diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/.env.example b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/.env.example
new file mode 100644
index 00000000..d75d8fb2
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/.env.example
@@ -0,0 +1,44 @@
+# ============================================================
+# RenAIssance — environment template. Copy to `.env` and fill in.
+# `.env` is gitignored; never commit real secrets.
+# ============================================================
+
+# --- AI / OCR provider keys (passed per-request to providers) ---
+GEMINI_API_KEY=
+OPENAI_API_KEY=
+DEEPSEEK_API_KEY=
+QWEN_API_KEY=
+
+# HuggingFace token for the "Local fine-tuned (Spanish)" corrector. Its base
+# model (google/gemma-3-4b-it) is GATED, so the first-use download needs a
+# token whose HF account has accepted the licence at
+# huggingface.co/google/gemma-3-4b-it. Only needed if you use that provider.
+HF_TOKEN=
+
+# ============================================================
+# User tracking / auth  (BACKEND-ONLY — never exposed to the browser)
+# ============================================================
+
+# Local auth store (per instance). Leave UNSET to use SQLite on the storage
+# volume (the default; works offline). Advanced: set a postgresql:// URL to use
+# your own Postgres for local accounts.
+# DATABASE_URL=
+
+# Signs session cookies. Generate with:
+#   python -c "import secrets; print(secrets.token_hex(32))"
+SECRET_KEY=
+
+# Bearer token for GET /api/admin/users (this instance's local users).
+# Leave blank to DISABLE the admin endpoint (returns 404).
+ADMIN_TOKEN=
+
+# Deployed origin. Only effect: session cookies get the Secure flag on https.
+PUBLIC_BASE_URL=http://localhost:5173
+
+# --- Central tracking (Supabase REST + PUBLISHABLE key) ---
+# Safe to be public: the central `users` table has an INSERT-only RLS policy,
+# so this key can only add a signup row. Published images bake these in via the
+# Dockerfile; set them here for local/dev runs. NEVER put the Postgres password
+# or the service_role/secret key here.
+SUPABASE_URL=
+SUPABASE_PUBLISHABLE_KEY=
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/.github/workflows/docker-build.yml b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/.github/workflows/docker-build.yml
new file mode 100644
index 00000000..1cd8a579
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/.github/workflows/docker-build.yml
@@ -0,0 +1,315 @@
+# ============================================================
+# RenAIssance — Docker Build & Push
+# Builds backend + frontend in parallel
+# Versioning: vX.Y with rollover at .9 (e.g. v3.9 → v4.0)
+# Tags pushed: image:vX.Y and image:latest
+# ============================================================
+
+name: Docker Build & Push
+
+on:
+  push:
+    branches:
+      - main
+  workflow_dispatch:
+
+permissions:
+  contents: write   # required to create and push git tags
+
+env:
+  DOCKERHUB_USERNAME: saarthakg004
+  BACKEND_IMAGE:  saarthakg004/renaissance-backend
+  FRONTEND_IMAGE: saarthakg004/renaissance-frontend
+
+jobs:
+  # ── Compute next version (no tag push yet) ─────────────────
+  # The git tag is only created AFTER both images build successfully.
+  # This prevents a failed build from consuming a version number.
+  version:
+    runs-on: ubuntu-latest
+    outputs:
+      version: ${{ steps.semver.outputs.version }}
+    steps:
+      - name: Checkout (full history + all tags)
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Compute next version with rollover
+        id: semver
+        run: |
+          git fetch --tags --force
+
+          # Consider tags that match exactly v<major>.<minor>
+          LATEST=$(git tag --list 'v*' \
+            | grep -E '^v[0-9]+\.[0-9]+$' \
+            | sort -V \
+            | tail -n1)
+
+          if [ -z "$LATEST" ]; then
+            # No version tags yet
+            NEXT="v1.0"
+          else
+            MAJOR=$(echo "$LATEST" | sed -E 's/^v([0-9]+)\.[0-9]+$/\1/')
+            MINOR=$(echo "$LATEST" | sed -E 's/^v[0-9]+\.([0-9]+)$/\1/')
+
+            # Rollover after .9
+            if [ "$MINOR" -ge 9 ]; then
+              NEXT="v$((MAJOR + 1)).0"
+            else
+              NEXT="v${MAJOR}.$((MINOR + 1))"
+            fi
+          fi
+
+          echo "version=$NEXT" >> "$GITHUB_OUTPUT"
+          echo "Computed next version: $NEXT"
+
+  # ── CPU-safe test suite (gates the image builds) ────────────
+  # Runs on a GPU-less GitHub runner, so it installs the CPU torch wheel and
+  # the CPU paddle wheel — the exact deps the CPU image ships — then runs the
+  # backend pytest suite. Real-GPU paths are covered by backend/tests/smoke_gpu.py
+  # (run manually on a GPU host) and the container-smoke job below.
+  test:
+    runs-on: ubuntu-latest
+    needs: version
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Set up Python 3.11
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+          cache: pip
+
+      - name: Install backend deps (CPU) + test tooling
+        run: |
+          python -m pip install --upgrade pip
+          # CPU torch FIRST so requirements.txt finds it satisfied (no CUDA wheel).
+          pip install --index-url https://download.pytorch.org/whl/cpu torch==2.5.1
+          pip install -r backend/requirements.txt
+          # CPU paddle (no-deps; runtime deps already provided by requirements.txt).
+          pip install --no-deps \
+            --index-url https://www.paddlepaddle.org.cn/packages/stable/cpu/ \
+            paddlepaddle==3.3.0
+          pip install pytest==8.3.4 pytest-cov==6.0.0
+
+      - name: Run tests
+        run: pytest backend/tests -q
+
+  # ── Build backend (gpu + cpu variants from one Dockerfile) ──
+  build-backend:
+    runs-on: ubuntu-latest
+    needs: [version, test]
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - variant: gpu
+            runtime_base: ubuntu:24.04
+          - variant: cpu
+            runtime_base: ubuntu:24.04
+    # Folder ID of the public Google Drive folder containing crnn/ and trocr/.
+    # Anyone-with-the-link access is sufficient for gdown to traverse and
+    # download. If this folder is rotated or made private, replace the ID and
+    # either keep public-with-link access or switch to a service account.
+    env:
+      WEIGHTS_DRIVE_FOLDER_ID: 1NRbCcUhcr5wJOCarBNHRP6jlnLuzDVXC
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      # GitHub-hosted runners ship with ~14 GB free after actions/checkout.
+      # The torch + paddle wheel download alone is ~3 GB, the built image is
+      # ~19 GB, and the weights add another ~1.4 GB. Reclaim ~30 GB up front
+      # by purging pre-installed toolchains that this job does not use.
+      - name: Reclaim runner disk
+        run: |
+          sudo rm -rf /usr/share/dotnet /usr/local/lib/android /opt/ghc \
+                      /usr/local/share/boost "$AGENT_TOOLSDIRECTORY" || true
+          df -h /
+
+      # Pull recognition-model weights from Google Drive into the backend
+      # build context so the Dockerfile's `COPY . .` bakes them in at
+      # /app/models/weights/. Weights are gitignored (kept out of the repo
+      # because of GitHub's 100 MB file-size limit), so Drive is the source
+      # of truth — update the folder there and rebuild to ship new weights.
+      - name: Fetch recognition-model weights from Google Drive
+        run: |
+          # gdown 6.x dropped --remaining-ok; its folder downloader is also
+          # capped at 50 files per folder (ours has 2 subfolders, so fine).
+          python3 -m pip install --quiet --upgrade 'gdown>=5.2.0'
+          mkdir -p backend/models/weights
+          # Retry twice on transient 429/5xx — Drive occasionally throttles
+          # anonymous public downloads.
+          for attempt in 1 2 3; do
+            if gdown --folder \
+                "https://drive.google.com/drive/folders/${WEIGHTS_DRIVE_FOLDER_ID}" \
+                -O backend/models/weights; then
+              break
+            fi
+            echo "gdown attempt $attempt failed — retrying in 10s…"
+            sleep 10
+          done
+          echo "Fetched weights:"
+          find backend/models/weights -maxdepth 3 -type f -printf '  %p  %s bytes\n' | head -20
+          # Sanity: at least one .safetensors or .pth must exist, otherwise
+          # we would silently ship an image with an empty weights dir.
+          if ! find backend/models/weights \
+                \( -name '*.safetensors' -o -name '*.pth' -o -name '*.pt' \) \
+                | grep -q .; then
+            echo "::error::No model weights found under backend/models/weights after gdown"
+            exit 1
+          fi
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      - name: Log in to DockerHub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ env.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_TOKEN }}
+
+      # Tags per variant: always :<ver>-<variant> and :latest-<variant>.
+      # The gpu leg additionally publishes the bare :<ver> / :latest as a
+      # backward-compatible alias (the default variant is gpu).
+      - name: Compute backend tags
+        id: tags
+        run: |
+          V="${{ needs.version.outputs.version }}"
+          VAR="${{ matrix.variant }}"
+          TAGS="${BACKEND_IMAGE}:${V}-${VAR}
+          ${BACKEND_IMAGE}:latest-${VAR}"
+          if [ "$VAR" = "gpu" ]; then
+            TAGS="${TAGS}
+          ${BACKEND_IMAGE}:${V}
+          ${BACKEND_IMAGE}:latest"
+          fi
+          { echo "tags<<EOF"; echo "$TAGS"; echo "EOF"; } >> "$GITHUB_OUTPUT"
+
+      - name: Build & push backend (${{ matrix.variant }})
+        uses: docker/build-push-action@v6
+        with:
+          context: ./backend
+          file: ./backend/Dockerfile
+          push: true
+          build-args: |
+            VARIANT=${{ matrix.variant }}
+            RUNTIME_BASE=${{ matrix.runtime_base }}
+          tags: ${{ steps.tags.outputs.tags }}
+          # GitHub Actions cache for BuildKit layers (scoped per variant).
+          cache-from: type=gha,scope=backend-${{ matrix.variant }}
+          cache-to:   type=gha,scope=backend-${{ matrix.variant }},mode=max
+          # BuildKit provenance for supply chain security
+          provenance: false
+
+  # ── Build frontend ──────────────────────────────────────────
+  build-frontend:
+    runs-on: ubuntu-latest
+    needs: [version, test]
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      - name: Log in to DockerHub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ env.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_TOKEN }}
+
+      - name: Build & push frontend
+        uses: docker/build-push-action@v6
+        with:
+          context: ./frontend
+          file: ./frontend/Dockerfile
+          push: true
+          tags: |
+            ${{ env.FRONTEND_IMAGE }}:${{ needs.version.outputs.version }}
+            ${{ env.FRONTEND_IMAGE }}:latest
+          cache-from: type=gha,scope=frontend
+          cache-to:   type=gha,scope=frontend,mode=max
+          provenance: false
+
+  # ── Container boot smoke (CPU image) ────────────────────────
+  # GitHub runners have no GPU, so we boot the freshly-published CPU image and
+  # assert /api/health returns 200. This proves the image actually starts end
+  # to end (entrypoint preflight + uvicorn) on a GPU-less host.
+  container-smoke:
+    runs-on: ubuntu-latest
+    needs: [version, build-backend]
+    steps:
+      - name: Log in to DockerHub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ env.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_TOKEN }}
+
+      - name: Boot CPU image and probe health
+        run: |
+          IMG="${BACKEND_IMAGE}:${{ needs.version.outputs.version }}-cpu"
+          docker run -d --name rb -p 8000:8000 "$IMG"
+          ok=0
+          for i in $(seq 1 36); do
+            if curl -fsS http://localhost:8000/api/health >/dev/null; then
+              echo "Healthy after ~$((i*5))s"; ok=1; break
+            fi
+            sleep 5
+          done
+          echo "----- container logs (tail) -----"
+          docker logs rb 2>&1 | tail -60 || true
+          docker rm -f rb >/dev/null 2>&1 || true
+          [ "$ok" = "1" ] || { echo "::error::CPU image did not become healthy"; exit 1; }
+
+  # ── Tag + Summary (only runs when builds + smoke succeed) ───
+  # Creating the git tag here ensures a failed build never wastes
+  # a version number — re-running the workflow will recompute the
+  # same version and retry the tag push.
+  summary:
+    runs-on: ubuntu-latest
+    needs: [version, test, build-backend, build-frontend, container-smoke]
+    if: always()
+    steps:
+      - name: Checkout (for tag push)
+        if: needs.build-backend.result == 'success' && needs.build-frontend.result == 'success' && needs.container-smoke.result == 'success'
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Create and push version tag
+        if: needs.build-backend.result == 'success' && needs.build-frontend.result == 'success' && needs.container-smoke.result == 'success'
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        run: |
+          git fetch --tags --force
+          git config user.name  "github-actions[bot]"
+          git config user.email "github-actions[bot]@users.noreply.github.com"
+
+          # Authenticate remote with token
+          git remote set-url origin \
+            "https://x-access-token:${GITHUB_TOKEN}@github.com/${{ github.repository }}"
+
+          # Only push the tag if it does not already exist
+          if git rev-parse "${{ needs.version.outputs.version }}" >/dev/null 2>&1; then
+            echo "Tag ${{ needs.version.outputs.version }} already exists — skipping."
+          else
+            git tag "${{ needs.version.outputs.version }}"
+            git push origin "${{ needs.version.outputs.version }}"
+            echo "Pushed tag ${{ needs.version.outputs.version }}"
+          fi
+
+      - name: Build summary
+        run: |
+          echo "## Docker Build Summary" >> $GITHUB_STEP_SUMMARY
+          echo "" >> $GITHUB_STEP_SUMMARY
+          echo "Version: \`${{ needs.version.outputs.version }}\` (backend tags: \`-gpu\` and \`-cpu\`)" >> $GITHUB_STEP_SUMMARY
+          echo "" >> $GITHUB_STEP_SUMMARY
+          echo "| Job | Status |" >> $GITHUB_STEP_SUMMARY
+          echo "|-----|--------|" >> $GITHUB_STEP_SUMMARY
+          echo "| tests                | ${{ needs.test.result }} |" >> $GITHUB_STEP_SUMMARY
+          echo "| build-backend (gpu+cpu) | ${{ needs.build-backend.result }} |" >> $GITHUB_STEP_SUMMARY
+          echo "| build-frontend       | ${{ needs.build-frontend.result }} |" >> $GITHUB_STEP_SUMMARY
+          echo "| container-smoke (cpu)  | ${{ needs.container-smoke.result }} |" >> $GITHUB_STEP_SUMMARY
\ No newline at end of file
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/.gitignore b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/.gitignore
new file mode 100644
index 00000000..35730b31
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/.gitignore
@@ -0,0 +1,34 @@
+frontend/node_modules
+frontend/dist
+.env
+__pycache__
+*.pyc
+improvements.txt
+RenAIssanceExperimental
+# Runtime data dir at the repo root ONLY (datasets/transcripts/users.db).
+# Anchored with a leading slash so it does NOT also match the source package
+# backend/app/storage/, which must be committed and shipped in the image.
+/storage/
+# Large model weights — tracked out-of-band, not in git. CI fetches them from
+# Google Drive via gdown at image-build time (see .github/workflows).
+backend/models/weights/
+# Belt-and-suspenders: never commit a weight file from anywhere in the tree,
+# whatever folder it lands in. GitHub rejects files over 100 MB anyway.
+*.safetensors
+*.pt
+*.pth
+*.pdparams
+*.onnx
+*.ckpt
+*.h5
+*.tflite
+.claude
+
+# Local-only authoring artifacts (blog drafts, diagrams) — not app code.
+/assets/
+/blog_*.md
+
+# Test / native-run artifacts
+.pytest_cache/
+backend/.pytest-storage/
+backend/.venv-native/
\ No newline at end of file
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/PRESENTATION.md b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/PRESENTATION.md
new file mode 100644
index 00000000..12238728
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/PRESENTATION.md
@@ -0,0 +1,324 @@
+# RenAIssance — Presentation Content & Technical Summary
+
+An end-to-end OCR system for historical (early-modern Spanish) documents.
+This document contains a one-page technical summary followed by detailed,
+slide-by-slide content for a 7-slide deck. Every metric is drawn from the
+project's own training and evaluation notebooks; sources are noted so you can
+cite them.
+
+---
+
+## Technical Summary (one page)
+
+RenAIssance is a full-stack web application that turns scanned historical books
+into clean, editable, exportable text. It was built and evaluated on early
+modern Spanish printed books, whose broken glyphs, bleed-through, warped pages
+and archaic typography defeat off-the-shelf OCR.
+
+The product is a five-step wizard: **Upload → Select pages → Preprocess →
+Text detection → OCR & export.** Under the hood it combines four ideas:
+
+1. **A configurable image-cleanup pipeline** — ten OpenCV operations with live
+   before/after preview, including a new piecewise deskew that corrects pages
+   whose top is straight but whose bottom is warped, plus one-click "book type"
+   presets of pretested pipelines.
+2. **Layout-aware text detection** built on PaddleOCR PP-OCRv5, with automatic
+   model-tier selection that reads free VRAM/RAM at runtime and picks server
+   models, mobile models, or CPU accordingly.
+3. **A choice of recognition engines** behind one interface — cloud vision
+   models (Gemini, ChatGPT) and two locally fine-tuned models (a CNN-LSTM/CRNN
+   and a TrOCR transformer), swappable via a Strategy + Factory pattern.
+4. **An optional AI correction layer** — a `gemma-3-4b-it` model fine-tuned with
+   QLoRA into a Spanish OCR corrector that fixes the residual character and word
+   errors the recognizer leaves behind.
+
+**Headline results.** The fine-tuned CRNN reaches roughly **2% character error
+and 9% word error** at the line level on held-out books. The fine-tuned TrOCR
+reaches **6.1% CER / 21.7% WER**, and post-training quantization to fp16 halves
+its memory and triples its speed with no accuracy loss. The Gemma corrector cuts
+raw OCR word error by nearly half (**WER 20.9% → 11.5%**, **CER 4.65% → 3.2%**).
+
+**Engineering.** The whole system ships as Docker images with a one-command
+launcher that auto-detects the GPU and starts the right variant. A single
+Dockerfile builds both GPU and CPU images; a CI pipeline runs tests, builds both
+variants, smoke-tests the running container, and publishes on every push. Model
+weights are fetched from Google Drive at build time so the git repository stays
+small.
+
+**Stack.** React 18 + Vite + Tailwind (frontend); FastAPI + Python 3.11
+(backend); PaddleOCR 3.0, PyTorch 2.5, Transformers 4.46, OpenCV 4.10 (ML/CV);
+Docker + GitHub Actions (delivery).
+
+---
+
+# Slide Deck
+
+---
+
+## Slide 1 — The Problem and the Product
+
+**Title:** RenAIssance: Reading What Machines Cannot
+
+**The problem.**
+- Historical books hold centuries of knowledge that is effectively unsearchable.
+- Early-modern Spanish print breaks every assumption modern OCR makes: worn and
+  broken glyphs, ink bleed-through, non-standard spelling and abbreviations,
+  warped and skewed pages, decorative typography, and inconsistent scans.
+- General-purpose OCR (Tesseract, stock cloud OCR) produces error rates too high
+  to be useful for scholars.
+
+**The product.**
+- A full-stack web app that takes a PDF or image and produces clean, editable,
+  exportable text.
+- Designed for domain experts, not engineers: everything happens in a guided
+  five-step wizard in the browser.
+- **Upload → Select pages → Preprocess → Text detection → OCR & export.**
+- Output formats: TXT, DOCX, PDF.
+
+**Talking point.** The project is not a single model. It is a *pipeline* where
+each stage removes a different class of error, and where the user stays in
+control at every step.
+
+> [IMAGE: The five-step wizard, one screenshot per step, left to right.]
+
+---
+
+## Slide 2 — System Architecture
+
+**Title:** One Interface, Many Engines
+
+**Frontend (React 18 + Vite + Tailwind).**
+- A single-page wizard that manages global state and routes between the five
+  steps.
+- PDF.js renders and slices uploaded PDFs in the browser.
+- Live before/after preview during preprocessing.
+
+**Backend (FastAPI + Python 3.11).**
+- Ten routers: health, OCR, preprocess, export, layout detection, dataset,
+  recognition, LLM post-process, storage, auth.
+- **Strategy + Factory pattern** for OCR engines: one `BaseOCRProvider`
+  interface, an `OCRFactory` registry, and independent implementations behind
+  it. Adding an engine means implementing the interface and registering it,
+  nothing else changes.
+- API keys are passed per request from the browser and never stored on disk.
+
+**The provider factory (key design idea).**
+- The application talks to *one* interface.
+- Behind it sit Gemini, ChatGPT, the local CRNN, and the local TrOCR.
+- DeepSeek and Qwen are implemented and registered but hidden in the UI, ready
+  to enable.
+
+> [IMAGE: provider-factory.svg — Application → OCRFactory → {Gemini, ChatGPT,
+> Local CRNN, Local TrOCR}, with DeepSeek/Qwen greyed behind.]
+
+**Talking point.** This is what makes the system future-proof: new OCR or
+correction engines plug in without touching the wizard, the API, or the other
+engines.
+
+---
+
+## Slide 3 — Stage 1: Preprocessing
+
+**Title:** Cleaning the Page Before the Machine Reads It
+
+**A configurable OpenCV pipeline.**
+- Ten operations, each toggleable and tunable, run in sequence with a live
+  before/after preview: normalize, grayscale, deskew, denoise, contrast,
+  sharpen, threshold, morphology, remove blobs, remove noise (speckles).
+
+**Highlight 1 — Piecewise deskew.**
+- Standard deskew rotates the whole page by one angle. Real books warp: the top
+  of a page can be straight while the bottom is skewed.
+- The new deskew measures skew in horizontal **bands** using projection
+  profiles. If the bands agree (spread below 1°) it rotates once; if they
+  disagree it blends per-band rotations seam-free, using triangular weights that
+  form a partition of unity so there are no visible discontinuities.
+- Modes: auto / global / piecewise, with a bands control.
+
+**Highlight 2 — Book-type presets.**
+- A "Recommendation" dropdown offers pretested pipelines per book type.
+- Selecting a type *loads* the pipeline into the editor without running it, so
+  the user can inspect and tune it before applying.
+- First shipped preset: **PORCONES → Remove Ink Speckles (20px).**
+
+**Talking point.** Preprocessing is where most OCR wins or loses. Giving experts
+a tuned starting point plus live feedback is worth more than any single clever
+filter.
+
+> [IMAGE: The three-panel preprocess editor with a warped page corrected by
+> piecewise deskew, before on the left, after on the right.]
+
+---
+
+## Slide 4 — Stage 2: Text Detection
+
+**Title:** Finding the Text, Adapting to the Machine
+
+**Layout-aware detection on PaddleOCR PP-OCRv5.**
+- Detects text regions per page before recognition, so recognition runs on clean
+  line/word crops rather than the whole page.
+- The detector was fine-tuned on hand-labelled page bounding boxes from the
+  target books (PP-OCRv5 server detector), improving region proposals on
+  dense historical layouts.
+
+**Adaptive model-tier selection (engineering highlight).**
+- At runtime the system reads free VRAM/RAM and picks a tier automatically:
+  - Enough free VRAM → **server** models on GPU (best accuracy).
+  - Less → **mobile** models on GPU (smaller footprint).
+  - No usable GPU → **CPU**, still fully functional.
+- The same code runs on a workstation with an RTX GPU and on a Mac laptop with
+  no GPU at all.
+
+**Talking point.** The system does not assume a fixed machine. It measures the
+hardware it wakes up on and scales the models to fit, which is what lets one
+Docker image serve both a lab GPU box and a reviewer's laptop.
+
+> [IMAGE: A detected page with bounding boxes overlaid on each text line.]
+
+---
+
+## Slide 5 — Stage 3: Text Recognition and Results
+
+**Title:** Two Local Models, Measured Honestly
+
+**Two locally fine-tuned recognizers (plus cloud options).**
+- **CRNN (CNN-LSTM):** a lightweight ResNet CNN backbone → BiLSTM → CTC decoder,
+  fine-tuned on a Spanish line dataset. Fast, small, strong on clean printed
+  lines.
+- **TrOCR:** a vision-encoder / text-decoder transformer fine-tuned from
+  `microsoft/trocr-base-printed`. Heavier but more robust on degraded text.
+- Cloud vision models (Gemini, ChatGPT) available through the same interface.
+
+**Line-level accuracy on held-out books** (document-level 10% split, same books
+held out across all experiments):
+
+| Model | CER | WER |
+|---|---|---|
+| CRNN (fine-tuned CNN-LSTM) | ~2.0% | ~9.0% |
+| TrOCR (fine-tuned, fp32) | 6.1% | 21.7% |
+
+*Source: training/validation logs, notebooks 01 and 03; TrOCR fp32 from the
+quantization study, notebook 04.*
+
+**TrOCR quantization study** (post-training, notebook 04) — accuracy vs memory
+vs latency:
+
+| Precision | CER | WER | Size | vs fp32 size | ms/line |
+|---|---|---|---|---|---|
+| fp32 | 6.11% | 21.65% | 1274 MB | 100% | 197 |
+| **fp16** | **6.08%** | **21.59%** | **637 MB** | **50%** | **63** |
+| int8 | 6.29% | 21.62% | 471 MB | 37% | 246 |
+| 4-bit NF4 | 7.46% | 24.62% | 337 MB | 26% | 127 |
+| 4-bit FP4 | 9.25% | 29.64% | 337 MB | 26% | 126 |
+
+**Takeaway.** fp16 is a free win: half the memory, roughly 3× faster, and CER
+actually a hair *better* than fp32. 4-bit shrinks the model to a quarter but
+costs real accuracy, so it is only worth it under hard memory limits.
+
+> [IMAGE: Bar chart of the quantization table — CER and size per precision.]
+
+---
+
+## Slide 6 — Stage 4: AI Post-Processing
+
+**Title:** Teaching a Language Model to Fix OCR
+
+**The idea.**
+- Even the best recognizer leaves residual errors: a confused character, a split
+  or merged word, a wrong diacritic.
+- A language model that knows Spanish and knows *how OCR fails* can correct these
+  from context.
+
+**What was built.**
+- `gemma-3-4b-it` fine-tuned with **QLoRA** (4-bit NF4 base, LoRA rank 16) into a
+  line-by-line Spanish OCR corrector.
+- Trained on OCR-vs-ground-truth pairs generated from **two** engines (the
+  pretrained Paddle recognizer and the fine-tuned CRNN), so it learns to fix real
+  model mistakes rather than synthetic noise.
+- Ships as a small LoRA adapter on top of the base model; runs locally on an
+  NVIDIA GPU.
+
+**Results — the corrector nearly halves word error:**
+
+| Stage | CER | WER |
+|---|---|---|
+| Raw OCR | 4.65% | 20.9% |
+| **+ Fine-tuned Gemma corrector** | **3.2%** | **11.5%** |
+
+- Relative reduction: **CER −31%, WER −45%.**
+- **Critical finding:** the *off-the-shelf* (zero-shot) model made results
+  *worse* — it rewrote text it did not understand. Fine-tuning on real OCR error
+  pairs is what turned it into a corrector instead of a paraphraser.
+
+**Also available as cloud correctors:** Gemini, OpenAI, DeepSeek, Qwen, through
+the same post-processing interface.
+
+> [IMAGE: Before/after text sample — raw OCR line above, corrected line below,
+> with the fixes highlighted.]
+
+**Talking point.** This is the difference between "a big model" and "the right
+model." A general LLM hurt accuracy; the same model, fine-tuned on the actual
+failure modes, delivered the single largest error reduction in the pipeline.
+
+---
+
+## Slide 7 — Delivery, Impact, and Takeaways
+
+**Title:** From Research Notebooks to a Product Anyone Can Run
+
+**Packaged for real use.**
+- One-command launcher (`./run.sh` / `run.ps1`) auto-detects the GPU and starts
+  the correct image variant.
+- A single Dockerfile builds **both** GPU and CPU images; the app picks
+  CUDA / Apple MPS / CPU at runtime, so it runs on a lab GPU box, a Windows PC,
+  or a Mac laptop unchanged.
+- CI on every push: run tests → build GPU + CPU images → smoke-test the live
+  container's health endpoint → publish images and tag only on full success.
+- Model weights are fetched from Google Drive at build time, keeping the git
+  repo small.
+
+**What the numbers add up to.**
+- Preprocessing removes skew and noise before the model ever sees the page.
+- A fine-tuned detector + adaptive PP-OCRv5 finds the text on any hardware.
+- The fine-tuned CRNN reaches ~2% CER on clean lines; TrOCR is the robust
+  fallback, and fp16 makes it cheap to run.
+- The Gemma corrector cuts word error nearly in half on top of that.
+- Each stage attacks a different error source, and they compound.
+
+**Engineering takeaways.**
+- Design for pluggability (Factory pattern) so new engines cost nothing to add.
+- Measure the hardware at runtime instead of assuming it.
+- Bigger models are not automatically better — fine-tuning on the real failure
+  distribution is what wins.
+- Reproducibility and one-command deployment are features, not afterthoughts.
+
+**Closing line.** RenAIssance turns pages that machines could not read into
+searchable, editable text, and it does so as a product that a historian can run
+on a laptop, not a script that only works in the author's notebook.
+
+> [IMAGE: The final OCR & Export screen with a finished transcription and the
+> TXT / DOCX / PDF export buttons.]
+
+---
+
+## Appendix — Metric Provenance (for Q&A)
+
+- **CRNN line-level (~2.0% CER / ~9.0% WER):** best validation checkpoint,
+  notebook 01 pipeline run and notebook 03 recognition fine-tuning (fine-tunes
+  `latin_PP-OCRv5_mobile_rec`). Training CER reached ~1.3%.
+- **TrOCR (6.1% CER / 21.7% WER):** fp32 baseline in the quantization study,
+  notebook 04; consistent with notebook 01 best validation CER of ~7%.
+- **Quantization table:** notebook 04 stored outputs, verbatim (fp16 / int8 /
+  4-bit FP4 / 4-bit NF4 vs fp32, on the same validation lines).
+- **Gemma corrector (CER 4.65% → 3.2%, WER 20.9% → 11.5%):** post-processing
+  fine-tuning experiment, notebook 05 (QLoRA `gemma-3-4b-it`), evaluated raw vs
+  zero-shot vs fine-tuned per engine.
+- **Evaluation protocol:** document-level `GroupShuffleSplit(test_size=0.10,
+  random_state=42)` so the same books are held out across every experiment; CER
+  and WER computed by `src/evals/metrics.py`.
+
+*Note: the CRNN and TrOCR error rates are line-level on held-out lines. A single
+end-to-end full-page number is much higher for any engine because it compounds
+detection, reading-order, and recognition errors across a whole page; quote the
+line-level figures for model quality and describe the full pipeline
+qualitatively.*
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/README.md b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/README.md
new file mode 100644
index 00000000..f129f0a9
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/README.md
@@ -0,0 +1,200 @@
+# RenAIssance
+
+RenAIssance is a full-stack web app for reading historical documents. You upload a PDF or image, clean up the scan, detect the text regions, and transcribe the text with your choice of engine. It was built for early modern Spanish books, but the pipeline works on any printed page.
+
+The workflow is a five step wizard: Upload, Select pages, Preprocess, Text detection, and OCR & export.
+
+## Features
+
+- **OCR engines:** Gemini, ChatGPT, a local CRNN, and a local TrOCR (fine-tuned from `microsoft/trocr-base-printed`).
+- **Optional AI cleanup:** correct raw OCR with Gemini, OpenAI, DeepSeek, Qwen, or a Spanish fine-tuned corrector (a `gemma-3-4b-it` LoRA adapter). The fine-tuned corrector needs an NVIDIA GPU.
+- **Export:** download the transcription as TXT, DOCX, or PDF.
+
+## What you need
+
+- Docker version 24 or newer, with Compose v2.
+- About 15 GB of free disk for the images and model weights.
+- A Gemini or OpenAI API key if you want to use those engines. You enter the key in the app, not in a file.
+- An NVIDIA GPU with driver 560 or newer and the NVIDIA Container Toolkit if you want GPU acceleration. This is optional. Without a GPU the app runs on the CPU image, which works everywhere and is only slower.
+
+## Run it
+
+Clone the repository and run the launcher for your system. It detects your GPU, checks your machine before downloading anything, and starts the correct image automatically.
+
+### Linux and Windows (WSL)
+
+```bash
+git clone https://github.com/<your-org>/RenAIssance.git
+cd RenAIssance
+./run.sh
+```
+
+Then open http://localhost:5173 in your browser. The first run downloads the images and can take several minutes. Some models download the first time you use them, and the app shows a progress bar while that happens.
+
+Useful flags:
+
+```bash
+./run.sh --cpu     # force the CPU image
+./run.sh --build   # build from source instead of pulling published images
+./run.sh --down    # stop everything (your saved data is kept)
+```
+
+### Windows (PowerShell)
+
+```powershell
+.\run.ps1          # auto-detect GPU or CPU
+.\run.ps1 -Cpu     # force the CPU image
+.\run.ps1 -Down    # stop
+```
+
+## Running on a Mac
+
+Docker on macOS cannot reach the GPU, so on a Mac the app always runs on the **CPU image**. This is expected and everything works, it is just slower than on an NVIDIA machine. The local Spanish fine-tuned corrector is the only feature that requires a GPU, so on a Mac use Gemini or OpenAI for the AI cleanup step instead.
+
+Follow these steps.
+
+1. Install Docker Desktop for Mac from https://www.docker.com/products/docker-desktop and start it. Wait until the whale icon in the menu bar stops animating, which means Docker is ready.
+2. Install Git if you do not have it. The simplest way is to run `git --version` in the Terminal, which offers to install the developer tools if Git is missing.
+3. Clone the repository and enter the folder:
+
+   ```bash
+   git clone https://github.com/<your-org>/RenAIssance.git
+   cd RenAIssance
+   ```
+
+4. Start the app. The launcher sees there is no NVIDIA GPU and picks the CPU image for you:
+
+   ```bash
+   ./run.sh
+   ```
+
+   If you prefer to be explicit, run `./run.sh --cpu`.
+
+5. Wait for the download to finish. The first run pulls a few gigabytes, so give it a few minutes on a normal connection.
+6. Open http://localhost:5173 in your browser. The app is ready.
+7. To stop the app later, run `./run.sh --down`. Your transcripts and datasets are saved and will still be there next time.
+
+### Optional: use the Apple GPU (Apple Silicon)
+
+Docker cannot use the Apple GPU, but you can run the app directly on your Mac to get GPU acceleration for the local TrOCR and CRNN engines through Apple's Metal (MPS). Text detection still runs on the CPU because PaddleOCR has no Metal backend.
+
+You need Homebrew, Python 3.11, and Node.js:
+
+```bash
+brew install python@3.11 node
+./run-native.sh
+```
+
+The script creates a local Python environment, installs everything, and starts the app on http://localhost:5173. The first run is slow because it installs the dependencies.
+
+## Using the app
+
+1. **Upload** a PDF or an image (PNG, JPG, TIFF, or BMP).
+2. **Select** the pages you want from the thumbnail grid.
+3. **Preprocess** the pages. Toggle and tune the cleanup operations and watch the before and after preview. You can also load a pre-tested pipeline for a known book type.
+4. **Detect** the text regions on each page.
+5. **Read and export.** Pick an engine, transcribe, edit anything that needs fixing, optionally run the AI cleanup, and download the result.
+
+The app opens on a simple sign up screen that asks for a name, an email, and an institution. It exists only for lightweight usage tracking and never asks for an API key. You enter provider keys later, in the reading step, and only if you use a cloud engine.
+
+## Compose files
+
+The launcher just selects the right Compose file. You can run them directly if you prefer.
+
+| Host | File |
+|------|------|
+| NVIDIA GPU, published images | `docker-compose.images.yml` |
+| No GPU or Mac, published images | `docker-compose.images.cpu.yml` |
+| NVIDIA GPU, build from source | `docker-compose.yml` |
+| No GPU or Mac, build from source | `docker-compose.cpu.yml` |
+
+```bash
+docker compose -f docker-compose.images.cpu.yml up -d    # run (Mac / no GPU)
+docker compose -f docker-compose.images.cpu.yml down      # stop, data is kept
+```
+
+## Manual Docker commands
+
+If you would rather not use the launcher or Compose, you can pull and run the published images by hand. The commands below use the GPU backend. On a Mac or any machine without an NVIDIA GPU, remove the `--gpus all` line from the backend command and everything still works on the CPU.
+
+### Install (run once)
+
+Pull the images and create the network and the volumes that hold your data. The volumes persist forever, so you only create them once.
+
+```bash
+docker pull saarthakg004/renaissance-backend:latest
+docker pull saarthakg004/renaissance-frontend:latest
+
+docker network create renaissance 2>/dev/null || true
+docker volume create paddle_models 2>/dev/null || true
+docker volume create renaissance_storage 2>/dev/null || true
+```
+
+### Run
+
+Start the backend, then the frontend. The `--network-alias backend` on the backend is required, because the frontend proxies API calls to `http://backend:8000` and must be able to find it by that name.
+
+```bash
+docker run -d --name renaissance-backend \
+  --gpus all \
+  --network renaissance --network-alias backend \
+  -p 8000:8000 \
+  -v paddle_models:/paddle_models \
+  -v renaissance_storage:/app/storage \
+  --restart unless-stopped \
+  saarthakg004/renaissance-backend:latest
+
+docker run -d --name renaissance-frontend \
+  --network renaissance \
+  -p 5173:8080 \
+  --restart unless-stopped \
+  saarthakg004/renaissance-frontend:latest
+```
+
+Wait about 30 seconds for the backend to start, then open http://localhost:5173.
+
+### Stop and delete the containers (your data is kept)
+
+This removes only the containers. The volumes, and therefore your datasets and transcripts, stay on disk. Run the two `docker run` commands above again to start fresh containers with the same data.
+
+```bash
+docker rm -f renaissance-backend renaissance-frontend
+```
+
+### Delete everything, including your data
+
+This is the full cleanup. It removes the containers, the network, and the volumes. Everything you saved is deleted.
+
+```bash
+docker rm -f renaissance-backend renaissance-frontend
+docker network rm renaissance
+docker volume rm paddle_models renaissance_storage
+```
+
+## Development and tests
+
+```bash
+# Backend tests (CPU-safe, no GPU needed). CI runs these on every push.
+cd backend
+pip install -r requirements-dev.txt
+pytest tests -q
+
+# Frontend dev server
+cd frontend
+npm install
+npm run dev
+```
+
+Model weights are not stored in Git. Continuous integration downloads them from Google Drive when it builds the images, so a normal clone stays small.
+
+## Troubleshooting
+
+| Symptom | Fix |
+|---|---|
+| The launcher says your machine does not meet the GPU requirements | Install or repair the NVIDIA driver (560 or newer) and the Container Toolkit, or run `./run.sh --cpu` to use the CPU image. |
+| The GPU image logs that it started without a usable CUDA device | The container was launched without GPU access. Use the launcher, or run the CPU image. The server still starts, only PaddleOCR detection is unavailable. |
+| The local Spanish corrector fails on a Mac | It needs an NVIDIA GPU. Use Gemini or OpenAI for the cleanup step instead. |
+| Port 8000 or 5173 is already in use | Stop the other program using it, or remap the port in the Compose file. |
+| The frontend says it cannot reach the backend | The backend is still starting. The first health check can take about 30 seconds. |
+
+View logs with `docker logs -f renaissance-backend`.
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/.dockerignore b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/.dockerignore
new file mode 100644
index 00000000..432cbf28
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/.dockerignore
@@ -0,0 +1,24 @@
+__pycache__
+*.pyc
+*.pyo
+.env
+.git
+.gitignore
+.venv
+venv
+*.md
+Dockerfile
+.dockerignore
+
+# Test + dev scaffolding that never runs inside the container.
+tests/
+*.log
+.pytest_cache/
+.mypy_cache/
+.ruff_cache/
+
+# NOTE: models/weights/ is intentionally NOT excluded. The recognition code
+# (app/api/recognition.py) resolves models relative to the repo root, so the
+# weights must be present inside the image for CRNN / TrOCR to load. If we
+# later decide to download weights lazily at runtime (same pattern as PaddleX),
+# we can exclude models/weights/ and drop ~1.4 GB from the image.
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/Dockerfile b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/Dockerfile
new file mode 100644
index 00000000..daf7e5b5
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/Dockerfile
@@ -0,0 +1,300 @@
+# ============================================
+# RenAIssance OCR Backend
+# Multi-stage build — one Dockerfile, two variants (GPU / CPU)
+#
+#   Build the GPU image (default):
+#     docker build -t renaissance-backend:gpu ./backend
+#   Build the CPU image (Mac / no-NVIDIA hosts):
+#     docker build --build-arg VARIANT=cpu \
+#                  --build-arg RUNTIME_BASE=ubuntu:24.04 \
+#                  -t renaissance-backend:cpu ./backend
+#
+# The two variants differ ONLY in the torch + paddle wheels and the runtime
+# base image; all application code and other pins are identical. The app
+# already selects cuda/mps/cpu at runtime, so the same code runs on either.
+#
+# Stage 1 — ubuntu:24.04  (+Python 3.11 deadsnakes)
+#            Installs all Python deps into /opt/venv (variant-specific wheels)
+#
+# Stage 2 — ${RUNTIME_BASE}  (+Python 3.11 deadsnakes)
+#            ubuntu:24.04 for BOTH variants. The GPU variant does NOT need
+#            the multi-GB nvidia/cuda runtime base: torch ships its CUDA
+#            user-space libs as nvidia-*-cu12 pip wheels and paddle RPATHs
+#            into the same site-packages/nvidia directory, while the host
+#            driver (libcuda.so.1) is injected at `docker run --gpus all`
+#            time by the NVIDIA Container Toolkit. Dropping the CUDA base
+#            saves ~3.5 GB of system CUDA libraries nothing ever loads.
+#
+# WHY the same base OS for both stages:
+#   Native extensions (.so wheels) are linked against the host distro's
+#   glibc, OpenSSL and libstdc++.  Copying a venv from Debian 12 into
+#   Ubuntu 24.04 (or vice-versa) causes "GLIBC_2.38 not found" /
+#   "OPENSSL_3.3.0 not found" errors.  Both stages are Ubuntu 24.04,
+#   so they share the builder's ABI.
+#
+# GPU variant requirements on the host:
+#   - NVIDIA driver >= 560 (supports CUDA 12.6)
+#   - NVIDIA Container Toolkit installed
+#   - Run:  docker run --gpus all -p 8000:8000 <image>
+# ============================================
+
+# Global build args (consumed in the stages below; re-declared per stage).
+# RUNTIME_BASE stays overridable but ubuntu:24.04 is correct for both
+# variants — see the Stage 2 note above.
+ARG VARIANT=gpu
+ARG RUNTIME_BASE=ubuntu:24.04
+
+############################
+# Stage 1 — dependency builder
+############################
+FROM ubuntu:24.04 AS builder
+
+ARG VARIANT
+
+ENV DEBIAN_FRONTEND=noninteractive \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PYTHONUNBUFFERED=1
+
+# Install Python 3.11 from deadsnakes PPA + build tools.
+# Ubuntu 24.04 ships Python 3.12 by default; using 3.11 here to match
+# paddlepaddle-gpu ABI and keep numpy/other pinned versions stable.
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        software-properties-common \
+        ca-certificates \
+    && add-apt-repository -y ppa:deadsnakes/ppa \
+    && apt-get install -y --no-install-recommends \
+        python3.11 \
+        python3.11-dev \
+        python3.11-venv \
+        python3-pip \
+        build-essential \
+        libgl1 \
+        libglib2.0-0 \
+    && rm -rf /var/lib/apt/lists/*
+
+# Create a Python 3.11 virtual environment — all packages go here.
+RUN python3.11 -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+WORKDIR /build
+COPY requirements.txt .
+
+# Install Python dependencies in two steps to sidestep a hard conflict
+# between torch and paddle over their nvidia-*-cu12 pins (torch pins one set
+# of CUDA user-space libs, paddle pins another; pip's resolver cannot satisfy
+# both simultaneously). Both wheels bundle their own CUDA libs internally, so
+# the duplicate site-packages nvidia-* entries are not needed at runtime —
+# installing paddle with --no-deps avoids the conflict.
+#
+# Variant split:
+#   gpu: torch 2.5.1 CUDA build (PyPI default) + paddlepaddle-gpu 3.3.0 (cu126)
+#   cpu: torch 2.5.1 CPU build (pytorch cpu index) + paddlepaddle 3.3.0 (cpu).
+#        torch CPU is installed FIRST so `-r requirements.txt` finds it already
+#        satisfied and does not pull the multi-GB CUDA wheel.
+#
+# BuildKit cache mount keeps ~/.cache/pip warm across builds so rebuilds
+# after a requirements.txt edit don't re-download multi-GB PyTorch/Paddle
+# wheels. Enable with:  DOCKER_BUILDKIT=1 docker build ...  (default in
+# modern Docker).
+RUN --mount=type=cache,target=/root/.cache/pip \
+    pip install --upgrade pip && \
+    if [ "$VARIANT" = "cpu" ]; then \
+        pip install --index-url https://download.pytorch.org/whl/cpu torch==2.5.1 && \
+        pip install -r requirements.txt && \
+        pip install --no-deps \
+            --index-url https://www.paddlepaddle.org.cn/packages/stable/cpu/ \
+            paddlepaddle==3.3.0 ; \
+    else \
+        pip install -r requirements.txt && \
+        pip install --no-deps \
+            --extra-index-url https://www.paddlepaddle.org.cn/packages/stable/cu126/ \
+            paddlepaddle-gpu==3.3.0 ; \
+    fi
+
+# NOTE: no font pre-download step. paddlex 3.0 downloaded fonts into its own
+# root-owned install dir (failing at runtime for non-root appuser), which is
+# why older builds baked them in. paddlex 3.4 changed this: fonts are lazy and
+# cached under PADDLE_PDX_CACHE_HOME (=/paddle_models, a writable named volume),
+# so they download on first use to a writable location — same pattern as models.
+
+# Slim the venv before copying it into the runtime stage.
+#
+# 1. Deduplicate OpenCV: requirements pin opencv-python-headless but
+#    paddleocr → paddlex ALSO pulls opencv-contrib-python, so the image
+#    ends up with two full OpenCV builds sharing one cv2/ directory.
+#    paddlex verifies its [ocr] extra by installed-package METADATA, so
+#    contrib must be the one that stays (removing it breaks PPStructureV3
+#    creation with a "requires additional dependencies" error even though
+#    the cv2 module itself would import fine). Force-reinstall contrib
+#    LAST so the shared cv2/ directory is complete. Saves ~170 MB.
+RUN pip uninstall -y opencv-python-headless opencv-python 2>/dev/null; \
+    pip install --no-deps --force-reinstall --no-cache-dir opencv-contrib-python==4.10.0.84
+#
+# 1b. paddleocr hard-depends on the LangChain/OpenAI client stack for its
+#     PP-ChatOCR document-chat pipelines, which this app never uses (we
+#     only run PPStructureV3 + PaddleOCR detection). The paddlex [ocr]
+#     dependency check passes without them — verified by running layout
+#     detection after removal. Saves ~150 MB and a large dep surface.
+RUN pip uninstall -y \
+        langchain langchain-community langchain-core langchain-openai \
+        langchain-text-splitters langsmith openai tiktoken \
+        dataclasses-json marshmallow typing-inspect jsonpatch \
+        requests-toolbelt 2>/dev/null || true
+#
+# 2. Inference-only cuts (GPU variant; the CPU wheels never install these):
+#    - triton: JIT compiler backend for torch.compile — this app only runs
+#      eager inference, torch imports it lazily and works without it.
+#      (NCCL must stay: libtorch_cuda.so declares DT_NEEDED libnccl.so.2,
+#      so `import torch` fails without it even for single-GPU inference.)
+#    - C/C++ headers (torch/include, nvidia/**/include, triton headers are
+#      gone with triton): only needed to compile extensions, never shipped.
+#    - *.a static libs: link-time only.
+# 3. Strip all __pycache__ dirs and *.pyc/*.pyo files. Python regenerates
+#    them lazily on first import — the builder-time bytecode for torch +
+#    paddle + transformers alone is several hundred MB of dead weight.
+#    We intentionally keep pip and setuptools installed; paddlex
+#    introspects pkg_resources at runtime.
+RUN SP=/opt/venv/lib/python3.11/site-packages && \
+    rm -rf "$SP/triton" "$SP"/triton-*.dist-info \
+           "$SP/torch/include" "$SP/torch/test" && \
+    find "$SP/nvidia" -maxdepth 2 -type d -name include -exec rm -rf {} + 2>/dev/null; \
+    find /opt/venv -type f \( -name '*.a' -o -name '*.pyc' -o -name '*.pyo' \) -delete && \
+    find /opt/venv -type d -name '__pycache__' -prune -exec rm -rf {} +
+
+
+############################
+# Stage 2 — runtime (CUDA for gpu, plain Ubuntu for cpu)
+############################
+FROM ${RUNTIME_BASE}
+
+ARG VARIANT
+
+ENV DEBIAN_FRONTEND=noninteractive \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PYTHONUNBUFFERED=1 \
+    PYTHONPATH=/app \
+    HOME=/home/appuser \
+    # Surfaced to the entrypoint preflight + the app so it can tailor messages.
+    RENAISSANCE_VARIANT=${VARIANT} \
+    # venv from builder; takes precedence over any system Python
+    PATH="/opt/venv/bin:$PATH" \
+    # PaddleX / PaddleOCR model cache — mapped to a named Docker volume.
+    # PADDLE_PDX_CACHE_HOME is the variable paddlex actually reads
+    # (paddlex/utils/cache.py); the previous PADDLE_PDX_MODEL_CACHE_HOME
+    # was only read by our own code, so models silently re-downloaded to
+    # the ephemeral ~/.paddlex on EVERY container recreation instead of
+    # persisting in the volume.
+    PADDLE_PDX_CACHE_HOME=/paddle_models \
+    # HuggingFace cache for the fine-tuned corrector's base model
+    # (google/gemma-3-4b-it, ~8 GB, GATED — needs HF_TOKEN at runtime). Point it
+    # at the SAME persisted named volume as the paddle models so first-use
+    # downloads survive container recreation instead of re-downloading — same
+    # fix the paddle weights got. The LoRA adapter itself is baked into the
+    # image (models/weights/postprocess), only the base weights download.
+    HF_HOME=/paddle_models/hf \
+    # Disable experimental Paddle PIR to avoid ConvertPirAttribute errors
+    FLAGS_enable_pir_api=0 \
+    FLAGS_enable_pir_in_executor=0 \
+    # Plain-Ubuntu base: tell the NVIDIA Container Toolkit what to inject
+    # when the container is started with --gpus / a device reservation.
+    # (The nvidia/cuda images set these for you; ubuntu:24.04 does not.
+    # Harmless for the cpu variant — without a device request the toolkit
+    # never engages.)
+    NVIDIA_VISIBLE_DEVICES=all \
+    NVIDIA_DRIVER_CAPABILITIES=compute,utility
+
+# Central user-tracking endpoint, baked into the published image so every
+# self-hosted instance reports signups to the one shared Supabase table.
+# These are PUBLIC-by-design values: the publishable (anon) key can only add a
+# signup row (INSERT-only Row Level Security) — it cannot read/edit/delete any
+# data, and the Postgres password / service_role key are never included here.
+# Override at runtime with -e SUPABASE_URL / -e SUPABASE_PUBLISHABLE_KEY.
+ENV SUPABASE_URL=https://hljxjbcarzbsbvwffmzu.supabase.co \
+    SUPABASE_PUBLISHABLE_KEY=sb_publishable_wKkx48mKpDZBk9h3gwDfOw_c6MxHIiz
+
+# Install ONLY the Python 3.11 runtime (no -dev, no pip) plus the shared
+# libraries that native extensions need at runtime.
+# IMPORTANT: same deadsnakes PPA + same Python 3.11 as the builder so every
+# .so in /opt/venv resolves its symbols correctly. One combined RUN keeps the
+# apt cache clean-up in the same layer as the install.
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        software-properties-common \
+        ca-certificates \
+    && add-apt-repository -y ppa:deadsnakes/ppa \
+    && apt-get install -y --no-install-recommends \
+        python3.11 \
+        python3.11-venv \
+        libgl1 \
+        libglib2.0-0 \
+        libgomp1 \
+    && apt-get purge -y --auto-remove software-properties-common \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy the fully-built venv from the builder.
+# Both stages are Ubuntu 24.04, so glibc / OpenSSL / libstdc++ are identical
+# — no ABI mismatch possible.
+COPY --from=builder /opt/venv /opt/venv
+
+# Create non-root user and the model-cache directory up front so that the
+# subsequent COPY --chown writes into correct ownership in a single layer
+# (avoids a recursive chown over /app afterwards).
+#
+# At runtime /paddle_models is shadowed by a named Docker volume so
+# downloaded models persist across container restarts (lazy-loaded on
+# first use). We used to pre-download PaddleOCR weights here, but the
+# named volume mounted at /paddle_models shadows this path at runtime,
+# so the baked-in models were discarded anyway — bloating the image by
+# ~1.5 GB for nothing. Lazy first-run download is the correct pattern.
+RUN groupadd --system appgroup && \
+    useradd --system --gid appgroup --home /home/appuser appuser && \
+    mkdir -p /home/appuser /paddle_models /app/storage/transcripts /app/storage/datasets && \
+    chown -R appuser:appgroup /home/appuser /paddle_models /app/storage
+
+############################
+# App setup
+############################
+WORKDIR /app
+COPY --chown=appuser:appgroup . .
+
+# Normalize the TrOCR checkpoint's processor assets. transformers 4.53 reads
+# the shipped processor_config.json/tokenizer.json natively, so this is now a
+# belt-and-suspenders step: it re-derives the processor from the base model
+# (microsoft/trocr-base-printed, shared vocabulary) so the directory is
+# guaranteed loadable by the pinned versions regardless of how the checkpoint
+# was serialized. No-op if the trocr dir is absent. Ownership is reset so
+# appuser can read the regenerated files at runtime.
+#
+# HF_HOME is overridden for this ONE step: it normally points into
+# /paddle_models (the named volume), and this RUN executes as root — a root
+# -owned /paddle_models/hf baked into the image seeds every fresh volume
+# root-owned, so appuser could never download the corrector's base model at
+# runtime (PermissionError in huggingface_hub). Keep build-time HF writes out
+# of the volume path.
+RUN HF_HOME=/tmp/hf-build python /app/scripts/normalize_trocr_config.py && \
+    rm -rf /tmp/hf-build && \
+    chown -R appuser:appgroup /app/models/weights/trocr /home/appuser /paddle_models && \
+    chmod +x /app/scripts/entrypoint.sh
+
+USER appuser
+
+EXPOSE 8000
+VOLUME ["/app/storage"]
+
+HEALTHCHECK --interval=30s --timeout=10s --start-period=20s --retries=3 \
+    CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:8000/api/health')" || exit 1
+
+# Entrypoint runs a non-blocking preflight (logs a clear message if a GPU
+# image is started without GPU access, or if RAM is low) then exec's the CMD.
+# It NEVER aborts boot: Gemini OCR, preprocessing and export work without a
+# GPU; only PaddleOCR layout detection requires one. The hard "specs do not
+# meet requirements" gate lives in the host launcher (run.sh / run.ps1),
+# which checks before pulling the multi-GB image.
+ENTRYPOINT ["/app/scripts/entrypoint.sh"]
+
+# Single worker on purpose: PaddleOCR server-class models (~1.5 GB each,
+# loaded fresh per detection call and freed after) would otherwise be
+# duplicated per-worker — doubling RAM / VRAM footprint and OOM-killing
+# the container on larger pages. Detection is already serialized at the
+# app level (asyncio lock in layout_detection router), so extra workers
+# add no throughput for GPU-bound work.
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000", "--workers", "1", "--no-access-log"]
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/__init__.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/api/__init__.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/api/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/api/auth.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/api/auth.py
new file mode 100644
index 00000000..b5cb5d06
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/api/auth.py
@@ -0,0 +1,201 @@
+"""Signup / login / logout / me routes, plus a token-gated admin user list.
+
+Sessions are a signed httpOnly cookie — no JWT, no refresh tokens.
+"""
+
+from __future__ import annotations
+
+from datetime import datetime, timezone
+
+from fastapi import APIRouter, BackgroundTasks, Cookie, Depends, Header, HTTPException, Response
+from sqlalchemy import func, select
+from sqlalchemy.orm import Session
+
+from ..auth.db import get_db
+from ..auth.models import User
+from ..auth.tracking import track_user_now, update_tracked_profile
+from ..auth.schemas import (
+    AuthResponse,
+    LoginRequest,
+    ProfileUpdateRequest,
+    SignupRequest,
+    UserOut,
+)
+from ..auth.security import (
+    SESSION_COOKIE,
+    create_session_token,
+    hash_password,
+    read_session_token,
+    verify_password,
+)
+from ..core.config import ADMIN_TOKEN, PUBLIC_BASE_URL, SESSION_MAX_AGE
+
+router = APIRouter()
+
+# Secure flag only on https, otherwise the cookie wouldn't stick on localhost.
+_COOKIE_SECURE = PUBLIC_BASE_URL.lower().startswith("https")
+
+
+def _set_session_cookie(response: Response, user_id: int) -> None:
+    response.set_cookie(
+        key=SESSION_COOKIE,
+        value=create_session_token(user_id),
+        max_age=SESSION_MAX_AGE,
+        httponly=True,
+        secure=_COOKIE_SECURE,
+        samesite="lax",
+        path="/",
+    )
+
+
+def current_user(
+    ren_session: str | None = Cookie(default=None),
+    db: Session = Depends(get_db),
+) -> User:
+    """Dependency: resolve the logged-in user or raise 401."""
+    user_id = read_session_token(ren_session or "")
+    if user_id is None:
+        raise HTTPException(status_code=401, detail="Not authenticated")
+    user = db.get(User, user_id)
+    if user is None:
+        raise HTTPException(status_code=401, detail="Not authenticated")
+    return user
+
+
+# ── Signup ────────────────────────────────────────────────────────────────
+
+@router.post("/api/auth/signup", response_model=AuthResponse)
+def signup(
+    payload: SignupRequest,
+    response: Response,
+    background_tasks: BackgroundTasks,
+    db: Session = Depends(get_db),
+):
+    username = payload.username.strip()
+    email = str(payload.email).strip().lower()
+
+    existing = db.scalar(
+        select(User).where((User.username == username) | (User.email == email))
+    )
+    if existing is not None:
+        field = "username" if existing.username == username else "email"
+        raise HTTPException(status_code=409, detail=f"That {field} is already registered")
+
+    user = User(
+        username=username,
+        email=email,
+        name=payload.name.strip(),
+        institute=(payload.institute or "").strip() or "personal",
+        password_hash=hash_password(payload.password),
+        # No email verification — this is usage tracking, not a security gate.
+        is_verified=True,
+    )
+
+    db.add(user)
+    db.commit()
+    db.refresh(user)
+
+    # Push to central tracking after the response goes out. If it fails,
+    # tracked_at stays NULL and the retry loop picks it up later.
+    background_tasks.add_task(track_user_now, user.id)
+
+    _set_session_cookie(response, user.id)
+    return AuthResponse(user=UserOut(**user.public_dict()))
+
+
+# ── Login ───────────────────────────────────────────────────────────────────
+
+@router.post("/api/auth/login", response_model=AuthResponse)
+def login(payload: LoginRequest, response: Response, db: Session = Depends(get_db)):
+    ident = payload.username.strip()
+    # Allow logging in with either username or email.
+    user = db.scalar(
+        select(User).where((User.username == ident) | (User.email == ident.lower()))
+    )
+    if user is None or not verify_password(payload.password, user.password_hash):
+        raise HTTPException(status_code=401, detail="Invalid username or password")
+
+    user.last_login = datetime.now(timezone.utc)
+    db.commit()
+    db.refresh(user)
+
+    _set_session_cookie(response, user.id)
+    return AuthResponse(user=UserOut(**user.public_dict()))
+
+
+# ── Logout ──────────────────────────────────────────────────────────────────
+
+@router.post("/api/auth/logout")
+def logout(response: Response):
+    response.delete_cookie(SESSION_COOKIE, path="/")
+    return {"ok": True}
+
+
+# ── Current user ─────────────────────────────────────────────────────────────
+
+@router.get("/api/auth/me", response_model=UserOut)
+def me(user: User = Depends(current_user)):
+    return UserOut(**user.public_dict())
+
+
+@router.patch("/api/auth/me", response_model=UserOut)
+def update_profile(
+    payload: ProfileUpdateRequest,
+    background_tasks: BackgroundTasks,
+    user: User = Depends(current_user),
+    db: Session = Depends(get_db),
+):
+    """Update name / email / institute (username is fixed).
+
+    Saved locally, then best-effort synced to Supabase keyed on the old email.
+    """
+    old_email = user.email
+    new_email = str(payload.email).strip().lower()
+
+    if new_email != user.email:
+        clash = db.scalar(
+            select(User).where(User.email == new_email, User.id != user.id)
+        )
+        if clash is not None:
+            raise HTTPException(status_code=409, detail="That email is already registered")
+
+    user.name = payload.name.strip()
+    user.institute = (payload.institute or "").strip() or "personal"
+    user.email = new_email
+    db.commit()
+    db.refresh(user)
+
+    background_tasks.add_task(
+        update_tracked_profile,
+        old_email,
+        user.username,
+        user.name,
+        user.email,
+        user.institute,
+    )
+    return UserOut(**user.public_dict())
+
+
+# ── Admin: user tracking view ────────────────────────────────────────────────
+
+@router.get("/api/admin/users")
+def admin_users(
+    x_admin_token: str | None = Header(default=None, alias="X-Admin-Token"),
+    db: Session = Depends(get_db),
+):
+    # Disabled unless an ADMIN_TOKEN is configured — never left open.
+    if not ADMIN_TOKEN:
+        raise HTTPException(status_code=404, detail="Not found")
+    if not x_admin_token or x_admin_token != ADMIN_TOKEN:
+        raise HTTPException(status_code=401, detail="Invalid admin token")
+
+    total = db.scalar(select(func.count()).select_from(User)) or 0
+    verified = db.scalar(
+        select(func.count()).select_from(User).where(User.is_verified.is_(True))
+    ) or 0
+    users = db.scalars(select(User).order_by(User.created_at.desc())).all()
+    return {
+        "total_users": total,
+        "verified_users": verified,
+        "users": [u.public_dict() for u in users],
+    }
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/api/dataset.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/api/dataset.py
new file mode 100644
index 00000000..60862f01
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/api/dataset.py
@@ -0,0 +1,254 @@
+"""Parse transcripts and export OCR training datasets as ZIPs."""
+
+import logging
+import re
+import traceback
+import urllib.parse
+from typing import List, Optional
+
+from fastapi import APIRouter, File, Form, HTTPException, UploadFile
+from fastapi.responses import StreamingResponse
+from pydantic import BaseModel
+
+from ..services.transcript_parser import parse_transcript_bytes, parse_transcript
+from ..services.dataset_builder import (
+    align_boxes_with_transcript,
+    build_dataset_zip,
+    build_detection_dataset_zip,
+)
+from ..storage.storage_manager import save_detection_dataset, save_recognition_dataset
+
+
+router = APIRouter(prefix="/api/dataset", tags=["dataset"])
+logger = logging.getLogger(__name__)
+
+
+# ── Schemas ─────────────────────────────────────────────────────────
+
+class TranscriptParseResponse(BaseModel):
+    success: bool
+    pages: dict  # {page_key: [lines]}
+    page_count: int
+    total_lines: int
+    error: Optional[str] = None
+
+
+class AlignmentRequest(BaseModel):
+    boxes: list  # list of 4-point polygons
+    lines: list  # list of transcript strings
+
+
+class AlignmentResponse(BaseModel):
+    success: bool
+    pairs: list  # [(box, text), ...]
+    num_boxes: int
+    num_lines: int
+    num_pairs: int
+    warning: Optional[str] = None
+
+
+class PageDataItem(BaseModel):
+    page_key: str
+    image_data: str  # base64 data URL
+    boxes: list  # list of 4-pt polygons
+    lines: list  # list of transcript strings
+
+
+class DatasetExportRequest(BaseModel):
+    pages: List[PageDataItem]
+    book_name: str = "dataset"
+
+
+# ── Endpoints ───────────────────────────────────────────────────────
+
+@router.post("/parse-transcript", response_model=TranscriptParseResponse)
+async def parse_transcript_endpoint(
+    file: UploadFile = File(...),
+):
+    """Parse an uploaded TXT/DOCX/PDF/Markdown transcript into page -> lines."""
+    try:
+        data = await file.read()
+        filename = file.filename or ""
+        content_type = file.content_type or ""
+
+        pages = parse_transcript_bytes(data, filename, content_type)
+
+        total_lines = sum(len(v) for v in pages.values())
+
+        return TranscriptParseResponse(
+            success=True,
+            pages=pages,
+            page_count=len(pages),
+            total_lines=total_lines,
+        )
+    except Exception as e:
+        traceback.print_exc()
+        return TranscriptParseResponse(
+            success=False,
+            pages={},
+            page_count=0,
+            total_lines=0,
+            error=str(e),
+        )
+
+
+@router.post("/parse-transcript-text", response_model=TranscriptParseResponse)
+async def parse_transcript_text_endpoint(
+    text: str = Form(...),
+):
+    """Same as above, for text pasted straight into the UI."""
+    try:
+        pages = parse_transcript(text)
+        total_lines = sum(len(v) for v in pages.values())
+
+        return TranscriptParseResponse(
+            success=True,
+            pages=pages,
+            page_count=len(pages),
+            total_lines=total_lines,
+        )
+    except Exception as e:
+        traceback.print_exc()
+        return TranscriptParseResponse(
+            success=False,
+            pages={},
+            page_count=0,
+            total_lines=0,
+            error=str(e),
+        )
+
+
+@router.post("/align", response_model=AlignmentResponse)
+async def align_endpoint(request: AlignmentRequest):
+    """Pair one page's boxes with its transcript lines, flagging any mismatch."""
+    try:
+        pairs, num_boxes, num_lines = align_boxes_with_transcript(
+            request.boxes, request.lines
+        )
+
+        warning = None
+        if num_boxes != num_lines:
+            warning = (
+                f"Mismatch: {num_boxes} bounding boxes vs {num_lines} transcript lines. "
+                f"Only {len(pairs)} pairs will be used."
+            )
+
+        return AlignmentResponse(
+            success=True,
+            pairs=[{"box": box, "text": text} for box, text in pairs],
+            num_boxes=num_boxes,
+            num_lines=num_lines,
+            num_pairs=len(pairs),
+            warning=warning,
+        )
+    except Exception as e:
+        traceback.print_exc()
+        return AlignmentResponse(
+            success=False,
+            pairs=[],
+            num_boxes=0,
+            num_lines=0,
+            num_pairs=0,
+            warning=str(e),
+        )
+
+
+@router.post("/export")
+async def export_dataset(request: DatasetExportRequest):
+    """Build and stream a recognition dataset ZIP (line crops + labels)."""
+    if not request.pages:
+        raise HTTPException(status_code=400, detail="No pages provided.")
+
+    try:
+        pages_data = [p.dict() for p in request.pages]
+
+        zip_buffer = build_dataset_zip(
+            pages_data=pages_data,
+            book_name=request.book_name,
+        )
+
+        try:
+            save_recognition_dataset(
+                pages_data=pages_data,
+                source="dataset export",
+                book_name=request.book_name,
+            )
+        except Exception as save_err:
+            logger.warning("Recognition dataset persistence failed: %s", save_err)
+
+        filename = f"{request.book_name}_dataset.zip"
+        # Content-Disposition must be latin-1; the RFC 5987 form carries the
+        # real name for clients that understand it.
+        ascii_filename = re.sub(r'[^\x20-\x7E]', '_', filename)
+        utf8_filename = urllib.parse.quote(filename)
+        content_disp = (
+            f'attachment; filename="{ascii_filename}"; '
+            f"filename*=UTF-8''{utf8_filename}"
+        )
+
+        return StreamingResponse(
+            zip_buffer,
+            media_type="application/zip",
+            headers={"Content-Disposition": content_disp},
+        )
+    except Exception as e:
+        traceback.print_exc()
+        raise HTTPException(status_code=500, detail=f"Dataset generation failed: {str(e)}")
+
+
+# ── Detection-only dataset export ──────────────────────────────────
+
+class DetectionPageItem(BaseModel):
+    page_key: str
+    image_data: str  # base64 data URL
+    boxes: list  # list of 4-pt polygons
+
+
+class DetectionExportRequest(BaseModel):
+    pages: List[DetectionPageItem]
+    book_name: str = "dataset"
+    # "txt" → one "x1 y1 x2 y2" per line; "json" → array of [x1,y1,x2,y2].
+    bbox_format: str = "txt"
+
+
+@router.post("/export-detection")
+async def export_detection_dataset(request: DetectionExportRequest):
+    """Build and stream a detection dataset ZIP. Boxes only, no transcript."""
+    if not request.pages:
+        raise HTTPException(status_code=400, detail="No pages provided.")
+
+    try:
+        pages_data = [p.dict() for p in request.pages]
+
+        zip_buffer = build_detection_dataset_zip(
+            pages_data=pages_data,
+            book_name=request.book_name,
+            bbox_format=request.bbox_format,
+        )
+
+        try:
+            save_detection_dataset(
+                pages_data=pages_data,
+                source="dataset export",
+                book_name=request.book_name,
+                bbox_format=request.bbox_format,
+            )
+        except Exception as save_err:
+            logger.warning("Detection dataset persistence failed: %s", save_err)
+
+        filename = f"{request.book_name}_detection_dataset.zip"
+        ascii_filename = re.sub(r'[^\x20-\x7E]', '_', filename)
+        utf8_filename = urllib.parse.quote(filename)
+        content_disp = (
+            f'attachment; filename="{ascii_filename}"; '
+            f"filename*=UTF-8''{utf8_filename}"
+        )
+
+        return StreamingResponse(
+            zip_buffer,
+            media_type="application/zip",
+            headers={"Content-Disposition": content_disp},
+        )
+    except Exception as e:
+        traceback.print_exc()
+        raise HTTPException(status_code=500, detail=f"Detection dataset generation failed: {str(e)}")
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/api/export.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/api/export.py
new file mode 100644
index 00000000..fe72c8e4
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/api/export.py
@@ -0,0 +1,43 @@
+"""Download the combined transcript as txt / docx / pdf."""
+
+from fastapi import APIRouter
+from fastapi.responses import StreamingResponse
+
+from ..schemas.ocr import ExportRequest
+from ..services.export import build_txt_export, build_docx_export, build_pdf_export
+
+
+router = APIRouter()
+
+
+@router.post("/api/export/txt")
+async def export_txt(request: ExportRequest):
+    buffer = build_txt_export(request.transcripts)
+
+    return StreamingResponse(
+        buffer,
+        media_type="text/plain; charset=utf-8",
+        headers={"Content-Disposition": "attachment; filename=transcript_full.txt"}
+    )
+
+
+@router.post("/api/export/docx")
+async def export_docx(request: ExportRequest):
+    buffer = build_docx_export(request.transcripts)
+
+    return StreamingResponse(
+        buffer,
+        media_type="application/vnd.openxmlformats-officedocument.wordprocessingml.document",
+        headers={"Content-Disposition": "attachment; filename=transcript_full.docx"}
+    )
+
+
+@router.post("/api/export/pdf")
+async def export_pdf(request: ExportRequest):
+    buffer = build_pdf_export(request.transcripts)
+
+    return StreamingResponse(
+        buffer,
+        media_type="application/pdf",
+        headers={"Content-Disposition": "attachment; filename=transcript_full.pdf"}
+    )
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/api/health.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/api/health.py
new file mode 100644
index 00000000..5ac35dd2
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/api/health.py
@@ -0,0 +1,11 @@
+"""Health check."""
+
+from datetime import datetime
+from fastapi import APIRouter
+
+router = APIRouter()
+
+
+@router.get("/api/health")
+async def health_check():
+    return {"status": "healthy", "timestamp": datetime.now().isoformat()}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/api/layout_detection.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/api/layout_detection.py
new file mode 100644
index 00000000..bb9a3802
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/api/layout_detection.py
@@ -0,0 +1,188 @@
+"""POST /api/detect/layout-aware-lines — image in, text-line polygons out.
+
+One page at a time per worker: the models are ~1.5 GB each and loaded fresh per
+call, so two concurrent pages double peak memory and get the worker OOM-killed.
+Budget 8 GB VRAM (GPU) or 8 GB RAM (CPU, much slower).
+"""
+
+import asyncio
+import time
+import traceback
+
+import cv2
+import numpy as np
+from fastapi import APIRouter, File, Form, UploadFile
+
+from ..services.layout_detection import (
+    models_cached,
+    run_layout_aware_detection,
+    select_tier,
+)
+
+router = APIRouter()
+
+
+@router.get("/api/detect/models-status")
+async def detection_models_status(use_gpu: bool = True):
+    """Are the detection models on disk yet?
+
+    The UI checks this first so it can warn about the one-time ~15-20 min
+    download. Filesystem check only — never imports paddle.
+    """
+    return {"models_ready": models_cached(use_gpu)}
+
+# FastAPI runs async handlers concurrently; without this two parallel pages
+# both load paddle models and fight over VRAM.
+_detection_lock = asyncio.Lock()
+
+
+@router.post("/api/detect/layout-aware-lines")
+async def detect_layout_aware_lines(
+    image: UploadFile = File(...),
+    use_gpu: bool = Form(False),
+    region_padding: int = Form(50),
+    layout_expand: int = Form(2),
+    score_thresh: float = Form(0.5),
+    upscale_min_h: int = Form(60),
+    nms_iou_thresh: float = Form(0.3),
+    gap_multiplier: float = Form(2.0),
+    debug_dir: str = Form(""),
+):
+    """Detect text lines in an uploaded page.
+
+    -> {lines: [4-point polygons], count, processing_time_ms, tier}.
+    """
+    start = time.time()
+
+    try:
+        contents = await image.read()
+        nparr = np.frombuffer(contents, np.uint8)
+        img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+        del contents
+
+        if img is None:
+            return {
+                "error": "Could not decode the uploaded image.",
+                "lines": [],
+                "count": 0,
+                "processing_time_ms": 0,
+            }
+
+        # The sync work goes to a thread so /api/health stays responsive
+        # while a page is in flight.
+        async with _detection_lock:
+            gpu_fallback = False
+            resource_warnings = []
+
+            # Probe inside the lock so the readings match what we're about to
+            # allocate. Keeps 4-6 GB laptop GPUs off the server-class models.
+            tier = await asyncio.to_thread(select_tier, use_gpu)
+            print(f"[LayoutAPI] tier selected: {tier['tier']} on {tier['device']}"
+                  f" — {tier['reason']}")
+            effective_use_gpu = tier["device"] == "gpu"
+
+            try:
+                lines, resource_warnings = await asyncio.to_thread(
+                    run_layout_aware_detection,
+                    img,
+                    use_gpu=effective_use_gpu,
+                    layout_model_name=tier["layout_model"],
+                    det_model_name=tier["det_model"],
+                    rec_model_name=tier["rec_model"],
+                    region_padding=region_padding,
+                    layout_expand=layout_expand,
+                    score_thresh=score_thresh,
+                    upscale_min_h=upscale_min_h,
+                    nms_iou_thresh=nms_iou_thresh,
+                    gap_multiplier=gap_multiplier,
+                    debug_dir=debug_dir,
+                )
+            except (ValueError, RuntimeError) as gpu_err:
+                err_msg = str(gpu_err)
+                if "Out-of-memory" in err_msg or "out of memory" in err_msg.lower():
+                    elapsed = int((time.time() - start) * 1000)
+                    return {
+                        "error": err_msg,
+                        "lines": [],
+                        "count": 0,
+                        "processing_time_ms": elapsed,
+                        "resource_warnings": resource_warnings,
+                        "tier": tier,
+                        "resource_requirements": (
+                            "Minimum 8 GB GPU VRAM required for GPU mode. "
+                            "Minimum 8 GB free RAM required for CPU mode."
+                        ),
+                    }
+                if effective_use_gpu:
+                    # Crashed for some non-OOM reason — retry on CPU.
+                    print(f"[LayoutAPI] GPU failed, falling back to CPU: {gpu_err}")
+                    gpu_fallback = True
+                    cpu_tier = await asyncio.to_thread(select_tier, False)
+                    tier = cpu_tier
+                    lines, resource_warnings = await asyncio.to_thread(
+                        run_layout_aware_detection,
+                        img,
+                        use_gpu=False,
+                        layout_model_name=cpu_tier["layout_model"],
+                        det_model_name=cpu_tier["det_model"],
+                        rec_model_name=cpu_tier["rec_model"],
+                        region_padding=region_padding,
+                        layout_expand=layout_expand,
+                        score_thresh=score_thresh,
+                        upscale_min_h=upscale_min_h,
+                        nms_iou_thresh=nms_iou_thresh,
+                        gap_multiplier=gap_multiplier,
+                        debug_dir=debug_dir,
+                    )
+                else:
+                    raise
+
+        del img
+        elapsed = int((time.time() - start) * 1000)
+
+        resp = {
+            "lines": lines,
+            "count": len(lines),
+            "processing_time_ms": elapsed,
+            "tier": tier,
+        }
+        if resource_warnings:
+            resp["resource_warnings"] = resource_warnings
+        if gpu_fallback:
+            resp["warning"] = "GPU failed mid-run. Fell back to CPU."
+        elif use_gpu and tier["device"] == "cpu":
+            resp["warning"] = (
+                "Not enough free GPU VRAM for detection. "
+                f"Ran on CPU instead. {tier['reason']}"
+            )
+        elif use_gpu and tier["tier"] == "mobile":
+            resp["warning"] = (
+                f"Low GPU VRAM — using lighter mobile models. {tier['reason']}"
+            )
+        if use_gpu and not gpu_fallback:
+            resp["resource_requirements"] = (
+                "Minimum 8 GB GPU VRAM recommended for GPU mode. "
+                "Minimum 8 GB free RAM recommended for CPU mode."
+            )
+        return resp
+
+    except Exception as exc:
+        traceback.print_exc()
+        elapsed = int((time.time() - start) * 1000)
+        resp = {
+            "error": f"Line detection failed: {str(exc)}",
+            "lines": [],
+            "count": 0,
+            "processing_time_ms": elapsed,
+        }
+        # Warnings gathered before the failure often explain it.
+        try:
+            if resource_warnings:
+                resp["resource_warnings"] = resource_warnings
+                resp["resource_requirements"] = (
+                    "Minimum 8 GB GPU VRAM required for GPU mode. "
+                    "Minimum 8 GB free RAM required for CPU mode."
+                )
+        except NameError:
+            pass
+        return resp
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/api/llm_postprocess.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/api/llm_postprocess.py
new file mode 100644
index 00000000..3acc4cc7
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/api/llm_postprocess.py
@@ -0,0 +1,110 @@
+"""Optional LLM cleanup pass over OCR text. Providers live in the factory."""
+
+import traceback
+from typing import Optional
+
+from fastapi import APIRouter, Header, HTTPException
+from pydantic import BaseModel
+
+from ..services.llm_processing.factory import (
+    post_process,
+    provider_requires_key,
+    LLM_PROVIDERS,
+)
+from ..services.llm_processing.local_client import base_model_cached
+from ..services.llm_processing.prompt_templates import list_templates
+
+
+router = APIRouter(prefix="/api/llm", tags=["llm"])
+
+
+# ── Schemas ─────────────────────────────────────────────────────────
+
+class PostProcessRequest(BaseModel):
+    text: str
+    provider: str = "gemini"
+    model: str = "gemini-2.5-flash"
+    template: str = "full_cleanup"
+
+
+class PostProcessResponse(BaseModel):
+    success: bool
+    processed_text: Optional[str] = None
+    error: Optional[str] = None
+    model_used: Optional[str] = None
+    provider_used: Optional[str] = None
+
+
+# ── Endpoints ───────────────────────────────────────────────────────
+
+@router.get("/providers")
+async def get_providers():
+    """List post-processing providers + their text models for the UI."""
+    return {"providers": LLM_PROVIDERS}
+
+
+@router.get("/templates")
+async def get_templates():
+    """List available post-processing prompt templates."""
+    return {"templates": list_templates()}
+
+
+@router.get("/local-status")
+async def local_model_status():
+    """Are the local corrector's base weights on disk yet?
+
+    Same contract as /api/detect/models-status: the UI checks this first so it
+    can warn about the one-time ~8 GB gated download instead of looking hung.
+    """
+    return {"models_ready": base_model_cached()}
+
+
+@router.post("/post-process", response_model=PostProcessResponse)
+async def post_process_endpoint(
+    request: PostProcessRequest,
+    # X-Gemini-API-Key stays accepted so the older Gemini flow keeps working.
+    x_llm_api_key: Optional[str] = Header(None, alias="X-LLM-API-Key"),
+    x_gemini_api_key: Optional[str] = Header(None, alias="X-Gemini-API-Key"),
+):
+    """Post-process OCR text using the selected LLM provider."""
+    if not request.text or not request.text.strip():
+        return PostProcessResponse(
+            success=True,
+            processed_text=request.text,
+            model_used=request.model,
+            provider_used=request.provider,
+        )
+
+    api_key = x_llm_api_key or x_gemini_api_key
+    if provider_requires_key(request.provider) and (not api_key or not api_key.strip()):
+        raise HTTPException(
+            status_code=401,
+            detail="Missing API key (X-LLM-API-Key header).",
+        )
+
+    try:
+        result = post_process(
+            provider=request.provider,
+            api_key=api_key,
+            text=request.text,
+            model=request.model,
+            template_name=request.template,
+        )
+        return PostProcessResponse(
+            success=True,
+            processed_text=result,
+            model_used=request.model,
+            provider_used=request.provider,
+        )
+    except ValueError as e:
+        # Unknown/disabled provider — the caller's fault, not ours.
+        return PostProcessResponse(success=False, error=str(e))
+    except Exception as e:
+        traceback.print_exc()
+        error_msg = str(e)
+        error_lower = error_msg.lower()
+        if "api key" in error_lower or "authenticate" in error_lower or "401" in error_lower:
+            return PostProcessResponse(success=False, error="Invalid API key")
+        if "quota" in error_lower or "rate" in error_lower or "429" in error_lower:
+            return PostProcessResponse(success=False, error="Rate limited — please try again later")
+        return PostProcessResponse(success=False, error=error_msg)
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/api/ocr.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/api/ocr.py
new file mode 100644
index 00000000..5c91fec4
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/api/ocr.py
@@ -0,0 +1,244 @@
+"""OCR endpoints. Providers come from OCRFactory, shared work from ocr_helpers.
+
+Only Gemini is rate-limited server-side (free-tier sliding window).
+"""
+
+import asyncio
+import time
+
+from fastapi import APIRouter, File, UploadFile, HTTPException, Header, Form
+from typing import Optional
+
+from ..core.config import MIN_API_KEY_LENGTH, MAX_BATCH_SIZE
+from ..core.rate_limiter import rate_limiter
+from ..schemas.ocr import (
+    OCRResponse,
+    GeminiOCRRequest,
+    BatchOCRRequest,
+    BatchOCRResponse,
+    BatchOCRResultItem,
+    ChatGPTOCRRequest,
+    DeepSeekOCRRequest,
+    QwenOCRRequest,
+)
+from ..services.ocr.factory import OCRFactory
+from .ocr_helpers import (
+    parse_base64_image,
+    validate_model,
+    validate_api_key_format,
+    check_rate_limit,
+    run_ocr,
+)
+
+
+router = APIRouter()
+
+
+def _provider(name: str):
+    return OCRFactory.get_provider(name)
+
+
+# ── Model listing ───────────────────────────────────────────────
+
+@router.get("/api/models")
+async def get_gemini_models():
+    """Available Gemini models."""
+    p = _provider("gemini")
+    return {"models": p.MODELS, "default": p.DEFAULT_MODEL}
+
+
+@router.get("/api/chatgpt-models")
+async def get_chatgpt_models():
+    """Available ChatGPT models."""
+    p = _provider("chatgpt")
+    return {"models": p.MODELS, "default": p.DEFAULT_MODEL}
+
+
+@router.get("/api/deepseek-models")
+async def get_deepseek_models():
+    """Available DeepSeek models."""
+    p = _provider("deepseek")
+    return {"models": p.MODELS, "default": p.DEFAULT_MODEL}
+
+
+@router.get("/api/qwen-models")
+async def get_qwen_models():
+    """Available Qwen models."""
+    p = _provider("qwen")
+    return {"models": p.MODELS, "default": p.DEFAULT_MODEL}
+
+
+# ── Key check + rate limit (Gemini free-tier) ───────────────────
+
+@router.post("/api/validate-key")
+async def validate_api_key(
+    x_gemini_api_key: str = Header(..., alias="X-Gemini-API-Key"),
+):
+    """Format check only — the key is really verified on the first OCR call."""
+    validate_api_key_format(x_gemini_api_key, MIN_API_KEY_LENGTH)
+    return {
+        "valid": True,
+        "message": "API key format is valid. It will be verified on first use.",
+    }
+
+
+@router.get("/api/rate-limit-status")
+async def get_rate_limit_status():
+    """Current rate-limit status (Gemini free-tier sliding window)."""
+    return rate_limiter.get_status()
+
+
+# ── Gemini ──────────────────────────────────────────────────────
+
+@router.post("/api/gemini-ocr-page", response_model=OCRResponse)
+async def gemini_ocr_page(
+    image: UploadFile = File(...),
+    model: str = Form(default="gemini-3.1-flash-lite"),
+    custom_prompt: Optional[str] = Form(default=None),
+    x_gemini_api_key: str = Header(..., alias="X-Gemini-API-Key"),
+):
+    """Process a single uploaded image file with Gemini OCR."""
+    check_rate_limit()
+    provider = _provider("gemini")
+    validate_model(model, provider.MODEL_IDS)
+    validate_api_key_format(x_gemini_api_key)
+
+    image_bytes = await image.read()
+    content_type = image.content_type or "image/png"
+    if content_type not in ("image/png", "image/jpeg", "image/jpg", "image/webp"):
+        content_type = "image/png"
+
+    result = run_ocr(provider, x_gemini_api_key, image_bytes, model, content_type, custom_prompt)
+    if result.success:
+        rate_limiter.record_request()
+    return result
+
+
+@router.post("/api/gemini-ocr-base64", response_model=OCRResponse)
+async def gemini_ocr_base64(
+    image_data: str = Form(...),
+    model: str = Form(default="gemini-3.1-flash-lite"),
+    custom_prompt: Optional[str] = Form(default=None),
+    x_gemini_api_key: str = Header(..., alias="X-Gemini-API-Key"),
+):
+    """Process a base64-encoded image with Gemini OCR (form data)."""
+    check_rate_limit()
+    provider = _provider("gemini")
+    validate_model(model, provider.MODEL_IDS)
+
+    image_bytes, mime_type = parse_base64_image(image_data)
+    result = run_ocr(provider, x_gemini_api_key, image_bytes, model, mime_type, custom_prompt)
+    if result.success:
+        rate_limiter.record_request()
+    return result
+
+
+@router.post("/api/gemini-ocr-json", response_model=OCRResponse)
+async def gemini_ocr_json(
+    request: GeminiOCRRequest,
+    x_gemini_api_key: str = Header(..., alias="X-Gemini-API-Key"),
+):
+    """Process a base64-encoded image with Gemini OCR (JSON body)."""
+    check_rate_limit()
+    provider = _provider("gemini")
+    validate_model(request.model, provider.MODEL_IDS)
+
+    image_bytes, mime_type = parse_base64_image(request.image_data)
+    result = run_ocr(provider, x_gemini_api_key, image_bytes, request.model, mime_type, request.custom_prompt)
+    if result.success:
+        rate_limiter.record_request()
+    return result
+
+
+@router.post("/api/gemini-ocr-batch", response_model=BatchOCRResponse)
+async def gemini_ocr_batch(
+    request: BatchOCRRequest,
+    x_gemini_api_key: str = Header(..., alias="X-Gemini-API-Key"),
+):
+    """Process multiple images concurrently with Gemini OCR (max batch size 4)."""
+    if not request.items:
+        raise HTTPException(status_code=400, detail="Empty batch request")
+    if len(request.items) > MAX_BATCH_SIZE:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Batch size exceeds maximum of {MAX_BATCH_SIZE}. Got {len(request.items)} items.",
+        )
+
+    check_rate_limit(required_slots=len(request.items))
+    provider = _provider("gemini")
+    validate_model(request.model, provider.MODEL_IDS)
+
+    batch_start = time.time()
+
+    async def _process_item(item) -> BatchOCRResultItem:
+        item_start = time.time()
+        try:
+            image_bytes, mime_type = parse_base64_image(item.image_data)
+            transcript = await asyncio.get_event_loop().run_in_executor(
+                None,
+                lambda ib=image_bytes, mt=mime_type: provider.transcribe(
+                    x_gemini_api_key, ib, request.model, mt, request.custom_prompt
+                ),
+            )
+            return BatchOCRResultItem(
+                page_index=item.page_index,
+                success=True,
+                transcript=transcript,
+                processing_time_ms=int((time.time() - item_start) * 1000),
+            )
+        except Exception as exc:
+            return BatchOCRResultItem(
+                page_index=item.page_index,
+                success=False,
+                error=str(exc),
+                processing_time_ms=int((time.time() - item_start) * 1000),
+            )
+
+    results = await asyncio.gather(*[_process_item(i) for i in request.items])
+    rate_limiter.record_requests(len(request.items))
+
+    successful = sum(1 for r in results if r.success)
+    return BatchOCRResponse(
+        results=list(results),
+        total_processing_time_ms=int((time.time() - batch_start) * 1000),
+        successful_count=successful,
+        failed_count=len(results) - successful,
+    )
+
+
+# ── Other providers (no server-side rate limiting) ──────────────
+
+@router.post("/api/chatgpt-ocr-json", response_model=OCRResponse)
+async def chatgpt_ocr_json(
+    request: ChatGPTOCRRequest,
+    x_api_key: str = Header(..., alias="X-API-Key"),
+):
+    """Process a base64-encoded image with ChatGPT OCR."""
+    provider = _provider("chatgpt")
+    validate_model(request.model, provider.MODEL_IDS)
+    image_bytes, mime_type = parse_base64_image(request.image_data)
+    return run_ocr(provider, x_api_key, image_bytes, request.model, mime_type, request.custom_prompt)
+
+
+@router.post("/api/deepseek-ocr-json", response_model=OCRResponse)
+async def deepseek_ocr_json(
+    request: DeepSeekOCRRequest,
+    x_api_key: str = Header(..., alias="X-API-Key"),
+):
+    """Process a base64-encoded image with DeepSeek OCR."""
+    provider = _provider("deepseek")
+    validate_model(request.model, provider.MODEL_IDS)
+    image_bytes, mime_type = parse_base64_image(request.image_data)
+    return run_ocr(provider, x_api_key, image_bytes, request.model, mime_type, request.custom_prompt)
+
+
+@router.post("/api/qwen-ocr-json", response_model=OCRResponse)
+async def qwen_ocr_json(
+    request: QwenOCRRequest,
+    x_api_key: str = Header(..., alias="X-API-Key"),
+):
+    """Process a base64-encoded image with Qwen OCR."""
+    provider = _provider("qwen")
+    validate_model(request.model, provider.MODEL_IDS)
+    image_bytes, mime_type = parse_base64_image(request.image_data)
+    return run_ocr(provider, x_api_key, image_bytes, request.model, mime_type, request.custom_prompt)
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/api/ocr_helpers.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/api/ocr_helpers.py
new file mode 100644
index 00000000..6651dcf6
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/api/ocr_helpers.py
@@ -0,0 +1,157 @@
+"""Shared bits every OCR endpoint uses: base64 parsing, validation,
+rate-limit checks, and one wrapper that turns provider calls into OCRResponse."""
+
+import base64
+import time
+from typing import Optional
+
+from fastapi import HTTPException
+
+try:
+    import httpx
+except ImportError:  # only Gemini setups can skip it
+    httpx = None  # type: ignore
+
+from ..core.rate_limiter import rate_limiter
+from ..schemas.ocr import OCRResponse
+from ..services.ocr.base import BaseOCRProvider
+
+
+# ── Base64 parsing ──────────────────────────────────────────────
+
+
+def parse_base64_image(image_data: str) -> tuple[bytes, str]:
+    """Decode raw base64 or a data: URL. Returns (image_bytes, mime_type)."""
+    if "," in image_data:
+        header, encoded = image_data.split(",", 1)
+        mime_type = (
+            header.split(":")[1].split(";")[0]
+            if ":" in header
+            else "image/png"
+        )
+    else:
+        encoded = image_data
+        mime_type = "image/png"
+
+    return base64.b64decode(encoded), mime_type
+
+
+# ── Validation helpers ──────────────────────────────────────────
+
+
+def validate_model(model: str, valid_ids: list[str]) -> None:
+    """400 if the model isn't one we know about."""
+    if model not in valid_ids:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Invalid model. Available models: {valid_ids}",
+        )
+
+
+def validate_api_key_format(api_key: str, min_length: int = 10) -> None:
+    """401 if the key is missing or implausibly short."""
+    if not api_key or len(api_key) < min_length:
+        raise HTTPException(
+            status_code=401,
+            detail="Invalid or missing API key",
+        )
+
+
+# ── Rate-limit helpers ──────────────────────────────────────────
+
+
+def check_rate_limit(required_slots: int = 1) -> None:
+    """429 with a JSON body when the sliding window has no room."""
+    if required_slots <= 1:
+        can_proceed, wait_time = rate_limiter.can_proceed()
+        if not can_proceed:
+            raise HTTPException(
+                status_code=429,
+                detail={
+                    "error": "rate_limited",
+                    "message": f"Rate limit exceeded. Please wait {wait_time} seconds.",
+                    "wait_seconds": wait_time,
+                },
+            )
+    else:
+        available = rate_limiter.get_available_slots()
+        if available < required_slots:
+            status = rate_limiter.get_status()
+            raise HTTPException(
+                status_code=429,
+                detail={
+                    "error": "rate_limited",
+                    "message": (
+                        f"Not enough rate limit slots. "
+                        f"Need {required_slots}, have {available}."
+                    ),
+                    "wait_seconds": status.get("wait_seconds", 60),
+                    "available_slots": available,
+                },
+            )
+
+
+# ── Unified OCR execution ──────────────────────────────────────
+
+
+def run_ocr(
+    provider: BaseOCRProvider,
+    api_key: str,
+    image_bytes: bytes,
+    model: str,
+    mime_type: str,
+    custom_prompt: Optional[str] = None,
+) -> OCRResponse:
+    """Run the provider and normalise whatever it throws into an OCRResponse.
+
+    httpx errors (ChatGPT/DeepSeek/Qwen) and Google SDK errors (Gemini) both
+    land here and get mapped onto the same 401/429 shapes.
+    """
+    start_time = time.time()
+
+    try:
+        transcript = provider.transcribe(api_key, image_bytes, model, mime_type, custom_prompt)
+        processing_time = int((time.time() - start_time) * 1000)
+        return OCRResponse(
+            success=True,
+            transcript=transcript,
+            model_used=model,
+            processing_time_ms=processing_time,
+        )
+
+    except Exception as exc:
+        processing_time = int((time.time() - start_time) * 1000)
+
+        if httpx is not None and isinstance(exc, httpx.HTTPStatusError):
+            if exc.response.status_code == 401:
+                raise HTTPException(status_code=401, detail="Invalid API key")
+            if exc.response.status_code == 429:
+                raise HTTPException(
+                    status_code=429, detail="API rate limit exceeded"
+                )
+
+        error_msg = str(exc)
+        if "API_KEY_INVALID" in error_msg or "401" in error_msg:
+            raise HTTPException(status_code=401, detail="Invalid API key")
+        # A daily quota is not our short sliding-window limit — it won't clear
+        # for hours, so tag it separately and let the UI say so.
+        if "RESOURCE_EXHAUSTED" in error_msg or "QUOTA_EXCEEDED" in error_msg or "quota" in error_msg.lower():
+            raise HTTPException(
+                status_code=429,
+                detail={
+                    "error": "quota_exceeded",
+                    "message": "Daily Gemini quota reached. Try again tomorrow, pick a different model, or use another API key.",
+                },
+            )
+        if "429" in error_msg:
+            raise HTTPException(
+                status_code=429,
+                detail={"error": "rate_limited", "wait_seconds": 20},
+            )
+
+        return OCRResponse(
+            success=False,
+            error=error_msg,
+            model_used=model,
+            processing_time_ms=processing_time,
+        )
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/api/preprocess.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/api/preprocess.py
new file mode 100644
index 00000000..210797e1
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/api/preprocess.py
@@ -0,0 +1,134 @@
+"""Preprocessing endpoints — run the OpenCV pipeline over a base64 image."""
+
+import os
+import sys
+import time
+import base64
+import numpy as np
+import cv2
+
+from fastapi import APIRouter, HTTPException
+
+from ..schemas.ocr import PreprocessRequest
+
+# `preprocessing` is a sibling of `app`, not a submodule of it.
+backend_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+if backend_dir not in sys.path:
+    sys.path.insert(0, backend_dir)
+
+from preprocessing import run_pipeline, OP_REGISTRY, validate_pipeline_config
+
+
+router = APIRouter()
+
+
+@router.get("/api/preprocess/operations")
+async def get_available_operations():
+    """Operation names + one-line descriptions for the UI."""
+    return {
+        "operations": list(OP_REGISTRY.keys()),
+        "descriptions": {
+            "normalize": "Normalize image brightness and contrast levels",
+            "grayscale": "Convert image to grayscale",
+            "deskew": "Automatically correct image rotation/skew",
+            "denoise": "Remove noise while preserving text edges",
+            "contrast": "Enhance contrast using CLAHE",
+            "sharpen": "Sharpen text edges for clearer text",
+            "threshold": "Convert to binary (black and white)",
+            "morph": "Morphological operations (open, close, dilate, erode, gradient, tophat, blackhat)",
+            "remove_blobs": "Remove large ink blobs from scanned documents",
+            "remove_noise": "Remove small speckles and scanning dust",
+        }
+    }
+
+
+@router.post("/api/preprocess")
+async def preprocess_image_endpoint(request: PreprocessRequest):
+    """Run the pipeline over one image and hand back a base64 data URL.
+
+    preview_mode swaps in faster, rougher algorithms for live tweaking.
+    """
+    start_time = time.time()
+
+    try:
+        validation = validate_pipeline_config(request.operations)
+        if not validation["valid"]:
+            raise HTTPException(
+                status_code=400,
+                detail={
+                    "error": "invalid_operations",
+                    "message": "Invalid pipeline configuration",
+                    "errors": validation["errors"]
+                }
+            )
+
+        image_data = request.image_data
+        if "," in image_data:  # data:image/png;base64,...
+            header, encoded = image_data.split(",", 1)
+            mime_type = header.split(":")[1].split(";")[0] if ":" in header else "image/png"
+        else:
+            encoded = image_data
+            mime_type = "image/png"
+
+        image_bytes = base64.b64decode(encoded)
+        nparr = np.frombuffer(image_bytes, np.uint8)
+        image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+
+        if image is None:
+            raise HTTPException(
+                status_code=400,
+                detail={"error": "invalid_image", "message": "Could not decode image"}
+            )
+
+        result = run_pipeline(
+            image=image,
+            steps=request.operations,
+            continue_on_error=True,
+            preview_mode=request.preview_mode,
+        )
+
+        if result.image is not None:
+            # Answer in whatever format we were handed.
+            if "jpeg" in mime_type or "jpg" in mime_type:
+                encode_param = [cv2.IMWRITE_JPEG_QUALITY, 95]
+                _, buffer = cv2.imencode('.jpg', result.image, encode_param)
+                output_mime = "image/jpeg"
+            else:
+                _, buffer = cv2.imencode('.png', result.image)
+                output_mime = "image/png"
+
+            encoded_result = base64.b64encode(buffer).decode('utf-8')
+            result_data_url = f"data:{output_mime};base64,{encoded_result}"
+        else:
+            result_data_url = None
+
+        processing_time = int((time.time() - start_time) * 1000)
+
+        return {
+            "success": result.success,
+            "processed_image": result_data_url,
+            "processing_time_ms": processing_time,
+            "progress_info": result.progress_info,
+            "errors": [
+                {"step": e["step"], "error": e["error"]}
+                for e in result.errors
+            ] if result.errors else [],
+        }
+
+    except HTTPException:
+        raise
+    except Exception as e:
+        processing_time = int((time.time() - start_time) * 1000)
+        return {
+            "success": False,
+            "processed_image": None,
+            "processing_time_ms": processing_time,
+            "error": str(e),
+        }
+
+
+@router.post("/api/preprocess/validate")
+async def validate_operations(operations: list):
+    """Dry-run check of a pipeline config. Returns {valid, errors}."""
+    validation = validate_pipeline_config(operations)
+    return validation
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/api/recognition.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/api/recognition.py
new file mode 100644
index 00000000..40d2dcbf
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/api/recognition.py
@@ -0,0 +1,169 @@
+"""Recognition API Router — local (CRNN / TrOCR) model discovery and line OCR."""
+
+import base64
+import os
+import time
+
+import cv2
+import numpy as np
+from fastapi import APIRouter, HTTPException
+
+from ..schemas.recognition import (
+    LocalModelInfo,
+    LocalModelsResponse,
+    LocalRecognizeRequest,
+    LocalRecognizeResponse,
+    LocalRecognizeResult,
+)
+from ..services.recognition.crnn_inference import (
+    crop_polygon_gray,
+    discover_models as discover_crnn_models,
+    get_recognizer as get_crnn_recognizer,
+)
+from ..services.recognition.trocr_inference import (
+    crop_polygon_rgb,
+    discover_models as discover_trocr_models,
+    get_recognizer as get_trocr_recognizer,
+)
+
+router = APIRouter()
+
+# Weights live in a different place in dev vs the container, so take whichever
+# of the two candidate dirs actually exists.
+_THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+_BACKEND_ROOT = os.path.abspath(os.path.join(_THIS_DIR, "..", ".."))
+_REPO_ROOT = os.path.abspath(os.path.join(_BACKEND_ROOT, ".."))
+
+_CANDIDATE_DIRS = [
+    os.path.join(_BACKEND_ROOT, "models", "weights"),              # container layout
+    os.path.join(_REPO_ROOT, "backend", "models", "weights"),      # dev layout
+]
+_MODEL_SEARCH_DIR = next(
+    (p for p in _CANDIDATE_DIRS if os.path.isdir(p)),
+    _CANDIDATE_DIRS[0],
+)
+
+# Rescanning the weights dir per request is pointless; refresh clears this.
+_model_cache: list[dict] | None = None
+
+
+def _get_models() -> list[dict]:
+    global _model_cache
+    if _model_cache is None:
+        _model_cache = [
+            *discover_crnn_models(os.path.join(_MODEL_SEARCH_DIR, "crnn")),
+            *discover_trocr_models(os.path.join(_MODEL_SEARCH_DIR, "trocr")),
+        ]
+    return _model_cache
+
+
+@router.get("/api/local-recognition-models", response_model=LocalModelsResponse)
+async def list_local_models():
+    """Return all available local OCR model checkpoints."""
+    models = _get_models()
+    return LocalModelsResponse(
+        models=[LocalModelInfo(**m) for m in models]
+    )
+
+
+@router.post("/api/local-recognition-models/refresh")
+async def refresh_local_models():
+    """Force rescan of model directory."""
+    global _model_cache
+    _model_cache = None
+    models = _get_models()
+    return {"count": len(models), "models": [m["name"] for m in models]}
+
+
+def _decode_image(image_data: str) -> np.ndarray:
+    """Decode a base64 (optionally data-URL prefixed) image to BGR numpy."""
+    if "," in image_data:
+        image_data = image_data.split(",", 1)[1]
+    raw = base64.b64decode(image_data)
+    arr = np.frombuffer(raw, dtype=np.uint8)
+    img = cv2.imdecode(arr, cv2.IMREAD_COLOR)
+    if img is None:
+        raise ValueError("Failed to decode image")
+    return img
+
+
+@router.post("/api/local-recognize", response_model=LocalRecognizeResponse)
+async def local_recognize(request: LocalRecognizeRequest):
+    """Recognise the text in each line box with a local CRNN or TrOCR model.
+
+    model_id is namespaced: "crnn:best_crnn", "trocr:default".
+    """
+    start = time.time()
+
+    models = _get_models()
+    model_info = next((m for m in models if m["id"] == request.model_id), None)
+    if model_info is None:
+        raise HTTPException(
+            status_code=404,
+            detail=f"Model '{request.model_id}' not found. Available: {[m['id'] for m in models]}",
+        )
+
+    try:
+        image_bgr = _decode_image(request.image_data)
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=f"Invalid image data: {e}")
+
+    model_type = model_info["model_type"]
+
+    try:
+        if model_type == "crnn":
+            recognizer = get_crnn_recognizer(model_info["path"])
+        elif model_type == "trocr":
+            recognizer = get_trocr_recognizer(model_info["path"])
+        else:
+            raise RuntimeError(f"Unsupported model type: {model_type}")
+    except Exception as e:
+        raise HTTPException(
+            status_code=500,
+            detail=f"Failed to load model '{request.model_id}': {e}",
+        )
+
+    results: list[LocalRecognizeResult] = []
+
+    if len(request.boxes) > 0:
+        crops = []
+        for i, box in enumerate(request.boxes):
+            try:
+                if model_type == "trocr":
+                    crop = crop_polygon_rgb(image_bgr, box)
+                else:
+                    crop = crop_polygon_gray(image_bgr, box)
+                crops.append((i, crop))
+            except Exception:
+                results.append(LocalRecognizeResult(box_index=i, text=""))
+
+        if crops:
+            try:
+                images = [c[1] for c in crops]
+                texts = recognizer.predict_batch(images)
+                for (idx, _), text in zip(crops, texts):
+                    results.append(LocalRecognizeResult(box_index=idx, text=text))
+            except Exception:
+                # Batch failed — retry one at a time so one bad crop doesn't
+                # cost us the whole page.
+                for idx, img in crops:
+                    try:
+                        if hasattr(recognizer, "predict"):
+                            text = recognizer.predict(img)
+                        else:
+                            text = recognizer.predict_batch([img])[0]
+                    except Exception:
+                        text = ""
+                    results.append(LocalRecognizeResult(box_index=idx, text=text))
+
+    results.sort(key=lambda r: r.box_index)
+
+    elapsed_ms = int((time.time() - start) * 1000)
+
+    return LocalRecognizeResponse(
+        results=results,
+        processing_time_ms=elapsed_ms,
+        model_used=request.model_id,
+        model_type=model_type,
+        device=recognizer.device,
+    )
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/api/storage.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/api/storage.py
new file mode 100644
index 00000000..19ca7018
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/api/storage.py
@@ -0,0 +1,169 @@
+"""Persistent storage API for transcripts and datasets."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from fastapi import APIRouter, HTTPException
+from fastapi.responses import StreamingResponse
+from pydantic import BaseModel, Field
+
+from ..storage.storage_manager import (
+    delete_entry,
+    ensure_storage_layout,
+    get_transcript_detail,
+    get_dataset_detail,
+    list_entries,
+    resolve_storage_root,
+    save_detection_dataset,
+    save_recognition_dataset,
+    save_transcript_session,
+    zip_entry,
+)
+
+router = APIRouter(prefix="/api/storage", tags=["storage"])
+
+
+class SaveTranscriptRequest(BaseModel):
+    transcripts: dict[str, str] = Field(default_factory=dict)
+    transcript_images: dict[str, str] = Field(default_factory=dict)
+    source: str = "ocr upload"
+    mode: str = "recognition"
+    book_name: str = "transcript"
+    model_info: dict[str, Any] = Field(default_factory=dict)
+
+
+class SaveDatasetPageItem(BaseModel):
+    page_key: str
+    image_data: str
+    boxes: list = Field(default_factory=list)
+    lines: list = Field(default_factory=list)
+
+
+class SaveDatasetRequest(BaseModel):
+    pages: list[SaveDatasetPageItem] = Field(default_factory=list)
+    source: str = "dataset generation"
+    book_name: str = "dataset"
+    bbox_format: str = "txt"
+    mode: str = "recognition"
+    model_info: dict[str, Any] = Field(default_factory=dict)
+
+
+@router.get("/health")
+async def storage_health():
+    paths = ensure_storage_layout()
+    return {
+        "ok": True,
+        "root": resolve_storage_root(),
+        "paths": paths,
+    }
+
+
+@router.get("/overview")
+async def storage_overview():
+    return {
+        "transcripts": list_entries("transcripts"),
+        "datasets": list_entries("datasets"),
+    }
+
+
+@router.get("/transcripts")
+async def list_transcripts():
+    return {"items": list_entries("transcripts")}
+
+
+@router.get("/datasets")
+async def list_datasets():
+    return {"items": list_entries("datasets")}
+
+
+@router.get("/transcripts/{session_id}")
+async def transcript_detail(session_id: str):
+    try:
+        return get_transcript_detail(session_id)
+    except FileNotFoundError:
+        raise HTTPException(status_code=404, detail="Transcript session not found")
+
+
+@router.get("/datasets/{dataset_id}")
+async def dataset_detail(dataset_id: str):
+    try:
+        return get_dataset_detail(dataset_id)
+    except FileNotFoundError:
+        raise HTTPException(status_code=404, detail="Dataset not found")
+
+
+@router.post("/transcripts")
+async def save_transcript(request: SaveTranscriptRequest):
+    non_empty = {
+        k: v
+        for k, v in request.transcripts.items()
+        if isinstance(v, str) and v.strip()
+    }
+    if not non_empty:
+        raise HTTPException(status_code=400, detail="No transcript pages to save")
+
+    metadata = save_transcript_session(
+        transcripts=non_empty,
+        source=request.source,
+        mode=request.mode,
+        transcript_images=request.transcript_images,
+        book_name=request.book_name,
+        model_info=request.model_info,
+    )
+    return {"success": True, "item": metadata}
+
+
+@router.post("/datasets")
+async def save_dataset(request: SaveDatasetRequest):
+    if not request.pages:
+        raise HTTPException(status_code=400, detail="No pages provided")
+
+    pages_data = [page.model_dump() for page in request.pages]
+
+    if request.mode == "detection":
+        metadata = save_detection_dataset(
+            pages_data=pages_data,
+            source=request.source,
+            book_name=request.book_name,
+            bbox_format=request.bbox_format,
+            model_info=request.model_info,
+        )
+    else:
+        metadata = save_recognition_dataset(
+            pages_data=pages_data,
+            source=request.source,
+            book_name=request.book_name,
+            model_info=request.model_info,
+        )
+
+    return {"success": True, "item": metadata}
+
+
+@router.get("/download/{kind}/{entry_id}")
+async def download_entry(kind: str, entry_id: str):
+    if kind not in {"transcripts", "datasets"}:
+        raise HTTPException(status_code=400, detail="Unsupported storage kind")
+
+    try:
+        buffer, filename = zip_entry(kind, entry_id)
+    except FileNotFoundError:
+        raise HTTPException(status_code=404, detail="Stored item not found")
+
+    return StreamingResponse(
+        buffer,
+        media_type="application/zip",
+        headers={"Content-Disposition": f'attachment; filename="{filename}"'},
+    )
+
+
+@router.delete("/{kind}/{entry_id}")
+async def remove_entry(kind: str, entry_id: str):
+    if kind not in {"transcripts", "datasets"}:
+        raise HTTPException(status_code=400, detail="Unsupported storage kind")
+
+    deleted = delete_entry(kind, entry_id)
+    if not deleted:
+        raise HTTPException(status_code=404, detail="Stored item not found")
+
+    return {"success": True, "deleted": entry_id}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/auth/__init__.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/auth/__init__.py
new file mode 100644
index 00000000..ad525ab4
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/auth/__init__.py
@@ -0,0 +1,4 @@
+"""Minimal auth: account storage, password hashing, signed-cookie sessions.
+
+No OAuth, no JWT, no RBAC — this exists to track who uses the app.
+"""
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/auth/db.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/auth/db.py
new file mode 100644
index 00000000..70fe316c
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/auth/db.py
@@ -0,0 +1,71 @@
+"""Engine + session wiring. SQLite by default, Postgres if DATABASE_URL says so."""
+
+from __future__ import annotations
+
+import os
+
+from sqlalchemy import create_engine
+from sqlalchemy.orm import declarative_base, sessionmaker
+
+from ..core.config import DATABASE_URL
+
+Base = declarative_base()
+
+
+def _normalize_url(url: str) -> str:
+    # SQLAlchemy 2.x wants "postgresql://"; Supabase/Heroku hand out "postgres://".
+    if url.startswith("postgres://"):
+        return "postgresql://" + url[len("postgres://"):]
+    return url
+
+
+def _make_engine(url: str):
+    # check_same_thread=False is needed for SQLite under FastAPI's threadpool,
+    # and rejected by Postgres — hence the branch.
+    if url.startswith("sqlite"):
+        path = url.split("///", 1)[-1]
+        if path and path != ":memory:":
+            os.makedirs(os.path.dirname(os.path.abspath(path)), exist_ok=True)
+        return create_engine(
+            url,
+            connect_args={"check_same_thread": False},
+            pool_pre_ping=True,
+        )
+    return create_engine(url, pool_pre_ping=True)
+
+
+engine = _make_engine(_normalize_url(DATABASE_URL))
+SessionLocal = sessionmaker(bind=engine, autocommit=False, autoflush=False)
+
+
+def init_db() -> None:
+    """Create missing tables. Safe on every startup."""
+    from . import models  # noqa: F401 — registers the models on Base
+
+    Base.metadata.create_all(bind=engine)
+    _ensure_columns()
+
+
+def _ensure_columns() -> None:
+    """Hand-rolled migration: create_all never ALTERs, so older users.db files
+    are missing `tracked_at`. Existing rows get NULL and the retry loop fills them."""
+    from sqlalchemy import inspect, text
+
+    inspector = inspect(engine)
+    try:
+        columns = {c["name"] for c in inspector.get_columns("users")}
+    except Exception:
+        return  # table not present yet — create_all will have made it
+
+    if "tracked_at" not in columns:
+        with engine.begin() as conn:
+            conn.execute(text("ALTER TABLE users ADD COLUMN tracked_at TIMESTAMP"))
+
+
+def get_db():
+    """FastAPI dependency: one session per request."""
+    db = SessionLocal()
+    try:
+        yield db
+    finally:
+        db.close()
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/auth/models.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/auth/models.py
new file mode 100644
index 00000000..0a1e7071
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/auth/models.py
@@ -0,0 +1,48 @@
+"""User model — the single table backing user tracking."""
+
+from __future__ import annotations
+
+from datetime import datetime, timezone
+
+from sqlalchemy import Boolean, DateTime, Integer, String
+from sqlalchemy.orm import Mapped, mapped_column
+
+from .db import Base
+
+
+def _utc_now() -> datetime:
+    return datetime.now(timezone.utc)
+
+
+class User(Base):
+    __tablename__ = "users"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    username: Mapped[str] = mapped_column(String(64), unique=True, index=True, nullable=False)
+    email: Mapped[str] = mapped_column(String(255), unique=True, index=True, nullable=False)
+    name: Mapped[str] = mapped_column(String(128), nullable=False)
+    institute: Mapped[str | None] = mapped_column(String(255), nullable=True)
+
+    password_hash: Mapped[str] = mapped_column(String(255), nullable=False)
+
+    is_verified: Mapped[bool] = mapped_column(Boolean, default=False, nullable=False)
+
+    created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=_utc_now, nullable=False)
+    last_login: Mapped[datetime | None] = mapped_column(DateTime(timezone=True), nullable=True)
+
+    # NULL until the signup lands in central Supabase tracking, so the local
+    # DB doubles as the durable retry queue.
+    tracked_at: Mapped[datetime | None] = mapped_column(DateTime(timezone=True), nullable=True)
+
+    def public_dict(self) -> dict:
+        """Serializable view, minus the password hash."""
+        return {
+            "id": self.id,
+            "username": self.username,
+            "email": self.email,
+            "name": self.name,
+            "institute": self.institute,
+            "is_verified": self.is_verified,
+            "created_at": self.created_at.isoformat() if self.created_at else None,
+            "last_login": self.last_login.isoformat() if self.last_login else None,
+        }
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/auth/schemas.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/auth/schemas.py
new file mode 100644
index 00000000..d432130c
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/auth/schemas.py
@@ -0,0 +1,40 @@
+"""Pydantic request/response models for auth."""
+
+from __future__ import annotations
+
+from pydantic import BaseModel, EmailStr, Field
+
+
+class SignupRequest(BaseModel):
+    username: str = Field(..., min_length=3, max_length=64)
+    password: str = Field(..., min_length=6, max_length=128)
+    name: str = Field(..., min_length=1, max_length=128)
+    email: EmailStr
+    # Frontend pre-fills "personal", so this always has a value.
+    institute: str = Field(default="personal", min_length=1, max_length=255)
+
+
+class LoginRequest(BaseModel):
+    username: str = Field(..., min_length=1, max_length=255)
+    password: str = Field(..., min_length=1, max_length=128)
+
+
+class ProfileUpdateRequest(BaseModel):
+    name: str = Field(..., min_length=1, max_length=128)
+    email: EmailStr
+    institute: str = Field(default="personal", min_length=1, max_length=255)
+
+
+class UserOut(BaseModel):
+    id: int
+    username: str
+    email: str
+    name: str
+    institute: str | None = None
+    is_verified: bool
+    created_at: str | None = None
+    last_login: str | None = None
+
+
+class AuthResponse(BaseModel):
+    user: UserOut
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/auth/security.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/auth/security.py
new file mode 100644
index 00000000..a378a8cf
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/auth/security.py
@@ -0,0 +1,46 @@
+"""Password hashing and signed-cookie sessions.
+
+bcrypt for passwords, itsdangerous for opaque signed cookies. No JWT.
+"""
+
+from __future__ import annotations
+
+import bcrypt
+from itsdangerous import BadSignature, SignatureExpired, URLSafeTimedSerializer
+
+from ..core.config import SECRET_KEY, SESSION_MAX_AGE
+
+_session_signer = URLSafeTimedSerializer(SECRET_KEY, salt="ren-session")
+
+SESSION_COOKIE = "ren_session"
+
+
+# ── Passwords ─────────────────────────────────────────────────────────────
+
+def hash_password(plain: str) -> str:
+    return bcrypt.hashpw(plain.encode("utf-8"), bcrypt.gensalt()).decode("utf-8")
+
+
+def verify_password(plain: str, hashed: str) -> bool:
+    try:
+        return bcrypt.checkpw(plain.encode("utf-8"), hashed.encode("utf-8"))
+    except (ValueError, TypeError):
+        return False
+
+
+# ── Session cookie ──────────────────────────────────────────────────────────
+
+def create_session_token(user_id: int) -> str:
+    """Sign the user id into an opaque, tamper-proof token."""
+    return _session_signer.dumps({"uid": user_id})
+
+
+def read_session_token(token: str) -> int | None:
+    """Return the user id from a valid, unexpired token, else None."""
+    if not token:
+        return None
+    try:
+        data = _session_signer.loads(token, max_age=SESSION_MAX_AGE)
+        return int(data["uid"])
+    except (BadSignature, SignatureExpired, KeyError, ValueError, TypeError):
+        return None
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/auth/tracking.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/auth/tracking.py
new file mode 100644
index 00000000..01124d93
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/auth/tracking.py
@@ -0,0 +1,152 @@
+"""Push signups to a central Supabase table, using the local DB as the queue.
+
+`tracked_at IS NULL` means "not sent yet". We try once on signup, and a retry
+loop sweeps the rest — so an outage (or no config at all) loses nothing, even
+across restarts. Never blocks signup, never sends the password hash.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+from datetime import datetime, timezone
+from urllib.parse import quote
+
+import httpx
+from sqlalchemy import select
+
+from ..core.config import (
+    APP_INSTANCE_ID,
+    SUPABASE_PUBLISHABLE_KEY,
+    SUPABASE_URL,
+    TRACKING_RETRY_INTERVAL,
+)
+from .db import SessionLocal
+from .models import User
+
+logger = logging.getLogger("renaissance.auth.tracking")
+
+
+def tracking_enabled() -> bool:
+    return bool(SUPABASE_URL and SUPABASE_PUBLISHABLE_KEY)
+
+
+def _post_signup(user: User) -> bool:
+    """POST one signup. True on success, including "already there".
+
+    Plain insert, not PostgREST upsert — upsert needs read rights and trips the
+    insert-only RLS policy. Duplicates just 409 and we treat that as done.
+    """
+    url = f"{SUPABASE_URL.rstrip('/')}/rest/v1/users"
+    headers = {
+        "apikey": SUPABASE_PUBLISHABLE_KEY,
+        "Authorization": f"Bearer {SUPABASE_PUBLISHABLE_KEY}",
+        "Content-Type": "application/json",
+        "Prefer": "return=minimal",
+    }
+    payload = {
+        "username": user.username,
+        "name": user.name,
+        "email": user.email,
+        "institute": user.institute,
+        "instance_id": APP_INSTANCE_ID,
+    }
+    try:
+        resp = httpx.post(url, headers=headers, json=payload, timeout=8.0)
+    except Exception as exc:  # noqa: BLE001
+        logger.warning("[tracking] insert failed (will retry): %s", exc)
+        return False
+
+    # 409 = already recorded, probably from another instance. Not an error.
+    if resp.status_code < 300 or resp.status_code == 409:
+        return True
+    logger.warning(
+        "[tracking] Supabase returned %s (will retry): %s",
+        resp.status_code,
+        resp.text[:300],
+    )
+    return False
+
+
+def _flush_pending_sync() -> int:
+    """Push every not-yet-tracked user. Returns how many were pushed."""
+    if not tracking_enabled():
+        return 0
+
+    pushed = 0
+    db = SessionLocal()
+    try:
+        pending = db.scalars(select(User).where(User.tracked_at.is_(None))).all()
+        for user in pending:
+            if _post_signup(user):
+                user.tracked_at = datetime.now(timezone.utc)
+                db.commit()
+                pushed += 1
+            else:
+                db.rollback()  # leave tracked_at NULL -> retried next cycle
+    finally:
+        db.close()
+
+    if pushed:
+        logger.info("[tracking] Pushed %d signup(s) to Supabase", pushed)
+    return pushed
+
+
+def track_user_now(user_id: int) -> None:
+    """Try to push one signup right away. On failure the retry loop gets it."""
+    if not tracking_enabled():
+        logger.info("[tracking] Supabase not configured — skipping (id=%s)", user_id)
+        return
+    db = SessionLocal()
+    try:
+        user = db.get(User, user_id)
+        if user is None or user.tracked_at is not None:
+            return
+        if _post_signup(user):
+            user.tracked_at = datetime.now(timezone.utc)
+            db.commit()
+            logger.info("[tracking] Recorded signup %s centrally", user.email)
+    finally:
+        db.close()
+
+
+def update_tracked_profile(
+    old_email: str, username: str, name: str, email: str, institute: str | None
+) -> None:
+    """PATCH the central row after a profile edit. Never raises.
+
+    Matched by the OLD email. If the row was never pushed, this matches nothing
+    and the pending insert carries the new values anyway.
+    """
+    if not tracking_enabled():
+        return
+    url = f"{SUPABASE_URL.rstrip('/')}/rest/v1/users?email=eq.{quote(old_email)}"
+    headers = {
+        "apikey": SUPABASE_PUBLISHABLE_KEY,
+        "Authorization": f"Bearer {SUPABASE_PUBLISHABLE_KEY}",
+        "Content-Type": "application/json",
+        "Prefer": "return=minimal",
+    }
+    payload = {"username": username, "name": name, "email": email, "institute": institute}
+    try:
+        resp = httpx.patch(url, headers=headers, json=payload, timeout=8.0)
+        if resp.status_code < 300:
+            logger.info("[tracking] Synced profile update for %s", email)
+        else:
+            logger.warning(
+                "[tracking] Profile sync returned %s: %s",
+                resp.status_code,
+                resp.text[:300],
+            )
+    except Exception as exc:  # noqa: BLE001
+        logger.warning("[tracking] Profile sync failed: %s", exc)
+
+
+async def retry_loop() -> None:
+    """Flush unsent signups on an interval. First pass runs immediately."""
+    while True:
+        try:
+            await asyncio.to_thread(_flush_pending_sync)
+        except Exception as exc:  # noqa: BLE001 — loop must never die
+            logger.warning("[tracking] retry loop error: %s", exc)
+        await asyncio.sleep(TRACKING_RETRY_INTERVAL)
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/core/__init__.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/core/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/core/config.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/core/config.py
new file mode 100644
index 00000000..0fdee8c0
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/core/config.py
@@ -0,0 +1,91 @@
+"""Config constants and env vars, loaded once at import."""
+
+import os
+
+# Local (non-Docker) runs need .env loaded by hand. In Docker compose already
+# injected these, and load_dotenv never overrides existing vars, so it's a no-op.
+try:
+    from dotenv import load_dotenv
+
+    _cfg_dir = os.path.dirname(os.path.abspath(__file__))
+    for _env_candidate in (
+        os.path.join(_cfg_dir, "..", "..", "..", ".env"),  # repo root
+        os.path.join(_cfg_dir, "..", "..", ".env"),         # backend/
+    ):
+        if os.path.isfile(_env_candidate):
+            load_dotenv(_env_candidate)
+            break
+except ImportError:
+    pass
+
+
+def _default_storage_root() -> str:
+    """Resolve storage root for both local and containerized runs."""
+    this_dir = os.path.dirname(os.path.abspath(__file__))
+    backend_root = os.path.abspath(os.path.join(this_dir, "..", ".."))
+    repo_root_candidate = os.path.abspath(os.path.join(backend_root, ".."))
+
+    is_repo_layout = (
+        os.path.isdir(os.path.join(repo_root_candidate, "backend"))
+        and os.path.isdir(os.path.join(repo_root_candidate, "frontend"))
+    )
+
+    if is_repo_layout:
+        return os.path.join(repo_root_candidate, "storage")
+    return os.path.join(backend_root, "storage")
+
+# FastAPI app metadata
+APP_TITLE = "RenAIssance OCR API"
+APP_VERSION = "2.0.0"
+
+# CORS origins allowed by the backend
+CORS_ORIGINS = [
+    "http://localhost:5173",
+    "http://localhost:5174",
+    "http://127.0.0.1:5173",
+]
+
+# API-key format validation
+MIN_API_KEY_LENGTH = 20
+
+# Batch OCR
+MAX_BATCH_SIZE = 4
+
+# Persistent storage root for "My Files"
+STORAGE_ROOT = os.getenv("STORAGE_ROOT", _default_storage_root())
+
+
+# ── Auth (all backend-only; the browser only ever sees a signed cookie) ──
+
+# Local accounts. SQLite on the storage volume by default — zero setup, works
+# offline. Point at a postgresql:// URL if you'd rather use Postgres.
+DATABASE_URL = os.getenv(
+    "DATABASE_URL",
+    f"sqlite:///{os.path.join(STORAGE_ROOT, 'users.db')}",
+)
+
+# Signs session cookies. Set a stable value in production — the random fallback
+# means every restart logs everyone out.
+SECRET_KEY = os.getenv("SECRET_KEY") or os.urandom(32).hex()
+
+# Guards GET /api/admin/users. Unset = endpoint returns 404 instead of opening up.
+ADMIN_TOKEN = os.getenv("ADMIN_TOKEN", "")
+
+# Deployed origin — only used to decide whether the session cookie is Secure.
+PUBLIC_BASE_URL = os.getenv("PUBLIC_BASE_URL", "http://localhost:5173")
+
+# Session cookie lifetime (seconds). Default 30 days.
+SESSION_MAX_AGE = int(os.getenv("SESSION_MAX_AGE", str(30 * 24 * 60 * 60)))
+
+
+# ── Central signup tracking (Supabase REST) ──────────────────────────────
+# The publishable key is safe to ship: the table has an INSERT-only RLS policy,
+# so a leak can add a row and nothing else. Leave empty to disable tracking.
+SUPABASE_URL = os.getenv("SUPABASE_URL", "")
+SUPABASE_PUBLISHABLE_KEY = os.getenv("SUPABASE_PUBLISHABLE_KEY", "")
+
+# Optional label so you can tell which deployment a signup came from.
+APP_INSTANCE_ID = os.getenv("APP_INSTANCE_ID", "") or os.getenv("HOSTNAME", "unknown")
+
+# How often (seconds) to retry signups that haven't reached Supabase yet.
+TRACKING_RETRY_INTERVAL = int(os.getenv("TRACKING_RETRY_INTERVAL", "120"))
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/core/font_registry.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/core/font_registry.py
new file mode 100644
index 00000000..4431d39b
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/core/font_registry.py
@@ -0,0 +1,28 @@
+"""Register DejaVu Sans so PDF export can render non-ASCII characters."""
+
+import os
+from reportlab.pdfbase import pdfmetrics
+from reportlab.pdfbase.ttfonts import TTFont
+
+try:
+    font_paths = [
+        '/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf',  # Linux (Debian/Ubuntu)
+        '/usr/share/fonts/dejavu-sans-fonts/DejaVuSans.ttf',  # Linux (Fedora/RHEL)
+        '/usr/share/fonts/TTF/DejaVuSans.ttf',  # Linux (Arch)
+        'C:/Windows/Fonts/DejaVuSans.ttf',  # Windows
+        '/System/Library/Fonts/Supplemental/DejaVuSans.ttf',  # macOS
+        '/Library/Fonts/DejaVuSans.ttf',  # macOS alternative
+    ]
+
+    UNICODE_FONT_REGISTERED = False
+    for font_path in font_paths:
+        if os.path.exists(font_path):
+            pdfmetrics.registerFont(TTFont('DejaVuSans', font_path))
+            UNICODE_FONT_REGISTERED = True
+            break
+
+    if not UNICODE_FONT_REGISTERED:
+        print("Warning: DejaVu Sans font not found. PDF export may have limited Unicode support.")
+except Exception as e:
+    UNICODE_FONT_REGISTERED = False
+    print(f"Warning: Could not register Unicode font for PDF: {e}")
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/core/rate_limiter.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/core/rate_limiter.py
new file mode 100644
index 00000000..16e7eae9
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/core/rate_limiter.py
@@ -0,0 +1,71 @@
+"""Sliding-window rate limiter, sized for the Gemini free tier."""
+
+import time
+
+
+class RateLimiter:
+    """5 requests/minute by default; batches book several slots at once."""
+
+    def __init__(self, max_requests: int = 5, window_seconds: int = 60):
+        self.max_requests = max_requests
+        self.window_seconds = window_seconds
+        self.request_times: list[float] = []
+
+    def _clean_old_requests(self):
+        """Remove requests outside the sliding window"""
+        cutoff = time.time() - self.window_seconds
+        self.request_times = [t for t in self.request_times if t > cutoff]
+
+    def get_available_slots(self) -> int:
+        """Get number of requests that can be made right now"""
+        self._clean_old_requests()
+        return max(0, self.max_requests - len(self.request_times))
+
+    def can_proceed(self) -> tuple[bool, int]:
+        """Check if at least one request can proceed. Returns (can_proceed, wait_time_seconds)"""
+        self._clean_old_requests()
+
+        if len(self.request_times) < self.max_requests:
+            return True, 0
+
+        # Calculate wait time until oldest request expires
+        oldest = min(self.request_times)
+        wait_time = int(oldest + self.window_seconds - time.time()) + 1
+        return False, max(0, wait_time)
+
+    def record_request(self):
+        """Record that a request was made"""
+        self.request_times.append(time.time())
+
+    def record_requests(self, count: int):
+        """Record multiple requests at once (for batch processing)"""
+        now = time.time()
+        for _ in range(count):
+            self.request_times.append(now)
+
+    def get_status(self) -> dict:
+        """Get current rate limit status"""
+        self._clean_old_requests()
+        available = self.max_requests - len(self.request_times)
+
+        if available > 0:
+            return {
+                "ready": True,
+                "wait_seconds": 0,
+                "available_slots": available,
+                "requests_in_window": len(self.request_times)
+            }
+
+        # Calculate wait time
+        oldest = min(self.request_times) if self.request_times else time.time()
+        wait_time = int(oldest + self.window_seconds - time.time()) + 1
+
+        return {
+            "ready": False,
+            "wait_seconds": max(0, wait_time),
+            "available_slots": 0,
+            "requests_in_window": len(self.request_times)
+        }
+
+
+rate_limiter = RateLimiter(max_requests=5, window_seconds=60)
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/main.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/main.py
new file mode 100644
index 00000000..765c63da
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/main.py
@@ -0,0 +1,73 @@
+"""FastAPI app wiring: mount every router, start the signup-tracking loop.
+
+Logic lives under app.api / app.services / app.core / app.schemas / app.utils.
+"""
+
+import os
+import sys
+
+# `preprocessing` is a sibling of `app`, not a submodule of it.
+backend_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+if backend_dir not in sys.path:
+    sys.path.insert(0, backend_dir)
+
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+
+from .core.config import APP_TITLE, APP_VERSION, CORS_ORIGINS
+from .api.health import router as health_router
+from .api.ocr import router as ocr_router
+from .api.preprocess import router as preprocess_router
+from .api.export import router as export_router
+from .api.layout_detection import router as layout_detection_router
+from .api.dataset import router as dataset_router
+from .api.recognition import router as recognition_router
+from .api.llm_postprocess import router as llm_router
+from .api.storage import router as storage_router
+from .api.auth import router as auth_router
+from .auth.db import init_db
+from .auth.tracking import retry_loop, tracking_enabled
+
+app = FastAPI(title=APP_TITLE, version=APP_VERSION)
+
+
+@app.on_event("startup")
+async def _startup() -> None:
+    init_db()  # no-op once the tables exist
+    # Pushes signups that never reached Supabase, including ones from while it
+    # was down or unconfigured.
+    if tracking_enabled():
+        import asyncio
+
+        app.state.tracking_task = asyncio.create_task(retry_loop())
+
+
+@app.on_event("shutdown")
+async def _shutdown() -> None:
+    task = getattr(app.state, "tracking_task", None)
+    if task is not None:
+        task.cancel()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=CORS_ORIGINS,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+app.include_router(health_router)
+app.include_router(ocr_router)
+app.include_router(preprocess_router)
+app.include_router(export_router)
+app.include_router(layout_detection_router)
+app.include_router(dataset_router)
+app.include_router(recognition_router)
+app.include_router(llm_router)
+app.include_router(storage_router)
+app.include_router(auth_router)
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000, reload=True)
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/schemas/__init__.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/schemas/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/schemas/ocr.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/schemas/ocr.py
new file mode 100644
index 00000000..93a6c0f9
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/schemas/ocr.py
@@ -0,0 +1,75 @@
+"""Request/response models for the OCR, preprocess and export endpoints."""
+
+from typing import Optional
+from pydantic import BaseModel
+
+
+class OCRResponse(BaseModel):
+    success: bool
+    transcript: Optional[str] = None
+    error: Optional[str] = None
+    model_used: str
+    processing_time_ms: int
+
+
+
+class ExportRequest(BaseModel):
+    transcripts: dict  # {page_number: transcript_text}
+    format: str  # "txt", "docx", "pdf"
+
+
+class PreprocessRequest(BaseModel):
+    image_data: str  # Base64 encoded image with data URL prefix
+    operations: list  # List of {op, params, enabled} dicts
+    preview_mode: bool = False  # Use faster algorithms for preview
+
+
+# One per provider, each with its own default model.
+
+class GeminiOCRRequest(BaseModel):
+    image_data: str
+    model: str = "gemini-3.1-flash-lite"
+    custom_prompt: Optional[str] = None
+
+
+class ChatGPTOCRRequest(BaseModel):
+    image_data: str
+    model: str = "gpt-4o"
+    custom_prompt: Optional[str] = None
+
+class DeepSeekOCRRequest(BaseModel):
+    image_data: str
+    model: str = "deepseek-chat"
+    custom_prompt: Optional[str] = None
+
+
+class QwenOCRRequest(BaseModel):
+    image_data: str
+    model: str = "qwen-vl-max"
+    custom_prompt: Optional[str] = None
+
+
+class BatchOCRItem(BaseModel):
+    page_index: int
+    image_data: str
+
+
+class BatchOCRRequest(BaseModel):
+    items: list[BatchOCRItem]
+    model: str = "gemini-3.1-flash-lite"
+    custom_prompt: Optional[str] = None
+
+
+class BatchOCRResultItem(BaseModel):
+    page_index: int
+    success: bool
+    transcript: Optional[str] = None
+    error: Optional[str] = None
+    processing_time_ms: int = 0
+
+
+class BatchOCRResponse(BaseModel):
+    results: list[BatchOCRResultItem]
+    total_processing_time_ms: int
+    successful_count: int
+    failed_count: int
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/schemas/recognition.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/schemas/recognition.py
new file mode 100644
index 00000000..cfa30439
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/schemas/recognition.py
@@ -0,0 +1,40 @@
+"""Pydantic schemas for local (CRNN / TrOCR) recognition APIs."""
+
+from typing import Optional
+from pydantic import BaseModel
+
+
+class LocalModelInfo(BaseModel):
+    """Single available local OCR model."""
+    id: str
+    name: str
+    model_type: str
+    path: str
+
+
+class LocalModelsResponse(BaseModel):
+    """Response from /api/local-recognition-models."""
+    models: list[LocalModelInfo]
+
+
+class LocalRecognizeRequest(BaseModel):
+    """Request body for /api/local-recognize."""
+    image_data: str                        # base64-encoded page image
+    boxes: list[list[list[float]]]         # list of polygons, each polygon = list of [x,y] points
+    model_id: str                          # model id (e.g. "crnn:best_crnn")
+
+
+class LocalRecognizeResult(BaseModel):
+    """Single box recognition result."""
+    box_index: int
+    text: str
+
+
+class LocalRecognizeResponse(BaseModel):
+    """Response from /api/local-recognize."""
+    results: list[LocalRecognizeResult]
+    processing_time_ms: int
+    model_used: str
+    model_type: str
+    device: str
+    error: Optional[str] = None
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/__init__.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/dataset_builder.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/dataset_builder.py
new file mode 100644
index 00000000..f99bee73
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/dataset_builder.py
@@ -0,0 +1,311 @@
+"""Build line-level OCR training datasets as ZIPs.
+
+Boxes get sorted into reading order, paired with transcript lines, cropped, and
+zipped. One page in memory at a time — a book's worth of full-res scans won't fit.
+"""
+
+import csv
+import io
+import re
+import zipfile
+from typing import Any, Dict, List, Tuple
+
+import cv2
+import numpy as np
+
+
+# ── Alignment ───────────────────────────────────────────────────────
+
+def _box_centroid(box: List[List[float]]) -> Tuple[float, float]:
+    """Compute centroid of a 4-point polygon."""
+    pts = np.array(box, dtype=np.float32)
+    return float(pts[:, 0].mean()), float(pts[:, 1].mean())
+
+
+def sort_boxes_reading_order(boxes: List[List[List[float]]]) -> List[int]:
+    """Indices of `boxes` in reading order: top to bottom, left to right."""
+    if not boxes:
+        return []
+
+    centroids = [_box_centroid(b) for b in boxes]
+
+    ys = np.array([c[1] for c in centroids])
+    if len(ys) == 0:
+        return []
+
+    # Row threshold scales with the typical line height, so it survives both
+    # thumbnail-sized and 6000px scans.
+    heights = []
+    for b in boxes:
+        pts = np.array(b, dtype=np.float32)
+        heights.append(float(pts[:, 1].max() - pts[:, 1].min()))
+    med_h = float(np.median(heights)) if heights else 20.0
+    row_thresh = max(med_h * 0.5, 10.0)
+
+    order = sorted(range(len(centroids)), key=lambda i: centroids[i][1])
+
+    rows: List[List[int]] = []
+    current_row: List[int] = [order[0]]
+    current_y = centroids[order[0]][1]
+
+    for idx in order[1:]:
+        cy = centroids[idx][1]
+        if abs(cy - current_y) < row_thresh:
+            current_row.append(idx)
+        else:
+            rows.append(current_row)
+            current_row = [idx]
+            current_y = cy
+    rows.append(current_row)
+
+    result = []
+    for row in rows:
+        row.sort(key=lambda i: centroids[i][0])
+        result.extend(row)
+
+    return result
+
+
+def align_boxes_with_transcript(
+    boxes: List[List[List[float]]],
+    lines: List[str],
+) -> Tuple[List[Tuple[List[List[float]], str]], int, int]:
+    """Zip boxes (in reading order) with transcript lines.
+
+    -> ([(box, text), ...], num_boxes, num_lines). Extra of either is dropped,
+    and the counts let the caller warn about the mismatch.
+    """
+    sorted_indices = sort_boxes_reading_order(boxes)
+    sorted_boxes = [boxes[i] for i in sorted_indices]
+
+    num_pairs = min(len(sorted_boxes), len(lines))
+    pairs = [(sorted_boxes[i], lines[i]) for i in range(num_pairs)]
+
+    return pairs, len(sorted_boxes), len(lines)
+
+
+# ── Cropping ────────────────────────────────────────────────────────
+
+def crop_line_image(
+    image: np.ndarray,
+    box: List[List[float]],
+    padding: int = 4,
+) -> np.ndarray:
+    """Crop one line out of the page, with a few pixels of breathing room."""
+    pts = np.array(box, dtype=np.float32)
+    x_min, y_min = int(pts[:, 0].min()), int(pts[:, 1].min())
+    x_max, y_max = int(pts[:, 0].max()), int(pts[:, 1].max())
+
+    h, w = image.shape[:2]
+    x1 = max(0, x_min - padding)
+    y1 = max(0, y_min - padding)
+    x2 = min(w, x_max + padding)
+    y2 = min(h, y_max + padding)
+
+    if x2 <= x1 or y2 <= y1:
+        return np.zeros((1, 1, 3), dtype=np.uint8)
+
+    return image[y1:y2, x1:x2].copy()
+
+
+# ── Filename helper ─────────────────────────────────────────────────
+
+def _safe_label(text: str, max_len: int = 30) -> str:
+    """Turn transcript text into something safe to use as a filename."""
+    safe = re.sub(r"[^\w\s-]", "", text)
+    safe = re.sub(r"\s+", "_", safe.strip())
+    return safe[:max_len] if safe else "line"
+
+
+# ── Dataset Export ──────────────────────────────────────────────────
+
+def build_dataset_zip(
+    pages_data: List[Dict[str, Any]],
+    book_name: str = "dataset",
+) -> io.BytesIO:
+    """ZIP of line crops plus a labels.csv.
+
+    pages_data entries: {page_key, image_data (base64 data URL), boxes, lines}.
+    """
+    import base64
+
+    buf = io.BytesIO()
+    csv_rows: List[Tuple[str, str]] = []  # (image_path, text)
+
+    with zipfile.ZipFile(buf, "w", zipfile.ZIP_DEFLATED) as zf:
+        for page in pages_data:
+            page_key = page["page_key"]
+            boxes = page["boxes"]
+            lines = page["lines"]
+
+            page_num_match = re.search(r"(\d+)", str(page_key))
+            page_dir = f"page_{int(page_num_match.group(1))}" if page_num_match else f"page_{page_key}"
+
+            img_b64 = page["image_data"]
+            if "," in img_b64:
+                img_b64 = img_b64.split(",", 1)[1]
+            img_bytes = base64.b64decode(img_b64)
+            nparr = np.frombuffer(img_bytes, np.uint8)
+            img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+
+            if img is None:
+                continue
+
+            pairs, _, _ = align_boxes_with_transcript(boxes, lines)
+            # Two lines with the same text would collide on filename.
+            seen_labels: Dict[str, int] = {}
+
+            for idx, (box, text) in enumerate(pairs):
+                crop = crop_line_image(img, box)
+                label = _safe_label(text, max_len=80)
+                count = seen_labels.get(label, 0) + 1
+                seen_labels[label] = count
+                label_with_suffix = label if count == 1 else f"{label}_{count}"
+
+                img_filename = f"{book_name}/{page_dir}/{label_with_suffix}.png"
+
+                _, png_data = cv2.imencode(".png", crop)
+                zf.writestr(img_filename, png_data.tobytes())
+
+                csv_rows.append((img_filename, text))
+
+                del crop
+
+            del img, nparr, img_bytes
+
+        # BOM so Excel opens the accented text correctly.
+        csv_buf = io.StringIO()
+        writer = csv.writer(csv_buf)
+        writer.writerow(["image", "text"])
+        for path, text in csv_rows:
+            writer.writerow([path, text])
+        csv_bytes = b'\xef\xbb\xbf' + csv_buf.getvalue().encode('utf-8')
+        zf.writestr(f"{book_name}/labels.csv", csv_bytes)
+
+    buf.seek(0)
+    return buf
+
+
+# ── Detection-only Dataset Export ──────────────────────────────────
+
+def _polygon_to_xyxy(box: List[List[float]]) -> Tuple[int, int, int, int]:
+    """Axis-aligned integer-pixel rect around a 4-point polygon."""
+    pts = np.asarray(box, dtype=np.float32)
+    return (
+        int(pts[:, 0].min()),
+        int(pts[:, 1].min()),
+        int(pts[:, 0].max()),
+        int(pts[:, 1].max()),
+    )
+
+
+def build_detection_dataset_zip(
+    pages_data: List[Dict[str, Any]],
+    book_name: str = "dataset",
+    bbox_format: str = "txt",
+) -> io.BytesIO:
+    """ZIP of full page images plus their line boxes — no transcript needed.
+
+    bbox_format: "txt" (x1 y1 x2 y2 per line), "json" (array of rects),
+    "yolo" (normalised cx cy w h + classes.txt), or "coco" (one annotations.json).
+    """
+    import base64
+    import json
+
+    fmt = (bbox_format or "txt").lower()
+    if fmt not in ("txt", "json", "yolo", "coco"):
+        raise ValueError(f"Unsupported bbox_format: {bbox_format!r} (use 'txt', 'json', 'yolo', or 'coco')")
+
+    buf = io.BytesIO()
+
+    # COCO wants one file for the whole set, so accumulate across pages.
+    coco_images: List[Dict[str, Any]] = []
+    coco_annotations: List[Dict[str, Any]] = []
+    coco_image_id = 0
+    coco_ann_id = 0
+
+    with zipfile.ZipFile(buf, "w", zipfile.ZIP_DEFLATED) as zf:
+        for page in pages_data:
+            page_key = page["page_key"]
+            boxes = page.get("boxes", [])
+
+            img_b64 = page["image_data"]
+            if "," in img_b64:
+                img_b64 = img_b64.split(",", 1)[1]
+            img_bytes = base64.b64decode(img_b64)
+            nparr = np.frombuffer(img_bytes, np.uint8)
+            img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+            if img is None:
+                continue
+
+            h, w = img.shape[:2]
+            stem = f"page_{page_key}"
+            jpg_name = f"{stem}.jpg"
+            _, jpg_data = cv2.imencode(".jpg", img, [cv2.IMWRITE_JPEG_QUALITY, 95])
+            zf.writestr(f"{book_name}/images/{jpg_name}", jpg_data.tobytes())
+
+            xyxy_boxes = [_polygon_to_xyxy(b) for b in boxes]
+
+            if fmt == "txt":
+                lines = "\n".join(
+                    f"{x1} {y1} {x2} {y2}" for (x1, y1, x2, y2) in xyxy_boxes
+                )
+                payload = (lines + "\n").encode("utf-8") if lines else b""
+                zf.writestr(f"{book_name}/bboxes/{stem}.txt", payload)
+            elif fmt == "json":
+                payload = json.dumps(
+                    [[x1, y1, x2, y2] for (x1, y1, x2, y2) in xyxy_boxes],
+                    indent=2,
+                ).encode("utf-8")
+                zf.writestr(f"{book_name}/bboxes/{stem}.json", payload)
+            elif fmt == "yolo":
+                yolo_lines = []
+                for (x1, y1, x2, y2) in xyxy_boxes:
+                    bw = max(0, x2 - x1)
+                    bh = max(0, y2 - y1)
+                    cx = (x1 + x2) / 2.0
+                    cy = (y1 + y2) / 2.0
+                    yolo_lines.append(
+                        f"0 {cx / w:.6f} {cy / h:.6f} {bw / w:.6f} {bh / h:.6f}"
+                    )
+                payload = ("\n".join(yolo_lines) + "\n").encode("utf-8") if yolo_lines else b""
+                zf.writestr(f"{book_name}/labels/{stem}.txt", payload)
+            elif fmt == "coco":
+                coco_image_id += 1
+                coco_images.append({
+                    "id": coco_image_id,
+                    "file_name": jpg_name,
+                    "width": int(w),
+                    "height": int(h),
+                })
+                for (x1, y1, x2, y2) in xyxy_boxes:
+                    coco_ann_id += 1
+                    bw = max(0, x2 - x1)
+                    bh = max(0, y2 - y1)
+                    coco_annotations.append({
+                        "id": coco_ann_id,
+                        "image_id": coco_image_id,
+                        "category_id": 1,
+                        "bbox": [int(x1), int(y1), int(bw), int(bh)],
+                        "area": int(bw * bh),
+                        "iscrowd": 0,
+                        "segmentation": [],
+                    })
+
+            del img, nparr, img_bytes
+
+        if fmt == "yolo":
+            zf.writestr(f"{book_name}/classes.txt", b"text\n")
+        elif fmt == "coco":
+            coco = {
+                "images": coco_images,
+                "annotations": coco_annotations,
+                "categories": [{"id": 1, "name": "text", "supercategory": "text"}],
+            }
+            zf.writestr(
+                f"{book_name}/annotations.json",
+                json.dumps(coco, indent=2).encode("utf-8"),
+            )
+
+    buf.seek(0)
+    return buf
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/export.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/export.py
new file mode 100644
index 00000000..d4848414
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/export.py
@@ -0,0 +1,161 @@
+"""Render the per-page transcripts into one txt / docx / pdf buffer."""
+
+import io
+import re
+from docx import Document
+from docx.shared import Pt
+from docx.enum.text import WD_ALIGN_PARAGRAPH
+from reportlab.lib.pagesizes import letter
+from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
+from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer
+from reportlab.lib.units import inch
+
+from ..core.font_registry import UNICODE_FONT_REGISTERED
+
+
+def _page_sort_key(x: str):
+    """Sort key that handles numeric ('5') and split page keys ('5a', '5b')."""
+    match = re.match(r'^(\d+)(.*)', x)
+    if match:
+        return (int(match.group(1)), match.group(2))
+    return (0, x)
+
+
+def _page_display_label(key: str) -> str:
+    return f"Page {key}"
+
+
+def build_combined_transcript(transcripts: dict) -> str:
+    """One string, pages in order, separated by a rule."""
+    pages = sorted(transcripts.keys(), key=_page_sort_key)
+
+    sections = []
+    separator = '\u2500' * 20
+    for page in pages:
+        text = transcripts[page]
+        label = _page_display_label(page)
+        section = f"{label}\n{separator}\n{text}"
+        sections.append(section)
+
+    return "\n\n".join(sections)
+
+
+def build_txt_export(transcripts: dict) -> io.BytesIO:
+    """UTF-8 with a BOM, so Windows Notepad/Excel don't mangle accents."""
+    combined = build_combined_transcript(transcripts)
+    utf8_bom = b'\xef\xbb\xbf'
+    buffer = io.BytesIO(utf8_bom + combined.encode('utf-8'))
+    buffer.seek(0)
+    return buffer
+
+
+def build_docx_export(transcripts: dict) -> io.BytesIO:
+    doc = Document()
+
+    title = doc.add_heading("Combined Transcript", 0)
+    title.alignment = WD_ALIGN_PARAGRAPH.CENTER
+
+    doc.add_paragraph()  # spacer
+
+    pages = sorted(transcripts.keys(), key=_page_sort_key)
+
+    for i, page in enumerate(pages):
+        text = transcripts[page]
+
+        doc.add_heading(_page_display_label(page), level=1)
+
+        separator = doc.add_paragraph("─" * 40)
+        separator.runs[0].font.size = Pt(10)
+
+        for para_text in text.split('\n'):
+            if para_text.strip():
+                para = doc.add_paragraph(para_text)
+                para.style.font.size = Pt(11)
+
+        if i < len(pages) - 1:
+            doc.add_page_break()
+
+    buffer = io.BytesIO()
+    doc.save(buffer)
+    buffer.seek(0)
+    return buffer
+
+
+def build_pdf_export(transcripts: dict) -> io.BytesIO:
+    """Needs DejaVu Sans registered, or accents fall back to Helvetica."""
+    buffer = io.BytesIO()
+
+    doc = SimpleDocTemplate(
+        buffer,
+        pagesize=letter,
+        rightMargin=inch,
+        leftMargin=inch,
+        topMargin=inch,
+        bottomMargin=inch
+    )
+
+    styles = getSampleStyleSheet()
+
+    font_name = 'DejaVuSans' if UNICODE_FONT_REGISTERED else 'Helvetica'
+
+    title_style = ParagraphStyle(
+        'CustomTitle',
+        parent=styles['Heading1'],
+        fontName=font_name,
+        fontSize=18,
+        spaceAfter=30,
+        alignment=1  # center
+    )
+
+    page_header_style = ParagraphStyle(
+        'PageHeader',
+        parent=styles['Heading2'],
+        fontName=font_name,
+        fontSize=14,
+        spaceAfter=6,
+        textColor='#1e40af'
+    )
+
+    body_style = ParagraphStyle(
+        'CustomBody',
+        parent=styles['Normal'],
+        fontName=font_name,
+        fontSize=11,
+        spaceAfter=6,
+        leading=14
+    )
+
+    separator_style = ParagraphStyle(
+        'Separator',
+        parent=styles['Normal'],
+        fontName=font_name,
+        fontSize=10,
+        spaceAfter=12,
+        textColor='#6b7280'
+    )
+
+    story = []
+    story.append(Paragraph("Combined Transcript", title_style))
+    story.append(Spacer(1, 20))
+
+    pages = sorted(transcripts.keys(), key=_page_sort_key)
+
+    for page in pages:
+        text = transcripts[page]
+
+        story.append(Paragraph(_page_display_label(page), page_header_style))
+        story.append(Paragraph("─" * 50, separator_style))
+
+        # reportlab parses a mini-HTML in Paragraph, so escape the text first.
+        for line in text.split('\n'):
+            if line.strip():
+                safe_line = line.replace('&', '&amp;').replace('<', '&lt;').replace('>', '&gt;')
+                story.append(Paragraph(safe_line, body_style))
+            else:
+                story.append(Spacer(1, 6))
+
+        story.append(Spacer(1, 30))
+
+    doc.build(story)
+    buffer.seek(0)
+    return buffer
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/layout_detection.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/layout_detection.py
new file mode 100644
index 00000000..72c2d290
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/layout_detection.py
@@ -0,0 +1,929 @@
+"""Layout-aware text-line detection with PaddleOCR.
+
+Two stages: detect page regions (PP-DocLayout), then run text detection inside
+each text region. Models are built fresh per call and freed after — keeping them
+resident blew past 8 GB VRAM. Budget 8 GB VRAM (GPU) or 8 GB RAM (CPU, slow).
+"""
+
+import gc
+import os
+import time
+from typing import List, Tuple, Dict, Any
+
+# Paddle's PIR path throws ConvertPirAttribute2RuntimeAttribute on these models.
+os.environ["FLAGS_enable_pir_api"] = "0"
+os.environ["FLAGS_enable_pir_in_executor"] = "0"
+
+import cv2
+import numpy as np
+import psutil
+
+# paddlepaddle-gpu links libcuda.so.1 at import time, so importing it in a
+# container without GPU access kills the server at boot. Import on first use
+# instead and return a readable error if it fails.
+_paddle = None
+_create_model = None
+_PaddleOCR = None
+_PADDLE_IMPORT_ERROR: str = ""
+
+
+def _ensure_paddle() -> None:
+    """Import paddle/paddleocr on first use. Raises RuntimeError if unavailable."""
+    global _paddle, _create_model, _PaddleOCR, _PADDLE_IMPORT_ERROR
+    if _paddle is not None:
+        return
+    try:
+        import paddle as _p
+        from paddleocr import PaddleOCR as _O
+        # Single layout model, not PPStructureV3 — that pipeline downloads every
+        # sub-model it knows (tables, seals, formulas) even with the flags off.
+        from paddlex import create_model as _cm
+        _paddle = _p
+        _create_model = _cm
+        _PaddleOCR = _O
+    except Exception as exc:
+        _PADDLE_IMPORT_ERROR = str(exc)
+        raise RuntimeError(
+            f"PaddlePaddle failed to load: {exc}. "
+            "Start the container with GPU access: docker run --gpus all ... "
+            "and ensure CUDA 12.6 drivers are installed on the host. "
+            "GPU paddle install: "
+            "pip install paddlepaddle-gpu==3.3.0 "
+            "-i https://www.paddlepaddle.org.cn/packages/stable/cu126/"
+        ) from exc
+
+
+# ── Tuning constants ──────────────────────────────────────────────────
+TEXT_LABELS    = {"text", "paragraph_title", "doc_title", "header"}
+REGION_PADDING = 50
+LAYOUT_EXPAND  = 2
+SCORE_THRESH   = 0.5
+UPSCALE_MIN_H  = 60
+NMS_IOU_THRESH = 0.3
+GAP_MULTIPLIER = 2.0
+
+# Thresholds for the warnings shown to the user.
+MIN_GPU_VRAM_GB  = 8.0
+MIN_RAM_GB       = 8.0
+LOW_RAM_GB       = 4.0
+
+# Tier ladder (env-overridable): enough free VRAM → server models on GPU, less →
+# mobile on GPU, less still → CPU, picking server vs mobile there by free RAM.
+TIER_SERVER_MIN_VRAM_GB = float(os.environ.get("TIER_SERVER_MIN_VRAM_GB", 6.0))
+TIER_MOBILE_MIN_VRAM_GB = float(os.environ.get("TIER_MOBILE_MIN_VRAM_GB", 2.0))
+TIER_CPU_SERVER_MIN_RAM_GB = float(os.environ.get("TIER_CPU_SERVER_MIN_RAM_GB", 8.0))
+
+SERVER_MODELS = {
+    "layout": "PP-DocLayout_plus-L",
+    "det":    "PP-OCRv5_server_det",
+    "rec":    "PP-OCRv5_server_rec",
+}
+MOBILE_MODELS = {
+    "layout": "PP-DocLayout-S",
+    "det":    "PP-OCRv5_mobile_det",
+    "rec":    "PP-OCRv5_mobile_rec",
+}
+
+
+def _model_cache_root() -> str:
+    """Where PaddleX caches downloaded models (<root>/official_models/).
+
+    /paddle_models in Docker via PADDLE_PDX_CACHE_HOME, ~/.paddlex locally.
+    """
+    root = (
+        os.environ.get("PADDLE_PDX_MODEL_CACHE_HOME")
+        or os.environ.get("PADDLE_PDX_CACHE_HOME")
+    )
+    if root:
+        return root
+    return os.path.join(os.path.expanduser("~"), ".paddlex")
+
+
+def models_cached(use_gpu: bool = True) -> bool:
+    """True when this tier's models are already on disk.
+
+    The UI uses this to warn about the multi-GB first-run download. A directory
+    only counts if it exists AND is non-empty (a killed download leaves an empty one).
+    """
+    root = _model_cache_root()
+    names = SERVER_MODELS if use_gpu else MOBILE_MODELS
+    bases = [os.path.join(root, "official_models"), root]
+    for model_name in names.values():
+        present = False
+        for base in bases:
+            d = os.path.join(base, model_name)
+            try:
+                if os.path.isdir(d) and any(os.scandir(d)):
+                    present = True
+                    break
+            except OSError:
+                continue
+        if not present:
+            return False
+    return True
+
+
+def select_tier(use_gpu: bool) -> Dict[str, Any]:
+    """Pick device + model tier from currently-free VRAM/RAM.
+
+    -> {device, tier, layout_model, det_model, rec_model, reason,
+        free_vram_gb, free_ram_gb}. `reason` is shown to the user as-is.
+    """
+    _ensure_paddle()
+    info = check_system_resources(use_gpu)
+    free_vram = info["gpu_vram_free_gb"]
+    free_ram  = info["available_ram_gb"]
+
+    if use_gpu and info["gpu_available"]:
+        if free_vram >= TIER_SERVER_MIN_VRAM_GB:
+            return {
+                "device": "gpu", "tier": "server",
+                "layout_model": SERVER_MODELS["layout"],
+                "det_model":    SERVER_MODELS["det"],
+                "rec_model":    SERVER_MODELS["rec"],
+                "reason": (
+                    f"{free_vram:.1f} GB free VRAM — using server models on GPU."
+                ),
+                "free_vram_gb": free_vram, "free_ram_gb": free_ram,
+            }
+        if free_vram >= TIER_MOBILE_MIN_VRAM_GB:
+            return {
+                "device": "gpu", "tier": "mobile",
+                "layout_model": MOBILE_MODELS["layout"],
+                "det_model":    MOBILE_MODELS["det"],
+                "rec_model":    MOBILE_MODELS["rec"],
+                "reason": (
+                    f"{free_vram:.1f} GB free VRAM (< {TIER_SERVER_MIN_VRAM_GB:.0f} GB) "
+                    "— using mobile models on GPU for lower memory footprint."
+                ),
+                "free_vram_gb": free_vram, "free_ram_gb": free_ram,
+            }
+        # Not enough VRAM even for mobile — fall through to CPU.
+        cpu_reason_prefix = (
+            f"{free_vram:.1f} GB free VRAM (< {TIER_MOBILE_MIN_VRAM_GB:.0f} GB) "
+            "— falling back to CPU. "
+        )
+    else:
+        cpu_reason_prefix = ""  # CPU asked for, or no GPU at all
+
+    if free_ram >= TIER_CPU_SERVER_MIN_RAM_GB:
+        return {
+            "device": "cpu", "tier": "server",
+            "layout_model": SERVER_MODELS["layout"],
+            "det_model":    SERVER_MODELS["det"],
+            "rec_model":    SERVER_MODELS["rec"],
+            "reason": (
+                f"{cpu_reason_prefix}{free_ram:.1f} GB free RAM — "
+                "using server models on CPU (slow but accurate)."
+            ),
+            "free_vram_gb": free_vram, "free_ram_gb": free_ram,
+        }
+    return {
+        "device": "cpu", "tier": "mobile",
+        "layout_model": MOBILE_MODELS["layout"],
+        "det_model":    MOBILE_MODELS["det"],
+        "rec_model":    MOBILE_MODELS["rec"],
+        "reason": (
+            f"{cpu_reason_prefix}{free_ram:.1f} GB free RAM "
+            f"(< {TIER_CPU_SERVER_MIN_RAM_GB:.0f} GB) — "
+            "using mobile models on CPU to avoid OOM."
+        ),
+        "free_vram_gb": free_vram, "free_ram_gb": free_ram,
+    }
+
+
+# ── Resource check ────────────────────────────────────────────────────
+
+def check_system_resources(use_gpu: bool) -> Dict[str, Any]:
+    """Inspect GPU VRAM (device 0) and system RAM.
+
+    -> {warnings, gpu_available, gpu_vram_gb, gpu_vram_free_gb,
+        available_ram_gb, total_ram_gb}. GPU numbers are 0 when there's no GPU.
+    """
+    warnings: List[str] = []
+
+    gpu_available    = False
+    gpu_vram_gb      = 0.0
+    gpu_vram_free_gb = 0.0
+
+    try:
+        if _paddle is not None:
+            gpu_available = bool(_paddle.device.is_compiled_with_cuda())
+        if gpu_available:
+            props = _paddle.device.cuda.get_device_properties(0)
+            gpu_vram_gb = props.total_memory / (1024 ** 3)
+            reserved_bytes = _paddle.device.cuda.memory_reserved(0)
+            gpu_vram_free_gb = max(0.0, gpu_vram_gb - reserved_bytes / (1024 ** 3))
+    except Exception:
+        pass
+
+    if use_gpu:
+        if not gpu_available:
+            warnings.append(
+                "GPU not available in this environment. "
+                "Running on CPU instead — this is much slower and requires >= 8 GB RAM."
+            )
+        elif gpu_vram_gb < MIN_GPU_VRAM_GB:
+            warnings.append(
+                f"WARNING: GPU VRAM is {gpu_vram_gb:.1f} GB, but a minimum of "
+                f"{MIN_GPU_VRAM_GB:.0f} GB is recommended for the server-class models "
+                "(PP-DocLayout_plus-L + PP-OCRv5_server_det). "
+                "You may experience out-of-memory errors or degraded performance."
+            )
+        elif gpu_vram_free_gb < 4.0:
+            warnings.append(
+                f"WARNING: Only {gpu_vram_free_gb:.1f} GB of VRAM is currently free. "
+                "Other processes may be consuming GPU memory. "
+                "Consider freeing GPU memory before running detection."
+            )
+
+    mem = psutil.virtual_memory()
+    total_ram_gb     = mem.total     / (1024 ** 3)
+    available_ram_gb = mem.available / (1024 ** 3)
+
+    if available_ram_gb < LOW_RAM_GB:
+        warnings.append(
+            f"CRITICAL: Only {available_ram_gb:.1f} GB of RAM is available "
+            f"(total: {total_ram_gb:.1f} GB). "
+            "The system may freeze or be killed by the OOM killer during detection. "
+            f"At least {MIN_RAM_GB:.0f} GB of free RAM is strongly recommended."
+        )
+    elif not use_gpu and available_ram_gb < MIN_RAM_GB:
+        warnings.append(
+            f"WARNING: Running on CPU with only {available_ram_gb:.1f} GB of free RAM. "
+            f"At least {MIN_RAM_GB:.0f} GB RAM is recommended for CPU-mode detection "
+            "with server-class models. Processing may be very slow."
+        )
+
+    return {
+        "warnings":          warnings,
+        "gpu_available":     gpu_available,
+        "gpu_vram_gb":       gpu_vram_gb,
+        "gpu_vram_free_gb":  gpu_vram_free_gb,
+        "available_ram_gb":  available_ram_gb,
+        "total_ram_gb":      total_ram_gb,
+    }
+
+
+def _free_memory():
+    """Aggressively free Python + GPU memory."""
+    gc.collect()
+    try:
+        if _paddle is not None and _paddle.device.is_compiled_with_cuda():
+            _paddle.device.cuda.empty_cache()
+    except Exception:
+        pass
+
+
+# ── Layout helpers ────────────────────────────────────────────────────
+
+def _resize_for_layout(image: np.ndarray,
+                       max_side: int = 1500) -> Tuple[np.ndarray, float]:
+    h, w = image.shape[:2]
+    mx = max(h, w)
+    if mx <= max_side:
+        return image, 1.0
+    s = max_side / mx
+    return cv2.resize(image, (int(w * s), int(h * s)),
+                      interpolation=cv2.INTER_AREA), s
+
+
+def _box_area(box):
+    return max(0, box[2] - box[0]) * max(0, box[3] - box[1])
+
+
+def _iou_xyxy(a, b):
+    ix1, iy1 = max(a[0], b[0]), max(a[1], b[1])
+    ix2, iy2 = min(a[2], b[2]), min(a[3], b[3])
+    inter = max(0, ix2 - ix1) * max(0, iy2 - iy1)
+    if inter == 0:
+        return 0.0
+    return inter / float(_box_area(a) + _box_area(b) - inter)
+
+
+def _suppress_overlapping(layout_boxes, iou_thresh=0.3):
+    filtered = []
+    for box in layout_boxes:
+        keep = True
+        for kept in filtered:
+            if box["label"] != kept["label"]:
+                continue
+            if _iou_xyxy(box["bbox"], kept["bbox"]) > iou_thresh:
+                if _box_area(box["bbox"]) < _box_area(kept["bbox"]):
+                    filtered.remove(kept)
+                else:
+                    keep = False
+                break
+        if keep:
+            filtered.append(box)
+    return filtered
+
+
+def _remove_margin_boxes(layout_boxes, page_width):
+    out = []
+    for b in layout_boxes:
+        x1, _, x2, _ = b["bbox"]
+        cx = (x1 + x2) / 2
+        if cx < page_width * 0.12 or cx > page_width * 0.88:
+            continue
+        out.append(b)
+    return out
+
+
+def _merge_title_blocks(layout_boxes, vertical_thresh=70):
+    merged = []
+    for label in ("doc_title", "text"):
+        boxes = sorted(
+            [b for b in layout_boxes if b["label"] == label],
+            key=lambda x: x["bbox"][1],
+        )
+        cur = None
+        for b in boxes:
+            if cur is None:
+                cur = b.copy(); continue
+            if b["bbox"][1] - cur["bbox"][3] < vertical_thresh:
+                cur["bbox"] = [
+                    min(cur["bbox"][0], b["bbox"][0]),
+                    min(cur["bbox"][1], b["bbox"][1]),
+                    max(cur["bbox"][2], b["bbox"][2]),
+                    max(cur["bbox"][3], b["bbox"][3]),
+                ]
+            else:
+                merged.append(cur); cur = b.copy()
+        if cur:
+            merged.append(cur)
+    for b in layout_boxes:
+        if b["label"] not in ("doc_title", "text"):
+            merged.append(b)
+    return merged
+
+
+# ── Text-line helpers ─────────────────────────────────────────────────
+
+def _poly_bounds(poly):
+    pts = np.asarray(poly, dtype=np.float32)
+    xmn, ymn = pts[:, 0].min(), pts[:, 1].min()
+    xmx, ymx = pts[:, 0].max(), pts[:, 1].max()
+    return xmn, ymn, xmx, ymx, (xmn+xmx)*0.5, (ymn+ymx)*0.5, xmx-xmn, ymx-ymn
+
+
+def _nms(boxes, scores, iou_thresh=NMS_IOU_THRESH):
+    if not boxes:
+        return []
+    order = sorted(range(len(scores)), key=lambda i: scores[i], reverse=True)
+    keep, suppressed = [], set()
+    for i in order:
+        if i in suppressed:
+            continue
+        keep.append(i)
+        bi = _poly_bounds(boxes[i])[:4]
+        for j in order:
+            if j in suppressed or j == i:
+                continue
+            bj = _poly_bounds(boxes[j])[:4]
+            if _iou_xyxy(bi, bj) > iou_thresh:
+                suppressed.add(j)
+    return keep
+
+
+def _resolve_vertical_overlaps(boxes, thresh=0.30):
+    if len(boxes) < 2:
+        return boxes
+    rects = [
+        [float(b[:, 0].min()), float(b[:, 1].min()),
+         float(b[:, 0].max()), float(b[:, 1].max())]
+        for b in boxes
+    ]
+    order = sorted(range(len(rects)), key=lambda i: rects[i][1])
+    rects = [rects[i] for i in order]
+    for i in range(len(rects)):
+        yi0, yi1 = rects[i][1], rects[i][3]
+        hi = yi1 - yi0
+        if hi <= 0:
+            continue
+        for j in range(i + 1, len(rects)):
+            yj0, yj1 = rects[j][1], rects[j][3]
+            ov_top, ov_bot = max(yi0, yj0), min(yi1, yj1)
+            ov = ov_bot - ov_top
+            if ov <= 0:
+                break
+            hj = yj1 - yj0
+            if hj <= 0:
+                continue
+            if ov / min(hi, hj) > thresh:
+                mid = (ov_top + ov_bot) / 2.0
+                rects[i][3] = mid
+                rects[j][1] = mid
+                yi1 = mid
+    result = []
+    for r in rects:
+        xmn, ymn, xmx, ymx = r
+        if xmx > xmn and ymx > ymn:
+            result.append(np.array(
+                [[xmn, ymn], [xmx, ymn], [xmx, ymx], [xmn, ymx]],
+                dtype=np.float32,
+            ))
+    return result
+
+
+def _merge_into_lines(raw_boxes, img_w, img_h, gap_multiplier=GAP_MULTIPLIER):
+    if not raw_boxes:
+        return []
+    bounds = [_poly_bounds(b) for b in raw_boxes]
+    heights = np.array([b[7] for b in bounds], dtype=np.float32)
+    widths  = np.array([b[6] for b in bounds], dtype=np.float32)
+    med_h = float(np.median(heights))
+    med_w = float(np.median(widths))
+    h_thresh  = 0.5 * med_h
+    gap_limit = gap_multiplier * med_w
+    MIN_W, MIN_H, MAX_H = 10.0, 10.0, 6.0 * med_h
+
+    print(f"[_merge_into_lines] {len(raw_boxes)} raw boxes | "
+          f"med_h={med_h:.1f} med_w={med_w:.1f} | "
+          f"h_thresh={h_thresh:.1f} gap_limit={gap_limit:.1f} MAX_H={MAX_H:.1f} | "
+          f"img_w={img_w} img_h={img_h}")
+    print(f"[_merge_into_lines] height stats: "
+          f"min={heights.min():.1f} p25={float(np.percentile(heights,25)):.1f} "
+          f"p50={med_h:.1f} p75={float(np.percentile(heights,75)):.1f} max={heights.max():.1f}")
+    print(f"[_merge_into_lines] sample box bounds (first 3): "
+          f"{[b[:4] for b in bounds[:3]]}")
+
+    filtered = [
+        (b, bnd) for b, bnd in zip(raw_boxes, bounds)
+        if bnd[6] >= MIN_W and MIN_H <= bnd[7] <= MAX_H
+    ]
+    print(f"[_merge_into_lines] after h/w filter: {len(filtered)} boxes remain")
+    if not filtered:
+        return []
+    filtered.sort(key=lambda x: x[1][5])
+
+    lines = []
+    for box, bnd in filtered:
+        cy = bnd[5]
+        assigned = False
+        for line in lines:
+            line_cy = sum(b[1][5] for b in line) / len(line)
+            if abs(cy - line_cy) < h_thresh:
+                line.append((box, bnd))
+                assigned = True
+                break
+        if not assigned:
+            lines.append([(box, bnd)])
+
+    merged = []
+    for line in lines:
+        line.sort(key=lambda x: x[1][0])
+        groups = [[line[0]]]
+        for item in line[1:]:
+            if item[1][0] - groups[-1][-1][1][2] <= gap_limit:
+                groups[-1].append(item)
+            else:
+                groups.append([item])
+        for grp in groups:
+            xmn = max(0, min(b[1][0] for b in grp))
+            ymn = max(0, min(b[1][1] for b in grp))
+            xmx = min(img_w, max(b[1][2] for b in grp))
+            ymx = min(img_h, max(b[1][3] for b in grp))
+            if xmx > xmn and ymx > ymn:
+                merged.append(np.array(
+                    [[xmn, ymn], [xmx, ymn], [xmx, ymx], [xmn, ymx]],
+                    dtype=np.float32,
+                ))
+
+    merged = _resolve_vertical_overlaps(merged)
+    merged.sort(key=lambda b: (float(b[:, 1].min()), float(b[:, 0].min())))
+    return merged
+
+
+def _filter_boxes_by_page_size(boxes, img_h, img_w):
+    """Drop boxes too small to be a text line, scaled to the median box height.
+
+    Thresholds used to come from page area, which threw away every real line on
+    big scans (6048x4536) because page_area * 0.0001 dwarfed a line box.
+    """
+    if not boxes:
+        return []
+
+    heights = np.array(
+        [float(np.asarray(b, dtype=np.float32)[:, 1].max()
+               - np.asarray(b, dtype=np.float32)[:, 1].min())
+         for b in boxes],
+        dtype=np.float32,
+    )
+    median_height = float(np.median(heights))
+
+    min_area   = max(20.0,  median_height * median_height * 0.5)
+    min_width  = max(5.0,   median_height * 0.5)
+    min_height = max(5.0,   median_height * 0.5)
+
+    print(f"[_filter_boxes_by_page_size] img_h={img_h} img_w={img_w} "
+          f"boxes={len(boxes)} median_height={median_height:.1f} "
+          f"min_area={min_area:.1f} min_width={min_width:.1f} "
+          f"min_height={min_height:.1f}")
+
+    kept = []
+    for i, box in enumerate(boxes):
+        pts  = np.asarray(box, dtype=np.float32)
+        w    = float(pts[:, 0].max() - pts[:, 0].min())
+        h    = float(pts[:, 1].max() - pts[:, 1].min())
+        area = w * h
+        print(f"  BOX BEFORE FILTER: width={w:.1f} height={h:.1f} area={area:.1f}")
+        keep = (w >= min_width) and (h >= min_height) and (area >= min_area)
+        print(f"  box[{i}]: x=[{pts[:,0].min():.0f},{pts[:,0].max():.0f}] "
+              f"y=[{pts[:,1].min():.0f},{pts[:,1].max():.0f}] "
+              f"w={w:.1f} h={h:.1f} area={area:.1f} "
+              f"({'KEEP' if keep else 'DROP'})")
+        if keep:
+            kept.append(box)
+    return kept
+
+
+# ── Debug visualisation ───────────────────────────────────────────────
+
+def _save_debug_image(
+    image: np.ndarray,
+    boxes,
+    path: str,
+    color=(0, 255, 0),
+    thickness: int = 2,
+) -> None:
+    """Draw boxes on a copy of the image and write it to path.
+
+    Accepts either 4-point polygons or dicts with a 'bbox' [x1,y1,x2,y2] key.
+    """
+    dir_part = os.path.dirname(path)
+    if dir_part:
+        os.makedirs(dir_part, exist_ok=True)
+
+    vis = image.copy()
+    for box in boxes:
+        if isinstance(box, dict):
+            x1, y1, x2, y2 = [int(c) for c in box["bbox"]]
+            cv2.rectangle(vis, (x1, y1), (x2, y2), color, thickness)
+        else:
+            pts = np.asarray(box, dtype=np.int32).reshape((-1, 1, 2))
+            cv2.polylines(vis, [pts], True, color, thickness)
+    cv2.imwrite(path, vis)
+    print(f"[debug] saved {path} ({len(boxes)} boxes)")
+
+
+# ── Core detection ────────────────────────────────────────────────────
+
+def detect_layout(image: np.ndarray, device: str = "gpu",
+                  max_layout_side: int = 1500,
+                  model_name: str = "PP-DocLayout_plus-L") -> list:
+    """Run layout detection and return cleaned layout boxes."""
+    resized, scale = _resize_for_layout(image, max_layout_side)
+
+    print(f"[detect_layout] image={image.shape}, resized={resized.shape}, "
+          f"scale={scale:.3f}, model={model_name}, device={device}")
+
+    _ensure_paddle()
+    pipeline = None
+    try:
+        _free_memory()  # drop stale allocations before loading a multi-GB model
+        pipeline = _create_model(model_name=model_name, device=device)
+        results = pipeline.predict(resized, batch_size=1)
+    except (MemoryError, RuntimeError) as exc:
+        # Paddle reports GPU OOM as a plain RuntimeError.
+        _free_memory()
+        if pipeline is not None:
+            try:
+                del pipeline
+            except Exception:
+                pass
+        raise RuntimeError(
+            f"Out-of-memory during layout detection ({device.upper()}). "
+            "Ensure at least 8 GB GPU VRAM is available for GPU mode, "
+            "or at least 8 GB free RAM for CPU mode. "
+            f"Original error: {exc}"
+        ) from exc
+
+    layout_boxes = []
+    for res in results:
+        for b in res["boxes"]:
+            label = b["label"]
+            score = float(b["score"])
+            x1, y1, x2, y2 = b["coordinate"]
+            layout_boxes.append({
+                "label": label,
+                "bbox": [int(x1 / scale), int(y1 / scale),
+                         int(x2 / scale), int(y2 / scale)],
+                "score": score,
+            })
+
+    print(f"[detect_layout] raw layout boxes: {len(layout_boxes)}")
+    for b in layout_boxes:
+        print(f"  {b['label']:20s}  score={b['score']:.2f}  bbox={b['bbox']}")
+
+    layout_boxes = _suppress_overlapping(layout_boxes)
+    layout_boxes = _remove_margin_boxes(layout_boxes, image.shape[1])
+    layout_boxes = _merge_title_blocks(layout_boxes)
+
+    print(f"[detect_layout] after post-processing: {len(layout_boxes)} boxes")
+
+    del pipeline, results
+    _free_memory()
+
+    return layout_boxes
+
+
+def detect_text_lines(image: np.ndarray, layout: list,
+                      device: str = "gpu",
+                      det_model_name: str = "PP-OCRv5_server_det",
+                      rec_model_name: str = "PP-OCRv5_server_rec",
+                      region_padding: int = REGION_PADDING,
+                      layout_expand: int = LAYOUT_EXPAND,
+                      score_thresh: float = SCORE_THRESH,
+                      upscale_min_h: int = UPSCALE_MIN_H,
+                      nms_iou_thresh: float = NMS_IOU_THRESH,
+                      gap_multiplier: float = GAP_MULTIPLIER,
+                      debug_dir: str = "") -> list:
+    """Detect line-level bounding boxes for all text regions."""
+
+    print(f"[detect_text_lines] image={image.shape}, "
+          f"{len(layout)} layout regions, det_model={det_model_name}, "
+          f"rec_model={rec_model_name}")
+
+    _ensure_paddle()
+    ocr = None
+    try:
+        _free_memory()
+        ocr = _PaddleOCR(
+            text_detection_model_name=det_model_name,
+            text_recognition_model_name=rec_model_name,
+            use_doc_orientation_classify=False,
+            use_doc_unwarping=False,
+            use_textline_orientation=False,
+            device=device,
+            lang="en",
+        )
+    except (MemoryError, RuntimeError) as exc:
+        _free_memory()
+        raise RuntimeError(
+            f"Out-of-memory while loading text-detection model ({device.upper()}). "
+            "Ensure at least 8 GB GPU VRAM is available for GPU mode, "
+            "or at least 8 GB free RAM for CPU mode. "
+            f"Original error: {exc}"
+        ) from exc
+
+    img_h, img_w = image.shape[:2]
+    all_raw, all_scores = [], []
+
+    for region in layout:
+        if region["label"] not in TEXT_LABELS:
+            continue
+        x1, y1, x2, y2 = [int(c) for c in region["bbox"]]
+        x1e = max(0, x1 - layout_expand)
+        y1e = max(0, y1 - layout_expand)
+        x2e = min(img_w, x2 + layout_expand)
+        y2e = min(img_h, y2 + layout_expand)
+        if x2e <= x1e or y2e <= y1e:
+            continue
+
+        crop = image[y1e:y2e, x1e:x2e]
+        if crop.size == 0:
+            continue
+
+        sc = 1.0
+        if crop.shape[0] < upscale_min_h:
+            sc = 2.0
+            crop = cv2.resize(crop, None, fx=sc, fy=sc,
+                              interpolation=cv2.INTER_CUBIC)
+
+        pad_scaled = int(region_padding * sc)
+        padded = cv2.copyMakeBorder(
+            crop, pad_scaled, pad_scaled, pad_scaled, pad_scaled,
+            borderType=cv2.BORDER_CONSTANT, value=(255, 255, 255),
+        )
+        del crop
+
+        ox = x1e - region_padding
+        oy = y1e - region_padding
+
+        try:
+            crop_results = ocr.predict(padded)
+        except (MemoryError, RuntimeError) as exc:
+            del padded
+            if ocr is not None:
+                try:
+                    del ocr
+                except Exception:
+                    pass
+            _free_memory()
+            raise RuntimeError(
+                f"Out-of-memory during text-line detection on region {region['bbox']} "
+                f"({device.upper()}). "
+                "Ensure at least 8 GB GPU VRAM (GPU mode) or 8 GB free RAM (CPU mode). "
+                f"Original error: {exc}"
+            ) from exc
+        del padded
+
+        n = 0
+        _logged_raw = 0
+        for res in crop_results:
+            polys  = res.get("dt_polys",  [])
+            scores = res.get("dt_scores", [1.0] * len(polys))
+            for poly, score in zip(polys, scores):
+                if score < score_thresh:
+                    continue
+                arr = np.array(poly, dtype=np.float32)
+                if _logged_raw < 2:
+                    print(f"    [raw poly {_logged_raw}] shape={arr.shape} "
+                          f"raw_coords={arr.tolist()} sc={sc} ox={ox} oy={oy}")
+                arr /= sc
+                arr[:, 0] += ox
+                arr[:, 1] += oy
+                if _logged_raw < 2:
+                    print(f"    [raw poly {_logged_raw}] after_transform: "
+                          f"x=[{arr[:,0].min():.0f},{arr[:,0].max():.0f}] "
+                          f"y=[{arr[:,1].min():.0f},{arr[:,1].max():.0f}]")
+                    _logged_raw += 1
+                if arr[:, 0].max() < x1e or arr[:, 0].min() > x2e:
+                    continue
+                if arr[:, 1].max() < y1e or arr[:, 1].min() > y2e:
+                    continue
+                all_raw.append(arr)
+                all_scores.append(float(score))
+                n += 1
+        del crop_results
+
+        print(f"  [{region['label']}]  bbox={region['bbox']}  "
+              f"scale={sc:.1f}x  ->  {n} raw boxes")
+
+    print(f"[detect_text_lines] total raw boxes: {len(all_raw)}")
+
+    if debug_dir:
+        _save_debug_image(
+            image, all_raw,
+            os.path.join(debug_dir, "page_raw_text_boxes.png"),
+            color=(0, 0, 255),
+        )
+
+    keep_idx = _nms(all_raw, all_scores, iou_thresh=nms_iou_thresh)
+    all_raw = [all_raw[i] for i in keep_idx]
+    print(f"[detect_text_lines] after NMS: {len(all_raw)}")
+
+    if debug_dir:
+        _save_debug_image(
+            image, all_raw,
+            os.path.join(debug_dir, "page_after_nms.png"),
+            color=(0, 165, 255),
+        )
+
+    if all_raw:
+        print(f"[detect_text_lines] coordinate sanity: "
+              f"img_w={img_w} img_h={img_h}")
+        for idx, b in enumerate(all_raw[:5]):
+            pts = np.asarray(b, dtype=np.float32)
+            bw = float(pts[:, 0].max() - pts[:, 0].min())
+            bh = float(pts[:, 1].max() - pts[:, 1].min())
+            print(f"  [pre-merge box {idx}] "
+                  f"x=[{pts[:,0].min():.0f},{pts[:,0].max():.0f}] "
+                  f"y=[{pts[:,1].min():.0f},{pts[:,1].max():.0f}] "
+                  f"w={bw:.1f} h={bh:.1f} area={bw*bh:.1f}")
+
+    merged = _merge_into_lines(all_raw, img_w, img_h, gap_multiplier=gap_multiplier)
+    del all_raw
+
+    before = len(merged)
+    merged = _filter_boxes_by_page_size(merged, img_h, img_w)
+    print(f"[detect_text_lines] after page filter: {len(merged)} "
+          f"(removed {before - len(merged)})")
+
+    if debug_dir:
+        _save_debug_image(
+            image, merged,
+            os.path.join(debug_dir, "page_final_lines.png"),
+            color=(0, 255, 0),
+        )
+
+    del ocr
+    _free_memory()
+
+    return merged
+
+
+# ── Public orchestrator ───────────────────────────────────────────────
+
+def run_layout_aware_detection(
+    image: np.ndarray,
+    use_gpu: bool = False,
+    layout_model_name: str = "PP-DocLayout_plus-L",
+    det_model_name: str = "PP-OCRv5_server_det",
+    rec_model_name: str = "PP-OCRv5_server_rec",
+    region_padding: int = REGION_PADDING,
+    layout_expand: int = LAYOUT_EXPAND,
+    score_thresh: float = SCORE_THRESH,
+    upscale_min_h: int = UPSCALE_MIN_H,
+    nms_iou_thresh: float = NMS_IOU_THRESH,
+    gap_multiplier: float = GAP_MULTIPLIER,
+    debug_dir: str = "",
+) -> Tuple[List[List[List[float]]], List[str]]:
+    """Layout detection then text-line detection.
+
+    -> (4-point polygons in page coordinates, resource warnings). Show the
+    warnings to the user — they explain why a run is slow or about to OOM.
+    """
+    # Silently drop to CPU rather than fail if paddle can't use the GPU.
+    cuda_compiled = False
+    try:
+        _ensure_paddle()
+        cuda_compiled = bool(_paddle.device.is_compiled_with_cuda())
+    except RuntimeError as _load_err:
+        if use_gpu:
+            use_gpu = False
+            print(f"[run_layout_aware_detection] Paddle failed to load — "
+                  f"forcing CPU (will likely be slow): {_load_err}")
+
+    if use_gpu and not cuda_compiled:
+        use_gpu = False
+        print(
+            "[run_layout_aware_detection] WARNING: use_gpu=True requested but "
+            "PaddlePaddle is not compiled with CUDA. "
+            "Falling back to CPU automatically."
+        )
+
+    device = "gpu" if use_gpu else "cpu"
+
+    res_info = check_system_resources(use_gpu)
+    resource_warnings: List[str] = res_info["warnings"]
+
+    # Goes first — it explains every other warning below it.
+    if not cuda_compiled and device == "cpu":
+        cuda_warning = (
+            "GPU was requested but PaddlePaddle in this environment is CPU-only "
+            "(not compiled with CUDA). Running on CPU instead. "
+            "To enable GPU acceleration, CUDA 12.6 driver on the host is required "
+            "and the container must be started with --gpus all. Install GPU paddle: "
+            "pip install paddlepaddle-gpu==3.3.0 "
+            "-i https://www.paddlepaddle.org.cn/packages/stable/cu126/"
+        )
+        resource_warnings.insert(0, cuda_warning)
+
+    print(f"\n{'='*60}")
+    print(f"[run_layout_aware_detection] START")
+    print(f"  image shape : {image.shape}")
+    print(f"  device      : {device}  (cuda_compiled={cuda_compiled})")
+    print(f"  layout_model: {layout_model_name}")
+    print(f"  det_model   : {det_model_name}")
+    print(f"  rec_model   : {rec_model_name}")
+    print(f"  region_padding : {region_padding}")
+    print(f"  layout_expand  : {layout_expand}")
+    print(f"  score_thresh   : {score_thresh}")
+    print(f"  upscale_min_h  : {upscale_min_h}")
+    print(f"  nms_iou_thresh : {nms_iou_thresh}")
+    print(f"  gap_multiplier : {gap_multiplier}")
+    print(f"  gpu_available  : {res_info['gpu_available']}  "
+          f"vram={res_info['gpu_vram_gb']:.1f} GB  free={res_info['gpu_vram_free_gb']:.1f} GB")
+    print(f"  ram_available  : {res_info['available_ram_gb']:.1f} GB  "
+          f"total={res_info['total_ram_gb']:.1f} GB")
+    for w in resource_warnings:
+        print(f"  [RESOURCE WARNING] {w}")
+    print(f"{'='*60}")
+
+    t0 = time.time()
+
+    layout = detect_layout(image, device=device, max_layout_side=1500,
+                           model_name=layout_model_name)
+
+    if not layout:
+        print("[run_layout_aware_detection] No layout regions found!")
+        return [], resource_warnings
+
+    if debug_dir:
+        _save_debug_image(
+            image, layout,
+            os.path.join(debug_dir, "page_layout_boxes.png"),
+            color=(255, 0, 0),
+        )
+
+    text_regions = [r for r in layout if r["label"] in TEXT_LABELS]
+    print(f"\n[run_layout_aware_detection] "
+          f"{len(text_regions)} text regions / {len(layout)} total")
+
+    if not text_regions:
+        print("[run_layout_aware_detection] No TEXT regions in layout!")
+        return [], resource_warnings
+
+    merged = detect_text_lines(image, layout, device=device,
+                               det_model_name=det_model_name,
+                               rec_model_name=rec_model_name,
+                               region_padding=region_padding,
+                               layout_expand=layout_expand,
+                               score_thresh=score_thresh,
+                               upscale_min_h=upscale_min_h,
+                               nms_iou_thresh=nms_iou_thresh,
+                               gap_multiplier=gap_multiplier,
+                               debug_dir=debug_dir)
+
+    lines = [box.tolist() for box in merged]
+
+    elapsed = time.time() - t0
+    print(f"\n[run_layout_aware_detection] DONE — "
+          f"{len(lines)} lines in {elapsed:.1f}s")
+
+    _free_memory()
+
+    return lines, resource_warnings
+
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/llm_processing/__init__.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/llm_processing/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/llm_processing/factory.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/llm_processing/factory.py
new file mode 100644
index 00000000..61f092e1
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/llm_processing/factory.py
@@ -0,0 +1,128 @@
+"""Registry + dispatch for text-cleanup LLM providers.
+
+Gemini uses the google-genai SDK; OpenAI/DeepSeek/Qwen share one
+chat-completions client. `local_es` is our GPU-only Spanish fine-tune.
+"""
+
+from .gemini_client import post_process_text as _gemini_post_process
+from .openai_compat_client import post_process_text_openai_compat
+from .local_client import (
+    post_process_text_finetuned,
+    FINETUNED_BASE_MODEL,
+)
+
+
+# OpenAI-compatible chat-completions endpoints (same URLs as the OCR providers).
+_OPENAI_COMPAT_ENDPOINTS = {
+    "openai": "https://api.openai.com/v1/chat/completions",
+    "deepseek": "https://api.deepseek.com/v1/chat/completions",
+    "qwen": "https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions",
+}
+
+
+# Feeds the frontend dropdowns via GET /api/llm/providers. These are TEXT
+# models — Qwen uses qwen-plus/turbo/max here, not the qwen-vl-* OCR models.
+LLM_PROVIDERS = [
+    {
+        "id": "gemini",
+        "name": "Gemini",
+        "enabled": True,
+        "requires_key": True,
+        "default_model": "gemini-2.5-flash",
+        "models": [
+            {"id": "gemini-2.5-flash", "name": "Gemini 2.5 Flash"},
+            {"id": "gemini-2.5-pro", "name": "Gemini 2.5 Pro"},
+        ],
+    },
+    {
+        "id": "openai",
+        "name": "OpenAI",
+        "enabled": True,
+        "requires_key": True,
+        "default_model": "gpt-5-mini",
+        "models": [
+            {"id": "gpt-5-mini", "name": "GPT-5 Mini"},
+            {"id": "gpt-5.2", "name": "GPT-5.2"},
+        ],
+    },
+    {
+        "id": "deepseek",
+        "name": "DeepSeek",
+        "enabled": True,
+        "requires_key": True,
+        "default_model": "deepseek-chat",
+        "models": [
+            {"id": "deepseek-chat", "name": "DeepSeek Chat"},
+            {"id": "deepseek-reasoner", "name": "DeepSeek Reasoner"},
+        ],
+    },
+    {
+        "id": "qwen",
+        "name": "Qwen",
+        "enabled": True,
+        "requires_key": True,
+        "default_model": "qwen-plus",
+        "models": [
+            {"id": "qwen-plus", "name": "Qwen Plus"},
+            {"id": "qwen-turbo", "name": "Qwen Turbo"},
+            {"id": "qwen-max", "name": "Qwen Max"},
+        ],
+    },
+    {
+        # GPU-only, and the gated base download needs HF_TOKEN. See local_client.
+        "id": "local_es",
+        "name": "Local fine-tuned (Spanish)",
+        "enabled": True,
+        "requires_key": False,
+        "default_model": FINETUNED_BASE_MODEL,
+        "models": [
+            {"id": FINETUNED_BASE_MODEL, "name": "Gemma-3-4B Spanish (fine-tuned)"},
+        ],
+        "note": "Fine-tuned on Spanish historical OCR; runs 4-bit on the server GPU (gated base downloaded on first use — needs HF_TOKEN).",
+    },
+]
+
+_ENABLED_PROVIDER_IDS = {p["id"] for p in LLM_PROVIDERS if p["enabled"]}
+_KEYLESS_PROVIDER_IDS = {
+    p["id"] for p in LLM_PROVIDERS if not p.get("requires_key", True)
+}
+
+
+def provider_requires_key(provider: str) -> bool:
+    """True unless the provider runs locally (no API key needed)."""
+    return (provider or "").lower() not in _KEYLESS_PROVIDER_IDS
+
+
+def post_process(
+    provider: str,
+    api_key: str | None,
+    text: str,
+    model: str,
+    template_name: str = "full_cleanup",
+) -> str:
+    """Route the request to `provider`. Raises ValueError if it's unknown or off."""
+    provider = (provider or "gemini").lower()
+
+    if provider not in _ENABLED_PROVIDER_IDS:
+        if any(p["id"] == provider for p in LLM_PROVIDERS):
+            raise ValueError(
+                f"Provider '{provider}' is not available yet."
+            )
+        valid = ", ".join(sorted(_ENABLED_PROVIDER_IDS))
+        raise ValueError(
+            f"Unknown LLM provider: '{provider}'. Valid providers: {valid}"
+        )
+
+    if provider == "local_es":
+        return post_process_text_finetuned(text, model, template_name)
+
+    if provider == "gemini":
+        return _gemini_post_process(api_key, text, model, template_name)
+
+    return post_process_text_openai_compat(
+        endpoint=_OPENAI_COMPAT_ENDPOINTS[provider],
+        api_key=api_key,
+        text=text,
+        model=model,
+        template_name=template_name,
+    )
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/llm_processing/gemini_client.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/llm_processing/gemini_client.py
new file mode 100644
index 00000000..f221bab6
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/llm_processing/gemini_client.py
@@ -0,0 +1,33 @@
+"""Gemini text post-processing — same google-genai pattern as the OCR provider."""
+
+from google import genai
+
+from .prompt_templates import get_template
+
+
+DEFAULT_MODEL = "gemini-2.5-flash"
+
+
+def post_process_text(
+    api_key: str,
+    text: str,
+    model: str = DEFAULT_MODEL,
+    template_name: str = "full_cleanup",
+) -> str:
+    """Clean up OCR text with Gemini. template_name keys into prompt_templates."""
+    if not text or not text.strip():
+        return text
+
+    client = genai.Client(api_key=api_key)
+    prompt = get_template(template_name) + text
+
+    response = client.models.generate_content(
+        model=model,
+        contents=[prompt],
+    )
+
+    result = response.text
+    if not result or not result.strip():
+        raise ValueError("LLM returned an empty response")
+
+    return result.strip()
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/llm_processing/local_client.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/llm_processing/local_client.py
new file mode 100644
index 00000000..fd47a7dd
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/llm_processing/local_client.py
@@ -0,0 +1,297 @@
+"""The `local_es` corrector: gemma-3-4b-it + our shipped Spanish LoRA adapter.
+
+Runs in 4-bit QLoRA (same config it was trained with, ~3.5 GB VRAM) and corrects
+one line at a time, matching training. GPU only — 4-bit needs CUDA.
+
+gemma-3 is gated on HuggingFace, so the first base-weights download needs
+HF_TOKEN set and the licence accepted at huggingface.co/google/gemma-3-4b-it.
+"""
+
+import os
+from pathlib import Path
+from threading import Lock
+
+# AutoModelForCausalLM resolves this to the multimodal Gemma3 class; text-only
+# generation works fine and matches how the adapter was trained.
+FINETUNED_BASE_MODEL = "google/gemma-3-4b-it"
+FINETUNED_ADAPTER_SUBDIR = "postprocess/gemma-3-4b-it-lora"
+
+# Must stay byte-identical to the prompt the adapter was fine-tuned with.
+_FINETUNED_SYSTEM_PROMPT = (
+    "You are an expert in early-modern Spanish (15th-19th century) palaeography "
+    "and OCR correction. The user gives you one raw OCR line from a historical "
+    "Spanish document. Return ONLY the corrected line, nothing else. "
+    "Fix glyph confusions (rn→m, 0→o, 1/I→l, ſ→s), restore diacritics "
+    "(á é í ó ú ñ ç), and merge or split wrongly tokenised words. "
+    "Preserve the original early-modern spelling: never modernise, translate, "
+    "or add content. If the line is already correct, return it unchanged."
+)
+
+# (tokenizer, model, device) cache. The lock stops two requests from kicking off
+# simultaneous multi-GB loads.
+_CACHE: dict = {}
+_LOAD_LOCK = Lock()
+
+
+def _adapter_dir() -> str:
+    """Shipped LoRA adapter dir; same path in dev and in the container."""
+    backend_root = Path(__file__).resolve().parents[3]
+    return str(backend_root / "models" / "weights" / FINETUNED_ADAPTER_SUBDIR)
+
+
+def base_model_cached() -> bool:
+    """True when the gated base weights are already on disk.
+
+    Mirrors layout_detection.models_cached: the UI probes this so it can warn
+    about the one-time ~8 GB download instead of looking hung. Filesystem only
+    — never imports torch/transformers, so it stays cheap to call.
+
+    huggingface_hub only creates a snapshot symlink once a blob has finished
+    downloading, so a half-done shard leaves a dangling/absent entry and
+    .exists() correctly reports False.
+    """
+    hf_home = os.environ.get("HF_HOME") or str(Path.home() / ".cache" / "huggingface")
+    repo = (
+        Path(hf_home) / "hub"
+        / ("models--" + FINETUNED_BASE_MODEL.replace("/", "--"))
+    )
+    snapshots = repo / "snapshots"
+    if not snapshots.is_dir():
+        return False
+
+    for snap in snapshots.iterdir():
+        # Sharded checkpoints (gemma-3-4b is 2 shards): every file named in the
+        # index must be present, or we would flash "ready" mid-download.
+        index = snap / "model.safetensors.index.json"
+        if index.is_file():
+            try:
+                import json
+
+                shards = set(json.loads(index.read_text())["weight_map"].values())
+            except (OSError, ValueError, KeyError):
+                continue
+            if shards and all((snap / s).exists() for s in shards):
+                return True
+        elif (snap / "model.safetensors").exists():
+            return True
+    return False
+
+
+def _load(model_name: str, *, adapter_dir: str | None = None, quantize: bool = False):
+    """Load and cache a tokenizer + causal LM.
+
+    adapter_dir applies a local PEFT LoRA on top of the base; quantize loads
+    4-bit nf4 when CUDA is present, which keeps the 4B model near 3 GB VRAM.
+    """
+    key = f"{model_name}::{adapter_dir or ''}::{'q4' if quantize else 'full'}"
+    cached = _CACHE.get(key)
+    if cached is not None:
+        return cached
+
+    with _LOAD_LOCK:
+        cached = _CACHE.get(key)
+        if cached is not None:
+            return cached
+
+        import torch
+        from transformers import AutoModelForCausalLM, AutoTokenizer
+
+        from app.utils.torch_device import select_torch_device
+
+        device = select_torch_device()
+
+        # Tokenizer comes from the BASE model, never the adapter dir. LoRA
+        # doesn't touch the vocabulary, and the adapter's tokenizer.json was
+        # serialized by a newer `tokenizers` than the one pinned in the image.
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        if tokenizer.pad_token is None:
+            tokenizer.pad_token = tokenizer.eos_token
+
+        # NEVER fp16 here. gemma-3 is bf16-trained and its activations overflow
+        # fp16: the logits go NaN, generate() emits nothing but <pad>, and the
+        # caller below keeps every original line — a silent no-op that looks
+        # like "the model had no corrections to make" rather than a failure.
+        # bf16 needs Ampere or newer, so older cards take the fp32 path (slower,
+        # but correct).
+        if device == "cuda":
+            dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float32
+        else:
+            dtype = torch.float32
+
+        load_kwargs: dict = {}
+        if quantize and device == "cuda":
+            from transformers import BitsAndBytesConfig
+
+            load_kwargs["quantization_config"] = BitsAndBytesConfig(
+                load_in_4bit=True,
+                bnb_4bit_quant_type="nf4",
+                bnb_4bit_use_double_quant=True,
+                bnb_4bit_compute_dtype=dtype,
+            )
+            load_kwargs["device_map"] = "auto"
+        # torch_dtype, not dtype — the image pins transformers 4.53. Still a
+        # valid alias in 5.x, so this works both places.
+        load_kwargs["torch_dtype"] = dtype
+
+        # One local LLM resident at a time, evicted BEFORE the new load —
+        # otherwise switching providers holds both on an 8 GB GPU and OOMs.
+        # Safe because post-processing runs one request at a time.
+        _evict_all_except(key)
+
+        model = AutoModelForCausalLM.from_pretrained(model_name, **load_kwargs)
+
+        if adapter_dir:
+            from peft import PeftModel
+
+            model = PeftModel.from_pretrained(model, adapter_dir)
+
+        # device_map="auto" already placed the quantized model.
+        if "device_map" not in load_kwargs:
+            model.to(device)
+        model.eval()
+
+        _CACHE[key] = (tokenizer, model, device)
+        return _CACHE[key]
+
+
+def _evict_all_except(keep_key: str) -> None:
+    """Free every cached local LLM except `keep_key` (frees VRAM/RAM)."""
+    import gc
+
+    import torch
+
+    for k in [k for k in _CACHE if k != keep_key]:
+        _, model, device = _CACHE.pop(k)
+        del model
+        gc.collect()
+        if device == "cuda":
+            torch.cuda.empty_cache()
+
+
+def post_process_text_finetuned(
+    text: str,
+    model: str | None = None,          # ignored; adapter is fixed
+    template_name: str | None = None,  # ignored; per-line system prompt is fixed
+    batch_size: int = 16,
+) -> str:
+    """Correct OCR text with the Spanish-finetuned gemma LoRA.
+
+    Line by line, because that's how the adapter was trained — a whole page is
+    out of distribution. Blank lines are kept so paragraphs survive. Raises on
+    CPU-only hosts; the API providers still work there.
+    """
+    if not text or not text.strip():
+        return text
+
+    import torch
+
+    from app.utils.torch_device import select_torch_device
+
+    if select_torch_device() != "cuda":
+        raise ValueError(
+            "The local fine-tuned Spanish model requires a GPU (4-bit QLoRA). "
+            "Use a cloud provider (Gemini/OpenAI) on CPU-only hosts."
+        )
+
+    adapter_dir = _adapter_dir()
+    if not os.path.isdir(adapter_dir):
+        raise ValueError(f"Fine-tuned adapter not found at {adapter_dir}")
+
+    try:
+        tokenizer, llm, device = _load(
+            FINETUNED_BASE_MODEL, adapter_dir=adapter_dir, quantize=True
+        )
+    except Exception as exc:
+        # Turn huggingface_hub's GatedRepoError/401 into something actionable.
+        msg = str(exc).lower()
+        if "gated" in msg or "401" in msg or "authoriz" in msg or "token" in msg:
+            raise ValueError(
+                f"Could not download the gated base model '{FINETUNED_BASE_MODEL}'. "
+                "Set HF_TOKEN in the environment and accept the licence at "
+                "huggingface.co/google/gemma-3-4b-it."
+            ) from exc
+        raise
+    tokenizer.padding_side = "left"  # decoder-only batched generation
+
+    # generate() stops a batch only once EVERY row has emitted a stop token, so
+    # both knobs below are about not letting one long row bill the other 15.
+    stop_ids = llm.generation_config.eos_token_id
+    stop_ids = set(stop_ids) if isinstance(stop_ids, (list, tuple)) else {stop_ids}
+
+    lines = text.split("\n")
+    idx = [i for i, ln in enumerate(lines) if ln.strip()]
+    out = list(lines)
+    answered = 0
+
+    # Batch lines of similar length together: rows are left-padded to the
+    # longest in their batch, and a batch runs as many steps as its slowest row
+    # needs. Mixing a 5-token line with a 60-token one makes the short ones pay
+    # for the long one at both ends. Results are written back to out[i] by
+    # original index, so the visual order the frontend relies on is untouched.
+    line_lens = dict(zip(
+        idx,
+        (len(t) for t in tokenizer(
+            [lines[i] for i in idx], add_special_tokens=False
+        ).input_ids),
+    ))
+    idx.sort(key=line_lens.__getitem__)
+
+    for start in range(0, len(idx), batch_size):
+        chunk_idx = idx[start:start + batch_size]
+        prompts = [
+            tokenizer.apply_chat_template(
+                [
+                    {"role": "system", "content": _FINETUNED_SYSTEM_PROMPT},
+                    {"role": "user", "content": lines[i]},
+                ],
+                tokenize=False,
+                add_generation_prompt=True,
+            )
+            for i in chunk_idx
+        ]
+        enc = tokenizer(prompts, return_tensors="pt", padding=True).to(device)
+        # A corrected line runs about as long as the line that went in — this is
+        # glyph repair, not translation. Sizing the ceiling to the batch's
+        # longest input (+ slack for restored diacritics and split words) beats
+        # a flat 160, which one rambling row would otherwise drag all 16 rows to.
+        # ponytail: 1.5x + 16 is generous rather than tuned; the guard below
+        # catches anything that does overrun, so a too-tight cap can only cost a
+        # correction, never truncate a line.
+        cap = min(160, int(max(line_lens[i] for i in chunk_idx) * 1.5) + 16)
+        with torch.no_grad():
+            gen = llm.generate(
+                **enc,
+                max_new_tokens=cap,
+                do_sample=False,
+                pad_token_id=tokenizer.pad_token_id,
+            )
+        new = gen[:, enc["input_ids"].shape[1]:]
+        decoded = tokenizer.batch_decode(new, skip_special_tokens=True)
+        for i, corrected, row in zip(chunk_idx, decoded, new):
+            # No stop token means generation was cut off at the cap, so what we
+            # have is half a line. Keep the original rather than write a
+            # truncated one — a silently chopped line is worse than an uncorrected
+            # one, and the frontend has no way to tell them apart.
+            if stop_ids.isdisjoint(row.tolist()):
+                continue
+            # One input line must stay one output line — the frontend maps
+            # results back onto boxes by index. Never blank a line out either.
+            corrected = " ".join(corrected.split()).strip()
+            if corrected:
+                out[i] = corrected
+                answered += 1
+
+    # Keeping the original line when the model returns nothing is right per
+    # line, but if it answered NOTHING at all it is broken (NaN logits from a
+    # bad dtype do exactly this) — and silently echoing the input back looks
+    # like a clean run to the user. Fail loudly instead.
+    if idx and answered == 0:
+        raise ValueError(
+            "Fine-tuned model returned no output for any line — it may have "
+            "failed to load correctly."
+        )
+
+    result = "\n".join(out).strip()
+    if not result:
+        raise ValueError("Fine-tuned model returned an empty response")
+    return result
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/llm_processing/openai_compat_client.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/llm_processing/openai_compat_client.py
new file mode 100644
index 00000000..45f2f524
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/llm_processing/openai_compat_client.py
@@ -0,0 +1,46 @@
+"""One client for OpenAI, DeepSeek and Qwen.
+
+All three speak the same /chat/completions schema, so only the endpoint differs.
+"""
+
+import httpx
+
+from .prompt_templates import get_template
+
+
+def post_process_text_openai_compat(
+    endpoint: str,
+    api_key: str,
+    text: str,
+    model: str,
+    template_name: str = "full_cleanup",
+) -> str:
+    """Clean up OCR text. endpoint is the provider's full chat-completions URL."""
+    if not text or not text.strip():
+        return text
+
+    prompt = get_template(template_name) + text
+
+    payload = {
+        "model": model,
+        "messages": [{"role": "user", "content": prompt}],
+        # Output is about as long as the input page, so this is plenty.
+        "max_tokens": 4096,
+    }
+
+    response = httpx.post(
+        endpoint,
+        headers={
+            "Authorization": f"Bearer {api_key}",
+            "Content-Type": "application/json",
+        },
+        json=payload,
+        timeout=120.0,
+    )
+    response.raise_for_status()
+    result = response.json()["choices"][0]["message"]["content"]
+
+    if not result or not result.strip():
+        raise ValueError("LLM returned an empty response")
+
+    return result.strip()
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/llm_processing/prompt_templates.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/llm_processing/prompt_templates.py
new file mode 100644
index 00000000..596be831
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/llm_processing/prompt_templates.py
@@ -0,0 +1,87 @@
+"""Prompt templates for LLM cleanup of OCR text.
+
+Line count is sacred: one line per detected text region, mapped back by
+position. Every template gets _LINE_PRESERVATION appended to enforce that.
+(local_es skips these — it corrects one line at a time anyway.)
+"""
+
+_LINE_PRESERVATION = (
+    "\n\nCRITICAL — DO NOT CHANGE THE NUMBER OF LINES:\n"
+    "The transcript below has exactly one line per text region detected on the "
+    "page. That line count is fixed and correct — it comes from the layout "
+    "detection step, not from you. Rules:\n"
+    "- Return EXACTLY the same number of lines, in the same order.\n"
+    "- Each input line maps to one output line. Correct text only WITHIN a line.\n"
+    "- NEVER merge two lines into one, split one line into two, reorder, add, or "
+    "delete a line — not even blank lines.\n"
+    "- If a line seems to end mid-word or mid-sentence, leave it as-is on its "
+    "own line; the next line continues it.\n"
+    "- Read the whole page for context (to disambiguate glyphs and words), but "
+    "edit each line independently.\n"
+    "- Output ONLY the corrected lines, separated by single newlines. No "
+    "numbering, no commentary, no code fences.\n\n"
+    "Transcript (one line per detected region):\n\n"
+)
+
+TEMPLATES = {
+    "full_cleanup": {
+        "name": "Full Cleanup",
+        "description": "Fix spelling, formatting, and OCR artifacts in one pass",
+        "prompt": (
+            "You are an OCR post-processing assistant for historical Spanish "
+            "documents. Clean up the OCR-extracted transcript:\n"
+            "- Fix obvious OCR errors and misspellings\n"
+            "- Fix incorrect character substitutions (e.g., 'rn' misread as 'm', '1' as 'l', long-s 'ſ' as 's')\n"
+            "- Restore diacritics that OCR dropped (á é í ó ú ñ ç)\n"
+            "- Normalize inconsistent spacing within a line\n"
+            "- Preserve the original early-modern spelling and meaning\n"
+            "- Do NOT translate, modernise, rephrase, add or remove content"
+        ),
+    },
+    "spelling_correction": {
+        "name": "Spelling Correction",
+        "description": "Fix only spelling errors and character misrecognitions",
+        "prompt": (
+            "You are a spelling correction assistant for OCR output of "
+            "historical Spanish documents. Fix ONLY spelling errors and "
+            "character misrecognitions. Do not change wording, do not "
+            "translate or modernise, do not add or remove content."
+        ),
+    },
+    "formatting": {
+        "name": "Format & Structure",
+        "description": "Normalize spacing within lines (line count stays fixed)",
+        "prompt": (
+            "You are a text formatting assistant for OCR output.\n"
+            "- Normalize spacing within each line (remove extra spaces, fix missing spaces)\n"
+            "- Do NOT change any words or fix spelling\n"
+            "- Do NOT add or remove content"
+        ),
+    },
+    "historical_normalization": {
+        "name": "Historical Text Normalization",
+        "description": "Normalize archaic spellings and historical typography",
+        "prompt": (
+            "You are a historical text normalization assistant.\n"
+            "- Normalize long-s (ſ) to modern 's'\n"
+            "- Expand common ligatures (ff→ff, fi→fi, fl→fl, ffi→ffi, ffl→ffl)\n"
+            "- Normalize archaic letter forms while preserving meaning\n"
+            "- Fix OCR errors specific to historical typefaces\n"
+            "- Do NOT modernise vocabulary or grammar"
+        ),
+    },
+}
+
+
+def get_template(name: str) -> str:
+    """Template prompt + the line contract. Callers append the transcript after."""
+    entry = TEMPLATES.get(name, TEMPLATES["full_cleanup"])
+    return entry["prompt"] + _LINE_PRESERVATION
+
+
+def list_templates() -> list:
+    """Template metadata for the UI dropdown."""
+    return [
+        {"id": key, "name": val["name"], "description": val["description"]}
+        for key, val in TEMPLATES.items()
+    ]
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/ocr/__init__.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/ocr/__init__.py
new file mode 100644
index 00000000..2f397227
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/ocr/__init__.py
@@ -0,0 +1,4 @@
+from .factory import OCRFactory
+from .base import BaseOCRProvider
+
+__all__ = ['OCRFactory', 'BaseOCRProvider']
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/ocr/base.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/ocr/base.py
new file mode 100644
index 00000000..da5c968c
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/ocr/base.py
@@ -0,0 +1,21 @@
+"""Interface every OCR provider implements."""
+
+from abc import ABC, abstractmethod
+from typing import Optional
+
+
+class BaseOCRProvider(ABC):
+    """Base class for all OCR providers."""
+
+    # Subclasses must fill these in.
+    MODELS: list = []
+    DEFAULT_MODEL: str = ""
+    MODEL_IDS: list = []
+
+    @abstractmethod
+    def transcribe(self, api_key: str, image_bytes: bytes, model_name: str, mime_type: str = "image/png", custom_prompt: Optional[str] = None) -> str:
+        """OCR the image and return the text.
+
+        custom_prompt replaces utils.prompt.OCR_PROMPT when the user supplies one.
+        """
+        ...
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/ocr/chatgpt.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/ocr/chatgpt.py
new file mode 100644
index 00000000..0a3f3f66
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/ocr/chatgpt.py
@@ -0,0 +1,69 @@
+"""OpenAI vision OCR over plain httpx."""
+
+import base64
+import httpx
+from typing import Optional
+
+from .base import BaseOCRProvider
+from ...utils.prompt import OCR_PROMPT
+
+
+CHATGPT_MODELS = [
+    {
+        "id": "gpt-5.2",
+        "name": "GPT-5.2",
+        "description": "Latest and most capable multimodal model"
+    },
+    {
+        "id": "gpt-5-mini",
+        "name": "GPT-5 Mini",
+        "description": "Smaller, faster, and more affordable"
+    },
+]
+
+CHATGPT_MODEL_IDS = [m["id"] for m in CHATGPT_MODELS]
+CHATGPT_DEFAULT_MODEL = "gpt-5.2"
+
+
+class ChatGPTProvider(BaseOCRProvider):
+    MODELS = CHATGPT_MODELS
+    DEFAULT_MODEL = CHATGPT_DEFAULT_MODEL
+    MODEL_IDS = CHATGPT_MODEL_IDS
+
+    def transcribe(self, api_key: str, image_bytes: bytes, model_name: str, mime_type: str = "image/png", custom_prompt: Optional[str] = None) -> str:
+        image_b64 = base64.b64encode(image_bytes).decode("utf-8")
+        data_url = f"data:{mime_type};base64,{image_b64}"
+        prompt = custom_prompt if custom_prompt else OCR_PROMPT
+
+        payload = {
+            "model": model_name,
+            "messages": [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "image_url",
+                            "image_url": {"url": data_url}
+                        },
+                        {
+                            "type": "text",
+                            "text": prompt
+                        }
+                    ]
+                }
+            ],
+            "max_tokens": 4096,
+        }
+
+        response = httpx.post(
+            "https://api.openai.com/v1/chat/completions",
+            headers={
+                "Authorization": f"Bearer {api_key}",
+                "Content-Type": "application/json",
+            },
+            json=payload,
+            timeout=120.0,
+        )
+        response.raise_for_status()
+        result = response.json()
+        return result["choices"][0]["message"]["content"]
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/ocr/deepseek.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/ocr/deepseek.py
new file mode 100644
index 00000000..13d3ec37
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/ocr/deepseek.py
@@ -0,0 +1,69 @@
+"""DeepSeek OCR over its OpenAI-compatible endpoint."""
+
+import base64
+import httpx
+from typing import Optional
+
+from .base import BaseOCRProvider
+from ...utils.prompt import OCR_PROMPT
+
+
+DEEPSEEK_MODELS = [
+    {
+        "id": "deepseek-chat",
+        "name": "DeepSeek Chat",
+        "description": "DeepSeek general chat model"
+    },
+    {
+        "id": "deepseek-reasoner",
+        "name": "DeepSeek Reasoner",
+        "description": "DeepSeek reasoning model"
+    },
+]
+
+DEEPSEEK_MODEL_IDS = [m["id"] for m in DEEPSEEK_MODELS]
+DEEPSEEK_DEFAULT_MODEL = "deepseek-chat"
+
+
+class DeepSeekProvider(BaseOCRProvider):
+    MODELS = DEEPSEEK_MODELS
+    DEFAULT_MODEL = DEEPSEEK_DEFAULT_MODEL
+    MODEL_IDS = DEEPSEEK_MODEL_IDS
+
+    def transcribe(self, api_key: str, image_bytes: bytes, model_name: str, mime_type: str = "image/png", custom_prompt: Optional[str] = None) -> str:
+        image_b64 = base64.b64encode(image_bytes).decode("utf-8")
+        data_url = f"data:{mime_type};base64,{image_b64}"
+        prompt = custom_prompt if custom_prompt else OCR_PROMPT
+
+        payload = {
+            "model": model_name,
+            "messages": [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "image_url",
+                            "image_url": {"url": data_url}
+                        },
+                        {
+                            "type": "text",
+                            "text": prompt
+                        }
+                    ]
+                }
+            ],
+            "max_tokens": 4096,
+        }
+
+        response = httpx.post(
+            "https://api.deepseek.com/v1/chat/completions",
+            headers={
+                "Authorization": f"Bearer {api_key}",
+                "Content-Type": "application/json",
+            },
+            json=payload,
+            timeout=120.0,
+        )
+        response.raise_for_status()
+        result = response.json()
+        return result["choices"][0]["message"]["content"]
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/ocr/factory.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/ocr/factory.py
new file mode 100644
index 00000000..3a37b71e
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/ocr/factory.py
@@ -0,0 +1,30 @@
+"""Look up an OCR provider by name."""
+
+from .base import BaseOCRProvider
+from .gemini import GeminiProvider
+from .chatgpt import ChatGPTProvider
+from .deepseek import DeepSeekProvider
+from .qwen import QwenProvider
+
+
+_PROVIDERS = {
+    "gemini": GeminiProvider,
+    "chatgpt": ChatGPTProvider,
+    "deepseek": DeepSeekProvider,
+    "qwen": QwenProvider,
+}
+
+
+class OCRFactory:
+    @staticmethod
+    def get_provider(name: str) -> BaseOCRProvider:
+        """Instantiate a provider by name. ValueError if it isn't one of ours."""
+        provider_cls = _PROVIDERS.get(name.lower())
+        if provider_cls is None:
+            valid = ", ".join(_PROVIDERS.keys())
+            raise ValueError(f"Unknown OCR provider: '{name}'. Valid providers: {valid}")
+        return provider_cls()
+
+    @staticmethod
+    def list_providers() -> list[str]:
+        return list(_PROVIDERS.keys())
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/ocr/gemini.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/ocr/gemini.py
new file mode 100644
index 00000000..d4c843dd
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/ocr/gemini.py
@@ -0,0 +1,109 @@
+"""Gemini OCR via the google-genai SDK."""
+
+import time
+
+from google import genai
+from google.genai import types
+from google.genai import errors as genai_errors
+from typing import Optional
+
+from .base import BaseOCRProvider
+from ...utils.prompt import OCR_PROMPT
+
+# 5xx from Gemini ("high demand") usually clears in a second. 4xx won't, so
+# only ServerError is retried below.
+_MAX_RETRIES = 2
+_RETRY_BACKOFF_S = (2,)
+
+
+AVAILABLE_MODELS = [
+    {
+        "id": "gemini-3.1-flash-lite",
+        "name": "Gemini 3.1 Flash Lite",
+        "description": "Fast and light, best free-tier quota (recommended)"
+    },
+    {
+        "id": "gemini-3-flash-preview",
+        "name": "Gemini 3 Flash",
+        "description": "Fast, high quality for most documents"
+    },
+    {
+        "id": "gemini-3.5-flash",
+        "name": "Gemini 3.5 Flash",
+        "description": "Higher quality, good balance of speed and accuracy"
+    },
+    {
+        "id": "gemini-3.1-pro-preview",
+        "name": "Gemini 3.1 Pro",
+        "description": "Most capable, best for hard pages (low daily quota)"
+    },
+    {
+        "id": "gemini-2.5-flash",
+        "name": "Gemini 2.5 Flash",
+        "description": "Stable fallback when 3.x models are busy"
+    },
+]
+
+DEFAULT_MODEL = "gemini-3.1-flash-lite"
+MODEL_IDS = [m["id"] for m in AVAILABLE_MODELS]
+
+# Without this a thinking model can hang indefinitely, burning free-tier quota.
+REQUEST_TIMEOUT_MS = 120_000
+
+
+def get_gemini_client(api_key: str):
+
+    return genai.Client(
+        api_key=api_key,
+        http_options=types.HttpOptions(timeout=REQUEST_TIMEOUT_MS),
+    )
+
+
+class GeminiProvider(BaseOCRProvider):
+    """Gemini OCR provider using Google GenAI SDK."""
+
+    MODELS = AVAILABLE_MODELS
+    DEFAULT_MODEL = DEFAULT_MODEL
+    MODEL_IDS = MODEL_IDS
+
+    def _generate(self, client, model_name: str, image_bytes: bytes, mime_type: str, prompt: str):
+        """One generation, retrying transient 5xx."""
+        # Gemini 3 thinks by default and can burn the whole token budget
+        # reasoning about a dense page, returning no text at all. Cap it.
+        # thinking_level only exists in newer SDKs, hence the field check.
+        config = None
+        if model_name.startswith("gemini-3") and "thinking_level" in types.ThinkingConfig.model_fields:
+            config = types.GenerateContentConfig(
+                thinking_config=types.ThinkingConfig(thinking_level="low")
+            )
+
+        for attempt in range(_MAX_RETRIES):
+            try:
+                return client.models.generate_content(
+                    model=model_name,
+                    contents=[
+                        types.Part.from_bytes(data=image_bytes, mime_type=mime_type),
+                        prompt
+                    ],
+                    config=config,
+                )
+            except genai_errors.ServerError:
+                if attempt >= _MAX_RETRIES - 1:
+                    raise
+                time.sleep(_RETRY_BACKOFF_S[attempt])
+
+    def transcribe(self, api_key: str, image_bytes: bytes, model_name: str, mime_type: str = "image/png", custom_prompt: Optional[str] = None) -> str:
+        client = get_gemini_client(api_key)
+        prompt = custom_prompt if custom_prompt else OCR_PROMPT
+
+        response = self._generate(client, model_name, image_bytes, mime_type, prompt)
+
+        text = response.text
+        if not text:
+            # Empty means the budget went to thinking or the output was
+            # blocked. Say so — a blank transcript looks like a silent failure.
+            reason = getattr(response, "prompt_feedback", None) or getattr(
+                response, "candidates", None
+            )
+            raise ValueError(f"Gemini returned no text (finish info: {reason})")
+        return text
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/ocr/qwen.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/ocr/qwen.py
new file mode 100644
index 00000000..2a847da7
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/ocr/qwen.py
@@ -0,0 +1,79 @@
+"""Qwen VL OCR over DashScope's OpenAI-compatible endpoint."""
+
+import base64
+import httpx
+from typing import Optional
+
+from .base import BaseOCRProvider
+from ...utils.prompt import OCR_PROMPT
+
+
+QWEN_MODELS = [
+    {
+        "id": "qwen-vl-max",
+        "name": "Qwen VL Max",
+        "description": "Most capable Qwen vision-language model"
+    },
+    {
+        "id": "qwen-vl-ocr",
+        "name": "Qwen VL OCR",
+        "description": "Qwen model optimized for OCR tasks"
+    },
+    {
+        "id": "qwen2.5-vl-72b-instruct",
+        "name": "Qwen2.5 VL 72B",
+        "description": "Large Qwen 2.5 vision-language model"
+    },
+    {
+        "id": "qwen2.5-vl-7b-instruct",
+        "name": "Qwen2.5 VL 7B",
+        "description": "Efficient Qwen 2.5 vision model"
+    },
+]
+
+QWEN_MODEL_IDS = [m["id"] for m in QWEN_MODELS]
+QWEN_DEFAULT_MODEL = "qwen-vl-max"
+
+
+class QwenProvider(BaseOCRProvider):
+    MODELS = QWEN_MODELS
+    DEFAULT_MODEL = QWEN_DEFAULT_MODEL
+    MODEL_IDS = QWEN_MODEL_IDS
+
+    def transcribe(self, api_key: str, image_bytes: bytes, model_name: str, mime_type: str = "image/png", custom_prompt: Optional[str] = None) -> str:
+        image_b64 = base64.b64encode(image_bytes).decode("utf-8")
+        data_url = f"data:{mime_type};base64,{image_b64}"
+        prompt = custom_prompt if custom_prompt else OCR_PROMPT
+
+        payload = {
+            "model": model_name,
+            "messages": [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "image_url",
+                            "image_url": {"url": data_url}
+                        },
+                        {
+                            "type": "text",
+                            "text": prompt
+                        }
+                    ]
+                }
+            ],
+            "max_tokens": 4096,
+        }
+
+        response = httpx.post(
+            "https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions",
+            headers={
+                "Authorization": f"Bearer {api_key}",
+                "Content-Type": "application/json",
+            },
+            json=payload,
+            timeout=120.0,
+        )
+        response.raise_for_status()
+        result = response.json()
+        return result["choices"][0]["message"]["content"]
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/recognition/__init__.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/recognition/__init__.py
new file mode 100644
index 00000000..af537a81
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/recognition/__init__.py
@@ -0,0 +1 @@
+# Recognition services — local line-level OCR (CRNN / TrOCR)
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/recognition/crnn_inference.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/recognition/crnn_inference.py
new file mode 100644
index 00000000..0bbad15d
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/recognition/crnn_inference.py
@@ -0,0 +1,276 @@
+"""CRNN line-level OCR.
+
+The architecture and helpers are copy-pasted from RenAIssanceExperimental on
+purpose, so the backend doesn't depend on that repo at runtime.
+"""
+
+from __future__ import annotations
+
+import os
+import logging
+from typing import Optional
+
+import cv2
+import numpy as np
+import torch
+import torch.nn as nn
+from PIL import Image
+
+from app.utils.torch_device import select_torch_device
+
+logger = logging.getLogger(__name__)
+
+
+# ── Model architecture ────────────────────────────────────────────────
+
+
+class ResBlock(nn.Module):
+    """Basic residual block with optional projection shortcut."""
+
+    def __init__(self, in_c: int, out_c: int, stride: int = 1):
+        super().__init__()
+        self.conv1 = nn.Conv2d(in_c, out_c, 3, padding=1, stride=stride, bias=False)
+        self.bn1 = nn.BatchNorm2d(out_c)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = nn.Conv2d(out_c, out_c, 3, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(out_c)
+
+        self.shortcut = nn.Sequential()
+        if stride != 1 or in_c != out_c:
+            self.shortcut = nn.Sequential(
+                nn.Conv2d(in_c, out_c, 1, stride=stride, bias=False),
+                nn.BatchNorm2d(out_c),
+            )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        out = self.relu(self.bn1(self.conv1(x)))
+        out = self.bn2(self.conv2(out))
+        return self.relu(out + self.shortcut(x))
+
+
+class ResNetCNN(nn.Module):
+    """Lightweight ResNet-style CNN backbone.  (B,1,H,W) → (B,256,1,W')"""
+
+    def __init__(self):
+        super().__init__()
+        self.stem = nn.Sequential(
+            nn.Conv2d(1, 64, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(64),
+            nn.ReLU(inplace=True),
+        )
+        self.stage1 = nn.Sequential(ResBlock(64, 64), ResBlock(64, 64), nn.MaxPool2d(2, 2))
+        self.stage2 = nn.Sequential(ResBlock(64, 128), ResBlock(128, 128), nn.MaxPool2d(2, 2))
+        self.stage3 = nn.Sequential(ResBlock(128, 256), ResBlock(256, 256), nn.MaxPool2d((2, 1)))
+        self.stage4 = nn.Sequential(ResBlock(256, 256), nn.MaxPool2d((2, 1)))
+        self.height_collapse = nn.AdaptiveAvgPool2d((1, None))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.stem(x)
+        x = self.stage1(x)
+        x = self.stage2(x)
+        x = self.stage3(x)
+        x = self.stage4(x)
+        x = self.height_collapse(x)
+        return x
+
+
+class CRNN(nn.Module):
+    """ResNet CNN → BiLSTM → Linear → CTC log-softmax."""
+
+    def __init__(
+        self,
+        vocab_size: int,
+        lstm_hidden: int = 256,
+        lstm_layers: int = 2,
+        dropout: float = 0.3,
+    ):
+        super().__init__()
+        self.cnn = ResNetCNN()
+        self.lstm = nn.LSTM(
+            input_size=256,
+            hidden_size=lstm_hidden,
+            num_layers=lstm_layers,
+            bidirectional=True,
+            batch_first=True,
+            dropout=dropout if lstm_layers > 1 else 0.0,
+        )
+        self.dropout = nn.Dropout(dropout)
+        self.classifier = nn.Linear(lstm_hidden * 2, vocab_size)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.cnn(x)          # (B, 256, 1, W')
+        x = x.squeeze(2)         # (B, 256, W')
+        x = x.permute(0, 2, 1)   # (B, W', 256)
+        x, _ = self.lstm(x)      # (B, W', H*2)
+        x = self.dropout(x)
+        x = self.classifier(x)   # (B, W', V)
+        return x.log_softmax(-1)
+
+
+# ── Helpers ───────────────────────────────────────────────────────────
+
+
+def resize_keep_ratio(img: Image.Image, height: int = 64) -> Image.Image:
+    """Resize a PIL image to a fixed height, preserving the aspect ratio."""
+    w, h = img.size
+    new_w = max(1, int(w * (height / h)))
+    return img.resize((new_w, height), Image.BILINEAR)
+
+
+def ctc_greedy_decode(
+    log_probs: torch.Tensor,
+    idx2char: dict,
+    blank: int = 0,
+) -> list[str]:
+    """Greedy best-path CTC decode.  (B, T, V) → list[str]"""
+    preds = log_probs.argmax(-1).cpu().numpy()
+    results = []
+    for seq in preds:
+        chars, prev = [], None
+        for idx in seq:
+            if idx != blank and idx != prev:
+                chars.append(idx2char.get(int(idx), ""))
+            prev = idx
+        results.append("".join(chars))
+    return results
+
+
+def crop_polygon_gray(image_bgr: np.ndarray, poly_pts) -> Image.Image:
+    """Crop a polygon region from a BGR image and return as grayscale PIL."""
+    pts = np.array(poly_pts, dtype=np.float32)
+    x1, y1 = pts.min(axis=0).astype(int)
+    x2, y2 = pts.max(axis=0).astype(int)
+    x1 = max(0, x1)
+    y1 = max(0, y1)
+    x2 = min(image_bgr.shape[1], x2)
+    y2 = min(image_bgr.shape[0], y2)
+    if x2 <= x1 or y2 <= y1:
+        return Image.new("L", (8, 8), 255)  # degenerate box
+    crop = image_bgr[y1:y2, x1:x2]
+    return Image.fromarray(cv2.cvtColor(crop, cv2.COLOR_BGR2GRAY))
+
+
+# ── Model discovery ───────────────────────────────────────────────────
+
+
+def discover_models(search_dir: str) -> list[dict]:
+    """Find CRNN .pth/.pt checkpoints under search_dir.
+
+    Returns [{"id": "crnn:<stem>", "name", "model_type", "path"}, ...].
+    """
+    models = []
+    if not os.path.isdir(search_dir):
+        logger.warning("Model search directory does not exist: %s", search_dir)
+        return models
+
+    for root, _dirs, files in os.walk(search_dir):
+        for fname in files:
+            if fname.endswith((".pth", ".pt")):
+                if "crnn" not in fname.lower():
+                    continue
+                abs_path = os.path.abspath(os.path.join(root, fname))
+                stem = os.path.splitext(fname)[0]
+                models.append(
+                    {
+                        "id": f"crnn:{stem}",
+                        "name": f"CRNN - {stem}",
+                        "model_type": "crnn",
+                        "path": abs_path,
+                    }
+                )
+    return sorted(models, key=lambda item: item["name"].lower())
+
+
+# ── Recognizer ────────────────────────────────────────────────────────
+
+
+class CRNNRecognizer:
+    """Loads the checkpoint on first predict, then keeps it in memory."""
+
+    def __init__(self, model_path: str, device: Optional[str] = None):
+        self.model_path = model_path
+        self._model: Optional[CRNN] = None
+        self._idx2char: Optional[dict] = None
+        self._img_height: int = 64
+
+        if device is None:
+            self.device = select_torch_device()
+        else:
+            self.device = device
+
+        logger.info(
+            "CRNNRecognizer created (model=%s, device=%s, lazy-load)",
+            os.path.basename(model_path),
+            self.device,
+        )
+
+    def _ensure_loaded(self):
+        if self._model is not None:
+            return
+
+        logger.info("Loading CRNN checkpoint: %s", self.model_path)
+        ckpt = torch.load(self.model_path, map_location=self.device, weights_only=False)
+
+        self._idx2char = ckpt["idx2char"]
+        self._img_height = ckpt.get("img_height", 64)
+
+        self._model = CRNN(
+            vocab_size=len(ckpt["vocab"]),
+            lstm_hidden=ckpt.get("lstm_hidden", 256),
+            lstm_layers=ckpt.get("lstm_layers", 2),
+        ).to(self.device)
+        self._model.load_state_dict(ckpt["model_state_dict"])
+        self._model.eval()
+
+        logger.info(
+            "CRNN loaded — vocab=%d, img_height=%d, device=%s",
+            len(ckpt["vocab"]),
+            self._img_height,
+            self.device,
+        )
+
+    def predict(self, image: Image.Image) -> str:
+        """Recognise text from a single grayscale line image."""
+        self._ensure_loaded()
+        img = resize_keep_ratio(image.convert("L"), self._img_height)
+        arr = np.asarray(img, dtype=np.float32) / 255.0
+        inp = torch.from_numpy(arr).unsqueeze(0).unsqueeze(0).to(self.device)
+        with torch.no_grad():
+            log_probs = self._model(inp)
+        return ctc_greedy_decode(log_probs, self._idx2char)[0]
+
+    def predict_batch(self, images: list[Image.Image]) -> list[str]:
+        """Recognise text from a batch of grayscale line images."""
+        if not images:
+            return []
+
+        self._ensure_loaded()
+
+        resized = [resize_keep_ratio(img.convert("L"), self._img_height) for img in images]
+
+        tensors = []
+        for img in resized:
+            arr = np.asarray(img, dtype=np.float32) / 255.0
+            tensors.append(torch.from_numpy(arr).unsqueeze(0))
+
+        # Lines vary in width after the fixed-height resize, so pad to the widest.
+        max_w = max(t.shape[-1] for t in tensors)
+        padded = [torch.nn.functional.pad(t, (0, max_w - t.shape[-1])) for t in tensors]
+        batch = torch.stack(padded).to(self.device)
+
+        with torch.no_grad():
+            log_probs = self._model(batch)
+
+        return ctc_greedy_decode(log_probs, self._idx2char)
+
+
+# ── Global model cache ────────────────────────────────────────────────
+
+_recognizer_cache: dict[str, CRNNRecognizer] = {}
+
+
+def get_recognizer(model_path: str) -> CRNNRecognizer:
+    """Get (or create and cache) a CRNNRecognizer for the given path."""
+    if model_path not in _recognizer_cache:
+        _recognizer_cache[model_path] = CRNNRecognizer(model_path)
+    return _recognizer_cache[model_path]
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/recognition/trocr_inference.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/recognition/trocr_inference.py
new file mode 100644
index 00000000..b4fc5647
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/recognition/trocr_inference.py
@@ -0,0 +1,128 @@
+"""TrOCR recognizer for line-level OCR inference."""
+
+from __future__ import annotations
+
+import logging
+import os
+from pathlib import Path
+from typing import Optional
+
+import cv2
+import numpy as np
+import torch
+from PIL import Image
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel
+
+from app.utils.torch_device import select_torch_device
+
+logger = logging.getLogger(__name__)
+
+
+def discover_models(search_dir: str) -> list[dict]:
+    """Discover TrOCR checkpoints from local HuggingFace-style directories."""
+    models: list[dict] = []
+    root = Path(search_dir)
+    if not root.is_dir():
+        logger.warning("TrOCR model search directory does not exist: %s", search_dir)
+        return models
+
+    for dirpath, _dirnames, filenames in os.walk(root):
+        files = set(filenames)
+        if "config.json" not in files:
+            continue
+        has_weights = "model.safetensors" in files or "pytorch_model.bin" in files
+        if not has_weights:
+            continue
+
+        abs_dir = os.path.abspath(dirpath)
+        rel = os.path.relpath(abs_dir, root)
+        display_name = rel.replace(os.sep, " / ") if rel != "." else Path(abs_dir).name
+        model_id = f"trocr:{rel.replace(os.sep, '/')}" if rel != "." else "trocr:default"
+        models.append(
+            {
+                "id": model_id,
+                "name": f"TrOCR - {display_name}",
+                "model_type": "trocr",
+                "path": abs_dir,
+            }
+        )
+
+    return sorted(models, key=lambda item: item["name"].lower())
+
+
+def crop_polygon_rgb(image_bgr: np.ndarray, poly_pts) -> Image.Image:
+    """Crop a polygon region from a BGR image and return RGB PIL image."""
+    pts = np.array(poly_pts, dtype=np.float32)
+    x1, y1 = pts.min(axis=0).astype(int)
+    x2, y2 = pts.max(axis=0).astype(int)
+    x1 = max(0, x1)
+    y1 = max(0, y1)
+    x2 = min(image_bgr.shape[1], x2)
+    y2 = min(image_bgr.shape[0], y2)
+    if x2 <= x1 or y2 <= y1:
+        return Image.new("RGB", (8, 8), (255, 255, 255))
+    crop = image_bgr[y1:y2, x1:x2]
+    rgb = cv2.cvtColor(crop, cv2.COLOR_BGR2RGB)
+    return Image.fromarray(rgb)
+
+
+class TrOCRRecognizer:
+    """Lazy-loaded, cached TrOCR recognizer."""
+
+    def __init__(self, model_dir: str, device: Optional[str] = None):
+        self.model_dir = model_dir
+        self._processor: Optional[TrOCRProcessor] = None
+        self._model: Optional[VisionEncoderDecoderModel] = None
+
+        if device is None:
+            self.device = select_torch_device()
+        else:
+            self.device = device
+
+        logger.info(
+            "TrOCRRecognizer created (model_dir=%s, device=%s, lazy-load)",
+            os.path.basename(model_dir),
+            self.device,
+        )
+
+    def _ensure_loaded(self):
+        if self._processor is not None and self._model is not None:
+            return
+
+        logger.info("Loading TrOCR checkpoint: %s", self.model_dir)
+        self._processor = TrOCRProcessor.from_pretrained(self.model_dir, local_files_only=True)
+        # fp16 on GPU: ~half the memory, ~3x faster, no meaningful CER change
+        # (see checkpoints/trocr_quantization/). CPU has no fp16 kernels.
+        dtype = torch.float16 if self.device == "cuda" else torch.float32
+        self._model = VisionEncoderDecoderModel.from_pretrained(
+            self.model_dir,
+            local_files_only=True,
+            torch_dtype=dtype,
+        ).to(self.device)
+        self._model.eval()
+
+    def predict_batch(self, images: list[Image.Image]) -> list[str]:
+        if not images:
+            return []
+        self._ensure_loaded()
+
+        rgb_images = [img.convert("RGB") for img in images]
+        pixel_values = self._processor(images=rgb_images, return_tensors="pt").pixel_values.to(
+            self.device, dtype=self._model.dtype
+        )
+
+        with torch.no_grad():
+            generated_ids = self._model.generate(pixel_values, max_new_tokens=96)
+
+        texts = self._processor.batch_decode(generated_ids, skip_special_tokens=True)
+        return [t.strip() for t in texts]
+
+
+_recognizer_cache: dict[str, TrOCRRecognizer] = {}
+
+
+def get_recognizer(model_dir: str) -> TrOCRRecognizer:
+    """Get (or create and cache) a TrOCR recognizer for the given directory."""
+    if model_dir not in _recognizer_cache:
+        _recognizer_cache[model_dir] = TrOCRRecognizer(model_dir)
+    return _recognizer_cache[model_dir]
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/transcript_parser.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/transcript_parser.py
new file mode 100644
index 00000000..b1ed9a64
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/services/transcript_parser.py
@@ -0,0 +1,167 @@
+"""Split a transcript file (TXT/DOCX/PDF/MD) into {page_key: [lines]}.
+
+Page markers are matched loosely — "PDF p1", "--- Page 4 - left ---",
+"[Page 5]", "Page 6", "p1-right" all work, case-insensitively.
+"""
+
+import io
+import re
+from typing import Dict, List, Optional
+
+
+# Optional " left"/" right" suffix, with any of space/-/–/— as the separator.
+_SIDE_SUFFIX = r"(?:[\s\-–—]+(?P<side>left|right))?"
+
+_PAGE_PATTERNS = [
+    # "PDF p1", "PDF p2 - left", "PDF p2 left"
+    re.compile(
+        r"^\s*PDF\s+p\s*(?P<num>\d+)" + _SIDE_SUFFIX + r"\s*$",
+        re.IGNORECASE,
+    ),
+    # "--- Page 4 ---" or "--- page 4 left ---" or "--- page 4 - right ---"
+    re.compile(
+        r"^\s*-{2,}\s*[Pp]age\s+(?P<num>\d+)" + _SIDE_SUFFIX + r"\s*-*\s*$",
+        re.IGNORECASE,
+    ),
+    # "[Page 5]" or "[Page 5 - left]" or "[Page 5 right]"
+    re.compile(
+        r"^\s*\[\s*[Pp]age\s+(?P<num>\d+)" + _SIDE_SUFFIX + r"\s*\]\s*$",
+        re.IGNORECASE,
+    ),
+    # Bare "Page 6" / "Page 6 left" / "page 6 - right"
+    re.compile(
+        r"^\s*[Pp]age\s+(?P<num>\d+)" + _SIDE_SUFFIX + r"\s*$",
+        re.IGNORECASE,
+    ),
+    # Shorthand "p1" / "p1 left" / "p1-right"
+    re.compile(
+        r"^\s*[Pp](?P<num>\d+)" + _SIDE_SUFFIX + r"\s*$",
+        re.IGNORECASE,
+    ),
+]
+
+
+def _match_page_marker(line: str) -> Optional[str]:
+    """-> '3' / '3_left' if the line is a page marker, else None."""
+    for pat in _PAGE_PATTERNS:
+        m = pat.match(line)
+        if m:
+            page_num = m.group("num")
+            try:
+                side = m.group("side")
+            except IndexError:
+                side = None
+            if side:
+                return f"{page_num}_{side.lower()}"
+            return page_num
+    return None
+
+
+# ── Text extraction per format ──────────────────────────────────────
+
+def _extract_text_from_txt(data: bytes) -> str:
+    text = data.decode("utf-8-sig", errors="replace")
+    return text
+
+
+def _extract_text_from_docx(data: bytes) -> str:
+    from docx import Document
+
+    doc = Document(io.BytesIO(data))
+    return "\n".join(p.text for p in doc.paragraphs)
+
+
+def _extract_text_from_pdf(data: bytes) -> str:
+    try:
+        import fitz  # PyMuPDF
+    except ImportError:
+        raise RuntimeError(
+            "PyMuPDF (fitz) is required to parse PDF transcripts. "
+            "Install it with: pip install PyMuPDF"
+        )
+    doc = fitz.open(stream=data, filetype="pdf")
+    pages_text = []
+    for page in doc:
+        pages_text.append(page.get_text())
+    doc.close()
+    return "\n".join(pages_text)
+
+
+def _extract_text_from_markdown(data: bytes) -> str:
+    return data.decode("utf-8-sig", errors="replace")
+
+
+_EXTRACTORS = {
+    "text/plain": _extract_text_from_txt,
+    "text/markdown": _extract_text_from_markdown,
+    "application/vnd.openxmlformats-officedocument.wordprocessingml.document": _extract_text_from_docx,
+    "application/pdf": _extract_text_from_pdf,
+}
+
+# Used when the browser sends a useless content-type.
+_EXT_MAP = {
+    ".txt": "text/plain",
+    ".md": "text/markdown",
+    ".markdown": "text/markdown",
+    ".docx": "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
+    ".pdf": "application/pdf",
+}
+
+
+def extract_text(data: bytes, filename: str = "", content_type: str = "") -> str:
+    """Get raw text out of the uploaded bytes, by content-type or extension."""
+    mime = content_type.lower().split(";")[0].strip() if content_type else ""
+
+    if mime not in _EXTRACTORS:
+        import os
+        ext = os.path.splitext(filename)[1].lower()
+        mime = _EXT_MAP.get(ext, "text/plain")
+
+    extractor = _EXTRACTORS.get(mime, _extract_text_from_txt)
+    return extractor(data)
+
+
+# ── Split into pages ────────────────────────────────────────────────
+
+def parse_transcript(
+    raw_text: str,
+    default_page: str = "1",
+) -> Dict[str, List[str]]:
+    """Split raw text on page markers into {page_key: [lines]}.
+
+    No markers at all -> everything lands under default_page. Blank lines go.
+    """
+    pages: Dict[str, List[str]] = {}
+    lines = raw_text.splitlines()
+    has_any_marker = any(_match_page_marker(line) is not None for line in lines)
+
+    # With markers present, anything before the first one is preface — drop it.
+    current_key: Optional[str] = None if has_any_marker else default_page
+
+    for raw_line in lines:
+        marker = _match_page_marker(raw_line)
+        if marker is not None:
+            current_key = marker
+            continue
+
+        if current_key is None:  # still in the preface
+            continue
+
+        cleaned = raw_line.strip()
+        if not cleaned:
+            continue
+        if re.match(r"^end\s+of\s+extract\s*$", cleaned, re.IGNORECASE):
+            continue
+        pages.setdefault(current_key, []).append(cleaned)
+
+    return pages
+
+
+def parse_transcript_bytes(
+    data: bytes,
+    filename: str = "",
+    content_type: str = "",
+) -> Dict[str, List[str]]:
+    """extract_text + parse_transcript in one call."""
+    raw = extract_text(data, filename, content_type)
+    return parse_transcript(raw)
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/storage/__init__.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/storage/__init__.py
new file mode 100644
index 00000000..6e4bdb8c
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/storage/__init__.py
@@ -0,0 +1,27 @@
+"""Persistent storage package for transcripts and datasets."""
+
+from .storage_manager import (
+    save_transcript_session,
+    save_recognition_dataset,
+    save_detection_dataset,
+    list_entries,
+    get_transcript_detail,
+    get_dataset_detail,
+    delete_entry,
+    zip_entry,
+    resolve_storage_root,
+    ensure_storage_layout,
+)
+
+__all__ = [
+    "save_transcript_session",
+    "save_recognition_dataset",
+    "save_detection_dataset",
+    "list_entries",
+    "get_transcript_detail",
+    "get_dataset_detail",
+    "delete_entry",
+    "zip_entry",
+    "resolve_storage_root",
+    "ensure_storage_layout",
+]
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/storage/file_indexer.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/storage/file_indexer.py
new file mode 100644
index 00000000..ac7ff04e
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/storage/file_indexer.py
@@ -0,0 +1,52 @@
+"""Utilities for storage indexing, IDs, and metadata loading."""
+
+from __future__ import annotations
+
+import json
+import re
+from pathlib import Path
+from typing import Any
+
+
+def ensure_dir(path: Path) -> Path:
+    path.mkdir(parents=True, exist_ok=True)
+    return path
+
+
+def next_numeric_id(parent: Path, prefix: str) -> str:
+    """Return the next ID like `prefix_001` based on existing directories."""
+    ensure_dir(parent)
+    pattern = re.compile(rf"^{re.escape(prefix)}_(\d+)$")
+    max_id = 0
+
+    for child in parent.iterdir():
+        if not child.is_dir():
+            continue
+        match = pattern.match(child.name)
+        if match:
+            max_id = max(max_id, int(match.group(1)))
+
+    return f"{prefix}_{max_id + 1:03d}"
+
+
+def read_metadata(entry_dir: Path) -> dict[str, Any]:
+    metadata_file = entry_dir / "metadata.json"
+    if not metadata_file.exists():
+        return {}
+
+    try:
+        return json.loads(metadata_file.read_text(encoding="utf-8"))
+    except json.JSONDecodeError:
+        return {}
+
+
+def safe_rmtree(path: Path) -> None:
+    if not path.exists() or not path.is_dir():
+        return
+
+    for child in path.iterdir():
+        if child.is_dir():
+            safe_rmtree(child)
+        else:
+            child.unlink(missing_ok=True)
+    path.rmdir()
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/storage/storage_manager.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/storage/storage_manager.py
new file mode 100644
index 00000000..e13406dd
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/storage/storage_manager.py
@@ -0,0 +1,630 @@
+"""Storage manager for persistent transcripts and datasets."""
+
+from __future__ import annotations
+
+import base64
+import csv
+import io
+import json
+import os
+import re
+import zipfile
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+import cv2
+import numpy as np
+
+from ..core.config import STORAGE_ROOT
+from ..services.dataset_builder import align_boxes_with_transcript, crop_line_image
+from .file_indexer import ensure_dir, next_numeric_id, read_metadata, safe_rmtree
+
+
+def _utc_now_iso() -> str:
+    return datetime.now(timezone.utc).isoformat()
+
+
+def _safe_label(text: str, max_len: int = 64) -> str:
+    safe = re.sub(r"[^A-Za-z0-9\s_-]", "", text or "")
+    safe = re.sub(r"\s+", "_", safe.strip())
+    return safe[:max_len] if safe else "line"
+
+
+def _safe_name(text: str, fallback: str = "session") -> str:
+    safe = re.sub(r"[^A-Za-z0-9\s_-]", "", text or "")
+    safe = re.sub(r"\s+", " ", safe).strip()
+    return safe[:96] if safe else fallback
+
+
+def _page_sort_key(page_key: str) -> tuple[int, str]:
+    match = re.search(r"(\d+)", str(page_key))
+    if match:
+        return int(match.group(1)), str(page_key)
+    return 10**9, str(page_key)
+
+
+def _decode_data_url(image_data: str) -> np.ndarray | None:
+    payload = image_data.split(",", 1)[1] if "," in image_data else image_data
+    raw = base64.b64decode(payload)
+    arr = np.frombuffer(raw, dtype=np.uint8)
+    return cv2.imdecode(arr, cv2.IMREAD_COLOR)
+
+
+def _image_ext_from_data_url(image_data: str) -> str:
+    match = re.match(r"^data:image/([a-zA-Z0-9.+-]+);base64,", image_data or "")
+    if not match:
+        return "png"
+    subtype = match.group(1).lower()
+    if subtype in {"jpg", "jpeg"}:
+        return "jpg"
+    if subtype in {"png", "webp"}:
+        return subtype
+    return "png"
+
+
+def _decode_data_url_bytes(image_data: str) -> bytes | None:
+    if not image_data:
+        return None
+    try:
+        payload = image_data.split(",", 1)[1] if "," in image_data else image_data
+        return base64.b64decode(payload)
+    except Exception:
+        return None
+
+
+def _guess_image_mime_from_suffix(suffix: str) -> str:
+    ext = suffix.lower().lstrip(".")
+    if ext in {"jpg", "jpeg"}:
+        return "image/jpeg"
+    if ext == "webp":
+        return "image/webp"
+    return "image/png"
+
+
+def get_storage_paths() -> dict[str, Path]:
+    root = ensure_dir(Path(STORAGE_ROOT))
+    transcripts = ensure_dir(root / "transcripts")
+    datasets = ensure_dir(root / "datasets")
+    return {
+        "root": root,
+        "transcripts": transcripts,
+        "datasets": datasets,
+    }
+
+
+def _build_tags(
+    model_info: dict[str, Any] | None,
+    mode: str | None = None,
+    fmt: str | None = None,
+) -> list[str]:
+    """
+    Derive short, human-readable chips from the pipeline/model info so the
+    My Files UI can show at a glance what produced an entry: which
+    preprocessing ran, the detection / layout / OCR models, and whether LLM
+    post-processing was applied.
+    """
+    info = model_info or {}
+    tags: list[str] = []
+
+    if mode:
+        tags.append(str(mode))
+
+    pre = info.get("preprocessing")
+    if isinstance(pre, (list, tuple)) and pre:
+        shown = ", ".join(str(p) for p in pre[:4])
+        if len(pre) > 4:
+            shown += f" +{len(pre) - 4}"
+        tags.append(f"Preprocess: {shown}")
+    elif pre in (None, [], (), ""):
+        tags.append("Preprocess: none")
+
+    if info.get("detection_model"):
+        tags.append(f"Detect: {info['detection_model']}")
+    if info.get("layout_model"):
+        tags.append(f"Layout: {info['layout_model']}")
+
+    if info.get("ocr_model") or info.get("ocr_provider"):
+        provider = info.get("ocr_provider") or "ocr"
+        ocr_model = info.get("ocr_model") or "model"
+        tags.append(f"OCR: {provider}/{ocr_model}")
+
+    llm = info.get("llm_postprocess")
+    if isinstance(llm, dict) and llm.get("used"):
+        provider = llm.get("provider") or "llm"
+        llm_model = llm.get("model") or "model"
+        tags.append(f"LLM: {provider}/{llm_model}")
+    elif isinstance(llm, dict):
+        tags.append("LLM: none")
+
+    if fmt:
+        tags.append(f"Format: {fmt}")
+
+    return tags
+
+
+def save_transcript_session(
+    transcripts: dict[str, str],
+    source: str = "ocr upload",
+    mode: str = "recognition",
+    transcript_images: dict[str, str] | None = None,
+    book_name: str | None = None,
+    model_info: dict[str, Any] | None = None,
+) -> dict[str, Any]:
+    paths = get_storage_paths()
+    session_id = next_numeric_id(paths["transcripts"], "session")
+    session_dir = ensure_dir(paths["transcripts"] / session_id)
+
+    written_pages = 0
+    written_images = 0
+    sorted_pages = sorted(transcripts.keys(), key=_page_sort_key)
+    image_map = transcript_images or {}
+
+    for page_key in sorted_pages:
+        text = (transcripts.get(page_key) or "").strip()
+        if not text:
+            continue
+        match = re.search(r"(\d+)", str(page_key))
+        if match:
+            page_name = str(int(match.group(1)))
+        else:
+            page_name = re.sub(r"[^A-Za-z0-9_-]", "_", str(page_key))
+        (session_dir / f"page{page_name}.txt").write_text(text, encoding="utf-8")
+
+        image_data = image_map.get(page_key) or image_map.get(str(page_key))
+        image_bytes = _decode_data_url_bytes(image_data or "")
+        if image_bytes:
+            ext = _image_ext_from_data_url(image_data or "")
+            (session_dir / f"page{page_name}.{ext}").write_bytes(image_bytes)
+            written_images += 1
+
+        written_pages += 1
+
+    display_name = _safe_name(book_name or "", fallback=session_id)
+
+    metadata = {
+        "id": session_id,
+        "type": "transcript",
+        "created_at": _utc_now_iso(),
+        "num_files": written_pages,
+        "num_pages": written_pages,
+        "num_images": written_images,
+        "source": source,
+        "mode": mode,
+        "name": display_name,
+        "book_name": display_name,
+        "model_info": model_info or {},
+        "tags": _build_tags(model_info, mode),
+    }
+    (session_dir / "metadata.json").write_text(
+        json.dumps(metadata, indent=2),
+        encoding="utf-8",
+    )
+
+    return metadata
+
+
+def save_recognition_dataset(
+    pages_data: list[dict[str, Any]],
+    source: str,
+    book_name: str,
+    model_info: dict[str, Any] | None = None,
+) -> dict[str, Any]:
+    paths = get_storage_paths()
+    dataset_id = next_numeric_id(paths["datasets"], "dataset")
+    dataset_dir = ensure_dir(paths["datasets"] / dataset_id)
+    images_dir = ensure_dir(dataset_dir / "images")
+    labels_dir = ensure_dir(dataset_dir / "labels")
+
+    csv_rows: list[tuple[str, str]] = []
+    total_samples = 0
+
+    for page in pages_data:
+        page_key = str(page.get("page_key", "unknown"))
+        boxes = page.get("boxes", [])
+        lines = page.get("lines", [])
+        img = _decode_data_url(page.get("image_data", ""))
+        if img is None:
+            continue
+
+        aligned, _, _ = align_boxes_with_transcript(boxes, lines)
+        seen_labels: dict[str, int] = {}
+
+        for idx, (box, text) in enumerate(aligned, start=1):
+            crop = crop_line_image(img, box)
+            label = _safe_label(text)
+            seen_labels[label] = seen_labels.get(label, 0) + 1
+            suffix = seen_labels[label]
+            sample_name = f"page_{page_key}_line_{idx:04d}_{label}_{suffix}"
+            image_name = f"{sample_name}.png"
+
+            ok, png_data = cv2.imencode(".png", crop)
+            if not ok:
+                continue
+
+            (images_dir / image_name).write_bytes(png_data.tobytes())
+            csv_rows.append((f"images/{image_name}", text))
+            total_samples += 1
+
+    labels_csv = io.StringIO()
+    writer = csv.writer(labels_csv)
+    writer.writerow(["image", "text"])
+    for image_path, text in csv_rows:
+        writer.writerow([image_path, text])
+    (labels_dir / "labels.csv").write_text(labels_csv.getvalue(), encoding="utf-8")
+
+    metadata = {
+        "id": dataset_id,
+        "type": "dataset",
+        "created_at": _utc_now_iso(),
+        "num_files": total_samples,
+        "num_samples": total_samples,
+        "source": source,
+        "mode": "recognition",
+        "dataset_type": "recognition",
+        "format": "png+csv",
+        "book_name": book_name,
+        "model_info": model_info or {},
+        "tags": _build_tags(model_info, "recognition"),
+    }
+    (dataset_dir / "metadata.json").write_text(
+        json.dumps(metadata, indent=2),
+        encoding="utf-8",
+    )
+
+    return metadata
+
+
+def _polygon_to_xyxy(box: list[list[float]]) -> tuple[int, int, int, int]:
+    pts = np.asarray(box, dtype=np.float32)
+    return (
+        int(pts[:, 0].min()),
+        int(pts[:, 1].min()),
+        int(pts[:, 0].max()),
+        int(pts[:, 1].max()),
+    )
+
+
+def save_detection_dataset(
+    pages_data: list[dict[str, Any]],
+    source: str,
+    book_name: str,
+    bbox_format: str = "txt",
+    model_info: dict[str, Any] | None = None,
+) -> dict[str, Any]:
+    fmt = (bbox_format or "txt").lower()
+    if fmt not in {"txt", "json", "yolo", "coco"}:
+        fmt = "txt"
+
+    paths = get_storage_paths()
+    dataset_id = next_numeric_id(paths["datasets"], "dataset")
+    dataset_dir = ensure_dir(paths["datasets"] / dataset_id)
+    images_dir = ensure_dir(dataset_dir / "images")
+    bboxes_dir = ensure_dir(dataset_dir / ("labels" if fmt == "yolo" else "bboxes"))
+
+    coco_images: list[dict[str, Any]] = []
+    coco_annotations: list[dict[str, Any]] = []
+    coco_image_id = 0
+    coco_ann_id = 0
+    total_samples = 0
+
+    for page in pages_data:
+        page_key = str(page.get("page_key", "unknown"))
+        boxes = page.get("boxes", [])
+        img = _decode_data_url(page.get("image_data", ""))
+        if img is None:
+            continue
+
+        h, w = img.shape[:2]
+        stem = f"page_{page_key}"
+        image_name = f"{stem}.jpg"
+        ok, jpg_data = cv2.imencode(".jpg", img, [cv2.IMWRITE_JPEG_QUALITY, 95])
+        if ok:
+            (images_dir / image_name).write_bytes(jpg_data.tobytes())
+
+        xyxy_boxes = [_polygon_to_xyxy(b) for b in boxes]
+        total_samples += len(xyxy_boxes)
+
+        if fmt == "txt":
+            lines = "\n".join(f"{x1} {y1} {x2} {y2}" for (x1, y1, x2, y2) in xyxy_boxes)
+            (bboxes_dir / f"{stem}.txt").write_text((lines + "\n") if lines else "", encoding="utf-8")
+        elif fmt == "json":
+            (bboxes_dir / f"{stem}.json").write_text(
+                json.dumps([[x1, y1, x2, y2] for (x1, y1, x2, y2) in xyxy_boxes], indent=2),
+                encoding="utf-8",
+            )
+        elif fmt == "yolo":
+            yolo_lines = []
+            for (x1, y1, x2, y2) in xyxy_boxes:
+                bw = max(0, x2 - x1)
+                bh = max(0, y2 - y1)
+                cx = (x1 + x2) / 2.0
+                cy = (y1 + y2) / 2.0
+                yolo_lines.append(f"0 {cx / w:.6f} {cy / h:.6f} {bw / w:.6f} {bh / h:.6f}")
+            (bboxes_dir / f"{stem}.txt").write_text(("\n".join(yolo_lines) + "\n") if yolo_lines else "", encoding="utf-8")
+        elif fmt == "coco":
+            coco_image_id += 1
+            coco_images.append({
+                "id": coco_image_id,
+                "file_name": image_name,
+                "width": int(w),
+                "height": int(h),
+            })
+            for (x1, y1, x2, y2) in xyxy_boxes:
+                coco_ann_id += 1
+                bw = max(0, x2 - x1)
+                bh = max(0, y2 - y1)
+                coco_annotations.append({
+                    "id": coco_ann_id,
+                    "image_id": coco_image_id,
+                    "category_id": 1,
+                    "bbox": [int(x1), int(y1), int(bw), int(bh)],
+                    "area": int(bw * bh),
+                    "iscrowd": 0,
+                    "segmentation": [],
+                })
+
+    if fmt == "yolo":
+        (dataset_dir / "classes.txt").write_text("text\n", encoding="utf-8")
+    elif fmt == "coco":
+        (bboxes_dir / "annotations.json").write_text(
+            json.dumps(
+                {
+                    "images": coco_images,
+                    "annotations": coco_annotations,
+                    "categories": [{"id": 1, "name": "text", "supercategory": "text"}],
+                },
+                indent=2,
+            ),
+            encoding="utf-8",
+        )
+
+    metadata = {
+        "id": dataset_id,
+        "type": "dataset",
+        "created_at": _utc_now_iso(),
+        "num_files": total_samples,
+        "num_samples": total_samples,
+        "source": source,
+        "mode": "detection",
+        "dataset_type": "detection",
+        "format": fmt,
+        "book_name": book_name,
+        "model_info": model_info or {},
+        "tags": _build_tags(model_info, "detection", fmt),
+    }
+    (dataset_dir / "metadata.json").write_text(
+        json.dumps(metadata, indent=2),
+        encoding="utf-8",
+    )
+
+    return metadata
+
+
+def list_entries(kind: str) -> list[dict[str, Any]]:
+    paths = get_storage_paths()
+    if kind not in {"transcripts", "datasets"}:
+        return []
+
+    entries = []
+    for child in paths[kind].iterdir():
+        if not child.is_dir():
+            continue
+        metadata = read_metadata(child)
+        if not metadata:
+            continue
+        metadata["name"] = metadata.get("name") or child.name
+        metadata["id"] = metadata.get("id") or child.name
+        entries.append(metadata)
+
+    entries.sort(key=lambda item: item.get("created_at", ""), reverse=True)
+    return entries
+
+
+def get_transcript_detail(session_id: str) -> dict[str, Any]:
+    paths = get_storage_paths()
+    session_dir = paths["transcripts"] / session_id
+    if not session_dir.exists() or not session_dir.is_dir():
+        raise FileNotFoundError(session_id)
+
+    metadata = read_metadata(session_dir)
+    pages = []
+    for page_file in sorted(session_dir.glob("page*.txt")):
+        image_data = None
+        for ext in (".png", ".jpg", ".jpeg", ".webp"):
+            image_file = session_dir / f"{page_file.stem}{ext}"
+            if image_file.exists() and image_file.is_file():
+                mime = _guess_image_mime_from_suffix(image_file.suffix)
+                payload = base64.b64encode(image_file.read_bytes()).decode("utf-8")
+                image_data = f"data:{mime};base64,{payload}"
+                break
+
+        pages.append({
+            "name": page_file.name,
+            "content": page_file.read_text(encoding="utf-8"),
+            "image_data": image_data,
+        })
+
+    return {
+        "metadata": metadata,
+        "pages": pages,
+    }
+
+
+def get_dataset_detail(dataset_id: str, preview_limit: int = 18) -> dict[str, Any]:
+    paths = get_storage_paths()
+    dataset_dir = paths["datasets"] / dataset_id
+    if not dataset_dir.exists() or not dataset_dir.is_dir():
+        raise FileNotFoundError(dataset_id)
+
+    metadata = read_metadata(dataset_dir)
+    if not metadata:
+        metadata = {"id": dataset_id}
+
+    dataset_type = metadata.get("dataset_type") or metadata.get("mode") or "recognition"
+    samples: list[dict[str, Any]] = []
+
+    def _to_data_url(image_path: Path) -> str | None:
+        if not image_path.exists() or not image_path.is_file():
+            return None
+        mime = _guess_image_mime_from_suffix(image_path.suffix)
+        payload = base64.b64encode(image_path.read_bytes()).decode("utf-8")
+        return f"data:{mime};base64,{payload}"
+
+    if dataset_type == "recognition":
+        labels_csv = dataset_dir / "labels" / "labels.csv"
+        if labels_csv.exists() and labels_csv.is_file():
+            with labels_csv.open("r", encoding="utf-8", newline="") as fh:
+                reader = csv.DictReader(fh)
+                for row in reader:
+                    if len(samples) >= preview_limit:
+                        break
+                    image_rel = (row.get("image") or "").strip()
+                    text = (row.get("text") or "").strip()
+                    image_path = dataset_dir / image_rel if image_rel else None
+                    image_data = _to_data_url(image_path) if image_path else None
+                    samples.append(
+                        {
+                            "name": Path(image_rel).name if image_rel else "sample",
+                            "image_data": image_data,
+                            "text": text,
+                            "annotation": text,
+                        }
+                    )
+    else:
+        images_dir = dataset_dir / "images"
+        fmt = (metadata.get("format") or "txt").lower()
+        annotation_by_stem: dict[str, str] = {}
+
+        if fmt in {"txt", "yolo"}:
+            labels_dir = dataset_dir / ("labels" if fmt == "yolo" else "bboxes")
+            for label_file in labels_dir.glob("page_*.txt"):
+                try:
+                    text = label_file.read_text(encoding="utf-8").strip()
+                    lines = [line for line in text.splitlines() if line.strip()]
+                    if not lines:
+                        annotation_by_stem[label_file.stem] = "No boxes"
+                    else:
+                        annotation_by_stem[label_file.stem] = "\n".join(lines)
+                except Exception:
+                    annotation_by_stem[label_file.stem] = "boxes available"
+        elif fmt == "json":
+            labels_dir = dataset_dir / "bboxes"
+            for label_file in labels_dir.glob("page_*.json"):
+                try:
+                    arr = json.loads(label_file.read_text(encoding="utf-8"))
+                    if not isinstance(arr, list) or not arr:
+                        annotation_by_stem[label_file.stem] = "No boxes"
+                    else:
+                        annotation_by_stem[label_file.stem] = "\n".join(
+                            [f"[{', '.join(map(str, box))}]" for box in arr]
+                        )
+                except Exception:
+                    annotation_by_stem[label_file.stem] = "boxes available"
+        elif fmt == "coco":
+            ann_file = dataset_dir / "bboxes" / "annotations.json"
+            if ann_file.exists() and ann_file.is_file():
+                try:
+                    coco = json.loads(ann_file.read_text(encoding="utf-8"))
+                    image_id_to_name = {
+                        item.get("id"): Path(item.get("file_name", "")).stem
+                        for item in coco.get("images", [])
+                    }
+                    values: dict[str, list[str]] = {}
+                    for ann in coco.get("annotations", []):
+                        stem = image_id_to_name.get(ann.get("image_id"))
+                        if not stem:
+                            continue
+                        bbox = ann.get("bbox", [])
+                        if isinstance(bbox, list) and len(bbox) == 4:
+                            bbox_line = f"[{bbox[0]}, {bbox[1]}, {bbox[2]}, {bbox[3]}]"
+                        else:
+                            bbox_line = str(bbox)
+                        if stem not in values:
+                            values[stem] = []
+                        values[stem].append(bbox_line)
+                    annotation_by_stem = {
+                        stem: ("\n".join(lines) if lines else "No boxes")
+                        for stem, lines in values.items()
+                    }
+                except Exception:
+                    annotation_by_stem = {}
+
+        image_files = sorted(images_dir.glob("page_*.*")) if images_dir.exists() else []
+        for image_file in image_files:
+            if len(samples) >= preview_limit:
+                break
+            image_data = _to_data_url(image_file)
+            samples.append(
+                {
+                    "name": image_file.name,
+                    "image_data": image_data,
+                    "text": "",
+                    "annotation": annotation_by_stem.get(image_file.stem, "annotation available"),
+                }
+            )
+
+    return {
+        "metadata": metadata,
+        "samples": samples,
+    }
+
+
+def zip_entry(kind: str, entry_id: str) -> tuple[io.BytesIO, str]:
+    paths = get_storage_paths()
+    if kind not in {"transcripts", "datasets"}:
+        raise FileNotFoundError(kind)
+
+    root_dir = paths[kind]
+    entry_dir = root_dir / entry_id
+    if not entry_dir.exists() or not entry_dir.is_dir():
+        raise FileNotFoundError(entry_id)
+
+    buf = io.BytesIO()
+    with zipfile.ZipFile(buf, "w", zipfile.ZIP_DEFLATED) as zf:
+        if kind == "transcripts":
+            base_prefix = Path(entry_id)
+            for file_path in sorted(entry_dir.glob("page*.txt")):
+                if file_path.is_file():
+                    arcname = base_prefix / "transcripts" / file_path.name
+                    zf.write(file_path, arcname=str(arcname))
+
+            for ext in ("*.png", "*.jpg", "*.jpeg", "*.webp"):
+                for image_file in sorted(entry_dir.glob(f"page*{ext[1:]}")):
+                    if image_file.is_file():
+                        arcname = base_prefix / "images" / image_file.name
+                        zf.write(image_file, arcname=str(arcname))
+
+            metadata_file = entry_dir / "metadata.json"
+            if metadata_file.exists() and metadata_file.is_file():
+                zf.write(metadata_file, arcname=str(base_prefix / "metadata.json"))
+        else:
+            for file_path in entry_dir.rglob("*"):
+                if file_path.is_file():
+                    arcname = file_path.relative_to(root_dir)
+                    zf.write(file_path, arcname=str(arcname))
+
+    buf.seek(0)
+    return buf, f"{entry_id}.zip"
+
+
+def delete_entry(kind: str, entry_id: str) -> bool:
+    paths = get_storage_paths()
+    if kind not in {"transcripts", "datasets"}:
+        return False
+
+    entry_dir = paths[kind] / entry_id
+    if not entry_dir.exists() or not entry_dir.is_dir():
+        return False
+
+    safe_rmtree(entry_dir)
+    return True
+
+
+def ensure_storage_layout() -> dict[str, str]:
+    paths = get_storage_paths()
+    return {k: str(v) for k, v in paths.items()}
+
+
+def resolve_storage_root() -> str:
+    return os.fspath(Path(STORAGE_ROOT))
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/utils/__init__.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/utils/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/utils/prompt.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/utils/prompt.py
new file mode 100644
index 00000000..4eeedb5e
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/utils/prompt.py
@@ -0,0 +1,101 @@
+"""The OCR prompt every provider sends. Tuned on Spanish print — edit with care."""
+
+OCR_PROMPT = """
+---
+
+You are performing **high-precision historical OCR transcription**.
+
+Your task is to transcribe **only the main body text** of the page with maximum fidelity and zero commentary.
+
+---
+
+### PRIMARY OBJECTIVE
+
+Produce a clean diplomatic transcription of the main content exactly as printed, with standardized long-ſ normalization.
+
+---
+
+### TRANSCRIPTION RULES
+
+1. Preserve original line breaks exactly.
+2. Preserve paragraph spacing exactly.
+3. Preserve original spelling (do NOT modernize or normalize spelling).
+4. Preserve capitalization exactly as shown.
+5. Preserve punctuation and special characters exactly.
+6. Convert the long-ſ (ſ) to a standard "s" in all cases.
+7. Preserve ligatures as standard character equivalents:
+
+   * “ff” → “ff”
+   * “fi” → “fi”
+   * “fl” → “fl”
+   * “ffi” → “ffi”
+   * “ffl” → “ffl”
+8. Preserve hyphenated line-break words exactly as printed.
+9. Do NOT merge, reflow, or restructure lines.
+10. Do NOT summarize.
+11. Do NOT explain.
+12. Output only the transcription text.
+
+---
+
+### LAYOUT RULES
+
+* If the page has multiple columns, transcribe column-by-column from left to right.
+* Preserve visible indentation.
+* Preserve headings and section titles as plain text.
+* Maintain original line structure even if it breaks mid-sentence.
+
+---
+
+### CONTENT FILTERING RULES
+
+Include:
+
+* Main body text
+* Headings and subheadings
+* Page numbers only if embedded within the body text flow
+
+Exclude completely (without marking omission):
+
+* Marginal notes
+* Side notes
+* Running headers
+* Running footers
+* Catchwords
+* Page signatures
+* Printer marks
+* Decorative elements
+* Stamps
+* Handwritten annotations
+
+Do NOT indicate omissions. Simply ignore excluded material.
+
+---
+
+### RECONSTRUCTION RULES
+
+* If a word is partially faded but context makes reconstruction highly probable, output the reconstructed word normally.
+* Prefer historically and linguistically plausible reconstructions.
+* If multiple interpretations are possible, choose the most contextually probable one.
+* If text cannot be reconstructed with high confidence, omit that word silently rather than inserting markers.
+
+Never insert:
+
+* Brackets of any kind
+* Uncertainty markers
+* Editorial comments
+* Added punctuation not present in the original
+
+---
+
+### OUTPUT REQUIREMENTS
+
+* Output only the transcription.
+* No metadata.
+* No explanations.
+* No uncertainty markers.
+* No additional formatting beyond faithful line preservation.
+
+The output must be clean, normalized (ſ → s), and suitable for direct OCR training use.
+
+"""
\ No newline at end of file
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/utils/torch_device.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/utils/torch_device.py
new file mode 100644
index 00000000..f9a3aa46
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/app/utils/torch_device.py
@@ -0,0 +1,20 @@
+"""One place to pick the torch device: CUDA > MPS > CPU.
+
+MPS only kicks in on a native macOS run — Docker there has no Metal passthrough.
+PaddleOCR ignores this entirely; it has no Metal backend.
+"""
+
+from __future__ import annotations
+
+import torch
+
+
+def select_torch_device() -> str:
+    if torch.cuda.is_available():
+        return "cuda"
+    # getattr: some torch builds have no mps attribute at all.
+    mps = getattr(torch.backends, "mps", None)
+    if mps is not None and mps.is_available():
+        return "mps"
+    return "cpu"
+
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/main.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/main.py
new file mode 100644
index 00000000..b76da332
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/main.py
@@ -0,0 +1,19 @@
+"""
+RenAIssance OCR Backend — launcher script.
+
+Delegates to the modular FastAPI application in app.main.
+All business logic lives under the ``app`` package.
+"""
+
+import os
+import sys
+
+# Ensure the backend directory is on sys.path so that
+# the ``preprocessing`` package (a sibling of ``app``) is importable.
+backend_dir = os.path.dirname(os.path.abspath(__file__))
+if backend_dir not in sys.path:
+    sys.path.insert(0, backend_dir)
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run("app.main:app", host="0.0.0.0", port=8000, reload=True)
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/preprocessing/__init__.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/preprocessing/__init__.py
new file mode 100644
index 00000000..06964521
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/preprocessing/__init__.py
@@ -0,0 +1,15 @@
+"""OpenCV preprocessing ops for historical-document OCR.
+
+The ops themselves live in operations.py and are reached through OP_REGISTRY;
+pipeline.py runs an ordered list of them.
+"""
+
+from .operations import OP_REGISTRY
+from .pipeline import run_pipeline, PipelineExecutor, validate_pipeline_config
+
+__all__ = [
+    'OP_REGISTRY',
+    'run_pipeline',
+    'PipelineExecutor',
+    'validate_pipeline_config',
+]
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/preprocessing/operations.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/preprocessing/operations.py
new file mode 100644
index 00000000..87cb87fb
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/preprocessing/operations.py
@@ -0,0 +1,766 @@
+"""OpenCV preprocessing ops for OCR.
+
+Every op is (image, params, progress?) -> image and copes with both grayscale
+and colour input. OP_REGISTRY at the bottom is what the pipeline looks up.
+"""
+
+import cv2
+import numpy as np
+from typing import Dict, Any, Optional, Callable
+
+
+ProgressCallbackType = Optional[Callable[[float, str], None]]
+
+
+# ── Basic ─────────────────────────────────────────────────────────────
+
+def normalize_image(
+    img: np.ndarray,
+    params: Dict[str, Any],
+    progress: ProgressCallbackType = None
+) -> np.ndarray:
+    """Histogram-stretch brightness/contrast. params: strength 0-100 (50)."""
+    if progress:
+        progress(0.1, "Analyzing histogram")
+
+    strength = params.get("strength", 50) / 100.0
+
+    if len(img.shape) == 2:
+        normalized = _normalize_channel(img, strength)
+    else:
+        channels = cv2.split(img)
+        normalized_channels = []
+        for i, ch in enumerate(channels):
+            if progress:
+                progress(0.2 + (i * 0.25), f"Normalizing channel {i+1}")
+            normalized_channels.append(_normalize_channel(ch, strength))
+        normalized = cv2.merge(normalized_channels)
+
+    if progress:
+        progress(1.0, "Normalize complete")
+
+    return normalized
+
+
+def _normalize_channel(channel: np.ndarray, strength: float) -> np.ndarray:
+    """Blend the channel toward a full 0-255 stretch by `strength`."""
+    min_val = np.min(channel)
+    max_val = np.max(channel)
+
+    if max_val == min_val:
+        return channel
+
+    full_normalized = cv2.normalize(channel, None, 0, 255, cv2.NORM_MINMAX)
+
+    if strength >= 1.0:
+        return full_normalized
+
+    return cv2.addWeighted(
+        channel.astype(np.float32), 1 - strength,
+        full_normalized.astype(np.float32), strength,
+        0
+    ).astype(np.uint8)
+
+
+def to_grayscale(
+    img: np.ndarray,
+    params: Dict[str, Any],
+    progress: ProgressCallbackType = None
+) -> np.ndarray:
+    """Convert to grayscale. No params; already-gray input passes through."""
+    if progress:
+        progress(0.2, "Converting to grayscale")
+
+    if len(img.shape) == 2:
+        result = img
+    elif len(img.shape) == 3 and img.shape[2] == 1:
+        result = img.squeeze()  # single channel, but still 3D
+    else:
+        result = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+    if progress:
+        progress(1.0, "Grayscale complete")
+
+    return result
+
+
+# Below this spread (degrees) between the most- and least-skewed band, the page
+# is skewed uniformly and a single rotation is cleaner than a piecewise warp.
+_PIECEWISE_MIN_SPREAD = 1.0
+
+
+def deskew_image(
+    img: np.ndarray,
+    params: Dict[str, Any],
+    progress: ProgressCallbackType = None
+) -> np.ndarray:
+    """Straighten a rotated scan, handling skew that drifts down the page.
+
+    params: maxAngle in degrees (15); mode 'auto'|'global'|'piecewise' ('auto');
+    bands 2-12 (4) for the piecewise path.
+
+    A single rotation cannot fix a page whose top is straight but whose lower
+    half tilts (common near a book's spine, or when the sheet lifts off the
+    platen). 'auto' measures the skew separately in horizontal bands: if it is
+    roughly constant it rotates once, otherwise it corrects each band by its own
+    angle and blends the bands so there is no visible seam.
+    """
+    if progress:
+        progress(0.1, "Preparing deskew analysis")
+
+    max_angle = params.get("maxAngle", 15)
+    mode = str(params.get("mode", "auto")).lower()
+    num_bands = max(2, min(12, int(params.get("bands", 4))))
+
+    is_color = len(img.shape) == 3
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) if is_color else img.copy()
+
+    if progress:
+        progress(0.25, "Detecting skew angle")
+
+    # Whole-page angle: the fallback for uniform skew and for bands with no ink.
+    global_angle = _detect_skew_contour(gray)
+    if global_angle is None or abs(global_angle) > max_angle:
+        global_angle = _detect_skew_hough(gray)
+    if global_angle is None:
+        global_angle = 0.0
+    global_angle = max(-max_angle, min(max_angle, global_angle))
+
+    if mode == "global":
+        return _apply_global_rotation(img, global_angle, progress)
+
+    if progress:
+        progress(0.45, "Measuring skew per band")
+
+    band_angles = _band_skew_angles(gray, num_bands, max_angle, global_angle)
+    spread = max(band_angles) - min(band_angles)
+
+    # Uniform skew (or an explicit request for one rotation): rotate once.
+    if mode == "auto" and spread < _PIECEWISE_MIN_SPREAD:
+        return _apply_global_rotation(img, float(np.median(band_angles)), progress)
+
+    if progress:
+        progress(0.6, "Applying piecewise deskew")
+
+    result = _piecewise_deskew(img, band_angles)
+
+    if progress:
+        progress(1.0, "Deskew complete")
+
+    return result
+
+
+def _apply_global_rotation(
+    img: np.ndarray, angle: float, progress: ProgressCallbackType = None
+) -> np.ndarray:
+    """Rotate the whole image about its centre by `angle` (already clamped)."""
+    if abs(angle) < 0.1:
+        if progress:
+            progress(1.0, "No significant skew detected")
+        return img
+
+    if progress:
+        progress(0.6, f"Rotating by {angle:.2f}°")
+
+    h, w = img.shape[:2]
+    center = (w // 2, h // 2)
+    M = cv2.getRotationMatrix2D(center, angle, 1.0)
+
+    # Replicate the border instead of filling black — cleaner page edges.
+    rotated = cv2.warpAffine(
+        img, M, (w, h),
+        flags=cv2.INTER_CUBIC,
+        borderMode=cv2.BORDER_REPLICATE
+    )
+
+    if progress:
+        progress(1.0, "Deskew complete")
+
+    return rotated
+
+
+def _detect_skew_contour(gray: np.ndarray) -> Optional[float]:
+    """Skew angle from the min-area rect around the largest contour."""
+    _, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
+    contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+    if not contours:
+        return None
+
+    largest = max(contours, key=cv2.contourArea)
+    rect = cv2.minAreaRect(largest)
+    angle = rect[-1]
+
+    # minAreaRect reports in (-90, 0]; fold it into (-45, 45].
+    if angle < -45:
+        angle += 90
+    elif angle > 45:
+        angle -= 90
+
+    return angle
+
+
+def _detect_skew_hough(gray: np.ndarray) -> Optional[float]:
+    """Skew angle from the median slope of near-horizontal Hough lines."""
+    edges = cv2.Canny(gray, 50, 150, apertureSize=3)
+    lines = cv2.HoughLinesP(
+        edges, 1, np.pi / 180,
+        threshold=100,
+        minLineLength=gray.shape[1] // 4,
+        maxLineGap=10
+    )
+
+    if lines is None or len(lines) == 0:
+        return None
+
+    angles = []
+    for line in lines:
+        x1, y1, x2, y2 = line[0]
+        if x2 != x1:
+            angle = np.degrees(np.arctan2(y2 - y1, x2 - x1))
+            if abs(angle) < 45:  # text lines, not vertical rules
+                angles.append(angle)
+
+    if not angles:
+        return None
+
+    return np.median(angles)
+
+
+def _band_skew_angles(
+    gray: np.ndarray, num_bands: int, max_angle: float, fallback_angle: float
+) -> list[float]:
+    """Estimate the correction angle for each horizontal band, top to bottom.
+
+    Runs on a downscaled copy (angles are scale-invariant) so estimating many
+    bands stays cheap. Bands with too little ink to trust inherit the median of
+    the confident bands (or the whole-page angle), then the series is lightly
+    smoothed so one noisy band cannot kink the correction.
+    """
+    scale = min(1.0, 1000.0 / max(gray.shape[:2]))
+    small = (
+        cv2.resize(gray, None, fx=scale, fy=scale, interpolation=cv2.INTER_AREA)
+        if scale < 1.0 else gray
+    )
+    _, binary = cv2.threshold(small, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
+
+    h = binary.shape[0]
+    raw: list[Optional[float]] = []
+    for i in range(num_bands):
+        y0 = i * h // num_bands
+        y1 = (i + 1) * h // num_bands
+        raw.append(_projection_profile_angle(binary[y0:y1], max_angle))
+
+    return _fill_band_angles(raw, fallback_angle)
+
+
+def _projection_profile_angle(
+    binary_band: np.ndarray, max_angle: float
+) -> Optional[float]:
+    """Angle (in [-max_angle, max_angle]) that makes this band's lines horizontal.
+
+    Classic projection-profile deskew: at the correct angle the rows of text
+    line up, so the horizontal ink projection has the sharpest peaks and valleys.
+    Score each candidate by the summed squared row-to-row change in that
+    projection and keep the best, coarse (1°) then refined (0.25°). Returns None
+    when the band holds too little ink to give a trustworthy answer.
+    """
+    if binary_band.size == 0 or int(binary_band.sum()) < 255 * 50:
+        return None
+
+    h, w = binary_band.shape
+
+    def score(angle: float) -> float:
+        M = cv2.getRotationMatrix2D((w / 2.0, h / 2.0), angle, 1.0)
+        rot = cv2.warpAffine(
+            binary_band, M, (w, h),
+            flags=cv2.INTER_NEAREST,
+            borderMode=cv2.BORDER_CONSTANT, borderValue=0,
+        )
+        proj = rot.sum(axis=1, dtype=np.float64)
+        d = np.diff(proj)
+        return float(np.dot(d, d))
+
+    coarse = np.arange(-max_angle, max_angle + 0.5, 1.0)
+    best = max(coarse, key=score)
+    fine = np.arange(best - 1.0, best + 1.0 + 1e-6, 0.25)
+    best = max(fine, key=score)
+    return float(max(-max_angle, min(max_angle, best)))
+
+
+def _fill_band_angles(
+    raw: list[Optional[float]], fallback_angle: float
+) -> list[float]:
+    """Replace unconfident (None) bands with a sensible default, then smooth."""
+    confident = [a for a in raw if a is not None]
+    default = float(np.median(confident)) if confident else float(fallback_angle)
+    filled = [a if a is not None else default for a in raw]
+
+    # 3-tap moving average: a single outlier band bends the correction less.
+    smoothed = []
+    for i in range(len(filled)):
+        window = filled[max(0, i - 1):i + 2]
+        smoothed.append(float(np.mean(window)))
+    return smoothed
+
+
+def _piecewise_deskew(img: np.ndarray, band_angles: list[float]) -> np.ndarray:
+    """Rotate each horizontal band by its own angle and blend the bands.
+
+    Each band is corrected by rotating the whole image about that band's centre,
+    then every output row is a linear blend of the two nearest band rotations.
+    The blend weights form a partition of unity (they sum to 1 per row), so the
+    transition between bands is gradual and leaves no seam.
+    """
+    h, w = img.shape[:2]
+    n = len(band_angles)
+    cx = w / 2.0
+
+    # Band-index coordinate for each output row: band i's centre sits at p = i.
+    p = np.clip(np.arange(h) / h * n - 0.5, 0.0, n - 1)
+
+    channel_shape = () if img.ndim == 2 else (img.shape[2],)
+    acc = np.zeros((h, w) + channel_shape, dtype=np.float32)
+
+    for i, angle in enumerate(band_angles):
+        weight = np.clip(1.0 - np.abs(p - i), 0.0, 1.0)  # triangular
+        if not weight.any():
+            continue
+        cy = (i + 0.5) * h / n
+        M = cv2.getRotationMatrix2D((cx, cy), float(angle), 1.0)
+        rot = cv2.warpAffine(
+            img, M, (w, h),
+            flags=cv2.INTER_CUBIC,
+            borderMode=cv2.BORDER_REPLICATE,
+        ).astype(np.float32)
+        acc += rot * weight.reshape(h, *([1] * len(channel_shape)))
+
+    return np.clip(acc, 0, 255).astype(np.uint8)
+
+
+# ── Enhancement ───────────────────────────────────────────────────────
+
+def denoise_image(
+    img: np.ndarray,
+    params: Dict[str, Any],
+    progress: ProgressCallbackType = None
+) -> np.ndarray:
+    """Denoise without smearing text edges.
+
+    params: method 'nlm' (best, slow) | 'bilateral' | 'gaussian' (fastest),
+    strength 1-20 (10).
+    """
+    if progress:
+        progress(0.1, "Preparing denoising")
+
+    method = params.get("method", "nlm")
+    strength = params.get("strength", 10)
+    img = img.astype(np.uint8)
+
+    if progress:
+        progress(0.2, f"Applying {method} denoising")
+
+    if method == "bilateral":
+        d = max(5, min(15, int(strength)))
+        sigma_color = strength * 7.5
+        sigma_space = strength * 7.5
+        result = cv2.bilateralFilter(img, d, sigma_color, sigma_space)
+
+    elif method == "gaussian":
+        ksize = max(3, int(strength) | 1)  # kernel must be odd
+        result = cv2.GaussianBlur(img, (ksize, ksize), 0)
+
+    else:  # nlm
+        h = max(3, min(30, strength))
+        template_window = 7
+        search_window = 21
+
+        if len(img.shape) == 3:
+            result = cv2.fastNlMeansDenoisingColored(
+                img, None, h, h, template_window, search_window
+            )
+        else:
+            result = cv2.fastNlMeansDenoising(
+                img, None, h, template_window, search_window
+            )
+
+    if progress:
+        progress(1.0, "Denoise complete")
+
+    return result
+
+
+def clahe_contrast(
+    img: np.ndarray,
+    params: Dict[str, Any],
+    progress: ProgressCallbackType = None
+) -> np.ndarray:
+    """CLAHE — local contrast without blowing up noise.
+
+    params: clipLimit (2.0), tileSize (8).
+    """
+    if progress:
+        progress(0.1, "Preparing CLAHE")
+
+    clip_limit = params.get("clipLimit", 2.0)
+    tile_size = params.get("tileSize", 8)
+    tile_size = max(2, min(16, int(tile_size)))
+
+    clahe = cv2.createCLAHE(clipLimit=clip_limit, tileGridSize=(tile_size, tile_size))
+
+    if progress:
+        progress(0.3, "Applying CLAHE")
+
+    if len(img.shape) == 2:
+        result = clahe.apply(img)
+    else:
+        # Colour: only touch luminance, so hues stay put.
+        lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
+        l, a, b = cv2.split(lab)
+
+        if progress:
+            progress(0.5, "Enhancing luminance")
+
+        l = clahe.apply(l)
+        lab = cv2.merge([l, a, b])
+        result = cv2.cvtColor(lab, cv2.COLOR_LAB2BGR)
+
+    if progress:
+        progress(1.0, "Contrast enhancement complete")
+
+    return result
+
+
+def sharpen_image(
+    img: np.ndarray,
+    params: Dict[str, Any],
+    progress: ProgressCallbackType = None
+) -> np.ndarray:
+    """Unsharp-mask the text edges.
+
+    params: amount 0-100 (50), radius in px 0.5-3 (1).
+    """
+    if progress:
+        progress(0.1, "Preparing sharpening")
+
+    amount = params.get("amount", 50) / 100.0
+    radius = params.get("radius", 1.0)
+
+    if amount <= 0:
+        if progress:
+            progress(1.0, "No sharpening applied")
+        return img
+
+    ksize = max(3, int(radius * 2) | 1)  # kernel must be odd
+
+    if progress:
+        progress(0.3, "Creating blur mask")
+
+    if len(img.shape) == 3:
+        blurred = cv2.GaussianBlur(img, (ksize, ksize), radius)
+    else:
+        blurred = cv2.GaussianBlur(img, (ksize, ksize), radius)
+
+    if progress:
+        progress(0.6, "Applying unsharp mask")
+
+    # original + amount * (original - blurred) — adds the high frequencies back.
+    sharpened = cv2.addWeighted(
+        img.astype(np.float32), 1.0 + amount,
+        blurred.astype(np.float32), -amount,
+        0
+    )
+
+    result = np.clip(sharpened, 0, 255).astype(np.uint8)
+
+    if progress:
+        progress(1.0, "Sharpen complete")
+
+    return result
+
+
+# ── Binarization ──────────────────────────────────────────────────────
+
+def threshold_image(
+    img: np.ndarray,
+    params: Dict[str, Any],
+    progress: ProgressCallbackType = None
+) -> np.ndarray:
+    """Binarize to pure black/white.
+
+    params: method 'otsu' | 'adaptive' | 'sauvola' (best on degraded scans),
+    blockSize (15) and k (0.5) for the local methods.
+    """
+    if progress:
+        progress(0.1, "Preparing binarization")
+
+    method = params.get("method", "otsu")
+    block_size = params.get("blockSize", 15)
+    k = params.get("k", 0.5)
+    block_size = max(3, int(block_size) | 1)  # must be odd, >= 3
+
+    if len(img.shape) == 3:
+        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    else:
+        gray = img.copy()
+
+    if progress:
+        progress(0.3, f"Applying {method} thresholding")
+
+    if method == "adaptive":
+        result = cv2.adaptiveThreshold(
+            gray, 255,
+            cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
+            cv2.THRESH_BINARY,
+            block_size, 8
+        )
+
+    elif method == "sauvola":
+        result = _sauvola_threshold(gray, block_size, k, progress)
+
+    else:  # otsu
+        _, result = cv2.threshold(
+            gray, 0, 255,
+            cv2.THRESH_BINARY + cv2.THRESH_OTSU
+        )
+
+    if progress:
+        progress(1.0, "Binarization complete")
+
+    return result
+
+
+def _sauvola_threshold(
+    gray: np.ndarray,
+    window_size: int,
+    k: float,
+    progress: ProgressCallbackType = None
+) -> np.ndarray:
+    """Sauvola: T(x,y) = mean * (1 + k * (std / R - 1)), R = 128 for 8-bit.
+
+    Means and variances come from box filters, so it stays O(n) per pixel.
+    """
+    if progress:
+        progress(0.4, "Computing local statistics")
+
+    mean = cv2.blur(gray.astype(np.float64), (window_size, window_size))
+    sq_mean = cv2.blur(gray.astype(np.float64) ** 2, (window_size, window_size))
+
+    variance = sq_mean - mean ** 2
+    variance = np.maximum(variance, 0)  # float error can push this negative
+    std = np.sqrt(variance)
+
+    if progress:
+        progress(0.7, "Computing threshold map")
+
+    R = 128.0
+    threshold = mean * (1.0 + k * (std / R - 1.0))
+
+    if progress:
+        progress(0.9, "Applying threshold")
+
+    result = np.zeros_like(gray)
+    result[gray > threshold] = 255
+
+    return result.astype(np.uint8)
+
+
+# ── Morphology ────────────────────────────────────────────────────────
+
+def morph_operations(
+    img: np.ndarray,
+    params: Dict[str, Any],
+    progress: ProgressCallbackType = None
+) -> np.ndarray:
+    """Morphological cleanup of text and artifacts.
+
+    params: operation open|close|dilate|erode|gradient|tophat|blackhat ('open'),
+    kernelSize 1-9 (2), kernelShape ellipse|rect|cross, iterations 1-10 (1).
+    """
+    if progress:
+        progress(0.1, "Preparing morphological operation")
+
+    operation = params.get("operation", "open")
+    k = max(1, min(9, int(params.get("kernelSize", 2))))
+    shape_name = params.get("kernelShape", "ellipse")
+    iterations = max(1, min(10, int(params.get("iterations", 1))))
+
+    shape_map = {
+        "ellipse": cv2.MORPH_ELLIPSE,
+        "rect": cv2.MORPH_RECT,
+        "cross": cv2.MORPH_CROSS,
+    }
+    shape = shape_map.get(shape_name, cv2.MORPH_ELLIPSE)
+    kernel = cv2.getStructuringElement(shape, (k, k))
+
+    if progress:
+        progress(0.3, f"Applying {operation} (k={k}, iter={iterations})")
+
+    # dilate/erode aren't morphologyEx ops, so they're handled separately below.
+    morph_ops = {
+        "open": cv2.MORPH_OPEN,
+        "close": cv2.MORPH_CLOSE,
+        "gradient": cv2.MORPH_GRADIENT,
+        "tophat": cv2.MORPH_TOPHAT,
+        "blackhat": cv2.MORPH_BLACKHAT,
+    }
+
+    if operation in morph_ops:
+        result = cv2.morphologyEx(
+            img, morph_ops[operation], kernel, iterations=iterations
+        )
+    elif operation == "dilate":
+        result = cv2.dilate(img, kernel, iterations=iterations)
+    elif operation == "erode":
+        result = cv2.erode(img, kernel, iterations=iterations)
+    else:
+        result = cv2.morphologyEx(
+            img, cv2.MORPH_OPEN, kernel, iterations=iterations
+        )
+
+    if progress:
+        progress(1.0, "Morphological operation complete")
+
+    return result
+
+
+# ── Blob & noise removal ──────────────────────────────────────────────
+
+def remove_large_blobs(
+    img: np.ndarray,
+    params: Dict[str, Any],
+    progress: ProgressCallbackType = None
+) -> np.ndarray:
+    """Punch out big ink blobs, keeping the letters they touch.
+
+    Only the eroded inner core gets painted white — erasing the whole connected
+    component would take neighbouring characters with it.
+
+    params: minArea (3000), minSolidity (0.55), maxAspectRatio (4.0),
+    erosionRatio (0.35 — higher removes less, safer).
+    """
+    if progress:
+        progress(0.1, "Preparing blob detection")
+
+    min_area = int(params.get("minArea", 3000))
+    min_solidity = float(params.get("minSolidity", 0.55))
+    max_aspect_ratio = float(params.get("maxAspectRatio", 4.0))
+    erosion_ratio = float(params.get("erosionRatio", 0.35))
+
+    gray = img if len(img.shape) == 2 else cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    _, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
+
+    # Invert first: connectedComponents wants ink as white foreground.
+    inverted = cv2.bitwise_not(binary)
+    num_labels, labels, stats, _ = cv2.connectedComponentsWithStats(
+        inverted, connectivity=8
+    )
+
+    if progress:
+        progress(0.3, f"Analyzing {num_labels - 1} components")
+
+    result = binary.copy()
+
+    for lbl in range(1, num_labels):
+        area = stats[lbl, cv2.CC_STAT_AREA]
+
+        if area <= min_area:  # small enough to just be a character
+            continue
+
+        w = stats[lbl, cv2.CC_STAT_WIDTH]
+        h = stats[lbl, cv2.CC_STAT_HEIGHT]
+        aspect = max(w, h) / max(min(w, h), 1)
+
+        if aspect > max_aspect_ratio:  # long and thin — a stroke or page border
+            continue
+
+        # Ragged outline means text; a real blob is close to its convex hull.
+        component_mask = np.uint8(labels == lbl) * 255
+        contours, _ = cv2.findContours(
+            component_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
+        )
+        if not contours:
+            continue
+
+        hull_area = cv2.contourArea(cv2.convexHull(contours[0]))
+        solidity = float(area) / hull_area if hull_area > 0 else 0.0
+
+        if solidity < min_solidity:
+            continue
+
+        k_radius = max(3, int(erosion_ratio * (area ** 0.5)))
+        k_size = 2 * k_radius + 1
+        kern = cv2.getStructuringElement(
+            cv2.MORPH_ELLIPSE, (k_size, k_size)
+        )
+        core = cv2.erode(component_mask, kern, iterations=1)
+        result[core == 255] = 255  # white == background
+
+        if progress:
+            pct = 0.3 + 0.6 * (lbl / max(num_labels - 1, 1))
+            progress(min(pct, 0.9), f"Processing blob {lbl}")
+
+    if progress:
+        progress(1.0, "Blob removal complete")
+
+    return result
+
+
+def remove_small_noise(
+    img: np.ndarray,
+    params: Dict[str, Any],
+    progress: ProgressCallbackType = None
+) -> np.ndarray:
+    """Drop speckles and scanner dust. params: maxArea (20)."""
+    if progress:
+        progress(0.1, "Preparing noise detection")
+
+    max_area = max(1, int(params.get("maxArea", 20)))
+
+    gray = img if len(img.shape) == 2 else cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    _, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
+
+    inverted = cv2.bitwise_not(binary)
+    num_labels, labels, stats, _ = cv2.connectedComponentsWithStats(
+        inverted, connectivity=8
+    )
+
+    if progress:
+        progress(0.4, f"Filtering {num_labels - 1} components (threshold={max_area})")
+
+    keep_mask = np.zeros_like(inverted)
+    for lbl in range(1, num_labels):
+        area = stats[lbl, cv2.CC_STAT_AREA]
+        if area >= max_area:
+            keep_mask[labels == lbl] = 255
+
+    result = cv2.bitwise_not(keep_mask)
+
+    if progress:
+        progress(1.0, "Small noise removal complete")
+
+    return result
+
+
+# ── Registry ──────────────────────────────────────────────────────────
+
+OP_REGISTRY = {
+    "normalize": normalize_image,
+    "grayscale": to_grayscale,
+    "deskew": deskew_image,
+    "denoise": denoise_image,
+    "contrast": clahe_contrast,
+    "sharpen": sharpen_image,
+    "threshold": threshold_image,
+    "morph": morph_operations,
+    "remove_blobs": remove_large_blobs,
+    "remove_noise": remove_small_noise,
+}
+
+
+def get_operation(name: str):
+    return OP_REGISTRY.get(name)
+
+
+def list_operations() -> list[str]:
+    return list(OP_REGISTRY.keys())
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/preprocessing/pipeline.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/preprocessing/pipeline.py
new file mode 100644
index 00000000..699c4a92
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/preprocessing/pipeline.py
@@ -0,0 +1,211 @@
+"""Runs an ordered list of preprocessing ops over one image.
+
+Reports progress per step and either stops or keeps going on failure.
+"""
+
+import traceback
+from typing import Dict, Any, Optional, Callable, List
+from dataclasses import dataclass, field
+import numpy as np
+
+from .operations import OP_REGISTRY, get_operation
+from .progress import ProgressCallback, ProgressAggregator, ProgressInfo
+
+
+@dataclass
+class PipelineStep:
+    """One step in the pipeline."""
+    op: str
+    params: Dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass
+class PipelineResult:
+    """Result of pipeline execution"""
+    success: bool
+    image: Optional[np.ndarray]
+    progress_info: Dict[str, Any]
+    errors: List[Dict[str, Any]] = field(default_factory=list)
+
+
+class PipelineExecutor:
+    """Runs the steps in order, timing each one and reporting progress."""
+
+    def __init__(
+        self,
+        on_progress: Optional[Callable[[ProgressInfo], None]] = None,
+        continue_on_error: bool = False,
+    ):
+        self.on_progress = on_progress
+        self.continue_on_error = continue_on_error
+        self.aggregator = ProgressAggregator()
+
+    def execute(
+        self,
+        image: np.ndarray,
+        steps: List[Dict[str, Any]],
+        preview_mode: bool = False,
+    ) -> PipelineResult:
+        """Run `steps` ([{op, params, enabled}, ...]) over `image`.
+
+        preview_mode swaps in faster, lower-quality variants of some ops.
+        """
+        self.aggregator.start()
+
+        active_steps = [
+            PipelineStep(op=s.get("op", ""), params=s.get("params", {}))
+            for s in steps
+            if s.get("enabled", True)
+        ]
+
+        if not active_steps:
+            self.aggregator.finish()
+            return PipelineResult(
+                success=True,
+                image=image,
+                progress_info=self.aggregator.get_summary(),
+            )
+
+        total_steps = len(active_steps)
+        current_image = image.copy()
+        errors = []
+
+        for i, step in enumerate(active_steps):
+            step_name = step.op
+            self.aggregator.step_started(step_name, i, total_steps)
+            op_func = get_operation(step_name)
+
+            if op_func is None:
+                error_info = {
+                    "step": step_name,
+                    "index": i,
+                    "error": f"Unknown operation: {step_name}",
+                }
+                errors.append(error_info)
+                self.aggregator.step_completed(success=False, error=error_info["error"])
+
+                if not self.continue_on_error:
+                    self.aggregator.finish()
+                    return PipelineResult(
+                        success=False,
+                        image=current_image,
+                        progress_info=self.aggregator.get_summary(),
+                        errors=errors,
+                    )
+                continue
+
+            progress = ProgressCallback(
+                on_progress=self._handle_step_progress,
+                step_name=step_name,
+                step_index=i,
+                total_steps=total_steps,
+            )
+
+            try:
+                params = step.params.copy()
+                if preview_mode:
+                    params = self._adjust_params_for_preview(step_name, params)
+
+                current_image = op_func(current_image, params, progress)
+
+                self.aggregator.step_completed(success=True)
+
+            except Exception as e:
+                error_msg = str(e)
+                error_trace = traceback.format_exc()
+
+                error_info = {
+                    "step": step_name,
+                    "index": i,
+                    "error": error_msg,
+                    "traceback": error_trace,
+                }
+                errors.append(error_info)
+                self.aggregator.step_completed(success=False, error=error_msg)
+
+                if not self.continue_on_error:
+                    self.aggregator.finish()
+                    return PipelineResult(
+                        success=False,
+                        image=current_image,
+                        progress_info=self.aggregator.get_summary(),
+                        errors=errors,
+                    )
+
+        self.aggregator.finish()
+
+        return PipelineResult(
+            success=len(errors) == 0,
+            image=current_image,
+            progress_info=self.aggregator.get_summary(),
+            errors=errors,
+        )
+
+    def _handle_step_progress(self, info: ProgressInfo):
+        """Handle progress from individual step"""
+        self.aggregator.update_progress(info)
+        if self.on_progress:
+            self.on_progress(info)
+
+    def _adjust_params_for_preview(
+        self,
+        op_name: str,
+        params: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Trade quality for speed in preview mode."""
+        adjusted = params.copy()
+
+        # NLM denoising is far too slow for live preview; bilateral is close enough.
+        if op_name == "denoise":
+            if adjusted.get("method") == "nlm":
+                adjusted["method"] = "bilateral"
+                adjusted["strength"] = min(adjusted.get("strength", 10), 10)
+
+        return adjusted
+
+
+def run_pipeline(
+    image: np.ndarray,
+    steps: List[Dict[str, Any]],
+    on_progress: Optional[Callable[[ProgressInfo], None]] = None,
+    continue_on_error: bool = False,
+    preview_mode: bool = False,
+) -> PipelineResult:
+    """One-shot PipelineExecutor.
+
+    steps looks like [{"op": "grayscale", "params": {}, "enabled": True}, ...].
+    """
+    executor = PipelineExecutor(
+        on_progress=on_progress,
+        continue_on_error=continue_on_error,
+    )
+
+    return executor.execute(image, steps, preview_mode=preview_mode)
+
+
+def validate_pipeline_config(steps: List[Dict[str, Any]]) -> Dict[str, Any]:
+    """Check a step list before running it. Returns {valid, errors}."""
+    errors = []
+
+    if not isinstance(steps, list):
+        return {"valid": False, "errors": ["Pipeline must be a list of steps"]}
+
+    for i, step in enumerate(steps):
+        if not isinstance(step, dict):
+            errors.append(f"Step {i}: must be a dictionary")
+            continue
+
+        op = step.get("op")
+        if not op:
+            errors.append(f"Step {i}: missing 'op' field")
+        elif op not in OP_REGISTRY:
+            errors.append(f"Step {i}: unknown operation '{op}'")
+
+        params = step.get("params")
+        if params is not None and not isinstance(params, dict):
+            errors.append(f"Step {i}: 'params' must be a dictionary")
+
+    return {
+        "valid": len(errors) == 0,
+        "errors": errors,
+    }
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/preprocessing/progress.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/preprocessing/progress.py
new file mode 100644
index 00000000..c4b17e28
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/preprocessing/progress.py
@@ -0,0 +1,121 @@
+"""Progress reporting for the preprocessing pipeline.
+
+Ops call a ProgressCallback with 0..1; the aggregator turns per-step reports
+into the overall percentage + timing summary the API returns.
+"""
+
+from typing import Callable, Optional, Dict, Any
+from dataclasses import dataclass
+import time
+
+
+@dataclass
+class ProgressInfo:
+    """One progress report from a single operation."""
+    step: str
+    percent: float
+    message: str = ""
+    step_index: int = 0
+    total_steps: int = 0
+    elapsed_ms: int = 0
+
+
+class ProgressCallback:
+    """Scales one operation's 0..1 progress into overall pipeline percent."""
+
+    def __init__(
+        self,
+        on_progress: Optional[Callable[[ProgressInfo], None]] = None,
+        step_name: str = "",
+        step_index: int = 0,
+        total_steps: int = 1,
+    ):
+        self.on_progress = on_progress
+        self.step_name = step_name
+        self.step_index = step_index
+        self.total_steps = total_steps
+        self.start_time = time.time()
+
+    def __call__(self, percent: float, message: str = ""):
+        """percent is 0.0–1.0 within this step; every step weighs the same."""
+        elapsed_ms = int((time.time() - self.start_time) * 1000)
+
+        step_contribution = 1.0 / self.total_steps if self.total_steps > 0 else 1.0
+        overall_percent = (self.step_index + percent) * step_contribution * 100
+
+        info = ProgressInfo(
+            step=self.step_name,
+            percent=min(100, max(0, overall_percent)),
+            message=message,
+            step_index=self.step_index,
+            total_steps=self.total_steps,
+            elapsed_ms=elapsed_ms,
+        )
+
+        if self.on_progress:
+            self.on_progress(info)
+
+
+class ProgressAggregator:
+    """Collects per-step timing/status for the API's progress_info payload."""
+
+    def __init__(self):
+        self.steps: list[Dict[str, Any]] = []
+        self.start_time: Optional[float] = None
+        self.end_time: Optional[float] = None
+        self.current_percent: float = 0
+
+    def start(self):
+        """Mark pipeline start"""
+        self.start_time = time.time()
+        self.steps = []
+
+    def step_started(self, step_name: str, index: int, total: int):
+        """Mark step started"""
+        self.steps.append({
+            "step": step_name,
+            "index": index,
+            "total": total,
+            "start_time": time.time(),
+            "end_time": None,
+            "duration_ms": None,
+            "success": None,
+            "error": None,
+        })
+
+    def step_completed(self, success: bool = True, error: Optional[str] = None):
+        """Mark current step completed"""
+        if self.steps:
+            step = self.steps[-1]
+            step["end_time"] = time.time()
+            step["duration_ms"] = int((step["end_time"] - step["start_time"]) * 1000)
+            step["success"] = success
+            step["error"] = error
+
+    def update_progress(self, info: ProgressInfo):
+        """Handle progress update from callback"""
+        self.current_percent = info.percent
+
+    def finish(self):
+        """Mark pipeline finished"""
+        self.end_time = time.time()
+
+    def get_summary(self) -> Dict[str, Any]:
+        """Get progress summary for API response"""
+        total_ms = 0
+        if self.start_time and self.end_time:
+            total_ms = int((self.end_time - self.start_time) * 1000)
+
+        return {
+            "total_duration_ms": total_ms,
+            "steps": [
+                {
+                    "step": s["step"],
+                    "duration_ms": s["duration_ms"],
+                    "success": s["success"],
+                    "error": s["error"],
+                }
+                for s in self.steps
+            ],
+            "final_percent": self.current_percent,
+        }
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/requirements-dev.txt b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/requirements-dev.txt
new file mode 100644
index 00000000..0d5f8b4c
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/requirements-dev.txt
@@ -0,0 +1,5 @@
+# Dev/test-only dependencies (never installed into the runtime image).
+# Install on top of requirements.txt:  pip install -r requirements-dev.txt
+-r requirements.txt
+pytest==8.3.4
+pytest-cov==6.0.0
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/requirements.txt b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/requirements.txt
new file mode 100644
index 00000000..1efda2e8
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/requirements.txt
@@ -0,0 +1,88 @@
+# ============================================================
+# RenAIssance OCR Backend — Python dependencies
+# Pinned for Python 3.11 + Ubuntu 24.04 + CUDA 12.6
+# ============================================================
+
+# --- Web framework ---
+fastapi==0.115.6
+uvicorn[standard]==0.32.1
+python-multipart==0.0.12
+pydantic==2.10.4
+
+# --- HTTP client ---
+httpx==0.28.1
+
+# --- Lightweight user tracking / auth ---
+# DATABASE_URL-driven: SQLite by default (on the storage volume), or any
+# postgresql:// URL (Neon/Supabase) with no code change. psycopg2-binary is
+# only used when a Postgres URL is supplied; it is harmless otherwise.
+SQLAlchemy==2.0.36
+psycopg2-binary==2.9.10
+bcrypt==4.2.1
+itsdangerous==2.2.0
+email-validator==2.2.0
+
+# --- AI / OCR providers ---
+google-genai==1.0.0
+protobuf==5.29.2
+
+# --- Document export ---
+python-docx==1.1.2
+reportlab==4.2.5
+
+# --- Image processing ---
+Pillow==11.1.0
+opencv-python-headless==4.10.0.84
+# numpy 1.24.4: satisfies paddlex 3.4 (numpy>=1.24), paddlepaddle-gpu 3.3.0,
+# transformers 4.53 (>=1.17) and opencv-python-headless. Kept at 1.24.4 to
+# avoid a numpy 2.x ABI bump across the pinned native wheels.
+numpy==1.24.4
+
+# --- Utilities ---
+python-dotenv==1.0.1
+psutil==6.1.0
+
+# --- paddlepaddle-gpu runtime deps (installed with --no-deps in Dockerfile) ---
+# These are the non-CUDA runtime deps that paddle 3.3.0 would normally pull
+# transitively. Torch/FastAPI/pydantic already bring filelock, typing_extensions,
+# networkx, numpy, Pillow, protobuf, httpx — the only one missing is opt_einsum.
+opt_einsum==3.3.0
+
+# --- Local recognition + post-processing models (CRNN / TrOCR / Qwen3 LoRA) ---
+torch==2.5.1
+# transformers 4.53.x: needed for the Qwen3 architecture (the fine-tuned
+# Spanish post-processing adapter is Qwen3-4B, added to transformers in 4.51).
+# It requires tokenizers>=0.21,<0.22 — which is why paddleocr is bumped to 3.4
+# below (paddlex 3.0.0 hard-pinned tokenizers==0.19.1; 3.4.x relaxed it to
+# >=0.19, so 0.21 satisfies both). Still supports the same
+# VisionEncoderDecoderModel / TrOCRProcessor APIs the recognition service uses.
+transformers==4.53.3
+tokenizers==0.21.2
+# safetensors must be >=0.6.0 to satisfy paddlepaddle-gpu==3.3.0;
+# transformers 4.53.3 only requires >=0.4.3, so 0.6.x works for both.
+safetensors==0.6.2
+# QLoRA post-processing: base Qwen3-4B loaded 4-bit + LoRA adapter (peft),
+# 4-bit kernels (bitsandbytes, GPU-only), device placement (accelerate).
+peft==0.19.1
+bitsandbytes==0.49.2
+accelerate==1.10.1
+
+# --- PDF parsing ---
+PyMuPDF==1.25.1
+
+# --- Layout-aware GPU detection ---
+# Requires: NVIDIA driver >= 560 (CUDA 12.6) on host, --gpus all on docker run.
+#
+# NOTE: paddlepaddle-gpu is NOT listed here — it is installed as a separate
+# --no-deps step in the Dockerfile. Reason: paddlepaddle-gpu==3.3.0 hard-pins
+# nvidia-nccl-cu12==2.25.1 (among several other nvidia-*-cu12 ==X.Y.Z pins)
+# while no torch release on PyPI pins that exact nccl. Letting pip resolve
+# both packages together is impossible under the modern resolver. Both
+# frameworks bundle their own CUDA user-space libraries under torch/lib/
+# and paddle.libs/ respectively, so the nvidia-*-cu12 site-packages copies
+# are redundant at runtime — installing paddle with --no-deps is safe.
+#
+# Bumped 3.0.0 -> 3.4.0: 3.0.0's paddlex hard-pinned tokenizers==0.19.1, which
+# blocks the transformers 4.53 needed for Qwen3. paddlex 3.4.x relaxes it to
+# tokenizers>=0.19. Runs on the same paddlepaddle-gpu 3.3.0 wheel.
+paddleocr==3.4.0
\ No newline at end of file
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/scripts/entrypoint.sh b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/scripts/entrypoint.sh
new file mode 100755
index 00000000..64871318
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/scripts/entrypoint.sh
@@ -0,0 +1,19 @@
+#!/usr/bin/env bash
+# ============================================================================
+# RenAIssance backend container entrypoint.
+#
+# Runs a non-blocking preflight check (RAM + GPU/CUDA availability for the
+# image variant) that only LOGS — it never aborts boot, so the server always
+# starts and the non-GPU features (Gemini OCR, preprocessing, export) keep
+# working even if the GPU image was started without `--gpus all`.
+#
+# The hard "your machine does not meet the requirements" gate lives in the
+# host launcher (run.sh / run.ps1), which checks before pulling the image.
+# ============================================================================
+set -euo pipefail
+
+# Best-effort: a failing preflight must not prevent the server from starting.
+python /app/scripts/preflight.py || true
+
+# Hand off to the CMD (uvicorn ...).
+exec "$@"
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/scripts/normalize_trocr_config.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/scripts/normalize_trocr_config.py
new file mode 100644
index 00000000..141b630c
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/scripts/normalize_trocr_config.py
@@ -0,0 +1,85 @@
+"""Drop a known-good processor next to the fine-tuned TrOCR weights.
+
+We fine-tuned from microsoft/trocr-base-printed, so its tokenizer and image
+stats match exactly. Copying them in at build time makes the checkpoint load
+regardless of which transformers version serialized the original processor.
+No-op when the weights aren't present, so dev builds still work.
+"""
+
+from __future__ import annotations
+
+import shutil
+from pathlib import Path
+
+from transformers import TrOCRProcessor
+
+# The base model the user fine-tuned from (see
+# RenAIssanceExperimental/experimentation.ipynb).
+BASE_MODEL_ID = "microsoft/trocr-base-printed"
+
+TROCR_DIR = Path("/app/models/weights/trocr")
+
+# Files that belong to the fine-tuned weights — we must not overwrite these
+# with the base model's copies.
+FINETUNED_ARTIFACTS = {
+    "config.json",
+    "generation_config.json",
+    "model.safetensors",
+    "pytorch_model.bin",
+}
+
+
+def main() -> None:
+    if not TROCR_DIR.is_dir():
+        print(f"[normalize_trocr] {TROCR_DIR} does not exist — skipping")
+        return
+
+    weights = TROCR_DIR / "model.safetensors"
+    if not weights.is_file():
+        print(f"[normalize_trocr] no model.safetensors in {TROCR_DIR} — skipping")
+        return
+
+    print(f"[normalize_trocr] pulling processor assets from {BASE_MODEL_ID}")
+    processor = TrOCRProcessor.from_pretrained(BASE_MODEL_ID)
+
+    # Save all processor assets (preprocessor_config.json + vocab.json +
+    # merges.txt + tokenizer_config.json + special_tokens_map.json +
+    # tokenizer.json) into a staging directory, then copy only the files we
+    # don't already have — this prevents clobbering the fine-tuned weights.
+    staging = TROCR_DIR / ".processor_staging"
+    if staging.exists():
+        shutil.rmtree(staging)
+    staging.mkdir()
+    processor.save_pretrained(staging)
+
+    copied: list[str] = []
+    replaced: list[str] = []
+    for src in staging.iterdir():
+        if src.name in FINETUNED_ARTIFACTS:
+            continue
+        dst = TROCR_DIR / src.name
+        if dst.exists():
+            replaced.append(src.name)
+        else:
+            copied.append(src.name)
+        shutil.copy2(src, dst)
+
+    shutil.rmtree(staging)
+
+    # The newer-format bundled processor_config.json confuses 4.44.x's loader
+    # once preprocessor_config.json is in place ("multiple values for
+    # image_processor"). Remove it — the dir is self-consistent without it.
+    stale = TROCR_DIR / "processor_config.json"
+    if stale.exists():
+        stale.unlink()
+        replaced.append("processor_config.json (removed)")
+
+    if copied:
+        print(f"[normalize_trocr] copied: {sorted(copied)}")
+    if replaced:
+        print(f"[normalize_trocr] replaced: {sorted(replaced)}")
+    print("[normalize_trocr] done")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/scripts/preflight.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/scripts/preflight.py
new file mode 100755
index 00000000..554f1cda
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/scripts/preflight.py
@@ -0,0 +1,80 @@
+"""Startup warnings about missing GPU access or low RAM. Always exits 0.
+
+The hard spec gate lives in run.sh / run.ps1; this is just the in-container net.
+"""
+
+from __future__ import annotations
+
+import os
+import sys
+
+VARIANT = os.environ.get("RENAISSANCE_VARIANT", "gpu")
+MIN_RECOMMENDED_RAM_GB = 8.0
+
+
+def log(msg: str) -> None:
+    print(f"[preflight] {msg}", flush=True)
+
+
+def check_ram() -> None:
+    try:
+        import psutil
+
+        total_gb = psutil.virtual_memory().total / (1024 ** 3)
+        if total_gb < MIN_RECOMMENDED_RAM_GB:
+            log(
+                f"WARNING: detected {total_gb:.1f} GB RAM; "
+                f">= {MIN_RECOMMENDED_RAM_GB:.0f} GB is recommended. Large pages may "
+                "run slowly or hit out-of-memory during detection/recognition."
+            )
+    except Exception:
+        # psutil should always be present, but never let this block boot.
+        pass
+
+
+def check_paddle() -> None:
+    """Probe Paddle/CUDA. For the GPU variant the import itself fails when the
+    container has no GPU access (paddlepaddle-gpu links libcuda.so.1 at import)."""
+    try:
+        import paddle  # noqa: PLC0415
+
+        cuda_runtime = False
+        try:
+            cuda_runtime = paddle.device.cuda.device_count() > 0
+        except Exception:
+            cuda_runtime = False
+
+        if VARIANT == "gpu":
+            if cuda_runtime:
+                log("GPU image: CUDA device detected — PaddleOCR detection runs on GPU.")
+            else:
+                log(
+                    "GPU image started WITHOUT a usable CUDA device. PaddleOCR layout "
+                    "detection needs a GPU and will be unavailable. Re-run via the "
+                    "launcher (./run.sh) or add `--gpus all`, or use the CPU image "
+                    "(./run.sh --cpu). Gemini OCR, preprocessing and export still work."
+                )
+        else:
+            log("CPU image: PaddleOCR and torch will run on CPU.")
+    except Exception as exc:
+        if VARIANT == "gpu":
+            log(
+                "GPU image: PaddlePaddle could not initialize CUDA "
+                f"({exc.__class__.__name__}). This usually means the container was "
+                "started without GPU access. PaddleOCR layout detection will be "
+                "unavailable; re-run via ./run.sh (or add `--gpus all`), or use the "
+                "CPU image (./run.sh --cpu). Other features still work."
+            )
+        else:
+            log(f"Paddle import check skipped ({exc.__class__.__name__}).")
+
+
+def main() -> int:
+    log(f"variant={VARIANT}")
+    check_ram()
+    check_paddle()
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/conftest.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/conftest.py
new file mode 100644
index 00000000..ff12f302
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/conftest.py
@@ -0,0 +1,31 @@
+"""Shared pytest fixtures + path setup for the backend test suite.
+
+All tests here are CPU-safe (no GPU required) so they run unchanged in CI on
+GPU-less runners. Real-GPU coverage lives in tests/smoke_gpu.py (run manually).
+"""
+
+import os
+import sys
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+# Make `app` and `preprocessing` importable, mirroring app.main's path setup.
+BACKEND_DIR = Path(__file__).resolve().parent.parent
+sys.path.insert(0, str(BACKEND_DIR))
+
+# Never touch real user data: point storage at a throwaway dir.
+os.environ.setdefault("STORAGE_ROOT", str(BACKEND_DIR / ".pytest-storage"))
+
+
+@pytest.fixture
+def sample_color_image() -> np.ndarray:
+    """A deterministic 64x64 BGR uint8 image."""
+    rng = np.random.default_rng(0)
+    return rng.integers(0, 256, size=(64, 64, 3), dtype=np.uint8)
+
+
+@pytest.fixture
+def sample_transcripts() -> dict:
+    return {"1": "Hello world", "2": "Second page of text"}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/smoke_gpu.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/smoke_gpu.py
new file mode 100644
index 00000000..0a76c2d9
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/smoke_gpu.py
@@ -0,0 +1,62 @@
+"""Manual GPU check — run by hand, not part of CI.
+
+`python backend/tests/smoke_gpu.py` on a real NVIDIA box. Exits non-zero if
+torch or Paddle can't see CUDA.
+"""
+
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
+
+
+def main() -> int:
+    ok = True
+
+    # --- torch ---
+    import torch
+
+    from app.utils.torch_device import select_torch_device
+
+    dev = select_torch_device()
+    print(f"[smoke] torch device       : {dev}")
+    if torch.cuda.is_available():
+        x = torch.ones(1024, 1024, device="cuda")
+        y = (x @ x).sum().item()
+        print(f"[smoke] torch CUDA matmul  : {y:.0f} (ok)")
+    else:
+        print("[smoke] torch.cuda.is_available() == False")
+        ok = False
+
+    # --- paddle ---
+    try:
+        import paddle
+
+        compiled = paddle.device.is_compiled_with_cuda()
+        count = paddle.device.cuda.device_count() if compiled else 0
+        print(f"[smoke] paddle cuda build  : {compiled}, devices: {count}")
+        if not (compiled and count > 0):
+            ok = False
+    except Exception as exc:  # pragma: no cover - environment dependent
+        print(f"[smoke] paddle import FAILED: {exc}")
+        ok = False
+
+    # --- tier selection ---
+    try:
+        import app.services.layout_detection as ld
+
+        tier = ld.select_tier(use_gpu=True)
+        print(f"[smoke] selected tier      : device={tier['device']} tier={tier['tier']}")
+        print(f"[smoke] reason             : {tier['reason']}")
+        if tier["device"] != "gpu":
+            ok = False
+    except Exception as exc:  # pragma: no cover
+        print(f"[smoke] select_tier FAILED : {exc}")
+        ok = False
+
+    print("[smoke] RESULT:", "PASS" if ok else "FAIL")
+    return 0 if ok else 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/test_deskew_piecewise.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/test_deskew_piecewise.py
new file mode 100644
index 00000000..346dbb30
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/test_deskew_piecewise.py
@@ -0,0 +1,67 @@
+"""Piecewise deskew: the estimator finds a tilted band's angle, and a page
+whose skew grows toward the bottom is detected as variable (not uniform)."""
+
+import os
+import sys
+
+import cv2
+import numpy as np
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from preprocessing.operations import (  # noqa: E402
+    _band_skew_angles,
+    _projection_profile_angle,
+    deskew_image,
+)
+
+
+def _ruled_page(h=480, w=640, line_gap=24):
+    """White page with evenly spaced horizontal black 'text' lines."""
+    img = np.full((h, w), 255, np.uint8)
+    for y in range(line_gap, h - line_gap, line_gap):
+        cv2.line(img, (40, y), (w - 40, y), 0, 3)
+    return img
+
+
+def _rotate(img, angle):
+    h, w = img.shape[:2]
+    M = cv2.getRotationMatrix2D((w / 2, h / 2), angle, 1.0)
+    return cv2.warpAffine(img, M, (w, h), borderValue=255)
+
+
+def test_projection_profile_recovers_tilt():
+    # Lines tilted by +6 deg need a -6 deg rotation to flatten, so that is what
+    # the estimator should report.
+    tilted = _rotate(_ruled_page(), 6.0)
+    _, binary = cv2.threshold(tilted, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
+    angle = _projection_profile_angle(binary, max_angle=15)
+    assert angle is not None
+    assert abs(angle - (-6.0)) <= 1.0, f"expected ~-6, got {angle}"
+
+
+def test_variable_skew_detected_as_nonuniform():
+    # Straight top, 8-degree-tilted bottom, stitched into one page.
+    page = _ruled_page()
+    h = page.shape[0]
+    page[h // 2:, :] = _rotate(page, 8.0)[h // 2:, :]
+
+    angles = _band_skew_angles(page, num_bands=4, max_angle=15, fallback_angle=0.0)
+    spread = max(angles) - min(angles)
+    assert spread >= 1.0, f"variable skew should register a spread, got {angles}"
+    # Bottom bands must be corrected harder than the (straight) top band.
+    assert abs(angles[-1]) > abs(angles[0]) + 1.0, angles
+
+
+def test_deskew_runs_and_preserves_shape():
+    page = cv2.cvtColor(_ruled_page(), cv2.COLOR_GRAY2BGR)
+    out = deskew_image(page, {"mode": "auto", "maxAngle": 15, "bands": 4})
+    assert out.shape == page.shape
+    assert out.dtype == np.uint8
+
+
+if __name__ == "__main__":
+    test_projection_profile_recovers_tilt()
+    test_variable_skew_detected_as_nonuniform()
+    test_deskew_runs_and_preserves_shape()
+    print("ok")
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/test_export.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/test_export.py
new file mode 100644
index 00000000..f700a435
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/test_export.py
@@ -0,0 +1,32 @@
+"""Export builders must emit non-empty, well-formed TXT/DOCX/PDF buffers."""
+
+from app.services.export import (
+    build_combined_transcript,
+    build_docx_export,
+    build_pdf_export,
+    build_txt_export,
+)
+
+
+def test_combined_transcript_includes_all_pages(sample_transcripts):
+    combined = build_combined_transcript(sample_transcripts)
+    assert "Hello world" in combined
+    assert "Second page of text" in combined
+
+
+def test_txt_export_has_bom_and_content(sample_transcripts):
+    data = build_txt_export(sample_transcripts).getvalue()
+    assert data.startswith(b"\xef\xbb\xbf")  # UTF-8 BOM
+    assert "Hello world".encode("utf-8") in data
+
+
+def test_docx_export_is_nonempty_zip(sample_transcripts):
+    data = build_docx_export(sample_transcripts).getvalue()
+    assert len(data) > 0
+    # .docx is a zip container — starts with the PK signature.
+    assert data[:2] == b"PK"
+
+
+def test_pdf_export_has_pdf_magic(sample_transcripts):
+    data = build_pdf_export(sample_transcripts).getvalue()
+    assert data[:4] == b"%PDF"
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/test_health.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/test_health.py
new file mode 100644
index 00000000..1d9fccef
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/test_health.py
@@ -0,0 +1,20 @@
+"""Health route, plus importing app.main as a check on the whole import graph.
+
+Real HTTP health is covered by the CI container smoke job.
+"""
+
+import asyncio
+
+from app.api.health import health_check
+from app.main import app
+
+
+def test_app_registers_health_route():
+    paths = {getattr(route, "path", None) for route in app.routes}
+    assert "/api/health" in paths
+
+
+def test_health_payload_is_healthy():
+    body = asyncio.run(health_check())
+    assert body["status"] == "healthy"
+    assert "timestamp" in body
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/test_layout_resource_logic.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/test_layout_resource_logic.py
new file mode 100644
index 00000000..72ee7fff
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/test_layout_resource_logic.py
@@ -0,0 +1,64 @@
+"""Unit-test the device/tier selection branches in layout_detection without a
+real Paddle install or GPU — Paddle and resource probing are monkeypatched."""
+
+import app.services.layout_detection as ld
+
+
+def _resources(**overrides):
+    base = {
+        "warnings": [],
+        "gpu_available": False,
+        "gpu_vram_gb": 0.0,
+        "gpu_vram_free_gb": 0.0,
+        "available_ram_gb": 16.0,
+        "total_ram_gb": 32.0,
+    }
+    base.update(overrides)
+    return base
+
+
+def _patch(monkeypatch, **resource_overrides):
+    monkeypatch.setattr(ld, "_ensure_paddle", lambda: None)
+    monkeypatch.setattr(
+        ld, "check_system_resources", lambda use_gpu: _resources(**resource_overrides)
+    )
+
+
+def test_gpu_server_tier(monkeypatch):
+    _patch(monkeypatch, gpu_available=True, gpu_vram_free_gb=10.0)
+    tier = ld.select_tier(use_gpu=True)
+    assert tier["device"] == "gpu"
+    assert tier["tier"] == "server"
+    assert tier["layout_model"] == ld.SERVER_MODELS["layout"]
+
+
+def test_gpu_mobile_tier(monkeypatch):
+    # Between MOBILE (2) and SERVER (6) free VRAM → mobile on GPU.
+    _patch(monkeypatch, gpu_available=True, gpu_vram_free_gb=3.0)
+    tier = ld.select_tier(use_gpu=True)
+    assert tier["device"] == "gpu"
+    assert tier["tier"] == "mobile"
+
+
+def test_low_vram_falls_back_to_cpu(monkeypatch):
+    # Below MOBILE_MIN_VRAM → CPU; ample RAM → server models on CPU.
+    _patch(monkeypatch, gpu_available=True, gpu_vram_free_gb=1.0, available_ram_gb=16.0)
+    tier = ld.select_tier(use_gpu=True)
+    assert tier["device"] == "cpu"
+    assert tier["tier"] == "server"
+
+
+def test_cpu_low_ram_uses_mobile(monkeypatch):
+    _patch(monkeypatch, gpu_available=False, available_ram_gb=2.0)
+    tier = ld.select_tier(use_gpu=False)
+    assert tier["device"] == "cpu"
+    assert tier["tier"] == "mobile"
+
+
+def test_check_resources_without_paddle_reports_no_gpu(monkeypatch):
+    # Simulate paddle not yet imported → no GPU detected, no crash.
+    monkeypatch.setattr(ld, "_paddle", None)
+    info = ld.check_system_resources(use_gpu=True)
+    assert info["gpu_available"] is False
+    assert info["gpu_vram_gb"] == 0.0
+    assert any("GPU not available" in w for w in info["warnings"])
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/test_llm_postprocess_factory.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/test_llm_postprocess_factory.py
new file mode 100644
index 00000000..cb03b928
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/test_llm_postprocess_factory.py
@@ -0,0 +1,40 @@
+"""LLM post-processing factory wiring (no network, no model load)."""
+
+import pytest
+
+from app.services.llm_processing import factory
+from app.services.llm_processing.local_client import (
+    post_process_text_finetuned,
+    FINETUNED_BASE_MODEL,
+)
+
+
+def test_local_es_enabled_and_keyless():
+    es = next(p for p in factory.LLM_PROVIDERS if p["id"] == "local_es")
+    assert es["enabled"] is True
+    assert es["requires_key"] is False
+    assert es["default_model"] == FINETUNED_BASE_MODEL
+    # keyless providers must not demand an API key
+    assert factory.provider_requires_key("local_es") is False
+
+
+def test_local_es_routes_to_finetuned(monkeypatch):
+    called = {}
+
+    def fake(text, model, template_name):
+        called["args"] = (text, model, template_name)
+        return "CLEANED"
+
+    monkeypatch.setattr(factory, "post_process_text_finetuned", fake)
+    out = factory.post_process("local_es", None, "raw text", model="x", template_name="t")
+    assert out == "CLEANED"
+    assert called["args"] == ("raw text", "x", "t")
+
+
+def test_finetuned_requires_gpu_on_cpu(monkeypatch):
+    # On a CPU-only host the fine-tuned provider must fail clearly, never OOM.
+    # The function imports select_torch_device at call time, so patch the source.
+    import app.utils.torch_device as td
+    monkeypatch.setattr(td, "select_torch_device", lambda: "cpu")
+    with pytest.raises(ValueError, match="requires a GPU"):
+        post_process_text_finetuned("some ocr line")
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/test_local_client_batching.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/test_local_client_batching.py
new file mode 100644
index 00000000..4c52cb8e
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/test_local_client_batching.py
@@ -0,0 +1,122 @@
+"""Batching behaviour of the local fine-tuned corrector — no GPU, no model load.
+
+Fakes the tokenizer/model so the length-sorted batching, the per-batch
+max_new_tokens cap and the cut-off guard are all exercised on CPU in CI.
+"""
+
+from types import SimpleNamespace
+
+import torch
+
+from app.services.llm_processing import local_client
+
+
+STOP, PAD = 106, 0          # <end_of_turn>, <pad> — the real gemma-3 ids
+PROMPT_BASE, OUT_BASE = 1000, 2000
+
+
+class _Enc(dict):
+    """Stands in for BatchEncoding: unpacks as **kwargs, indexes, .to(device)."""
+
+    def to(self, device):
+        return self
+
+
+class FakeTokenizer:
+    """One id per prompt/output string; word counts for the length probe."""
+
+    pad_token_id = PAD
+    padding_side = "right"
+
+    def __init__(self):
+        self.prompts: list[str] = []
+        self.outputs: list[str] = []
+
+    def apply_chat_template(self, messages, tokenize=False, add_generation_prompt=True):
+        return messages[-1]["content"]
+
+    def __call__(self, text, return_tensors=None, padding=False, add_special_tokens=True):
+        if return_tensors is None:
+            # Length probe: token count == word count.
+            return SimpleNamespace(input_ids=[t.split() for t in text])
+        ids = []
+        for t in text:
+            ids.append(PROMPT_BASE + len(self.prompts))
+            self.prompts.append(t)
+        return _Enc(
+            input_ids=torch.tensor(ids).unsqueeze(1),
+            attention_mask=torch.ones(len(ids), 1, dtype=torch.long),
+        )
+
+    def batch_decode(self, rows, skip_special_tokens=True):
+        return [
+            " ".join(self.outputs[i - OUT_BASE] for i in row.tolist() if i >= OUT_BASE)
+            for row in rows
+        ]
+
+
+class FakeModel:
+    generation_config = SimpleNamespace(eos_token_id=[1, STOP])
+
+    def __init__(self, tok, correct, no_stop=()):
+        self.tok, self.correct, self.no_stop = tok, correct, set(no_stop)
+        self.caps: list[int] = []
+        self.batches: list[list[str]] = []
+
+    def generate(self, input_ids=None, attention_mask=None, max_new_tokens=None,
+                 do_sample=None, pad_token_id=None):
+        self.caps.append(max_new_tokens)
+        prompts = [self.tok.prompts[i - PROMPT_BASE] for i in input_ids[:, 0].tolist()]
+        self.batches.append(prompts)
+        rows = []
+        for p in prompts:
+            oid = OUT_BASE + len(self.tok.outputs)
+            self.tok.outputs.append(self.correct(p))
+            # No stop token == generation hit the cap mid-line.
+            rows.append([oid] if p in self.no_stop else [oid, STOP])
+        width = max(len(r) for r in rows)
+        padded = [r + [PAD] * (width - len(r)) for r in rows]
+        return torch.cat([input_ids, torch.tensor(padded)], dim=1)
+
+
+def _run(monkeypatch, tmp_path, text, correct=str.upper, no_stop=(), batch_size=2):
+    tok = FakeTokenizer()
+    model = FakeModel(tok, correct, no_stop)
+
+    import app.utils.torch_device as td
+    monkeypatch.setattr(td, "select_torch_device", lambda: "cuda")
+    monkeypatch.setattr(local_client, "_adapter_dir", lambda: str(tmp_path))
+    monkeypatch.setattr(local_client, "_load", lambda *a, **k: (tok, model, "cpu"))
+
+    out = local_client.post_process_text_finetuned(text, batch_size=batch_size)
+    return out, model
+
+
+def test_batches_group_by_length_and_cap_scales(monkeypatch, tmp_path):
+    long_a, long_b = " ".join("ab" * 1 for _ in range(10)), " ".join("cd" for _ in range(10))
+    text = "\n".join(["x", long_a, "y", long_b])
+
+    out, model = _run(monkeypatch, tmp_path, text)
+
+    # Short lines batch with short, long with long — never one of each.
+    assert [len(b) for b in model.batches] == [2, 2]
+    assert {len(p.split()) for p in model.batches[0]} == {1}
+    assert {len(p.split()) for p in model.batches[1]} == {10}
+
+    # Cap is derived per batch (1.5x + 16), not the flat 160 it used to be.
+    assert sorted(model.caps) == [17, 31]
+
+    # Original line order survives the sort — the frontend maps back by index.
+    assert out.split("\n") == ["X", long_a.upper(), "Y", long_b.upper()]
+
+
+def test_blank_lines_are_preserved(monkeypatch, tmp_path):
+    out, _ = _run(monkeypatch, tmp_path, "one\n\ntwo")
+    assert out.split("\n") == ["ONE", "", "TWO"]
+
+
+def test_cut_off_line_keeps_the_original(monkeypatch, tmp_path):
+    # A row with no stop token was truncated at the cap; writing that back would
+    # silently chop the line, so the raw line must survive instead.
+    out, _ = _run(monkeypatch, tmp_path, "keep me\ncorrect me", no_stop=["keep me"])
+    assert out.split("\n") == ["keep me", "CORRECT ME"]
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/test_local_model_cache.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/test_local_model_cache.py
new file mode 100644
index 00000000..641834cf
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/test_local_model_cache.py
@@ -0,0 +1,63 @@
+"""base_model_cached must not say "ready" while shards are still downloading.
+
+huggingface_hub writes config.json / tokenizer.* / the shard index up front and
+the multi-GB weights last, so a snapshot-dir-exists check reports ready almost
+immediately and the UI never shows its one-time-download warning.
+"""
+
+import json
+
+from app.services.llm_processing.local_client import (
+    FINETUNED_BASE_MODEL,
+    base_model_cached,
+)
+
+SHARDS = ["model-00001-of-00002.safetensors", "model-00002-of-00002.safetensors"]
+
+
+def _snapshot(tmp_path, monkeypatch):
+    """An HF cache laid out like a real one, with only the small files present."""
+    monkeypatch.setenv("HF_HOME", str(tmp_path))
+    snap = (
+        tmp_path / "hub"
+        / ("models--" + FINETUNED_BASE_MODEL.replace("/", "--"))
+        / "snapshots" / "abc123"
+    )
+    snap.mkdir(parents=True)
+    for name in ("config.json", "tokenizer.json", "tokenizer_config.json"):
+        (snap / name).write_text("{}")
+    (snap / "model.safetensors.index.json").write_text(
+        json.dumps({"weight_map": {f"layer.{i}": s for i, s in enumerate(SHARDS)}})
+    )
+    return snap
+
+
+def test_missing_cache_is_not_ready(tmp_path, monkeypatch):
+    monkeypatch.setenv("HF_HOME", str(tmp_path))
+    assert base_model_cached() is False
+
+
+def test_index_without_weights_is_not_ready(tmp_path, monkeypatch):
+    """The exact mid-download state — the bug a dir-exists check misses."""
+    _snapshot(tmp_path, monkeypatch)
+    assert base_model_cached() is False
+
+
+def test_partial_shards_are_not_ready(tmp_path, monkeypatch):
+    snap = _snapshot(tmp_path, monkeypatch)
+    (snap / SHARDS[0]).write_bytes(b"weights")
+    assert base_model_cached() is False
+
+
+def test_all_shards_present_is_ready(tmp_path, monkeypatch):
+    snap = _snapshot(tmp_path, monkeypatch)
+    for name in SHARDS:
+        (snap / name).write_bytes(b"weights")
+    assert base_model_cached() is True
+
+
+def test_unsharded_checkpoint_is_ready(tmp_path, monkeypatch):
+    snap = _snapshot(tmp_path, monkeypatch)
+    (snap / "model.safetensors.index.json").unlink()
+    (snap / "model.safetensors").write_bytes(b"weights")
+    assert base_model_cached() is True
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/test_ocr_factory.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/test_ocr_factory.py
new file mode 100644
index 00000000..9b9cda9c
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/test_ocr_factory.py
@@ -0,0 +1,79 @@
+"""OCRFactory strategy/registry behavior (no network — only construction)."""
+
+import pytest
+
+from app.services.ocr.base import BaseOCRProvider
+from app.services.ocr.factory import OCRFactory
+
+
+def test_list_providers_contains_all():
+    names = set(OCRFactory.list_providers())
+    assert {"gemini", "chatgpt", "deepseek", "qwen"} <= names
+
+
+@pytest.mark.parametrize("name", ["gemini", "chatgpt", "deepseek", "qwen", "GEMINI"])
+def test_get_provider_returns_base_instance(name):
+    provider = OCRFactory.get_provider(name)
+    assert isinstance(provider, BaseOCRProvider)
+
+
+def test_unknown_provider_raises_valueerror():
+    with pytest.raises(ValueError):
+        OCRFactory.get_provider("not-a-real-provider")
+
+
+def test_gemini_default_model_is_gemini_3():
+    from app.services.ocr import gemini
+
+    assert gemini.DEFAULT_MODEL == "gemini-3.1-flash-lite"
+    assert gemini.DEFAULT_MODEL in gemini.MODEL_IDS
+
+
+def test_gemini_retries_transient_server_error(monkeypatch):
+    """A 503 ServerError should be retried, not surfaced on the first hit."""
+    from app.services.ocr import gemini
+    from google.genai import errors as genai_errors
+
+    class _FakeResp:  # shape APIError expects for a non-requests response
+        body_segments = [{"error": {"message": "busy", "status": "UNAVAILABLE"}}]
+
+    class _Ok:
+        text = "hello world"
+
+    calls = {"n": 0}
+
+    class _Client:
+        class models:
+            @staticmethod
+            def generate_content(**kwargs):
+                calls["n"] += 1
+                if calls["n"] < 2:
+                    raise genai_errors.ServerError(503, _FakeResp())
+                return _Ok()
+
+    monkeypatch.setattr(gemini, "get_gemini_client", lambda api_key: _Client())
+    monkeypatch.setattr(gemini.time, "sleep", lambda s: None)  # no real backoff
+
+    out = OCRFactory.get_provider("gemini").transcribe("key", b"img", "gemini-3.1-flash-lite")
+    assert out == "hello world"
+    assert calls["n"] == 2  # failed once, succeeded on retry
+
+
+def test_gemini_empty_response_raises(monkeypatch):
+    """A thinking model that returns no text must error, not yield ''."""
+    from app.services.ocr import gemini
+
+    class _Resp:
+        text = None
+        prompt_feedback = "BLOCKED"
+
+    class _Client:
+        class models:
+            @staticmethod
+            def generate_content(**kwargs):
+                return _Resp()
+
+    monkeypatch.setattr(gemini, "get_gemini_client", lambda api_key: _Client())
+    provider = OCRFactory.get_provider("gemini")
+    with pytest.raises(ValueError):
+        provider.transcribe("key", b"img", "gemini-3.1-flash-lite")
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/test_preprocess_ops.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/test_preprocess_ops.py
new file mode 100644
index 00000000..32d6db9d
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/test_preprocess_ops.py
@@ -0,0 +1,42 @@
+"""Every preprocessing op and the PipelineExecutor must run on a synthetic
+image and return a valid array — guards against op-registry regressions."""
+
+import numpy as np
+import pytest
+
+from preprocessing.operations import OP_REGISTRY, list_operations
+from preprocessing.pipeline import run_pipeline
+
+
+def test_registry_matches_list():
+    assert set(list_operations()) == set(OP_REGISTRY.keys())
+    # 7 documented OpenCV ops at minimum (normalize, grayscale, deskew,
+    # denoise, contrast, sharpen, binarize/threshold).
+    assert len(OP_REGISTRY) >= 7
+
+
+@pytest.mark.parametrize("op_name", sorted(OP_REGISTRY.keys()))
+def test_each_op_runs_with_default_params(op_name, sample_color_image):
+    fn = OP_REGISTRY[op_name]
+    out = fn(sample_color_image, {})
+    assert isinstance(out, np.ndarray)
+    assert out.ndim in (2, 3)
+    assert out.size > 0
+
+
+def test_pipeline_executes_multi_step(sample_color_image):
+    steps = [
+        {"op": "grayscale", "params": {}, "enabled": True},
+        {"op": "normalize", "params": {}, "enabled": True},
+        {"op": "threshold", "params": {}, "enabled": True},
+    ]
+    result = run_pipeline(sample_color_image, steps)
+    assert result.success
+    assert isinstance(result.image, np.ndarray)
+    assert result.image.size > 0
+
+
+def test_pipeline_skips_disabled_steps(sample_color_image):
+    steps = [{"op": "grayscale", "params": {}, "enabled": False}]
+    result = run_pipeline(sample_color_image, steps)
+    assert result.success
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/test_torch_device.py b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/test_torch_device.py
new file mode 100644
index 00000000..60bdcdc1
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/backend/tests/test_torch_device.py
@@ -0,0 +1,28 @@
+"""Device selection must always land on a real device, cuda > mps > cpu."""
+
+from app.utils.torch_device import select_torch_device
+
+
+def test_select_returns_known_device():
+    assert select_torch_device() in ("cuda", "mps", "cpu")
+
+
+def test_cuda_preferred_when_available(monkeypatch):
+    import app.utils.torch_device as td
+
+    monkeypatch.setattr(td.torch.cuda, "is_available", lambda: True)
+    assert td.select_torch_device() == "cuda"
+
+
+def test_mps_used_when_no_cuda(monkeypatch):
+    import app.utils.torch_device as td
+
+    monkeypatch.setattr(td.torch.cuda, "is_available", lambda: False)
+
+    class _FakeMps:
+        @staticmethod
+        def is_available():
+            return True
+
+    monkeypatch.setattr(td.torch.backends, "mps", _FakeMps, raising=False)
+    assert td.select_torch_device() == "mps"
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/docker-compose.cpu.yml b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/docker-compose.cpu.yml
new file mode 100644
index 00000000..26ccfd75
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/docker-compose.cpu.yml
@@ -0,0 +1,59 @@
+# ============================================================================
+# RenAIssance — CPU variant, LOCAL BUILD (no NVIDIA GPU required)
+#
+#   docker compose -f docker-compose.cpu.yml up --build
+#
+# Use this on Mac (Docker on macOS has no Metal passthrough) or any host
+# without an NVIDIA GPU + Container Toolkit. The launcher (./run.sh --cpu /
+# run.ps1) selects this file automatically. It is a STANDALONE file (not an
+# overlay) on purpose: a `deploy.devices: nvidia` reservation cannot be removed
+# by an override merge, and requesting it on a CPU host makes `compose up` fail.
+#
+# Builds backend with VARIANT=cpu (CPU paddle + CPU torch on an Ubuntu base).
+# To use the Apple GPU on a Mac, run natively instead — see ./run-native.sh.
+# ============================================================================
+
+services:
+  backend:
+    build:
+      context: ./backend
+      dockerfile: Dockerfile
+      args:
+        VARIANT: cpu
+        RUNTIME_BASE: ubuntu:24.04
+    image: renaissance-backend:local-cpu
+    container_name: renaissance-backend
+    env_file:
+      - .env
+    environment:
+      STORAGE_ROOT: /app/storage
+    volumes:
+      - storage:/app/storage
+      - paddle_models:/paddle_models
+    ports:
+      - "8000:8000"
+    # No GPU reservation — this variant runs entirely on CPU.
+    healthcheck:
+      test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8000/api/health')"]
+      interval: 30s
+      timeout: 10s
+      start_period: 90s
+      retries: 5
+    restart: unless-stopped
+
+  frontend:
+    build:
+      context: ./frontend
+      dockerfile: Dockerfile
+    image: renaissance-frontend:local
+    container_name: renaissance-frontend
+    depends_on:
+      backend:
+        condition: service_started
+    ports:
+      - "5173:8080"
+    restart: unless-stopped
+
+volumes:
+  storage:
+  paddle_models:
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/docker-compose.images.cpu.yml b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/docker-compose.images.cpu.yml
new file mode 100644
index 00000000..47c2d9d0
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/docker-compose.images.cpu.yml
@@ -0,0 +1,48 @@
+# ============================================================================
+# RenAIssance — CPU variant, PUBLISHED images (no local build, no NVIDIA GPU)
+#
+#   docker compose -f docker-compose.images.cpu.yml pull
+#   docker compose -f docker-compose.images.cpu.yml up
+#
+# Pulls and runs the published CPU image. The launcher (./run.sh --cpu /
+# run.ps1 on a host without an NVIDIA GPU) selects this file automatically.
+# Standalone (not an overlay) so no GPU device reservation is ever requested.
+# ============================================================================
+
+services:
+  backend:
+    image: saarthakg004/renaissance-backend:latest-cpu
+    container_name: renaissance-backend
+    env_file:
+      - path: .env
+        required: false
+    environment:
+      STORAGE_ROOT: /app/storage
+    volumes:
+      - storage:/app/storage
+      - paddle_models:/paddle_models
+    ports:
+      - "8000:8000"
+    # No GPU reservation — CPU-only image.
+    healthcheck:
+      test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8000/api/health')"]
+      interval: 30s
+      timeout: 10s
+      start_period: 90s
+      retries: 5
+    restart: unless-stopped
+
+  frontend:
+    # The frontend image is variant-agnostic (static bundle + nginx).
+    image: saarthakg004/renaissance-frontend:latest
+    container_name: renaissance-frontend
+    depends_on:
+      backend:
+        condition: service_started
+    ports:
+      - "5173:8080"
+    restart: unless-stopped
+
+volumes:
+  storage:
+  paddle_models:
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/docker-compose.images.yml b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/docker-compose.images.yml
new file mode 100644
index 00000000..c434e5be
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/docker-compose.images.yml
@@ -0,0 +1,67 @@
+# ============================================================================
+# RenAIssance — run the PUBLISHED images (no local build)
+#
+#   docker compose -f docker-compose.images.yml pull     # get latest images
+#   docker compose -f docker-compose.images.yml up        # run
+#   docker compose -f docker-compose.images.yml down       # stop
+#
+# Use this on a deployment / self-host host where you just want to pull and
+# run. For local development against your source tree, use docker-compose.yml
+# (which builds from ./backend and ./frontend).
+#
+# The service is intentionally named `backend` so the frontend's nginx can
+# reach it at http://backend:8000 (the bundled nginx.conf proxies /api/ there).
+# Compose handles the network + DNS automatically — no --network-alias needed.
+#
+# GPU: requires NVIDIA driver >= 560 (CUDA 12.6 capable) and the NVIDIA
+# Container Toolkit on the host. See the README.
+# ============================================================================
+
+services:
+  backend:
+    # GPU variant. `:latest` is kept as an alias of `:latest-gpu` for backward
+    # compatibility, but the launcher pins the explicit tag so the variant is
+    # never ambiguous. CPU hosts use docker-compose.images.cpu.yml instead.
+    image: saarthakg004/renaissance-backend:latest-gpu
+    container_name: renaissance-backend
+    # Optional .env (SECRET_KEY, ADMIN_TOKEN, DATABASE_URL overrides). Not
+    # required — Supabase tracking creds are baked in, so compose starts without it.
+    env_file:
+      - path: .env
+        required: false
+    environment:
+      # Pin storage to the mounted named volume regardless of layout.
+      STORAGE_ROOT: /app/storage
+    volumes:
+      - storage:/app/storage
+      - paddle_models:/paddle_models
+    ports:
+      - "8000:8000"
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: all
+              capabilities: [gpu]
+    healthcheck:
+      test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8000/api/health')"]
+      interval: 30s
+      timeout: 10s
+      start_period: 90s
+      retries: 5
+    restart: unless-stopped
+
+  frontend:
+    image: saarthakg004/renaissance-frontend:latest
+    container_name: renaissance-frontend
+    depends_on:
+      backend:
+        condition: service_started
+    ports:
+      - "5173:8080"
+    restart: unless-stopped
+
+volumes:
+  storage:
+  paddle_models:
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/docker-compose.yml b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/docker-compose.yml
new file mode 100644
index 00000000..56b1ea0f
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/docker-compose.yml
@@ -0,0 +1,77 @@
+# ============================================================================
+# RenAIssance — full-stack orchestration
+#
+#   docker compose up --build      # first run / after dependency changes
+#   docker compose up              # subsequent runs
+#   docker compose down            # stop (named volumes are preserved)
+#
+# Topology
+#   frontend (nginx)  : host :5173  ->  container :8080
+#                       reverse-proxies /api/  ->  backend:8000  (same origin,
+#                       so no CORS and no hardcoded backend URL in the bundle)
+#   backend  (FastAPI): host :8000  ->  container :8000  (also exposed directly
+#                       for debugging; the app itself only needs the proxy)
+#
+# GPU: requires NVIDIA driver >= 560, CUDA 12.6 capable, and the NVIDIA
+# Container Toolkit on the host (paddlepaddle-gpu has no CPU fallback in this
+# image). See backend/Dockerfile for details.
+# ============================================================================
+
+services:
+  backend:
+    build:
+      context: ./backend
+      dockerfile: Dockerfile
+      args:
+        VARIANT: gpu
+        # Plain Ubuntu for both variants — torch/paddle bring their own
+        # CUDA user-space libs and the host driver is injected by the NVIDIA
+        # Container Toolkit (see backend/Dockerfile Stage 2 note).
+        RUNTIME_BASE: ubuntu:24.04
+    image: renaissance-backend:local
+    container_name: renaissance-backend
+    # Provider keys and auth secrets come from the repo-root .env at runtime.
+    # .env is gitignored and dockerignored, so nothing is baked into the image.
+    env_file:
+      - .env
+    environment:
+      # Pin the storage root so it always lands on the mounted named volume
+      # regardless of the in-container directory layout.
+      STORAGE_ROOT: /app/storage
+    volumes:
+      - storage:/app/storage          # "My Files": transcripts + datasets
+      - paddle_models:/paddle_models   # lazily-downloaded PaddleOCR weights
+    ports:
+      - "8000:8000"
+    # NVIDIA GPU passthrough (Compose spec device reservation).
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: all
+              capabilities: [gpu]
+    healthcheck:
+      test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8000/api/health')"]
+      interval: 30s
+      timeout: 10s
+      start_period: 90s   # model normalization + first import is slow
+      retries: 5
+    restart: unless-stopped
+
+  frontend:
+    build:
+      context: ./frontend
+      dockerfile: Dockerfile
+    image: renaissance-frontend:local
+    container_name: renaissance-frontend
+    depends_on:
+      backend:
+        condition: service_started
+    ports:
+      - "5173:8080"
+    restart: unless-stopped
+
+volumes:
+  storage:
+  paddle_models:
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/.dockerignore b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/.dockerignore
new file mode 100644
index 00000000..ebc6803e
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/.dockerignore
@@ -0,0 +1,7 @@
+node_modules
+dist
+.git
+.gitignore
+*.md
+Dockerfile
+.dockerignore
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/Dockerfile b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/Dockerfile
new file mode 100644
index 00000000..0fa6867d
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/Dockerfile
@@ -0,0 +1,61 @@
+# ============================================
+# RenAIssance OCR — Frontend Dockerfile
+# Multi-stage: Node 20 build → Nginx Alpine slim serve
+# ============================================
+
+# ---- Stage 1: Build ----
+FROM node:20-alpine AS builder
+
+# Do NOT set NODE_ENV=production here — vite and other build tools
+# live in devDependencies and must be installed for the build to succeed.
+# NODE_ENV=production is set only in the runtime stage.
+
+WORKDIR /app
+
+# Copy manifests first — layer cache for npm install
+COPY package.json package-lock.json ./
+
+# Install ALL dependencies (including devDeps) needed for the build.
+# BuildKit cache mount keeps ~/.npm warm across builds so rebuilds after a
+# lockfile edit don't re-download every tarball.
+RUN --mount=type=cache,target=/root/.npm \
+    npm ci --prefer-offline --no-audit --no-fund
+
+# Copy source and build
+COPY . .
+
+RUN npm run build
+
+
+# ---- Stage 2: Serve ----
+FROM nginx:1.30-alpine-slim AS runtime
+
+ENV NODE_ENV=production
+
+# Install curl for runtime health checks.
+RUN apk add --no-cache curl
+
+# Remove default config
+RUN rm /etc/nginx/conf.d/default.conf
+
+# Copy custom nginx SPA config
+COPY nginx.conf /etc/nginx/conf.d/default.conf
+
+# Copy only the built static assets
+COPY --from=builder /app/dist /usr/share/nginx/html
+
+# Non-root nginx
+RUN chown -R nginx:nginx /usr/share/nginx/html && \
+    chown -R nginx:nginx /var/cache/nginx && \
+    chown -R nginx:nginx /var/log/nginx && \
+    touch /var/run/nginx.pid && \
+    chown nginx:nginx /var/run/nginx.pid
+
+USER nginx
+
+EXPOSE 8080
+
+HEALTHCHECK --interval=30s --timeout=5s --start-period=5s --retries=3 \
+    CMD curl -fsS http://localhost:8080/ >/dev/null || exit 1
+
+CMD ["nginx", "-g", "daemon off;"]
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/index.html b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/index.html
new file mode 100644
index 00000000..1deb63ab
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/index.html
@@ -0,0 +1,16 @@
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <link rel="icon" type="image/svg+xml" href="/vite.svg" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>OCR Preprocess Studio</title>
+    <link rel="preconnect" href="https://fonts.googleapis.com">
+    <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+    <link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap" rel="stylesheet">
+  </head>
+  <body>
+    <div id="root"></div>
+    <script type="module" src="/src/main.jsx"></script>
+  </body>
+</html>
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/nginx.conf b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/nginx.conf
new file mode 100644
index 00000000..390998aa
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/nginx.conf
@@ -0,0 +1,74 @@
+server {
+    listen 8080;
+    server_name localhost;
+
+    root /usr/share/nginx/html;
+    index index.html;
+
+    # ESM modules — Vite emits the bundled PDF.js worker as `.mjs`. Browsers
+    # refuse to load module scripts/workers unless served with a JS MIME type;
+    # nginx's default mime.types maps `.js` correctly but not `.mjs`. Re-include
+    # the default mappings before overriding, or this block would replace them.
+    include /etc/nginx/mime.types;
+    types {
+        application/javascript  mjs;
+    }
+
+    # Reverse-proxy API calls to the FastAPI backend container. The frontend
+    # issues same-origin `/api/...` requests (see src/config.js), so there is
+    # no CORS and no hardcoded host. `^~` makes this prefix win over the
+    # static-asset regex below. Using a variable + Docker's embedded DNS
+    # resolver means nginx still starts if the backend is briefly down and
+    # recovers automatically when it comes back (instead of failing config
+    # load with "host not found in upstream").
+    location ^~ /api/ {
+        resolver 127.0.0.11 valid=30s ipv6=off;
+        set $backend_upstream http://backend:8000;
+        # $request_uri keeps the original path + query string intact, which
+        # the variable form of proxy_pass would otherwise drop.
+        proxy_pass $backend_upstream$request_uri;
+
+        proxy_http_version 1.1;
+        proxy_set_header Host              $host;
+        proxy_set_header X-Real-IP         $remote_addr;
+        proxy_set_header X-Forwarded-For   $proxy_add_x_forwarded_for;
+        proxy_set_header X-Forwarded-Proto $scheme;
+
+        # OCR / layout detection / model loading can take minutes.
+        proxy_connect_timeout 60s;
+        proxy_send_timeout    600s;
+        proxy_read_timeout    600s;
+
+        # Matches backend MAX_UPLOAD_SIZE (100 MB) for PDF/image uploads.
+        client_max_body_size 100m;
+
+        # Stream responses instead of buffering large OCR payloads to disk.
+        proxy_buffering off;
+    }
+
+    # SPA fallback — all routes serve index.html
+    location / {
+        try_files $uri $uri/ /index.html;
+    }
+
+    # Cache static assets aggressively
+    location ~* \.(js|mjs|css|png|jpg|jpeg|gif|ico|svg|webp|woff2?)$ {
+        expires 1y;
+        add_header Cache-Control "public, immutable";
+    }
+
+    # Gzip compression
+    gzip on;
+    gzip_vary on;
+    gzip_proxied any;
+    gzip_min_length 1024;
+    gzip_types
+        text/plain
+        text/css
+        application/json
+        application/javascript
+        text/javascript
+        text/xml
+        application/xml
+        image/svg+xml;
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/package-lock.json b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/package-lock.json
new file mode 100644
index 00000000..f0831586
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/package-lock.json
@@ -0,0 +1,2924 @@
+{
+  "name": "ocr-preprocess-ui",
+  "version": "1.0.0",
+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {
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+        "pdfjs-dist": "^4.0.379",
+        "react": "^18.2.0",
+        "react-dom": "^18.2.0"
+      },
+      "devDependencies": {
+        "@types/react": "^18.2.43",
+        "@types/react-dom": "^18.2.17",
+        "@vitejs/plugin-react": "^4.2.1",
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+        "tailwindcss": "^3.4.0",
+        "vite": "^5.0.8"
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+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/package.json b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/package.json
new file mode 100644
index 00000000..b369309b
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/package.json
@@ -0,0 +1,26 @@
+{
+  "name": "ocr-preprocess-ui",
+  "private": true,
+  "version": "1.0.0",
+  "type": "module",
+  "scripts": {
+    "dev": "vite",
+    "build": "vite build",
+    "preview": "vite preview"
+  },
+  "dependencies": {
+    "lucide-react": "^0.303.0",
+    "pdfjs-dist": "^4.0.379",
+    "react": "^18.2.0",
+    "react-dom": "^18.2.0"
+  },
+  "devDependencies": {
+    "@types/react": "^18.2.43",
+    "@types/react-dom": "^18.2.17",
+    "@vitejs/plugin-react": "^4.2.1",
+    "autoprefixer": "^10.4.16",
+    "postcss": "^8.4.32",
+    "tailwindcss": "^3.4.0",
+    "vite": "^5.0.8"
+  }
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/postcss.config.js b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/postcss.config.js
new file mode 100644
index 00000000..2e7af2b7
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/postcss.config.js
@@ -0,0 +1,6 @@
+export default {
+  plugins: {
+    tailwindcss: {},
+    autoprefixer: {},
+  },
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/public/vite.svg b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/public/vite.svg
new file mode 100644
index 00000000..384ad2a1
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/public/vite.svg
@@ -0,0 +1,13 @@
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 100 100">
+  <defs>
+    <linearGradient id="grad" x1="0%" y1="0%" x2="100%" y2="100%">
+      <stop offset="0%" style="stop-color:#3b82f6;stop-opacity:1" />
+      <stop offset="100%" style="stop-color:#1d4ed8;stop-opacity:1" />
+    </linearGradient>
+  </defs>
+  <rect x="10" y="10" width="80" height="80" rx="16" fill="url(#grad)"/>
+  <rect x="25" y="28" width="50" height="6" rx="3" fill="white" opacity="0.9"/>
+  <rect x="25" y="42" width="40" height="6" rx="3" fill="white" opacity="0.7"/>
+  <rect x="25" y="56" width="45" height="6" rx="3" fill="white" opacity="0.7"/>
+  <rect x="25" y="70" width="35" height="6" rx="3" fill="white" opacity="0.5"/>
+</svg>
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/App.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/App.jsx
new file mode 100644
index 00000000..851298d8
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/App.jsx
@@ -0,0 +1,563 @@
+import React, { useState, useCallback, useEffect } from 'react';
+import { FileText, Database, BookOpen } from 'lucide-react';
+import Stepper from './components/Stepper';
+import DatasetStepper from './components/DatasetStepper';
+import HomePage from './pages/HomePage';
+import MyFilesPage from './pages/MyFilesPage';
+import CombinedUploadPage from './pages/CombinedUploadPage';
+import PageMatchReviewPage from './pages/PageMatchReviewPage';
+import DatasetGenerationPage from './pages/DatasetGenerationPage';
+import UploadPage from './features/upload/pages/UploadPage';
+import SelectPage from './features/upload/pages/SelectPage';
+import PreprocessPage from './features/preprocess/pages/PreprocessPage';
+import TextDetectionPage from './features/ocr/pages/TextDetectionPage';
+import TextRecognitionPage from './features/ocr/pages/TextRecognitionPage';
+import LayoutAwareDetectionPage from './components/LayoutAwareDetectionPanel';
+import { usePdfPreview } from './hooks/usePdfPreview';
+import { revokeObjectUrls } from './utils/imageUrl';
+import AuthPage from './features/auth/AuthPage';
+import ProfilePage from './features/auth/ProfilePage';
+import { fetchMe, logout as apiLogout } from './features/auth/authApi';
+
+
+function App() {
+  // ── Auth gate ──
+  const [user, setUser] = useState(null);
+  const [authChecked, setAuthChecked] = useState(false);
+  const [showProfile, setShowProfile] = useState(false);
+
+  // null (home) | 'ocr' | 'dataset' | 'files'
+  const [mode, setMode] = useState(null);
+
+  // ── Shared state ──
+  const [currentStep, setCurrentStep] = useState(1);
+  const [files, setFiles] = useState(null);
+  const [selectedPages, setSelectedPages] = useState([]);
+  const [processedImages, setProcessedImages] = useState({});
+  const [detectionMethod, setDetectionMethod] = useState(null);
+  const [detectionProvider, setDetectionProvider] = useState(null);
+
+  // ── Dataset state ──
+  // 'recognition' needs a transcript and runs match review; 'detection' is
+  // boxes only, so it skips both.
+  const [datasetMode, setDatasetMode] = useState('recognition');
+  const [parsedTranscript, setParsedTranscript] = useState(null);
+  const [allPagesBoxes, setAllPagesBoxes] = useState({});
+  const [alignedTranscriptByPage, setAlignedTranscriptByPage] = useState({});
+  const [isProcessingBook, setIsProcessingBook] = useState(false);
+  // Survives step 4 <-> 5 navigation.
+  const [detectionCache, setDetectionCache] = useState({ pages: {}, alignment: {} });
+
+  const [ocrDetectionCache, setOcrDetectionCache] = useState({ pages: {}, alignment: {} });
+
+  // Both end up as My Files metadata/tags.
+  const [preprocessing, setPreprocessing] = useState([]);
+  const [detectionModelInfo, setDetectionModelInfo] = useState({});
+
+  const {
+    pages,
+    isLoading,
+    error,
+    progress,
+    extractPages,
+    loadImages,
+    reset: resetPdfPreview,
+  } = usePdfPreview();
+
+  const handleFilesSelected = useCallback((selectedFiles) => {
+    setFiles(selectedFiles);
+    setSelectedPages([]);
+  }, []);
+
+  // ── Dataset: combined upload ──
+  const handleCombinedUploadNext = useCallback(async (bookFiles, transcript, mode = 'recognition') => {
+    if (!bookFiles || bookFiles.length === 0) return;
+    setIsProcessingBook(true);
+    setDatasetMode(mode);
+    setParsedTranscript(mode === 'detection' ? null : transcript);
+    try {
+      const isPdf = bookFiles[0].type === 'application/pdf';
+      let loadedPages;
+      if (isPdf) {
+        loadedPages = await extractPages(bookFiles[0]);
+      } else {
+        loadedPages = await loadImages(bookFiles);
+      }
+      // Detection mode uses step 2 as a Select-Pages step, so preselect
+      // everything and let the user deselect. Recognition mode picks in review.
+      if (mode === 'detection' || !isPdf) {
+        setSelectedPages((loadedPages || []).map((p) => p.pageNumber));
+      }
+      setFiles(bookFiles);
+      setCurrentStep(2);
+    } catch (err) {
+      console.error('Failed to process book files:', err);
+    } finally {
+      setIsProcessingBook(false);
+    }
+  }, [extractPages, loadImages]);
+
+  // ── Dataset: after match review ──
+  const handleMatchReviewNext = useCallback((matchedPageNumbers, updatedTranscript) => {
+    setSelectedPages(matchedPageNumbers);
+    if (updatedTranscript) setParsedTranscript(updatedTranscript);
+    setCurrentStep(3);
+  }, []);
+
+
+  const handleUploadNext = useCallback(async () => {
+    if (!files || files.length === 0) return;
+
+    const isPdf = files[0].type === 'application/pdf';
+
+    if (isPdf) {
+      try {
+        await extractPages(files[0]);
+        setCurrentStep(2);
+      } catch (err) {
+        console.error('Failed to extract PDF:', err);
+      }
+    } else {
+      try {
+        const loadedPages = await loadImages(files);
+        setSelectedPages(loadedPages.map((p) => p.pageNumber));
+        setCurrentStep(2);
+      } catch (err) {
+        console.error('Failed to load images:', err);
+      }
+    }
+  }, [files, extractPages, loadImages, mode]);
+
+  const handleSelectionChange = useCallback((newSelection) => {
+    setSelectedPages(newSelection);
+  }, []);
+
+  const handleStepClick = useCallback((stepId) => {
+    if (stepId <= currentStep) {
+      setCurrentStep(stepId);
+    }
+  }, [currentStep]);
+
+  const goToStep = useCallback((step) => {
+    setCurrentStep(step);
+  }, []);
+
+  const handleReset = useCallback(() => {
+    setMode(null);
+    setCurrentStep(1);
+    setFiles(null);
+    setSelectedPages([]);
+    // Object URLs leak unless we revoke them before the next book.
+    revokeObjectUrls(Object.values(processedImages));
+    setProcessedImages({});
+    setDetectionMethod(null);
+    setDetectionProvider(null);
+    setDatasetMode('recognition');
+    setParsedTranscript(null);
+    setAllPagesBoxes({});
+    setAlignedTranscriptByPage({});
+    setIsProcessingBook(false);
+    setOcrDetectionCache({ pages: {}, alignment: {} });
+    setDetectionCache({ pages: {}, alignment: {} });
+    setPreprocessing([]);
+    setDetectionModelInfo({});
+    resetPdfPreview();
+  }, [resetPdfPreview, processedImages]);
+
+  // ── Mode selection ──
+  const handleSelectMode = useCallback((selectedMode) => {
+    setMode(selectedMode);
+    setCurrentStep(1);
+  }, []);
+
+  // ── Check for an existing session on load ──
+  useEffect(() => {
+    let active = true;
+    fetchMe().then((u) => {
+      if (active) {
+        setUser(u);
+        setAuthChecked(true);
+      }
+    });
+    return () => {
+      active = false;
+    };
+  }, []);
+
+  const handleLogout = useCallback(async () => {
+    try {
+      await apiLogout();
+    } finally {
+      handleReset();
+      setUser(null);
+    }
+  }, [handleReset]);
+
+  // Must be logged in before anything else renders.
+  if (!authChecked) {
+    return (
+      <div className="min-h-screen w-screen flex items-center justify-center bg-gradient-to-br from-slate-50 via-white to-blue-50">
+        <div className="h-10 w-10 rounded-full border-4 border-blue-200 border-t-blue-600 animate-spin" />
+      </div>
+    );
+  }
+
+  if (!user) {
+    return <AuthPage onAuthSuccess={setUser} />;
+  }
+
+  // No mode picked yet -> home.
+  if (!mode) {
+    return (
+      <>
+        <HomePage
+          onSelectMode={handleSelectMode}
+          user={user}
+          onLogout={handleLogout}
+          onProfile={() => setShowProfile(true)}
+        />
+        {showProfile && (
+          <ProfilePage
+            user={user}
+            onClose={() => setShowProfile(false)}
+            onUpdated={setUser}
+          />
+        )}
+      </>
+    );
+  }
+
+  if (mode === 'files') {
+    return <MyFilesPage onBack={handleReset} />;
+  }
+
+  // ── Dataset mode: upload -> match review -> preprocess -> detect -> export ──
+  if (mode === 'dataset') {
+
+    // ── Full-screen steps ──
+    if (currentStep === 3) {
+      const backStep = 2;
+      return (
+        <div className="h-screen w-screen overflow-hidden flex flex-col">
+          <PreprocessPage
+            pages={pages}
+            selectedPages={selectedPages}
+            initialProcessedImages={processedImages}
+            onBack={() => goToStep(backStep)}
+            onNext={() => goToStep(4)}
+            onProcessedImagesChange={setProcessedImages}
+            onPipelineChange={setPreprocessing}
+          />
+        </div>
+      );
+    }
+
+    if (currentStep === 4) {
+      return (
+        <div className="h-screen w-screen overflow-hidden flex flex-col">
+          <LayoutAwareDetectionPage
+            pages={pages}
+            selectedPages={selectedPages}
+            processedImages={processedImages}
+            transcript={parsedTranscript}
+            preprocessing={preprocessing}
+            onBack={() => goToStep(3)}
+            onHome={handleReset}
+            datasetMode={true}
+            initialDetectedPages={detectionCache.pages}
+            initialAlignmentByPage={detectionCache.alignment}
+            onStateChange={(cache) => setDetectionCache(cache)}
+            onDatasetNext={({ boxesByPage, alignedTranscriptByPage: alignedMap, modelInfo }) => {
+              setAllPagesBoxes(boxesByPage || {});
+              setAlignedTranscriptByPage(alignedMap || {});
+              if (modelInfo) setDetectionModelInfo(modelInfo);
+              goToStep(5);
+            }}
+          />
+        </div>
+      );
+    }
+
+    if (currentStep === 5) {
+      return (
+        <div className="h-screen w-screen overflow-hidden flex flex-col bg-gradient-to-br from-slate-50 via-white to-emerald-50">
+          <DatasetGenerationPage
+            pages={pages}
+            selectedPages={selectedPages}
+            processedImages={processedImages}
+            transcript={Object.keys(alignedTranscriptByPage).length > 0 ? alignedTranscriptByPage : parsedTranscript}
+            allPagesBoxes={allPagesBoxes}
+            preprocessing={preprocessing}
+            modelInfo={detectionModelInfo}
+            onBack={() => goToStep(4)}
+            onHome={handleReset}
+            bookName={files?.[0]?.name?.replace(/\.[^.]+$/, '') || 'dataset'}
+            forceMode={datasetMode === 'detection' ? 'detection' : null}
+          />
+        </div>
+      );
+    }
+
+    // ── Steps 1 & 2 share a layout ──
+    return (
+      <div className="h-screen w-screen flex flex-col overflow-hidden bg-gradient-to-br from-slate-50 via-white to-emerald-50/30">
+        {/* ── Compact header ─────────────────────────────────────── */}
+        <header className="flex-shrink-0 bg-white/90 backdrop-blur-md border-b border-gray-100/70 shadow-sm z-40">
+          <div className="flex items-center justify-between px-6 py-3 gap-6">
+            {/* Logo */}
+            <div className="flex items-center gap-3 flex-shrink-0">
+              <div className="w-9 h-9 bg-gradient-to-br from-emerald-500 via-teal-500 to-emerald-600 rounded-xl flex items-center justify-center shadow-md shadow-emerald-500/25">
+                <Database className="w-5 h-5 text-white" />
+              </div>
+              <div>
+                <p className="text-sm font-bold text-gray-800 leading-none">Dataset Generator</p>
+                <p className="text-[10px] text-gray-400 mt-0.5">OCR training data pipeline</p>
+              </div>
+            </div>
+
+            {/* Stepper — centered, full width */}
+            <div className="flex-1">
+              <DatasetStepper currentStep={currentStep} onStepClick={handleStepClick} compact datasetMode={datasetMode} />
+            </div>
+
+            {/* Home */}
+            <button
+              onClick={handleReset}
+              className="flex-shrink-0 text-sm font-medium text-gray-500 hover:text-emerald-600 hover:bg-emerald-50 px-4 py-2 rounded-lg transition-all duration-200"
+            >
+              Home
+            </button>
+          </div>
+        </header>
+
+        {/* ── Error/progress banner ──────────────────────────────── */}
+        {(error || (isLoading && progress > 0)) && (
+          <div className="flex-shrink-0 px-6 pt-3">
+            {error && (
+              <div className="p-3 bg-red-50/90 border border-red-200 rounded-xl text-red-600 text-sm animate-fade-in shadow-sm mb-2">
+                <span className="font-semibold">Error: </span>{error}
+              </div>
+            )}
+            {isLoading && progress > 0 && (
+              <div className="p-3 bg-white/80 backdrop-blur-sm border border-gray-100 rounded-xl animate-fade-in shadow-sm">
+                <div className="flex items-center justify-between mb-2">
+                  <span className="text-xs font-semibold text-gray-600">Extracting pages…</span>
+                  <span className="text-xs font-bold text-emerald-600">{progress}%</span>
+                </div>
+                <div className="h-1.5 bg-emerald-100 rounded-full overflow-hidden">
+                  <div
+                    className="h-full bg-gradient-to-r from-emerald-500 to-teal-500 rounded-full transition-all duration-300"
+                    style={{ width: `${progress}%` }}
+                  />
+                </div>
+              </div>
+            )}
+          </div>
+        )}
+
+        {/* ── Page content (full remaining height) ──────────────── */}
+        <main className="flex-1 overflow-hidden">
+          {currentStep === 1 && (
+            <CombinedUploadPage
+              onNext={handleCombinedUploadNext}
+              isProcessingBook={isProcessingBook || isLoading}
+              initialBookFiles={files}
+              initialTranscript={parsedTranscript}
+              initialDatasetMode={datasetMode}
+            />
+          )}
+          {currentStep === 2 && datasetMode === 'detection' && (
+            <SelectPage
+              pages={pages}
+              selectedPages={selectedPages}
+              onSelectionChange={handleSelectionChange}
+              onBack={() => goToStep(1)}
+              onNext={() => goToStep(3)}
+              isLoading={isLoading}
+            />
+          )}
+          {currentStep === 2 && datasetMode !== 'detection' && (
+            <PageMatchReviewPage
+              pages={pages}
+              parsedTranscript={parsedTranscript}
+              onBack={() => goToStep(1)}
+              onNext={handleMatchReviewNext}
+            />
+          )}
+        </main>
+      </div>
+    );
+  }
+
+  // ── OCR mode ──
+  return (
+    <div className="min-h-screen flex flex-col">
+      {currentStep === 3 ? (
+        <div className="h-screen w-screen overflow-hidden flex flex-col">
+          <PreprocessPage
+            pages={pages}
+            selectedPages={selectedPages}
+            initialProcessedImages={processedImages}
+            onBack={() => goToStep(2)}
+            onNext={() => goToStep(4)}
+            onProcessedImagesChange={setProcessedImages}
+            onPipelineChange={setPreprocessing}
+          />
+        </div>
+      ) : currentStep === 6 ? (
+        <div className="h-screen w-screen overflow-hidden flex flex-col">
+          <LayoutAwareDetectionPage
+            pages={pages}
+            selectedPages={selectedPages}
+            processedImages={processedImages}
+            preprocessing={preprocessing}
+            bookName={files?.[0]?.name?.replace(/\.[^.]+$/, '') || 'transcript'}
+            onBack={() => goToStep(4)}
+            onHome={handleReset}
+            initialDetectedPages={ocrDetectionCache.pages}
+            initialAlignmentByPage={ocrDetectionCache.alignment}
+            onStateChange={(cache) => {
+              setOcrDetectionCache(cache);
+            }}
+          />
+        </div>
+      ) : currentStep === 5 ? (
+        <div className="h-screen w-screen overflow-hidden">
+          <TextRecognitionPage
+            provider={detectionProvider || 'gemini'}
+            bookName={files?.[0]?.name?.replace(/\.[^.]+$/, '') || 'transcript'}
+            processedImages={
+              selectedPages.map((pageNum) => {
+                const original = pages.find(p => p.pageNumber === pageNum);
+                const preprocessed = processedImages[pageNum];
+                return {
+                  pageNumber: pageNum,
+                  originalPageNumber: original?.originalPageNumber || null,
+                  isSplit: original?.isSplit || false,
+                  splitSide: original?.splitSide || null,
+                  original: original?.thumbnail || original,
+                  processed: preprocessed || original?.thumbnail || original,
+                };
+              })
+            }
+            onBack={() => goToStep(4)}
+            onHome={handleReset}
+            onComplete={(message) => {
+              alert(message || 'OCR processing complete! Transcripts have been saved.');
+            }}
+          />
+        </div>
+      ) : (
+        <>
+          {/* Header */}
+          <header className="bg-white/80 backdrop-blur-md border-b border-gray-100/50 sticky top-0 z-40 shadow-sm">
+            <div className="px-6 lg:px-10 xl:px-16 py-4 flex items-center justify-between">
+              <div className="flex items-center gap-3">
+                <div className="w-11 h-11 bg-gradient-to-br from-blue-500 via-blue-600 to-indigo-600 rounded-xl flex items-center justify-center shadow-lg shadow-blue-500/30">
+                  <FileText className="w-6 h-6 text-white" />
+                </div>
+                <div>
+                  <h1 className="text-xl font-bold bg-gradient-to-r from-gray-800 to-gray-600 bg-clip-text text-transparent">
+                    OCR Preprocess Studio
+                  </h1>
+                  <p className="text-xs text-gray-500">
+                    Prepare documents for text extraction
+                  </p>
+                </div>
+              </div>
+
+              <button
+                onClick={handleReset}
+                className="text-sm font-medium text-gray-500 hover:text-blue-600 hover:bg-blue-50 px-4 py-2 rounded-lg transition-all duration-200"
+              >
+                Home
+              </button>
+            </div>
+          </header>
+
+          {/* Main content */}
+          <main className="flex-1 w-full px-6 lg:px-10 xl:px-16 py-6">
+            <Stepper currentStep={currentStep} onStepClick={handleStepClick} />
+
+            {error && (
+              <div className="mb-6 p-4 bg-red-50/80 backdrop-blur-sm border border-red-200 rounded-xl text-red-600 animate-fade-in shadow-sm">
+                <p className="font-semibold">Error</p>
+                <p className="text-sm mt-1">{error}</p>
+              </div>
+            )}
+
+            {isLoading && progress > 0 && (
+              <div className="mb-6 animate-fade-in">
+                <div className="flex items-center justify-between mb-2">
+                  <span className="text-sm font-semibold text-gray-700">
+                    Processing...
+                  </span>
+                  <span className="text-sm font-bold text-blue-600">{progress}%</span>
+                </div>
+                <div className="h-2.5 bg-blue-100 rounded-full overflow-hidden shadow-inner">
+                  <div
+                    className="h-full bg-gradient-to-r from-blue-500 to-indigo-500 rounded-full transition-all duration-300 shadow-sm"
+                    style={{ width: `${progress}%` }}
+                  />
+                </div>
+              </div>
+            )}
+
+            <div className="min-h-[600px]">
+              {currentStep === 1 && (
+                <UploadPage
+                  files={files}
+                  onFilesSelected={handleFilesSelected}
+                  onNext={handleUploadNext}
+                  isLoading={isLoading}
+                />
+              )}
+
+              {currentStep === 2 && (
+                <SelectPage
+                  pages={pages}
+                  selectedPages={selectedPages}
+                  onSelectionChange={handleSelectionChange}
+                  onBack={() => goToStep(1)}
+                  onNext={() => goToStep(3)}
+                  isLoading={isLoading}
+                />
+              )}
+
+              {currentStep === 4 && (
+                <TextDetectionPage
+                  pages={pages}
+                  selectedPages={selectedPages}
+                  processedImages={processedImages}
+                  onBack={() => goToStep(3)}
+                  onNext={(method, provider) => {
+                    setDetectionMethod(method);
+                    setDetectionProvider(provider);
+                    if (method === 'layout-aware') {
+                      goToStep(6);
+                    } else {
+                      goToStep(5);
+                    }
+                  }}
+                />
+              )}
+            </div>
+          </main>
+
+          {/* Footer */}
+          <footer className="bg-white/60 backdrop-blur-sm border-t border-gray-100 py-4">
+            <div className="px-6 lg:px-10 xl:px-16 text-center">
+              <p className="text-sm text-gray-500 flex items-center justify-center gap-2">
+                <span className="font-semibold text-gray-600">OCR Preprocess Studio</span>
+                <span className="text-gray-300">|</span>
+                <span className="text-blue-600 font-medium">RenAIssance Project</span>
+              </p>
+            </div>
+          </footer>
+        </>
+      )}
+    </div>
+  );
+}
+
+export default App;
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/assets/home-image.png b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/assets/home-image.png
new file mode 100644
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Binary files /dev/null and b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/assets/home-image.png differ
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/BBoxEditor.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/BBoxEditor.jsx
new file mode 100644
index 00000000..3481525c
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/BBoxEditor.jsx
@@ -0,0 +1,837 @@
+import React, { useState, useRef, useEffect, useCallback, useMemo } from 'react';
+import {
+    X,
+    Check,
+    Undo2,
+    Redo2,
+    ZoomIn,
+    ZoomOut,
+    Maximize2,
+    Plus,
+    MousePointer2,
+    Trash2,
+    ChevronLeft,
+    ChevronRight,
+    Copy,
+    RotateCw,
+} from 'lucide-react';
+
+// ── Constants ──
+const HANDLE_RADIUS = 6;        // corner handle radius in canvas coords
+const MIN_BOX_SIZE = 10;
+const MAX_UNDO = 40;
+
+// ── Helpers ──
+let _uid = 0;
+function uid() { return `bb-${Date.now()}-${_uid++}`; }
+
+// "2_left" -> "2L", "3_right" -> "3R".
+function shortPageLabel(pageNumber) {
+    return String(pageNumber).replace('_left', 'L').replace('_right', 'R');
+}
+
+// The resize/rotate math assumes [TL, TR, BR, BL] vertex order.
+function orderQuad(poly) {
+    const byY = [...poly].sort((a, b) => a[1] - b[1]);
+    const [tl, tr] = byY.slice(0, 2).sort((a, b) => a[0] - b[0]);   // top: left, right
+    const [bl, br] = byY.slice(2, 4).sort((a, b) => a[0] - b[0]);   // bottom: left, right
+    return [[tl[0], tl[1]], [tr[0], tr[1]], [br[0], br[1]], [bl[0], bl[1]]];
+}
+
+// PaddleOCR polygon -> editor box. 4-point quads keep their rotation; anything
+// else collapses to its bounding rect.
+function polyToBox(poly, id) {
+    if (!poly || poly.length === 0) return null;
+    let pts;
+    if (poly.length === 4) {
+        pts = orderQuad(poly);
+    } else {
+        const xs = poly.map(p => p[0]);
+        const ys = poly.map(p => p[1]);
+        const x1 = Math.min(...xs), y1 = Math.min(...ys);
+        const x2 = Math.max(...xs), y2 = Math.max(...ys);
+        pts = [[x1, y1], [x2, y1], [x2, y2], [x1, y2]];
+    }
+    return { id: id ?? uid(), points: pts, selected: false };
+}
+
+function boxToPoly(b) { return b.points; }
+
+// Axis-aligned quad from two corners (draw mode).
+function makeRectBox(x1, y1, x2, y2) {
+    return {
+        id: uid(),
+        points: [[x1, y1], [x2, y1], [x2, y2], [x1, y2]],
+        selected: true,
+    };
+}
+
+// Point-in-convex-polygon.
+function pointInPolygon(px, py, pts) {
+    let inside = false;
+    for (let i = 0, j = pts.length - 1; i < pts.length; j = i++) {
+        const xi = pts[i][0], yi = pts[i][1];
+        const xj = pts[j][0], yj = pts[j][1];
+        if (((yi > py) !== (yj > py)) && (px < (xj - xi) * (py - yi) / (yj - yi) + xi)) inside = !inside;
+    }
+    return inside;
+}
+
+// Which corner was hit (0-3), or -1.
+function hitCorner(pts, px, py, r) {
+    for (let i = 0; i < pts.length; i++) {
+        if (Math.hypot(px - pts[i][0], py - pts[i][1]) <= r) return i;
+    }
+    return -1;
+}
+
+// Drag a corner, keeping the box axis-aligned. The opposite corner is the anchor.
+function resizeRectCorner(pts, cornerIdx, nx, ny) {
+    const opposites = [2, 3, 0, 1];
+    const oIdx = opposites[cornerIdx];
+    const ox = pts[oIdx][0], oy = pts[oIdx][1];
+    const x1 = Math.min(nx, ox), y1 = Math.min(ny, oy);
+    const x2 = Math.max(nx, ox), y2 = Math.max(ny, oy);
+    return [[x1, y1], [x2, y1], [x2, y2], [x1, y2]];
+}
+
+function rectSize(pts) {
+    const xs = pts.map(p => p[0]), ys = pts.map(p => p[1]);
+    return { w: Math.max(...xs) - Math.min(...xs), h: Math.max(...ys) - Math.min(...ys) };
+}
+
+function rotatePoint(px, py, cx, cy, angle) {
+    const cos = Math.cos(angle), sin = Math.sin(angle);
+    return [cx + (px - cx) * cos - (py - cy) * sin, cy + (px - cx) * sin + (py - cy) * cos];
+}
+
+function getBoxCenter(pts) {
+    return [pts.reduce((s, p) => s + p[0], 0) / 4, pts.reduce((s, p) => s + p[1], 0) / 4];
+}
+
+// Rotation handle sits off the TL-TR edge midpoint, along the outward normal.
+function getRotationHandlePos(pts, offsetDist) {
+    const mx = (pts[0][0] + pts[1][0]) / 2;
+    const my = (pts[0][1] + pts[1][1]) / 2;
+    const [cx, cy] = getBoxCenter(pts);
+    const dx = mx - cx, dy = my - cy;
+    const len = Math.hypot(dx, dy) || 1;
+    return [mx + (dx / len) * offsetDist, my + (dy / len) * offsetDist];
+}
+
+function hitRotationHandle(pts, px, py, r, offsetDist) {
+    const [hx, hy] = getRotationHandlePos(pts, offsetDist);
+    return Math.hypot(px - hx, py - hy) <= r;
+}
+
+// Corner resize that survives rotation — the angle is preserved and the
+// opposite corner stays put.
+function resizeRotatedCorner(pts, cornerIdx, nx, ny) {
+    const opposites = [2, 3, 0, 1];
+    const oIdx = opposites[cornerIdx];
+    const [opx, opy] = pts[oIdx];
+    const ncx = (opx + nx) / 2, ncy = (opy + ny) / 2;
+    const edgeDx = pts[1][0] - pts[0][0], edgeDy = pts[1][1] - pts[0][1];
+    const angle = Math.atan2(edgeDy, edgeDx);
+    const cosA = Math.cos(-angle), sinA = Math.sin(-angle);
+    const lox = (opx - ncx) * cosA - (opy - ncy) * sinA;
+    const loy = (opx - ncx) * sinA + (opy - ncy) * cosA;
+    const lnx = (nx - ncx) * cosA - (ny - ncy) * sinA;
+    const lny = (nx - ncx) * sinA + (ny - ncy) * cosA;
+    const hw = Math.abs(lnx - lox) / 2, hh = Math.abs(lny - loy) / 2;
+    if (hw * 2 < MIN_BOX_SIZE || hh * 2 < MIN_BOX_SIZE) return pts;
+    const local = [[-hw, -hh], [hw, -hh], [hw, hh], [-hw, hh]];
+    const cosR = Math.cos(angle), sinR = Math.sin(angle);
+    return local.map(([lx, ly]) => [ncx + lx * cosR - ly * sinR, ncy + lx * sinR + ly * cosR]);
+}
+
+// ── BBoxEditor — multi-page, supports angled bounding boxes ──
+export default function BBoxEditor({ pages: pagesProp = [], onSave, onCancel, initialPageNumber = null }) {
+
+    // ── Multi-page ─────────────────────────────────────────────────────────
+    // Frozen at mount. A batch detection running behind this modal reorders the
+    // parent's list, which would slide currentIdx onto a different page and save
+    // edits under the wrong page number.
+    const pagesRef = useRef(pagesProp);
+    const pages = pagesRef.current;
+    const [currentIdx, setCurrentIdx] = useState(() => {
+        if (initialPageNumber == null) return 0;
+        const idx = pagesRef.current.findIndex(p => String(p.pageNumber) === String(initialPageNumber));
+        return idx === -1 ? 0 : idx;
+    });
+    const pageBoxesRef = useRef({});
+    const [modifiedPages, setModifiedPages] = useState(new Set());
+    const currentPage = pages[currentIdx];
+
+    // ── Refs ───────────────────────────────────────────────────────────────
+    const bgCanvasRef = useRef(null);
+    const overlayCanvasRef = useRef(null);
+    const containerRef = useRef(null);
+    const outerRef = useRef(null);
+    // Scrolled into view on nav so later pages stay reachable.
+    const activePageBtnRef = useRef(null);
+
+    // Focus on open so arrow keys work straight away.
+    useEffect(() => { outerRef.current?.focus(); }, []);
+
+    useEffect(() => {
+        activePageBtnRef.current?.scrollIntoView({ inline: 'center', block: 'nearest', behavior: 'smooth' });
+    }, [currentIdx]);
+    // ── Box state ──────────────────────────────────────────────────────────
+    const [boxes, setBoxes] = useState([]);
+    const [undoStack, setUndoStack] = useState([]);
+    const [redoStack, setRedoStack] = useState([]);
+    const boxesRef = useRef([]);
+    useEffect(() => { boxesRef.current = boxes; }, [boxes]);
+
+    // ── View ────────────────────────────────────────────────────────────────
+    const [zoom, setZoom] = useState(1);
+    const [pan, setPan] = useState({ x: 0, y: 0 });
+    const [imageSize, setImageSize] = useState({ w: 0, h: 0 });
+    const [imageReady, setImageReady] = useState(false);
+
+    const zoomRef = useRef(zoom);
+    const panRef = useRef(pan);
+    const imageSizeRef = useRef(imageSize);
+
+    // In the render body on purpose: effects fire too late.
+    zoomRef.current = zoom;
+    panRef.current = pan;
+    imageSizeRef.current = imageSize;
+
+    // 'select' | 'draw'
+    const [mode, setMode] = useState('select');
+
+    // One of: move | corner | rotate | draw | pan, each with its own payload.
+    const dragRef = useRef(null);
+    const isPanRef = useRef(false);
+    const panStartRef = useRef({ x: 0, y: 0, panX: 0, panY: 0 });
+
+    const selectedId = useMemo(() => boxes.find(b => b.selected)?.id ?? null, [boxes]);
+
+    // ── Page management ──
+    const saveCurrentPage = useCallback(() => {
+        if (!currentPage) return;
+        pageBoxesRef.current[currentPage.pageNumber] = boxesRef.current.map(b => ({ ...b, selected: false }));
+    }, [currentPage]);
+
+    const loadPage = useCallback((page) => {
+        setImageReady(false);
+        setUndoStack([]);
+        setRedoStack([]);
+
+        const saved = pageBoxesRef.current[page.pageNumber];
+        const initialBoxes = saved ?? (page.polygons || []).map(poly => polyToBox(poly)).filter(Boolean);
+        if (!saved) pageBoxesRef.current[page.pageNumber] = initialBoxes;
+        setBoxes(initialBoxes);
+        // Synced here, not in an effect — flipping pages fast would otherwise
+        // save the previous page's boxes under the next page's number.
+        boxesRef.current = initialBoxes;
+
+        const img = new Image();
+        img.crossOrigin = 'anonymous';
+        img.onload = () => {
+            const w = img.naturalWidth, h = img.naturalHeight;
+            setImageSize({ w, h });
+
+            const bg = bgCanvasRef.current;
+            if (bg) { bg.width = w; bg.height = h; bg.getContext('2d').drawImage(img, 0, 0); }
+
+            if (containerRef.current) {
+                const cw = containerRef.current.clientWidth, ch = containerRef.current.clientHeight;
+                const fitZ = Math.min(cw / w, ch / h, 1) * 0.9;
+                const fitPan = { x: (cw - w * fitZ) / 2, y: (ch - h * fitZ) / 2 };
+                setZoom(fitZ); setPan(fitPan);
+                zoomRef.current = fitZ; panRef.current = fitPan;
+            }
+            setImageReady(true);
+        };
+        img.src = page.imageSrc;
+    }, []);
+
+    useEffect(() => { if (pages.length > 0) loadPage(pages[currentIdx] || pages[0]); }, []); // eslint-disable-line
+
+    const goToPage = useCallback((idx) => {
+        if (idx < 0 || idx >= pages.length || idx === currentIdx) return;
+        saveCurrentPage();
+        setCurrentIdx(idx);
+        loadPage(pages[idx]);
+    }, [currentIdx, pages, saveCurrentPage, loadPage]);
+
+    // ── Overlay drawing ──
+    const drawOverlay = useCallback((boxList, drawState) => {
+        const canvas = overlayCanvasRef.current;
+        const { w, h } = imageSizeRef.current;
+        if (!canvas || w === 0) return;
+
+        if (canvas.width !== w || canvas.height !== h) { canvas.width = w; canvas.height = h; }
+        const ctx = canvas.getContext('2d');
+        ctx.clearRect(0, 0, w, h);
+
+        const z = zoomRef.current;
+        const lw = Math.max(1.5, 2 / z);
+        const cr = Math.max(4, HANDLE_RADIUS / z);     // corner handle radius in canvas coords
+
+        for (const b of boxList) {
+            const sel = b.selected;
+            const pts = b.points;
+
+            ctx.strokeStyle = sel ? '#f97316' : '#22c55e';
+            ctx.lineWidth = lw;
+            ctx.fillStyle = sel ? 'rgba(249,115,22,0.12)' : 'rgba(34,197,94,0.08)';
+            ctx.lineJoin = 'round';
+            ctx.beginPath();
+            ctx.moveTo(pts[0][0], pts[0][1]);
+            for (let i = 1; i < pts.length; i++) ctx.lineTo(pts[i][0], pts[i][1]);
+            ctx.closePath();
+            ctx.fill();
+            ctx.stroke();
+
+            if (sel) {
+                ctx.fillStyle = '#ffffff';
+                ctx.strokeStyle = '#f97316';
+                ctx.lineWidth = lw;
+                for (const [px, py] of pts) {
+                    ctx.beginPath();
+                    ctx.rect(px - cr, py - cr, cr * 2, cr * 2);
+                    ctx.fill(); ctx.stroke();
+                }
+
+                const rotOffset = Math.max(8, 30 / z);
+                const [rhx, rhy] = getRotationHandlePos(pts, rotOffset);
+                const tmx = (pts[0][0] + pts[1][0]) / 2;
+                const tmy = (pts[0][1] + pts[1][1]) / 2;
+                ctx.setLineDash([3 / z, 2 / z]);
+                ctx.strokeStyle = '#f97316';
+                ctx.lineWidth = lw;
+                ctx.beginPath(); ctx.moveTo(tmx, tmy); ctx.lineTo(rhx, rhy); ctx.stroke();
+                ctx.setLineDash([]);
+                const rhr = cr * 0.95;
+                ctx.beginPath(); ctx.arc(rhx, rhy, rhr, 0, Math.PI * 2);
+                ctx.fillStyle = '#ffffff'; ctx.fill();
+                ctx.strokeStyle = '#f97316'; ctx.lineWidth = lw; ctx.stroke();
+                ctx.strokeStyle = '#f97316';
+                ctx.lineWidth = Math.max(1, lw * 0.8);
+                ctx.beginPath();
+                ctx.arc(rhx, rhy, rhr * 0.5, -Math.PI * 0.8, Math.PI * 0.2);
+                ctx.stroke();
+            }
+        }
+
+        if (drawState) {
+            const { startX, startY, currentX, currentY } = drawState;
+            const x1 = Math.min(startX, currentX), y1 = Math.min(startY, currentY);
+            const x2 = Math.max(startX, currentX), y2 = Math.max(startY, currentY);
+            ctx.strokeStyle = '#3b82f6';
+            ctx.lineWidth = lw;
+            ctx.setLineDash([6 / z, 4 / z]);
+            ctx.fillStyle = 'rgba(59,130,246,0.10)';
+            ctx.beginPath(); ctx.rect(x1, y1, x2 - x1, y2 - y1);
+            ctx.fill(); ctx.stroke();
+            ctx.setLineDash([]);
+        }
+    }, []);
+
+    // imageSize is in the deps so we redraw once the image loads.
+    useEffect(() => {
+        if (imageSize.w > 0) drawOverlay(boxes, null);
+    }, [boxes, imageSize, drawOverlay]);
+
+    // ── Undo / Redo ──
+    const pushUndo = useCallback((prevBoxes) => {
+        const snap = prevBoxes.map(b => ({ ...b, points: b.points.map(p => [...p]), selected: false }));
+        setUndoStack(s => { const n = [...s, snap]; return n.length > MAX_UNDO ? n.slice(-MAX_UNDO) : n; });
+        setRedoStack([]);
+        setModifiedPages(prev => new Set([...prev, currentPage?.pageNumber]));
+    }, [currentPage]);
+
+    const handleUndo = useCallback(() => {
+        setUndoStack(s => {
+            if (!s.length) return s;
+            const prev = s[s.length - 1];
+            setRedoStack(r => [...r, boxes.map(b => ({ ...b, points: b.points.map(p => [...p]), selected: false }))]);
+            setBoxes(prev); return s.slice(0, -1);
+        });
+    }, [boxes]);
+
+    const handleRedo = useCallback(() => {
+        setRedoStack(r => {
+            if (!r.length) return r;
+            const next = r[r.length - 1];
+            setUndoStack(s => [...s, boxes.map(b => ({ ...b, points: b.points.map(p => [...p]), selected: false }))]);
+            setBoxes(next); return r.slice(0, -1);
+        });
+    }, [boxes]);
+
+    // ── Coordinates ──
+    const screenToImage = useCallback((clientX, clientY) => {
+        if (!containerRef.current) return { x: 0, y: 0 };
+        const rect = containerRef.current.getBoundingClientRect();
+        return {
+            x: (clientX - rect.left - panRef.current.x) / zoomRef.current,
+            y: (clientY - rect.top - panRef.current.y) / zoomRef.current,
+        };
+    }, []);
+
+    // ── Pointer events ──
+    const handlePointerDown = useCallback((e) => {
+        // Middle-click or ctrl+left-click pans.
+        if (e.button === 1 || (e.button === 0 && (e.ctrlKey || e.metaKey))) {
+            isPanRef.current = true;
+            panStartRef.current = { x: e.clientX, y: e.clientY, panX: panRef.current.x, panY: panRef.current.y };
+            e.preventDefault(); return;
+        }
+        if (e.button !== 0) return;
+
+        const { x: imgX, y: imgY } = screenToImage(e.clientX, e.clientY);
+        const z = zoomRef.current;
+        const cr = Math.max(4, HANDLE_RADIUS / z);
+        const rr = Math.max(5, (HANDLE_RADIUS + 2) / z);
+
+        // ── Draw mode ──
+        if (mode === 'draw') {
+            dragRef.current = { type: 'draw', startX: imgX, startY: imgY, currentX: imgX, currentY: imgY };
+            return;
+        }
+
+        // ── Select mode: handles first, then bodies, then empty space ──
+        const selBox = boxes.find(b => b.selected);
+
+        if (selBox) {
+            const rotOffset = Math.max(8, 30 / z);
+            if (hitRotationHandle(selBox.points, imgX, imgY, rr * 1.8, rotOffset)) {
+                const center = getBoxCenter(selBox.points);
+                pushUndo(boxes);
+                dragRef.current = {
+                    type: 'rotate',
+                    id: selBox.id,
+                    origPts: selBox.points.map(p => [...p]),
+                    center,
+                    startAngle: Math.atan2(imgY - center[1], imgX - center[0]),
+                };
+                return;
+            }
+        }
+
+        if (selBox) {
+            const ci = hitCorner(selBox.points, imgX, imgY, cr * 1.5);
+            if (ci !== -1) {
+                pushUndo(boxes);
+                dragRef.current = {
+                    type: 'corner',
+                    id: selBox.id,
+                    cornerIdx: ci,
+                    origPts: selBox.points.map(p => [...p]),
+                };
+                return;
+            }
+        }
+
+        // Top-most box wins.
+        const hit = [...boxes].reverse().find(b => pointInPolygon(imgX, imgY, b.points));
+        if (hit) {
+            pushUndo(boxes);
+            dragRef.current = {
+                type: 'move',
+                id: hit.id,
+                origPts: hit.points.map(p => [...p]),
+                startX: imgX, startY: imgY,
+            };
+            setBoxes(prev => prev.map(b => ({ ...b, selected: b.id === hit.id })));
+            return;
+        }
+
+        setBoxes(prev => prev.map(b => ({ ...b, selected: false })));
+        dragRef.current = null;
+    }, [mode, boxes, screenToImage, pushUndo]);
+
+    const handlePointerMove = useCallback((e) => {
+        if (isPanRef.current) {
+            const dx = e.clientX - panStartRef.current.x;
+            const dy = e.clientY - panStartRef.current.y;
+            const np = { x: panStartRef.current.panX + dx, y: panStartRef.current.panY + dy };
+            setPan(np); panRef.current = np; return;
+        }
+
+        const drag = dragRef.current;
+        if (!drag) return;
+        const { x: imgX, y: imgY } = screenToImage(e.clientX, e.clientY);
+
+        if (drag.type === 'draw') {
+            drag.currentX = imgX; drag.currentY = imgY;
+            drawOverlay(boxesRef.current, drag); return;
+        }
+
+        if (drag.type === 'move') {
+            const dx = imgX - drag.startX, dy = imgY - drag.startY;
+            setBoxes(prev => prev.map(b => {
+                if (b.id !== drag.id) return b;
+                return { ...b, points: drag.origPts.map(([px, py]) => [px + dx, py + dy]) };
+            }));
+            return;
+        }
+
+        if (drag.type === 'rotate') {
+            const [cx, cy] = drag.center;
+            const currentAngle = Math.atan2(imgY - cy, imgX - cx);
+            // 0.35x so the box turns gently, not wildly.
+            const delta = (currentAngle - drag.startAngle) * 0.35;
+            setBoxes(prev => prev.map(b => {
+                if (b.id !== drag.id) return b;
+                const newPts = drag.origPts.map(([px, py]) => rotatePoint(px, py, cx, cy, delta));
+                return { ...b, points: newPts };
+            }));
+            return;
+        }
+
+        if (drag.type === 'corner') {
+            setBoxes(prev => prev.map(b => {
+                if (b.id !== drag.id) return b;
+                const newPts = resizeRotatedCorner(drag.origPts, drag.cornerIdx, imgX, imgY);
+                return { ...b, points: newPts };
+            }));
+            return;
+        }
+    }, [screenToImage, drawOverlay]);
+
+    const handlePointerUp = useCallback(() => {
+        isPanRef.current = false;
+        const drag = dragRef.current;
+        dragRef.current = null;
+        if (!drag) return;
+
+        if (drag.type === 'draw') {
+            const x1 = Math.min(drag.startX, drag.currentX), y1 = Math.min(drag.startY, drag.currentY);
+            const x2 = Math.max(drag.startX, drag.currentX), y2 = Math.max(drag.startY, drag.currentY);
+            if (x2 - x1 >= MIN_BOX_SIZE && y2 - y1 >= MIN_BOX_SIZE) {
+                pushUndo(boxesRef.current);
+                const nb = makeRectBox(x1, y1, x2, y2);
+                setBoxes(prev => [...prev.map(b => ({ ...b, selected: false })), nb]);
+                setModifiedPages(prev => new Set([...prev, currentPage?.pageNumber]));
+            } else {
+                drawOverlay(boxesRef.current, null);
+            }
+        }
+
+        if (drag.type === 'move' || drag.type === 'corner' || drag.type === 'rotate') {
+            setModifiedPages(prev => new Set([...prev, currentPage?.pageNumber]));
+        }
+    }, [pushUndo, drawOverlay, currentPage]);
+
+    useEffect(() => {
+        const up = () => handlePointerUp();
+        window.addEventListener('mouseup', up);
+        return () => window.removeEventListener('mouseup', up);
+    }, [handlePointerUp]);
+
+    // ── Scroll zoom ──
+    useEffect(() => {
+        const el = containerRef.current;
+        if (!el) return;
+        const onWheel = e => {
+            e.preventDefault();
+            // ~5% per tick — 10% overshoots constantly.
+            const f = e.deltaY < 0 ? 1.05 : 1 / 1.05;
+            setZoom(z => { const nz = Math.max(0.05, Math.min(8, z * f)); zoomRef.current = nz; return nz; });
+        };
+        el.addEventListener('wheel', onWheel, { passive: false });
+        return () => el.removeEventListener('wheel', onWheel);
+    }, []);
+
+    // ── Keyboard ──
+    const handleDelete = useCallback(() => {
+        if (!selectedId) return;
+        // Land the selection on the box above the deleted one.
+        const sorted = [...boxes].sort((a, b) => {
+            const topA = Math.min(...a.points.map(p => p[1]));
+            const topB = Math.min(...b.points.map(p => p[1]));
+            return topA - topB;
+        });
+        const delIdx = sorted.findIndex(b => b.id === selectedId);
+        const prevId = delIdx > 0 ? sorted[delIdx - 1].id : null;
+        pushUndo(boxes);
+        setBoxes(prev =>
+            prev
+                .filter(b => b.id !== selectedId)
+                .map(b => ({ ...b, selected: prevId !== null && b.id === prevId }))
+        );
+        setModifiedPages(prev => new Set([...prev, currentPage?.pageNumber]));
+    }, [selectedId, boxes, pushUndo, currentPage]);
+
+    const handleDuplicate = useCallback(() => {
+        if (!selectedId) return;
+        const src = boxes.find(b => b.id === selectedId);
+        if (!src) return;
+        pushUndo(boxes);
+        const dup = {
+            id: uid(),
+            points: src.points.map(([px, py]) => [px + 12, py + 12]),
+            selected: true,
+        };
+        setBoxes(prev => [...prev.map(b => ({ ...b, selected: false })), dup]);
+        setModifiedPages(prev => new Set([...prev, currentPage?.pageNumber]));
+    }, [selectedId, boxes, pushUndo, currentPage]);
+
+    useEffect(() => {
+        const onKey = e => {
+            if (e.target.tagName === 'INPUT' || e.target.tagName === 'TEXTAREA') return;
+            if (e.key === 'n' || e.key === 'N') { setMode(m => m === 'draw' ? 'select' : 'draw'); return; }
+            if ((e.key === 'Delete' || e.key === 'Backspace') && selectedId) { e.preventDefault(); handleDelete(); return; }
+            if ((e.key === 'd' || e.key === 'D') && selectedId) { e.preventDefault(); handleDuplicate(); return; }
+            if (e.key === 'Escape') { setMode('select'); setBoxes(prev => prev.map(b => ({ ...b, selected: false }))); dragRef.current = null; return; }
+            if (e.ctrlKey || e.metaKey) {
+                if (e.key === 'z') { e.preventDefault(); handleUndo(); }
+                else if (e.key === 'y' || (e.shiftKey && e.key === 'z')) { e.preventDefault(); handleRedo(); }
+            }
+            // Up/Down cycle boxes in top-to-bottom order.
+            if (e.key === 'ArrowUp' || e.key === 'ArrowDown') {
+                e.preventDefault();
+                const sorted = [...boxesRef.current].sort((a, b) => {
+                    const topA = Math.min(...a.points.map(p => p[1]));
+                    const topB = Math.min(...b.points.map(p => p[1]));
+                    return topA - topB;
+                });
+                if (sorted.length === 0) return;
+                const selIdx = sorted.findIndex(b => b.selected);
+                let nextIdx;
+                if (selIdx === -1) {
+                    nextIdx = e.key === 'ArrowDown' ? 0 : sorted.length - 1;
+                } else {
+                    nextIdx = e.key === 'ArrowDown' ? selIdx + 1 : selIdx - 1;
+                    nextIdx = Math.max(0, Math.min(sorted.length - 1, nextIdx));
+                }
+                const targetId = sorted[nextIdx].id;
+                setBoxes(prev => prev.map(b => ({ ...b, selected: b.id === targetId })));
+                return;
+            }
+            if (e.key === 'ArrowLeft') goToPage(currentIdx - 1);
+            if (e.key === 'ArrowRight') goToPage(currentIdx + 1);
+        };
+        window.addEventListener('keydown', onKey);
+        return () => window.removeEventListener('keydown', onKey);
+    }, [selectedId, handleDelete, handleDuplicate, handleUndo, handleRedo, currentIdx, goToPage]);
+
+    // ── Fit view ──
+    const handleFit = useCallback(() => {
+        const { w, h } = imageSizeRef.current;
+        if (!containerRef.current || w === 0) return;
+        const cw = containerRef.current.clientWidth, ch = containerRef.current.clientHeight;
+        const fitZ = Math.min(cw / w, ch / h, 1) * 0.9;
+        const fitPan = { x: (cw - w * fitZ) / 2, y: (ch - h * fitZ) / 2 };
+        setZoom(fitZ); setPan(fitPan); zoomRef.current = fitZ; panRef.current = fitPan;
+    }, []);
+
+    // ── Cursor style ──
+    const [cursor, setCursor] = useState('default');
+    const handleMouseMoveForCursor = useCallback((e) => {
+        if (mode === 'draw') { setCursor('crosshair'); return; }
+        if (isPanRef.current) { setCursor('grabbing'); return; }
+        if (dragRef.current) return; // already handling a drag
+
+        const { x: imgX, y: imgY } = screenToImage(e.clientX, e.clientY);
+        const z = zoomRef.current;
+        const selBox = boxes.find(b => b.selected);
+        const cr = Math.max(4, HANDLE_RADIUS / z);
+
+        if (selBox) {
+            const rr = Math.max(5, (HANDLE_RADIUS + 2) / z);
+            const rotOffset = Math.max(8, 30 / z);
+            if (hitRotationHandle(selBox.points, imgX, imgY, rr * 1.8, rotOffset)) { setCursor('grab'); return; }
+            if (hitCorner(selBox.points, imgX, imgY, cr * 1.5) !== -1) { setCursor('nwse-resize'); return; }
+        }
+        const hit = [...boxes].reverse().find(b => pointInPolygon(imgX, imgY, b.points));
+        setCursor(hit ? 'move' : 'default');
+    }, [mode, boxes, screenToImage]);
+
+    // ── Save all pages ──
+    const handleSave = useCallback(() => {
+        saveCurrentPage();
+        const results = {};
+        pages.forEach(page => {
+            const bxs = pageBoxesRef.current[page.pageNumber];
+            results[page.pageNumber] = bxs ? bxs.map(boxToPoly) : page.polygons || [];
+        });
+        onSave(results);
+    }, [pages, onSave, saveCurrentPage]);
+
+    const selBox = boxes.find(b => b.selected);
+
+    // ── Render ──
+    return (
+        <div
+            ref={outerRef}
+            tabIndex={-1}
+            className="fixed inset-0 z-50 flex flex-col bg-gray-950 outline-none"
+            onContextMenu={e => e.preventDefault()}
+        >
+
+            {/* ── TOOLBAR ────────────────────────────────────────────── */}
+            <div className="flex-shrink-0 flex items-center justify-between gap-3 px-4 py-2.5 bg-gray-900 border-b border-gray-800">
+
+                {/* Left — title + mode toggle */}
+                <div className="flex items-center gap-4">
+                    <div className="flex items-center gap-2">
+                        <div className="p-1.5 bg-gradient-to-br from-orange-500 to-amber-600 rounded-lg text-white">
+                            <MousePointer2 size={16} />
+                        </div>
+                        <div>
+                            <h2 className="text-white font-semibold text-sm leading-none">Box Editor</h2>
+                            <p className="text-gray-500 text-[10px] mt-0.5">
+                                {boxes.length} box{boxes.length !== 1 ? 'es' : ''}
+                                {modifiedPages.size > 0 && <span className="ml-1 text-amber-500">· {modifiedPages.size} page{modifiedPages.size > 1 ? 's' : ''} modified</span>}
+                            </p>
+                        </div>
+                    </div>
+
+                    <div className="flex items-center gap-1 bg-gray-800 rounded-lg p-0.5">
+                        <button onClick={() => setMode('select')} title="Select / Move / Resize / Rotate  (Esc)"
+                            className={`flex items-center gap-1.5 px-3 py-1.5 rounded-md text-xs font-semibold transition-all ${mode === 'select' ? 'bg-orange-500 text-white shadow-sm' : 'text-gray-400 hover:text-white'}`}>
+                            <MousePointer2 size={13} /> Select
+                        </button>
+                        <button onClick={() => setMode(m => m === 'draw' ? 'select' : 'draw')} title="Draw New Box  (N)"
+                            className={`flex items-center gap-1.5 px-3 py-1.5 rounded-md text-xs font-semibold transition-all ${mode === 'draw' ? 'bg-blue-600 text-white shadow-sm' : 'text-gray-400 hover:text-white'}`}>
+                            <Plus size={13} /> Add <kbd className="ml-1 px-1 text-[9px] bg-gray-700 text-gray-400 rounded">N</kbd>
+                        </button>
+                    </div>
+                </div>
+
+                {/* Center — info + undo/redo + zoom */}
+                <div className="flex items-center gap-3">
+                    {selBox ? (
+                        <div className="hidden sm:flex items-center gap-1.5 px-3 py-1 bg-gray-800 rounded-lg text-xs text-gray-300">
+                            <span className="text-gray-500 text-[10px]">Drag corners to resize · Drag body to move · Drag ○ to rotate</span>
+                        </div>
+                    ) : (
+                        <div className="hidden md:flex items-center gap-1.5 px-3 py-1 bg-gray-800 rounded-lg text-xs text-gray-500">
+                            Click a box to select · drag to move or resize
+                        </div>
+                    )}
+
+                    <div className="flex items-center gap-1 bg-gray-800 rounded-lg p-0.5">
+                        <button onClick={handleUndo} disabled={!undoStack.length} title="Undo (Ctrl+Z)"
+                            className={`p-1.5 rounded-md ${undoStack.length ? 'text-gray-300 hover:text-white hover:bg-gray-700' : 'text-gray-700 cursor-not-allowed'}`}><Undo2 size={15} /></button>
+                        <button onClick={handleRedo} disabled={!redoStack.length} title="Redo (Ctrl+Y)"
+                            className={`p-1.5 rounded-md ${redoStack.length ? 'text-gray-300 hover:text-white hover:bg-gray-700' : 'text-gray-700 cursor-not-allowed'}`}><Redo2 size={15} /></button>
+                    </div>
+
+                    <div className="flex items-center gap-1 bg-gray-800 rounded-lg p-0.5">
+                        <button onClick={() => setZoom(z => Math.min(z * 1.25, 8))} className="p-1.5 text-gray-300 hover:text-white hover:bg-gray-700 rounded-md"><ZoomIn size={15} /></button>
+                        <span className="text-xs text-gray-500 font-mono min-w-[42px] text-center">{Math.round(zoom * 100)}%</span>
+                        <button onClick={() => setZoom(z => Math.max(z / 1.25, 0.05))} className="p-1.5 text-gray-300 hover:text-white hover:bg-gray-700 rounded-md"><ZoomOut size={15} /></button>
+                        <button onClick={handleFit} title="Fit to view" className="p-1.5 text-gray-300 hover:text-white hover:bg-gray-700 rounded-md"><Maximize2 size={15} /></button>
+                    </div>
+
+                    {selectedId && (
+                        <button onClick={handleDuplicate} title="Duplicate (D)"
+                            className="flex items-center gap-1.5 px-2.5 py-1.5 rounded-lg bg-blue-900/50 hover:bg-blue-800/60 text-blue-400 hover:text-blue-300 text-xs font-semibold transition-colors">
+                            <Copy size={13} /> Dup
+                        </button>
+                    )}
+                    {selectedId && (
+                        <button onClick={handleDelete} title="Delete (Delete)"
+                            className="flex items-center gap-1.5 px-2.5 py-1.5 rounded-lg bg-red-900/50 hover:bg-red-800/60 text-red-400 hover:text-red-300 text-xs font-semibold transition-colors">
+                            <Trash2 size={13} /> Delete
+                        </button>
+                    )}
+                </div>
+
+                {/* Right — cancel / save */}
+                <div className="flex items-center gap-2">
+                    <button onClick={onCancel} className="flex items-center gap-1.5 px-3 py-2 text-gray-400 hover:text-white hover:bg-gray-800 rounded-lg text-sm font-medium transition-colors">
+                        <X size={15} /> Cancel
+                    </button>
+                    <button onClick={handleSave}
+                        className="flex items-center gap-1.5 px-4 py-2 bg-gradient-to-r from-emerald-500 to-green-600 hover:from-emerald-600 hover:to-green-700 text-white font-semibold rounded-lg text-sm shadow-md hover:shadow-lg hover:-translate-y-0.5 transition-all">
+                        <Check size={15} />
+                        Save All
+                        {modifiedPages.size > 0 && (
+                            <span className="ml-1 px-1.5 py-0.5 bg-white/20 rounded text-[10px]">
+                                {modifiedPages.size} page{modifiedPages.size > 1 ? 's' : ''}
+                            </span>
+                        )}
+                    </button>
+                </div>
+            </div>
+
+            {/* ── CANVAS AREA ──────────────────────────────────────────── */}
+            <div
+                ref={containerRef}
+                className="flex-1 relative overflow-hidden select-none"
+                style={{ background: 'repeating-conic-gradient(#1a1a1a 0% 25%, #222 0% 50%) 50% / 20px 20px', cursor }}
+                onMouseDown={handlePointerDown}
+                onMouseMove={(e) => { handlePointerMove(e); handleMouseMoveForCursor(e); }}
+            >
+                {/* Background image (drawn once) */}
+                <canvas ref={bgCanvasRef} style={{ position: 'absolute', left: pan.x, top: pan.y, width: imageSize.w * zoom, height: imageSize.h * zoom, imageRendering: zoom > 2 ? 'pixelated' : 'auto', display: imageReady ? 'block' : 'none' }} />
+
+                {/* Interactive overlay */}
+                <canvas ref={overlayCanvasRef} style={{ position: 'absolute', left: pan.x, top: pan.y, width: imageSize.w * zoom, height: imageSize.h * zoom, imageRendering: zoom > 2 ? 'pixelated' : 'auto', display: imageReady ? 'block' : 'none', pointerEvents: 'none' }} />
+
+                {!imageReady && (
+                    <div className="absolute inset-0 flex items-center justify-center">
+                        <div className="w-10 h-10 border-4 border-gray-700 border-t-orange-500 rounded-full animate-spin" />
+                    </div>
+                )}
+
+                {mode === 'draw' && imageReady && (
+                    <div className="absolute top-3 left-1/2 -translate-x-1/2 pointer-events-none">
+                        <div className="px-3 py-1.5 bg-blue-600/90 text-white text-xs font-semibold rounded-full shadow-lg backdrop-blur-sm">
+                            Drag to draw a rectangle · Press N or Esc to exit
+                        </div>
+                    </div>
+                )}
+            </div>
+
+            {/* ── BOTTOM: page nav + shortcuts ─────────────────────────── */}
+            <div className="flex-shrink-0 px-4 py-2 bg-gray-900 border-t border-gray-800 flex items-center justify-between">
+                <div className="hidden lg:flex items-center gap-4 text-[11px] text-gray-500 shrink min-w-0 overflow-hidden">
+                    <span><kbd className="px-1 py-0.5 bg-gray-800 text-gray-400 rounded font-mono text-[10px]">N</kbd> Add box</span>
+                    <span><kbd className="px-1 py-0.5 bg-gray-800 text-gray-400 rounded font-mono text-[10px]">Del</kbd> Delete</span>
+                    <span><kbd className="px-1 py-0.5 bg-gray-800 text-gray-400 rounded font-mono text-[10px]">D</kbd> Duplicate</span>
+                    <span className="hidden md:inline"><kbd className="px-1 py-0.5 bg-gray-800 text-gray-400 rounded font-mono text-[10px]">○</kbd> Rotate</span>
+                    <span><kbd className="px-1 py-0.5 bg-gray-800 text-gray-400 rounded font-mono text-[10px]">Ctrl+Z</kbd> Undo</span>
+                    <span className="hidden sm:inline"><kbd className="px-1 py-0.5 bg-gray-800 text-gray-400 rounded font-mono text-[10px]">Ctrl+drag</kbd> Pan</span>
+                    <span className="hidden sm:inline"><kbd className="px-1 py-0.5 bg-gray-800 text-gray-400 rounded font-mono text-[10px]">← →</kbd> Pages</span>
+                    <span className="hidden sm:inline"><kbd className="px-1 py-0.5 bg-gray-800 text-gray-400 rounded font-mono text-[10px]">↑ ↓</kbd> Boxes</span>
+                </div>
+
+                {pages.length > 1 && (
+                    <div className="flex items-center gap-2 flex-1 min-w-0 justify-center px-3">
+                        <span className="shrink-0 text-[11px] text-gray-500 font-mono">{currentIdx + 1}/{pages.length}</span>
+                        <button onClick={() => goToPage(currentIdx - 1)} disabled={currentIdx === 0}
+                            className={`shrink-0 p-1.5 rounded-lg ${currentIdx === 0 ? 'text-gray-700' : 'text-gray-300 hover:text-white hover:bg-gray-800'}`}>
+                            <ChevronLeft size={18} />
+                        </button>
+                        <div className="flex items-center gap-1 overflow-x-auto min-w-0 scrollbar-thin scrollbar-thumb-gray-700 scrollbar-track-transparent">
+                            {pages.map((page, idx) => {
+                                // Unvisited pages have no edited boxes yet; showing 0
+                                // looked like detection had been reverted.
+                                const pageBoxCount = idx === currentIdx
+                                    ? boxes.length
+                                    : (pageBoxesRef.current[page.pageNumber]?.length ?? page.polygons?.length ?? 0);
+                                return (
+                                <button key={page.pageNumber} ref={idx === currentIdx ? activePageBtnRef : null} onClick={() => goToPage(idx)} title={`Page ${page.pageNumber} — ${pageBoxCount} boxes`}
+                                    className={`relative shrink-0 min-w-[2rem] px-1.5 py-1 rounded-lg transition-all flex flex-col items-center leading-none ${idx === currentIdx ? 'bg-orange-500 text-white shadow-sm' : 'bg-gray-800 text-gray-400 hover:bg-gray-700 hover:text-white'}`}>
+                                    <span className="text-xs font-semibold">{shortPageLabel(page.pageNumber)}</span>
+                                    <span className="text-[9px] opacity-70">{pageBoxCount}</span>
+                                    {modifiedPages.has(page.pageNumber) && (
+                                        <span className="absolute -top-0.5 -right-0.5 w-2 h-2 bg-amber-400 rounded-full border border-gray-900" />
+                                    )}
+                                </button>
+                                );
+                            })}
+                        </div>
+                        <button onClick={() => goToPage(currentIdx + 1)} disabled={currentIdx === pages.length - 1}
+                            className={`shrink-0 p-1.5 rounded-lg ${currentIdx === pages.length - 1 ? 'text-gray-700' : 'text-gray-300 hover:text-white hover:bg-gray-800'}`}>
+                            <ChevronRight size={18} />
+                        </button>
+                    </div>
+                )}
+
+                <span className="hidden md:block shrink-0 text-[11px] text-gray-600">
+                    {imageSize.w > 0 && `${imageSize.w} × ${imageSize.h} px`}
+                </span>
+            </div>
+        </div>
+    );
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/DatasetStepper.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/DatasetStepper.jsx
new file mode 100644
index 00000000..740e4c79
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/DatasetStepper.jsx
@@ -0,0 +1,124 @@
+import React from 'react';
+import {
+  Check,
+  Upload,
+  GitMerge,
+  Wand2,
+  ScanText,
+  Database,
+} from 'lucide-react';
+
+const recognitionSteps = [
+  { id: 1, name: 'Upload', description: 'Book & transcript', icon: Upload },
+  { id: 2, name: 'Match & Review', description: 'Verify page matches', icon: GitMerge },
+  { id: 3, name: 'Preprocess', description: 'Apply image transforms', icon: Wand2 },
+  { id: 4, name: 'Detect & Align', description: 'Detect lines & align text', icon: ScanText },
+  { id: 5, name: 'Export Dataset', description: 'Download training data', icon: Database },
+];
+
+const detectionSteps = [
+  { id: 1, name: 'Upload', description: 'Book images', icon: Upload },
+  { id: 2, name: 'Select Pages', description: 'Pick pages to detect', icon: GitMerge },
+  { id: 3, name: 'Preprocess', description: 'Apply image transforms', icon: Wand2 },
+  { id: 4, name: 'Detect Lines', description: 'Detect text bounding boxes', icon: ScanText },
+  { id: 5, name: 'Export Dataset', description: 'Download training data', icon: Database },
+];
+
+export default function DatasetStepper({ currentStep, onStepClick, compact = false, datasetMode = 'recognition' }) {
+  const datasetSteps = datasetMode === 'detection' ? detectionSteps : recognitionSteps;
+  const progressPercent = ((currentStep - 1) / (datasetSteps.length - 1)) * 100;
+
+  const inner = (
+    <nav aria-label="Progress">
+      <div className="relative">
+        {/* Background line — spans between the centres of the first and last step nodes */}
+        <div className={`absolute ${compact ? 'top-3.5' : 'top-5'} left-[10%] right-[10%] h-0.5 bg-gradient-to-r from-emerald-100 via-teal-100 to-emerald-100 rounded-full`} />
+
+        {/* Filled progress line */}
+        <div
+          className={`absolute ${compact ? 'top-3.5' : 'top-5'} left-[10%] h-0.5 bg-gradient-to-r from-emerald-500 via-teal-500 to-emerald-600 rounded-full transition-all duration-500 ease-out`}
+          style={{ width: `${progressPercent * 0.8}%` }}
+        />
+
+        <ol className="relative flex items-center justify-between">
+          {datasetSteps.map((step) => {
+            const isCompleted = currentStep > step.id;
+            const isCurrent = currentStep === step.id;
+            const isClickable = step.id <= currentStep;
+            const StepIcon = step.icon;
+
+            return (
+              <li key={step.id} className="flex-1 flex flex-col items-center">
+                <button
+                  onClick={() => isClickable && onStepClick(step.id)}
+                  disabled={!isClickable}
+                  className={`relative flex flex-col items-center group transition-all duration-200 ${
+                    isClickable ? 'cursor-pointer' : 'cursor-not-allowed'
+                  }`}
+                >
+                  <span
+                    className={`relative flex items-center justify-center rounded-full border-2 transition-all duration-300 ${
+                      compact ? 'w-7 h-7' : 'w-10 h-10'
+                    } ${
+                      isCompleted
+                        ? 'bg-gradient-to-br from-emerald-500 to-teal-600 border-emerald-500 text-white shadow-md shadow-emerald-500/30'
+                        : isCurrent
+                          ? 'bg-white border-emerald-500 text-emerald-600 ring-2 ring-emerald-100 shadow-md'
+                          : 'bg-white border-gray-200 text-gray-400 group-hover:border-emerald-300 group-hover:text-emerald-500'
+                    }`}
+                  >
+                    {isCompleted ? (
+                      <Check className={compact ? 'w-3.5 h-3.5' : 'w-5 h-5'} strokeWidth={2.5} />
+                    ) : (
+                      <StepIcon
+                        className={`${compact ? 'w-3.5 h-3.5' : 'w-5 h-5'} transition-transform duration-200 ${
+                          isCurrent ? 'scale-110' : 'group-hover:scale-110'
+                        }`}
+                      />
+                    )}
+
+                    {isCurrent && (
+                      <span className="absolute inset-0 rounded-full bg-emerald-400/20 animate-ping" />
+                    )}
+                  </span>
+
+                  <span
+                    className={`${compact ? 'mt-1 text-[10px]' : 'mt-3 text-xs'} font-semibold transition-colors duration-200 ${
+                      isCurrent
+                        ? 'text-emerald-600'
+                        : isCompleted
+                          ? 'text-emerald-700'
+                          : 'text-gray-500 group-hover:text-emerald-500'
+                    }`}
+                  >
+                    {step.name}
+                  </span>
+
+                  {!compact && (
+                    <span
+                      className={`hidden lg:block text-[10px] mt-1 transition-colors duration-200 max-w-20 text-center ${
+                        isCurrent ? 'text-emerald-500' : 'text-gray-400'
+                      }`}
+                    >
+                      {step.description}
+                    </span>
+                  )}
+                </button>
+              </li>
+            );
+          })}
+        </ol>
+      </div>
+    </nav>
+  );
+
+  if (compact) {
+    return <div className="w-full">{inner}</div>;
+  }
+
+  return (
+    <div className="w-full bg-white/80 backdrop-blur-sm rounded-2xl shadow-lg border border-white/50 p-6 mb-6">
+      {inner}
+    </div>
+  );
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/ImageCropper.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/ImageCropper.jsx
new file mode 100644
index 00000000..25c73ec2
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/ImageCropper.jsx
@@ -0,0 +1,607 @@
+import React, { useState, useRef, useEffect, useCallback } from 'react';
+import { Crop, Check, X, RotateCcw, Maximize2, Lock, Unlock, Copy, ChevronDown, ChevronUp } from 'lucide-react';
+
+// Draggable crop box with optional aspect lock. The same crop can be applied
+// across several pages at once.
+export default function ImageCropper({
+  imageSrc,
+  onCropComplete,
+  onCancel,
+  initialCrop = null,
+  availablePages = [], // Array of { pageNumber, thumbnail } objects
+  currentPageNumber = null,
+  onBatchCropComplete = null, // Callback for applying crop to multiple pages
+}) {
+  const containerRef = useRef(null);
+  const imageRef = useRef(null);
+  const [imageLoaded, setImageLoaded] = useState(false);
+  const [imageDimensions, setImageDimensions] = useState({ width: 0, height: 0 });
+  const [displayDimensions, setDisplayDimensions] = useState({ width: 0, height: 0 });
+  
+  // Percentages of the displayed image, not pixels.
+  const [crop, setCrop] = useState({
+    x: 10, // percentage from left
+    y: 10, // percentage from top
+    width: 80, // percentage width
+    height: 80, // percentage height
+  });
+  
+  const [isDragging, setIsDragging] = useState(false);
+  const [dragType, setDragType] = useState(null); // 'move', 'nw', 'ne', 'sw', 'se', 'n', 's', 'e', 'w'
+  const [dragStart, setDragStart] = useState({ x: 0, y: 0 });
+  const [cropStart, setCropStart] = useState({ x: 0, y: 0, width: 0, height: 0 });
+  const [aspectLocked, setAspectLocked] = useState(false);
+  const [aspectRatio, setAspectRatio] = useState(null);
+  
+  const [showPageSelector, setShowPageSelector] = useState(false);
+  const [selectedPages, setSelectedPages] = useState(new Set()); // Pages to apply crop to
+  const [isBatchProcessing, setIsBatchProcessing] = useState(false);
+  
+  const otherPages = availablePages.filter(p => p.pageNumber !== currentPageNumber);
+  const hasMultiplePages = otherPages.length > 0 && onBatchCropComplete;
+
+  useEffect(() => {
+    if (!imageSrc) return;
+    
+    const img = new Image();
+    img.onload = () => {
+      setImageDimensions({ width: img.width, height: img.height });
+      setImageLoaded(true);
+    };
+    img.src = imageSrc;
+  }, [imageSrc]);
+
+  useEffect(() => {
+    if (!containerRef.current || !imageLoaded) return;
+
+    const updateDisplayDimensions = () => {
+      const container = containerRef.current;
+      const containerRect = container.getBoundingClientRect();
+      const containerWidth = containerRect.width - 48; // padding
+      const containerHeight = containerRect.height - 48;
+
+      const imageAspect = imageDimensions.width / imageDimensions.height;
+      const containerAspect = containerWidth / containerHeight;
+
+      let displayWidth, displayHeight;
+      if (imageAspect > containerAspect) {
+        displayWidth = containerWidth;
+        displayHeight = containerWidth / imageAspect;
+      } else {
+        displayHeight = containerHeight;
+        displayWidth = containerHeight * imageAspect;
+      }
+
+      setDisplayDimensions({ width: displayWidth, height: displayHeight });
+    };
+
+    updateDisplayDimensions();
+    window.addEventListener('resize', updateDisplayDimensions);
+    return () => window.removeEventListener('resize', updateDisplayDimensions);
+  }, [imageLoaded, imageDimensions]);
+
+  useEffect(() => {
+    if (initialCrop) {
+      setCrop(initialCrop);
+    }
+  }, [initialCrop]);
+
+  const handleLockAspect = () => {
+    if (!aspectLocked) {
+      setAspectRatio(crop.width / crop.height);
+    }
+    setAspectLocked(!aspectLocked);
+  };
+
+  const handleResetCrop = () => {
+    setCrop({ x: 5, y: 5, width: 90, height: 90 });
+    setAspectLocked(false);
+    setAspectRatio(null);
+  };
+
+  const getCursor = (type) => {
+    const cursors = {
+      move: 'move',
+      nw: 'nw-resize',
+      ne: 'ne-resize',
+      sw: 'sw-resize',
+      se: 'se-resize',
+      n: 'n-resize',
+      s: 's-resize',
+      e: 'e-resize',
+      w: 'w-resize',
+    };
+    return cursors[type] || 'default';
+  };
+
+  const handleDragStart = (e, type) => {
+    e.preventDefault();
+    e.stopPropagation();
+    
+    const clientX = e.touches ? e.touches[0].clientX : e.clientX;
+    const clientY = e.touches ? e.touches[0].clientY : e.clientY;
+    
+    setIsDragging(true);
+    setDragType(type);
+    setDragStart({ x: clientX, y: clientY });
+    setCropStart({ ...crop });
+  };
+
+  const handleDragMove = useCallback((e) => {
+    if (!isDragging || !dragType) return;
+
+    const clientX = e.touches ? e.touches[0].clientX : e.clientX;
+    const clientY = e.touches ? e.touches[0].clientY : e.clientY;
+    
+    const deltaX = ((clientX - dragStart.x) / displayDimensions.width) * 100;
+    const deltaY = ((clientY - dragStart.y) / displayDimensions.height) * 100;
+
+    let newCrop = { ...cropStart };
+
+    if (dragType === 'move') {
+      newCrop.x = Math.max(0, Math.min(100 - cropStart.width, cropStart.x + deltaX));
+      newCrop.y = Math.max(0, Math.min(100 - cropStart.height, cropStart.y + deltaY));
+    } else {
+      const minSize = 10; // minimum 10% size
+
+      if (dragType.includes('w')) {
+        const newX = Math.max(0, Math.min(cropStart.x + cropStart.width - minSize, cropStart.x + deltaX));
+        const widthDiff = cropStart.x - newX;
+        newCrop.x = newX;
+        newCrop.width = cropStart.width + widthDiff;
+      }
+      if (dragType.includes('e')) {
+        newCrop.width = Math.max(minSize, Math.min(100 - cropStart.x, cropStart.width + deltaX));
+      }
+      if (dragType.includes('n')) {
+        const newY = Math.max(0, Math.min(cropStart.y + cropStart.height - minSize, cropStart.y + deltaY));
+        const heightDiff = cropStart.y - newY;
+        newCrop.y = newY;
+        newCrop.height = cropStart.height + heightDiff;
+      }
+      if (dragType.includes('s')) {
+        newCrop.height = Math.max(minSize, Math.min(100 - cropStart.y, cropStart.height + deltaY));
+      }
+
+      if (aspectLocked && aspectRatio) {
+        if (dragType.includes('e') || dragType.includes('w')) {
+          newCrop.height = newCrop.width / aspectRatio;
+          if (newCrop.y + newCrop.height > 100) {
+            newCrop.height = 100 - newCrop.y;
+            newCrop.width = newCrop.height * aspectRatio;
+          }
+        } else {
+          newCrop.width = newCrop.height * aspectRatio;
+          if (newCrop.x + newCrop.width > 100) {
+            newCrop.width = 100 - newCrop.x;
+            newCrop.height = newCrop.width / aspectRatio;
+          }
+        }
+      }
+    }
+
+    setCrop(newCrop);
+  }, [isDragging, dragType, dragStart, cropStart, displayDimensions, aspectLocked, aspectRatio]);
+
+  const handleDragEnd = useCallback(() => {
+    setIsDragging(false);
+    setDragType(null);
+  }, []);
+
+  useEffect(() => {
+    if (isDragging) {
+      window.addEventListener('mousemove', handleDragMove);
+      window.addEventListener('mouseup', handleDragEnd);
+      window.addEventListener('touchmove', handleDragMove, { passive: false });
+      window.addEventListener('touchend', handleDragEnd);
+      return () => {
+        window.removeEventListener('mousemove', handleDragMove);
+        window.removeEventListener('mouseup', handleDragEnd);
+        window.removeEventListener('touchmove', handleDragMove);
+        window.removeEventListener('touchend', handleDragEnd);
+      };
+    }
+  }, [isDragging, handleDragMove, handleDragEnd]);
+
+  const applyCropToImage = async (imgSrc, cropParams) => {
+    const canvas = document.createElement('canvas');
+    const ctx = canvas.getContext('2d');
+
+    const img = new Image();
+    img.crossOrigin = 'anonymous';
+    
+    await new Promise((resolve, reject) => {
+      img.onload = resolve;
+      img.onerror = reject;
+      img.src = imgSrc;
+    });
+
+      const actualX = (cropParams.x / 100) * img.width;
+    const actualY = (cropParams.y / 100) * img.height;
+    const actualWidth = (cropParams.width / 100) * img.width;
+    const actualHeight = (cropParams.height / 100) * img.height;
+
+    canvas.width = actualWidth;
+    canvas.height = actualHeight;
+
+    ctx.drawImage(
+      img,
+      actualX, actualY, actualWidth, actualHeight,
+      0, 0, actualWidth, actualHeight
+    );
+
+    return canvas.toDataURL('image/png');
+  };
+
+  const handleApplyCrop = async () => {
+    if (!imageRef.current) return;
+
+    const cropData = {
+      x: crop.x,
+      y: crop.y,
+      width: crop.width,
+      height: crop.height,
+    };
+
+    const croppedDataUrl = await applyCropToImage(imageSrc, cropData);
+    onCropComplete(croppedDataUrl, cropData);
+  };
+
+  const handleApplyBatchCrop = async () => {
+    if (!imageRef.current || selectedPages.size === 0) return;
+    
+    setIsBatchProcessing(true);
+
+    const cropData = {
+      x: crop.x,
+      y: crop.y,
+      width: crop.width,
+      height: crop.height,
+    };
+
+    try {
+      const currentCroppedUrl = await applyCropToImage(imageSrc, cropData);
+      
+      const batchResults = [];
+      for (const pageNum of selectedPages) {
+        const page = availablePages.find(p => p.pageNumber === pageNum);
+        if (page) {
+          const croppedUrl = await applyCropToImage(page.thumbnail, cropData);
+          batchResults.push({
+            pageNumber: pageNum,
+            croppedDataUrl: croppedUrl,
+            cropData,
+          });
+        }
+      }
+
+      onBatchCropComplete(currentCroppedUrl, cropData, batchResults);
+    } catch (error) {
+      console.error('Batch crop failed:', error);
+    } finally {
+      setIsBatchProcessing(false);
+    }
+  };
+
+  const togglePageSelection = (pageNum) => {
+    setSelectedPages(prev => {
+      const newSet = new Set(prev);
+      if (newSet.has(pageNum)) {
+        newSet.delete(pageNum);
+      } else {
+        newSet.add(pageNum);
+      }
+      return newSet;
+    });
+  };
+
+  const toggleSelectAll = () => {
+    if (selectedPages.size === otherPages.length) {
+      setSelectedPages(new Set());
+    } else {
+      setSelectedPages(new Set(otherPages.map(p => p.pageNumber)));
+    }
+  };
+
+  const renderHandle = (position, cursor) => {
+    const baseClasses = "absolute w-4 h-4 bg-white border-2 border-blue-500 rounded-full shadow-lg z-20 transition-transform hover:scale-125";
+    const positions = {
+      nw: 'top-0 left-0 -translate-x-1/2 -translate-y-1/2',
+      ne: 'top-0 right-0 translate-x-1/2 -translate-y-1/2',
+      sw: 'bottom-0 left-0 -translate-x-1/2 translate-y-1/2',
+      se: 'bottom-0 right-0 translate-x-1/2 translate-y-1/2',
+      n: 'top-0 left-1/2 -translate-x-1/2 -translate-y-1/2',
+      s: 'bottom-0 left-1/2 -translate-x-1/2 translate-y-1/2',
+      e: 'right-0 top-1/2 translate-x-1/2 -translate-y-1/2',
+      w: 'left-0 top-1/2 -translate-x-1/2 -translate-y-1/2',
+    };
+
+    return (
+      <div
+        className={`${baseClasses} ${positions[position]}`}
+        style={{ cursor: getCursor(position) }}
+        onMouseDown={(e) => handleDragStart(e, position)}
+        onTouchStart={(e) => handleDragStart(e, position)}
+      />
+    );
+  };
+
+  if (!imageSrc) {
+    return (
+      <div className="flex items-center justify-center h-full bg-gray-100 rounded-xl">
+        <p className="text-gray-500">No image to crop</p>
+      </div>
+    );
+  }
+
+  return (
+    <div className="fixed inset-0 z-50 bg-black/90 flex flex-col">
+      {/* Header */}
+      <div className="flex items-center justify-between px-6 py-4 bg-gray-900/80 border-b border-gray-700">
+        <div className="flex items-center gap-4">
+          <Crop className="w-6 h-6 text-blue-400" />
+          <h2 className="text-lg font-semibold text-white">Crop Image</h2>
+        </div>
+
+        <div className="flex items-center gap-3">
+          {/* Aspect lock button */}
+          <button
+            onClick={handleLockAspect}
+            className={`flex items-center gap-2 px-3 py-2 rounded-lg transition-colors ${
+              aspectLocked 
+                ? 'bg-blue-500 text-white' 
+                : 'bg-gray-700 text-gray-300 hover:bg-gray-600'
+            }`}
+            title={aspectLocked ? 'Unlock aspect ratio' : 'Lock aspect ratio'}
+          >
+            {aspectLocked ? <Lock className="w-4 h-4" /> : <Unlock className="w-4 h-4" />}
+            <span className="text-sm font-medium">
+              {aspectLocked ? 'Locked' : 'Lock Ratio'}
+            </span>
+          </button>
+
+          {/* Reset button */}
+          <button
+            onClick={handleResetCrop}
+            className="flex items-center gap-2 px-3 py-2 bg-gray-700 text-gray-300 rounded-lg hover:bg-gray-600 transition-colors"
+          >
+            <RotateCcw className="w-4 h-4" />
+            <span className="text-sm font-medium">Reset</span>
+          </button>
+
+          {/* Full image button */}
+          <button
+            onClick={() => setCrop({ x: 0, y: 0, width: 100, height: 100 })}
+            className="flex items-center gap-2 px-3 py-2 bg-gray-700 text-gray-300 rounded-lg hover:bg-gray-600 transition-colors"
+          >
+            <Maximize2 className="w-4 h-4" />
+            <span className="text-sm font-medium">Full Image</span>
+          </button>
+        </div>
+      </div>
+
+      {/* Crop area */}
+      <div 
+        ref={containerRef}
+        className="flex-1 flex items-center justify-center p-6 overflow-hidden"
+      >
+        {imageLoaded && displayDimensions.width > 0 && (
+          <div 
+            className="relative"
+            style={{
+              width: displayDimensions.width,
+              height: displayDimensions.height,
+            }}
+          >
+            {/* Original image */}
+            <img
+              ref={imageRef}
+              src={imageSrc}
+              alt="To crop"
+              className="absolute inset-0 w-full h-full object-contain select-none pointer-events-none"
+              draggable={false}
+            />
+
+            {/* Dark overlay outside crop area */}
+            <div 
+              className="absolute inset-0 pointer-events-none"
+              style={{
+                background: `linear-gradient(to right, 
+                  rgba(0,0,0,0.7) ${crop.x}%, 
+                  transparent ${crop.x}%, 
+                  transparent ${crop.x + crop.width}%, 
+                  rgba(0,0,0,0.7) ${crop.x + crop.width}%
+                )`,
+              }}
+            />
+            <div 
+              className="absolute pointer-events-none"
+              style={{
+                left: `${crop.x}%`,
+                width: `${crop.width}%`,
+                top: 0,
+                height: `${crop.y}%`,
+                background: 'rgba(0,0,0,0.7)',
+              }}
+            />
+            <div 
+              className="absolute pointer-events-none"
+              style={{
+                left: `${crop.x}%`,
+                width: `${crop.width}%`,
+                top: `${crop.y + crop.height}%`,
+                bottom: 0,
+                background: 'rgba(0,0,0,0.7)',
+              }}
+            />
+
+            {/* Crop box */}
+            <div
+              className="absolute border-2 border-white shadow-xl"
+              style={{
+                left: `${crop.x}%`,
+                top: `${crop.y}%`,
+                width: `${crop.width}%`,
+                height: `${crop.height}%`,
+                cursor: isDragging && dragType === 'move' ? 'grabbing' : 'grab',
+              }}
+              onMouseDown={(e) => handleDragStart(e, 'move')}
+              onTouchStart={(e) => handleDragStart(e, 'move')}
+            >
+              {/* Grid lines (rule of thirds) */}
+              <div className="absolute inset-0 pointer-events-none">
+                <div className="absolute left-1/3 top-0 bottom-0 w-px bg-white/30" />
+                <div className="absolute left-2/3 top-0 bottom-0 w-px bg-white/30" />
+                <div className="absolute top-1/3 left-0 right-0 h-px bg-white/30" />
+                <div className="absolute top-2/3 left-0 right-0 h-px bg-white/30" />
+              </div>
+
+              {/* Corner handles */}
+              {renderHandle('nw')}
+              {renderHandle('ne')}
+              {renderHandle('sw')}
+              {renderHandle('se')}
+
+              {/* Edge handles */}
+              {renderHandle('n')}
+              {renderHandle('s')}
+              {renderHandle('e')}
+              {renderHandle('w')}
+            </div>
+
+            {/* Dimensions display */}
+            <div className="absolute bottom-4 left-1/2 -translate-x-1/2 bg-black/80 text-white px-3 py-1.5 rounded-lg text-sm font-mono">
+              {Math.round((crop.width / 100) * imageDimensions.width)} × {Math.round((crop.height / 100) * imageDimensions.height)} px
+            </div>
+          </div>
+        )}
+      </div>
+
+      {/* Page selector panel (collapsible) */}
+      {hasMultiplePages && (
+        <div className="bg-gray-800/90 border-t border-gray-700">
+          {/* Toggle header */}
+          <button
+            onClick={() => setShowPageSelector(!showPageSelector)}
+            className="w-full flex items-center justify-between px-6 py-3 hover:bg-gray-700/50 transition-colors"
+          >
+            <div className="flex items-center gap-3">
+              <Copy className="w-4 h-4 text-blue-400" />
+              <span className="text-sm font-medium text-white">
+                Apply to other pages
+              </span>
+              {selectedPages.size > 0 && (
+                <span className="px-2 py-0.5 bg-blue-500 text-white text-xs font-semibold rounded-full">
+                  {selectedPages.size} selected
+                </span>
+              )}
+            </div>
+            {showPageSelector ? (
+              <ChevronDown className="w-4 h-4 text-gray-400" />
+            ) : (
+              <ChevronUp className="w-4 h-4 text-gray-400" />
+            )}
+          </button>
+
+          {/* Page selection grid */}
+          {showPageSelector && (
+            <div className="px-6 pb-4">
+              {/* Select all button */}
+              <div className="flex items-center justify-between mb-3">
+                <span className="text-xs text-gray-400">
+                  Select pages to apply the same crop
+                </span>
+                <button
+                  onClick={toggleSelectAll}
+                  className="text-xs font-medium text-blue-400 hover:text-blue-300 transition-colors"
+                >
+                  {selectedPages.size === otherPages.length ? 'Deselect All' : 'Select All'}
+                </button>
+              </div>
+
+              {/* Page thumbnails */}
+              <div className="flex flex-wrap gap-2 max-h-32 overflow-y-auto">
+                {otherPages.map((page) => (
+                  <button
+                    key={page.pageNumber}
+                    onClick={() => togglePageSelection(page.pageNumber)}
+                    className={`relative w-14 h-14 rounded-lg overflow-hidden border-2 transition-all ${
+                      selectedPages.has(page.pageNumber)
+                        ? 'border-blue-500 ring-2 ring-blue-500/30'
+                        : 'border-gray-600 hover:border-gray-500'
+                    }`}
+                  >
+                    <img
+                      src={page.thumbnail}
+                      alt={`Page ${page.pageNumber}`}
+                      className="w-full h-full object-cover"
+                    />
+                    {/* Page number overlay */}
+                    <div className="absolute inset-x-0 bottom-0 bg-black/70 py-0.5">
+                      <span className="text-[10px] font-medium text-white">
+                        {page.pageNumber}
+                      </span>
+                    </div>
+                    {/* Checkmark for selected */}
+                    {selectedPages.has(page.pageNumber) && (
+                      <div className="absolute top-1 right-1 w-4 h-4 bg-blue-500 rounded-full flex items-center justify-center">
+                        <Check className="w-3 h-3 text-white" />
+                      </div>
+                    )}
+                  </button>
+                ))}
+              </div>
+            </div>
+          )}
+        </div>
+      )}
+
+      {/* Footer with actions */}
+      <div className="flex items-center justify-between px-6 py-4 bg-gray-900/80 border-t border-gray-700">
+        <p className="text-sm text-gray-400">
+          Drag corners or edges to resize • Drag center to move
+        </p>
+
+        <div className="flex items-center gap-3">
+          <button
+            onClick={onCancel}
+            disabled={isBatchProcessing}
+            className="flex items-center gap-2 px-5 py-2.5 bg-gray-700 text-white font-medium rounded-xl hover:bg-gray-600 transition-colors disabled:opacity-50"
+          >
+            <X className="w-4 h-4" />
+            Cancel
+          </button>
+          
+          {/* Show batch apply button if pages are selected */}
+          {selectedPages.size > 0 ? (
+            <button
+              onClick={handleApplyBatchCrop}
+              disabled={isBatchProcessing}
+              className="flex items-center gap-2 px-5 py-2.5 bg-gradient-to-r from-blue-600 to-indigo-600 text-white font-semibold rounded-xl hover:from-blue-700 hover:to-indigo-700 transition-all shadow-lg disabled:opacity-50"
+            >
+              {isBatchProcessing ? (
+                <>
+                  <div className="w-4 h-4 border-2 border-white/30 border-t-white rounded-full animate-spin" />
+                  Processing...
+                </>
+              ) : (
+                <>
+                  <Copy className="w-4 h-4" />
+                  Apply to {selectedPages.size + 1} Pages
+                </>
+              )}
+            </button>
+          ) : (
+            <button
+              onClick={handleApplyCrop}
+              className="flex items-center gap-2 px-5 py-2.5 bg-blue-600 text-white font-semibold rounded-xl hover:bg-blue-700 transition-colors shadow-lg"
+            >
+              <Check className="w-4 h-4" />
+              Apply Crop
+            </button>
+          )}
+        </div>
+      </div>
+    </div>
+  );
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/LayoutAwareDetectionPanel.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/LayoutAwareDetectionPanel.jsx
new file mode 100644
index 00000000..4ef4be47
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/LayoutAwareDetectionPanel.jsx
@@ -0,0 +1,2419 @@
+import React, { useState, useRef, useEffect, useCallback, useMemo } from 'react';
+import {
+    Play,
+    Loader2,
+    AlertTriangle,
+    CheckCircle2,
+    ChevronLeft,
+    ChevronRight,
+    Layers,
+    Image as ImageIcon,
+    FileText,
+    Info,
+    ChevronDown,
+    PenLine,
+    ArrowUp,
+    ArrowDown,
+    Split,
+    Link2,
+    Sparkles,
+    Cpu,
+    Download,
+    FileText as FileTextIcon,
+    FileJson,
+    FileType,
+    BookOpen,
+    RefreshCw,
+    Home,
+    Plus,
+    Wand2,
+} from 'lucide-react';
+import ResizablePanels from './ocr/ResizablePanels';
+import BBoxEditor from './BBoxEditor';
+import {
+    getLocalRecognitionModels,
+    runLocalRecognition,
+    exportCRNNResultsAsText,
+    exportCRNNResultsAsJSON,
+    downloadBlob,
+} from '../features/ocr/services/ocrApi';
+import { saveTranscriptSession, fetchStorageOverview } from '../services/storageApi';
+import {
+    getLLMProviders,
+    getLLMTemplates,
+    postProcessWithLLMProvider,
+} from '../services/llmApi';
+import { API_ORIGIN } from '../config';
+
+const API_BASE = API_ORIGIN;
+
+// ── Layout model options ──
+const LAYOUT_MODELS = [
+    { id: 'PP-DocLayout_plus-L', name: 'PP-DocLayout+ Large', desc: 'Best accuracy' },
+    { id: 'PP-DocLayout-L', name: 'PP-DocLayout Large', desc: 'High accuracy' },
+    { id: 'PP-DocLayout-M', name: 'PP-DocLayout Medium', desc: 'Balanced' },
+    { id: 'PP-DocLayout-S', name: 'PP-DocLayout Small', desc: 'Fastest' },
+    { id: 'PicoDet-L_layout_17cls', name: 'PicoDet-L 17cls', desc: '17-class layout' },
+    { id: 'RT-DETR-H_layout_17cls', name: 'RT-DETR-H 17cls', desc: '17-class, high acc' },
+    { id: 'PicoDet-L_layout_3cls', name: 'PicoDet-L 3cls', desc: '3-class layout' },
+    { id: 'PicoDet-S_layout_3cls', name: 'PicoDet-S 3cls', desc: '3-class, fastest' },
+];
+
+// ── Detection model options ──
+const DETECTION_MODELS = [
+    { id: 'PP-OCRv5_server_det', name: 'PP-OCRv5 Server Det', desc: 'High accuracy, slower' },
+    { id: 'PP-OCRv5_mobile_det', name: 'PP-OCRv5 Mobile Det', desc: 'Fast, lighter' },
+    { id: 'DB', name: 'DBNet', desc: 'Classic, reliable' },
+    { id: 'DB++', name: 'DB++', desc: 'Enhanced DBNet' },
+    { id: 'EAST', name: 'EAST', desc: 'Efficient text detection' },
+    { id: 'SAST', name: 'SAST', desc: 'Segmentation-based' },
+];
+
+// ── Default tuning parameters ──
+const DEFAULT_PARAMS = {
+    region_padding: 50,
+    layout_expand: 2,
+    score_thresh: 0.5,
+    upscale_min_h: 60,
+    nms_iou_thresh: 0.3,
+    gap_multiplier: 2.0,
+};
+
+// ── Slider definitions for the tuning UI ──
+const PARAM_DEFS = [
+    { key: 'region_padding', label: 'Region Padding', unit: 'px', min: 0, max: 200, step: 5, type: 'int', tooltip: 'Padding around detected regions before OCR' },
+    { key: 'layout_expand', label: 'Layout Expand', unit: 'px', min: 0, max: 50, step: 1, type: 'int', tooltip: 'Expand layout bounding boxes before cropping' },
+    { key: 'score_thresh', label: 'Score Threshold', unit: '', min: 0.1, max: 1.0, step: 0.05, type: 'float', tooltip: 'Minimum confidence score for text detection' },
+    { key: 'upscale_min_h', label: 'Upscale Min H', unit: 'px', min: 20, max: 200, step: 10, type: 'int', tooltip: 'Upscale crops shorter than this height' },
+    { key: 'nms_iou_thresh', label: 'NMS IoU Thresh', unit: '', min: 0.1, max: 0.9, step: 0.05, type: 'float', tooltip: 'IoU threshold for non-max suppression' },
+    { key: 'gap_multiplier', label: 'Gap Multiplier', unit: '×', min: 0.5, max: 5.0, step: 0.5, type: 'float', tooltip: 'Gap threshold multiplier for line merging' },
+];
+
+// Monotonic, not Date.now(): rows created in the same millisecond would collide
+// as React keys and silently remount.
+let _rowIdCounter = 0;
+const nextRowId = () => ++_rowIdCounter;
+
+// Word-level LCS diff for the before/after view. Whitespace splitting is fine —
+// OCR corrections are small (diacritics, glyph fixes) and we only need to point
+// at the words that changed.
+function diffWords(before, after) {
+    const a = before.split(/\s+/).filter(Boolean);
+    const b = after.split(/\s+/).filter(Boolean);
+    const n = a.length;
+    const m = b.length;
+    const lcs = Array.from({ length: n + 1 }, () => new Array(m + 1).fill(0));
+    for (let i = n - 1; i >= 0; i -= 1) {
+        for (let j = m - 1; j >= 0; j -= 1) {
+            lcs[i][j] = a[i] === b[j]
+                ? lcs[i + 1][j + 1] + 1
+                : Math.max(lcs[i + 1][j], lcs[i][j + 1]);
+        }
+    }
+    const beforeTokens = [];
+    const afterTokens = [];
+    let i = 0;
+    let j = 0;
+    while (i < n && j < m) {
+        if (a[i] === b[j]) {
+            beforeTokens.push({ text: a[i], removed: false });
+            afterTokens.push({ text: b[j], added: false });
+            i += 1; j += 1;
+        } else if (lcs[i + 1][j] >= lcs[i][j + 1]) {
+            beforeTokens.push({ text: a[i], removed: true });
+            i += 1;
+        } else {
+            afterTokens.push({ text: b[j], added: true });
+            j += 1;
+        }
+    }
+    while (i < n) { beforeTokens.push({ text: a[i], removed: true }); i += 1; }
+    while (j < m) { afterTokens.push({ text: b[j], added: true }); j += 1; }
+    return { beforeTokens, afterTokens };
+}
+
+function shortPageLabel(pageKey) {
+    return String(pageKey).replace('_left', 'L').replace('_right', 'R');
+}
+
+function getPageBaseNumber(pageKey) {
+    const value = String(pageKey).toLowerCase();
+    const match = value.match(/(\d+)/);
+    return match ? Number(match[1]) : null;
+}
+
+function getPageSide(pageKey) {
+    const value = String(pageKey).toLowerCase();
+    if (value.includes('left') || value.endsWith('l')) return 'left';
+    if (value.includes('right') || value.endsWith('r')) return 'right';
+    return null;
+}
+
+function resolveTranscriptKey(transcript, pageKey) {
+    if (!transcript) return null;
+    const keys = Object.keys(transcript);
+    const asString = String(pageKey);
+    if (asString in transcript) return asString;
+
+    const targetNum = getPageBaseNumber(asString);
+    const targetSide = getPageSide(asString);
+
+    if (targetNum == null) return null;
+
+    const exactSideKey = keys.find((k) => {
+        const keyNum = getPageBaseNumber(k);
+        const keySide = getPageSide(k);
+        return keyNum === targetNum && keySide === targetSide;
+    });
+    if (exactSideKey) return exactSideKey;
+
+    return keys.find((k) => getPageBaseNumber(k) === targetNum) || null;
+}
+
+
+// ── Thumbnail item ──
+function ThumbnailItem({ image, index, isActive, isDetected, onClick, pageLabel, processedSrc }) {
+    const imageSrc = processedSrc || image?.processed || image?.original || image?.thumbnail;
+
+    return (
+        <button
+            onClick={onClick}
+            className={`
+                relative aspect-[3/4] rounded-lg overflow-hidden transition-all duration-200
+                border-2 group
+                ${isActive
+                    ? 'border-teal-500 ring-2 ring-teal-200 shadow-md'
+                    : isDetected
+                        ? 'border-emerald-300 hover:border-emerald-400'
+                        : 'border-gray-200 hover:border-gray-300 hover:shadow-sm'
+                }
+            `}
+        >
+            {imageSrc ? (
+                <img src={imageSrc} alt={`Page ${pageLabel}`} className="w-full h-full object-cover" />
+            ) : (
+                <div className="absolute inset-0 bg-gray-100 flex items-center justify-center">
+                    <ImageIcon size={20} className="text-gray-300" />
+                </div>
+            )}
+
+            {isDetected && !isActive && (
+                <div className="absolute top-1.5 right-1.5">
+                    <CheckCircle2 size={16} className="text-emerald-500 bg-white rounded-full shadow-sm" />
+                </div>
+            )}
+
+            <div className="absolute bottom-0 inset-x-0 bg-gradient-to-t from-black/70 to-transparent pt-4 pb-1">
+                <span className="block text-center text-white text-xs font-medium">{pageLabel}</span>
+            </div>
+        </button>
+    );
+}
+
+
+// ── Main component ──
+export default function LayoutAwareDetectionPage({
+    pages,
+    selectedPages,
+    processedImages,
+    transcript = {},
+    bookName = 'transcript',
+    preprocessing = [],
+    onBack,
+    onHome,
+    datasetMode = false,
+    onDatasetNext,
+    // Restored when navigating back from export.
+    initialDetectedPages = {},
+    initialAlignmentByPage = {},
+    onStateChange,
+}) {
+    // ── Model selection ──
+    const [selectedDetModel, setSelectedDetModel] = useState('PP-OCRv5_server_det');
+    const [selectedLayoutModel, setSelectedLayoutModel] = useState('PP-DocLayout_plus-L');
+
+    // ── Tuning parameters ──
+    const [tuningParams, setTuningParams] = useState({ ...DEFAULT_PARAMS });
+    const [showAdvanced, setShowAdvanced] = useState(false);
+
+    // ── UI state ──
+    const [viewingPageIndex, setViewingPageIndex] = useState(0);
+    const [loading, setLoading] = useState(false);
+    // null = not checked yet, true = cached, false = needs the big download.
+    const [modelsReady, setModelsReady] = useState(null);
+    const [downloadingModels, setDownloadingModels] = useState(false);
+    const [detectedPages, setDetectedPages] = useState(() => initialDetectedPages);
+    const [error, setError] = useState(null);
+    const [warning, setWarning] = useState(null);
+    const [processingTime, setProcessingTime] = useState(null);
+    const [imageLoaded, setImageLoaded] = useState(false);
+
+    // ── BBox editor state ──
+    const [showBBoxEditor, setShowBBoxEditor] = useState(false);
+    // Drives the "edited" dot on thumbnails.
+    const [editedPages, setEditedPages] = useState(new Set());
+
+    // ── Process-all state ──
+    const [processingAll, setProcessingAll] = useState(false);
+    const [processAllProgress, setProcessAllProgress] = useState(null);
+    const cancelProcessingRef = useRef(false);
+
+    // ── Alignment state (dataset mode) ──
+    const [alignmentByPage, setAlignmentByPage] = useState(() => initialAlignmentByPage);
+
+    // ── Local OCR state (OCR mode) ──
+    const [localModels, setLocalModels] = useState([]);
+    const [localModelsLoading, setLocalModelsLoading] = useState(false);
+    const [localModelsError, setLocalModelsError] = useState(null);
+    const [selectedOcrModel, setSelectedOcrModel] = useState('');
+    const [recognizedByPage, setRecognizedByPage] = useState({});
+    const [ocrProcessing, setOcrProcessing] = useState(false);
+    const [ocrProcessingAll, setOcrProcessingAll] = useState(false);
+    const [ocrProgress, setOcrProgress] = useState(null);
+    const cancelOcrRef = useRef(false);
+    const lastSavedSignatureRef = useRef('');
+    const [ocrDevice, setOcrDevice] = useState(null);
+    const [ocrTimeMs, setOcrTimeMs] = useState(null);
+    const [ocrError, setOcrError] = useState(null);
+
+    // ── LLM post-processing (optional cleanup pass) ──
+    const [llmEnabled, setLlmEnabled] = useState(false);
+    const [llmProviders, setLlmProviders] = useState([]);
+    const [llmProvider, setLlmProvider] = useState('gemini');
+    const [llmModel, setLlmModel] = useState('gemini-2.5-flash');
+    const [llmTemplates, setLlmTemplates] = useState([]);
+    const [llmTemplate, setLlmTemplate] = useState('full_cleanup');
+    // Keyed by provider so switching doesn't lose an entered key.
+    const [llmApiKeys, setLlmApiKeys] = useState({});
+    const [llmProcessing, setLlmProcessing] = useState(false);
+    const [llmProgress, setLlmProgress] = useState(null);
+    const [llmError, setLlmError] = useState(null);
+    // Local corrector only: null = unknown/not applicable, false = base weights
+    // still to download (~8 GB, one time), true = cached.
+    const [llmModelReady, setLlmModelReady] = useState(null);
+    const [downloadingLlmModel, setDownloadingLlmModel] = useState(false);
+    // Stamped into the saved transcript's My Files metadata.
+    const [lastLlmRun, setLastLlmRun] = useState(null);
+
+    // { [pageNum]: { [boxIndex]: textBeforePolish } } for the changed-lines view.
+    const [llmDiffByPage, setLlmDiffByPage] = useState({});
+    const [showLlmDiff, setShowLlmDiff] = useState(true);
+
+    // One source of truth for image<->transcript hover. Rows derive their
+    // highlight from this, so hovering costs one state update, not two.
+    const [hoveredBoxIndex, setHoveredBoxIndex] = useState(null);
+
+    const imgRef = useRef(null);
+    const imageWrapRef = useRef(null);
+    // Row refs by box index — used to scroll a row into view on image hover.
+    const lineRefsRef = useRef(new Map());
+    const setLineRef = useCallback((boxIndex, el) => {
+        if (boxIndex == null) return;
+        const map = lineRefsRef.current;
+        if (el) map.set(boxIndex, el);
+        else map.delete(boxIndex);
+    }, []);
+    // Needed for the SVG viewBox, so polygon coords land in the right place.
+    const [imageNaturalSize, setImageNaturalSize] = useState({ w: 0, h: 0 });
+    // Stops the seed effect from clobbering alignment that already exists.
+    const seededPages = useRef(new Set());
+
+    // Push state up to the parent cache. Skipping the first pass matters —
+    // otherwise we overwrite the cache with empty state on mount. The ref keeps
+    // a new onStateChange identity from causing extra syncs.
+    const onStateChangeRef = useRef(onStateChange);
+    useEffect(() => { onStateChangeRef.current = onStateChange; }, [onStateChange]);
+    const didMountStateSyncRef = useRef(false);
+    useEffect(() => {
+        if (!didMountStateSyncRef.current) {
+            didMountStateSyncRef.current = true;
+            return;
+        }
+        onStateChangeRef.current?.({ pages: detectedPages, alignment: alignmentByPage });
+    }, [detectedPages, alignmentByPage]);
+
+    // ── Derived data ──
+    const availablePages = useMemo(() => selectedPages || [], [selectedPages]);
+    const currentPageNum = availablePages[viewingPageIndex] || 1;
+    const totalPages = availablePages.length;
+
+    const currentLines = detectedPages[currentPageNum] || [];
+    const isCurrentDetected = currentPageNum in detectedPages;
+    const detectedCount = Object.keys(detectedPages).length;
+    const transcriptKeyForCurrentPage = resolveTranscriptKey(transcript, currentPageNum);
+
+    const currentAlignmentRows = alignmentByPage[currentPageNum] || [];
+    const currentRecognition = recognizedByPage[currentPageNum] || [];
+    const recognizedCount = Object.keys(recognizedByPage).length;
+
+    const currentRecognitionByIndex = useMemo(() => {
+        const map = {};
+        currentRecognition.forEach((item) => {
+            map[item.box_index] = item.text || '';
+        });
+        return map;
+    }, [currentRecognition]);
+
+    // { boxIndex: beforeText } for the current page.
+    const currentDiff = llmDiffByPage[currentPageNum] || {};
+    const changedLineCount = Object.keys(currentDiff).length;
+
+    // Sort top-to-bottom by the midpoint-Y of the top edge (average of the two
+    // highest vertices). More stable than min-Y once boxes are rotated.
+    const sortedBoxIndices = useMemo(() => {
+        return currentLines
+            .map((poly, idx) => {
+                const byY = [...poly].sort((a, b) => a[1] - b[1]);
+                const topMidY = (byY[0][1] + byY[1][1]) / 2;
+                return { idx, topMidY };
+            })
+            .sort((a, b) => a.topMidY - b.topMidY)
+            .map(item => item.idx);
+    }, [currentLines]);
+
+    const getImageUrl = useCallback((pageNum) => {
+        if (processedImages?.[pageNum]) return processedImages[pageNum];
+        // find-by-pageNumber, so split pages ("1_left") resolve correctly.
+        const page = pages?.find(p => p.pageNumber === pageNum);
+        return page?.thumbnail || null;
+    }, [pages, processedImages]);
+
+    const currentImageUrl = getImageUrl(currentPageNum);
+
+    const toDataUrl = useCallback(async (url) => {
+        if (!url) return null;
+        if (url.startsWith('data:')) return url;
+        const response = await fetch(url);
+        const blob = await response.blob();
+        return new Promise((resolve, reject) => {
+            const reader = new FileReader();
+            reader.onloadend = () => resolve(reader.result);
+            reader.onerror = () => reject(new Error('Failed converting image to base64'));
+            reader.readAsDataURL(blob);
+        });
+    }, []);
+
+    useEffect(() => {
+        if (datasetMode) return;
+        let cancelled = false;
+        async function loadLocalModels() {
+            setLocalModelsLoading(true);
+            setLocalModelsError(null);
+            try {
+                const data = await getLocalRecognitionModels();
+                const models = data.models || [];
+                if (cancelled) return;
+                setLocalModels(models);
+                if (models.length > 0) {
+                    const preferred = models.find((m) => m.model_type === 'crnn') || models[0];
+                    setSelectedOcrModel((prev) => prev || preferred.id);
+                }
+            } catch (err) {
+                if (!cancelled) {
+                    setLocalModelsError(err.message || 'Failed loading local models');
+                }
+            } finally {
+                if (!cancelled) {
+                    setLocalModelsLoading(false);
+                }
+            }
+        }
+        loadLocalModels();
+        return () => {
+            cancelled = true;
+        };
+    }, [datasetMode]);
+
+    // ── Arrow key navigation (BBox editor closed only) ──
+    useEffect(() => {
+        const handleKeyDown = (e) => {
+            if (showBBoxEditor) return;
+            const tag = document.activeElement?.tagName?.toLowerCase();
+            if (tag === 'input' || tag === 'textarea' || tag === 'select') return;
+            if (e.key === 'ArrowLeft') {
+                e.preventDefault();
+                setViewingPageIndex(prev => Math.max(0, prev - 1));
+            } else if (e.key === 'ArrowRight') {
+                e.preventDefault();
+                setViewingPageIndex(prev => Math.min(totalPages - 1, prev + 1));
+            }
+        };
+        window.addEventListener('keydown', handleKeyDown);
+        return () => window.removeEventListener('keydown', handleKeyDown);
+    }, [showBBoxEditor, totalPages]);
+
+    const runOcrForPage = useCallback(async (pageNum) => {
+        const imageUrl = getImageUrl(pageNum);
+        const boxes = detectedPages[pageNum] || [];
+        if (!imageUrl || boxes.length === 0 || !selectedOcrModel) return null;
+        const imageData = await toDataUrl(imageUrl);
+        return runLocalRecognition(imageData, boxes, selectedOcrModel);
+    }, [detectedPages, getImageUrl, selectedOcrModel, toDataUrl]);
+
+    const handleRecognizeCurrentPage = useCallback(async () => {
+        if (datasetMode || ocrProcessing || ocrProcessingAll) return;
+        if (!selectedOcrModel || !isCurrentDetected) return;
+
+        setOcrProcessing(true);
+        setOcrError(null);
+        try {
+            const result = await runOcrForPage(currentPageNum);
+            if (!result) {
+                setOcrError('No detected boxes available for this page.');
+                return;
+            }
+            if (!result.success) {
+                setOcrError(result.error || 'Recognition failed');
+                return;
+            }
+
+            setRecognizedByPage((prev) => ({ ...prev, [currentPageNum]: result.results || [] }));
+            setLlmDiffByPage((prev) => ({ ...prev, [currentPageNum]: {} }));  // fresh OCR → drop stale diff
+            setOcrDevice(result.device || null);
+            setOcrTimeMs(result.processing_time_ms ?? null);
+        } catch (err) {
+            setOcrError(err.message || 'Recognition failed');
+        } finally {
+            setOcrProcessing(false);
+        }
+    }, [currentPageNum, datasetMode, isCurrentDetected, ocrProcessing, ocrProcessingAll, runOcrForPage, selectedOcrModel]);
+
+    const handleRecognizeAllPages = useCallback(async () => {
+        if (datasetMode || ocrProcessingAll || ocrProcessing) return;
+        if (!selectedOcrModel) return;
+
+        setOcrProcessingAll(true);
+        setOcrError(null);
+        cancelOcrRef.current = false;
+
+        try {
+            for (let i = 0; i < availablePages.length; i++) {
+                if (cancelOcrRef.current) break;
+                const pageNum = availablePages[i];
+                if (!(pageNum in detectedPages)) continue;
+
+                setOcrProgress({ current: i + 1, total: availablePages.length });
+                const result = await runOcrForPage(pageNum);
+                if (!result) continue;
+                if (!result.success) {
+                    setOcrError(`Page ${pageNum}: ${result.error || 'Recognition failed'}`);
+                    break;
+                }
+
+                setRecognizedByPage((prev) => ({ ...prev, [pageNum]: result.results || [] }));
+                setLlmDiffByPage((prev) => ({ ...prev, [pageNum]: {} }));  // fresh OCR → drop stale diff
+                setOcrDevice(result.device || null);
+                await new Promise((resolve) => setTimeout(resolve, 100));
+            }
+        } catch (err) {
+            setOcrError(err.message || 'Batch recognition failed');
+        } finally {
+            setOcrProcessingAll(false);
+            setOcrProgress(null);
+        }
+    }, [availablePages, datasetMode, detectedPages, ocrProcessing, ocrProcessingAll, runOcrForPage, selectedOcrModel]);
+
+    const handleSaveTranscript = useCallback(async () => {
+        if (recognizedCount === 0) return;
+
+        const transcriptByPage = {};
+        const transcriptImages = {};
+
+        for (let i = 0; i < availablePages.length; i += 1) {
+            const p = availablePages[i];
+            const byIndex = {};
+            (recognizedByPage[p] || []).forEach((r) => {
+                byIndex[r.box_index] = r.text || '';
+            });
+            const sorted = (detectedPages[p] || [])
+                .map((poly, idx) => {
+                    const byY = [...poly].sort((a, b) => a[1] - b[1]);
+                    return { idx, y: (byY[0][1] + byY[1][1]) / 2 };
+                })
+                .sort((a, b) => a.y - b.y)
+                .map((item) => byIndex[item.idx] || '');
+
+            const pageText = sorted.join('\n').trim();
+            if (!pageText) continue;
+
+            const pageKey = String(p);
+            transcriptByPage[pageKey] = pageText;
+
+            const imageUrl = getImageUrl(p);
+            if (imageUrl) {
+                try {
+                    const dataUrl = await toDataUrl(imageUrl);
+                    if (typeof dataUrl === 'string' && dataUrl.startsWith('data:')) {
+                        transcriptImages[pageKey] = dataUrl;
+                    }
+                } catch {
+                }
+            }
+        }
+
+        if (Object.keys(transcriptByPage).length === 0) {
+            setOcrError('No transcript pages to save.');
+            return;
+        }
+
+        const signature = JSON.stringify(transcriptByPage);
+        if (lastSavedSignatureRef.current === signature) {
+            window.alert('Already saved — this exact transcript is already in My Files.');
+            return;
+        }
+
+        const defaultName = bookName && bookName !== 'transcript' ? bookName : 'My Transcript';
+        const chosenName = window.prompt('Name this transcript:', defaultName);
+        if (chosenName === null) return;                       // user cancelled
+        const finalName = chosenName.trim() || defaultName;
+
+        try {
+            const overview = await fetchStorageOverview();
+            const existingNames = (overview.transcripts || []).map((t) => (t.name || '').toLowerCase());
+            if (existingNames.includes(finalName.toLowerCase())) {
+                const proceed = window.confirm(`A transcript named "${finalName}" already exists in My Files. Save anyway?`);
+                if (!proceed) return;
+            }
+        } catch {
+        }
+
+        try {
+            await saveTranscriptSession(
+                transcriptByPage,
+                'layout-aware ocr',
+                'recognition',
+                transcriptImages,
+                finalName,
+                {
+                    preprocessing,
+                    ocr_provider: 'local',
+                    ocr_model: selectedOcrModel,
+                    layout_model: selectedLayoutModel,
+                    detection_model: selectedDetModel,
+                    llm_postprocess: lastLlmRun || { used: false },
+                },
+            );
+            lastSavedSignatureRef.current = signature;
+            window.alert(`Saved "${finalName}" to My Files.`);
+        } catch (err) {
+            setOcrError(err.message || 'Failed to save transcript session');
+        }
+    }, [availablePages, bookName, detectedPages, getImageUrl, recognizedByPage, recognizedCount, selectedDetModel, selectedLayoutModel, selectedOcrModel, toDataUrl, preprocessing, lastLlmRun]);
+
+    const updateRecognizedText = useCallback((lineIndex, value) => {
+        const boxIndex = sortedBoxIndices[lineIndex];
+        if (boxIndex === undefined) return;
+
+        setRecognizedByPage((prev) => {
+            const existing = prev[currentPageNum] || [];
+            const next = [...existing];
+            const foundIndex = next.findIndex((r) => r.box_index === boxIndex);
+            if (foundIndex >= 0) {
+                next[foundIndex] = { ...next[foundIndex], text: value };
+            } else {
+                next.push({ box_index: boxIndex, text: value });
+            }
+            return { ...prev, [currentPageNum]: next };
+        });
+    }, [currentPageNum, sortedBoxIndices]);
+
+    // Autogrow each line textarea so the transcript reads as one page.
+    const autoGrowLine = useCallback((el) => {
+        if (!el) return;
+        el.style.height = 'auto';
+        el.style.height = `${el.scrollHeight}px`;
+    }, []);
+
+    // Fetch providers/templates only when the feature is first switched on.
+    useEffect(() => {
+        if (!llmEnabled || llmProviders.length > 0) return;
+        getLLMProviders()
+            .then((data) => {
+                const provs = data.providers || [];
+                setLlmProviders(provs);
+                const firstEnabled = provs.find((p) => p.enabled);
+                if (firstEnabled) {
+                    setLlmProvider(firstEnabled.id);
+                    if (firstEnabled.default_model) setLlmModel(firstEnabled.default_model);
+                }
+            })
+            .catch(() => setLlmError('Could not load LLM providers.'));
+        getLLMTemplates()
+            .then((data) => setLlmTemplates(data.templates || []))
+            .catch(() => { /* fallback <option>s are hardcoded in the UI */ });
+    }, [llmEnabled, llmProviders.length]);
+
+    const llmProviderMeta = useMemo(
+        () => llmProviders.find((p) => p.id === llmProvider) || null,
+        [llmProviders, llmProvider],
+    );
+
+    // Switching provider resets the model to that provider's default.
+    const handleLlmProviderChange = useCallback((nextId) => {
+        setLlmProvider(nextId);
+        setLlmError(null);
+        const meta = llmProviders.find((p) => p.id === nextId);
+        if (meta && meta.default_model) setLlmModel(meta.default_model);
+        else if (meta && meta.models && meta.models.length > 0) setLlmModel(meta.models[0].id);
+    }, [llmProviders]);
+
+    // Same top-to-bottom order as export/save, so polished text maps back onto
+    // the same boxes.
+    const buildSortedRecognition = useCallback((pageNum) => {
+        const byIndex = {};
+        (recognizedByPage[pageNum] || []).forEach((r) => {
+            byIndex[r.box_index] = r.text || '';
+        });
+        const orderedIdx = (detectedPages[pageNum] || [])
+            .map((poly, idx) => {
+                const byY = [...poly].sort((a, b) => a[1] - b[1]);
+                return { idx, y: (byY[0][1] + byY[1][1]) / 2 };
+            })
+            .sort((a, b) => a.y - b.y)
+            .map((item) => item.idx);
+        return { orderedIdx, lines: orderedIdx.map((i) => byIndex[i] || '') };
+    }, [recognizedByPage, detectedPages]);
+
+    // Whole page to the LLM, then split back onto the boxes in order. Extra
+    // returned lines get appended to the last box rather than dropped.
+    const polishPage = useCallback(async (pageNum, provider, apiKey) => {
+        const { orderedIdx, lines } = buildSortedRecognition(pageNum);
+        const text = lines.join('\n').trim();
+        if (!text) return false;
+
+        const res = await postProcessWithLLMProvider(
+            provider, apiKey, text, llmModel, llmTemplate,
+        );
+        if (!res.success) {
+            throw new Error(res.error || 'Post-processing failed');
+        }
+        const cleaned = (res.processed_text || '').split('\n');
+
+        // Remember what changed so the UI can highlight it.
+        const diff = {};
+        orderedIdx.forEach((boxIdx, i) => {
+            const before = lines[i] || '';
+            const after = cleaned[i] !== undefined ? cleaned[i] : '';
+            if (before.trim() !== after.trim()) {
+                diff[boxIdx] = before;
+            }
+        });
+
+        setRecognizedByPage((prev) => {
+            const rows = orderedIdx.map((boxIdx, i) => ({
+                box_index: boxIdx,
+                text: cleaned[i] !== undefined ? cleaned[i] : '',
+            }));
+            if (cleaned.length > orderedIdx.length && rows.length > 0) {
+                rows[rows.length - 1] = {
+                    ...rows[rows.length - 1],
+                    text: cleaned.slice(orderedIdx.length - 1).join('\n'),
+                };
+            }
+            return { ...prev, [pageNum]: rows };
+        });
+        setLlmDiffByPage((prev) => ({ ...prev, [pageNum]: diff }));
+        setShowLlmDiff(true);
+        setLastLlmRun({ used: true, provider, model: llmModel, template: llmTemplate });
+        return true;
+    }, [buildSortedRecognition, llmModel, llmTemplate]);
+
+    // The local corrector runs on our own GPU, so it's the only provider whose
+    // weights we host — and its gated base model is a one-time ~8 GB pull.
+    const isLocalLlm = llmProviderMeta ? llmProviderMeta.requires_key === false : false;
+
+    // Probe the HF cache so we can warn once instead of looking hung. Same
+    // contract as the detection-model probe above.
+    useEffect(() => {
+        if (!isLocalLlm) { setLlmModelReady(null); return undefined; }
+        let active = true;
+        fetch(`${API_BASE}/api/llm/local-status`)
+            .then((r) => r.json())
+            .then((d) => { if (active) setLlmModelReady(!!d.models_ready); })
+            .catch(() => { if (active) setLlmModelReady(null); });
+        return () => { active = false; };
+    }, [isLocalLlm]);
+
+    // Returns the key, '' for keyless local providers, or null if invalid.
+    // Check `=== null` — '' is a valid result.
+    const validateLlmReady = useCallback(() => {
+        if (!llmProviderMeta || !llmProviderMeta.enabled) {
+            setLlmError('Selected provider is not available yet.');
+            return null;
+        }
+        if (llmProviderMeta.requires_key === false) {
+            return '';
+        }
+        const key = (llmApiKeys[llmProvider] || '').trim();
+        if (!key) {
+            setLlmError(`Enter an API key for ${llmProviderMeta.name}.`);
+            return null;
+        }
+        return key;
+    }, [llmProviderMeta, llmApiKeys, llmProvider]);
+
+    const handlePolishPage = useCallback(async () => {
+        if (llmProcessing) return;
+        const key = validateLlmReady();
+        if (key === null) return;
+        if (!(currentPageNum in recognizedByPage)) {
+            setLlmError('Run OCR on this page first.');
+            return;
+        }
+        setLlmProcessing(true);
+        setLlmError(null);
+        if (isLocalLlm && llmModelReady === false) setDownloadingLlmModel(true);
+        try {
+            const did = await polishPage(currentPageNum, llmProvider, key);
+            if (!did) setLlmError('No recognized text on this page to polish.');
+            // It answered, so the weights are on disk now — don't warn again.
+            else if (isLocalLlm) setLlmModelReady(true);
+        } catch (err) {
+            setLlmError(err.message || 'Post-processing failed');
+        } finally {
+            setLlmProcessing(false);
+            setDownloadingLlmModel(false);
+        }
+    }, [llmProcessing, validateLlmReady, currentPageNum, recognizedByPage, polishPage,
+        llmProvider, isLocalLlm, llmModelReady]);
+
+    const handlePolishAllPages = useCallback(async () => {
+        if (llmProcessing) return;
+        const key = validateLlmReady();
+        if (key === null) return;
+        const pages = availablePages.filter((p) => p in recognizedByPage);
+        if (pages.length === 0) {
+            setLlmError('Run OCR on at least one page first.');
+            return;
+        }
+        setLlmProcessing(true);
+        setLlmError(null);
+        if (isLocalLlm && llmModelReady === false) setDownloadingLlmModel(true);
+        try {
+            for (let i = 0; i < pages.length; i += 1) {
+                setLlmProgress({ current: i + 1, total: pages.length });
+                // Sequential on purpose — provider rate limits.
+                // eslint-disable-next-line no-await-in-loop
+                await polishPage(pages[i], llmProvider, key);
+                // First page answering means the download is done; drop the
+                // banner so it doesn't sit there for the whole batch.
+                if (isLocalLlm) { setLlmModelReady(true); setDownloadingLlmModel(false); }
+            }
+        } catch (err) {
+            setLlmError(err.message || 'Post-processing failed');
+        } finally {
+            setLlmProcessing(false);
+            setLlmProgress(null);
+            setDownloadingLlmModel(false);
+        }
+    }, [llmProcessing, validateLlmReady, availablePages, recognizedByPage, polishPage,
+        llmProvider, isLocalLlm, llmModelReady]);
+
+    useEffect(() => {
+        if (!isCurrentDetected) return;
+        // Seed once per page; handleBBoxSave owns alignment after that.
+        if (seededPages.current.has(String(currentPageNum))) return;
+        // Rows restored from the parent cache may contain user edits — never
+        // stomp them with the raw transcript.
+        if ((alignmentByPage[currentPageNum] || []).length > 0) {
+            seededPages.current.add(String(currentPageNum));
+            return;
+        }
+
+        const transcriptKey = resolveTranscriptKey(transcript, currentPageNum);
+        const transcriptLines = transcriptKey ? (transcript[transcriptKey] || []) : [];
+
+        // One row per transcript line. boxIndex is null when lines outnumber boxes.
+        const initialRows = transcriptLines.map((line, index) => ({
+            id: `${currentPageNum}-${index}-${nextRowId()}`,
+            text: line,
+            boxIndex: index < sortedBoxIndices.length ? sortedBoxIndices[index] : null,
+        }));
+
+        seededPages.current.add(String(currentPageNum));
+        setAlignmentByPage((prev) => ({
+            ...prev,
+            [currentPageNum]: initialRows,
+        }));
+    }, [isCurrentDetected, currentPageNum, sortedBoxIndices, transcript, alignmentByPage]);
+
+    // Clear hover on page change.
+    useEffect(() => {
+        setHoveredBoxIndex(null);
+    }, [viewingPageIndex]);
+
+    const updateAlignmentRows = useCallback((pageNum, updater) => {
+        setAlignmentByPage((prev) => {
+            const oldRows = prev[pageNum] || [];
+            const nextRows = updater(oldRows);
+            return { ...prev, [pageNum]: nextRows };
+        });
+    }, []);
+
+    const updateLineText = useCallback((lineIndex, value) => {
+        updateAlignmentRows(currentPageNum, (rows) => rows.map((row, idx) => (
+            idx === lineIndex ? { ...row, text: value } : row
+        )));
+    }, [currentPageNum, updateAlignmentRows]);
+
+    // Re-pair text with the current box order — needed after box edits.
+    const realignCurrentPage = useCallback(() => {
+        setAlignmentByPage(prev => {
+            const existingRows = prev[currentPageNum] || [];
+            const texts = existingRows.map(r => r.text);
+            const newRows = texts.map((text, i) => ({
+                id: `${currentPageNum}-${i}-${nextRowId()}`,
+                text,
+                boxIndex: i < sortedBoxIndices.length ? sortedBoxIndices[i] : null,
+            }));
+            return { ...prev, [currentPageNum]: newRows };
+        });
+    }, [currentPageNum, sortedBoxIndices]);
+
+    const assignBoxToLine = useCallback((lineIndex, boxIndexValue) => {
+        const parsed = boxIndexValue === '' ? null : Number(boxIndexValue);
+        updateAlignmentRows(currentPageNum, (rows) => rows.map((row, idx) => (
+            idx === lineIndex ? { ...row, boxIndex: Number.isNaN(parsed) ? null : parsed } : row
+        )));
+    }, [currentPageNum, updateAlignmentRows]);
+
+    const moveLine = useCallback((lineIndex, direction) => {
+        updateAlignmentRows(currentPageNum, (rows) => {
+            const targetIndex = lineIndex + direction;
+            if (targetIndex < 0 || targetIndex >= rows.length) return rows;
+            const next = [...rows];
+            const temp = next[lineIndex];
+            next[lineIndex] = next[targetIndex];
+            next[targetIndex] = temp;
+            return next;
+        });
+    }, [currentPageNum, updateAlignmentRows]);
+
+    const mergeLineWithNext = useCallback((lineIndex) => {
+        updateAlignmentRows(currentPageNum, (rows) => {
+            if (lineIndex < 0 || lineIndex >= rows.length - 1) return rows;
+            const current = rows[lineIndex];
+            const next = rows[lineIndex + 1];
+            const merged = {
+                ...current,
+                text: `${current.text} ${next.text}`.trim(),
+                boxIndex: current.boxIndex ?? next.boxIndex ?? null,
+            };
+            return [...rows.slice(0, lineIndex), merged, ...rows.slice(lineIndex + 2)];
+        });
+    }, [currentPageNum, updateAlignmentRows]);
+
+    const splitLine = useCallback((lineIndex) => {
+        updateAlignmentRows(currentPageNum, (rows) => {
+            if (lineIndex < 0 || lineIndex >= rows.length) return rows;
+            const row = rows[lineIndex];
+            const words = row.text.split(/\s+/).filter(Boolean);
+            if (words.length < 2) return rows;
+
+            const splitAt = Math.ceil(words.length / 2);
+            const first = words.slice(0, splitAt).join(' ');
+            const second = words.slice(splitAt).join(' ');
+
+            const firstRow = { ...row, text: first };
+            const secondRow = {
+                id: `${row.id}-split-${nextRowId()}`,
+                text: second,
+                boxIndex: null,
+            };
+
+            return [...rows.slice(0, lineIndex), firstRow, secondRow, ...rows.slice(lineIndex + 1)];
+        });
+    }, [currentPageNum, updateAlignmentRows]);
+
+    // Insert a blank unassigned line. atIndex is clamped, so "at top",
+    // "below row N" and "append" all go through here.
+    const insertLineAt = useCallback((atIndex) => {
+        updateAlignmentRows(currentPageNum, (rows) => {
+            const at = Math.max(0, Math.min(atIndex, rows.length));
+            const newRow = {
+                id: `${currentPageNum}-ins-${nextRowId()}`,
+                text: '',
+                boxIndex: null,
+            };
+            return [...rows.slice(0, at), newRow, ...rows.slice(at)];
+        });
+    }, [currentPageNum, updateAlignmentRows]);
+
+    // ── Update one tuning param ──
+    const updateParam = useCallback((key, value) => {
+        setTuningParams(prev => ({ ...prev, [key]: value }));
+    }, []);
+
+    // The overlay's viewBox must be in source-image pixels, same space as the
+    // polygons the detector returns.
+    useEffect(() => {
+        if (!currentImageUrl) {
+            setImageNaturalSize({ w: 0, h: 0 });
+            return;
+        }
+        let cancelled = false;
+        const probe = new Image();
+        probe.onload = () => {
+            if (cancelled) return;
+            setImageNaturalSize({ w: probe.naturalWidth, h: probe.naturalHeight });
+        };
+        probe.onerror = () => { if (!cancelled) setImageNaturalSize({ w: 0, h: 0 }); };
+        probe.src = currentImageUrl;
+        return () => { cancelled = true; };
+    }, [currentImageUrl]);
+
+    // Reset zoom/pan on page change.
+    useEffect(() => {
+        setImageLoaded(false);
+        setHoveredBoxIndex(null);
+    }, [viewingPageIndex]);
+
+    // SVG overlay, not canvas — canvas meant a rebuild + base64 encode on
+    // every mouse move.
+    const displayUrl = currentImageUrl;
+
+    // Hovering a box on the image scrolls its row into view. The microtask
+    // keeps rapid hover changes from fighting.
+    useEffect(() => {
+        if (hoveredBoxIndex == null) return;
+        const el = lineRefsRef.current.get(hoveredBoxIndex);
+        if (!el) return;
+        // 'nearest' avoids yanking a row that's already visible.
+        el.scrollIntoView({ behavior: 'smooth', block: 'nearest' });
+    }, [hoveredBoxIndex]);
+
+    // ── Navigation ──
+    const goToPage = useCallback((index) => {
+        if (index >= 0 && index < totalPages) {
+            setViewingPageIndex(index);
+            setError(null);
+            setWarning(null);
+            setProcessingTime(null);
+        }
+    }, [totalPages]);
+
+    // ── Image URL -> blob ──
+    const urlToBlob = useCallback(async (url) => {
+        if (url.startsWith('data:')) {
+            const [header, b64] = url.split(',');
+            const mime = header.match(/:(.*?);/)?.[1] || 'image/png';
+            const bin = atob(b64);
+            const u8 = new Uint8Array(bin.length);
+            for (let i = 0; i < bin.length; i++) u8[i] = bin.charCodeAt(i);
+            return new Blob([u8], { type: mime });
+        }
+        const r = await fetch(url);
+        return r.blob();
+    }, []);
+
+    // ── Build the detection FormData ──
+    const buildFormData = useCallback((blob) => {
+        const fd = new FormData();
+        fd.append('image', blob, 'page.png');
+        fd.append('use_gpu', 'true');
+        fd.append('layout_model', selectedLayoutModel);
+        fd.append('det_model', selectedDetModel);
+        fd.append('region_padding', String(tuningParams.region_padding));
+        fd.append('layout_expand', String(tuningParams.layout_expand));
+        fd.append('score_thresh', String(tuningParams.score_thresh));
+        fd.append('upscale_min_h', String(tuningParams.upscale_min_h));
+        fd.append('nms_iou_thresh', String(tuningParams.nms_iou_thresh));
+        fd.append('gap_multiplier', String(tuningParams.gap_multiplier));
+        return fd;
+    }, [selectedLayoutModel, selectedDetModel, tuningParams]);
+
+    // PaddleOCR lazily downloads several GB on first detection — probe the
+    // cache so we can warn once instead of looking hung.
+    useEffect(() => {
+        let active = true;
+        fetch(`${API_BASE}/api/detect/models-status?use_gpu=true`)
+            .then((r) => r.json())
+            .then((d) => { if (active) setModelsReady(!!d.models_ready); })
+            .catch(() => { if (active) setModelsReady(null); });
+        return () => { active = false; };
+    }, []);
+
+    // ── Single page detection ──
+    const handleDetect = async () => {
+        if (!currentImageUrl) return;
+        setLoading(true);
+        setError(null);
+        setWarning(null);
+        setProcessingTime(null);
+        // Warn about the download if the models aren't cached.
+        if (modelsReady === false) setDownloadingModels(true);
+
+        try {
+            const blob = await urlToBlob(currentImageUrl);
+
+            const formData = buildFormData(blob);
+
+            const response = await fetch(`${API_BASE}/api/detect/layout-aware-lines`, {
+                method: 'POST',
+                body: formData,
+            });
+            const data = await response.json();
+
+            if (data.error) {
+                setError(data.error);
+            } else {
+                setDetectedPages(prev => ({ ...prev, [currentPageNum]: data.lines || [] }));
+                setProcessingTime(data.processing_time_ms);
+                if (data.warning) setWarning(data.warning);
+                setModelsReady(true);  // a successful detection means models are cached now
+            }
+        } catch (err) {
+            console.error('[handleDetect] error', err);
+            setError(`Request failed: ${err.message}`);
+        } finally {
+            setLoading(false);
+            setDownloadingModels(false);
+        }
+    };
+
+    // ── Process all pages, one at a time (memory) ──
+    const handleDetectAll = async () => {
+        if (processingAll || availablePages.length === 0) return;
+        setProcessingAll(true);
+        setError(null);
+        setWarning(null);
+        if (modelsReady === false) setDownloadingModels(true);
+
+        const total = availablePages.length;
+        let lastWarning = null;
+        // Snapshot to dodge a stale closure. Keys stay strings — Number()
+        // turns "3_left" into NaN, which would silently re-detect and wipe edits.
+        const alreadyDetected = new Set(Object.keys(detectedPages));
+
+        try {
+            cancelProcessingRef.current = false;
+            for (let i = 0; i < total; i++) {
+                if (cancelProcessingRef.current) break;
+                const pageNum = availablePages[i];
+                setProcessAllProgress({ current: i + 1, total });
+                // Don't force viewingPageIndex — let the user browse meanwhile.
+
+                if (alreadyDetected.has(String(pageNum))) continue;
+
+                const imageUrl = getImageUrl(pageNum);
+                if (!imageUrl) continue;
+
+                const blob = await urlToBlob(imageUrl);
+                const formData = buildFormData(blob);
+
+                const response = await fetch(`${API_BASE}/api/detect/layout-aware-lines`, {
+                    method: 'POST',
+                    body: formData,
+                });
+                const data = await response.json();
+
+                if (data.error) {
+                    setError(`Page ${pageNum}: ${data.error}`);
+                    break;
+                }
+
+                setDetectedPages(prev => ({ ...prev, [pageNum]: data.lines || [] }));
+                alreadyDetected.add(String(pageNum));
+                if (data.warning) lastWarning = data.warning;
+                // First page worked, so the models are cached now.
+                setModelsReady(true);
+                setDownloadingModels(false);
+
+                // Breathing room for GC and a UI repaint.
+                await new Promise(r => setTimeout(r, 100));
+            }
+
+            if (lastWarning) setWarning(lastWarning);
+        } catch (err) {
+            console.error('[handleDetectAll] error', err);
+            setError(`Batch processing failed: ${err.message}`);
+        } finally {
+            setProcessingAll(false);
+            setProcessAllProgress(null);
+            setDownloadingModels(false);
+        }
+    };
+
+    const isAnyLoading = loading || processingAll;
+
+    // ── Left sidebar ──
+    const leftSidebar = (
+        <div className="h-full overflow-y-auto space-y-2 pr-0.5 scrollbar-thin scrollbar-thumb-gray-300 scrollbar-track-transparent">
+            {/* Parameters Card */}
+            <div className="bg-white/95 backdrop-blur-sm rounded-xl border border-gray-200/80 shadow-sm shrink-0">
+                <div className="px-3 py-2 bg-gradient-to-r from-teal-50/80 to-white border-b border-gray-100">
+                    <h3 className="font-semibold text-gray-800 flex items-center gap-2 text-sm">
+                        <Layers size={16} className="text-teal-600" />
+                        Parameters
+                    </h3>
+                </div>
+                <div className="p-3 space-y-3">
+                    {/* GPU Required Info */}
+                    <div className="flex items-start gap-2 px-2.5 py-2 bg-blue-50 rounded-lg border border-blue-200 text-xs text-blue-700">
+                        <Info size={14} className="shrink-0 mt-0.5 text-blue-500" />
+                        <div>
+                            <span className="font-semibold">GPU Required</span>
+                            <p className="text-blue-600 mt-0.5">Models require GPU for stable inference.</p>
+                        </div>
+                    </div>
+
+                    {/* Layout Model Dropdown */}
+                    <div>
+                        <label className="text-xs font-semibold text-gray-500 uppercase tracking-wider mb-1.5 block">Layout Model</label>
+                        <div className="relative">
+                            <select
+                                value={selectedLayoutModel}
+                                onChange={(e) => setSelectedLayoutModel(e.target.value)}
+                                disabled={loading}
+                                className="w-full appearance-none px-2.5 py-1.5 bg-gray-50 rounded-lg text-xs text-gray-700 font-medium border border-gray-200 focus:border-teal-400 focus:ring-1 focus:ring-teal-200 outline-none pr-7 cursor-pointer disabled:opacity-50 disabled:cursor-not-allowed"
+                            >
+                                {LAYOUT_MODELS.map(m => (
+                                    <option key={m.id} value={m.id}>{m.name}</option>
+                                ))}
+                            </select>
+                            <ChevronDown size={12} className="absolute right-2 top-1/2 -translate-y-1/2 text-gray-400 pointer-events-none" />
+                        </div>
+                    </div>
+
+                    {/* Detection Model Dropdown */}
+                    <div>
+                        <label className="text-xs font-semibold text-gray-500 uppercase tracking-wider mb-1.5 block">Detection Model</label>
+                        <div className="relative">
+                            <select
+                                value={selectedDetModel}
+                                onChange={(e) => setSelectedDetModel(e.target.value)}
+                                disabled={loading}
+                                className="w-full appearance-none px-2.5 py-1.5 bg-gray-50 rounded-lg text-xs text-gray-700 font-medium border border-gray-200 focus:border-teal-400 focus:ring-1 focus:ring-teal-200 outline-none pr-7 cursor-pointer disabled:opacity-50 disabled:cursor-not-allowed"
+                            >
+                                {DETECTION_MODELS.map(m => (
+                                    <option key={m.id} value={m.id}>{m.name} — {m.desc}</option>
+                                ))}
+                            </select>
+                            <ChevronDown size={12} className="absolute right-2 top-1/2 -translate-y-1/2 text-gray-400 pointer-events-none" />
+                        </div>
+                    </div>
+
+                    {/* Advanced Parameters Toggle */}
+                    <button
+                        onClick={() => setShowAdvanced(v => !v)}
+                        className="w-full flex items-center justify-between px-2.5 py-1.5 bg-gray-50 hover:bg-gray-100 rounded-lg text-xs font-semibold text-gray-600 border border-gray-200 transition-colors"
+                    >
+                        <span className="flex items-center gap-1.5">
+                            <Info size={13} className="text-gray-500" />
+                            Advanced Parameters
+                        </span>
+                        <ChevronDown size={12} className={`text-gray-400 transition-transform ${showAdvanced ? 'rotate-180' : ''}`} />
+                    </button>
+
+                    {/* Advanced Parameters Grid */}
+                    {showAdvanced && (
+                        <div className="space-y-2 pt-1 pb-1 px-1 bg-gray-50/50 rounded-lg border border-gray-100">
+                            {PARAM_DEFS.map(p => (
+                                <div key={p.key} className="flex items-center gap-2 px-1">
+                                    <label
+                                        className="text-[11px] text-gray-500 font-medium w-[90px] shrink-0 truncate cursor-help"
+                                        title={p.tooltip}
+                                    >
+                                        {p.label}
+                                    </label>
+                                    <input
+                                        type="number"
+                                        value={tuningParams[p.key]}
+                                        onChange={(e) => {
+                                            const v = p.type === 'int' ? parseInt(e.target.value, 10) : parseFloat(e.target.value);
+                                            if (!isNaN(v)) updateParam(p.key, v);
+                                        }}
+                                        min={p.min}
+                                        max={p.max}
+                                        step={p.step}
+                                        disabled={loading}
+                                        className="flex-1 min-w-0 px-2 py-1 bg-white rounded border border-gray-200 text-xs text-gray-700 font-mono focus:border-teal-400 focus:ring-1 focus:ring-teal-200 outline-none disabled:opacity-50"
+                                    />
+                                    {p.unit && (
+                                        <span className="text-[10px] text-gray-400 w-5 shrink-0">{p.unit}</span>
+                                    )}
+                                </div>
+                            ))}
+                            <button
+                                onClick={() => setTuningParams({ ...DEFAULT_PARAMS })}
+                                disabled={loading}
+                                className="text-[11px] text-teal-600 hover:text-teal-700 font-medium px-2 py-0.5 disabled:opacity-50"
+                            >
+                                Reset to defaults
+                            </button>
+                        </div>
+                    )}
+
+                    {/* First-time model download notice */}
+                    {downloadingModels && (
+                        <div className="mb-2 p-3 rounded-lg border border-amber-200 bg-amber-50 animate-fade-in">
+                            <div className="flex items-start gap-2">
+                                <Loader2 size={16} className="animate-spin text-amber-600 mt-0.5 flex-shrink-0" />
+                                <div className="flex-1 min-w-0">
+                                    <p className="text-xs font-bold text-amber-800">
+                                        Setting up detection models (one-time)
+                                    </p>
+                                    <p className="text-[11px] text-amber-700 mt-0.5 leading-snug">
+                                        Downloading PaddleOCR models on first use — about <b>15–20 min</b> depending
+                                        on your connection. Please keep this tab open; this only happens once.
+                                    </p>
+                                    <div className="mt-2 h-1.5 w-full bg-amber-200 rounded-full overflow-hidden">
+                                        <div className="h-full w-1/3 bg-amber-500 rounded-full animate-indeterminate" />
+                                    </div>
+                                </div>
+                            </div>
+                        </div>
+                    )}
+
+                    {/* Detect This Page Button */}
+                    <button
+                        onClick={handleDetect}
+                        disabled={loading || !currentImageUrl}
+                        className={`w-full flex items-center justify-center gap-2 px-4 py-2.5 rounded-lg text-sm font-bold text-white transition-all duration-200 ${loading || !currentImageUrl
+                            ? 'bg-gray-300 cursor-not-allowed'
+                            : 'bg-gradient-to-r from-teal-500 to-emerald-600 hover:from-teal-600 hover:to-emerald-700 shadow-md hover:shadow-lg active:scale-[0.98]'
+                            }`}
+                    >
+                        {loading ? (
+                            <><Loader2 size={16} className="animate-spin" /> Detecting…</>
+                        ) : (
+                            <><Play size={16} /> Detect This Page</>
+                        )}
+                    </button>
+
+                    {/* Process All Pages Button + Cancel */}
+                    <div className="flex gap-1">
+                    <button
+                        onClick={handleDetectAll}
+                        disabled={processingAll || loading || availablePages.length === 0}
+                        className={`flex-1 flex items-center justify-center gap-2 px-4 py-2 rounded-lg text-xs font-bold transition-all duration-200 ${processingAll || loading || availablePages.length === 0
+                            ? 'bg-gray-100 text-gray-400 cursor-not-allowed border border-gray-200'
+                            : 'bg-gradient-to-r from-blue-500 to-indigo-600 text-white hover:from-blue-600 hover:to-indigo-700 shadow-sm hover:shadow-md active:scale-[0.98]'
+                            }`}
+                    >
+                        {processingAll ? (
+                            <><Loader2 size={14} className="animate-spin" /> {processAllProgress?.current}/{processAllProgress?.total}…</>
+                        ) : (
+                            <><Layers size={14} /> All ({totalPages})</>
+                        )}
+                    </button>
+                    {processingAll && (
+                        <button
+                            onClick={() => { cancelProcessingRef.current = true; }}
+                            className="px-2.5 py-2 rounded-lg text-xs font-bold bg-red-100 text-red-600 hover:bg-red-200 border border-red-200 transition-colors"
+                            title="Cancel batch processing"
+                        >
+                            ✕
+                        </button>
+                    )}
+                    </div>
+
+                    {/* Edit All Boxes Button — available even during batch */}
+                    <button
+                        onClick={() => setShowBBoxEditor(true)}
+                        disabled={loading || detectedCount === 0}
+                        className={`w-full flex items-center justify-center gap-2 px-4 py-2 rounded-lg text-xs font-bold transition-all duration-200 ${loading || detectedCount === 0
+                            ? 'bg-gray-100 text-gray-400 cursor-not-allowed border border-gray-200'
+                            : 'bg-gradient-to-r from-purple-500 to-pink-600 text-white hover:from-purple-600 hover:to-pink-700 shadow-sm hover:shadow-md active:scale-[0.98]'
+                            }`}
+                    >
+                        <PenLine size={14} /> Edit All Boxes ({detectedCount})
+                    </button>
+
+                    {/* Result stats */}
+                    {processingTime !== null && !loading && (
+                        <div className="flex items-center justify-between text-xs px-1">
+                            <span className="flex items-center gap-1 text-emerald-600 font-semibold">
+                                <CheckCircle2 size={13} /> {currentLines.length} lines
+                            </span>
+                            <span className="text-gray-400">{(processingTime / 1000).toFixed(1)}s</span>
+                        </div>
+                    )}
+
+                    {warning && (
+                        <div className="flex items-start gap-1.5 text-xs text-amber-600 bg-amber-50 rounded-lg px-2 py-1.5 border border-amber-200">
+                            <AlertTriangle size={13} className="shrink-0 mt-0.5" /> {warning}
+                        </div>
+                    )}
+                    {error && (
+                        <div className="flex items-start gap-1.5 text-xs text-red-600 bg-red-50 rounded-lg px-2 py-1.5 border border-red-200">
+                            <AlertTriangle size={13} className="shrink-0 mt-0.5" /> {error}
+                        </div>
+                    )}
+                </div>
+            </div>
+
+            {/* Page Thumbnails */}
+            <div className="bg-white/95 backdrop-blur-sm rounded-xl border border-gray-200/80 shadow-sm overflow-hidden">
+                <div className="px-3 py-2 bg-gradient-to-r from-teal-50/80 to-white border-b border-gray-100 flex items-center justify-between shrink-0">
+                    <h3 className="font-semibold text-gray-800 flex items-center gap-2 text-sm">
+                        <ImageIcon size={16} className="text-teal-600" />
+                        Pages
+                    </h3>
+                    <span className="text-[10px] text-gray-500 bg-gray-100 px-1.5 py-0.5 rounded-full font-medium">
+                        {detectedCount}/{totalPages}
+                    </span>
+                </div>
+                <div className="p-2">
+                    <div className="grid grid-cols-2 gap-1.5">
+                        {availablePages.map((pageNum, idx) => (
+                            <div key={pageNum} className="relative">
+                                <ThumbnailItem
+                                    image={pages?.find(p => p.pageNumber === pageNum) || null}
+                                    processedSrc={processedImages?.[pageNum] || null}
+                                    index={idx}
+                                    isActive={idx === viewingPageIndex}
+                                    isDetected={pageNum in detectedPages}
+                                    onClick={() => goToPage(idx)}
+                                    pageLabel={shortPageLabel(pageNum)}
+                                />
+                                {/* Edited indicator badge */}
+                                {editedPages.has(String(pageNum)) && (
+                                    <span className="absolute top-1 left-1 px-1 py-0.5 bg-amber-500 text-white text-[9px] font-bold rounded shadow-sm leading-none">
+                                        edited
+                                    </span>
+                                )}
+                            </div>
+                        ))}
+                    </div>
+                </div>
+            </div>
+        </div>
+    );
+
+    // ── BBox editor ──
+    const bboxEditorPages = useMemo(() =>
+        availablePages
+            .filter(pageNum => pageNum in detectedPages)
+            .map(pageNum => ({
+                pageNumber: pageNum,
+                imageSrc: getImageUrl(pageNum),
+                polygons: detectedPages[pageNum] || [],
+            })),
+        [availablePages, detectedPages, getImageUrl]
+    );
+
+
+    const handleBBoxSave = useCallback((results) => {
+        // results: { [pageNumber]: polygon[] }
+        setDetectedPages(prev => ({ ...prev, ...results }));
+
+        // Re-sort the new polygons top-to-bottom, then re-pair them with the
+        // existing text lines by position. Keeps manual text edits intact while
+        // fixing the ordering.
+        setAlignmentByPage(prevAlignment => {
+            const next = { ...prevAlignment };
+
+            Object.entries(results).forEach(([pageNum, newPolygons]) => {
+                // Keep the string key: Number("3_left") is NaN.
+
+
+                const sortedIdxs = newPolygons
+                    .map((poly, idx) => {
+                        const byY = [...poly].sort((a, b) => a[1] - b[1]);
+                        const topMidY = (byY[0][1] + byY[1][1]) / 2;
+                        return { idx, topMidY };
+                    })
+                    .sort((a, b) => a.topMidY - b.topMidY)
+                    .map(item => item.idx);
+
+                // Prefer the user's edited rows over the raw transcript.
+                const existingRows = prevAlignment[pageNum];
+                let texts;
+                if (existingRows && existingRows.length > 0) {
+                    texts = existingRows.map(r => r.text);
+                } else {
+                    const tk = resolveTranscriptKey(transcript, pageNum);
+                    texts = tk ? (transcript[tk] || []) : [];
+                }
+
+                // One row per text line — the panel is text-driven, so surplus
+                // boxes get no row and surplus texts get boxIndex=null.
+                const newRows = texts.map((text, i) => ({
+                    id: `${pageNum}-${i}-${nextRowId()}`,
+                    text,
+                    boxIndex: i < sortedIdxs.length ? sortedIdxs[i] : null,
+                }));
+
+                next[pageNum] = newRows;
+                // Mark seeded so the seed effect leaves this alone.
+                seededPages.current.add(String(pageNum));
+            });
+
+            return next;
+        });
+
+        setEditedPages(prev => {
+            const next = new Set(prev);
+            Object.keys(results).forEach(k => next.add(String(k)));
+            return next;
+        });
+        setShowBBoxEditor(false);
+    }, [transcript]);
+
+    // ── Center panel: image preview ──
+    const centerPanel = (
+        <div className="bg-white/95 backdrop-blur-sm rounded-xl border border-gray-200/80 shadow-sm overflow-hidden flex flex-col h-full relative">
+            {/* Navigation header */}
+            <div className="px-3 py-2 bg-gradient-to-r from-teal-50/80 to-white border-b border-gray-100 flex items-center justify-between shrink-0">
+                <div className="flex items-center gap-2">
+                    <button
+                        onClick={() => goToPage(viewingPageIndex - 1)}
+                        disabled={viewingPageIndex === 0}
+                        className="p-1.5 rounded-lg hover:bg-gray-100 disabled:opacity-30 disabled:cursor-not-allowed transition-colors"
+                    >
+                        <ChevronLeft size={18} />
+                    </button>
+                    <span className="font-semibold text-gray-800 text-sm min-w-[80px] text-center">
+                        {shortPageLabel(currentPageNum)} / {totalPages}
+                    </span>
+                    <button
+                        onClick={() => goToPage(viewingPageIndex + 1)}
+                        disabled={viewingPageIndex >= totalPages - 1}
+                        className="p-1.5 rounded-lg hover:bg-gray-100 disabled:opacity-30 disabled:cursor-not-allowed transition-colors"
+                    >
+                        <ChevronRight size={18} />
+                    </button>
+                </div>
+
+                {/* Status — simplified, no per-page edit button here anymore */}
+                <div className="flex items-center gap-2">
+                    {isAnyLoading && (
+                        <span className="inline-flex items-center gap-1 px-2 py-0.5 bg-teal-50 text-teal-600 text-xs font-medium rounded-full animate-pulse">
+                            <Loader2 size={12} className="animate-spin" />
+                            {processingAll ? `batch: ${processAllProgress?.current}/${processAllProgress?.total}` : 'Detecting…'}
+                        </span>
+                    )}
+                    {isCurrentDetected && !loading && (
+                        <span className="inline-flex items-center gap-1 px-2 py-0.5 bg-emerald-50 text-emerald-600 text-xs font-medium rounded-full">
+                            <CheckCircle2 size={12} /> {currentLines.length} lines
+                            {editedPages.has(String(currentPageNum)) && (
+                                <span className="ml-1 text-[9px] bg-amber-100 text-amber-700 px-1 rounded font-semibold">edited</span>
+                            )}
+                        </span>
+                    )}
+                </div>
+            </div>
+
+            {/* Scrollable image container — actual size, scroll if too large */}
+            <div className="flex-1 overflow-auto bg-gradient-to-br from-gray-100 to-gray-50 min-h-0">
+                {!currentImageUrl && (
+                    <div className="h-full flex items-center justify-center text-gray-400">
+                        <div className="text-center">
+                            <ImageIcon size={32} className="mx-auto mb-2 opacity-50" />
+                            <p className="text-sm">No image selected</p>
+                        </div>
+                    </div>
+                )}
+
+                {currentImageUrl && (
+                    <div className="p-4 flex items-center justify-center min-h-full">
+                        <div ref={imageWrapRef} className="relative inline-block">
+                            <img
+                                key={`${currentPageNum}-${displayUrl?.slice(-20)}`}
+                                ref={imgRef}
+                                src={displayUrl}
+                                alt={`Page ${currentPageNum}`}
+                                className={`rounded-lg shadow-xl select-none block ${imageLoaded ? 'opacity-100' : 'opacity-0'}`}
+                                onLoad={(e) => {
+                                    setImageLoaded(true);
+                                    const el = e.currentTarget;
+                                    setImageNaturalSize({ w: el.naturalWidth, h: el.naturalHeight });
+                                }}
+                                draggable={false}
+                                style={{ transition: 'opacity 0.2s ease-out' }}
+                            />
+                            {/* SVG overlay — matches the displayed image via viewBox
+                                and absolute positioning. Hover is handled by CSS +
+                                a lightweight state update; no canvas re-render. */}
+                            {imageLoaded && currentLines.length > 0 && imageNaturalSize.w > 0 && (
+                                <svg
+                                    className="absolute inset-0 w-full h-full"
+                                    viewBox={`0 0 ${imageNaturalSize.w} ${imageNaturalSize.h}`}
+                                    preserveAspectRatio="none"
+                                    style={{ pointerEvents: 'none' }}
+                                >
+                                    {currentLines.map((poly, i) => {
+                                        if (!poly || poly.length < 3) return null;
+                                        const pts = poly.map((p) => `${p[0]},${p[1]}`).join(' ');
+                                        const isHovered = hoveredBoxIndex === i;
+                                        const strokeW = Math.max(2, Math.round(Math.max(imageNaturalSize.w, imageNaturalSize.h) / (isHovered ? 300 : 500)));
+                                        return (
+                                            <polygon
+                                                key={i}
+                                                points={pts}
+                                                stroke={isHovered ? '#f97316' : '#22c55e'}
+                                                strokeWidth={strokeW}
+                                                fill={isHovered ? 'rgba(249,115,22,0.25)' : 'rgba(34,197,94,0.08)'}
+                                                strokeLinejoin="round"
+                                                onMouseEnter={() => setHoveredBoxIndex(i)}
+                                                onMouseLeave={() => setHoveredBoxIndex(null)}
+                                                style={{ pointerEvents: 'auto', cursor: 'pointer', transition: 'fill 120ms, stroke 120ms' }}
+                                            />
+                                        );
+                                    })}
+                                </svg>
+                            )}
+                        </div>
+                    </div>
+                )}
+            </div>
+
+            {/* Loading overlay — only for single-page detection; batch shows a badge in the header */}
+            {loading && (
+                <div className="absolute inset-0 bg-teal-500/10 backdrop-blur-sm flex items-center justify-center z-20 pointer-events-none">
+                    <div className="bg-white rounded-xl shadow-lg px-5 py-3 text-center">
+                        <Loader2 size={28} className="animate-spin text-teal-600 mx-auto mb-2" />
+                        <p className="text-sm font-medium text-gray-700">Detecting lines…</p>
+                    </div>
+                </div>
+            )}
+            {/* Non-blocking batch progress badge */}
+            {processingAll && processAllProgress && (
+                <div className="absolute top-2 right-2 z-20 flex items-center gap-2 bg-blue-600/90 text-white text-xs font-semibold px-3 py-1.5 rounded-full shadow-lg backdrop-blur-sm pointer-events-none">
+                    <Loader2 size={12} className="animate-spin" />
+                    Processing {processAllProgress.current}/{processAllProgress.total}…
+                </div>
+            )}
+        </div>
+    );
+
+    const alignedTranscriptByPage = useMemo(() => {
+        const result = {};
+        Object.keys(alignmentByPage).forEach((pageNum) => {
+            const rows = alignmentByPage[pageNum] || [];
+            result[pageNum] = rows.map((row) => row.text).filter((txt) => txt && txt.trim().length > 0);
+        });
+        return result;
+    }, [alignmentByPage]);
+
+    // ── Right panel: alignment (step 4) ──
+    const rightPanel = (
+        <div className="bg-white/95 backdrop-blur-sm rounded-xl border border-gray-200/80 shadow-sm overflow-hidden flex flex-col h-full">
+            <div className="px-3 py-2 bg-gradient-to-r from-teal-50/80 via-white to-emerald-50/50 border-b border-gray-100/80 flex items-center justify-between shrink-0">
+                <h3 className="font-bold text-gray-800 flex items-center gap-2 text-sm">
+                    <div className="p-1 bg-gradient-to-br from-teal-500 to-emerald-600 rounded-lg text-white">
+                        <FileText size={12} />
+                    </div>
+                    <span>Step 4 — Alignment</span>
+                </h3>
+                <span className="text-xs text-gray-500 font-medium">Page {shortPageLabel(currentPageNum)}</span>
+            </div>
+
+            <div className="px-3 py-2 border-b border-gray-100 shrink-0 flex items-center justify-between text-xs text-gray-500">
+                <span>Detected: {isCurrentDetected ? `${currentLines.length} boxes` : 'Pending'}</span>
+                <span>Transcript: {transcriptKeyForCurrentPage ? `${(transcript[transcriptKeyForCurrentPage] || []).length} lines` : 'Not matched'}</span>
+            </div>
+
+            <div className="flex-1 overflow-y-auto min-h-0 p-2.5 space-y-1.5 bg-gradient-to-br from-gray-50/50 to-gray-100/20">
+                {!isCurrentDetected && (
+                    <div className="text-xs text-gray-500 bg-white rounded-lg border border-gray-200 px-3 py-2">
+                        Detect this page first to align lines.
+                    </div>
+                )}
+
+                {isCurrentDetected && !transcriptKeyForCurrentPage && (
+                    <div className="text-xs text-amber-700 bg-amber-50 rounded-lg border border-amber-200 px-3 py-2">
+                        No transcript page matched for {shortPageLabel(currentPageNum)}.
+                    </div>
+                )}
+
+                {isCurrentDetected && currentAlignmentRows.length > 0 && (
+                    <button
+                        onClick={() => insertLineAt(0)}
+                        className="w-full flex items-center justify-center gap-1.5 py-1.5 rounded-lg border border-dashed border-teal-300 bg-teal-50/50 text-teal-600 hover:bg-teal-100/60 text-[11px] font-semibold transition-colors"
+                        title="Add a new line at the top"
+                    >
+                        <Plus size={12} /> Add line at top
+                    </button>
+                )}
+
+                {isCurrentDetected && currentAlignmentRows.map((row, lineIndex) => {
+                    const assignedBoxIdx = row.boxIndex;
+                    const isHighlighted = assignedBoxIdx !== null && assignedBoxIdx !== undefined && hoveredBoxIndex === assignedBoxIdx;
+                    const isUnassigned = assignedBoxIdx === null || assignedBoxIdx === undefined;
+
+                    return (
+                        <div
+                            key={row.id}
+                            ref={(el) => setLineRef(assignedBoxIdx, el)}
+                            className={`rounded-lg border p-2.5 space-y-1.5 shadow-sm transition-colors cursor-pointer ${
+                                isHighlighted
+                                    ? 'bg-orange-50 border-orange-300 shadow-orange-100'
+                                    : 'bg-white border-gray-200 hover:border-teal-300 hover:bg-teal-50/30'
+                            }`}
+                            onMouseEnter={() => {
+                                if (assignedBoxIdx !== null && assignedBoxIdx !== undefined) {
+                                    setHoveredBoxIndex(assignedBoxIdx);
+                                }
+                            }}
+                            onMouseLeave={() => {
+                                setHoveredBoxIndex(null);
+                            }}
+                        >
+                            <div className="flex items-center justify-between gap-2">
+                                <div className="flex items-center gap-1.5">
+                                    <span className={`text-[11px] font-semibold ${isHighlighted ? 'text-orange-600' : 'text-gray-500'}`}>
+                                        Line {lineIndex + 1}
+                                    </span>
+                                    {isUnassigned && (
+                                        <span className="text-[9px] px-1 py-0.5 bg-amber-100 text-amber-600 rounded font-semibold">
+                                            unassigned
+                                        </span>
+                                    )}
+                                </div>
+                                <div className="flex items-center gap-1">
+                                    <button
+                                        onClick={() => moveLine(lineIndex, -1)}
+                                        disabled={lineIndex === 0}
+                                        className="p-1 rounded border border-gray-200 text-gray-500 hover:bg-gray-50 disabled:opacity-40"
+                                        title="Move line up"
+                                    >
+                                        <ArrowUp size={12} />
+                                    </button>
+                                    <button
+                                        onClick={() => moveLine(lineIndex, 1)}
+                                        disabled={lineIndex >= currentAlignmentRows.length - 1}
+                                        className="p-1 rounded border border-gray-200 text-gray-500 hover:bg-gray-50 disabled:opacity-40"
+                                        title="Move line down"
+                                    >
+                                        <ArrowDown size={12} />
+                                    </button>
+                                    <button
+                                        onClick={() => mergeLineWithNext(lineIndex)}
+                                        disabled={lineIndex >= currentAlignmentRows.length - 1}
+                                        className="p-1 rounded border border-gray-200 text-gray-500 hover:bg-gray-50 disabled:opacity-40"
+                                        title="Merge with next line"
+                                    >
+                                        <Link2 size={12} />
+                                    </button>
+                                    <button
+                                        onClick={() => splitLine(lineIndex)}
+                                        className="p-1 rounded border border-gray-200 text-gray-500 hover:bg-gray-50"
+                                        title="Split line into two"
+                                    >
+                                        <Split size={12} />
+                                    </button>
+                                    <button
+                                        onClick={() => insertLineAt(lineIndex + 1)}
+                                        className="p-1 rounded border border-teal-200 bg-teal-50 text-teal-600 hover:bg-teal-100"
+                                        title="Insert a new line below"
+                                    >
+                                        <Plus size={12} />
+                                    </button>
+                                </div>
+                            </div>
+
+                            <div className="flex items-center gap-2">
+                                <label className="text-[11px] text-gray-500 w-14 shrink-0">Line box</label>
+                                <select
+                                    value={row.boxIndex ?? ''}
+                                    onChange={(e) => assignBoxToLine(lineIndex, e.target.value)}
+                                    className="flex-1 px-2 py-1.5 bg-gray-50 rounded border border-gray-200 text-xs text-gray-700"
+                                >
+                                    <option value="">Unassigned</option>
+                                    {sortedBoxIndices.map((origIdx, sortedPos) => (
+                                        <option key={origIdx} value={origIdx}>Box {sortedPos + 1} (row {origIdx + 1})</option>
+                                    ))}
+                                </select>
+                            </div>
+
+                            <textarea
+                                value={row.text}
+                                onChange={(e) => updateLineText(lineIndex, e.target.value)}
+                                rows={2}
+                                className="w-full px-2.5 py-2 bg-white border border-gray-200 rounded text-xs text-gray-700 resize-y focus:outline-none focus:ring-1 focus:ring-teal-200 focus:border-teal-400"
+                            />
+                        </div>
+                    );
+                })}
+
+                {/* No rows yet — let the user start adding lines manually */}
+                {isCurrentDetected && currentAlignmentRows.length === 0 && (
+                    <div className="space-y-1.5">
+                        <div className="text-xs text-gray-500 bg-white rounded-lg border border-gray-200 px-3 py-2">
+                            No transcript lines for this page yet.
+                        </div>
+                        <button
+                            onClick={() => insertLineAt(0)}
+                            className="w-full flex items-center justify-center gap-1.5 py-2 rounded-lg border border-dashed border-teal-300 bg-teal-50/50 text-teal-700 hover:bg-teal-100/60 text-xs font-semibold transition-colors"
+                        >
+                            <Plus size={13} /> Add a line
+                        </button>
+                    </div>
+                )}
+            </div>
+
+            <div className="px-3 py-2 border-t border-gray-100 bg-gradient-to-r from-gray-50 to-white flex items-center justify-between text-xs text-gray-500 shrink-0 gap-2">
+                <span className="shrink-0">{currentAlignmentRows.length} lines · {currentAlignmentRows.filter(r => r.boxIndex !== null && r.boxIndex !== undefined).length} assigned</span>
+                {currentAlignmentRows.length > 0 &&
+                    currentAlignmentRows.every(r => r.boxIndex !== null && r.boxIndex !== undefined) && (
+                    <span className="flex items-center gap-1 px-2 py-0.5 bg-emerald-100 text-emerald-700 rounded-full font-semibold shrink-0">
+                        <CheckCircle2 size={11} /> All matched
+                    </span>
+                )}
+                {isCurrentDetected && currentAlignmentRows.length > 0 && (
+                    <div className="ml-auto flex items-center gap-1.5 shrink-0">
+                        <button
+                            onClick={() => insertLineAt(currentAlignmentRows.length)}
+                            title="Add a new line at the end"
+                            className="flex items-center gap-1.5 px-2.5 py-1 rounded-lg border border-teal-200 bg-teal-50 text-teal-700 hover:bg-teal-100 hover:border-teal-300 font-semibold transition-colors"
+                        >
+                            <Plus size={11} />
+                            Add line
+                        </button>
+                        <button
+                            onClick={realignCurrentPage}
+                            title="Re-pair transcript lines with boxes in their current top-to-bottom order"
+                            className="flex items-center gap-1.5 px-2.5 py-1 rounded-lg border border-teal-200 bg-teal-50 text-teal-700 hover:bg-teal-100 hover:border-teal-300 font-semibold transition-colors"
+                        >
+                            <RefreshCw size={11} />
+                            Realign
+                        </button>
+                    </div>
+                )}
+            </div>
+        </div>
+    );
+
+
+    // ── Right panel: local OCR ──
+    const ocrRightPanel = (
+        <div className="bg-white/95 backdrop-blur-sm rounded-xl border border-gray-200/80 shadow-sm overflow-hidden flex flex-col h-full">
+            <div className="px-3 py-2.5 bg-gradient-to-r from-blue-50/80 via-white to-indigo-50/50 border-b border-gray-100/80 shrink-0 flex items-center gap-2">
+                <div className="p-1 bg-gradient-to-br from-blue-500 to-indigo-600 rounded-lg text-white">
+                    <Sparkles size={12} />
+                </div>
+                <h3 className="font-bold text-gray-800 text-sm">Step 4 — Local OCR</h3>
+            </div>
+
+            <div className="px-3 py-2 border-b border-gray-100 shrink-0 flex items-center justify-between text-xs text-gray-500">
+                <span>Detected: {isCurrentDetected ? `${currentLines.length} boxes` : 'Pending'}</span>
+                <span>Recognized: {currentRecognition.length} lines</span>
+            </div>
+
+            <div className="flex-1 overflow-y-auto min-h-0 p-3 space-y-3 bg-gradient-to-br from-gray-50/50 to-gray-100/20">
+                <div className="space-y-2">
+                    <label className="text-[11px] font-bold text-gray-600 uppercase tracking-wider flex items-center gap-1.5">
+                        <Cpu size={12} className="text-gray-500" />
+                        OCR Model
+                    </label>
+
+                    {localModelsLoading ? (
+                        <div className="text-xs text-gray-500 flex items-center gap-1.5">
+                            <Loader2 size={12} className="animate-spin" /> Loading local models...
+                        </div>
+                    ) : localModelsError ? (
+                        <div className="text-xs text-red-600 bg-red-50 border border-red-200 rounded-lg px-2 py-1.5">
+                            {localModelsError}
+                        </div>
+                    ) : localModels.length === 0 ? (
+                        <div className="text-xs text-amber-700 bg-amber-50 border border-amber-200 rounded-lg px-2 py-1.5">
+                            No local weights found in backend/models/weights.
+                        </div>
+                    ) : (
+                        <div className="relative">
+                            <select
+                                value={selectedOcrModel}
+                                onChange={(e) => setSelectedOcrModel(e.target.value)}
+                                disabled={ocrProcessing || ocrProcessingAll}
+                                className="w-full appearance-none px-2.5 py-2 bg-white rounded-lg text-xs text-gray-700 font-medium border border-gray-200 focus:border-blue-400 focus:ring-1 focus:ring-blue-200 outline-none pr-7"
+                            >
+                                {localModels.map((model) => (
+                                    <option key={model.id} value={model.id}>
+                                        {model.name}
+                                    </option>
+                                ))}
+                            </select>
+                            <ChevronDown size={12} className="absolute right-2 top-1/2 -translate-y-1/2 text-gray-400 pointer-events-none" />
+                        </div>
+                    )}
+
+                    {ocrDevice && (
+                        <p className="text-[10px] text-gray-500">Last run device: {ocrDevice.toUpperCase()}</p>
+                    )}
+                </div>
+
+                <div className="grid grid-cols-2 gap-1.5">
+                    <button
+                        onClick={handleRecognizeCurrentPage}
+                        disabled={!isCurrentDetected || !selectedOcrModel || ocrProcessing || ocrProcessingAll}
+                        className="flex items-center justify-center gap-1.5 px-3 py-2 rounded-lg text-xs font-bold text-white transition-all duration-200 disabled:bg-gray-300 disabled:cursor-not-allowed bg-gradient-to-r from-blue-500 to-indigo-600 hover:from-blue-600 hover:to-indigo-700"
+                    >
+                        {ocrProcessing ? <Loader2 size={13} className="animate-spin" /> : <Play size={13} />}
+                        Run Page
+                    </button>
+
+                    <button
+                        onClick={handleRecognizeAllPages}
+                        disabled={!selectedOcrModel || ocrProcessing || ocrProcessingAll || detectedCount === 0}
+                        className="flex items-center justify-center gap-1.5 px-3 py-2 rounded-lg text-xs font-bold transition-all duration-200 disabled:bg-gray-100 disabled:text-gray-400 disabled:cursor-not-allowed bg-gradient-to-r from-cyan-500 to-teal-600 text-white hover:from-cyan-600 hover:to-teal-700"
+                    >
+                        {ocrProcessingAll ? <Loader2 size={13} className="animate-spin" /> : <Layers size={13} />}
+                        All Pages
+                    </button>
+                </div>
+
+                {ocrProcessingAll && ocrProgress && (
+                    <div className="text-[11px] text-blue-700 bg-blue-50 border border-blue-200 rounded-lg px-2 py-1.5 flex items-center justify-between">
+                        <span>Recognizing {ocrProgress.current}/{ocrProgress.total}</span>
+                        <button
+                            onClick={() => { cancelOcrRef.current = true; }}
+                            className="px-1.5 py-0.5 rounded bg-red-100 text-red-600 text-[10px] font-bold"
+                        >
+                            Stop
+                        </button>
+                    </div>
+                )}
+
+                {ocrTimeMs !== null && !ocrProcessing && (
+                    <div className="text-[11px] text-gray-500">Last recognition time: {(ocrTimeMs / 1000).toFixed(2)}s</div>
+                )}
+
+                {ocrError && (
+                    <div className="text-xs text-red-600 bg-red-50 border border-red-200 rounded-lg px-2 py-1.5">
+                        {ocrError}
+                    </div>
+                )}
+
+                {!isCurrentDetected && (
+                    <div className="text-xs text-gray-500 bg-white rounded-lg border border-gray-200 px-3 py-2">
+                        Detect this page first, then run OCR with your selected model.
+                    </div>
+                )}
+
+                {isCurrentDetected && sortedBoxIndices.length > 0 && (
+                    // Lines flow together like a document rather than sitting in
+                    // cards. Hover still drives the shared bbox highlight.
+                    <div
+                        className="bg-white rounded-lg border border-gray-200 shadow-inner px-3 py-3"
+                        onMouseLeave={() => setHoveredBoxIndex(null)}
+                    >
+                        {sortedBoxIndices.map((boxIndex, lineIndex) => {
+                            const isHighlighted = hoveredBoxIndex === boxIndex;
+                            const changed = showLlmDiff
+                                && Object.prototype.hasOwnProperty.call(currentDiff, boxIndex);
+                            const beforeText = changed ? currentDiff[boxIndex] : '';
+                            const afterText = currentRecognitionByIndex[boxIndex] || '';
+                            const { beforeTokens, afterTokens } = changed
+                                ? diffWords(beforeText, afterText)
+                                : { beforeTokens: [], afterTokens: [] };
+                            return (
+                                <div
+                                    key={`${currentPageNum}-${boxIndex}`}
+                                    ref={(el) => setLineRef(boxIndex, el)}
+                                    onMouseEnter={() => setHoveredBoxIndex(boxIndex)}
+                                    className={`group flex items-start gap-2 -mx-2 px-2 rounded-md transition-colors ${
+                                        isHighlighted
+                                            ? 'bg-orange-100/70'
+                                            : changed
+                                                ? 'bg-amber-50/50 hover:bg-amber-100/50'
+                                                : 'hover:bg-gray-50'
+                                    } ${changed ? 'border-l-2 border-amber-400' : ''}`}
+                                >
+                                    <span
+                                        className={`select-none w-6 shrink-0 text-right text-[10px] leading-6 tabular-nums transition-colors ${
+                                            isHighlighted
+                                                ? 'text-orange-500 font-bold'
+                                                : changed
+                                                    ? 'text-amber-500 font-bold'
+                                                    : 'text-gray-300 group-hover:text-gray-400'
+                                        }`}
+                                        title={changed ? `Line ${lineIndex + 1} — edited by post-processing` : `Line ${lineIndex + 1}`}
+                                    >
+                                        {lineIndex + 1}
+                                    </span>
+                                    <div className="flex-1 min-w-0">
+                                        <textarea
+                                            value={afterText}
+                                            onChange={(e) => {
+                                                updateRecognizedText(lineIndex, e.target.value);
+                                                autoGrowLine(e.target);
+                                            }}
+                                            ref={autoGrowLine}
+                                            rows={1}
+                                            spellCheck={false}
+                                            className={`w-full bg-transparent border-0 outline-none resize-none overflow-hidden py-0.5 text-sm leading-6 text-gray-800 rounded focus:bg-blue-50/50 ${
+                                                isHighlighted ? 'text-orange-900' : ''
+                                            }`}
+                                        />
+                                        {changed && (
+                                            <div className="mb-1 space-y-0.5 text-[11px] leading-snug border-t border-amber-100 pt-1">
+                                                <div className="flex gap-1.5">
+                                                    <span className="select-none text-rose-400 font-mono shrink-0" title="Before">−</span>
+                                                    <span className="text-gray-400">
+                                                        {beforeTokens.map((t, k) => (
+                                                            <span
+                                                                key={k}
+                                                                className={t.removed ? 'bg-rose-100 text-rose-600 line-through rounded px-0.5' : ''}
+                                                            >{t.text}{' '}</span>
+                                                        ))}
+                                                    </span>
+                                                </div>
+                                                <div className="flex gap-1.5">
+                                                    <span className="select-none text-emerald-500 font-mono shrink-0" title="After">+</span>
+                                                    <span className="text-gray-600">
+                                                        {afterTokens.map((t, k) => (
+                                                            <span
+                                                                key={k}
+                                                                className={t.added ? 'bg-emerald-100 text-emerald-700 rounded px-0.5' : ''}
+                                                            >{t.text}{' '}</span>
+                                                        ))}
+                                                    </span>
+                                                </div>
+                                            </div>
+                                        )}
+                                    </div>
+                                </div>
+                            );
+                        })}
+                    </div>
+                )}
+            </div>
+
+            {/* ── AI Post-processing ───────────────────────────── */}
+            <div className="shrink-0 border-t border-gray-100 p-3 bg-gradient-to-r from-purple-50/40 to-white space-y-2">
+                <div className="flex items-center justify-between">
+                    <span className="text-[10px] font-bold text-gray-600 uppercase tracking-wider flex items-center gap-1.5">
+                        <div className="p-1 bg-gradient-to-br from-purple-500 to-pink-500 rounded text-white">
+                            <Wand2 size={10} />
+                        </div>
+                        AI Post-processing
+                    </span>
+                    <button
+                        type="button"
+                        onClick={() => { setLlmEnabled((v) => !v); setLlmError(null); }}
+                        title="Toggle LLM post-processing"
+                        className={`relative w-9 h-5 rounded-full transition-colors shrink-0 ${llmEnabled ? 'bg-purple-500' : 'bg-gray-300'}`}
+                    >
+                        <span className={`absolute top-0.5 left-0.5 w-4 h-4 bg-white rounded-full shadow transition-transform ${llmEnabled ? 'translate-x-4' : ''}`} />
+                    </button>
+                </div>
+
+                {llmEnabled && (
+                    <div className="space-y-2">
+                        <p className="text-[10px] text-gray-500 leading-tight">
+                            Sends the recognized page transcript to an LLM for cleanup, then maps the result back onto the same lines.
+                        </p>
+
+                        {llmProviders.length === 0 ? (
+                            <div className="text-[11px] text-gray-500 flex items-center gap-1.5">
+                                <Loader2 size={12} className="animate-spin" /> Loading providers...
+                            </div>
+                        ) : (
+                            <>
+                                <div className="grid grid-cols-2 gap-1.5">
+                                    <select
+                                        value={llmProvider}
+                                        onChange={(e) => handleLlmProviderChange(e.target.value)}
+                                        disabled={llmProcessing}
+                                        className="px-2 py-1.5 bg-white rounded-lg text-xs text-gray-700 border border-gray-200 focus:border-purple-400 focus:ring-1 focus:ring-purple-200 outline-none"
+                                    >
+                                        {llmProviders.map((p) => (
+                                            <option key={p.id} value={p.id} disabled={!p.enabled}>
+                                                {p.name}{p.enabled ? '' : ' (soon)'}
+                                            </option>
+                                        ))}
+                                    </select>
+                                    <select
+                                        value={llmModel}
+                                        onChange={(e) => setLlmModel(e.target.value)}
+                                        disabled={llmProcessing || !llmProviderMeta || (llmProviderMeta.models || []).length === 0}
+                                        className="px-2 py-1.5 bg-white rounded-lg text-xs text-gray-700 border border-gray-200 focus:border-purple-400 focus:ring-1 focus:ring-purple-200 outline-none disabled:bg-gray-50 disabled:text-gray-400"
+                                    >
+                                        {(llmProviderMeta?.models || []).length > 0 ? (
+                                            llmProviderMeta.models.map((m) => (
+                                                <option key={m.id} value={m.id}>{m.name}</option>
+                                            ))
+                                        ) : (
+                                            <option value="">—</option>
+                                        )}
+                                    </select>
+                                </div>
+
+                                {llmProviderMeta && !llmProviderMeta.enabled ? (
+                                    <div className="text-[11px] text-amber-700 bg-amber-50 border border-amber-200 rounded-lg px-2 py-1.5">
+                                        {llmProviderMeta.note || 'This provider is not available yet.'}
+                                    </div>
+                                ) : (
+                                    <>
+                                        {/* The finetuned Spanish model does full cleanup only —
+                                            no template choice to make, so hide the dropdown. */}
+                                        {llmProvider !== 'local_es' && (
+                                            <>
+                                                <select
+                                                    value={llmTemplate}
+                                                    onChange={(e) => setLlmTemplate(e.target.value)}
+                                                    disabled={llmProcessing}
+                                                    className="w-full px-2 py-1.5 bg-white rounded-lg text-xs text-gray-700 border border-gray-200 focus:border-purple-400 focus:ring-1 focus:ring-purple-200 outline-none"
+                                                >
+                                                    {llmTemplates.length > 0 ? (
+                                                        llmTemplates.map((t) => (
+                                                            <option key={t.id} value={t.id}>{t.name}</option>
+                                                        ))
+                                                    ) : (
+                                                        <>
+                                                            <option value="full_cleanup">Full Cleanup</option>
+                                                            <option value="spelling_correction">Spelling Correction</option>
+                                                            <option value="formatting">Format & Structure</option>
+                                                            <option value="historical_normalization">Historical Normalization</option>
+                                                        </>
+                                                    )}
+                                                </select>
+                                                {llmTemplates.length > 0 && (
+                                                    <p className="text-[10px] text-gray-400 leading-tight">
+                                                        {llmTemplates.find((t) => t.id === llmTemplate)?.description || ''}
+                                                    </p>
+                                                )}
+                                            </>
+                                        )}
+
+                                        {llmProviderMeta?.requires_key === false ? (
+                                            <p className="text-[10px] text-gray-500 bg-gray-50 border border-gray-200 rounded-lg px-2 py-1.5 leading-tight">
+                                                Runs offline on the server — no API key needed.
+                                                {llmProviderMeta?.note ? ` ${llmProviderMeta.note}` : ''}
+                                            </p>
+                                        ) : (
+                                            <input
+                                                type="password"
+                                                value={llmApiKeys[llmProvider] || ''}
+                                                onChange={(e) => setLlmApiKeys((prev) => ({ ...prev, [llmProvider]: e.target.value }))}
+                                                placeholder={`${llmProviderMeta?.name || 'Provider'} API key`}
+                                                autoComplete="off"
+                                                className="w-full px-2 py-1.5 bg-white rounded-lg text-xs text-gray-700 border border-gray-200 focus:border-purple-400 focus:ring-1 focus:ring-purple-200 outline-none"
+                                            />
+                                        )}
+
+                                        <div className="grid grid-cols-2 gap-1.5">
+                                            <button
+                                                onClick={handlePolishPage}
+                                                disabled={llmProcessing || !(currentPageNum in recognizedByPage)}
+                                                className="flex items-center justify-center gap-1.5 px-3 py-2 rounded-lg text-xs font-bold text-white transition-all disabled:bg-gray-300 disabled:cursor-not-allowed bg-gradient-to-r from-purple-500 to-pink-500 hover:from-purple-600 hover:to-pink-600"
+                                            >
+                                                {llmProcessing && !llmProgress ? <Loader2 size={13} className="animate-spin" /> : <Sparkles size={13} />}
+                                                Polish Page
+                                            </button>
+                                            <button
+                                                onClick={handlePolishAllPages}
+                                                disabled={llmProcessing || recognizedCount === 0}
+                                                className="flex items-center justify-center gap-1.5 px-3 py-2 rounded-lg text-xs font-bold transition-all disabled:bg-gray-100 disabled:text-gray-400 disabled:cursor-not-allowed bg-gradient-to-r from-fuchsia-500 to-purple-600 text-white hover:from-fuchsia-600 hover:to-purple-700"
+                                            >
+                                                {llmProcessing && llmProgress ? <Loader2 size={13} className="animate-spin" /> : <Layers size={13} />}
+                                                Polish All
+                                            </button>
+                                        </div>
+                                    </>
+                                )}
+
+                                {changedLineCount > 0 && (
+                                    <div className="flex items-center justify-between gap-2 text-[11px] bg-amber-50 border border-amber-200 rounded-lg px-2 py-1.5">
+                                        <span className="flex items-center gap-1.5 text-amber-700 font-semibold">
+                                            <Wand2 size={11} />
+                                            {changedLineCount} line{changedLineCount === 1 ? '' : 's'} edited on this page
+                                        </span>
+                                        <button
+                                            type="button"
+                                            onClick={() => setShowLlmDiff((v) => !v)}
+                                            className="shrink-0 px-2 py-0.5 rounded border border-amber-300 text-amber-700 hover:bg-amber-100 font-semibold"
+                                        >
+                                            {showLlmDiff ? 'Hide changes' : 'Show changes'}
+                                        </button>
+                                    </div>
+                                )}
+
+                                {/* First-time base-model download notice */}
+                                {downloadingLlmModel && (
+                                    <div className="p-3 rounded-lg border border-amber-200 bg-amber-50 animate-fade-in">
+                                        <div className="flex items-start gap-2">
+                                            <Loader2 size={16} className="animate-spin text-amber-600 mt-0.5 flex-shrink-0" />
+                                            <div className="flex-1 min-w-0">
+                                                <p className="text-xs font-bold text-amber-800">
+                                                    Setting up the local corrector (one-time)
+                                                </p>
+                                                <p className="text-[11px] text-amber-700 mt-0.5 leading-snug">
+                                                    Downloading its base model on first use — about <b>8 GB</b>, so
+                                                    expect <b>20–30 min</b> depending on your connection. Please keep
+                                                    this tab open; this only happens once.
+                                                </p>
+                                                <div className="mt-2 h-1.5 w-full bg-amber-200 rounded-full overflow-hidden">
+                                                    <div className="h-full w-1/3 bg-amber-500 rounded-full animate-indeterminate" />
+                                                </div>
+                                            </div>
+                                        </div>
+                                    </div>
+                                )}
+
+                                {llmProgress && (
+                                    <div className="text-[11px] text-purple-700 bg-purple-50 border border-purple-200 rounded-lg px-2 py-1.5">
+                                        Polishing page {llmProgress.current}/{llmProgress.total}
+                                    </div>
+                                )}
+                                {llmError && (
+                                    <div className="text-xs text-red-600 bg-red-50 border border-red-200 rounded-lg px-2 py-1.5">
+                                        {llmError}
+                                    </div>
+                                )}
+                            </>
+                        )}
+                    </div>
+                )}
+            </div>
+
+            <div className="shrink-0 border-t border-gray-100 p-3 bg-gradient-to-r from-gray-50/80 to-white space-y-2">
+                <p className="text-[10px] font-bold text-gray-500 uppercase tracking-wider flex items-center gap-1.5">
+                    <Download size={11} /> Export Transcript
+                </p>
+                <button
+                    disabled={recognizedCount === 0}
+                    onClick={handleSaveTranscript}
+                    className="w-full flex items-center justify-center gap-2 px-3 py-2 rounded-lg border border-cyan-200 bg-cyan-50 text-cyan-700 hover:bg-cyan-100 text-[10px] font-semibold transition-all disabled:opacity-40 disabled:cursor-not-allowed"
+                >
+                    Save to My Files
+                </button>
+                <div className="grid grid-cols-2 gap-1.5">
+                    <button
+                        disabled={recognizedCount === 0}
+                        onClick={() => {
+                            const resultsByPage = {};
+                            availablePages.forEach((p) => {
+                                const boxes = detectedPages[p] || [];
+                                if (boxes.length === 0) return;
+                                const indices = boxes
+                                    .map((poly, idx) => {
+                                        const byY = [...poly].sort((a, b) => a[1] - b[1]);
+                                        return { idx, y: (byY[0][1] + byY[1][1]) / 2 };
+                                    })
+                                    .sort((a, b) => a.y - b.y)
+                                    .map((item) => item.idx);
+                                const byIndex = {};
+                                (recognizedByPage[p] || []).forEach((r) => {
+                                    byIndex[r.box_index] = r.text || '';
+                                });
+                                resultsByPage[p] = indices.map((idx) => byIndex[idx] || '');
+                            });
+                            const blob = exportCRNNResultsAsText(resultsByPage);
+                            downloadBlob(blob, `layout_aware_transcript_${new Date().toISOString().slice(0, 10)}.txt`);
+                        }}
+                        className="flex flex-col items-center gap-1 py-2 rounded-lg border text-[10px] font-semibold transition-all disabled:opacity-40 disabled:cursor-not-allowed bg-white border-gray-200 text-gray-600 hover:border-blue-300 hover:bg-blue-50 hover:text-blue-700"
+                    >
+                        <FileTextIcon size={15} />
+                        TXT
+                    </button>
+                    <button
+                        disabled={recognizedCount === 0}
+                        onClick={() => {
+                            const resultsByPage = {};
+                            availablePages.forEach((p) => {
+                                if (!(p in recognizedByPage)) return;
+                                const byIndex = {};
+                                (recognizedByPage[p] || []).forEach((r) => { byIndex[r.box_index] = r.text || ''; });
+                                const sorted = (detectedPages[p] || [])
+                                    .map((poly, idx) => {
+                                        const byY = [...poly].sort((a, b) => a[1] - b[1]);
+                                        return { idx, y: (byY[0][1] + byY[1][1]) / 2 };
+                                    })
+                                    .sort((a, b) => a.y - b.y)
+                                    .map((item) => byIndex[item.idx] || '');
+                                resultsByPage[p] = sorted;
+                            });
+                            const blob = exportCRNNResultsAsJSON(resultsByPage);
+                            downloadBlob(blob, `layout_aware_transcript_${new Date().toISOString().slice(0, 10)}.json`);
+                        }}
+                        className="flex flex-col items-center gap-1 py-2 rounded-lg border text-[10px] font-semibold transition-all disabled:opacity-40 disabled:cursor-not-allowed bg-white border-gray-200 text-gray-600 hover:border-emerald-300 hover:bg-emerald-50 hover:text-emerald-700"
+                    >
+                        <FileJson size={15} />
+                        JSON
+                    </button>
+                    <button
+                        disabled={recognizedCount === 0}
+                        onClick={() => {
+                            const rows = ['page,line_index,text'];
+                            availablePages.forEach((p) => {
+                                const byIndex = {};
+                                (recognizedByPage[p] || []).forEach((r) => { byIndex[r.box_index] = r.text || ''; });
+                                const sorted = (detectedPages[p] || [])
+                                    .map((poly, idx) => {
+                                        const byY = [...poly].sort((a, b) => a[1] - b[1]);
+                                        return { idx, y: (byY[0][1] + byY[1][1]) / 2 };
+                                    })
+                                    .sort((a, b) => a.y - b.y)
+                                    .map((item) => byIndex[item.idx] || '');
+                                sorted.forEach((text, i) => {
+                                    const escaped = `"${String(text).replace(/"/g, '""')}"`;
+                                    rows.push(`${p},${i + 1},${escaped}`);
+                                });
+                            });
+                            const blob = new Blob([rows.join('\n')], { type: 'text/csv;charset=utf-8' });
+                            downloadBlob(blob, `layout_aware_transcript_${new Date().toISOString().slice(0, 10)}.csv`);
+                        }}
+                        className="flex flex-col items-center gap-1 py-2 rounded-lg border text-[10px] font-semibold transition-all disabled:opacity-40 disabled:cursor-not-allowed bg-white border-gray-200 text-gray-600 hover:border-violet-300 hover:bg-violet-50 hover:text-violet-700"
+                    >
+                        <FileType size={15} />
+                        CSV
+                    </button>
+                    <button
+                        disabled={recognizedCount === 0}
+                        onClick={async () => {
+                            const transcripts = {};
+                            availablePages.forEach((p) => {
+                                if (!(p in recognizedByPage)) return;
+                                const byIndex = {};
+                                (recognizedByPage[p] || []).forEach((r) => { byIndex[r.box_index] = r.text || ''; });
+                                const sorted = (detectedPages[p] || [])
+                                    .map((poly, idx) => {
+                                        const byY = [...poly].sort((a, b) => a[1] - b[1]);
+                                        return { idx, y: (byY[0][1] + byY[1][1]) / 2 };
+                                    })
+                                    .sort((a, b) => a.y - b.y)
+                                    .map((item) => byIndex[item.idx] || '');
+                                const text = sorted.filter((t) => t.trim()).join('\n');
+                                if (text) transcripts[p] = text;
+                            });
+                            if (Object.keys(transcripts).length === 0) return;
+                            try {
+                                const response = await fetch(`${API_BASE}/api/export/pdf`, {
+                                    method: 'POST',
+                                    headers: { 'Content-Type': 'application/json' },
+                                    body: JSON.stringify({ transcripts, format: 'pdf' }),
+                                });
+                                if (!response.ok) throw new Error('Export failed');
+                                const blob = await response.blob();
+                                downloadBlob(blob, `layout_aware_transcript_${new Date().toISOString().slice(0, 10)}.pdf`);
+                            } catch (err) {
+                                console.error('PDF export failed:', err);
+                            }
+                        }}
+                        className="flex flex-col items-center gap-1 py-2 rounded-lg border text-[10px] font-semibold transition-all disabled:opacity-40 disabled:cursor-not-allowed bg-white border-gray-200 text-gray-600 hover:border-rose-300 hover:bg-rose-50 hover:text-rose-700"
+                    >
+                        <BookOpen size={15} />
+                        PDF
+                    </button>
+                </div>
+                {recognizedCount === 0 && (
+                    <p className="text-[10px] text-gray-400 text-center">Run OCR on at least one page to export transcripts.</p>
+                )}
+                {onHome && (
+                    <button
+                        onClick={onHome}
+                        className="w-full flex items-center justify-center gap-2 px-3 py-2 rounded-lg border border-gray-200 bg-white text-gray-600 hover:bg-gray-50 hover:text-teal-700 hover:border-teal-200 text-[10px] font-semibold transition-all"
+                    >
+                        <Home size={13} />
+                        Back to Home
+                    </button>
+                )}
+            </div>
+        </div>
+    );
+
+    // ── Render ──
+    return (
+        <div className="h-full w-full flex flex-col bg-gradient-to-br from-slate-50 via-teal-50/20 to-emerald-50/30 overflow-hidden">
+
+            {/* BBox Editor Modal */}
+            {showBBoxEditor && bboxEditorPages.length > 0 && (
+                <BBoxEditor
+                    pages={bboxEditorPages}
+                    initialPageNumber={currentPageNum}
+                    onSave={handleBBoxSave}
+                    onCancel={() => setShowBBoxEditor(false)}
+                />
+            )}
+
+            {/* Header */}
+            <header className="h-12 bg-white/95 backdrop-blur-md border-b border-gray-200 shrink-0 relative z-10">
+                <div className="absolute left-3 top-1/2 -translate-y-1/2 flex items-center gap-2">
+                    <button onClick={onBack} className="flex items-center gap-1 text-gray-600 hover:text-teal-600 hover:bg-teal-50 px-2 py-1 rounded-lg transition-all text-sm">
+                        <ChevronLeft size={16} />
+                        <span className="font-medium hidden sm:inline">Back</span>
+                    </button>
+                    {onHome && (
+                        <button onClick={onHome} className="flex items-center gap-1 text-gray-600 hover:text-teal-600 hover:bg-teal-50 px-2 py-1 rounded-lg transition-all text-sm">
+                            <Home size={16} />
+                            <span className="font-medium hidden sm:inline">Home</span>
+                        </button>
+                    )}
+                    <div className="h-4 w-px bg-gray-200 hidden sm:block" />
+                    <div className="flex items-center gap-1.5 hidden sm:flex">
+                        <div className="p-1 bg-gradient-to-br from-teal-500 to-emerald-600 rounded text-white">
+                            <Layers size={14} />
+                        </div>
+                        <span className="text-sm font-bold text-gray-700">Layout-Aware Detection</span>
+                    </div>
+                </div>
+
+                {/* Center progress */}
+                <div className="absolute left-1/2 top-1/2 -translate-x-1/2 -translate-y-1/2">
+                    <div className="flex items-center gap-3 px-4 py-1.5 bg-gradient-to-r from-teal-50 via-white to-emerald-50 rounded-full border border-teal-100/60 shadow-sm">
+                        <div className={`w-6 h-6 rounded-full flex items-center justify-center text-white text-xs font-bold ${detectedCount === totalPages && totalPages > 0
+                            ? 'bg-gradient-to-br from-emerald-500 to-green-600'
+                            : 'bg-gradient-to-br from-teal-500 to-emerald-600'
+                            }`}>
+                            <Layers size={12} />
+                        </div>
+                        <div className="flex items-center gap-2">
+                            <div className="w-40 h-1.5 bg-teal-100 rounded-full overflow-hidden">
+                                <div className="h-full rounded-full transition-all duration-500 bg-gradient-to-r from-teal-500 to-emerald-500"
+                                    style={{ width: `${totalPages > 0 ? (detectedCount / totalPages) * 100 : 0}%` }} />
+                            </div>
+                            <span className="text-xs font-bold text-teal-700">{detectedCount}/{totalPages}</span>
+                        </div>
+                    </div>
+                </div>
+
+                {/* Dataset mode: Continue to Export button */}
+                {datasetMode && detectedCount > 0 && (
+                    <div className="absolute right-3 top-1/2 -translate-y-1/2">
+                        <button
+                            onClick={() => {
+                                if (onDatasetNext) {
+                                    onDatasetNext({
+                                        boxesByPage: detectedPages,
+                                        alignedTranscriptByPage,
+                                        modelInfo: {
+                                            preprocessing,
+                                            detection_model: selectedDetModel,
+                                            layout_model: selectedLayoutModel,
+                                        },
+                                    });
+                                }
+                            }}
+                            className="flex items-center gap-1.5 px-4 py-1.5 bg-gradient-to-r from-emerald-500 to-teal-600 text-white text-sm font-bold rounded-lg shadow-md hover:shadow-lg hover:-translate-y-0.5 transition-all duration-200"
+                        >
+                            Continue to Export
+                            <ChevronRight size={16} />
+                        </button>
+                    </div>
+                )}
+
+            </header>
+
+            {/* Main 3-column layout */}
+            <main className="flex-1 min-h-0 p-2 overflow-hidden">
+<div className="h-full hidden lg:block">
+                    <ResizablePanels
+                        leftPanel={leftSidebar}
+                        centerPanel={centerPanel}
+                        rightPanel={
+                            datasetMode
+                                ? (transcript && Object.keys(transcript).length > 0 ? rightPanel : null)
+                                : ocrRightPanel
+                        }
+                        defaultLeftWidth={280}
+                        defaultRightWidth={300}
+                        minLeftWidth={240}
+                        maxLeftWidth={420}
+                        minRightWidth={240}
+                        maxRightWidth={450}
+                        minCenterWidth={350}
+                    />
+                </div>
+                {/* Mobile fallback */}
+                <div className="h-full lg:hidden flex flex-col gap-2 overflow-hidden">
+                    <div className="flex-1 min-h-0 overflow-hidden">
+                        {centerPanel}
+                    </div>
+                </div>
+            </main>
+        </div>
+    );
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/PageCard.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/PageCard.jsx
new file mode 100644
index 00000000..df5d321f
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/PageCard.jsx
@@ -0,0 +1,97 @@
+import React, { useState } from 'react';
+import { Check, ZoomIn } from 'lucide-react';
+
+const PageCard = React.forwardRef(function PageCard({
+  pageNumber,
+  thumbnail,
+  isSelected,
+  onSelect,
+  onPreview,
+  isShiftHeld,
+}, ref) {
+  const [isHovered, setIsHovered] = useState(false);
+
+  const handleClick = (e) => {
+    if (e.shiftKey || isShiftHeld) {
+      onSelect(pageNumber, true); // Range select
+    } else {
+      onSelect(pageNumber, false);
+    }
+  };
+
+  return (
+    <div
+      ref={ref}
+      data-page-card
+      className={`relative group cursor-pointer rounded-xl overflow-hidden transition-all duration-200 ${isSelected
+          ? 'ring-4 ring-blue-500 shadow-card-hover scale-[1.02]'
+          : 'ring-1 ring-blue-100 hover:ring-blue-300 hover:shadow-card'
+        }`}
+      onClick={handleClick}
+      onMouseEnter={() => setIsHovered(true)}
+      onMouseLeave={() => setIsHovered(false)}
+    >
+      {/* Thumbnail container */}
+      <div className="relative aspect-[3/4] bg-gray-100 overflow-hidden">
+        {thumbnail ? (
+          <img
+            src={thumbnail}
+            alt={`Page ${pageNumber}`}
+            className={`w-full h-full object-contain transition-transform duration-300 ${isHovered ? 'scale-110' : 'scale-100'
+              }`}
+          />
+        ) : (
+          <div className="w-full h-full shimmer" />
+        )}
+
+        {/* Overlay on hover */}
+        <div
+          className={`absolute inset-0 bg-blue-900/40 flex items-center justify-center gap-2 transition-opacity duration-200 ${isHovered || isSelected ? 'opacity-100' : 'opacity-0'
+            }`}
+        >
+          {/* Preview zoom button */}
+          <button
+            onClick={(e) => {
+              e.stopPropagation();
+              onPreview(pageNumber);
+            }}
+            className="p-2 bg-white rounded-full shadow-lg hover:bg-blue-50 transition-colors"
+          >
+            <ZoomIn className="w-5 h-5 text-blue-600" />
+          </button>
+        </div>
+
+        {/* Selection checkbox */}
+        <div
+          className={`absolute top-3 left-3 transition-all duration-200 ${isHovered || isSelected
+              ? 'opacity-100 translate-y-0'
+              : 'opacity-0 -translate-y-2'
+            }`}
+        >
+          <div
+            className={`w-6 h-6 rounded-md border-2 flex items-center justify-center transition-all ${isSelected
+                ? 'bg-blue-600 border-blue-600'
+                : 'bg-white/90 border-blue-300 hover:border-blue-500'
+              }`}
+          >
+            {isSelected && <Check className="w-4 h-4 text-white" />}
+          </div>
+        </div>
+      </div>
+
+      {/* Page number badge */}
+      <div
+        className={`absolute bottom-0 inset-x-0 py-2 text-center text-sm font-medium transition-colors ${isSelected ? 'bg-blue-600 text-white' : 'bg-white text-gray-700'
+          }`}
+      >
+        {typeof pageNumber === 'string'
+          ? pageNumber.replace('_left', 'L').replace('_right', 'R')
+          : `Page ${pageNumber}`}
+      </div>
+    </div>
+  );
+});
+
+// The grid can be 100+ cards; memo stops one selection toggle from
+// re-rendering every thumbnail.
+export default React.memo(PageCard);
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/PdfPreviewGrid.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/PdfPreviewGrid.jsx
new file mode 100644
index 00000000..bfaaadaa
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/PdfPreviewGrid.jsx
@@ -0,0 +1,288 @@
+import React, { useState, useCallback, useEffect, useRef } from 'react';
+import { CheckSquare, Square, Grid3X3, LayoutGrid, X, ZoomIn } from 'lucide-react';
+import PageCard from './PageCard';
+
+// Rect intersection, both in { left, top, right, bottom }.
+function rectsIntersect(a, b) {
+  return !(a.right < b.left || a.left > b.right || a.bottom < b.top || a.top > b.bottom);
+}
+
+export default function PdfPreviewGrid({
+  pages,
+  selectedPages,
+  onSelectionChange,
+  onPagePreview,
+}) {
+  const [gridSize, setGridSize] = useState('medium'); // small, medium, large
+  const [lastSelectedIndex, setLastSelectedIndex] = useState(null);
+  const [previewPage, setPreviewPage] = useState(null);
+
+  const [isDragging, setIsDragging] = useState(false);
+  const [dragStart, setDragStart] = useState(null);
+  const [dragEnd, setDragEnd] = useState(null);
+  const dragModifierRef = useRef({ ctrl: false, shift: false });
+  const preSelectionRef = useRef([]);
+  const pageRefs = useRef({});
+  const gridRef = useRef(null);
+
+  const gridClasses = {
+    small: 'grid-cols-4 md:grid-cols-6 lg:grid-cols-8 2xl:grid-cols-10',
+    medium: 'grid-cols-3 md:grid-cols-4 lg:grid-cols-6 2xl:grid-cols-8',
+    large: 'grid-cols-2 md:grid-cols-3 lg:grid-cols-4 2xl:grid-cols-5',
+  };
+
+  const handleSelectPage = useCallback(
+    (pageNumber, isRangeSelect) => {
+      const pageIndex = pages.findIndex(p => p.pageNumber === pageNumber);
+
+      if (isRangeSelect && lastSelectedIndex !== null) {
+        const start = Math.min(lastSelectedIndex, pageIndex);
+        const end = Math.max(lastSelectedIndex, pageIndex);
+        const rangePageNumbers = pages.slice(start, end + 1).map(p => p.pageNumber);
+        const newSelection = Array.from(new Set([...selectedPages, ...rangePageNumbers]));
+        onSelectionChange(newSelection);
+      } else {
+        const newSelection = selectedPages.includes(pageNumber)
+          ? selectedPages.filter((p) => p !== pageNumber)
+          : [...selectedPages, pageNumber];
+        onSelectionChange(newSelection);
+        setLastSelectedIndex(pageIndex);
+      }
+    },
+    [pages, selectedPages, lastSelectedIndex, onSelectionChange]
+  );
+
+  const handleSelectAll = () => {
+    if (selectedPages.length === pages.length) {
+      onSelectionChange([]);
+    } else {
+      onSelectionChange(pages.map((p) => p.pageNumber));
+    }
+  };
+
+  const handlePreview = (pageNumber) => {
+    setPreviewPage(pageNumber);
+    if (onPagePreview) {
+      onPagePreview(pageNumber);
+    }
+  };
+
+  const isAllSelected = selectedPages.length === pages.length && pages.length > 0;
+
+  useEffect(() => {
+    const handleKeyDown = (e) => {
+      if (e.key === 'Escape' && previewPage) {
+        setPreviewPage(null);
+      }
+      if (e.key === 'a' && (e.ctrlKey || e.metaKey)) {
+        e.preventDefault();
+        handleSelectAll();
+      }
+    };
+
+    window.addEventListener('keydown', handleKeyDown);
+    return () => window.removeEventListener('keydown', handleKeyDown);
+  }, [previewPage, pages.length]);
+
+  // ── Rubber-band selection ──
+
+  const computeDragSelection = useCallback((start, end) => {
+    if (!start || !end) return;
+
+    const bandRect = {
+      left: Math.min(start.x, end.x),
+      top: Math.min(start.y, end.y),
+      right: Math.max(start.x, end.x),
+      bottom: Math.max(start.y, end.y),
+    };
+
+    const intersectedPages = [];
+    for (const page of pages) {
+      const el = pageRefs.current[page.pageNumber];
+      if (!el) continue;
+      const r = el.getBoundingClientRect();
+      const cardRect = { left: r.left, top: r.top, right: r.right, bottom: r.bottom };
+      if (rectsIntersect(bandRect, cardRect)) {
+        intersectedPages.push(page.pageNumber);
+      }
+    }
+
+    const { ctrl, shift } = dragModifierRef.current;
+    let newSelection;
+
+    if (ctrl || shift) {
+      newSelection = Array.from(new Set([...preSelectionRef.current, ...intersectedPages]));
+    } else {
+      newSelection = intersectedPages;
+    }
+
+    onSelectionChange(newSelection);
+  }, [pages, onSelectionChange]);
+
+  const handleGridMouseDown = useCallback((e) => {
+    if (e.button !== 0) return;
+    // Clicks on a card or control aren't the start of a selection drag.
+    if (e.target.closest('[data-page-card]')) return;
+    if (e.target.closest('button')) return;
+
+    e.preventDefault();
+    const pos = { x: e.clientX, y: e.clientY };
+    setDragStart(pos);
+    setDragEnd(pos);
+    setIsDragging(true);
+    dragModifierRef.current = { ctrl: e.ctrlKey || e.metaKey, shift: e.shiftKey };
+    preSelectionRef.current = [...selectedPages];
+  }, [selectedPages]);
+
+  useEffect(() => {
+    if (!isDragging) return;
+
+    const handleMouseMove = (e) => {
+      const pos = { x: e.clientX, y: e.clientY };
+      setDragEnd(pos);
+      computeDragSelection(dragStart, pos);
+    };
+
+    const handleMouseUp = () => {
+      setIsDragging(false);
+      setDragStart(null);
+      setDragEnd(null);
+    };
+
+    window.addEventListener('mousemove', handleMouseMove);
+    window.addEventListener('mouseup', handleMouseUp);
+    return () => {
+      window.removeEventListener('mousemove', handleMouseMove);
+      window.removeEventListener('mouseup', handleMouseUp);
+    };
+  }, [isDragging, dragStart, computeDragSelection]);
+
+  const rubberBandStyle = isDragging && dragStart && dragEnd ? {
+    position: 'fixed',
+    left: Math.min(dragStart.x, dragEnd.x),
+    top: Math.min(dragStart.y, dragEnd.y),
+    width: Math.abs(dragEnd.x - dragStart.x),
+    height: Math.abs(dragEnd.y - dragStart.y),
+    zIndex: 50,
+    pointerEvents: 'none',
+  } : null;
+
+  return (
+    <div className="animate-fade-in">
+      {/* Toolbar */}
+      <div className="flex items-center justify-between mb-6 p-4 bg-white rounded-xl shadow-card">
+        <div className="flex items-center gap-4">
+          <button
+            onClick={handleSelectAll}
+            className={`flex items-center gap-2 px-4 py-2 rounded-lg font-medium transition-all ${isAllSelected
+                ? 'bg-blue-600 text-white'
+                : 'bg-blue-50 text-blue-600 hover:bg-blue-100'
+              }`}
+          >
+            {isAllSelected ? (
+              <CheckSquare className="w-5 h-5" />
+            ) : (
+              <Square className="w-5 h-5" />
+            )}
+            {isAllSelected ? 'Deselect All' : 'Select All'}
+          </button>
+
+          <span className="text-sm text-gray-500">
+            {selectedPages.length} of {pages.length} pages selected
+          </span>
+        </div>
+
+        {/* Grid size controls */}
+        <div className="flex items-center gap-2 bg-blue-50 rounded-lg p-1">
+          <button
+            onClick={() => setGridSize('small')}
+            className={`p-2 rounded-md transition-colors ${gridSize === 'small'
+                ? 'bg-white text-blue-600 shadow-sm'
+                : 'text-gray-500 hover:text-blue-600'
+              }`}
+            title="Small grid"
+          >
+            <Grid3X3 className="w-5 h-5" />
+          </button>
+          <button
+            onClick={() => setGridSize('medium')}
+            className={`p-2 rounded-md transition-colors ${gridSize === 'medium'
+                ? 'bg-white text-blue-600 shadow-sm'
+                : 'text-gray-500 hover:text-blue-600'
+              }`}
+            title="Medium grid"
+          >
+            <LayoutGrid className="w-5 h-5" />
+          </button>
+          <button
+            onClick={() => setGridSize('large')}
+            className={`p-2 rounded-md transition-colors ${gridSize === 'large'
+                ? 'bg-white text-blue-600 shadow-sm'
+                : 'text-gray-500 hover:text-blue-600'
+              }`}
+            title="Large grid"
+          >
+            <ZoomIn className="w-5 h-5" />
+          </button>
+        </div>
+      </div>
+
+      {/* Grid */}
+      <div
+        ref={gridRef}
+        className={`grid gap-4 ${gridClasses[gridSize]} ${isDragging ? 'select-none' : ''}`}
+        onMouseDown={handleGridMouseDown}
+      >
+        {pages.map((page, index) => (
+          <PageCard
+            key={page.pageNumber}
+            ref={(el) => { if (el) pageRefs.current[page.pageNumber] = el; }}
+            pageNumber={page.pageNumber}
+            thumbnail={page.thumbnail}
+            isSelected={selectedPages.includes(page.pageNumber)}
+            onSelect={handleSelectPage}
+            onPreview={handlePreview}
+          />
+        ))}
+      </div>
+
+      {/* Rubber band overlay */}
+      {rubberBandStyle && (
+        <div
+          className="border-2 border-blue-500 bg-blue-200/20 rounded-sm"
+          style={rubberBandStyle}
+        />
+      )}
+
+      {/* Lightbox preview */}
+      {previewPage && (
+        <div
+          className="fixed inset-0 bg-black/80 z-50 flex items-center justify-center p-8 animate-fade-in"
+          onClick={() => setPreviewPage(null)}
+        >
+          <button
+            onClick={() => setPreviewPage(null)}
+            className="absolute top-4 right-4 p-2 bg-white/20 rounded-full hover:bg-white/30 transition-colors"
+          >
+            <X className="w-6 h-6 text-white" />
+          </button>
+
+          <div className="max-w-4xl max-h-full overflow-auto">
+            <img
+              src={pages.find(p => p.pageNumber === previewPage)?.thumbnail}
+              alt={`Page ${previewPage}`}
+              className="max-w-full max-h-[85vh] object-contain rounded-lg shadow-2xl"
+              onClick={(e) => e.stopPropagation()}
+            />
+          </div>
+
+          <div className="absolute bottom-4 left-1/2 -translate-x-1/2 bg-white/20 backdrop-blur-sm px-4 py-2 rounded-full">
+            <span className="text-white font-medium">
+              Page {typeof previewPage === 'string' ? previewPage.replace('_left', 'L').replace('_right', 'R') : previewPage} of {pages.length}
+            </span>
+          </div>
+        </div>
+      )}
+    </div>
+  );
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/Stepper.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/Stepper.jsx
new file mode 100644
index 00000000..f2743694
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/Stepper.jsx
@@ -0,0 +1,103 @@
+import React from 'react';
+import { Check, Upload, Layers, Wand2, ScanText, Sparkles } from 'lucide-react';
+
+const steps = [
+  { id: 1, name: 'Upload', description: 'Upload PDF or images', icon: Upload },
+  { id: 2, name: 'Select Pages', description: 'Choose pages to process', icon: Layers },
+  { id: 3, name: 'Preprocess', description: 'Apply image transformations', icon: Wand2 },
+  { id: 4, name: 'Text Detection', description: 'Choose detection method', icon: ScanText },
+  { id: 5, name: 'OCR', description: 'Extract text with Gemini', icon: Sparkles },
+];
+
+export default function Stepper({ currentStep, onStepClick }) {
+  const progressPercent = ((currentStep - 1) / (steps.length - 1)) * 100;
+
+  return (
+    <div className="w-full bg-white/80 backdrop-blur-sm rounded-2xl shadow-lg border border-white/50 p-6 mb-6">
+      <nav aria-label="Progress">
+        {/* Progress bar background */}
+        <div className="relative">
+          {/* Background line */}
+          <div className="absolute top-5 left-[10%] right-[10%] h-1 bg-gradient-to-r from-blue-100 via-indigo-100 to-blue-100 rounded-full" />
+
+          {/* Filled progress line */}
+          <div
+            className="absolute top-5 left-[10%] h-1 bg-gradient-to-r from-blue-500 via-indigo-500 to-blue-600 rounded-full transition-all duration-500 ease-out"
+            style={{ width: `${progressPercent * 0.8}%` }}
+          />
+
+          <ol className="relative flex items-center justify-between">
+            {steps.map((step, stepIdx) => {
+              const isCompleted = currentStep > step.id;
+              const isCurrent = currentStep === step.id;
+              const isDisabled = step.disabled;
+              const isClickable = !isDisabled && step.id <= currentStep;
+              const StepIcon = step.icon;
+
+              return (
+                <li key={step.id} className="flex-1 flex flex-col items-center">
+                  <button
+                    onClick={() => isClickable && onStepClick(step.id)}
+                    disabled={!isClickable}
+                    className={`relative flex flex-col items-center group transition-all duration-200 ${isClickable ? 'cursor-pointer' : 'cursor-not-allowed'
+                      }`}
+                  >
+                    {/* Step circle with icon */}
+                    <span
+                      className={`relative w-10 h-10 flex items-center justify-center rounded-full border-2 transition-all duration-300 ${isCompleted
+                          ? 'bg-gradient-to-br from-blue-500 to-indigo-600 border-blue-500 text-white shadow-lg shadow-blue-500/30'
+                          : isCurrent
+                            ? 'bg-white border-blue-500 text-blue-600 ring-4 ring-blue-100 shadow-md animate-pulse-soft'
+                            : isDisabled
+                              ? 'bg-gray-100 border-gray-200 text-gray-400'
+                              : 'bg-white border-gray-200 text-gray-400 group-hover:border-blue-300 group-hover:text-blue-500 group-hover:shadow-md'
+                        }`}
+                    >
+                      {isCompleted ? (
+                        <Check className="w-5 h-5" strokeWidth={2.5} />
+                      ) : (
+                        <StepIcon className={`w-5 h-5 transition-transform duration-200 ${isCurrent ? 'scale-110' : 'group-hover:scale-110'
+                          }`} />
+                      )}
+
+                      {/* Glow effect on current step */}
+                      {isCurrent && (
+                        <span className="absolute inset-0 rounded-full bg-blue-400/20 animate-ping" />
+                      )}
+                    </span>
+
+                    {/* Step name */}
+                    <span
+                      className={`mt-3 text-sm font-semibold transition-colors duration-200 ${isCurrent
+                          ? 'text-blue-600'
+                          : isCompleted
+                            ? 'text-blue-700'
+                            : isDisabled
+                              ? 'text-gray-400'
+                              : 'text-gray-500 group-hover:text-blue-500'
+                        }`}
+                    >
+                      {step.name}
+                    </span>
+
+                    {/* Step description (hidden on small screens) */}
+                    <span
+                      className={`hidden md:block text-xs mt-1 transition-colors duration-200 max-w-24 text-center ${isCurrent
+                          ? 'text-blue-500'
+                          : isDisabled
+                            ? 'text-gray-300'
+                            : 'text-gray-400'
+                        }`}
+                    >
+                      {isDisabled ? 'Coming soon' : step.description}
+                    </span>
+                  </button>
+                </li>
+              );
+            })}
+          </ol>
+        </div>
+      </nav>
+    </div>
+  );
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/UploadZone.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/UploadZone.jsx
new file mode 100644
index 00000000..87082c17
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/UploadZone.jsx
@@ -0,0 +1,180 @@
+import React, { useCallback, useState } from 'react';
+import { Upload, FileImage, FileText, X, Loader2, UploadCloud } from 'lucide-react';
+
+export default function UploadZone({ onFilesSelected, isLoading }) {
+  const [isDragging, setIsDragging] = useState(false);
+  const [selectedFiles, setSelectedFiles] = useState([]);
+
+  const handleDragOver = useCallback((e) => {
+    e.preventDefault();
+    setIsDragging(true);
+  }, []);
+
+  const handleDragLeave = useCallback((e) => {
+    e.preventDefault();
+    setIsDragging(false);
+  }, []);
+
+  const handleDrop = useCallback((e) => {
+    e.preventDefault();
+    setIsDragging(false);
+    const files = Array.from(e.dataTransfer.files);
+    processFiles(files);
+  }, []);
+
+  const handleFileInput = useCallback((e) => {
+    const files = Array.from(e.target.files);
+    processFiles(files);
+  }, []);
+
+  const processFiles = (files) => {
+    const validFiles = files.filter((file) => {
+      const isPdf = file.type === 'application/pdf';
+      const isImage = file.type.startsWith('image/');
+      return isPdf || isImage;
+    });
+
+    if (validFiles.length > 0) {
+      setSelectedFiles(validFiles);
+      onFilesSelected(validFiles);
+    }
+  };
+
+  const removeFile = (index) => {
+    const newFiles = selectedFiles.filter((_, i) => i !== index);
+    setSelectedFiles(newFiles);
+    onFilesSelected(newFiles.length > 0 ? newFiles : null);
+  };
+
+  const getFileIcon = (file) => {
+    if (file.type === 'application/pdf') {
+      return <FileText className="w-8 h-8 text-red-500" />;
+    }
+    return <FileImage className="w-8 h-8 text-blue-500" />;
+  };
+
+  const formatFileSize = (bytes) => {
+    if (bytes < 1024) return bytes + ' B';
+    if (bytes < 1024 * 1024) return (bytes / 1024).toFixed(1) + ' KB';
+    return (bytes / (1024 * 1024)).toFixed(1) + ' MB';
+  };
+
+  return (
+    <div className="w-full max-w-3xl mx-auto animate-fade-in">
+      {/* Drop zone */}
+      <div
+        onDragOver={handleDragOver}
+        onDragLeave={handleDragLeave}
+        onDrop={handleDrop}
+        className={`relative border-2 border-dashed rounded-2xl p-12 text-center transition-all duration-300 ${isDragging
+          ? 'border-blue-500 bg-blue-50/80 scale-[1.02] shadow-xl shadow-blue-500/30'
+          : 'border-gray-300 bg-white/60 backdrop-blur-sm hover:border-blue-400 hover:bg-blue-50/30 hover:shadow-lg'
+          } ${isLoading ? 'pointer-events-none opacity-60' : ''}`}
+      >
+        {/* Subtle background pattern */}
+        <div className="absolute inset-0 opacity-[0.03] pointer-events-none rounded-2xl overflow-hidden">
+          <div className="absolute inset-0" style={{
+            backgroundImage: `radial-gradient(circle at 1px 1px, currentColor 1px, transparent 1px)`,
+            backgroundSize: '24px 24px'
+          }} />
+        </div>
+
+        {isLoading ? (
+          <div className="flex flex-col items-center relative z-10">
+            <div className="relative">
+              <Loader2 className="w-16 h-16 text-blue-500 animate-spin" />
+              <div className="absolute inset-0 blur-xl bg-blue-400/30 animate-pulse" />
+            </div>
+            <p className="mt-4 text-lg font-semibold text-blue-600">
+              Processing files...
+            </p>
+            <p className="text-sm text-gray-500 mt-1">This may take a moment</p>
+          </div>
+        ) : (
+          <div className="relative z-10">
+            {/* Floating animated icon */}
+            <div
+              className={`mx-auto w-24 h-24 rounded-2xl flex items-center justify-center transition-all duration-500 ${isDragging
+                ? 'bg-gradient-to-br from-blue-500 to-indigo-600 scale-110 shadow-xl shadow-blue-500/30'
+                : 'bg-gradient-to-br from-blue-100 to-indigo-100'
+                }`}
+            >
+              <UploadCloud
+                className={`w-12 h-12 transition-all duration-300 ${isDragging ? 'text-white animate-bounce' : 'text-blue-500 animate-float'
+                  }`}
+              />
+            </div>
+
+            <h3 className="mt-6 text-xl font-bold text-gray-800">
+              {isDragging ? 'Drop your files here!' : 'Drag & drop files here'}
+            </h3>
+
+            <p className="mt-2 text-gray-500">or</p>
+
+            <label className="mt-4 inline-block">
+              <input
+                type="file"
+                multiple
+                accept=".pdf,image/*"
+                onChange={handleFileInput}
+                className="hidden"
+              />
+              <span className="btn-gradient inline-flex items-center gap-2 cursor-pointer">
+                <Upload className="w-5 h-5" />
+                Browse Files
+              </span>
+            </label>
+
+            <p className="mt-6 text-sm text-gray-400 flex items-center justify-center gap-2">
+              <span className="w-2 h-2 rounded-full bg-blue-400" />
+              Supports PDF documents and images (PNG, JPG, TIFF)
+            </p>
+          </div>
+        )}
+
+        {/* Animated border on drag */}
+        {isDragging && (
+          <div className="absolute inset-0 rounded-2xl overflow-hidden pointer-events-none">
+            <div className="absolute inset-0 bg-gradient-to-r from-blue-500 via-indigo-500 to-blue-500 opacity-20"
+              style={{ animation: 'shimmer 1s linear infinite', backgroundSize: '200% 100%' }} />
+          </div>
+        )}
+      </div>
+
+      {/* Selected files list */}
+      {selectedFiles.length > 0 && !isLoading && (
+        <div className="mt-6 space-y-3 animate-slide-up">
+          <h4 className="text-sm font-semibold text-gray-700 mb-3 flex items-center gap-2">
+            <span className="w-2 h-2 rounded-full bg-green-500 animate-pulse" />
+            Selected Files ({selectedFiles.length})
+          </h4>
+
+          {selectedFiles.map((file, index) => (
+            <div
+              key={index}
+              className="flex items-center gap-4 p-4 bg-white/80 backdrop-blur-sm rounded-xl border border-gray-100 shadow-sm hover:shadow-md hover:-translate-y-0.5 transition-all duration-200"
+            >
+              <div className="p-2 bg-gradient-to-br from-gray-50 to-gray-100 rounded-lg">
+                {getFileIcon(file)}
+              </div>
+
+              <div className="flex-1 min-w-0">
+                <p className="text-sm font-semibold text-gray-800 truncate">
+                  {file.name}
+                </p>
+                <p className="text-xs text-gray-400 mt-0.5">{formatFileSize(file.size)}</p>
+              </div>
+
+              <button
+                onClick={() => removeFile(index)}
+                className="p-2 text-gray-400 hover:text-red-500 hover:bg-red-50 rounded-lg transition-all duration-200"
+              >
+                <X className="w-5 h-5" />
+              </button>
+            </div>
+          ))}
+        </div>
+      )}
+    </div>
+  );
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/ocr/OCRLayout.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/ocr/OCRLayout.jsx
new file mode 100644
index 00000000..490c4c5b
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/ocr/OCRLayout.jsx
@@ -0,0 +1,140 @@
+import { ChevronLeft, Sparkles, CheckCircle2, AlertCircle, Check, FileText } from 'lucide-react';
+import ResizablePanels from './ResizablePanels';
+
+// Full-viewport 3-column shell for the OCR workspace.
+export default function OCRLayout({
+  onBack,
+  onComplete,
+  onHome,
+  processedCount,
+  totalPages,
+  hasAnyTranscript,
+  backendOnline,
+  leftSidebar,
+  centerPanel,
+  rightPanel,
+}) {
+  const progressPercent = totalPages > 0 ? (processedCount / totalPages) * 100 : 0;
+  const isComplete = processedCount === totalPages && totalPages > 0;
+
+  return (
+    <div className="h-full w-full flex flex-col bg-gradient-to-br from-slate-50 via-blue-50/30 to-indigo-50/40 overflow-hidden">
+      {/* Header - Fixed height with truly centered progress */}
+      <header className="h-12 bg-white/95 backdrop-blur-md border-b border-gray-200 shrink-0 relative z-10">
+        {/* Left: Back button and title - absolutely positioned */}
+        <div className="absolute left-3 top-1/2 -translate-y-1/2 flex items-center gap-2">
+          <button
+            onClick={onBack}
+            className="flex items-center gap-1 text-gray-600 hover:text-blue-600 hover:bg-blue-50 px-2 py-1 rounded-lg transition-all text-sm"
+          >
+            <ChevronLeft size={16} />
+            <span className="font-medium hidden sm:inline">Back</span>
+          </button>
+          <div className="h-4 w-px bg-gray-200 hidden sm:block" />
+          <div className="flex items-center gap-1.5 hidden sm:flex">
+            <div className="p-1 bg-gradient-to-br from-blue-500 to-indigo-600 rounded text-white">
+              <Sparkles size={14} />
+            </div>
+            <span className="text-sm font-bold text-gray-700">RenAIssance OCR</span>
+          </div>
+        </div>
+
+        {/* Center: Step Progress - truly centered */}
+        <div className="absolute left-1/2 top-1/2 -translate-x-1/2 -translate-y-1/2">
+          <div className="flex items-center gap-3 px-4 py-1.5 bg-gradient-to-r from-blue-50 via-white to-indigo-50 rounded-full border border-blue-100/60 shadow-sm">
+            <div className={`w-6 h-6 rounded-full flex items-center justify-center text-white text-xs font-bold ${
+              isComplete 
+                ? 'bg-gradient-to-br from-green-500 to-emerald-600' 
+                : 'bg-gradient-to-br from-blue-500 to-indigo-600'
+            }`}>
+              {isComplete ? <Check size={12} /> : <FileText size={12} />}
+            </div>
+            <div className="flex items-center gap-2">
+              <div className="w-40 h-1.5 bg-blue-100 rounded-full overflow-hidden">
+                <div
+                  className={`h-full rounded-full transition-all duration-500 ${
+                    isComplete 
+                      ? 'bg-gradient-to-r from-green-500 to-emerald-500' 
+                      : 'bg-gradient-to-r from-blue-500 to-indigo-500'
+                  }`}
+                  style={{ width: `${progressPercent}%` }}
+                />
+              </div>
+              <span className={`text-xs font-bold ${isComplete ? 'text-green-600' : 'text-blue-700'}`}>
+                {processedCount}/{totalPages}
+              </span>
+            </div>
+          </div>
+        </div>
+
+        {/* Right: Complete button - absolutely positioned */}
+        <div className="absolute right-3 top-1/2 -translate-y-1/2">
+          <div className="flex items-center gap-2">
+            {onHome && (
+              <button
+                onClick={onHome}
+                className="px-3 py-1.5 rounded-lg font-semibold flex items-center gap-1.5 transition-all text-sm bg-white text-gray-600 border border-gray-200 hover:bg-gray-50"
+              >
+                <ChevronLeft size={14} />
+                <span className="hidden sm:inline">Home</span>
+              </button>
+            )}
+            <button
+              onClick={onComplete}
+              disabled={!hasAnyTranscript}
+              className={`px-3 py-1.5 rounded-lg font-semibold flex items-center gap-1.5 transition-all text-sm ${
+                hasAnyTranscript
+                  ? 'bg-gradient-to-r from-blue-600 to-indigo-600 text-white hover:from-blue-700 hover:to-indigo-700 shadow-sm'
+                  : 'bg-gray-100 text-gray-400 cursor-not-allowed'
+              }`}
+            >
+              <CheckCircle2 size={14} />
+              <span className="hidden sm:inline">Complete</span>
+            </button>
+          </div>
+        </div>
+      </header>
+
+      {/* Backend offline warning */}
+      {backendOnline === false && (
+        <div className="px-3 py-1.5 shrink-0">
+          <div className="px-3 py-2 bg-red-50 border border-red-200 rounded-lg flex items-center gap-2 text-red-700 text-sm">
+            <AlertCircle size={14} />
+            <span>
+              Backend offline. Run:{' '}
+              <code className="bg-red-100 px-1.5 py-0.5 rounded font-mono text-xs">
+                uvicorn main:app --reload
+              </code>
+            </span>
+          </div>
+        </div>
+      )}
+
+      {/* Main 3-column layout - Takes remaining viewport height */}
+      <main className="flex-1 min-h-0 p-2 overflow-hidden">
+        {/* Resizable 3-column layout */}
+        <div className="h-full hidden lg:block">
+          <ResizablePanels
+            leftPanel={leftSidebar}
+            centerPanel={centerPanel}
+            rightPanel={rightPanel}
+            defaultLeftWidth={260}
+            defaultRightWidth={340}
+            minLeftWidth={200}
+            maxLeftWidth={400}
+            minRightWidth={280}
+            maxRightWidth={500}
+            minCenterWidth={350}
+          />
+        </div>
+        
+        {/* Mobile fallback - stacked layout */}
+        <div className="h-full lg:hidden flex flex-col gap-2 overflow-hidden">
+          <div className="flex-1 min-h-0 overflow-hidden">
+            {centerPanel}
+          </div>
+        </div>
+      </main>
+    </div>
+  );
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/ocr/PagePreview.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/ocr/PagePreview.jsx
new file mode 100644
index 00000000..ce262496
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/ocr/PagePreview.jsx
@@ -0,0 +1,620 @@
+import { useState, useEffect, useCallback, useRef } from 'react';
+import {
+  ChevronLeft,
+  ChevronRight,
+  Play,
+  Pause,
+  Loader2,
+  CheckCircle2,
+  AlertCircle,
+  Zap,
+  Image as ImageIcon,
+  ZoomIn,
+  ZoomOut,
+  RotateCcw,
+  Maximize2,
+  Move,
+  Minus,
+  Plus,
+  Clock,
+} from 'lucide-react';
+
+// Full-res scans blow up browser memory when displayed. Downscale for the
+// preview only — OCR still gets the original.
+function useDownscaledImage(imageSrc, maxWidth = 1200, maxHeight = 1600) {
+  const [previewSrc, setPreviewSrc] = useState(null);
+  const [isLoading, setIsLoading] = useState(false);
+  const [error, setError] = useState(false);
+  const [dimensions, setDimensions] = useState({ width: 0, height: 0 });
+
+  useEffect(() => {
+    if (!imageSrc) {
+      setPreviewSrc(null);
+      setError(false);
+      setDimensions({ width: 0, height: 0 });
+      return;
+    }
+
+    if (imageSrc.startsWith('blob:') || imageSrc.length < 100000) {
+      setPreviewSrc(imageSrc);
+      return;
+    }
+
+    setIsLoading(true);
+    setError(false);
+
+    const img = new Image();
+    
+    img.onload = () => {
+      try {
+        setDimensions({ width: img.width, height: img.height });
+        
+            if (img.width <= maxWidth && img.height <= maxHeight) {
+          setPreviewSrc(imageSrc);
+          setIsLoading(false);
+          return;
+        }
+
+            let newWidth = img.width;
+        let newHeight = img.height;
+        
+        if (newWidth > maxWidth) {
+          newHeight = (maxWidth / newWidth) * newHeight;
+          newWidth = maxWidth;
+        }
+        if (newHeight > maxHeight) {
+          newWidth = (maxHeight / newHeight) * newWidth;
+          newHeight = maxHeight;
+        }
+
+            const canvas = document.createElement('canvas');
+        canvas.width = Math.round(newWidth);
+        canvas.height = Math.round(newHeight);
+        
+        const ctx = canvas.getContext('2d');
+        ctx.imageSmoothingEnabled = true;
+        ctx.imageSmoothingQuality = 'high';
+        ctx.drawImage(img, 0, 0, canvas.width, canvas.height);
+
+        // Blob URL, not data URL — much easier on memory.
+        canvas.toBlob((blob) => {
+          if (blob) {
+            const blobUrl = URL.createObjectURL(blob);
+            setPreviewSrc(blobUrl);
+          } else {
+            setPreviewSrc(canvas.toDataURL('image/jpeg', 0.85));
+          }
+          setIsLoading(false);
+        }, 'image/jpeg', 0.85);
+      } catch (err) {
+        console.error('Failed to downscale image:', err);
+        setError(true);
+        setIsLoading(false);
+      }
+    };
+
+    img.onerror = () => {
+      console.error('Failed to load image for preview');
+      setError(true);
+      setIsLoading(false);
+    };
+
+    img.src = imageSrc;
+
+    return () => {
+      if (previewSrc && previewSrc.startsWith('blob:')) {
+        URL.revokeObjectURL(previewSrc);
+      }
+    };
+  }, [imageSrc, maxWidth, maxHeight]);
+
+  return { previewSrc, isLoading, error, dimensions };
+}
+
+// Big page preview with zoom, pan and navigation. Zoom can be controlled by
+// the parent so it survives page changes.
+export default function PagePreview({
+  currentPage,
+  currentIndex,
+  totalPages,
+  isProcessing,
+  isPageProcessed,
+  isAutoProcessing,
+  isWaitingForRateLimit,
+  rateLimitReady,
+  waitSeconds,
+  error,
+  canProcess,
+  onPrevPage,
+  onNextPage,
+  onProcess,
+  onToggleAutoProcess,
+  zoomLevel: controlledZoom,
+  onZoomChange,
+  processingPageIndex,
+  pageLabel,
+  pageLabels = null,
+}) {
+  const pageNumber = pageLabel || currentIndex + 1;
+  const processingPageNumber = processingPageIndex !== null
+    ? (pageLabels ? pageLabels[processingPageIndex] : processingPageIndex + 1)
+    : null;
+  const imageSrc = currentPage?.processed || currentPage?.original;
+
+  const { previewSrc, isLoading: isDownscaling, error: downscaleError, dimensions } = useDownscaledImage(imageSrc);
+  
+  const [imageLoaded, setImageLoaded] = useState(false);
+  const [internalZoom, setInternalZoom] = useState(1);
+  const [isPanning, setIsPanning] = useState(false);
+  const [panStart, setPanStart] = useState({ x: 0, y: 0 });
+  const [scrollStart, setScrollStart] = useState({ x: 0, y: 0 });
+  const containerRef = useRef(null);
+  const imageRef = useRef(null);
+  const lastClickTime = useRef(0);
+
+  const isControlled = controlledZoom !== undefined && onZoomChange !== undefined;
+  const zoomLevel = isControlled ? controlledZoom : internalZoom;
+  const setZoomLevel = isControlled ? onZoomChange : setInternalZoom;
+
+  const MIN_ZOOM = 0.25;
+  const MAX_ZOOM = 5.0;
+  const ZOOM_STEP = 0.1;
+
+  // Only the loading state — zoom is the parent's business.
+  useEffect(() => {
+    setImageLoaded(false);
+  }, [currentIndex, previewSrc]);
+
+  const smoothZoom = useCallback((targetZoom, instant = false) => {
+    const clampedZoom = Math.max(MIN_ZOOM, Math.min(MAX_ZOOM, targetZoom));
+    const roundedZoom = Math.round(clampedZoom * 100) / 100;
+    setZoomLevel(roundedZoom);
+  }, [setZoomLevel]);
+
+  const handleZoomIn = useCallback(() => {
+    smoothZoom(zoomLevel * 1.25); // Multiplicative zoom feels more natural
+  }, [zoomLevel, smoothZoom]);
+
+  const handleZoomOut = useCallback(() => {
+    smoothZoom(zoomLevel / 1.25);
+  }, [zoomLevel, smoothZoom]);
+
+  const handleResetZoom = useCallback(() => {
+    smoothZoom(1);
+    if (containerRef.current) {
+      containerRef.current.scrollTo({ top: 0, left: 0, behavior: 'smooth' });
+    }
+  }, [smoothZoom]);
+
+  const handleFitToPanel = useCallback(() => {
+    if (!containerRef.current || !dimensions.width || !dimensions.height) {
+      smoothZoom(1);
+      return;
+    }
+    const container = containerRef.current;
+    const containerWidth = container.clientWidth - 32;
+    const containerHeight = container.clientHeight - 32;
+    const widthRatio = containerWidth / dimensions.width;
+    const heightRatio = containerHeight / dimensions.height;
+    const fitZoom = Math.min(widthRatio, heightRatio, 1);
+    smoothZoom(Math.max(MIN_ZOOM, fitZoom));
+  }, [dimensions, smoothZoom]);
+
+  const handleSliderChange = useCallback((e) => {
+    smoothZoom(parseFloat(e.target.value));
+  }, [smoothZoom]);
+
+  const handlePresetZoom = useCallback((preset) => {
+    smoothZoom(preset);
+  }, [smoothZoom]);
+
+  // Multiplicative, so each tick feels the same at any zoom level.
+  const handleWheel = useCallback((e) => {
+    if (e.ctrlKey || e.metaKey) {
+      e.preventDefault();
+      const factor = e.deltaY > 0 ? 0.9 : 1.1; // 10% zoom per scroll
+      smoothZoom(zoomLevel * factor);
+    }
+  }, [zoomLevel, smoothZoom]);
+
+  const handleDoubleClick = useCallback((e) => {
+    e.preventDefault();
+    if (zoomLevel <= 1.1) {
+      smoothZoom(2);
+    } else {
+      smoothZoom(1);
+      if (containerRef.current) {
+        containerRef.current.scrollTo({ top: 0, left: 0, behavior: 'smooth' });
+      }
+    }
+  }, [zoomLevel, smoothZoom]);
+
+  const handleMouseDown = useCallback((e) => {
+    const now = Date.now();
+    if (now - lastClickTime.current < 300) {
+      handleDoubleClick(e);
+      lastClickTime.current = 0;
+      return;
+    }
+    lastClickTime.current = now;
+
+    if (zoomLevel > 1 && e.button === 0) {
+      setIsPanning(true);
+      setPanStart({ x: e.clientX, y: e.clientY });
+      setScrollStart({ 
+        x: containerRef.current?.scrollLeft || 0, 
+        y: containerRef.current?.scrollTop || 0 
+      });
+      e.preventDefault();
+    }
+  }, [zoomLevel, handleDoubleClick]);
+
+  const handleMouseMove = useCallback((e) => {
+    if (isPanning && containerRef.current) {
+      const dx = e.clientX - panStart.x;
+      const dy = e.clientY - panStart.y;
+      containerRef.current.scrollLeft = scrollStart.x - dx;
+      containerRef.current.scrollTop = scrollStart.y - dy;
+    }
+  }, [isPanning, panStart, scrollStart]);
+
+  const handleMouseUp = useCallback(() => {
+    setIsPanning(false);
+  }, []);
+
+  useEffect(() => {
+    const container = containerRef.current;
+    if (!container) return;
+    container.addEventListener('wheel', handleWheel, { passive: false });
+    return () => container.removeEventListener('wheel', handleWheel);
+  }, [handleWheel]);
+
+  // On window, so releasing outside the image still ends the pan.
+  useEffect(() => {
+    if (isPanning) {
+      window.addEventListener('mouseup', handleMouseUp);
+      window.addEventListener('mousemove', handleMouseMove);
+      return () => {
+        window.removeEventListener('mouseup', handleMouseUp);
+        window.removeEventListener('mousemove', handleMouseMove);
+      };
+    }
+  }, [isPanning, handleMouseUp, handleMouseMove]);
+
+  const showLoading = isDownscaling || (!imageLoaded && previewSrc && !downscaleError);
+  const showError = downscaleError;
+  const showPlaceholder = !imageSrc;
+  const zoomPercent = Math.round(zoomLevel * 100);
+
+  return (
+    <div className="bg-white/95 backdrop-blur-sm rounded-xl border border-gray-200/80 shadow-sm overflow-hidden flex flex-col h-full relative">
+      {/* Header with navigation */}
+      <div className="px-3 py-2 bg-gradient-to-r from-blue-50/80 to-white border-b border-gray-100 flex items-center justify-between shrink-0">
+        <div className="flex items-center gap-2">
+          <button
+            onClick={onPrevPage}
+            disabled={currentIndex === 0}
+            className="p-1.5 rounded-lg hover:bg-gray-100 disabled:opacity-30 disabled:cursor-not-allowed transition-colors"
+          >
+            <ChevronLeft size={18} />
+          </button>
+          <span className="font-semibold text-gray-800 text-sm min-w-[80px] text-center">
+            {pageNumber} / {totalPages}
+          </span>
+          <button
+            onClick={onNextPage}
+            disabled={currentIndex === totalPages - 1}
+            className="p-1.5 rounded-lg hover:bg-gray-100 disabled:opacity-30 disabled:cursor-not-allowed transition-colors"
+          >
+            <ChevronRight size={18} />
+          </button>
+        </div>
+
+        {/* Status badges */}
+        <div className="flex items-center gap-2">
+          {/* Auto-processing indicator - shows which page is being processed */}
+          {isAutoProcessing && processingPageNumber && processingPageNumber !== pageNumber && (
+            <span className="inline-flex items-center gap-1 px-2 py-0.5 bg-amber-50 text-amber-600 text-xs font-medium rounded-full animate-pulse">
+              <Loader2 size={12} className="animate-spin" />
+              Processing #{processingPageNumber}
+            </span>
+          )}
+          
+          {/* Current page processed badge */}
+          {isPageProcessed && (
+            <span className="inline-flex items-center gap-1 px-2 py-0.5 bg-green-50 text-green-600 text-xs font-medium rounded-full">
+              <CheckCircle2 size={12} />
+              Done
+            </span>
+          )}
+        </div>
+      </div>
+
+      {/* Zoom Toolbar - Enhanced with slider and presets */}
+      <div className="flex items-center justify-between px-3 py-1.5 bg-slate-50/80 border-b border-gray-100 shrink-0">
+        <div className="flex items-center gap-1.5">
+          {/* Zoom out button */}
+          <button
+            onClick={handleZoomOut}
+            disabled={zoomLevel <= MIN_ZOOM}
+            className="p-1.5 rounded-lg text-gray-500 hover:text-gray-700 hover:bg-white disabled:opacity-40 disabled:cursor-not-allowed transition-all"
+            title="Zoom out (Ctrl+Scroll)"
+          >
+            <Minus size={14} />
+          </button>
+          
+          {/* Zoom slider */}
+          <div className="flex items-center gap-1.5">
+            <input
+              type="range"
+              min={MIN_ZOOM}
+              max={MAX_ZOOM}
+              step={0.01}
+              value={zoomLevel}
+              onChange={handleSliderChange}
+              className="w-24 h-1.5 bg-gray-200 rounded-full appearance-none cursor-pointer accent-blue-600
+                [&::-webkit-slider-thumb]:appearance-none [&::-webkit-slider-thumb]:w-3.5 [&::-webkit-slider-thumb]:h-3.5 
+                [&::-webkit-slider-thumb]:bg-blue-600 [&::-webkit-slider-thumb]:rounded-full [&::-webkit-slider-thumb]:cursor-pointer
+                [&::-webkit-slider-thumb]:shadow-md [&::-webkit-slider-thumb]:transition-transform [&::-webkit-slider-thumb]:hover:scale-125
+                [&::-moz-range-thumb]:w-3.5 [&::-moz-range-thumb]:h-3.5 [&::-moz-range-thumb]:bg-blue-600 
+                [&::-moz-range-thumb]:rounded-full [&::-moz-range-thumb]:border-0 [&::-moz-range-thumb]:cursor-pointer"
+              title="Drag to zoom"
+            />
+          </div>
+          
+          {/* Zoom in button */}
+          <button
+            onClick={handleZoomIn}
+            disabled={zoomLevel >= MAX_ZOOM}
+            className="p-1.5 rounded-lg text-gray-500 hover:text-gray-700 hover:bg-white disabled:opacity-40 disabled:cursor-not-allowed transition-all"
+            title="Zoom in (Ctrl+Scroll)"
+          >
+            <Plus size={14} />
+          </button>
+          
+          <div className="w-px h-4 bg-gray-200 mx-0.5" />
+          
+          {/* Zoom presets dropdown-style buttons */}
+          <div className="flex items-center gap-0.5">
+            {[0.5, 1, 1.5, 2].map((preset) => (
+              <button
+                key={preset}
+                onClick={() => handlePresetZoom(preset)}
+                className={`px-1.5 py-0.5 text-xs font-medium rounded transition-all ${
+                  Math.abs(zoomLevel - preset) < 0.05
+                    ? 'bg-blue-600 text-white'
+                    : 'text-gray-500 hover:text-gray-700 hover:bg-white'
+                }`}
+                title={`Zoom to ${preset * 100}%`}
+              >
+                {preset === 1 ? '1×' : preset < 1 ? `${preset * 100}%` : `${preset}×`}
+              </button>
+            ))}
+          </div>
+          
+          <div className="w-px h-4 bg-gray-200 mx-0.5" />
+          
+          {/* Fit and reset buttons */}
+          <button
+            onClick={handleFitToPanel}
+            className="p-1.5 rounded-lg text-gray-500 hover:text-gray-700 hover:bg-white transition-all"
+            title="Fit to panel"
+          >
+            <Maximize2 size={14} />
+          </button>
+          <button
+            onClick={handleResetZoom}
+            className="p-1.5 rounded-lg text-gray-500 hover:text-gray-700 hover:bg-white transition-all"
+            title="Reset to 100%"
+          >
+            <RotateCcw size={14} />
+          </button>
+        </div>
+        
+        {/* Pan hint when zoomed + zoom percentage */}
+        <div className="flex items-center gap-2">
+          <span className="text-xs font-bold text-gray-600 bg-white px-2 py-1 rounded border border-gray-200 min-w-[50px] text-center">
+            {zoomPercent}%
+          </span>
+          {zoomLevel > 1 && (
+            <div className="flex items-center gap-1.5 text-xs text-blue-600 bg-blue-50 px-2 py-1 rounded-full">
+              <Move size={12} />
+              <span>Drag / Double-click</span>
+            </div>
+          )}
+          {zoomLevel <= 1 && (
+            <div className="text-xs text-gray-400">
+              Double-click to zoom
+            </div>
+          )}
+        </div>
+      </div>
+
+      {/* Image Container with zoom and pan support */}
+      <div
+        ref={containerRef}
+        className={`flex-1 overflow-auto bg-gradient-to-br from-gray-100 to-gray-50 min-h-0 relative ${
+          zoomLevel > 1 ? (isPanning ? 'cursor-grabbing' : 'cursor-grab') : 'cursor-zoom-in'
+        }`}
+        onMouseDown={handleMouseDown}
+      >
+        {/* Loading skeleton */}
+        {showLoading && (
+          <div className="absolute inset-0 bg-gray-50 flex items-center justify-center z-10">
+            <div className="text-center text-gray-400">
+              <Loader2 size={24} className="animate-spin mx-auto mb-2" />
+              <p className="text-xs">{isDownscaling ? 'Preparing...' : 'Loading...'}</p>
+            </div>
+          </div>
+        )}
+
+        {/* Error state */}
+        {showError && (
+          <div className="absolute inset-0 flex items-center justify-center">
+            <div className="text-center text-gray-400">
+              <ImageIcon size={32} className="mx-auto mb-2 opacity-50" />
+              <p className="text-sm">Failed to load image</p>
+            </div>
+          </div>
+        )}
+
+        {/* No image placeholder */}
+        {showPlaceholder && (
+          <div className="absolute inset-0 flex items-center justify-center">
+            <div className="text-center text-gray-400">
+              <ImageIcon size={32} className="mx-auto mb-2 opacity-50" />
+              <p className="text-sm">No image selected</p>
+            </div>
+          </div>
+        )}
+
+        {/* Zoomable Image - optimized for smooth zoom with high quality rendering */}
+        {previewSrc && !showError && (
+          <div 
+            className="min-w-full min-h-full flex items-center justify-center p-4"
+            style={{
+              width: zoomLevel > 1 ? `${100 * zoomLevel}%` : '100%',
+              height: zoomLevel > 1 ? `${100 * zoomLevel}%` : '100%',
+            }}
+          >
+            <img
+              ref={imageRef}
+              src={previewSrc}
+              alt={`Page ${pageNumber}`}
+              className={`rounded-lg shadow-xl select-none ${
+                imageLoaded ? 'opacity-100' : 'opacity-0'
+              }`}
+              onLoad={() => setImageLoaded(true)}
+              draggable={false}
+              style={{
+                transform: `scale(${zoomLevel})`,
+                transformOrigin: 'center center',
+                transition: isPanning ? 'none' : 'transform 0.1s ease-out, opacity 0.2s ease-out',
+                maxWidth: zoomLevel <= 1 ? '100%' : 'none',
+                maxHeight: zoomLevel <= 1 ? '100%' : 'none',
+                objectFit: 'contain',
+                willChange: 'transform',
+                imageRendering: zoomLevel > 1.5 ? 'auto' : 'auto',
+                backfaceVisibility: 'hidden',
+                WebkitBackfaceVisibility: 'hidden',
+              }}
+            />
+          </div>
+        )}
+
+      </div>
+
+      {/* Processing overlay - positioned relative to viewport, not scrollable content */}
+      {isProcessing && (
+        <div className="absolute inset-0 bg-blue-500/10 backdrop-blur-sm flex items-center justify-center z-20 pointer-events-none">
+          <div className="bg-white rounded-xl shadow-lg px-5 py-3 text-center pointer-events-auto">
+            <Loader2 size={28} className="animate-spin text-blue-600 mx-auto mb-2" />
+            <p className="text-sm font-medium text-gray-700">Processing...</p>
+          </div>
+        </div>
+      )}
+
+      {/* Process Controls - Compact */}
+      <div className="px-3 py-2 border-t border-gray-100 bg-white shrink-0">
+        <div className="flex items-center gap-2">
+          {/* Main Process Button - single page */}
+          <button
+            onClick={onProcess}
+            disabled={!canProcess || isProcessing || isPageProcessed}
+            className={`
+              flex-1 py-2.5 rounded-lg font-semibold text-sm flex items-center justify-center gap-2
+              transition-all duration-200
+              ${!canProcess || isProcessing || isPageProcessed
+                ? 'bg-gray-100 text-gray-400 cursor-not-allowed'
+                : 'bg-gradient-to-r from-blue-600 to-indigo-600 text-white hover:from-blue-700 hover:to-indigo-700 shadow-sm hover:shadow-md active:scale-[0.98]'
+              }
+            `}
+          >
+            {isProcessing ? (
+              <>
+                <Loader2 size={16} className="animate-spin" />
+                Processing...
+              </>
+            ) : isPageProcessed ? (
+              <>
+                <CheckCircle2 size={16} />
+                Done
+              </>
+            ) : (
+              <>
+                <Play size={16} />
+                This Page
+              </>
+            )}
+          </button>
+
+          {/* Auto Process All Pages Button */}
+          <button
+            onClick={onToggleAutoProcess}
+            disabled={!canProcess}
+            title={isAutoProcessing ? 'Stop auto-processing' : 'Process all pages automatically'}
+            className={`
+              flex-1 py-2.5 rounded-lg font-semibold text-sm flex items-center justify-center gap-2
+              transition-all duration-200
+              ${isAutoProcessing
+                ? 'bg-amber-500 text-white hover:bg-amber-600'
+                : !canProcess
+                  ? 'bg-gray-100 text-gray-400 cursor-not-allowed'
+                  : 'bg-gradient-to-r from-emerald-500 to-teal-600 text-white hover:from-emerald-600 hover:to-teal-700 shadow-sm hover:shadow-md active:scale-[0.98]'
+              }
+            `}
+          >
+            {isAutoProcessing ? (
+              <>
+                <Pause size={16} />
+                Stop Processing
+              </>
+            ) : (
+              <>
+                <Zap size={16} />
+                Process All Pages
+              </>
+            )}
+          </button>
+        </div>
+
+        {/* Auto-processing waiting for rate limit - show prominent timer */}
+        {isAutoProcessing && isWaitingForRateLimit && waitSeconds > 0 && (
+          <div className="mt-2 px-3 py-2 rounded-lg bg-gradient-to-r from-blue-50 to-indigo-50 border border-blue-200 flex items-center justify-between">
+            <div className="flex items-center gap-2 text-blue-700">
+              <Clock size={16} className="animate-pulse" />
+              <span className="text-sm font-medium">Waiting for rate limit to reset...</span>
+            </div>
+            <div className="flex items-center gap-2">
+              <div className="text-lg font-bold text-blue-600 tabular-nums min-w-[40px] text-center">
+                {waitSeconds}s
+              </div>
+              <div className="w-16 h-1.5 bg-blue-200 rounded-full overflow-hidden">
+                <div 
+                  className="h-full bg-blue-500 rounded-full transition-all duration-1000"
+                  style={{ width: `${Math.max(0, (60 - waitSeconds) / 60 * 100)}%` }}
+                />
+              </div>
+            </div>
+          </div>
+        )}
+
+        {/* Rate limit warning (when not in auto-processing waiting state) */}
+        {!rateLimitReady && !isWaitingForRateLimit && (
+          <div className="mt-2 px-3 py-1.5 rounded-lg text-xs flex items-center gap-2 bg-amber-50 text-amber-700 border border-amber-100">
+            <AlertCircle size={14} />
+            Rate limited. Wait {waitSeconds}s...
+          </div>
+        )}
+
+        {/* Error message */}
+        {error && (
+          <div className="mt-2 px-3 py-1.5 rounded-lg text-xs flex items-center gap-2 bg-red-50 text-red-700 border border-red-100">
+            <AlertCircle size={14} />
+            {error}
+          </div>
+        )}
+      </div>
+    </div>
+  );
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/ocr/PageThumbnailList.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/ocr/PageThumbnailList.jsx
new file mode 100644
index 00000000..3eeaaf48
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/ocr/PageThumbnailList.jsx
@@ -0,0 +1,233 @@
+import { useState, useEffect, useRef, useCallback, memo } from 'react';
+import { CheckCircle2, Image as ImageIcon, Loader2 } from 'lucide-react';
+
+
+function createThumbnail(imageSrc, maxSize = 200) {
+  return new Promise((resolve, reject) => {
+    if (imageSrc.startsWith('blob:') || imageSrc.length < 50000) {
+      resolve(imageSrc);
+      return;
+    }
+
+    const img = new Image();
+    img.onload = () => {
+      try {
+        let width = img.width;
+        let height = img.height;
+        
+        if (width > height) {
+          if (width > maxSize) {
+            height = (maxSize / width) * height;
+            width = maxSize;
+          }
+        } else {
+          if (height > maxSize) {
+            width = (maxSize / height) * width;
+            height = maxSize;
+          }
+        }
+
+        const canvas = document.createElement('canvas');
+        canvas.width = Math.round(width);
+        canvas.height = Math.round(height);
+        
+        const ctx = canvas.getContext('2d');
+        ctx.imageSmoothingEnabled = true;
+        ctx.imageSmoothingQuality = 'medium';
+        ctx.drawImage(img, 0, 0, canvas.width, canvas.height);
+
+        // Blob URL, not data URL — easier on memory.
+        canvas.toBlob((blob) => {
+          if (blob) {
+            resolve(URL.createObjectURL(blob));
+          } else {
+            resolve(canvas.toDataURL('image/jpeg', 0.7));
+          }
+        }, 'image/jpeg', 0.7);
+      } catch (err) {
+        reject(err);
+      }
+    };
+    img.onerror = () => reject(new Error('Failed to load image'));
+    img.src = imageSrc;
+  });
+}
+
+// Scrollable page thumbnails, lazily rendered.
+function PageThumbnailList({
+  images,
+  currentIndex,
+  processedPages,
+  processingPageIndex,
+  processingPageIndices = new Set(), // Support batch processing
+  onPageSelect,
+  pageLabels = null,
+}) {
+  return (
+    <div className="bg-white/95 backdrop-blur-sm rounded-xl border border-gray-200/80 shadow-sm overflow-hidden flex flex-col flex-1 min-h-0">
+      {/* Header */}
+      <div className="px-3 py-2 bg-gradient-to-r from-blue-50/80 to-white border-b border-gray-100 flex items-center justify-between shrink-0">
+        <h3 className="font-semibold text-gray-800 flex items-center gap-2 text-sm">
+          <ImageIcon size={16} className="text-blue-600" />
+          Pages
+        </h3>
+        <span className="text-[10px] text-gray-500 bg-gray-100 px-1.5 py-0.5 rounded-full font-medium">
+          {images.length}
+        </span>
+      </div>
+
+      {/* Thumbnails - scrollable */}
+      <div className="flex-1 overflow-y-auto p-2 min-h-0">
+        <div className="grid grid-cols-2 gap-1.5">
+          {images.map((img, index) => (
+            <ThumbnailItem
+              key={index}
+              image={img}
+              index={index}
+              isActive={index === currentIndex}
+              isProcessed={processedPages.has(pageLabels ? pageLabels[index] : index + 1)}
+              isProcessing={index === processingPageIndex || processingPageIndices.has(index)}
+              onClick={() => onPageSelect(index)}
+              pageLabel={pageLabels ? pageLabels[index] : index + 1}
+            />
+          ))}
+        </div>
+      </div>
+    </div>
+  );
+}
+
+// The parent re-renders on every zoom/hover/progress tick; memo skips the work
+// when nothing here actually changed.
+export default memo(PageThumbnailList);
+
+
+function ThumbnailItem({ image, index, isActive, isProcessed, isProcessing, onClick, pageLabel }) {
+  const [thumbnailSrc, setThumbnailSrc] = useState(null);
+  const [isLoading, setIsLoading] = useState(false);
+  const [error, setError] = useState(false);
+  const containerRef = useRef(null);
+  const hasStartedLoading = useRef(false);
+
+  const imageSrc = image.processed || image.original;
+
+  useEffect(() => {
+    if (!imageSrc || hasStartedLoading.current) return;
+
+    const observer = new IntersectionObserver(
+      (entries) => {
+        entries.forEach((entry) => {
+          if (entry.isIntersecting && !hasStartedLoading.current) {
+            hasStartedLoading.current = true;
+            setIsLoading(true);
+
+            createThumbnail(imageSrc, 200)
+              .then((thumb) => {
+                setThumbnailSrc(thumb);
+                setIsLoading(false);
+              })
+              .catch((err) => {
+                console.error('Thumbnail creation failed:', err);
+                setError(true);
+                setIsLoading(false);
+              });
+          }
+        });
+      },
+      { threshold: 0.1, rootMargin: '50px' }
+    );
+
+    if (containerRef.current) {
+      observer.observe(containerRef.current);
+    }
+
+    return () => observer.disconnect();
+  }, [imageSrc]);
+
+  useEffect(() => {
+    return () => {
+      if (thumbnailSrc && thumbnailSrc.startsWith('blob:')) {
+        URL.revokeObjectURL(thumbnailSrc);
+      }
+    };
+  }, [thumbnailSrc]);
+
+  const pageNumber = pageLabel !== undefined ? pageLabel : index + 1;
+
+  return (
+    <button
+      ref={containerRef}
+      onClick={onClick}
+      className={`
+        relative aspect-[3/4] rounded-lg overflow-hidden transition-all duration-200
+        border-2 group
+        ${isActive
+          ? 'border-blue-500 ring-2 ring-blue-200 shadow-md'
+          : isProcessing
+            ? 'border-amber-400 ring-2 ring-amber-200'
+            : isProcessed
+              ? 'border-green-300 hover:border-green-400'
+              : 'border-gray-200 hover:border-gray-300 hover:shadow-sm'
+        }
+      `}
+    >
+      {/* Loading state */}
+      {(isLoading || (!thumbnailSrc && !error)) && (
+        <div className="absolute inset-0 bg-gray-100 animate-pulse flex items-center justify-center">
+          {isLoading ? (
+            <Loader2 size={16} className="text-gray-400 animate-spin" />
+          ) : (
+            <ImageIcon size={20} className="text-gray-300" />
+          )}
+        </div>
+      )}
+
+      {/* Error state */}
+      {error && (
+        <div className="absolute inset-0 bg-gray-100 flex items-center justify-center">
+          <ImageIcon size={20} className="text-gray-400" />
+        </div>
+      )}
+
+      {/* Image */}
+      {thumbnailSrc && !error && (
+        <img
+          src={thumbnailSrc}
+          alt={`Page ${pageNumber}`}
+          className="w-full h-full object-cover"
+        />
+      )}
+
+      {/* Processing indicator - shows spinner on the page being processed */}
+      {isProcessing && (
+        <div className="absolute inset-0 bg-amber-500/20 flex items-center justify-center">
+          <div className="bg-white rounded-full p-1 shadow-md">
+            <Loader2 size={16} className="text-amber-500 animate-spin" />
+          </div>
+        </div>
+      )}
+
+      {/* Processed indicator */}
+      {isProcessed && !isProcessing && (
+        <div className="absolute top-1.5 right-1.5">
+          <CheckCircle2
+            size={16}
+            className="text-green-500 bg-white rounded-full shadow-sm"
+          />
+        </div>
+      )}
+
+      {/* Page number label */}
+      <div className="absolute bottom-0 inset-x-0 bg-gradient-to-t from-black/70 to-transparent pt-4 pb-1">
+        <span className="block text-center text-white text-xs font-medium">
+          {pageNumber}
+        </span>
+      </div>
+
+      {/* Active indicator ring */}
+      {isActive && (
+        <div className="absolute inset-0 border-2 border-blue-500 rounded-lg pointer-events-none" />
+      )}
+    </button>
+  );
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/ocr/PreviewZoomViewer.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/ocr/PreviewZoomViewer.jsx
new file mode 100644
index 00000000..72021630
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/ocr/PreviewZoomViewer.jsx
@@ -0,0 +1,254 @@
+import { useState, useRef, useEffect, useCallback } from 'react';
+import {
+  ZoomIn,
+  ZoomOut,
+  RotateCcw,
+  Maximize2,
+  Move,
+  Loader2,
+  Image as ImageIcon,
+} from 'lucide-react';
+
+// Zoom + pan viewer for preview images.
+export default function PreviewZoomViewer({
+  imageSrc,
+  isLoading,
+  hasError,
+  pageNumber,
+  className = '',
+}) {
+  const [zoomLevel, setZoomLevel] = useState(1);
+  const [isPanning, setIsPanning] = useState(false);
+  const [imageLoaded, setImageLoaded] = useState(false);
+  const [imageDimensions, setImageDimensions] = useState({ width: 0, height: 0 });
+  const containerRef = useRef(null);
+  const imageRef = useRef(null);
+
+  const MIN_ZOOM = 0.5;
+  const MAX_ZOOM = 4.0;
+  const ZOOM_STEP = 0.25;
+
+  useEffect(() => {
+    setZoomLevel(1);
+    setImageLoaded(false);
+  }, [imageSrc]);
+
+  const handleZoomIn = useCallback(() => {
+    setZoomLevel((prev) => Math.min(prev + ZOOM_STEP, MAX_ZOOM));
+  }, []);
+
+  const handleZoomOut = useCallback(() => {
+    setZoomLevel((prev) => Math.max(prev - ZOOM_STEP, MIN_ZOOM));
+  }, []);
+
+  const handleReset = useCallback(() => {
+    setZoomLevel(1);
+    if (containerRef.current) {
+      containerRef.current.scrollTo({ top: 0, left: 0, behavior: 'smooth' });
+    }
+  }, []);
+
+  const handleFitToPanel = useCallback(() => {
+    if (!containerRef.current || !imageDimensions.width || !imageDimensions.height) {
+      setZoomLevel(1);
+      return;
+    }
+
+    const container = containerRef.current;
+    const containerWidth = container.clientWidth - 32; // Account for padding
+    const containerHeight = container.clientHeight - 32;
+
+    const widthRatio = containerWidth / imageDimensions.width;
+    const heightRatio = containerHeight / imageDimensions.height;
+    const fitZoom = Math.min(widthRatio, heightRatio, 1);
+
+    setZoomLevel(Math.max(MIN_ZOOM, Math.min(fitZoom, MAX_ZOOM)));
+  }, [imageDimensions]);
+
+  const handleWheel = useCallback((e) => {
+    if (e.ctrlKey || e.metaKey) {
+      e.preventDefault();
+      const delta = e.deltaY > 0 ? -ZOOM_STEP : ZOOM_STEP;
+      setZoomLevel((prev) => Math.max(MIN_ZOOM, Math.min(prev + delta, MAX_ZOOM)));
+    }
+  }, []);
+
+  // passive: false so preventDefault actually works.
+  useEffect(() => {
+    const container = containerRef.current;
+    if (!container) return;
+
+    container.addEventListener('wheel', handleWheel, { passive: false });
+    return () => container.removeEventListener('wheel', handleWheel);
+  }, [handleWheel]);
+
+  const handleImageLoad = useCallback((e) => {
+    setImageLoaded(true);
+    setImageDimensions({
+      width: e.target.naturalWidth,
+      height: e.target.naturalHeight,
+    });
+  }, []);
+
+  useEffect(() => {
+    const handleKeyDown = (e) => {
+      if (e.target.tagName === 'INPUT' || e.target.tagName === 'TEXTAREA') return;
+
+      if ((e.ctrlKey || e.metaKey) && e.key === '=') {
+        e.preventDefault();
+        handleZoomIn();
+      } else if ((e.ctrlKey || e.metaKey) && e.key === '-') {
+        e.preventDefault();
+        handleZoomOut();
+      } else if ((e.ctrlKey || e.metaKey) && e.key === '0') {
+        e.preventDefault();
+        handleReset();
+      }
+    };
+
+    window.addEventListener('keydown', handleKeyDown);
+    return () => window.removeEventListener('keydown', handleKeyDown);
+  }, [handleZoomIn, handleZoomOut, handleReset]);
+
+  const showLoading = isLoading || (!imageLoaded && imageSrc && !hasError);
+  const showPlaceholder = !imageSrc && !isLoading;
+  const showError = hasError;
+
+  const zoomPercent = Math.round(zoomLevel * 100);
+
+  return (
+    <div className={`flex flex-col h-full ${className}`}>
+      {/* Toolbar */}
+      <div className="flex items-center justify-between px-3 py-2 bg-gradient-to-r from-slate-50 to-gray-50 border-b border-gray-100 shrink-0">
+        {/* Zoom Controls */}
+        <div className="flex items-center gap-1">
+          <button
+            onClick={handleZoomOut}
+            disabled={zoomLevel <= MIN_ZOOM}
+            className="p-1.5 rounded-lg text-gray-500 hover:text-gray-700 hover:bg-white hover:shadow-sm disabled:opacity-40 disabled:cursor-not-allowed transition-all"
+            title="Zoom out (Ctrl+-)"
+          >
+            <ZoomOut size={18} />
+          </button>
+          
+          <div className="px-2 py-1 min-w-[60px] text-center">
+            <span className="text-sm font-semibold text-gray-700">{zoomPercent}%</span>
+          </div>
+
+          <button
+            onClick={handleZoomIn}
+            disabled={zoomLevel >= MAX_ZOOM}
+            className="p-1.5 rounded-lg text-gray-500 hover:text-gray-700 hover:bg-white hover:shadow-sm disabled:opacity-40 disabled:cursor-not-allowed transition-all"
+            title="Zoom in (Ctrl++)"
+          >
+            <ZoomIn size={18} />
+          </button>
+        </div>
+
+        {/* Action Buttons */}
+        <div className="flex items-center gap-1">
+          <button
+            onClick={handleFitToPanel}
+            className="p-1.5 rounded-lg text-gray-500 hover:text-gray-700 hover:bg-white hover:shadow-sm transition-all"
+            title="Fit to panel"
+          >
+            <Maximize2 size={18} />
+          </button>
+
+          <button
+            onClick={handleReset}
+            className="p-1.5 rounded-lg text-gray-500 hover:text-gray-700 hover:bg-white hover:shadow-sm transition-all"
+            title="Reset zoom (Ctrl+0)"
+          >
+            <RotateCcw size={18} />
+          </button>
+        </div>
+
+        {/* Pan indicator */}
+        {zoomLevel > 1 && (
+          <div className="flex items-center gap-1.5 px-2 py-1 bg-blue-50 text-blue-600 rounded-lg text-xs font-medium">
+            <Move size={14} />
+            <span>Scroll to pan</span>
+          </div>
+        )}
+      </div>
+
+      {/* Image Container with zoom/pan */}
+      <div
+        ref={containerRef}
+        className={`flex-1 overflow-auto bg-gradient-to-br from-gray-100 to-slate-100 min-h-0 relative ${
+          zoomLevel > 1 ? 'cursor-grab active:cursor-grabbing' : ''
+        }`}
+        style={{
+          display: 'flex',
+          alignItems: zoomLevel <= 1 ? 'center' : 'flex-start',
+          justifyContent: zoomLevel <= 1 ? 'center' : 'flex-start',
+        }}
+      >
+        {/* Loading State */}
+        {showLoading && (
+          <div className="absolute inset-0 flex items-center justify-center bg-gray-50/80 backdrop-blur-sm z-10">
+            <div className="text-center">
+              <Loader2 size={32} className="animate-spin text-blue-500 mx-auto mb-2" />
+              <p className="text-sm text-gray-500">Loading preview...</p>
+            </div>
+          </div>
+        )}
+
+        {/* Error State */}
+        {showError && (
+          <div className="absolute inset-0 flex items-center justify-center">
+            <div className="text-center text-gray-400">
+              <ImageIcon size={48} className="mx-auto mb-2 opacity-50" />
+              <p className="text-sm font-medium">Failed to load image</p>
+              <p className="text-xs mt-1">Image may be too large or corrupted</p>
+            </div>
+          </div>
+        )}
+
+        {/* Placeholder */}
+        {showPlaceholder && (
+          <div className="absolute inset-0 flex items-center justify-center">
+            <div className="text-center text-gray-400">
+              <ImageIcon size={48} className="mx-auto mb-2 opacity-50" />
+              <p className="text-sm font-medium">No image selected</p>
+            </div>
+          </div>
+        )}
+
+        {/* Zoomable Image */}
+        {imageSrc && !showError && (
+          <div
+            className="p-4"
+            style={{
+              transform: `scale(${zoomLevel})`,
+              transformOrigin: zoomLevel <= 1 ? 'center center' : 'top left',
+              transition: 'transform 0.15s ease-out',
+              minWidth: zoomLevel > 1 ? `${imageDimensions.width * zoomLevel}px` : 'auto',
+              minHeight: zoomLevel > 1 ? `${imageDimensions.height * zoomLevel}px` : 'auto',
+            }}
+          >
+            <img
+              ref={imageRef}
+              src={imageSrc}
+              alt={`Page ${pageNumber}`}
+              className={`max-w-full h-auto rounded-lg shadow-lg transition-opacity duration-300 ${
+                imageLoaded ? 'opacity-100' : 'opacity-0'
+              }`}
+              onLoad={handleImageLoad}
+              draggable={false}
+              style={{
+                maxHeight: zoomLevel <= 1 ? 'calc(100% - 2rem)' : 'none',
+              }}
+            />
+          </div>
+        )}
+      </div>
+
+      {/* Zoom hint */}
+      <div className="px-3 py-1.5 bg-gray-50 border-t border-gray-100 text-xs text-gray-400 text-center shrink-0">
+        <span>Ctrl + Scroll to zoom • Scroll to pan when zoomed</span>
+      </div>
+    </div>
+  );
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/ocr/ResizablePanels.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/ocr/ResizablePanels.jsx
new file mode 100644
index 00000000..518dfd72
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/ocr/ResizablePanels.jsx
@@ -0,0 +1,191 @@
+import { useState, useCallback, useRef, useEffect } from 'react';
+import { GripVertical } from 'lucide-react';
+
+// Three columns with draggable dividers.
+export default function ResizablePanels({
+  leftPanel,
+  centerPanel,
+  rightPanel = null,
+  defaultLeftWidth = 260,
+  defaultRightWidth = 340,
+  minLeftWidth = 200,
+  maxLeftWidth = 400,
+  minRightWidth = 280,
+  maxRightWidth = 500,
+  minCenterWidth = 300,
+}) {
+  const hasRight = Boolean(rightPanel);
+  const containerRef = useRef(null);
+  const [leftWidth, setLeftWidth] = useState(defaultLeftWidth);
+  const [rightWidth, setRightWidth] = useState(defaultRightWidth);
+  const [isDraggingLeft, setIsDraggingLeft] = useState(false);
+  const [isDraggingRight, setIsDraggingRight] = useState(false);
+  const [containerWidth, setContainerWidth] = useState(0);
+
+  useEffect(() => {
+    const container = containerRef.current;
+    if (!container) return;
+
+    const updateWidth = () => {
+      setContainerWidth(container.offsetWidth);
+    };
+
+    updateWidth();
+    const resizeObserver = new ResizeObserver(updateWidth);
+    resizeObserver.observe(container);
+
+    return () => resizeObserver.disconnect();
+  }, []);
+
+  // Center takes whatever the two side panels leave.
+  const gapWidth = 8; // gap-2 = 0.5rem = 8px
+  const resizerWidth = 12; // width of resizer handles
+  const rightTotalWidth = hasRight ? rightWidth + gapWidth + resizerWidth : 0;
+  const centerWidth = containerWidth - leftWidth - rightTotalWidth - gapWidth - resizerWidth;
+
+  const handleLeftMouseDown = useCallback((e) => {
+    e.preventDefault();
+    setIsDraggingLeft(true);
+  }, []);
+
+  const handleRightMouseDown = useCallback((e) => {
+    e.preventDefault();
+    setIsDraggingRight(true);
+  }, []);
+
+  // On window, so dragging past the divider still tracks.
+  useEffect(() => {
+    if (!isDraggingLeft && !isDraggingRight) return;
+
+    const handleMouseMove = (e) => {
+      const container = containerRef.current;
+      if (!container) return;
+
+      const containerRect = container.getBoundingClientRect();
+      
+      if (isDraggingLeft) {
+        const newLeftWidth = e.clientX - containerRect.left;
+        const maxAllowed = containerWidth - rightWidth - minCenterWidth - (gapWidth * 2) - (resizerWidth * 2);
+        setLeftWidth(Math.max(minLeftWidth, Math.min(maxAllowed, Math.min(maxLeftWidth, newLeftWidth))));
+      }
+      
+      if (isDraggingRight) {
+        const newRightWidth = containerRect.right - e.clientX;
+        const maxAllowed = containerWidth - leftWidth - minCenterWidth - (gapWidth * 2) - (resizerWidth * 2);
+        setRightWidth(Math.max(minRightWidth, Math.min(maxAllowed, Math.min(maxRightWidth, newRightWidth))));
+      }
+    };
+
+    const handleMouseUp = () => {
+      setIsDraggingLeft(false);
+      setIsDraggingRight(false);
+    };
+
+    document.addEventListener('mousemove', handleMouseMove);
+    document.addEventListener('mouseup', handleMouseUp);
+
+    return () => {
+      document.removeEventListener('mousemove', handleMouseMove);
+      document.removeEventListener('mouseup', handleMouseUp);
+    };
+  }, [isDraggingLeft, isDraggingRight, containerWidth, leftWidth, rightWidth, minLeftWidth, maxLeftWidth, minRightWidth, maxRightWidth, minCenterWidth]);
+
+  const handleLeftDoubleClick = useCallback(() => {
+    setLeftWidth(defaultLeftWidth);
+  }, [defaultLeftWidth]);
+
+  const handleRightDoubleClick = useCallback(() => {
+    setRightWidth(defaultRightWidth);
+  }, [defaultRightWidth]);
+
+  return (
+    <div 
+      ref={containerRef}
+      className="h-full flex items-stretch select-none"
+      style={{ cursor: isDraggingLeft || isDraggingRight ? 'col-resize' : 'default' }}
+    >
+      {/* Left Panel */}
+      <div 
+        className="flex-shrink-0 min-h-0 overflow-hidden flex flex-col gap-2"
+        style={{ width: leftWidth }}
+      >
+        {leftPanel}
+      </div>
+
+      {/* Left Resizer */}
+      <div
+        onMouseDown={handleLeftMouseDown}
+        onDoubleClick={handleLeftDoubleClick}
+        className={`
+          flex-shrink-0 w-3 flex items-center justify-center cursor-col-resize
+          group hover:bg-blue-100 transition-colors rounded mx-0.5
+          ${isDraggingLeft ? 'bg-blue-200' : 'bg-transparent'}
+        `}
+        title="Drag to resize, double-click to reset"
+      >
+        <div className={`
+          w-1 h-12 rounded-full transition-all
+          ${isDraggingLeft ? 'bg-blue-500' : 'bg-gray-300 group-hover:bg-blue-400'}
+        `}>
+          <GripVertical 
+            size={12} 
+            className={`
+              mt-4 -ml-0.5 transition-colors
+              ${isDraggingLeft ? 'text-blue-600' : 'text-gray-400 group-hover:text-blue-500'}
+            `} 
+          />
+        </div>
+      </div>
+
+      {/* Center Panel */}
+      <div 
+        className="flex-1 min-w-0 min-h-0 overflow-hidden"
+        style={{ minWidth: minCenterWidth }}
+      >
+        {centerPanel}
+      </div>
+
+      {/* Right Resizer — only when right panel exists */}
+      {hasRight && (
+        <div
+          onMouseDown={handleRightMouseDown}
+          onDoubleClick={handleRightDoubleClick}
+          className={`
+            flex-shrink-0 w-3 flex items-center justify-center cursor-col-resize
+            group hover:bg-blue-100 transition-colors rounded mx-0.5
+            ${isDraggingRight ? 'bg-blue-200' : 'bg-transparent'}
+          `}
+          title="Drag to resize, double-click to reset"
+        >
+          <div className={`
+            w-1 h-12 rounded-full transition-all
+            ${isDraggingRight ? 'bg-blue-500' : 'bg-gray-300 group-hover:bg-blue-400'}
+          `}>
+            <GripVertical 
+              size={12} 
+              className={`
+                mt-4 -ml-0.5 transition-colors
+                ${isDraggingRight ? 'text-blue-600' : 'text-gray-400 group-hover:text-blue-500'}
+              `} 
+            />
+          </div>
+        </div>
+      )}
+
+      {/* Right Panel — only when present */}
+      {hasRight && (
+        <div 
+          className="flex-shrink-0 min-h-0 overflow-hidden"
+          style={{ width: rightWidth }}
+        >
+          {rightPanel}
+        </div>
+      )}
+
+      {/* Drag overlay to prevent selection */}
+      {(isDraggingLeft || isDraggingRight) && (
+        <div className="fixed inset-0 z-50 cursor-col-resize" />
+      )}
+    </div>
+  );
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/ocr/SidebarConfig.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/ocr/SidebarConfig.jsx
new file mode 100644
index 00000000..6502cca9
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/ocr/SidebarConfig.jsx
@@ -0,0 +1,308 @@
+import { useState } from 'react';
+import {
+  Key,
+  Eye,
+  EyeOff,
+  Loader2,
+  CheckCircle2,
+  AlertCircle,
+  ShieldCheck,
+  ShieldX,
+  RefreshCw,
+  Zap,
+  Minus,
+  Plus,
+  FileText,
+  ChevronDown,
+  ChevronUp,
+  RotateCcw,
+} from 'lucide-react';
+
+const PROVIDER_LABELS = {
+  gemini: 'Gemini',
+  chatgpt: 'ChatGPT',
+  deepseek: 'DeepSeek',
+  qwen: 'Qwen',
+};
+
+const KEY_PLACEHOLDERS = {
+  gemini: 'Enter Gemini API key...',
+  chatgpt: 'Enter OpenAI API key...',
+  deepseek: 'Enter DeepSeek API key...',
+  qwen: 'Enter DashScope API key...',
+};
+
+// Provider + API key panel.
+export default function SidebarConfig({
+  apiKey,
+  onApiKeyChange,
+  selectedModel,
+  onModelChange,
+  models = [],
+  isKeyValid,
+  isValidating,
+  onVerifyKey,
+  backendOnline,
+  batchSize,
+  onBatchSizeChange,
+  provider = 'gemini',
+  customPrompt,
+  onCustomPromptChange,
+}) {
+  const providerLabel = PROVIDER_LABELS[provider] || 'API';
+  const [showKey, setShowKey] = useState(false);
+  const [promptExpanded, setPromptExpanded] = useState(true);
+  const isCustomPrompt = customPrompt !== null && customPrompt !== undefined && customPrompt !== '';
+
+  const getKeyStatusBadge = () => {
+    if (isValidating) {
+      return (
+        <span className="inline-flex items-center gap-1.5 px-2.5 py-1 bg-blue-50 text-blue-600 text-xs font-medium rounded-full">
+          <Loader2 size={12} className="animate-spin" />
+          Verifying...
+        </span>
+      );
+    }
+    if (isKeyValid === true) {
+      return (
+        <span className="inline-flex items-center gap-1.5 px-2.5 py-1 bg-green-50 text-green-600 text-xs font-medium rounded-full">
+          <ShieldCheck size={12} />
+          Valid
+        </span>
+      );
+    }
+    if (isKeyValid === false) {
+      return (
+        <span className="inline-flex items-center gap-1.5 px-2.5 py-1 bg-red-50 text-red-600 text-xs font-medium rounded-full">
+          <ShieldX size={12} />
+          Invalid
+        </span>
+      );
+    }
+    return null;
+  };
+
+  return (
+    <div className="bg-white/95 backdrop-blur-sm rounded-xl border border-gray-200/80 shadow-sm overflow-hidden shrink-0">
+      {/* Header */}
+      <div className="px-3 py-2 bg-gradient-to-r from-blue-50/80 to-white border-b border-gray-100">
+        <h3 className="font-semibold text-gray-800 flex items-center gap-2 text-sm">
+          <Key size={16} className="text-blue-600" />
+          {providerLabel} Config
+        </h3>
+      </div>
+
+      <div className="p-3 space-y-3">
+        {/* Backend Status */}
+        {backendOnline === false && (
+          <div className="px-2.5 py-1.5 bg-red-50 border border-red-100 rounded-lg">
+            <p className="text-[11px] text-red-600 flex items-center gap-1.5">
+              <AlertCircle size={12} />
+              Backend offline
+            </p>
+          </div>
+        )}
+
+        {/* API Key Input */}
+        <div>
+          <div className="flex items-center justify-between mb-1.5">
+            <label className="text-xs font-medium text-gray-700">
+              API Key
+            </label>
+            {getKeyStatusBadge()}
+          </div>
+          <div className="relative">
+            <input
+              type={showKey ? 'text' : 'password'}
+              value={apiKey}
+              onChange={(e) => onApiKeyChange(e.target.value)}
+              placeholder={KEY_PLACEHOLDERS[provider] || 'Enter API key...'}
+              className="w-full px-2.5 py-2 pr-16 text-sm border border-gray-200 rounded-lg 
+                       focus:outline-none focus:ring-2 focus:ring-blue-100 focus:border-blue-400
+                       transition-colors bg-gray-50 focus:bg-white"
+            />
+            <div className="absolute right-1.5 top-1/2 -translate-y-1/2 flex items-center gap-0.5">
+              <button
+                onClick={() => setShowKey(!showKey)}
+                className="p-1 text-gray-400 hover:text-gray-600 rounded transition-colors"
+                title={showKey ? 'Hide key' : 'Show key'}
+              >
+                {showKey ? <EyeOff size={14} /> : <Eye size={14} />}
+              </button>
+            </div>
+          </div>
+
+          {/* Verify Button */}
+          <button
+            onClick={onVerifyKey}
+            disabled={!apiKey || apiKey.length < 10 || isValidating}
+            className="mt-1.5 w-full flex items-center justify-center gap-1.5 px-2.5 py-1.5 
+                     text-xs font-medium rounded-lg transition-colors
+                     disabled:bg-gray-100 disabled:text-gray-400 disabled:cursor-not-allowed
+                     bg-blue-50 text-blue-600 hover:bg-blue-100"
+          >
+            {isValidating ? (
+              <>
+                <Loader2 size={12} className="animate-spin" />
+                Verifying...
+              </>
+            ) : (
+              <>
+                <RefreshCw size={12} />
+                Verify Key
+              </>
+            )}
+          </button>
+        </div>
+
+        {/* Model Selection */}
+        <div>
+          <label className="block text-xs font-medium text-gray-700 mb-1.5">
+            Model
+          </label>
+          <select
+            value={selectedModel}
+            onChange={(e) => onModelChange(e.target.value)}
+            className="w-full px-2.5 py-2 text-sm border border-gray-200 rounded-lg 
+                     focus:outline-none focus:ring-2 focus:ring-blue-100 focus:border-blue-400
+                     transition-colors bg-gray-50 focus:bg-white cursor-pointer"
+          >
+            {models.map((model) => (
+              <option key={model.id} value={model.id}>
+                {model.name}
+              </option>
+            ))}
+          </select>
+          <p className="mt-1 text-[10px] text-gray-500 leading-relaxed">
+            {models.find((m) => m.id === selectedModel)?.description || ''}
+          </p>
+        </div>
+
+        {/* Batch Size / Rate Limit Setting */}
+        <div>
+          <label className="block text-xs font-medium text-gray-700 mb-1.5 flex items-center gap-1.5">
+            <Zap size={12} className="text-amber-500" />
+            Concurrent Requests
+          </label>
+          <div className="flex items-center gap-1">
+            <button
+              onClick={() => onBatchSizeChange(Math.max(1, batchSize - 1))}
+              disabled={batchSize <= 1}
+              className="p-1.5 rounded-lg border border-gray-200 text-gray-500 hover:bg-gray-100 
+                       disabled:opacity-40 disabled:cursor-not-allowed transition-colors"
+            >
+              <Minus size={14} />
+            </button>
+            <input
+              type="number"
+              min={1}
+              value={batchSize}
+              onChange={(e) => {
+                const val = parseInt(e.target.value);
+                if (!isNaN(val) && val >= 1) {
+                  onBatchSizeChange(val);
+                }
+              }}
+              className="w-14 px-2 py-1.5 text-center text-sm font-bold border border-gray-200 rounded-lg 
+                       focus:outline-none focus:ring-2 focus:ring-amber-100 focus:border-amber-400
+                       bg-gray-50 focus:bg-white [appearance:textfield] 
+                       [&::-webkit-outer-spin-button]:appearance-none [&::-webkit-inner-spin-button]:appearance-none"
+            />
+            <button
+              onClick={() => onBatchSizeChange(batchSize + 1)}
+              className="p-1.5 rounded-lg border border-gray-200 text-gray-500 hover:bg-gray-100 transition-colors"
+            >
+              <Plus size={14} />
+            </button>
+          </div>
+          <p className="mt-1 text-[10px] text-gray-500 leading-relaxed">
+            Pages to process at once.{provider === 'gemini' ? ' Gemini free tier: 5 req/min.' : ''}
+          </p>
+        </div>
+
+        {/* Custom Prompt Section */}
+        <div>
+          <button
+            onClick={() => setPromptExpanded(!promptExpanded)}
+            className="w-full flex items-center justify-between text-xs font-medium text-gray-700 mb-1.5 
+                       hover:text-blue-600 transition-colors"
+          >
+            <span className="flex items-center gap-1.5">
+              <FileText size={12} className="text-purple-500" />
+              OCR Prompt
+              {isCustomPrompt && (
+                <span className="inline-flex items-center px-1.5 py-0.5 bg-purple-50 text-purple-600 text-[9px] font-semibold rounded-full">
+                  Custom
+                </span>
+              )}
+              {!isCustomPrompt && (
+                <span className="inline-flex items-center px-1.5 py-0.5 bg-gray-100 text-gray-500 text-[9px] font-semibold rounded-full">
+                  Default
+                </span>
+              )}
+            </span>
+            {promptExpanded ? <ChevronUp size={14} /> : <ChevronDown size={14} />}
+          </button>
+
+          {promptExpanded && (
+            <div className="space-y-2">
+              {/* Toggle between default and custom */}
+              <div className="flex items-center gap-2">
+                <button
+                  onClick={() => onCustomPromptChange('')}
+                  className={`flex-1 px-2 py-1.5 text-[11px] font-medium rounded-lg border transition-colors ${
+                    !isCustomPrompt
+                      ? 'bg-blue-50 border-blue-200 text-blue-700'
+                      : 'bg-white border-gray-200 text-gray-500 hover:bg-gray-50'
+                  }`}
+                >
+                  System Default
+                </button>
+                <button
+                  onClick={() => {
+                    if (!isCustomPrompt) onCustomPromptChange(' ');
+                  }}
+                  className={`flex-1 px-2 py-1.5 text-[11px] font-medium rounded-lg border transition-colors ${
+                    isCustomPrompt
+                      ? 'bg-purple-50 border-purple-200 text-purple-700'
+                      : 'bg-white border-gray-200 text-gray-500 hover:bg-gray-50'
+                  }`}
+                >
+                  Custom Prompt
+                </button>
+              </div>
+
+              {isCustomPrompt && (
+                <>
+                  <textarea
+                    value={customPrompt}
+                    onChange={(e) => onCustomPromptChange(e.target.value)}
+                    placeholder="Enter your custom OCR prompt here..."
+                    rows={6}
+                    className="w-full px-2.5 py-2 text-xs border border-gray-200 rounded-lg 
+                             focus:outline-none focus:ring-2 focus:ring-purple-100 focus:border-purple-400
+                             transition-colors bg-gray-50 focus:bg-white resize-y
+                             placeholder:text-gray-400 leading-relaxed"
+                  />
+                  <button
+                    onClick={() => onCustomPromptChange('')}
+                    className="flex items-center gap-1 text-[10px] text-gray-500 hover:text-purple-600 transition-colors"
+                  >
+                    <RotateCcw size={10} />
+                    Reset to System Default
+                  </button>
+                </>
+              )}
+
+              {!isCustomPrompt && (
+                <p className="text-[10px] text-gray-500 leading-relaxed">
+                  Using the built-in prompt optimized for historical OCR transcription.
+                </p>
+              )}
+            </div>
+          )}
+        </div>
+      </div>
+    </div>
+  );
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/ocr/TranscriptPanel.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/ocr/TranscriptPanel.jsx
new file mode 100644
index 00000000..84056190
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/ocr/TranscriptPanel.jsx
@@ -0,0 +1,419 @@
+import { useState, useEffect, useMemo, memo } from 'react';
+import {
+  FileText,
+  Copy,
+  Check,
+  Download,
+  Save,
+  RotateCcw,
+  Type,
+  Loader2,
+  File,
+  FileType,
+  BookOpen,
+  Edit3,
+  Eye,
+  Sparkles,
+  ChevronDown,
+  ChevronUp,
+} from 'lucide-react';
+import { getLLMTemplates } from '../../services/llmApi';
+
+// [illegible], [margin], [unclear], ...
+const MARKER_REGEX = /(\[(?:illegible|margin|unclear|damaged|torn|faded|stained|missing|blank|unknown|(?:\?+))\])/gi;
+
+
+function HighlightedTranscript({ text, monospace }) {
+  const parts = useMemo(() => {
+    if (!text) return [];
+    
+    const result = [];
+    let lastIndex = 0;
+    let match;
+    
+    // The regex is /g and module-level, so reset before each pass.
+    MARKER_REGEX.lastIndex = 0;
+    
+    while ((match = MARKER_REGEX.exec(text)) !== null) {
+      if (match.index > lastIndex) {
+        result.push({
+          type: 'text',
+          content: text.slice(lastIndex, match.index),
+          key: `text-${lastIndex}`,
+        });
+      }
+      
+      result.push({
+        type: 'marker',
+        content: match[0],
+        key: `marker-${match.index}`,
+      });
+      
+      lastIndex = match.index + match[0].length;
+    }
+    
+    if (lastIndex < text.length) {
+      result.push({
+        type: 'text',
+        content: text.slice(lastIndex),
+        key: `text-${lastIndex}`,
+      });
+    }
+    
+    return result;
+  }, [text]);
+
+  return (
+    <div 
+      className={`w-full h-full p-3 text-sm leading-relaxed overflow-y-auto whitespace-pre-wrap ${
+        monospace ? 'font-mono' : 'font-sans'
+      }`}
+    >
+      {parts.map((part) => 
+        part.type === 'marker' ? (
+          <span 
+            key={part.key} 
+            className="text-red-600 font-semibold bg-red-50 px-0.5 rounded"
+            title="OCR uncertainty marker"
+          >
+            {part.content}
+          </span>
+        ) : (
+          <span key={part.key}>{part.content}</span>
+        )
+      )}
+    </div>
+  );
+}
+
+// Transcript viewer with copy, export and LLM cleanup.
+function TranscriptPanel({
+  pageNumber,
+  transcript,
+  originalTranscript,
+  isProcessing,
+  isProcessed,
+  hasAnyTranscript,
+  processedCount,
+  onTranscriptChange,
+  onReset,
+  onExport,
+  onSaveLocal,
+  exporting,
+  savingLocal,
+  onLLMProcess,
+  isLLMProcessing,
+  llmEnabled,
+}) {
+  const [copied, setCopied] = useState(false);
+  const [monospace, setMonospace] = useState(true);
+  const [editMode, setEditMode] = useState(false);
+  const [llmExpanded, setLlmExpanded] = useState(false);
+  const [llmTemplates, setLlmTemplates] = useState([]);
+  const [selectedTemplate, setSelectedTemplate] = useState('full_cleanup');
+
+  useEffect(() => {
+    if (!llmEnabled) return;
+    let cancelled = false;
+    getLLMTemplates()
+      .then(data => {
+        if (!cancelled && data.templates) {
+          setLlmTemplates(data.templates);
+        }
+      })
+      .catch(() => {}); // Silently fail - templates will just not show
+    return () => { cancelled = true; };
+  }, [llmEnabled]);
+
+  const hasChanges = transcript !== originalTranscript;
+  
+  const markerCount = useMemo(() => {
+    if (!transcript) return 0;
+    MARKER_REGEX.lastIndex = 0;
+    const matches = transcript.match(MARKER_REGEX);
+    return matches ? matches.length : 0;
+  }, [transcript]);
+
+  const handleCopy = async () => {
+    if (transcript) {
+      await navigator.clipboard.writeText(transcript);
+      setCopied(true);
+      setTimeout(() => setCopied(false), 2000);
+    }
+  };
+
+  const handleDownloadTxt = () => {
+    if (!transcript) return;
+    // BOM keeps accents intact in Notepad/Excel.
+    const utf8Bom = '\uFEFF';
+    const blob = new Blob([utf8Bom + transcript], { type: 'text/plain;charset=utf-8' });
+    const url = URL.createObjectURL(blob);
+    const a = document.createElement('a');
+    a.href = url;
+    a.download = `page_${pageNumber}_transcript.txt`;
+    document.body.appendChild(a);
+    a.click();
+    document.body.removeChild(a);
+    URL.revokeObjectURL(url);
+  };
+
+  return (
+    <div className="bg-white/95 backdrop-blur-sm rounded-xl border border-gray-200/80 shadow-sm overflow-hidden flex flex-col h-full">
+      {/* Sticky Header */}
+      <div className="px-3 py-2 bg-gradient-to-r from-blue-50/80 via-white to-indigo-50/50 border-b border-gray-100/80 flex items-center justify-between shrink-0 sticky top-0 z-10">
+        <h3 className="font-bold text-gray-800 flex items-center gap-2 text-sm">
+          <div className="p-1 bg-gradient-to-br from-blue-500 to-indigo-600 rounded-lg text-white">
+            <FileText size={12} />
+          </div>
+          <span>Transcript</span>
+          {isProcessed && (
+            <span className="text-xs text-gray-400 font-normal">
+              #{pageNumber}
+            </span>
+          )}
+        </h3>
+
+        {/* Actions */}
+        <div className="flex items-center gap-0.5">
+          {isProcessed && transcript && (
+            <>
+              {/* Edit/View toggle */}
+              <button
+                onClick={() => setEditMode(!editMode)}
+                className={`p-1.5 rounded-lg transition-all duration-200 ${editMode
+                    ? 'bg-amber-100 text-amber-600'
+                    : 'text-gray-400 hover:text-gray-600 hover:bg-gray-100'
+                  }`}
+                title={editMode ? 'Switch to view mode' : 'Switch to edit mode'}
+              >
+                {editMode ? <Eye size={14} /> : <Edit3 size={14} />}
+              </button>
+
+              {/* Monospace toggle */}
+              <button
+                onClick={() => setMonospace(!monospace)}
+                className={`p-1.5 rounded-lg transition-all duration-200 ${monospace
+                    ? 'bg-blue-100 text-blue-600'
+                    : 'text-gray-400 hover:text-gray-600 hover:bg-gray-100'
+                  }`}
+                title={monospace ? 'Switch to sans-serif' : 'Switch to monospace'}
+              >
+                <Type size={14} />
+              </button>
+
+              {/* Copy button */}
+              <button
+                onClick={handleCopy}
+                className={`p-1.5 rounded-lg transition-all duration-200 ${copied
+                    ? 'bg-green-100 text-green-600'
+                    : 'text-gray-400 hover:text-gray-600 hover:bg-gray-100'
+                  }`}
+                title="Copy to clipboard"
+              >
+                {copied ? <Check size={14} /> : <Copy size={14} />}
+              </button>
+
+              {/* Download single page */}
+              <button
+                onClick={handleDownloadTxt}
+                className="p-1.5 rounded-lg text-gray-400 hover:text-gray-600 hover:bg-gray-100 transition-all duration-200"
+                title="Download this page as TXT"
+              >
+                <Download size={14} />
+              </button>
+
+              {/* Reset button */}
+              {hasChanges && (
+                <button
+                  onClick={onReset}
+                  className="p-1.5 rounded-lg text-amber-500 hover:text-amber-600 hover:bg-amber-50 transition-all duration-200"
+                  title="Reset to original"
+                >
+                  <RotateCcw size={14} />
+                </button>
+              )}
+            </>
+          )}
+        </div>
+      </div>
+
+      {/* Transcript Content - flex-1 takes remaining space */}
+      <div className="flex-1 relative min-h-0 overflow-hidden">
+        {isProcessing ? (
+          <div className="absolute inset-0 flex flex-col items-center justify-center bg-gradient-to-br from-blue-50/50 to-indigo-50/50">
+            <div className="relative">
+              <Loader2 size={36} className="animate-spin text-blue-600" />
+              <div className="absolute inset-0 blur-xl bg-blue-400/30 animate-pulse" />
+            </div>
+            <p className="text-blue-600 font-semibold mt-3 text-sm">Extracting text...</p>
+            <p className="text-blue-500 text-xs">This may take a few seconds</p>
+          </div>
+        ) : !isProcessed ? (
+          <div className="absolute inset-0 flex flex-col items-center justify-center text-gray-400 bg-gradient-to-br from-gray-50/50 to-gray-100/30">
+            <div className="p-3 bg-gradient-to-br from-gray-100 to-gray-50 rounded-xl">
+              <FileText size={36} className="opacity-40" />
+            </div>
+            <p className="font-semibold mt-3 text-sm">No transcript yet</p>
+            <p className="text-xs">Process this page to extract text</p>
+          </div>
+        ) : editMode ? (
+          <textarea
+            value={transcript || ''}
+            onChange={(e) => onTranscriptChange(e.target.value)}
+            className={`w-full h-full p-3 resize-none focus:outline-none text-sm leading-relaxed 
+                       border-0 focus:ring-0 bg-transparent overflow-y-auto ${monospace ? 'font-mono' : 'font-sans'}`}
+            placeholder="Transcript will appear here..."
+          />
+        ) : (
+          <HighlightedTranscript text={transcript} monospace={monospace} />
+        )}
+      </div>
+
+      {/* Footer with stats */}
+      {isProcessed && transcript && (
+        <div className="px-3 py-1.5 border-t border-gray-100 bg-gradient-to-r from-gray-50 to-white flex items-center justify-between text-xs text-gray-500 shrink-0">
+          <div className="flex items-center gap-2">
+            <span className="font-medium">{transcript.length.toLocaleString()} chars</span>
+            {markerCount > 0 && (
+              <span className="text-red-600 font-semibold flex items-center gap-1 px-1.5 py-0.5 bg-red-50 rounded-full">
+                <span className="w-1 h-1 bg-red-500 rounded-full" />
+                {markerCount} uncertain
+              </span>
+            )}
+          </div>
+          {hasChanges && (
+            <span className="text-amber-600 font-semibold flex items-center gap-1 px-1.5 py-0.5 bg-amber-50 rounded-full">
+              <span className="w-1 h-1 bg-amber-500 rounded-full animate-pulse" />
+              Modified
+            </span>
+          )}
+        </div>
+      )}
+
+      {/* AI Polish Section */}
+      {llmEnabled && isProcessed && transcript && (
+        <div className="border-t border-gray-100 shrink-0">
+          <button
+            onClick={() => setLlmExpanded(!llmExpanded)}
+            className="w-full px-3 py-2 flex items-center justify-between text-xs font-bold text-gray-700 hover:bg-gray-50 transition-colors"
+          >
+            <span className="flex items-center gap-1.5">
+              <div className="p-1 bg-gradient-to-br from-purple-500 to-pink-500 rounded text-white">
+                <Sparkles size={10} />
+              </div>
+              AI Polish
+            </span>
+            {llmExpanded ? <ChevronUp size={14} className="text-gray-400" /> : <ChevronDown size={14} className="text-gray-400" />}
+          </button>
+          {llmExpanded && (
+            <div className="px-3 pb-3 space-y-2">
+              <select
+                value={selectedTemplate}
+                onChange={(e) => setSelectedTemplate(e.target.value)}
+                className="w-full text-xs border border-gray-200 rounded-lg px-2 py-1.5 bg-white focus:outline-none focus:ring-1 focus:ring-purple-300 focus:border-purple-300"
+                disabled={isLLMProcessing}
+              >
+                {llmTemplates.length > 0 ? (
+                  llmTemplates.map(t => (
+                    <option key={t.id} value={t.id}>{t.name}</option>
+                  ))
+                ) : (
+                  <>
+                    <option value="full_cleanup">Full Cleanup</option>
+                    <option value="spelling_correction">Spelling Correction</option>
+                    <option value="formatting">Formatting</option>
+                    <option value="historical_normalization">Historical Normalization</option>
+                  </>
+                )}
+              </select>
+              {llmTemplates.length > 0 && (
+                <p className="text-[10px] text-gray-400 leading-tight">
+                  {llmTemplates.find(t => t.id === selectedTemplate)?.description || ''}
+                </p>
+              )}
+              <button
+                onClick={() => onLLMProcess && onLLMProcess(selectedTemplate)}
+                disabled={isLLMProcessing || !transcript}
+                className="w-full flex items-center justify-center gap-1.5 py-2 rounded-lg text-xs font-semibold
+                         bg-gradient-to-r from-purple-500 to-pink-500 text-white
+                         hover:from-purple-600 hover:to-pink-600 shadow-sm hover:shadow
+                         transition-all duration-200
+                         disabled:opacity-50 disabled:cursor-not-allowed"
+              >
+                {isLLMProcessing ? (
+                  <>
+                    <Loader2 size={14} className="animate-spin" />
+                    Polishing...
+                  </>
+                ) : (
+                  <>
+                    <Sparkles size={14} />
+                    Polish with AI
+                  </>
+                )}
+              </button>
+            </div>
+          )}
+        </div>
+      )}
+
+      {/* Export Panel - Compact */}
+      {hasAnyTranscript && (
+        <div className="px-3 py-3 border-t border-gray-200 bg-gradient-to-r from-white to-gray-50/50 shrink-0">
+          <div className="flex items-center justify-between mb-2">
+            <h4 className="text-xs font-bold text-gray-700 flex items-center gap-1.5">
+              <div className="p-1 bg-gradient-to-br from-blue-500 to-indigo-600 rounded text-white">
+                <Download size={10} />
+              </div>
+              Export All
+            </h4>
+            <span className="text-[10px] text-gray-500 font-medium bg-gray-100 px-1.5 py-0.5 rounded-full">
+              {processedCount} pages
+            </span>
+          </div>
+
+          <div className="grid grid-cols-4 gap-1.5">
+            {[
+              { format: 'txt', icon: FileText, label: 'TXT' },
+              { format: 'csv', icon: File, label: 'CSV' },
+              { format: 'json', icon: FileType, label: 'JSON' },
+              { format: 'pdf', icon: BookOpen, label: 'PDF' },
+            ].map(({ format, icon: Icon, label }) => (
+              <button
+                key={format}
+                onClick={() => onExport(format)}
+                disabled={exporting}
+                className="flex flex-col items-center gap-1 py-2 rounded-lg border border-gray-200 
+                         bg-white hover:border-blue-300 hover:bg-blue-50 hover:shadow-sm 
+                         transition-all duration-200
+                         disabled:opacity-50 disabled:cursor-not-allowed"
+              >
+                {exporting === format ? (
+                  <Loader2 size={16} className="animate-spin text-blue-600" />
+                ) : (
+                  <Icon size={16} className="text-blue-600" />
+                )}
+                <span className="text-[10px] font-semibold text-gray-700">{label}</span>
+              </button>
+            ))}
+          </div>
+
+          {onSaveLocal && (
+            <button
+              onClick={onSaveLocal}
+              disabled={savingLocal}
+              className="mt-2 w-full flex items-center justify-center gap-2 py-2 rounded-lg border border-cyan-200 bg-cyan-50 text-cyan-700 hover:bg-cyan-100 transition-all duration-200 disabled:opacity-50 disabled:cursor-not-allowed"
+            >
+              {savingLocal ? <Loader2 size={14} className="animate-spin" /> : <Save size={14} />}
+              Save to My Files
+            </button>
+          )}
+        </div>
+      )}
+    </div>
+  );
+}
+
+// Typing is the hot path — memo keeps unrelated parent state (rate limit,
+// zoom) from re-rendering the panel on every keystroke.
+export default memo(TranscriptPanel);
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/ocr/index.js b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/ocr/index.js
new file mode 100644
index 00000000..4f625399
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/ocr/index.js
@@ -0,0 +1,7 @@
+export { default as OCRLayout } from './OCRLayout';
+export { default as SidebarConfig } from './SidebarConfig';
+export { default as PageThumbnailList } from './PageThumbnailList';
+export { default as PagePreview } from './PagePreview';
+export { default as TranscriptPanel } from './TranscriptPanel';
+export { default as PreviewZoomViewer } from './PreviewZoomViewer';
+export { default as ResizablePanels } from './ResizablePanels';
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/preprocess/BeforeAfterViewer.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/preprocess/BeforeAfterViewer.jsx
new file mode 100644
index 00000000..61b1bb30
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/preprocess/BeforeAfterViewer.jsx
@@ -0,0 +1,372 @@
+import React, { useState, useRef, useEffect, useCallback } from 'react';
+import { 
+  Eye, 
+  EyeOff, 
+  Maximize2, 
+  Minimize2, 
+  ZoomIn, 
+  ZoomOut, 
+  RotateCcw, 
+  GripVertical,
+  Move,
+  Search
+} from 'lucide-react';
+
+// Split-view comparison: draggable divider, before/after toggle, synced
+// zoom and pan across both images.
+export default function BeforeAfterViewer({
+  originalImage,
+  processedImage,
+  isProcessing = false,
+  processingLabel = 'Processing...',
+  onOpenZoomLens,
+  className = '',
+}) {
+  const [mode, setMode] = useState('split'); // 'split', 'before', 'after', 'toggle'
+  const [splitPosition, setSplitPosition] = useState(50);
+  const [zoom, setZoom] = useState('fit'); // 'fit', 50, 75, 100, 150, 200, 300
+  const [isDraggingSplit, setIsDraggingSplit] = useState(false);
+  const [isFullscreen, setIsFullscreen] = useState(false);
+  const [isPanning, setIsPanning] = useState(false);
+  const [panOffset, setPanOffset] = useState({ x: 0, y: 0 });
+  const [panStart, setPanStart] = useState({ x: 0, y: 0 });
+  
+  const containerRef = useRef(null);
+  const imageRef = useRef(null);
+
+  const zoomLevels = ['fit', 50, 75, 100, 150, 200, 300];
+
+  const handleMouseMove = useCallback((e) => {
+    if (isDraggingSplit && mode === 'split' && containerRef.current) {
+      const rect = containerRef.current.getBoundingClientRect();
+      const x = e.clientX - rect.left;
+      const percentage = Math.max(5, Math.min(95, (x / rect.width) * 100));
+      setSplitPosition(percentage);
+    }
+    
+    if (isPanning && zoom !== 'fit') {
+      const deltaX = e.clientX - panStart.x;
+      const deltaY = e.clientY - panStart.y;
+      setPanOffset({
+        x: panOffset.x + deltaX,
+        y: panOffset.y + deltaY,
+      });
+      setPanStart({ x: e.clientX, y: e.clientY });
+    }
+  }, [isDraggingSplit, isPanning, mode, zoom, panStart, panOffset]);
+
+  const handleMouseUp = useCallback(() => {
+    setIsDraggingSplit(false);
+    setIsPanning(false);
+  }, []);
+
+  const handleMouseDown = useCallback((e) => {
+    if (zoom !== 'fit' && e.button === 0) {
+      setIsPanning(true);
+      setPanStart({ x: e.clientX, y: e.clientY });
+    }
+  }, [zoom]);
+
+  useEffect(() => {
+    if (isDraggingSplit || isPanning) {
+      window.addEventListener('mousemove', handleMouseMove);
+      window.addEventListener('mouseup', handleMouseUp);
+      return () => {
+        window.removeEventListener('mousemove', handleMouseMove);
+        window.removeEventListener('mouseup', handleMouseUp);
+      };
+    }
+  }, [isDraggingSplit, isPanning, handleMouseMove, handleMouseUp]);
+
+  useEffect(() => {
+    if (zoom === 'fit') {
+      setPanOffset({ x: 0, y: 0 });
+    }
+  }, [zoom]);
+
+  const handleZoomIn = () => {
+    const currentIndex = zoomLevels.indexOf(zoom);
+    if (currentIndex < zoomLevels.length - 1) {
+      setZoom(zoomLevels[currentIndex + 1]);
+    }
+  };
+
+  const handleZoomOut = () => {
+    const currentIndex = zoomLevels.indexOf(zoom);
+    if (currentIndex > 0) {
+      setZoom(zoomLevels[currentIndex - 1]);
+    }
+  };
+
+  const handleResetView = () => {
+    setZoom('fit');
+    setPanOffset({ x: 0, y: 0 });
+    setSplitPosition(50);
+  };
+
+  const getImageStyle = () => {
+    if (zoom === 'fit') {
+      return { 
+        width: '100%', 
+        height: '100%', 
+        objectFit: 'contain',
+        transform: 'none',
+      };
+    }
+    return { 
+      transform: `scale(${zoom / 100}) translate(${panOffset.x / (zoom / 100)}px, ${panOffset.y / (zoom / 100)}px)`,
+      transformOrigin: 'center center',
+      cursor: isPanning ? 'grabbing' : 'grab',
+    };
+  };
+
+  const imageStyle = getImageStyle();
+
+  return (
+    <div
+      className={`bg-white rounded-xl shadow-card overflow-hidden flex flex-col transition-all ${
+        isFullscreen ? 'fixed inset-4 z-50' : ''
+      } ${className}`}
+    >
+      {/* Controls toolbar */}
+      <div className="flex items-center justify-between px-3 py-2 border-b border-gray-100 bg-gray-50/50">
+        {/* View mode toggle */}
+        <div className="flex items-center bg-white rounded-lg border border-gray-200 p-0.5">
+          <button
+            onClick={() => setMode('before')}
+            className={`px-3 py-1.5 text-xs font-medium rounded-md transition-all ${
+              mode === 'before'
+                ? 'bg-blue-500 text-white shadow-sm'
+                : 'text-gray-600 hover:text-blue-600 hover:bg-blue-50'
+            }`}
+          >
+            <Eye className="w-3.5 h-3.5 inline mr-1" />
+            Before
+          </button>
+          <button
+            onClick={() => setMode('split')}
+            className={`px-3 py-1.5 text-xs font-medium rounded-md transition-all ${
+              mode === 'split'
+                ? 'bg-blue-500 text-white shadow-sm'
+                : 'text-gray-600 hover:text-blue-600 hover:bg-blue-50'
+            }`}
+          >
+            Split
+          </button>
+          <button
+            onClick={() => setMode('after')}
+            className={`px-3 py-1.5 text-xs font-medium rounded-md transition-all ${
+              mode === 'after'
+                ? 'bg-blue-500 text-white shadow-sm'
+                : 'text-gray-600 hover:text-blue-600 hover:bg-blue-50'
+            }`}
+          >
+            <EyeOff className="w-3.5 h-3.5 inline mr-1" />
+            After
+          </button>
+        </div>
+
+        {/* Center - Zoom controls */}
+        <div className="flex items-center gap-1 bg-white rounded-lg border border-gray-200 p-0.5">
+          <button
+            onClick={handleZoomOut}
+            disabled={zoom === 'fit' || zoomLevels.indexOf(zoom) === 0}
+            className="p-1.5 rounded-md hover:bg-gray-100 disabled:opacity-40 disabled:cursor-not-allowed transition-colors"
+          >
+            <ZoomOut className="w-4 h-4 text-gray-600" />
+          </button>
+          
+          <select
+            value={zoom}
+            onChange={(e) => setZoom(e.target.value === 'fit' ? 'fit' : parseInt(e.target.value))}
+            className="px-2 py-1 text-xs font-medium text-gray-600 bg-transparent border-0 
+                       min-w-[4.5rem] text-center focus:outline-none focus:ring-0"
+          >
+            <option value="fit">Fit</option>
+            <option value="50">50%</option>
+            <option value="75">75%</option>
+            <option value="100">100%</option>
+            <option value="150">150%</option>
+            <option value="200">200%</option>
+            <option value="300">300%</option>
+          </select>
+          
+          <button
+            onClick={handleZoomIn}
+            disabled={zoomLevels.indexOf(zoom) === zoomLevels.length - 1}
+            className="p-1.5 rounded-md hover:bg-gray-100 disabled:opacity-40 disabled:cursor-not-allowed transition-colors"
+          >
+            <ZoomIn className="w-4 h-4 text-gray-600" />
+          </button>
+          
+          <div className="w-px h-4 bg-gray-200 mx-0.5" />
+          
+          <button
+            onClick={handleResetView}
+            className="p-1.5 rounded-md hover:bg-gray-100 transition-colors"
+            title="Reset view"
+          >
+            <RotateCcw className="w-4 h-4 text-gray-600" />
+          </button>
+        </div>
+
+        {/* Right - Actions */}
+        <div className="flex items-center gap-1">
+          {/* Zoom lens button */}
+          {onOpenZoomLens && (
+            <button
+              onClick={onOpenZoomLens}
+              className="p-2 rounded-lg hover:bg-gray-100 transition-colors"
+              title="Open zoom lens"
+            >
+              <Search className="w-4 h-4 text-gray-600" />
+            </button>
+          )}
+          
+          {/* Pan indicator */}
+          {zoom !== 'fit' && (
+            <div className="flex items-center gap-1 px-2 py-1 bg-blue-50 rounded-lg text-xs text-blue-600">
+              <Move size={12} />
+              <span>Pan</span>
+            </div>
+          )}
+
+          {/* Fullscreen toggle */}
+          <button
+            onClick={() => setIsFullscreen(!isFullscreen)}
+            className="p-2 rounded-lg hover:bg-gray-100 transition-colors"
+          >
+            {isFullscreen ? (
+              <Minimize2 className="w-4 h-4 text-gray-600" />
+            ) : (
+              <Maximize2 className="w-4 h-4 text-gray-600" />
+            )}
+          </button>
+        </div>
+      </div>
+
+      {/* Image container */}
+      <div
+        ref={containerRef}
+        className={`relative flex-1 bg-gray-100 overflow-hidden ${
+          zoom !== 'fit' ? 'cursor-grab active:cursor-grabbing' : ''
+        }`}
+        onMouseDown={zoom !== 'fit' ? handleMouseDown : undefined}
+        style={{ minHeight: isFullscreen ? 'auto' : '400px' }}
+      >
+        {/* Processing overlay */}
+        {isProcessing && (
+          <div className="absolute inset-0 z-30 flex items-center justify-center bg-white/80 backdrop-blur-sm">
+            <div className="flex flex-col items-center">
+              <div className="w-12 h-12 border-3 border-blue-200 border-t-blue-600 rounded-full animate-spin" />
+              <p className="mt-3 text-sm font-medium text-blue-600">{processingLabel}</p>
+            </div>
+          </div>
+        )}
+
+        {/* Original image (shown in before and split modes) */}
+        {(mode === 'before' || mode === 'split') && originalImage && (
+          <div
+            className={`absolute inset-0 flex items-center justify-center ${
+              mode === 'split' ? 'overflow-hidden' : ''
+            }`}
+            style={
+              mode === 'split'
+                ? { clipPath: `inset(0 ${100 - splitPosition}% 0 0)` }
+                : {}
+            }
+          >
+            <img
+              ref={imageRef}
+              src={originalImage}
+              alt="Original"
+              className={zoom === 'fit' ? 'max-w-full max-h-full object-contain' : 'max-w-none'}
+              style={imageStyle}
+              draggable={false}
+            />
+          </div>
+        )}
+
+        {/* Processed image (shown in after and split modes) */}
+        {(mode === 'after' || mode === 'split') && (
+          <div
+            className={`absolute inset-0 flex items-center justify-center ${
+              mode === 'split' ? 'overflow-hidden' : ''
+            }`}
+            style={
+              mode === 'split'
+                ? { clipPath: `inset(0 0 0 ${splitPosition}%)` }
+                : {}
+            }
+          >
+            {processedImage ? (
+              <img
+                src={processedImage}
+                alt="Processed"
+                className={zoom === 'fit' ? 'max-w-full max-h-full object-contain' : 'max-w-none'}
+                style={imageStyle}
+                draggable={false}
+              />
+            ) : (
+              <div className="text-gray-400 text-center px-4">
+                <div className="w-16 h-16 mx-auto mb-3 rounded-full bg-gray-200 flex items-center justify-center">
+                  <EyeOff className="w-8 h-8 text-gray-400" />
+                </div>
+                <p className="text-sm font-medium">No processed image</p>
+                <p className="text-xs mt-1">Apply preprocessing to see results</p>
+              </div>
+            )}
+          </div>
+        )}
+
+        {/* Split slider handle */}
+        {mode === 'split' && (
+          <div
+            className="absolute top-0 bottom-0 w-1 cursor-ew-resize z-20 group"
+            style={{ left: `calc(${splitPosition}% - 2px)` }}
+            onMouseDown={(e) => {
+              e.preventDefault();
+              setIsDraggingSplit(true);
+            }}
+          >
+            {/* Visible line */}
+            <div className="absolute inset-y-0 left-1/2 -translate-x-1/2 w-0.5 bg-white shadow-lg" />
+            
+            {/* Handle circle */}
+            <div className="absolute top-1/2 left-1/2 -translate-x-1/2 -translate-y-1/2 
+                            w-10 h-10 bg-white rounded-full flex items-center justify-center 
+                            shadow-lg border-2 border-blue-500 cursor-grab active:cursor-grabbing
+                            group-hover:scale-110 transition-transform">
+              <GripVertical className="w-4 h-4 text-blue-500" />
+            </div>
+
+            {/* Labels */}
+            <div className="absolute top-3 -translate-x-full -left-2 
+                            bg-gray-900/80 text-white text-[10px] font-medium 
+                            px-2 py-1 rounded shadow-md pointer-events-none">
+              Before
+            </div>
+            <div className="absolute top-3 left-2 
+                            bg-blue-600/90 text-white text-[10px] font-medium 
+                            px-2 py-1 rounded shadow-md pointer-events-none">
+              After
+            </div>
+          </div>
+        )}
+
+        {/* No image placeholder */}
+        {!originalImage && !isProcessing && (
+          <div className="absolute inset-0 flex items-center justify-center">
+            <div className="text-center text-gray-400">
+              <div className="w-16 h-16 mx-auto mb-3 rounded-full bg-gray-200 flex items-center justify-center">
+                <Eye className="w-8 h-8 text-gray-400" />
+              </div>
+              <p className="text-sm font-medium">No image selected</p>
+              <p className="text-xs mt-1">Select a page to preview</p>
+            </div>
+          </div>
+        )}
+      </div>
+    </div>
+  );
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/preprocess/ImageEraser.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/preprocess/ImageEraser.jsx
new file mode 100644
index 00000000..2a4e4537
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/preprocess/ImageEraser.jsx
@@ -0,0 +1,600 @@
+import React, { useState, useRef, useEffect, useCallback } from 'react';
+import {
+    Eraser,
+    Check,
+    X,
+    Undo2,
+    ZoomIn,
+    ZoomOut,
+    Minus,
+    Plus,
+    ChevronLeft,
+    ChevronRight,
+} from 'lucide-react';
+
+// "2_left" -> "2L".
+function shortPageLabel(pageNumber) {
+    return String(pageNumber).replace('_left', 'L').replace('_right', 'R');
+}
+
+// Full-screen canvas eraser. Erase across several pages, then save them all in
+// one go. Undo is per stroke.
+export default function ImageEraser({
+    pages = [],           // [{ pageNumber, imageSrc }]
+    initialPageIndex = 0,
+    onSaveAll,            // (results: { [pageNumber]: dataUrl }) => void
+    onCancel,
+}) {
+    const [currentIdx, setCurrentIdx] = useState(initialPageIndex);
+    // { [pageNumber]: ImageData }
+    const pageDataRef = useRef({});
+    const currentPage = pages[currentIdx];
+
+    const containerRef = useRef(null);
+    const canvasRef = useRef(null);
+    const lastPointRef = useRef(null);
+
+    // Brush cap is ~5% of the image's longer side, so one stroke can still wipe
+    // a stain on a 12000px scan. Recomputed on every image load.
+    const [maxBrush, setMaxBrush] = useState(200);
+    const [brushSize, setBrushSize] = useState(50);
+    const [isDrawing, setIsDrawing] = useState(false);
+    const [canvasReady, setCanvasReady] = useState(false);
+    const [strokeHistory, setStrokeHistory] = useState([]);
+    const [zoom, setZoom] = useState(1);
+    const [pan, setPan] = useState({ x: 0, y: 0 });
+    const [isPanning, setIsPanning] = useState(false);
+    const panStartRef = useRef({ x: 0, y: 0, panX: 0, panY: 0 });
+    const [imageSize, setImageSize] = useState({ w: 0, h: 0 });
+    const [cursorPos, setCursorPos] = useState({ x: 0, y: 0 });
+    const [modifiedPages, setModifiedPages] = useState(new Set());
+
+    // ── Save the current page before switching ──
+    const saveCurrentPageData = useCallback(() => {
+        const canvas = canvasRef.current;
+        if (!canvas || !currentPage) return;
+        const ctx = canvas.getContext('2d');
+        pageDataRef.current[currentPage.pageNumber] = ctx.getImageData(0, 0, canvas.width, canvas.height);
+    }, [currentPage]);
+
+    // ── Load image ──
+    const loadPage = useCallback((page) => {
+        setCanvasReady(false);
+        setStrokeHistory([]);
+
+        const img = new Image();
+        img.crossOrigin = 'anonymous';
+        img.onload = () => {
+            const canvas = canvasRef.current;
+            if (!canvas) return;
+            canvas.width = img.naturalWidth;
+            canvas.height = img.naturalHeight;
+            setImageSize({ w: img.naturalWidth, h: img.naturalHeight });
+
+            const dynMax = Math.max(
+                40,
+                Math.round(0.05 * Math.max(img.naturalWidth, img.naturalHeight)),
+            );
+            setMaxBrush(dynMax);
+            setBrushSize((s) => Math.min(s, dynMax));
+
+            const ctx = canvas.getContext('2d');
+
+            // Restore saved strokes for this page, if any.
+            const saved = pageDataRef.current[page.pageNumber];
+            if (saved && saved.width === img.naturalWidth && saved.height === img.naturalHeight) {
+                ctx.putImageData(saved, 0, 0);
+            } else {
+                ctx.drawImage(img, 0, 0);
+            }
+
+            if (containerRef.current) {
+                const cw = containerRef.current.clientWidth;
+                const ch = containerRef.current.clientHeight;
+                const fitZoom = Math.min(cw / img.naturalWidth, ch / img.naturalHeight, 1) * 0.92;
+                setZoom(fitZoom);
+                setPan({
+                    x: (cw - img.naturalWidth * fitZoom) / 2,
+                    y: (ch - img.naturalHeight * fitZoom) / 2,
+                });
+            }
+
+            setCanvasReady(true);
+        };
+        img.src = page.imageSrc;
+    }, []);
+
+    useEffect(() => {
+        if (currentPage) loadPage(currentPage);
+    }, []);
+
+    // ── Page navigation ──
+    const goToPage = useCallback((newIdx) => {
+        if (newIdx < 0 || newIdx >= pages.length || newIdx === currentIdx) return;
+        saveCurrentPageData();
+        setCurrentIdx(newIdx);
+    }, [currentIdx, pages.length, saveCurrentPageData]);
+
+    useEffect(() => {
+        if (currentPage) loadPage(currentPage);
+    }, [currentIdx]);
+
+    // ── Coordinates ──
+    const screenToCanvas = useCallback(
+        (clientX, clientY) => {
+            if (!containerRef.current) return { x: 0, y: 0 };
+            const rect = containerRef.current.getBoundingClientRect();
+            return {
+                x: (clientX - rect.left - pan.x) / zoom,
+                y: (clientY - rect.top - pan.y) / zoom,
+            };
+        },
+        [zoom, pan]
+    );
+
+    const getClientPos = (e) => {
+        if (e.touches && e.touches.length > 0) {
+            return { clientX: e.touches[0].clientX, clientY: e.touches[0].clientY };
+        }
+        return { clientX: e.clientX, clientY: e.clientY };
+    };
+
+    // ── Undo snapshots ──
+    // Only the stroke's bounding box is stored, not the whole canvas — roughly
+    // 100x less memory for a typical brush on a full-res page.
+    const strokeBoundsRef = useRef(null);
+
+    const beginStrokeBounds = useCallback(() => {
+        strokeBoundsRef.current = null;
+    }, []);
+
+    const extendStrokeBounds = useCallback((x, y) => {
+        const pad = brushSize;
+        const b = strokeBoundsRef.current;
+        if (!b) {
+            strokeBoundsRef.current = { minX: x - pad, minY: y - pad, maxX: x + pad, maxY: y + pad };
+        } else {
+            if (x - pad < b.minX) b.minX = x - pad;
+            if (y - pad < b.minY) b.minY = y - pad;
+            if (x + pad > b.maxX) b.maxX = x + pad;
+            if (y + pad > b.maxY) b.maxY = y + pad;
+        }
+    }, [brushSize]);
+
+    // Clone of the canvas at pointer-down. On pointer-up we read back just the
+    // dirty rect from here.
+    const preStrokeCanvasRef = useRef(null);
+
+    const clonePreStroke = useCallback(() => {
+        const src = canvasRef.current;
+        if (!src) return;
+        if (!preStrokeCanvasRef.current) {
+            preStrokeCanvasRef.current = document.createElement('canvas');
+        }
+        const dst = preStrokeCanvasRef.current;
+        if (dst.width !== src.width || dst.height !== src.height) {
+            dst.width = src.width;
+            dst.height = src.height;
+        }
+        dst.getContext('2d').drawImage(src, 0, 0);
+    }, []);
+
+    const commitStrokeDelta = useCallback(() => {
+        const bounds = strokeBoundsRef.current;
+        const pre = preStrokeCanvasRef.current;
+        if (!bounds || !pre) return;
+        const w = pre.width, h = pre.height;
+        const x = Math.max(0, Math.floor(bounds.minX));
+        const y = Math.max(0, Math.floor(bounds.minY));
+        const rw = Math.min(w - x, Math.ceil(bounds.maxX - bounds.minX));
+        const rh = Math.min(h - y, Math.ceil(bounds.maxY - bounds.minY));
+        if (rw <= 0 || rh <= 0) return;
+        const data = pre.getContext('2d').getImageData(x, y, rw, rh);
+        setStrokeHistory((prev) => {
+            const next = [...prev, { x, y, w: rw, h: rh, data }];
+            return next.length > 20 ? next.slice(-20) : next;
+        });
+        strokeBoundsRef.current = null;
+    }, []);
+
+    const handleUndo = useCallback(() => {
+        if (strokeHistory.length === 0) return;
+        const canvas = canvasRef.current;
+        if (!canvas) return;
+        const ctx = canvas.getContext('2d');
+        const last = strokeHistory[strokeHistory.length - 1];
+        if (last?.data) {
+            ctx.putImageData(last.data, last.x, last.y);
+        }
+        setStrokeHistory((prev) => prev.slice(0, -1));
+    }, [strokeHistory]);
+
+    // ── Drawing ──
+    const drawDot = useCallback(
+        (cx, cy) => {
+            const canvas = canvasRef.current;
+            if (!canvas) return;
+            const ctx = canvas.getContext('2d');
+            ctx.fillStyle = '#FFFFFF';
+            ctx.beginPath();
+            ctx.arc(cx, cy, brushSize / 2, 0, Math.PI * 2);
+            ctx.fill();
+        },
+        [brushSize]
+    );
+
+    const drawSegment = useCallback(
+        (from, to) => {
+            const canvas = canvasRef.current;
+            if (!canvas) return;
+            const ctx = canvas.getContext('2d');
+            ctx.strokeStyle = '#FFFFFF';
+            ctx.lineWidth = brushSize;
+            ctx.lineCap = 'round';
+            ctx.lineJoin = 'round';
+            const mx = (from.x + to.x) / 2;
+            const my = (from.y + to.y) / 2;
+            ctx.beginPath();
+            ctx.moveTo(from.x, from.y);
+            ctx.quadraticCurveTo(from.x, from.y, mx, my);
+            ctx.stroke();
+        },
+        [brushSize]
+    );
+
+    // ── Pointer events ──
+    const handlePointerDown = useCallback(
+        (e) => {
+            if (e.button === 1 || (e.button === 0 && (e.ctrlKey || e.metaKey))) {
+                setIsPanning(true);
+                const { clientX, clientY } = getClientPos(e);
+                panStartRef.current = { x: clientX, y: clientY, panX: pan.x, panY: pan.y };
+                e.preventDefault();
+                return;
+            }
+            if (e.touches && e.touches.length > 1) return;
+
+            const { clientX, clientY } = getClientPos(e);
+            const pt = screenToCanvas(clientX, clientY);
+            clonePreStroke();
+            beginStrokeBounds();
+            extendStrokeBounds(pt.x, pt.y);
+            setIsDrawing(true);
+            lastPointRef.current = pt;
+            drawDot(pt.x, pt.y);
+            setModifiedPages((prev) => new Set([...prev, currentPage?.pageNumber]));
+        },
+        [screenToCanvas, clonePreStroke, beginStrokeBounds, extendStrokeBounds, drawDot, pan, currentPage]
+    );
+
+    // rAF-throttled: mice fire pointermove at ~500 Hz, and repainting the canvas
+    // that often visibly lags at large brush sizes. getCoalescedEvents() replays
+    // the skipped points, so no pixels are lost.
+    const moveStateRef = useRef({ rafId: 0, latest: null });
+
+    const handlePointerMove = useCallback(
+        (e) => {
+            const { clientX, clientY } = getClientPos(e);
+            // React pools synthetic events — copy what we need now.
+            const native = e.nativeEvent || e;
+            const coalesced = native.getCoalescedEvents
+                ? native.getCoalescedEvents()
+                : null;
+            moveStateRef.current.latest = { clientX, clientY, coalesced };
+
+            // preventDefault must be synchronous to stop touch scrolling.
+            if (isDrawing) e.preventDefault();
+
+            if (moveStateRef.current.rafId) return;
+            moveStateRef.current.rafId = requestAnimationFrame(() => {
+                moveStateRef.current.rafId = 0;
+                const m = moveStateRef.current.latest;
+                if (!m) return;
+
+                if (containerRef.current) {
+                    const rect = containerRef.current.getBoundingClientRect();
+                    setCursorPos({
+                        x: m.clientX - rect.left,
+                        y: m.clientY - rect.top,
+                    });
+                }
+
+                if (isPanning) {
+                    const dx = m.clientX - panStartRef.current.x;
+                    const dy = m.clientY - panStartRef.current.y;
+                    setPan({
+                        x: panStartRef.current.panX + dx,
+                        y: panStartRef.current.panY + dy,
+                    });
+                    return;
+                }
+
+                if (!isDrawing) return;
+
+                // Replay the coalesced points so fast swipes stay continuous.
+                const points = m.coalesced && m.coalesced.length
+                    ? m.coalesced.map((c) => ({ cx: c.clientX, cy: c.clientY }))
+                    : [{ cx: m.clientX, cy: m.clientY }];
+                for (const { cx, cy } of points) {
+                    const pt = screenToCanvas(cx, cy);
+                    const last = lastPointRef.current;
+                    if (last) drawSegment(last, pt);
+                    extendStrokeBounds(pt.x, pt.y);
+                    lastPointRef.current = pt;
+                }
+            });
+        },
+        [isDrawing, isPanning, screenToCanvas, drawSegment, extendStrokeBounds]
+    );
+
+    // Don't let a queued frame fire against a torn-down canvas.
+    useEffect(() => {
+        return () => {
+            if (moveStateRef.current.rafId) {
+                cancelAnimationFrame(moveStateRef.current.rafId);
+                moveStateRef.current.rafId = 0;
+            }
+        };
+    }, []);
+
+    const handlePointerUp = useCallback(() => {
+        if (isDrawing) commitStrokeDelta();
+        setIsDrawing(false);
+        setIsPanning(false);
+        lastPointRef.current = null;
+    }, [isDrawing, commitStrokeDelta]);
+
+    useEffect(() => {
+        const up = () => {
+            // Always try to commit — it's a no-op when bounds is null.
+            commitStrokeDelta();
+            setIsDrawing(false);
+            setIsPanning(false);
+            lastPointRef.current = null;
+        };
+        window.addEventListener('mouseup', up);
+        window.addEventListener('touchend', up);
+        return () => {
+            window.removeEventListener('mouseup', up);
+            window.removeEventListener('touchend', up);
+        };
+    }, [commitStrokeDelta]);
+
+    // ── Keyboard ──
+    useEffect(() => {
+        const handleKey = (e) => {
+            // Scaled step — a 5px nudge is invisible at brush=400.
+            const step = Math.max(5, Math.round(maxBrush * 0.05));
+            if (e.key === '[') setBrushSize((s) => Math.max(5, s - step));
+            else if (e.key === ']') setBrushSize((s) => Math.min(maxBrush, s + step));
+            else if (e.key === 'z' && (e.ctrlKey || e.metaKey)) {
+                e.preventDefault();
+                handleUndo();
+            } else if (e.key === 'ArrowLeft') {
+                goToPage(currentIdx - 1);
+            } else if (e.key === 'ArrowRight') {
+                goToPage(currentIdx + 1);
+            }
+        };
+        window.addEventListener('keydown', handleKey);
+        return () => window.removeEventListener('keydown', handleKey);
+    }, [handleUndo, goToPage, currentIdx, maxBrush]);
+
+    // ── Zoom ──
+    const handleZoomIn = () => setZoom((z) => Math.min(z * 1.25, 5));
+    const handleZoomOut = () => setZoom((z) => Math.max(z / 1.25, 0.1));
+
+    useEffect(() => {
+        const container = containerRef.current;
+        if (!container) return;
+        const handleWheel = (e) => {
+            e.preventDefault();
+            setZoom((z) => Math.max(0.1, Math.min(5, z * (e.deltaY > 0 ? 0.9 : 1.1))));
+        };
+        container.addEventListener('wheel', handleWheel, { passive: false });
+        return () => container.removeEventListener('wheel', handleWheel);
+    }, []);
+
+    // ── Save all pages ──
+    const handleSaveAll = useCallback(() => {
+        saveCurrentPageData();
+
+        const results = {};
+
+        // Pages we aren't viewing need a temp canvas to export.
+        const tmpCanvas = document.createElement('canvas');
+
+        pages.forEach((page) => {
+            const saved = pageDataRef.current[page.pageNumber];
+            if (saved) {
+                tmpCanvas.width = saved.width;
+                tmpCanvas.height = saved.height;
+                tmpCanvas.getContext('2d').putImageData(saved, 0, 0);
+                results[page.pageNumber] = tmpCanvas.toDataURL('image/png');
+            }
+            // Never visited or unmodified — parent keeps the original.
+        });
+
+        if (currentPage && modifiedPages.has(currentPage.pageNumber)) {
+            const canvas = canvasRef.current;
+            if (canvas) {
+                results[currentPage.pageNumber] = canvas.toDataURL('image/png');
+            }
+        }
+
+        onSaveAll(results);
+    }, [saveCurrentPageData, pages, currentPage, modifiedPages, onSaveAll]);
+
+    // ── Render ──
+    return (
+        <div className="fixed inset-0 z-50 flex flex-col bg-gray-900">
+            {/* ====== TOOLBAR ====== */}
+            <div className="flex-shrink-0 flex items-center justify-between px-4 py-3 bg-gray-800 border-b border-gray-700">
+                {/* Left */}
+                <div className="flex items-center gap-3">
+                    <div className="p-2 bg-gradient-to-br from-red-500 to-pink-600 rounded-lg text-white">
+                        <Eraser className="w-5 h-5" />
+                    </div>
+                    <div>
+                        <h2 className="text-white font-semibold text-sm">Eraser Tool</h2>
+                        <p className="text-gray-400 text-xs">
+                            Paint to erase • Scroll to zoom • Ctrl+drag to pan
+                        </p>
+                    </div>
+                </div>
+
+                {/* Center: Controls */}
+                <div className="flex items-center gap-4 bg-gray-700/60 px-4 py-2 rounded-xl">
+                    {/* Brush */}
+                    <div className="flex items-center gap-2">
+                        <button onClick={() => setBrushSize((s) => Math.max(5, s - Math.max(5, Math.round(maxBrush * 0.05))))} className="p-1 text-gray-300 hover:text-white"><Minus size={14} /></button>
+                        <input type="range" min={5} max={maxBrush} step={1} value={brushSize} onChange={(e) => setBrushSize(Number(e.target.value))} className="w-28 accent-blue-500" />
+                        <button onClick={() => setBrushSize((s) => Math.min(maxBrush, s + Math.max(5, Math.round(maxBrush * 0.05))))} className="p-1 text-gray-300 hover:text-white"><Plus size={14} /></button>
+                        <span className="text-xs text-gray-300 font-mono w-12 text-center">{brushSize}px</span>
+                    </div>
+
+                    <div className="w-px h-6 bg-gray-600" />
+
+                    {/* Zoom */}
+                    <div className="flex items-center gap-1">
+                        <button onClick={handleZoomOut} className="p-1.5 text-gray-300 hover:text-white hover:bg-gray-600 rounded"><ZoomOut size={16} /></button>
+                        <span className="text-xs text-gray-400 font-mono w-12 text-center">{Math.round(zoom * 100)}%</span>
+                        <button onClick={handleZoomIn} className="p-1.5 text-gray-300 hover:text-white hover:bg-gray-600 rounded"><ZoomIn size={16} /></button>
+                    </div>
+
+                    <div className="w-px h-6 bg-gray-600" />
+
+                    {/* Undo */}
+                    <button
+                        onClick={handleUndo}
+                        disabled={strokeHistory.length === 0}
+                        className={`p-1.5 rounded ${strokeHistory.length > 0 ? 'text-gray-300 hover:text-white hover:bg-gray-600' : 'text-gray-600 cursor-not-allowed'}`}
+                        title="Undo (Ctrl+Z)"
+                    >
+                        <Undo2 size={16} />
+                    </button>
+                </div>
+
+                {/* Right */}
+                <div className="flex items-center gap-2">
+                    <button onClick={onCancel} className="flex items-center gap-2 px-4 py-2 text-gray-300 hover:text-white hover:bg-gray-700 rounded-lg text-sm font-medium">
+                        <X size={16} /> Cancel
+                    </button>
+                    <button
+                        onClick={handleSaveAll}
+                        className="flex items-center gap-2 px-5 py-2 bg-gradient-to-r from-green-500 to-emerald-600 text-white font-semibold rounded-lg hover:shadow-lg hover:-translate-y-0.5 transition-all text-sm"
+                    >
+                        <Check size={16} />
+                        Save All
+                        {modifiedPages.size > 0 && (
+                            <span className="ml-1 px-1.5 py-0.5 bg-white/20 rounded text-[10px]">
+                                {modifiedPages.size} page{modifiedPages.size > 1 ? 's' : ''}
+                            </span>
+                        )}
+                    </button>
+                </div>
+            </div>
+
+            {/* ====== CANVAS AREA ====== */}
+            <div
+                ref={containerRef}
+                className="flex-1 relative overflow-hidden cursor-none select-none"
+                style={{ background: 'repeating-conic-gradient(#2a2a2a 0% 25%, #333 0% 50%) 50% / 20px 20px' }}
+                onMouseDown={handlePointerDown}
+                onMouseMove={handlePointerMove}
+                onMouseUp={handlePointerUp}
+                onTouchStart={handlePointerDown}
+                onTouchMove={handlePointerMove}
+                onTouchEnd={handlePointerUp}
+            >
+                <canvas
+                    ref={canvasRef}
+                    style={{
+                        position: 'absolute',
+                        left: pan.x,
+                        top: pan.y,
+                        width: imageSize.w * zoom,
+                        height: imageSize.h * zoom,
+                        imageRendering: zoom > 2 ? 'pixelated' : 'auto',
+                    }}
+                />
+
+                {/* Brush cursor */}
+                {canvasReady && (
+                    <div
+                        className="pointer-events-none absolute"
+                        style={{
+                            left: cursorPos.x - (brushSize * zoom) / 2,
+                            top: cursorPos.y - (brushSize * zoom) / 2,
+                            width: brushSize * zoom,
+                            height: brushSize * zoom,
+                            borderRadius: '50%',
+                            border: '2px solid rgba(239, 68, 68, 0.8)',
+                            boxShadow: '0 0 0 1px rgba(0,0,0,0.3)',
+                        }}
+                    />
+                )}
+
+                {/* Loading */}
+                {!canvasReady && (
+                    <div className="absolute inset-0 flex items-center justify-center">
+                        <div className="w-10 h-10 border-3 border-gray-600 border-t-blue-500 rounded-full animate-spin" />
+                    </div>
+                )}
+            </div>
+
+            {/* ====== BOTTOM: Page navigation ====== */}
+            <div className="flex-shrink-0 px-4 py-2.5 bg-gray-800 border-t border-gray-700 flex items-center justify-between">
+                {/* Left: shortcuts */}
+                <div className="flex items-center gap-4 text-[11px] text-gray-500">
+                    <span><kbd className="px-1 py-0.5 bg-gray-700 rounded text-gray-400 font-mono text-[10px]">[</kbd> / <kbd className="px-1 py-0.5 bg-gray-700 rounded text-gray-400 font-mono text-[10px]">]</kbd> Brush</span>
+                    <span><kbd className="px-1 py-0.5 bg-gray-700 rounded text-gray-400 font-mono text-[10px]">Ctrl+Z</kbd> Undo</span>
+                </div>
+
+                {/* Center: Page nav */}
+                <div className="flex items-center gap-3">
+                    <button
+                        onClick={() => goToPage(currentIdx - 1)}
+                        disabled={currentIdx === 0}
+                        className={`p-1.5 rounded-lg ${currentIdx === 0 ? 'text-gray-600' : 'text-gray-300 hover:text-white hover:bg-gray-700'}`}
+                    >
+                        <ChevronLeft size={18} />
+                    </button>
+
+                    <div className="flex items-center gap-1.5 overflow-x-auto max-w-xs">
+                        {pages.map((page, idx) => (
+                            <button
+                                key={page.pageNumber}
+                                onClick={() => goToPage(idx)}
+                                className={`relative shrink-0 min-w-[2rem] px-1.5 h-7 rounded-lg text-xs font-medium transition-all ${
+                                    idx === currentIdx
+                                        ? 'bg-blue-600 text-white shadow-sm'
+                                        : 'bg-gray-700 text-gray-400 hover:bg-gray-600 hover:text-white'
+                                }`}
+                                title={`Page ${page.pageNumber}`}
+                            >
+                                {shortPageLabel(page.pageNumber)}
+                                {modifiedPages.has(page.pageNumber) && (
+                                    <span className="absolute -top-1 -right-1 w-2.5 h-2.5 bg-orange-400 rounded-full border-2 border-gray-800" />
+                                )}
+                            </button>
+                        ))}
+                    </div>
+
+                    <button
+                        onClick={() => goToPage(currentIdx + 1)}
+                        disabled={currentIdx === pages.length - 1}
+                        className={`p-1.5 rounded-lg ${currentIdx === pages.length - 1 ? 'text-gray-600' : 'text-gray-300 hover:text-white hover:bg-gray-700'}`}
+                    >
+                        <ChevronRight size={18} />
+                    </button>
+                </div>
+
+                {/* Right: page info */}
+                <span className="text-xs text-gray-500">
+                    Page {shortPageLabel(currentPage?.pageNumber)} • {currentIdx + 1}/{pages.length}
+                </span>
+            </div>
+        </div>
+    );
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/preprocess/OperationCard.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/preprocess/OperationCard.jsx
new file mode 100644
index 00000000..58b06048
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/preprocess/OperationCard.jsx
@@ -0,0 +1,202 @@
+import React, { useState } from 'react';
+import { ChevronDown, Settings2 } from 'lucide-react';
+import TooltipInfo from './TooltipInfo';
+import OperationSlider from './OperationSlider';
+import { shouldShowControl } from '../../config/preprocessOperations';
+
+// One operation in the sidebar: toggle, tooltip, expandable params.
+export default function OperationCard({
+  operation,
+  enabled = false,
+  params = {},
+  onToggle,
+  onParamsChange,
+  isInPipeline = false,
+  compact = false,
+  className = '',
+}) {
+  const [isExpanded, setIsExpanded] = useState(false);
+  
+  const hasControls = operation.controls && operation.controls.length > 0;
+  const visibleControls = hasControls 
+    ? operation.controls.filter(c => shouldShowControl(c, params))
+    : [];
+
+  const handleToggle = (e) => {
+    e.stopPropagation();
+    onToggle(operation.id, !enabled);
+  };
+
+  const handleParamChange = (controlId, value) => {
+    onParamsChange(operation.id, {
+      ...params,
+      [controlId]: value,
+    });
+  };
+
+  const handleExpandClick = (e) => {
+    e.stopPropagation();
+    if (hasControls && enabled) {
+      setIsExpanded(!isExpanded);
+    }
+  };
+
+  return (
+    <div
+      className={`
+        rounded-lg border transition-all duration-200
+        ${enabled 
+          ? 'border-blue-300 bg-blue-50/50' 
+          : 'border-gray-200 bg-white hover:border-blue-200'
+        }
+        ${compact ? 'p-2' : 'p-3'}
+        ${className}
+      `}
+    >
+      {/* Header row */}
+      <div className="flex items-center gap-2">
+        {/* Checkbox - styled via index.css */}
+        <input
+          type="checkbox"
+          checked={enabled}
+          onChange={handleToggle}
+        />
+
+        {/* Operation name */}
+        <span 
+          className={`flex-1 text-sm font-medium cursor-pointer select-none ${
+            enabled ? 'text-gray-800' : 'text-gray-600'
+          }`}
+          onClick={handleToggle}
+        >
+          {operation.name}
+        </span>
+
+        {/* Tooltip */}
+        <TooltipInfo 
+          text={operation.tooltip}
+          position="left"
+          iconSize={14}
+        />
+
+        {/* Expand button (only if has controls and enabled) */}
+        {hasControls && (
+          <button
+            onClick={handleExpandClick}
+            disabled={!enabled}
+            className={`p-1 rounded transition-colors ${
+              enabled 
+                ? 'hover:bg-blue-100 text-blue-600' 
+                : 'text-gray-300 cursor-not-allowed'
+            }`}
+          >
+            <ChevronDown 
+              size={16} 
+              className={`transition-transform ${isExpanded ? 'rotate-180' : ''}`}
+            />
+          </button>
+        )}
+      </div>
+
+      {/* Expandable controls */}
+      {enabled && hasControls && isExpanded && (
+        <div className="mt-3 pt-3 border-t border-blue-200/50 space-y-3 animate-slide-up">
+          {visibleControls.map((control) => (
+            <ControlRenderer
+              key={control.id}
+              control={control}
+              value={params[control.id] ?? control.default}
+              onChange={(value) => handleParamChange(control.id, value)}
+            />
+          ))}
+        </div>
+      )}
+
+      {/* Collapsed indicator showing active params */}
+      {enabled && hasControls && !isExpanded && visibleControls.length > 0 && (
+        <div className="mt-1.5 flex items-center gap-1 text-[10px] text-blue-600">
+          <Settings2 size={10} />
+          <span>
+            {visibleControls.slice(0, 2).map(c => {
+              const val = params[c.id] ?? c.default;
+              return `${c.label}: ${val}${c.unit || ''}`;
+            }).join(' • ')}
+            {visibleControls.length > 2 && ` +${visibleControls.length - 2}`}
+          </span>
+        </div>
+      )}
+    </div>
+  );
+}
+
+// Dispatches on control.type (slider, select, checkbox, ...).
+function ControlRenderer({ control, value, onChange }) {
+  switch (control.type) {
+    case 'slider':
+      return (
+        <OperationSlider
+          id={control.id}
+          label={control.label}
+          value={value}
+          onChange={onChange}
+          min={control.min}
+          max={control.max}
+          step={control.step}
+          unit={control.unit}
+          labels={control.labels}
+        />
+      );
+
+    case 'select':
+      return (
+        <div className="space-y-1">
+          <label className="text-xs font-medium text-gray-600">
+            {control.label}
+          </label>
+          <div className="relative">
+            <select
+              value={value}
+              onChange={(e) => onChange(e.target.value)}
+              className="w-full text-sm bg-white border border-gray-200 rounded-md 
+                         px-3 py-1.5 pr-8 appearance-none focus:outline-none 
+                         focus:ring-2 focus:ring-blue-500 focus:border-transparent"
+            >
+              {control.options.map((opt) => (
+                <option key={opt.value} value={opt.value}>
+                  {opt.label}
+                </option>
+              ))}
+            </select>
+            <ChevronDown 
+              size={14} 
+              className="absolute right-2 top-1/2 -translate-y-1/2 text-gray-400 pointer-events-none"
+            />
+          </div>
+        </div>
+      );
+
+    case 'toggle':
+      return (
+        <div className="flex items-center justify-between">
+          <label className="text-xs font-medium text-gray-600">
+            {control.label}
+          </label>
+          <button
+            onClick={() => onChange(!value)}
+            className={`relative w-10 h-5 rounded-full transition-colors ${
+              value ? 'bg-blue-500' : 'bg-gray-300'
+            }`}
+          >
+            <span
+              className={`absolute top-0.5 w-4 h-4 bg-white rounded-full shadow transition-transform ${
+                value ? 'translate-x-5' : 'translate-x-0.5'
+              }`}
+            />
+          </button>
+        </div>
+      );
+
+    default:
+      return null;
+  }
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/preprocess/OperationSlider.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/preprocess/OperationSlider.jsx
new file mode 100644
index 00000000..93a50391
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/preprocess/OperationSlider.jsx
@@ -0,0 +1,64 @@
+import React from 'react';
+
+// Compact slider for a single operation parameter.
+export default function OperationSlider({
+  id,
+  label,
+  value,
+  onChange,
+  min = 0,
+  max = 100,
+  step = 1,
+  unit = '',
+  labels = null, // { min: 'Label', max: 'Label' }
+  disabled = false,
+  className = '',
+}) {
+  const percentage = ((value - min) / (max - min)) * 100;
+
+  return (
+    <div className={`space-y-1 ${className}`}>
+      {/* Label and value row */}
+      <div className="flex items-center justify-between text-xs">
+        <label 
+          htmlFor={id}
+          className="font-medium text-gray-600"
+        >
+          {label}
+        </label>
+        <span className="font-semibold text-blue-600 tabular-nums">
+          {typeof value === 'number' ? (
+            step < 1 ? value.toFixed(1) : value
+          ) : value}
+          {unit && <span className="text-gray-400 ml-0.5">{unit}</span>}
+        </span>
+      </div>
+
+      {/* Slider */}
+      <div className="relative">
+        <input
+          id={id}
+          type="range"
+          min={min}
+          max={max}
+          step={step}
+          value={value}
+          onChange={(e) => onChange(parseFloat(e.target.value))}
+          disabled={disabled}
+          className="slider-compact w-full"
+          style={{
+            '--slider-progress': `${percentage}%`
+          }}
+        />
+      </div>
+
+      {/* Min/Max labels if provided */}
+      {labels && (
+        <div className="flex items-center justify-between text-[10px] text-gray-400 -mt-0.5">
+          <span>{labels.min}</span>
+          <span>{labels.max}</span>
+        </div>
+      )}
+    </div>
+  );
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/preprocess/OperationsSidebar.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/preprocess/OperationsSidebar.jsx
new file mode 100644
index 00000000..5981171b
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/preprocess/OperationsSidebar.jsx
@@ -0,0 +1,159 @@
+import React, { useState } from 'react';
+import {
+  ChevronDown,
+  Sliders,
+  Sparkles,
+  Contrast,
+  Eraser,
+  Layers,
+  RotateCcw
+} from 'lucide-react';
+import OperationCard from './OperationCard';
+import {
+  OPERATION_CATEGORIES,
+  OPERATIONS,
+  getOperationsByCategory
+} from '../../config/preprocessOperations';
+
+// Accordion of operations grouped by category, plus reset / recommended-preset
+// shortcuts.
+
+const CATEGORY_ICONS = {
+  basic: Sliders,
+  enhancement: Sparkles,
+  binarization: Contrast,
+  cleanup: Eraser,
+};
+
+export default function OperationsSidebar({
+  enabledOperations = {}, // { operationId: { enabled: boolean, params: {} } }
+  onOperationToggle,
+  onOperationParamsChange,
+  onApplyRecommended,
+  onReset,
+  isProcessing = false,
+  hideHeader = false,
+  className = '',
+}) {
+  const [expandedCategories, setExpandedCategories] = useState(['basic', 'enhancement', 'binarization', 'cleanup']);
+  const operationsByCategory = getOperationsByCategory();
+
+  const toggleCategory = (categoryId) => {
+    setExpandedCategories(prev =>
+      prev.includes(categoryId)
+        ? prev.filter(id => id !== categoryId)
+        : [...prev, categoryId]
+    );
+  };
+
+  const getEnabledCountForCategory = (categoryId) => {
+    const operations = operationsByCategory[categoryId] || [];
+    return operations.filter(op => enabledOperations[op.id]?.enabled).length;
+  };
+
+  const getTotalEnabledCount = () => {
+    return Object.values(enabledOperations).filter(op => op?.enabled).length;
+  };
+
+  return (
+    <div className={`flex flex-col h-full ${hideHeader ? '' : 'bg-white rounded-xl shadow-card'} ${className}`}>
+      {/* Header */}
+      {!hideHeader && (
+      <div className="flex-shrink-0 px-4 py-3 border-b border-gray-100">
+        <div className="flex items-center justify-between">
+          <div className="flex items-center gap-2">
+            <h3 className="font-semibold text-gray-800">Operations</h3>
+            {getTotalEnabledCount() > 0 && (
+              <span className="px-2 py-0.5 bg-blue-100 text-blue-600 text-xs font-semibold rounded-full">
+                {getTotalEnabledCount()}
+              </span>
+            )}
+          </div>
+        </div>
+
+        {/* Quick actions */}
+        <div className="flex gap-2 mt-3">
+          <button
+            onClick={onApplyRecommended}
+            disabled={isProcessing}
+            className="flex-1 flex items-center justify-center gap-1.5 px-3 py-2
+                       bg-gradient-to-r from-blue-500 to-blue-600 text-white text-xs font-medium
+                       rounded-lg hover:from-blue-600 hover:to-blue-700
+                       disabled:opacity-50 disabled:cursor-not-allowed transition-all shadow-sm"
+          >
+            <Sparkles size={14} />
+            Recommended
+          </button>
+          <button
+            onClick={onReset}
+            disabled={isProcessing}
+            className="px-3 py-2 bg-gray-100 text-gray-600 text-xs font-medium rounded-lg
+                       hover:bg-gray-200 disabled:opacity-50 disabled:cursor-not-allowed transition-colors"
+            title="Reset all operations"
+          >
+            <RotateCcw size={14} />
+          </button>
+        </div>
+      </div>
+      )}
+
+      {/* Accordion categories */}
+      <div className="flex-1 overflow-y-auto">
+        {OPERATION_CATEGORIES.map((category) => {
+          const Icon = CATEGORY_ICONS[category.id] || Layers;
+          const isExpanded = expandedCategories.includes(category.id);
+          const enabledCount = getEnabledCountForCategory(category.id);
+          const operations = operationsByCategory[category.id] || [];
+
+          return (
+            <div key={category.id} className="border-b border-gray-100 last:border-b-0">
+              {/* Category header */}
+              <button
+                onClick={() => toggleCategory(category.id)}
+                className="w-full flex items-center gap-2 px-4 py-2.5 hover:bg-gray-50 transition-colors"
+              >
+                <Icon size={16} className="text-blue-500 flex-shrink-0" />
+                <span className="flex-1 text-sm font-medium text-gray-700 text-left">
+                  {category.label}
+                </span>
+                {enabledCount > 0 && (
+                  <span className="px-1.5 py-0.5 bg-blue-100 text-blue-600 text-[10px] font-bold rounded">
+                    {enabledCount}
+                  </span>
+                )}
+                <ChevronDown
+                  size={16}
+                  className={`text-gray-400 transition-transform ${isExpanded ? 'rotate-180' : ''}`}
+                />
+              </button>
+
+              {/* Category operations */}
+              {isExpanded && (
+                <div className="px-3 pb-3 space-y-1.5 animate-slide-up">
+                  {operations.map((operation) => (
+                    <OperationCard
+                      key={operation.id}
+                      operation={operation}
+                      enabled={enabledOperations[operation.id]?.enabled || false}
+                      params={enabledOperations[operation.id]?.params || operation.defaultParams}
+                      onToggle={onOperationToggle}
+                      onParamsChange={onOperationParamsChange}
+                      compact
+                    />
+                  ))}
+                </div>
+              )}
+            </div>
+          );
+        })}
+      </div>
+
+      {/* Footer hint */}
+      <div className="flex-shrink-0 px-4 py-2 bg-gray-50 border-t border-gray-100">
+        <p className="text-[10px] text-gray-400 text-center">
+          Enable operations to add them to the pipeline
+        </p>
+      </div>
+    </div>
+  );
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/preprocess/PipelineStack.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/preprocess/PipelineStack.jsx
new file mode 100644
index 00000000..91aaaa73
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/preprocess/PipelineStack.jsx
@@ -0,0 +1,323 @@
+import React, { useState, useRef } from 'react';
+import { 
+  GripVertical, 
+  Trash2, 
+  Settings2, 
+  ChevronDown,
+  Power,
+  Plus
+} from 'lucide-react';
+import TooltipInfo from './TooltipInfo';
+import OperationSlider from './OperationSlider';
+import { getOperationById, shouldShowControl } from '../../config/preprocessOperations';
+
+// The ordered operation list, reorderable by drag. Each step can be toggled,
+// expanded for its params, or deleted.
+export default function PipelineStack({
+  pipeline = [],
+  onReorder,
+  onToggle,
+  onUpdateParams,
+  onRemove,
+  onAddOperation,
+  isProcessing = false,
+  currentStepId = null,
+  className = '',
+}) {
+  const [draggedIndex, setDraggedIndex] = useState(null);
+  const [dragOverIndex, setDragOverIndex] = useState(null);
+  const [expandedStep, setExpandedStep] = useState(null);
+  const dragCounter = useRef(0);
+
+  const handleDragStart = (e, index) => {
+    if (isProcessing) return;
+    setDraggedIndex(index);
+    e.dataTransfer.effectAllowed = 'move';
+  };
+
+  const handleDragEnd = () => {
+    if (draggedIndex !== null && dragOverIndex !== null && draggedIndex !== dragOverIndex) {
+      onReorder(draggedIndex, dragOverIndex);
+    }
+
+    setDraggedIndex(null);
+    setDragOverIndex(null);
+    dragCounter.current = 0;
+  };
+
+  const handleDragEnter = (e, index) => {
+    e.preventDefault();
+    dragCounter.current++;
+    setDragOverIndex(index);
+  };
+
+  const handleDragLeave = (e) => {
+    dragCounter.current--;
+    if (dragCounter.current === 0) {
+      setDragOverIndex(null);
+    }
+  };
+
+  const handleDragOver = (e) => {
+    e.preventDefault();
+    e.dataTransfer.dropEffect = 'move';
+  };
+
+  const handleDrop = (e, index) => {
+    e.preventDefault();
+    dragCounter.current = 0;
+  };
+
+  if (pipeline.length === 0) {
+    return (
+      <div className={`${className}`}>
+        <div className="text-center py-6 border-2 border-dashed border-gray-200 rounded-xl">
+          <div className="text-gray-400 mb-2">
+            <Plus size={24} className="mx-auto" />
+          </div>
+          <p className="text-sm text-gray-500">
+            No operations in pipeline
+          </p>
+          <p className="text-xs text-gray-400 mt-1">
+            Enable operations from the sidebar to add them
+          </p>
+        </div>
+      </div>
+    );
+  }
+
+  return (
+    <div className={`space-y-1 ${className}`}>
+      {/* Header */}
+      <div className="flex items-center justify-between px-1 mb-2">
+        <span className="text-xs font-semibold text-gray-500 uppercase tracking-wider">
+          Pipeline ({pipeline.filter(s => s.enabled).length}/{pipeline.length} active)
+        </span>
+      </div>
+
+      {/* Pipeline steps */}
+      <div className="space-y-1">
+        {pipeline.map((step, index) => {
+          const operation = getOperationById(step.operationId);
+          if (!operation) return null;
+
+          const isActive = step.id === currentStepId;
+          const isDraggedOver = dragOverIndex === index && draggedIndex !== index;
+          const isExpanded = expandedStep === step.id;
+          const hasControls = operation.controls && operation.controls.length > 0;
+          const visibleControls = hasControls
+            ? operation.controls.filter(c => shouldShowControl(c, step.params))
+            : [];
+
+          return (
+            <div
+              key={step.id}
+              onDragEnter={(e) => handleDragEnter(e, index)}
+              onDragLeave={handleDragLeave}
+              onDragOver={handleDragOver}
+              onDrop={(e) => handleDrop(e, index)}
+              className={`
+                group relative rounded-lg border transition-all duration-200
+                ${step.enabled
+                  ? 'bg-white border-blue-200 shadow-sm'
+                  : 'bg-gray-50 border-gray-200 opacity-60'
+                }
+                ${isDraggedOver ? 'border-blue-400 border-2 bg-blue-50' : ''}
+                ${isActive ? 'ring-2 ring-blue-400 ring-offset-1' : ''}
+                ${draggedIndex === index ? 'opacity-50' : ''}
+              `}
+            >
+              {/* Main row */}
+              <div className="flex items-center gap-2 p-2">
+                {/* Drag handle — ONLY this is draggable, so interacting with the
+                    sliders/selects inside the card never starts a card drag. */}
+                <div
+                  draggable={!isProcessing}
+                  onDragStart={(e) => handleDragStart(e, index)}
+                  onDragEnd={handleDragEnd}
+                  title="Drag to reorder"
+                  className={`flex-shrink-0 text-gray-300 group-hover:text-gray-400 transition-colors ${
+                    !isProcessing ? 'cursor-grab active:cursor-grabbing' : ''
+                  }`}
+                >
+                  <GripVertical size={16} />
+                </div>
+
+                {/* Order number */}
+                <span className={`
+                  flex-shrink-0 w-5 h-5 rounded text-xs font-bold flex items-center justify-center
+                  ${step.enabled 
+                    ? 'bg-blue-100 text-blue-600' 
+                    : 'bg-gray-200 text-gray-400'
+                  }
+                `}>
+                  {index + 1}
+                </span>
+
+                {/* Operation name */}
+                <span className={`flex-1 text-sm font-medium truncate ${
+                  step.enabled ? 'text-gray-800' : 'text-gray-400'
+                }`}>
+                  {operation.name}
+                </span>
+
+                {/* Processing indicator */}
+                {isActive && isProcessing && (
+                  <div className="w-4 h-4 border-2 border-blue-400 border-t-transparent rounded-full animate-spin" />
+                )}
+
+                {/* Actions */}
+                <div className="flex items-center gap-1 opacity-0 group-hover:opacity-100 transition-opacity">
+                  {/* Settings button */}
+                  {hasControls && (
+                    <button
+                      onClick={(e) => {
+                        e.stopPropagation();
+                        setExpandedStep(isExpanded ? null : step.id);
+                      }}
+                      disabled={!step.enabled}
+                      className={`p-1 rounded transition-colors ${
+                        step.enabled 
+                          ? 'hover:bg-blue-100 text-blue-500' 
+                          : 'text-gray-300 cursor-not-allowed'
+                      }`}
+                      title="Settings"
+                    >
+                      <Settings2 size={14} />
+                    </button>
+                  )}
+
+                  {/* Toggle enable */}
+                  <button
+                    onClick={(e) => {
+                      e.stopPropagation();
+                      onToggle(step.id, !step.enabled);
+                    }}
+                    disabled={isProcessing}
+                    className={`p-1 rounded transition-colors ${
+                      step.enabled 
+                        ? 'hover:bg-green-100 text-green-500' 
+                        : 'hover:bg-gray-200 text-gray-400'
+                    }`}
+                    title={step.enabled ? 'Disable' : 'Enable'}
+                  >
+                    <Power size={14} />
+                  </button>
+
+                  {/* Delete */}
+                  <button
+                    onClick={(e) => {
+                      e.stopPropagation();
+                      onRemove(step.id);
+                    }}
+                    disabled={isProcessing}
+                    className="p-1 rounded hover:bg-red-100 text-red-400 hover:text-red-500 transition-colors"
+                    title="Remove"
+                  >
+                    <Trash2 size={14} />
+                  </button>
+                </div>
+
+                {/* Tooltip - always visible on right */}
+                <TooltipInfo 
+                  text={operation.tooltip}
+                  position="left"
+                  iconSize={12}
+                />
+              </div>
+
+              {/* Expanded settings */}
+              {isExpanded && step.enabled && hasControls && (
+                <div className="px-3 pb-3 pt-1 border-t border-gray-100 space-y-3 animate-slide-up">
+                  {visibleControls.map((control) => (
+                    <ControlRenderer
+                      key={control.id}
+                      control={control}
+                      value={step.params[control.id] ?? control.default}
+                      onChange={(value) => onUpdateParams(step.id, {
+                        ...step.params,
+                        [control.id]: value,
+                      })}
+                    />
+                  ))}
+                </div>
+              )}
+            </div>
+          );
+        })}
+      </div>
+    </div>
+  );
+}
+
+// Dispatches on control.type.
+function ControlRenderer({ control, value, onChange }) {
+  switch (control.type) {
+    case 'slider':
+      return (
+        <OperationSlider
+          id={control.id}
+          label={control.label}
+          value={value}
+          onChange={onChange}
+          min={control.min}
+          max={control.max}
+          step={control.step}
+          unit={control.unit}
+          labels={control.labels}
+        />
+      );
+
+    case 'select':
+      return (
+        <div className="space-y-1">
+          <label className="text-xs font-medium text-gray-600">
+            {control.label}
+          </label>
+          <div className="relative">
+            <select
+              value={value}
+              onChange={(e) => onChange(e.target.value)}
+              className="w-full text-sm bg-white border border-gray-200 rounded-md 
+                         px-3 py-1.5 pr-8 appearance-none focus:outline-none 
+                         focus:ring-2 focus:ring-blue-500 focus:border-transparent"
+            >
+              {control.options.map((opt) => (
+                <option key={opt.value} value={opt.value}>
+                  {opt.label}
+                </option>
+              ))}
+            </select>
+            <ChevronDown 
+              size={14} 
+              className="absolute right-2 top-1/2 -translate-y-1/2 text-gray-400 pointer-events-none"
+            />
+          </div>
+        </div>
+      );
+
+    case 'toggle':
+      return (
+        <div className="flex items-center justify-between">
+          <label className="text-xs font-medium text-gray-600">
+            {control.label}
+          </label>
+          <button
+            onClick={() => onChange(!value)}
+            className={`relative w-10 h-5 rounded-full transition-colors ${
+              value ? 'bg-blue-500' : 'bg-gray-300'
+            }`}
+          >
+            <span
+              className={`absolute top-0.5 w-4 h-4 bg-white rounded-full shadow transition-transform ${
+                value ? 'translate-x-5' : 'translate-x-0.5'
+              }`}
+            />
+          </button>
+        </div>
+      );
+
+    default:
+      return null;
+  }
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/preprocess/ProgressBarLabeled.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/preprocess/ProgressBarLabeled.jsx
new file mode 100644
index 00000000..541c079b
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/preprocess/ProgressBarLabeled.jsx
@@ -0,0 +1,75 @@
+import React from 'react';
+
+// Progress bar with a label, percentage and optional detail line.
+export default function ProgressBarLabeled({
+  label,
+  progress = 0, // 0-100
+  subtitle = '',
+  showPercentage = true,
+  variant = 'primary', // 'primary', 'success', 'warning'
+  size = 'md', // 'sm', 'md', 'lg'
+  isActive = false,
+  className = '',
+}) {
+  const clampedProgress = Math.max(0, Math.min(100, progress));
+
+  const variantClasses = {
+    primary: 'bg-blue-500',
+    success: 'bg-green-500',
+    warning: 'bg-amber-500',
+  };
+
+  const sizeClasses = {
+    sm: 'h-1.5',
+    md: 'h-2',
+    lg: 'h-3',
+  };
+
+  const trackBg = {
+    primary: 'bg-blue-100',
+    success: 'bg-green-100',
+    warning: 'bg-amber-100',
+  };
+
+  return (
+    <div className={`${className}`}>
+      {/* Label row */}
+      {(label || showPercentage) && (
+        <div className="flex items-center justify-between mb-1">
+          <div className="flex items-center gap-2 min-w-0">
+            {isActive && (
+              <span className="flex-shrink-0 w-2 h-2 rounded-full bg-blue-500 animate-pulse" />
+            )}
+            {label && (
+              <span className="text-xs font-medium text-gray-700 truncate">
+                {label}
+              </span>
+            )}
+          </div>
+          {showPercentage && (
+            <span className="text-xs font-semibold text-gray-500 tabular-nums ml-2">
+              {Math.round(clampedProgress)}%
+            </span>
+          )}
+        </div>
+      )}
+
+      {/* Progress bar */}
+      <div className={`w-full ${trackBg[variant]} rounded-full overflow-hidden ${sizeClasses[size]}`}>
+        <div
+          className={`h-full ${variantClasses[variant]} rounded-full transition-all duration-300 ease-out ${
+            isActive ? 'animate-pulse' : ''
+          }`}
+          style={{ width: `${clampedProgress}%` }}
+        />
+      </div>
+
+      {/* Subtitle */}
+      {subtitle && (
+        <p className="text-[10px] text-gray-400 mt-0.5 truncate">
+          {subtitle}
+        </p>
+      )}
+    </div>
+  );
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/preprocess/TooltipInfo.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/preprocess/TooltipInfo.jsx
new file mode 100644
index 00000000..d579bd90
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/preprocess/TooltipInfo.jsx
@@ -0,0 +1,107 @@
+import React, { useState, useRef, useEffect } from 'react';
+import { Info } from 'lucide-react';
+
+// Info icon with a hover tooltip.
+export default function TooltipInfo({ 
+  text, 
+  position = 'top', 
+  maxWidth = 250,
+  iconSize = 14,
+  className = ''
+}) {
+  const [isVisible, setIsVisible] = useState(false);
+  const [tooltipPosition, setTooltipPosition] = useState({ top: 0, left: 0 });
+  const triggerRef = useRef(null);
+  const tooltipRef = useRef(null);
+
+  useEffect(() => {
+    if (isVisible && triggerRef.current && tooltipRef.current) {
+      const trigger = triggerRef.current.getBoundingClientRect();
+      const tooltip = tooltipRef.current.getBoundingClientRect();
+      const viewportWidth = window.innerWidth;
+      const viewportHeight = window.innerHeight;
+      
+      let top, left;
+      
+      switch (position) {
+        case 'top':
+          top = -tooltip.height - 8;
+          left = (trigger.width - tooltip.width) / 2;
+          break;
+        case 'bottom':
+          top = trigger.height + 8;
+          left = (trigger.width - tooltip.width) / 2;
+          break;
+        case 'left':
+          top = (trigger.height - tooltip.height) / 2;
+          left = -tooltip.width - 8;
+          break;
+        case 'right':
+          top = (trigger.height - tooltip.height) / 2;
+          left = trigger.width + 8;
+          break;
+        default:
+          top = -tooltip.height - 8;
+          left = (trigger.width - tooltip.width) / 2;
+      }
+      
+      const absoluteLeft = trigger.left + left;
+      const absoluteTop = trigger.top + top;
+      
+      if (absoluteLeft < 8) {
+        left = -trigger.left + 8;
+      } else if (absoluteLeft + tooltip.width > viewportWidth - 8) {
+        left = viewportWidth - trigger.left - tooltip.width - 8;
+      }
+      
+      if (absoluteTop < 8) {
+        top = trigger.height + 8;
+      } else if (absoluteTop + tooltip.height > viewportHeight - 8) {
+        top = -tooltip.height - 8;
+      }
+      
+      setTooltipPosition({ top, left });
+    }
+  }, [isVisible, position]);
+
+  if (!text) return null;
+
+  return (
+    <div 
+      className={`relative inline-flex ${className}`}
+      ref={triggerRef}
+      onMouseEnter={() => setIsVisible(true)}
+      onMouseLeave={() => setIsVisible(false)}
+    >
+      <Info 
+        className="text-gray-400 hover:text-blue-500 cursor-help transition-colors"
+        size={iconSize}
+      />
+      
+      {isVisible && (
+        <div
+          ref={tooltipRef}
+          className="absolute z-50 animate-fade-in"
+          style={{
+            top: tooltipPosition.top,
+            left: tooltipPosition.left,
+            maxWidth: maxWidth,
+          }}
+        >
+          <div className="bg-gray-900 text-white text-xs leading-relaxed px-3 py-2 rounded-lg shadow-lg">
+            {text}
+            {/* Arrow */}
+            <div 
+              className={`absolute w-2 h-2 bg-gray-900 transform rotate-45 ${
+                position === 'top' ? 'bottom-[-4px] left-1/2 -translate-x-1/2' :
+                position === 'bottom' ? 'top-[-4px] left-1/2 -translate-x-1/2' :
+                position === 'left' ? 'right-[-4px] top-1/2 -translate-y-1/2' :
+                'left-[-4px] top-1/2 -translate-y-1/2'
+              }`}
+            />
+          </div>
+        </div>
+      )}
+    </div>
+  );
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/preprocess/ZoomLensPreview.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/preprocess/ZoomLensPreview.jsx
new file mode 100644
index 00000000..e070a33b
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/preprocess/ZoomLensPreview.jsx
@@ -0,0 +1,247 @@
+import React, { useState, useRef, useEffect, useCallback } from 'react';
+import { X, ZoomIn, ZoomOut, Move } from 'lucide-react';
+
+// Floating magnifier for inspecting text detail, original beside processed.
+// Both the lens and the window itself are draggable.
+export default function ZoomLensPreview({
+  originalImage,
+  processedImage,
+  isOpen = false,
+  onClose,
+  initialPosition = { x: 20, y: 20 },
+  className = '',
+}) {
+  const [position, setPosition] = useState(initialPosition);
+  const [isDragging, setIsDragging] = useState(false);
+  const [dragStart, setDragStart] = useState({ x: 0, y: 0 });
+  
+  const [zoomLevel, setZoomLevel] = useState(200); // percentage
+  const [lensPosition, setLensPosition] = useState({ x: 50, y: 50 }); // percentage of image
+  const [isPanning, setIsPanning] = useState(false);
+  const [panStart, setPanStart] = useState({ x: 0, y: 0 });
+  
+  const [showBoth, setShowBoth] = useState(true);
+  
+  const windowRef = useRef(null);
+  const imageContainerRef = useRef(null);
+
+  const handleWindowMouseDown = useCallback((e) => {
+    if (e.target.closest('.lens-content')) return;
+    setIsDragging(true);
+    setDragStart({
+      x: e.clientX - position.x,
+      y: e.clientY - position.y,
+    });
+  }, [position]);
+
+  const handleWindowMouseMove = useCallback((e) => {
+    if (isDragging) {
+      setPosition({
+        x: e.clientX - dragStart.x,
+        y: e.clientY - dragStart.y,
+      });
+    }
+  }, [isDragging, dragStart]);
+
+  const handleWindowMouseUp = useCallback(() => {
+    setIsDragging(false);
+  }, []);
+
+  const handleLensMouseDown = useCallback((e) => {
+    setIsPanning(true);
+    setPanStart({ x: e.clientX, y: e.clientY });
+  }, []);
+
+  const handleLensMouseMove = useCallback((e) => {
+    if (isPanning && imageContainerRef.current) {
+      const rect = imageContainerRef.current.getBoundingClientRect();
+      const deltaX = ((e.clientX - panStart.x) / rect.width) * 100;
+      const deltaY = ((e.clientY - panStart.y) / rect.height) * 100;
+      
+      setLensPosition(prev => ({
+        x: Math.max(0, Math.min(100, prev.x - deltaX)),
+        y: Math.max(0, Math.min(100, prev.y - deltaY)),
+      }));
+      setPanStart({ x: e.clientX, y: e.clientY });
+    }
+  }, [isPanning, panStart]);
+
+  const handleLensMouseUp = useCallback(() => {
+    setIsPanning(false);
+  }, []);
+
+  useEffect(() => {
+    if (isDragging) {
+      window.addEventListener('mousemove', handleWindowMouseMove);
+      window.addEventListener('mouseup', handleWindowMouseUp);
+      return () => {
+        window.removeEventListener('mousemove', handleWindowMouseMove);
+        window.removeEventListener('mouseup', handleWindowMouseUp);
+      };
+    }
+  }, [isDragging, handleWindowMouseMove, handleWindowMouseUp]);
+
+  useEffect(() => {
+    if (isPanning) {
+      window.addEventListener('mousemove', handleLensMouseMove);
+      window.addEventListener('mouseup', handleLensMouseUp);
+      return () => {
+        window.removeEventListener('mousemove', handleLensMouseMove);
+        window.removeEventListener('mouseup', handleLensMouseUp);
+      };
+    }
+  }, [isPanning, handleLensMouseMove, handleLensMouseUp]);
+
+  if (!isOpen) return null;
+
+  const zoomLevels = [100, 150, 200, 300, 400, 500];
+
+  const getBackgroundStyle = (imageSrc) => ({
+    backgroundImage: `url(${imageSrc})`,
+    backgroundSize: `${zoomLevel}%`,
+    backgroundPosition: `${lensPosition.x}% ${lensPosition.y}%`,
+    backgroundRepeat: 'no-repeat',
+  });
+
+  return (
+    <div
+      ref={windowRef}
+      className={`fixed z-50 bg-white rounded-xl shadow-2xl border border-gray-200 overflow-hidden ${className}`}
+      style={{
+        left: position.x,
+        top: position.y,
+        width: showBoth ? 500 : 260,
+      }}
+      onMouseDown={handleWindowMouseDown}
+    >
+      {/* Header */}
+      <div className="flex items-center justify-between px-3 py-2 bg-gray-50 border-b border-gray-200 cursor-move">
+        <div className="flex items-center gap-2">
+          <Move size={14} className="text-gray-400" />
+          <span className="text-xs font-semibold text-gray-700">Zoom Lens</span>
+        </div>
+        
+        <div className="flex items-center gap-2">
+          {/* Show both toggle */}
+          {processedImage && (
+            <button
+              onClick={(e) => {
+                e.stopPropagation();
+                setShowBoth(!showBoth);
+              }}
+              className={`px-2 py-0.5 text-[10px] font-medium rounded transition-colors ${
+                showBoth 
+                  ? 'bg-blue-100 text-blue-600' 
+                  : 'bg-gray-200 text-gray-600'
+              }`}
+            >
+              Compare
+            </button>
+          )}
+          
+          {/* Close button */}
+          <button
+            onClick={(e) => {
+              e.stopPropagation();
+              onClose();
+            }}
+            className="p-1 rounded hover:bg-gray-200 transition-colors"
+          >
+            <X size={14} className="text-gray-500" />
+          </button>
+        </div>
+      </div>
+
+      {/* Zoom controls */}
+      <div className="flex items-center justify-center gap-2 px-3 py-1.5 bg-gray-50/50 border-b border-gray-100">
+        <button
+          onClick={() => setZoomLevel(prev => Math.max(100, prev - 50))}
+          disabled={zoomLevel <= 100}
+          className="p-1 rounded hover:bg-gray-200 disabled:opacity-40 transition-colors"
+        >
+          <ZoomOut size={14} className="text-gray-600" />
+        </button>
+        
+        <select
+          value={zoomLevel}
+          onChange={(e) => setZoomLevel(parseInt(e.target.value))}
+          onClick={(e) => e.stopPropagation()}
+          className="text-xs font-medium text-gray-600 bg-white border border-gray-200 
+                     rounded px-2 py-0.5 focus:outline-none"
+        >
+          {zoomLevels.map(level => (
+            <option key={level} value={level}>{level}%</option>
+          ))}
+        </select>
+        
+        <button
+          onClick={() => setZoomLevel(prev => Math.min(500, prev + 50))}
+          disabled={zoomLevel >= 500}
+          className="p-1 rounded hover:bg-gray-200 disabled:opacity-40 transition-colors"
+        >
+          <ZoomIn size={14} className="text-gray-600" />
+        </button>
+      </div>
+
+      {/* Lens content */}
+      <div 
+        ref={imageContainerRef}
+        className={`lens-content flex ${showBoth ? 'gap-1 p-1' : 'p-1'}`}
+      >
+        {/* Original image lens */}
+        <div className="flex-1">
+          {showBoth && (
+            <div className="text-[10px] font-medium text-gray-500 text-center mb-1">
+              Original
+            </div>
+          )}
+          <div
+            className={`h-48 rounded-lg border border-gray-200 cursor-crosshair ${
+              isPanning ? 'cursor-grabbing' : ''
+            }`}
+            style={originalImage ? getBackgroundStyle(originalImage) : { backgroundColor: '#f3f4f6' }}
+            onMouseDown={handleLensMouseDown}
+          >
+            {!originalImage && (
+              <div className="h-full flex items-center justify-center text-xs text-gray-400">
+                No image
+              </div>
+            )}
+          </div>
+        </div>
+
+        {/* Processed image lens */}
+        {showBoth && processedImage && (
+          <div className="flex-1">
+            <div className="text-[10px] font-medium text-blue-500 text-center mb-1">
+              Processed
+            </div>
+            <div
+              className={`h-48 rounded-lg border border-blue-200 cursor-crosshair ${
+                isPanning ? 'cursor-grabbing' : ''
+              }`}
+              style={getBackgroundStyle(processedImage)}
+              onMouseDown={handleLensMouseDown}
+            />
+          </div>
+        )}
+
+        {/* Show processed only when not in compare mode */}
+        {!showBoth && processedImage && (
+          <div
+            className={`flex-1 h-48 rounded-lg border border-blue-200 cursor-crosshair ${
+              isPanning ? 'cursor-grabbing' : ''
+            }`}
+            style={getBackgroundStyle(processedImage)}
+            onMouseDown={handleLensMouseDown}
+          />
+        )}
+      </div>
+
+      {/* Position indicator */}
+      <div className="px-3 py-1.5 bg-gray-50 border-t border-gray-100 text-[10px] text-gray-400 text-center">
+        Position: {Math.round(lensPosition.x)}%, {Math.round(lensPosition.y)}% — Drag to pan
+      </div>
+    </div>
+  );
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/preprocess/index.js b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/preprocess/index.js
new file mode 100644
index 00000000..4ac8cc87
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/components/preprocess/index.js
@@ -0,0 +1,12 @@
+// Preprocess components barrel export
+
+export { default as TooltipInfo } from './TooltipInfo';
+export { default as OperationSlider } from './OperationSlider';
+export { default as ProgressBarLabeled } from './ProgressBarLabeled';
+export { default as OperationCard } from './OperationCard';
+export { default as PipelineStack } from './PipelineStack';
+export { default as BeforeAfterViewer } from './BeforeAfterViewer';
+export { default as ZoomLensPreview } from './ZoomLensPreview';
+export { default as OperationsSidebar } from './OperationsSidebar';
+export { default as ImageEraser } from './ImageEraser';
+
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/config.js b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/config.js
new file mode 100644
index 00000000..d6423e86
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/config.js
@@ -0,0 +1,4 @@
+// Empty means same-origin: nginx proxies /api/ in production, the Vite dev
+// server proxies it locally. One code path for both. Only override for unusual
+// deployments.
+export const API_ORIGIN = import.meta.env.VITE_API_URL ?? '';
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/config/preprocessOperations.js b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/config/preprocessOperations.js
new file mode 100644
index 00000000..161e1791
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/config/preprocessOperations.js
@@ -0,0 +1,443 @@
+// Operation definitions for the preprocessing sidebar: params, ranges and
+// defaults. Must stay in sync with backend/preprocessing/operations.py.
+
+// ── Categories ──
+
+export const OPERATION_CATEGORIES = [
+  {
+    id: 'basic',
+    label: 'Basic Processing',
+    icon: 'Sliders',
+    description: 'Essential image adjustments',
+  },
+  {
+    id: 'enhancement',
+    label: 'Enhancement',
+    icon: 'Sparkles',
+    description: 'Improve image quality and clarity',
+  },
+  {
+    id: 'binarization',
+    label: 'Binarization',
+    icon: 'Contrast',
+    description: 'Convert to black and white',
+  },
+  {
+    id: 'cleanup',
+    label: 'Cleanup & Morphology',
+    icon: 'Eraser',
+    description: 'Remove artifacts and refine text shapes',
+  },
+];
+
+// ── Operations ──
+
+export const OPERATIONS = [
+  // Basic
+  {
+    id: 'normalize',
+    name: 'Normalize',
+    category: 'basic',
+    tooltip: 'Normalize image brightness and contrast levels to improve overall quality.',
+    controls: [
+      {
+        id: 'strength',
+        label: 'Strength',
+        type: 'slider',
+        min: 0,
+        max: 100,
+        step: 5,
+        default: 50,
+        unit: '%',
+      },
+    ],
+    defaultParams: { strength: 50 },
+  },
+  {
+    id: 'grayscale',
+    name: 'Grayscale',
+    category: 'basic',
+    tooltip: 'Convert image to grayscale. Often improves OCR accuracy for color documents.',
+    controls: [],
+    defaultParams: {},
+  },
+  {
+    id: 'deskew',
+    name: 'Deskew',
+    category: 'basic',
+    tooltip: 'Detect and correct scanning skew. "Auto" and "Piecewise" also fix pages whose skew drifts down the sheet (e.g. straight at the top, tilted near the spine).',
+    controls: [
+      {
+        id: 'mode',
+        label: 'Mode',
+        type: 'select',
+        options: [
+          { value: 'auto', label: 'Auto' },
+          { value: 'global', label: 'Global (one rotation)' },
+          { value: 'piecewise', label: 'Piecewise (variable skew)' },
+        ],
+        default: 'auto',
+      },
+      {
+        id: 'maxAngle',
+        label: 'Max Angle',
+        type: 'slider',
+        min: 1,
+        max: 45,
+        step: 1,
+        default: 15,
+        unit: '°',
+      },
+      {
+        id: 'bands',
+        label: 'Bands',
+        type: 'slider',
+        min: 2,
+        max: 12,
+        step: 1,
+        default: 4,
+        showWhen: { mode: ['auto', 'piecewise'] },
+      },
+    ],
+    defaultParams: { mode: 'auto', maxAngle: 15, bands: 4 },
+  },
+
+  // Enhancement
+  {
+    id: 'denoise',
+    name: 'Denoise',
+    category: 'enhancement',
+    tooltip: 'Remove noise and grain from the image while preserving text edges.',
+    controls: [
+      {
+        id: 'method',
+        label: 'Method',
+        type: 'select',
+        options: [
+          { value: 'nlm', label: 'Non-Local Means' },
+          { value: 'bilateral', label: 'Bilateral Filter' },
+          { value: 'gaussian', label: 'Gaussian Blur' },
+        ],
+        default: 'nlm',
+      },
+      {
+        id: 'strength',
+        label: 'Strength',
+        type: 'slider',
+        min: 1,
+        max: 20,
+        step: 1,
+        default: 10,
+      },
+    ],
+    defaultParams: { method: 'nlm', strength: 10 },
+  },
+  {
+    id: 'contrast',
+    name: 'Contrast',
+    category: 'enhancement',
+    tooltip: 'Enhance image contrast using adaptive histogram equalization (CLAHE).',
+    controls: [
+      {
+        id: 'clipLimit',
+        label: 'Clip Limit',
+        type: 'slider',
+        min: 1,
+        max: 10,
+        step: 0.5,
+        default: 2,
+      },
+      {
+        id: 'tileSize',
+        label: 'Tile Size',
+        type: 'slider',
+        min: 2,
+        max: 16,
+        step: 2,
+        default: 8,
+      },
+    ],
+    defaultParams: { clipLimit: 2, tileSize: 8 },
+  },
+  {
+    id: 'sharpen',
+    name: 'Sharpen',
+    category: 'enhancement',
+    tooltip: 'Sharpen text edges for crisper, more defined characters.',
+    controls: [
+      {
+        id: 'amount',
+        label: 'Amount',
+        type: 'slider',
+        min: 0,
+        max: 100,
+        step: 5,
+        default: 50,
+        unit: '%',
+      },
+      {
+        id: 'radius',
+        label: 'Radius',
+        type: 'slider',
+        min: 0.5,
+        max: 3,
+        step: 0.5,
+        default: 1,
+        unit: 'px',
+      },
+    ],
+    defaultParams: { amount: 50, radius: 1 },
+  },
+
+  // Binarization
+  {
+    id: 'threshold',
+    name: 'Threshold',
+    category: 'binarization',
+    tooltip: 'Convert to black and white. Choose between automatic (Otsu) or adaptive methods.',
+    controls: [
+      {
+        id: 'method',
+        label: 'Method',
+        type: 'select',
+        options: [
+          { value: 'otsu', label: 'Otsu (Auto)' },
+          { value: 'adaptive', label: 'Adaptive' },
+          { value: 'sauvola', label: 'Sauvola' },
+        ],
+        default: 'otsu',
+      },
+      {
+        id: 'blockSize',
+        label: 'Block Size',
+        type: 'slider',
+        min: 3,
+        max: 51,
+        step: 2,
+        default: 15,
+        showWhen: { method: ['adaptive', 'sauvola'] },
+      },
+      {
+        id: 'k',
+        label: 'Sensitivity',
+        type: 'slider',
+        min: 0.1,
+        max: 0.9,
+        step: 0.1,
+        default: 0.5,
+        showWhen: { method: ['sauvola'] },
+      },
+    ],
+    defaultParams: { method: 'otsu', blockSize: 15, k: 0.5 },
+  },
+
+  // Cleanup & morphology
+  {
+    id: 'morph',
+    name: 'Morphological',
+    category: 'cleanup',
+    tooltip: 'Apply morphological transforms to clean text edges, fill gaps, or remove small artifacts.',
+    controls: [
+      {
+        id: 'operation',
+        label: 'Operation',
+        type: 'select',
+        options: [
+          { value: 'open', label: 'Open (Remove noise)' },
+          { value: 'close', label: 'Close (Fill gaps)' },
+          { value: 'dilate', label: 'Dilate (Thicken)' },
+          { value: 'erode', label: 'Erode (Thin)' },
+          { value: 'gradient', label: 'Gradient (Edge outline)' },
+          { value: 'tophat', label: 'Top Hat (Bright details)' },
+          { value: 'blackhat', label: 'Black Hat (Dark details)' },
+        ],
+        default: 'open',
+      },
+      {
+        id: 'kernelShape',
+        label: 'Kernel Shape',
+        type: 'select',
+        options: [
+          { value: 'ellipse', label: 'Ellipse (Smooth)' },
+          { value: 'rect', label: 'Rectangle (Sharp)' },
+          { value: 'cross', label: 'Cross (Directional)' },
+        ],
+        default: 'ellipse',
+      },
+      {
+        id: 'kernelSize',
+        label: 'Kernel Size',
+        type: 'slider',
+        min: 1,
+        max: 9,
+        step: 1,
+        default: 2,
+      },
+      {
+        id: 'iterations',
+        label: 'Iterations',
+        type: 'slider',
+        min: 1,
+        max: 10,
+        step: 1,
+        default: 1,
+      },
+    ],
+    defaultParams: { operation: 'open', kernelShape: 'ellipse', kernelSize: 2, iterations: 1 },
+  },
+  {
+    id: 'remove_blobs',
+    name: 'Remove Ink Blobs',
+    category: 'cleanup',
+    tooltip: 'Neutralise large ink blobs from scanned pages while preserving adjacent text.',
+    controls: [
+      {
+        id: 'minArea',
+        label: 'Min Blob Area',
+        type: 'slider',
+        min: 500,
+        max: 10000,
+        step: 100,
+        default: 3000,
+        unit: 'px²',
+      },
+      {
+        id: 'minSolidity',
+        label: 'Min Solidity',
+        type: 'slider',
+        min: 0.1,
+        max: 1.0,
+        step: 0.05,
+        default: 0.55,
+      },
+      {
+        id: 'maxAspectRatio',
+        label: 'Max Aspect Ratio',
+        type: 'slider',
+        min: 1.0,
+        max: 10.0,
+        step: 0.5,
+        default: 4.0,
+      },
+      {
+        id: 'erosionRatio',
+        label: 'Erosion Safety',
+        type: 'slider',
+        min: 0.1,
+        max: 0.8,
+        step: 0.05,
+        default: 0.35,
+      },
+    ],
+    defaultParams: { minArea: 3000, minSolidity: 0.55, maxAspectRatio: 4.0, erosionRatio: 0.35 },
+  },
+  {
+    id: 'remove_noise',
+    name: 'Remove Speckles',
+    category: 'cleanup',
+    tooltip: 'Remove tiny scanning speckles and dust particles based on connected-component area.',
+    controls: [
+      {
+        id: 'maxArea',
+        label: 'Max Noise Area',
+        type: 'slider',
+        min: 5,
+        max: 200,
+        step: 5,
+        default: 20,
+        unit: 'px²',
+      },
+    ],
+    defaultParams: { maxArea: 20 },
+  },
+];
+
+// ── Helpers ──
+
+
+export function getOperationsByCategory() {
+  const grouped = {};
+  OPERATION_CATEGORIES.forEach(cat => {
+    grouped[cat.id] = OPERATIONS.filter(op => op.category === cat.id);
+  });
+  return grouped;
+}
+
+
+export function getOperationById(id) {
+  return OPERATIONS.find(op => op.id === id);
+}
+
+
+export function createPipelineStep(operationId, order = 0) {
+  const operation = getOperationById(operationId);
+  if (!operation) return null;
+
+  return {
+    id: `${operationId}-${Date.now()}`,
+    operationId: operationId,
+    enabled: true,
+    params: { ...operation.defaultParams },
+    order: order,
+  };
+}
+
+
+export function getRecommendedPipeline() {
+  return [
+    createPipelineStep('grayscale', 0),
+    createPipelineStep('deskew', 1),
+    createPipelineStep('denoise', 2),
+    createPipelineStep('contrast', 3),
+    createPipelineStep('threshold', 4),
+  ].filter(Boolean);
+}
+
+// ── Book-type presets ──
+// Pre-tested pipelines for specific book collections. Selecting one only
+// populates the sidebar/pipeline; it does NOT run — the user reviews, tweaks,
+// then processes. `steps[].params` override the operation's defaults.
+export const BOOK_TYPE_PRESETS = [
+  {
+    id: 'porcones',
+    label: 'PORCONES',
+    description: 'Pre-tested pipeline for PORCONES scans',
+    steps: [
+      { operationId: 'remove_noise', params: { maxArea: 20 } },
+    ],
+  },
+];
+
+export function getBookTypePresets() {
+  return BOOK_TYPE_PRESETS;
+}
+
+// Build pipeline steps for a book type. Returns [] for an unknown id.
+export function getBookTypePipeline(bookTypeId) {
+  const preset = BOOK_TYPE_PRESETS.find(p => p.id === bookTypeId);
+  if (!preset) return [];
+
+  return preset.steps
+    .map((step, index) => {
+      const built = createPipelineStep(step.operationId, index);
+      if (built && step.params) {
+        built.params = { ...built.params, ...step.params };
+      }
+      return built;
+    })
+    .filter(Boolean);
+}
+
+// Controls can declare a `showIf` predicate against the current params.
+export function shouldShowControl(control, currentParams) {
+  if (!control.showWhen) return true;
+
+  return Object.entries(control.showWhen).every(([key, value]) => {
+    if (Array.isArray(value)) {
+      return value.includes(currentParams[key]);
+    }
+    return currentParams[key] === value;
+  });
+}
+
+export default OPERATIONS;
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/features/auth/AuthPage.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/features/auth/AuthPage.jsx
new file mode 100644
index 00000000..63de15c5
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/features/auth/AuthPage.jsx
@@ -0,0 +1,212 @@
+import React, { useState } from 'react';
+import { BookOpen, Loader2, AlertCircle, Eye, EyeOff } from 'lucide-react';
+import { login as apiLogin, signup as apiSignup } from './authApi';
+
+// Login / signup gate shown before the app loads.
+// onAuthSuccess(user) fires once the session cookie is set.
+export default function AuthPage({ onAuthSuccess }) {
+  const [mode, setMode] = useState('login'); // 'login' | 'signup'
+  const [form, setForm] = useState({
+    username: '',
+    password: '',
+    name: '',
+    email: '',
+    institute: 'personal',
+  });
+  const [showPassword, setShowPassword] = useState(false);
+  const [loading, setLoading] = useState(false);
+  const [error, setError] = useState('');
+
+  const update = (field) => (e) =>
+    setForm((f) => ({ ...f, [field]: e.target.value }));
+
+  const switchMode = (next) => {
+    setMode(next);
+    setError('');
+  };
+
+  const handleSubmit = async (e) => {
+    e.preventDefault();
+    setError('');
+    setLoading(true);
+    try {
+      if (mode === 'signup') {
+        const res = await apiSignup(form);
+        onAuthSuccess(res.user);
+      } else {
+        const res = await apiLogin({
+          username: form.username,
+          password: form.password,
+        });
+        onAuthSuccess(res.user);
+      }
+    } catch (err) {
+      setError(err.message || 'Something went wrong. Please try again.');
+    } finally {
+      setLoading(false);
+    }
+  };
+
+  const isSignup = mode === 'signup';
+
+  return (
+    <div className="relative min-h-screen w-screen overflow-hidden flex items-center justify-center bg-gradient-to-br from-slate-50 via-white to-blue-50 px-4 py-10">
+      {/* Decorative background blobs (matches HomePage) */}
+      <div className="pointer-events-none absolute -top-40 -left-40 w-[34rem] h-[34rem] rounded-full bg-blue-200/40 blur-3xl" aria-hidden="true" />
+      <div className="pointer-events-none absolute -bottom-40 -right-40 w-[36rem] h-[36rem] rounded-full bg-indigo-200/40 blur-3xl" aria-hidden="true" />
+
+      <div className="relative z-10 w-full max-w-md">
+        {/* Brand */}
+        <div className="flex flex-col items-center mb-6">
+          <div className="w-14 h-14 bg-gradient-to-br from-blue-500 via-blue-600 to-indigo-600 rounded-2xl flex items-center justify-center shadow-lg shadow-blue-500/30 mb-3">
+            <BookOpen className="w-7 h-7 text-white" />
+          </div>
+          <h1 className="text-2xl font-bold bg-gradient-to-r from-gray-800 to-gray-600 bg-clip-text text-transparent">
+            RenAIssance OCR
+          </h1>
+          <p className="text-sm text-gray-500">Historical document processing</p>
+        </div>
+
+        {/* Card */}
+        <div className="bg-white/90 backdrop-blur-sm border border-gray-200 rounded-2xl shadow-xl shadow-blue-500/10 p-7">
+          {/* Tabs */}
+          <div className="flex p-1 mb-6 bg-gray-100 rounded-xl">
+            {['login', 'signup'].map((m) => (
+              <button
+                key={m}
+                type="button"
+                onClick={() => switchMode(m)}
+                className={`flex-1 py-2 text-sm font-semibold rounded-lg transition-all duration-200 ${
+                  mode === m
+                    ? 'bg-white text-blue-600 shadow-sm'
+                    : 'text-gray-500 hover:text-gray-700'
+                }`}
+              >
+                {m === 'login' ? 'Log in' : 'Sign up'}
+              </button>
+            ))}
+          </div>
+
+          {error && (
+            <div className="mb-4 flex items-start gap-2 p-3 rounded-xl bg-red-50 border border-red-200 text-red-600 text-sm animate-fade-in">
+              <AlertCircle className="w-4 h-4 mt-0.5 flex-shrink-0" />
+              <span>{error}</span>
+            </div>
+          )}
+
+          <form onSubmit={handleSubmit} className="space-y-4">
+            <Field
+              label="Username"
+              value={form.username}
+              onChange={update('username')}
+              autoComplete="username"
+              required
+              placeholder={isSignup ? 'Choose a username' : 'Username or email'}
+            />
+
+            {isSignup && (
+              <>
+                <Field
+                  label="Full name"
+                  value={form.name}
+                  onChange={update('name')}
+                  autoComplete="name"
+                  required
+                  placeholder="Your name"
+                />
+                <Field
+                  label="Email"
+                  type="email"
+                  value={form.email}
+                  onChange={update('email')}
+                  autoComplete="email"
+                  required
+                  placeholder="you@example.com"
+                />
+                <Field
+                  label="Institute"
+                  value={form.institute}
+                  onChange={update('institute')}
+                  autoComplete="organization"
+                  required
+                  placeholder="University / organization"
+                />
+              </>
+            )}
+
+            <Field
+              label="Password"
+              type={showPassword ? 'text' : 'password'}
+              value={form.password}
+              onChange={update('password')}
+              autoComplete={isSignup ? 'new-password' : 'current-password'}
+              required
+              placeholder={isSignup ? 'At least 6 characters' : 'Your password'}
+              minLength={isSignup ? 6 : undefined}
+              rightSlot={
+                <button
+                  type="button"
+                  onClick={() => setShowPassword((s) => !s)}
+                  aria-label={showPassword ? 'Hide password' : 'Show password'}
+                  className="text-gray-400 hover:text-gray-600 transition-colors"
+                  tabIndex={-1}
+                >
+                  {showPassword ? <EyeOff className="w-4 h-4" /> : <Eye className="w-4 h-4" />}
+                </button>
+              }
+            />
+
+            <button
+              type="submit"
+              disabled={loading}
+              className="w-full flex items-center justify-center gap-2 py-2.5 mt-2 rounded-xl bg-gradient-to-r from-blue-500 to-indigo-600 text-white font-semibold shadow-lg shadow-blue-500/30 hover:shadow-xl hover:-translate-y-0.5 transition-all duration-200 disabled:opacity-60 disabled:cursor-not-allowed disabled:translate-y-0"
+            >
+              {loading && <Loader2 className="w-4 h-4 animate-spin" />}
+              {loading
+                ? 'Please wait…'
+                : isSignup
+                ? 'Create account'
+                : 'Log in'}
+            </button>
+          </form>
+
+          <p className="mt-5 text-center text-sm text-gray-500">
+            {isSignup ? 'Already have an account?' : "Don't have an account?"}{' '}
+            <button
+              type="button"
+              onClick={() => switchMode(isSignup ? 'login' : 'signup')}
+              className="font-semibold text-blue-600 hover:text-blue-700"
+            >
+              {isSignup ? 'Log in' : 'Sign up'}
+            </button>
+          </p>
+        </div>
+
+        <p className="mt-6 text-center text-xs text-gray-400">
+          RenAIssance Project · sign in to continue
+        </p>
+      </div>
+    </div>
+  );
+}
+
+function Field({ label, optional, type = 'text', rightSlot, ...props }) {
+  return (
+    <label className="block">
+      <span className="block mb-1 text-sm font-medium text-gray-700">
+        {label}
+        {optional && <span className="ml-1 text-xs text-gray-400">(optional)</span>}
+      </span>
+      <div className="relative">
+        <input
+          type={type}
+          className={`w-full px-3.5 py-2.5 ${rightSlot ? 'pr-10' : ''} rounded-xl border border-gray-200 bg-white text-gray-800 placeholder-gray-400 focus:outline-none focus:ring-4 focus:ring-blue-100 focus:border-blue-400 transition-all duration-200`}
+          {...props}
+        />
+        {rightSlot && (
+          <div className="absolute inset-y-0 right-0 flex items-center pr-3">{rightSlot}</div>
+        )}
+      </div>
+    </label>
+  );
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/features/auth/ProfilePage.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/features/auth/ProfilePage.jsx
new file mode 100644
index 00000000..abf0c627
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/features/auth/ProfilePage.jsx
@@ -0,0 +1,138 @@
+import React, { useState } from 'react';
+import { X, Loader2, CheckCircle2, AlertCircle, User as UserIcon } from 'lucide-react';
+import { updateProfile } from './authApi';
+
+// Edit name / email / institute; the username is fixed. Saving persists
+// locally and syncs to central tracking.
+export default function ProfilePage({ user, onClose, onUpdated }) {
+  const [form, setForm] = useState({
+    name: user.name || '',
+    email: user.email || '',
+    institute: user.institute || 'personal',
+  });
+  const [loading, setLoading] = useState(false);
+  const [error, setError] = useState('');
+  const [saved, setSaved] = useState(false);
+
+  const update = (field) => (e) => {
+    setForm((f) => ({ ...f, [field]: e.target.value }));
+    setSaved(false);
+    setError('');
+  };
+
+  const dirty =
+    form.name !== (user.name || '') ||
+    form.email !== (user.email || '') ||
+    form.institute !== (user.institute || 'personal');
+
+  const handleSubmit = async (e) => {
+    e.preventDefault();
+    setError('');
+    setSaved(false);
+    setLoading(true);
+    try {
+      const updated = await updateProfile(form);
+      onUpdated(updated);
+      setSaved(true);
+    } catch (err) {
+      setError(err.message || 'Could not save changes.');
+    } finally {
+      setLoading(false);
+    }
+  };
+
+  return (
+    <div className="fixed inset-0 z-50 flex items-center justify-center p-4">
+      {/* Backdrop */}
+      <div
+        className="absolute inset-0 bg-gray-900/40 backdrop-blur-sm"
+        onClick={onClose}
+        aria-hidden="true"
+      />
+
+      <div className="relative z-10 w-full max-w-md bg-white rounded-2xl shadow-2xl shadow-blue-500/10 border border-gray-100 animate-fade-in">
+        {/* Header */}
+        <div className="flex items-center justify-between px-6 py-4 border-b border-gray-100">
+          <div className="flex items-center gap-3">
+            <div className="w-10 h-10 bg-gradient-to-br from-blue-500 to-indigo-600 rounded-xl flex items-center justify-center shadow-lg shadow-blue-500/30">
+              <UserIcon className="w-5 h-5 text-white" />
+            </div>
+            <div>
+              <h2 className="text-lg font-bold text-gray-800">Your profile</h2>
+              <p className="text-xs text-gray-500">@{user.username}</p>
+            </div>
+          </div>
+          <button
+            onClick={onClose}
+            className="text-gray-400 hover:text-gray-600 hover:bg-gray-100 p-2 rounded-lg transition-all"
+            aria-label="Close"
+          >
+            <X className="w-5 h-5" />
+          </button>
+        </div>
+
+        {/* Body */}
+        <form onSubmit={handleSubmit} className="px-6 py-5 space-y-4">
+          {saved && (
+            <div className="flex items-start gap-2 p-3 rounded-xl bg-green-50 border border-green-200 text-green-700 text-sm">
+              <CheckCircle2 className="w-4 h-4 mt-0.5 flex-shrink-0" />
+              <span>Profile updated.</span>
+            </div>
+          )}
+          {error && (
+            <div className="flex items-start gap-2 p-3 rounded-xl bg-red-50 border border-red-200 text-red-600 text-sm">
+              <AlertCircle className="w-4 h-4 mt-0.5 flex-shrink-0" />
+              <span>{error}</span>
+            </div>
+          )}
+
+          <ProfileField label="Full name" value={form.name} onChange={update('name')} required />
+          <ProfileField
+            label="Email"
+            type="email"
+            value={form.email}
+            onChange={update('email')}
+            required
+          />
+          <ProfileField
+            label="Institute"
+            value={form.institute}
+            onChange={update('institute')}
+            required
+          />
+
+          <div className="flex items-center justify-end gap-2 pt-2">
+            <button
+              type="button"
+              onClick={onClose}
+              className="px-4 py-2 text-sm font-medium text-gray-500 hover:text-gray-700 hover:bg-gray-100 rounded-lg transition-all"
+            >
+              Close
+            </button>
+            <button
+              type="submit"
+              disabled={loading || !dirty}
+              className="flex items-center gap-2 px-5 py-2 rounded-xl bg-gradient-to-r from-blue-500 to-indigo-600 text-white text-sm font-semibold shadow-lg shadow-blue-500/30 hover:shadow-xl transition-all disabled:opacity-50 disabled:cursor-not-allowed"
+            >
+              {loading && <Loader2 className="w-4 h-4 animate-spin" />}
+              {loading ? 'Saving…' : 'Save changes'}
+            </button>
+          </div>
+        </form>
+      </div>
+    </div>
+  );
+}
+
+function ProfileField({ label, type = 'text', ...props }) {
+  return (
+    <label className="block">
+      <span className="block mb-1 text-sm font-medium text-gray-700">{label}</span>
+      <input
+        type={type}
+        className="w-full px-3.5 py-2.5 rounded-xl border border-gray-200 bg-white text-gray-800 placeholder-gray-400 focus:outline-none focus:ring-4 focus:ring-blue-100 focus:border-blue-400 transition-all duration-200"
+        {...props}
+      />
+    </label>
+  );
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/features/auth/authApi.js b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/features/auth/authApi.js
new file mode 100644
index 00000000..3ff64b88
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/features/auth/authApi.js
@@ -0,0 +1,60 @@
+// Every call sends credentials so the httpOnly session cookie goes back. The
+// cookie is opaque to JS, so no secret ever lands in the bundle.
+
+import { API_ORIGIN } from '../../config';
+
+const BASE = `${API_ORIGIN}/api/auth`;
+
+async function request(path, { method = 'GET', body } = {}) {
+  const res = await fetch(`${BASE}${path}`, {
+    method,
+    credentials: 'include',
+    headers: body ? { 'Content-Type': 'application/json' } : undefined,
+    body: body ? JSON.stringify(body) : undefined,
+  });
+
+  if (res.status === 204) return null;
+
+  let data = null;
+  try {
+    data = await res.json();
+  } catch {
+    data = null;
+  }
+
+  if (!res.ok) {
+    const message =
+      (data && (data.detail?.message || data.detail || data.message)) ||
+      `Request failed (${res.status})`;
+    throw new Error(typeof message === 'string' ? message : 'Request failed');
+  }
+  return data;
+}
+
+// Current user, or null when not logged in.
+export async function fetchMe() {
+  try {
+    return await request('/me');
+  } catch {
+    return null;
+  }
+}
+
+export function signup({ username, password, name, email, institute }) {
+  return request('/signup', {
+    method: 'POST',
+    body: { username, password, name, email, institute: institute || null },
+  });
+}
+
+export function login({ username, password }) {
+  return request('/login', { method: 'POST', body: { username, password } });
+}
+
+export function logout() {
+  return request('/logout', { method: 'POST' });
+}
+
+export function updateProfile({ name, email, institute }) {
+  return request('/me', { method: 'PATCH', body: { name, email, institute } });
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/features/ocr/config/providers.js b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/features/ocr/config/providers.js
new file mode 100644
index 00000000..f5503e65
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/features/ocr/config/providers.js
@@ -0,0 +1,108 @@
+// Provider metadata, fallback model lists and UI config in one place.
+// DeepSeek and Qwen are implemented server-side but hidden in the UI.
+
+import {
+    Sparkles,
+    MessageSquare,
+    Bot,
+    Globe,
+} from 'lucide-react';
+
+export const PROVIDER_LABELS = {
+    gemini: 'Gemini',
+    chatgpt: 'ChatGPT',
+    deepseek: 'DeepSeek',
+    qwen: 'Qwen',
+};
+
+// Used when the backend can't be reached.
+export const FALLBACK_MODELS = {
+    gemini: [
+        { id: 'gemini-3.1-flash-lite', name: 'Gemini 3.1 Flash Lite', description: 'Fast and light, best free-tier quota (recommended)' },
+        { id: 'gemini-3-flash-preview', name: 'Gemini 3 Flash', description: 'Fast, high quality for most documents' },
+        { id: 'gemini-3.5-flash', name: 'Gemini 3.5 Flash', description: 'Higher quality flash model' },
+        { id: 'gemini-3.1-pro-preview', name: 'Gemini 3.1 Pro', description: 'Most capable (low daily quota)' },
+        { id: 'gemini-2.5-flash', name: 'Gemini 2.5 Flash', description: 'Stable fallback' },
+    ],
+    chatgpt: [
+        { id: 'gpt-5.2', name: 'GPT-5.2', description: 'Latest and most capable multimodal model' },
+        { id: 'gpt-5-mini', name: 'GPT-5 Mini', description: 'Smaller, faster, and affordable' },
+    ],
+    deepseek: [
+        { id: 'deepseek-chat', name: 'DeepSeek Chat', description: 'General chat model' },
+        { id: 'deepseek-reasoner', name: 'DeepSeek Reasoner', description: 'Reasoning model' },
+    ],
+    qwen: [
+        { id: 'qwen-vl-max', name: 'Qwen VL Max', description: 'Most capable vision model' },
+        { id: 'qwen-vl-ocr', name: 'Qwen VL OCR', description: 'Optimized for OCR tasks' },
+        { id: 'qwen2.5-vl-72b-instruct', name: 'Qwen2.5 VL 72B', description: 'Large vision-language model' },
+        { id: 'qwen2.5-vl-7b-instruct', name: 'Qwen2.5 VL 7B', description: 'Efficient vision model' },
+    ],
+};
+
+export const DEFAULT_MODELS = {
+    gemini: 'gemini-3.1-flash-lite',
+    chatgpt: 'gpt-5.2',
+    deepseek: 'deepseek-chat',
+    qwen: 'qwen-vl-max',
+};
+
+export const METHOD_OPTIONS = [
+    {
+        id: 'api',
+        name: 'Gemini',
+        icon: Sparkles,
+        tagline: 'Google\'s multimodal AI with state-of-the-art document understanding',
+        gradient: 'from-blue-500 to-indigo-600',
+        lightGradient: 'from-blue-50 to-indigo-50',
+        borderColor: 'border-blue-500',
+        accentColor: 'text-blue-600',
+        shadowColor: 'shadow-blue-500/20',
+        recommended: true,
+    },
+    {
+        id: 'chatgpt',
+        name: 'ChatGPT',
+        icon: MessageSquare,
+        tagline: 'OpenAI GPT-4 Vision with powerful multimodal OCR capabilities',
+        gradient: 'from-green-500 to-emerald-600',
+        lightGradient: 'from-green-50 to-emerald-50',
+        borderColor: 'border-green-500',
+        accentColor: 'text-green-600',
+        shadowColor: 'shadow-green-500/20',
+    },
+    {
+        id: 'deepseek',
+        name: 'DeepSeek',
+        icon: Bot,
+        tagline: 'Cost-effective AI with strong reasoning and open-source foundation',
+        gradient: 'from-purple-500 to-violet-600',
+        lightGradient: 'from-purple-50 to-violet-50',
+        borderColor: 'border-purple-500',
+        accentColor: 'text-purple-600',
+        shadowColor: 'shadow-purple-500/20',
+        disabled: true,
+        disabledReason: 'Does not provide API support for images',
+    },
+    {
+        id: 'qwen',
+        name: 'Qwen',
+        icon: Globe,
+        tagline: 'Alibaba\'s vision models with dedicated OCR and multilingual support',
+        gradient: 'from-orange-500 to-amber-600',
+        lightGradient: 'from-orange-50 to-amber-50',
+        borderColor: 'border-orange-500',
+        accentColor: 'text-orange-600',
+        shadowColor: 'shadow-orange-500/20',
+        disabled: true,
+        disabledReason: 'Does not provide API support for images',
+    },
+];
+
+// TextDetectionPage card id -> provider id.
+export const PROVIDER_MAP = {
+    'api': 'gemini',
+    'chatgpt': 'chatgpt',
+    'deepseek': 'deepseek',
+    'qwen': 'qwen',
+};
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/features/ocr/pages/TextDetectionPage.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/features/ocr/pages/TextDetectionPage.jsx
new file mode 100644
index 00000000..e9906910
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/features/ocr/pages/TextDetectionPage.jsx
@@ -0,0 +1,206 @@
+import React, { useState } from 'react';
+import {
+    ArrowLeft,
+    ArrowRight,
+    Check,
+    ScanText,
+    KeyRound,
+    Ban,
+    Layers,
+} from 'lucide-react';
+import { METHOD_OPTIONS, PROVIDER_MAP } from '../config/providers';
+
+// Not a cloud provider, so it isn't in providers.js.
+const LAYOUT_AWARE_CARD = {
+    id: 'layout-aware',
+    name: 'Layout-Aware',
+    icon: Layers,
+    tagline: 'PaddleOCR layout analysis + line detection pipeline — runs locally on CPU or GPU',
+    gradient: 'from-teal-500 to-emerald-600',
+    lightGradient: 'from-teal-50 to-emerald-50',
+    borderColor: 'border-teal-500',
+    accentColor: 'text-teal-600',
+    shadowColor: 'shadow-teal-500/20',
+    isLocal: true,
+};
+
+export default function TextDetectionPage({
+    pages,
+    selectedPages,
+    processedImages,
+    onBack,
+    onNext,
+}) {
+    const [selectedMethod, setSelectedMethod] = useState('api');
+
+    const processedCount = Object.keys(processedImages || {}).length;
+
+    const allMethods = [...METHOD_OPTIONS, LAYOUT_AWARE_CARD];
+
+    const handleContinue = () => {
+        if (selectedMethod === 'layout-aware') {
+            onNext(selectedMethod, 'layout-aware');
+        } else {
+            onNext(selectedMethod, PROVIDER_MAP[selectedMethod] || 'gemini');
+        }
+    };
+
+    return (
+        <div className="h-full flex flex-col animate-fade-in">
+            {/* Header */}
+            <div className="flex items-center justify-between mb-6">
+                <div className="flex items-center gap-4">
+                    <button onClick={onBack} className="btn-ghost">
+                        <ArrowLeft className="w-5 h-5" />
+                        Back
+                    </button>
+
+                    <div className="flex items-center gap-3">
+                        <div className="p-2 bg-gradient-to-br from-blue-500 to-indigo-600 rounded-lg text-white shadow-md shadow-blue-500/20">
+                            <ScanText className="w-5 h-5" />
+                        </div>
+                        <div>
+                            <h1 className="text-2xl font-bold bg-gradient-to-r from-gray-800 to-gray-600 bg-clip-text text-transparent">
+                                Text Detection
+                            </h1>
+                            <p className="text-sm text-gray-500">
+                                {processedCount} preprocessed page{processedCount !== 1 ? 's' : ''} ready
+                            </p>
+                        </div>
+                    </div>
+                </div>
+
+                <button
+                    onClick={handleContinue}
+                    className="btn-primary"
+                >
+                    {selectedMethod === 'layout-aware'
+                        ? 'Start Detection'
+                        : 'Continue to OCR'}
+                    <ArrowRight className="w-5 h-5" />
+                </button>
+            </div>
+
+            {/* Main content */}
+            <div className="flex-1 overflow-y-auto">
+                {/* Intro */}
+                <div className="text-center mb-10">
+                    <div className="inline-flex items-center gap-2 px-4 py-2 bg-gradient-to-r from-blue-50 to-indigo-50 text-blue-700 rounded-full text-sm font-semibold mb-3 shadow-sm border border-blue-100">
+                        <ScanText className="w-4 h-4" />
+                        Choose your detection method
+                    </div>
+                    <p className="text-gray-500 max-w-xl mx-auto">
+                        Select a cloud AI provider for OCR, or use Layout-Aware detection for local line detection.
+                    </p>
+                </div>
+
+                {/* Method cards */}
+                <div className="grid grid-cols-1 sm:grid-cols-2 lg:grid-cols-5 gap-5 max-w-6xl mx-auto mb-10 px-2">
+                    {allMethods.map((method) => {
+                        const Icon = method.icon;
+                        const isSelected = selectedMethod === method.id;
+                        const isDisabled = method.disabled;
+
+                        return (
+                            <div
+                                key={method.id}
+                                onClick={() => !isDisabled && setSelectedMethod(method.id)}
+                                className={`relative group rounded-2xl border-2 p-5 transition-all duration-300 ${isDisabled
+                                    ? 'border-gray-200 bg-gray-50 opacity-60 cursor-not-allowed'
+                                    : isSelected
+                                        ? `${method.borderColor} bg-gradient-to-br ${method.lightGradient} shadow-xl ${method.shadowColor} -translate-y-1 cursor-pointer`
+                                        : 'border-gray-200 bg-white hover:border-gray-300 hover:shadow-lg hover:-translate-y-0.5 cursor-pointer'
+                                    }`}
+                            >
+                                {/* Recommended badge */}
+                                {method.recommended && !isDisabled && (
+                                    <div className={`absolute -top-2.5 left-1/2 -translate-x-1/2 px-3 py-0.5 text-[10px] font-bold text-white rounded-full bg-gradient-to-r ${method.gradient} shadow-md`}>
+                                        RECOMMENDED
+                                    </div>
+                                )}
+
+                                {/* Local badge for layout-aware */}
+                                {method.isLocal && (
+                                    <div className={`absolute -top-2.5 left-1/2 -translate-x-1/2 px-3 py-0.5 text-[10px] font-bold text-white rounded-full bg-gradient-to-r ${method.gradient} shadow-md`}>
+                                        LOCAL
+                                    </div>
+                                )}
+
+                                {/* Unavailable badge */}
+                                {isDisabled && (
+                                    <div className="absolute -top-2.5 left-1/2 -translate-x-1/2 px-3 py-0.5 text-[10px] font-bold text-white rounded-full bg-gray-400 shadow-md">
+                                        UNAVAILABLE
+                                    </div>
+                                )}
+
+                                {/* Selection indicator */}
+                                {!isDisabled && (
+                                    <div
+                                        className={`absolute top-3 right-3 w-5 h-5 rounded-full border-2 flex items-center justify-center transition-all duration-200 ${isSelected
+                                            ? `border-transparent bg-gradient-to-br ${method.gradient} shadow-sm`
+                                            : 'border-gray-300 bg-white'
+                                            }`}
+                                    >
+                                        {isSelected && <Check className="w-3 h-3 text-white" strokeWidth={3} />}
+                                    </div>
+                                )}
+
+                                {/* Ban icon for disabled */}
+                                {isDisabled && (
+                                    <div className="absolute top-3 right-3 w-5 h-5 rounded-full bg-gray-300 flex items-center justify-center">
+                                        <Ban className="w-3 h-3 text-gray-500" />
+                                    </div>
+                                )}
+
+                                {/* Icon */}
+                                <div
+                                    className={`w-12 h-12 rounded-xl flex items-center justify-center mb-4 transition-all duration-300 ${isDisabled
+                                        ? 'bg-gray-200 text-gray-400'
+                                        : isSelected
+                                            ? `bg-gradient-to-br ${method.gradient} text-white shadow-lg ${method.shadowColor}`
+                                            : `bg-gradient-to-br ${method.lightGradient} ${method.accentColor}`
+                                        }`}
+                                >
+                                    <Icon className="w-6 h-6" />
+                                </div>
+
+                                {/* Name */}
+                                <h3 className={`text-lg font-bold mb-1.5 ${isDisabled ? 'text-gray-400' : 'text-gray-800'}`}>
+                                    {method.name}
+                                </h3>
+
+                                {/* Tagline */}
+                                <p className="text-xs text-gray-500 leading-relaxed mb-3">
+                                    {method.tagline}
+                                </p>
+
+                                {/* Bottom tag */}
+                                {isDisabled ? (
+                                    <div className="inline-flex items-center gap-1.5 px-2.5 py-1 rounded-full text-[10px] font-semibold text-gray-500 bg-gray-200">
+                                        <Ban className="w-3 h-3" />
+                                        {method.disabledReason}
+                                    </div>
+                                ) : method.isLocal ? (
+                                    <div className={`inline-flex items-center gap-1.5 px-2.5 py-1 rounded-full text-[10px] font-semibold ${isSelected ? `${method.accentColor} bg-white/80` : 'text-gray-500 bg-gray-100'
+                                        }`}>
+                                        <Layers className="w-3 h-3" />
+                                        No API Key Needed
+                                    </div>
+                                ) : (
+                                    <div className={`inline-flex items-center gap-1.5 px-2.5 py-1 rounded-full text-[10px] font-semibold ${isSelected ? `${method.accentColor} bg-white/80` : 'text-gray-500 bg-gray-100'
+                                        }`}>
+                                        <KeyRound className="w-3 h-3" />
+                                        API Key Required
+                                    </div>
+                                )}
+                            </div>
+                        );
+                    })}
+                </div>
+
+                {/* Bottom spacer */}
+                <div className="h-8" />
+            </div>
+        </div>
+    );
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/features/ocr/pages/TextRecognitionPage.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/features/ocr/pages/TextRecognitionPage.jsx
new file mode 100644
index 00000000..a11e0b3a
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/features/ocr/pages/TextRecognitionPage.jsx
@@ -0,0 +1,682 @@
+import { useState, useEffect, useCallback, useRef, useMemo } from 'react';
+import {
+    OCRLayout,
+    SidebarConfig,
+    PageThumbnailList,
+    PagePreview,
+    TranscriptPanel,
+} from '../../../components/ocr';
+import {
+    getModels,
+    processPageOCR,
+    processBatchOCR,
+    getRateLimitStatus,
+    verifyApiKey,
+    downloadBlob,
+} from '../services/ocrApi';
+import { saveTranscriptSession, fetchStorageOverview } from '../../../services/storageApi';
+import {
+    PROVIDER_LABELS,
+    FALLBACK_MODELS,
+    DEFAULT_MODELS,
+} from '../config/providers';
+
+// Safe under the 5 req/min free-tier limit.
+const DEFAULT_BATCH_SIZE = 4;
+
+// Three-column OCR workspace. Pages process in the background while the user
+// browses; Gemini goes through the batch endpoint, everyone else sequentially.
+export default function TextRecognitionPage({ provider = 'gemini', bookName = 'transcript', processedImages, onBack, onHome, onComplete }) {
+    const [apiKey, setApiKey] = useState('');
+    const [isKeyValid, setIsKeyValid] = useState(null);
+    const [isValidating, setIsValidating] = useState(false);
+    const [selectedModel, setSelectedModel] = useState(DEFAULT_MODELS[provider] || DEFAULT_MODELS.gemini);
+    const [models, setModels] = useState([]);
+    const [backendOnline, setBackendOnline] = useState(null);
+    const [batchSize, setBatchSize] = useState(DEFAULT_BATCH_SIZE);
+
+    // Which page the user is looking at — independent of what's processing.
+    const [viewingPageIndex, setViewingPageIndex] = useState(0);
+
+    const [processingPageIndex, setProcessingPageIndex] = useState(null);
+    const [processingPageIndices, setProcessingPageIndices] = useState(new Set()); // For batch processing
+    const [isProcessing, setIsProcessing] = useState(false);
+    const [isAutoProcessing, setIsAutoProcessing] = useState(false);
+    const [isWaitingForRateLimit, setIsWaitingForRateLimit] = useState(false); // True when auto-processing is waiting
+    const [error, setError] = useState(null);
+
+    // Guards against two batches running at once.
+    const batchInProgressRef = useRef(false);
+
+    const [transcripts, setTranscripts] = useState({});
+    const [originalTranscripts, setOriginalTranscripts] = useState({});
+    const [processedPages, setProcessedPages] = useState(new Set());
+    // Real failures, not rate limits. One attempt per page per auto-run.
+    const [failedPages, setFailedPages] = useState(new Set());
+
+    const [rateLimitReady, setRateLimitReady] = useState(true);
+    const [waitSeconds, setWaitSeconds] = useState(0);
+    const [availableSlots, setAvailableSlots] = useState(5);
+    const [dailyLimitReached, setDailyLimitReached] = useState(false);
+    const pollRetryCountRef = useRef(0);
+    const MAX_POLL_RETRIES = 30; // Stop polling after ~60 seconds (30 * 2s)
+    const lastSavedSignatureRef = useRef('');
+
+    const [exporting, setExporting] = useState(null);
+    const [isSavingSession, setIsSavingSession] = useState(false);
+    const [sessionSaved, setSessionSaved] = useState(false);
+
+    // Lifted so zoom survives page changes.
+    const [zoomLevel, setZoomLevel] = useState(1);
+
+    const [customPrompt, setCustomPrompt] = useState('');
+
+    const pageLabels = useMemo(() => {
+        return processedImages.map((img) => `${img.pageNumber}`);
+    }, [processedImages]);
+
+    useEffect(() => {
+        async function loadModels() {
+            try {
+                const data = await getModels(provider);
+                setModels(data.models);
+                setSelectedModel(data.default);
+                setBackendOnline(true);
+            } catch {
+                setBackendOnline(false);
+                setModels(FALLBACK_MODELS[provider] || FALLBACK_MODELS.gemini);
+                setSelectedModel(DEFAULT_MODELS[provider] || DEFAULT_MODELS.gemini);
+            }
+        }
+        loadModels();
+    }, [provider]);
+
+    useEffect(() => {
+        const initial = {};
+        processedImages.forEach((_, index) => {
+            initial[pageLabels[index]] = '';
+        });
+        setTranscripts(initial);
+        setOriginalTranscripts(initial);
+    }, [processedImages, pageLabels]);
+
+    useEffect(() => {
+        if (waitSeconds > 0) {
+            const timer = setTimeout(() => {
+                setWaitSeconds((prev) => {
+                    if (prev <= 1) {
+                        setRateLimitReady(true);
+                        setError((e) => (e && e.includes('Rate limit') ? null : e));
+                        return 0;
+                    }
+                    return prev - 1;
+                });
+            }, 1000);
+            return () => clearTimeout(timer);
+        }
+    }, [waitSeconds]);
+
+    const viewingPage = processedImages[viewingPageIndex];
+    const viewingPageLabel = pageLabels[viewingPageIndex];
+    const totalPages = processedImages.length;
+    const isViewingPageProcessed = processedPages.has(viewingPageLabel);
+    const hasAnyTranscript = processedPages.size > 0;
+    const canProcess = apiKey && apiKey.length >= 10 && isKeyValid !== false && rateLimitReady;
+
+    const isViewingPageProcessing = isProcessing && (
+        processingPageIndex === viewingPageIndex ||
+        processingPageIndices.has(viewingPageIndex)
+    );
+
+    const handleVerifyKey = useCallback(async () => {
+        if (!apiKey || apiKey.length < 10) return;
+
+        setIsValidating(true);
+        setError(null);
+
+        try {
+            if (provider === 'gemini') {
+                const result = await verifyApiKey(apiKey);
+                setIsKeyValid(result.valid);
+                if (!result.valid) {
+                    setError(result.error || 'Invalid API key');
+                }
+            } else {
+                // Format check only; the key is really tested on first OCR.
+                if (apiKey.length >= 20) {
+                    setIsKeyValid(true);
+                } else {
+                    setIsKeyValid(false);
+                    setError('API key appears too short');
+                }
+            }
+        } catch (err) {
+            setIsKeyValid(false);
+            setError('Could not verify API key');
+        } finally {
+            setIsValidating(false);
+        }
+    }, [apiKey, provider]);
+
+    const handleApiKeyChange = useCallback((value) => {
+        setApiKey(value);
+        if (isKeyValid !== null) {
+            setIsKeyValid(null);
+        }
+        setError(null);
+    }, [isKeyValid]);
+
+    const goToPage = useCallback((index) => {
+        if (index >= 0 && index < totalPages) {
+            setViewingPageIndex(index);
+            setError(null);
+        }
+    }, [totalPages]);
+
+    const processPage = useCallback(async (pageIndex) => {
+        if (!canProcess || isProcessing) return;
+
+        const pageLabel = pageLabels[pageIndex];
+        const page = processedImages[pageIndex];
+
+        setProcessingPageIndex(pageIndex);
+        setIsProcessing(true);
+        setError(null);
+
+        try {
+            const imageData = page.processed || page.original;
+
+            const result = await processPageOCR(imageData, selectedModel, apiKey, provider, customPrompt);
+
+            if (result.success) {
+                setTranscripts((prev) => ({ ...prev, [pageLabel]: result.transcript }));
+                setOriginalTranscripts((prev) => ({ ...prev, [pageLabel]: result.transcript }));
+                setProcessedPages((prev) => new Set([...prev, pageLabel]));
+                setIsKeyValid(true);
+            } else if (result.error === 'rate_limited') {
+                setRateLimitReady(false);
+                setWaitSeconds(result.waitSeconds || 20);
+            } else if (result.error === 'quota_exceeded') {
+                setDailyLimitReached(true);
+                setIsAutoProcessing(false);
+                setError(result.message || 'Daily API quota reached. Try again tomorrow or use a different API key.');
+            } else if (result.error === 'invalid_api_key') {
+                setIsKeyValid(false);
+                setError('Invalid API key');
+            } else {
+                setError(result.error || 'Processing failed');
+            }
+        } catch (err) {
+            setError(err.message);
+        } finally {
+            setIsProcessing(false);
+            setProcessingPageIndex(null);
+        }
+    }, [apiKey, processedImages, selectedModel, canProcess, isProcessing, provider, customPrompt, pageLabels]);
+
+    const processCurrentPage = useCallback(() => {
+        processPage(viewingPageIndex);
+    }, [processPage, viewingPageIndex]);
+
+    // Gemini has a real batch endpoint; the others go one at a time.
+    const processBatch = useCallback(async (pageIndices) => {
+        if (batchInProgressRef.current) return;
+        if (!canProcess || pageIndices.length === 0) return;
+
+        batchInProgressRef.current = true;
+        setIsProcessing(true);
+        setProcessingPageIndices(new Set(pageIndices));
+        setError(null);
+
+        try {
+            if (provider === 'gemini') {
+                const items = pageIndices.map(pageIndex => {
+                    const page = processedImages[pageIndex];
+                    return {
+                        pageIndex,
+                        imageData: page.processed || page.original,
+                    };
+                });
+
+                const result = await processBatchOCR(items, selectedModel, apiKey, customPrompt);
+
+                if (result.success) {
+                    if (result.quotaExceeded) {
+                        setDailyLimitReached(true);
+                        setIsAutoProcessing(false);
+                        setError('Daily API quota exceeded. Please try again tomorrow or use a different API key.');
+                    }
+
+                    let quotaHit = false;
+                    const failures = [];
+                    result.results.forEach(item => {
+                        const pageLabel = pageLabels[item.page_index];
+                        if (item.success) {
+                            setTranscripts(prev => ({ ...prev, [pageLabel]: item.transcript }));
+                            setOriginalTranscripts(prev => ({ ...prev, [pageLabel]: item.transcript }));
+                            setProcessedPages(prev => new Set([...prev, pageLabel]));
+                        } else {
+                            const errLower = (item.error || '').toLowerCase();
+                            if (errLower.includes('quota') || errLower.includes('resource_exhausted')) {
+                                quotaHit = true;
+                            } else {
+                                // Mark it so auto-processing moves on.
+                                setFailedPages(prev => new Set([...prev, pageLabel]));
+                                failures.push(`Page ${pageLabel}: ${item.error || 'failed'}`);
+                            }
+                            console.error(`Page ${pageLabel} failed:`, item.error);
+                        }
+                    });
+
+                    if (quotaHit) {
+                        setDailyLimitReached(true);
+                        setIsAutoProcessing(false);
+                        setError('Daily API quota reached. Try again tomorrow, pick a different model, or use another API key.');
+                    } else if (failures.length > 0) {
+                        setError(failures.join(' • '));
+                    }
+
+                    if (result.successfulCount > 0) {
+                        setIsKeyValid(true);
+                    }
+
+                    const status = await getRateLimitStatus();
+                    const slotsAvailable = status.available_slots || 0;
+                    setAvailableSlots(slotsAvailable);
+
+                    setRateLimitReady(status.ready && slotsAvailable > 0);
+
+                    if (!status.ready || slotsAvailable === 0) {
+                        setWaitSeconds(status.wait_seconds || 60);
+                    }
+                } else if (result.error === 'rate_limited') {
+                    setRateLimitReady(false);
+                    setWaitSeconds(result.waitSeconds || 60);
+                    setAvailableSlots(result.availableSlots || 0);
+                } else if (result.error === 'invalid_api_key') {
+                    setIsKeyValid(false);
+                    setError('Invalid API key');
+                    setIsAutoProcessing(false);
+                } else if (result.error && (result.error.toLowerCase().includes('quota') || result.error.toLowerCase().includes('resource_exhausted'))) {
+                    setDailyLimitReached(true);
+                    setIsAutoProcessing(false);
+                    setError('Daily API quota exceeded. Please try again tomorrow or use a different API key.');
+                } else {
+                    setError(result.error || 'Batch processing failed');
+                }
+            } else {
+                for (const pageIndex of pageIndices) {
+                    const page = processedImages[pageIndex];
+                    const imageData = page.processed || page.original;
+                    const pageLabel = pageLabels[pageIndex];
+
+                    try {
+                        const result = await processPageOCR(imageData, selectedModel, apiKey, provider, customPrompt);
+
+                        if (result.success) {
+                            setTranscripts(prev => ({ ...prev, [pageLabel]: result.transcript }));
+                            setOriginalTranscripts(prev => ({ ...prev, [pageLabel]: result.transcript }));
+                            setProcessedPages(prev => new Set([...prev, pageLabel]));
+                            setIsKeyValid(true);
+                        } else if (result.error === 'rate_limited') {
+                            setRateLimitReady(false);
+                            setWaitSeconds(result.waitSeconds || 20);
+                            break; // Stop processing on rate limit
+                        } else if (result.error === 'invalid_api_key') {
+                            setIsKeyValid(false);
+                            setError('Invalid API key');
+                            setIsAutoProcessing(false);
+                            break;
+                        } else {
+                            setFailedPages(prev => new Set([...prev, pageLabel]));
+                            setError(`Page ${pageLabel}: ${result.error || 'failed'}`);
+                            console.error(`Page ${pageLabel} failed:`, result.error);
+                        }
+                    } catch (err) {
+                        setFailedPages(prev => new Set([...prev, pageLabel]));
+                        setError(`Page ${pageLabel}: ${err.message}`);
+                        console.error(`Page ${pageLabel} error:`, err.message);
+                    }
+                }
+            }
+        } catch (err) {
+            setError(err.message);
+        } finally {
+            setIsProcessing(false);
+            setProcessingPageIndices(new Set());
+            batchInProgressRef.current = false;
+        }
+    }, [apiKey, processedImages, selectedModel, canProcess, provider, customPrompt, pageLabels]);
+
+    // Kicks off the next batch when there's room. Ref-guarded against re-entry.
+    useEffect(() => {
+        if (!isAutoProcessing) {
+            setIsWaitingForRateLimit(false);
+            pollRetryCountRef.current = 0;
+            return;
+        }
+        if (batchInProgressRef.current) return;
+        if (dailyLimitReached) return;
+
+        const unprocessedIndices = processedImages
+            .map((_, i) => i)
+            .filter(i => !processedPages.has(pageLabels[i]) && !failedPages.has(pageLabels[i]));
+
+        if (unprocessedIndices.length === 0) {
+            setIsAutoProcessing(false);
+            setIsWaitingForRateLimit(false);
+            pollRetryCountRef.current = 0;
+            return;
+        }
+
+        if (provider === 'gemini' && !rateLimitReady) {
+            setIsWaitingForRateLimit(true);
+
+            const pollId = setInterval(async () => {
+                // Too many retries usually means the daily quota, not the window.
+                pollRetryCountRef.current += 1;
+                if (pollRetryCountRef.current > MAX_POLL_RETRIES) {
+                    setDailyLimitReached(true);
+                    setIsAutoProcessing(false);
+                    setIsWaitingForRateLimit(false);
+                    setError('Daily rate limit reached. Please try again later or use a different API key.');
+                    clearInterval(pollId);
+                    return;
+                }
+
+                try {
+                    const status = await getRateLimitStatus();
+                    const slotsAvailable = status.available_slots || 0;
+                    setAvailableSlots(slotsAvailable);
+
+                    if (status.wait_seconds !== undefined) {
+                        setWaitSeconds(status.wait_seconds);
+                    }
+
+                    if (status.ready && slotsAvailable > 0) {
+                        setRateLimitReady(true);
+                        setWaitSeconds(0);
+                        setIsWaitingForRateLimit(false);
+                        pollRetryCountRef.current = 0;
+                        clearInterval(pollId);
+                    }
+                } catch (e) {
+                    console.error('Failed to poll rate limit:', e);
+                }
+            }, 2000);
+
+            return () => clearInterval(pollId);
+        }
+
+        pollRetryCountRef.current = 0;
+        setIsWaitingForRateLimit(false);
+        const actualBatchSize = provider === 'gemini'
+            ? Math.min(unprocessedIndices.length, batchSize, availableSlots || batchSize)
+            : Math.min(unprocessedIndices.length, batchSize);
+        const batchIndices = unprocessedIndices.slice(0, actualBatchSize);
+
+        // Breaks a synchronous setState loop.
+        const timeoutId = setTimeout(() => {
+            if (!batchInProgressRef.current && isAutoProcessing && (provider !== 'gemini' || rateLimitReady)) {
+                processBatch(batchIndices);
+            }
+        }, 200);
+
+        return () => clearTimeout(timeoutId);
+    }, [isAutoProcessing, rateLimitReady, availableSlots, batchSize, dailyLimitReached, processedPages, failedPages, processedImages, processBatch, provider, pageLabels]);
+
+    const handleTranscriptChange = useCallback((value) => {
+        setTranscripts((prev) => ({ ...prev, [viewingPageLabel]: value }));
+    }, [viewingPageLabel]);
+
+
+    const handleResetTranscript = useCallback(() => {
+        setTranscripts((prev) => ({ ...prev, [viewingPageLabel]: originalTranscripts[viewingPageLabel] }));
+    }, [viewingPageLabel, originalTranscripts]);
+
+    // Export
+    const handleExport = useCallback(async (format) => {
+        if (exporting || !hasAnyTranscript) return;
+        setExporting(format);
+        try {
+            const timestamp = new Date().toISOString().slice(0, 10);
+            const orderedEntries = Object.entries(transcripts)
+                .filter(([, text]) => text && text.trim())
+                .sort(([a], [b]) => {
+                    const aNum = Number.parseInt(a, 10);
+                    const bNum = Number.parseInt(b, 10);
+                    if (!Number.isNaN(aNum) && !Number.isNaN(bNum)) return aNum - bNum;
+                    return a.localeCompare(b);
+                });
+
+            let blob;
+            if (format === 'txt') {
+                const text = orderedEntries
+                    .map(([page, value]) => `--- Page ${page} ---\n${value.trim()}`)
+                    .join('\n\n');
+                blob = new Blob([`\uFEFF${text}\n`], { type: 'text/plain;charset=utf-8' });
+            } else if (format === 'csv') {
+                const escapeCsv = (value) => `"${String(value).replaceAll('"', '""')}"`;
+                const rows = ['page,text'];
+                orderedEntries.forEach(([page, value]) => {
+                    rows.push(`${escapeCsv(page)},${escapeCsv(value.trim())}`);
+                });
+                blob = new Blob([`\uFEFF${rows.join('\n')}\n`], { type: 'text/csv;charset=utf-8' });
+            } else if (format === 'json') {
+                const payload = Object.fromEntries(orderedEntries);
+                blob = new Blob([JSON.stringify(payload, null, 2)], { type: 'application/json;charset=utf-8' });
+            } else {
+                throw new Error(`Unsupported format: ${format}`);
+            }
+
+            downloadBlob(blob, `transcript_${timestamp}.${format}`);
+        } catch (err) {
+            setError(`Export failed: ${err.message}`);
+        }
+        setExporting(null);
+    }, [exporting, hasAnyTranscript, transcripts]);
+
+    const toDataUrl = useCallback(async (url) => {
+        if (!url) return null;
+        if (url.startsWith('data:')) return url;
+        const response = await fetch(url);
+        const blob = await response.blob();
+        return new Promise((resolve, reject) => {
+            const reader = new FileReader();
+            reader.onloadend = () => resolve(reader.result);
+            reader.onerror = () => reject(new Error('Failed converting image to base64'));
+            reader.readAsDataURL(blob);
+        });
+    }, []);
+
+    const buildSavePayload = useCallback(async () => {
+        const entries = Object.entries(transcripts)
+            .filter(([, value]) => typeof value === 'string' && value.trim())
+            .sort(([a], [b]) => {
+                const aNum = Number.parseInt(a, 10);
+                const bNum = Number.parseInt(b, 10);
+                if (!Number.isNaN(aNum) && !Number.isNaN(bNum)) return aNum - bNum;
+                return a.localeCompare(b);
+            });
+
+        const nonEmptyTranscripts = {};
+        entries.forEach(([key, value]) => {
+            nonEmptyTranscripts[key] = value.trim();
+        });
+
+        const transcriptImages = {};
+        for (let i = 0; i < pageLabels.length; i += 1) {
+            const key = pageLabels[i];
+            if (!(key in nonEmptyTranscripts)) continue;
+
+            const imageSrc = processedImages[i]?.processed || processedImages[i]?.original || '';
+            if (!imageSrc) continue;
+
+            try {
+                const dataUrl = await toDataUrl(imageSrc);
+                if (typeof dataUrl === 'string' && dataUrl.startsWith('data:')) {
+                    transcriptImages[key] = dataUrl;
+                }
+            } catch {
+                // Transcript still saves even if the image snapshot fails.
+            }
+        }
+
+        const signature = JSON.stringify(nonEmptyTranscripts);
+        return { nonEmptyTranscripts, transcriptImages, signature };
+    }, [pageLabels, processedImages, toDataUrl, transcripts]);
+
+    const handleSaveLocal = useCallback(async () => {
+        if (!hasAnyTranscript || isSavingSession) return;
+        setIsSavingSession(true);
+        setError(null);
+        try {
+            const { nonEmptyTranscripts, transcriptImages, signature } = await buildSavePayload();
+            if (Object.keys(nonEmptyTranscripts).length === 0) {
+                setError('No transcript pages to save.');
+                return;
+            }
+
+            if (lastSavedSignatureRef.current === signature) {
+                const proceed = window.confirm('No transcript changes were detected since the last save. Save another copy anyway?');
+                if (!proceed) return;
+            }
+
+            try {
+                const overview = await fetchStorageOverview();
+                const existingNames = (overview.transcripts || []).map((t) => (t.name || '').toLowerCase());
+                if (existingNames.includes((bookName || '').toLowerCase())) {
+                    const proceed = window.confirm(`A transcript named "${bookName}" already exists in My Files. Save anyway?`);
+                    if (!proceed) return;
+                }
+            } catch {
+            }
+
+            await saveTranscriptSession(
+                nonEmptyTranscripts,
+                'ocr workflow',
+                'recognition',
+                transcriptImages,
+                bookName,
+                {
+                    ocr_provider: provider,
+                    ocr_model: selectedModel,
+                    batch_size: batchSize,
+                    custom_prompt_enabled: Boolean(customPrompt && customPrompt.trim()),
+                },
+            );
+            lastSavedSignatureRef.current = signature;
+            setSessionSaved(true);
+            window.alert('Transcript and page images saved to My Files.');
+        } catch (err) {
+            setError(`Save failed: ${err.message}`);
+        } finally {
+            setIsSavingSession(false);
+        }
+    }, [batchSize, bookName, buildSavePayload, customPrompt, hasAnyTranscript, isSavingSession, provider, selectedModel]);
+
+    const handleToggleAutoProcess = useCallback(() => {
+        setIsAutoProcessing((prev) => {
+            if (prev) {
+                setIsWaitingForRateLimit(false);
+                setIsProcessing(false);
+                setProcessingPageIndices(new Set());
+                batchInProgressRef.current = false;
+                pollRetryCountRef.current = 0;
+                setError(null);
+            } else {
+                // Fresh run — let previously-failed pages retry.
+                setFailedPages(new Set());
+            }
+            return !prev;
+        });
+    }, []);
+
+    const handleComplete = useCallback(async () => {
+        if (typeof onComplete === 'function') {
+            onComplete('OCR processing complete. Use Save to My Files to persist transcripts.');
+        }
+    }, [onComplete]);
+
+    return (
+        <OCRLayout
+            onBack={onBack}
+            onComplete={handleComplete}
+            onHome={onHome}
+            processedCount={processedPages.size}
+            totalPages={totalPages}
+            hasAnyTranscript={hasAnyTranscript && !isSavingSession}
+            backendOnline={backendOnline}
+            leftSidebar={
+                <>
+                    <SidebarConfig
+                        apiKey={apiKey}
+                        onApiKeyChange={handleApiKeyChange}
+                        selectedModel={selectedModel}
+                        onModelChange={setSelectedModel}
+                        models={models}
+                        isKeyValid={isKeyValid}
+                        isValidating={isValidating}
+                        onVerifyKey={handleVerifyKey}
+                        backendOnline={backendOnline}
+                        batchSize={batchSize}
+                        onBatchSizeChange={setBatchSize}
+                        provider={provider}
+                        customPrompt={customPrompt}
+                        onCustomPromptChange={setCustomPrompt}
+                    />
+                    <PageThumbnailList
+                        images={processedImages}
+                        currentIndex={viewingPageIndex}
+                        processedPages={processedPages}
+                        processingPageIndex={processingPageIndex}
+                        processingPageIndices={processingPageIndices}
+                        onPageSelect={goToPage}
+                        pageLabels={pageLabels}
+                    />
+                </>
+            }
+            centerPanel={
+                <PagePreview
+                    currentPage={viewingPage}
+                    currentIndex={viewingPageIndex}
+                    totalPages={totalPages}
+                    isProcessing={isViewingPageProcessing}
+                    isPageProcessed={isViewingPageProcessed}
+                    isAutoProcessing={isAutoProcessing}
+                    isWaitingForRateLimit={isWaitingForRateLimit}
+                    rateLimitReady={rateLimitReady}
+                    waitSeconds={waitSeconds}
+                    error={error}
+                    canProcess={canProcess}
+                    onPrevPage={() => goToPage(viewingPageIndex - 1)}
+                    onNextPage={() => goToPage(viewingPageIndex + 1)}
+                    onProcess={processCurrentPage}
+                    onToggleAutoProcess={handleToggleAutoProcess}
+                    zoomLevel={zoomLevel}
+                    onZoomChange={setZoomLevel}
+                    processingPageIndex={processingPageIndex}
+                    pageLabel={viewingPageLabel}
+                    pageLabels={pageLabels}
+                />
+            }
+            rightPanel={
+                <TranscriptPanel
+                    pageNumber={viewingPageLabel}
+                    transcript={transcripts[viewingPageLabel] || ''}
+                    originalTranscript={originalTranscripts[viewingPageLabel] || ''}
+                    isProcessing={isViewingPageProcessing}
+                    isProcessed={isViewingPageProcessed}
+                    hasAnyTranscript={hasAnyTranscript}
+                    processedCount={processedPages.size}
+                    onTranscriptChange={handleTranscriptChange}
+                    onReset={handleResetTranscript}
+                    onExport={handleExport}
+                    onSaveLocal={handleSaveLocal}
+                    exporting={exporting}
+                    savingLocal={isSavingSession}
+                />
+            }
+        />
+    );
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/features/ocr/services/ocrApi.js b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/features/ocr/services/ocrApi.js
new file mode 100644
index 00000000..e7dfc83c
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/features/ocr/services/ocrApi.js
@@ -0,0 +1,321 @@
+// All OCR provider calls in one place. Gemini uses X-Gemini-API-Key and is
+// the only provider with server-side rate limiting; the rest use X-API-Key.
+
+import { API_ORIGIN } from '../../../config';
+import { toDataUrl } from '../../../utils/imageUrl';
+
+const API_BASE = `${API_ORIGIN}/api`;
+
+export async function getModels(provider) {
+    const endpoints = {
+        gemini: '/models',
+        chatgpt: '/chatgpt-models',
+        deepseek: '/deepseek-models',
+        qwen: '/qwen-models',
+    };
+
+    const endpoint = endpoints[provider] || endpoints.gemini;
+    const response = await fetch(`${API_BASE}${endpoint}`);
+    if (!response.ok) {
+        throw new Error(`Failed to fetch ${provider} models`);
+    }
+    return response.json();
+}
+
+// Gemini only. Falls back to "ready" so a backend hiccup never blocks OCR.
+export async function getRateLimitStatus() {
+    try {
+        const response = await fetch(`${API_BASE}/rate-limit-status`);
+        if (!response.ok) {
+            return { ready: true, wait_seconds: 0, available_slots: 5 };
+        }
+        const data = await response.json();
+        return {
+            ready: data.ready ?? true,
+            wait_seconds: data.wait_seconds ?? 0,
+            available_slots: data.available_slots ?? (data.ready ? 5 : 0),
+            requests_in_window: data.requests_in_window ?? 0,
+        };
+    } catch (error) {
+        console.error('Failed to fetch rate limit status:', error);
+        return { ready: true, wait_seconds: 0, available_slots: 5 };
+    }
+}
+
+// Format check only — the key is really validated on the first OCR call.
+export async function verifyApiKey(apiKey) {
+    if (!apiKey) {
+        return { valid: false, error: 'API key is required' };
+    }
+
+    if (apiKey.length < 20) {
+        return { valid: false, error: 'API key appears too short' };
+    }
+
+    try {
+        const response = await fetch(`${API_BASE}/validate-key`, {
+            method: 'POST',
+            headers: {
+                'X-Gemini-API-Key': apiKey,
+            },
+        });
+
+        if (response.status === 401) {
+            const data = await response.json().catch(() => ({}));
+            return { valid: false, error: data.detail || 'Invalid API key format' };
+        }
+
+        if (response.ok) {
+            const data = await response.json().catch(() => ({}));
+            return {
+                valid: true,
+                message: data.message || 'API key format verified. Will be validated on first use.'
+            };
+        }
+
+        return {
+            valid: true,
+            message: 'Could not verify with server, but format looks valid. Will be validated on first use.'
+        };
+    } catch (error) {
+        if (apiKey.length >= 20) {
+            return {
+                valid: true,
+                message: 'Server offline. Key format looks valid, will be verified on first use.'
+            };
+        }
+        return { valid: false, error: 'Could not connect to server' };
+    }
+}
+
+export async function processPageOCR(imageData, model, apiKey, provider = 'gemini', customPrompt = '') {
+    const endpoints = {
+        gemini: '/gemini-ocr-json',
+        chatgpt: '/chatgpt-ocr-json',
+        deepseek: '/deepseek-ocr-json',
+        qwen: '/qwen-ocr-json',
+    };
+
+    const headerName = provider === 'gemini' ? 'X-Gemini-API-Key' : 'X-API-Key';
+
+    if (provider === 'gemini') {
+        const rateLimitStatus = await getRateLimitStatus();
+        if (!rateLimitStatus.ready) {
+            return {
+                success: false,
+                error: 'rate_limited',
+                waitSeconds: rateLimitStatus.wait_seconds,
+            };
+        }
+    }
+
+    const endpoint = endpoints[provider] || endpoints.gemini;
+    const requestBody = {
+        // Page images are blob: object URLs in app state; backends want base64.
+        image_data: await toDataUrl(imageData),
+        model: model,
+    };
+    if (customPrompt && customPrompt.trim()) {
+        requestBody.custom_prompt = customPrompt.trim();
+    }
+    const response = await fetch(`${API_BASE}${endpoint}`, {
+        method: 'POST',
+        headers: {
+            'Content-Type': 'application/json',
+            [headerName]: apiKey,
+        },
+        body: JSON.stringify(requestBody),
+    });
+
+    if (response.status === 429) {
+        const errorData = await response.json().catch(() => ({}));
+        const detail = errorData.detail;
+        if (detail && detail.error === 'quota_exceeded') {
+            return { success: false, error: 'quota_exceeded', message: detail.message };
+        }
+        return {
+            success: false,
+            error: 'rate_limited',
+            waitSeconds: detail?.wait_seconds || 20,
+        };
+    }
+
+    if (response.status === 401) {
+        return {
+            success: false,
+            error: 'invalid_api_key',
+        };
+    }
+
+    if (!response.ok) {
+        const errorData = await response.json().catch(() => ({}));
+        const detail = errorData.detail;
+        const message = typeof detail === 'object' ? detail?.message : detail;
+        return {
+            success: false,
+            error: message || `OCR failed (HTTP ${response.status})`,
+        };
+    }
+
+    const data = await response.json();
+    return data;
+}
+
+// Gemini only.
+export async function processBatchOCR(items, model, apiKey, customPrompt = '') {
+    if (!items || items.length === 0) {
+        return { success: false, error: 'No items to process' };
+    }
+
+    try {
+        const rateLimitStatus = await getRateLimitStatus();
+        if (!rateLimitStatus.ready || rateLimitStatus.available_slots < items.length) {
+            return {
+                success: false,
+                error: 'rate_limited',
+                waitSeconds: rateLimitStatus.wait_seconds || 60,
+                availableSlots: rateLimitStatus.available_slots || 0,
+            };
+        }
+
+        const batchBody = {
+            items: items.map(item => ({
+                page_index: item.pageIndex,
+                image_data: item.imageData,
+            })),
+            model: model,
+        };
+        if (customPrompt && customPrompt.trim()) {
+            batchBody.custom_prompt = customPrompt.trim();
+        }
+
+        const response = await fetch(`${API_BASE}/gemini-ocr-batch`, {
+            method: 'POST',
+            headers: {
+                'Content-Type': 'application/json',
+                'X-Gemini-API-Key': apiKey,
+            },
+            body: JSON.stringify(batchBody),
+        });
+
+        if (response.status === 429) {
+            const errorData = await response.json().catch(() => ({}));
+            return {
+                success: false,
+                error: 'rate_limited',
+                waitSeconds: errorData.detail?.wait_seconds || 60,
+                availableSlots: errorData.detail?.available_slots || 0,
+            };
+        }
+
+        if (response.status === 401) {
+            return {
+                success: false,
+                error: 'invalid_api_key',
+            };
+        }
+
+        if (!response.ok) {
+            const errorData = await response.json().catch(() => ({}));
+            return {
+                success: false,
+                error: errorData.detail || `Batch OCR failed with status ${response.status}`,
+            };
+        }
+
+        const data = await response.json();
+
+        const quotaExceeded = data.results?.some(r =>
+            !r.success && r.error && (
+                r.error.toLowerCase().includes('quota') ||
+                r.error.toLowerCase().includes('resource_exhausted') ||
+                r.error.toLowerCase().includes('rate limit') ||
+                r.error.includes('429')
+            )
+        );
+
+        return {
+            success: true,
+            results: data.results,
+            successfulCount: data.successful_count,
+            failedCount: data.failed_count,
+            totalProcessingTimeMs: data.total_processing_time_ms,
+            quotaExceeded: quotaExceeded,
+        };
+    } catch (error) {
+        console.error('Batch OCR error:', error);
+        return {
+            success: false,
+            error: error.message || 'Batch OCR processing failed',
+        };
+    }
+}
+
+export function downloadBlob(blob, filename) {
+    const url = URL.createObjectURL(blob);
+    const a = document.createElement('a');
+    a.href = url;
+    a.download = filename;
+    document.body.appendChild(a);
+    a.click();
+    document.body.removeChild(a);
+    URL.revokeObjectURL(url);
+}
+
+// ── Local recognition (CRNN / TrOCR, runs on the backend GPU) ──
+
+export async function getLocalRecognitionModels() {
+    const response = await fetch(`${API_BASE}/local-recognition-models`);
+    if (!response.ok) {
+        throw new Error('Failed to fetch local recognition models');
+    }
+    return response.json();
+}
+
+export async function runLocalRecognition(imageData, boxes, modelId) {
+    const response = await fetch(`${API_BASE}/local-recognize`, {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({
+            image_data: imageData,
+            boxes: boxes,
+            model_id: modelId,
+        }),
+    });
+
+    if (!response.ok) {
+        const errorData = await response.json().catch(() => ({}));
+        return {
+            success: false,
+            error: errorData.detail || `Local recognition failed (${response.status})`,
+        };
+    }
+
+    const data = await response.json();
+    return {
+        success: true,
+        results: data.results,
+        processing_time_ms: data.processing_time_ms,
+        model_used: data.model_used,
+        model_type: data.model_type,
+        device: data.device,
+    };
+}
+
+// ── Export helpers (pure client-side, no backend round-trip) ──
+
+export function exportCRNNResultsAsText(resultsByPage) {
+    const lines = [];
+    for (const [pageLabel, texts] of Object.entries(resultsByPage)) {
+        lines.push(`── Page ${pageLabel} ──`);
+        texts.forEach((t) => lines.push(t));
+        lines.push('');
+    }
+    return new Blob([lines.join('\n')], { type: 'text/plain;charset=utf-8' });
+}
+
+export function exportCRNNResultsAsJSON(resultsByPage) {
+    return new Blob([JSON.stringify(resultsByPage, null, 2)], {
+        type: 'application/json;charset=utf-8',
+    });
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/features/preprocess/pages/PreprocessPage.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/features/preprocess/pages/PreprocessPage.jsx
new file mode 100644
index 00000000..9eabf9f3
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/features/preprocess/pages/PreprocessPage.jsx
@@ -0,0 +1,1129 @@
+import React, { useState, useEffect, useCallback, useMemo, useRef } from 'react';
+import {
+  ArrowLeft,
+  ArrowRight,
+  ChevronLeft,
+  ChevronRight,
+  Crop,
+  Play,
+  SkipForward,
+  Sparkles,
+  Layers,
+  Search,
+  Undo2,
+  Redo2,
+  RotateCcw,
+  Info,
+  X,
+  AlertTriangle,
+  Eraser,
+  BookOpen,
+  ChevronDown,
+} from 'lucide-react';
+
+import { getBookTypePresets, getBookTypePipeline } from '../../../config/preprocessOperations';
+
+import {
+  OperationsSidebar,
+  PipelineStack,
+  BeforeAfterViewer,
+  ZoomLensPreview,
+  ProgressBarLabeled,
+  ImageEraser,
+} from '../../../components/preprocess';
+
+import ImageCropper from '../../../components/ImageCropper';
+
+import { usePipeline } from '../../../hooks/usePipeline';
+import { useHistoryStore, HistoryOps } from '../../../hooks/useHistoryStore';
+import { preprocessImage } from '../../../services/api';
+
+// "1_left" -> "1L", "2_right" -> "2R"; plain numbers pass through.
+function shortPageLabel(pageNumber) {
+  const s = String(pageNumber);
+  return s.replace('_left', 'L').replace('_right', 'R');
+}
+
+// ── Dismissible tip banner ──
+function InfoBanner({ message, onDismiss, variant = 'info' }) {
+  const variants = {
+    info: {
+      bg: 'bg-blue-50',
+      border: 'border-blue-200',
+      text: 'text-blue-800',
+      icon: 'text-blue-500',
+    },
+    warning: {
+      bg: 'bg-amber-50',
+      border: 'border-amber-200',
+      text: 'text-amber-800',
+      icon: 'text-amber-500',
+    },
+  };
+
+  const style = variants[variant] || variants.info;
+  const Icon = variant === 'warning' ? AlertTriangle : Info;
+
+  return (
+    <div className={`${style.bg} ${style.border} border rounded-lg px-4 py-3 flex items-start gap-3`}>
+      <Icon className={`w-5 h-5 ${style.icon} flex-shrink-0 mt-0.5`} />
+      <p className={`text-sm ${style.text} flex-1`}>{message}</p>
+      {onDismiss && (
+        <button
+          onClick={onDismiss}
+          className={`${style.text} hover:opacity-70 transition-opacity p-0.5`}
+          aria-label="Dismiss"
+        >
+          <X className="w-4 h-4" />
+        </button>
+      )}
+    </div>
+  );
+}
+
+// The preprocessing editor. Ops chain onto each page's current working image,
+// and every edit goes through the shared undo/redo store.
+export default function PreprocessPage({
+  pages,
+  selectedPages,
+  initialProcessedImages,
+  onBack,
+  onNext,
+  onProcessedImagesChange,
+  onPipelineChange,
+}) {
+  // ── State ──
+  const [currentPageIndex, setCurrentPageIndex] = useState(0);
+
+  const [isProcessing, setIsProcessing] = useState(false);
+  const [processingProgress, setProcessingProgress] = useState(0);
+  const [currentProcessingStep, setCurrentProcessingStep] = useState(null);
+  const [batchProgress, setBatchProgress] = useState({ current: 0, total: 0 });
+
+  // { pageNumber: { current, original } }. Undo history lives in `history`.
+  const [imageStates, setImageStates] = useState({});
+
+  // Seeded from the parent so leaving and returning keeps progress.
+  const [processedImages, setProcessedImages] = useState(() => initialProcessedImages || {});
+
+  // One timeline for every page's edits.
+  const history = useHistoryStore();
+
+  const [showCropper, setShowCropper] = useState(false);
+  const [showEraser, setShowEraser] = useState(false);
+  const [showZoomLens, setShowZoomLens] = useState(false);
+  const [showPipelinePanel, setShowPipelinePanel] = useState(true);
+  const [showTipBanner, setShowTipBanner] = useState(true);
+
+  // ── Derived data ──
+
+  const selectedPageData = useMemo(() =>
+    selectedPages.map((pageNum) => ({
+      pageNumber: pageNum,
+      ...(pages.find(p => p.pageNumber === pageNum) || {}),
+    })),
+    [selectedPages, pages]
+  );
+
+  const currentPage = selectedPageData[currentPageIndex];
+
+  const getOrCreateImageState = useCallback((pageNumber, originalImage) => {
+    if (!imageStates[pageNumber]) {
+      return {
+        current: originalImage,
+        original: originalImage,
+        history: [],
+      };
+    }
+    return imageStates[pageNumber];
+  }, [imageStates]);
+
+  const currentImageState = useMemo(() => {
+    if (!currentPage) return null;
+    const state = imageStates[currentPage.pageNumber];
+    return state?.current || currentPage.thumbnail;
+  }, [currentPage, imageStates]);
+
+  const originalImageState = useMemo(() => {
+    if (!currentPage) return null;
+    return currentPage.thumbnail;
+  }, [currentPage]);
+
+  // history.version is in the deps so the memo invalidates on timeline changes.
+  const canUndo = useMemo(() => (
+    currentPage ? history.canUndoPage(currentPage.pageNumber) : false
+  ), [currentPage, history, history.version]); // eslint-disable-line react-hooks/exhaustive-deps
+
+  const canRedo = useMemo(() => (
+    currentPage ? history.canRedoPage(currentPage.pageNumber) : false
+  ), [currentPage, history, history.version]); // eslint-disable-line react-hooks/exhaustive-deps
+
+  const isModified = useMemo(() => {
+    if (!currentPage) return false;
+    const state = imageStates[currentPage.pageNumber];
+    return state?.current && state.current !== currentPage.thumbnail;
+  }, [currentPage, imageStates]);
+
+  // ── Pipeline ──
+
+  const {
+    pipeline,
+    enabledOperations,
+    toggleOperation,
+    updateOperationParams,
+    togglePipelineStep,
+    updatePipelineStepParams,
+    removePipelineStep,
+    reorderPipeline,
+    applyRecommendedPipeline,
+    applyPipeline,
+    resetPipeline,
+    getActivePipeline,
+    buildPipelineConfig,
+  } = usePipeline({
+    debounceMs: 600,
+    onPreviewRequest: () => {
+      // Auto-preview off — too slow on full-res scans.
+    },
+  });
+
+  // ── Book-type presets (Recommendation dropdown) ──
+  const [showBookTypeMenu, setShowBookTypeMenu] = useState(false);
+  const bookTypePresets = getBookTypePresets();
+
+  const handleSelectBookType = useCallback((bookTypeId) => {
+    // Select-only: fill the pipeline, don't run. User reviews then processes.
+    applyPipeline(getBookTypePipeline(bookTypeId));
+    setShowBookTypeMenu(false);
+  }, [applyPipeline]);
+
+  // ── Effects ──
+  // Push processed AND crop-only images up. The callback is pinned to a ref so
+  // a new prop identity doesn't re-fire this — the data maps are the real deps.
+  const onProcessedImagesChangeRef = useRef(onProcessedImagesChange);
+  useEffect(() => { onProcessedImagesChangeRef.current = onProcessedImagesChange; }, [onProcessedImagesChange]);
+
+  useEffect(() => {
+    if (!onProcessedImagesChangeRef.current) return;
+    const combinedImages = { ...processedImages };
+    selectedPageData.forEach(page => {
+      const pageNum = page.pageNumber;
+      if (!combinedImages[pageNum]) {
+        const state = imageStates[pageNum];
+        if (state?.current && state.current !== page.thumbnail) {
+          combinedImages[pageNum] = state.current;
+        }
+      }
+    });
+    onProcessedImagesChangeRef.current(combinedImages);
+  }, [processedImages, imageStates, selectedPageData]);
+
+  // Report the applied op list up for the My Files tags. Pinned like above.
+  const onPipelineChangeRef = useRef(onPipelineChange);
+  useEffect(() => { onPipelineChangeRef.current = onPipelineChange; }, [onPipelineChange]);
+
+  useEffect(() => {
+    if (!onPipelineChangeRef.current) return;
+    onPipelineChangeRef.current(buildPipelineConfig().map((s) => s.op));
+  }, [pipeline, buildPipelineConfig]);
+
+  // ── Image state ──
+
+  // Read a page's working image without touching state. Falls back to its thumbnail.
+  const getCurrentImageFor = useCallback((pageNumber) => {
+    const state = imageStates[pageNumber];
+    if (state?.current) return state.current;
+    const page = pages.find((p) => p.pageNumber === pageNumber);
+    return page?.thumbnail;
+  }, [imageStates, pages]);
+
+  // Write a page's current image. Does NOT push history — callers that want
+  // the edit undoable must add the op themselves.
+  const setCurrentImageFor = useCallback((pageNumber, newImageData) => {
+    setImageStates((prev) => {
+      const pageData = pages.find((p) => p.pageNumber === pageNumber);
+      const entry = prev[pageNumber] || {
+        current: pageData?.thumbnail,
+        original: pageData?.thumbnail,
+      };
+      if (entry.current === newImageData) return prev;
+      return { ...prev, [pageNumber]: { ...entry, current: newImageData } };
+    });
+  }, [pages]);
+
+  // Restore a processedImages snapshot, dropping nulls to keep the map small.
+  const replaceProcessedImages = useCallback((nextMap) => {
+    setProcessedImages(() => {
+      const out = {};
+      Object.entries(nextMap || {}).forEach(([k, v]) => {
+        if (v != null) out[k] = v;
+      });
+      return out;
+    });
+  }, []);
+
+  // Redo: replay an op's `after` side through the normal setters.
+  const applyForward = useCallback((op) => {
+    if (!op) return;
+    switch (op.kind) {
+      case 'crop':
+        setCurrentImageFor(op.pageNumber, op.after.image);
+        setProcessedImages((prev) => {
+          const { [op.pageNumber]: _removed, ...rest } = prev;
+          return rest;
+        });
+        break;
+      case 'crop-batch':
+        Object.entries(op.after.images).forEach(([pn, url]) => {
+          setCurrentImageFor(pn in imageStates ? pn : Number(pn), url);
+        });
+        setProcessedImages((prev) => {
+          const next = { ...prev };
+          Object.keys(op.after.images).forEach((pn) => { delete next[pn]; });
+          return next;
+        });
+        break;
+      case 'erase':
+        setCurrentImageFor(op.pageNumber, op.after.image);
+        setProcessedImages((prev) => {
+          const { [op.pageNumber]: _removed, ...rest } = prev;
+          return rest;
+        });
+        break;
+      case 'erase-batch':
+        Object.entries(op.after.images).forEach(([pn, url]) => {
+          const key = isNaN(Number(pn)) ? pn : Number(pn);
+          setCurrentImageFor(key, url);
+        });
+        setProcessedImages((prev) => {
+          const next = { ...prev };
+          Object.keys(op.after.images).forEach((pn) => { delete next[pn]; });
+          return next;
+        });
+        break;
+      case 'pipeline':
+        setProcessedImages((prev) => ({ ...prev, [op.pageNumber]: op.after.processed }));
+        break;
+      case 'pipeline-batch':
+        replaceProcessedImages(op.after.processed);
+        break;
+      case 'reset-page': {
+        const page = pages.find((p) => p.pageNumber === op.pageNumber);
+        setCurrentImageFor(op.pageNumber, page?.thumbnail);
+        setProcessedImages((prev) => {
+          const { [op.pageNumber]: _removed, ...rest } = prev;
+          return rest;
+        });
+        break;
+      }
+      default:
+        console.warn('applyForward: unknown op kind', op.kind);
+    }
+  }, [imageStates, pages, replaceProcessedImages, setCurrentImageFor]);
+
+  // Undo: walk one op's state changes backwards. The store already moved its cursor.
+  const applyRevert = useCallback((op) => {
+    if (!op) return;
+    switch (op.kind) {
+      case 'crop':
+      case 'erase':
+        setCurrentImageFor(op.pageNumber, op.before.image);
+        // The forward op cleared it and we can't re-derive it.
+        break;
+      case 'crop-batch':
+      case 'erase-batch':
+        Object.entries(op.before.images).forEach(([pn, url]) => {
+          const key = isNaN(Number(pn)) ? pn : Number(pn);
+          setCurrentImageFor(key, url);
+        });
+        break;
+      case 'pipeline':
+        if (op.before.processed == null) {
+          setProcessedImages((prev) => {
+            const { [op.pageNumber]: _removed, ...rest } = prev;
+            return rest;
+          });
+        } else {
+          setProcessedImages((prev) => ({ ...prev, [op.pageNumber]: op.before.processed }));
+        }
+        break;
+      case 'pipeline-batch':
+        replaceProcessedImages(op.before.processed);
+        break;
+      case 'reset-page':
+        if (op.before.image != null) setCurrentImageFor(op.pageNumber, op.before.image);
+        if (op.before.processed != null) {
+          setProcessedImages((prev) => ({ ...prev, [op.pageNumber]: op.before.processed }));
+        }
+        break;
+      default:
+        console.warn('applyRevert: unknown op kind', op.kind);
+    }
+  }, [replaceProcessedImages, setCurrentImageFor]);
+
+  // Undo the latest op touching this page.
+  const handleUndo = useCallback(() => {
+    if (!currentPage) return;
+    const op = history.undoPage(currentPage.pageNumber);
+    if (op) applyRevert(op);
+  }, [currentPage, history, applyRevert]);
+
+  // Redo the next op. Global on purpose — the store's branching already keeps
+  // the next op coherent.
+  const handleRedo = useCallback(() => {
+    const op = history.redoGlobal();
+    if (op) applyForward(op);
+  }, [history, applyForward]);
+
+  // Ctrl/Cmd+Z and Shift+Z. Skipped while a modal is open — cropper and eraser
+  // handle their own keys.
+  useEffect(() => {
+    if (showCropper || showEraser) return;
+    const onKey = (e) => {
+      const meta = e.ctrlKey || e.metaKey;
+      if (!meta) return;
+      const tag = document.activeElement?.tagName?.toLowerCase();
+      if (tag === 'input' || tag === 'textarea' || tag === 'select') return;
+      if (e.key === 'z' || e.key === 'Z') {
+        e.preventDefault();
+        if (e.shiftKey) handleRedo();
+        else handleUndo();
+      } else if (e.key === 'y' || e.key === 'Y') {
+        e.preventDefault();
+        handleRedo();
+      }
+    };
+    window.addEventListener('keydown', onKey);
+    return () => window.removeEventListener('keydown', onKey);
+  }, [handleUndo, handleRedo, showCropper, showEraser]);
+
+  // Reset the page, pushed as an op so the reset itself is undoable.
+  const handleResetImage = useCallback(() => {
+    if (!currentPage) return;
+    const pn = currentPage.pageNumber;
+    const beforeImage = getCurrentImageFor(pn);
+    const beforeProcessed = processedImages[pn] ?? null;
+    if (beforeImage === currentPage.thumbnail && beforeProcessed == null) return;
+
+    history.push(HistoryOps.resetPage({
+      pageNumber: pn,
+      beforeImage,
+      beforeProcessed,
+    }));
+
+    setImageStates((prev) => ({
+      ...prev,
+      [pn]: { current: currentPage.thumbnail, original: currentPage.thumbnail },
+    }));
+    setProcessedImages((prev) => {
+      const { [pn]: _removed, ...rest } = prev;
+      return rest;
+    });
+  }, [currentPage, getCurrentImageFor, history, processedImages]);
+
+  // ── Crop ──
+
+  // Single-page crop. The op captures before/after so it can be undone.
+  const handleCropComplete = useCallback((croppedDataUrl, cropData) => {
+    if (!currentPage) return;
+    const pn = currentPage.pageNumber;
+    const before = getCurrentImageFor(pn);
+
+    history.push(HistoryOps.crop({ pageNumber: pn, before, after: croppedDataUrl, cropData }));
+    setCurrentImageFor(pn, croppedDataUrl);
+    setProcessedImages((prev) => {
+      const { [pn]: _removed, ...rest } = prev;
+      return rest;
+    });
+    setShowCropper(false);
+  }, [currentPage, getCurrentImageFor, history, setCurrentImageFor]);
+
+  // The eraser hands back every page it touched. Folded into one op so a single
+  // undo rewinds the whole save.
+  const handleEraserSave = useCallback((results) => {
+    const pageNumbers = [];
+    const beforeMap = {};
+    const afterMap = {};
+
+    Object.entries(results).forEach(([pageNumStr, dataUrl]) => {
+      const page = selectedPageData.find((p) => String(p.pageNumber) === pageNumStr);
+      const pn = page ? page.pageNumber : (isNaN(Number(pageNumStr)) ? pageNumStr : Number(pageNumStr));
+      pageNumbers.push(pn);
+      beforeMap[pn] = getCurrentImageFor(pn);
+      afterMap[pn] = dataUrl;
+    });
+
+    if (pageNumbers.length === 1) {
+      const pn = pageNumbers[0];
+      history.push(HistoryOps.erase({ pageNumber: pn, before: beforeMap[pn], after: afterMap[pn] }));
+    } else if (pageNumbers.length > 1) {
+      history.push(HistoryOps.eraseBatch({ pageNumbers, before: beforeMap, after: afterMap }));
+    }
+
+    pageNumbers.forEach((pn) => setCurrentImageFor(pn, afterMap[pn]));
+    setProcessedImages((prev) => {
+      const next = { ...prev };
+      pageNumbers.forEach((pn) => { delete next[pn]; });
+      return next;
+    });
+    setShowEraser(false);
+  }, [getCurrentImageFor, history, selectedPageData, setCurrentImageFor]);
+
+  // One op spanning the current page plus every batch target.
+  const handleBatchCropComplete = useCallback((currentCroppedUrl, cropData, batchResults) => {
+    if (!currentPage) return;
+    const pageNumbers = [currentPage.pageNumber];
+    const beforeMap = { [currentPage.pageNumber]: getCurrentImageFor(currentPage.pageNumber) };
+    const afterMap = { [currentPage.pageNumber]: currentCroppedUrl };
+
+    batchResults.forEach(({ pageNumber, croppedDataUrl }) => {
+      pageNumbers.push(pageNumber);
+      beforeMap[pageNumber] = getCurrentImageFor(pageNumber);
+      afterMap[pageNumber] = croppedDataUrl;
+    });
+
+    history.push(HistoryOps.cropBatch({ pageNumbers, before: beforeMap, after: afterMap }));
+    pageNumbers.forEach((pn) => setCurrentImageFor(pn, afterMap[pn]));
+    setProcessedImages((prev) => {
+      const next = { ...prev };
+      pageNumbers.forEach((pn) => { delete next[pn]; });
+      return next;
+    });
+    setShowCropper(false);
+  }, [currentPage, getCurrentImageFor, history, setCurrentImageFor]);
+
+  // ── Preprocessing ──
+
+  // Runs on the page's working image, which may already be cropped.
+  const handleApplyToCurrentPage = useCallback(async () => {
+    if (!currentPage || isProcessing) return;
+
+    const activePipeline = getActivePipeline();
+    if (activePipeline.length === 0) return;
+
+    setIsProcessing(true);
+    setProcessingProgress(0);
+
+    try {
+      const pipelineConfig = buildPipelineConfig();
+
+      // Working image, not the original — crops must survive.
+      const sourceImage = currentImageState;
+
+      // Fake progress; the backend doesn't stream any.
+      for (let i = 0; i < pipelineConfig.length; i++) {
+        setCurrentProcessingStep(pipelineConfig[i].op);
+        setProcessingProgress(((i + 0.5) / pipelineConfig.length) * 100);
+        await new Promise(r => setTimeout(r, 100));
+      }
+
+      const result = await preprocessImage(sourceImage, pipelineConfig);
+      const pn = currentPage.pageNumber;
+      const beforeProcessed = processedImages[pn] ?? null;
+
+      history.push(HistoryOps.pipeline({
+        pageNumber: pn,
+        beforeProcessed,
+        afterProcessed: result,
+        pipelineConfig,
+      }));
+
+      setProcessedImages((prev) => ({ ...prev, [pn]: result }));
+      setProcessingProgress(100);
+    } catch (error) {
+      console.error('Processing failed:', error);
+    } finally {
+      setIsProcessing(false);
+      setCurrentProcessingStep(null);
+      setProcessingProgress(0);
+    }
+  }, [currentPage, isProcessing, getActivePipeline, buildPipelineConfig, currentImageState, history, processedImages]);
+
+  // Same, for every selected page, each from its own working image.
+  const handleApplyToAllPages = useCallback(async () => {
+    if (isProcessing) return;
+
+    const activePipeline = getActivePipeline();
+    if (activePipeline.length === 0) return;
+
+    setIsProcessing(true);
+    setBatchProgress({ current: 0, total: selectedPageData.length });
+
+    try {
+      const pipelineConfig = buildPipelineConfig();
+      const results = {};
+
+      for (let i = 0; i < selectedPageData.length; i++) {
+        const page = selectedPageData[i];
+        setBatchProgress({ current: i + 1, total: selectedPageData.length });
+        setProcessingProgress(((i + 0.5) / selectedPageData.length) * 100);
+
+        const pageState = imageStates[page.pageNumber];
+        const sourceImage = pageState?.current || page.thumbnail;
+
+        const result = await preprocessImage(sourceImage, pipelineConfig);
+        results[page.pageNumber] = result;
+      }
+
+      const pageNumbers = Object.keys(results).map((k) => (isNaN(Number(k)) ? k : Number(k)));
+      const beforeProcessed = {};
+      pageNumbers.forEach((pn) => { beforeProcessed[pn] = processedImages[pn] ?? null; });
+
+      history.push(HistoryOps.pipelineBatch({
+        pageNumbers,
+        beforeProcessed,
+        afterProcessed: results,
+        pipelineConfig,
+      }));
+
+      setProcessedImages(results);
+      setProcessingProgress(100);
+    } catch (error) {
+      console.error('Batch processing failed:', error);
+    } finally {
+      setIsProcessing(false);
+      setBatchProgress({ current: 0, total: 0 });
+      setProcessingProgress(0);
+    }
+  }, [isProcessing, getActivePipeline, buildPipelineConfig, selectedPageData, imageStates, processedImages, history]);
+
+  // ── Navigation ──
+
+  const handlePrevPage = () => {
+    setCurrentPageIndex((prev) => Math.max(0, prev - 1));
+  };
+
+  const handleNextPage = () => {
+    setCurrentPageIndex((prev) => Math.min(selectedPageData.length - 1, prev + 1));
+  };
+
+  // ── Reset ──
+
+  // Pipeline config only — page images stay put.
+  const handleResetOperations = useCallback(() => {
+    resetPipeline();
+  }, [resetPipeline]);
+
+  // Current page only, via the undoable handler.
+  const handleResetCurrentPage = handleResetImage;
+
+  // Everything. History goes too — every op's before-state is about to vanish.
+  const handleResetAll = useCallback(() => {
+    resetPipeline();
+    setProcessedImages({});
+    setImageStates({});
+    history.clear();
+  }, [resetPipeline, history]);
+
+  // "Undo All" is just the global undo — the last op is usually an apply-all.
+  const handleGlobalUndo = useCallback(() => {
+    const op = history.undoGlobal();
+    if (op) applyRevert(op);
+  }, [history, applyRevert]);
+
+  // ── Computed ──
+
+  const activePipelineCount = getActivePipeline().length;
+  const hasProcessedImages = Object.keys(processedImages).length > 0;
+  const currentPageProcessed = currentPage && processedImages[currentPage.pageNumber];
+
+  const hasModifiedImages = useMemo(() => {
+    return selectedPageData.some(page => {
+      const state = imageStates[page.pageNumber];
+      return state?.current && state.current !== page.thumbnail;
+    });
+  }, [selectedPageData, imageStates]);
+
+  // Cropped-only pages count as progress too.
+  const canProceed = hasProcessedImages || hasModifiedImages;
+
+  const canGlobalUndo = history.canUndoGlobal;
+  const canGlobalRedo = history.canRedoGlobal;
+
+  // ── Render ──
+
+  return (
+    <div className="h-full w-full flex overflow-hidden bg-gray-50">
+
+      {/* ========== LEFT SIDEBAR ========== */}
+      <aside className="flex-shrink-0 w-[272px] flex flex-col border-r border-gray-200 bg-white overflow-hidden">
+
+        {/* Sidebar header: step badge + title + description + quick actions */}
+        <div className="flex-shrink-0 px-4 pt-4 pb-3 border-b border-gray-100 space-y-3">
+          {/* Step badge */}
+          <div className="flex items-center gap-2">
+            <span className="inline-flex items-center gap-1.5 px-2.5 py-1 bg-blue-50 border border-blue-100 rounded-full text-xs font-semibold text-blue-600">
+              <Sparkles className="w-3 h-3" />
+              Step 3 of 5
+            </span>
+          </div>
+
+          {/* Title + description */}
+          <div>
+            <h2 className="text-base font-bold text-gray-900 leading-tight">Pipeline</h2>
+            <p className="text-xs text-gray-400 mt-0.5">Toggle and tune operations. Preview updates live.</p>
+          </div>
+
+          {/* Quick actions */}
+          <div className="flex gap-2">
+            {/* Recommendation: pick a book type -> pre-fill a pre-tested
+                pipeline (does NOT run; the user reviews and processes). */}
+            <div className="relative flex-1">
+              <button
+                onClick={() => setShowBookTypeMenu(v => !v)}
+                disabled={isProcessing}
+                className="w-full flex items-center justify-center gap-1.5 px-3 py-2
+                           bg-gradient-to-r from-blue-500 to-blue-600 text-white text-xs font-medium
+                           rounded-lg hover:from-blue-600 hover:to-blue-700
+                           disabled:opacity-50 disabled:cursor-not-allowed transition-all shadow-sm"
+                title="Load a pre-tested pipeline for a known book type"
+              >
+                <BookOpen size={13} />
+                Recommendation
+                <ChevronDown size={13} className={`transition-transform ${showBookTypeMenu ? 'rotate-180' : ''}`} />
+              </button>
+
+              {showBookTypeMenu && (
+                <>
+                  {/* click-away */}
+                  <div className="fixed inset-0 z-10" onClick={() => setShowBookTypeMenu(false)} />
+                  <div className="absolute left-0 right-0 mt-1 z-20 bg-white border border-gray-200 rounded-lg shadow-lg overflow-hidden">
+                    <p className="px-3 py-2 text-[10px] font-semibold uppercase tracking-wide text-gray-400 border-b border-gray-100">
+                      Select book type
+                    </p>
+                    {bookTypePresets.map(preset => (
+                      <button
+                        key={preset.id}
+                        onClick={() => handleSelectBookType(preset.id)}
+                        disabled={isProcessing}
+                        className="w-full text-left px-3 py-2 hover:bg-blue-50 disabled:opacity-50
+                                   disabled:cursor-not-allowed transition-colors"
+                      >
+                        <span className="block text-xs font-semibold text-gray-800">{preset.label}</span>
+                        {preset.description && (
+                          <span className="block text-[10px] text-gray-400 mt-0.5">{preset.description}</span>
+                        )}
+                      </button>
+                    ))}
+                    <p className="px-3 py-2 text-[10px] text-gray-400 border-t border-gray-100">
+                      Fills the pipeline for review. Nothing runs until you process.
+                    </p>
+                  </div>
+                </>
+              )}
+            </div>
+
+            <button
+              onClick={handleResetOperations}
+              disabled={isProcessing}
+              className="px-3 py-2 bg-gray-100 text-gray-600 text-xs font-medium rounded-lg
+                         hover:bg-gray-200 disabled:opacity-50 disabled:cursor-not-allowed transition-colors"
+              title="Reset all operations"
+            >
+              <RotateCcw size={14} />
+            </button>
+          </div>
+        </div>
+
+        {/* Operations list or Pipeline Order (toggled by showPipelinePanel) */}
+        <div className="flex-1 min-h-0 overflow-y-auto">
+          {!showPipelinePanel ? (
+            <OperationsSidebar
+              enabledOperations={enabledOperations}
+              onOperationToggle={toggleOperation}
+              onOperationParamsChange={updateOperationParams}
+              onApplyRecommended={applyRecommendedPipeline}
+              onReset={handleResetOperations}
+              isProcessing={isProcessing}
+              hideHeader
+              className="h-full"
+            />
+          ) : (
+            <div className="p-3 space-y-3">
+              {pipeline.length === 0 ? (
+                <div className="mt-2 p-3 bg-blue-50 rounded-lg border border-blue-100">
+                  <div className="flex items-start gap-2">
+                    <Info className="w-4 h-4 text-blue-500 flex-shrink-0 mt-0.5" />
+                    <div>
+                      <p className="text-xs font-medium text-blue-700">No operations in pipeline</p>
+                      <p className="text-[10px] text-blue-600 mt-1">
+                        Switch back to Operations view and enable some operations to add them here.
+                      </p>
+                    </div>
+                  </div>
+                </div>
+              ) : (
+                <PipelineStack
+                  pipeline={pipeline}
+                  onReorder={reorderPipeline}
+                  onToggle={togglePipelineStep}
+                  onUpdateParams={updatePipelineStepParams}
+                  onRemove={removePipelineStep}
+                  isProcessing={isProcessing}
+                  currentStepId={currentProcessingStep}
+                />
+              )}
+            </div>
+          )}
+        </div>
+
+        {/* Sidebar action bar */}
+        <div className="flex-shrink-0 border-t border-gray-100 p-3 space-y-2 bg-gray-50/60">
+
+          {/* Apply buttons */}
+          <div className="flex gap-2">
+            <button
+              onClick={handleApplyToCurrentPage}
+              disabled={isProcessing || activePipelineCount === 0}
+              className="flex-1 flex items-center justify-center gap-1.5 py-2 px-3
+                         bg-blue-600 text-white text-xs font-semibold rounded-lg
+                         hover:bg-blue-700 disabled:opacity-40 disabled:cursor-not-allowed transition-colors"
+              title="Apply pipeline to current page"
+            >
+              <Play className="w-3.5 h-3.5" />
+              Apply
+            </button>
+            <button
+              onClick={handleApplyToAllPages}
+              disabled={isProcessing || activePipelineCount === 0}
+              className="flex items-center gap-1.5 py-2 px-3
+                         bg-gray-100 text-gray-700 text-xs font-semibold rounded-lg
+                         hover:bg-gray-200 disabled:opacity-40 disabled:cursor-not-allowed transition-colors"
+              title="Apply pipeline to all selected pages"
+            >
+              <Layers className="w-3.5 h-3.5" />
+              All
+            </button>
+          </div>
+
+          {/* Edit tools row */}
+          <div className="flex items-center gap-1.5 flex-wrap">
+            <button
+              onClick={() => setShowCropper(true)}
+              disabled={!currentPage}
+              className={`flex items-center gap-1 px-2.5 py-1.5 rounded-lg text-xs font-medium transition-all
+                ${isModified ? 'bg-orange-100 text-orange-700 hover:bg-orange-200' : 'bg-gray-100 text-gray-700 hover:bg-gray-200'}
+                disabled:opacity-40 disabled:cursor-not-allowed`}
+              title="Crop image"
+            >
+              <Crop className="w-3.5 h-3.5" />
+              Crop
+            </button>
+            <button
+              onClick={() => setShowEraser(true)}
+              disabled={!currentPage}
+              className="flex items-center gap-1 px-2.5 py-1.5 rounded-lg text-xs font-medium
+                         bg-gray-100 text-gray-700 hover:bg-gray-200
+                         disabled:opacity-40 disabled:cursor-not-allowed transition-colors"
+              title="Erase artifacts"
+            >
+              <Eraser className="w-3.5 h-3.5" />
+              Erase
+            </button>
+
+            {/* Undo / Redo / Undo-All */}
+            <button
+              onClick={handleUndo}
+              disabled={!canUndo}
+              className={`p-1.5 rounded-lg transition-all
+                ${canUndo ? 'bg-gray-100 text-gray-700 hover:bg-gray-200' : 'text-gray-300 cursor-not-allowed'}`}
+              title="Undo (Ctrl+Z)"
+            >
+              <Undo2 className="w-3.5 h-3.5" />
+            </button>
+            <button
+              onClick={handleRedo}
+              disabled={!canGlobalRedo}
+              className={`p-1.5 rounded-lg transition-all
+                ${canGlobalRedo ? 'bg-gray-100 text-gray-700 hover:bg-gray-200' : 'text-gray-300 cursor-not-allowed'}`}
+              title="Redo (Ctrl+Shift+Z)"
+            >
+              <Redo2 className="w-3.5 h-3.5" />
+            </button>
+            <button
+              onClick={handleGlobalUndo}
+              disabled={!canGlobalUndo}
+              className={`flex items-center gap-0.5 p-1.5 rounded-lg transition-all
+                ${canGlobalUndo ? 'bg-gray-100 text-gray-700 hover:bg-gray-200' : 'text-gray-300 cursor-not-allowed'}`}
+              title="Undo last global operation (e.g. Apply All)"
+            >
+              <Undo2 className="w-3.5 h-3.5" />
+              <Layers className="w-2.5 h-2.5" />
+            </button>
+
+            {/* Reset page when modified */}
+            {isModified && (
+              <button
+                onClick={handleResetImage}
+                className="flex items-center gap-1 px-2 py-1.5 rounded-lg text-xs font-medium text-red-600 hover:bg-red-50 transition-colors ml-auto"
+                title="Reset to original image"
+              >
+                <RotateCcw className="w-3.5 h-3.5" />
+                Reset
+              </button>
+            )}
+          </div>
+
+          {/* Pipeline order toggle + pipeline count */}
+          <div className="flex items-center justify-between pt-0.5">
+            <button
+              onClick={() => setShowPipelinePanel(!showPipelinePanel)}
+              className={`flex items-center gap-1.5 px-2.5 py-1.5 rounded-lg text-xs font-medium transition-all
+                ${showPipelinePanel ? 'bg-blue-100 text-blue-700' : 'bg-gray-100 text-gray-600 hover:bg-gray-200'}`}
+              title="Toggle pipeline order view"
+            >
+              <Layers className="w-3.5 h-3.5" />
+              {showPipelinePanel ? 'Operations' : 'Reorder Pipeline'}
+            </button>
+            {activePipelineCount > 0 && (
+              <span className="text-[10px] text-gray-400 flex items-center gap-1">
+                <span className="w-1.5 h-1.5 rounded-full bg-blue-400" />
+                {activePipelineCount} active
+              </span>
+            )}
+          </div>
+        </div>
+      </aside>
+
+      {/* ========== MAIN CONTENT ========== */}
+      <main className="flex-1 flex flex-col min-w-0 overflow-hidden">
+
+        {/* Preview header */}
+        <div className="flex-shrink-0 px-5 py-3 bg-white border-b border-gray-100 flex items-center justify-between gap-4">
+          <div className="flex items-center gap-3 min-w-0">
+            <div className="flex-shrink-0 p-1.5 bg-blue-500 rounded-lg text-white">
+              <Sparkles className="w-4 h-4" />
+            </div>
+            <div className="min-w-0">
+              <h3 className="text-sm font-semibold text-gray-800 leading-tight">Preprocess preview</h3>
+              <p className="text-xs text-gray-400">Active page · before / after</p>
+            </div>
+          </div>
+
+          <div className="flex items-center gap-2 flex-shrink-0">
+            {/* Modified badge */}
+            {isModified && (
+              <span className="hidden sm:inline text-xs text-orange-600 bg-orange-50 border border-orange-100 px-2 py-1 rounded-lg">
+                Modified
+              </span>
+            )}
+
+            {/* Page counter badge */}
+            <span className="px-3 py-1.5 bg-blue-50 border border-blue-100 text-blue-700 text-xs font-semibold rounded-lg whitespace-nowrap">
+              Page {currentPageIndex + 1} of {selectedPageData.length} selected
+            </span>
+
+            {/* Zoom lens */}
+            <button
+              onClick={() => setShowZoomLens(!showZoomLens)}
+              className={`p-2 rounded-lg transition-colors flex-shrink-0
+                ${showZoomLens ? 'bg-blue-100 text-blue-600' : 'bg-gray-100 text-gray-600 hover:bg-gray-200'}`}
+              title="Toggle zoom lens"
+            >
+              <Search className="w-4 h-4" />
+            </button>
+
+            {/* Skip */}
+            <button
+              onClick={onNext}
+              disabled={isProcessing}
+              className="hidden md:flex items-center gap-1 px-3 py-1.5 text-xs font-medium text-gray-500 hover:bg-gray-100 rounded-lg transition-colors disabled:opacity-40"
+              title="Skip preprocessing"
+            >
+              <SkipForward className="w-3.5 h-3.5" />
+              Skip
+            </button>
+          </div>
+        </div>
+
+        {/* Tip banner */}
+        {showTipBanner && (
+          <div className="flex-shrink-0 px-5 pt-3">
+            <InfoBanner
+              message="Tip: Crop images first before applying preprocessing for best results. Cropping after preprocessing may change output quality."
+              onDismiss={() => setShowTipBanner(false)}
+              variant="info"
+            />
+          </div>
+        )}
+
+        {/* Processing progress bar */}
+        {isProcessing && (
+          <div className="flex-shrink-0 px-5 pt-3">
+            <ProgressBarLabeled
+              label={
+                batchProgress.total > 0
+                  ? `Processing page ${batchProgress.current} of ${batchProgress.total}`
+                  : currentProcessingStep
+                    ? `Running: ${currentProcessingStep}`
+                    : 'Processing...'
+              }
+              progress={processingProgress}
+              variant="primary"
+              size="md"
+              isActive
+            />
+          </div>
+        )}
+
+        {/* Preview area — fills remaining height */}
+        <div className="flex-1 min-h-0 p-4">
+          <BeforeAfterViewer
+            originalImage={originalImageState}
+            processedImage={currentPageProcessed || currentImageState}
+            isProcessing={isProcessing}
+            processingLabel={currentProcessingStep ? `Running: ${currentProcessingStep}` : 'Processing...'}
+            onOpenZoomLens={() => setShowZoomLens(true)}
+            className="h-full"
+          />
+        </div>
+
+        {/* ===== BOTTOM BAR: thumbnail strip + back / next ===== */}
+        <div className="flex-shrink-0 border-t border-gray-100 bg-white px-4 py-2.5">
+          <div className="flex items-center gap-3">
+
+            {/* Prev page arrow */}
+            <button
+              onClick={handlePrevPage}
+              disabled={currentPageIndex === 0}
+              className={`p-1.5 rounded-lg transition-colors flex-shrink-0
+                ${currentPageIndex === 0 ? 'text-gray-200 cursor-not-allowed' : 'text-gray-500 hover:bg-gray-100'}`}
+            >
+              <ChevronLeft className="w-5 h-5" />
+            </button>
+
+            {/* Page thumbnails */}
+            <div className="flex-1 flex items-end gap-2 overflow-x-auto min-w-0 py-0.5">
+              {selectedPageData.map((page, idx) => (
+                <button
+                  key={page.pageNumber}
+                  onClick={() => setCurrentPageIndex(idx)}
+                  className="relative flex-shrink-0 flex flex-col items-center gap-1 group"
+                >
+                  <div className={`w-[52px] h-[66px] rounded-lg overflow-hidden border-2 transition-all
+                    ${idx === currentPageIndex
+                      ? 'border-blue-500 shadow-md shadow-blue-100'
+                      : 'border-gray-200 hover:border-gray-300 group-hover:shadow-sm'
+                    }`}
+                  >
+                    <img
+                      src={processedImages[page.pageNumber] || imageStates[page.pageNumber]?.current || page.thumbnail}
+                      alt={`Page ${page.pageNumber}`}
+                      className="w-full h-full object-cover"
+                    />
+                  </div>
+                  {/* Status dot */}
+                  {processedImages[page.pageNumber] && (
+                    <span className="absolute top-0.5 right-0.5 w-2 h-2 bg-green-500 rounded-full border border-white shadow-sm" />
+                  )}
+                  {!processedImages[page.pageNumber] && imageStates[page.pageNumber]?.current !== page.thumbnail && (
+                    <span className="absolute top-0.5 right-0.5 w-2 h-2 bg-orange-400 rounded-full border border-white shadow-sm" />
+                  )}
+                  <span className={`text-[10px] font-medium leading-none
+                    ${idx === currentPageIndex ? 'text-blue-600' : 'text-gray-400'}`}
+                  >
+                    Page {shortPageLabel(page.pageNumber)}
+                  </span>
+                </button>
+              ))}
+            </div>
+
+            {/* Next page arrow */}
+            <button
+              onClick={handleNextPage}
+              disabled={currentPageIndex === selectedPageData.length - 1}
+              className={`p-1.5 rounded-lg transition-colors flex-shrink-0
+                ${currentPageIndex === selectedPageData.length - 1 ? 'text-gray-200 cursor-not-allowed' : 'text-gray-500 hover:bg-gray-100'}`}
+            >
+              <ChevronRight className="w-5 h-5" />
+            </button>
+
+            {/* Status indicators */}
+            <div className="flex-shrink-0 flex items-center gap-2 text-xs text-gray-400 border-l border-gray-100 pl-3">
+              <span className="flex items-center gap-1">
+                <span className="w-1.5 h-1.5 rounded-full bg-green-500" />
+                {Object.keys(processedImages).length}/{selectedPages.length}
+              </span>
+              {Object.keys(imageStates).filter(k => {
+                const pg = pages.find(p => String(p.pageNumber) === String(k));
+                return imageStates[k]?.current !== pg?.thumbnail;
+              }).length > 0 && (
+                <span className="flex items-center gap-1">
+                  <span className="w-1.5 h-1.5 rounded-full bg-orange-400" />
+                  {Object.keys(imageStates).filter(k => {
+                    const pg = pages.find(p => String(p.pageNumber) === String(k));
+                    return imageStates[k]?.current !== pg?.thumbnail;
+                  }).length}
+                </span>
+              )}
+            </div>
+
+            {/* Divider */}
+            <div className="flex-shrink-0 w-px h-8 bg-gray-100" />
+
+            {/* Back */}
+            <button
+              onClick={onBack}
+              className="flex-shrink-0 flex items-center gap-1.5 px-4 py-2 text-sm font-medium text-gray-600 hover:bg-gray-100 rounded-xl transition-colors"
+            >
+              <ArrowLeft className="w-4 h-4" />
+              Back
+            </button>
+
+            {/* Continue */}
+            <button
+              onClick={onNext}
+              disabled={!canProceed}
+              className={`flex-shrink-0 flex items-center gap-2 px-5 py-2 text-sm font-semibold rounded-xl transition-all
+                ${canProceed
+                  ? 'bg-gradient-to-r from-blue-500 to-blue-600 text-white shadow-sm hover:shadow-md hover:-translate-y-0.5'
+                  : 'bg-gray-100 text-gray-400 cursor-not-allowed'
+                }`}
+            >
+              Continue to Text Detection
+              <ArrowRight className="w-4 h-4" />
+            </button>
+          </div>
+        </div>
+      </main>
+
+      {/* ========== MODALS ========== */}
+
+      {/* Image Cropper Modal */}
+      {showCropper && currentPage && (
+        <ImageCropper
+          imageSrc={currentImageState}
+          onCropComplete={handleCropComplete}
+          onCancel={() => setShowCropper(false)}
+          availablePages={selectedPageData.map(page => ({
+            pageNumber: page.pageNumber,
+            thumbnail: imageStates[page.pageNumber]?.current || page.thumbnail,
+          }))}
+          currentPageNumber={currentPage.pageNumber}
+          onBatchCropComplete={handleBatchCropComplete}
+        />
+      )}
+
+      {/* Image Eraser Modal */}
+      {showEraser && currentPage && (
+        <ImageEraser
+          pages={selectedPageData.map(page => ({
+            pageNumber: page.pageNumber,
+            imageSrc: processedImages[page.pageNumber] || imageStates[page.pageNumber]?.current || page.thumbnail,
+          }))}
+          initialPageIndex={currentPageIndex}
+          onSaveAll={handleEraserSave}
+          onCancel={() => setShowEraser(false)}
+        />
+      )}
+
+      {/* Zoom Lens Preview */}
+      <ZoomLensPreview
+        originalImage={originalImageState}
+        processedImage={currentPageProcessed || currentImageState}
+        isOpen={showZoomLens}
+        onClose={() => setShowZoomLens(false)}
+      />
+    </div>
+  );
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/features/upload/pages/SelectPage.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/features/upload/pages/SelectPage.jsx
new file mode 100644
index 00000000..99645e1c
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/features/upload/pages/SelectPage.jsx
@@ -0,0 +1,86 @@
+import React from 'react';
+import { ArrowRight, ArrowLeft, FileImage, Layers } from 'lucide-react';
+import PdfPreviewGrid from '../../../components/PdfPreviewGrid';
+
+export default function SelectPage({
+  pages,
+  selectedPages,
+  onSelectionChange,
+  onBack,
+  onNext,
+  isLoading,
+}) {
+  const hasSelection = selectedPages.length > 0;
+
+  return (
+    <div className="animate-fade-in">
+      {/* Header */}
+      <div className="flex items-center justify-between mb-6">
+        <div>
+          <div className="flex items-center gap-3 mb-2">
+            <div className="p-2 bg-gradient-to-br from-blue-500 to-indigo-600 rounded-lg text-white shadow-md shadow-blue-500/20">
+              <Layers className="w-5 h-5" />
+            </div>
+            <h1 className="text-2xl font-bold bg-gradient-to-r from-gray-800 to-gray-600 bg-clip-text text-transparent">
+              Select Pages
+            </h1>
+          </div>
+          <p className="text-gray-500 mt-1 ml-11">
+            Choose which pages to include in preprocessing. Click to select, Shift+click for range selection.
+          </p>
+        </div>
+
+        <div className="flex items-center gap-3">
+          <button
+            onClick={onBack}
+            className="btn-ghost"
+          >
+            <ArrowLeft className="w-5 h-5" />
+            Back
+          </button>
+
+          <button
+            onClick={onNext}
+            disabled={!hasSelection || isLoading}
+            className={`btn-primary ${hasSelection && !isLoading
+                ? ''
+                : 'opacity-50 cursor-not-allowed hover:translate-y-0'
+              }`}
+          >
+            Continue
+            <ArrowRight className="w-5 h-5" />
+          </button>
+        </div>
+      </div>
+
+      {/* Loading state */}
+      {isLoading ? (
+        <div className="flex flex-col items-center justify-center py-20 bg-white/70 backdrop-blur-sm rounded-2xl shadow-lg">
+          <div className="relative">
+            <div className="w-16 h-16 border-4 border-blue-200 border-t-blue-600 rounded-full animate-spin" />
+            <div className="absolute inset-0 blur-xl bg-blue-400/30 animate-pulse" />
+          </div>
+          <p className="mt-6 text-blue-600 font-semibold text-lg">Extracting pages...</p>
+          <p className="text-sm text-gray-500 mt-1">This may take a moment for large PDFs</p>
+        </div>
+      ) : pages.length > 0 ? (
+        <div className="bg-white/70 backdrop-blur-sm rounded-2xl shadow-lg border border-gray-100 p-6">
+          <PdfPreviewGrid
+            pages={pages}
+            selectedPages={selectedPages}
+            onSelectionChange={onSelectionChange}
+          />
+        </div>
+      ) : (
+        <div className="flex flex-col items-center justify-center py-20 bg-white/70 backdrop-blur-sm rounded-2xl shadow-lg border border-gray-100">
+          <div className="w-20 h-20 bg-gradient-to-br from-blue-100 to-indigo-100 rounded-2xl flex items-center justify-center">
+            <FileImage className="w-10 h-10 text-blue-400" />
+          </div>
+          <h3 className="mt-6 text-lg font-semibold text-gray-700">No pages extracted</h3>
+          <p className="text-gray-500 mt-2">Please go back and upload a PDF document.</p>
+        </div>
+      )}
+
+    </div>
+  );
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/features/upload/pages/UploadPage.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/features/upload/pages/UploadPage.jsx
new file mode 100644
index 00000000..34cf917d
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/features/upload/pages/UploadPage.jsx
@@ -0,0 +1,110 @@
+import React from 'react';
+import { FileImage, FileText, ArrowRight, Zap } from 'lucide-react';
+import UploadZone from '../../../components/UploadZone';
+
+export default function UploadPage({ onFilesSelected, onNext, isLoading, files }) {
+  const hasFiles = files && files.length > 0;
+  const isPdf = hasFiles && files[0].type === 'application/pdf';
+  const isImages = hasFiles && files[0].type.startsWith('image/');
+
+  return (
+    <div className="max-w-4xl mx-auto animate-fade-in">
+      {/* Header */}
+      <div className="text-center mb-8">
+        <div className="inline-flex items-center gap-2 px-4 py-2 bg-gradient-to-r from-blue-50 to-indigo-50 rounded-full text-sm font-semibold text-blue-600 mb-4 shadow-sm">
+          <Zap className="w-4 h-4" />
+          Step 1 of 5
+        </div>
+        <h1 className="text-4xl font-bold bg-gradient-to-r from-gray-800 via-gray-700 to-gray-600 bg-clip-text text-transparent mb-3">
+          Upload Your Documents
+        </h1>
+        <p className="text-gray-500 max-w-xl mx-auto text-lg">
+          Upload a PDF document or multiple images to begin preprocessing for OCR.
+          We'll help you prepare your documents for optimal text extraction.
+        </p>
+      </div>
+
+      {/* Upload zone */}
+      <UploadZone onFilesSelected={onFilesSelected} isLoading={isLoading} />
+
+      {/* File type info cards */}
+      <div className="grid md:grid-cols-2 gap-5 mt-10">
+        <div
+          className={`p-6 rounded-2xl border-2 transition-all duration-300 ${isPdf
+              ? 'border-blue-500 bg-gradient-to-br from-blue-50 to-indigo-50 shadow-lg shadow-blue-500/10'
+              : 'border-gray-200 bg-white/70 backdrop-blur-sm hover:border-blue-300 hover:shadow-md hover:-translate-y-0.5'
+            }`}
+        >
+          <div className="flex items-start gap-4">
+            <div
+              className={`p-3.5 rounded-xl transition-all duration-300 ${isPdf
+                  ? 'bg-gradient-to-br from-blue-500 to-blue-600 text-white shadow-lg shadow-blue-500/30'
+                  : 'bg-gradient-to-br from-red-50 to-red-100 text-red-500'
+                }`}
+            >
+              <FileText className="w-6 h-6" />
+            </div>
+            <div className="flex-1">
+              <h3 className="font-bold text-gray-800 text-lg">PDF Document</h3>
+              <p className="text-sm text-gray-500 mt-1.5 leading-relaxed">
+                Upload a PDF and we'll extract pages as images. You can then
+                select which pages to process.
+              </p>
+              {isPdf && (
+                <div className="mt-3 inline-flex items-center gap-2 px-3 py-1.5 bg-green-100 text-green-700 rounded-full text-sm font-semibold">
+                  <span className="w-2 h-2 rounded-full bg-green-500 animate-pulse" />
+                  PDF detected - Ready to extract
+                </div>
+              )}
+            </div>
+          </div>
+        </div>
+
+        <div
+          className={`p-6 rounded-2xl border-2 transition-all duration-300 ${isImages
+              ? 'border-blue-500 bg-gradient-to-br from-blue-50 to-indigo-50 shadow-lg shadow-blue-500/10'
+              : 'border-gray-200 bg-white/70 backdrop-blur-sm hover:border-blue-300 hover:shadow-md hover:-translate-y-0.5'
+            }`}
+        >
+          <div className="flex items-start gap-4">
+            <div
+              className={`p-3.5 rounded-xl transition-all duration-300 ${isImages
+                  ? 'bg-gradient-to-br from-blue-500 to-blue-600 text-white shadow-lg shadow-blue-500/30'
+                  : 'bg-gradient-to-br from-blue-50 to-blue-100 text-blue-500'
+                }`}
+            >
+              <FileImage className="w-6 h-6" />
+            </div>
+            <div className="flex-1">
+              <h3 className="font-bold text-gray-800 text-lg">Image Files</h3>
+              <p className="text-sm text-gray-500 mt-1.5 leading-relaxed">
+                Upload multiple images directly. All images will be added to
+                your preprocessing queue.
+              </p>
+              {isImages && (
+                <div className="mt-3 inline-flex items-center gap-2 px-3 py-1.5 bg-green-100 text-green-700 rounded-full text-sm font-semibold">
+                  <span className="w-2 h-2 rounded-full bg-green-500 animate-pulse" />
+                  {files.length} image{files.length > 1 ? 's' : ''} selected
+                </div>
+              )}
+            </div>
+          </div>
+        </div>
+      </div>
+
+      {/* Next button */}
+      {hasFiles && (
+        <div className="flex justify-center mt-10 animate-slide-up">
+          <button
+            onClick={onNext}
+            disabled={isLoading}
+            className="btn-gradient flex items-center gap-3 px-10 py-4 text-lg"
+          >
+            {isPdf ? 'Extract Pages' : 'Continue to Preprocessing'}
+            <ArrowRight className="w-5 h-5" />
+          </button>
+        </div>
+      )}
+    </div>
+  );
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/hooks/useHistoryStore.js b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/hooks/useHistoryStore.js
new file mode 100644
index 00000000..25174650
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/hooks/useHistoryStore.js
@@ -0,0 +1,176 @@
+import { useCallback, useRef, useState } from 'react';
+
+const DEFAULT_MEMORY_BUDGET_BYTES = 150 * 1024 * 1024;
+
+let _nextOpId = 1;
+const nextOpId = () => _nextOpId++;
+
+function estimateDataUrlBytes(dataUrl) {
+  if (!dataUrl || typeof dataUrl !== 'string') return 0;
+  if (dataUrl.startsWith('data:')) {
+    const commaIdx = dataUrl.indexOf(',');
+    return commaIdx >= 0 ? Math.floor((dataUrl.length - commaIdx - 1) * 0.75) : dataUrl.length;
+  }
+  return dataUrl.length;
+}
+
+function estimateOpBytes(op) {
+  let bytes = 200;
+  const walk = (v) => {
+    if (!v) return;
+    if (typeof v === 'string') bytes += estimateDataUrlBytes(v);
+    else if (v instanceof Blob) bytes += v.size;
+    else if (typeof v === 'object') Object.values(v).forEach(walk);
+  };
+  walk(op.before);
+  walk(op.after);
+  return bytes;
+}
+
+// One undo timeline for every page. Each op records which pages it touched, so
+// the same array serves per-page undo (filter) and global undo (walk). Ops past
+// the cursor stay around for redo until the next push truncates the branch.
+//
+// Trimmed by byte budget, not op count — otherwise a run of cheap crops evicts
+// while a few huge erase snapshots survive.
+export function useHistoryStore({ memoryBudget = DEFAULT_MEMORY_BUDGET_BYTES } = {}) {
+  const opsRef = useRef([]);
+  const cursorRef = useRef(-1);
+  const bytesRef = useRef(0);
+  const [version, setVersion] = useState(0);
+  const bump = useCallback(() => setVersion((v) => v + 1), []);
+
+  const enforceBudget = useCallback(() => {
+    while (bytesRef.current > memoryBudget && opsRef.current.length > 0) {
+      const dropped = opsRef.current.shift();
+      bytesRef.current -= dropped._bytes || 0;
+      cursorRef.current -= 1;
+    }
+    if (cursorRef.current < -1) cursorRef.current = -1;
+  }, [memoryBudget]);
+
+  const push = useCallback((op) => {
+    if (!op || !op.kind) return;
+    const pageNumbers = op.pageNumbers || (op.pageNumber != null ? [op.pageNumber] : []);
+    const normalized = {
+      id: nextOpId(),
+      label: op.label || op.kind,
+      ...op,
+      pageNumbers,
+    };
+    normalized._bytes = estimateOpBytes(normalized);
+
+    if (cursorRef.current < opsRef.current.length - 1) {
+      const dropped = opsRef.current.splice(cursorRef.current + 1);
+      dropped.forEach((o) => { bytesRef.current -= o._bytes || 0; });
+    }
+
+    opsRef.current.push(normalized);
+    bytesRef.current += normalized._bytes;
+    cursorRef.current = opsRef.current.length - 1;
+    enforceBudget();
+    bump();
+  }, [bump, enforceBudget]);
+
+  const topIndexForPage = useCallback((pageNumber) => {
+    for (let i = cursorRef.current; i >= 0; i--) {
+      const op = opsRef.current[i];
+      if (op.pageNumbers.some((p) => String(p) === String(pageNumber))) return i;
+    }
+    return -1;
+  }, []);
+
+  const nextIndexForPage = useCallback((pageNumber) => {
+    for (let i = cursorRef.current + 1; i < opsRef.current.length; i++) {
+      const op = opsRef.current[i];
+      if (op.pageNumbers.some((p) => String(p) === String(pageNumber))) return i;
+    }
+    return -1;
+  }, []);
+
+  const canUndoGlobal = cursorRef.current >= 0;
+  const canRedoGlobal = cursorRef.current < opsRef.current.length - 1;
+
+  const canUndoPage = useCallback((pageNumber) => topIndexForPage(pageNumber) >= 0, [topIndexForPage]);
+  const canRedoPage = useCallback((pageNumber) => nextIndexForPage(pageNumber) >= 0, [nextIndexForPage]);
+
+  const undoGlobal = useCallback(() => {
+    if (cursorRef.current < 0) return null;
+    const op = opsRef.current[cursorRef.current];
+    cursorRef.current -= 1;
+    bump();
+    return op;
+  }, [bump]);
+
+  const redoGlobal = useCallback(() => {
+    if (cursorRef.current >= opsRef.current.length - 1) return null;
+    cursorRef.current += 1;
+    const op = opsRef.current[cursorRef.current];
+    bump();
+    return op;
+  }, [bump]);
+
+  const undoPage = useCallback((pageNumber) => {
+    const idx = topIndexForPage(pageNumber);
+    if (idx < 0) return null;
+    const op = opsRef.current[idx];
+    opsRef.current.splice(idx, 1);
+    bytesRef.current -= op._bytes || 0;
+    if (idx <= cursorRef.current) cursorRef.current -= 1;
+    bump();
+    return op;
+  }, [bump, topIndexForPage]);
+
+  const clear = useCallback(() => {
+    opsRef.current = [];
+    cursorRef.current = -1;
+    bytesRef.current = 0;
+    bump();
+  }, [bump]);
+
+  const getTimeline = useCallback(() => ({
+    ops: opsRef.current.slice(),
+    cursor: cursorRef.current,
+    bytes: bytesRef.current,
+  }), []);
+
+  return {
+    version,
+    push,
+    undoGlobal,
+    redoGlobal,
+    undoPage,
+    canUndoPage,
+    canRedoPage,
+    canUndoGlobal,
+    canRedoGlobal,
+    clear,
+    getTimeline,
+  };
+}
+
+export const HistoryOps = {
+  crop: ({ pageNumber, before, after, cropData }) => ({
+    kind: 'crop', pageNumber, label: 'Crop', before: { image: before }, after: { image: after, cropData },
+  }),
+  cropBatch: ({ pageNumbers, before, after }) => ({
+    kind: 'crop-batch', pageNumbers, label: `Crop ${pageNumbers.length} pages`, before: { images: before }, after: { images: after },
+  }),
+  erase: ({ pageNumber, before, after, region }) => ({
+    kind: 'erase', pageNumber, label: 'Erase', before: { image: before, region }, after: { image: after },
+  }),
+  eraseBatch: ({ pageNumbers, before, after }) => ({
+    kind: 'erase-batch', pageNumbers, label: `Erase ${pageNumbers.length} pages`, before: { images: before }, after: { images: after },
+  }),
+  pipeline: ({ pageNumber, beforeProcessed, afterProcessed, pipelineConfig }) => ({
+    kind: 'pipeline', pageNumber, label: 'Apply pipeline', before: { processed: beforeProcessed }, after: { processed: afterProcessed, pipelineConfig },
+  }),
+  pipelineBatch: ({ pageNumbers, beforeProcessed, afterProcessed, pipelineConfig }) => ({
+    kind: 'pipeline-batch', pageNumbers, label: `Apply pipeline to ${pageNumbers.length} pages`,
+    before: { processed: beforeProcessed }, after: { processed: afterProcessed, pipelineConfig },
+  }),
+  resetPage: ({ pageNumber, beforeImage, beforeProcessed }) => ({
+    kind: 'reset-page', pageNumber, label: 'Reset page',
+    before: { image: beforeImage, processed: beforeProcessed }, after: null,
+  }),
+};
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/hooks/usePdfPreview.js b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/hooks/usePdfPreview.js
new file mode 100644
index 00000000..9950a7d4
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/hooks/usePdfPreview.js
@@ -0,0 +1,248 @@
+import { useState, useCallback, useEffect, useRef } from 'react';
+
+// Turns a PDF or a pile of image files into page objects.
+//
+// PDF rendering happens in a Web Worker: background tabs throttle main-thread
+// canvas work to ~1 Hz, which made extraction look frozen. Images can't use a
+// worker (<img> decode is main-thread only) so they run with a concurrency cap
+// instead. Everything comes back as blob: object URLs — see utils/imageUrl.
+
+const IMAGE_CONCURRENCY = 4;
+
+function makeWorker() {
+  return new Worker(
+    new URL('../workers/pdfExtractor.worker.js', import.meta.url),
+    { type: 'module' }
+  );
+}
+
+export function usePdfPreview() {
+  const [pages, setPages] = useState([]);
+  const [isLoading, setIsLoading] = useState(false);
+  const [error, setError] = useState(null);
+  const [progress, setProgress] = useState(0);
+
+  const workerRef = useRef(null);
+  // Revoked on reset/reload.
+  const objectUrlsRef = useRef([]);
+
+  const revokeAllUrls = useCallback(() => {
+    objectUrlsRef.current.forEach((url) => URL.revokeObjectURL(url));
+    objectUrlsRef.current = [];
+  }, []);
+
+  const trackUrl = useCallback((url) => {
+    objectUrlsRef.current.push(url);
+    return url;
+  }, []);
+
+  useEffect(() => {
+    return () => {
+      if (workerRef.current) {
+        workerRef.current.terminate();
+        workerRef.current = null;
+      }
+      revokeAllUrls();
+    };
+  }, [revokeAllUrls]);
+
+  const extractPages = useCallback(async (file, options = {}) => {
+    setIsLoading(true);
+    setError(null);
+    setProgress(0);
+    setPages([]);
+    revokeAllUrls();
+
+    // Fresh worker each run, so a stuck one can't poison the next.
+    if (workerRef.current) workerRef.current.terminate();
+    const worker = makeWorker();
+    workerRef.current = worker;
+
+    const requestId = Date.now() + Math.random();
+    const collected = [];
+    // Batch the flushes — one setPages per page re-renders the whole grid.
+    let pending = [];
+    let flushTimer = null;
+    const flush = () => {
+      flushTimer = null;
+      if (pending.length === 0) return;
+      const batch = pending;
+      pending = [];
+      setPages((prev) => [...prev, ...batch]);
+    };
+
+    try {
+      const fileBuffer = await file.arrayBuffer();
+
+      const result = await new Promise((resolve, reject) => {
+        worker.onmessage = (e) => {
+          const msg = e.data || {};
+          if (msg.id !== requestId) return;
+          if (msg.type === 'page') {
+            const { blob, ...rest } = msg.page;
+            const page = { ...rest, thumbnail: trackUrl(URL.createObjectURL(blob)) };
+            collected.push(page);
+            pending.push(page);
+            if (!flushTimer) flushTimer = setTimeout(flush, 200);
+          } else if (msg.type === 'progress') {
+            setProgress(msg.progress);
+          } else if (msg.type === 'done') {
+            flush();
+            resolve(collected);
+          } else if (msg.type === 'error') {
+            reject(new Error(msg.error));
+          }
+        };
+        worker.onerror = (e) => reject(new Error(e.message || 'PDF worker crashed'));
+
+        worker.postMessage(
+          { type: 'extract', id: requestId, fileBuffer, options },
+          [fileBuffer]
+        );
+      });
+
+      return result;
+    } catch (err) {
+      console.error('PDF extraction error:', err);
+      setError(err.message || 'Failed to extract PDF pages');
+      throw err;
+    } finally {
+      if (flushTimer) clearTimeout(flushTimer);
+      setIsLoading(false);
+      // Left alive for a likely next extraction; replaced or torn down later.
+    }
+  }, [revokeAllUrls, trackUrl]);
+
+  const loadImages = useCallback(async (files, options = {}) => {
+    const { splitDoublePages = true } = options;
+
+    setIsLoading(true);
+    setError(null);
+    setProgress(0);
+    setPages([]);
+    revokeAllUrls();
+
+    try {
+      const total = files.length;
+      let done = 0;
+      const slots = new Array(total);
+
+      const canvasToUrl = (canvas) => new Promise((resolve, reject) => {
+        canvas.toBlob((blob) => {
+          if (blob) resolve(trackUrl(URL.createObjectURL(blob)));
+          else reject(new Error('Failed to encode split page'));
+        }, 'image/png');
+      });
+
+      const processOne = async (file, index) => {
+        const fileIndex = index + 1;
+        const fileUrl = trackUrl(URL.createObjectURL(file));
+
+        const imgData = await new Promise((resolve, reject) => {
+          const img = new Image();
+          img.onload = () => resolve({ width: img.width, height: img.height, img });
+          img.onerror = () => reject(new Error('Failed to load image'));
+          img.src = fileUrl;
+        });
+
+        let produced;
+        if (splitDoublePages && imgData.width > imgData.height * 1.19) {
+          const halfWidth = Math.floor(imgData.width / 2);
+
+          const leftCanvas = document.createElement('canvas');
+          leftCanvas.width = halfWidth;
+          leftCanvas.height = imgData.height;
+          leftCanvas
+            .getContext('2d')
+            .drawImage(imgData.img, 0, 0, halfWidth, imgData.height, 0, 0, halfWidth, imgData.height);
+
+          const rightCanvas = document.createElement('canvas');
+          rightCanvas.width = halfWidth;
+          rightCanvas.height = imgData.height;
+          rightCanvas
+            .getContext('2d')
+            .drawImage(imgData.img, halfWidth, 0, halfWidth, imgData.height, 0, 0, halfWidth, imgData.height);
+
+          produced = [
+            {
+              pageNumber: `${fileIndex}_left`,
+              thumbnail: await canvasToUrl(leftCanvas),
+              width: halfWidth,
+              height: imgData.height,
+              fileName: file.name,
+              isSplit: true,
+              splitSide: 'left',
+            },
+            {
+              pageNumber: `${fileIndex}_right`,
+              thumbnail: await canvasToUrl(rightCanvas),
+              width: halfWidth,
+              height: imgData.height,
+              fileName: file.name,
+              isSplit: true,
+              splitSide: 'right',
+            },
+          ];
+        } else {
+          produced = [{
+            pageNumber: fileIndex,
+            thumbnail: fileUrl,
+            width: imgData.width,
+            height: imgData.height,
+            fileName: file.name,
+            isSplit: false,
+          }];
+        }
+
+        slots[index] = produced;
+        done += 1;
+        setProgress(Math.round((done / total) * 100));
+      };
+
+      // Each lane pulls the next index until the queue is empty.
+      let cursor = 0;
+      const lanes = Array.from({ length: Math.min(IMAGE_CONCURRENCY, total) }, async () => {
+        while (true) {
+          const idx = cursor++;
+          if (idx >= total) return;
+          await processOne(files[idx], idx);
+        }
+      });
+      await Promise.all(lanes);
+
+      const loadedPages = slots.flat().filter(Boolean);
+      setPages(loadedPages);
+      return loadedPages;
+    } catch (err) {
+      console.error('Image loading error:', err);
+      setError(err.message || 'Failed to load images');
+      throw err;
+    } finally {
+      setIsLoading(false);
+    }
+  }, [revokeAllUrls, trackUrl]);
+
+  const reset = useCallback(() => {
+    setPages([]);
+    setIsLoading(false);
+    setError(null);
+    setProgress(0);
+    revokeAllUrls();
+    if (workerRef.current) {
+      workerRef.current.terminate();
+      workerRef.current = null;
+    }
+  }, [revokeAllUrls]);
+
+  return {
+    pages,
+    isLoading,
+    error,
+    progress,
+    extractPages,
+    loadImages,
+    reset,
+  };
+}
+
+export default usePdfPreview;
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/hooks/usePipeline.js b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/hooks/usePipeline.js
new file mode 100644
index 00000000..d34d07d7
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/hooks/usePipeline.js
@@ -0,0 +1,277 @@
+import { useState, useCallback, useRef, useEffect } from 'react';
+import { 
+  getOperationById, 
+  getRecommendedPipeline,
+  createPipelineStep 
+} from '../config/preprocessOperations';
+
+// Preprocessing pipeline state: the ordered step list plus the sidebar's
+// enabled-operations map, kept in sync both ways. Preview calls are debounced.
+export function usePipeline(options = {}) {
+  const {
+    onPreviewRequest,
+    debounceMs = 500,
+    initialPipeline = [],
+  } = options;
+
+  const [pipeline, setPipeline] = useState(initialPipeline);
+  
+  const [enabledOperations, setEnabledOperations] = useState({});
+  
+  const debounceTimer = useRef(null);
+
+  // Rebuild the pipeline whenever the sidebar toggles change.
+  const syncPipelineFromEnabled = useCallback((enabled) => {
+    setPipeline(prev => {
+      const enabledIds = Object.entries(enabled)
+        .filter(([_, val]) => val?.enabled)
+        .map(([id]) => id);
+
+      // Existing steps keep their position and params.
+      const existingSteps = prev.filter(step => enabledIds.includes(step.operationId));
+      
+      const existingOperationIds = existingSteps.map(s => s.operationId);
+      const newOperationIds = enabledIds.filter(id => !existingOperationIds.includes(id));
+      
+      const newSteps = newOperationIds.map((opId, index) => ({
+        id: `${opId}-${Date.now()}-${index}`,
+        operationId: opId,
+        enabled: true,
+        params: enabled[opId]?.params || getOperationById(opId)?.defaultParams || {},
+        order: existingSteps.length + index,
+      }));
+
+      const combined = [...existingSteps, ...newSteps].map((step, index) => ({
+        ...step,
+        order: index,
+        params: enabled[step.operationId]?.params || step.params,
+      }));
+
+      return combined;
+    });
+  }, []);
+
+  const toggleOperation = useCallback((operationId, enabled) => {
+    setEnabledOperations(prev => {
+      const operation = getOperationById(operationId);
+      const newState = {
+        ...prev,
+        [operationId]: {
+          enabled,
+          params: prev[operationId]?.params || operation?.defaultParams || {},
+        },
+      };
+      
+      syncPipelineFromEnabled(newState);
+      
+      return newState;
+    });
+
+    triggerDebouncedPreview();
+  }, [syncPipelineFromEnabled]);
+
+  const updateOperationParams = useCallback((operationId, params) => {
+    setEnabledOperations(prev => ({
+      ...prev,
+      [operationId]: {
+        ...prev[operationId],
+        params,
+      },
+    }));
+
+    setPipeline(prev => prev.map(step => 
+      step.operationId === operationId
+        ? { ...step, params }
+        : step
+    ));
+
+    triggerDebouncedPreview();
+  }, []);
+
+  const togglePipelineStep = useCallback((stepId, enabled) => {
+    setPipeline(prev => prev.map(step =>
+      step.id === stepId
+        ? { ...step, enabled }
+        : step
+    ));
+
+    triggerDebouncedPreview();
+  }, []);
+
+  const updatePipelineStepParams = useCallback((stepId, params) => {
+    setPipeline(prev => {
+      const updated = prev.map(step =>
+        step.id === stepId
+          ? { ...step, params }
+          : step
+      );
+      
+      const step = updated.find(s => s.id === stepId);
+      if (step) {
+        setEnabledOperations(prevEnabled => ({
+          ...prevEnabled,
+          [step.operationId]: {
+            ...prevEnabled[step.operationId],
+            params,
+          },
+        }));
+      }
+      
+      return updated;
+    });
+
+    triggerDebouncedPreview();
+  }, []);
+
+  const removePipelineStep = useCallback((stepId) => {
+    setPipeline(prev => {
+      const step = prev.find(s => s.id === stepId);
+      
+      if (step) {
+        setEnabledOperations(prevEnabled => ({
+          ...prevEnabled,
+          [step.operationId]: {
+            ...prevEnabled[step.operationId],
+            enabled: false,
+          },
+        }));
+      }
+      
+      return prev.filter(s => s.id !== stepId).map((s, i) => ({ ...s, order: i }));
+    });
+
+    triggerDebouncedPreview();
+  }, []);
+
+  const reorderPipeline = useCallback((fromIndex, toIndex) => {
+    setPipeline(prev => {
+      const result = [...prev];
+      const [removed] = result.splice(fromIndex, 1);
+      result.splice(toIndex, 0, removed);
+      return result.map((step, index) => ({ ...step, order: index }));
+    });
+
+    triggerDebouncedPreview();
+  }, []);
+
+  const addOperation = useCallback((operationId) => {
+    const operation = getOperationById(operationId);
+    if (!operation) return;
+
+    const newStep = createPipelineStep(operationId, pipeline.length);
+    if (!newStep) return;
+
+    setPipeline(prev => [...prev, newStep]);
+    setEnabledOperations(prev => ({
+      ...prev,
+      [operationId]: {
+        enabled: true,
+        params: operation.defaultParams,
+      },
+    }));
+
+    triggerDebouncedPreview();
+  }, [pipeline.length]);
+
+  const applyRecommendedPipeline = useCallback(() => {
+    const recommended = getRecommendedPipeline();
+    setPipeline(recommended);
+
+    const newEnabled = {};
+    recommended.forEach(step => {
+      newEnabled[step.operationId] = {
+        enabled: true,
+        params: step.params,
+      };
+    });
+    setEnabledOperations(newEnabled);
+
+    triggerDebouncedPreview();
+  }, []);
+
+  // Select a predefined set of steps WITHOUT running. Used by the book-type
+  // presets: the user gets the pipeline pre-filled, then reviews and processes
+  // themselves. `steps` come from createPipelineStep (id/operationId/params).
+  const applyPipeline = useCallback((steps) => {
+    if (!steps || steps.length === 0) return;
+    setPipeline(steps);
+
+    const newEnabled = {};
+    steps.forEach(step => {
+      newEnabled[step.operationId] = {
+        enabled: true,
+        params: step.params,
+      };
+    });
+    setEnabledOperations(newEnabled);
+    // Deliberately no preview trigger — selection only, per the preset contract.
+  }, []);
+
+  const resetPipeline = useCallback(() => {
+    setPipeline([]);
+    setEnabledOperations({});
+    
+    if (debounceTimer.current) {
+      clearTimeout(debounceTimer.current);
+    }
+  }, []);
+
+  const triggerDebouncedPreview = useCallback(() => {
+    if (debounceTimer.current) {
+      clearTimeout(debounceTimer.current);
+    }
+
+    if (onPreviewRequest) {
+      debounceTimer.current = setTimeout(() => {
+        onPreviewRequest();
+      }, debounceMs);
+    }
+  }, [onPreviewRequest, debounceMs]);
+
+  const getActivePipeline = useCallback(() => {
+    return pipeline.filter(step => step.enabled);
+  }, [pipeline]);
+
+  const buildPipelineConfig = useCallback(() => {
+    return getActivePipeline().map(step => ({
+      op: step.operationId,
+      params: step.params,
+    }));
+  }, [getActivePipeline]);
+
+  useEffect(() => {
+    return () => {
+      if (debounceTimer.current) {
+        clearTimeout(debounceTimer.current);
+      }
+    };
+  }, []);
+
+  return {
+    // State
+    pipeline,
+    enabledOperations,
+    
+    // Operations sidebar actions
+    toggleOperation,
+    updateOperationParams,
+    
+    // Pipeline stack actions
+    togglePipelineStep,
+    updatePipelineStepParams,
+    removePipelineStep,
+    reorderPipeline,
+    addOperation,
+    
+    // Quick actions
+    applyRecommendedPipeline,
+    applyPipeline,
+    resetPipeline,
+    
+    // Utilities
+    getActivePipeline,
+    buildPipelineConfig,
+  };
+}
+
+export default usePipeline;
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/index.css b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/index.css
new file mode 100644
index 00000000..c24e7774
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/index.css
@@ -0,0 +1,495 @@
+@tailwind base;
+@tailwind components;
+@tailwind utilities;
+
+* {
+  font-family: 'Inter', sans-serif;
+}
+
+html,
+body,
+#root {
+  @apply h-full w-full m-0 p-0;
+  min-height: 100vh;
+  min-height: 100dvh; /* Dynamic viewport height for mobile */
+}
+
+/* Ensure full viewport coverage */
+html {
+  height: -webkit-fill-available;
+}
+
+body {
+  @apply bg-gradient-to-br from-slate-50 via-blue-50/30 to-indigo-50/40;
+  min-height: 100vh;
+  min-height: -webkit-fill-available;
+  /* Allow scrolling by default - full-screen pages handle their own overflow */
+}
+
+#root {
+  display: flex;
+  flex-direction: column;
+  /* Allow scrolling by default */
+}
+
+/* Custom scrollbar */
+::-webkit-scrollbar {
+  width: 8px;
+  height: 8px;
+}
+
+::-webkit-scrollbar-track {
+  @apply bg-gray-100 rounded-full;
+}
+
+::-webkit-scrollbar-thumb {
+  @apply bg-gray-300 rounded-full hover:bg-gray-400 transition-colors;
+}
+
+/* Scrollbar for dark backgrounds */
+.dark-scrollbar::-webkit-scrollbar-track {
+  @apply bg-gray-800;
+}
+
+.dark-scrollbar::-webkit-scrollbar-thumb {
+  @apply bg-gray-600 hover:bg-gray-500;
+}
+
+/* ========== GLASSMORPHISM & PREMIUM CARDS ========== */
+
+.glass {
+  @apply bg-white/70 backdrop-blur-md border border-white/20 shadow-lg;
+}
+
+.glass-subtle {
+  @apply bg-white/80 backdrop-blur-sm border border-gray-100;
+}
+
+.card-premium {
+  @apply bg-white rounded-2xl border border-gray-100 shadow-card transition-all duration-300 ease-out hover:shadow-card-hover hover:-translate-y-1;
+}
+
+.card-glass {
+  @apply bg-white/70 backdrop-blur-md rounded-2xl border border-white/40 shadow-lg transition-all duration-300;
+}
+
+/* ========== BUTTON STYLES ========== */
+
+.btn-gradient {
+  @apply relative overflow-hidden px-6 py-3 rounded-xl font-semibold text-white bg-gradient-to-r from-blue-600 via-blue-500 to-indigo-600 shadow-lg shadow-blue-500/25 transition-all duration-300 ease-out hover:shadow-xl hover:shadow-blue-500/30 hover:scale-[1.02] active:scale-[0.98] disabled:opacity-50 disabled:cursor-not-allowed disabled:hover:scale-100;
+  background-size: 200% 100%;
+}
+
+.btn-gradient:hover:not(:disabled) {
+  background-position: 100% 0;
+}
+
+.btn-gradient::before {
+  content: '';
+  @apply absolute inset-0 bg-gradient-to-r from-transparent via-white/20 to-transparent -translate-x-full;
+  transition: transform 0.6s ease;
+}
+
+.btn-gradient:hover::before {
+  transform: translateX(100%);
+}
+
+.btn-primary {
+  @apply flex items-center justify-center gap-2 px-5 py-2.5 rounded-xl font-semibold bg-gradient-to-r from-blue-600 to-blue-700 text-white shadow-md shadow-blue-500/20 hover:from-blue-700 hover:to-blue-800 hover:shadow-lg hover:shadow-blue-500/25 hover:-translate-y-0.5 active:scale-[0.98] transition-all duration-200 disabled:bg-gray-100 disabled:from-gray-100 disabled:to-gray-100 disabled:text-gray-400 disabled:cursor-not-allowed disabled:shadow-none disabled:hover:translate-y-0;
+}
+
+.btn-secondary {
+  @apply flex items-center justify-center gap-2 px-4 py-2.5 rounded-xl font-medium bg-white text-gray-700 border border-gray-200 shadow-sm hover:bg-gray-50 hover:border-gray-300 hover:shadow-md transition-all duration-200 disabled:opacity-50 disabled:cursor-not-allowed;
+}
+
+.btn-ghost {
+  @apply flex items-center justify-center gap-2 px-4 py-2.5 rounded-xl font-medium text-gray-600 hover:bg-gray-100/80 hover:text-gray-800 transition-all duration-200;
+}
+
+/* ========== CUSTOM RANGE SLIDER ========== */
+
+input[type="range"] {
+  @apply appearance-none bg-transparent cursor-pointer;
+}
+
+input[type="range"]::-webkit-slider-track {
+  @apply h-2 rounded-full bg-gradient-to-r from-blue-100 to-indigo-100;
+}
+
+input[type="range"]::-webkit-slider-thumb {
+  @apply appearance-none w-5 h-5 rounded-full bg-gradient-to-br from-blue-500 to-blue-600 shadow-md shadow-blue-500/30 hover:shadow-lg hover:scale-110 transition-all duration-200;
+  margin-top: -6px;
+}
+
+input[type="range"]::-moz-range-track {
+  @apply h-2 rounded-full bg-gradient-to-r from-blue-100 to-indigo-100;
+}
+
+input[type="range"]::-moz-range-thumb {
+  @apply w-5 h-5 rounded-full bg-gradient-to-br from-blue-500 to-blue-600 border-0 shadow-md shadow-blue-500/30 hover:shadow-lg transition-all duration-200;
+}
+
+/* ========== CHECKBOX STYLING ========== */
+
+input[type="checkbox"] {
+  -webkit-appearance: none;
+  -moz-appearance: none;
+  appearance: none;
+  width: 1.125rem;
+  height: 1.125rem;
+  border: 2px solid #93c5fd;
+  border-radius: 0.375rem;
+  background-color: white;
+  cursor: pointer;
+  transition: all 0.2s ease;
+  position: relative;
+  flex-shrink: 0;
+}
+
+input[type="checkbox"]:hover {
+  border-color: #60a5fa;
+  box-shadow: 0 0 0 3px rgba(59, 130, 246, 0.1);
+}
+
+input[type="checkbox"]:checked {
+  background: linear-gradient(135deg, #3b82f6 0%, #2563eb 100%);
+  border-color: #2563eb;
+}
+
+input[type="checkbox"]:checked::after {
+  content: '';
+  position: absolute;
+  left: 5px;
+  top: 2px;
+  width: 5px;
+  height: 9px;
+  border: solid white;
+  border-width: 0 2px 2px 0;
+  transform: rotate(45deg);
+}
+
+input[type="checkbox"]:focus {
+  outline: none;
+  box-shadow: 0 0 0 3px rgba(59, 130, 246, 0.3);
+}
+
+/* ========== CARD & PANEL STYLES ========== */
+
+.card-interactive {
+  @apply transition-all duration-300 ease-out hover:shadow-lg hover:-translate-y-1;
+}
+
+/* Loading shimmer */
+.shimmer {
+  background: linear-gradient(90deg, #f3f4f6 25%, #e5e7eb 50%, #f3f4f6 75%);
+  background-size: 200% 100%;
+  animation: shimmer 1.5s infinite;
+}
+
+@keyframes shimmer {
+  0% {
+    background-position: 200% 0;
+  }
+
+  100% {
+    background-position: -200% 0;
+  }
+}
+
+/* OCR Workspace specific styles */
+.ocr-workspace {
+  @apply h-screen w-screen overflow-hidden;
+}
+
+/* Panel styles */
+.panel {
+  @apply bg-white/90 backdrop-blur-sm rounded-2xl border border-gray-100 shadow-lg;
+}
+
+.panel-header {
+  @apply px-4 py-3 bg-gradient-to-r from-blue-50/80 via-white to-indigo-50/50 border-b border-gray-100/80 rounded-t-2xl;
+}
+
+/* Thumbnail grid */
+.thumbnail-grid {
+  @apply grid grid-cols-2 gap-2;
+}
+
+.thumbnail-item {
+  @apply relative aspect-[3/4] rounded-xl overflow-hidden border-2 transition-all duration-300 cursor-pointer hover:shadow-lg hover:-translate-y-0.5;
+}
+
+.thumbnail-item.active {
+  @apply border-blue-500 ring-2 ring-blue-200 shadow-lg shadow-blue-500/20;
+}
+
+.thumbnail-item.processed {
+  @apply border-green-400 ring-2 ring-green-100;
+}
+
+/* Image preview */
+.preview-container {
+  @apply flex-1 p-4 bg-gradient-to-br from-gray-50 to-gray-100/50 flex items-center justify-center overflow-hidden rounded-xl;
+}
+
+.preview-image {
+  @apply max-w-full max-h-full object-contain rounded-lg shadow-xl;
+}
+
+/* Transcript editor */
+.transcript-textarea {
+  @apply w-full h-full p-4 resize-none focus:outline-none text-sm leading-relaxed border-0 bg-transparent;
+}
+
+.transcript-textarea.monospace {
+  @apply font-mono;
+}
+
+/* Status badges */
+.badge {
+  @apply inline-flex items-center gap-1.5 px-3 py-1.5 text-xs font-semibold rounded-full shadow-sm;
+}
+
+.badge-success {
+  @apply bg-gradient-to-r from-green-50 to-emerald-50 text-green-600 border border-green-100;
+}
+
+.badge-error {
+  @apply bg-gradient-to-r from-red-50 to-rose-50 text-red-600 border border-red-100;
+}
+
+.badge-warning {
+  @apply bg-gradient-to-r from-amber-50 to-yellow-50 text-amber-600 border border-amber-100;
+}
+
+.badge-info {
+  @apply bg-gradient-to-r from-blue-50 to-indigo-50 text-blue-600 border border-blue-100;
+}
+
+/* Loading states */
+.loading-overlay {
+  @apply absolute inset-0 flex items-center justify-center bg-white/80 backdrop-blur-sm;
+}
+
+/* ========== FLOATING ANIMATION FOR ICONS ========== */
+
+.icon-float {
+  animation: float 3s ease-in-out infinite;
+}
+
+/* ========== GRADIENT BORDERS ========== */
+
+.gradient-border {
+  position: relative;
+  background: white;
+  border-radius: 1rem;
+}
+
+.gradient-border::before {
+  content: '';
+  position: absolute;
+  inset: -2px;
+  border-radius: 1.1rem;
+  background: linear-gradient(135deg, #3b82f6, #8b5cf6, #3b82f6);
+  background-size: 200% 200%;
+  z-index: -1;
+  animation: gradientShift 3s ease infinite;
+}
+
+@keyframes gradientShift {
+
+  0%,
+  100% {
+    background-position: 0% 50%;
+  }
+
+  50% {
+    background-position: 100% 50%;
+  }
+}
+
+/* ========== PREMIUM UPLOAD ZONE ========== */
+
+.upload-zone {
+  @apply relative border-2 border-dashed rounded-2xl p-12 text-center transition-all duration-300 bg-white/60 backdrop-blur-sm;
+}
+
+.upload-zone:hover {
+  @apply border-blue-400 bg-blue-50/50;
+}
+
+.upload-zone.dragging {
+  @apply border-blue-500 bg-blue-50 scale-[1.02];
+  box-shadow: 0 0 20px rgba(59, 130, 246, 0.3);
+}
+
+/* Responsive utilities */
+@media (max-width: 1024px) {
+  .ocr-sidebar {
+    @apply w-56;
+  }
+
+  .ocr-transcript-panel {
+    @apply w-80;
+  }
+}
+
+/* ========== STEPPER PROGRESS LINE ========== */
+
+.stepper-line {
+  @apply absolute top-5 left-1/2 w-full h-1 bg-gray-100;
+}
+
+.stepper-line-fill {
+  @apply h-full bg-gradient-to-r from-blue-500 to-indigo-500 transition-all duration-500 ease-out rounded-full;
+}
+
+/* ========== FOCUS STYLES ========== */
+
+.focus-ring {
+  @apply focus:outline-none focus:ring-2 focus:ring-blue-500/50 focus:ring-offset-2;
+}
+
+/* ========== COMPACT SLIDER FOR PREPROCESS ========== */
+
+.slider-compact {
+  @apply appearance-none bg-transparent cursor-pointer w-full h-5;
+}
+
+.slider-compact::-webkit-slider-runnable-track {
+  @apply h-1.5 rounded-full;
+  background: linear-gradient(to right, #3b82f6 var(--slider-progress, 0%), #e5e7eb var(--slider-progress, 0%));
+}
+
+.slider-compact::-webkit-slider-thumb {
+  @apply appearance-none w-4 h-4 rounded-full bg-white border-2 border-blue-500 shadow-md;
+  margin-top: -5px;
+  transition: transform 0.15s ease, box-shadow 0.15s ease;
+}
+
+.slider-compact::-webkit-slider-thumb:hover {
+  @apply scale-110 shadow-lg shadow-blue-500/30;
+}
+
+.slider-compact::-webkit-slider-thumb:active {
+  @apply scale-95;
+}
+
+.slider-compact::-moz-range-track {
+  @apply h-1.5 rounded-full bg-gray-200;
+}
+
+.slider-compact::-moz-range-progress {
+  @apply h-1.5 rounded-full bg-blue-500;
+}
+
+.slider-compact::-moz-range-thumb {
+  @apply w-4 h-4 rounded-full bg-white border-2 border-blue-500 shadow-md cursor-pointer;
+}
+
+/* ========== ANIMATION UTILITIES ========== */
+
+@keyframes fade-in {
+  from {
+    opacity: 0;
+  }
+  to {
+    opacity: 1;
+  }
+}
+
+@keyframes slide-up {
+  from {
+    opacity: 0;
+    transform: translateY(8px);
+  }
+  to {
+    opacity: 1;
+    transform: translateY(0);
+  }
+}
+
+@keyframes slide-down {
+  from {
+    opacity: 0;
+    transform: translateY(-8px);
+  }
+  to {
+    opacity: 1;
+    transform: translateY(0);
+  }
+}
+
+.animate-fade-in {
+  animation: fade-in 0.2s ease-out;
+}
+
+.animate-slide-up {
+  animation: slide-up 0.2s ease-out;
+}
+
+.animate-slide-down {
+  animation: slide-down 0.2s ease-out;
+}
+
+/* ========== PREPROCESS SPECIFIC STYLES ========== */
+
+/* Card shadow for panels */
+.shadow-card {
+  box-shadow: 0 1px 3px rgba(0, 0, 0, 0.05), 0 4px 12px rgba(0, 0, 0, 0.05);
+}
+
+.shadow-card-hover {
+  box-shadow: 0 2px 8px rgba(0, 0, 0, 0.08), 0 8px 24px rgba(0, 0, 0, 0.08);
+}
+
+/* Tooltip arrow fix */
+.tooltip-arrow {
+  @apply absolute w-2 h-2 bg-gray-900 transform rotate-45;
+}
+
+/* Progress bar animation */
+@keyframes progress-pulse {
+  0%, 100% {
+    opacity: 1;
+  }
+  50% {
+    opacity: 0.7;
+  }
+}
+
+.progress-active {
+  animation: progress-pulse 1.5s ease-in-out infinite;
+}
+
+/* Indeterminate progress bar (e.g. first-time model download with unknown ETA) */
+@keyframes indeterminate-slide {
+  0% { transform: translateX(-120%); }
+  100% { transform: translateX(420%); }
+}
+.animate-indeterminate {
+  animation: indeterminate-slide 1.6s ease-in-out infinite;
+}
+
+/* Pipeline step drag indicator */
+.pipeline-drag-over {
+  @apply border-blue-400 border-2 bg-blue-50;
+}
+
+/* Compact scrollbar for sidebars */
+.scrollbar-thin::-webkit-scrollbar {
+  width: 4px;
+}
+
+.scrollbar-thin::-webkit-scrollbar-track {
+  @apply bg-transparent;
+}
+
+.scrollbar-thin::-webkit-scrollbar-thumb {
+  @apply bg-gray-200 rounded-full hover:bg-gray-300;
+}
+
+/* Zoom lens window styling */
+.zoom-lens-window {
+  @apply fixed bg-white rounded-xl shadow-2xl border border-gray-200 overflow-hidden z-50;
+}
\ No newline at end of file
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/main.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/main.jsx
new file mode 100644
index 00000000..54b39dd1
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/main.jsx
@@ -0,0 +1,10 @@
+import React from 'react'
+import ReactDOM from 'react-dom/client'
+import App from './App.jsx'
+import './index.css'
+
+ReactDOM.createRoot(document.getElementById('root')).render(
+  <React.StrictMode>
+    <App />
+  </React.StrictMode>,
+)
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/pages/CombinedUploadPage.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/pages/CombinedUploadPage.jsx
new file mode 100644
index 00000000..0dda64bb
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/pages/CombinedUploadPage.jsx
@@ -0,0 +1,569 @@
+import React, { useCallback, useState, useRef } from 'react';
+import {
+  Upload,
+  FileText,
+  FileImage,
+  BookOpen,
+  ArrowRight,
+  Check,
+  AlertTriangle,
+  Loader2,
+  UploadCloud,
+  X,
+  FileScan,
+  ScrollText,
+  ScanSearch,
+} from 'lucide-react';
+import { API_ORIGIN } from '../config';
+
+const API_BASE = API_ORIGIN;
+
+// Book (PDF or images) and transcript uploaded together, before matching.
+export default function CombinedUploadPage({
+  onNext,
+  isProcessingBook,
+  initialBookFiles,
+  initialTranscript,
+  initialDatasetMode = 'recognition',
+}) {
+  // 'recognition' needs a transcript; 'detection' is boxes only.
+  const [datasetMode, setDatasetMode] = useState(initialDatasetMode);
+  const isDetection = datasetMode === 'detection';
+  // ── Book upload ──
+  const [bookFiles, setBookFiles] = useState(initialBookFiles || null);
+  const [isDraggingBook, setIsDraggingBook] = useState(false);
+  const bookInputRef = useRef(null);
+
+  // ── Transcript upload ──
+  const [transcriptFile, setTranscriptFile] = useState(null);
+  const [parsedTranscript, setParsedTranscript] = useState(initialTranscript || null);
+  const [isDraggingTranscript, setIsDraggingTranscript] = useState(false);
+  const [isParsing, setIsParsing] = useState(false);
+  const [transcriptError, setTranscriptError] = useState(null);
+  const transcriptInputRef = useRef(null);
+
+  // ── Book handlers ──
+  const processBookFiles = (files) => {
+    const valid = files.filter((f) => {
+      const ext = f.name.split('.').pop().toLowerCase();
+      return (
+        f.type === 'application/pdf' ||
+        f.type.startsWith('image/') ||
+        f.type === 'application/vnd.openxmlformats-officedocument.wordprocessingml.document' ||
+        ext === 'docx'
+      );
+    });
+    if (valid.length > 0) setBookFiles(valid);
+  };
+
+  const handleBookDrop = useCallback((e) => {
+    e.preventDefault();
+    setIsDraggingBook(false);
+    processBookFiles(Array.from(e.dataTransfer.files));
+  }, []);
+
+  const handleBookInput = useCallback((e) => {
+    processBookFiles(Array.from(e.target.files));
+  }, []);
+
+  // ── Transcript handlers ──
+  const processTranscriptFile = async (file) => {
+    const ext = '.' + file.name.split('.').pop().toLowerCase();
+    const validExts = ['.txt', '.md', '.markdown', '.pdf', '.docx'];
+    if (!validExts.includes(ext)) {
+      setTranscriptError('Unsupported file type. Please upload TXT, DOCX, PDF, or Markdown.');
+      return;
+    }
+    setTranscriptFile(file);
+    setTranscriptError(null);
+    setIsParsing(true);
+    try {
+      const formData = new FormData();
+      formData.append('file', file);
+      const res = await fetch(`${API_BASE}/api/dataset/parse-transcript`, {
+        method: 'POST',
+        body: formData,
+      });
+      if (!res.ok) throw new Error('Server error: ' + res.statusText);
+      const data = await res.json();
+      if (!data.success) throw new Error(data.error || 'Failed to parse transcript');
+      setParsedTranscript(data.pages);
+    } catch (err) {
+      setTranscriptError(err.message);
+      setParsedTranscript(null);
+    } finally {
+      setIsParsing(false);
+    }
+  };
+
+  const handleTranscriptDrop = useCallback((e) => {
+    e.preventDefault();
+    setIsDraggingTranscript(false);
+    const files = Array.from(e.dataTransfer.files);
+    if (files.length > 0) processTranscriptFile(files[0]);
+  }, []);
+
+  const handleTranscriptInput = useCallback((e) => {
+    const files = Array.from(e.target.files);
+    if (files.length > 0) processTranscriptFile(files[0]);
+  }, []);
+
+  // ── Helpers ──
+  const formatFileSize = (bytes) => {
+    if (bytes < 1024) return bytes + ' B';
+    if (bytes < 1024 * 1024) return (bytes / 1024).toFixed(1) + ' KB';
+    return (bytes / (1024 * 1024)).toFixed(1) + ' MB';
+  };
+
+  const bookType = bookFiles
+    ? bookFiles[0].type === 'application/pdf'
+      ? 'pdf'
+      : bookFiles[0].type.startsWith('image/')
+      ? 'images'
+      : 'docx'
+    : null;
+
+  const transcriptPageCount = parsedTranscript ? Object.keys(parsedTranscript).length : 0;
+  const totalTranscriptLines = parsedTranscript
+    ? Object.values(parsedTranscript).reduce((s, l) => s + l.length, 0)
+    : 0;
+
+  // Detection mode needs no transcript.
+  const canContinue = isDetection
+    ? bookFiles && !isProcessingBook
+    : bookFiles && parsedTranscript && !isProcessingBook && !isParsing;
+
+  const handleContinue = () => {
+    if (!canContinue) return;
+    onNext(bookFiles, isDetection ? null : parsedTranscript, datasetMode);
+  };
+
+  return (
+    <div className="flex flex-col h-full w-full overflow-hidden">
+      {/* ── Page header ───────────────────────────────────────────── */}
+      <div className="flex items-center justify-between px-8 py-5 border-b border-gray-100/80 bg-white/60 backdrop-blur-sm flex-shrink-0 gap-6">
+        <div className="flex items-center gap-4 min-w-0 flex-1">
+          <div className="p-2.5 bg-gradient-to-br from-emerald-500 to-teal-600 rounded-xl text-white shadow-lg shadow-emerald-500/30 flex-shrink-0">
+            <Upload className="w-5 h-5" />
+          </div>
+          <div className="min-w-0">
+            <h1 className="text-xl font-bold text-gray-800 truncate">
+              {isDetection ? 'Upload Book' : 'Upload Book \u0026 Transcript'}
+            </h1>
+            <p className="text-sm text-gray-500 mt-0.5 truncate">
+              {isDetection
+                ? 'Detection mode: only images are needed — line bounding boxes will be exported, no transcript required'
+                : 'Upload both files together — only pages with matching transcripts will be processed'}
+            </p>
+          </div>
+        </div>
+
+        <div className="flex items-center gap-4 flex-shrink-0">
+        {/* Dataset-type toggle */}
+        <div className="flex bg-gray-100 rounded-lg p-1 flex-shrink-0">
+          <button
+            onClick={() => setDatasetMode('recognition')}
+            className={`flex items-center gap-1.5 px-3 py-1.5 rounded-md text-sm font-medium transition-all ${
+              !isDetection
+                ? 'bg-white text-emerald-700 shadow-sm'
+                : 'text-gray-500 hover:text-gray-700'
+            }`}
+            title="Aligned line images + transcript labels"
+          >
+            <FileText className="w-3.5 h-3.5" />
+            Recognition
+          </button>
+          <button
+            onClick={() => setDatasetMode('detection')}
+            className={`flex items-center gap-1.5 px-3 py-1.5 rounded-md text-sm font-medium transition-all ${
+              isDetection
+                ? 'bg-white text-indigo-700 shadow-sm'
+                : 'text-gray-500 hover:text-gray-700'
+            }`}
+            title="Pages + bounding boxes only (no transcript)"
+          >
+            <ScanSearch className="w-3.5 h-3.5" />
+            Detection
+          </button>
+        </div>
+
+        <button
+          onClick={handleContinue}
+          disabled={!canContinue}
+          className={`flex items-center justify-center gap-2.5 px-7 py-3 rounded-xl font-bold text-sm transition-all duration-200 flex-shrink-0 whitespace-nowrap min-w-[210px] ${
+            canContinue
+              ? 'bg-gradient-to-r from-emerald-500 to-teal-600 text-white shadow-lg shadow-emerald-500/30 hover:shadow-xl hover:-translate-y-0.5'
+              : 'bg-gray-100 text-gray-400 cursor-not-allowed'
+          }`}
+        >
+          {isProcessingBook ? (
+            <>
+              <Loader2 className="w-4 h-4 animate-spin" />
+              Processing…
+            </>
+          ) : (
+            <>
+              {isDetection ? 'Continue' : 'Review \u0026 Match Pages'}
+              <ArrowRight className="w-4 h-4" />
+            </>
+          )}
+        </button>
+        </div>
+      </div>
+
+      {/* ── Main layout: two columns in recognition mode, single column in detection ── */}
+      <div className={`flex-1 grid ${isDetection ? 'grid-cols-1' : 'grid-cols-2'} gap-0 overflow-hidden`}>
+
+        {/* ── LEFT: Book Upload ──────────────────────────────────── */}
+        <div className={`flex flex-col ${isDetection ? '' : 'border-r border-gray-100'} overflow-hidden bg-gradient-to-br from-slate-50 to-white`}>
+          {/* Section header */}
+          <div className="flex items-center gap-3 px-8 pt-7 pb-4 flex-shrink-0">
+            <div className="w-9 h-9 rounded-xl bg-gradient-to-br from-blue-100 to-indigo-100 flex items-center justify-center">
+              <BookOpen className="w-5 h-5 text-blue-600" />
+            </div>
+            <div>
+              <h2 className="font-bold text-gray-800">Book / Document</h2>
+              <p className="text-xs text-gray-500">PDF, images, or DOCX of your book</p>
+            </div>
+            {bookFiles && (
+              <div className="ml-auto flex items-center gap-1.5 px-2.5 py-1 bg-blue-100 text-blue-700 rounded-full text-xs font-semibold">
+                <Check className="w-3.5 h-3.5" />
+                Ready
+              </div>
+            )}
+          </div>
+
+          <div className="flex-1 px-8 pb-8 flex flex-col gap-5 overflow-y-auto">
+            {/* Drop zone */}
+            <div
+              onDragOver={(e) => { e.preventDefault(); setIsDraggingBook(true); }}
+              onDragLeave={(e) => { e.preventDefault(); setIsDraggingBook(false); }}
+              onDrop={handleBookDrop}
+              onClick={() => !bookFiles && bookInputRef.current?.click()}
+              className={`relative border-2 border-dashed rounded-2xl transition-all duration-300 cursor-pointer flex-shrink-0 ${
+                isDraggingBook
+                  ? 'border-blue-500 bg-blue-50/80 scale-[1.01] shadow-xl shadow-blue-500/20'
+                  : bookFiles
+                  ? 'border-blue-300 bg-blue-50/40 cursor-default'
+                  : 'border-gray-300 bg-white/60 hover:border-blue-400 hover:bg-blue-50/20 hover:shadow-md'
+              } ${isProcessingBook ? 'opacity-60 pointer-events-none' : ''}`}
+            >
+              <input
+                ref={bookInputRef}
+                type="file"
+                multiple
+                accept=".pdf,image/*,.docx,application/vnd.openxmlformats-officedocument.wordprocessingml.document"
+                onChange={handleBookInput}
+                className="hidden"
+              />
+              <div className="p-10 flex flex-col items-center text-center">
+                {isProcessingBook ? (
+                  <>
+                    <Loader2 className="w-14 h-14 text-blue-500 animate-spin" />
+                    <p className="mt-4 font-semibold text-blue-600">Extracting pages…</p>
+                  </>
+                ) : bookFiles ? (
+                  <>
+                    <div className="w-16 h-16 rounded-2xl bg-gradient-to-br from-blue-500 to-indigo-600 flex items-center justify-center shadow-lg shadow-blue-500/30">
+                      {bookType === 'pdf' ? (
+                        <FileText className="w-8 h-8 text-white" />
+                      ) : (
+                        <FileImage className="w-8 h-8 text-white" />
+                      )}
+                    </div>
+                    <p className="mt-4 font-bold text-gray-800">
+                      {bookFiles.length === 1 ? bookFiles[0].name : `${bookFiles.length} files selected`}
+                    </p>
+                    <p className="text-sm text-gray-500 mt-1">
+                      {bookFiles.length === 1 ? formatFileSize(bookFiles[0].size) : ''}
+                    </p>
+                    <button
+                      onClick={(e) => { e.stopPropagation(); setBookFiles(null); bookInputRef.current.value = ''; }}
+                      className="mt-3 text-xs text-gray-400 hover:text-red-500 flex items-center gap-1 transition-colors"
+                    >
+                      <X className="w-3.5 h-3.5" /> Remove
+                    </button>
+                  </>
+                ) : (
+                  <>
+                    <div className={`w-16 h-16 rounded-2xl flex items-center justify-center transition-all duration-500 ${
+                      isDraggingBook
+                        ? 'bg-gradient-to-br from-blue-500 to-indigo-600 shadow-xl'
+                        : 'bg-gradient-to-br from-blue-100 to-indigo-100'
+                    }`}>
+                      <UploadCloud className={`w-8 h-8 transition-all duration-300 ${isDraggingBook ? 'text-white animate-bounce' : 'text-blue-500'}`} />
+                    </div>
+                    <p className="mt-4 font-bold text-gray-700">
+                      {isDraggingBook ? 'Drop here!' : 'Drag & drop book files'}
+                    </p>
+                    <p className="text-sm text-gray-400 mt-1">or click to browse</p>
+                    <div className="mt-4 flex flex-wrap justify-center gap-2">
+                      {['PDF', 'Images', 'DOCX'].map((type) => (
+                        <span key={type} className="px-2.5 py-1 bg-gray-100 text-gray-500 text-xs rounded-full font-medium">
+                          {type}
+                        </span>
+                      ))}
+                    </div>
+                  </>
+                )}
+              </div>
+            </div>
+
+            {/* Book info cards */}
+            {bookFiles && !isProcessingBook && (
+              <div className="grid grid-cols-2 gap-3 animate-fade-in">
+                <div className="p-4 bg-white rounded-xl border border-gray-100 shadow-sm text-center">
+                  <p className="text-xs text-gray-500 uppercase tracking-wide">Type</p>
+                  <p className="text-lg font-bold text-blue-600 mt-1 capitalize">
+                    {bookType === 'pdf' ? 'PDF' : bookType === 'images' ? 'Images' : 'DOCX'}
+                  </p>
+                </div>
+                <div className="p-4 bg-white rounded-xl border border-gray-100 shadow-sm text-center">
+                  <p className="text-xs text-gray-500 uppercase tracking-wide">Files</p>
+                  <p className="text-lg font-bold text-blue-600 mt-1">{bookFiles.length}</p>
+                </div>
+              </div>
+            )}
+
+            {/* Accepted formats hint */}
+            <div className="p-4 bg-white/70 rounded-xl border border-gray-100 text-sm text-gray-500">
+              <p className="font-semibold text-gray-700 mb-2 flex items-center gap-2">
+                <FileScan className="w-4 h-4 text-blue-500" />
+                Accepted formats
+              </p>
+              <ul className="space-y-1 text-xs">
+                <li className="flex items-center gap-2"><span className="w-1.5 h-1.5 rounded-full bg-blue-400" />PDF — pages extracted automatically</li>
+                <li className="flex items-center gap-2"><span className="w-1.5 h-1.5 rounded-full bg-blue-400" />Images (JPG, PNG, TIFF, WebP) — one image = one page</li>
+                <li className="flex items-center gap-2"><span className="w-1.5 h-1.5 rounded-full bg-blue-400" />DOCX — converted to images</li>
+              </ul>
+            </div>
+          </div>
+        </div>
+
+        {/* ── RIGHT: Transcript Upload (recognition mode only) ───── */}
+        {!isDetection && (
+        <div className="flex flex-col overflow-hidden bg-gradient-to-br from-emerald-50/30 to-white">
+          {/* Section header */}
+          <div className="flex items-center gap-3 px-8 pt-7 pb-4 flex-shrink-0">
+            <div className="w-9 h-9 rounded-xl bg-gradient-to-br from-emerald-100 to-teal-100 flex items-center justify-center">
+              <ScrollText className="w-5 h-5 text-emerald-600" />
+            </div>
+            <div>
+              <h2 className="font-bold text-gray-800">Transcript File</h2>
+              <p className="text-xs text-gray-500">TXT, DOCX, PDF, or Markdown with page markers</p>
+            </div>
+            {parsedTranscript && (
+              <div className="ml-auto flex items-center gap-1.5 px-2.5 py-1 bg-emerald-100 text-emerald-700 rounded-full text-xs font-semibold">
+                <Check className="w-3.5 h-3.5" />
+                Parsed
+              </div>
+            )}
+          </div>
+
+          <div className="flex-1 px-8 pb-8 flex flex-col gap-5 overflow-y-auto">
+            {/* Drop zone */}
+            <div
+              onDragOver={(e) => { e.preventDefault(); setIsDraggingTranscript(true); }}
+              onDragLeave={(e) => { e.preventDefault(); setIsDraggingTranscript(false); }}
+              onDrop={handleTranscriptDrop}
+              onClick={() => !transcriptFile && transcriptInputRef.current?.click()}
+              className={`relative border-2 border-dashed rounded-2xl transition-all duration-300 cursor-pointer flex-shrink-0 ${
+                isDraggingTranscript
+                  ? 'border-emerald-500 bg-emerald-50/80 scale-[1.01] shadow-xl shadow-emerald-500/20'
+                  : transcriptFile
+                  ? 'border-emerald-300 bg-emerald-50/40 cursor-default'
+                  : 'border-gray-300 bg-white/60 hover:border-emerald-400 hover:bg-emerald-50/20 hover:shadow-md'
+              } ${isParsing ? 'opacity-60 pointer-events-none' : ''}`}
+            >
+              <input
+                ref={transcriptInputRef}
+                type="file"
+                accept=".txt,.md,.markdown,.pdf,.docx"
+                onChange={handleTranscriptInput}
+                className="hidden"
+              />
+              <div className="p-10 flex flex-col items-center text-center">
+                {isParsing ? (
+                  <>
+                    <Loader2 className="w-14 h-14 text-emerald-500 animate-spin" />
+                    <p className="mt-4 font-semibold text-emerald-600">Parsing transcript…</p>
+                  </>
+                ) : parsedTranscript ? (
+                  <>
+                    <div className="w-16 h-16 rounded-2xl bg-gradient-to-br from-emerald-500 to-teal-600 flex items-center justify-center shadow-lg shadow-emerald-500/30">
+                      <FileText className="w-8 h-8 text-white" />
+                    </div>
+                    <p className="mt-4 font-bold text-gray-800">{transcriptFile?.name}</p>
+                    <p className="text-sm text-emerald-600 mt-1 font-semibold">
+                      {transcriptPageCount} pages · {totalTranscriptLines} lines
+                    </p>
+                    <button
+                      onClick={(e) => {
+                        e.stopPropagation();
+                        setTranscriptFile(null);
+                        setParsedTranscript(null);
+                        transcriptInputRef.current.value = '';
+                      }}
+                      className="mt-3 text-xs text-gray-400 hover:text-red-500 flex items-center gap-1 transition-colors"
+                    >
+                      <X className="w-3.5 h-3.5" /> Remove
+                    </button>
+                  </>
+                ) : (
+                  <>
+                    <div className={`w-16 h-16 rounded-2xl flex items-center justify-center transition-all duration-500 ${
+                      isDraggingTranscript
+                        ? 'bg-gradient-to-br from-emerald-500 to-teal-600 shadow-xl'
+                        : 'bg-gradient-to-br from-emerald-100 to-teal-100'
+                    }`}>
+                      <UploadCloud className={`w-8 h-8 transition-all duration-300 ${isDraggingTranscript ? 'text-white animate-bounce' : 'text-emerald-500'}`} />
+                    </div>
+                    <p className="mt-4 font-bold text-gray-700">
+                      {isDraggingTranscript ? 'Drop here!' : 'Drag & drop transcript file'}
+                    </p>
+                    <p className="text-sm text-gray-400 mt-1">or click to browse</p>
+                    <div className="mt-4 flex flex-wrap justify-center gap-2">
+                      {['TXT', 'DOCX', 'PDF', 'MD'].map((type) => (
+                        <span key={type} className="px-2.5 py-1 bg-gray-100 text-gray-500 text-xs rounded-full font-medium">
+                          {type}
+                        </span>
+                      ))}
+                    </div>
+                  </>
+                )}
+              </div>
+            </div>
+
+            {/* Transcript error */}
+            {transcriptError && (
+              <div className="flex items-start gap-3 p-4 bg-red-50 border border-red-200 rounded-xl text-red-600 text-sm animate-fade-in">
+                <AlertTriangle className="w-4 h-4 flex-shrink-0 mt-0.5" />
+                <div>
+                  <p className="font-semibold">Parse Error</p>
+                  <p className="text-xs mt-0.5">{transcriptError}</p>
+                </div>
+              </div>
+            )}
+
+            {/* Parsed transcript stats */}
+            {parsedTranscript && (
+              <div className="grid grid-cols-3 gap-3 animate-fade-in">
+                <div className="p-4 bg-white rounded-xl border border-gray-100 shadow-sm text-center">
+                  <p className="text-xs text-gray-500 uppercase tracking-wide">Pages</p>
+                  <p className="text-lg font-bold text-emerald-600 mt-1">{transcriptPageCount}</p>
+                </div>
+                <div className="p-4 bg-white rounded-xl border border-gray-100 shadow-sm text-center">
+                  <p className="text-xs text-gray-500 uppercase tracking-wide">Total Lines</p>
+                  <p className="text-lg font-bold text-emerald-600 mt-1">{totalTranscriptLines}</p>
+                </div>
+                <div className="p-4 bg-white rounded-xl border border-gray-100 shadow-sm text-center">
+                  <p className="text-xs text-gray-500 uppercase tracking-wide">Avg/Page</p>
+                  <p className="text-lg font-bold text-emerald-600 mt-1">
+                    {transcriptPageCount > 0 ? Math.round(totalTranscriptLines / transcriptPageCount) : 0}
+                  </p>
+                </div>
+              </div>
+            )}
+
+            {/* Transcript page preview */}
+            {parsedTranscript && (
+              <div className="space-y-2 animate-fade-in">
+                <p className="text-xs font-semibold text-gray-500 uppercase tracking-wide px-1">Preview</p>
+                <div className="space-y-2 max-h-52 overflow-y-auto pr-1">
+                  {Object.entries(parsedTranscript).slice(0, 8).map(([pageKey, lines]) => (
+                    <div key={pageKey} className="p-3 bg-white rounded-xl border border-gray-100 shadow-sm">
+                      <div className="flex items-center justify-between mb-1.5">
+                        <span className="text-xs font-bold text-emerald-700 bg-emerald-50 px-2 py-0.5 rounded-full">
+                          Page {pageKey}
+                        </span>
+                        <span className="text-xs text-gray-400">{lines.length} lines</span>
+                      </div>
+                      <p className="text-xs text-gray-600 truncate leading-relaxed">
+                        {lines[0] || '—'}
+                      </p>
+                    </div>
+                  ))}
+                  {transcriptPageCount > 8 && (
+                    <p className="text-xs text-gray-400 text-center italic py-1">
+                      +{transcriptPageCount - 8} more pages…
+                    </p>
+                  )}
+                </div>
+              </div>
+            )}
+
+            {/* Format guide */}
+            <details className="bg-white/70 rounded-xl border border-gray-100 group">
+              <summary className="px-4 py-3 cursor-pointer text-xs font-semibold text-gray-600 hover:text-emerald-600 transition-colors flex items-center gap-2 select-none">
+                <ScrollText className="w-3.5 h-3.5" />
+                How to format page markers
+              </summary>
+              <div className="px-4 pb-4 text-xs text-gray-500 space-y-2">
+                <p>Use page markers on separate lines to split pages:</p>
+                <div className="bg-gray-50 rounded-lg p-3 font-mono text-xs space-y-0.5 border border-gray-100">
+                  <p className="text-emerald-600 font-bold">PDF p1</p>
+                  <p className="text-gray-600">First line of page 1…</p>
+                  <p className="text-emerald-600 font-bold mt-1.5">PDF p2</p>
+                  <p className="text-gray-600">First line of page 2…</p>
+                </div>
+                <p className="text-gray-400">Also supports: <code className="bg-gray-100 px-1 rounded">--- Page N ---</code>, <code className="bg-gray-100 px-1 rounded">[Page N]</code></p>
+              </div>
+            </details>
+          </div>
+        </div>
+        )}
+      </div>
+
+      {/* ── Bottom status bar ─────────────────────────────────────── */}
+      {(bookFiles || (!isDetection && parsedTranscript)) && (
+        <div className="flex items-center justify-between px-8 py-3 border-t border-gray-100 bg-white/80 backdrop-blur-sm flex-shrink-0 animate-slide-up">
+          <div className="flex items-center gap-6">
+            <StatusDot
+              label="Book uploaded"
+              active={!!bookFiles}
+              activeColor="blue"
+            />
+            {!isDetection && (
+              <StatusDot
+                label="Transcript parsed"
+                active={!!parsedTranscript}
+                activeColor="emerald"
+              />
+            )}
+          </div>
+          {canContinue && !isDetection && (
+            <p className="text-xs text-gray-500">
+              Both files ready — click <span className="font-semibold text-emerald-600">Review &amp; Match Pages</span> to continue
+            </p>
+          )}
+          {canContinue && isDetection && (
+            <p className="text-xs text-gray-500">
+              Book ready — click <span className="font-semibold text-indigo-600">Continue</span> to proceed
+            </p>
+          )}
+          {!canContinue && bookFiles && !isDetection && !parsedTranscript && (
+            <p className="text-xs text-amber-600 font-medium">Upload a transcript to continue</p>
+          )}
+          {!canContinue && !bookFiles && !isDetection && parsedTranscript && (
+            <p className="text-xs text-amber-600 font-medium">Upload the book to continue</p>
+          )}
+        </div>
+      )}
+    </div>
+  );
+}
+
+function StatusDot({ label, active, activeColor }) {
+  const colors = {
+    blue: active ? 'bg-blue-500' : 'bg-gray-200',
+    emerald: active ? 'bg-emerald-500' : 'bg-gray-200',
+  };
+  return (
+    <div className="flex items-center gap-2">
+      <span className={`w-2 h-2 rounded-full transition-colors duration-300 ${colors[activeColor]}`} />
+      <span className={`text-xs font-medium transition-colors duration-300 ${active ? 'text-gray-700' : 'text-gray-400'}`}>
+        {label}
+      </span>
+    </div>
+  );
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/pages/DatasetGenerationPage.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/pages/DatasetGenerationPage.jsx
new file mode 100644
index 00000000..0eb11b32
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/pages/DatasetGenerationPage.jsx
@@ -0,0 +1,653 @@
+import React, { useState, useMemo, useRef } from 'react';
+import {
+  ArrowLeft,
+  Download,
+  Loader2,
+  AlertTriangle,
+  Check,
+  Eye,
+  Database,
+  ChevronLeft,
+  ChevronRight,
+  ScanSearch,
+  FileText,
+} from 'lucide-react';
+import { saveDatasetToMyFiles, fetchStorageOverview } from '../services/storageApi';
+import { API_ORIGIN } from '../config';
+import { toDataUrl } from '../utils/imageUrl';
+
+const API_BASE = API_ORIGIN;
+
+// Alignment preview plus the export controls.
+//
+// "recognition" exports line crops with transcript labels; "detection" exports
+// whole images with bbox annotations and needs no transcript.
+export default function DatasetGenerationPage({
+  pages,
+  selectedPages,
+  processedImages,
+  transcript,
+  allPagesBoxes,
+  onBack,
+  onHome = null,
+  bookName = 'dataset',
+  forceMode = null,        // 'detection' to lock the toggle to detection-only
+  preprocessing = [],
+  modelInfo = {},          // detection/layout models carried from step 4
+}) {
+  const [isExporting, setIsExporting] = useState(false);
+  const [isSavingLocal, setIsSavingLocal] = useState(false);
+  const [exportError, setExportError] = useState(null);
+  const [previewIdx, setPreviewIdx] = useState(0);
+  // If upload already committed to a mode, honour it and hide the toggle.
+  const [datasetType, setDatasetType] = useState(forceMode || 'recognition');
+  const [bboxFormat, setBboxFormat] = useState('txt'); // 'txt' | 'json' | 'yolo' | 'coco'
+  const lastSavedSignatureRef = useRef('');
+  const isModeLocked = forceMode != null;
+
+  // ── Detection data ──
+  const detectionData = useMemo(() => {
+    if (!allPagesBoxes) return [];
+    return selectedPages
+      .filter(pageNum => (allPagesBoxes[pageNum] || []).length > 0)
+      .map(pageNum => {
+        const page = pages.find(p => p.pageNumber === pageNum);
+        const imageSrc = processedImages[pageNum] || page?.thumbnail;
+        const boxes = allPagesBoxes[pageNum] || [];
+        return { pageNumber: pageNum, imageSrc, boxes, numBoxes: boxes.length };
+      });
+  }, [allPagesBoxes, selectedPages, pages, processedImages]);
+
+  const totalDetectionBoxes = detectionData.reduce((s, d) => s + d.numBoxes, 0);
+
+  // ── Recognition data ──
+  const alignmentData = useMemo(() => {
+    if (!transcript || !allPagesBoxes) return [];
+
+    const tKeys = Object.keys(transcript).sort((a, b) => {
+      const na = parseInt(a), nb = parseInt(b);
+      if (!isNaN(na) && !isNaN(nb)) return na - nb;
+      return a.localeCompare(b);
+    });
+
+    const data = [];
+
+    for (const pageNum of selectedPages) {
+      const rawBoxes = allPagesBoxes[pageNum] || [];
+      const boxes = [...rawBoxes].sort((a, b) => {
+        const midTopY = poly => {
+          const byY = [...poly].sort((p, q) => p[1] - q[1]);
+          return (byY[0][1] + byY[1][1]) / 2;
+        };
+        return midTopY(a) - midTopY(b);
+      });
+      const pageKey = tKeys.find((k) => {
+        const n = parseInt(k);
+        return n === pageNum || k === String(pageNum);
+      });
+
+      if (!pageKey) continue;
+
+      const lines = transcript[pageKey] || [];
+      const numPairs = Math.min(boxes.length, lines.length);
+
+      const page = pages.find((p) => p.pageNumber === pageNum);
+      const imageSrc = processedImages[pageNum] || page?.thumbnail;
+
+      data.push({
+        pageNumber: pageNum,
+        pageKey,
+        imageSrc,
+        boxes,
+        lines,
+        numBoxes: boxes.length,
+        numLines: lines.length,
+        numPairs,
+        mismatch: boxes.length !== lines.length,
+      });
+    }
+
+    return data;
+  }, [transcript, allPagesBoxes, selectedPages, pages, processedImages]);
+
+  const totalPairs = alignmentData.reduce((s, d) => s + d.numPairs, 0);
+  const totalMismatches = alignmentData.filter((d) => d.mismatch).length;
+
+  // ── Preview ──
+  const previewList = datasetType === 'detection' ? detectionData : alignmentData;
+  const currentPreview = previewList[previewIdx] || null;
+
+  // ── Export ──
+
+  const handleExport = async () => {
+    setIsExporting(true);
+    setExportError(null);
+
+    try {
+      const pagesPayload = await Promise.all(alignmentData.map(async (d) => ({
+        page_key: String(d.pageNumber),
+        image_data: await toDataUrl(d.imageSrc),
+        boxes: d.boxes,
+        lines: d.lines,
+      })));
+
+      const res = await fetch(`${API_BASE}/api/dataset/export`, {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({
+          pages: pagesPayload,
+          book_name: bookName,
+        }),
+      });
+
+      if (!res.ok) {
+        const err = await res.json().catch(() => ({ detail: 'Export failed' }));
+        throw new Error(err.detail || 'Export failed');
+      }
+
+      const blob = await res.blob();
+      const url = URL.createObjectURL(blob);
+      const a = document.createElement('a');
+      a.href = url;
+      a.download = `${bookName}_dataset.zip`;
+      document.body.appendChild(a);
+      a.click();
+      document.body.removeChild(a);
+      URL.revokeObjectURL(url);
+    } catch (err) {
+      setExportError(err.message);
+    } finally {
+      setIsExporting(false);
+    }
+  };
+
+  const handleDetectionExport = async () => {
+    setIsExporting(true);
+    setExportError(null);
+
+    try {
+      const pagesPayload = await Promise.all(detectionData.map(async (d) => ({
+        page_key: String(d.pageNumber),
+        image_data: await toDataUrl(d.imageSrc),
+        boxes: d.boxes,
+      })));
+
+      const res = await fetch(`${API_BASE}/api/dataset/export-detection`, {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({
+          pages: pagesPayload,
+          book_name: bookName,
+          bbox_format: bboxFormat,
+        }),
+      });
+
+      if (!res.ok) {
+        const err = await res.json().catch(() => ({ detail: 'Export failed' }));
+        throw new Error(err.detail || 'Export failed');
+      }
+
+      const blob = await res.blob();
+      const url = URL.createObjectURL(blob);
+      const a = document.createElement('a');
+      a.href = url;
+      a.download = `${bookName}_detection_dataset.zip`;
+      document.body.appendChild(a);
+      a.click();
+      document.body.removeChild(a);
+      URL.revokeObjectURL(url);
+    } catch (err) {
+      setExportError(err.message);
+    } finally {
+      setIsExporting(false);
+    }
+  };
+
+  const handleSaveLocal = async () => {
+    // Name first, so cancelling doesn't leave a spinner up.
+    const defaultName = bookName && bookName !== 'dataset' ? bookName : 'My Dataset';
+    const chosenName = window.prompt('Name this dataset:', defaultName);
+    if (chosenName === null) return;                       // user cancelled
+    const finalName = chosenName.trim() || defaultName;
+
+    try {
+      const overview = await fetchStorageOverview();
+      const existingNames = (overview.datasets || []).map((d) => (d.name || '').toLowerCase());
+      if (existingNames.includes(finalName.toLowerCase())) {
+        const proceed = window.confirm(`A dataset named "${finalName}" already exists in My Files. Save anyway?`);
+        if (!proceed) return;
+      }
+    } catch {
+    }
+
+    // Carried from step 4 into the My Files metadata/tags.
+    const combinedModelInfo = {
+      ...modelInfo,
+      preprocessing:
+        modelInfo.preprocessing && modelInfo.preprocessing.length
+          ? modelInfo.preprocessing
+          : preprocessing,
+    };
+
+    setIsSavingLocal(true);
+    setExportError(null);
+
+    try {
+      if (isDetection) {
+        const pagesPayload = await Promise.all(detectionData.map(async (d) => ({
+          page_key: String(d.pageNumber),
+          image_data: await toDataUrl(d.imageSrc),
+          boxes: d.boxes,
+        })));
+
+        const signature = JSON.stringify({
+          mode: 'detection',
+          bookName: finalName,
+          bboxFormat,
+          pages: pagesPayload.map((p) => ({ page_key: p.page_key, boxes: p.boxes.length })),
+        });
+        if (lastSavedSignatureRef.current === signature) {
+          window.alert('Already saved — this exact dataset is already in My Files.');
+          return;
+        }
+
+        await saveDatasetToMyFiles(pagesPayload, {
+          source: 'dataset generation',
+          bookName: finalName,
+          bboxFormat,
+          mode: 'detection',
+          modelInfo: combinedModelInfo,
+        });
+        lastSavedSignatureRef.current = signature;
+      } else {
+        const pagesPayload = await Promise.all(alignmentData.map(async (d) => ({
+          page_key: String(d.pageNumber),
+          image_data: await toDataUrl(d.imageSrc),
+          boxes: d.boxes,
+          lines: d.lines,
+        })));
+
+        const signature = JSON.stringify({
+          mode: 'recognition',
+          bookName: finalName,
+          pages: pagesPayload.map((p) => ({
+            page_key: p.page_key,
+            boxes: p.boxes.length,
+            lines: p.lines.length,
+            text: p.lines.join('\n'),
+          })),
+        });
+        if (lastSavedSignatureRef.current === signature) {
+          window.alert('Already saved — this exact dataset is already in My Files.');
+          return;
+        }
+
+        await saveDatasetToMyFiles(pagesPayload, {
+          source: 'dataset generation',
+          bookName: finalName,
+          mode: 'recognition',
+          modelInfo: combinedModelInfo,
+        });
+        lastSavedSignatureRef.current = signature;
+      }
+
+      window.alert(`Saved "${finalName}" to My Files.`);
+    } catch (err) {
+      setExportError(`Save failed: ${err.message}`);
+    } finally {
+      setIsSavingLocal(false);
+    }
+  };
+
+  const isDetection = datasetType === 'detection';
+  const canExport = isDetection ? detectionData.length > 0 : alignmentData.length > 0;
+
+  return (
+    <div className="h-full flex flex-col animate-fade-in">
+      {/* Header */}
+      <div className="flex items-center justify-between mb-6 px-4 pt-4">
+        <div className="flex items-center gap-4">
+          <button onClick={onBack} className="btn-ghost">
+            <ArrowLeft className="w-5 h-5" />
+            Back
+          </button>
+          <div className="flex items-center gap-3">
+            <div className="p-2 bg-gradient-to-br from-emerald-500 to-teal-600 rounded-lg text-white shadow-md">
+              <Database className="w-5 h-5" />
+            </div>
+            <div>
+              <h1 className="text-2xl font-bold bg-gradient-to-r from-gray-800 to-gray-600 bg-clip-text text-transparent">
+                Generate Dataset
+              </h1>
+              <p className="text-sm text-gray-500">
+                {isDetection
+                  ? `${detectionData.length} pages, ${totalDetectionBoxes} bounding boxes`
+                  : `${alignmentData.length} pages, ${totalPairs} aligned pairs`
+                }
+              </p>
+            </div>
+
+            {onHome && (
+              <button
+                onClick={onHome}
+                className="ml-2 px-3 py-1.5 rounded-lg text-sm font-medium text-gray-600 hover:text-emerald-700 hover:bg-emerald-50 transition-all"
+              >
+                Home
+              </button>
+            )}
+          </div>
+        </div>
+
+        {/* Dataset type toggle (hidden when upstream locked the mode) */}
+        {!isModeLocked && (
+        <div className="flex bg-gray-100 rounded-lg p-1">
+          <button
+            onClick={() => { setDatasetType('recognition'); setPreviewIdx(0); }}
+            className={`flex items-center gap-1.5 px-3 py-1.5 rounded-md text-sm font-medium transition-all ${
+              !isDetection
+                ? 'bg-white text-emerald-700 shadow-sm'
+                : 'text-gray-500 hover:text-gray-700'
+            }`}
+          >
+            <FileText className="w-3.5 h-3.5" />
+            Recognition
+          </button>
+          <button
+            onClick={() => { setDatasetType('detection'); setPreviewIdx(0); }}
+            className={`flex items-center gap-1.5 px-3 py-1.5 rounded-md text-sm font-medium transition-all ${
+              isDetection
+                ? 'bg-white text-emerald-700 shadow-sm'
+                : 'text-gray-500 hover:text-gray-700'
+            }`}
+          >
+            <ScanSearch className="w-3.5 h-3.5" />
+            Detection
+          </button>
+        </div>
+        )}
+      </div>
+
+      {/* Stats row */}
+      {isDetection ? (
+        <div className="grid grid-cols-2 gap-4 px-4 mb-6">
+          <div className="bg-white/80 backdrop-blur-sm rounded-xl border border-gray-100 p-4 text-center shadow-sm">
+            <p className="text-2xl font-bold text-emerald-600">{detectionData.length}</p>
+            <p className="text-xs text-gray-500 mt-1">Pages</p>
+          </div>
+          <div className="bg-white/80 backdrop-blur-sm rounded-xl border border-gray-100 p-4 text-center shadow-sm">
+            <p className="text-2xl font-bold text-indigo-600">{totalDetectionBoxes}</p>
+            <p className="text-xs text-gray-500 mt-1">Bounding Boxes</p>
+          </div>
+        </div>
+      ) : (
+        <div className="grid grid-cols-4 gap-4 px-4 mb-6">
+          <div className="bg-white/80 backdrop-blur-sm rounded-xl border border-gray-100 p-4 text-center shadow-sm">
+            <p className="text-2xl font-bold text-emerald-600">{alignmentData.length}</p>
+            <p className="text-xs text-gray-500 mt-1">Pages Matched</p>
+          </div>
+          <div className="bg-white/80 backdrop-blur-sm rounded-xl border border-gray-100 p-4 text-center shadow-sm">
+            <p className="text-2xl font-bold text-blue-600">{totalPairs}</p>
+            <p className="text-xs text-gray-500 mt-1">Line Pairs</p>
+          </div>
+          <div className="bg-white/80 backdrop-blur-sm rounded-xl border border-gray-100 p-4 text-center shadow-sm">
+            <p className="text-2xl font-bold text-indigo-600">
+              {alignmentData.reduce((s, d) => s + d.numBoxes, 0)}
+            </p>
+            <p className="text-xs text-gray-500 mt-1">Bounding Boxes</p>
+          </div>
+          <div className="bg-white/80 backdrop-blur-sm rounded-xl border border-gray-100 p-4 text-center shadow-sm">
+            <p className={`text-2xl font-bold ${totalMismatches > 0 ? 'text-amber-600' : 'text-green-600'}`}>
+              {totalMismatches}
+            </p>
+            <p className="text-xs text-gray-500 mt-1">Mismatches</p>
+          </div>
+        </div>
+      )}
+
+      <div className="flex-1 flex gap-6 px-4 pb-4 overflow-hidden">
+        {/* Left: page list + preview */}
+        <div className="flex-1 flex flex-col bg-white/80 backdrop-blur-sm rounded-2xl border border-gray-100 shadow-sm overflow-hidden">
+          <div className="flex items-center justify-between px-4 py-3 border-b border-gray-100">
+            <h3 className="font-bold text-gray-700 flex items-center gap-2">
+              <Eye className="w-4 h-4" />
+              {isDetection ? 'Detection Preview' : 'Alignment Preview'}
+            </h3>
+            <div className="flex items-center gap-2">
+              <button
+                onClick={() => setPreviewIdx(Math.max(0, previewIdx - 1))}
+                disabled={previewIdx === 0}
+                className="p-1.5 rounded-lg hover:bg-gray-100 disabled:opacity-30 transition-colors"
+              >
+                <ChevronLeft className="w-4 h-4" />
+              </button>
+              <span className="text-sm text-gray-500 min-w-[60px] text-center">
+                {previewList.length > 0 ? `${previewIdx + 1} / ${previewList.length}` : '\u2014'}
+              </span>
+              <button
+                onClick={() => setPreviewIdx(Math.min(previewList.length - 1, previewIdx + 1))}
+                disabled={previewIdx >= previewList.length - 1}
+                className="p-1.5 rounded-lg hover:bg-gray-100 disabled:opacity-30 transition-colors"
+              >
+                <ChevronRight className="w-4 h-4" />
+              </button>
+            </div>
+          </div>
+
+          <div className="flex-1 overflow-y-auto p-4">
+            {currentPreview ? (
+              isDetection ? (
+                /* Detection: bbox count per page */
+                <div>
+                  <div className="flex items-center justify-between mb-3">
+                    <span className="font-semibold text-gray-700">
+                      Page {currentPreview.pageNumber}
+                    </span>
+                    <span className="text-xs text-indigo-600 bg-indigo-50 px-2.5 py-1 rounded-full">
+                      {currentPreview.numBoxes} boxes
+                    </span>
+                  </div>
+                  <div className="space-y-1.5 max-h-[60vh] overflow-y-auto">
+                    {currentPreview.boxes.map((box, i) => {
+                      const xs = box.map(p => p[0]);
+                      const ys = box.map(p => p[1]);
+                      const x1 = Math.round(Math.min(...xs));
+                      const y1 = Math.round(Math.min(...ys));
+                      const x2 = Math.round(Math.max(...xs));
+                      const y2 = Math.round(Math.max(...ys));
+                      return (
+                        <div
+                          key={i}
+                          className="flex items-center gap-3 p-2.5 bg-gray-50 rounded-lg text-sm"
+                        >
+                          <span className="text-xs font-mono text-gray-400 w-8 text-right flex-shrink-0">
+                            {i + 1}
+                          </span>
+                          <span className="flex-1 text-gray-600 font-mono text-xs">
+                            [{x1}, {y1}, {x2 - x1}, {y2 - y1}]
+                          </span>
+                          <Check className="w-4 h-4 text-green-500 flex-shrink-0" />
+                        </div>
+                      );
+                    })}
+                  </div>
+                </div>
+              ) : (
+                /* Recognition: alignment view */
+                <div>
+                  <div className="flex items-center justify-between mb-3">
+                    <span className="font-semibold text-gray-700">
+                      Page {currentPreview.pageNumber}
+                    </span>
+                    {currentPreview.mismatch && (
+                      <span className="flex items-center gap-1.5 text-xs text-amber-600 bg-amber-50 px-2.5 py-1 rounded-full">
+                        <AlertTriangle className="w-3.5 h-3.5" />
+                        {currentPreview.numBoxes} boxes / {currentPreview.numLines} lines
+                      </span>
+                    )}
+                  </div>
+
+                  <div className="space-y-1.5 max-h-[60vh] overflow-y-auto">
+                    {currentPreview.lines.slice(0, currentPreview.numPairs).map((line, i) => (
+                      <div
+                        key={i}
+                        className="flex items-center gap-3 p-2.5 bg-gray-50 rounded-lg text-sm"
+                      >
+                        <span className="text-xs font-mono text-gray-400 w-8 text-right flex-shrink-0">
+                          {i + 1}
+                        </span>
+                        <span className="flex-1 text-gray-700 truncate">{line}</span>
+                        {i < currentPreview.numBoxes && (
+                          <Check className="w-4 h-4 text-green-500 flex-shrink-0" />
+                        )}
+                      </div>
+                    ))}
+                    {currentPreview.numLines > currentPreview.numPairs && (
+                      <p className="text-xs text-amber-500 italic pl-11">
+                        {currentPreview.numLines - currentPreview.numPairs} unmatched transcript line(s)
+                      </p>
+                    )}
+                    {currentPreview.numBoxes > currentPreview.numPairs && (
+                      <p className="text-xs text-amber-500 italic pl-11">
+                        {currentPreview.numBoxes - currentPreview.numPairs} unmatched bounding box(es)
+                      </p>
+                    )}
+                  </div>
+                </div>
+              )
+            ) : (
+              <div className="flex items-center justify-center h-full text-gray-400">
+                {isDetection ? 'No detection data available' : 'No alignment data available'}
+              </div>
+            )}
+          </div>
+        </div>
+
+        {/* Right: export panel */}
+        <div className="w-80 flex flex-col bg-white/80 backdrop-blur-sm rounded-2xl border border-gray-100 shadow-sm overflow-hidden">
+          <div className="px-4 py-3 border-b border-gray-100">
+            <h3 className="font-bold text-gray-700 flex items-center gap-2">
+              <Download className="w-4 h-4" />
+              Export
+            </h3>
+          </div>
+
+          <div className="flex-1 overflow-y-auto p-4">
+            {isDetection ? (
+              <div className="space-y-3">
+                {(() => {
+                  const layoutByFormat = {
+                    txt:  `${bookName}/images/page_N.jpg\n${bookName}/bboxes/page_N.txt`,
+                    json: `${bookName}/images/page_N.jpg\n${bookName}/bboxes/page_N.json`,
+                    yolo: `${bookName}/images/page_N.jpg\n${bookName}/labels/page_N.txt\n${bookName}/classes.txt`,
+                    coco: `${bookName}/images/page_N.jpg\n${bookName}/annotations.json`,
+                  };
+                  const sampleByFormat = {
+                    txt:  'x1 y1 x2 y2 (one per line)',
+                    json: '[[x1, y1, x2, y2], ...]',
+                    yolo: '0 cx cy w h (normalized 0–1)',
+                    coco: 'COCO JSON (single "text" category)',
+                  };
+                  return (
+                    <>
+                      <div className="p-3.5 rounded-xl border border-indigo-200 bg-indigo-50/70 text-sm text-indigo-700">
+                        Downloads a ZIP with full page images and bounding boxes
+                        for each detected text line.
+                        <div className="text-xs text-indigo-600 mt-2 font-mono whitespace-pre-line">
+                          {layoutByFormat[bboxFormat]}
+                        </div>
+                      </div>
+
+                      <div>
+                        <label className="block text-xs font-semibold text-gray-600 mb-1.5">
+                          Annotation format
+                        </label>
+                        <div className="grid grid-cols-4 bg-gray-100 rounded-lg p-1 gap-1">
+                          {['txt', 'json', 'yolo', 'coco'].map((f) => (
+                            <button
+                              key={f}
+                              onClick={() => setBboxFormat(f)}
+                              className={`px-2 py-1.5 rounded-md text-xs font-semibold uppercase transition-all ${
+                                bboxFormat === f
+                                  ? 'bg-white text-indigo-700 shadow-sm'
+                                  : 'text-gray-500 hover:text-gray-700'
+                              }`}
+                            >
+                              {f}
+                            </button>
+                          ))}
+                        </div>
+                        <p className="text-[11px] text-gray-400 mt-1.5 font-mono">
+                          {sampleByFormat[bboxFormat]}
+                        </p>
+                      </div>
+                    </>
+                  );
+                })()}
+              </div>
+            ) : (
+              <div className="p-3.5 rounded-xl border border-emerald-200 bg-emerald-50/70 text-sm text-emerald-700">
+                Downloads one ZIP with line images and transcript labels.
+                <div className="text-xs text-emerald-600 mt-2 font-mono">
+                  {bookName}/page_N/images/line_label.png
+                </div>
+              </div>
+            )}
+          </div>
+
+          {/* Export button */}
+          <div className="p-4 border-t border-gray-100">
+            {exportError && (
+              <div className="mb-3 p-3 bg-red-50 border border-red-200 rounded-lg text-xs text-red-600">
+                {exportError}
+              </div>
+            )}
+
+            <div className="flex gap-2 mb-2">
+              <button
+                onClick={handleSaveLocal}
+                disabled={isExporting || isSavingLocal || !canExport}
+                className={`flex-1 flex items-center justify-center gap-2 px-4 py-3 rounded-xl font-bold transition-all duration-200 border ${
+                  isExporting || isSavingLocal || !canExport
+                    ? 'bg-gray-100 text-gray-400 border-gray-200 cursor-not-allowed'
+                    : 'bg-cyan-50 text-cyan-700 border-cyan-200 hover:bg-cyan-100'
+                }`}
+              >
+                {isSavingLocal ? <Loader2 className="w-5 h-5 animate-spin" /> : <Download className="w-5 h-5" />}
+                Save to My Files
+              </button>
+            </div>
+
+            <button
+              onClick={isDetection ? handleDetectionExport : handleExport}
+              disabled={isExporting || !canExport}
+              className={`w-full flex items-center justify-center gap-2 px-6 py-3.5 rounded-xl font-bold text-white shadow-lg transition-all duration-200 ${
+                isExporting || !canExport
+                  ? 'bg-gray-300 cursor-not-allowed'
+                  : 'bg-gradient-to-r from-emerald-500 to-teal-600 hover:shadow-xl hover:-translate-y-0.5 shadow-emerald-500/30'
+              }`}
+            >
+              {isExporting ? (
+                <>
+                  <Loader2 className="w-5 h-5 animate-spin" />
+                  Generating...
+                </>
+              ) : (
+                <>
+                  <Download className="w-5 h-5" />
+                  {isDetection ? 'Download Detection Dataset' : 'Download Dataset'}
+                </>
+              )}
+            </button>
+
+            <p className="text-xs text-gray-400 text-center mt-2">
+              {isDetection
+                ? `${detectionData.length} pages, ${totalDetectionBoxes} annotations`
+                : `${totalPairs} line images will be exported`
+              }
+            </p>
+          </div>
+        </div>
+      </div>
+    </div>
+  );
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/pages/HomePage.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/pages/HomePage.jsx
new file mode 100644
index 00000000..34acfddb
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/pages/HomePage.jsx
@@ -0,0 +1,152 @@
+import React from 'react';
+import { FileText, Database, ArrowRight, BookOpen, Archive, LogOut, UserCircle } from 'lucide-react';
+import homeImage from '../assets/home-image.png';
+
+// Landing hero plus the two workflow cards.
+export default function HomePage({ onSelectMode, user, onLogout, onProfile }) {
+  return (
+    <div className="relative h-screen w-screen overflow-hidden flex flex-col bg-gradient-to-br from-slate-50 via-white to-blue-50">
+      {/* Decorative background blobs */}
+      <div className="pointer-events-none absolute -top-40 -left-40 w-[34rem] h-[34rem] rounded-full bg-blue-200/40 blur-3xl" aria-hidden="true" />
+      <div className="pointer-events-none absolute -bottom-40 -right-40 w-[36rem] h-[36rem] rounded-full bg-indigo-200/40 blur-3xl" aria-hidden="true" />
+
+      {/* Header */}
+      <header className="relative z-10 bg-white/80 backdrop-blur-md border-b border-gray-100/60 shadow-sm">
+        <div className="max-w-7xl mx-auto px-6 py-4 flex items-center justify-center relative">
+          <div className="flex items-center gap-3">
+            <div className="w-11 h-11 bg-gradient-to-br from-blue-500 via-blue-600 to-indigo-600 rounded-xl flex items-center justify-center shadow-lg shadow-blue-500/30">
+              <BookOpen className="w-6 h-6 text-white" />
+            </div>
+            <div>
+              <h1 className="text-xl font-bold bg-gradient-to-r from-gray-800 to-gray-600 bg-clip-text text-transparent">
+                RenAIssance OCR
+              </h1>
+              <p className="text-xs text-gray-500">Historical document processing</p>
+            </div>
+          </div>
+
+          {/* Signed-in user + profile + logout */}
+          {user && (
+            <div className="absolute right-6 top-1/2 -translate-y-1/2 flex items-center gap-1.5">
+              <button
+                onClick={onProfile}
+                className="flex items-center gap-1.5 text-sm font-medium text-gray-600 hover:text-blue-600 hover:bg-blue-50 px-3 py-2 rounded-lg transition-all duration-200"
+                title="Edit your profile"
+              >
+                <UserCircle className="w-4 h-4" />
+                <span className="hidden sm:inline font-semibold text-gray-800">
+                  {user.name || user.username}
+                </span>
+              </button>
+              <button
+                onClick={onLogout}
+                className="flex items-center gap-1.5 text-sm font-medium text-gray-500 hover:text-blue-600 hover:bg-blue-50 px-3 py-2 rounded-lg transition-all duration-200"
+              >
+                <LogOut className="w-4 h-4" />
+                <span className="hidden sm:inline">Log out</span>
+              </button>
+            </div>
+          )}
+        </div>
+      </header>
+
+      {/* Main */}
+      <main className="relative z-10 flex-1 min-h-0 grid lg:grid-cols-2 gap-8 items-stretch px-6 lg:px-16 py-6 overflow-hidden">
+        {/* Left: decorative image only */}
+        <div className="flex flex-col min-h-0 h-full justify-start">
+          <div className="relative flex-1 min-h-[18rem] rounded-2xl overflow-hidden border border-blue-100 shadow-xl shadow-blue-500/10">
+            <img
+              src={homeImage}
+              alt="Historical manuscript"
+              className="absolute inset-0 w-full h-full object-cover"
+            />
+            <div className="absolute inset-0 bg-gradient-to-t from-blue-900/30 via-transparent to-transparent" />
+          </div>
+        </div>
+
+        {/* Right: mode cards */}
+        <div className="flex flex-col min-h-0 h-full justify-start gap-5">
+          {/* Dataset */}
+          <button
+            onClick={() => onSelectMode('dataset')}
+            className="group relative text-left p-6 rounded-2xl border border-gray-200 bg-white/90 backdrop-blur-sm hover:border-blue-400 hover:shadow-2xl hover:shadow-blue-500/15 hover:-translate-y-0.5 transition-all duration-300 focus:outline-none focus:ring-4 focus:ring-blue-100"
+          >
+            <div className="flex items-start gap-5">
+              <div className="p-3.5 rounded-xl bg-gradient-to-br from-blue-50 to-indigo-50 text-blue-600 group-hover:from-blue-500 group-hover:to-indigo-600 group-hover:text-white group-hover:shadow-lg group-hover:shadow-blue-500/30 transition-all duration-300 flex-shrink-0">
+                <Database className="w-7 h-7" strokeWidth={2} />
+              </div>
+              <div className="flex-1 min-w-0">
+                <h3 className="text-xl font-bold text-gray-800 mb-1">Generate OCR Dataset</h3>
+                <p className="text-sm text-gray-500 leading-relaxed">
+                  Build line-level training data from book pages and transcripts.
+                  Export in TrOCR, CRNN, YOLO, or COCO formats.
+                </p>
+                <div className="mt-4 flex items-center gap-1.5 text-blue-600 font-semibold text-sm group-hover:gap-3 transition-all">
+                  Start dataset workflow
+                  <ArrowRight className="w-4 h-4" />
+                </div>
+              </div>
+            </div>
+          </button>
+
+          {/* OCR */}
+          <button
+            onClick={() => onSelectMode('ocr')}
+            className="group relative text-left p-6 rounded-2xl border border-gray-200 bg-white/90 backdrop-blur-sm hover:border-indigo-400 hover:shadow-2xl hover:shadow-indigo-500/15 hover:-translate-y-0.5 transition-all duration-300 focus:outline-none focus:ring-4 focus:ring-indigo-100"
+          >
+            <div className="flex items-start gap-5">
+              <div className="p-3.5 rounded-xl bg-gradient-to-br from-indigo-50 to-blue-50 text-indigo-600 group-hover:from-indigo-500 group-hover:to-blue-600 group-hover:text-white group-hover:shadow-lg group-hover:shadow-indigo-500/30 transition-all duration-300 flex-shrink-0">
+                <FileText className="w-7 h-7" strokeWidth={2} />
+              </div>
+              <div className="flex-1 min-w-0">
+                <h3 className="text-xl font-bold text-gray-800 mb-1">Transcribe Pages</h3>
+                <p className="text-sm text-gray-500 leading-relaxed">
+                  Extract text using Gemini, ChatGPT, or local PaddleOCR / TrOCR
+                  models. Export as TXT, DOCX, or PDF.
+                </p>
+                <div className="mt-4 flex items-center gap-1.5 text-indigo-600 font-semibold text-sm group-hover:gap-3 transition-all">
+                  Start OCR workflow
+                  <ArrowRight className="w-4 h-4" />
+                </div>
+              </div>
+            </div>
+          </button>
+
+          {/* My Files */}
+          <button
+            onClick={() => onSelectMode('files')}
+            className="group relative text-left p-6 rounded-2xl border border-gray-200 bg-white/90 backdrop-blur-sm hover:border-cyan-400 hover:shadow-2xl hover:shadow-cyan-500/15 hover:-translate-y-0.5 transition-all duration-300 focus:outline-none focus:ring-4 focus:ring-cyan-100"
+          >
+            <div className="flex items-start gap-5">
+              <div className="p-3.5 rounded-xl bg-gradient-to-br from-cyan-50 to-blue-50 text-cyan-700 group-hover:from-cyan-500 group-hover:to-blue-600 group-hover:text-white group-hover:shadow-lg group-hover:shadow-cyan-500/30 transition-all duration-300 flex-shrink-0">
+                <Archive className="w-7 h-7" strokeWidth={2} />
+              </div>
+              <div className="flex-1 min-w-0">
+                <h3 className="text-xl font-bold text-gray-800 mb-1">My Files</h3>
+                <p className="text-sm text-gray-500 leading-relaxed">
+                  Browse saved transcription sessions and generated datasets.
+                  View, download, or delete persistent artifacts.
+                </p>
+                <div className="mt-4 flex items-center gap-1.5 text-cyan-700 font-semibold text-sm group-hover:gap-3 transition-all">
+                  Open file manager
+                  <ArrowRight className="w-4 h-4" />
+                </div>
+              </div>
+            </div>
+          </button>
+        </div>
+      </main>
+
+      {/* Footer */}
+      <footer className="relative z-10 bg-white/60 backdrop-blur-sm border-t border-gray-100 py-3">
+        <div className="max-w-7xl mx-auto px-6 text-center">
+          <p className="text-sm text-gray-500 flex items-center justify-center gap-2">
+            <span className="font-semibold text-gray-600">RenAIssance OCR</span>
+            <span className="text-gray-300">|</span>
+            <span className="text-blue-600 font-medium">Historical Document Processing</span>
+          </p>
+        </div>
+      </footer>
+    </div>
+  );
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/pages/MyFilesPage.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/pages/MyFilesPage.jsx
new file mode 100644
index 00000000..b2428b4a
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/pages/MyFilesPage.jsx
@@ -0,0 +1,497 @@
+import React, { useCallback, useEffect, useMemo, useState } from 'react';
+import {
+  Archive,
+  Calendar,
+  Database,
+  Download,
+  Eye,
+  FileText,
+  Image as ImageIcon,
+  Loader2,
+  RefreshCw,
+  Trash2,
+  X,
+} from 'lucide-react';
+import {
+  deleteStoredItem,
+  downloadBlob,
+  downloadStoredItem,
+  fetchStorageOverview,
+  fetchDatasetDetail,
+  fetchTranscriptDetail,
+} from '../services/storageApi';
+
+function formatDate(value) {
+  if (!value) return 'Unknown date';
+  const date = new Date(value);
+  if (Number.isNaN(date.getTime())) return value;
+  return date.toLocaleString();
+}
+
+function TagChips({ tags }) {
+  if (!tags || tags.length === 0) return null;
+  return (
+    <div className="flex flex-wrap gap-1 mt-2">
+      {tags.map((tag) => (
+        <span key={tag} className="inline-block px-2 py-0.5 rounded-full text-[10px] font-medium bg-gray-100 text-gray-600 border border-gray-200">
+          {tag}
+        </span>
+      ))}
+    </div>
+  );
+}
+
+function EmptyState({ title, description, color }) {
+  return (
+    <div className={`rounded-2xl border border-dashed p-8 text-center ${color}`}>
+      <p className="font-semibold text-lg">{title}</p>
+      <p className="text-sm mt-1 opacity-80">{description}</p>
+    </div>
+  );
+}
+
+function computeDynamicPreviewHeight(aspectRatio) {
+  const safeRatio = Number.isFinite(aspectRatio) && aspectRatio > 0 ? aspectRatio : 1.5;
+  const estimated = Math.round(460 / safeRatio);
+  return Math.max(280, Math.min(620, estimated));
+}
+
+export default function MyFilesPage({ onBack }) {
+  const [loading, setLoading] = useState(true);
+  const [refreshing, setRefreshing] = useState(false);
+  const [error, setError] = useState('');
+  const [transcripts, setTranscripts] = useState([]);
+  const [datasets, setDatasets] = useState([]);
+
+  const [viewingTranscript, setViewingTranscript] = useState(null);
+  const [viewingDataset, setViewingDataset] = useState(null);
+  const [detailLoading, setDetailLoading] = useState(false);
+  const [transcriptImageRatios, setTranscriptImageRatios] = useState({});
+  const [datasetImageRatios, setDatasetImageRatios] = useState({});
+
+  const totalItems = useMemo(() => transcripts.length + datasets.length, [transcripts, datasets]);
+
+  const loadData = async (isRefresh = false) => {
+    setError('');
+    if (isRefresh) {
+      setRefreshing(true);
+    } else {
+      setLoading(true);
+    }
+
+    try {
+      const data = await fetchStorageOverview();
+      setTranscripts(data.transcripts || []);
+      setDatasets(data.datasets || []);
+    } catch (err) {
+      setError(err.message || 'Failed to load saved files');
+    } finally {
+      setLoading(false);
+      setRefreshing(false);
+    }
+  };
+
+  useEffect(() => {
+    loadData();
+  }, []);
+
+  const handleDelete = async (kind, id) => {
+    const ok = window.confirm(`Delete ${id}? This cannot be undone.`);
+    if (!ok) return;
+
+    try {
+      await deleteStoredItem(kind, id);
+      await loadData(true);
+      if (viewingTranscript?.metadata?.id === id) {
+        setViewingTranscript(null);
+      }
+      if (viewingDataset?.metadata?.id === id) {
+        setViewingDataset(null);
+      }
+    } catch (err) {
+      setError(err.message || 'Delete failed');
+    }
+  };
+
+  const handleDownload = async (kind, id) => {
+    try {
+      const blob = await downloadStoredItem(kind, id);
+      downloadBlob(blob, `${id}.zip`);
+    } catch (err) {
+      setError(err.message || 'Download failed');
+    }
+  };
+
+  const handleViewTranscript = async (id) => {
+    setDetailLoading(true);
+    setError('');
+    setTranscriptImageRatios({});
+    try {
+      const detail = await fetchTranscriptDetail(id);
+      setViewingTranscript(detail);
+    } catch (err) {
+      setError(err.message || 'Failed to load transcript');
+    } finally {
+      setDetailLoading(false);
+    }
+  };
+
+  const handleViewDataset = async (id) => {
+    setDetailLoading(true);
+    setError('');
+    setDatasetImageRatios({});
+    try {
+      const detail = await fetchDatasetDetail(id);
+      setViewingDataset(detail);
+    } catch (err) {
+      setError(err.message || 'Failed to load dataset details');
+    } finally {
+      setDetailLoading(false);
+    }
+  };
+
+  const handleTranscriptImageLoad = useCallback((key, event) => {
+    const img = event.currentTarget;
+    const ratio = img.naturalWidth > 0 && img.naturalHeight > 0
+      ? img.naturalWidth / img.naturalHeight
+      : 1.5;
+    setTranscriptImageRatios((prev) => ({ ...prev, [key]: ratio }));
+  }, []);
+
+  const handleDatasetImageLoad = useCallback((key, event) => {
+    const img = event.currentTarget;
+    const ratio = img.naturalWidth > 0 && img.naturalHeight > 0
+      ? img.naturalWidth / img.naturalHeight
+      : 1.5;
+    setDatasetImageRatios((prev) => ({ ...prev, [key]: ratio }));
+  }, []);
+
+  return (
+    <div className="min-h-screen bg-gradient-to-br from-slate-50 via-white to-cyan-50">
+      <header className="bg-white/90 backdrop-blur-md border-b border-gray-100 shadow-sm sticky top-0 z-30">
+        <div className="w-full px-4 md:px-8 xl:px-12 py-4 flex items-center justify-between">
+          <div className="flex items-center gap-3">
+            <div className="w-10 h-10 rounded-xl bg-gradient-to-br from-cyan-500 to-blue-600 text-white flex items-center justify-center shadow-md">
+              <Archive className="w-5 h-5" />
+            </div>
+            <div>
+              <h1 className="text-xl font-bold text-gray-800">My Files</h1>
+              <p className="text-xs text-gray-500">Persistent OCR outputs and datasets</p>
+            </div>
+          </div>
+
+          <div className="flex items-center gap-2">
+            <button
+              onClick={() => loadData(true)}
+              className="px-3 py-2 rounded-lg text-sm font-medium text-gray-600 hover:text-cyan-700 hover:bg-cyan-50 transition-colors"
+              disabled={refreshing}
+            >
+              {refreshing ? <Loader2 className="w-4 h-4 animate-spin" /> : <RefreshCw className="w-4 h-4" />}
+            </button>
+            <button
+              onClick={onBack}
+              className="px-4 py-2 rounded-lg text-sm font-medium text-gray-600 hover:text-cyan-700 hover:bg-cyan-50 transition-colors"
+            >
+              Home
+            </button>
+          </div>
+        </div>
+      </header>
+
+      <main className="w-full px-4 md:px-8 xl:px-12 py-6 space-y-6">
+        <div className="bg-white border border-gray-100 rounded-2xl shadow-sm px-4 py-3 text-sm text-gray-600">
+          Stored items: <span className="font-semibold text-gray-800">{totalItems}</span>
+        </div>
+
+        {error && (
+          <div className="p-3 rounded-xl border border-red-200 bg-red-50 text-red-700 text-sm">{error}</div>
+        )}
+
+        {loading ? (
+          <div className="h-64 flex items-center justify-center text-gray-500">
+            <Loader2 className="w-5 h-5 animate-spin mr-2" /> Loading files...
+          </div>
+        ) : (
+          <div className="grid xl:grid-cols-2 gap-6">
+            <section className="bg-white rounded-2xl border border-blue-100 shadow-sm overflow-hidden min-h-[72vh]">
+              <div className="px-5 py-4 border-b border-blue-100 bg-gradient-to-r from-blue-50 to-white flex items-center justify-between">
+                <h2 className="font-bold text-blue-800 flex items-center gap-2">
+                  <FileText className="w-5 h-5" /> Transcriptions
+                </h2>
+                <span className="text-xs font-semibold text-blue-600 bg-blue-100 px-2 py-1 rounded-full">
+                  {transcripts.length}
+                </span>
+              </div>
+
+              <div className="p-4 space-y-3 h-[calc(72vh-4rem)] overflow-y-auto">
+                {transcripts.length === 0 && (
+                  <EmptyState
+                    title="No saved transcriptions"
+                    description="Completed OCR sessions will appear here."
+                    color="text-blue-700 bg-blue-50/50"
+                  />
+                )}
+
+                {transcripts.map((item) => (
+                  <article key={item.id} className="border border-blue-100 rounded-xl p-4 bg-blue-50/40">
+                    <div className="flex items-start justify-between gap-3">
+                      <div>
+                        <p className="font-semibold text-gray-800">{item.name || item.id}</p>
+                        <p className="text-xs text-gray-500 mt-1 flex items-center gap-1">
+                          <Calendar className="w-3 h-3" /> {formatDate(item.created_at)}
+                        </p>
+                        <p className="text-xs text-blue-700 mt-1">{item.num_pages || item.num_files || 0} pages</p>
+                        <TagChips tags={item.tags} />
+                      </div>
+                    </div>
+
+                    <div className="mt-3 flex items-center gap-2">
+                      <button
+                        onClick={() => handleViewTranscript(item.id)}
+                        className="px-3 py-1.5 rounded-lg text-xs font-medium bg-blue-600 text-white hover:bg-blue-700 transition-colors flex items-center gap-1"
+                        disabled={detailLoading}
+                      >
+                        {detailLoading ? <Loader2 className="w-3.5 h-3.5 animate-spin" /> : <Eye className="w-3.5 h-3.5" />} View
+                      </button>
+                      <button
+                        onClick={() => handleDownload('transcripts', item.id)}
+                        className="px-3 py-1.5 rounded-lg text-xs font-medium bg-white text-blue-700 border border-blue-200 hover:bg-blue-50 transition-colors flex items-center gap-1"
+                      >
+                        <Download className="w-3.5 h-3.5" /> Download
+                      </button>
+                      <button
+                        onClick={() => handleDelete('transcripts', item.id)}
+                        className="px-3 py-1.5 rounded-lg text-xs font-medium bg-white text-red-600 border border-red-200 hover:bg-red-50 transition-colors flex items-center gap-1"
+                      >
+                        <Trash2 className="w-3.5 h-3.5" /> Delete
+                      </button>
+                    </div>
+                  </article>
+                ))}
+              </div>
+            </section>
+
+            <section className="bg-white rounded-2xl border border-emerald-100 shadow-sm overflow-hidden min-h-[72vh]">
+              <div className="px-5 py-4 border-b border-emerald-100 bg-gradient-to-r from-emerald-50 to-white flex items-center justify-between">
+                <h2 className="font-bold text-emerald-800 flex items-center gap-2">
+                  <Database className="w-5 h-5" /> Datasets
+                </h2>
+                <span className="text-xs font-semibold text-emerald-700 bg-emerald-100 px-2 py-1 rounded-full">
+                  {datasets.length}
+                </span>
+              </div>
+
+              <div className="p-4 space-y-3 h-[calc(72vh-4rem)] overflow-y-auto">
+                {datasets.length === 0 && (
+                  <EmptyState
+                    title="No saved datasets"
+                    description="Generated datasets are persisted here automatically."
+                    color="text-emerald-700 bg-emerald-50/50"
+                  />
+                )}
+
+                {datasets.map((item) => (
+                  <article key={item.id} className="border border-emerald-100 rounded-xl p-4 bg-emerald-50/40">
+                    <p className="font-semibold text-gray-800">{item.book_name || item.id}</p>
+                    <p className="text-xs text-gray-500 mt-1 flex items-center gap-1">
+                      <Calendar className="w-3 h-3" /> {formatDate(item.created_at)}
+                    </p>
+                    <p className="text-xs text-emerald-700 mt-1">
+                      {item.dataset_type || item.mode} • {item.num_samples || item.num_files || 0} samples • {item.format || 'unknown format'}
+                    </p>
+                    <TagChips tags={item.tags} />
+
+                    <div className="mt-3 flex items-center gap-2">
+                      <button
+                        onClick={() => handleViewDataset(item.id)}
+                        className="px-3 py-1.5 rounded-lg text-xs font-medium bg-emerald-600 text-white hover:bg-emerald-700 transition-colors flex items-center gap-1"
+                        disabled={detailLoading}
+                      >
+                        {detailLoading ? <Loader2 className="w-3.5 h-3.5 animate-spin" /> : <Eye className="w-3.5 h-3.5" />} View
+                      </button>
+                      <button
+                        onClick={() => handleDownload('datasets', item.id)}
+                        className="px-3 py-1.5 rounded-lg text-xs font-medium bg-white text-emerald-700 border border-emerald-200 hover:bg-emerald-50 transition-colors flex items-center gap-1"
+                      >
+                        <Download className="w-3.5 h-3.5" /> Download
+                      </button>
+                      <button
+                        onClick={() => handleDelete('datasets', item.id)}
+                        className="px-3 py-1.5 rounded-lg text-xs font-medium bg-white text-red-600 border border-red-200 hover:bg-red-50 transition-colors flex items-center gap-1"
+                      >
+                        <Trash2 className="w-3.5 h-3.5" /> Delete
+                      </button>
+                    </div>
+                  </article>
+                ))}
+              </div>
+            </section>
+          </div>
+        )}
+      </main>
+
+      {viewingTranscript && (
+        <div className="fixed inset-0 z-50 bg-gray-900/45 backdrop-blur-sm p-4 flex items-center justify-center">
+          <div className="w-full max-w-4xl max-h-[85vh] bg-white rounded-2xl shadow-2xl border border-gray-100 flex flex-col overflow-hidden">
+            <div className="px-4 py-3 border-b border-gray-100 flex items-center justify-between">
+              <div>
+                <p className="font-semibold text-gray-800">{viewingTranscript.metadata?.name || viewingTranscript.metadata?.id}</p>
+                <p className="text-xs text-gray-500">{formatDate(viewingTranscript.metadata?.created_at)}</p>
+                <TagChips tags={viewingTranscript.metadata?.tags} />
+              </div>
+              <button
+                onClick={() => setViewingTranscript(null)}
+                className="p-2 rounded-lg text-gray-500 hover:bg-gray-100"
+              >
+                <X className="w-4 h-4" />
+              </button>
+            </div>
+
+            <div className="p-4 overflow-y-auto space-y-3">
+              <div className="grid md:grid-cols-3 gap-2 text-xs text-gray-600 bg-gray-50 rounded-xl p-3 border border-gray-100">
+                <p><span className="font-semibold text-gray-700">Mode:</span> {viewingTranscript.metadata?.mode || 'n/a'}</p>
+                <p><span className="font-semibold text-gray-700">Pages:</span> {viewingTranscript.metadata?.num_pages || viewingTranscript.metadata?.num_files || 0}</p>
+                <p><span className="font-semibold text-gray-700">Source:</span> {viewingTranscript.metadata?.source || 'n/a'}</p>
+                {viewingTranscript.metadata?.model_info?.preprocessing?.length > 0 && (
+                  <p className="md:col-span-2"><span className="font-semibold text-gray-700">Preprocessing:</span> {viewingTranscript.metadata.model_info.preprocessing.join(', ')}</p>
+                )}
+                {viewingTranscript.metadata?.model_info?.detection_model && (
+                  <p><span className="font-semibold text-gray-700">Detection:</span> {viewingTranscript.metadata.model_info.detection_model}</p>
+                )}
+                {viewingTranscript.metadata?.model_info?.layout_model && (
+                  <p><span className="font-semibold text-gray-700">Layout:</span> {viewingTranscript.metadata.model_info.layout_model}</p>
+                )}
+                {(viewingTranscript.metadata?.model_info?.ocr_provider || viewingTranscript.metadata?.model_info?.ocr_model) && (
+                  <p><span className="font-semibold text-gray-700">OCR:</span> {viewingTranscript.metadata.model_info.ocr_provider} / {viewingTranscript.metadata.model_info.ocr_model}</p>
+                )}
+                {viewingTranscript.metadata?.model_info?.llm_postprocess && (
+                  <p><span className="font-semibold text-gray-700">LLM:</span> {viewingTranscript.metadata.model_info.llm_postprocess.used ? `${viewingTranscript.metadata.model_info.llm_postprocess.provider} / ${viewingTranscript.metadata.model_info.llm_postprocess.model}` : 'none'}</p>
+                )}
+                <p><span className="font-semibold text-gray-700">ID:</span> {viewingTranscript.metadata?.id}</p>
+              </div>
+
+              {(viewingTranscript.pages || []).map((page) => (
+                <article key={page.name} className="border border-blue-100 rounded-xl overflow-hidden">
+                  <div className="px-3 py-2 bg-blue-50 text-xs font-semibold text-blue-700">{page.name}</div>
+                  {(() => {
+                    const previewHeight = computeDynamicPreviewHeight(transcriptImageRatios[page.name]);
+                    return (
+                  <div className="grid md:grid-cols-2 items-stretch gap-0">
+                    <div className="border-b md:border-b-0 md:border-r border-blue-100 bg-slate-50" style={{ height: `${previewHeight}px` }}>
+                      {page.image_data ? (
+                        <img
+                          src={page.image_data}
+                          alt={page.name}
+                          className="w-full h-full object-contain"
+                          onLoad={(event) => handleTranscriptImageLoad(page.name, event)}
+                        />
+                      ) : (
+                        <div className="h-full flex flex-col items-center justify-center text-slate-500">
+                          <ImageIcon className="w-5 h-5 mb-1" />
+                          <p className="text-xs font-medium">No saved page image</p>
+                        </div>
+                      )}
+                    </div>
+                    <div className="p-3 text-sm text-gray-700 bg-white flex flex-col" style={{ height: `${previewHeight}px` }}>
+                      <p className="text-xs font-semibold text-gray-500 uppercase tracking-wider mb-1">Transcript</p>
+                      <pre className="whitespace-pre-wrap text-sm leading-relaxed flex-1 overflow-y-auto">{page.content}</pre>
+                    </div>
+                  </div>
+                    );
+                  })()}
+                </article>
+              ))}
+              {(!viewingTranscript.pages || viewingTranscript.pages.length === 0) && (
+                <p className="text-sm text-gray-500">No transcript pages found.</p>
+              )}
+            </div>
+          </div>
+        </div>
+      )}
+
+      {viewingDataset && (
+        <div className="fixed inset-0 z-50 bg-gray-900/45 backdrop-blur-sm p-4 flex items-center justify-center">
+          <div className="w-full max-w-5xl max-h-[86vh] bg-white rounded-2xl shadow-2xl border border-gray-100 flex flex-col overflow-hidden">
+            <div className="px-4 py-3 border-b border-gray-100 flex items-center justify-between">
+              <div>
+                <p className="font-semibold text-gray-800">{viewingDataset.metadata?.book_name || viewingDataset.metadata?.id}</p>
+                <p className="text-xs text-gray-500">{formatDate(viewingDataset.metadata?.created_at)}</p>
+                <TagChips tags={viewingDataset.metadata?.tags} />
+              </div>
+              <button
+                onClick={() => setViewingDataset(null)}
+                className="p-2 rounded-lg text-gray-500 hover:bg-gray-100"
+              >
+                <X className="w-4 h-4" />
+              </button>
+            </div>
+            <div className="p-4 space-y-4 overflow-y-auto">
+              <div className="grid md:grid-cols-3 gap-2 text-xs text-gray-600 bg-gray-50 rounded-xl p-3 border border-gray-100">
+                <p><span className="font-semibold text-gray-700">Mode:</span> {viewingDataset.metadata?.dataset_type || viewingDataset.metadata?.mode}</p>
+                <p><span className="font-semibold text-gray-700">Samples:</span> {viewingDataset.metadata?.num_samples || viewingDataset.metadata?.num_files || 0}</p>
+                <p><span className="font-semibold text-gray-700">Format:</span> {viewingDataset.metadata?.format || 'unknown'}</p>
+                {viewingDataset.metadata?.model_info?.preprocessing?.length > 0 && (
+                  <p className="md:col-span-2"><span className="font-semibold text-gray-700">Preprocessing:</span> {viewingDataset.metadata.model_info.preprocessing.join(', ')}</p>
+                )}
+                {viewingDataset.metadata?.model_info?.detection_model && (
+                  <p><span className="font-semibold text-gray-700">Detection:</span> {viewingDataset.metadata.model_info.detection_model}</p>
+                )}
+                {viewingDataset.metadata?.model_info?.layout_model && (
+                  <p><span className="font-semibold text-gray-700">Layout:</span> {viewingDataset.metadata.model_info.layout_model}</p>
+                )}
+                {(viewingDataset.metadata?.model_info?.ocr_provider || viewingDataset.metadata?.model_info?.ocr_model) && (
+                  <p><span className="font-semibold text-gray-700">OCR:</span> {viewingDataset.metadata.model_info.ocr_provider} / {viewingDataset.metadata.model_info.ocr_model}</p>
+                )}
+                {viewingDataset.metadata?.model_info?.llm_postprocess && (
+                  <p><span className="font-semibold text-gray-700">LLM:</span> {viewingDataset.metadata.model_info.llm_postprocess.used ? `${viewingDataset.metadata.model_info.llm_postprocess.provider} / ${viewingDataset.metadata.model_info.llm_postprocess.model}` : 'none'}</p>
+                )}
+                <p><span className="font-semibold text-gray-700">Source:</span> {viewingDataset.metadata?.source || 'n/a'}</p>
+                <p><span className="font-semibold text-gray-700">ID:</span> {viewingDataset.metadata?.id}</p>
+              </div>
+
+              <div className="space-y-3">
+                {(viewingDataset.samples || []).length === 0 && (
+                  <p className="text-sm text-gray-500">No dataset preview samples found.</p>
+                )}
+
+                {(viewingDataset.samples || []).map((sample) => (
+                  <article key={sample.name} className="border border-emerald-100 rounded-xl overflow-hidden">
+                    <div className="px-3 py-2 bg-emerald-50 text-xs font-semibold text-emerald-700">{sample.name}</div>
+                    {(() => {
+                      const previewHeight = computeDynamicPreviewHeight(datasetImageRatios[sample.name]);
+                      return (
+                    <div className="grid md:grid-cols-2 items-stretch">
+                      <div className="border-b md:border-b-0 md:border-r border-emerald-100 bg-slate-50" style={{ height: `${previewHeight}px` }}>
+                        {sample.image_data ? (
+                          <img
+                            src={sample.image_data}
+                            alt={sample.name}
+                            className="w-full h-full object-contain"
+                            onLoad={(event) => handleDatasetImageLoad(sample.name, event)}
+                          />
+                        ) : (
+                          <div className="h-full flex flex-col items-center justify-center text-slate-500">
+                            <ImageIcon className="w-5 h-5 mb-1" />
+                            <p className="text-xs font-medium">No sample image</p>
+                          </div>
+                        )}
+                      </div>
+                      <div className="p-3 text-sm text-gray-700 bg-white flex flex-col" style={{ height: `${previewHeight}px` }}>
+                        <p className="text-xs font-semibold text-gray-500 uppercase tracking-wider mb-1">Label / Annotation</p>
+                        <pre className="whitespace-pre-wrap text-sm leading-relaxed flex-1 overflow-y-auto">
+                          {sample.text || sample.annotation || 'No annotation text'}
+                        </pre>
+                      </div>
+                    </div>
+                      );
+                    })()}
+                  </article>
+                ))}
+              </div>
+            </div>
+          </div>
+        </div>
+      )}
+    </div>
+  );
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/pages/PageMatchReviewPage.jsx b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/pages/PageMatchReviewPage.jsx
new file mode 100644
index 00000000..507580f1
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/pages/PageMatchReviewPage.jsx
@@ -0,0 +1,619 @@
+import React, { useState, useMemo, useCallback, useEffect, useRef } from 'react';
+import {
+  ArrowLeft,
+  ArrowRight,
+  CheckCircle2,
+  XCircle,
+  Eye,
+  FileText,
+  Image,
+  Filter,
+  AlertTriangle,
+  Layers,
+  ChevronLeft,
+  ChevronRight,
+} from 'lucide-react';
+
+// Review which scanned page goes with which transcript page.
+//
+// Keys are normalised first ("3 - left", "3_left" and "3   left" all become
+// "3_left"), then matched exact-first, falling back to split-image/whole-
+// transcript and whole-image/split-transcript pairings.
+
+// Lowercase, delimiters collapsed to '_'.
+function normalizePageKey(raw) {
+  return String(raw)
+    .toLowerCase()
+    .replace(/\s*[-–—]\s*/g, '_')   // "3 - left" → "3_left"
+    .replace(/\s+/g, '_')           // spaces  → underscore
+    .replace(/_+/g, '_')            // collapse multiple underscores
+    .replace(/^_|_$/g, '')          // strip leading/trailing underscores
+    .trim();
+}
+
+// -> { num, side: 'left' | 'right' | null }
+function parseNormKey(normKey) {
+  const m = normKey.match(/^(\d+)(?:_(left|right))?$/);
+  if (!m) return null;
+  return { num: m[1], side: m[2] || null };
+}
+
+export default function PageMatchReviewPage({ pages, parsedTranscript, onBack, onNext }) {
+  const [filter, setFilter] = useState('all'); // 'all' | 'matched' | 'unmatched'
+  const [selectedPageNum, setSelectedPageNum] = useState(null);
+  const [detailTab, setDetailTab] = useState('both'); // 'image' | 'transcript' | 'both'
+
+  // pageNumber -> string[]; undefined means not seeded yet.
+  const [editedLines, setEditedLines] = useState({});
+  const initializedRef = useRef(false);
+
+  // ── Build the match table ──
+  const matchData = useMemo(() => {
+    if (!pages || !parsedTranscript) return [];
+
+    // Two indexes: exact normalized key, and a per-number bucket holding the
+    // plain/left/right variants.
+    const exactByNorm = {};
+    const byNumber = {};
+
+    Object.keys(parsedTranscript).forEach((k) => {
+      const norm = normalizePageKey(k);
+      exactByNorm[norm] = k;
+
+      const parsed = parseNormKey(norm);
+      if (!parsed) return;
+      const { num, side } = parsed;
+      if (!byNumber[num]) byNumber[num] = { plain: null, left: null, right: null };
+      if (side === 'left')  byNumber[num].left  = k;
+      else if (side === 'right') byNumber[num].right = k;
+      else                  byNumber[num].plain = k;
+    });
+
+    // -> { transcriptKeys, lines, matchType } or null.
+    const resolve = (page) => {
+      const norm = normalizePageKey(page.pageNumber);
+      const parsed = parseNormKey(norm);
+
+      if (exactByNorm[norm]) {
+        const tk = exactByNorm[norm];
+        return { transcriptKeys: [tk], lines: parsedTranscript[tk], matchType: 'exact' };
+      }
+
+      if (!parsed) return null;
+      const { num, side } = parsed;
+      const bucket = byNumber[num];
+      if (!bucket) return null;
+
+      if (side) {
+        // Split image against a whole transcript page: "3_left" -> "3".
+        if (bucket.plain) {
+          return {
+            transcriptKeys: [bucket.plain],
+            lines: parsedTranscript[bucket.plain],
+            matchType: 'split-image→unsplit-transcript',
+          };
+        }
+        // Only the opposite side exists — that's a mismatch, so skip it.
+      } else {
+        // Whole image against a split transcript: combine left + right.
+        if (bucket.left && bucket.right) {
+          return {
+            transcriptKeys: [bucket.left, bucket.right],
+            lines: [
+              ...parsedTranscript[bucket.left],
+              ...parsedTranscript[bucket.right],
+            ],
+            matchType: 'unsplit-image→combined-transcript',
+          };
+        }
+        if (bucket.left) {
+          return { transcriptKeys: [bucket.left], lines: parsedTranscript[bucket.left], matchType: 'partial-left' };
+        }
+        if (bucket.right) {
+          return { transcriptKeys: [bucket.right], lines: parsedTranscript[bucket.right], matchType: 'partial-right' };
+        }
+      }
+      return null;
+    };
+
+    return pages.map((page) => {
+      const result = resolve(page);
+      return {
+        pageNumber: page.pageNumber,
+        thumbnail: page.thumbnail,
+        isSplit: page.isSplit || false,
+        splitSide: page.splitSide || null,
+        transcriptKeys: result?.transcriptKeys || null,
+        lines: result?.lines || null,
+        matchType: result?.matchType || null,
+        matched: !!result,
+      };
+    });
+  }, [pages, parsedTranscript]);
+
+  // Seed once, when matchData first lands.
+  useEffect(() => {
+    if (matchData.length > 0 && !initializedRef.current) {
+      initializedRef.current = true;
+      setEditedLines((prev) => {
+        if (Object.keys(prev).length > 0) return prev; // already seeded
+        const seed = {};
+        matchData.forEach((d) => {
+          seed[d.pageNumber] = d.matched && d.lines ? [...d.lines] : [];
+        });
+        return seed;
+      });
+    }
+  }, [matchData]);
+
+  const handleLinesChange = useCallback((pageNumber, newLines) => {
+    setEditedLines((prev) => ({ ...prev, [pageNumber]: newLines }));
+  }, []);
+
+  const matchedCount = matchData.filter((d) => d.matched).length;
+  const unmatchedCount = matchData.filter((d) => !d.matched).length;
+
+  const usedTranscriptKeys = useMemo(() => {
+    const used = new Set();
+    matchData.forEach((d) => (d.transcriptKeys || []).forEach((k) => used.add(k)));
+    return used;
+  }, [matchData]);
+  const transcriptOnlyCount = Object.keys(parsedTranscript || {}).filter(
+    (k) => !usedTranscriptKeys.has(k)
+  ).length;
+
+  const canContinue = matchData.some(
+    (d) => d.matched || (editedLines[d.pageNumber] || []).some((l) => l.trim())
+  );
+
+  // ── Filtered list ──
+  const filteredData = useMemo(() => {
+    if (filter === 'matched') return matchData.filter((d) => d.matched);
+    if (filter === 'unmatched') return matchData.filter((d) => !d.matched);
+    return matchData;
+  }, [matchData, filter]);
+
+  React.useEffect(() => {
+    if (selectedPageNum === null && filteredData.length > 0) {
+      setSelectedPageNum(filteredData[0].pageNumber);
+    }
+  }, [filteredData, selectedPageNum]);
+
+  const selectedEntry = matchData.find((d) => d.pageNumber === selectedPageNum);
+  const filteredIdx = filteredData.findIndex((d) => d.pageNumber === selectedPageNum);
+
+  const navigatePage = useCallback((dir) => {
+    const newIdx = filteredIdx + dir;
+    if (newIdx >= 0 && newIdx < filteredData.length) {
+      setSelectedPageNum(filteredData[newIdx].pageNumber);
+    }
+  }, [filteredIdx, filteredData]);
+
+  const handleContinue = () => {
+    // Unmatched pages count too if the user typed a transcript for them.
+    const selectedPages = matchData
+      .filter((d) => d.matched || (editedLines[d.pageNumber] || []).some((l) => l.trim()))
+      .map((d) => d.pageNumber);
+
+    // User edits win over the parsed lines.
+    const mergedTranscript = {};
+    matchData.forEach((d) => {
+      const edited = editedLines[d.pageNumber];
+      const nonEmpty = edited ? edited.filter((l) => l.trim()) : [];
+      if (nonEmpty.length > 0) {
+        mergedTranscript[String(d.pageNumber)] = nonEmpty;
+      } else if (d.matched && d.lines) {
+        mergedTranscript[String(d.pageNumber)] = d.lines;
+      }
+    });
+
+    onNext(selectedPages, mergedTranscript);
+  };
+
+  return (
+    <div className="flex flex-col h-full w-full overflow-hidden bg-white">
+      {/* ── Header ──────────────────────────────────────────────────── */}
+      <div className="flex items-center justify-between px-6 py-4 border-b border-gray-100 bg-white/90 backdrop-blur-sm flex-shrink-0 shadow-sm">
+        <div className="flex items-center gap-4">
+          <button
+            onClick={onBack}
+            className="flex items-center gap-2 px-3 py-2 text-sm font-medium text-gray-500 hover:text-gray-800 hover:bg-gray-100 rounded-lg transition-all duration-150"
+          >
+            <ArrowLeft className="w-4 h-4" />
+            Back
+          </button>
+          <div className="w-px h-6 bg-gray-200" />
+          <div className="flex items-center gap-3">
+            <div className="p-2 bg-gradient-to-br from-emerald-500 to-teal-600 rounded-lg text-white shadow-md shadow-emerald-500/20">
+              <Layers className="w-4 h-4" />
+            </div>
+            <div>
+              <h1 className="text-base font-bold text-gray-800">Review Page Matches</h1>
+              <p className="text-xs text-gray-500">Only matched pages will proceed to preprocessing</p>
+            </div>
+          </div>
+        </div>
+
+        {/* Stats */}
+        <div className="flex items-center gap-3">
+          <StatPill count={matchData.length} label="Total Pages" color="gray" />
+          <StatPill count={matchedCount} label="Matched" color="emerald" />
+          {unmatchedCount > 0 && <StatPill count={unmatchedCount} label="Unmatched" color="amber" />}
+          {transcriptOnlyCount > 0 && <StatPill count={transcriptOnlyCount} label="Transcript only" color="blue" />}
+        </div>
+
+        {/* Continue button */}
+        <button
+          onClick={handleContinue}
+          disabled={!canContinue}
+          className={`flex items-center gap-2 px-6 py-2.5 rounded-xl font-bold text-sm transition-all duration-200 ${
+            canContinue
+              ? 'bg-gradient-to-r from-emerald-500 to-teal-600 text-white shadow-lg shadow-emerald-500/25 hover:shadow-xl hover:-translate-y-0.5'
+              : 'bg-gray-100 text-gray-400 cursor-not-allowed'
+          }`}
+        >
+          Continue to Preprocessing
+          <ArrowRight className="w-4 h-4" />
+        </button>
+      </div>
+
+      {/* ── Mismatch warning ──────────────────────────────────────────── */}
+      {unmatchedCount > 0 && (
+        <div className="flex items-center gap-3 px-6 py-2.5 bg-amber-50 border-b border-amber-100 flex-shrink-0">
+          <AlertTriangle className="w-4 h-4 text-amber-500 flex-shrink-0" />
+          <p className="text-xs text-amber-700">
+            <span className="font-semibold">{unmatchedCount} page{unmatchedCount !== 1 ? 's' : ''}</span> have no matching transcript entry and will be skipped.
+            {transcriptOnlyCount > 0 && (
+              <span className="ml-2 text-amber-600">
+                {transcriptOnlyCount} transcript page{transcriptOnlyCount !== 1 ? 's' : ''} have no matching image.
+              </span>
+            )}
+          </p>
+        </div>
+      )}
+
+      {/* ── Body: thumbnail rail + detail panel ─────────────────────── */}
+      <div className="flex-1 flex overflow-hidden">
+        {/* LEFT: Thumbnail rail */}
+        <div className="w-52 flex-shrink-0 border-r border-gray-100 flex flex-col overflow-hidden bg-gray-50/60">
+          {/* Filter tabs */}
+          <div className="flex gap-1 p-2 flex-shrink-0 border-b border-gray-100 bg-white">
+            {[
+              { key: 'all', label: 'All', count: matchData.length },
+              { key: 'matched', label: 'Matched', count: matchedCount },
+              { key: 'unmatched', label: 'Skip', count: unmatchedCount },
+            ].map((tab) => (
+              <button
+                key={tab.key}
+                onClick={() => { setFilter(tab.key); setSelectedPageNum(null); }}
+                className={`flex-1 py-1.5 px-1 text-[10px] font-semibold rounded-lg transition-all flex flex-col items-center gap-0.5 ${
+                  filter === tab.key
+                    ? tab.key === 'unmatched'
+                      ? 'bg-amber-100 text-amber-700'
+                      : 'bg-emerald-100 text-emerald-700'
+                    : 'text-gray-500 hover:bg-gray-100'
+                }`}
+              >
+                <span className="text-sm font-bold">{tab.count}</span>
+                {tab.label}
+              </button>
+            ))}
+          </div>
+
+          {/* Page list */}
+          <div className="flex-1 overflow-y-auto py-2 px-2 space-y-1.5">
+            {filteredData.map((entry) => (
+              <button
+                key={entry.pageNumber}
+                onClick={() => setSelectedPageNum(entry.pageNumber)}
+                className={`w-full rounded-xl overflow-hidden border-2 transition-all duration-150 text-left group ${
+                  selectedPageNum === entry.pageNumber
+                    ? entry.matched
+                      ? 'border-emerald-400 shadow-md shadow-emerald-500/20 bg-white'
+                      : 'border-amber-400 shadow-md shadow-amber-500/20 bg-white'
+                    : 'border-transparent hover:border-gray-200 bg-white/80 hover:shadow-sm'
+                }`}
+              >
+                {/* Thumbnail */}
+                <div className="relative w-full aspect-[3/4] bg-gray-100 overflow-hidden">
+                  {entry.thumbnail ? (
+                    <img
+                      src={entry.thumbnail}
+                      alt={`Page ${entry.pageNumber}`}
+                      className="w-full h-full object-cover"
+                    />
+                  ) : (
+                    <div className="w-full h-full flex items-center justify-center">
+                      <Image className="w-6 h-6 text-gray-400" />
+                    </div>
+                  )}
+                  {/* Match badge overlay */}
+                  <div className={`absolute top-1.5 right-1.5 w-5 h-5 rounded-full flex items-center justify-center shadow ${
+                    entry.matched ? 'bg-emerald-500' : 'bg-amber-400'
+                  }`}>
+                    {entry.matched
+                      ? <CheckCircle2 className="w-3.5 h-3.5 text-white" />
+                      : <XCircle className="w-3.5 h-3.5 text-white" />
+                    }
+                  </div>
+                  {/* Split side badge */}
+                  {entry.isSplit && (
+                    <div className="absolute bottom-1 left-1 px-1.5 py-0.5 bg-black/50 text-white text-[9px] font-bold rounded uppercase tracking-wide">
+                      {entry.splitSide}
+                    </div>
+                  )}
+                </div>
+                {/* Page number */}
+                <div className="px-2 py-1.5 flex items-center justify-between">
+                  <span className="text-xs font-bold text-gray-700 truncate">
+                    {formatPageLabel(entry.pageNumber)}
+                  </span>
+                  {entry.matched
+                    ? <span className="text-[10px] text-emerald-600 font-semibold flex-shrink-0">{(editedLines[entry.pageNumber] ?? entry.lines)?.length ?? 0}L</span>
+                    : (editedLines[entry.pageNumber] || []).some((l) => l.trim())
+                      ? <span className="text-[10px] text-indigo-600 font-semibold flex-shrink-0">{(editedLines[entry.pageNumber] || []).filter((l) => l.trim()).length}L</span>
+                      : <span className="text-[10px] text-amber-500 font-semibold flex-shrink-0">skip</span>
+                  }
+                </div>
+              </button>
+            ))}
+          </div>
+        </div>
+
+        {/* RIGHT: Detail panel */}
+        <div className="flex-1 flex flex-col overflow-hidden">
+          {selectedEntry ? (
+            <>
+              {/* Detail header */}
+              <div className="flex items-center justify-between px-6 py-3 border-b border-gray-100 flex-shrink-0 bg-white/80">
+                <div className="flex items-center gap-3 flex-wrap">
+                  <div className={`flex items-center gap-1.5 px-3 py-1 rounded-full text-xs font-bold ${
+                    selectedEntry.matched
+                      ? 'bg-emerald-100 text-emerald-700'
+                      : 'bg-amber-100 text-amber-700'
+                  }`}>
+                    {selectedEntry.matched
+                      ? <><CheckCircle2 className="w-3.5 h-3.5" /> Matched</>
+                      : <><XCircle className="w-3.5 h-3.5" /> No transcript</>
+                    }
+                  </div>
+                  <h2 className="font-bold text-gray-700 text-sm">
+                    {formatPageLabel(selectedEntry.pageNumber)}
+                  </h2>
+                  {selectedEntry.matched && (
+                    <span className="text-xs text-gray-400">
+                      {(editedLines[selectedEntry.pageNumber] ?? selectedEntry.lines)?.length ?? 0} lines
+                    </span>
+                  )}
+                  {selectedEntry.matched && selectedEntry.matchType && selectedEntry.matchType !== 'exact' && (
+                    <MatchTypePill matchType={selectedEntry.matchType} transcriptKeys={selectedEntry.transcriptKeys} />
+                  )}
+                </div>
+
+                <div className="flex items-center gap-2">
+                  {/* Tab selector */}
+                  <div className="flex bg-gray-100 rounded-lg p-0.5 gap-0.5">
+                    {[
+                      { key: 'both', label: 'Both', icon: Eye },
+                      { key: 'image', label: 'Image', icon: Image },
+                      { key: 'transcript', label: 'Text', icon: FileText },
+                    ].map(({ key, label, icon: Icon }) => (
+                      <button
+                        key={key}
+                        onClick={() => setDetailTab(key)}
+                        className={`flex items-center gap-1.5 px-3 py-1.5 text-xs font-semibold rounded-md transition-all ${
+                          detailTab === key
+                            ? 'bg-white text-gray-800 shadow-sm'
+                            : 'text-gray-500 hover:text-gray-700'
+                        }`}
+                      >
+                        <Icon className="w-3.5 h-3.5" />
+                        {label}
+                      </button>
+                    ))}
+                  </div>
+
+                  {/* Navigate */}
+                  <div className="flex items-center gap-1 ml-2">
+                    <button
+                      onClick={() => navigatePage(-1)}
+                      disabled={filteredIdx === 0}
+                      className="p-1.5 rounded-lg hover:bg-gray-100 disabled:opacity-30 transition-colors"
+                    >
+                      <ChevronLeft className="w-4 h-4" />
+                    </button>
+                    <span className="text-xs text-gray-400 min-w-[48px] text-center">
+                      {filteredIdx + 1} / {filteredData.length}
+                    </span>
+                    <button
+                      onClick={() => navigatePage(1)}
+                      disabled={filteredIdx >= filteredData.length - 1}
+                      className="p-1.5 rounded-lg hover:bg-gray-100 disabled:opacity-30 transition-colors"
+                    >
+                      <ChevronRight className="w-4 h-4" />
+                    </button>
+                  </div>
+                </div>
+              </div>
+
+              {/* Detail content */}
+              <div className="flex-1 overflow-hidden">
+                {detailTab === 'both' ? (
+                  <div className="h-full grid grid-cols-2 gap-0">
+                    <ImagePanel entry={selectedEntry} />
+                    <TranscriptPanel entry={selectedEntry} editedLines={editedLines} onLinesChange={handleLinesChange} />
+                  </div>
+                ) : detailTab === 'image' ? (
+                  <div className="h-full">
+                    <ImagePanel entry={selectedEntry} fullHeight />
+                  </div>
+                ) : (
+                  <div className="h-full">
+                    <TranscriptPanel entry={selectedEntry} fullHeight editedLines={editedLines} onLinesChange={handleLinesChange} />
+                  </div>
+                )}
+              </div>
+            </>
+          ) : (
+            <div className="flex-1 flex flex-col items-center justify-center text-gray-400 gap-3">
+              <div className="w-16 h-16 bg-gray-100 rounded-2xl flex items-center justify-center">
+                <Eye className="w-8 h-8 text-gray-300" />
+              </div>
+              <p className="text-sm font-medium">Select a page to preview</p>
+            </div>
+          )}
+        </div>
+      </div>
+    </div>
+  );
+}
+
+// ── Sub-components ──
+
+function ImagePanel({ entry, fullHeight }) {
+  return (
+    <div className={`flex flex-col bg-gray-50 border-r border-gray-100 overflow-hidden ${fullHeight ? 'h-full' : ''}`}>
+      <div className="px-4 py-2.5 flex-shrink-0 border-b border-gray-100 bg-white/70 flex items-center gap-2">
+        <Image className="w-3.5 h-3.5 text-gray-400" />
+        <span className="text-xs font-semibold text-gray-600">Book Image</span>
+        {entry.isSplit && (
+          <span className={`ml-1 px-2 py-0.5 text-[10px] font-bold rounded-full uppercase tracking-wide ${
+            entry.splitSide === 'left' ? 'bg-indigo-100 text-indigo-600' : 'bg-violet-100 text-violet-600'
+          }`}>
+            {entry.splitSide} half
+          </span>
+        )}
+        <span className="ml-auto text-xs text-gray-400 font-mono">{formatPageLabel(entry.pageNumber)}</span>
+      </div>
+      <div className="flex-1 overflow-auto flex items-start justify-center p-4">
+        {entry.thumbnail ? (
+          <img
+            src={entry.thumbnail}
+            alt={`Page ${entry.pageNumber}`}
+            className="max-w-full max-h-full object-contain rounded-lg shadow-md"
+          />
+        ) : (
+          <div className="flex flex-col items-center justify-center gap-3 text-gray-400 h-full">
+            <Image className="w-12 h-12" />
+            <p className="text-sm">No image available</p>
+          </div>
+        )}
+      </div>
+    </div>
+  );
+}
+
+function TranscriptPanel({ entry, fullHeight, editedLines = {}, onLinesChange }) {
+  const lines = editedLines[entry.pageNumber] ?? (entry.lines ? [...entry.lines] : []);
+  const lineCount = lines.filter((l) => l.trim()).length;
+  const hasCustom = !entry.matched && lines.some((l) => l.trim());
+
+  const handleTextChange = (e) => {
+    const next = e.target.value.split('\n');
+    onLinesChange?.(entry.pageNumber, next);
+  };
+
+  return (
+    <div className={`flex flex-col overflow-hidden ${fullHeight ? 'h-full' : ''}`}>
+      {/* Panel header */}
+      <div className="px-4 py-2.5 flex-shrink-0 border-b border-gray-100 bg-white/70 flex items-center gap-2">
+        <FileText className="w-3.5 h-3.5 text-gray-400" />
+        <span className="text-xs font-semibold text-gray-600">Transcript</span>
+        {entry.matched && entry.transcriptKeys && (
+          <div className="flex items-center gap-1 ml-1">
+            {entry.transcriptKeys.map((k) => (
+              <span key={k} className="px-1.5 py-0.5 bg-emerald-50 text-emerald-600 text-[10px] font-mono font-bold rounded border border-emerald-100">
+                {k}
+              </span>
+            ))}
+          </div>
+        )}
+        {hasCustom && (
+          <span className="px-1.5 py-0.5 bg-indigo-50 text-indigo-600 text-[10px] font-semibold rounded-full ml-1">
+            Custom
+          </span>
+        )}
+        <span className="ml-auto text-xs text-gray-400">{lineCount} lines</span>
+      </div>
+
+      {entry.matched && entry.matchType && entry.matchType !== 'exact' && (
+        <div className="px-4 py-2 bg-blue-50/60 border-b border-blue-100 flex items-center gap-2 flex-shrink-0">
+          <span className="text-[10px] text-blue-600 font-medium">
+            {matchTypeHint(entry.matchType, entry.transcriptKeys)}
+          </span>
+        </div>
+      )}
+
+      {/* Notepad-style textarea */}
+      <div className="flex-1 overflow-hidden p-3">
+        <textarea
+          value={lines.join('\n')}
+          onChange={handleTextChange}
+          spellCheck={false}
+          placeholder={entry.matched
+            ? 'Edit transcript lines here…'
+            : 'Type transcript for this page to include it in the pipeline…'}
+          className="w-full h-full resize-none text-sm text-gray-700 leading-relaxed font-mono bg-gray-50 border border-gray-200 rounded-xl p-4 focus:outline-none focus:ring-2 focus:ring-indigo-200 focus:border-indigo-300 placeholder-gray-300 transition-all"
+        />
+      </div>
+    </div>
+  );
+}
+
+// ── Helpers ──
+
+// "3_left" -> "Page 3 · Left"
+function formatPageLabel(pageNumber) {
+  const s = String(pageNumber);
+  const m = s.match(/^(\d+)(?:_(left|right))?$/i);
+  if (!m) return `Page ${s}`;
+  const num = m[1];
+  const side = m[2];
+  if (side) return `Page ${num} · ${side.charAt(0).toUpperCase() + side.slice(1)}`;
+  return `Page ${num}`;
+}
+
+
+function matchTypeHint(matchType, transcriptKeys) {
+  if (matchType === 'split-image→unsplit-transcript') {
+    return `Split image half matched to unsplit transcript entry "${transcriptKeys?.[0]}"`;
+  }
+  if (matchType === 'unsplit-image→combined-transcript') {
+    return `Unsplit image matched by combining transcript entries: ${(transcriptKeys || []).join(' + ')}`;
+  }
+  if (matchType === 'partial-left') {
+    return `Only left-side transcript entry "${transcriptKeys?.[0]}" found`;
+  }
+  if (matchType === 'partial-right') {
+    return `Only right-side transcript entry "${transcriptKeys?.[0]}" found`;
+  }
+  return matchType;
+}
+
+function MatchTypePill({ matchType, transcriptKeys }) {
+  const isCombined = matchType === 'unsplit-image→combined-transcript';
+  const isAdapted = matchType?.includes('→');
+  return (
+    <span className={`inline-flex items-center gap-1 px-2 py-0.5 rounded-full text-[10px] font-semibold ${
+      isCombined ? 'bg-purple-100 text-purple-700' : 'bg-blue-100 text-blue-700'
+    }`}>
+      {isCombined ? '⇔ Combined' : isAdapted ? '≈ Adapted' : matchType}
+    </span>
+  );
+}
+
+function StatPill({ count, label, color }) {
+  const colors = {
+    gray: 'bg-gray-100 text-gray-600',
+    emerald: 'bg-emerald-100 text-emerald-700',
+    amber: 'bg-amber-100 text-amber-700',
+    blue: 'bg-blue-100 text-blue-700',
+  };
+  return (
+    <div className={`flex items-center gap-1.5 px-3 py-1.5 rounded-full text-xs font-bold ${colors[color]}`}>
+      <span className="text-sm font-black">{count}</span>
+      {label}
+    </div>
+  );
+}
+
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/services/api.js b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/services/api.js
new file mode 100644
index 00000000..da8713c5
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/services/api.js
@@ -0,0 +1,50 @@
+// Preprocessing calls into the Python/OpenCV backend.
+
+import { API_ORIGIN } from '../config';
+import { toDataUrl, dataUrlToObjectUrl } from '../utils/imageUrl';
+
+const API_BASE = API_ORIGIN;
+
+// Runs the pipeline server-side. Returns the original image URL on failure so
+// the editor keeps showing something instead of blanking out.
+export async function preprocessImage(imageUrl, pipeline) {
+  try {
+    const operations = pipeline.map(step => ({
+      op: step.op,
+      params: step.params || {},
+      enabled: true,
+    }));
+
+    const response = await fetch(`${API_BASE}/api/preprocess`, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json',
+      },
+      body: JSON.stringify({
+        // Page images are blob: object URLs in app state; the backend wants base64.
+        image_data: await toDataUrl(imageUrl),
+        operations: operations,
+        preview_mode: false,
+      }),
+    });
+
+    if (!response.ok) {
+      const error = await response.json().catch(() => ({ detail: 'Unknown error' }));
+      throw new Error(error.detail?.message || error.detail || 'Preprocessing failed');
+    }
+
+    const result = await response.json();
+
+    if (!result.success) {
+      console.warn('Preprocessing had errors:', result.errors);
+    }
+
+    // Re-wrap as an object URL — keeping base64 for every page of a large book
+    // bloats the JS heap.
+    return result.processed_image ? dataUrlToObjectUrl(result.processed_image) : imageUrl;
+
+  } catch (error) {
+    console.error('Preprocessing API error:', error);
+    return imageUrl;
+  }
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/services/llmApi.js b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/services/llmApi.js
new file mode 100644
index 00000000..a4b1b0dd
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/services/llmApi.js
@@ -0,0 +1,48 @@
+// LLM post-processing client — cleans up raw OCR text.
+// Provider (gemini/openai/deepseek/qwen) is chosen server-side from the id.
+
+import { API_ORIGIN } from '../config';
+
+const API_BASE = `${API_ORIGIN}/api`;
+
+export async function getLLMTemplates() {
+  const response = await fetch(`${API_BASE}/llm/templates`);
+  if (!response.ok) {
+    throw new Error('Failed to fetch LLM templates');
+  }
+  return response.json();
+}
+
+// -> { providers: [{ id, name, enabled, default_model, models, note? }] }
+export async function getLLMProviders() {
+  const response = await fetch(`${API_BASE}/llm/providers`);
+  if (!response.ok) {
+    throw new Error('Failed to fetch LLM providers');
+  }
+  return response.json();
+}
+
+// -> { success, processed_text?, error? }
+export async function postProcessWithLLMProvider(
+  provider,
+  apiKey,
+  text,
+  model,
+  template = 'full_cleanup',
+) {
+  const response = await fetch(`${API_BASE}/llm/post-process`, {
+    method: 'POST',
+    headers: {
+      'Content-Type': 'application/json',
+      'X-LLM-API-Key': apiKey,
+    },
+    body: JSON.stringify({ provider, text, model, template }),
+  });
+
+  if (!response.ok) {
+    const err = await response.json().catch(() => ({ detail: 'LLM processing failed' }));
+    throw new Error(err.detail || 'LLM processing failed');
+  }
+
+  return response.json();
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/services/storageApi.js b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/services/storageApi.js
new file mode 100644
index 00000000..7aaf9c80
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/services/storageApi.js
@@ -0,0 +1,113 @@
+import { API_ORIGIN } from '../config';
+
+const API_BASE = `${API_ORIGIN}/api/storage`;
+
+export async function fetchStorageOverview() {
+  const response = await fetch(`${API_BASE}/overview`);
+  if (!response.ok) {
+    throw new Error('Failed to load stored files');
+  }
+  return response.json();
+}
+
+export async function saveTranscriptSession(
+  transcripts,
+  source = 'ocr upload',
+  mode = 'recognition',
+  transcriptImages = {},
+  bookName = 'transcript',
+  modelInfo = {},
+) {
+  const response = await fetch(`${API_BASE}/transcripts`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify({
+      transcripts,
+      transcript_images: transcriptImages,
+      source,
+      mode,
+      book_name: bookName,
+      model_info: modelInfo,
+    }),
+  });
+
+  if (!response.ok) {
+    const err = await response.json().catch(() => ({ detail: 'Failed to save transcript' }));
+    throw new Error(err.detail || 'Failed to save transcript');
+  }
+
+  return response.json();
+}
+
+export async function saveDatasetToMyFiles(pages, { source = 'dataset generation', bookName = 'dataset', bboxFormat = 'txt', mode = 'recognition', modelInfo = {} } = {}) {
+  const response = await fetch(`${API_BASE}/datasets`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify({
+      pages,
+      source,
+      book_name: bookName,
+      bbox_format: bboxFormat,
+      mode,
+      model_info: modelInfo,
+    }),
+  });
+
+  if (!response.ok) {
+    const err = await response.json().catch(() => ({ detail: 'Failed to save dataset' }));
+    throw new Error(err.detail || 'Failed to save dataset');
+  }
+
+  return response.json();
+}
+
+export async function fetchTranscriptDetail(sessionId) {
+  const response = await fetch(`${API_BASE}/transcripts/${encodeURIComponent(sessionId)}`);
+  if (!response.ok) {
+    const err = await response.json().catch(() => ({ detail: 'Failed to load transcript details' }));
+    throw new Error(err.detail || 'Failed to load transcript details');
+  }
+  return response.json();
+}
+
+export async function fetchDatasetDetail(datasetId) {
+  const response = await fetch(`${API_BASE}/datasets/${encodeURIComponent(datasetId)}`);
+  if (!response.ok) {
+    const err = await response.json().catch(() => ({ detail: 'Failed to load dataset details' }));
+    throw new Error(err.detail || 'Failed to load dataset details');
+  }
+  return response.json();
+}
+
+export async function deleteStoredItem(kind, id) {
+  const response = await fetch(`${API_BASE}/${encodeURIComponent(kind)}/${encodeURIComponent(id)}`, {
+    method: 'DELETE',
+  });
+
+  if (!response.ok) {
+    const err = await response.json().catch(() => ({ detail: 'Delete failed' }));
+    throw new Error(err.detail || 'Delete failed');
+  }
+
+  return response.json();
+}
+
+export async function downloadStoredItem(kind, id) {
+  const response = await fetch(`${API_BASE}/download/${encodeURIComponent(kind)}/${encodeURIComponent(id)}`);
+  if (!response.ok) {
+    const err = await response.json().catch(() => ({ detail: 'Download failed' }));
+    throw new Error(err.detail || 'Download failed');
+  }
+  return response.blob();
+}
+
+export function downloadBlob(blob, filename) {
+  const url = URL.createObjectURL(blob);
+  const a = document.createElement('a');
+  a.href = url;
+  a.download = filename;
+  document.body.appendChild(a);
+  a.click();
+  document.body.removeChild(a);
+  URL.revokeObjectURL(url);
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/utils/imageUrl.js b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/utils/imageUrl.js
new file mode 100644
index 00000000..73c59149
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/utils/imageUrl.js
@@ -0,0 +1,36 @@
+// Page images live in state as blob: object URLs, not data URLs. A data URL
+// parks the whole encoded image on the JS heap for every page of the book,
+// which is what made large books crawl. Convert to base64 only at request time.
+
+// blob: / http(s): / data: -> base64 data URL.
+export async function toDataUrl(url) {
+    if (!url) return null;
+    if (url.startsWith('data:')) return url;
+    const blob = await (await fetch(url)).blob();
+    const bytes = new Uint8Array(await blob.arrayBuffer());
+    let bin = '';
+    const CHUNK = 0x8000; // avoid call-stack limits on large images
+    for (let i = 0; i < bytes.length; i += CHUNK) {
+        bin += String.fromCharCode(...bytes.subarray(i, i + CHUNK));
+    }
+    return `data:${blob.type || 'image/png'};base64,${btoa(bin)}`;
+}
+
+// data URL -> object URL. Anything else passes through. The caller owns the
+// result and must revoke it.
+export function dataUrlToObjectUrl(dataUrl) {
+    if (!dataUrl || typeof dataUrl !== 'string' || !dataUrl.startsWith('data:')) return dataUrl;
+    const [header, b64] = dataUrl.split(',');
+    const mime = header.match(/:(.*?);/)?.[1] || 'image/png';
+    const bin = atob(b64);
+    const u8 = new Uint8Array(bin.length);
+    for (let i = 0; i < bin.length; i++) u8[i] = bin.charCodeAt(i);
+    return URL.createObjectURL(new Blob([u8], { type: mime }));
+}
+
+
+export function revokeObjectUrls(urls) {
+    for (const url of urls || []) {
+        if (typeof url === 'string' && url.startsWith('blob:')) URL.revokeObjectURL(url);
+    }
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/workers/pdfExtractor.worker.js b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/workers/pdfExtractor.worker.js
new file mode 100644
index 00000000..7e1aa823
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/src/workers/pdfExtractor.worker.js
@@ -0,0 +1,121 @@
+// Renders PDF pages on an OffscreenCanvas, off the main thread, so a hidden
+// tab doesn't throttle extraction to a crawl.
+//
+// In:  { type: 'extract', id, fileBuffer, options }
+// Out: 'page' (streamed) | 'progress' (0-100) | 'done' | 'error'
+
+import * as pdfjsLib from 'pdfjs-dist';
+// Vite resolves this URL at build time and bundles the worker.
+import workerUrl from 'pdfjs-dist/build/pdf.worker.min.mjs?url';
+
+pdfjsLib.GlobalWorkerOptions.workerSrc = workerUrl;
+
+// PDF.js's default canvas factory reaches for `document`, which doesn't exist
+// in a worker. Back it with OffscreenCanvas instead.
+class OffscreenCanvasFactory {
+  create(width, height) {
+    if (width <= 0 || height <= 0) {
+      throw new Error('Invalid canvas size');
+    }
+    const canvas = new OffscreenCanvas(width, height);
+    const context = canvas.getContext('2d');
+    return { canvas, context };
+  }
+  reset(canvasAndContext, width, height) {
+    if (!canvasAndContext.canvas) throw new Error('Canvas is not specified');
+    if (width <= 0 || height <= 0) throw new Error('Invalid canvas size');
+    canvasAndContext.canvas.width = width;
+    canvasAndContext.canvas.height = height;
+  }
+  destroy(canvasAndContext) {
+    if (!canvasAndContext.canvas) return;
+    canvasAndContext.canvas.width = 0;
+    canvasAndContext.canvas.height = 0;
+    canvasAndContext.canvas = null;
+    canvasAndContext.context = null;
+  }
+}
+
+// Blobs, not data URLs: structured-clone is cheap and the pixels stay off the
+// JS heap. The hook wraps them in object URLs.
+
+async function renderPage(pdf, pageNum, scale, splitDoublePages) {
+  const page = await pdf.getPage(pageNum);
+  const viewport = page.getViewport({ scale });
+
+  const canvas = new OffscreenCanvas(viewport.width, viewport.height);
+  const ctx = canvas.getContext('2d');
+  await page.render({ canvasContext: ctx, viewport }).promise;
+
+  const isDouble = splitDoublePages && viewport.width > viewport.height * 1.19;
+
+  if (!isDouble) {
+    const blob = await canvas.convertToBlob({ type: 'image/png' });
+    return [{
+      pageNumber: pageNum,
+      originalPageNumber: pageNum,
+      blob,
+      width: viewport.width,
+      height: viewport.height,
+      isSplit: false,
+    }];
+  }
+
+  // Split double-page spread into left / right halves.
+  const halfWidth = Math.floor(viewport.width / 2);
+  const out = [];
+  for (const side of ['left', 'right']) {
+    const sideCanvas = new OffscreenCanvas(halfWidth, viewport.height);
+    const sideCtx = sideCanvas.getContext('2d');
+    const srcX = side === 'left' ? 0 : halfWidth;
+    sideCtx.drawImage(canvas, srcX, 0, halfWidth, viewport.height, 0, 0, halfWidth, viewport.height);
+    const blob = await sideCanvas.convertToBlob({ type: 'image/png' });
+    out.push({
+      pageNumber: `${pageNum}_${side}`,
+      originalPageNumber: pageNum,
+      blob,
+      width: halfWidth,
+      height: viewport.height,
+      isSplit: true,
+      splitSide: side,
+    });
+  }
+  return out;
+}
+
+self.onmessage = async (e) => {
+  const msg = e.data || {};
+  if (msg.type !== 'extract') return;
+  const { id, fileBuffer, options = {} } = msg;
+  const { scale = 1.5, pageRange = null, splitDoublePages = true } = options;
+
+  try {
+    const pdf = await pdfjsLib.getDocument({
+      data: fileBuffer,
+      CanvasFactory: OffscreenCanvasFactory,
+    }).promise;
+    const totalPages = pdf.numPages;
+
+    let pagesToExtract = [];
+    if (pageRange) {
+      const [start, end] = pageRange;
+      for (let i = Math.max(1, start); i <= Math.min(totalPages, end); i++) pagesToExtract.push(i);
+    } else {
+      for (let i = 1; i <= totalPages; i++) pagesToExtract.push(i);
+    }
+
+    for (let i = 0; i < pagesToExtract.length; i++) {
+      const pageNum = pagesToExtract[i];
+      const rendered = await renderPage(pdf, pageNum, scale, splitDoublePages);
+      for (const page of rendered) {
+        self.postMessage({ type: 'page', id, page });
+      }
+      const progress = Math.round(((i + 1) / pagesToExtract.length) * 100);
+      self.postMessage({ type: 'progress', id, progress });
+    }
+
+    self.postMessage({ type: 'done', id });
+  } catch (err) {
+    self.postMessage({ type: 'error', id, error: err?.message || String(err) });
+  }
+};
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/tailwind.config.js b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/tailwind.config.js
new file mode 100644
index 00000000..8587c29b
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/tailwind.config.js
@@ -0,0 +1,106 @@
+/** @type {import('tailwindcss').Config} */
+export default {
+  content: [
+    "./index.html",
+    "./src/**/*.{js,ts,jsx,tsx}",
+  ],
+  theme: {
+    extend: {
+      colors: {
+        primary: {
+          50: '#eff6ff',
+          100: '#dbeafe',
+          200: '#bfdbfe',
+          300: '#93c5fd',
+          400: '#60a5fa',
+          500: '#3b82f6',
+          600: '#2563eb',
+          700: '#1d4ed8',
+          800: '#1e40af',
+          900: '#1e3a8a',
+        },
+        accent: {
+          50: '#faf5ff',
+          100: '#f3e8ff',
+          200: '#e9d5ff',
+          300: '#d8b4fe',
+          400: '#c084fc',
+          500: '#a855f7',
+          600: '#9333ea',
+          700: '#7c3aed',
+        },
+        surface: {
+          50: '#f8fafc',
+          100: '#f1f5f9',
+          200: '#e2e8f0',
+        }
+      },
+      boxShadow: {
+        'card': '0 4px 6px -1px rgba(37, 99, 235, 0.1), 0 2px 4px -1px rgba(37, 99, 235, 0.06)',
+        'card-hover': '0 10px 15px -3px rgba(37, 99, 235, 0.15), 0 4px 6px -2px rgba(37, 99, 235, 0.1)',
+        'elevated': '0 20px 25px -5px rgba(0, 0, 0, 0.1), 0 10px 10px -5px rgba(0, 0, 0, 0.04)',
+        'glow': '0 0 20px rgba(59, 130, 246, 0.3)',
+        'glow-lg': '0 0 40px rgba(59, 130, 246, 0.4)',
+        'inner-glow': 'inset 0 0 20px rgba(59, 130, 246, 0.1)',
+      },
+      animation: {
+        'fade-in': 'fadeIn 0.3s ease-out',
+        'slide-up': 'slideUp 0.3s ease-out',
+        'pulse-soft': 'pulseSoft 2s infinite',
+        'float': 'float 3s ease-in-out infinite',
+        'glow-pulse': 'glowPulse 2s ease-in-out infinite',
+        'shimmer': 'shimmer 2s linear infinite',
+        'bounce-gentle': 'bounceGentle 2s ease-in-out infinite',
+        'scale-in': 'scaleIn 0.2s ease-out',
+        'progress-fill': 'progressFill 0.5s ease-out forwards',
+      },
+      keyframes: {
+        fadeIn: {
+          '0%': { opacity: '0' },
+          '100%': { opacity: '1' },
+        },
+        slideUp: {
+          '0%': { opacity: '0', transform: 'translateY(10px)' },
+          '100%': { opacity: '1', transform: 'translateY(0)' },
+        },
+        pulseSoft: {
+          '0%, 100%': { opacity: '1' },
+          '50%': { opacity: '0.7' },
+        },
+        float: {
+          '0%, 100%': { transform: 'translateY(0)' },
+          '50%': { transform: 'translateY(-10px)' },
+        },
+        glowPulse: {
+          '0%, 100%': { boxShadow: '0 0 20px rgba(59, 130, 246, 0.3)' },
+          '50%': { boxShadow: '0 0 30px rgba(59, 130, 246, 0.5)' },
+        },
+        shimmer: {
+          '0%': { backgroundPosition: '-200% 0' },
+          '100%': { backgroundPosition: '200% 0' },
+        },
+        bounceGentle: {
+          '0%, 100%': { transform: 'scale(1)' },
+          '50%': { transform: 'scale(1.02)' },
+        },
+        scaleIn: {
+          '0%': { opacity: '0', transform: 'scale(0.95)' },
+          '100%': { opacity: '1', transform: 'scale(1)' },
+        },
+        progressFill: {
+          '0%': { width: '0%' },
+          '100%': { width: 'var(--progress-width)' },
+        }
+      },
+      backdropBlur: {
+        xs: '2px',
+      },
+      backgroundImage: {
+        'gradient-radial': 'radial-gradient(var(--tw-gradient-stops))',
+        'gradient-conic': 'conic-gradient(from 180deg at 50% 50%, var(--tw-gradient-stops))',
+        'mesh-gradient': 'linear-gradient(135deg, #667eea 0%, #764ba2 100%)',
+      }
+    },
+  },
+  plugins: [],
+}
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/vite.config.js b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/vite.config.js
new file mode 100644
index 00000000..edd35844
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/frontend/vite.config.js
@@ -0,0 +1,31 @@
+import { defineConfig } from 'vite'
+import react from '@vitejs/plugin-react'
+
+export default defineConfig({
+  plugins: [react()],
+  // Local dev: the app issues same-origin `/api/...` requests. Forward them
+  // to the FastAPI backend so `npm run dev` works without hardcoded URLs
+  // (the nginx container does the equivalent in production).
+  server: {
+    host: true,
+    port: 5173,
+    proxy: {
+      '/api': {
+        target: 'http://localhost:8000',
+        changeOrigin: true,
+      },
+    },
+  },
+  optimizeDeps: {
+    include: ['pdfjs-dist']
+  },
+  build: {
+    rollupOptions: {
+      output: {
+        manualChunks: {
+          pdfjs: ['pdfjs-dist']
+        }
+      }
+    }
+  }
+})
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/run-native.sh b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/run-native.sh
new file mode 100755
index 00000000..fb59383f
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/run-native.sh
@@ -0,0 +1,73 @@
+#!/usr/bin/env bash
+# ============================================================================
+# RenAIssance — NATIVE macOS run (uses the Apple GPU via PyTorch MPS)
+#
+#   ./run-native.sh
+#
+# WHY native: Docker on macOS has no Metal/GPU passthrough, so the Docker
+# images always run on CPU there. Running natively lets the torch-based parts
+# (TrOCR, CRNN, local LLM) use the Apple GPU through MPS — the device helper
+# (app/utils/torch_device.py) selects "mps" automatically. NOTE: PaddleOCR has
+# no Metal backend, so layout/text *detection* still runs on CPU on a Mac.
+#
+# This sets up a local Python venv (CPU paddle + MPS-capable torch) and runs:
+#   backend  →  uvicorn on http://localhost:8000
+#   frontend →  vite dev server on http://localhost:5173 (proxies /api → :8000)
+# ============================================================================
+set -euo pipefail
+cd "$(dirname "$0")"
+
+[ "$(uname -s)" = "Darwin" ] || { echo "run-native.sh is for macOS only. On Linux/Windows use ./run.sh"; exit 1; }
+
+green() { printf '\033[32m%s\033[0m\n' "$*"; }
+bold()  { printf '\033[1m%s\033[0m\n' "$*"; }
+
+# ---- prerequisites --------------------------------------------------------
+PY="$(command -v python3.11 || command -v python3 || true)"
+[ -n "$PY" ] || { echo "Python 3.11 (or python3) is required: brew install python@3.11"; exit 1; }
+command -v node >/dev/null 2>&1 || { echo "Node.js is required: brew install node"; exit 1; }
+
+VENV=backend/.venv-native
+if [ ! -d "$VENV" ]; then
+  bold "Creating Python venv ($VENV)…"
+  "$PY" -m venv "$VENV"
+fi
+# shellcheck disable=SC1091
+source "$VENV/bin/activate"
+
+bold "Installing backend dependencies (first run is slow)…"
+pip install --upgrade pip >/dev/null
+pip install -r backend/requirements.txt
+# torch on macOS-arm64 from PyPI already ships the MPS backend — no special index.
+# PaddlePaddle CPU wheel (no GPU on Mac). Best-effort: if the wheel is
+# unavailable for this macOS/arch, detection won't work but MPS recognition will.
+if ! pip install "paddlepaddle==3.0.0"; then
+  echo "WARNING: could not install paddlepaddle (CPU) — PaddleOCR detection will be"
+  echo "         unavailable on this Mac. torch (MPS) recognition still works."
+fi
+
+# Regenerate the TrOCR processor assets (needs network the first time).
+python backend/scripts/normalize_trocr_config.py || \
+  echo "NOTE: TrOCR config normalization skipped (weights may be absent locally)."
+
+# ---- env ------------------------------------------------------------------
+export STORAGE_ROOT="$(pwd)/storage"
+mkdir -p "$STORAGE_ROOT/transcripts" "$STORAGE_ROOT/datasets"
+[ -f .env ] && export $(grep -v '^[[:space:]]*#' .env | grep -E '^[A-Za-z_]+=' | xargs -0 2>/dev/null || true)
+
+# ---- run both, clean up on exit ------------------------------------------
+PIDS=()
+cleanup() { bold "Shutting down…"; for p in "${PIDS[@]:-}"; do kill "$p" 2>/dev/null || true; done; }
+trap cleanup EXIT INT TERM
+
+bold "Starting backend (uvicorn, MPS-aware) on :8000…"
+( cd backend && PYTHONPATH=. exec uvicorn app.main:app --host 0.0.0.0 --port 8000 ) &
+PIDS+=($!)
+
+bold "Installing frontend deps + starting Vite dev server on :5173…"
+( cd frontend && npm install --no-audit --no-fund && exec npm run dev ) &
+PIDS+=($!)
+
+green "RenAIssance (native) starting. Open: http://localhost:5173"
+echo "  Press Ctrl-C to stop both."
+wait
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/run.ps1 b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/run.ps1
new file mode 100644
index 00000000..fe06ce26
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/run.ps1
@@ -0,0 +1,121 @@
+# ============================================================================
+# RenAIssance — one-command launcher (Windows / PowerShell)
+#
+#   .\run.ps1            Detect GPU, pre-check specs, pull & run the right image
+#   .\run.ps1 -Cpu       Force the CPU image (no NVIDIA GPU needed)
+#   .\run.ps1 -Build     Build locally from source instead of pulling images
+#   .\run.ps1 -Down      Stop the stack
+#
+# The hard "your machine does not meet the requirements" gate runs HERE,
+# before any multi-GB image is pulled. GPU on Windows requires WSL2 + an
+# NVIDIA driver with WSL support + the NVIDIA Container Toolkit in the WSL
+# Docker engine (Docker Desktop with the WSL2 backend handles this).
+# ============================================================================
+[CmdletBinding()]
+param(
+  [switch]$Cpu,
+  [switch]$Build,
+  [switch]$Down
+)
+$ErrorActionPreference = 'Stop'
+Set-Location -Path $PSScriptRoot
+
+$MinDriverMajor = 560
+$MinVramGb      = 4
+$MinRamGb       = 4
+$RecRamGb       = 8
+$CudaBaseImage  = 'nvidia/cuda:12.6.3-base-ubuntu24.04'
+$Url            = 'http://localhost:5173'
+
+function Die($m) { Write-Host "ERROR: $m" -ForegroundColor Red; exit 1 }
+function Info($m) { Write-Host $m }
+function Ok($m)  { Write-Host $m -ForegroundColor Green }
+
+# ---- docker availability --------------------------------------------------
+if (-not (Get-Command docker -ErrorAction SilentlyContinue)) { Die 'Docker is not installed or not on PATH.' }
+try { docker compose version *> $null } catch { Die 'Docker Compose v2 ("docker compose") is required.' }
+try { docker info *> $null } catch { Die 'Docker daemon is not running. Start Docker Desktop and retry.' }
+
+function Get-RamGb {
+  [math]::Floor((Get-CimInstance Win32_ComputerSystem).TotalPhysicalMemory / 1GB)
+}
+function Test-NvidiaGpu {
+  if (-not (Get-Command nvidia-smi -ErrorAction SilentlyContinue)) { return $false }
+  try { nvidia-smi -L *> $null; return $true } catch { return $false }
+}
+function Get-DriverMajor {
+  try { (nvidia-smi --query-gpu=driver_version --format=csv,noheader,nounits | Select-Object -First 1).Split('.')[0] }
+  catch { '' }
+}
+function Get-VramGb {
+  try { [int]((nvidia-smi --query-gpu=memory.total --format=csv,noheader,nounits | Select-Object -First 1)) / 1024 -as [int] }
+  catch { 0 }
+}
+function Test-GpuInDocker {
+  try { docker run --rm --gpus all $CudaBaseImage nvidia-smi -L *> $null; return $true } catch { return $false }
+}
+
+# ---- down -----------------------------------------------------------------
+if ($Down) {
+  Write-Host 'Stopping all RenAIssance compose stacks…'
+  foreach ($f in 'docker-compose.yml','docker-compose.cpu.yml','docker-compose.images.yml','docker-compose.images.cpu.yml') {
+    if (Test-Path $f) { try { docker compose -f $f down *> $null } catch {} }
+  }
+  Ok 'Stopped.'; exit 0
+}
+
+Write-Host 'RenAIssance launcher — OS: Windows' -ForegroundColor White
+
+# ---- common spec checks ---------------------------------------------------
+$RamGb = Get-RamGb
+Info "  RAM: $RamGb GB"
+if ($RamGb -lt $MinRamGb) {
+  Write-Host "Your machine does not meet the requirements: only $RamGb GB RAM (minimum $MinRamGb GB)." -ForegroundColor Red
+  exit 1
+}
+if ($RamGb -lt $RecRamGb) { Write-Host "  WARNING: $RamGb GB RAM; ${RecRamGb}GB+ recommended." -ForegroundColor Red }
+
+# ---- decide variant -------------------------------------------------------
+$Variant = 'gpu'
+if ($Cpu) { $Variant = 'cpu'; Info '  -Cpu given → CPU image.' }
+elseif (-not (Test-NvidiaGpu)) { $Variant = 'cpu'; Info '  No NVIDIA GPU detected → CPU image.' }
+
+# ---- GPU gate -------------------------------------------------------------
+if ($Variant -eq 'gpu') {
+  $dmaj = Get-DriverMajor; $vram = Get-VramGb
+  Info "  NVIDIA driver: $dmaj   VRAM: $vram GB"
+  $fail = ''
+  if (-not $dmaj) { $fail = 'could not read NVIDIA driver version' }
+  elseif ([int]$dmaj -lt $MinDriverMajor) { $fail = "NVIDIA driver $dmaj too old (need >= $MinDriverMajor for CUDA 12.6)" }
+  elseif ($vram -lt $MinVramGb) { $fail = "only $vram GB VRAM (minimum $MinVramGb GB)" }
+  if (-not $fail) {
+    Info '  Verifying NVIDIA Container Toolkit (GPU access inside Docker)…'
+    if (-not (Test-GpuInDocker)) { $fail = 'Docker cannot access the GPU — ensure WSL2 + NVIDIA Container Toolkit' }
+  }
+  if ($fail) {
+    Write-Host "Your machine does not meet the GPU requirements: $fail." -ForegroundColor Red
+    $ans = Read-Host 'Fall back to the CPU image instead? [y/N]'
+    if ($ans -match '^[Yy]') { $Variant = 'cpu' } else { Info 'Aborting. Fix the above or run .\run.ps1 -Cpu.'; exit 1 }
+  }
+}
+
+$mode = if ($Build) { 'build' } else { 'published-images' }
+Ok "Selected variant: $Variant  (mode: $mode)"
+
+# ---- pick compose file ----------------------------------------------------
+if ($Build) { $File = if ($Variant -eq 'cpu') { 'docker-compose.cpu.yml' } else { 'docker-compose.yml' } }
+else        { $File = if ($Variant -eq 'cpu') { 'docker-compose.images.cpu.yml' } else { 'docker-compose.images.yml' } }
+Info "  Compose file: $File"
+
+# ---- launch ---------------------------------------------------------------
+if ($Build) {
+  docker compose -f $File up -d --build
+} else {
+  Write-Host 'Pulling images (this can take a while the first time)…'
+  docker compose -f $File pull
+  docker compose -f $File up -d
+}
+
+Ok "RenAIssance is starting. Open: $Url"
+Info "  Logs:  docker compose -f $File logs -f"
+Info "  Stop:  .\run.ps1 -Down"
diff --git a/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/run.sh b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/run.sh
new file mode 100755
index 00000000..0c094291
--- /dev/null
+++ b/RenAIssance_CRNN_OCR_with_LLM_Integration_Saarthak_Gupta/run.sh
@@ -0,0 +1,185 @@
+#!/usr/bin/env bash
+# ============================================================================
+# RenAIssance — one-command launcher (Linux / macOS / WSL)
+#
+#   ./run.sh              Detect GPU, pre-check specs, pull & run the right image
+#   ./run.sh --cpu        Force the CPU image (no NVIDIA GPU needed)
+#   ./run.sh --build      Build locally from source instead of pulling images
+#   ./run.sh --native     macOS only: run natively to use the Apple GPU (MPS)
+#   ./run.sh --down       Stop the stack
+#   ./run.sh --help       Show this help
+#
+# The hard "your machine does not meet the requirements" gate runs HERE,
+# before any multi-GB image is pulled. The container also runs a lighter
+# preflight as a safety net.
+# ============================================================================
+set -euo pipefail
+
+cd "$(dirname "$0")"
+
+# ---- tunables -------------------------------------------------------------
+MIN_DRIVER_MAJOR=560     # CUDA 12.6 needs NVIDIA driver >= 560
+MIN_VRAM_GB=4            # below this the GPU image is not worth it
+MIN_RAM_GB=4             # hard floor; app recommends >= 8
+REC_RAM_GB=8
+CUDA_BASE_IMAGE="nvidia/cuda:12.6.3-base-ubuntu24.04"
+URL="http://localhost:5173"
+
+# ---- flags ----------------------------------------------------------------
+FORCE_CPU=0; BUILD=0; DOWN=0; NATIVE=0
+for arg in "$@"; do
+  case "$arg" in
+    --cpu)    FORCE_CPU=1 ;;
+    --build)  BUILD=1 ;;
+    --down)   DOWN=1 ;;
+    --native) NATIVE=1 ;;
+    -h|--help)
+      sed -n '2,18p' "$0" | sed 's/^# \{0,1\}//'
+      exit 0 ;;
+    *) echo "Unknown option: $arg (try --help)"; exit 2 ;;
+  esac
+done
+
+red()   { printf '\033[31m%s\033[0m\n' "$*"; }
+green() { printf '\033[32m%s\033[0m\n' "$*"; }
+bold()  { printf '\033[1m%s\033[0m\n' "$*"; }
+die()   { red "ERROR: $*"; exit 1; }
+
+OS="$(uname -s)"
+
+# ---- docker availability --------------------------------------------------
+command -v docker >/dev/null 2>&1 || die "Docker is not installed or not on PATH."
+if docker compose version >/dev/null 2>&1; then
+  DC=(docker compose)
+elif command -v docker-compose >/dev/null 2>&1; then
+  DC=(docker-compose)
+else
+  die "Docker Compose v2 (\"docker compose\") is required."
+fi
+docker info >/dev/null 2>&1 || die "Docker daemon is not running. Start Docker Desktop / the docker service and retry."
+
+# ---- helpers --------------------------------------------------------------
+total_ram_gb() {
+  if [ "$OS" = "Darwin" ]; then
+    echo $(( $(sysctl -n hw.memsize) / 1024 / 1024 / 1024 ))
+  else
+    echo $(( $(awk '/MemTotal/{print $2}' /proc/meminfo) / 1024 / 1024 ))
+  fi
+}
+
+free_disk_gb() {
+  # GB free on the filesystem holding this repo (proxy for docker storage).
+  df -Pk . 2>/dev/null | awk 'NR==2{print int($4/1024/1024)}'
+}
+
+have_nvidia_gpu() {
+  command -v nvidia-smi >/dev/null 2>&1 && nvidia-smi -L >/dev/null 2>&1
+}
+
+driver_major() {
+  nvidia-smi --query-gpu=driver_version --format=csv,noheader,nounits 2>/dev/null \
+    | head -n1 | cut -d. -f1
+}
+
+vram_gb() {
+  local mib
+  mib="$(nvidia-smi --query-gpu=memory.total --format=csv,noheader,nounits 2>/dev/null | head -n1)"
+  [ -n "$mib" ] && echo $(( mib / 1024 )) || echo 0
+}
+
+gpu_works_in_docker() {
+  docker run --rm --gpus all "$CUDA_BASE_IMAGE" nvidia-smi -L >/dev/null 2>&1
+}
+
+# ---- down -----------------------------------------------------------------
+if [ "$DOWN" = "1" ]; then
+  bold "Stopping all RenAIssance compose stacks…"
+  for f in docker-compose.yml docker-compose.cpu.yml \
+           docker-compose.images.yml docker-compose.images.cpu.yml; do
+    [ -f "$f" ] && "${DC[@]}" -f "$f" down 2>/dev/null || true
+  done
+  green "Stopped."
+  exit 0
+fi
+
+# ---- native (macOS MPS) ---------------------------------------------------
+if [ "$NATIVE" = "1" ]; then
+  [ "$OS" = "Darwin" ] || die "--native is for macOS (Apple GPU via MPS). On Linux/Windows use the GPU image."
+  exec ./run-native.sh
+fi
+
+bold "RenAIssance launcher — OS: $OS"
+
+# ---- common spec checks ---------------------------------------------------
+RAM_GB="$(total_ram_gb)"
+DISK_GB="$(free_disk_gb)"
+echo "  RAM: ${RAM_GB} GB   Free disk: ${DISK_GB:-?} GB"
+if [ "$RAM_GB" -lt "$MIN_RAM_GB" ]; then
+  red "Your machine does not meet the requirements: only ${RAM_GB} GB RAM (minimum ${MIN_RAM_GB} GB)."
+  exit 1
+fi
+[ "$RAM_GB" -lt "$REC_RAM_GB" ] && \
+  red "  WARNING: ${RAM_GB} GB RAM detected; ${REC_RAM_GB} GB+ recommended. Large pages may be slow / OOM."
+
+# ---- decide variant -------------------------------------------------------
+VARIANT="gpu"
+if [ "$FORCE_CPU" = "1" ]; then
+  VARIANT="cpu"; echo "  --cpu given → CPU image."
+elif [ "$OS" = "Darwin" ]; then
+  VARIANT="cpu"
+  echo "  macOS detected → CPU image (Docker on macOS has no GPU passthrough)."
+  echo "  To use the Apple GPU, run natively:  ./run.sh --native"
+elif ! have_nvidia_gpu; then
+  VARIANT="cpu"; echo "  No NVIDIA GPU detected → CPU image."
+fi
+
+# ---- GPU gate (only when we intend to use the GPU image) ------------------
+if [ "$VARIANT" = "gpu" ]; then
+  DMAJ="$(driver_major)"; VRAM="$(vram_gb)"
+  echo "  NVIDIA driver: ${DMAJ:-?}   VRAM: ${VRAM} GB"
+
+  fail=""
+  [ -z "$DMAJ" ] && fail="could not read NVIDIA driver version"
+  [ -n "$DMAJ" ] && [ "$DMAJ" -lt "$MIN_DRIVER_MAJOR" ] && \
+    fail="NVIDIA driver $DMAJ is too old (need >= $MIN_DRIVER_MAJOR for CUDA 12.6)"
+  [ "$VRAM" -lt "$MIN_VRAM_GB" ] && \
+    fail="only ${VRAM} GB VRAM (minimum ${MIN_VRAM_GB} GB)"
+
+  if [ -z "$fail" ]; then
+    echo "  Verifying NVIDIA Container Toolkit (GPU access inside Docker)…"
+    gpu_works_in_docker || fail="Docker cannot access the GPU — install the NVIDIA Container Toolkit"
+  fi
+
+  if [ -n "$fail" ]; then
+    red "Your machine does not meet the GPU requirements: $fail."
+    printf "Fall back to the CPU image instead? [y/N] "
+    read -r ans || ans=""
+    case "$ans" in
+      y|Y) VARIANT="cpu" ;;
+      *) echo "Aborting. Fix the above or run ./run.sh --cpu."; exit 1 ;;
+    esac
+  fi
+fi
+
+green "Selected variant: ${VARIANT}  (mode: $([ "$BUILD" = 1 ] && echo build || echo published-images))"
+
+# ---- pick compose file ----------------------------------------------------
+if [ "$BUILD" = "1" ]; then
+  FILE="$([ "$VARIANT" = cpu ] && echo docker-compose.cpu.yml || echo docker-compose.yml)"
+else
+  FILE="$([ "$VARIANT" = cpu ] && echo docker-compose.images.cpu.yml || echo docker-compose.images.yml)"
+fi
+echo "  Compose file: $FILE"
+
+# ---- launch ---------------------------------------------------------------
+if [ "$BUILD" = "1" ]; then
+  "${DC[@]}" -f "$FILE" up -d --build
+else
+  bold "Pulling images (this can take a while the first time)…"
+  "${DC[@]}" -f "$FILE" pull
+  "${DC[@]}" -f "$FILE" up -d
+fi
+
+green "RenAIssance is starting. Open: $URL"
+echo "  Logs:  ${DC[*]} -f $FILE logs -f"
+echo "  Stop:  ./run.sh --down"
diff --git a/RenAIssance_SyntheticImageGeneration_Saarthak_Gupta/.gitignore b/RenAIssance_SyntheticImageGeneration_Saarthak_Gupta/.gitignore
deleted file mode 100644
index 9e715d67..00000000
--- a/RenAIssance_SyntheticImageGeneration_Saarthak_Gupta/.gitignore
+++ /dev/null
@@ -1,11 +0,0 @@
-data/GAN-DATA/2_splitted
-data/GAN-DATA/3_processed
-data/GAN-DATA/4_bounding_boxes
-data/GAN-DATA/5_mapped
-data/GAN-DATA/6_word_data
-data/GAN-DATA/final_dataset
-data
-models
-proposal.pdf
-output
-CRAFT-pytorch
\ No newline at end of file
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diff --git a/RenAIssance_SyntheticImageGeneration_Saarthak_Gupta/assets/upscaled.png b/RenAIssance_SyntheticImageGeneration_Saarthak_Gupta/assets/upscaled.png
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diff --git a/RenAIssance_SyntheticImageGeneration_Saarthak_Gupta/experimentation.ipynb b/RenAIssance_SyntheticImageGeneration_Saarthak_Gupta/experimentation.ipynb
deleted file mode 100644
index 0853ae22..00000000
--- a/RenAIssance_SyntheticImageGeneration_Saarthak_Gupta/experimentation.ipynb
+++ /dev/null
@@ -1,4187 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "58fa8778",
-   "metadata": {},
-   "source": [
-    "# Data Generation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3c778a0c",
-   "metadata": {},
-   "source": [
-    "Modified the transcript file so that my splitting function can easily detect PDFs containing a single page or double page.\n",
-    "\n",
-    "An empty new line marks seperation of the page from left to right. if no new line detected means that it is a single page.\n",
-    "\n",
-    "New Convention in transcripts:\n",
-    "- leave an empty line to differentiate between left and right page content\n",
-    "- leave one empty line if either of the right or left page in a double sided PDF is empty or if transcript for that particular side is not available\n",
-    "- No empty line need to be left for single page\n",
-    "- Do not leave line otherwise(even after the ending of a paragraph)\n",
-    "- for new pages `PDF p{page-Number}` marks the start of page and make sure that the `page-Number` matches with the page in the PDF file.\n",
-    "\n",
-    "<img src=\"assets\\image.png\" alt=\"Alt text\" width=\"650\" height=\"500\">\n",
-    "\n",
-    "\n",
-    "<img src=\"assets\\image1.png\" alt=\"Alt text\" width=\"300\" height=\"500\">\n",
-    "\n",
-    "- Note : if the page width  > 1.2 page height it will be considered as a double page and splitted into half.\n",
-    "\n",
-    "For example in the above image as left page was empty a line was left and 1 line was left to tell about the shift from left page to right page.(Total 2 empty lines)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "87a970ed",
-   "metadata": {},
-   "source": [
-    "## Splitting pages \n",
-    "\n",
-    "splitting the provided data into individual pages and their corresponding transcript."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "d6577e5a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from src.data_utils import process_books_with_transcripts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "5f74f059",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Splitting Images and transcripts: 100%|██████████| 12/12 [00:33<00:00,  2.83s/book, Status=Completed book 12]                                               \n"
-     ]
-    }
-   ],
-   "source": [
-    "process_books_with_transcripts(\n",
-    "    input_books_folder=\"data/GAN-DATA/1_raw/books\",\n",
-    "    input_transcripts_folder=\"data/GAN-DATA/1_raw/transcripts/\",\n",
-    "    output_books_folder=\"data/GAN-DATA/2_splitted/books/\",\n",
-    "    output_transcripts_folder=\"data/GAN-DATA/2_splitted/transcripts/\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a8e89eae",
-   "metadata": {},
-   "source": [
-    "## Image Preprocessing\n",
-    "\n",
-    "Various transformations\n",
-    "\n",
-    "    'correct_skew': correct_skew,\n",
-    "    'normalize_image': normalize_image,\n",
-    "    'ensure_300ppi': ensure_300ppi,\n",
-    "    'remove_bleed_dual_layer': remove_bleed_dual_layer,\n",
-    "    'denoise_image': denoise_image,\n",
-    "    'sharpen_image': sharpen_image,\n",
-    "    'enhance_contrast': enhance_contrast,\n",
-    "    'morphological_operations': morphological_operations,\n",
-    "    'binarize_image': binarize_image,\n",
-    "    'apply_binary_mask': apply_binary_mask,\n",
-    "    'upscale': upscale\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "31461d58",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x17f0e24d970>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from src.data_utils import *\n",
-    "\n",
-    "img = load_image('data/GAN-DATA/2_splitted/books/book7/page1.png')\n",
-    "plt.imshow(img)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "793a47b7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## A function to copy all transcripts and images from the source to the destination \n",
-    "from src.data_utils import copy_all_transcripts, copy_all_images\n",
-    "copy_all_transcripts()\n",
-    "copy_all_images()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "386cf34f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Processing 12 books with 98 total pages:\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Transforming page Images: 100%|██████████| 98/98 [09:16<00:00,  5.68s/page, book=9, page=31, step=4/4, transform=sharpen_image]           \n"
-     ]
-    }
-   ],
-   "source": [
-    "from src.data_utils import process_multiple_books\n",
-    "\n",
-    "book_transformations = {\n",
-    "    'book1': {\n",
-    "        'denoise_image': {'method': 'bilateral'},\n",
-    "        'denoise_image': {'method': 'nlm'}\n",
-    "    },\n",
-    "    'book2': {\n",
-    "        'ensure_300ppi': {'target_dpi': 150},\n",
-    "        'remove_bleed_dual_layer': {},\n",
-    "        'denoise_image': {'method': 'bilateral'},\n",
-    "        'denoise_image': {'method': 'nlm'},\n",
-    "        'denoise_image': {'method': 'nlm'}\n",
-    "    },\n",
-    "    'book3': {\n",
-    "        'ensure_300ppi': {'target_dpi': 300},\n",
-    "        'remove_bleed_dual_layer': {},\n",
-    "        'denoise_image': {'method': 'bilateral'}\n",
-    "        },\n",
-    "    'book4': {\n",
-    "        'ensure_300ppi': {'target_dpi': 300},\n",
-    "        'remove_bleed_dual_layer': {},\n",
-    "        'denoise_image': {'method': 'bilateral'}\n",
-    "    },\n",
-    "    'book5': {\n",
-    "        'ensure_300ppi': {'target_dpi': 300}\n",
-    "    },\n",
-    "    'book6': {\n",
-    "        'ensure_300ppi': {'target_dpi': 300},\n",
-    "        'remove_bleed_dual_layer': {},\n",
-    "        'denoise_image': {'method': 'bilateral'}\n",
-    "    },\n",
-    "    'book7': {\n",
-    "        'remove_bleed_dual_layer': {},\n",
-    "        'denoise_image': {'method': 'nlm'},\n",
-    "        'denoise_image': {'method': 'bilateral'}\n",
-    "    },\n",
-    "    'book8': {\n",
-    "        'remove_bleed_dual_layer': {},\n",
-    "        'denoise_image': {'method': 'nlm'},\n",
-    "        'denoise_image': {'method': 'bilateral'}\n",
-    "    },\n",
-    "    'book9': {\n",
-    "        'ensure_300ppi': {'target_dpi': 300},\n",
-    "        'remove_bleed_dual_layer': {},\n",
-    "        'denoise_image': {'method': 'wiener'},\n",
-    "        'sharpen_image': {'method': 'laplacian'}\n",
-    "    },\n",
-    "    'book10': {\n",
-    "        'ensure_300ppi': {'target_dpi': 300},\n",
-    "        'remove_bleed_dual_layer': {},\n",
-    "        'denoise_image': {'method': 'wiener'},\n",
-    "        'sharpen_image': {'method': 'laplacian'}\n",
-    "    },\n",
-    "    'book11': {\n",
-    "        'ensure_300ppi': {'target_dpi': 300},\n",
-    "        'remove_bleed_dual_layer': {},\n",
-    "        'denoise_image': {'method': 'bilateral'},\n",
-    "    },\n",
-    "    'book12': {\n",
-    "        'ensure_300ppi': {'target_dpi': 300},\n",
-    "        'remove_bleed_dual_layer': {},\n",
-    "        'denoise_image': {'method': 'bilateral'},\n",
-    "        'sharpen_image': {'method': 'laplacian'},\n",
-    "    },\n",
-    "}\n",
-    "\n",
-    "stats = process_multiple_books(\n",
-    "    books_base_dir=\"data/GAN-DATA/2_splitted/books\", \n",
-    "    output_dir=\"data/GAN-DATA/3_processed/books\",\n",
-    "    book_transformations=book_transformations\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "01910848",
-   "metadata": {},
-   "source": [
-    "## Text Detection"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "e8aa7d9e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Generating bounding boxes for each book page...\n"
-     ]
-    }
-   ],
-   "source": [
-    "from src.data_utils import text_detection\n",
-    "\n",
-    "input_root = \"data/GAN-DATA/3_processed/books\"\n",
-    "output_root = \"data/GAN-DATA/4_bounding_boxes//\"\n",
-    "model_path = \"CRAFT-pytorch/weights/craft_mlt_25k.pth\"\n",
-    "\n",
-    "text_detection(\n",
-    "    input_root=input_root,\n",
-    "    output_root=output_root,\n",
-    "    model_path=model_path\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "babf8a83",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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VVth9+lk9/sykhrAPz9LokzWfgZIbXvZOY1Q+UcmDyKG+vIZZoSItVBhdm+6vJGKVQ5bporRm/8j4wb4aBMk6iuj5AK/Xo9vt0ut5iMBP8RxjZqTaxPcEXS/AlwLLtrAt7dRvIaVNIGF2SNVNeoRgK2RHMmR6umEMORrFQxLmckE8QWKGTMzIZJx7okmGWZjSaaLaHYJh6DIGgbG+/DFqqIXFr+4YCyVM47bsL1WHdTCZ9MiZ9wNL814aXQkZboE2AZMhTPoq0D8WDs7g+1DfEBA3mOJWGwCU1A8gBDrmJogEAxCAF+XTD7QOSxZx/+m+juuEJBSWQdJSlOqrRxKMNMVEBfYgcUcy3tg+y1YDlJtoVOj6mD5HUR1H2xsbt0MItELrZ8x/hjmnKyErkcpo6R+yTeOKxJ9DwsLEby8HVMmcy+m5lCgo/ovmsp5rMozHFqj5GqTzeFj8y+SN5nUFTLAgYf3o4+2pekTjfxC4SJPse+chuaYUbB3TENTSsvkOtuozKRN9FUsyFXKgb9xigJihqycDamZYeeSe1s5BecbXEs8OGrsDwVrq+8AlPj22UuX2/ZbJ9H1lG20jiTCAlLE1Ma6i2rAFhJ8yFD0Bge/TbnfotNt0u92Qb4PyaVXLidIwDkkZ4AcSLwhoNn1W1jdZ29zFdlxcx0UIG9/ok3/6zG/1t12KHrnPljCsVGnBqcHWnkqtFafVj6o5OCTw48g1O8wzacZglj6AGaSY9K8KaCV30iSZia7a4VoiTYPaIyog/ipFvGTXt9Q3QhmHIZkad3rDxYH7IDX+IvIh2rZs3EszEyOPdJ6HF1wCtYQY7yRVjEdbaEJBEgKuQ9Og9tqr/fqERjj+hzgbqzY9WP3SYjHiAuZShGa0hpAaqaWFFe0WToDtIUqS1ChIB1a2nMO3txX6fgHRcr4Y/r6Du2H4WybzMUCd/kwcf6YBlwwtTxgKxT4teeiVBrN+YY0jH06IrG8xWhlQbgxv9utvmeKLA9Nr8B69v0imTCkWUUaGe0K0VIlEHc6n8pBmXgkfsWQt1cewtzEB4360D1iFPfpswHUNhKS6P2B29F0VRv7SzFYafWeKU6ktxWH7SB1ySIZTxbhm/AXhp1o6VC5MXq9Hp9Wm2WrT6/bUvdB/KwgCfD/A930838f3Vbgqzw/wPJ/tnRYfXb7FB59ep9X2sG21QiVBAbNA8k//4/90SNvF9Cs46TLlbhbJQBFHkDdSmGds6UWOvkCQFsjAQnVybDDe07plLOlFWrU1gImmfse5mR5n5swy0+vlnfj6owRY6aXCpIk6doZ9WDWI8kkw1BFy77Mo/WpA52cnEXZneowQDgG1hdgMoxDzRSMgLOk89mGOgwB9+poWABGQMceniFndIMCll97S/o6mBUnfO8j47Zt/AUOtS0KC8Acq7UPzHrikF5eldmAGYMdLeyOTZaGXmRO9s8f7x8FitaCQe/Og4Rmhw6vE1xIJMPtW1TE194dk3Z8qzSP6KhM9KfWwE0BghrJJjWVjmTxZ+GdR6ETf1/QybdLSrIXygXMfMfWgMTcgnQR8Efv7hfMohkQmSDGRxfA67n0Y/OhtnCzNBGqDmMuQ3wZKEvv0b/q+GRNMsU+zTQcpphquBfFY1SDKyE8G4c5CFMiypAJAavVQfQ98qbLwJQQBQeArYOYHUbzPwPORvof0PQWi/ADfk3heCL48Sa3exJeSQCqgJYRgYnx8z3bQ9CsAW9pEF/KhyOwXDkKhZ3TYcNKAZ0MYndpKT7SZZZQdUjo/GYKhPUFQYqkiHBDCnHDJgRH7SNlGFr8iQKEHYcjs4m3LDxdoqcwOokklHjxAUhPIDbh3EAZu8hGRvBxflAO+mw8MqXvEIzSgUM+ry2bsskHP7zP2ROp3ouKGBihsQ75o30adTu2kjAAXJMHVflarfcbvoJYaTH4ihUSFpRD7zcF0vcxlPQ0+RciEDZ+1uByBsCWEu4j2ra+A5K7DeL7vyytkCEykVPNw1DGq+0AAeH0VS7xvoub9AnLAxvkBdptR56FRTgS8CT+DFOCSRpBmw/olRKx5mGDf8LE7DA3sizDW1qOlA7ZdoAGXSN8xaLRx8rAlSRLE7Q3To7v7LXXuQ1JKYzNJOpyDoeSlgF0c5FzlEf2SEgtlubJCaxeBxEIg/dCAE0gsKcKlRIElVcQDIcNd6DJAyNBzNlwmDrARQmIJHyksbOI4kkqnsKLIQIHvMTle5e/90XdGaoNHALZSaDaQIcDSDaoBAYpJybi94wjyQicdyug04FInzMcRzAckVPVIAa2B+SaYelSDCGgl/coG1OdXSIOCnOpB+EiAliYT8BxGi98v7/38Bkwwtmebi4ENoHKKNyrEYFVodeuAddZ56DrKqNy9HVhF8mv0uIy/WyJ+z75m1g8Ns94YmZoAa+h7jDZa+gV4StAnxgfqe3ioceSwLgYEod23YLO/Q25ntHW6TuhqiKQSlHwHc3/hIKA1CpntECD0rtpR/Ecji1YwgHsZdUhYjUL+ONCHaZgN5DCcIAwWij5HcxDgIga45pKwVgqEUPkMs64Oa6OD8JTEmBj9sX3JtPQeig4whvZYjXlUtP+bxeOpD3AdtsAIPBk8PJ1negXA5CVoniqxCMJDX/XyobqqprG6HyAJZGAsKcb+XVaYfaBmPlq+W0LtHA6QCCHxhMTWrDQa13oqSGw7wysvvTxSEzzy0A/alBf+TFC0tqpBlkw+O1DDNzon3qW4Tz1M7WII0IoO+VSp+j4PHCD1EVH/DpqQflXV04J/mEB5WABsEKja61qKTDwYaehG3nGIDDPCPyhfqNhaOBIo0GUMrEh8JMXQZ9PWLPMF+hPrBPuM+4cIgvtyNOdHPFGjpctBgEvAwKWmkQpOA2tjWWEgifgjase+Bjb+p55LW40G9EWyP+M+kfgIK7Q4DpsHUXDRZJy0tHI3uDwjtEuaTCuBzjRhGU3fH0KRIrIX4NLBmtOMWv/U/MnY6ZmclEZaTdoKGo4rXd29qnzQpe6D0mfCW4MVvmT+MvlpPvsIaPAcTtMI8/rQpZuapVmkwdcS10cFvRZChpttwpNBojc0+kEgsZEhyDL8vsP7lpChC5lAColtGcM5svTqmiromMlkR3n5R72MKIi3J8edFznGm9YHYz2XQISHJafOmlMPR2kPfoBkion2TdQ0k1P3BTaP3ky9N0UHQEsYAV2OkOYAZE6yURjfIOvXMGC21+902Xul6yMd0BOS2nfcx31R+aM6Hz48R3zw84hMYpCwSAtTOUCgwT7dHGum+tdnoSSTHgw01M90vfdox0HLlsPaLbFUeIC5GAGuNPAYlriv4GT50VUreT9Wd2PANShAaOQIH1pD00WOMPZGas9hb5IAn6l5EY672KkbEuElMB6NU+xDOh6W5g3GcyK8r/szlmrxbyEU6NsjFMRDJz0GtSVDHkZJMADjwGElB39/hDRwDqcUyiTfMpDuIB51EIqggOHLmjJ8JDctyLjIUAYIIZS9VRIqsHEIZCnVb4tk11lC7XW2hMAWhEDLqJbm1ejN8oJACLDUbn7TH1e9BMnfCPwRdz0/YrAVN5iaR+q3ZVnYtvoT0QuEuwzCxlaA1mBGQzpXBsbxMSMMgH6NNA2urGTaROMegoZpA1Z68Ib/BsoZyaAApfH3sJ4iyRjT83y06dHPXGOtJvoxREsd8n3Q72HXBlbps0IFRXu9fwxm08DsAPmH/WQuToU3wu4ZcTxpbqHHTnqsmOmG0sF7fmh1EnkOq7sW0KHABlX/Yb45g87oSxQq+39rYTxqCA/dh0JHro/nf1Jn2aN9hi51DXEuj2SsrwSZ6S9nQQQuhlV/ULDOh0Q6NlCivsa94coOA0F0Is3AAiHyH42OpvKJfRzjXZd9z5llCT8MQ5FKaGnwZoQWGpDFoUimQdYhcvdFaDRI5z2CvNrXTeKwZAKt/vxjv8/YNUIkE+yd/T48KdpAJJOb27T0jwtTV01srkkBKEkIvVRYOR03TUhzVIe/AywhcHTcbEFs8JGx/6gaYtrNyMKywNIBgaMmi+fDoKmxFz0asBXJ66QjrJIzIgRbNpZlDOZIm1Brq8IKHzAF1UOk2KplAoRhyz1Gb5sauP6t3zc90CxjUA/SzNLm9AF5xJqGifrTj+nJOyCQJTLFGvYQlul3AwZaESLNc4Q+abTgf/gLsGzVHnb4KSywQ38OO5wFVvjbTKefi9Lp5wZcE8Y1y0LaDti2Ch+SCfMKr6m/sDw9FiwR5+vYYIv+PjKBptkkESAeBJjT4GAQ0A4vS+N5XY4+UiKaFAwEMf2W3n7gfBBKgqx0vQcJnxhkypCRDcpVRmdPgha8ey/T67RaUJvX9npOhAw52Y8Ht4gfhET8bsYRQpEQGImNjQgmD0GH8pWLH45BwKhtGA0J49iU6EYyXlyiWOP5SHrYxkW1BBKnTwhp/fwAsDBqpYUxzszxqi19wR7z2aS9lnxH1ZUeKhntHw2zlPIeLt3tWYVh7zQ0uYg2tcXFx4DLSJmqp1GWsWogEGoPQthXmn9IKWLdNFAAzELiCOiG8j3RcxpwoUWAULwrBGSW/h5imfjMXBE+L5HDTqxI0SO3bAWBPq5HOZoJRIwuw1SB1OY/A+lKok4/kAXkMIw0rNBwRhSiW9PCENs4B8gag1ma1gkNuA4MHGOLS4qnJNP0fRepKya4TAnitBPuXm15EBne7sCffHfExA+PojeN2lqmfvenH3wjCeASfwZYExoAavBnGaDQtkPQqEFhCPYECtSlAaZ+Prpm5pdKb9ZPiAGgcwDAFca1dB4m4DWv6XoI/R0FRqP62HG6kLFGZQmdn1CavmMBDrHlAxJO7Br8mksaIjzyqA/kDQKY5neLgb17YD6xX3oD0CWmX/zcoBwelkwdJZr4Q6G0hWuU8pSWTX8g37B/5DBLnwZOxvFI2iQp0u1p8ra+wodXawgJ84gjjPfUwsmWRFH8hy3zD7LaET4fnQCwR50fHeY2ykmDGxm1+mD6bBXScTO1SFM16DcJDKqhuZwIeswPa0NABMqoHaBCTGnDjwGmIjclEQYBj3ZKy0j/Bh0milDcx0Gkk5B/b3rEDvKSdqdFp91RWzQd05KlHeQDpLTCo3N0DwQgbYQMXdhGtW4Nsjol7g98iD2Blr5sWqkGJUrC5cH1svZIY5aXGOda4Fihlg6Rg7Ax/1XKwey8z8CeYJiEg88YwOb5cLJ/zuvI9I+Yrf/qKa3F62u+OlNrhAyGXE+31D7jciAQ2L+1xdBnD0dJqD6g/MSgGCVHQ2EQDAGuBrBLAUehwWwEBlPW0gjsWQrQWU6cv2FJFeHzUj9jiRA4GoA5BaaTVtgQXNqCJCB2EI6FTFhaBXz15XjZNNVQw8XM6BQtgaPxz69oZh6oHFOomyNrrzkzAEz3FSniz74jtZQo3LuMQZf3AvD0A69gCOBKY8sIz1h7AK0wYw1QzZ3OD7VbpfFpKteK30VDSphKqvhMdUj6XuuizePGYgCkApoq6YUuP8XbouhlCRBmIaVSzIQVRpS3QAZxTM+kqI7LV0uEIrZimZI1tdoz+KzTvekhg6140qsKBjSbTXZ3d8lmM7iWq5iQTiFVDK4gkEhLIvQnRjaE4AubaFeMQX3H0gwDOp+FEkBrCNgaqCWkkw14ts83xbxlWLSkFWv6whj0wnxskFA366VRvfk7XUWDEUogXBdXZQajW7QG0YWzyH/v76gJ4Outu8Z3X4bej/E14YdpgkHXQiAUSBV0UecTBAq8ByqNCnDpE8VkCojuRUBK5y/De76nvifKStYj8d33++qpPmX8/MA+GpUeuZq7Bw0CWoPqs5dQk8mfELdh4vGH9Y77CIaE0SFtgRhgPZPm72EZicEpfvTfkViWSjggm54rqUl9EDL4iN6t+PBAV6ywxa+6f96JJjNi//Wh9KEKrIgyGaZIxhqnFVYrqSLoHZWDaVC5+9laBmShz3eMAIrJnzWDVkBApRvRFhKAOgYo/DHUAjgaxbPQFDJ+XGckQc+jXW/TqDfxPI9cLku5UsbOumET78GHBrqh7FEfIfpkuvaJklFNRfQ/vZyoyIplYmRpDU/XIB4PQQiydCBzDai0ZUsHKBZRHeLy1YqbVMAzEvGqs5UFbBRF/BFbtmQQ0Gg2WF3fYHpygmImLk4Sgq2w0nptNQIYUqJ2A4Q/hwEtkih4TwvYwDEwZPBYqYETAa1k2sHHPgzQyAYWHaYZ4M+VdNg2gJZ+LnEETvrpIUz7UI63QzTMQ5AcryJfeSaZl4GsRaQdxiqhTJU/NJ5a1DYBhJbK6BBhKzy3TEhldbCUA6T2sxKRkLdj5i58oojgQvv+6Dkm6fR6tFttpJRksxlyGScxEdU4tGKZry2GHgbYC0D2QgAYxACwpwFkEIM9X4M3EYPDwAfpqdPse77K14vzEQkQ6sd5QAgOQwAZ5ZcGk6oOUqfXwNFTZUrPU/4OUpcV5pUGo74GnOkyzOsyvmaUrcsNej38noc03kX6PkJKHNvBskS/0B44tbU2rIdUejwNE+rDaB/pZ1kI2xjPWrBFx+MYQOuhOMY/ZFAeCR5V11FzT3PE4Q/1A6bB5Q/bkGAZSQfJBxh+3M2w+hwCcIXCOH4+PY400DoAH43ShkBNg9bPCLgGFIT2lW5s17l48TKfXrmB5wecXJjniXOnOXLsCG4hp/jvQx1eKXmqgc1e/N6wZEn923iVEJnpDKPL0UwLlxEVzjcsa8R4Q1MgQ1mcqEZSkZMjgudHfBC1oNvpsr27Q6mQwS9mASecXjKJHaJnjOf1cSQhEtVnJfVNKkvERp8hDGHwtBnCOkytxLQCDQVaGtnayMhQGUne1Hulq7DfPYkYqA0JxX8sjbCTezsizdCo80E13aFxzAyNcz9KtrBmSMkUSsswHkg9b+ZijhMhZcy/tOUKBaSiNxci1HIspLSi/ow1dV2wHeYT0Ol2qTea9HotpJSMj1fJlYogLYIg4M7dRd546wPu3FsCKZmfm+ZzLz7NudML+H7A3XsPuHL9NnOzUzz/3FNksxmUhdABx3iPIEBiE0h1LpeUEmEJXDfL0MCf0ZxRQCoIevQ6HWq7dXwPbNum2+1SKBapToxjWfbQJVAJ+FLSabVxbAvXdWO/Bt1KQlDbrbO9s4sEms0Wq2ubbG5t02o3cR2bYjFPNpelkC9QrVRZODpHqZhXfi/Sii3Pwsf3PVrNFlvbO/S8gFw+R7lcpFQuYQnNGQQRawoCNje2+P5PXufTy9fxPB/f8+h2u/Q8j2KxyJdefpavfP4lpibHww1/EqnBZADtZotbN+/QbLYoZB1cx6JcyDNRKeEKYQA8H9Oq2tips7tTJ+e4VIoFbMtGRJbTAOl59DpdvHabQPrYUpLLZtV7/PO/hPUt5ZNnULxcbwKt/vsHofQuw4dl0UoeR6TpIEpXP88cmCas77B6DwZMoRK0z7umVz7CixFvja/pftL8LimR9m3RKMGA9kmItQMqrYnVjL2sdJ+RpCToBSwvrfCLtz7g3Y8u0+50OTI3zdZOja9lcxw5fsTgnzE9LOwVBzvX+Q6Q2qYFSwOugfss1EUTTEWBIoQGXLKvO/p/64CpWl7ouplSbbQ+fbQHUQsIfJ9avcGDpYBSPksu4+JkHOW2GoHPsDFCq4EMtWXV8GGjhOkGaS/GL3Q0+ZEmSXT4V4oikJXSPPvSpUy70qe/SfdgVAlEfhhANgxw6edC7fkzLSnEk6s/bMYBGUdff0t6PY9Ws0G700EArp2hWCiSyWSwbAWaZLqP1cN0uz2Wl1e5/2CJVrNFr+cxMV7h9MkFxsbHsW0nVHwVWJUS1je3WVvfolgsMDU5TqGQR9g2AjVW7y8u8dob73L56k06nQ7FYoEvff55vvbVl8nmc/R6Hh9fvMI/+5/+iqXldSxLsHB0jozrcGR2ivsPlvmTP/ser7/1IS899yRH5mc4dnQOpdmqlpRS4nse21s73Lx9h8UHD2i1O/R6HtlshmeePs/Zs6fJ5XNAuu8kkgCv2+bevSVu37rLyuo6iw9WCXw1Bja3dlk4Osc/+HvfYXpuZvAIlJJOp8snV67x/oefsLAwzysvPMP4WBW9g08Czd1d/uqvf8TP33gP23GoNVpsbu1QqzdVnwlwHQfHcchmMjz95Bn+8b/1HZ5/+jyOY4VzOqDbafNgeYXrt25z+dot7txbQgLZXI4zpxb49u98jbm5mfBdQwVG2hBIWu02d+7e59Vfvkuz2WZuepxSIc/axjY79QYPlleZmplifHoSK+P2KUgNGfAnr73JR59cJp/PYFs2Z04t8O/8wz/k7Onj/a0joLHb5Lvfe5XX3niP40fn+Xt/9DucPHFM+WdJBeZWVtf55NI1Pr17j3qjwdzcJH/nt7/C5PQk/MWPFNiy0447umrCKPKA82hgdhrEDYhNOPyhcBoPUKgGP6DKGNXiE6aOLAGRP6g0vo8ImAbsQh3lHWN/NkOKD3pX6SOE2twxqEsO7VunweqBrGuD8pGhvDoE3x2RvG6PWq1BoZDjzMljrG9us7m1w7Wb9/j8514ktiIml4MHHRWVrLo09P+Dt0MfsEsBLsJrUctEOoxFFNdLDIRuiXwlySwT9/VSY1gjaV4f8T0euYN8EAQ0mi1WltfIZTNU8nlK+WzfHqHkpDBptMEVrf9iRXn1bys10stwC/rAepuVG6ZtSfri5WiG0vdM+ndknjCe26Mi5hIiQTwqdL6BpSxGVjyc1BzXzytm9dkAV/xe2hExDlA3mHQvxEqfEgSe71OvN7m/tMK167e5desua+ubCKBcLHHy+FFeeO4CZ04dJ5vLRpqp1lKCIGBtfZN33/2AH776JnfvPaDb7dHpekyMVfj8ixf4nd/6GufPniSbzUTle70e77zzAX/6r39CuVTkG1/5HK+89Cyz01M4GRfpeVy5ep1/9dc/ZGllnVa7Szbj4nk+zz/7JNNhXnOz05xcmGdza4dsJsOFJ89w/twppJR8cvkGH396jSAIyOezBL4fxoJTfSelxOv1uHd/mR/97Je8+vpbtFpNxsYq9DyfWr3Jm+9+zB//wbd4+ZXnKRTyJHZWSEmv0+bixSv82V/9kIufXse2BeVCnna7R63RYrfeYvLGHV556VmmZqb6Rl+AgCDg9r0H/Mu//BGvv/kezz1zntm5aYrlAlknG/VVrd7gvY8u8d7HV8hlMxQKeTo9j3qjieM4FIs5LGGxW2tSqzdZ39ym2+0pcOuDjwLFFy9e5s/++kdcvHyDbrdHNuuSz2VZ39rlrfc+odvt8cd/+C2mZqeJ2GYQYCEpl4r8xlc/z/LKOrfu3OePv/1Nzpxa4Ac/fZ2f/fI9HiytcuvOIi+/8Axuxu0bh/l8npMnjvCLt97jzr0HTE5UOXpkBts2BLjJpaXF8tI6b77zEe988CnXb97jyfOnOTI/SyZnRe1pAZ7n8cnlG9y+94Anzp7gd37rGwokeqFAcpyB5hHtgDv6wtxDJAEJH7KHTNL8Fi3DyyQITikQ+7VBws/0M9Rq+NNSKc8aGCJSTXPYnhIo9wSThx+SNECV/sMdMqEok4Ekk3U5Oj9DsZBnbmaSWr3JE+dPMTMzRb+g2sOQEFVZRifIIFGroUPkUHolxYig2VeO6VelcJWlXCf00VeSsLAgVkTUg9FvzWfMGHPSvBaWIyyrz+IPIEUQiuHROuPRgK1Igw9od7r0uj22dxvcvrfEqaOzTIxVYlNe+MhAA1P032jgPSjpcCdCITeK2TU1jfqKGVJuevbtO5cGoKoBWqUZs0mYGpG5M8WcuFKEkxDFMAwFKA4it8+xMSORDF/B7LUhkww9cJPXavUGN28v8uY7H/LhxassLa/h9TwC36fV6tDt+eRzWb5x5z7/9B/+EccXjhIImZhcD5ZW+R/+5V/zxlvvk8m4PPPEaYQQrG3scOvuEn/5g9fY2KnzT/7tP+D8mZPYjo2OmVIpl7CF4IOLV7lzb4mVtQ1+5ze/yrEjc9i24MlzJ/kHf/Tb/OKtD/n40nXOnDzG5164QD5XQPpKwC4cm+eZp89z9cZdut0e01PjzM1OEwQB6xtbtNodxiplTp84RiGfV5YGwjNCA8nigxX+xV98j5+8+iYzk2P83d//Dc6cWqDR7vLLdz7i7fc+4U//8ge4uSwvPndBAcaw3/xej08+vcr/+Kd/w0cXrzAzNc5Xv/Acp4/P02l5fHzpBm9/dJl6o8XyyhqB52E5yWkuJfR6PW7eWeTqjTs0mm3W1rdYXdvkzMkFsraDlGGMGtulVCxw5sRRvv7lV5iemeKTy9f5/k9+iW1bfP7FC7zw3JN8+PE1llY2ePHZJ5mZnlTuaPj4Em7eWeTPv/sT3vvoMseOzPDis09x4tgcuVyWDz+5zt/85Bf8xXd/Rr6Q54/+4Lcpl0uJcZ/P55gYG6OQK+DYDrPTU5w9eZLLR26Ty1yk0e5QqzXo9byBIzGfd/jCK8/y09feZGt7lwtPnuHv/MFvcWR+lkDGXoFgIxB4ns/yyhprG1uIEEh+dPEar7z4PDOzOTW/bJuZuRm+VimTz+V4892P2N7ZxdebLLxwg4Vth8vXQYQ9THrYOwf3zc8KFSWh2magfjsCDQMesfhLh1pRIDb+bmYgHzHe1D28x4saAldIUse36Ue1P9FBqhtbXh4K2Bq2GvOZKcC2bSYnJrCARrNFt6cc5E+cOkGxUg7H1qBzPB8FDTNyGN80ftAiMXRviX311CBLxvo0DAZpY0vKPUZjFEuo2KCWHsvmUBLqxIhR6BGBrSCyanW7XTzPRwjodDr0el00crQ04DIG+n6U3D6aeiZtPheEVhH1I4rx0Z9rKk/zby+Kdz0oGhLT5zORyZWGDXMZ1sMm0qJEoBzBObxO1kfRskP4fQTrllmu5/m8+8En/MV3f8rV63cp5DKcP7WgQEnW5e7iMp9cvcXi0hrXbt5ldX2TY0fn1Tb9MI96o8mPX32D7//kF0xUy3znW1/liy89TalQYHVtmx+/9jbf++kveOfDT3ny7AnmZqYYH6sAYFsWz104z+9/6yu0/vrHXL+1yF9+9yf0ej2+/VtfY+HIHMeOHWG8WqVWa3D1xh2+9MpzfPMbXySfy+L7yhpaLhU5f/Ykc7OTXL1+h7uLS2xubZPPODxYWsXzfBaOzfLk+VMU8jllJQqlWrvd5dXX3+ZvfvQ60+NV/uEf/y5f+9LzFMolvAAmJsbY2q7x6eWb/Ognv2RqcoJTJ45FVpj1zS1+/OqbfPzpNeZnJ/nj3/8mX/7881QqJQgETz/1BLlCge//9A22tnfwPA8nBbZsAmrtDutrG/Q6HRzLYne3xurKGu1Wk3zGRXselot5fvNrn+frX36ZF55/BtdVwO/VX7yH5/WYnZnkt3/zqzzz1BNsbdWYnZlkbnJMhW0JBLV6i7fe+YgPL15lYrzKH3/nt/jal1+hWCwgERw9doz7K2u88c5HfPcHr3P8+DG+8sVXVDDBaATZiHA3slZO2p0OtXoDy7KZmqgyOz2O6xg7kyJSGx0c18EOGWa1UubY0Xlc14mW3JTrgpJlXrfH6vomzWYHS1h0uz2u37zL6uom0zPTCBHzmUKxwJe/9ArVapnv/eg1og0+XjgvnHDThd7QctipOIx3HeQYGyvkTykeGZ2BMox0sKLoARjEVfpy0HxdosC75+F1ewS+j+tmsDKZmPfLRwO4Yp+dUNjKwFhuMmpsWcp1JeRx0fdoqUwvfwbG24dlDCpYpu4KEYf/MJ2VD/Yy/QBhUJF71WtgvoBl4WQtpqYnqVbL+J4PlkWukMfJZtXxUwh0M8SyTkafSiyEpWqrkxDJkEcHJZGU+XoYqlJ12QaYTi0tRsCe+Dlt3RLEZ+RGo1rjkQSW0GnNBlNXHcdGiNFg1MMHW3p7PSrGRRCezJ3NZCgVC9i2jbCSkeVHVu7MdMMAmjmADSuMAlqCKAKwJYxEqTJGqI+ITJwO6HPOiCPZ7v8iB5loo6VVuyJC65U0GORnXuc3zMCHnDkSaDbb/PCnb/LuB5eYmhjjG19+ha994SWOz0+TyTisb2zz5rsf8bM33qdSLuM6tho/lh3166UrN/jxz9/Ctiy+/Lnn1FJhJsPEWIXJsQkA7tx/wDsfXeaTKzf5/MvPUq2U1LgTgnK5yDNPnePt9z9hbX2L7Z1d/upvfka72eH3f+vrnDp5BM/rEciAQAYIS5DNuOEp8qpdbWEzNzPH0fk5rl6/yyeXrvPq6+8wVi5w/dYiuWyGZ586z/GjCzhOJvJbEgKWV9d554OLdDpdPv/S03zxlWco5HPIboCwLE4sHOHl55/ixs17vP/hJS48eZrZ6UlKpQIA9xeXuHX7Po5t89Uvvcw3vvoKlXIJ31fHwxw/dZx/+Pd+n3KpwMLRedX2JuOIlgfrrG9uUSzmmQ7G2K03uH3nAdvbu5RLJdwQqOeyLl/+/ItgW/iBwPN9KuUSGdeh0+2wtV2jVqtx8sRRjh9TCpSjzexBwOraOpeu3qTT7fHsU2f5wkvPUirkCTwVC+fUiWN89Qsvcf3mXR4sr/Lz19/luSefYGysCqgdyXoYW0LQbLV4/+Ilbi8+4JMr1ykXC3zlCy/yyrMXyGUcNeY1Q9bCKQRAEhCWRalYUG0eDusgvC/wEdJhN1zizmZc5mYmWVpZZ2V9k/WNTYLAj0++CH1LHcdmfm6Gs6ePU8jn1T0NtiKfLc1YDungLGOhNjrDTNPDsorspfyF7ynisqQU9Jotbly7ya07iwRBwMnjRzl16iQF7SM44vb5w1XXtCqJSEZ1Ot3Q/7BBtVJmZnZKWYEHtG+0WUBoXmT+pSjik4YlT9ejrz6MDrqGWLaGwb7R/cxCGekIXCuPo8ew0OcXCwW4BeGniC1Iui2iQ8uJLZom4DoACZF8R9MfUW2GU+0Q9aiwDHCk0aBmAvFSogb2keRPlRH3cVxmBDNS+oke5sVikVwuN9J7PTSwFflJpXZ5CCGwbYtyKc/s1DjFQg7LWAON/qAPqCQ1p8NxidiaZalYVZoCoQCXVJ0TGWuCEIiNCLhUPa0D1jANtVP5Cr2DJpVpQsMcnK/W4gYP8IOp1aYfXfTUgWVF/II7u3Wub27gewFz05O8+MwTPHX2BNlcBiyLUrnE5MQYZ04u0Op5HDsyhyUEfqB8KZrtNm+/9zH3Fpc4OjfFF19+jiNz05TzWQV1LcH05AQnjx/hvU+u8mB5jeWVdc6cPB6NOYSgkM9TLBY4MjtNe2yMO4tLfPdHr9FqdfjOb3+d6ekqvq8c6j3PwxzS+mupVOTI/CzlUoH1jW1+9LM3GCsV2Njc5vyZkzzz1BNUKmXAQsXVUyEmHjxYZnllHSEElWqZTNYNFRIBAeSyOU6dOMbszCSXr93mypWbvPLCBfL5LALY3tllp1ZX1rVzJ8kXi/TCkAlSSvAls3Oz/MN/6w/xAxlZtcygfJ7ns7q2ydLyGpVSkUqpyPaVm9y4eY/799eZnpzGzVmRQmLZFp4RfNB1XYRt0fN9Vja22djcZWpiKukbISWe57G5tcPm1g62bTM7M02hXCbwFVMOpMTNZDh79iSTE2Osrm9x6+Y9Fu8vMVYphWlsFYcvZKae73P1xh3aHQX0vvjys3zti68wPzeDcGxi64NA4se6bxgB2hKCfD6L6zqJsUnIaH0/YHltk7uLy0xOjCkrZ71Js9Vit94g8HtIJxMLobBNxserfOWLLzNeLYeNnAZb8JkcnKUxl9LOwSPnoZSx+OiZGIgmkkWWF3OyDwEWI5ULi3cX+Rd/8X0++PgKtm3x/NPn+f3f+QbPPP8Mbj5P5Dj/CA6cjhzsQ4u/VEse7O7Weevdj7lz7wEXnjjNV8olCuXiUDCbGN/o9o+DpiafsuLmGpSdSI6fkfsykP3BUqNC+gsaPXxFaAGyBhkMTHBtnHuKAUJNC5cQsWXwgGM0DbT67umaRIDLirvLsGqrKO/6t40QPtq9KM4rxh/auqV9jIVRZmwYEkk7iYRCoaB2fI9AD9eylQICCmjZUWULhZwyuxErPqYjWnRDZ8cQG9Awq9YgE6bOSKaW+PQ1bQUSYf2FvglDez2RiU51aCPu/mVYUjnBj7CjJTatEjVgOAXUpVF3KkXFm8AwadUatZV0Pq1mi1q7CVJSyGWZqJTJZFSgW4EA26JcLfPSC08TBBJhq+FpIfGlCjuwvr5Jr+dh2w7TU5NMTU4gAl/hZnw8fCzbxhKCVrtDvdHC87xQuKp6+EFAr+cxOT7GkbkZbMvi1r0H/OS1t2g2Gnzjq59jt9ZQsZtCUpYui0BKOl1fPT8xzvjYGNs7NR4srbNqCarlMs89/QTHjx2JytRN6Pk+G1s7tNpdQEVk6PQs3HCOBBIEDrlMnnwuT6/rsba+zdbmDjNTk1iWiHyCbMfGdRx6nt7eLEDaeJ6P7HkgLBwHuj0Peh6ObYXAS9Bud7i3uMTy8jpCoOaklCwurXLt+m2Ozc/QKxXC80ttAqGit9uWTWBZCNvBsix8P2Bza4fVjW3OnlJhA6UQ6mQI36NWb6j37XTURpl2h1a7i1PIAVJZCi0LN5uJxmar06HZbOH5AZYlCPyAbq9Hp9vD93pMjo/x9S+9zPZOjZ+/8S67tTqNZpNu1yOT00GTNdtUljEtDtUKpMS2rJQMiEdyt9fj/oNlVtc3ybourqPAcK/nsbW9Ta/Xi+qrSUiJ6zjMTk/GAsYL/cc0ABSGleNhLJkdxv9Huxdo675ulEQSDb7SQCtJe1df5xFyxkDtAl5aWWNrZ5dut0e353Hm9HHOX3gSNz+0mMORliVpf9hQ8KpTAXwc1yHjOoxXy7iOo6zDI2VvKhWmvDKBjQfCSb2TTH2m5NjIFi4daHpg5Yj5vx+Vth/Ej6w1kTuKiO9Esnk/jh+Dsth/KmF6Cf8fEGAOojBI7MC8QguXjmKgIhqIKBZf7AQf2ltMq5cQYXqGyEmRKMa27IQiuxc94t2I4Ng2rutQbzRpNFr0er5aGghrm3wpE3jpBiACV4dyJh0EtGCAA6QgGcdkFKA1qJFHH5AJMs9OhHCUitBqLFFm9vAoksh0Oyj7Perd588yCu2nGe09jYXBYGQQxpMKJH7Xx+9JwFYWx3C297odHtxfIpvNMDE5ie04UeDMoBdgW+oA83qjxeZWPRRw4IRgpdVWvjye5+M4tlqKlPFxC1JK2u02tXqDRrODbducPXUc13W4e+8Br7/9IStrG/hShs0VA9cgCGi02qxu7LC+sYlj2UyOV3mwtEq32yUA5uemeeqJM5TLxVhDlND1A2q7NbZ3a9FZoeubW2xubGFPVCMneD+QNFptWq02Emi3e6yu7VCtbpALrWC2Y1OvN1laWefEwjFKxQJSCmq1XTa2tmm12gDkc1ks26Ld6jA1McbRuVksy2FrY5fbtxbp9TzyuQyBL8lns9QbTT74+BLlYo6Z6UmKhTyO6yIsBzfjUikXyRdLykpkKdDQanXY3W3g+xLbIgxk3GZjc4vllTXu3V+KhOvtu/d5sLTC2dMnIvAhgUa9SbPVRgiB6yjfKhkE9HxJbbfB2voOd+/dZ2t7FwEcmZ3hzIkF7t5b5PL123zvJ6/jug4XnjxFqVxCOGHIEGkhhcT3fHqdnhKmEoJAzR+tzerAAkEQUN/d5fbtu8ggIJt18fwe5VKRRrPF0soqrXabQqmIXo7tExhSjXNh+mxppSzB5x6+BWckCoZbqGKhEW/E2ZdbmLgBrdQZSm8I1I/MzfCFl5+jXCywvVNTy3ZTkzh2aK0fNar6MEop6kMVb02WRXW8yhc//wKNRpOM65LPp5eDRuGVA/h5wtJi7vqUJANCGdYSgZJTg8bUsCITpcZjK1reM1ZCBgdDSpcvQ11Ag20Vs3BgwXtWLhzfwpQe8Zjvq8mBLbQp2T20KnrnpkDqQ6jT4yRVc5ms6oj1+dsGW6FpMZN1KReLtNpd6s0OnW6AlIZZTjda9DXWGgz9IW6g/cBCapIlykikiwX8oYHcwyDTbNrXycYFAVHsodhumvrUR1agNNg+UhNZMgy1H7zqiXruOebU8t2YI9itNVjf2mFpZY0nzp0k4+QVsAwC7t5Z4l9970eMVyv8zre+xtzMtNI0ArCFxczUZMSw337/YyCgUMgwOzOFbVmsrm9y9/4yfhAwPTnO2FhFWY2CWBg0W22azRbdrke316NcKvDys08xNzXOux9d5uKVm2RcJwr7oMnzfXZ2ayytrFKvN8hkXeZmp7l5e5FOp0M2m+H4sTmOzM3gOg46vkwgJfVGk+u377O5tUM+l2Vnt861G3e4dPUG3eNHKRZKFAp5AiS37yyysrqhfMUswdLyGr4fkM047OzWcR2Hre0ar77+Hlk3z1NPnKFWb/CLN9/jk8s31DsVC5w+tYBtWWxt7/LihScZK1XIZjLcW7zPrTuLzEyO8bXPv0A24/LGex/z1vuf8MnlWwgheOGZ8xydnyGfyxEIZQlqNptMTavYaCIcAN2eR73WwPM8LNeh0WyztLzK/aUV1je32N2tY9sK8N68vci7H37C5MQ41UqZbq/L5tY2V67eYGe3hm3bzExPKOd5Cb2ux9KDVT68eIk79x6wurGlYqutrfPMk+d46vwZbt9f4a33L+J5Pp73DV587gKFsh1q1gporW/vcuvOfeqNFhJJO7Se5bMZtJ7rBwH1epsbN25z/eYdJsarfP3Lr1CtlHjvw0/54c/e5P7SChsb20xMThD4Po1GMwa2GTVehKPifGW0ZasvzlbIjx6GdetRkNwDaJksNZrrxtLtwPdRQn9+4Ri//7u/wfNPP0Gr3aZaKbNw/BiZXG5QSYcgMVg2hEpBP18V2I7D+NQE4xNjYdrYH2mkEoW5602RATUN0uAl3UYG8JWElqNwvOwpwAeNnWTJ8fJeOqFup2RaEknTVqIRyDQCDOwLQ2aF6dS5z9r8NVqjKyxqADqZqm+q3rqPdBgJEc34ZM0SLk37ibJEMaPP44cHtlK+WqoiFrlslvHxcQQWpWIJS+hAlVY8d/VLJjpJgSHVrimn7IMArpRVS0K8K+dvjdIA06C0OTSybkGkGel3EulBI8MJ2z8AYq0zjOrNIQCXTE7ovvqmyIySIoRgfKzC89PHWdnYUo7iH37CqYU5Tp5awLJdNje2+Pmb7/Lamx9wZH6Wz73yPHMzMygGFKiAn0+e5catu7z70SV++LM3WHywzJH5SZ5+8iylYoH3PrrE3cVlCvkcJ44fZWKsGm/ZxSIIfHZrDWqNJq7jUszn6Hk+E+NVzp9eIJ/N8trbH7Bbb9LzfbpdP1r7l0FAr9vD9zxyuRzFYp6Tx49w+cp1dnZ2KZcKnFg4QqVcjtpWhgJ/Y3OLB0srlEtFXnzuKd545yMW76/w2hvvsbW5zVhljEqlRKfT5d33LtJqtXnizHFefu5JTh0/QqvdZmVtQwVuHRvjerDIBx9fQQZQrzd4sLLGd3/4GvcerACQzWS4eXuR+blpioU8m1uhNc5x+PTKDW4vLvH0uZM88+Q5KuUS65s7fPTpNZbXt2g1O8xMTnL82BFyuSy9AJqtNo1mm83tGrdu36XT7mFbFt1Ol7X1jRB0ZJVP2W4N13U4OjdDuVjEDy2aG5vbvPqLd3Edh/m5GXZ2atx7sMzFT6/RanWZnhjn9IkFSqViyA9sHNum3mhy9/4Su7U6vZ7Hp1duMDM1wfz8LAtHZrl4+Qa/eOdDxqplTp1YIFfIKod+z2d7a5tPL13njbc+UsBPWNy+fY9bt+4yVnoCy3Ho+T7b2zWuXb/H62+8x+27Dzh+bI6JsSrnzp5EIPj402ssr65z++4iJ08cY/HeIr98+wPWNrZxHZvjR2Y5f/Yk+UIRCRztdmPfVKnOUItn4sF5z15LFYPm8bD0e855Y/mwDxMAGCEsIuYchMJSgxphPkCUl+04TB2ZZ2puVoXk0QeJa3DzKNixaVXqAxI2hAGwzfN6k7U+QDmGZS724QqiKxpoDe3FCHgoFwDDuJgkS++KVLG2+vRzQ5mOfX+NBjZ8lYZW5YByoc+XbY88IhGix6cGWsMAV5/xRBcUnjwSgbehlTPGpYj6QJj3jfpo4JVsVe2LPih/2KNXE/RIlxEtIchmslTKZfIZl1IxTyaXBSs81kab9QgRpwYVenkRDbYOYXkyLWH6HDyN6FPLlor0hDgIM+wb6gPyHbnCibySmokM2yusZ/pA6EHF7WkJPAh3G3ZG2N7wf1DuxUKe3/jKKyyvbXDxyg1ef+cjAt/jleeeIl8ocuPuIq++8S47tQanTmb6QhbkslnOnznJ73zzK3R7PT785Bq/eOcjKuUCH316jUIux53FZTzf5+zJY5w8NmfEqFLWmU7HY3llna2dmgIudxaZmZogk3E5uXCUcrGA49i8+f5FNnfqygfKqINlCfJZF8uyKRcLHJmZZHpyjAdLqxydn+H0iQVyudgHSUpJu9NmY3ObQEqeOHOK5y9cIJPJ8dov3+Gji9dYWt5gYqxCJuOyu9tgZW2Ds6cW+OZXX+Gl5y8wMVZla3uHS1dvYVmCl597kp3tXS5du8XHl67RaLbYrdVZ29hSW7aFoNFscfPOAybGx3nmiWOUivnQWX2b9z++xMbmFo3mDPVGk07H58HyOq1OF6Sk0WgSeJJquUqlUsCT0On22N6tcfvOfd56+wN2dneVP1Wny9LKOls7u0CFRrOJ49hMjM9QLOYJAsnczCzj1XHeeu8j7j9Y5c//+sfkclna7Q61RlNtmJiZ5tmnzjE/Oxsel6jGXC7rMl4tMVYp05v16PY8tndq3Lu/zLnTJzh9YoH7S2ush8un7XZbtQGSRrPJrTuLXLp8ldu37+IIi2q5xNLyOu+9f5FTC0cZq1bZ2q5x6cpNXn39HT785Apb2zUK+SzXbt4hkAFrG5tYlmB7p8YHH1/m3OkT/PAnv+CvfvAqzWZb9evpk+RzBaYmJ+i2u0x3e2rXou/j9nrYWUvFDou5PCMx6Uh5F8QS2BAQEZ9IP9Sv/CLSvmrhZb3sNBRoCQW0grBsrazqQ8SlDC3u3nCuIhSPx7KTunPkIJ4u+IDU96zBx/uEpya9q9QERYNkwx7FRkAjfdB4GDQ7imC+Z2WJLD26PlZYPznI0DDoXfoFQkLmDajzw6ZR8u2Xa3s8NwBoRZhgFHwT8n2ExBIWCKuv5aPeFqFj/ADMp/l4utDo16/8IOpEA4To1rLIZFwK+RyBY1HI53AdVx0GbD4aLaWpBtE+Wljh7pk44YErJaPYMgPWng1TowJaMi4/WcH4e2J58rPQqCMmTCtS3Gi/4ge2VbxzJBnR+RAk4aBbE23b5tkL5+l0e0xNjHH5+m3e+/gKN27fx3Edmu02gQx49qkzfOGlZ5mcGMPUlBzHplqp8PwzTyKEYKxa4eadRTa2trh55wGOY1Mu5nnh6fM8df4UkxNVVMT5GBju1hrcXVxScZyE4O7iEq7jkDnrUirkmZqoUioVGB+r8rM331fLgb4PoS9RPpejmM/j+V4U3fzYkTm2d2o89/QTLBydi5bNAGQQ0Gy2qTeaVMoljh09yuT4OOVinmohx1vvX2RtY4u19S0CGVAs5Hny3Cm++oWXeOHZc0yMV7Btl07Hx/d9Mm6WJ8+eJOu6VMoFrt26z/LqOpal5len08P3AyxLAYsTR+c4cXSOQj7P9m6Nt96/yI3b90CqmF/tTodGo8PSyjoAuVyW9a0dFh8s8dKLz+DYGWxLYofLots7NdY2tqkUC3ieT89Ty4iNRpNCLkcQBJTLJaYmJ8nnlOdztTzG+FiVyfEx3njnQ+7ef8DG5g75XI5j82rZ9Ymzpzl2ZJZWu02j0SKY6BF4PXbrO3R6HZ48d5Ivfe4FMq5y8p+aHGdifIxmq0Wz1cLzfJ67cJ5sJkPg+3i+x9r6JssraxRyOV549kkCP8B11dFC05MTdLs+u7sNrt24ywcfXebS1Zvs7jbIZTOUS0WklFy/eZcbt+7heT5TEwpUf/zJZW7cvsfObj08wzugVCwwPzPNxMQ4tc1tQO1s7PY8es0WZdeN4yzFE2iEORZakCIBFd6IlvvSKCUpFIwnYsHfZ53WvEimwjiFwkoawAoRWrNGqDraipJWzOLyou+PRv6nykzVUAOXRJDmQ+YeWbIxJLZeadirHqk6mUlkKJOimFZmPWNrmjbaRH5ZYQDr+Lg2Ur5Xf7uU2B06ypKliYAEobHBiu+l3WqkMT8Mo0NCjBv9EEliAZZMhSgN/WsD7cObmG+jym9FD9GyNaChhNoB5boOvcBXpmORrKJGlKB2MkVBFPROgr2Az8j1GmCtSgCt2Myb1DxJduYAc2f8LR3ctB+U7R2DK4Wah5otHzlXStEeA+oQu0kq5TJffOk5js3PcuP2Pe7eu8/ubg1fQr6Q5+j8JKdOHGXh2FGq5TIYIQcU4LKolsu89NwFjszNcO/+A5ZWV9ja3qXd6VLI5zg6P83k+BjFvLKs+D7IMHyOYzs8ce40pVIBIdTus0ajg0SFOCiXC5wpHqdUqTA3P8vU5Bh2uO5v2Ta5XJZcPkujrsBPIQRHE+NVnr3wBNVqJbmEGB5X1e32mBgfp1wsks+6nD2xQLVU5Imzp7h+6w5LK+u02x2OHZnlhWee4sypBUrFApawkAG0Wh0WH6xRKec5dnSOV154mqNHZrl5e4nNnTrdXo+trR0WH6ywU6tjW4Kjs1OcPDZPIZ9HCNjY3KLVbnP29HFKuRynjh9hamIMKQVffuV5zp1ZoNfz8AOfc2dOUiwUsIQdrmb7OI7L0blZfufrX0RKSafdZWtnV8Xdclx6nodAUMgXyGWUYgXgOi5H5+fI5XJkMxmu3rhFsZBnenqSuekppqcmmRirggy4e2+RdqNJ0OvR6bbZ3NoBCc88dYazp06SzeZBKgtju9Ol0WjiOmpn6tzsFIV8jm63S63ZYGV1A8d1OH/2FONjVYqFHLZj49iKJ2Vcl/tLy9y6fQ8pA15+4QJSQqmY5+TxY4xXK1y9eRvLErzw7JMcPTJLxnUpFwucOn6EpZV1Ah/mZqf4nW9+JVzCzFF1XFxX+ft5SJq7dXL5HG7OCZl1IjJjapqZDtUhQ4qEUsLGGibzSe4Si2GOSdFvaRYmonzik+VSvDLQgitFAzYGaPAS1yAEapEyLZPlC+P7IBrVl0ejjcRbD+OT5nsenH/tW5X0ktoBHf8T0DlqM584bpd+TzOOl7KmCYL4CDrTWidVr+xneYpDfqTHmglowisPRQ6ZbTX62ZPmKItJ11nL8dDCGI4/NUSEkdoI+RAOhTQcNTdV6adk6p75HvvRQ15GTA5gKQMCX0ckNMLdD1mbjQOoiZDB6+/9nT0K9et3cVnqIw209vHlSlm4ZNTBhqlYQv8R5GqC6L1PyUOK9ABnAAMztdfU/UMNdmm0MQkQM5yMOkYa6meYaEJQKBY4e+o4C0fnaLWewet0kEJguxkK+SzZrItl2/hSb6tNlmfbFsVCnuPH5pmfnaTWOMnG5jZb2zv4QUAhn1fO5bZFEAT4XoDMKGZTrZT50isv0um1ANjc2uHt9y/S87p4BASWhes4HD06zfTUJEIoK5FuAssScVBL1G7b8fEqlXKJ6alxnJRDdBAEtDsduj2PSJWQAZZtMTM9yVi1zFPnz7C1vcPW9g7VaoUjs9Pk87moX3w/oF5vsrq2SRCoo66q1TJj1Spnjp+i0/PwfY9ms8XWzi71RpNarU692aRaLilg1O3i+T7PP/0ER+dnGa9WmKgUGauWsSyLudlJOj0fz1dzoZDPkw+D9QkEtnDIOpLTC0eZn5oMI9oL2mFYh3K5xE69oXzcgrjLtJwVtk25VKRSLjI9XuW5C+c5fvwY+Xw+PDDcIuh1GKtW2NzaptXu0u31qDeaVKtlji/MMzZWAqF2r2ogWymXyOWyTE+OR5sB6o067W4H13HU0nCpRLlUplDIYgsZnVxRrzfY3tyi02py/tQxnjh3mnw+j5vNkstl6XZ6tLtdWu0OC0fmuPDEGcYqRXrdLpPjVY7MzuM4DsePzXPy+DG1S9GyKGQz6kxEQNq2Ol0gn2XczYDtGFYGPwWyBoGF4QDKvCKN/0Mpum1u7TfLSAGWUXbGmWn1iRJGFn285SASdWQ8ZCrERgFW6l0SmT86wAXES7Om31gfJk6DZ8Xr+4CEUDwjTjcIWOpnjKXmw1BiSZX4u8n3Q7echwe4DtgHAhK+g0C0AUHTgHHbJz6l0U8kw0AIEeMDvdEpgUU1UOzrx+H00MCWWhoWRvgCSeD5dHtdpJTR+UL9HaS4cYDA1qBKiBChp+I6DejcYd29F9CK66CBlgwnZnoQ6xcb9LL6+RSwGlihEDBJGN7kMtVnsTarFByjt7XGN8pgTzMZM6BhBAL3Alw6jXbyPeAES5cfgRa1JJfLZmPTbALMmsLG0Bij9XOBYzvYDthOOboupSSfy2E7VrgD0cYPTcGgYkpVymUkeYLAJwh3LVqWFYEoLdrcrDr+yA/r7Qc+nucT+AGO65DNZhECXNvBsW1sO97mr+seBOB7AV4voNPp0uv18P1slMJxHKqVEiDZrdfp9XphfKt47KuQEy1a7TYwFt23cMnnBfm8apeJsSpH5mbodLrcX1pmfXObbDZDxnXpdl0sYTE+VuH4saOMVcrYthXJIyeTww+CMCxC/3iwBWRsm1KxQD6XxXUcHMeOZEan22V7Z5d6vUU206BaqajgpwiirTBej06rhe97FPI5qqUCjuMQhIqI4zoqwOrOLjs7NTq9No1Wi6Pzs5SKRQKhQILigSLqT8txaHV6rG9scu/+A4LAY+HYPMeOzpLJZPE9cJ0MWctFWD4CSSAVCN6t1XEchxMLRzixcET5lIa+Hp1Ol5mpCdbXN8lmM5TyGSrFArKQp1KscObkKWUly7hYjq1AlBAqgJrmd47Nzm6dYrFApVLBsR1iAaPHywC+Y9KoFp5+M1lMEpLWkWHPHpL25JOHoEHK516JNdhDEC0xBZLBu7JTddzvuKJDUnIOiT581L96EY6LhAAPYbSQBuDSGfXZYlL39c/9z8Q1z+NN59cHh2RA36a1z0Qhb9/LujVwDhha3SCDRCp/Pc/0DsXYshWCK2nMRg26wjyDIFDnvfbthBydHr5lK2wUifZZ6CEJsC0jEmtC81HPxfcsFcRToM72i8DXYIa098Gg+wEtqVo4ZK4HYg4CosPURmUKAqX5pZtdysRkj+P3mEALFdiUsFwNmobVWfYPvhjUmJqwYk6DAJcalKlBpd/5ECRRgSxFdKSTChaqX0PIuA4BYTlaYZUq5MDWzg6er3ybctkMwlJTtBeowKelQp6xShnHydDr+ni+AlSarMhKqiKIZzMZ5uemcWybbNY19MN4gmoQ53kenY4C1/lcjmIhT7fXw3ZsAl8i9NmUuoGlhW255HMFspkszbYCD5VSITrrEKDXU5HWHyytMjc7nVBKpJR4vkej2cL3A3JZBXRiBhX3FSgQq8IbdCkW8kxNjlPI52l3OmxsbmFZFrZt4bhO3N9SCSUd7NW2LAWkwuERSBX/q+f5BIHEdV1yuawKWKzbRwhcN0u312NjS1nostkMwlZgyg98tnbrbNfq5HJ5cvkiUjhIYYetDNgOhWKBbC7Hxk6N3d1tPM9ncnwMO5PV3UEgJQEST0I3kLRaLbZ3atxdfIDX63JyYZa52SkqlTK9rqTjqfAMgRDY0kYKHymh1eyxuVmnXm9hCQW29bFeEollCTKZTNgW8QG8lmWRyzohrzL4lm7PIPal8YU6Yqjd7kR+fMRvbHzuT8NUnX25j4TYmvUwrBEDKL189qjKGURRw0j0iSBqDobAS+wRluJvkYYFzew7dVLHyuoDXMJgABBbFoeArpGpX6YqePEZLGYDKQ3jhm3GSj2lV2aM54XxHzDknoyBoYhBlLQsRBBb50SIA4SBEXTuQSDwfRkpojpfCcZO9/3pEYEt9cv3vVCT98JglCIGXRiDLRKAJhCKhZGMJu+wFxuGZnVz6fzN9NqqpRnloCz2bkgdMXvfracpi40M9KEAe5EBlkyghYiviUAB06FZDL8Xt3TYDlKHdiQh6PueGgS0hiwLJ5chlHk8MNaYpAGChAzPvQpCNxHUBBC62ECysr7O2x98Ss/zOX1qgdMnjpLLubS6Hts7dRqtDpVSmXyugOu4dKxeuNuuSzfXI2+cZhDt7hSCarVCIV/AzeYJhN03cSXQ7fVoNtv0vC6ZjAqAmM2EQUZt5ZsisBTgMnYQObbDzPQUrXaH9fVNtrZ3qZbLWI6rjiLyfbZqDe6vbmC7GWZmZrDdDIGwlEVISjzPp9PpkM/nqFbKuE4OETqhyXBs62gyXs9jbWOT9c0tJifGKRYLVMsl2u0Mu/UG2zt1tnYbFEolstksejk8END1JfVWh8BXAT1tS1muPN+n1e7Q6XQoFgtM5KsIJxOWD1IG2G6GUrikt7NTY31jk1w2Gx7gHbBTq3Hz7iJdz+fEsWOUyhWEbSWs1QKBm81TLJXY2N5mdX2TSrnI+MS4shoKc9QGSDy6vQ4ra6ssrawBcPr0AqeOz1OplHEch067Q8/rEQQBQlj44fgLhCAgoNFqsLm9w/ZuTR3aLTMh2xB4nk+r1aLZbpPJZOj6Eg+hgq6CYtYByuxnzGgZ7oYMgoCO59Fqd0LL6T7Hemgzo57fyQkVvXmakyWBm+J3yXT78U+dmUx+P4hPZmRpt/TkTYIJc0nvEViRogbrUwaHvbMpa0S4indwa8WjoDQIk+b2u3BsqtAVxBsXwhhhIgJrByTDL3DvUfLZAZfpAxVTEngNUksSFLZD+olE2w3ydYywh8pDncqR2EsKaBtwCN4lUWDsKHcZejmGq3aj0EN2kA8nnCXAh8DrsbVdY3Nzh/FymWI+h2sJnNDEHi3daDOnyRNEbNWKeUkKkO3JDELrmAxA2P0WLdMhXkv0g76vjnwu9qiLwXTi7dUm002je/MVwnz1oJIhAtfjXe8S+gxjP6GxRNGGDRNxZCGDeNnDUKgGWND6QWo4Ea7cxP4v/isQttolKiykHZ7BJyx1bI9QlhdpWQjLAke1cSDA2dhi7MZtvEDizk5jnVrAKRbIdHtUd3YoBoLqWJVspYztOhAEeO0OPhCUCshiEcLjgRTz9xGNNjnfJ1cqYhdyCNtB2OGuWEtpQEEQ0G636XV7uI5NNp8ja/fAD7DbHTJ+AJaDI2XYHyI8Y0w1a7GQ5+TCUfK5LLV6nZ3aDk5G4DgOrVaHnVqNUrHI6ZPHmZ0JD8MFAukjhI8veyBgcnyM8WoF13HDeRNrt4FQIH59e5frN+9Sq9cp5PPIIMC2bdxMBtvJ0PMDao0m3Z5PJkOkDAipljSDQLJTqyFryrLjewH1hlrCLBbyjI9VyWQyWJYRqVqq+Vwu55mdGafdbrO2vhlF8W80lC9Zq93m6NwsZ04tUCzGPmkms3IdtVQZyAAvgNm5OUpG3DKTPN+n3miwtrmJm3E4fWKB48fmKZWKofUPOr02nU6bQPogAoSlxroIbAqlIjOz02zv1tipNWh1euQKqk2kVMFvVUT+DlMTKuaXCMdtICW+9Am8AEc6uG4shIJeG4JA7dTsdLFtde6n7bp7T8Yoiz14gpksQWnAdUgK9ll2G0bRMp7mbbK/oqb7wyE218RFxc8OtA4lfEv3KkfE6RNyZUBSax8mq7SOvdMclgwDRlgZ+t4t9A/rx+gjCocIxO2toCe5/8Fp8LmbqTRRaerXMHgXh5EwVYxUWgHK1Sd0Mwmf00f4RL5nKV3EEsogIhBYQvEoK5TzkcwMv1sj7sp/RHG21ODodHrcXVzmzp0HPHHmOOOVIpaIrVuWsUxoOsdHeaTzxAQuGL2/l2VJ0m9WDRJzKwJm6TIH7bjpo4D+7b3JZxIWoqhfDJBi3I/WzhPgRYL00UuW0WC05N5zaZh6MBAYGW2UnggJ/ynjmJJIQdmvndRzYmMbXnvXqM4AUDZkZtlSMuN5lFptgkCScW+Q/cX72LZFVkpK4UYM2/C9cqUkFyjLjSUElj6DMZxbtpSM6UOOI4trf50cKckE6mBqy1wOl1CSATPhQdmWZal4LqA0TUuBLmyLnBCMWeAFKp6OHR7m7AJlBLbrYmdchGUjbBupz/GyodTzeW6nxpl2m9IbH1KcHMfKZhGOjY0IAauyElWaTZ7crWE7FtlcjlK1glUskrUEC90ek+0ObiZDvlpBZF2ErQClJSyyCCY9j2KvRy/w8FFLs2VfYrkOxVKJyvoWTjaDcCx1BIallqfc/+ZPqGxucb5YYCGboe5maBdyNLMZgmwGt1SkcHSO+XyOaq2B3ekiHEdFWLctRPgJgoIfMOY4eOUS8xPjZIWIQb0x4H1f+V3lslkWjs5zYuEIpWJRWRqFYHe3xr3FB7TbHSYmKgi7pDR/CbYtKJdLnD19gnqjSb3VZmltAztcIvV6KnRErdagWikxPTVJoZAP+1j1TRAEdLpd2u02hUIe13WRSDq7dexuj063S2AJ5ufnGZsYRwz0WR0wbwaxk4TA1QrjsPg+6Tmt0+9T9kMDC0oE9VkVIOy/IYBmBDJdLBJ+Q9HrhTv3hL4oU/x5j3Ywl9VNsgT7g5bDv9PByfBVsqwYwCb6Wdd51BiV+vnPCNb3oChW1Z7jLIV61JPKcV1lolIJw4IXfcTjvi8HMxaWMHi9UKFJos1riZWPEKcIS/nJGuNN55/NuiPbOh4B2NJMEbpdj42NXVbWt5gcq3BsZhIA2xLKOTcUjDKybInoWf0GMn0hJBPV7k9666xRv6i6eoCGzCExMEfRwAbdN4GLOSAE6ixAfUH2+2oR/wlpAps0wwAZWJHG1TehZDjkQh8GgZl3mvQEM3e69A8h04waLVMMa59B9e1PRN/k7is2ftaxbfK5HFJKbNvCDk+oF0IgnHCymFkIgWWHQI8BQEqIcAV5MPSLk8U7EBPr+QJ1IkJ0zRi/MgAfhO9DT122Q5uh6me1G9IK84z7L8zL8NnLBgGTXY+q72OvbeIuLkeAIt1UY1JSjfzgkj4IZaAkZciHk3tidV0cIG+M2Qjap/KKntNdd+seeB42kpyEsVS9okIcR50X6Njqu20b19RnVQheCgICGfQxuVDNRAqLDJKq54Flk8lmsF1HAThLLRNmmy3ma3XsjEtlahJRLCjAYwmwLQoIzgcBM7U6tUYT23VoFQsEuawCrr0eLwaSXCFP8b3L5PI5hKvOJxW2RUaooKVdz6NrWQSuQyAEreVVcr0elhBMzs5QOH2CYrEwZGybsQTFwOkZ72yL70fRgPYQpFGz6/GYNDMNeGDwXB4WhsbckSYiq1WKh0jf2HxklK1fedCSoozTqRkQpNjyAP5kXjKVxYHNswfPClK8aGD7pnjWQwWqe5PqU4k66Drc8GDubk0kTsuz4aTGWKhMsycsPxQNDiuRLmlYW+u0YFpFkytGyXSRm0iUVk8fgSWU+4/QYSFE/Gn6letNfbZt4dqWUrTDkvQwGa+W+4JvD6NHZNnSnWxhCYnX67G1vUOj2cT3fWXdEiIKLKkHRFofw7yaYvTqkl5eG8Gxbk/rE8Rne5laQXow7DFJzfknjPTRI+HE0IMtxWRi1K+sWkmgpR1uY+gZGTMDlK9TegAC0g9T6okkQCS2x4Y+Z9agXUqD3lsadY2vJFKmLBByvAL//P9J4PtIP8D3PPVOXqBi1sqAMDIkBH7I8ALwAwLfI+h5EARYUgWV8z1f3SM0Euo4QL5y9hJBgAgCrCBA+GH7B74yDob3kD4yUM7eBBLCuqk6hE5jfmDUTcbXpYRAHaat6xnVNzDqEaZTz/pRPYWv6iAD9T16XzMv/Ux437IE2axLJlDTVVnhkj0T9ZoQyjpGmgQqkvLwWRKBL1PpIZ6fQx8KJPheohxzCiQ2TXm+atPO8CwRKoCtAmI20nGU1ctxQoBmgWNjCUFOSoQvEZ6H9scAtQMy1+0x0e1itbu4nZ4CYmYxUpIBJv2ASq+HF+ZhhxaogkgqhelWEEKQlRI3UH0vAxX80JESXBfHscnPzuCMVxFOCKAD3WgaaEmUkDR3svbzmQhnRVZu9hGkmg9onhEK6MjqlEy3N9BS495MoWGQOudWx/yS9K8ipPmoILFsN+QgOql9kqLSYgViyKsO/z0yiWTTqAL3KMQEXIctc1QypGMCVJoxHfW9Q8KlcHlN5RDLms9CCaCe2O2Y/hwkb0neO8jys9E9AlQgdRFjhmjp0EiTVCQVyNdhYkSEV9SQVR7OglKx0BfuZxg9OrAF5HNZpsbHqJQKdDpd2t0eQagNKbcY0y8rNN0NzG+/Lk8O/EMPE4nBMNKWgyHaSwjKRKTdaK5oRLiVehDvowHJ9ACMcZzGbUIqk6rUwQJBCf6oPmEVgnjwRkAMgZQW+pw2icYAMlI+4zfWeZpLikl2q6aOwE4BXRVeL4SGloMcr4avnnx/b4+2kFIig0CNFyQyPEdNTwqzx/tzUZGwbcASTqTRRODcCh2YsbD/4/8bYnunP49hVROk3tX4l+qGGKykYWw4zqMdiTZyvIr3X/5n6td//J8htnbitpISoUMy6HL0X9hWad85iRENXMZ9GY0nfV0LUSlji6skUXaf5m5ek4CnADESZKmA9+/+Xag3EPUm1JuIRgPRbEOjiWi0od2O2izWgcz2k3Gb+r6yDqaW2ISUYFvIjIt0XYTjIF0nAmfCccnksrjCUsBA19cEzeHypAJUGVzXjQrQywzKiBIrMAmuElp69e5RaUnsMJEllAVNFnLK6joQVJiCU1vfw/k2BFPEykz6ZhoUpNozgX51fC+tADKQL5n+NWJwleK8pYRWG9HuIJttaLah1YZGHZot9b3ZUn/1lvG9CX//9+Abn0++aVQngw/pY9eiVzY0jlHYvfmOQ0HbMGE/QLsxFfgU9nqUlHxdj/4lWw1k9w/50E9J0DPY2f4zQ7C+HEaxpx0UQqbTCxGPGd2jMfASif6L421pOSujeocu6YDAEg52H0IfTA8RbIWdrRmZFCrY4NQYU+MVPN/HsSxcK7ZQCaFDOoQU/ZbRb/03bNAoAWqFQiM12MImHWXAmYLb9EEHbRnSc18vyZmVsJDCC8+ANi1EfWJ5JJKpmSsjB3kDzInBA08HYFNp7f57IrZtCRGe34XamWUNY6l74ETFuvV6eli0PgwbkNGxCvojzn/QENVOjEiJ5ytQ5DgOwhLJNXPDhCygb1u9FGrnmCN87DDSk9QCPHQCDgiwt3ZgYyv11mbbG7mmQJ7xWhGQjB0uwzc16qkApPIPs63hE1Rs7cD6Vl+D6/wScjNdj9TvdAJ9PZAqlEUQSBUnyrKi9kk8q+tvWI36Cvd99I5eWS7R+T/+b9SSHiCDwGxN7CBA9iRyaxe5tYOt/za3EZvbsLGN3NyEjW2oN2LQNYAiMWz40ykABXgelja2jVVgchymxmFyDCbHkRNjMFFFVivK+loqqlcNLYzSDwj82NIqtNV0gCVSBOGWfF/9qbkQgrb52SH1T/MQ2O/4q7SbQerm/mWky9Nbfxsd1dbNJjRa0GgqsNRoQbMRfg+vaZAU3hcaSLXakbV+IGYZxoMDCW99BOdOQKUMlSKUi1AuQ6UE5UJ4raR+V0rqfqmYzDNlUR9Kgdzf2b2voXQZgLTNHzHgeghAa1/Hf2PuKZCgr+uNTTqNOBTQMleKkmWmgdzo+cZ8elSHeEj64umyU7jAXMmxMAwOkc01GocaUKk9RWppMAg3MkVL9GFaE2BpcGYOFx0+SUdkVNJ+tPZ4KGArDogWRJq31gq18T2fzVLKZ8i6jmKKAyoYN2efLXeEWgxB90Pra3SisFRcJVN5kaEgF/EzsXJtCByh7DtRBAbhqzzlaKbFuD7hhJGaZ+kuDOPUp5lp+A6JMBp9r67NzCLMR+3w8gMVFd12+pdGhjebtjzGk0ZG18OtsYGCXggZxisCGZ7HJrSm78eF9G9xjt9VHXPTwPc9yuUC2UwG7Q9iDm792paQxkaqJMiWoUOpMDo45ifhOLAsJXylpNXusLW1Qy9c8tTxVQqFHONjVRUyIUWBH9BoNKk3muRyWcqloloKCyvYbDZZXl2n2+2Ry2aZHK9SqZQQW7txXCZz4oNyeh+r0O50qNUb+NHZfsqPKZvNksm4UR/EfWI0TnhVfw2AdqvD+sYWu7U6mWyGI+NVioV8ql91NyU4DclEYcrNneiiZ1n84Kevs7W9y8zUBE+cO8XszBS2basICUgCy6dZcOhlKljzYypafbaIbVn4gc9us87Obp3axja1u0vkdmucr5YodLqIjW3k+hbBxhZyY4vug1Va27v0PI9iIU+pWOgXMjs19XfzbvJVpDGTbQvGq8jJcQXMJscQE2PIEKDJyXHE5JgS+gmLikD8R/8pbG3HQDaQIQBUZ0patogHatS4MYNHosbe//s/D7ttD36nx4fnxSDIBEKNlgGOWsqSqAFSQ3+mQFOiIkZ3HwRADKxyCgwNei/fh1od7i0BS8knzTEmEnfUfNXAqxICs0oRquUYlEXgrATVkgJo+ZzKYyRwFsqpaHLZxnX924vzGXWJawAlZZIS44lxICzSfnem1an/LeTAqyORCGVp9D4p8CX2GaO6BglrePQPxa/6o2SFGSfylulnh7Rz5EqT1oQNLGyFbgH4IhyOyrdTb9aTgYVlyXBpXMQx9MyqE4eO0HJ3VFD7mcHW4MizyjTtex7NVotur0e5WCCfzeI6Nnaigkm/reiV0kt4ewycCJFHJpjoRn9d1ZcIaStDmo8QlrLCpASMlGmgYzIlgYiC6FmJ63G99yEt9aXxDjJQQ1EGYVSJ+BBMK4J6+l0VWFRV1BGUdenx4BWIcGlNKteoQFmNCNtORVkPsG0ndL7WZaglSw1V9BFK2ilRtZ9ehlTMIiAgCPSz4W5AQrhjgZR2BA4HOd/qAew4Nn7g4fk+rgwQUoRAvf8ZZUyVKMBvgbTD+irLmy2jkKqD+2GiSue//S+4ev02f/Knf8OnV25y+uQxjs1OA4JrN+9wf3mNV154kr//R7/HmTOncWx1nFAgJY1Gkz//qx/yJ3/+PY4vzPO//Sd/nyfPnSGbcbCFZOvBMv/9f//nfP+nb5BxHf6dv/87/Lv/9h9S+A/+E1jfSkHIuE47/9V/yg9+8kv+9K9/hOM6XDh3Cj8IuHbzLs9dOMc/+rvfYW52FgiDfUoFh9WuOd2eIKxAtYUf8M67H/H/+W/+Zy5dvcWF86f5P/yH/4QXn3sqjqBvao0m4xMi3P1E6F+nxoj8s+9j/Z/+c/A8PNviZ6+9zfsfX2ZmaoI/+vZv8M2vfYHx8Wo4JiU9z2O31uDNdz/i9r37/PY3vsyzTz0JwPLqOj969XUuXbvJbq2J70teef5pTn758+SreWqNBvfuL1OrN0AIfvnWB7z3xgfM2ja//+KzfP7kcQqtFmJzByu0lImNbdjcUpanYeQHqh/Wt9S7D0kmspkIfDExDpMT8Ol1aLfAdvAF1Dod2p0ulmUxVi2TyWb7sYzng9eLrWTrm/CnfwPNDrLZQrTaSStTs4XQvxtN6Hmpmn1W08ohns9loJCHQg5ZyOFlM3gZFwp5spNjiHIJigVEMQ/5vEpbKkAxryy3/8n/Q4HGQf2SRPz9dQwC2N5VfyztA5oMEiIGaRqYpQFbpaTulcvqerkMhWJKNpgAQdfNBHH010lpkqNUMlVlc4PEIFk4aLHvcEArudvfkGMJsLQ/Dd95OCgP451SrxftOtzDeT8JSnWWgvjg9TidFYXzkQgRRLIm2oVoWrawlHFBG16kcg9QYExGVi9rREvpZwJbA83axofX7dHudPH9gIzrYDs6To1GocklOfNbvPNN5b3fQZqJ3VwDkunyAhmEofe14FXC0rIsbMsBy4lBRaIAXd84OnS8pKNBRaJEFNwxNagkGJT6oFYjCGbg+0hpRQ63BMqy0ul28Dtt5VhtqZ0RXs+n3e3R63mRgOz5yjLTbHfo9bww6KYdHs2iIoNblsDz1bmVfuDTaLTY2a3h+z7FYp5SKU8+l0HYFp4vCAKBkAFuGHm82+nRareRgcR2bErFPFPjFUqFPJ1Oh936Ln4QUK5WKBWLSCnpdLrYjh0ebuwQ+BLLVgeVRzvjRBhzSQLh+YfZrIPv+/Q8DysT7gi0wng+Mox1FI8QlC1VIuiF7atCE/QkuLoLEmMt/n3l2i3+m3/+r3jvw8v84e99k3/wx99mvFxASMmlqzf5//73f8ZPXnuPbsfnH/2DP+LcmRM4ofqfy2aYm52iXMxz8+ZdfvnW+0xPjDE7NY7tOhyZm+Hf+wffYXu3xr37Kzx1/pSy1hmVGTS+bdumWimRyWSYnhzjN776OcbGqvz5X/+I19/6iPNnTvLbk5MqBhREYNzSfjYhgxZaK7Ysjs7P8uS5U9xfWiWTibcuD92A0nffmKlSwNpm9Nsp5Hj5hadZXFpmfXOLK9dv8ezTZ6lU8ziWi5TgeT7Xbt7lX333Z9xZfMDR2VmeeeIcQkju3rnHz994DzfjMjc9zcmFo3zl8y8xVq3Q6bZ4/6NL/OX3fka70+XI3BSO4/DtP/wWFy48wZH5WexSgcB1w0PvDcYppbJubW7D2hZyY1MJ/M1tVf+NbdjYUmn2EoidLjxYRTxY1a0C9+4r8KQaifuFHPVej3KpSLFaJmMueemsH6zA/RVl2ZGAYyP+y/+WfYWkTP04qEwVQll3Cnko5pH5LE0p6Vg2xelJMpNVRLGAzOeQeQWkrKICSrKQCz/ziEJOhesApPRZWlvnx6++yadXblIqZvnONz/HM888geVkSB4TFJ6dWC2rdvb8yLIr/8nfofft36S3sQW7NaxaHbvZxml1ld9frQH1OuzWodZQf9s1tYQ5zHKWJhOk3Vsani6xRCkMS1oIxJ46B//R/4qIp2tThwkS+wwG+utgBVPqe2LwXNQWoWQ+KUNAmO7gvlr99THzlWHg6z5r916UeE8tDw9VG2IUNnxuJi1isi+pXkKUloVlqfeJnN8tKwK0liUI9MYPobCDdlXQ+QgZLzM+csvWoK2cUZEhqpRIPD/A8xVAkJaNHwZei9l2UqMXpA/ijMGcNv0dqq4ywPN8trZ3w4CLHp1ul0ajSa/nk3Ez5PN5SoUi5XKRQqGA67pxXCULlGOpDJ22wYp2KtmKuacGl0bF8ZKa8i8J/ADfU+8jw2Nmep5Pr+fR6XRV4MudGu1WE6/n0e31WFldZ2N9g263ixAC27bpdLrs1hp0ur3QUqdi/7S7XVqdHl5P+eSIMNRGhOB1j0llDel2u3S6PYQQuI6D69g4ro1tC71ZDwFkMy62bdNoNGm11XYy27bI53NMjlcpF/M0mi1q9Sa2YzE9Pc7M1AQCaHc6FAsFji/Mk83k6XQ9HNsim81EO+T0jg/btnFdl0qlQKmURUpJt6uElz4mBhx8gtD3yMf3/YSVzHWc0KrkIXAjraXfNK4+fc/n+z/+BW+++wnVSonTJ48xVi5iC2X1O3tqgd/7za+wtV3jvY8vc+b0cY7OTlKplLCAjGtzfH6aE8fmeLC8yvd//Dr5jMu3vvFFZqcncV2XuZkZzp06Tq/nMVapYNnWvsqi4zhMTo5RKuXJZFymJsc5eeoEr7xwgfc+uszy6gbdbg87p4Co5wf0PB9bqHMDbdtWCnEQRH1frZSZm52OgG44FFIKg+p77QphWZbyZQrHmS30vBKI9c3oGTuX45kLZ7h8/TpvvXuRO/fus/hgmaNHZ3CyStlstdtcvHSFlfV1pAyo1XcRjur74wuz/Pv/+O8wNzdNtVKhkCuo8zMtaLSaXL1xh2a7w+dffJrTJ44xOT7G7OwUhVIZYVlDfeGkEMp3a6wCp49HfEGPmUBb570ecmsHsbED69uITWUZExqMbe4oH796c0Bn2fhHZ/nL73ydH7/xHi8+9xT/+3//H3Hq9AmE68RHIwD82ffg//Vfq6VNDdQi68EAhdGyIoCkrEkKCKnfufh6MQfFgrpmpi/m1fVcJgIBEthcXed/+pd/w6dXbvFPfvtLfO5zz6vwGOklGd2OaI0liK5IAu7du8/3f/JLrt24Rz6X4YlzJ7nw1DksWxKHg4DQwUZZkyC0bKn3bxXy/Oz9j7l05Tq9jjqpYXJijDMnFzh96kUmp6dwsxkVMFgAQq1QdOt16ktruO0uRQR2o6WA2G4NdhsKnO3WFZCuNeLvrTZDKQ3eEiANyGWJGsgUaCLJV5JmmqRBIlmcabAYblxIWnEE8coSaF+nzwq0BpEGHoem6J3NsSCJTnGJCxpWA/rmxihWwmh5llDX1GBLrWRZytlTGV8i9yY1/gNLhsd4idDAoWM4CgLi3480gnwyCmxo6ZHGQArDF1goIS+DIAo2GccismIBmPgbOBKJtIdDkgx8dnZ3+eVb7/PTV9+k2WrTaneo15t4nrL+ZFyXQiFPuVSkUilRyOWwHTvqLD2IgzBMRMZ1yOez6py+XDYSbrrxbZRvlBs6eLfbHZqNNvV6i0arTbvdodPt0e0p4NfpqECIzWaLWr1Bu9MNrU/hIcbdLkIoIOG6Dq1Ol164nBCF0SA2l+oJ4tg22VwWEUJZr+fR7vbwfXWwspSSjOuQybh0u10ymQx5MmzvNvH9gGKxROD7bO/sKuDTU8cwgVrq2601WFvbVAcZy9hh/fqtRXLZDOVSHsu2aLc6uK6LYzuRQ7sIhbrqZh2YVJDJuIxVyswfmaRcLLJbb6jYWo6L4zjKyTHUOHw/wPf96N0d22Z8rMLc3DQzExVmp2eYGBvDdh3Co5PVNBfxBO50uly6chOQHD0yzez0JEGvRxflqC8lvPT8UzxYXuV7P/klN27dZWtrm0q5GGmlQRBQKRd56bknubu4wp/99Y9ZXVvnO7/1dc6fOYmQgvpug05bW91CxpHgNSLx3bYtysUihVyOdqtFq15HdjvIIKBcKjBeVQdKSylptdrcW17l0rXbBF6PY/MznDt9kkIhix/06Ha7tDtdtmpNtnd2lQM44PkeOzs77OzW2W206YZj6tjReeqNBq1Wm8mJcdZW13nw4AFCWBw7Msv83DTlUpnM2nq0k1G6LrmcS7VSolIusrG1w9Ubt3ni3Ekyk2MEXsDtuw/Y2t7l7Klj3LpzX3EQWwnw2ZkJnGyG9Y0ttrd3VT/OTJPLZ6nXG2xsbnPy2Dzf+saXmJ+dptvrUWu0WdvYwnUccrksuVxWHYJtJxUgKQnHSWxxtlCnEyBCJc+xCCaryPEK8syxEMCpMy/N+UWrDRs72Bs7iP/z/x2u31b5VcucO3eK1979mIxtkQ1PQJDRxqGwPrlM3O3FPMxPw//lP4R8IQZIpUIMrLIhSEoLGCGJ4yv1qfLJ39GzcSysVqvHvcUV1ja2qDeaIbpOgoJ4xmiQllx1QAZMVMs8dfY4q2ubIAQTYxUs2zHcLCAhKHMZcJ047AtQz2Z5+72LvPbLd5maqHL21AJr65t89MlVpifHee7p8zz/3NPMzk1jZTIIKeg2u7z93iV+8tqbnD97gm//3jeojI+nLDDaqijDaBshWPQ8tTFgp678xrTFTIOzekOBtZ3w+tZObMWtlJJ5D6QBlq1hQCvtijOqpS5d4iMAWp+ZJKi2SPm8IYnA977VlvHnoXzjJEi16mFbFtg2gV4PCQVQgAy3UoXbxSTYtq+Uf9vGsu3wqC7lIG+Fp5yI/Y7hCunAYCsJtEKBqa8FIpyT6rft2FTLJQqFHK6rKiyEjSC0BAkDfKUsXBGIlTLeoSiNcPsHqK+UAa12l6vXbvPjn77BOx98SrVcZGysQrPZYWNrJ/JBsi0Lx7axHScCDdpR3hIi8VuEqNe2LRxHadWWrR0bBY4FhVyWUqmAY1sQBEgpCALlAF6rN9nZrdPu9pR1JgQO2s9JAJlwO7qUklwuQ6WYp1IqALCyvkWQzWDbNvmcOodOCLX0lM1kosOOs9kME5PKwiSlsiysrm3SbLXZ3tml0+1RCJ2LZRAwOzPJ8YVZFh+ssLVd48SJY+TcDNvbu9i2he8HLK2s0Wi2yef0lnllAQqCgGarTbPZQkpJsZBnbnaSSrnInXtLbG3XKBaUNXB7p0a90cR1XCqlEp7nUWs0QAY4js3yyhq3793HcWxqjaay4BHv5PNCa5bC4rFVxrIE2UyGYjHPeLXM177wIr/3rW8wMzkWra/LMAyClGq4dnseG5u7gODI7AynTxzDdWxlJWy1abe7ZLMZzp0+wVvvf6LesaWWUoVazKfneTiOzTe/8gpBEPDdH/6c7//kDZr1Nv/4j7/NiZMLtNod2u0OyDAMh0ZbJuAyfljCwnUd8rksd+6u82B5lXNnT+J5HtOTYxxfOILrOHS7PT6+dJW/+ckv+PjyNXq9LlPj4/zjP/42584ssLm9ze5unaXVda7evMulKzdpNFsgFAC59+A+3/3Ba1y+do9Gs0WpVOA/+F//A4rFHO998Am37yyxvLyGlAEZ16XX63HuzEn+4Hd/g/Or6wqoWBaBIFRk2oyPVWivrPPJpRs8/cQZSvkc6+vr/PyNdyPn+eXVjWiXqef7fHr5Oj/4+dusrG2yub3LqYUj/Nt//LucPnWcRrPFbq3B1OQYuVwWCaxvbvH+xavcXVzGzbjMz0xx5tQJZuZnKefVod2Wpd7xxu17LC2tkHVirXRirMLMzBiFQg6BoNvrUa832dipUWu0yGUcjs7OUi5XlSUyZH3StfGnxvAmxslNVNG+NLJSivRGyw795gKfXju0onseGdelGJ4eACh/ptlp5Le+RuD7IY8J4VC4C1dISeB5dDsd5YAvJZlslkw2A8JB+j3anTa+5+G6DplsJlxuD1mzr6y/AgvbdiOvhkq5zDe+/DKff+kZpRAQziUJPd+n66kYddoFJD7IXWLJmFcdOzrLF19+lo8v3aDeaJHPuug9W8nlLq1Uo6xbi8uRZSuoVnBsi0zG5cVnn+R3f/PL2LbN7Tv3+OCTa/z1D17l1p37/N3v/Cbzx46AY9NqNHj3/U94+71PyWVzBL4GVqDPSdRHqyhsqOecraTfeFVtjBi4tOdE1iKEgCs34H/3f1W/K8U9AMKgG6EMi5YVTDoMeEhU9LM9/ygpAlpiwPnYhqY5EuAy8x3FqpX6Li0QvlomDPTSoQ34yh1CKAd5VTNJYIVBTS0rAlrCEoSn1mIJG8uyIzm7Hx0IbPUDLWMN1tcFxtdty2Z8vMrCkdn4TDcRAi6hLS9a2MRaU7x1U0QvrrEnWCMvJ2qNodfrsby6zhtvf8inV25gW4JXXnqaZ546x5tvf8zHF6/Q9ZT1wrZsctkMGdel2+vR6XTxAx/Lssm4yn/I8z0c10EiaHc6+H6A5yutM5BBJPyzrkMhl1UgzfeZmqgyPzuN7wcsPlij1e6QzbghE1eWqVa7Q7fbIwgUuPriy89SKubZ3d3FdW3KhSzlcMfV/aV1dmpNPN8nl8sxNTFGLpeJ2ifwA5rtNpYQnD65wE69webWDkePzDJWrbCxuYUtlJA7MjvN+HiFdqfL7OwUZ04do1wqcO/+CsVCjkqxRD6bIZvNMFatMD01zurapjqUOZfFdR1y2SyFQp6VtQ2u3bhDp9OlXCoyPTnOsaMzjFXLLK9ucGx+FiFsLl+9ydrGNkdmp3nq7Clq9QaXr93C833GqiXKpTw9z2d1bYNWu0NPeOoMvlKRXs9np1an5/lqGy/gug4I8LyAbrfBTq3O8so6hVyOF597momxMhZ6uy/I8MwsDWZ73R6+5+MIi3wuC5bA95VVsd3p0O50uLu4xNZOjWqlpKwkUYBJNRltIagW8zx74RzFXIZ//i+/x6tvvk8gJd/51tdotttKcEVWNVMQDSa1c9Rmt9Fic6dOs90lk83yxZef4+TCEYQl2Nze4V/85Q94472P0NucNzZ3ePO9j5mZHuPB8iqvvfE+2zs1splMGFYD8hkF5Lodj8vX7vDxpzeoVkrksi6uI1g4MsP3f/hzvvuDV/niy8/xB7/7DcrFPN/70ev88s33mZuc4NzalprDoYWtXm9QrzeZGKsAcO/BCu9+8AnjlSJvvP0Rl67e4h//vW9TqzfodHpkwjARnU6Xt9//hBu3F/nGl1/mF29/yEefXuU3v/oyJ48fo9vt4fl+ZLpvttp8dPEq//oHP6fZ6lApF7l56x73l1Y4efI4C0dmmZ6aoFIqsry6zj/7H/8VFz+9RrlUIJtx6fR6HJ2b5qtffI6Xn79AtVpieWWdt9//hCvX79BodchlHF589mm+8NJzVCtltnd32d6pEQSSe4srBAH8breHGwpzv1JSyxUiDtrstdosrazz5nsfc+3GHSbGx/hWscjZQIeJUCsBjVqdW3cWWV/fCPtdKRalcokjszMsLa3x7gefEAQqKvuLz1/gheefxvN9FheXePeDi6ysr3Ph/ClefP4pxserSAnb2zvcufeAjc0dysUyC/PTTE2M4+ZzFIoFnrlwju2dGpmMqyxwFjSbLS5dv8W9pWXGKlUmxsbI5XNMjKldtAhBr9NlZW0d3/cZH69QrVTIui5N2lhG2JlouTYIwiVAoayOlVIY5DYMxTJeQUpJPpfh5PEjnDq5QLFc5OzZkzxx/gx/9b2f8Yt3PmR8rMwf/N43KY1VEQImxyt87sVnOH1yAdfNoAPEykAFK+602tRrNXxPMjZWJVsoKNkTyhkpJb7v0Q39i13XCV1IbBAynqu7jXhSlkskacCy4cD7DJjvoh+EjQigtGL/60vaX89Y7jStdoL4/mcFnSals5IpTKF9Oa049qQldGy98F7ijGCi51QaK4wTaiXT7UEHX0ZMAy2JAbSSdZNI8tkMx+ZnKBfy6gDb8AWTS4e64eMWSoApqc1cHNC8qnY+raxu8MbbH/LeB5/SaLTJZl2KxQKO64CUTEyM0QuX84JAWZPy+SxZP6O2o/s+rutSLOTxfI9Ot0OlUgYhWN/YwnVdxqtlENANI1FLGVApFjh3ZoGpyXE6nQ6zk2McmZ8GCbMzE1RvFlld26bRaiEsm3w+x+5uneXVDXqex9TkGI7rMD09wYvPnqVUzNPpdrEETFRKdLuSpdUNVlY3CCRMT41j2zbrG1usb25TqzfZ2NphrFKmmM+yurZOq9lkfnaKY/OztFszHJmZoJDPMzM1Qb3RpNVpc+r0AvOz0+TzWYQQbO022NjapNVqMzE2zsy0y8LRecYqZQqFPIV8DoDxsSoz05O02m1OLhxhbX2L8fEyC8dmmJwYo9lss/hgmdW1Ta7fXERKeOrcSc6cOEqlVKFcLCClMvPOTo0zVi3R83rs1hvUGi1+8fZHLK1tGJYwP9qVZjsOs9OTlMsFer0eu7t1mq02Xq/Hbk0tP3mej2uYfKPxCyoGnGPT7fW4dPUmt2/f5cknzoSWK4tcNsvig2Xe++gSy6sbnDg6B6jDn6WlFmgdIfG9nqoT8NIzT9Nre/zJX/6Qn73xPncWl1nf3Obo/Iwa3xqopfhMfOaXDH2JlMByHJtcLodtOzz71FM4DhSLBWQgube4xLWbd8hlM5w5eZRKuUjGcTi5MEs247K6tsGtO/f5xpdf5vzZk7z2xnu82fyEYiFPMZeh0xG4lk2lVOAPf/frvPLiM5w+vkDgeaytbSAQfPlzz/P5Fy9QyBVwLZcr1+5w9d2LeK22Eq2uYieNRodms83U1DhnJ6qsr2/y7gefsr6+xb3FFU4cm6NULHDt5l063S6lQg5hCTKuw4UnTnPkyBxnz5zg6o27bG/tqN3LoVAMAjWnPd+n0+ly8/Y96rU6X/vK5xmrlnntjff42S/e5Y13PubEwjzPXjjHk2eVxcb3PXbrDc6eXuD0iaO8++ElfvH2RywurdDudHnh2Sd4/6PLvPHOx4xVypw7dZTFB2t8/ye/BODU8WP87BfvcPHydXLZLKtr2xTyOX6r1Q6tVJJeqYDn+WZnUq83+PDjS/z41TdZXl3H9wNmnn2KU36gGHAg1S7R9y/yF//6R7TbypdoN3QlODIzzbd/6+t8+MkV/ur7r5JxHSqVIoVchifPHOfG3Qf8+Odv8cml66ysbXD9xj0q5QovPF9lY2OLH/z0NT66eJVCPoeUguPzM/zeb32FowtHWVle4U//6kdcvHydb371c/ydb/8m2WyG+0sr/Olf/YAPP7nKzNQEszPTzM9M84VXnueZC+dxXYel1XV+/st32NjcYWK8yvZOja3tehRqkRBktVttFheXaLU7lEtFJifGKJaK2KVi6CAvIZB4pVIEpguFnALhlkUmn+HU6eO8/PwFfvH2h7z53kW++LkXKFXKFHJZvvnVz3NhY0dZKvxAiSg/oFlrcP/BMrfuLHJ/aQVLWHzu5Wc5d/Y0bi4PUuD56mzLre1d7i2uICWcPHGEaqVINiNwXDcUyhbs7Mb9WikPkDcjyCVB33KwVv6iAfNvCskYaMVBv/W9AYBLqrBBh/XNjrOON9iFwzCsSxADKQN3aEd3dYRPuIJlAvEgwAstzkRgTK1AiGgT1v40MtgyY2mBFlSS9CHF0fcgwPOUY3yhWCSXy+C6FsJWgTQtE5Gb/loCBg3axD6MfZYTTef9bqfHxU+v8oMf/4Lb9x7gB2qJqrZb5/qNu8qZ2xLY2QyO69Dr+uiwBNmMS7VcYnpqnGqlzG6tzsrqBs22zdz0BOPjVS6H1T8yP0Mhn0dYatky8H1sSzA3M8WJhXkyro1tCcWIpeTkwhxHZqfY2m5wb3GZtc1tHMehkHVpt9sIITixcIT795dZWlrBe+48T507yfRYFd/32d1tsbG1q+rTajE9OYEtAx48WOPe/WU63R75fI5quUin2+XGrbu4GZcXnnmCI3PTZLMOhfwRGqeP4XmS23fvc+/BchQXSQhBtVKiWCywvlUjl89TzOcoFnPq2UKeqUnVLoHv0+6oZTYpAyYmSnzu5afxehLLkmxub9FuK8vDxFiV9z68xPVb95gcq1Au5tkKNe9CLo9jCQLfY3Nrm0a9QbGY59ypBY4dnWd5dYOl1XV2dmuKIeezNMODqV3bYqyiDhYWQrK0vMqdxSW2t2u02h1q9Tper4t0cpH1Wq9mAGRzGZ44e5LVjW1u3l7kX/3rn1AqlXBsC8/z8IOAjy9d58rNu5RLBU6fPEalXCQIgnA7cBifa7tGrd6k1epSLVf54svPIyybP/3uT7h0/Ta7tQbjYxVa7TbtTod8xBRk5KBuR/NNquOJjDFt2WqZu1AtIfEIZIDne6xtbtHpqh16f/f3v8mx+VlsS1DKZ2g229y/v0zg+8xNj1Mq5JWPouOEVtcw7Iel/N1mpyd58bkLynr6YJlWuxf6Hlr0Oj1kBsphENDO4gO6vS5ZKQksC8/z2djcod5oMzNtcfbkMTbWN3n/46v88p2LlIsFZqcn6Hke65vbBBKy+Tw6TMmx+Rm63hIffXKV5dV1QCkw2r8sCAIajRbNZot2u8Py6gau67BwZIbV9S2u3bzL+sY2gZRcvXGHK1dv8sWXn+FrX3yF40fnuXz1Fl9++Rl++5tf4dSxWf7r/+EvWVnbZG19k6WVNS5euk4hn+PrX36JE8ePsLy8zj/7n/+G19/8IAQrktv3llg4MsfzT5+nkM9h37wf8T6/UopWrLRFpN5osr2zw9PnT/L0uVO88e7HrNUbykIFyCCg1Wrzr3/wKjfuLPJHv/dNxqpl/uaHP+fqjbtMVsfIhlZlPwh44uxJzp85wfzMLIv3V3nr7Q/Y3dnhN7/2Oa7euMOdu/dpNpoEXsDHH1/mRz9+gyfPn+Jbv/Fl7j9YYXt7h263BwJqtQZXrt/mvY+vsHBkjna7jWPbLK1usLG1y3i1wvZunftL63zhZRe9M6vX9bh5+x6vv/kB9WaLI7PT7OzW2Kk1qJaLahOLlNS2tvjp6+/w09ffwfd8xsYqPPf0eb76xZeZLZWiJUSKeQKhNngA4dJyuOQnJZZtc2R+hvFqhVa7g9frglRW59t37/P2h5dwHIfx8QqFUpGN1XVef+Nd3nrvYza3duh0e+SyGfL5LDNTk0zOZKg3Wly/cYvbdxZZWdtgeWWdc2dOcuzIPK1Wj2Zzl2IhT76QV0aCmmHZqhQHyp69yTAqpDBVvBvxENn+2tI+LyPjNaxohStcP9eAa9i5nP1ZaZmvlVWpf4TuTcnwIvE+BiP0A/1lJv3rtcWLBFhjjwDVJo1u2ZJxXJcExJGpK/qrlPiBakzHcbBsR/l0RI+YFq00vIoDiMZBxQzrVujQZR6EGjdQDLQkylfjzp373L23hOf5kRlwY7OG70OpWMDr+TSbbXxfoVjbshHColwsc3R+hvnZKWxbUK/X6fV6eL2ARlP5pFgC2u0O7VaLbqeD69jhWrAKd9FutdnZ3iabUUDE99QuPC+bJZvJMTs5Rj7j4joWm1s7SN+jWiyQy2URYfpaq83PXnuXna1dnr9wjo2NLS7fuM3S8gaNVotsxuX4kXnKJeWQ7Dg2Z44fpVwpEgSSza0davUGx47M8cTZk5SLBbpeJ/Jfuru4xMVL18lkXMarFdqtjgoILoQKwDkxhm0Jtre3kYGkVMxTrVYYK1dxHIfV9Q1sz6LVbrK5vUk+n2W8OkaxUKLVbvHRxWtsbe9w7Ngcu7t1Fh+sqqXXnsfW9i62ZZHNuGQcm63tXbZ3a8rZOeNQLBaoVsp4vYDt7RoCQa/jUdut4zhOtDSdzbh0Oz3u3r1Pq91mt9ag1VQ7Jn3Pp9NW/ix+uCvPCpGWWrSWZFyX3/nml9na3uGTSzf52S/eI5fJce7McfK5DBtbW7z7wSf0uj1eev4pXnnhApVyMTw6SRL4kq3dBkurG0xOjLG5VSPj5ijkcnz+pedwXZc//95P+eW7H7O9W+fG7TvkcjZnOx1cX0Um9311+DJ+QOD5eG3lT6h8bdQS88bmNuub21RKCtT3vC4CByklruuyubXL4oMVxiplXNvC7/VoNrt0Ol26vR73H6zQ63lsbm6H7b/DxuY2ruuGmx+6rK1v0u12kUHAyvJqGI4j4JNL11mYn2fhCNy4dY9Go8VYuONVosBWs9ni5u1F5Wy/UyfrZDi1cIwr1+7S7XpMTYzh+ZIHS6ssLa/jez7NZodWvcHSg2W+/+PXuXHnPmdPn8CxbXZqde4uLnPm9DbNVptOt8vWjs/q+ibdbo/NHRXQdHt7m6WlFZqNVsSoO50ut+89QCB44swZvJ7aSGFbgmIxz/kzJ5meHKPRalMsFBAIavUmE+NVZmcmGa9WsITNeKXE0so6ruNw5uQxMq7D6RNH+ePvfIuJSgnnL34c8XW/rILZ2kKEB48ri+TJhaN4ns+DpXVy2Sw924kVfd+n0Wxy/dY9ioUCzz79BCcWjnDz1l1u333AF15+nmeePMfW9i5j1TKvvPA0v/X1L9FqNllZ2cC1M7zy3LOcPHGEtbVNVrIZMo5Nq17n2vXbdLs9nnniLM8+eZbzpxaUT16xAIFkZnqSc6eP8/Gl6zihP6ZaYha88OyT5HMub7xzkRu1RcqlIuWSCj5bqzf49MpNFpdW+corz/DVL32Oe/eX2djaRQjIZDO0mi1+8rM3+Gf/4l9Tbyl+efvefZZX1zl2ZI6ZYh6hlYliUVkPgjAAsRDKGhGoLS3SlxTzJaYnx6nV6ljSggAajRYfXrzMj3/+JsfmZ/nNr32eTrPBO+9/zF9//1Vy2QxPnjtFo9ni+s27fHrlBs88dZ5yucTFjz/lT//6h9y+84BGs0W5VOCp82fIZFwCX7K2sUkhn2N+foZ8Pq+c6DVVyqEYCmKgYNLQzQlaSA7yERP9z/2a0f5R7qOUxh+p909kiBDakGKHOENGOagv8c7XPUs0j62LLKukmtq0TvWdBdNXe4SIIw6k3lfvUnzooR8OMwSCQIZBwlQGKr58CI4Mj6yBucv+y0nrljorC/QAMM2U4cOh82Y3jDelG8XzA6VVh7vHxseqyjen1lC78yyJ6zgcnZ/h5LF5er0e9+7d597iEt2O0qjqtQYrK+s0G006oebd7XRYbzTJZzNkMi5exmVr06HVbOMHahCNlQtMTYxhWzbdbpPdWgPf9xmrlCgV8sxOTVKrq0jkG9u7yCBgarzC/eU1rt28x26twW6tTrlUoFxWDKrWaHL11j2Ozk4xPTnB0SPT5PM5ao0mjmNzYmGeWrON62a4d38J3w/o9ro0mk3uP1hn8cEKu/UGM1MTXLtxh7n5GY4dmwHUyRadbpfbd+6ztbXN6ZMLnD1vMTM7RblUpN1us7m7TbfTJZfNcPHSNdrtDt/4yhdwbBffDxDC5uqNu3z4yTWlFXsepUJegbZKifFqORRsUK+rsBK+G9Dp9tiuNWm2u2zv1rh55z6FfF6BDyGolIpMhn4pdrgTbXl1nQdLqyCkClVhWeHmBBnuWgywbSuKrW/SS889Ra/XZaxU5NK1u/zk9bd4+4OLZFyHVqdNt9vl6fMn+c2vfo4zJxfIZjOAwAe63R5bOzW2duu8//EVJsfH+crnXiQ3O00hn+Ol5y4ggZ3dOhvbNS5dvUG322G23qTU7WJ7Pt1uF8/3yXR7eO02dSH59PJ1/n/U/XewZFme34d9zrXp/fO+6pW37Xt6/MzO7MxaAAtwCQQBimBIEEOUGApREhkKkVKIEQJDLoIQIkCGSAECQAILYP3M7I7vnmnvqrrLV72q5216f+3RH+dmvnyvqqd7dnqXwIl+Xe9l3rx58+S553zP9/f7fb92zKbTU5Ia731wh2Q8xdLCHJZl0mjWMQ2bbCbFzOQ412/d5fe//WNu3llhLJ9hcW6GqfExspk0QRDy7od3mCgV2N2v0HdctnfLbO7sMTFWVFWvrsfa5g61ehPT0Ok5LmeWl6jWmty48wDH8ZibnmJldYNYzGbRMA+LDjSNbr/HnQeP6PX67O6XuftglXRafU+pZIJL55aRSN5+/xarGzs4jsfq+g4bm7vcvHOfN96/Sb3ZYW52mkQ8RqvT450P75Ir5Gm1OxGTGfLg4TqmaeB7SiD14eomvh+QTsbxvEBVFhkGtqWqVz3Px/N8gkCyu1+h1WoBcuimYOgmA5HCWr1Jrd6kkCuwvVOm3miSy6RIx23qtRrdbg8nGu/5bFqZYEetZeiqUleouc4PJc1Wm7WNbVbXd9jaOaBcrdMvFhgoXsvoGhLxGI1mi/srq2TSKSSCVDLOpfMnyeeyJOI2IsqJS9omzbqDDHwmxwoEEt54+wOu37iHaRi0231W17fYP6giAd/3VY5mVHWNFPiOTzxyO9Aj71HP91XxRanA6TDkzv0V9varKsxrmcqxQAiqtTqP1rdwXY+56UmevnSW8bEiP3njPQ4qNXTToFpr8PLr77K7X+bcmZOUinkq1TrpZEKJS5rGsLjKt01c1xuyu0P7qyCg3+tRqzfZ2SsThtDuOLhegIw8zRPxGKEfEAY+br9HvVzlxs27SCRf/eKLnD+7zKPVDQ7KVba29zkoV8lkUrz59jVu3rpPp9dHApl08lAiRxO0Gg3KlQqZTIpYzEREYwZQlaLiqGjmY+1YuPDo4vZvcBtZez+6RZGqgVn2R/bF8d4YVCc+AQDwRFhw+I6aOAq4gKEnMYPowSgf9Pj1DMdf9LeuKW1KVWgzKpsk0XVDgfBPiI4+BbueoxYCqikAJTSBiFgiBuAqQpNDsDWamsUAuz5GdR1rh6DqEF8dhbCjtGIUFUSxYeB6Ac1WG9d1hzvZTDaFYRo4jku318fzXeyYyo2o1mq0uz1Akkkn6TsufafPzl6Pbq+vJqpQYuga3U6PQjZDLGbR77tU683IiylAyoB+3yEIIZ9Rti0P1zbZ2NpjcrwYhfiSPFrfYq9coVxr0Ok7pDMpkokE1UabnYMqxVyGC2dOEkrJ1s4+m9v7NNsdmt0upbECPcel2mjS6fZJpRI4fojn+XQqdd69fotWu4ttm/T6Ds1WRy1Evk+92cZxXaamxkglE2xu7XHr7kNu3X3IfrmGrmnkCzmCMMSybQzLQng+ewdVavUmczOT3H+0RToZi4CYQ6/fJwgCDN3AtsOovzRSSZupiRIzU2M4jsfOfhnbMkkkYqpsXNOo1pq0Ol2EELS7PQr5HCcXZmm02rTaHRZmp0kmYiAEQRAQi9mk4ja+69KMdqG6pmGZBqZhIaLE2cdGU/RQIhHjM89cYqKQ5dqNe9xb2eCgUsfzfMaLeRbnJrl8/jTnz50ilUwMFddBheKK+RxPXTobVZg6IAblwYJ4PMbVS+dpdnvcW3nExHiBdEoJvnq+T+gp2Q/P8xGeR+D7eJ7G7fsKuDQabUqFHAflOtdu3CWZSDBWyrO9u4/jeJw6scizV8+ye3BAtdbg/sN1khdOkU7FkPiMl3LMzYyzuX1ApdZEE4JsJkkymYhEh02K+QzjxTye53NQrjI+VmRqcpyxUpH52Sk+vHmH7Z197j1cZaxY4MTCLOdv3sdY21b9GNkITU+McXJhlmTcHhZIXL5wmk5H3WsbW3u02j1KhTyWaVKp1jgo1xgrFrh0/jTrmzuYhs705BhnlhcRQrCxtUu706XvuLiux9rGDvOzk6pQo1Lj5p0V0qkkuWyaRCKJYRoKDKVTzM1Mks2khwUv735wh8nxIp1Oj1arw/REiVw2QyKeZKxY4IPbK/zg5bdZXdtjdW2TXs/h6sXTJBNxao0WnufTaLbpdHoEfoAZSWVICVUB1VpDsZRAs93l3eu3+MM/fZl6o42UgiAM2e92cT0fG5WvZBoGs9MT7B1UePu9GwB0ez2evnyWhbkppPSBgCD06XY6OI5DGKVElLd3efWdD9naPaDZ7jA9Mcadew9pNCeQUuC6Htdu3FXFGZpGMpFgYXaW2ekpdF1thkMZUqk1ePhog1Ixz365xhtvX+eda7fY2asQj9n0en1VMBQoptiLwFGv31c5i7qOpmtRqgT0uj2arTamaTIxXmRhbkalQpSKTE9NIMXA0xZ6YagsqQa+pRJCGdDtdFnf2mJ1fZP7D9fY3Nklm05jWjZBIGk027Q7fVXBidIrrDeb7O2XidkWuqHTanfwPU9t5jpdmi2lY9hotgjDINL7sxkrKECro/wuu902+9Uqi4tT5AtJtOZA8FZAOs5Qa2yQSjNcs0ajMSP/Hk/O/De1fSzQGj0uBBmq7zqaL4/kb2mHMiTq5xdIlj9kYjjkrIZJGgoLRM8rNv7JMOMIexcVqRhRxGrAHElUAVsxn1PyUJ+gfTpG1CLgMYNOVF6WpmlPiGkeJgQeFTU9DBV+lDLrEQJ2AFOl5HDgRyHGKMzohyGOqxLfpVTvG4ShEv0MgmGZeiaVxI7ZpKwEgR/Qane5G+XYeI5DpVbH90NitgAU1R4EAXHbJhFXwouu59PtuVRrKgzWcxwsU6NUzJHLpuk7ferNNgeVBtlMirnpcarNJrvlKj3XI5FKkUrEWFndYH1rj3qzo87Td3A9H9f3FbAIQ9Y21eLTaHXodHvKMqbbZ3Vjh63d/ag8XOX4KHZJva7V6gzLz03TUMntcTUJtTs9qrWm0kdD5+GjLW7decR+WeUDmYaOjiogGGimhaGa8O6vrLG1vcf+QYWTi5cBwZ37KzSabbV4mibnTi+xvbevWAjPo+/2OahU6PdVErsZaZSFocTQFVi3LZNcNs3C3AxjY0XmZ6fZ2Nphc3uPfDYDKBX7AdMwNz1J6Pusbu5Qb7YRQmCbNrZlq9Ldj6F84/EY506fYHpyjGev1mi3ewDYtkEmnSKdShBPpkYqComet7hy8QxzM5P0+n1M06SYy6lK0yj5Mp60eemFK5w7s4jvB/Qdh4HCu5Qhoe8TuA5B4EdMR5yF2Sma7Q4zU+OYhsneQY1YzGZmaoJCPsvO/h7dXo9YzODqpbPs7B1wUKkxMzXOU5fOMTNVYn/vANuy+NzzV9kvVylX6pimweR4kbnZaZZPLmJbJs9evcDp5SUmJlVBQzqZUJWvmmBivMjsZIn1jS10TWdyfAIpYeLmg0P9PNMgnUrwm9/8MplkHNNUemj9vkun06fXc6nXO0yOj/P05YvYlkWz1SIIQtLJFKVinrFiiUqtSi6bwrZtnrl6gU6nhx/47B1UuHh2Gcs0OXf6JNOT41im0rnrOy66pmOZFvlcllKpRDYVx7ZMkomEGudS2RXV6y3efPdDHNcjl01z9dI5pifHyWbTXDx3hkq9zerGLnsHNWzL5PzZE5w9fZIAnb2DJmEo2N6rcP/hOqVinljEbAlUGHF3bRM/0rDb2y+zsrrBQbWOJjQSMVVhu7pfxXFcUlGVngQMS+n8xWIWjuOytDDLycU5TMvE9Rx0XccwdNq9DvW22sTZlkWl1uDmvYf4foBlGVRrde6vrFIsFDi5NMfW7j5vvX+L6zfvAYIT87P8yi8lyGYyOI5Lo9HBdX1WHm3w7ge3OX/mJPdXN/nJG+9HzJgan3sHFRrNNoEfkIjHKBXzaCtr3H+4we17DynXVKWm6wW4fQcsczi+1yO19hNzU2TTCSzTwIlyFiXgoMCl5/mKiQsCOt0O++Uqt++u8Gh9i/XNHeIxi6cunSGdSuL7PvVGi4NyHc8L1FItFLjs9h2a7S4PH22oz1hv0Gp1osrDkHanixCKFWu2O8gwIGZbxCJHBc/zabXbdDvdyKGDkTCiVAbZ4QiI0oQCXYPKxSdihdF0m4865l/v9klDZkcshogYLhFViTJIzT4OUgd/6AyS5Q/bCFiSR/tOEvnyymPnkdHmelANy7HoGKPASg6vbfQcA5FtPaqAHoA1pMTQDeKJxJ+fztZjbdhRkQHzwI5BhGi6jHz2tKMf8CPVaKPA4kcArSe2EUZrQEnLyFtQSug5Pq2Ogx8MOlIiQ2UZIsMAGUo816fT7qhONQxkEOC4Ho/WNlld3yIMVS6BaehIciQTMYIwoNd3SCRiw4RQ6XqAZHvvAFDl96VClljMJhG3CUKfetMbLuCJuDI0nppUIbl2p0u5Wqfe6hKLxZhJJIbGw/2+y8z0OOOlPBrKdxKUgnsqqUyEbcsiHrPQDAM/CGl3lDiq43rDnYQeibN5rkcopfKrtAxSEcPRdxzWNnaQElYerqELwczEGI2Wqu5TXaiU8fuOw8PVDR4+2uD+yno0AHXSiSR37q7w45+8hee5pJIJxgpZSoU0u3u7GJpKvjcNnW5XVWKapkG/70Y7UI1UMsHpE/MUCznmpqeYnJpkvKRCL7ZpKoV426LTVVZDrXYHz/MwdV31kSbY2ivT7faxbQvTMo7oDo2q/g6YT4FA03RMS6NYyJFJp0ZySEYGu64zcIscjlUByUQcPdrZa5FeWzhUdZbomiCbTmJZJptbu+wfVFgMAjRdCWcKlJq/FQQIKTFNgwvnljF0HcuyCEO4v7KO7weMFfNKUDceo21bJBJxbMtiaX6GEwszXDi7zMR4iV5PJZJblsmpE/Pomka5phb+qfFxxko54skEjufz9OXzmKZBJptRIdhBPiQC19CQxTzxmI1txrAsm16/T6zVieZBiTAU2FmamyYZj2MYKly1d1Cm1/fodvvMz05z9tQJJsdLStdNSqVXhsQwTMZLJYr5PJatY1kGU2MTtLt9KtUa6USac8unyOeyavPSd6jVmzx96QLFQo4gkOwdlEknk5xeXiITMYe6Bo4XEo/bGLrO2eUlvvCZqzTbbcrVOgvz02QyKZX7ODvF5194ik63RywWU+H6VJJ0MkWz1VPJ4JNjzE6Ns727T6PVZsIbWPVAcmaKyV6P566e48TiLI7jIqXk0pmTTE+NUyqUaHV7bO8e4N9+qF4X6WY1mh08PyCdTqLp0Oq0uXVvBcd1uXh6iZnpCZ65ep6YZdFsdxUYTiVZWpjjuasXidlR3mOjSSoRJ5dJMTszRdyO8ZM33+X+ow10TaNUzJPPZen2+ly/cZubdx8Si9lomkG369LpuTSabbrdXnSvaPi+0gX0fI8wDMimkpxZXuTh2hYPVrf573/3uwRhQKXWxNA19g/KzExNkE4l1eZwY5vd/TIrK2vU6k2KpRJat0csugf1mK2qwG2bUiFHr9vj3soqm9t73H+4TrfbI5fNUCxkmZosoWlCiUE7Ls22YudVFaQCU5oQNJptVlY3CCJB5lqjycz0FLlsmo3NHfbL1eFGwXE8mu0uruupwg/Xpd3pqsV2MFX8LOmHUB6Gvj6O+RlNBn/suZF55rH2bxZAO6yyHKzRAaAf/XhPXOcjwBUOSJQRxksK5ct6pMmjOAAiQKZ04LThyn8k9qU0PIlCh3LwGnmkmlGtCYd4ZODTOACMuvbJscqnw2wNP2F45G9NF6hUACVvHzKiGB8d9DiV97MvXD52TBR8HKVsozEZBhLXCej1lOL24HEpJQQ+MhDRzRnge6pTTctUE1U2TTaTIpVMYBg6vqvCi2PFPCcWZgCVWO75QSSBkMDpO2RTMRxPAU9NE2TSCSYnSqSSMfpun+nJMZWInstgGjqdbg9NU4BJYFCrt2hEjEwiHqPvOOwfVJmdGufC2VNMjpfQdQ3P8+j3VNmy6zpD25xEPIZmmioJutagVm/iuIoZa7batNs9glBGE1CLRqutdnZROMv3A1596xrvXLtJvdkmEY+Rz6qqvMD3VWiw06XdarGxvcf3fvQ6K4828D0P27SI2TaO43JQqRGGyh6pmM9yZnkey1KgcayU59KFZUzDYP+giuP6EXhzyaSSXDh7gqX5eYq5PPlchmw2gxWLIaNQQyaTZnysSCqZ4OHqBo/WtyiXq8Rsi0TcJhGLkUklQWiUqw3iMUuJvA40foYlvyOAq9bA/Pf+98ORZSDRh5OhODrJCYZj+LBAA2QQYPk+Fkq1XxvaPxyOXAAjCJhutim5HnHPwzBMBQSlxHFdYkGo3ltK4jGbTDqFbVn0eh6JuE0YBsTiJqrmRH0mTYvET+M2nucTj8fQNI16o0292aaQy6hcQV0nlKBrJmPFIrlcFs0U2KFUSeLaoWjsUGbBi8KbQpBOpYhZ8eHO1aw3lbacphFE4RRTtzENS4EtwLZtbEt9B+OlApl0UlXXWRYIJRyshF4lmiYIQoFAx9BsREw5DLRMk1gsxlipQD6n9JjqjRaO45FKJpmbmaHT6bC7d4DvB9iWAqDqvg+RIiCU4IcBxUKOK5cu0um0uH7rHgPXAdMwMAyD8TGVHJ9Jp9Ai6Zd2p0+702VqYoxzp09w+sQitm2TSaWUEnn0TRcWZ3g+m6TT6VEq5vH9gHOnT6AJwdLCHMVCiXbPYXt3n9ib15T1jwwxdI1iLsNtx+XmnQfs7O3RandptbpMjZcwf/PrXDy3zG/88pfo9R0yacWuJmIJrlw4rVg+QyeUIbv7ZVzXY356nInxMYqFPHOzUzxc3cA0DZaXFigVctSbLXw/4Pzpkzx96VwUhs0gCYnbFs9cOYcQGhJJo9lierJEIpZAQ8O24pxYnOP5py5RqTaUq4WlYdvmUAgym0nx9OVzrG/ucFBt0Os5NPQ2TiTi7PecYZGTHbMp5HOcPb2E73lMjBWGjHwhn2G8VGC8VMDQ9UjrMEDzfbq9HtVaA13X6fc8Gs0e2XSSibESXUeFjTd39vBcj4lSgacvnyORSLB+/Rbrm3t0e32CQK0NKhfPQUrwPBfXc7FME0NTuXw0I2bLtpGRbNDRFnlwavJngKZBbEZE+OEwmjMMpQ1fdjQUppgz+fhTo4d/wuq9v9A2ZLcG4Ek7FDE4POgJf4/OnyJ6rVRgdvRzytHXRHiAo1O2Om4UTEUs1wi44thrRo0Dh2TaCN6Vx1/wMe3TA1ujbQh4IrNGLURoOiEKdA0PA4adKZ7YPUfbYwjyKFYd/QIGtGIYyqElDREToWwMo7itBE1IpM6wHD6ZiLM0P8MzVy+qSkRN0O+qEGAqFWd6skQQBEyMFwlDSbGQxTRNWs02QeARi8UjcT3FbsXjSjMmCAP8wMWKBECDwKfX7RKikUwmsIwYfcel0+2pqildp1pvkU1nyecyLM3PqWRW/XAxlGGobIEMDd1Q4peaphOEKgne8xz8wCcIFOjs9Z1hOWut3mZ7Z59KrUGr1eagUmO/UqPV7nLguirU1XeHYpKu59NudVlb3+agXOXajXs8eLiO47jEbZuTi3NMTZSYn5tmZqLEzESBuw/WWJid5MypBe6vrIGAKxdP8cLTV2i3FSuysXVAs62KBWamxrh4TpXUr6xuohkLYJi4NSXhsHdQRhMKzLhuVGG3e8DufplCNs3ywgzjYwUQGn3PR9c1rGihP670eyRBMpTK/25kbI2y2GJj57BMfXTcjhxvSAXQPo6Z1SQUA+UdOgzB7VfI/Af/GTHPx1zbRvMDaLSJ/a/+i2E4Tg9ClnXB/f/o30UIVYKPUMxDGAQYhk4mnaJcrbG3X6bRaLF3UMGXkC/ksWIxfC/A8yS+8FW1sNAQQkczpPKxYxCZl4RhEHmIetF41LBMCysa2zIIMBstgghomtu7yEaT5D/4x5gTY4hSHgoFMskYy6kUnWwGL6oIHSiRBxKCMBIy1jQ8P6DRbCkGzbYxBwLImtp8JBMK6DWbbSrVGq7nkogXopyintrYyXAYrPB8n1qjRaVaY3N7Fz8I6Tqe0tHUDUJURZvrepiGytkUqBy7ZFK9l+8rfS7HcZieGOP8mWWmJsfQtSiPI8rZQtMw0ynyEXDNplPqKoRi0vO5DMmEFeV4jGGmU8MwmxCCeExVLB+U64wVczx1+SzVaoO3373JyuoGT106x8ml+WHumef5WDGTZDxBLpeDUKU2TJRUsUMiHsO0FAN8dnmJk4tzgJrnur0+lmly9dI5TMM4suiUqzVOzAuunD9DIpWg13fY3N5FoJFLpzGEUl/PJNPMTU9y8expCvksQeizs7dHv+8wNzNFJp3icy88Rdy2WN/eUyyzZfL0lXPkM2l03x/eRZqmjOfnZybQgbnZKeIxm7mZSWr1JtW60vLa3StTj/LmDF3No+dPn4w2rwYylGiawcWzpzl9agnTMtGEQAY+uiaYmppE001AsdOKBdOIxyxy6ZQSRo5SYHShgk6KuA4Pw4jpJMelBKJPEWk1DQDU41TCcO4YYq5RcDUKLI6/Vjz25+PtGBD7FNvxZPKfL5zIgG5imDD/MaTKyBkGV/AJj/852jGiUI6AsdF8u4HciYpyjH4v0Xz5F85sPaFJovLI46zXz2ifLIR4jN8a7aSRQ4JQKu9B11U3oS7QDZ24ZeEHodKaGbw2hCCQ9Pou1Fo0Cz0KuTyLMzP0+33aVpd0KkU2kyaXS9Lr9/A8D8uyooo4qUInpkE6mVDlw6HKdQCiKrwAz/OQ0kfIgMBzabe7dLo9aoZNKp0hHo8hhLKPqR9UKVebGJqOZdps7+7j+74SWI0AnGXqJGMx0pYKT6laBA3TNInFTSB+iNrDQUGBWsBUoqtL33Xp9x2qtTp7+xX2ylXKlRp7+3X2DyrUG01a3Z7SRqrUef/6HXqOx0GlGuWJmORzWabHi1w6t8yZU0vks2kWZie5cGYZTZds7+1z98EapmGwvDTHeDGL0++Qy6bYO6gSBD7pVEKZNlsmtx+s8u3vv8pLL1zl1MlF7t5/hG3ZbGzuKHNsP8A2dQqRVlfFUP0+MznOeKlAq91VuRqttmINhUDTxRCvh9ECRy4T2UEd3kBD2B+FxwC07T1EcGwMi8NjBuD9Z0YBRl6mKmdGNrZhiFZtYoYhRqikIHBcjA/uIhwHJGhISkKQf+/W0DrmS9FGQjdUvttkePQ+GEwNAwZMIpmKck0Gjx2fxwfXaEmJCaSOs3rR0ckwRAwWoMEGq+eQ/Kd/gOYopgohsGI2n5HwggyRuo6wTIRlgWUiTYOcriNNE2GZePksO/+b/xmaYeINNkxCJ5FSUhYKMHpDRjYRj5PLZiIh4jiFQh7dMNAMCxktprt7B/zkjXd58GidMJQ8WlP5P/l8JrLlUxWynu+jaYJEIqYM0kdYQ1XmLSkV8xTyWSzTOmRIR6oRM//Bf04i+tsw1IJeCpWOmYj8YNNSUgxD7PurEYBX9+Laxja9vqNEh2cmOX1ygQ17n/eMO2qzYOiqItBU+mgD2zDd1LBsHRmGSD8kZtvDL1QSaVZFrw1CNe/5fqAAZSIOUlKu1lWVcDJBMqEM5W3TZqJQpNfv0Wo0cVxfjUsBstdn7P/yXyFdl/TUBLm5KYJkjIzn0TZ0CnaMmONRjNk8f/40Z5YXqdQadHt9FmanSMXjiEAON9Ch6+M7HqEfYsftYZVkIsrHa21s02x22NjaJRFPIKXacJ1YmCObSfPu9ZsEQcD4WJFkIs7i/Ay6DrFEDF0IVenr+6RSSfpuwPRUiXOnllTY3jQJw5BCPk0pl8bSNSxdw9Q1+q6ncrbCMGK2ZKSxFYW3jmCAkRrnjwRao/fSz0JPH0Ms/I/Rjhlef9I2ULmXA2ZKhsAny3N68nV8gn4Y5mgduxbk0HKPY8zW6FlDBEjl9etHTOrAqWTwusFn+6Sw8c8VbKl2KP0wmGx+sbM9ecipEK0csq0Sge+HVCo1ZborQ4QUUahCI2aYaELD0JVytet6uK7KF3I9n/2DCuVymYunFjGSCUxDdVUiESces9F1bWgyPcg9sW0bTRPDUvNOt0u93qTR6tCN2CrX8+h1OlHlmVJHrzeU7YxlWaRSKv9rv1xlZ1fZ8cRjNvlsZlgpadsKbIHSMJsoFViYm6ZUzCAEaLpOsZClVMoSi1kIVJKfbduYZgyhK29KQyp/tbSUBH7IxFiJ5aUFXFdVTbbbXQ7KVTZ39ni4vsXDtQ1298tsbO2qBHwgm04xPztFJhlne+8gqqo8qwoRgoC56Qk2t3d47c3r3F/ZYGqixEG5wVSxied4VKtKYLGQTzM3M8nS4jSrmzu8+8Ft1jZ3lEJ9Isl7H97B83x8z0cIKOazZCfHCGXA5HiRMAiYGiuQz6XxAw/DEFw8s0C73aTZatJzOgghh2NwsHtx/h//KULTCDVd9R0hOmo3FkYxfyTYf/s/QVbqCE1DFrJqzKEENzudnrJwCQKy2QzJhEoOf/LGQeJ5Pu1Oj3a7oxaVdDKyB1EKxmLvAFz/cC5WF6PGvwDd9YbskzbILfCDQabDE+8Pcezfo0886Z588mT12CHDkm51ZwpkZC4c7Q4FSiVcSrUMBSHC86HTG57GcL3h75aAp954HywDYVlI0yRhGuRME0wTLJNQ11gIQ6akMlC3LQvDMIhJSXEU6AgNIww51+tTLFf4UrOlKvi2Dpi48UAZeTsuWlQtqmsaE9En0XR9OJFKGTIfBEw63tB3dFiJKqUyKZYS/ADj/ZtHJtXBd/KkJvquSo63TDxXFQD4QYDn+ezuVajVWqysbjA7PcmFC2cVgJQSoUsM00BzdPxA7bgHvm3S1KK+F8M0FqUFpLT/ZMT0G8bATzWG67nDHKVB8YtKIu9EIVkbQzfpeH1cT21Q/VoT89EGJcclvr1P/MZdCCW266pwelTsYvoBMc+jFAQsajrCMrATcXTDhK1dNTYE6DfvIX7vTzECHxZmCJMJpB0DXaPeaHFvZZ39cg3fD3j+qUsY0feTSiVotduqWCkCaIr9hHa3RafdxnFcOp0eiUSMYiFHPKFEiW1DbXg0oXFQqWHbRmQrpw9DTfV6k4NylbFMmsxgnKbiUb7PCAMiBEfEZD4Ju/WJ2ycEWYMUnQEz82ljs19w7R4N8/3C1/EEpu2JUg4ci7QeCYTJo4/Jo+ArlESMlkq7CYcFEQMJCAlagNA+GZH06YGtJyKg6H8y8nfT1Q5xGDn8iJf9vG9z+MzRM4ZhMEyeRh6KnPYdh3gsTjadJBm3idkqL6bZalNvtuj3HVrtDmsbW7SvXGCsmI/UoxW4MQwdwxDKP0+IyLRaYlomrXaXm3dXuH3/EXt7B7TaHfp9Jwplgud7OP3+kdCmYSr9Gk10Oag2CPyARrtNMhFnerKoVMo1SCbjSkU6CBSw0zX2D6ps7e5z7fZdFdWWKkl9crLEwtwktq0qgizTJJ/LM1bIU8yrBGM7MpG2TAtdM5TSrpCYlkbSMilmUipMcOEs7U6Hja0d3rn+Ae9ev8327oECp+kkk2N5Gs02Wzv7LMxMEUT9/gff/iEvPH0BzwsAjbOnTqgy/s090vEEuWyaUOoEAWRSGQqFHFJo7Jf3qDebpNNJHm1s0+716XR66LpOvdFkvJhj+cQ8tmXy/rWbdLo9ZmfGGS9kEZqk0+miaRpnT5+g03N478M7dHs9teAPEx3B/o//LlqjNYQJ6j9VH3t8nGkf3gXXQybj+H/4X4OAntvntbeu84d/8jJ37q0xMzXGN3/pC5w9tUSppEJbxmjVolS6Tu9dv80//Z0/Yn1rl9/+y7/MX/rVX8K2FVMShiGp/+3fRdSbw7lN//AuotFWs4KuQTxGICV+lGOnaQLTMKIw6ZMnRAnR5zv2/BM20GEYqhL8iJnSdW1oFv1R5x4YvktNx1+cwb69gggCpBB08xn6rQ56EGJKiYnE0CIzegDPG6nsEoggQPQC6Dkj8/thH2oSEjIkgUCYhmLKDB0x6ANDB8MA3QBDJy4l04ZBKZ0enkN2ewSdLrqUWJaJ6XojUh6Pd4sKEav+07RDoCj9QxHGUeD6UZyFBJXjFQ52+FELA84bJslclkq3x/2VdYqFHMtL83ztSy9xYmkeoWkEoepjzbQxbFXB6vl+pAOkDKulONypi0HIRghkoIEmsCwLy7awdB26fWi2SNea2PUm9vYBdrdLrFpH7zvkb9zH9H3i5QpBq0Pm5beJA7H9Mtp+hWSg1BO16FNGyodqnEVYQ0g5BJxDJkAIcN3h9x67s8LCyhrzg82DYSB0HWmbPGtZnNcEXcPATyVI7JSJX7sNhTx6MUc6ZnFeaMSzGeJegG0aiFQcz3c4qNSo1RsAFAtZEvE4tm2zODdNPhWn2+vR6zskkzbJpJqTBveRoZt0Ol1eefUdRLnOc5EeGunUIaCJPpMcfLsyPOyNT8JufWw7Bk6OA4onAaDRxz4JC/QJ2qfhwaj6aQCUfp6+kUf+OQ62BkDrCOB6AiA7csYh0BoAK3lEe1FKZdk5mNvCMDxks4b/G7zXJ+vjTwdsDWbyyFWbCPGHMhzqWWhCGymhjCbvCIj//Ih5sGCOKMoKgRDySP9qkUDdYAeujeRraZpgeqKEZZrsl5Xq96XzpzmoVLn7YJVypcrG1i7VWoPxUvEIUyEUfQRCVzloUXgmlILVrW3+1be+z+7eAaVCHtf1hvY7nqcmxZnpSabGS8MkZtu2SMRVQrEy8u2ytbOvWKuxglLg11RoUEOJd+ZzKnTSbLd5uLbK+x/cYb9cJZCSYjHP6eUFZVBca9Dr9anWm1RrLYr5HLNTE6SScWzTwDR0xseKnD99mvFSUSn9R1TrACQnbZ1UKsHEeJGTJ2Z49soFrt+8y3sf3GZtc5c33vlQ7YBtk0wmSa/n4LguG9t7bGzvMT5WIJdJs3xigWQyTuiHBGFAu9tH11Uira7r1Jptmp0eAnj26YvohsmHN+9x/cZdxkoFLp8/w627D2g2VS7S2eUlzp5eQgAT40V816VWq9OM/BK39spYtsX01PgQGI8CH1FvIqqNwxElDmHW4ZIZDSjXhyBARMwaQi3Sp08tcuLOA27dfYQQglhMJX3L8HAhHVrtAJoMyaXj2JZBq93B0DWScZtB5bIQgs7//T89ksSf/Pf/E/R3byiS6JmLHPy9/5wfvPw6u7t7zE6ND43BpyfHidk2mjAQ4pBPGYjxCaEd2fRouow25GqKcV2XN9++xo9++jbJZAzPc3m0vs2JhQX+nb/+lxkrFj4SnPX6fXb3yjRbbbKZFKd//X8KPQcZt3nv//2f8c9//09578N7mKbBN77yEr/2wtNM6DqP3rvB7N//J0zWW2hIhK5DJoX0PKUuLo+/nao28zylPG1Kieb54PsInCfuxgxdU6K3poE0DTwh6IchLcelGwQkjTSFbBrLto+8z/GcjpHCbwQqTaG1f0BS11U16WQRFmYfm88OGcoorHfvEaLWiGxE1Dtk+i7/u1sPCYMQz/fwhEDkasTKTRL7DfTvvYZMJRCpJDIRQ7cMdASeiqfhxyxMw0C4ijXUen3o9hSY6vag00Xv9jG7PUSvD90+oqccFmwkqdE0DAmSUAHLKMycGIzhATPc66MFSrJCZtLITArCAOmHiDBU4z8IEUEAYYgY+X242B7i65GNgAL4hKF6nedh0CUNDNwIxYO10eFHClgQQm1EdF2B7XiMQjzGfDyGG7cJs2nsyTGsmQmYGidfzJNOxHAtg34igVPIE8+kyGTSGLpONpPmmSsXWJifZvugjOh2D7/Q9ONWPYczxeiI+TTA1hPaYGOi/YxzD5PSP/23/8VahMBlVJn4sf0zGJPRmAk/mkU6mkYUzeND1ophSsiond/xt3pSdwWhHOaBDjblf5aO/cXBlkSBrGOBioHL+3Ch0TTM44oPI/TdxwIuMboMHs5ecuQRKYhcvKP31AWJhI1pmRHAUv5uuq6RSlgUC1nq9SbVWo1ut4MfuJw6sUTcNlU1XqNJtV4fqs9rQ6CFsmkRh+X/Eug7XR6urrOxtcf01Bi//JWX2D+ocO3DO/iui6EnmZuZ5MqFsyzMTRO3baRQ6s7tTot2p4/jeEMx0s3tPR482gCBsq+xLaQULMzO8NUvvMjk+Bh+4GGagt39Cq6nwhyXLp3l3JlFur0+2zt71OrK0uTR2hb9vgtESbLdnlLSD0LOnlrk6sVzzE9PMTUxxlipGLFtEcA0BGgapfEi+UKOs2dPcuXSWV7+6dtcu3mfZqtKz3G4dfehSty1VCWaHwTs7JXxIyHIs6eXiNs2W7v77O5X6HYVY+UFAc6Oyq0zTZNiKc9YqUgum8ayTHq9Pn2nh2Uqpu6t9z5kbWObC2dOkEnG2dkt0+p0qNcaVGt1hKbT6jq0Wh00TSMeiw1ZJsnA4T3a2QhBmM+qMKIY7OIODVEFAla3DoeiJkEDE4PJiTHOnlriJ69fQ9f1KOdF5ewppipKmEcysDcdL+Y5uTjDjTsraIAhwBveBocg6yNuNiq1Otc+vMP5Uwu8+OyViNXShxuC6HY5fNUgP22YoxAtkpEu0KAEulKpc/POCmdOLXH10inCMOC7P3ydl1+/xudeeo7xseLRq4lCKYNK1k63S6/XJ2ZbR+aixfkZvvy559gr19jeLYMQWBNjGKU8bddhD0lRgGVaBM9dpvff/Zeqz8IQ0WgjqjVEpY4o1/D3y1QfrLH6/k0SnR6nchnSx+cP+dgvSHloEm+ZJgkEmuVgdHoYrodRbyHyApnPIgs5ZClPWMghc2nCfE49VixBLoMwDDQZ0ur2ePO//iec/v/+jjL0/hu/if13/sbhOIm+x1FLsVBK7P/o/4z+/dcUoxcdZ3V7WEIoI+UgIPQDRKuLvrmnStdDOQQgHAFFHHk/NO0QdEQah1KPHtM05ODx4WPGMMyIGK2/0o+yc48BSAGGrqpoZyeRZ05AIo5MxpCJOCIRV/9GfxOPI5JxZMxGGgZuv8fed19j9dotcq7HUiqF3engHFQxen1ino/uOAjXU6AtPAq8JagcVAYgMEpM94OBaBc6EJcQG/TNCPMIYOkaCV2P+kEH24RUEtJJstkUmWyGxVwGv5BBeB5au6v6Toz0gfqFv9CmiU++1o/sF//1apLDHLeP6T8p1ff/2OtH/lWT+fGX8VEffqC3dbwScZD5MLh3YYSRGxwljwK7T+rf+CmArVEF2AH0GVTLhVHSsKrs0AcltKMvj+bJATn2WL8PQNaQxhSHv0uAcCQEFHWYEIgQQnmYlDc4v2EYZFIJZqbGSaVibG1tE4Yqcf3eg0e4fYe56SlmJsaU/Uq9ied7xLAjoCWQIhwmVYsIdEFAu9Pl4do2ru+RSicYH8+TSNhsbe8R+h7FQpaFuWmW5qcpFbIYmkrALHe67OyVlW5Po0u13hxW99UbTVY3d9iJqnB6fZf7DzeIx2y++oXPkMnEKeSzXL14hrFintWNLT68eY/7D1cp5DMU8zmmpyYiv74mMTvGi89eVSKm7Q6bO/vs7ZW582CNG3dWSCYSzE5N8PSV81y+cIb5uWlSqUT0tQqE0DF0i6wd45lnMiwuzPOZlVXeu36Td6/d4sPbD7h+6z6WaTAxVmR+dpLJ8RIC6Pb61OpN/FRyyPKNlwpMTJRotDts7e4pQcJen41NpTJuGDrZTIr9gyqP1jbJZtKMFxbZL1fZ3a+wt19hZrJEqZDFdX22dw+oNZqkkwkQgu3dA+VhGTGqIpqohC6Gk2TbNvlHX31eld8XUiwvzZPJJHm4ugkIrlw8zdRX/vawxN8LQzrtPp1uj26/Hwne+hiGAluJhGIsXc/Dcz2EEMQtHTNmAyGxgezB4EaPForBza0qaJXJtK7pR6aVMJRsbu/xaH2L00uzmFFl6yCJG6mqG497mA2gnojuS6SqCDR1bcgA1xtNdF3nsy8+w/RUEQhxfZ9X3rjO3QernD9/mrhtKf0wqfIYOp0e5UqVarVBt9cjGfl6jjbbMnn68jkOylV+91s/4ubt+3zxs8+RTiW5/2CNz0ZgUUTFNKr6UEMzDRgrwFjhMJgSBHS3d/nR7/4JlWqTv/nbv87l04uYjQbBQV1JKVQbUK5BpYoo16HaIDyoUDuoEAQBpUIO27JI6kqbTAilVydaHUSrg1xXivhHcq1GQhgym4ZCjmw2zfP7FTqmge8HuMkECU17Qt8fLghCguh0h+fC0vHzWQhCNBmihQps4ftoYYg2XDCOrZqja8zIQ0KG4IfDsTo4Zvh/TYBQ4UQZaSCiaWCbyHgckYghkwlkOonMphTLmMsg8xkoZJGlAqI0hvbeLfR/+M9BCIK/8zeQ3/i8ugYpcT2PfmR0bVsWMUtH1w21G47Afqfe5OWpEr/7ns+lS6f4G7/1a0gp+fDWPeLJJOfOLDNWyNM42Kezs0/KD8l7AbFKFX97lzuvv0t3c4dcKLF7fQqGQd7Q0RxXMWiBYtaiVOfh5z/ShsdErQOMMN2Dlcw8/rrt/cPfHwNcP1/y+CdrI9c+YKyeiE+OjZERFv9fuyZAhdGPkjRPPvT49UsQAxmIkYd0iQx+MXQ5cDVAHIqya1EkQDwRoISEeE861WPtFwRbIwBo9DGpIaQ2XEyk1A7DM0Ic6d9hGFE8gd0aApnHXhD9qQ1mGIU2xeGthaYYCz/y+oJDYBeP2ZiGocynZUg+lyERj0VDNarMCQJ6/T6Vah3HccmkB0CLIcodAK1QqEcc16Xe6tBzHO6vrPGDH7/OmVNLpFMJWs0WUkI6HieTiGHqGq7r8ejROitrm1iWhecG7O4fsLV7gOcFzM9M0mx1qDWaaJpgdlopewe+5PV3rtHt9fjiZ59manIsUlmfptPtcf3WCpKQyQnFRORzGZZPzFOrNXm0toMQsDA3zcRYkd39Mu12l2q9zsbmLqsb29y4c59b91aYf/1tXnj2Mp//zLMsLMxhGIfDRUgwLIuJqQlKY0UunDvN889c4f3rN7l7/yHbu2XKlRr9vsOlc8ucPrlEq9NBRgB8vFTAti2VoOu6XLt9F8s0VbJ7KKk12xxU6sRiNudOn0AgaHe6LM5NszQ3w/lzy9RqDR482mBre4+LZ0+yODfN7fuPWF3fBpTCr66rJTMcoZ+HMf5ojAHcvPOA2/ce4vk+n3/xKn/z3/oV3njnQ27cWeGv/5Vv8FdGRuFBpcYPXn6T6zfu4rgem1v7VKoNTi7OY9s2MpTs7h2wurHN+x/cplqrk8+k+NqXXuTKhTNDcdXBzkqgxPH6fY+VR+tcv3mPTrfLycU5LpxdJh0OoQZBGLK5uUO/72BENhGD3Vm359DudPG9aEwnYriepOe4IFTSc7PVwnH6WJbSlMqllPhlKCWb27t8cOs+Ukp+6YsvkErHWF3bptnq8J3v/4St7T2uXj7Llz73AqZp0G53eO3N9/mXf/g9+o7DpXOn+NqXXyIeix1bHwTpVJLPPHeVB482eOO9G/zxd35INpdhbWOH35ocw2z3hsyDYRgEYUiz2R7m35mGjm2rAhIlwCmQMgQRotsmTI2jz0zh+CGO52NoQulO+QGBLzgoV/i7/9e/RzEI+Pd++UvMx2yo1tGqdUS9fgjQag3FpkRtOG0PJ/Uo/FxvYgI536cnFMDyU0n0iB0Kh2riKndUiBBBBPjbEdjSNELdoGnomDGLeMxG03T6jku3pxK6Y7atijIsCywDaRjRPKRF664kDBS4EkGI8H00zwPHjWKWYshwHc6dx5WyR1ooh6CT7b0nHiKEgFoTUamBbUEkYdHr9bl+8y7vXr/J/kGVUjHPzFSJqckiU5PTTE6Mk7AtVagESBmSiNtMjpdIJuLs7B1QbzbRDYMgCFjf2ub3/uhP2dja5XOfeZZf/soXsPIZfvrTN/jvbt/lpa/9Nb7+1c/z/o2baBp8/sVnyKWTaN0+NNuEzTai2kDbryDKVUS5hlatQ60JjSai2YF2R6UIhCMALXw8fH2k5XNH1iopjq5PYvTvX6g9xlvy8aHJ42BDKPx33DPwL7A9KbwHjACuj6lMFNEYHkgvHAda0SEqBXqQa6ohhCQU4kgo8ZO2gdyMpmkYhoFpmQhdY2TrpDCNkJ8YzP5iYEv+LBSvgdQJQy1Sao/UXAc715EBeXj7jzz+JKA1OH6Elh82MTjXsNeVWro28FoSUbm0AkXdXp9EzKaQVfkGnq8qxJAqib7b69NzHFodperMABSiTDIfo9Wjnb4mFL3danV55/3b9HouxZwyAW63ukgphiXh3V6f1c0dvEBSzGSo1FsUC3lOLMyhaRrdXp93rt3CNAzmZyc5uThHMhln5dE2mzv7vPfBLXK5JPlchlQqgQRisRjzs1N0e13GigUmx8bQhEG/H3D21DK37qzyw1fe4t/+rV+h01X2GLV6g0q1ztXL5/jM81fZ3Nrj7oNHPFzd5A+/82PWNrb52pc/y6WLZ0ln0kOQMvjXsCwKpQIv5LNcOH+a3Z1dbt66y3vXb7OzV2Z3r8LEWEnllBg65WqNTrdHLpum3e7y7ge3+PDuA4Ig5NL5UywtzKDrOo1Gk5hpsDQ/he+6vPXeDa7fuEO1WmdpfpZiIUc+m2Zze4/rtx4wNTXJ2VMn8Dyfh2ubaDGNmG2p0vF2R3kA6qaqTBWHi2EqkeA//Pf/Or//7e/zre+/iuf7zExPcvXSWcqVOumRHA0JXL9xl7ffu8n83DTnTy9z+95DXnntHXRNYEYA6K33PuQPv/MjNrb3kWGIEBCzbc6eOsHoyQZFEp7r8O77N/hnv/cn3H2wihAaLzx9kYmxArMjVZGe51FtNOn2HbxhkQV0uj2++6PXePPdD2m2Olw6v8y/89d+Dc8P+aPv/oSN7V0M3WB9c5t6s8Xs1DjPXD3P05fOEIYhG1s77O1XsEyD7/3oNWzLIJtN8rvf+hHNVgfLNHn/g1s4jsOzVy+SzWa4ffchf/DtH7K5vaf0uByX5RPzTI6Xjt4bUT+PlfI8c+U8d+6v8oOfvEUyEee3//I3KGweDNeIAQP6k9ff5sc/fYt2xG4KTTAzOc6v/vKXKBXzSqtqYHkSbYK2Nnf44U/e5M69h5QKeb76pc9wenkRTTPY2Ttgu9HEmJ3CO3+Ke0HAHddBy8Y5e/bzLC/OousaMghp7ldpPtog0e2Rclz0WhNRayDKNajUEdU6slJHhsrYPAiVY0CYTTPYhwdhSLPVptVuIwmIxVXlX8KIYUXK7JgG3XyOfzk3zpkLpzn/zBXsfI4fvnudP/rpm/zSN7/EX/rt3yCMQueMzHuB7+P5SvvMNCPuRcpIHFcxosJ1CesttE4HvdODZhvZbI/820K0uurfdhfR7ih5gyHbMzrHHQ3JioHmnBCQUdY5L7/2Dv/0X34L1/P5ta9/nqcunUVoIQ8ebfCnP36by+fP8kuff55MOommKf27mG0rPTDDIJdJc2J+llg8jmEYNBotxkpFNY9NjKMbOu1Ojzff+xDTNPjs809xcmmOqfEsjucTsy2V85dNEWQTSDGuNOgilW9N00aA0eFmJ+z2odFCa3ag2UXUmohqHfbK6juv1uH2A1U9GYTIsdzjxMBIROfTaR+BEOSx535mCDMiMf4CAdcTQ2pP1CQbPskIbfyzTjxy7EcxeyPnlFEayIDwGUk5etL/R1sohKryFiiGfZjneuxwISKQ9xcVRvzINorADwVEj/BgYqB7NOjAjxA8/VlU4+A1MohOecg6aVJZnui6sghQPoEq9NGMdUgnVLk3QpkQm4Yx9KxzHIcgCJU+S+CqXbTUEEINnGHRQ8TkSakRBAPxM5X76nkBK6ubWKcWSSUSkRWMjdAN3ED5M05NTWKaJp2uqorZ2S2TTiU5f/oEMcsknYpTb3ZUhVgQcPfBGg9Xt6MEZ0Gt3qTb7xOPxzgoV6nUGirJvtPHcXwS8QRCaKytb9NotkgkYuQsC9d1WV3f5NGaykWan5kkDEIazTYz0+OcPDHH3n6F1956nxu3HlCrN1ld2+KzLz7N7Nx0VIE58jUAuhBkUklSJ5eYmRxjYqzI9378Otdv3mNje4/lpTmWl+bUxJpNk89lef+D29y885AwBNf1+eDmffwgIBmPMVbMMz01zuLcNL1en1ffukal2sD3fGqN5rDsP59Ls/Jok+/9+HViMZuVRxsYmmBhdpJQKjuYdqeLHwRYKMmPUeE6oQmWFmZ46vI5Xnnjfaq1Ju9/cJutnT0W5iY5tTR3OC6l5M7dBzh9h6fOn+bZpy6SilncufsAGQYYQhL6LtvbO1QqNXQBdjxGNpNmYXoKS7foe240ZkCGalPSaHT4yWtv0253eP7pi2zt7EeMrNJoGlbNRGX+vqfUs9VmT2dvt8Jb794kmUjQ67m88/5tfu1rX8a2bT68cZfrt+6zMDdNNpNib7/K3QdrLC3MEo/F2NrZ4+adB1w6f5rnn7nEw5VHzExNoJsmS3P3WdvY47d+7aucOjFPOpUkk7AJfQ9Cn4lSnqnxAuVqna3tfTqtFtJ3H58NpETXdeZnJ5mbmWR7TxkFLy/MqSTskfl2bX2LP/3Bq/Qdh6996SXWNrZ59c1rWIY5zJ0cTL5qwRQ0ai2+/d2X+eFP36bb7VGpNdjY3uPv/E/+LU4uLdBqd+hGoqduIHnnvQ/5nT/4Drqh8zd/+zeYm53G1g2qjSYvv3Od7//4dSbHS/z6N7/M2a9+FiMS/vQ8jwcPVrl+7Ta9gwrN1S3WPwg5X8zxq9NjSE0JzG7t7PGH3/kBN+88IBazOHVins88f5UTpTESg0XPsujlM3wnbuNMljj7wlW26i1eq9dZkxInmcLTdfoROFM5m7C1s8v9h2tUqw2SyThnT5+IzKph5dEGK482lJOEptFstSkWcpw6uYg2UYpsiBTASSTiQ+ZXCGVt1mh1oN0mHfrEXU+xXO0OotlRjFyrgyjXMH//u2qW1gQyk+T2vYf8wXd+xObOAb/9l77ON776uegaJOlshg9uP+SV199lcW6Ki2eXFTOn6wRAz/Hoez75fI5UKkkoFBBLJRPMzkxG+mcaMdumUq2xe1AlHk8QTyQIfY9EPIYVV5tqIQAtkniRhxv7UT2k0aUxQBDGbIjZBBMlVUQSFZhomjhMQv/7/z/E7/6J+v0LLzy+Jg1tuQ7Jgl+8yWO/R5v9UdA0BFNPer+RTyz+/AHX4Zw6WBy1jwFa0SXKgdXfaErSE99BMVdPePow92oAMlSI8qj3cvSWETEzwB9iOJ8cXlQYReAOTXtGQXr0mBi891+09MNHtCNdF3XGY9BpEEYc/fsjQodHLFaOMFwDBku9Xkql3q5HejtIkGGAF0osQ0cTKjHddT2kDIbJq34QEAYKGIZhQKfbpe/0kNKHqMJLRJ9FChXKUDkyEPiSvuMq30EjJB4zcR2P+w83yKVSpJNJPN/H9UMOKlX29iuEYTj09lIeh5J7K2tIKTl/+gSfe+FptnYP+P1v/zBagAN8X6IbGppQ4qegKPztnX32KzUOKoo5KldqvPLaO7TaXYQmME2TeDzGxMQYe+UaB9UGDx5t0un1SCQTPFzbYn1zm0w6xdlTS0xE9L7re9y5v8raxg4PVzf42lc+y4Vzp0glkyP382A3oVi/RDLF5UvncVyfP/jOD7l5d4X7D9e5cOYE42NFpqYmsOwYjzZ26bkeY8U8tm1SrlRZebiBHYVVkok465vbNJttLNNE1zUlSheGdHt9spkUsqf8F+/cXx3edDOTJTRNw3HcYQjxqNhnRAePgJjFuWnOLM/z9vu3+S//q39EKhnnb/zWN8gO2DxAhiHbO2Vcz8c0dAw9coQXAsdxhz87e2Usy+Ty+dNcPn+akwuznF9ewtQEnji8L4QM0ZDELYsLZ05w8dwpMpkM3/3Rq8TjMQxDG34mKVU1WDGfRdcEnXaPMBAYQuC5Ls9fOccXPvc8P3z5dX7y5vsQhtiWRamYJ5dJ8eLTl3ju2au89ua7vPLa23iuq8Q017fp911VqTo7xcxYIZIYCTk5P8VP3zI4e3qJpy6do9VqR4BH8szTlzi5NM/9B4/4V3/8fYSmxDmPt1FR3WQiQamYHT4XjiZ8RzvSh4/WeLi6wTNXzvOVL36G7Z09NrZ2mJosUchlCaXSKhuIBodhyMqjdd589wPFRKYSGIbB5vYetUYToRF5tCr6zDAFV6+c5drN26ysbQ6dBRzH5dad+9y9/4h2p8tb73/IzPQ48zOT5LIZvCDg/Q9u8/f/P/89nuPypZeepzc9zo3VdZy5ab4ZSUv0eg5vv/8hP/rpW+SyKZrtNjfvPMQPAiZffP6wY3RtOA7anT43b93juy+/Tvmgyt/6q7/O5196ltBXqQaBlExNjPHeB7f4oz/5EQhJIZel2Wrz1nvX+Y1f+TLpZIJ/8jt/xE/fvK4Yo5iN66mCgPm5KQxdo+8oB4vJiRJf/8rnuHzhDJrQWN/a4Q/+5GXFlmeTfPNrn+Ol568St43hhnIwasXWLtYAeGgaNS/gW997hQ9vr3DhzBJf+9KLpFOKDdZ0GB8v8dUvvsi1D+/RaHfpur4CW5rA8zzuPFjFtCyScZvxYp65uVlsW0kx9Lp9DspV4vEYmbSKQMho1x6KMEo5U9pi3V4fZIhmqJXA0HU1Z4wYBXueT89x6bkqvSQIAoSm7mHbsrBtC8tUa4fEGMZdRLN1+L1l0gxMlZ+0Lg3bJwJcx8HPR71m8LgG2gjgGjBdMno/ebgGHic8/vVqI7urAeEyrFDk8DkOP9pHCaOPVtEethAh9Mgb/PE+Hd3fPfmco+cTDJCLHAFeum5gmibhE+a8J7VfEGx9AvovSs5VlNyIdMKRszwhDj1En8d2DE98i8cL9gUCIUPlbWUYSKko/2Q8xvREifmZcfLZDIHrEwQ+QoNqpGUUS8cJgpB6S7FGbpTDoXJE1MAeCJ4JLZILkEos0PeUHYmBztR4iRNLs9y9t8ba5i66pikV9mod13WpVOuRSrWmpAkQ5LIZDio13n7/JmsbO8xOjdPudikVcsRjNh/eXqHT7Q3zjlw/UHkqjRY7BxXanS5SSor5LJMTJZqtLls7ByAElmWTSSdptruEQcjM9ARnTi2xsrrBy6+9C1KSiMco11SYcmZyDNM0OHfqBLqu8eDhOq++9T6b23s8/8wlXnr+KWZnptAjkUcN0KMQqwTseJznnr1KaazIm29f470P7rC6tce9R5uUijmmJsbYPahgWxa5bJqxYo5MOkGz1abbc1jf2OXegzXGxwrMTI5xZnmBrd0DTNNicWGaEwszjBUKvPHWdYqFPA8ertPp9chl0pF+mhXZbwjMSFZjUD3nez5aECDCkMAP6Hb7ZNNpnr96nhu3HtDt9njpuctcOn9qmBs1GPG9vkez2abRbCMBz/fpOSqs1+v3sS1LmStLyYUzJ/nNb3yVsVKeZr1Os9VRbGp4yFYRBiRjNp977io37j3kBy+/zv2VNV54+jymLkY0XlQeQT6bxjQNao0WruNiZJIszKrq1h+98gYvv/5eVBGoPO5mJsd58HCNXDZNKhEnk0piGgY9x6HRbLFfqdLp9tg7KON5HqmYRTIRw7RMXM8fzoUD8U8BuI7H29du8Nrb17n3YI2NKL9nc1tVvy5EoqZhGNKJQvZCE8TsGIZuHJvoxLG/QjQhKZcrbKytY9sWybiJbWiYpo7j+vhBMBQa9Dyfzc1tOp0eX/78c7z47BU6nS6tdodiPqc2ANog4RUsy2CsNMXkxBjrW7tKIBMIfQ/b0Di3vEA8ZvH62x/QaXXw+n1EOkGtWuVHr7zOwUGFv/O3/hq/8qtf52C/zM7egap09JSYa7fd4voHdwiDkPGxgmJy08p43ej2GMybMmLVlWWTRq3WZmt7n6sXTvEbv/pVEjGbSrXMysNVAgSu6/Gd7/+ETrfL//xv/zVOnz7BrVv3+f1v/ZCfvv4eLz53GdNUFbdz0xN89QsvUK7W+f6P3+D9D27z4rOX+Su/9ku89tb7/MkPXyMRj3P+zDL9foff+b3v8L1X3iKVjLO1s0MmneTC2ZPEx/JqfhYhhApg6J3OUIRIahp3dst8cGsFBJw/c4J8NjP8VmUokQIunDuNZcai7yxUMkC68l3s9nrs7h1QqdaoVuucO3OSb37tS8xMTfCDV17nez9+lV/+yuf50ueexw/8SFLIV7lx0fpQqdV55dW3ebi2EaWoCRbnFVs9PzuFbVs0mi0+vHmPOw/W2CvX6HR6BGHAicU5LpxTxu2lYj4SsFZjRrVA5XYNxmc2fYwQeFKLWJ7H1q3RNfN4qPA4QDp+f0SkhDzGUo0efgRs/cW24Tr8Z8V1AiBQn+8ITSNRklJH2xGpkiGREwHPx+YUcaRLFCb5aLcadSY5GriMzqINH43bNolE4ojMz89qvxjYEqMyYE88IPq/HMbMh+JoQ7p18DPqrH3sLJ9g4IweEy1LCE15ucVtG9syWZybIptOogGu4+N7gbLY0ZWJbzadodXu0HN86noLgTb8IoMgQIYaofRUSE9KAlS+hBEpDvcdl8BXk9CgHL/bcXAdj3ani65prG3uYNsWhZx6L9MwlM6UptHqdLEsg4vnlqlU69x5sMrufoVnrpzll774GVZWN9gv19jaPcD1A0zLwjCVWr3vOtTrLVqtzjBZPp/LcvfeGoVcVhk3p1M4rsvK6joH5RrpdJKnLp8jnU7x+lvvE4aSpcVZavUG7XYXyzLRdZ1kIoZhGKRTSVqdLvcfrrG2uc2tOw/42lc+x9mzp0gmEoqNMg00IpYw8oybn5shkUiQzWV5450PuL+yxoOHG0oGALVjL+SzOJ7KpQulJGYrRfVWp0e7s0Wv5/C5F6+SyaS4dfcR2zv71Kp1TMNiY3MXx3Up5rOkknF8P6Raa6k81zBiNZDDUKwMVdK45gdoQYjv+1TrdYIgZHxsjBPz0+yVq1w+v0w2nUYTI4UBQmN2eoJ7K+v88fd+ws5+mZ3dAw7KdQr5DP2+SzKZIJVI0Gh1efWt6xTyObKpJK7T5/TJRTKpFF3HIYi87PqOw9bOPr/zR9/llbeu0Wi2MXSdWqNFt9vjUKCSyMdNjedao0mn26Xbi/Pj197m2z/4KUEQ0ndcYrZFt9unVm/geaoiMmZbmBGrq+saMlTm391un/dv3KXWaDI1MUY2nSSfTXH65CKdKHfx3soqpUKOfC5HKmGxsbXJP/wf/pDb99cYhm+E4N7DdW7dXeFSqMy0w8guZ2KsSMxO4PuSTrcfhRyUy8OwWAHFHM5NTzI1Mcbmzj5vv/chi/Mz5DJpxkoFPMeh3e3jRf6AnU5XjX9fLeLFfI6TS/NDZsWyTGQ4ECUcbI7AcZRwsef7KnQEWLbJycU5XNfjnfdv4jkehqaD1PH9gL29A/b3y8P5xhKCQjZDJp3kweoG1UqVhZkJOu0urXabmakJfvObX2FuehzX6QOC1G7tcNLStOFMbhgGVy+exXFc3rx2jT/81vd54dkrmKagUquDEBQLObq9PuNjBU4uzVPIZVhanGVyvMT+QYV+3yGXzZBKxDl7eonPv/QsN27d45VX32Z2epy//Ktf5bOff5FWu8sPf/I2G1u7NJstJbVSyPFXf+OXyOUz/PGf/AjXcZCBHxHW0UVKAE1ZNEW7eWkaVDpdBVwCiesq5v1wLtYIAzANi+nJcVrtTlQZrMSXk/EYLz57hW987Yvs7e7x+3/8PR6ubrB/UGZ6coJQSmq1Br1eF7SAUPoqEgHRuFEepk6/z8qjdWq1OpcvnWbl0QY/ePlNmq02v/6NL5FOp3jltXf4vT/6IdVag0LkZ3tQrnHv/ho7u/t86fMvkEzEVXgVUJXuUSFC6xBsPUln68ntaGjqcNkeAVpHwm7D/z2hjQKu6HUDCYjo3jv6Pv/jtEPi42OPfPzvUUwwCM0N0NugbwYseZTDKkf6T4z+GzkoPDmE+Pg1j/57KAUhGcRAho8JkFJhFdu2iVnxqALy49svHkYUAcgRcbKf0dNi5P9HHz0WQhwCsj+7vY8KUauddCJuRwDIZHKsBKGk3e5QLtfw0kmK+RyWaeL7Ac1Wl539MpV6Cz8Iht+v63qEoY8fuErFWUpkxFcPfMoazaZiO0IZ6WLFsEyTmG1imSalQpZnr17g8vnT9B2HG7fv0+31SaWSZFJJdg/K1OotLp07zdWLZ0km4jRbHU4szlGu1HjjnQ8UaDMMDAQTYwVOzM9gWSa2LsjnM6RTSaYmxpiZnlALgmWytDDDudPL5LIp/NDDNFReyczUOKZpEoQBsZhivbLpFNVanfGxIhPjBRqNFp1ujzCUJBIxnrp0ln7fYS2y1Gl1uvxyz+H82VMUCzksTaAZOkHE8oShEv8sFfM8dfk8lqm83T64eZ/9cpVkIk4qmSCXTbFfLtPt9dA1nbFSfmhjs39QxfN9SqU8xUKOO/cesbO9Dwh6fRfP94nHbIJQYkiNVktJdtTrLRzPY6yQU3IKQaASmgNlR5QYJhEK/CDEcz2StkWpkKfveExHxQViZEOhCcELz15lc3ufeytrrG3sIFEaTkIIHNfFtizmZqbI3n3EvZU1/pt//K+wDJ3nr57nxMIs+9U6uwdVeo7H5s4e61u7rG1s89b7N9nc2Sces/GDgHsrGzxa2+bCkUpKlSTv+QH1Rpv9chXX6fPt7/+UG/cekk2nCAIlZXLz7gqzs9O0O116fYdGs02n26fv+VENiYZlx5mbneLajbu8+d5NzMgh4cKZE0xPTYLQcByP3/3jH3Ltw/t85fMv8PkXn6XV7hOPxUmnksMJT0YixtV688gOu99XDJoXk9x/qMLRgwKR9c0dwkDZ00sJPUcBhrPLS2ztHPDOtVv0+g4nFuYYKxZ4tL5Ft6e8PJutFuubO5xaXiKVToEQvPza21SqNZZPzPPM1YtMTowpBiQICWWI7wd0uj38qNpYRpWeMgxotLtcu3GHuw/W6PVd5UNoW6oaUKowfCIex/WUZl2z2SKWUFZerVaHW3cfMF4q0O31og2di9t3yCTj9HTlvap3eoeLQCQTMZD6ADizvMgb71/jH/+LP2K/XOXrX34R0zDodHuqMlNoSmbC9XH7Pr6nRF59X9n8KOkPiJkGhgAClSKRz6QYL2YxNMhlUyQTcdoR+zc1Oc4vf+Vz7FcqvPz6OzRbHZYXZxg4GoTh4feroSNb3aG4ZBiPo2mqGMnzfPYOqnS7DvFYDEmAMAwQOpomaXe61BtNFQY3lDm0qQtilo6tCeanJ7l07pTKQ213EDIkYZvYlomhDzYbAk3XIQgJg0BJXQCalKQTNlNjJ/jmV1/i7soa3/ruT1jb2GG/XKXXd/jgxj129ytcOneaL3z2WZJJm5+89i6vvnmNd967STadYnaiSDadUHmBOuiaVGxSIwojJhNKw+zjV6BjK/toXGok7DXIcxrmXH1CoDQKTI697fC8A0r6F20/96UdBS2H7SMiYdF6f5QwifK9RmDFYxzhyPlHlbDUA4rgCUdw2iD897hmnBpX4ZFrZBh5CEL1c/h2g+tVm1/5CfPgPp2crSHgGn1s9LIYSVQ8POTIJR5htT6Oov0ElxSdS488ujzfZ3Nrl7hlcWpJJeXu7x9QrTWo1puUCjnaUZ6TDKOqSSlx+h79voPj+OqEGuiDSVIwzPWSYUi90aLeaAEKbOmaRiJmMzVRpNfrk04lsS2TVEJ5K/pBSKXaIJNOKQNmw1Q5CUhitsXpkwvU6k2klPz0zfd56/2bFAs5SoUcQRhy7vQJ5mamCIKAZMzi7KkTOK6PYRgqBU5Kxkp5suk0yWScbk+xVRfPniQeszB0TVkKtTpD7SeJjBhIdb+mknGCaMKdmhyjWMjh+wH5XIZbdx8q0dXvv4IQgsuXzmIbGgIL3/fxPD+q5NBBSpKJOGdOnSCXzaDrOm+88wFhGGKaBplMkm6/Nzy2kM+Qz2Xp9RzqjSaJeIyNzd2hqGkyGefy+bO4nsfN2/dJJRKkUynWNnbodrvouk6706XvesxMjmFZ1pDZ8n0FujRNUyE9XSdu2wjAt1Re2OLsFIV8Dnhcs+rZpy9RKuS4c2+Far1FICUy8EnFY0yOl0glElw5fwbX8Vjf2kXTNOZnJnnu6nlCoXPnwQoHlRqmYXDr/ir3H66RSSWZKBVodXqUiln8aJHsdJwj90kYSrai6r9Gq82teyucXJhBaIJ0MsHUxBipZJz9gwrv3riDHY9jmioceP3mXfKFPP2+iwT6fRfLsHn60gW63R4//Mnb7JWrzExO8PUvvcT83DSNVpunLp6mWm/TbHVoNpXX3OR4ib/yq1/j1t0HqqrVV/56pUKO2alxBtWqA0a71e7S6ykdNE3TWJybJJ1K4XreMKculOFQZ63X75NKxllamOHEwiyFfJad3QMa7Q6GaeK6Ls1mm1t3H3Dl0lnmZqc5e/oEr755je9svsrszANVxTY+huu47OwdEAQh5WqN1fUtcrkMrXYX1/MoV+t0+33u3n/E7/7xD0in1KZja+eAnb0y1WqddCpOMZ/l9Ml5bt5d4cPb93n32g2uXDqPEIJWu8v7H96hVCxQyKUxTZON7Uf83rd+yP2VNeqNJgtzM/yyJxnwIlKoELQf+HT7PSq1Gpl0gmevnOfW/Ue88sZ7xOMWpqnR6ztsbG4ThiG1Wovbd1fpzrlR+K0JCBxHAa5QhgS+j+97hOFhxarv+UjXJWEp5wjHcXFdj1arzfsf3OLHr77N7fsP6fZ69PsO27sHhKHEdV16fQddM8hlc4xV68QCVX7vxW1ymTQTYwU2t/fY2Npja2efvuMSSh8rFsOOWYSh5Ls//Cnlap3f/OaXGcsrZt/1fCrVBo/WNsmkU8PxE4tZiKisXhISBD6B5+G6LjIy0/ZcdzjGPM+j3emhaTqddo/xQo6JsSJbO/s0Wx2khHqjg21ZLJ+YZ352GtsyGSuVEEK9Zn19l/39OulUhlgCTNMmbgl0Q8DAcP3nYbUkPJ47dfyY6O4OZRQkGoQKo+dHF7ThQ3LkteLoqYcL/xMAwCdNjh8wZsdDkp+w6u7xNnpNT+qPx8N5iiGL8rBHsKkYsHqgBMxHPtPwrEJEQC04luN9+FkG1b3DUKII0bTIpmzEZkndSwGeHxCEg5jZIXBDfHI4+ykwW4NPqar1DgfK452nDWonxdEXP5F4/EVYLQCUH5cfTRTKUsRhY2uXmGVSzGcoFXK02p1ox68qfmzbYmYyR7reiBaRAMdVybhWZDKNLgjCkACBpg80k6KcLV99wZZpgAypVGt0ul2y6QSpuE21UuXho3Xy+fwQyGxs73Hx3DKL89P4gUrar1QbaJpJIp4EqVHM55ifmcSI8psS8ThjpQLdXp+dvX0ScWXuOj01jibANg2CIKSQz5CIx1VYTpfIUA4/K1LS7/fpdjoIKWk020qU0rZpNtvYphIUzaUzxGxVpu30HfYOKgRhyBc/+yyr61vcuLPCn/7gJ4RBwLNXz1PIZaLdeoBhwEBJ3Yusbs6cWooWqA73Vtao1Zu0272o2kt9fsfxaLU7HFRqeF5AJp1UAp4xm2wmRb/v8YWXnqVYyPFHtsntu6tcPrdMIZum0WwxOzXBrXsPufdwXZlWZzLDKsRQKrumgRCojHIJe32HlbUttnYO+MZXXiKRTKrnj42vZCLO5UtnuXD+FJ4XEIQh/X6fTquFoeuYhsnJxXkKuSzxeIxUMkk8bhP4Aa12h4W5ab7+xZdYnJsGIBZPMD5W5EsvPcuF86eYmRqj2+tRrTU4sTirTIYZzBNKo+yLn3ka0zSZn5tgZmqc3/j6F9jc2ePqlfOMl0o8fPiInusyOz3NWKkQ9Xcb29B5+uJpMskYfsQMJONJXnzmaSzTplyp8+zVC1w8t4xp6Vy5eJaFmVkFKv2ARCKGECq38Jkr5zm5NEe5XKXVaZFOJ8ikU8qOK0oZ0KINVq/fp5RP8ktf+AwLs5PUmg1OLMyyND+P/qM3GSqBaxphGLKzV6bV7nL/4TqrG9tRKD9kaX6GEwtzLM5OgZSYhk6v77A4N8Nv/cbXGSsVuH33IZl0knw+i6YJ9g7qrG/tMFbKkUrEh5InAshnMxyUq7TavWERSbXWoGP16fUd3r1+i0vnTjEzM0Y8HuPZpy5GUiMrfOf7P6HeaFKp1shlU1F+lk6/7zI1UeLh2iZ3H6xx98EaiViMRDJNiD7MHQwidq/Z6XDv0Rorq6tcPn+GEwszXD5/ims37/O9H7/B6eV5lpfmOH1yAduy+PYPXuWf/PM/oljIIoSg3e6ytKCYqCAICPyA/XKNcqVGq9PF9wOqtSY37zwiEU9y/+EGzVZbSWjoOrfvP+Qf/rM/wPN8ivkc/b7L/ZVN3nznBhfPnWR9Y5trN+4yViry0nNPYaxvkwqV12vfNsllM5xcnOPugzX2KzVef+cap5bmkVJixgyCEFY3tnjz3Q+ZHCtwcFCm225zUK7hOD5r6yrfLh6zKebSnJyfZnJ8DD0aC2GgBKObjSa7O3v0uj0cVwEvog1vLwoL+0HAvQfryow6VDr4vuPTkT0Cz0cDfNdR1bSmQafdJQgCzMi6THnZuoRSEPjKnzaXTmI0ozBiJjUsAvqZ7TF89YQ17vhDoTyy0B/a8hx70RHQJJ+82o+CpZ+nHQnljV7nL8qQPQkE/uw+VHnJI68ZuaZBzvLPfj1IcfT9xBFgd0gAaQMt02OEpIxYZ9fzFKkyuI5h9O1JDN6T26fEbBF9sZHY2JCuGz1EAEf9ev48c/gGl9Tv9en1+gySdRutNvdW1liYneTsyXmW5mfY2tlj96CCpmmUCjlyuSxdx0NoqprF0LXhLkKg6MZBnskwyVkSad4Y9B21iNuWosDDMEbCUpWArutxd2WNhfmARDyuQpBhSCoCOeubO3TaDrNTacIwpN3u0u32idlxJicmWN3YotFsU8hlSCcTGELS6jTZ2OzjeYohmJkaJ2ZbKqxAj2q9TqfXxTQMXNfl7v1HrG/ucO70CVKpBJPjRUqFLCuPNul0OiQTMfq9HuVKHcs0mYlyJ4IwZGZ6kvm5abKZNEsLc7zx9jUerm3xaHWDP/j29+l1u3z5Cy+QiMVU0nAQYFsuYZTT5rjKUuj82WU6XRVS2dk74K13b9Bqtzmo1Egl46qQoFKjWmsCh5IHnuchgEq1zvd+9CpnTs4TRKEhKSV/6ZtfwfMD4rEY2YzKURsrFsikU0olfKRiRwzzAGBtc5sPbt7j7v1H5LIpzp85ORQfPT5MdaRS+9YEWCZ6FJq0Y3EIA4QGyVQM07awLJuYqUQaA0Js22Z2eorZ6SmeunIep++QzaSJ2SbPP3uVIAwwLZ3N7V0yqSRjxYKqpo32U7qu8/wzl7h0/hRSqmpb6XtcubjMlYvLJDNZtWMvJAkCiWaY+D5MTYxTqdaYGC+Ry6RVccLeAaahE8qQRDzG8tIC8zPTnFiYIxFPousC24qRS+eZnvRodTqEoSQWUyxozLZV8UkoSSSV/ZVtWSqMODKpBpGBbyJhk8+lOL08PwxfZbOpERZcAeBMOsX87BT75ZqSCMmmcR2X1c0davUWcy9N89xTl9ne3cMPfMZLBXRdZ/nEApMTY2zv7NHrOUxNjhNGRR/f/OrneebKRbrdLjPT42SiHLBKrU48ZqObyrP0177+BTzfJxlPsLl9wO5+mYXZaUzDIAgls1PjfP7Fp5mdmiKfzVLIZ/n6lz+LlJK5mSly2Qy9XperF88wNVli76CG6/uUigWWTyxgvntz2C9hxCI/dfGckp6xbHqOg23bfP7FZ9ENg7sPVlnf3OWZK+c4dWKBqYkxTEPn2q37aJpOsZDjqUtnWZyfIZ1Osb2zz9RECdM0aDbbeJ7PxFiBXt/hw1v3aHe6fHDrLqZpKH9V2yQZj5PLpocsejxmU2+2KVfqmIZNs93lxu0VdH0VXRNMr6wxE9lQyWyKZNLmzPIid+4/4sGjDX7407eo1hpMTZRotFo8XN9mr1xlbnqCF56+iKFrlKtqE6VpGuvbu9x+8AjX9chnUnzxM89w6ZJS9g/DEC8IqDVaVGp1NrZ2aHd6hECz1cZ1PSzTHDIc/b7D3fuP8PyAg0pV+b0C9XqDRNxG13W2dw54+Ggd1/W4c0/5UY6PF5meHCeVTCqrsTBkb+8Ax/M4PztDMYxCWukEA7Pp4VrzZ13IjmXQwBP+Hjz257hWHmlDBm3kQv7MjNZHnPpTOdtHn/9ok0MGazRgNiquLkSkqTXMG1e+q4NrDcKQMAifAKqOSkJ8XPt0pR8eC8WKYSKjlDoCDYHB8aR6GVGif+ZB+xFNSnXzua4zvLwwjHZJrRbxmM3Z5UXFlDgujVaHnusjGy2q9UaUy2Ri2SaIkFAKQhkQ+OoLQNNRIX0Dz+/TjpShLdPANs0o3qsENA3DUJOCYVCpN7l2465SpjUNLpxdZmpigs3tHTqdPqbRJZNOKZAISsNod592Twk89toO5WqdiVKedifJ/kGVtc1davWmysEKfBZmp1Uul2nger5KRk3EMU2D6ckx+o7LfrlKoZDl5OI8mqbYp0q1Ti6dpBR5BU6Ol0inkuyXK6STSc6dOcnEWBFN13Fdl3ZXgbiF2Sk63R4/eOUNctk0z1y9SBiESuk8smLp9nq4rvKny2bSPH3lPAA/fOUNVlY3OahUcVwXMwInjWg3GY/HVDJ0t4dlGLiOS7fT4/qNO3Q7HaUubpnU6k0mx8dIpRJRQvQV+n0XXVdl3bquD1m2UUX5IAx5+70b/PSt62hI/sqvfInJsQJ+qHJhwmM3WSi0IxOG9HzouxBIpKYTCo1QkwhVCMmgjkbTBKZpRNVnkEmlCOLqOzEMg7RhggjoR+M1NbS+OXpfWKayzHE9j263j+97mJqGYZpRxZ3AtGMgQ3wpkDIgkbCwzBL5XE7lIlkWmq7TdxxarbYSmTRNctk06VTy0GcxYgKFEBi6jmZGhuiaqsoxTZ143MQwlSdkGIZUa8pvU5MSPI+L/8U/wDB0Bc50nUyoqgiNqIhE/+CuEovUBLqm0e506PX6TE0U+caXP8ulC6epN9r84Xd+RLnaIB6Lkc9m6XZ7CF0oNk05zJNKxFlamMX3AyxTacElEnGWTyxQzBfZ2N4mZtvks1lymQzFeg4JWKZJPBnj+acvDlWjz53u0m53KRaKR5JoZ2cmmZ+dpVTMYehGlNOknvc8H9vWSCQsTizOoRs6uq5jmCZCaFivvT/8Hs16i9mYzX9cbyO8EK7fpXfzAY5lcjGbYXlxnjuWze3tXdI9F9F3yaZTfPGl51icn8O0Y+SzaWJWFKrXdF54+iLzU2PkshnmZiaYmCgxNzNJ33FJxOPEbYtsJs7p5QVKpSLJeJzU/Ax/9de/xu5+Gcs0FdvfanPh7DKT4yXGikWymQx7+2WVY3dQHW7uRTGPaerMzYzx4rOXyaSTrG/tcuvuQ+6trNHrO0gky0tzfPMrn2N6qkSlVlNzyekTBIHkfgR8fD+g0WpTj2QWpFRMZzGfhVBSqTbYO6gMWdD1zR3a7S6FfBZN1whDycO1TeU8YVvMz0xy8dzJoQPB6ZMLNJsdNrb2qTeU7lql3mB5aZ6L509xYnGWYjFHIh7D9wN6jsPKw3UmgpDCIPcnnQQZjOREiSHwemz9+rNWBj4J3DyxsvHPsf2CiGg0cf14U3DmI/K3nvgC8XhfHgklDt7pMAlp5EoY5GsNWK1hOtNIfvihFptAQ0MTIcfz3gdYbZAdNihc+qTt0wFbEoY6LCIcPjgQOZNyQPlF7JYUH9PHg1j9L7BriN479F1V5i+UN6NAhaQDP1Bxf9+n7zi0u1229w6QexXyuTRB4GMYGom4rXa+ukBnoGofAcOhb5ISD1zd2KLd6ZJJJSjmlQVQp9PBdVykpZTLs+kUsZjNvUeb7B5Ueem5K7zwzGXyuQy7+/tkMkmSidgQEKZSSZKJOGubOwSEzEyOMT87RSadYnKiRLfdYn1rlyAMmJ2ewDB0dB38wCNlJhgr5MikA7RIc0YTMFbIcXJhhrsPHpFLp8hn02i6xqmTC+i6TiadZHpqglQyjiY0Gq02/b6Lpmm02x0s0xjS+NlMkhOLM2TSKUzT4M13PuQ73/8Jjutx6fzpYeKspqnSddd18X0FPzKZNM89fQnLMvnej16l2+sOTb+73T5hGDIzNTak7bs9l8KEClnYlk2r3cFxXfLpAnPTE/Qdh26/TzFKiC8W8mrH3mhiGAaabiCEJP9//H8haw2MWw/QPJ8Y8L/+4A7/y0g80b52H/F3/9vhjlIglLK2lOD6mJe/eXSoui7xnkNegIzZIMShUrV2eEOjRWmc0U0uoxt+6LlJZAclJVOhROgCwzTR98qRabEg9v4tzL/6vwAEISq0hgChKU0ZTdeH2nIDa6kQSVYqdXHdNBBCI+b5TDguUkh0w0DXFQg0TVMBwmEOpUSTYIchpozkE4aTlMSQEAsDJFLlMoaSC90eYgBSQ4ndaquq3Z6DEAJdSjQZ5QZqWvTZlBClpmncW1nj/Rt3yaSS1JstXFd5Ufb67rByd3f/gEazzfh4CdtWwHFw3+u6jq5paBG41gF00HRJEPjR+FMJ306/r8YGMtJlMoYTbzoVJ5Gw1bwhDif7UrFAzFIaaEJoyFAQhCpfKggCpNSJ2QmSSTuqqlUaX2EYYvR6w6lP+D5GL6R46z5q0ZZk/ABJOLSaOuF5fKHTI37tDvH/9l+CrpOxbSZtEy2dQqSSBHGbIG5DKk4hmWA5mUD3JGYQMpVKcmp6ijBhQyqBsCwuXVAFOmEoicctVfTx9EU60cZkIF+TTMTxfZ90Mkk6leSgXMOybJJeMBzTWiGHoevEYzEunFnmzMklqvUGO3sHlCs12h0lW/O5F5/hzPIStUaTZrNLLGYzP5MlnUwhBGztKL/BUi7Nc1cuUCooCYalhSm+8Jmn0YSg0WxhmgbPXD6LZZkUcpkoHUAtxq7n4bgu8zMTnD9zkpOLcyQScZotpdGXz2XJZbM8WNmgWm+STiW4evEsF86foVDIIJGH0QZ5GD7yKrXD2z2VVMUBw3BfRE8Jfch8DNesQQjsSWvYIDH+k65lw/CeGIkk/evZDiUZPhpwfdJ2GEo8nI/EkefkEcA1TPoYBWgfcV4BkZ2PzsA5VoGtYJCerfDCiGfryIcE5M+GMcfap2BEDUf4ucHgi1gdJVo4StEdb4cDdhiaQwwH82h8/Hii8s+4IBAqaT0MlGiihiAZj5NJxSnkMiTjMVrtDqvr29QaTfqOhxeEdLodgtCjVMiSSiaIx2wsy1SLHnIY3JXR78qoEhzXodns4HkeMcskZtu0Wm36vR6JeExZSQCGoVMo5HH9ECm3SCbiWJZJGAZk0ikWZqcwTYONrU129sucOrHIzPQEc7OTbG7v4jgeC+dVMnAYhLz1zruUKw2mJkucXp4nlUxElGeI57ukkkkS8Th+oARaZSgp5LOkJktYho7jenQ7bVUppSmg1e31qdUaw+qyRqNFvdFkfWsHy7JYmJ3G812C0Ccet3j66lmkVK/f2Nrjzv1H/N4ffx8pJc89dRE9qk4c6CKFYUgoVU5dKqXkJ6T0cdw+t++tIoBWp0smneTE4qyyT+o6+K6yVJocKzIxVqTT7eG6LrZpY1p9Nnf2uL+yythYiZilPOUcz8dxfQIpkMJA1yV6s42otxCeD36AAGwkFgIRSnC8IzspoQbf4WPd/pHxJvwgmoAFREDyo0bnx92cgzFuDI4VAuG6hxO346KvHRolG4MJnpFogzgktz8qQ0IH4oMNTfSaQ7p98DOYlNRko4/kKQyPJzKXH6HlU4AYuVctBFooESIEofzFhu+jDVILFOAUukZpr8oJ16O5vceN775C58EGjgxJux5nTi7itzs82t0jmVEsnGXY0W0Z5eMJJfvCyJwzGHODCVqGISIM0AjRCaPJdtBPYrhrHXikKUCsoxkKxElNEAilK6c0j9R7BFG4O2bbxOwYpjkoNol0p3MZ5PQEYmsvsrwRh/knEVuiEZmKR9+fritPRUAZVrc62C3Qqo3DzzMyTx7K7DxhgBk6MhknlYgjE3FkUv1Y8RipeByZSiJTCcJ4DM+2qLTalO/cxz+oMJFNc/rkIrkPV4aLlTQMqDXxXIeYbVGYzHPq5CKepwoddnb3kUhOLM6RTqXUfJJsqQrKIMDzVUHPmeVFlhfnObM0y/TUBFYsTigli/MzaBo8eLTB7p4K6V65cJp0OkUqmSCVTCAlyvmj75JJJTl7aonL509TKubVZgRVTR6z4xRyRZYXF+l0uoBgbKxAPpum2evS7fURQLvTo9Fs0Wx1yOcypEfTjVIpZCgQWrSwi8GtGZEMQjvW4R9xxw+/HPnz45HB/fJpAy5x7Pdf5PRHVNUfByVHTi8HEhtP7isVIXsyuzXEBuLn78ej76ejnJUViBoFbjIczP0D/mxEges4CPsZ7VNgtgSPeSRKOUwsC3wlm69rhxPIY2eQ0SIzfDr6IHJQjBlN8sMBPQruRinLaIEYMGNBiJAhtmmiaRCzDM6eXODc6RP0+n129ysITXD14lkWF+dZ29xhd/8Ax3UihXJHqQvrOkZk+SOEWpB9VFWirmkITQ490nRN5W0V8hnilolp6FEel4ttW2QzGayYTTqVIJdNU6032NzeJR6zVagmCjM5rkur3cbzHAzDZG52Eomq6EsmE9EO1CMes8ikkypUN7LIqsTukE5PVeDEY6qcudnt0nMcfM8fJpv3en0qtQZuILEttdMNZUiv5+B5StB1amKMbFYpZJerNaQMomR+FfYzTZN6o0kqEePs8iLVepM33r5GqZDj3JmTBL6q6gjCo+J0UkoSiQRXLp2j01OWReubu5TLNQr5LBfOnSQWs+l3+lQrDTa2d9nZL6vdtG2RjNl0HZdqvcbW7h5vXf+Qk6cWmZ+dIRRgWAaGZapFTAMiBkdoQpWlW+bRcYjSB/ODEMsylPq0QIGpwQ4zETt6Y3e7SuRRCGQyfphtGQ6YXbW4E8oIhMiRDe+o7cNg7DMcywDSO3RHEJqAgcjq4Ww1/HVgTyGftJkmYumO3j3Dv8TRJ0YuSh4S1p+g6YA0o6lF1zHi8ZGPIxDDyUwgwmEMABGq0Npn/odvcamjXBXYLKO/ewdT1xXr9vI7SCm56HvouoEVs9EsC0wdqetIQ1f9YxhoN+5CNoOcKCIlTPk+pahIwzQMhIBCtBjrEUM9ClL1UF3n4HELhsBmAG4Gdl9SQiyUZKTK/9AihfTD/fhof6KYTF1XYCWbAs8H30f3ldCuJkEEPqbnYYQSvddH6/YQQYDuK9NkERkmH19jBt/jz1oCPomLn5RQDALOBQF/KwyJhRLx+odqkQtDpKGT+md/TPJ3vkURAfEYWi6DyKYJcxm8dIqMJuhaJsn1XazpCfKJOIHrsu84lCtVNnb26Pf7LM7PcOn8GaYnx9B05QCiE5BOJZgYL7G5vYcQsDg/zcLcNJZlHQJpqQpr+o6q3G13VGGTbdlDZqTdVjpnSqcuRjxmk0ioXDXT1Ag1VbkeSkmz1Wb/oIKh68wtzVPY2j/sy0yKiDJmuE4BRxLbP7Id2wI9xpR81MvE43+PopVPy4JnyFh/gmv6+U7Mk5HQyFw3kgf3caTMk99hCI0ee/zwuxp5PEpxGhTxSCGGH19ylJ1TdmkjOOPY+f5iwJaEQxuHwYVEC0wY4ro+QRAqj6rBxCNk9COOnUhEn2PAboWHoOp4Z0XM0pNoPbWzUNcgZAgypJDLKJE+CbsHFSRqp6NF5fInluaJxeM8+9QF+k6PZqvDG+9cZ3VzR4UAo9CE+gwCTygQp0VhRID9gwrVWmOYnzU9Oc7S7AStdodHa1vs7lcoFnKko6q4eCzGeKmAZZlUqnUsy6TZ6rC7d0ApCn/p+ixBKFld36LRaHF6eZFiPqfAW88BJNPT49GgYZgU7wfBkDFzXY9ev49lWiQTahdYqdVpNNvkMmlSyQReEFBttPD8gEIhRzGTV9YYns9BuYptmxQL40Oz4dD3CVEVeM2WEoZ0XY/AD8hk0pxcmmf/oMrO3gGvvfU+uWyGfD6LH0lIHM+BQkoyqQzPXrlMrdam2fwp1VqTZqtNu93FNE1SiQT5TIaJ8SKb23vsHVRotDq02x16fYcglDiux9rmLgflSh9hVQABAABJREFUShRSFRQLWWIxi1jcVN/VcMwIwqvn8P/R/+3IpNJotflv/tG/4PV3PuA//Pf/bb7w0jNYpoVEEEYL6JFyYymxvvY30da2kIZB5+/9n3CeuoCUIfvlKmsb21SrDXr9PppQ1WK1epNUMsFzl88xWchjCk2xpp5P4DpoocQIVajN7fa4feMuH35wm5nxEk+dP0U2mUD4AcIPkL6H8HykHyA9H6fbo7x3QNB3mB4rkrIttCCEIBj+qwcBeD4iCAk9dR4RBMjoeREt/MMfL4BAsYAfr5b8+GQohvfvAAGMppWO3MNBgL62hS4V0zgMy4weIw4n1uPg8LEpr9uHcg2xsQ0RAWUdmUeiCfAJ55CANvImh9P8wOc1HLo/HFtCD083et7j3RIEw3FndPoMhBMlKBuwaDOgDzcDgz334M/BqhAqoB+qHxEl8orw8LHhT3D071Gge/zaBx9dkxJzkDIRRgzuAPiHIXrkoqADotFC7B4MP6IFJGH4WYSuY+k6U0IwIcCXkuekpGfoaLkMyanvok8WIZdBlPLITAo9kyJnm5zp9snG4szZMYwR+y0hVIGRBMZLRXRNUw4SiQSGrvI0MykDQ7fo9x3l7qHr2LZFJpPAjlsgBCmhYcdsvEiKZKxUYGZ6glIxT/zh1uGQSSdVzwx2M09QNn9yO0oQHAVcg/XvI8CNGBx3/PE/B3brSAjwF2mDa1Ofb7BGHTtidBuixvJjzGB0rFAAnGirpm7LUbYrmhfk4Z06nB/E4TGH51IYRBz/GVwKEZQZRGIGm+1oLhsQ0TL85LnmvyDYOm6DPbjCQ7XuQYcr2waJqlh8gijcAGiJw4n0KNs12j4K+R4DZEGA7/tk0glOLz2N63psbO+yvrWL63hRqfcNmt2uEszMZxkrFZidVuX0uq7j+Ur2QdcjY1IEPhqmEQ4TfP0goFKt02y2kWGIJpRBdLvVYnN7j82dA7wgYH17n1v315ifneTsqSXGxwsEQUir1eHu/VUq1Tqu55FMxNH0hPIGczzWN3dotTo8dfkchUIOx3ExLQPT1DGtEqah40ZVfo1Gi3ZHVeYlk3HikT9az+kjQ6FKoiMRQkNXpc71VkeF9XSdfq+P01d0ukqUVonwvU5HhWl8H8uykMLAD3xs26LXd5RWVyxGKhGn13fYOyhjWxbbu/u8+uZ7fP6lZ7FMg1DXo77UhnlBGqAhGctneenZy+zs7vPqW+9z78E6AsHC3DSGpjM3Nc7SwiwTYyUc16Xd6bKzd8DD1U22dg+QCPzAwwtcJAGarhGPxzBNU/Wp9jhVHUYAYDCSdU1TGlbNDr2+C0KLjok4ivAQBADDvKQBjhCWUr6/e3+dH/30LcIgYGFuhpnJcZLJBO2OkjN45/otSqUCk7NTCNNAGAKEVOUjQkMTOlIKQrdPtdngT155nQKS0je+SOLUUpQHJNH0AUWvgFx5r8y/+P3vcWN7m3/3lz/P5158WumuiUPdKyvS85FAQBQKFwo4PLYgCAlS5WKFUiJ9BezwfYQfoPmeCod5PiLwkX5kf+X7ClBEQBBPgcIBIBBBMASM+j/4J9DsHG6YoplORD/IAUseKEZKE0MduSM/g/knYhGHv/sBGPpQlWYgJHqYY8Mw9Dj4Zkfm8GGPDBi5MAwiWRMDwzg84rFZ6WetV3r0XpaJLOXV66UkiPTTQilJJxPYUfrB4Sm1j6CyDt9SMZtH50PP8/A8X6VEGMbh0RHAJgLcBCH4PqHnE3oe+AG62p1CtcFINjLSNAnTyYhlUwBOjoC4Yb+FaiNB5CghQOXtAaaUijGrNtBWt44iVU0DXSOhaSxrghOAZhgIQwfbUvlT6RQik+JiIs6spuHmMiR3amTkPazSATKTxkon0RJx7HQSP5Sqatw2MG0Toau+sG0dy1ZafIlEDClVnquhG2jd7mH/ppMjlxgOP89HftUfuQ6PvEooZufj2/GTjQzSTwEbjZDcv9D5hiryQ+3NaN2XwUf0x/E3/ohOEwOQKx8Dmuo9JcgBfhjZEEcfbpB3JUZeMwrADnPt5BBIKZu/YGgTJQmH51Pnlsqr8hO0PzvYUluwY4+FR77/x9D8MNwXEjlEwhBQRam8oTYEt4fdf5wJI2K3jl/Q4BrU+/iBRxhKSvkMF88tcXJhnlq9Rblap1ZvsrG9w8bOHj9+/R0s02RqosTc9ASTE2PUG60o9KnYjAGLJRGYmlDgJAIMQtMwLeMwEVZKdg8qNOoNKtUGaBrjpQKaJqg3VC6AZZoszs9SqdYol6vU6or9iMdj2LbFYAftuB6dbp9Gs43r+Up4VMqoYkqHUF1jEIQ4rhJorNaalIo5ioXcoQdZGFKvd/BTAdm0MlYOpcR1PWzDoJTLDKURao0mB/sH+K5Dr92h1eni9PoqdBe3h9cnpBIT7PcdYjGLZCJOtd6kVm9SrtZJJRK02l3e++AWhXyWS+dPE4vZUeL9aA6f+i51TWNmaoIXn7vKxvYOt+8+YmV1U6nDB5J2p8dBtUE2k2ZyosTs5BhzM5Mszs/w5vs3WNvaiXwMJUHo47g9HLdHq9VTorLp1LERozJ1pESlRErlNekHoVqrhWKzwgHrKgOVqxaArumH5sbDiVKCqbG5vcO3v/cyO3tlfuMbX+L08hKGriEiQ1wpJY3v/JDd/TL9voNlqecGYEdGGnNCCkxTZ3ysQDadou84OK57uAuL8kY0bbAb1CgUsizMT/LaW9dot9tDUDsMWWoa/ujNNbhuIXksVjg8JlDATgo0Q0MaGmA+vvsVg8osLzJpf+IdCtF3HaEceOVNqDWOMlgjR/t+wN5+RVUoTo6TjAzbBxc5OhNIgGodbWUdXKHUvhMxwstn6Xb7Ssne9yPRYSV23O+r8FM2nSYTiVZKVLWkpulHJ2Ek1WqD9c1t0qkUi/PTWKY50l9PWigO6aPBFD0EfvkM/f/n/wEdSRgGlCt1/um//BbX///U/WewZVmanoc92x3v3fUuvc+syixfXV09XTVtxmAMZzAgKIECSUlBBRWyCIFBShH6AYqEfkihP2AoxAAFkAJmBtB0z/TMtC1fmVWV3mde793xftulH2ufc8/NvFmmuwaEVkTmvfecffbeZ++11/rW+73f+957zNtvvsLv/uZbBA0dIaQulxB7piSK8JG1Hq9sEC9UAKRActe0+MX7V3j3w09547VLfOfXXiMQ0CSvzZ8Qe4illGgxeTy7yIdXrpFMxPnmay8yUqkT/0//MdryOiISxpmZoPl/+k9Qmy2UZgsabdRGE6XZRmu00RpNtHoTtdZEabSkKGinu4eu+QGesB08x5GVq3tnL394Hvi0A3UweOv/lCiaCsT9f/IFKVSMpvZ/11SVoKZCJCwDpmQcUnGUTAqyachnIJNCS0XR4hGUeFSmDBMxRLOxNyknYl8ayejfemWgDzzZJ/bdMDHYrdlbwe0P+Z/+nPf0s/hl2yBqtp/P8Cu2wYVbD5HqcaL2H37fK88EWHrn9cXnNqgmL3rrRfxh+gu+m6pI0jz484Mn+tp1Xg/V7a3tFBA8K4B8un190g8DEFtv8lSVPbRA17UBzlbvTEVvGYai9HgcHkIM5G7FALlO2R+vHnjhBm6IbTlUazVi0RD5bIp4PEw+l+KEcgjHcaU+1dYuD2YXufdwnlK5SjIeQ1VVtnZKWLYj+Uia7lckKKBoEjJHkZCnKoXz4jGpz6IqGgE9wOToKMbEGJVKjd1qFcevssukkwzlM4TDQWLhEGoqTj2bJGholKoNkskEo8MFbMeh2WzjuC1fwVmqagN4rken00HxQhi6SjwWQVNVqvUmiqoSCUuNKV3X6HSkkXYqmSQckjpfKBCNRHA9yWcLBgK02h2arRajw0NMjA1TKlep1hpSwNT1+qieqqkoLUkmleKjXSLhIIFAAITKdq1CNBLiyIwMmFY3tmm1O3xw+RqZdJLzZ04SDAb3DVh9WVsF9ECQU8eP8MoL59nZLVOq1Fhe3SQWi9LtWtx9OI9l24wNF3juzHFGh3JohsHQUJZsJim9AX17mN1imdmFZTY3S4wOD5HLpAgFjL2VF+C6Ls1mm+2dIp1uVxYCbOzIgF8VqJoHqotlWqyub/J4folOxySfy3F4epJcNrPv+W/bFlc+u87dh7NcPH+SC+eOEQqF8FxZlet5HseOTPNrb7yMbUvh1ngsjII0uC7XGhi6RjKRIhgIoCgBQsEwoVAQr+P10alehUwPClf9hUE4DJl0kkBAxwjo0htQuMhquj1+EYI9+QtN2zcIuq6EzXsBsaJIbTFFkUK5na70r+x9JBIOoek6rpCB+vbuDvF4hExa6m49uVBSFQXHddjc2sVxXeL/+X9CNBqWZuEIdF3rc6qEgEqlxp/92V8xN7/M//jv/A7nTp/YTy3xn3sPDxcP5WcfY/zj/xfq4ioincQ7PMXH/6t/nx/+5c+p1hu88cpFLj13hnA4RLPZ5v7DWT68fJVwKMT33/4Go0N5otEwiXgcxQj0j9NLP6w8nOX//F/9E6YnR/kH/8v/gEI+209pKU/wUoUnUNnzeRP+YtDr8VGFvLbyukgktpDLsFMss76xLVFfRWr7Kf6guv/J8VCUHl9zcAHjSXTLT3k3W23uPZxnZDjLt964SDAUlX2i5/6hSpa/4km+ret6LK9u4nrrnD9zgrHWALqjaRKRmx7HdmQFoKQRODQbDZrNDpl0mqnxESKREBjS8gjHgVYHmi1EvYVVqrK7uMLOwjJpTWM4FMKoNVArNajWUWpNaDblZxwX17YRjosqBIonU6VKL73z5KLeccB+QmBAUfyg/uCmqIofoPmBmh+sKQPBmxLQ+jFQ/3CfF3z1nxOFp3PJgwfvBTwHvLb3wrM/O2hM/VXakzytr6n10dV9c7SGEM7npBO/6Dy+4vcTILNvn488yYWtLKzpCVxLrpc8M8/1leX3HV+gaUp/vfhl2q+AbA3kViXmNngeCOGhKAJNA11T0XSe6mz7HgQ/qOrfoAOQrN624sAV5P7VrYJEEGQVSrDvL+a5HoGgy8bmLpZtMZzPkMtliMeiXL15D8u2pSmyZfdFSg1D96um/MFUSN/F/jdWeoGh/BkIGBw7MsXRqTEWlta4ee8Bm8UKAcOg3emys1tmY2uXydECmVSCdiFHuy0nekVViceiuJ5Hq9VBUxUiYWkE3SfnIvV8LM0mHIyQiEsNL9txGB8poGkyuJVaVU0q1QauK42IO10Ty7JJxKNIxWmPdrtDpebbFmXT5HMZTNNme6dEuVpn2Bf863RNVEUqrZfKVYSAXDbB2GgBQzeoVJvMzq8QCBicPXWMYNDg0dwypXKVx/PL/OzdyyTicU4ePyQLDva1vUA8mYhz6bkzzC+t8v5H11nf3GWo4DI2Mkwum6LTMWl3utx9NM/jhWVqjRaFXBpQCAR02r7t0tzCMtduPqBSbXDxwmk81+6v5BWkvtbq2gafXL3NnfuPSaeTCOGxtLrRR8gUFBzH4cHjef7VD3/G3QdzWJbNcCHL2996nbfffJXYQN9f2drh8me3abe7TIwNE4/HEMLDUz08V6IQ4XCIC2el6XAoFAJUavU2n964wyfX7hKPRfnWN17hxLEjGIaOp2g+2iUHfq0vN1JnY2sL1/OYGh8lnU7geWo/SOp2uywsL9HpdEmnEuTzGQzdwHE8dnYrzC2sUK3XiYbDTE2OMT05RsAwWF7d4O6DWZrNFuFwiEwyxsToEKOFEWYXl/nok+t0uxYgSMRjfPuNV5gYG2Ftu8g7H37C5c+uk0rFeeXSeV68dJ6Cr2A/GGCvb2zz3/3JX7CzW2Ion+Xk8cNkMynWN7bJZJK88Pw58tm0vFf+Z3suBL0nXAiB43nYjoT3VU0+n7ozwKNRpL7bf/enP2JufpE/+J23+NYbl0gmE6jopFNJErEoIUNnfnEFTVVIxKPEohECAYOO7UjPTV8vq4fYC4QvgUH/e/UU3B3H9Z9BObz2kVHEnhXYk4icbwvSs6/qI9cDT4ei9lK9PhLVH+jlWKv4w/keECIn0oChk0klCIeD/cesn1IX+Ihmr/JbmnenU0ky6RQbWzt0uqY0n+41n6faNS2Ku7vYtiNTb56gXGty79E8pmnx9puvcOLoDMFesYShQSoGmbgcK00Lb3oE6/QRnHQKdziPout9Hmy/eR6i0WZrbpGr718h5glOj42RVjWURgu13kArV1EqNckbqzdQmi1Eo4ndNVFcD11VJHrmDnDXnmye8JG0Z/OwFF3dQ4AV8fmB1uB+e1Y8n9c+d18HBRoDAMcvS5LvndvXHnAJn1bYC7iU/r9B+Qa5JXzRtfmyKu1Pt/377XX5Z27dW7gq9Gd4rzfv9j8oQaJYNLKHan+J9isEWwPo1BOBVq8TKEIqsSqq9+x7KWSH3SPR+hfE7wRPkeAHUk+DE/Rg670aCASYmhxjd2cXAYSCUsbB7JrcezTLxlaRTDpJMhmn2WoTDAbY2S3JVb1faRgJh2S6aHD/gwibz09rtdo4jk0sEiIY1AkEdJLZJMOdDsO7WfCFNZutNhtbu9y5P0vI0Hn54hky6SSziys0Gk0cV1bDOK5LqVIlGYty7PC0L5yINDoOSnVl13Homjb1RpPVjU22d4oIBEP5DNlM0te4cqnV2tjWFtFoBNt2iEbCZH19nNX1LcrVBrbtYWgBLNNjYXGducVVdoolqdbspy2j4RBAX3S03e7S7phEwhEmRkdo5jpcvXmPx/NL5HwPxVQiTqVSw7Rsbt97TDaTIp9LMzYyJIUYn2i9+z02MsrFC6e593Cejc1d6vUWY8Nw4exJHNthfXMHVVUoV2ssLq+zvVMiFAqQSSfwBLRaHZZXN2m22uiajj5IbPefGsdx+OiTG/zZj37OcCHHb373TXRN5/HcCivrmxL5UaBeb/DOB5/y4ZWbOI6D8D+7vLJGu93dN9Atb26zsLTe94RUVQXhyao1gYqtCBTFQE8k+ppijuPyeHaZubllyuUqyysbHJ6e4vihKTRDasBovVSEEKiKimlafHb9Lj955yOKpQrf/uaL/P7f+g6hSBhPUbBsh/ev3OTOgzl2d8scnhnn937rLSbGR1hcWeXHP/+Yx3MrBAwN6cUZ4te+8TIvv3CBer3BX/30fR7NLRMOBRgbzvFHv/M9cukcdx/M8i9/8BMffYsxOTrMmZNHCQQM/vpn7/Hhpzeo1uvMLqxQLFUIBAK8/srzRCORffc5Go2QSsT54PI1ZhdWKOSzeK7Hz9+/wlA+y9FDU+QyqX4A4/WkAmx5/YUQtNsdFlY2uXXvAZ7ncPb0cY4cnkL3uUG9waDZbPF4boFMOsGFsyfIZlK+XphHQNMx0glefP4sx45MkUomSCYTWJbF/Udz3Hk4j+W4PH/+NIempEip8OkS/cWiIj0r640maxtbtH07rJnpCTKppN/XXErlCitrG0TCIQ5NjhEKGbgoOLbD7s4uaxtbVGsNHs4u4roehu4j6n4f6po21Vod03IIBg3SSWmjpSj4aUTBHqdFBnamZdHpWrS7XVRNJRgMIlD6wZYQEt3FDx4VRUPXPd8lwOiTg5XmHrKFquK4LjfvPKBaqXL6xGHfR1RKuURjUW7eecjS2iaTE6MYwaCcz3vjt5BpVFWBZDxCMBQkGJLoaJ+8PDCsK4qKlk5QDAX54eIKZ08d4/S3XsPxaQGqouAM2Lv1ZC+63RZby6torTZDoTAR10FttKDZRqk3oVhGKdegXJVG07UmNBrQbEvttx6frffT82Ck0C/lFPQKbhQGItwnBjS+IJA5eA47uD0j4PpVqhEPOrUeUvbEffjK+xXu3ncXAN7Bx/tl2oFZrf5//p9PH+xzYzZ/EQQKPc0/wf5FYu84qqIwNlIgEY996azrLxds9WBRxMA/+oFXr9S9t/pT98GJYt9FEUjuTy9nrQy889XuzD6czOfGypLNxZUN2u0uJ4/NkM+m0XXpsacqknO0vVOk2miTSSUxsimWlrck56rRQlM1eeqeAG0PdVOQpG4Aq2uys1vCsmxymTTDhSyGobO5VWRhdYNSTXov1hstPM/rVx7euPMQEJw7dZQj05NsbZcoVuvcvPOQerPFxtYuJ47OAApd02RpZZ2t7SJDhRzHDk3SsUxmFzaYW1xhe6dE00fuNjaLHDs8xdTEKKlkCst0+1IO7U6XVqvN9m6R+48WuHn3MaFggInRYZKJONV6k8XlVVbXt4nHIhi6PNeNzR2pdK5pBAKGVKoXHebm1zBN6WUoVekrVCp1Prl2B8PQyWfTjI4UWF7dpNFqc/fBHC9dOkchn91bXQ+gA1KPSCEYCHD88GGOzEyyuVWiUmuyuLJBLCon7a2dIrFohGKpSqfTxTQll6neaGEYAabGh9ktVul2LGKRsLRcYnCFJFcrCwvLbO0UOX1shsmRPNFohKMz48wtrmJZNkIIKtU6i8sbGIbG+TNHmRwtoKkKRw9PEgmpsurVb2pAVj22Wl22tou0m11i4ZA8noLk+yngKeAICZjapo2qwIvPn2N8dJifvnuFdqslzXcdh267g+e6fZI6CqyubXD/4RzZTJLt3RLXbj7gO299g1hcii6qqkImFefsyeN89Ml1rt16wIWzJ0gk4nz8yQ0uf3qTC+dO8NabL9Nqdfjzv3qHv/7Z+xQyCYYLORKxMJZlMjMxzLdef4FTx6ZJxKOMFHIoikI2k+KPfue7FHJZJsdGUTWVeDTMxXPHcAV89OkN2u0O3W4H13F6T2X/+cxl03z/7TdYXF7l9v05NFUB4RLQVWYmRhgbyqEpAscTfT0m13VxXQeEoNM1efR4gY8+vcHt+4/Z3CmyvVMin88Qsux9o4hj27iuIR0c/LFLVSQ6jSIdH9LphF8BrNHtmlz+9CafXL9Du9NlY1vyxfLZNKlU8onBVY5dzVab6z4ynkzGefhonnanw0uXzqMogoWlNX727hWu3rhHPpvmD3/3e5w9fQwFwfrmDj9750PWNrawbYe5xTVc10XXZYLRcjy2i0U+uHyNa7cf0zVtThyd4e1fe52ZyTE0Za/SW+DiAbZpsbq6zt2Hc2wVSzx4NE+j1UZRVTyh9IPY9e0SlXIV13HIZVMUcoU+DwxF2Rds9UZjD4VarcHP3r3MCxdOMZTPEfEXYuFQkEg4RCwakbQH4Q/GPiIvPA/HEz6jRyESCRNR/NQd9AsXXN9jVtd1BHJBu7K2werGNqeOHyYcDu+hg4p8rl3P8cdr+f1C4RDjR2dAgG5ooGl7SJSyVxAh8McfXz4H4YJpIupNqDdR/o//N9jcAV1HxCIDwZMCPs/yqQnZH9f6v+9/84nfnzXXPTmLf5XA7Eu03jkqB5zPYHqxVwT0lfffw7Fgz8rviz7zZURJ2Duvgw/rv6f2+96XbYoCqqqhqp5/T5V+X5HH9GMWRSHmU4fcL3ltfkWCPAPRb+9Lif7v/ZXMgR8+qAOKL97sy56a8Gh3JdLy4We3+f63X6NjOSytbpCMxxCeIJWMy7SU6RCNS4HEWDhKJpXlFx9+Snu7SKvTkRWJwkMTsusIf3LtDUidrslusUy7Y7K5U2KokOHO/VlqjQYra1usbWzTanf3eE9+XlhXFcqVGqtrO1w6f5p8NsfC6haut0UyEWO3WGFrt0IkEqZUrpKIx9js7HL73mPqtTpDuRQLS2tsbhXxPI90MkE0EmYonyEWjdJqdUilEhw5NOkbtC7xaG6JVrtDfD3CZzfu02p3OH5kyifRCwKGTiQska/x0SG6XZN2t02r00HtKn3EpmtaVOpNKrUG65s7FMtVaatj2Rw9NEkhn6XebJJMxBgu5AiHQjyaW6RYqrCwtMbxI4fRklpfH6x/PQe6QiGf49ypE9y885jdYpVSucrK+haFXJpgMIBA+rj1FOMt20Z4glqtTikcot3s4LrStkWmLYVfUSIP0SOOe65HqVxlZ7fMsUSceCyC6zpUay0sy+6X/Rqaxulj07z1zRcJBgIEDIN4LLTvYZ6emeDIzATXbz3k1t1ZXr64xvmTR+QDq4CuyHFrcXWDUrnC4ZlJQqEgI/ksK5s7PJpdots1MXQdBNJSpFyha1r+YyZw/erQsyePMDU1xr/413/JxuauvwbScRwPVVF54cIZXnnxecyuxQ//+h3K5QY721Vm59cQQuHooSmmJ8cAWFhY4ifvfMKNOw/55isXfV05hZGhHN97+w2y8QiKIkjFI0TDIbLJOK88f4ZcLovmc63e/tbLzC0s8d6V65gdk2gwSFDXCGgqhgauUPa8dRWF8eECF04f4869x9y4dc8X9xVcOH2MaCQkeRS9hVvPYslzAQ/HMtnZLZJKxnn7zZf5yTsfs7K6QaPRIu/LtfTUpUHB0HVs26HVNFFc+uk4OVFKJF1TFXRdVqMKITh1/DDjo0P89c8/pFFvYJpm/x70U4B+WuL+/UcsLq3w67/2GtlMimjQQODhCZtKpcZ7H33Cp9dv026bdLomc4vLHD0yjWM7fHjlGg9nl3j9xfPEo2G5IOp0/CpSQaPd5OrNu/yrP/8ZjifwPKjXm5w9fZxD0xP+xLjXBy3L4v6DWX788w/omCaTkyOoqoJtOzIN649fj2YX+dMf/JRYNEyt3iQRj/L7v/VdhocK/eDYE9JQXq036HnAOsKj3mjS7cpUedgPtHrPbyAQYHRE7kMW9Mggq16XvrRbuyUyqQQjw3nGhgsEgjrVRpOl5TXKtQbtdodOx2RifJgLZ09KCRtbeqW6bq8KXAaEmqbRbDVYXduk1W4xOT5KNpNDR8N2pVahlKSJEQ6HsW3bNw+v0u2afsZAMDoyxMTEKKohOZ2EgyihEAzl+lWUJKIDwYDS//2pQEtVnjHv9Qa3z5v7nvX757Qvudkz25MZoycQol+VML+XTuztdvAa+EHY14F4+dehHzx/wWnLeWD/9Zby5z4X0idjKb399TJa/gSiDgBEX6b9CgR5/wLtuxFiH9CFj2opfkXf3jc86PS+gmriAe2p/iag1TFZXttkpygJ6sFgkM/uPiRg6Bw9NEksFmG3WGF1Y4dQJCwnt5ZFLBbl3KljFCs1zK5Fx7JxXIGq7mnHAn1SrNSbaqIoCsl4jEazxd0Hs6iayshQjtHhPEsrGxTLVemd5ssDWKZNrd7m4ewSkXCYeDSC63hs75SIhEOMjRS4evthXxU7GDCYGBvm2q37fHLtFm++eomhfBaAUCjASCHPUCFLMhFnYXmVnd0yAMlErK9t1ZOqUFSNdCpBMGiQiMcIBgOsb+0CCkOFrAwMNI1CLo3txBEDKxPXddktVaUorKJw8tghXn3peR7PLxGLRUkm41LHhlg/VTg5Pszm9i47u2Vu353l1NFjTE2OYASlZ14wENiXNpbcpiBnTh5lZnJMIlhdk7WNLRo+l822bV+ZX8V1HSKhMIV8mlQ8TLfbxXZshvNZJkaHSCWlvlkvcBJCCr/ms2kUReHB7BI/fe8yuq75fpoOxd0SrVZHCtvqKvVmi48+uUmpXGW4kOW5cyeJRKL9Z0AAo+PjvHLpOZZXt5ldWOUn716mkEtTyKZxHDloF8tVfvjX72BaNrlsmlK5ynsfX+XytbssrW5SyGcwdN2f0Ezqjab0jNR1P0gUTE2MMjE2zPpOiZ3dCtFQiIAWQKDKYEvVCIfCJOIx6R2nKjRabbnPjomuqgT0ALomJUQScanuXSpV2fSrJIWPkAUjIVDA7Jq+16VcdEhPRykE2Wq3mV1Y4p0PPuXmvcd+utvo8486XRlkGIEQoXAQRYChGZw+foyxkQJ3HswD8PpLF5gYH5HOD6rSr7K1bBvbsrFtG+F5REIBLp0/ieUK7j2cxbZtdE3F6nbAD7r9jiS5T0KwvVNifn6RS+eOYxgxuSBUoN3pcvP2fcxul/Onj5NKxHjhwkk2dys8mltifWuXsZECwnVQPLePjvbm1K5t8un123iuSyoZJxoOcfLYIYRw0QwVTdWYnhpjdHiIZrPLZzfuYJomjm2zubXD9Zv3yGaSPH/2GPFohO2dIqtrWxi6LFxwLJNYNMxLF8+SyaR5NLfC2voWVreDhrenLu/3wmq5yi/ev8Lj+WX+8He+wzdfv8S7H33KysoGVreLgqBSrfHnf/UeH165wYvPn2a3WKVSaWB3u+g9tEPIhYjrONBo9oNM2/MwHWkALSVVBpAdRVbw6qoqeVKaihCCYrnGX/70fT65dodoNEzWT6++dPEcLz5/locPH/PP/+RHbO6UyPrK78OFLOlUUgaljuunZyXtxFNVgrrO6vo6P/rJL7hy7TYK8M3XLvFr33iVbCbD4/k53vngE6KRCN964yXGR4eYnV/mp+9+zOO5ZWxb9ivHcXn5hfP83T/4LSYmx1A0jX0zd0N6tBKP0Q8UnlwgDk5Ez5yhnrVBH19jbxY7KFJ4RvTwy8ZCPfL+k6c0CKJ8TW0vPSxzQnuHUgD3qwMrg6jb/jfYdw0/J4UrfIS7d36KEChoKIrbr/AWIPmyverp/id7AaLoF7h8Ufslgy3/yhyEavn/VAW/QkrZK/Pvh4XPQLb2QZrKAZt80d3Ye18IQbPVpd3uEo2EeOeDz7j86S2KpQrT48N9s9HFlXWK5SqhcASEQigYot3p4rjSxzAQCOA5LsJz8VwFocgS7H7ljyJotdo0Gk2CQYPxkQK6odA1LYbyGTKpJK7ncfrEYVKJOJ2uycPHCxRLZcyu1MZ68fkznDp2mFKlykghx+XrdyiWq0yNjzA9OUqxXGNru8jswgpd06LZbFGr1dneLfHmay/TbrWx7Q7hkBQuLVUqPHy8iKIojAzlAWnInc2k+Du//z3iiQTlah3dMHg4u4jjCaq1BrfvP+Leo3nGRwq0212E5zKUzxAKBbEdaZDcNS1M06LZ6ZLPZTh3+jhnTh4lEg5x5dptypU6pmmxsbVLLBrm+NEZrt54wMLSGrZj+2mSVe4/nicej2HZXUKhAMNDeWKxqBy4e/dQVRkfHebSc2d4NLdMrdnCFVBvtqjXm2iKJPOGgiE0TaNab1CtNQgamq/pBvFohOFCllQyLrusJ8N6BRl4njl5lPGRAktrW/zknStsbO6wU6xgWTYra1uUizWCkRDRcAjbdrj7cJGHsyvksinMrkshVyDpazp5nkfb8Th98igXZhf56JObvPvhZ3TbbWYmx7B9UdiV9S2u33nEySNTtNodrt9+wJ/88Gc0fRugnd0yv3jvMo16nePHDvcHqnZHmk7vlqr87N3LPJpfRlEUllc2ODozSbVSJZVMYHZkoNTpyopR15OlyzXfgikRC7O43GF9fZNmo0vAUNnY2MXQdYYLWdm/Gi1c18O2Xamojy7TydtFupZN17Io1xpoRpBO1+Tardv88Q9/Qr3RJJ2MY1t23zx8t1Tmk6u3eTi3xNlTx3n91RdIRqK4rsvo8BDnTp1gbmmNRCzC4elxOt0uu8WyrMAEtrZ3Wd/YplpvUKk2sC2PgG7Q7Zj8/MNP+cWHV1lY3mB0JEelXAUf2eqNCfFYlGOHx/nk2l1+9sGnDBdyfPMbLxKPRQCVnd0Sf/GT9ylXqsSjEcZGCnz4yQ3uPFygWm+yur5NPpPG7JoIz5VBl/B1pRwHq9VhY2uXYMCg3W4Ti4T8DI0GHmRSCV54/iyVcpM792clB8nXd7Is2/cp9HBdQaSPTIf9hYpCLBLiuTPHmJ4cZ2Fpg5u3H6GpPdshsU8OXghBrdFgZ7eEpqmkkgkCRpCx4WHSqUTfp3RpcYWbtx8wlM9w6vgRAmcCjOSlxqCq+HZRwsN1bKmb1mz3pxRPUXA9D8u2cV33CRmXHt96b/wWQnD91n1+8s5lzpw8wt/5/e/Tbnf48x+/yyfXbjM1MdJXhB8pZPnbv/8bGEaAn793mTv3HjExPoIe0InHoihIhE5RoGWa/PCvfsZf/uwDXM8laBjcezjPsSMzxOMRqvUGS6sbctztdCmVqrzzwSdcu3mPk0emOXF0mvuPFrh2+yEff3KD4XyW3/9b3yGRTOxprtkeio8qk4jR42Y9U/5hMP321Lz0RfPX1xvgfOnWC65+VYTsS7W9IEY2BTAA5+DNAbEvvvhqrWcb9ixj7P2xmo9UshdID/57+j4qA/v+4vZLcrYOyh2LJ36VML4nQO+drHLAtv32BXf6wE7c29cBn1MUP7XkMDM5yvhwnstXb+N5HuPDWfAc7j+ax7RsLpw5jqbr6HqIcCiE47pS02coJzlemioryvrKxeAqe6Kc7bZUUq81Wtx+MMtQPs25U0e5cPY4nU6XH/z1e9TqTb7/7de4dOE0wUCAqzduY5o2qURMcsiCASbHRqToqKZQqlaxLJszxw8zu7hCsVihWqtjmRbbPon/8fwqZ0+e4MjkGM1mA8dzsG2bxZV1qrUm4XCAdqfDxNgwqaSH7Thoqsq9B7OsbWzRandQFUEiFkZXpVG1Zbts75YJh4KcPXGE86ePEwwGsRyHbrfL8uoG9x7Ncer4GG++/iKjwwVMy+bKZzdZWFolEglx7Mg0mXQSRZHcjoavMu84vtdivcHy6gbnTsvrUy6X0TSVUCiAqkqpgF4HjkTCvHTpPLfvPebK1dsEdI3nL5zm8dwilUqNQMAglYiTTiZ5PL/EbrnqV2pJRkcwaJBKxjh14jBD+SyBUIjAwLjy/LmT/M///T/gnQ+uMbuwRL3ZltsZhl/hCJFQhMMzkzyeX2Fjq4gQ0mdT0zVsVwo5yuy5wBSCXDbNhbMncF15La/fecR7l69j29IHLhIJEYuEiMUk/yxgGDKN61fkml2Lx/OrpBIJpicnqdYa1OotTNNke2uXWrXJj9+5wurGNsGgdAm492iBf/Gv/pp/9/e+T6VSpdlqs7yyxsbGFuvrW9KCpFgiFgpy8dwpZhdW+cWHn9FoSb/Mm3cfMZTPcGhmos+Bsx2XB7OL3LnzkFNHZ9gp1Xi0sNIv8vjzn77Ld7/1DdKpBJ1u1/eqdChV6zRabZrtDgtLawzls9y6+4jL1+5w79ECAC8/f4GAIXlUJ47MkErEGcqnGR8dolyuydSPiNPqWtx7OMf88hqVSo1Hs0u88FwF27L54x/8Fb/48BOqjRaqorC9XWZufo2LbRPNE31gIhwK8Qe/9RatVoeHc0v803/xQxaWVrh04RTpdIqbdx4xu7DC+Egew9B5NL/MD3/8PpbjcubkUUqVGuub26xtbJNOJ2k2WziuR7lao1yucOjQNOMjBT7+7BY/+Mtf8OJzZ/ouDvlchmazzS8+vsbc4gqWaVGt1fsjl6broCgsrWxyf26JVCrh0xaEX0Ch0enYfHz1JldvPWJ9s8jWdolMOiH7imUhTDnW9rTLOp0ujuP4foFdVN8oWtcMbMvB7lgEdENahiXivPWtb1AoZMGy8PwiBISK54LjCDxHQam3+kOrqkm0rlyts7ld5PjRGQxN61dK90bz3h/tTpcr127TNS3OnjpKJpUgGY9y9NAkt+4+plSpEQwEJDXA88gkY6TTKXRN4eHsAq+9cpFsLiPFlAHXk5pk27s73Hs0RzIR5aWL54jHohTyGfLZNIahcXh6klPHDrG9WwKg3myyvrnDSD7L7/3Gtzl99jgff3SV9c0dlte2+OjKdS6eO8XZM8dRelVmtere9BKPfjEC80zqzNeRJ2MA4ODrD47+jcV6B10Ljc+rAh1Eqz73Sj5xTfYuk+jHk4Pv9nqspB0oftaj99pezNKTwFGUpy/6l71sX4NdjxjoAEL6vyH6HIueWN5B6t3wNAwrQ6cDUK2B959u+8uoEQqK0PqyDZVqne+++RJDuTTX7zzkxJFpMqkEP//gGl3LksTXZJxyrY3teAQDAYaHcnhCkE7F+76F0CPdS7Kzp8oJ0nPdvsxENBwiEY8yOT7MzNQ4wUCA7d0yP33vCleu3ZFl+hOjnD15lK3MLrulOh9+coPltU1evHCaY0cOYXsun928y+rGNgqCYzMTtJotYtEIoWCAVruN5wm6XYv5hRXG8lmi0SidTpet3SLrWxU0TadWb/P+5evU6i0uPXca23F45/J15pbWyGVSzEyOkk8nCYVCJGJxUskkG1s76JEww4UcZ04d4+WXLwGC4m6JrZ0iG1s7HJoa541vvMiRw1N0uiaLK+s8eLxAPBYlEg4xXMhx9vRxqrW6H2CEuH77PivrW7TbbWzHZWe3SLPVJhQM0G46dJotXMtCNXTfEnhPP2p8dIhvvnaJpZV16o0Wk6MFLpw+ysPHi1RqdTRFJRQKE/FFVXtVfgCNZod7j5Y4eWyFI4emSQeDA/1EEAoFeeOVS0yOjfPex5+QzSR49aXnKVdqpH1NtHa7ywsXzpCMRVlZ26LebBOPRiQ5WdP21MpR0MOSIDw5PiK9J+MxdktllpZWqTeaZLMZDk1PYGgqtmUSCYc5eWyGly+dZWu7xHAhj67rBAMBzp44SjqZ4O4jiVrGwkFMy8Jx5PfTNc23fgrTbncol+tsbO6yvLpFt2vx6PEiQ9k0C0srRCNhKdcgNF58/gLlapOfvneZjz65STgc5OjMJK+9eIGxkSEUBV5/+TlU32lgdm6JmdERLMtkeCjLKy+ck+m3dodipcTU5DAvv3SRWrPDo7lFKZXieZiWxdhIgeGhHG++/gKW43DzziM+uXqbk0cOk0klUX2pk3g0wsTYMLlsGs/1ZAGLrtNoSQusMyeOsLG5TbPVZml5lVZbBk7VestfRCiYlsXcwjJt1SDouf0xQQEuXjiNoqr88Z/9mNv35/iXf/ZT/voXH5NJJWi2u7iOw/FDU4QjEYqVOpqmsb25S615C9O0cByXOw9mCYWCXLt1n0q1TqvV4ZNrtzl8eJo3Xr3EwvIaf/mzD/ngyg0Chs5rL17gpYvnJAH+3ct0TYtEPEa13uD+wznGRobIpJOkEnE+W7jPv/7RL5hfWmNre5diWQoDO55gc7vEX/30Ix7Or6EocuEXi4XZ3N5lfmGZRqPFw9kFOp0OU5Nj6JpGKGBQq9b5yc8/pNVq0+l0qVardDptPrl6h5nJMabGRnk4t8TVz27w6kvPYZvSlSEckmOJDNqkRZPSbPvRoYam64SCgkq1wftXrjM9OcbRw1MgBK4AxxM0anWE55JOJag3mmxu7SKEIBwM+obg0qzbcaWobCgoC5a6vvWXbZoyY9DqUKnUfPswu8/2cT1pe1Wp1knEo7x48RzDhVyf0N/pmMRiEfK5DMVSpT+BBgwNF4n+tetNkokEqUSSVXWXdrtLp23imq6PGKqotUZ/1hGJOF8Ybf1NByyDKb7/gYCwX6btpRAHEaKvt+2JDz8RD4CfiaJ/zXr2P73slPys90SstueDug+9Up7c95drv6Ko6TPutr+s8TyvzxWSXJMeOvW0lMK+pdDg6888wCCUJ574m74MmCcEzXYXXdd47uxx7j9ekD6F6QTJRITmVpu7D+c4e+oYqqbiWg660H1VdkeuvoOBJ3gRSJK8L5iv6xqhYABdU0kmogzls1IQUZEaWSeOzrCyvsn2Tol3P75GIvaQQi6FZZpUa01s2+Ojz27xeH6ZP/q97zE9PUpyIU690SKfS9PuSIXrYMBguJBjYXldphZ3inx28x7ZVIwLZ05i2w5LK5tUKjWpRB8IML+82k/pHZ6Z4NiRaU4cP0wiEccwDBqNFs1GA8/FR/AEhVyacCjIZzfuUqnWqdbqrG9uU67WsSybN169yOjIEODzt4pltnfLuK4j9YUMHeF53HswS9eyicUSFAo5TNuhWqlSbzQplqvs7BY5NDmGYUgxWNkzRC8j3r+duq4zMiwn7VK5xp37s3zv7W/w8qVzNJotqa5vOf2S+27XpN0x6ZpdPM9D0zQCwRBovUDO33WlTvDv/x8AhUOmTaZexzA00v/yx0zoe2rvhhCcE4Jz/uccx6Xd6aD85DLhcAjFtPZW9Lrhf06e9/TkGBcvnGZzY5NWq83IcJ5oNIrZ7VIsV3Fdl7GRIf7e3/5tPM8jm06hqjrNVgdNVQmFDKKRl7l49jjBgEE2k8K2PAwjwOb2rtRkcxwcy+Kli+eIRKOcPHqIQj5DwNCIx6P8+puv8J1vqdi2SzqVJJVM8Guvv8zk2AiaplDIpxkeyhEOBfvVYN9/63UunDlOOpkgaOjomk4ikeC3v/smf/h738UypXCsqqoIRSWfz/K3fuPblCo1woaGoWu0O10Chi795UIhPrtxl1AoxEghTzQSJhKRVkqVah3Ldjh2eIp4LEqn1SbseujlGtHNHS64gte/8yZqLkWn3cXQg6hqldPHDhGNhOVqFEGlViOViCNKdVzHN2mW7Hd0TePShdOEgwF+8s7HNBpNbNuh0zVl6nA4z/EjU3iuQzab5td/7VVu3Z+V1lh+0cjxozNomkapUiWdjKOqKptbuzTqDY7MTPJHv/c9btx6wKP5ZabGRzg0PUE4FCSVSjA8lOfuwzla7S6e8Lh59xG5bJrvfvsbvHDxHO1Oh7WNHT769BbhUMCXd1HodNosr62zW6rRNS1CoaBvy9Xk6vXbCMdFQeWn71xmdWOb6YkRfv3Nl7h0/iSu63L/0QL3Hi0ghEet3kLXNK5cu83ocJ50KsHK+jb/9X/7p1y9eY/hQpZCNsvYyAiP55e4+2ieSq3O9Tv3ebtYJiQEwnekSISijI3kuXL1DpZl8xtvv87IUB4PlWKlxl//7AMKmSTff/sbElXTdWzHoVyt0Wl3ZZVvrUGr1fV5Uw6u753q2A71Rot6Q7pmmKYlSfNdUwZqeGiqQjwqF7a1epPb9x7RaDaljpmmMiUEhXxOZgoUBddxiIWDhAIGW9U62zsl6o0Omqpj6BqaolDIphnKZWUVsuVhhAwCtcbeoJ/Yc6Do8XW/uH2Z4GLw/S+IoPrTnT9nfl0m1P8m2pOOM3tv8Ktytve1HmVJmjLLLMdgZaEiEazenelrICg8FUgJP2Ph+Uj5gYf7kgWUXzrY6oczQjzjoj0Nre1LZR7UMZUnOuLndd7P7dhPQHsKIDwcR3JVDEPDcVyqtQbtTpfrdx8hPI9YJMyJI9PyYfQEyWSUTsdEUV2EI61ZbMfFsUVfe6tXFCA8EL64XSAYJBaLomkqgWAAzTCwPYHtCqp+ymy3XCUWjzExPsLm1i43781SrzeJRUJMjBaIRgJs7Ozygx+/w2+8/Q3OnDxKMGDQbHW4cfsh80trtDtdgsEAwaCBEIJGs8XG1jYbW7ucP3WcerPF5vYujmODCJDPxHnl4lk8f9vdUoVkMk613uLh3BLFUqWvPba+tsXG1m4faRBCsLyyRrFUIhaL8dyZk4wM56nVG4wM54mEwgADA2GXaq3B8toW5UqdUCDA+bMn+LMf/YK5pRsk4wkOTY+zrMhVZa3eYHZ2gUw8SiAQIBAIomsyUFGFwB24o8ITPjlalv/fuveYrmny/LlTjI4UyKSTxONRDs2MUyxW2d6tsLK2LosSKhVZ0p7P7kMoQXJSlHIVUFAth3DXrwBzha+o3Rsq9wTthBAojotumaiKitY1+wKJAgXbk/3ONLvgSZHKbrtFrdagY5rkHRfbsnBcD8dxaDRbRCJhRobyfhfvnZ8UoIyEQxxJTHL00KRf3i65PaPDQ3ieJI93u12yiRihUADbFUxNjuM4DvVmg3gsTDgU6RPNgz43JpGIcf70CaLRCKGQgSI8hOtiKwq2axONRJieHMUwDHRNo1xpUK03iCfDxCJh3GBA+ki6Hh6Se6QoqtR+MlQ0zyNmO4hGC2dzl8VPb6F9cJ2/m0rwTdMh9xc/R9SblFc3yd57yD+0HU79yY8x/vmfI5odDE1FQSHmymvY+p/+EYGTh7BtF9P0iEUjFHLZPtJsOw6WaZJKJYn8838tX+8hEkgBW01TOXJoimBQooSxSAgE2L6Ol23bbBXLKKrKKy9c4KWL56n7E3g6mSYRj1JvNvjOm6/ya6+/SDIRZ2ykQCoRR1VloUixVCGbSfHN116Q1ccCEskE/94f/hb3Hs769kzS2ur40RmCwQBHDk1yaGKETrfbFzcGOH/mOAiPSDTEubPHGB9vMDpckD6WeIwN5RkfHUUIePPVS1y/81AaxXsKRw/NMD0xTqkqpVEazRbFUpVgMMCLz59hamKUrmlRrtSYX17jzoM54tEohy9O47puX1olGAiwtr6FXa0TjITBF3dNJuL8/m++xS8+/JS7D+dZXd9iZCiHYRgUy7J45vUXz1OrN0jEYxyampCp6w8+lVZJmsbNOw994Wj5mWqtgfAEHT+QL5WrhMNhgsEAlWqdlfVNX/5D8sSymRSnjh/mJ+98zLWb97Bth1QyTioZ76MRmgKOZdFuNilkU8SiEXaLc9x9OE8kHKNeb7CzUyQUNHjh/EmGcik63S6265LUFPDvBYASi8rUEp5Pdfg8QGDwWf4y7SvkBNVe4AB9L5p/m5tgD/04mI1P75p+pXYASb6vgekT3mXwtLfffX89dcl7QZ/iB1hS28+yHRw/tT+YKpfHEXx++nOvfQVk6wnl18+7wQdAb/thOPYu1BPX18elEEJ5avXwVfqU5x+zF/Q126YvcKmxUSzz4dW75HNpLp47yemTR0gkk6ysb7G0ui4riiJhbNuh27UwTbt//nvEePqoXTgcJJfLIBSVclVqamm6hNvbrSYr61ts75RIpZOcPXmUC6ePsbK+JQNBTWFzu0ir3fXRtig7xTJTE6MEAgY37jxiZV2mhTxPsLldpFSuEQ0FSMViGIYkjoZCQYTwCIcDJBMxAoZGJBxkLJ2i3mgxt7TG/UcLaLpGx5SBS7vTRVVVCrk0O7tSpyudSmAYBsGAwSsvPsfk+Cidbpdjh6Z9ZKmKqis4rkA4glK5yurGFg1fQywRj9Jotbly9RbxWJStnSKqIlX189ksG+tbPjLUZX5pleFClqOHpomEQ6iKjucqoEj8qZdO7GlLBQIGiir1gSrVOuVqjUw6yfBQjnw+Byhkc1lGRlvEExECAZ3gqk4kHBqohgGRSshHSunHztjdLt2WSsg3uBXqk0imtDBxHEdWxtky1SfCIcROWXq8aSqW7aB2TSzTIhQwMHTN/w5en5SvahpYNp2uiW27aKrWF2JUfPKx50seuH2eYO952vsetuNK5EU3pLq5Ir0UHVdgmhau44FQ+76SlmWjKtIVAUH/mniu7/bhCtpdk0azTbvdpNuoEzAtEih0d0pYm9uQz0EggNpsESxVCDTaGJaN2u4QqTdRWh20dmePVIxEAk81mhz3PBLVJoGNIoqi0Gy2sCo1hmybdDJOpNqQfJwnBk9FUdA6JiDTyrqmEDBkFa0C/cEwYGjoRgDXtKX5MfvXha4rA67hoTy6qhAydFB8DqYQ7BbLVOotDENnGAiHw3S7NqbpEAgEARVVMSjks+SyKTLppC8p4lMMXI+Hs4tc/uw2tXqDly+dI5dJE4lGmZ4cIxwKUalWGSrkyOeygGBruwgg5VHCQYqlCjvFEMP5HIV8BoDTx48xOjyC43iEQiE67TaRUIBoOApo1BsN6o0mK2tbNNttKd2SzpDLpKRNmqbhuo6f0pd9zXEdjs7McHhqikqtSrttMTM1IZHFTpc/zHyX7337DZqtFmqzQ/SBLLgRfnWxpmm8dOksRw5NcP/hPNfvPGR5dRPLshkbLfAHv/UWx48ewrLk+Pm3vvttDF3no89u8M/+5EekknGG81leuniWZCrFnfuPCYeCEqlEBlzTE6OMjcrU8qfXbrO+sU0iFiUcCoLnYehan5LR6nSo1uoEDJ2Ev4DzXId6Qy4S6s0WoWCAmckxbt59xKfX73Hv4SK246AIwQsXTvPyxfPoqsrOzg6WC/FYWNoF9abomM/Z6pXYKGp/nj9YfPuLCF5ftSlPRAr4KM6/xdGWjEg4+Dr0Aq2ev+DnFB58XjvwGigS1VYGEooKUtOTAURrMLco9sZZz1/AycxJg2ZbygiJva379fkeXw7a+hq9EdmXS5bVNTLi609q/Y2fFd1+/u73PtvraE9YBtHr5IpUrdfkxRSeIB6P4Xou+Vwa07LIZdO8/uIFTp84TCQapd7ssLtb5sHjRcLhEEP5DKZp+dVuQTRN70ORKgqqKvlpnhCEQyEKuSyhYADXcYnHouQzaYKBAO2mIBYJMzZSIJ6IEo+FmZkc5+jhabLJGAFDZ35xlU9v3GVzu4gRMEgn48QiIVLJOEdnJmSVU6crf9dVup0OyXicdtvEsh2SiTjtTpfNnV1UFbJpyS2wbZt2q4Vj27Q7HTa2itQbLaYmRjhz4gjFSo1SucroUBbbctjZrfoCoBrhcIgTRw+RSib45Not2u0O58RxcpmUVDVXNVzXQ0GTxGhbVlamEjES8RiNZputHTmR5LIpHMfhzr2HbG7v9stqLctGVVWSiTiJeKyvQ9ZbPfYok4rQiITCjA0XmJ1fptWWZOx2u0Ot3uCzG7fxgHQ6xfjICKMjQ6iahuNJDontOLQ68hwDAYPW//UfykkciPzv/hFU6n7XlTpQnpAp756uEMjXXF9c03E9dMPACARkkORjKKrrcfg/+k8pvvUqk50ukXiM5OwqqqEzUm9gux6hpU1Uw8DtdIk0mqSjYeLVJlo4hKKpqIaGpqi4to0tBMQiKIYh+YGahlBVXAUsR9AxHRwEgVgUz/FwNYEtXGr1Btu7RVzbIuLZqLZLd3Ubu1YnousYloNaraG3OwRcD8O0UFptRL1JvFb3g6Y2uA4KCpqmSrTOsmURQ0jqKgVdty+hIZ9RKUh70ApVppIkKiIDV5dWu9OXtDCCQUQ8hhsKYIeCeMkEqueh3X0sg62uiVB6HpC9sUUijv1UcSCIokhBT0WI/j3dP4YofSK5648frufRaLRY2yzSancYHS4QMEJoioGm6iiK0xdIlnG/DFh6Fh5yrBeEwyHeeuNlNreL/OLDz4jHozx/7hSRaAQFxUdlPDRVQ1HANB3a7S6tVhc3kQRkpZ3XGzdVycsLiyBuxKXRbFGtlPFcj3gkRCwaw7Jtms02y6vrrKxvMjZS4NDUOMP5tB/oy/uiqhpaQBaNeICuGSQTCTxPkEwk+otIx/eDzaRTkifluCibRUnkF6Jv1dNbfGQzad58/QWeO3eS+cUVgsEAh6Ym/FS548u9VDl14gi/8/23ScSjrG/tcOrYDCeOzhCLxWiZFqdOHOHkkUmS8TjZTIpWu83Jo4dIp1PEkwkmJ0b5/ttv0Gi2OHf6OMGggeM6DBVyfP/X3yCTShIKh6hWKrRabUKGhtnpsLVdpNFs+9IgGtOTo7z0/BnWN2WRUTQS5sjMJM+fPcHocJ6OabKwso7tekxOFIjUmv3eI+JRyUlW8a+i3798lAvop6j2tX2I9Re1L0C4xMBm/7Y3//kT/WBEPPHmQKD1ywRZsBdoDaBcEt0aAHNUGWQpigzAFP94yoFB6h6k5DrSN3e3VKHZauN6g4Hbnm/sl83kfn3BljzPgd/9oU1RDiDHHxDx94K1Z6YboR9oHVR+e8DnXFemnRRVIgsra1usbRWZnhjlzdcucvr4EVRN5fbDeR7OLlGu1GUaUFNptTvomk4mlSTmGz3LQEB+F1XxIUpXKh2Hw5J/omkqhXyWVCqJ4zgUixVsx2GkkOXwoXEOTY1jGDrbO7uY7RaT4yOMDuc5d+oI504d5YMrN/jpOx8TCeiMFXLU603y2TSFXJrRoSyGptI1LdKJJAsrG3QtWZ1YLFW4evsulu0wOpQjm05IPpmmMTmeodHu0DUtctkUb7z8PKeOH2Z5bYNqtcFQIUu7ZbKdKJGMxwiHg4wO5xgdKlBrSDPq+eUV7j16zKULpzl98giZdJZwKMTk2AjD2QwhXzldCMn9qlRrlCp1wqEgtmXjuB7NVgfTtEjEoj6iJIhGImTTSR/ZUvoTpI9JogoVXdOYGB/hjVeeZ25hmccLK3Q6kr9x7PA0nW6bze0ilUqV5ZV1otEIwvNoNNu4novnabiO2zdrlgGS322qDZRSlaDroNgOqmkhOl1alo3la6KFQkGp0u15qK5HEFnwoZm2fOAsG92UXoHG4iqh//ef9ft+r1vGfPHEnphmQgjGntim3zSN0NhQn+iveEKmKl1f1NP1EL6FiCJ8U17hofiVkSnXY9LzpGGvJvtttvfM+YNELw5Reqlxf9WsDQ5S/VSFgiEg4LkoXRNF78htBzIYHgJ0DSJhRDiMiIQgHJI/g2GMqHzNCgURkQh1RfDJwzmuLa5w+uI5vvmdNwjmszTaHTqOQzQew9jYJnbnESqgNVs4jge6NH+3LAfHlDIGwYBOMBhA0yW3QsND1yRS6PUKW+TVR6gqqm+D0wuYLNtha6dEuVIlnUoylB8iHJJcn3g8RiAYQB8IEi3b8hcagzdO8WVJ2iQScTZ3yn0hWlVBajqZFqoQaMhrpwl8zlAQDw3Pd9LQfE9F4QmEKiM8x3XodLrYlk0sFiUW9dWrXY+OadJqdWQV7JkTHJuZIGhoA+Ox6D9PAt+JRZHOb5rmB8pCqrablo2iSL6homkYmoFq2/0+IzS9z6n0PIHwJKIbjYQ5PD0JCuiGgWVKj8jdUoVWu02z2SYcCvLic+dRVYVcTo6P7U4XXdcZHxkiEQn2dfnC4aBP3ZA6bkcPTzM+OiQtwJIJdF2jTadfKDI8nEdBYWNtHcfXYqvW62zvllEUadXWbHeoVOucODrDN15+AV0zSMSj5DJpgqEguqZSLVbY3i3juA7NVpNUvbEHfMSiMoj35GJe9vre4n8v6FJ6cCn9i/1UOuvgNoiGHRwI+PALoP3/R8CFegCnqAeM+Om3Z4nDftl2QDpRBmFPOpD23vqCgFaR46706BQ+cr4HHPU+qihP7/vz2ldKI36VRF5P3VfXPHRNmovuS0H2r4/8o38BPheCfUagdUDzXEGj1aJrmnS7Fu9/fI3t3TLHD0/x3W+/ypHpCQIBg3bXZHt3l89u3kVRZUoul04QMAzqzRYo0ouu50/Wi5rV3hmqeyX7iqLIknlbEsXbnS5bO0W2d8s0Wk2GhzJEIgaVao2rN+4Sj4Yplqs0Gi3uPV7g+bPHCYeC7JYq/OyDT/nmK89jdru0mk0i4RDbO0VymSRjw3my6Qxd06LRbHHj7kM818X1XITwWF7bpGuaDBeyBIMBVEUqaM9MjDI1Ocr4yBCNVgtN0wiFZHXe5MQQrU6bgGEQiQRIJqK0u22WVtcJBjQmRgusrG/xJz/8KVeu3eb44UM8d/YUE+OjEpmKRQj7AdNusUwwYHDp3HGK1QYPZ5dkCkOT13con5U8G9PEtCxfKVtBwUcPessRfzDTFUjHwpw6NsP5U0dY39yh3TVpNlvkcxl+4zvfZHNrh83dMj/4y3e5c38WQ9eJxaIAaP6DEwwYfgVf7xh73UozDAK5DAJZZbdcrtJqd0gl48wUsrJaCtGnSPT4VQJQ1rf2+jWg9k1uB8xL+4uPnizFvsPvLVhtW064y+tovgDrVxlVFfC5CvIvxfX6rz+53YGfV1XQVISq7v2uKCi6iqoa4CNsqCqo8vubjk3HslEVhbAQBBwHpdHqi0H2/cb6oxREBHzPsnnLtgl9eA39k5sIDwzXkQCKpqI4DuryBgDGf//neL+4LC+lopBSVRlI6RqqrqMYOoomzy1YriJ6/Dx/vPAG9H0UTfeRLQUhPISqEY7FmJyaIplIkozHpZK48CRnKBDwb6FA4DH9j/+fxCxLmtAqexrUYdfj6G6J0VaHcDhIaqdK+AfvomkqnoCE7fhG07IfRj2P9E5Jfo9Ugtb/4n9EQbikVAi1WuiGgRIJomoagUCAZDJOJpMkFo0Qj0UJ+AbPQ7ksFy+c4WVdY3JijHgi4fdv30jXHzc9IWRQLL/MvqI2gYpQQSiuT0JXUBUNVVHQW+1+YKl4LnRNaSLdaKNEw75jjUIwGJCcQkegKnL8bfhFPsGAVGbPpFO4ntOXCjFNi0gkhEqo70WqqbLKFkUGdJ5w9vxZIxJN7lU0xqIRPE8WQNTrMp2qqop0qlheY7dUIZWIk8umaTZbuK5LJp0knYoRCUdIxONEIhFUXUe4Uh6nY3bpmpKDOlZv7T0riegA2q2A6lfe9+Yl2bn2P11eL6hQP+fBe3I0GGwDc24PWv1qQ8L/gK03Vw/G/TLxJi/HXmbqWfP50/pYnxOHDAZdB2E2/gi0P+47OKumAIauE4lIxwxN00A5QAvsK6Rwv15kq3d85MXTdU1C/Lr2VDXfAP6+d5E82YGf7Ew9+B//5xcFWpLc5tJqtrFsm1qjye37c4yN5Hn95ec5efwIKgLXE9RbLYrlKq2OSSSiYgQCgMLWbomdYoVz1ZpExxRdTi6eP3kovrC/P4j1CHWdrkWt3kD6++lEIyFy6QRdq0u7I0Umh3IpXnzuNLV6k6WVdcnZ6nTZ2inRbLe5eO4EyXiMD67cwHZcNFVFV6RFRz6TIhoJ4zgu+WwagGarzeZOkZGhHKlknMWVDcrVOnEfkavWm9RqdSYnx5gYGerbXSRiUba2i3RNk5mJcUzToVytYjsO1XpDEnmjQV596QKxaISt7SK3H8yyubXLg8fzdE2LTreL69ok4lFZGee6mKZNOBQiHoui6gblSl0GfEYABMRjUbLpJJvbuzQaMiDuDWIqPbqhgsRZ9laFsUiYSxdOs7ldZGF5g6XVTTa3d5meHKNcqXPrzkO2d4p9AvhQPkur2UYIGTCrqoqhqb6npSJTlIr/EGZSWP/Nf4njuuzslvjj/8+f8c6HV3nhwin+1//x32N8JIci5PTlPFEgoiyvE/6t/xC6Jmga3olDWKZFq9WWiKgQNBpNDF0nnUxIno8/cErzYPk8KM02ym5JBjTxKJ7r7aWU6IkD9x4Ise9nbz8CpMeb7nvBaaoMjBRFenv6fbb3LMm4VpMOD/tUkv0FOb1gqfcc7vmXCs+j0+lI6QTPQ3imn9KTyvIy3uut/BnYk3xZFnsE/EMKLMehUq0BSJkTQ+9vLwb8JxW/SEEF1MVVaRLM3jDcszdXuibK2hasb5N66fc+d8wY2rsi+9q+oVTXiB+eJHB/DtUnafc+oQAGgrG9OBc2dvr70wBdsMdD7aUxOt3+BQn8x/8Z6WecX07x74+mgh/sKqpKUFM5rSicUuR7iqZJNFOVAbMMjv0+MPAaA4H04JftaaTvXWzQbz1AaTb3X5CVdUa//x/Ia66poOtyf5rf73SNNDCpqujBAKphgK4hNA1PVXAUQdyWnq2GrhMIBHw00k/1DGAS/RQdyH7qn3PK8zjnIw6qqpJyHPKdLq4r1eZD9SbHXZdsJkV+bh1FUTjem49UaRKv9d1N5BGHuxb/Tq2O63kkfnoFdXNXLhomR1ESkb1z6nkqq3vXS/RO9qBOJLx+0c3+9sxlz/4dDAZag6/9MtWI6sC+/6aqGffFiE8eY+B79B+WJ7borwT2Fq5P7fjpD+3/+4m4TMglxR53199ICAZeU8BfEGuaJn0+YxG5WOhY+3aoqs+6dwe3v5FgC/YCrh7xd+/1vcFp/7XoBVzCXzH8ak0BPB/p6XE6Ir4hcNeSyFO11uDarQfcuDtL17RIJhIYus52qcL84iqdrkm13uyTlOUYKWcgVVFxXYnEeC5Yvoig63p+isvDcz02t4usb+3gum5fDDLhcy06XZNdv0Iom0lRrtRkNSSSu+G4LuVKjVg0QiAYZKiQxfMEswtr6LpOwJAprnAoRKXWoN5oEwqGUBWVerNNqSLFJSvVOqVKjYmJUdrtDju7JcZHh1A0jbnFVXRd49DkFNFwmKWVdUzTIhQMMpTPMjM1RjIuifiFfJbh4TxVXw5it1hlcWWF9c0dao0WgUAAQ9cwAjqBgE6pUmdzp0Sz2SKdSjAxOkS90WJrZ5fdUgXbtlnZ2KJYqjCUz6IHDJ688z0kUdJFAkyNTzA1McLK+ha1psmtuw9RgIAexNACuK6QytmJGOOjBeq1BtVac4+gPvBwP7EGBaT1QiGT5PzpY1z+7DbRaJhgUAccFEVjr6pm4FOTo3ISVBUIBdj9p/+Yv/zxO9x9MMsf/s53mJ4c41/+8Q/pmg7/7h/+Npl0EtGXRVH6D238P/qHKNW6DOrzWezRApvNFiu1Oh1dIzs1zuSJw4SzGUQ8ioiGEZEIJKJoiRjr9SaPVlcZHxvl6OFJjIAhgzQUGWD6A4onvL3Vues/c35aEtdFdV0U15XCoJ6LcH0/UMeWTgqOi205rCyu8P6Hl4mHgjx3+jhLC8u0Gm0unj1JIZ1G83x/VNdDdUFxXVTPQ3EdmQp1XD/1aSNch261yb2rt1hf2+RIJsmZIzPEdysShUnGcU8dlQ+bI88Px0Xd3u1Xg/Yno8GJyXXlPXaerVC91yOeuK9Pvq5rqJU6iuPI1K6CDHz8PQzOG0/uT3L+HJnm9OUT+ufZ24PzdFXTvjnpgB0rT7+0l+Y44Ds91RSZWkVRfP8hvx/7rwlFQel0DpiUFXkPkPcVy37qmBrPbgEh8Pz9o8nUbm+B0ENUFU3zn6u9ABNd6wdbir+f3ldWhUB3XUzLolutE3dcIqEgCSBQb/aLLXqp9P75DuSGAkKQE3KeUttdaHX2gvlYtL9dP304iIz4+zxQFkLtHeOrzmwDaM3gLfC8A7f+srvrw029FM3fCMl+YIbfg/j3/9z/44n3xNPbf1E7gCj/uZs/828XFAVD13zRaa1/u4UQflbIL7z6GzeifsYp7qmvPlGNyN5D8eyvv/cAiIM2Vr4Y1drbk+g/XIoCAUMnmZC+hbMLy4yNFCiVq1z+7A5zi+tEwhItarbaLK1u9YMgy/GV4xUFxVP8Acjrn4/wPCzLpl5vYtnStNiyHbqmxc72Dp/euMfswiq6rlIs16jXO+B5kn/hSJ2fXCZJtdZga6cktXSCQXLZJJ2uiRBgBAyOH51huJBjbWMby3Xpmja2XScaCZNOJcikEtRqdarVGu2OiesIYpEIqiKr29LJOJqmsblb4vH8MlpAqndv75Y4NDWGJ2xWNjZ4OLco0RddTgjNVtv3P1OliGW5gmXJqqbx0QLSHqRJpVonEDAYyWfJZyX65gmoNuSKuNXuSDRQVWm3u7S7XUxTkuo3d4ocPTxNMBjsE9cHe42HTH8IIBKPg6KxtVuh1e7wweXrlCo1krEozWarnxatN9s8nl/GcxxAxbEduW8fafI76b7+67kuniO5WpoiNb8UwPA5h8IflGSs8uwHbHN7l0+v3yUcCpBNpwiFghyZnmS3VMaxbfBcVOEH6qg+uOsP3r1JOB7F/r//5yzdvMt/+y//gvuzS5xQBX/3/EnOnTnuV8HJMdMIqCyvbfIv/+Kvuftgnt//7W9z+NCoHxxKhFBWX8prq6Li9WppNEDVJDIBqHh7C18/ldP/iejPS816k5/fucNVBH/7e99k5s1X2Lx8jVtXbzP53CkyRw+Dnx5WFRXXRyl7mT13MExQHIQCIc9j5Y//nH/yT/+E3/jmJY78T/6I2PwytLt4kyOY/8X/3r9tgl76Lvz3/wFKqSJRm3SCdqdLrd7EcRyGdsoEegu+g6KUg5p4+k/FdmRfNHScdAJ9c0dy5gIG4tzx/iTbNW1MyyIcCvq+hvIG9ayWVte36HRMRocLstCk00W9eV/eb0PDnRiR1mCOIxMsrotr2wjXQ0dSFxTh7XH4vuDcv8TX2988Abj7KtkVQBgyfayADMz86+8l4zKYdQcC4CcP9AyoUI7vMuhXXA+EvS8gfup+DZ6QooChywDMR8t6gZqmqRiaRjQWxVUUwrEoeiQEhiG38Z/lZ80jCor0uhMCyjXf+BwwdEQouH/jPher97fY//dTO983mT3jAh3Q+pPhr9AGUZjeWNPnkR0Y8fxqTcFHpVSeQukOCnGeQVbfw5++oB30+f5LXz7AfZJeoqqymrdX1KWwF2zFIlHp+/wlA9+vEdkaDLj8c+13amXfVs/+6gOT4FMPw4FruH2f7avUCk+C0KLn0SgJ8rlMCsd1+fTaHV66dI6AYdDqyEm/ZwUhleCtvq6W2uOuiKeP3ztF23ao1hrYluSbSH0vB8uySCXijI3kabbamJZDuVJDBar1Bo7jEouGqTVaNJptag2p6RMMmrieCyhkUgmeO3eKF54/4+tMyQ68tLRGtd4gGAjguVJyIRwM4LgO9VaHTCrBockJspk0kUhYVlWGI+yWK0yODRMJhag3W0TCIQr5LJoutcgChjTJdhyXar3ZFyMMGAam7bCxU+beg1kmRoc4feIIO8Uyrg/VD2XTTI4PEwwEpLCpohAMBVBVBdfxfNRL68ta3H+0QLPVplyR1kQHCwXK69BLOUfCYY4fO8RwIcuj+RU/OA1g2jaqpvHChVN0zb1qxe3tBrpuSMmAHjFe2S8rIgDHcVhb26TekPo+t+89lgRnz0dGhIptO5Qqdda3S3Q6XaLRCMNDORKxKIPD1ubWNlu7JQ5NjsriDFcG7GsbOzx8OEvgzPF+UI6qkYjHCYf9gVxV5QTW6qBrKqeOH+b7b71Gx7RY39zl3oNZpifH+nZIqq7QbDb5q5++z3sfXcN1Pbpdc+/qeS6mLR0ODE0jqO+RUXvGwijSnqU3efSeXw8Pz5Nik6iaLz4rn+lavcHte49xXY9kIt5XH98tVdgpljk0PUnAkDwdhNon+wtN7RPB5WCmItBxXYum/wyArF4MGAYiEkbpdFFa7T00ot9HxF4aIpNk5//xn/GnP/gJn924TywSYnV9m+//+uv87m9+m2AgBGJvolUBzf+usspoLzoQQvN/Cjzhkvif/UPUchU3nWTt//IPGP/f/iOCjSZKLoP13/wXaKpGp2vyg7/6BbfuPuKtb77My5fO+yR7eZ7lnRJ/8Wc/5d7DOf7gt7/D6y9fJHz3IcG//w/AcfAmRin/4L/mvY8+ZXZ+me+99QbRaJj5uQWu3X6AQGFspMCpE0c4fvQQoUAAxbKhY0LXQumaqK0Oomsi2h35umkiOh2UTlf6RXa6/usWdEwU05T8K/8fpv/aEzxBoWkojoOXSVP9J/8ITziS6+bzvvqLas9D6XRQm22UZhut1cZodfGqDbqlCmaxjFuro7Y6hEwLvWuiNFsYHZOgrqEb+t49HoAIBzCozwmS9+YOXQjimobnCZnubXVQlA5C1yEeQ0klpKl0PIpIxCARQ0nE+q8Rj0I0Av/wv5KIL0AihthHhVH2n8uBc9YB5zfYdw9sz8BYf5VAaCD1euCx+qf0NaNbCgjRU0zsHeTLInJfItB68poOnr9fkPRsIdUvaAp9cEDXdVTVXzwJiQhqqiqrn79CIPc3lEaU8aGmqniqJwfoLx3JH3TDvyjQ6n20d4H3dDIURSUcChAJB8jnkziuxcb2DsVShVw2TSgkc7KJeJRELEowGMDwV+SaAgFdQ0WRfLLBDqvIAUFRJcnWtCxcz/W/p4eu6xw9NImqwNzCKrfvy4np3KljjA/nqNcbdC0LTdHY2NllcWVDln0Lqa9UqTXIpJI8d/Y4r7xwgWwmyep6T0cnSCGXoVKrS/Vl10VRIOQT+TLtrkwxBgOUq3WikaBEqwKy0jIRjfSJ/aFggE7HRFNULp4/iWlZVKp1Mpk0gWAAy/Vod22EopHJpElX6rgCCkN5tECQWT/oSyWjZLNJhBCsbm5j6AbJVAzd0Mik47Q7Fh3TwvNc0qkYiUScxZV1Oh2fy2bbB/QDt3+t95B6jamJMQ7PTLC0usFIIcfLl86TiMfodE1UVWFja5NGo0WpXKPTsRAexCJhDG0/IbM3SbiOS6VS409/+OM+7+zBrNTgEfRkTDx2dsu89/E13r9yg0azRSoZ4/WXnuPVly6SGOi67VYb2xrQZlMUKrU6n928T9OXyajWGhTLVaKxGK+8+BwzU+Oyh/vBFn5qJpGIcfrEUa5cf8ja5i537j/m6OEpnjt/SlraKB4PHi/weH6ZoUKGer0l+VNC2kdtbReZW1inXm8yMznK8UOTxHwpAk/IwKzV7mLbNgiZRlUVjXAojO3Y7JSKlCo1FE1ncnyEoUKOQChEq92i0WyhqSqu61IslVlYXGN1fYe792cZGx7myLSsvK3UGqysbaEqCuPDefLZtOTwKCqNZouV1TXK1QqNZouFpTUAXx9KhVgEShVpF/NE/xDKXoWQQArsbu6UUTWNsbERSuUqruPh2C7NZgXTdEjE4kSjERRNxXalivnahkScksk4Q4Uc8VhEooKK91T6LBoJSz6aD/H1KgZX1ja4+2CW7WKZX3zwKSNDeY4fPiItX1SXUChILBpE09R+JSSt5t6ONZVOt8vlT2+xUyzxu7/5FqlkQppEh8LcfTDLlau3uXXvEX/vj36Xo0em0YMBCMogUo4brr/W7AXMvQWozykFXLEnUyDXMP7FUzyUHn3DtAj8sx9g/MXP5XYDqIjyRPDTo93K7TSIRVFiYSCH0DSKtSaXP7vFvWadjnCJjeRI+oT1TCqFrqsEDJ3RXIrhcBija6I12zJAarZRWzJwU5otlEZLSpLUmyjNFtRbMDhuCPzrIBdSvUpLkAG12zHRLBu1Wt8nWjwQbezvYgsr4LgydRmPoQwGW4qQ/aM/rX3BHPVMlOFZ890TfyvP2PSXaoPn+jWgWoOffdZ3VHpzc+8llSedZJ7eqXQL2FtUDb49gIY9EST2FpEShVJggO/5eV+zH94JeWRFVVB1qUuo6z2ErldiInwQRkP5khfvb4YgL3qdXZZLS4ToywRZsvMKn6fTr2LooZ6fW7HQkxiTX71el35ciqIQjYQp5NKkUwmWVzclKVJReDy3RLvTZWJsmHwmRTKZwLIsAgHdX8GrqL4X2YGniwwoe2TgPc6/HNxymTSRkFSpXtvcJpmI8/rLzzM9MYrnl4/btmBze5s7Dx/zaG6ZUqVGsVKj1WwznM+SzWVl1WSny8raJiurm0RDUi5hemKUVqvdV9E2DINUIoamqrQ7HeZXVqjWG0yMDmHoOqFIBIRHu9Wia9lYtksqGWd0OMehmUk8z+Xh7CILy+vSY1BIc1tNk2TSkaE8lUqVUEhnZCRHvVlnbnGZjmmRzaYIhoN0bZPFlTVabZNsNkk4HPKVzl3aHUuWZQuBJ7w+h8W0bB/J298X+miDGBzRBbFIlIJvFt1stSmVq5w4dghN1Vhd36TVNClXGrhuL8jSyKbikidzQN8xLZPtRosHjxf5o7/1FoemxtFVaaJt6NK6yTItHs8t8eNffMS9x4v9e48QHJ6e5NBARrIvpuu/1NOpqtQauK5LwNDZ3i3x/pUbpJIJjh2ZliiYXKFAb/5otlHDIWmB5FdTrqxv8fGnNxkZzjMzNU611uDWnUecODqDAvz8/U/9yVZQLFX4xXuf8MnVO9QbLY5Mj/Pv/PbbnD15FE3TKJYq3Lz7iO2dogx4uyYKgvHRIY4emmJheZWrtx5I823g2JFp3nztEs8/fwbbkbIGPQsW07JYWF6jXKkzv7TG3MIShUwCQ9e4cesBV67elf6Dh2f4/ttvkM1lKZar/PSdD7hz7yGjw1lMy2Z5ZQ1V8YnLgBKVTgWYtuQFBQx5ZRXPL6roCSiDFgiSz2e492COu/dnmZkaY2ZqlFKlyu27j3g8v8JrL17k4oUzKApsbe/yweVr3H0wS6PZJptO8s3XXuDihdOEw6Gn+oqiyIq7XkpBviiR7fmlVUaHCxw/Ms27H13jxp2HTI5NEImEoTdwq8rAJO3IwKHXdA3TtKQgsp+2CBg6ejzCscNTZDMpNAV+/M7HfPzJNUaG8ySTCYQraLdbVKp1Op0Ohm/MHotE+vQmx3Gp1Ju0uxaJRIxwyPAXW1CvdyhXq3ielNHQdI1ELEphcAIZGLtdz8GxLTmm++O753ry+ylSEkfyKxXa7TY/f/8y/9+/fBfLsnn9pQu8cOEsQ4UcekAiubZt4QkXLRbDTSfRArLC0/PTv56q9tHU3hwwuFDCtKDeRNSb2OUqpZU1ysvr5IwAKUVBbUukrbVTpL62RUpVSaoqmmUfEP88me4aaIlYP8CWm3p7Kc19H3/GPNfLnx/85jNeH3hb/JLR1lOo1i+J9BzUBifnXjQo9gJQ+XwOEtx7SJWG4AmU8KkdK358piKrOZ/47s9A4XpzrzLIcRukND25G5R9L4sn4jrV17rb813c21hTZIHTl3Tr+ZtCthRQVDRNRQifDHrATe6dt7LvFQ7Y9tmrhv1VC3sy+8ur69y6+5hWu0M8EiKZjBMMBqk1WggEAUNndmGFlbVNRoYLslLN0FEUuXrVfGXop6oooZ9uQcg0iBHopVhA+DZBpXIV2+ximiblao3xkSGeO3eCI4cmMQwDqyPwTBnYHTk0wcR4gdHhPNdvPyAQkOkaTdf42XtXqFTrjI0UWFzeIBaNEVBVul2TfCaF8DyarfYeaS8YJB4LM7+0QqlSwxOg6Tpd28VpdXAch6WNHcqVGkP5LGMjBc6eOkZhOM/WxhadrlTq9oRHNBIhEg5hWTbNVptGq0UyGeXY4Smi0RBrG1uUKlWi0TDxeJRIRFprjI7WWdvY8X32wLKlVYw0tRXUGy3K1Tpd00RVNQxdeu/1+sBgqNIrt+8/FELq+MRjUVAUNneK3H0wx7lTx9ENg3KlSrvVouGno4KG3rfweHawZdM1LVKJGEemJzg0PU690eTjz27LB0oB27JYWV1jY2uX0aEs+WwaRVE4NDlGIhbet8/+ZCzoDwqqohAKBDh57BCvvfQ8kXCYa7ceEAzo5JIxND9I73NjAKXVxsulpWK9YzFUyFKrNbhz/zGHZyZIp6LceTDL1k6J3/mNX+PRXE9iQ0FXFBrVBtdu3Ed4gkwqzv3Hi8wuLHPs8DSu2+WTa3e4fPUWmVSSTqfL7fuzIDy+8+bLxONRfvLuJyysrPdTRe2OychwlvPPnZDSBsk42ztlms022UyKZKKn0ZZnamwI13XZ3N5lZXWdcFBnu97gxt0HvPbSc6SzGe7efcCf/tlfMzU5zBuvXsQyLVbWNiiWqnsUhFhkb65pd/xga3CswO+vgvX1TXRNI5WMs7S8zpGZcUKhIPOLK9y4/ZBO1yQY1NB1Qafd4MqnV7l6/TbZbIZQMCCLSWp1HJ97dNB4vi/QQqLbi8urfPzZLVKJOPFYBNOyuPdwgddeqDAVCfn0lUEOq+xTg8GW0DS6poXla1qh9CpP96zCDk+Pk00luP9wjm+8WiIcDlEuN7h55yF37j2iUq0TCgU5e/oYLz5/jqF8Gsdz2Nja4dHsAivrW5w4OsP5syfl9222+Pn7n3DzzkNCYRmkdbsm6XSS39/aYUT4HL1+ut1jcXGZldU1hvJygdZoNtnc2iGXzTJcyCKQ3oaKYbC0us67H11lt1jh2994gT/47e8yMTaCUBQcTy62ur78SyDQ8xQVPX12hOeh9Uj6vWuIkNWDvfSlrkMmBZkU3tgQ9nCO+sQIwVwGwzc69zyP67fu8c4Hn/LKixd487UXSUUj0O7gNToorRZKvS6RskYb6g1otFD+2b+WlYiGDlNjMnARHr1qNRlrPBnIPIMPpjz5y9ecsjuoPXUaB2WX/N+/qu2PgD0+1v4DDfbzvZ8DKUHhQyIDKdWnwDEhBgpflD1k7MvEij6aq/hI52CY2gdwPv+LDQRfwi98c/0YY09ZTVVkUcaXpJH/m6hGlKtU1dfkUZ4IHZUnVwY9nLq3Evgy3+QJ5MnzPLa2i2xtS8J5OBSk27VwPEEwGMTsWgSDAQTS1qTZbLG1U8KybbKpBIVsSjrFewq6LjV5vF607A+C+/gKAoJBA93QZVDhe4ut2zauY2M5Dtlsho5pM7uwQjgYoFyusL29S6PVJZOKceLoDIlYlHw2jScE5WpDbr+0znaxwtGZCd/SJ0Uin8UyLdodk2K5SqdrkU0nCQQMgkEJeZqWTTqVIJfNkM9lsGyHup+KsUybRrPN5PgIuq5Tb7Qo7ZZZ2yxSa7SJx2MkEwnp+5ZMsL6xRbXewLRMhgppItEQlUqd5VWpgVTIZ0jEpQaNaVroukY6nSSVkppAtXqDUqVGq9VBVbXeukWaOKMQiYT91C0I9ipIe6ioXDD2BgWpwxMJS0Xyer3D9m6JjmmiWha1Wl1+Dmh3uziOSzadJJmIoyjqvnu3v7+KvkCkpmkEg0H/+9h+WkLqbxmGxgsXTnPpwilc12O4kGOokN03jvX6tDRily+btoOh62RSCSKRMMlkHF3XSMZjJBNx+o+3OgBKtzp4nicLCjomw/ks6USMB48XuPzpdRrNOnOLq8xMjTE6nOf9jz7DtiypK4aKoRscOzRJKhmnVK6yvimlNizLYmNrl6s37uE4Li9dPAvATrFCo9lkeKhAvd5kbXOHTCrB9PgIsViEkeEC508fJaDrZDPSvHpjcxfX8xgdzpPLptFUlUImxfjoUP/eTYwNMzKcp9O9T6XalErqlsXK2jrbxRJvvfkCx48fAdfl7N1H3Lz72MenhUwjDlwPkYzLyW4fdihotzu8895lMukkv/H26/zs3Ss8nlsmFgnRbLVREPzm269x9vg0AVWwtL3Np9fukExE+O3vvkYwEKRSqZPJSk0or2f3c0CKwr/NCKBrWrx/+Tr3Hy1wZHocx7KIhIJsbO6wtbXN9FgB1V9I7N+VgCeCrVarTbPVlui0AhoCRwipoedI94NQKEi5WqfVaNJtdbh//zH/6oc/Zn5xFduR/pRbO9sMFVLksnGq1RoLSytomkaz2eLmnQccnpF8Os8TbO3scPPOA04cneLQ5Bir61t8dPkaF5tdCq4jCzH8Sjrheayvb/DT964wMTpELBqmUq3z0ac3OHvqKPlMkkarRblSY3J8hN1ihe1imVAoyIUzJ5meHCNgGFieh4aL6ykoKmi6RK9QJHLmOg71ZotuxyKbSZJJxREYVGsNFpdXCYeCjI+N0Gq3pU5XOEw8HkFVIRYLk82kfDP6LrFopG/v1Q/qFAWha6ipBCKTZq8ApHdn/ftz+RrsliGfQfxv/kP2bGUGhICfDFwOmq96XoaDCND+XrV/H19Xewq6exrL23fsrwyeye/Tf0Z6kZTY/2z2A60Bbl//WvU5eoOnJJ54WBQf4fKehp76H/Gf155htBBI+eDBczngcwe85grJHvA8SUXqWhaW7fRRs15oqKAMahV9Yfuagq2nO1Ev6u+px/cuqYxNfFuP3in34Vm/zN9/Tb767M4nxJM3tvc6jI8M8f1ff50rn92Wg62mYTsO6VQc1b+QQcNganyI4aEcwWBYch6Ex1AuQ6fTpViq4bnSd03yIXwrGQU/syVwfEkHx3Eo5DI0W22CAYNOt4vZNXF84vb61i6f3rzHSD7DyaPTdDpd1tZ3uPdoHoHg/MoGmi/nEAiGUFQp2Hf6+CHKlRoPZ5dQVYWtnSKtVpNYJMzWtvQzDAV9Erpf9mw7svovFA6jqGrfR7DRbBGNhGm02riuIBqKoqkG69tFTMdjaXWdWqOFoipEwiEmx0cYGS4QjYZpddr9IKhWa7K9I61/dF3HdT2qtSb1Rgvbdmi0OqAo8lxsiRpJnS1ZUyWEIKCr2HZI2vwYCpouQJFEbsu26HRMXM8jFAwQCEnJDvkMuXjCxTBkqlcApmnS7ZpkMymp3G9L78dmq43jugzls0Qj4f4qXYF9k6ih6RiazuZOkVt3HxOLRlha3qDdNqnUWpQqLdKJGEKoeC7oWoBwKEKtLtG5funvwIDt+YiZY8ve3ul0ZfDl9Qo35LMQDAYwAvoe/aCPbAlEs4XjuNSbLVrtDmOjBYbyWSqVGvOLaywsbzA9NcLFC6fQVJVSqYrjeAhPwXMFsWiE6YlRllY3eDS/7HMYFSzbZqdYZqdUIRoJkU4mCIckImXbaQqFLLPzy9h+wUQ+l+Hw4SmeO3uSTCaGoakEg5ITaNkOrXZHehp6HqYvtNtud0mnEsRjUdY2ttnaKfr2KCEUYdLtdmg2WziuI9NnjSbRaJhEPOaXWStS0DIURPXTsmapTCsaIhDQCYX2hq6eKfvy6iZnTh3l/JnjCCH4wY9+wUdXbjA2kufX33yZF587TcRPa+8UK5SrdSbGhxgu5MikEjA91l8gOgI8T3lmsNVrpXKVa7fuMz02xHe++RKJeJSJ+WV++u5nrG1sYp874euq7Q1OfZufxkAaUdOo1Ru02x2GcykMtXdsBV1TUYVCt2PTbHaRytga3bbF8uo66xtbZFMJhodyMhAZLZCKRTBUGbCNjxQIR6PMLiyzur5Fu9MlnRJEI2Emx4aJRYMcnZngu996hYWlVdY3tqgvbkhbKk1j0BYtm0kRDgWZW1ztm1XrPqIOsLq+xebWLmMjQzi+HI6mKsRiEblwlSN+f9rX9T2BCEXx6HS7bG7tMr+4ysbWDlNjw7z56vPEEhm2dnb57//0R4wM5fjD3/0ej+cWuXHnIYl4lO+99RqFQppmq8H7H3/C9k6Fly+d47lzp/r9UlEUwqHggFPFIGuod0aD2ZXBYMrz0ZUBQ+UDAqs+eKA+8flnokziid+/5ib8/54i5g9+V7H/ra+y+32BkcceAX7/vpXedmLfhw+Gjw9svYDrAHkUf789fqKCh9LjJj7F7TpoEaXse01KDcm5znFcuqaF7bjPSEV+eYWEXzLYOihC3v9uX09roOLrqY/v242QAYyy10lFP/L9PJiyBxnuxZuqpnLk6Az5fIZarcGn1+/SancoV2qkEjF0RePx3BL1RotDU6OMDOeIRqLYlkOr1UbXNYZyacrlKvV6HcfsIpwgQtVA1fBrHfGEtO9odzqYls1wIYvjpEgm4rK6Dmnhsb1TYmllnZ3dMpVqAU3XScaiWLbkMRUrZcrVOqqqMjqcZ3Q4x/hogZGhHKFQiIWlVeYWJXHYstvMLztyAFYUCrkMCgr1RpOu2UXXpelzs92V1TPVOp7nUanVabU6lKt1KtUGh6bGOHx4gnQmTdUnCS+vbvQJ8rou0zFjo0OcOjGD7XSZnV9me7fE+sYOrudK3pR/LNPX2QkYBqlUglZbDpqmaaNAP6BQhIrnuXt2KsLDdXyfOwS2Y9NqdWk0W9i2TSQSJqNpqEYQ15ETe6tl4thev+Ck1epSrTY4ND1FMlFjY2OTar1Bs9UhnYozMpQjENCf6Jui3z8DAYNkIgaKwk/f/5T7s0s0m10abZONnRL3H81z8ughopEouq7z4ac3WF7bQAjBhTMnmJoYIz/QHbt+sFetSUmMkRGpj9bqmNx5MEsuk2Jje1caUVu2b2USkmPBQApSNFu02x22dko0WlKN/cjMJJVyhc1fFKnWmsxMjDM2NAwutNomtuNSqzfZKZZZXt3gw09usra1TavV9dW4JfqoqgrhUICdYomHs/McOTTNyFCOZCJGNCLdADRNY3Vzh1A4hOV6dE2L588d48iRKWnS7qe9treLbG8XsSwb07KZnV/msxt3mBgbZrdY4ePPbhEOBYlFI6xvbHHv0TyeomHaMgC9efcRY+9dJpdJM7+44nP8bHZLFRKuS9Jx8ATc+Pgay3OLnDtzjKnJYQxD9+cSWQxQq8vrvbG5QyoZZ2p8mA+u3PT7cUFynDwPx3Wx/cKSSrXBbrFMwND7aJaqaZRrLbZ3qkTCIS56A+5u/oDdW+XOL62yvVvmj37n13nlxQsEdZ1QMMwHV+6wsLJBvdEkFApi2R6O60jzcMuWFZ6tdr8feqpCuyOfo9559CsnVelFWqrU2S7KamJd07BsG9O0CAYCvP7yc7z9zVdJJeMEAjqJuPQ4DYWCpBJxml2LnWKZdruL5zrgySAoaMhUkKJIseGhQo5IOISwbDxF7ZvA46dLCvkMxw5P8d5H11hZ32J6YkSawRdyoEDbHwt6xHkh8LmpDj1Sa99XsPf9NAUhJKLd7cqFUyIRo9Vqs7yyRvnUYRKJJAFNZbdYlor8nvRlLFdq3Lr7iIsXTlAoZLAsm0ezSyyurHN4ZhRF9fCE119o6LqO47p0TZNgIICm+UTnwbnmqclTGbDd+RLWMurgvHXQdgek8Z76/cmPKF8hMBloQhzwfQ4I8H6ZXfcTvv4O+nytATTrq+/2lzmRgUBLPHFFxVPb7vtTHADn+OiVJ+Qz69gOntvzRn0CUIIvl33jlw22lN4D+Iy3YY+otu8df6B6AmIUAl8Z2P+82iPH70e/9gVZvSh1ENkSHh5qfwXT1nV/FV+h0zVZXFxjKJdia7vE2vq2L9Q5RdDQ6XY7NOotms02mVQCQ1N94dGu7/bdi8wFnkB6liG5OLquY/jpuEgkhKpBo9nEshwarRaLS+sUy1UcR6YXHcclGY8RCgTQdc3350rRNS25AkPh0NQ42XSSVrsLSKTJ0DUc1yOXTlBvtlChn/JwPWnXU6030HQdRZUWH+FQkGDQIBoN43ly0EvEY7z95ss8d/40wXCIZrNNLBbBCAYxAiGu37rL3MIqnU6HjtnmjVcvEotG6HS6rK5uggJjI0MMFbKMjxao1hsUixXp2ZhKkM9l2SpWqFbrRCJSSd6ybNqtri9cre2ZF/vnLgR4/oTe8h3We/fc8zxUT07uu6UKtuViWQ6uIx+Aar3J/UeLKCg8npvjwewCju0wPTnKhbMnyGXTvqq9AE15EldFN3SysQi/9xvfZqdYxuzapFMZ8vksiXiMTCqJ67qMjQ7zwoUzfHz1Fg9mpR6ZqqpoWo9HJPv9wtIatu1QrtTZ2Nrh8KExkok4kXCIdz66ysbWLsGAQb3RYmFlg5v353jx4jlSnvArd2Vza00WV9a5dfchpXKN1fUtLMviwpnj3L4/SzBYZ2S4wNr6JmvrW2xs7mLZDo/mlznyeJ6FpQ1u359HUcAwdEzLYmVtUwYx8TgT46Nsbhe5cecR0WiU40emZf9VVY4fmeHlS2d558PPuP94iY3tImsb20yMDnP4ENRqDbZ3SjQaLW7dnSURl9rjhmHwcH6ZerPFuVNHSSXj7BYrhEIB6RqwU+Kdj66hagHy2SwXzhxnbnGVf/bHPyIej1KvNyVRfnWT+YUVpm2HqOPgeoL71+5wNRllaChHPp8m7D+TAjnelSs13vngM+4/nAchcGybqfEhtnfL/OVPP6JSbZBOJRguZEkmYkTCIe49WkT/0bsU8mlM0yIWi5JKJXk8v8Ls3CqjIwXOWCZ6DyEQstrN8zzMrsXlz26hqgqHpidQtRDVVpv17Qpd22ZhZYP17RKpdJpmq02l2qDWkKhwo9Ei7qf1BWD72nuO41Ks1KnWGuRzaUmWV3Q6HYuHs4tYlsX0xIgvmSHQdR2BoGtKakQ4HJKBpCMrpO89mGVxZYNDh6bRVJWuadJsNLHMLqqi0mx1sG0H07KxbRvXcbFsG9V1pRqIJ6uv/OGOSDjM5PgIiXiUm3cesbSyztGZSdKphD8WarIKXQjisRipRJx133Hi5YvnyWQToPjXEAXbddjc2gQhyOeyhEJBJsZHZNDV6VIqlnwPRjB06QCBANOyGcpnGR3Ks7S8jmU7km8bDZPJJFhZ25TVg0LFcaQWonw+YbdYpt3ukEjEpYWXrhONholGI74vpfpElDCIBn1BoNXb/EvNv8+KcJ4RoH3lNJ+/q54Ar4/s7z+2v9Pee19WUV5BBldiEDkZ/NeLuwZRpa+/Dab1nnhnH5I2uM2eM8HAz/7vfoDmP+euK/zqbq8fufT+eULBPUAS6lntV0wjHnz3BdIQ1vPEPi01DgrAnoq8D/p9r4R5f3C1h2rtO7q/WavVZXV9F1XROHlshlBAp9nqUK+3KFXqTI7lyaXiRMMBFpY3WF6RAVgiHsUIGAQMvV+5qCiKRLSEkNWSwhdN9d+3bIdao4mmS10uVYWuaVKp1Kk3WliWjWHokjxfqdJqSzTDtkxCwSAzk5J3E41GsEwLTwh0Q2pcAYwM5VBVhWarIyucVJmaKlbqBAyDWCyCaVrUmx10Q9qlGIYuy/tNq2+jEo4E6Jg2qxu7hO48ZHSkQCad5OiRKUbGhjhyZJJQUOGTq7cplat8/MlNwqEgAUNjd6dIKGgwOpInFArR6XTRdI1wKEQ2kwIgEgnLMlkhiIRDZLMphofyLCytUSnXiYSDhIIBXyHf8zt3L7iWZri2baNpqtRa8osFEALHkWa8qqL7fCh531utDu9//BnXbtxG11TS6TgvXDjNi8+fYaiQo95oUqs3pd6YGuwvXvupBEUGs29/82U8IXAcD9eRKY5QKEgoGKTVkiTwN169xLHDUxTLVYIBg/NnThCJRPt9VSCYGh/h+2+9hoJCLpvGdV0unj+JJzxu3XvE1NgIkxPDpFMJNnfKWD6a4Qmpqt0jdlrlGo+Ew/rmDrqmUSpVqVQqPHfmBG+98RKO63Lu9DHmZhf4wV+9SyCgMzaco9XqYNkuI0MFYtEw9UaLoXyS8ZG8z3XTiScSkpuXTDA1PkomlSQUDPgyCVUOTU/w+7/1NoGAwbsfX2dsuMC3Xn+B6ckxXFtQLjVoNmUlbyGXxXEEhWyWS+dO8HB2kXAoSDIeY2J0mEq1Tte0OHF0hunJcZrNDpVai3w2xR/+9ts8mF3kweNFdF3jyNQkjmczNTVBKhUnnMugahqecEmq0rVhfmEFRYGJ8QIpIW2zotEImXSSO/fneBwIMJRN8s1Xn+fcqaO89/ENLl+9y9WbDzk0NcK/94ffZ2ZqnFdfPM+PfvYx712+QTgUYqiQ4czJI9iux90HcywsbdI1rb4JrRxhJQrruC47xRLvfnwNVVEpV5rUai1u3r3Pj372Pqvr2yQTMTZ3SxyamaJWa7C1U6RaazC7uMr65jYjjVZ/9LRdl1q9ge04LK1u8sMfv8sbLz2P68HWdokbdx9y98Ecz587yUvPnyPg+3wW8hkChsFHn96k4lt0pRJx3nz9EulUjE+u3+H2/TkuNlrsFCssrqxz/dZ9MqkElmVz/9EC5WqdUrkqEe7NbbZ3y2iOg6sre2nv/rgq09NjowU+vXGPWiPO6y89RyQs1bRDISlv4dgORw9Ncf70Mda3dnn/ynVGhnO88doLZLMZNE2ja9rcezjLz9/5gOnJUb7z7dcJhQKsbWzz2bU73Lz9iEjYkAbkPgXF8akSPWPrnruHnFfkOB0wAhLj8CTtoxdMKoqC58L9h4t8dv0OQgg0XUPXVM6fPc43XrlIPC7Ra03X96Mi/aCC/tj/9bQn589nBVqCX6oisY9PiL0g8GkoR/74EoHWXirNn4eVgfl48Iz/hoOs/SfV/69/zN5Z9e7hnseiGEDC8ONMidH1hdj9f24/2JJ+oXt7kM+C1/9uX7uoqXjirx5s94yte9yEfX8/tZuBNhBhD740ePwnLthTgVY/kFVwHNjcLrO2voNtO3TaFq7lkIhHyaTilMoRYtEwrucRDgU5PD1Ot2PTasuKPdf10FStX8HWu7iu8BCa50PfMgDYLZbZ2inS7nRQdZV6s0UgmCIWDWNZcXZLVerNFoo/qYeCAYLBIKlEjHQiSjQaZnJshAtnj5NMxFlaWZd2QZ0uqqqS9AeAdqdLuVJnaW2TWCRMPBalXKlRb7axHKfvTq7rGgFDJ51KSPgeKT9QrtYJhkKUq3UW3/+EZrtNo9kkEgmRz6cYGRnm6OEJdrePUqs1sG2baDRCuVyhWCzjeYILZ48RMALslqp0bUeusIXXF0e1HYftnRK7xTKKqpDPZjBNi8XFdUqlCvF4lEQ0iushNbCEwHY8XE/Q80NzXLfPhxMW6JqG4fMsPM9D1fC9+ER/DEnEY7z0/Gni0Qgjw1lOHT9EIZfpWx5Vaw1SyThGMNjvI4OrFVVViUYlGVtSCFU0TUdTtb7dk64bpJJxpifHUJDcp3gsijHAO1EVlbfefFVy/DyBpuo4nkUqFefli2eZmRwln00zVMhx/MgMKxs7DBXy/nUUfhpdIidKp8OJF8+QTCSotdooQnBkZpJ4XOp7GQGDaCyOKjyOHprk/OljJBIxTNNiOF8gHovxu9//NqVKVaIAw3mGhzKMDOVod00s06TRaLG8tsnWbolSpYbreuQzKX4zFOTksUP83m+8RTgUYmykwGsvPkcqGce2bYK6xrdfv8TE2DCJeFym5iyLmfFhXrl0jmAgQC4npU+mJ8ekhVIy4ZsE11lZ3SAY1DlyaJLTJ46wurFFu90hl80SjYYIhiMYhk5oY6fPeZrKpEgFdT64cp3ZhRW++9YrHPOLF6LhEC9dPEsqmSCTTHDhzFGeP3eCaq1FLBKjVKmxsb1LJh0nk06RTKd469deI56Is7C0jmFo0lJqbFi6SdguyXiCQ9PjBD+4LiUGEL5XpdKvSjx17BDJRBxd03E9l2gkwszkGJqmkU0niUUi1BtNyrUajWYbTwjJa9reRfgVs8KXUYjHohyeHmd9c4db9x5TqdRo/P+Y++9gy7I0uw/77eOvt+8+b9O7yrJdrqu6pqd7emYwFjMABAYcIREiFWIgSAlBhSCKEikQEiMEMQhJQQGCECAGgwEwg/GmvStflZWV3pvn/fX2uK0/9rnmvcysruruAWZXvMr3rjl2n73XXt/3rdXsUq7U6PZcnjl3gp//yk9QKuaREgzDYGl+hjMnjvCttz7g++9+RDIZ5/UXnyUMJZVqHSFUvy5XaiTiDtMTpYEX6eb2Lhtbu2TTKdqdHg9XN7h99yGNZhvND/CkwLaGYEZKVUQSc2wWZqa4fuvBoC/rkaOBbVlYlkkQhuRzWb742ufY3N7hw0s3+PV/+8fcW1nnpefPUxorcOPOA7795rssr6yTz6URAu4/XOPb33uP3b0KW7v7xGyTtfUdFmZnAZW2IaWk1W5jVHXq9SZ+EOD7wSAFQoVKJb7rEkRFSoHvRyy5ZH1ji0tXb1CuNiJDayXSe2RhlnwvQ6/nkk4lKYUhg0KNfsgsEuTtTztPrDz81OzWZwBtPwyzdbjJ4dz52b86jCCNMlj9Qzu4j38HIAsOnMvhEGK/cO4AQBySXQfA1cEcLjn4ehCEBIHqN4+/ZFF16qdonxpsfVrhLlUWG6CqylRy8ODkRqm8PsN1qK/1Vyf9bww+MMJs9Wm+0Ta8cBoSjSCAtfUtylXlN3j52h0sUyedjDM5lifm2FRrTe49XFPJ4NMTVKst1jd3aHd6bO+WaXU66LoyKvWlkkMIpDoWDZW71Wx3ebCyztbuPghBu91hZXVLDbaZFMlEfGATZJoG8ZhDPpdmaWGW82dOMF7MEndsMtkM2VwGr+dibe1imkZE1ye5e3+ZtY1tur0e9WaLnd0K2UxS2fTklNVPz1VCmZ7vo2s6pWKOfDYzSHbPpFM8XN0glUpRKua5dO0GnV4X27YIw4CPPr5GcOEK8XiMlbVN6s0WtmUSBD7LKxts7eyTSsRpNnsI4bGzWyGZSlEs5HF7PfxACWiubWwfIJLXN3e4c3eFnZ19pUcmBY1mh57r0nM9DF2n2erQ8XzSqM7t+z6eVBYnfuBTyOfI57L4vlLqt01nEGrs3/sgCEjEYowV8hTzeZKJBIZp0um57OzuU603VLJ/Oo2uQViuIN+5CJW6MpB2PYJOD82x0DQ9WgEG0YpH4Loe3W5XmXPHlLCl6yll/VHJXgTE43E67ZbKTTN0eh1VUYiAuekJnJiDlJJcNo1uWpimOSB3pRYVkYQSvdNjcW6Go4tz9PyQwPcwI82XZDKhKnxlSMxRferY0hxHl+apVlt0uy6JeJwvv/EKzVabbrdHPpclnYoPqlVt06Td7nDz7kNOHFlgrJDj1t2HPFheZ2lhhunJcfK5DPMzk2TSqUF5vh+EJOIxnjl3ksmJEgKoVGt0uj3i8Rhxx4kYQlWBWSzkBuBE15V1TrlSjoQ+4wNNq0arTTFXIJGwCSIW2cxnIheHkKyuY1km5UqNTEoVLPSX6rpucObkcc6eOsnS7KSSRUHS7QYszs/w2svPoRShIZ6IIwyDiVKR1195npMnlui0u/i+TzqdIp/LMD05wRuvNEkkHKx3rwyHo/5YE0qymTR/66/9KoVcHsdxsAyDZ86d4uTxJfUseh6apiQd5mcm+Uu/9BW2d/fxg4CZqQm0jlL6F1ISX9/mpz64xrPolHM5zFQSUzOoWTadyQli6RTzxxbJuR7hXgVP1xHxGFOZND/zE69SKuZwPZ+JUpGnTp9gYjxPtVHnpRfO8/yzT6EbatEgg4B8JhWFxkN+5ed+kiCEZDzB1EQRO6p4nP7nv4dWbxHo2oF8NU3XKOSyKrd0vMjC3BSZVBJNU1pyuq5hW1b0+ZDF+Sl++c/9BJZlcPHqbf7462/y/kdXyaZTVGp1PN9jfmaCpYVZkHDtxh0ufHxD6fMFIeub+7z53secPXWcIFQs/er6Fl/95psEYcide8u0Ol06bZdGo82Dh+ssr6lwe7VWp9Vs0uu0aTWbNOotvF6PhekJXn7uKcX4S8Wmz89MoElB4Aesb2xzr7fCGz0Xpz8rSZBiZNKO+p3kMYArlCM5Xp+Gufo070Xvf1aJhh9TexzQGqZby8Mf/nd3YFEbAK2RfQvEAcAl+gTQ4GeISeQBwCUU1AhRXrBRZfzhPSqFhU/v4/zDhREfx1AJgaoUUDdDohITNaGpuP9jDHwf2einWg0cBFpDkKUoYzl8g3a7E2nUqCWBHrnK15tKCNQwNJLxOJZlsl+ps7tXptXuMFEqkk4l6PZcCvk0hqWELUWfTdMFaDq+77K9v8duuYzruhiGgWNbyiKl5xKEIbZtkUzEyKZT6IaqMJwcLzE1UWK8VGRhdhJT1+j0XG7fvs/tuw+492B1MHBapsnpE0fJZTPcuHOf5bUtxscE+byya4nFlJ9gu9OlUq1DxP50ey4ISCcTKp9C06IKbmWb43kB9x+sk0qkmJwoUG+2+PDidbZ3VI6EEnMzMHUd09QJgpB22+Xjq7cp5rPEYw4xx6JWa9Dt9QjDkP1KhZu3H+DEHI4fW2R1bZP7D9ZBhqSScVXRgRiUZSMUuGo223iuT6ic+dB1Hd/3o8pKlZwYIuj2euzu7SOlzs7ufmQBo/JRd/b2+b2vfYdUIs7EWJGJ8RzTkyVSyQT3Hq4Sc1SSdxCqkAh3ljH+4T9HPFwDP0Bf36b4hf8AdA1pKK/A/r+hrlMMQ3KAGXMw4zFCXScQgK6rKqtIyVp4Pvbf+Dt4rRZhEBJLxIkJSdZXLKCu6QcWHZOh6vSaJtBrDeU9Fz1eIgJooZToAkQEtPqVukIICJV6e7PZVsboIViWBUIoMdRIlNUwLUzLimwnBEgdXdeZGC/y0vPnePrMCXquh2NbvPPBZWQEdputNjt7ZZrtDjPT45imqZ5rXVP7iSh1PwgIQlW9ZprG0A0hOk9NU8etaxq6ofZtWxamqfIdHcfGdUOEpqPrtqLowxAZTwxWn0GtzlrFJ5mI8+z50xxZmB0807quMzczTRgGpFMJdEMfAGTP97Ecg2Sk2RVE8EFYgkQyjlWrU/M8LNMik06RTCQxkxqlXPbgIBvltUgpo0VMjIXZWXWNRyZc0zKxLYtao0mr1UbXoVjMMTtTotPt0el0iTs2WqszGKv0eoPch5dJByELUl1HTWhD03Ah0L75trrnUg4YGSEE41LysmEg4g5aPAbfeofAMsgYBrOWCTGH0LIILAMt5qAl4uBYxC2TeceGeALNcaDrMZvLcuL588T/4LtK+LOvfxWdm6HrxB0HwzQojeVZnJvGtq1BfrmhKUAsUJ6sQsDC7DQ/9+XXWJybYmVti1anh9B1Th5bYGamRLGQ5djSPIZhcGJpnsqzNcUq6zrLqxsk4zEkkkQ8zrlTR7n7YI21jW3yuQzZTIqYo/JC6/UOl6/dZW19ByEl65s7kRZgjbsP16jVW+yXy5w+fpSJ8SK5bAbHtpHRPJVKJfF9H0ODu/ce8nyrjS1lNO7rCBkMAddgshr1Nh1poQQtund9cDaoumckQ2ZAkXHwjdEmh9v5MwK0Rt789wKuRttjiaDHsHcqfe0QgyV5BEgJMbJVqRYNB6J0B3YyWiTwye2zgS2JQuuPBU7hAHUrAKTKQA+DrH7y/CPHN7IKGMTEB+Dr0dPsf25Qdh8lqvXzAQ1d5XE4to3nBSAFjm0zM1miEwGTpfkFnjp9gljcpFyp02x3QQimJ8ewLRPf90mlU1i2iW6AQBvIBwQIQhmgaYKjR+ZwPY8PLlyl3e1GAqomUobYtk0+l430pwzyuTT5fIZavcl33nyfsWIe29R58HCNm3cfsL65G4lEqhyai1duqlDByaN0XY+VjR0W5qY4dmSO1Y0t7txdptXrks2mmJos4VgWvV6PBysblCs1psbHVLFAp0u367JXruJ5PjMTBUqlIq12i+u3qkxPjnH8yDydTi8SSVUTqGUYjBXy5DIpPD8gEXeUzZFt0Wx1KNcamKaJYegqaX4sz8dXb3Pv/grVWpN8NsVkqUil2mB7p0yn26XT6ZFMxCjmc6pwIFCVhUruIoalC/wgwI2qmtKpJLplYtsW46Uinheyu5tUOWlhQDIe5/zpY5w4usjNO/fo9bqsrG3x/XcvApBKxnntpWcxBiVSICu1Ibvab5FNjoiSaQfDn5TEogfygKillEqqwdCh041WQyHG3Qc4XkCgCex2d1Ap2+87w349GhcYMrii28Ne38L82vex7y7TBzRCQJhJU/t7//mg//fzUY4tzZHNKg0qXZcYIaBFgrBCYtg6mqGqrqTQcANJvalsnY4uzJJIxLly4xJ3H6wyOVFkcW6aIAi4ePkGl6/foTRW4NjSHMV8DlCTrq5pyFDi+SqMY+gKRPVBgBGpfw+e/UgKRtM0TMMgiMI/EgWA2q6P6PSwnDhCCIIAPMNGj8Zzs9sjmUnw4nNP8cqLzzAxlh8UFOi1Oov/u/9OsSuui7FXwURywvPRTQM75qBFRsSaEArUaoKslMR8n3k/RDcNLMdG1w1lw9WXX6jUBwNyPwdV1zREo4X5n/zdweDcH/tCKdEDH8sPKIF6PnSVdB3rL9jCEG1lA4LgwKimaQINPdKEE2hCfyTMMdpj1GcEer+6sdUBlM2QiPwoh5WBqhMN5/khYBttiTDE81Q1tbTMwaQjBANHiI2tXVXck05iRPZmfdkMTSig1T9QXdeYnhqnWMwRBEqg1bJtYvE4gfRpt1vYtoMmDE4cWWJ+elotDIRgv1yh13OJ2TapZIL/6K/9KvuVGhINy7bY2t6l3e5w6sQR9soVctk0X3z9c4zl0uSz6YH92tHFGTqdHmP53IBJLeQyJBOJwfkLIeh0u9imRaPRwHVdpB7lbgUyuvkjISM1malb/yTANXrXRid1bfi8qzwGDuUuH/rev2NQ8wiTMwBaqv24crIeBTqfBFwes6/HZB+NbHywDzFy/YYamUSJK8OP92Uk+tGKfn5WH2yNkl+qSIVPfQ0+Pdjqg6aBjEa/fDfi2x6TJNZPGT7w2iFadTTICCNAa+S90W8cztE6+G6Ux4Ma0HPZNLZt47o+p48v4fs+65t7GLrG9ESJU8cWyGXSdL0uhXyaMycWiTkOM1PjuJ5PLB7DcZSavK73k7g1wii7yDJN5mamGB8vMj87zb0Hq9y9v0oi7pCIHOINQ2d2apxUIkYqGcdxYhhR4v3y6jp/+LXv0mi0lLdiEOI4NuOlAsm4CjU1Gk2+/+4FOt0uiXgMTYNYzKZQyLK2scX95XXa7S7jY3mef/Y0uVSKTqfH9NQ4tVqDdquFDHyIbHe6XZfFuSleePYUlm3yze9f4NqtZWr1OseOzPPay8+wvLpJu9PD66kctm6vRyw2xrnFWbKZFPV6k5X1LVbWtsjls5w8vkQhlyEIPbZ29pAypN3psjQ/zaljS4yXCnx8+TbbUTK4EIK56SmW5ib58PL16F6rSTDu2MRt68BDKIQgEDrFfI7k0wlCX+J2ulimQa/bI5lI8NJzz/CVN15hdXWdntchlU5z+eYtvvG9d9nYVFIVlqmjo9gfUa2rjes6YSoJuobwA/B9pOuBHwwn0QFVLA4+1f2HfPRYwxC91SEWDbT6wNgXwj5bNhp2PNiZh7sIQvT9KlqjfXD1axro/+l/PfhOGEpejbwxzd/6hgIJA2Plg3tRIqPqObZ8nz+/vcN+uUrq+x/h2BYvtju8CBTzWdIrv4YmYLzZ4rV2l1jMIfXeVSzTAAmpMIwW64IgCMlGKue6rj+yaNdkNPFLiQgCrJ7H+U6H0POwNQ1dCApeQM7zEEGIJqUCvn4QJaSDIeFUrcH/Y2Yca7OM+b0Lig28ckvZ+KDmKysaGEUQoCHRJQfsXgb3qX9NJOhyeL2EEIdvsZpMhQZbu2T/N38f885DhKeeKTa2GdCrYjiamVJiahqaHhnZa32gqz4nhCAsFdU9lBBmklT/9/8rZBCghxJbCDTXRXR6BO0OQbON4bnoro9sK0Npw/PRfV+Fwftm0t0usq0MqRkFUiMn1QeESDnQQTzQE6UcgOBQ09AHoEGjVmvz9vsXePO9iyzOTZFKJrBtZfTuel16npK1cF13MI2FUmIaBrZlEUT9JJVMYJomtWYTQhWm06PjkhI6XRcjyjvtM59CCGYmx5mcGKfr+eztVxgrZJk8eYRM0kEjw+svPYtAkM+mAHBdj1w2w8L8NIauk8tkaTQ6SvcuGD63Sk+pR7vTYX1zG4HAtsyRdByp+AQhUC9G07QIo4W+NgTBnyZ5PpTDhdcT278foHVg3495/Ynhwx92T9F2fviig9FFa3+bw1dl9B9yNKbQL9Y7jEdGSEeICkSG1mD9RZUWGVEjBeGPPUFeavTzpg6g0ei1w00MDvvTX8AhOBOPeZ0DN3coLRE90ChdDIVE1en3V5RCCI4szGJZJq1mU4UQhTIg3itXsW2TeNwkk05gmTaappHNZpieCkinkkrdPBosBQJNqhsoDF3lQrRCRVeXazi2xcRYPspvkcQdk0TMotPW2draZSuSodA0Ddd12dmrEIYhZ04e4ezJo+SzGcby6SicEWd7e5u3P7jEnfsrJBNxtrZ22djc4eHKupJy8H2q9QaNZovQC7h9b5ntnX2klGxt77K2DudOHWVmeopivkW5UsUwdHZ21XnnMyk1uaaSzM5MUsxnOX50kWq1ye7OLnfurbC9u8/te8s4tkU6lSSUKt9ovFSgUCiQL2RotlqsbW6zurZJzLE5ffIoM5OT6Jqg0WgxUSpimqZKPk3GWZgZpxjZDYWR9INa3D260u73CMMwiOs6XtenUq0psVnAMnXm56ZI5DOk6xX2y8qQ+rWXn2N6coyvfuutQZ6VlCqUqVfrasKbnaT13/xnBGeO43k+9x+u8hu/9UesrW/y2pmT/PxLz5LXNPR6A1FrojWaaLUm1OqIagNRb6DVm48xSo766PK6Ag1Ab24KL5OKdKSUKrGh68RiNkbEpgDErt2BIFTl99EDIIKRB3qvEoVtNHQBkVvg4KmT/bDDgYenP5ipFwwpGZNgGwZ01cSd1HVVeRpIdU5ATkqyponwfES5hggCCEO0MIQgVL8HIVaofhdKennwngjDATPTDysKIYj16RbRt6UZFZh8/ArfdF0yro90/eHLYX/i639VohTB+yC5D7JExECMiIpGYLGfOK2qiw8Ov2Ek5qbmWIm+taeAVt8s/NAxC+RAJuFJudPDO6Vukw7oe2Xy/9V/Pzxe00BYlmKWLBNpGmBZeH/jVwifPg1AoOuKWR3JcQ1Died5eD0XXBfLDxBdl7Ddxm+2CVsd/FaLXq1B0O6SAGIoYKf1eohOl6DTpbtbxm00CdMpslt7gyNfXd/kq99+m1any+svP0shn8U0jEjqpEatXiMMA1xPFRkpYUh/4LGqG8qey7Yt9RoR4EWFZrVQ3Tc/CNF0HcNSxUSGZtOfe/xIS67ZbJFNp0inEhiaTioRRxsv0usppXgVqjYH5uGGodIhGs22MjkXwwm+57pcvnaLi1evs18uc3RxVhm2H3iuI3Ak9ej+SsVBCAkEB0DXpwIOj4CVw2zSvy9G60n7HIUhP752UNbpx9BGma4BA/e4/RLds8dtQx1TEAmbHlj8QzRuO0ipferb8+nBVngQvUn6sWw1gB9gnwYrSe3AgHMANY6GCn+YNspqSTUQjjY/CKk0WvhBQKfbY2u3zPGlOU4emWd3d49vv/0huqkzMz2Brms0Wy1llBxAY32be8vrtNodzpw4Eq1c1VSgRecqhdK5Uas4D02GFLJJ3LhDMhFTvpChxHV77O3ts7m9R7nSYG1zl1qjpXJMpif4wsvPcubEEseX5uh5Hg8erlGu1Oh0ulimjmOZkf5Uj/GxAsV8jv1Kje2tPY4enef1l5/l1t1lLMPk2s173F9eR4YBRxdmWJqboue6hKHE0gW6UINlr9dje2cf3dA5eWyev/KrP0OhmCedSWNoGp4X0m622dzcJh5zuHz9DuubO9y484B4zGGskGNmapypyTFa7Rarq2usbe6wur5Nq91hZmqcMycWcd2At9+/zMOVDabGS/iByuNJpxLKIy1S6SWUSBkgZPhYoDW6YhRS0my3eLi2GQmpquT43f09fvu3/4D3LlxiY3sXXdc4fnSeU8eXmBwfY7xUwDQFyltBQ9Qaw36Zyw72q2karXaHG3eXmZ4pIRfGCbOZIegR2uMXEp6PqDcRfVAWgTHtv/tHaLUG0tBp/5/+Nq0Ti+zulfnat97k6999j2QiwV//y7/AM0+dHsiMlP6z/wv6bhk2tvGnxlVO0sY2eD6+gAetNul0kkIuM9Qri54wyZAmV9esfyEV2yOCcPATQ2DpOvg+mpRoYYjouSpXLPosga+A00g7zPw8cVoRAqkruQZ/UD0ahZFMU+npPbJFlGWRboChg65+hKEjHQtZyB44irCQG66MKzWV5yWHY6j/C18ifO4soucSdFtcv3yTB7cf0KrU0PyAc0cWmR0r0KrUkD2XfCKOgwDXxWt3aJZr0OuRNE0sQHg+weIswvcPHPIQao2unZ9wWTjADww+rTG8j/gB0u8i2t0Do6v+D/4JmOboTke2qf5njY7+oj/f9MMhysUgCAL8IEDXFAgZfe4MwJShCu/vVojdXUbzfFjf5LW/9Xd5JQhU6PbDG1GYWO09KyUn+2GYkUVTP21kCHyHh56VI3NB9KI0DNxjC2oxoWsIoaEh6KeZxJBMBiETSAxNHyxSDCmxRlNLRgiPYQhfkowqy3RDH3jfpmTI080WR1ttDNMgdeEGVlTA8OjdG9wyRXJpEQkhABTTJXmUMfzENhjkRsCBDJ/ciX5MTT4C5g4DvpHfRwsEfkhwJA/+L9pcP4l9ZFc/NMsVfV8efBYP97v+X6OcWB+OSKnEtn0/UO4kvR5BfwyMPqxrivkEDrsFPrH9iDpb/RX04y7MSI3WyIp6lNr70fY8ZLWGF6z/ZKlckI3NHerNNpoG95ZXaTabtFotsqk4mVQS1wuoN5qk0ymceIJsTjFSe/tVgsjgudXpKMpdE1GkVIycrhoyY7bF+XMnqFbrPFhZB6kqXFrtNuvrDfbKVZKJOOdOL7EwN8HK2jZHF+f44usvceLEUXRNcOnSdd589yNWN7Yjv8Ymuq5z/vRRbt9fpd7qkM9mlD6VH+B5PsvLG8r7ywtZ3t3kVu8hAPlMCt/3mShNUas3uX77PncfrNJsd6jUGpw5vsS508fouj4zUxOcOrmEE4886EIIQ510Ik4i5tDp9tjc2adSbeB5AavrW3hRsvf1W/f48NJ1fD/gzKmjnDt9jPc/ukqj2WZ9e4/dnTL75Sr1RpO1jR0ALNNgcqLE1NQk9x88VGKEmvJI68fHH3nQovyV/iDabLfZr9Tx/FDldVRr/E//6vdY39ql1WqTTMRIpxO8+d4lHq5uMjs1zpHFWXQtEiuUchhGBMimB/lExXyWZ86f5Nqtu2QzKWzbUoajyAHA7m8jOjgEGrqpQyGDLGSinhkxwP/DP4MI2IlchrhtMjmW48Vnz/LhxWvslqvIMFTgI+paMh4bsFkCCHNpwmwasVdh23X5bxI2X372DL/w2vPYfoBotgmbbWh2EK0OotmCZhvRaqu/W20FBqOVvCJ61DXXHAuwhpc6ugf9RfzjOabhpx/7bEqJjMcI43H2ej3u7u7hFPPkZya5tLzKw0qN17/0Kuc+9zRGNg3JBGEyiZ+IQzwOpjUYLTSUVp/QQIb+4LhGD8aPWLPYf/hfIPYrik2LJFvCo/OEz51D1+HChY/5t80GJ7/wApl0kg8+ukpw6iSZV16gXK2yvbvP7NQEU5MldF2n13NZXt2gXK4yMz1BqZjHMM1BXhKej3A9RBTO6zZaLN++x+7WDkYgmchlmMikcYRAeB64Hlr0HTwP0XVVCNTzEZ6L7LngBuo91xt8R9SbKkQoUDlZQfOx1/2xd0MOIaCI7o0iYgJC31OvGkYkJD0MoepETKMEzfcHLJ7uB+j9fUWMbf9+jATSnnhcj58pDv1t+BjVxoC97ffXUYj6pImrX14yTCmK5oiRMcXs77XPeEbHnkaSMlSYXGt1n7iKOMxyDKsP5YFtfqbWB4PR8Q/n1ifv/8ej9TW6nH3MWwOzaSJUEfzITNuoOPmoXEMf6PSZrk9/fp/ueD4tg9aXOHE9j3anR7vrDvTcBsuhaL7gMaHIJ7XPDLYOh+8e84HoYIYvDfrRD0x++8R18oEm6CuBR4lrh7QuQgI6nS6e55NIJghlqCbjdofjCzNMlAqsbmyzub1LNpdmdnpSudZL6Ha7Kvk3uvsDn29NIWZ1gYeBgHqtxocfXaNcqaFpGj3Po9Fo0ag30KOS6N29CpoQ5LJpXnr+LF/+4uvMHVlke3WDP/7ad/n6d99F0wSpRBzLVJT4w/UtPrh0A0M3AMHK+hZrGztRFZ6gWmug66qku9PtqTwzx6Ld6XLh8k2u3rxPMhGj53pKe0ZKnj59jC9+/gUW52eIp1I4sRh6NNdKP6Be7yCF0vyJxRxi8TiuF1CpN8mkksQTcTzP5/b9ZaqNBnuVOtMTY0q52vfxPI/tnT1i8Rh7exUmSgXiMYeVtU3a7R6WpaorJyfGuHrjJmGkc2ZGg1x/JT6aaiL7q2QNCMHzvKFXI4Jmu4NRqVHIZTh7conTJ5ZYmJ3k3QtXuHDpOpqmhA37rA8wAEAYOiKVGHg3FopZFuYnicXsQbKkrgk0KQmRBCNLJOX6HrnBS8XgBFGOkRn5wDHCgIhsWlWZ2TaL8zOMj+Vpdbqk4g5WJNUlpYSEM+hbxuqmyguKcpgmgP+zoZMu10lfuDZkD0afpEOs74HFaXSRRwe44XvR/wWqKjMZRybiyEQMmUxA3EEmE8hkHJGIQSqhfhJxSCWQiRhhLEYYs0EYbG7t82u/+fusb27zt/7qX6BwZIHlf/mbvPnuR5z/wucIX3wGGSVXD1GURMePTCqiCVYTowd4cIQQChgIKYfjTBAgI7AlYjYyDHFDn++9fYEwCPnpr7zO5OwUr7/6QlS9qQpIej2Xhyvr2LZFIZfFNnWmSnlipo5tKLEXrd9JhUDYFtjq4en6AW/eusvHD5cxTZ1Op0trf4dnz5/mtZefI5PJoAudgMdPkiLK5QrRIq21KM0LsH/tt9H/5e+rD2oiImc1yGce2c7hdnBuUYyEDJUGVa/biyYREYkI25h9lksOR2Rtr6yA34EtqXsThMpuKwiVHpGyv1IVuo85y8fMGsNlsog0AaVpIgtZ9Fv3IwZRjGzhE8/2wOeGc5UYFAo82g5OVI/9SKU++NyQCZJqBYAAqamQuTZyNiMh88/cPgn/jIT5+iZSPxroGuV2HvfeCNhSS78fYV+Ht/6ow8wo4PpR2uCMPun04MARDBlgOUjR6Y2YUPfX2f0dGEKFv8NPSW39SMzWyGEePoMf8PlDH+/nbsAPAGSHNtafT/qDExAi0BBoGmjRBKZpGmdOHmV7Z49btx+wvbPH1HgRw9Bottts71eo1VusbmzRaLQIpVSJ4d0erQikIFQSZ//4tChW4fsSt+ezsrrJtVv3SKcSjBVy9LpddF1jolQgnYqzurbNzu6+CvHNzxB6Pmv3l7l2/TZvvneR1fVt5qdLEIbMzU5w9uRRbt1b5uHqJu1Ol0wmw+R4kanxIpqm43kelVqdRrM9UEf2/Ca+56FrGlY8RqXWIAgCCvkMc1PjnDy2wPmzJ5ibnkQzTVw/oFKpAiG2rdPzJLfvrZGIxxkvFWg0W2xGGlWhlHR6Pbquh6FrVOp19KjKp693I6XENM2BQKdtO+zslmm22qRTSaSE6ckJnj1/lkTCptPtEnccZibHyCQcpAwJAi2SCYhus+hXmMrBBKBpGom4M7CgmZ4c41f+3BcZy2fJZFLMTJbQNI2NrV0uXbutwrK9VpQbFnWKigJbMpNGCkGr1ebW3Qds7+6wurapFOoDJWqooZiTbs+l1ekRAjHHJhVXatmhLyjXGly6epPL1+8ghOCZcyc4d+o4CW8ItrSM0jZqtXusr+/SanWxTTMq8x8BTcn48GnxfRScoA/5D84P/YQnhqu2/iuhbRPGbIJ4DJFKQiKGlk4g0gklqZBOKkAVj0EyAkuJGGEiTqBrBKGHTiTMGgTIUCqhUVtJM/QrC0MgkOqe6TIcMCI9t0ej2aKYz1HI59A1NaGnkgmy2RSWY0dVQiBkJA8ho2dLqI2M5lf1x4p+RagQERCXAl0Ew+symvLgWOhIdvcr3Lm3guNYuJ0uQatNPpMglOB5gkQiRtyxefhwlZhjEY/FSMctEjEH0zTVznTj0HJQHbjQBOurG9y6d48Xnz/LqRNL1OpNvvatt/no8nWWFmbIZFIg9BGYO3L/DiSxSyAcPANCEwhPCaoKGEiDkM8Q/Iv/nn7Vdx/8D6G0BlJECecyuixqEnG7XXZ399jc2qHZanP5+h0u37jLG688y5/78usD0/ZQKn1BTQYDINufCIWQKr1gY4PJ8QKNdofvvX2RB8sb/MyXXuFLX3iRRDyOwKBfpTjsr8NFTz9fzpeC7H/yX6KVa8hCltr/5++R/Y//D2jl2vCKjfSB0UrKcAC0RydOief7dDtdtMiNQhUDHHqARjd9mCHo/1LMDe93dH/UnyGDcKEcMm9y8Bn9wLl+IoCQo9t9PKv1SD5VP/z2WUOWn6kd3q6OFOEh15bPuMWIYepfrVEoB59FuWqwQQ5UlgtUaDcgIkUiWDfymA2qdEfC3cNLGPXNUAlrh8HjwrkCTRgw8nz9oPaZwNbhjQ4HnoM2PI+Eg37AtRuMrZ+5DVdKQoyu2dXDbRg6mq5Mmn3f5+SxBXZ396nV6whNcnxphmqjRdfzObowg+d7g+RylYipEpnDIByONH0aEVUNHPghmmaQy+bodnrYhk4qbpOem8K2LBKJGF6vRzJms72zj2mauL0e3/n+ezxc28KxLY7OT2Fqgs2dfeqNFhOlAqeOL/HyC0+zuq5A3N2HGzRbbaYnS1iWiet6ZCOmRNc15cO3rcJohq5FiedK+dyxLBLxGOlUkr39CisbO/Rcnxu372NZJoVCmtnZSVwvZHNrn+mJcdY3t6k3mmxu75KIxzh9fJHb95a5eOUWU6U8TsxSqvbtDo1mm1SfHTJUkrXv+bRbbbZ29um5KmHVsS1eev4pThybY3e/TKvdZnKiwInj88RilgpPYiBMbWQwHbIxQRDQarWp1RuEYYhp6thWghefO8dP/+TnsU01sBuGTs/1ePrcSfarZa7dvEe3q6o91TgYIGoqjBikEtx7sMJXv/kmd+4tE09Y7OyUqdYaIJXyl5SS/UqVqzfucu/+GruVGo5lcubEEs89fZp0KsOV67f5rT/4Bpvb+wRByOXrd/gbf+nnmewzW4bB1s4+H125zp17y0qt+/4qpWJuUKHV10cLQknKdYlJVGGGbYGuIz0fV9d4NxVn9sQiz33+BaxcBhmPsdHpUfZ90pNj2MUcWjKFL6BarVOp1djbr1DMZzmxNEs8HkfoxsA4WvV1nyAIIjslna3Nbb75nbcJA58zJ4+wsrrF6uY2C7PTfPknXqOUV3ImV2/e5aMrt2i2O9iWxfHFaY4fVQKprueqbaZTWKZBu9ul0+3iui4rq5tks8rAOGbbuJ6HH0oIfXrdHrZtYZnKP3JtY4tOtxeplReIx2JIoFpvcP/hGtVGm6nxImfCEF1yMMcs7qBrgvXVTTY2d5mbHsfv+eCHKk8QDUMI0nGbhZkJbEMjFo8RBAHdnke32wPDQBMa3V6Hbq9LIh4nnY6j6WqgDoKA/UqZfC7D2dPHyOeypJIJjh2ZY219i71yhU6nR9wxBj7jRCNJX7pCjV+KO5UIoroC5WAR5Uj1Q7/974rB5yV63yA5elcQIoXKMRXRKiVEEgqJ1AXxmE0hlyWVjHF/ZYX1jS32yvuE0iWUBgKNINSiS6kKBDTCwZi/tbvPt773NuOFDF/6wgskEnFOHZ1nZ79GMZ9F1zU831Psr9CQsg8IhiEklRujAKAv+6rsEcAOhxVgUteQuUwkalyn2WqjCSXtk06rMS30fTKZFOlUUk3hYUiv06EpwDJN7HQSTdeV1dqQw43OLZpHDi/4y9VHLGyE8tIa5eOGK6CRPz8zFOlTOk96e5RRG8560ZsjoO9PtY0yXT8c2BJSUypPDO/3o+3Q4lE8eX8KuD3p/ZHXI0A8WNwcCgWPKiCMLuzUoyMfd9UZXA85Wijz5PZDhRFBPc6HSeGBwe/Iy0L0AdDIa3zCrRpdxn7q9vjPa0Ktlrtdj1v3VshmUrz0/Dlk4KNrAssyOXlsEcu20A3B7bsP2dkvI1ArcFCxW9m/mAN/qv6KLBwwHo1GAw1BGEgCP2R2poRlGggp8Uyd3T1lyXF0cYZY3Oa7717i9r01TizN8vz5kxSyaTa292i1uywuzGLaDtlchlwuw/zsNLcfrLG9uz/wV/SDgEw6yUSpqPK2gpBGs0Wj2aLZbPFwZY3NrV3mpifI5zKYlsnYWIFytcH95XX2azWu3bhHOp3g+fQZKrUGO7sVbNui0WpRq6lqvXQyQTGfo9Zo8HB1k063gdCV4XDfR3J+bopkIs7efoX9cp2Y7dDzXOqNJrlMkmw6iR9KTh1f4ouvPU8i7nD3QQPf91lamGZmchxAWXEIgRbqg0kpDOUgkVoC9UaTew9WWFnbwvcDSsU0yXgMy1QVTp7n02orschjS3PsV/Z5uLqJ5/uDqhLR6gwSqZumzr/5na/y4cWr/OJXXuelz53ng48u82B5PZpwe8Qcm0tXb/O1b70zEMy8+2CVW3cesl+u8carL1Iul6lW6zz31Ani8Th37i0TuL1BaCTUNP7Zb/wOW7v7nD6+NNBx63R6NBtdul2fZqvBlWu3uCgku6eP8Hd8nwldx54sKeC5X6WhCf71WJYvvXSec7/8U+iOw+17D/gX//ZPuH1vhRNHFzh76iife/YpbMdmv1zh6995m2s37zEzNc7/7Je+zJnTJ/HcLt97+0Pu3FvGMLTIv7PHa688y4vPP0W5UuF7b12g53lMTYxRqdX57lsf8bZ5hVQywS/83Je5c2OZX/+tP+SDSzeVvpWmcXxpll/46S/w4nNPUa3V6HQ76HqeruuytrnJvYerbO2W+Z/+zR8y//YF/tKf/2lOHVviq996k62dMt1eD5B86fWXGCvmefv9i3zr+x+wX6lx/MgcP/fTP8GpE8fouS5vv/8xX/vW22zt7jExVuD/2miSBVUx2R9OHIee63LzzkP29muMjxVotzsqHUAYdLo9Vta2uH7zLs1Wm6WFGSZKY9i2zYOVVd557yNaXQVIu70elgGvvfo8586eIGaoUPPOXpn7D9e5cfs+tm3x7PnTdDtdLl+9zer6Nt976wLbOxWef/oci/MzSCnZ2tljd7dMNpNiZnoS27ZAKPBRbzTZihZOhVyGk50uA0OokbGx3enSaDaQKL9C27JURbZUUgvKQkcSBiG+p+y8wshU19B1crk0nt8jn00Rj9nYloWU4HqeAuK9ANdT7K6uC2xbLaSEECyvbHD3/iqOZVCr1vF6LqauMT1ZQhM6qjhVIvDRhEDTNfSICVUTpE8QimHFoozss2TfFsiPQmWSMJPixv/xP+Xr336LN9+7yOljz3Hm5BGmZ6Y4ujjPO+9f5OvffotSIcvf+mu/yvTkON1ej9VVpTVYyGexJktYlkWIkuEYZYT7x9fXLBswYH/5b8NemSHAGbLr/fFD6MAwi031ucOz048NBH1SuO/H3D4RS/2Q+1OIlmHyz/Dl0S0K5AGgpRbeoNxpHnc4YgQJ9UGvUJ//FJhw9COCIYMoJU+w6ZFD9lEKGD6dn9h+hDBiP6gR8UkjdK5691FqFx4PtAYATYon38cBsu//PdzPkGviwPtBKAc3ar9SoVqr87M/+SrjxTx7u7vcX14nmUwwNpblwfIat+6usLtXRQLxmEPcsQfVfDxmH/3XjYhZane65LNpWu0eK2s7ZFMO+YwSRTVNg8lSgWI+y8fX7/JwZQNdqHwg1/VYnJvmpc89g2072LZFz1MAsZjLkM9lOH/uJJ1OF9f1cD2PIAixI6FP5d8oSKcSkXegCl9+7TvvECKYnZ0mlUxQyGeZcrukc0l29irUGi129yvUm23qzZbS6yoV6HaVEGfcsYjFYkhNIwgCctkUhi5IJROUxvLMTk+QTCgPu0w6Rb3R5Or1e3R7HqGUNFsdLEMnn8uwuDDH66+8wNTUJO1Oh909FZ4cLyorF2XTE6BUxsMo50atOvt1CX1zas/3abTaUehUMXQ3bt/n/JkTrK5v8p23PqTeaHL29FGazSYyDPE8f2Aoqu3XBvewZehcv3mHyVKer3zxFRaW5tE0jT/6+ttsbO5RLtdJxGLs75UJfJ8XnjnNwvwMl67c5PtvX+Ty1ducPX6EQjaNY5ncurvM8SNzvPzcGY6Mjw36bE+GvP3hZX7q9c/xl3/xp3iwsk671WJzZ58wCNjZLXPr9l2+/t13ebi6QaenfN4y8RhmxFAOnpdocabrGnvlCr/5+9/g/QtXaXW6rG1sU63VOXvqGMlkguW1DW7cfsDRpTkScYdypRYxBE0+vnKDje1tnjl3inqjybWbd0klHU6fWMLUNQxDyVIcPzLPsaU5rt9+wJ37azSaDbrdNh9+fI2rN+8TBiFxxyYMQ+qNpvIAjJhVkAShRxh6SAI0oZHNpHjh6dOUClkKuQz7lSp/8q232S/XmJ0eZ2ayROj73Ln3kO+/exHX83Acizv3VtjY3Obo4jw7O7t8fPkGUoacPXmUWq2u8vIkKkG+/4zaFr2eS6vdwTB09spVtrb3OHF0EdfzePfDy3zzu+9yf3mdZqvDc+dPkc9lGbMsLl+9yR9+/U38MCSTSlKt1/E8DyfmsLQwS8y2WF/f4nvvfkSr3SVm23x86YbSIkNw687DiJnbplJV/Wh6cpztnT1+74+/xeXrd5idnuCXf+5LnDt9HN3Q2N+v8Edf/R6Xr92i3emxNDfF3+kFmKBCN6Lv1Sp5uLLBd958l0q1pqRjTh9TZtLrW5w4vsjRxXk6rTZ3765wf3mNcrUOUmLbNrZlcO7MUUpjORX617XBxNZottjfr3Lr7gora9t4nkc+n+bUsQUWF2awLJOVtQ1q9aYaq3ouu/sVdnbLlEpjTIxPoglBGPh0PZ9ez6Xb7ZFOJyPbJ40g9HF7Pt2eT89TbF4xDJWsjlQFRn3g1W63+Uf/9De4fvs+b7z6PP/zv/orjI8X8QOJYRj87FfeUALQH1+lWqszNVFC1zQmx4sDo27TNOjLch5kJhhcz0dBkRx+bKCnpV7z/YBQhpiYoOmfnR/4YZqqEhk5bjEA3386rNaPe5sjV34QTtYihvZwG7JbQ22zJ7THhPhEnxT5TEc1fEWxrmph0scXB3nZYQTv07YfAWwdxoPDoF6fdFPSVI+/YQdTdEcfATn88wlnIuVhYu+RjQ9jshGa9n2far1G1+3Rc12SyQTT0+O0ux1a7TaZdJKlhWl6rtJwcT2PTtel0+kNPPgGHP7IfjUhIn/BkN39Kn4QsLNXxnEsZqbGCGdhvJBVmlJIltc2uXH7AYmYTT6rVOL9ICCVTjM3O0W90eLdDy9z/fZ9dE3ji699jvNnThDKkHKlRqPZUv5zMYdGs0Wt3iCZTOAHAbt7ZRrNJsVCToVhLJM7D1coFjMcP7pA122jGaoa0LJtpqdKVKp1rly/QxiGFPNZEvEYgRfS7XQRpLEsC9f1QYSkU3FMUyMWU7lKQRhSqzfoK5k7to1pmHS7Ltl0CsMw2KtUWd3Y4dlnzpHNZeh0u9RqDTa2t9neLTM9MabCsVGY0I8qnNB0pT0lVNYUUV9yHBsplc1PKhknlUywsbXHH33zbSwnxjvvfcQffO27NFttPrh0TYWv2l0FUH0IQ4FWaQ66lpdM0NncxLZMdvbKjJeKJGIO6XSC/XKdSqXJZKmE21OaWNlUktnJKXqtHg/ur7NbrtLr9ZiaGOPFZ8/ynbcv8L13LvLKc2f5/MzUoLd4ElrtNp2uKtqYnZ5grJBjr1IjZpuIMGBvv4JtmXzljZdxbIvCr/0eVvi4Pq6ekSBQ4EYAR5fmWNtQ17RWb9LtuWiaRrPVwfN8Yo7N80+f5cjcFJah47oqxLcwO8VXfvIVVtc3efBwhZXVTZqNxmApZeoa8ZhDKpVkerLE6voOyUSc/b19Hq6uAZLPPXOa86ePKfFK0+Do0qySCCnmSKUSeJ6P0GBucoz5qRJSSn7157/E3MwEViLOw/srCCRz0+P8L//an2csn8XQdZbXN3nuqZM02x3u3F9hZ6+ijkuGBJ6H73tUKjUmSwW++OrzpO6uIRqtKIyorlsYs7Esk1PHFhkrqNBnGCqtpnq9yb37Kwgh+OJrL/DBxWtU6w16vR62rqFrqmji+TPHeeWFp3i4tsbvffX73Lh5j0atiqULvvHtt2l1e3z5J14lk4yxsbWD49hUqg0ScYf52Ql+/qe/QCGbIp/NQuizvLKO63nMTU9w98Eq33vrAxbmpsnnk+xs7/Lt779PaSzP2VNH2NvdJ/BHJo6RnCfPd9nY2uH9C9doNNtMTpZYW9/io0s3yGbTzM1M8fHVG3ztm+9SKubRNI0LH19nc2efo4vTLC1MIwvqmS/mMtGiTdJtt7l24w5f+65iFCu1BjHH5pd/5gtMTRQhDNne3lNMn2lQqTX4w6+/yYeXbvHKC0/xq7/4M2QSMQLfY3+/wrfe/ICbd5d54enTfOmNz1HIp6NihA1u3V8jCCWlYoEZ10OPwocqv1Il89fqTb7/3sfks2l+7iuvMzU9gZABav0hicccXnvlBabGixTy2SiVwMC0bLqtNtVGHUvXyKQTGJbDaPJ6v58/Mks9aXKPGDE/COh2ezh2iKXpaIauQoyjQOjH2AaVdEJ75PUfrT15Dh1NmxnSJOGTv/ODdiNHk+0PAq7+Nh91G1QuGIN9jvItUfHDgXwtCYyIqPQPXchH8cIoIXawiSHDGvZZNjm4BHIQAoWBxtqnaD+i9MPhQ1StH4tXtVvaIJGxr6Ui4YCNx6CLDphaNZmIQ7FVte2Ri/4o3ot+VeKEWl/nK4rbN1sdVtY2iDvKKmRzR1Un5nNppidLvPHK8xSyOT6+epvVjW0836fd6dDrRirgYrijvh88Qihz4kQcLwjY2q2QjDtMlgq4rk+l1iSViJOIx9jeLXPt9jKapnP2xDyJyPetWMgzMT6G6/lcvHKdO/eXMXQ9spOIg4Dd3QrffvN9NrZ2Obo4y+z0BOub29i2zdL8FO1Ol/XNbXb2lH7WZKmI4xiAZGV9k5mpEolEjCBQqvDlSpWxYo4zJ49w884D9ss1mnab/UoNLaxFjF4YlURp9NweQjAQBQzDkHx0fBtbu9QbLbKZNNu7+1SqdcZLBRJxh0pVsFeu8t6Hl1jf3MY0VN7Kzdv3FchMpTAtGyFU9afnu9ANEcIiNEKEbqAZylMQIAh8qtU6QRBgmganji1y6+5DLl6+SShVon4YgmM7bG2X6bkeY4UMnhviugFhIJEjsg8iq+Qd7txf5V/85h/xF37BI5tJR4rSLrV6i3a7x+b2HstrO9y7v87MxASe52OaBvnId3J1fRvHsXj5+af41lsXuHLzHrtTE8OVkKkjZciDlQ0erq4zPlag23PVCjmUxGM2hq6TSSX4/IvPcGRhltTX38Go1hGa9sjQHURikYVcnl/8mS/z+1//Dtdv30dogna3R73RRNM1ji3NcWdxho+v3iIMQibHcsqupj+pBR5ISSrShut0e7iuG01EUlnBGDpG5H/XfwK63Z7SaYpC4z/3lTcQQlBvNAZK8oZhYOgqf85zXTTbVCA9CGg2W8hgmHitC4Fl6JTG8hSyaTzXY2p8jG63x1vvX2J1fXsghBnKkEwmxYvPnKVarfPBxWtoQvCVfq5WoERyEQIZVQvmsmn0yMg6EY8BkoRt8uJz53j2/GnKlRofXLw+GJcMQ8eyLBzbYmFuihefP8PMTJEPLl5nv1qn03XZ269w485DXn7hPHOzk6TjNtOTY/R6Lleu30EgScUdzp5cYm5a9ZleN2BhZpyJsTzrO3ssr26ws1em2+khwwQyDNGi67E0N8XTZ45hf+v94fgXSSEIAdOTY7z0/FNsbO5iWUqhPZtOcfzIHFMTY7Tbbd7/8Aq1eoO/9Es/zfhkiWa7y8rGNmdPHWNpfo5Or0ciHlMeg7aFgaRRb3Lp6m2qtQYTpcJAFDSTUlZaBL4ycQ5DTE2QdEwsQ2dzp8zq+ja9dhviEdNZryEErK5v0WjUOXNqnkw6wX6lxu9/9XtcuHKbZDzG0aU5Xu32cKKCiMAPUKGcUDH6uuqjiXhcVQBHRQEqr8YjmYxz5swJjIgN93yf1c0d3rtwWeViOjZvvPIcZ04fj8YbNXNqDGUuDrouSA4+dFHebqhe1CIrJzeUmEIqH8TBlCQeAUU/jvanx149CTz1wZU28vuIs8aPkCT/+OM4fCT9ST5y/RDDPK7++6MSUAOgdTjyJw9+Y/D7ocM/WGDBQI8uHGHKpRhSSuo78Mjg/IT2YwVbgwOScvAzEHPuN/FoqFBjWF0SbeETQ4qyr+qsYBsHO4wY9IP+hdWEIJ2Kk8um0KLVarPVZnN7j3QqQcxxqNYaA4ufdCoRVbp51BstlSQbBgxLfQUhglDoCA1sy6JYyBGPOfRcj3QqSTaTwjBMXDek2wvwXJ/ltW2a7R6z01Mszc8ShBLbcThxbIl4zOH6zbu02i0+9+xZpifHSWfSpJNxTFPQddtcv32XlbUthAipN+vs7JU5dXwRL3Cp1Ks02m0CKdnb2afb7ZFIJogn4pRrTSrNNmMT47TqTTa2dqhU69TrTZLJGJl0iu3dMrt7VTw3IO5YIGHfqOH5AZZjoWkqR6Td6UYm1TqZdJJsJs3axjblepMwlGxs7bK1s0ej2VasS8RGra5v8XB1AyHAtkxq9SaWaVIqFXEca3Dr+g9YGEp6rkcoAmwbBZ41TWl4be3gRzlYjmMzNVnC9wPefv8S+Wya0yeX8Ho99it1qvUG0xMlNE1XSdiRDU6/vyXnpnhek2xt7/P2B5fxfZ9nzp2k3uizm10M00AIjf1KjY+u3MSyTCrVOjt7Fc6eOkoymeTN9z/m46u3SadT+EGIZRn4e2VkqHy5hGmSz2a4dX+Ff/k7f8J4Mc/D1U2arQ4bu/ssLs4Bkodrm3zw8TUATrsuWhiiw8CYOUT5zpUrNVrtDo4VZ2V1g4+v3qbV6mBZJvV6g+XVjYGB+YvPPcXefpUPL11naW6SuZlJ+lWW7VaPXrs3UD3XhDJ11oRAaBF7WWtQrbVYWdum0+2yX66jC5NsOoPvB3x05RbTk+PkcxnqjSaTpSLpdIp2u0On00NKSb3ZxrFMPN+n3mzxYHkVDcmRI/N9S1VanQ71WoNkzKHZaPLhx9d558IVPNcj5li4XkC13mB3r6wS5ot5fvqLr/In33qLdz68TK3exNE09D7ociyCUBLKIDJuBj0yyg7DkI7no+sqtHjj9n0qtTqGodPpKjY7iMy1w9Af9HnTNJTJfBCgaRqe51OrN9ndq6AXlDl8pVpjd69Cq93Bti2CSBtPoKPrkIjH2Nja5d33P2Z7t8yRxTkkEs8LiMccnj57nAsf3+B3/vBb/PSXXkXzw5GV9HB8NS2To0tzHD86z/bOPrfuPCARczixNEshm6LRaLJXrgIa6UyKbD5LNp3E0HXGIva70WzT66nSdtvUCaVke6/M2uYO05Nj/OyXXsUwdMIgoFTIRCF/NQHJUKV2ZFIJji/NUsymMXUjei9EJ2SilOcnX3+OhytrPFjZoNvpIqWkXKlx+94qe/s14jEnih6o7UmpvBnDUA7DitH7vuchZeTDOxj3ldWXYejoSGQQUm+0uHT1Flev36HV7nC/VmeylOfI0jymaSGDINoOhEIMFO41Q6hQnTw0Y/dt6aJEf9O0SKZ00DU0vT/pHkqZ+XcSW/xTbAKUSGsfbITDUxw9t8flWX9KIHa4OlXttv975K8ccY+HgdZozvjQH3wECzzCgqn3ZdintIJD2+x/rc9qhVEBhzy4tychuR/Q/lTA1mg7SK1+QpMSKfux1r6X0ZM67Ci1+ZiIqxCRJYoXyTaAbVscWZhhaqI4qHZJpxIU8xk8z6PRapPPZpiZHldeXLbNx9du0Wp3cD3vUH9SN07XlCaO5TjkMirkFoaSZCqhwJauDxLpa/Um++U6uXSK8bECqXSaWEyZOieTCT66fIMHK2ucP3OcublZUunIRFWXyNClWm+wX65FivhdtnfL2LbJeKmA0DR6fohumKQzadrdHl3XJ2OYxOMx9vYrtNpKimJtY5vV9U2WFmZwIz0woanE/j5z4HlqMK3VGrieT3Esx0SpoLS3HIfF+SliMYd4LIZpqMlnc2uXWMxRq0ovwPM8mu0OYShJxGPMTo0zNpbFsgz2yzU+vnILx3bIZ9UArqonVVm/aRqDisKO6yEB3VB+klvbu6xv7kCUo1bMZ5mfncK2TBqtNs8/c4ZSIcvOzh6ObaPrglQqhmMrMUqkRI4YCye7Lr946hh2q8O1zW2CQAGZ40tzgGBmaoJsOsXpk0e5dus+69v7fOedi4RBwGSpwOmTJxkfn2R8vIR76SY3764AcGRhhqCiqiY1QJgm588c5/K129y+v8LqxvZA0HWvXKXRamNaFvVGi9/6o2/x9oeX+C83d5g2TZxIdiH0PDphSLNp8GBlnf1yDdet8Htf/Q73l9cRCDw/oN5os7m9S6fb5cOLV9nY2iGUSg+p0+nSbneQYYhj22xW65Fem1qJm1HVaDzmMD6WZ2V9i9/94+/Q7fosr27i+yGb2/t0ui5z0xMUchlu3HnI7l6FQi5DqZjjy2+8jO/7NNsdWu0OrbbKJYs7Dm4g2d4t8/tfe5PZyTF++ed+kmIhp9iuVoeNLaX+f+PWff7g629y+eY9js5PE4Yh23tlvv/OR0gJ7U6H/XKVdCoJQK/nqjJtBFo/BOWoSbznunQ6XQQC11OM1MbWLtdu3uO9j67g+z7pZAJN01jf3OXS1VukkgkFuF2Paq1Bvdmi1e4OjL9brQ7TkyWymRTff/cifhhy5sQSbq9LuVLH8306XZf9co1LV2/T7njks1mqtTpvvneRDy5eY2NrF8/z0JB0Wm2qhs72Tpkji7PEYg7fefNDvvmd9/hl3cGM/C7DyCqpn/TuODYzUyWuXr/L17/9Ds8/c5qjC1PKJ9I0GB/Lc7u2yp27D3B7LvvlCqViDtuyFKgMA3Z2Kzxc3WRhpqTkTVodGq0OTiyGaZnEbItKrT5IsFeaWmEkJRGNiOFwwvQDPyr+6LK9s8/mXoX9igpNB77ahmNbzM+Ms7G9T6/nkksnVBU1amx2PS96dkKlIi9Dmq0OO7tlFltTODFLVReigxQDJw+Q2JrAc11ijs0XXn0BCXz3+++quUGGdNstllc3qdZq6IaBaShdwcnxMdKpJIZpoGkj8gajURQdkAJNMzANE/TwEZzx4wohDsJXT9jej6xF9ZgcaLXhA58aWQQ/cgAH/338Tvrx7yd+5BE5EASCgH5F7Se1g28/PlFdKZIJCPtCaCEyHHFl7ifjM8pMPnp8gxdF/3ufLaT6pwq2xOAm9anIT2ojHVsc+GX4iShJvl/KPNQf6sez+58Dz1MaK2GgVlntdkd5c2ka7XYHDSU82e70SMQdivksxXyOTDpNKpFkc2cP67YZrWzDYRhxBOdpulIPBxMr0nHRNI1kPEY2m0VDGUtbpkm700UISKeTxGI2yWSC+dkpej2X9z+6wjsfXmF2apxkKoXtxDDNqJpIqHDW8soG1XpDeS4KgaFrzEyWyGczNNsdpATbsfA9H89TuU9CEyTiMfbLVcIgRNc0dvfL3L2/ytLCDOdOH6NWb9JotNE0neNH5pidKLG2ucXefnVwqo5jMTGuQGqz1WasmKfXcxWN7nns7FVY29ghkYij64JjR+ZIJeKK2bIFs9PjTE+OkUrHScbjtFo9PC8kn42RTMQABlVUmjAG5x5KJU0gNIHjOHjAnXvL7OxVQAjGx8c4e+YEvudz98EKJ47O8+JzT9FuNulGgrb1ZhOBqjw1DVM9OBHYEkJg/fPf4aSUHPE8QglBwiHc2CdIxiGTwnnrI8Irt3k+DMkfXWLH82jZFmRSLJ09zrEjcyQTMV576Tmy6RQbW7uYpkFprED2G28zMEA1dM6dOsbpY4uD8Wdvv8z2XoWF2WlM0ySXz3L0yCy3769Qa7QHofe+WKr0fbq+sjzar9SoVOtIqVGtN5BhiO3YSrrBsVX4GSV7cuHj6wRBwPzsBEsLs0hU5efS/DQAtXoTPaoWK0V5VslEnJ/50qvcub8SGaTHyKQTlKs1luZn0IRgbmaS82ePY9sWMdsil01z4ugCpWIez1N+eOOlAjfvPOTG7QfMzUyyMDtFaazA8vo2zVabn6g2mZ2aZGlxjtv3lrlzf4Vmq8295XWy6STzM5M4sRgZ20Ki4fo+lVodTQgFmG7dVw4GJ4+QvHoP0fMGPpKhY0XnGnLvwSrlap1ur8c3vvcePdelXKmxGh1HMZKy2CtX+ejKTUrFPHv7VdqdLpev3+X40TmqtTp75ZoCj1s7nD93glc/9xQPfuur/OHX3+TCxWvksylOHl0ilUpgGiYrazv8m9/7FqeOLXDu9DEazTbffvNDVta3SCaUntXaxhYbm1vI0Oed9z5mc2ef82dPMDs9ye7OPtLSBs9HECrmJgzUuCAlZLNpLNukGuVWJaOUBice59Txo2xs7vHehSs8WN3EiSf40k+8QmksT7fbw/MDNrd3WVnbYXltm3qrSzIZp1TI8WB1i9/83W9Gml+CL73+PKVSCddVYUTPD9UzE0Cj2abdUeCt23Vpttq888ElvvH2R4yP5Wk02tSbHdY2djiyOIth6Jw5uUSl1uTew3Vu3HlIu9PF9tW5tdudaNxVTOCYo7O1s883vvcuuVw6SotIRNWAsLO7z9Ubd8hm0pw5voBu6CzMTtF1PW7duU8nEq3dr9R4uLLON7/3Hhvbe8oiS9MYL+b44muf4/jRBdLJBLG4SUrKIWNyIBcrktmIzMcPTFMiMpr/cbFaj8k1inb+49t+X3sQQZ9Nit48sO/PfEYHwNzBfK3DbQi4QLFZh8O4Bzb8mD8jINWPqoUq0Udhhgho0c//Gj2mg/lWqiI1SkUapD5Fxy6G9M5ALoRDoPUT2p8y2Bphqfqv8UScHv3bj4U+/lN9uwkxAG+PClAQUYCer6wFZChVVdDaJpPFLOlkjJ6rVrgIwdTEGJ6vBolO18XtBWzvllVi7yh9KYfHAFEBADq6YSgldtMEV+UP5LJpkjEbx7bU6lXTEJp6EGO2TamYRwjBR5ev895H19iv1llcmEEz9QHLoHYmqNdb3F9ex/V8EvEYUkocx2ZyYgyJpFKt02q26ESDXLlSAxTzZlmqEseyDAzTwLEtOl3FNOSyGaRUYqXxmINtmjiOxZGFWXRNhVe8ICTwJYahSsU73S7lisrpMiJT2f1ylU5XCVimU3EW5qZVkvEDk0wqwVgxw87+PncfLBNzHNY2dmh3uqSScRKJuApNyH5IWN3TQILrh/R6yrRbIqjUm9y8t0Kr0x0M8q2WS6/bJZPKYOgmtmXSkhLLNJUCvuMAAk0Mr6uo1Q8+IFIqjzhNINwA9qqI/arqg1duE4aSaRkyM9onhUD7vW9DJg2ZFIuZJLOpBEEqTphK4tZaCNdH13SEDDBiNpMTJdKpFKVigTAMWFnfpNFoMjs9RTzmUCrm+Nyz53jq9HFSqQST//g3cVodvCh/wDQMsvEYb7zyHJomSKdSOLbDay8+g2PbZLIpji7NUshlWJqbwtQ0XnnuHIYAPwh46sxxTp84gq5ppJIJnnnqlPrb0NnZLTM/O8nRpVkSsRiJWIyXXjjP+XMnCfwQXbdwXSXn4dg2iUSceNzh+fOneWpumiMSzFwGPZPCyaTQTZOpiRJvvPo8hq5H7LDk5RfOk0knuXDpBqZpMjU1TSye4hd/5ot8cPEKlmmQSiZ44ZkzPPf0aaoNZQRsmkqmoddzsUwDx3GYnZ5gd6+C63kszk8Tu7sOXXdQlh86Dl4IARr1dpeZ6XGKuQzZdJIgCDl2ZJ5EPM7qxhZ+ELK0MEOn02VqYoyxYo5ao8kzZ08Qjzuqui6ULM5OMjc9TiGXwTINXnr+PKHQeeeDS1iGweljS5w5eYxKrcarLzzN2uY2tmUxPzNJMpEgCEKmJopRdW8a11V5e51OlyAMaTSafHzlJqvrW8RsixNH5zFWdwfzlozCiKEE35OEgUYmlWVpboZarcH81ASmaeB5QRT6VCHwUEo03eDMyaPE4w7taKxAQiad4tjiDIl4Es+T5HIFnn/6LF035Nqth7iex/kzx0gmM0ips1dpsbNXVd6kElw3YGN7n0q9SavdVeFYXePtC1e5cPkOn3/hLEEYsLtf472PrrM4N8Xmzh43bj8klYyRScW5v7xBs9UhbRhIqUSl/SDAQIGtV589wde+9z7f+N772LbBK597mnwuh2nHQEre+eBj3rtwhZ964yVOLM6wvbPPhUvX2avUqFbVwmRnv8KDh+tcuXGXm3eWqTWagyKqcrnK5HiRRDxGLaHG70Qo0fvjfigG/rjDMeAQHaTpB4Svf/Q2CnZGx54fYz7YAAQdYm+ecDSj7QeeZf86/ABm68DHR/KyDsudHtz36Jws+g/Go+8Pvt8HXo+ghYNbj/LRpBCDVIqBb+fg6yrVQgEuJXj9adqfehgxApsH8rgOU3RDEo9BLF5yMIl+8PnBLejHXSUHvt7/nNTQZKTpIQWu67O/X8N1Fcu2tbPPyvo2tm2q/KaEKsu2LRtDlxRyGUzLeCxq7ZfeK/0niY5GPO5gWybttsB2HNLpJLl0ilarSbvbQ+hGlDCsMVbMYVkmH168yvsXr6MbBmOFnJJRMOyBQnYQsRiVaoN6o0k+m1JVVI0W42N50skEzWabnd19dnbLNJptHMdmLKc0u27ffYhhKP2pQl6FDqYnx8mkkmxs7qIJwerGNssra1gmdLstdnb3yOWyzM1MqjBju4vr+lFoUVKu1qnVGvRcj+3tfSSKGfEDNWEA3L63rMKvUQjw9t1lytU6jm2RTgWUqzVMQ6M0ViCdSg5lDTSTMPRxQ4kMfNqdLp7vE9M0NMNkY3uPlbVNfC8g8APu3lvmN3/nT7As5SsYhpKPPr6BbWq0O126vR5a/6GI8g6EENR+5nXE06dI+QFmq4O3VyEsVzFabbR6C63eQI9kKIa9WESrHm0Q0ycIEZXawGdx6DAI8T41rQkIBcKyBnkD8cjaKO44WKZJNpNC13RMyySbSZKZnWRyfIx4zEGLJuEgDLE1jXQqwSsvnKfVapGIO2QzOd549UUy6RS6LpifmyTeB5kSVeUWGVbnc1lMw8Dzo+T+nArh9tXhjx9ZYH52HNM00DSBY6sE8TBUeQ7dnoGhjzCPQcjSwiwpL2T8H/z/1JXqh9stizAZZ9wyWOx0aOk64/UOmelxSvEYJxMJ2ppJcXWToNVhOm6hnz1JOwzJpJPEbAspNMYj0VXd0JFhSL3eoVqvEY87zM9O0e32qNYbZNOpQQJtv1o5cGyCQNluTJTG+Nkvv87M1DhmVGmYTiVZnJ/BdX3arg9hgK5B3HHw/ADX9ZifmWSiVCQet1leXSOdTjI7Na7y3oTAsR2eP3+O6YlJUnGbfC4b6e+FfP7lZ3Fsm5hjY1umyktrdZgYL7K1va+KIgydILqOQgiOH5nHD9S+x4o53njlBfR//rsKFIh+sVE09ksVyrBMk8mxArl0kolSCaSOiJT8u90uu/tVKrUG1XqL5dUNPM/H0AVfePlZZqcmOHPyiCrGSSZIxOMYusGRxXk03WBxThXfnDy2SDGfw/d96vUWuWyGk0eUjEez06XTc0nEHXbLNe49XCObSjA7WWJpbh/T0JmZGMP3gyhc3ObhyiZvf3AFP1CMnWHoynMyciTodLtKdy9U7335i6+gGRofXbnFR5dv0mg0yWUzCN2i0Wxx98Eq2XSSY4uzaMLgzv1VvvHd93A9n1QyTqPZYXNrn8ZSh7npKV75XIBhaDiWjSBgfXNHhdYdNf4OqqL7I0AEuBQgCAaTgBSA0KLF/yGW60dofUHNR+BANCb9eJiziLUZTHH9KNTI+4y+N/qafATOHNz0yCuRzdqnibgJ0Tel/hTg7MAOfvCnDwiaClTsOzqow/O8QEnrGFE07MB7miCdSROLxyPw9e8ZbB0AUXIkdAuPD0MLECJyTv8B1/nA1+WIAjBDqw9thH7VNZUA6Xoql2N3t8qlq3eo1pssLc4wMV5gcW6ahblpMuks3a7L7l6ZK9fvMuhUYmQFoEn6AnjRTonHYjiOhZRhZCcTp+t6rKxv43senueRy6WZm54km0nzwcWrfP2779FodXju6TNoQgyS7Pt+h412B0MTNJodEvEYx4/McfXmfZqtDoV8lmwmyfbOHq1Wh2qtQRCETE+OkUzEabW77O1WGBsrcP7sSeZnZog7SY4szHPu9DbLaxvs7ill64lI7LRcaxJKiReETE9NMjk5gbe+CahVtOeGNBtdKtUGnW6P1bVthCaIOWrSOHViiSAIef/CVVzPw7YsbMui5/nUm238SIk/CCSzMxOcPnGEbCY9AJeGaUR5KXKgi+U4DvFEHITg3oMVdvfKkYaTSuRtNFtMjhdVzpxp8mBlA9PU8X0lbtrudLGjxGyiu9lenMGfncQq5MBWXpKtdhehCVwvoNdzSegaeQRGo4W7u4e/X8Vud7BaHcJKjbBcw2p3sDo9RK05MOodUMv0WVjV+b1KlbN/7/+lZDschyAMiXe7hGGIbVkIIUi4LnOeh20ruxjr+l3FlEZJw0II2N3nhX/wT/E9n0Qijm3Z5DyfqUYTTRdYpqn0xEKJYyvZg8kRsChEf2BRLgsCVdlotjqMtzvYjkkslcQwDEYHkVBC2HXB87BMZeVrhCFZXcdsttBXNh55TnVAD0MWwuh5uXhjMEkclSPPcbRqzBMhNUNX1ji6Fv3o0Ws6PgKPEN00MRybQAh6YYhhGujt7kDxW0rQ6k3sb7+D9APOtFqYiRiJ3SpS1/DCAKPWJJZMICwTX1PbF6aB0HWqrkfctkimU4yXirh+gNQMUqkUk5NTxOIppNQjEK+AbDrhYJkG9UZLnU8uQyadHPRvKZVMQSadZHF2etDnjSgfsVypszg3z9L8LImEQzKRoFjIIvxALRqj694fS6WUuK7HfrlCrd5kcW6KTDqFEBpGJONgmiaFnKquTSUTJJMJtnf2uH1vjaMLMyzOz1DIZcllM0PLKAnpVILJ8SKFSBIin81gWSqtojRW4Cs/8TKB7ytJCd3k6bMnaXd6VBst5VphW3zx88+xMD+t+qJjD57FfD7H1NQEp44vUK7W0XWd+ZkJUu9dQ3eVh6nn+wRhiIw0mGamSvziz/4Ez54/yeb2PuVyTUnt+JJYPMYrn3uap04d5cjCLM1ml3arq3I2NQPLtNB1nXKlThCGnDiyyPzsJNlMikwiDoS8+d4FNrd38T2PQjZNKhF7/DQk5GAaUHhAI9KledynP3U7kCA+2NYo8BnOej+uEKUApJAR4JIHwMfBT33S3/2j+wSTHUFUrRldtM8imfWp2g9IUxJRQUU/BUmEI68N6Zs+MdQ/5n5VtSJVxBCKCo2J8RLpjIoM6f+uwVYf4B3gAgYrMW0Y2n4c0CLqQELdEPkJMe+hcn00MI8SW4OZTgwS01X1ocXEWJ7nzp8il07x/keXiTk2588c5dSJJaanJslmlS6U2wsi/RhThQ8GasN9oKWBkMOAV3Ri8YRJPGYRhkGUa6Vx+94DtrZ2yKUT6LrO4tw08zNT3Lm/wrff/ICdvTLJZJJ6vcHUZInJidJAJLReb/BweY3p8SK9bi9SiLYBNfAdW5wnk0iwLXcj1iJDPOZQyGep1ZtoQmNxboanzhzn3NmT5HI5dF2nUCjw8gvPMVEaZ3tvB4BCIUu5Uuf+wzW8IEQ3DerNFn4QUMwrxk0pK+vEHIdcNkPK96jWmwrI6jqe51MaK2D1WRHHplZrUKu3MB0bzw9pttpsbO+TSsR4+YWnePrcSbVtXeW6OU6IYUTK00KQSaXQDJ14LEa71ebWnQc0WiqsJDRVBOH6PpqmqzwVTyUz+75PIBU7UG+0sCwVglI5YRpTf+9/RKs2ME0DoWnEpCQXZfqGoTIW1YQS9RRC4AwEOhkUX0ipkvmloRNmUgOjaHxfqcb7Priuej0IiG/uEkdEOSICQ0rMUBIKQXe8oJjcIMQGbNdD73rgecqqZZCrqHq6VWui+T6666FZFoYfYI9UiYqoWky3LDTLPJhSIoZCgYNHLAjRuz1ivo/huuD6oPdLrKLvSYnhB0jPQ0TsniZBN3S0VgcRMQHSMtUzEobIIESEIbqUkYH7gJN+0npLNT8A3OiZ7j/g6l1dSqzB8atzsft/GxFD2nMxtvcw9srYH18nFILx6H2h60gtMp7RdXRDR/RN5/tjCpANQmYDH6HrGI5DoGukei6hrpHMZjAcG2no+ELgBAG2oZNIpzBsExGG2H5APJ3AjMcRpoE0dNVXIqbVNHQ000SzLYRlEmqClB9wtOdjJhzihomhdRHb+2iuqy6OaQ5CIVJKut0eaxvbfHzlJp7rMjkxhmmo+yaAnuuxV64xVsxx/uxJFhbmiccc3vtQSWmAklSRoUQfSVLWIncNgRLvTCbjxGLO4D6psGqOTqsVVebpLC1MU603qDdaLM1PE4Qh2UyKMwnFvmu6jmOb6LqOH0rmZibJZpRfKiJKefjoNnjK41JKHzlaci818tkchVyesydVTle5WqNeb1Eo5hWDm1HV5q1Wh3g8xpdef4l0KkWzpYRlk4k4p48fpVTMIyUkEw5xy6DWUIvMTrdHtVJFQ6JTGElQH7aD/VYxQz8K+BmCrGGIS9IPiY16Lf7gMN9nboKoCCAYITn6wqmP+TBwQGty9Ng/xb4GYVGdKFXq4DmpUx7RsDq068HAcfi9w/nUj3xEEIoArQ+yIj/RA1FO+tE3kITDe9p/LgZbk4MKe8M0QLo/+Nyj9uNjtqQc6STRoC6gr7P1xK8RAaiRFcMgTnqoDUBPvwyXgxOCaiovKowqZpBQyKR54+XnePmF81y6coP1zV1+4vPP87lnTyrNHS0KO3Vd1jd3uXd/ne29fdqdbmTKqvUPYNBphgBMHW8sFiMejyufO11QrTe4decBfuCTzaQYK+aYn52m0Wjy0eUb1BpNFSqKx6jVm8xMjVMq5gcVgavrWzxcXmN2okjX9QgD6Lkqh2x+ZorpyUmCQLId+fhNjo+RSSepVJvcu79Op+fy1JkTTJSKtFsdQsoYhkExl2F6qhSt9Cqqas9ShtKe67KzV2FivIAMNS5eukGxkKW0MEM6nSIECvksjmPjei75XBpT11he26JSrfPeh1dIJmNMjo8NdKS2760wOzvFyy+eZ3Vti4uXrjM1UeSps8cpjRXQ9KFZq21bmFHOhpSSQEpCoWOZJnfuL3PvwQq+7yutJNOiWlWq4Zvbu0ipCgDqjWaUT6ZFOTDtgSQHKGrYbrUR9aZasUSAXRvJAQgHg6yinXVFZjJgSvtvDajpR/u10ATYFtKyQAui7UfsUren5CCkCpXH9quDazC0CxEI14uYGjlcyEgPZ7+iJkhdV9WbUpLqT0wC7Ij56Cc2H7bLGh6k2rSBJCGH/VrQeOS5EoApwRxMCNExCYFwXfB9pGMTzE8jsylgaHxMGKrVnx8qKx0/QIQBKqFqBKQGgUpuDwL1uSA8FKwYhs4GYRwpoypW9TyKMESEEi0KaROBwsHzO9hOBF5FtIDSNWXyrCs7F00Tym9Q1xDtLgiB7XlITcPo9NBNZUodej6Wr4oBLFsxlLFQ4kg5ANaDCVM+icFQzQklhej10YjHoBl69Lra5vbOHm+9+xE37z7guadOks9lB0U6QtOotXs8XNvi4co6pWKB8QkVIl7bUJpl4+NFgvBQuCzav2mohUir3cayTDJR1aeUqvJzbWObaqXK1GSJmKNCpbNTE1TrdcIwpN1RNleWZaFpyvnCsU1Mw8D3AizTJp9TDJjQBI1G61BvG0YSBs+Z7F8GnXQ6SSqZiHLfMqSScYQQ9Ho+rudRyGcZHyuSy6bZ3NqlkM8xMzXBZKk42IttG6ApbbhGQxWJ9FyX+8trtNptZoJgWN8mD0KeHz0vq09PjiakR5seZX0P3Jk/hSZG/+kDiyezRAcS2YVQxz96aD/osvTBkdY//z7ry8i/o+LhcgCmhjpbQ62rx+5u5HiGqQUjPsqD1/sfPzhCDr05GURR+p9SJXFyMJ5/lvZjAlsHO0L/ZBDaQFh0VNR0+MHIYlgT9PU0oiLNwfuPwFwtoiEFIJX2VR//H1hzRGE+KUMKuTTHjsyzvbvPOx9cRo8kGW7eXUYg8QKJEZk73767wvrmHoZuKLAGyL5YWP/4R0OK6qDQTRPbtpGhpFqrU6nWMAydZquN0DQKxSKW7bB86z61ZodiscDUeJGe59Fud5meKFLMJLENDddzuXP3/qCCstVqE4QhrXYXw9CZniwRj9k8uL/MjdvLdLodjizMEngBa+vbbG7vEUoGiuLdbo9UOkUmneKnfuJl8oUc0ndp1Bq02z0812eimGN/L6NEQYOQsbEskpCtnT1iMYf9chU3ktLIpJOkUnESMRMNpYGzs1thf79CvdFkdW1baeeESgDV93w2N3dZW98CiRr0xscxDGMYbose4kHIJbq+hhC02h3eu3CF9c0dDF1nYX6aQjbHW+9/jOu6eJ7H2sY2uqbR8zxMQ2dyvKAqNyWkooIF01Qim5qmI3QNNIHMZR7tzqHEdV1q9QaGbpBKJjGMg2XFauyP+u8j3XT4R5jPDhKaA99XDN7N+4g+GNA1wqnx4Tc1Qdh/VvrbCJVXXBiGCAR+oGxpHNtCGEYfgQxYgv4vMqqW+rTDdD/EOMQmIwMrsr/YPnB+EtDKVWi2Ceen6f7Xf5vw9NHoe3LACGojC6g+mA4DxSgFUll9hTICiJo6jaDXobtfoby8Rmt7l6JpkQwlfrWG1e7heB5BtY6sNbBdTzGC9SZapUbg+wR+gKFHY1B/YO5LFMhwhA0fhnuRqFw8X6L3F45SIsKQWKAqnLRKPQpHaOgCLCEQuq6YNV3lHklNUyxaPwwaiQOji5GJWozcLyLAyIGJXFvbRASB6meWiSElwvOR23uc+a//IcWdfYQQlFZ3yL15UfVtNQBjJ+L86uoGrbVN+Ma7WJaFb+j8LIK/lEqQ/X/+2mPzYvuh/IVej3a7ixbZZJmGCvO7rsdks4XleZQairVCE5wGPMNg66nj6DEHKxFH2CYFJJ7Q0GIOmmXREwJPAKaBFrMJdR3L9TG7HrrrE/Zc0o2uOtcwCqGHoerrQhXPCAmmqZNOp0glY9GYDgKdeMxhbnqKsUIOI5KmicUcSsU8sViMUPpIAkxTIwwlbbeHE4tRsCx6vgKKna7LI4+0iB6Az/BMPb71QUQwMmM9Ooce/H8fsP/pm05/mm0fFBgVKJLD/4w7AgiRcrjgHnmDIV31BE/EA20UYXH4coKAUGoRqdMHV4/exf4rfYlaGQ4T4BXcihixPgB87FYe335IsHV484f+HqBlBUrE6OTxCOBSYbn+5CW0Q1f1cYCLSNRfMEh8liOroVGwJoQa5D66dINGo4Ftm7TaHd758DJTE0XiMYtYPI4Tc9jdq7BfqVHIZZmaGOPqjbtD6YcB2JIR0zW8XVJEKtu6hm1bbG/voUmJ41jktQzZTJpCPkez1aJSrWMaiq1xHIdqvUkqmeDY4hzxeAxkSL1RUyHEyfEoobSFbZuUK3XSqQQzUxP4fsC9h2v0XI98NochNKXltV+h1+thWhbtdhdJh3sP15ieLDH17Dllji1UBdLczATNVpunz51gfnZahSU292i2O1i1BlOTRRrNNtu7uzxc6eE4NvlchvFSlnNnjlHMpuh1u6TTyaiqa5tuz1OhtiBkY3tPeTm6LpVaXfnDxR2OLS2QSWUeqeJQbGhkACoEQlOD0rXbd3n3g4vUGy10XaNUzFMqFhWL4NhMjhVodToEQUjPdem5Lu2uWlnH4w5jxTxTE+M4tn2gf8pchu4/+ftI9QhhCKmwfChZWd3g//1PfoNQSv7mf/BLHDsyH/VyQThSDST6M/gBACIHIBLA9wIuXbnNN777HufPnOAv/Ks/wvr4umJvLAsxlj84wA0W8WLYs0OJ9H3qjRb7lTaObTGeSWFE4R1tBCSpfTNczIiRk+5/Qj76FAehT7XWiMy3Y6SS8QHIVMKSIWEIRpTIjABRqUX2ONF+UskI0KoKuJ7r0o6KJTqdLj3PwzBNxooFHN2IBiwtuoIBo6XZmm0hMym+v7HFd978gM89e5Zf+JkvDljWTjTShVLSi8K6QkCz1uQPfusPeXD1Fr/6xVc5Oz2BaHbQmq3o3y5as4XWbqnXWm20ZhvR6hwc00f+6DPlQgil+zQCHA8A0P7F79+Pft5eEKjJ1Q1VqNVxkLalfrctsEykaSJNQ4E2Q4UezWoduj21bV1HNNsQBGhBQOnBGmN9QNhsDxiv/sHrlsHxIET2emp13uqqRaimoY24KDy2SUhISVaqFX6fJQWQoSQTsZVGFPbrNwvB4vc/GkxEI5sbuaCPmZ4kA09Lu1zlc//w14ht7qrXdvZZ+I//K8U+WhaYBlgmwrHRHRstun6YJpZpEdc1hGWixRyEaTKhaYS6hhmPocdspGUgbAstZiF1g9lej0QiSaBrBLpOkEqRzGUVixsEMCr9c6B9dsDTD00KOcooSkZWSoeu2ig4/7PYhoQD6IoAGX35cOsvavp/IpEyiBiufi8ZzQBTQE79FY2zcqQfDX59MuxRWEE5MIaDZZfCDxH8ePI1/gQ0dai07we2H5rZGuRNHWjqQgqi5HGhDej9g0fVL6dUE6pKjB92qkHVwGOBVh9h9hPXhyzIyJJcfS4aCHs9j07HJZFIkUqmeLiySqfjYpkWiXiMWNyh2e5Srzcp5DMszExTzOeVxlDYN8JUTMgjV1bTEAhM0yQWswcl6htbu0yUipw+scTJY4ukUglW1zfxgwDTNGk0W7iuG6mwp8gXcpSrdZZXN/ClT73d5ulinmarzX61hh9Kuj2PI/MzTE+V2CtXWd/eJ5fLUizkQGhKdBCYm51icnyMRDxGq90hm0lxdHGWp586TSJKNlcVZxrLa5vEk3GyhQyeFDz3zBnWN3d478IVQIGyXC7NxLgZ2XYY9FyXW7cfsJlKc3R+imNLizjxFLMb2zSaLYTQ6LY7VGpKwfrOvRUsy6TXczm2eJyTx45gWubj+5XWB9IhQmjsVeu89+FFllfX8XxlFeH7AbquceLIPPMzk/R6Pe4/XBsAg71Khd3dMs1mG9syKBWyjEeGtId7sQIFquuqVb4K/yQTcRKJGO1Ol0JehWcU6B52y1CG6jgfcx6D8mWpcsBW1tb53tsXMAydX+knces6wbnjdP7xf6v6P6hqx0PgSETb6XbavPXOBf7pb/weT50+xv/ir/x5piZKaiDpLzok+HL4HD0uJN+nyQ+/Vm1U+YM/+TZ//PW3GCvm+Jt/5Zc5dWJpULHYanbpdT3SCYeYoyxP4v/R30Xb2Bk+DukkjWaLa7fucfPOA8IgZGpijHwuw8PVDT66fIN6o83rLz/Lay89Tz6bQ4+I4yAi6RHRcCgFlm2Ry6RxXY/N7T38IMC2zAMWGqAAsi4U85fOJCGT5I7rslcqIF58WimPAyEamjAQQkHswdAkBFoYIlodiICX1u6gN9uIVgvqTWS9hdZqIxsRQGu1EY2WAml9D8vHdIbHBhyCANHuINqdx/SeYRPVusqHExBMTRC22ggkhm6oYMtITsmAB4nU1oVLlLwrFIurj7KzI+CQfojs8EIXhIxG2tGTiBbPYRAOcObwLTkATeLgpg7v+RNbv6J0qKkIhCFaz0XzPLROD+rNQd8e3f5haUtn5PfRYqr+zJQDsocXfgAP11Tu5comfPtdxFde/vSMUiSg/NiTHcmJGuSFBSEDGQYt0ohCgoiAxihZ8Weq9UkOGIYfg8c9Bqo9Zj4fsMwjW+QA4CICU/pBoDVYmB3CCaNgTo72cwYLa3WsASpf7dFQrhrn+wuMx/fYz8pv/hBgSw4p+Sc1QfRA9g+Ywe9yBGjRPxERdfvDDNjov4duknzsozxMnB0mAKvER8MwyGbSxByTYi5JPptmvJSnWq+xW2mwV64SSsl4Mc/UVBEZSrq9LpZtoxvakB3QRvcpBq+bhkkioUx327KDaWjKTDiXpVhUE73v+3R7PZqtNq7nRaKEku2dfd565wK7+xVc12NudoJ8OoXj2NxbXmev3CCTTiIESifHcbh24y69Xo+ZyTFMXdDpqIrF2ckSsXiMsWKBWMxW+3I9ZibHyWdTKqk7CNgvV1jf3Kbb6+J6Lt1ej1q9QTaTQhMae/tKR6fbdXnu6VOcP3sS27ao1xvs7JW5fW+Z1bUdXnn+HF987SXGi2OYusUHF69w7eY9HMsACbpmUG+0kUgK2QxnTx1narJ0IHdlcEUjlqivcdL1Ay5fucnFSzdwXQ/DUAn627v7NFttYraJoWlgWYOk3kIuQ7PVot3p4odtLCNJLp0kFSXijw6WAkAz2NnZw/M8psaLGJoCfLqmwk/xmBOJtfYZnn5PC5AyIPBV9anWZ5HokxvRwkMIdKGTSqYGIY3D63wt6qcAGiHBMIMsepRUf7Zth2IxRzoSHXUc+5HBXxMCUyhtqT6LoVgGVbEjUWN92C9nln05FkEqmeTk8SW+8d33lTp9L0CGqtpKR2BbDpZhYhxmn8NwwMH5MZsLH13hN3//G3Q6Xd549XlOHFskk04yXipiGAa//yff5d0Llzlz8gi5bAZltyIR6EoBXvTVoyWObbG4MMPM1LgaPyKxwuFpRyBVk+gRi65rgsmxPPlsCl1IdAGBQm/ohNH2FTA48DTrGqQTkE6oRaLQ8KOY5mhOyUBxWqiKThkE0O4q8NVsIZqKKdMa6nfRGvm90YRmSwG6RnPACv7ApuvU/savsPkvfxdRrbM4N43j2IO3h4VDoF2+qYocogWNwAPLQJ47iR8GuD0P1/PQNCVuq4zalRxIIh7HskylKyYlXpTm0Nf2M02VU1mp1mlv7bLY9bAsQ3EPQiBNne6ROdUnIpZPRD996yoVFpQqHzHqf/1cOyklLvBm3OJZKclEUiNGpQphtKyWDItGQskjeUNPaofX/I++dbCNyj84xoH7/sn7GZm7BIMK2eF3tSEDJCXl3Qq1Sp14PKYKoaLcP6EJhClB78Pg/pzzZwVwiZGfg00e+tTwj4PASOVO9sGWduA7Bzi+AXAd3VpfU+Lw/h/TGR45xChZvv+NaCxQR9PHKH0Q2V+MDH9DDvVDP+3d+NRga6CR9Snb4QPo+6uPWh5KBFp/wHvSER+iHSECbSMvHQg99vMfdA1DVzkGnu/h+S6NZoNkosDxxUVKxTxB4FOuVlTJrx8wPVlSoAZottu0Oh1sx1ZadqPhz9Gci6iFSLzIr88wdCxDWc5U603WN7aYmiwxXioyW60RBv2ku75Ba5ONzW18z2dqYkyJb0q4ceseD1bXabRUybRlGcxMjbNfqXHx8k3u3F9he1f5IHqeTzzmEI87LCTjgKTbdalWG1y//YAHKxt0ej3OnzlBKpVkb7/K7n6FYj7DxFiOeqPFzTsP2Nurksuleflz59ne3mNjaw9llp1jvFRQuVuez+z0OL4fcuPOA3bLNeqNFlu7+2xu7iKRfOm1F3jlhfN8/72PuXbzHt2ey+z0BOdOHVPhvHA0/MsghNhfQodS8uDhKl/75pusrW8zOzNJPpPGtiwerqyzsroJMmRtc5sjCzMMqgRRujyxmFLu94OQVsQejD7AArVIunztFr/5e1/DNAx+8WfeYGaiqCxgRBSecj06vk8MJZEgpUTq6iF3XVcBv2abbDZNIZfBtm31LIYSQh9N6MRMGztKblbM78EWhhI/sjGxLBOhofJSon5m6Oq7uq4r0BetTwbCelKFtrUoaVsEIaEcWXRoEi2SUSAICX01EYahGlBEBLZ0Q6dYzJOIO7RaSkxWAJoMBwyvput0u11cz4uKR8QQMDg21U5XFYDUm/y5L3+en/vKG+SyacIwZKxYYLxUACRXb9zFtixMYwgKNaHybvxA9QNNAzvS+zIMfZAYb2hKPkQKDU3vpw5IRGTTI4QYSH1YpoluGuhBoHIwxcgUMQgJatEcIB8BUiBV2FhEbLqU+Kr+G8NQOWdS05CJODKVQDKm+rWmERCxpf37MBii+j5tIWGnp4o1mi1oqB/RbKE129BoYf9//xXUGmpMy6T4w5//Sa7cuM3f+V//dU4eWxzoUgVRrqsmwPzr/1vEXgVZzKn97VWQ+Sytf/z32Nmv8O77F+m5XU4em8c0DXb3Kly/dZ8PP77Jc0+d5stvvEQ8FiOUkla7zcOVdTa2dpmdmmByoohpGNy6u8ylf/Kv+Zu3VyiN5YkbOtIwCLIpVv/+f35w0Rs9carqXxtc537Y2I+q8AUavu9z+/4K//TXf5f/Igw5Eo9RyGeUOLAAofXFiSP+QwzH/CH46v/A0Oew/7ccAEAe99MPGYYSekrqBICYNWT3ZJ8geMx8eDjvrh9+PPDRkXkjSln43lsfkkomOH/2JONjBRzHwrYdnKQdyXj8GW2PXAI5PPfBK59Azoxe00NtEL16DJiKNAuGbCDDSMKj2xluCSK8hxxhyVTfG4jUiqGg6fA+juxd9rngUPk6f0rw+xmZrYPmj6On80ltdBE6Wp14+DwO0O1PAHYHEvMGzNnIhoQakPuGs2pTaiXcarcpV3Ty6QSdTpeQgGq9SafbIxGP4USaS+VKjU63h+t6+JEZ7fCAH39coR/QanfxgwDP8ynXGnRu3WN5dYvl1RLPPn2CdCrJWDGH73vs7JbZ2Npjc3uPIFBhMU0IXM+j1e7Qc5XC88raJkIz2Nsrc+rYIqlkgtv3HvLRlZvsV2pRErgk4di0u130ulK0N0wVfkkkYpSKOfYrNX79N/+Ib3z3PU4dWyCbSUUyFD6/+8dv0my1qTeV+fIXXnmOn/zCi2xu7fLr/+aP+Nq332V5dZPjRxfQdY3l1U263R5hGLK+scP7F6/Ranc5fmSeTCbJ9m6Zj6/dZntnj5XVDeqNFvG4w7PnTnBkfgpNBgPWJyAKm0nlRdbPh2q12rz3wSWuXL1Nu92h1W7juR7NRhtNE7z28rOYhhaFmFyq9fogGT+fSzMW+e1tbO3S7nTpdLsH7pdEWTp99Vtv8u3vf4Dn+7x34TI/+fnn+Rt/+RcRKGPbC5fu8X/7v/8jUsk4pbEszzx1kpMnj2EYBqvrW/zBn3yXm3ceksumOXPqCK+/8hxzM1OAEnvd2Nhhd68Wled7A1ao38/DMOTegxU2tnawLYvjRxfQNI2rt+5z6dptioUcn//ceWYmS8qLMxI4HTJsKset1XJ576P3abfbHDkyz8LCnPLqlJJKpcLqxibVWpOYbXL0yHzkSQlIVbW5u7PDhx9f49K1m2xu7VEq5LENDU2G0QpPcvXaLT6+epPQ93nu6dOcOXmUpOyHjQQkE+zt1Vhd22aiVOCFZ86Sz2fRAENTJsfpVJLXX36eUjFPLpvGiMCWBG7ffcjFKzeJORZPnTnG1GQR0zTpZ3MMQrNRSL/numys7LCzt8/U+BhLsxOKyZPQ6fbodntYtkmgqSozTdOwDQspdQXkHINWq61yFE0D27IigVw12NabLba298mmM6RTKWV0rAsCGVCtt0hn0lGepWKApFQhaNM08cKQwPORERk+KlWhCYGBKgggZiNjDmJyjHAE7IVCha2tf/2HiAhskYqhaSDlUIqEKFrQ38fBJGM1XgVBSLPZ5JvffZdut0cmm2F66gj5bJIwCMlm0oyXCupz7Tau55NMKBAedxxmpiaU7ptlomkK8BdyGdIxe5AnNzqhjYKsPqjtH9MwctFfRAt0EbGuUhVSzEyWeObcCVqXblMzDVKpJKallPCVWbQxZEjE6NPUn0MOzCTDyzGIRKjf1cL/EBiSI7PRyiZs74Ohq3s0wlg9dibop8wMjmkEeBxgB1DPVBgiNEE2mySZiFOu1Nje2SOViGPoGqZ1KEXhzwyjRfRAPulNceiDT9iEHN3IcKH9g3b8qHr8oX08Ji+sf1Q6YfSciZGj6/fUcGSBqvXh3BPOIxws7D5t+/Rga6Ry4tHND9Yu6qOyD/5DhNAHKzsZubOrD/eXmBHjxaES9U9i0Qb9+BClOjjAEN0QUQ6PwPf9KOQC1XqTVlQJFotZlIp5QqGkH5qtNr4fROWeIX6fgepT3Z9weUKpmK0gCOl2XYQQJCI3+42tHWK3TI4szqgE5/0yO7t7BH5APpOi1mgOQo6xKDRw6vgS127eo5jLEkrJ5vYeT50+hmWZVGp1pbIdlVznsmmy6SSOZdDueaSSCWXSXMhxsrDE5196liAI+ejSdR6urFNvtGh3erz0/FOMlwqMFXOECBqtDqtr6ziOxdXrd1jf2KFaa9DruZHqtIZt2zixGPlCDkIlPeF5HhLJxtYOzz9zhr/4Sz9NNpPi3/7eV9ktVzgyP8W5U0f5/IvPkIg5hIHKQRGyr8AcTTDRYiMMQzY2d7h05QbNVhtd16hVG5FgZJtE3GG8VODVF85z++5DNnd2SSXjrKxu4vo++UyKXs9V/m2ex8PVTR6urDM3M4lhGINHS9cEmWQcXdd4/pmnOHNiiUI2hROL0eu5hGFIzLFIxmOsbWzx1gcfc/fBGv9hMsnExBhvv/sR33nrAvVGGyFgeXWTYi7D3MwkG5vb/P4ffYtOp0en4/LuR9dod7pDtfyoi+/sVvhXv/0nxGMOC/PTTIwXWVnb5N/+4bdY39zBNA22Nrb41V/8MgtzM+qxkHKg9eV5HmvrO/zGb3+N77z1Aa7nMV4q8Bd+6Sv85BdeRkrJH33te/zuH3+bdrtLIZ/hL/7yT/FTX3wV21IWPLdu3eWjS9e5fW+Z2/dX6LR7BBn1HIho4fLehSv8D//419kr1zB0neXVTbKZFEWJYrZ0HVIJ6rU6nXaHifE8tmUoXa6oWk25OcD4WIFcNj1ILdA0xfD+i9/6A77/3sfEYzY/9caL/NW/+HPkskNtqbAfgtI0KtUy3/j2O3zre++ztbvP6eOL/M2//AucOr4EQrGB1XqT3/6Db/Ldtz7k4eom508f45d+9kuMF8cQmuDm7Qf8+m/9Iavr26RTcfLZNK++cJ7nnz1LzLb5+rff4g++9ha2baNrOmdOHuHP/7kv0el2+R//2b/CC0LOnj7GqeNLPPPUKcXYCsGN2/f4xrffpttzefXFZ3n6qVPYthUNe9HgJRX7ub29y72Hq/9/5v47yLYsve7EfvvYe643edP7fN6/V952VVd3oR3Q6EYTBDAgOBxSQ40UE0FppAkq5h8FQxNBTWhGEhnSDIPD4AAgQQIEwAbaodFdXd3l65V53pv0Pq/3x+qPfe7Nm1mvql9VNyTtiPcy85pj9jln77XX931r4TguYyODTI6P0u50cH3QdJ24txvKChIxoM+Jo2/MbHc6NJstdF0nG+yOxfI+86nWGvwvf/QXnDl5mL//e98kahm4IROPIpXgX3zuUZqtDolEtCfkqAsp+2BZEZzQEQJkNEHp1g0RVr6Gw7muabRtqZO2J09Skf6bXRPrbh6kzDkU4UcUYlGLTDrJPz0wwcmjc/z2N79MLivdD6xYnHgihhZWlHdlLvqBpgjHFiVQ8IW3x3KlG/JHCaTUh+hWncoKzMD3u7MY6v/zD1C+/5o89oj5APz2gDlqD/jr+ym8vvm6C8rk/DUxOc43v56gUm6gaxrJRBzD1KSGXbcKWoTFXg8dtPqbbXtBSn/bg1z5KEh5mC33L0j7odIu0No1Cd+7/R57vO+93cIEuQ0VWZ0I0m2hS8h0JZ0EMt/cD7rZ6f1ged857f/7Y9pDg62HvcT9K9Dde6pbFdUHsMJy7AAR0nDiwTvZF+PtvazsA1r03QBC6nZ1w38SNPm02h3WN3eYnRjhcMjClGs12o6LbTvU600azZZki5CJ2Hqo8NxPOPZxn0C/SKR0nxeqInVnxkcYzg8AARubW9y9u8Cxw7PMTpxD0zQsM8qla7d59fX3GB8ZYHAgQ7PZZmp8GF2TlhO6JgeV0eE8x48eoNls0Wq2MQyd7UIJXdfCfJgcpq5Ra7SIx2LkMukwoV0lnUyQTCbIZlJcvXGH6zfvsbqxxfd//Dpjw4NMjo8wPDTAysYOQkA8HundWtmM1LB56XNPMDM9zuWrt5lfXEUIwfTkGPFEjFjMIhcxGR8dIp9LkUzGyKUTOI6LaRi8+OxjfP65J8jn0ru6PiFY9rvKvkJAIHA9j3q9yaWrN5mfXyFqRZibHsMwNOqNNgtL68SsCLVqjb/+yZtUazVKlSqD+QwvvfAYpXKVQlEyk6oqyKQSVGsN1jZ36NjSozIIdmnoiGGgCHjk1BH+k9/5Bl67jRUx2e508DyXcyfm+F/9/d9kYX6Zf/WH32Z1bZNyucxANkm5XCURszh1/CDpRIxk3OLQ7ASBY/PT197h1p0F/vY3vkQuk6FjO7z+zsXQbzPsgiCgUq2hqApfeOEpUokoEV2lXCqTz6V56tFTzC+usLC8yvrGNjOT471nyfd9mdfWcfjuD3/Gd//6NdodG0VVqNxb5JWfvs3Zk0eIWBa37iyysrbF6PAA2UyypxyvioCF+QX+9C//mtGhPL/9G1/m1t0F/uQvfkTHdnBcD0VRqJQrvPr6u6xt7ADgqz73l1YpFMt7VpFBLEqn08F1ZTjPcxwCzyVQBMIXkpFCTngR05QMjeeC53H9+m22twt88yufZ35piY2N7d5E3BvuggDPd3A9m5W1NRaWVzh6eJaDc5Pcub/E0so6h0NmUAgZfpyeHOXE0YPsFEr8+LV3OHJgjoFMlq2tIt/+3ivcvb/EC0+fJRGP85M33uPf/tn3se0OTz5yilazyebWDrouw5jD+TSObZiri3oAAQAASURBVFOvN7lzf4WtnQIbG1tsbW4xOzFMbGSYO/NL/It//cfcm1/GdlxW1zdIxC2OHTn4EWbC9TwuXb3FH/35D9jaKXHy6AF+7Uuf44OL0tPvpRee4Uv7wVaPKJGTg+f7bBbKfHDhKhev3ODwwVm+5bp7rKOEUCCQlaaZdJJE3JLCvYoiF8SKiqYG5HOZ3SklvKyNZov7iyusbWyRy6QYyg+QSkrNLa2/SEHsnt7iyhpvnL9EKhHn7MnDDGQzRCIGXgA7hRJ355fxfJdjh6dJJuL4rrTfsm1pPF+uVClXqiBknlgXWLmeR8e28Wvy/KNWhEioS9gr+ujOJYAXRjk+Oq10a14D8KVggRJWm0ohbKmPRtvudiCYJn5YjYrSxVTK7pzVNaV+EAAJLej2zmO7YEszNNIDWdK5tFxxhs/nrgGy+Mi98//L1sufemAFoGSIwk/2Xun7cm8bu+Nw+KHAQ5Y3PIjh6gsDPiC16Ocf9Me9HoKrrrgZElsEQvSg3UdDk0Hf4ckg/sO0X75dTx81GPQ6Xia+qiJciXVvnp+HCD/mBvsk6i4QsjowjKYSBAG6rrG5XaRSreG6HhErQiIeo9l22ClV2SmUKZRrlMNVue24ED7cEniFN0YQQN9F6Z5ll2UwDR2QhrWe76OqKsODA6RTSTQVLl65yeLyOslEnIFcmvHhYeYXVwl8j4FMkkQsyr35FdY2dljbKrJdKBG1TAJgfGwIQ9PCiiyfbCpJtdZA0zRarTZ2x8a1bdY3C7TbG5QrdUaGc+i6RqPR4sDsJI7jELMiPHLmGKfcQywur7O8usHmhQIjQwNEo5YEnTUpMGpZJkIRrK5v8+4HVzAMjenpYSaXh3n3vStUKtWecXBAwNhIHj/w+fd/9gMS0Qir61ukkwlmp8fJZpJhwq3bmwwDNQjvW4HnB3g+NNttLl25xU9++o5U2I9aeJ5HpdJmcXmDWr2JAswvrrC+uUMkopNKximWKhw6MMXhA9PML65yf3FFFjm02j1g0p+T0427e770a2s1W+A6WGboTSk8BB6uB/GYxczkKPlcmtXNAo1GC8dx6XRshvNZvvHVFzl9/KD0zIrHWVha5fqteQYHssxNTZAfyPLCU2e5evOeDC31hQC7VYQj+QzpRAzbcXjszDEeO3cCxw3Y2ingb3gEnosQ9IASgFAVGo0m12/dJ2aZHDkwRcQ0iRgap08exTJ0CXY8F01VGcoP8Ksvf46nHj9JLGriOS5rm5tUqnWee/IRDs3Jye/1tz9ka6ck9ycEq6vrLK+u86tffJZUKoGha4wN55mdmuzZFAH4sSiu69Fst9neKbO5VWB8ZFBa/3QnvTCfqtlsoSgKUUMjQDAxNsx/8s0vkR0Y4D/8RZGuel6PuxHdaxegqgoTY8P8zt/6Culkig8u3GB5ZZ1O6KMIMoQ7kEnyxecf5+Qjp0haJv+Pf/nv2Nzawfd9rl67yfuXrvHMY6f43b/9a1jxGKah8Qd/8j1ee+cCRw/NMj0xysRonlNHD/Pis4+TSSXIplMIAblMEtPU+C/+s9/kwMwkQwM5fNfhzp17lMtV/sHvfoNrN+9y7eZdNjY2OXF4jv3cuBCCmakx5qbHubewSqVaJ5tOUa3La/ryi0/3lPkBgnhsb4AmCCiWyrx9/gILS6t0bIcLl6/z1XYHvTtCdSNZQoSskwyTigA0EeCLsBpcEQihsquiLYVGX3ntXb77o9dZ2yxw7sRBvvby88TjUaBriRYyDkLmWZYqNf7wT7+HHwRsF8qcv3iVf/A7X2dqYoQPL13jOz96i63tEo7r8uWXnuALn3sCoWisbxb48NJ1rty4x9pmgc2dIp7vE49GUDW1F20obm7xvR+9ztZOkSceOcWXXnqOXDYt/UM9D1VRUDVtz1zth+E6IRTpt6gqOI5Lrd6k1WoTtUxScYtKpUaz1SY/kJXVth27NweJqMxh83wfvVs+2wVDnwS05LeRCM3bN+mLntuC/Loa5oj3g43//wFZe1o3hPcg9ioM737kyPezUA8ETB67/dh9+j8d0NqTQ9X32R5ztee1bni/7zOE5xZeb3/P5/v3HdCfZP/z2t+cEXWvjwL8EHDJ1/tYrI+7Nz+yrQezWw/6nAi9nvwgCEVJfbzARzdUspkkO4UKb79/mTfeuUDbcdA0BV3TaNkOlWod27ZRFRVFVXFC49Rmq4XvuZLS7Zbth8Si6wd0OjaNRlMKCipCGq6G5dq6oaMoClPjw1imzk6xwtZ2kXvzK9RqLTzXxbJMGs0Ohw9Mk07GabWlP9dOoYTjuMTjUVzH4633LlKrN4lZEc6dPsqZk4dxHIdW2yZqmQg/wPMU7swv88rr5xkcSHPmxGFarQ5rm1s0m9Lc9bFzJ5meHOOJx89RbzS48OEVbtyep1ZrEHgem+UKlWqVdDpBNp1kYXGNS1dvc+r4HPl8iuHBNF966SkiYWhke7vIB5du8N6F6yTiUVZWt+l0OgzmMnzp809xaG4aIUSYi+ag6QZGRMcwZD8HgO26NFst7t5f4Ht//So37yzi+7KSdGFpjVbbRuZRadQbLbZ3Sni+hxwPJVi7cv0ulWqdZrNNvd6k3bGxO/J73aIDLzS+FcjJyvM8bMdlfXOHzY3tnop/N9TRtNvYzZbUWfJ9XNcN8/lsXM9jfmmNDy9cZWZyhKHBAQJB7z3H9Wh3HHw/wDBNdK0rlNtPb0O71e4Jniqhm8HmdoHVzQI3bs+Hnggy7OT3iex1+7TVavHMY6f4vd/5FrGYhRZ4WBELVddxvA5f/sKz7JQq3Lh1n+9qGpqicPL4AeKxCLVai3qjSblSpVpr4IR5ioqioKsanueysr5FtdbgmSfOMDE2QhAEDOezaLol7VXC5sajWJaFruvcWVjlg8s3mZ4cxTAMDF0aMfuBz4XL1/nej17niXOneObRs8Qsk8GBPPfm1/gP332Vyzdu8eipwzSbLfxsdzFDjwHVdZVMuNj4y++/ypvvXqTRbIWJxHLwbLZamKZJ1LIQ7Q6JeDT0R3XD3DeZFjE+kieiaRhBwMzECNlUnJ2dEuVKTV4v36fZajM6lCefywCwUyz3TNfHRgaZnZmAUC3/kdPHOHpolmwmxfziijRfj8fQVAVfSEHO7limKArDQ3mefuwMV27c64XPxkbydDodDsxM7KmI82NWyNTLe9b3fVqdJrGoznNPnWNru8zP3jzfyx/rJoULBFpY7GB3WmjIgh436Eor7LINBKCGWXIrq2u8d+Eqc1NjHDs0w06hRK1Wl0N2AKof7GIMRcFudyi123zhhcd57plHuXPjDivr28RiJo7TYXVtg4FMki88+xivvvk+b757iSNzk+QHBnjjrQ944/wlTh6d48lHTvDuh1e4eXcRVZVhZlUIbt6Z5w//7AdETINWq8N/+PZfEYtafP2rX8BzPVzPQ+hCciN957SzU+T6rXvsFErohsZQPsd2scCHl2+xtV3kwOwEv/WNl3Ecl/uLKxi6RiSSR3T1zQAsU1Yz78ZR908+P2dugj06VP3fEaJvSOgCrb7tidB/8dM2RXRDTZ/+u5/UgoBdBirYC5o+TQhs//JD9L3ZA/LKQ29SbkN8DONGWAn74L7oKST0jkBIIerQDL17eP1BTfGQjFa3/WJgaz8tCj1qVNKtUkkZRaAqfclkYrczehpZXdTZ21z/jRjs/v2gztqTLNknsBjG85vNNpGIybFDU7z5ziUWltcxdI2WbaMqUoi0Wm/SbHVQFTmhCyFzvcqVOts7UoU9Zpiyw4UPgcBzXVq2S6PRolptygqoMJ/GCSdbz/dpdzo4tkM0ajGAIBGP4nQcyuUa1WYTTVFYWF7D8z1mp8aYGh9naXWDrUKJiKEzMjLA+OggnXaH9z68yrHDMyQTMWxbJtPvFMtEIyaWYWJZESbHh7m/tML9pTUmx4cZHcn3wMXmdoFrN+5g6Bq6ruN6Hq2OTbFcRQjID+TIDWQpVUr4QcCBmQnSqQRb2zusbWwxOzPO80+do9V0uHt/iY2tAq7nkUzE6HQcqdDt+1imweeffYzPP/8kyUSMIAiwHZdCpYYQAbG4RSIex7QsfDzqzTrzC8v81Y/f4NKV2yiKIJ9LYZoGtZqUrxgeHMBzXYIgYGgwR7FcJh6zGMxnaLdtNqs1Ll6+RS6XJhGPSfmGIMDzdwdex3Gl92IggYttyzy7dz+8SrPV5ujBKZ5/+hx+4FOs1FCEoFZv0Gp1pO+iF9Bs2niuYHx0mA8v3+Y7P3yLrZ0y46N5Th0/FFa0CuaX1nn7vYscPjjDxlYB23FxPW/vLRwENJotao0m6WSCze0Cf/FXP+X2vWVGRvJ0bBvP9VhYWedIuYLndWULhBRx7dh0bJdCqYLn2IjAZKdQwnV3GBsbpVKtgu/zu7/xZX70s3d454OreGFl3vGjs3Q6NpVqnbfeu4SiKjihnpUQCq12m8CXfpL1Rov/+L1XGBkawrEdMukEj549zRO20xt2/WiUZCrJ7PQ4qxvbvPXeZYYHszz5yEmSiTiBorGxtcN3fvgab56/RCIW5+DMNM1GnX/zp9/jw6u3mZkcY3R4kGK5ylahRDKVlAUrzQ4ocmHTbjtcvnKdP/3Ln7C9U+75ZDaabdqdDlFFVklaVgRNN3BcPwSlMmneDwJUTcN1PeqNlgQwvi/zE22HXDaGFTGph2NCqVLtFYQoiiIH324eme+HjLdkHePxGKVylbfOX+DKjTtEIibJRJxutazr+XiuixOa/ipCYXJ8hLmpMe4trvKHf/Jd1je3eeqx0wxk0z2w1VOhB5qtNpVqDc/zyOYynDxxhLWNLa7dukOpUpO5R30MgBAQsUzSqRi37iywurbF2OgAXasj35diwPV6E7vtkktJ2ZnBbIpvfPkFYvE4t+4uUKlUZdVs4MswZNDV0xK9Md3QNeamxjA1haMHJzk8N4Hr+QS+z+eePI0XCIJA49K127TbTVRFoVZrcHdhhXjM4tnHT/PI6SNETMFOsYRtd1BwUBUD33OZHB3iSy89y9Ubd/jej99gfW0D3/N2iwsCPqIhZzsON2/f59U3zqOqKudOH6VYqfLBpRvSO1dTWF3bJJVKsLNTpFiukstl0PrAlohZsjptd3LZM+ftmQMf2EKCQVH5qMWNKgObwa6Mwe4M2QUcnwJyCOiFOPvJp1826KJvu905OmDXiufnHeMnfaaL6PeA04fMWesDr2IPkO077H19odAvpyz37YVzR4/ZCmGGgpC+iF09wJ9/RMAvjdnai8S71TwBUr1dZvfvJ0Y/4RCFePDPbtuDpPvCkj2RQh9d1xjMZ4nHLLlidz3mZiZJxGPs7JSwLJP1zYIMmzVbXLx6l3jMYmpiRCahl6tsb5dkRdn6NtVqjWg0KpN9hWQ97E6Heq1Jo9XB9wLi0RimaeC6Hq7rSUkG18UyDXkPCqkFFQQ+ih9gmjqJuEzMXd8qcOXmPRzHZ2hgkHK1Ttyy0DSFVrNDu9UhFpGVT512m1KxxPpWQQ6SrQ5DA1lUIT0ax0eHOXn0AHfnl4jHomRSSYrlCrVGA893KVXKfHj5Ool4jKnxUbLpJNl0kp1imVjUZGJylHghwtr6JoVSBUVR0HSVtfUdmk2bfM5iYXGDS1dvs7G1A0FApVqn03FwbAk4z548yuOPyERjNzSL1iM6lqVTazRpttoYkSia72O7Hba2dnjtjQ9474NrNBotMukkE2PDHD44zd17i9y4NY+uKghUFCFIJqNUalW2tksk4jGi0QixaIRWu02j0WJyfAjL1Nh0HIQbYBpGb1IOwmRrz/cYzGcZHsxSLFX54PINgsDl8MEJggCq9Radjs2l6/dACJbWd1CEQqUqDXjPnjhKqVTjjXcv8vo7lxgfzTM7OcHk6CiTY6PcvrfM9378Jh9euUm90aJWb1KrN+VqKXwEAqBca7K6WSCVSrG5XaRQrLC6tcN2qUKrY+PYDq+/e4mpyfHevby1U2RpaZVWu41paFy4epv/97/6IzLpJIVihVwmyTe/+hLzS6t890evcejAFIauY0VMcpkUA9k0qtDIZdKkUgkuXbvD4so6ruuxVShh6DqXrt3mwMwkE2PDDOdzvPvBdeKxeSn6Go/ieHCu3e6FrBxLx4rqPPfUWXRN4f1L1/mz7/6EheV18rk0tUaLuwur3LizwOBAmnQqztZOgTt3F7h66y7VegNFFZimTqVWZ2Vti3QqyY3b89ydXyEet1hY2iBixHj7vWtcvHpH6qvp8vMXrtzk5NEDjI8N02y2qdbqrG9u4zgO712+xcZOmdXNbcrVGtFoDEVVOX/hOsePHCCVSnD+4nVabUdW1Sbi3JlfomPbtDttag1pzK4Cru/hB+B6Po7tSBWTQFCpVfn9f/cXXLlxhy9+7kmGBrI0W206tk2hVKbTsVle36bVapNJxYlGTHRdJx6PcvTQDNfvzPPDn77NcD7HgZlJOb65rpzPNAlpXdel3ZYAueO41Fp13v3gCusbO1SrdcmqdwF9GBJUggArEuH44TneePci3/vR63z9K8+TiEclCG1LJvj8B1e5d3+FL7/0NMcOzfaS+a/euMOla7dpNBo4ro3tdOjYHdRu+Csc1F3XI0D6xnUaTRzb7smlREwDVVVYWt7g/MXbnL9wnYPTI+iqRqMplftVRVbGttodmTPouFSqdeqNJlHTYnpihHQ6ieMF3FtYCXP4PHzfCxX+PXxN2zuRCkEiHuPwwRnuzC+xtLxGqSwrlzVV4dFzx/nKy88zMzXK1naRSrXOTqHE+OgwZrsjpxNDlwUgvW0+yJP3IVsXqAVdhru7nX4/wD2Bs8+2D9H33R5w+QzHu6/t9q0Ehv3MqPzpy+iP4BMjUg99Vv0fDPoiZA/1xS5c3e2HbhhxVxHhYzpFSAkQP+iKp4ueT4hQFOKxSLjw8j4xram/fXaw9XNQsqIIZL6i+Jj7UnZGr2A42E00l28/JIL9CBCTmzY0jZHBHFErQr3R6lXrHD8yh2noVKp1YtEV6o0Gy2tb5AfSjI8Nc/rEIcrlGhev3GRHlGi12mwXSlSrdfIDuV7eSeC7ciBuNXFtH0NVGMplyKQStNsdgkBqNHm+h2UZCFxcOyxdFgJMi0wqhR/4dDoOuq7hui6Xb9yh07E5c/wwI0M5Vte3abY63A2WKaYrqGFpe73ZZGl1A8dxGRjIcmBmguGBLEJoNNo2iXhUChcSMDqYo9FsUq83ScQtTp88xMLiOt/+/k+Zmx4nP5AhIKDZbrO4vIqiq3Q6NvVGSxYM6Bq6bhCLRiEQbG0X2N4pUipXaDRbpBIJHNulVKpiGjqPnjnGN776ecZHhmmFq0MrpmFZkXBQdnECScm6rkelUuW9D67yznuXqVQbKGFZf6vVRhAwOpTDdRx2ihXa7Q75XIaDsxOUKzWWV7dY39zh4Kxk4GTlpI+hqXiuQrtjoypaz2JFUXfL0hWh8OQjp4jHonx46TqJhMXZk0eZGBul3mjw1KMn2Nop0bE9MqkUj589SbvTkZWfkQiH5rKkknGmJ0ZZWd/iwOwEZ08dJx6L8vnnH6dUqXLl+l0KpSrZdFLaQ0UtWbYfPj4R0+CJc8eZGBvG8TyqjRajo0N4CNqdjizHb7RIxGNs7RRZWFqj3mxxb2GVDy/fYGRogNMnDlKpN3n7wjVUIcikExw//DyxaJR8LouiKHz7Bz9D06Xh+OeefoSJsRF0XWNuZpLnnjiL60rJEjNpkMukZNVssRyGfVUGB9JUanX8AKyIwVOPnWZmapTAcegO5LYlQ6VHDs4yOpxnfGSQN85f4L0L19E0qQkWjVo88/hpzp06ytz0hATdpsa500dZXd9G11XGRsaxIhHGRwcB2C6VcX3JEq+sbpBJJ2nbbXRdJR6LkkrE6dgOqxvbbBdKqLpJtdFifmmdDy5dZ3xkkLfeu0S92WJ1fYu1je3wvB/lx6+/xX//P/4bEvEoO8UKs5OjnD5+CNt1WdvYJmIYlCo17i8tMzY6iGmatBwbw9BwWg4bWzu4rouqqKxvbvPOB5fZ3C5y4/Z9tnZKlKs1fviTN3nikZM0m23+8q9fo9ls8fyT5zh2eJZUMkEiEWNibFguikpVjh+eZXJ8BNd2dnPiVJVmq0O5UscNUxvaHZsbt+7zo1ffYXx0mIhpUqnWqdYapEwDzZdgSwQBmqbxpS8+z3ahxCuvvUer3eLcqcN4nk+t3mBpdZMf//Q9FEXwhedcdgpl/uKHP+Nnb18kmYjSbts4nkO7LWVYNra3cdvt3vgeIMFWw3FYWd0g8DxKlWoYgpX3/R/9+Y945c2LFMp1OrZDJhlnc3MHVBmuLVZqLK9uEDEN1tZ3aDTb3J1f5fbdZcScxt35ZV5/9yKlap2dQlmmcNgOvufR6cjE+kwmhWJZIQshqxV1XSOXTTM2MsjC0ioXLt8kAOmsMT1BPpvCsiI9wemt7QLNZpNMK2S2TKNvepHEwmeEWn3zFh8FP6ILwn6BpnRZtz0bDt8L/ww+G/DaZUu7//o3inxNBLsA6W+ESfv52xRCyqTuD1H23BD2NUXsHvYucAoIcAkC6cbQey2QpEksaqF2fZofsv1SwFbPWmHPeewFWTIE+5C3aH/Y8EHtYxFzVxVbQCAfwq6uzc5OiQ8v3SAIAtKpBKvrW5TKVdqdDtl0kt/6xsvE4nG8MJxgOw6uJ6tZXMeVKyjPJ1DCOhYPPMfHcaThre+BF+aB+J6HbUOxKJOEc+kEAVI0Utc1KdCHIsupHQfqzTD8IhgbGaLd6TC/ukajbRONWTL/C2h37DAEojI2PEit1mR5YxvTMEilkqTTCYqlBo7jIBRBvdHk/Us3ODA9zuz0OGvrm6xubqOpKudOH+fO/RV2iiXato3j+qAIDCtCuVJjc7tAp21jGDq6rmEYBpqqShBaKLGzU2KrUJYTtKbjOB7ZdJKnHzvNr3z+GSbHR2TlZLst9c4UBU2ROkQ+YNtu6JvY4cKlG7z21ods7ZRRFNFTSN/Y2uHt822OHZrhsXPH2dwu8s57V1nf3Ob9C9dZXd/i2KFpIhGTWr1JPGbh+wGObdNqdUKfRAdNDWiGifJmqKPUFdLMZFK88OxjPHrmKO1Oh1g0hqHrZDNpfusbeVotG03TiEYjHDs8R6slxT7joSL98GCeL3wug1AEUSvSU9g+enCW3/nmlzk/c5VsOsWRgzNUa3Wpwv7f/cvuI0IkYnLu9DGGB/MUSxWEonDy2CF+9Usv4nketXoD3w9IxGN4nocVkYa6Uj9NOhN88cVnOHHsMJeu3qZaa3D2+CE+99SjxKMWiXiM3/jqSxwMWd0TR2c5dGAKTZeh8mQyzvNPPcLhA9MkEjFiUYtWq8Od+4tYERNN12jbLgdnJ3nmyXOkUykiEYOoZVGu1lD6gGMQs3rVu4ORAZ575jFmZyYxDI1YROZtReMJyYo5Lts7ReqNBkODOaYmRzENowfGY9Eo6VQC1/N58bknmZudwjBURkfyaJrK7PQ46WScuZlphgcHuXT1BqqAqYlRDCvKN3/1izz7xFkG81mSiTi/+fWXefT0Cp7noyiCWDTK5z/3BJouKJUrGLrOU48mOTA9wUAug+t6PHL6OIfmpilVG4yNDUvTcBGQyST51q+9hKqqHJydCvOWAjLpJM89eZZbdxdIJuIcPTTD0sqG9EENgZCuqgghn/1u1bPnSSbeNHVGhgZ45omzpJJxOoVyLzsiUFXKlRrbhSJWxKRardPpdMCXuV+VSh1VU3tWRn4YItzNvgg4emiO/+Lv/Savvfk+V2/e5fqteZrtNu1WJxSthTPHDpBOxalUa9y6u8jC6ibTY4O4ritdL9o2jWaLhcV14qFdTtd/1jB0sB3+X//62xiGRrnWYGwox3/2W19BCMG9xTWW1wuYhjSE3yyUuLe4yuz0JOlUgtvzy3z/J+8wdPkWW4UShXKdZsvm/Yu3cByfn771IReu3iGdivcW8+VyjXZLWoO9df4iuUyawwdnSKeSGIYeaoNJOYl8LkM6maBSraOqKpNjQ2RSCVzbkRIYAfKe11TJyHWZLWuP4c/DzWE/r4k+FXmgJ13xsPnJD2pdoNXPagHsVkmw+7B+xn10NxEWigViv5n23wDA2tP62cBPaDLfp3c8HxdO/Ni9BKEfqu+FRUx9jKMQqLpOl9R7WFjz2cDWgy7UvpjwfmpNdKlM6Mut+owUaf/Pj34AANdxKZYkE2KaOnOzk5w7fZRms8UPf/IWG1tFcpkUVsTg7KkjHJybZHOnwvLqBts7RZrNNq4rfdjkJCR1uvwQ3Xu+T+DJAc1zfWzbDcFFR4I0oFypsbq+yWAuhRUxUQVYpo6qKjQakip3Xa8nuGhZUaYmMtiOy6Vrt1lcXWd6YkSW+Lds1rcKCBH0lMPbHcn+2I6k3OtNGe7QdY1MKk4+l8E0dF55/TzFchVNFdy5t8SPf/YuX3zhSaYnh6lUGkxNjXPjzjwLKyskElFc16VYquI6Dgghk8htF0UBy5Sl2IMDOSzT5NbdRZZXN4laET7/7GN86aVnGcxncV0P23HkQKxqvWvf3Vaj2aLdaXN3foHX3/yQpeUNKeKoa8RiFgfnJtjaLrO8ssGla7dxPY9UMk48FqHVarG4sk7HdpmaHEFVVa5ev8fWdpFWu4Nj2+wUyyTiUaJWhHbHoVSW+ThRK9LTepPWLgoEAZZlSL9IEdLUgYJpRNA0uapVhEIsGsU0TPwg6Kk6B4qCGZHl56qq9Gh2XdcYGc5z7NAsXauTdEraJfU3JVRIB5k8rqkq8WiUeCwq2dhWG9NQiUQMaTmVTqKpKpqmylyvWoNUKsHE2DATY8MsLm1weHaSTCpBgGTOjhyY5fjhA2QzaRQVOo6N7XpSkV5RsKIWw4MDRKwIlmURjXZwHIdIxMTQNQxDZ3R0mJHhQcZHhjBNIxT+bUuwFT6K8Vffwbg1j4jHCOJRFF0jZupEhweJJJKIZByiFh3XY2u7yE6xBEAmkyKdSmAaBr7vY9uOZIAVBQXBYD5HPBZFM1Qs08R2bA5MTyNmBJm0NDQfHR5E1xRU3cAwDA7OTTM7MwmBfF4GBnIcOnSAWr1BMh7H9jpkUjE+/9zjxGNRKQDr+3Q6MvSv6DqjYyM9LaiuqKcQgmw6xa98/hkikQhqWFkrhCCXSfOtX3uZer0RLqRc6o2mlLkA6o0mB2bGWVhexzR0NFVD07VQ3kUyQxOjg0xPjslBvNHcvVFUlXQqwZc+/wylcpWxkUF0TeX44QMkkinJLDkuhVKZkZ1/h9ns4O8bIl3HYWp8hN/+1ldYWl7j6o27LK1tIIBEPEY+myRmRVBVFdMwOHfyMK7nyfwUmQxFJp2k2WqzuLLOCU9Weqvhs2AaBkMDJrlsiht3lkgmLGYmRjB0E0M3ePqRE1RqLanlFbMYGsgwOpxnZDjPicMz4SKvGBYBWQzlMqSSMdKpOJ1OB01VGcilSMWj1BotAhVa7TbNZotKucLb712iWKrw6JnjPPnYaQ4fnOlZGsXjUQZyGTLpJKsbWzKFYmKUmBVDJq4r4RgcIZ/LEo9FEV3ph4jB/zdaryquH3B1858feiO9//a/yC8KhLoaVEF4L9B1/FD6QokPe6g/J8z42Vp3W5/cZx8V/+3bQq+wQhZEeaGMTf/HZMrmpzxf/oaqEUVYFRgEu27xvWx/0RPa3/elPoD2WeLh4cnLcHGA67iUqzVcz+PAzARPP3GaU8cPc/nqLVbXdljb2sH3fOZmxhnIpanWqiyurLC2sU290UJRVbkCjlkMDeaIxWIybtvN+fGkuq+uGLhKgKL4mIacdBNxyShoobWKH1qduL4ntZ5C0+hmsxWaGSuoiko0qpJMxImGuUcLy2uhlpKN57mMDOXIZZJMjg5JurtQkvlUmobv+0QiEUZH5cAWEDA9OcLE6DDtVovX37nAwdlRjh2eJp/LsLaxxdb2DtuFMh4BlWqVYqlC1IowMTbERJiQX6pUKVc6dEKLFsdymRwdYWJ0iOW1TRzXI5WI87lnHuHlF55meGigJ7Gg6wZ6xJKCsoro5UohZBXd5as3OP/BFTa3iniej6HrxKIRouGAn0rG8UcGqVSqXL1xD9PQKVVqRKNypek4MkdlbnoC23a4duMe9XoDVVXQVJVYNEo00sLzpF1NjyYWoG4VUJfXyTz/2/KeFFJgNeiu1BURJiSL8HfR+5y8jxUCJVSJUaSPWbcgo6vTYno+CcdGCEUySaqKUBSMuwvS4DbMbVQUWZLuui5CCDRdlSA8BNOKouL7AbqhyUlf1xFCejdqmgQlqqoSj1nEYhaeJzWDuu8ZuiaFOVUVx/epNWQIKh6Lous6tuNRa9n4ioaq+zRaNtV6Ey1kYa1IBE3TKJerJBNx4kEgGTdP5h4SJv/qV26jXb4dCmJCNHxWulWW3b43w+ub11T8eBQlFcfIpFEySfx4DNcyCWJR/HgMLxJB0VV0XccwIqiqhuZDJOJJM2ZFodOS3n1CUeX1o38wlNp3AWAYBqmkgmEYBEIWxnSZuC6o8nyZT9dlo7oegV2Tc0UINFPv7bt/UFdVlWwIHAmkBlYumyYIoFSu0Gq2qG4V6ISeqM2WfP4t06RtO7iutOtKxKOyEKLW2B0eHYfR/+qf8jXPw7alaLIRakwN+UFfnrKP1rYfkOYaYNs2gS/Zr8mJUXw/IJtJEY9GMMNcUjf0SIyYJs8/dY4zJw7hBD6u5+J2OiQTMVY3i2TTSQ5PjxLdKCHCZ0FRBKlknNNHD9BqtTl6cJqnHjnByGAO09R57omzJFNpllY3iUZ0ZidGZLg0EAwPDvD8k2cwTYNMKoEQSmhqL8eKRDzKl196io2tAqVyjZ2iLJ44e/IIkUgEz5UyPa12h8vXbzMynA9FgGXgTxUK0YhJMh7F0HUGcxnGR4elnZOioioqngudjouiaKhCg26CvBXpozA+hmj4ZRFeD5gDd82Ug08GX7/E4/hU7eP2GQCBIvOfHjzhf3qw9TGX4IH7/pjvi2C3n0Wogba/T/1w3vaCrhRK/4aDnmbg3tDjJ7dPB7b2JMI96DweFBHdv43w32dNMNx/LPt+l/5l8opoqkoyEWMonwME7bZDJp0mm07RarUZyqcZH8mjaRrb2wU67RamqZLNxGm2WpQrNVLJBAO5jExs97ueXvIBUFWFmGWiahq2bjM2NMDgQBrT1BnMZ6UOlq5hmgaRiBkmt7YJXA9dUxnIpQFBq9XuhRUsK0LUshjODzAxOiSr/GyH44dmmBgZpNOxiURMrt++j+O45HPpnlBlMhEnnU6iCCl1cOf+Er4fMDQ0yPU7C7iex7NPnuXA7BSNZpullU1UVZXijFsFrEgE23FIJmJMjuSxHYftnVLI1nlUKk2WVja5v7QaGlfXyefSfPGFJ3nhmccYHMhJ4OD7CFVDi1ihQGBAIAJZiYVCq21z4dJ13nznIjvFMqqqkEwmmJoYYWZqjGw2QbVaw3E8ZqZGUcUYi8vrLK1uyr6KmFIg1fNpNttMjg8zNTFKxNB4/e2LJONRjh2aw3ZcVla30FSVbMic9BwMXA8cF+G4e+6d3cXRZ78/u99UgwAjHAFF35siLCJQBQwurpH6P/33qJEIMd9nJAjQrQiqFSEZBGQDH3QdYeh4qooWMYkk4yiGjuYHRBWw4jEiiRhqEKA0WmhWFAayCMtECwIifoAetRCxKIgAu1LG8QPcXIYgGqUVVswaho4TSlu0bJdm28ELIJGIM5TPUa03aDZbeKG4ZMSK7J5XQK8iTA0UeoKA3X/dyjgESqNFxHFQw/C4ELICeNfzLggf6WC3sg4gFiWIW3QiEdq6RhCPomZSdAwd3fcRyTjxUhU9m8GPRSEexY9FCULGsWtxoyhd3zMNRfi9ax0gGUwJ7HQpERHeE/3PvugNzmJfyoZAERJs+4GP2le2rqoKdqvNP/yLV1FUDevGAm7EpKmqiESMwUaTbxaKHIwnSP7V6/hRC39tS+ZsdUHrdhEEaI4rzdLDquluyrAfzgoiZDe6mlkilCzptNtosWiYIyvTETzXxXVsTF2Viz/fJxIxCIKAWNQiGrVw8Wg2W1QrFQrlKo1Wi5PHDnDcDjBLEhAFfQvqjm1TLNfYKVawIhHJEgnIZdKcPXmE0eE8vu+TTcXRDAO7Je3SDsxMkkkniZhGj63f3C6wvrlNOpXg0OwkRw5Mc+vuIvXmMKdPHmN8bFh6dCpSwHg9YoQMuYwg+J4Hnku9VqVSLuO7LhFdY3hogKgVoVguk82mCAKZprG1WWB7s4A9PhL6twqZsxV075BuOEndk9/zYKDzoDEk/Oynzc3qff6T0mseZkMPi1YetomPTuc9nBA+z4FKT/hT7P3upzoc0d32J0e1ZG7ZXomdT97sLm4JwnFMkir+R6oW5Yd6/32q9umZrQfsPHjInfcYzgf01aeiSj/mWLrXQq6mVRKJOKqi8sGlm5QrdfyORyIeRVMFwwNZJoaHSMYitBoNtgolnI5DLBKh3ZbhPUVRMA2dSMTsqRT3C5xpmkZUUdHNAM/RGRnKMziQZXV9i665bavVplarYxrhw+96VCo1TNMknZJAr2PbBAQ9mQFVkXH3REyGk04eHePJR06ytLzKjTvzDA5kaLXbGLom9XvCJNdavUEsFg0NqS1cV1oFpVNxRocHaLZtllY2SaeSpFNJRkfyoCi02h0arTaOK01X640W9UaLSMRgIFyZG7q0IsEPeO/idSqVOmMjeb700jN8/rknGMhlpJggUjCxq3XjB1LnzPN9fN+jWKry7vuXefe9yxRLFRQhyGVSnD0ldcMGcmmEIlhaWcdzA0xdI2IaRKMR/CDg5u0FCCCXTVOuyLwVhYCDs+N4TodKuY4VkflVC0trWBGTmKqSzUgQLIQgSCcJDB0RVnpJwzk/jMH3A3exe2Ptue8++ms/ex/sebM7OIu+z7nduxXNdlHnVwDQ/YA4eydL2DeQ7VmFdSdXOWhFgGR3l0Ievw5E+7hkk0D6GbZtutYXw1GLoBsOAyaDgBNhsYoS2rYM9wPRvgdOqTUIY2AY69sEioI7NoTwPITno4Q/he/Ln56H8DxMx8Vw3R5h0AUvvU23O3QdJnzTCPtN9lkkgNS+XIzx7jk+aBxRpAm3ryqyqkzTEJpKoKnSnFtV0da3oatALgBdxz0y2xtP5GbCXD+xz6S2v4XdpPbuI/lz0PP4crlGotkGVcpOuKUqtNp0bId8x2ZUV8lfuol5477sl3oTxZbPpFKpQbOF0o1oaCqKriE0jUBTQJGsaaAqoGmgCgLXw280oNlGOA7j/8f/K5qu9wiFmOMw1WojkHlfXVkHM8zP3AWhQQiwnZ5wqGHoJFc2URstQOAN5+QzBaSTCZptm/cu3+a5x09z9OA0CAWhqSTiXS25Fn4IcLpFNNFoBCsEeqVylYXlNdY2djB0jUwqgWVFJPurKBKwRiPEohadTof5xVXuzi/TaLQ4dfwQs9MTMoToy2KC9Y0tVte32NwuSomOaIStbVnNPT05JnXxOh3q9Tr35hc5PJhloFuwZZoyvUoJwrwoOZkHXZ9fP5DpwkHvwWMXRYg994H81f8M4bOHmB97g9HPo7h+UcDVP+rJB/jBe+x7pgMVhPfRtz+iPfZJu/001N1+bBDsea1nhh7sas0Ffe/1drfnW/1jlBx3hPLw7NwvKYwYIHzRO6r9aFAOpKHgYO/FT9pc0P3ix7//QNAnL4YfAIqKaVk0O9L02fd98pkU0YjB6voWgwNZ2h2HYqmKrmuUyzVqzTbpVIJqpUa1WsdzJevRBT97jTMJB12BogR4QiOVSpAfyEjbiU6bVCpJgE+xVA6Tm01cW1a8OI6Hk5Ul2o4jtZ6WVzfZ2ily9OA08ViUWCyKZRqMj+TJpRMsLHhs75SwbZtOxw7teDRcR+YorW9uY5oGQkggODyYw/MdstkEmWKC9y/eoNZo47guI8N5Oo6HYRg02x0ipoauq3iu36vK8X0f1/UoVxo0mi18P2Bjs4Cm6sxOj/OFzz3OU4+fYSCXDc1hpRiiCGSI1UdIltGTK/3tnSJvvv0B77x7gVq9ga6paJrGkQOTnDt5CM9zuXHjDoZhIBSFVEIqZq9vSpuYuelxVte2yWXSTIwOc39+hXpdVs3NTk3guZBMJFAVgec4DOVSHD0wRcuWbJ2cLH3s/+G/xvhH/xRlYYVA16j/yf9daiV5Lu1qh+31LVq1OgagCYHm+cQNnXgkgt8JCJwOOA50HJxOBzo2OgIt8BGOKwGG6xI4Htg2ge0gXBdsB1wP68dvQqu99z72fYJACuipirSy6hcL7B8ed4EHe97/uNYPBHvG9n37Vmxnz/ZEsCs22DMU/rjxuas5JeR2Ak0FzwsXKwKhGZLlCgFVFyjKxafUXxK+j/BDQ2vPB99H3S6C78kdm5JVFp4sKxe+TGbec16f0AtBqH/Xbwj9kT6ynb1jihDoH1wjUKR0YSAEdIGMpkEYFkZVJFgPdbCCrtXLHqANagDxTgetuwsrghsJIJSHkUn7UhA2CPcvfJ8gTKIPBnMEiZiUm3AkANN1mfzdzy72rhUBaqWKubSO8GQ/itUt+aYqj1MTgkh4bt3z9EAyZroqw7KqZK00RQlZWsn+iY6LVqqFJuTIbWsqImLyZKuFWahSMw3GrtxBWDFELo2STaHGZRGGogiarbZMkQhA0TQUTSdQZIGJUHUUVWdwcICRoTz5/IA8HgVi8Rg+QhYm+T6Vap2VtU3aHZvx0SGefvwsE2PDoShtQK3eZH1zh9X1LQrlirQvi0dRBAzn0iRjFiIIiJg6E2Oygnp7eYMjQch6GAae4yI0RTobKArBfskAv/tg0ffzYwDXp8Q6+wU3P7ZJGpO9z8F+sPfgufNhW/CRubf/HPvm7Y/sQoRMV39Isc+8/KEP4LMc9d7vfwSfAD4+AZ7sP7H3/ET4UPWglhDsJt9/nNrCR9vDgy3/wbRnV0hO7lohCPrCMn0rVoHafWEXIH7SUXYv6sPeaL3DlB3Qzb3xw3yNTsdhu1gmGYvghflW7U6H1lZL2tKAtLxxPUxdRwtXeooi0FQVNSB0pwc32GPzCuEKSJpJp4jHLLLpBOl0knjCQtUUCsUyuq4TMTRsRxq6uo6L4ziy8tFxuXDlJuVKjWwqQfpgkrnpcSlq6UMgFEZHBhkezEnbIc8nGo3IQUNVabc7MscrzI/JpJOcPXWUZDJKrd6gEwKO8dE8nh9we36ZIPAZHBiQkgmOFO1st6X4Y63eoFJpsF2osLy2SbFURVNVMskEzz31CM8/9QhHj86RTCVRVDW8C6X9h6wPCEMYQgKttfVtfvzqW/z4p++ws11EVQUD2RRTEyOcPHYQTVO5N7/Epau38XyfseE82UyKZDJOsVSlWKpy9uRRRgZzxKIRZsZH+TAWY7tQolbrQCBXmaqqhMyi4LGzxzmbzXDr7kI4ye6GjAgnLDQNoSj4fkDb8bi9LEvDPc8jFso0bG4XSacSvPzi04yMjYYq8OxZ7ZuG0asuU8L8le5t3H1Our9r5Sr21o4Uyo1G0TQF23FY39im2WozMpQnHrMIQrZNCVdRdrtDq9nCNHQs00QVsgK4C5yE5MB7z04PUHX/yUQEOZmtbBBeKLDMnmBmEAS4joftytxC09B7+W7yqnZhTXgynhdONAFoKugaVUOnEqq2J+IS5Pb09h4AErvhud3+ClAqVXAFgaZiHz3QfdIIY5LS4sT3Ea5kyvA8aW3TZdI8T4aKXQ+308Zt2+hCoKvqXjuP7nEIh3BF1Ruj/PAZ7bd5EopACe+ZBwkACJBizpoasmjyp1BVlGY7BJQewfQYtV97iaVyjZ1qlYhpMDsxLI3K6y3UZhvzp+cx3r0Ino935ih+IoZfrUO1gdJsIVrt3Um+13/dXFnJVqGpu+CtO/aCBK5IBq7b+2pvpR/mIH60k+Q5qSqBkIuqvhW27Pu2zejqFnnHJabqxN++gv7+jR5oDxQFL5XATcboJGK04lHaMQs3lUDJZxEDWdxUglwmRSxmAXJcNQwdoSiYhsbE2Ai240jbICHQDINjRw+RzWUZHsxx/OgholGLrq+m60q9rpX1LQRw+MAUh+emGRjIkEokSKXimIbBobkpsukkrVab4TDvLQAC0yDwPHwh+/QT5/se+9K/GugClI9QJQ/d9jDuDzUfBg/+/WOIik/X9qPFj0GPSrBPGuGjxx34+02sPqb1Eqd+/qeDgL7xCvYnN+0/Uvm+z67P4V65hz0Ieg9b9OlQ8y+F2eplLgSKpAuBveaMom9genjg1GvdAbC3ObH7eu8joRpyj7YMV85BED40Pq7nkYjHyWVSCCGwOzaNVpt6o42uaZKpQiZ5Rky9JxVghvkbQN/2P9rJuq6TSiXD0KBkC1LJOJoC5UpdGgWHatKO41JrNCGMD9ebUkDTsiKMDkt9oXKlxvzSKtVag+nJMfKDeQ4dmuXK9Ts07Sq6YRKPx7Adj2qjyU6pa8AsE8RTyQSKULk3v0Y8anHiyByHD86g6Dq37syzvrFNPBonkYiRiMVRVZ1qrYCulYloGo1mm7XNAqVylYhpcvTgDE8+eoazp44xPDKIEdF73l6+8AkFQELQL2/MTqfD3ftLvPLqW7z62rtsbhVQFMFwJsMTj5zkzKkjWBGTUrlKfiBDOpXg5p1F6vUWuWyKuelxsuk0G5tF1je2URUl1H+yeeKRE3x46QbxmIUVMTl2+ACmafLW+YvcuruIZUU4cnCWdtuWodreWCfkZAwIQyabb21vcf3GHZaXN7EiJtOToyQTCVqtNps7RV557TwR0+DXf/VlLCsilYV90csF6lHSfeEwhaA3YPthvwQBFP4v/xVvvvsBl67e4mu/8jwzM6OUy1W++1c/4/L1O3zrV1/m0bMnw1VqgCI0bMfjJ6+9ww9+9DoHZyf4za+/3PMd3GWfRM+gXdkHbrqVTj7gBRB55luIQglUlcZP/wBSSQIC6rUOFy7f4F/8L3+Crqn843/0n3P04LSsmAO6hd5qyLxp/+l/jSiUEKub+KNDuOkE3/n6i/zglTd5/slzfOGFp6Rdja6BqqEoKqoI7xP29l//czzy4m9LsKRrbP/z/3MPkHXzrvYmuHb/ib5/ANI669vf+zG//+//kkdPH+Uf/b2/zYCmI+oN1EYLpdFCaTRJ/k//FqXWQKnUCKIWniK4lYqjtdpkVQ3Ptmk2WwghyGXTRKOWrAoO99O9s/aMCsHeX8RWQZ6T56O/c5Ghu0vkw8VgEASgKnhWBDdqEcSjiEYLsmkA7H/wm3iHZ3C83THO9zyCVhu11YF6g8rqFks37uCVyszlMwyYJnahzMqdeSzXYzyVQmu3od5E1BoEttN370oZG8eVPpq6poUguetN2e3RsOCp3YFKA1mVRhiKD/ANjYwvnytdCJSODbYj82CEQNU19HYHbauACSTCjQdiL8DzNQ03lcBLJ3DTSbxUAi+TRMlnyeaziGyGIBJFaAaptMajZ0/gui6GIYseuiFwFYWoZYULIIWjh2Z49MxxDsxNkUrGMXQdLax8zeey5LIZeX/fXQrPFoKI0Xuug95yI5zGgweAnz1zQx8Y/YwYpxdRCQIQuxXPH9nvA6fW/UDrsx1DbxdCsJ8ZevAHuz/Dffae0/7DeciD6Q3cDwO09oHJbl894Nx7fsfhgqObJiQ/2v1wd1nZxTCixxd9WizzC9v1iDBxb08ppa9AEKrLsq/2UMCnMW/cT+1/4kf3nbwf+L0JsL9E2zB02u0OAp9Wy6bZaJNIxDE0HVM38AK/19WxaIRYxEQRYdJvOKZ3rRu6fksiCFAMvSe3sLFZRAiFeNTANDSqtQaKEEQMHU0RCFWhVKli247MSbIiZDMpavUGjusyv7TK3ftLbO6UKJSqHDs8x+PnTjI3PcnquhTyJAhC64461XqLVsem0WhBENBq22RSCRaW1yhV6gwNDRGJGDTaLvm4THj2PJ9CqcLiyjoC0QtJVKsNmskkQgjy2RTHD81w9NABjh85xMT4KFY8vivkK6ShtLxBQwNwIbWDyuUqV67e5NXX3uHylVtUqnVZOacIjhyc5eypoxiGTqPepF6toQQwNjyE63nU69Kzb3unwszkGDOTYyyurFMsSW2v2/cW+drLzzM1PsL46BCGoROLWlhHDsgEW9vh8vU7bGwXEGHIISBAqJKREK5U/g10nTv3FvnjP/8BnXaHLzz3JKdPHCGdSqCFvo2xmMX6xg7Xb8/zQqlMKjaCA3h9IGf3FpWDvCZ2GV/JKoAXiB7bOzKUxzQMMpmEZEUjJqmktBjyfVBV6WAnRwgVKSAeUK83aNQb2B177yMVjgD7gVYvbAf4olsJvHdAEpomc1H8gEhEZ3J8lHQijuu6skotzMnw+4CMD7vFBvseT8dxQ5ZRDatlZbWXL3aHKCFJ+16fAb1ndZ86xu55BH6YM9PdSrgiFd2/upMSdG9QTVUZyKZRFIVqvUlbCILBLEE+I4UPw36L/8cfIgydwLbxhnLsCME/mRrkiXMn+Fu/9jLCcVm6dIPm5jZj8RjjqSTxQKA2Woh6A2ryn6g1oC7ZJ+qNUD5gHxsIMuQYBNK/05aixoaug13D2yrI18MKU8MwICUXaZoq6Fc28qMWftSCXJqypvLDi5fYqJT52y8+zlOPnaFarvBnf/RtDNPg9377m0StCL4vrcSa1RrtnRJBtQbVGqWVNRqbBbK6Tk7TsVwX3bZRWx3UZhu11UZtSrCm2Y0e8Aj6bGyCeAxFUyS7GMjQcGC72I0meJ6sJgUZKXlQKkgv8iGBXq/6F8KqXxm2FUJB1OqSjOyybZqKP5ANK4eVXpVwXAh+z3b4TcdBvbeKef4aumGEbiByLKZXLRvuq1ZHzK/I0KgVmmELBSF2Z5meLtaDWpeB/QXbXiFR6E+UD9gHuPpJmL+hY3pogER4LErw8fsM+oian79j9i6kPmm3D9rf7mKu/yfBbu5W92M9dlfsAitBv4pCd8AJKy0fsk8+A9jq37DfO9jdt+WateeaFexa9sgjD/b0V/CQHfiRtucmC3rUPwG9TvKDXVRqRgxiMUk7B4RWG2EYz3FdAt+Xti8Rk61ihXbbRdd0sqk00Ui0LxwqGTMnLNWWpfqSFVNUlbGRESYnRlhZ26LVksr1tXqDQrFCIhaV+RaaRjoeI/AFzWaLjm0zmMuSTMRoNtusbWyFnmw1qbBerXH73iLHDkuD24OzUyyurLO1XaRUkn55hmmga1L5/d7CCpvbRTRNZadYRqgaC8uruK5H1IowNzspzVbNCCtrW9xfWMVxJegLAkGr3aFQKjM2PMjj507yyJnjjIwMYVoxhKIjRBh6EGJ3glNCu2Q/oFSqcH9+iUtXbvDBh1dZXt2g1WoTi1qMDuep1RrYtsvd+yusrG2QS8fIpJLsFKqsrG2Sz2WYHBumVKlRr7VY39phaDCHqqlSh6fR4va9RTRN4+nHzyCEgq7pCEVWUB2cnaLV6lBvNrl1d5FsOhmGoKRuky8EhFWIniJ49fXz/OS193nuidOcOXmUkeH8ntv88IFpPvfMI6ytb/VEUVUR4DqOVFX3fIyMiip0IKBRr7O1uU0kYjI6lCcAqrU6iqYTT8SJRAxmpyfJD9SImCq6quLrGqYmmZJdhkyVoE2VE3EyEScSVrbK1XuAIgI58QiBEuoddcFJEE44QRDQbNu9cKeqKXvBliF1hFzHplKuUygUaLc7pFMJIqa8t73wWXIcm1qtQbvTIZVMkPsIay3z9IQQqJoi/SkVWSwiffhchK6idZmSkNnosr2uK6UH+puiCDxH2hYJIVD0MI8pHFKUEOB2aUQ5eCq9YcEydZnH53nge+jK7pARIEFc/3DSNSBvd2zyuQyxmEUsGiWRy1AO/QFFOoUbjYKuoyhqb1L0+tItgiCQwpg1Cbwi//C/QV1YAUXBffocy9kUty9eIyUEh4byGK6HWmuiFcsE7U4Y/rfJZlKIZDy8J7om6l08sstcZlJJ8rkshe1iT/JDVeWx+Z6PKkAVgTxfAnnd0wn8eAQnn6GRilOZqEEyTpBKYUaMHusjhI9QoNXu8O77V8m8/iHferuB3rEJDAMRhuWFqqIZBr3gjJByOSXXod5wyadioTRGIMPYrqwWFK7M1xOuK0OcvcIKrweruwuX7p2G44bzjdO7l0SzvXeaCH9qQHTfax/5UHcS7ZpM+558yYoQKFJqpUsUBH3H8rGtRwn+oozSLw7a6DuUX7z1s1Q/rw/C9/cBkv7854ea/YO9n/xYdq/74T6GS5Ih9EDVR5Lj+3dBF0p0mcvd8wx9R3p/SaSv8LCd+inAVrD3Z8BH8wWQvliBCNgVlt4djPf0i+g7zYfFWvtWEru5FyG1qYRsJd2JZhfoxWIxMul4WBUnfQlN08D1AhIxaZehaRq1eoOdnaKs7ItaDOVzmKYZMgZyfAiAZrvF9k6JIAjIpJIkEjF0XWcgl+GFZx9lc6tArdZAV3VqtRa27WCkpX6SNFXOkUtnyWXTFEsV6g1pynry2AEeO3scx3F55/3LXLp+h0arzer6FnfvLzE6MkR+IMsLTz/GtZt3mF9YpmM7JKIWuqGhqhrxWAxNV7l9b4n5pTUy6UzPZyyVjGM7Doau4wcBlmmRjMdZXFmjUKgihGBoMMupY4d45omzHDk0SzyVBKV7q/i9gaYXMlQ0XMelVK4wv7TK+xeucvHCNVbXNmSivecjhGB0JM+Lzz5Ju9nk2s17/OS185TKVQ5MjzE3NcbS6ib3FlepNaRfoarKqqWllU1mpkZ54tFTLK9ucOP2PEtr22xsF5meHNtddYQgOxazmJuZYGZyjHsLK7Q7tlTVF6r8RyCZLQQuglu3Fmh3bDKpJOlsOvT13G3RqMULzzxGs9UmP5AFoFqtc/7CFS5ev0vE0Hn6sVMcPSj95N57/xLf//EbHD00w69/5fMsrazzyuvnmZ4Y4QsvPEUul6HQavCT194inYjyxRefRFO6rNSu9pYqAnzXpVypUazU2dmRIVhVETheBz/wQCj4BLSbDp0wvyidTGBELFCkXdG1G3f44OI1mq02hw/O8MjZI4z2h+A1hVqlyrUbt3nvwxvcvb/C4so6uWwKQ9NQkEKjGzslLl29xXsXrlCtNTh59CD/abuD1f8IB7tgSwv95PwgoF5rcOHabSrVOs8+cYbBXBotBA4ecHdhlfMfXKbV7vD042cZ6uv/Zq3Gh5eus1MsMT05xujIIIoiJ/VozEDRVGzboVAs0wiFQDPpDPl0Gl1TJJMsV0kIQhcD38d2HNbWtylVqjzjOHR1wl1Pyl8YusZATooMz9+4w8bWDuOjw4wM5zFNKSNSqzWot9pYpkEqEe/pcoEEgb5pEJgGYiCDiBhyTNJUyqcO88eNOjfHB/nNX/8VjKceQeg6QRCgeD6N1TUuvX2B6+9f5vmTR3g0FiMQSsgMKziOQxAQCunKkHU8ZpFOxkNrKqn71e3/gEDm+IXDqEJIEvoeBFLGJpWIE7NMDE1F16WtWD+Y604+hqExaOo4uRQoKvbkMMbCKvhQ/LvfwM0kCTo2iuOguR5eq8U7r7/H7VsLPHNkljOHZ1FdFxwX37FlcYLtSRkW20GEkizCdREdyazRsVE8DyUIpLab5yFK1V2GTKJumaMW7E7m/QUEHz/NPOAdbzcNJohGkCr5ojfu9b75SYCrSzDIld4vDTN1t/2RfSv7iYv+30O42sUGvzDr1oW/+3DBxxyr/Mh+wPWZaJbd7z7gy92iot2j2mUG+2WboAvCdnMWu9hYCBn98P0Az/PDPHD6PiP2AvSH9Gv8DGCr++cDOjdEikEgQVfviH4u+H0Ildy+3t27Z9HrJXkfBTKBXSihWKF83/d9tgtlXMemWq+TiEldq1qjRaPVRjd0olaEQqlMsVyhWmswOznG6HA+ZDPkPjQFAhQsSyeZsGi37d0LGAQYps6jZ09Qrzf5zl/9jBu35vGDoOfh1m532Nwu0mh2GMgWGRvOo6oqS6ub+L7P3MwEuWyGarXO+OgQd+4vU6nWWV3f4qdvvs/05CjZdIpUMs6jp48yOphleW0TAmi2O5iGzuNnj+EjWFrdpNNx2CmUKFdq+IEXeh42w5vIJx6P0my0sB2XTDrJ3MwEzzxxlsfPnWR4cECuUntl7ruBYYRAKOAGAevr21y4eI2Ll2+wtLTKxuZ2WM3p4Xmy0nIwn+XIwRnyAxkmhg8Tj0VZWFpFVaX1Tb0hGaxWu02l0sCKRIiYhlR/r9bwFlZ4+vGzZNMpxkYGGR3OI1SNjhegaaGeixDsKjzJVXipXCMei+J5YV5QoKAKevpagaYQ+D6+50uTZ8eBiLnn/laEwlB+QMb1fSkOeXd+ibffuywrN+tNOu02A5kM42NDdGybhZV1BvNZrIg0LL126y63789z6MA4+VyCdrPGlau3SCZjPPvkGWLRaC8EqasCUxVUa3UuXrnJ7XuLrG5ss76xTa3eoNluUSyWGR7M0Wp3+ODiNe7OL+N5AdVak9Mnj/HSC08Ti0W5e3+R3//3f8G1G3dJxKPcunufWExntMvACEGxUuOVV9/k3vwymiolSmzHxQ0nHNt2mF9c4fuvvMXP3v6Q7Z0SfhBw684CXyvVGQ+Zpv5nVRFKqHUk2dZrt+7ynb/6KbV6k1TC4rknH+mxhMvLa/zBv/8271+8jut6FApFnuwbiLe2C7zy+nlu31/i5ReeotlqUypXGRkZ5OjRWer1Bm+dv8ji8hrNZpvV9W3SqSRffvFpnn78TDjh7R1fWu0OH166zl/+8GdUqnWOlaoMCgUFKUbqOC6qqmJZEeYXV/mDP/5L7i2s8pvf+BW+OfkFhAi4fP06P3vzIvfml8lkkvz6V17i1IkjPTC3vrXD9Zt32SmWGMpl+KLt9qKgq6UKV+/NM5jPcmB6QmrAheE4TYOp6Uly+QFmTh8lkUwgdBNVEZQrNe7cW2BxeRXLinBgZpKx0SF0XUXgh0yW33sedoEHBIHo5Rh5ns/axjaLi0vELIvR4SEiZhRPcwl8We0csBsKFopcMMVjFk8+epLcnWXUW4sEQsFPxAgiJgTQPnsMbzAri5B8D0WA7Ti8u7XNX29uYz52nJlf+wKaquIGAS4erhfsss6+jxdANzfJ82TFoaaq6AiSEZOkaaC4HpF/9E8QhTLK6ib+SB5vcpT63/kGa8trbK6soboOkwMDjA1kZDVw25Feno4rK3Bdl6Dj9PT2hO0gHFk5LM5fgo4tx75otBeV2SVYuppwD9H6Adcvo3WZt/6m9M+1HwO49r/8mXO4HgJgPUz7VEzbXlbrYwsG9m9z//b3Aa2+l/q4uu72uvmksB/OdD8T8NF0io9rn92uZz9K3feR7qHsslei75P9nfNZse1eFN8Dz91JVwlCbSuVwA8kyGi20BSwXZeAgGTcotlqs7S6SalSY2J0kNHhQSZHh9jcLjIylGMwn5MrRNElEQWaKr22TFMPPbWk35kcLH2SiRjPPHEW3/f5q1feYnl1AyEEpUqNdrtDoVSlVGmwsVVkdW2LbCaF63jMr6zSarfZ2Nyh1e70mJqIaVJvNLl47TbrmzvMTY+TTiUYyWd72l6lSo0PLt3gzv0lRobyzM5OyerBdIJ0KiXDb7U2rXaHYqWG7wehOn2HsZEhnnr8DCePHWJuZoKR4cFQiFDs6+uuYrqK63UoFCtcuX6H1984z6XLNygUyrihHyXI8I3nybCkaWgUimVu3rpDp92kUCoSBAG6Jj3KJsdH8H2fyzfvyVyWtk211qBWbxIE0Gi2Wd7YxvE8jh2e47d/4ysMDmT3hHc9z6fabNFutRBBQC6TIpmQXodCkSFPPwhd6UNZD8UwSCbiAKysb7G2tkn6aFKu+Pua60lhx4AAXdOIWhHOnTpCxDT52Vvvc39xhUKhwMzUKPGohWXoGKqCaeqMjuQZGkhzb3GNZquNoipELZmj1bGdUM1dVu2pqhqGCOHe/DJXrt9hdmqMsyeP8MGl67z53kUcx6Ud5ue9d+Ear7x2nkfPHOPk8YP85LXz/Mfv/QjLivDi555gY1Myorqu8+yT54hGDSJmH5gUgmvXb/PqG+/z5CMneOlzz7CxscPWTqlXGOI4LqvrWz07lIFcmpgVQdMURKm+54nvti5QMXSNRlPqHB07NEOxXOHt85c4NDvF1MQoEHDj9n3eff8K8ZjF8EQWx7b3jC+qqpLLphD3pW1Vo9FiY3M7FKMMuHD5Bhev3OKJR04yOpLnP373J7x9/gpDuQznTh2lKwmjhCtWz/e5N7/En3/vJ1y7Nc9AVlrQeKaJvu9MFCGLXJKJOM1Wm47dRjcV1je3+Hd/+n2abYfJ8VF++sb76KrGzNQ4mVSSWrXKj199k2u37mHbNqoQPN9uo4XzW6nVptlqMzU+QiaTCuU+dqMBiiLdJE4cPSjHM1VhcWWdP/rT73Hr3gKWaRKLWly9fofHzh7nyUdOyKT2oOuL2M2l9XsOFkIoaKpBvV7jx6+9zbsfXmF9U6YsfPnFZ/ncM49BAMVSlRt37lMJi1SS8TiGKceSbCbJ0soGufeucKrRJBa1woFXFjbcXl1jc2GZ6clRhrLJEDR1Q+NIm6vwb9tx2SwUmF/ewPNgZlK6eQghi2Cu37rL1naRk0dmZeQhHkdJxhGxqMy3ikQkMlUUsCyCuSnKxw9ysdkgyCYpFsvcj0f50hc/RyqZwPc9QDpyNFsy2uB5HrFYlHQq2Qt5Kypov/u/R+yUYSADX/0cKL7E66EPJHwKG53ubC3Cicp/eJAihCDo0lHB3tf37oO+fEZ5D+3bUt+v/XPwpwVcveX2x7y9O658UnvoRPvdb4Sb75I5oS7iPtPtB1UI975HH0x8KFbto9c4AFRFxYyYISbo5qb//PbL0dnal8TXQ557DnTvReheMvFpbto9rQ8I7AstKgH4Sh+FLOQx+X6AGwShGa2GFbGYGovQbLXZKpTYKZSZmhghEY8yNpzn5LFDxGNRbMcBIfVnuqsUP0xUVFUFz92tklJU+TMatTh7+iiapvLOe1e4t7DC8to2nufSbHXIpJLkMils25FGuIZBp+OwuLLJ5k5JCpZqGpYVYWQ4j23b7BTKlCo1FlfWWVxZZ3s4z6HZSZLxKKZpkstlWFjdZGlti5HR4VD1u0kkEqFYruK4LkJIu5JsNsHk+DCPnDrK2ZNHGB4cIJGIE4tZ6N0Kty5lLsKkd9ej2XHZ3ilw/eZN3r9wlSvX77Kxud2zIOmqgRu6xtBgDtf1ZDix3mR5ZR3fdXAcm9X1LRyng+t6OI5LNp3i+acfIRqPce3WfVzPw/W7IUvo2DaXrtyg0ZTFDEMjw6TiUQn/whX85vYOFy7foFqrc2huSk5S4SrQ78+lcX1ZcixAsyIcPjDFa+9cYHV9m/cvXOXgoVlpKO24QEC70+H6rXtcuHyDuekJzp0+yvT4MLlsmuu37lEoVQg8n8D3UJB5VKoiiJg6mhIQMSBiaBiaSsSUjgOmaWBZJvVmG9t2dplTVcE0DFzXZWFplUq1xujIECePHSQg4Pqt+3iezNO6eXeB7//4TYYHc3zxxScZyg+ws1PhvQs3+enr73Lu9HFGR4bJ5zLcX5RVrWdPH2Z8bGh3hScEa+ubNJttspkUw4MD+F5AKhmXE5Cq4noeO8UypUqVRDzK80+e5fEzx8hkUgz9t/8TSrXee8S7pLxAoKD28smGBwcYHR7k6s17vPvBFTY3t5geH0YISMQsLMtkp1jh2KEZvvTSs4g/f6UHPpLJOMcOzXLn3jL3FlbwAxff94lFTRzH4d7CCq7rMjkxwpGDM1y9fpebtxc4ODtJMtRq644VQgCey7Ubt7k7v8yBmXGOHpwmsvX6A8YWWeqfiMdIJWMYhoquSmY08ANmJkc4ODfD8NAIH168RuC76IpAwcfpdFhdXUdXBI8/fhrX9VDfuNjbrmfIZySXSRGNRHpa892QuFzYBD1SudNp89PX3uYnr73Lc0+e4asvf45Wu81rb33AD195nVQ8yrHDc4DMz/I8r8foB74fjrPyerx1/kP+7Ds/ZnAgg2kYfHD5NgOpFI+fOYZpaFy/dZ8f/uwdKrUGhh5WVhNw9uRBfuXzTzO/uIq+skEbBSsSQSAZqGq9yfffeJ+bK+ucPXmY3/za50kl4r3uVELbKgiwHYc79xb4/qvv8N6FG6iqxtdefo6XP/8sQghefeNdfvyzd1EVhXZThoZnpyfwfb8XbpfXsi/cl0qyuLLOnfuLnD5xhJW1DeaX1zh29BDHjsRQFMmyvn/hKuc/uEylKv1SR0cG+coXnuPg3BSqJh0vereBQCZ5d0OCXV7jYeasfsokYHcbDwrl7U5UH9lMfyXk7t/7P/TzD+cjH+z9+mkA175jedivPXBL3Xv9wTvfu22fnlZnP6sV+CFwErts3YNa38s9ppe9n+8u22U+srJrNbhnO4G0lErLopvAe5AV0YPbL9UbsR85uuEsqRKKhCldRWJ1Ly7+NFdrD2oO0XlfCLI7yBP+lPlZfo+e39opEXgeiWiEkaFBhoaHKBZL3F9aZbtQptZoUq7WQVF45OwJThw7hOO4bO9IUdRkMkE0nqBjdyiWiziuixUxsTserueTTsWJxSL4gd/LqZgcHyGVTLC6vsXyygY7xTLFUo1isczSykZvFeoHMmnf0jVqjRYEoOkaU/EYczMTNFttqrUGrucxv7RGo9mmENphDA5kSCbjJBIJhgdzWFaEe/NL3JlfYm2zQIBkZuamxxkdGWJqcpy5mUmGhgYYzKWJRUxs26HRbEmvuNDDL1AUXD+gWq6wurrB/OIK9xZXuX13nvsLixRLFRzX7/O/VKTqfC4jPSITcUQQUK3UwksW0LFtNjZ3KBQr+IG8E9odG9txGRwa5IlHTcrVGmubO9K+Rcgra0UMdoplad7tu7TaLSngiugJJN64s8D5Dy4TtSJMjY+CUGm02miqgut25UnA6dgY3bwa0+DJR0/zs7c/5MqNe/zlD19jbnaKR544i+L7NGp1rt64w5/8xV9z884CX3v5OaanxmQRwOIKW9tFmdjtOBTLVZphjlqArMpzPA9VVXBcLyxK0FEUWREbj8VYXtui3mjSLb9XVVm91+nYlMpVao2mLMfXtJ4FieO4tFoOy2vr7BQrPHrqMIlYjMBXSCWTmIbBwtIapXKFqakJfuubX+bPvvMjzn94BV1XGB7KMdldXSuCer1Frd6mWm3h+QGKrvVYxyCQodN6o4ltO9QbbS5cucnIYI7TJ4/sSWYPIDRzltfTcSSTEAQBhWKF9y5c5erN+1iWSeD7KCEwOH3sIL/3m1/lD/74e3xw6SaPnTne2x5Ig+OZqXFmJsd59c33Wd3c5JFTR/D9gMJOgUqlJm1dNjY5fHCaIAjIZZJMTYygdRdI3eWdEKhCEPgB6WSM3/z6F3nxuSeIv38dpVTtOxPCsK7MkfIDn97a2PcZG8nzrV/7AoVilcvXbtFotsjnMhhhrlMyEWdkaIC//OFrCCH45tdewujp0UEynyMeqppXKnVikf7MN7nAsTsdrly/QSoVJz+QZWF5DdPQeO6Js5w9cZhWu816mMu5sLTK3PSEHE96zJbYTQIOx+N6vc6HF69SrdUZGx5EIBgdzJGIRzF0jXKlxjsfXgPgyy8+wcZ2iXc/vIZpakyODTI2KmVp8qoKno+vqiiuK017PY8XXnyK2KXrvPP+ZV584hSD2STdwtmuZiEIiuUK33/lTb7743epNVqMDed6Btib2ztcv3WfVCLGE4+c5PadeUaH84yNDGLbTu/agOjphSEELTPCG29f4PwH17Bth51imZ1CifsLyxyYncSyLGzb4dadeX7yxnl8zycatdgplDhyaJIDc+MIoYUE1G7MSOwBWp+i7QdU/WG7fpar+3v/9x7QPhsp8cAtsTt/9r3UbT+XeeuPUn2a3e4DkyHAfBA++ghbJEBWYqrsZ9Z6s/+nPJxdfm/vMYnwWOXl2yvnEiAFw5OJBILQIutvDGw9IIS4521kibgP7IpI9nQC2D29z3Dz9u+kd739fVTibqJ0V+5BETJ02KzWiUYMBrMZKU1QrbGwssHdbnhHCNKpBF984UmeevR0qDqu0Gq12S6UKBQrjI6N4HgeK2ub2LZDNpMEoN22URSfiGVASJFL3RedocEcA7k0B2cn5cTseSwvrfPehWuUKzVUVcXzpNmrNCWWoCQeszDDXLLxkTyrqxvcvrfUk7S4u7DC6uY2I4MDHD8yix7a5Ri6TrPVQQAHpidIpVKyCGB8hINz0+TzeVCg0Wzgh8yS40pjbMdxGApztTY2N7h87TYXr9yUulyb21RqTRzXwdDVUOPJ7y3INE0jEY8T0Q1K5QqdVod0Ms7w4ACe46LrKpoiZTBs28E0TWwcOo5Ntdkg3okRsTTS6TiLqxsybNOR4oID+Syu69MKc0ne/uAid+8ukk7GSSVSeJ5HuVKl3mgyMTbMYD6H7TgkEnEc2w6NsKHjuFSKFQzXRUCPEfk7f+ur/Mfv/4SrN+7z3/2zf83Tj31AJpWk3mhw8dptbt5Z7F3rO/cW+NHP3mV9c4eTR6XUxI2lVS5du82h2UlsR4YnLl2/y1/96A2CwGdpdZMggGqtIUO4ihQyLRSrVKt1giDoOQnYjoPrefhBQLFUZXF5jeNHD9AdmDqhPlLENNFUhXpTVs7F40k815NMLIJCocTyygaWFeELn3uSf/un3+fytTs8/shxTvu7o77jBlSqdS5cucXxo4cIBDRbbe7Or7CxtcPgQJZcJkXENGg0q9ybX+F7P3qdRrPF71bqpEJhQt/3aXdsFlc3qNTq1JtNXM/h5p15fvzau1TrDVRNJWpJvzzdMNjeKfLqm+8xPjLEP/jdX+d//jff5vyFq/xej++XrKauGYyNDEqTZMMgmYixsVmgUCxiGRrbO2W+81evUarUuL+wwunjBxgZyofXYlcHLQgChCKYnRojHrO4fus+p08cJtlnKt0NeynhqtYPZIGA73cNqQXzC8v8xfd/ynaxSiwa64ngyu8HGIbOk4+d5vb9Jd798CpCCB6xHYxwbB6aGOVArcb5C9dQhMa3vvYSE5PjqJrem5Ov3rjDP/uX/47PP/coX//yi6FvoUkqmegBQU2TSfXtjo3ne71czN116W74hSDA6XSwHYeZiRH+7m99nbhlsri8jGVGyKaS3Lo7z8rGJofnJjl59CDxxAYLKxskYiaHD0yTSiYolqokPbkPNxbB7DioqkoylyGVTrK1Iy1xpMiujyzhCHrAxQ9cNre3mV9aY3Qkx6G5KaYmRjhz8kiv8EBRFIrlGqVylZNHD5DNJFnf2CaVTkstubDooaeXB+y4DktLqxyem2ZqYoxo1KJWa7C0skalWicWi6JpKqlUnGQixrFDs7zw7BNAwMT4ILbto6qBjEwGDym2+WmbH7JkXZuXLtDazYPhU6OGbutd9O4Ln3QGfYzSfhDX1cZ6mO8+aP8P/Ir4yOd2AW1fSPHnnv9D9k1fXlc3+iE+9vj2nr8Q0svRD/o0ovt23Y3edJv/Sefd1z6bgvzHblvgB4pkLERXkVV9wLUX+/7+FO1BSHKfqacfJsMahoGiylR9x3axbYeoaWK7Lo1mi40tmRulaXIln0wlefrxMzz/9KM98+luKFLXNVZWNzGsKKlUgmQ8LuUSIiYEgnbbo9l2sT05kXooKLpJLGJhaAqdjo2qqhi6RjRiMD02yMToEPFYjGwmBYBt271VabXaYH1zm5+8fp7t7R1OHz3Ar7z4FKeOHcR2HOr1JrZtY5k6B2YnOH3yMPF4FFXVMI0I9UaTZ588i6lbqHoE1/PwPI9EIo6pa7IiU9NDlxF5fMVShc1taWuxtrXDu+9f4trNuxRLYbk7Ak3XGR0eJJdLsb1TpFypSTVtIYiYETRFYXunCEDMMrFtG891UYQgGjWxHSkOqWsqipCed23HYbtYRFFlBYjj+liWCQI0TSHmRxgeylJvtNnaLnL95j0WllZZXd2Uq2VFshdWxMTQFKJRi62dAol4jNHhPBtbBQKh4ngKjufhNJphhUlAxw9oNVo8du4EUxOjvP3eRc5/eI3zF66xsVXAcVwMXePgzDhPP36aw3PTbBdKVKt1doplrly/Qy2sJO3YDjulKqWqBFS37i/zP/7+nxOLWtQaTRqtNu9+eI1zZ45z5dodPrx8i0KpwmbIjnV1z1ZWN5kcGyGTTtKxHb7/4zdYXZcM2OLqBqqicHd+GSHAcX3OX7jBkYNzTE6M8YNX3mRrp8SZE4cJEPzwJ29y+fod4jGLQrHM0GAOIbTdgVERTI+PMDI0wPkL11hYXiefS7O+WUBVFd6/eI3Hzp7gxNGDPP/UOX7wylu02h3ml9Zptt7i15stkuHz05UpuXTttszT2tqmWquzvLbB2uYOEcNAUQSVWp27CyscP3aM7UKVf//tH2FZFudOHUZVlV4VXbe12za+FzA4kGNmYoRsKsZIaCSciFucOHaAje0i24UyN28v8tjZYzxx9jj5gSzlcpW780s0wwXT+tYOk2NDjI4MMpBN88rr5/EDn/+y3iDueShByEg6LoVSlYXlVVrtDvcX16g3Wriuh+d6vPP+FX740/M8/+Q5ZqfHeev8Je7cX+LilZucOHqQze0CxVKFl194kmqtzsLyGq5tY4TnlBjI8uiZ49xfXOV7P36dxZV1vvrFz3HkkAwF3r47z59/7xXWNrbJpJO0bBfX9Wg229QaDTzfp1JtsL6xQ7XWCPPrVNyQZeoNjYAXTvCe51Gu1ihX69TqTdqtFscPTpDLxGk2Jf2kaTJUurSySblSY25yhOuDWQxdZXAgja6piCAg6nigqTgxCyM0qt/p2PzF917h4tXbpBMxfN+n1elIVjkkBR3Hpm23qdTqeL7P8cOzfOvrXySVjKMqOo7jkEwmOHFkjqWVdb7z169z/NA0s5OjHJyd4uTxwz0QLKAXRgx8n4VKBUVV+dvf+AonTh1hfn6BRqPJ4soGi8urDGQzEghrWi/+MTKUp1otU9gpogiNaNQiHo+Q2Qe2PiP8+Wjr5xj6QVb3zZ8LdD6m9THVv1B7qKT5jwFa+0HHA8OdYvfz7AKu/kVB4D94H/vDfh97dH2f6YnQ9kGObmVtP5DYQ0CGvJofSCmXgN30iD3J+Z+yqx8ebD30DRCAkCXW3RDfnkRr0ePp9r338E3so/Z23wCB3yMZuya6BIRgQ3aeZVkMDuWxOzYBFYYHMxw7PMUjZ47x5GNnGBzIoWnarmp84BEEHutb2+TyA4yPDZFOxyAspa5UG1SqTRwPfD/U4VAUaazatVUJk1wjpkHM1PFMi4OzEeLxKKlkfFerJ/wcikq5VMaypKDqiWMHeSJ2mlarTbvdodOxaTRrVCoVkokYB+YmiCbiIBTwVQLPI/B9PDfAdgJ2SpVQI0jqg9XqDbZ2tqlUpbbV6vom1VqNTsdGN3TK1RpLKxthcrtOKimlLcZGR3j80TNEIgZvvH2eer0h81E0TQq2airJeIx0Kk6lWqfZapOIRsikk+iayur6Ou2OzWAug6MIHNfDdVwpk6FptNs2hWKZer2FUAIiEQPP9Wg12yiKBL2lUoV2u0MiEaMVsjpCCDnhaDora5u89tb75LLpMPxl43q+9BlWBBFVQws1rbyoRRNwHY+RoQG+/uXP88UXnqZaq/PaW+9z9eY95mYmePrR04yODLK8uo6/7fP0Y6c5dniObCYZ3gMyT2xgIEe51uDsySOk0inmpsY5e/II80sr/OztD1AUQbvVplZvcGh2guOHZxjMZwkCSKcSxKIWm9sFqrUGc9MTPHLqCJeu3eH9i9cBiBgGI0MDjAwNMDE6TKPZ5tU33udf/uG30TSVaq3BsUMz/K2vv8yhA9McPTzL7XsL7BRKpJJxTh8/xNT42O6gqiicOXkE23W5eOUWzVabY4dnOX5kDtt2GBkaQNc1hgcH+K1vfIloJMKVG3cZyGZ46rGz5P7oL6WOFJIB8oOAibFhXNcNQ1M6QwNZBnNZNE3j6MFpCqUyqxtFGo0O8XiS2alJ3n7/Mosr66RTCR45dQS+87PuaIKq6FiWRioZZ2xkiImRHJOjgyQSUnLl4tXrBH7AE+eO88wTZ8hmUtIbWAgq1Tr3FlbID6QJ/ICFxVVOHj2IYUY4cmiW+eUNfvDKW/zGdoUpK4IW5h8hBK7nsblVoNFsU2+00FSNRqNNgCAeT2BFLK7dmmdjq4jtuJy/cA1VURgfHWJ1bZM/++6PiVoWpXINw9BlXl84VKlxi9lskl/70uf44NINLl69wz//n/+IdCqBrmusb26jaRp/61df5viRg7TbNrlsjlq9xZ995yfcubfCTrHM+xevo6oauWyGWqPBehii39opUq83KJYqrG5sk27ZzC+tUCmXMTSNje0C//ZPv8vyylnisRi6rjE3PcHI4AAHpsf52btX+O6P3+T4oWkMXePE0Vmy6RS2bVNe25Tq9aqCHbWIbhWoVhus2Ta5bIqRwRzLq5vcur9EOpUgUFRqdemgsVMq02i08DwfzwtYWFrj1dffY3Agw/jYKLNTk6yuyXSL2akxPM/n8o17jA3nOTg3SSoRo1IsYVkmWhD0wojtjs3ltQ2UdAohFDrNFgO5DNOTY7z21vtcv3WPibERLCsS3qtQLFfY3tlBVwXRSATXtlkqlNB1jbOOi9EDXP1pL78AmNnDYu0PxfXH8z4D2gpFieUm+wmJhzzeT5Ws/knboTe/9xJueQA42R9S/KTj2heK/XRXIFRIeMA73WsbhOHeAMnb+H6A5we4ri/njT3grVvRu/+Efn77peZsASA8hCLR6S8vxvyA3YQ3VL/oWKjyBchO6YI5xw1XP4HswGKxzM82tqjVm4wND/D0YyeYmZkgm0nR7NjsVKrEInpIhQtK5TIXr9xgaXWdiYlxtneKtNptjLDasVCssLS8hmVZPWPWRrNFIGRidBC4tNsdQJDPpjHSKRQE5UqVcqVKEAzJkFqn00vC7zgOlUpNessJha3tIkqx3EvsLpYq3J2fZ3O7SD6XxhcKQ4MD2K5Lu+XSbLbphKBsa6fM3fvLFMsVVFVDNwxKlSrrW1u9XJyoZRIEPo1mi5HhAUaH8vgjecrVBslEjFhYnTg+JhPvp2fGGRsb5Ec/eYMPLlxDURRcz0X3JXOXiEvxwkqliue6VGt1Go0msajJmROHmJke59a9Za7dvEe7Y2OEVYH1xjbtdhs/kArUjaZNvd6i2e6EIFhBKAq6rmHqBk7HQbVkxanjuBBITbTb9xZZf+sD6vUmg/nsLrgIFFlxJkItIlOylz39NcPo6aupqkIyEefY4TkOzU3R7tjouk4um+bQ3DTDw3k0XQd/1xy43mgyUM3wq19+kYFsmoF8jngqSavZ4hu/9gViUYNcLs0XB3O8+OJT0oBZCFzX5eDcJKVyDdMw0TSV6ckx0qkEzzxxFkPX0XWNdqfTA2aTE6NMjI+g6xpXrt9lfHSIsycP89i5E0yMjaJoOr/xa7/CmZNHKJYqRK0II8OD5LLpPfmPg/kBvvrF5/nVL38eP5CA1vM8PNelVq0RhK8dPDDDf/m/Hmdzc5tEPE4mm0P787+GegOEzK0aS6f4x/+7/5xGvcZAJk42k+IR/TjZTIZMKs2BuWkajTrNZgfD0LGsDP+bv/fbvPjsEzRbTSbGBpmbnoD/9l/0nnVD10FArdZA01TGx8bI57Kouh6aoxe5eW+J+0tr3FtYodNxCALBU4+e5PFzp/j6l19ga7tAxDSZHB8hCAJM0+SpR08zNjLE4vIayT/+qzB0JIjHo0xkkvzD3/smB2YmMQydVDLOTqHEiaMHMQ2L5558DCsiw1SGrnPiyBztjs2BmQmiVpTZqSkGsllu3J4nn8ty9uQh1Hev0Zt4TJNsOskzT57j7Klj3Lu/yvsXr3H15l3qjSaH56b5yhee56nHzuB5LtVGiy++8Aye6/HK6+e5cPU2mqoyOJDhpeeeYHJslLv3lljb2KbeaHHt1j3OnDzMvfll6s021VqTH/3sbZ44e4KvfOE5mu0O71+5xfU7C8SiEb7+8vOcPn6YIAh49MxxSpW6zA3s2Jw+cZjTxw9hWhZrG9vcu3iTU45MkfASMVjelJXArscb5y9TrtaZX9vh/MUbnDx+mHK1zL3FdYqVOhev3eXJR0+QSCYwTZ0rNxZY2ygwPTnCr3z+aaYnxrl68y5vvHuRgWyaeMxC1/J84fknOXn0EIsr63z/R69z6vhBvmw7GGH1c6Va49LaBkNjI0BAu91GUQW6Lm3HVlY3qFRrRCJmaLXlUyxV2Nkpcvr4IdLJOAFQrjW4t7DM0Y6NHoAIwjSYrpwGwWcHXHvJlAe/2QVknxX8fFxh2p7WBUB9QOahGK1P2Gd3V4F0j9mVC+o2n561U/f8Pu48e4f3C4ZT92+2S+4EAYoioyi7ijAKBC5h0BHfl6lAtuuGm9ub9iSUfrLolx1GfKjWtc4QCBHskli7h/jL3V0fyu2V9XcTiFAwQwNT1/N67Gqr3WZ5rY0Qgmg0Qsd2uHTtHm+/f5Vmu40ViZBOxUnGY+jhxN5sSmHNkeE8y2urfP9Hr3NvYYV4zOppOIHM0/B9n3bbxgmlBQI/wPU8hIBkIs7oYDYcRKLcvrdItVrj1LEDxGNRllY2yGZSeEHA+mZBDpzNFgJ6IcihfJahfI6F5TXml1bxfI9EPMr12/OoqsrWdgk/kMbb1XoDXVNptWyq9SbxqIWqabQ7Nu1OB8+TivKTE8NMjA3R7sjKuIFcGgK5WjQNnQDY3C5QKtcoFKtYEZOBXJLZ6XG+8OLTqCGLd+36Xe7eXcLzPVzX5aUXn6CwU+De/WWWVzcpV2vMTAySzqQYGRlic7uE48iQrm1LbZ9CsUy90WJkKEcqGWN9cwfH8YhHYxI42y6aohDRjZCYkWHDiGng+z6qIkgmYpJNGciwXSgDEix0dbK6GlsA6rW7xP5v/4pAVzEsC80yEbqGr2lESxVGlteI311GnR4nqmmMN1sME5BxfSJrWwjDINBkQUHL9yjtFGkVywyPjZAf0om4Hn61TsQ0mBwdRShSRFITAl3X8QJw/QAFQSaTZnZmAnxBIh6T8hvhcUZMCUbbHZtiqdwL/aaTCR47c5xMKsmLzz7GzNS4ZD0bDQJFwzAMDs7NyKpZz5MuAv/kn0ml7SCARgv9X/8ZImYRxCwc08CORlCTcYRlySVMPIpIxPAcBzxPJoIbBkrg0nMLdVzUxVWU9U0O/Df/nayAC3MnTc8j73poalhh2/fMEsAsMNsbGmTVMH3XyHYc1ja2eO/iVVLJBMNDeYxIJLzvLJ587DSLqxvcvLNAPBrjxJExrt28xx/+hx8QtSzOnT5Kp93h4Nw0Q4M5CMDTfUxTFnMcmpsi+YM30OpNcFwia1sMaRpf+4O/DItrAqY9KYKr//AtVFVlWAi+hsxTkyFgWf2nvX4B9d98j5wQ/GNH5taJcgMxv4bWNT8HgogRFpYoJBMxzpw6xOkTR+jYDvV6A0VRyGUlG+chiJom6fEE/9u//zt88YWnWF3fBGA4P8DUxCjRqEU0avJ3kzG2d0pYVoRcJk3sRJTBfJZqtUG90SaZTHB47iT5wTyvvnGeUqXC2FCezz31KAOZNJ2Og2kYHDs0zWA+KyVZAkGt0SYXKKRTSU4M50nEo1gRE2V4kMj1+8RjFqlUjDMnj5DLJHnz3YthLtYozdvzpFNxjh2ckvmkbYdDB6Z44elHMCMWG6FfajqVJJNOMTYyRCaVYGllA9PQ+fwz53pm9fOLK7z65vt8cOkGT9caDLiyMrXZarPteSg7BTa3C+RySdY3t7hw+QYA+YEsUctCDXMlPc9na7vIa29/QDxqcebkYSKmief5rKzKfNyuZEgQCBAaCCki/JnbR6a//ZN0HwO0/61PAib9G94D6D6ez9nTHlKKYq910D6GqQu0uqBkf+J/r992Cxr2n0/QWxDveXF3+/5ux3yygnx/EyAURBeb9EXThJD2ckL48hqHZyRxnt9Lvena9+yeT9Bj3BQhUB4S1/xywVZfvHmPL9vfVLJh/67D3fsEoSOQJ61NTCM0pFYxDA0rEsHUtVB5WR5voVShUKxgmgaO47OxWaDRkvkZ3XOQekmDVKp1FldWWQ2tWzRNsiqGId/vdGyWVjaww2pEWaoMVsQknWywvS1zYQQyd8U0dAYGskStJjfvLXH4wDS27bC1U6RQqlCq1IiYBkP5LIP5LKZpsLK+yer6Fh3bJhIxqNWb3LyziKqqaKom3eszSS5fu0Ot3iQSMRkfHeLpx06TG8gyv7jOG+9+QLVWxzQNOh1pwZJIWEyNjxCPR7l6/S6bWwUmx4cxDJ2NzR0816XdarO8ssbyyiD5gRSpuMWRg1PEY1FiEQP8gLv3l1lYXuP+/BLRiBHm4ShhZYegUm1w/eY95hdXabXaqIrgw0s3sZ2rVOoNms0WW4WizHUKdbY0tUWz1ZY5Ka7fSygHgaaomLqBqkoBTgl2PaJRi1irTbtto6kKfiAV1mm3gdBEtlDC3C7KKjVVCUG5QAkCcq02kWYL69pdjKiFEJB35cMnBXOV8P728V0f3XEYcVzGBKGlzq4YLiDVrTWVQFcRuo4wdFRDR9U1Ak0jJyDqygovKx5Ds0wU30dxHJSIiZVISKuZVgcjZmGmU3R8j9zqBs3NbWLxBO76Dn4QoOoawjTxNA3fNNAsC2HqCFVFvXa3p7wt2h3Mf/77vefI3BfViHcHNUVBqAqBohKoCkLTEJoKOyW5rW6ekOuiLq9LKx2ZEIgfBCi+v/sd+Mg8sMtQy3IX0fsz4MqN23z/R6/jeh7PPnmWbDbRG4Qd16VQKDOYy3DyyAEeO3eCw8cO8/rP3mZx+ffp2A6tVpuO7aCIXVV7JZD3Y1e009ClITkEvaRrrVztTQqSDe33WA27yQ9QPGkurXR1msJQkbl7crIPgr6Z0Oxmb+2eu6IIolYEQ9dlUr+/6+tKaF+pqgrHjxxkdmoCN6xS1TR5DsODQ4yNDsm5Otxn4PtMjY/S7nTY3CpK5X1d49FzJzh56jC1SgXX8UlEIr1V+pUb93jv0nWSIaCyHYd4PMqLzz7OC88+yrNHDpEcHJCl736AsB0s3eDoiQMc+z/8Q9yOzWOnj5JKxMikUxyam+LvfOvL6KbJ4MAAoyMDGKbKl7/4LI88cpJbdxbQNY2Txw4Ti1ocPTTLl196lkq1xuBAhqcfO8Xw0ACu63Hk4AzPPn6an7zxPvVGi4FwjI7Fo5x75hGSqRS6LquYs5k0L/x/iPvvaEuOPL8P/ETa6827z/vyFoWC9+hGN9p3T/f0TM8MZ2iGlLhcaUmd5R5y91ASyZXOUuIa7uHuSlpqKVJLasQZzZDdY9AO6AbQaHhTAMr7et6b60262D8iM+99r14BhR40N+q8uvfmzZsZGRkZ8Y3v7/f7/p54WEVejg6RCk2Ik+MjfPGZx1hd22RufpFX3zrD+OgQA/1FlpZX2Nqu7JpcwolYdNNhfeISi45Gk/puM+LHlL123+2fFTndf2TZg17rNUF+7G8VsbADz0kRAqFdx43Amei23W3f7zp8bxqfHaeVokeEtMvI9e555/uy04wZaXxFeVHjL2XEYHYjeoPQkrQT1IrYHBwEQc9g9dHlU2a2emi2CGjttZuEncJqv3iJHdZiM3U3YaSpq8StQkAmneTUiUOMDg3gOi6B71Ot1inks4yPDnLt1jwzc8uYpkE2naLRbNJotvCDANsy6SvmGB8dJJmwGR3qp1ZTUW+NZktJOHSUbTibScXOu4FU0VmpZILJMEJOdRilWN7udEgmEzxw73ESts3o8ACHD0zTbLVpNls0mi0uXr3J8GCJ/VMT2JZJo6miAbeP76fdUWxQuVIjk04yOFAin8vQXyzRbLUJAsnswjIz8yt0nA5C03Bdn82tbTa2KgyU8owM9bNdrnJrbomO41DMK32lja0KjUab1fUypWKOfVNjTE+Mk0mnOH/pOmfeO8/YUImBwQH2T41jGAYjwwOkEimq1RozC8tcvHwDISVrm+XQl2WA0eEBMukU2Uya8bEh5hZXWF7botNxaLsuMlAUb73RQrKNaehk0mmCQDLYX6CvkFe5+doujUaLseEBxkeH0TQlnlqu1KhUlRiq56movv5iAV3XEQKK/9n/DX1xFX12qbcz3h6QAyR8H9MP0PUaWjhJG1Ff03qSkiLRgwAtkNio7yIgdhuhL0PlYiFuUx7WicBOt06mDAFPuCpDQj76jCAjJcUg4AFAf+5n8T49VdtVCYlodbrgKAgQkZl91/WH63r12Q/A7Zkmov8cZ8cPRSDRwgCJqJhSokuJ0HWEaUCYOBhdUwmEIyAXvkfXkOFiLdAE5eV1BvoKPHT/PZw6cRghlO+lF4qt/vilN1hcXuNXvvwZcpkM2+sbvPXeOSzbYniwFMsFyPia1NVJCb4EKTRkMUegabC6oS7HNPGL+XhgBsWiSl2BrShVmAwC/FAcUymta+iaHvt7RmyqH/hoaxuqHU2TQDNA9mZak+GYGGlribgfSRnmmg2P6XseuqahW5baD0EQRiH6vozntUh9PapHKmUrdXyvTbsdxNGVhm6g6Ur13jJNHrn/JH2FDIMDKl/rwtIq7569zFvvnWNqYpThRjt8ngS8/Bba4io6kKjWkH/p7xDkMtxnGohiltTwIFY2RUoIjMF+MrqOcH38VIKgL48v4cTRAxQKOQZK/diWzcH9kwwN9iOlSkGUNHXa7Q6tdjsMu09j2xamqfIoSgH2QB+PPXJ/LKbr+z6lsM9slSsYugKZuq5zYN8koyODeJ7P6vIK29sVOo7D8so6c/NLKujGMBBBl4mM+/deE/pu0LOXSe626W4PUPGxpYfyinKB7j7vbU73e5Wec0f7xsTTXdQp3iVisT6qylF9umlxol/uZrd2fB8BqsiEF9wOtMINO69j9+nZSRRGZsQYcLEzobeEHqAVJZkWO44nw2N80jv46YKtKCljiA6j2nWrGyHd27648yF7GvY2NVepBrLeooU3NZC9ilsCyzLo78sxOT6kxPWEYHOrTDKZYLC/j7X1LZABzWYTXdfpy2fJphM02x1y2QzT40MM9OUxLZNMOoEANja26C8VGOov0mq1kX6AldQ5cWQfxw7vx/OUHIFu6AwPlBgaKNFoNllZXVdaWoDruGxvV1SC5nqDW7MLDPQX6SvmGRsdJJdVzqumYeIHPoMDJYaHiiSTdqxp1G53kBBGgzXY2qqxtV2mr5Cjr5CjVCzgez6e41EtV8mlkzz18L1k0ilyubTKG4gkkLBdqbGytqFyOVomSyvrXL42w4HpMaYnJnA9n/XNbc5evIplGzzx+AOMjykzhpQBTz/5IJoQXLx2g4RpsL6+xYF9kwyUlNr7wtIymiY4cewg05OjzC+uMju/ikQy1N+HHkZuup6n6heaaXPZDOmEMvuePnmUpJ3gT370EomETTqdVMmgTROkYjt0XcPzQu2wMC0MgFapoTkO3sRIDxUcTsDxA676ldtx8H0PDB1NN8LtilWL12mKc1ZRUUGUfj3uoD3UeJi03VevWvh91zdBxgukHaJ7dAemGADtWhFq4SAr/OCO6+XeOu0YrEwD78QhhO8jPV8p6/u+8j301DbhB2gyiBMHR8wPAEay+16CNDSC8ZEdz6jnKTpeMZxGl4nvqWW3SlIBwLCeuuPyu3/0Q9A09As34Ls/RqYSBKkEfjJBXgi+vbjGh7OLtP7g+1w6e4WGEDRvzvKd0yc4nM1SCyQDA/2YdgIfjeJ/+n9FbFfDsO4uBOttrWhrPJaH9wARCjHG85qGpmv4fhD6hvpYpmKpQDHbfthnRI8Dc/Zv/Gfh3LhzICdum+451YIyshZAkM9R+8d/TzGAmohzH4qQzSJQ21wn0nkzEJqGZlpIz8XzJTKMbgwCH8swkULHQ7K+VabR7PDYQ/dx78lDSAQLi8u4vmQlZOX1WqM7t4XRWgIUaG40EbUGluMoE4tpkgAGfKXHFy1CABKWSTZl42XTmIP96P19yEIeCjlShSxeLkvbNlkIPLZabRAqS8T1W/N87okHGXjzPMwuIaQku7DCZ/7b31MTpR9gGDqWZSEEDIYyFXoIlAuBcnrWhOBIoBa9MlBuKL/dbCvXBE2xt+omRIzOjidJlb1Az84O3f0s7vR07trv49ipO53zo+qxA9jc4fgfAbR2mviU/Env2HXHErNHvc/Z7fv3MtuxxpYE5UslUBlU73D8vbb1XLdE7HiQe2ugIQmiBZVQfmUyZLSCoJueKbZQxJT2nbTq71w+fQf5yEEubjBN/QVaT8OHX8Uc1MccMlDaOHdTRPwXYGgC2zLIZVM4HYefvPw2M3NLPHzqGBNjI2xuV1i/PsNQfx8dx1UmvNCen0zY1Bp1llbWaTaaXLmukjr3FXO02w6moVOtN3ji4dOkUwnmF1cZKBUQmtK5sgyddquFZeh0nA7Xb9zgxo1bNJptVtY22a7UaLba1BpNXnn9PUaH+1lc2eDowSm+8JlHMNNJdCTDA0Ucx8M0DZUiRdfotJp0Wg010AuBIUQYkKCTS6XQJaRsA4RGOp3myUfvJ5PKINBodzoEQcDS6gYrq2s02022trZJpxLsmxpXCaA1qNcbBBKG+otUajU6nTbPv/gqlVqThG2Rz6V57a0P+ODcFY4e3scD9x7n8KF9DJZKfPaJh7AsnRs359A0naGBftKpJPOLy8hAUirkKeYzCAEjQ/0cOzSFruuMjw+RSilF/2q1Tjadoq+Qp9XucOTgfjodlx+88Crr69scPbSPbDrNpau3WFvfinMsplNJTEMn8JV5wTJ1dE1gGTo7HnJdQ4aSG0GPybu3aKHwpgACTd+R1Hb3ENNLf/dCrnh/IQl8pWmmhQ7+sVhiOMAGgcTtuHTaHTzXRfrKBy1pq+TAGmryDjxfgbZYHVyih0+SAilBVyAmlLiQgdouvACxua226xre6BAz/+1/gWWapNJmyLAotW8ZAlFD06jW6uTyOUzLRhOgOR7Um9B0kNUGst6AWoOgVkfWGsppvt5E1hvIagMaTWi2ka0WotFSwK1n2Nv9LhqW1UIpNLk124hWe0fLp4CnPI/7PYm7tI5Y3kBK+KJlktp8E+3Ft9WBohx36STmxWvgergDfcgweW98/10vBJMSPRY5VbdJE9pORjMczoyQHfTCPmfoBkIPwX14H4RA5d7z1b3TL1xXddI1NXlqWs+fUAxf+Ce0nWOnhlTpY9DwhTJjBL5EEiACtV8QSNquH4ouSkxdx7ZtAtOKgaBmCHRhYZsahmYQ+JKb80t870cvc989R8gXVQaBi9dnWV3fYnJshOmJMRgfxTkwCZU6XJ/F+v0/UwuNoX7k8AByq6xyDPY8U5HwcW8RHQer2cLerKDNrdwGPCWQCiQ5z2VC0wjyWTqpBH/T8xjLFbFWN4j8djUpsasNFREbsuNayL51FydRQnJl0hVCoIfPuQxBY1qorBNGxNwBXV+tPYDWrnQx3c4Sjgc7Vkx3We6EXQR3YHB6+ZvdIC/6St6lqfDjKtaN8L8dGO5xsUEQgkO/B3Tt/O3evlqgAhP4WPB5J/NhL8u/w6Wph93aCZokEuUmovxbvdAfu3tNgp3HE5oW5oH++PLpg60dZQeyQgZaFxxKBbXu1tFNBhK0PfbbGygTJZpMWiaDpQJCKNOU77qcu3yDi1dnaDZbFPJZ5XcFYRRaH7qus7FVpjHTwrZsHM+n1e5gmjqz8yvYlsXoUD+5bIZSMcfBfZMcOTCJH0hW1zZxnA7NRgNNwP7JUSZC86MQAtf1aLZatNodGs0WaxvbOK7L+sY2V2/Mc/7yTaYnRsidOkZ/XwHP89hsV9jaqrHmKZ2cRqOFaSm15cBXk2MkFJlOJclmUvT3FZQavGFgmiaWaaBrJgN9WRaXVnnl9bdwPY/JsSGGSgVKfXkSlkng+0yND5HPpilXa+iawDBGqDdbrKxtYq5tkUmnGRsZYH5plVvzyyyvbHD+4jUeeeAenv3MkwwO9HHmg0u8+ub7aEKwurZBo9miXKnzyP0nmBwdwXd8Zmfmcdptxkb6aTsuK6sbsZ9cEASsrm7huDN02g7r62WefuQBPvf4g2xubbO9tU3SDnW6Wm0s26Req+M5DsVCHss0lKyE55NLJylk0+j0PODFHOX//v9IJMmhCT3spgHK5VENrivLK1y5dpOjh/YzPTmGoRtIwJcyZkZUBoDdXHpoJhQghPq+1Wozv7BMtd5gdGSQocFSKESrVuzNZou5+WXmFtbQNY35hWWW1zZ45qlHePC+e8hkUnHAhe/5YQSlD0KGrFE4AAiIUhhFOeniwWF+heR//F8gbi3gWybLtsm/+v0/5tjhfTzxyD3kM0k0oaEbgs2tqgqwaHf40x++zLOfeZQjRw8p0JkyIPSBQar8d5EpS7ELfpzBAdTkHzN2QUDQaqPVm9BqQU0BMxotZK1Oe6tM/x88h9Vuo6GhbVcQgWLY8ENmreeZNwQkdjz8QKMFEVjaPWS0VHCA1u7g62rBoul6fL+kriNNA6+YU1NL1H6hT1aXdQr/kxL8AOGryKVA13eYluJ3i6tx9JPodG4ftHb0nu6rDG+qaLbVRl2j+Nm/EJthA00ghQJuwjBC06zAFwJ0Hc00EIahgKVQGRd2Wl9EnEHm89sVjiyt4p+/Tva5l9A1nYdclycsk/5Skdy//B7C87rN7/mqXgmboNVGuh4ynUJYNlrgK8Y3SvTseoo5dT2E56GFCaGjCO6d16+uXKNnklpYAWAAEO9fgnYnfp41QKys7zJVidvuPcBuTKD1EJu3/WZ1A/G//gfw//mvdlUwBFrdjrD7AojR+G3M6S8AeuLVR2/P2POkO88R/ebj2LKPPX8EHCPQuQcDt4O966kDyrc2hjV7sHc78iTG59l9nLsrKs0cPefQiJgx9Z2M/RMR3ffRvyBQ80bHdcPcx9HCSsTHiJeGnwBEf8pgS4YoL9KTkeFA3KW5kbpC2MqOohiZj2CtPipZZdfOKm+7H1JK1je3WN/cZn2zQjadZN/ECLlMipm5FbbKVdKpBEITbG1X2a5UGRsZpFKtsbFVUWCr0SKbzTA1MYogwDIMNrYqOI7HQH8fI4MlarU6731wjoWlNYr5LKPDA9imQbVeD9cbpTghdr3eYKtSpR0mmbYti/2ToyQSNkurG7z8xvvcnF3ij557iY7r8eXPPsry2gY/ffVdtrcr5LNpbMskYdvYtkkqmQRkDFCiNk8mEippbCFPIa9eleNtgBAB7509x9tnztFXyCKkz77JUR667wQjYyO0Gw08r0210WZ9sytyWms0sUyTkaH+MPWNZGykH00onTGn4/DOmQu0Gm2OHJyiWW+QTtpk0ymOH9qntH5yWQZLRWbnl3jz3Q95+/0LKuGxbZHLplRqpM0yzXYnJCM0fC+IBRyrlRrFfA5D09mq1NnerlDMZejvK2DoGn7g0+44IAPSqYRKIRIo341CPhuv//YqARJfKmbI0NTg7zo+Z89f4cVX3yaTTjExOqxWvCh2QUMSEAAeUvpIIdjxrew+mAKJoYHnOly+fI21tU0ef+Q++gpZdQ9dj3K5woWLl8nncjz2wP0UMkkWlpbZ2NzGD1kToQvMMO2JxMcPBFKGkX/dByN+6U6q4bZ2d5IPhGC7UuWFl9+g02lx7/Fp8pkUpmlSrlT54+//lEIuy6EDU7z74SUmxobZd2CaZCKENrHfZUCEInoHOUEE+rqrSSlDtjBhgp0jkFnivKW+z41b8/z4pXl+y3UYS9jkshlE5JgcP9yhL0WgxDJF6KAvQrZPST8H8fZoW2QajVbRrusyi0EiYVMq5rFtG4HS2ClbBj/7y9/i6KF99BWyGIaJZdtYlhlfl4iSR0tJs9Xm9bfOsLq2wWeeeIiB/j400TVHIiW53/27iDDgo3uv7tAhiaa0aAeJuHQ9Ziy1sgKSQU87RD/SQid+o2eSj9pYaALNNHYxaYpNE5pGSQhSEtq+T1Cpo+k6pm2REBpGo4VothTgjTtReH7fU76Am2V0KcOcIT0rYU0HWwfLImIuJWoRHQSBCqCI7mEYcKBew7/IfO33ALMeFW9ZyBKU+mKTbWQyvMNavKfZo4mU7szZM5kigK3KnW/QHW9g977dDp33ACu9595L2PQ2INN7vL22RWDiE6CBjypd2//e5TYTZXQ9/h32+9gTxi8SDeQdzIgfeYgwA0TMke9ktnrOAiKI+2IQ+LieEkH3fD/m2XtrHgTyE1yLKp8q2IqmGbljcRCGVUYIP+40kRnhE5TdCwTCQa+nQ8XgLBA0Gi0syyKdTCIQ7JscQ9c0Ll2bRQjIZlIM9BVIWCb9xQJDgwOMDA1gmctUa3XWWi3ajsv99x7n1InD5DPKLymSdhBApVLlg3OXuHRthgNTY5w8eoBAShZX1vE9j3qjxeVrSg9rdmGZpdUNGs02uUyKgf4iqYRNMplACyULctk0Jw5Pc3DfBCvrm7z13jkuXr6JFeagmxobZmS4n0q1TgSsDEPHi9O0KOZgdW2T2fllHMelWMhx+uQRpsZHWd3Y5J0PLhJIyeTEKK2Ow/OvvEMgBd/8+rN0Oh1kEKBrNkMDgxQLBaYmRjl14jAdx6HZcLh8bYbN7TKppEWpmGd2fpWVtU06bYc33j3Hux9exjZNOh0XQZtWy8E2LTpth3OXbnD52i3qjSbblRqOq1hDpKRULDA2bNJsKWmOidFh2u0O7baj2BDf5+KVW3Qcl1Ixz+hQPyOD/eSzGerNOoZhsL65zcraJtVanaH+PjJJm8mxYXK5TM8ALFQfDU0bSmS0iQhzsyVsU0WnETA40MeTj9zH5NiIEpsN+5iU6uGUBGogkAEaKpQ/WicpIWT1VOgiIGGajA8P02kr/SLHcQg8F13T8J0Oi4tLXL46w/33HsU0dWzbUqHqjtv1+wnrj1CAV7EUt0dJRQlVdzPBWgi2BKAZBplUEk3TWFlZp1KtMz46hK5pXL42wytvfMATD5/imdEh/sK3v8TEWAg2exZHEaEXLXy18JI1IQmEJMxkFzNsWrjw2r2IEmG/XVha5sateRqWhZNN4xfyYSCBuDOJ8LHDSHegNK/cRDhKLV2aBhuawBaQSCUwwqTJzWabuWqNC5eucWBiBI2sak0pb3Np0IQC0iLwWF9bZ3llDc/poEkfTWj4Qgvvl6D2r/+JAibtDqLVRjRaaK02NJVpVTRbaK3Ozm3tjjK7NppoV27GDS7DKGjP91UgGAp4CSkww3yO8bQnlPnZ8V20QCixzgiM7mpQAaSRpNRZFGPnuFBrqkknYtdixiR81Q1kLoMsFdkJKHoOzO75KQTZnjLbxtGcdwouQfUR4XkKNLtuzJrJdJK2bbFdriKlpC+TUdk97jDHxNWIAJm2q39FIGuPNvpkZa/f9rI4HwGIPpaRiiDrTtnOnmXPL6HcqS1kuAgSXcAoPspz/uNOIekRwQKhQbio7L2yjzIhdtP1RI4WESmh/PUCuQs7REcOzYhOGPii2lf0PE/iF7LI/pLNiKrsTiopIYzIisX09yyxLTf+nQzB+p1/E32n6xr3HD/MX/5Nk/m5BVZWNzhyYJpcLoOmaaQTFof3TzI8NIAA5pa2QAjGR4c5fc8xDkxP8MZ7H7C+WWagv8iJw/vIZdJ0XId6vUmt3qDdbjNUytPpdKjWmyoNyPAA2+UqtmWyVq3z45ffpFpvUG80aTTauJ7y2aln05SrdTRNU/IPpQLT48NkUglGBvuYn1+kXKvx2jvnaDbblIp5rt2c59b8Moaus12pAWAaBpl0gmQigSYEmXSSUl+eTDqJ7/lcn11kbmGVm7OLPPPEg9ycXeTarQUK+Sz7p8ep1RrcmF3kgwtX47xmvh90gYkQaLrqnLqug9QoV2psbJYZ7C/Q31ckaW8T+D66JvA9n41Q20ogaOgtLl65gURSrtRptTuU+rKcPLyPU8cOsL5Z5uqteZX2Rwjy2YyKdEKwf3Kc9c1tKpUqmVSSgVIf9ck2m9tlkDA2MsRgf1+YkLnMwX0TFPNZkgmbc5dvYBoGp08e5vjh/XHYtxblVEOZvRaWVnjznbNcuzlHIZ9jYnyYE0cPMD0xSsoyOHpwmsmxYQr5nGKTpKTWaLC5VQ4fRg+ETy6bIZtJY5sJEOB6Ls2OqzS0hCBhKbHXQi7H8aOHWFrd5Or1OVazCfZPjeF5PguLq2xslUPnTJ9mqx2bncuVKulUkoRth0xxEPun9D4Oka+Vkh5RYEzv9T8JddsAhK6RStmUChnWNrZZWlnn8MFp3Hqddz+4wPBAH089ej8jY8MUCjk2t8qsrq0zPDSIaUcBA6H4n6MmTlMXOxgdz/MVWJYS09CwdMX4tdodFVQCsYRKEARMT47x29/+CrXfSbA2UCLoK6DrBsgof6iDpqMYph0LrSDM9KAGaSGMsA+rlW00kBb/t/8V1lsfQCCpTI/xD3NJPM/jL33nqzz92AMIIXj3/XO8deYCDx2YVArogOsH4HlEEiGKOSKMEAywTIPPPPEgzWabYiGMYkTge0qvZ8fYZFtoyQRaf1/XOTcsURRjr09ItBI3fv42tNqQTLD4o/+RH33vR6zNLvD1Jx6kP5lkY24Jt9ZgLJ8jFUhkvYloNzE6LqLZ4vIHF3G2KxwbGyaDQLSd7rjZO4ZGn3vImUBCo9XCXljBADTTVN/5vjJb7hvH+xu/jfeNz4d3PiDyf4nnMCDwe84gJZ7rUKnWqdYa6LpGIZ8ll0ljmAa9OW+75nq5a5vqaUEguXTlBv/i9/4drVaHv/YXv80jD55SQTO3lXAS1tUYpWkami5CB37RbfPf/NtK2uRTIodU6WW8CIHJncDWzl1vL7Ln/4j50e+w66cAvuTe9YwiBHeCvHAEuI2Mux1AKhM9YbBb10ITW/qi8ST0Bb/j/K+FJk7p7/jdjr2lWmRLX8advHs8gbLECRXt7Pv4kZN8eH1qHDNuiyS/m/LvBWwRrgqFDF9/0d67B7N1W4mc3zSN4eEB+ot9NO85SrVWJ5mwSaeSHD+8H036pNMZOp7P0vIquqGTTqVIJROk0ylKfXmOHJpidX2DfC5HwrbQDQ2v6bO5XWZ7u4L0fZIJi1Qywf33HGFqYpSp8REmRocZGRrg0rWbfHD+KroO+VwKXdNC5kSl4HE8JXrY31dkYnyEvmIBx3G5fO06Z85fZnx0kLFhFQI9NT7EjZklytU6uqYxPFCg3VE2Zcs0SSeVY/naZpmN7YpizBK2UjYXcPXmPJ7vU6s11eSm61y6ekuFU+saruty/tI1FpbXKZerygdJaIo5CfzQVKUhhArDdl2H9Y0tUgmbcrVOuVInn81QKuSQgVQ6Zb5aiSyvbaCc1ZXvWKfjUq6GGl+OQ7vtKJmNULVcSvD8gNm5ReaWVplfWiWdTDI63E9fIYeu6axtblNt1NkqbzO7uMJ2pYptWZimQb3ZIghUkMO9xw8zPT4cax+Jnofr+q05/vQHL/PKG+/TbquE17lsmm9+5TOMD/fT8j3efO8sM/PLfOmZx8hkbDxPcmtmgRd//jattlrlN9st9k2O8ODpe5ieGMdxXC5eucHl6zOUa02klBw+OMXTjz+E57q8feY8b777ITPzSxSySb76xac4dHCarapKaSI0QaPVYnltla1KGTkryH+QwjTvZWxkBM/zWd/cYH1zGxkEDPT3MTDQh2WY+EHAhcvXuHjpBoEMGBka4N57joTJi+n6/UAYzGHQX8pz+dos127McfrkEZZW1jl34ToPP3CSg/sncByHH//0NX76yjsMDRT57e98jUOH9xG4HptbFa7eWmB2dgXP89k3NcbB/dOk00k6HY8bt+Z4/+wlhEAp8U9PIoTOjVvzvPvBBaq1OsVCjomxYY4e2sfwYD+NejMU/K2TzaRJJkwajSa3Zue5cuMmmUyS+08dD/M8KjNqtV5laXmNdqdDImEzPDBIsZCDEPSoCwbRUlHAaIJk0mZooI/rt+bZLldih9iNzTK5TJLxkUEsy6TR6rC5XUXKgFQqqYQOA8ngQIn+UjHMQCDjJOCu5+Gj0em4zMwusrqxgWWaoY9ywGB/iakJ5T4gpaTVarNVruB5PoV8LswTqOF6Hh3HxTINbHPnUH1zYZm3b84wNjJI3yP3kS/mSbkqN6sQgg4g8ULnewPPc/m3/+z3uHpjlv/93/orHD96QJEPTRfRaKG3WmiNluofrTay2YKQeZONFl61zuqVG5SW18gIgbAtROSTFTVuwqbL6fawGj2gQUbjeDil6kKQMA1amlBMr+shQpglNaHyOobzxkdhDiEEQ4MlTp04zHsfXAyTce9a5Mc29QChSaIAIxGHAaMARQjslC7anUxn8u7mo48qHzVhx8zhRxtBd27bxSR9GgBr5wHv/FXMcmpdVkv0fNfDSIl4/12gK2SMlTZ5GPARgvLIZzJyRejWKBZvIPa36w3Q+8gi6Bka0NDjIyIVGyxjc2FYNynJ5bKkkkk+miq6vfzSwNYOx3cJcofwl0RGg+CntWro7biR/4quYSZM8rZJvpiL/R1U8mifQGr4zSYIgWnqFHJZigXFCgk9YGioyMngkHrwdGUeKhQ0Egkbd9xFD2+846h8g7quK2d7KUNdrT4eOHWMRquB60VMg6aQcSj2aBgGyXSadCqJpuk4jkN/f47jh6fpK2TpdFRy6nw2TbXejGnyKNw88CWGYcaMh+/7oZihpN1xFDCs1LAMld8um0mrlbmmIXRDOTJ7LsmETbGviON6lLe2aTsuUigx0sD38D0PP4BWx6FcrlGv1zENHds2qdVbrG9USCVt+vuKeJ5PrVaj47pomlppBVIqrRtDJ5AevudTramEuqVijmTCwjJNkkkbzw3YrtS4cPUmjuuRsC183+fG7CKzCyskbEtNaH7A/NJqnMvu2sy8EjNN2IwM9XP65BGOHtpHwrYJRM9ThRI//eHzr/LzN95H1zU++8R9NFttrlyfY319EyEDHMfj4pUbvP3+RR44fZQD02MEnk95u8zVGzMkEzZHDu7DWXV59c2zVCotfvVrWeXv9MMXuXZzHk3XsSwT09A5fc8xzl24wv/0h8+xsLSKEBoJ22B6aoKpqXFM0wyTMAvarsd2tUGz7ZALE6e32x3anRbXbszyk5ff4ObMIq7ncfjAFL/69c+zb2qctfVN/vB7P2ZhcTVU+i8wPNSvwJYErdEkXhFrOrquM9BX4GIww625JTbD5Nqu53Py6H7StkG10eDazTne/eASyaTN8SP72Xdwinq1xs9ff5cfvvgGm5s1Gs0W999zhN/5zV8hlRxhbn6RP/vRy7z61vvIQPLUY/cx/J0SA6US84sr/PEPX2a7WmdsuJ+nH72P44cP4HQcXn79XV57+yyfefx+jh85hOM0uHT1Bm+fOce1WzPoumLBnnnyYbKZNJVanRdffZt3zlyk2WqTyST51a8+w/2FY3HgQ3TjY5+1MCJ0YmyYazdmVULnap1Gs83a5jaD/SX6inlc1+Pq9Rlefv1darUG/aUC7Y5Do9Hk+JGD/MpXP8fwYD/VWoPvv/AKM/NLfO3Ln+X+e0+wtrHOCz97laWVDQ5MT7BdrrG2vsnJYwcpFnIkEzar61u88/45Ll6+QavV5sihfTz9+IMM9vcxM7fIK2+8y8ToMCeP7ONIuBiXUnLl6g02tyucOnEYXdfYrtSp1BrYlkUul8EwdaQUVBottkP/xtX1zdiPUEYr/IyNTKcIEOhC5ZOU0ZgdtpznKW2+n/zkFZ6+eI0J2yY9NabG3I1tgkKW9X/0d1SwQbWCnTCwwwjSKNhFjZESGYhQEFhtF4GPYRpkMikc11XjTRDJpPhK3DWaR8Jr35FoWEoF0qQkYdtkM2nMKNG0pOc3Cuj6gQNITNOMZMt2FCkljXqLra0yw46DFUQGpF3ISplndvStT1x2Ril0t8k93u/84Y5PIp72te73kRN6dOxPA3cJdgCPeHOvOXkPoNWFJOE9jLCQII4U3REtqCmGKfb9FoqoUe4fAqTe41LxyS5gtw9ptE0IpcMYL8qFikqNL0RVBAkU8zmSiWS37nd59k8VbMmYgJbdCyKOPu+53913u32u9iq9PhJ77nsHKlZIVP8LNHWDRRCjZBm6MivVdZ1EwiSdTpBImOimCDlm1Yl0GfT0HRXa7Qc+ge8T+B6GruOFEToR3S0DiRXm0esXRVQqo27H6n0G/OghkQLbstk/Pc6+yREMTcSaH8o3J8xCHqVCCM0l0UpBE93OFIQPquu51OoNHMcjn8uQz2VJJRNohqLptdDIEg3Anqf0dzwpo0QsiJ576vkBjuPgu46SBTAMHEcxZkEgyaRTGELgdFz8QK2sA0To66KpxQkerqucEBvNFptbZWqNJprQsEwbx3Gp1up0HMX8RTIJrXYnpnE9z6fjOLHoo+8HuOG9GBnsZ3JshP3T4wwOFJBiZ38ESaPR5N33L+A4Dp976kF+5ze+Rq3e5MyHFxkqFbFME82Cwb4cpq6B76GhfBJty2BkoI99U2M88/SjLC2v80d/8mOWVtao1pRpuOO4JBIWTz96P/unJ5gYGwbf482336dcrnDfqaMUcllStsn+iVFsKxFOFDoCSS6dYLAvz1B/kYceOMljD52mv1RkdW2DH/3kVV54+S3a7Q6eH7C4vM6pk4eZnhpjbWOT6zfnyecyPHDvCfpL+Z5IzECZoaISRvf2lwrYtsnq+hZXb86ysLTK0YNTTE+M0ek4JEyDzz31MGcvXGdmYUWBZM/nxs15Xnj5TVqNNk89ci/Lqxv09+VJ2Sae0+HylWtcvzFDEATU6k1uzixSrTUYHxmhWFBCs+lkgqceOc1Dp09gW0ZowtEoV+vKbBvAytoqq6tKWqVYOMGHF64yO7dIvd4gl0mysb7OG2+fDYMwMqxvdGh3OkrtPbxnQTQQhaYzGZrFpydGsC2T67fmee/DS7iOj+sETIyOkkgkaHc6zC+t8PaZ8wRBwEOnT1Ao5Lhxa57F5XVOHj/EyGAfmhawvrnJlRszPFnZxg9cqrUaS8ureH7Agf1jbG/XmJ1fZGllnVarTbXW4MVX3uQHL7zC0soGnu9z/eYshgZPPv4Qy6vrPPf8K0yNDZGyDQ4j4/luYXkVx/HIZtJ0Og6vvfU+F6/NcPzoQT7zxEMIYXFzZoFL125QbbRUftJKDUs30IShWkVKOp02G5sVOh2PUl+RQi6rwKyUNENh53q9ybUbs1y4fJ2n4vFYTfG+51GuVPnuz99ACDAtg+NH9nPk8H4StkWr1ebCpevMzi9jGDpTExNMT4xjWSaddpvAc2NV/1arzeZWmUJe6QOWqzVW1jbJZHNMTIygCcHGZplypYrn+5i6jhsCwUw6xfBgP4HnIkQ4XqJSPbVabRrNNq12i3aniWWZjAwPkMtmdswXEYxoNlvMzC3S57hYt33b+4MIMdw+Jf1CJQJIUfkEjkGiZ+6NjyPDY/456hcD21g7M+COchewJ6MV1XBHReL5L2SmdoMuAPQQRgbqVUhEmJJPxIr0t1+cFMqhfvc30R3cEZEYmesVUFCfNS2M7ta7QDJqC6lSvyl3De5a9gF+CcxWEBkJe/pmr2W5u2UP3ZJdJbLlxu/33ilusC7ODN/9R/9QOTlKJSYZo2HR7ZrJIGDUMqn8zb+IH7h4voPpq1DwKLZSXU94FRG9GNLavlQCgl6o1hx9H4S6LZGvU+zHEi42NKHChgO6CTulRNmKPZAyQteKHnY9Pz6253lK/Vm3sC1ilizofVhDU6ptWfjJAE3rhJ1EJWfVDJOO6xFIQg0qYjFRIZR2l+8H+FIgdLCizqdpIDIhSFM+Jp4ncUtBnJtQC+f1QAZIAqTQVJIHKdE0iWFq8QDr++p63CjEFo3AD3BDeYPI70hCeN1BvFruOColkq5rsbOwoetxXkHTMtA0JXArY/OGDH2aVHJsGUjGhvuZGh0CYHpsKAR4OjqSVDKBHqXBCPtPPpthqL9IIZchZVukkxaFXBrP97ENDdM0yWczNJot7j1xhNP3HEXTNG7OzrOwvEapr8C3vvo5xoYH0YXKbqBbCQTKbBJIJeVRLObVqj+Vor9UJGGZLCwsceXaDIVchuFDU4CgUMgyOFBCBpBMJBkbHuLGzAIrq5ucOnqQQjaDEq2VyEar26dD0D800EepkGNtc5vnX3oLyzT41lc/Ty6bpdVukUgk2LdvnKHBPuYWV9Uk1nC4cPEmK6ubfP7pR/nGlz7L1naFdDrJQKmoInnTaR667ziLKxucOXs51hlDQKlYUMl/peT0ySNMT4zgOh0y2Rz9pQKmoaMsCAGCgJHBfkzD4NrsHG4omCkCHyGV+c73fVzP59D+cQ5OjXFwciQcGkTolE8MtADQ1LM1OT5KXzHH3MIqP/35O2TSKQ7vn2JkcACBkmqxDKUIPzo0wJeffZID+6YIgoDX3nqfSmULIR1SKZvxsSGu3JjDNk10AjrtFp7rMThY4tCBKTY3t3nn/RStdptGq02rvcqH5y/j+T5TEyMq/2U6ydbWNq16HT1c0acSNmMjQ3v6nkS5X2/NLfHehxcpFvIEgWRuYYWXfv42hmFwz/HDGLrO9RuhbyQCgU6r1eCDcxc5e+Eq9XqL6YlxnnnqUfr7+yiXq7x95izlcpXhoX4WV1aZX1yN3UCEUNGc9XqDhVabl159m2TCVtHEz7aZGB9B0wTXbszyh3/8fJybs9FsMTE2QhDodDod2q0W6VSSZqvNG+9+yNXrsxw/vJ97jh/i6o1Z3nn/Ag/cd5LBwRLb5Qo/+snPWV3bRDd0fM9XuoGez+MP30spn1GLOkAX4HsuC8urnL14je1qjUajScfpcOjApBJDTiXRDO22uSWbTXNg3yS2bSm/NtHT6DvYrZDFkR8hAfFRZc85rccEtpcZcQdb1cOs9VJ5u97+oqzWzlyIUYkCBvaq++59e+fkHjC4C3T1/iKaa3cAIqmrsTsCjgKEjORfevIxftz1CC1ko2RcFYkIWdbQZy9ktBQJY3Z9s2SXShIyAqGfjFn7JYAtVKb0HgChXiPk2oO076KmH8d6wQ6erOdVwlYZubaF66owTiHANEwMQ4uRsQgC9HQSx3XpOC6Oo6LDDFMidAWWoqMGYWioYrWC2EEzkCqiptXuqAEeEZq5fEzTIGHbSg8ndPCUEgKUUJ6ugWF0TW2e59FoKP+lbOgs3uq0qVTrcfh/o9Gk3e5QzKsISgMRA7zoL8r3pus6Umi4AbgBeAiC0O9qdW0T21YRhVJK1rereK5HLptSbF2kii0gMPTQJ0r5OMRh1VLiuA61mkrhkUzYyjyhBXiujxeo1ZDredSbNaSUFAs5CrkMmq5Al22ZYYoEGYbfhilIpPpdu9Oh1Wri+R6WZSnnfz9gc1v5e+WyBSzTplJVQQOJhI1lGSACFUFOEE68IeAmeuQ1PC/g2s1Frt+cI5NOsVWuYhgaljFE0jZiYOf5akUnpQoFVgmhK7TbbTY2tphfXKWQyxAEPoEv0JA0Gi0qtVpsYt4uV8Ok2y6VSpXR4UE6joMELF9Sb3TouB6OH+CjEyBod1yqtQadjoOhQblcwXNdHrr/BJ996iGSiQSpZJJSsYTvQanQx6MP3sva+javvvk+MvAZ6C9yYN8oAFqrR/ohdAQe6C8yNNjHwvI6l6/N8uTD93LkwL4dLGkErFX6GRVcUKnWcB2PQi4bgqcsemhGb7ZaSCR26MgeCcL6IYjWNC00mRKLT/qBAsN2mIbGDZlN27KoVGvcnF3k/NXrbGyVOQG0Oh08z2Ogv8iD9x5haXWTS1dn2T8xopIk94wNUkolWxCV0Jl2eLDE5Ngw12cWuX5rnsmxYQZKRdLpVMy++uEzW8ilGR8dVlHEyYTSNVMe39iWSSGXwTKj1bCk1WzRcRyVJDqZoBKat1zXo93uKPDpdDhyYJInH3tQ6cPp6rnKZFKYmzqmoVPqyzM8VOqOhUKtrqNVeiaVYniwRCrU8vODgOs3Z5mdX+ZzTz/K/feeoNVqk04lqVTr8X3d3Kpw5doMhmFgWSYvvvoWgwP9PPHI/dyYmeOHL7xCNp3i1PGDpGyTixevoV28Gc/1nY5Dvd6g7XqkksqEt12uxqbAarXB+2cvce3mHF/+/OMU8llGhgZIJPQ4zN7zVMLxZMJG13RuzS1RLOR48L4TpFIqO8Fgfx++53Pmg4u89Oo7PHz/cUaGBjh/8QZnL1wjm07x6MOn1eI2nDCllFS2y5z58AI/eeVtdMOg3e6wWS7TaDY5dGCKYHgwnD9CP50gQMoAU9cZ7NHAU50oBAm9YEcN2iGbc7cJqnsB0R4oqAdr7Xgfn6tnY8R8xR09mmzvsiofVcsYZMVPEF2Q4oPYDR12Aa3IHB2joz2YqF5tQhFZT7rvY7kYAUJqMfgL94SI6QoX6dHxo+CU3RY0JLGJPIxJj2sdB6SEBIeuhTlHw4Cj28HKJ/c9/6X4bAVEtk/VeF4gFTsQUoGKglblbsDU3ZW9EZzjulTrDSqGQTKZIGHbJG0LyzLRK/U43US3LkozhxakUmp/IGRnu+HfQeQ70HMjpFQ+VCIM1+84DkmZwLIstbqWEtcLcH0V6eD5yrk9k07FisVSStxQxV6BGUGz1WFtY1sl1k7Y1Bptmq0WmWxOsUZCw5cSPwDXC2g0W4qByWWxdQMvkLiuH6rhKiBcqTWYnV9icDD0TfF8llc3cF0Xyx4jYeu4cQqSkNjRfNANZd4k7N5BQLPdZrtaJpVIqsTfQlcOwzIMR9fAky5rW2XqjRa6ZZLJpZV6cw/NLEQAWtRH1ITuSp9Gp6MSVDeaGIbO5NgIpmXSaLZY39wmlbLxfJ/5pRUQAjuRwEqYaELlvvNDB3+JTypQBmTLsshmkmxX65z58DKmbmDbFvOLK0yOD/Gb33yGwVKRar1Brd6mvN3AdZWJdWOrzM25JcWmuB6zCyssr21SKuZpNFX+trbjsrq+zfvnLnPk4D6GBkq0Wm0812NlfYt/+6c/4cC+y8ggYGR4kAP7p1haWWdru87aWplKpU6n47JdrvLB+SskkzbDpQIra5s024oRGAzZrpW1MoEvGBzo48qV68wvLJGwTZqtFhcu32Bzq4Jp6MwtLnJ8cYWBnjQUoMR8B0JHbyEEx4/sp1jI0e441GsNEl5AMpUikbARQK2mzEsJ28LzfS5eucnk+EhoZvZJJRPMzC3x0mvvsLFZRtN1pcjsK1HeVrtNx3FUcmhNww914pK2Tbvdod5Qvnzlap1aTfk5Pf+zt7h8YzaUN5Fcvj7DiSP7yWWU+O7E6BCHD0zy5rvn+PlbH3LP8YMUiwVuzi2yur5JPp9lshd+hWaAYj7L9MQoSfscjuMzMjTA2MgQuq7jOG3K1TqVagPXV76DSctQz6jjoIestUQBRtu2YgDphf5Gfijuqpg3D8f1kFJQrzeQYcSpH0Ahl+XEkQNoMqDdbqMbOk64f5SUeueQHwqTSolpqnyBKkpS+ZE26nVazWYMPjzPx3OVHIVKIB2QSSd55MF70HWDD89f4fW3P2R1fY22047B2gOnjlDMZUglLAb6CypaL6yBF6rmp1M2v/LlpykUCmxsbrF/32TIwvtkM2ks0+TSlVt88XOPMT05psyP0o2ZC03TSNg2UxMjZDMpqrU67baDbZncc+wgB6bHqNbqfHD+MkHg8/TjDzAxPkIqleLarXkymRS2bfcQPcr9YnO7zM2ZBQLf58C+SSSSze1tBkp9mKYR9lcPr+2rBWOliuM4FAo5PCkYcl1MGfpsBUE4weu3T7GfeArrASW9psI4WTVELiy3/1Tevj1yK2GP7/48ZQer1eWo1MS0h6xDZELc7aclbmcP1RF7AGov40SEEXrOGZ8jBE1E5sUQTcid5s3eOV3Gx+85huiC5qiusVlR09A1A13T0bQwN4cUPZJWCkAqALcbEd+5/LnB1s6Ijwjl+3FcRBA2ovKZ8kPE+zGrgL3o04+vCTtvS3hTAkkzmeDffecL7Jsex9B1+rJZDu2bYuTv/GPk+lZ4SoFpqMatVGtsV6oMlPoYKBUxDEOp30sZOlkGoTaMQAsEQorQOd6Ilb0dT0UlpYRiBOKcYBqIQDmyN5tt2qF5T08m45WqFt5kZR7041x/hUIO27ZwXY9EwiKfy2AYYTSNocfmtiCsn+/7amUQgp7I8U8GMnS2dmJnfT9QIC26rujeqfpEzMTOByZA4EvouArMaJpU9xjFbgRIpC7QDA1dWkqlWxcYpobQBUIj1I8KqWAReZCFoFb0mlgF21U1CfcPDFDKZEim05j1JlLTqTZbLG9sUsjnkBr4SKIQXtf1FBCWrvJ/k5JMOs3jD92D98b7rG1u85Ofv6NWNIZOImFRqbUxtDpLK1s0Wh1mFpZpNFvomka11mBjq8JWuaLa0LJ44NRR9k9PEKCxWamzsrbFdqXGa2+d5fCBKb78uScZGRpgenKU7WqD2cUVltc2mBgdYnh4kK2tMptbZUzDYHV9ixs3F0Bq2LbFB+eucP3mHA+cOkLCMtE1jTMfXiGb/gnNZptao82zzzxBwjJ4890Pee75nytxV2BsZJB8LsP1G3P80Z/+mN++NMNDLQclpSoIAkkymWD/9Bj9fXl0XefwwSkMQ2ervM3KqgIqwWaF9Y0yrbbDpWu3WFxeY3pqnEI+x8tvnOHG7IKKbOwrcPzIfq7emOP85ZsEfkAqnaTVdljb2OLazVscOTDF+sYm5WqNeqPFuYvXKPXl6e8rcOHKDV5+7T3qDQWkN7bKOK4X92nC/ru5XaXV7lCtN3j+5Te5emuOrUpdMYF15T9Yb7V57vlXeOPd8+ybHOWv3H+SscgdQFMr22TCYnpylHwug+9Ljh/ez9BgP4Hv02x1WFhe58bMAo1Gi42tCssraziuy8qaSgskQj0rx3XZCBcT2xVVt3xOMdNXrt3ildffw9A1Wq0OruuxsrpO0jJwXI/5pTmee/5lZuYWKWaV79HAYD/VRhvH9dgqV0Mtuu5QV6+3w3gfnSAQXYV+KTFQfoWtVpulJRWp22q1cBwnHmMilwopYXZ+iYuXr9Nqd9A0gec51GoNmq126MfZ1RyKNMPUMxnlbNQ4enCK0eFBNrfLNFodqrU6lmly8tgh1jbL/Oy19/jRT17H931OHjuEaarFgOdErgGQSadIJmwuXr0Fz/2ERMLiwXuPk0mnaKxv0QyjjFvtNsmkTSadwjJ1BvryGELiOJ3YzQAh6Dgu9WaLibEhPv+ZR8lm0qxvKpX5dCrJ5laZy9dusbyyTq1aY2Nzm+HBEp996mF002Rgt8/UXvOWJvbefrdlNwjp9XP6JHPgJ54vP+pQu82He4CeHb5bu1iteOc7Ay3YRbSIXgmUCDQqPBGRiSL+NvT0kl3f593XLzRxm8+bEIIArZtLNrTQqHkmCAkiTbn4aF3rF1LE1VEgK7wmocSrNXF3bf/pMlthIsedmEeGDosydqxU9b+DY3xMld894OrehNuPZZompUyCZ556GMPQ2dws0+60w4EkHHilAj/RTe10HDY3tzF0XZm7QidsKbtmuugy1XYFkIzIcbvjKkFPITANs+uoq2mYoY+AqxyzVLJY10Mmohuv/nQt7GiB8luybJtEKo1lqqz2ESiIUrRoQlP51FASC77fVZWOVpCRho8XDkj5fJZMOhVTrJomsG0zNmuC6rSapvUwUBIFZZQZDpSTfDKZIJGwQ8DnI7QATQdN05VZQQ/CekMyacfHU8DKV067EW2M8mULCAjQcAJY3aqwuLbJ+MggZiKJbtr095fCummUqzXanQ65XJpEwuq5N7vBproK2zL5zje/xOED05y7eI21jS00XSNpWxw6MEUhXySRSvDA6ZOMjAwzPjKoNFekH7M0uWyGxx+5j+nJccWiBQG2beEHAUcOTmHbFtuVKvMLK1RqDUaHB/nK559gZHiAlfUtAil55P5THDtygFq9gef7PHD6BIMDJYrFPKVSka9/8WmuXJ+h2Wqzb2KEocES6XSan7/+AT9+8U2y2TQP3Huc/lIR1/U4uG+Cxx46RbVWJ5VI8OXPP8H05JjKsZiwqa1v0dQNMlKGNL/yS3vovhN4nvKpGRroUxFkroPr+wihAEOz1WHfxCgJ28b3fY4fPcQXPvso3/vBy6ysbVLqy/PQ1BhTE6PKwbrVZmFpjUarozIfJGykDGh1WliWzn0nDzO3uML65haVUOah0WyRzaQ4MDVKOpmg1mgxOTLIN7/0Ge6/9zjNtjLNlYp5Dh6YxrYsHNfn5uwK9WaTwf4iJ44dwEok8PwAXdfZ3KqyVa7x1f5Sdw6J8gMKwfTEKPceP4QfBBw9NE06lcTzXBxXSZJUKjXSqSTrW2Xe+eACpmVx9eY82+UqH567xmefeojllXWu35xH1zUuXrnJiaMHGBnuZ//0GO+dvcLFyzfYv2+cVNJmqVylXqsxsn+SowenqNYavPH2Wd794BLTY0N88ZnHSOfzsdP3drnG1naFKVSyct8PuHZzjmazTbPZol5vsba+TavVwXFU1PNgqQ8J/PjF11la2cDUBZeu3CCTSVGrN6jVmzz/0mu8+e6HTI4PU2+0QCrzJCEY0XWN67fm+OD8ZVLJJOVybcdAGzGhzVaHn7/wOiPDJTa3KwwP9TM9PcmNm3Osb25z9OA0Qmi89Mpb/PyN98OFpKDZbDNQyNNXyCOAfDrJ9Pgw128t8Ma7Z5meGOH+e47FhEfCMqjXGrz0szexdJ2l5TVy6RQJ26LT6cTuIpEgpW1bmIZBEARk0in6igUajSr1ep1Ox2F5dYPnfvgSl67cwvUUSz05NsTU5Cj3nT7ZzV96V7PPL2ihUQNVt10jS0nM8NzNMcTtp4+P9YuWLoDa+8qihXVUh4gxjlitXtPe7npExMjOI/fqXcU8rlTHjqmUHn+uHaxSzLb1mivD73f4XQmVC1dCLAMUAy6hMimEQsRRgqHdNRXQDUZDhAnePr58umBrD9GzWBhT09QKQOxV/Xjn7uungNQlCkAkEgkOHZhSlL4fsL1RjnO2gWKC2u0OjuvheQEdx6FWb5BKJXFcD9M0w4btAhhVxW4aAU3o6MIk8F3aHQ/PVQOWadphFJ6I2d4oCWqvU7sMJz/Pl7QdB0OXSJkkkAFeAJqu8hxGx1J/OqCprhn6UAmhBkyJ8gWLmKzu8kDgBqBZFrlCDtO2VMYNGYR5FK0wOAAI4U6USsUPAnQDdMMILd7KLKnpOpZtq8EpBJZCgKaLOJ9clKBb1wmjnXpvu1QPqwzDxL1ABVoIgeP5bG6VWVpZo5DPMTo2EJ5HYCdTZDIu6xubrKyuY5omfYU8hmmFKyrFiAWaDB0gtXC76ofjo0MMD/bz5KP302wp02y5XMO2TQb7+8hmUowMDxJFmGpCsLmxydLKBivrW0ovTdM4uG+CekMJ3abTKUZHBhkbHWJtbZOLV2+SyaQJULpO9586zsjIMAvLa7TaHU4eO6QmgWaTYiGHbVnkc1k0TaPVbnHfvcd56IGTNBtNmo06uUyGwwf2c2BqkivXZxgZ6ufQgWmKfUVsQ+fpxx/k0QfvpdlqEfgBw0P9JJMJhFAMheX5CF099ubiKvr6NhP/53+JUcwxYhrUhCD5Zy9hlkrkZUAQePQ5kgdLfRz6jW/QMQxagc/QQIlsOsVnn3yYvmKBSrXGyFA/hw9MY1kW+ybHue/kMZbXNijXGkgJfYUs+yZGyKSVA/JfHuxncWkVx1UZASzL4vTJo0yNj7BVrrC+sYXjuti2zaMP3c99nke708D1fEzDIJtJYRgG3/jKMxzYP8nC8jK5bJp7jh0kn1dRt089eh9Xrs9x/vItaPY4yIsoGwAMDJT4+peexvN8RocHEJpPgEcqZXPq+GHGR4dpt9sIGTA2MkCr7fDZxx9gbbNMKpPGdVxMQ+crn38cx/NodTySKQWUfuObX+CR+0+SSCbQdZ1MwqZWbSlNsaF+kokEo8ODrKxvYxgG46ODjI4OY5kGRw/v59d+5QvMLyyHaajCpU5ojk0lE1SqNT44d4l3PjzPxlaFcpgObKC/yKMP3MOFKzeZm1/EcV00XePA1BiJhE3Hcbhw+QavvX2Wer0BQlBrNFlcWqXVdtg3Nc7Rg1N8eP4qv/dH3yebSbO4sq7Ecm0bpMpekU4l2Wi1+OMf/gzbtugrZPitb3+JkaF+Xn3jDD/66etMjg9jmgaO4ypfSgRvvnOehYUVHjp9jImxEfpyWfryBR689xhzS2tcujaD47iUK1WcMKl1ImGTTqdptVyWVzYp5LI89dj93JqZD5XnBZvbFdY3t1lb32Sgv0hfMcc7H1zkT7//U3K5NCur6+RyafL5HIV0godOH6OQy7AcPtOO6zI7u8iJI/vjhN1xR8EnSsX1qbm/7Blx+AnNgbv9yCK/rr2OsRcw212HHlbrI69yB8LoMclFc3y0uBUBXTX5sG6Bxh0FWOmyr5FyPGGNRC843X1dyHA+DtlGLXQbiLMAyJ46dvMlRvVSjFVIDgnQdBWVqLQmZXRJ4Z8IZb/untW8a7D1UTkKeyusMnX78YUpE6hiH3bfuN004m2U6t1VbOfH3uNLiet5tBpN1tY3SaWSuGGqjlhALZxI2x2HdqetciNublOrNyn1eTHdvrvemhamXAuRrVIiDh19feWgnkzYWKYRPrRRNKJy+u50HFzXRTd0tB4/j1anRaPZUHkbBSA0dN3AElqYLkahaYW+dUQoxIYM75FQORct01S+YlEKDF3rovFQx8YwdKQGbiDZKNeptToMpDOgGcq8p6koP+XUHGBJn0SgIWVXkTwy+blegBdIbCHQBASBul49NENGoqYIQ4GtEAzpQhClwXXdgFq9wVa1QSaTIZNO4fsq4qjUV2B0eJBWy6NcVmrquqZMv81mi0azzejwALlcFhGCxcD1ka0Wst5Eth3wHWSzqeQPqnXMMxewXI/EE/eTdzOsbW4ph+98lnQ6iWmZodaKKn7g02h1VComP6DtOHxw/ipHDu5TApqowSWdSobsiEdfMUexWCCbVjkH83kTy7ZotNpszy7g+0Gohp6KEyLboa4YKGY0lbIxEHgdB103KebzPPXoAzzywClc10USqbCraFMtq4EoAurZa7c6nL94jY3tKiPZNCnDRNRbKt+c45CYWUSfX0H3fWzXQzd0TMPE8jz6fB/LtGLVdCklnmkgsmlELsN4KsmgbRFkUpgbVZhfxQ+vv5hKMJbN4A4OYBTzWNkUCBHqoqnnaGJsmCAMmkglk2iaCFk6l43NbVbWNrCTSexEgoSATMa+7ZmcnhxjanyESq2C47lkUkls20JKKFcalKsNsukUpYTdfYh7JlLLMDi0f0o56Eup2KFAOffnCwWmp8ZVCiddCxcwcP+9J2h0HHRNI5tOk8ukmRgbRgiNjutB4KPrOvsmxxgbGaTRbFGt1skkk1imrdJBCcHoyCB9xTy6rRLJI6HZaqksF6kkTz32IOsbmwRuFyjqhs5/+DvfJpGw6SvkqdXrHD2wn+GBElOh71xfscDXvvg0n3/6Eaq1BtVanXqjycTYCEP9JYSu8eDpk6xvbpHNpMhmUtRqTba267RaLsND/Xz9C08xPT5CvdEkaVscPTBFofwz9HACtEyDQi5DI2kzPjLE1MQID54+xv2njpFK2ExNjjI0WGJ1fYtkwubo4WnuPXGYUrGAYRgsr21w7eY8Tz7SwtAFRirFof3THNx3k63tCqauUa3WQrAFjuuTyaQ4cnAayzRJpdO4nocUGpnQN8w0DdKpJB3HJZNK8dDpkywsrfHKm+/TcV1StsWDp4+hCUGpr8AXn3mcZz8TsF2usrSyzs3ZBSqVGuUwQ8ceE84dtv//ucTO8h8zh0Yr4V42raf0OsZ/5JE+bq6OzYcyBFu7uKEeUurOpxDxnBYzXLKHSxTKlIggzEgTHVciYiCl8n5GgEuE/wdS7NDUEppABqEGcGgFskwVOGIYGoHXzQIhtC7z+ElA990zWx8DtmKPfQnIEABElSNCsXeoWETlqQ/suIi7AXlnr8D/6b9DZNKQSSEzaUQmBTOLiHoDWa2z9ft/RjWfxRUa2WKBdLGI8INYxsEwdDodhw1ni1a7QyGfJZvJhLR6WE16OqkAhI8UyjcJIUHvYkbTUNpdhtEFWr7sSjf4gZpkU8kktqVSSnheh3arjgx8EpYyq2lCmd0kyvcrCAIl8hn6WnUr0zUV2gm7m14HSCVtDEMLo7wUi6VpOpZtIgTUmzWWVpbpdNokE1Z4DaGQqyYQQmmW6IaG0HR8tHBRs9NW78vQJi4giB4MIRB+gN5xSDsuhpRYa9vowRbCddAdBxwX2erglqtU5ldway2SpT5SloXVaJHYLuO32nitNs1ylb50ilQ6he66pNodMtUax+pNUpqGjUB4PsJ1FWPYG8wAWAvLKtnt+ibaP/p/A9D63n+H4/u0Og5oWsxC9KrNx/1cCI4c3s/42Ihi18J7J4TA8zxWVtfQdQ1DN1haXiMIJAOlvlBCQoWIJ8II0EtXb3Lj1hzFQg4Nie86GIbKVdjpOHiOo9hAzUCgKQAr1T02DB1NVwOClMRgKAIx3WdPXcPo6DC/8a0v0f/2OYx2B/nTN4F2vAoNpIwjbCNfhd52i0lnQHdcxFYZtipogCG7z7p6ZGW8iAlC6l7TBMI0lIBmJoVjWzR1jYxtYfblSQ6U0As5ZCaFTKfwkjaeDAgMg6SlhyYrCWhhWo9u/RxHOc0nEhYJrFDiRKfV8njn/YssLK3xlc89Tt5UMglRmh2tXKX4t/7Lnfc3dClwwzyjpmlhhNIPyjlctXEK6KPHZyR6HqTEoLuIk0gMCYkgoBBq5MU+H+E1BL6P0PUwHZZikSPxY4Cc66rB3lWRe7qu8cyTj2BZimF2XJd7jh3GC1xMQ8MwDba2K+i6ztBgPyPDg3iuh+t5WJapFm2axqMPnWZqYhQvcEBKHn3wNP19ffTlsxAEnL7nKA/dd5KEZYRjjyT57kX07Up8vZZpMpjP8vUvPsXRw/uZnhhVMg+Bx9GD0/zar3we27LIZFQGDSEEmXSGRx88xdycYtyiZL+e79BoNhUDZirB5U5HRW0C+J7H+voWr771Ppap3B0qtQbpVFIBzL4Cjz54inQqSTJp4/oehw5M8Re+/RXOXbnJ2sYWBAFHDkxRzOdVBKSu7m1fMc/wUD+VqjLZmrErRQRKwvi2T4nQ6j30p1L2YqzueF75iTS8/twlMvdFp/wEDRn5FXZNj92xJjIlyvCYtx1RQKQPJjUlo6LkLrsmwIilEiHI0oQISTGBZeokLAvT1AkIrVA+IFTQl4qSvPtm+FTMiEpzavdZFQLUIrMNKmGuYOfAvHfZlUT34wDXdhU2yrBR3mmgXNvEdD3ywMP/4nvx/dZCdCr8AA1J0TT57P/wbyGbhnSKIJVAZNMY1xcwP7wMmQxkUpBOkvgH/1RFplgm0rKQpqGSwhoGUtdIA8UgQBo6ViqJmbTVd4ZBoOsEhkprW9IE0jTQbRvNMpGGgaVJNLdDSdNJZFKYyQSBrmNqEOgGesJG6gJTSgLNxHRBJGwwDERYB6F3Q+qjdk7YNrZloRs6IgjQ6m1SnRay08bcrkKtzuDSKiOex8DVG9hXb4DTgY6H5nnguoi2i+b7GEEAro/mOipBreORaDnIdgfN99E9H+G4JBwX4bgK9IQPdq/OWfcmhd8FkqTjMNLqoOkaqWQyvo50yK5F+f4Stq0YhvAI6SAgEQq/dpPYhkkcIt2vPaJGxPwywvex/9rfwxSSlKuEaU3DDCf3aC2k6qkDB12XidD5PGJtDV35dkw4Dh2ng5/LcO1v/zUcz2NqcpyBwQElIitCbwZdp6/Ux9FD+7l2c5YzH16gkM+SSSUZSmcAlduw2XGwE0mkFHgB+KGGkIjYSsCPLqsXZCG67YwyK9938ignD+1He/phfF/izy4jN7Zwsyk2/v7fglqN+tIqZrNNSTcxOh1qS6u4WxWKpkHKk+idNtSbKhlxs6WSKQdSMaXRcypRq0gp0YNgxxJLxLdbYkpJyg+TaUegNlpVKwqYopTsczw0z+0uyMJXf6hf/WVSBJk0XjZJJ5XAz2cwRvrRBgbwcln2lYo8dOooB6bHaV28ged6EJrdkVIBh55uIQF8nyCMCMY0EKaJ0LsA6W5Ws0IG4CspAb3jYi6vq7WoGtUh9LGU4Tah6whdU/6aUoafdSQCXSr9uqhdhePS95/8Q9Wevd06fKy8wCfRbCOE0muLfE6RPYw+giIw0uPG0PXNjJa9ojuHh8+CuHANHBeW1pDjwwAkbYuH7z2Gadtxv9PRyKRsjhycJpfLYpoGlUqNrXKFTDqtFiAJm1QqEYpC+6ytb3Dm3GWu35qnVm+SsAz6inkVpdpxMXQN09BJJhIcPjBJXyHP22fOc2N2gSvXZhgfGUKgGHTbsjB0g1QywZFD+9l/YB+Ly2tcunwN09TRhI4m1ELJcX0alTpLK6s4jhsHSewaono6Sa/Zboct5WN6RU//DoH5p1Yixir+3GV57rh/hF+iqu3a4U7paG4/Yu9BdszA4WaNGBEBuyMH71R2Cqrufebd9VJ9tQc3iN4RXCK0QIEuqZ47rTtTqGcr9M2WktDP1SRp20iE8l/F7fovi1AI9S7LL0X6IXqgewVE1c3vqdzu+xFvvAt+MfpNtKKu3onyJaQYQe9F8tGkEBYtkKSrdag2Pvac4tZ8FzykEgQjg11H+/BYZhCJceo77f6767/HlqKUe3eavS5sr/cCMAykqatXwwBDB9NQE0ezjXX2cjxpRQ/pqAS/VECEq2nZw9fuONOeE83t23b+Rl1CRNvuVaRUWmWu52HrdribagkhVPJfWyg958gxN+aUE6GOmW1B9GdZSMsE2wTDUN+ZBvpzL0K7g9ZoKeAgJfr7F9CB3pS1EYNBVI2wJJDYvdsE4SQIFijHc0Mn/5//U8UuCsX0xavJcGJLSsmgH/Cop1IhCRRjYYT5v/K+hEBJImgIcoHPiB/EpmARTpJm+Nr9I/6u98/a3YV8lYPS1HVG/9N/Eif0jf0rATvMJmAYOnpo9orvrWFAVomS4vvgB0pzygvA9xFBAL5Qr0EQfg5C23uAFuxcnu3uFfEzELZtBMSi/fSVdfT1re6NkMQLvt6Iuf8Q+GsC/B+/ARGQcVyVA1DRxUjlSIjUNfXZ0PA0QdvzcBI2mXQaYZkQA3ni4/fOub3XI0PGKggCRL2h6ip7dgwCxbASTjvCC58TGacBIbxsi1A/ylF+W3ge5nvnIrQf2j66r1p4nQiBZqoFmESoAJowVVf023h82vVc7rguEUm0CAW0QpNKNA4KTTA8VMLxuvX2wwTs6aQCU0EQ4LoutUqdleVtfvb6u8wvrfHQfcexLJOW4zG/vM57Zy+zsVWmr5jngVNHeOSBU6QzKa7NzLO5XWFyfJi/9jvf5MjBfaQSCUaGB/h//vPfZ2F5lWs3ZvB8lSv0wP4piqUSZuhKgacyX5SrddptI4y81ELpEeXCYBgGx47sZ2RogFIxz20ltmrszkjxScsvgVnqnSN6aeje0+yYi6Kxf4/f70BiO+vZnaejV3Hbt7fPTbvb6eMFzbuH3Almdv8qYr5UbeXOGvSaS3vrEubGlEikHy4+wnk3kJG1S7HL6XSCbDapLFjtSK8xBBXhOHO3veBTB1u9HXB3NeL7+gkquLvszosFQLnWPfbf/48J7jlMY20T93/3X+Msr+M7LoZUKugZ28LUNEQ0QXi+GqhyWUS9Gdp27wD3oskrWuplM8iBPqTjgueB6yFcF+FG6FjJGtzpoYwj8qJBeI+28VyPQCrWxtDv5FDYXaIIAM9DeB7QFbCMO0XHgbazk22I3gyWVDRkeL7dZ9j7jHSfSdNAhqCGkPkTlhluM5GJ8LNlEdgmwjLA1Gl6Potb25y/McdWq83J08c5eOwgVjoFpo20LBqex1vnLrOwuUVpaIAvffkZkqkkvibwJLRCs4/yXdLjVAu7s7Ob568gNstIdw3RaKnVjOcrQEo0rytTjpRSpW3Qets9FHPt4Y9F6LwcmRpwBMlb83e+VT3F2LUpBhO72ljr/bwXMuktd/Nwhd1YCmIpEyV90u2vxg5Tdc/hdyFpKYxwJJGxBIEfBhTosZ/hHiUGZUHsR6gW/F2QKKr1MHl2F6T0XuKOJNN7nEIAupTooQlOOQrqyFwGmbBu2xc/wECSDBRAFW0HI5CIbCZ0UVBmTjLp8C/aloSUek8mhZ9K4icsXNvGfO8c+v/9XwASP2GrsJJWu3stoVkxAltRG8qwLbQwfYhYXO1J/NxzIwMZHyu6DmPXvPexXSI0LaJpqn1CYCY1xbphKEDaBRwCTFNJtZsmZqWOtBNIu+vaYBgG6KG0jO9jmibpVIKbM8sAPPnIaY4cnCKRsFhd3+SdDy9xc3aR8dEhnnniIU7fc5RCPgsIVlY3WFrdYGSwn7HRYfpHh6lvV1hZ3WCov8gTD5/m0H6l+p5OpbFTKRVVrYVsoQ5Dg/08/MApNE0jXyzE7W5ZJsV8jnw2TSJhkEwl4hyld5gIPq41//2XaEESD+x71D129ZE7f7dn+SigFREn0aki2QaxY9udS/e73b7gva4Iu5Yvtx8lZu8AGZkVoau/RXdR1HuEcLHaC856a6YSXQfoukYqmSCdTNBotbtNq8kdzfvvFWzdKR9712QUNiB3JDZu/+1e54kYpN2rgh5nxqCYw0kmePnmAv+ykGGmWsFPGAgBlmnwV77zFf7Sr3+NfD6nJgp6fMWkhHYHWWtAvQG1BjSaUK8h6y1ktY75//o9RK2OTNg43/gczf/kd5V/RxCm6/ED2q02la0ynUaTjGWQSVjKnOL76J6H8D2k47GxskF5o0x/NstAPot0HAg8cFyE57OxusGrr76L22pzZGqMU0f2o3m+AgdegPACRAis1DZfmf38AOF6CN+PvxO+j3Q9NMfttptpIJMJlb9QSq49eJz5WoNK20HYJql8jtJIP8WBAslCnlQ+g55IIBJWzBQJy0TYFtLY7ZOnHkhd00OzrRZKTwg8P2CrVsf3WuQzKWbnl/n//v6f8sbyKkcOTzP5yCm8E0cQpgHoCDSq65v8ye9f5v2zlzh6eD+PPftZkqaN9H00JKahx/53UYlYoG4JoJBT/bBchXIVEMiETXD6aNzHmq02t2YXqVTr9BdzHNg3gWma8SAVT/DhZ+38FUTHVY73kYlB/+hHS/b8v3t7D7G9o+z56PQwlF22cq8/9tzm2ybXJke4tbDMyNgwBw9Ok8ikVNuHjE+gCZqBxEjYmAkLzbIUAxpNZuE+rpRs1xu8f+kq567cojhQ5LNPP8LI2AjouvqNqaOZRmj6VseQIaskNR3Pj4CGapvUP/99Es+/CkDrN75C8n/5AQQB9S88wVxfjq3rsxjlKpmOS973yQtBsu2gtzporTai2UZ0Our+eF6XKbsT4yxU+3cBewiv6w1Eo4m2Gu8U35M7z8chS11roG1sITWN1t/9q3R+7Yv4nofX7iCbbXTHReu46I6nEmU3WohWJ/xz0DoOQbuD11bbYhNuu6Nem21EmAZpVwfrRaU9E5Dcs9K92Se0sH90Gd4oskxpEiEB1wUXxMo6ib/+n2NHh01YyISNTNoEqQQymUAmbLIJiyHT5IBuQn8/WiqB1vHQrs1xYWaezdfeY9gP+OzBfTx0eB+ZTBqhaXieRyqZIJdJs7Syzvd//DNOLSwzO7/E5eu3+PoXn+aRB06prAGaBpoywYqevq5pGqW+AqW+AkKImGnThMQwDBK2hdAUQNS1nnEjftR7QGbcpFJlS4lWLjsa/E4lbE+NX47f1I6BQ97+3V0BRbnj3Z36eHdslbvm9Y9ug516WjuXmjGWCAHU7vrsVURPX1Wi4xpiD/OeQAW0dc2AArQgTokYnUUjlEGyDCzTRPkThnOLkFihD6NqF3XUuymfDrN1t31G9ExU8TbBzju1G9HuOtVeHbRS777PZlhd3+L1tz5gdmGZQi7D0UPTtNttZueX8SMKvGciju+3EJBMKLPUQF/YuD5hch2CloPxP/0JolZX15JKEHXHIJRgQAh000BLWLTbbVwh0dJWGGmlKY0PoNXu8KcfXOCFl97mc089xF946gGSCRuEH9OiCzdm+ddvnmG13ebbB8c58Jd+BSEDZBCer3d1QRRFoTqd6FF61kJnPjQNbXGV/N/+xwB4Tz2A2CzjvH+JcqXG3zx/hbrnU+rLk5Q2re0y7avXmBwb4kufe5THpg6TzaQV67Vj0NnzTiFEpLocOjcLkFJjY3OL7/3gRba2t/jVrz1DXzHP6PAAqVSC6YlR+ktFosTbAokmJAN9eZ58+BS3Zhdotzo0G01kX0GxHwIsIQiEFmpH3RmYtP/p3wPA/r/8D5j/+k/A8wmOHaD1P/7Xqo6BT7Vc4+fff5F/+W/+jFPHD/IP/u7/ionRkXD1JPAIqeuQTUj8R38fbXEFbXYJmVXshvfXvwO6gdTNLisQMgbCEIo1EJoCqbqumAVDB91AWDqBboJpIUz1vWYqc6gydekEERPRex96Bq6PWlkKKRHSQwaSaq3OK3/2An/8g5f5/P3HyH/9WfpLKlosWixdunqTn7/xHvdMTfNAKDIpehivKIWV23ForG1yc3mF5ze3KcmAY/ksw9PjcUSuiEAf3VctpPv9cNBWeDVkWJvdxNkypaIZXSl5Z3GZ/+biZQD2T41iGAab29u0Ow4HTu7jm1/5PPunxrssERC0HbR6A71ax8+mER0P0Wwhmk20RhNRbyjw0mgpYNVsozXaaK0OWqOpQE6jtWOSvDMx0ANoQhZd+AFOKsmZs5dYXF4jl1U5PGfnlxFCcP/p44wfPwBEEbwq2tjr8XW8U1S48ANEu4PW7oIxWiEYa7W675vqs9ZW74NGi/rmNn6lhlerQ6tNUkIymcQKdaYk7PFARdTornoAsu0g2g6iXA1NpL07CjLxnt1yf8fhUKOBrmmk/833Mf7wR+rcpoFM2HxB17iv1WaxUqP2r76Hn8tyuFTgifERCksbaD9+FT2bRqQUw0g6AakkJJOQTSOyaVVr2cMahvWIJmst1Fdir3D+3SAm9jvqgSN3xSTIHS+farltAXHHpcDHlJ33a2fugp2slrruoLu/3M3J9+y5g1G7gx9WPP3vXrx7CkD9Of3ceu1GO1uny6gJJLqu5lLfd2g2m7iug5TKjJhOpxVrG1d4N9u8d/ml+GzB7YN9dJFdKvIuy15mw92lWo/3ddMJLp+9wpXrt5CB5OH7TvAXvv0VhICl5RUmRoZIp1Pd1T5wp8gIoRAMoNCKHkhl9oi+TFpKvFOLWHxlBxZCkEom8T0fz/d6rjsiGNRKK2UnGOov0F/IYmoijGISSBEgdIPh/iITIyVc16WQy2DoOtIPVGREEKg8fIEydRmGgS5CZ+Mdc7B6GLSQxROmodRxAaFrVCo1ahvbtNsdfE3ji888xLNPP0I+l+XK9Zv8wR//iFvzy1Rrjdi8uMNUvIP23fk52hZI2WOe9QkCD9dt47oqKjOXTXNgepxSMUcqaZNKJojChlXuK4llaBzaP0kmnSTwA3zfQ8gAIX0F4GNtFNXIkX6ZtqOeXR9CYfR4aIWUtRYOmKmExdjIIIVchoRthqmmAiJdmDAtahihQmyCRIAsFZClAt5vfAUwwjB51N4yHLrCgcb3ZajDHPGrfrjaEgh0AqGiRQ3hIYWBjNw5IzPbbf1150DSC4cj41s0mAgEUnQDW0p9efZNjZEI055E987zfN4/e4l/+9yLdByXk8cOks2k40lYSgiErlJ8C4NEKs2Jowe4emMWTShtJGVKpMt076qzQLFyMlD3S4jQvQ0QjWb3KtJJOh2HcqXG2bOXmXzifr767JPsmxpD1wWb29s89/zPefWtD9k/NcmB6Uk0rcuUSduERJGgvwhC4BP5kAZ7DkldjUBDRYSGddU6Dlqrg2i2Qt+/hgJhjTrU61BvqnrXWtBoYbz6bnxT/EKejY0yP/3Z20xODPOVzz+BbVv8+MU3mJ1f5te/+SwTYyPoAhChkp2IJnV523waA4bQJ1PmMju+v33NrTIoaAIcx+UHL/ycH/70Vdp6mmZCwzT7OHl4H7/2hac5NjaM4XohcHPR2i20f/TPoFxFphJ43/4ifq2B1u6ghcyuDBk3mm1oKYYOGeyY5G4vEssy6TPzcbvH98BVTH8CGJOSYd0gEBpa20Vf2UQub+IHZ2P/q5iJ7Dmd9+h9tP4Pf2PHGVV0rIlBlOe1N6hEQLDzCRI9Da8sZhEqENGGTzStfRIQtHtsvWO5Tf7hkwOTGFwjegDR7eftZbXi50dCnJj7TmWvND+94CwGWjsBnUAD6d3lVXwEWQPqXNGqbo/9I0LI9T3qjQb1ZhPP87pze0oRJ5+0dX9pYOsO4PYXP1x3KcJtjVmtg1QRRou1Bq++8T6r61v09+V5+L6THD9yACl9jhzZjy4lhh4KgUpwOi4d1yeRtEPfHPUQ+aG5QQgVvaCBisDrAVsymYgnfN/z8ZxOqDKuoucSyQQJ28K0xA4fKA1B0rR59qmH+NzjD9BfKmDbNp7rIYSKuLHTNl6QwzKM0HdIjx8EzwtYWt1kaXUDTdMYGigxPNiPlTDZXRRxGK6KBaG5TxW30WJzbRMjCMhm0/xv/oPf5LGHT9PfXwSgry/D4soKr775AcpJvfvbKL+a5/lomvouEoqVUrK2voXrKc2qVDKBaRhIlNNsqZTj177xeRzHDVksjVQqgWUYSjIwZE00lLOw43k4DRdDV7SukAJTAy2cjHRdCbW6nkdCtxFIldcxTPMSBCEIBhIJHdswuu0gQMgALYwU1DSNRCIRmySzmXQ3P2YIUqJlg+e5uL7E1nU1NMRmRpAywMCPo7uiBNjRSSUSTYNmQ/kjWaaOrsswelfJO/hSoOEr8IyPL0UMhER0c+MjqvPHQ2XI+MUSA4DjBzSbHXzPwdTBtix8X9LpOCQTNgOlYhwdFhWlqr6Pv/kf/CaH9k2GppromgBdR5cCP/AxNEiaOqlkEtNUqasIAaYmNOX7A3Hy4Rj4alqsPQeRirnaVzTb8cDY0HW0RpNqvUHfaD9PfudrnDx2UD37SCanRhjoL+J7fmgOExiGieM4eF6AbZrohg4SOqEvpArz1nvG3jA5rZQ9k3Z34RCx3zKVhP4iQczwKbAcyCiXqCTw1YSd/eJfUdt0HbNQYLyYo5DPYhkGwwN9TE+OsrC0ymtvfcj42BCjQwMYtgUIhFSgXqBAflSlaNEW97eIaQonkh2Lnp5eF7km64bOuQtX+e5zLzI6WOKBpx7h9bc/QBLwyIOn2Hd4H0YmpcZEIfAjhqNUiNto+9/9mGqtQV8hTz6XuX28T9jKX1OGEau+rwBByPIRBEjXJ/BcNKnEnuNAC78bUKHFARgBRs9zFN4ujHCyuRMWMWoNMtduxZ/lrnkkasvuX7h1q7zn8fby9fnkc90vyjr98stHMah77B2Cq6Dn/R1YrT2BFnvs3/s5prr4uDbbiRF2fLHryGIndyd6RVOFco2Q0HFcqrUmnY5DIIP417re64d99yD7lwe2dpXwceBOjRUO33d9vGhSkQhE6LMl00ne/fAib585j+f5jI0MMDzUz8rKKm+/d5ZKtc5jD53iyMF96EKj1Wzy8mvvcubCNY4d3s9Tjz1AsZAhCDwVJnxllma7SbGQZWJ0iHHDUOwW6sbWPJfWdhmAeqPJytoGnbZHX7FItVrnxsw8gZTYtoGuayoNycQYQ6U+NF0nadssrqyzsr7J4f1TZNJJpIT1jTIzHyxy7dY8swurtDtOt/NL6DgOV27M8sOfvkbCtvniZx8lm0mRNLN4MqDZalOu1PB8n0I+TX8ph2WGzsA9YGt7s8zWxjaTtkVpaICvfPFpLF1Fw/lALpPiiUdOK/Pe5Ci2pSLS2h2Hmbkl3n3/IhubVY4f2cfjD99DLpcmCCRnL1zlhz95jVw2zel7jrB/epxiPkcioSQbms0Wi8trVGsNRChiGd9XoUAWKPamXKlx/vJ1Ll6+wcLyGiur6wz3l2LHYU0ELK+s8eGlq6ysb3Fo/yRHD+0jmUpSb7TY2i6zvrHN5Wu3KFdqjI0M8JnHH+Sg0dP1A4mp6ziOy/L6JvOLq5z58BL1RksJ01pdytjzPOYXV7h49QbNZpvhoQEeDALSIdskQ1+Qmzdn6cvnkYHGxtY2+VyW/lIx1m3zfZ+ZuSV+/NLr1OsNjhya5NSJg2QzGZaW12g7LqVCkWIxQy6bxrYs1rfWWVrZJPAlxXyOseEBLMvCk+C6Llsbm9yaW6BarZPPZZieHGNooB/TMKhVG7zx7lne+fASrtNh/+QoTz/+EAnLptXqhjM3mk0M3QjTNhlYhsE9xw9xcP9k6KgtQFMpi5bXNplbXCOVSjEy1E/KUr6RkQnY830IPAwB7U6LueUN1ta3kMD+qXFKfQVs22Rjq8xrb73PzdkFbNvi8YdOc/DANFJoiGZLNb1lUm53yDhKTXxqqJ/RoX5EoMBNgMRpdxjoK/Kdb30BTajFyVvvneWVN84wPjrI/fcco93p0Gy1KeSyDA4PohlQLlcpV2s4HSUyPDjQRy6rGKJ2R+UJtM0EfaHKffQs+oHE6zgq56AQJFNGyOJ1V8wyAKJr0DVkKoFbrSltMBkQeC5CWiRtE13Xu6a7kI3zAg/XU2yoISSGrmMaBkLoSnA4UMA1kj3RdKmA0W0TTMioAwgNzw1458xFGo0m3/rqd3jskfs5cXg/QkgO7Z8gnVa6WGiKdxU9bCeA57h4W2UI5T9EDKA/GYRwA59mxyGRsLAtm526SlH9e1ilIIpoDcP0o+CAIAzpD1+F6yFqKrpcW92Aze2PrMce3I0aK6dGezaFADZiv3b86m7RVjgHfgKsdddRj1oPAFA/VK+f0PQm5Scx1wmU5adX87H3WBELfzfH283Q9PxO7vH17vPA3iAxXEl1F56SrmtLmF5I+LFAOeHz57oebcfBixXo1V+8SBShd+3HCcmG5ZdqRtzRSfYwPd3xt9Gbu73hoRlRZtNsb1doNls4jotA6cy8d/Yy/+qPfoDjuJT68hw5uA+kZLtc5cKVG7z+zlmuXJ8hm0nx9BP34XkuH164yh/+8U9ZWFolnU7w8H3H+evPPkkqZLw83+f5N89Qxeexh04xt7DMc8+/SiAlv/qVzzG3sMLvf/dHbJVrSiFcExRzab75xaf50jOPYVsWz//sTb73w59RyKX5m3/1Nzhx7CAzM/N89wcvcWt+maWVDWaXVsmmk/Eg5ssAx+2g6zqO63H15gJjIwPsmxojaeqsbWzx+jtnOXfpOpV6gxNHpvjNbz7L9MSoMh2ZXUbHbbbxWh2ErmMkbNCFYpNCBs2yTI4cnGZqfARN07Ask07b4d0z5/nTH7/K2YvXqdYbHD0wxWApx6OPnObcxWv89//qe1y5PsvE6CDnL9/kyMEpnn78fo4enMY0Td4/e5n/+d/9ECT8zq9/mWI+p8ynoS+FJiS6lNTrbV554wxvv3eWheU1llY3aDTbjAwOKGX5doefv/ker7z1HtV6g0w6xc2Zed5890Meuk/lGFxb3+LtM+d568x5Gs02fYUcU+MjHOoBWwLY2q7y/IuvceXaDNuVGucu30QiSdhh6DgSp+Pw/rlr/NmPf8aVG7M02x1OHNnP0XabZBAoD45AqVH/N//8f+HwgUk6HZ+zF64xPTHCt772OU6dOIwmYGNjiz/94Uu88Mrb6Jqg0WowNjJAuVLnu8/9lCvX5xgd7ufwAdV2E2PD/PiFV3jxtTMMlvKcPnGUZz/zGAP9JWrlKq+/9T6vvf0+zWYLz/dpthw+9+SDfP1Lz2CZCV54+U3+3fd/gkCZBsvlKvccO8JgfwnX9djcqvKnP/oZhqmTS6e49/ghTp08Si6b4swHF/j+T1/n0IFJvv31Z7ATBjNzS3z/+Z/z5pkLJJMJvvaFJ3ny4VNoQusKBWvK0Xxzq8xPXnmL1989R62h6ve5Jx7k29/4Ap7n8Sc/fJHv/eBltsvVkLn1GB8dIpFKh5GIIFNJ2oFPKmSssrZFyg6DFkR38SV0jYPTExAe5+dvvMcf/vEL3HPsANl0ig/OX+G9Dy9zz/ED/OXf/Bqu7/OHf/ICN2cW8YOAUjHPA6ePc+zwPgKhceHSDc5fvIqhaTz7mcd44PQ9ZLNpXNfjnTPnuHjlBpVqDcs0+fxnH+LEsYMYuoYWroS9erMrlaDrBEkbd2uLZqPF1naFhaU16o0mZy9cY7C/wJGDU+i6hut6bG1XWFpZZ25pjUazRV8uzfHDBxgaGELTAlbWNlhb3wARYJg6xXyOwf4+0pkMEoHjenQcR63M/YBs2iadSGHoJq1Wg9mFJaWSn0kjHZcjB/ahGcoNIRAqs4UQAs93aLccZACZfFaxY55HOpMiGQTK9GxZOxbLMcd7mztJtwSBpFGrs9luk7MsCoUshq4Y8OhQH4VJIl4qYiJ7dfz0Wh3t+iyg5EfkXaIbKVX4vxLiNaC/D/pyPRewx9z1Sc2H6iR3OP9u01bIxdwt4Npdn09IoN3ODvV+uFMd9t6+N9Dak3ra4zjd30Wsfbz5ToArkq9BfswtiRwpdp5LIhXYCksQROn02EFgRb+MrQt3eW/+3GBrb0HTXSXyjwqRJGLvznN3Vd55LuG5auUoQBRyHJweZai/SKVaBwnJZIKRkUFs22Jjs0yr3SEQmrIKa4Jnn36EfVPjvPLWGTa2tgiCgHq9xbUbs7RaLUrFPOVajcWVdYKOQ5zgUAjOzS7hDZd4/OFTVGsNbs4ucXBqhIRtoAm1Kk4nE9x7/AAIwaVrM8wtrtBoNlXqGqDZamNoAiF93HaLMx9eoNVs8Vvf+iKb2xX+5+/+iK1yFcs0Y72mIPBJJUwevPcoy6sblKtVOu025arg0rVbnLt0nXQqSavTYW5hlUazpeQjhARDj7uaJZRjuSbUKk5I2dXgkSqC0LLMWCfHDyS1epOLl27SbDR58NQRLly9xcbmNhsbZTzPY2l5jZm5JabD1B03Zxd5JTTr/u5vfYPJ8WE0DdyOS73RpNVsIoIAPXy2gkCC1JBS49zFa7z65nscPTDFN7/0NG+dOc/3f/o6yIBGq8n80jJ/+GfP03EcfutbX+TUiUO89+El/uBPXmBpdY1vfOkz2JZNrd5kdLif0yeOxGZX7UZXmiHwfP7oj3/Mz14/w5efeZgv7H8YZMD7568TheL7QcDs3ALffe4FPrx4g1w2Ta3eZH5xBSdQ5jZDKm0lxw24cn2WTCrJ9MQYge/x/MtvIgiYGh2kVMxT2S7juy7f+OJTuK5HvVHD83wF7oRGx3FJJRMMDfSRTNi0Wm0Wl9eYmV0kn0kyMtxHOm3heS5vv3eWf/5732VqfISvffFpkrbN9194lTffO88D957C931+8NOfk0kl+d3f+hamaVCrVikV83i+UhZPWBZJO4Gma1y5PsPi0irJhMXo8ADvnb3EK2+8T6XW4OnHTyM0wRtvf0Cj2eLQ/knOX7rB2fNXuP/kIdLpVGyy1XWddrvDi6++wx/92Yu02grsLq9t8cG5y3z9i09RqdR46edvs76xxcToEKlkAttSLKxOgNZsIYAgnVTMUHhsISVuIEmGI4ciQ1Qn0jRlcjMTFuND/WQzSbKpBMcOTDBQzHD56g2u35qj1W6r/ILlKrMLK+ybHA2BlEuj2eLW7CJvvXuOTDrJ3OIKzz3/M4YGBzh6eD/1ep0//eGLXLsxx70nDnJhbomBUoaD+ycwk3boeiDRt8tK2gKUiGkiEfepmfkVXn7tPVrtDsurm5w4up90SiWGr9UbvPXeWX7009dptjskbItKtc59J4/yra8+S6PR4ns/+Cm35haxLQNNFxTzWb7wmUd58rEHkAguXb3Bux9cYnZBSS089ch9PP7AfZT6ClRrdcqVGpVag1ffep+Z+UWklGxXqoyODfHEI/eTy2VYW9/k/XMXuHjlJoZhcvK3vso9xw9RyGfROg5ra5vUG01GhgdiJfeO67GwusrG5hZTE6MMDfbHLgYqCtCj1e6wurbBex9c5P2zl7n3niM889TDDA/1h2NcmIFC6uhCxpNtEEiarRatdhvHk/EY1e44OI5LOpVU2T/OnCf1T/6Fwhqer4SnhcDLZcIAjB5mgq7prNHqEKxvYpsGyYlRtH/z/wj9+1WKqe703+NT1Ov/+2mUHQBFsTB3VaK5qfc4v5ClcjfgixDO7s8fcYQ7+EPt3rYTXt2+b/y9iEyUd+eMfqfSFU/eqzZdR/8dKgUClF+3eh9H6oqdv/648ktjtm536vv0euMO5NojRCryaUp9ebLZBBDEiZiHBvsZGiixsroRUvAChEqjkk6l2K7UyGZSpNMJBOB6Lp220tXYNznK+NgAB/ZPUEwnY0dvzdDJD/ZhDJZIJBKYlsnB6VG+/uzjjA31sbGxQX9fnnw2za9/4/O4nket3qBeq9NqNBgq9XH04DTTY0PUGsr5vFypcnNuicP7x3nqsftot12WV9d57ievqTyGqMFG1zSSCYNWW/n72JaBrknq9SZbWxVqjSYTY0M8cPowqaTNyNBAnCqD0IwlECR0HVuoXI6BpqHv6DiKSvY8n0q1TrVWV4DL9bn35GEePH2cSrXG/NIqlWqDVqvD9sYWgZSkUglKxRxTE8NMTSiV6Zn5ZWYXlhka7COXTtFXyNNotvA8HyJWK5D4boCUgk7H5YOzl9jaqnDyawd46tH7MQ2Dl18/Q6VWZ7tcZrtcZm1ji6ceOc3nnn6ETCqJ4zi8/Ma7LKxsUC5XFevWl2NmfpFmu8NjD55ifGQQrK5/W7vV5kcvvsnE6ABfevZJBob62djY4urNBdqOoyLtHIe5hWVm5pZIpRLsmxpleLCP/VPjJC/eQoapaaSUmKbB0GAf9508yJc//xkO7xvnn/3r7/LB+avcnFlgYLDEyPAA3/mVZ/Gk4Gevv8vNW7NUyg0GSv0MD/aTn1vi2OF9PPX4/RRzWWqNZuj/ZlPIZTgwPU42m6Zeb/P+uctsbVf5i7/+VR5/6DS+H7CyvMEfPfcTrt6YwdBgc3OLh+67hyMH91Eq5pC+yvt5Y2aBTrvN5NgQv/bVz1Es5PmDP/4B5y5dp9Xq0Gi0GCwVGRnsI2FbtFpqUhsdGaRYzLNdbVCtN9B1Dc/zifRxBKrv3JhZ4J0PLmKZOp957FEG+/tYXt1gZLCPpKHh2CaH90+wuLxOtVbn+KEpHrz3GIZhIH1fOV0DQTKBHgJRPwioblXY3K6STtgqgEFoSDx8L0DIAN3UMQydVMrGNHVKfVlKpSzZrM3YcInVjTJCCIqFLPsmR1hcWedzTz/EgX0TGIZBNpOm3XH4zOP3Mzk+wkuvvsOt2SUaIfgTMqDdbuN5HtPjI5w8eoDDByZJmkbsZhAEAq1SU0EeoKJRk7ZK2eX7JOwkRw7uYzRMFn5jZoFzF68xNDBAs+lw6eotrtyYZXx0iMD3qdTqzMwt0aw3qDdbXL81S6Xa4DOPP4Bt61y+eoubswvcd+9xGo0mP3npdV545R0qtQaaJiiXK5QKRR7O38Pq+iYb4Vjx/Ctvkk2n6DguW5UqX332CR5+4BSbW2We+/HL/PCl1+k4Lulkglq9zshwP5lMmrMXr/IH3/sRAsFf/e1vcejAJO2Ox/mrN/jBCz9jZm6JRx+8h1//1hcpFvJ4vs/GZpnrN2eZW1jm6vVZbs0ugUD5vLoeXsjGNZttfF9imTaZZALLUICj43Q4d/Eqb7xzllqzRSKRYGJ8hCMHpzENA0PXlfJ/M9JFEkrIFXCzGX7wu7/KzPwytm2FvpgBvutiWSb1lkOz1eK3v/sCKV/uiLjdGXmoZtpfTNS0h3jY0+LVu3AI9/8kpRcH/UJA6/YDKpjVUx+pPn/s9d/RR+ujrqrHXhiZk++C1erWVe5kwu5Ux5Aw2eHPJVSAlQB8KXukhKIgnu5vY17sE+Tr+ffms4UgjH66PSH17up+VHveRhHGkYgQZFI0Gi3aHQcQeIHPjVtzrKxtsri8hm2pNCyB9PGDgO1KjTNnL/HyG2fYrlaxQiCWTiU5MDXG2Qs3uXZrjoHBAvunxki4oRK2BKFppEoFbi2tcWtukes35xgbHmBsZEDl+/IUKDJ0XeXx0nVsy6TRbNFsdvBcB8fp4HkejuPSbLXpdNRru+NQLddodZwwR5ek1WpTqzVoNxtcuT7DG+9e4J0Pr1APAUvHcREB9OXTFPNZ3njvHOVqha89+ziZVCLsu1KlHdFUhFzKMEhZJp1ag4brYHccbNtGaBoSpeY+M7fIa299gOf5TE+NYhsmjuNSqda5fH2Gja0KpmHQ7jisrG3RarWxLZNASnw/UA7VQuB7fgw2PU8BYc9TLGKr1aHd7tDudKjW6rRbnXiV3Wy32djaVpNcGKrWaneo1htYpqkCElB501qtNoahkUra6JqGLgTZdIp7jh3k6s05Xnj5Daq1OgPFPKUeJ3DHcVjfLHN4/zidRovqxja5bAbT0HEcD8/10aSkHjKEg6Uin3n8AQYH8hRyRRJzq6EDJRAQn1sIlZdyemKU0aF+3j9/lbmFJU4cO0Ct3uDm7CIXr89y+dot2q02tVod3w/QdQPfU+1nhamDzEgLSAglGBqmYAlkQKPZQgtZn07HiSM5O45Do17HMnR836feaKp0KUkrzv3Wbrep1xsU8rkw6apKneL7AUEAiYTN5PgwmXQSECQSCXLZDCND/bz61gdcuTGP7/vYtq3y/AUBjtMhCFQS+HKlyna5StI2OXpQSUe4roehSWzTIJu0+cLTj9DueLz61gdcvDbDZ8sVfM/Dqat+gBAEyQTpfBbbNhUrsrzG8rnLJBMWuUwaoek4XpvF5VXm5xc5cmCSg4cOxfc48hes1RwCqXTZIr8L0zQRQMIymRgdCgMqYHpylFw2zczsEjNzih2SMiDwfZWi5vQJ1je2+eFLb/DlZx5jfGwUU7cJ/CBKFoBWrnYzTiRspKbFuf4mRgd58N4TjI0Mkk6l+Fd/+Bw/e+0D7rvnJDIQNBot0qkkjz90isFSkVbHYXJ4kANTY9TqTe45coDFlXW+8aWn6evL8SfPvYAulBDydrnKjdlFXNdlcnSQRMLC1HXqjQae52EaCoz2F/P86pc/y+TEKO99eJEXX3+XZDKBFDqrq6u8++EFgkDyra89w9jIEAnbIJdLAgGO6zK3uIpp6HiehyY0KtUaL77yFqlUgtGRAd4+c56Txw/xxKP34Xc8Lly6xrmL1zhyaJrpyTFm5pbpOA5mCI6brTbnL13j8tVbtFsu2Wyak4f3c+zwfhK2SsHj+T7vnb1Ef6nA8SMHWVxaYXiwxGB/H5alklHrrR5BZ12PF8nzS6s8/9LrNFttjh7ax0B/kXMXr9LpuP8/5v47yLIkO/MDf37V0zJeaJURqXWlLi1boRrdQKOBBmYwwAzXxmaFkUbb3X92l2u7tsKMRluaDWk07uwOOSSGQxALYBqiBapFVVd1aZWqUuvM0OLF0/Iq3z/8PhGRkVXZAjN0s8wn4r4r/Po9/vl3zvkO2WyKybERurU86Q+6f4T/pjNpd2N6Pq/9Aj69LaKhj90eM37oC4/dl4nYk374AgATtIcFSbfZZushNx2hz0HcFet99D47C/W+L/oAmnxo287xHgpTCpgqiVTxmH4gjtrJmKYvY1V7nPu9uf39x2xt8Wluuk+bPwSvj7hJ8hF/C9yFIGlZJnfuL7CeLwGSjY0ib773CcureQqlCmHLxHE9avU6zWaTjz+5xLVb9xQF7biUKjWarTZSwsjQEIf3zfLh+au88fZZBtIZpnbNYvp+10gPTIzw2kfnEUiabZunTxwkFApRr7VptWwcx8VxHBzHwbIsQqZBqdGiVmvh2BLXVi6cerPN+kaR4VwW23a4dO0uu3beRtMEK+t5qvUm12494OD+RZaX1/irH77J4soGrutj2y537i9w68484yODJBNxnn/yGPonOpeu3WVsKMvhPTtotdrkixUM3eCgpqN7HpbQSIZDVEtVljeKFC5eY9+eGWKxGI50WVvf4N0PL/CTtz5iZmqUPTunaDTb/OznH/FgYZXBgTQhS01+hVKFaq2J70mSiRjjo4MMZFNdV0WrbVOvN6lUqt2ss2q9yb25FRaX11gvlKjWmyyurLO0usbggOqL9Y0y731yiemJMVbXCioFV0pabVvpPQm4+2CBsxevsnfnDppNG9fxyKaSJOIxFpfXaLXbHD2wm41ShUtXbzP/5Cp7++tHSpUBd29umY/OXmXH1Bh37i/SaLapN5pU6wrYVatNPM+n2Wpj6DqxaJRarY7t+5h941cC1VqdB/Mr1GsNFWjZauP5PvlCiavX7/D2B+d456ML3TRi13VZzRcoVZQ70fV9Wi0XJwA+Erpik/V6k2KpwmAui++56LqGbTvce7DIwuwKmXSK1XweISTRWBhdqGu9fXeO9z8+z/qOcaKRMNl0SrlyW22iEYe2rYLBa/UmzbZNs9XGNA1iUcV61BtNLNMiXyjx/R+/zaWrdyAQq62lmpQrNZrNFg8WlqnU6oRMg2QiTjqV4PK123xy4SoT4yOqpqNj02q2uXDlNosrefbMTPNgfpn55TWWV/Psa7eYu3CVSL1BKGThhkxCsSixaBTX9fFth5+89QH1ZpPZqQmi0RCuZ/PphatcunKT3/vGy+zctbsLAF3XpVFvspYvUq7UFUvrKekUz/NwXA+73VbyKZqkUCxz5cY9zn12g/vzy6ysFRgbzlIul2m02ni2zf49s9iOzY/f/JAf/ex9jh7aSzadpFSuUipXSSZi5ArlnuxJLAJS0mrZAWAWXVs5kE0Tj0Z5sLRG27YJmaZaRHg+YyODfPmFp9VAdX0MzcR1XVKJGIsra1RrdbLpBJ7nU6k1aLXV/h3HZXgwy2+/+hIzU+qe5wYy+NJheDjN4EAKgeSZM0fZc2A30vf4+MKVwBr7VGsV2rbN7I5xzpw4zPjYML7vYwqJL10y6RhDA2maLbtr68Mhi2NH9jM1OcyFS9eZm19ho1BC+upaCsUy8WiEp04fp9V0KBSqXLxyA02YmEaY9fUyH396hfxGiYmxYUxNsLK6ztjwCOGhKAKBqYcIWSFc12dyfJhsNoNlWV1Xout60Gj2iJBA+d4wDZ489QTziyt8euFqsGgY5Nxn10gm43zzay8pm/L2WZRchQLXaB33XKArI7a4EH+tPsSOGdnKrD1G+zUArW4motD63JkPe6e+mNV6FNja7ndb3Yrb1E7ssIHbHupxY7V6xxPS7zoNezFYwb2UPVDWkRTpYE+xZT//s3Aj9jeVQi0Dt+dmVfnHDS57ZAsETX1fstRo8uGn8xSKFaSUrBfKcOt+1/1mOy7La3lW1vJUqjU+On+F9Y0Smq5TKFa4eXue40dK1Gp1Pjx3hVAkwq6ZKT6+cIW7DxZpjYyQ9IIVjhDM7N2J9/453vv4Mk8eP8Dk2DCaUKnS6ngOlVqDUqXKQCaN70sq1QYbpQqep4IwPc9XsT9Laxzau5NYNMrZyxcp/fkPmBjNsby6Qa3e4s6DRZaW11hcWefBwiqO6yuXiutzf36Na7fuI4TKZEwkYuzaMc780gr5QhnX87n3YIm//P6btG2P/1ejQday0DyPVCSMFw1zt9rgT7/7Gs8/eYyZmUkq9Ro37zzg/Y8/o1qrkxtIMzyUo1Sq02jZrBdKxGJhTNNgbaPE9Vv3OHJwD0YQixayTDzfZ25hhdX1Ao1mm0q1geMq91KrZVNvtJhbXOHe3CKraxvUmy2WV/MsLK4yPJhjdscUF6/d4dOL12m3bZWKW2sQCSvmanZqnD2zk9y8O8/3fvw2X3mxTSQcxjRMDuydIJmI88n5y1y8eotIOIyUAsPQVPa5rnUqzGCZBgf3zHL+8k2++3dvMTs1xv2FZTZKVTaKZfIbJRXoXSwjpWR+aY0fv/kB0YjKoPontk2ssywLXImNZpvL1+/y/icXqdebFEoVZiZH2TE5pkD0mnLjTEWjWJZFuVrn9v1FJifGiIRDhEMW6xtFSqU6Icuk1W7Rbts4rsvc4iqXr99hIJsmGU8wMTpEyDJ57+OLNJttRodzXL5xl9GRIUaGc5imweyOCS5cucVffv8nDGXTTI8Pc+b4YRzXo9FsY5o1Vtc28H2flfUNKtUaxXIZI8jSrDVaaLpBuVIjXyjyozc+xJeSsdFBavUGd+8tcOX6bSzL5Mr1uyyt5EknYwgB+3fPcG9uiQ/PXWGjWAZgcnSQ3Tsm+fFbH3PtzhxT48MUyzUmRobYtWOSYqnCO2+8x2ShRCIew/E8/IAlTiXj7MjEqdTq/ODH75BJJ4hFw/jSZy1fYGggTSqdUiZb1zFNk3tzy3z/J+8wv7jK/fkVEvEY5UqTkeFAc9T12ShUaTVcwpEwS8sbvP7zj7h07R6+VCz1Wr7IlRt3OHxgH7du3eGTC1fIZdNMT4ywvlHqxlX91Q9e59qte+zfPcPvlurEO6vzWJRavcH88hrlSp2VtQILS6tEoxFu3ZtnbaPIzh0TKvbJl6SScVptmw8/vUQmlSQWjWAA0xMTNJotqvWGYiuLJYZyKVrtNtVag1arRcg0CYcs6o0WQ7ksRw/tRaASe0BNJl6g1Wc7Nm6jge3aih21bTzXod1u07Zd3ACUdmRU3EC9S9M0hKaY1k4wtKZpjAwNcuHiTd79+DzVWgPXVX3cbDrgC3KZNDo6uu4Tj0cBZS+lL5lfXGE9X2RifIRTTxwgm0pQKFVptx18z6dSrZPfKDI6lGMtX+SNn3/E1770LAMDWRaWVvBcD8PUiZcqWJ3kCV0DVwGEyfERTh47xOLyGovL6xSKFZDwynNneOGZUywsrXbni4DjCCZhbdNM22M3ts/C+5XbLzMv9sds/QouxH5Jke2u65evCdndw0PfdHlDhXCD4xAIN/eJpiIeAnLbZbBudRF2jyNlIJHWuzoh+o4nFTZR5fGMQNxZBxFEcQmVsSg7QVvi8dnKXz1A/jEOpNCj5GG02heEtt0o6Uvn7IkxbmmdTERg1VZuh727ppBSEgmHGMoN4Loe9+eXKZWrLCyvUyyVicfi7N05jWWaNNtqNdRstWk02tQbbW7fX6RWb+B5PrFIhKFcVum9yI4mh2B8dgdT4yNcqtxhdChHJpVB14xu+jYImm2bxZU8IPCCdPhiqYLjumxU6t3V4Op6Ac/3efrUYTaKZe4vrmEaGsODGY7unyGbTWNZFnt2TvEbL51hfnGdekPFs4RCFiNDA/i+5O7cEstreUKWpVxegwPoZoh8ocql6/dZWtlgXWqkMikM1yOs6WTTSSohk0azxY/f+pDYJ59RqdUolCs4jsvenVMcPbibVDKBoYfYt2uaxZV1BIJdO8aJRyOk00lMyyLiuUTCIc5fusm9uSXW8kUKAbgsV+okkmnatk84HGJ0eIBoJIzn+yQScabHR3BdD9t1icdjPH36CfLFEncfLOIjSKUSHNgzA1IyMJBlanKM73zzy/z05x+xtLpOfqPEnp3TvPzcaQZyA8QjFrlsGl3TWM8XMU2dmalxstkUstoTy7R0g6+98gxt26FYqrC2USaVSrFzeoJcNo3neRi6wfjoMAf2znLx6m3OX7pBLBbmyROHsILAYFXYtDfxVKoN3v3oAo1mk4FsipeeOcnxI/sxDYMnTxzCdhxGR4cZHx1iYWkVx1Es1ewONZmqeLkqlqVTrVcxTYPJ8eEuu5XfKJJKpnjq9BMsLq9x484ciytrVKo1opEwRw/tYWx0iHQywe9+80uk00nmFlaJhEOkkgkc12NxNY/tuhTLVa7evEsg1UoqGcN2XcKRCI1Wu+taazRbhK0Qg7kstm2TSSUZGwnRbrVwHA/LMkklYgwPZnBsl7V8kUP7dvLS0yc4d/kGq+sFxoYHicdiOL7P+NgwhUqNhZV1PM/nqZOH2TE1xv3FVUSjqZIHanVK9QYJVxVrtiyTnRNjfPO5E9y4fY/llTyr60U0IZgcG+G5p44yNTGG57SZmhzhqZOHWMsXubewgut6TIwOoek6pXINTegc3LuLZlMB9VbbJplMEI1EGcnlqE21iUbDRCIh2i0bQ1exkksr63xy/iqRcIhYNMyXnj/N9OQYtu2wUShx7eZ98hslvhqLK5kCwE8opnV5NU8yGcfxfD797Dp3F1ZYWFpjZsckLz93hkwqSblaY2w4RyaV4PzlG8wtrJBJJzh5ZB9DQ4MsrW9w+/48a4Ui80sr5AbSVGtNXFeBo1QqwdTECPfml3n95x9RqdTIpJNMTYwyNjJEpVpXQMhzsW0F4uuNFo1Wm0KxTLXWRNOU1MvS8jqXr91SWX66RjwaJh6LYPtK587xArezlNy8fY8//9ufMr+0iut5uK5HpVyh3WyyML/M2QtXSCXjDA/m0HWdaqWG76mwDttxyG8UaTSaxCIRctk0iXiU1bUi+UKRsZEhSpUK127dY3R4kJNPHOLtDz7h03OXOHH8MPcfLFIuV0kk44yUK/i+SpZA17piudFwiMnxESYnRnj/44s4tsPhg7s5dnQ/pmlSqdYYdh3ouBC1/km1nyQI/hPdD4/R+mKPPmfa/JXAzK8cp9Vjsn51UPXLtM4xvb6PAVETsE6iD5B1f6UJpK/0vrpB8NtIXwihKif2B8pvErMNwpz0IPHCNE2UqHBHM7DHoUmto7r4eAzkv5uYLeEHddvY5FbcBLT6Ll6C0kx5nBZobGlCMHt4H//B9Ai27WI7DrZtEw3HcFyP+3OL3H2wyOhwjmQiwejQMKNfyVGtNylXa6yu5jEtk9HhQbKZFC89e4pLV29RqdbYv2eGY4f3YVZ6haoRkBzK8eLTJ4iELHZMjgWaTIriz2aSnDy6V8V/7ZhgKJfhqy8+ST5fZmpsCCsUYmZqgt/62vNsbBRJxaOEQhYnnjhEbmCAW/fniMfCTI4PUSrXKNcapFJxxoZzzE6MsricZz1fJBqNkEkl0DSN1bUN9u6cIpmI4boeYyMDnHriEKZusXtmmpeePs5fv/Y2zYpKvzdsBw0FSqd2TPCP/8FvcufenHJleS6u61JvNpkYHWIwl0XXDUzTZ8fUGMcP7WHf7hlmd0xQrdaIJxJEo1EikRBffvEMn164xtUb94jFwpw8ug/pw+7ZaZLxGLqm8dxTJ9gxPUHIMhkZGWHv3t2MjgyxuLLO1MQohq4xmMvy9KmjPH36CQYyKcKhEMur68wtrjAwkMHx4eDBvaTTSe4+WGR2aozR4Ry6YSI1Hek6PHXyCDt3TFCq1GjZHtFomJHBHCytd4eQJuDowX1IX+loWSGLkGXiun4QQD2OlJBKJBU4Ghvh1p0HTE4M8vJzZ4i99m5f8WDZdVkdIKngAAEAAElEQVQcP7KHE0cOcv3WPRKxKKePHSSdSuC6Lgf37sR2XHK5AaanxpmZLlAqVxgZyhGNRghZFrbjEImEqQWT6BOH97J7dgoEtNu2iv9rNpgaH+GrrzzD0UN7ScRVWZK19Q1GRwaVKGs4xIknDjI+NszcwgqO7REJhTBME6EbfPmFp/Fch3giQiwWYWZ2CkDFEsXjTEyM8du/8SLRaJiJ0WHi8Sj/+DvfYGlllXgyyuT4KBo+2UwKQ9c5sGcnh/bt4cbNu0FNugxfeekpDh/YjYdgcCBDOh7B9312z05z/MheLl+/Q6lS5emTh4jGIiSTcQ5MjGIaBq7nM/bxJWL3lzAWV0HTyLge30gl+IppUM0OUM1KWoZOaCDFsGaSXCshWw57hgf5B9/6KhvlKl4AChzbDxiYHGHL5NQTB9g1M0mz1SaVjGNZJrt3ThOLRshvFDEsk3BY6WslEil0TSOTTvL0qSPomsbk+CinTxwKWFWDMyeOcPXmPWzbwfL6SonFIowk43z1xSdptI8rcK1puJ4XMLkTjAzlgsxhpT/3/FPHWN8oslEoMzqUY/fOHRiWCZokl8vgBS5123GZnBjFMA1isSjRSIRjh1W28r25RUrlCof372Igm6Jt2yytrFGp1Wi12ty6v8TI6Cgr+SK27VAs11jZKBFPpJienODcZ1f5/o9+zoVLNzi4byenTx7GDIVYXslTqtSo1RuU601cz6fWaCpZlGabZCJKu+2wuLii3O3tNmsbRS5fv8P84irRSJiVtQKtVhvfcfBcl4hlgPSp1aqUyhUKhRIffHqBwYEB9u+ZRdc07s0tkohHmZkaJ5mMYZkmlmHQbLWZv3qLkeFB9tQa+L6HEPomd5xlaCTjMUaGcuiaKpd1eN9OxoZUpY5KpapCFYIwgy7L0wVV24fHPH6TDwGFf6+t43oUviIS/O1Ce/5dti6a2hopRgfcbhaI7nd9gpSiy0Cp3clHsFx9gFIG3jepA72KL4os21pibIugad/LF7W/v2xE+hSYu+em9TqvP6hQ9FG0cnP3fmGr1IJ7IBg5e5XhlTxuOETTMMjbbcqujxcOsy9skR4bIT06RC6TDWr8aaTTabKNJvFYFMs0u6vYF585xYE9s5TKFUzLZGJ0GP3KrV6wa1A898mTR0jGo0RCyhjruo6maQzlsrzw1AmGchkGsmnCoRATo8M0Gg6+LwlHwuyYjDE8lKHRaCCQJFMJwqEwu2anGMylkbgkEnGGcjaLy2s4rkfI0InEE+iaQSIWI5mIkYjHWN8odmM0jh/ZRzIeIxqNkErGMUyDTDpJPBrB9XzcYLD4tq1EAIUgHI9x8thBDu7bRdu28X2fVrvFwvIK+Y0itVqberyFbTvEEwmeffok05PjJOIxpPTxPJ+2bRMOm2SzCYaHBkjEI1imyd7dO0gl44wMDndV6FNJBRBjsQgjQzmSyTjRSJihwQGSiTiaptFstQmHQ2TTKcZHh9F1tdoAFT3hV2uYnk+25aJrJkPFGqm6KsrrNFrQaJFwHKbaNn7Lxm00Ea02oU+uYCz3wBZSkohHObhvl3KLuB6Vao1kMkFuII1lWvi+r+LuQiHisSgz0+OMjgwwPTGGNIxuajtSMVu+5zM+MswTB/dSLJVxXQ/TVEKhUir3Vst2QAjFNCXiSCkJhawgmULV0wyHQmrREI2STiUIWSZt2+HB/BKup8RFMcHUdUZHBpmdnsQNAtO1IEGjk1GTzaS7QcydklKjIzkO7d9FuVqlVq+TTiXIpJNKRV6IYEwN8O1vfhld04hGwhiGwaljh7h0zaLVbjA5OkgspsCTEIJMJkUsEmejUKZaqxGOhBkdHmRoZFhR9EJpH2lCks2kGB7Mks0kKZYqjI0OItGUTai3lAtP1zCQaKUKIugzM18g8fFnJIHB4B76wbOpa8ExgKiUZAE/ZCFDIbyQKpIsgkLJWshCREJkQya+aSINAxkyiRsmOV3D1TTlWeiU6vIe4LRaxEoVTkpJ1DSx7i5i3V9GkxLTcdg9t8h3rt0lalmMtRxVcgaJ+e6nzN5+wKymdQs1q1dVqJloBBmx8CwToQsOt9ocMC3MyQnaszswEzFCjoexuMJMOMLvf+k57uc3CEXCjAwNMDw4gK7rpBIJNE2we3aab3xFI18oYVkWM9PjDGQz+L5PLBrhyROHqdUbSMBxPXbOTPLqK8+QyWaIRSMkE3FOHTtMIh6l1lDu+0wmSTQSplqtc/P2fQiyb8vVGrYn2bFjiheeOcmde/M0Gi3ClkVuIA1SMj01xsvPnuLt989RqTWQUpBKxJXOni/xXIfds1PcvP2AS1dvM7ewgut4VGsNhgcHVcKIaTGcy7JRKnPxyg2S8RjHg+xV23G5P7/ER2c/45limZjnAaJbakcG8Xue72MagdB0IsXO2R2Eo1FaLZtWu61iJLU+cdo+Vuvh7PpfEnD9z6UJ6LDZyuUaFBn3f1X36GPO47KzrQo12nxMuRlodfb5CMYK+XieNuEHwCrwlvVU772uS9J1lVZhs+3g+f1xawTH6Vzf4/fPrxFs9V9kD2n2+KutiLmP0dKUdqsUArxH3SS57SdRroHjQq2O9sZHSphT0zGEIOJ7jAT0NprgaJB5ZIVCqjBpPIqMRYhZFoZloCXiWNkUxKLoIZOQppEzNLREnEi5hlGu9Zgt30d7/xwJX7KzXKXh+RieRM8k0R0Xq9EkbejEQhbRcKgby2Tqym3W6YVYJEYkHEIIMAwF1AwhiMWieL5S9u7UUtR1iRA6um4Qj8WwTEtRnYaBZSmgaFkm46NDZNMpDMPoKkvnCyXml1bJpuNE9bZaUTfa+LarJDIMHcMwiMd1YlK5xVzXIxqJMjRQDQKKlZGaHBshHFagVJXhUQrmuq6CCXXhEvIyvHhgN8J2GI1ESaBhLK1DcwFZLDGWL6I5DgnDJHb1HqbnkWy2GK430B0XzXaJVWtkanViuk5ECIRtk6g1mK420FHuJE3TiHoeg66rmDdD5e7qnlqbdPpv0+jcuiKVEsuyGMimcT2PQqFE23aUMKeu+tD3VUZooVim0WyRy6YZzA6gaTqOlJjBRN9R2m7bDvlCmUarRTqVZKNQYnV9g1gigeO43HqwyNJqnunJcQzDUJIkqEwyJLTaCmgqmY8QuhFcr67TspvYjqvutxnC8zzK1SqgAuh931cJGrbbA4GBebAMnYiuowtBUtcIa8rgRASsVmu4xTKNkKWKJrdaaL4kl06SDofQfAmup0QiN4o45y7Trtfx7y5hpRI0a3VazSa+7dHIlxicX2LKNBncqBKNRJS4p+epZ8h1Ea6H8D0itkO02QbfJ/zae+D7DOaLaDfvEvU94kaISKONVmt2gYtwXKz2Uvf+9ayC6D5b/U3v26K/fZ6p3LRtp/C3poqd5zShAq+Dz+iqHJEPZKs1TrfaxBMxIrWmumaUrdJ8qZTJNa1bjFztW6jC5JrA1DVMICZ9NE1HCwR4ZRBHpAWT/4SUnPJcfNNEi0chEkKGwxCL4IcsYrrOiGWqsjvxGLLRxru7gIhG2BUJkds1S833iOeyJH2fY/t2MjM9iWaYJJNxNCHYu2eGqckxtdAxTWKxEJal02o7zM5Mks2kME2TXTPTAAznsvz2q69w98E89+eW0JAcP7wPTdOIx2N87ZVnmRofUbGl8TjRSEQlaTRbNBsthoaynD5+CNdzqdaaIAW7ZqbYt2smkO2Bo4f24roug7k0Q4NZlYBx4w5CQLvtcOf+AvVCjaTtIizQO0yJlBSLZVZWVsnnC7iux0AmxchglrCpU6+7uI4dxF3S0csNvDIBGBA+CP1z3IePERj+iHiif19NAa1+bxOg+wgpFPnRr3nwGO1h+YpHHhjlggvK/cj+GqW/BMPWj9c2HacvHMknuC4V1iSkrxZ3+OhC4gkF2GzHUcoGrbaS9KE3hyjZi+4FPPb5/VqZLT3AlT0VVvnQoOwO3D6wJfCCjpJIIR/BbvUK9Kj/g33WGoi2Deul7jEFyrjqEqzgO6kFkge6rgImNU19p2tYmoYpRGAAddBV6n5YKL8sQRaYqNYQrocE9EqN5H/xr/F9STrQLNJ1XdX+k5IJL9hO09BMA2GaSMtEmp1/QeFYy0JaBlg6wrKQlolvGoR1Hc/UwDKxNIHpeXimQWShgB4JEzINfEOHUAhMnZyEUDaNEQmTsCys4No6lHcyHuPlZ09y4ugBRr//c8RGGbvZhGYbwzSgXEU/exnZtqHZgraN0WwTarbItlrQsqFto9mO+ue4aLajmIa2rYrP2rb6HEyI4/0+8e5IlSR8n2FfDVitExvRWZUEKsDSB8tXwMPQNJW5JcHwZRA7JxGOKt2i+z6aHxS77YDTTkmPPnK1Sz93giltRzF7S2skvvpPAu5YMuB57Pb9LlATgPR9kp7PWBAMrHUYGimRjZYag1KirxfICY3/QoPovVWG/uZNpgyDD/6P/5T1jTKl2jVK5So37zxgIJtmciBD9m9fJ9pokijXwHXB90i2HcK6ptx9UiJdB01KfMcjUW+yt90mHgoRtSy8ts2h9Q2E75OMRYnaDi/lC4QNlQ1oALgu0vNJeh4yYKA0rUeJJ4Gc6/bVkwxU4A2dcCgU1ObsPcvpts2xchXP90ieu04kEiLi+di2cmnFPI8xqRIlInMrqvQLnd/LbkCrBAwpsWQwToJSP4mGKgNjppLokTAOKmjVDDJZfcvEGxpQbFNQskV0SrV0S7hIVWfP79TZCz7L3pl8XttkhTq1+nqWq9v6a1VqSLKeTxbQyjXVZx3pgU78j+uBdNWCzQ/qBm7Zp64eCLW9rgCdDMBeB/hJ0Ykr0hDVOlLv2TgpNEwhiQX91pvgReABE2QDUCGCgGApJSO6rti2WAQZDpG2LGREATgZVkycHwlhWRZpS8cPm+ihGEahjN5qQSTMsGFQi8UpJ2IMDqQZyKSo1upomkYmleTQ/t2s54sk4lGSSZW9eevuPWzHRtd1ZndMEA5b1OstqrUGiXiMqckxDFPH8x0kLoYp2Lt7B6PDOUrlqmKjpCprJKXErdbUAjAWAaFq3fm+z+LKGvfuz3N/bhGAidEhUokoQkrazTq1WrWrlxcYL8Drxcf3Y4/grvWIhEcFY/VXU1SIQGjikSry/87atsHdonuhqt5mH8zw9V+MlNskIbGlSehmd3bjnrZu+wVP6DagtcM4bf9L0bP9BIhC9t53+C0hVOJaq+1gO2rRLQPGS9M0zG5M9hefYn/7e47Z8oB+H+eWJrapBR/QgY/MJuijb4UQyP/7f4x86hjaf/6vusVKZfBKp4CpFxjibl0tHxy3Z6i33pyHxp8yeHg+OE7g35VoG0U0oaFrqDpu3dWvQBO9lbCQsgtExLb738IKAr20W/V/WnZ82Nuv3DtNX1pDBPQ5gSGWQjCgCfYEBlur1RGuj4lUfSOAuWXMH7/z0HX7sWhAt9K7H/33pf+7voGMlEoVvjNp9J/jI859azM7TIUQ3YlKE0IBdk2owEVNC0Qt1fei2/8Caaj3XfahM2EFIFqrNhCep+7L6kbvsre8dt6rtRAPbYfrdWMMpS/RhE9c0xmpNAjXWxiGwTP/4i9wfB/bdrBdl+eFIB6LEvnx+xif3SAGavLTNNpDA3RYuY4YrfR77knL8xGawDQMBfg8j1SrjQBMo4zu+2TbNkITaM22GpfB/dH8TlmTLaBBSiwpMTpAKxCK0oRAb7To1jUN7qnlSwYcF1/66MUyellDl2D4PpFuQguIRgtRqm4ZK71xIfr22Vs6SkwpSYG695UanfGsCogLZDhEe2IUYeqgG4pZMgw0y0SYhqqIoOtqe03D1/QApAT3zvPVc+J5CE8iHE8BXcdVCwbbBid4bdnq+W07iHa7g1nUOJKoGCtNoAWuV8/z1Fg1dHyhIRZXVNkhw0CO5NRr59mWKHmBDmBUqCAo3Nz3OQCLWnAfRfB3vVsXECWeunUCFwLZx8ptAmvBs4KudVk6IYS6/loDhOiCPsVWbHnGuxPz5mcl6vuEmy12uS5GPIaVjGMbBkYyRiidwjB0IkisdJJQOonh+diVKloiSqJQJZWIEXYcSr5PKx4hNTxAMh5CCB/PV3p99UaLcrnK6PAglmVimSb5jRLRSISD+3aSXS4QDvuYyXhXYsPzfdbzBeYXV8kXikyODnLy6H4yqSQIVb9TCDADJtGXEl0QKD3ILvujWk84QKL3Aa6HAUzPivcplAvt3z/g6mCPbdmkzsctz+wvgi4e9ySAh4HWNtt1u/bRfdZxJ/Z9oV67C3+6C/v+3anRrQCXWkgHhmLTVOcTDofJZjLouvG557Fd+3sCWxLwUAVMO0+j2HxTu4FmW05Y6jxakv/hwSB0TRlEwwAD5Aun8IcGlFux2kDWmohaHepNZK2OqDdU+YzuYWW3En1vFSx7oMzbAtB0Tb3XdYRhqEnFlwjpgqOKo4qOMQwMowzcBAhtsxZKMLluNYBCU+4EdK171bIDTD/v/go1iXRW4Kp5D4EG4bjdDKnu7dqu+KgQiGbrcw7IJiCkgI0OXWCzGSh1ABGdkkB9wKcbcLrle8V0qJR1I3BTds65g8n7x8V2OF1uee22oITHJpZim/ci+NQdrV12luB6HGgH6fCACUyFLKy2jeZ64HqEr9wmDOqeB/vq3s8AKIlg35HF1f6reeT5dc27EMQIYiSD8WUF+xKNVvfZE4heoG8AFjpsYOezTpCP0w2cRY3bvm0kym1sayBRYpSmZQVFmMWmeyKFUGa0ux96BrBrEx5+lcHk1nXl0Ok31VP2yCCL/8//nSqYHdTwMwyj6zrvBjbLTuJC570q6NxlLUVn8SIIIgHVkaTf/W1nPvQR+J6H32hArYHeaOFXa1CtEXIcLNtFq7eh3kCrN/HqDUSjiV4sQyCy2frj38H9yrNQrSBrdWS1jl0o4RQrhGwHs22jN1qKsa/V0etNqDeVPWu08TtjT3bqtql+Et17SZDa3rfo9GUXiAlfgvT7YtAktP3u93iBHei6n0WXMeuw/FttFn1/E4aGQGB2QiJ8H71Sw/ClAuULq+i+T8gPCskHrH3cddGEwPR8NNcjIwIPiS4QhqGAczhE3DCYQOKGLCI35wllkoRCIc602wzaHu5gjolInJxpYmgapJOKdUcxW2vrBe7NLeI4Lk8c2sOxw/uIxyNoumRsdIiXnjvN8N++jVWtd4uzb5q7Ns1jBIbGCwDXo5raZpPBlR4IXT1n/z4Al+i3aJuWXQ+3TYukL25dVrDLam1jtUTffr+Iau4Cn34JiP7f9465NXh+0276cZjc/Jv+zERN13qhHVrPfej7knAoRDqTCmzdL1Y66NcOtjqORMSWLhadG7r1xooAYPn0Koc/3LoZB30/67Zqr2SP/6Wn8U8eQkiPlu1Rr7colVRmi+O6xKMRhpMJMpaFXm/il6v41Tr5uQU25pbIGia5kIXRbCPqDWTH2FXriEYT2VQlcvrvmiKheuuX/tdenzyywzrLW7qy01IiXA8cqVblVscFaSgXpN5ZuWvdVasIJjMtX1IuLbVE69t3x+gG77XOAFeDSQQAUp1TcC2ahj8yuBkM6boCKYY6BwyjuzKW/aDRCECX3gGOKqGgE9+CpgW/1ZFaXwxLZx+6gdQFrvS4cW+Ou/PL7NgxwcFDe7FCFkI31HXretf1q/ap4s/Qgr7RFcPhC/C14Bx0ozuBiM51dH7ficMJ9iF0DU1Xr76u40oFWASgBcKHxvfewPzvv7vptlqAVrvbLULcAVIBct48hjvoUBPKLTw+rAqO2y5t2yEUtgiHLTq+DAUQtSCuUwQA7mFwuHl49d3vfnfSNuxE51lrtZSUSic2rvN9rd6g2rZBN/A8F1yPVFQPRGZF9/nonIGvBaA2GCtCDyYZw+hN3JpQSSdBQHrb9VgtFKk2WmQH0gzksghLR+gGvhDYqQRC8xHCRzOU21/TfYTmd8d0h8baZDaEhhCy60rrXK9630ly6O8N9c7rXI8uIBbCNjX8VJTw7JhKQjB0HCHowFWgO1lH/tn/CeO9s2pniRgyHUemo0oWoVpjYSnMiqkSHCYnRlQd1GBRoaFqLCp9IIHedPCrdexiGVmuobfa6G0Ho9mGWp3aap7G+gZmq01SaEQcF6PVgnoTEYjjdm3zYxAVAvA9rwfOfH+TPel4EDpue6RE930ivuze1003IRDB1bouTw09WHgLHfRaHVGpd4vjbD4ZgaFphLvxckIt7nSNMSEY7YR8XL4VjDEN39CV2xy1iMgXiqyuFxgezHLsyH4GBlKYpgJ9A9k02WwaLR5FtOzeuftCAaN+vad+N9ImYy956MzF1u+2/n27i/0lW/9iBrYHcp1tuqu97QaB7F2LRLkQH6NtD7Q2W6ZNxwiy/z63A7a6IvvA0ebNtun7TuvY2C1BXV1ZqS3QQtcFur61WDVqcRCUAfxF268JbHXN6iY7peYVrTvR9LMCm0+2A7i2bx0Kux9wdQykRCrKG0BK3HCIYqHI/NwC167f5/78Mq7rEY9FVSkSz0P6kn17Zjhz4ggDk6NI3+eS2+b7l65x4uh+fvMrL5JOJQAfDy2IQVNPhHBcRL0GlRqy3kBWaviVulp51huIWgOvUsWv1NRKt9FENJqIWlOxag/R/H19o/ch9e6bAKl34qNk86Gf994ItEYLXBdXwFrIZPnMEZL/h39GLMi+FIAuJboHloS52/d57bW3+MPXP2AgHkOMDUOjAcUKfiZN6b/6vyKD4F3RmSj77kv/6/ZNqU13rsjvlECAgKpVZhA2g+nOnN1sNvnghz/hf7p1lxMhnf/9V59hZGgATWp9dq834MRDY6sDOLYTs6O3atrUjX3n0T/pSuXalP5mcIPZq7ModAUiRQdEiCDo8+g+ZDiE7bos5Qu0XZexsWHiiQTaO5+oMawJSCWQZ57A81zWN4rcnV9mYDDLztlpQtEwwtDxtR7ARRcBWBXq2B2AK5TLtOW63J5bYH55nZ27ppmengBNwxMamCa6aaKban+6oaMFIPPC1Zu89tYH+AK+/PIzHDiwm1A4zI378/zV373J0SP7OHhgN2ev3uTN98/yzFMnePUrzxNNRJGawJU+rqbhSpTUglRZa0LTFODpA2/d+yQ7au8ehWKZH/7kLd589xOeO3OI3/nGy121fd+T2HYbIT0QJgLFgHYAk+yOJb9vkpCKYOXhtHGtbxx3V7t940q5FlQJDx9J07Z570Ml2Pnis6cYHhrYBNi0bvRHsA/b6V1j2ELis1EocfHyDR7ML9FstnAcl/VCkZXVPPF4lN07p0km4gAYmooHLZXrLK3lqdUaxGNRcvtnFQg3TGzf5dK1W3x0tkV8cojxsWGmJsYYGR7slsyibSPqTWWn6g31vtFE1Brd1873olZHazRpF0q01gtEhFC1BDdljHUm1r4ElL7O7QfyvWdIBgxbz2MgfInueeD4iGZbhX/oOiTiaqLteBg67JznB3GhvfaoiUzkC8pu+j4hTeN/e+0O/6H04c4Soc9uo3fcznqwSDOM3rPoOOq4eqdqrIHAowsg+oxvf/j0VhsvQC046QWAi44/+3PwQafJbYAFPMLu9rNxsDnuoT8OrUt+PHS0vpfAZnafoccEGNvEaanRsgV0PXZdwb7ttmGuOuV6Ag794bPsd3U84hK23jGtQxp1zr3bLZ3ZS/39MdYr3fZrBFt+gPzVTRSBO0j031jRmVi3NrFlX9tsse3ELtXgr/YJm7ZavPaDN3jng3OsrBWYnhjj1VeeZe/uHYQsi0q1xlvvfcL3XnsL6UteeeFJQpYVlGFRRk9Kv3uquqdKB3SuTDd1SCXxk3EIYmh8z0NIF9dXBvnGrXtcvXmXM8cPMTk+HGSEBcvJVlvFsQTATDSaiHoT6nVEvYlfb27+vhYYv0arS4dv12tb/yCBRqPJhRt3Gbx1j2NHD6jVslAA2AhZ2K7PG9du8mcfn+ebbZtMNIpu6GCaClQZOkTCD3GRmyjbvvuh671763dWGcLfNFhVsKrse9Y7q5R+kCSDotiSiGUwPaZW+7om0FATdwfAbx0j/e6UrX+TnXiXvu9AgT6/byWmaZ33Okh1vr0MFBHEckoFwgV4X3sO/9XnEZqOHsRRoWnoz3wHVvOKCfzn/wlMjnL98g3+0//yX7GwvMb/+ltf4ltff4Xo7/6HcHtObbdvBv8//Y9x2w61uRU++vFbpBJxkl96jpGhXHAPReCYEEEuT4dJUrUSO8bH930WF1f47l8uk4+H+c6Zowwf2IMQAttxkaiagSHTUJpDmsAXSu7h6t37XEAyNjxIZWyI9tQYbeDTc59xuV7nt84cY2rPLA8cB/fSdexICBmPIsNhFYMUsH5aUJZGlyorrFNSTD4CqAtNohuSZDLG7I5Jbt65z2AuTSQS7tkATao4yeCedVkqsRkwqZWsBzJYZHTvYW+sdNyJvUkw+BwsCjpjouPO1JBo0ufy1Vvcub/IwX27GB0Z3DwGNz0xEuH2gIGMWOQ3ivzwxz/n/GfXObh/F2dOHiEaDSGlz8LiOj998wPW8wW+9OJThC0TTcB6vshffu+n3Lj9gJBpcezIXl569iS6YaFpHovLq7z7wVlsx+XlF04xNjqMYRhYhtVLhAgkJryB9Lb2dOvc3W7b/M0P3+DdD87y9ZdO89UzT2C22rTyBerrJWIIQraDVmshGq1gsdlENJto9SZarYFoBnZM9rm5teDZ2tokKtnEcZHRMPU/+DrNSg290SLUbqM1m+h1ZTuxXcVYSdn9h5S9ONzAVYqhQ74YADwPy/WUi73tQKP58Dn0egMWVuHBEsxO9hkwHYGnErm6k24QI/s5066QYsufOyjrlwQwYrt5FDpu5d52j3jfs4L05tz+136g1Xmm+hfDj3PewX4eY8uHf7fNd1ItlpB9CQyB3Xsk0IJNLFj/1fa3/u879kTXxEPPhPp7h+jpB9lf3H5NYMsHIfsYoA5DoPVWfJ2JahvmYbvWXVk+AtX3tgJqdUDp7Jy7fY+/fe3nLK2sMz4yxO/85iu88uLTmEE2lfTVKvHBwjJLq6v40lUp17h9roXOjKCKQCvHgOwznz2jrokgtqhj+ANctb6xwd35BUZGB9QEI1VAtxaLIOJRpMzg01EbV3EinufjeB5CN1VhYdkpkaHKZOB6yh1Qa2O02pgNxZZp9aaKrao1if3Fa2jlKrrjELJMHpSrXHv/HDumJxgcyKhz0ASGqfHZlRuc++yaCgjsZPxtaZqmbZYPkCA9l7ZtoxsGhmkFAFUSCpldHS21PxkwDkb3WtqOulbH89CFHxTE1ZBS7wqDdh4qoSldrUw6hWWYGLpS8xV9zEGn9cfo+NKn3XbQDR3D7J2TQBnlbhFn38dzPWzP6clkBG4mdbVSpeZ3XCf9I08otg8h8DW1b03Tukxor5itaq7jIT2f+/OLCCAaDnPx6k2eefIJpk0jwJ1BNJcmCIVNhocGmJ2ewHacbo2uHosnuusvj6DWV58buuOGSiXj/M7XXyEWizI6rECB67m4nku93sRzXURcacwZQUq7Yei88uKTHDuyH9NUsQtSQrlS5cH8Mu22TbFcoWW3VR1HT8UFquLVHghV8BzPp9VqUyzWCBk6uUwaTVMik50A4U4ZmG59M6F0tSIRjXg8iudJGo0Wvg+GoWHoGr4rqdQbNJotYgmXdDpFJIgR6j4zW1yleueOStGVKBGdcSPo61etq2jeGVddUK6BECaRcBRdaFQqdZpNB4EBwuvaDyGDwHlf2UXsdne4NpG8/9EF3vvoAjtnJvnKS08xPjaMFAqQjY2MML+4yuVrt3nuyePEwiZSQq3eIJ2M89tff4lEPEYqHiURj2HoOp7nM7+4wvzSKnt3TTM8lCURj6oAbNm1WOq52jLhbnWn9jfDUNUwNCHxNQ1SSZoxhx9dvMJ7H3/Gl54/zQsvP4llWnRy1iQdmx/onQUJQrJpozdbaAEo0+oNZK2hQkA6sbRrGxh/85PA8Ohc/9LT/On3fsq602JycphnTz/BiaP7kWjUq1U+++AcNy5cJWQ7nJiZ5tDUGGHH7bJ3WuBt0N/4QGVYu55KiugsfruA6RHN0CEW6Tzx3fEj0ILJvd8meDwy9UfQC9vofidBqgSyzzuHzvywzR8eRsfQx1h9EYHxqGvvAVeltdUHtLrs0KPDfbadr3sr7cfAlpuBX3fzbVyHveLTW7Z95Hl1+jKIhZay72idfag53TBUaTBD79iJ3q5+CQ8i8GsBW1uowa2te2bbdPQjwdSj8Oc2xxZAtY4EXNfl4xu3KRRVTbfDB3Zy8vhhLNMIyqgon+vOmUn+8HdfJZmMEI2EuoVq1UTRfxYCzdDwO9IPvo/ficGRIEwTzTBo1Gp4jocmNDQNxkazTE2N0bIdHF9iGQaGrsyR7bTxPA9d1zFNQ7mkpKpTVihXWM8XCUeVIc2kk+iGQTtwQ3horLccaq02qUSUwSnF+HSaB4TeP6+KZRfKmKaBEzY5e+EaRw/t4bmnTxIJhwhZBhuFIm9/+Cm5bJrJsWHIV7e5dQow2LaLCOIlpOewtLzK8mpeCVUO5bj7YImNYomZ6QkGB7OEw0r7q1ypcu3GHRqtNtFIiJHhQWKJJJVajWqlzsrKKguLy2TTKVLJBFPjE0yND2MaOq7rsZ4vUCxVuH13LhBalQipI4SOpmvqoe/GpSlALD3lgrpy7RY37zygUKp0XchCCHZMjTMzOUa5WuPG7fsUihXGRwd55swx0uk00tDQBCwurXLus2u0Wg7DuRx7d8+QTifRNPA9SalaZ3lljVq9SSoRZ2x0mGQihtGN5dxiDF2XcrXG9Vv3OH5kP1JKzn12jTv355ky9C7YUhSMQdtxadoKBOqahmHoSgHd9xVYAVQCnZqkTdNgYXGZO3fus1EosWtmkkP7d5FMxHE9j0tXbnLrzgNOPHGARCzGfGGZc59dp9VWRbUHsmklCzI8iGVapJJJ7s8tce3GHaYnxzh2eB/FUoUHi8tsFCtcu3mXg/t2UipXKFdqrG1ssLS6wthIjmarxZ0Hy9y6O8/cwjLlSo0njx/mlefO4Hoet+484MHiCiMjQ+zeuYNoJILQwPMdQNXmK5UqLK+orLHVtSLttkvICLG4tML7n5zn7MWrFEpVUsk4J44d5NUvPwcCPjl3GcdxGRzIEApZDOWyjI2OqnJcjSYP5pd4sLBELpvm0IHdDA5k0ISmRDrv3OfG7QdI6TM7PcHu2WkSCaXI73lKdR5dp1QoU67UQYIufUwBvtDwkXiepF6rIaUMSn1IYu2eG/Hug0XevHAZ0zR45swxRkeGFDgN7GMqGee5p45z9epNYhFLBXkDO6bG2DEziRmPorkeTrMVFGWHZqvFWr5Ivd4kEY+SScYJhSx86avFSQA4fSmDOocqPrYjVeP7XjDZKBdv59m3LFOJPVsWumagaQaagHQyxeF9exgbGsbQe/FldK12x2UbMIGaBokIpOJq/0L0CZX3MZE37mJ890fqh6bByOQ4e3fP8NmVm4wMZhgN3LVIHysaYvbkQS6trPP22UtMf+0F7NPH0MKhLtjrspn/yf9ms11zXfRmA2o1RNNG1FvdEBCqdcSP34X1gto4yMbuzlXbApzg2cXfHoj0k1hd8Nv5bYcVg564ZvBxE5v0MBPWH1Kz+Xw2M6uPP5/Sm48lgPYw0IKAHPgclYHeDjaDpMdGKX2Ab9tTlN34rP6Cf4+x224T9Pq2sw+1VlXvdU3Nz11vTdCNv0oJo18JbHVvxNZ7DQEX18mzDrLQ+jfsBk5uu+df7ESqdZCSlgb3l9Zwg2y8wYE0EcvsIliBj5RKtfzEE/vQ9WDSaqlAZF/KIONNgQzH87h7Z54r12+j6zrHjhxgYmwocDt5NNotbl+6x8/f+5TdO0Z57sljCAFrhTLXbz8gGglz+sRhwiGLfKHIBx9f4KOzV6jWm+i6RiwSZsfUKL/x8lMk4jHe/fACb753Dtt1AcHOHRP83m99mbGRERzfY2llib/+4c84f/kmg5kkr77yFC8/e1qpffc3X626hBAMTo9iV6q88daH7Nk9xczUJLquc/bCVTzX4+QTB7nzYHHbPm+12vzpX3yfjWKZZ588zuG9s5TLVX761oe8/+lnjI8M8tSpI1y+dpuzn93gwN5Z/ugffIN9e3awtLzKn//Vj/j5e+eo1htYpsmOqVGePHWUVDqBrul8+PEF3n7/HKZhMDyY4du/+SUmRgbYKJf53o/e4tMLV2k2WxTLFaq1epdh03WdWr3Bux+eYy1fwJc+kVCIwcEBBjIpYpEItXqTn779EbfvLRCyTGKRsBICjYTJZVO0HYdypU6xXGVidJB0Ksnpk0+A6/Hmex/wl3/7U1bXCkQjYUBw4omDfPubX2JqcpiNUpm/+cGbvPnOJ1RrdSbGhvmD3/kNnnnyuCqB083k6xvvTpu5+TnmF5d58ZnThEMh3vnoPDdu3+cFXQvYT/A9nw8+usjrb33IzTvzSOnz8rOncX3Jjdv3OPfZNTYKZYqlKsVqA9/3yaTjhCyTW3fmWF3fwLYdjh3aQyadZGxkiA8+vsif/Pn3GRxIE7JMEPCDn7zDxSs3sR011sKWxddeeYo/+NbXGB8b5uz5S/yLP/lLllc3+J2vv8STp47SbLZYWy9iGjq6pvH+xxf4yVsfspov8e7Hl0ilE3z5+dNcuHSDP/vrn7C+Ucb3fcIhkyP7dlKp1vjk3GVe+9l7LK6sE41G+PY3XuE3v/Yipmlw++593nz7E+YXV8gXSuQ3KlRqDQ7u3Yln29xZXeO733+dO/fnOXpoF4f2zXL24g3+6vtvIIB9e2Z4691PuXpdlQk6sHeGf/h7rzIyPEy1UuWvf/A6V27cIZNKsri6zoHdM/zRd77B8FCOS9du8WfffY0bdx6AlLzw5BMMBmrpbcehUCxz/rNrXL5+h6WVdW7enSOViCnQj3IuFYpVfv7ep7z38QV0TWNmepwXnz1FNgBbUtOYu7/A/bllzpw8yI7psSDbNmDFJAroTY0yMZwNxI6VN6BSrnD9lgLSiUSMnUFpH13XKJWqPFhYJl8oc/7STWLRKEcP7WVoeDAAYXWazRbxWJRds1P4wJ2789y4dQ/TNDm0byeTk6NKS01TEWe24+J5PpGwRThkBWZZEg6HePr0MZ5/ylCLxX6XTrBNz85rgXdAUxIKgbtH67CaqH+dvARRrSvXH0AkTCabZnJ8mGg0TDIeJZmIoQmBh49hagwNDrBvzyw37twnlUx0q0tAx3klA5a555XwEYiQiYyk0HJZJY/SJw2DAPHJZwpsCQFhq88i9hELW4khQcCadJiTLZOiH8yHPcHBvv11wIgEqfcwRhdsbbMtwfH6wzK2PbfHAVq9fXcXfVLv/eqhoH/RBVyw2auw9Vw398IXMIl0ANTnbCH79/2YLsqHxmdfEz5S9MXhfcG5bbeLx22/hkLU2wTDdf2ZdI1F8JcvRoZ9AcyP3aoNpJQ0DZ1qrRGs3NRqtBcM2LvR0gchlIuwkw7uOA5IMHQDXddxXI9Pzl3if/ru33HpmlInfub0E/wv/vBbzOwY54P3P+Vvf/w29+eXWcsX+e2vPc8zp47hS5c79xZ496PPGB7M8qUXSwgh+Nk7n/DuB+cZyKbQhODsxZtUaw2eO3OYZ888wZ0Hi/zw9ffZv3uaPTunuHDlNn/3xvvYjsc//v1vkk4lePv9T3njnY8pVRqsrm0wPpLjzPHDJJOJQOcpWMUG8Sa+7zM0O8mucoXL1+9w/sIVdu/ewWq+wNmLVzmwe4bR4QEajdZDPe75PpVqjbMXr7KWL5BJxtk5OapcZR7ML65RrdZ5+sxxwpEoa/kiQtyj2Wrjug4/e+sDfvLmh4wO5/jSi2dYXF7j3MXrLC2v88oLpzl57CBjIzlSiRgT48M8deIQB/fupNVq89O3PuCHP32HM08c4MjB3Xx8/go//+AclqGj6zq+9Pngkwv8iz/5SyzTYGpiFJUl18Q0db716svsmBpjdCjHwtIae2YneerEYdbWN3jrg3OsrW/w7Omj7N45zQdnL3H2s5usrK7jOTY3b9/nz7/7I1bWN/gHv/MVZqem+P5P3uFnb39IJGzwx3/wmxQ3Njh74TNa7TbxWIRKtUaz0egC9c4zgNbLqXJbba4/eMCVG3cplmpkMykq1Tp37y/i+sGDKCW1WoP//k+/x627c+zcMdEt1L2yXuSNn3/IG299QLPVZiCbwfF81tY3MAydXDbNS8+dJhGP8sMf/5zFlXVV/mZkiIFsmmQ8RqPR4u7cIvmNIhev3gLgy8+fZnJ8lHc+PM+5z65x5vghRoZzZDMpouEQtuPQbttIKRXbmkrguC6jwzlGhnPs2TlFoVRhamKU6bFxNKExOJAmlYjhOC5PnzrMzNQEB/fuJp8vcO6za5w4sp/Txw/x2uvvsby8TsQKU6k3eOPnH/P9H73NmRMHefm503x89iqXr90mYhl4rsulq7e4fP0Oh/fP8p3f/gqjwXn+6Xd/zI1b99i/d4ahgTQf1RpMT4xw/MheJoYH0HyfuYUl3v7wPMVylRNH4pTLNT65cJWXnzvDQDbN3Pwi1WqNp44fwHU9MqkUphHCtT0WFtb42bsfcX9+IejPFK2rNtl0MtBBU/FNr7/5Pn/+tz8hm05hmgY/e/sjRoYGeKLdVuSApuG3WiB9krEIUcvsBlMLIdAFELhRzWgkMIeShaVVvvv912m3bZ598hiLS6tcuXaHL71whr27d7C2UeTu/SXqjRa37sxTrzcxdI1Cscxffu91Vtc3EMCBPTP80e//Josreb77g5+R3yjRajY5t2OCf/KPvsWundM0Gm0ufXaNdz48r2qyVioUS1XFLAcxmqtrG3xy/jLJRJzTxw+RSSXxfJ+llXVKpQqpZIJsJkMkHEE3VEWPer3BwvIStuMwMjTA4EAW07ICAkWxW6JW77n4YhGQKjTEcT3qzVY3zlAF5Ascx0PTBPFopDfhd+YeqXSyHE/FDBq6ktwWQkPXBVL6NF0Hu+Vg6gbJRLTnMq4HsVzRcPAMf85ctcm/1PlOskX5NNhe9L4P3MabPH4ddgyNzTIH2wSFd5mjPjZNuCCNLczWo5tKL9Po6H8pNkn0AuIlD51HD7yJTcfeGtO1HTsl+s/7887vc1gtdcm/ANrp24/sHrbjRu7/ez+OUZjAl36gHi8334FfEm396m5EKfrQej+g7q0Uet+KLRtt04T4xYgtKbvSD04k1CsI7EsWl9epVOskgqweiUD6kla7jaZBKKQGle962LaqWSg0get5XL1+i7/4mx9x4fJN2rZDq23z1ntnefGp44wOZvj0wjXeeOdTjhzYybe//hJHDuxSK0MpGMxlGcgkaTRb1GoNfM/j0tVbpJIx/uHvvUokZPEnf/Y9rly7y5dfOMVAJsUbb39M2DL43W+8zIF9OzmwZ4Zbd+d57+OLnDx6kNPHDrCwuEKt3sTQBcl4hNHhnFpddtLZOx3rK5ep5/lkxkd4+cgebt9b4OOzl3numZNc/OwGhY0SR39rD3fuLdDuC7zvPEqe56EZBmdOHObt98+yvLJKuVIllUgyOJAhlYjhBaAuFosSi0Y4cmAXOyZHuX9/nrfe/QRDE/zxd36Dl15+ho18gf/u3/w1P33rI1rtNpl0kqmJEUaGB3jm1GF+5+tfIpVIcvX6LT45dxnL0Hnl+dM8+/QJpsZHuHz9jip7pAny6xt87+/eZHVtg3/6R9/iN776Anbb5p33PuV7P/o59+eX2LtrBzOTY9y9P8+O8WF2To+RTcW5evMuyUSMr730FDtnVODr5Wt3ILjmj89f5tbdBf7oO1/jm199gXAohvQlt+/OcfnqTVZX1giHTOLRCMawyfGjBxkfyXHy6AEiltlbh24xxNVCiUtXb5NNpzhz4jBjI4MIIVhaWafpul0Nrnqjwd1784wMDfDtb3yZcDiEY6uyTa7r4XgeTxzawx//4e/QbNr8t//6L7h59wFfefFJ/vif/B6teoPzF65w58EC1VoDD43R0WHGRnJcuXGX+cUVhnNZ9s5O0WrbvPD0SY4e2E2r2eDj81doNZtIz2VmapSjB3Zx+/4ibiDSOTY2xK7ZSRZX80hgz64drK4XuHL9DmeOHeTIgd1omhr/YyM5PM/j2OG9PHn8KLFImPV8jH/yna+za98uLl+5yXsfXSAaUWWfbt+d4/zFG6RTCX7vt77G8ScOMZwbJJ/fUONMKsmJSMjkwO4phnMZ0skE05OjZNNJVbg5nWBseIBELMKzZ47wlRfPkE6mQGpcunqL1XyR0aEssWiUl589yejIIDumx7EMnZBpUG80uD+/zHOnj/HSs08ynBug1ba5deceb733Cd/48vP83u98hUKpwuraBpUgC1oTGnMLy/zgxz9nYWmVkGViWUr/q1EoduU/0DWErwCE3XZwHSdIvaHLvmt9Af5SSiq1Bt//0c/56OwV/tkff4vnXzjD8sIK/7/v/ojX3niPgYE0U+MjzE6Ps1GocOrYfl5+/hQz0+PcvjvHer7A2HCOV7/8LDPT4yTiUc5fvEo4ZPG/+g9+lx+89iafXrzOb24U2LNnlrff+YTv/d2b5AbSZNNJbt65T7XexPfUAtb3fe7cn+fffv8NJsaGmBwfIRaN8u5H53jtjfdYXs2TiMf4B7/9KqePH8EyTTYKRf7sr/+OT85/hu24PHvmKH/8+98kl8uiI/FQ8ayiUu3qe8mgoHqntW2PZrMVCJo2EbqJEIJypdbtL+VZUoCp1WqzsrxMPr9BOGyxc2aaRDyOrglKpSrvfPAxZy9co1xRhdy/+erL7N+zE00KRC3QFoxFHz3v+IFmnLbFvSe17YHW1ib1wIW4ybeFkkDq//327M2j+Sqfx5ON7gcPfQDK72flNrNpm8FS/1l0rn0rs7WldVjPX94T1yMBu9xl70w+92ebwGCHlet913FrK5JC9ry9W0lF2X/MXyz4/9cvahqALCkEWscf3UHaHYZJ9Lset3Kxqkn6/KP9PnO2bN60VeCjJgjnsgyEdJbXNkCDKzfucOPWXUZHBrsPZL5Q5Obt+xTLJcbHBtk9O6VWQK6H7bg0m22Wllf5+Xufcv3WPcZHBxkfGcSXkE0lyGWTlEtlWrZNyDJ56sRh/uBbX8O2HcrVGpZpkE2nGRxIs75Rwvd96s0WzWabqfFh0okYVshiIJ1kZDDD/j0zxGNRmvUGiWiYUrlKpVJn144dPHn8ED/62Yds5DeIhkx+9zdfRkrJh+euACpeRwW8a92SH+qBUaNCSomVTXLyiQN88MlnXLt5n//Hf/bfYOkaX3vpaSKhMLfvzeO4vUrnHYCga5pyf24UWV3fIJ2IUiiWiEfjZNIpctkMd+cWeOudjyhWasSiYQ7t3Uk6k+LiZ1fIFyq4ns/8wgql/AbJaITx4UEsQ6fdbFIpldjYKFKr1qlWa7TabZIxn+XlVTaKig1sNlt4jktuIEMkHMa2XUrFGhuFIncfLGEaJrMTE4wMDtNqNRkeGiAUMnFsB0PXScZUBlsnyNs0DAxN65qi9Y0i+UJJTeSNJqvrBQqlCrquMTSYYS1fIBZ1yGTSDGRTeJ5Lo9li5/AEe3ZO8YOfvke13uQbX34WTQPPc5BSCbAKTWL2rVjv3r7P6voGf/z7v8mXXnoWXVMK8v/t//jXVFotMgHDatsOtmMo4C8EgwNZ2nabRCLGxPgIA5kU+3fPsGd2knq9TS6b5u6DBWYmR0mYJrbrYVlGtyizpgksyyIWiaAJVfh3985pNopllpZXMQ0NTUgMTSMStghZSp4hFIkQi4a78T6ITn1OnY5IaMi08DyPtuMQDlukU0mk9KnXK0hfUqk2yOeLNBsNLF0nGY+haxpXL1/nb3/4MyrVOslEDKTEc1Xx83QqSTKRRCBIpRJEI2GagSCoZRpdc6CjVNs7yQ/hkEkyroqym6ZBOGSRiMUJWWEAHNfFdV1OHj3AH/7+NxgYyGAGY8NA8sJTJ9golPnr197i7Y8uMjM9yVAui5SSuYVlao0WyXiMkBFiaGiI8dFhyjfvdAPpF5fX2CiW2Tk9zh/9/jcZH85hGjpjmkD8y3+rxoGmE9I1XM/j8vW73L43TzqVRDd0JT0slbhAq9lkeTXP9OSYCj/49BKRSIiZ6XE0z2c4l+HkkT289+klNjZKjI8OMT4yzJXIPQYyGXbPTBGLhSmWKjiOy8TYEKeOHSSVTNBut/mNLz0Dmk613iRfKBMJh4iELFaXV3n9rQ/wfJ8/+ParTE9PMDI0wHsfX+i66DRNI5dNk0klAmJAkt8o8ta7nzAzNc6pJw7xb/7ih5y/dJ2jB/cRC4fI5/O89/F5Du2bYWZ6QrkDO6XEZJ+0ZqHcs+3JHtjSdY0bt+/z//03f8Xe2Sks02BhNY+m6SwsrVAsVWk2W0ih3Nuu63Lp6jXOXbhCu9WgUqnxwjOneP7p03iez3sfnuMv/vonLK8UkEgezC1xcP9u9u6aUR6PeiAl1A2O72tBEosQmhqI3hbA8YtMvx19yT5JCNkZ4Ftdat1YKtnnrdmmbXJTfpHLrs/dS4d12w4o9WDWdls8zGrxOTFXX3BeaqX66L8HJ9Bfq7D3wy173nIOUnYglkSyBegG+wjCf+lkoD/qvDe7bh+v/RrA1mYf8ebjd+Qg5JY/BCBM9n8v6FTifvgQYvNr93stKCsBQgpiI4Ocnh3jwfwS1VqDfKHMT978gOHBASbGhhFCcOvOA9758BwXr97g0L5Zfu+3vsxANo3jqOK9hWKZpZV1VlbXcVyP2alxvvG1F5meGicaMhG+x1q+gOO4eL5PvdFUafNSaeeEQxbtthO4UhUgisUiWJbJjTtz/PStD/E8j+u37rN3dorhgRyu7VGqNKjWmsqlJzXMUIhUIoGmaTSbLc5fus69uSWeOXWEVCLOhSs36ZRxaTYaVKs10HSyoHRogm60IyFSyQRPHN7L9dtzXLp2h1dfepLnnzzO+kaRar2xJQhSgbS2bbNSq/Pjn71PuVpnMJumUq2jGxq5gRST40M8WFzik/NXEZrg2KE9TE2MYAB7dk5zcN8MH356hYtXbvLMmScIhyLUak1c18e2PYrFCo16A9txWFkrsLFRYTCdUqPG9ylX6+Q3ipRKZR7ML9JstnmwsMq9BwvUG018qRS0y5Uq9WKZ9UKZufkVWi17U3kO13GVa9PzaAV1+2zXo1Kr07YdVtbyNNs2t+48IBaJsBYExuY3SsoNrRksrxcolWukk1E8z6NQqlAP0sZv3nnAazrsmBrGtDQWVzdo2Q7ZdIq9UmWy+lJy4exlagF9/eD+fDfGREpJudkKul7iBQHMHVV011XxhIp59XEcl3yhxMryGr4naLWUi8+XEt9xupmrKqNGZVjarhqrIctkfGyIkaEcQgi8ANB5viq86ntBfUlNqEysjo+jS5iqRBLZSSiRKoO21bJZyxeo1uqkknEcB1otl0KxyrsfXiQRjXJgz04Wllf52bufKCAUjyKQ2G2ber2B43qELZNSpcbC4jKjI4PdbLZytUa1Vu8WPX4wv8L88hoN2+Xu/QUajSbZbIpIJEyrrTIk27aN6/gQUcxTLpPuMYmttkr40JW9qdTqzC0us3/3Dmz7GV5/+1M+u3KTQ3t3EQpZ1BrN7mKq1bKxImEioTCep7JZEaIbQB8JWxzdv4sdU2O4roe8db9n43SN8YEcEyND3J1b5GfvfMLocI7x8VHlyJGSlm3z5lsf8enFa/xH/8t/iOdLCqUq4yO54H6qxVU8FiUcsrAdB9t2cVwX23EpV2oUSzUQqOLOrTbFUpVKpUY0Eub+/BIffXqZW/fmuT+3wv35JbLpBPV6g7t351lazTOQSeEHpYw6cWN+YJc1IbBMi5BldbM/Y9EIf/CtrzE8lKNQrPHdH/yMarVGrVohFQtjaBrJeJR6s8WOyTEO7t9FIhbrxU3LIBhlo9g17TKZUAH+mq+0fiMhnjlzjFPHDmLoOktrG7iOy0/f+oDV9QLFclU9N8JgYWmZdz/4lLZtE49G8DyfWr2O7djYtsedews0WzYvPnuSA3t3YZoGh/bvVmWXbEfpogkU2Nom+LwXCiN6c5zsPC+P2/qYMBGweX3xSNuCnn7A1d3FFrJCAptkKWDbfW06i47b8fNcSo/gkbqH+gJWa9Netm6zmcn7oiY6+jH9v5KyqwgQfLHpb+ooff0hOnIuIui24F4GP/V9lezi+1vOaOt4+AVcir8esLXdt5vGQJDiKqWiXbscXd/Pt0HHD+9oa9Og2ujuJDSQ4enTTzC/sMzHZy9TrtZ57+OL1BtNnn/qBLtnp3gwv8Tl63dYzxdpNFrUG03CIYtKrUat3qRSraFrGom4mlhv3p3j3tyiAmQtDctQ6fGe5+F5PuVag5bjBTdIZYrZjtvNIEQIhnJZDu3fyWuvv88PfvoB6UScybEhzpw4SjwWo9FsEQmHqTXaFIo1KrUGV27c5ePzV9g9O8nOmQlW1wv83Rvvs3N6nN2zk6RTcY4c2E2t0eCt9z4NNH92sjNIbZZIPCkp2jbNVkvtZ3qceCzC115+hlg0yoXLN1jLF2m22ioFX0o0Ca7rUas1KDaaVGOWEpksV2m0baSuY4YsJseHGRzIcH9+mYgVYmx4kGwmhZAwPTnKS8+c5OqNe1SqdRqtNitrG9y6O6cy3zJpUskE6VQCIQQLS2s8mF9idnqMmekJJseGWVjO8+7HFwmHLT69eJ18sdztY9M0SMSiFEtVPjl/mbHhHPfmlvj5B+do2w6RSIRKrc5Kvkil1uDGnTkSsSiNZotCuUrbcZlbXGFoIEOpUsdxXJZW8xw+4HH8yD5u3n3A2YvXOXRgP8JocO7iVdY3ShzYv5N4PM6nF67y4aeXiUXDIASWaSAEXLl+h7/43huEwyFOHz/EjOsSlhLX9bh14y5r2SRzC8sMDw6QL5R498PzLK6sU2m28KWv+t7zcFyPZqvFrTsPWFxeJWRZNIYGuHNvno1SheW1PPceLCKlRrVWRwCVao227VAsl5X70PdpOS6O67O6mmdpNU+zZQeCrJK27VCpN1gvlMmVqhQqdYrVBuV6Cx+d8kaJe/PLirmyVTFWz+swby537s+zspbHcV1qjSbnPruOQDA9OYbjumwUy7Rsh0q9SdvxWNso8PrbH/Ph2cu8+spTRMJh2rbD3blFbt15gOd5xKIRbtyZ44c/fZv5xRXu3ptjdb2AlJLV9QLTk2Ps2z3DlRt3KP3p9xgezFKtNdg1M8ETh/bSaLaYW1ih1W7zYH6FheU19sZjaIbGzPQEw7kMF67c5J//1/+aIwd28/JzZ9i5Y4Jipc6PfvYhc4vLIDUaLRUfJIQgZFmkkgkkkrfe/5TpyTF27pqlUKpQqze7ixXLMghZJhvFChcuXcMyDTzPIzy/xHDHzOs6E0MDvDJ0htZP3+H8ZzeJR6M8eeoo0XgUx3V5MLfI3/zgTXLZDKYRJhzySMSilCt1VlbzjI0P02q0WForoOtKtb9cqbO2XqRWa9BoNmk02iTiMQRKTqXRbHWZvfc+vMD/+G9/hC9V5mM4ZFKvN9kolEin0hC4a1fX8mTTKWqNJq1WGzcA7B2b7QdSI57noRs6uYEMq+sbfHz2KvVgEbWwuIxlGgwPDfC73/gyb7z7IX/5vZ+wvlHg+adPkstmVKFfBGY4DH3Mlkwl8DxHLT6kz9jIIM8+dZLZ2UnwfWZ3zlCvN7l09Rae51GtKnbLdhyu37xLsVTmlRefYtfsNBvr6wxk0miahm23kChXbjIR56nTTxAJh4nHovieT3OjSEyqxQrx6JY56BEOva47MQABkl+wBE8fkOn73SZW6wsnddH3D7YFRr9M21JM+uGYq4fP7fGP+DCztPntlmsRfd6ubXe3BTBuAVwdJnbryXbCb1T6RiCs7Pu4rh/oL/aD116dWrkNv/Z57e+xELUIhGk6n9UKULkHt9+8mzorOxf/GD7RSrPbv1oqwe5dO/gnf/gtjhzYxfsffcatuwt8cuEqV2/eY2w4h+3YFMtVhgezjI0OYpkGq+sF5haWadk2rueRSsbZu3uGTy5c48H8Ct/70c/56NPPMHTBP/r2qwwPDQSuIkGt3sR2XeWGiYQxrRD5wnxAz1tEwiHC4RDZbIajh/YxMjzE2HCOdDyKZZmUq2rF/tJzp1ndKPGjNz/k4tXbtNs205OjvPzcGSLRCPnCdcWQXL1No21z5thBRody3Jtb5PV3PuHu3DLNVovvOA7hgB0peh4/e+csuq5zYO9OTh8/yIP5ZXbNTrO+UeCnb3/EhSu3cFwf23FwHBcjGIy6rmMaBqlEHCEEk+MjZDJppK8C0UOhEOMjOZZW1okHmUJ6oGVmmRYnjx3k+JFLfHL+Kv/q3/w1hmGwUajw3JPHOHJgNyNDg7iez66ZKTYKJe7PL1Es1xibGOXVr7xAvdnmzoNFlr+7QbvtEI2EGBxIE4tGSCcTfOWFJ/nJ2x/ywbnLzC+vUa7W8XyfF546zvEj+5VMwfwKVsjCdlxu3ZvHddVKXdMEG4USyWQSiSA3kCYaCZPNpDi4dxeu6/G9H7/Fv/zX3yUaVQHwT59+gt945XmGB4f4+OxlavUmrucRskymJ0bJpFPMLSyzspJnNV+k2WzxTc/D8iWu7xPSdZUJCEyMDfPJ+St8dvUmuq7hdeJ0pMTQdeKxCCtrG3z3B68TsixOHz+I53ssr65j2w7VepP7iyvkNyosrxdotGze/+QiM1PjXLl+m7W8mog7jNCd+/MsLK1iGDq249Bstmm12uQ3Spy9eBWkpNluY1kmzVYbz3V5/Wfvc/bSDbygnly72VKaWaUamtC4fW+B67fuEg5ZDGbTXL15j6WVdY4c2M3E6BDhsMVAJsn4yBCTE2NYhsZAJsXgQJrrtx+QTMSJxaLcuD3H7XtznDlxhCcO7WF5Lc+1m/e4duMufhDcPJTLkE0nmZ4Y45tfe5GpiWHuPpjH9yVPnzrK3t3TDA0OUKnUiEUjjAzlyG9UWFxeY/fOaXzfZ9fsFK88f5q/e/09rty4y0axzOz0hGJjTYNINMK9+RWqtSbpZJyp8VEi4TBWyOKl556kXGvw/scX+P/8D99lamKUm3cf4Ho+tXqDVqvNQCbN4f27+PDsJf67P/seb7z9Mblsim8nEgwHpkrqGjHL4jeeO8nYyCBvvPMxH52/ykfnrqrED9/HDdikF585SSYZp2kaPPfkE7z2xvv8yZ99n3sPFvB8nwfzy+zbPUMykeDchRtcv32PcrXG/NIaDxaWSCRiCKEjpYprqgWgsFZv0mrbuAFwVtmiFiAYHxkml01z894873x0HttxKZbKtNo2tu0owOX5OLZKmhBBHOGlqzf57g/eIBmPAYJW20YTgnyhRNt2SCUT7N09jW4I/uyvfsSb737Cnp3TWKbJ3MIibddn1+wMmWIHbElkJoltOwooOh6Waaq5IMjU1oSG7ThUqjVsx6XVtmk2m9iux9rahgrqb9uELIORoRyRcKir2eZ5SodvZS1PtVZnIJ3EsgzqjRbrt+4x4ziYpoG2nRtxuya1XqjMF226abIPiIeOurwv1b6+aN77PMCxJURne8jVDx63uPW6LFUP2HTjm7b1MHb+thXE9IUA9X8nOiXtt7rotpznFhkMxR5u9ZBtbZ8HfDbHePUfTwYgzhMaEg8JeJ5UYMv3H9qTH4RRgOQhrdrPab8msKUuQ0DXfbapdTI0+CIRNxl0quj/5tEXI4FKry4iiRjxeJQ9u6aZmRjm9LEjnLt4jQfzy9SbTRX0rUE0EmFqYph9u3eQTMRxHJf9e2YYHxnmwN6dDOaypJIJTh+fo1j5kAcLKxTLFU4c2UcqnUQ3TIaHBnnuzDEO7tuprlkIorEYCI1SuUImlWB8dJB0KkGt3uTmnQfUG23ClsXc4gqfrK7TbLZ48sRhXn72FMeP7Kfdtrk3v8ToyDA7ZyYZGxnC8yWLy6vkBgf4vW9+BU3XCFkmmVSSeDzK+OgwT588TKttU601cF0PPKWvExkcYO8uZdSSiTgvPnOSWq1NLBqlVqkyPjJIPBrHRyP0N693XUOGYZBMxJnIpPj93/4KkWiEqYkxpifHqDearOU3qNbqxKIRotEI2XQK3/dpNFt4Hkip4jq+9PwphnIZTNMgGg4zMzVFMhGj2WoTDpscObCLoVyaK9fv0Gw5VGt1BnMZnjlzjPHRIe7PKQHQSMjEdT1ikRjDgzlcT8khHNy7k0vX75Avlqk3muzdNcOTJw+Ty6UoV2r81qsv0hGfNXSdZDyCoWk4jksoZJEbHGTfnp3kC0WKpTIDmTS5XIZ/+J2v88SRXVy/vYDrSYaHcowM5RhIJwhbJsePHGR+YZWF5TV27pjgxWdOMjI0RCwW4cWnj/E3P3qHpZW8AuQB8/V7r77I154/ydjQEMlEnFw2zVMnjzA6PMi+H7yF9t45kB5DAxn+2T/+Nleu32GjUEI3NKYmRkkm4uyYHEMIQSQcwnZcCqUi0UiIVHKc4cEc4VCIeCzKC08fZ3pijEMH9mBqgO+RTsY5dngfp44dIhqJ8NSpI+zdNc2eXTuYHB9hanIMpGRsVEGDeDTCN7/6PJbZC/a2bYdkPMarrzzL80+fYHw0h2maZFJpPj7/GdVanSMHdnPk4F6ePHWUB/PLhEMWuYEMrUaDg/tm2TM7STQaIRQKceHqbRzH5cmTR8ikU5w4ekDVVwx07Qxdx7JMZndMMDk2guu57JgeZe+eSTqSAyHLQghwHJdMJsU//PbX+cqLz9JotMgNpBGo+x+PRXnpmVOMDuUIh0OMDOXYMaX6MxGP8Y2vvsATh/eS3ygRCYc5emAvZiiE7Up2z+7gn/6j3+XM8SPMLS6TTsU48cQ+HMdl364dOI7D8NAAv/UbL5FKqiQMKSV7d+1got5Wwr6A0HWE65FMxnnmzHF2z05z/tI1Ll27DRKSiRiT4yPMTk8wu2MCXdNIxmN8+xtfIpmMcfX6Xe7cXyCdTHD86H6OHd6LoWvkCwV8XzKUy+A4LovLa0yNj5JJJziyfxeOZ5PPF5mZGufQ/llO3N3LnblllUCTjLFv9zTTk6MM5rI8//QJ8sUyH316mftzS0pmpdFkJSgT1Gi0OPvZVZbWNhjKZWm22swtLPPZlds8eeIQkbBKUipWalQbLSq1Bj/5+YeMjObYNTOJaRpYlknIsqjW6py/fI21jTK6YZIplII5QODGotSbDZZX1rFtm8VlBSInx4aU+1cTVKs1VvMFvMD9bRkajusSDodpt11+9taHrK+tMzk+ws6ZKbJGGNfzaLdtHNclv1GkUCgxMZxDWib1eoMHN+4w2morMdZorMt6fO7cJ1GB5V1R6C9gO7Zm+PmPB9S+sEkChq0ny9Bx7an0qf7jbMPWiWD7h3CT6IKLh85z2xqIm3687al+8dVu6ZMtiXif2z6PCdwE/kDKXkF6VQJYiZj6XlBaTqJEioM+0DWdjhadj0D/onvd134lsNUpTdF1IXdjqx5Ol93eV7vdRlt1SuTnA66+ItQiGVPjTROYpkk0GmF4cIBcNs3oyBDRqIltqwfNNA08T8kbZDMpvv7l52g0WphmiFDIIhqJ8PTpJ2i02ui6xvHD+zm0fxeDuSzlSpVD+3dzcP8uds1OEo2GaTRaaLqBYRgcP7qfsdEBPM8jGgnjOC66ZnDzzhz355cwTZNmo0GxVKVab7B7dorhwQGyAxnS2TST42NK7V3TKJaUQOvOHZNkMylsx1XxWUA4EmEyEefUEwe5cvMe5UoN13MRUmIYOqOzU7zy/FPEY1ES8QSapjM4oO5TJBbjmSdPYBohJBrxn76PdBw8z8dEiWRm00leeeFpdEMnGglj2w5r63kKxTLVRpP55XUikTCDuQxu0Je2rZTrfV8jk0rz5ReeZnxsSIlyulIJztar6LrAsgxSyRijwwPYttctyRIKWezdPcPunTuC51/i+x6241GuNKnki4RDJgf272XXrp1sFEtsFEoMDw6QSiktntxAmhefOYXn++Q3irRaNoO5jHIXuC5tx8EwdUZGMsxMj7BeKOJ5HkiPkKXu4f49e2i2HNq2jWM7mIHy+PTkGL/xpeeo1xvsnJliIJsOtKd00qkEmiZIxSNBfAcYus6hXTvg5CF8T6PVtvE8j1w2zZGDe8h8+BmdygVhy+RLz53m9IkjrOcLrOcLxGNRYrEoE2PDRCMRTNOkUqmyd/cMv/1qiGwmxVAgt7BzRgGRSDgUMK8NpsaHOXXsAE+feoLpyTHCoTCjIzlA9bWm62pVHVDqjuvw/NMnu0rrSm8phOe5jI8OsWtmmh1T44RCOrFohMGBAXbv3EG+sEE6lWBwIIOUkumJMdq2cgWVi0XabYfx0UHGRoYoV+tMjg8zkM2QTiVxXQ/LspiZHmfXzBTRjvSBr6QQHNeh1Vb6dJGwiRWy8H2J67rYwbg1dJ10KsVAJtPNSu7E70kpabdtMukkRw7uJZNOqgoCgSbf8FCOocEBOiWoNE3D9V2QAtf3SSbDPPvkUWzngHItSLXC1YNKD6ZpsnNmglwuwdz8IZLxOLtmp4j/zU+6E46o1rHe/gTPdZGxKJOxCKPpDM8+eRIjncDMpLDSKfSwFayeVSjA6HCO3/vml8g/W0L6klgsQjIRV/3ieLzywmkO7N1F23bQNKWZFotFGRoaYPfOaVzXJhpVz9bUxChffvE0x0vVgCWOMzyYZSin4vhefOYUyUScq9dvM5jLBoCqpWosRsIsLK3SaLbZv2cWyzSpNpqMjQzx9KnDCCFIpeLsmhmn0WyiGwau54AGd+7Ns7yyjmUaHDmwm2QiTt1uU6zUuXD5BqPDwzxRqSmsoGvYlsnyyjpzCytEoyoE4INPLnL04D6SyTi+43Dz9n3W1jdIp+I0Wy3abRtNKOb44L5dXLh4hcXFVSYnhnn1ywZDJ4ZIJmLEomHFqi2u8P3XfkYsZLJ/7yzFUpnVe/Pdihifm4340MT1yzS5ZV4Uff9/3s9kH4jpn1M1kK4K2+mWzuttJjG/GDz+It7PTT/awsZtBTy/aDT51vZFQOsL3KyCTnUSuenbLRt17fDm/QZ3SSqdOSvQKvxFr+hXZraUZFynfEHny47vus9nGmz9GDtUHbudz3a7VuuBLRmPKmPp+zSbLRaX1lhZyxOLRjAMnWxGGdhavUmhWCJfKCOlTyhkkU2niISj2LbbjQ1KxGMcObCbqYlR9u+ZJRaNYjuOSj0WkEzESSYiXSmWToZCJp3CcW0ajSZuwDINDmQ5cmAXYyODRMJh8oUiFy/fwA5kJTwpCUfCuJ6N4zo4nocZIG7TEkQjJqGwjuc72I4NCBotm3aryfXb99kolEkk4uiyorrR80lcvsnB/8t/RfjefK9OpSZAaKQF7ACkEPgSjHxBLSo3SshUUAC3bTP61odI0wRTR3Nd4qt5JvMFzEKJjXuL7BnMsDscJoogvrqB/2ARP5vG81wqi2vEMynEYA4zZCCQRGMWhpnAstSE4jgqgWB0JE4mE0PT1coMlLBsR0JBCB1DCnQDhCaRQqLpEDUtJHFiMUs9CKYq22IaBoau02rbtFo2pXKFcMjCNNXNcjwbX2pIfNptm3qjEbj5BJ7j49pt7LqNXWvgNJsYvnrQzGYLv9EiWyiTaLZIzq8Q3ijittpsLCzjvHOW5xyPo7E4US+Q4/A8jP/6T5H/9sfoCDTP42C5ym7bIZP+Ccb8MsLzurGthq6RiIRwE1F0fEzLwgyZmKZBPJpAC/S8MukkuUySeFCguaOF1Wm+VAHcbdshZJrdx0kICFkWnWgFGQAOJHi+R6lUoVKrk4hFSaVUkoYdsAGxaAjDEJSrZdp5m4nRYdKpNJZhkYgmiMei6EHcouO6NJpNKpUac0urtFsqNlHVuVTxRonuhKYMoa7rGIGemuu4tNptPM+lZbdwXIdkIq5kMJpt2s0mdrNNq95A831i4TCaboIPmucjfB/N98HzEM0W+vU7xBpNzLaLCIfQXQ/d85Guq9xTvq9kGlwP6Xnqt66HZ9vg+RgSQr6PZzsI30OT6jkTnof0PXBdko7DuOOhIzAk6DfuoG2UVL83Wxjnr6DfX+jeo7CA+Fa7aBnIWAwZjyBjUfx4hFgsQjQWgXgUGYtALIKMRzHjUQbCUZKpFG7YwjUNbMdF1/VgvJuBOfaRUsU7JuNxspkUA5k0SyvrlCs1BgdUKadIOMShfbsYyKRIpxLkBjKYphmAShifGOd3vzWMbduUylVM02R4IM2umUlabZtIOKRc8Z7H8GCWy1dvsXPHBBPjw5imCRLGRobQLQsTn6nJUW7fm1fsVDUouyYE/so62WyKF8Yn2JvNoiWTDI4NdZNBPM8nHotweP9uYrEIhw/swQrYslQywUvPniaXSTK/uML0xChT42NYlkFMRtgxOc7+PTsolirkN4pcu3mHgYE0yytr2MVyMOkC8cd0I8LDU9s2PqvNop/buM4e+1iPxws9TFN8Lm3xS7ZHMFoPbSYf67w/tz3i5w+DqF9u153KA6oiggjisjrHUMfpsPIK4fx7kH7QAmXcrpsWVeD1l7mxQgikJoOUWrr7eGT8Vh+zJWMqjdyXUK83KZbKaJpGJp0kGg0rQ67rhCwXwzCIRkKELBVXZVkmlqUCYmUAFAdzWQZzWdLJBPF4DKFraJ6LrukkEjFyuRShkFothCz1qmhGVJkLU5Wtufdgias37jI4kGHf7lk838N2HcJhxQrFk0niiSTRWBTXczF0E9PQ0TTFPCQTccygfp6mCcJhldpvuy1u3b3H+cs3SCXjPH3qKJGlN3t9absYhTKi0ep9t+VVolRZREcLqOV3RVFFtU70z36gPP8Cor4kbtvMNtvUG01ebLVJ5EskF9ZVaYNAGkDTBDFf8lSrjRB061Ki68Q0DalroGtITSOFYEYXaIaBbhoqzk8ToOldHz+dwQ+Y0icbuOdM0wAEUalkEzR6MQGd6zNcj5lmC9exMQ0dyzDA9/BdH036CAme7ZBq25gITE0gPBUAGfUlqT4JhU5twrCURILsPdNUbKbueQzVG3y90cQyDWLVq4TK1d6JVOqIIMNPeD5Wo4kpJYYQiLYNnXCEgNHwfQ9NKHmIUCgEutY1VtL3MXWVrKEJged7Dz8XQXZiNV+ksLCMqDVwVzfwcnn8sAWeWpTIAFwIzwPHwStXKF6/y8bCEno8TjIeR9g2RrXG+NIaifUNookY9YO7cStVGMgi4nFEuYZWqaDHouiRECHbQdYbyEoVr1hmR7VOKhph6KMrhHWdcKvNQL2JpWlELBPftrGqdYTnEw+H0KXEc13clo10HYTvo0tlsDq8ue/7SoMr0MfTgwzO7fgB3/c50Gzh+5JYYAs2dVfn/21sti97IRKdz0CviHW3yzsTT881IdYK4PQKUX9ROT4AbBdhlxHFsmLqHzGZdeZz3/cxXZWkg64hYxG0ZAItEYNETAV6x6J40RBRxyZbrRIaSEMiRmluicjgAF5mAEJhPKFqZ+p9NVGFELSaberNFpqhwJquJ4nH47iuiqdMB1IN7batMiQdh7n5ZeYWV5gaH+HwgT1EY3Eq1RpIH8+XuI6KLR3KZZnKZVUBaqmCTSI/foedr7/PjO8pz4CuY0TCaP/D3yISMfR4lMMIUuUy4cEsOanj3l2g1WiSikYYnJlk16F9lA7sJTyYITuUw9I1XAF7ZicZHxnA93wKQR/fuTfPymqekViMcCikxkc8vOVeBS45yab7vm3bDmjJzkLyC6iHRwGH/mN+btD+tj/e5sR+wfl5S7D831uTKGZOalu+3GZTxYzwELO2TeuE+/T6d3NfKMvhsV2yXmffuq73Svj8gu1XZ7a2SY3tvpUdg9Zhun7RgfE5LRi8slLrbq39n/85MjAsKVPnQMumhiQ2PEh6ahQrk0DGo8SjYSzLZDgSJvGf/4kqjmoYwWGD8xQoGQXRqfytjhJGMuupVWKndqCUEOoGzalzG+gblIlqndxKHs/3SPzljzFNkx22zYu+TzadIn35HobRKZVLd+KVgCUlKdmbTJNSMhQcSEoYb7V4stVWbtON9wnPLSGcIBOy0cBw7OCzuiYZDm3fn47oja+gsDC+j768ClIVoNalRPd9LNclHoAzs9pAb7S6hGS/IYlsfa63eV7Mh7/63PbLDNj+YzySKxWqf4Smdd93wFv/PyEUGxgK9iE0VXpERxD3PaKaQJOgtey+AtYChETqAYA0DCxdlQgRkTDUGshAWFZbzRP9z/4lYccl4bgI6aP5IINU/A5TIx0XzfMxBAED44Ltgu0gHBc8F8OT7HAcxtu2AiuahiGC8tVeEJDr971Kxd7sCdgDFccguv0+ajuMSgmawH//QuCmU9cfkZJBv1N4XP0mIiEdZK113PsdEBLuHyui8wwFJby13jGtvolnq63RUGMSAF3Hnxju3luxzSq+U4ZL7UZumfIk+sIqeA8DV032TEPnc2fIPKp1/+S63QwzqWv4+2fxjuxTtquTV+55aE5w3xotJWfTaKpafW37kcfoX3V3QKcmffRaA73ZRqxtbOozXUpyjk3KUWDK833G2g6hkEn4z36kis6HQ8QtgwHDQE/GCQ9k8KIRWtLDlhIzk0KfGEEkk7jSx4uE8EeHVOkby8T3fdbzBW7cvs/yWp5oNMz+vTtJJlOgaeiGqWrQImg0bWq1BlMTo0wl493nRQZxlgrgaj3JC89DFErqHxD1PHa02ui357DOXsH3faKOi6ZpqmasgPHO42cayESMcDRMKhrpAlA3GqYGrLXaJARMahrhSEixG9u5ET+XoekMjP7Jeiuz9PC32+/qES64btD6I/bSidgOnt9eQH6vmkXvDPqfkQ4YfASwFz0vQ/f3W4P9f10tOH+lYvDopgCsOvYj70i/d62DGTrAVyo7sd2Zd5is/nNS4zHogq737fHb30M2ouijTzrARVcoVfQH7X1ek8HFfH5FdEAZpE5zPUSxAsUKlu+Tsx2SbRvt5gNC4RB6IKRnAh3Iod1b6BlYTev901XR2U2fg1dd05Ca6H4WWq8oZyeQsB/7JhyPcdOk3nCR5RqmaRAzTVUU2vPRyxW2Tg8CAs0kPwjc6+x/s6WPSUlMgnA8hFuHVEIZbECmk8hwCGmrlbU3Pkzx+/8NwnExfQ/huvi2A65P/D/6vyEazWAwEpT/kWolI/sGqi/BdRGeryLrOuCks+LrdIKk+9v+AS62DHghO522/eCVwf62mokvXFn22RCx6bve+PQlChx0s2aDlc/mp6oHxPqyaTrAiyBuUQgQmtldVyAE5DKBN1xAyOq6CjUpsIITcI/uxz66H+PnHytA5ftY75wF6YEvEX7AQPmKcewAI+l56m99fbrpulGPkMY2gPbzuk6oNZ4mOllDdPfd/ZmUaJ1j+j7C78PS3sOGT+vcX68HxLZuI4Ljbgoh6Ovzbp/2Wz1648CPhGieOoTUNPSQhWYaCF1XemGGjidUrVOpa4hIGGlZ6vnWNQWCDZ3o//vPENVGb3ygxr3Q9C6I7N5P2DRu+l975ywgX0CUVZF3oRto6wXE6+93OvLhGxK2kLEoMhbFG8oho2GkoYFlgK4ru4PoThR4Hn7Lxq43kY0WZttGa9vIZhvRbG2aEAWg60Y3wBfPIxwSQT1P9RxrrTahRgtTSrSNEtr9JZU047qkfb/r5hUI4gHz1REoFaaBFYtyHMmM6+JEwiSGB0nYZ9Ev3caPRjAtEz9g3rLA4cEcoUyatCPx41F8TVDeOY331DHivlQ2qVpHb7bQmi1EvYmsNRBtFcQeDocgYBJ1TUezgnPpls0K+tl1oVBGL5Qx+pBzGIhKSa57LbqKYYSe9MMXtc4zr3hG9arJXtmbbnuM2flRsU79DOpDB+97L3UFWjUJssPWaNts25ltZN/nzwcQW+OZ+m3DF7auPX3M9pibSulvIR97/derxOD35hkJiiv2tjDGmrKlm3bVqfvZmc+2gNFf4HLg70VBfutZbDmjjhbGF51oB91u1/o7dO+MEqGr1lVNq2pd1diqK3eOqnbvf84Y6oM3vWrAasD20f+P+lX3fDrAS9cD4x2ANV3H1HWSEaXS7AnQLQsjHMIImaAbYOibPN8Cgeu55Atl6s0W0UiIXDatsOvWLuq+9gy87LBkyQS+ZaKxrj4Lge9LdMvE9TVWiiXuL64SjUZ42jQwQqHuWfStB3rvDHU9mq6jGQbS0JGGjm+oaxCGAaYBhqlWksEkJnUdaYhNE5s0dKTW+4yhgwiGv9abTNY3inx25SbtVhvPU66lbCrJoYN7ySYT3bEgAnDXe6gC11gQdyN9D+F6CNvGb9usr+R5cHeeTCzCzPgwhpTq746KvcH1FNPgeN3g8e2b3Fpd46F+2/SN56MtrHYZJW0trwBbpbZNj2++15smdom635pAip6LUWwCJgQF4Nls7PrdX5aJtEwIWRCykCELwiFkKIRvmRRabe6uruFZFk9du4veqTawe0f3ODIIWpTQfY9Qa0bX9ag1mlQbTZV0kU1hWVZvrPatlDct4LsA5nOMm0QtrnwfJ5Xgo689Tatts2/PjKpbuMVV2PnkCA2f/lW/auE/f01NS0LQikcolaq0HYeBTFolb9AD2v1ntnkOEZtetGodqLHp6RYdFlx0jXlXeLHZRrTaiI1SV+36cZolAU0L4rmiyLEUXjQCIRNhml2g5hNkWXkenuvi265azHg+WstG1BuIegO91lDPjzpjTENNFSqzUl2cKsLdd98cF71UISElcSmh0kBbL8Llm90h2x8FlZIw1sfRyJCFZpnoz5zA+YNv0A6ZXde9r+v4ulKI7xyLegMqNWStDrU6oqrOXdTqaPWmmgeqNbUgr6m/Ua1D2+67Z2phoXXHbd8JPnaAfH8Tgf3pAyUPua3Us/GQSXkM92Fvi57chBCd3pUgvCAuVwTnoD3aJvVjrS6z9Yu2TTt55Hn/ult3sf4oVit4vhTuDPqgc0+kWgCKgFiQvkBIgSqLLrtSZ/1zYMfcdrxcIlgX/iLt1w62Hl3JqbOBeNhobt06MNTKb/v/5+6/gyRL8vxO7PNkaJFaZ1ZmVpaWXVWt9YjumdlRuzu7RwjieAYeBAEzwA68A3l2NOAIHIlTtOPRDjhIcqUtdnYXsyO6e1rLqurSuiq11pmhxZPOP/xFZERWVouZWaMZvTsrMuO95+7P5de/PwVNsZ52D4gfvIr4jVfqpctG9aRlXb6It53DLZQQ5SpYtpyEpQqiVIFSBeUPfyono6EjTh+W3xVKUszyqNdoqkJDHKWGG2ohTWrvoxE0dm3DdNxmXY5oOBCBRhCxGE7IYJo5rmaztHW18so3XyTUEseNhRHRiFxU41H8eBTF2PEsbbz+MaF/9ccAVP72X8bXVBL/2f8dPBdfUfBcF1PTcTzpH+c//OxdOjpaSf3tv8TY/qE6AHJUubhhUGf1aq+tIs8GCsFJUNVQFak/pGgqaIbU46s3lo8vpHPCxrhvtZAyj0q24/Dzn77D63fuc/LMYZLxOIvLG+i6RstLj5M4MCrFXIpUaqyNPck0uqgBS+JD4LBVim6qls2VC1f5f/3rP2bfQDf/4O/8VXq6OoKgwooMnVJjK4UCnoLhulKMEeg2Ka6DcB1wHHBc+b3tIGz5t7BshONIkaztyu9dyUKE/pc/3AFXioLfloaWZP1vqK0Lu0+1SrCWB8EmYhHURAwlUJgmFkWJhiEagVgYEY3hhkxc00BLxNFScYiEEbEISiwKkVA90K66y4JYKAqlqs3tm3f4f//hn7G4tMFPQwZR3wddw/73/xO+YdSrJQKnuDtrtwisV7f44PwVfvTGe/R1t/Of/rVfZ2TfgOy3YLp6QqAFG55CjS0KFtIG0ePuZSP0n/23qLfHwXYxDZ1j/+RfkssXSaaTxFKJ+sYs0gkq//3/sc5MKSiPlAIogN+S5Nrf/yv8m9//c7Yyef7OX/9tzpw8jKaqqJqKX1s2FdFUL+myQn5q8k3Q/+m/QP3DH4PrIkYHcP+Lv4EolrC2sxRWN6msbyJKFbRyBa1SJey6pFQNo2rJQ+QXTHK/9evAgrXNRy6zjXOwKYNwCBGLIFIJRG8nfiSMq6hUfR9XCEIRaamtQMC6BsYHli1/SlL0qZQrqBXJrisLKzKihSyx4d9Hp+Sf/ZzEx1fq/V5/v11vVP/qkX1ZX4B3vkzEJIjypa4iXsOnFxhIePKwxX/3b+B3/tnn1JbaIJYgx4dGJkmCgoZQap+b1x6sVkMxO2uCGtzSsCk1it1qp/faGrsbEzXuZb8QyPoS6cuCLkX5zDrtiAH9vVs0YK9k4O/GsResrcILeqhu2YaK33Cw2QFljUrysmrBeEQ0vNcXa79fLdhqELc8Wszzi3TsHmaljcX+H/4xSib3EII3hEBv+q7x9Bw0WqEkN0xdw//X/1cg6IJg8aBUkYtX7bNYhkJReq4vlRGF4LRUrF0vQRDLzfc9fF+6YVD3CDHQlMpVSZnLWhIRgmOFEh2ZAtGqTfJ3/8OOdV79FYL8dK1+olUsq/6uxKNo2Xx9VVI0GQdPQSqXd3e1U7VsZueXKaUT+F3t9ax1IWGkJ9y6nanwpRWQqoCh1TbFIMQBAoGGItQdEWrtgIVA8XdOxPVTvNL8Po2/q6rCxsY2N28/IBIy+PpLT9LR1s7Wdh7Xcenpakerk3l1bk+SPY3LkkJADwcO6FTpp6qnq510Mla3smpUdq6JVHbEQTp+EJdPVQUozXHgm4Z6AOhqVlON7+YEZwfj37+Oli9KoNXZinf6CH40LEVGEfnpR8P44TBEQ4hICD8SCa7FcSM6NgJNVQmFTKkYrqooClKHK6iP7ymsb2ZYWFqmvbWF/t5udC2Y8rWbgjr6DdbEgWAUXdMZGuhhbHSIielF3MY7yhakzbpuTUOXB+NBAuBQOEQ6lUDXNCzbCQIai/paAUigpYAMpu43ZxT8GpzhaUxKtoBiu/VAzynHxbdsvPUtqbtWY7aV2gGu6ZWbgj7X3NjUxmw8FqU1naBQLKEqoq5FoCqg4AXBk5X6aXfnd/npE3ghr1Z2KpxKYJ87wfzKOjdvP2AhnyN0YEi6oRA+84vLbGxl+PpLT/LCM2dQHQ9RriBKVSk6K1YC5kmCGrVcRRRKO9+VyhLsBICHPc6L8l1rXdDw/gBVC8WyULaz9Q5QPQ/PslE9j1AoFERLUOp9XO8RVZXiz0QUWlP4gahWzxWgHui+YcevPRyIooXnSWMNz8fJ5VGEwND1JiZtr+VTobkeMqSUF6hfKHXL1oce3wO8oapgqqAEh4hqlYfSZwGH+uGxYZ1r2qu+wOa8G2jsYlEb82neYxUQOuAHxmW76rm7yF8ZyPqMPJp0vL5g+iJAC9hTUb/e3o/ea3eep06I1FpYPinq460JS9WHYA3FBjfsDib+GelXBLYa6dFHefJQ+KxG+MKp1gKNugiZPGxm6g3oB5tdTYG9sczmASoku+QGoiOoKwQTDkll8vaWnU13d/mBQqcvBPiaZE6EtPCyCyVWp+ZZnZ5jtLOdFk1DKVVRywEoK5QkDV6UoI1CGcoBWHN9UBVUbQe87p2CI53jomQLkCs2t24ihrK2KasNKLqGrgOKh67q9HR10N7WwnY2TziIgQbUrZDkfhywQ0LBcRy2t7MoqkZLSxJT13F8gaJ4mJrA0OTSV99LFRVVC+LvuZ7caD8n1SaD5wlmF5ZZWFojlYiSiMdJJhKkk0lAqRsU7G4PIeTmqjY2hBKMy8CowQ+ASs16rbFsXxCEa3EIBf6KJGBUEAq4wsNzpTWkrmtS1CN2gF6t/eyarx7kmNJ0WZbvCwib8kZdw/32S9j/6W/X9QN29AQA4UuneULqbiEUFKFKfRrfQ1Ukg6IhT2YICUhqj/u+oFAsMDk9h+1YtLUmMM0QnkvglqGKbTuYhkFHewvhUCgQue/8Fwqb6JqG73lScRk5y/1yFdHWItmNJieNNL2HqqjomlafV40AVA2YIklGS8UvGZBZ1NUjFEVIiXy9n2p6nI2uZpDi6/YWXKBatYglYuiWA0Kg3Zsm8tf/q/p0ETvLVdNGrt2fRnS2AhKQRyPheoxKNXC0qirS07RaAyN13aBG54hSD1AIAZWdDdsHJqbneOujS6ytbXBodIiTxw/R1dGK4zrcG0/wZz99l/OXb/LYycMyeoNpQFqO+Vr7BxVvaO/mZUkRPprnQ9VBKVagEYwFB0RRrB0Yd9gopVRBLVUQpZIEscF76ZqKpsrPR65Fvo9SKErRXcPeo2bycm1VFKlW0cBu+SODiJYEnu9TLJbJ5YsoikI4HJJ+4kKhYJ7vWnsbur0uGQrie2ZzBcqOjefJWKAtsbh0lNsAtpXmfx5OmVxg4barcRv+ru0vn5++IKD5HD2t4Is96iwaflSapED//5pqVrJ7XauxWo0wfDc50wiYoH7Aazp4NGUp9xVB42MN7f4FIc2vAGwJdqKWPzywZAUbK1QbMF9gEH4OypW31PkFXOGT01XKZQshIGQawUlM+l0KhUxcz8cNrPVM08BsyEsNRGW2ZVEuV9F16bRRNQwZdLpUxgn0GExdC9xJqKhu8KbB4q0YGkoswnZIZzEaov/wfkRnOygKFcdhaXlN+n0Khxns7yEWjeChIBTpvkCrOljZHB/87G3+5I9+wrmRQX7wtRcI2RYUi1CuQLGCXyhBoQCFCoZlY1RtRNWSYElA1dCJVaydttI0VE3FdR2ypRy5fE4e5gwdGjZ5z/OpVC2KpRIoHrGEDJpsVcqsrKziCnl6D4dDTM8usr2dpaM1yf7hAdrSbWiqjo5KsVQik89iWxa27dDakiadigPSweTaxhaVShVD1/GCAMptbWmScRkvcnp6nny+iFW1eOPtTxga6OPwgVFa0yksy6ZcrUpHlskEZsjA8zzWt7Ksb2UJGTDQK50Y+khpvBuItWZmF7l26wFbmSypVBSBVwdIM/PLXL09ztZ2ltaWNE89fpKxkRGkHp3D0soKM/NLKCgM9Hcz2Ncj/Qch0AKgv76Z4c79SbYzeWzHob01zcljB4OYjGuc9TwSgOL5VDyPXCaLpmqEwiZmMNasqvQ8HtE1BALLchG+QjgsAwDXGDnXdbFtW5rSB7HqEtFw3QlppVKhWCpTKJbJFYrkC2vcfzDH4so6m5kC+UKJlmSMrzz/BE+ePUk4ZOJ6Hstra0zPL7Oytsr41ByeL/BVdWcaV0q4nsXmZo5SoURHW5pELFqf1cVSmaXlddY2NpmaXaRi2bQbaTRNDX40XNdlI5NjZX2LfL7E2P4hOtpbsSybfL5INp/HsV06WpN0tbdg6AZ1lV9lR4QgNI3i2CCv/dVvcevuJJqm8uKz53j6f/p9tExe6uJt5QIAJ/A9D8d1QRC47ghcRtT00YCqZVOuVOtgo7bW2I5LvlzFFxCLholGIk0HIsdxyeUL5HJFIuEQ+wrlQA9MxXIcfv7uJ0zOLvL8U4/xzOOn6GhvQdPB9zxCoYNsZXIsLq9RKleC8De1JPUSlQDI+QEFV1MXrcWudFwXFYGp6qiRCESjQBvlqkUmm8f1PMJhg3QqKq0PPRABYlFUFT0Qn0vLyAqiUMLO5CTLbzuopao8HJYraAF4U0o7B0alVJGsmtew5itIa9F9/fV3EUDlv/57uEcPsLq+zp/86E3ml1b52otPcezwAexIGDssxZY1cXCNva3tHzUjDt9zKZfK3Lw7wbU747S1pgmHQ7Qk4xwYHiDc0xWA5lrpj9hHFAlolb/89+XhHer7TyOwQ/j1tq8/+5lpV4m1k8RD31E7pT7iaYW9HIbXxYfiS5IZgUTgFxI2UWNGP2MvbzzIflY+j3p2r70/QDyPfMsaW9pYhS/J4snbd0ibHby9ox4kW/rLNdyvltkSzd8oTb/sutIIwPZo2Lre1hdJimycnK7xD/b3MjO/jAKkElGikTCW5dHT1c6B0SHWN7aYnltC1VSeOXeSv3FnAjVYZEulMg8mZrj7YFoGuB0Z4Ozpo0QiEa5ev8NH56+SyRVk0NxIiMdPH+W5Z84RjyeRSq+CUqXC6uoGK2sbTEzPy87xXBA+ru1w7eY9/vRn7zG7sExrOslvf/8Vnn78NGYojFAVVtc2WF5cZWpmgTdv3uOWELR0tWI9dwbPULFti0KxxOpGhrWtbUrFAi3JJMND/XS0pZmeWuD8e59SXt/m8Pgk38zldpRSNZVCocj5T29w/dY4m9s5JqYX6OpoxQ0Ca1uWzer6Jheu3OTugylCIY0zp4/x2MkjOJZFsVTBE7CdzXHn3hQffHIFx7bRVJUzJw/xvW+9TH93LzNzC/z4zfeYW1xF1zXyhQoHRvfx9Zeepru7janZOX78+vvMzi1jGDqu52HqOi89d5aXnj2L67hkM1lK5QqZXIE33r1IZ1sLi4sr9Pf1sJXJMTE9z9BAN9/46jN0dXQwM7fEn7/+Htdvj6PrCi88/Rjf+Oozkr3LFbl05UYQVDrD7OIq29k8w/QhFLBdh/nFVX7nj37C1VvjRMJhVFVhdW2Lv/6/baejrZXFlU1+9LN3+ejCdRQFjh3ez299/xUOjY3Uh6JtO8wvrnDlxj0mpheYmV/h4Ogg7W1pyuUqv/fD1+nZznEwYEZnl1b5w3/9R+i6xtFD+zhyaD9LKxvcvT9NNBLiwHA/4VCIqekltrN5Du7fx8ljh4iEQ4xPzXLlxl0K5RKu52PZDrZl8/JzZ3j63Ekcz2Viep7LN+6jqiqj+/o5f+kGr711gXLFIhGP47gu49Uq4ZDJyFA/A33d3LzzgNff/phsoUguX2B6bkmO4wa6ML+8xuv3xrl64x521ebZJ07zlReeJB6LsrG5zfufXOb2vUkc12VpZYNsrsBgXyeKouL7gum5eT759Ab3JuZYWt2gUrX4wXe/xg++9worK5u8/cEF7t6fwrIdTh7dz3dfeY7+3u5g0fPrC51AMolrG9u8/vYnZHNFqraNZhicdVw59muMlgKlSJjNrW0KRRnAviWVoEtRZID5hmTbMlZofYEB8sUSl6/d4dqdSekcNBHnyKFRThw9QCQiA2vfvjvBG29/zHYmRzIR4z9f3aBNyqmxLJt7EzPohsHY6BCdHa2S+QUUVSGViPPys+fIFYok4zF8IT3kb2xmWVhcx/NlRIqurlZa21sRAuYXV9jYzBAOh9jayjC/uEoiHuHxU0cZ6utH01TWNrZ496NLfHLpBtWqRXdXC7/+ay9z/MgYjmuTzZaZnJmnUCgzNNjL8GAfsWgEkUxQMnS2FfBScdpa08TjMYSi4fk+haIMk6WqCslkgkRcMkj4Pl7Rxs8Vif29f4KWySLiMey//VfQX3sfbXJWtmo8hu/5FAoVNreyrK1vMju/zPBgP6lkvA6CJUMqGVwfta4HJXwPH2mEUa7aJJIJnjp3ir7ebsIhE02Rob5q4uI6YRxYSdcYa6kcHzBVjTio/ilvVkTNGEbsAJsAdO0JuOqMb+PGGACFR23+jaCrKcnydsBm4zN6ALi+BNCqz+XPJzR+obQn0NqrfntEiHkE0NoJ/vx5aXcUmt0lfrYf/Zr41/N3dIx36iZz+EXSr1hBXgT/Bw3yKMT/ZaI3fl55Db+7rstWJkcuX8LQdQ7tH+Tg6BA37k1z6cZ9VtY2+PqLT5FIxHn7g4t8dPEafz1wYYCA5eUV3v3oEuNT82SyBRaWVhke6ME0dF77+Qd8ePEmne1p2ltSjE/Oc/32JLpm8vILT6HpGhsbW/zotXfZ2s5Srdrcm5hleLCXZ5+ooAqfrVyOK9fvUCwWOXxgmNv3Jrl09TYnjhygIxrl3sQ0/+Gnb2HqOiHTYGFpDTcwt9Y0lWq1yvTcItdv3md1Y5tCqczy6jpnThxi/75+Nje2eOPdT7h9fwZN1bDuT/EVb6ehHd/nT370Nldu3ufogRFi0Rjj0wt4ga6B47isb27x5nvnOX/lFqlEjLnFLDPzK1TKFYYGunEcB8MM8WBihp+//TGD/V088/hJ7j6YoRLoZqxtbvKHf/Ya41OzfPWFJxns7+PKzXuc//Qa29tZvvPNl9nazjExOc/GVoax0QFMoTM7v8Lc/BJW5Rgt6RSjw/2kk3Fcz+f733yJRDTE1naWy9dvc+veNCP7+ujqaMXQdUqlCu99fIkrN+4Rj0dYXdvkg/NXOXxgH7FohGs37vDGO+cZGuzk159+iZt3Jnjt7YvoqoKGimU5XLt1n2u3HuA4HogqjuOysZGhWqmiqAqT03NcuX6XZDxKMhlndW2T9fUtDu3fhx6wC7YtIwwM9nXT29XO5laOQqlcZ+9M0yCSjqNsyJOzrqjMLSwzu7CCZVfo6mrn5u1xPjp/nb6eNor5Ap4nmJheZGMzw5Ub97Adh5Ghfv7kJ29z6cZd0sk4nuexuZ1D0zROHBmV4joh+zSbzVO1LHRdDyIWWIwO9fHqV56lUq3y0zc/lGxMvkA4bPKztz5kYzPDr3/rKwD88MdvcvX2OH7DfF6dnOf68jKpeIzFXJELV27y+JnjaKrKxxev8db7Fzg8OsiTZ09w/c442Vwey7LxfR/Ltvno/BX+9KcfUKla6LpGsVRhcXEN31OYmFrgvY+ukIhFZfD1mw84e+Ig/X3dgZhuR+lVCLnZbmfyzC6sIhAUixUmp2XgcVHj/oXASSX4V185y9vvfyqBhuOwvpnhf5xeoX2XsUYkIsWIigKWbZMvlvj4wjV+/MYHJJNxzp05wfjUHJ9evc1vfe8VXnzuHMViiXc/uMj45Bxff/kpZuaWsHIFuZmoARPpuRimiabrKKrfoDclWYuWdDLQ4ZLr2fTsEn/2k3fZzhY4NLaPcrlCuVrlhWfPMNDfx8cXr/P2+xdpTScJh0Nksnl0VSEZjdLb1U0mV+anP/+An739MZqm0tHWQqlcpRxYh07OLPCjn73H9VuT2I5DT1c73/768zz/9FkUReHB5CzvfXQJIXy+/tLTHD20H89zuXV/kovXblIIAkH393Xxja8+R3dXOz6wkMlw4dObvJzNEi1X0JNxeOFxEhevA8Hyn4iiCI9Y2GR0Xy9z84u8+8EFtrYzPP/0YxwaGyYRT0rphCZ12quWzdLqBuVSia6ONkLhMK4PFdvFF1CpVplfXCISDhMJmbSm4qSSCQxdD+K3Vljb2CabyaEiaG1N09PTRTgcrut27r3VBIBrZ+Q1bEECwW4R6w5iawJXX5JhYae1QK2F4NnDlcMX9mEZpJqF9V8AzmpMzYK5R9XxS1RCBC0qdkOmBoauzm7VQFszTmiKdiMkY+zv6peaAVfdWriux6XUi/qyEOZXbo3YZNRa59uUpjsakzT9lr2+d+U/75WC04OiEIuGObh/H4vL6+iayunjh/jer71M/LX3GZ+cJ5mI8+xTZ9B1nZm5ReYXlnccCyqQisd47slTHBob5t0PL1EslrCqVdrSHSTiUcIhk1//5ks8ee4k7390hX/zB3/Ohcu3eOzUUWKxKB+ev8r5Szf5zW9/hc6ONhQEhXKFqmWBAoZucGhsH0cPjuAJwczsAtlsjlKxSCga4cOPLrK6usFvfe8VRoYH2dzK8trbH9et5HzfZ2ZmkRt3Jujpbsf3ZGw3Q9OIR0LMzi8zNbuE5/mcO3mYo4dGiVy6K9sZyOSK/Py9i5w9eZjvvPI8oXAY23GYnpPhQ3zhsxGIwDLZPC2puGQGbZuVtY26WMOqVrl9Z5xSucLhsX08de4kZ04dQfiCvp5O3vnwEjfvjPP048f56vNPEo1GiEfD3Lk7zu17Ezx++gjhiAyts6+/i9/41ssYusaPXntPmpgrYJo68Zhsc8f1GRnoYWx4gFKlwjsfXeLC1Ts8fe4kT545RSIeoVIqY2gqJ46MMTY6yFvvX6BSLqMrKtWqxeT0ArbjcuLIGGdPHSEWi/Dp1XsoioqiaJTKVW7enURRFL799WcY6u/Dtl3GRgbpaE2hCJ9qpUI2V6Szo4UDo4NEwiZt6XigPK1KEVWgmJtOJejuauf2gxnmF1YoVywWFtdoTbeQam9DmVgAoLu9hbMjvaxvZrAsF001iITD7Bvq4fknTuJ5Pu9+eJW1ja06ta0qCp7nkcnlODA6yJPnTnDp6m3ml9YZGeqlrSUlFxFFIRIy0FVFhp9BEI+ESSeidHe2cmhsH9lcjlg0TNWy2dzOsLmdZXxqjuOHxzh5/BCapkrxzO3xHbcOKHTHYvz2917FNAx+8vP32djYQvFdMtkcDyZnKBRK0nP44f0gBBev3JbSF8B3HNbXt6hUKnR2tNHe2kK5UqW7vRXF86iUS+RzBXq7OxgZ7EPBJZ1KSJE9UtTe6HfO9yVoefaJk3S2y7za21rQl96mQcsJFcGh0UHS8SiHDoxwb3yaxaVVqVag7ugTgRSR14wPKlWLiak53nr/Uyzb5juvPsvxo4c5MDzAv/39H/HhJ1c4d/Y4qqJQqVTY2s7iui4vP3uGlp9faFjn5MLtODa2Xd0BWRBYcipNm/12JieZ1Is3+K1vv8w3v/Y8y6sb/PAnb/Pamx/xystPETI0bMsiFg3z3W+9jOu6fPTJZQrFEo7jMj45y9sfXERB8Ju/9lWOHdkPvkd7exrf8xmfnOXdj65QKlsYhk65UmVxaRXP9lENlfWNTW7eHcc0TJ5+/BQAkzPzvPvBBWLxMC8+/RiFYon3P77C3b4uOlpTGIbBvfsPeOv98zznuFSqFqJQJOp6QUieICVjaKpCe2uKrzz3BOlEnHc/usTla7dZWVnlxWfO8uTZ03R2tFAsWFy7eY+Z+WUmpubIFUq8/Nw5zj52HE03WVxe5c33PmZjcwvTNFAUBVM3eerMMZ5/6iwt6SRrG1tcvHKTK9fvsbmZwbYdWtIJXnz+cZ5/6gytrekdnd3a/tSom9UknmrkVhXJtKE2bHWNm3oNaDXrNn4+CKldUyTQUmp0nC/ZrPotHnXz2odQgLqTR8Cw1tPnWIP/smln5ik1GpHm92++9wuBF4UGi89GsakI+sdtYJ98doNnIQIXZJ/BLNZzrOlj13Gb2PmuXpkvnn5FOlu/SKotPp8jZ1bUvTuprkS486GqKqlEBE1VpTJ3Jsfd+5NMz8zjODaOI2X7PT2dxCJhbMfdaTghrQez2QLzS6tkcgXa0wn8wLw0Eg5h6BrxeITurk4O7h8iHA6xvLbB2toGkXCYKzfukkpEOXZ4P+l0moX5Ra7eeoDtuDKYbyTEQF83N+6M897Hl5ldWEFVFG7fHSe9ssb5yzfpaGuhp6uDjvY2Dh8Y4b2PL0sFY1VaEFYtm0goxEvPnKViWWxncpw4NEJnRyvlskVvdycffXqTlfVtXnr2HHrNfFwIMsUShWIEVVOZmJplaLCfkaE+FpZWcRwXz/VYWl1jYztLyDTYv6+fo4eHg8C3CYSvsLC0wq27E9yfmKNUrrKwvM7axhaj+/oxDQNFVVheXcfzPLo62vB9qSTdmk6RSsZZXtskXyhiGLVQMz6qIujpaqO7o5VMNsfq2iYh02Q7mw/0kHwq1SqmaSCEQNelHp4MsB0DT7I1Tz52jIpl82Bqnu3tHKlkDFM3qJSrbG5lsao2CBXdMAgFyrfy9OJjWQ7bmTye59OSSvCtV18ilkzIMEa+ZD8P7h/i6IF9fHzpFrZl89yTJ+t6UbFotK73YRg6xVIZ3/cZ6O3k7v1p/vz193EcV8Zoi0fr4zas64wO99OSSjA5vchrb36I5/mcPHqAU8cPceP2A7L5Av09nTxx5gTDQ30cPjCMqvp85xsvkE6n2MrkWFhaI52K882vP8vRw/sBaRxiGtJrfblcoVIuI4SPoWsk4mF0Q8EXnnx/22JtfYuVtU0ymTzxaATd0NE0DcPQ5CLTsBG1mAbF9lY+uXiNOw+m6W5vwbJsLMcnXyjhui6armGaZt3pZSRsEtIMhK9gWS4tqQT/m++/yukTR7CqVWKxGLbtEI+ECYdMbt+bJB4N84PvvMTY6JCMiejKQOuKphJqWDdUVeGJM8d4/LFjEvhqOqE//6DpCKdpGqePH2QmGefmnQec//QWpVJlb7IhWJA1VQXPY2FhmfX1TXp72xns7yYVj3JobIhD+we5fX+afCZLIh7l6MERbt2Z5Ic/eptXn36MZ0XgHLbmwgKVre0c62vb2Ps9wpEdb+kgda7cwABjdXWDS9fuYho6Rw+OkIpHSYwMcPLwKH/8k7cZ6OtiZKiP9pYUqUSU4YFedF3lzp0HFEolcoUCG5vbbGckKD994hDDQ70ITzoEzRWKASNt89iJgxwYHcI0DE4fO0A4ZCBUha6OdlpbUpTLVVRVxbId7t6fQtc1Xn35KQ4cGMGybcYn57hzd0LGf+3vpVqpAjJWZ0zT8NMp1Eh4x+VJNIzQdfClInt3ZztfffEpRvb189b7F7h49Q4/eu19TMPk6y8/zf2JGX73j38qraFVhY3NDJ9cusHwYB8tba1oqkomW2BxeZ1zp4+QTie5e3+a85dvMTzUDwq8/cEF3njnPK3JBIdGh1hYWePB1Dxzy2usbWzx7VdekC5g6hs3O5KaJguEhg2+cR8TvgTMjaKmGrMifB4eaLue/0zdL2UHQ4jdels10aO/B7lRA3z6l2e/GvNuqEf9Sv19xK67d5VTI1QaRaBN1oSyDT5PnLhjBbsHZhC1+xur6cs2afQ11th1j0yBvpbvP9RlX8woYu/0S4MtEcBppbERlT0aI7hb/qhywHwRs0nxUBfsyZQJIZWul5bXURXwfMGnV+9w694k6xtZQCGXLzA3t0AiEUNRwAuU3UVQztWb9/nRGx+RyZfI5QokomHyuSLFZAIvYJHW1rco5LMI4WEYMkbh1naWStVidnGFwZ5OFKQ/K8t2qNoOlUqVtY0tbt+b4PV3L7C1na2/w/zSOnceTHP6+CGKpSqCLJlMjn0jQxLgGQaarqFrOsI0MAyDTK7IxPQ8vd0dDPR10daSxncV2lpb+cbLT+N5PtfvjPPHf/4WBzYbdLZUlUKpzMzsImFdY3l1g9XNDPlCSVrxVKt1wBEyZWDneDzGUH8viViCTC5PoVimo62NZDzO+kaWC5fvIITg7MnD9HR3cGD/EI7jk80XGZ+aY6C3l+7OdlY3tqhaNiKgZoUvY2kWimUWl9cxDYNiucLM3AoXr9wmly8yO79CoVCW531fMLuwxNWb97ly6wGu61Eolsnk8szPLXB/cobF5XUWltfY3M6xnc3Tlk5StWw2tnK4nkehVObarXF6e7vRNRNdM2TfrG8RiYTp6mzl9r1p3vrgMo4raEun6O/r5vTxw8Q1lYH+fv7KD76Fqml8cP6aDI+iaORyJUIhMwClMfQgGPD6xhbpZBzTNHjj3YucO3mYJ84cxwhYLQDHstjcyuK6HmsbGd5671OOHBrhW197Bk1VyeUKCN8nGjIZ3dfHwbEREvEwiip4+vGTLK9v89pbH7G5neG5p04z0N/FnfuTtKaTDPZ2EYuE0XWN7VyBXL5ItWrVlb+rVpWqZeE4Lr7wsV0L13PkWJ5fZHlpmVgsGohcpBi6RqvfvXyDf3frDoauoyoKiyvr3B2fobWtFc8XVKo2m9tZstk86xvbWJaNaaTRNA2pribwfSjkCzjVKpoiCOkqnicPQPsGepiYWeCjizeIR03SqQTRSJg796fIF0oc3D/E8QAEo8iYfD967X0s26anq4NQOMxh10PzPdRAJOC4Ln/0H37OW+99SltrknyxTKlSxXEcPEVBEzvrmeO62LaD53mBeNjFdj0sy8X3QFFkSJhYLILjumxubjMzs8DS8jrPPH6S2/dnuHz+OsVSiZCQzJaqqrS3ppmeW+bugymOHxmjQ0sh8OsK7itrG3x69TYHx/aRK5SoVCzaW9OkkgnJfokgtqOqInxfBl83DSzLplDIY+g6+UIJRZHBwh3XDeZZkdWNdVKpCIYK0UiYbDbP8tomIdPgay88xTdeeQFVCIwAFLrCo601TVtLikKxHBguFNjKZGlJJ0km4viuKy1lFbjzYIaZuSW6uztpSScpl8usb24zHI1I9hRQ8tKbvh+LUq1auJ4HAjRNujE5dniM7q4Oers7+fm757n7YIqnzp1kYmoO13X5xleeobuznYXlNcwgCofrekQiYdpakqxvhBnZ189AfzeWZTM5vcTS2ha+53Pz7gSGpvHCU2cYGxnk/sQMuUKJiZkF/uyn71KpVPnm157ngO/XRX+iJq5qklgFkGIvvWIRiPoCvR9BEF7L91FQd0KaNW1hNaCl8jDgCvL3G5wXi53H5C0aMqIkOyK0RkV7ARJ4qDt1+8KslthhzdhDOX9veNX8TR1LPuxIeFfD7rRmoCP3sP7WjiSsOZtG8qYB/Cmg4NXrXuOOa5KixndorK4QArfupoZ6HQzTbAqt92Wg1y8FtuoKaw0lNopL904N1KloGju/YJKN7/uCatVmbWO7TvHOL63R2d5CJBwiXyyzlckxu7CMj8Lc4ipV26l3vgBu358hly9xeGwo2LjXWV7dZGigD0M3kJFqBFvbBTLZAq7rYZoGIdNgbWObYqnCyvoWqxtbdPqCqu1SKFUolStsZbJ89OkNLl2/R193Oy2pBKqqUixXKJYqpFMJ0qkEswvLXL1+m9bWNJlcnlK5wvTcEvOLqwz2d9DakiKbL/LGuxc5fGCIro5WIrqJpuicv3yTrUyOp84ep1gqs7K+iZW3ZPsqCpFohJCpc2d8loWlNXq62rAdl42tLPNLK4yNDnH6xGEmpueZXVhhYyvL+x9dIxq5x+OPHaenu52OthZaziUxDINK9T1W1rZ4/5MbXLnxgAOjA/y13/5OwPKoXLkxTn9PD6VyhY8uXmNyZpGxfX2MDvVTtR18n8BFgo/reZRKFSxbxoIrlaosr2xSKJZRFIXLN++xf18/27kiAoVSucLM3CIt6QT3x6fZ3Nzmk8u3sWybzvYWwmGTmYUV3vvkCsePHKC3q5O7D2Z47e3z3H0wQyIeZXZhBUPXuX57nKcfP8nB0SE+vnCTB5MLzC2uYRo6zz95msMHRilXKrz13nlAobu9jXQijuv6rG9kmJ5d4t7EDOGwyTe/+jTDQ31EwiF6uzsYHurnxp0pVta2OXxghJF9A2ihnfiUmlAY7O9h/+gA65sZXM/j4P4hBnq7sG0bIaQrgZv3JgmFDCzb4fiR/ZimxsraFm+9f5GrN++TSiYYHuzn5u0Jrl2/y8mjYzxz7jgyjInK+kaGja0sVcsmkyty894UB/bvw/N9svlCcJjw6O3poK01xafX7rK1LUWM96fmqVg2Vc+tH0w25pe57TmMDQ9g2Q7zi2v87O1POHvqCNFICNf3efvDy8wvrrK0usHCygaqqpLJSsVxQ9fZ3M7y+3/yBm+8ewEBfO/V5zl7+ijvf3KFja0M7a1pCvPLPJicZ3J6HkVR+IM/eYPp+WWee+Ik/1W5QkJRUBXp0PfTa/d4MDVPNBLi7Omj9BdLhCpVIp6H40gLxzffvUg2X2RsZIDtTIGNrRyZfIWWWAwjOAR4ns/84ipzi6tSlcB2UAN/TTPzq0zOLDLY10sml2dxeR3bdlla3uTBxCwfXLxGb1cHvufhFopYllNf1DVV5cjBEe5PzfHptTuMDg9wcP8wllPB9z2qVZuPLl7n06t3+Bt/7ddpa0mRTiYolauUyhV0TWMzl2d6bgnbdohFohIUOS65fIm1tS0ikRC5fBFVVSmXK5imRiwW4f70Av/yd/6Uk0fHiIZDnD5xiJHhAbraW7hzf4bzl67T1pJieKiPdDxGPBoLLDE1DF2nXK6wvLpBazpJLl8kl5cHpZ6BHuxilXy+QrlSpVyuUiqWSCbjHNo/hH11gs2qRSqdlPEfS2W5kcUiFEsViqUS2xmpaJ9MxOnr6SQcMunt7iAcDjG3uMK98SnKlQqaptHakuLksYMcOzxGpWpRtSw8z8OybCrVKooiLUxb0klaW1M443OsrW9SKRXZ2s4RCpmBLp5Cb08XrakkvoBSucLi8ho3bt+nr1QhVmcbG92Q7GZTdhivJtBV21OEoJwvkM1KXcpkNEYoYHplwM0GPa9Gg7GGna0pb7GT98OK+bXYh7tZpiCjugNQ9UtuuAp7WTk+Ugz3UOk7IGjH79jnPRs8sxtoidq1eiU+u1hZKrV2EQFb3ShGbPysu90R1A82u98zEg4H1udfnuH65ZmtYJAou/7eO+2+0Og3ZjeC/aJJIlbf9ylXq2xuq4HoSqmLiSzPkUjWkzpJmXyJtc0Mxi4LJAWNXKHMjdtTWLZNIhYhEokifIVsrkihWGZpZYM796cC1wE5llbC9U27t6uD+1Nz/MGfvk53RyvzS2uoqkoiFiMSkqbIyXiUje0chUBHIuz75IslSuUK7a1pFpfX+NHrH3Dpxj0yuSKlSpWFpTXujU+zb7CP3u4uutpbuHLrAWsbWxw9OMKZY4cBWFxe473zVzEMg2KxzNBAN+GcDBKNqpBOJzg20sv12+Nk8gVUVSFkGlLEmC1QKpXp7erguScfY2X9Td47fw3X8WhvTZJOJWhvSxMJh9ENneeeeoxiuczP3z1PJlfAsV329fehqdLp4ZEDw8zML/Pm+xeIRSOsb2YY3dfPD777NY4c2s/1W/epVG3yhRLrm1laW1IoqoJpGMRjUZyAuQqHpKuDm/en0HWDzo5W2ttasWyHXL4YWGKFOXhghLnlde48mGFtK4vnetiOy4PpBY4dPcipY4dY29jm6q17bGxl2c5KFqCnq5321jQd7S08duIQ9yfm+PjTWxiGztEDIzz35GOkknFW1ze5fOMe12+P43k+mqZy7tQRxkaHuHL9LrlCic3tHDfvTJKIRejqbKe7qwNDM2hJpUjEowwN9JKMx1CMnWkXUlQOjQ6ztrrOzdsT2I7LvsFeqStXddg32M/pEzne+fAS73x8lVyxQiqZoLenjVv3Jvn44nXWNzLEYxE+vnidbK5ANldgbGSAqdlFfM9D1zUS8SiaphKLRmhNJyhXLOYWV+hsb2GgrxvX81BVlf37hvjGV57l8rXbFAolSuUqh/cPcW9yDqPioSgVUBS641G0YpH5pTVSyTj7BnsxQyY9XR0M9veSzRWZmJrj6q0HwI5ftKplYxoGYyMD3J+cY255nWyhiGnoEiQo0p/b1NwyuqZimgb9vV2EQia5fAHTkEr+K2ubuEHAdF3XaG9L09aSpFAq09vVztFDo2gfXAv8Y6n1sC/xWJTtbIEHU/N4vk8iFsHdLmI7Dn6g/2XZDjduP2B1Yxtd05hbWqOro40Do0Ncvz3OW+9/ivAFC0trXLlxn9GhARKxGOl0kmrV4vKNe2iaxqlIBEWV7EeN2RrdN8CRsWGu3x7nT3/6DqP7+iUJoQoqFYvrtydIJWNomkZ7WxvPPXmaP3vtPd58/wK27XBvYoYPLlyntSVFf08n2WyBtY0MhqGytLpOT1c7pbI07qhWHYYHBzh1/CCFcpWJ6UWmZpdJxCIkk3GefOI0Lz57ljsPZrhw5TaVqsXwUB8njxzgqTOnMcJ6HYRXLYd8oYSu63S0t3Dt5n0uXrmFAyzMr3L3wQx9PV30dHWwtLzOhcu3iEbDxKJhnEyecrlColhCeL402hCCfKGAqqgsLW/w3seXcFyXIweHiYTDPJicZWlljcHeTtZW16mUymSzOX725ke0plMcPjgKwPrGJrFYHFVVqFSqgEIoZBIKhfA8D99zqVYrGEEA8krA7KaSicAZsOyfSFgyYrqmkS8UCSsKRk2cqAiEr9aJHaW2VzWArz3lOL7Pyuo6E1Nz6JpGWzpBT2cHLWnpqgbN2HlW2QNEBN/tCNkaWIwG/FWz0K2JEEX92WYWRMYRlD4cJRG2c49oAkHKznt+Fqh4SAftC6TP0d3aeatHXdrFaj2EGVTA2/N9ZM4P593kfBoQQWSI3eQcSHdRuq79IljrLyIQtUwP4+u9EG0D7fcLAS1BTUFeURRCpsn+4QEUBRLxKAO9nfT1dLK6lmVtYwtdVThyYITW1hZ6ujvwXA998nelt2ng8ceO4bge4UiYjrYW9vV1cmj/MIVSCcMwGBnqY3V9C4DVjW0OjQ7h+T5ziyscOTDMd199gSPT80zNLTK7sEJXeyvPPHGKY4f3U65UOXn0AP29XcQTcXTDoJgv4DguI/v6Obh/mHRLC889cYrpmXnml1ZpSSUZ6O3k4OggZ08dwTQNBvt7ePUrzxKJhEnEozx17iTHDu4nFo1w9tQR5pelHkIsFuHw2DCReRkXEUUlGonwW9/9GqeOjOF5rgROmorr+xw+tJ9IJIxpGpw9dZRsoczP3/sEx3F5/LHjnDp+WJqDCzB0nUgqxLlTR1lcXqNQKPHUuZN869UXWVxcYX5xheeePM1/9P1X664D5haWOXZoP8cOjRIJm/T1dvHs46e4dP021apNOBTi+SdOkc2XGOjrJZcvcuTgCM8+cZquzjZi0QibWxmEEBw9vJ+nzp3E930yuTy27dDR1gKKQmd7KyHTIJmIEw7YpaOH9tPR1sq+wT5mFxbIF6Xz10gkzL7BPjo7WhG+TywS4Te/8zJdna3s3zfAs0+fJZVIoikqne2tvPTMOSpVi61MjoOjQ3z3Gy+STMSxbYdiqczE9DwbGxmK+RJj+4ZIRJPki0Uq1SqRcIhYNIKm62DshIY2fJ9kLEpPZydDfd2EwyEOjw6hazqaqtHT1cGrX3mOrs523vnoEulkglgsRtlyuXVvko0tqSskBMzML2PoOn09HRw5OIyu62xuZXjlxSd47MQhujrbyGTzHBgZYG55ndbWNMePjPG1F59iZW2TYqZIbyTC0bOn+P7YCHY+j161sbdzTKsm3WtX6mtMbyzO3/7NVxno66GjvYVywNAmE3Gi0QhdnW2sr65JlxSOy8bGFq0tSYYHJev3nVdf5PCBEd549zyXbtzj4MggT5w5TjqZ4NSR/YEFp0F/byfPPXmS7mAMPHHmGCvrW8COh39VUejubOMH330J3/M4e/oIrekUhq5LMaemous6iXiU737jeUBh32APiqKwuZ1l8H/4XaLVKkq5Wle47+/r4q/+4FWqVRvfh0g4yndffZlzp49x4844F6/coWo5vPDUWU4dk5aSqUSUUrHI5Mwi0ViUr6TTpH/8Hmqpghf46mptTfOV5x/n5NEDPJic5d7ELFXLwjSlBfKZkwc5NLaPalX6+fv2K8/jOA6b21l++tZHrKxv0d3ZxtdfeILRff18fPE6sUiYaDSEqeuB3mqUTK5INlfkwOgIv/W9r7FvsIcLl29RqdocPTTMkYMj2LbD8eOH+dYrz/NnP32XW/encD2fp86eRNHl5izFKR6qqpCIR+nv6+alcAjLdvjgk6v89M2PcGyPwb5uXnr2HL4H96fneP/ja+SKJb5ZrpIK9BjJ5CiWymxnctxQfGY/uMjQQC9Vy0FVNcanp7lxdyLQlVM4PDbEf/Tdr9MZuKYxdJ3rd8ZZ38zw/W+9xJNnT0r9QM9nZSPD4so6mqZi2w5bWxlm55axbBtFQHt7C6ND/Vy//YCV9U22Mjlu359ken4Zz/PZN9jLicNjzC4uU6lWsXUdTQhUaoYLPsJXJTirMVq7QNHupCBIJqTLjFKxSCZTwHOlHmBLSwJV0erRKmrylc9yCS7v2EsMJ+pAa6cuuz8bSBARKNsHSvY7wZYb9+odcdvu1ByCqOH7PculzuDVWaI6XvqCe35TWz8sKnw4BWJV4cnX9JtFlbsyD3TARBMEURoOaKLeO0hfb78I0uIvEGw1If89lQplUurf7X68Jid+1IsFQCu4rOkaXe1p/tE//Fs4tkUuX6BQKGGaBscOHaBSdaiUK3R2tNLX281zz5yjWCii/T9/v16TsdEhurs6aGltIR4No/geruthmAb/yV/6LlXLZmVtk8XVTb7Z2UZ/XxeaplHMF6hUqnS0t/LsE6exHAfXdYlEIkRjUXzXxfF8Do4NEwmHiESilKsWlXKJcChEIh7FsmziEZNjB4d58rGjGJEwqiYVk01NQ9cNXNcjFglz4vAo4ZDUYzl2eIx0UlL+Tz3xGIePHCCfL1ItlykUS/DjD2SLa9I548jQAMfGRgmFwly4dB3P93nm8ZMITaVq2TLEimny0rPnSCbjrG9uc/bkEfYN9MpQGJ6LqkhzeMdx2T88QFd7K2dOHSUZixINhxge6qOzvZXjRw4QiYZZX9/ik0+v4XkeruejqRqD/T382ivP09qSJB6LMjq0j2QshIKC48PVW/fo6+nk6XOn6O5qp1gq8/N3P6FYKlOtWoyNDEnW0rYplyvous5zT57hyIFRXNelt6eTdDqFgsB1PFzPI5mIc3hsjKptoakq8ViEkGkiNSs8qr5NOBziqy88ztjIEJFIuK4kGQ6H+eYrz/P4meNks3lCIZOWRJRsLkdvZytDvV0sLK7KsagZ0tklcH9ihrmFFbo72+hoa5GOGBuYLRwXQzdob2vh6XMnOTC6j9HhfTi2g6bp6IZBKhGnr7eLgb5uPM8nlUpiudL9Q293Bz2dbUQVldzKOobtMNLeSruiszK5wBFVYVCoRM7fQilXSBXL9OaLnFzbgnKVpPoGpu3Sky/hWRa6JsVltZiTCuA4Hm2ui7mVrVc7jsJLT50lHDHxPIEdCVGMRqhWLJxCif5kkuFkAuG4ZDYzrNgO6VCI9GYGdTtHzBcct1yinR2EjjgMH95Pd0cbmqZy+vhBurs7GB4aIBIx8HwpJipXqmxnc3ieT3dnK6G1nJy5nkd0fI6//Gfvoiigv/YJvudBvojSYH1l5Ev8xh/9XAKwevBsqWzvh8MoSgEUhVCpwnd/77U6G2cH3tTDIRNd0/mthjVLmVnFv3gbEejAPIZfdz6uFu6hr6yjeB6KUotBqdDd1cHYyD6effIxllfXKVeqtLWkSKVjpJNxcoUiyyvr2LZFa0ua/+QvfRfDMMgXimxtZzEMPbDO1Ng/PMBvfOslUsk4hw6OoBsm6USUpdVNQmYE13PpaGvjpefO8djJw7iuFzDaJoVShUg0wnPPnEUIyOTyfO2Fpxkd7ENRlMBJqlP3xp4K9A97ujv5zisvcvLYKOvrGZKxKMePHEAzTNbWN+nqaGdseJAHU3OoqkI8FiEZj1HZzsn4oEIwmyvwzseXeeGpx3jq7GlGh/sYu97P1Owitu3Q39PBN156iiMHR/E8FwWwbIf7Uwtk80XmF1c4dngMw9DJFspcv30f3xeYhsbC0iprG1vcuD2ObTvMLa4wNNjL0UOjLK2u8/MPPuWTK7cpFEtULItjh4b55lefIRqNkM3lUTWtLrKrGS8IpRlwNWxUO7/vZqdUhba2FsIhk3KphF2RYnhDN0EETlabnmnY7h5BPjR6wv/8tAcoqYvyePj75i9q/+8BuD6LQKmVuzv7RhFi4/Of8VTdSKERpAU64Y8MRiKBmRJYLNYwXT2aR2O5wfcKOy4eQDoJ1nRpUFNn4hr0tn7R9CsAWzVKdde3SmCFUxMR7oV4Py/VG/TzZbyKomDkywz+5/8DQvh4rke5UqFUrkoLM0XBNHTisRihkPQbHxICxZUTWTgO7X/v/0Y7UllTCRrXrL2aouALnzbH45gCpmFIJVXA93x835PhPDSVWFBlOV8FwvOJ+h4xRakv9BFf4HquVJxUFMK+oMtx0FSVcDjUHI4myMsEdN+jv1wlXSojgHAihhKJSP0KoF1RaAds2yaTyxOaWpQZ6AZqrkDLvWnWs3k2iyUya5t09nahrmyhJyQ4cDUFT9dRhE9vVzud7a30dXcQDptSr9JzpcKn65JKxjl9/BCd7W20Bu4GOtpbeez4ERxXugEolspsBvo37W2tRKNRHM9jaWWdB5MzCCGkHlg6SSomvXFnCxUMXceybGzHAaRDyEpFikdyuQJVyyJkmigoVKoWlWoVXW/DcaVieiwaIRoJI4T0iF/zBux5Hgpys1FVKWaWi4l0oNiaThGNSqVyP7gXpI8fRUBrOkk0EqZSrbK1nWF+cYWbd8a5cXeCSDTMmZNH6GxvIZcvMr98kx+9/h6ZXIGnzp5goK9b9mtj31oWeqlMW9XmhBmiazuHcekWaq5AajODVqkQcTz8QoFT69uoVYuoD0q5yl8qFNBtG92WFnqeKw1OFEW6euj2JbDVVKW+efieh+Z5JB1XWrjqMn6g4vtogVsJJRhztUVIdT0M30ep2nKuOy7GB5fQ/vf/pVSodhxCjkvUkeNZTpeaewaFpOfSVbXRdJVIKLQDdITPWNWi/6tPkEzIjdw0DFpSSVAgFjMJhUwqgSL19Owil67do7uzlReeOYP5YLE+PxTHJVwoB2VLtQKv6cAny9OzhXr5cuWgXl+5VMm2MBruMz0fRZEHOhS1ORSUAK2u3yG/kO0noFiW1qzIzbrmu8f3fTzfCyJURIlFpdg5Ho1QtqTriO1sjpZ0EgUFPfARFY2EifR0gUI98PzwYF+9/oZhkkgkMM0hNN2kUKxIZ6CajKDR3taCrhuUyyU8V65Xvi/ZlOGhPo5ERunsaMULwmq5rlc/3KiqNAhASMvPVDLByfRBwo9FiITkPCxWqmxndPKFIv29nbz4zFlG//kfEi6W0Qwd37IJJeIAeCGNdDLOiaMHGBroxgxJtzi5fEEatyTjdLa3AAqeL0gk4jz9+Gleev4peQA1DMqVCrlckc7uTn79177GM0+eZG19k0Q8RiqVwHUFuUyOliA2Z3trC0+eOcHt+1O0ptN0dbQSj0dob0vT2pKqWzAnYlEM221ir3YAVzNZQK2vdwOtAKRphk4ilSQRj+I7vsQKQZxNlJ2IDA+JEmv5NKagPjtsyyNEao8AR4/eeR+1v+7xrr9UEk2/N+W8G7TuAQp3wpgF9doD/AghZOgoQOy4/Aeka6OdubrzozTM31rWiqo0MFvyUPZwa3y5tvnVgC3YoVa/wL07Sd2bjmxitXYovD1za7zg+7CZQUEGoI77PiFXBiYFMHyB7hdRijXLBNE02PVsXv7eyMg11FoRgpCyEwOt9r0qBGrtXqWWc3Ay8H25kQkJ4modqAB6zbpLCBTfR3dded00gtnY/K4iqEPY9dBdV4r08iUoWw+DXcdFLxRRPE/m5bqo0wu0/8+/Q9JxGXIczgbgT//j10GBuAgI0+BzUNMgZKKFTNSwKcVfhga6TkLTadV1MHXUsIlqmhAySeoaUVXFEj4V38dRVFo0le5EnHiuhLm+jS181EyWzmKZ/kiEThRimTyiaiMMDd31SUcjFJNx9FoQWUWhJZ2iYll4KDieIKxqtLW1YJg6yXhcxjtUVSLhEFrdrYMIvON79ffSNK1hwxegCBTFR9c1otGodDFBTZZf+5FWRrWYiJrtkvrp+wxsbhPZyHDa8uhuSdG2tIk9Ps/WxhbVhVWeyRf4jdY0h8/fpOPeLIrvo9weB9uRG9cfv074T37OgPDp9xuMmoUgsavvY7sW09Cu680jpjZ2lWZJghBovo+G3DQVTZPjQ1GCI50qY9ipKkKVFlCO62J7Hrrny4VJU3FTcZRMHqHVNoyaI+MGb9xB2ULItlR8Qc0tI0hnpLbj4lSquJ5br4KqCjzhUapUsB0H25Y6lwf2D9Hd2U5vTwejwwMoP3ofTF32kGHgt7XUD6LSB52LUBX0QgmvJYkX6KWhawFg2hFtCCFQMnncdAKEkKG7NA18GUoHRZFK8rW2apgnwt8B8/XWF6D4PkLT5D2qArZN6OZ9lJCJ2t6KSCfxbAtXlaKvIrCyusGtO1PoukrICGNoqtQlcz184e+sH8EaomkaVcsimyuSTKWIRKOUyhYbWxl5j6pgO9LRrhEKY4R1KayqhXzyfSqVqjy4haVyfVkpk4jHcD2Pjc0t8sUSIVO6dcnlpZNW09BJJSUzrChyjpqGQcg0yeTyDPX3cOTgKOFwCC0In6aXK7LfPZcsGqlknHAoxPzSCtFImP7ebjra24hGI3iOTaFQQdN0BC6GrtHWmsYMhSgWy0zNLrC5nQEBHa0punu6SSSiKEAqlWCov599/YPMzS1QyBcQQuA4Dh3tLbz07FkOjO6jJZ3CF/L9q5ZNOVwmHDbrIaVqorTaHAuETeyZGjf+Rn0hRQki7ASHHj+4JhX1dubz5+6du8pQ6jtCsC/QNM8Ds0eaQM0jmZk9GLA9i99hhL54+hLv9VAb7nGrvwfIbLqB5msN+TTFRGxgu2ogSwi5PgkBnu/j+W6TZFXU+u0XTL8SMWJNqv2oq3snhaaR/NBjjaNnrxsazhitKZo6IABrKpK9MvcA/PUhuL4tg1EbGkpHawNlSN2hWe1EIw+/cgFrlIoqYmcK1vFvDXw1ATGlCZDWNlUhBJ7r4gRiPCMcQmha0+RudBSrI9ACL/zKrqasJR2FdEsKLVfaab4g/EWNQVAWV1A875E9ZOz6e3cvGLuvBR7kzXCIKNAiGsZGw54fBgkkGrq4cZ2IggxxAlKnQVVJKAqDyL4gAAmKqtIa/F1bsNprC5cW0P2KilCC5xRVPquqMl+tBjIUhKoQUhSEGjynqkiHp7teXCiYQNR20G49oEvAwcZG0DSsrnbi5QotBRlYN205xBdWUJfXZVbbuR3Ta88D4dfXxsaidv8uGgdw8I4EdRZBnYXS/KnqGoouFYF9RcHxPCzXQzN0DNOQVDmKBEO1fJsWFCF1Gj0fEXyiKGibmZ22bDiAUFMsDdpaAJrvE3I9FE1F9X3QNJRiGa1YIu77vPrOp2hX7tGSTqJpGgOuS7fr7ewnSCV4Xdd3NsFgzvn93eCB15oi/7/+03qtHcehWi2h4knP+Y5DoVAiHA6RTCUxdB0tqJ/nyliJlUoVx3UxTYN4NEI4ZOK5CsVSBSGkz6hwyGwSI/m+j207WJWqZPWC4Nqu56P+6c9J/tFP68yHWiyz/x//zyieXwejg8JHKCqKoSFUlZTvM+oLjEiIUCSMousITZUOZTUN9AAEapIhjaSTGP/x97DdPPlimTbLDtxsGMTjETQN8vkCy+ubtLa1EovH8ISMaaeoWr2/FEV6X7dth5AmlYDXt7a4cfcBuUKB0aEBTF1nY3NbitBbEsH82ln7NE0jHo/S3dFGW2sLoXAIeUCRA9jdzlItVygUy5SSUVKJOEsraxi6Lq10NRXT0BGRCCVfUClXEb6NYSqEQiGMgBV1wy7JRJRo1KS9rZXB/i4MQyMVD7N/eIBUKhV4ni+zHo1i2zbRSIRwOEQ4HCIRjxGNRiQzli9SKpdRVXA8qSZSqVokUJqCzDfNu0elJoDQzKrIvwMrRNGQT/1zL7cPn1NOnXlTG4oTDSpOYu/nBDQezSR7o/BoMFYDJjXqpwGBwEP7QlNSgvz3IFWa6/RooFUHRQFQlcPp4VIbw/k0H3xq3z/8fg9/I5lUy3awbAdP+J+JLRtRyuelX43O1meG3/msqnxeNWsxjgIfIvUBUnvFAHD9i/86uD2wjvD9YDPTgo2soUP9YJHe4RCCwSkCv3OyjFpQV0TAhGigqH7dskkNFtwaIm4yHa3VXuycdutxtxoBX3DNth1W1zZZWFolEY8xsq+fWDQi6+U3ADkhvRKrtUo31H83JgDAsuH8NXzbkYFlGz6F7aL/4Y8RpUojtN/58cXOoPf3uL5XeQGAqIlOmu7ZtX8/qucfgtee/9C1ve5/1Pd7TZQvdTZR2AEgwU8N9CiVanOOCqCqmNk8hhAkAievmuOiFoo7p1nPCzw5KxCPQiRw8Gjo+KYOpgmmgTANRCiEHzIQZggRNhGRMIRDKKYOug66iuMLKq6L5fkUrCpVXxCNxwjFIrR3dRBNRMDQEJoauCFQ8AwDT9MkW6nJDV0xDVTDQDF0hK4jVBXVlM4tXV/g/83/CrazzQz/HkD/oSYMRJRKDSACVKqBArJCu6qC66FnC1IcJkTTwiS7oOGgojQc3JH9I8P4KKi1uayqRMwQQrhSBCAEtuPgoRCOeiiqRs0Y3kfB9cFXNXRTwwyF0AwTTdVQdZVoNIzj+hKg70qqqmIYOkKEmth9RfFQXbcOQOWGA6onWez6mBEChFePzxoOfhTbQckVm8ra3c4KoOs6T25s45gmSiKKlkqSiobpi5ioyRh+LIJTqWIWyxhDvaiFCqai4IRN/FSCUCRMe2uaWGA5CVKvrFws8cEnl7h07S77Bnt46bkzjOwbwDB0TFMlFovI2IXsrG+KohCLRujp6QZFxdsVn87dzmIHqhIjxw8yfO4kfT1dJOJR0qnkjud3Q0dEpVqD5zjyYBAKYRhSBaOlJU46PRI43DXq7Hd3Zwvd7e0IVZfRaBIKY/v3IVyXsLmjp6coClXLYjuTlT7gHAtVFVQtW7olMXRw/aB7guPzL8xoNHiaV5Qmprk+hVSVL7co1fiARqASgCWJND4/O7E7dmDDyabpPukZfy+gtbuMpjp9JvnyGSLEpq93s1CNgEl56N6HFPebAJw80O64ehL15xrzFEhVC8uysWynrotZk1TRcGdtIRRfsPv+4hTkPzcJ6h5v90q1Y61ovCdoyGBA1T3i/s1/JNmCpsFX+3PXIAquqQ0F14tqKF7zBXrt3tqCWZ8wQbbI75rGSk20IARa/bbPPg3pvk9v1aI1OJFGIqGAcWiuc72+u/P4nJ7eyxUdApRsARwHoWt4Jw7VG8D1PQrFEq7nBSFzGnx1105MvvRJhBAowkfJ5tEWVuS793ZBIibNvG1buuGotVstPJLvy4nsN4BjEXzWwJ0fiK2Ca0rt3qbd/hHN+gte2512N60E5YFeUi0jFck2AGgqIpWQ7E9wqlNUpEPCYDNTknHo6QTA+x//Szg08hBY93wZGBxVxxcyKGrjG8hzsmyHre0s07OLLC6vMTkzTyZX4EBvB48/doz0/n2IZBiQbE+5ZOF5AsMwmjafOshVZaDfXL7I8op0HDnY3y31HNPJZr3Uhin4mUkINHb88QkA20YpliVgaE0jkvGd6crOyVXZmWxNy6xcemuTVoF0AiMIcioCWlszDHyhIYSP60qA47geru9hBm1dAwmGodc3czVgPj0ki60ZIRRdyLZpbCsCRk9TUEylbkJe08N0Xn2B0onDqKUKSqWCUixj/NsfohZKKMLH13TsahXPsjFUFR1JfDy0Ge/RpErgDBQERrmKUa5CNg8Lqw3tJsdr2PXoDSwKVU2jxRd1kaQalY54RTwG8QgiHsWPRsh7LueWVjnS2sJQfx9drk94ewstmUQxImhqDTiI+kSo640lExRLZTzfofGEaZSrhGJSR+2V736VRGDhrGnNcQVVTSVkGmgq+J4JiooaGG1IslpBVfT6M0LIvjGkVjNCyL7QDJ1QS6q+MdYOv0IIbNdDN0P09nYQjUiOvlSuUK1aRMNh1FKlnrf4JYBWLcljxR4siao9dO8XSqKmuyVzR2iguJ/zEOxmtb5YWY3OPxsO2o0M2yPTL9Z2jT6vHs6xef2v6UISwMLd+4OMXLlT91q+O7CxAYQJgStE3eVJnSlrqkDDQQnAfwRrtyv9ysCWqBWuNC+Mn93We297dQXA2jytUTdNNwWfvoDtLGxmGrLbG2yJwHrNdT1CITMI+7Iry4Y81KBT6uKR2j0NG0AdpTd2XiBacF0X0zQxDX2PAVnrXKnzZQbernXbQataDYvP3gOusQ32amLxiO+bXtFxwPXAMCj9y39SR/V3xyf4t3/wY+YXV/nt73+FX/v6c0TD0QD/iHrbNEaeSv6t/wvq0hoAXk8HD/7R3+WTT2+wsLTCobFhOttbaW1J0dXZRluL9CTueR6u62L7BKd/As/yNfGd0jCRgrrXJqHv4zsOiu2gej6KY6M4Pqpjo1gWbtVic2WNrdUN2hJxOlJJqeTtuCiOA54Ljiv/dj0UxwmuubJdHBfVdlFsO/jORdg21UKJ/FYWzXXpCMKqEDZlO7qBfpzZIGANmJ9m0NDQO7t9vSky7uHyyjqr61v0D/TRkk7R7H1ZqUEKVEWhUCzz0cVrLK6sYdsOy6ubLK6sMzzUw+GDQ/JZ32d9Y5trN8YxDIMjB/fTkk7UdYBqyRcCx3G5fX+SP/vp20SjEf7mf/wD+nu6qPz3/7DeB42fu8XjtVEWwMa6CKHxHcw/eRPz938CQPUf/O9wzx17qB2kj6xdovdgLRC+qDserM1zuZkpwZyV32mKhu/LzTlkhkFR0VBRawuwIsXGqqYGOn9KAGR3VNk0VUEIFaEE4rz64r3DeKiaWmfQRY1p6+mAno5mxvv1DxBbWfy2NEv/3X/Bm+99ws/e/piQafL9b77Ei4+fJuJ7iGIJpSgBmloqQ6mCUqpAqYJaKmP+qz+Sxj2+j7q6AZ7XxCg3JhWkw9amDarWnuyA7oZnwkBn7cafX3zousw4EMXrGkQj+C89SSgaoSNsENdVvEgEdSuDUqmiWBaRiVmUojRiGPlf/hBMo4Hpaci9Xk+x9/IXLMd1o4RAnF27qKCgIYKIAI1Z7ozbFs8n4XooikAPdPhc16diWbjbObxwSPJye43thn3u81JTm++xiX3uYfyzM6+3XT0i8V4Lf1MDBtFbdt0k99wGENbEYu2uNTuA5pF1312RRzBnn5U+63Zl56TXyHop7GrzujTmM4oJ8ql5DduZG7W+b4yQs5NR7coXTb+cB/ldaLHxpFFD3g2QoenJz7Dd/OLJ35WPqgb6W7sbV1KslUqVxeUClapFXyIWONJsRPkNIK0BHYuGCbd742x8thbVwLUdNje2yJTLdETCdLSmpGJtcyk7tRMCLfipi6qoIXjqVPbOk8rDbbvHBNvz+8Zs5pZ3fSlQVCGBqKZSKJbZzmSxLItIOITvy41MDZzZ1Qbb7gHneT7vfPgpv/fD1zh+eJR0KsG98WkKxRJDA718+5UXGRroleAOHxFskjUlT3kS0R6aOE2voCgogciLwCpUU0BRpUKzVbW5sb3Nhc0tDrUk+OrZI6STiR3VCEXBC0Q70LBpqgJFkYBPFaqMkxYk1/O4Nz7Nn/z4HayqxT/96CaRUAhvuB91O4e6nUOkEhT/0d9FcVw0z0XxHFTHlRuj58vfHQfF9sD1EW3ph7rIcV2u337AjdsP+OpLT5NKBvoxuzYkTZGOeq/fvs+texN859UXGezr5s9ff4+b9yaoWtVAJU3BFYLV9U0uXr1Fe2sLI/sGCDTnmtq4FsZibWOLja0sA6EQqvLwSbhJPN6QGpdUhUAfUFHZCbIcjGRNQ11cA88j8t/+W/x0YiePhvm191xrqEfTwWpXTYS8LoQg7vu0+jU1gJqOGQ1zbRfDp+zOq/nA13hZAKST2P+P/3P9YmNeD9UsAOCqIp34Cl+wsZnBsiz0iAmGCukovh+wR9QONju6cebv/AfJLusafk9HvSKS/fUCtlj+CNfHsx1sy0IDDFVtuB6s1f5O39Tfr95+Df3Q+FKV6s4j2znUH76OivRyFAGZb9Xac6PTbo0/onW+WBLIeV4DW3VkvCvV3kNperIGQNkBFYH4WgsMHTRNk+/0Gf34hevq78yIv5gkAgClg7JXDMZg/DZwsjtzvqFuTaH2GvJQ9jBk283u1L9utJT8IlV/eHDsveaLR/6pNKzjjRdqulq1ve0hseSuchr3Ak2Tupe1Mb/jI+wX78NfOlxP8NtD13bxPnUQUUOKnylC3J1lIzKtKafuWhwQQFsK+3f+GZ5tBeAPVEVH16QuwOzEDO98cBHLsXnhqbMkjx7EbGAhGjvZ8/yHNpSdDXnXSTuADQIf3xeUC0XuXrnN/YlZTh8/TOLkUWk23VBOXWmvUbYSRG8XAhRPQfgujmvXgyXbtovt+Ji6TihsSrP93SevGjBTAhikaAExGLBQYicUQeTJ35asTb3PfBRVoa9HBtoen5rHdV3pidkPlnuxy4IrOBE1tp2qqiQTMQCOHRrlt7/3KltbW/z+D3/KhxeusW+wh4H+TqkDp4ogXiL1TaquJyB2jBTkmBdN64AifClWkLNnB6AqPoahMhJ4504kIui6gqKKet95QuD51C3JUGRYG6H5wT1S1KDhS99TAcsy0NfND777VTzPw7w2KXXKBFLHSdfwQybeUB+KIiSzrzQzOnIia+iaiqJoqMpuMwTQNY3hwT7CIZP+7k5MTdbXry2EQqAq8r6NbJ4Ll28SjUQ4cmCESCSMGvhUCzXE8dI06dvs26+8gGmGpFsBJbBUK1WwbQfTNEgmYoRMg0P7h4n/ZpSernZaW5IP1bF2AFCD/pZYMJiTwsf1fLlRK6pkjwKAIa0VhLRqDdgYJV+UivPUuzq4t1lfxvf9+t87oGgHAARC/WAxDcZire19Ke7eUQPYAXM1ZfUmAr0JVzzMSOz+SwDG3/9vULL5er0fYpIUgXZrQoKc1jSarhCJGMRjEfKFIp5wUdTGMb7jRkJRRHCQ15s3I0VBtKYbt8z6e9W+8wJWM5eH3p5OjHisXidfiJ0g2b6Q49n1UDwP33EoF0r4tkPY0GXMRN+XIkzXg62s/ETIfm0QbTacSh9ODXXbu433PmA1pUDU4wsfRTGowdJHlbeTbYMD0aAeIvhH6u/UDKB2xslu63QRDLAvqsfVzEr/RaRgwCsqMk7i59xdBxpNm+wed+7s2WIvz+118LLr2T3b5cu9/w5ADdbN2qdgF3BsLmF3KSIgMRr3rB29LZlUBH7DN9KFk4amavX51zTlvvzrAL8E2HoU0BINHVRv9Hrj1z5/CVbL32uAyGa2HYebdx6wsryK4zioiko4FOHA/mGG+nsZ7O/hu998CU2DVDKBbuiBlRp4jkelUsHzfGKxKHrgULRebSEX+2rVwnYcotFwHUApQevX6Mxw2OSpx09w5tRhNE3F821yhbIMCitUHNfDsiwURSWViteVU62qRaks/Wdpqobt2NiVMtFIGMPQyeRyLCxtoKoa3Z3ttKRiUlFVM7Fsh82tDNl8gXgsTHdnC+FwqL7wO670T+V6HrFoNHC62diWEvy6nodtO3X9FekXSPoGUoMgq54n8wN5AvRQm0GRAj2d7UQjIVQE7a1J2tpSnDg8xv2pBbYzWVxPeu3fzuTYyBbxfZ90KkEqlURVFSyrQqFQolqtSh2OkEksEsEMGdKpqmWxtb1NKhEnkYhTLJUplcvE41GikTC6rtLX3YFpajiuU9/8HEfqo5WrNq4gMHt3aUm1yLhXIQVVVcjny9y5N0O5WKStJcW+gR66OtupOXsslsq4Qsg28WRb+L6gUq0yPjVLSzpGW2sS0zCwHZd8scT2dg7XdYnForS3pSXTpmnUg6QG80SK+UY5ODYszfNxUVwfz1PwhRIwMwJF01hZ3WB9c1sGao5GUJAGF7XlxHFcQqZ0zKqpKu3tKcJmNPBTVGV6doE796fY2Mpg6DpnTh7h7OmjdHe1kcsXWF3bJBaNMjwk/Tn5ns/qxiYra5soQE93B+2tLRimFFUWS2VW17fI5vJEI2E6OjtJpRJ1ZlcJfHKjNi89fmsqcJrr1f2iua4MuxEOS794+bzUI5Sx7cBxXHRdIx6NYBjSXYcXzNGqZWPoOtFIuD6OLcuSSq81QAAYhkYoFELXdTzPw3FkvFRD1zEDa01Fqe0t8tCj1GPaKSiZ3I5hSDYv1RmgwXdPg8qwrLTsGSHQNIVkMoYZ0vGCgNkqPqpQcIIYjb7t4TguQgHVUDGNELrWANB1jey//MfSdUG5glO10DWVSCxKJBpFVVXWNjb5X//dH7OxmeHv/a2/wsGxfei6bH9NqHhCxieVjjZrxgYqtuMwNT1PNpenu7OdttY00WgYw5AuQYy/9g9RL9+SgCsSxj+yH89yKOYL4HnETFOy9Z63A+J8DxQVf7iv1igELdm0rjcyaw3f1Jdjz/dZX9tgO1dgsK+HZDJe93tIU047/9RBVeN1IRC+V3fdoSig52UgbzpaHg2ogrp9HuCqMT0KuwBXjf38JZiSIJsvte/vyWjV61A76kDNLYXSuId/wYJq9EMzIKrVtAn5Up9cTZXcnV8NcO19/Ysm2de7fWw9HHJI1HtMaShu1zxGfOlq/EJga7fV3SOPFI3It7GlFPHoZ5ryVBp+D/KpF9vcAQIoFsv87h/9hLkgUKuiyFhwf/k3vklfdyeLy2t8fOEq6ZYYLzx9lmg0ji8EWxtb3B+fZm5xBdf12D88yLEjY9IDuZC6Icur60xMz7G6tommqTxx5gR9PV1oWqA/E7SJYzvkCkU2NrfZzsjApyHT5N74DCtrGxzaP0qxVOHarfuoisrXXnySY4fHKJUrvP/JRW7fn8LQNOLxGJZt0dPRwuOnj9Db3YFl2Zy/fIv5xTX6ezs5dGCIpx4/RcR0uH1vkrc+vMz0/Aq93W1862vPcPLoAaJhk2rV4sLlm1y5eZ9yxWL/8ABff+lJ6Xw1aD2BYGNjm8mZeeYXV7l5ZwLHdevKq76Q7ikyGQn4tjPSAeDI8ACGGQlYhx3mKxaT/qpc16NYKNaDgKuqQntbGl3XmJ5d5L2PL7OwsonruuwfGeT5p8+STMa5eecBt26PU65axGNRDF2jNZ3g8dNH6elqZ3Fzkz/6szc4cmiU5596jNv3J7l+8wE93R08/9Rp2lrTbGxv8cY7n7CVyfH1l55kbGQf98anuHL9HvliGR/I5Yu0tSZ45SvP0t7WiuGqFEtFXn/rPJev3kML1pjnnzrNd7/xImvrm/zpT95hZn6Zg4UCHWYI1XXxhY9tVVktl/i9P/4xo/v6+PoL5+jv7aJUrnLr7gQfXbhOuVIllYjx+JljPPvEaZIJLTiN1hSOFUrlMuub2wB0tKcwTY1CscjkzDK2K2hJJehqTxOORljb3KZStUnEZcy5UrlMuVLFdV0eTM7R29MpQ7M4Lucv3+TmnQlOHz/M6eNHWFxa5efvfsLC0hqmaVC1ZDzQ40fGWFnb5Ic/fovVjS2+88rzDPb3YOgaKxtb/PDP32RyZgFD13nq3AlefOYs6XSUbK7AtVv3uXjlNtuZPF0dLbz8/BMcP3oATTUalgQV0cD0+tEw4//473Dr3jgLC1K0bTke00H4oe998wXC4RC/+0c/ZTuT59WvPIWu63x44RqxaITf+PbLnD5+mHKlwief3uDC5Vssr24Sj8b49V/7Kk+cOY5l2fz56+/y3seXCYdDRMIhCsUSyWSUl198krGRIW7eusfFK7ewbZfuzjZeevYsj585hqHrrK5vcffBFPl8kc7ONlKJOC1tHQz8g3+G2uBZH+TB207EqVoW1apk2UNhk7jlNN6Fpqm0taaIRcMgZIgaFek4uFqxmV1cZWlxjVK5iuu7tLWlOTQ2SltLa1N5pVKFxeVV7tyfYnFxBUPXGNs/zKGDI7S3tbC5mQkCZjuUy/JAqetSSOA6DpZVoVq18TyIBs6AZcQJjdZ0knyhyL2JaTraWhke6sM0NSzXpc22CQtQNA3/xEGK/+KfsJ3NcfPuOLqmcfTQfoqlMtlcAcPQ0TWNcNikpS2FGQ7VDQlMXcPUdIRQ6oAHAZ7nBYHqZXBpTZNsd82a8I2f/Jw33/2Uv/c3/jKPnTyCahr4St1eOxhvO4dFKXbcYTVq+n6aKtDVwIBAVdH/2v8JNpv7tL4tBQfqPYHWXsCBBtHaXyS59QUw297uG2jeU5tw12dk+ggx4udX8svGU2xQqaklVYDfXDdVVXZ0ex/qh4fLE7t+Ew1tUiPtvB3+oP6v1HGVFszqw5l9ZvrFma06affokpSGO3c//XmpUTwAQWM8YjA33rO5mWF1fQtd1+nv6WT/8CDtbWkc1+PilZv87g9/xqGxQY4cGCadbmFhYYU33/+E6bkFYpEonieYmJ5ndX2Dl55/krb2VnKZHH/++ru8/8kVCsUyIdOgu7Odvp6uoIN9hOJLB4GWxcTUHG9/cInpuSWOHhzmzKkj3Lk/xZ17k3S2tpKIx1le3WB+cZWjB4c5MDLI0vIqr79znis3H9DRkmJ4qJf5pTVCpvSk/v1vvUTFsrk/OcvE9BIb2xlUXeXUicNsbmd57d0LfHr9HsIXzC+t0tHWwtBgH6ZpMj27yA9/8i7rWzmSiRi5YoVTJ4/Q39C+9ydm+PD8VTK5PNlcgQeT89iOK/0RaSrCF0zPLfHRxRvMzi+TzeUJh0N87aWnOH5krIliF0IOflWRThDPX7rBnQdTzC+tcubEYY4fOYCqqiwsrfLBJ9colCp0d7WTLxQplStYls1b715gem6JY4dGScSj5AtFfv7OeWzL4tdeeR7f97nzYAYUhXOnjuA6LpMzC9ydmGF0n1QqL5bKjE/NM7+0xrFD++lo7eDS1Xv8+OcfousaA/3dZLIFrt9+QCqZ4qsvPsnGZomPzl/lrfc/5eyJgxw+MFwP++ELgeN6MmTT8gZeILYSrovjuNLTuQKLK+usb2xx+ugovd0dZLI5Ll6+xbVb4xwYHSCTK7C0so7rBSIYxZP+2FCxXXgwOcub755HNzS+9uKTjA73sri8xlvvf4IvVNKJGMePjPFMS5piqYTlOOSLFW49mGZjfYOtTA7H9VhYXiNfKOL7gkKhxNTMAtdvPaC7o51D+y0mpudYXlnjiceO8djJoxSLBZKxCBHTIBoOUSpXWF3folSuopsGwveZmV9icmaBzvYW7o3PcuXGPY4d3k8kYrC4vMZHF64zNbtEd2crtuNSLJbYCVi7s5IrDYYBju3wxz96i6s37jLYJ2MhlisWd+5PUfQ8TEMnGYuSiIYplcr0dLXR3dnOg/Fp1ja2KZfK4AtmZxd498NLwdzv4sLVO0zOzPHEmeOomsrK+gbT80s88dhRzp46yr2Jae4+mGJ1ZZ0DI4NkMnnmFlaJRSMkE1Es20YIWFpZ5ydvfMDt+1OYhkFHewuZbIHu7g7+VrlMounILXCTCT742z/g7Q8+JZMtYDsOB0YG+bvvX8PctcaFQ6HAi/8OQ+44LvfHp/nRGx/i2i6RSJiVtQ18ReHlZ/O8/NyTtNXP25Id/vjCNd58/1M2NrZRFIX9I1P8hvJVnjh7kkrVolSuBMyuWxf/A6ysrpLJ5jEMHcfxaWtpkdExNA3HdXkwOcuPXn8P3/d59eWn6e5sZ2Z+nbsT03x1ZYNBz6tbTs/ML/HJpevomsZLzz1OuVzhh3/+JuNTcyTiUfYN9nJ4bJgTYWn5vLq+ju8LkokouqqTiAXsbABOMrkCt+5OsJ3N0dPZwYljB4jHogFekNEgPL8WgPnhvaD2KYTAtmzWN7exLEs6WtU0GeGiWMJxXdrTUUaH+6U04DPS5+5cn7NHPZTZL0dsNRb82UUJ0TAP9+T/ajfuyuuXrWAtP7lPPlT2F2qrBv6ukSCrY6saa/honu8hgmjXtVrWNXGy74lAfUdeEYEUR1rP6g3FfPH2+VJgq1Ypv0lZrvkFmhCosoMJJVhqpOe+OLqtWxw1UaC7y4VYNMKxQyMsr6yj6xrPPnGab33tebo62zEMnbaWNIaukS+U2MzkSK1v8uGFq1y8covHThyUgVR9hTff/4QPLlwmHo/y4rNPkMnkuHV3gkrV4uTRMRzXwQxpSOs9H4Gk4V3Po1yuUqlW2dzOMjG9QEs6weOqQioZIxQyiEVMDo8NcfzgMLlcHlPXUBG0peKMDffzYHKec6cO8s2vPcNr71zg3Y8uc/32OM8/dTrwUaNwZGyQV15+gr7+XkIhk+WVdSzb4eTR/aSTcS5evcN2No9tOXiex+rGFjPzq3R0tPLiM2foaG8hnYxTGyi+EPzotfeYnF7gK8+f4/HTR/E9n8mZxQBsaeTyBT66eI3zl+/IeIJCMLuwyr3xGQ6MDj0EjG1bnuIt26FqWbS1pDkwOsThg/vp7+tGURUi4RCmadCbTPD1l59iZF8/rekU98anmZpdJJ1M8OJz59g/MkghX2B8fIbzl29x5uQRIpEwsUgIXZU6FmOjQ+wb7OXO/WnKlSqe72MaJslEHFXdwHHkphCNRjF0nWOHRvn2N19gYmaBP/jha6yubVG1bGbnl/n0yh1S8Si//b2vs2+olzMnD+P7voxrGQ6RSsZJJWOYLmieAFda3OmaSsQ0MHUpgvEC0USxUGRjM4OiwKGxfRwcHaSnq0NuHI0DWPgIocqg1jPzaJrGYycO09PdxuLKGitrmwz191GuVJmYmuP08cPEIhE0VaNq2TiOQ19vN92d7WRyeQ6MDnJobBhd03Bdl3BIOnU0DZ1yucza2gaqorB/Xz8HRgcxNAVNUdA0g/Z0ks7WFHMLy8QiITRdx7Ns0qkE3//my/T1dvLP/92/p1yp4Lg2AumewnGkjl9rOsmRQyP0dLU3MQBKoGyrajvOV6oVizfeOU/INHjm3Cm+9vzTbGdzLC6vMzGzgKmpJKIh0qk4hWKJVDxKV1uK1nSC7WwOTQFN8YmGQzx++gh9vd0srW5y7dY4+B7C89A1lZZ0gnQyztmTB/nWV5+ksy3B6toGtm0TCRmkUnF6u9p58uwxHjt5mJ7OdjQFrt64x4Urtzl6cIRTxw9Qrdq8+f5F3n7/Ir+5sU0sFqUW2kiudT6ZbB5FUXjsxEGm55aZmV/Gdd2A5Zcbsl/zPdZwIFVVlWy+wBvvXWBpeY3vf/NFujvbmZpd4GfvXOCnb37E0EAvQw1r39LKGpev32NjY5tkPFrXe1TY0S9VUPB8v66vKcVaPnfuT1EqV3jy3InAQNfFdey64n6pIgF3Ih4lHo+haSo3b0/w8w8u8GSp3LQGZ7I5PvjkCn3d7XznGy+ysLjC7fuTzC+tEY2ECYVMjh8eQ9dU8rkid+5OYOga4XCI5dUt9g8PcebkUeKxKJ7w2NjM8PYHF9nYytCaThIKGZw9dSyQuisP7dO+L/Xa/LqaihuwZZArFPjwk0s8mJxleKiPkX0DXLt5j6mZBQrFEofHhvjt732VwYFudD57C93TAq2mNvMZ4KEGCmr+zH5ZEaKsC/wigGjn2LOHaK/23UNgcA8BYSPD1ag69MgSd2e5R5s9Ah58FjatqwY06JJJtLEXg7cDvlQhaFQP8wMm1HM9vNp8Rbr7iURC9PR0YZgG4hdQhfrCYKtRqftz762h2Lq4UKEemkco0nBiLxr2sytQz71piIn6b5iGzoGRAT7+9AabWzkcx6W9vYVIVAYU7ulsJR6LUqlaZLJ5psUC12/dp1gq09qSIpmIk06lObh/iGu3HnDn/gSHx4apVm2pSC8EHe1pjh8Zpb+3k0q1hPClTo3ve1Qtm2y+gGkYjAz1cn9illy+iO24mHXnewLHrVK1ZNyymgJzPBalt7udSNikv6eDowdHuDM+gxCC7Uye2fllyZy4HmPDPTxx5hjReALP8zF1lQOjA+QLJRaW1ykUK/XToROwU6lkjNn5Fe7cn+bbrz5HMhVs9Iqk6y9du0sqHqWnPc2h/YOsLK+yuZXB0OVk2NjYZGZ2GU1V6eluJxGPcfigwqGxfYRCZtOpwfVc1ja2ZODsWIzHz5xEVcBzXYxQGOF7qKaMe2cYOqGQQWdHKx1tLSiaCOKx2RhtOl4QuLqzo5WWdDIQx27Skk5QtWwUBNWqTThsUvM74/sC13XrQa+FL7CDWGfRcIhoNEJvdyf79/VTrlqYhiH9gSF1koqlMkN9XXR2tBIOhRgZ6sey7bpOUc0vk2roKMJFuF59DFqWzcr6Np1taal3Z9vYlkVXW4qZ+RU+OH+NzrYUJ4/sRw08mPueDM6rKSq+kDHw4rEIlapFsVhmdn6Rial5FEVheLCPXKFANlugVK7Q3pYmFg3jCxgZHmT/UB9TM7M8mJollZDhUJxATygaDRMy5Bi0LYtSqUImW2BxaYX+3k5a01LHzNBVNK3md0oJ3CHI+Z+Ix9jazvLp1ZusbWzS291BKCSXkZZUkoP7h1hYXufWvSmSiRij+/pxHDfQ2ZJOMA1VkQ5Zg+nrex7likVLWjIbjuuiqRqRcCRgY6QOoWlIQ5easYIf+GxTVakH1tnewsi+PlbWt7l55wGlclWyNAqgK4RCBtFIiGjYJGzqtCTjxCIhGZw5yFdXFfo6Ozg8Moxh6pTKJVaW1xC+z+kTh3jizDEURSGRiPHP/90PyRdKuKEwBrVDoZwHo8MDMl4oMLewGuiK7RJt+N4uZWU5HuYWV7l5d4rjh0Z46dmz6LpGZ3uaWw9m+OTSbaZm53m5QUVjaytDoVBkbKSfp88dpyUtg7uPjQ5iqKAH4jHhuniuS9WSAdeFK1hdy2CaKh1trajorK5vUi6ViZgmuiHjCbamZdDrkCl9sxVLFTzXIxGPohYrwRKvous6juNSKJQoZHNUy2V8TzA2PMiTZ4/T09VOd1c7ni+4c2+SW3enOHPqEKBy9/4MpVKVkaEBYtEICKl7OLqvn5PHDnD1xj3uPpji2OExIuEwtuNgWU6dpavpRNXO5b7wQfEDgCNVIcqVCncfTOH7Pp3tbSyvrDM1u0AsGsH1BNl8kbZShcijGJDP3aSCtAeAqOtu/Qr0tOpl/CryARrfbG8Q1lRw/frnv0cwputt8cVJFkVVGpTkoWbNXK9Vg+uHnTv2rsFD3wX1UQMpTCO2EQJcR1CtWpLZrsnwhEA3DNKpZKAb+BcItvzdpp+PeAmlpkin1Lwk134aOi9wDN+kgP7ZGdOMuh8GWiAHcUdbG/FojMXlTSan59nc2CQei+A7DpVKBcd28BVBLldEEZLlsmyHUqlMqVwlmRCYugQPrudTtRyE73F4/z4WltZ4/Z1PcF2Hnq52hA+O7aGoAl94lCtV8oUSKAqpZBzD0CmVZQgQNXAWadkO+UKJje0s5arUrbFsG5BI2vN8GVLFtqlaFr4QVC2LlfUtSuUKxXIFz/exLAdVl9fXNzNMzy0zv7TGyuom2bwUxxWKJVRVArqvvvA4n16TJ/SqbdPWmqC9od8KxTJt6QSGoWPbNpWqjevKgN6+LzfDimWDohCLhjl8YB/7RwaJhEPS+VttlVOkIUGpXCEaCREKh4jGYuTyBTY3tkgkU6RaooGjUlm263pUqzaZXA7Pd+vgIJPNMzWzQFtrmpZkjFK5QjgcYjubw7IsHEe2ayabwzQNSkFYFdeTwNeq2tKJpSv7xrYd2RfBCbNatVECNsJ2bDSVunXq3NIa7398hbHRAVzXI5sv0NneSr5QCoJjgx+wM77rUqpWsUsVyqoEAZZls7C8RtWymJ1fJhaLcOLofm7cmeRPfvIeyUScM6eO4bgOW9vZgKKO4tg++UIRQzcouGVWNzZZ39pkanYR0wiRiEfZCAwhtrazeJ4v+8zxiEQiRKJRQqaB7wvWNraZW1gmkYjhOC7hkImqaVSqNrYtA0NvbGf56dsfs7y2SUdbmmg0wonDh0in4nVKvVAsYVfKrK1t8sY7H/Hux1fYymSxLJuh/m5URaVq2WSyedLpBEcO7OPqrXFu359mbHSIWDRKsVRGCIXujg7aUnFMRakrLKtAJGyyuZXlyrXb9HS20ZJO4vs+lYpN1bIDz/cC23HJ5YssrW2ysr4tA80H8+H67XHe+vAyvkDq5AmfQrFE1baJmjFURcELHPY6jhfoefjYjoPvC3RNwXV9yuWqdPyKhhAatuOhqpKBMQyDcMikq6MN0zADpk6OY18I8GXkiUwmx/rGNlXbluzbLn9mckXbAQi1TVgernIUimVMU4rwS6UymXxBigCRzFmj0MR1HRzHJRYNc+zwfg7u39e0FutqYGnrC6qVCrlMFlM3CelGYIUqDXCsqi+ZzJAhA4MbBvFYlEgkLOPLCsmUmaZBJCw9ujcu3b4vrQNd12N1bYvl1S08z2ewr4dXX3oeT7iUyyXWN7e4fvsBK2tbmEaIWCSGABaW11hZW6ezoxVd0+jt6SQej2JZNvcnZllZ2ySXL1IslnkwNcvSynqdHWx0zePXYlUilfEVAZFImIH+XtKpBKvrm1y7eY/VjS062lp45SvPMDY8iO97bG5nafWlQ+qmLSjYY/zAOEJRG8COshNVAkWRel2PAFz/v007+3DNL1cj/NkTrDSInHeU5PcQ2T3SkEDURFvN939WLRsYwEa3GfXjSD2/3eLOL0AEiYAuEqKuj91YV+kj0w7UWQIP8kqtbEUGo4ddNNsXcxL7K/Ug/zDr+BmDy1fZ8Qi/RyPVtCTr9KD8bDKcEGqd6PJ9n0q5wuzCSn1xnp1f4s7dCXo62rFtm5nZRfLFEtFohErFpqPNoK0tzeZ2hnyxgu9JVmVpeQ2EQmdbG4lomKnpedLJOL/+ay/zwx+/zfnLtzl1dD/hAZ1b96awPY/ennZCpomhayTiUbItaSJhUwbDDfQKCqUK49PzbGVyLC5vkMuXmJ5bYqC3m1QqSj7w2i5P7bCdzcuQGIpCKGQSj0UAheX1La7cvEd7Rzsd7a3cGZ/h40u3cBwXPxDl3Lo7xaVrdxnZ18fde5O0tbXwa19/lu1Mnpt3JpmaWeJUfQmRIr3tTJ6VtS2KxQrXbk+QyRYl6PIERsjEB5ZWNwmFTFpbUtiB24iujlb2+aLe+Yqi4roepmmysbnN3QdTCN/Hsm3Sra1oqoZj2+QKJUoVi1LFYnMrgxA+pXJJ6tz0djI5LZlHTVVwHZtCocTB0UEqlSqmrmMaOksrG9y4PY7vC2bmlymVKmRzhcBq1KVUrlCqWBRLZflTLOP5PoVCkbX1LbLZHLbtUKlY2JZNIh5l/74+Pvr0Fv/8//On9He3o6oq7W1pvvbiE1SrFtlcgVKpQtmRjJRv2eQLZUTFwjY0bNtldTPD1Rv3GejrZHxqgWK5ysnjB8nmijyYnGNlTVrsffzpdcan5jl2aJS+nm4qFZk/BEDU8TBNHc8T6GGpSJ/NFahULOkLa3NbihBdj0KhiOv7oGp4vuD+5BwCQX9PJ0MDvdiOQ7FUZW1zm6rjMTTQx/DgEg+m5lla3SQaDnFobIjujnY2trZY3diu605NTM6SyeT46MI17k/MYZoGvu+zsZUjk81jhgyu3LjLxPQC8ViUREx6GTd1lfXNLd754BIbm1nOnjjCy8+eJWXbhINQWpqmcvjAPi7fuM+bH17G9XyOHd5fZ4R8X24L4XCIre0cP3nzY+LxGJMzi5QqVabnlunv7ef67QnOX75DW1sKQ5PWllduPuCl556gp0dlfTNDJlvkweQCkzPLzC+us7mdIxaLUa1ahIwQlarNzXuThEyT4aE+OjukE95IOMz8whrtrSlM0+TClVsUixUS8SiaVrPa9cHzKBTL/N4fv4Zl2Rw/sh9Vkcyb5/lNa6TtumxsZsjkChLQ5wp4vk8qKQ89iyub3J+YwfcFU3NLLKxsSFF2JNyw6YEeHOSmZ5f55NMb5AtF4rGotCBsacHzpGNW3/Mp5CtkM0UMzaQ1naJcqbKxtc3Sygarq9t8evUWJw6NSofMQkYycF1ppSnZXSGZRfXhtb1q2XKc6hqlssXswgqVqkUqkSAaiZDJ5tjazuO4DtkgOLRfdzTtUiharK1vsZ3JyUNVJsfVm3e5fW+S6flljh4YplAoMjmzwPTsItlcUQaNVqXgzxc+tuNhByG+fOHi+wJVkd7nuzraaG9r4eadCRaX11FVha88/wQvP/ckhq4yNTNFsVRqUpaut7JSix6wY2mvBPuUUJqVxmpeEpQ6KPgVp4f2VbEjxnvkIzUl/R1QUhuLj34qEKGJRr0o2FOcuLu8+h00Aa2m+xuY4D1L3+V5oC4SbCJZGuv6iHz2LONhdxACEL6PYztULEtasdfB2d5snoLyGSU3p1/a9cMO+R0g9xqSbahOM/JrrJpay4wd8WANuT5U4k62tcZuuMd1XFY3tviDP32D9Y1tFGAzk+fT63cYGuilUq1y4ept8sWyPOm7Hi3pJGeOH2Z9fYvrt8YBFVPXeTA5W1eA9wRMzMzzYGqeU8cPE4uECQUnv6rl8qc/e4+FlXVefOY0r778NO1tLYRCBu1tJaKRCMVSCUVRSScTOI7LOx9dIZmIkc0VyRfLnL98m56udtrb0ty6P0OxXKVQqrK6mWFyeoliqYqmqMwvrtHd1Ua5anHl5jgLyxu88MxjvPDMWaqWi205VCwbVVUwdB3P91ld2ySViDE5u8QHF27Q09VOJlcgmYhJ1w9Bc2uayuGxEd4/f43f+5M3MQ2D5bVNfF+wuZ3HdV26O9sZ3dfP/Yl57k1IsBAyDQ7sH+SJs8ebBrSmqhSKJeaXVtncyqKqKqePH5Rx0GJRNF2jWnHJl6S4s1SuShcDsQhCqHS0JfnKC0/gOC6z8ysUiyUSsQhPnDlGe1uaQrFELBZheKiPe+MzbGWLmKbB+mYGRVFYXtnAPuZKVxuKQntrilg0Iq2xKlWKpTJT88t8eOGaXLTzRdY3sywtb9DSkuCbX3uGSCTEpWv3mFtcoyWd4LETh+hsb+X2vUmKpYp08+A4CB9URWCYBr4uRS1jo4MgBCPDfQwN9LK2lWdyfpXqlTvkiyXi8ShtbWnyhRIXLt/hweQ8ITNMd2dXYGll43oygHJnRyud7a0sLq+xsr7FzTsPmFtclsxXscTC0hrZbAHbcfnwk8u0pJOEI2Fcz+PWvWnmFlc5cWQ/iXgsuM8J3I4onDpxhI7WFOcv3eTO+AzpZJxnHz9Fd1cb7350idX1LZLxGKVylZt3xunp6uDMiUOEw2GSiTjLaxtYtkupbNXdoEzNLmPZNrqm8tixMVqS0uLXNHQWllbJ5ooM9ndxsFqlLWAVw4bBd195nrBpMjE9z53xGTa2cyyurGOa/1/q/jvKsiS/78Q+cc3z/qX3lVWV5V1XV3s3PdPjZzAzHBBYgBC42BUpSiJ1RIm75JKrw7MraKlDiYe7kqjlUivRgACWwGCA8T090z3dPd3VprrLe5eV3r3M5901oT/i3vvey8qq7h4MoKMAeurle/dGxI2IG79v/Mz3Z2IaSsMys3uCO/eWsB1JNpviuSdPsLZeAKHRshzGR0cCTWQuk6HWUM9Zb7aZW1jh9t1FEIJGy+bcpRvcujtHqVz3HMjbZFIpEvEo75+9wq278zz/5CO89MKTnDxxhFK1wdvvXeDMucvEYhGuXJ9VVCWpOsJ2lL+Q56PnOA6VWp1Ktc69hWW2SlWWVwtslarkgg0eGs0W5y/f4PrtebaKZd46c5HHHz3M9NQYT546yukzl/iXv/cdxob6WVnbpNFo8uixfcSjkR4/1mgkzPjoAJev3+WHP32H9z68wt7pcX7l8y+SjKeYm19hfaNIvdlis1im0WhRKlcJh0KUKlU+uHAN0zRptlqEvAhEx5WUqzUuXrnJ/NIqmWQc21I+oLZPJhrs70oe1Gp1T0CpSK5qraE0+dUqd+8tYLsOxWIVoUsajZai65CS9cIWjWYLx3GpN5pKC17Y4kevvsVb719gq1RBui5jIwOsF7ZY39gkkYgRiYQUkIvEaLddllaW2djcIpvLejQoKpJRaCGVXDweJ59No2lqz5kcG+aRYwdJpxI06zUEIqAb6SlCZQ8IiIX9vLhK6BFAlm5tViCeek1dXZVua2PHb3cEIz690v11dsOfndoSD7nmAaUHxG0XzL5h78EGx4f9/vB2d/66gwE+usYACHXjkgCv3c/L5munLdvGbluePzY9Oh9tW+DeJym/MPUDdJCn1HoRnyKZ1JCuptTsmv+U24FYd+laAP6T3bcmuhazP9geUNN0jUgoxNTYEMloGMtWZpNmo8WFy9cxDINIJMze6XGmxkfIZdIk4jEePXEYXdc4feYCt+/Ok0klOHxgD5PjIyQTMarVOgidxZUNiuX3CYdDvPD0SQYG+qnXLRKJOBubJX7+7gUeOXqA0ZFBDM/pM2SqcGZNExw5uJevf6HGvYWVwDG8WmuQSiTIZVKsF5QWIZ9N0WxbLK+uA7BrYoihgRy6qVOpKR+URCxKLptmbGSQWDTC/j270HWdRrOpuIJsm6nxEWZ2TyCE4OihvQgEW6UKB2emODCzi1QyEbx2mqbxtS++iGka3JtfotlsMT7cz9BAnv17p4lEFMB89skTFMtVzl++hWEYHDywhz27xmm27CB3n3/yGxkaYHSon3QqwejwALrHWyY0DRcdwwxx6MBeNF2n3bYYGR4gZBpUqjUymRTDwwPYjsO5C9fYNTHMkf3TjI8Ns1Uss7yyTj6X4Suff459eyZZXd8klUoQMg00XWfXxAhhM4yh6zx24hDJuErunUwmePTEocCR23VcspkUjz9yiPGRIaQUVKt1dk2N8ju/+Ss8+ehRSuUqA/k8e3dPkUzEsCyXzz7fprBZIv7+ZUSxgpCQy6TAdojHo/za118iHouSzaSo1eocPzJDXz7DeqHI0so6mXSS/lyOXCbHsYP7WF4tUK83SSUT1OsNbNuh3W5jGjr5XIbpqXEKm0UazYssrxVoNtvYhhIK2UySybEhHKmE4+raBuOjgxzaN83i8hrpVIJ8PotuGMzsmSSXS5NMxEkmYqRTSXIHZxjIZ5nZPUEsFmXvrnES8Rj9+Syfevok4yODgUkvlYzz6WdPcfTQDOlUiqXVddVGMkk0EuHpx49jGAazc0tEImEO7B4jHDJJpRI8/cQJ1jdLnL90m7mFFXalPaJUoaJWD8xMk0rEWV3bwDB0qvUmr7zxntL2OC6apjM1PsxXPvsM6WyG4cF+TE3QaDQQhkk4FCMSNpmYGEITOrFojNNnzoOUZNKKnf7JU0c4emgPk6OjaLqmfN5yWfbNTDPc34c2IPn6Fz/FrbvzRCNhDh/YQzwWI5vP85XPv8DQYB83PG3hYycOsWfXBJF/9x2kVcfn5gFFn/Cb3/wCa+ub2LZNuVJjeDBP5PTlnj3UT3Y/OtRHfz5NLBZlY6vC2Pgo3/jyi6RTCc5dusHC8jpmyOQzz53i2OEZHA8Q+MfTVCrBM48dY2pihDuzizSbLQb6siSTcer1JqvrBRLxGNl0klarTaVaJZGI0Wg2abfaxCIhHNtmbLCPowf3Mjk+AkJjfnGZC1du0my2SA4PoJsmrXabYqlCtdag3bbp5pCq1OpYlkOxUsMIGUTjMRzX5c13z7K0ssYjR/eTy6YJa6bymy2VeefMRYqlCosr6wz0ZRVQcxyqtTp355ZYLxSJhEOEY1HmF1e5fO02yUQMV0rK5RpjI0OkUknmF5b5/W99j1uzc5w8tp+Txw/Rl0uTiMcxwmpedF1T3Gmahq7rTE2MMjUxFmhtNE14vGpdAErzSXlFx09qmyZLAS0tWM89bOv3KR4eVnbwiO4KMoPtAK7rWz/9RlDDw4DUw0FWpw3RefagPz6bfDdw+ygPrweUhwUUCIJ11emXakt1x8FPOxSY4j3NUy9YVs/RMYWiTIiiVyOlzi0S6TjKLG9bKO456WkuHzZmv2QzYq+zmryv7e2aLlcKtK4UF8FZ4GF9FgLQCRzsO4cECKzoysneP9UFD6JrDOYy/L2/9ddYWV2nWK7QbLYxTIOBvix9uSyHD+2nVKkGeQs1z7fqsZNHGBkeoNVuM9CXJ5dJUdgssVZQJpr+vhwzuycY6Mtx7PA+piZGlVrerfNXvvpp7i2oTaFUrniP4TEQa8rh2XUlw4N9fO2LL1Cp1qnWGoAMInQazTaxlRDf+NILGIZOOp3Eats89+Rx9u4eY2gwT63e4NatRY4d2sPYcB9HD+9neHgAQ9c5efwgj508gsRhZXWdpZV1RoYHGOzPY1kW/X1ZTp04pJz/bIvNYplytd4zfnumJ/iPvvEF7t5bZGFhkcG+HMeP7GdgcIBwRMd2HCbHRvj8p59icmKUTDrJgf27aTXb3L47j2XZ3uQrsTPQl+OpU8fYv3eK6alxllbWaDSatFptT3hqjI4Mks9nqFbrbBVLzC0s025b9OVzxCIR8tk0M3smeeLkEXaNDwHg2A7lWJV4LMr46BCDA31cvXGHTDrJzPQkpqlMCsVSDelKhgb62DU5Rj6XQdM0Tp04zMF9u1lbL1AqV0mnVIRaLBZlZW2DuYVFTMNgdHiA8ZFBlWNUKqDYtiyy6TS7p8bJZdLEr91DlGoIAZFQCCJhZDTC6PAAyURcRYKubpDPpTl8cC+6pvH+2cusr28RjyXI57IcPTTDhxevkohHCYdDlEoVmi1lAjV0XZl4oxH2z0wz0J8HoFSu0GpbHJiZZn1jk2wmxdjoMP0D/cTjEdpWi3wuQ8kz1YTDIZLxKP25NId0nVq9geuot8oIhcnm8xwxTUKmQdwjRp3ZPcm+PZPksxkkUKvVFUBwXcLhCGYoRCQSxjD0wCl5amKUsZEhmi0VgLK0tEJhq4QZDrFVLLO6vkmt0aRUrVGNRnv2lkbbJhwOcWj/HvryWQqbRc5dvsl6oUjbauO6KjBhoD9HLp8jlUpgaBrpVBwHDavtkkomSKaihM0o0pUcmJlWiYWjUeLxCJ998UmkK9G1EI1mk8G+HPFIiIHBAWIhxcz+2COHefyRI0G0pOvtPoP9eb740nN85vl2cNhvNtvov/99/LBzTQiEppNotvnKv/1eYJbw/UJ8lwC/JOIxPvvCk5w4vA/Ltsll0/Tnc7iuZGxkkM+9+CRjIwMYhkEukyKRiHtptGo9+2oumyadSnLi6H6q9QZr6wXSqSTxWARdN3jy1DGmp8awbZuwaRCPhBSNRqVKrVbj6UeP8LkXnkTXddLpBNFIGKFrDPbn+cpnn+fE4X0oP9QkpmFyeP8e5Su1sB64zkgpmdk9yVc++yy6rjM+MsCFy9fJphNEwiHisTBSuh7Z8ACPHj/Iux9eZG1jC00TxKIRdE1TZkvHJZdNc+qRw5ihEOOjQ+i6FhD4RiPqMJ3PpTl+eB+mYdBsNnAdh81imZ+/e450KkF//jihUCjQRNmOIoh1pUTTNKKRsHLZkBLHk2+mafRiGU0E4EsRYGsesNpmTvJ9xu4T9J+kPFiod1Fq7liv8AFXd/uuq8BiT90dk97H6U9vJLH/jNs1ZHyCOrtvefA9AfWT9LHHTmPjQpd3ncJRH92PYCR8/7ruGj2fQ8d2FAGqb6J9kAIRdtCR7Vw+UTTig3/sNK7k7Uc88Ef8LqBzUth+qexd1MJ1grsM02DXronAP0UBKg3TNDEMHReNtm2xVSyyWawQCoXQNI14LMbY8CCGaZBMxHEdlzV3E9PQFUN1IsbBmWkOzOxmfGwIQ9doteq02g1WVtcoVxVzuRnSAVdtGo6j1JCS4BQaNk0y6aSXZ9DF9E5Zpmmga4MMDg4QDoexHYfNrSKjwwMMD2cxDJ2l5XUkgnwuzcTYMBPjIyqiSErCZgShabStJo1mm1K5ysjQALFoFC0eJ+WoMWq12swtLlOrNhga6u9SD0OlUmN1rUCz2WJ0ZIhHjuxjaHAAYehIqaLJotEwI0P9GCGfAiGJHbNptdpKBYuX295x0XWNIwf3MjE2TDIRp1xRjq1KVauSBmuaoNlsceXaLRaXVmlbNmOjgyTiURzXpVyuUi5X2SyWGB/pR9c0Gs0mSyvrbJXKZDMp6vUGK6sbJOIxlVBW05ASXEepgx1X9cXwTqxS03Bs5UCfSsYZGeonkehw9zRbrYAQV9d9ojxBq+mwUdji9t15CptFNd8ek7kQXl5HT+BGwsps0Wg4Kq1PyAwirCzLZtOLJLwzO8/Lr77F8mqB/XumWF3bYHVNabmazRaarlH2CHKrtTqZdJKhwX6lwdOUU/r6RoF8LsO+3VNksmkcHFpWm3RKaXPq9QabxRIgEYZ6D3SthdW2PafzzvYrhMAwFOhIJmKBNljXNEwv0sxxHVxH+fE063WE4xDCRWu3wdbAsrHKFdZnF1meW2Qgl6LQtDj35vs0lzd48eBujo30IzfL+D4b0rJhaRW3VEZLJtCabcLlCsONJpu1JqGFVbR8lkiphGE7REoNjFgEzds2NUdCq4VTqmFqgnA0jus4ZGYXaLZaRJo2oahJWHrpZFxBzHZIttoYQhC9t6wiFl3pgTF/UCQym8E9eThI6RQOmwgBji1piQ5JqXqVOsJJbHlpe6SXs7Nrb+vWTuRzGTLpNEgwTE1x2nmn6WQiTl8+i+M4RKNhTEOx2fvs734Jh8zAr9PXqBuGgWkqTc7k+AhDA300mk1cy0LTIBIyuHHrLstrBU4c2UciEQuc4IUQGJrwyHcPcvjgNJtbZcKhMJm0MvHv3zdF5u1ziJWC9/yC3bvG6ctnaDRaLK+us7ZR4PGTh3n2iUfIpFOUymXq9Sa5XIavfO55Hjuhko+3bYu5BRX9nIgnCIdC9OWzfOEzz/HMk6dIJuIsLq9y6/YstWqV/r4suWyawwf2Mjw4QChkMjY0yBc/8yxNy+bKjVu02lawD6ssTYJWq+35lTkgJdV6g3KlSj6fQboujqMCW9yeVHBdJkIfbG0DIfeXLmBwnxbsQWUHRUS35kcKAg1Kj1eOx13lXSu91GoS5c4hugFXV/0P6lGvVusXtJf9BZWP7x217T7v3QSQwg8ckTs+n+N4WSwCmhR1T6D58uSEtpOf+UeUX0K6HuH/f2/kAp2fu276qEp7b3zA9R1/BQ9oBZepCAbD44ZCiI593VX5xUxNkE7HCYVMhFBgSgBGOumxHBtYlkU2kyaTThEKmUQiYUaHB+nPZ4mEw4CDLkyazRa//yc/JmQafPXzz3Jo/x7C4TCuVIlli+UqiXiUZFLx0whdhZYLEfYmrJNY1kjpuF7CXpVSJ6o2TVOAkKSSScZHB8imk2SyGbWZappSdXovh9RM8qk0csQll0oRMUII3cA1JG69ibtaZGh+g4limdz8BqLRUmk02hYz/+D/wlizBa4kGgkRDpneHKC0Vd5Cy0mX3Y4SPJoXLbLLcaFSQ/i56+p1RgolQoZOYmUDUyswWa3Talskb95DLKwhBOjSIVqu0nd3jli1Ti6TZKBuEb9yh3azxZ75ZbKFIn1nr2GsFTE0SK1uMHhnDtdVxJgRx2Gf4zJZbRFd2FAvpCvJNy2MrTKu65K7t6KeR4JuO8TLFcYbDRKxKPFoBEMHx3bJLC4ztrxG9Ow1zNFBdKF5G5iLZlmkNkrsLlXYp2ukEnESdxfQvfQslCqIZotopcbov/0Ohqbj2DapSg1NCJLxGFJKXljd4NhWhcE7ywjgS0urfMFxSf/0XfTX3mfYsthTqXGsVCGViDG09i1ChkFGQCwSVrxaQmBZFna9zkyjSSysqCJ0L8Fw2IuqkY5DvG2Tbquoy5BpoCFItdtIx0XXdXQhSFg2ectSyXkNXZlFXQeB4t0S3vujBJIT5Moc8nyu1GFGgVzLtom3LHKWxaMe8AQ40miiGzqxxh2Mq/ew6030lkrZZG5ssvd3/3ts28E0dEzDIGXb/EahSK3RpP///W0yqQSpriTBmtarug87LlHbQvOAoetK9tQbACplj6517Sf+gc3fIjrmGN8Z2C/WkRmaJw6qZOeuxJU2jmvTbLSo1hqkZSfHq5tOdtKJCYK8m8KLBkQI9OUNcF20Ypm+v/27wfbVnYvP72FGSjLNFq1mC01TQMwXqprtBH0UQnjM1iJ4HkVlgaLZ0DSECCsgZ1sBsKhU6zSabeoNdcDodhRWGj0HiUbI1EkkoiB1L7o5RDadwvSoOPw+6LrEslosr65x7uINYtEIzz7xCCeOHkDTNO7NLyFEEdMw6MtnGBzoU9GgbYs9uyao15tIb74i4TDJSIRcXrHlu65LsVQmm06we2qcvpzKQqHrOrpm4GgCITRarRaaB6y2imU0XSMaUzQ3lWqVcqXqBe+YgW+rf+ANmSGKpRLNZgvDlb3GoS7T4oOAVsexvhdofbwoxAckcO72l5J+sm3Xu3qbFgtFEPzemfPUag1OPXqUbD77ID0Y3dDq/kse0uf7zH+d9+djl49wju9tDw+L7kT/0Gtm3A7I/G+CUfQ0ZgpwiZ6HV5p7R9EGeZot1XC3Vq0Hjn6i8oun66GzuHxN1s6LSnzyXqlGHti2+uCbGXvVohIHKSQK/sugj9IDDEjpaasi4GoITZlqhKbygQkhCIVCDPTlgudJJRNKo6z79muJEAb5XIbPfeoJpidHOHnsoOfgLVlaWeft9y9QKlf50ktPMzk2jK7puKgNQejdnCBKG4RmBJu+4SeYhuClyqRSHDk44wFarZNgGEM9arNFbGOLPVtl9m6UMW8tYGyWEBtFxMYW+u05tLVC78w0W+pfxyF25Ta+Yefhp54HThjCIzKN3Zpn7//+n/S8sL2pjDuK1wiSPqnU5NvXT/B91xqaBCa6XzDp0eQKetpLAv29sjUo6WDd9K7N/VKyT+7MhRMBEr5vgFBPINptBVYB4ZkfRLNF8r1LwYYU84SvLwwnXcmY4yCqdXRNY9CLYnM2ili2TaPRJNpoMRoyyLgSs1D0/EkUj5EPMnTXIWY7xAUYegOtUFRrRYLmCU286DhhK657P7Gq5rrgh8xL9bfhbSyBUl7Kng3Fz/WH46J5Sdo1X6vnXY+UCMfFcF3i3mFC88YhIf0tvgRSYvoHS0+DFFla64ytEEjbIW/b9AHmRhG9WOn0bYd9RkiJ3nW/JiVJTwD5Y++Tjm6zAXRKsC46P4QKRYy/8V8GZz9FXmwTsh1SgFFreLJAUvw//R0VcehqOI5LrV6n2WoSiykwLIQg/7d/F32zpADXZil4nm6g5RcNiLsuRls5pqt9CjQ/bx2A7TD9X/x3gQBxPSClQKeOpun+dOKziPsHp5cqVfYvrZFZ2GDgh28RMtUhVfPyQaouqTrDjuKS8/emkOtiXJ9VSal1HSEkoZChfAHTMfbPTNCXy3Fw3x6ikYgiv7VUXtBUMo5pGh7lS4t6o6nWk67RbFmUa03McATT0y4LIUmlo/T1pXEcl2wu4+XHFB49gMu1W3f4o+/+mOu3Zzm0b5fSwBs6sUiEWDRCoVTlyjXlLyiB3VPjPP7oUcUTZ2iEkwkmx0eIFkLeePmapk8ovHoA/Sfj1HpoHkZfsyb93XPn3bhaq/NH3/4xq+sFotEIzzxzysu/2t2vnT739OQT993r5kePViC/fzEtVQ+g8vYWITTUObNLhnQpflwp0Om8Xr6s2e5j70pFUu5s02z5JLrQRYDa1aOPWz4Bz1avgOtpruvBPo7N9JOUnvr8zdADWsK3p3b9HECvgNDOw/Cuv8l0CVMBoJxvAz8rlAOebmhIqSgYQl5odacVHVfA4ECe3/nNX/ES3aqNfW19k//wp6/w9nsXeOLRw3zm+ceVSUdqIAVqu3fxEz+7EvCzi3P/CyeEQLguoljGXN9CrG+hbaj/xMYW2kYJvbCFVm0E/es9r6hXQEi584vgv8Sehq8Xun7C4lFBAAEICZp5wC2d5+7q7kfct327+HP0uKdOX2j6ppQHXRcUt7P+hOtrGF20Si0AEZrsmg0JupSYgclABtchFWtx1LLISonZRgll7/egXU8IaEjMbdrlTv86YFagst7jHSi2P4yU6h69I123PWynYiG9I4xU74h0lY+DWkIaQtPUgcUza3SDZO9peye67CptqGEg/TQpQuACjmUjTQPd0BGGgdQ6M9Tbx86+I/32hAiY0gGkpuE718ruZ/JBul+P7GJy9/IdimYLsVnsGhKJIdV/AdD0iu94rQmB4yi3hoQTVdoXXQkDJ530/Jx67+3sR53x8T/pjoNrWdiO0jcZug6FIlI4SE3DKFWCsZZdfVOHx+466XpGScqymYyGCQtBtFrvOIeL3vWvyc6BOtCgSYlmd73vCEKmSS6bJpGIsXd6QqUiCpm4bptao4oZEmSzA0TicSxXUmu02doq0/AyPjTbii8snUoqoSpACLWPhEyDaCREsVzBxUHqnmUAgRSSeqMGwuWxRw7x+COHmRwfoT+fUxxhjs3lqzd458x51gtbxGNRjh3ex4kjB8hm0sr9QEBfX4ZsNoEZi6I12/4q3bYH7USFsEPZ4cD2CxeBN/4uvmarswkKOk7rqEwRkRDZTAohFJG01tMP/8b7+/axd9E/r4x/yP3dfI09f9OhfggOJoEm8WOULqERmBHxDs3+37L38u3zvkNvH/Lb/eWX5rMlu/ZAl51Pap+4LV+TsB0C+MKnp08SsVlC+2v/ede1vRJc9pxcu8t2R8DO3b4vTlcFXo2qT2F652mw2eR/trHFf6xp5M9cJ3zxLjst7O5+4TjQthGWBW0L0bYQbRss9RnL5qELXKqQVekhe03TepvUdXW/46gTkmmoenUdQiayL4vz+edwcZVm1WPkVs6gakCkp1GReKcsNCW4hIYEXKER/dHrSjj5AM4fsp7NuyMQO3JA+APds27U6yU79wSj1nlzAgHec6Dc1lZXM3Lb9+p29Ue11mBjq4TrOqSSCXLZtDI/BTCsq15AW1xF84IiusdaRiNdlXvjBwjUWHVr4dR7rjQWwpXorhuAlm47Rm+4d/fjdXamnhUiVY8d76TWMTV1LnEcRfDpOC7JuG9a3zYv2zZm/8DSbLfZ3Cp5aZkEhi6UWTaVwAiHcHUdTF0xxesaGAbS+xdTR+o6+vuXcJstrEiYZi6NaeiEPT9KHYkhlHbacSXuNgDwoOLHxKoh6Foj2wfO26N6qux+x8pVtf+ETGQug379DlgWwUGXzj4lhEBb3yLzv/o/KuB6/a4a3327Ou1776jjODhSevOr8dFPpACOrunByV1oAmkYuJ5W11wpbF/S95cdxs4AohJoWYhKrTM022ryXdi6fxEArbY6cGgO4sotzL/+DzGAsJSQTeH887+PdJXvpG4YDA4PEo1G0TBotS0cVyJ0g3gygaYpbj6JJB6NEomEApAgBGi6TihkUipVKJWrxONRhJDo3owf3Ldb5aqVkkg4RDIRIxxSWqpSucKtO3MsLq9j2w7Dg33M7J7y+MwUZYmuSxA6hufu4b+z0hNswj+VSFftecFh6QH7slQerKr/vwTQJbz2ulZzZ5+VwcTE4zF+9WufQ9MEUxOjXtaSno7dV3FvDOT9MOP+vogdnrvzzv15n1ZuAz5+L3esV9DVF/UsPml155IupOXdE+wA2+S+5pn8e/ZKudOI+KTMbgACP6r8EkhNd5o8sQ1Nf4La/BOplD0DfB+48k+t3ad+14XC1gOBpi8yt3/nj77Y9q3/NMHAdhrv2c6D9wCISMlERKm4qTWgWgfbBdtB2DbYDth212fH04r84sXVNVquS8N20MMhYskYejTsCTpdqfkrNQXiADeTRBRKoIMMm5QH8/zh9ABrG1uMjw7yzOMn6O/L3teOH66OFPg5x3xVq4Og8tvfACS3Z+dZWl5n/95dTE6MemaNDgAXnnnJT1YtNA0NCymd3vYA3C6WX09guV4/VKJQj8DQVXOh+RtQ18nSX0fqbBjMOEiJoUG90eTK9TusrG5ge+l6FpfW+NWvfYbpyXHlm+atS9f1NJRSEv0X/57w6+/tMCPBEWqH3/yFJnr/RKB14yshwTSRmsDVNDTTANPA1TSkoaOHQoiQifQ0Pxi60hB1gRlXE7Qch3rbQpgm4URMpRgyNNB1qq02b565wOzSGs8/9zgz+/cQikbQTB3MEFLXwVD/Ca9+W9NYKWzy+995mfnVDU6dOsry5hY//+Ay/f05/uPf+CrHj+wL5mInQRMKmRRLWJ4iQAABAABJREFUFc6cu4xhhhgfH+He/BKLi8scPzTDwX2KVLNcrfHqG++xUSjyhc88zfjo0I71+b5KndEXOCjt1k57gdLwaehSKOd1b3PWcYN9JfKbfw/RauOMD1P9v/4Dkv+L/7JHw+W4LltbZVrtlopmjUaVKVcIRL0JEJgJA6Ft2zTqDWzLRmiaojQIhwJt0QOLlMrs6KpjrCtdNkMGddchEg6RSsaVUBW9O9h2EeofcLr3PHXQ3GGYHtAd2b1u7y6CbSsgPZiHwhbC82+RAhwflOgmiVQo0JwpbjrFRJ9OJdB13YtqVdkkNE9DiOiYjDShAgZcV7K0skYmnSSVjCOQaJqK4E6nkt5eIQOXENtRXHsTYyM8eeooUsLxI/uY2TNFyPMn9E/VHS1gz2Lq0oLgue90Ux/sCEO9e31g9glT0/1CRfUnlkxw8uQR7+DNfWsCuuf6/sMU9O6dD25uG91BF93Fznqz7nt3AmvdVfferaICt69o1Vfh+20JPwODBx69NoSnGFBfdfrYsW5tf1Z1mNH1zjvZM14+PnElLq53fP545ZfDIL/DyAqcT27z9TZH0TVxPSBrB/MlADfnoK6Son6SIIH7eveg7u6IbDu39CyutqXAlCs9G6HsmEjYYcFrAun5Vey0nJT2yUCGTCV8Q+o/QgaYJpg6bdthZa3AemGLVCLO+OggkXC4axMVar15wkIm4sh6E2HZ2I7DvWqVtY0tClslpJSUD1Z3BFt+caWD9Myi/uAIL/3rwuIy/+PvfYtzF2/yV778aX7r175KMhEDb4P1jSe60NBQf2tSQlfEBxBoWPwm/OfwRtTbEjuj5b+fwkO+O53PBEHOAjSkh8kEZy9c44c/eYvf+rUvc/TYARzbYWlhmVgs2nO/f5r1l5392DHkcH8ASgJwYmpIQ1PmL9MAw0CYBpqhgxFGmCZayFR+XqaBMNRnLWQiQhoYGlJTgRJ3ZhcobBaZmhglGgmzsrYBwNjIEIlELHC89oGknwIGCY5jY1WrNCpVhBC4MXW9JlWEpttoUoganDt9ln0nZpg+dRLdNBFCIkW38JZqU5Hg2g5zq2ucXVrla198nm/8ymfZLGzxyKmjSCmZmhj5yO1HCMHp9y/w+9/6EV/74gs898xjICUXL15lYWmV40f24zgO5UqVm3fmCVict62PTn0dwOTPka7RGYttbft+lwiJ6NI++CYFKaWaxxZg2wSOz1IiNQ03ozwQNdOgsl6g4TiMpzzNYLHcaUwTuJlUcEip1utUHQc9FsE0DOxQCDMaVprknUAkHa2Y9A4WUioKg0KzxZZlk0vEiGVSCFNXz9LBQt7MeV3x/xbQ6+N4/+l+e9l+PA00AgurnW/9z5at+r24Svi3/0FQaU9/JBhSEnFl13x2IsGEUCZJXe/SqQlBv+vyTLVGtVLDjEYwknFM3d87uw4xXXNuSMmQ6/Jl2+WL0lWBA+dvoP/BD3pZ8Lufe7PUEcLbB8TXcN03Mg+AGB7g+sspHjgVAqF/XNl7/y75UXJ7++9K+9cxZT5Q26FufjjQ2qHu4N3e1lcFtADhIrzgAR9siW11Su4/UcgdPiEIglFEEIQjkbhe1719gN4z88cpv9R0PapbnuATrvffx1tovoCQXrb6+0CWhGD78R7a13hoeycQG1vBxtRbOhBmu7DsRNJ0v6z+PbDTotmu4fL/N6hzdQNRcZSPiQYYOs7ESFc7nT5JXUfmM7h9WWRfFvozyP4sbp/6V+YzuB4fkebBDP+Z/cmWUlKvN3jrjff44+++xsz0OL/zm19lfGQQn7jNQcN49wKR//P/CED7t76KceYS4sptCptF3vjmS/wvf+tr1Cs12m2LdCoR5HLsLq5nEmi12t6GGPFOkA6aNydhQ2P/7jFS8Sgz0+OEDSXcu8fXJ6uTXo5MFxfRldhTdiYIF4GDEpxecCDSFUEIbzd9gT9rvgq4249QSqUi1lCpVRwUQ0ut1uD9s1coVWok4jFatbrKyzbUr8w0nvlN1WEjpYvrej5BJw+hP3bkvjUhcTqmaE+Dh1DPIYSBJnQlXD0OHwkE5L+a748h2SpWOH/pOq22xdBgH4l4lGgkgmXbXRrabqDVaVOijBihkMp2oOSqJ7ykUpObpuHRCShhqOsi0ChomtI4StfFdbzwcunSqNe5fPUmAsnuyRFcyyKTTvLcU48CvsbRi0rdYdPWNI1mU6UAKmx10lHpmqZ8eMo17zpBPpvhN7/5BZLJOJlUwpvLzgbsC2jHlR1qD28yXAccD5x0TqletF6X+U5KL0BF6UjwiRH9RNnCJxD1BtdJJ1n/v/8jDF0nbBqsnLvEv/q33+Zv/vW/wokj+0n+zX8MdxdV3dk01X/5j6nUapy/dIMzZy/Tn8/y2MnDDA304eoalq4HWqeditKmugEBqr/eX3nl57z86mmef+oRvvL5Z0kkImjCDNwudKHMKa63/+q+hshL4i10HTTdM1F647MN9Pka4e6eaRgI4c3Zb/89hBeNK303gDsLKihDAp7fG+xwKJUSLdjevXsdB81xlO+baah3AhDzy+A4aEDalcQ9XzHla7UdMPUKVYHEBMxt49rjMfcggbm6cZ+WZfuBeMcD8l962fYADwE0HR3dAx76F9XAbVeE7NyzT1w673T3SHd9Dp5VXRNEGQaXfXRPuruuCYGh6yqYyB8Lr3pN03pzYn7C8ksDW0H7PR2RCnDdl9aztwSOrDstECnp4RjxNv1OC76uQ6rNIpP22Mz9ZSWCl1ainJBb7TYCnyJCQ9d8PyfR02wXsA3+8TSWHZu9953rfdDqTUWpYDvqZBuN0H70CHIgh+jPIvpz0JfFyadpx6JoXRFmQoDQ1AlWCA3LcpBtB9MwPASrzByWbdNuWxiG8mPQdYNIyFPTB6kkFEC1HEnbttAdh7CvVrVtGOyjde4q7bbF3kSMVDJB1ONY8he4b7LzTWjtts1GYYvCZsljGe9XlApCIqR6MSZGh/id3/waUkpMI0QkpFLH+PNreU70pmkok5YQCE1FlLiOi23bWJaD0DSlZdE0NF3DsmwqVaW9dF2XVqOBQIWNRyNhYrEohq7jug5bxTK1RoNELEYmnfQiNyWW49K2nc68GTqrGwVu3V0gFlWJdX3TKFLSbFusb2yyVSwTDocYHR5E0wQbhSKFzQq6ppFMJIjHogH7dDQaIRzSg8Ttuq55gMAmGg6r+fFTgnrrzbYdhOYlDEatd8uBuZU1Ll2/zchgnzeeBoMDeaSUGIYRaLKUFsv1MgiotS+EIGoaQdRqpVqj0Wh5ufXCAejQfNOSUFQBQpNsbpa5fnvWY1jPsGfXhJeVwWFhaYW33jun8mNmkjhWm/mlNQrFCnt3T5JKxvFpTVrtNpVqHUPXgvkxDJ3CVomVtQKRsEpoLJCsrBdYWt1gaLBf+TRpGuFwCCEEF6/cJBlXTtehcIhSucLtu/PMLajcieMjgxw9tJdcNo1t21RrdVY3tihW6vTlswwPDaCHQh2epXYLXdOIRSNEo2HCulA0H926Uj/hrOME69+PyLS95OZIycTYECeOzKj11eMw7u8jLusbm7z6xvvcnl3gpRceC8xmpve+uVKqeRcdo4QPKvF3MeGZyDV14hBCJZhPxGOYhqn2ENl9CHWV75FXXH8/lOqzY7uEQjoBpPKCCLoPotITXkKq6FVFPaCU7QBkUh3xd+UWWFavH9fiCt1CrkfbICXSNHD27wkeWLgOujf3dJtFl1bA8zDQhAJZgYnovgNy18cujej2a3qPKg8Rnj1g7v7rOvU/BMD8EstOmt1O+35/uvIOe99+NCjsgIiH+Wc/MDDgPoDXJYfYYWR2VKE+oN0dO9+dmFp460H94uWO3kGTpSlT4rYxvD8obZvPllfCYZOQGfqojj2w/AVotgSe5O3q0Me4zzO59TjCBRqtbsbezkAJ1OnVo5lDZlPM/tO/yzvvX2B1dR1D14lFouyZnmD/3l3EoxFu3Z3nz374MwqbRfLZNJOjQzx+4ggjQwOUyhXmV1ZZ3yoSj0U4fngffdkUbduhVK6xVSrjupLx0SHC4RCu4yCBjWKFy9fusLlV4ujVu+z58duY8ys42RTW2BCbf+e3QMLS0hJ3ZheorqzStiw0IXji5FF271IpI1otC9PUMQyDZqvNG29/SLla47EThxkbGaDRbHHr7jwfXLjCZrHME48c5sSx/V0mAYkrXRwhsXGxLIdbdxb52VsfkLxxj18rlhXfU9vCzqdpNtu02xaxUhXXExRSSkqVKitrBUqlipejTyeXTWMaBjdvz7OwuMbk+DBIBSRCYU3l5IuEcaXL2nqB989eptm0+MKnn6Mvn6VUrnDxyg3u3FtEE4JcNs0jRw8yPjaIbdvMzi1y7eYsq+ubuAgmJ0bYvWucwYE+NCF47Y13efP0OQUwTZPNzS0sy2Z8pJ+nHj/K/j2TNOs2H5y7wpXrd1nbLJFIxHjx2VMcObiXcqXK1VtzFDZLOK5NLBbm6OEZCsUSW6Uy6eSwZ7bwxqBc5fSZC7zx9ofcm18hn0vzq1/9NPv2TjG3sMw7Zy5ye3YJJCSTcWWe0HWOHZ7hxWdOkcslEEJQrzd5850PuXF7jmceP85jjxxF6MqAWipVOHPuCjdnFxgYyHHi6D7Gh/uIhEO40qVUKjO/vA5SUixVCHn561LJJMlMGiGV465l2dy4NcvPT3/A0so6IUPnkSMzPP/MKZCSdz+4yJlzV2k2W+zbO8nTj52gL59RWkmt88yudFlYWuG7P3qTn775PqVyldHhfr75lc/w/JMnqdYbXLs9z9JagfGRfhwpuHj9Lv/mD75Hvdnir/3qF3j68RNomka90eDt987z3oeXGejL8sLTj6o1A6ysrrOyVqDuJd2+cfseL//0Le7cXWSgL8vKyjqD/Tmq1Trf+dHP+Mnr7/PkqSMMD/Vjti1++vp7vP3+eSzLoVpvMDKYJ51OEI/HuHztFm+ePse9hWUKxSqf//RTfPGzz2FLwc3b9/jg7GVu3LwLSPbtmeRTz55iemKMqNnL7i5MQx1+bMdXswfbqmXbbGxsMb+wxMLiIs1Wi1t35olFI2Rct+do2bZsFhZXuT27oFJr7d3F0EAe0zCoN5osLK2yUdgkHY+yZ3qSSEzxsTWbNlvFCvVmHctqY9kW0UiEXCZFKBzyQLXA9A4sriNotVVC96XldeKRELunRoknE7gurK8XuDe/hJSoRO7RCI+fPEIimcJF0Gq0KJermKZBPBYjFDICjW6t1mBufpVKucLoUD8jQwNEwmHsf/ZfIF1FMxH6nb8PG1uI2YWOH9fIkCfDfQ1BrzlY5jO0/s0/BanoGzQcpVvUNBzhm4ME5l//e0pLpgnIpb3IPEG3xbBb5HWTT/o8hB3zsSqulDhe7lTTIwkOft8sKondn9vRvNspYtvnX1zr8VGlO2is+7nvA12BmaVbXm7XCm3XhHVM6d0m8/uK6PJd9hUeQnswAHuQhehhY/QAwCU03yH9wbfe310RWLs6vltu8Ft3Wz0zKejkwex0jHBIkVXfD9k/Xvmlgy3NM1M8zC4Lvei5G2h1XeB9eLB6vedv1Mb2xtsf8IffeoW1tQLJRIxELEoyEeeZJ47zlc8+S6lU4sPzV7k7t0IqEWP35CjDA/20LZvvvvw67527QsuyeOzEAaZGh0jFIpy/doeXXz3NrbsLaJrGb37z8zz12DF0XcdqtfnRT37OH33nVSrVGp9x4O9Ua+SUugGkpF6r8/7ZS/z0jXdVaG44xN35Far1BtFohPGxIer1Bj9/7xxHDu5iemqUUq3Ct37wKqvrW+QyaSYnBrly7Qb/+j/8kNv3lj2Hc8HMnklisYjSwnkadCHAsh1u3Z3jj7/zGj9+/QxTxSqfrqjciZF6A21sJDBJhLaqSjviuiwsrfLjV09z8eptpHSp1ZvU6k2OHtrDc0+eYHZ+iTdPn+PxRw8hNMn5yzdZXt3g1IlDvPD0o2ia4AevqPEY6Mtz/NABkvEoly9f41vf/SnSswVuFsusrW/ytS+/QLlS4Y/+7BVOn7lMrdHE0HUO7p/mmSdO8MSpY6RTCYrlKu+cucjo0ACffeFxcF2+8/Kb3LyTYHJkgIPTk1y9foc/+f5rQR7GC1duEY2EGRsZZH5xhW9956fcubeIYWjM7B5nYmwYp2uZ+ebNRqPNG6c/5GdvfcDRg3uYHB/m9PsXOHP+qpdBQGe9sMW1W7O4rmSwP4d0JavrW9xbWGZ0qI9TjxxEaIILV27wJ99/jeu35lhcXvPSB03SbLb42Vvv82/+p++xsrFFXy7N4tJjfOOLLzA+NqSyz9sOjm2zvFbg3MXruK5kbWOTE0f3c+rR46TTBq7tcvb8Ff79H/2A5ZV1BvuzVKt1BvIZpIQLl67zZz94jZHhASbHR5hbWGF0cJ5cNhUEKIBSXJRKdX76+vv82Y9ep1iqIpGUb9Z4+dW3mZ4YxgybuNJWLPMINF0dDNq2w+r6JlulSrCmFhdXmL23QKvZ4t78MgtLK4wM9REOh3BcF8d1PfMqFApbWG0LV6qsAaVylb5cRjlQJxPoHsA2DYPZuUXeP3uZoYE8n3vxKWrVGrVajaH+PMVSmR/+5G1m55bZt2eChSWVukoIaFtt5uaXePP0h6yubTAxMsDNO3Ps3T3J5OgwmASaEiGE8odEIBxHMcqLjuHp3twir7z2DvOLK+gCUskYZ85d58ML15mp1cl27Ut+bsR6o0kmnSQSDqFrguLmJm9/cJnX3z7L4tIquWyKb375Uzz5+AkcF948fZbTZy4gXZfNUpmtUpl4LMrTjx3lSy89g6+t0L1oWcuyWVvf5INzV/jwwjXSyQS/8vln2LNHpY368OIN/ug7P8W2XSzL4viRGY4c2Es0kWBpcY233jnL9VuzxOMxpiZGefTEISbGh2k1mrxz5gLf+9GblEoVPvv843z6+SfIplPML65wb36RwYE+TjpOB2TqOo6uUQrpaEIjZBqYITOITBSbJXQh0FyJ6zhE/+5/g9gq4WsHa/UmtuN4aXyimNfvqIjsWAT3+/+ScrnOylqBaDxKvi+ruL88mdBstVjf2GR2bolSuUI0GmF4oI/+vhzxeDSwIjRbLba2ymiaxkB/jlQy0eEX+/X/Laxv7SB/ukHMNqD1AC3JL6P0cEv2dONBpkBf/naA1v2feloIrD9egw/qCPcDjI/QDHZd1dPFHfvdAeW9X98/tq7rPgTrPEzj1DufPZZC6f0ufN9i32qmkIkfpajG0Pk4j91TfulgS5WuXmxjae0u20FWj0YLurRawQ9wn2+PZxJyXcqVGq++/j4Li6sM5NO8+OxjTE+NcubsFX74ys9JxcPsnhwlk0pwcGaSTz/zKKNDg+yZHKXRarOxWaLeaPHkyUN8+pmTZJJxytUa12/c5fqte0QjYa7evMdPXn+XXRMjDA/1s7S6wY9fPc3SyjpPP3aUyo17KhxeEvh73Lx1l+/9+A0E8Gtf+yxTE6O88rPTfO+VtyhXqlQqVa7cuMuf/ehnRCI6kxMjGKaBpgmVy9FqI1EaI0PX+MYXn6NSrVMqV6nWqkSjhuKewTMH6XogeN589wKO7aCHTBynTqvVxmy0kPmct7gFoc0Sjg2FQoU/+8HrvHH6LCcO7+fooT2cu3yNV372Hol4lKdOHUHTBNV6nUq1Rn9flj27xjh/+SYfnr/KyWMHGOzP0d+XJRI2FVu+dKiUy3xw4Sqr65vsmZ7AMFTod6VWY6tYotFsUqs1CIdNjh6eIRIJeaaaTiqgXDZFNBrmyKE9fOlzz3Dz5hyXr93m7r1F7s2vUq+3uXJtlsJmmUeOzjDYn2P/3kkO7ptWSXgzKRLxMLoheOTYPp594ii5TIK2x87tC1opJfNLK3xw7grjIwP8+q9+Eatt0W63WVpZp1yuMtCfY9/uSeYXV0kkYnzzK58mHovy7e+/xr2FFdY3N1WqIMdlaWWdA3unmBof5uKV23x44SpjIyM4jsPi8hor3qFgcnSIvOdM7Xog3XXUCd00FCVDs9Xkzr1FopEw+w/MkEolWFnb4Ls/eJUPzl3h177+Wb72xeepVGqYpoHrOLz3wUXarTZf+9wLjIyOcvPWTRLRMCFD66JCU46lxVKVqzfuUqs32bNrjNHhfsKhEBOjg0QjBrppkExECYeVB4xh6EyNj3Dy2D42Clue7w/gOoQMnacePYw4JXj1zffZWC9gtVroqSRDA3n68xnqjQaZdIJHju6n2Wiwulbg8P5p9kxPIJABUM4k4xiGys+5tLyKbbU5sHeK44f3EQkZuLYFusH80hqTY0McO7SXsdFBZueXPeEq0KRDrVqjXm8Sj0V56vHj7JocJZ9Nqz1EEJizBRrCS42D46AbPtBSYPz9Dy/x7geXeOLkYXZPDrNn1zh/9J2f8vP3LrDRcsni6w8kriuD6DpD19CExG63OX/pBq+8dppCsUq92aZwZ5G337/A8cP7uDu/xn/405dJJuJ8/jPPcO7SNc5euoEmBAf37VKnbm+L9P1ILMuiUqkSiYQ4tH83c/PLrK4V2LVrEl3XaFkWqxtbpBIJHj12kJPHDpDLZlhf3+SP/vTHvP7WBwFFw/tnr4CmMTDYz9LKGm++/QFr65tk0kmqtTqWZWPbNucuXeM7P3qdqfFhDrfaxABME3d0gGokxO+emGFxeY2Z3ZN8/Suf5u69RX5++kP+xo/eZtgwCLsu0nXQtkpQ2FIJqMtVqrW6Yt4XApFKEmlbijzVdmjbgnc+vMJ3f/QzIpEwx47s46nHjzE6PEiz2eTi1Zu88dYZ5haWcRzF3j81PsKzT55k7/QU0WhEmex1nVg0EphzEd08dA8wT3YDrW4t6F+QNqundCe39lbXgyJzgY5EDe67n1v9gYCtx9n9FyndWqEHKEt6etPRgKku7Qy6elro1qZ1a6iEilDcWdEm6GYy7YRr+dXI4HulEfOr7mj6VNfcBz3WQ8ufE2x9HDXaw67pAlo7DFzH16uDMrv/1ITA9R6hbVlslZvcnVODtm/vJJ/71BPM7N3FxMgg/+xf/B7vfXiFqbEhMqkEmiZ47slHGB0cxBSSrXKVsZEBHMfhy599loP7p2g0WhQKRXLZNL/29ZfIZdL8D//228wtrDK/uEIymaBcriKEIt0byGc4+oVJkv/u+4r2wUttcv32PVbXN/nM04/w+PEDZPN5lpbXeP30h1i2HeToKhRLNBoqbNw0Te8UrMwb7UaT/XummJ4cA13nJ2+8T7PVplqrkk7HsSw78LcCWN8ocu3mPUKmzmeee5Q9lkPuJ+8SCYeRlkM7HcP0nIz1QpFKtc7V63f52VtnyaYTfPGlZ9k1OUQkYnDj9jxhL71FOpkglYjj2Mp8MDE2TD6botW2sCyLWDTCnqkxcpmU8o9rtVlcXmN2bpm+XJrjh2aYGB+hbdkITZ1Ebdsh6eUp/PTzjzM00Mf1W3cZ6M8RCpmdnG2odDLlUoVWu83U+DDziys0W23alkWpVGG4P88LT51iz+4xRYQYi6EbJvVGk1wmQSIWZXJ0gF0Tw8QiIdotC9t2vDxqLhubW7x5+iw37y7wjS++AI6L7QUNXL81y3phi3wuQy6XxjQNMsk4e3aNEY1GyWVT3Lq7QLXqCSTHYWxkkKmJEW7enuOD89cpbJWwrDaGYbJ3epLJsSEqtTqTY0McP7zf83lSa9hxXEwjxK6JUZ554gTxWJSBviyVWgPLY+u/MzvP9Vv36MuleeTIDMNDA0yM6eC6FDYKLC6veSd/F+E67N8zhWFohEMmtaZyuFYUGurVc12XWDTCp555lBeffVw55UdNNA22ShXalh2sNdtbA6ZpEg6HFGO5B/YH+rPUahGu377H2kaRUCiEbdsqU4Omew7taj7T2bRKqaMJ4rEo0ViEVqPZ45PjOC7tdptqpUa11qRWb1AqVxDpJJp0kdIhm05x8vhBFpZWOf3+BSrVOvlsWoGNlkNfLs301AjXb97jzNnLZDNJxkYGlbkJG4nm5TyT4CU0F47TyejggfG795bQdY2pyRH6+zLsn9nF4QO7efv9i1TrjV6/R8chFDKIhE2U47rAsR3uzi3hui5f++JzJKJR1teL9PWlkNLl7PlLzC+t8etfe4mvf+ML7Joc5dyl64RCBqeOHyQSVub6QAsnJZq0yKUTRKNTzC2scPvuPLV6U5nnvOTT0XCYmd0T/NVfeUlFs8aTnD5zmTffPku11mRyfJhkMk4yESOTjKFJl3K5wmaxzEB/jheffpR9uyfpz2c9c76hgixMX4z4JigVmNHfl+PMuauEw2FCponVtlhdKxAOhwh5gQGiy7/WchxWXRc7ZBKPRag3WrimTkZ2ZJuLxsLyGheu3ETTNDY2i+QyGXKZHPOLa/zgxz9nYXGFxx89TF8+y8UrNzl74TqaMMhn8wyHI+hCw3VVAIhhGB543Vnno+TN/aa3vxSAxf0WHNX4R9/XAV1+UExXPV1mw4fe9yDAtcN4BPVKf7Ie1skHmCmhx7e98+VH4IztJkGhDo94vqu+KdGvWAg/wKqrCY/0vKdKr6/3tf4L5EWEX4pm6yEIWErVsY/QNPYArZ48RNvb6OXJkCizpZQS23JoNNs06k0Fwly4euMuK2sFpGNj6Dr1elP5F4RM1gsl5hbWkLYkFjFpttrUanUarZa3sVvU6nUajQblao1SpcatO/Osrm/iOA7zi6uMjQyRTCZ46tQxNrfKvPKzd9GePMkXdS++yVa0/4XNIi0v91ir2aRaqVIslWlbSmPSarXRdUVk6biuzy9Py3OCNwxN5fWq1lha3eDS9XucvXSDRCJKsVRloD9Ps600Ka7s8E8JBIlYhBNH9/F4NkP+g2tIKSm2WlSjEfp8jc7KOoXNIkur6xTLFUYG81RrVe7OLVCu1HBdFVTgetwtvn9Pu91ms1hhvVBUAMxVEVOrq+uUK1UioTBbpTKVao31whaxWJR4LMz+PVNommBpbZVKrU6tVqdeb9Jut7EsC91QjuaZVIpYLKJeAgFCaGx5ztWlchXN87cY6M+RiMeClyeZiJPPZnEcC9t1cdoWxVKZwmaZza0Sd2YXmJ4cwZwKU/OSPgsh2NoqcW9hmcvX7rBRKLG0ukGxVKZaVWaLSrXBRqHI1oCqp95ooQtB23O8rlSV5rBaq7NVLFOtVLg3v0SlWufG7Tlq9QaWZVGt1dE0g0w6ySNH93Pm/FU+vHCNcMjkU8+eZPf0OAKhAKyXTFsTgr58liOHZrh5Z55mq4XruFQrVVpti3DI5O69RcIhk8G+LMlYGOm6mLrO3MYWp89cYO+uEtlsgr58FtM0qTcabBYrVOtNap42wTAMbMemVK4iNAiFdVqtFoaXiLpeb9L2ghwcR62LZrOlnOJ1XeWwK5a4fP0Od+eW2CqVsSwbTRNYjoPrSuWs32ojpaDRaGM1VW6+RrNNsVSh7N0jXeU7V2+0aDSb1OsNNF2jVm/wzgeX0Q2DVCKOdB0OH5xB13V+9tYZ5hZWMD0H91pdzYltK9/DJ08dJZtJ8fY75/jZz88wNNDHUH+WSrlCo9miL59RQRWmrvYuV+VT9QNoJND0khU7jmLaEZoerFHfmV4KlcS80WzSaitA74NUV7rUG2rNjY8MceLQHqQUuNJB90yrjuuwur5OYXGRVqOKbVmMDOYZHurDsiyk43QAiOtSqdaYX1xlaW2T67fmWF4t4HqpeVzHxbFtHMchHFJ5T+PRMFbb4t7cEqVyjdHhAT7z3BOMjw/Rn08xPDSggHAqxa6JUa5en2VpeY0jB/YG0Z1HD+0jk07R35clfHkWp1JDOMpMadsOuWyKfDaltG7VGrbjsHvXOLmlTYxaA1dK9SzSi3rMZ/nwb/06jusSj0V5/e0PiCfi/O7v/wCt5SURRxIKmRimweTYMJ978SnSqQQ3bs1y4fJ1bty6x4GZ3Tz31ClMU63He3OrXL1xh6OH9zHQn8eybDY2t9jY2CIcMRgcyJHLponHYxghk8BpXjnvBPLmoSVQ5Gy77v78Lh+79JoPu11sdmjnQd3y/ax8QCJ21oh9zMr8D0Hd3e34Xdv+2/YiH9T/7dQR9zndf6LO0jNmQV0aQbRFd9NdnzWtK9eoX5XY6cpPVn4pZkTf+XDHAf54Jt0uoLUtBBB4oNrOe0nVGhIqt5enCl5YWuPNd86RScUJmwaFYpnhwT5i0Ri6ZrC2vsWV63eo1WpkknFAUKrUaHqCU0oVrehK+ODcVT64cJ2RoT4sS/kULK1uUK+3aTRaZFJpnjh5hLfeO88Pfv4Bf9NxiIAKZRaCbCalhOH8MnfnlxkYsLkzt0i90cSyLVpWk1a7hWXbXLl+l/6+s+i6zuZWmVDIIBw2aTRafPsHb3Dp+l2isQhty6ZYqtJstxFCAbNGs0Wl2qDeaCjTWTbJ3bllPjx/nYmj+5jwIqikZWNEwrjZFKJUJrxVptpoKjMQguX1AosrK+zbO0Wt3qBSbWDoRsCBYxg68ViUcChEuVylUq0TDpk4VptapcLs/DKVaoNoPupFGDpYtsPNO/P84bd/zPrGFkcO7kY3DUKmQdtUAr7ZbFEpV2i12lhtC8d1POZsFcKvaYKbt+d4WdOo1ZvcvjtHfy7N2PAA0WiEsZFBLly9xb//1o+Y2T1GJhVneHSIibFhr58NWi2baq2p/D5WN1ldL1CrN6k3mqwVNlnf2CKdipNJJ7h+6x4fnr9KYavErbuLjAz2Mz46hNVW/jH1RgtbSmrNFtVqg2KxgivBsmzmF1c4c/Yy75+7yvTkqMfkrpL/lis1SuUaP33jHcLhMHt2jfH2exd4891zDA3mGfbyupXKFcqVGmvrm6wVtujLZ2g0miwurlKtNRgbzGPqSktV2Crx3Zff5OKVm+yZGmWwL8PTjx3n4L5prt68x/d/8jYTo7fpy6U5dniGx04eZXVji7mFFTY2i1y9cYf+fJZMOoGh6/zkjffYLBZJpRTtwovPPYZtKaLRdtuiUquzsLRKNpNiaWWDUrlG0QPW12/f4zsvv0mxXGV8uB+ArWKFwlaFVLrK3dkFCpslqtUG127OcurEmudv0+bsxRukU3H6cllSyQRXrs+yWaywsVGksFVidHSYifFhzl++zdziGolYhMnxIQYG+iiVq7z65geAZO+ucTaLFd49c5HnnnqUZDLOO2cuoGkag/15sukE7bZKOFut1Xnv/Qu8f/YKhw/u5qXnn2Ba65gYNG9/cRHYXiBJo6mc0TOpBPcWVrg3v4x0VcSowAKp2OJrtTrrG1uUKzWS8YgybQlFrbGytslPXn8XIV12TQyTSSeJRiMcnJkim05w8eptXn7tTUrlKiFT58DeSdLJBNJVGnMpXYSU2I7LndllfvLGGRptFXjjuC71epNWq4V0oVJtYNs+hYfaTl3HoVZv4DoOmVSCAzPTjI8NI4TKH9tuW5imwfEj+ylX6nx44RqZdIJYLEI8GiUaCXPs0D6SyThCEyqK1NNaSNel1WoRMg1WVjd47Y132doqsmtilJBpgmwgkbTaVhCNaRg6UxOjXLl+m8vXbnNvYZnJsWF8njXhiwVv3+/LZ3nk2GFK5TJnzl7i/KXr1OoNUsmYMknWaspHTECpXGVpaZXlkTUWllc5f/EaC0urGIbGscN7OLBvNwP9eUWxsk1Gd5u5vG+Df1QWje7vt13rr6OdQMPHMgx1A61uYfpxBWsX8PmYIEvRMO2gRJFd/fBA3P2RfD2Gue092eGarjZ3Kh/DpLitJlW/p5gTbDM5dvXT99MK2BC833Rdx9C0gIdtm7Gx62k+GfD6C/LZ6ipdpoD7f9KghzXc98nyF9JOWq7eok5FgnDIpD8e5dC+ES5cvsNGoUij0SKbUpuq68C+6SkSsQSRcJjVjS1++Opp9u4a4+jBvfT3ZdF1nVbLYnl9i/2OBN2g2mixsr7JxmbJS6BqEo1IbMslHApz8eotvv2D18imEyQTCVbWihTNEIOgkvZqGs88dpxKtcZ7Z6/wB995hfGRAS5du0OjqU7Imq6RTMVJxqP89Ocfsri8TiadZG5pjVg0QrVWp227LKyss7BaYNf4MAkv6XU4FMKVavMvVxssrxZYXt3g8P7dHDmwm6s35njr/Ys07i6wd6tMNBJWxIO6jtOXg9lFIo0mxXqDXZNjHD6wmwtXbvDmuxdotm0VPVlrkM+mcFypNE7pJOuFLd5+7zzzS2vU6k0FvKot1jcr3PNC8h3HIRoJEw6HmBwbom3ZzC2t891X3uLu/DInju1jZvcE0UgEQzdoNFqsrhUYHlHXtlst5dMhDDSpoQtNAZ878ximwfjIAHunxpWpSNN4+olHKNcanLt0g9NnLrFrcoRMLottO4E2JhQysS2Xjc0y64USt2cXEJqiKWi12tiO4yWxNbh45Rbf+t5rWLZNLp3ixedOMbNnksWlVUqVmqKpsB2WllV03dLqBrqukUrGabXa3J5dZHGlEJhdXM+vUNd1HMfl3KWbGIZOMhHDNA2GB/vIppUpqdlylJanUmN5ZZ1LV25SLle5eWeOd85cZGxkkGefOE4um2L/nknuzC2xUShy9uJNbt6e49jB3Tx+8gjPPnGcjc0iZ85f487cMsVylYH+LFulCqVyhXQqzt7pMS9/n86pE4dotJpcvnaHG7fvkcmkeOTIPgxdw7YgZJqMDfej6RqLy2uUKzVW1wtKU7myQWGrjK7rxGMRSpWqGifHoVyts+ZpN2/PLhAOmeQ9s+v5yzfIpJJMjQ9x7eYc33/lbR47foh8Lsvs3DKhkEnbtqk3WuzbM8kTJ48obi1DZ3Son4P7phkayGPZNof3T7NVqpBOJ5gcGySZTCgCV9fFsV2u37nHysoGmXSKPdNjGIaOZSkN3c27C6xtbDI6PMCE0Ah525BrKy2S6yqN997pcdrtNldv3GVpZZ33PrzM7NwiM7vHyd9bAy9PqetKrFabtbUNKtU6jp3FcRzP/D5EOpXg9AeXKZbKfOa5Uzx24hCRaIyhoUGGB/Osrm9yZ3aRSDjES88/xoH9u1VwgXcwaTRaFMtqHtc3y2x4ZJzJZJxGq8WFq7fZPT1FNpNldn6VRqtNq6W0vUIIdNMkk0oQCYdYWlnnzdMfMD42hEAwOT5MPp/hvQ8uUyxViMei1JtNllbWaTZa2JbNhSs3cByHxx45whQdB2JFzmuSikeVBs2yeOPtD5Rv18E9GD863WMe9qMHq7U633/lDeYXV9VYWypookfIe3/rmsA0DBLxGM1mE8uygwPNVrHM2kYBXdOoVGo0203SqTiaITh/+Qqn3zvP1Rt3aTRbZFJxBvIp0okY8wvL7Joa95z9u9xWAk2L7xStKTPbdv+dnRzIhWK883neOtfR+/kjRV2Q3dP7+y+LKPUBxdOSPeQCNV7dAFUqqpGPVX3X549opaN9kg6943I/wWlwn+wQILtSeg73MkjXE2Q72NabB5OJfHT5iwNbUtynduwuPvGiSs7sX9q1WKUkUPc9zATpadQMQyedSvAffeML5LOnuXJ9lmq1hmM7DOQzvPTcYzz7xAmi0QhHDuxmvbDFxmYR23FwXIdsOskzjx9naLCfWDSK7di40sU0TQ7s3YVtO2TSCQbyeUzTZGb3FJFImEwqSavV5uLVO0TCIaYnRwi5AhbXQEpcy2J0eIBvfOnTDPRluTO3QLVWD5yMm+02hmFwYO8Un//UE1y9OcvM7glSyRgbmyVsx1Ebtevy+COHiMdjDA3kyecUwOjvy2LZDrbtkk7GkK7LRqGIBA4d2M2nNspcunqHarGicK8QOJat+LdyaS9NBsiVdfY88Qh/5cufJhINsbJW4PzlmzQaTRXxJAQtyyaVjHN4/zRziyssrW4Aknw2RSIeI2SGKVWa1BsWeyZHmZ4aI5tT2piZ3ZPs2zvN2sYmtXqDcMRU/kISpK7T35+jbVkkEjGUPx7gOmhSKv4pIZC45DIpPvfik4yPDpGIhGi12ypth6YxPjLIr37lMxw/so9ipcrQYJ7RkUF0L+LoxWce5cDMFJqmYVkq6e2uiRGGBvJK25FJsbi8TjaT4tHjBxkezHPt5j10XeNTz5zk2KEZDF33AFkfyUSM8ZFBopEwpqGzZ9cYkUiYyfERMukURw7uJRQKMTSYJxmPUq01iETChEIhBgfyHDu8lyvX7uB6c3v8yH4G+vJIqTQoqUSMvdPjhE2d9Y0t1ja2mJ1fxnUlu6fGFGDJZfjMC49RqzVYXt1gbWOL9fUCpmlgWRZjI4N88yuf5uC+aeZXNhDSJZdOIoHdu8bp78vRajkYukEum8YwNIYGs9w6Ok+r1WZwIM+eXePE4zHKlSpHD+1leKgPgSCTTtJotHn2iRNomhLQyUScaNjghacf4e78CprHRZZKxRnoywFw5OBepidHkcDS8hobhSIjQ/1848svcvHybTaLFaYmxlSUJ5LVtXWSyRijwwNkM2mOHdnH0GCefC5DXz5LNBJWFCShELmvpbEsm0Q8SrXeIBIOMTLUz9pmkaefOMGxwzM4rktfLkMqGScSMomETY4emuHilVvcnV+mVK7g+vuRFNgtC83bkE0zzJc+9xwnjx/i3Q8ucPP2HJqm8eyTJzi4d4rsf/9HiPlVb+NWLguRUIh8JkUumwyc5Y8fnkEzDM5eukmz0VTaXNvBkbBWKFKs1BgZzDMy1IcQglQiTqlUYWFxmb6MChaJx6IsLK7Ql88SCoc4dngGoWnEoxH6cmk2NraYW1jGNENksgn2To+pd8Xblw3D4NHjh7hx+x7vn7vKT15/h3QqQTaT4osvPUcqleTe/DIXr9wEJOGQydBAH8lknEq1xvlL17l9b5F0KsGkB4B81nVd18llUorKY32TarXOgZldzExP9Ozfmhdi7x9E3jlzCV3XGOzPoQnNS9XSEQkqMYfElbC6XuDK9VsM9ucZHOij0WgGVCKxeJhMKsHV63cJmSanTh5ifHSA9bUNYhGTbCpONhXn8IHdPPfkCZKJOOcuXefmrVmOWJbKGSl9mSQ8oOVpdLoDtboBx4PklHQU4KIrZDyQcf7nbbcEwG0HABfc8JfjN/aLF/kAIPogPLCTn9jDEOh2UyEETNlC+eThOp0r7jNLdmmpvAAJ3zezm3Yz4AoTssdfS3mc/6XmRuxOU9o9jL3fP7AIb8B21jh+xK1q8NShQQGuk8cPMtCX4/T753nvg0uMDvbz/JMn2Ts9SSIexbIdjh/ax66JUUqVKrVanZBpMtifZ3pqnEMH9mKGQkTCJrVGg2Q8wXNPnmLP9DiZdJKh/kEFwnQD23bYMz3B7/zGr/Dhhas0W21OHj1A7vx1+OCyMiW02jiuy8ToEF//0gsUvTDuH//sXS5dn2WrWKZcrpBJxvnM84/x/FOPkM+lCYVMpsZHqFRrZNNJkok4Tz9xnInxYYYG8sS9VDLpZBypK1+UXDbF6lqBeCyCZdn053J85oXHFU1FuU72n/5/QILl+RHoI4OEolHlj7W8DprG9PQUXw0brK0XcF2X2bklllY2aDZbVCsVcuk4u3eNMTE2hKZpVGsNctk0/fksA/15CptF9u3ZRS4d5/D+vQwPDXL5+m1cBI8c3kd/f4Zmq0m1pnxpLMcBKdg1Ocr+PVOMDg0icVletiiVqzSbNqlkxMtPqPyxnnrsOCOD/VTLZe7OLXbIOYUgm0twMrNfJUMXAildbMdlaKCPVDKhzDqFLVbXN4lGQkxNjJJMxAmHTDY2i4RDJol4lKnJUQYHcuzZNY7jOBzYt5tYLBr4rs3snmRkqJ9sOkmj2SYaDbNneoJcJkU2k6WwWWbv9BTHDs3Q15fB9cxHsWgUgXIE/8xzj3No326SiTh9uUxAIOs6glg0wtOnjjI23I90XTKZJM1mm/58lnDY5KlTx4h6OTinPNPMcSmpN5pcv3GbufklLxJOMDzYRz6fpVCqcufuHOViGaRLLptmsL8f1xXKLOWlT5menGRwoA/XdYlEwgHADIdCgcO/EDrttkO7bbF7WgG/kGniuA6bW20y2SxHksrnLhaNKMJZD4DM7JkMEuTem1/i3sIKUkr2Tk8y2NfP/OIqfbkMQ0P9DA/lWV1fR9M0xU9lGuQyCtwn4lHC4bDnkG6TTMSD90IIwaC3J7iuJBoy2Lt7gkw6GdxTrVSxWk1wJdVanXqzTSQSIRKJIL1DCAKkx6cnvf6PjgwyPjrM8FAfV67dZniwn4nRAVLxKIbHPO8Djnwmx6njh4lFY0yNDzM6NEzYA9tPp1McO7yPSrWGISRGKKR83soVNrcqNOoNxNmrSmvjupiGwYnDM3zm+VM8/9QjTI2P0LYsIuEwiXiM8dEhkokYkUiYer3J0tIK8UiM0cF+vvTiU8zsnqTdVtQzmhBoAmb2TPFrX/scmXSS985exjR0jh7Yw5EDe4jHopw4PEMsbFKuKDqOo4dmSMRjuK5aP8trBdpWCz+VEIArXSzLpm1Z9OUz9OXSZNNJHj1+kFgs2vHR8nwu/XymPmlvPB5jenKUYrFEKBTqEdRSyoAb7t78En/6/Z/y+MmjxONR9s9MEwqFOHfpGpeu3MI0DCKREE89dozDB/fi2jbDg3kGXnySxaU1HMfh8ME97Nk1Qa3eIBoJU637xMBeCTQ4XTqOQDZ9HLAjAhCglpPvsP4xbpU7ydX/Py0CwAH5YMhxP+DytX4PMCX609L9u6dtFFKR9PqRlfeZEmWv/awnw4Qu0XT1fkjcHt1Yb+Ipzwon7vcB26n8OcHW/bpPTaCI4HpWh3866EWWDyZD+2QlGLStCpm/9V8RbVsMlCp8ulojHr1H+oPriukc1V5Ouox5tlrXdYMX3WeNVUzKLinHod9TTx9FYpoGum4Edl6frXnYdXnRsgBB5MpdwqsFRfsAaLajNDgI5esQj6AbOq22RbutiAs3CkVi0QhjwwP092UVeNA0jh7cS73ZpNFoeU65LoP9OQb6ckS8JKpCqJQco0NDJBMJtoolkok4phEmZAiikajiQ6q3CJlqMw9pGk1DRx8ZIBQyFfdWqYLlSoyQztjoIIMDedpti1bbIhIJU6k3KVZqTGkauYyKxJNIKpU6ffk0+WyWcNjAdR3yuSR9uQzZfFb5lpUrtNttJA5DA8pcu7FZZGNjC6EJXAn5bIpcOkt/X57l1TUKhRLra0VikQT7906rUHBU9FokHCJsCpoeE7fPnYPmBklLpecz00lFo6NpKomvYeiMDPUxNNCnnMU9AdlstcnnMiTiUbVRZ9IcmDGxHYdEPIqhCaqtFq1Wi1g0Ql8uQyIRQ9OapFNt4vEo+WyWdsuhWq1hGgZTE+Ok0zHuzM7TaltkM1kAQqEQ46PDDPbniYfD4Lqsrm3gtG2MZouIFiarCVohk5DQGEqlsaMWoUwRbJd0uYZhu0TaFk6tjg7EwiGi9SbDhTIpB1KzS7BeVBGVjRaNlQ3cxWWySNKNNtr8mho3GzTHYwl3XDTXQrcslc8SxSButNtorTYaYGo6tuWg1RvENY2wIdAR4LrYlgWVKolmG10o05IhNAwhPBZyF11o6BKk6xKp1Rmr1kjGYsQjIfocl7FGG0ODkMeXNtJqgeMSMnQ0BI3//D9F6J5G23PKdzXFmu24YFnKZyocVjlJg7WjqZx7ipDYJRo2EdKh0VDpgyq1Bof272ZoaNDj2VK7i2xbSFetJ9t2lNtC2CSdSrJraoyhgT7SiRjadpOFVNQPQwN9pJMJhof6yaZTCE1pdHQjRDweJ5/LYLfb6IaJYejks2lymSTRiDK/h0yDrVKFS9dmuXx9ls88/xjTE2OMDg1Qb7YoVhuetjxENp0iHAmTSsTJJuMIdJLxGFoqjuNKNoslImENNAekQ8g0ObBvN/F4jHEv3+apE4fJexrpR47s58iBPTQaLRzHJp9TJvFsJsWnnnmM4cE+hgYGEF7CZaSk3baCIJFYVB0IZnZPsGtyNOBW6968JaBrgkwywa//lS8CgrHRIaSjIozF733PExgqtVckHGJ8dIihgTzjo8NYtsVW0WJ4sJ9HTxxiZHiAy9duUqs3OHZ4hj27JhAC6q022XSadCpOJBxicXktEJ6NZot6o4kQWm8KoEDC/HlKB3ApDso/Z3W9Htt/iaV77j6Oo/1OttGPq5FTAPtBV/ak0ul82fWHi8rHoTSJ3X5ZwVVd3wPBgV0TWu86lZ0bAo2jcOiArY/xOPyFMch7i6F7TQjROxiBrdobDA9V/uLRBygEWygi2hbhepOQdDGbLQwp0TyB2gtu1ZvuO6x2j5oADOmiebne8ARHEHXhAS3dm7AQygdNNFsILywfIQgjCIfDuEKl+0BCpdGiXG3iupJqrUW12vSoFDp56pAS3VBcMFJK6o0GmtDIpNXJNWQaXtShqlPTNBKxGPFoDMPQA5szto1RrGCsbKDV62i2S+L2POF/+Yfoi6ugq7QoeasNUplLQghCuo6p2WQNg76QydpWGVGsEm+0yRgmpgQchzRgmCHCzTbU18hubbLHcYiWq8RmF5GuZHe5xmjIZHRlg4hXN9U6kWoVIb0caY4gul4lPLuMW9jiiUIV6dhkP7iIc+km8txlnl3eoK/ZJva91zAyCVJNiz21OgKBGQkhvPkyHFex+zsOQqpxd2yHULNFrNlixHUJh0JEDVNZ+F0FNMbbNn31BromiH7vLTQgYyuhhOOC6xBptEg3mhiaRjQcwtA0cm2bkbaFLsDQFO9TrtFCAyIhE+G6hCtVxqs1YqEwyX//AwwBWVcGC1JKSd4Dh/7cxR2bnOfjY3h+XrFmC1AcVIZpgIR44G+gEXFdDrXbKt1NKKQApisJt9oMNFsMSEk4HFL3G8ozRZfbX7v71f1SSsI+95FQZp9Q0LeOzt10XUxLmeB9vhohUBQmgcMpwXtvOg5J6WLoKnWWBMLeAcgXeoYfIOFF+Uq7jSV1j4upEz3kpwnyP/vbjeM4NBpNLNsmlU57aW9UGi9DNzBNl/GREb74YpLJsWGGs2mVOxBACrCcID+hViyT/hv/BwXymy3SrTaxaMQD7BLNT0QtwXZcKrUauqYxOjxIOp3E9Hz3QHjs5Kq/mmGiGzqO67CwvIyua7z47Ck+/eyjaALuzS0xv7juaUfVOy60MIZpoofCaJrwfA5dIoChaxjRGEIos6XrupSKVQqFLdrjQ146KQchFGHs+OgQpmEQjYTJZtPBaT8ejxEOKZN/s9VGvS5qbPvzWUxzH7lsqrMvuhKrbWObBn25LMur67RbFgf27iabySCERNM6qXhsLx0SCKK1Jl/6198myH2JAsqGN16qbZt4LMbxw/s4dHA3E2PDzM2vMLewQrPVYmJsmLHRYaYmRimXq+RyaWLRKI7VIhWLeaZyndX1Aq2WRbVep1ZvsFHYot22GB0d7mgne6RB51O3IWn77w8uvntM51k+XvHG9RO19RdUHhQEt/PFIHX1vGI7R9jHuPdjI1Lv2p2wRZABt/N9kIP5gfV768y/tnPzNjD38UGWX/5CElFvB4Q9Nk1fu9Wt5dIUL9B9mPcjgJcfpaLdnIN6PTCnhqTEdH0EKnpOKjuNz30RFf6/0puqWh3hh/BqAjeuTBX6fWhZAUxh2QjbRgCRlXX6/+4/6VQoYapS5R/enOM/a7XRFwukL88SSiWQLz6hOINA8c+4ElyXsOOQsSykrRI+K4CAykPWbCGaLWhZiGYb0W4j2haiZSEsC+E5hiNBND0Or7UC4Vv3gjGOIkn88Ss4b3zYGVsPiI62Wpwslmk2W6QWC6S//3MVVi+6BKcaRJCSPg+ECm/chRCM+LhaE15ouyAlISnltrFXn4ekZDBIswHttsWJzSIHWm0ipRrZP/kxhmlgABEP7HaD4M4zdGqVQNQ7eSvAcL+DqQlEvPvv31TUetJdScT1I8o84S/BcNXLp3naWsML3NAaihYhatuEdNVn3UvkLYJ6lZASft+8/Iaa62JK0ISr0sa4LhEPWOnNtppbKdU69B5Wd11CrquclS1bAXHAtGw0LzrWaFvottO1JFVuy53eN/VOee9ZVzsKJHfMQf4PQkqE6waExf71fsaDnjUjJd0us8G+IbtOtLajtGzBqVow+nf+Ca6po+sGWsgAoRIoS68S6WnRhARcBbxzbQtcl5BpqjyI0iXpjSkS9tDZljQPiEmvx7ItA5OiBmibJYSuobfbhG1XzYWu9e4tXj2JeIxIWPlXhUJm8HzqCTXlxeMd9nRdp1ptcO7iTVbWN9ksllnf2KJer/POmUu0bYvHThwkn83gaxY0TSNkKqb9mt5EPbrSXgtdD3Jfuh4tS7OlqDpcR3GT+fxiuq588PDWr78SVL+0QPPf8gh+VYSxTSadVLQr/gRqyjQYCSs/uuu35smk0xw+MEM4FEbiBAmvhdDQ/D76YL1UCdZGz1oQgGWT/J//Iz7VavOc7WD++C1M02DcsnnMshBCHTB0XWe8S1gGx2ihGPellCRaLQ61LUxD5Q/Nti0OOQ6RSASz3gymMTiQB/kGu/MMdkk60f33TkV49390ruDOLV0yshuAfGzA88sucscIxAcXtcaR/tiJjtLlQfuN7xPXg3PuOw0GoGlHUPYgs2PXc/iA6/4nkSoIxbNI3f/rdpn18ctfnIN89+Lb3rmuRRQISU1TgMvfUx8wUPel6ZESuXcCsbEVjH0gxPzDROdqr2ui53t/yB9oGp5dAM8siK7heuHscof+CEC02uhLykQjY1G0zVLQmgTMWoNkS5k6sF2Mah3RsnDfOd/R7tkOOI4Sst5nbLv37wdi0e0/iJ7xAKnu9x9a11Raklq9e2CREgX0JFiahtlqY1brHsjoakP2ftC3gf7tS7NHuAaT0KlPdI+plISkZMh2cIWGZjnoQSoNiXRcRUiHFw3VvSlt61fvC7ztC7nTdff/rXfvfdC1tkXXg4r7fte7rxXd13Svvd469J6BEuh4QDu4VW1cQtJDBig8v5bg3RIKSBqGjvD+z3WU8PWTJ5uG6WUt0HralL6k8l7MYHsSEuF/132tq3ik3O4TpFDJ3jt9Ej0Pom7vbTdQjm9V1Ptj2QoYAeE7C0pYs3NRDDKa8mnwBLvpr3Nd937z+qIJddjTteB7wOd7UB8dB5HPegAahGkghXIREFKCrvXm0FtaA1eiGwZ9+Zxijtc0hNBw8KiXpAzipgwPcGheuHk2ncayHH7w09Ocv3QDQ1M+fF9+6Wk++/wTRCKGoqCQAukKNE2ZTFWaKjB1A0NogcMvgOZqTIwOk00niUbCKjegC+guQkiEJgmHDaQUwUHAMAwvOssz1Qu1X7fbFlJCLKoApO9/h7e2NF2nWqvzx999Ddd1+caXPs3UxKhad3j+lZoGhk4qlUDvyyLM7ZFqnmBHwtIqWAJ0HX2zRMwDg8LTtEWkJOzjkEYruL+7KM+WDtiPSklESkTbW/+ev4+w673CVErAQXoASeCCKxT4EgKERAoPDDxQBv+C4Ch4hO3HkT8HV9bDmvP33QeBFM+E5q/cj9cH/xpfU/yQ+j9p+YT1+L3vXWWdPcg/MyotttPru4dnZkT4J9RPXH4JYKtLuHV943//iTg+upH8Nv+uTpGeJkO7/3mFBvmUd5siHWy2WkipQv5Nj4zPH+4euSlEj4ztziYlFlcCcGNpGsuWRTwWIZlMdCHsTmXCstHXCupvU0fmUh2oZTuYho6xWgDXRXNdpcVqtxELqh3h7ISoHzZu6n+C86ifHNQ0wDSRIRMZNtGX1r1VpSG1rlOGB/DczRKOr2EUyrnY1DQMQHo+LKLd9hZc77z6ArcDAvyfe0GFI2WQisMw9C4Nky/M1f0dzKHOlJontCXS472VAbdY28u1FzJ0ksk4hm7sAIbU5yANiJ9Msmv8dgRLnuDvyvKgNARNtalHIuEgN9v2TfVBm5EQvdc+cKl3dcc3tagie7rYixu7UlU8QGNrOQ7Vap2G5eKgcmJGNUE2EUMYxg6vqrjvU88hw998pHrnHNdBE1pnXbkS6YGtACBuG5CeZ+l5OE+Idcubrqi37jr8+6VUp1Nc0HXwI4wUurB7Ad329rrXhK6DYRD+r/47ZC6DNjUGsSgkojjREDISRsYiWLEIRMIQi0AsTOR3/wdESRF86rqOMBRVgD9iuq4hZCcBOHT8DuOxGJ//9JPK7FeuqqwB+RSPHN3Pvr3TxGJRbEcdPFwEbrAfCo/jS4E2XXScgqWUGLpOXz5DPp9C10FqOo4H4EFpsEJhHaSXrsjTOik8qvqmazqaqUzabsRP8iwRWof+QNg25vwyfVLyD8tV0qkkg3/wfULf+rFqBxCbJfVZCMKREPw//7F6wQJOKdHlF+ui/W/+a4Rnnu1eBsEh2VsnOx1+g7W1ba6F3LZ8ts89qKTXeLLAj3Kja7+SgFQCWGqSh6Zx+QTAoPMMOxyaP5Ep7+O00fPlDm3udLNnBufjAq5tpUs+7Ez66r/wH9GXj3FJMNGBdqz3gO0nOw/2D08+2Y46MHZXHwqrA+kvWn7pmq2dt/dtwCb4uSNlOrT6O//eqchP8SO3jxvkUtj/5r9BSslWqcyPfvo2b7z9IaZp8NSpo3z6ucfJZ+LK1IPA8STodkJWiVRmEKlst5H/5B+hf3gFJNzLp/jriRDPPH6Yv/nXv0kkZIJ0e+bdcF1yv/2PVE2JOO5ADrFZQtsqg2UTgs7pznGRhjpZu0N9PcBk588SNx7FzaVxcxncXAonm6YZj/D+/CKX1jeZeeo4R596hFA4iiEIBFPut/+BMi9umxcpvWishuIHajWV70cqElJs2nrHbCi9Oeid5i54LejU3yPU1MbfbDQpl6vomiCbTCiCw21lx9c3qFJps2r1OjXHIZqME5bQbLZoOg4x00SPhMHXWgT/ajQti61SBTMcIp1JqhfHBwW+VkNonfuUTcn7G6SmVt5mqcKduSWkgOmRfnL5LEL3gGNQj7pXaJ328cC+0FTuvaBuvHZ8AQ+BRgYhFD0NgOEBRF3do+k6aB7I1vXAlOZKxWAuNE1pjD0zldB1bCm5cWeOc5duMDCYZ2pylHbboWqaGEP9ROMxNK9vQoDwtD7SHxuhfI1cTXa0XkLgAtVGkwtXbrK8VuDY0f1kMklmF1e8tvo4fnQ/uVwGMxJGExrC9/XSlT+X364UOhgamoDwv/wPmC+/hVgrIMNhMDTsv/XrEI1AvYFotBC1BtQbyHoTWa1R3ygyd+MuZttiIpNWfnP3S9aOwJYPXHWAVJo1DyB0NJWy5z3ocbS9t6QCZJZWiX3zb0MkhIxGIBZBxqIQjSBjEdxYBBmNIKMKtMloGDcSZlrT+MbuSXb94Y8IWzZ6yER7/Ty+ia+7TekfDKXyf3JsF8PQgqCP+14j/9l90Cu2gU7v327w2z0yvW++J5QcF3HtttISNZpoEqICDpZqiEodsbgWVN9dl1hcRfzG/+7+2v19GU+ki067O81SIBIfBCB2AgTBo35SsLDz9cJV61hK5/6DkCevhAvInfmxOlQPvszbvsd+Em3Sw4vcCXj0XkHviG/TJvTUta2bH9U/7ZP03+PJ2sHk1MELOyCubrOjFL1uCd2XAd6kBO+S76vVu+ZVH1KJBLFo5BMB5+7ySwNb3U6NAVr8KNj5sE7v+OIA6N7749Hu+5olqRw3bdtRJ0cEpqGz5JF8ZtNJnn/yBJpI4IeYKiy37eQctON956uXUfNqemlLqrU6gRyFYCMUAIaOTMUR5SqiWke/djeoWmz71xdWaBqETWQ2hZPLIHNpBajyGWQ2hcynIZvEzSZxwpGOds4bp3a7zdzLb/Dza7cRm0X2Ow5RTW2b/rw0v/k5ldDVbw9whWB+ZZ0rN+4wMjLI0EAfs4vL3Ly7QCaT5MTRAyRScYQORshENw0U3BUITVdmGaEEuWbo6IaGZhgIT5hqhoHu+Xg5UrK4tMp3X34TzdT56heeZ3JiTIFOzQyAjhAamiaDutE8E5WpSBAXVwv8q9/7FpFIhN/6ja+RTCZoVKpslcq0MikSiXjwzH5xXcn7Zy/xB3/yMqPDA/zWX/0iw4P9ytzVVTQ8DZ4PIIVKbOq/lI7tcOfWLP/+j39IvdHir/3qFzhycC9hXUNigNB7NhzfQV1hCr1DU6GpqJftm5Pyl+nuk/+XMvtalsXqeoG25TA8MkQsbCr9hvccrnS8BNad9SGlRNd0DM2gWW9wpdngdr3Kvs88w/i+vQgEjqs0t66uK/8jfzMTnfXjdI2olFbQSz/ys7KxyaVmg6vLqww+ephof46Ll67zrfcvMjY6wOChvaQzKcxwKACSwTj7mi9/zP3xqzU6L0osAiED+dgR5NRo573z+qDSv9isrm7w//gXv8faxhb/2f/6r3Fo9xRao4XebKM1Wsq/s95Ea7aRtRZuvQm1BqLWQNQb0PD+bjQQ9SbC+xu3m1Nnm4vtDtudcBzE7CIPK9v0lQAkpKQf0JqtzpeRkAK8O1bi+dTZTkCaqnt5Kns6+BDMcf8X3rxsf6bgg9p3ddf1XBwcelCRp2F7cEMos3Bhy3uG7ufpPmc+SPOh/rdlWbSaLUwvDdEDixA9Y92p5mHCf5tQ6NJ07AgqXII8i50afLAnPKB1//uuPmyjO+jpxv37xJ+/fFzA4ClDgr/8UfSVHb1A7CO1XZ8ofdEvAGq6DyN4gTLbtVmyE1Wo9lu1t3Z8tLzDqRDKTcA7rMbjUUVF0t29TzAtvxSw1e2g3PVlz58f1SepYGVnAnc6NUo8ezm4Dt4LJINrLMumsFUkk0oSi0V4/NGjfHjhOvMLKySiYWKRUKDFElIl0hWgNARdE+LzDQXXdvXe8ABFOGwQCusITSJtj7LOi4JypcTJJNFL1c4ijUdxsynIZXDzacLlV9W4VGq4fVnc/iyV/9d/3fEZ2VYUPFQLR7qd/gSkrrpB1OMPUszrLrroFejtX3mx5z6AUqXKn/7xD1nMpfgbv/YF8hMjhKp1BkplbNvBCYdYazVoNBvEEzEy6RSGF5KvoJweUFXouo7rLUw/gg5dVyYkb8wj+TT2jduUKzWqg3mciSF0/5mlQUdqyUDYSwmucMCLuFprNrm7XuSFp06gRUI4miCZThJPJYK1tl1bCS57do3zn/zmrxAKh8hm0sG6abetDmOwUM7l/gupogOFB5rUWolGIyQ8PqdwyAwUYWq37XV+DXLlSRBConv7bbC2dtiYhHeiU2tJ+ccJocZ8c6vMj376FusbRb721Zc4uGcyqMeVEuEnU/bqdXyzKcoHzLVtms02uhCEjRDSVZwyIcPc1q/OK+GDGuGdujVvl+miC1TtuxLbsr1cgCof6chgH88+cZxhjwQ22LxFJwK4W1vTPR66piGq9U4nDBU277TaSMf1DlX+fRoabrBJKlCrIUQIEYrimlFEVg+iDDVDx/Wex5XCiyx2gsOiv48G/p3ShZYCa6LewK3XkLUqVJvIWh3qDQXQ6k1Cf/B9xSLfFeQiZOezqrBrbOn9rNqWnYOkN/4PLEIgXZUXVXqXdnDEJxTSQXtd+7d/0uy5QBVlcnGQuopqDLoZMnGP7u/qouh5hu1d84XiffK764btTyIllApbzK8VyGaSjPQpWorOHtLR2vk+ftKrS6NrkLZXXCh15qnTGoFcEg/gkJQCXH8v8yfYQ45ubyO9nE/bch/uuCr+8stOLfvvx85XfEL08cDS0ax9dG07zYOvgBFqbCXBfGznhBf4DPJdTPKeJqvLYKy2H1330gEKkDoI+xM91S8tN6JfgiHqWS8PH7LOwnM7m0z3b9INFnHnNfK3RGXXd10X27ZZXd/Esh1y6SRjE6Mc3LeL135+Joiq8etstGxm55cwTZPRYcU3JT3NmOO6mCEToWkqAqu3t8GELa+uMzbcH6SpaLUsNoslXFdS+tIzREMRzL4s7WQcGQkTiUYxQyFcaWO+fVZRRDTbyHAIaRi0HYewJ42llLStTrSM63ipD4QIoppc1w3CyH0RIVHpPKRjqQVi6ITCYdA1HMvGsW2kxzvkuJKNjS1u3p5XDraajgBymSTZTJJ226bValGvChy7Ta3WwDQMxV6v69i2g5TKp8PPpVbYLBGJhMhnM8qHxD/V0VmwiqNL0VX49AXKyd1L1Os6tNo2jaZFq9VG0wVmSMORAttxWV5Zp1qtsVHYYnWtwMiQmj9NSmr1OhuFIgiNXDZFPBpB13VMTZBOxjDNIcJhlUIIYGWtwIfnrjAxNkQ8HiObTpPLZHEcl6WVNe7cW8AMaRzaN01/Lq02aqHWkp+KSBMiWF+u8DzM3M4hwF/+mudIrWkaurZTRJKf6FtNdatt07QcdE2iofjeFpdX2SgUqdbqbGxsUhnqIxmPdjRmuq5ck7y/9UDroTYdf3232pZKYSTANE11WLFtpY1wpddHDYTaeFwEmmGoDci2ArnjekBCOg6bhU1uzy5Qr9UVTYapceLwDHunx1nd2KLRVEnedS+q7e7cImvrmxzYN01fLqO0oR7gk1IlrE4Xy5jIwMeu3W7z1lsfUJqdZ8+ucbKZFOVKFcuyg9yCrusElgSldXNZXl3nzuwCK2sFpidHicdjLC6vkUknOXr4AIl4TIEzTWDbDq1mC00TmKbpUb7oyGgEEYtSChncLRfJDfV561ynbdk0mk1MwyBz+RbS90vyE9p2YxXXoxLxA11cBxwZ+Gu63vf6/IoCaALs4/s7JuauvVGBE+WjZlu24q0zDOxAO3i/aFR/d7iEBMIDI0qLe1/oQY/WzhOoQmmZV1fXWVrZIJ9LMzY8hOnRiZBN0/xn/1AlttEEaIa3Jn3zdC/A0KSLlMr3cidHbT+4YpuuibXrd/hv/2//hmw2zX/6299kdHgg0Kj5a8mXT5ZlY1k2pqGTikXQTUMFRuCDak9u/frfhfUtuqTZNhHt0iPjerRyAfzv/B08jg8iumXc9n9F7+cHHMj+4orq330t7mTCuw9c/TL7+WCOrQff0jmgSHQlLruAm+8c372UuyNv8Q/GdGGS7gOBJ/uFj9h7a/vI8kszIwaRT+yQtclfZA9ZND4A2Blo9QaNd9C1utZ11Wbjema8zc0tVpZWkEBhY9PTnCinT4GGK13WNzZ54+0P0XWdl154guGhfhzHYaOwxdLKOnfnliiVqhw7MsOTrhuEqAtNoBs6s/PL/ODHb/Hll55kYnSI2XvL/OjV01y7dY/dU6OcPLaf3eMjxCMRNgpFzrz9AbOLK/Tnszz12FFOdtmFbcdhdWWdf/7f/mv27ZngqceOsbS0yo9ePU06leDFZ05y9OAe2rbD3bklbs+plC39fVnGRwfJZVLYjoNlWSrE27Jptdo02y3K5SrXb9/j7r1lSpUq0xPDPPf4cWLROI7jcm9+ibnFFfbvnkKTLoX1De7OL1HYKjM2Osyu8SEi4RCGrrO8VuDmnXlSyTgHZnaRy6bVwU26LC2u88NXT3Pp2h2+/Nln+NQzp9CiEYQwEXRyZAXbletSLJa5euMukXCIbDpJIhbFcV3m5pc5c/YSd+8uUqrU0XXBkYPTnDx2kEQySa3epNm2mJ1f5vbsPP19OVzpcunydc6cu8Kde4u02yoVz3NPnuDgvl2Yhs7b737A++eucOLoIR579BjNZpOf/Owdfvzau0QiIfpyGb751RfJZ9OUKhXe+/AiP3rtbVrtNi89f4pf/dILZDJpT4PgYpiCcFgPwslv3L7HhWt3qdUbTE+OceLIQSJhRepYrzd4762L3Lgzx2B/jkePHWTv9CSa3mVaEK6naXGZX1zjJ2+8z+3ZeQYHshw7uJtUIs7VG3eZnV+msFlmfeO7fLBvii997jn27ZlCILh7b4n3P7yEbTtks0ksS3FMTY0Mc+TAXhzHoVSucun6LBtb3+HEob08/9RJ7t5b5Mz5q8G7NzUxwhdefJp8LsPt2XnePXMRzUuj8vjJI4TD6iBSrlQ4c+4KH5y/xvXb89yeXWRsuF9F4AnByuo6r7/9IVdvz3Pq+H52jw+xuLjEqz8/wzsfXmFhaZ2nHz/Kb//alxka7EMTOuuFLV5+7TQXLt/k71y+yZimEQsrzZvtOLz33nl+ulXiyUcPc+TgHs5fusHt2SUef+QgX/zss8qhe9seuLZe4A/+5IcsrxX4q199iWarzcuvnWb31Bi5TJqD+3YjpWR+fpnT759naWWNXDZDfz5LOpVgYmyY/v4Ms/cW+J/+9GU+vHCd/Xsn+bWvf5ZD+/ewsbnFK6+dZnV9i9C+caq1PM8/dYLjRw4QCYcoV2qseDkSBTAyPMBgXw5dqPfg9JmL3FtY5djBGQ4d2EssFiPzqd9Cq9SRps7yP//7rKwXcByH/nw2iAiORqMITaPVarO4tMLsvUUikRAH9kySz2UC4Oq6LuuFLUrlKslEgmyun0Q8iqYrF4RSvYmu6yTiKkdrYN7Fp/foCCB1sHXYKpX5/f/wfV55/T2+/qUX+Ktf+xzxaALpSlyhe8LMxfY0DDoCR3i5HVt1pHSJRiOEDOWILz3KED9Fj+qC9DeM3in1TPED/TlGBvvY2CpTqVRxBvt6xQ542SckjUaTaqVKJGRgahAVEaSlntIwlW+i2O5T1ePz5YEL6XraLdHxf5Iy0GrvBI4CoNUNuLaVh2uN/n9UtgNf/zT4AD+4Py8w/Lgk5x2/s52K7g1xr3ayg1C8OnxljdsLsvy/u+/zlWM+zPSjuj+JMu+X7yDvy41u1Of7RT3sPuR9If/q6OybCrZrzwhOdSBxPfLR+aVVzpy9zMXLt9gsVmi1LeoNC03oCAwEBvPzi3zr+z/lx2+8TzQSIpGI8blPPYmua6wsr/K9H7/FpWt3KJWrXLhyk2ONJmmvTUPTiYQUr41p6pQrSsPye3/8Q37y5ofYjsPSygbjIwPsmxzDdSVXbt7lez99m4WVDY8AUfCIdAOkLKWkWm/w8mvvUi5XeOrkIer1Oq/+/EP6c2mOH9qDpmlcu3mLP/j2T1jd2KJUqSGl5MiBXTzxyGFm9k5j2w6up1GxbZeNjS3OX7jGH/7ZT1lcKWDbNtMTw5iawTOPP4IULoWtEqVyDce2abVsLl27yb/745exbJvf+PpLTI70U6s3OH/pJuev3GRlfYutUpUjB3fz5c8+ze6JUeq1Ft//8Ru8/LP3WS+UOHl0X6ANCE6MPtj2VLbFUoWfvXWGjc0ijuPw+MkjfOrpU7Qsiz/5/qucvXCdQzOTPPvkUWbnlvj+K2+TTiV58tRRlVoGPLb3GJqm0Wg0eevd8yyvbXDi8Az1RouzF2/gOBaZdIKxkTylcoUbtxdIJtMcPjhDtVZncXkN3dDpz2eIRMJEImGQDlub61y6ep1YJIRpaFy9fofis496hI/e3AkvolJ3uXjpFn/2wzcoFKu02zavv/0htVqDp0+dIBQKcW9+kW//4FVm55dJxqMUt7aYGO4jmkgCEuFxSriOS2Fri1feOM13fvQGhc0yuyaHGerL0LYsbt6e4+7cMuVKjdn5ZW7enScaizAxOUZxq8S/+rd/EuSXSyZiNBpNpJS8+PSjjA4PYBoGjuuqtEWetqnRaHL20nVe/tm7niZR59jB3Tz3xEmisSbfffl1vveTt5CuZN/uCabGRxgf62Njs8hP33iPV372HplMEkPX0HUtMOXZjsO9hWXeeOcca4Uih2cm0TWNC5dv8OqbZzhycDd7pyc4f+kG12/NMtCfx8bh/bOX+dZ3X6PVatMqFKlEI4RSSQyUpmlisA+nsKUAZSYFQjC/tMqRg7uJRSOK5d3TCGhCsZMnIiFarTaRUIiJ0SFGhge4N7dIuVqn0WzSbre5PTvPd370My5evUWz1Q7yFSbiEX796y/x1GPH+Mnrp/n5e+exLYf3zl5h/94pDh3cS7PZ5o13znHl+iymaRCLhpkY6+PwwWnWC1X+v7z9Z5RkSZqeiT12lWv3cA8PrSMyMiK1riytuqtaK4iRwEAD5C6wBLnkWRwSWHJ5+IP7g+eQZ88KYkEsBmJmMJjuaVndXVVduiurKrXOjMgMrYVreSV/2HUPj8jIquqenrWTkeFx/Qq7du2avfZ+3/d+b713kV9+cgPLklpXh8aH+Ktff4lENMIPf/Y+3//Z+2xl8vT1XOIf/v63efm5J6TMC4CqcuXGPf7tn75GsVSmuzMFHvR0Jvnaq89x8MAoaxtb/NkP3uT9j69z5OAwyd/+KqmkHLVcP1p3+uECP/j5+wz29fDtr71CLDqA69jcnZ7lp299RC5XZGS4jxefPcehibGmmVZrjulQLJW5OzXLrbsPWFpZ5970PEIIIqEQilCbQMnzXAqlClsbG7gedPf0EI2E8DzYzmaZmZtjdn6J0aFejvupgRRFwbGROSJdV2a+kElSfXdOac+QvrmS6a1Uqli2LaOSTasZbew4DuVylUKhhKZrpFNtKJ6L69jUag6lcoXNrTwLi2sEAgaHDo4Sb4sg9psRd7nJ7AYfjmmxuLBMqVSmr6+bZLqdBt2wGws8CrQeb6bbfe0m/PpNMlxNCp0ds+he8LIPw/hYoAV7gEfruX7Fen9OwPXYOrR83Gvw3du+rbcoFxW7uVNpaRa7DmrGFXvi0/ijR8pfgs6W+Eyz4SNlv4fsuVLHp3HOvYc02YCd6+XyRf7nf/t9SsUyJw6Pc+7EYT65epup2R0n1Wqtxo9ef5cf/vwD0u1teHi89+EVDowMcGBogGyuRCaT5eyJCfp7u8gXS4iZ1Ueqd2C0n7/2rS8SDweZX1imIxXnH/7+17l6a5pb92dZWd3AcW2EJdjazmBbFi89eYLOjhSd7cld59M1jY50kq6giqpKIcR0e5KudBt/9esvcv70Uba2s/z5T99DVRX+yT/6XR7OLPKv/+hHvHfhOqlEnAOjQ00tH03V0fUAxXyRW3dnyOVLWKZNuVpnemaF67enOXfyCMFggGQsTDgoTWqqqpBsixPQNQrFCgFDR9fl5Ly5nadUrtKRirO+keH+1BxPnprkwEA/xVKFja0shw4M8uWXujl3YpKQYaD6DooI0TQPOL7qdzCoc+r4OJqm8tobH3Lt1j0OjPZLs96Nu4wND/A73/kSo2ODTD9Y4OMrd7h49Q6njh5E01Q0TWNspJ/J8VGCAYNSqUS5UqU9meCrX3wWVVOxbZsHc8usrG0y0NdBR0eSRDzSHJQNXccDJsYG+Ed/569j6BqaInAci0Q8wpNnDpPLF1le28KxHXRN2/EvQy4oNE2lUCjz0aUblCs1Th07SDZf4trNKebmFjl37BC6qrC2tkYul+Ps8YMcGh9meLAb3RAI4ewaei3b5tbdB3x8+RYTB4Y4cWyCns52ejtT3H8wz8LyOuVKDUVRGBvuwrJtrly/x4vPLFIuV5iZW6Zar3N0YpSvvfoMD2cWeOv9y2xn89R9tfN0Ks6BkT6+9oVnee7Z8yiKoP/uA6KREMlEjG+8+iznTh2lt6eTlZUVioUS//nf/et8cvkWa+tb2JZJqWLx7/70p7z+7ieMDffyd3/3G9RNkz/67s/ZzOSluVURctExNojtOMSiYXRdY/LgEP/bv/ttTp4+SjZbIhLUCQd0FM/Bs1wWF1fIF4o8c/oIXcvb0ocuoIPnoSoKgz2dtC2tUiiVyRdKWJbF2ZOTvPrSkyTiUbYzeX9y9lDxUDxXmlQVhY72Nvq6OxgZ7GFibJCH88sYuk4uX+T1dy5w4458N9qTCd765UVm5pc5MjHE2HA/hqbQlW7j//if/w0y2QL/9k9+IoMVTJNIJEgkHEDXVV58+hRffuVJ+no6AI/phwu888srbGfzGLrG6vo2uVyBE4dGGR7o59qtaRQBz5w9SqFUZiuTwzQtGakMoEnTezZXoFqt8eKTJwC4cecBP33jl0SjUUIBg3AoIBlu20ZRBMVSmfmlNcLBID1dHYwODRAJh/jpWx8x2N/P5MQYG5sF3nznY954+2Pqpsm1W/exLJuuTpnoW/FpLYFgK5Pjrfcu8t6FK4RCAWq1OpvbOX/RoSNQMC2L2/ce8MEn15lbWCGTydGWiPG7f/0bHJoYw7JtllfX+ODCJd6/cJVgIMATp4/wtVeeZWRokFKpyr3pGW7cuk+xVGJkoJcTRydId7QTDgfwcCkWy8zMLzO3sMzi8jpTDxcJR8JybHY9TNukWKpw7eZdXn/rArbt8OUvPs3TZ4+hKhqVSp2bd27y87c/ZHZhhVRbnL/2jVf4yhefJZoIPbrob8wzj8xRHvVajYtXbjIzt8SrLz9DW6oNoTZwVevUvsNs7Ts7PjJnthzXDHbZmeseC7x2aMjHWrgabOfue9zDuO0HtHZVr0Gg7AYnTbcJ0RBwFcjAgMcEd+xfQfB2/LGbgKelPvuqFzR2/pxYrcGOtUY8tlroHt3fbbqHtCCQz11+o2BrR1Npp7KtDfVYrPvInTU65qPHyW9Ey//S2dHDo1SuMr+0xnPnjvN3fvcb9Hd3EAsHWVje9M2U0kn70Nggf+OvfAnD0Png4k02t3Pk80U8x21KEdy484D2ZIxXX3iC8PvXm9dvpK1Y38gwNT1Hb2eKoaFe/vE//htk1jNcvH4Py95J7xGPGnQkE3iex9rGNudOHub08UkU5cfg+RFgrkfAMBge6GRpZZPph/PMLKzQnopz5vgk5UqVlbVN5hZWOX/6CKPDA9i2zVB/N5tbGQb7uohHQzL6yAPLls7x9VqN7WyeybFBOtpTbGxlqdXqjA/1Ymgyr2AsKtNxNJyKVUUQDhqoqmg6iIeCAc6dPMSRySGmZ5a492ARkP4/mqYRCslQ/gdzKxw7dJDJg+PoukyfU7dsNjM5SpUikXCIYqlMtVYlnUoweXCYcrnaTAK9trFJJpunoa+ztpVD1zXyhRKKIsjmCpTKVZlvDajWTIJBo5l2xvOgUq2RzRfo7eogEg7hOvJ5eZ50uG449CqKgmXXqVXrRKNhggGDeDQCjo3nufT2dBIMBvmzH77JrXszHBof9h26fd9ZTy4IHcdlbWOLtY1tOtuTPPvkKcLhMF979VnaEzES8Ri2JcPBq7U6D+eW+OLzZ3n6iRMEQ0E8pKciCGzXYytX5v7DBcqVGk89cYIvvvgUwYDBxto6i8trbGVyjAz2cPbEJAN9Xdy5P8vl6/f4+NINvvGVF3nlxfP8h+/+jO6OJM8+eZKJ0QEezCw2/XBUVSUcChAJB0klE8RjUSzbJhIOETB0Usk4T509zvGTh1GEQndXmr/5W18jGAlz6+6UBMtAJpPn8o0p8oUS3R0p2uJRHNelM50kmy/6RKYglUzQ3dnOw7lldE1D11UmxgcYtnq4eWuKH/38QwSQTMTA89ANnZ6udhQhWJqeQ1MVyV5GQnglKTjZlWpjZLCHKzenmHq4iKap/Na3vsDwQG9T58t1panXEzYIB094uJ5DKGgQNDQc20bx/R81TSWTy/NgZgFNVenqSNHV0U5/TyflcoVnzp9gcKCHZCLKN7/5RXK5In/2/Tf8dgtgWTVU1SMSDhAwNA6ODXLu1BE8311hbnEZ07J49cXzDA/2srqyJk19B0dQ0EhEQ1iWTWc6ye9+50v09nRiIHxdMPBUlUg4RDgUoC0W5gvPnW3m8Hz/o+vcm5rhmfOnODg2wCdX72DoMmL66s37/OL9S5w7eYR4NI6m6pw/dZTb9+dZWFzGth1yhRJTD+cpV2skE1GOHT7A4clRdE2jVdvNcT2u357mJ298SHdHij/4vW+iaoI/+o8/5OrNB2hC+tPlCmX+7Edv8s4vL+O4cozWNJVDkwfo7emkWquyndmWrKVpEYuGScQjaKpOLl/izXcu8NGlG/R0pggFA7z30VWu3Zria196gcmDIxTKRX76xgdMPZwnlUxw8+5DVtYzHDww6JvkoVSu8Pb7H/PhJ9dxHJdcvsjb711kqLeLjlQ7D2cX+cFrb+G5LhMj/Vy+eZ8f/fwdJsaHOXbkwOefRIUgEAxy7sxxDk0eoL+vW86BLVFtzVnLRwZiP/CyxzS3c9QuG478xhMglObY/EhpDISfUh41wX0KoHrM9sf6bXkNYfKG5piHjKRuBIZ8OvASfkDU/g5x7GbkwG8L9zOfWTMC8VcBYn4tvJZn87lPsE/5jTvI73ZhbDH9NffZB3o+ssnP/t0ije01ouqa9nGveZwHzTyBnifY2M4x9XCB7e0cG1s5gCabEQiHefG58/TPLfH9197mwewiA71dJCJRmTS2I80LT53me6+9w5/96B0ECgcdp6WhpBP90soG96bnKJfK0hciX+H6zSnuPVhEEVJgMJsroKsGh8dHOHFojNffv0yl9guEgEHH9Z3vvaZPxUBvB/cfzPHvv/tz6qbFwdEBEEgHYF/t++7ULO++9xGzi2tksnmi4SCRcBDbd353HYdyqcT2doZcoUShWCGdauPFp89I3wXXIxLUWVvfZHCwr/luGoZOtVbjxp0HPJxfJdkWIxIOoWkamqoSCYdZWtngyvVptjMlOtoT5PIVarU6PZ0dfPPLL5HNV/jzn76L53l856svE49FuP9wgVv3ZwiHDXq602xnsmxn8gwN9hAMBKQPixDU6ha27ZBqixMOBZlfWuPB7CLRSIjl1Q2qNZNTx3pIJOIo6jqu67G+laVeN4lFIwgkEN7O5FnfzJBMxMgXyxJQRsJoqo5p2dRNG8s3MxWKZXKFIqFQANO0KJbK5PIFtre2WV7d5MqN+9x/uEAmV6SjPUehWMZxPRw/csWzHQrFEtVKjWKpTi5fYW19m9Mnu+jsSFEpFnkwu0ChWKZWrdHf08GVm9P8+Wvv0N/byZEj4wh/GWw6LplcgXvTs2xs5ZrAzrYdLMWhUKqwtpGhUq3T05XmidNHGRsZIBgIcP32NFMPF9B0XUb8eR7lSpUH03PYto2iqNTqJuVKFUVR2NyW91IolTFrNWly801/pmlRKJaplyqowuPmrfu8+9F1jIDG1VvTeK5HJl+iPdVGLBJC13VKlSrlao1gwJBBD5oqE1CrKgHDwDA0P4JXx7Ic3v3wKh9evEHACDAzvyLfKn8At22HIxOj/PY3X+bKz98nXyihaRrhcBBKFTw8YkGDkaE+Ll67x8raOkcmhulMp6jVTRTLolarY1qW71ogg0aEcGnEajS0tVzXlUlYXBdN0zB0jVKpzMbmFm3xKIoQtCdjdHekCOgqa6sbfHz5Jr/44DLTs8vUTYtIJOSPXQJcKQ1SLlcpl6s4rsP6Robl1S1M06IzneKJ00cJPHFMBlUIhXrN5ssvPkk2X+SdC1dxXI/f/c6XULRqc7xBVaVQL/isrophaAQMHc9zyeYK5IslVEUhHAoSCgbQDZ1ypcZ2toBjy4nOMGTybF3T2NjOUiyWiPmJsBvA8/zZY5w7fYygb45FAVWomJbF1laOer3OcH8XvR3toHik2hIEDQNVCObnFvnk2h2u357G82B8pJ+JsSE8oLsjKX3DCnnsep1QwKCjvY1XXjjP1199gUg4wt2pGT6+chNdU/nKy0/T39vFJ1du8cPX3+P9j66g6SrzS8v84v2LfPNLz3P21GH6erv50x+8iWla5HJFqrU6uqbR3ZnmpWfPkWyL8+a7H5PL5igWy3R3dKKrCpZpyQAITSUWCRGPhCgWS5h1i+CjE9z+5jMhUA2d4bGh3dtd95EpbW/g167yaaa5X2X/5hT76cyW3Ed5lLXz5NIPvD3b9qnC4ysnz/FYpu7zlhaW7TFlB5jRwsS1fM/u7/bAQhQ/zdejuSi8Bgm55xn+xcy4f3npenZzW49+bn2Ye2yi+KhZNKOkGxE9O1Sf62tj7BwiBy/XdVlYXufStTv0d6epVGooiqBUqlKt1QmHAly6cpPv/exd7j2QDIIi5IBbKlfY2M7Q39vNqy+c4z/+8C0+vHSDP6iZzRdQYj0P07JJJRNEYxHev3CdDz6+ybmThzg8Psid6Xk2tnOsbWYIBkKUKzUCAQNd15maXebO9Bzf8gf6xh0KIU0uoWCASzemSScT/NY3X0EVmh9dmWSgt4tPrt6lZlo4noeuqRw/NEYkHKVWs7FMG8dxKJWqrK5t47mS9VtY3mBtc5unzx4nYOhMTc9QrdXp6emkVKpiWjbJWJhSqcLte7NUaiYDkRC6r5a7srbBG+9+wo07D8nkiggBhaIEFpVanXQ6xfmzJ2hLxPhv/7s/5PV3P+bJM8cA+NlbH5DNFzl76hC5QpGV9U2qtTqeJ8P0gwGDQMBAVRUMXWOgv5vhwR4uXbvPrbsPcRyHu1NzVOsmwwO9GIZBpVKlblrUaqaMqJMtiAdsZwtcvzXN1naOxeV1+ns66e/vxfYEm5kihVKVSqXmR1J6WLbD4vIGv/z4KoqikMnm6Ui1cW9qgQ8+vkk8HiFgGGRzRTa3sxz0J2ZFKNRNU7KXsQSRSIgrN6Z4/Z2PEIpA0zRWV1ZRFQ08wfW7UwC0p+JMzSwy9XCByYlRTMsiVyyRKVSYX1hhY2sb3dBwHJd7U7NEwiFCwSCu42CZLo7jsbGZY3pmkcOTY3R2pGhvi9IWC5PP5iiWygR0jUqlytzCitS/KpVRFY2Hc0vU6nWu3ZpifSvD8uoG+XyRSEQyK7bjUKnW2NzOkM0VKJfL/OmP3+KTa/f40otnURVBplDi4vU7PP/kaYb6e3gwt8Tt+3O8/cElBvq6WNvYxnHky+w4LsVSmUKhjOtKFfNcvsgb715i6uEiLz9zhs50kjtTc1y4dJNEPMbG1jbvXrjK6FAfzx85iH1HvqOqphHyxwjVg7Z4lEg4SDgcpLuznXKlxtSDOQIBg1y+SLlUplypkskWmr6MQghWN7a5ef8hhXIPq+tbVH2GJd2e5MSRg6yubTC/sIKmCFzb4uSRcbo6UpRKZd545yO+99q7DPZ1kW5PMLewxtpGBtv28FyFuungOB7VWh3XEZTLddY3climTS5f4o13P8a2TYb6uoj7WnAbm3kURfCdL7/Af3rtHd65cIWjE6N0jQ7tjKGqiioUdFWjXrcoV2pEKjXpt4lcaC6trLO0tkHdrMsJRNUwAgHZ5oUim9s50qkEpZJcINXrJlvbOZJtksnczBSYW1jh3tQsx49MMDzYR75QpFap0JFKNMdfQ9exbZtCroBQoFisYloWiqqwnc1x9/5DisUy8ViY5548xReef5JYNIKiCAqlClZdpvqpmzKiNRgIoiiaDBLa3KZQKHNwtF8mURcKAUOnVje5ePU2pmUyv7SGZTlMjA9RrdYplMpysVarc396FiFgZHiA40cnWFnb4N0PLnF/eo6udJu/sPTo7kpzcGyAdy5c4+HCCpFQkHg0zPr6Jusb2ww1ndlbBv39AFfDJLiLafkN+lXJEz76eW+EYsN0uJ+pc78zip3z7NodWqxJnyLn1Fi1NH/2qXMD2XhI86fPyH2+1tmp9yP7fxpr98h3u/9u3lnTFAkoqu9j6uw6qjUco5mr0YOdaNPGGT+/3fIvEWy1lF3KeC0P89MOaXVtE7Dbpip1rdxmowkMXSMaCREP6RSKJenMvbGFKqS4XyZfoFKpIlyH//C9n/PRtbt0d6QwdI2F5XU+vHSDk0cOcuHSDUrlCl0dbb6W0i5JNGzHAU1prho70kkcz6Nu2mxnC5iWRalS4/rdWcZHBzly8ADvXrjK2x/foFI36WhPMDrYh6Le9AXeGrpAgu7OdgZ7u1ha3Waov5uJsWHqdRPX9Whvb+eJ00e5eP0+c0vrHDs0xtNfeIrJ0RGEEFi2Q61uUa1ZlMs1dE1KWoyPDfLm+5e4cOkmruNi6Bq4Dn09neTyRZZW1qnV6gghKFdqeJ5LNBzEsV3WNzOUyhVW1ra4dvsBm9s5NE3qahXLVbayBVzXY2V1g5m5NdKpNrrSKZbXNjF0nfmlVT66fJtcoYhlW5w+cYhKpU65UmNjK8d2Ni/NfLU6lmVTrZm0xRM8de4EG5s57j+c5/6DeTykHIWmapRKNVbXt3A9j+2MBBeum0YmoQ1SqVq899F1opEgXR1Jzpw6THd3J7lcgZn5NXKFCtu5Atl8wfdfauPqrSn+8E9eoy0R5dihMc6dPI5tw0dX7kj9LUX+1C0bTyhEwkHi0SjbuTyaatDd1cHJY5PMzK/w8ZU7PJhdIhwK0p1u48svP01XR5qL128zt7QGCLo6UrQnEziOw6Vrd/nR6++TyZXo6+3g6KEDTBwYYX0jwydX73Dlxn2OTI5y/NABkm0x2pMJiqUKUw8XuHT1Ng9nFhgfGeC5J0+S9WVHwqEg29kCiyvrKIpCvlgGT3BnaoZyucrGVpZ8scLFa3cZGx7k4NgQW5k85aqJbVvcfzjPuVNHQSjUTZu6aXL/4QK27VAsV7l8/R5PnDrCKy+cZ2M7w4eXb/Eff/Q28UiITK5IV2c75WqNtY1tPrl6mzvTc2xnCzycW2Z4sFcmUhZLXLpxD9eV4OS1X1ygLRHDNC1ef+cig33znKrb/mDtYftSHR4gLBs9oJNOtdHd1cGxI+NoqgS/QhFsbGZkRJzrMb+0yomj49RNk97uNDPzq7z57gWGB3vIFwukUikJNG2HjnRKCt26Lrqu88KzTzA2Mkg8HmI7m6NcreG6LplckaLvmD23sEbdlMeXq5Il3M4VJOByXaKRMONjg8wtrnLz7gz3pueJhoMcmxxhYmyQlfUsG5sZnj53nPGRARaW18kWiji5ws585fsoGobOzPwGH12+xcTYIMurmzJBtKqwur7FzPwKW5kC7SnJ8glFoViu8M6HV8nmSxwY7ufO1AyWbXNwbIhqtcr7Fy7j4fHKi+f5+VsXuHztLkcPHSAei/DmOxe4ees+z54/wXNPnaGrs51kIsrC8joXr932zf95qjUTz/M4dHCU+aVVrt99iOt6xKIROtqT2LaD7djguQQNA10PUC7XMS2HYqlCoVgmHo1QqdSoWyae6/kpsWqYlnSELxTLTD+cZ30rSygUZDuTJ5MrsL6ZQVEUYtEIXZ3trG1syeTd1RoXr9zm4dwS1VqdgZ4ONFUyvMGAwQtPnaFaq/PLizexbPn8llbXCdwJ0u+4OzlQG2U/NqnVeb0xW/26zt2P8w1rnv1TSgvQajrXCF+M+VetziNmusdcW/j/PcKACaSZ0PHxqtK0INLwvfiLlL31269eLebv3V89jv3yQdMeIP3oVVoteHvEUj9H+V8HbH2OItVd9lReUZvftO7ZgM2i2akEqqqSjsf42jMneOv9T9jKFiiUq8TCYTyEr4NTJxQ0pKOuplGrm6iqQqFU4drN+wz395BKJrh9/yF3p2fx8DhzfIJA5qq/KnKp1UySXR2YlsXC0hqHJ0aIR8P0dafRNY2hgR6y+RK2Lf2CIuEwqbY4MmeZ4MlThzl1/BDqf3xDoummKdjD0FWG+ru5/3CRZ584QXuyjYWlVTxPgrxiWfo6HT00ytHJMfq6O6j6QClgSBPScH83He1JYtEI/b3dfOmlp9jK5JmeXWJucZXBvi6ef/IkqqazsZ3Dw6O3O02lblEzTdqTcYSAmlln6uEC/b2dmJbNyKBMYqupKnXTYm0z02QEq1WLS9fusLa5jaHr/M63X2Ggv5v703MM9HaxncvzcH6FQxOjeJ5HIh4jEgpRLpuEggEikTCW5ZLLlVGEysmjh4iEwkzPLGCZLpZjsra+haZpBAyDU8cmsSybjmSSVCKJqugIT8V1XNLJOC8+c4bx0UHAYWioXwqP+mD2mSeONxnEYCDAF54/x+T4CKVSlVg0RDQSIRqJ8NQTJ6lU62xuZwiHQoyP9XHs8AGEohAKBjl6+AD1uklbIoaha5w+cRjLtpmdXyGRiBEKGriOQzQcpqezg2fPn2B1I0MmX+Tl55/g8OQYpuUyu7DC5ev3yOZLJNtijA4PEItG/Ggrh2AgwJPnjhM2dFKJBJ4HH1+5xeXr97gzNcuxiSF+/69+mZ7uNFduTAEe8ViYtY0M0zNLJNti6JpKqVxnaWWDvp4Ojh8+QLlSJRQMUqlU8VyPocF+Th4dZ31zi3giSjgcJhGL8tIzZwmFDI4cPkAkGODu/RmGB/oZGugjHInwO0CqvY2FpTUEMDrcz+GJUYYGesjni6ysbiIQpFMJH8x7fPNLz3Hu5CE0XaNarXH5+j2SiRiHJ0ZRFMHM/AoXr91lO1f2I/uCqIlYc0XqWjZeQCVg6IwN93F0YoRwOISh6wQCulQSdx3a4lEOT4xi+Lkrnzx7jC9/8VnaEjHpY2bJtFRGIEomm+fG7Sm2MzmOTo4y0NtFyNApFksEghrBQIBTxyfRVIW1zRw10yIalu9b0NCpuQ6vPHeW00fHSadTPmsbpLMjhaIonD4+gWU7VGt10qkEoyMDTIwP09/bxYcXb/Pme5+QL5Y5PD7M0YkxQsXqzqinKqgCDH/Munz9Hvem57Bsh8MTIySTCVZWNlhc2aJSMymUKtRqJsMDPRydGOWjK3d578JVLl2/67snDHLq2ASmaTG3uMydqVn6ejukST4SImjoVCpV8rkCd6fmKJWr9PV0MTzQx8kjB3n7w0v8yfdfl36U+ZLUbDNtutLtnD5+iLc/vMK9h4tcuHgdy7YxTZPTxw/TnkwQi0aaCX6LpQp3788wMtDLwfFRKQZtO+SLJSrVKtVwkFq9Tt20MAydUDBAWzzKRibPBx9dJZVMUKvVsW0bw9AZHe5nO5Pn4pVbXL5+l83tHJFQEF3TsB2p4VepVLlw8SrVSo22eIx4LEImV8S0LCzL4d70LF+0rGbOyF2+UQ1zVKtLjFDAa+FBmvu2zleNbAwNN6ZfZZLeYyH6FObMa/pHtdTlV7befYr5bte1G+Bkb70arj8au+itXx310bijxl/7Ai0BzUCE5vcuzcTXjylN53j/dIoi9mRcaL0Xdp7dX6D85YEt0dAi2bWx5fejje/t2UP+p7R8u7On8HfwWtitUDDIH/z2Nzk6Ocr8wjKKUEinEtRNGyFktF04HOb3/upXOXn8ULMW+UKJaCREV0eK7s52xkf7cF2HSDhEf18PynvXfcpYTtj/5//dH1Cp15uMWjgU5OVnz3Du1BES8Qhzi6sUSxWG+nuIRiM8ceoIpXKVfKnMi8+co6+3h1ZFZdd1qdbqTM8ssZXJM9Db6TvRi2auJtuyyOXLbGZyXLs1zdSDRRkW4MHwQC+vvnCeZ8+f4szxwxRLZaLRCIauc+LIBKFQkDtTM4SDQfp6OptJbgfbu/nWl1+QIf5B6bz+oC3G8+ePceLYBOlU0m83jWfOnaSnK0U0GqFSrXLj9jSrfiqkU0eP8Nvf/hKZXIF4PEZfdwfBoEF/XxdPnzvG2sYWmWKR1fUtEvEoX3z+PBPjw3R3ylyQX3rpGYYH+hgfHSThA7pDE6OMDg1SLFW4Nz1LKBCit6uLaCTMoYPj1Go2iViU9lQSTdOomyaZXIFwOMTxo4c4c+IIuUIBTZNAvCOd4vmnTmM7Dn3dnQRDASzT8nM/SuX9lbVNVlbX0XWN9mQbz5w/xczcIm2JGEcOHfBD110Mw2BsuB/d0Em1JdBUlUQ8ypmThzl36ghdHWm2MlkufHKdummhaQpnTh1hbSvHw9klTh2fJBKLoigKT507zuz8Mq+/e5FSuYJpWaiqwtBgLz1dHXR2tGMYGlvrW3iuoLszTV9PB/cfzmPWazz35ElGh/pY29gmk81xcGyQA0O9zMwtsbGdpa+7kydOHWZtI0OhWOXcycMcGOlFURXK5TqKoqIZBudOH6U9lWBqZoZjh8cRqoLreRyZHCYaDzJ+YIQ2X3ojFAyjG0FM02J0ZIBvBDWWVzbQdY3urjSd6RSe62CZFs8/fZqXnz+L7jMz4WCQYDDA0EAPwYBBJlfg4NggnekUkXAIz/P4u7/3Dc6cmKTtvSu0128SDAYwoyGp8uy6VEtlCrgUyxUs2ybkM5SapqGqKsFAAF1VSCVidHemsR0bI2CQbk8xOtxPsi0uGVrPxXEcaqZkSXO5PKubGcrVGvcfLvjjCkyMD/Hic+fo7Ejz6hGBu7ROSQhqJw6R6u8ioilE22J87ZVnm9IDqNL8FQoEiEbCHJkcY/LgCIlY1A9MiKAqAsey6O3u5MadaSqVOieOTDA+NoR4//LOcKdp2LZNpVrj0Pggf+u3v0rAkOb3VKoNoah0d7YTCgVZ38wQDgVJxKMMDfTxd3/32zz/5FkezC5Qr1t0dqQ4MDJANBohX6ryhefOoygqt+8/IBYN8/JzZzl+aIxYNMqZE5M8mFlgczvH1naOwwcP8sqLTzLY18XS6gaWbVMsVtjK5ujr6UAIGOrv4ezxSVbWt5meXaRYqnD8yDjhUBBN16RIrKrS293J3alZHs4t8tGl67S3Jxnq7+HwxBgLiyv88uPr6IbGrXszKELwxKkjHDt8kEKpyM/e+pArN6cwDINqzaRSrRGNhKjW6lLGxvEIBgPN9D1SGsakUqnTnlTJ5kt8dPE6dcumXKkSCQUIh0N4QKFQwrRsAvjO7N5jTGqNmWqPszaKD3Y8lV3JtX1gsuNs3WLZeYwP1uMMVI+XgPBBTeNA0Uie/avDhM91hD9h70Q37szFjbM0sdCvBVV2WmB/rmqnGgixJ/jgs8HdTsTo475rXLj1Sfz6zNxfGtgSLf/L0qBJFfaKjQGI1hQIu+6Uls+tGddbHqz/MIWARDzK+OgwPR3taKqklzVNpVarEwpJgcnR4X7aU20UiiUMw5AO5q5HLl+gUqnQ091Hsi2OJxTKVelsq/t3EwwEGBocxLJN8oUCM/PLRCMRjhwcZXiwl4Ch05aIU6+bcmDRDDo62nn2/Emqps3wYB/BoLFrIWDbNhXH5t70AnOLa5w5PslQvwRkwYAhGY5QiAMjA/R0dhAKBohFw0TDQeYXV7kzNcOxQ2OcPDaJ0WVQrlQxbRdPSGX70aF+PE9qW+ULModgMhEnnWojngjR3dmJZTo8nFvE0HTGR/o5dWwSIQQra5sEdJ1UMkFHWjJmlYoEppvbOW7cniYcDHP08AQ9vd0y+bHnYTke+XKNuw/m2coV8PCYXVhlYmyQ0eE+xg8Mo6kqrutyZHKM4cFeIuEwASMgfWwck2yuyOraBpVKjaGBPrq70hiGTqZQZn5xhcH+Xqkr5knAXChXfJX1DUaG+tF1Xco5+KHz/X3dOI5DNBKWZLcQ6LqBphoyslJVmwyJ53nNxNztqSTRSEQqxDsuhh4kkUigaBqaroPrIYTip4kKoakyOGJlbYtkIo7letiOVGWPxyO+L5xoRnoKRcGybDY2s1y8fIuezhTBYIDBwT6SyTjlYknqDgmN9lQbT6dOcejgKPlcltGhbizLplAs47kuyUSM/u4OOtrbuPdgjr6eTkYGetnczrK4vEVvVwepZBJVEwSCjgwWUFUUIWU/hgZ6iIRDWI6F5zg4rkObDygBQuEwtu1h1+q4jku5XMNzoburg3gsQjQSQlVVaqaJrmt0tLeRiEUJBAxq9Tr5Qglh+knJayalckUCsZaE5O2pNg6M9MOFm+i6hqIo2MEgwrYxTYuHDxaYTcVIpdro6e7A0HXpR6cITLPO5naWrUyWcEgH4SKE5+t/KU0fJ8eRUVOKUAALVXgM9nWhqQptiTg9Xe2oqsrlG3e5cPEmw0N9nDg6QfDWA9J/9JqfNUDxxUWBoEE4EpIJpSNhnHCQQMAgoKnEAzp94RAiEkKrmCh1GydXpCI83JBBIhrk5NEDqKpGR3s7QlHxSpWdCUZTKZbKZAtF+nvSDA/0MtjfjW07mK50YRgJBujtkpp4ruvSnmrDUFW6OqTK/fjIAJYtVfYDhsFmtoBeMxkbGaS7u4P7Dx4Si4aZHB8mFo2hKKoESJqMhIyGwwjPJRYJc/rEYU4encR1XSq1GrW6SVdHCkWVDvoHx4Y5NL/CQG8XJ49OMD46RCgUpG6aqJogEg7y8jNnGOhJs7Syjqoq1Ks1enu7+OoXnuHG7XtkMnm2M3ni0TBHDo5w7MgERiBAIhHlt771CjfuTLO0usFWpkBfTwcnj02QL5RYWd1koK+Hgf5OHs4u+QyuQU9HmngsSjBgcO7kUSxLBhsl41HGhvvp6UpTqtTRGlGYzanoUaAlPK8FQLTAAH9eEw1TlpAJ1vZ4We+aEXed+VMB16fBjca+rYmmPL8+4pHjdmQfvBYw8esUn7VrlYkQ4pE2+4vpg+1MlM0W2MNwiT1/I5DpsfY506NwTLQ8tx1fuObVmqSZaKZ0+3XLbwBs7Ye/PXb7aX06ymwg41ZJB/lxJxXN7uvJ341maT6ObJ7oP/oXjNRNmZBV4DeQBGmKIvwUJGDYDilfAkDTVFwgZVk4joPmuIQeLOAJQdIDtVJF+Glx9Iu36Pv6f4brybxzE56L6q/ahaqCIggjqWahKPKFE5Dya6pqCkJRUWYWwPNwhQBdQ3gu9tQ8varCC8P9xGsmJtARCmMJqNXqLK1ucGxyjJefOy8d4ctl3nMvMT27KE2NLhiKQjAcRLNdFFWmxtB1lVAwwMeXb5LNFRkd6mNksJd4LIYqPFwErmfjAcFQEN3PsaiqCv03H6DlKgSiYULxMFo4gO7BwVKZqO2RK5Twbt7HcjzUWBgRMEDTwDCorq1T3NgiFQnR099Fb08Hhw6O0NvThWboCE/C50gkQjgUpZGhvflMXQdDUxgd6Kavr4dUWxxVUXBcl1hUTuyNnICmaWE7LuVylbnFFQ6MDDLQ142u6yiKTGgtgavUXJLmZ6UZ9QcQ8c8XCgWxHQfbluxmsi3eBBtCEehGgJDwVa9RcD0HVdHRgwYB3cCyHGo1B1XVsGwPx5HO4sGgVMp3/Cg506xx8eptrty4h2HotCVirK6uUy4VOTwxSiwSxtB1KtL2gOu61P2+bdsO7ck2YtE4IPt4yE9N1EjoHAwEpIN9KERPt048JsP/LculYrqYlivlGHyhU4BIOOxHBcp0Pq5QiMdkkABAuVqlUCgTNEIEg0EpqaEahHQNXdNxPZmeRVGlHIdtS58vz/MolSuUK9XmoGVZkq2R13cwxE4aqlK1jrqVwXGkvpkTDqA4Uozy6tXbzI0Pcur4JN9+7ZfEfvAuDSFL0zTRa3V6gXgsQjgYwPU8hqt1bNuRiuW6vmvQVB2HkUKJttyOH1/AMLBsm+c2tsgXy/T+y+8SCYdxsjls20HXtZaBGUTNhFodtna4BMXzMBxnlwtJI5DH82R6Mc+flN2QgRKLoiaieJEIYnFVpvNyPcgVCL/1ISc3c4Q9D/PaPTzHxTF07GAAVwXbkZImuq4TDgeJRiPSn0sIdF0jlUzINGR+Ci3dMOTEiEdfTycdaek+EDAMVEWVMiU+MzV5YJCBnjSar8SuCIGiqXioJHSNdEqa9xupuRJtcZ5/8iQnjk7S292FEIJKtYqigKJ4BA3pLtHblSaTzbGxlcXQVQKawqEDg4wMdFOrmVRrNRaX19jO5qnV6yyurBOPRTl3+jhHJg4yM7/A9Mwi3d0ddHWmWFndINkWZXx0hLZEkP7udlbXtujt7mCwv5dYNI4iBJMHR+jr7WRxeQ2rLl1LtjJ5HK/AQHsS4+Obsu39p+v5ZqrmpC4akt2tZMCeSVg8TmpgHwICaEbZN3drNc+1Arp9mDa3Md8KX8XzUSmE/XW1APzcgSg8NufjvsW/3l5Q08LS7acG//nAyp573nXNT6nOfpsbpMw+O3nwiBRHI8LR8xfxDZShKCqRSKgZFfzrlL8w2HoUNzfJw1/9RJ5/tsbKYNd3e3f2nfBai+uhZPIE/JyBgpaH69OdjcbTPA+18eIoMsJJeFKqXzg2mDbC85M/u40O6SFsG7VQQgW0Blkndh7K48ojFmTTBE+yK2EnT5fr8k8LJRKxKOl/9T20f/MDAh5EwWcB6nxrcxuh66TevoTQNYqmyWixjKcqdP7gbZIfXkcLGni6hqepoGkoAR00jXEhSJer2EIQW88S+vAaauAOnqqiaCoBIegqljhbqKHbHsaNaQKRIMl3LqOvbDalAdBVgkIQ8WDYT6EhV/m/2Glnvy2OWjb/d19aQFvJoAYeor1zDfXf/gQR0EHXEbpkhzxNw9M00P36KAoJoeCoKkpARwvfRw0FQNfpUwRfdV3E3CrBrQKuptK/us5XYzFyqs5wtU56O0c8GMCo1fH6OvB0FdWQE7ai0ASTrq8FpCgKyUScRCxGIKDjuh49XR0gBLGEZDld2cVQVIWAoja1W4RQUFTVz02ogGvT0dnJmZNHScZjhMJBIlGD82ePU65USbenUNAolYrcuT9DKBjg6196jlNHpGOyrksGK5WMoWoa8WgYK5XAcTxURcOyHAxdIxkLk4jFURRBIhanv8ciGgnjCYVgMEhvdwfJtgR6IEgwrBAMhbEsD8t2wQVDV5vRoLbtEIuG0HXRZN5URZFsQDCAYehYpkU+X5KMcLuOpkMkEpCabMLDcx35/ggh1fhDYSrlMtVqjWqlRq1SRReCmKaiWQ5e3SRkOSiOh5Mr4RiWXIBUa6jbBWLFCortIlQbzcNnkgSK46KqKt2daaKmjVEoySSylg11E8N1pESC4+L6EgqaaaN6HlrNQmjqrrdV8Tz0Uhk9X8IrV7FMG8XQqZsmWrlKt64Rq1uoVhFRqVP3Abpz/CAYukyWXa5CqSw/W05zlJKLAbc5fonWyVMIf1B3UWsmmpVHyRXlO7WZRTjSyVjJ5Bn76Yf8g0yewHaRjv/hTwkHA4TwZWNUBStgYBkGXjSMkYyjJuIo0QgiGsYLB1FCQbxwCBGL4EbDRBSNmOMi/JyZEkz7k7iQ4qSmZXFwbJAnzx6XbKj/vjQU6UHmOtVUFeG3iaLC8FA3A/2dxGNRNF0yO8GgiqoFUIQgqKuoQrKZhq7RlojjuC7hYJBAQPcBv2Qg47EoD2bmyRfL9HSmGB2S+TDTqTbaEjGG+vtQVQXLNtEViEbDdHV2EDCkQHGqLUFHe5JUMtEUXgWFVDJBf08Hjm1SrdbJ5kvUTCnmGvj+L+TzhJ15Z5/hvcU4sc8+LZPDHuardefHBoo91mH+MQxXY35qAq/GZdwdU9kjbM8eQqR1oftpdWrWq7Vxdp9LnsrbvV2Iltv6VRgib9evR/y2Wpm1xm9v725yg9fygy+0vZfK8XxfrtbtmqrJ/qztgUye2EMsPb78xZmt/XvKPtt8+/E+9F7ruRqPWjzuNK07/5P/ByKbh9sPwbIRPi2q4PngZncnED6Qk4j20fo3k067LsKydr7wOzKKKvf3G/wxa5S9tdy/mD6oVARoGsJxiUfDJOJR1EYy18ZzFEJS9AED07SobGYwdI2ggD5kEqLAVhY9X2qmStlbNKDnsTXy0D0Iei7dbmt7gb662bLKA68njRcOSWVpdR8nRA88v3GlaGpkx78OD1E3Zdvucd7c6a+eHwnkNcHs3udoeB6B1kt6HuOuy4gvJqs+WET/dz/y2UjwDo6AobMTwus1f3YocHkegW9msm1ifjLmvQ6yzZe5cb6W3411QtJzOep6fqCQPD7ZAGd+vYOuw39ZM8Hz0G/Po6hv7PIIaOwX8zw6Xa/lOjvfNwaHEf+nUTrcHdp917i4555bv2trrq53rtNIJ9XIWZf0XBRksEdzfb9rEPQDWjyPXr9ueB6eqmB2p1FVtdmmsp38YxSlOa+EXI+446CubaHVTUAQ/1d/hicEOoJvt6d4dqCPdN0iUKmh1E2ZgDsRA1NKOVQ9l4qqEAoahEMhFN/nUvjJwHfoc1lvLRJCaBq5coWyZRFSFVxFYKTihNviaJU6uK4EbD5jVP1b38Yb6ZOssi3T8DiOQ0AR6HULShWcfBE7X8ItlKBcRalUUUpVNNNEq9ahVEGUq4i6BbU6XrkqxUzd3eOkEIJwKCA1tDSZyL05VtoumlVB86oo2QLK8kazzzXCjvYO9CE8kv67LkIBvEgINxyEaBgiEfSQzjeEwDJ0gjPLhLJllFgENxzCDQUhFIBICC0W8ZlUOcmrmiCdansEEiiqIU3CgOpzf0J4KIqBYeg+Q6rsgFEBiiroTCeIRA5i1i0MwyAUDCIUmfA9EY8SjYSlY71j4rhpNFWVVgYBXZ1pOtpTGH4mjAarKIRAVQQKBqDTlgjT2dGGpygSkLWw3UK0muZa+3njxWotO1F4zd7Vuo9Qd5vc/PN6ouXs+7FB0MKiPW5G8Vp+frWyc37ns3Zlt39Zo11aryv2gCxv97EIPjsicZ9j8aP/9mkfedW9AKzlmpKq2nMb3s7Y1DImup6/mHB250eUAS8BmeS82R32o5oeX/7iYGvPPbqIz4gD2Ocg/AnJa+1U+++3q2QLsJXD60xh+5E+9bqJ64cYS/8ZZVeXkGdtsF2tHaHlvLaNmFvBA9xwELsjhdfeRv5/+q/lMOF6YNkoritBnmUhHBdhy23CdhG2hbAcFNtGmA6K44Lt4JkWnm2j/3/+HcKy8EJBrCMHKOcKqHUTEQriKQo4DtiOTNnhZ6pPKSp2rY7ni6yqnr+SqVvgR3q5eCgN7ZDmfT3mPlvt3p63M9H72z3PQ1RrOwN/MIAXMPCCAYQr6yZ//M+e508UHsJ1ZN1cOXF4rotrWniui6YoUkzSn1SU1sFp7ypm54uWD60Dj3yaKnvYw7rVvA9x92GTMN1V9uleHuA5Dqrn+Yr6yudD1a3lc75/ivcZL6DY9etTd/vUxcmeZnvkfDvz2yPFoyVRq7fDAgrl8Zo5j26XA7Tum++aaeWFXGvsfThCiKYJrLlvSTINCjCysMaw/7e6sCrfE01F11Tq5QpqrY6rqRQ8j+1ane54jER3B0RCuJEQXigAkbD8HZaMD0YApW6yvblF1rQw2mIkezoZGuqDVAz+3n8N21kUX2dNCIEXDQHS7GlaNsVSmVK5QiQcIhaNYMQiiN5ONJCO+LU6i8vr3L73gGg0zJnjh2lri+20kifbw6uZtP0//xXqGx+C4+J0JIkEDMIBQ/bLuoldtJr9VPU8NMdF7APS9n8eNNvy0Ucln5UBhD/lHDvzjQBVkQsvVbpkBPzPqCqeqja/8zRVulqoCiiqZN99k/7u63j+nC07bGs9vEd0jhpzRstYtl8HF+zqd3vvyQA/ylDIBfyuo108P0H1Iwuv1ot5HrjapzIdu9LBtKbz2Xue1s+7wI3c5rXWpXny/cb5/SQMGju0tNlja9xSD2+//b19Pu3dsgcoeu5u4AktTvbsA6g+ZdDdO7/JkzVWoZ/vHC1FAncXy7KbeYZ3Tuunn0OlRc/ic5fffCLqR9cBLeVzou7Gg913dtxnKBeCnK6xnstTrtSwLJkGYqQjRdRXePbYObQpJtHSOXc9F9PEW1qX96PIVbzbwlCgKaCp0i9LCBxfxVlTQ8RjUbmCE7ItXK8xl+34eDieh/7vf4TnmxLVB/No5SrVUplSwCARj6FpO9ChIZcS0FQC0XCzU3oeOI6NZUl1+WpVinXGQ0FikTBK6zvawuZIFOm/bI2/vUb0zc424blQlSv6JpCqmwhN3Y3fGgMqO4k5XW+nz4OUrshm89TqJsm2OMGAIRXMTZtEPEo4GNxdP8/d87e3u4603IO7s0Jp7reR2dmvMajv+7B3mkh+5WG7UpxSV6Vvyq9lFv8c5fHvyV9O2R+/+lBN7PlCCFxf78hTJMCyHQfHcZtMAb5vWCMUvvnsESiFknwO/jMSrofwcya2okjhr+wfQcNCSLbHbay2RfN/JVeEUkX+Xa3JG7MsxNIaZV8gNxUIEDFN8pkCXm0WdSO7yz/vkSIEETwGPH+SbzjLhoLSDHfnAVg2mBZ6rY4nFNT/9DpuIopn6NRtm+1ika1qHUJBIh1J0oO9dAz2EvDND5ouBWYvXLqJrusM9HWTTMZ3fFY9PzV9wMCsVAkKOfYQCaODjEp0HMo1k6rj+JGrKvFYmGAwKBcGjXegAbxct7noEa7bst2Tbev4CyLHwXMcXMf1w+A/nX3Y1ZLWrkf66H6f9ur4YM0d6t0NRGgxZz32eNHy/6fs2AK0Wi+7+yzNHXef2/PkOL8L9Hxaafh47V+nX9fBemcE8pB+VoJGrsSGr5xMhSP2XFLsjKfN473mOR9/G58GcD5jLNzz4PYbPXeAlVQt2PX3PszWvmmOmif3RdAbAGs3jnz0ms1DH03y7Xpg29L/0W1iEPGZt/x5yl8C2Hr89k/FwJ6fEPJTXphd1KvX2NeDVBvb/81/weXLN/nZLz7kk2t3OHN0nP/qH/8BRw+NNztHI5+TKxTpD+jnb2o4MjYfxmaGwN//FxJw5ItyFa8ou3Q4hBCoQta4XK7ywUeXSSakIKehaygtEQ4Nxk48Mjg0bk1uqVTrmJbtp8lRW3egVZ+uQXMLz8P1FITvuOq4Mheh6Ti4QqBo2q80m3t7/hIgJzV/MJYUipBmVOFPsn7KA1SlOXB6HlTqJoqmEgwFEIpMCp2zLHKei97ehhKLsL7iknPLjHSkCHR1oKoqnn9vns8oCUUGHchzNz7Le0bxr6006uHXT1EQ//o/QbUGioL7D39LOu775/R8PxpP+PnbhcBt5D1EkTpWt+7zhefPc3jyAJqhSZ8kRfH3VWQ9G6tyRdmpoyId72V7gOebnoVfVw/kil/2Yv95+uZpdfd5harISEH/ek3zl6KC8JomY69Zp5a2kpmY8YQEuluZPBev3yUWj3Lq9HES8bDPMqkgNBwEAgsVafp0HJc33/uIn/3iQ77x6nM8de44H31ynT/+/hs8++RJvvrF5wiFAjvmqsZE6b9DkX/wf0VkcihzKwifefLSbZ/S+1qnTNnZnXhUvrsNoNDClDY+C8uWAEIRVHWdO9EQwx1J4vEYbGWpVaoEGo7on/oy7IwRuybVag1RrSGqdcmg2bZkbvHQfvoeCIFiO8RqdQ755n48yXa5nidzMQaDEA7hhgzO2zbJ1U2qmsqYDdHeLggFcUMGbjCIGwxQVQSlhVWCjoumazjJuB9pKRMsu2EDEQliVaoU6yYEDIxkHPGYaKnm3N+64vTZI+FPObZls7GVYXs7R1s0Qm86hSbw2XUHbJ85k9Ee0p/M/1v4rD2Os7NI/lUmJ12DdLKFjGnAij0jktfaR/xxc9ftPv6iO3JJO/e/K8ABmguPplkvGfePafTJzwO4/Jt/ZHYW/lyzt66NOrSASyH2Ob71ye3Mg3hKC8sl2L+Pf362c5erxKeVx5k7xe7vdt3TrntumdB2mfj2AYOfUacdoPVIbXbvhByfGnJKu29nh713XBfLzzCy+4x/MUHW3xjY+jyIvdlpeczD9nj8d/4OHoCfm07xGvO/YKi/F1VVuXF7ynfQNZrh4J4rTWQu/mJcgNdMlNkAYX5yzYYfA9LpFyFfTwUZ2ei6LeG9fnFcl3KlQrq9DaEIypUqwnMIBgz0hn9XU/NEFut/8zty5a9IkLC8sMy//+5PUY0Av/2dLzE2MuCDKoGnCBzfQVsoQjr4Kh4oCo5jU6mZbGTzfHLtFp9cvcupY5N8+eVn6O5Ko2gKnlB3AxLVn9yFbyJrggUJQj3h4ioqKAqhv/fPURZWEYUS7lAftX/297GfOIZt2+SLZeqWTTgcIRAwEH6054OZBd754CKjw/08c/Y4kUiQXKHEn//Za1y9cZ+/83vf4ODYEH/+3Z9x+94Mv//XvsaLzzzRjHjzPFc6FSOBS+MZt4bmNpLOtpq4Wm3vxp/+RE6Mmorzj38Pmol15SAlkxXv9qFyPQ9F6MxGgryZyXDkpfNMPvsEtipwHBMETfkAOSgrzbdc9QfYRl33dGl5hOfhICTz4zl4jgue75+HyyPmhxYw1YiEcpsDwM7CxOeO2P/NcXDMOgtbGd67dJMjh8c5hoyuaS5W/FTYDaAlFIXMVparN+5TqdZob4uhKwJNU7AtC0UozfZvLXsBVwP7oqlgaNDe9sgxHrvH0NZ5pvVz8649r0nvK4qQ/k61Op6isB4P8z8d7Oe3v/MKkfOnuHz9Lv/Dv/5PHDk4yP/h7/w2bbqOnS8jKlUJoCo1lFodKjWUBqiq1BEV+ZlKDVGuQrWGt7CKwGk+Y09RcFwP27YoliuoivQharzvjuNSqdao1S00VUOvVFErVUJ1k4GCTDHTfusBxv35nfvzbzbmulTqdSodSbSBbur/3/8LQgguXLzO5Wv3+MLz5zgwMoRTrLK+ukYpEkAd6CMUDDcXhI3we+mn5C8kPAmuZY+1m2OY53lk8wVef+tDvv/au4yPDfJP/sHv0N3Z3hQgxcN3LW+Aa+k3JZkVT/pfCaR/Z6kCJRkw4BXLUKlKM3CpIoMIylVEsYzyyU055/Z3Yv/3/9yvi+KPsS4eO++zfO6Ap+Ai8Pxo4J2AA5/xETx2Lmq0id5cG4ndbdRYPCrqzrY95sL9RE53JoPHAS2/Ab3WntyyvbU8Yhp89F72BV2fq3xOAPUXoHEel2i7wRA26/HIbi3j2t5z/Br12YfO2akOLYuqPcDW8zwcn8F3W7Y/+hj2Av3PLr8BB/ndV2w++seIhX162e8hPLoLntNcoTR2l1ECHtV6DTwPRfj+Nv4kpKgCgYLtWS0Pfaeoquo3sPsIfvXJKQr5Apm8jBaKRkKEgwHC4TCxaJiXnjsHnkepXGJtfZtsNksqEWegt5tgUEZ76VqAgCGd7NwXnwCgUq1RrlR56Dl8EDBItsV45fABho5NoiAnV9t1KZVrbGdyxKJh2lNxNF3en66AqNWJZXLYmW22ZhYoppNYfZ243Z1oClQsR4prqqqfh3C3vVzxtbFk0mIki9NYKVkOElhIQOq6LqZpkcnlWVxaw7Qdenq76UynMISGikd7IsqzT56iPZkgGNCbYLXRyffXK3EAKT9hOxabmRyW7RKNRqUEgiZlAUzTpFSpghAEdK3ptCjwUDwH2/PkOq5hb/egXK+zsbzG+uY2gYBBf08XhVKZjc0MbYkoQwO9TWkHRWlx1PUH4ly+wNTMDAHDoK+nE9O0sGybjnQ7RkgGC6j+/g6NlZYAV0boNUGiECjI1Eoenj+Y+yBc7DAr3h700dgmfODveTIApLFd8fu553r+BNU4VjaA67oUyyVK5Yq/r6yj6j93xzGxXfnMdU32jZn5Je5MzRLSNTRVwbJtVte2KJermGadQr5AJBJpymV4npTfcFzXf+/8qUDT8Ho7cVMJMv/v/wrTDzzRNalppuoBavU6c4srLK9u0JVuJ5WMU6pUiYZlwIhh6DiOy/rmNleu32V6ZgHP85g4MMy3LYvQrQd4nkfdtLAsG13TqFRrzC+uUipXyRUrzGxuMT46jBYJUyyVWVhaw4kGGZgYJpVM+Kb/nbZ2fXNcA1yH/v6/QNnKIRbX8Ho6sNsTzPzOl/jgrY9YejDHV546zanRYVzThEpNRrNtZckurlK2bNKhIIZl42bzPhnqX8cXQRXNoAEPRVWI+KnC3HQSDJ1KtcaDmSUWltaJRsOEwyHCoTDpdBwP1/dxaz70Xf2o0XdURW2Oy0KOLvKzIohGIvR0dxAMBqQvpbcnc0cTOUtA7uIvhpQWqR4hIBiQP+mk//55uyatxjZMi8Bf/afy1Hdm0P7uv2jttOA1HNObr/EeoqI1EGgvYGm9VuN0O+4tDaDVaKfmIf5/nhDQFof//l/sOqXbCrRa72VfEPF5AMI+DFBzQ8uWvauRz3Pm1mOai0x5zU89w6cAm1YB8V+pNMzynrcPLNinb/zK4OoRWmvXpuZ47PfpVpDt+qC6gQlcD2y3IcvSOFHraeW8KImQXwXo/oakH1o/+7fDZ3W21mru1iN5fGPLF84fIPZQo57nUfeTynr4g5miSN8KQ8W0TKamZ1jZ3OTA2BD9vV3NYy3LZn5xlemZeVRF4Wi6naGWO3Ecl+1sjv/uf/5jlte2cD3o7+ngW195nsOT48zMLfHhxatsbud46dkzWJbFtdvTZLazPPvEKXq7O7j/YB5FUTh2aJLhgV6EgNv3HvLOh5dZXFlnYyvbVEC3/AdtOybVeo2FlQ2u3XrA+sYmxw+N8tQTJzACUYSAxcUVLl+/y92pWe4/XMCybEJBA8XXo5pf3eDdj65RKMv0HyODfTz35KmmwCueTGJ7b3oW0zQ5fnSM7q70ztOxbf/hSl2kqZl5tnWFWq1OLl9EUVXqtkMmk2N8uJd0e5J4LMJWJsfM7ALhwCjJtkTzHEI0Itm85o+iNFayLjXT4tb9Wd54+wLlSo3nnz7N2VNHScSjeLbNpWu3+ejSDUzToquznS9/4Rl6OjubqyGBH4naMA0rMvnwT15/n0vX7nJgpJ+vvvIsH1++yaVrdzl/Rqrfh8OhZn9ohLY7jsxev7K2yZ//+G2CAYNvfPkFypUKt+4+5Klzxzl6ZBJN1yiXy0zPLLCyuoFt22iaJoVJ+3uk3pemYlo212/d5+6DeQZ7uzg8MUp7KgF4qJqOadvYdVMCBl0e73keG9tZ1jczBAyd7s40kUgIfLo7XyiRLRQRQiGdjBMOh5HMnYdpWWxlt1laXuP2vYdU6zUfSMoxu25abGxts7GZoVarE49FGejvJmAYzMwtsb6ZoSMZp1qts7mV5ebdh+RLZT6+fAvHsnnu6TOEwr1UK1UezCww9XAe23YZGujhRcvG8F9j25H941/+mz+jUq2hqlIo88yJSc4/cQrH9fjk8m1+8LN3eenZsxw/fIDX3/4Ix3E5cXSc558+g+d5vPbGB/z4jV+SyRbwPI/B3rs8n83T6zOatu1gWTb1uslHF2/w/oWrlMpV7j9Y4H/8X77Lt7/yIkMDPbx/4SofXb6FZVmcOjbBN7/8AmMjAzieRzZXYGMrQ6VSo1avk25PMjzQS2inc4CuoXSnmY9F+EkuR/rgEO2//02UgV4cvx+6nodbq3Pn46tcvHaH5548xbHDB5lfXOGP/vQ18mubjHW0ExGQUFXGOtNM9HXRpmlYhQLZ5XUKa9soXSn6HBn8s7SyQa1W3zWCKopC3bQo+1IF0UiUgBHwra8yp+DK2gblco10KsXoQC+6YeB4Atd1qdVNqrV6sy9VKjW5qGqZWBsaZo2sGaFgQAJ1vF2gZS+z2SiK8N03Wpz3vXKLaKvjILbzu8f5BijYF7w84oG1s1hp+WYnWpgmI7nDajRh6Z75UjyKZz7DfPjobOc9suVxx33u8ljA5fnAdC+z3DqPfr76fL56sOu5f/7jWgHX7hYTje9bf3/O0niWeEgLQKOSj7BWDStBo5/IRYbwdkfvSwd5D8fda2oUzWtJf8aGZUx87qb9zUg/+HVtsgGP2XXnNnf+an7nNSbfTyvy5dkPyrmeSy6fl6yHv6sQ0sG5Uqnw05+9w/d+9i7buTzPnD/B3/ytr9Hf24Vtmnxy+Sbfe+1dllc2iEZDOOdPM+h6TfRarlRZKVe4fOM+IwM9rG5sM2dZqIpAUwX3p+d47Y0LOK7DmRMTtCVi6LrG7MIawwPrJNviPJxf5t70PLWaRbItzvLKOn/4n37CxmaG8ZEBCsVyoyHkwCA8iuUyP37jPS7fuI9tu8wtrpDL5ZgcHyIUDHD3wTw/ffMD8oUyiXgETVUxfY0fXZN53X70+vu8feEqg33dzC+vEwndpKO9jZPHJlFVlZt3p/mTP/8529kc1VqdTD7Lt776MoFgEM9zwbZp+BNUa3V+9osLfPLBJXq706RTbayub7GVzdOVTvEHv/01Eok47314mR/+9F1sx+G/+Ae/Q1sivstcp3oKqqeiNDLM+2OiZds8mFvm+q17ZHJZ7k7N09XZxtHD48S8CNuZLD954z1WVjfp7+3ivQ8vc/zIOD0dbUj4pjSZn52oLKlYraoKG1tZaeoVgmq1juO4dKRlaHiz/+JhWRJgqqr0DXNdm5XVTYIhKTgRDoVYXtlg6sE8k+MjuLbFu7+8xJVrt4lFw+TyRe5OzxMwDH7/t75KX08n2VyZDy9e589++AuWVjaJx8J888vP89e/9SqRcIh7U7O8f+EK29k8hq5x7vQRTh0/xMzcEj998wPuTcuFwBOnj/Dqy0/R191BpVTm+6+9w617MziOy9dffYannziBoesUCmV++ck1bt57gKrA7MIahWIF17HAdTHrJnfuP+TCxRvU6yb5Yom+7k6+FI3Qnkr4JlVBrW6yurHVfA8cx6VcqeLhEQ4alIsl3r1wle+/9i5rG9u4nsvYYD9nShVS/mBlWTZb+QrX7zzg3MlDpFNtfHzlNluZHMPDA/T39xD0E8hnsnl6utIcPjjCT978kEKpzImjE1iWxZ17D1hb30ZRZTTrdraA6cuSCKTUiOd52I5Df28XB0ZlYucDI/28+MwZhgZ6qNXqPJxb4uHcMocPDpNub2suTJaW13j7/UtkcjmGBrrI5Apsbxc4f/Y4X7EdFNfxxx4PEYuwsblFJlvk2KEDtCfbmv3HX/uiaZqfcaHG2voWRycPoAiBg0cBUHo7CCRiXJ2a5c2703y5N81XXzhP3arz05+/z0/vPeCpsW7+keNQrVQpFsvNCFmBoFqrM7u4yO17D5hbWMO0LI4dPsBz58+QTiUplSu89f4nvPHOJ2SyecYG+/jbv/MNjh+ZoG45zC0tc/HKLZZXNzFNiwdzS2xu55g8ONyUSKjV6jyYXWTq4Ty5fJGAoXN4YpQjk2NEIjvBOq2ARAhBsVRmZn4Zz/WIREJEQkFM25aBRJrGkKIQ9Mdx0fBPVQSe7yfVykQ1Tu8P6370p4Vty9yhUtah8X0L2PJcP4amca4d06qcJ1tYwMa1soVHJ/zW7/eWR5EZe2e6v6zS6ItNeNpa7V1s1s7+jy2fh1HydlhSecij+38qCPM+o11+XfOlAM/bJ7dyS3ERDcGRlj4gfIa5AaSUJrv9yCV8YCX2A+OfE3h+brDV6hzeeBGUxwGrhgmqpXzuZnxcg3seTZMLgNhRyW28gNvZPMVSZedFVTxQ4PadaX7xwUVcX7X5wsUbPH32CAPdaSqVChev3GL64TwHx4bo7mwn4k+qAukfUrVMTNPiydNH+M5XXmZjK4OLw9hwP5oiSERDKIqC6TMSmirFIhVVoOsqA/1dHD44wv0HC2TzBbK5PL+8eJ0792c5dfQgX/nCM6xtbDG3tCZZH8/Fc2xs22NuYY2AbvDVl5/guz9+g4WlNRYWljHrJj/62TvcuT/HV158kmeeOsP7H13mzXc/AU9G0s0vLnP11hRtsQj9Xe2USmVUVcGxbamkrrhcvXGP6dkFvv2V58nkSyiaju2B7nky+qrxOBSFgKETDgXY2NgmEYuQTMRYXF6nWjd5/slTdKSTeL5Q3NpGhkBQ+nE5jmSKvAaNy87L6rpy8s4VSk3fkaGBLvr7OskVSuiGhoeLZZrk80WWVzalCWl8iGgkhKFr4Emjhocjlf1tB9UXtUURJBNxDh0c4aNLt8gXSty6+4DtbJ5DB4eZGBtqqmoLIbAdG9OuI4SHobmoqo2hCTRFEAnqRMNBVE0jHDJ8mRGPzY0trly7g6aqfONLz7OyusH07DKmaREMSCfyja0MP33zQ/KFMqeOH+TmnYdcvz3Nl195lmAwwNSDOX7+9kcUSmUGejsYHe5lfmGFn77xAa+/+wmFYgXHT0Y+NNBDR3uSpdUNZuaXmTgwSLVax/Xpb0UI7k895Ge/+JDJA4O89NxZrt2e5udvfYxtW+DabGczfHLlJtdu3m+alTvTKT+1kc5gXzfpZAJFQFs8RldnOyODPUzNLtLblebg2CCBgMH8whLvfnCRBzMLBAwD07JZWF6jXjelrxY0J7eRwR6+8oWn6UgnyWQL3Lj7gFw2z+BgL7FIgGBAx/MkAD5+9CAfXb5FIGAQCQWZ39wimyvS25Xi7PEJUkmZ3zDxkw8RG1kQAk1VUQQENZVjE8Msr67z4cWbjAz28cqLTxEOBZlfWiUSDhIJBXnp2bN84fkniEbCFIplLly8wcVrdzh7YpKnz59gcyvLf/zzN/nBa+/wbKlM0mlhZiJBHNvBsW3Meh3PtdEUcB2v6XagKR6KkMLIuA66piDjOhSG+rt56dmz9PV2MdjXxR9/92fcvD3Fy0+dIpmKMzbUSyhg4DoNXykPVQiptK6rqAIK+Tw3bt4nWyzR09XO/QfzvP3+JQxN5+XnnqRcLDE1PUu9VieViLK2scXdqVmOH5mgVq/xxtsf8ea7F3n2/HFOHZ9gdWMbD4jHY6iGgeN5zC+t8oPX3mF9c5sjk6MIAe/+8jLlSpWnnzhJMBggly+yuZWhuzNNIiGlLPLFMn/2gzeZmV8m3d7GS8+cYTub5+LVO/R0pfn7506QbLRlQ68vGaf+r/5vZLIFbt17SLlS48DYEN1dHdLML8BxPe7cf8if/eBNsvkiT549yhefP09/bzdCCCzHZTuTZXFpla1MDoDOdIr+3k5CoSBBXUVTJCMoWXZfKNcPgFL+9j+H7dz+c1DrRLz3dyt71FhY7mXEWn28HvH3Yv+/927f9/sGg9eAhF7Lz84C4DdV9moT7tTxc5oY/wI+YfI6Yp9z7KFghNvcVy7AfUwidgC3h4Jo+bsJvlH2bWe53375nn/DYGtvI+42H/qChEI8do/dP9Ck+h7b8C1LGT8ctFX4UjQoruYmj1rd3FnpCt827zp0daT4e7//LWKxEH/+2ju8/csrbG3lqFSrmJaF53kUCmXy+QJfeO4MI+1Jn/IWuL7mhhCCwweGCQYDTBwYoS0RJRwOUKlUAA9d06iZZrM6qu8sbhhyYo7GQhiGTJg8s7DAjbtTmJZNRzpJe6oNVVOJRcNSi2dplb7uDhLxKH/v976JhyCTLWA7DqVShUw2hwLcvP0QoSgkEzHaIiGCuk7A0AnoGp7rsL6ZoW5afOnF83z9ay9TzBdxHYdUQoabu650ys7miyyubvDqS0/T19tJMGBIwNoiZooQzVVtNx66rlGt1bBsm/Onj/CF588RCgZRFIXJ8RG6fO0zTdNkehnLbqpOo8iIRelEXGd2foVQJEIiEQHhkiuUmFtYoVqtk0zEqNfqrJY3MC2Tjo4kV27c5+PLt/jqF5+ht6u9Ca4t26Zet6jW6nS7OwlgjYDB+NgQB0b6+fjKbf7TD9+iLRHhhadPSwkDIZoRnY38kdWqNJPatt00KwYDASJBA8t20BWFgC6djRump96uNMlEjGDAoLerne1sgWg4iKII0skELz17BpD5O1fWNtE1lUAgAKpCPB4lFg2RbIvw0nNnGB/rZ3FplbvTc+i6xvhIHwHD8JkYgWvbbG5lWVjeINkW5ytfeJrOjlQz1crVW1PU6ybnTx/l8MFR6jWLC5/cpFIxqdVMCuUiy6ub9HZ38PLzT8g8mfGozKmna6Tbk0SjYVQBnZ0p0u1Jkm1xFF9MsqM9iQBm51eYWVjl7PEJzp6apFa1AIX4Tz9AqTXeB/niqqpKOByUgRACTEvmOnQdB9dxUIRAUxSZQkiXAQ0BQ0dVBPW6BJMTowP89rdfYWioD891ib97ozmOCLGjMh8IBjA0FU1V5ETteQR1lXBDIgGPYMAgHo8iECwtr3H15n3qpsXIcD893Z2kkgkOjg7w45//kkqlSluLH6AbDhKLhAgYOg/nllnf2KIj3dYMrBICPNclm8lRKlcJh0JyksfDUAXRSIj2thhdqQRnjh3kgwtXWFnbYjOTpa+nk7ZoDEPXfRHTAJqio2sSjBqaBFtm3cS2bSYPDHHu9BFu35vhez96i+s373HqyEHak3H+yldfJOznJfzj773BdiaHg8fm5iYfX7lNNBria68+x4GDI2xsZplfWicejxEMBqnULW7eecDM/BKnjo/z1771EkbA4Ac/eYdPrtygrzfNxIFhSuUit+9NUyiWODAiQXgwIPvq+x9fJ1co8fVXn6VQKrO6vs3oUB9RtxF9LmR0rif1jaYeLvDj1z/g8o37VGom504d5htffp6B3m50XcOybO5Nz3Hl1jSWbeMCw4N99PZ2Ydsu92cW+PFrb/NgdgFD16nV60TCIV554TznTh9BiUXxNBXVc30frIYKvoIiFHTP2x2n0jIJfxowan7ayxDtN7991vePK48FMjumst1Aq/W732DxMdx+zNauAILfdNnvOTSut5+5a9ehoukY5IlGHX1fWdEwXDeRxT5M3yN0Fjtg9vPBqF/LjLh/Y8oL73y3czOyyr6LdIP18h0gd2z+PjrfD4AJH5s3+9QeoOU3UcDQ0bWGo7OfJFZV6O7pom6aXLx2k0+u3adWt6jWpMO5qiicOT7J/YeL3L4/xw9/9h7uyUP0WpY0L1jS6db1PJbXtkinN/zUD6o01ZVqbGzlsGwHBQXhqTiOh+1K59qAEUTRglTrDrW6Td20mF/eYH0rh+u5pNuTtKWSZAplPA+qdZMHc0u0pxLE42FyhSI3bj/kztQc9x8uMtCTxrYdtrNFQPpROa5LtSYT8ErGSqVUMzEti7opzUCuC4PDgzimiWs7TSbguSdPcvPuND96/UOW17b47W+/yrHD4zJSr1yVL09zEaYwNjzAQLHI4vIGuUKJjvYkzzxxnGAwwOraBkJAsi1GWyLG2sY2lUoN23Gazsuu5+K4YAOma1OtmyyvbRKJRUmVYqysrXP99jTb23lfP8xhO5tjdWWL3q4UT545SiZb4P6DBeKxCGdOTCJUP1FvJkelWkfVDbobDr6K0kztcuzwAW7cecBWJs/4SB+GrmE7Jo28Yrl8kemZeemzU60xv7RGuVSVibFdqT9Ur5tsZbLYjiN9V3AJatLRfHZxlamZBRQhyBdKxCIhEtEwwvNoi8UZHx3k3Q8v89NffMjy6hYnjx4kGJTMl6oKdF2hqyPFYH8X4VCQ7WwWXVN55txxzpw8RCIWwbIdErEotm0xPNjD5PgQv3j/EosrG3z1C09xcGwIRVHYyuTw8JrJsz0/X1omWyBfKMmFSd0EoRAIGDRC213fV207kyObLxKPhvG8HYK+sfDQfRBdKleo1up0dCT54vPn6erqBCD8/hVEXYKtRjh1NlugVKoQj0ao1WW+xLplsba+zcz8GqZpYwQCvsyBzM1o+vkTBYJwKEShWGV6boW2VIJYOLxrSmlEo5qWjeP3Ocdx0Q0d13WwHYeAIR3hXXfHob5hWlxZ20JTVSzLolSsgXCJRCIyaACkxIFf3HCIyfFhDh8c5srNaa7fmuLgwRGMgCHHL0Uhs77F3el5KpUa8WgMEM0E1q7rUK/VME3TN4nJ+8znZeohy3awbRtVU3E9j1K5SrVWlwEseAjFQ1FloIGqyYCTcEiyg8VShWKpTG9Xmlg0zP0H8yyvbWJaMhG1Y1rMzS+xvpHh8MQwkXAIQ9OIxWSy9UbS7mKpzPTsEnXbZnCwxzcbunR3prj/YJ6FpRXGRvro6Upx5tQkS8ubXL91n7a2GAEjwOT4MCODUyytblKu1IhGwnSm2zg8MYpWqcnIb4EvFyPNv7MLKwgh+NZXnmduSY4FB8cG0XXNZ04tipU6tuNy/Mg4f+f3v83YYC8A0zMLfO+HbzIzu8A3v/wshycOcG9qlh+//gE/fuMDgkGD44cPomoqG5vbLK9uoioKA72dpNqi6LpOl+2g7TF3fW6j4F+Usfm0czwGwDyKMbxd333m+Rvz7WfUvSHN1Pzb3Wf/x9paP3/ZK3T62PKpRA1Nk+JuuLRjNm6ARU8oEnz5+7iOXARatt2CWxqZQPZjtT5/+c1IP/j0YdMu2jQjipaO2gK0mtu8lk/7IcfGL7ky9QR4now889gRUnBdj7mFFe4/mKdaraEqcgJRFBXTNPnp6+/xh3/6EzL5AnXLRlMVCsUym9vST6lYrtCeakM3dBZWNimOjzRNKw15ANtxeOeja5iWw8SBIQKGQTgUpFYz2c4UqFRrGLpMO1Eq1SgWq5QqdeaWNrg7Pcetuw9Z2dimt6eD4eEI/X1drG1mee2tC5i2Q6FYJpMrIvAIhQMoqscf//nPuXFnhkDA4Mj4MOFgANt2KJdNhvpTjA71cunGPR7MLhEKBlhY3iBfLFOu1anW6jIZra7z1i+vEgiG6O5MEwwYnD99lPZUG/NLK2xtZ/jt77yKEQzywcfXOTg2xNBgL+VyhfzsInHLxvAZIs/zSCXjHBgZYHpmkXK5ygtPnWR8bIhgIEBeFKlUqkQiMkLzwewiuXwBgcC0LMqVKqVylXyxgOW0YztSz6RWN4lEQmRyeT65codsrkAiFqXkVVlYXGW4vw/Pc/nlx9fRVJWx4V6CAbnq91CYX1rjT777M0rlCs89dZqjh8d9nwxAlWArGAoyNtxPZzqFadkypYjnUauZcuKz6jyYXeDW3YcsLK1j2Q7rWzmyhRKan9x3YzPLx1dusbGVZXZhlYG+bsy6TXdnmtPHJnjrg0v86//wQ9oSMr/ik2eOkkwmMGsOt+5O8y//3Z+TK5UIGDqub1YtF0sYWoLGSGU7LtVqHdtxmkxouVKlq6OdjnSS+YUVqrUatuPSmU7xW9/8Ipqq8sZ7F4mEgkTCITrak6iKyuZWnpW1LUaH+qn4grfZXIFCsUw0GiYYCHDr/gw//vl7dKaTnDh6kEg0ALisrm+Ry5dwbIep2SXa29twfMmVlbVNPrx0g1AwwOrGNpqqMj2zyPVbU3RtblMolHi+ViPkD4a2LRNnr29lyWQLpNuTWJZNuVLj9v1Z1rdyPJxbwbSlw/bs/DJ37s+QyRVQVYVMroAeCNLb1cHtqVn+6Luv88a7HzM60MM/LJZItYCs7VyRuw8WOHH8EPlSlUKpwgcfXePBzCLPPHGcc6ePYrsetbrNdiZPoVhiK5NjfmGFet2kZDvcnZqlI51ieWWDdz643GTYhGk3Ryc3HKQ9meD86cPcuDvDa29eoD2Z4PChA6iayurqBm+88zF37s8wMjRIPJHAtKTpwnFclle3uHn3IZlskZn5ZWbmV+lsj6OrCrZt+xGvDuVylY2NbZZWNiiVq0SjgaYcg+PaLK1usLKZBQ9y+SLlcpVELIpjO3x8+Rbfe+0dQgGDnu409bpJMGAgFIXtrByzKtU6hVIZx7KxbQchZP5D27apVCTAM3SDUCiM5YJtWj6IdTBNWc9KrcbWdo5qtUayrY1YNEIuXyQYDHBgpJ/F5XVef/sjPM+juyvNxIEhvE9u7UysflSsQHDqxGHOnT7Gdq7I8to2tm2TyeUoFstU9brMY5jL4zgO0UiYkeEBouEAs7OLvPHWL/n40k16u5MEAhrJRIyDB4bounqHO1NzLK1s0NfTxfVb93nvwlW2MnkADo0P8+qL54nHo7SZFornNbNaNMqO8zW72a7mDt7u3/8rFjl/7nLY+mz/LPhMwPLocb963T7XaVvNkg2TaJPE2mOGba2z/1susvYm3/YZqz3K/wI/StfHLK3gznPlIqfuByk1m6kFv+x+7J9Bp+0pvyGdLSGdqVtNiS2Aa6diouUGfTAm8NV5YVf6gkeu0HCfF3ieQtMmi0yF8ea7UoAxl5d+SUsrG1y5eQ/N0Lh66z73Hi7SnoqjKAqVap35lU1O1kwWltb47k/eoVKziEfDBAKG9FlQVVw/oW1IUzEsi/mlddY2swz3d/G73/4S3d1d5MtVVjey5AplArrO9OwisUiYfKHCVqbIa7/4iCs3p1jfzLKVKbCwvM7wYA8HhvtZWtnwV9QbOK5HqVyjLR5BN3RsF9Y2s+SLZQJ1i7mlNWp1i0o1x93pOU4fm+CJk4eYmlnk5+9+wi8v3WQzkycckrnTNFWjv6+HwwdH+fEvPuQ/fO91NE3lmbPHOHboAFHL4fbUDN//6ducPnmYVFuccCiIrqkUi2Vu3J7i+psfMpovkhZCChwCQcPgwFAvQcMgmy3S05UmHo2gaiqGbpDNF6nWTCqVGlsZaV4Dj63tLMurmxSKZdY2N0mn483JoVAsy0lAyEmzUq1j29KX6879WU4eO8xAfw8fX7zBnak5Um0x3+waQVEEy6tbfHLtHtlsgXg8zvGjEztaLUKG1SueR3sqSSoZp1gqE49HqNcle+J6DpZtUjdrLC6vsrmdJRoJkskX2NjMMDzYy7NPnuLdX17mJ29+iCIUanWThaUNstkS3V1pDo6OUCpLk6jreDx5+ijjo8MYgSggWFhaY3puCQ+IRoJ4nsed+7N8cuUWLz57CqHQjDhbXduiv7eTrs522tpiPJxb5tbdB/T3djI7v0xPVxrb9bhw6RYBQyeZagME6fY24rEoqqJzcGSYi1fv82c/eocHs4tUqnVWN7Yl+LMsOtNJzpw6xOLqJh9elEETQ4Nd8v0TLpFIgHAoQMVnTBEq1bpFOBTg9tQ8K+vbfPPVZzlzfJLV9W1u3HnIv/qjH2HoGgN9nTxlOYR8BtuyHRndA2xlcgzVe4hGQnjA0uomyWSC8dFBtrbzXLp2lwezS1QqVTYzeZKJGOVKlUg4TF9fF8VqjUw2T7VmEggGUFtERG3HJRwKMj2zxMLSGsm2GIlYhNmFVWYXVulMJzkyOYauGWiaysLyGptbWWmmPTBEtVbn4rW7fHTpNoVipcnuPXXuGNEPbkJBqtZ7eFgBg0AgyNmTh1lYXufCpVv8j//me6Tb2wgaOlvZPKZpc+7UIZ598hzhcIiN7SyZXJla3WJmYZX6Lz4iEYuxsZUhEg7z8rPn6OvtlO9ArUahVGErkydfKLK2sU2pXEXVhGS/el1K5SpLq1usbeYol6tYtk2hWGZ0sA/Tsng4v8TUw0V0XWN9K8tWJs/g4gr5QoG2tgTtyQSLy+u8/f5FsrkCc4urkg2v1ylXayiKQiIRk2Bwbpnenk6ikRDBYIBjh8fo6kxRKle4cPEmP33zQ1554UlOnziKosgAlLKo0tWRImAY3J2aIxIJ8vzTp+jqTCNKlR03I03Fs6XuV65YZurBPHfuz3DjzgNqdZNSuQwC2pNtVMNWcxJsBLJYlkWlWqVUln6NwYBOqVylUCoRCGgEg4Z8TxIxIuEQC0trzM6vUPb9HOV4XcK0bZxGxoJ90Mq+TMtvCGR9pqzC43y8du/0F6rDZxWh7OhQtuKMHT+2X/2cTW3EvVV/HEv2mHbe3zne24OHpNxJE4P4gGvHh0v6F9u2jW27vtM9u/ZrgLfdZsTPV34zYMvzfV580+lu5N8wH+4kGRW+8noDXDXbQoj9owo8kHBLoRmjsmeXWq2ObUu/j4CuMT27xC/ev8hgfxearpFOxYnHI5QrNUzTIpsvYjoufX3dRKMRltYWCAYMzo0PMdDf1bTfqqpKIhJCCQb4znMnufdgAU1TKddNhKJQqZrki2W6O1I4jsPs/ApPnzvGqWMTGIZOoVhmZLAXTde5e3+Gummi6zqnjk1ycGyQa7emWFxep1Kr4zouvd2dHJk8QHdnipGhPgxN5cjkGKMD/Swsr5HLFzhx6AADvd309XSRTMS5fX+Gje0sxVKF/t4ujh46gKIqtCcS/Na3XqW/r4uN7Ry93Z2cOXGYdEcHQlVJptowbZc/+fM30DSV3u40w4O9TbOGZ1oyBZCh+34uoKmC0b4BejrTmKZNb1cHhq7iIlXZ5xZWuHbzPrfvz0hfuGKZummxvLpJsi1OV0eKeCzCwtIahWKZUCiAYei0JWKk24coFCs8nFsgFAxQrlQZ6OumuzNNQNc4MDrA1Owim9s5vvjiE7z83Dki4RC93R2cO3mI9z66xtLKOsVieUdny+9TjeicYMCgu6udVDLB5laGWs3EcVxCoQBHJkcJGAbHDh+ULIc/eIeCQb78xWcZHuhheztPLBolm82TL5YwLTnB3Z2exXU9xob7uTs9y4VLt5lf3sJB4+ihAxwYG+LsycNMzSwQMKRJZHM7x3YmC3jUa3XKlRrlSo1SpYrreiTb4hwY7mXqwSL/yx//mHQqwchgD0MDPTiOy93pea7cuMfaRpZjh8f54vPnaU+2oSoaTz5xinypzGu/+JBfXrxFqi1KJBykp6tdmo0MnZPHxrFtm/c/usFgXxfjo9JcIzSdQ4fG+MoXz/NgdomO9jjRWIDDk6PUajXuTc2RbIvyyovnSSbiGIZO3TTJF8oM9nXx5ZefIjy7Ab4Mi2Fo9MU7+Ad/8zuMjw0ST8T52qvPc+L4IRLxGO2pBI7jcvrEIa7cuEcuJ6UdqnWTIxOjHBgdpFKtMzbSx6njE9J3TJHvpv7mRf8xCzra2/gHf/PbdHe2M9DfTTrVxje//JyUsGhPcmCkn0AgwKsvSXNrRzpOe6oNTVOJxyKMDfXR093BrbsPyeWLDA/28u2vvsjJwwcIXppqJjbHAysQQhUag719/MFf/xqHDozw8ZU72I5Nsi1Gf2+nZAuPHaKtrY1Cscp2JkO9VuG588dpi0eYW1wjVyhx6OAILz51mskDg+iG6q+qPYb6pfO8EAqzCytU6yYdepyNTJ6RumTxCqUqlmVJdjsU4PDECCPDA1TqFqODfbzw1EmW1zYJBQNoqsrc4jK37k1zYGyQ73ztBV5/5xN+/PovuXj1DkJVMXRdBqzkCvR0d/DCM2fJZPN84L9bo8N9HJ4Y5eTRg5LZr5tEI2Ecx2N+aR3LsohG5KK1bsrUXKlUnO2HeQ5PjHDy6CQBQ8erygTjnufhKlISoFqr8//7d9/n2u1pJg8M0RaPsp3Jsbq+Td2U7Leiy1RRqqJIVq1SJZ5OMtjfw+SBYW7fm0HTNCLhIJoGhVKZSq1Cuj1Bb3cHXR0pnn/6tAyKKMnozuGBHpLJOJls/lMxU9Mf6XGO779m8Txvx6RKY9j6FGC3T2lEVMKnTP97j997H59Smlp/rSmvPqu+8ov9r926S9NW9auXVqC2uwbC/yczhey9vvCjWHZlrWks2uyGcPIO1lAURUatCx+3/BrA8jeYrudRT/2meKjqyTx7DTQoXHnpZoTAjlhjE0o+8nD22Es9pekHqKkaf+VrLzM5PszC0horaxtYtsOxo+OoqspLz53lzMlDKIpgdX2bcrnC4ckxhvp78PD4+3/j26yub6Eogs6OdtoCAZluw3XRHBfd9UgFg/zvv/4qy1sZgtEIHV1pRLVOZzjE7379C0TiIYygFAwtVaoEAwYvPHOGSDhMIBzCcWF7a4t8rkAoFCQajnDiSJDnnz5HNlskk8tjGBrRcJRQWGNtfZNkIsb5k5O8+spzBIwQi3NLAHR3pjFUGSH3wtNneOqJk2xsbLGeyROLRkBIIU48j8H+HkYPjDQz2uM41Ot1HMfmyOQo/+yf/m3W1rewLJuO9iRdne1UqjWGBnspdKWl7ojrIjwBlgXbWfoH+/jOM2dZyeQYH+xDVwSm41CrmVSrdYYGehkfk0zB0clRgsEAX3v1Ob708tNsbmWwHRtD1xno7WJheR1VVent7iLdnuSpcyfo60nT1hals6OdSDhK0DCoVSr09XRycGSAUCjIl156mv7eLoQQtCVijAz1ceHSTRzXpZDJS+dXfzDxbI+6aTK/tIplWRwaHyaViLG5sYXjyBdKVVUSiRhnTh7i7OnjLC2tc/P2FNFIGCGgXKlSMy36+/vo6kizsS610RpyA9WayTsfXsFxHOkn5XrcmpqjWK7RmU4xOTHGf/mf/S0KpTz1usmNO1NMzyxyeHIUTVNpa4vz5NnDLC5vEAxIB3JVUejp7uQLz52jZtl0phIM9nfT0yXFJ199+Sni8ShTDxZ5+txJEomkTEKu6SRCYb799Vd49snTmFaVSqXK2voW6Y40QwM9qKogFApw7MgYXZ1pEvEo8USMmuUS0j16ejp55eUnObGxzcGxIXRN4fSJCQ6ODrGwuAyeS3syAULQ19PJl196io7OdibGR0in2tD+8MdyqLJsIusZogGDr/7hjxFCmv1TwCF/fHD8PGTjqsKLQsowOLaD4zpo9xZRfvgepmUxajtN53mEwHNd9PuzCNdBAbpWNvn6v/0Rhm7IIALXZdJxpfOzouD84iJ4gnEheEo0FnqejKBFDqgn/TrZjgMLm2if3JU+TPniriTPbiSEKgSKopJqS/Hck2c5e/IoyWQbRjiAcC0c28Fy5PvjuC6Vqk4q2cFI/zFefu4cswvLCDQGert9wGL5PoSCsycPM9DTjabppJJxvvDcOZ49f4JoLEp3dxrXFeSLkml74ZlTvPz8OVJtceKRENuZEsVylYGudk6fOMT65jamaREKBgiGQs20Pl//0gucP3ucufllqqaJUFQMw6C7K017qo1QMMDEgWH+yte/wFvvf0ImlyMQMOjp7iCZTOA6kgV65pmzrK1nWd/Ypl43Cega21tbbG8X6OpMMzrYx8raFmdOHGKgrxuEQK3WZDt6nhSW9KROWjZfQFNVKlW5KHZdj8XlDQqFIq4/xyhC4HoeUw8X+Hd/8kN+769/jZ7Odk6cOMzd6Tnml1ZYXN5AURSmHi6ysZFjYnyYaDSMpmmcP32MyYMjqIoUj3Uclzv3H7K8urHHpOVPR5+JYn5DxdsBdM1MK48zV7aWpons81xjH8D1Ocuv5fj+KwC6/Q9/vMP9jmivb0Lc1/dsL7v1qPVM4LNrrgS9exNQI2RwTzAQ8DW2fj1g+BsDW5I42K8SfuWUFhFSrxVxNkyNj6HkPHmO3e24Y6Js6GaMDPbR19NJpVojlytQqlQIRYNEwuGmjk6lUqWrsx08iPupNVRV5fDkGMcOj2NaFqZpw2am+SC1chVRLMFGhrb/039L3H/4ih/Nd8BxGVdkRItQBLYHFdMCTSUci6AFDJkqRlXoUQRdQviJmzUUXSOsa8QVhV7AVRRcIciUipRW1nnB0BnPlogsbVEo1ymtrqMHA7iDfajxKIqmSp8kz8MoVUjbDkYoQN11iZgWgXgUsZlDiUQQmoqnqpiuQ6Faw/RsEm1xDiXbONzVievnf3QFrDsuW1tZluZXUISMqhTlKmKhivh//Vu0SJiXfX828cc/AwUcRXDYcZlQFYLRCEowgC0Eyoc30IIBRDCAqyjEQUYDhYOgqYxX6+TrdUL3FwjEo7SVK7iVCpG2OD2DvQQjYYSmYgnBWKGCk4jT2dtFr2mhb2URhk65VGbu/iyeaTPU101uOyMd2v1nWK6UuTv1kDff+Zh8ocRAjwQXk+NDDPR1ofupfCqVqs8s1Zmansd2bRLxGJ7nkc8XqdctVFWwvrHN4vI6oYBBwDCIRcOcOT5JKGCg6RrtqTaCwSDf/fEv2NzOUiyVSaXiKIp0WC+Vy9i2w/joAB3tSVzPY7C/mzMnJlGEQjgYaOocDfT1MHFglGQyiW3WKVUqUlZECAlOz55gdHCQzo52EAquUNFVFVVRCAWD9PV1Y9t16qbJwEAfwWAAQ9cRqotwZJBEsi2C63lsZQtEozaaruG5HgvLGzx4MEdnR4pQOIiuq4QjIYxAALNWawq/rqxtcO/BPKohU2Q1hwL/g7AdwMRZ35JBK43cno0VrePiOQ6eqqKoKorr4joOqivV1IWioNk2wnVR/X2a2ki20xwnFNshUKigqjXwJ/DGAOcBajPNT0NFf7cJQ5oK5N+6I33qVCGkMrvnQd2S+QArNfTZZbRqHd11qT5zGtM0CQWDkkFSFFBUXFVFcaQjvqpAKBQknWojHA7huS5D/T0oQiMSjiAE2LYAdBQ0IqEI3Z0aIIjHIjx59hSWK6PvdFWlVjep16UZrbuznYOjg8RiEXBcDNWgI+kQi4YxDBmhbNsORsBA0XRqdVNqVAUNOtJJRkcGqVs2iqo20w2B1JkLBAIcmhgjEgmhqSp9fR2EQoaUp3FdTMvh5sVb3L43w4GRfjzXZWFxmfmFVUKBED3pJNFQkInRfs6dOkwkHMLDQ6nWmpOwp0oAFQ4FeenZc2xsbtPT1UEqKVn7+cVlhKqgahqaC7FYhOHBHgSCaMv4nk4lePGZM7x3AR7MLDO3sEYyEee5p6SwdDAg04HVTJONzQy6phEIGL6PmkO5Iv0aG6KgTeD1OJbpLynqrtEnd7FIn36A/P1Z9flVGLjPwUh97vKYczQZvccd5u4Az8cCLtdtOhjt+eZTPzfMl8JzfWwhAbztOHLx16LTKPBQVVX6bT5yIW/P+R9ffiOippJiBLwWIbqGKVHsYxYU0MwF57aYBB/jstVoyv1CTcGDbB7lb/8zAngEPGhr2IH9SEjPXzE4juvLHfi54URDDVZe2HEdPNeVqSSWN2RdGjb81qoj83TJVCliJ72LB8JxMDwPxXFQSxVEpdY8TngyR+MuW7ePIjV/8nBcl3StTtyWSvDBTBGhCOK2y6RlyUiwy3d3XdPwPDqaOlY7HUlRVV+fRjT9CoVtk7AswCNgGH7C650UmxrQ4zi8nC/yVL5Id91CtWwe0WwWUhDOfzAolkPAdVEcDzVfgnwJfWdn+ctrpJnZmWiTlk3EtOSEqioEbIe0bcuVhG/6AAgABxyHYdtB853eGy9ie7XG38wW+HuGTnTjbdB3dLM8RWF5bZXvv/YWH1++xckjB+jtStPT1YHqugT0AKoLjueSL0ghxlKlDqrC+Mgw8UQMoSh0daaJRMO4Dmxu5ohGI3Slk4TCQVbWt5meWyKVTNDX200gGCRXKCKEoK8nTbIthu2YLCyvMDO/RL5QwnVcxkb6CQUDCGSU4/ziGsVShWg0hqrI1otGwgR0HQUZDampqp/zU0EIjWgkRnAgJEGIL3gpPzfaXEXoupTg8MGR7TgYqtZcbLieoGramP9/4v47SrIrz+8DP/e58BEZGekzK8v7AqpQBW8bjUZPz/T09EzTzEg7dEeido8kniOJK4pcrrSUKIl7DrlcSWcpikOKQ1IiOaQ4PT3TGLRFN0zDFVAAypusSu9dZPh49u4f970XkaaAQje4e8/JzMgXz9x7372/+70/8/05KkIwn8viuR5b1TqblTq1epMBP0AgcF1X+bc4Do2mzeZWhRt3plnfqtK27c5OtJjH933EnK40jIZBPaVSK6WSiRjwBGEkrR8ofyuh6wS+j9O2CTwPyzDQEXiOwPc8hK4jdSOcN4FKDh46WEsh0FwX4QqQnWTVMpAqSXIQdPL7Sbk9qXWkCY1THnWAWTzybUfdY3md3O/8K4RpIHSdZj7DRquN2ZMjZZqkshmEEHjSp1ZvsbZZZmNzK6Zy0HUdKQTZTBYZbE/HFL3bIIiCc9T70nQDIQzMMJ2U5/s0mzau6+M6Ac2mSzIRYArC/hWYpqncKhIWuqXkqO24KrpTCFIiiWZYWJqBbvpq/Og6MlCyMIrozuezHDtyECHANFX6Lc+3MQwDgcbGpuKsO3H0AO1Wi7X1TTLpFPtGRlhd3+Te9DxnTx/m0MF9MaUHzXaYdF4Dy0Rr2SSTFt/61S8TSImuabiuRz6Xobcnz/DgAMmEYsbfNzrE/rFhDh7YF2p5TXzbplzeQhOCR8+dpljI0VfqIZNOE0iVYiybUcCs3bZZ3yij63qYwUERxCpNWtClMYlXHrVW3c989Hkdzfe8hdi1vv3c6XG+yPIFtO3Tb7+73d3lfpqtGCztCbQgflF7YIq4X2OgFCag9nwcx8d13ZCmKFTqSBW8oXVb7iL/8s/xar5AM2JX6e6cbXpYVSK6fkXdH4QvlD1UnKAyI7NbtUfX+UGA3CjH13TAnexoEUNhKkM0GCU4js4LAqmAVXieH6ZEiTqaVAL7uUcQXoAIAhUGHma8l1IifR88HzwPw/PRggARCXlPJR2WngdeAJ6v/D+EQLTaaIurcb8ZQmCJsA/jBNECM/yJE0bHn5WwNrrArYy+C5TWAA21KEEYReTGpphotHSPmUi9ntA0aNtqMQv7Ozh7Avp7wfWRjqOIT10PaTsqCa3vI10X4fvgqO9EBMK71d0ChAz9bkJUpmkC01DP707/EV2jCYERgcyuYpomfb09Kr9d2E5dC+30miCTSnLkwCgEAc8+cZYD46OkU0m0P34T4x/+fvhwwUFdaRh9XUNPJTEzaYRlgWWSNnSkoePrBuOaQJomejKBsCzWV9Ywr9/GTycxjx8iSCRYmJrjTKvJM8cP0T89i7RM9jWaCB+aySTpQk6l3glAcyUpK8WBffsYGRpibHgwzPWpWq6FofG6YZDNqChC07AQQiedEnFqFSllTC0Q5btESHTU4u0HAUHgI4SukklbFqlsFjcw8WUDkKQsE933aVfqsFGh35OI+XU8D0xfotdaDC2sELRsvJUyztIqp5fX+XJPnvFyk8wrbyFcj+DkEexqA+PuLHrbRiYsWgO9aH6AmbAwJci2jdNo4dk2VgCGr+aWFkhMzycIfDRdB6EhPE/NKYNw0VNj1x8bwvM8XNcLx5IRLuJqvCr8JAmiTZOuIyLNlowErwinjggd7RXpr65pmKapnPABsbCi8h4CehjJJoDs3/h7HAtzYCYTCbRUAplOoacTpAXkXY+kqZMb7KdneAAtl0ZmlNuBTCYhk0YmE8hkAlJpgnwGX9NwfEXFkQzA1KNUV8pvxnFcyltVHEf5DNbqTXoKeaVNM5VmE00L0zmH4iBQ+StbrTaJRAKhaQqghv0RWQkIN6ER6acAtKTW5bMEhpZA0y16iwleev6JeO45dpt0MkHbdllZ3eK1Ny/iuC6Pnz9NPpftzN16U83xdBJhmqoOQiOdTsWyKEgEnDh6kLHRIfLZDMmEiaZrPPnow2iaRi6XIWGaCOljI8lnM5j7DPK5bJwUXBFZ+3EOSi18RyNDAwhNYJlK65fNpjl57CDJ5HuIVpQSKbK3RGtVuJ7EO1fRASO/ACjp1t5s0/SEa9WDgq7up/9C8Cxq17abf/GA635aq70c5vcEZJ9VJaGpTZcIQOjEi//2G8cywg8U3YrjejHPJhC7Q3WIT+9jhfuM8sWZEQVdVPcdYaga2nVivMDHKGjHjXZ8lKHWbA/DuSwWus6V8UXh3jS+JN6phL/idAOhdiWidvB9HyFAFxpefy9B+CI0TSMxu4h1Y3KbejUSUp22sV0DJELcbRiqpy2rU99ucBmaA4ORwRDsRScFMSN73PJ4IMoOQI0AZqiWFUiE1923nXNN3wfXQzMMdFuZO0Naa/U3FLhJTUNmUpBRCXGjTV1waIzg2CGwEgSWiUyY+L09BKODXUJB8QDF4Mr3wVN+OMLx0AMfzbaRTkDQdnBtB1wH3XURngJuWhAQeEGoqfCQroe0bdyWTfqP30A0W6rdQYApJWYQCkSlcgy1EBKxvsXof/7/4s/6AZ4XYFyfQfsnr6g+WdlAzK+oLtJ1tH1DJJHggGi7UK4pU6noZFCItXXhG5FScsB2KFSqtFpttCsTaLrGiK6crtNrr6F95ycgIQ2MhP5BkeAPhwlJ4Cuahm/oSMMAy0CaBtI0EZYJlomwLETCDAGghTRNkoaOtExlqrZMpKGDZaqxZhpISwHlxP/xAwLTQBo6mush/ADhumQcl37bRToKLAvfD0GHpN91kYHENN+ONalpKemRnd3kUKA0XrqhdelHVUkFAVqtoUx9rkf/7PI2E3wQBBiBREcqjU4k4ASx5jL6iTR32zRNRABBixnB1eoklCYi7F8t1MKqRVeia8R9imEiLQssAywTaeqKYkbXFfjJpAiSCZUP8Y9fh80qEoFotmIeMQ1IWInOOLFdhO0iypJMEJAIfZuM2ZXdMgO2yap4XBk6qYSFTCUxCjnIpBCZNGSTkEnSIwQvl+tcGB9jUDcZnF/Fcn20bBqyabRMWsmdaMMoQNOF4nXTNDTDROhmDMpF2O9CCNBQnIFR/6MSrcdVlBKpqfdjGibFZFLJHyFI5zIkkxY//PE7vPKjd1hcXudLz5zj+NGDIXFweI96I0Sq6YjmDlGuYPzZvxbPCSmhH/UTdVAKQUfqd8SxISHTtUZ0W8ESOxaVFJI+GV2v1oCByMRsu9vMd7F2K+aB3PX6tgOTXwBwqQqwDXBtAx7ajnO7SuwfHeviOveIz35QQLiXWfLfkobrs/yxgE7wwI7HK7Kp+944PL/L/V4o2dCtTZN0tGRBIAlkoDTKRM1XwRs7N/8/T/lCwJbaUbP95ahvPvM6dUqggE+klQsbLwNBxB4fuaXF00lK/P/h/w6A/p/8TZXTKlb3drq4AwAiICR2TBj1FpXZRTGCCy08sxvoNNuYn9xC6hrNvh4cx4sj6aJJuCt50a5/xe4uqdaURgwBw/1qEXVdhO3gNVo0ylt4rnpWlPpldw/vFcshuv50XRNIcF2kEMiIPX3Xtd132H5n/Yfvov/w3W3H3BefxP6Pf3vHlZ23JQwDTKHMNyF7twwkSA0hQZdR/X0ICeaidnpdk07tzB0S712GchURLrzxub5PI8yNmW+20fwAdA2xsYUuBXpXWyQgIiAAyISFv39EAQ7Xw7cdahtlvGabTEJxqu3q2/DZiYRJb7GAHTrbGrrieTNDc2Z3l2xbbOOh2Blvwg6Ub1Bj+7uIdoDbEu3ueumdcbDtnQYSbXIOWSp0NijxOAr37vFORNVP0wQJKxF/3xl2EQBSTzI0vatO21cjrVtDKZQJ1PN92o5L0/No+j5mOkWhrxctm1ag0TIJDBPfNAh0DWmaHRAUAc+EhZboAE5bQrnRxNM08qUiiXwWLA3N0sPrLcptm3/xne/TN9TPt775MtlsOgQaSnOmhUBJCh9dBiqZeahdlkI54wcvP4XcrCDrbfQ/+gn6h9eUVv2pc5BJQ6OFbLWh2UI0WtBsorVsrK53LuaW1eZjjyK7X6pQPmfoOnLfcNe8V0IyJ+GxSHN/eQLxR29su5cQqPmdTkE6hcyqjZORTpHKpJDpNGQzaLm0ApRp9b1Ih5/TSbRsGqFZsSZw26K9a9x1tIO27XD15l3WNsqMDPXxwtMXKPQWQv8YpYnVak3V4FwGqip6WPo+YlVRxcRmXYlaA+LPstNH0edog7rt+7BvZLD9eHyujJ/T/b2QqHfeVwyb2kFtIkKFcbPl7r74AkDJbrAX+g8FosNJthMIqQ+whzSPne1/3vp9Adq7z112gqs9ZF30+1MBF0L1216XdwO6MKNA9OztMm1nxX6+8ouDrfu0ViHJPUX/HmdGAEl9joTOLqL48HzR9Vci1MIbmhEl6rogCBRtgeeTSJhYltXZ+dIBvoIQ9AUynpjRMRkmntV1A+F6ISiCZT9go1pjNJNksFiIVe/bFuH7NVtuPygq1fiz9xf+JMGzF2JV/fLqGn/vH/1zrl69za996Un+5C89T0rX1Y46/nHBttVu2nEhNOcJx0WzXfA8cBR4E7b63mjZSNtGuj44Tud+XQzZXTXks0Cz0n7tfk8776Kh+lR93fGV67yVzu4x2GNSC0HoYyTBVz5MorcnXtAd22a5VsfzfY7rXTvzhdVdNRIS1W9RSiJLR9SbRBXwgJVGi1qjwWimj2Qxp4JoZQABoZ9PEP+1TAMrmQjHkTomgmDbeTKIjodCPtLCxVo5iRb7GqlzZRAQeH6oYd1tWv30Xo/aKtUY2KggKnV1UNOUuS0EFEqjIUCEx6Nnie7z1P/x526NqBDhecqhXR0HOTYESGSpyMx/8x9z8coN9FSCnkKe9y5dY3Z+iX/vt3+d08cPK/ATdq0fzuHIiTWIFhihqXyDsRuAANfFXllnfXMLu5Clr1TEClMCRbvYpYkprs0u8HhfD0JXbVBM7EFn7IVCVjN2i0UhBPL4wVDTDHJhGXlvFgD/m18hOHcqHqNC2ewIZADSh2ZbaWIbbRL/xd9RG0O/y18s9B0LPB/PcdAR6IKYSlB2a5XCbSQi9D2LzRp7FMcDpwZbNTXHonEYa7rDsSGlAnbhAqR3AxqhQSIEvJYBpgmmoTTihgG60sqjaeFfQV4G/MX1Mv9Oo00uqFL6J3+E9o+/E7oc+Gi2g7g9pWT80lrsfvGFlD3WyAdZhbaVroTY8WZOPAA9wRdgeovGqwypB7ppwSVSubpoYST+p2padm+AYjej7ssetI6/SNvuV8/73OPBeLvCNgUSQjDaDYy3ndc1RbrhigZ0b3vUegGdDSLxhb+gUgv4Ih3k2UXOAEh2U9x/xu0irRYSbUdv7/pvp5wRAnoLIBVv0XylSq3epFTMM9JXVFqosJZdKWWVgA9knKPNDxP6+n6gtBMJCxZW1PNNg1f+3K/yr//wNf7in/km3/rVl8IkptHLDpeAyEF5m4oZUC7i8aHEV/98/K/IZjpOeAL6S72ce/gk7310g3ubZfzeHkQmtQ2IbHPm7HpWlFhVanqsYo79eLqKJkCPAKynwFcE5KTtQNtF2rYCZG0X2VKATbs3i/7DnyHmljB//wfo73y0611GYLtbG9N5Zdt3b9s6SWz/t7tIKdHuzuIU8/j5HK1/8P9QJighqNUbvPJvvsdHV27xd6dW6Jddu0S5c7ZIxMYWtDfU5HQDtI0toumYAAYAw/Uxqw2EYT64GlmDyKQWA/qutuz2s93mZbitxM68mkZvj8qXF+34o00CMujs1qVyqo536c02NFodcKdbyH2DCkRte6KI+3evEm2AZFdDto9tifDDFRy/I5xMFeAgDZ3LU7NML63wK195loNHDpBKp/jjH7ypGNEjkyoKlAsBgVBqfSULwu/ivGahj4sMMAyNVDoBmwGVao1USM6L3kmEbYcReAnLQg/zJ0ZRZxGqCWRoSd/Ztp39IkCmE53/W22VcD1MTJ20jDChvMAXJmRMZCYL/RKZzagNkia2u0FIyVa1xvrGFv2lHgpegD63rM7rLah3F/qJEgRKO+b74bHweOhDSpc/Kb5KzSVHB+HevDq+6+1+1rjunrH3O7fTPxowGP7E99eE0jJrIVgPtXuSCJDvXZ0ICO+5ifjU2skwJZPKlxsFZuhhOrddLYnNTkDCIvif/voeYkhsP5/d8zX6LqKd6dzgwcBJBLQibZbouja+WxCEFoJdrdirMl0tEBBxXMLuOj5I+Xm1W5/nOTs1d/c9TYZEq11BeLsAV5xnRn0d14cYu8T36/4qus1eygbB7mMPUH5hsBVV9n5RqrtUnrGtcM+TO/f9jLYolB4K3ehYbx73d/87hPRxmi2mP7zKP/6Xr1Aq5vlL/96f5tD+fQhlIMBHEAg14QMJbiDxHAe73abZatMKI3byuQzZbIbUC39OgRGhIsRMw+iKTlDpHXypkiF7nodh6FiWGZtSlMlUCx39gw567tImiZBVO+o3TdNC3izQdTPmx4n6SdmZg1jr0V0EhNvs8P9oMYuERHdfRx8MHYwUsstkFjtfA1IKCELA9uYHCmz5PrLVRtvcil+heufEmoIdtdrx+uUOoda1BdmjCEB4voqa0tQS7Ps+PuA4LpZlIBDYmSS+lYjJWKO6dRet0Yrt8pg6slSI6yMluDKg0myiJy16ewtdJsCuQIv7DObuiRvhvG7Ztn2PIHaAQTVHpJS4jSb1Vgtd18mXerBSyS4B0Bn/e4EkCUpzYDvgCGWSsgxkX288NCIQte26SG4RK0DiCDUVFq3cBgzDiKNd47Z07aI7AhDcTJrZ+UVSSYuhgV4MIahWlZYtnU5imgZBGC3shxqtIJB4vgeyk2CasK0SkGFdANKpJD09KjtAo9kkkTDJpFKxr1nkexP5jGmhtiLu/+530VW0SNMQzrt4/qWScbM128bxA+5NzzO3sMSZk0cYGigpjWF3v3bxdMliAfsf/7dxe8qVKq/84E3qjSZ/4htfofBf/F2YXlCW9XBu7VnHSItoGvE7j/ahahABfT0E//Rvo/2Z/yusbSiZE4JvEQSRGnH3z07t256a786bv/8CJEMMHt4PobRiQkBvD/LofjU2DUO1w9TB0MAwCMLAFKErX0Rhhn6Jhq6ijiMTs65MxrppIiyDQAiWN7d49c33uXTjLocO7eNbv/FV9h8a366ZM9WP0DTM//T/ibg7B2HUd7cJPPrZtjjvMt91Zkz3dI4/PjBI2QXtuiZlOP5k6PS98x1Ea1K0kdhdi676737yA0GiPTVIP0fZC1B9jnt+ViQj3AcscZ92hl0m401l52w1r6KDwWcDlD3KFxiN2DH/yS77+F7NilKpRLtkwf36eO89mIyuEoQ7gM7FEvUSMpk0Tz91ntff+Zjl1Q21udLVblOdpxzhEIocVNc1pJ4gYeqkUknatk2z2VIaux2mm46DfZRHXMmQcrXBnXvTLK2skcumODA+wujwIOl0EqRKs7G6vkmt3iBpmYwO9jHkdRSZfipBrV6n3bJptFpsbG5x5fpdPC8gkbAQmtJBaULgBwGb5QrlrSqWZZLPZUmnkvHi4vk+nuvh+mqBiICfbatce47joOs6A31FDLMzad0wX2Hc7jDVjQz8UPsXRrvVm52XEkYSBcU8QaDS7GiaiEP8I0ATA7Hw3al3HkZldr3jIAjiBNu6pmGYSnOlbVWVyQ0VbRhoCmi5dptqrc7M3BJrqxtomuCjv/gneO7J8wih6ApcV2LbCghn0imy2Qzpf/h7WP/wXyM8H//4QVr/6L/G8x1abZt6o8VP3vyAb7/yBl/90pP8n//8n0RKxcfjOA5tx0EGkmQioZyOdR3XcbBth7bjEfgepqnysyXCwIjYLCZUhCFEztuh75AUBNInkKGwB6YnZ/lX3/4BiYTFn/71lzk4PooI37+A0A8tcvBU1A5RImEhBNo//gO07/wEMTmPHO5HHh7H+52/oc6XMgYd0fWe58eh95pQAR5OIKnW6kzPLlDeKlOuVDE1jXOnj7FvdATTCKk2kMr81bUJiUy/K4urVH7vuxTzWUzTUMnUN7eo1Bo4oSlX0wRblQYraxtkMikSlsVmuYLreZR6e8hnMwBhjk2VbLnZbAOSnnwWKSW1ap2Z2UWymRQnD4/T119CN02kVP6AjmMzt7iMYRjksynyuZyaW0Lg+x5OqAEzTYN0Jg26apvnqlyBnueRSiVJppOxfNJsG9/3mV9c5s69GfaNDjM40NeRSbFZaLtMiwJzGs0W96ZUrtEL505R7MlHeDsU8oKgWIjfM4jQ+ivCeaUCQyJuoGh86WGuVUkIDBKWAhmWRfDcowhTBWH4moaDROo6ZjqJlU6p4AFdo+X5uFJiJE2sdApdaCq62nWRkVnQdRGeq4JKHEf5rbVsaLUQbRsZaVcbypdNBbGEc/03Xsb9C39SbepkJFPVptb1PDY2t2g022Hqpew2n8fI1NMxOwlC71cCKbEXV9iYmOT2zXsUinn80UGCwVLcb91FggJf0AGX+g4w0/WcDlCIdpVdmiapNtRdd34ggNJNZ9B1kMh/WfGCSzRNgtBD/7Kuugita22637M+DU51UTc9SLmflus+WqlPgycP/Mz71KMbS2x7oNIS7FmXXZtUoXogCGVj99li25XdN3/wmn9BYOtTrNkPqqrcdcrejQn1QuoSEahw3K5x3GrbNJotmq0mtt2mbdtxGKeqRkC1Xmd+YYVytY5mGAwO9DE0UFK8NJpGrVbh/Q+vcnd6nkI+y3NPPkIfO0vX9l8IypUKb7zzCddvTypCTU1w4/Y0jz5ygodOncAyLCYmZ3nj7Q9YWV3HMg2efuxhvuV5cQvbmuCdi59wd3IWTROsb2zx4ZVbSviHC1ogBIHnc+vONG+99wlLq+sM9hd55KHjHBgfRdM0VlbXWFvfotZssbFVQ0rJ6PAA6WSCldV1FhZXaLdtDoyP8LWvPEtmsA/fD1hd3+TGxAxb1RqlYg/DQ/0kUwmQ0Gw2KZdrrK5sIIAL03OMRQNSU0Cr9r/8N8zMzPHqj96i7To8/fhZRkcGKBZ6SKcyzC0sMzUzz9BgH4ODA6xubFDe2mJoqJ/BgT4sXaNeq3H52m0uXb7JRrlKbzHPuTPHuHD2FCN/+W+jhSH36n1LVlbXuHt3mmq9wd2peW7fmyWbSamUIFKyvLzK5NQsG1tNVje2qFTrHN4/xgvPPEY6jCYDwtRLba7evM1muUo6leDmnWlczyOZVOmKao0m6xuKM2mzXKXZapHLZjh8aJyeYpHFxSVu3rrHRqVBvVantyfHSy88zujQgEqSXN5ifWMLXdfixNIiNO22221WVsusrq+BkPT25Bkc6CNpWSSTFpZp4vsBlWo91tIkLJNUUsVHtto2C4urLK6skQ7JM/v7eik02x0tp6YhXAVsao0m8wsr2I5DTz5HsSfPRrnCzNwiuqaxf98IgwMldE1D+j4T96Z54+2LbFVqrKxuksukGB8bYP/4MEITBL5Ps97AdmwEgnQ6RSadRA8XLM91Yv4az/NpOXXKWyqCs7xVxXVdEokEc4vLvPqjn3Hq+CHOnDzCwtIqd+5Os3/fMA+fOY6Uko+v3GJmdlElSw5zJfbkMvT05KhU6yytbNBs2fzKV57il778OKYpkYFDwjJotprcuDPB3NwyQRBw7vQJLpw7RcIymJpZ4O7kHPVmk4Pjo1w4d5JEMsnK2hZ37s0wO79EtdZgaKDE055kKNJ0NZoYhuDU8cOMjQwyPNQfj09kGFbuK7LMRBjwI6Wk1mgyPbvAvak51tbL2K6L7wfYjku2I2EIinnW/+f/ktm5JZXtwfMo5jOMDA1gJhJMTc9x9940jUYLKSWGoXNw/yhf+1+/Q6LWDJ/nIE21QEvTwP0r/z6u5zEzu8CN2/fYLFcIgoDB/h7OnT3O+NgY9XqDn751kdt3pujJpTl6eJwTR/fRatmsbJTxQmqKnlyaE8cOkk6nFYB0Xer1Jp7nkjT1kM+to3GRtoOstxANmyCZwvccXB9l/iZA5a/TqFRrXPrkBstrmzz2yCkYCaGjhGQyQTKhTMJxEFOcVERJ1IRlUchlsEwjziLwqStRl4kRV3G6dST9Z69h24FOR/ssd4KATytdy0qssAgEvuvjOg5S00hYJrqQoEUKDYh89zoY4VOgTQyS9qrVAwKu6B47AVc3IN37wm3Pio59rj7aVo1I2919v+5n7X5eHJi34/TIGhMri+jWzstt99jelAer+RcGtn5BbKpKl91cdg2I7j1hsENdG9dASlrtNj/86bvMLy0DEt/zuDM5x/BACV1TpGRzi8v85GcfMD27hK5rVOpN8rkczz19nnMPHccydO7eneLipavhTrvK8SP7uyvZMT2Fj3cdjw8+usaP33ifpx47y7mHTlCp1njz3Uv89K1LlIp97BsZZGVlldsTU5SKBZIJi1q9GfsugGTTcfjpWxdZXF7jT33zZY4cHGdzq0q93owZ6qWUTM0s8O1XfsrFT25SrTexTJ2tSo2vvZggkPDDn/6MmxPTDPSXMEyTmbklZBAwPNBLImGxtl5mZn6Zh08dCQHRICvLa7zywzd5+/0rlCs1hgb7eP7pC4wM9yOA8laFjy7fYmp6Ed/3yXsw5PvKKKurek3PLvLm2x9yZ3IaX0pq9QaagMcfOcNTjz/K3ckZvvO913n07Em+9NzjXLl+i4uXrvL4hYd4/plHMXSNSx9d5duv/JTrt6ewbZdk0uLu5DxDAyWGd4w5x3H47vdeZ2Vtky89c4H9+4a5N70AMnI096hWq7zzwRU+unaPSq1Jq21zeP8o4/uGGXU7Trm+lPz4jfd4/9JVnrhwhp7hfpWjDomuC9ACGo0GE/dmuDc9TxAElCt1gpDV/JFCHs/1+ODjG0zNLVHIpTmwb4T1jS0KuSyz88v87P1PsG2HVrtNqVjg17/+ZYaH+mk0mly5PsGPXn+P1fVNshnF+fPyl57ENA0sUwHHe1NzfHT5JkHgM1AqcuTwAUZHBtjYKHPpkxt8dPkmrutS7MnRbjucOn6Ir29WSEVzS9fAU3n3ms0271+6ytWbd+kr9vDY+dPYts07H1wlmbD48nO6Mp1aFpubm/zwtbeYmVvi4dNHKOYztNp2SIqrEfgOk5NzXL1xl5btIBAMD/Zx7swx+sOors4GP9QGhWZywzBIJhLomo5mmTi2w72pOQZKPRSyaQZ687y5vMba+iYH942QzqSYnV1gbn6JR8+dpLenwFvvf8L7n9zk6IExnnvyHOOjQ3zn+28xMTXHV/wLGJqGDCT9xTwnjh3goVNHMITk+z95n7X1LYYHe8llM1z86Bo370yzf98Q2UwKXdOoVqr86Cc/4633LqPpGvVmi0ajjXHqKL8avntaNqapMTzUy/BgH0JoMSiQXQJecX2p457vq03Fx9exwhQ4C4ur/PSti6SSCUpdsk0ImJ1b4pXvv8GtiWkc12VsqJ9Hz5+mv6+XS5/c4K13PqKQzzDQV1SbtE9u8lStQV/QRSUTmhrxPGzH4ePLN/ij77/OxmaF0ycOkUomefvixywsL/Nrv/wSQSB5572PefeDK/T25Hhqq0ZvocjkzDw/fP0dGi1Fv3L04D5KxQL7xxU3YaPR5M7daSrVKoVsmv1jwxTy2Y6rg2UiiwZ2JsPWVp3567fZrFRxQrCZsEwOjI/guh4XP77O6nqZQwdG8f2AiclZWi2bM6eOcObEEXRT9XWt0WRtfRNdM0MCVEtxyWkdLidFTRStHtvXLBHWK/pHuB4ilVTr0DZN1rbh/CklOl/Gv+NNz6eavmTXHwFSU5rAQGnLpTSUO0e0/mzDEl2Kjfs+QnSNz51m4e11/szSDbi6jz1Qkff5/AWVsP8ijsfOsQ5o6lYRaZqII5JFqEDZWT3lBvDzY50vBmyJLvux6H5hn6NiEXaJB4tAasrZV4RO5crq12FDV34hauB5ns/aRpl/9q/+mEwmwVdeeIxk6AeStAySCYvV9Q3+8Huv8+6HV/nSMxd46NQRbk3M8Nb7lylXqmRSCR46cYh6o4nn+5w7cYSRoX72jwx0VbSjxo84OSrVKh9evoHnujz35Dn279/HVrnK5My8El4LyxwYGUALVeOJhMUTF85waP+YikwD0AS3p+eYmlng9PGDfPnLz5JOJvF8n+m5JRKWgQY06nUufnSDS1dukcukKGQz+IHSjrXaNq7rsbaxhef5PPvEWY4fPci3X/kJr71xkcMHRvj1X3mRmblFfvdfvMLqelntaDWNhcVlbt6eoq83Ty6bZnJmgcmpWUxdoGuCuYVlJqfm6MnnGRroo/fuXOfNahqe5/GTt97j5u27DA2UOHJwFNf1+c733ySTTvHouTPkc2larRZtp00ioZFJKjPR/MISzXqdSrXG629/wN2pOZIJlQZHoIhY/R2ktjKQ1OpNXn/nY04ePcDp44cQQrC+XubmxLRig/Z9bFslCjd1jWMHRjEMndGRQZXzMIpEBFq2zbdfeZ2+UoGnHn+Y/lKRe5PzzC2uKB8iIXE8m9m5ReYXVzlz8jA9hTyu61HqLZBOJRgbGWRooMTqepmnHzvL2Eg/gfSYX1rmjXcu8dZ7n3Dy2AHabZu1jS0WllbpK/Vwb3qe7/7gTSZnFnjo1BF6CzmSpgFS7fAVP60yOa1tbHHl2h36evP09xUZ7Ovh7fc+5t+88hP6inm+/pVnKBbz/PEPf8b3f/IuT2y1GJUdkzCu4tEq5LPs3zfCxY9vcOnyTfp68/QWe7Bth9GhfgqFHEjV95PTc9y+O8MTF07zZ37zV1Rqk0aLsdFBTF1jvVzmzuQUzXYTy7RYXt1gdmER09R4PHOKXE5FdsmwHpZpYqQNUqkECctkoL+XRNJCaMpVJ2EaZNNJ8pkUfl+RdCpBrdHC0CCXSpDNpCj1Fjh76igjwwNsblWZnFnk9IlD/NKLT7KwvMbP3r+CbTsKDBsatuOQTic4cmgfhw+NU8ylWFha5/bdWdq2TU8hR6PZJp/L8Mzj5zh54jCmabIwOcely7cIgoBHzhxFApMzC8hkhzNPtG2EEOhxeghlhpCaREiBCNOOCK0jD1utNm+/9zG6Jvjqi09y8MA4Nyfu8bv//Lv89K0PuOCH9BPhWP/oo6u8/+EVXD9A1wTTs4ukUwlOh0nTE5bJ0489zCMPHee1N9/no8u3VEYA3UCLZHEMtnwqW1W+9+O3ef/D63zja8/xp379a2RyGf7olR/x/qUrHDmwj1PHDpFJJRgd6uPJRx/iqQvnODA+wtLyGgtL69iOzYmjB9g/Pkwq9PMMwrRotVqNOxNTVCoNvvnLXyaVSGOYOkIECAGu67O0ss6b737M3ekF8vksgQyYnF7Asky+9atfZmigRLXeZGllg3q9ScIyVZL7W5O4rsfJI4eRhsbi0ipvvPchd+7OkM/l+I2vvxQmawfXC8KgX4FK+xaSCO1YliQgDaOzlY/dOwSS7RQmHR2MFqtDdvr9dcx4XevhZwEtqZ7WcQ2KaiPQdYNkQuALPdQSavexBu383H1sZx135hvuUl48qKbpU0Dobp2S2PGt3PPcn69EiEh2+l0KkH7XsY6CRv3blZKJyC9UUynC7vuE+7Xws8sX4CDf0Tt1284fHK12J93sJHkUIQO4umX0qrR4zCo+pog9W2I7Nlu1OtPS5bFzxzmyf4TBvh4++PgWpiaoblW4PTnL93/6Hn3FAs8+doYTx48yPjrE6kaZdz+8xq3bk5w+up/RoQFSCYtLV25jmiYnjh7YZnJ3HGV+8v0A13Upb9XYKFdJWBamYaEhcD0Px3FpNNrU6g18z6e/t8jwQB9ziyvcvDPF2PCg0mxJkJrGlZsTbFVrZHNZrEQCXROUij3kcxl0TcfzPKrVOpPTCwgheOGp84yNDJJI6BSLeTKpDEvLiuagt5jnxNEDjAz20ZPPkE4lOXZonEfPn8H3fCzToNVqs765ReDYFPMZfunFJ8jnc1y5PhEmbXbwXBfNMsmm0xiGju04HD4wxr61Sod5WtNotlp8fPUmA6U8Rw+NcfzIASzL4O0P1KLnuja5dIpMKkEukySTTjLQ30suk2Zrq8bGZpmtSo2NcoW+Ug+PnjtJPpfB0DRGhwfo7+1RkyMcNYGUtGybraofpy/JZtIMDZS4dW9WJbJ1Xaq1Bu22zaNnj/PS80+QSSdIptLkcnl8u8MUbTseq+tlzpw8SCGfBWBkuI9MOhWOTRVtVqs3sSyTRx46QU8hp9KZ5BRbdSadVPnpgFKpwNjoALVGncnJKa5ev00yYXLy6AGKPXk838cwdFbXNrl46Ro37kzx2LmT/Olvvkw6lcRzbNKpJFvVBr7vk0panDx6kEP7x9jc3GJ6doFKtU690eT67XssrazzJ7/xPF956TE0w6BSqfMP//c/ZG1+g0HNUFkJNAGehxDKn+7MySNMz8zx4zcuUt6q4Ps+uVyaE8dUHWO/mfIWrbbDYH8vfaUipqHHvmGu7TA3r8g6X3zuAulUkntT8/z4jQ+4cuMOw0O9HDuUwvN9bMcNgatACsW5JZEqBUzoaB+EvmO6rnIoOq5HICWZdJJUmO5HE0IlpA5NxZomSFompWKeZFJFCZqmju9L/EAtjq7roWkalmkS+AGGYZJOJ0inEiQSOumURTJhUchnGRwokUgmCHyfSrVKs9XmsUdO8vILT5BOJ2m3bVLrVbTvK765iHE8Gp1B4OGFUX8i9ifxY8EuUVGm84srPPPYQ4wNqZQz+8b6yWSS3Lo7g+d6RPGOnudx9cZdAL7x1WcoFvJsVaokEhaarnzNeot5zpw8zMEDY6QvfoJldoCDqpOMI/EEUN3cYmpmAV3XePj0McZGh9B0nQPjo7z59gfMzi1w9OA+EgmTsZEBvvTsY5w5dgTdMsmlkyQsk2JPll968UnOP3ScYj6j/OICD1ODowfGuHpjgo+vTfDQqePkMmks08S0NExLJZq+emuSN977hEI+y7NPPUIhn6XVdphbWAYgkUhgmSrNVKvVpq/3AMcP7+PDj2/gOi6GEASuy5Vrt7hybQLLsrg7NcfU9DzDg/0qwMLtgC3PkwSBQNOJF9qINFpomopgDd+PdKYyBFYAAQAASURBVN3YJLkrYk2AAjtaLBt2rWoRKHtAh2+5zc8LVNR6tBaq5+mapnzmNG3bWtl1FwUuttd2j2d12NG76y+3//oFym6tXudY93fd/frzmRE7T9yJOQTIoAtMhZq86L3vsIqJ2HdbVyZn0bmjMgooYN2NR6J6d7fq08oXoNlSFe28vvC3eNDu61RWCpBSQ4gwdD3aIYQ7AuU4GSavFpIALW625/m4oVmo0WxzZ3KOdNJibHiAqZl53nr/Mncm51jdqHBo3zDZTBrdUHnDkgkLGUhcz8P1fA6MD/PC0+f57g/f4kdvXGRooMTBcDMkpWRpZQPX82m2VORio9XGMAzWNja5MzVLIpkInWVnsR0PQ9dZ31Q+Gc88/jDLqxtcuXGX3p6bPOIqrVQgBCtrmzTbDtOziyzOLbBvdJhavRFGK9XY2KqiE6Zu0TT6SkUeP/8QmYyF7/vU6m2WltfwQgAXBJJ6o0mz2SYIAuW/5rq4jq00c4FPo1anVauTtFTKm3rLprxVRaA0EFHEx76xQR6uHeXNdz/mw8vXeblaI4oKCwTYjkul1iBpGcwvrZJOpygV87EGqlqrqQFndGgyUgmLVCpJvdFkeWUdIQQJy6SQS3Pq2EEOjo9iGhqGYWK7Lp7nYcXBFTLO0D67sMKN25OUioXQ4Vrl9Wu1bCq1BpVaAz/wGR7spbdYYHOrwtLqCslyhVTo/4SmzAwbmxU2yxUGB0ohmPa2jVTbVYDX8316iwU0oWEYKkrU83zabZt6s4Xv+6TTCVp2m8XldZZWNjhz6ijPPnWeQi6rxk2zxdzCMnen5mi1bEq9PaSSCRKWhWUaoGnYjovrBaTDVCalUg9HDu1jaWUdL5C0bJdqo4lhGgwMDGCaKVzHJZtN4/kBXq2Bn8uGzr9CcR2FpaeQ5eTRA3x0+Rb3phcY7Cty+tRR9u8bQde0MFdcZ35evzXJO+99Qj6foVKtk89lKeYzTEzO0rYdMukUPYUcA31FLMtgcmaRm3emKeRzrG5U2KzUSGfSVBttMhkFgOp1lUtvsL9IOpMM3QFspmeXuHF7koXFFVbWNjk4PoIQGhvlLVbXy7RtWyVjdlzabYe27VDeqrJZrlCrN+L56bo+EhH7Qm2WK2xsbjE5NcfdqQVy2TSpVJKl1TXl75ZMKh9PX3Gc6bpiu6/WGtiOo3JZCkFD+niej24ATeUr5TguG+UKy6vrrK5tMjTQx4H9o1imget5cYBETNAcOuJWqjXQoNls44egVM1htbMMAkm92ULTNI4dGufpJ85RrdbZKFe4dVeZ1VJJS0UuS+KAAzVmZfxXGnrolCEwgkC5JkQaG6ncLtq2qoPne9QbrZg2IR3mKZW+jx8oipwo9U+9XidlGaRSqu+arTbzSyvcuTdHuVJjcXmNvt4CzZZNPpdkoL9IveVw7fYklVqDU8cPMTLUTyqZoL/UQ6VaU/kcNQ2Vk07g+SpISAUB6WGOSQ1d0xkd7ueFpy6wVavz5juXWFpao91ylGwKN+NKPqq0RoH0aDQbNBot2m2bZCpBsZBnEIEVBvBIxyWIgFYor2KT4mcArU75PEqHzhWxsImPie1UD9tMhd3r56dri2Sk2YnDEBUclxCPg93ZDH7estdNdmq2Pu3cz/s0GXPGhZOrA3RlZDLt6pvwe5V/NFL2KOJl0zQ6UeyhcieTTpHOpDqKJNGZW9vZuu5fvhCwta3jRNfPA5VO5dWgAqQix4vs6hG67NAChMelSiODEDHhoyYEG+UqC0sbnDt9knQqycLKJj35An2lEsV8nnK1Tq3RJEBQqzdZWd0kkbAYHijRarWZmVsik04yPjrIe5duUN6qxbX1g4CFpVVs22FufpnNcgVD1xkeHOD6nRn+t3/9CocPjFGrN5icmefQ/jFGRgbZ3KrywSfXSafUrtB2XGr1ehgKrTRbxZ48+Vyayzfu8I//+e/z2LnTfHTlNgtLa6RSSbW7K2TJ57JslCu88c4ldE1jbLSfXC6NZVi4nku7bdNqO5S3KlimQbPVxnZc1tc2aTbqKj8e4AcS13VpNJq8e+kqb7zzCYlkUmnifJ9qrcH80ipt26G/1MPgQC/Dg71Mzy1Sq7YpBr5ifxciThw7Pb+Mrmsc2DdMJp0imbTY3KqytrFFqZjHMHSqNRVF5noeIGnbDhvlCv2lXhJWgsnpZd567xPWN7fQhEZPIcfwUB+jbRvN9xXnkhBYCYtcJsnM/Arf/uPXyWZSrKyV2dyqsri8Tr3RwjKVT9An1+9S+N7rHNo/im276LrOSL1D055Opzh1bJTbd+f4P77zGk8+eoZPrt5ho1ztSk6rkUxYTM0t83vf/iHnzhxlfN8wxw7vJ5tJs1GuMLuwQnmrRrmirkslLQzLwvUD1tbLrK2XSSWTVGv1WMOUTimQMTE5ywcfX6dULJDJpBgfG0bKIHYqD6TENAwMw4j3wLquUyoWkRLmF9dpNh2azRbX7kzj+gFFUyWbjk1IfqCYujUNU9cYGexjZLCPT65PcPLIfk6fOEwmncILNWAA2UyK8bFBJibn+d1/8QqmZeC5Ps8/fZYnLpzBdjxuTsww0N/LYH8vN+9MsbSySSJh0my1WVhe4/a9WSrVBsWeFovLa+wbGUQIwdrGFn/8w7fY2qry1KNnyKRT5PM5btyZZmVtk3qjxfrmFsODJaq1BvVWSyWu9n1qtQYJywzN/gH3Zha5NTHN0so61VqDVDKB63p4TkAyjOa9dPkWN+9Mc3dyDj+QXDh/GiuR5OOrE6xtbHHy6EGVTij0pezv62N4sI9Ll29TrTUY6Cvi+z4F0+I3XY+EpiHqLVzXY3Oryq2JKa7fuscnV+9Q6i3wy195hodPHwtlf0dzkUomKPbkuTs5T19vD8WeNFNzyyytrHNwfAxtva7knlTmjdHhfu5OzfP2xcsM9Pdih+Cy2VSbKdd1w0jjJm3bjvO7BUJDi6xmZifZ1GAhx/Gj+5meW+LK9TscP3qQVCrBrdtTWKbB+OgQruNQrTWoVOvcnZpjqNRLMpHAcVy1ufV9NssVKpUamhDo1RrVWoO33vuEyZkFZuaWSFgmg/29mIbJ/NI06S0LwzJptGxW1jbRdZ2+Uo+Sb+02tu0oDZhhIASYpkEhn6W/1EMyYeF6qg9N3SCQYArB8EAfC0trfBD6d5XLFRr1BuhqAxRF2q5vbLK4vMbcwhLLa2tUq3VabZujh8d56tGHyPkeRhCo6GBPmfCF1jEfql7UQw3HZy9wsamue6HvNpHsOJfuc0W4Nu4KMOv+vF279Vk16kCxHeZG2TGV/qIGvS4d2Y7n7sAJ287rXtc/X9kzghMJAWzXasmOVqtrw94NmYQQGIYK6ND0WK+FEIJisUBPTz58F90aSBXl/CDlFwNbXQ+JeUkUaupopB7oPp0PMUKPEKrYjvZVcl31T2doCBKJBNmMSmuztl7h5sQsQwO3uXzjHpvlOtlslifOn6HRtPnZxcv86+++ztPLG0zOLHB3ep6TRw9w8thBPM/n8o0JJu7NsVmp0lfqobevN3o8UkrqDZUS5ubEDFdv3uX4kf0cOjDG+a0a5UpNESsmk3z52cc4feIIo8OD1KsVHMfl4kc3kEiKhRyH+no6bdN1zj10nGw+zbsfXuPytTusrG3gezJO/ZKwTDKZNGdOHuLdD69w+foEU7OLHBwf4kvPnOfIoX3U6nVc12N9s8KHn9zkxNEDbG5VsR2XmfllpmcXabVsao2mGlSahu/73Jta4JPrE6TTKaSUVGtNLl2+zfBgiUI+w/ziClLC6tom65sVWjUbT9OwNGX6SSYTHNw/wkdXbrG+WWFjs0KxJx+SjTZZWllneLDEYH8vdyfn+NHr71LeqjE1s4jn+5S3ahzaP8rhA2NMzixx8eObXL52l0I+wwvPnGd4qE/RQYSLoKZp5FIpvvzcI8zMLWI7LtVaE9f16evtoVTMk7BM9o0O8sSFU/zs/St8/6cXKRaynDlxmFMnDhPYTjyerJBa4Q/++KdcunybtY0tVlY3yKST9BRyBH6AJjSOHh5nfbPC3ak51je3eOHp8+wfGyYdLhZKw5Bi4u4MQwNFenvyjO8b4tyZo3x89S7/8J/9AeP7hijkMjx86ij5fJbxsSFGZha4eWeG1bUyA30qunR0ZBAplTN1s+0ozUPbZnW9TKVap9Fskc9neeHpC9y8M8Vrb16MBcn7l64xMtRH/2pVaSRMg1ioeT66pVTihXyWoYESPXNLHD64j/6+XoRQCZgTocZ3oK/EExdOU601WF7dYHl1k9HhPh556ASjI4OsrW1y6fIt/o8/+imphIXn+4wO9XH2zFGGB/vQTeWX9bWXnmagr0ghnyWVSnD4wBgnju5XmkFNEEhBJpvl8QtncBw3Ztx3HIe+Ug/oOql0hjOnjkAQkMmkCIKAgb4iT5w/RX+piK5r+IHP6eMHOHX8EPlCgUqtSf9AH88/fZ4gCKjVmxw5tI8jB8c4eugAfiBJprI8+dg5Hj59jEJPQVFrCMFgXy9feeFxWm2b+aU1hBCMjQ7SN9AXc3DRauF6Pp6vKCN6iwVKvQWmZhf58OMbjI0MYllmrBkHSKWSPHbhId565xI/fvOiij7erNCTz/Pis49iXp+JhZ6uazx+/jTXb03ys/cvs7q2SU8hR3+ph0wmja4LGk2Xm3emWN8ss7ZZpVyp02zZBGmto5kxO+I+axl86dlHWVsvc+3mXYSAYk+ByekFjh4a59jhg1SrDZqtNnfuzfHdH7xJOpHkoZNHqTXbuH5AvdmkWm+SSqXQhGB2fpnX3vqQj69NcPLIOLlsmmq9GVJpaAgkvh/g+Ur7pesalmmEVCAelWqdcqWGDOc3oUYhm06SSFhUa43YH9XxA1w/oFYt8+1Xf8L125N4vsTzPLaqNZrNJsLQaDRbsXxq2w5vvH2Jj67dotFsKc1kSLR6/Mh+Go5LNtQ8al2bja5F7oGBFnQv9g9YhAgzhofgoJsQXHad07mAncBmj0p0/oamsegeOzkEYpLfnY/8fK3YXYXP+Gbb/T9vn0nCzC8yppTaZgqNcER3LSLN1x6P1YQI6ZPUeRJFj5OwLBKWuUdrHry+v6BmS8Y9tf3FfXYFOuGVXbg3RPNd2Gt76RpbMoisi+pAwrIYzWf4jS8/ysdX7tCybd567xPqjSb7x4Y4MDasHD7TKRzPY2Jygc2tGoaucf6h43z1xacYGuzHtm3OnT4Wq9iPHN7PmdPHOlFUmsZLzz/G2dNHSaUSFENW76OHD3Dy2GEVWaXrMampYRhYpkEuZfG1F59m38ggjuPSU8ixP5+Om6b5Po9MzPJIwuTlh05SbrXQ02lEMsnSVhUSFoWtKoam8fiRw9jf/CVe/+ATqvUmRw/t4+D+EXLZDIP9JZ598hzLqxukUwmazRYDpSIXHj7KYF9RURkkLM4cP8iBfUOcPHoAUD5eRw+OMTYyQCaTZnNzi1rTJptJMdjfi+f73JteUAvpcD89zSW12OiK6cmyTH7l5WcJ/IBKpcr80hqmaeK4Hi3bYW2jjKZpPPXoGd65eIWZuSUc1yOZTJDJpBge6mNwoI98VnHpXLlxFxA8fOoILzx9nlQqQUR0GZVEwuLrv/Q8gefiOi5b1RpblRp9pR6OHdpPJpNSfE3JJCMjwyyvrJNKJRgZ6qfVthVPEGrgCSk5fewQxUKe1fVNDMOgXK7gBR4PnTqC70I6leLIwXEGB/rY2NxC1zQOjKt+F0Iw0NfLb/76V3nqsYcpb1Votx1Vn74i3/jaC6TTaWbnl2k2Wgz297JVrdG2bcbHhvj6V5/lk6t3WF5ZJ5NJsX/fMMkw5Uwy5IFyXUV1kLBM8tkMrZaNrmk8fOYYv/71L/Pamxd57c2Lyp+mkOeZC2fI3phVY9cyO/LF81TqFQSariMEDPb3sm9smIRlxRo03TTxXZfeniKnjh8mn0vhuB7rG1uMjQxwcHyUQEoOHtzHU48/zMzsotJU9BY4feIQg/0lAnQM02L/2LgKwTcMLMtEBlL59Zkm2Uya8bFhTNPANJU/XC6XwTJNpbEOk3cHQtBsthkdVknPk+kMge9T7FHBAmPDgwRSUm+26e8rceL4YYSmUa02yGZy9Pf1YVkGruvieQGZdAbLtLAdl0P7R0kmE/T1FhGajudLdE2i6zrHjx5kvVxlaWWDU2G7NE1DpJMqSXpo/ksmLMbHhinkc6ytl7k7tUDLVkErjusqjZlUqXF0TeOZx8+SSSW5fH2ClbVNxkaHeebxc5w+eXhbfjyE4MzJo3zzl1/g8vUJkgmLY4fHKfbkqNYaPHruFLlshnw2DUJw+sRhEgkL452rXaJTIEwjFqqB7XB4fITf/OZLLK2sU67UsAyNX37paY4cGGNkaIBUsspTjz6MaZqkU0kcz8cLYN/oCI8/cprp+QV6CjkyuRxSBiyubnJ7cp5qvcnCygattoOuaczMLzEyXApBtXL90HWdU8cOkc+tMjWzwNp6mUTCZKtaIRnm5NR16OnJceXGXV75wVvkchlm5paot9osrqzh+QHlao2f/OwSrutxeP8IpqF4Ei3TYG2rwvr6JpoQFHJZDoyPsrq2Sa3ZoNFs4fsBuWyKR8+eACmptFr0h/xamud1pawSXQSmn7PsMF99domeEyVl30O7FbvXdDnqdIOHbu1Z9/E9gJeMHikjhrLtgOuBy17P37NdO491g6Kf47kdw1hXXaL/dt8v0mzFyKNL4SgiB3lNC3FuqNmKmhQpOOPbfr76/sJgK65sqHWS3TbhXSrQ+5UIcAVEPln3uyxW7nV4E9VxIci7Pn/5nWu0221abQfH2VCOsWKd/MIG+T/4CcO6znnHxXZs5GoVQ9cw7y2jf3Abx3WxfJ/zhsHjRphyQ/tAPTVcmHU/4Lf/8A0i0skgeF8JD01H1/WYADXSgnXX+1EZ8EjoVO+4HpbnxazMom3T9+0fo+saQ90OmRLc0CRg/eFP0DSNLIJvScmvui6+rmMurKO9e5XANDhsGDxrmZAw8Tcb2GKBl3QNvacXM5nCvHyPhudz6uB+Cn1FEovr1GdWOGf7nHz4BONH9pPtydMMJBvNJrMbZQLDYHR0iC89c4H1zQrpVJL+//4fobueSsER9v+ZE4fZ2qqwtrrOkYP7GOgrcvPONFLK2EH5oZNH2DcySKttU2m0WFxeJ5fLMDLUj2WaFIayfPn5xzl6aB+WZTE2Mkgmk2JtbZNixGAdblb0So2T//Xf7+zUIqLPkNg1kJJMIBlC8FA4mQQdssS++VWV0kQIjMu3Kf27f4VSyEwtLRNpGEjLQHz3Z0jDwNIEJV1HTyXREpYCLJ9MgGWAZVI0DAqGwWFNULVt6p6NNAzSlRbpXJ5DZ07QPHkUPWlhA7W2Q4CLlTYYHB/l4OgwK5tlevI5Dh0YQ9d10qkkB/aNKHCRTJDPZXj2qUeUb9RACdeXZHNZvvLlZzl+/DCzc0shtUQPx4f60fRvqw5LdKLnAsdVCYcRbFbqLK5uUurrpVTqVYI8CPClJPAlga80PIVcjnxeBUmUigXyIcGkkJJCLsf5s6c5f/Y0xUKWbCqBphs0bZ9ms006naKQV0EEUfF9n2w2zUB/b0gym1JO3LpOLpvBMAwkEj8AyzSVY7uU+J5PKp2KKTfabZtqs00xnyORSuEHklw+F254DGzHCf3uBIZhkjATmEaCIAgwdFUfw9DpLRZi4t8gCsQIJAECQzco5bJQbzGgGZScMN9owlJyodlGhgA1l0mztLzG9OwihXyaM6FZdn2zHI7RcDdfrjD0n/1tviYlL7kebccBKUlemVQbvXInZypSUshl+eqLT/HcU49g6DqWabKytsHc4gqD/SUOHxiLSYSffvwstXqT/qm/hVWtxxqZbjMiroeVTTE6MsjRw/sVebChY5omhq4yXfQWC3zja8/z7FOPUK83SacUIe2B8RF+7ZdfoN6oMzLUTyadYqO8RSDhkYePs1muUm80yWbTHD+0j/MPnyIQGi3HI6UpJ3TLNBgaKDHQV2R1o0x5qxqmUApluwgwDINHzhwj8H1aLZt8PksukyYIJMVCHl3XyWYynD11hNnFFbZqdc4/dJyvPP84uVyaq3fuMT2/TL3ZZnZhmVqtwZmTRxkd6aOnkCWRsEglExiGwQcfXWOj1uBQ5DAf+tLGffcFlL0MaZ9eAjoRj9vv0+071PniQddbYpQR+0XvAFkPXM9uAHe/sg2hdAOhnwNcbXt0ZELcXZ3PrH9sWeyAvMhyq8UWOqlItLuZ+reZDD8to8Lu8oUyyKs/qhOFjMyBxN7+n1ri9yFD5H6/89WJ25Sp0b0DiVWpY6EWWScAJ1TJJpttNM9HExpJAXFCGs+HtoPne/gttQvVEiZmMoHQ9C4zpow/m5V6aP+N8rgp7pJOShy6VMEirjVSqtxknoo0MrsITeOTRMcpM2rTzvkesUXrmoYpBFrLhlY7DuTVwutlIEn5fryTjIBgOkxGG/2fDCTFkIhVD9X3JQmjMuBU6POgJxOIVILAUiDD8JTjMZoe+8rpuo6m6WRzWYYGS9i2S6ttk0omGOgr0lssUMhlyWbSSuPVchnu78Nxlco+bVlkkwnymTTFnjx220YCtVqDza0KBw0dPQjivhGBRO9alGJhFvV5qFrucOwowaJ7PulGG93bw7HR88HzES17xySWSMeBIEC3LJWmJtxsRKA6iN6v61GQUAoJHaOozVJ8LxUi7/leyJbvo2tayESvKYBnWZCwSGuCousiLIvM6x+hpxIcBQYcB2laJN+6jJ7LkLdMTpoGh6XERmLW21hTix2zu2XGidSloyKtavUGH125xcpameeOHiSfyyBCx/iW0yaQNvgefuCTSqVIJtJoAnxf0rY9hHAhgFbTwTAs8rks+WwaQ9fwXBfbbimn8ogWoGttUP6CyncwmUygCQ3bU4TE7bYKOIlKKkrTYru0VtdpLK1iBhI9n8Ws1knfm8WwTPTVMokA+lbX0TyPVG4CzfXQaw0CxyGl6SQ1Dc3zwXXRXA/N85GOC46L7qm5KVxPkVo6LrgeScflCcfB9wMs01RRfbLL16PViWptNFvcuTeL5/s8/9R5Hj59DMPU1YYp2oyGnaFtbCElGDIg7atxrdsqe0DEBg+gbdXo+Q//ZigHRDweC22bcdvpchzX4jFekECjGeZREyq6u8uMiOcj6DgEp5LJ8NRw7migSWU+GejrjVntdV2gBzpjI/1I+jBNM/ZpHN83xPGjSlu5Wa4wO7/E8GAfIyODzC4s0mzZZDJpUskEpmlQqdaxTJPxsWE0XbCyusHM/FI8mw1DZ/++EUZHBmP5Va03eOGZ8xQLKip2oFTkP/gz32J+aYWZuSUOjo8y0F9ko7yF5wccPjhGbzFPo9nkoyvXQxO2xZFDYwwP9qNpGluVGghohhx0aq67HT+gzwNi9iw7NC338dvadYmUxIBrpzeUCB7MnBlpxtix+e9aVH5hKLmzHTu0XA9g7NzjlrJzUSTvtykh7g/ypPzsNqnwvc61sZM8O9bfuO73GQOfo/O+MLAlIqgIoSDqGmCy4+y+E5R19JhdDZSSTgLJHa0Ru9WNspgP7bVdgymQ6K6H5ii/HJGwEIa5PYljl29Y4Do4ZgPPDzBTSWQqodI1iHC3tbIBrqf8Hvp6kFHCV8dFOB7CdRG2C26YqNlx8QdLyKH+WABKKRFBgBGicSED/P4SBD5+Kknrz/86ppSYfoBwXGTbJmjatLbqBK02aKB7AX6jSdBso7ueMrE4bpw8WrgueEGM0jt5GdkmSEVXYizFb6SHA63T7RoahqG6SPMDaLTRGu2uCQ+EkXgRoBsbGaRt26RTKcpbNVq2TSadZN/YELlMWkUQ6TqGnsA0fJKJpOIDkjL0S7MQuo5pJvFSLo6rHP5HhxU3luHXkYZOUMx3TPFi++SMudiiPlfblc57CAL0bArP8ztCxzTxjx9SC6znIRxHLbiuC45H0GrTbjt4vgLsepyGSD1D+aJ4KvotbIthGMpM1z1Ww7kSmcBtWzkbK1qD0ITueOqn3kQD8lIBY219C1AAv+SrRUEB3ChpFJhI0ruEH5C0woTUELQdlhZXef3tD/npWx+QSiWU2TKZCNvi03JtXNdF1zUyiSRpXSfhuGqs1Rq4LZtW28ZrtQnaNj2GQc4y0cM5IRstEtU6lu2SMQx0IcD1lSbIcdHabbK1BhnbJqkJND9Aa7YYaTQJ2g66H6g55froKAJUGUhyrsuwqwIcTNNQi7zjAoJEQkXP5kIwmbCU+TXjhymIdA1N0+PxoYZwF/UM99eoq8W+k5+xW4aItr1NJo0M9fPS849z4ugBioUcEsjns4r3q5AjCPmcomjEOEoxzHMohUD6Piyvq/ErO5uK7jFnuC5J18No24hmG6FrEI+Erkka7tZJmPG8EJ4XO38LIUJSx46cin6itCVGyKauaxpSByGS4SZavdJ8TgXuWJaJrusUe3KUegtYlkU+n6ZYzzE+NkRfqUixp6DGqqHeXyadwvcD1ta3cF0fz1eA3jIsUsXMtoGcz2XRNTBElCIK+kpFsllF+9JotqnXmwhN4+EzR3j4zGHqjSaLy6vU6k0MXaOvt4d0KhXLRt/3yeeyJDJpfF9xJ8q2jZBBKCe7ezQg4qd6UN+tXVqc+5jMIi1LfNcdgCvWv8QRD3s9ag9YE9IoIcO0UaGW/4vS2HUDuvuewl6AKzy6cz2XskueyxDnaDtclbaDx+57qrvu/K6rBvGm5f7tF4hfVPG2q3wxYCvunOiHcEZ3f+62+0WAq2tl7+qIbd91OwnGLybszgix/73/SkX1SZBShUpLP8C1W2xtbtG2HYqFHLlcNgzT75DRgRJ21a0qE1OzuK7Pvn0jDA4UMT0HsbKhgNbyuhJ+K+uIlQ205Q1otTv1D5OaSpmKW+l/5Wnc/+i3O4AnOlsogkNJuNiH2hETJRT9GCSBDEB3JRoSX9MIUCYYfI9AEwRaVz6sEFhoSITjErRsvGaLoNVGcz1M3wfHhbaDbNtI20FzPXAchO0qgOG4MdCj7SBtB2ybwFF5z7AVsBO2i/CUGTFsFJZpcPjgPkVC6ntsbtXYLNfIZVPkMhl03YCQoDbaaGvhdXG/hFpNITQMQy2QyYTSmpimgRAa7rGDLP+Pfw0CX0VcdeWFMwxDcTCF4bu6rocpYyJ/LxlrDlpSw9E7CcUj/hkFwPz4XCkVK/bMzDxOo8VYqZdCKqn6yXbw2m3sWgO70SJo2VhAWtdBV5o44XjgqnOF6yEdF7/VplVt0KrU0IKAVDKh8rp5vupfz481K1oI+mQQCWKhWO3Dz1FQSjfY7B5vEiCRQGu0kALM//X30TfKHJyYor9c4+BQP4d+598oXifHI+O49NoOhJuIeF8dPiNDBygQaQ63jUPQpSQR1qE7P2ZUdNkBhVGUtZASzQ9Cd4SOyFScQgKVF04LfblU2D9IhCVCTa3Symrh+473cLFmU9uxvkR727Delom0zNCMHCU5DvMHCo3A1PEtCyx1TBoGImmhWZba7GkamZD9P5CSVCKBGZruBvtKivftv//L4TjuJNuORFqkZRJCw3MDxF/92+hb9d1rQvh+hechfE9tCk2zMxd3lmIh1GyZRPBS83wSlmJZt0wTEVKoyi6epgh4RWMq0g5rQiAMFZkNYJki5qiKxoFl5inkc/GxA+OjDA/1Y+g6idCknUolYh/MZrOFHwTUm208z6PVctB1I5YNUT1MQ3WHJhXJdVSSSZP+RJF828b3QTdNTFNH01UA0MH9o8pPU0pSCYt0OoWO8l3LC8HpoX70wT5ggkCqza5Sxcpo6IVLUrBjPfuUEisAukfZZ12zwzS1DXBBxPv1qUBvh2ZpZ3lwkPgA5QGA1vYSrd874NdO7ZvcqXkKYkwQ1z88J6Z8eIA6xUF825/eqdsujVb0+RdHXr8w2JKhijVqsNqJAYRU+ZGgjOuq0W3r7ODQ8CXsNINGOsEulNvdDTIITUVSEAQdLY4ENMMimckSiCZeALYX4IcLvRAatFuI1Q1YWic5v8ThqTn09TLZWgOzXEF0J1um89wdVSAiWN35frUwegn2GODhNTGPyx5TUQBoOobZ0dpEviG68emTXaQNtFQSq7egUhzJ0MkYBUgjDUtU5e4Ne2wWC7oXBAUOPSD/1/7fENJhiKU1tK0a+tIaPf/h39x27TPrZY6uqfQzo3/nn5FKJNiuVu68q66nx1rRyAQWnW/cuIdwPfSlNQb+0n9HpO2N+l3h58ihUflsOY6LEErL0Z3ENlZNR1M6fnl7AX9IA31S0jXP4w+79lDd5uQdeq3uMZKQUIgWs1ozHgNRpjAphHJst0wEaYgES9ARMCLa6AQSMmncv/TbSNuOTWPSUaDJ/OQmYnMLAZiXrjPi+RRtH89KkGrZJGaXt3VN9zYo7pJt8kwobdV9tEGftSB0a5eiB4mEhWYo/7fANAhMA3Q9/GwiDR1pqshKaZkECQtpGASmTqAbYBkEho4nBK4mkKaJnrTUfawEWjoZm2exTPXZtPBDYKwZIvQbkjGwUFNHKjJW2XGH0EXIlyU0lXw7fG+aZmDmsuHQkDEljRAWppHoGjeh6d3oANUoKbnQBIYG3t/960SsaLJrnHWHr2saSB0CywyDHbo69S/9t7BZhXIV8X/6K7CwDItrqv7/5f9EJhdlYNyxoe2al0FPTrWti/7mfmtZZ+aKro23GptpGYTRpV2LY6AyhBBAj+/T02xxulxBSEnvtRkyyYS6XxRtFkTriwwFWdBx1wh/sjueu+15UsabcnVPVe8c0AdIXccfVdlCpOd1zo/cSaJ3EFpXJA8OXLZj/J1C5EEu7hZyP18RmoYQ8ovTaEGnXx/s5N3/73VpELDTkUjEGKFzGwHbgZZEATIZWXa6Np0iotUI/xU7yCJk5G/eqVsk+/bqrZ8HfH1usNXNgizuU5HOcqohpBZire7FLJ6S9yk7tVldn6MFZ9f3HW1XJBSF65Jc3UBfWIGlNUS5gr6xhb6+iba6CdW6GncSDCSp0JcpNi1tWyy7nqnryL4icrCEHCghh0rIwRJBfwnZVyL5n/0tqNQRy2t79aCqZxBsjzja67RtT1cHdNERaJ81ZeKFAUBoKkE2qOTd3eftcV33wqK00AJfCjQCtEo9duAVrhf7Ankr67iuInE1TINSICkUVLSmWW8hGu34gd1V6HbRjDYXku3dL6TsPEtK9HJl1y6l21gNYDfblCu1MBw/j26aMbCNJlYcrGCZMTnpXj0bLyTd72UvISN2fdj1tSKN9NE0Pfaz2TnKOjOkc0R03bvbDBZdLLNp/MfOdC3GEi0I8CXoK+voH3euNwyDXFbvNEEPVY2mCq7ANJUGx1KfpRlqbk1T+X9ZCsDor74BbRsis0T0VwiF4EPw0P29EAKpafH33ZaaCDwLz0MP/ZyQki4X1a7O3L7qR6DZ931FexAEpNPJOLK4o83eXqKxsNNXY/c72P5d9A70W1MA+CcOdn27bTe2zV9t+33CzZboPG2vxbgbaHWer8VN6h4bcbl8S2myDR3Gh5UZ2VPwTSysIsxNBRoCCHxFVBqbx0OQoiUTCrS02sQmrth6EYKOGPhHdb7PIvopRaLM5blwQyPaa9vGdty6z7/G7V22T634T5Q4XSm5Q9kiO35TSnKEfqOyQ6q9J+iKbLY/d8U6JVz5PlurtfP5saDY8ff/D6Xz5J3r924QtvdcedCndCW27rb6dMkLGcrQnX5gESdbHCSzq8j7fP7s8sBgq5sILH5EEMS4SIjtg0SGAEh0DeSuJXWPjgvtytu20TvPio6HxXY75r2lNVheR1teg5V1xMo6xlYVS6rJEjNhxztWsWvxFGE0TCAEdjGH7O/FGBtCH+knGCwh+0sEA71QKiikLLZXLdaqDfWhVWqIjTKJ//S/RVQbnZM66OH+nb2zh2T86zOuiIDHfc79OQRgJDhjYY/EuDahIrXGh2KnWyklYmEFUyqzga7rGFo3bNn+qXss7D7jU6oeRgv6vYVwwRHxAu35yvQXaQqqgc9aVVLMJJG9BaUlIiTClUr7tryyxnqlxr7RAXrCKLZttdoBzuJekB2QuLuILjm7fZB4fsDK6job5SqlYoH+3gKmqUyrO0Z317ZElSCeT50FWgJis7JLYHWr1zXA/+aXCb78BCKptDqYBliW0hSFmp34Wm23QI8Y+7c9Q+jo71+Gja3uJnZaEsiYtLerM+O2dbYNnZZHimwRPlP7HCYKGV6D5yFatkp23bbRjI5/T/e52wDuTnTPp69L275qqk2E2Kjc9/zQy2fvRaTrudu1oV1t75ZTQrXTDyNFdb3L/6b7AaH/IQB9ReVcVamr/0OuwOh9BWH2DBmZMsMsFETO/yG/HeH8UKZ7GWJqBajlzv67b2/sLYr2hsI/Z1ECXWmHo01tlHJFE9s3BLYLAmQqifzSY0rrOdy/bVMvZaBMsYQWGwIlw6U6JtkDcMldo6yrfrsB9f1K54zP2TvbVP7/FsqDarVC69cD3vTz16MLRAkhkZrWlTQ6Pmnv63Y8O5A+QXean1A4xJovovX189fzgcFWsEenynAnE2lCunB+Z6fY9cLFtgv3KF12cCWMdw/W+L///Y8Qv/vtLswiYxex+PxY8KuM3kEQKMQrdCj1EAyWYEBppRjoJRgsEvT1MNO2+aPX3iGXy/BLX36aoYG++MERoIrq0YE3odO7kDBcgttT6vuVDUSztbOhO8DWZwzEBwRb8SLc1Sc/b5Fwf+1TSIMR9OTxCzm85XVczwv9pZS2xA9NItARQnuLHbnHp221iDUWUQl6C2z9f/46vqd8tlptm62tKi3bpljI0dtTwNQF7777Ed/53hs88/jD/MavvBjm+1MaOgmK5PBHb/FPfu+P+dWXn+HXvvYCgwMlHMdlZW2DbCZNb09BmZbomFWllAS+rxxod9S443SsEezQpMjQrPmj77/B7/7LVzh76gh/6S/+JiPDA3FrvXBB1qLxFN1XCHyhcqIpMNvxkUr+ub+K6AI8yf/kv0eUK+zotr3lndzd759HNmtX7kAQIMdHoLcQ32wvwB9vBkT3xqCrIl2AKxZw0SbmsyonO/w5+D5WBPJ0PfZrjHa6cud8kt31VXNIOTvsNpF2j2EhQJQ75jU0DVnM7109uoBkvDHdrm0QcB8+p47gl4Djeaytl6nW6uSzGYZKJcyIQ6v78ql59dcy4V/8Hfj2j+Dv/96uWwdS0qw1WF3fjHOWGrNLscY6LrqOs2+ISq1B3XZwNYEHGMkEfUN95Io9CsTrOtLQwdTBMBBG9L8yBWOo4xHQl7pOYCitqZZQkaci1KQKy0ToAmGYSMNS14f3F4aOMHXQNVpSslar4gnB4HA/iWQyXpdg+wZCC022EdzX/9z/DTYqyFIP/l/597b5H3beudyxV5Y70uN8ynv7AsovljnwCy6fYwMUA629gN+eYO1ztnPbvQEZqPVd0yDwO1aaT7l85ySXHVGk/o9O2faF/7mr+gX4bNGlouscj73540ruuGhHiYHZfYTprqOlnuhmndt2f+otIPt7kQMlgr4iXn8Rt1jA7y+iDfRhhElto2cLTdmJZRBgrqyTzaQwDZUENZASDbHNVwIIaQXUgPFcT1EYALKvp0Mt4YQ7Q00gi4VO3+woMtKWRAtRvFFVk/x+jB57mg+6uuEXcuzbCV4BuqgWhBC00glWXJdUKsFgf2nb2JcdMdW5pqvSQsptiG73sJDbhEz0tSzkMHUdXdNCFmg3JEdNU8hnSIdMvyp/mq7GZ5ewjepo6DqlYg+5TArbcZVjrJC0nSYffnydYk+eZ588v405eBcpXle7uqO7gi6o1K2tyOVznD55hL7egmLQDrmdIh8QHbEHiJPhOipBKCf+jj+biE8B1I5usxJrm/YcHjs32d1fyc6+LQYGO1Fy95oe5eHrLeD/7t+K50fgB9ums9LCyVgj4/k+aAZWSKOhnhlQ3iqzur4RBjZomIZBMmGFvFuwvlGmWmuQTiUp9uRxHJdavYFpmhTyKhoOFAVDrd4glUySzaTU9VJSrbeo1pskkwkK+RyaruG5HvVGA9t2MS0TyzRIJwxM04qpTYRQgSeGUBrUIFDUBPn/y9+AqbB7innqv/M3VPsD9Z60bVGwEY1LBK4EQii6FC3Stkc/dPorfDHxPWbmFvmXf/B97k3P8cxj5/jWN15icKCoTLNdb1U7/XUVARqV86fgP/mzIeAxwDAJhI7UNWYmZ/lnf/Aqx44f5Lf+9K+Q/Rt/D7FVUxphTUOUq/i9Bf7gN7/Kd3/0Mx49e5JnnjxHvdFkamaBI4fGefjhU2iartwVpB+PkYhiZueGfVvUI4IADU1Xkb5ad6SkDJQ7SiRPQi2rRKKj5K/TbHPv3iT1RpNkLkN/IhErrtQc0YGASKmluihUD4gOy34Eyvc21UUyumvOhXP7/qa9HSv3zrKXKRy4390+VwnH1P2e/qnP+DRA9XnAVvf5nwm0umvWtbnnc/RHiDeijc02t6dtVZKdpWcXPungGiIcs03a73zmg9Xu5wJbHaezHVhqp0Df3rrPuun9t97d10eAZ3igc+zIfoKvPa/MfIMlRKmgdk1BJ7Q6CALlgxBE0WiSCMIIEUY1CPW8gb4if+IbX8ELfMV2LcP7hAsFqHuYurrPjTuTvPfhNZZWN9CE4FcNkwuRT1bbUVQFhTxrf+evhKp3Qatt4zgqea8QgqmZeZZW1jh6aJwjB/eTSSeUf5WUKl2L4+GFEUSmqRPIgJbdRghFECiFSodh6DqGrtjCI39Az/dxXRc/zPuVDEPiI9u0rmkq6bLvkUomOwg+1MwoO7aHjyT57/9X8eIihODGf/zv8j/+g3/JuYeO8ud+69ewYuJEgcCITR6e7xFIL+bjklLgO20kUjGL6xqer5xW4+g1obQLgRRxyL0fEpvqQsVPKUGudrFJyyKbskIeMkW/oOsatu1SrdbxQ6qHZDKJblkEQaAiOwUhkaOH49roukZPMYPrOfi+Bxj4gaJo8NwAP3S8NUKfIzMEJL7vx8mRk+lMmLSYeLyARhD4yPA9FApZUqkkuqZ1JSlW/d4RFMpcITSB47g0mi1c1yeTSSnyT90KZ4maF67roXseuu+jhRQZ0dwSOzYM3UAgElCe59O2bZXcOJUgk051/FikjElCDUMn2Wx3CZDOwtlq2axvlNmsVEkmFE9TLpPBcV2WVtZYL29gmQa6mSCbTjMy2E86naFcqfPd77/Jhx9f48ihcXLZDOVKhdHBEo+df4h0Js2V63d478OrHDowxjNPnGNpeY23379Mb7HAl59/jOHBPibuzfLD19+jWmvwwtPnOXvmOJomuDc1zzsfXGFxdYO+3h6eeuwhHjp9nFa7zTsXP2ZmdgHLMkmYFgN9ijV/aGQopJAAx24ztbjKzTuTaJrGIw+fIBt0/MmiVEDLK+u0bZv+UpGenjy249Bu2ZimST6XQdM0bMehXK5QrTUoFHIMDfSTTKRwPY9KpUa77ZBMJrBtRbFR6u0hn8ti6DojQwP8hX/nmziuSz6bIZ/L4fkqz2kQukwo8uMdknT/CO3BEo7nYxk6lmEh0PE9n03PYTKTZHRsEA7tg2QCzJbSikGEWlROWSnJ5TIMDg5wqpjnwtlTChSFm9NoIZV0gImU3YtWNF47ATiBDPBkAL6KbFRRnHLHtR0NX+dOqli6RjqVZn1ji2arHRIch5pJqSsZL8LNihAhk32X6RyUxlXsDZzUfNxJ+xCv1h0NWtzeznd7lvush9usQduev33j+flLtPn7jHp1l88Lqr6gIrvqKXeBij2A104Qv8exbdRU8Vly96Gu8bmzRr9of/xcPludEoEVYvOaqtrPicx3dtBOu3a0UEiQI/2dDXchB7/0HAQ+jUaTtaUVmq02AkinkzSbbXRDZ9/IEFJKKrV6yGYtWV0vIzTJYH8P2UyaVtvm3vQcd6YWGBkucfrEYRUNFUClWuf67QmazRYHx8fYPzJAJp3io8u3+Cf/+ntsbtU4enCUZ557LJwaAmE7+FqSja0Kf/t//md4nvKz2CirXIn7RwfRNbh+e5rl9U3OnjzCb33zJZ569GGSCQvHdrk7PcfbH15lo1LnkTNHeez8STzP5+rNCSYm5/E8D9dXwuvEkXGeeews+VwWBLRtm0uXb/Lxtbs0my36S0Veeu4JxseGmF9Y5dLlGwhNUG+08FyPX/va85RKaoGYn19jem6JVMrk4Pgw+Z6ezrsJ3/tWpUaz1aZabeA4jgIYCDRNR0pFkGk7Ns12A00IUskkrucxNbPAjduT5DIpzpw4iN22mVtcpb/Uw+GD+8ikktQbLW5OTDO3tE5PIcvZM8cp5LMhKPBYWdtgbaPMwuIqU7OL9OSzfOW5CwwN9CGESioqEMzML/EHr75Opdogk07y2COnOHHsEFYyqQCoELiey9rGJghJq20zPbNIImGyvLbKvtFBqrU6y0urTM+v0my5pJNqQR4aGqRUyOB7Hvem57l0+Rar62UuPHKapx8/RzJpIpHYts3y6ibrGxWuXJug1bYp5LIxOA2CgJW1TcqVKqZh0N/XSy6bAqGAWbXa4I33PuGTq3doNNucOn6Ql198irHhfroFp91uI2wHy/ORhSzv/Ed/Atf16Cv1kLRS3Juap95oMTo8wJFD+8hmMgRBQL3R5N70HLcnZlheXWNico7eYoFf+cqznD97kkw6xfLqOq/84C0+unKLnnyWv3rxJiPb5rpks1zloys3uHx9gvmlVQxD46XnHuepR89SqdV479InJBIm5x46jud6XLt5m5WVdR4//xBOu82NW/e4c2+O82dOcPTAPn705iJT0/PsHxvi2JEDJE2daqVGo94gnbQo9WRpNlu4ISFlq21zd2qOdz+8hq4JxkcHyecytNo233n1DT66chvDNCjk0rSaNfr68ghN44OPrjI5tcD+fYMkExafXLvFvelZvvaV5zly+ADraxv85I33effDa0yHCZZfXljhP7IdjFA+uZ7H0soa3/3+G9QaTX75pWdoux5Xr91iem6Jc2eOc/TQOBvlChP3ZpmYnKVSrVPIZ3nqsYd57JGHqNfbvPqjt7g3PUd/n0o4vbq2yZOPPsSLzz3BYH9JpeLRdTY2t1S6I99nfmaFGxPT1Bo1DF1ncKDEl4Igdu0IfJ+l5TW+/9rbbJYrPHz6GA+fOkFfby8g8XwXTRMkLHM3l1iXPO7rK+K6Lq/++G1cz+P5p86zb3SIZDKBG4QyO/BxfeVqYOg6aDqu44MQGLoWvicHx7XxAw8/zCvZcjwQglJvD7lsxGmmNh6BrwTOrrqFQ18TAhkIavVWyHWngdSIAkWI1PSa0kBvA02RdoqOFu6+pUsp0IlO7NBBdDSRAfH6GF33KYt1hzpptw0jgh6CABloO/ykH7R8hoatu25fNMj6XPUMteARzUOXtlZR33wWa7vaoO6ElnueGWuvOvXssANsL9o2/+Ofr38+N9jafhClZZNd50jZGYSRefFT7re3UyG7X1C3qlVKRDGvdl9tG5ZWabdb3L47zZvvfsTH1+7g+z49ecXNNL+0xqNnT/Lnf/MbrKxt8Aev/oQDY0McOjjO629/yPT8Es8+8RAvv/AEaxtb/P53f8q7H9/gV77yJIcOjNJqt5m4N8/swjLfe+1dFpfWOXfmKP/Bb3+TM6eOcmBskEImBUHAb37jy5x7/CzaH72lKms7BAmLSqPFH7/2LgnT4vCBEXRdZ2Jqnis377JvqJ/x0QE0TfDOpWtkM0lOHDnAQKnI7YlJ/pd//ofcvjeH7bpcuzVBOpNgaKDEBx/f5Hs/eY9UKklPT556rcGNW5MUCzkePXcKPwj44JPr/M7/9ofcmJjB9wOy6STttsOf/61f4970HP/bv3kVhMboUB8DfQXKlS1yuSRXrt/h1R+/w93Q7+Px86f46kvPcGLbXkPgOA6e51OrN2m1bPp6i1TrDWbnl7k3ucjM3CK+9BgfHeT0icNYloXruswvrPDaWx+QSSVJpxLMzi3ys/ev8MJT51SKnlSSDz+5zquvvUujZWM7HglD5/ELZ9A0naWVdf7+7/4bkpbBl55+hNPH9nPx0nV++Pr7fOOrz4YJlQWe7+N5PqViAV3X+fCTm9yZnONP/OqXeOShk6FZQQlx1/NYXd9kdXWDP/z+z0glLcZHB+nv6+HuvRl+9JP3WN2o0HZ86vUWg/1FvvKlJ3ni/EmQHo1mE03TmJpdxDA0Hnn4KKl0gUazxSdXbzM1s8jqepmLH91gbaNMIFVUk9AEc4sr/OGrr3P73gyD/SWef/oRHjt/klTSQkrJrYkpfvDjd2i2bWzbZWurytnTRxkfLtEtSB3XxfA8TBkQyIAfv36RheV1vvL8oxzcv4/L1yeoVOuUSj1IqUym5XKdt9+/zDsfXObQgRFefP4CTzx2mh+89i7//PdfpdFq8sIzF1hYWubS5RtICdms2ph0Lz7NdouLH1/jrfc+5uC+YYYGjjO/uESjUafZanNr4h6bWxW+/tXnOHrkALbjMTE1z6XLNzl2+ACWZajUPZk0+8eGeeThE2xuVfnxW++xvrHFiaOCZMIimbQo5NLkMyk0GVAsZJBCw7IMNCEYGeqnr7eAEDA8qID3jduT3J2a5+D+YY4e3IdhaOSySXQkCVMnm0pw+OAI3/r6lxgcKPHGOx8xO7dMtVrGd4f44KNr/MGrb7C0soGmaXi+z/d/+h5/tuaQjUx8MiCbtUilTO7NbFBrNSjJIm2njWlplHpVgusr1+/wk7c+pNmy6SlkmZicY6tSY3x0kEwmy8SkSnD/+LlTnDp+mNXVdb77g9dJp1N87aVnabVtfvzme7z17ke8+OxjfPn5J7h1e5J/9Z0fsrlVIZdNceLofp4Po4+RksWlFb79yk94693LNFtt3rl4jT/zp77Or7z8vNJySxWJmLDMmHduZxFC8NCpozz/5DI/evMDvvPqG8zOLfHLX3mGh8+cQBgGlz65wbVrt5EyYGxkgIdOHWV1bYuPL9/myKFxTh0/zN3JGT65epN6s43jOnieR39fkaGhfnL5HJalEqBvblVY2yjTqDYggEMH9jE+NoRpmp2FVKro07bj0Wiq9F/VaotAqalwHJtGU5ERCwSGJcjlsyQTZpy+S+xoY9c/u/tgG31Ml2aYECgJbdta9bngkJS76tN9n+g5IR3g7vrep3zmGV3KjH+r5UHqKqJgpI6G8IE5xSJMEUSuFnSB3E9vW2yyjnIjRtaFsNpWIqQO2mnFE+KB2gVfEM9WpzP+f6d2lEIgRgfg3ixyaZUPP7rK7/3Rj3n/4xsEgeT5J8/yxIWHuHVnipsTMwwP9hFIn3KlwpUbE+gCHjt3ioFSD2+8+xGFXJInzp/G81SyX9/3MQwdz/e5c3eG3/v2j1hc3qBcqVOu1Ll8/S7rG1uIIODA2CDDg72UK3VFpNeTQ6YSKo2H7SCESibcl0pz7NA4f+G3vkEqmeSf/qs/4tKV2/zyi0/w3ONnuXZ7kt/5F3/E2sYWjUYL2ZNjbmEJ23b4c3/ql7l6e4JLVydYWl6jVCxQa7TIpFO8/KXHefapC3x85SYXL11lfnGVh08fY219k5/+7EPqjSZPPHKSSrXB8uoGK2sbtNpt0qkEgQzIZdN869deYnykn8G+ItVGiztTKun0U4+e4aOrd7h6c5KHz5zg2LaINInn+cjAJ/A9dAGe7/Ha6+/yzsUr7BsdwXZdJiZnmJ5dpLdYoLdYUDvkUg9DAyVqtQZ+aCot9RY4uH+UVDKBFwTcvjeL63q89OwFQCVLVrtEn6mpGS5fn+Dl5y7w5ONnKfTkSVoWr/zoZwyUinz1xSfRdZ2EZXLq2AF+41eeB6FR7MnxL779I965eJXTxw+RNHUCP0Dln9VxbIdMyqKvmKVeb2HpGq5tc/nqHZZWN3nxuQv4gcarP3qH2YUVKlWV6iOVTPDw6aMcPDDO9PwSjusgZYCua3x85RY/e+8yTz3+EI9dOAPAxY9ukEjoGIZga6vKm+9c4tLlm/QUcpSKeZrNFhubZUaGB7Bth4XFVbYqNQ4fGufwwTGSlkFvIdPZtKBU336o4RGaSrWyb3SQa7emaIaatHwuw2B/L6ePHyafS1Nr1JianeONdy6xvrnFn/jGC5x/5BQJy6LdavM//IN/zU/eusj5s8dwXJtGq0XSSnBo/wilYgFCVnuAzc0trt6cQErJs0+e5cC+ITY3NggCie+73Juex3E9ioUcgR9gmiaZTDo0vTroejIGvpGVJpm0cF2P9fIWrueF403545mGgeN6tG2HdCaDoQtMyySTSSkzpa4zNjrIYH+Ji5euIQS88NQjfOWFJ0CAbkCxkKNarauE5QmLUqmH4aE+hgdLbGxuYRgGjUabWxPTNFttzpw4SKm3gG27GKaOfnVqm99NLpflyNEDXLszw0a5yuCgTSDhyIEx9o8P0Ww6VGsNKrUGQwO9nDhygHqzSU9IuJxOJyjk0xQLWV564Qmeee4pPN/n977zA+YXV1QSbdej3bap1OrU6k18PyCRTJBKJhga6OOpxx/i4Pgw2j97NX43bdshlUzyG1//MjNzi7z647dZXlWBLbou8Dwv1gZ/mp/n0EAfv/pLz9NbLPDamx/w7qVrrKxt8tu/JTj70Alq9QZvX7zM3ck5vvzcBY4c2s+du7O8+trbfNM0GB0e4PW3LvKTtz9kcKBEqbeH1bV1Ll25xSMPHeOZJ88jZMC1G7eZuDdLsVhASMnN29Ncv3WPr375aQ6Oj2IYBkHgs7m5xfzCEivrW0xMzTIzt8T80gonjx/C82BxaYkPL99ifnEDz/NIpU3OnTnOIw+fUBswo3sJ3LFwdn1WGqwHYI5/gIX9fkUIDaR//+/DmiiQuYf65ecpn6Fxu/9l97/mvgoU9eXnfBBKoRM8YL9KHxUhuvP4Z10b8t6F/oIdV1iJEBrZTFql6Yqqrz3AWNhRvsDciF2la7f7Wcj0M5FrVPZyJBzpR96bxW07XPz+G3x4+Ra1RpOzJw/zqy8/zVNPPcojZ45z/fY9kgkTy9IwDI18NkMmnaS3kOPI/hGG+osUshmEEKTTSQb6imRCfp7NzQrXbt5jfnGNQiHL+NggQgiOHhxjfHQQApXqobeQp95sc/3WPR49d5qewRL69ALYLkKq1BS5XJq+3gIDfb0YhkE2nSKbTtLXWyCRsLBMg6Rlogmh2KYbTfp6e/jTv/oiY2NDXL59l3Q6pfy8wjDmXDbN/n3DHD64j7X1Ta5cu02rbRP4AcsrGyytbPDkhdN8/Ze+xMbmFrNzC5R6ighNp1DIUuzJUyjkOHvmGOlUEtdxsF2ffWNDlHp7CAKfO1PzSAR+EOB5PoqHOvpRk0AtkJKVpRXeePsSQgi+9Wsv0j/YzwcfXOa1Ny9SqdZxHDdcENMMD5YoVyrcnZqhUqlxaHyY3p48ruthGTp9RaUJmFtY4aXnHmNsZEClC5GSzXKFhKkzPjqICALsRpOeQhbH8bhxZ4onLpzBCFPZSN+n3WqTTqc5vH+UfDbN2uYWjqOSY7uux1alTrXaIGnp5DJpUgmLdttB0wTtdpullXU0XePwgTFy+TzzCyusb2xx7PB+stmM8veyW9ycmGF+aY2D+4dBCFzX49LlW8zOr/Brv/w8Dz10XDla6xr9pSK6Lrh1d4qfvfsxg31F/tRvfJWhgT4WlpaYne8QjSYSJv2lHianFxBC8MJTZwFote2YqR0pVeojZAxazp89weXrE1y7OYn0NZrNNhfOnlRASYBlmdi2S9t2yGZSyk+oZWNqGvlclmTCUlkN/IBCPsfJYwf42btXeO3ND/hWF3kgQlCp1dgoV9AEtO02bceht1hAE4K67ZLLpqk3W5QrNVKZDLph4vudVDXd/iqBJE6d4rg+zaaNY3s4jovjedQaLcpbVVbWNqnWGqTSaUDl+tMjklBNYFkmqWSSQkHRelSqDVKpJAN9vWi6Gs/rG1tx7kLP9XAcj0q1QaNhIwONVqtNs9nG0HWeffIcLz77GJ7nqY3Kf/0PtsmyWqNFIZ8jk0pyd3KOhGXRsh2Gw4TNzaaDHwQkLJMTRw7w1RefJJfLoGs6pVKP8rnTNUxDJ5lMovk+uWwWy7RiEaiFTPXJZJJAaHgIjIROOpMgn83x4nNPsG90cJvJbXRogJe/9CTzi6tcu3mXXCZFLp0k8FW7my0b3/fvixUilykpJX2lIi+/+BQDQwO8+sO3eO/Da7zx9iUOHxrnmSceoV5r8Tv/9N9gGiaZVIZ222Gwr8j42DCGppFOJSjkMjz/xFnOnD7Oex9+wtsXr+C6HtlUgkatxgeXrmJZJs88/jC9xQLpZIof/vQ9vvuDN/j6y88xNNDH4vIa3/vxWxiGTsKyuDUxTblSo1ar02o3qdUbvPX+Zd567xNy2Qz5XJqJqXWu35pkdn6Jl154gtHhARJdJMGd5WaPNWkvv+Jw/doeVrLjmu6/9ymdABK1y3igJVwSz/X7n7NHnXfW7XOW2O9zj8ujduyq0+cAJUIIZSrt6k6hiV3UM/etvwhA7mTm232uEJ33LEK/mL14FjUByUQi9F1VkeYqUOnzlV8QbO1lFe0ibPi0Fw1xnqb4bvc7dw+nQSkhGB5ESOWY7M4sYTtKkEkk9UaD1eVlQDm5i1ADaDuu8nHyfMqVGuVKDdt2VeqP0MHYdT2CQIZmpTJzC6v4geTY4X0cPbSPYk+Oowf3MzjQh0TQbrs0msrUdevuPLMLK+wf6EObXlTN83ykZSADieN4lCs1dE2j0Wrjej6NZpuNcoWtal05iSNottosLK+zuVXl3uwi33vzfd77+BbDAyWshInQwtxwQL3ZplarU6s3aLSUc7PreXi+rxbSto1pGhSLBaTv4fkBi6vrOJ6Hput4nk+90VRO+7ZD23Go15ssLK2xurbJ8lqZTCrJ9OwijzSaJINAhbl3jYMgkLiOy8raJhubFR575CSDfT2kkwlOHT/ER1dusbC0ytEj+ykV8+i6Ri6botFo8ua7nyhfrf0jtOw2G+UtEkYvj549wdLaJj97/woLS2v81m+8zOnjh0iFkWhCCJrtdhiR6Kt2u65K0h2mLvH9gLbj0LZdhOZSrbXihMKBDHBdL0wP0kIGklIxjwwC3ChvIsq/QzcMVlbLvHPxCgcOjJJKJTn30AkOjI/iOC4TUzPMzi8zObVIs9UGocBpq21TrTWpN1qsrG4wP7vA/NIa65sVhgb7cF3J4vI6qxtlenoUAaxlmQSBZH5xBd/3SSYsdF3n/MPHeeeDq7zz/mVazSYvPnsBx3VJuy5mGBTQarUpdsn34aE+9o0M8upr73F3cpHnnzzHvtEhtLB/TF0t0Plslq1qjUqlge/7VKt17k7NYTsuw4N9SBmwWa5w9OAonuvx4eXb1BtNeulMYdeXNJtNNraqXPz4Bksr6+SyKYb7exkYHODY4XHWN7a4cXsSoSsS2aWVdRIJK8yP5+O4XmiWblCt12m3bWQgcZ2AZsvB9wWtls1HV27jui71RouF5XVyeeUkrgkNJwRMnufTbjvopsnRowcZHrzCpcu3yOXSHD00ztBAkcHBErVGi0q1gR/4VOtNeho26xtVGs02rbZNvdlG0wSBDFhd20TTNEqlItV6S0V/hv3tBwHliqpzJpPi3uQ8AsETj53hwP6ROOtDFJyyUa6wUa5Q7CmQz2dIJZPYdhXb8VQuTxUho6L4BEAQm56NeHz7eL6a461WG8u0aLbCBOAQ0uFIpmcXeP3tD/nwsvIfk0GgfKY8l6Zjs1mu0LadmB5np6wVEryQl67Vdij25Dl7+hiO43J3ap6Z+SVWVtY5fvQgj114mLffv8zSyiZXrt1hYnKGA+Oj9JeKeL5Pb7Gg0lP5AYauk0mnsCyDZFKlN1pcXmN+cZVDB0ZpttrkMmkG+nsIgoD3L11jfHQQ09B5+/2PefuDK/zaLz3P04+dJZ1O8vrbH9K226ytb3D77gzvfniVhGXya7/8PCePHuT9S1f59h//lD949Q0ajRa/8vKzjNs2mm2DbSNsGyuR2LV2RZrj3R0jur6Hbevh5zDPdSiFPgOUhc+ILGQPbGK7n7bpcwKuvYFWt4vPfer0c/htdWODODKQHfXe9neHMmaPIIVuS8DuEkbGdrcvfKxaT34xy90XALbEnv8pHpK9K7eNQiHcLX0qm/p9ihwdQAC6oXPQMrAsA9EW1OpNVtbLrG+WcRwP13WxTIMgCGi3FRWDYzvcnJji9r1ZZTYMJK4f0Gq3sR0ldJotG93Q6Cv1AJJbd2ZiwbC+ucVAsYeEmWB+aZOFZeXLMb+0ztUbk1zoyWFFzXc9pKFhuz5b1RrLq+uhsK3SbNnKwbzeYGFljUbLZqtWo1ypUizk+ck7H/PGB1doOy4t2yWXSbNVqbNvDNKZBH4QUKnWsR2lnWg229SbTWxHCcR8PsOlK3eotxwc18P3PEYG+zl/VpDOWLFz9MbGFpmMyutYrdZ5692PmZiaZ//YILqmMbe4ykeXb/HVVotSJBSk8lOJBmkU5el6KiqvslXF8wLu3FNApL+viGYYaKaO0AIMQ4X2zy2uMTrcT76Qw3ZsqjXlTN9otnn45GFWVjf46OoEt+/OsG90CCthcfjAGH29PdyamOHW3WlKxQLXb09iGDoPnTxCTyHH5MwCzZbNernG7MI6bdvlg49vYpkWZ04cQtd1Fd3nKcqOgb5exkYGuHN3hnqjhQgBUyqdYv++YW7cnub6nWkabZu+3l5OHj9EKplgeW2VK9cm2CxX0TSdXDZDu21TrzfJZTMUchmq9QZXrt8llUpw8/YUN25PMzY8iGN78bzZqtRZWd0gn8sQeH5InyCpVOvcmpghlUzQVyxw594ccwurVKo11tbLjDVaZFxFYNkKx3e0O0sn0xw7vJ833rmC5/kcGB+mJ8xZp6JEAzKZFMcO7+PKjXt8fHUCiQp8eP/SdQb6ejh2ZBzfD/jk6h0Wl9fp7e2hWMxjVp1tsqCnJ8/42BCTs0t894c/I5VM0FPI8fyT5/jql3o4tH8M23FZWy+zsLSKaRrk83mOHthPJpNmZW0TIaCQy7C6vsncwjKe5zM61E8hTOSczShN7tTcEldvTWNoAtNU0beu69G2VaBFs20jg4Dl1XUOHjrAgf1jPPvUeX742jv87L3L3JqY4cLZkzz5RJZmO8BKJGk06iwtb6ILk0q1gSY0Gi2bngDGx8e4MTHDOx9eo952KOSzNJtt/vNWm3S4Bgihk0ykSKccctksjusjNKECPrLZ0PdZkMukSSYsbtyeUgD20DiPXzjNo+fO0Gy1qVRrYeSzas/q+gae7ykamkBpn9yQY852XFpNm/WNLaq1JqlkinZbuUF05C289+FVfv+V16nWGyQTFr7vs7G5RavdZqtWY2FpjXqjRdt2cByXdNe1impByc4fv/E+s/PLHBgfYf/4CCurG/hBQKPRot5s4fo++VyGk8cO8r0fvcl3f/AGmUyKC2dPkkxYtG2lLfaDgGqtgev6mKYZm3CEUH6HruuxWa7Sbtvoho7nqb7w/P8vc/8dLFmW3/eBn3NN3vTueV/vvfLetDfTPT3Tg8HMYAZuAJAgIRKkQrva1cYySIq7IiSRDG2QokiuFCFxBQZJkSIhgiBmgBkMxqHHtHdVXdXlvXveZb70mded/ePcvJn53qvq6p7mxp7o6nyZefPcc8495vtz35+HZZk0nRY37s7hOC7RqMXQYD/790zz7pnzrBc3WVnb4M69BVbWCuybnSSfTTPQn2NqYoRkIsb9+RXeP3eFg/tnGHFcNNdFOiovbMQ0O+BgC+DqKVsAxI4n2McAMyFX40dc97HNiR+h9PjYRW77A9EDNT/Fe/Xc6iM0W+0UTFLb0sbu+EaCFsogm1NvHX43NukpP3ufPgUzYtdk3Iaeu7RXovf7kJX7ZykjA4GUp3Min+V4Js6HV26zurHJa2+fY3l1A8PQKZQqWJbSFrieSyaVoFCq8MZ755lfXqNYrrJZrlGv2aytl5hfWmezXGOjWMI0DB4/cYC19SLvfnCF0utnGB7MM7trnJG+PMl4EitqceTALPF4jFg0QtSK4KS6yA0dFxlT2oliqcK1m3dptlqsb26CJlhY3SCejLGyvkl/Pk0qFQcd0pk41WaTiGkSjVpYLYfNSpXrt+fYvXuadDKJpmkUimUaQVJsCTQaLVpNl9GhQV585iSr60XeOXMJIQT5TJJcNoNhGliRCP35LHMLK9y5v8CRg3uIRS3q9abSArZsNE0jnUooOoOoha5poYAhpU+1Wse2lXlH03VyuTS5TJIPL93kX/3+d0jEo9y+t0S5UuOzzz3G6HA/UrqAyj9nWRF0TSOTThKPx/A8RdVR2Kzw49ffp1JrsFmukkknyKST+L5Hy7YZGx7iS597itfePsePXnufgb4si8vrnDy6j8dPHMQ0DbTAZLG0ssGrb39As+lQqlR5+rEjPHXqCEjJRqGEHWzYfX05pGawuFqk0XIY7MvgSbCsCJmMMreqtD4Go8OD5LIp6s0WvgfjY0P09+VxXZ9Wy6HRbLJe2GRqcpTDB2Z47+xlPrhwHU3TWVvfxHV91gtlavUGffks8XiM9cImd+8vkkrGcRyXyfFRRkeUuWR1rcDSijrYspkkh/ZPs2dmEssyQw0Otko9pHLkucFepDM2OsLk2BBS+szsmlB0Jr4MhY9Wy2b39ATpVJJKtcb9uRUqlTpT42P05VOMjQwQtSySiRilcpXNSl2ZGGOVHj/RkaEBPvfCU/g+XLx6m41imUajwMZGhUbDIZNOcGDPNMODfdiuJGJFGOzrJ5NUvmfxRIKf++wzVKpVkokEyWSC/XsS7J4eJ2KaxOMx4rEo6UyM+aV1ytUaEUPHilqkU0nisagKhshneOHZU5i6YGxkSJkWdY2jh/ZiB2C0vy/L1MQo8ViMbMbjiVNHWVldR9M0PN/n8P5ZHNcll8vS35fj8ZOHWS9s8u7pi5w5d4VEXGlXu/c+Q9fJZ7PEYjF2z0zh+5InTx1hdGgEhInreRiGwaH9s0SjEW7emWdtvcji8hqlUlUBimYrXAu6rlPcrLC2USSbTgc0NIqeY6Og/DqbTaUV3yhU1P7meqyubTI9Nd6zVY6ODjIzNcpGsUQ6GcfzfGKJOI7vKe394gqNps3c4gobhRLZYJcOBamgnrWNTd4/d4UPzl8jk07ieIrKZM/spNI2A7oumJoYwdB1bt6d55e+9Fn2zEzRaqkUSo7rh1pMSZt3TQkWuq7Tn8+RTiVYXt2guFmmUq1z7dY9iqUKw4N5pibH0AyDeqOJ7TiUShUcx8E0DXypuNSk0DAjBlIqzrW1gJ+tWqvTajn4UmJZEVLB3JMyYMLfSQP0MDNct9Wl+7qP6QsV3ldoW7Iu/IzlYdai7vIJTYrdqdwfCLQepun6pE75O/1OBi2SWyxhW61iyB3Bm0CgdRkRlXJNvXtQhO7HKT+7ZksqXx2gZ1Fuvazn7YPsuh+z+GPDaCgfhkmh87Uvf5Z4MsG163dZXS8ipSSZiFGtNdCCsNzhwT6OH9nHvbllas0mM1NjpFNJ0qkkrufTsj1i0Sj9+SyWGQEJe3dPYxoWhmFw+dod5b+SSmEFqvDRkUF++SsvsbZeJJmIMzzYT3R1o9Nf1yUei/LVL34Gp2UTjxqYpuAzTx7DMHRSyThCg8MHpnnpM6fIppNMTY5hRUyef/o4J47uo2k7lKoNNsslRoYH0HVNOcM+doSxkUEMQ6cvl+Gpx44wOzlCPBYnFo3zmaceI2JGOHf5Bo2WTS6TYu/sFDNTI0SjFl/83NPML64yPNRPLptWWgKh8/hjR8nlMkyMDJBMxBW3TjJO8sOboX3bcX1WNjZDIslGo8lAPstnnj7OuYs3WFhaBSAai/Hic6c4dfwgyWiUZktF7RmmTiIeJZWMM9CXJZGI0azbio8omaC/L8u9+RWQ8LnnTrF3dpJIJAK+wIxE+MoXPsPk2DAbxRICncnxEfbvmWJoIA8SpsaH+dLnn2F5ZR2JwDItBvvzzE6Pk8slaDSajI0O8Mxjhzm0fzeWFaHVshkYyPOll59hZKiPmekJpIRSuUK5XCWXTVGu1Lhx6x6Vap2RoX7SqQRT4+MBnQiMjwyxtLKGrhm0Wg4H98/w2PH9XLl2D9+XzEyNhaSY9UaTsZFBJsaGuXbrPnOLK6TTSWanx5kcGyYaM/F8n1PHD3D56m0kMD46yJOnDjE82B8QeqpDxXNcZULtYsCWUmIaOtlMkoG+HKPDA7RZlW3HpVSu0LIdhof6OHxwd+jDpvylPBrNVujL9cLzjzM9M8Xi8gZCCqLzxZ6NNJGIc/jQXvqyaa7fvMela3dYXt1gaDAfaEdUEEIiFiXiQzQWxYrEiEYspKaTTMZ54tQxpOdiB1QmuiDwYXRp2g5In1Q2yejoCJVqDU0TJJMJxVUWHNZHDu3jwL7dGBpYEROpGVRrDSKmwePHjzAynCebSaFpGjXbpdFsks8pv8lcJsVgf56ZXRM0bQcplCCQz2X40heeZ2RIjXk2k1Z+WL/7HzrrXNOIWIrD7sDeGcZGBpndNaHmrATPl+imyezMJHtmp6hUa2wGQGFooI9IxGRoIM+v/MLnABgdGQYMnnn8OHtnphjsz+NJQctRuR8TgZ9pvd5kdHiQl198WvHoWdY294xDB/ZgmSa1ekOBUs8jEVevjUaT4cE88ahFLpPCdpxtm7YQKhL0uSeP4zgOd+eWkVIylMlz8OAennzsGLsmxwJtsEM2lySZTuD6PkcO7iWRiNOybRAoAUUKWo5LtVqnVK6GBL+mYdCXz3Jg7zSvv3OWH79+mnfPXGRuYQWhaRw7coC+/n50HaYmRrh1Z55rt1T0ZstWUYdSQjKVZHhokFQyzspakWs375HPprl1d55SpUY+m+ax4wcYHR4I+6frGlpAnhu6wXQGQL228yR+FFj4BCCiQwy+k3vOz1Aeds7u4KLz8KqUP1W3/1Rv7Wr0tvlib73Hxz37t6mmHtDunZzj2W5N6wro7LlHrzHy09XQfToO8u0wTejkkwpsrh1t7PbFu2PZQX37wOv6shCNQrOJVShx7PA+6o0WwwN50sk4k2ODXLl+h9PnrytHXN9XUuqJw0xPjWM7CgTJQMrpz2fJplOk0wkK5bKSolNpolaMIwf3ks9muH7rHtGoxZ7pCXJCR7+7QHKtyIHVDQ6uFzELZfRCEW2tGDZVcz2SLYe/+r038XwPz/WQyMCBWwt8hpRfVSIeC5ntPd/na54XTpR28kzj0n20P/4puz1F3KfrOkLAtOPykoCIaaL/3g9UFKSEL0vJ5x1HbaICoq+eRdd1fCkZQqF3oamURhJIeZIBx8HzPKyIqRi+pVT+cLVGaMf2fY+hwX6+9PlnmJ0aZWiwj1jU4vmnT3Ly6H48T+kuTcuiL58hm06jCYEQBgKdiBmhP58hEY9ycN806WQK4dexrAj5XJovff5ZDu2bwfN8xkYHSSbiWBETXTcQQiMRS/L4iaPYtoPr+kg84vFICD76chmef+IY9WYT35MIEcGylE+Upit/kYP7ZpmYGMcwdOYWVnDcFpNjQ8zsGicetYjFojRbipBSCIHjOKytFbl3dwmfq+zfM8Xxw/uY2TUe+JxESCQTDPTnEAIcW5JOZnj2ieMM5PvYMztFMhFjaLAvIHhVeSSnJ0eJx6McPbiH6akxhof6SSWUMac/l+eJE4c4fGA3bTNULBYNwIiBoanUJVITXSmFgv1JyuBAdxkfHSQWtXBcF88LfBY3yyqKbWiAfDYdMq0rx3SHarUWMH8bDA+NMDQ0SqVSo1ZvYPzJa531WCgh/sJ/SQKY9jyGG01O1hp4nkf83HWif/hKMK8lKc9DBvO2HWrdnvPtEm/7TqipRkxKkl473Zba7D0/COcPEoi7AeGtFmhf2ySuvu+T9DyGpSK6NQ0jvF/S80g3W4y2bHRdC0iDDXRND3+raSokvF9KTjjKnKfrOoauoVXrvdtSIOvHY1HS6SSJ4BmG5J2eh6Gp6NV0KsHo8ABu4B+o6xqWleDIwT1BbTqOr/PU48epVKo0HQ/dMInFdZ5+8iTjE6Ohz9PY6BARU2ldEwkLKxLpaVc6leTE0f3hIdgmyC1uVolGI7z0/OOkkglymTRDg31dYD3sGJqmcXDfDH35DOVKDcMwyGVS9A30Ew/66Tguju0oUmFDZ//eaWamxwPQr8b+4L5Z9YxjUVKpOIcOzJDPpxjoyxGPR8mkkzx24iC+VNxzEsGhg3tJJVPsCoTQdDrOs0+dYHmlQGGzxGvvnmNoIEc2m2FkuJ9YNMrY2DBHDu/l/MUbXL15n3rTplSuMjzUx77ZST7z9ElisWhwnCiiU5Uf96O0WTt/9Ym1NN0lCDZ55Loedkw+KqD5GFqmDmgJiMC3NUWx+/e4Be0EtD7RWH3KIHRLaZvMewMlPr36PxWwpQjkHjwIH0uD9YjXCkAKgRwdRNy+j1jZIGFZ9OezVKt1Bgfy9OUylE5fotVywkmiaYJEIk4kEsF1vZAl29CNAEBIMrksbq2MXC1gLReI31jA3CyTWd/kwOoG+noRo1hGBDkCH/j8211xFXO6ValBe7J2mVU9z0N3XHTHxXC8cC4K6aP5bX8oOodSMOfMYLzah4oRvNdo0j0xBYIoEjNQT2sN5WsjZFcUGB37teZLIu1Dpqmu9T0P6SqNR1vy03WdU8cOYBo6A7k0UUvFKY6PDqIJDU2Y+EKBtHbpjvhqNBwikQhHDu1h9/QkETOKaUSwTJ1YNKo2/2waz/NDgkMNHV03lcPiZgXrwysqL1wkApaBFjXRoiaYOkLXMQydZDSKb5rYUgehIlI1zUdoOjE0bE8yt7DMtRt3yWYTTE+O0pdXFBVa4IdmmgZ7ZsaZ2TVGJBLl1q37XLl5n+XVAo1mK3QalUKA0InFYmjBM5MSpibG6cvlMU2DRrPFxNgQqWSCRDxGo9Ekn1dO0kcP7WGgLx8CCIB4LE48FuvMF18GRJAuZhBxqesq91zblNLeNArFErfuzGMYuvIzaqgowXpd+QZphkk+myadSoZM8aDU5qahnJZt2wlY/FWbEkFEbDu/HKhIIbG+CUiELzEch7it5o5muwi9gdauvw2i2tE/mhZyHnW5snYloRYI30eTHZOWDO4jhBYkYgYjMI12aghcFXwfI3A3MPRgXIOx1H2fiO2guS6armHodgAC1fe+rxjHNSHQAL1nI27n3lMt0jfL5P/Pf0eNv+8H9XWeo+/7ZALNi653SDvbY7iVvVwCuhREgaT0u6xVggHfx3YcZKAhNdrJtkX7l4Q5THFc4n/ld4KRbNctSTkemUaLSekTiymAJrQgGqtQIvT/oPO8olGL8dEh9CBhtaZpyk/TUa4Btu1QrlS5fvOuoo554ii5fIpWvUkqFceKWAz059k9PU65WiUet5iJjLBv9xQAuWyGZDzG9OQYViRCqVonHo+j6TrFQiX0o41GI0xNjvLCs4+xvrFJLpsi35dmcmI4iDpVCbcPH9hNLpvG91QfpyZHSaeSTI0Nhhxs7Tnp+z54fs862LF82j5QW4sQBOqjLX5G2y78iDr4VNv5sCjELVeGCkDVhAeYLD8GsAwd5mUXl/7W3wpQOiuNrQMX5mKVnTOxXW/3+ef7vlIqhNLqRzbtkcunArbaUVtt8xJ02W8/baDV1gQGgyZHBtB++h64Lpm/+t/wuPQ5WG8AAt/z+Pp6kS9Uaoz90U/Jv3c59COTtoNwXHTXQ/d8hOOA7YJjI2y3k+X+k5Yg8arwffBBX1ztbn7PMtEAc5vdPzi8uw6m7knz0BJMyu67qeW7w8zp9qOTahPuzbAWTG6pDjdp6CqFR2CyGRsZVFK5poGQAUmohhB6YGIWaHow5lIGSZ4LnD1/ldPnriCl5MjBvSQTKaJWhHhMcS0ZkYjiTTLNIOVSsIClCDdDsbiC+T/9m55+q7NGReYhJaYQgZQliOmq7cKKIC0TyzSI6ToxzyPabDLm+aT7s/Rfm8eIRyEaQVgR7M0KxpmLHBQwK0yi6TSZuk205RJv2kw3bdIbm+gNG+JxRDSCMAyVVFdTKXo0oRGPRwMTnZpbmqYRMRX9wejwgPJbikWDQ6x90BFqVtr+cp7rYf0//99QLKtcnjfuq4N1vciJv/8vid6eR3M9mF9m91f+L8wEqaP0P/wJ7TQj4ZbTBfp3mlnZh0wx4XrQaCnAvxDQVAR7VMT30T0/0E7JgL9G5bfTuysxDPwDs6DrPZtbj9ofVAqmoL7uma1pGrKdlLt9TVeEoEp3JIjdmkPzvM4m3f69lFiySxARWiiVd55Ab7+3jUNL0W2Y9xbh3hId8NR7bbvfobtFjw9rb2lDuK7doOdaCUR0HW98eAtI6+RYDQ8iKdEKpeBtIFABuqeAptAEluOhac0d70fgySIhNFEbhh6kmVIceXbgq1iuVLl5+z7vf3CRkaEBjh3ej2lG0OKAVNxqhm4Sjxvkm6nAT0sjG4DWNgWOaZoYpkkzSFEGkEqWaDZtkokohmHQn8/yxGOKty4eiyI0KG6WuHt/kfWNYuCikeDZJ0+QSSexIoqYUmiCqGmQTsQUhYeudkbf8/FcF7OdouhBpa39+I8FuEKgpHeBiW5BAx7gsNP5/Ue0rddU1mXyewDwCUHWxwAf2zRc2yt9eFu3acRA+bR5imdz23VBXwiShIefa0DnPBdCdKVm6r6HDAHXw3jEPmn5FHm2wmO5c1j/R5iM2zbhsSEFjDwPbb2oktY2W/i2jV1vMlStM+R6pNaKRDYr6oAI8hu2t7LuVn6UkgpQSaXDZK46UjeCTPY6MvgMrZ1Mtm9LnXLbfdQeuO2TAG91Nu7QpB+iCrFDD7ZUI7q/bwO37f19oOsB7b0lyGOWzyrikWIJIUTopwSBplG0U14ovwZl+pQhb4rreZy9cJU//bM3WVzZYHgwj+soeo6IaYbs0JquqQUTFCFUmgoh22BQIh037FrYQ6noHlqtVmjuscyIchr3PGj4iMBJWkcdtinfJx4ctnqhjHZzvmdEc40GT64VFHnkhzfRdY0hKXkCpeWJX5sL+iy2jDf4ho4jwBFqznimQVYTeLpONJ0kkU2TjlrkhLo28sFVtFgMrAjCMhU4jFpoUQusCFgmmmmgL60jSopQVTiBcCAgWqmhd81xIzjEFC1MW+uzE+TvFLHl7x2l6/Yfpprv/uhgTx2+52PbKrJNCEWwqgfpkzrcNgL6c/j/+n9AM4ywTt/v5DNtS5q27VCt1bEdJ9gsdXRDmd2jAbuzL2UIZpXZ3Qc8dF0n+n/6b5Ftbc02ZKM0UW3zkaYF4ELwkZuulBLNWwbP66r2weMq2/3eWg9bV2n332LLFcEnuobozwfrrNMXRLBrrG6oNWIayL5szz4jAemD1tYaG3pvPe37ZJKIYpn2HqTrOlogaGnBYSWReBIK9QYffHiZ198+y9zCKp9/8SmGBvMgFAO3HhzqQoOI3k4u3qUtEaBrOkLo6LogFjew6KTQMYeUe4BhqEwBqWSCeLxjBlQaaBUIVas1iERMcrk0+WxGBRwE3IRIP2yLYeh4lqVSsukaUttBnN1BEJZdqpuf1fd4x9K9RmSbf8vbcsmncd8OIP/IKz8WAAnmWeji9hFtfZD2q+eStolXI0xrFJqB2qLJVs2bgI9M8fMJSu9yfKTyKZkRZW8f26jzERB2T3kYwu1W30OwSEGMDXU+X9nA0ASGr+zGpucR9aVKFiwlwlNSLkavmnjbY42YyIgJpomMGOq9aUJw+KErCf1hc6+9WT+gQztOXAUk/YAl20c32j5dXpgaxTB0BUwikeBh7+wQGHasW2shghnSI8T0bucP6pNWLIPvIww9kLjFtoPD+uv/ELFZpnODQJoXYWMwPY+X1wocXlmn2bJJb1SZ+F//A6l/+90uM0h32aJf6DosxGYZ7e6Cet+fw/2tX8Sp1lhbXGT+zgK1UoWEoTOayzGYSRNFIBwHabtg22ArLabueQHvVOc+3WvJNE2S8Rj1RitsU9SKEItaRAOH6M5I9jZVtmx8x0EPTNhIiLY3+pWN8OCw2gtI9G72PRG8nWFFu7MArgJYeB4yElHApz8LG5sqAhZCwCIlobmxPSMeIMM+8NNOapLOk5GAp2lsGirVi2VZQZ46ieZ6GJ6nCG81DSnUYSc0Df38NSX4LK2h//bf6qlRD7A5ss0nJDFdj4jjAAEQlwSHpRFw4OykG+pUq1+4rjTXpo5//EA4zu3ZpLWl9vYYP0TS7wE2gNeXRfYsdlWZ9H2arRa1mqJi0A2dZDyGFbXC1ra1ruF7oQhd/SCKu5dkUb1qxRLi/iLBBb2NE9DeePwDs9va3j1OSmPaWV87ady0zQriwjU1dosrWL/9O3QuUSNoSLCkJOa4GEurzG4UyaSSTHzzR2g/9wJtf9OeVgihGLlpnx9C/RMaUgZ7Czp6+DMPK9Jx/JdBsu3OXibRNIjHLCbHR/Cl8omNRkwihhaSQAe9CrYkiabr6KaBjESQloW0rJ2BwVbAJWWAVh+y/36KRezELC92ADHBmbsNNH/Ee+ChgGsboeg2May73i3aoiCf40OJYh9RUxjuQbJruYZarLbk7QN6TzMePB6i56KOMuNhbflkIPfTMSOCkqbkRzTjYarKjwHKuh3z/OH+9t6GL8EeHUQP1OqybYPXNISu4wMym0b2Z5H9efyBHH5/Hr8vi9+fQQ5k8FLJBy4gxZLeOfF8X+sBLO2x6N4EOp92Fx9fuj3D4Ps+lWqdGzdvMb+4wsLiGo7rMtSfJ5dNMbe4yqWrt/F8n6cfO8zXv/oy8VRy53DloKj6teCwI8wDKDTlY+f7fuiE3O6FL8H1t8P2xF/524j14gOfr9CEAlobpR4tU8e5X91HE4J+IJ5K0IpEsKwIyUZL+fXsCLS2jl9nnolqXQEOACuC/ZtfYXFpiVd++hZvVcuUozq1epPjsyP8xV/7Knt371KmD01H4iPbG5jn4dsOvu2B7YDtIGwbvdVCd1z8lo2/VqC5uoHuesR1Dcs0MQC35UDLRga/ky3F1UPLQdo2XqOBX2uiex665yktVMtWGjqhdfVQbJ8mDxrs7uWj68h0EtmfQ/ZlqP+zv4NE4Hge5y5c4+KVmzQaTe7OLdGyHb7yhed45vFTRKMxpNC6zFUSIbxQrS4INkch8KVAinbesF7folarxU/ffJdvf/dVJsaH+NqXXmT3rjHwfWxbmXPbmgnlBK4TMaMkn/xV8FxwgPXi9vkUbqZK0PJaLVzbCZictdAPUDOMwN+ra7B2GjfHVfeLGLi/94/C9sBHSOzBHG5L1e1r/UAAlEikr5IgCyRC+vjSwXUdiptlzl+6wWaphuO0uHLjLjNTY3zp5efIZTMK/Hb5q7UPT99T/Fm+VNGLemB+RehoQif2238L7fYc4CE2to/ddj+WrWPa073wwHrgDmwHY2eD2NgM6xXBrQSovdZxyNiO8kWMWUQbTZq+F8whkELS9i/t3keF1JFo+KKdj4LAXCwDTai6oQKDD6cM0jSh8h4Kga7pmN2H/A4HegfkBu4GD/LX+ght5H/s0msWY+c794Cs3u9l1/93/PVHaK1EO3oqrE9p23YS2bbPL//RANfHKUEOyh7/LSWGohJR+0q4k53P1GVq4krR3se65mL7ZaetuEurqf4TdGvMHqX1HwtsPTB5tJABoO6aqN3X7IS+ezaEzvcPnE7bJltw/fgQoCKXbM/lcqNJM5MgOj5ManocfWgQ2ZfBHO7DGhsmmkyEJIhuoJ1qtVo07TrxWDTISt9zhy2t6bwXmo/0JAiJTvvQ0nomoKQn93v4+45zrSrNls1b753lwsXrnDiyl7GRAf7ou6/Rsh3+8m98iS++9DTjo4MsLK0x0JfDNA0MIYPJ0o34JW3zXSi1ayJQ+Xch9gB0aV2AS6D8ugzADwdfHZbqMO4de1EsYf3274QHhXb+ugIrQigtS9Aex1aRjRLlpByJmGSDkRC2g6jWdnriDyztZSUCgAMg5peJ/tXfob9a5bNrBb6gKyqBVsvGeOci+WvzRCKRLlNsp4SuLVvuEQwTAEMShrq+UMpc2fUj2TVv2lpPid52cjVUxCBWBJmI43mekr6FUL50gTZWSB/a6UP8QHoO3/vqxp5UINN2wHGwNYHTaBIjQ1tRcefuIj9+7T327p7iuadOcPf+Ijdv3ycWjYYajNCHT0gEO/soCkBrm6V6xiyYM9Ink0pgOw5z8yuKNFgqbZoQSvsQzj3a2pQta6o/t7OgHGymruOyueHiCoHVl0UaOm6QLUJYUUSXCTJ8aFv2ChEkVG+3vVdj+OB516aoUXt5YI71RRB1rQyVUqi9oK2ZlJ5Kb7S0sk65Uufk0YPE4gZLqxusrhep1Rvkc5meMQ5uptaRptL1eL6PhkRrrz3ND0FO+FtNQ+YyXV3uncwK4ND72bY+dvaP8NruMVlYBlt9KRZWum6uABygxsN2iLoeiYAkmHtLxE/96vZ+dr+R277pHoodSu8q3Qq8Hirjbvsg+K0dBBIsLMNv/k3IZeB//p2un4ne1621bJlPn3pRD58A+YPQHoxNQnPaw9sTgpRHNA1uY3T3Hiag9II65UriI32hQFI4nN3oZgeM8IC6w7MumPQSP1iP7foCcKX54LcVJzrgdaSDHpVN12233VsdGIJgrrfXWHg7/5GB4iODrXbocvtgD5siAKGynUuhDvWPHLytSLYtYXZ3sHsZCRFSSgjRGSQBkM/iH9qDXdjkdr3Ob8cM6pubPLFrlN84dYShgT6a9Spzd+dYeO9Dcpk0zz11gl0To2iA7frcvjfPh5eucPTQHvbOTnWFoXcdSqLj4Kpurgbc19oam3aLgnxxbXt+8D9JN7hqh6h3pMlCscQrr75LKhFj/55dJOIxrt+e4+yF6+RzSXbPjDM5PkyrpaKl4rEomtZJKaPmnezMNyHxA/AbarPYCSgH5pSuBaJpvcbJyF/7B+gBkJILytFfBCAHOocYAbEqsajypRNQr9VZWS3goRGNmCSTccxUEk3vRJ89XDba6b0CNrJcRasvBwBESfmiWidRb5JJJUki8E3l7GpUakhR31ZbuGbk9rv0XCx7/966VHu1up1nrQXzWHZrEaUMI0G1rmi1ne8vApUkgB6udfROBHCjZVOv1jBdT+Ux9H1On7tEqVTm+KG97J6dYnJ8hMeOH8SyIsSsGD2SGt5D92YBgTnHx/O1EIDo+GhAIhohGY8ihEYkANkKJrXZbDrasO5IP5DKLP/v/lHXQKKAZ7CWPM/l7t15/uCPf0g6leSXv/Iy8bjJtZt3KZaq7Nu3m6HBPkzDDIY20DZJwo1ZCEH02FfBdbuelwwAYTD2W163gTUJulBSciehTeCfKtSKluHPJNVanQtXblIsVnjy1FEipoFARR57wX3Fln1SBFpDiQwjj9uRpe19SAt3/GBC5jP4//s/Ctdwt/ZNSh8p3GCBKXHQ93bwXwkAniY6q75be6n/J38T1ouIHUB3+5HhSyrFTYqbFXY7MjiYfER7T/iYRWz7q4MUwzZ0n/8PlNI/ZulKrK7q2gK0ehQOO9Ac/EcqPQADtu/j3W18cC3stNs+KuB6aNlaRw/gCmiLAAV4RK/16EGara2fddcZaqV3+E1bAEAiNW+rq1unurDa3nrbo9QteOiGohtSXwSBCjLQOD/i8D0y2PK32WzbDel8LhHh4d0DuB4Vtaovg9cO4OnpTTASCrsGm148ilk1yUUyjPQnuXbzPr7nMTLYx8z0JK7dJB2Pc/nmfb71g9c5f+Umf/k3foH9u3dhCOUkbehGOKHDg1KorVUEmp1uibm9KUqpuK4QHSd2LVBHCwH4EtvzOzb3NjgKDmQdBVSLm2VW14pMT4wQjUZIJGJkknHl/xL4M1iRCAnLBE0PfBAIpnDXAhSqbl/TlI5NSmWm6NYetk0gUm5LT7B9EQfmQdtRgMZmm8/btocTMaE/p45bK0LcihCNRohFVXJvTe/QV2yrIZQ6ts6HzmYng1f8NkgQYBi4uTQV16FsmySyKcikMHo2wq2gvreEa37Lht79vXqV4Xei68sOcGvfR4bXe56HG6RYUVhJU47lAdjqVsZ0630U/UTXxtC+x9wSbX23lkpgjQyg9WWQmkaj2eLazfvEoxbptCL8TMSixGNWYNrVQx8Mz1e59UDxs2lar5knPMADQUoTSuhyfR/H93Fdl3qtheu4xBOK9VwRmKoxclwP17VDX0NdbjWxt1e4xPNVIm0dsAKQ7Ps+6xtFiqUKqYDEs1Ktc/f+IhuFEn35LNkgybT0Jc1Wi43CJpVKjWjUIpGIkYjHiYZTQN18fmGZUqmCHrDLJxLx0JEaAY5t02w2QBh4vo8hpMriEIkrAB2ax9uaIU2lKPMlLpJypcrde4tqDxEqIbnreYpXrYtXTI2tcgzH0NW+5nmh5ltK5fSvaYKIiGwbu/bccwMQpwWAVmmdRGhKUXOwmz6ia/SlInz220Kg6BI2NSCXVk/oAdu2CNogBZQbTWjY7ckLsejOP9pew0cWxfXn4XlqTzYC/9FuhZ4I7ttTo9j2R7tGOhuReDBz+8MUBbSjP/9/A7ge8uXHqOlTAFddNXXvgQ8qokejJHcYx0csO2itd7pGtk2HUoII6Iq2ClDIHjca1QUZRLJ37cNCC2lRuq6kHeP/qOVn9NmShLwW3UDrE5ZAVuiq+2G19WoWNE0QMXTi8RhSKrNcs9VSRIHRGHtmpvnFn4eF5TV++tZZTNPgr/y5r7FrYpRD+2c4cHQfmvCpV2o4wWFjCBlEUCkfAk0qk5qmaaAZuI5DvVEnYhqKSFTXcF2fazfvcPveAslkjGOHD5DJpgPiQnAcB9dVTsNWxEQTGp4vqVRrtGyVw9E0TDzXo15vBACurSVTk7RSrdGwXRLxKOkg3YRm6HgIGs0WjUZgYgF1yMZjPepR13WpVmsqXYaUWJEIsZjVidYJQKfTUjxJ3fSI0jTwj+4LJbvw2RXLiNtzapIe24/3b/4BrutiAnHbpmm3sBHgecHBH6XtN6aaJhC6jqsJPCnxHRdDaOiGqcBLIEU0my02ipvYTovht86Q/Lv/H3A9/F1jLP3Dv8a//cPvcenqbX77N77Mc889hqFr+J5Hy/YC7iqNhuNRKldxXFdpwBJx5awrlPnH91QEmx4AD8+XzC8us7pWoNWySacS7JocIxmLgFQH63phE01o9OWyxGMJNVbCw5WiA7Y8T5kPAw2xlARRmOpw9DyfWr1JqVQNw9YT0QimrlGrN1hYWkXoBiNDfQz/p38H7fJtMHRi//lvYP72LyKlh/B9ytUyhWKJdCqBJqDlOLQaTXzfJxoLorKkZHV9nbfPfMjN23Nk0kmePHWYA3tngrQwkmbLprip8tNFIga5TAYrGsG2HUrlCrfvzrO6XmRhcYVSuUo2myTItYztOCytrHP5yl0uXr1NxDR54uQhjhzcQ18+S/cR3Gq1uHDxOhcuXWd1o8i+3VM8//RJctmMmqs1lVjZdT2KpTKXr97gu6+8ifQlmXSS4cE+DE1QrTe5cOkapz+4SCtIXTQ4kOdzLz5NX9ducvbDK/z+N75HYbNEPBrF832GB/v4/ItPcfzoATRN4+KFy9ybW2T37mlW1wucPnOBvlyWr37xc4yPj4DvUyiUuHLtFstrG1gRkyMH94YcVLbtUG80iJomc/cXuD03z5XrdzEMnTPnLhOPRYlGLRaX1tgoboYpaybGhtg7O4UeMPkvLK9y++48mqYxvWuC0bFhrJ6zX3JvfpEz5y5RKlfJpJPEooo+ZHAgi2UZbBRKqq+xKHtmJhno60Mg0HRBrdFgfb2EphmkEnES8XhACixp2g0MXcP8J39L7cxSUXjohh4Asg7o9DyfM6+/y+/+q2/y+29dVIFJ8Sj19/8g1NiJLeeDJKDaoKOVbINMt0t3rKPM62sbBV598zTrG0WmJkbZNTHKRnGTcqWG5/tErQiT4yOMDA2oNGC6RNHRaMF67gSzILxgTw0ER+ErE2Lbf/ATKQr+/7C0gXM7em+rpvATVyu2O80/ChAKS7cCpVcIfoAcjtip/vBiQYfuqIMdVOSyUO4ZwuvRfHXXq/4FEfSy42OM7E0rF0ocn2AafApgK/irbUKkDYNEzwH/qGWLzMBOvQqGMRSe2p+2JWpQTMau64YtkdJnqF8xcV++cY+79xeZX1xmcCCP70sun7vE0toqU5NjDA30oSNxXZe5hRWu3LhDpVqnL5dhZtcYvidZWNqg3miyf88kh/ZPIyylUfjTP3uTP/7ea8wvrZFMxPj6Vz/HL/3C50nEo9y5t8B3fvgGN+/M05fP8rnnT/HYkb1ous7qeoF6oxVYxDzev3Sdtz+4EpofAMqVKu+ePs+P3jxLrdHiiZOH+OVf+Cy5TIbVtQJvvH2Wc5dvsV4oKV8lQ+fY4b18/WsvMzjQB0JQKBZ4/a3TXLl+h0qlgaYbPHHiEI+fPEQ2nUJocOv2fc5duEZfPsOh/bPE2/NO11ke7effvXCEpx47TDqd5PzFGyyvFvitb71Of/txiXZ7a7zz/oecv3iVZtOmVm/iOC5HD87y2eceU6bcwJzo+ZK5uXneev88N24rKoXHTx7k1Ikj5HIZkBrr6yX+9M9e4/1zl6g3Gvwl3eAlqWQYKWBxdYOb9xa5u7DG7/3xj7h2Z4ETR/extlHkzr1Fjh/ei+35vPvBZZZXNzhyYJbPPHOKRCJOvd7g7ffPc+HyDeqNJgf2TPH806fo78tx7eYd/rff+zb35peJx6L83EtPMTEygK3BtZt3eeW197h68z4R0+T5p07wueeeJJtJIRAYusB2fBaX15BS0rJt5hdWQMDs9Bj9/Rl0X3GJraxtcObsVRaX10kkosxMjXJg9y7i0SgffHiVP/nhGziez+PH9/PbzSbRYAXObRR470/+jGeeOMLQYB8bhSKb5Qp351b4p//iD5BCo9lo0p/P8uUvvsC+2SlW1lb51vd+ymvvnKNcqeN5HisrayrzwsQoxc0yt+7McX9+mZt35ti/d5onTh4ir6m59q3v/ZRzF6+DlBQ2K9QaDSbHh8JIxKvX7/LDn7xDuVIjEY9zb26dm3fm+NxagS9+7hlSXav80uUb/O6//oZiC/d9Tp+9TKPR4vMvPqXmv6byDkqkYijQFCgeGsgzNjqIQFIulfng/BW+84PXcT2VvHpxaZXL126zZ2aS4+HtJGsbBW7fnWN6cpQnTh5iIbju4sWrzEyNkYhFuXbjLkur6+zfO8Nwf46NQomz56+zd2aakbFhCoVNvvHtH/Kj195jo7CJpus89/hRfvNXv8jU1Ig63IWKlmy2bJaW16nW6gghuHlnnt0zk7iex7e+9yo3b8/huipbw+dffJJdk4oZ/vL129y5v8Di0irnL99ibHSIX/3Fn+O47Iyd7/ucPnuR3/+jH1Cu1OjvyxK1ItTqDTLpBJGIwfrGJsVSlVwmyW/9+pd58dksmmlSLlf59vdf5Z3TF5BScPzwPr7yhRcYHuqnWCry/R+/ydLyGqlUAtt2yGZSPH7iELump4jFY2jSx27ZbJbKuK7H2lohIIHttE/TlOC7ul4I8lCmMAydVstmo7BJtdYgZiWU1jNwjzAjEVwpVAYLKxJELQpi0SjHDu/DNAySyTiVSo16owkChgf6sKIR6o0m84vLpNMpMqkYmk5Aimxg6ipzh+t6aDrEojFMQ1duRHLLEf+gc2unz2UHKP4s5eEWho8oH3V9D1DpaN5/thZ3jdmjAK0utWq3mZ+wlo+wPLDVdUN2Gb66LWFto/5OpYttlfY4yxBLbNXStWtRpOc/22j9DGCrY97rmK/otFh0abx+5tJ23gwkH01T0S/tb0TH7NQdLSTbiJeAQNKIMD4yyPBAHttxqdYbtFo2C8ur/Kvf/xNW1tb59V96mVwmhfA9XnvnHGc+vMLsrnHisSh/9up7bP5JDRA4rstmucqhfbv423/9r5Ab7OOtH53jD779I3ZNjPDC08dZXiuQzySJmAbrhU2+88PX+aM/fZXCZgWA6zdu89f+s9/g2KE9AahSk1DXNTKpJAP5DBvFMrarct7dvb/IT974AFBJon/yxmmOHtrDqeMZFpfX+cFP3+H6rXkO7p+lvz/L2fPXWVrZYN/uKT772WcobhT5/W9+j5++eZqZqVFymTSXrt7lzr0FNE3wzBPHMA2Ns+ev8L//wfcYGezjP/tLv8Jo15PwPI/3PrhMOpUgl03xze/8lI1imV93HOi5zufVN0/zR9/5Mc88foQjz+zhw/NX+eafvsrd+SWGh/oZGxkkqkdA01hYWOTfffMHvPbOOYYG8uQyKf79H7/C5ev3+MJLzzI1NsZ7H5zn//jG95hfXsP3JcdNkxd9HxGYoK7fvMfcwirVeoNrt++TSsaZ2TXGzdvzvHX6AoMDfUxMjFKu1Pjg/DX681klxXse585f5jvf/ynVWoNqrcGlKzfpz6V5+okTLC+v8eHlm5QrdZ46dZCp0UF83+Py9dt84zs/4e3TF6nUGjiOx8LyOplkUiVCNgW+Kzlz7gq//0c/pNFsEomYClRIny+9/DRfevlZdKFxf2GJH/7kXS5euU06GUcCV67foVSucuLQLEurq1y/PUehVEXi8VuB1lFKybvXbvHvLl9lfHRA+S+ZBv19GeoNG9fzcT2XKzfuEY8u8/zTJ2g26rz+9hneOX2Rk0f2sn/3FB9eukEmrTRTnt1iaXmVWqNJX1+WP/nh60RMg+efOoHjuLz3wUVef/sszz95lHw2zZnzV7l2e04Rr+oa1VqdMx9e4c69RV5+4QlefPYxLl+9xb/9wx/w0zfe58DuScaClSklvHfmAtdu3FPaVV1jZa3Ilet3eOzEQZWjNKKY8YWAeDzG+OggE2NDjI8OMj05Riadot5ocufeAp7v8dLzp5iZmuD+/BI378wzkM90tiEBu8aGmJ0a5fiRfXzt519kfnGFVrNFuVqjWa+TjFtIKYkYBlErwvBgP/t372KzpDQobrPJuXMX+Mnr79JotojHY9Rrda7euMP8/DK7xgeJ6MrMlYjH2Ld7F5YVYW1jk76+DC+98ARDg33cubdAuVzFcVyePHWY3dMTzE6PY0UiXLt5l5+++T7HDu3myMEnqTXqnL1wgzfePs1h1w30MapTZpDPz7IiPHHyMDNT47zxzlneP3eJdDrOEycO0rIdzl+6qfKIBhrbt98/z2tvnyWdSnDn3iLvnXV4/unjjIzkKVUqXLp6izMfXmUg4OjyPI+WY5PKZRixTAqlCu+9d45zF69Sb7S4cWuOwma5Z98uFEv88Cfv8uM3TrNrYoSvf+1lZqfHqdUavP72B/z49TMqwjJmoWkaY8MDDA0PUKrUKG5WGB0Z5KXnnmRqbJBEPMZgf56zF66yUdjk8RNH2bd7N41mDSsS4fa9ea7dvIuUkj0zkxzYN00kolOp1qnX7IAc2adSqeFLn4P7ZhkeHMAQ+paD9NHOrE8LsnSXT90HTD7ANNr+mnbwxyfQdAmUi0t7BB5Vs/WRwLQXFPZ+trWqwJwXguW2ae9B/e6Y53tNzR1NWNuVpetjhNDpuOw8SPf28PKxwZYMUTy0O9fdcAFI0XaM7Urj8zFRYQfpBh1rD0B3fVICbnh1j09NMGhapzKVaDabxtB1NmtNvIB3K5VMMDSQ5/7iMssr67QaDcqVGmfOXWFkuJ9f+6UvkB/II32Pf/Hvvket2SKbUhFl75+9yptvf8BAPsvlG3dBwl/89S9x7MQhGvUmhpRoms75i9d4690PSSZiTE0M4zguA7k0lmERMU2yqTi6rgUaKYNTxw9w9dotvvHd16hWavi+T9yK8JWXn6W/L8u3X3mLd85colgsApKBfIapsWEEgv/kz32FiYlR/rf//Zv85PX3uXtvAcdx+ODcZV576wP2zk7wtZ9/gdnZSc6fv8n//M//A6+99QFH90/T35elL5OiL5PEMnUsU6c7s3ssajE0kCOZiJHLpkklYxw+MEP+3StwfyV8VOVajTfePcfRg7v5C3/+q2RyaXbvGuP2vUXWNorksylFYKpptGybsxeu8c6ZSxzaN81v/fqXGRjq50+/9yrf+v7rCDT+3C99kftzC5TKFWJWhIG+LLuEjra6qcyRSGIxi/GRARDw8mdO8otffpFsJsW9+UWVqiVisn/PNPOLK5y7eD0wPRsUCgXefu9DAJ48eZDrt+ZY2yiqvJmei2XqDOQzzE6N8vxTxxkZ6sO2HW7emefqjXtIKZkY6UcIjcH+HIZpoEwsglq9wb35Ra7evEsmneDrX3sJ3/f55nd+yutvf8iJI/vIZlL88Xdf5fylW8xMjbJ/zxRSSs5+eJUzH15lYriPRNwim0mwb+8Un3v+FNbF++EyyAz3EVlbx7YdpO+TSibIZ1OMDvXzW3/+q/gC/uA/fI/zl25jN1usrG5w/dYchqHzzONHeOLkYZ598ji+75PLpADB2FA/U5NjXLtxl1q9iW3bWIagWqnw7unzGIbO559/nF1To0xPjvCH3/kRibhi9i4USywurJBNxdk3M87IYB/4koN7d/HOmUtcu3GHZ9qSo+9z7eZ9YjGLZ584ytTEMBI4sGeafDaDHeQibBcBAfBS2i1ApavRdTzPxzR0piaGOX70AIcP7qFeb5JOJehGW6auJFvXdbEdJ9SetVo2nq8ilnzpB878Op4naNle6CrQqNW4efs+EcPgl375s0xMjlEqbhIxDfbt3oVE4HmBid6MKKJlQw8S2CdUonpLBRUM9OWQUvK5zzzOof27MU2DzVKZV988zdLKKl/90vPs3T3F1zyP4maFt945y29WaljBLimEYHpqnMmxIQqbZQ7v383JYwfZLJW5efc+p47t4y//5i+ysrpOrdZAoMat0Wjh+S5f/sKzPHHyCL//je9z7dY91X+Un5hh6MzuGuNXvvo5TMPgT37wKmsbRRqNGivrku/94E0uXLrG048dZWCgD8/zKVdrgfCtDrTv/fB1/v23f0I8HmWjsMnaepHJsVEazRaLy2vMLa4w2N9HPB7l7v1F3j93mWQyRn9fjpbt8Ma7Z9ncLPObv/pl8pkk5y9d43f/9R+i6zp7pmfYPTPF2QuLvPLqO1y5cYdSpUrUilCt1Rka7Md1W7z29jlu312gUquHFo+RoX6ilkUmnSKZiBNaYj7mGaXAyqcHjn7WutRpuRNY2elKCCmbPpYJsNPOQEHFIwOuR/TV2lHHFfga/2xFtW9nm5nWuaa7FZIw+0Xv9x+v/GxmxF6d3s5/d79/xIkkd3zXQZMdJlm/9zrZ5mfZOiydhLRr60XWCiXlEI9yqo2YBkMDeUxDZ219k5XVDYqlCr6UHNw7TToRJ6rpHDmwm8H+DEcOzKrcXpUaruexe3ocz3GwWw6armzChoRUQHjZaDRxbYdUIspzTx3n8ROH8H0fUxMMDfTjOj6GbuD7kmKpSqVSwzQMag2bWr3FvfllSuUqu6ZGGRsb4pXXTvP26UsIAbZt02rZCL1NdqqY2A1NIx5TBH1m4NA+t7CEpmk89+RxpqfGSCXjHDm8l4nRQe7NLVEqVxge7Oex4wcYHR0kakUYG+rvUW8bhoHn+dy5t6hMAFGLY4f3YJy50fW4JeUgyfFTpw6RyqSgaZNMJhgZzFOt1VUy4MBRvlqtc+vuPLVGk9Gh/iAhbopjh/bwg5+8w41bd1kPiBLz2TSLKxsM5DMcHxhAXLwZpHAXTI4NcWj/NKVKhXQ6QX9fRvmmBK5ohm5gmkaQEsQMogE16tU664USB/fs4rPPPsbk+DCaJhkb6QOU43g2nWBsdIj+vjRWRNCyWxQ3y0jgiRMHePmFJ+jvVxq5XCaF49qUyk10Q2dmapyhgTy7Z0b54uefoVZv8MGH17g3v0yrZbO+scntu4vkMklmd42RTMRIpxLMLyyztlGiWKqEhKr7dk/y+MlDIVmsQNA3PkSsXGFpZZ2VtQLVWp1iqcrYsEUiauBKsCyTWqPB0so6lmXSaDaDdaQcuPtyGRACXROKksLQeeOds3z3lTdZWS3gOEq76noujUZTpZgyDZLxOOlUgqgVIRIx8X2faq1OtVYnakXQdYFt20QiJqlkXOXU60qS7EvJemGTeCzKM08c5YlTBxTjuxWnWq2ztlFkY2NT5Wf0ZZi6yfM8arUGhWIJwzBoNJWJenFlg4tXbnNw/z7SuS7m8K59olKrU6k1uH1vkbv3FClupVrHMHSkVE79rZaDL31cx8dx3DD/pUD5RZbKyq9ubHSQU0+cQA/oXzQpadUbFIoVSqUqfdkcrZaN3bJVahg/MGNFIRYxMQwdx/Wo1Zs0mk3qDTh74Sqvv32O3TOjCvz6koGBPAP9Wc5fvkW90ei4dktJo9mi2bRxHZdSqRwCK6Qkm0lhmga2bVNvNFndUCZmTYPHTh7Adn3mllaZD8zc7eL5Ps2mTTwWZWxkSAmqmaRKNO24XL9xl7feO8foyABPPHmMyYkRpO+ytlFEnL8T1pNMxPhzv/IFdk2McvrcZarVJo7j02q5OK5HNp3k8595gmeeOMGb757lD7/zCrsmR/iFn/8suqHzjW/9iOu37lHYrJBJp4JgBGVidFwFnC5cvslP3zpDvdFUtBmmwdLKOqVymdv37vPDn7xDy3EY7Mvheh4bhRKlSpVzF64yOTGKaRpEzAgPCvvZqYRA60FR3p+0fJJqttz7gYCr238rCNRSfz+k7oc5vYvOKasY9R9BxxdYwbrb3G1CDOvr+qbz6QMbSUgip3Sw2+7Zvkebt00TQpmOu3wJpezghK3ce8pvOmiBFJ/oOX166Xq6Bu+B8667Ax85OYPvNT/QCO7g9d+tzZOozTEI7+6OGJNIfM9js1Lj/JUbbBRKTI0PY1kmbSlO+ioYpdFqUW80qTeaOIH5zvM8bNsGTcMwDMZHBnnx2VNYkQhWxEQCK2sF6o0maxslXn3zDJlkHMuKIIBkMoEVMTECp+uJ8REEko31DVbWN/BlwEECbJYrlMpVzIgROnHfmVvirfc+pFwqc/nmfa7dmmNxtUgybnHh8h327duLoeu0HJdKpca9ucXQb0PTNayoRdv523U9Wi0bTRN4jk8rSErdbLWo1ZsIAYl4jMnxkcCXyg8dBgXKNJJMxjlz/hqe5zMxOsDQQL5ns5FSUqvVqVQbXL56m+LyGn2D/fg+tGyHZstWpI2eF2gOPGxb+dc5rsvmZolMMs76RhHP83Fcl+XVdaJWhMePH+C9s5e5fX+JO+UGM+11JiCZiJOMx3Bd5QDfbLSwrEg4V9qRdp7n4/leuKja9xAarBeK9OfTpFMxfKnIZ3VdpQLRdR3DVOS4rVaLZsvG91WqoYH+PHtmJpFSsrq2wfLqBi3bYaAvh+u6oVbVdVyWlhRhref5NFsKiACKMwkVWabeq4N4s1xneX2TYrnG3bklbt2dZ9J1w7nf1DRsRx1e6xubrK4XgtQ2WaXt0vTgkPcplisM1LMYhsFmucLpc1cCDY8LUrJndopsJsXNO3Msr6yTiMcYGsiRTsYRQpBOJhgd6md5tcDNO3M4jsOHl26wUSyTyaZpNFsKDElY2yhx/vJtypUmq+tFzl++RX9flr17psM1297cytUa9+aXOXRwBk0Imk2HZtNlYWmVi1dvsbJWCMfS91UgweKyykIwPNTHkQO72bt7igtXbvKTN84gEGTSKUaG+zl17CDpLu334tIahWKJSqXGa29/QDqVCCIbM+EBrmmCckVpsGKxVe7PLwXblqDRckBAsVThe6+8ydzCKtGIwczUGPv3zmDbNnfnlllcKZBOpdgollQC9kDzM7+4Qj6bDgBWK9g3CqysbiCRlMtV9VqtsVmqMtCvAJuUkvHRIaL1xfbw4boeV67fZm5xhVQiTr3ZolypUqvX1doFHE/du9FosrS6xvLGBo7jcuXabe7NL2O3HOYWV8mlkyiNLLiOQ8t2aDSbXLh0HTTB6sYm8USMerPJRqFEtdag2Wxxf26RiKlTrdfDvJ/tsmfPNLfuLvDWex9y9cY9cpkstuPQbLVC7Vk0apFOJcikk6QSMYYH8oyODACCRCJGtdpAILFMnWwmRV8+w+ZmFcdxWFxe5cbt+8SiFgf3ThOPR0nG4zx2/CBTE2PMLy2TTMY4sWsvX/3ii2wUNvnj7/2E2/eXuH1/gZWVNXR8orE4/b78GLFlXcfQTmdZ+7NH1MSEWVikDM28nwTAPfxu3d9u1dLsoHF6kBZqi29ZmKRPeo/eb9FFfSK2tuWj2t5xFwodiHq+FrTNiO3I+14rG6GPluz6uUQGgSreNrLvT6N8qomou4sMRC/R80H4gwdqu3o6FpD4qdO0o8XqoM4OJvZRB3WtVu/UITR8TcNpOhSLJc5cvMK5SzeQSGamRtk1MUzENGnZNtV6Hcd10XWdTFqFkreJCVuOgx4xuXNvkWKpyvnLNzl2eA9HDu1F6DrVUplrN+/StG18X/KtH7zJ5et3iccsRobyfPkLL4Cm0WjZvP72OSKRCGMjg2wWi2RSGVzH5f7CMo7rYtsOtUaTARRfkq5p9OezpFIJ/uDbP+bS9bvEY1GsiEGtaXPtzjxLy+vkc2nK1QZLa0Vu3J4jk0pSLFWDcHoH6bj05bPU6k3OfHidg/t2Yw5a3L2/yOLyOtFoNNBKNDhz7jLXbt1j9/QEe2cniflKapcoUtKhgRxvvnsex/U4fng3qVSi5/lJCf19ecZHBnj9nQ/ZNT7C80+dorhZZn5pLTT/KLWwTyxq0d+XxTB0NstVKrUG68USN+7M43k+B/bMEI9FefWtM6STCb7wwpN8+4dvsLq8FmwIIAMwubJepFpvUm+0aNkO0ZilaAEQ1BtNFpbWmFtYplZTgNq2XSKWRSJu8e4Hlzhz/irTkyMcPTjL2MgA6WQCIdp8WIGxQdNIJBMMD/YRiUS4cv0eP3n9fW7fm1daTUMnn02TTiVoNJssr27QbNrYttKQbBQ2qTeatFo25UqNkaE+TENns1SlUq2TSSdxXI96Q5m6a42mMuU5Ltdu3OeV6Hs802gRD+TXpqHj+z7ZdIpcNs3tewuUK3XVP8fDtAxkkBTcth1cz6cvl8Z2PP70R2/z2jsf4vs+o0N9fPkLzzExOsjy6gbJRIxD+6YZG+pjdmqUiGkSi1ocPjDD+x9e4/e/9SMG+7KsFzZZWS+SSieoVGtkMykmJ0Z4893z/MmfvUMuk6LRaNK0HV56/gnGRoZ61np/Psvc4io/fess9VYTXdfYNzPJrolxCoVN7s+v0GwpzcxmuQoogeDG7Xk2SxWefuwIyUScsZEh1jeK/Oj193nl1ffJ51K8/OJTNJst0l0uBvfnl2g2bWJZi8XlNW7cus/SygaaJlhbK9CXyzA9NcbK6gbvnDmPQFE3TIwNE49HsR2PqYlxrty4z7tnL/PuuStEIyZPHj9AXy5LNB5jYaVIy3FZLZS4t7BEMh5TbgfnrrJWKHHy6D7y2QzlSo1Ktc7qWoHdM6P05XPsmZ3k8P4Zzl+5wSuvvkskYnDz9hyVap1TJw4RX9wM90/P8yiVKzSaLayISbOl/PQaLUcJX7UmlVqdesuhYTt4QZqb5dUN/vSHbyKlZP/eXUpAqjdZ3Sgx3bJpOS6O41Cp1lleK2JGDKr1JkbExLEdlYha15hfXOXajbsYhsHC8jqNZrNnL/jgwjXOX7xBtVansKk0tI7jUqnWQwBZrVbZ3Kxg2w6gElMbhh6sGaVdi1nKby+bSSstqmmiazqlcoVSpcpAX5Zf/NJnGejP02y2mN01QTRqETEtopEI+WyKXeNDJGMRrIiBQJJLJRgb7KNWq3Pn/gJPtFpEt/ryfJLSBhAf0+QV8qN9Qr+tXg1R16tS27RvErz6dJ/ZO5rVHgS0tvStDbiQbeD1cdvcbmeXtq2nDw8YR7+tTZPhJcrxvqO1klIEpNCqHim7fL3DLgU0SJ7K3esGEePdpQ3OfpbyqeVGFN1/S+jm39rhB+q1/dB27IXXNcYKd/YQvcg2eaHE831qtTrrtTplXAxdY2mtwHd++BqXrt5ko1BhfnGF2/cXKJTKPHXqML/8Cy8zNTlOq9WkUCxzf2GZSrWBhkYimWZ8YpRD1+/y/rkrbJarpJJxzl28gSYEF6/f5X/5377J2HA/AvjCS08jhc7e3dMYZoS3T1/k7KVb5DMJxkcHSSQT7J6N88IzJ/nej9/h3//xKyQSMfbsGuWlZ5/AdlyWV9aJx6xAatxkz8wEkYhONKrMM4f2K9Pl3NKqmkyahmkIDN3AMHTqjRaFYolCqcqlq7fQNcG9+WXKlTrFUgXHsTl2eC9PnTrCBxeu883vvsbYUD/XbtwhEjF47PgB0ukUhWKJt09f4NW3z3HiyF76chlG25NZqE1gcnyEfC6NbTuMDPWTCLh0QggsJflsmp///LPcX1jh3/zhD/jBT94jakVYWl1nsD+r8tkJleYkFo3y+IlDXLh6myvX7ynTWD5DoVji5156hheefRLP87i3sEqj2WLv7klisSg5NBCFoG0C1/NZ2yhhOy4bxTLFcpVMPkcu3wdC4/s/fodzl26xvLpBo2VTqtRwPZexkSGOHj7A2YvfYaNYYW2jTDxm0deXpWH7+EJneDBPMhFXfGGxGIZmcujgXq7dnuf1d87x3R+/SzIRY8/0GD/32acZHxum1WqxuFJgdb1ArdZgZbXI0vIaK2tFyhVFvbFZqrBndoKJsSHuvP0hl67cCahBNJZXC6RTCfryGVzPIxGzWF3f5Mz56zjNVqhqryDwfEmt5WBYFrarQHq1ZrO6XqW/31QpdxCsbJSYnvY4cewAjid5470LbFZqpJIx9uyZZnR0mHgiRj6fZfXmPer1JslkPDRHRSImzz55gpWNMpev3cZ2PUZHhpiaHGFwIEfUijDQn+czT5/CtlVQh6ZpjI4Osmt8hCdPHiVqdYgfNE3jSy8/hxWLcff+Aq+9dZYD+6Z54uRhpHRwPJdjh/cwPNjPQF+WVDJOzLL4+tdeZv+eXZTKFZ44eZiRoQF0XePZJ48Ti1q0bIep8WEmJ0ZDDaaS2ySP7Rons38Wf2mdzI05YqUq6VqDvrk1jJqD9vf+bxw7tJfR4QEKxQq2rfwo+/JZsmmlLTx8cC/xRIzTZy+zXigy0J/nmadOkEyn8aXP0UMzxOMWmVSKoaFB+tIJnjh1iPfOXVaaZaGCCXLZFJ7vM9ifY3iwj2wmTSaV4eihvSwsr3Lh8i1aLZtGo0kuk+LxE4cxvv9Ox0gkYM+eGR5b2WB1bYNCcZP1QgnXl5imwdzCCrduz7FeKOJ6PslkPDT3Ck3QqLVYXF5HCMF6scxP3jjD8NAg5QCkVetNao0maSMRaGOV1D/Ql2NkqJ8Ll29x7cY9UqkEq+vFkMoFFEfa2+9fIJdJcmRylnrjCqVyJaS5qTdb2I7LWmGT+aVlGk1lBtQNZdZzA5LeocE+4ok4QtcxTUOZZV0nnD9RK8JmuczcwjKZdEoJo9EIjuvSbLao1pusrBVYL2wGvG8ehq4xMJAnm1dC3o1789QbTUzfx3hUXcaDNFodr+qe8+pBpZuIduvnvVU/1JDGjj5FYVva/+syL4Y0CApq7dCCrbXtrP0K2iY1DXyPdlKlj25nWGnva3ei6QeVoN1Cyp4+qO8CICl1CPLCCumFbVV7Qe+YtzXsnu8HfHSy14+tR6H0yXRdn44Zsd3Hj/u7HYBWr725u9JgQkhF7yDbvwd8z6fZbNFq2giRIha1aDRavPn+ed47exnbcRVtw9QoX977LM8/8xh7Z3cFvFgOc4sr3L6vcsfpukEsGmdoYJBf/spnGRnMc/32HGsbJZ5/+iTPPiU5f/kmi8vrrBXKnDiyl+HhQeXv4fs8dvIQzz99gqWVdbLpGEf27yaXz+F5Hi8+/wR9fTnWCyUiEZM9U6OMDQ8DPj//+ad4+onDNJot0sk4pmHwtZ9/kVPHDhKLWRiGzp/75S+we3pcOQ1LuDe/zLHDe5ndNYHQBL/+S1/g6ceXiccsYtEo9UaLoYE8E2NDSF/Sn8/x9a+9TDyRYHW9gJCSoYE8p44fYGJsmL6+PIaQDA/1kUknyaQSpFOJbYt8aCDP6FAfjusxMtxPLBbd4r6nJvLzT58kEY9y9eotpNTIZdO8+d5ZKrVaT52aJpidmeLrv/gFvvdnb3Dn3iKFcpWXnn+cZx4/RSaV4v7CMo+dOMT5y9e5cXuOowf3cLRcR9wMGOyFAqB9fVmmp8YwTZNSpYGpWxw9uI9arclqoczoyCAAg4N5xkYG0QKw+tzTJ6k3WjRbLbLpFCPDOQYGBtF0k+Ghfr78hc+oDXowq3jLpMHwYD+feeYk2YwiMhjszzM7Pc7E2DCe71Opt5BCmSAjEZOllXXOX7pJvdHE9yX9fRn6+vNMjo/w+RefoFiqcOnqHQqbZfK5NLsmRzh2aDe7pycYHR5gcWmdW/eXmJoYRr8yHy6hZH+OE8f2kc0kA98ujc89/zhjo8OKNgOY2TWOEIJ8LsPo8CDDg/1MToxx6vhB5haWadkOB/ZO05fPEIvFiMVjZDMZavUG84srgcZSPdeBgX6+/rWXWVhaYbNUpS+fIZWMY1mKG0zXTSbHR/ncZx5HSkgnE5imgabppBId0geB8p04cuQAI6MjnL90jfsLizx+8iBjY0MU1pW5a2SonydPHSGRiAXWDsHu6QnyOQXIB/tzITdUNGqxe2SQfsdlsOUh3jqHXFqDehMcF+G4nPr7/5L99QaO63Y48jxPkcz6EjseRdM0ksk4k+PK1OsF3GvSF7Rsm0Q8ymPHD3H04B4q1RoRM8LI8ACGrlNv1pmdmWBkeJBUPEUkYmJFNL78c5/h8VOHKW6WyaaT9OVz7J6ZxHEcctk06XQU3TDwPMWh9/jxQ0zvGiYfmGctK8Lo2HDv2tE1jhycZdf4CPV6HU0IovEEwyODPP/McYSAwYE+RoYHGOjLk0xaxGJRJsaG+dxnHqfeaJJNp9A0g1q9yWB/nkwmhSYkn3nqBMtrBdKpJI7rkozHGRroCzTrEQ4dmKFUrrK2UeTd9y/gOC6jw4MgrqjnBEStCNdvzYVpilbWChQ3y1jRKC3HDT7b4PL1O9xfWKJcqVGtN2i2WsokLX2GBvqIxeP4UrBWKLOxWabVtFkvFkgm4gz257h9f56fvPE+64VN9u3exejIIL7v02i2aLVsStW6Wo9SYrsevoRiqcrS6gb5TArDMIMACRn6bj3oaA0zmIgu5/iu1x5j3UMAV7cmq1Nz+7OdgBMBRtoBTHVfJHqNoW3Lk+zWSnW3O9RybbdQqVb16J+6frddyxWmymEHzZ5QJLshyfYOwHRrGrue0e8CS4pMfOs1chtxadjmEFiGV4b36iZw9v02WbFGhzq9/aOAZ+sTarg+pUTUwUBBMIAP0lZ9dAmRr5CKwj1ErtBxglMXth3adE0jlYwzmUvzW7/2MvV6E9OIMNSXIx4AlUwqSX9flv7+PtKZNFrwwKOxOIf27+aLn32ajUKRx08cIhNwJE2ODTP05TylUgVXQiwWVxxM+2ZoNltkM2kmRoeUtGU7SKmkyaGBPvbvaeJLh6QVQ/guhoDBfIbnnzqBYRiK0BSJ7/n40mdwIIuh67ieF0YkTk+NMz01Fmo6dk9PMDE2jC/B8SVrG0XSySTRqOrjk6eOcPzI/nARHD28l2q1Tj6XQQiB7/sM9OX44ktPU2s0yWeSxKLKid7QdeKxCJah8fMvPcPh/bOBViXbfsi0F2OpXKPZchgdVqBMNwxkD3eJ8vXSNMGp44d47OgBDDNK03a4cfs+pXIN15XKN1NXCy4SMTm4d4ZUIs7NO/fJpFMcO7SfdDKN6ykOon27d7F7eoJkIs746DD9/+IPQ7V19MZ9jvz1f8xBIbABVywRuTxPLJVgxjSZ1DR800C7Noena3ymVoe1MumFDYx4lBHT4M8LAaaO9Gr45Tr+7WUMyyIXtRgydFxNg0IJI2riayai5TBWbzGxb5aBkUES2TS+Jqh7PnXPI5WIE4/H6MtnOXxgN47jks0k8HyX3dPjIATJdIpKtca+Pbv4jV/+AmfPX0NK2Ds7weTEMFbgz9WXz/Cbv/ZFFlaUP5bxjVeDsRYcPHmQqeP7sSImy6sFbNvj0P5Z9uyexrKUtnRibIj+fJZMOkkiEcMMUielkgkG+3OsBQmNK9U6kUiEeCxGPBZjs1Tm1t15ytUGtuMRjWloOqRTSZqtFp7vK42fZeFLH9AxDZN4LBqSSybiMUUy3HDQNK1XaBWCaNQilYqTSafIVdPkcxl0TcO2HRzHRU8qp2fTMFTydMeDtQKJuWWs+RVimyVixQra8hrRhVUGy1WsgLdJOfV7CFf5pOGDMbdEKjBli3be0GZL7S23F4i+/1tgmUjLwo8YREwdGYmgWxGklCQcB02oNintbCcrgBCCiJQkA588GdjVlWnMYBiJ47qAIGIaPUzy7WNNd1xO1eo8LtS+piIZPaSUaLqBVir36CjilkF8IAfkw71xsD+L0EfRgvRIApidnkBKH893iEUtMpkkIIhGohiGChixIhEsU6c/k6Dvy59ls1Sh1rSp1BocO7Kb/r4s+VyGlm1z6MAsu6cnAm2Ujud6tBottD9+HYmH0DS+9vMvsLJWwDQMYjGLob4BJidGcX2XL738PPfuzRGNRkmlEmTSMaYnh4nFlX+gYztYVoRsVvGFOY7D/YVlPF9ixaIsr29wdCDLyy8+hdAEZy9c4/S5y0Qti2ceP47vS1otxWZfqze5eec+hq4r8OxLTp+7gmHonDp2iMXlVQ66LlJrZ7ntBVm98fb0Hrjtw3qb79GDot62Aq32qSfD5yq6D7rwN0L5Rm1VLoVaFwW0Hurr1WNGbJcHeaptrWdLf3YyJ+7kHhScR1I8JOKzfY3sjMfDQNT2ejw6lA+y96pgULvB1TZIJlWwkPKfpqtfHWzT6Xe7jx/Pw+9nA1tbZ1LXYH5y82YwMmGeMLbYm7vwrNRAakGmdwszl+brX3sZz/MxdJNoxMIyNHzfVw7AQiMStXrygGmGzp49M1ixGGvrG4wNDwahwMofwjQMBvrzSMCVAisaIRGP4gbf6UGW+LYjdjvprmVFcBzl7NxOzxExdCKGHtjj1aQSAnShITQRpgkwg1Ql7Q1WSUsiCINX2RdN02BkaCBUf/qBJGR0Za0P2dE1LVSNSinJphP05zMk4iopcbNlg1QT2vdVBFMiHlN5j30RWnER4HuSGzfn8TzJ0MAAESOKL7dOIxXR5jgevuuiayYaDk6jQaPRwrZdHNsL2mwG9nAX0zQYHuqnL58lnUoQi8aRUqD5glw2zd7ZKYyA0FATGtJuZ8cNtCSahi4EZjAmolJHq7dAoDiqAzO0Ln36PQ8B6O20SoAeTjMZLsruVDqh06RQm2TEl+SlxNC70q8giUpJLjhkpWmAaSBME2no+IaOb2j4hk7T9yk3WsTSCVLZNEcF7HY8jFiUZNlGXLqHLXwarocNJDWN4ZZDoV5Hc1QgiPA8Rv/2/4L0VVRatFpnV+DIHomYCCFI+T55xwnIHYMchZqGriuKipzjMus4SF+BAtNUUXJISbJl87nNMqZpkP697xExTYQA0/UYajTpc10iEQVCHMchallEoxYxKcl3mZQ8VyWn1nVd1e108hQ2Gk0WFldZ3yiSzWRIJ9PopTrRO8tMXr/PoCdJnb2BtbGJWNlAFDbBb0uhSopWz0iiuS5C18KN1QuEl6jsEgYjJh7g6+pZaBGT2MJqINhJROAXFugD1LxQEwxpGCri2DTRo5bKqKDrar8KwJYmpaJ88X3VRt9Xe45pgATDC56doSgn2o6OaqOX+K6H5boqqtj1EJpADyTuMG9rMCn1zQrZ//zvdbbH7lUoCNvUlpXasquUPoN+J+y/nWi4+1CJA4NBTk8/AAVh/kYpmQhyPOpByinf93EdF63LSf7A3hlmd02AQGVEiMURQqPeanLowG7GhhRAzOcyZANfWcfzMS2LZCLOFz/3LKPDgximAOkxOz3Gr//SF2k2bcbHhpmZ2UUuk2R8bJg90x9wb36JwwdmFRGr4zA1PsKTJw4RiSgBwLIiHNo/y8hQiVbL4e79RVqtFpousCwLo30oa3rI5Rgu+uBlWx7N9mB3zZrwHAs0IzsCrh2AFg+4NvhF4Bu15d5hGx4RaG3/4QM+bie/7m3DI5edzKmPooTZotna+RdbJzuAF+TrDvIvSm2bRktVL9tpvXsqCJbh9jvKoLruJyTaGq5HRzo/A9jaWeXYaaD8OO1QP+mpu+tDueUzCO2umuxsJMoHIq3MjFJDl2oI2wDIF51DsV0HKIfmibFhhgb6MHUV/t52umv7hanEuyA1jWjUCtWNYbLWrkkkpUoRITQjWG4dENp9f6DHEbKdJECpMQV+OAE6v3faoAnChL69auv2XA2u2QIYhFDA0DSUVK0OYEUS253vrc0rEuLboH/Vep3L128z2J9jZmqcTCYVHNo9vcKVAsfz8RwX09CwdYPCpnJmdVyXetPGdX0iZtBXX0PXDNLJBFqQP07KYKMSKq1NNpMO54RE4u+bgUwS2WxCJILcPw2uh3QchK1MRngewvXAcZGOG25UvXQA3XqC9jh2Uoy0L+mZgYKAUkILDyplOmlvxAE+dT2E5yNaTviMlVZWorsuluthrCjJ35KShOxKCSXA8iUxT4XbO65L2vOZ0jR01wsboW2UVHqmZouYq6IjI5WOqVaXEnwf3e/4ImhakKMSMD0fzQ80J0Kga3oYEaXbDvF6Q3FOIRBG8Kw9D72l8hgahuKnko6HFmlBJIIuFA1CG6RKzwNfoukaWvd8sV30b/4ZA9fvMLpepK/lkPxn30C4LknHZaxloxs60YBGpZdlu/3sOh/I/hxyqA97eACnP8d9z+FyqczX/u13sTwfEY3gHdqDbTs0AzqHhASWN9QmDaBrXVoKOgun3W9HIloOoq6cwTUpkYYOUQsRtSBiIiwTohYyiF5F0xTwlir3IYCm60g9gHRSRUx7nofnKhOJFjHBMEJwLwG/fdAvroLvI6REFMs7HqRb56966YyhH7hkiABAbJ3fqm+oe4T7XXvf6dTTvofmS8wtvjZSSiq1GrqmkUzEkdKDIN+saRrkshlSiTiZTBLLskI9gosgGo0yPNSv6ByCeXdw/wx7d0/TarloAb2NoQumZybpz+dYLxTJZzMkgzRmLzz7OI+dPKLAVjyG57kcP7qParVKuVJjbb1Is9VioD9H7t3L6OXalkHYomWR7ekQOIM/zIwXbp5d82dHM1fX8d79/VZz3w6lDdUe6bANgcdHAKYejVV7b3vAb7aaRndoSXhFkA5nZ0DYvY57Qd5OTvcSDdEmLg3bKwKtU/u5eSC3GoUfAFSD30jph33qgR6yfaJ2g+mPVz4dB/mdbv0oCLYboPR80c5B1A5vN1Ruo3b3pUTDB+mrZNQBrhOFMtZf/p1eE3JQe8fO3vVN16BGpCTZpbnYuV0EZIJbv+nuq+xYPrc1ZLsaVezwux7beniheh8qmUSHM4TwfW+7tmp0O+97WXJ7DrDuOgJNkHbpFsLz8DWNQqHE6nqKJ08dYc/sFIP9ObQtiaml6ExUX4LveziOy73FFRoth2Qqge16OD4BqCQwZZqIQL3khzVIEBKh++hCadba7XK+/kWsP3sDUdhE9mex/+HfCCR5Qp8ATWhIoYV7jLQdfMfGa7YUGHNchKvAmbRdcBVI0zwPzXYRno+0HaVFc1yE7SBdF9myFZByXHAcaAM810HaDtiuenUcNFcBPgX6HHA8hG1jojSRQtOChOMdjrjuQ1JpT0UIgDVdQ5Rr4PnIiInflwVfgTcdwujLXoFUAS58H18IhK7hi7YJy1cRO8FK9oP91ZcSz3aQMQtHSpxUAiNqAQLpugjXRWg6QtfA9dB8H800wdCRgTZWBpodN8gJaho6WiSCvryqutdsMfxP/hVDWw5ugRJSI3LLgabrSMtERkwFsE0DIibSUn8bAfikUEY4Dn2lCk+0bJzBPkzLQgzkqP3z/y6IbKygazrm5VvE/5v/CVEsI/MZ7H/0N7EH8vjzK8i5JcTSGsbKBsbyOlpB0ThoQmmyZACcu+ecRAEUw3bQWzZeNo0/MoA3MYz56vuYroe0IohAC93G6kJXOQd1TfEg6ZoGuTS1f/y3cAO/Hi2oP/7X/wGiWOkSAjqHU3ttyy5zSVumk10gqZPeTIaN6N4j2r+QUoE9P+in0qAQ7leiXZcMxMrVglorUjL4f/8HSousaSoJfSDkRKQkFdSnzK9az5FhBv/CRRB0TAdMCTF6I980IBP86y79QH8PKJfkA/AQCpe+VGuw1ug8DDWC7AxMZGjS6wECon0q7+DcHWhNH1qCa3rOjuAsEEH9as/vAmAi2DE+SqtFGyC2u/Sgvokd2il2vnYr0JJy22UPBFrdbW9riujMu26cGjYh+KJNmhtK4j3taO8cgeAk/OC86B0buXUsQSkcfH8bz1ZbyJP4IPSgLx/xLHconxLY2vKQw0FUPhwPLT0TpDPwAkAawcTdMnm7xrq3IRLWN7uOmW40293SrgdKIFl0CxRbWvTgsgWcBW/1+RXYwjfziUvXQ98OGHe6vPfQ2ql8bEzeaIIEDcnu+Rb/x1qRyHtX0aIR5IFZNeeLnTQdWrFE+q/+1z0bsBCCp1s2h2t1kJCc/ybxf/fdUGOogwI6d+ZVB7X2P6VlkEJDtk0YwUGHpiFqDTUZKg24MY+0TLSo8rcRloVvqUOZQFMkrIjSGMRjPZq88FDSNHVgiA4I7B5XdW0ngXZn3ANtRGAKkhJU8my1NnVDhAd0u/iuAmTCcfEcB992oOUEgEyBPt928Fs2flOBPVwX31URM5rjIOIW7s89Gx76yqwM0u/4AnU2Dh8XHx8R+hcBgRlakvuv/gnaZoX2wS1QWl/LioS0JKZhYAT/dMOgfdqH5usQ9Ldzl4ogb6raAHc6b0S92XVgAYl4aH7F0JGGod4bhop4op2KK5jrnhdqmbohpnBd9HIV03HwU8nO50IQCxJB67qG9dpppUECZMREbpQozE5wKx5hJZegGtGI7B5j355pdg32EyuWMVbWYWkDsbiKtrSGvrSKCNJwtQ/G9ngYpaoyTV67g3Z3QYHu4EKp6xAxwDSRuRSuVKm4IhFTAVdNQyJCLXY7Qqr+j/8fPXNSSF8lbG4fWpoeOCMrSBBwLeNJFbnafu6ubSM9N2DL18I6tS5zoUS5ULT9uNuYIv23/hFaQOwrEKFrRvgsXQ/rw2udce965lu5uneQ1nvf6jpMjj7sio8uW4+pR6jjwd8He1uXFktAT8B8OD+D7x/5HlJ2fPQ0RTuz/Ueiy1fr0UuHpmHbTbfX1SY/lTsAswf0Z+eYxi1Wgm337dyre1137tv9PuzIzlix/V2A1B4AEwOY3N0dNebtf21ydCllkDlCD8+FDlj4eGP/6ZGatkv7/t2DsQ19PlpFO4XDAj2jJ30fcumOik/2DuLW5yu3vJE9SP7Bbfxo9pCu3y6ubiOx/VlKexk8rAXdUpofLFJdE9sH4BHuxbZ7taesQANivgTbQfg+/sbm9gntS7TCZpfkHBxwQhANLtTqDUSj2Xsnx1X+ODs1SspAOn+Ak+Vqkejf+IfB2d8j4qti6Ap0RQxkJIIMXjENpBVR2hHTQEQjCEuZgohElKN0+++Iqa6LmAjTUHUFGhVh6mimgWcaeJqGp+vokShmxAw2kQ5Tc2h2MQIA4SvVtdY1d7sjY7ql4tCHrw0G2d7VECn1DIPSIhjCCEWXbodRXRfopSpasdRugDIzuR6m9NH9gAy2WseIRLaYw7ukw21rSPkb+a6L4TjouoYwTaVla4PqdoOiEYha+Mf29XSlDSk6gFLdJ5SkRTArRFdLpMR3XGqOQ8Xz0GJRYvk0ei4d3LajAdUXVxSwAzANWvNLnI0ZLCyuqtQv1XrAvB9hcmwYffck7J7ER4TLXEoJjSba0hra4hr68jpaAMS0pVUoVbcPl66rP20XbBd7YojVcoWa4zCm60TMzvUiAFwyFPk7zw8p0UUH3EJbTpE7AIzO4aULdWHT8XFcT3FnaW1/t+CwCuqNaFqodW9rXrTNMlqhxLbi76DZYcfjvGs8RPfDVn/3aDQ1ZH92xxrktj+2b3vhLXo+eEAJInh3LG1htv3WlyrIp6faj3fWPWhcpNy+mna88IFA5kFl6479UOm9+03vlTucz23ah25Xf/EIWrfe+h4wfjtqseQD8MXOMGt7tGPn1gpwqYCaUAiXkmw2TSqVUi4/otOvj1s+dbDVHuSOxNKR8D4acHVQrgxFhIdIBP/F/wujWOqqtvfaj3q+svv/2RTN//5v7Hwf0YvXZdfn7U1eZedTB0D8P/1vERuboAn8bLrrh23gAdsflgwvaUsePsq/Q8pA4xL4SqjDohNA0GzZlErlAGBpbBRKaLrGyFAf8Vis13TaPpgeNiqyc70QoF25Da5De6sWAdu4jJjIvmznp6sFJCizVhss+Mq3SAuCBLp9Q9p+MeH7Zgsxr3djO6QfMKoLwjp23CYMDSG2LoKuK12lFaIeAFe57YquOsVHr6X2c+q5owICnufh+T6GaaJFLYgYeL/0eeSvfVFF1WxVmXePidw6hztj1G3+6T5Ye5oVSoNu77pQP0AT3YYPn05YuN+zZt1MMgybj8eiCCGwm01cTcdIJxGGHrimiK612jtAqs0q8MNptmg2m5imjhGPIQfzqt+Lq4iFVaUJHhlATo7g/eu/T9tkooQHZdL0PQ/P75AO+oGfGdDRqnQ1pd5o8u0/eYW33jvPV77wHD//+edIJuJoSLxQ2gZtYaWj2TIMKjfucdsUTI4Pc2j/bqQvmV9cIZ1JYlmRjh+klJ3cqwL8mIWcGYfZCTyh4Xa3qVpHW1oj+jf+e7SNTaShIx87iLg5p/yhNMFP/ouv84PXTjMzNcFvf/cttCt3EEtrJP/K3w4OMUKg0zvSsmdN9wLw7euh++dS+sQ8H9/zUL58WqiF2Pb7LeeXfvGG0rYaOs6RveGVTj4DUkUC2o6DZVlELbMz7+l4KXXPYyl9lb2j3sCyIiQTMfRSDSF9ZH8e99/+o/Yvwna1p/u2AzQA0t3rR2gSITpO/g8tDzurOiaR4O1OnEwfXZfQRGBx3P69FgQdaO1cY/Su987O0/7nE6avk52IygeWbVxWO1zf1hB9FOdVT7VC3f9htw9NiO3SLeJLdhqPB9UTcnw+EF90zoQQmwT9anNtea5DvWHTaLZwHCfIHCODeeUz0J8nl8sodwngwUmuH14+NVLT3k2gC2B1v3Z+sK2Ozjp+hIEWAqRAbJaRG5sddtjg572bzcMa3j7E1Jvof/k/QGAK2Nq2B4lkO7Vbu3obOZjHz6Wp/+7fCcBjlzNql4q+U7VQ6WF8iRuEjTuOo1iVNQ0rFg8S5moIlF+OrmtUqjW+/6O3uHnnHp9/4SnGRof4/W/8KXfnlviLv/YlTh7bj6WrAwKhhSpdAsCm9ahaA6Dla70aW00iNDVpfQTGX/rbiI1NxGqhZzT8AzPBxt8FKtsTe5uo2Qu0gMAvL2hMPoM/0s9qs8W/HMvz/KlD7BkfIa6b6I6n/KAcV/lVOb7yjbKd0K8Kx1avtg220/nXsntfvd6F8yCgtW1rEF1Pv+fwaof2A9JHtFqIm/cU+P7ua+0f9hQtBFFh7eGNOrKeRL9yGwBv/8z2RnX9tNu89OA+9K5VCRiXbijTZczizv/8X/FnP30H6bl89YufwTRNPrx6g1K5wgvPPs5Am5gWgScjKNCltI9a14bp+z6VhsP83DzLK2uMDPUzPTWuQKgQ+D94G+N//Fdod+aDCMW2qTXYtNWEVBu+1NQakj6aUH42bWoHHz/UVmmaIgyNx6KKmkTXAgqEXjOXOqilAnumDoaB50vq9xaxd48xMTbM6PAAQihiTU3TVIRnV1BLB9+LTgC9UPk4lQgWjG8qjkztgnxG7QX9Wdx//DeI/MpfB9uhmU+zsFrg8IFZnn3iJNYrpxGepwI8NjZDMNXZrzpPcus02AlI9BzTW35v+EE+OLoDarau1/ZLG+TIwN/RwzcNNn/376KhwKeK/nZ4/+wl3jl9gVNH9/PCs48RDWhmBDqeMnpiCFApUoXay378Bt975U2OHdrDL375JWZ/539EK5QIKKyhZ94q394dLSBtYU3K4LjoClMKBKWPpw1q17v1XupG3cFS2659FAsNvYIVoHz62oJYsH93gEo3CJYgvM4hKtRaeRDi6aVoeAB9QZcGdcfvdjrDw/N+55/t2BZ2Ovfltmt2utfW7fKhNwn/UKBMChEK3CqjQS3MxGDbKi9qe2VFLQvDNLYN+8ctn5pmqzPhtzwkscPfD5jkWwSnRy+aQAaq3zZY0YTK46ceSu8xI7rbWyyHam9Rqihn686liJ3PXlXLtrkVgAzX7arD23FspATpK8lGD/xo0HR83wkc9EAEkY9mxEQz2uH1LiCxgjG8P7/M+cs3SCaiDA/2E7MsDD2CJjomAaFJtJ4jUE1WTXQ5ZEsCIKaDHjg29/j7tMeiazRdV4GIrhIeZMiQSiGEk8Gh1KNpQ9UpAVqtjq+b7eBXalQbDS5N9fOZxw5i7N6FNE0gYJ/XDDzMjqQaqKvVGSqRwkV2SWWa1qEEkAEdiOt6aJ6P5nohiKNlQ8tGOg6y2UKzHUzfC8Cbg99yoGmDbSNsGxn8LVs2rUqdRqlCVAhFpLmyDm7gU7RlrHpmjdw+j2S34ACqPtjR1NpW3ocCBOxI8Neege153b02hOOqtroelVqdG7fu05dNoWsapqmxsVFgeW0T21b5CQ1dDzSris5D+h6WqYeaMM/zKJUqKoVSrUY+l6G/LxcwmEvWNgqsbm6yp94gLqW6f9Ac1/Go1xu4rks7cs33ZUAI6xCNKk1JuVKlUCypZOzBdfFYlIH+PNGAPkCRcJrouvJJcnxJrdGgVm9g1hrEKjUljCRjOK6DWFVJtDOpZJjw2vN9Mulk6Dvl+35IgpmIRRUAC6aa7/vUGjYt20bXNBKJgLW9M+XVeG9sIls2juOyGqSj2T0zSX9fTrWnfZ2m4ecyCnb6Pp6rDlZd08P2hDjCl8onSyoaj7ZmpP3ce5x/A4AQuiAEh1Bb09q9TctAu0jgoiA0De4vde0ngWY5oIMwAhPpylqB1Y0ijusSF1Ha3OKdmdlrFtZ1XeVNdL1gnrVZwgM6l3DTVZ+1fRPbQ9C9tYgg7Y0AfHyUp9gnzzv4oNKTXmfrmvtIa06w73affF2a7gdqDHo0AFs2Ctnu66MUidgCuELteM+59YlO5kcq3WdG9/tPrXLND9x6upQj4WRRAUe6ruaF63phfs92O9qBST9ryz59ny3YckI8BCE/sDwY5Yafdptechncf/n3aLkec4urXL5yg75sikP7Z8nnMkhNaYQ8QPoemq/SQPieh/6X/+sAMHQvf7CTMSRqQzMD0NbuUuDOEIZOawHHDoUSdGlnHMfh4pWbCCEY7M9jGga1RkMRCqayCATziyssLS2Rz+cYHBzg6vXbGIbG3tlpolYM09SoN5tcvnGT5dUNolaE0ZEBRocHMA2DQrHE+sYmUWsATdOwHSc4oNQEafPg1CoNVjc2cX2IxSyy6RTpRDRcy57vUyo1iJgm8Xg0AIMKkLTsFo7bQtN1konYtkkjhaCVjIUs221zphYkVg7ENnVtl8SkhUg2kFkrNaU+V1+GBK8qR2AQLk+gPdEEzWYT265jGCaxRALNUPV6nsvGRpGltVVsV0XBgWR8dJjBgXxIbLi0ssaNu4s0mi12TY4xvWuMeJB6qNm0WVhc4dbqGpqmcXD3JFMToximgRb4M0HHvOW4HtVanc3NMkKofH/m+xfR//G/Ipw4moCAJFadH344r9parfDg8VUaKkFgPt2s9GzAXialDo22ljQE6Mr85gdpJxBqnB3XDROrWxETKxJRQp5PqNHQ7y2Gz3SzVGFto4iuoZJcey1u3JqjXFO5HlOpGOmUxr25Jd4+fZXiZhlTF0xNjPDckyeJx6Lcn1/izXfOcvveAvlsmqceP0o8FgPgxu17/PDHb7N44Tr/12KJiOdhOG4wTIKNQpFX3zxLoVhiZHiAXZOjNJs2t+7cI5tNsH/vNJVKjYtXblKtN2i1bNbWN3Fcl6mJYX7us8+QGFb3Mk2DWMCv12zZ3Lg7z5Xrd6lUawytFOhvNBW5bz6LV28QrTVImwYxU2d1ZZV3P7gEms5zT51gdHgAKWFtvcjpc5coblZ45oljzOwaR9M0HMfjxu153v/gIpvlMoMDOZ576gRT4yPoXcINgHN3EbvRpNFosWTqFDcrvP/BJQw9QpYOTbDMpSn/7t9haa3A+YvXuHt/UbHAjw5x6OAehof6kNJjYWmZDy/cYGlpA19KBgfyHDu0j6nJUQzDYG5hiUtXboZA2fdcRgb7GBrso9Wy2SiWsCIRBvpyivg2MK1W63XuL6xy+94i1UqVsbEhjh7ez/TP/dWQL00FaClByvMUmInHY8SiFpHAH045HSuHC+Wkr4Wa/nYxApoH0zAUF9y1O2j1BnJxFeMv/M0QALQ1/G0TthrXLWdFF1DRe8znW7R/vbI4MpdG/tP/dnt9W+oOVm4g3HXV+wgAq5suRoVv+ztq28KaNH0HgBjspVsj4zShwsB3NO937v9x026HtT1C/x5y44+u/xFK6Mu6Y5u2gsNAO64mjNJ0AgiJrglMM0ImnaTVcllcK4bjKQJy9l6N3Sfv+6ev2epCyV06kW3vHlQ6V37U9W1JQt17cXWDucVVVtY2uHV3gfpAjsnxYVLJBE2nya27i9y8ex+72SKXTnLk4B7GRga21S2RNOMx/uCXX+Te/DKWGeHkob2cPLKPbCbFvbllPrx8XR1GhU3KlToTY0O89Jmn2Pt3/ykUimGbarUG/+zffItEPMoXP/sM0YjFOx9cYGZymBeeeZJEPMaHl67xxjsf8PKLT5PP53jnzAXuzi/zm7/y85w8sp96vcmfvvIG3/qzN7FME9txOHl0H7/xSz9HOp2i3mgGvjWOYnuu1VlZK2DbDtdvzTHQnyOTTPKDV97g7KVbOK6KtHjy5EF+7RdeIpVMUG80eevd87z1/kXy2TQvPnOKmV0TCGB5dZ2zF69w8eoNHNfjqVOH+bLnBvnD1L9GPMr/9NxRrt+8TyIRI2KazO4a4/mnTwYM04J6vcGHl26wtLIeSLEwMzXG7NQoxcImd+YW8T64xvNrBRJxC9NTYbie5+HYNp6jNCdIgevBtZu3OXvhGgIdX0r2ze7i5LGDJJMJypUKP/jxG7z2zhnaCkjH9Th6cDdf/PzzzEyP02w1eP/sBf7s1dOsFUrsmRnn1375CxzcN4Pv+1y4fJNv/emr3J9bJmIaPHZsH3/h177C4EAfoABBW9Jvtmzu3Jvn4pVbFDdLpOJRDh3Yzf5ao0MVIgTkMjT/+d9hcWmNu/cXKG5WGBsZ4MCBvViRCKvrBW7fnWejsMlmucL84iqD/TmefuIYB//u/xpujc1EjG/86os0mi2OHdpNNGrxzumLWJbJY8cPMtjfx725RU6fu8z01Ch9+Qw/ef0Md+eWGB8Z4LmnjjOza5zVtQJ37y+TTCQYGxnk4K/8tTBSrtlo0my2aDRtSpUqpXKJe3PLVOs2Fy/fYrBPCQ/vf3CJP/ijV3Acl1QyzlOPHebkkf2A5PK129y4PUckYuJ5Ps1GEyEUkPvRa+9x6dptBgbzHc2e4wRAW8OMGCytrPLqW2c5fmQvmXSSldV1Ll+/zYmje2m1bD68eJ35xRVOHN1PMhHnldfeZWO1xLFDe0gkYgjRAZ26oebJtZt3+e4rb1Ku1sll00QuXqfeaKrov7Eh5K37gCTVsjEMndJalctXb5NKp3ACMIjvUi5XWF7d4Oz5a5TKVX7r179CNp2hWq3x1rvn+PDSDQb7c8SiFp7nbyMXRUoW3v2QZKWGYeiMnDrE4yf38f0fvc33f/QWh1t2uDk3WzZvv/sBV27c5e79JQ4f3EMmk2Zto8i9+4sM9OVYWlnn+6+8xdLKOrO7pjBNg8tXb7G4tMrPv/w8lhXhW9/5Me+cvkAiESOTTlKpVIlGTCbHh3Fdl5W1AhHTZN/uKZ5+/CijI0OUK1XOnr/K6fPXKJbK1OtNIqZJqVxjl+yQQwrA9VyWl1dZ39gkl0lRLleJWibpVFJpQQMT++rGJgvLBZpNl/5cismxYRLxGG0fHCAU2traVgC5WqDVUtxokUgEwzA6Y7p1Fw/2YGV16+Vj79bYt/1At51BDwIUWyw1IeD6hGlcwuhANNo8b70wQWzTPIUXte/na0p706PhEh+JCx6s3ZMPeOVnA1o9t9ihno+rbWz7a20DXXJ7/W2gJJTgDb5yjZHKAhaPWcTjSgOuLCC9SqJPo9ePDLa0NsP5A4raRHonoiqy+woepdnd0Gyn4d8aUWDbDt/54Ru8ffqCykNn6mRTcSKmyfpGke/9+G3efP88pqERj0ap1Zuc/vAKX/viCxzfcj/pSyq1Om++d57iZplm0+bchevU6g0+++xjvHv2Iv/+2z9icmyII/tnuXFnnvc+vKLS6fg+Wpdt0fclSysbDPTlyKRUglrbtrFMDV34SOlTKG7SaLXIJGMqC/1IHz996wyXrt7i1NEDXL91j2/94HVu319manyIjWKZDz68yueePUU+pwhcPc9XiYjrDZZXN9gsVTEMnUajSctu0WiaXL5+l7dPX6QvnyERjzE3t0S93iSRTPDaW+f49vdfJx6zuHbrHuVKld/+c18ll06wtrrGB+ev0LJtTNPg7v1FXM8LJ44EqtUaZz68wt4gH9zps5c4e+Eqiyvr/Paf/xoTowNcuHiNf/5736ZUrpFJJ/B9ydOPHSafjnPm/BV+9NppTrk+z8ogrF+0TZng+RLfc8H38FzBhxev88OfvsfsrjFmd43xwfnr/N43v4emaTz95HHqzSZzC8vcW1jmqZOHmN01xtunL/Lq22fp78syMtyP6zmsrG3Q35dmZnqMi1ducf7iNYaH+gDBmXOXeffMBY4d2sPxQ7tD01j3/BMorc+N2/d5/a0zRCImu3eNsbKyzrtnLjLoQqK9FAKz2k9ff58f/OQdlVy4ZeP7kl//pS/w5GPHuHHrHt/8k1eo1utMTgxTKleZX1rGskz2uG7IO+R5Hu+eucid+0sYhsbs9ASvv3MOKxJhZmqM/nyW9UKR9z64TDqtgFQkYjK3sEo6GScej7JR3OSVn77DvfkVHjt+GE3TOCD9ENBZZkdDGTFNkokE0WgEoensmhwlEY+hCwMrEsVzPUYG+3j+6RMcPjBLJhkHJH2ZJCeO7FOM/0iG+jJENKg6DtWaSpBumgZONoXetEOgJxFkM2lOHtvP+cs3Wd/YpNFQJtS+XIahwT50XWdto4imaQwP9TPYn2fw0nWaTZuZXeOhBk2Cyp7gS4rFMu+fvcj6RpF9e2cZGe5n153FkDDVGxuEW/cBSFTqIf2BMr2pw0zHxxcwPtzPM48fY3WtwK3b96lVKvRnM0jXoVyuUK3XmUmMcWjfLEP9OUKH80DjbDsuK2evEnU9kokYsaN7Se+Z5vbdJb7/o7epNZrEg/a7rseHF6/zwflrPH7yEF/+/LMYEYtytYYI0hqd/fAKN2/P8dTjR/js80+Hflfvf3CRe3OLHD6wB8M0KAepoV564QnmF1b47g9eY2l1gxeePsGRg3s4c+4KZy9eY3JihIH+PJeu3uSnb54mkUjwzBPHKBZLfHD+Ku+cPs+f99R8UYBG4+r1O7z6xvvkMikO7pvhzv0FWi1HacMDLdnSyjrf+JMf8+Hl2ziOy8TIAF/6/PM8cfJwsK7U/DMNvRcECUE9EWOxVMHzPKK+JBYTpJLKTKwHgTO+51OrN6g3GgihMovEY1F0XaNl21SqDRot5ZcjhEp4n02nFBgulD4aTHyK5sdOlSIQQLtVDR3z0NYAiFA7o1TZAbbQA02cD/7PaPKS2998ar1+GIiV8mOO7w6gCsk2h/6uazpDHABwoawLmibCTCChaT5ABm0t2M9aPpZmq4frpasxQEdy686LGEhwvTD80cqDru7mfmlfVW82eePdc1y+fg/X8xgdzGNFTJKJOK++/QH/4U9+wszkCL/21ZcYHx3i/XOX+db3XqfRaLGv2SIemNuQKvKt3Ghw9/4ipmnQaNpslqosLq3QbLYoV2oUSxU+//xjfOXzz2LoGjfvznP1prq3FbZfpezZNTFEudrEtm1832d4MMeuiRGScaWlqtXrgCTgZiSViAJKvWmYBgvLa2xslDm0ZxeHD8xSqVZIJmIkE1EMAclElFjUQkpJLGrRl89gWSa6pjE1McLo8ACWHgkdfY8dmOVXf+ElUsk46XSSeq3O8uo6e2fHefqxY3zjOz9mbX0d321hmilcr0WpVGZosJ/nnzpOLptG/8kH4egLlAkym47zhRdOcfjgHvbPjvLP/s23OX/pOrdv3yWdsLi/sEShWGJsZIjZ6XHiMYtjB2bJZ9JYERPT1Dk4OkLq5iKGrinHYBkG1gcbC6yuF/n+j9/Fipi8/OJT9OWUw/G7H1zi/8vcf4dblp3pfdhv7XT2yfHmWLfqVu6q6lDd6IQGuoEBBsAMZobUcMRMk0NKtiWZlmSLfEiLlKjwWBT5iKYtWbSYTHFIkRORBhnonLu6K+d0czo57bT8x9r7hHtvdQLIx+vp23XO2XntFd71ft/3ftdu3uGRM8fRdY14wiKViHPi6AJPnz1NuVpnbXMH13UhcEjETB45dYSZ7Qqrm2WCkInsdh0MXccQoddD4HP8yDyLB2fJZeMorSg1CQRSUqnWOX/xOu1Ol8cfPs6xxTm2d6q89vYH7FxbZjpabApBp9vlx6+8wzsfXGNsJA8SytU6K6vr+K6D9F0ct0s2neDJR0/Q6jq8fe4ym9uKqYyHg6yua6TTSTodB9uOEQuZo5FiVv3e7dJqd5Rav+ORTic4cmiWd967hK7rmIbB2uomN28vkc2mmZkao5jPDg3oVujjJDTRMwUZuomVspmeHCOdtDF0jUQ8TjIZp5DPcvbh4xxamCVhW7iOQyaV4M7SGlvlKlOjBRKJOIZpkMtmePLsKba2K1y5fod1TbAAEARIXyKFhmGZHFmc5+SxBd55/ypvvKMYmXw2RTadUv6YQLfTodlo0oir5NEzU2MhGNNottq02x1cz8PzfdY3t7h1d4VE3ObsIyeYnBij8NN3eiZG58AU8qdvIaXE3KnQandwXRfPV6K8rusRhH4dtUaTlbVNKtU6VpgEO/ADLMvk2OIBrt1e4sbt+xyamwjfrYcvBWagNP1cx0Ov1npjmjs+ghA6qWQSx1GplaI+ZoV11nVcRkt5SiMFpO+TTsXxpTK5rqxtEgSSudlJivkcmqYxPzvJO+cusbVVxrJMRksF4naM2elxzj58konREm+8eY7ltU0Ozk8rMFWpsbqx3ctYcPvuCts7VU4dX+TYwVnW1rfY3Nzm7tIGfhAtugQbG9v84CevsbVd4amzDzE/Pcb5C1doNlt0Q2Djuy4XLl/n9XfOU6400DUdp9vlxu17nDqxSNy2kagsA7YdU7kno2IaXP2b/xF/++/9Q1LJBI+eOcb95TUajRbPPf0Yzz/7BKZp8O77l/iXv/c97i6tkIjH+cyjJ/nFLzzL5HiJt859wP/2Bz/k/vIG3a6Drmv8yi9+ll//lS9RGClh/In/ZI9LCXu+PWhukkPz/oMkBobOswtYCE2DaG7bbQUTEVMXWXQkfVYsZLGESl/3M5ddptA9z/sxTaWfqHxioLXr2A8rQ/cretYOpMIrEfsZaZppQ1IVu8zNaue92z5G+dhg6yPVaenbgcWgZ2+v7I+QP4zB6u/UR7B90c7w6BDQOV0HQ9co5jOcPHKAifESErh64y6bWxW+9oUnOXJonkTC5uTheX7yyru8d+E6zZZLvP+QeEGA63ocPTTLc089gqlrdDsdFucmSSVMYjEDKX063TZd12VyrISpG9QbzWGhSwGxmBp4f/TyO7zx3gWSiTjFXJJiPkssZtFy2zie2x/EZUC76/ZkAySCSrVBICVnzxznF7/wDK12nZhpkE4lcRyHdDJOIm4rs+bmlhL/lPRyHGYzaYQPuWwqXC0KFuYmicdtJZ5oGvzq118ACffvrtBoNYmZGn7gIUTASDFHsZDlnfNXse0Yv/zlZ9H0AeXksKG6rsfNO0tk0gmKhTRjpRzXby/TaDWp1WsqGa1p8OyTD/PCZ89ix2LELZ1arUGr1VExCp4b+r8JhNTAD32awhyYvgt37q1xd2mNL3z2sTDSTKdYyJFOxak16nhBF4SPFoISQ9eJx9XqNkpRFCDptru0O10uXrnFW+eu4rgesZiFZZpIGXD2keOsbWxz7vw1fvsbP+ZP/vqXKeazgJIf8H3wPI+1zS2WVtdJp5KMj49gx2IkE3FisRjNSpUgCNAk+FLSane5c2+VibEizz15hlRSRZiePHYIPYxeMwwVgadpgmxG5bZULhiRE7MCt9OTI5y/FGN9Y4frN+/TdVwOLcxQrzdZW9/i1p1l5bQeDspxO4ZpanS7XTa2yty6u0yn0+WhE4vMTI1TyGWH+ngkdOr7KrKs0+nSbneJJxTDp+kGgQx6C7BWp4PnB+iaRuB5LC2v8ZNX3uaDK7cxDZPazCjjIwXGRks0Wx0MXeczZ4+DCFi6v6FEWoUAx1E6cUCxkOP0iUXOX77BG+9eYGqixBOPnkBKSTIR59DCLG+9e57X3/6AXC6LbVscXVwgl0njeR7LK+tcv3mPSrVOpVInmYjTanUUkJEBvu8hl9YUU2uZuHOTSmoikDjLG1y+fpfN7QqVWoMAQbXWoNnqcP3GHc5fvslmucba5g6TIwWarRaVao1KtcH01Dif/cwjvPT6u7z+7gWKhRTxuE2t0eLJZpOErxLLj7S7BEFAww+4s1Em0eyyvLKBRITtIDJ79ftvpVqn1mjih0m6pW7geX4vN2un08HzPaQHrVaHVlv52G3vVHDC/JhCCMxYjHjcxjB1pfergeMHvTRGjWYrNCN38X3l55eI2+SyaUaKee4srcOApePmrTvcurPMaDFHPpvGMg3SqQStdod6vYkgoN1uc+vWfdLJBF949jNk0kl0XefowTmS8bjKWOArf7J4KDg7WIxwdTc+VuQXX3iGWqPJ//dffoNvfPdFxkdL5LJpPrhwlc2tHcZKRSq1Otdv3eOx7TJjozkSCeWHls0kKRWniVsm+VyanXKFVCoZ5k+N5qlhZxZJNAH3TZByYL/oh962/VW3Bz4LxZZ8pCkv/BaEoGo/0kpIBsHex3X+308Aey9AHFbq/zcCtKLzftrjhhz5P2p/+oLo4QQW4SdNJ/Q1DmVn2EUuCY2e1kvPH+Dj3/fPxWfrQc6GoucMvT+ztQ94/1hld3qZ6PrJeIzPPXmas2eOUcpnQCoHbylVkt5qrRF2/saQgvagGZHQ+TlAMDM9wcRInma9gS4kXqBWk3bMolyt03W6oZK5wA8np8H7MnSDI4fmeOmN9/nhy29xfHGO2WceJRaLIYXAMAyymRSJeIxGu0vH8SjXGni+Sm4sfZ9EPEan43D73gqe65JOJuh0u5SrSrlZC9PTbGyWOXf+GoZlsr1TJZ1KoGs6umaC9FUC4bBxuF7AzsoGnu8zUsyxXW1w8dJ1bt6+z3a5yuRokWqjRb7VYWKsxG/8yhdJJF7hnQ+uUMilWfSDPa6V2+UaV2/eY2y0QKvVYWunytzsBAsLM+RyKQxD0Gx1eP/CVRYXZjgwN0XgOtxfWWdlfYu1zW3u1Zo4jqvMVb0oKXV+zw3oOh71ept6o0UQ5tnTDR3TNDAN5fQe+EGocyUxDRWZFilj66EkgERw694Kv/utn1JvtJiaHOHGrSW2tytsbO2wvrbJ0tIqX3zuLJomeOm19zkwN8nhhVkCYHVtk07HxTRMVtc22dqu4Lo+XccjEBrlWoP1rTKlRgs/CNBRAKndcdiuNHjk1GGee/pRJkZLxOPKp2dzu8J2uUa93qLretxb2aRUzCmzs2UO+ZF4ns/8zCTZTJJv/+A1XNfjzMlDJOwYFy7fpNlqc+vuCl3H7a2Im6029UYbz4cLl2+wvr5Fq92l2WzRbnfoxOND77PdUYxO13GoVOp0ug6tTpdyvcmNW/eYGCthaMoc1uk6lMs1llfWmZ0axzI0rt68z1vvX6VSazIzOYrnB2ztVKg12ly6dpv/9V9/Gytm4LoeaxELIASy4yClxAcMw+DA3BRT4yW2tiuUClkScZtOV+1zYH6aSq2udOU0jbnpCaanRgFotjo0W23K1Tqb21Xev3iNUjHLoYVpXnvrPN/+3oucOrbI82FQQDAzTreYV0nMpUTbqnJreYOV9U0q9RZ+oHS76s0OP3zpbTzfZ2yshCY0ltY2ub+8jqbpvP72BW7cWeXUiSMszE1w6+59Lly5RaPZ4sKVW8ysbDBtmcSyKUZ8SVlKrna7/OF3XySdTHD+8k0KuQyJcqs3HjmuSyadYnJilFff+oBavdnzLTl4cI7TDx1jYW6aq9fvcOHSLWanZug6Du++f4l6vYlp6DQaLRrNVi/iT4gQUEuVaNrxfdpdj67j0mp32ClXabU7aJqg6zisbmzhui6JuN1TN0f0R04nNM012x1cL6DaaFFvtHo+okJodB2XeqPJ+GiJX/7qC0yMj+J1HSwd0HQazbbS1AsH0MDfO3nquurvCMHk2AjzM5PcDP0cJ8aK/MLnnuDJJx5me6fMt3/wMoEfYISJyVOJOLYdI5/L8NnPnObEkQXSqSSVap2VlTUWfF9lsggvO2jKE4OmpWi+C2QPcPUiqsPvHz3vh/vs57r0kceoK/ZcuaQ/ANw+ieO7ZN9bHTLD/RsAVvveyidktqKJYchfi70mxA8tqv1G/lm9oAUG0+BFew4yiJ/gEgPl55YbMSp97aHQnvxA0jUqkYvlA17qg1D0rp+DUDk9n00jhMDzfHRNOTI7rsv6ZhnHdanU6rz4xgfcvrdKzLYw2n7PARP6wO3d89cJgm/x2SdOYRkaqWQcO5GkUmvQ6qh0GulkAsd1aXccNndqSnkWhl7UzOQoE6NF7i2vk04nmZ4cIxZX9L9h6FiWxfJGme/99C1u3l3l5Tc/oNnqsF2u4Xkehw7McGBuknfOX+F3vv0j5qZHyaSTjI3kSSTitDpd6s02rU6XaqOFZZl0Qn+gi1duMTM9wXgxixEm8y0Vi3gBrG8qX7Ibt+/z9//hb6MJwdkzR0kl46xu7rC8vk0ul+XOvRU0XeeFz54NTVN9Bq8/uAi6jket0WZsfIztrR0cz+PwwVlmZybIJG1OnzjMy29e5sqNu/zhj15lanyE2YkRhFCmtFa7i+OHgEKq5N2epwQso6i6VDLB6EiBbCbFi6+f49SJRU4cX6TddWg021iWgaYJHMej0Wzh+T7tTleFn2+V1cAfDortjkO5UiedTlLIZ+l2b/Hqmx/QcVziMYs33j6PlDA+WiSZiBMEPl4Q8Mrr5/j977xIMmHz0PFDCCFotJqsbWxx8cpNspkUd5fXaXe6FNPJfnSS0EJJgjy376/y3oUbGI/Y2CHTslWusb5VpeN4dF2X9Y1t2u0O95c3KBXyPYYqisKZmR5jYW6Km7dXsG2LR04fJZmI0Wi2yGfTTI6XuLe0zuraNlev3+Gd9y6xtlGmVJRkMynGR/JsbJZ57e2L7FRaHD44y58NAozwvbY6XUxDmRwdP2BktMT01Dhvn7vCb3/jx5hGjEfOHOmZXn0/4P2L15mfnWJ6coyZmSmeevw0SysbjI8WmRgfoVAqKqmEZJyRUp5rt+/RbHV5aGoUfa2sFmeO08s64AGpTJqpqXG2yzUW5qeZGB/Bti3qjRZvv3eBV978IGQrUiytrHP91n2OH1ng4IEZxsdKPP/sWQ7OT+O4Lrpu8KXPP8VIMc9b713i1W/8iKebbcxkHG9iBCefxtY1FfGraQSzE0yOl4hZJu12V7Gjlolumty6v4bj+eSyKe6vbHDh2l0KxSKxuDKZX7t1l7htMTs5wszkKPlchlw2jf/2VboSYl2HZDxG4KfoZpNcu3mPVquDaRp85YtPYy33New0TXBoYYavWyZ/8Icv8tZ7lwAVYHLy+CFSyTinTh6mWqvz7vuX+cf//PfxfZ+dSo2nnjjDl194GssyQwY5YGu7zMb6FmsbW2zt1HA9n67j4vs+XdcL80Y26HYd8rkMpmny1nuX6XZdhCa4fPU2hXxWmZlDc2epmMcwdFbXttgpq+jo7XKVIJBkUkmEpmGaBul0kmu3r3P+whUyyQSmruNLELpakHieh+N4dDpdHNcdHugFGLpGzDSx4zE0Xcf3PWKmQTIRJ2nHKLc7rKyu8/a5S6xtbHN4YVY56BsqIbjneRi6xrHFAywenMP3AxqtNrfuLjHZ6SoJkqGZpf9tP9PgoBSO2NeHqHdwOG/Jvb8Nlo/BGvV8uIKQcdMEECjGRqjPDNzVg87S/yj2/rbbEvXAyL/9HuHB2z+UdetTcx99/k/NrkUAK2SlIsJQRE88QJhE+8BQmrWPL6sxXH5GsBX6aA2WwRVPn6tT3wcpv4EHic41HJy765yoSpY99mhvZbuOx/3ldQq5DJlUAsOwOX38KN/76Tu8d+Eax4/MY8divHnuCs12l6cfP4X95mVotHvn0HWdZCJO3LZ48Y0PuHTtDgdnx3nyUeXjcePOCrVGm+t3Vlhe3+bKjXtUag0a7S4t0yajReq9CikXi3kOH5pldXOHI4sHmJwYxbZjaJrAMlUkUDJhc+veKul0ikQijm7oVOpNAs3gwPwMX/zcY/zz3/0B333xLabGijzz+EkOzk9j6DrLq1usbSifnnanSyGfoVTMcvveKr/1u9+nXKnxx37lC2xtV0kk4iwenKfr+HhSkC/kuLe8zvLqFnHb4ta9FZrNDmubO6ysbfHQ8UVW1rf44NINTNNgp1LjwOzkcMOLGqsmuLu0wTf+8EVc1yeXzfDYw8dIJuJIBA+dPM5zT93nm99/mdffvsCRg3NMjY9g6VrP92g8m8XYqveaTddx8FylhJ5IJLBsm+mZKT779CN863uv8M9++7s8cv0u95fXGBsf4YXnPoNtx2m0NxRr0+7y+nuXWNnY4crNJcq1BvVmqyd/ELctBWx3qpSKOUZKeYoFNYkUClm+/9O3iNsWo6UciwuzNJotrt+6z8Wrd0LNJjh2eJ5MOsnNOyv84KdvKFbpzpIyGadSfV5XEySTcc6cPMy3fvAq3/nha1y9eZdE3OaxM8fJpJNomoZpWrS7LmsbO6yu73B/eYuuo2QgBtvoaKnAwtwklmUyOV7i6KIykU9PT+D7AcXVDa5cv8tLr7/PlRt38T0fy7IYLeVZmJ9ipJinUm3yo5fe5catJXLZ9FBfKhRyPP7oSVKJOPl8jlwuy5NnT1Gu1vE8ieN4tNvKDH3mxCJTE6McPDBNKhlHCI3x0VGef/ZxqrUGyWScTDqFZZkYus787BR/6t/9FW7du8fyyjqPBBrGe9eVxlrXHer2mm6QSCYYGysxHfqWZVIpNXGnkhiGYsfa7Q5bOxXev3iDC1du8hu/9iWOH17gs089SrPV4fV3LyI1jVIpz5eef5qTxw/TeukdMldVtGRnchQjncQq5tDKNXQJxuwUqVSC0ZEiWztlSsU86VSCX/vaC2ztlCkWcrRaHa7euKOc9EeLBFLy7JNtdio1piZGmJ8cI51S0X+droMQSoiVThdd08lmUpz68jP85ukjbGztELdjPHbmOOJ3f9IbJw3DoJDLMj42wuTEKOcvXcfzfA4tzDA5PkpM10jl0vziC08pAH5nCc/3OTA7xfGjB8mkEmxXaoyOFJifnaTRbHF/aZVGs42uK6HWZkv5thmmSTKZAKna3MLcFE8/cZrzl2/x3oXr6JpgbKTAC889jv6Hr6MgMczPTrEwN8XLr7/H73zzR5TyWe7cX8UwDUZHCgghiNk2xw7N8+rbF/mdb/2Ie/dXmZoY5cyJRYqlIs1mi61tFYlbrTWVf+U+ZXNzm2qlSiDh9p0lFhdmmJ+dYmNrh9/67e9wZ2WDk0cPMj5axPE8PM8jQOB4ih1XJqO+Ur7neSytrNNstbGl3Debrwxkb46Nknf3TIi7gcig35R4sLP2HjbnI4mFyKwVkRMhsApBF5oIp9mQJY6O2TOpDtlyhj7vC64+7N56m+XQvw8kxMJp/xPrnO0BWA/ACnuU8cOiaaGe5q4bE8quGIEtidpnUO5hPx/1T1N+DszWrkeWcgAp73NzQ8g++t+HPMQ+QEtK2RNslNE2Kem6Lu9dvIHjeuRzGbpdn2eeeJh6s8Vvf/NH/Ks/+DGmaWDHLH71K8/y5c8/iXXuBtAHW4ahM5kv8X/5S7/Gm+9dwvN8jh6aI5dJ4gVw8vghWl0HAexUGxQKOV549izNVgftyt3hZxOCZDrNE2dPMzk5zvEjCyTT6VDNWsHpz5w9w/TEKIYVY3x+hpW7S7z2xjmOLh4gHo8Ri5l8/SsvMD83Q7PVZnK8xGghq4QUdY1SMceZk4s0mm0Ozk/z0IlFdE2n03GIx22SSQV2JsZKvPDs4yzMTdNotMhm06QzaU6fPMx/+Bf+KOubOxTzWZJJm7v31xgt5YlZJmdOHqFcrXPl+l1mJsd46Pgh9G+/Sq/yUY3RNA3y2TTZTIqJsRK/fvgLHD08H0ZzCdLpFE89cZrVjS0OHZjhybOnyGcz3Lp1m4W5KRYPzXN0q4Z2cyU6KYZhks2mefaph8lkMziuTyIR5+tfeZ6HThzh5dfe5dqt+xw/fIDnnjnLaKlAs9nCisX46pee47lnzpJOJ9GEoFTKU280mZkaodPpMjk+wh//o1+iNFJkfGKMbqeDkJKdaoPVtQ0eP32Ure0yrXaH8dESs9PjGLrB5555lEq1zqtvXSARj7G4MM3UxAjFQpZqtcXFK7ewYyaPnDpK5pX3+4OppvzHvvT8U6xvlPng8g22d6p84bmzTE2MkojbnD55mK7jcPXGPTxPYpkGC/PTFPI5tHubvaal6zqpRJqzj5zk7tI6hw9Oc/jQXC8iy/M84vEYZ04uslOu8ejpo4yOFPjg4nUSCZuJsRGSiTjHjhyk1fYYGynw2Jnj6P/g91TVux7P/v1/wWdCx2bzu28Agq94Hs93uxiGif0Pf4cgCPh8p82zvk8qcQfDeEdNYGGa10yYOUFompoHelSoICcEc66j0gFpel/MNjQRquYlw/QtHSYnRsnksjiumixTyQQPP3SU6ckxEok4mhCsbWzx+lsfcOHKLe4vrXH88AJ2zKJSaypmNBEnYccwDIOjh+bQr9zBSih9OK/bxdgsI0YLmPUWeqtLx7ZJ2DalQo54PEY6lUDTdGamx5meGuuZpw8emEEIQbvdoZ1J8dzTj5HLphXj22nRarbZ2Crz5rsXGfd9dNNEK9cQtSZCCEb+8FW++OYFlZbIDxD/7NtQa/RGRC3sX5oQzM9MMjM13hvzInkDpCSVsDl75hinjx/CcT1M00AATqeDKQRnHjrC0cUDjI2WyGVTSCCdtllaWSOdToKEL7/wNE+dPUXge2RSCabGRzl+5CC//FWXzdCUOD5aYrSUH1xGk89l+NLzn4HA5+qNewS+z1OPnyKbzTA+WsILsyqcPnmYzz+zzmtvXeBHr7zNqWMHOXpwhm7X4fqtu1y7eTfU9nP6loLwOkGgIg03N3f4rX/1Ldpdl67j8utfeJZcNs3te0tcunGPSq2OHbNoNttYlsmteyuMjBdpttqhOb3Jd370OpVqndmpce4vrbG6thUuaD7EiLcLZIloftptyupvVcBLBB8CpD7u5K0PubqICBT0eC4f/DA59eBfxIIN3t5++OsTgqv+bgMgS/ausE8RQ/98zJPvuZcPZbUGQdGgq9EQIxmK9u4+h+iPOdFbVmOZ6An3D9zYJ3wQVX6+oqZRRQSEWU732R6WB8Grj3wMuXtVQcgQjbGxXWa7XOX8lVs8euoYAIm4za985XmeeOwUm5vbdB2HIPAZG8kzOT4yIKwZ3pcQxJptvvSPvsEvSNXBvXeuIaXEMA1Oazq/ESg/B+PuHyCECi33fR9TiCEULMo10n/xb/CYlDwq+2g5uqREkAfyPXoIcoHkaBCgaa8hxG+pZwDGA0K6eIDdE/CLAr4UKkZrm3X8F89xaqfCH3ddivkciR+/g/jJexwIfBzXx/vea/hBgB1TwpZpDf5o712o+wt8X6nw/+NvUtQ1DqOeMbi5iv7mZbRKncEeq9ToBROjRX71a58nk06pSC7PR9N0QCPwlQL3Zx49ydHDB5gcH6XZapNJp3j80ZMUigVKf/hy/0Voyuw2nUvxv//zfwxDVyHuUkrFBj1ykodOHaXb6WLHLJyuw9Z2uedYnsumMQ2jl0XgxNFDdDpduq7SCRIxOHr4APG4TSIZR0vFVXRos8V4KcfczCSnTiziOC6O66pULabB/OwkiwdnuHjlNvlcmq6jkit/7ReeYbQ0qsRzPY+YZWG8fK63lBBCDY9zM7P8qV//JS5fu0khn+Ph04dJJezQZDJOPG4zPzNJq91hanKUYqmEbcdInLveq2td13ECycLcLP/+n/t3sCwlArm9U+lFfm1sq1yZjz9ynMcePoHv+z0fHClhc6vKnbtrtDsdioUspVK+N6IIwKjU0MJ2poWDlR4EJCXouododQhcF91xVVdvOXgoxsDQ9RBc9WKlBqKo6K0WjcBHC5QvXW9EcBz8QOUDbbc7nL94ja3tCmceOoLjuGzWGsTjqr7efPciibjNoQMzlKs1tncqBFIyNTFCJpNiu1yl0+mysr5J3LYZD1PuRCl+tBt3CTrKST31nVcwfvAG+sYOdLoEhk7+H/0uHFsgnrSR+Sz6aKmn7N6ThAjrqNVqq+t1u4wWCyTidhjFaCJli7WNbeqNFpl0AtOXUG/1AKZstZG+D1KJ9gohIGKPpURUaiR/8z/vTRqRBMjgmDXIFMSkxIyyYoQMh+X7HAv1xgzD6A15T8rQx9H3AeXTqAklkhsJ92qa+m1equABpUwvVK7R3kuVHD80R0zXWDq1wdhIkYUDc+iGjq7ruK6H7zpk0kn+5K/9Il99/hlaLSUmO1LM40tJIZflhc8+wfLqOsePHFTSD7I/T3S7Ds1Wh9mpMU4cP4wdi3H44ByTE6N0ug75XJYvP/8UV27cpt5oITQVYdZstylXatTqDYqFLDvlKt/5/iu8e+4iZ04shr62UqViCtX5ewPs0EQlh8yGDwYkEbPy6cxNsj+8984TTa29iMTQXCgGgINEhumj1Kgjo4waYnDuGQYiQ9amT2Ca2w9k9YMKhhXxBzkoGcg98+0DK+AB9ySHT7FPeQDA7bl0POC8PdpNfYraf79/RYzipys/Hwf5fW5AhouEj88WDq6Too9y1x4h0JKqIqOtya7Df3X5Hjstj07HJeY2KP7Wd0n8/otKPwPBPKjBIsw/qGlKW0OU++HXPdgvpdJcATRJmMWrn5hV34UUNcAkGiAHHjiQiJ0K2ocB8d3tgV1dVAByWLhPMFDnAvShawZkHRcpwW60EO2O2l+C6Qf4joMlBFbMQgwkde7XvoRATawIQqV+9czKQCp61aTabD9j2eZ2mQ8uXCceV5FORw4fwjK13n6GbjA2WgIEnU4X3/exYjFGEgls2+7pLIFArG5iej56o4n8u/8ETIO4aYBposdMAsvCtEyErtHodNmu1dHjNqXREmbCRuo60tAJDANpGmDq2LpOzNCRpokTBDjdLq7dRRTAsmP4gUQXOqZh4fuSVtuh2+2i6xqmqUQxb9y8x1vvXgrz7+ncub9KNp3iyKEDymQqJZ6uqSTcXbe3SpJCIMp1kn/xr3Go22W60yERjxOPxwCJ6fnIZgur4zApIG7HiMfjaGFElqg1egOTpmm4UuB7AXE7DkLD9QLqjTZXr9+l3uyytVMllcqycGAey06ysblFs+3iuR7tzhJbWzusb+4wPTnG/OyUUpQ3DTB0pGUgCzmlcxYS6yo6MUDTdISm98CbY+pYpkWg6ziui2UaxMOgBNVH96piqwlAgAzURKHpsL6tclWG4KVcqfLqG+f4wU9ep1jIYlkmjUYL1/Xouj7tTodrN+5y6+4K46MK3GztVLBMk8898yhz0+PslCt0ug6pVIr5UpFcmBQ+CFTaK3HrPriuchjXRC+KWngeuuuR+P5ryJffw/KVEKJuxxBTYzA5ipwcJZgo4Y2XaOczbMmAcq1BIpHADjXZVNSmhmnbHDgwy+joCPEr93HqLeLNNlJXBitvpKBExOmDaYRAW9lA+gHSD1R+xEH3jGiSk8pXR4R9X8oA4QWIQNVtlOpG8wPM0B9JRFaCIECXEi2bRk/EwuFWRJaVPrAQoteHtZCpFCEQCgcNRBBgmQZjo0VM0ySXzxELcyH2orSlpNN2MXTl76oJldZH0wSmYTBSzOEHAZPjI+Tz2Z42V3SNVruj5HVsm0dPH2d6clzVXwhak4k4X/nis/yRr/8CrWZLRW7WG6SzSVKpBCePLjI7NcHdpVXu3F1ia3ObSrWObceYn5lUEiBNpeeGpoVjfpTe62MyU4NN/RM5aw+dFBn0fbGU+rno1bcacwP6GYIikNNn2/q+ogp0qQxn+wCtBz3Hx7nL/Zg+hvHAvmeNQKMm9tn2CQAf+zxTb2LSok7Rqw+FCUXYhfyBOT/8eYAt7lndBhOXCwlDJtxPVn5OYEuVQEr0XXbVXoX8rGUXo9W7ilCDR6rtYGk6XiymUuy0Ouhdh90cmgZ9m/zQbQlkPtO7b3XJXQ1z4DmigSnC8T26CgGVGpTyQBRN11+eyYHTMbwJOfhFMATwhm5WhvuKoZpQh0nCyUExYVGNiYgRczxl27fM0JlyoH4kSIL+ij28B3W//WiN3hU3dvCDgI7nUq4E1GpN3vngCkcX5zh0YA4hTHypoYftIB6PqTQ2ltHrH2Ys1mOgDL+fvka0OuD5aF2H2Mvv9Yk0EdG74fMGASnXIxEEGKFD9/Ag0v+nV5Xhuy2Ezr09ITtdJ6MJAk1Dsy0CXUNqOlrMRLNNAl1nttHgj69t4WkCc+dNjLhNcaxI8foqhm0TGGrilrqBdm914MVoCnxvVzA9Dy3wsVwfmm0QEj2Q2F0HzVVMmRWA3vWG/GqHRFUJ188iyn9pUhoZw/E0tnbKTEyMMTE2wuhIgVjMIpvNMzXpsr1Tod5sIXSDw4sHOLY4z9jYKJph4h9dQGxXCAo5qv/T38LzPFxPSV24nocMwI5ZmIZBq9Xi9r1lGs0WY6MjxGIWzWaLTCZFqZDHMJREioorVPescuqpCVbXtXD+kGhCJ/5X/y7aheuqDbba3Lx9nx+++Aa1eoOnnjjN2GhJ5cYTglw2jes4HDt8QLGOQukyHT44x+GDszx0fJFYzFJJ3AHLtjENE61WR2zuYG7uYKxv05kaI3A87J0qermG1mip9DNRpXv9fgQC4fmIe6twb7W3KDIAK5BkTANntIA2PY6YGUdOjGCMFbALWWKmQc4w8XSXxp//I3RrDVL/r3+BaHchmaD9qy8gW21Ex8HwfKTno7ke2vIGtNroO1WV508GaL4CSVE+190lmmwHk2I/cL+oiyRiBMVcNMSG2+VAn1GdPsqYIHUNNE2BQVAgPUw9Zccs7DDJuB8EIWup0vAYVgzP68uJuGFbFyHoyqdTaJoyl8UsK/Sr6o9Qjusphl0GuJ6KiAykApiWaagUZqaOYWjk0olemwmERA/75bhXYmpyjMX5KdbWN2k0W8TtGAdmp7C/8VKYfzRiMnYtEoY4mo9RPmTnIWZIRLko++YuKXxkmM4oujYaA1YN9XYiSZg+ENOUMnpvCRzOIVLfd+HzaUDWx33Ufc8czSmfEhIo1hdkMMCe7QFcDLR7DVCscX+Rtz/Ik4RZjmR4fCB7bVPbDxh+wvKzgS3Rt3FHkRhDmE8GIPZzNxwse1HibrblgUflMgq1hhVtyQAjGAAGAzSwHHj1Q1bs6EMug/93/jOVYy6kIKM8fkLTegNuOD0ToY6ok4gBw676qOLeFHAJ+oBqV6MY9EMLwj9PgiEEpq71HTkx92+9wmNwQ3QepRcUhPnIVAl8gR+ZIw1jaCIPwpE2CHw8zw3NCsofxBBR+gwVVhyE9Zv4C38dNnewmx3+h80yUkJh51VKxUvEbRtdj9JpqPuLS0lpt/2dPojQl9YQm2W1cSAZ6KAFSuWi7P8YhOyLGTpf762f4dfc+1mInpN7bwDyfXRfsZmi6/QYyWizBoyEIM33A2TDQdOaGBtVNHF76PzhGKqGZ0NHFnMQrtS18E91fhCh8IsZBGhBgBBRXsnhVy5X1hGB7AHO/pOpz7ZtMz83xfTUeMi8ab3JLJdNE4/bTE2O9SQy4nGbdCqFqStNN/3yLWi10VY2yP57f314pTdQb0IIClIyESY1ViKAw3U7cNcD70D1x+gcvb3yWeTcJKBMpeZWlfGxEo+dOU7cjvHk42coFHI9k78mBL4f8IXPP8lnn3oUTdMwHBdjp4pVbWC+dRFtu4LY3IHNMmKrjNiqqDYV9gcJxHsLIQm2jR+z1GgbBAjPwzl9FH9qDH1jG327jNiuIip1ZT7rJUgO0AKJEUhid5bg9ffVNQK1DQloQiVQNw2kqSN1HW2rop690SLzT/9gd+NUNRePQTzWY4+JeOUBcy+72ghR/w9X89quReJ+pflnf5nWl58lCERv3BDSV4BS0xBoBIGa0ISmYRoKLGf+/f8cfacCmiD2F/4GQkAskKRcF4Ry7B9KqB3ecS/hb+ArAWFDRRHbQFpGjEV4xIBZr9PpYBg6o8UCqWSiBzI0DSwrWnSErI8mMHUdoakxKAh1EkzTIGFbFLJJDh6YBgm6qczwikUeWDQPlYFa3mN222dgHuq4fXARmQOjuDKFE3yVADySN5eRdSPYA+804YdNNvTWkhKB1gcQEgiU6VA9SoQeVHLqIcD1KYFWz1eQvvDufo8/GKcJ9Jisn4V8iXy2hoITPvI5BggFIAzbRAUyDKdIGvQXldH8Z+jo2u65ZS9u+ajyc/XZUmNXH6lH31Xn+Tg3tmuQHtokdjVgif/3/qoa7AMfKYMeiyT701mP/lZNQ/SrSID1l/9rREWZEanUMP7cXyU6Q+8WBjve0CPsXvXsutfwHHqP3drnmdjbTvo5JqPVTv/sD8gCNnTeqBnKoE+fRluCAEQ4Me5+H4MT6tAEKwZqctcr0C9cR3c8RoOgD6I2ymiblaHz77nrIAgZBA2ZTdFjDTWBTCfDvqGAiEzFaf/pr+NrGo6nGBY7bmNYSjes1emwXa6QsG2K+ZwyMTseuB7CU3nVhKe+4/lhrjVPDeJufz/h+QjfB9dF+AEyOt711eTqej2RVcPQMIwHdbYIQEavURBMj1P9H/9LRCzWSwHTm0xEgCZ8DE21Tz8QBFKtvuQA1SAlpH/zryC2K2jlGtnf/L/1L7nfQnFX/UtU6qDhBqcNvR/RVmyiBLSdSm8yk0Nvva9Bswfa9hbYDxr8lM8PApVwvF9JyCdO9Z9lY5u5xx/ij/zyLxAEAbZloFfqsLGN2CwjNrfRNsqkN7cRmzsKoDda+1+yXEN0nZANCgFQCKjCVYbaz9AVKDYMpKGDrmNdvqnS9wwwD+TSqj04HtJ1VXtyPfDD9rLfowdS3UMYWDO0ydzFxO5ThBDqvmwLGVN/QcwkMA2kHUNLxNXvlolvGbSFoKNpxFIJ7FwaPZEgsC0C24Z4jCBmYVy4TvJ//BfqAk6AlBqFv/p30Cp19b6jRVq4aO2NQAPA2bh4TT0/IFjusX16D1GIB44Dhuy3Ku3Dnr/dVffiuHz1L/+3fCkIsM7fwvyn33jgXLe3Lwy6Wqh+qwPW4D6mARMjD7yNHqu1H9CS0JdbEOHn8Pdg7yQd6R2K/u3ArnFc9VmpmKoeOApCkBaBMgUYBGGao954EYpySgk6ymw86Ef26bHOwGNE8H+fTURAa+Dd/xyAVv8CDwZ5e0t0H3LXr31gGxErUvpq8d6rRzVW9dwMfsbb/rmBLSGD/s0MAIZBfaA9vhvsHsh7J/jo6w2sjAMhCLOWMYgIompWZpfdgEU5nrJd2XNpKQP8UIxPodrBFc++d7P/LyJMiDrgrLrbwrVbOSOqk775bvc1dtfh8AlkECh9Ks/ryUsITRCE+eGiSa432SOH+zvDwCu6ZFR7UQMUCPAHKnqwzsWen4eLlCiKTaoJ/gFFADRbJP7ZN0Ay4G/Xz1+VkJKc5xMEgYq+ihiEmPJBkpaJtEwwTWT4HUt9JmEPfZeWCTFLncO2ENGxlql8vwwdqQk0wwgfVFHzgRAKqEUg0vWU/1EI7oTngyFQJjWtV8eaiPzg6K3QFUXeH7ilHGwH6kUIqXwBP6zs19f2voddWwf7bSEHyNB01N9d4eAHDzvD5pGBtYoE1/MoV6roQmd8YxstnEhZXlfs051lAMz/8G+pqOGug3BcVZefYhUudV1Noq2OAlOmjjRiEPrzReAK4yPY9xA49PqlYYQspT3UvoPQvKZAXKDeu+MiOl3oOmqnUARZhosL5/nHcZ98BGnHwLaQsRjStiBmEdgWXsyGmKV8iGQQMs8e5UqNy1dvYVomZx46onzuUIK3tboSb86kkohMOhQBBX/AGzS43zdxR4BJq9bRy9Xe+5OBxA98QPmRabvAUyTq2aum6HwDn3sUL7u4ADHo2P3xJk7LD4gJVL0OaCPuWz7pzGgYQ4r4w2XPinh400BasaGHHzosABGo9hOxUKIPWPowaWDMF32/MVU0ekCuf3GQYfvdZ4E1/D0EwT8XtLX/6L5fDfZ8nn4eZWCe+fhln71FgGK21Al9CZ4f4Po+nt/XkRSIPYnKP235+UYjIveAqshXaI8z3BADtht1sh/l8yHXjV784CwfNWT1W+QKPpxVMTpcQCFLEKbq6ToOnucretuOoYURbbsb756XOPDc0dVa7TbVagPTNMhm0yrv39A97+q7u5gtGYGiwYXZnsajOrmUUoWZh5IFuUyKkXwGQ1cOzYHvIwyzZ6LSIlw6MJlG73DXyKi+imjFItV9FbMEgc/2doWV9W2EUBIT+VwGw1Dmh/2qRyytIZY3QNcJDkztHcv6FEm/pgQD2S0GwagSh41WgJrvg+8jO10+fRkGxYN3tns/CN+Zpg2Bu57ZyDIhZmL99E2wTETMQsTUNmIhoDMNhG2BZaJbFrpp4pvqHIFhhGDQUgAxmUDqAoSGfv2Omih/XgNZ11GNMZOi87/816ESv6fYqAE5l0BovVyoUQlCDRuFo32k9HpVFO23vLLO737zB+Tl4JFlAAEAAElEQVQyGf6jn75L7P0ryrTnCmh3etFtHwUkeyVqoxEbZYYgyAiDI0oFvLMnMX/6Vv8QTfQZIstExkJTXcxCxkyIxfAtU+1jmQQxC2HHEIk4WsJGS9gKyNvqOGHHwI4RxEwF4naZXCPWJug6sL6NtrqBtrqJtrqBWN3E/eXn8U4c7tXdEMMcuUIQRikCQtPwEGzvVHj3g6tkM0mOH17AMs3eNQ1DydsYpqGiY4UA4Q9MtBIsHSHC5MeuF95n2Kc0QZDN0Gx32AnfRSGVI5mID71zWcgOMe/916LGUc9XTsimYfTcEHrDmESZLGU/WGm/diwu30K4Xn9hKvb2wgf1S7H3p737ua463DJhJK9+zmf7Zw7HQtH/YdeVdxn69txcNJ4HAyvaYcQggSBMBxTIQM1Vvfx8u+YUZXfssUe9oDGxHyB44HL3Zyp9v6kI/O26plDM2s8F0/08StRGh95dn4CJ+p0f5kD1wgWG2k32xrqfFab+XMFWFM0TAa6P+6of+AAfsZrV/oP/Cso1ooFq99X2BUSD2z+4qlbNyTjNf/Xfc+Hy9TC6qcihhVny2QzCslU0h4gSYKtrDCar7K0zIv+08JKdrsPvf+eH/OtvfJ+zDx/nT/+xrzFazPfqJfLRkiF1GUjZcwLWtb6/liZE/4VHrEe0mkH5VkX1vnp/hf/3P/5t3nzvEn/0a5/nj/zSF8hkUgRhChvD0NGECv02wncUhbhG5kPfD/rvcJCNExpSEyDCPHZCEkiPq+9f5m///X9Ordnm3/tzv8Znn3qETMJGF+bQ4BxF1llf/z/23or/q1/A//zjyI4ys0jHRTouwnGRnS7SdaHrIhyPoNsl6HTRHA89CNBcD+G6CFeZ/3Bcgq7b+9xjRSK26WOUfdbgu7Yx8M6jjPAoVqvrILruHrZx6PhBX6XdiHJw9N11F/uuGFsdZCaFLGT3bvsYAKx/bjUQKWZJIo8dRPn+BEj88Ck1dE0nQLD7rgYtKpoIkMInEGFWBy+g0+3SbLZZXl2nWmuSywwLqGIaMFpUEYmDRYI0tJBdtMAyCEyzB1oxDQJDx/V8XFexC6ZlEms01VgUtyGX6Z2u8V/8h7QOz1GvbtNqtXG9ACE1ksk46VSKmGUipaTjOLRbnZ6quqYJMukU6VQSXdfw/aC3zY5ZxGIqvRRS0nUcHMfFMAwsy8QMZRakaeCOF2lnU/iH54hZJpZlINBwXBfHifTFxIC/nQJL2kBdR4sLgFa7raJZI0AXSFzPx0P0/CV9GZp7I3+g6J2ZoW6TkAjP7bUHgSDIZSj/P/8aF6/c4A9/+CqmYfDFz32GI4sHMAbMvxpqDIkYGFBq937g4/ou1UaLRqutMifkM9iWMeRrKAPwfYnQApUHUTOQAwAVIqf8MAVNCNCixhFyEqGT/F6QGwVkRJ+JxtLwTwoN8Sf+E9iqqICmf/F32bfsMR1GX9W880CwJQei06XOHmAycHDUq3rKWaHrS9+9auAe5OD+D87SMoRM/w0AH0Wg7M8K/9yYrA8rH8tf60NPAKg25vlKDsYLrSQDrUmB4IEa/7TlZwBb+1xUyqEJ4kPLJ30Zu322QEX9bZcHVh4PBlv73pfjhitrj83tHX7yyjsEUvLo6aOMjxaU+VAOrPjE/pnc+6seHyl0tLATGbrO8cMH+CNfe5652Qky6SQCtUoNpMTzAvwg6A0AhpD4A5RlBHp0XVODWs+23E83IAaWXUIIxkdLHD44y5Ubd7FtO7yvAEMDXTd6A5Qmtf5KaaBEoG9wOg0Cia6F1FK0wg7BlmkazE6PMzqSx/UDUqlEL5myChCQBGG6HQMDXeubDwBkMYc/O6mCCGQ/SEATiuJVGkBSTUxCmUN9KXFktC0I0x4pYBclUHY9D9dxw5yVKkw6JjR0PwJpoanPcRVI8n1M3wcn9OHqKn8c6ShTlnB9hNsFx1H+Oo6L7DrIrovmeQrwOV64r6fOGV1joD6jPjJQ4z3qTuwaGQffTP997GrJmjJdBbn0UGRSZGrtm9vVQBEB+sifTwtBvChX6UNIWF3fYnltlU63i+M4GKbBwtwMpUIBQ1eAOwgCnI6SYhCawFDknmorUuB4Pmvrm1y7cZeV1XXWNndYW99idnq8f/+Gjjx9hO7//F8gfuubMDYC4yOI0SLNVIJLd+9TLleZn50iHre5e28ZwzBYmJsinbS5d3+F19+5wP2VLer1Fg+dOMwf/53vY1Xrqmkn+zkfRafL8soab7z5Dq12BxlIOl2XsZEiZ04eYWZ6AoFgZXWTy9duUas1aHcd0qkETzxykkQiTrvT5f7yGtdv3VMiuROjnDy+SCGfo9Pp8u4Hl7l9d5kDc5MszM8wNlLENHTqjSaXrt7i1p0lfN9nYmyEI4sq+8Dd+6tcu3lX5f0LWfR8LsNoqUAmnSSZSGCaijVzXI9Wq0OjqfKDWqaB0HQcL6DZbHH73gr3lteJmTqHF2aYmZnG1nU836PdaStNwCBQ2l2er/xRXI8o0nhwHBgtFfjS809h2xbjIyWEUKb8QVcM5XZthsy36LGgpqGTzaSJ2zYClUs1siiqf1W71w0F5CNZi57PadSWey1SsXB9q7bKBzi43+DYKKOxOlAL196YqesDkZo/G+sT3lX/Sw9YDbJMg5U6CEykYht37TXYz6XczZqFch6I3jU+2pMo1Jz6OZd/K2Dq32DpN61wjgoC/EDi+X5Pdy16pX2Lzs9WPhHY2h09thvCRN1liLITYs9+H+NCH2u33vU0DZnPDF2yf1XV0fwgzLEHoSifjhb6iACsrG1y9eZ95mcneivVVrtLq9XBMm1s2yIIfNodh1gsRsK2w8HP7WlG6VHiY9MEBKYBRw/PkculaHe6dLoOCcsgCCTrm9tcvnmPeqPF/MwEh+anMEyDjuOwU6nT7SoxzWq9wcH5GUaKORzXY3O7TLPVZqRUIJNOKfCCGuQcVyV69TxF3+u6UCs7VDZzqQkc16fd7mDpBvGYPVSfruuxXa7iOE6YIDuuzEKACP2NoqhLIUAKidNVecyEUJOsFSbIBZVuZ3Orwtr6Jp2OQz6fZfHANPaAb0TTdbly8SqO66FpGo1GEyklI8URDF1jbXOTZqvN7PQ4Rw7NY5oGvu/TbLVZWl7n/so6dsziyOI8pUJenbPV5ur126yubyEh9F/TODg/zczUOHY2BRJVv7U6Gx0lgDg+MUYxn8O2LRzXpVyusVNR+d3GRwsUixmEgE6ny/LqBmsb26RTCcbHSmQzaTTdRJMqACECfI1GE7fTJSZB9wOcRhO/08UWGnERUN7apttokbNjxHWDVqWGcAJShknC0MFx8Vod3GaLoOOA62Kvb2Mvb6gK1ARuJsnrf/lPU63V8cLcdom4zdHFeeamxjD0GEiVD/PKjRtslyu9SW+0VODo4jyT//F/1x/oA8nrb7/PD158XZmDNUGj0ebE0QU+9/SjHD2sGI6VlTXOX7qJQFP5G/HJ59NMjI1gmgbVcpl33v2Am7eX6TguG1tlqrWGEqeNWBapTJB3V9dZPjJHImFz+NAsqXQKp9rg/fNXeeOd8/zC55/k+NGDXL91j8DzyKfjWHqR9Y1tVlY3mBwtULEN2u0Gg/pGIpUIF0mKCVxd7/DyGx9QKmQ5cmgWBNxdWkZokM2lSSeT3F9e4zs/eJVqvYltmRxamOGRU0cJ/IAPLl7jm997iXvL6zRbHXLpBF954Ul++Rc/T7XW4Ls/fIWL1+5w9NAcn3/6ETKPnyaI2Vy/dY/f//ZP2NqpEI/bZFJ3EQIWD87xypvv86OX3lIMUDFHo9EmmYxz8ugBDsxNc+jgDPlcimq1we27a6ysbXD77jKVWoO52UkkglqjzfvnL/O9n7zBvaV1DE1w6sQhfvVrL7C4uMD95Q1ee+s9ul1HgaKb9/iLzZZSjndddi8B2u0Od++vsLSywehIgWIui5Q2nufRbndpd7oIAmKmQcK2idtJdE2j03XodLsqO4qQOK6PqYERmgqlVKKyXccN70XJPMRCs6dE4LpqTOmGC5V43MaOWYDyYQykJMDrLfqkVJNkt+vgeV64iFBmy0gORteV760IghDUGh+fpAivMcRsD7IqkgEW61NOygP+cMGANIEcaMvaEND68OsMMjOD0ZD/f2Pa+9RlkOH7mC9QMryvDCmP8J1GuCboAa/QvN7DLVr/uJ+h/j422IqASt+s9PHQ8gOJtwHHyaHf9vtM/ySRc25kqwYIcmnu/bd/mXtLa+i6xszEKIVcCtuKITC4t7zKW+cusrlVJggkC/NTPH7mJKNf/HPgeUgpWVrdpFxtcNhUYcidTpf3zl/l8vU7nDp6hFIpw9LqOpeu3ObMyWM8euoYmqZx7cYdrt68Q6Op6PKTRw+xMD8das34rK2t87vfeYlqrc7nnz3L2VNHKJerfPN7L/Hq2+dxHI/jR+b5d3/1S5SKOV59431ef/cSmq7C/ze2yvzZ3/glCvkM127e5Vvff4lqrcGjp4/zxKMnmRwfxQ98llc3uXlridX1dS5cuUk31BgTgK4rrZCV9W3OX7nF6vomC7PTPPrQMSxTgaNmq82771/i2s27ICSPnD7CkYMHkMIgIMBHRw9ZEk0T1Botbt29y/ZOmbWNbTa3Ksr8oWnomkr+feXaXV5+7X2WVjbY3qkwOlLkz/z6l8kFQW91ubJd5rd++zL1RpNkMsH2jhKiPDg7jRVTSX13ylXmZsb583/yVzhyaB7X87h09RYvv/4ey6sbdB2XZ544w1e++AzJRIL7y2v8zrd+zM07y0yMFkHA9k6V0ycW+eUvP8fYaBHX9bh9d4nX3znP5pYCH/lchuefPcvpk0e4fXeZH/zkDZbXNonHLD7/7GM8efYkEsmtO8v86KW3uHl7mbGxAk88coKHjh8im84ihbIo+r7P2voW735wmUq13ktovb6xzfrmNocWZnn41CLvbm/x/r0lnn3yYQ7OT/PWuxe5t7TOqeNHeOT0MWr1Jhev3KRcqfXEIR+3TU7cX0MHpKaU9X/nD37A5naZQwdmME2Djc1tlpaW+dWvfoHxkTG2KxV+9NIbnL98lamJUSbHS9xfWuO9969Qrzf5quf3orMkks3tHcrVGmfPHOPggWnefO8Sb5+7RKvdYWykiB2P8b0fv87N20sqh2La5v7yKitrCgSOlgrcuHWP6zfvMTs1zuHFea7euMtLr71H4LlD7GYQSFbXN9U2GZDLpkmnEpgajOTT+J5KqmzoOnHbwnPUOOR6HrqhEwQB9UaLQwemmZ4cQ//D13omW5IJoo+i1caIh+mbClmeeuI0vu/zzrnLrG9sU66UyaRtEgkLwzAo5jM8/vAJFg/OMDqSpd6s8+ob5zh/6Qb5XIZ4zGKnXOX9i9f4xReeQvouzWabbCrB4vwk8ZiJ5zi4rsfV63dYXttkrFSgVMySTsfJ5ZRZ0nUcms0W89Pj/MLzn+Hqjbv8+OW36TodCoU0Uk5QrdZ4/e0L3Li1jBCCqzfvUa018H1l5t3c2uHVN97n5q0lstk0QgZsb1eo1+po0md1ZZ0fv/g2jVabqfESmWpDiYFKED2frf5A2+50uXjlFq+9fZ7jhxcYGykQj9usb6rE5pVqHRn4+J5POp3g1LEjTI6N8f7Fq1y+fptcJoUXuOxUGqSSMZ587BQH5qbQNY21jS2u37pPp9Mlk06SiNtMjJUYGSmCZrC0ssadeyusb26jaRqPnjnO/MwkSGi1u2xul6nUaiTiNlMTY8RiKjH5jZt32dqpYMdixG0L3dBJJRKMlFTyej0cU01Dhm4a+1hLdpW+GObebdFUrISgwy/sM4dFc9ZHsEF9SYWeN9HQxT4u0NrnKRgEKfsFq32y0/Vsm5/+HJ+iDMs+SPoY5OOB3B5AFuH72v1SZcQoDv74swGswfKJzYg9O/U+NxDyXuqL2NNcPu4F9v9dgBxw0kV4vTrodB3+4Ds/5e33LwNw9OAMTz32EI+dOUmzWedff+MH/OClt7DDTjkzNcb4aJHRgdM32l28cNWjazq+H/D+xeu89vYFxkdKJDNx3rtwnW/84UuYpsFDxw6iaTqXrt3i5p0ldF1g2zau54GQHFqYQjNMNndqvPP+ZXbKNY4cmqe1OMf7F6/x/RffotXuMFrM4/tBmEy3y/Vb9/jJK+9SKuV4/JETjBRzWKZOtVrn2z94hW9+71Usy2Bjq4xpqKTZN+/c55XX3qdSrdPqdLhx6z6O4yvzJMqcubaxzb/4nT/knfPX2NqpcezQHFOjI8xOT4CUXLl+m3/9rR8yNV7k+JEDyukcqTTHoloXShG7Wmvwoxff5INLVxkdybO5VWZ9s8xIKd/zl7i9tMY//9ff5fylm+QyKSrVBpVqk52dupJnCNvGTlOJYwopWZidYLyY5c33rvDGuxc5dvgARw8d4MoNlQT36o07LMxN0W53WV5ZJxazeOzh4/z0lXd5892LfP7ZsyQTcbpdh3qjhSYEp08uUsxn+f5P3+wN4KlUgvX1Tb7zg1c4f+UW8zMT+L7PW+eukEomOHl8kZu37/PdH79BLpviycdOUspnMdCo1upcv36PtbUdhNC4dXuVhB1namKCVCKDpqnBrN3pcu7CVb7zw1cZKeYp5rOkkgmW3FXev3AVwxCcOrmA63qsbezQaLQwTSNsA/eZGB3F83zuL6/x4itv4/s+xUKOeNxGNluhujugK72wja0y2zsVfuWrn+fQwiwvvvwmO+Ua7U6HQPrcuHWXV958l9GRPE89fprZ6Qmu3bjD7337J5y/dJ0vut5AKLyk66g0SKdOHuZzTz/G7MwE/+x/+zbLKxtUa3VW17d49a0POLY4z1NnTzFSKpCIx3jn/UvcX1pDADdv36fruBw6OMtjD5/Atm0+uHBtT3SPEDA2WsSKmdxfXscPJTYs02BmaozRUp6NzW3u3FvGcVzGS3nitk2n06WQyzA/O8nb5y5TrtYZLRV7E4lAMVu967TaaCMZpJRU600ARkcKjJTyrKxt0W53kFJimjqmqZPLpHn0zDHmZiZIJEwuXb3D7furFHIZPv/MY9i2RafdYnKs2NN403WNA7MTfOn5J4mHeRi3ylVW1jaJWRYnji4wMz3O+FiO8bEividIxm0MXaeYT3Pm1CKJhMVrb5+nWm+i64oJevn1c7xz7gpPPHqKE0cPEovFeOXN90PhVkGz1WJju0Ihn+PZzzxMMZcml0tx8MAsGpCwLWzLxPU8Pv/Zx5gzLZJ/939Vi2fP2zOhGIZOJp3sndt1Paq1BucvXuf9S9eZmRxD1wRvvHsRgMnRMSZGR7ly/Q7f/ckbPHR0gYnxIrfvr7C+sU0qlSRfKNDpdPjxy29z4/YSiwuzxBNxrt28x627y5w5fZxSscDtu8u8/Oo7rG+VOXRwjkfPqMV+o9ni/KWbXLp6k0qtDkLjK7/wWRYPzbFdafDSmx9w9dptRkfylApZavUWmqZx9uFjPHrmBKlEAk0P5VuCYKAdfnrQIHp2Tfng04QT5h6QIwdMfLKvKzj4b++cPxNzpkClModJHjiBf6xTib2fB8/3bwiA9U3Eoa6kHIxGjaSeYG/99EmZ3nc5hFR6gRvKtcfHD4KeyXrQSLf/+T9++bn4bAkR+fDQX1EO/rv7Bh/0kj7yRUXnHSRJBY7j8ta5i1y+dpdYzGJjc4dMKsHpE0d5490LfOfHr6sVeamAEUYMyV2pFIIB05aUEsMySCZsYjETXYdiIcdIqYDn+zTbbVzfQRMG7W6Hzzx2kkMLU0gJr715nguXr1EspskXCqTTSUaKObbKZTrdFu1Oh6XVDTodh0ceOsKp44eYHC+Ry6ZJJeM8cuooP3ntHIl4nK9+8RnsmMVIPsP7F69x7vxVDh2YYny0SDGbJGmbLC+v8offf5m1jR2ef/oshUIKz3O4euM+GgFIRa+/+e4FXnnzA0zTRNcEN+8ucenaTaYnxhC6xvZOmXvLaxyYm+D0ycOkU0k0XcMNhunzIJC8f+Ea3//J68zOjPLCc49TLle5c3+NZtsJRRAl12/e49yFGyAlxVyWdCLBSDFPNp0ZIkW7vk/gB0yMFnjh2cewYxabW1V8f5nPPfUojz92mh+++Cp37q/Sbnfx/YBYzOKh44scO7KA5/lcu3EPIVSKGyEEtm1RKmR7ibRHijnu3F3i5u0lWu0OjUaDpeVVbt6+T8w0GRsp4HoeCdtiYqyoEhP7SkxxcWGGLz//JHOTo2iaoN3pIoOAsVKOar1JuVyj0WgTBBI/UP4wvu9Tqda5dvMuaxs7PPuZMzz28Amy6STtVhPL1Gk0m3ieirKSgezlfYwkLZQZWMfpdqnV6pSKOY4dmqVUKjK200/dg1BsYiJhU6nqxOM2I6UCczNTxEwTXdfodNvcX1lha6fC8SMHlIq/oZNLxUkmYtSbLQK/P3hJUH49Agxdw47bpFPJUIBSx/MDllaVGS2VjJPNppUoZczCdT3uL6+TSsapN1p4no+uqXRCru/jBTJkY/rF83x2yrWeiWuwt2dSScZKOS7fuEun02FqfITxE4sYhs6de8t0HZf5mUlq9SavvvkB5y9d44kg6GUtkIkBn61GG88L6Doe95bWuHVnSflT1ZXp2rJM5fzeDd0CdE1lNjCUo3q73aXd7pJKJXj41BEmx0fRCbBti2TCZqdSVelkLJNMKkksZtHtOqGPVZtABoyOFDh59CCGpYEMaLXaOK5690JTiaijyM7IkXttfZufvvLekCn84PwU71+4Fk5A9PYVQjAzMcpjZ06QzaaJxywC6ZFOJojHbFodh5mpcQ4VC5imoRZUYRTqYD83Yzbj4yMU8xllEvcDNrcrXL15j3a7y4mjC6RSSa7fvEfXcSjkM4o50hWoWFyY4XNPP8yb713kt7/5Eza3q3Qdh3vL67x17grFfJbTJw8zPTmGEII337mAFDrPPPkwruexuVMhl0ny+MPHGS0W8H2f1fUtrt24hwwk2XSK9y5e5/7yKocPzdPudNkp11he3yabyzA2Nkqru8LFKzeJJ+IcODCPFYthoCvBT0ct1s0BkPRJGB/5wC8POkD2J24RWmbER+hFRU5D8kFA4iPubWj+lUPbPxVkiAiUXX10DwAbdOYfApefHohJKRFS+en13AR6pw7Fg9kNnkPmMtpf9l0Xhh4rPL/nqbnS9SMH+X3scp+68n7e0g9iWCTxY1Xtp0bZPYUiNCGImepRThw5wMMnD3F88QCu53DuwjW2yzXOnj7CE48cVz42o0UOLUz3blKZdGXP9u/7PjJMkKtrGoFUjuB26MvluB6OL4lbgm63gx94pFNJmq02lVqVZrtDvdEmnVXOdlbMRIY+PJ4XqXfHOH3iEM8/exZdU6JppmkyMT5CIh7D9TySyQRT4yMEnsPd+6s0Wm0+/+xjPHLqKKVsikTc5sKVm9y8rUwHUxOjlIoppidGuX1vjUa7S63epFqtc+78NRJxmy8+/ySe51OpVCnks2iahuN6xGyTYjHHi298QKfr8ovPP8nC/DS+DBktqYQPO50ul6/dYqtc5XPPPBwCxRRHF+f44PItZJiio9Xq4LouB+cmefrsKeK2TTabIp9NhYKS6t13wqTXQaDYICmh67iqLsZGKOazTI6PYJmmCs31PBzHxQ3918qVGu1Ol/mZCUzDwPcVOxmzDDq6huc6BJ6LHTnQ+x5u16HbdQgCydhIgUdOHSWZjFPIJCkWcgSejwgCDF3DMk0y6SSmaeC4HvVmi1a7w+r6Nkurm3Qdl1w6odjHepVWq4uhm9QbTTodBySkEgkK2RS6EASBWjkFfoDveSowIPDDpMXK38rzBgYHGdDpquTSI6U8o6UcsVDdP2z8avAWEtd1qNUb7OxUWV3f5O69FXZ2quSzGXRdOcOXK3W2dsq4Tpe1jS067Q5WLDY0gESq093Qz6rRbHHj1j2W17aYmiiha8qvRtc0ytUG5UqNRCLOlet3uH7rPsVCtufX12p1uHNvmSAIuH1vhUajyU6ljucpT0ApAza2yvz0lbfpdLo9BsXzlHSAYRoUC1majRbVSp3F+WlGivnw2nXeOXcJKcFxXLqOSlQc+H44rgbIAQd5Wm06jkvHcSmXq7z02vusrW+zuV1hbKRAIZ+l03XY3K7SbHXw/SCMTpLIQCOZTBKPx9jYKvP6OxdYmJuimE0xNV6ikM+qttt1e07oXceh3engeSqicXOrwstvvE8sFmN6YoRYzKRab1KtNfH8gCAQIYPTptnqkA5NoI1mm83tCoVcFt8PMA2DZDIeauhpaEIjk06Tz+W4cPE6P/jpG7TbHZ547BSz0xMYukk8Hkc3NNzw2eupJONhE/O7jkrHNDAkIzTseAIrZtEN81G6rke5WqPeaFGrK99Qx/MpFvLEkzYBHkGgmIdYzCCdSlAq5tANnVa7Gz6Lylk4MVYkFrNIJhOMj40gNI2rN+/y0PFDKvDC8dB1nbFijphl0gn77OzUOIYpuL+8hhe2lSBkqUzTUCnjdI3REZV0/Mat+7Q76vlqjTbtTgek6jaZVJx5P/jQeL6oRJI3+8OXBzNOg+LQPbCApnKlRlgg3GE4yXV40Cc0G8rwLiXs0kXTFMEQESOfsgzKkajrDc/y+5MtH3XSj0YKg2E+D9pj//OGVpQBMmcPhBJKSNpxPNrdrloEh7hAPa7Y51k+eR1+OrA1ZFqKWC3VVPbIISCGNbZ2040REv6oCt+zuW+KEJqKOPE8n5Fiji8+9wTFfIZGs02r3UYIGClmOXH0AAfnZ8jlMgjd6Ds9Dlw7GuQ7nS7Vel1pbvkBnhfQ7joEYUSQ6/nE0ajWm7x97jKJRJxao8n5yzc5MD8Nmk6n41CtNei0O70HCMJHbre73LyzzNHFAyTiNvF4TLEGnsr/VWt0qNebiPGRXlRivdFiZXWTwwsz5JIq6bHjuHQch5Tv47gOXqDUuQMp1URYb+F2OmyVqximycLcNHOzk7QaTQqZLIEMqNTqGIbBI6eO8sMX3+a7P3kTwzT5ejpFPGbR6Trkcxn0hIbrKedVL1Rzj5zx47aF7/l0Op2wOlUqnE5HJfrNhk7p2+Uq856PgeoA5WYb1/NxXJedchXTNGm1u8qJ1nV74EkPZSMcx+XC5Rt8+wev4LoeB+en2CnXSCbi3F9Zp5jP4nsevucpVqHZom5b1BotBWgch67jhE1P4Ps+XcchGzKQqWQCx3FotdrIIKBcqbG5uU02GafRanPz9hJvvHuJa7eXcF1PTQROh82tDeqtDrZpMj8zTT6TZHSkgBBw6849lpcPEI/HWNvYodnqKgZQSmxTSQNUKzWWVza4v7ypGCHfRxMQD4HQjdvLzM/cJQgCxqqNfp46TQVHeJ4Caq2WYlBW17e4dO0O2+UKRxbnObq4wOnji9xbXuP1tz5grJRjeWUDkIyNFns57KI+qwmdRrPNW+9dwtB1Xn/nAq7rMloqEEhJIm4zNlqgWmtw7eZdTMPgzXcv4Hk+x48sMFLMq+i9bpf3zl/j5u0lJZcQqITNkXBgICU75Rrlap1MOsnq+jYbm9u4rgLgpmFQKuZ77OPBA9OkU0k6nS7jo0XyuQzvX7xOrd5kpJhnYX4aeK9XP749oBPeVCrzmqahmyY71Qa37q4wOzXKmYeOkEwm2dmpsL1T7/XVRkuBpa4nKY2McOLoQe7/6DW++b1XGB/JU8xn+OxnTit/o9DB9vrtJV5+/RzjY0WymTS6aTI+WiRmmbx7/hr1Zptnn1DmQNfxqNQadF2PrZ0qW9t1qtUWuqaRSsbRNINEPEkxn2d1fYv3L16jWMhRrTZ6DuRSSgq5Ag8/dJR2s8XK+iavvf0BkxOjTI6PYhg6na4Coo1mm5u37lNMp1gM17lBJ+wTsj9Rd7pd2p0OjVYHx/FottqkU0lKxQJbWxXefPciyUQcTdM4eewguRCotjtdHFcBX9f3CUJH5CDoRzFKKamEIH1qYkzl0SRKTh/0rA/NVlstfKUKy4/FTISmEt7vlKtKUqTVxg8CEgmbdCqhzEFhhHIymUAPz+UHAXfuLfPmO+fZLldDxj3Nf1RvkPtYjIvYO3eFPz/IMTmSCpIyQBOD4tFBSExEc6VK9yZlmONEMmQ23DcC/kOAzLCRbPe2T6mEvgdwRA/9AGpll7/ans+793tAGZK82HeHwfsZ+NxjsR58fgV7VQkCQmbL7fmnq1w0uwRZf4YI1k8FtqTskVjhTSlRtshHiEHwFQ7iQ/mYfk7UIgOd1/eV3kqz1cE0DOK2jef6TIwVMA2dy9fvMDczjuP6dB2XqalxTjLcRPwgoBOyJq7rUWs0qdVbrK5vc+P2Pe4vrSl2o9FWdGPcZrtc4/L1uySSCbqux427qxw/vohpmbTabZZW1lnf2lEJfX2VqzCVTqAbOj96+V2WVzc5cXSBZx4/jWkY3LqzTLnaoFpv8dZ7FxUAiFsUCjlMw+DVt86zs1PhiUeO8+RjD2FZJoZusLaxze37y6BrNFpd2l03jArS1AoybnPr3irf+v7LPHzqCKamMTMxzsLcNNvlMldu3mVpZYOpCZW89f7yGtvbFVzP5fylG8zPTnL24RMkbJvJUTWxfHDxOkcOzeEHAbfvrrJdrrKxuYPnSWKxGLqucePuMt/8/iuMjxYo5DIcP3yAh0OnXimh3Ongej6drsN2uY7r+ezUGrRaHe4trTE3Pcn2TiWMtvTw/IBO1+HqjXt0ukrTaKtcpdZo8tJr7/Lk2dNslausbVZod7qsbVUo15vcXV6n3XGo1JoUC12SiTjJhM3S6ia/9+2fYugan3/qYb74uSfw/ICdah0JOJ5LvdlWbGZo6os0lSzLJGaZrK7tcOHSbRKpOMcOzpFLpwgQHJybYrSU5/qtJX740ptMjo9w7eY9ms0OhmaiCZNSUU3IV6/fo9V2qVabJOwYli4QBIyNFDiyMMsb713iwpVbZNNJ9E63N9RJoSazYj5PLLZOq9XBNEz00JncDAMgZqfH+dqXP8urb5xjaVX5XRVzaZ5+/AwjoyX077850KuUTpMds7BjFveW1pgYK/Ho6WMsLsyQy6TxJwIeO32Mar1B4AeUGzViMYuji/OcPHYIXdOYGh/h2OF50skEoyMFNE2jWMyTSiUxV8u9HpzJJJmdGkfTNR46rsygKnItTG5sxyjkM5RCMGWESYXHRkt8+YWnOfPQETa3ytixGAfmppQpNlya+jGrN2CLZoeYZWLHYoyWDJ598mFGClkmRouMj46gYWCaNkcPzZPLZUmlUhSLeaSmEQSSZDLOM089ShD43Ly9rPL0jRRJZzK0Oi7pdIpHTx3m5Tc/4KevvceTj57gxLEklqVY2odPHWG7UkcLz2caBkba4NDCDH4gidsW1Wqd2ekJfu1rn0cIQT6bYWpijC+/8CTf+t7LvPjqe9xfXmenUqNabyA0XYGNpM1Tjz/CkYVZypUqhq4zNzOJrmt4nket1kbXNOJ2jKXlDc4cW1TafYDwPLpdt1dPUkrK5SrnPrjK3ftrZDMqEjKfyzA7M8n2dhnP8ynkMzx0/BAPnzpGLpPm7v0Vtso1Ol1H+cB1HLqOi+sHSnPMMhgp5SkVcyytbPDe+askE3E2tyvUGy2mJkbJ5zN0ul2SCRspCaMGFWi5c2+F3/nWj5mfmSCZiNPuOGxtV+h0utixGMlkIpQyUYnJG812yBi71GqNkDX26XRUQMLm5g7tdpesrn/oPNSTxdhvl4iBGvypB7Ii3ioI5WzCqNhw/FCH7gPgej3jAfPkA4BWxGqB6JMgQ+cj3PYpma0HWqE+DN59+tIDWpFZHVT6IwlRTsMBuNS/Axl+/7jYQqpzeqGVZciRfg+jJv8tmxH3mDH7aHnI52KXynT/gE8LsOTAP30CNpqIAdY2trly4w6ZdIJUMskjp47xylvnuXZrid/5zkukX3qHZCLOb/zqL3CydzuSu0urVGtNtnZqlGtN4skkdjxJs93luz95kys379FqddREXKn19IeqjTYrGzv85NV3VYdPxEmnlJ5WvdHi5p0lNrcqOI5HpVrHcT3m52Z44bknWF7ZwDTVyj1mx6jWW6xs7DA6UiCXTffAzJFD80xMjPGZx07y1nuXWV7botFsI4SgWCpweHGOt89d5ocvv0Pu/DU2NndwXRfTMEnEbUxD58jiAW7cXeXilVvcubdCLpPm13/5i3gBBELj1t0VXnnzPCOlHJZpkM2kicVMbt1Z4kcvv0M6eRkhBM8+cYaHHzrMtVv3OH/5Bv/yd79POpVgp1IDCbVqi1arQy6bZnpylNt3V7l5b5nNnQqPnzmKrus9G7oQggOH5nkil0ZKFaSwU66SSsSJ2zHqjSbblTJdp8vRQ7MkEnE8qfzn5qbH2KnUmZ4a5cDcJKZhkM2kcEL1/7nZSXRNoOk61VqTUjFPYtrGjseRaIyPjfDYw8e4enOJRrPF5naVpbUtWo4StkPoTIyWODg/TS6vzGLpVIKTxw5iGAalYpZWq8PC3BT5fIZao42pW6TTGUxbsY6FQpZHTh0hmYiTy6bRdCUn4rgeUgh8oSmH5ifO4DgupZESx48eotPuMD8zjq4ZFPJ5nv3MIxRyyjl+fnaaWPBmr28JTUPXdZ58/DSZdIpDC7OkUglOHlsklUwwGgKXuG1z+OAcxXyWja0dpJRkUwls28YbchZW46ltK/PpZ596hMMH57Ask4Rto5uGSjEIjI8VKRayLMxPKbOS7zM9NUbCtvECn7HRIslknOnJMQr5LNVanXQmw8RYCeulc4BimSbHR/jKF59RTA0qkhGpnKId16XT7hAEAVMTI+Qymd6YYug6pWKe0VJBSQ50uj29NRVFJlUy5+i5QhbEDX0zhDCYGh9jtFTAspRcSyqd5vjxwxxH66UZiSZbTWjMz00zWsjSaLZAKlmCVDJBLBbDStr86lef56Fji0gCJsZKmDEbx/WYmhilWMii6QYdxyVpm9h2jFjM4oXPPs4jp45SrtbJZtOMFPNMjo9SqdawYjFymTTPPfUoI6Uil6/fxnUcRkp5Fg/McPTowVDqQFIsZJkYySmNNSnRPQ+tWsdvdTik6/zmZx6hWamStUzmKk2VSSKQaK6vzNkDY6rjKqf5R04dZXJihGIxR7frUKnW6DgupWKOQi6DHbNoNJtkMynFCAjBzNQorY5Dudak3e6QTMSZHC+RiMeZm5nkuace4aevvsulq7epVOsIIchlUzx86ii5TIZO12FqchTL0EllUgjdwPdalCs1ms0OO+UatXqLZqvD9Vv32NzaQdM07i+v0XXUIrPRbLG2vkW1WieXSbG1U2FueoKvf/V5Op0uW1vbrKyuE1v+kUqrFUUdPqg8CGwBDEk1DAKtXfNU+FuP0+pdMwJAIjT1RaxM33w2CHL2m1P75kOx/z4fxsjs3ne/+TkEWnurQe7zaS8e+bBtQ35eu8sg0JLRvQ2aVvv122fahoFW34Qo+yC3V7SeYU353Q5HI4pQPkSJ4g5e65OXn9lnSz2/YE/erJ/BLrznGrLflHr+bb1VmLoJz5N4nuTGnRX+8b/8Dts7NX7huac4dPAgX/3SZ9F/+AqtdpdUMsFnHjvJiaMHe+8rkJJ2u8vUxAimabJdqVMo5jl57FAvUnB0pEC70yWTSXHq2CHSqSSO49JotjlycJbDB2eI2zFSyQRTEyrOsRM6Uz90fJHR0QInjy1gmTajI3Ge/2wWXdMwDYNYzMQPAmq1BvNzk4yPlUinkgBMjo8iNI24bfPoqaPMTI4Ri5nMz06CbpDPZXnu6UcZHy3R7nQxDJ0Ds5O0O10eO3OcTDqFEILFQ/M8HUZgBUFAJp1hfn4WwzRJJhI8dOwQlWqd7Z0qI6U8Tz1+ivHREWr1Jgfnp7h89Tbvvn+Fo4vzTE+O8fWvfJ75uSl8PyCVTDA7pa758ENHAEGpkOOP/tIXqFQabO9USCXjnDiyQC6dRBsYEI6dPMJEPoMfKNNts9XhybOnsWM2pWKOdCpOKmnzyKljxJMqOkoIeOzh42TTSQ7MTamciIieuTGbSTM/O4nnetQbLRrNFkcW54nbMWWqCALsRJynn3iEo4cPshmmNzp0YAbLNHAcl4nxEqlknPGxIpVqg0wqSTGXplQqsYhGo91lc6vM8eOHKBVy3L6rnLURWgj+Hbpdl0MHZjhx7BDZdJLtSpVL1+6A0PBDsVdD11mYnyGTSmAnU4DsKZDrRgxTlxycm2WkUMCyY9jxGEZnIM+eAL1S5wv/07/is65L4pX3sUyTL3Q6eK5H6kdvEQsBSAxISpiREhk680fh/1qzHfZb9V6yWRWSn0klmRgb6ZlxPQldx8MJfXhy2TS5bAYQJOyYEqMNR+ToXYwU82TSKbqOSzqdpJjP9M2WQjnWj42PUa2pSFU7ZuGEzPL2ToXNrTK6rjM5NkIsjKjzPOXnZoNSaw9AEJmbw2wPMrTGWIbKQtBq44bMda3eZHllg4NzkxTDrA1SaGgy0mhSvpq+H0WJKfBm6DqFXJZCPkskGKsyPgg0IRkpFMhlcnh+l47jUm91EALyuTSmkQMf3HYH0Wxhrm9jaQLb9UnVm4zWm9jlOqnbK/jtDvGdGnoQkLQtsl7Asx2Hp9ptglYHras018TFu2jud9A8D8P10V0lrBtlTJAywJaQETAfjqU9y4xQ3wPHHeAmlFVipJjni597kkTcxrJMKrUG9+6vUK7WWV7dYHVti1t3lrEsk6mJEZ598mHGR4vKB1XXyWZSTIwplvyzT57h8KF57JhF3I7xmbOnyWQV+I7HLHKZFKMjBcZKBSzLIJVIsHhwnmwmSTKZ7E12B+am+PpXnqPV6VKtNak1Wti2TddxQlkQm0MHpinkszQaLRzH6S1ckVL5wE6MoukaU2NFRgpZzN9/Een5+5rq9pZhqLEHUAwwg0P/0jeh7k77skcsMzQZRS7aWsSEfUjpATZN651t91wsCfbmNP0w36oHAS4Gbv5DKJ5Ikmo/zmsoxdGHAa1w756rz9B+A6bCCBQIAP8BDOQAIAtN78rXXgHcQPYDnCJ/LaTKw2vbNrqh/0wmRPjUPluDzxDlMB9oQKEZ8eeqMjtQWWJXxcfjNl954Wksy2J5fYtavc3WdlWJiKZSPHr6GEaY+ubg/DQL89Okw5BwgRL8/PVf+SJbYVLq6clRLNNkbnqCyfERDMOg1e6wvLrJyWMHmZ2eIJ1KsL1TxfU8DsxO8Bu/9mU8z2Nzq0wmo4CSZZosHpzj4TMnmJ+bIRGPIT3l7BxFd2mhz021VqdWbxKzLGanJyjmc4oFQomDdrpdJXQ4UkBoytG11e6SzaaZmRxnbKRI3LYxTYNO16Faq5NKKjV331dJmg8fnFVUfS6LHYsTt22VcsQ2OfvICQ4uzLC0ssH4SIHFg3NYpsHB+RlOH1/k+q37bGzt0O4o0+TC3DSz05NIlC/Z6vommXSSqfExhAZjIyWKuRLxuJqA/UDR+YbwI6IbKQTpfAZ7rISKBvHwAwVAzDDXWxAEIVuh8sQ2Oy4xy+LU8UUy6aTKX6n1ldCB3mfP9YjFLErFHLFYDJBUaw1arTaGYSiNn2TIOmmCYj4Xnk9jpJhnPNTjWlndIJ1Kks1klFlcCJX3TRPEYhbFgmIT1ja2aXU6bGxts7K2yfLqGrMzYxTySWzbwpcJMpkEuqEhUZ270WzR6TqYsRipMA2L47gqIk1AzLbJaxqmZeEEAYH0kM22GjBCYEEgsWoNzCDADMVhbdcjkAGG6yF0rbcqFWFX8oMA6XoIqXziVHLtfn/NZdMcPjhLNpNSYFaA5ylWqNXusFWpYdsxJsZGiNsxmq02VszE8zyazTbtTpdu1yGZTPSi+dB0LNPEMK2ha0kh8AJJvdnGFJBOJXE9jzv3lnnvg6usb+4wOzXG+FgJ3dCVaSoMqkDo6JqpVO2lNtAOQrVxKZGJuMoI0GxDoCZWZeraYHVtk/FijjgC6foqaXXXQXM9dMfFcByVHspTWQdEp4vuqO0iTM+key6a66J1XXB84l0Hul3cVod8s6VSUTkuuuspEWKh9ZgkLXK5CKOhNV25Y/hBQMJ1EUDMivXArhz+H4Hsq7pruywJ/dRbAbqmh+m/whW8H/QSuwftDqYm+2mBwtV/JpMilUzg+z7tdptGs4VlmkyMlSjls0xOjNDtdLl28x4//Omb/PKXn+OxMyeI2bHeXKDpOrGYRSqZRBgmpmlQLJV4LJlCAqahYwp6AqRqcrOYmRrDNI2edl8yEWdqYpREKqVSEnk+Zx85SamYZ3J8BCEEf/aPf51qrU4QSHLZNA+fOsYTZ09TbzSxTIN4yCTGTB1LgOe5gBp3dD9ABBJdf8CcNVDn/Z8ikdHBaWo3o6L6saQf6SjD+hWBRGgyBL1DF9oFTz5sHo0AkACxn0dWZA4L0YjYdeQ+c/SQy89HlgeZFQXD2S4GmaiPLkr9fzBQaLc5rc9UDV32I088+EX0gFcQpuvxvSDsi4qh1HUdO2aFi0P5M9lKPxXY+ngY6udtwR0+4yChF7NMvv7lz3Hm1DHuLa+Tz6Y5NDdFOpXAJyCVjLMwP0UsZjE1MUYibg9JPQghmJ2aUNF5QpBIxDF0lQKm3e5QrTXY2i7jeiqEOmaZBIGk1e7Q7TpcunqHtfUtJidGSKcSxCwLTdfIZtOcOHoQqRkKdGgahOyLoes9MCWEMuO4YSJeyzTDCU6ln/E8D8/zSCUTpFNJPN9DiFAJX4geYHNdF00TPbtz5HvWdRwaTaU5Y8dtBVJ0HSkDfKnMBbqmkU4lWFyYIZNWwo/tbpdrN+9w7qLSRjq8MEs2nSZSp9cNHSnBs32ScVtNpIZS54/HdBpei2azjWkaKqVOTMfUjSFBS/PuKlqrQ2DoyrFf1xAxlSg4MAyVODjyk0Jg6Oo+3TBIAcCOKT0jBST6IfOGaZAO89OZpoH0AwwJGV3HAOyOg93uEu84aFIS72xiBBLRbGPV6gSOR7fZITlWJJNKh+fVSNg246MlZR7MpInH7XBCSdBstdneqVCt1hkfKzA3O4EZG0wSrIfgUGIYmrpvxw3ZEQWKlJ+VwDA1DMtCN0zQDdrdFp6H8tnSNIjbyGJODQ6NJnq7C6apkjLHLIRhIC2VpFmEPpQDaAvpuooRNA2ErsPqBviq/hYPzjEzPcnk5BhSV1kPXBmE/nWKIR4rFRgpZLAsi0TcVrlEhaDd6dBqd4hZFplMGt2KITWdRDzOSEmQSieH3Q3Cnh0EAb6gVxfrG9u8+uYHFAtZPv/MY5SKeQzTQjM8DCuGEAaGGVc+XELDMILeu4/+NE2DhA3lGlq5xuf+2bd4aG0Lv9kh8dI5Mm9cIhlO6hCN3YOTRDRGhL/unbPUPLdn9Q5mECDC6GbfD1QOBlM5eQtN9CPWpewxihHzIQOJ9CW+DDAMXwEl9lqzBKiE5ZaJtG1kPNbrP76u0ZEST9eIZZKYqQQiZuDqOnUvoNrt4GgCezSHHTMG3IcCAs/BMEwMTesF7bTaHbZ3KiwcmOFrX3iaibESO+UqP/zpG9xbXgOUX5tpGL06yAoFuBC6MnlrGvGYkp/xpURlflIyI9E7sGMWI6UCjuv2TG8xy6JUyJPJpBXE0fWe8nxUN+l0kkl/tAeChIDx8RHarQ6O64a+kJpSs49ZvcWVmtcDlWYteqH9xjDwRncVqe0BYZGRKkqbxcBbG8zAoj4HqKxFonfNva3ow2UfJGoXlf4ouqY+sDX8i84fAT4RXXMQDEVMnGrkIhorPnGJzjV4/sFtu9it3c80YPYTH3b9XfJNw9uif2TPFBmxZL0rh8KmSoXAwwl9gJUslOrwmhC9BfhHGJo/svx8pR/+LZbewAewUyX9f/ibnJBwTEa5+/p7TgeSydBUoDpm2PDC5LUIiMdjPYATmSJUfjkttNeCUalhmIaSahCCpZV1dso1EqHGztzMBNlsWukR6TqGbmDHYgRqJFHACFMpsff6c0RVxsimUwRSqpDu0E9ECKEisgp5EGCGEghB5HQa3jNCoGtKjFHTNCxLOUkbpspbNjpSJJCQTKURuoHQoqTWSuIiSpcBhPo7Kmx/bWObmGXyK7/4HJ957JRKaj2QhFuI/uBoaEKtEDsOZsfHbjTpNlroMsDSBXoQoAdSJZwOAvAh89/8g/7KJRqgolVLBIh1HTSBDMGIGqbCIVUTvfcjImo5pKZFeC4BSki1N/6Jfuh15DehCUXha9Fg0FeO3vn3/zhaoYBuqP0z2QynTh7B8zzsmNWbXMy8STqdJJfLMDM1TjxpYdm28s9CEoSDrOsqh+SYFWNybAzP97FMA9uOoYkw5ZEQxKLUR0KQiAssS8fvdjClVPkQn36Y9n/zf8bxJfrvfp/kP/19hO+D76shuusgWgIZtyGdgHQSkU4iUkm0pI2ZiCPSSbRMEtIp+O//EdSb0HGYzWYJ4nG0ECgSToamaZDLZCjkspimSdxUbEkmnWJuZhIpJYZpkPVU4nMrHicet9F1nVwuQy6XwY7F+mDL8+HcZTK/+deJu0o2wDR04hK+1mrzuWYLc71KZulfE/vHfwBCIx0EJMO2IoSGxrAfhqjUVUJjTZmWZTIeLvwl2e0qGd0gCJltlax42F/tgRFbsj+FDMOxCMQOHKULiMdA11XEqKETWBZ+3EaPx5CWSWDoSMtExkykaaLFbTQ7hm/qdAW0/ABH08iU8mRKeTTbJjBjeLoOMZMgZuIKaHZdPF+SCpXTDUNHQ9Jtd6jW6wihkUknkTG1OpdSIhwPo9VBCzxs28SyrN6AGklJmKaBbhhous74aInllXU2NnfodB12KjVGijnsmMWxwwc4fGgulJnQewnnkWBaMVK6qcYnXd2XpvKH4csw4fJAtLpq/xalQg7X81TuRyEwTJ2UrtKHaWE+UDkAUtS7E3sEc03DIBEzkYGK9NNDAUvD0BXzb1lhQk+9x1oPmdf2Y1V67SHaj94+AkEgIxQdboxSv4gB5itkmgfPPWz6iz4EDOdU7F1cDZVC9uaKfvHptVJtsI6Glgj9v0HQ1dOwisTLxZ7n3w3498L/3deKfgrZoY8D4AaB1CcFfBKQGlKKgfNETFi4apIBENCTfPICuh1P+dPKfRisn9GECJ8abIX4UAw0tn+LZTCtQfgDbFcQMnKdG64YgwcwjFEHcT1Sf+lv7L94Ca9VchyazTaFlU2McEJ/oevwjONimgbW+b+zVxn7gd/2f3Gj4bX6/m+qnjPAyD77R2fcfTa12jXwjiz0esbkIN0cHVupoTVaCtREdRomKUZKxgPJoSCAKFk29E24wSBAGgBKAyW371OCaHfDWFuBvrHzoXWyD8mw/wN/yA8fXfMPulEBmmDkf/4XBN/4oWKJLBMsNTlGDBJm+LtpImImadMAy0DaFtI0wLKQlonedZndavDFsVHmNIvERpm0FUPYKu+bkBLNNLC00MlbU4nQFVbXsTQd2h1ENGgl42hCmWCsRjOM4FNPOThwB802st5EaFtK6VyIIc3lXrm9BJ6HtrRO+rN/qlcH0tAV4NV1ZbrUdcROBRFIiKmJNAHk2dsGhibDwe+1BoQh1jgu2tsXiPXekNonA2R2sw2w9973GxuRyJV14n/+r6NdvwO1pkpCrAkIwgleU+Baiuizyh8qws/R+5e9z+H+mkBoOuiCgDDpsQA0ZY7VDL03AeshoDD8APPyTZy5qf6jhI8aZV2IUqEN+oxEz66FQN3zAmIhkIx2yQWRvEC0QFR9NS4lSVcFHajVOehX74DrkQ0vj6HjHJ5DygDj0m2E56GvbjL1H//fh+pzJJDMd7o8u7lDvdEk9tI5SNikLYunUgkSto0oZnH/7v+1324AoWtYeh8s+EL5IWmaQCMya0ZCriG7ByTitgL4oQ/gbsZSSkmAJJIF7S/++pWrEZpGRbiQivYFhKZh6mrxTfgn9T09YqBBiY81eAwxW/2DobcQ3O+gPvOk8NsQ//KAa0gCIYcWCr1LhXvtfYQHASD6bNs+4GrPvQ5dZ7/r7YVj+/4WnUvuqrMg9JN8IMjdfZ7d2yOgFY0lWu+8aqrq6zwqWRLl7x0pEUSwebcr/fBUtN/E9OHlY4OtfufelZAzHAiGVgP/ltCXzGd6FT2wuOiRoVEZzFLPwCe5sqGO03S07erAtv6J1HklouugdzponW6vEViBxBRC0c9dd9973DWuDt//wBbVJCKquV+fu9cJctf3B10P10MvVx+wV7jv5g6i2hjqBn2qW53tAfnrP1Z54L3q/UkryKb6YG0AwEWgLnJkHAR1IrSp7/NE4cBKf4Uaslh9ulP0KfTefvsc67iIVkeZ1eoNtI94+IHx8gFFkETyy37AL8kA7fY6+ovvqYi3od0EmKYCcSGwG/obbBGJfiJxUW30PjvPPKoYv2aboNags7ENjRaW4/RW9URsTO8BGP4SmrQBhNtv271Dum74zvz+RCRlxDfSW7kPHTTwQ7C/CUCZdPxwnFGgYs+Ess/p9j6HUOap7Qoyl1F/pZzasl3tgRnfD3A9pa1jWSaGYQyccGC86HcQRABC+nSaXRzHDQGDJJCSeCxGPIyAjPqVJtWIorW7GJXa8G0L0UvMrIIV1L6WaSoR5RBA+75Po9nCdTwSYbRuD2AESsdKIiFk6oRQ4F33PBVQoGlYpgGtDsKLwuYlgabRvLeK47gUWh0MQPMCzPcu76nmGJAN6yxothHlumIPtRBcWib6/+6vDbwKgXb5Zs9Zf78yCErE8P8+crz5KOzz4ceH7T+MgGVpHfHH/k/9owpZ+J/+5j5nHLhfTULwcUZFQV8FPjqNDAmvcGEUBHQ9l2qliu/5ZNJJ4skEQjMQIiDS3YqAQBAyWmq4+ngj84MU8odTCEUMEHuYt+F9GKrgfs0M7r8fKNo1dg8CLSlBqsW+2LV98BlA9lK+haaKofodPqQP6ML45N7vUqo+64ZyIFE2h32O3gWeP1352GArQr7qxQwDmX549CeYjvt2tI99yIdhyajO+6uC4T175uzoO4Bh9AFBKPAY7bv73JbnYfgBmueBpRx8Reg4LCMwsOv5h9cnoZVahGeUhAO9h6YpUyFCEIQCppF5rAfFRHRYvxYkfce+yLZMOGBjmlDMDdcPw3WgtTqIZrv3m/IPUxS0aRh7OqaMKOnBv0HzmwAptD37DB9HyAQIuqbB1WcfIZlJkcln8DUNV4BmmUhdx4jbYBkIwwDDINA15X9kGkhdmXK7jsP1a7dZXl7n+IFp5ifGsIWG7npovo9wPXBdcDy08N8oWku4LnQcNRk4jgIQ0b731zAv31SDYjKBTCVV9FcoJLm7fFTT15fWwPcH8h58RPkYM43eaGJfvIklQb9xF1FTgMvIJJEjBVp/+z9lc2OLtY1N8tkMMghIaDrT2TQxL1AO4/UmstFS5sN/8K+g1kB4PtJT5kjCf4XnK4AUPagTTqCmgTRN/FDkVtOUP2JfyFiE/+0CdwNAEdOAE4dwHZe1jS2qNZWOyLJMRkt5MpnMsDByZA6A/uSAGH4tMhpWA9iphuBucHYQeLk07XaH7Z0urucxms+QSib67V4Or22jAVfTVCaF1UaDeDxGOp1Ek5LA88GOIe1IbiI0xZRrijEO70BqgiCXQQaSeqPJTr1Bp+Ngmio1kOO4BJ0uBT3DWKhP1u10WanUaLbbTOUyWPlsj8mMfE58X7FbpmmogCAE0vOpVapUaw3ysRSjZugn6vtIoeEAZUNDM+N0yzWV8ULX97KevWoTIVjvs1XC70eAye3K0GsWzbZqR4OvLqzLIAh6i3ehhVIbuxp+EC24eov6gXM/4B4/tAzMA/0+G37YKn/s06j5cO+Codd2NHpBD4PXUABJDeZS+r151el0eOu9C7x97jLNdpeHji7w1NlTFEdHQNu1cNHkUET3py/7HdvndT5sbt7vyGFksP8vQ8fvYaiCB4Is9VNk0RoAWhEI3dX3dx25987Cvh2l6Wl1OtSbrVBCSgz8DYLInw1wfSIz4iBFG/6wp9b7Q9+HnuiTXDY8RgMZ5lQLV22iXIftykdeURCuPEM9od5YOlrYd/9BZguUsmwzVObO1VowPQrFPDt/7z/j7v0VNrcrbG2XSSbjHF2cZ2RkVPk6aDqa0NQKCAiEUJOtABkEXLp6k//H/+d/o9Pp8h/8xd/g1PFFypUaaxvbmKbB6NioUkHW1BQto4DgQEeXAa7r8vLr7/JP/uU3WJib5i/8qV/j8H/+99B2qshilu7/8rd61RK9l0CCHzbG2D/5PWK/90OQkuZv/lFuJmP809/5LrFEnD/1J77O+NSYMo2FPg2aNhxh2gPZA9/74c6DFdp3Do06TLfr8u4Hl/n97/yEyZTJCycOkEokuHT1Fu+dv4oQgi889wSLB+dUyqRwYO4BUKE6TG2nwg+u3KTV7jB++ihzCzMEdkwxAmLYvCDF4IBHb2UzeM8S8BGYr55D+9v/CH1pTa2IYhbELKJErtIP1OoqkBAEqvMGA8ybVObXHlvneep7LjOwiqNvlg1XdH12D/Ywfrs7e8dBbFfQUE79wlP5HPVaA2kaSM+j3W6Tz2YYGymq1Eaui5vLEEsle/2w16t/9QtqMpIBfuATSBdfagThgCZcD1ptYn/176L/5C3wfYLHHmLz7/81vvf9F3n3gyt87Uuf5bEzx0OHUhAonx/TMNDNEHwPLM6EkL32cef6bX740zc4tDDL9OQYZszCHClCOqnY0OgNhftLX1P13TMLKNkNIVR7CWRAIF3MP/NXFMM12CTzGVb/u/+Y985f4Xe/+WPicZs/8xtf48ih+V6bUefot49AuqgIQoPf/v0fsry2yZ/5d3+JkZlJPM/HCs17nTC/ofqTpP7S30ALI50RApnLsPk//BXefOc8/+Rffot22uKXvvQszz31KMlMivpOhW997yXOnb/Kv/NLn+erX3yWTtfhJ7/3Pb774zf507/xSzz1xJnQuVviex6tdpdGowVoFHJpcqkEpmngeT7vv3WO7/3kdU4eO8Cf+P0XMWsNtJVNaukka9Jn53/4Tzl69CA7f+KvUL23ykgh14uw3VN2MZYCgXb+GsJxlSm9mBuu5+UNhBM+t6kESj0/wHEcZGjy7EUTh76P0TWCIKDd6eL6ft8ZfmC8edDU98AFuVT+ko6rFrhKEFqZ1TENKBVgp/JA1nXPhC0EUgQIlKO8CBneaP4YHA8fdLoAxbjslCtcuHKLG6Go9eZ2mXwuw9lMBjsRD/1jNAU0PsX8OZjd5UNvqP9w4U8Pvv8Phx4R0Nq1SOr9M3B0NP59xPXUbYnhQxH9sfJBdyjV7BnNPdG4GpkQXc+j3e7QbLV7mS160hU9ZL4PgNuL2j+0fGoH+cFJbEhj66PMiEP7heVjVLASilONTSCHk2FqmjIp7nddIeh0HdY3tuh0HSbGRkgnEwyuWYcGh8FKRKq51AuwQsZIaG3VPWTAt77/Ej986R2+9LnHmZwYCfMQNhgp5tHCDtjrjIS+JkL0yKDp8SJzkyOcv3ILJ0xVkc+msE0dT0IiZmJqvTVRDwxEqR90XSeXTZNJJ2m2W3SdjgIS91cRK+vE/vxf40FFItFWNtFWN5G6DmNF9JE8TI7S8n3cdAIZrfIfQD3vR2MLIQgGaeBd9TsozxCzTKQMuHrjLocWpjh59CDVep3V9W2mJ0dJpxI9XxVkoIBiKLZomzqGLshlM/za155npJQnl0mHoeL9MPh9BQAHVjWDPiARfR2AGnxBsTr1ppJGCAeJwRDuDytC/P+I++8oy7IsvQ/7Xfu8D+9deleZlVnemzbV3dPjjQYUMAsECREURAJyoJaEJQgAQWFRBiAlcREgMBjOYHzPtK3uqi7vsrLSexuRkeHN8/a6oz/Ove+9iIysyu4aSmetzHjvvmvOPffcc77z7b2/LX2D2tFGmobozSAyKf816Zo4dhjnOqyQv08AuvzcnagKQvNTLi+uSd+qrnbe2Cywtp5namIU0zQoVaq0Whau47CdtelcWLQnDhVNykb4LIanaRAOy8AShXYwge3rmS0ur3Pt5iwzk6NkMyk8IfA8Cx0NTRdoGHICbTRB1QiHTRmM4g+gfT1ZfuG1F8mkk74siupLUkA7RZjXeXa208JzQUMGeSiKR73ZYHFpHdvxSMSltMmw48igAtmK7WfXbFnkCyXK1TqJRKyttr8FfPsMjG3bCBypOVUqcv7KTSKhEIoA13Hbcg2uH80ZgE3RvVrvKvMLy7zx7kmqtQbf/vpzvPLCCSLhEAJBb0+Gb331ORTg5p17VGs1wqEQkXCIcMggGtFRFIHnBTncBIahEo2FQGioWoexNwydpx57hMceP4quCvQffNSeJyzbxvJcGs0WjVqDc3/7l/n41CVeePoEx47sbzunB/1TLna6FiyKjBoN/83/I8pmEXrSWP/mv+ruUJi/87+HzQIil6bxP/xj1jbL/MUP3+LChav8tV/7OocP7JapfBQpo2LoepvFvDu/xD//b3+P2btL/Ce/8yu8+uKTRMIh6v7EaBoG0VhURtK2fbIEMvlPVy2EiiqgUCrzh995nb94/T1CoRB/87d/ka9/9VnCsbA8XlHg1/8z2PD9SLsXjg961xVQcKVfnyc6LC7yubTbzxPb2NmgdCLuYtEw/b0ZYtEIlm2ztlmgZVkSbAkA/737WTk9IfwxZWdSYseUOJ83tm0ZU7cRMPd9F9v27SyY2tcWXXJOn8emtcGrSlu7bEeg1Q3qZBCY2H5v/nvtui62bclUZ74Go2g/v24osAP4bC+Av8DNwS9fMhrRd4IMVjtKkKLnC1C00nFhf1hNj+6Ii8BI2L5ENon3b/8JwlOQNy6bS0UgVI133/6If/mv/5hoNML/6m/9Fo8dO+hH4smm8pB0tUcQsSYkKeHKv8LzSP4n/xA1X2zX1/UEH526hOd5nDhxiInxEZqVGk3LQtM0HxSCIhQ/FZYcfF3Xbb/X3S2ka9I51tQ1Qsk4jpATtOo7SQsRtDbyhfYjAkMhE8OQIQCKj76DiDQlX6R9k/c9OVDqDameDGieJ1eYioJuGhiaiqYFE4bYMph113wnQKMFAAY52XTkUoRfTw9Nh0wmSU8uRaVW94VWY7z03HEef/QwuUyacDhEq2VJR1ddpViuceHKTZoti6mxQWYmRolFI4wND7BZKCGEoK8nh2HoGL6QZ6vZwrZlfkbdl0BAyNQMpXIVEETCYRRVwXFcX6bCkE7gdG5ZKApOMkqj2cJ1XT/ZddD28nl6vvlXUVR/peaDrHyp3TYik8L5d/9lG0jYrku9WsNz3fZkI/uXlAtQVa0dneg4rky068thNFsWxVIF27YZ+9v/CM0TqGubKAo4nstHn55H1zUO7JvBsmyu35qj1bLp683K+odMX7TXxrJsytUahqHTm8tgGAbVusXS8hrVWp1EPMrQ0ACxWAzFzzGID2gT0TATY0OET1/krfc+o1ZrcGj/rnby9snxYXrNLI1mk4uXbvDZeekPtGdmnGOH95HJpgBFZlGo1lhcWgUhtcDi0TC5niyhmImiyfuulKtcu3GHO3OLtFo2/bks+/dM05NLc/rcFf7wOz/Btm3GRwdpNFr8nc0C/UondDsYnxuNJoViGcu2MX3TW1Acx2F1fZPZ+SUq1Ro9uSTjowOYpsFmvsTq2oafJ1KaghaWVjl19jLRaFjmKKzXcV2XSCxGrGvxoSDv68yFa9y4fY/piWGOHJxG06DRqCMFonXSqQTffPVp8oVi2+yEAoqqYPp9JGDOBBLoNZstLMsHT3WTbCouBWItm81SBUOXfoPBu2hZNq7SYf0dxyOdTNLn53l0HLctTxMsTCRzKN8fAN0wCQzCQsiFdtvvdCsFgedAPl/i5s15Wi0Hz5OuD4EosWSIJNsQROWmk3FymSSDvt6h7bjM3l3gwpVbDPT2cuLYAcLRcIdNQo7f3VO/igvI6MOZiVHSyTge+JHjatfY1l26AEg3ILivKH5EoHTXVzxf+27b9Kc8SL9LnoF0KsEjB3aTSsQpVao4jstAT4aQ0SXjICfbB55nxyJE+5iuj12/d7N4/vjcfewDK33/cjpoe7q+bb3cDufrZvW/AANsOU/AiewEtAKQJTpBdAGrFYC7gNWSWSocmq0WLctuv09CbMMoimg77t9fnQds31Z+DrAltlBqD81oBft0AS3/bA8NuOTpVaShR+na1gX2RPCC+ZoansfkyCB/7z/6TcbGR+nJpBGui6aA44Hjm6e6QaMQ4DmuP7AoUgvmPpMZ5DJJSuUKpz67RLVUZaAv1w7fD+7Osmw28yVmF1aoNVpMjA4wPtRH1HeiFUKqeXueB67DvdU1AAYG+rA9wfLKBgoqPbkcsZjpgzRpJmm1LGzbQVM1QqFABqKbIlHxMok2U7Ft+EMpVTqf/fsFMDSNarnCbLPpq+LHCEejmIaOhpQwcBy3AzpCJqqq4Loe1WpdJhsullhdz5NNJxkZ6iccDknF/XoVIaQsQKFUwLJtEvEIqYQUz5RUf4trN2fJZlL0ZtPourzeyc8u8MM3P6LWaHF43zR//Te/QV9viM+u3OQvf/Quzz7xCL/w9RcIhUxqjSZXrt1mbX2TRDyKrmlSAHZ4gGgkTKVU450PTnHn7iJhX4SxWKqyf88kX3v5aaLhoK1lh/DSST74O7/OD37yAbdmF4hGQzx14jBfe/lphgZ6yRfznL14jVhCMo2378yjqQqPHNnPzD/4l3BnoQ1cb9yaY2llA13XWN/Ic3dhkd5clscePcTk+BD1Rp1Pz1xibS3P9MQYkUiEzXyJfKHE6HA/B/fN0Gi0eO/j05y7dAOvXOM/X8uTTESJ+H6IrZbFuUs3OLR/mpBpsLqW58bteWbvLrG0ss5zTx5jZmqUG7fucu3mLLV6k3uLq+zfM8k3v/o8+UKRtz/4jNX1PCHToFprMD0xzNdfeYbxuq82r0kAk4hFeOzYQRrNFj9880N++t4pPjx1gXQyztOPHWFsZBDLsnj/k7N85wdvs7FZpFpvkMskUf4aPP/MY6AqfPjxGf78ez8lm06QScUpV+qYpsELzxzn2KMHCEVDFDYLvPn2J5w6cwXHdSlXajQaFs8/dZxf+PrzlMoV5hdXWFsv4Hoeg309beAfvNuSJ/doWhb1hkyqrutSsiBgshaWVvnRmx9y8eptCqUKu6eH+Wu//nVy2TTNllSHBylKqYU0zly8yv/wB9/jyRMH2b97itd/+iE3bs2zZ/ckf8txCHWNHY7jcmdugWarRU8uSTwekWyYpiG8QB/PJpNKMDzYi6aq7UW05ydHP3P+CtVak2w6ydjEKAjBpWu3OPnZZZpNG9PQePr4YZ594ihXbszyJ999g9HhPv43tkPI74eu5yFUFQWNWs1ifmGd23OLfPTpeXZPjzE+Okg2ncKyHZZX1ymXqzIfaMiUuR0NnaHhIeJ0Rn4hBLVag9n5RQxd54Drovnzn+O4bG4UWNvYxNR16Wjvs1q25VCuNGm1msTiIZLxWFvIdGOz6AM9OeatrhW4O79KNJrEFQaep0kLghIsBbs9ZX1GTkhx6HqziesJBvp6GOzvkZGjwYBO10H+e3/fRrHD3NeWdlBAFSieQmCH+kL22z9PJBxiz8w4/b1ZqrU6uq6TzaYIRwPrQtfi7XOK2Kl+O+9JZ+G8Ewv1xaUbXHUvJuja/oXloQHWloP8d3mH+xOAcFEedH0BihA+6+61F7WyL3bPnWznFb50+VLMlrr9YSo7POBt38WWB9sF0B4CcEkbuZBIcltDe56g2WpRLtdwLItYJCxNA6EQM3umGBzs5e7iGivrm4wO9OApUCxXuT2/zEa+SDqVZHpihGRCpkxptlrUG00ZFRQObbmW8Os7PNjLeyfP89/86z9l99QIX3nuOE8cP0Q6k0ZHw/VcFhZXeffjM5y/fIuFlQ2ePnGIX//WC4wN9aMrUuQvmNCXVtb5t3/4fSIhg7/1P/8VVvMl/tXvfQfX9vjmV1/gKy89Dgqsrxf47NxlZu8usry6zsraBnt3jfkqt512cVNx3vwPf5m5+SV6ckkOHdjFYH+O6zfnWFkr8JV//K/aq1JsByHkYFYolvno9CXuLa5hWTb7dk/w1ONHGRsZQNMUNvMFZueXWVxZJ2QaHD96gFwmxcnPLvL+ybNEQiHyhRK35haIRaP82rdf4fmnHqVSrXH5+k3K1RoImJtf4u69VbLpBKZp0LJszpy/yo/e/JhavcXXXnqSieF+XNdlfaPAx59dot6QzJLtOIRMoy2oaVkWS6sbkrFotvjokzN88tlF9u2eYmpihOs353j/k7M88/gjnDh2iM18iTfeOcnqRoGDe6eIRSOcu3idy9fvMDjQyzPxRNcDl34xH396nvOXb6IoCpuFEh+JC+zfM8XQQC/rG3l++u4pIpEwh/ZPc2dugTMXbrCwtM7fFV7bndhxHN7/5CzvfXSGZCJGf1+GhaU1Tp6+jBnSGRvppVgs85O3PiFfKJNIxLAslzfePsnSyga//M0XmZoY4aNPz/Pv/viHrK0XGHY9iqUKjmPTHwqh+ZOy9BWU75muawwP9uE4rkyfk4xRqzU4efoiP33/M+KxCEMDvfTk0uQLRX7/T39EuVzlqy89xeT4MJ98doH3PjqLU2vyv7BseT9qJ2w8m0nx8vOPMzTQywefnOX0+WtUfLAUDodYWFrl9Z9+yPXbdxkZ7MM0pVBqrd4AXcO1beYXVzh/9TYHd0+wb+80mUyTj05eQNfPMjU1Rk5TOH/hGu9/fJbhwT6OH93H+kaBDz45z8efXeDw/hlGhwfo78lQKFZ46dkTvPzcY/RevINWKLUFMtvPNEjF4zPEpmH4jGGLC1du8tGpi9i2FBBeWFonXygxPTnSXiG7PmWrhXTAxTBkwu+hoT4mxof46fun2tpqAQAIhjnLdnAcudALd11bCBVVcSmVSgjXIRYNo2taO6OAZTlcvTFLqdZg7t4KmVSSb3/zJfbtmabRtLhyY5aWZTPY20OhVPYzMqgUy1V0Q/NX7rJbyEWmdOIvV2rML61x565MybOxUSARjxEOhTh78Ro/ffdTWpbFQH8PruMSChkcPbSXXG9P27Il783j1p15/j+/+yfEohH+Sb1B0h/zPU+wvlmkXKkz0JchFJZix57nceXGbd776BzlapVXnjvBsSP7fKHgCK7rsbq+SfhOiEqlRrEkc52OjQ4TCoewLIdKrUaz1URTFVKJCLGwjOT0PLngbTYcFpZWuXDtDpV6gz2pOIl4dOt81TYLdW/qYlx+BmBwv79WN+OztQQLfTNk0tefo1fkZL00je548LZ/0vY60mEe23XGd/Nqz6uwPbGytFhsPdcXYott8/mD9n94jLKdAvjivT+/iPuA1s7PofukKqrvttBlPtt2te2A9GdHYV/KZ8uDLn8tIdF3IHi5k29Wu7I/D5r1T9cGaKLTLkKQL5Q4f/EWH5w8S7FUIZmI8cSxA7z8wpOous53f/QOf/z9t/naS0/ym99+iUKhyNsfnuXyjbssrKyTiMf4jW+/yqOH97UdT2v1Bg2l6Zvy7u+Uzz91jHgswrsfneHStTluzy1xb2mNb7z6jFTe9jwqlTK6pvDq8ye4eO02hWKBQqHIUF8OKbIn2zBwAp+7t4yhqViOTTaTxLFtzl26zZ6ZCb76ylOsb2zw+psfcu3WXUaGBlBVBdu2ZPqSHQaDzXyBP/3+T3Fdl7/529/km197jsvXb/PdH73P081mxwRgW/J4RSGZiLFvzzS5XIa3PzjNT945iWma9PdmKNbqvPPRGTYKZRaW1rg1u0Cj0eK5p47x7ken+fE7n3JgzwTHH9lPOBLmnQ9Oc+XabR4/dpCWZXHl+h00TePA3ikazSaaJu8bIf1u4tEosWiEer2JYahU6jU+O3OZQrHG6FAfjxzaTSIeY/fkCLlsBlSNTCZJzE8rIlSFzXyR23P3mJke5oXnjpNOZylV6nz06Xmu3Zzj2LFD6LpU8p+ZHOFv/4e/QU9/L3/we3/Gn/3gHW7N3uOpRw52GlFAsyXTkui6Rk82ha5pDPfnMDXFXyF5PqDp4YWnH+XE0f2UyzXuzC7g2DZGe/ErE1pXqg16cml++Zsvsr5Z4A///CcsLq/JrAR+Mu1w2GSgP8fIYB8ra+sUy1VUVaFQLHPh8k1K5RrZTJL9qkp4pShNPoEIpK6RyyYxDHmfQ4N97Ns1wcToIMePHSSTTlEslhgZ7ieXSfLEo4d45fkTjI4McPHidebuLvKNV57ipWeOEY1FGerPkS+UuHr+qjTLQhtsyddcIRmXid+HB3sZGxngo0/Py4mu2WJ+cYVytc6hvdPsmRknGg3T35vj2OF9aEKg6jozk2NMjA7S39fDU489gqGprKxuYFk2rWYLq2WxvLKBpmkcPbyHXVNjZDMpVtfzfHb2KtV6jXDExPWkaS9kmqTTqY5JO1iF+/1cU1V0XZqXo+Fw24RbKle4eXsegOnJYQb7MwwO9jA6MoCiqGiqhq6puK4rHWoVKdipayq6DnpI5cC+SXZPj+DuMDmqqkI8GkFVFRoNC9uWzJrjuKiKimqA8FzmF9aJhEMM9PcAMsuDoioMDvTy1OQoJz+7xJmLN5i9u8jumUmi0SipZIy+niy/+q1X2TM1RjoRw7Zthgd6aNrW1jEsmJxRiMdi9PvM/JEDu3jluccYGezj9t0FfvTmh+QLJb764pMc2DfDxSs3eefDMzK5+a7JrjNKps8TMqVTsVz1AW1wHVA0Fd2QQROaYcr0S02LO3cXuHDtFsViiYnRAfbvmSIajaCpKi3L4u78MvlCmUtXb1GpNjl6eL9UgNc0zl24ypvvfsxmvkgsGuGRA7t49onD5HIpiqUyV67fYX5+lZX1PGcvXafmR2DbQup0qXISIfDZbN+NEG1Qqn4u0Or6zdvJwiM1vwC2e/AJuqRptj0Xgthl4dcPD+8+k2fn2kIEZ5R/ReC4D213zMBXc8u2hzVNPnBO36lssaF8zjm31eFhAa0SXGMnQLRDbdpmRJ/Z7rqOqup+1hbjwUSfeDi/rM8rP1+6nvs2CFAcULQO3flAyrMLKP18DOLWSgjpoPvRybN898cfsrC8zuhQH2sbBRRF4bETR4jHY/53QSoZQTdUSpUqjWadRw/tpq8ny0efXWBhcYXDe6fRVQXTMMikkrQsq7MC3Fbh0aF+Bvt7OHZghpNnLvP2R2d5473PcByH/+DXvk42k2BsOEd/fxrDDIHqcfvOvbb5T1E6aSUUVSEei5BJxVlbL9BotpiaGeLI/hmu3ZwnZMrosjNnL/HJZxd45fnH+da3XuHqlWsgHLQdpBpUVeHokd2cuzTDe59cYHl1E0VVSSWlL4dUivfvyZHOtqahMzzYx9HD+3B8E+Wf/OUbXLp6k6dPHGT27hJ37y2xb++Mn5y7SbPZlLncomFi0TAvP/cYv/ztr3D58nWu3ZyTOac8F8/zyBfK7Joe48jB3QwP9nHz9j3WNopUGxZC1dize4KNfJEPPjmPpqnE4lFKlRoty+LQgV2MDA8Si0VJxCLgK/nLdDwamh/dVPETGA8P9WCGTDwhSCYThMMhGs0WAjD99DqGaRAJh4iYBrFoGMc3hXp616shpCSGEHDs0G6OHt5LKhEjm4qRy6QlWNaken8sGkHTVJqtFigK+WKJlmUT6Xouhq6jaVKNPRaNIoQgEg5x994Ks3cX/ezzXjsDQSqVkOfVFSzbZnl1nbXNPKap8+TxgzzjQnpesoyaKl8s1/OoVOuUKhUcz0JXdDY2i+SLZQ7t24UdsRCe8FMs6SQSUUYH+2Q6FB/4moZOwddqiyei5NJJ7par2LZDGCEd9IWg0WxRbzT8+wiTSafYPTPO/MIKmqbSaDSpVOsoKBw+sItvv/YiyVQS17ZxXZd6vYFpGsxMjfKtrz7rK+xbtBTJALmuS6Np0WpJ59WQaRAyDeJ+xF0qEfMTR7s4rtVmWOqNBlbLwiBwwA0GDmnmCfwe5fOTn13Po1iqUihVSMSjvPrCYzx+/ADRaBhd1/FcmTpHVWUycRn+70+kvh+I8GSKsHQyzlq+vG1lraBrGiPD0rQ+d2+Fq9fvkkomMI2QHGsUFU3TuDW3yPziGr/42vMk4jLFka6pDPTlOHJgD7btcvXmXdY3i2zmpWnWcaV5ryeXJJGM4rg2zVYdV7hbF2NdrhGekDkBE5EQmqowNNDL7slhavUGly7fYHFpjb3To+zbNc7M1BihkMmnZ65w4cotnjh+iJltIGX3zBh//Te/Rb3eJPK7f4mo1Np9aqi/tx34ovvjXzhscuLoQWLxBO99eIqV1U3WNwvkhIwUi4RDTIwPMTEmk87/5Y/eY8P3R51fWOYP/vT7nDp7GduRmRjuLSwxNJgllYpz+dptXn/zQ2KxKEMDfWRSCdY2Ctiei+M6tJoWrtdACCmkqndNtAHYehBTIvx+1WaJtrEhQfLyYGsAtJT2GdUuVwW2FQ08rWsO9cmMwE8G2gCu+/DuunpdbFf7OOHJebq97X86oCVkgqp2zTr/bzuv2P79c0DTlrPtZOe7P5J0u3N8ALw8P+m0ptBOKbc92lFBMrJ/BQLyP28iar/h2g3frWETMFzQMRN2N4iCL0LCDk3/uaUTtqmiKK6/zaNcqfK9Nz7gxuwCTz16kN/5jW9iOy6tlkXENHBbFrumRqg2akQiYcrlGgO9Wb7xytOAyvd+8hEIQaNex7ZaREwdIZR2OhzP9WTiWG9r5643ZA44IxTilecf59HDe/i9P32di9dmyRdK9PYksB2HpeV1bswucebCTRCCzakyLcvCMAwZ1Y+ku8Nhk1g05Pc3gabJND6mrqNrOq2mxa3ZBSrVGpGIgWdbJBNxRgZ7adouZoDMu5o6l03x2KMHuHxjjluz97h+Y5aVtU36ejPo5nL7bkTLRghBy7LZzBdZWFohEZOTmK7rtFoywXC+WCKbTnJo3wyGaTIzPkwobLYn7mAidG2bUMgkEg5802RQgeN6tCyLlq+8n0rGWVrdZHl9k+W1TVKxSNv5e3Z+iVq9SbFSo1ZrUCzXyGabrG3kqdYanDh6gEQ8RqUqTQiu5+B60hzabLVYXF5nbb1ANqtQrtZwXMk+yTFG7TgJOw6u7VBvWm0fCkyt7csXvNOhkMH46CBPnjiC5zjYVqudJFsJ7tF1cVyXRqPl949W21wF8vdSueqnS/IDMJBO02vrBa5cn6Unm0J4AlXrKIXbflJi17VZz2/SbDYRwmN0pJddFcvPj+khDBPXcanXmyytrGMaOvV6A0012SyUuLe4ys3Zear1OpblUCpLjadarUGz2SLpuqRTCRLxKJev3/FTMWUQQnDzzj3Crtt+nijST29haYXzl27QaDY5uG+Gvp4c1WqdWDRCLiPTrgRk96mzVzBNgz0zExLUCo/B/h7SqQS6prNnZpyFpTXWN/LU6g3yxQpCQN5PfJ2MJwiZIebuLRP3k8HPzi/Tk00TD3zzknGEEMzOL7KytsGUJ1e0nuiM7Z7fRxqNVnuAbVkWpmViOQ6GYVCrN1he2ZA6dqraCarxV8iOI3OPIkQ7aXK90aRUqlCtNdo5EbeP06qqsmtqlOnxIa7dvMv3f/IhrudxeP8uP/epy+LyOqfOXqVab/DkiUPEo9G2/Inj+znF41EEUCxVWFhaYX2jQKNlUWu0KJTK9PVmcR2XhdUNNvIlotFIpy4i0LBSqNebzM0vMXdvCdf1UITKxkaFG3dmuTu/jKHrRCJhanWZjBrk5LS2UaDoi+lKiC+BazIR4+VnH6feaKH//vfb9x0sMBA+UFFos4vDg31sFCt4Au4trXLzzjytlvSpi0bDDAz0MTU9wcBAH5v5IvlSnXyxxPWbdzh/+QaxWJTRoX4S8Sh92RTRSJhiqcL5yzdY3yxy4tgB9u+Zpt5osLAixaxt3xdtfSMPKOyaGSfXNcJvFc7szEHCZ5mUrm2ygylb9pPt0mG1JBDyo6RR/ODFnZiqndgxRUZVBoCLrbinAz62nmXrGQIrZMdH+T5gs+Wk93/u7L2dLdn+OQByAQiV9769XgogFBUhvE59vpDl6rr2faRNm8LbVivRPl/gHB/8pmkqmqZj6JqfKUUerwiBpihyAat0t/HPbkKEv4LciN0V6EiJSgdU+etOeZ26tz8cZJSO5H4jdDbieYJqo8Gdu8t4nsfYcC/jY0NEIhE5GVgyvFtRFFbWC7TOXyOXitOfS1MsV7h68y7vfHKW9XyRUqVMuVJF13XqLRmZoPmJgx3XRfM8maoHKcVy/uotNtbzhMMm/b05pscGGejNsr5ZRNVUmpbFBycv8MmZK2iawZ27KzQaTWbGhzmyfxfhCDRaLRzH135R1LZQoecJHFfqe4H0L3EcSyZw9VxK5Qr1RkMmyq5UcYVclW9H5eGQyZ5d4+zfPcGFy7f593/2Y1qWzd5d46gnrwfNiOebruqNJo2GzIloZ1wZDaUqDA31kUzGJZPoq4unUwl0xR98FMm62LYjtXEsG8dx2/fmOE4bkGxsFMkXSjiOQ73RxHVdisUKd+8uETY1zl++xfpmkWQiRjadpLcnw+z8Kq+/9Qm7pkb9NoGD+2Zotiyu3ZqlUJSm41q1QixmkohHuT27yMDALQb7+7l89TYCGB0ZRtN1CfpsG+EJLMtP3O3ISdW2HdbyZYb9HINCCFRFxbJsPjl9mVg0QjwWwXMcJseHiSUS2LbXTo6t0Mm44DruFtkegUycHg2HyBcrrK3n0Q1NTnKKQsSXQ5BgvsXq2iaJWJR6o4knBJbj+hGIDo7j8tn5a+xteujVOoauoUdCuLZN04Vm05ZyJJUaphmlWm9w5+4irTc+YGZylN3TY23fkmq9IZ+rEAwPDnD04F7OXLzG2Us3CBkGiyvrlKoNvjI1jrlR8xmYQJJFUCiWuXN3EV3TaTZbbGwWSKfi9PdlMQydXCbFQF+OU2evsL5ZaIt1jo8Moj2i4noejXqTsxevsbqeZ++uCcqVGmubRZngu1anWquTSSeZGBtm/t4Kc/MrFIpVYrEwzz19jKGBPlq2w67pMdY3i6znS1y+Mcu4b6ZukwL+qnZ1bZOWZTE63E80GqZWbxJPJukf6Ofggd2sbxZ5/5PzeLg899RRhgZ60TWNpp+wtlIVVGsNENDb30s2k+Tm7Xu89e5JWpbN4mqeUMi8bz5QFIWJsSGeOH4Iz/O4u7jKn33vba7emKO/J0OpUuP6zVnmFlaYHBtEQcF1XZotC9uRLJ9lS/Os63rUag1q/vOzLJv1zSJLKxuMDg+AgOXVPJvFKoqqyUWjkItUz5WuIMVKlVZTYyNfwrIcavUms/eWWV7JE4vGyKQSeJ5Hs2XRalnk80WqtTqGsVW8VjL0Kmvrm8zfW0NRVI64MsGz8JmicqVGo9UiFg+BIs2ijWaL67fmeO+j09y6cw+EYH5hRTqwqwqNRovNfEkmtdd1JsaGiBeqhMOmP34IJsf6+I1f+hqTY0NEQwa5XIKllTU2N6Wvnq7rkunRNDRNb4+tzVaL5dV1hCcYHOjpAlvdU2pnopbbvS2/C/AJla0+s0K47UCtTvEArcO2+IuWna56P/MkAVenN3kPnPdln5OO4G3fLdF1HBptaYpgezfY2YHBEl1n3sogdcDN1tvwz6EpvlZg8Fs32xZcpwtwdf+23X2ni3mSQE2w3X+bLVYoDxS/zyvB4tmvrwjEcn0XAD8Cvft9DZkm0XAIXVW/DM4CvizYUmCr053SeVBKQNdLRLv1INjxAX3h5QKwpSCTc8pzBUKXjuv6zEGRRqOBgoqh63hCZoTv68lgOy7VRpNbn13k1LlreJ5HsVyh3mwxv7TG0pqMBjx7+TbXbt9loC/Ho0f3knY7WeGD/0+du8aZc1eZmRginUrwwclzfHbuOsODPcTicSzbYyNfYu7eGrmsZAeK5Ro3ZxeoN5pUGi1W1wtUGy1K5YoUYfQEhVKVq9fv0Kg3uXn7Ho2WJQGV5zI1PsRn569y5sIN4okEpWKJG7fvYYZCFEvl+9pU13X6e3M8eeIgN+/c472Pz/PokT0cO7wX5Y/fbu/nNm1s26ZabVCqVLl2Y5aebJq5+WUyqSRHDu4mk82QTMY5c/E6P377Y4YH+xCey8zUGMNDA+i6FFEslipYtk25UqXeaFGr12m2LEKmSSaV5M7dBT7+9CKRsEmpXMUTYFk2G/kitUqVpZV1bNshGg2xf88U05MjtFo2V67fZXUjTyIW5fmnjpFKJbk1d4/FlXV6ejJEIxHK5Rr9fVn27Z1m5YPTvP/xWZKJOJqmcezIfg4f2ouqKNQaTRxXoKsK9UaDZqNJoVRBVVVuz93jYk+awYDJ9JmL8dFBzl2+yQ/f/JB0MsFgf45sLkvLsqk3Wli2Q7Vap95oYdtSC0tRNDy3A4IVRSGZSjE82MfcvWUuX7tNf18OVdEYGepnbHwMx7GJhEOsbxR475PzrOXLlCo1UBSq9RYoKslknBEh2MyX2VjcZMSRzKanabiOh6vCyMgAA31ZLFegh0Ls3TPD3YU15u6toGsa05Mj9PdmObR/mpGhfhTNQAhIJVO89NwToGhcviZ9wzKZBK+9+igvCAX1Mz+Vi59cPZdJc2j/LsJhk1QyjucJYrEomXSKTDpJJBymJ5dh/94pGi3JwpmGNJEO9ud8VktjbbPAyppcvKRTCRZX1qVDf09GsjKeIBwO8cihveRyWRkVGktwcN8M01PDaGaIsG5w4vhhRkYGcVyPTDotF01bxizJLCQTMZ44fkia1cMhEskkhmnSEwnzzJPHCIVMzl+8zsLiOoVihaGBXgRgWRappBRRKJcrNOsNBvt7eeKxw1y5cosbt+dJJmKk0wl6smm4vthe4Af/4rEYx47spa8nw43b88zNL7G6tsnSyjr1ehNdhadPHOKxo/vp68lQrtZYz5doNC2WfTPb2nqeWqNJOBIiGg4z2N/D6HAfzZbFytomzWaLsK/dlUnFCYdNKRAqJEvvqQJPKFKLbXyYvt4eFpc3pUvF0iozkyMcObQPRVPZyOdZXNkEReXqzTlaLYvJ8WFS6VR7SBdAs9niR29+yJvvnmJidIg9rRaG/6tl2SwsrlCt1knGI7RaFo7jML+wzJvvnuTMhetyYeA78kciMjig3myxkS9iWTYNf4E5OJAjk46TzaQImTqqopBOJRgfHUIRHqomwYBkMC3K5SqlcoViqUKzKRcxi8tr7N01SSgkx6FWS7qMbAFRfqdRgpViewbqsCQ+zStnqDZ74t0HtAIA1PXl5yhdcECoPAzgEl1jjxIskrpNjEF5AMC5H3jtdM3PQSFti9DONx34VCLk4k0Jrrkjs7WNTdjxdwG4Xc/Cf/k8D0W4eL6UUsDuyZRTiq/VJ8+hIPcJh8MkYhGZo/Pne2Dt8qWZrW64F9iwH+yr9bBbd7pMF6vV7uCgqDJstieb4vbdGqcv3pTNLQTRSISvvvgUmWyWZCpBNpOkXJUqsZeuz/HR6Sv0+fnSXE8wu7DGeqFMPJHg1Pkr/PjdU0yODdDTl2HPNlVhRVHozWUoVmp8cOoS0UiIWDRMbzbNC08fJ51K4gmHI4f2sLpZYnE5TzwWYWZySDpYGzrCFeyaHmV4sI/+3hyqojI5PsKN24u8+9EZbg8usO4rT6+ub1IqV5mcHJVOqlfv8OHJ86SSMXp7ssTjsbYNesuTURUikRCH9u/iwN7rlMo1Thzdx9jIQFuUEsDzc0JFIiE2CyVu3rnHxmYRFIVjh/cwNT6CaZpMTIySuHCdT89cIR6bZXpimPGxYRzHZWyon+mJYVqWTaPZolptoGmqlISo1Rno62H/ninqjRaFYpVwf44De6coVRtEoxFfIVpw9NAe6o0Go8P9ZNIJctk00UiE85dus7JRpL83y/NPHycUieB6LsNDfRzcv4vhoT76BvqIRMM8cmQ/QlG5M7dI2AwzMzXGrulxMulk26x3YN802VScVCJOq9FksC/HUycO0bIsbCS9DKD4YCuYmCvVGgDpdNLXVINcJs3jRw+RTMQwdJNoOMquqXGG+vsw3z3bfkuEgHAkzFOPP8IjB3dLoGHoPHbsIH29OUaHhlnd2GD3rgn6+7JkMlky6RSHDuxmeHiAcChEuVpjZGSA448eABTG/vwtEvGozJfXssCyiMUifP2powyMDtI70Itp6hw/dpBcNs3s7DwID02VoO25p44xMjRAKhlHUWTWg96eHHt2TbJRKDEzHeWpx44yMzWC+dZH0sdH4Ptsyf6TSSWYmRwjFo1IfS1fTiNkmjIiMWQyPNhHKBxhaLCfdDwiWU/LIh6PoaoK/b1Znn78CLlMilA4xMZmkccePcChvTPksmls28YwDOKJBOlslp6eHLV6neGhfsKhCJpvds1mJMgLmWGZYairnt2mg727pwj5+RBlQmnV9/tTyWXSPHniCOMjg5iaIjW2DBMQTE+M82vffgVDV5maGMFqWoRCJk899gi7J0eIhE0S8Ri1ekP6Wb174b4xzzQNBvt76c1lmZ4cZWOzSKlc8fMjQjhk0JNJ0O/rxtVbFhPjI9iuoCebpl6XgTvTE8OkUglSqQTDw/30ZNO0LJueTJJwOETINJmZHMV7QZCIxwjNf0/mNRSCkBkiFY2gaxqGpvDo4T2k4nHu3luiWm9gmCYTY0MkEmGu355DVRWqtQbpVJwnThxmeHiAnp5sZxQXgpZlUa5UWV7dIBGLbfGXyRdL3J67h6JINmujUGqz3/VGC13TmZ4YIZeOMzM1SiqZQDMMyXzdnCOZjNNsNNnIFxkeHsQ0dSbHhxkf6WdxeZUf/vgd7s0vkEknOXRgmmgkTE82zZXrs5w8c5lbcwvcml2g1mgyd2+Z02cvMzE2hKaq2JbUmtvylBTVxx9bLQbdYKszNwVuB6INtB5UBIGv7hfrUX7Oj5/z2/b9HqQjtsM5HgZo/Txlp+boAlSK4id8b5tpHwC6RNfJBF2sls+2KS4Ib5ufJARpfoRvdWv/LFQ/6EXqSm5VWBAYvuO8ZHB/znv3y5cDW/6Nd5wAvwTy6wJQ9/8kHrifokgwsWfXOCsbRa7evsfNu0uYhs5jR/fxCtL5U+rvrFFvWQwP9dHfl+PYod2EQgamYdBqWWTSCdKpBIqqMNCfYWSwh/HRQRLxWFdt/JWFovDcU8cAOHn6ErlMkkP7ptg7M87k2BCmaeC4HrumxzAMncXlTUAlGYswOthDf28OT1H51W++jKIqZFIJDF3n5WdOkMukpNK36zE5NsjM5Aj79kziqSqmKYHT8GAfmXSSbDZN2JQh0r092W3vRof56+vJMjzYy+hQH/t2T2KaxpYXSbU9MukkTzx6kNZB2UYh09iiUq8oKqPDg3ztlacZH5tFUzWGBntJpxK4rsuumTESiRjxWFRGxilw9OBupidHSScTGIbOrulxhgZ6iYTDxONRPM+lXK9TLFexWi3SiSjJZILNQplCsYztgqpqTE2Mksn2UCrXSCbiZLMpqrU6pqmze2aC0ZEBctlUO5K0J5fh8eOH2L9nF7qmEwqZfpJhaaKNhMOcOHqQocFeenMZXNfltVefpVZvsLq2QUSwldIG+nqzZDNJEvEY5UqN9c0CkUgIwzAYGxkgm0mhayrRaIRELM4rzz+OZTtEP73aXtMp+RKP/fN/Qywalm0kpBOw53oYuoamqPS6HntcW0a9GYZv6nNkGLstU0sEPju6pmPcvIdabwJlhO1iCkGsUOHFf/57UkcoFoVYlEgsSioaZrcCVQR2OIQTCaEm4+Q2K4TXi4hEHKJR6fNYrbF31yR7d01KsGO5aOWa70Tsr5A3i8T/o39IyHEYhnb7237yYdMfqKK2Q8qy0XWdSETS8q7r+qZvOYIM+6ttzWeqJyzpR2i8dVquVX1Tq65pCCBr2Xie1067ItvJ6whuajJ3KaUqQYCLIuTgrRYrDP39fy5XrNvfGf9hpRGMCz/tp9LZvkcIZjyp66T6q/IYkBPC99/zOQV/mFLL1ftGRjUI7PADW0zDYGig19fKk2ZDRbh+HkKIxmI8efwwB/dNE4tGCIdCJOIx0ukU5ZqU2DANg6FBmeIrHjYJmQaqqjA6PEAmnSQcCqHrP0T4Y0IiESOeS3Pi2AEcq0UkHGHX5CTVep1SqUIyESeTTtLXl2Z8bND3szQQKDSaFqqm+lI5nbk0HDJ5+bnHiMdipJMpzLurMueo//z2756kv68HVfUY7M2iaSpjI4N8+7UX2CxUCIVNktEwfT1SWHd4qJ/xkQEazSZ3ZheIhAyikTAKAsdxGB3M8ZUXHufHb5/k2s1ZNjYLHH9kP/v3ThKNRjh6eC+FUoW795YplSu4rktvLk2zaVGtSZZMCvvKhN1tCNJmQCHQc2r7iG+fp7ZMT6LNaN1v3JInDoDWA8t9PtGfV3w2rc0uBlYmX2PdZ3E7gtg/hx3MJ1DaiZy/hCmtU+0uk2U34Or6TeA9QEh2x0rSBkP+O7jFVCs6jvFtFyfR9Y4GOV3b9ep6zu0OITU+f97y5XS2/K75xW3/JY2dwFal2wCpyquHTIPHjx5AAWr1Jp4n6MmmePGZ4wz09tJs2jSbDplMilCjhesJHjm0h6OHdtOyfCdXZERYyDRwHIcnjx9iemKEbC5LNpuSDMa20JFkIs6Rg7vRNJWp8WH27JokEQ2hKiD8PIyRcFgOdqkUmiojqOKREIqmYSgq/b3Z9kslgJGhfnLZNIVimc/OX2FsZIAjB3YxPjFKyDRZXd/ENA0O7t/FYH8PkXBYhq9rUpSxuyhIPzfbciiWyqyt5xke7CGXTclkt51bQRMe0UiY3dNjaJrK4OAAAqhWqqgKfqSGdIzdv2eayfERms2W9IfQpLxAX0+O3pz00bEsqcp96MAu9u+ZIpdN4wlBLpOivyeLYZoI3w/EQ8PzVMyUQjabxgiFKFUtmq0izZbAdjUMQycSUVBVnXDYBCEdlOPxKKlUQqZ46YrI1HWNRDxGNBz3/bEkWAl8GBLxmHS4TsQQKGieIGsYpCKCTDaDXa52aGhPIDaL7Po//TcYuoGh67Qsi2ar1U6jomkqqS62XAhIBzpMlVr7mXiuS6hcxbBs3/wtwBfU02t19I0iOrSjF4M+F6SnCgMJf7uiylQESr2xJZ+btO4L1PWCb9LPg+K74ioKmucRC+5NkRkfNE1DDQCLopBxPZ5pWQjTQIlH8CJh9GQcvWmB64An/Qi9RhOv0UAREmjpuibX7a5M6Kr5QqGqL6ZpmgZGy/L7nED1AjZA+GZXOfChKKhesEL1zR7C8/0a5TlNV5oKZHSg1jb1KH7C7CBljuefQw6efr5Dz0PZLH1xtgu26glKwNY94Yot7++WM3WZnTrUmkAplEn+7f+zv1YVxIKEzF1Mh/DzMqr+fYBC2nX9wBoZqt7vCYabTRpNqz2HaqpKLBJG706143ltk4l55bZMvO64JNbyUKxw9J/+99iO40egmgQCoiigqVq3woe8FwUZNAQy4bXvJA8yhdjUxCi5bBrLcjF+/wd+PRR6chlefu5xHNdFCIdkQi7KNE3jwO4pbGTAhXBsXNeh1rDo7cnw4jPHSSbjJJMJFOFRrtRQAKfVJBKP8eRjh8hmUqytS22wQ/tm6O1JA4Jd01J/8PbcArZtEw6HQQHHdhgeGSIei7K6vokb5M3tenyK75OoKCp0Jc1GUXwA9vAmwfbPO6rVty/Yvu7DFUWaElXXP+/WubgdB7hjKN1DXuM+S9UD5vLAShLY57Zv98T9bfQAwBVcBRFkqKHz/ijQDrBT/DmunbHjAbeA2LHWwv8NZOYPVe0wW+040m2BcV+mfHmfLb6gnz1ER/zC0t3OXUhX+Bs1TefxRw+xa3oM1xMomk4iHiWXzmAoKk3FIZtJc/jAbizLJpWKMzzYh2kY1GoNytUawnOlOroQRCNhUsk4u6bH8BS5UjcNwxcN7Tw0RaEdWTU40Es4ZOIJpf3gFUVONuGQ6duNJXr2ZNa59j6C4MHLvImmadKwbAqlKtMTw0yMj5BKJkGBUChEMikF+cIhCQ7lan/nSaPebLKxWeTDk+eYX1jlqy89TjwWba/igqK6HpqmEYmE2cwXac0vYhg6tmURi0phwfa+qrwnVVH9SDNpvtH1YGKAaq2O47hEwyHiZgjdtnEtm1DLQpP5kcCyMGp14o0WMcsmomuEVovYLZvc8irhcoXUyibRK3fQhcC0LNyWhe56aEIQsWx6HZuQqmCggOOg2A7CdlEtG9N2ELYjBVttG2wX1XXBcYjbcl/FcdsTZwCuYu0VEP5qSCaVDpfrUmlcVRGOg+q4GC0HrdFqEyLdnXNLaIguw7i9bFI+p3BIDhKAcD15DUAUKzu+Mm1mrKv/4fkRUboGQtvK+ioKuJ4EHp4Hrv9X+LkEt40+StcNKIAuZAonAbBKh7EJBiPLxsiXEarSrliguScUBV1RpIi2oiIUZGCJKsUDfarI3xdELIY70IPwXNlG/jvhdQ10QUJoz3+ngI6zt6Lg+qmmEPhtqSDUrnfLE3gpycIo/rNVVVWmHOpis9oN0NUu7bRoCP/83To9W1mF4PlskYkREixqG4XOc8uXZD3859G5rDzaE0LuF4BqQNgOomWBqaOYITQg7Diotg22Q6xYAV1DNXQUTUfo8v48VZWJvDUdLBvFB0qq6yFsB71YBseVkYFtJrXTjbqHlbYXkidNMoraeZ5yf6nxlkomcByHIM8zCkTDIdRoRL4rakdQNvhrIPOausLFcxU0FXrSCXKpOIlYTFoL/KCaILWWXORlyaZTgCpTaJmaXPAKQW8uI4FpX853LZGMsny/FdYLZZrNFrFoxDejbx1D2+KkXT47cg+VdpoWRZGfFRUV1QdiHUmQ9lEBINo+Tj8AZHX1sB2ZsuDhyPZ9OPDU7uIPscjoHNQ9AXdfn/vr3gb4ojMefd78v91M2N7s64+x/fjto6MARWx5d7+QDBPqlvfXt1rSEXkNtm1nAztj3c9TvhTY6h5v2n9/noo8NFW4c1GLZfr+3j+nr6sq8hnJ1WxKCA4HJguERLD+ABbI9nfXoZ2FvkO+olSqW0Uckau43p4s/b1KG/QIBG7wanjg4aHqOoYusG1ZB094qELxo7lkPYPruCjE/w//gj2rmwxV60QiIV+7SU7dU57HhB+K3k62TNAfFJQLN6DRlHW8cIPI7/xDwptFjhUrPBsNM7KQx/z3b0rzzNwSim2jOA7RP/gB4R+8y4Dj4tiODHP3GQAVBdMPi/VnLn+lIjp//X+Kv63H85j2JA2sqJ+zmntASQUrnftQzNYSrDR3LD9jv+qea9ufWxZC01B6sxJMaBpCAdUTGP5qWPiOlWLbydrnUMDNpfFScRr/+O/KEGPTbA8olidZHfGXPyX87/5SNvHoACIWAR8U4vjA0ZFgEceVQNFxJYjyB5yuVttyT+26+cBLeC6KJ1A8OanLfwIlAGWui3BchCNZLCUYkYLzmQbucB+ub+ZSVdVPCNw18You0CECBqijPQTIpOm6DpqCpuptkBvIJsj2Uzr9OzCLKIpMrxM8Z58BVBT8iK1O48s+qKBUZOYCoapd5+oq28EnOyzqg2bsWol3YoS6W/3+SRsU1OX1Ldv9G2B7ad9XUAkBiudhCNHWkwMFDUFIAK6D1rLlBOVaKFjt83QrHSmW3Wa5UBTwPNRGS+YzNRTQVISuIwwNNA10zRfd7DAOwhNYtt1ehOpB8uhMsn0nqs+WdmxvtJOOy00aMn9dAE0FquLJyDld88dXlWg47C/kdBmp7ZlEwmE83+dQ87WRTNNoM1OdeV+aarPpZNvcGVgAhBA4vqlbUUaJRaP09/V2oRqlDQSCcX2LAzf+/KL4z0kRKD7gQlFByGi/IFK7058eHmh18g7IFmqnoPOPk+Oq+3BAS/HPJanjL95/h7IDJHowYAsqet8AtHPd5D7i/u07HrfDNQPQtkMKn+D5dYy7Ch1zoDQruj6T3BlDRfvvjquwn6P8FTjI+2XbYLzDDnRudAfK7wETo+g+7077BIjUdyRvd0YRDJi+XoYImIat51DYykDsBN53KkqxTM//8p9u29i9BOxa1Qg/F5MbyAKobVPJfccB+qWbhCxbmnqUrZPCTkIaW67vOO1bVCwHNoqIjQJJT9ATixGuNlCQ0gmK03H8VMs11HINARh0Ol079YPaGTAetrTXA9sSeD5M193+W/casT0VPmx1gkneX+3iK3G3gUH3cwhWVP6oLaIRvHSCxr/5Z+iK12Y3gS2mn04IfFedfVZG8cU/hSeI31c1BUNTEZpkGoJi/86v4D1+uM2saX7+TAHtHHGu68k0M7oGPmMnbE8mIrdtmrUGtXIVYdtENI2IaaJ7AuG4eK7cT3V90Oa6aI6L4vi/WzatepNGtYZotdABwxNoLQut1URpNLEjIRQBqvCkL1QA0lwP7A4oxHVRfcCII+tGAKQcF5ot1M1Su02FEAjLxnNdP0Ko40ysqoEY6UM++y1jxnYI+iAgr2w5fwAHAvDYbLRotaRTfMif5IOzKv4z3Sknnteb6VRrh/ptBesBe6a0zXqBXpnQ9XZqruC9UGoN2CzijA0Gd7D1HncEdf6E4noI15WTtydQbJ8tAzB0vGQckYzjphJ4yTh2IkZZU2hGwkQHe4mPDKH3pFAySUDxpQZUGbDgX18plAn9zX+wwzPYfveKPyl1tYvYimXvIxzaZQeA7JfQDg/aBMaFYAx/saAq4PfDLScKLui3T9dSoQP0g0oKn21WJMP1hU7w/nUeNHN2nxrYeu2HKGLL2BaA3W3zzkMvSsXWy35RFVSlKxLxC8pOddmxXt14oFMNGW8c1E8lkOdo650pCp7oJDvfWvyx1dvJsf5nXrM/sPzVga0HlWAScgNU7eFTPiDUzkrhAXQi0LGVd30XQqCkk3Sc3LofkrIFKCCClegXw+yd0PuW21kvyK2eQMmX7tt7x/P59Q98UJSuCWSnotiONAEB6A+/ChF+Ytku6Rui+TIjnjRw6OWqXN1335oPLIS+A4zzmS3w2YCu+rbHb5CDkKJ0zAnBP7UbvHT+bt/ePr79m9qpm6riIXWgGi0pBJtIxqS5Q1NRVFWa47ZfP7iGX0XHdtjIF6hU6yTiUXpyWemHxs69QUFAvgzCg1wa3dDQAv/Lzxk8hVQq3AJMRTBAb2dS/OuoipRIVOyuaChT31ozxb+ukM7n12/NsZkvsnfXJIMDvWi6DrqOEpYSKCtrm3x66Rqz80uAYP/uKZ56/BFSCT9tsAJCUXF9P4VghS57m4rrIRO2t2wc28YSAldT0TWFjUKe85ducG9hhYH+Ho4d2cfoUD9myJQgQFFxlWBhFXSljk6eEKDcuYf5n/6joEFlZGMmhRTWtSgUylRbTRRFRdWCaCGNWDhMLBZpA5wu1BI0U/vbg6b1riZ9wMYOAHddVy6SVLWdUmuztcF6vU5P2KQ3FZfixJ5HuVzFcV0/GMS4/9zbFmD3/yTbxrItSuUqjuOSiMdIJGKS6fOZGNU0utgJBbVYloykX/3it16gODGCu1FEKVcxaw1CjRZ6pYZWrqJWa5jVOoZtd9h4x5UrOX1bizkuWr6EyJfaelmmAhEf/Guq1lbfVxQFEYsg0klEOgGpOMq9ZWhZ0oxZqfnmTJ81UzvMjWzu7mXYziOxgI6a945Y+QHv5gP2bc89Oxze/c7K10PIsaktaBoAXgGa7xPYnmdcv57qVral+71o97Wu67Y7rbrl5/sYG0WA0mXG3FbElmsE4yud8XHLuR4893b2gY54uf92eUJWa8vk/Hkn+YJrdC88AkvJTqfo8s8SQgtYAUAD4XR1nw5BIMMFAtNvIMzcwQMCVwqd3tcOX0QiPXz5UmAruI2H2U8oKopvD0VREKrwfUjkpNuWjNjW4BJo3d/oAvD+xX/RBgNS9kABVGnl8mnBQJZfplsRXaDL/UJkrigqapCfSl6R+D/4r1GLlQcdEFS8q56iA8RF52F6PuC6v6VAXV6Xnwwd9+CezkQbXKJ7qYNUQi5Xqu2Imr7FdUxNRRkfQoF2CgrR3bwKchVbrSOScZr/67+B0HU5GBoGrqpSrNeZXVrBUzUmp8fI9vWgGBoYBoppSF8hf9Dv5MjcuQRt38HFnRUH/nOTAnNbRx2pJt/ix2+/z+/+4Q8YHxvgP/uPf4uxkSHfZ0Nr08Od12LrYK0Kl3KxzHe/+wY/fvsTTjyyn7/x136RXDaDIjxcga9iLI/RFAUND/2v/xcomwVAIfb3/5kEXw8o7Wp3zfIPsyLaMs4vrKCsbAJg/D9+l9bv/tNOSyiAIgGL47osrazz8akL6LpOT08G1ZDRpa7rsrK6welzV6jVG2TTSRqNJvVmC8dx/QTVqhyr8SQzpQSmuaDeLooi0HDxVMCPMFRUwdy9BX700w9pNJps5sucv3yTO3cX+MXXXmRmakyytp11ZlcRvgCh7Lue626dALJpmv/6/8Lq+iYfnDzHxcs3AYVEMtZOaXTt5hy6rvELr73IIwf3oBt6u80VfMXvrnfsQf1xuzN6YKYMvgeOsitrG/zwzQ9ZXtngyROHOHZkP4au8ZMfvs3v/cmP+PbXnuNbX3uOVDLB8so6//Jf/RGNRou/9R/8IocP7L7vusF4tNXnyx+rPNHOsTl/b5nf/9Mfcfn6LL/02jP8tV//Bq2WRa3ewHFdopEwIdP0QY5K7j/9x+2FnwDmQjo/nbtHy7KJRiPUHY2NmkW0J8HA/klqtTqrG3mmh3r5+onDeJtFbp65TJ+hszuXw6zWUSs11FIVtVLFWc9jrRdw/KCOSCTkmwjVzhgtByaUWkP+W1yVbZsvSTbzAZ1fMXQ57hi6HE/8vyL4Ho/JMYnOsNF+xZSdIdnOj13ZinnoAJJO4FPwC5BNsWUcUZT2t+3RiQRjmAJCeCidAT94wn5l/bGqeyxXfCeS++rsdm4S6BiDhQ+0/N/uY8W2AbuuPh5s2wm3fS7gUnYGv+17fGgs8gXnue/7g5it4I/SBbSCoksQKJyuqwViHd59xqfOu+hLQnxh/X/+8iUd5DszS/dKcsddg5VL+5n7gMvt2mFHM+EXP8mAbu8o+G5fNYquv5+/eurUd2u+9cQ/+K9RiveLhn5Bzba0idLV+eWK5QGtput4g32IXJrGf/+P2srygeKyioeiyIirWr3Jux+e4tadeY4d3kupUmb4v/w3DIVD9GUS0gzTDUS29+meDCKdwHnykS3bPU/glcpYqgzZ9vpziGyqwyh0rRk6PjXbL7D13lTfvCKUHX7zJ+hgq9e1XQpZ7uM7P3iHWq1Bq0sPR/iRZ3JfZBv5S1+fZEJRZYj66PAA0UgY/GerK53XMAhpFv41OxO1P1gVKpAvfiFNsuNK7HPKFrBVa0hzIKBU67IVFAVV8VCEJ8GKn7jcMHRW1qS4pfB9ERFQKlf59OwlFBRefu4xMokoLdvBcmS0qesJv/8oaMEgKu6vTHthoviqzopHpVrl/U/OUq7U+NZXn8N2HD757CKXrtxifHSQ4cE+KcsQ+M7Qee8CBsUL+ryfhUBOgjJ1ydJ6gTfe+oTVjU2OHN7H+OgQyUQM0zRptWRk2qVrt1lf36TRaJLQ4+3FlKJI/yWJ3Tr30Y5u6hpHurdJ60pQT8WPapS/t2wXx58nHddtAzJVlfITiXhUBs4o0in82SceIRoJMzoy4CdY3/rwfQnKLW0c1EeSuHLBMTrcz/TECFdvzsn+6Ps/RSPhbUCxyzna7bwHpBIMKCrJhBRV/fjUeT49e4XhgV4O7d/FxNgw4uotao6LlU4QHxkg05PFjEawBnsk+PHLytoGP3nrYy5cukFSUXh0YoSndk2QRcUrlLE38pi1JtGWhVGto5QqqKUqNFryHkcHg7uVz7qrSbqMoP6GznipADgurf/b/w66za8iiFrd2XT/oEWfsm1xq7bZdq0dkasELDudSdp/QFvmIUWRvlLt2rb7NyjdKWm2V0P4z7r9wnWBrPvYpu030M3C+Dt0VmFbNBM79ewalPx3XbR7YFdLB/e3HYkE9VIkM7Tj7931vW/o314nmSvxi7CCeCDQ+qLSPZhqBHlLpW9s8FfZdhvdizP/DN0L5vvq8fMDrv/pzYhfVBQPaU6kbSZpF9HlOt5lPA320P7uP4VCaetz7wbabSalG71uLQ968Mq2T/rlmzJkWlPxhvsfsitsRzbdJ77fiVkJtndFCrY9MwSoeLK/KgI06UOysrLK9Vtz9GRTHDmyi+XlNf7dq8cZGxngV7/1EulUEoTazgUlCEjwzoSzk3+JokAkIlWpXdcjGo34rIW0iNM2OMk7EMgoMglSRSdzmOhMtt0t6m0ZvDQ0Zav0Xlt9WQXDMIiGO6ajNrjzn287TF6VfiLCH9S6xx9VVdv56wxda/sCtSevbTffDSAVJXh0foRbJtm+62AA7d5PtmunbdvP0n+bt2jAIJ+noiiotTpKwJqqsh2FkGyU53oy5ZFfb13T2veiqqoEjgrcnrvHx6cu8torT5PNpAgbGuGwwHZlOqJyRUpahEOmVOhW9LY5NnAQDRhGVVHwFAnsXVfh3uI6d+4uMjY8QF9PlmbLYniwl9uzC6ysbpIvlNE0ra1pJoTUQgp0jITwiETCaLoB3QKSivRJ+vT0Rd545xNeeu4xXnjmMcJhsx0Y4jgOiUSMfXum5P0aOq6fn7LZbFGu1HBdh95chkQ85vcFmXqp5et1WX7i65BpEg75aZEUD8fxaFk21Vod23aJhCLEYlFymSTf/vrzKEh5k5Bp4npyf1VViEbCmLqKpgh6s2lee+UZwuEwmqbiOFJOpgOOwHHlc+wGCd2ASwgp9RCNRkin4n7ka6c/B/IzWyIh/XFEcTvRfUN7p+nt6cU0DIQQXLl+G03TiMci7JoaZ2ZyjONH9lNrVGVUczjE/t0z8ni1AwcD9fVMOsHTTx5lZKifibEhYsk4rh/R12q2aHoCNxojFg35MhwKqmVDqQrFMhTLiEIZpVRFycu/FMtQrkLB/xv4N95b2cKEmX///3ofkGgT+zvOew9mtrZ8a+OQrgVwNgX/3T/atvfWyUVR/MWiUDsV8aNNA8DyQMcPBbbA7TYrpTzgXnY+yVZGruvzjrvLhVK3BlX7vgRI09sOs1k3ULvPx7qb3Og+17Zzd5ExnckuMEV2gOrOfwPgsxUPdOpHV/vvUHeh+s261Q9L2Q74FV/yQZUBN1tZzgCA/fwAq7v8/xVsBYhTPgA12Hjfw3/grRbKsFHcyq2JrhVT+1mIdrh3Z1/8Tw9uSqXrf3yZAAGIXKq9T2fx2o2k/AhHZPQLitIOU+/cO+1Jvc3rFkr3ORR2s3JNf4IKhXUMTcouLC6vs7KWZ3ykH90f5D1P4Ngy95mChic6/hztlxvojgtT/EoFZjzPn5D7e3OgKERCJoZhovqZ6oPVdHCc5z83gdMW0wtOrAgFT3hSqFGRJquggyuKQqNmUarWiEbDxKIRAku74vtrNRoNlpaWqVbrDAzkMLv0tDzPo1ZvUq3VSSbiRH0TRxu8+21XqlRZXdvEdT1f5dzotL+qyIg48O8p8APsrB4DbtDLJNn8F/9A5n90HISAeDRCLCaVuBVVpWHZXL91l+u35nD9fIPRaIRjR/Yx0JOhZdncW1zh7r1lXNelWq+RSkR59cwNkv/jD9vsVqvZpFytsbC4ymahRDQSZmSwj1RSJnfWNJVoJIKhBqAR5hdWWFnbxBPS6dkSLrbtkC9VuTm7wPpGAdPQmZkeZ3J8mEo1z/zCMvlCiarPGmbTKQ7s2cXwYH+bQSkUy3x29gpXb9xlbn6Z1fVNkokYC4trrK7ncT2P8Jvvc2j/LvbvmSYRj1Kt1rl+a5aLV2+ysVkkk07y9GNH2bVrCtPuRAMpioLjOJy/eI1KtU5PLkM2k/JZCg/Xk7pphmHIdECOg+vYNGpSqfzM+atcvz1Prd5gbGSAl597jP27J1lbL/LxqQvML65IXytfsXx0qJ+nHz/C5PgghqGyWShx6uwVTp+T15+ZHOWpE48wMT6Mazusrm8Si0UY6OtBVdQ2k2bompQ5dF02NwucOn8NgGeeOEomnaLZslheXefSlVssr20QNk2mJ0fYPT1OIhFHUWizX57n+fkWa9I86Otl5dIpNFXDRUYh1ht11jfzOI5LJp0imfAFl73OOx7q7yUUjrZN8N3vd6CJl0zGSCTDuK7bdkMIh0KoqlyOxWIRdFWmkOrvzcn2998R4S9sPM8jEY0ifCEbz1NRNTkOeIaO0ptB6cttGQ+FECiufKPcYLz0PKjXUcslzP/8v0K9PiffAUVFKZQ6jHwwpHSRLjuXHX7wx6H7fu5GbZ31oV98ZiSY+X0zePchHdzrT/AiWIQ9YDmubLt418k60Ll7fupcpM2efRHA6r5WQNXscOtbyhYmq5sG8HzWf/uR3QFT238TnTa477Bgvu+yY4ht9xsw9dvrtv3z5xUfcAVMXgCiAti4lemWenJy/N56T/ejgy/i5R5c/orB1v0PBGi/LAFdv3UXpf0A7gc+O99U0B3bxjFVRWRSW44R/kW7fSSU9rPtdF4I8E3HUT+gmGkfC+r8kjzY0Gn963+EqusIT+a/czwXy7F9ZkDQqDWYnVticXmdYlmuHndPjzExOoSha6xvFjhz8TquK6ToassiFgnzzP/rD9G3+IPJBMgt22J+YYXFlTX6cllGh/tJJiNYts1moUSxVEGg4NgOxXKVUrnG+uZNerIZXnzmOJl0sn2/5VKZcrVBX0+GcMiQ4peqQrXRoukrRJfKFRDQ25NB13UazaZM2tuyMRSVRrNFy7Lp68nQm8uwvlFgeX2TeCLOyFAPqWQcVVWp1eucu3SDubuLWI7D4ECWE0f3E43EuLewyuraBvlCkZu377G+UeTJ44d56ZkTZDMpPM9jdnbBT0y8ydUbc2wWSkxPDskVPwJVuMwtrPLBpxdYWV1ncnyYxx49yEBfD6Zp4rouS8tr3Jlb4PzlG3z82UXqjRbxWBTdT80gDJ1ypca163faatu5XMZX9vafQnv1KnO/fecHb3Hn7hK1mkz+u2tqlK+8+CSTE6MID27PLfL919/lo1MXcV3Xzyqvce3mHL/9K18jGgnx/sdneOPdTwmZBpZtMzbSxzNCJ+k/eUcIzl28zuVrt6lUG2wWShiaxqsvPk4ykaLeaIKAUMhoOyijqjTqDam0LgSe69Bs2iwtr/PRqQtcvnmXcrnGxGg/I0N9NBpNPjp5ju/++H1K5SrRSBjHkZPvS8+e4Bdfe4lsOs3aRp6fvvcJN27f5akThymWSmzmS0TCIeLxKIqiYFk21WqdfKFEuVwFIfjs3GV++OYHNFstopEQ127Nsb5R5Fe/rbPLFzYNxgAhPPL5IpoKkbCJrmtoSD9MRfXfThFElHo0my0Wl9f4wRsfcPPOPfZMj9Hfm+Xytds0Gk2q1TqXr83yk7dPslkoMjiQQ3iC5bVNPtOvYZgavb1JHMfhg0/O8d3XP6Raq9Pbk2EjX2R9U+ZovH7rLj9+6xNmJkf4jV/6Cr25TCcVi5ATkeO6nL14jd//09fJpRPsmxknHotye3ae13/6EYsr64wM9pG3S1y/fZfFlXWe9QFZYAbdzBc5c/Yyn52/iuXn0gyHTQb6e9BUA0NT2CyUeOPdjzl36Qau6zAzNcJzTx6lX4i2gzymgeqbysVWRZv2Qsp1PTRNjrsty+KDT87x3kdnSaeSqKpganyIr7z0FPFYFMdxmL27SLFcJhmLMtSXI5lKMru0yqmzlxEuDA8NMDY8yNBgH/FYGAeX5dV1mQHCdsimEgwN9mGGzPb825EPdeU6Ox5BJKOQiHUqHQ0jcumu+a2LdQhATfB1+0J2y2xBe7YIZGiU7p/zxR2i5vy5RemeXu+fjzq4xLcQiG2BRm2kuK3+7eM7G3b6tN28pz4IYO20XQmW09uX2cFXP7lyN9ACApApgh39BddWIuTzQE8AGT9vn8//7eHhTMCU0ekj7fuBtvE+sIJ0iLMubIDvGmD4bjf4t9tZrG99jj9f+ZLpevyHEHxlW1/qBsgPhUi3ovr2527kGwCl7v0ySbx/908QKNiuh4uKgori2ShI6r5eb7C2nsd2HbJpmQ6n0Wyh6xrJeMw3kym4jsudu4tcv3UX23HZNTXC9MQIA6/+x216+5NTF7hyc5aNzRI92SQH9k6wd/c4eiiM47rMzt/jz3/4HhevzqIqCuFwiIHeDN/++nOceOQA5UqNH799kivX7xIKhag1mjxyYIbHbRst6BT+/V2/Pce7H5/h9twChWKZPdPj/PI3XiQWDdNqWeSLZWr1Js1mi4tXb/HmO6e4cWeBluWQSsTYv2ecTDqB63pcuX6H7/74fW7fXeK5J47wzVefobcnS6Va489/8DbnrtwmGglTqzc4/sg+vvHyU+hmiBu37/Kd77/N0tI60UgIy5a5zIYHehnsy7G0tsHy6gaRcIinThzil775Etlcmk/OnOP3/+QnbGyWaDSb7Ns9xtT4EE7K5d2PTnH2/HWGB3LtPHKnzlxi3/QYmVSSC5dv8EffeZ1sOsmu6QkWllblqt3Q0OUymqZtc/bCZT45dQHbcbhyfZZSpcy3v/4y2bTOrdl53vvoNMlEnL6eLPFoRAJGw2cbgc2NAn/8Fz/h+z9+H0VRGBsZ4MkTh3js2EH2+Bpj7XcYaLUsPjp1gfXNIpOjQzSaLd5491OK5Sq/+SuvkUwl+eDjM3x06gK6pnFo/zSpZIL5hWXe/fAMY0N9fPOrzxEJh6hUG4zs6uOFZx5laCBH7Pvvt7u9Zdu88e6nnL90i2QiSrNpoasKjz+6HxSB50pGr1Kt4zouWkgydVKiQ7SZDs8TbOSLVGp1pscGuXxtVkpQREJSQNLzqNUb9PVk+cZXniYei/Kd773F2toa5VKBcrHEex+f4cPPLvCNV5/j1Rcfx7Zb3L23TKVap95oylRN02O8+MwJwiGTUMhgeXWDT05fBATf/vpzDA70cPKzS3z82SXe+/A0w319hIL319eMUlWVVsvGtW0ZkOqbZxQEmvBwbBu7JYF+uVLl7IVr3LwtfRVfek6C9J++m+L0+avMzS9haDoh0yAZj/Ltrz7PoYO7eOu9k7zz4RlsyyYcMpldWefk6cu4rsu3vvoMjx7Zi6ppCFehXKnRaLQoV6oUC0VwbDQkQ4vn+YEFskTCIUIhabZzXIdSucJb75/k3KUbvPjMcb75lWcplit878fvc/L0JYb6ezh2RC5KVtby/Pn332J+fon9eyaoNizOXbyBGTIwdUOy2E2Hj0+e4c++/xblipRomVtYRtNUjruuzwQpkE74C0EFoXgPnJyDCUZVVUrlChev3iIZj3Nw3xTxaAgdj2ajwcenLvDuh6dBgVjYZKA3y5OPP8Li8gYfnjxPoVghZIZ47sljfOPV5wiZBmcuXuL7P36PRqMpIzkV2LdniscePcTU+AixWDTwJ+8M+/7YLjwh/c90DffFx2j9s79HrdFgcWlN+icKQTwWpViqMDE2xFB/L1dvzjK/sExPNk0yEaPeaJFKxujrzZFKJlAVjVK5ztUbd7gzt0AkHOLRR/YzNT6KHjJQfvvvwUahU5FAkDRghbZMUTu0pw+IulQZu/bf9v1nLA/FXj2oXu0KBArrYtv27np204Zel1+t/6/blPsQU/k2aLf1IAU61qyHaJtukLfls3/eQJVf9e/RC+Qdtt6jwv3guPNPQVVU3x+vc4yu62i69mUeYedcX+bg+9tc2fbZDwcOGlb4LNbnn+QBF9sKuLpFFgWwsVnk/KUbnL98k1K5iqbqhEI6u6ZGeP6pR6nWGvyPf/o6pUqVX/vWS9y4Pc8b750iZBp89cXHefGZE+i6xqenL/Fn33+byzfmaDQtvvLccX7nt77JAB00/PHp83x46hL7ZiZY39zkrQ8/4xuvPMnXX30G2xO88+E53j95kcG+LL/5i68SCYd4/a2P+YsfvEPY0IlGI2iKwmaxTDgUYqAvy/BAD8ri5pZbtm2H3/uTH/LR6YskYlHqjSZrm0UeO3aAqYlh3w9FRiC6nnSATqcTpBIxdk2N8gtfe5rhoR40XeXe4ip//oN3uDm7wMp6nu/95EP2zIwz0JejVmtw+dodPjl1iX17JhnoyxIxpeOvoSm0mhb3FlfJF8q8/MwJRgb7+PDUeU5fvMb48ACPHz3AxMggb7z3KWcvXeerLz1BridNq2Xx9GOHyKYz/PidjymWapTKVdKpBL54N48dPcBTjx3hnQ8/4+NPL1IpV6hVK7zx9kdcuTnH3/nrv8yrLz7F6EAP12/dbUfUCSHQNI3JsUG+9bUYum7w+k/fZ31tA8dq0Ww2uX7jDvVajeeffpSxMdleb773KY4rTTa61uLO3D1Onr5EJBLmwN5J8oUyv/8nr/Ppmcv8s3qDxLYOqigq2XSK4cE+fuVbL9Nstvju6+9y4/Y8N27O0tffy/nLNyiWqrz8/GP8wtefJ6RrXLh0nbt3l/jks0s8cfwQyUQcXVOZHB/iq688Qzxqov7ow86zd13m762gayozkyOoqkJ+s0ij0cRxbEIhydwtr6yztpGXjIumEg2ZmLpOoVim0WwRCZnMTIxg6DpXb87Rsqz26xkJhxgfHWSwL0d/X47jRw+SSsY5d+EaxWIJRZGsy527i9i2zfTUMLlsCtf1sGwXVtdpNKWZamSon4G+HjRNwbYbLC6vkC8U2bd7gkcO7Sbl58+8emOOc5eu89JeQVp0NNd1XWNkqI+LV2+zsLRKo1YnHo20l1/C82g0GqxvFmhZNrZts7aRJxIJs3/vFMl4jJBpMjrcz5kL19A0lcmJAVLn4zRbDQb6MuzdPUk+X+DWnQXi8RiGYWDZDo7jcmj/FK+88BgDfTnqdYt8oUKxVCGditOTSZHLpgiHQ/KdsyzpN+dnBVCA/t4cmWScZstCeA7VapHbs/dwhUtvT5pIJEQkGuLQ/mlW1zZZ2yjIlFamyeVrt/no1EVGB3J8/eWnCYVltOHFKzexWy0MTWV9I8/Js5colSrEohFCpk40GqJRb8qoYs9DaBoilUBVVN+lqEvnqcvXS26TnyPhEHt2TdDfm2Wov5ff+a1vkkvHCZkG1+/c450PP+PG7Xv0ZFMsNVssLa8zMz1OKGQQi0aYGB1ksL+fiZFBkvEISytr/Ns/+D7NVoNf++aLJBIxPjx5nu//5EMuX5/jt375qxw9vA/TUHYGCLbT/ugl45LNe+cTTp6+iGU7eJ5H3U/J9jd+65vkMine//gMn527womjB+jvzfLOh2dIJmK8/NwJHjt2BM+FU2cv8Wfff5Prt+cxDZ0nLx/if/Yr32Dv7skO6A/YLLz78dJOU1JnUNi635cjQB6ubANZO8C8rhL4VnX5S7UJqi5CY0fAto2Na0+7DylJdF9bdNV0S3j8VtPe1kMebEZsQ1zFJ2DEg1OAd5t3pe+rnEdcz/M1nQOTo+TWFJ8oCYdMtrbyz1f+ysyIgaf/lge2HXvteCBd9/DQyKu9r+cJiqUy/8//7t9z5cYsyViE6clR6o0WH5++wLnLN5kaHyEei7C4skahVAEU9u+Z5OTZS5y/cod9u8cplsosr27wlz96l3OXb+G4HpVaU5rU2v4Qkm+KRkI8+9hhfutXX6NpN/m//7e/z4enLvL4owdQdYMbdxYIhQxeee5RXn3hcUxDppn40+/9lDPnr/HEowcwDY1kLMI3XnqSY0f30ZNNopy90Xa0FJ6gVKlx8uwVMskER/bPEDINEokYPdm09BkxDFLJBLFohFg0wu6ZCaq1BrfuLLJrcpT9e6aJx8MgYG0jz9BAD7/6Cy/z9vuf8cb7p/AcqRRvGhq9PRl6cym+8cqTPPX4UTQ8zJCBiiCbijEykKMnneRrLz3J9NQYrutQLJZ4+dkT/NovvIqiSxah3mwQDhloGrz0/OOEUwk2V/NcvHqdW7MLNFsWmqYSi0WIR8OEQiaGoRONyByPjutSLJZZXtskl0mSTSdpNZoI4WHqGtVaA8uypWnOMNi/bwr36hxvvnuK1fU82VQCq2WxubHJwsIyChDWNYTjYpqSJm61LEqVKsVyhSvX7zAxOsi3vv48u2cmKOSLvP/JWer1BsqNhfs6q6rKlCBBRGAsEqWvp4eV1QIoYJiGH5pvEAmbNOpNXE0hnUoQChk0WpIpWVnbpFytc2dukU9PXWD/3ikGmx3TmqKqRMIhIpEQzz11lHQqwaUrt4jGoniuVK7XdI2l1Q0+O3+V4f4ehof6qDdbtGyb+cVV5uZlQvb5hVUuX5/j5uw8hVKVRLzE2QvXWVrNs7ZRoNmycGyHZqNBJGRg6BqVap21jSKuI33KqvUm12/OcejAbuKJWDvgwHU9CqUK5UrVlx+xKZXLzC+sUqu32u+ooeuEQyGfaStR2ih0BldVtuUTjx7i3MUbXLhyi8/OXOTY4f0kfLOSZTtUa3VOn7uC63pMT44gRCCb4GH4qVw28kVaLct3JhcEaZAc16VYKFGq1BAIDENrO/CrmkoqGae3J0sqlURXW9iWi23bVGshaXL2AxM8z8N1vTYrFIxGhiF9oTzh4XguzZaN47poqoaiCmyn5V/P8h3PW+1F0uzdRRp+9F6t0SKRSpJMRHFcF8eTkjVrG3k2NoscPbibb7zyNJqmYJoaY+MjhM/c7BgBUnFczwEhE323LItGs+mv6iXgarUsUFxMU2/LXKiaQjhsEItFME0D23a4M3uPjc0ijx3dz4mjB6hUqniuzfjIIGv5IpqqMjLUz8vPPkUyFsbUNS5eus6duwv82jdf4Btfe55wPMbu6XHyxTI37yxy+fpt9uyaQNNinfRncnaTxiM/SlUA9bDJ2x+c4i9/9C4To4N8+7UXCYUM/vg7b3BnbkG2Sb5IvdGk5Sc5n54c5fbcAheu3OLm7XkmRsdYWd3grfc+ZnVtg0Q0Qq3R4MNPz9NsWfzSay/yZMvCDHyxfiYTlhwTOnOS0mFZtkxXD7Rxbt2+3Ul8++9B7XY8RfD7TvX3FzXdmRV2uobougcRAE5BoPLfbUr7ojbq8HzBdbbmnXyQtSvQ1LzPStY+sbLtuB0YyO3grNt26J9iy7k9xR9H3PZ4EewXCoUxTdNvf++Lb/xzyl+B9MO2Tdu+t+2d2/rkfQf9TKAxWKlBo9licbPG629/Sjwa4sCuMZ45cRDTDFOpVCiUyoRNnWqlSqPeIGRouK7L5PggRw5Ms7KWJxoJU6nUuHT5JksrGxzYNcbIUC/JeIyD+6bpzaX9e5GDe95npDTfx+eJR/fz6dmrLCytEUvEabRaRMImuWyqXeO+niyGrlMoyqStuqYSDpuYpo7dbOJ6MYSQOfI8T1CrN9i0bZxcnBeeOsYvfeMlbMsi4os62rbt54GUIpIKKlbTpVJu0GhYWLbTdkj3PMG+XZMMDfSxvLbBlZtz1BstZu8uMjEygGnoREwdFAgZOplUgmq5zPpGQcoF+KmODNOg2WyxurJBrVYnEjJJJ2OYIQXDNOjvy7K2sUmhWCaWiFK3WswvrrC8uE6t3kRVpQO/qqqETRlpFuihqX4aDUVRsH32KhaJEItGuTV7jx/89CM2ixXiyRgVP+/i6nqeN9//hI9PX2VxSaZBiYTDKJouwfPapvRTq9VR8kUKxTIty6ZlWdTrDc6cv8r3fvwBu6ZGmJocRdckgIiETZZX1mm1bCKu11HtFwLX9Wg0W6gq3Lo9jxAqa+sFYtEI2ZRkFRPxKJqmUqnUMfQQPdkEK+ubCCHIppPEolEsSwpKNlsWtXqdSqVGb9PG8F8HVdeJhEPcmV/m7IXrPHJwN+lUgmQyjqrq2LaDpipMjQ8zOTFKIV9gfnGVtc0S1VoTkP44739ylpNnr/lq8waO61GuNKjVLao1izt3l8kXK2TTSZpNC1DQdI1KrcH12/MoikomneTu4ho/fvtjIpEQE2NDXL85Rzoleb98scKFyzeJhCVH4Loum/kS5XKN67fu8cihDdLpFPMLq6ysFYhEwni2I8GI78SjKApHDu7hKy8+ztsfnOb3/+RHLC6t8fzTx+kf6qdl25y7dJ13PjzN0EAvu6bHmJkaZXZ+iTPnrxGLRQmZBssr6/T3ZenvzVIsV2i1LDwhqDeaNJotiqUKlUoNy7bk9TUNBYXrt+b56NPz7Joax3PlmGUYMum421bsd7EdF8dPlyX9rUQ7jVWgbO+4HpqpkEzEKZSq5AtlXE9QKJa4cn2OfKGMbbtU61V0XaNltQC5wrZsG+EJGV3pSQFkITwarRaWbROLhtkzM8HE+AC6oYGqyUhEfwJxUgkZ9ei41BtNbt65y7Wbc3h+ainbcShVKhRLBTKZJNlMGteVAHI9X+TDj0+TTMTIZVOUq3U832w3NjJAKGSgeC7xRIK1fAkUsCybYqlMrVohGg6h4MlFlGlQr9ZQEaSTcYYHerlzd4lSqcpmvoimajIpeZBrVKi4rsBr2ai+H9vFxRV+MCvHquefPsZTjz9COBqmXKrye3/0fdbX89TqDZKJGJoqQ/2TiRj7dk9ya3aBtc0iFy7f4MatWZZW1nnq0SPs2zVFqVJmbTNPw3I4de4y+0plUpaD4Xi+LMoDpp3uibvtxxSAkPuZmvunrR1MftsB1vbjt5AXwecHMFBfYGva8fw7/e4HMQWphuT2bbv5/3eDqp0kmzuf2rbi++qwXZZFiECOp8uMGtQlaGu6j+nScVM8Hyx2V2H7c7nfAid8nbvtzaOqWpfc0ZcrfwU+W53SwVMP6q3blWboJqke/rIQBIZgWTaNZgsnbpKIySzy8wsraKpOoVTBNAxMXWW1VKZUqUt7f7nC/MIq+WIF1/VklI2iUKnVsWyHwb4szz/xCJPjw6QyaZlOxZN35QnB/OIao/09CE/QrDWwbBsFWF7dpF+RXj6uJ7AsG9f1qNkN5hdXKFdkrkNdl9Fytu1QLFUoVSoM9Gehq+MGQElTVRZX1qnU6hiqyka+gIeDqscplOusrReoN1s0bYdytc7aZpF8UbJ0dxc3CIejIFwuXL3Fex+f5eSZK9xbWufI/ilcz+Pu/Aqjw/1yILYdavUGjWaLUrlKrdFEVRUajRa1WgNV1fwoPJtG06JQrrK6sUmz1UJRFO4uLHP33hJHDu1GW9vgj/7iDVbX8nzj1WdJpxIsLq9TrtYRQjIZjickA+DJ3qOqUl06EovRk8ty/vIN/ui7b1BrSNDW8s0IlmWxtlHgrfc/5d//xU9xXI+x4X428kXypRr5UgVVUTFMg1t37vHT9z9jZKiflbUNqrU6xZJU+g6e29z8MmfPXWGwv4dLV2/x3dffY2OzxK9Xa8Q0TebWQyaAkNGPLeqNOtduzqEoGsVShaHBXrLZDI7r+VFdKgtL66yu5/Fch5OfXcYTgqOH9pDLplEUMHSNfKFEs2VhmoZMXeMXXdfpyWW5dG2OTz67gqYaDA/2Ymgmji+lUKu30DSNoYE+VFVldn6Rtc2CZHEqddY2CtR8nypVVYlGI1i2SzqdYu+eGSbGRtjYLGJoOtV6k0KpTCaTRAgp+xGLRXnk0B4efWQf4UiYk6cv8wd/9uM2I/naK0+TSiUxTZNTZ6+yvlHk6cePcPTwXnRdZ35xhdm7y3z3R+9x8cptrt+cIxIO8exTj9K7WW4r6yu+WaMnl+bbr71ITy7D2++f4ruvv8ftuQVGRwZYWFxlbn6ZWCzC3l0T9OQy9OQybGwWOXnmMvMLKySTMcKhEIf2z2AYOrdnF9gslChXapy5cJ2R4QEKxTLrm0XyxQq1pk1PT5aD+6Z5/a1P+X//6++QTiUYH+nnuSePMTLcT6PZolZv0GpZ2I5DoVhieW0T13Wp1Ro4joOuaczeW2ZhZR1DV8mXKkymkzz26AHK1QbvfniOQrFGrVbnzt1lBvp6iEZClMoVerJphgZ7iMfDFMpV7szfQ1FhaWUDBKiaSsOxQPFIxCPcnL3Hv/mj7zI9OUxPLsOu6TGOdJneRDKBUBRs12JldZ2Tpy8yd28ZFBncUSiWyWWTbWbXMAxaliXZwFSSWt2SUZuuSzwWQQj46NQFQDA6MoDruGTSSZrNVlvvqlZvUK/XGervIZdLE4mGuXJzjoWlVabGRyiWKqxvFgmHQ4yNDBAJh2lZFs1WC01TCYdCIHQazRZmsyUFcQXUTB2rZqP7i7ByqYLnOMSiUl6jXm9gGgaD/T2oqoJtO7iekEwEUK3WsR2HcMj0ddFiHD+yn1QqhmVbLG8UOHPhKo1Gk7DjoTgOhvBQlAeZorpyIQYBV34mlIeiPD6PufqiY7YgwJ2Of4hzPsx1AxDpp7xpA6EAtLQBpoB2qIicxLvhVof/Clix+0GWrJJoVz1IaxYQKTsmMVJk3do+aPexO2wDWFsxx3ZJn6BIUeEOwBNbTvhXU/4KzIg71Py+XRS2aH1sP7yLnuyg188rov1P01UprwD092YYHerDdQWlcoV6vYmZiqMqqswhZ+g0WxZLKxusruWZm1/BcV1AIRGLMNSfQ1UVbs4tsW/XBPWmRSabYdf4ENngmgLWNopUKw02NvNsFBTOXLiBqiikM0n6erKMjw6wvJbn8o05Bvr6WMuXeOv906iaxu7pceLxGJqmS90fzyMUDmOEtmbvMkMGPbEIo8M9fHLmEqVyheGBXnp60rz47KNE4zHKFRn6XixV2SyUqDZaeAIaLYvTF2+SL9f5+itPMTUywB/82Y85d/kWtu1i6BozkyOMDA/gKiouKo2WTb3RYsP3C7KC1CCKiqprCEWlWpcmvGgkQiQSplxrsriyTrFcR1F11vMFNooVzFAI1/O4fnOe+YVV+nt7yZcq5EtVlpbzuELHDEexfEkC23HxANtxaVoWiUSEF545yp35RT44fVlqQoVDKIp06NYNA1dRSaZSpFIJVtfz5ItlbMflzMUbRKMRvvLiE+zdPcWtuUVe/+knJBJREIJiqcri0hq2ZXPs8D7CoRDvf3yWP/7OTxgd7mdheY1qrc70xBDhUkPKfXSzsYqC43osrm7iAYlYjLBpcGjfLtKZNPVGk10zY8zOL3JvcY0/+LMf+OALvvry07z6whMkEjH27prg8PwS8wurrK3nZVsHYEuRSc6PHtpLvd5kdaNAtVoHFAzDpF5rEI9HOLx/mnDYpFiu4HoexVKVer1JNBrm5uwCoLBv1xh7ZyZoWA7RSJi7CyskYnFfxkBhdKSfY0d2MzzYx9BAL6qisHt6nHQ6QTadRNNUZqbG+O1ffY3JsWFW1jYk0MmmmRwfoieb4VtfeYbzl64zNNDLkYO7GR8ZxNB17swso6gajZbNxmaRfXumeO2rY0xNjpL8wTu0o5V8M4CiqvT35vjay08zPTnCex+d4catu2zmS4TDIZ44fpADe6cZHuwjHA5h6Drf+Mqz9OTSXL52h0QswuEDu5kcH6ZcqaJpKhNjsi6D/T3omsZAXw+PHNrNxNgIhh4ilI7w6JEDNJous3el8/TumXH6erM4jlTqVxSVcDhEtdbwWRmVQ/umSaekqKrj2cwvrWA7DvFYglK5jmMLDuzZhWMLTp6+zL2FFTLpJC8+fYzhoT457Hkeuq5x4uh+llc2OH3+Ot97/QMy6QRLK5sM9veSSibIF8tousa+3RN8fOYKZy/d4OrNOaYnhunrycr0Xv6I6KbiWLbDRj7PuctXuHlnjlBIZyiWQ9cVNvJ59u+dIJ1KcG9hhVK5Qsg0mBwb4tD+PRzYt4u19Q3SqSjZXI6xkQE++vQCb7x7imQiiqHr7N8zSW9PhkazxfXbdwmZYRLxGIMDGplsmlQyzrXb97h04y6pVJIrN+eYvbfC3t0THD6wm2Qyjm3bbOaLbGwWqdabREIR+ntz9LuSWVJVhcE9U/TdnuPK9Vmu3phlcnxYssa6jqKouEJg+j6wArkQrjctqj7z7bqCVCJONGRy684C127OcWrgMqlUlEq1StP20Hw2WxOOFDa9b3La+vWBrk3/Py0dZudnqsp9Jrgdyn2RfNAGS+2LPYhZu+9knXN+znVFwG7TkQzqYLAdqJs24No5M8EXG8p8PUhF8VlVsB0Hy7LbEiUPDjz4+R/+zwG2AqCzw0WVB3xRPs9UrciFgQcyPDPQnhKfA7yU9rHhUIhUEpLxMPVGk/WNIqqqETKksKJcpajksjn6ezJcuDbH2Ys3AMHcvVXfB8NF13VmpsbYNTnCh59d5t/+6U/QNI0XnnqEwd7slkvrmsGl6/P83p+8DghW1vI8fmw/oyODDPb38s2vPoNlOZw6d4PL1+YxQwb9PRl++Rsv8sjhfXhCCjSGTJM795aZnhrZmpgayWgl4zF+/Vsv8u//4k3OXbnN7MIqr73yBLFYHNUXOAyHTIQQ3Lxzj6OH9zI+NsRQfw/nrtwhX6qya2qU4b4suq7jeR66oeG6Hp+cvkKhWOH5J4/SslrcXVih0bT49NxVkukUU6P9xKIRWpbN2oaMZvNcj7pls1kss75ZwHEc1jZL5EsVPAHVeotoVKpcx+Mxnn3yEW7cmieVStI/kEMgSKfTKKpKNpNheLCfeDwKQDaTYv/eGXp60miGxqOP7CcUMrl7b5VkPI6u68zdWyAUkoyPaRg8cmgvKAo3bs8Tj0WIRkIsLq8zMTpIOpng8IHdRCMR7i2tYFm2D7gNJsaGGOjvpVqtcXD/DJl0kh+88QHXbt1lcmyIE0f3s3tmguSd35Vq2O2+Giz8BLouV8r7dk1w9MA+9u+dxlMh5Hk8cfwIo8OD3Lx9l2KpQiQiTT8H983Qm0lg2zbPPHGUsZFBrly/I01JLQvRzo2ooJVrPNqwmJyeoDkxjtBVdFcQz5cJxRyO9/XxyNf60EMmVrPF0vIGIUXhtZefQigKq2sbWLbDvj3TjI0M4nrSlNaTlcxDNGbgOC1SqSiPHT3A+NiQnLgVhSMHd7O4vMbq+iZz80tUKnXGx4b49msvYPlRfJVqHdd1SacSvPbK0zz+6EEcx5EiukjZgpHhAXpyGUaGBxga6COdSrYd+92WJZmtttmiUyKRMAf3zjA00Mfq2gamaRCPRaXPl+/Tofl+VEMDvbz07GPMTI6SiMcY6OtpT9YvPfsYv/haDNM0CJlmmwV55omjhCIRDMNEIBgdG+UXenpQgbApfQ+FEJTKVQ7tm2H/7knSiSixaBhdU/nlb7xAOBwilUyi6gaRaJivvvQM+/fM+D6IBi3LIRaNcPyR/RzavwvTNHwhVY1SuUqxXMIwDT8woJ9vv/Y8A305Ngsl+vt6MEyTod4ehgf7yBdK6IbBs08fZ2xsmDtzC9TqTQ7tn2HXzHhb0BRAVOo079xleVOarV989jhDA70k4lKPK9CiC4dMHNel0WgyNNjHb/7y18gk07ieR7wRxwyF6ckl+dZXnyWTjDM7v4Sua/T35SToSSYYH5dBSaVyjVefe4JIxCAclv3p9bc+5kc//Yhzl26QL5aJx6M8eeIw/X05wn4faLYsbt6Z5+NTl7AslxeeeZR9joM/7aL3ZJlwbOYWVvjg5HkAnn/qGMsr61iui0x2rREyDRRF4d7SGm+9f4qbt6VMTCqRwHYcUukko8P9nDxzmT/4zo+kBp3wGB8d5vCBXYRME11I147Pn1zF1plcCAJJmDY06ApC6P77pcoWJ3il6594gI/WF59LnvDzIInS/v8LuY/AnUh0cqJ8UZ0CRqujh78N5nYFdHRMmQ/LMn0xqJQZCFQUxcVxHclgN5o4QcqI+y73ObjnIcuXZ7a2J1raVh66s30O6t75FiUdqOsa/YksX3/2MCdPX+KHb58iEjLpzaZY2yyRiMWwHIhEouzfM8212wtcv32PidEBIuEQ+WKFfKFMvdGiv6+HF555FFdAoVhhfHSA40f2EQ+EA/3KKKrCkf1THDu8B8PQObB3gr7eDLFoBM/zmBof5ld+4SUO7duFrqnkshmmxgYY6OkFFFpWg6+8+ATDfT1tFe54NLLNPC9B5wtPP8bwwAAXr93Bcl2OHt5NLCa1jdLpJK++8ATHDu8lHA4xMtRPKhYhGQ1z6dodao0WRw7MMDzQy6vPn0DTVPp6MuiGyZ25e6xtFCiVq0SiYZ48fpDR4X4yqQTJeATTMDB0A8dxiUXDPPfkUQZ7MxzeN4XruEyMDvLk8QNMT421nfSPHtrDxmYBIcB1PJ56/DDPP/UoyVSGRqvFrqlxenI5dF1ncKCPF585QTwWQVM1JseGGR6QWjweCmY4zPGjBzl8YDcKGpqu86S1X5oKULFsh0gkzLNPHuPxRw/hOJ10KoEjvABymTTHjx7o+IqFQpiGTrVel35h0QhHDu4hmYyzuLTK5PiwTOvjm6RBbF3NKnKAiIRDHNo3zddefIbRoUE84VFt1BBCEAqZHNy/iyMH98iVkvAwzZB0oHZdNFUlEonQ35fDsm1sW77sjUpNOuoKgTa7QM+/+H1yXe9GO8l19+rP9/PJ2Q6GoRMKmSiqii0EludhfO99jHAYdJWWJ+j1XFTDQA+buIpKDgGGTiQeQw+HUQ2dqALRVouBRgtLeNgCmrEw8b4c2XgMT1Wolqqomgp9OUKhEGnHpmE7GMk4ejRCzLLZ07JRwxFSAsxSBbXexNI1mq6Lsl4g5vkDm6Ig8mX47f8tQbYEDegRgp6uoT6g+kF0nNOFwHAckpaNYeiYflJoo9lE1zRposIf1Ntzwnb+4n7jhxCCfiHYTSfNlOt1hKuCPiHFeVXSisI+ZM7HRrOF63lo/m9Buh3VN4/YjoPjOuiaimkaqKpKr+dx0Gc2VU0KPmqq9D3J2TaTrouh6zymKti2jeN6RC/PEfrue1Kp3S+xP/sJ4T/5EcOexwumAekkajaFkk0hknHcZAwrFkWv1UhWa8TNEOlshtToMC4qm5tFHDdIyqswNT7CUH8v1Vrdj0KVbHw4GmV4cIBD+2YoFIr09aUxTR3DMHj0kf0yEMSPPJ4cH8Z1XcaGBwiZZtv31XVlBHUqmeDcpZu8+e5JfrvRxPCfl5JJcnywh6HBPu7cvsv6RoH3PzmLbTsym4L/LgwN9DLQm2V2fpmar6M3MTpIKhnHcRxMw2ByYpiNfIFqrUE4bDIzOcqRQ/uIREw0XUO1lK7FffDCb5+POn0ooF6EULtMYV3GJ3+eeNiyUzop/4t8P9o9U6HtwO5ve2jA9bPMxd1f2x5UWzbSUYPfsvHhrtF17u2BjUFqL0VR/eT1HfHsrcEG3e3QXcR9+wbzqei6veA9tx2belPmjxWik5boZw2V+KLyM4KtnRqyC3VuoRofvgSN22mgzkkEKgr304XCd+JTFYVIJMRv/OKrjI8M8v7HZ2m1bGkybEonWMeRK7jh/l6eOnEYTVM5sHeSVstiZW2Twb4ctm0TjYTZNTVGJBJGVVWmxobIZtKBq167XpqqMjrcx7NPHCUSDnN3YYGWbfsRSzKhzWBfH2PDw6QScTRVxbEt7FZHVX7P9BgzYyM0G00sz5XOp93DvhAohQrJv/OPOaEoHPM8bMdB+cF77ZQtSSEYEtKvI8gJpiDIAUcdGdKuv/EphqHzmu3wfKOBaSygGwbNZpNarYFx9wek/vJdptROLjc5sbgyIEVVmBCC54SMKNO0NxFCMGnbWLaDeXWe0BsnAfjNWh3bcoh8cB5PCHK2TTQcxjBNXNdjxJZO4YaukxaCPtdBVRR0TUdRZUSI8B15gyS7YXyQAUT9Htid/LYNPLp6Y/d7FrxYco4O/AHkBJ0BdMNAUxV6HRfbcTB0HcPQAQW3WG77DrRTZLSvo5KKJ4hFItiOvC/T0GhYMtrR86SJQ1EUDBU810FVZRCCJwSObbO2nmdxaa3dR4dKFZJdA4TwV2ja6jpC0xGaKpPz6jpCVxGKihNE4xkapmH4ztoCzfUwXRe1WkdpWiiA6XqoXTn+DBRCSOHctjAqoAlIuQ4xx/UzH8g6maaBrusgBAO2jRBym6oohDyPlOf50XcKYdcj5bp+pFtbrQyBaKcekuys/5A8DzYKW0YYZUtbyO+qEJ2gKRQZQWQ7qK6LZhqouo7nuGiWJfVxDIMtPUQEPhsPSuvSuXb3HCBAyisEX2wbrVSlnYFAVghPCCKuu8X3QwnAlm+y0ITopJcyZNt4wlcdUoKcf/55FVA9ge55bUkHXBdDKBihJv9f6v47TLIsPe8Df+e68Da9K+9td7XvmXbjegYYYAAMQIAEQUkUKXIJUc+KFCmRS+6SFPlIgmikJUXtrkgtaJciARIDjDc97X11l+vyVZmV3kWGd9ed/ePceyMiK6u6ume01J5+qjPixjXnHvt+7+eEroNtQwDShZAYmobUNKTnQ6mCKNfQZpeQUmJI0D2PqU6HSV+S/O6bKoQFAi+VJBWPMWyZiGIOa6SAl0thpZOks2mcdIJ2zMROJ4mnkyTSaaanxmg16riBt6+UkphlcmjfLsZGh5iZGsf3fFY3SgwV8yqWoefR7SpHFV3XKeazxGIm1VoTaTvR5NXyWXYN5Tl2eD/ys49QD+KLLS2vceX6nLJpNXSGUwVe/NxTLK5sMD09wd7pMSrVOuVKnUQ8iWmaZLMZnnr8ISbHRxnKZxjKZ9FMi6XV1d7eJQnIA7W5yx0AlwhVaqqGweDoSyvXN156a9CDbYrb05rdt/QDwwcs/WtkdI8HLv2Lat+xgZt/MqA1oPzaDvBEL5WaDOfsANC6l9Zrp6IAG8JXzqJS4TQ8lFOK6wbhZFT2k582yArLp2C2tqHZbS+8bXm8i1b9+CIVkJLBNf35lcJJKNTiJOmlfhkfH+W5px4ll0rTsW02t6pcubWg4uh4HoahY8VinDi8n2Ixx+7pcWJxg06ng22r1Cdd28ZxPPKZHMmkRSoIdOp6gyAyEY8pA2/Xw3VdXA88XwOpg9QR0scMpJpWq42QHq6jYo3puo6uqdhjmmaQTiZxpI+8y55NqMVzqwII5REXbKwEqD/cNPuTryog4qP5kligkxaawJIS3feRHSWZ6o5L0nVVxFzXQwtSj6i1RKL5PYgZSjWRu7sU6h2lRHSdyDMrbjtY0ke3HWUz4TgQ6yIsUw00T0XcF5qaKLpUG0gY1Vm6Lp7vga6YLBFE8x1IFto3+u43pravB3rwXuGaYCAjnb0QAlNK9D4AJ1FLqdQGx54vCXLjaVFaoKhumkEiEafT6SpDfsdFF6Cb+uBiLPtyQwrFdEihsfDlzzLyB68g2l38ZILmr/wsWqVG+l9/ExEGe/Q8REdtrpqU6LqGYZkQsxC6hm+ZYBpITVOaeUOlEBKeD46DsB2VespTbswq/dLdxL8mNHRNzUUJQVvpYcOj60YkhCjVf5gsvQeqpFSjWoRIQvb6Ug/S7wjDQBbzkEqETbMjwx2pakJmKTjmeS5ukPtQWhbS0JGuC46L1HV8I/QlDfNUBgAmNIIW91rL5EA91H7sR2NINJqYa6XeEAvuo0mJ6csBJiAcU+HWHYLGcO4iBHoA7vrvFfUFCqCFahc9qFeY35OQIfQlWqkKrof0PJV6yvd7oDXoS1OCHiQj1yphwMYgOTuQCs7t389k70XVMw0DmYgF/+LqbzKOn4iTtUx2JWJYxRxGMY9MJchrGsJYgGCdSXa65DZKrM0t4t6Y50CtwcSR/VhiLnqOn05GdU8nE+SzGazA4zWTTjI8VCCTThKzLB556BinT3gkEnESMYvuWOA45apAxKlEjGTMZGioQCoRRxOCjuMqT8bAJENt6OFc+Hjgsx1fDPKjn6IM8gyDz4lYrU8GBLZDRXVM9n1jx/mmTtjOWoUjuH9UiL7fd6i+lHfdPxKWo7ncY7fubvO7geyOJapSMIuiMe8j8AIyR+V5RfPRpE/oSRqyrJ4vo1caFN5/OuDrU4AtMfCpF2kWwgVKiIAJ8HuT/IHAYr8gEdITQhDF+aCHeMOiul6gGwb5XIYzDx3H930+vHgVXdexXQ/bdhgdLnL4wB48z1cBBuNxDBNymRSu5+M6Ll3bRddMkglBIm5iBvE1whD+YcM//vBRxoaLigERKhaHbvqKoQkkeQsTqeloAnwXVITaEBQJCPKJCSEwRV+eMCHANGAojx8tAiGQUgyJL/1o8xOaiDZEoI+JUDpptBCpq0Hr+z5O16HV7mDbtjIOTafQLJNwO/CljNhZ2f9/TSBFT30jZeCaHoAtEbnyC8U2+D5YFr6h3hVfIn0fT4aB5JSqxTAMfCnpdDo4jotlWYh4rMe2BGxGv2dMtHENjKsehyEGTgygbN9GLQLmAE1TEqyUveUklGhlwK4UciqZrlSsjGmYpJISX/RsPHypcoSbho6WiOMF/aAJgWbo+EJHSHBQCaZNw2ByfIRsNoPrK/CSeOazaO9fRW5VkMU87q99FePyDeQP30Di9wBH30QJp0i4BgrbAdvFEApgIoBkAn+4gFfM4eQzONk0/lAev5BFFnJohQwiFkd4HsJTTJHX7eJ1bKTjRv2q+SotjBac49sOuu+jeb66zlUMU/J3vo/e7ipbohBEBP0ngsVXJbruQjIO+se7VodsFEL0LdIqQbQWD5Kih8DFMIjperSW9AzxlXCm7tXfcH3Dpx8TB2hDPTocNcFckoCu4UyORkAqnBdeHxuopGgRASPPV56koZ1oLFAj9hKWDzJu4Xrn+z6e6ynGWSphy7LMyDmov0hkGBowAHQoQOb54LpI10O4HsJX4D0E8bgewusDZ6GM37/X9a/31f7UYr2S2H46qq2krqu+1nXSmsawgMO+z+dtB18T2JMtzPAqQ0foehCWwsP1NMBFdAWVSo1Ox2ZkKE8qmUQIyOcyA22XNHQSiRhSCjxXseOmLoL8d6peCd1gbKRIIh5T4zXa1nuM1V37Vt/e83EKvP61+16lP/ftDjfYAVyFgcJhEBLsIKBEv/X/7f06gO12en60uBAJoNuqwt2D4wGhpuyBNoFE+trOatePZbD6nimVVin4EvwbJDH6qSKJh48CWU4AtkIh7i5h6afAdj0w2Io6ZtszwwjQH3PVx5aoM4PNceA6EUjPEIC5ILN6/ymastUpFnIR/ej5Pk6Q6y0WsxgeKgDgo6Ehg7Es0TWBZUAiHiZMFaCDpguyf/n/jlapoTXa4HuYrst/9vf/dfRMEEz0sS7R5Ij66l4j5W45SLiu2uNNM5K2BttIealpQYC4kPnpfyya+lXs2Flq0bdMA9c1ovASCleIqP5a8P4yl6X13/55QCI0iSZ0hFBsjicBTaJpRMEJ/QCAORKSf/63iFVqfXUQKB8ALXD5VzYh/UUGUobaeAZkapSqRUZhDMIk1js3bR/4zGVo/O2/GABI1eeapmEYOoZuqHbWxGDfMbgISimxfuMvghAMrWzwWyg3+vjcKtn/7TvRhid3iDs3UC3CBThY7CUM0U9bS/RL1xC2g7a8Rv5P/xXodiOppv38o1SG8ujlKtl2F6NcRStV0LZqUfLq/teIpPV2B21hBX1+GXO7OB4+OxFTAGwoj1fM4w/l8Iv5CKR5hRxuSnl9hTZMfh87E443IQSJH76lQFnQdp7vB3ZAihE0DQPNNFQKAUA0WshGq782OxfZP2t6QSj1HfYcLeq77e/bOzc8XdnVqfncS28jov0nfE6oaRJIaLUVSAHqzz1K7enT4AZgJnh3TQrwFVC1NA1NSlzbwW53kK5HwjQRMUuFOnBVyh3pSaQ3yEzhefhdB7fTwXdddZ7rIUxT2Xf1gSbh++perkopFMYPIgTEngJUMgBefgicHRff9YJJKMH3wPURvqfexZcIXyL84DleH1C7f5epElwTruwi6CMDSIZz7eL1yLMSQ1fsqQ++r2IF+hrYrmSjVKNcbVCu1vF9D0M3Ahy+fb1UfahZ6omCcLOV6JpEoJFMJZQjlab1DT7Rk+Si4J47vZtE4gV33A407l7f+28UGYjfi1Xa4epwfrHt2F3AaYfr7r6m9w7R2vcxgCu6NgShd52+7cAOrFbvNr3wDhJtx7W8R6zcA8SJYBRFXonBTB1QN8ro0jCTQv+zwjiPvvTUHkEPpg3cUzwokrl3eWCw5QcSudY/qPsHJ9sr8+BAa6BsFzbDjTMSFO+B4qVicjTps1Wtc+3mHbq2jWXmAuZHMUGhtOpL9S4hmA5fSdcN3GBaSnxEtY4o19SkCyqi9SfRjTDv/Yf7PcsOc1EAerlGZMu27Sk9qTyQpAPbGD2wgRmw/br79hjSJ247gQpQYDoemmH0waJwgGmB/VL4QxDsLmQIlMgcBU3WUQbMug96tYFWrg2wMVGdpEovovmBKkPT0ICE62I4LnrHRm93EJrem+dS4nZtms02uq6TTiWVTc42ZqI/QxkE+4aUEYDTNF0lOQ5saHpjubfRhiBiex8JIdC6NqOzixFg/DjptV8FGt1j2+I10EcBGwQCrVRW6jBT9U3j+H6+12xQ8WK8+AufY3SkGLFFfqONsVXF2Kqhb9XQyxX0rQCMlSqIUgVsJ1hTd1jYOl30pTX0pbWAXeh/h+BvzMQv5hQgK+QUIBvKw1AeOVxADuXxsmkIwb7Q6KTi1OpNGu22shkyDOKmQT6VUPlIt7VdvyH7/cvOC3lU17DP5YCMGuyhvSNd26ZareP7klwmQzxmofVFAR9Ye4Kba5VaL56zlJQTFm9Xqgggm01jGiqMi6ZpGKZBKpmgkM+SSiYwdJ1En+G8KwSeGGS9PSmVLV6o9pSA7+J7Pr70aTbbNNttRocKiEwGTSjgO7iRBEm72R4zKngHKfFcl3qjye25Re4srLC5UaXTbmMIgfAVMJSuzehQjlNHDzIxMowuBJrnBxPLRzgOWr2FVmsianX0Rgut1kDUm4haE9FsKbvB4B+OS2TjJAHPQ6sEDFk/s+H7TP2F31IaAU0jdNaWUvBktca+zRK5xU1i331bzWO2YYKPKaGNHUIgytXo6GBTiR4I2eHeaq8PbErlDrY+AqI8ixHjcu9K3nsdCeDpJ3nB/uvueu42AHMvoBWd3ndeeK/o9Hsh0XsDrcHwDiJitPpNYe5ZPqauHwde++NsQTiM/UjIv/uCj7/ng5YHVyMKDSn7peftHRl04I4DQvb11/0HW7Q539Wo4eIhEL/519UEOXcVbAe5uAq//hfQDRNNQMF2+NVSmZ9ptkjZ6wz/nd9W0ktQ1QHgK+4ehmEd/Hxm8EQNBdzisQB4PMi+sNPm0bvSDSJSh+o0IQSYOn4xd3dbBUg/RNme71Ot1qk1u2TSSQr5DIauD0z67XNCBoDIaXewO10M08BPxJWEHDSQApcyYmL6S2i7K0TQFiJgdfCCRUcGrGEAEjWBX8gNNIcyP/LxPRdCkIhqeC0wLvYNI4iOr9rK9yWNco2FrQq6rrFvbIhMKkkvbbeI6g+o8eFLjKu3yP3Jvxq1QwgSI9WP6ONmg7GxXaklpUQ7r8YanS4YwbTRtaB7d7bvCIGw74csqoaubz8v5BRl756E9jICbBth6kgBXjFLa32d5bVN2p1OcI0LuoaWSyNzGfz9AqlpOFpPBS2EQPq+UvuslxCbZcRWBbFVQd+som1V0beq6Jvlgdx029cYrWujrWzAysZd7xrNB9NAW1hF+D7OSJHv/Ylf53svvUEykeDxR0+iCcHi8irHjxzgs08/QjqZjNpKssPCJqPeRReqUlIqRwrPv5tVCZkcz/ejuRWBJJXdGh8fNBV4+ObteX7/2y/TaLT4xa8+z8ljKhSAJzWVzDm4NmRV8QUjf/a/xqj1PABL9Sbf+M6rxGMWzzz5ENl0kmarzfpmmXK1Tiad4pHTRzh2eD9GUo0d3/dxHAWeTMNQQW0J16PAEcBTIi5SRGpBFVS3RTeICaRJZdKrIyLABb11TQjwApAZvosezhkpmb2zxI9efYdELE4mleLW3Abrm2UeOXWU3XunmVtY4Mr6FpnpBmNH9mNaFn6oaQiFkz5V08d64HW6aNU6eqWGXmug3bhD/H/5HcXezYyjLa0hmm2kYWCUq4SLTcicagJyrkcykcBAoJdr6tm9gRj8ub+Zswj/37dm0D+PRbiXhcaG2xM5h6cJ0Pq5EAFSC27di5unrtcH1Mt3eSCKbetttMf277UM/r6TwLH9TftAeMQU9Xkz7rzf7vC4YHzJu1t7WwXuw2j1369vrAzaHA9eH4mjD1DH6PmBMCak2Knb6JmnKGzi+X4QEuQui7ZgbyLaiz5t+UQ2WyJ4WIiLfSkCD5reJN+x9LEb99qYIBwID/Ay5RpsVsBxFAXetdHe/wgRpH6IAVNhh1UaiJXNT/KaPUnWUs0jbAfXNNjIpOhmEnzrj/0s2Uyafbsn2T09QSIRH7jUkwItTL0jJb6nbCikDA2NJYZQAfVeefMD/t//+jsc3DvNn/r1X6CQzytJTjdUKo6+hQxQOuagDRvNFt/49o/5wSvv8vxnzvCHfuFFhnJpNKkP9IcAfKGu9aXKjba0ss76Zol8Lsvk+CjJhEo/lPwLfxdx8QbCcWBpndif/Cs7NlHEU0Ugrrd4SCT6ldvI0SH8Qpb2P/ob0XW+L3FdSavj0HG6SoVrKXVIf40NQ8cyDcwAtHW7Plc+usbf/Yf/DM/3+Jv/1X/CscP7oiB0LlrEkgghiP3xv6LYHNdF26oOCGf9bTowEgX9rPNA++HLaAGWgZebfWx/lLrFCNW+PcoDu+tQrdURQCxmEYvFiFlmpD4DkHI7tFNFu3pTPdt2kOkEQoI+Nkxsdh7LMIM4U2oORvfq3RTpeXhCwwwMxIUAkU3ipxP4eyfxZV/CX0AIQ9nYNduIUg1KVbRSGW2rilaqogefjVJFAc67W0h9clxE11Y2QV2b5dUNFpc3OHX8AIcP7GFibJillXViQVTv6LqIQRi0yUQEgXWRaNJXfLPfY60Grt9eq4Ax6p0VgrYg+jcBWJEqPIeQvnJoEBKBjyuUl17/c7xgQe4fJNIwqFTqDA/l2bNrkmIhR6fTJZNJc/b8Fc5duk6r1Safy7Jvz3QQ2qH3bqFt4sC4FCDQogS5vidBU8+2LIt4PIZlmSidQ5jipD+id/gIiSEI2HoRgDMCG8ku737wEYvLG/zSVz/HzPgIjt2l2Wpx5OAMj54+xsG946xslBkeKmIYimnWg83Zj5o8FLT7PofH+zAICIjH8OMWYkyFiRaFLNbvfB8A+/nH0a/NIso1BRxDtk41SrSm6UJgbOvznTZIKQZ/3TY6toEbAX3p1QbOk9Edd/h929iTEhXAW/SukSDRe5J8sM/1j9he//XtjzswagNY475bpaDXJ/c7J9zsPmbfDYH8dtb/U5b73iNkPh/oRtCLKL/TvcKTdkRcg7Bl4JRAQNN6Dlw/CdCCTwC2IkmbcNOP3AcejN3sZ6x2+jkchPd8n3C3HKgVniawvUAKFKKPUg42xgeo2l0lsH8RTu+76/u02h3MXIpqrckb717AMk3+6K98mTMnj/SBIVWvyFMSQAeh9TZhHYKQByoBdBjkT9NFZIvUWyfCjUEdV+BWPSceizE9NcZQMRepJTRNQ8jtBn0SIdSijFQebMVCjkQiRsyyIiNdITS0Sl0BrSDmjyhVdmigQaAVfhSyz3YgSCjbf04PTHuYhkTXVdgFTdNwpQTfx9BUnkU0DaNP6tQ0QT6TUsmdO50AbIRuwQItCC6o3Uuy1gSykA0EFBFJknC3fVtPpAhbj95Cs7iKnBrDL+Zo/r/+BnbgCWcYRrAZ+YjAgbjVatFpd0ink2imhYOOMEwVn4pwuGv4Ukd5lyolqCbA/A/+IpTKik0DsEz0obxy0xcSX7p07W707HAvCkFIudbgzuIqxVw6ANPxSDKTyCDlUhfP9UDTSSYzxA2BTMfxUikaI0U2S3kSiThDhVyQ19Kj02xgV6oY5QZ6uYpVqZFu28TrTfRyDX1xDXFnWdU5ZpJIxBAaXLpyk1jM4IVnHufIoT2k05korUoUwyroG9t2aLaUDVcqqXIeIn1a7TblumJ1hPQwdJ1UKkEiFuuzaZY4rko75boelmkGoQ2I2hwkju1Sb7RYW9+ktFVVHsYBY+kH65AufeV9GmySYcgKP2DWwtGixcwoEnk+m2FsZAgpJZPjI4wMFfjeS28xv7TGtZtzUYBRw9CJBWpsXRPoQirgLJSxfKfj4gVer0iQAXC0LItiQcWRsywTKQS+9LGdjsrMYJn0nEYV29WoNynVVAy4sWJWCYcSNrcqXLs5h2HoTIwPk8+nSSZjisnVBJlMgnhiiunpSQgCiPaHJ1C7Qc92TmPbptTH3ki0gZCMfrCd6q4XzT1pmjR/67/AD3KxAuT+8v+AqNQj0BVpVLbtA33wrre1ymiK31Xk9sP3PLGfZRF9N71fEX3/eg+UfvjWg7eIaIidgIykJ8BFpMX2E7atVf1P6GM7o/U3ep8HBFrRzXuA68E2/QcvkUDjSwa5s48r/XuRDzsKr5ErdO+qYA/Qha7i4AltwKRBShU4OUzM/tN420/AbHn0DzQJfYaxOyWh/BQlRP4Peq9knMb0GPOLa9QbTSbHhpmeHFO5B2UoUfTsdfoFrUHJIlg2gs4wLt9E2MpWRp0qcSU04hYz02P8wleeIxG3+Jf/7odcvXGHU0dVhGghAoAtJa6vJPHBDrx7MumapoIfBi4yQlMhIkJA5TgOli7QTLUxhWuNJsHQIJNKkMukyGZS0WbRW3iCfgmBYKhO0dQmlUolQAYUqi/VIt0n6GGZyCAJd//q1N/XA6BESsU6Sj9giFSD+76k0WzS7nQD1ZoKZphMJtA1DdtxWFxcod5oMTUxyvBQHkvTooXJ7nRZWy+zvLKmYmGZRsSKCKFCJyyvlVhZKzE6WmRqYoxYfyNLiZ1JcOm//A0S8RQT46NommBjcwvf95mcGCObSeN5ynO10+nSardxPZ9CLkMimcD8Y39JgZ+weYQgbmhIF7bKNeotFZ29mA+jgYFpGDSlZHllg2qtSaPZZWZynP17pjFMA5DoGjieQ63eYnOjTKPRZGpinGmCRcdW9oH+cEHZJGo6nu8yN7/MlWu38aVkz8wku6anSadSCCFotTu8+/4FXnrtPVKpBI8/fJznPvMomXQKx3FYXlvn6o1bLK9u4jguw0N5nnz0IcZGCkjpsVWu8e6HV/jo6m2OH9nHs08/im7ofHj+Cmc/uEi5UiMRV9Hc87k0j5w+xukTh0glE8S+8RLG2Y9UG8ViHDu8j2s3b/P+h1f48etnubOwwrOfOcOzn3mE6YkJLCvGZmmL+cVlfB9mpieo1Ru8+sb7WKbJM089zN5dk2xtlXnjnXN8dPUWXdtBIhkpFjhxdD/HDu8jl8sgpaRSrXPlxixz80vYtsPE+AiH9u+mWFBqed93qdSqzN5ZYm5hhWs35lld3+LA3qkBUKZycKpYdbYnlerbU7HYPM/rA1tShZcQvXGhB2DaMHT27Jrk5LEDrKxtMje/TKWm1IqO61IqVeh0ukyMFsgF87fe6nD+o5vcvL2EYegUchmEbuB4PrrmsXtqjJmp8ciWst5sM7+wxOLKBkLo7J6eYt+eSeJxFZD4+q07vHP2IvPL65imyQtPn+HRh4+DlNxZWGFhaZ39e6bQNY1O12Zzq0Kl1sAOxp3KG+vT6jrUGy0MQycZxCEMhaFIbeuokC+GoROLWQGBE6x5vocbeKdbhlrzfAnCdiFgc4RpqPWyz5ZSrzYQW7Wobfvn304l5PYULApB4U4AKeqwvq/32HeE6N847l8EDKroBOFmfy8QEYHD7T/2CdqDQUXDxXjwgruAFoDYBj7uAlafEnD971BCMCq212cn9eEA+xe08T3fQYAM53YP3GtCV6FzdA3L1FUOzj6QYJomQ8Uc8Xi8B3h/gvIJbLZ6H2Vv1402/zAq80/cDQKQPePjfqnirmFy7AAbf/M/5bf/19/l1Tc/5Fe++gJ/8jd+iVwmHehfAXx86Qbuwz6OD8LvqVA0TeC54LoyYkXyf/Zvom1V8YtZ9YKbFebrdX7r4QP8+T/z6xyameAzT5zkG99/nUvXbvNivcFQIcdGqczC8jquL2m22wzlUhzcuxshBFuVKiurmyAEMxPDDOfTSMDtk+JCYCJwqTTaXL29QKVS5cDuKQ4d3DtgQNrtdljbKLG6uoGuCbLpVBQ0UaI2i1q9odLqeDaZXJZ8LotpKhbENHTqjRa3ZhfYKJXZt3uafbumejNW1/FPHqL9//xrbJSqLCytArB7eoJ8LsPKeonFpVXanU6gGvQQmuSL//j3SLQ6qseCuiwur/Ly6++zsqbUublsmmeeOsOh/btwXZdLV27w1nsX2CxVeeKREzz9+GkK+ayySavUuHlrjnOXbnL52iybWxUVIy1mIYTAdT0ufHSd7/34HW7cXuDgvhm++uIzPByoS0BtkqWtCv/kX32LQ/v38DNffIbNUpkfvPwOuWyaX/rq54lZFrN3Frl5e56u7bC0sk65UuOR00d5+omHVG7MPnJVSkmr3ebajTk+vHCVjVKFIwf38MKzj5DJJJBScvvOEj9+/X1arQ7NZoeVtS12T0/wh7/+ZQ4f3IOmaWyVq5w9d5k7iytslsosLq+zd9cUv9lqku7z9pLDefVX+thdFRDV930uXb3NB+ev8au/+CWOHNiPEILV9U2+/+O3g4TJPo1mmyMH95BKJphbWObNd8/RarcwTYP5xVVW10ocPbifkaE8qxslzp2/we07y1y5eotGvc6Zk4cZHhnm2o1ZfvjqeyQTKk+h47hcvHyLeqPFzNQYmXQKbbPSm8oxiwP7Z/jKFz9DLpvm0pVbzN5ZZnW9xNr6Fl//2uc5sG83a5sbfONbP6RW7/D1n/8ShUKW9z+8TMwyefzhowAsra7z4zfeZ/bOMtOTozSbbT68cI3V9U2KhRyZTJpqrc47H1zk9twi2UwSocF7H15k9s4in31CJZbe3Crz6hvvc2N2kVw2RbPVVmEYdF15SQpBvd5UufSuz1Op1TEMg+JQkfGxITKpFLsGbMUEvqZFqYd6aqAAWFimYrMySUpbFRq1OkyMUipX+d5Lb7K2XuKrX3ya08cPUW/U+P7L7/DBxZsMFwvU6k1mF5aVR7XrgpAc2T/DL3318xw9tJd2p8sHF67w3tmL+FKFE1nfKJNKJdg1PcrcwjLf+PbLXLhyE00Iao0WlqFx7Mg+YpbF6vomlWo9CpK8vFbi6s15Wu0Ojh+KapJytcEHF69y7eYdhot5Hj9zgpmpcVzXZ2F5jWs3Zmk023Rth3Klyuhwkec/+xhjI0MIIag3mly/dYeFRZU/sljIsn/PDNOTYyQ8t7e5GgqUaFrPRq1vocYvZCMVshawdiELFwbTDSVqX8rIXk/X1IYqNDEASNTeJWArcICKBM2egPrAdkL9JVqmQzalT4KNYurI6DmKb+rZV36CB7AT6IrqzQ73+zTv8/+jctdb3Kue/f0TnScgMA9QwaDhboQsQBoBiFXjxjRUMOhUMkYirua/HxA0mi5IJhOBEPYgjOb9yyeOsxUNjIEjg1JCT90YHNfu9fI7lftJF4OslxAC01CxZnpRz32EJvBsn3KlxkapxObWFkIIpqfGKBYL6LqupFMpWVha5+y566xtbHFw3wzHD+8nJ+VAVZSwJbBdZZTasdvcuD1PvdEmm1apcyq1Ov/8d77DOx9eUSlDpM+ZEwcZGSrSaLZ47a0PuXJjjq7t8pnHjvHC0w8xMTKq2quPgfN9n7XNLX706ge8fe4qdrfLi889zsH9u9ENjVa7y/Vbd7gWJOi9MbtIvdnGdlxC43nX9ZlbWOGVN9/j6s1ZWu0OQ8UCLzzzKKeOHyKTTlHaqvCt77/GBxeuslWpc+bUYf7DP/SzpPv7WkrmFhd5450L3JpbptXq8PDJw5w6dpB3PrjES6+/TzIRp5jPUipX2KrWeKLRIKH1bFI8z+U7P3idi1du8chDR0nEY1SqddwgB9pWucL1G3PEYzGy6RR37ixx/PAehgo5llbWee3N92k0msGGmsB1PRJxK0hLI2jU67z/4WU2S2WGizkuXbnFrqlRTns+EU8hlCPCncU1piZGsUyTpZV1NkplTh07QKGQo9Xu8OqbZ5mdX+bZp84wMpTnwkc3qFTrTIyNUNhhZDabbVqtDlOTo7Q7XW7NLnD6xEGV9BrJzdsL3Lq9xGcef4jhoTw/ePkdzn90g0dOH+Hg/l24rscPX36bt967wKMPHePJR0/x9vsXee/cZdY3y8QQWMGs8UcKIKUyqLYMjh7ay76903i+5Acvv8vyyjrHDx/AlxCzLPbtmWbv7knOf3STer1Js9XBdhwuX73N5laVZ586w/TUKB9evMKlj24HBtfQaLRJpRK88MyjOI7DtVvzlCsVZmamGB8bxrJMZibH+ENf+yKe5/O7v/8Dbs4usr65xe6ZSbTNcm/uWCapZJKHTh5hZnqcJ+eX+eD8Vd569wKvvPEB05Oj7Nmzh6FiAYTg2s07rG+WyGXTaJpgcnyY8fGRgPBWm+boSJEvf+5pOl2b3//uq2yUKjSaLRzH4eLlm7z21occObSbp584STqd5EevvMu3vvc6vu/zyz/3Ra5eu8Vrb37IieP7efHzT3Pj5h1+9Mq7pBMxUnGTRq3Gm+9d5OU3L1Aq19A0jWarTdd22TU9weOPnOCUlD1BV4A0DEJbs7sFcJXhIh6LUa3WcTyVSN0wDJqtDluVKkKoqOrnLl3jR6+9z8ljR/jFr36BldV1/un/9gd8cOk6hqmBL7lkOxw5uIdD+3axsrrO2XOXSSViPP/ZRzF0nU7XI5mI0e50OXvuMuc+uoFp6AwFzJ4AXEclJTeDkAcho59JpygUcmxs1RSTJzQ81+Xm7ALnL12n0Wiztr5FOpVkbHSIZrPNd3/4Bm++d5FCPoNlGswtrGBZVpDmZ5RKtcn3XnqLH7/xXpDtQ+WwHR8Z4ue+8iwPd+zeOhvkOIQBs0YA/HyW2b/1n/O9l95gaWWd4WKBeqNJpVpnbHSIR04f5cTRQxQyCeyuzc25Bd754CM2S2X27prk8TMn2LNrAhHYnYVmJrqmof+x/wpKlQffnj4lYIl2r/DafkPSB9rJ+9WZ/d/7j0WV/BjgNqh+/MRlp/f/SUGcpqmQI/1lJ7AYPq7vezBqQPPB1yKqMBQYgosDbY8WYCcPXVMsbKQd2izj2gHDFowPhd38B+qh+5VPAbaCl4teWnlZDICr/loNxFbQeDArqnAQahENLe8J1vozkweffMmV67f53W/+CCklmXSSza0ymUySxx46xplTh4nFLBaXN/g3v/8jXn7zPF3b4eSRfWTTKfbu8BQhBN2uzfXbs7zy5nucvXCd5546yVe/9BmymRStdotao8mN2WWO7p/h8TNHOH54L77n8fa753nt3fM4jku52kDXfI4f2sX48EhgMN+L6eN6Hksr60h8fvYLn+H3v/sy65tlEIKu3eWt9y/wxrsXOHZwDyePHeDO4irVWkNtOK6L6/nMLSzzre+/QblS4czpo7iexytvfsg/+9ff5us/1+JLLzzN7J0l3jl7kX27p9i3Z1qpzjqdgb6zbZsfvfoe7527QjxmsbJWotVpk80m2KqUKZWrHDm4i2eeOM2VG7P83ndfp9N1kPHeStlud/jw4lWOHNjNz734LIl4nHq9Tj6bIm5qpOMWDx3bj2FYXLx8m6XVNTzXxXNsXn7tHd5+/xIvfOYMRw/upljIcmdxRYVuMJTBqaFpHD+0hyOH9jA7v8LqeqnPHkuNVk3TSMRjpJIqDMHaRomllXUO7pvhkdNHSSTiNFpt5uaXcVw3Yp3mF1e5cn2OWr2xzRhXLVTJRJxjR/YjpaRabXBzboF2u4uGju34rK5vEY/FefzMCaanxlnb2OKja7Osl8p4vmR5ZZ23z14kkYjz2JnjTI6PYBoGt+eWqNeb2LqhwjBIkMMK7knfVylOijmGR4qMDCuHikqtji9VSqCJsWFe/NxTvP72B3S6NqmESg7uOC6lchXpS4YKOYbyeXLpjArqqCmboz0z04wMDbG4tEal1lAqomBqFfK5yIbBskx8X7EpjWaLSqWG7/uIEGwB0tSxnQ5Cg8nxEUZHiuzdPU0+m+bf/sHLLCyt0e12GR0b4vEzx7lw6RavvHGWazdmsW2HE0f2k8tlIfLW1UnELMbGRzEMg9GRi7TbHXwpsR2X23OLLK2s88jDhyjksxRyWcYDULCysorrdFhd26TZ7jA9PsL0xBibG6UgerSP78PN20u8+uZ5PE/y4gtPMTk+zJ2FFV5+8wNuzi4wOTFCKFIGKwMiZg3YCip1pfpdSoltO9i2g9A05SQRi5FKJsjl0oglld5pfbPM2fNXaTQ7HD6wh7HxEYaKBY4fucyNuXm++MJTFHNpPNdl355pfClZXF6nVq3z2LOPcuLYQQxdV2yP0JlfXOHqjTm6XZvPPvEoTzxyEl3XKOSypAIP0GQyjmkqwRNgYmyYibFhrt2cj1gjz/OJxSwePnkE0zR4670LrKxt0mp3EJrADvJ6vvjwE5w8fpA33z3PD199j0qtga6bzN5Z4ts/fBPXc/jDv/QlpibHePfsJd545zxvvnOe/eNjpAhME4L0VmEbhn8VLpI0Gi0uXrnJletzPP3YKfbvmWZ1vcT3X36b+cVV4pbFmRMHqdbqgRq5S63e4LW3P6SYzzIxOU5c16g3G2xsbtHudEmlkux3PfSQuNjOmvQ6NTihLx7XAFjqKxGYUuNj4KeQUYts2aJhdN/S40r7r+ozxBc+CD2K4XlPoLUNEH0qAPG/AzO2Y0ytfnbx7isUOJPAQDo/gRB+EHw7uF9ffbcLREITxCyTTDpFPpchZll0nDah1bPQAOH9FFR2nxhs9bJ6B1Xv++1eKFtNJCkFSA2p+ezoOSAHP+xsA3b3sV5MnB6S94DzH13n7IWr/PJXP8czT55hcXmNP/j+q/zz3/kuqWSch44fpFatcuX6LLV6k6cfOc7JYwdJp/uSTocTA9Vftu2ysVnGcV2++NyjPHzyMMVCDtfzME2TQi6Naegc2DfF177yWUaGCpTWy9y+s0S3a1PIZ5iZHOHUkT0MFbIgwHG9YLFXqgIhBNMTo+zZNYVhxXj77Hk0XYDms762xWtvnaPZ6nD6xCF2T08wv7TGeqmC49o4js3mls2PX3uX8x9d4wvPPc7nnn2CdqfLytomr7z1ATdn53nm6TNYhqDT7rC2XuKZp8+wd9ckI0O5XndIaLbafHDxGtV6k0Iuw+T4MLumxhgu5kinEiSTFhNjRU4eO0A8YfHKW+dgqzkwmNsdm3qjxVAhRyGfJWZZFHOpaMPKZFKk0yk+vHidcx9dI5tWG4HruaxvbGEYOpMTIxiGwchQgWI+SzwWGJkLQSab5sTxQ1y+dpuLV27SdRxlZyL7LBuEIB6zGC7muH1nid/79ks0Gh2++qXPMpTLoAmwHYdmqx14eEE8ZmFZFn7gUNA/OqWvNqFO16bT7XJ7bpHL12/jeT7SddCFwLZd6lVllKzAiUU6lcT3JdVaA8d22CiVWV3f4sSR/VEMpuGhPLlsWt3LcyLVlFvMYbtuNF5836fdbFOtNrBth1q9Sde2ScQN7iyu8OPX3+P23JLaSEOCWdNIJmJcub7Fex9eZqNU4ebsIrquE4/HcByX+cVVPrx4lY+u3OLKjTsUC1l0U4/UNZqm0e50Wd/YwnU9as027a5DqVzFdpyeGtEw8H3J2Q+vMb+0xOTESJAnD1ptO4qy7nsOppHg1PED7JkZ4+KVmywsJNk9M8GhA7vRDR3XdfF85dHro4FQYDtMExTZ9+gahqHAS6vdIZVM0Gi20TSNbCaD47g0m108D2zbo7RZZXOzhm17dDoOpVKdhaV1ypU6+/bOcOzwXkaGi+zZPYPjS7730luBGl4biLIuTVOlneqz13KDYMrtTofbc0ssr21yaN8Mo8NFNR5Mk2QiTrvT5cbsIrV6k1t3lunYNu1Oh3ajRSxmks2lyKRTHDu8nzOnlUrVEBLbcdQ48jwScRW6RTMNNKlinLbbHRqtNp2uTbPVIZfNsGfXJBDYozk2lqkCzLqui+f5UegZz/PwPC9KOzU+NkIsFuPCR9dZDDxJbVvlkk0mYsTjFpMTIzx88qhi6C5eUzNFg/XNLTZKZZ545BhPPXaaTDpFrdbg7Pmr3JpbpKwbTIQT1TR3BgnBIdPSyGXSCCE4dmgfLzzzOFMTo/z2v/p95haXWVhe4eHjB0gk4pw8epBD+/fw6ltneffDyziOi/Ql84urvPzmO1y+Pkur2WZkpMD/uVqn4Hn33gzFdsCkjol7/K5OCtDbQEw70fe1R0yEQT0/ToU4yGfJPrMS9SyBr+zDZX/8wZ8CSvj3VbbbbfWX0K5PoJgs+uzkhFTgM0i9dD9oKITACuwQM6kEsZiJ1urgE9jLiYDsGWDIPl35FOl6BosinOQg1toJkvkBYvUk9zU26x+c90DQA8NX9ICb0rPqdLs2cwsrZDMpTh07xOhIEQGkU0nev3CVjc0yMUMjn0kxlM9y3V8im0nx2EPHGA3sDBA9rk6pMVTYhMnxYQ7sncE0dDzfZ7NUIZvOEI9bkSSIVMmVNU3gBF6SD588wJOPnGBkOM/4UI5MWslzna5Nu9Ol3mhTqTcp5LMqCr7QuDm3RKlcY6iYw3FdZdOzusFIMR8F5SzkMiTiynjedhxKWzVmF1bwPI/J8RF0XaPRbBKzDB5/+CiT4yMsr6wxOlzgiTPH+f4r77K2WebXfuELHD2wiyj/m5S0O11q9RaHD+zha195lmRMeQKqND1Keq/VW7Q7XeKWRSIeI7S5A7XhdB1HhZpY3aBWbzI2lqDVaKALgWnqLK2s89Jr7ymWw+7S7mi0Wh00IRgbKbK+WabV6mCYVuCl5GFaBuganpTcnl/h7IWr3J5bolJtIBDUGgp4aL4y1BdAzDLZv3eKm3NLvPXeRR576BgzU2MgVNwmz/WwbZdmq8PV67NYlsnmViUYY32LWqDqbbba/M43fsD6ZpnhoRyO4+K6Lo1mC9/zcByVEslxHFyvZw/i+T6NRot2EOTT8zw2tyqsb2wRj8dwXS8aeFpfdG47n2V9q8rqRoV6vUmr1WF9c4typYbn+ayulSht1Rgumrz65lm++6O3mZkaRdM0FQzT9zFNg7HRIRzH5Xs/fot8NoMQcOjAbuKxGCurG/zuH/yI989dQdd1Wu0u+byk3e4EKWZ8XNenVlfqG10PPBS7DksrGzQ2ygzXg/hTho7v+1y+epvvvfQmmiaYmRojZsX46NrtiBU0TR3pOoyNj3Dq2AHOnr9Ord5k/54pJiZGwfNxPZ+uI3E8pSJ3bBU13PelCgAqwTAMhot5PF9y/tINRoYLdHZ1uT23RCIeZ3JshHbbxvd9urbNhxdvoBsmpaCPm60Oq5tlWp0uvoRSucadhRUkkM5m0QPnk1jgCBOOcd+XVNqdKJGt47hUa4o5WdvY4tbsAu9+eBkpJQ+fOsL42DAiSLRu6Dqtdofb8ysBaFXA6Nylqxw6uJtkKkW13qTV7nLz9h1OnjhMIhlna3OLZr2O4zjUm20uXL3N1MwUIyMFTF1HoJFKJhgZKuD5Pucv3WDvrknyOcViup6H5zm4QQYJz/PpOi7dII1QOHcr1Tobm1tcvTHHnYWVINegF7y3H4FL3/fpBAGSY5aFZZmRl64SDFRy6ng8RrvTodXu4AVzyG62e+t3MGYG5lxEbghilkE8bmGaBtlsmuGhAocP7mVspEi90VRmJBDY2RjcuD3P3MIKAKZlslEq8wffe5nvvvQmtUZTqXhnY/yxcpV0PK5yo+644/SKDA2xdwh3FMGbSC2o9e2Las2UEkU89Omhw7h/O5Ydj/eev/06xc4FTE9kFP7/x4Brp7KjajFksISiozRPtZEnkFIjjHkmhIjSs4UlZPXj8Vjkma9sZWXg4PbTab+fEGw9gBfiADUYNkr/YNsO1Poj7faoeLkjqgz0zsGOKqVEMwzKlRrLq5vELEXvt9tdXM8D3yMRj5FJKW+aPTMT/NFf+iKNVpcfv3kOx/X5+RefYcxX4Rkip2apYvt4vk8qmSCfS1PaqlKu1olbcdLJNJ4v8HwRhHRQgSl9P6TABavrZcrVGvG4pVJeSEgnlQeQ63psVRtU6y3K1TpvvHsBD8GxQ3vp2g52sIgLlJt4vdGiFUitnW4X11VA0HWV91Quk2LTMmk0W5TKFWzbxjC0wJ7CZGy0SC6d4pmnHmJiYpR//jvf45s/eJMDe6c5ajtYYTodoG07tDpdstkMI4UMt2YXlEQdBL9stTs0Wy1cz488Z/rD/8ViJolEgnfPXWXkmz9iYmyY1dUNnn7sJHt3TfHhhWu8+vY5CjnFPKyub3F7bpHjR/axe9ckr7x1jm/96C12zUzQ6XSZW1ij3u6wWaqSTqZ4+Y0P+MGr7zIzqcIbbJQqXL81T7PZxnQ91Y9SJT7ev2eaQu4KC8vrxGIGUrq4roPveXS7XRqtNq12F9txKOSz6JpGq91hY7MceMoEwxfo2g4fXrzGynqJwwd20Wi2qddbrKyV6DoetUaLju1SrbeoVGs47hjtThfHcdkq16jUGuRzGWamxrh05Tbf+dEbPPPkw1y7OcfK6ibJeAyz2QmkKnALOe4srHBzdpHllTU+unqLQwd2s7ZRplpvcuXGHVbXSmQzWZXs3NC4s7CKbuiMDOWJx6xopuZyaQ4f2M3B/bvodLpYlkUyGUcgGCrk0HUdwzAo5LMMFXLYtk2j2aLRbNFqt2k0PK7emOPksQNsVWq4rsfq+haNO0uRQI9pqATxY0OYpsHc/Crzi+sITTA1PsIvffXzPPHIaQxNQ3ouMcvk0ME95HMZPM/jyMG9pJIJ5eHm+zRbHTZLFUrlKh+cv8xQMc9WuRqEeejgSziwb4bJ8RHeP3+FSq3B7ulxLnx0i7gVY2i4SCaX5YkzJ7izuMb5y7e4MbeMaRpoSDLZDLVmC8O0SKWTXL15h82tKlMTI8Qskys355ESctlMb+kJyuXbC5TKVWr1Jv/on/8euUwqUFeXaTRbTIwN8ZXPP82zT50hmYjjuS7lao2llXUazTa+75PLptk1Pc7aRpm3z14knowzOTHKBxeuUqs3+N5LbwcspIo+//CJg2QyKQq5LD9+/Sw355aYmRhl98wETzx6inwuw6MPHeP23CI3Zhf5nT94iQuXb/LsUw9z9NA+0HxW10vU6k1cz6NUriF0g1KlRtd2aAU2fvOLq/zw1XcxdJ0jB3dTDvobIaJk652OTb3RDDw4HaVSn12g2WwRs1RYjPVN1RZDxXxkklHMZ4jrGtriGnge5j/4l/BPvwEo0wpN19E/ugGexBsqYFlxYpYVeCNr6JZJLK7ykCJA0zWk8Nks1fjo2i2q9RaJRAzP92i0WrQ6HXI5te51Ol3ml9aYnV9VwDAW20YR7FT6xG/p9/Ykse062XduAMwHg5VG/7t/+ThjeUlUhygo6MA+6YE0iAar7LfT2mY39n+0co93V02rBT+HQWODnIgDJE4Qe0sLsg5LiQhCtkjhI4SnbLGFymVsGnowtkwFfsMgcmLw2T8J7PqJwZYaUPepQtS/4Tmid1wwODCjMjDdNQABAABJREFUM+6+X0+nO3A0UOsE23vgobKwtMLiyjqpZJz1zTKjw0V8KanWm1imSSadodFs8cGFq2xsVdk9PcGla3OcvXiNk0f384T0B4ZkmBW83bGjIJa+BMeHRICKEYJ210ECtuvS6bpoQiMWi2NZFheuXGJ+aZ1cJsXDx/fy81/6LL6vMTu/TK3Rwug6OK6HK+GtDy6zVamztFZirVQml0lRqdUZGsqQz6W5cHmWf/V7P+TIgV1cuXGHjVKFarWJbau6DRVyzM6vcPbCFdrdFs1mi/fPX6VaaxKPmSTicVbXSywurnPs6EFmpsZYWF6j3mgplWiwY5qmSTadYm5+he/+6E2G8mls2yGTTrGxVaPearOyscn6VpV6vUm9qRgQX1fDStM0ctkULzzzKN/8/uv8m99/iXQqwdOPnSSdzdLo2FRD423bpZjPkMum2CxXqVQbnDpxhOeX1nnlzQ+4eXuRsdEioyNDGIZOqVRl9+QEEmWvs7q+RTqVQEpYWSvhuG5EuKrXUQ4SKryB5MD+GXRDVzmxXJd2uxNIzyZSSvbtmebwwd3MLSyzvLqh1CyBJj887/FHjnPl+hxDhTyHD+yhXm+SzWfRAs+2U8cPMDe/Qq3eZH29RK3eoJjPIpG02h3275nml3/u8yQTcZZXN/nw4lWEELzw2UcYXiyhN5aj+SBHCww7Do+fOc7S0hAjw0UmxoZ59KEj5LJpbMdhq1JBSo8XX3iK0ZEiS8vrpFNJ9sxMsHtmknany9Xrc1y6fIv57BrzS2tRrknP83ji0ZN85omHmJwYYXJ8RCVS1wRTUyPoOuyemeDXf+VncLpdpVKenuAXf+Z5NrYqpJJxch27p3Y3DHRN4/jh/Xz+2ce5cXuBVrtDIZfhuafP8MjDJ8jlMghf5SQTCPKZDLlMikw6yaH9uwkTpyMhm0lF9oe7d03RajXZs3uSmGWSiMXwPI+piTH+yNe/wsF909y8PU9pq8bRg/s4c/oIhw7sIZfNcPLEYayYxQ9fe5/5pTX2zExwcN8uksk43a6N47ocPbiXWr3JwvKaAqy6Riqd5Ge++FlOHDuI9o2X+9SIYCVinDq6n0QijmUZlLaqaJrgxJF9HD28l4dOHGbv7hmSCaWWbjZbnL90nVt3lojHY2i6RjqV5KlHTzE9Mcalq7e4cWuexaU1pJScOX2Ejc0KC0urJOIxdk2PE4vHGB7O8eLnH+f8pVu8/+EV1je2GBsbIhYzMU2To4f28tzTZwIWXDA+WiQes7Adh063ze07SyCgXKlx49Ys6xtb3Ly9SLPV4c7CCk89foqx0SHSqSR3FlbwgsTxiUQMKxBql1Y2aHdstraqLCyt8eGla6xtVlhcXqfRaHFg3y4eOX2US1dv8G++8QOOHd7H1RtzZNJJ5axjq5yQwvXwK3VqWxUcxyEej5FOJbFsN5oDlqHY83a7w7sffMSePTNsbm6xsraJFaRFcn3J9Vt3+MEr7zA5OYLjeaysl3j93XPMzIzz8195HoFimJdXNzh7/iqF//X3VIqie+9i0V7Q26tCUsCjF12xv/RZ+PfZonyst+H9QMa2T3dpkKRUwZdl+HzBoD3T4PWid+H96/Tvq9y3rbzBz0JXoKs/T2LISgk/ooUiokqA0FSqOV1TMeRMQ1fMcGQn+NN9nU8HtrZRvPctA0ArLBoKdavB0pcw5R6PC4DWNoIMAbV6g3qzha5reCgJOJlMMTY6xJUbc/zg1XdwPY/ZO0ucv3Kb44f3MTI8QrnW5Rvff5M3z15G03XaXZvJeCyIBj+o6/WDFCCVWpN/9+3XyKSSFIoF0mmPRDyJblrUGi2WVjaQwHvnrzM8lOfXv/4i+XyWQwd28/aHV1kv1RgdLrJ/zy6ymQybpSoLy+tks2mmxkcYHx0iH9hWbFauMb+0SjIRp+vaVGoVJsdHSCZidLo2b75/icvXZxFCGak2mu2IXUomE+RyGa7evMON2/O0OzaJRIyfefEZ9kyPKluQbJb3z73KK2+dZ2OrykMnDrJrZlKxgYHdSTqZ4NGHjvL9l9/h2z98k7HhAmdOHWJ8YoSJiSH2753CcT22qlUazTbDwzm02bWBNCq6YfD0Y6cxDQPHdSnkszz6kDIGX1svUchn+YWvPMvM1DiTY8OKOWm2aXe7ZHI5fvZLzzI9NUaz1eHgvl2AWignx0fwfZ9nnnyI3dMTZNJJYjGLzVIZKSH99/8VWhCCQg1DychQgScfO4nrupw+cSiK/O86Lvlsmj/8Sy8Si1nKJT0W49GHjpNOJcll04iX3u+NXiGIx2P8zBc+y4ufe5p4LEY6nVKqOsMgmVAxzB45fYznnn6E4eEiju3w8Mkj7JqeYHS4yK4pFb7i4VNHmBhTRthCCMZGh8hl0mS+9Qaap9g0mUkRy2U5EE+wa2YCHUk8ZmAY8OLnn+YLzyuVzNpGCQSMjQ7x/GceZWNzC13XGRkukkom2CpXyWVTWKbBZqmC47hYpk6z2aZr24yOFBkbKfL4mRMMFfNBTC8PX3j4UrBvzwwH9u1C85U9UjIZZ3pyjE7XxrYdht650JvHhlq0JseKPPXIcU4c2kMsZpFKJZmZHCOVSgbecZoKZ9G2mV9cRQBPP35KeSEGalRD19i1a4JfKLyAZZnousHG5iZTkyPkchmGiwViMQtdwIG9M4wM5yhtbeH7guFikXQ6ieO4aLqGFY9x4vhhJibGKZUrQf9m6HS6bJS26HRtZibH2D0zzvlLN1jfLJPNpjhz6iinThzCtKye8TbKu+kx4PGjB8hms3hCUm20MBNxhkeKFIp50p5EX1gDU2UfsHzJyaEiE195nq7n0XVcfF1jYnSYM8cO8blnHmVlU9nTrW+UmJ4aZ6iQI5GIk8slicdj+J6HLiSpZIypyVGymRQbpQonjhwgEY8HjkEpTp88zMhwgeGhAsVCDsdxAEEmk+SLzz/B0UN7cRyH4WKRVDLJiy88wdrGFrumx0nEYgwX8zz39BmWVtYZHS4yOlxgZLhAPpdhYXkN23EYHc5TbzS58NE1arUGTz5ynEdOHyWVijE8VODXfvFL/Pa/avPaW+e4cWuemelxfvaLn2F6aozEe5ejbcJDsi4lja5NUtPYG/PorcbKJs+KWbiuxxvvnWdhWYWj6XRsjh/Zw56ZCcViWzqe9LlyfU7NSctkbbPMVrmqMmUEJhi5TJpcLkvud3+E3mzfFwgNwpH+8/TQlqbvrD7tTGSBcLfa8a4iPs4qKNTkqHMR3KfOfaYP9/jl/5Al2OvvoTwNzgm9APp+kx70JbRW/1dG8lLTVEJ3NISQvdy+QqBpfpAjWXkeCq0fj2j8NFvrU4GtiKMSvcEk+ySDngLwfkXrO2uHF9ruNSHEAG0K4Dgub713gfmlNRCC+cVVrt64xWMPn+LP/PFf5V/87rd579wV3v7gIxIxi2OH9vDLX/1cZMvw1KMPs7CyxfLaJk88dJQvPPcERw7tj8Lzh+PYMHSGijm++vwZknELw7BIp1MITcMy45iWRToFP/elz/LcUw8hNJ1dkyPEYzEymRRf/tzT5LIZmq02p48fZGw4TypuUSwU+M9/8zdoNNp0u13yuQzpTII//R//Ii1XUt7cYqtcJZVMsHf3JJ1Wm6Filhc+owBGOpkgmYxjxeKMjQ0zMpRnY7PMnl2T7D+wm4UlFdNobGSI06cOU8hlmJ9fRNcM9swM86XPPcn84iqJRIJnnz7D9MQIuqFHgVgN0+Dk0QMqL5zrMTZSZNfMKNNTIzx25hhf/vxT3JpdJJ9LMzkxyhOPnGDoz/9djEbPDkMrVzn6N/5nDnk+iXgMwzDg915B+j6W4zDatdUiGqh8XcfF9TwMw0DXdbJC8EKQKNTQVQLpvkHCYd/nYJh7UFP2FI7rQbNNGNAvXAsTiTgvfPbRKF+hYej4CGzXJx6Lc+zwPtKpJPGYhQQy6SS7psdJJ5OqTSIBVaJX6uz6L/9eL59l36CXUlK0HXKeF4ADZYvymOsigpg/EOQ/lJJdnseEo5hQMwzsWGuqpNS6jj9SVFHeEzqZtKqLjocvXUzLxHPVczJptRHHLRVqIJlIoGkCyzRVPLmgXZ9+7ARPnDjM2PAQdbvL5RuzXLw2S6PZYvfMBPGAKQKJHlpcI9B1MDWNZqfDjVtzaLpOzDJxHJfRkSKjpWqvawIbp2Q8zuT4KGt6SWVMGCqQSMTR+2IigcZWuc65S9cpFnM89dhpYpapwoNINeOTcYtkTL2H60nMiVHGx4cxg8j9uiDIySnIZVNkMgkMzULXDWr1BmvrJYaKORLFGIYRY2JsmJHhgjJs1wTtmLK5dD2XZDLB3t3THNq/m3qzxchQgUIui+t5bFWqSD8UFJUN38kfv48ZJDeXgB/YQglNRaVWwZ+VF5uGxECwT8A+ROSt6LgOhq5jWRYTQrAfyRnbwXY99JiFbhnEEnH0mIXUVLJqHxCGjtQEP9ex8YD8tUXMmMrAYOkaByXsF6AZBhg6XdfDE2AlLCZjJggNW/q4KxWEbvCYaSATafRaB177ANvzmNJ1rNExFTah2UV0S+ibNR7xPPY/fgbb90llUiTSSb40MoJumlhxC5bW6TgOBwydP/dzX6De6ZLKpIjFLByhzHdNJ2SuQOSzfOfnn+G7L72NZRr8dqVNNprpEtvt0Om08XyfYj7Lnl3jZNMpPvfMIxw6sJvJ8VGEEBw/coDpqXGaLWUXubyywUapzPjoUDDv1ES2HYdms3W3cXofMOqxWdFOROglPwiutu14EQALNpKPZaz67n/PMyX9m2ykOgz3Sj+s08Bi1Lvjjs4H2471e1ju9Pl+1/yEJTSZuC940IIT7noVpdrd4a59N1QgSgihDOyljLqml8mh5xSnMq78ewZb/UUgIqQ4WLaj/HtdvUPZHocEIqPt/tu3W21ef/sc5UodgeDS1dvMLSzxmSce4fSJI4wMF7l87Ra1Wp3piRF2T41jGAZSSlLJOD//5ec4cfQA75+/TCaT4cihfVEU7v6i6zojuRx/9Fd+lnw2SToRp1Jr0Gp2kXGBlodCLs1XPv8UWpAI2ndsWq02zWabbDbNc0+fYX1ji1jMUl48cQuhaSTSaYp5l1arjRA+ibiBaSWxbI+N1XVM02Dvnmksy+T8xetUa032757ii88/ScxSG4QZi6EbJu1OF9M0GR0ukM6lmZ4aoVTaUl5u+QzVap1Ou0MylqSQy/DzLz6rjAWFwBQqrEBkH+d6GJducPq/+Uc8HrNIJhMIwHUdhCbQdYMRKZl2HIQQmKYZdEq3L5cU+J6PXq5hahp6uxtNVAGYvo9wXWVsq+sKiMggnZEWbFAoalwLKHih96SXCHz3BZT0PB/HcRAItJgVDRZRrpH9U/81fYgJQroYjbjn0+l2AYlmqnQoBelTkEqdKmrNYG8NnCAAo1wbeNf+8at7HsL31SYnBEJKjICd7RnQShUM03XRpUq7owVeNtJ1e+vlSCFaEDQhMIQEKRDoSN/D91WeTcuMK7CmchcT0wy1wQf2NYvLa7zy1oe4jsvXbq1w8Oocvudx0HH4gudTuDhLMp+FeAxiJsQtpGUgLRPiMbREHGJxDF1jslxlqVyhpuukillSB/eif3RT5U0M1PrCcdBabdKWRTebRkqIxxWo9oOFDinp2g5Xr89xZ3GFvUFuQTcYF+FY0fs2FB2JZWhIVCRyXSjQ4wUMtLJtVO3oui7rm1vMLSxjWSbFfA6hiyACulp/Qo9TXddwnCDcQeAOHkZD1zQNp9OhGTDI+D1TA0PXVW7PYFPSQmEtGHsqyKIMhoa6TgvsTqRUsdO8wJHGlz4aAtdx8Ds2pibQbRvNcdBsR80TKVVqrPCeviTrewihYWxUImFJQ2IEeDacMlYQ5iL0LEUILGW1DUKlOgs3VymlCsuwgyE2AuIIsqEMMgBW1P88z8fwlG1TVtd7MYuCfvL72kphbsHE2DCZdBLbdu7axNfWS8wtLNPt2pw4up8/8Rtfo1KtUa7WIlW663nEYxYzk2MqnqLvc2DvDI7jKvYzYO0916NcrdNqt4MUb4PrwsCL9v8Nvd/7AEg/l7LT3hWdv63soNC7x/cHKL5EpasJAMW96rFTXXYCUzudc7/vDwq4xLZ22vEcxXb3PyGCkeJuVazKFhM4Ick+YVwodkoE7BaeF7wrhM4FKs2dxAzstgzT6I8djrL90hm0M/905ScGWwiQg8PtPgP23mTsXWUb0Or7IbpNPB7jP/2Tv0qj2aZSrdNqd3jqsVPEEga+A6MjReUB0+6QTMSJGbrKO2foaELDsgT79kySy2dodx1igWqGARNvVTRNo5jPoAuVyLnVatFutdEA33XQY2ZgA6RaoxPkUlTu9vFAavagawfRkYOlODC8jscshKYWNteT1Gt1bt5WxuiVSp1Ktc65i9exdJ2ZqXGyGeXN6Lou0vfVQuO6Su9sGSQsHV8KmqbBytomm5tbOI5Ds9Uhk0qphVjvBwq9uDqE/2yHZLNN3PMxbBXQVQ+8lbRAijf9Xk7KqK+EAEPHzWdwXeUhpJkGUtOiVhXBudJzVc45IdCDZNQhUyQRylPQ95G+8uYhXLRDsAJB9G4/AFpuwOCoZNEEqT6QEi383D/+QhZPgmXbdDo2bSSWaWJZhmLYND2oDyqFUxDbShj63YtOMETxPLXg6VrwHr3cZ1L05oNUeWDUBqlraqEBaLTA0MHQ0Te2iP+7HyCHi2gjBRjOI4s5MHQ0YYYkUpRXU+IHoK5XHYkgl82wa2qcG7fucOPqdbLlejTMc+kk6XYX3d66a+wrbYi6oQAs4ICEPZ6H9CWarmH84F205XVod6DrIJY3YH0L6w//F5gSMgJkzELEYwGYsyBmImMWTqdLYn6Jr9Qa7K51yPyLP0BLJBAxCxIWWBbSUucSs8AyEcF3LW5BPKZ+Nw01d0UoJcsgI4Oa82E6qzCVl8piqZYaX+gYsTiGVC8cgh+VPFwF0NU05eWnaxpeJoWMx2j80a9iCg1T19CkxLMdfMdB+BLd99F8H02C8CV2p0O9WsO1HTKJOKl4HOl6NMpVWo0GyZiFkUrge5JWrY7d7qILgS4lptAwTEONdc/Dd1y1eXg+uC7CU/EciWZFX9/1f9VEkMMwGI8hdxMxt33jWfTnI9ypqHaUQgzYsEHodRdAzT6tR4/sEduUNJKO7XD2/FVq9SaH988Qay70QR2lujeCdWdlbZN2u0OxkCedSvYJKkRhYSQqXEksphJ3R08K5mIIyizTuHszuo/ab8eYUOE1/X8f6H6h4NXXOJ+qhMBPBMBrp1O21aGfuer/HlXtE4C+7UzY9ufudN8d2qmf3dr+6/bMQ9seEgk3969j2H89AUIJHmr/UWnggneJXkOBtl54iU9XfjKwJXsS3M7dIvv+9R/7FI8KB1Df5VajxWN/+59EmxmA9u9eBiQaoPsQi+j+noSn9LUS5lewqg1Sho7UdaSuqQ2u64CmWBRpGCoZsOthIfGkSv9imSbDxTyxmNWXp0/RkCAwTYNMOqVcvE0DoQkKuQwIgWboeGhRm4VSdRjV2EcjnUxz4ugB2u0OrufRaHY4dng/+2YmOXxgl4pSH2wknlS523RdJ5VKYpo6pqG8IXNZFRO+2WrTaLRo0sFxbMIQD3e5DhdzYJlqoTUMrIkRdNNACqmYr2ATkoHKLoTZMnBr9oO4Tu6+Kdb+r38K15Uk4nHisViQTihc3NV4dl0P11PG7GE7aIjeOuYr8ORLEJpOzDLQDAMtACVSShJ/4W8jyjV0VO7AYEb12ndls2/g3D1SwyNxJJangJsQfX3Sf55pBLGWdpD++k7UdG1grIbM1ODzBZoGmmb0LgzOjRJQux7aS+9gvfSO+m7oyJkJ9TmfQQ4XkEM55Ehe/R3Oq2PDORjKq/yWUgUe3rNrkj/zx3+FjVKZ/N/5bRIXbmA7DvbEMImhAjoSYTvq2bbLQBFR7aI9OczPFx11vejcba+pUje1OshWJwKFoPrParaZrtTYo+sM3Vwktrj+qTUTMmYGIM4EK4a0LA4ZOvtNAyP1AVoiBrEYMm6hxRRI8y0LI2ZiGgbpgMkTpk7SNCBuofsSEbPQDYPEcEG5h2dS+EN5jF/5Mpqu4Qcg2g/yjKo2Ugu2FEqFu7axxeLyGr7vMzM9jhkEsTW6Nma3jRazcGMW4vUPSf/2H+Al4wpoGjoiHkPGLFzTpOv7dJAKbMdjYGi4mkCYJvFUGjNmKpWfriEDxlgGgoEUAg+Jh1QMn6EroV03AzW8hnQ9ZGC0LiRo0kf4UjGWrge+j/AUkBQoBld4PtoP31YgO9ywNIEQOrqpbKTQNAXMwnkQTiFdB9/HbLQ4fHuR48UCp3ZPk740pwAlApptCuU6MwiGPI/uxhbdco3xYh6ZjEcsnZo/Igoy2x8UFQiEYYlmSLLZBJpQjhzRRv2xTFAYFHPb3ibvcW34Wz8YiUDPdrakH8DdC5hJtlEvP53ySYAV0IP0ISgRO6xvP8FzgoTsvfVXgPB3vnyAlbtXMupttx8AyT0ALn2Qvui9Vt8V9+yWByw/ObNFmPxzMGt5D6v3Rbntq+lPNlyChvIlbFaiO/fDunDyaJLo10FgLxHVBjTb9wSLslJX93I99MVVRp7/42rBMHQwNKRuKHbDNJSNiqGDboCpkzB0soauFspAHZaOgMa2bgw3nr5jFpCNDkge9dVk1rRB1WsgpAfSwDaQcW2WEceNJkVo6C+EwND1bQO3T7ryffWeuoYWswgTSmuaBoUcrf/uzyKEwPMlrh8sYEIEefiUs0IyEcfyJfGYjqGbkTTYz1IKITAMPUocnvlLfw+tUg8VLwFAHhRxlC3MYI/pF6+D0wcOdA1vagwAL0hzs72DB1+9z95QIThFTgXAvB8cgEBsVe87frdL8Z+miGo92GjuUyp1RKWOuHnvB8pcWiUTHy5gDuVJDheYGi4ghA65DL6u0fiXfxuRNHB0qNebbJQqdNptEmiM5TLEJOi2j7A9hOMiujbCdgJgZiM6DrJjo/1P/xIcV6lcUwlk3MJ/+Biy28VrdWiUKnRrDbKmQVLX1X2AWMwiGwgmpmX8RCYgoutAcN9wbdTv01ty25do7N2naPMr4PnoKxtk/7O/BbEYxHusW6SGDZg7aZp0Hz1KU1NzoFjIkc9mVD4/IUjFY8QTZqAKB63ewljfChI991VPBrlTOx3MIOGzroVu8Ip503XF3n+cb13/e/b2EKHAvGkokG7o6n1MA2mqv+F3LAtMHWIWwtQRMaVqF7UmMptGGLpS/SMVaBN+8Jzt7lBS2Sb6ktRWlT/1Xl0x/G9cUMe7SujQrt5i6C/9PX5lc4ufaXYwVyuM3vxbpNKB2UdYr746KtAcftcH/xo60oohdQO5sKLGzHABfE8Jkn1CxcAmGwGtvr/3A1r9nbftuyJawgf02qNXtkksIeMiw4/bmLRo8xMfX5/+eoVr2wMBoT6Qta3ePdF7cD8T28Bq9Drh+22ra8hubc8mcDfjFLZB35yV20DtIM+rbuEPznEVNsmj27XpdDsqXtvAq/0EC1JfeXCwtSMxsDNouPuM/qndO7LzXe7zbEBIHwq5HU4avEs/AOlJLf2DW8L6lgJLdz9GFV3vbXhSgS7wAtYhzAD5AANUV1IduhZ9lroefFbADV1D6Hp0nqLme7fodZTcVtG7lLi9Fml11EKHam9NSvADQLwNsAzMedH3p1SN2it8X13TIoAlRBBbV0ocx6Faq5NMJkglE1imHt1MSgmOg2i10ZsttGYbrdVRdWy2Ec025ke3oNFEeL7aqLNpHCcIFumqWGOmYZDJJDENs9dnjqv6RqDaVdOQxRxSSjrdLs1WRyUVTcSxTDNYVFQ79gLYqv9sRyXTrTdUguJ0OsnYcDFIfK3Ok4j7200OLMjb5+rdE3eneSOH87B3WoFIx0FcuQ2uq1iFSh2cgIFy3PsuBvesZtdWKkoEuT/912A4Ty1mstRqsWUabOmCetzi9POPcfCxh9Az6ShhcfQWAUshJVBpwr/4prq3rtofoSHuLCFQtnudcpW19RJuMUdsbBg9GUfZt/kkMynwfGSoRvCVXVTEqPi+Eq58L/grlYeRr2Ln9H8nuCb6fjfWvg/Pef8iQK0JEnBctJXN6Ied52EAoMaHsCeKgTCiM1y0SSUTkXmMJn2kUBnYhOPsWDEB+L5Hp2Nj23YQD00nHrN6jhoP/B69CT+whrmK0aLd7R2967Z9QKT/6OIadLqIVAIZONrIvvGyc+36BXClMlVe/dsEDc/DWlxj1LbVumM7aKslNKPy8e99r5/DpdT1FCgLmS3fV+syKPWouJfc9AAg635lB81CcNd7lGCQ9TNl4Vojg+AG/WDjAZ7/CSt8F5t1d8VFcFZvfN091wICRtyzYdW64m+fuH2Ac+B4yDgE7x/2i/QZ6NOB/goRglq/PFdlTOh2bPwgoKmuKfMApQb/GMH3AcoDgy0Z6CzvGhr9lN12uhSC78rt8u4ptx3V9//Ud5+o/QIbg3/wV7bVDaRQtjF+f2NKVADR4LsMDG5V0mof60/+3xCrm5BK0P6Hf1V5gNUaUA/+1hpY//wPEPWmos9dF2m7+LaD76hs9bqhBZ5qHwO7pAzu4UWvdL8idD1g0PSANQsYNV0dk7quVJy6Htn2ENldBGV5PRz/+LoeARdNU2pO0zD6hJkdJr1pIIdyqi3LNbXJuB6sltDqTbRmG73ZQdZb0GgjN8ukl9fIaTpZTUNrdRWQanXRmm2wnZ3W7d7H9VIPNCVjtFJxtso2VjKO7/tslWvguUwn4mQzfY4M/e+5axJZzNH6f/xVOt0uH1y4yu9+88e0Om1++eee58zpI9i2w+ziGo7jsW/PFCPDRUxDp1Ft8Z0fvsGrb59D1zSW1jY5vG+G/9N/9HUO7d8dqYt9LbTxCG37+heVQBXTF/5ie3y4kMEAAk+1u2eF+hvMFwHi9NdV++sa5NMD9wvH5uDfbZ/v6lwZVgDRbOPVmni1Bruk5Egyrpg9z8M6exPT/B1EOoEcyiNHCko9OZSH4aKyHxvOIyqN8MaKOQCQPiIA67ovSTRaZDo2RqWBMEwlXATvaUWL9A4CjBBqnOsA9xaO7rl1SElonCVlAMYCNkKGgC4QRBj4PQBxsve7DOzwhC+Rho5MJxG2PcisDlYegcRIxCjkMpQrNWr1BpVqnWw2HRhsBwwQSkugOb154v7lP4732DG8rku9XOfO7XnOn71KtVRBuA4W8OiJg4xl8+i+T8owSekCzQtYSNtBOA7CcZGOq4Cc44LjIh1HgXbHR9iuGi+2g3C8yHSi/3z8vvV/m5wWtTOAJpB/8A/wg2f4nS7NapPy+iZOo4khJUnDJGHoWFKS+J//DdSbUbuLvj4Qi2uq3bNp8HzMuIrwLSKgQQ90SLnzv/uUXiD3bSyL76uQAcE+JCAAjr373fXe933QDiDonkBL9P3r+0XISIUY2TYNCN8frz7bcY8eVPl8TPGJJIRt1VN18fsPqDrLUNwP34OgTfs9y3coO24Wge1UqCWKgJYWmFSF/e711l0/SBEow+vk4PUodsvz/SBskXK2Mgwd0zLVGr1jfT5ZeXCw5YdAVA5QfKrSfWxRdMFgB4Ybx86R4LeVvsEg+yZVeLee3c/2+4dD1I+GqtRC9dXgIzQhFIgSIHMZyGXU3/BNpHpT5+svAGrjuXZjjjfe/oBmo8vi0jqrt+Z5cv8ufuW5J5jJ5tAbbQXUGk1171oD0WgptUqtoYye74b59y/yrg8BiJRg24PnppLITBKZTSEzabS5ZbBtZDbN1n/8Nf7t2hr/6HtvkM+m+dN/7Bf54nNPBIxNz8B94GlSov21f4i4s4x2Z0VR+oZO8jf/u7vqJCVYrkui6yggZ5nbXvGBOEB1L13HfvZRvvnYERaW1/nic08wOlKgu7ZJp2NT2DWJlctENmDxP/nXEKUqYnm9B9+FsmHLZdNYlsnCyhr1RpNarcHiyjrf/uHbIDR+8avPMzJcxHZc3jp7no+uz/KrX/sCB/bNcOnqLeWVmMv2tY9EQwWv3W4uGXnFSIGk5+GkmIt+AWSwnbVAUosOR8/aqcUEDOV75JnYduZOgq2UwUbqQqmimMDNMjKdVOqW0SG6i6t0bcW2WFaQo072ranNtlK5z68OPix6i77H5TNqc9zWNr6hUW+1kYkYw8Uc2gCrLPo+36vcreKTg//bsfQ2pn6gu/15O11/t5KHvmtlPkP3v//zav4gI3BDV6lYtd97Ce0HbwECK5VgbGQI0zRpNFpkM6nA0SN4dv+L9dvLGQZCM/B1n0sLy1y8ucD+h45yMJXEdhw8z0NmM6wJlTpncrRIIpuOxpAG6EJG7x2ubap4wTjuV7SKIInv4PqqaSIKPipcBeRwPTTbQfo22j/+PQhS4xCyx6aBK2Bps8yr757jwuUbSM8jEbfIZVKk00mK+Sxn/vqfZs+emcEcpEEdzT//30O5EbW6joxCvAz0X2S+sq23+kFXBKqDTdeTcHVWfY5byHy2D1jJQcBFyHKJu+/9oOWTqPeC83sxJgXKZmkHQWQnEPdTUn31l+iOwebaXxcZLhYf+9gHRyw7M5Yh4ApqFISlGWS0AqAV9bsX/SZlwICjxpgWrikyzJoSeFIjMXSduGkFZis/eflENls72TPIgGMVUgYMptwGxEA1UBBULFyoPslzI72smiSRCiv6jQh56mGHDxwfjD4vhFDSfqut+iCbCu7ls939GXQQPptbVb7/43dIxU1+/Q+9SLnS4m/81v/CxWaLXzt9CHNqDN9TsXW8PvfUfrdp6boKcNX6wFitBbU61FvB92Yfs9ZUxx+gCITaDJttWC2p9tqqIlwP2WhT+CffZP/XnyMZj1HIZ9i3eyqKWSX6J0nf5i0BUW0gNitBrqiw/QgEqt5WJIRygSdGTxIAZDyGTMYhnUCmkpBM9L4nE/hpdYxMgvj/8M/Rr84B4NYa3JpbIpVULFYmnSKfzUT9OLhp3F1Cz0pd14NQG3GkRKWdabQp5NJMTY2Tz6l71moN3nj3AuVKnYP7Zti/b4ZdU+NIKZVBfPg8AVGUYhmoFIONzA9CZ4hgW9C03ibmSeV56A/UWYLQMLXQQVk9w7WdKCm5Sk3StxmaOpX/6S8H6YEa2I4KFDtczCuvKgSe72E73YA5F4GDgSQRj5H4T/46zsoGmhC4+Sz6wd20//af4zs/eJV3fvwOX3/yIR6ZHsco1xGlCvpmDblRxtvYgs0qmuspdWJQfwlRUmktsKfzfvMP47/wGL6vEia3Wh1s2+HqzTl++199k8MH9/Kn/sNfppBNoQlwPEmjpRxBTMPAsszAplC1iePadLtdPM8jZpjEYhYITWVccNVCamoE9kqK3Q6TdYsAhGjBBt1sdyjXmjiuTzqVDFTSwTIoXAUKfR8pFcAwtcHtwZeSTtul3bExdJ2k42CZptqU4zEIAuUiBARJ1QGEaZD6S/8jqXJtB2FxcByLpXXEakmxeZYFUqNa6/DG2xdYWSvxlc9/hmQiodpEyiA/pa3yD1pmMO50EIGzik/ASskoJIZ6qq68SenVRwilwLt7fomARTciZiNa4zRf2amFp1uG6jfH5dKVm/zB91/j4pXbHDu0h4eO7cMyDdY3yly+Psd7latoQjA1OYqeSERbhuepfnD/27+ApukBAyxB86M1VYRt+AnUpwNFSsRXf1N9PrwH+T/+5R6rHOxnBO2w4xMeFNCEdXxgANQvxAafpeqVgTv42/onfFY/KPy0oCu8bru91T2aOhrL236PHn9XNRS0EaGL4SeqZ/huEGTe3nZfdT8ZgGoROINFrGkAukITg7AuvSqoD5qmoenhXvaTeSLCJzWQlyr/UCghRRRC74SdrwtowwFR/FMUJXEI9D/7N5VaKzwW/T5Yl/6kptsJS2k7iIVV5PQ4ZFJoeMHkVXYTMno3gfQkV6/Psriyzq997XPsnpkglagzXMyRTsZJp1MKCvoBq6EHwI3QK0bV0BcCNxlHxmOI8aFokffDgRDE3vGj4HQoFN5oISsNZK0B1QayWserNvDLNWStjtnsYLS7aI0mWr2FCIKKRgtH0AaJiRFSyTijQ3kV0LEvXIOUAT0rGPDkIddjKGTMhHQC/5mH8VMJvHgcPxHHTZi4iRhWMYebiGPHTPxkAj2TpNm16bZaxGImqUQyCO+goUmV/qjZsTFNk2QyTiydjCptV+vcmvM5uG8mer7n+3Q6NitrG5TKVRIJi93TY4zJQV5ESp+tcpWVtRK35hbYKFVUbCdfPVPXBMPFHKZh0Gy1VSymjRKLy+sYuo4vHYTwMC2BY0uazXbQJgKEh2UpQIAI7tm1qdcbNJptul0bwzDIplMk4ililgUCOu0OpdIWjVYbTTdwHQcNSS6fY2y4QCJuYTsuK6sbXL91h9X1EpZlcvzIfg7snYmCO/oSPrp6i4+u3mZ2fpnV9S2mJkb4Q1/7Agf37cJ2HK7fusOFy9eQvoo91Wh0SKcSPPbwMU56Kvq76bq0Oh0Slkm92eb2wgorXZvm1DD+4yfwDYNms4XbUQDkg4tXmZtfIS90nt6/n2nTRGxsUZldYO3aLUSpQsHxKDgu/nAe6UvqjSbzS6vcuHWH5bUS84urbG7VOB6kt/LRaHdtLl25yQfnr9JstxkZKnDs8D4O7J0hEY9Trla4dmuW+YUVbMdlamKco4f2EY/FuDW3wOraJrqmkU3F2TU9TjGfo9FssbC8xuLKBrqmMTE2xO7pcdKpJJev3ealNz5go1Rl9/Q4n3nsJIcP7sW0LDp2l5XVdVbWN/E8X+WunBihkE1G8fnKlRrXbiyysLRGIhHn2ME97Ns1gRmLIwPpOmIj+lWLpgnlGqJUUf3o+yr4LjKI9SOixV6vtwL7UPANZTi/sVlWz4zHMARkkzFsU8fxwfU8kom4yompK6ClCQ1f+mxVKiwsr9FotMilE+yeGieTTtLuqlRZKrGySlmVSadIpVP49EByp+ugCZ1EwiSVSqhk8ygg1bVtkGCYGilbhbsAIIiyf/3WHf7pv/42124t8KXnHuOXfu5zTI0Pownodm1u3prn9XfOYZpGtFYD2F2fcqVGuVJT+TmL+SjUTP+m3B9vL2Ja+jUifWXH2Ez9Zg2mEYo7SMJQFqIXcqZ3IZ+6PAAA6u0WkkGVXGhz9AmeE5adnnc/8LedsYtYw752fUCAG4ZgGaz3Nsn+U5QB8mQ7/xyAKV9KtIDl6gdbfuhsJn18X0SOYyrYdR+oCsfUT4fY+jTeiMHIln3Joe83APo7Z6CB7sLpd1/TV/rVhJRrsFkOxkTv/kJEZyCR2F0bz/OxgqS4Wp9RuLDtyD5I5JT9j+/7eI6DJ0HoRhDlWtDoOly/qXKtWaaB67hUa3Vc11O2Qwgc28d1PHzp0fVdWi3FSOVzGZLJOI7jsrZRYmVtk06nSyadUuySYbC+UaLebDE1PoKu62yVqzRbHfK5DGMjRWQiQcV2aAmJVsig61OsrG2ytrGFrmuMFPNks2kV00pKUrEYQ5ZO7s/9HfRrc5EE1M0kEEIQswx0XangHEd5YdQbHVrtFgiVpieTzahcb7lUb7CNDSEnhtn4E7/A4so65a0mXcel0WgSF4InD+7DsHRu3JylU6myb880c/PLnL9wleFCljOnjzA8lMf3fGrVFrfmlrhy4w7xuMWZk4d5JKDtAfxGi7KuApSGzKZtu3x48Rrfe/ltbt1ZJBGz+OqXnuKXXZcwio4vVZLo194+x/mPbnD91gKLq5vMTA4jhMAyFVC6fnuRxZVNvvj8Y0yNjzK/uMryaond02ORe7rvu9yYnef8pVsMFbKYpsFWpcLk+BDHjx8ilUqpfIM3Zjl34Rq249JqdajWGuyanmTf7mn2zEzi+j6Xr9zkg3OXqTbbpJIJKtU6uqbx5GMn+czjDzE2lOPm7Xm++f3XeevsR2xuVbFMgy+/8Di/9otfIhssWFJK3nz3At9+6W0AbMfl8o07HNg7zf4906xtlPin//pbvH/hGmPDeUaG8qxvKoN/x5cc9fwo2rllmpCIYbu+ymvp9sBB17Z5/9xV1ta2ePT0Ec5fvs33Xn4PELh/JMsf+oUv47RbvJ+J85bXwTi8i1w2xdOPnuTQvt24XZurN+b4/stvsbZRIpGIU6k26dgqcKwIFrLL127zj//F73P7zhLdwPPsqUeP8x/9+tcYKmY5e/4SP3r1fZqtNr6UjI+skIzHSSTifOeHb/DR1VkKuTR7psd4TldBTt98/yLf+tFbLK5sogvB+EiBX//6l/jsEw9x6cotvvvS23Rtl9uzC+hCMjUxjBlL8N6HF3nj3XNUa00AkokYJ4/t5+lHjjM5PoznqxQwr755jmbLpms74PtMjw9hWrFBVl91Tm+57FeZCkHVNCg1mvi+JJNOEDMMWp0uvu8z4Ti98azreK7H2kaJerPNcOD4ETJ21UaDG7OLrG9ukc+mObJvholRlRtxcWWVH7zyNpeuqKTM6WScX/zKszz9+ENcuDrLD197D8swsG0HQ4PPPfMYe/fMUK03WFxe48btBaq1JslEgj0z4xw/so/J8REc1+P2/DJXbtxha6vMcDHNz1UbZMK12DToui5vvneBsxevc/rYAb72M8+ze2YSPAchJalEnBNH9jNczOH7kkTMwvd9Wi2H5ZVNrt+6w3ppi6MH95JIJlVCac8G6WMYOp4v8aTEME2VYkwo21kfH9fu0ml36do2hqGTiMeJBarxgf5xB8GwoBcaQtnghiDnY2yL7lc+BfM2ALiigx+PsnakMu4H8B6kXgPgqo+J9SXonxSF9OOFBzcruefd/B1syAHVZx7gIqUfhHKQ6CigFZp7+AEgC3Mfu66L5/mRWloT/WTJT464fvLQDzKQBD72vLsP3Zea3WFw9KcECqdBJ53A8zzlRq2pkAZGYPhtN5q0Wh08X6ILyCQTJBIxRGjsHZZsmo1ShVuzC6yul3Acl4nxEY4f3kcun6PRaLJVqasAolJSqTT54MI11ksV4pbFG29f4NiRAxSzaerNFtfnFrh+a452t8sTD5/goVOHaTZb/Pj19/nRa2dptTscO7SbP/JLXyaZTPDdH7/F7bklfv3rX0bXNX702vvcnF3k6cdO8vNffpZ2p8vr75zjarCp7t09yZvvXuC981c5cWQfJ4/s5+rNO1y7eYeu7TJczPHI6cN83jKiyNt+OoEtdFzPx/fB83y6XZtao8ntO0tcunKLza0Khq6TiMeYmBjl2NEDHE0looVfej6tTpfv/PBN7iyt4rmSUrnO7PyKUhGcPEIMix+/+h5rG2X++G98jWazzRvvXsTQdYaGVSLctY0S128usrxa4v3zV1nbKLO6sckJ28HU1MQwug66nsKXLhL1b2Vtld/77itcvHqbZDLO4kqJb/7gLX6208UKlJqe51Gu1PjgwlWGinkmx4fZLNcCdWKMbBR3rMPqeolqtUK708IPomqblgmaioNme5Jrtxd46fX3efLMMfL5NO988JFS78XiHDuyn43NLd54+zw3Zxd59KEjDBVz3Hh1gQ8u3OCR00cpfCWLbXd55fX3ePfcNXZPjzF8eB+1eoMLV26RSiV54pGT1BotXnv7Q370+lnanS66ruF6HpVag3anS6TB9CWzCyu0213y2bRSDcYtMqlksDB4NFtt2m2bJ88c45mnH+bs+ev8u2+/xur6Fr4vsYSGFvSzl00rlilQC0vAkYJatc67H15mda3MqROH+fyzT/HRtTtcu7VAs9HC833anQ5rm5vousaxw3tVuql0CikEtUaTsxeucO3WPGdOHeShE4eYX1plq1oDIRGaT7vT5uz5K9y4vcDE+Ajdrs3aeilwv7ZZ29jgo6uzjAwXePHUk2iaht3pkksnqDXbrG9WaLbanDl1mIdOHGDX9Ahziyt875V3WN/cYnpiCMfxqFQbLK1s4Psyiha+e2qEh08cVMyphAuXrvKN77wCSF54+iFSqQRvvqeAnq5rfPGZRzAMnZu3F7k9v8xwMY+ua6TTKXR9cBmNVN3hZi63ga18lvN/5pf5Z//mO9ycXeKRU4d49KEjvP7OOW7Pr/A75UZvzhk6nufRaneoVOvUCiptEECz2ebdDy7x8psfsr5ZJp1K8OXnn+CLz+Zpttr88NV3eO2d8xw7uIdDB1RC+a1qA03XWF7d4KXXPyBmmRSyaXZNj+F4Kjnze+cuc+HyTQxdp1jIslEqs1HaIptNkS8WWN/c4ls/fJ1zl67TbCnW9LmOH4EtGTOpVupcuT5Hq92lkEuTSSdVGqNwXQ/m28TYcLTc11odrt9e4Icvv8dH12bxkfhSsGv3Lhzf48btWRr1OvlshlKlTqXeZGykyKljBxkaKmA7iqG+eXuexaUV7K5NLGYyM6mAYrGQi+zkpJTKIzcs5n22wn613IOUT6vWJCQggg1ehPZIwY+auEfA0n5eP/y+Q5qenWy77lXuwXxt45AevEQXhjUMQto+AFO4k7mIDNTid7+BH6jP1e8SH+krEwIfOcBySd/H81SwcddTJgn9eX2VqjpQX/8U2K1PCLZ+Av2vEDyQt0R4fl8RmkD6QdpX0YvB5GSSvPGbv8ry6gaO62LbLvG4xRNnTnBg7zRLs/P8zjdf4v3z19CE4NHTh/n1X/4Ke/4vf18ZrAelYxq8/vaHfPtHbzG3sIrrekxPjPAbv/JlnnnqDLZt43tK1dZouSytbHLl+iyVWhMp1/nRG2cxYxaPnjrEzbk5Pro2S7XW5Pzlm7TaHfbtncT3YW2jzMZWheFijkI+i2WZuJ7HwtIaW+U6sZilcs25Hu+fv8bu6XG6XZV6plpr8Mpb50DA4QO7o8CaM5OjCAE3Zhf58NJN0qkE0vcplav4nq9stnQNP5+J6GHH9el2bDzX49KVm/z+996g1mhx5MAMqVSCOwurvPXBZW7dWWLcshhDjTXfdSmVq4G0up8TR/YzO7/KRqkSeTaamkan08V1HWKWyb49UwwX81y+PsdWpUa13uT9c1eYvbNKNpMmEbfwpU9pq4rnudEkN2wHQyewvVLs26Wrt7gxu8C+3ZMcO7yHaq2BoQm0tSvR6LRth1qnw+hIka+9+CwLQRDJVrtNzLKwTIN8LsPkWJGbswuB7t5mbDirpGwZRB0XAk9Co9XBMA2OHttPIZ9hca3EO2c/4urNO0xMjLKwuMbsnWUmxoY4c/oolmmwsVnm+q0lllc3sAyNbDJLIZ8lZpk89/RD/OwXn+H67CIbpQr1RhPX9ag3mswtrpLPpvj8M4+QTMRwHJfD+3dTyGWjsSqRVOtNRoYLPPvUQyTjMXKZFKeOH0Q3FBszNlIkmYixd88Uhw/vp9lxSL78Ho1WG18G4RAIKP5kHIGyp5aAJ0VgA+SwuaUSjJuWycTkOKPDBW7NLakgvYBumiQScZaWN6jVmjzz5EPKfglJu9OlVmvg+Uodt3tmHCEEhXwGw9BxfZ9qbYs7i8tICSeO7mdifIR6vcbkeJF0KsGt2UXqjSZnTh/h1IlDKjVPIBUvLK+TTCj7wxNH93PmoSPkMgneOXeVrXKNpx47wWefOE2z1aZaqTMzMRYxQjHL4NjBPbzwmUeYmR5HIvnoynVWVjd48fnHePaph0ll0tiuz7/5xktc+Ogmj54+wkgxh0SyvLZFqdzghafPsGt6XIWrCOaW5/XWLj0IEQMoz+GQVRcwPTnOkQO7mZtfxbZdQKAJg0Iuh2ls9LZc08D3NXTNwPcl1VqTbsdF+jr1RofNUpnxsSHGRopcvHyTOwvL1Bo11je2eO/DKxTzWX7mxecYH86zVa6SiMdU2qIAeEyODfOLX3mW0ZEiI0N5Ll+f5a33L+H5Pr/685/nyME9XL0xx9tnL3FzdokDB/bSbDe5s7iCRLJ7ZkxlWZhd76nwLJNavcFWpY7vS5XVwQ29w5SKbrtWw/Ul5WqdN987z3sXrlCptWi2OgwVb/P555/Cdjp86/uvc+XGHHt3TWA7rlK1myb/wa99lc8++TBr6yW+9YPX+ODCVSUwDudZWSvheh/yM194mmefPEMul4nMJLDdHmdlGv26k5+8fByQ2VZ67RGYkGhiECRFNlH3ACgRmNkOvD7FG91TxTgIOnYKir3z7UIVuQxu0c+S8WCAy++l94Kepuvuh6n/hbZ8Qgg8QWAv6/fZwEnlKImy7XRdH9vx8PyQ1VT1lD8BcN5ePjXYuutVP67BdrKU2464txvjEQZ+A6ETsGi903zf5+r1Wd589zzVupK2jSCY3uRokUtXbzF7Z4WHTxxkq1Lj7bOXeezMcXYjEF4v79qm6/LGuxe4enOeMOBntd6kVK7SbndUNOXAsLRSazI9NszeXZPEYhYnju7j8888yq7pceqtNp6UnDi6H4C5xWU2t8q02k2SiRSFfJqHTxzkM0+c4tih3QwV8iyvbiIQ5HNp4jFL2awc2sP3UsorrFZvkM9nOXxgN0PFHJ2OTavdIR6zOHV0Pwf2TrO5VUX6Pom4xbFDu3n+M48wNTEM/5/vE6UmyWdUJGkJjutSrTVot5q8/d5F3jr7EU+dOcrXvvwsI8NF3j57kX/5b3/Apau3WDm4j9GQHbMdmq6DZRocPbSHo4f3kMvm+OD8NdKpRGR8a5oG+Wway9BJppKMDCnbNsMwAnC5Ttd2OHpwDwf2TrK5VWV8tIh2fakXX8zzwHZxPJ96s03Xdrhw+TbxmMWXXnicU8cPAmAZOolzs9BRudTCmFyFbIZcLoPjuuRzabp2l3anS7PZJpNJMTM1RjxmKZDuOORzKVLJOJ2OMsQeNMBV482yLPK5DK7nsbK6yVa5yvrmFq12l/HRIQr5LL7vMz05TjGfVXY+qIVU1zUy6QRT46MU8jmGh5qkU0la7Q6NZotkTKkxioUsTz16ksmxYdqdLsVCNkjNFMwICa4nsWIWJ44dJJ9LY3c7JBKWMlTXdKyYyrvZ7Ng4jksiHsM09ICZJQJbEvBjsZ5tAorx7HS7uAHgNE2dRDKBbdt0bReVXUhR7bqhsXf3JNO3x3jv3BXanS6JRIyHMyllE6Fp2I7LnYVVFpbW6Lo+8XgMyzRptm22qg26XRuJckI4emgfQ0MZDKHo/rWNLRZXNkink0yOjzA2MkQ6rmJTOa5aJHVN79nQaSq3mWHopFMqebumabRbbUzNCBZVF9+XJBNxMukUcctSXqr1Jr7vk0jGEIaBL2Vkl1ert+jYLq12l7GRAhOjQ9yYXWZ2YYXV9RLFfBYRZDRwXQ/p+ximgd61I/HS03V0ZSEFCDK5LPv37yH3wWVuzS9Ta7aoNVo89egpjHO3o/5x0PF9IkNi23HodFU+0lQiwUMnDtHs2NyaW+TaDYN2p8vmVpnZ+SW2KjWOju4lnUqSzWUp5rM4rkulWg/YU518PsuZh46RTSdZXd/kzuIqpXKNQ/tnOLR/N5PjI3i+z+Xrs6yub1Jr1EilEoyNFFnbKOO4HkcP7SG5UIpAga8rWzzloACVepNKTQkIQYg0FYzedZRAqKkYg1IIcrksTz56kkarzfvnrtJsdbAdm0QiRqdrM7+0zv490zzz5EkuXrnJq2+e4+qNOc6cOsLl67f5/o/fwZc+P/P5pzl1ZC/vfXiZ77/6Ht9/+V0KuSwH981EjkHG+hbDvo8mtABs9Tbovk3qk7NanxBoRQArUF/2h3e465oATERe+iEQC4FIGGYpCgS4LXrApynR/ry9zg92z9AMpFf6jOIf0AZOhXi4j6NCdJ8+C64gRIimSfwgKrwfMFtacJ2y7XJwPOWU5HnbbbZ4IBXug5SfSgT5sNy36UNku5O3RL/qsN9zQ/QAkfrQo1SFAN0wSCZilKsNytU6+3ZNMDU+wsTYEL7nk4jH+NkvPM3pE4f4/o/f5vadZSLU27fhrLTbLK+VOHpwF6eO7iebTZNNpzi4bwbPDw3q1MJRrdUVozQ1RiIeY8+uCZ545ATtdpd6o8lQsUCt3uTO8hrtrkMmkwz0xGCZJhPjoxzYt4vh4TyeowKpeUEi4a7tRB5WEoltO6xtlIjHLEaHi0xPDHP15p3IU+3ooT10uzaNRhMn8EiSeEyMF8ikUso9OyheLoP0lIThuh6NVpt6tU6t0VKqJcukkMtQyGfZPT1BMZ9hZXOLUmBQqAG+4+LoQsUj8Xxs28H1nGgyh6EYfF/ZBXmeR7PZxvN94okY8ZiFRMP1VN7IQj7DzNQIm+UKvhckEhYq1pCUEtP16NgupWqDdrvD4soGjWYHicp72Wq1aTSauEEiZ7XeqPuUq3U63S6249LpKqC1vlmmkM+QDOJ0maZOp6PyIaaSCeW15oXeWmqgmYZOp9NlYWk1SFTsACJiblrtLu1Oh0azrZLdWhZxS71rOpXAl1BvtKg3WrQ7XVrtDl2nZ7RpOy7tjlLFpFNJLl65zbsfXubEkb24ro8TGC6Hk0gIyGXTXL15h2//4A3Gx4aQ0sOKmeSKWWzXo9lWgVznF1W4C02FFMd1HaI4U+F8S4QKK4EbqKvqjRau52NZJrZTZ3Vlnc1SheW1Eo7nUWs0cR3Fct64vYAVsxgeKnBzbpkr1+c4fGAPpmWRyWaQvuTazQVS6RTpVBLP8/EkVKoNbNcjnozjeh5Xr88Rj8fYv2+STDKO77pslWvU623eevcj2i2Ho4f2snt6nHw+y8aWUiH2LS5oQjk+JOIxzl28wfTEKONjQzQaLYq5LLlsBj8QNtY2trg9vxQJB7quY9sOS8sbrG+UMEyT+cVV2u0OsbEiuqZx7fYC80ubPPnoKXTd4PK1OQ7vv8WuqXGEpsbGytom127OUyxkea7aIBt4RLqahhXuj4BlGkxPjjI9McrbH3xEqVznyMHdPPrwMbR/9u1o3rr6tphtvmJ6QzXu+sYWd5bXmV9cww7ylzaaLRrtDl3HZbNcY6NUJpdN02w02CpXaLS63JhbpmM7eL7EDlinZqtNtdag1XWUesVXJiJa4PnZaLVpttqMjQ7x8KlDrKxvsbC0Rj6TRnaUsTyawDeUuUImm0bTdOaXN7g2u8D09IQKQ+GrAMiVWp3SVhUpJYVCHnSdQwf3Ek+uceHyTbq2gy+UEYEwDOLJBKZpcGj/Lh45fRTXdXnz3QuUtip0ujYbm2XK1Qa7pkbZu3uK4aEC46NDpJMJFpbXOXvhCkur63S7Dv9f7v47yLIsv+8DP+fa5216n2WyvK9qb6a7xw/GgARAkFwQoigEqeCutKSCsRvajdBGiFxypaWo3ViR1JKiQEIkQBCEGWIw3eiZNtPeV3eZLl+VWZXevnz22rN/nHvfe5mV1V3V06QUeype5XvX3+N+3585318oJcWNKl+vNUglEyphvOzKhPMFYq22yLAHKmHnntp2sLdzaQeex8HrQnQZtqRKf7El6IYHf667ju92Uj5gaeOtbfFvnxWov+0C9wnt2tcVAJqI5ElExxPHaIVhFCwfRjLDJwh8ZflXF4j+fDlAC74A2IpNh1tfPGxzs8B94t17dcruir8XeI7QvWUZPHr6CO9+eAHHdXnykWM8/dhJyqUCfqBil1wvYPrOPOcv3yBhWSo/ISgqg+ja1ciPf+TAbn7lz3yDYiFP4HnUag08L0QXOrZlYegGjutTbTTbwdaa0Kg3WqxtVPE8l7c/OM/swjLZbAbH9RCaYlEPgpBmZDXx/YBW0yHwIYhIVzVNsFmtkkxYVKo1vAiIVWsNQinJZtNMjg3xzkeXWFmr8NQjx5gcG8J1HJqNJp7v4wdqSXoQKLCGF7SrMchnCYIA1/fxvQBkSCGfYaC3RCphRZQIdfKFHIvLa1RqDWzLJMgk25qJCEOErrO2XmV+cYV0OsnM7UXWK1XKpbzqCZEgb7ZclpZW2dhU8TL1eksF+Po+veUC127O8qevvs2xQ3vxfR/bMjnkB4SRZcsPAmS9he8HaBFj/chwP9enF/ijH7/GZlXF4zWbLf5avYUV9Rs9SlR97tPrDA304AceSyvrbFQbzC2tM9DfgxeGOF6A6wU0mg6+H6IJnW4+tiBUAZS6rlHZrPPBx5dpNB3mF1fJZ9MMD/ai6xrpVAIhNK5cm+HytVv0lcssLK1hWSajQ/2kUkk2qwEhgtX1Ktdn5nmopiw6ruvhuh6eF5BOp9iza5S3P7zAC6++y8cXr5FO2Tz1yAmGhgbbQ0EgOHJgN3fml3jz/XMkbJvRoR6efvwkCIHnKZDScjzOXbzOzJ0FNE3D0HVcz6e95DmalMKkje8HkbLQZH5hhQNTkxi6TqGY492PLvOPfvP38X2f6bkFWo7LyloF3/epVGu8/OZHzMwuEUoZJUE38UJIpFIcObSP5ZV1zl+6wblPb1Au5kinkmhC0Gg0KZfyHNq/mwuf3mRmdoHV9Q3efj/B7olhjuyfpL+3l9PHD/LeRxf52dsfc/HKLR46eZAzJw5FK0Q1TFOPFoao9hodHuDgvkl++tqH/O4PX2awv8TIYC8PnTiCE0iKpQKZTJqPP71Bs+Xw3FNnOH3sAHt3jfHBuauc+/SmIjO0bW7cmiOfSzO1e4JkIsVG5TY3phcYGxlgeKifRrNFb7mIbZs0my02Nqu8/s7HvPDKu5SLefZvtkj7Abph4EWuRtGWBRrlUoGp3WOcv3SDpuMyMTpIf2+prQiCJDQMwmiVlBCCEEW+6Hken1y8yu/96Kckk0nS6aQKt5ASzTApl0sU8lluzczz05+9y/TteTYqVWzbZHiwj2bLwXE9apFFrZjLIjSdVDpNNpNicXWDOwvLjI4MsLK+wcrqBvmCcsHdvrNAs+mwa2KY5dUKV27cxqsr4CuFQBo6qbTFqWP7uTO3xPzSGj9760OGB3vo6ykopc0LWK9U+fj8FTzf58D+vTSaLc5euMaFyze5M7fIZrXOwEBP29ogUcpPNpumkM9hWmZbeWzLJQGGoVKRtRwXz/ejBNWCtfVNrt24w/TsEp7vM9Z0ObVeIZSSFEoYbvet7CiAuuXWg8RB7VhkBzQ9MBYSO/7uUNQ84KNsvdi2DdEMFF/7C5Vt2U66DS/3aeH6/LINJAvFoKBpIQERtXqo4UfhFIrINOahU5QvMnpWROR65EHsePcuDwC2YrfeZ93yMzpmBJC2+AE/s0Tm1O3xW/F1hBI86cjdBpBOJSkX86STCW7cmOb5n7yJLyUH9k4oa5AmaLUcwiBACzoINjnYR8K2mFtYYXWtgmEYrK6s4TgeuUwWy7LI59JomsDQNZLJBPPzy9QaTW7dnuenP3uPUrlIIZvknQ8v0Gi57N87QbXWZG5hhcWVTUYGbDYqNS7fuEMmm+Lg3hEGenuxTMWIvLi8wuXrt5hfXOHCpevUGy02aw0sS60sMk2DibFBUkkbTRMcObiHidFBbs3MESAIfBXkt7a+ycrqBrlMTq22iaqvlUowfXuO9UqNUqGB73n09/cwOTpEMZ/lxsw8P37pLYYG+3j/7CVcz+Pk0SkKYwPt5tck5LJpQuCt988zt7DCWrREW9eUG8U0NfZMjvDOhxd4492P2diss7i8ztJqhU8v3ySXSTExPsTi8jrnL91ifnGN0eE+9u4abTPxxxafNJJCPkupkCORsDhz8iAtx+Ojc1f5N3/4MsVClicfOU4iYSOjyd6yDMqJHMVClo/OXaJcyjHYXyaRTKgcWKH6tByPfC4T1aeJH4DrKetgo9lSFphAafMr65vo03PIMMS2LR49c5iD+3Zh2xa9PUVOHJlicWmNa9fnmJ5eYqNS5fSx/Zw8fpBMOoXv+5SLBbLZNDN3FllYXEHTNIqFLJXNOlIqzqcTx/ezvL7Bux9eYLPaIJdL0dNTVEH7Xf3/yKG9FPJZrt28jev67Ns7xvjoIGGgnm//vkkStsnwYG/bKnbk4G5y2TTGzEqbX0ZKiUwqdv7BgR6CMCCfz5BM2piGsiB8enmGWr3FoalxioUc07cXmRgdROgGxVKRA/t2sbpRxbZMThzdx8njh0ilktH54+i6RqlUpNFsMjHWT19viXQmo0BPOsXpk4eUi/99tYgilUyQz2coFnMUcwVGh/opFnJ88PGnpJJJRob6KZfyBEHAkQO78byAfC6jgEgQUijmeerJM9SaDp9evkWz5THQX6ZcLiBDyd5dYzzx8DEuXLqBrmtYpkEqleDYIWWpuXT1JsurG2QyGfbuGmN4sJ/RIRWXdGj/XmQIV65P43kezzx5huNH9mPoBs1mnc3NOpZp0N9TZHZhhfWqQ4BAkxKHTl5QKSW+75NIJJicHKWnXGCjUmNsbEi1dXvKE2AaNJsO127eptZokUzaeJ6H7/tUa3UWVzYwzTrpZIKNzSrLa5tIYHJ8mDMnDvGztz/izffO8db75xnq7+E7X3+cibFh1jY26espsLSyxpXrNxnsL5POpNm/dwIEfHz+Ci+88iYLSyvMzS8RAvundpHNZjj/6cf89NX38UNJo9nENHS02IouVELsTCbFoYOTrKyt8eqbZ/no3GXqjQb7945TyqXQNLXq+ub0Avv2TGDbFh98/CmvvqloOXxf5XStVhvUG02KhVw7JkkTAi1aTS0j8KlpGqVCjmwmxWa1oSx9LYcb07PUG01GBnvZNT4MCNYqKpYsT72zivFe6Y62uxW7LV73MBh8fiq6bRane93r5ymajDIeifadfr4r7wC+5BdxUXY9SbdH615Hd9DPA5Qovhva7SU0EXEZCnQZIqVGINXCoCAMCWTMgag+nf/V9aKkdA/6IFvKA4Ct4K76VrinO6GmGgidA7o7JqiVAtr9VZ6IGiX2RW+9a/v6zZZLs+Xg+QEblSq1ehPLNLh8bYaX3zrL0EAvE6ODBH7I9J1FfucPX+RorUE+VNnkhRCMH9rLvuk7fHD2Iv/kX/xBe2XX8cNTHDu0D8/1aTkejWaL5dUNghCajtIM33z/PJ9em+HP/MIzlIs5glAFMM8tLNNTKpDJJKk1HKSmUyoXcS/f4p0PLuD7Ln09JYqFHPundrGwvMo7H15goLdEImEx1F/GcV3FqxORNeYyGQo59ZnaNUbCtpXrLpTYlkkhm6bRdJiZXWZ4aFi5EWPrnW2yvLjEUF+ZQjYNQieTyzIxPsSBqXHOXrzGB+euMLu4Qj6X5le+/yx7do9SMox2jWtSks9n+OZzjzA9M4/jupHW2GluTRc8duYIw4O9NBotNmpNhob6WVxaY7C/rOKTkkm+8tRpDh7YjZSSnnKB/t4yhmkgIzt+MmHzS88+inb6IJlMCs8P6Sv38IvffpZTRw+yuLxGJpPi9PGD2C99oBjOUZatUiHLr/+5b9NqNUgmEwTAeqVGrdFUCY81nSMHd5PPpZGBj2GaGKbJI2cO43lqkUXsv6/Xmxi6zqEDuzh+eC/Dg330lAsEQUi12iSXyfG1rzwWuUZ8NjarSCkZ7O+hVCyodsnnePbJM+yaGKZabxAEAUMDfXz3G08xO79EMZ/DMk1Ghvr53ree5MjB3czOLZHPZ9g3pQRRd5/v7y0z0FfmxNEDuK5LOp0imUwgpSSdTPL0Yyd44qEj9JQL5HNZqrU6Tz9+Qi3K+OmHEGlrAPaPX2P8gwv8BiGBmcC8vYK18RaBafC4H3Bo7y4cBP0jA7SQrB1uMDgxTnJpFU0L+cbxQ0wN9mFl0wyPDFIq5LBtC13XMQ2DXeMjyrUchAwP9ZDLpdEMFb2ka5CwCjzz+EnKRUVzUi4WsEyV8ihhJ8ikU5RLBQ5MTZJM2PT3ldE0jSAIOXPyMIVclkI+g2VZaLqGpWkcmJqkWMhx+84Cvu/TVy6QtG2CMKS/r8wPvv0VHj55kI31jchCqdPfW+a7X3+Cx04fRuJj2SmSyXSbA0rXdEqFHGdOHqa/r8zaeoXx0UEymTRevYG3uo62vMaJXJYDxw7w0+W3yFXXEELFI9kvvo22uIqo1tEaTex//K8xWi5Hl9f4jUszZDSN3X/0Cl4yRYdfCfTz19F9j+Fmi0cHe8Ey0Wo1tMBlavcIX33qNC3XJ5NO4jjKHZ7NpOkpF3ny0ROMjvQwN7+iwNzIAEcPTVHIqUUFv/y9Z1heXSebSSK0kHTGZmJ8gFIpTV9vnstXb3H1xjT5bI4nHz3Fwf27SCVsSsU8pmmwOLeEoWmcPDKF/fLZ9vQsTQNT1ynkMjxy5ggD/T1cu3mbmTuLvPnuOWxTxdQV8xkOTE1y8tgBTMsmn8vQU8rTciKLr6+4vFzXxTQMioUsQhPMLizRaDk4XkAYqsUYjuMyPjbEyaP7ePO9c/zpK++QTtqsrVfIZtI8+tAxTh7dz/59u7k5M4freuyqNuhbfUFlu0glIuJUcf8uxHsCrU6MUocIPFp9R9j1HSRBHBj0+fe7z9LmedOi5wi/vGureLAu/i9JO2PfZ4Ku6B3bKfdiK9n9lK7xcM9rd18rCjfquFa1CKcI0HSEIdGlJAh1QoI2z1YYBdF3AGHcRl8OYL1/sLXjXVRCXl9oaDHz+mecrzS7LpZ25L0rXMrP9Zf6ns8nF68yv7BKrd7k8rUZzpxYU7wrusmeiRH8MGRuYYV8LsPE2CChjPInxpYtTdC3a5SvP/MIjusxN79MSJNd48P0DfShmQa+9Ni/dxJd1wmjeKNdE6M8+/hJpu8skIwsauVSka89/RCNlsvk+DCFfJZkwqanXEQX8NDxg0r4IOnvK1AulBBC8NSjJ+jtLbK6usb48AD9vSVOH5umFnEyxeSjoVScYbvGB+ntKbaXq6ZSCR49c4THHz5KGErGRocwLButKw9janSAXzi6t71ibLC/B4kgk01z+sQBdk0MMzE2wMBAiUI+RzKdioRkoJijBYgwJGFZPHbmGEcO7mFpaZWr12e4dG2GltsilB5gKgqAdEScWGtGrkAVE7S0rFxsg/09HJjapYLREe1M61IItDAks7TGV/7kLeSb55SQjDiYEgmbh4RiRZdSYvzwNaxPb6gcbwBCoBsGk+PDSKkCiQM0+h2XymZNuSxtk0ymV7n4NiqKFiRp88wTp/ADZWVqtRQflJSQsC0mRwc5MDVJJpMmDAKaTQ8hdLKZFMXIpeH7inbBdVwSCZtEwsI0DAzDYM/uMYYGe7k9u0AiYZNJpzhycC9DA70U8lnFfWXqDPSppfCu65FKJUinElsY5IXnM/F//gcdQlqI4uWiyU5KCpErV2hq6XIhDBkOA4TQ0DZrHfJJAfr0HMbMPJaInSYi6msSOwwphSo9SrxkfoyI9VmAJSW7/YBx31cWAssA20IkbYRtI20LwpBdLQctZVPoLWNmkoS2gbRMRMIC22LY89E2quTqHsVWSGjqbLRcRCJBsreEaRokSgW0ZIJEKknLUUpIPpcln8tgGgaaLomXaKdtk7HBHorZJPV6U+UFrDeRGzVFchr4lL2AmuOTvTlPZm4Vww/ItBxGWy1ko4lsuYQtl6DeAsfFCkJMP0A6LkPVOmGzhemH6EEQLSFX/VfTBC3HY+/GJsOe4n8Tmkbuf/l36PPLKt9gtU7ipXeUq7rRYk+lSiaTIre4Sm1ts714ByD39/6/5JD8YhjyHdfF9XySF25hWRalMOSkpqn6TtiElgm2hfbpDCJh02fp7Dd1QkMnsCzE0jrWy2+DZTFuaPShEQ72Y6NhXruNp2loAgqWycDULo5OjtCSkEylyWay2LaJxGdybIhf+Prj3JyZwzJNzpw82AW2BBiGircKJQN9Zfp6ihw5uJvNap3FpVU21jfpKeYZHx2iv7eMblpsbNbYPTlCJpthfnGVxeU1lpbXGB7qo7+vh2TS5uEzR6jWNjEMwWZ1k2azRTadYmy4H8sy6UnYfO0rD2PoOjem53A9j+Ghfk4fP8D+qV2EEnp6ChiWzspKhaKvYlUNw0BGZK1b2OK3g4ctvztWj63WgwjgiG3bZfeXGHDdX+lYd7pir+LfXTFbWx+1KzWeCNmSw/iz7xZfYYd9ousTsa8LGb3OvaV/G/x1X/9z3IfdVuCdKR4eoLSNAXFdaUhNKrqotuuVTnYPQbRS8cstXzhAXmkAMuK+2qHBxQ5V1O5/XRV/T9/yPUyMXZurtTrvf3wJw9QpF3M0HZd6o0UYwpGD+zh0YDcISTabIZSCRsshmUyQ+1t/vwO2MimsRIKD+3aTy2bU6q2kjW1b7ZQfhUKeJx/t5djhKd5672OWV9Y5dfwgf/03/nx7ZU8iYZNNp/jKkw8BQjGiW6YSTEik7zE63Mfo6CCGYaJrihMuDEJy2TQH9+2m1Roln02TSlikU0mWVjYwLQvPUwGdcwuK0+jA3kkyqSRrG5t4vq/On5qkVCwAklCCL0UnAXEQUv5//St6Ela7uuNA21wQMByoVZyWZSoma2gTvQkh0O8sgh8iDNA0nXwuSyaj0pzMzC4hEaxXalTrTXLZtIoRMiApbPSITVoTGq1WC8dRjPG5rMpZGAQqNm1js6VW0wml9YlAYtYaqn8FgcrnpwnMlosQAkN2pb7xPEQQILtAiW4akaaqzMUSaJoGMnI5GLquYoxMA0PX0HSNbCZNvdGiWq0T+AG5XIbB/jKD/SWy2XSUDLuCDEHXFajMZzOkkgk836darVHZrIEA27basSKhVO+4sraO5/uUUwVF82FbKhbQ0NF0ZekRlkpHk0yq/qRiBbcGlRob1Y4GHk1acciHROWPU8MtGpdRMH7seo+1vu7P3WO0M/RixTtinaEdDRBZhrVIsASuB66H3mi10+PQbJFsOdi2RSI5F4HEDpsQCHTfJ+2q9DemphMGAcloJZ+haxBKSmGIwlLKVhAKELqqtzgYtn3NUHHolPwAGXTiFqUQiCh1j0RlBtR0Hd3Q1colTUTXViA7DENCqd4zTvkTquW8hDJE01RNx+mKdF3F/TVbLQxDxwhlBJwiDbtrVZYMQ1zHpV5X5MeWaWJWapT+x99Fq9SUq9cTGHfmQWjoQmAKSAmBaLoIXW0zhEBs6krYaZqyDMfu+Ph7N41ABJoTQLaryVW1yS0xuZLI1axrSNMitAykbVI0DcYMHV/XMNJJEndWO2EZmsB4+xP63/mEggyRpolmm2i2jZawkIaBj0BYBkbqApptExgGOU0wrGlgGTRDWPMlm6kUPckU/R9fxkze5JQGhw7vQ7NtwtsL7HM8fuXgHh49OEW55RIaBrvyBVJPnmbu4Dq+hIG+Mj2lApu1OkvLq2RzyXZM1+aqGo+6riNNlSFgi2emu3K2/O60I+3e3J2ke+tJd1tE4t73AGVLLHPX/aPx34ZI3XFbMUjbfqMtA/sB7ts+XhBFP3WMItvAyZa8ifeQ4TsBrW5qho4V7Esu0XypaWpc67r6a+gautCIUwB1vYE67ee87RcGW5KI80roO0zYYlvDbJvMkR3z6o4mwi5oKbduV6kUVGWlU0l+7Ve+g9NsUKs3QQry2Ry6bjLQl8c0dSxbx7BM0Aw10WgCYRqKWkDTFCD5p79HKptmIp2EQhZdaPimRd0yCTSNVDKBaRpsbFYxdB2JjEBYjmw2TaPZAknkclD74vxuEhCBT2gamIYOQgl2gRIIMhSYmiAdWTts20ITAiuRJJl2CaWk0XK4fPUWr775IaVCjj27RtGjVD9xoL4R5ZTTNBWML4KwazWiRKs1EE2HWNSFoYQwQIsEr65pCK0F8b4gRAf1rGG00i9QE4qmGSAkhmFE/FqClfVNNqoNhvtBcz2E46F5Pobrq7Qlng/1Br1rmySEILO4jh6ESMfDrDcxqnXsKJWOiCeKpqOIaoMQPVSr6IQAUknCgR5kzAa8sKK6imkQlPJQzCpAIIxoYMkomNoEKTE1DUMXJC0DkU2TsBXgaTVdNiqbuJ4Kqk2HIfv2jOO6HhNjQ+SyeTxPWeLSqSTJqF9oQlBrNJlbXKZeb9LXU8K2rHY6piAIqTeaBEFIT6lIqZDDinjSjAgsxJO8JnQKuTwHp3ZjmAappAKvmAZ4hvrbU2yPrzbXTTfaiiMOtky8UU8QIHsKyHwW57/9Gxi+QPd8cFyE66rk5k0Hr9agvl4lqDdICQ07CNAbLUSjCfWWyivaaEGtgag3oOVCswUtB83zEb4Prkey6WAHoUonValFyYDDdlJgEaq0GZYy0UXTuIzIeMU2eRD/kIpvS6htO02Csuv/+GvXKSC7Jj+x9SpafKSIeHY09RGahmboaLqyFmGZKoDdMCBhIdIpfENH9pVIFrJw6Sah4xOmbNb+yi9S+Gd/iNFoEhSyfPiXvsfzr7/P2evTfHOwn1+/dgfRVMCYrvlJZtKdxMlhqMaiVEmv4/prj5e4bneUbdE7aTH40u76LoTyVAhNa8+VMhLkMu6jQoChkdD0iLJB0M5vJ2gLMj0IMVyXsKEoJswoi4cgwgCxBUkIzK2PSyoIKEQLYwzjMpqmrpmEdtoqKSVPRcqk/uY5YoqWbCgZDkNORKSpmmWBZeIDLSShpiEtkwkpCesNZbmXEs00HiD7XWydUr0ltjDtnBFFdB0fZaG8q0+LB5Dkn2GEuMu6FN1bgAodEPcYLdvP+azbdwOPaIzIoGOp6lbGugdvvMiqm/bhXrfoIiz9QgCnXUVdgLP91BKkhiBEE2BGhgAjUsBFOzm8hqEbEUH6zwuzVPmCYCuIGvbeaQweCI/e9S6y60+Xf1u2/wPAqjU5+n//p2pfGOL7yv+qa1rbqqAMJds69OqGmsA0DRotxO+9gC4hEXckIbCQJCWQTkIhhyhkGc2k6E1YhLk0iaaP1VsizGXQMknCXAY9WuofBGqiUG4clIsl7OjeIprxpaZjGApcaDJACyR6rYnwfVKNFlrdwW82Wbgzz+u//6eYaxW+8fQZxm7Nk7i9RF+tgb1eoTXST9a20FsuIgjQXK/9Xc18GiRs5ZWVasLWhHIrqd8hBKqDEyqgpkeWBA1QyyYV+7Nxa47c3/rvwHPJNB2KaxucmltCD0JGL9wmnbDpVnParRVpKr2BWnKrRYCu8wkgCNE8r93hpWUg1qvqPbp7RwTuY+NJdzcSQqJtVMn/tb+zpWNJKSlKJYy6k5h3lk8Lcr5Prt5ASshmUpimScHzGHd9xQ9lmV1W3O67SnrCkJyrVvvpht5OpAyQkpJiF5Fod5zbToMgS5fVIdqsyGl15NEpvH/2d7pxh+raYYhwPWTTQTQdaDnguYiWg2w64LQQjotouYo5u+WS+Od/jGh5CM+DloNw1F+aDqLZwmg0kU0H3fejia8z/YkoR6oRgbtYgGIYalZJ2KrvS6namkhTlWrpfwweZAwSov4mZayEhJFFjjY4E9Gxsvv39gl1B4tOKFXYiqYrC1YbQAlAaIp/Tu+2BmltoCUjgKEASKdBYr4sTUr18XxEpYolIQUqoXwqCUmQxRz+06fhD15Sz5hOMW0ZfLi2QXKwj0OPn8SaXkSL3kUauuJ90nVagz2qzaLFLpqmoRsqbKMzHrZ2h8g0HdVv2CG2kp063P5dAbfoWD9AyA6Yi9uqA+joMMF3d1I/QLg+VGroqPlUAsILEC03Uq5lBMhUG0hNIITesb5pGoYQijg3mkN3HCVCUXZs3aPyGGpCQ+pRqEoYKgVASswwGu8ytlqGaFEaHyyjM5i6i1TK6dY8jN02j3CbYWGnvyjDxBZage57ya2NeM/SAVodtSRqn8idd5e3UHROu/tykh0mom3n77B/C1BS7Sfb6ctR78IO134AUPBzwZu7fIDRbxnxj8kgWlQZuShF7KqMR7U6xzRMLMOOZr0HQjQ7li+WGzEKXr/L8xybr2Nwozbe4zqi04k/C+XGMBk6S9ZjCS5DWNlo38WMJ2e2CcN2n452xqv0QqlifaJJRMguzTCeUOoNWFxBSIklVZxKxzyrJlqjbfIETBNp6uqvoSN0HYzuySTC7PFk5/nI4G6dKgnkUZNCtt7gr6ysY9sWPT99j8TrnyBaLRLzq+RlSFjIQUS90D1+tYYTAXkdzfe3aDVC01WMS+yiioV6u8vJjiB3XAiUu4NMSq3MkxIzDEk3mvRpGhoCu+WqyTYI2uBJBAH4IYTqd6et7yPFUwTC4qS87dJ00Nc3lX1USmVFie5pnb3SBrM7F7njV1D9x4qAXazlWFItC4+XlW8Ta+0/Gmow7TBd33+JT/Q7LO/t+oqW/mtXpkn8F/+NAkURcBItZ2uetwe94Q7CWkONiTAWmJq25cXktusIsfObCyEQeuc+aBpYJiRsSFgqriuK3VJ/bWTCgoRNEMV9hZapPrb66+k6vqGjp5MYmRRaMqFcyEKBUlwP4bgEjSafnrvMe2+fZWV2iZP7J3ni2CFsJDgeuusqAOopS6xwIiDquOB44LodAOpEoHR79QmB3t2Xt8lMKUCzLbLZnFroommIzTrf/a0/5skoP2Z56SXM2wvqvpFy5I4M4hQzXPvb/weQ4LsuuutTtE1KiQSWH6rnbjqq/VtRP2i1oOkimq3ou4NotZCtFt5mHdloYYYS3fWQjVanf7UbVH3ZOiNt7/tRP98G1toKrGUCMnL7bj2n/a0950aCLL5G4KNHgA7TAFNH6gp8okffdS1ymUaxGIiI9iKa92P3Uxyb6AfKAhiquUR6PrrrEwMCAUjDILZNtUFhXBOiyxrTFiw6Xaok9zPq244pGd+la9RtA0tb4pxiA0PbqhSnulH1K9syVG5vuK5K77p3t7zdLnu3e6jusqDt9FtZN6WM6ZQUeNyZYX7netoeo/XAZcuz7nz+9pWNMQ6VIchQLTTrDhM3DB3LtNrK+c9bHpxBfkvQeqzl7rBkNWaw3f6Umog6hGqkzmqNnYrqUDKUEbQD8cLrML+kXBjdgz0mHt3ipoBul8WWjmIanVxTQdAGBTL+3mVtIQi6uG/u+aR3fb83zOz6pkfsyYamgI8e/1XfNV0jZZoM9JYwTAPLNDsTX2wtQXYIg+Prh13AxtgiDroO7Kg98d4wDPGCQIHX2IQaBcgDaLHLzvfRpCStaaRj7b/WaAPuNvCOJsiQSFMVWmSZIrIaaLRdYF2WBNJJgqdOob13DrFRU+R0s0sASNuimU4QIlW8VURI2LF6ftbQ6J7gom8SwkClauiAqmj3XQNtu3Da+epfqMSP7vudmML4AeNnqdXh+u3PuPP9TVSx9rb9iQWAZSgQZJkEuoa0LYx0Cj2dRiRtBYiSFq4u2HQ9QsvEzmVIFrJoSRtsi9C2EAkLYScgkYCkoQLiI1b29mNG1sX2HLJNdslQRq55lXg4CMK26wdNA0NvWzrjOgJFbHvp6k1+OLdAvbfAxfUNrjca7H3kCIP9Pejtvh/1/y6Xa7hdoLQfSQlq5XJVbnIFxoI2OMPx0KL9wnFVMuqEjW0ZbXeYkJJ0o0XajGge6i0VzhBGVpLIiqZpGr3lEoauE0YEjKZpIhK2UuSi+Eq5re21LvCi6Toraxt8cPY8127e4fbcEuOj/Xzz2UcZGehFtpTrV7ZcdMdR4NNxwfEJG03ChgJyfrVOfa1CIlTjXXM9Bfa7gV5sUXVc4ti+qIk7dRjPXULAVvx+T0kgwrArj6HoSMm72if++xljQNNUP8ymwbaQtgmWhfjZB4iznyKTSeTDRxFPnIpus8O1pHqRzxxp8ZwWRmMsHsI7BMYr5TtsAy624IYYIAhAa2OZ9j66AFe8rXscIaPzxN0ycKfSba24n+O77qlwbjyvbmvL9jPdPee0QWXkPrznbbb9vudce89LCIhcnoKONV6GiqstCHzFZxcdq2nqI2JX8c9ZHpBnS9EldFD11gcQ3Ye20Xe4DaBFB8amxnuNHKk6YQy02trGhWtgmpDf7unf+TKfLf3ioWRuOfeuywi18tGpNxEyJGmYakLzwy4rTgTM/Oj7ThpDd2zNtt8KJEbXEIpmIxYiZvQhlMpKlkh2LDpCqOco5pGpJGHSxrVMdAT2yoaypO2dwP/qo2DpYJhI0wDLQJg6wjTB1HGBO8urLFaqXJ6eZa3R5PjxAzz2+BkyL76F+P0X1aQgwHE9Nio1EgmLbCYd5Rrb2htiLTEIAiqNJusCiqMDZIb6IJ9G5jKQzxLmsshcBpnLIHJpZDYN2ZSyCAKJ3/i/IRoOYTEb5ZcDZ984v3lsF6+98zGT44P8p4FPT0RKqoIejXY1dzTIztNtD9h0XJcb03PUG00mRgcp5LOqng1DDbSd+lA0cYjYArhNbLB9032V6ISbd5SrOy6NVnuCj0G4SNhqBZVtKkuQraxBIvqrrEZqdVq8QlBG27EtSCbaf0XS7pwTcXopLqiAarVOs+mRy6RJp1NRwncVc9iqbXLh/GXmF1cYHezl6ME9JOxoQUTXO6k6lB2lWcZtItqys6O5d0CQIIpWEMrUL6VU1nRNQ6Nba1YTpyYUF45AEgY+8/NLnDl5iIdPHeHCpes06nWKuTRG9Cyd/tHRptV2Ecu8ra0jDLB1VW/R2OzYgbuAjwC9SxirmEgQxRw7Fs+H2UUFsoUAUycs5QmzKTQktmVEumQYYckQHYEW3VuLnrkty4jmDk1jvVLlj/7kZW7PLnFw3ySLS2v8u+ffwNJNfu2Xv4Vm2UjLRqZ9mr6H0DUCARvrFbyWR6mnhK5rvPfRp7zw0h2OHNzNd776JPl8BoFSnuJ3bJcwVCAzAmCyGQOxlnInRm7s2CKnAJuDaLnIZqsT/9dU4K99DU9xb3musjAahr7FnXpfJQzVeGq02q3fbvi4N/SW4IlTnXNkiETreGG6j22P9Vguys6Uc1fZeYe6jqbm/+1FKrCkjrsHAIrdiiL2mnS/2RcECfcDsrYcDwg9AjGRzN9iSIrqpT3Gt51+z8TS20qUFkvsGOsdKU/38JbJyBMVg6xQqkVggR+2ycZj2RDPA6LtpfsPCra28myp/EI7gSTZ6XgRGt8CuDrKpNq/wxrLtjAMYmEWnysjE/U9ihCR6dlQgCIKYiX+bhrqt9H5LQ21EqVzjNm+BoYBpklo6Hz46VX+8E9fJ5XP8Bd+9bvsmpqMgIrR+eg60tAJDQ3p+Yh6HVFrIDfrUKnCRhWxWYONKlRqiGgb8eqjzyhhoFK36AiMlouoNjrWj2YLsb6JXKtQr9Wp1RpkM2nM3pJaXfjVR5C//n3lrpadrOcacYqbkHffP8c7cwt845lH+Oa3nuSFn7zB+8urTIY+ewZ7IgELQSjZDEPWUwn694whxocgl1bAKZ+FXAaRz0AuhZ9J8cntOf7rf/SvmF1a5b/8i1/nmSdPK2Ep4mBNxbDf0Urv1bSdPaaUPPbQYd764DzPv/Qe2e8+xa/9yrcwdF0RVcZxGNEVBbrqQlEdh9FgEwSgqeS+/+Kf/Vve//gyf/Ov/QpffeohNNsCXSfE5O5l2lGQcpzYVNJ+j+5jOoP/cwZqDLrj9/x//0v48Wtq3//pr8D/51+pPtJTQP6Lv0tomu2xF0oFDUT3GGm/+ZbBdrdW3W2B7D4/+q5rOqlECqSjLEuByiMnFLIhm05yeN8kw31lstlUZHUVXZeP36nzDEo46pHCvhWed0qH4b77UaMFd1EsYWyBjGzjQqAJiS6US8MJfObml8gXVMLl08f2owvaKzvllnqiPRYgwoL3lJixBYE20Nh6tIxWwW6rZiD4B39ry33Ucwik46P90t9AzMypPrd3jM1/+H+hWqtj+R6hrxOGIa7nRcG8CaUoQXthRSwcuuW95/u8+8E5Xn79fR46fpBnHzvOYE+BWzPztJotVWeoBTWLyxu8e/YiN6dvU6kqstRD+8b5s999jlw+S6PRZHl1g8XlNZpOkzwpZZTuiqntNLKKEZW2BXHGjvh944cl6h9dFsktSX+jfii7znMdj5d+8iZ/8G9fYKSY4z/6xW8wXMwrINdsQbOFFrtSYxAXuVllswPoYuBH/D0IthpcElb7GTpitgOyOtaf7S/WJeN2LPeGEmqPjowpFdoPFAO7z4EhMYjpHn/x/9vckP/+SteYkZG60o357lJKv8D1256GbYBKgJo37iFHZXSeoB0zKiXIUKgY0VguxkpUG8b8PM+7tfycPFsxWI264z19tF0zwE6IU4itAZeADARChndtpx0UKeH/+tfhyB7lgjNM0M125UhAig7Cjg2BnS4sVYcIu1doKEAZdL2sFsUjFY7sYW52jlwmhf74CRgfod16dL1XhJ4JAwjz7ViuMIyfKxbAHWEkg0CRclZqiEqtDczYqCpAVqkRrK7TmF0i0XLUCr/uwNGuuAsZduqrjXnLOYTo0s6inapJQhoNh4uXbyAljI0MkstlOLRvF42PL+H5ATxxEnl4L+TSTFeq/N7zP6NUyPGD7zyLLGRVXQqhCOOi5J8EPrgu5maFJx87imHo7N091q6vdlt0T1KwQ//pehldU3FgYciu3WPs3zvG/OIqPeUClmViRX1DhY+EEY6IBqiM21ziS4Eu2uEeWKZOPpcGCbqmKY1Zi5+zy5rbeZiobrX4ZnS4c/TuwyJtdQeNNS7x6pcY9GgC0onO3XqKKn9hvQmGga/rhH6A2V7i39HAZCzVJR3QFAU6I0WbMJZ4nHaNlThbQzwRKQ43NVps24rSAFUwTUOtmrUsRAS4zMjNrOtd4COyrGwFf/G+SHuMA9i7YvnUHTvzSWduiTz68YTdBjQdF6Ky5qjzZSi5cPkmCI0zJw4xMdqPDFTAuPJAaghDVwH3vqeCyaPnvpdIirnN4qcUQqWnuluR3go4NV1H6KouROi3ec5kZJEQpqnqIpSKiDKZIJlMdBIm67qKT4pKnPB9K2jbOoaklGxW63x65Sara5sqB1yoUiqlU4kONUmk4d+cmeOl1z6g0Wwy0FtkdKiX44f3Ui7m8EOJrqnViGoRUtil8d9dtsuDncb0lm3a3dYxNS1sA8S6hqMLLq9v4GRTsG8CBvuQUQxuV2/e0gxKz+osUAFFc9EGqn4AL7yB+Ee/q0RVwu6Mkc5LbZVv3eCmHavUdeOOH3BnkLGDcoTorOzcSo6yw1x/j3KXvNzhnO5x1THMiS0y6ecpsX0ZZAf7tC10WwfLXbQQ9yhb1LItbtJ4r6R9s52uGRGbxqIqtlyLiB1A07SIfmfLMqD7erb7LQ8es9XuAyICyyFKU90OtOQ9vm8vYst+BcK7X7jrKlKCabQnYxI2ZNIIEYK827LQsYpJCJSJU2hB51oSpNSiBugMHA0IhUBHBVUiNIrZFOV8hnIxRyZpgwjbArLb5yBkvMItEigiDhRU7yo0oWIVIreglFLFbGXTiFwGMRaz3kYXjHL0aUAGEJqmcqW9/C7mf/q31bsNDxD8lV+CSpXE6gbmaoVEy0FUa4jKJqJcADrWi3bwpVAWgmqtztzCComEmoB9z6feahEEAYahQzmPKBeUS3BxBcPQ6e0pouuCUPqqvqTG0soGt+7M43sepWya4f4eJsdG+I1fG6Zar9FqtvA8D8PQo9yAIQJNGSwtg2QygbElEHtbDzAMZODgNprMzqrEuwCZVLJNuhmGkkajwdLKOq7n099bpJDPYaAjwxDPD1hcqzB9e45iPsnE6ACWoZNLJzGMCIzqKvi2Pahlh5qh5Xo0Wi0MTZBJKgZ/NA3X9Wg6LrpuIlBJiR3HIWHr5LLpNhgKw5DKZpXAU6lxLNNEAs1Gk81aHcf1yDVb5PwAQ9eQzZaaI0JJo97g9VfeRtM19u8eY6C/B8MwQSgrxtLyGmsbauGALhQlyGBPAds0mV9a5dbsEpvVOuOjA0ztHsc0TRzH5fr0HW5Mz1HIZdg1PkSpkMfzfD48d4nFpTUOTu1iYWmNn7z2Hgnb4uGTh3n8oWOYpuDazdu8+Oq75HNpvvXsY/SUi0gkm5s1VtYq1OsNEgmb0eF+MukUmpBIAjzfY3F5jSs3buM6LkcP7qWvtxRxV3XSCYVhyPLKOtOzi2zWW7Qch77eIlO7JtqAQbW7svxiCkxdQxoQIpmemePytVskbYN0MqGSDmtQb7RYWl1ncWkVAp/BvhJ9PSUSthXxsskoZkOlhdmoVJm+M0/MK7a6XkEIQamYJ2FbrK1XaDku2UyKPZNjlIq59pxSrdVZWF4DKRko5VR/gCihtE8YgG2ZaKFS/khY6JrKuwkqaffq2gbFQk6lrWnPcOoacc5VyzSxLUuBXiFY29hkfnEVBGTSKonzZpQUPZVMoNkmsuUQhIrI9yuPnWDvrlEmJ0YwExa2JTBQOQYTlqEEkRCYpqlWdkbWgc5wVf07DNRKv+38cPHcEyf/1TWtPc9t4YdSX+4617RMDkxNUMxlMHQtclcLQLk+NRkDE0ms4Nw159Ehr2wDLtPYKqLaydk7s9C9JJgCFl2s6mLb0REo2yLR7vWOXQDunsJ+OxiS2+613frG9sM7ng11hxgYa3wZQCt6SDoeLeg2cezoDAs7bvx7IwXRUUy7jmtb70UX5cld2k/3fUVHARA6uq5WHZpRPLTW1Sc1bVtGnJ+zPPBqRDVZxg8g1QQht65LVO8adG2TO/abdi/e1kGkbEcgtEscuCqsTvoY6bhIqZLQqlt0L90ERIiUAUEAvu/jBwGakCrIVAuRITiOS8tx0XUN2zLRDcUfJaToivEPcRyHZstRKU9MHYIQqWtAqLQRCWp6D2k1mzTqTRK2RTJiJkZImo5D03HxgwCn5aicefkcmUxKaY1BwOpahfnFFRqtFv29ZcaGB9B1Dc/zWV5db2tp9comh0T0zukE4S88A5rA1DR0IJAeIer9qtUa7vwSgVQTTBiqwPLeUgHd0tmoVFleXWdsZAApJbV6g5nb86ysVVhd22C43iCdShEGIc2mw8rqBo7rsm9qlEx6iBDJrekl/uff+WPFJt9yKOQy/PX/+M+yb884b79/nj/56Rvkcyn+wp/9OuVijoWFZW7Pr3FrZoGVtQ0O7ZvkkZOH6Cnm0NAIxd28Na4McWoNNhdX+Fe/9zzvfnSZIAixbcUvFoaSWq3B+UvX+ejcFSqbdfp7izz16HF2jY0QuB7Ts/O89s45Lly5SV85x6987xkmx4YwNIFpGNy6Pc+LL71NJp1i965R+vr6MDQIfJ/bd2Z59c0PuXRthr5ynqceOcHhQ3vREJw99ykvv/6ByrmWzbJe2SQIA7761BkePnWEIAj59OpNbs3McWdukY1KjUdOH+Ghk4cIw5BX3/iAn719lkazxVcW1vlGrU42m0ZrtpBSUm82ma9WefGVt6nW6hzYO8Gf/d6zDA/0Ua01eO2tj3jv7EVWVjdYXd8kCHxy6SR/6Ze/xa7xYX704uu8+PqHaELj2197jPGRQVzX4+0PzvMHf/IKH1+4Ti6b4le/9wzf+fqTBBJ+/JPXef/jy/zSLzxDMpng/KVrTM8ucWd+gd2Tg/T1FPno3GX+9Q9fYtfoIAf3TlLIZ5ldWOZPX36buYVl8rkMQRAwtXuMR08fpb+vjOf5vPvReV5+/QMuXrnFylqFX/3BV/nl7z1HJp3a0uaO4/H2B+f4tz96lc16g1w2xXNPnWJspB+EZHpmgaWVNUIZkkqZTI4N099Xxg+UVfvOwjK/+Ts/ZKCnyHe//gSPnD7K9Zt3eOWND6KE7z6VjU2GBso8+8RpxkcHmZlbZnZhBcs0ogwQBa7fusNv/8FP8IMAQ9eZnVcEw6NDvdi2xcLSKpVqg6H+Mt//xhN895tPkU4lmZ1b4sc/fZM33jtHwjL4znOP8NwTp6nWm1y4fINqrY7r+Txbq9EfKMJdmVAgvlZvREm4z3Lj1h32TI7wna89ztGDewFY26hw9twVLly+gef7TIwMcvr4AcZGBgG4NTPHzOwiYShJJRNsbNb55OI15pbW+OCTyxQKWQ7uncC2TNY3NpkYHWTP5CiGYVCvt2jWAhK2iWnoWIaqC00Irt24w+3ZRYqFPAO9vaRTSVotlys3ZpidW6TRdNA1wdFDe5kYHUbTlZLZaLZYXdug1XJI2BblYoF0WmXIkEJl9gg8D0KprA3brGIxUBBCkDAtDHSEVIBOF3QJWDV3eJ5DqxXN7V3AXIvutUWQtpyO4EhsBVvR3XfYpu7VAVJdq+XbZ8RxfV0A4LOsOXe5xrbfTtz9+0Hdg5K27an9nJ+HKba7eL9A6QDh7ZcWyuix5Zm6XPHdrtG2Z6Yrxk10hx107tEG2DK2asVgVES0IiruTzcVX6Rpmuh6x2qsiZhb7v4Z2D6rPDDYigNQu6tMp+PvBLoaoxvtbyva9mO7SmwV3Gmf0Xlkp1HjxpXrLK2sY5s2epTIdrC3SC6fQ+gQBD7LyxXOfXqDmzOzhGHIsUNTHNw/gWkY3J5d5s7cImvrm7iew9BAmUP7d9PXW2qbb2MNG5Qmubi0SqPRxHVdevt6SKRTqmHDkPXVDV58+S0+vXqL08cP8NSjJ0kmbO7MLfLKGx8wO7+M63ncmV/GMk3+0q98i1PHDoCuc+7iVX704hvcnltiZnaR8dFB/uqv/YDDB/Zwe3aef/Jbf0it4ZBK2mRmlzgYKl4Z/IBQhjRqDucvXePm7dvsGh9m/9QkH5y9yMuvf8DU3nF6esqcu3iVT6/c4tD+3fz6L38Lu5RDSgVAUwmVlHhpZY3zl25y5eYsrudTa7R47MxRLNPA9wPuzC/BfMhzlRNoDNJotnj59Xe5cWuWrz5xiqXVdW5Oz+I5LTzP5833PuGl1z9i764h6vUmxWyKjY0KV67d5Pr0AtdvzjI/v8TEUC+lXAahx4bBeCKRuK7H7PIaScch3ZNjuK+E53oc3j/B8GAvmqbhOC6Xr0/zs7fPEvgBG5t1zn16A10T9BazhEHIG+99zMWr0zz3xInIpZLE9wMqtQabtQZvv3+BV988y9pGjTMn9vMXf/kX2D+1h/n5BX7791/g1Tc/itIPaTQaLXrKBUqFHFevz3D9xgwBgtW1Te7ML3Pm+H56ykU0XWf69jz/4z//Ay5dm+HQ1Dir65tU6w3GRwco5LJcvzXLxxevcXjfLoYmhjFvL6n+12i1+186meAH33qa5196i08+vcozT56gXM7y7ofn+Ie/+fs89cgxvvP1J3j+pbd45Y2POHpgF7ppsry+yfvnrjLY38Mzj59iavcYuq4xMzPL629/SLmY41vPPcyb757j6o3beJ5LKpWinM8igKRt8b1vfYW+niJ//x//Do7rtbXwTDpFuZAlCAM836fluHxy4QofX7jKsUN7eezMUeYWlrl+6w4Xr9wgm03jez6vv32WT69O8+zjJ7kxM0+1WsdvU3yoNpdIQunhBz5rGxVWN6rs3TXCvr0TJGyTW7fv8Fu/+2M+PHeF3lKeMycPkLAtCvkMrudhW1YU+OrTU87R31egUq3whz9+mdtzS/zqL36VfbvHeff9T3jh5XfJZy+h6zp/8tO3+elrH2AYyr38tafPYOgGq+ubLCyvc3hqkqMH9/DxhSu89/EV0qkEu8cHsW2La7fm+MPnX+PIwb2MDvfz0flLXL15h/GRfubml5hbWMFxXS5dvclv/u6PmV9cVTkCN6r0RfxiJCxabpMXX32L/+m3f4Sh60xNjjAzu8h7H11kavc4G5UqP3z+Z7z61lkStso68MmFa0zfWeD733yKYiHH7PwSq+ubyjtpGNxeWOHyjTtUa00uXLlFEARkkja5bIbf/eOXKeYy/NVyAQG88Mo7rG1U+frTZzi0bze2nWRksJ9ivsDb71/k6o3b5LJpnnr0BE88fJzF5TX+8T//feW6BWzL4rtfe5Rf/cG36CmXmb49ywsvvcHHF68hhODw/kmefeIMuyZGsCyTlZU1Lly6zp25RcrFPMcOTdHXW9piHZNS0mp5tByPUiGHDmysbQCSVDaNpSlrWxiGbGxWOXvhIp9evklfb4lTxw4wMtSH0SU/trheW07neyq5s5VHbku8HJ/fdqnGFpQYOLTRwv0Bra777AiiPivEovvcHS/ZjQC3H7Ozq3fLtbvf+T6sPQryxPWxk58qvqzowkddoKp7/2feKPwc02N8bxmFCIiIFF0gNSNa+G9gWSqjhyIxjTjhvkSrFjwg2OrqOjs0104d4x5mULHt9xZTaNxR71F78WqpUHLjynX+p1fe5MKVmyQtC03TMUyDrz5+ku987SlKPTmqtTo/+slr/OjFN2m0XNYrVXaPf8Rf/fXvc+LwflbXN3j1rQ95/+NLVKpVxkf6+cu/+h2eeeI0dsQXI6Uyfbuux3sfXaRUyJFO2rz53ic89dgpvvbMY1ipBK16k9ff/pBX3/qImdlFlpbXmRwbYnJsmFsz8ywur1Eq5pi5s8jNmXmOH9xNPptBCEG93uAnr7zDy298xMhgD+uVGrfnznFk/y6m9u2iVqvz8YVrBGHIM4+f5NTkCOLVs6pOPB/fD5ienee3/+AFLl69wa/+4KvsnhhhYXmNS9emGRnuJ5dJY9sWl6/dxvN8vv/1xykVsgwN9LJ/95hiixdKgI4M99NouYyPDpDL55AyRCCwLaXher6r2kgTzM4v88mn1zl9fD+/+J1nCIKAjfV1Ctk0hhAcmJqg/NZZlSvQNJBITNPg6MHdPHTqMD9+8U0WFteiVEthtERedEzzQKPZYqPeIJ9MkLZtvvO1R6nUmtyZX6bVcgmCkM1qnbffO8fN6QUO7RtH01QC72IuHfnn1erWZrOFQHLiyF7KxTye7+N5Ppahc+rYPvp6ivzhj1/jtbc/Yc/kCLt3jdFotbAMg28++wilQo6XX/+AjUqVymaVUiHHoalJ9k2OsF6t81u/9wI9Tp6vPHaCsZEBfN/no3OXuHrjNuVilkMHdpNNJ8mkkmTTaSzLJJ/LUC7kePKRY5wxbOz3Lqqx0XQQQjHWJwo55pM2fhBQKmQxLQM/CLh+axYpJY8/eoKHHz5BJpXkyvXbnDy6n317J7kxPUsrYsF/7KGjlArKxZXNZvna0w/TUy4yO7/MxUhQxhkQMukUhq6TTCZUEujeMrlsWrlto+TovRHY9HwfIaBS3eTy9VuYpkr2PT46wNBgL5ZpUK01aDSa6LpOuVgg8ANqjSbffPYR+ntLpKJk2kSuRgBNF/T3FRkc6CGQksnxQQb7egil5Ob0LHfmlDtZ1zUK+QymaaiUSOUiRw7s4t2PPuWbzzzE/+4v/AKpTJo3f/YeV27c5vSxKU4enVKprvZP8P7Hl1he20A3DHZPDPP+x5fIZVKcPn6AM8cPMju3gmWZDPSU+I///PeYGBviv//H/4I3PrjI048c5S//he9xZ26R/+Gf/VtajofjukgpGRvp48//ma+hCcELP32DXDal0sNEc1ipkOUH33ySkebLaOs1FYaQNJlfXOLFV9/FcVz+8n/0fZ55/DRz86uEQYjv+7z13ie8+tZHjI8M8Cvf/yrJpM0fv/AaH52/wvBgL199+iEmxobo7y2xsVkjm0kzOT7M/qkJLt+4w3NPnuZXvvcshWya9Y0qlmniuH6bjLdSrXPhyi1OHt2PJwGh+sHYyACPDfVzc2aOn7z6Ds+/9BZDAz2Uo6TrLcfj8L4JDuwdZ9+ecZJJk7X1dX7nD17gh3/6Os2Wi6YJbs8t0d9bZnioj2arxU9eeYfnX36bzWqdseF+ctk0haifdpdmw8H3A/K5DHfmlnjr/U9wHJfTpw5y8shBMokEG5VNnn/5DX74wqtMzy5RKihL8/e++TQ9pUJH1LS/SIxmq3OTHS1b91PEtg+dMJb29/ssOwGtLeDuAc6NN0fkwnfLVhXjuJ1Adsd73TcAuTfA2glIfiEXpujKRPN5z9K+p0AKRSotpKJYMU0D0zK3ZP2ISyex+M8PvB6I+uHBycZ2eEixtWI72kUHBe/kQmyXKAg6DENuXJvh7UvX2disMdBTJJtJcf3GPK1Wi6k9o5wqHuKjc5f5/T95hUqlwYGpcTQNllbXWV5ZR9MEhqaxvrFJEAmw2GUXBCoJsxLQ4Lo+QRAw0FNk78QQtXqT85dvsbBc4cDULibGh6nV6ziOyzeeeYSFxRVef/cT1tYr7BofYnJ8kJGhPoQQ/P6PXmZksJdvPP0QY8P9aJrG+to6V67PMDnSx67JUTKZFJvVBsVCFilDSqUCo0O9JGyLv/Sr36VfSMTf+Z9V/fgeQehRyKWZGBvgxswdWi0HCZTLJUrFPEnbYnx0kFIpz1vvncf3fJrNFo7rtnO+qRVKMNDfw7GDewiCkGeeOM3U/r3oeIS+ylWn6xphqBH4AYGUzM4vMTe/zKkj+0gmbUxDp1zIUKvWcFyXfDZDMmHj+ypWIwhVcmeh6SyvVVhaXW93k+54Ct8P2iSyQRgiopQf0vXI5nKcPLaPsxeucWduid2TI2xUatyeXwFg98QIw5GQ7y2qJLqeF3BgaoKr0/P8/o9fZ2WtwteePkM+n8ULQgr5LI8/fIw9kyOsV2r81r/5Uz69cov5O3OU8jl+8O2v4Pk+Fy5dp+W4NJoO1Yhb7MDUJMvLq7zw6nvMzC7x9KPHOXFkH1JKqtU6V27cxjB0vvHMIzzy0HGyKZtCNkM2k2KzVscPAoJAZUFwbE2lr5EqpYgmpQpQX1vnX//RS1y6eouEbTA7t0Q6mcRxPBzXY/bOIu7hOvlchmJOUXLU6g3CIMQ2TZqtllpRGLlTentLZPNZbk3P8vYH51lZq3Bg9whSBggByaQNQuAHamn0ZrUeZUfoKEy6DkKTSBnQcpssragkwp7voWmSIPQxDYOm43B7dpHK/jqlfI5Tx/Yrd+Mr77G6VuEH334SP3AQmlTO+EiDlkInl8+Rz6sExSKKe3Q9T8VbAV95/DjHj0wxPjpIPpdFCIGu6+RzmSjXp0B6PkGrxZ35JTY2a8RM/qGUSKFhmDqmbZDLp9m/Z5zhgR56Snmee/I0k+Oqb1mmgW0qt5oAivksCdtibHSIiYlRAt/HMg0q1Toraxvs3TXKyHAf12/O8eY757h2a45iPhvFWCmNvJDPcOTgHkqvn0OIayAhNE2qDZe1Sp1yucCRQ/vo6+uht1RWStXtOT48dxnX83jk1GFOHNlHGIacOXGQ+aVVms0Wlm3TP9hDNpui1mhgmippej6XxbYt8rksQyODiCBgvdpQJMe6jmZaJGyLUqmIaZqECDw/YKNaZ355neGhfoYHeimX8iwsLfH8y+/wycUrfO0rj9DXqwDX0cP7+NXvP0cuk0ICn169whvvnSOdSjDUX8Z1fVJJGzdKVl2tNbgzv0Q6meDZJ0618xkGfoDfJivVkKHAcb0oxtOi5bvcuD3Lz946y6UbtygXcxyYmmRlbY33zl6g0XKYGOlnfmmVs+evcvrYAbKZDJquIYMgGnPKSp2pt1Q2BAEidQ+wJXawAG2x9NzDwvQF3W533Sd6hi9alFstlsrbgjQ+w2Upt8nxB4FbO17z564PpZDFVp/20+147Y5lrh0XCCjGfuVOVKvYDfSIu07QqeZQBl8W1noAsBXlE7r/tr7HE4ayTbOzJQVCd3zXXQGGsRCWiisKEJqgJ5uhkE+TsA3+3He/wv59u/jdP/opH1+4xq3bc+ybmuTC5RvML64xNtjL1K4RTh6doq+nyMmj+9Ugyybp6ylwY+YOCdvixOEpdk0M02g4XL48Ry6Tpq+niOu4mIbOgb3j7N87iRcEHNw7zsWrM8wvrtBTKuA6Hvv3TNBoOcwvrqjJOGGi6ZKB/jLr6zVeePkt3v3oIrsnhhka6m+v2Nqs1qnWGzz3xCkePn0Uz1eBr+OjgyRMnYRtkU0naTQdPMdBppOdevJV3FpPT57Tx/dz4fINpJR4vnLtaJogkVAJjeOP1ARBGOJ5PrW6Cii3LRMZKsG+tLrBwvIafqB6cyg1fN+l6XjtoNzb88sMLay2g2bnFlZYWV0nYdvUazXW1yuEiIiM0SCInqdWb3Lh8i3mltaQQKVaxzYM6o1m2yIwM7vAwtIajzabJEOJYekEQuA4LrbrISFKAB1w+foMB/dN0nJVfIZlGowO93NgagLHcUEqELO2sUkmneT733qSF156m5ff/JjB/jLHjuzH89RigCACPeWiYvyeW1jh7Q8+oVQoUqnWmF9c4dxF5bo2DJ3llXWazRahafDxhSu8+9FF+nuLPHTiII7jcunKTQr5LAlLLSdf36yxsLSKV8iSSigrleN4EfD1qNbqbJoWycBHCIFbrSObDpW1DW67Hjem5wgl3JxZ5KNzV0inUliWief5/Ognb5BOJQilZLCv3BbsEqmstBK8KKVVGIbcmpnjlTc/4MLlm9yYnqNSbdBwHDaqNUrlMslUCj8IuXRtmqkr1/ngkwusVTYZ6CsShgEhAW4Q4Ho+nh+wur6J5wfomkY+m8YwdEIZEPoBmVSS/r4ypmFQbzZxPY9vPfcImgZ/8tI7NJ0mf/XXf5FEKsXKegUk5HNp8rmsShat6RFQV+SegR9EFiSPvp4Se3eNEgQh6xub6LpGOp1qA9iNzRr1egOzvTDDY3ZhheWVdTUuN6o4ns+uvjKJhIkfKktxrdGk3mjSchxlcRVKDXQ9j43KJo7rqYBvKaPjXDRdp9F0OHfxGqahc+n6Ta7fmCUMYaNSZX5pjcXlNRzHjcB1SK3p4ANGNJ7DKFG2Yeisrm9y7eZtBvt7adZaBIEaP/VGC1AJdNtxTLYCSoZlRZkqOnp5y/PYrDWoN5pqzhCKSsFxXBqRYtZeiSkERpSkXUbvtlGpUqmoRQ+r6xUazRaNhovvh9yaXWRuaQVNV3kj640mTcfDtjzWK1UuXbtFvd7kmSdO8NCJQ9Qbqv2ndo2pQHfAskyajgrW3zU+3E787vkBhIqdvBOzBbqhM9hf5slHj3P+0nXmF1dYWF5j39QkyZTF6WP7GRnqZXllg6XVDaq1BkurFUo9m4RS4jSbVKs1Nqs18tkMhyvVSBgKRcK7TZJ1OwW3lHu4G+/pCvzM0h139OUWIboytnRb3aLnvNeq0U4P6n62L//5HrQIwuipFC7ZWtc7Ikf1f/sdo4wTXQszwkDNjepakRPuS3zVB7BsxXwqXagyKu1UNfHvz7tUHAgPCmGid1IrbOuc6pigY+8yVZoETQh6c2ky6QRBENBTLjI+OkhPOYfjeiyvbrBRqeJ5HRfD/r3jHNq/m96eIpZpEgQBtmUwPNhDuZhnvVKNXB0tlldX+eMXf0Y+m+XrTz+M53voEUngwuIyxXyWTCqJ47qsrK6zur7B7Pwyb7z7MWvrm3ieTzaTxLbNdkD8levTvPPhBUxDZ2rXKOsbm1y6epM9k4oSwXE9Wq7P4EAvoMhDC7ksmqY06SCUNFsutUaLIJ/tdLAgIJAhWrSqKGlbOK6HG8XASCRBoASV0iZDxT4tNHRNo1ZvsraxST6XVcLS81lYWmNtfZP1yiatloNp6LQcj7WNTSqbddYrm3x6bYZSqUAymaKnXOT1d8/RaLmUSlnclotlGkxOjOC6HlJKGq0Wlc0aYRhy9uINbs7Mc/jALlLJBMurG1y/NcvUrjFcz+Pf/PAl7iyscLTRwg5DTFMn1HU2Nzfxltb46K2zXL91h4WldTRNZ3ZxhXIhRyqZ4Mr1Ozz/0tssLK2SsC16ygX6yyVm5lb46PwVDu6b5NihvaysVnC8AD8I8SNwEwdxxnkTRdQOb39wntff+YRcNkU+l8a0dCrRirtmy6GyWePD81dZ2ahyoKeI63m8//GntByXrz79MEcP7eXlNz/ih8+/zuVrM/T1FDl1ZC9PPHKiPV5arsv0nUXmDYuyH6DrOmG9FT2LKnFC6zCUrKxWSCWS7IssMYvL67z1wXkmRgc5ffIQI0P9aLqJ0A3K5SKlUh40HTcIaTSbvHv2Av/6j15is9Ygk1bxep98epNjl24yMjxIKmURypA33z+HJGBlbQMZhjiuS73VIh8ENB3VZ13HY3llg6GBXo4fncL1fISu47jKvTgxNsTUngly2QxXrk/z+z96haHBMoODPZRLOQIpcIOQ6WvTPP/TtwnDkCMHd/PEo8cjK1THwoYE0zTp7SmAgNff+Zi19QqJhEUqmeDksf0Uclk2qw18P+DmzDy355Yp5LPs3TXKvt1jXLl+hx++8BqH9u3i8rVpAj9kdLhfJSR3HBzXw3AMXE/lu3RcFzdK7O76Li23xWajiev7NFtq8cxqpRqB2ZDl1Q3OXbrO8y+/QzJhcXDvOH4ouTkzz8z8Mp6vMhbUmi1W1zfxRWfejFMPGbrOylqFP/nJWyyvbOA5Pj3FHFO7xxgZ7OX69BwXLt1gavc42VyaG7cXaDi+WlihC+qNFo6nrEdNx2VhdY2F5TW8IKDRanJzZk4tHPKDdjqmzWqdRtNhs9ZA1zX8IMDzfOU21jQWl9e4dms2chm65HMZCvksLdfDjXKDNpotGi2HTCYFuoHrh/gRR9uhA7vRdZ2NjQr5bCbK9agz0FvC8wJ+/0evUq3Wee6phyiX8kgZk9WabWAW9wfD0Bka6G33/VZL8cGZdoJiqcD5K7c4d/kW1XoLXTdwvICLV25w+84Cm5s1NiqbLK+ss3/POFPVGskYUrUXNXVJ2y8idR/YghODmh24y76Esh1MxcB6p32dd/8coLUloP8+nvnLsPJ1lS9cS+13U3LV8VS8qded9uxLboIHDpCXMtKk4riaGB1u/9wvApafh/7VvjixT5vhGsB18SOyz3qrQRB6kTYnqdab1JvNCPBYLK2uc/7SdRZX1hjs7+XksYNkUkk2KlWCMOQrj53gyo0ZZu4sMDO7yL49YwRhyOUbtzh6cBeZXJYgcm21HBfXC9B0Ddf1qdabuJ7P/MIyb39wAcs0GewvMzO7xIVL0/T39bC52eD9s59y7dYsY0P91BstXvzZe3iex1/8pW+Ry2fp7Snxxnvn6OkpkkzY1BtNvvL4aRJJmwCBbhhs1pssLa8xMTLQyYnlhyBFRDCvkpOuV6o0Ww4DfWWymQy37iwwfOs26xtVNjYV+3uz5aBblrI2NRyarq9cBQO9OK7L/NIqz7/0NtV6i+NH9iNlyOLyGn6g3EyraxVW1irsGs+yb88YFy7f5IVX3sUwdDLpJI+eOkQuk+b8pevU6g0kkqs3Z9m/exTbttQSciEYG+knYVv4EuqOR6vZZLPeZHV9E9fzCRGYhsnAUB9hrUmjWuff/ruXqdabBKEkn8+QTKZIp5MM9Kmg2lffPsel67eZHOnn6cdOMtDTh2FYLCyvc316nnQqwchwP+NjQ1iWildxPR/HdQFBK1o1Gkiw7ATZdApdVxxcSPC8EM91aDkuYSi5M7fItVtzyFCytrHJS6+9T6PZYmJ0kBDB7t0TPPHIcf74xTf49Motbs8tkc9lOHHsMJlsml2TY5w4vEYilWQtstwhBIbno9sWWj5LKwwZ7C+TSiXJpqc4dXQPAwP9OAGECAYHeuntKSOFRtPxqNQaFIoBmUya/VOT9JQLJJMJglC56jLZNCPD/dTrTcqlPJZtUKvWWFxep95s0d9XZP/eUW7dXsROJnj4zFH6e8uYlkq95AUBuq4xOTZIwrYYGOxlYLCfQqlIrVYnkUjghwLTsMjkVABqEEocP6Rab/H6uxco5NPs2zvOQycOouvK8rS8us70nUVajsP+qQnyuQy95QJ7dw3T11tEN3QSCYujB/dwc3qOj85d4ez5awgBU7tHOXX8AGsbFWbuzGOZBnfmljh/+SZ7dk+wf/9e/vwvwitvfsjC0gb1+qdIKTl8YA+jQ0P4oUa14dJ0FUVJHMLQajkIAa4fsrRWIZ1O4PsBpmGwsr7B2sYa16dv02i2GBjoYXh4gLGRPg7um2R+cYWNzRq95QKaptFsudi2RSadxPV8qtX6FipGKQS5bIYTR6fwfJ/pmQWuXr9DIZfhmcdP8tDpo5w6cZhbs0t8eOEqTc+nVMyysLTK6HA/e/aM40m4eXuRtY0qru+zsLRKsZglkCESyVsfXGRxpcLjD59gfHQQ0zRZXa9w9uJ11jc2effsJQB8CcIwyeYyBFLy8ac32Ky36C0XWF7bZP/eSR4+dQTbsqjVW7iez0alRsvxEJpOPpflwL499Ped5Z2PL1MqFxkZ6gcZcPjAHkKh6kMi6O8rcWd+mU+vTnPy6H5W1za4euM2/X1ldo+PkM9lcDyHONm7EMoFZJoqY4SilID1SpU33v2EN987R6WqQKNhqRXLN27e4cVX31WuZBnieUoh9etdeS+7qB9i6bOj5edLDqD+D201uv84qR3cpNCxjKmLfb6rs/v4L6sIwZbE1zs9T/vYzmHKoqcMRcq74NJsue1FOkJETBPbY8x/jvIAYEsQEPGaSNpxVR3j6oNW4v29QXtFYLzB6MRsVdY2aEaxM8trypLVbDr4UXxJs9VUsVKDvVy6PsMb751HAgf2TjA5OUYimaTWaHH91iyJhK0sHH4QWcR8BUiaDo2Wg5VMEASS1Y0q1YZDwCbrm3U8P6Baq6MbBiPD/Xz7uUdZWd9ks97EX9vk5u0FKjWX+cU1rtyYIQgUE/TVG7dZXd+kVMwRBAHZTJpnnzzND59/jR++8DpJ22JibJCnHj3ZJn2c2j2GEMr9Fyi7v6rHsEOqJ4RGKJWGWm+0GB0e5NTxA3z48SXefPfjqL9LVtcqXLp6i4P7diOFTrGQY7Na5/bsAv29ZQYHesnnMu1VR7smRtA0xd30+ENH0TTIZjKUCkUSdpKHTx4mlUgozdkPCAOfwb4yuq6Ry2Z46pFjJJMWpVIBTdeVNrl7jPHxIXLpJM2mQ9JOkstlsSyLR88cZWW9SqO5QGBbmJqgb6CHYLWC47p87anTOJ7P3NI6uydH6O8vI8KAvZMjWKbJ4moVIQR7JgYZGx7CNA0G+no4dXQ/03fmKeQyHD+yl10TwzSbDoZhkEza1Jsufqgsp6NDfWSzaXTdoK+3xP49Y/T3ljBNg55yAU0Ido0PYxo6rufTWy5QyGcpF3NqtWKzxehwP7Ztk82mefbJhxT30cIK+/aM88jpo2RzaSzT5NCBPRQKOeqNJolqAy2yOgrXQ9d1EokEfakE33jmEeyEzeToIIN9eWxLx3XVRLFZq3P5+jSe71GrN0gmEzzx0DEePnWU4aE+RVtimYACQAemdmOaisCxVMxjWjrLKyvYpkEgBUODfTz35Glu3Vnkua88TF9Pkdn5JTY3FS2FlJLecoGvPfMQ5WKeYiFHMmljmSbpZBLDMLEtG9uyIqJYDcd1KRXzfOcbT7K8sk4yaTMxNkBPOYeu6ezeNcovffdZfvzTt6jWGlQqNYYGennk9GEOHdjFQG9Zucp0g7HRQX7hG08yMTbI7PwyjWaL/XsnGBnqVwswDk1RKhSxLJPd4+MIDExDcOLIPsZGBiIKEw/T1MllM2QzaQKpYVsJ9kyO0lvK0VsuADDY38NzT54mRDA5Pkw5l+aRU4fo7yszOjKIruuMDg3wzWcfIZVMkMtlGBsZ5pd/UKZSqZGyTUzDwmu06Eknqa5u8NTYMHnT5LBpYTWc9oyHplHIZ/n21x7nyIEprt64zez8EoVclpOHp8gkE+yfmuQXvvEkb733CZVqDd0QHD6wixNHDzEyMoznK8vS6RMH2VhXdAua0Hj0zBEMQ2dhaQ3Xc0klbPp7yuzfO85b753j2s3bbfdrMpoTJYJcLke5VGBheZ3bc0vU6k12Twzz+MMnmBwforJZY++uUVbXK4wM9SreME3Fd07tHuXZJ0/x09fe48WfvUtfucD+PePsm9pNKGFlo8qFK7dYXtskmUzQU8pjWybTdxZ46Y0PkTLkz33/qxw5OMXtuSVc38N1lTt4aXWDlbUKEtpxcPVGk5nZRRoth1w2RTqVaPOMTYwNcfLofuYWlvFcZX0/dmgPyTfPd8SSbX2+rPp5LF73LP/ru+e2l50ZL7kbON3vKsv7ummXmzP+veVc5Q0TBNsjz7r238sq17YfK07zyNvjei6u60ZuRHXLZCKBbX9GxpoHLA9u2SJqABmbH+kI/fu9QPxFqe+dXXH0Xncbbq9M21TGHN9nfnaR9Y0a9ZbDrZl5zpdusLRaUWbqWpWV9Qrjo4N845mHKOQzVOtNWo5Hf18PtmUhpSSTy5LOpJm+vUAyabN31yiZtLJ4NVoulm2DpgTfoX2TtBwH1w/wa3V6SgXOnDhAIplEN0z27t3F2OgwS2sbrK1v0my2SKdTJGwbO2Fz9OAe9kyOkIrIFVPJBIP9PQwN9GFZJk89epJCLsuVGzNYts3p4wfJ5QuEaGSzWb7z1SeoVOsU8irwF01DxImyVQW2idgU4AnIZdN85fEzlAp5KlF8Qn9viZszc+SyaRpNNSl//ZlHAJgcHyaTsnns1GF6S3l8KRge6qdcKrC2XmF4qI+D+3aRyaSiFC4Btm1RyqUZHujF9QPqLZeZmTusrW0QhpLdE8Mq52Axg2lb1Ko1hod6KBUL5IsFxU4dhogo5Y3juAiheHkc1yMwDIxQotsWVtImmbT49jOPsN5oMru0RiqZwDZNfE9jdHiIyfFx7KRKH5O01ArLMAzJZTOcOX6IR04dIZ/NYtlKGDSaDgf3TXL8yBRDQ/2ECI4c3ENPTxHNMCnkCly7foupXaM8cvoIxUKO9Y1NqrUGA31lEgmbXePDfPurj2MYOqViTvXBShXbMtXzGTqjQ72cOXGQWq3ByWMHGOzvIRURyfYUc+i6xupaBSL2cKEJtJZKwKsJgWWaDPT3EAQhuVyWhK3ikpAwOTaIH3qMDvcRBCHTt+e5ObPAe2cvsnvPOKZpkLDtNneRrun0lkvksllCqZjFwzCgWMig6xqJhI3rGiRTWcZG4xgsg0I+h2kY7Zx02Uya3ckE6VQSTY/45nQwDZtkIoFlme0VbqEMMQ2DcqkQ5dRU3HaGoSHx1USHSoD+3kcXldLj+yRsi9HhflbWKh2LqCZI2Db79owzNjJAtdaIxluSXDaNoZs888TDykUmRAfwyRB0nXKpQKmYJwhCWi0H11OWDV0GDBdyPHdkiv50iv6Gg3n9Dns3m4yVS9gIkvMraDdn6a802XRDcneWSc+/xmStwbMtB6/WoLa6TjGZIKPr6J6H1ooSVfthO/XO0SDEMg0Mw0CrNdpTnLLY2PSVe+kr93L4wF5q9Qa+65JJ2Xieh21oHD04xUBfD9VajXI5y0BfD+lUGlfR7HNo/25Gh/upblRoOQ695SK7J0fYPTFMo9kiDCW95R6SCZuvP/MouydHFcu8aXDq2AF8P6C/r4yhK3fdt7/6OEcO7qHRdCgWsuzbM0F/b1nFA+Xg2ace5sjBvei6ptpXCHRNI5NJ87VnHmGgv8zla9M0Gk36+nraHIS6pkerXYscmJrg5JEpSqUCdirFqfkVfvTi68wtrbF/v0QKwdTuMfwgZGl9g7nlZYJQUu4pEEifwPfJplPs3zNOTynP8GAfPaUCmXSSnlKB3t4SU3vGWV2rUK9V0YVgbHSQxJvnlaUnYW/z0LAluLoj9PiSgdZ9ls+y3HTv//d9/8+6zxd9hgeuVw1FXvs592unBIyfHYhiAH0/wPU8XNfD8/22JVs3DIrFPMlUiti09PO29gODrc7zd9vkuE9/LXQeWbbffWuKgZgY7u5GFUBoGMQxNWnD4MjBXTiOSy6XpeF47N09wq6xAcrlIolkAjuR4PFHT3Fo/24WltdZ29hkdLCPjGWiOR79yQTfPnWE6t4JtFBiC0joOmJtk0dyWXzLZmR1k5Ib8ot9vbSaTRLT89i6zkSphJPKoK3XKf7sA2xNI+0H5F0PfB8RhAoMvXeBfMthV71O6PoYgCkEBgJDSrTwFQhCkkHAVz2fZz0fghD9Z2cRYdi+Ttr3GfF8Ba78ABGDrKDjhIjJX5ESXVP1l89leOjkYTzfxzJNThw9wGa1hmWaJBIWyaTN6eOHsG0TyzIxNRgdGWRgoBcpVAqeIAhI2Ba95RLJhK0sgb5iAlfXsbEMXVnctAbJhE0mnVImfk0nlD6GaZGwbRqNJkJTuSSV8NcQukCTOp7vM7+0wuvvfEyz5ZJK2srci1RL36KeELg+aDrZTFpp0bqBHmqkkkkSto2ZSESplkLitCiWZWJZeQxDjwAASBliWBZHDu2lkM9SyGcxDYOR0SEGB/tYWF7n0pXbzC2usmd8iL6eEtlMmoRtk800FBGuplEq5lVORiCTSSMEpCO+npRlYOpalCJIYNkWuWyGVCKBoan+bZsR51cQomfTilwPgezi/9F0jYH+Xmr1OkEY4vghzZbL4so6PeU8zz19mr27R1lZ3eDsuctob3xEPp8FAZZtbCF2tF57n+TF66Rsi8C2CC2TwDIxLQ09k8TMZnADiXN1GleG1Af60XpDnJZasapHpJO6oatPxFoupUTXTRKWSgljRKl84jnD0NV7ppOJLa4ZKRTYc12P6dvzbFRqTO0ZY7C/BzPid2s5Dp7r0pPLgOuheT5a0yHTaJFpeciWi2wtoDkewglIOB44Ljgq+bHWchBOlB/PiRMgu6QbTWSU9Fj4AT1hyJSU6h011Q75rri5WD9OhQE9Mkr9IxRljYqLdDFdj2QygWnb0TvKKCBexUgFYRitgtKjJfeyHZcq/AA9CNXqQE3RrRi6Rr3eIAyCNjCUoaSnVGBosIdsJoGuazSbDtVaE93UsBI2pVyaQjqhtPUooD5OHu95PmGg4foepVKGYnGfSsYbzbuxFVyRPRpksykmxofx/ICkbZGIyFd9PyBhWSRtm55SQfGwQbt9TUNnqK/MQE+BM8cPsLS8hmEaisVeaJRLBZ57+gyaDvlsllQyTbPVUuA+l8GyLEDFaT1y+giHD+xBCoFhagz29/AL33qCfC7D5OQIiIC+/iLf+urjtByXQiFLOpmMnsNQz5wTlIsFfNdBIJWi4ERuxOTW4Ph2g9+1rctI8BmH/XspO1mUvpRVfp9xn8/a9mWX+7pHbP26n1qPoFLUn9v4QgYEgYsfuISBUvZkZEcydJ1MJqUWjXE3Q8IXKV8IbIn4f6lt2Xr3a3fhwfazKtb1z3r2NhyTcstvpERYShsyDIPT1RYH9Dpus0U4u4b+3mVsTcPSNLRQIsIQXaJykfkBoeshfR9DRLE3QAEYlopnJG6IWAAcikCLiBIny+4XEdqW991Kzrb9RdSfdJSXLV4F0a6xnay03ZtFV+22raBt5zNEK6VCGapVUS2HVDKBZcS5AhUrfpxrLWFb2Fax/dxxWhJd12mnh9Y1NM3El2q5MJpGMpqI9EhISCnRdJuEbWHGaUJCZZmwEglyCJKpFI7nKx4XoUfEs0mCUGIaRntCluiEKKZfiWCgv4dd48OU/vh1rKZygQR6J/V54IcI3cQ2E5iGiS4MpB4gpQm6QWw2jo+PV5NpURoe1dbKHG0ZSYYG+jBNHdMwIhZh5RqcmV3gztwCfT1l9u2dJJfNoBk6lkyQCCUIjVDTMQRkI3Jb27aQQpAWqr4100Rl9VFJsv04IXZXZwmFjmVZFPMKuAjDgCBANLvAlhD0RFahWOHw/YD5xWUuXZ1mfHSAYiFLy1ExQXt3j3Jg3yTjQ70Yhk4qYbdlhHH+KuZL77QVvja9StzXBCSDkGeaLWQoSf6bn6IJQZ8AkUygpZPIhIVvGsiEhUgm1CquZAKRtNEtC82Kcv4Zap1dnGhYxMAlDBV5re+1wY9erbH3zgJ/fX2T3sw6uStzGJ5P2GjRU2sgXA9L1xUnXLdVvHtsbBs3MUzakcFaEuUHVGM9ph5RFuJOCpP2uV2nC6GpxNjtFU20k9qahoEepRMjYSFtC08TVP2AJhJsm0wxh1bKYaSSiMs3Ee+cQwoN86dvY776Phga2DYkLGzDIG3oSMtAJG1kwia0bbRMCjOTRM8kCSyDpq9Sp1uFNMlCTt3XMpTFJmGhmQlM04wUDgPXVQBQoviGVDYGrT2fxf1CzX+Ki0jlgxTtcRQfr1ZGduondh1rAkwNNGGQtC16ClkkEsOwkJpGb7lAuZyJ5hxBGGjMzrc4e/4yb7z3CX09RcZGBshlMxTzORDghSGu7zI81Mfxo/sxDJ2kZWIaylq9d+8IgUc76X3cPiKy/Bu6TmgaqO6pq/4HnXitLhCzc87f7q6wQ7/69122A5J/X0Bru0vv32d54HsIVFogdpChcuth0UpWpXhHuVjDAKSPlCGaJiN+x0jtiayy3cnmf97yhS1bqgiQOoju5LvRtBQLMyFpcz20O2VX5/zc4PgI4rSBj2hraKlGC6vawHXdNqu3aSpQsZVWQhUNELreXnlD+6kE6F25A+PjF1ch0iK3WO62WPNE5wTRfVy8XaX9aVdD+xKxqZptx6Mi83Rdsewb0fMaOlLXwdBB05C6howAo9R1xJtnWa1sMnf+CoO3F9k/NUH/7DKm4xNqOqEAKbTOudF9hK4jDB3NUH/RNIJowpIC0FQwtABsTUNL2O2kuLquXDNmBORUa4Xohk65VCSMcivGmm7C0tHCgIyukbRMNC/AWNtECyX4oPkBhh+w1wsZ37MLEYZkghDD8WB2CfPqNKKhyAdL/8U/oBgFMIu4LWTMCNNZvBF1ni3V3NUKsO1nBwhDSkq+0nJ4MlDUCeYfvqLAg4QEkmzk9lKCJ76EaI+B5Lbukg9DvhKtzLT+lx9HQkrtMxB0s/to07OK0mN+GTnc176OaRqKUiEiKEynk/SWS/hByCtvfMDCkkqRk04leeT0YXZPjpKKQLISjoECObWaAulhRGgYqglISKlWBku1LeOr4zXXQ0iJGUqQGwowhSFWfF4Ya4tSAaidFM57DfV43GgaphBMCMDzVfLmrgtZyI6OsaNytyPeuncRgFDjQYtW52oC1b81ocaGrqkE6EJDaJpK5h3v1/T2d6KUM0iJDuimgW6aarsE4biYuQzh3/0biCj4XtgW0tQJNYH22z9GXplmy5sFITSayHoTPQyxwzCqKq3TT7utgzLEiOI74+S68Zho14UATFOBikSCwLII7Rgw22jppAJmSQWgZcJWxyZtpG0i7YT6JCy0pE1g2wjbgshqKgEhDGIeM02AEY0rlfpEomkqJiqmmtCtaI6DCJx33JBjIwOcOLKPk8cOUirkMUx1bYMAu02WEYtdxQUpQjVepaG1rXOxQtEGz0K0k2trmqaUGinvJjSN5o57Aa6tcu3/D8t/CJD1BUs77Kg7N2V757YfWzxlMspLrMaToesYhtbOtylENAXSwQRfRvm5wJbqfJoCXN0lroAtmuD2UPqtHVdKZaHZ8nsH9C6i1Yix9q1FgEcIoZJUJxPqGENHahpSNxRgMXTQdaSpfkvD6AAYXUPqusp1GB2HrmO+8DpirdIFrqL/YkEea3fb/Ptt4dEFxmS8D9WItIkhxbZzdqgboQyZQioLHQQdAReECMfF/nv/lOxmnYdaDo9qgsLSJqkPLqsrtTFrJFi3u2ijTivi9tqyXbb3p9ttIru0CdGuGhltz0pJ37ZrdzSluDvsAF63AFf1RavWt/QLVR8C8/oMdCUNjc+RXd/RdWQu3bl25wo73LurOaJfuoB0/NiuDzS7riDRu2b6bivlzmlkJZqEZAQYEM1t+7dN5LGrWDpIx1Wjv9ZA/+hTaDrguiT+ye9Btc4vui7faTrIIEC8+jG6EMp6G4F8whDCEBkBJ6RUbpPI/RyOqVx6ytKkQBSxZTiUCBm2ARhhuAWMEXb6g2xX52dCnLtL3D/CoC26IjNRLKUVb1QEeOJtMgJobPmrxlO8T2qia7s6RsQAabvwjK1bYUQxQcc6I7b1cxHVV7cLX0fNehZAy2ELp5FQ+wu5bNtNp1IYq/oTk8OEzz6szmu50HKh1VLfmy1EvYloOgpURCD9Lst4nDBdRHkFu4D/luIHUG3AZiMSANtoK7dd9q622mmPQAGzhKWsccmuT8JWQedJWylrSRstmUDaFiQTaCkbmbCUBTBlI5JpSobJU2eO8dDJw5RLRZXEXO9wMQokupDtR1L9PFBjY7OOVmsga03YbEKtCY0molpX9VhvIOpqu6g3EdUGXL4J6SQcmaKN6Lu9B2yzcHVQP1vmE+hsv4egjgV4d5Lsrac/4Pj5D1C2pDa6j+e7F0j5Mt6tO/VQHCLSaad7nkUbFEsFtECBe91QXh1N78hfSQy4vrzyQAzy8SBryxjRnRW78yIdnpAuQba98qPOvHX73XxdbSNe3EH7Ssjf+ruEmkag6zR8n7nlNaqNBtlsmlIxh2VbIEUb7LaTS7a1QLFVRhPP951nCcMQ7foM2o076t661hEyxFqlOrsNNOj87n6H7haLAYnsEurdf3YqsSvE99UqIS12g8bnBCH23DLlyBWqCYHmVe6h+99nuQc2+azjhNy6eetp99Ntd7hpBA62HCUlWtO5W1huK2EqgV8cumuAd9xK8QU7DqaORUxsAU5yh/PjIdGOtekaqN2gQ3RpVrElbmt/i6yfXe8vF1cQUrkQtdkltW1pjcR//Y/aD6NNz6n4HiIBf48+2N4ThioBryY69SqEEjKi857bJ8qOfO26amSSD6XE1QSVlkPd9Sj1liiU8miGEVl7IsDTxtqxCV8qQBlK8H0FLBtNqLcUEAoCsNQ1/HwmyuoQAAJNVwH+uq6ha0Y7Jqrr0VCvG3ZASdyskrY7c6ciw5BWs8XqegVdE5QyaRIJ+67xKbcP7C31FbsjonlifRPqTajcwfwf/mUEQpLIpIVI2MhUAhI28quPQNImTNiqTZI20lTWG8f18BwPXYIZhhheiGi1FChzHETTIag1CBpNRMvF8AI0NwJqrSg+LYpLo+moXIDtj4voGrz3rp3PGG8SZR1qOiCq93V2B6p0gZZIGTTCkJEgit+NlN+OchXJDSmVghGEqg95PiAi62MErmNLZAzIY5AduYcEKMAsleKqViJ2ZJ1AjYct6Ww+C2jdZ5Ghcmd1rCddciCeV/63BLpiYBO5VbfM+9vn17YlsVtJFlve7V7nfuYjdIPUaPxF2Q5Bdlu3ugVu51jiW7cNAOqQOFyge0jvNA/+vOW+wVbsJFJGctr+T9gOwdR3sf05u5Hxtm1brFhbjtuqAQDKzVUsKL4YCWYQ0m8YFF1FOhpzN0lAarIDioTSjKUQaF3PqgllMZK1BrJaR9QasFlTFhVHJaSUuk7lt/8bDE1g6Bqe47KxXqHZaKIBmUSCXDZNwjSU68cPlfHJV4llpR+AVH9D38f3fAhDdERbG441aiEDVXeRIBIyRAY+N6dvc+nSDVoNB7/lMthX5klNYK1XiScfTcouIRB3nh3qsEszaFuz6JzXcbtJ2ru72qft0u0WYvGXuNPG2LutIbJzabtTxQ7X6PzuACD1PUwnYyKUrWCsaxL09oyz/l/9Z4hI+KmJMyTEbx+r/PRaW3FQ8Wjalviu7tJegNBV4hV+WnSe8X/8e7CxScfOdveg3QYP1DN2bYndKlvOCQL0W3MdSNVsdUBMXG875HWTmobUwAtCHM/HTFgk6y31BIZO8PXHwDKRlkVgmQSmTmhbaKkEwo6tDrYCBlH8kbQtfF3HkSErqxu8/No7fHD2Ir/0C8/yzJNnSNgWUqo4PClRVjNCECFxFJWkI2gloL/6PuZ/98+31FJYyPHib/yA5196i2QywdSecVIJi43NKo16i3wmw+7JMfbvnaBUyEfdQRL4Ac1GHQ1IJhPomgboamHPFkGp+oYuABniOi7Tt+7w+jtnyWfTPP7Qcfp7S2gitkGFkdYbRh/a7ghNiLZAFpEVThMC6z/5rxAbVcRaBf1P3+xqmLua6u6iCUjamAkLEglk0kakkypGLhV9kom2tUhPJZClAiKdQKaSkFKuQJmMQJ2u363ShCHCaSirWqOlFgq0FIDD8RQgazjQ8pCNCLw5EbBqudFx8feWsiQ1W+B4kfUz7FgBt3yX6t7d23eSE59RdjrmvsR3DLgMvXPPIIRkcutx26xb93Wfnbwydx8UXaMzR6jEzV0xhv8bAVvdsU4yjoWJ2Qh2yqfYVba8n9wKaLafuzVZdvsCXdbE+JhO3XWwRPRMWzZsuTrdLSbb/1TbKt0v7Jrb46Tjn/l6D1TuG2zJMIxiWUI1gcYIFyDWVtG6T+CzNaFtQCuasLr3tfd3b5NEBICqknRNI2krbh8ZBMh6E63ehGpdmY1rDbRaE1FT30Wtgag20OLf1QbUGx13SPy/hDCfie4kMXVBGIRcujHDux9dYG5hBZAkbBV0nEomOHxgDyePHyKTtCHUOu3dDVSCkNDzCMJQxVqZSnvXVFgI2zNQarrG9ZvT/MsPzjJ4aJI9E6O8+8FF3lhcYf+xKYZ0nVDTcMKQludjp5MkMin8yDWKrqvYrq6YL6lrSN1U9zb0zl9DR1gmwtCUK9Y0CHWd0NAJNQgQ3Fne4O2zF7gyPcfjj5/kicdPk0wlIkOl4viSYYAfhviej+d5BIGPEP4O1gGVbEEGgNRUAvfIZaU01kAJ6DBABmFn4o610BBEENdroFZuegH5v/dPVbsZGoYWEKK14zkQst1L42ntbs447muUxbFdIhKybevpxiZidaP7RTvnxP0AtiokWyxhCqQKy2xr6LLLRRa70ETsFtQ1wsFewmKO5v/jb0ZB1TZa0lZUKUJQbzT44OxFXnnjfb7yxEm+9Z/9fWXdSCYI/vNfa7dHELG0S6mW2XfHuLRBnhDKYBBKdN+nnE8z0FNESpUwPAbrEokWKUUdIK8yQAgRRFWsFIQAEVkm4o6v7htKyeziCuc/vcGRg3vYv3eCdDrFzek7vPnOJ/zpy++SsCy+87XH+e43nqanlI9cA4oAObbkaZpAkyrVC1q3oIu04qj+LdNkZLCP5544g65rlAq5KFBWzXeSiNQZFY8URAmzVT/oWC0FIZqUSmvmHsL3fiZyKaHRQqu3gApd2s19X2JLMU1kSvWP8JGjhP/Jn1XTdtJWOQFLuc6to/8FUV+NwkXaADmau/W/+d925tFIARBCKCtR7IJWE0MbYLWtm+3v3a7qbcdu+d2Zp9tF1yKrVfTRtSjuLoq3i2lyonhVX4Lr+2qhitDILa+rNwrDaDXiF4ZwO5btMkxK2TXnbDmyrWTuCLTibTu5Kb9kK8zdZSs43Onp4yK2YIPtiuXn3KVtiYruIkUbVmy95v0koKYLKG+7z5aPIAijVGCyAy7VfcT93+tzyv2DLSCOM2gjXKkSNert2IQOIIon2M7WeGO3ZslWoLVFI+iqoDhmp9lC/MFPMap1qNZhswGRJUpU61BrqniTexWxVaBur0C5bbvm+9EcLPA9nzfe+5jf+YOf0Gw5PHLqEHt3jZJOJZlbWOaNdz7hzffO89c0jccfOrYFOO7ko9e6TOKIQKXPoStINCqO4/LBJ59y/eYdfvFbT7B/zwSmbvCTV9/l5lcfp//MMaq1Oq+8/j5vvX+Opx89zNOPncTQOysRuzup+ioQka0yLpqGEkJtS1I84CWIED0MkZ7PxsoaF2YXmF+vcBoR0TfEACFEkxCKEAiRIiQQEikUa3V7Em7fM1rlqQPCjKwOIKXKNyeRCE21pwxDkCF+qFaKqUUQOnFGA4D8f/nfo61VMGbmAdDXNin+7//ODjrpTv0i/rIVYOxUOpnm4+M6FxICxMXr0F9WO0p5to6LTl/Y4o1AjRblfhKIcn5L+8QaZZxGCFBxJxFACRM2YTJBo6/cFoIJQ8eMhL0mdNLpNL3lMulkWlkTQLlnpNLshADDMjFQQjKIuNr8IIzoHXQVLK9rmLYNYUDoCgJfxVG2Wg6O4yrXou/TanoIdKyIHgOIchtKDFNHt4TqL0GgQp+6wVZEpQCQSiUpFnL09Rbp7SlRLOQYHupj7+5xnn/xdV586R2ef+lthgf7ePqxk6RtFZiup1PK6hSGCrsJCMMo3lHT8MOQMAjQpOK6itsynUxgDvaiR+76OK9cO2YnsoIGIWgioHvuUyv11HvGCqkh2RIuEf75byOnxtvxWaLeUNQTLQcaLUTTQTZaar5rqm3KDdhS1qROt73v0h4DnoeoeMhKDe13X0B7/aOuI7afwZZ+fs8Lf3xZfR0d2Hq0QLXj5y3oSiYgk4R0CrIp5ULNptXvTAoySWS8L3K5tl2LSGi5yHoD2XIQrRZho6W+Nzvxb6Lp4NUbLE7PsTCzQKvZxPB8SrZFWtcw/EiRS2wjsWxb1ZXgb+cXjNtz+xyxDfR0GxPoEuAdyLrTNbh3lW+Jf7mPe/PlWMi6wWGsoEro2FTaODAGWjFW2H6dsP0NOnFw2wHpFq9A2C2z7wHbPg9siq6HbF9JMSNomorjjDEOcnt405dT7h9sRZIhfkQR82ERIsMo6JSQ9sgKg86E2e03FSLqtPEwDuMb7GABEB2kK1ButX/5o7vHbnRtyWcZJMRdv2Q6CZkUMpNG5tSgptZA++iSOqBr8v/k02v883/9YyrVOr/6/ed49PRhSsV8m8xxdLif/+c//G1ef/sjjh/aSyadid6zU38ychUCXe7NsO2u0jSlOXa7qVZW1/nkwjVq9SbJiNfG0PVIAVSBflJKLMtkZKiP/p4SuhYJCKmsFKZlgZSRxSJEoDKct5d1t1dn+l3Kk0DoGppuIEQAnov0JPlsmqceO0Emm2XvrjGSlhmBbVX5oamhYSqS0iiBb2yIkqFU8Ta6HhE7+kozNgyELjEiS1EgQ+r1FhubVVJJi3RGcRVVKlUWllbZrNZJJhL09/Qw0NeLESXM1So19LXNaBEBSMclWF5rc0vF7aDeWeksW1YtdveTCJh3JyrtPiIWwKo9RTtbPLGFRgLlPOFv/m2CULJRqXLt5gxhGJKwbUDSUy6Sy2ZUAlSp0thsbNbYrNYJw5DB/l76ynkIJcsrFc5fuUW11mDX5BBjI/30/ud/D211A21uGdfzaNYbfPTJJWr1OrqmsXtylL5SnkwqAVKRd66srrO6vK4s1QC6huN4LK9scuPWDHfmlpBAb7nAgf2TlIs5Pj5/hXc+OM/IcB8PnTzC5as30XSd0cEe+ssFkglb9bWoTcMg5M7SIm+++wmg8dCJwwwO9KJriuj18rWbTM8ugAgZ7Cuzd9coPb1l0lssW3QYYqTE9/028WDLcdE0QX9fmW889ygry2v89LUP+dlbH3Jw7wTJ4T5qtQaXrt7k9p15pnaNcvDgHi5evMrZc1cY6u8hnUnxyYVr+L7PicP7OXpwCtuyWF5d5fK1GYQQ7NszQamQY3l5hVu35xka7Afg06s3SSWTHD20CzuVJJQhIpQ0Wy0Wl9dYWl6l5Xr0lgqMjwzQK5WLP+pJeOP9XM2luLm+TjqdZPeBQ1i2xfKKyu/X31disL8HwzTwQ8nKyjr1RlORcyZthOsha02cyibuRg3d80iEoDsOYaOFaDnoTQU2/FqDjfll1heWSSPosy3MxVX1LK6HbFtg7xYud0+lnS0d1xIKuEdWQ7lnHLIKOMlMCrIZNa+mEhFwSiFzGWQ6icikIZNUKz7jeV5KZYUSd8/X9ypSdlbTxonKIZ5jO2d+evkmv/uHP2EmaVHMZZgcHeDkkX2M/87zGLfm1L1//fs73kN0g+3PK1ssTrSBVkeAd+2/63piZ9dcO9Tif4US40o6f2PABTIyvGx77m1gJe77nfOlWrTT9U5qTtrJIrbVjvaZtfB5mnW3IUEo3BK236T9Rlvm+8+/6P2VBwNbdOCUkBIRBhAIpOhC0BoQCmWlkF2V1G1dga0BmdtRZDTwpNQ6qFaGasD+/5j77yBJrjzPD/y4DK0jtRZVlaV1AQUUNNBoNBronuke2cNVM3PLPePyyD0aaSTPjrSz27tbuyXvyNu73btdDndndqZHtZzuBhoNXY2qQqG0llmpVWSGli7vj+cRGZklIBpjtg9WiIwID/fn7s/f7/t+4vuVpXXR6maLiIdYioRwIyHccAA3FBAgKhyAeAwSMdxQACfgR46IBFRXUqhWTZZWVmk0DLo6E6Sml1DO3xQPlme0LcviFx+cZnFljd/9tZd4+ug+gsEAuqZ5q3SZfbsnePHpg/h13fNSuR4Y2ujNALBsm2qtjmFbOI6F0WhQKleoNkzi8RgDfd1Egn5wbBaXMswvZrzyVAXTNMkVShTLFVzXpVwRxJpPP3GQp546LMgzAcMwOX/lJp+cv46mqQz1d7F1bIhYJMy9qSVW1vJCakZTqVZrpJJxYtEgoZAAdLbtMDe3yMXrk7iSw8RoH8mYkHvpTCUwLZtcNk/IpxEOBjBMk/mlDFdu3KVYqpBKxBge6CEU9KPpAmAtLmW4MzVPsVRheTXP0kqWjkSUp57Yx5axUVSfn1qtxvTcEtPzSywur9EwTPbt3kZ/Xxf5Qpn3jp/l5Jmr1BsW+3du4fd/7xsM9HV7CwFvnCB4hQumyVy+2JINURUZzZPlaRKuWpZFvlDCMIVkUjgc9MCsEB82LVEhJ8syAb8AfY7jUKvVyRZKVKsCBKeTcUINs5UP6HqLjHKlxqmzl3nvo7PCmDZMLMuiqzPJ73zzJfw+nZm5JQzTQtF13v/oLCc/uUI6GeXbr79AJLyDufkF/ubN43xw8hLVWp3xkX5e/+oxXvP65rpCciKfL/FHf/ojLNvm5WePcPP2PXDh9a8+RU9XB7gu+WKZaluI01VkVlezvPGLj/jw5HkaDQO/30cmm+e3vvki33j1OVZWc/zFj97h2GN7OLRvJzNzS7zxzgk6UzFef/kpnj56oPmUYzsOlm0zM7fIz949yfziGtdvT/O7v/5VerrS3J6c5k/++g0mZxboTMfw+zQkSeaFpw/x7ZqNKk5I0JQgDJRtGjTqDSrVGo2GgR20sSwby7KIhYOMjfRx+vx1pmYXmFtcorszzunzV/j//cmPuTezwN/9ja/SN9DHm++d5ns//YAXnz7I3p1bef/URSanF5iaXSaViNPVkeDMxav86fffob8nTSIWQQJ+9s4JfnH8Ewb6eihVqly6MUkqHuV/94ff4vHD+3Bdl8xanp+/d5Jfnr5ENl8EF7pScZ55Yh/fMQwCTY+7CzXH5vipc/zkFycJ+H288vxjyLLMiTNXyOaKvP7yMb719eepVGq8f+Isb394hlyhxJ7tY7zywlG2bx3FSercWlnl7U8uUK8bHDmwg96+Dq7fuoetwuGnD5BOxTlx+hLfn5/nSi5Lf08H/+Slx3nij3/SMjSAmFMTMVzXxTBNj5NORlVlFG8B2GgY1BqG8HZ6QEuWZSKGhU+WkGzh77X/+T9BkcUSOl8oMzmziGXZ7Ng2QiQUpFZvcOPONDO3J4lGQnSmk/h9OrFokFgsiq5qnqfV8wzKgntPRtgUt+kdVxSEv9wVi3BZFnbE08tVfToSYBmmKAhBIhGL8+pXniYaDpNOxQkHA+iqivbWxzC1KIaw3Qb4m/apZbO+gMFtBgdgkxv7wZ6TlubwZ24P98B8aTlf66hnw0fNozchigjHr39zP4zcvMumnW9+9ujfPLS1rmUTK9y3Aeu8SxuBroRwAti2g93k2vzynVrA56lGbDuf9dCU65WLS8KTJctgu+Aq63kazZBMaz/u/WHU+wae1CKSF00sc10QruVSBVJxCv/0P+XWSgbLdUkl4+C6LGWyyJLEyFAviVgU23HJ5Ip8cuE6d+7NEg74eeLQTraNDQlh0qk53nr/JHOLKyTiUZ7t7OBZSwjsuqaFpChU6w3OX75Fb1eKg3sniEUjLQkS13WxTIug38dv/9pXAJG/5Tg2hmmRWS2QzReJhEN0JOOoqopt28zML3P6wlVu3J6k0WjgOi65YolIJMyrLx3j9a88SaFQ4s7dKRaX1ujujKMoCnem5nj/o7NcuzXNX/3Nu9y+N8vzxw4TjYS4NTlDo2EwMT6MZdu8/cFpfvrOKQqlKof2bOWf/MPfxrJs/v333uTW5Ax/+J1vgAQ/+cUvCQb8vP7y07z4zEEsy+LcpZv86M0PmJ1fwcVlZLCLl597nL7ubqZnFzl+6gKD/d089+RBujtT3J2a4wc/+wDTstg6OsD8wgo/evNDhgf7+Mrzh+nqTHHr3jz/7s9/RqFUo7c7xWquSLVSQ5Khr6sDXVH45ccX+Nk7J9E1FU1VuHh9kmqtzne+/TUKuRK3786xb9c25hZWuDM1T61WR2sndnIFgCgmY7xpm/xffArdnSks2yazlqcjEeS1l57g1a88gWkZ/PStjzhz6RYLS2tsHUnzD77zOgf37WZlZY0/+rMfc+XmPfq6O3j22EGefuIguqZy7dYk3/+bd5icXsQwTVzH5Vtff47v/PB99GKlNdYdx+EXH5zir//mPfp7O/n6V46xmi3wwzc+oDG3jGnZNAyTKzfuoCgSh/bvYfuWEc5fvkkuX8Q0TKqVCp+cu8pbH5xmYSmLCywsryJLDi8bBqoH6C3LpmzWhe7i2AAhv4+5SpWZ+RWeOLyb/t4uwgGhU6jU6+uPmiyzvJzhxCeXsGyb33jtBQb6u7l2c5KRgR4CAT/9vZ0k4xH6ezrp6Ejy8vNPcOfuNBev3hE5WrgbChBc16XeMHAdh66OOH09acIhH41GgzMXr7OwvMrXXjjK00/sI5sr8G///Kd89we/YP/YCHuba9s2j2Kt1qBSq1Or1TENA0VysVxHpPtYNvFYmJ6uJJZlU6xUqDQaIEOt3iCTLVKp1anXhNh2PBokEYtwYM8Etm3zJ3/1BvNLGaq1GqYZpm4Y5AolFEUiW8ijaRpTc4tML2TwB/x0phJ0puLcujfHG+99zJbxEaLhEJeu3ebHb32Eris8fXQvjm3z8bnr/OSdk3ytUMLnOIigl4vs1wkGdUzLQDFlopEQtu2wli0QDQcZ6O1ClmUuXLnFv/nTvyGViAHw4akLJBJRhkeGhMSPqrGcyXLp+l06OxKEQgGu3pykVjfYvnWEbK7An37v59y+N0coGGB2YYXrV2/zuOdhbk3EiRj1f/N/4OylG7zzwRkM08Sn63SmEzz1+D6S0QhvvH2CN98/jWGYIMuUK3XSyRj/w71lemRJ5E46LtQMTJ/KmfNXefP908wuZKjWGvzur7/EC08LbdAfv/kBH566yGBfJzu2DDO7kCEc8vPVF49wYPcEPp+P1bUcl2/cZXFpVeiadqUZGxuhr7uHer3O9duTLCxmqNWFqoCmyGwbH6KrM8WN29Pki2UiQR893Z30dacJh4L09XTR2dlBNpunWKxgmhad6SRSJNQyO5SqEIs92AZKmwDMgwDThpSNNsPdHnp8wO/+luz7r9weSMHU1jZDmM9TwbcRBnyJV0By2zvkgd0HByAd18W0beoNg3rdi8R8eT3Z0D4Xz9Z6SE9cYsdxRb6NI7XIHttjsc3Qoyy135LPglXvH5Sud3wpEoJSBada45N7M/zlj95mbmGFqOeRWMzkiESC/EfffplnnzxEqVzhz7//Fm+8+zH1hoHjOFy/eZc//L1vMDo4yPTcAh+eusDcUoZUIkpy3w6ecrzQnmXhenIU1VqDaCSEYEpW1pOhJQnHBdOyiUUjgsndcTEti5n5RX74sw+4dmsKTdM4enAnLzx9mGAwgI3NzMIiF67cZue2IZ5/+jDTs4v8zS9OcPL0BZ5+bA/FUhmzCfwAw3bF5GKZGKZFwzAJBQOoisyde7P8mz/5EblCiX/4d7/JzokxJFkiEgmya/sYTz22m6GBboqlKrVGHcu2CQb9jA8PcO3mPd47cY4j+zM4rkuhUObkmctkVvN889VnCQR0MplVggEf8ViYSDhItlCkUqsx1N+BK9n88vQF5pcy/P7vvsbjB3dzd2qWf/Xvfsi5yzd4/PAOhof6iMUiaLrOxNYOfu1rz4iJ92cfsrySpVarE4u4lMpV8sUymqoQjYTYOtLP2GAPyWiQYrHMwtIqTx89wDdfeYYzF64R8IRCJW8ibEbm/T6NjkiU/f0dvPryU8iyzF/96BfcujvLUmYNJIfZ+WUq1Tq/82svcurMFc5euk0ms4qqODiORWY1R65Q4sVnjnB4/078Ph+OY1Ot1omEQ/zWN1/ENC0+PHkel81jFSrVOt/76ftEIyH+4Pe+wdi2UWq1OoViiQtXbiFJLqZtkc3n8fk0/D6N3TvGeWxqB9dv3UXXZKrVKnMLy7iOw94dI4wM9iLLEoN9XSgLp1rHdFyXhmEyPtLH73/ndTo7kuzZuaUlho0rQqe2beNUKuveY0UmXypRrdXZMjLAYwd3MTzYw5ED23E8KaNAwE/A06SUkYhGgnR1JPD7dSG54qkPKNI667rjukQjQqz5tZefJpmIcW96nsmZOTrTcX7ta88wNj6IA5RKFf7FH32P6bsz7LK98S4L3TMX15sIDeq1Oo16HRkHRRJi7JZlUSxVyawWUDUh9+S4LtsnRtm1fZjb9+aQZBdVg96eJLFIAL9fJRIO0N/XQTwaIuj3oXqkZIlYhHQygqYpuLj4/Qqjgz2Mzy3y0tMHOXpoF59cuM7//Ec/4PrtGZZWMtiOwbnLN7Bti6+/9BTf+NozlMsVqrUGp85eo1xZzyWVAD0UYGy4j97uNK7jMjLUzWq2QFdHnK+98AR7d21FViSCQR9HDmwnkYiTyxWYnluiUmvQpHfp6kxxaN8OVrN5/D6dUMDPYF8X4XCQ3p5OlldWeezADl565girazneePdj7FpdFA1AqwjBBSZnl/nu99/h1Nlr4ApS1FgkhN+vc2T/dkqVCrlCmbHhPkaH+7l28x63p+YpV6rroSAJKFcxpABvvX+aTy7c4NWXnuT25CwfnDzPru3jxGIROjpS2I5IfRgZ6qVSq3P1xj0Gb3Qw2NeFqqr87BcnOP7xJVxcVteKOMCuiVH+0z/8HWYXVvgX//ovmVtcaeU+OrbN+HAvHak4129Pky9W6OpI8NrLx3jq6AFwoVDMcOPOPS5fv8u96UU600m+9fUX2BX0tzJY3Wp93TmzIXT3ELv1EHDRCrPeR7b5CFP+hdjKH+Cx+TKbF5nZHJ1aP3w7+PTiXn8bXWk/9gM9di60UpIe9P3GvjVtBYBlOVTrDQ8fPBpc/irtC5OabqQSQMRtHe8EvPJukfjeNH9Nf6rywP1t2BnrKchiwnXEihlwwwEk16WRLfDjNz7g43PX0TWVUrlKoVShVjfYPTFCOBhAliTWsgWu3pjEskzi0RDLq3mm55bIrGTYMtSHY5nIksu+HeN8+7UX2D08gHrysuiHaYMuGGZjkSCVao1KpdYCket8IxKWAzguquyiSJDNFXjvl2c4c/E6e3aM4zgO56/cJB4LsXf3BD6fRl93Bx3pOM8cPcCvf/Mr3Lx5l48+ucziyipLK8v4fBpjIz3snBhmenaJhmEy0N/D4wd3kCuU+darz3L00B40TaHeaNDVkWAps0q2UMSRXELhAJ3pON967RmOPb4PnyrkBwZ6O1ldyxMJBRkc6OXQ3u2cvXSdYrlMo2GwnFljfnGF0eE+jh7eSzwRxqxXW7xGQwM9DPV3ks0VsRyblcwaswtL7N89zsG9E2iaSiIeZfeOUd47eR7bcQgFA0TCITrTcXZtH+PwgZ2srOY4e+G6CH2qCrIC6WSMpx/by/BgL52dSVLxKF0dKUJBPwf2TLBz6wh/9TfvUSpXeeLwLrrSCSHd0l6h43lfbVnCF/DR05VmdLiPzNoquXyBfKlMtW6ybcswe/fvwB8Mcu32tHDhyxLgEAr46elOk8kV2LtzKx3pZEulYOfEGN2dKaZnF3n7g9PML2awbMGZ4ziOR8HhUqnWWFw2OfDCVtKJGFaliiZJpOJRXNehWKnQLYPPp2EYFpV6nVAwTCIeJhoNo/k0qoZJoVRFVRUO7N3O119+msG+TnRNxff+ecFLJeFVzEGuWKZQLLFlfIjujhS6T0OG1kRimBZ2odJa9riyjGFamJZJrVHDsBrUjTr1eoNq3SASbU600NT/s2yHUrmKpimEgv51jT9FxvIqGTVVxe/ThSi0qnphWZNarU4qESESDmDWG8iyTDwWEUoDlSq2I8CWCMWL2+k4Ih+sUq1TrdY2zOWVWoPp2UVmFzJ0pGIeF529QVKmmQDr0zWQZGzHwXZMLMvEdh2RBC+LStp6w8AwLXw+HUWWBQj2a9QNg4ZhoKkq6WQMTVUwTJNqrUo2D9dvT6NqKv0DPQiSC4loNIRlezlErU5LKH6doVSUnduGOX/pFjfuTLO6lmfHtiH27hpD90nICuzZs4V0V5I/+6s3+eTidVRVEeeAANc+n4/BgR7SiRjnL98ks5bDcRwmtowQi4SIRcOkkwnOXbrBx+eu0jAMGuUqtqfs0J4vM7+YIV+ssG/3FhKxMJnVHJZlo+saSBKaphANB9gy1s+rLx2jpytFNl/AzhQ38paVqyjJKC89f5TnnjnM6HA/3/3eW9y9N8darkA0HieZiBMI6MSiQsw6nUyQL5YpV2rk8iXuzSzw0enLbB3t58Xnj/LTtz7ivV+eo1iuUrdMiuUaS5ksruvy8nOP0dWZ4oc//YArN6fo6Uxy5MAOfJrGlRuTXL15j9GRQXK5Ir94/xSnL1wnX6yIvE+fj2DIz4ii06rBLFXZDKw+u+ltpovQ4tFi8942A4V2MPdpVYj/IbSHAK4W5HMfcM6PaO0V4Z/6G9d9xLXwwFYb9txQGEYbaGx9IprQM7VaQtTtzsgv88p/cbDVfG0ieMdZJ4/zXI8b1wRu2y8fcAobwJsLzYo2T0i49W04KC6r4xLGZceWASbGBzFNiw9OXSQSCvDCsf3s2CJc7flCCcuyePzATnbuGMc0DAZ6Oti7cwuS5OI4Noos092ZYmJ8hHRXWpyH43hSPQEURcan60zPLXP73iw7to2i6N6kbTk4rkTDtMgVCrjYdKUSTM0scOHKLbZvGebVl54gEolw49Y9wKVQqoAkEQwKMd5KvUG1Use0XDRdpVY3KFdrgrdJVfD7dAzTol5vYAR9NAwTCZFTVK2XCUg+4vEQQ4Od3JsXuQemaWHZDqZpkcsXmVtYxjAMGjVBxeDzaeg+Bcc2qdRqGKbIXapUa2RzRVZWc6SScar1BuaqheQYSJIkRJ9lwawdi4YIBfyUylUqlTq6roIkXLKu6xII6Az2ddKRTqBqKtVanXyhTL0uKtZURegBqoqComsYpsn0/BKzCys8/cQB9h3eg2I7WJ6LNxGP8utff44//f5b/OjND8Gx6UwJrUBZaeZsCY+N47o0bEfE4x0bTZOFF9AjiUSSicXjrOULvPn2SU6euYqmaThINCwHFIVQKIhtORRKIjdLlkXlY7FU5tylG3xw4hyXr08SCQWQFRXHcTAtG61Z5aYqOK7E1Nwy+VKRQFDFsWVMy6JQqrC4kmViYoxoJMzk9DyZ1Rxqt+DJ0nVNVP1pMv6Aju24TM0ucvPOFC4usXCIQXed40tRRE7ZvZlF/vm//C5HD+7kicO7GRsZoK+7o5UI2mgYGIbdet5cRcH2ClZuTc5x+sJ1guEw96ZmsWyH3Tu3YpgWlm1TrlQxAdNxqdYa6JrQypNlGV0TFX2WJchHdV1D05oalQIwaaqC7hPjoFKt093XjWUYZHMFKtU6muajRSLrzSPNohLXFWHBUkmEf7xUIrLZPFNe9emu7aMM9HV5AukWqqf1Z5gWq2t5ytU6DdOiUKys509agrzBRqJu2awVKpSqDUKhQGuCtl0xXkzTplSpklnLU2+YJD2B53pdAGLDtCgUSjQMk0qtQbVutoS6pbrZmv0sTSMSjbB1bJAzF2/yg58dJ5WM8e3XniUYCiDJCoYtPK9vvH2CxeVVXFd4fXWfgu1YaAgNw0gkTDDg5+3jZ7l6c4qnHt9LZ0cSRVGZnJrlez95j+MfX6RQrFBrGJTzZVEp6SIqsDyv/HImi+04HHt8Hy+/cJRcNke5WCYej+DYDrbrguRi2RbVehW/TyEQ8Il71CpAcZHKFVRVZcf2ca7fvM2f/tUbvPfReZLxKNlihXi+SKVaxfHK7PPFEvliiUbDoFAqMzO3xLlLtyhXqvT0dIr0i1ScUDhIPB5B1TTSHSkikRC2ZfH4wd0MDfYyO7fMhyfO89j+XfzDv/9tKuUijb/8GWv5MpnVPDNzy3x8/gbxWITx0UEyazlm5pZZWF6lOthPRBKhJqlcfYCdkx6YtN+0f+sbrlfVP9Af9tDqZrxy8Ae0tuKjz9TaF51fRpPwPEUtl9BnCit+gYN8hs0e4tGSbZHG1PrI3fg9m8OK7ia4IYrHnDY1CFkW+b2fxiP2edoXBlvrSPbBF6ml/feZm8sGfSPXacVem5MegBsOIiO8Aa8f3cP5zBr5YpXJqXlM0+LxAzt46rG9BP0+KpUKK6tZKrUGqUSUHdtG6OqI0ZNOo0lQrdawbJOGYVIqV8jmc8TjIQLhIHKh1EqQV2SZVDLKjbsznPjkMgf2bGN0qJ9GwyBfEontlUqVtz84TcOo85vffIlytU613iAeC7OaLWDYkEonWVtdY2p2kVAwgOUBomqtQblSY2U1S61uoKiCC8ZGwjBFMr1l2ZTKFTRNpWGYuEC1UfdyZgRLvu7zicnTtKgbBqVSBcuycYFa3WAtmyeTKTC7uCoMkmnSMBtUa1VMSxiIbKFMwzQxLZvrt6c5ffYKfT1pbKuB49j0dKVFyMibHGr1BooiEw4HWFpeI7OWw6f7qdYaqKrG6HA/qWQcx4uL1xoG1XqDhmHSMAwMw8RRHQzTxnYcGg2TO1PzfO8n75IvlOhIJ4jHY9QbBm+//zHjo4P84z/8Lf7l//rXzC2uUKnUKFeqWLZNj+ugtE1+lixTLFfIrK0xvxjk9uQstbrBQF8nkUiID06c5efvfkyxVKFcqeH36RTLVZYzOUL+EIqieH3ywKEMhVKFn79zguOnLjA20s+TR3Yzt5ihVKlgWha+1urWJRwKsmdigMvX7vC9n77H808fpr+3i3K9TskbH5qq0tGR4tzFm5w6c5nuriUuXRO5UKZpkkhE2TExwqVrd7l0/S6ZtTzBgI/t24b5L0yTAMJIqKpKzK/R35Pk7r15fvL2SS5du8sLTx3k2994iXA4jGFZ1BoGtWp9fWrzuKT6ejs5d+kmf/b9X3Dq7FUMw+TrXvjVcRxSiSgfnhTJ2JIkcePuDDIwt7DM7u3jqKrwcgW8sGLA7yPg91EslanXG/h0jXgswvbxYX7y9gl++Mb7/FboFUrFMu/88izBgI/hjg60hbXWClYsqhxqNZHTWK01WFheZS1bIBIJUKs1uHL9Dmu5Ii88fYDf/MYLdHelBTAsV6nVDRwX7k4vsbiaJxAKIssyx09fYfu2UQH+i1V0XRcM7ZZFsVShWqtTrZtUGxaWKzQS04kYNcPkzOXbfHT6EuVancFghxCO9vvoSMe5N7PEpWt36exMcXdyjqs374mii6qNVGm0fPa2qiLJCumONIFAgCs37pFOJwhHwjiSgoOovP3rH7/DG+98zIHdW+hMJ5idX6HRMKg36vh1wW3n03XhBdXEVN7TmRJh/nyBv3nrl/zgjQ9pGGJx5SIRUFVxD5Hwa6pYlDSEtFDAr3P33iy1+gGGhvqwG6IoYXklKwo7bFcw+VsivCs4hT3G9mbz5rG/+MFbfHz+CgG/zxONdljOZAmHAlSqdWxH2AjTEsUdlmVTqdapePNZvWEyOTVPMhHDdl3isQjJRIxcvkitZiDLMqbjUq6KSENXR5KA30coGECTFeoNE9t1URSFlUyOKzfu4vPpfPv15zl8YDcrmTVu3LpHT1ea8FpxHSQ0wVZ7lRz3gwiRUtMql93wXXtgz+HBPE1u+9bNvOYH2cumN+fTwNMj8sF+lbZe9CYWQNIjvUu/yoFkmuTAn9KhtjdNp8wmcNXKKWc9p6P9e5pJ/eIv2StKcKFV1Cbsqd6yc19G+xWFqNsGyAb+jzbqfMkbam6zsPIR6P6h33rbuK4oI0bkn2h1g9nFDFdvTrOwnCMaDvLEoV10d6QolitkCxUq1Rq27XDm4k1UXaOvJ8XWkSFG+rs8Q+JiOTa2Y4Pk4Lo2TsiPVCiBZSHU6VVeffEJiuUq129P84OffcA3Xn6KUDDA0lqeumGyuLTK8Y8v0pGKsprN4fPr+HWNucUVxkYGsdwid+/NcPfeDLFojG3jQ1i2jeOIkMzKapaLV2+zlisRjQSp1hokYtFWLlK5WuPOvTl2+Xz4/H5qhsVSJsdiJkcsFiEWCa97WSUJ0zAplqtUanWWMzkG+rpwXRdFFQK1y2t5Lt+YxHYlrt+dIVesUizXqNQahMJhfH4/Zy/dJFco09uTIhoOMD7ci+7zEwgEcJEolqvkCmWGB3s4vH8Hx09e4PL1ewwPDjC3kGFxOceWsX6i4SDVhkmxUqdhWOSLFe5OLzIzu8DM/AqpRJRqtUYoGGB8bJDbU/McP32Fc5fvsHfXON9+/UVUVeX0hessZbIcObCLaDjEyEAX4VCAqzfu8tO3T/CfL2XoMS00byBZsszcwgp/8YN3CIf8rOWKbBsf5OiRvfj8fk6ducqde/NsHRsgX6ywupbnxOkr9PX0sHfnNkKhALIsUfM06SRJxjBMsvliK2RdN4TncHFpBXBbFV6O66LpGt989XlyhRJvvneayelFHju0k7nFjCe7JKHrGqMj/XR1Jjl76SYDfQXq9YbgH3Ndgn4/+3ZtZXp2kROnr2CYBrICkbDfq7ISYVNVlYnHwvyd3/oq7x0/x6Vrd8kWy4SjETTd1/JmjA72Ers13ZqMZGB7Is43ju6Hap2bs4vcuDPNvl1b2To2hM+n09vTyeMHd/G9n7zH9dtT7NkxRmdHklKxTL5YxjBNAgE/I4O9pBMxJEmiI5VgYssQeB6pWDSM3+fj4P4d3J6a5/0T55ldWMY0LdayBX77my+ydXoFFtfE3CiJHFDbdlhaESGjfKHMhycuMDu3jKop1GoGlWqNg3u38dJzjzE00Auug2manL14gzMXblIoVrl6Y4rVtTwH921nfHSAj05f4dL1u6STceEhcVwqtTqxWFQYacel4IVjmzyaluNw8dptypUaiytZkvEQgwNdKKpKLBrm2ON7KFdqXLo2ycpageWMCOk98+QBou+dQ1rOrs9jmgjNpZIxujqSrKzmOLhvgo500runiOerUqfesJiez1BvNDAti8XlNaq1OtGw4Pdq3p/hgW4ahklnZwpF1SmUKqwVysiKQjoVxnYc8oUya0sZiqUKqqrg1zWadAajw31MTs1z7vIt/vL7bzE61MvIQDepZBTDMClXxNxQKFYwLZtaw6RWF5WLTjswKZXJrOZ4/+R5VrMF9u3eQsBfwnEdMqtZksk4jivSSmzbplCusrxWoFypEYkEiCfiHDq4i6XVPFduTiGrCuVqg4G+LvbvmSBfKDI7mxELIM8m6JpGvdFoVUkapkmt3qBWE4s614V0IoZhNDzaGB/Dg31YpkU4HERrT6tqgq02UNEipd2cHN9mxB8EpgRvnrCF7RJh6xHl9eq9R1YO/i3lEH2e5jZTzDf3s/06fEEQJuHAw07xkSDSCx1uzotrXmS33avlNqfKVtgSRGGPLMme/qlMk25KeOu1NkqfX719cc/WA2LPwpvlSai0XqGp7fdoV2EbaHvE4HLD63IKat3EcaFSq+PTVYYGOtH9Gj9865dk8yUO79vBnp1bqTVMfvbuKd45foZIOMDOrSP85usv0NWRQtV1FEWhWCpTrdVxcXGCARTcdf4YRVA7SJLMX/zobT46fZmp2UWi4SCGaeO4sLi8hizLHD28i0DARygQYmiwm+OnLlOqNFAUhcnpBfx+jacf76UjncQFfD6dazfvkYxHKZcrdKYT5ApFrly7S3dnEtsSnq1cvsxHH19mfGSA3p5OVEXhR28c5+qNSZ46uo+9u7ZSKFVoNMwWH1E0EqRSbfA3b50gmyuwZ8cYrgu7doyRzRf5ydsnOX/lNtl8sXU//X4fvd2dHD20m8XlNe5OL5DJ5tm/exwHifmlNcAlky0wt5Slr6/MjnCYI4N9aLrOYiZHtS5Cswf2bmf/nm0EgkFKtQbxeJQtY4NUqnU+/Ogs2XwR3aczMNCDqmk4jsPY6AC/EQ4zu7BMo24wNtLPYF8Pq9kce3duIeD3USpXOLx/ByMD3S0gemtyltW1PB0+Hzqe3pWuojoKmiYqEp98bC/bt40wNDyAoqqkO5JEIyFcSWJ0pB/LskWI01vBbxkd4JUXnmB0uL8FzB0Hxob7CQT8dHSkkBWFkeFu+no6UC9OCnkmtxXNZP++nSiqxOUr11FUhc6OJPrdWTHUZQlVVejuSvPsk/uRZIWhgX66u+JUa3WGBnoIBHyMjw3yO9/6Ksce3yfCw5ZNT3ca9f2LGyZrn0/nxWcfY2LLGB+eOEexWObA3u2EQkFc12Vii6icC/74XeTT18B2kOeW6fon/4xXXXjJMDFtSxCCnruFfuv/jevTiPh0/gHw7XAMVdIILxcoaQGMVIDA7CrB777JHglGTBd1ZoVw9TRhv84rik4DCE8toK4VkFSVIRt+/7knKNSrZKsNDNmloyvN3l3bCPyrv9owcUsS6KrKay8+yeG92yjVRC6ZaVooikKwx08yEWWwX1x/WZZwbAnbdrAsi8cObOe5YwcAkayv6xrf+NozPH54N5FwkGDAx9hIP7bt0NnVgarp7Nq5FWSZUMDHlrFBfLrG6FAf+WKBak14cS3LIhDws3NijMG+bpDgmScO0t/bzaXrk6xl8/R0d7B31xYO7NuB76MrbXQ1EugaEhCLhnnuqYPs37uNndtGCQX9Lbb6WCzKt15/kXAoSLFUEfdXU9i3eyu+QFBQIziicCYQ8BMOBUin4nR0pFFUkTP5wtNH6OvuIBoNUa8bXLt5j94b9wiV6kLvUZaRXAdN04iEg/T1drDoeQ4T8Qgjw324koJl25TKVeoNg/mlVa7cmmYlk6VhWhiGha2tE4G65SrRaJjxkT5qHg3D2HAfi8tr1I0GgVCA7u4OgoEAF6/eYWklS8MQlaupVJxQMEB/PMqeneNcunoX03IYGe5j58QYPT2dzMwuki3ksWwLxxHcdA3TYn4pg2GZ1E2Dhm1RM0xKlTqGYRCNBDm0bwcfnqzy/kdnaXjSbhev3CIcDvIHe3Yw1sQRm8KI6+DoYU6Ch9W5sU6t8YjE9/9QJHk+vT0i1NfufXtUteYDzvUzBRA3pxi1frGZpqMJru7fo9vqY9umeFJdHiVQq/DNCwNLTTm4L6n9yp4toHURBWJsukWlTZvc/9n9+2kmz9sP2WDdswWg1g1qDZNyVbAqr+aK/ODN40zNLjM62MNjh3fR3dPFV16I40jw7oefoKoqvT2dBMNhkBW6ujo4cmAHkiSh67rI8Qn5aU0fjoukQMDvY/+ebUQjQW5NzjIzt8z03BL1hoHP72fvznF2TIwwMtRNOBwkGolw5NBubtyd487UvCcZIrNnxzg7t48Rj0VIxKP81jdeZC1boLMjwd5dW3nq6AFm5ueJxcIgSfj9Po9uIkytVkeWJLaMDfI733qJU2euUqvVEeXxdXRNJRoNUyiWsSybQ/u205VOEgwF6e1K0WgYZNZyDA/0sP3vjpBZy4mqxoZBqVyhv6eTgN+H36/xlRcOMzjQwdzCCuFQgL6eDlZWcywtr5JIRDm4d4ItY4OkUzFSiSh9PZ30dKaZmV2hWK7S39NFb08nuq55OWI6B/ftoLszRTZXFJ5JVUXTNVLJGMGAH6tRR1EUto0Ps2fnVvw+HU3TME1BaNnX08Gu7eOkkwkc16FWqYDrsGv7GF978Sjy9ZnWeJRlCdXvY6gjwrdfe45D+3cSCPiQJRlbFjxar778NDsnxrAdh3QyTrFQIhIK0tXRgaLI7Ng6xq6J8ZZeomXb+HSNPTu38NTRAyQTMVxFodaoiuT57/6i9TjIioyEeJB379rCyEAnPp9OIBjg2o1J8aRIYkz4NI2hgR4O1kxSiRSjI70oigBizfDQ8GAvfT0dZHNFFpczgkxXltefKG+C8fk0+nrSHDmwk+nZReLRCLqmgiQJbqFQACWZoEXm6v2TJQl/wIfPFQSlru1AodTad0CWCXreNndqgbS3uGp6yHxApPkYe6++9lwJ78MBFwYQnun1FShCdNpqe+69c1M1lSMHdmE7FnWjTrlmYDsOPk1DURQM0xCbexOlLMv4fTrHHt/Hs8cOChkvV3inZUVmZKiPoYEeYeBdl8H+HhQZcGRsB7ZvGWZsuB9dVfDrIg9veLCbnu54q2uGaSJJEqFwGFeSMQyDcCjAU0f3cejATnKFCoqqkErG0Zo5gu0GQFW8HDaV/XtEQYntJdJLTYJjCbZvG2Wgv5tsrsD8vGA9n9g6QjDox3Ed6vU6q6tZJqcXyBXK7OnvIRjweyFBH0cO7OLg3h24rkO1VuepoweIv3OK5F//QhD9yhKSI7V0QCe2DLNz2yi6rhEOB/H7fTi2QzDg57GDOxgc6CYZjzI6OsjwYA/dXWk6V36MWm0ANXGTK3WS6TjfePUZtowPisVhbydXr91D03XSyQQdqRTfePVZbt+exO/T6e/tIpWMEYsJpYDlTJbZ+RUi0TDpVIJwKMharshaoUIiFubY4/uwbZu79+YIBgOUKnXqdYNYNIwkS5geGO5IxVlaWQPJZWykl9XcKG+8c5I/++uftyppD+/fIXgam61Uac0hG9oGDPHZPU0PAlP/ocOrz3N+bT96sGfrU7xeTQ+gQA6PUH+5rz0IaLGuNrOpNUm+m82RvPCuJCPLCqoqnC7e1vxt3KUvJYzYys+SlC/uStyc2PcwD1c46G3msjazwNzCCqGgn45UjHhMhCr6utPs2bWF7u40Ljb+gMZLzx1hfLgHKV9mLJEgMbmAUiyzdW6JgZpNuGEQ/su3UYol5MXV9W7YDq4mJiSfpjHU38PQQC+yJAnun7qBI8mEAn4URcG0TDRNRVM1tm8d4zvffoVcvkQw4Mfn0wVRogSmZdGRSvDssUOYpiBoVDUVVZHZtnWASqUqtkkn+OpAD6VylanpeaLRMLqucnDvdkaG+iiWKiTiIoyYeDbKof07WjkzSDA00EN/bxeGYXL52m10TeTOTGwZxadrIj+lVhfJ3aZFw6hjWjrJRIyjR/bgeOKcq2s5kXMRDjIy1MdAbxeaqlFvmIRDQQK+ILIkMz4aoFarEwz40VTFu5UyiqQTCrqMDvczOiK1gIILOLZgN7clCde1qRs1DMPAsnUi4SCOCw1D0F2oHiWEaVm4liCElSWIhkP4fTpKW9mvLQuQ3NWZIpmMiao2r2pQUmQGB3oY6Oum3jDAcdFltVX5pqkquiYSJJsVcdg2gYCfSCREwOdD1VQkRZCkWrZHnthceDiexiA2mqYSi0VQFRnLFn1ojnlZEgLWiqIQDIicJ7/Pj6p50j2ui2vZVKtVllbzgp28YRCPRVqASTIt1NllpJUs8t/771BMk7Fag1HXJRZ9E9UDbIrroomsZCGH4jrg9+Ee2w+GhWtYUDdwanWchoFkmDj1Bm69gWqvV1k+cDKWNrzcB7Kan6+vudqBmLTOus/GDeV8icj/9v+M67hEHJukp9HaVD1wPI9RU0x8UzbMAxIXmudAC2Q2+/tIGyM1fyvuSbMwQYQZRZ9URSEmSXR6AtWKN8blbGFdhLlu4PsXf+7J/jkeXQY4phjP9rOH4cAEUsCHI0EoGECSJKrlMqumKBTBsSiWq1y5fpcz529w+dpdQXWSiq93V5bQZRVJF10PBfx0pBLI4VCLSqa10s8VGfmv/4VYEEpy68KJSIxDp+2w1Vvty7KMrMhe4YLIiVJ9euu4SqVKUFcYHxlA0zWikTA9nR1EQlHWsnlkSSaZiPOV545yaM9WZEkiEY+iqiqGWcMwhUdqaSUrvOcLK95i0kBWZL720pP89rdepb+3l5WVNcKhINVKha9/5RjVWp3e7g5i0TDRSJhXv3KMW3enSSVjpFMJnn/6CPFYhOm5JeLRMEMDPWzfOkqPpq2brXLVs2Wb7Jjj4rY7qFpVdw/wojxiGP2H3Fp50W2VlG4LeHyGs/qseWXwYDC7+ecP3V8zRam14abXh+26fTbw5mnXBVeM++Yz3XIbSV8u5PpCYKt94nSRBNtzc7J7FLr9XEe4/zeSJOO2ViESaU3j4I4xQqbNUDhEl6aScCDmuARvLyCfu4OTLeCsFUhVaoy35d7IXkJc0HZak2UzTwbXXa9CcIS2WuB//GMcWUaxbVxJQvXpxBXZK/NGVE8pXgxeEaLOyBKHDZO6aaL7fPgCfkx7jlypgmFZBOMRAokYYUXBkQQfrOnYqLaF3zTxyRCJRQiFQkQlSNngLKziLmcxbZuQ65IMBAjJVdSGTUKR6Q0EsF1XJMcXK8haHVNWMGybkGXTFwnSFQrgc110QPL5UGVZVCpVqpQrVTRNIxgICge5KxKUC8UyuqbSOdTHYF83qUQSVVFbt7ZJ8qprLqapYto2uuuiALKsIOMgOQK4uI6Da4uKONcbN5IsgayAa2FZJpbdQFYcwuEAiiJ5peJhDywhqAX8OrZlksnmMS2bZCKGWiyD62JZNoVanXpdp143xATSFnJrVhY6jitYsj3mc8dxUGS5ZVQkScJ1XDxRHlRFwaeJSjtFUZBlGUuSUBH7tL1hYzsOkusgSaJyE0mEjixLHKNFByB5Bsyw6L2zQHAQ5FgUKRVBVsXYsoGGadNoGOi6RiIRIxmPbfBqSbaDZFiwlkeyLAK2g6qoaE5p/Zls5k264Kbi4vN0DOe/+QPvoVZwbAfTsLG9a1MuV7Asi2gkjE+RsWp13JqBYtlCO9Q0kU0byTSRTAvJMJAaDaFX1zDAMES/DBPJ8N6b4j2GCQ0TqWHhGiYYBjS8z5shGMdBXiu0nkvZW0E376X8qIWzN41IzWQNHhLZaEvr2NBcd4NYsuSI6lap9d77zhNLllzBq+Tabe+b99kQ1YjK3DLKH/3o/n56ANx54wREQ+IzXcMJ+An5dSKSRFUCORIi0Jmkbpo4t6dIZHI8E/DTP9bHQGaN5M17aJUqUjSKEtSRQwEkf8ATJAfXWhfOdmUZFyFmruZKrXxPqXlhmsa37aLJzWe11XcZyfMISIBUqQmvtaYSDPjBEaTsPl0XMlWShKpI+FQdJxoRCxtNaGc6joZp2nR0JNkyPkQyW6CzI0k4FGR+IcPHZ69w5dokxRdLdKfjpKNhAc78OvFoGL+uEg768fl8OC74to3RkU4CEItF6PKlGOjtxjTNliMm4BdpB60BUio/ZBiJsK0kSw/0nribf/ElJlZ/mW2D7NgDHpp2oNXemlvev3i57wCf/nnbcaXmXj/L7x7UoeaVb9OefGT32qBFc+Fo297C2F2HlkLMTf6Uk/187VfzbEmCT0b2/rXOQ3pAUtmvkOS3PihciIa9sSyx5dRV/tNaA8cWYElRZM8INokVBemhZdmCXkBThWFvGzqyLCF5S5amDIQbC0E8ils3RHm0BNrpy+CCtsnztj5412Fwa7ICgo6glwAR6gCIWZYw6oqyzkSPd/Mdp5XoKYEw6EpL9lwAhLbvZU/NXmodWFymiOPQ5U0KiiwIGvttwUOiyMq6HqK3Unccl17XwfISuzWfDrKCK4nwUMpx2CWB5tPRdA1Z05AURVQiKYKnCC9UKtk2luuCroOmIakKuiyjSqC7rhjwiiyqWxQZVIUgYLkis892HBzJRfP5CAT9SKrKuGHRb1roU0vooatIqoLmOgQch5Bh0gekHRelIYyaZdvkKjXKAZ1SuSqKA2QFWXYR5HYKruuIimFLaO+ZpqAtaIIox1FQJBcZUYnoyBKOK7W0LCXvGqoS2LKoMGuWiGuqgqSqSJqCIgvhb0mCel1wXVmWTaFYotEwiYSCRDNFkv/2Ry3Qj6pCOo7SkUDuiJNMxQjHIzipOGo4gqIqnsg1MLcihpumQTqO7HlMFFlZzxtpjlJ3vezZlRDj3JHbUixF+FJBwnRcgqEgOA66riIrCrqmof/3/wwpV2ybiNqf9vXKq80esM2ep+bUdl/ysK4hX58E09owycufPtU3D4w38LGG+wSQsW1v4SResV0k2255nCTbFjmatiPKyG2hd/jItsECtRuQ9TnCbQIRy3r0HGgJz56cLUBx3eDL3v413FaYFiRCuPS6TQFxCWlyAemdsyIhuwmImpOCJAkCU8/Dib2uxiE2c8Visbld29huAYfWgmj9fesz1xWfWxas5pHKVUK4dCcTIGv4PM9Xkwk/4NfbzkJwsjUvY6XaYHp6iWy2xJ4d2ziwdwfBYICrN+5w9948hmFg1uteaFjx8h5VwEWXhQ0Q40iCUEAcy3Va+qiqIpQpGoYJiH3ImiYWyrYD5crGMXTffZXWqYEe5lzgwUCmtf2DgMemhPsvx2nx4OY6Lg+jNJBkCdeRW+ZMav7hJY5LrDOsf5len8/XJO+ZE0HIzeD3oaz3zWsqSa0/m4TkhiHSE9oXcrhN6PXltF8xjNgeQpRaD6y7yTP1RborSUIXq7mKBO8BCodb36tISD4dq42iQQAtMVhkBLCQgjpSMgaJKG4yhhOLQCKKE4vgRMPYkRBuPIwbj0E4gCIL1778X/xzpLuz61EPT6PLbUv3b03/Uvt5rsM5oXWltPoMwiujzK+IyX7zQ+W6GwfzAx/mDfGWB14/mc93cxUEkHTb+tlsWtvfm48mbfpQAnxtoLSdqbe951K9sWFCc/2+DVtJdWPDA6NLEtEN23ifA6Hmm1q9dYDE3Ar/2bLKfyLPoZ2+iaqqG2yj+MPd8LdUqdFc8rrh4H1u5ECr89Km85bEtTZMwURdN5DnVmB+BX3vb2zYSdx1+a8Nk//SdlD+X3+N9q9+KPK2DBO83MMNFzPgQ0LcA41NTVFErpMi4+pBSETg738DuSuJ1JVCSqdwPe6qlrbp5jCg2yau64Ikg+xx2/kUCc2vbwibSZKEXChBtnDfvdh0dcUs4LiYlonjiKqx1sKhdfk23hXJczG5nUnP4+S2vm4f9ZunUsl1UWaXWl4mcEFRkGv1jRvKsvinNiFR03vTttf7rMn9z9j6M78uL9QaE4qMG/Tj+H0Ymorl09AUBdXvQ9IUJFkVoNxbyMkrWZQ3fyn67tPBp6171LzXloesrQeKJKE0paocPADw2fJeJIBYGNcTRb9Pn7b13K4LaOO4SI7EBvLIZjMtmFqAxVWUq5OEgFYmlKqCphJWVdAU8V5VCSpy672rqgRlGa1RZ2xyFv9qjtTFW/g+OIMhy8SLJX6nVKEvGKDzzV+iRiPIuoasKWiqWPhJmviHpoCioqsKrreYczUVV5FxZJmG42A5DoFwCE0WYXzCQSiUoVx7oPtTWDS3NT80PVdfTC/xMy4Y2oHZFzlOa1fu+rP0CLzWOpdmXn/bIV0JIcuH4hk+UWG5wUb9CmBwvdDgIUB0Y0+9fjT9/o9qUsubJXnXUzg3vPnQEZx8pmkKp03ruC20+Std+/b2ObQR21ZK7afSbkwliWY4cV0Xqrlq/YI3om3/EkA0gjS7LG6AqopVuKaCqoiVvSutGyFVQfYSY0FCypcgX2rtup3LfsOiotlX24ZYWMTrQby6jqhK8xJamyKn6w9j2368sbMu1Nn83AsveDfXxQVZxvE8FS2D07p8my1N26Bs2+f6921/uO2/aT/RB33n9XXT563QU9NQt9/z+/on/pBbhrK5mtjwIoxIayJBAE+aCzz3fuMiIe7HA1rTUIrr3QbQXBfNRYSxzPWEyiZ43rQX0Qevy1KtsXmDjSP/Uc+fcKmKa2VaGybkJhh1ASwHyRYJ3lj2+gqtKX3lIvr9sIfdsqFhrHenWkP5H/9kHcRIEiSi0JlC6kziphPIHeLV7UxCRxLikQ37d12PvsJxWiCrtdprrfqaE5HseddcHjbR1mt1FpYrNBoG3V0pj86k/Xw2gh6JtnHG+r1tvW7Y+/qizgWUBUG/cZ9Q/X2/aX/nicY7Xnl+MADBAG7Ahxv0r78GA7hBPwT84jXoRwoFxGs4iBQKIoVDyCEfkl+hZlpcunKbS1dv4/fpbBntZ3xkgEg0hqb61kvKJXCu3EF5+yS4Mm5HAue3v4ZrWWDawiNm2WIcGAbURYhWaoh/IhRrgGEJEmbTEmFaywLLK4vfFO5shkVbqwlHeJOBFqCW2q7Pp11DYN3b0+ZlaHpFXNsG20bCe6bcB+3FRXYhbNlsLZboLlegWEGbWkSWJbpUVRTvVBqot+da8277XWzf1+YetqYp10VxmotzBVmWcL/+rLjvhbLHIL/5x+tzntD+lL+AYPSv2H5Fr9bnT+pvTkBtjPCSjPAvu+Jvp81Z8Ct73VyEwGbbSseV1oHPhv1L6/9aXqw2W+JFisR9Ex8KnLVujyRZAlssBh1HKE6I82rOKo8Gpl+kfQ7nh7IRPLUbEGmdFGyDG641F3on+TlvSGuftD2Y8Qjs2YqbK7LeGzZeGMdBahi4jfbH8VOO1f7mAYYYxM2yPTJQw7CQZImgJwt0fxx6/S/Js+6bp4TmZOvKMgYSlVRMsKFvmEge1BPvrw0Apvmm+W59Zdr8f9tYu685jgilObZI6FYVZdP3DsVylWKxTCQSIhmPtkKRG6Y2D7C5jityoDw+qWaycGv8XL0DTUDj13EmhsF1qdcNMpksHat5/LIscnK81Snjgy1c1+S+cR0hi1MqVeio1DcOaO3+MFr7DdggzQAeIBMTq+3XcJ02cClJtFaHCGoJpSXmK208gFvD8Vj/FUVuVQO2t/tuQ7XeAgmuX8N2XEwJGluHiPp0ZMtu5Ti18p2a/1phM2XTOHaFBypbhBv3NnkqmtdIg44Ebkey9eqk40jphABjHQkI+FvXsZWUD7ipGO6f/F9b3mdnk/veBean5/kX/8tfMb+U4X/ze9/g2WOHNo6tNq+a44lYC4oNp1Ws4Hr3uRlCf1CTgMh/9s+QltZQZhaFJyPop/HMoRZIIrQOoqRAADcUwFBlbiwsc/zyTRrAE4/tYee2USFn4yJC3d4Jt7Q3vT7LsizGteyNb0VGksUCbGF+iQ9++QnhUJD9u7aQTibQVQ1VlkRYWVqHmYpfX79uQT/Of/xbrYVKc46QXGHoN09OD5vXWgsK20aybLGQsGyRG2dZIp/OcqhUaty5PUmlWKQ7GSfs86Ei45cUfLjIjgOWJQieLRvXNAUI9PaLBwalH74rkstVFQ7uFAsjq/nPW+w0QWP7e0uAQ9cbCoIgOYisyNiePJamqeia1opabPQ4e3alBSbXQ8atogTHEYVOjgOWjWzbSI4jFmeOi/TvfgjphNhXtSZARFOioN3D1HqGHzGRPqx9FmC2+Tibw5T3LZg/z+Hbw/WP6ktz395Y2zC1eZmrm0Ohzb5+0b7hIkn2A4xw8zo/TG9RYt1l4iLQEw+4NeLcBUGr2zo18at1PNNcSErS+jzXOqcvAVh//jCiI4PiGZz70P1GAAYb8Nlnb203sx1w4brCa+XF5ZFlERpkfUXctASbD7kR5KwPvI293vSbTQPHdRwK+SLLhSL1hkEiFsGfiLQSRJt7at3vlvt5/evW9VhYaX1W60xyq1DkL4/u4D/5/d8UgMtDFQ7rN319xS8hyQ6uJACc3DSAkoSXqo0irwM92xHSH8sra4yPDRGOhFtrA1yXldUs5y/dYGZukXAowBNH9jLY39MqGGjq/P34zQ/54Zsf8sKxQ/z9332NeDyCJMmItGt5/Qo0AZ/jYHiVjj6PxkFurpp+77+ET66I8zowgfMn/xTHcZmZmuP/+a++yx++eZqtsQjhfAmprwvScdw/+b+19i3un4XrOty6fIt/+W9/yMF92/l733kdv1/1gIsErsxmks0AAQAASURBVJCMEcBqHaDZtkg4N00TGWE4I0/+R1CrY/t03v2f/ys+uXCdy9fvEouG2LtjK5ZlMTW3iGlaPHF4Ny89+zipjqQIC7UmSJGPNzM9z7/59z+itzPFP/i914W0iQS4Il8MEO56yRsd//n/Ha7cAdfFDAYozCyQVRRW/ts/5MjBXegBHUlqVgiJFZnkusIw5MuQyUImh5spwGoeOZODlSxk8m0hv+ZdbxvXpgkLK0jN8QiorYnI2zwcgs6kMEidSaTFDNQbSLGINxfK4HqGuXl/vJzCYNBPf08H4JLwAPoDE3PbQGD71+30EkCrsKW9tebEcBCZNQCc7jRuKkbtP/7N1n7a99F8Pze7wF+fPMfp8zfo70mzc9sQMg6qJPIVHSQPcK33pwW2JGmTh0MURDiOw+27M8wvrfJrX3uG/bt3eMeUURW5lTvtNs2ebz1ALLl4ocHWHdhwkp+WRtbyzEoqSF4unrYeXmydB2C7Dhc/Oc/ZQo5d20cJbhtB03yAKsCqrrakeFw23Yt24y3JuFMLSHdmQJZx//t/1ArXtl931ztnbwCvizQ7Dm65ipMtYuXLOPkiUiaHs5qDcgWnYWBW6tjVGkrNQDYM3HoDqVIXC7aGse7h8PZN09vaDKE1QTyukB5yXHTbppnjtqGVKxCNrr/fZGwfGaX5oqCjHWB92jafc//rC6RPAwxNgLO52q+9eYLOzf58CR4tV1oPS284pDDqINnC2+88cmmBgDObKSG872Rp3SZIEkjrRXGKJHL9ZE9fdt3k/qrntrF9sZyttoRySV4frKKjD7ogbcgSHn2DHnAD271mG1oiivvv/un6itpzG657kNqnFvGdLEk4rts2abncP/mvW5umX8h1HUzT4dqFq/w//r9/ztTsEv/o773GN7/2jEhyRxWAQ5JQwCMHFoZVAmRFsNU2Dxv4w/8OKVtAXsigKDKqolCuVHFsB80b9GJoeGz8kosjSU31Lc9z5SJ7S1ipichdT0ajdV7iiPlCias3J0nGoySi4dalNE2bqek57tybIeD3MzzYK7xWbUZFkkR1UTQSQpFlavUGpmWIhwCQUDY8zI4XflJkmUDAj2U1Q2niPDZfbYlm0rlLJBwkHgm1nRfCAknufbffBSRFprc7zehgt5C4sW38aHiuAFpTvHdtkBDeaVlC0lUUzzg2BZSbdq4jnWSwv4v3T5ynXK3zzVd6BN2CLHHj9hSO47Z5aFpwn+bKSFMVcFwsw0ZyZBxbEjRykro+HzfjzEhIS6vemI6guKLkX40E0Xs7cWwH13JxZSHIXjdsKtUaZqNBNBwkEI+gJKO4EyO4eGH8FkBwwbRw13KwnMddLiBl1iCTRVoRAI2VNag1NiwKaDsjyhWkcgUm58QXc8vCO9GiGnBxbBvTFKFTVVXWvUAIrqxIOEgoFPC8VtZ6Dl0z/CQGGk1aBsfzaK3fa/E8yNLDTYYbXudLkpw2r6U3qW7wxnuLEV3XGBnqpbszxZbRAbaODghNNKn5hLUvIEFqH4vN7HW885UFb0+9YTC3mKFeNwgG/IKJWtoYntuwAFfbPH2b8uk2Nq9P7qacKbf5pHj7dyUhh4PjHbd5xLY9KRJra1k+OHGOWDTMgX3bCQf8gIzrrBeBtN8Dx6PdaPatHRRKTf5Dx4HLt4XXqlKDSg2pUhW5UOUqcrkqPGDeP7csvpccR+TYej0NuN4934Quxb30jCKSl+Omc39rPltikdzUmzVNFwsXn0+jo9YQnriBbtg3IehQPL3L+3e3afH/QA/KI9rn9I5sSMFYh+Wtc/pygM6D2gPy8ZqtCX5onotMy5vU2uZT+rX5GkjCJojF+vo9ax5uvS/eMe/b9SYb39QW2vhjcEXlbfvRWzmostwqinpgP78ErxZ8DrC1sWbIe23d9yZavL/ctTnxt373BQdIy1PWHgvzJhHXtj3WhbbjeOjb8YyP5A0Sx10PTQAtCoCNx/F8Xy3g5iBJwoj0dXfQkYqxsLxGPBpGU72b5Da3lVor143o2DOqGz6j1QfBzSV7E+QG7Nga5LIkVtq2LXi5HNdC01R8uuaBHQVZVjau4gFFlhge6MGnayQS6yu25mDr6+kklYyTiEWIRUMENBXbbZsfXFEVGQ4F8HsTW3NR6kqA42IYhkiC1jUheuyKSk9FlkBVsS0Lw2yIqrYHGMzme0WW8Pu0lrHb2NoFRIXRAxm/30cqEaVYrWO35XWJVXTzIW7PPVhP9hYrmvX3SOJ+DA/0oCgS3Z2naRgm4yMDDPR1092ZZvf2cbq7UoTCQVzHwWgIDT5NU0X+IAqarKJrOpbtkM8XqTdETDuRiBOJRpBUGdc2KRaKVMt10gsZAVA7kijZArquYUgy84sZVFUlEg5Qq1cplMpMzi5z884MuVyeY0f2cOTQbqLhIJIi0zAsFpdWCQb8pJIxVEUCTcZOxSj7dCrJONaWfiLhILFYRJCBAk6ljrmYoTw5izG7SKBcJVwzcJZXMeaWUfMldI8So9U0FdsTOp+eXWB5ZU3IDw310dPVIc7BsDwi1jUuXLnF7PwysiQxPNTHYH8Puk9UcFqmSaMhqn8r5QqlcpVkPIoky2RzBeYWlnFdl56uNOlUwmNaF8A+my9RrtTowyXeHCAegLMsm3qjQcDvo8mF5fPprZBkZzrJV5876glV16g1BOu5LsvYjk2pZrSqWdOpKH7/OnCq1mqUyhX8up+OdEoU1sgylUqd5ZUchulgmuIhkiUJ23E8MfYStVqdQMBPPBrBL0m0Sj8cIXwtOQ61eoNCsUStbhDw+0nGE/h9PmE0XEHeWa3WhJ6qIhMOBb1zg2KpRDZXaBHy2o6F36eSTETRdR0kmemZJa7enOLZJ/YRCoVRJSFMLXk8Wk3wLMsSpmlRrdVbpLGqqghiSFnMrXIw2PR3IP03/9MDDdQ65msaTjEnbsYtzZx/yZXWH19vBxIe0NI0AY7CQQgFxGvzXyggClxCPtygj0ytxvErN7mdyWLqKpHOFF8Lhen6i1+AJOP+9itIv/FyG3j+DLQNj7K/XxgEtcdbNgKP9WvkCDvr8rcIuB7RWiswybtRiujTZjD5sH49BHQ27W7TI86Dqh4fdr5tYwlJ8sh613/YdJjQXPA3fSlNAC1LwuO8eTHvSl/q5f3MYOs+XOg+arQ9on2uAbIJzcMGIOE6LvNzCywsrdLTlaYrnULT/N4K1BOWtIXAsSyJCkPbNqlbNo7roMgKuq6t65G5LqbHgyQhAIaiKriuheTYSJImkjR9gjOpKVq5zi8mNR1obQPKaZ2C7YgvlU3PsuyVLCuyhKQ4VOs16nUhzqprOsGAD1UVE+FaNs/la3eYnlsiENDYvX2UbVuG8ek+gd69wzoeJ5CuiAkxGgkhyzKr2Ty2ZdORTgCgqAq9PZ0YholhGBi1OorrR1F9ovrScajV6+SLFQrFCvWGIbiuWvkDDvl8ibMXbzCzsMKuHVvYtX0cv64Bgtwxmy1xe3KGUrnIrh3j9HZ3rK9MEUavUChRLpWZX1gmly9tSJJuzjuNukGxVCGXL6Io0N+TQg8GWoahWChRrzUgGhLG1HIolYRQtU+VicciqLrIj7Esi4WlDCuZLNFIiFQyTpB1OKxpKqqq4tNFtavseR4bhkFXR5Le7g5wYXl5lZu3p8gXSoyPDjA2Mog/FEBVNYLBALfvzfHWe6colSvML6/Q3Zni5eeeYHxLP/l8kZ+8eZz8lbv8ZjaP36cTTEXR1wRvWCab56NPLpFIRtB0uHpjktPnryHJCqoic/naXWRgqL+byPgg1WqNDz78hLc/+IR4LMrrXz3GHo84spAvcvrcNS5dvUehWGbH1hFeevZxOjtTAnwG/ZTTcd6/fpOPZ2bYsW2EV77yDKVKnT/+8x8TDQf5Oy8/S8f/9O+R7gm5IVdVmLw3yy8++JjZeUEuHAr6mVtY4fFDuxkbHhCM3qbF9NwyP3nrI8H8X2swPNjDa185xlNPHcF1HD785Rlu3p1G133MLyyTTsb49msvsJYr8LO3P+Li1Ts0DIN9O7fw+lefZmigBySZW5PT/Py9U0xOL/D6nXleqNaIIihUXCRuTc7y7oen8ekatuPg9/t44vAetm8bxaep2LLN3OIKP3rjQzJreZ5+fB+JZw4RclSm5xe5MTnHwtIatm3z/FMHmdgyjCwLSaB3j5/hzIXr+HQ/Lz59hCeO7MTv85EvVFlayVGp1ikWy4JTTZJYWlnlxOlLXLhyi0KpQiIW4bGDu9jX18egZytc16FQrDA/v8CZC9e4e2+BSrVOwO/n6OE9vPDUY2iayuz8Ileu3+bm3WnWcgVikTCH9u7g4N7tqKrK8VPn+PDkOW8sC+3AaCTIi08f4vCBnfiVICuZPNlcWbBOOB4NiwTFcpWZuUUWlzKEgn5GhvrAhdmFJep1g2gkTMAvGLej0TCFQgklm6W/YeDTPb+87SB54LM5t1peviuApuuosTCEghh+nYaqYPp1GpqCoWtEutJEutNIkbBQDQkFIRJEjsSQgkGkpjdLEp4+17YwLQvbM9a6pqKoGmChZbNM9HWw16d787eP+KVbLe+mpDfDuPeZ9/ua+ynffz7rvBlQtf12AyARK98maJDaAdeX2TbG0LxDP+ogzW2bStVtXrdP/W1z/03Qtr74F/O+5+N0oWVDP1Of295vOL5no1sGcp1CiWYUqNVvj9lAVpCa+WJfgnPrc3i2vpTjffajrfvp4QGJoY7rUiqV+bMfvMXUzBJ9PR3s3jbO3p1b6e/pwjAtZheWuHpzkoWlDJqmsWV0gLHhHpFXIAlJC0kWnEiuCyurWY6fOs/0zALpZIw9O7cSjYSYnpsn6FPZuXUcn6YI745EK4nXdWVcxyGXL3FncoZoJMzwYC+hUAAkF8mxWVzOcubyLVzHYf/eLWxxNwQokCSJlUyWn799gmy+TKlUZ+toP+Ojg/T39RAL+ckWivzojQ945/hZSpUaR/ZPsGfHFnBl7kzOc+3mpBA4loVeYG9XmiN7J0glopimxelzV3nj3ZM8cXgXr331GKoiUynVuHFnhmy+RLFUxmw0OLR3grGRYSzbYWFhkWs3J5mdX+bi9busrOZaTk3XdTEaDT45d4UPT11kbnGFxcUV0rEQw4N9uLgsLmR464NPWFhepVypUK7W+OoLTyAIPMTDVC5X+IvvvUm+UKTRMJmZWxbyJe66M73RMPjRT9/h6q1pVlbzJGJB/t5vvcyWibHWg1IuV2nUquDGcSybmZklTp+9zp17cyTiEV5+7nGGBvpQZInl5Qw/+fmHLK6staRNXmwfgZLUur/5YoVPzl2hUq2xsJRh59YRujuTVCtlpqbmmJpZYHFljUw2Tywaoc+no8oyHYko5VKczo4k27YM4b+h8e7xc+TyJX7/915DUxWu3ryH8clVQV/ik7A7Erg3pwVlgmmxli9484PE7clZMqs5XnnpCcaH+xkb7sW2HUzLxLZsTp26wAcfnSMeDXNncpYTpy8xPjpAKBSgUKzw8blrnDpznWg4SDoVp+bxFeGFpzVNIxzyMzW3iKxKPG8dBdmhXK2we+co0ZFeQa8BIMuYrsvP3z/Fu8fP8NRj+zj2+D4qlSrnL9/k3MXrpFNJ/IEAHek4W8cGOOKpG5w6e4WPPrlCqVSlf6CXdEeSn797kpPnrhGLRdAVhcP7t+M4Dpev3ead42fo6Uyh60E+uXCd0eFeBvq6MC2L0+eucPXmXXq705g+FcurKJVs4SGeXVzh3RPn6OtOMT7cx5Wbd7l8Y5LfeP0FnjqyBxeXer3BvdlFZucz7N4+Cris5XKcPHOFpdU8juty4/YMqUSUkcFeIuEgK5kcP3/vNEsrWbaMDPD2B6cZH+1nsK8b07SoNwwaDYNKtY5l21imxbmLN/jeT9/nztQCpqeGMLeYwffcUQaaNseyuX1nku9+/y1mF1bYv2sCn8/HR59cwjAtDuyewDAt/vJHb/HJhev4fUIRYmkly/ziCrIiMzrYx60705w+f41gwM+W0QGqtToXrtwW2rDbxtB9PqFraDdDgxLIMpmVVX72zgl++fFl6vUayXiUY4/tZc+Ocep1g08uXGNuIYOmKXR1JHnyyF5m55eZvzPNVzrjDG0bJWtZTK7l6BruZ3z3Vpygn8uzC5y8dpfJtRwNn8ae/Tv4ygtHCYYjnDx9gbPnryAhkSuWKazk2NcZ45t7tzLQ392W5ychSRrrmbASjm1TKpW4dXuSC1dukc0XCYcCbN86wv69O0kkYvh8wm84O7+Mrmv0dncQbZjr1ehq0ww2tfAeYOnajfFD8g6/WGsDXK0F6MP2vQkMfhZP0mdpzf20XIoPO/xDjuHZapETuzGC8MDfbABlD/Nyib24XjRDaua5PnCf7fvwXKEtP80mT5okATYu64wCkhfNaKlJSKCqqojSPELX8vO2L0cbkc+yJmhrnxrXhXWg9eDtHdsml6/xzvFz5EsVbtyd5cbtWVRFaJKd/OQSP3rrOIvLq0QjIeqNBm++f4JDe7fx1JG9pFNxdEXCNVUsBHCoNxrcmZzl5+99zPBAN4N9XWTzRT48eY6J0X4Ge3tIpxIkIiGB5T0PjwTUqhVOnb3Eu8fPIkkSr7xwlGefPIiqqpSKZY6fPM97Jy+gqArVeoURy0ZvG3OyLGNaNiDCaB+dvszFa3f4zq+/RE9nioYhc/P2FNlsnu7OJI25ZYYHehgZ7KdWa/DRqXNcvHaXof4eipUKZy7e5NDeCSZGB0jGhURQoVji/OVbdHfGcCwTSdI4d/Eaf/6jdxkfGSTg1zh78QaO69Lf28VCJsfJ0xfBdRno6yKTzTMzv+Il34uVTHYty9JSht3bx+jrSXP91hSz88v093bjODZXrt/ho1MXOLx/O7snRtF0GdNY59hyHIflTJZrNyc59theEvEIpmUjX7gL3rUVckF5/vLH76JrGqFQgHszC8zMLTG2dUTkSGme1I5l4wKWaVIoFFtz5IUrtxgf7qe7M43fp3Ln7jQ3bk+xZ8cWujpTRNvyfZrNtGxq9QaNhkEuX0TTVBoNA1wHVZawLYtUPMyhPdu4fPMe07OLZLN5OtOJVg5Yb1ea7VtHSCaiDA10spzJcvr8da7euMvRI3uFMLQsEw4FCAX9In/EFd5OVVVaBL2VusHC8qrQfOtMk+5M88IzcRzb9ghYHfw+neefOsTIYC9/9N2fkC8UqVRrBIN+DMMks5rDcWx2bR/lycN7Wiz0jiu8wLquMT42yNBAF5m1Arl8nkbDpLsjKZL0NRlpNScujqLQaBh8cv46qqpy9PBu9u+eoFAssZzJcvPuDFtmFxkZ6iEc8jHc38UzTxygqzPB6HA3SyurTM0tMjUzQ99QN+lUnEbDZNv4EN98+Sni0RB+T2pp+5ZhNFWhXK1RrdcplErYjoVtW5TLJdZyRcaG+9iyf4Jg/qygd3FtJEnCH1AJ+nUeO7Cd1756jMvX7vK9n3zALz74mLHBHnp70vT2JBgd6mY5k8VxLVwcXEmitzvN0FAflmVzZ3KeQqmCaQkqjmKpzGq2gE/XePaJvQQDAWJhsXgL+H2tsCUID/zqWo4LV26xuLxK0K8T8Cg3FFnGrNTW5zjHYXp2kYvX7jI+PMC3X3sRv09nsLcLJJE7+cuPz/P+iXNEoyFe/+oz7Ng6yifnr/Lz909x4pNLdKYTdKQT+HSNgb4ufvP1l6hUa/wv3/0xq2sFytUqsWQMTVM8DU8BXmzb5vrtKd7+4DS5UoW+zhTlSpW7U3PCexoOEQz4mZlfojOd5Mkj/fR0pVlZzfFuJIDyzCF+65tfoVoo8uM//gFbO+N0P7GXO/dm+Zffv8SNuzPe4hSydYPhoV5GRweZmpnn+u1p9u4YZ9fEKB+fvcrJM1cY6u+moyMppMdaZsAGjyAYx6ZULHL67GV+8OYH5PIlQqEAq9k8t+/NkYzHUBWFcxevcPbSDQJ+H5Ztc/zjC7xQNthnCIk01PYq3gcAgOZ9BB4lKv3I1g7WNhxLgAO35b15ONBaz4Js6+OXFUpsAzjr+SttnzdByyMBF+DlGLvN1J1H/aZ5HEfZyMPU+roZ2WgFqB/iddvkBWy/NpLTlr8p08wzFs5CUTQh46LIEmqz4hqhMRvw+xEcmV+OC/FX10aE9ZN2HdbFpL2PvtBA2OxCvT9+20zaxvNQPf34HvZt38quLUNUKzU+Pn+VC1fv8PyxfXztBZGT8dNffMTP3z+DIsu8+PRhopEQtmW1KkFTiQjHjuzi7tQctmUTCQWp1A3SqThjw334/T40TSMRC6MqMqbHBC9JIjypKi7PHdvHh6cucfnaHQ7u3U4iGmJ1TTz8qWSM3dtH6e5KCMROU2tNnFMiFuaJw7sIBv3Yts1f/OhDsjkBGAzTIJ2M8eoLR1ktlDnxySWGBzoIhzTWViskY2FeePIAYyP9vPHuKfy6xo7xQWJRQfmpKzJD/T3Eo2HyuSKO6VAzDU6eucbC8hqPHdwNjk0sGgbXpVAs88m5y1y/NcWLTx9m18QouqYyM78CHteY60IwEOCZo/vR/X5++vYJllayVKo1kcRrW2DbFEsVpmcXObxvO/19HYQCemtsOK5LtVqnIxXnsYM7SSaiFEsV9O9/0CLAdByXUrnGyEAPLz1zhHgiTi6/xvhoP65pUq8bVKt1ag1D5GzZDpokMTbUw2B/Px2pBCurOYqlAka9il8L47guuXyRW3em2L5lkC3DveujTxJgx7EdTMsiFg2xf88EqUSMHVtHSMUjaJqGriqkk3HuTC9w8+4M2VyRXKFItVrHMAwya3kKhRKFYrmVVxePRag3TBEqxUVTFRINQyRpqgpuT7o1vpv5hLbtYNkOpUqNSrXO6lqe4YFeHFfCMCxUTUdzXLaODbKWLXDlxl1m5pbZOtqP6bGw+/06nekEV29Ns7Ccodao4mBi2xambVL3vByxaIg9O8b46dsfc/rcVSzTorMzSTqdwCmWkeqGqD1QZQzTolypEfDrhAL+FkD0+XQy2SIz88skElHypQqOayPJDqoq0dWRIBGPsJYrUq/VURVIxMPIskxXR4rDh/Zg1muUKzUioSADvZ28d+I8y6s5opGAuJY4aJpMf28nkiRx/ONLHBweYEdz0vdytmTvXvZ0pRgc7CMYCnHl5hSXrt5lOZOltydJMBggEQsjK1IrLSAai7JvzwT1hsHJM1ep1BqoqoTkJceHQkF6OpPcuDPL6QvX+eZXnyQc8uE6jge29FZYxHEcVtfyLCyv4vf5OHZkN3t2bmktHLoatlcoYOPaDuVqDcuyGOzrZGCwD9l1eeXFY9ielNTc4gqFUkV4b3ZtY2xkkFK5wi9PX+DezDzZXLGlTBGLhOjuTFGvNwiHgmTW8hRLFXodR9ApeLlpjm1jmBbziyuYps2rLx7jsf3bWVvLoagKkYjQHt2+ZYSbd2bo7Uqxc8sIqUSMSChIwKd5tBwunemEB2yEgPyFy7eYnFnk0J5thMNBbtyeplSpMrewQl9fD6qioGsqHekEzzxxANdxefeXZ5iZWyKbK9DTlUZRFM8LawvQ4UKjUef23Wl++u4J7kzN8+Th3Tx+QMzfkiQRDGjcuXuHd46fwefTObB3O5Vag7feO8VHl+4wmC8Sj0WE2sPDMI4Hklrhw2ZuZ/v37e0L2buHuCqk9q+8rOn2SONmD9evArpaHiDPM9RKhF8/fisv6iGAcJ05oFnU9SlAyzsLkXwJriz2L37m9cHFI1P9NFDZfrHa34vXVrGYC01v4vp/bTJUroC1qqqieYUtXocfcezP1j4z2HrQoZp4v4UlH9Yf13nI7fmsx5YRGRgbK9lk72YGAz727RjnsQM7iQYCTM8vM7uwgqoqPH5wJ7t3bqFULJEvFLl0c4o70/McKU+QTsYwLVOQ7imgqzLbxvo5vHcbH5y8yLVb9wiGggz1d9HVmRKyEhKtB7/RMGgYJn6fRjDg58kje6jUDM5fuc1aPk+lWiYc1LBsIQUzNbvE7h0jbNs6sj55NM/Rdb0Jwk9PV5rd28f4wRsnqNYbyKqMz6cwONBBLl/j7tyyYL01DFzbJBkP8fxTh/D7fExOz7O0vMq+HaM8tn87kWCgJcAcDPoJBv1k8xWqtQaqolCt1xke6Obo4T2Uy2VGBnroSMepVGvcvjfP/PIahmGK/C9dDL6GYWGaQjy6XK4xt7jCjbuzvPfROXL5Io5tCyFmy6KnK8WOrUNcvnGPk2eu8LX44wQCOrJlIbtuqxKwWmsIj5YsY1l2axXXXPyoqoxl23T3dDI23Idl9WCbJkvLa8wurTI9u0ShWKFarWMZJisrq9yZnGN5tcSNO9OsrGbJFYoUikWCfj97d27hpacP8/2ffci//vc/5ne++Rxf8cZp8zl3PIPt8+mEgn4huO33oWsqpUqV1cwal67fYWWtSLFUYS1XYG4hQ19PV2t8zi1luHlnGtu20XSJfKFEsVQVkj2GhWFahIs1DFkRIZ3uVFtgwaXRMKnW6iQTUXp7unjvl2c5fvIikXCIm3emmZtfZvu2Ebo7kly4dIOZuSX8Pp1iqUaxVBXahmaCUDDA00/sY3Ely/Xb07z7y7N0diQZ6FXIFRtk1nKoiksiFmTr2BDvn7zID37yAelkjD/8O98U+U2zS8hemMBRZGxbeNMq1TqZbJ5arc5arsDC8hqGaWFbNvOLGaZml4mFA5TKFSrVEHMLK1RqdeKxsEiCt21ROCFLVKs1LKuB7ZisruX44MQ53v3oHPPLa1Qqdfw+1fNCG9QbDTRNYf+ucT65cJNfnLvOs4aDDxFGRHJb1aar2QINw8R2oVZreKtaB8t2Wws3oYkpQG61Vmd6bp7lTJ5rt6epVGueJwhMy6AjFeN3f/0l/ujPf8ZP3v6YWrVGdzpBX18/fr+PWDSC64JhmtTrDXFsRwDneCzCgT0TpFMJ6g0T++4srezcJrkvUG8YGIZJLBJGVlXq1Rr5fNFz9m80LuFggHgsQrlSo95oUGs0sF3X83q6LWHsQrFINlfAqBut3xqmhdkwMU2TtWwBx3WFR3H7FqxGg1qtjmGauK6oFla8AhgHqNYa5IslKp5+ar5QIhwKUixXScSjFIplpuYWcV2XA3sm2L9vB/MLy0xNzxMOhyhXa9he9KIpqxYM+tE0DcO0KJYqpFOJDZQhjmtjmzarazmu3pzkxp0Z4tEwB3Zt5ejhvRzcv9O7NjanL91gaTXvLeTiJJMSA/3dVD+6RLlSxe/TkRW5jbjmEZZqM7B6oLeKzxA62/CF+Hffvtu2dds+bE5QCMsrfVqfP0vbEGpr7s/rV7Py1fW++xQJm3bA5X4Ou++6EjRlwzbl6m443kMS7NfbA/K77sNhEqKiuFm924LSrY3FYvdXvK6b2ufybLUXoLY+c1l3F37mHX0OlNg24Jp4tHkkBxfbcXEtm7ph4roOFi7VukGt3sCybWbmlpiZWSAaCREM+lFVmWK5Qr5QpFAuYJh1HElB01VCAT/hkJ+dW0f4+Px1fvDGh+zaPsbjhyawLJuGYRK0HZG8bjvUGya1WgNFllBwKZRqvPnex5w6e53x4R4ahoHtuoQjQfbs2sKNe3N876cfgqzwO7btyeOsm1bLsjFtGweRMG/ZDrliGRSZhmlx8cotFlYK1GoGkgSZbJG6YRGLBAkGQxSLVc5evIFhWTz9xD46u9LUGgbVWh1VE4CwIxUns5pnNV+mIxFBVRSKxQrlSpXOjhSpWJh6vcFarkihVGU1W2BucYXxkQGRRG+KxP1avcHkVIW33z/J5euT7N89wehgLyuZLFOzi2TW8uiKxOpankP7Jujt7uDqzUkUBQ7t28b2eqMlf2PbDjfuzHL1xiR+n042X6RhmBiK0HSUJAm/z8fF65N87yfv8coLjzE20ksmk2VmIcPcYoZiuUKxVBWGxDS5dXeOv/rxu1RqDWzHYWU1x+TMEiu5PMFggGKxwpNH9uAPBPhf/+Kn/OgXJ3jBdmhOFaZtUK5WMD1iUsM0aBgGElC1bGbmFjlx+gIXrt6hp7sDy7JZWc1z7dY9Bvu7CQZ8VGp1btyZRZI+4qnH9hCNBrh47S5+v05HSgDatVyRSKVGQ9dFcm8qLtaWjiP0E0tlypUquq6ze8cY5y7d5I13TnHmwg1qtTr9vR0M9fcxVVvh+z87TmcqzqG9E7iOy517iwL8dXdy594cU9NLDPR0MTm9yPJKlmqtTt20yBULVOoVUjHhXUp3phkb7uf25DxdHUn6ejsFOFnJtsCvpKpoukp3dwcXrtzixCeXcZEoVWrMLWYYHuhhoL+bcrlCpVpnamaBvhMXGB/t5+fvfszKap6vPneEbeODmHVDiHXjUmvUqTfqaIpMIKCRTkVJJwXr/GImx8JyjlPnrrFldIC1XIEPPrqAoqlEw0HmJxcwZFU8Tp7ntVJtsJYvc+HKHb5RqbGaK3BncpauzhQd6SSmKULEmbUCti30Sh0k7s0scPzUeeKJGN3dCWRVZmp2mcWVLAO9Kh/88jyyrPLK80dYXFrj1uQ82UKFri4XTdPoSMVxJcjkSmRyRQLBIFtGB5icWuDj89fRdJ1wKIgrSRwJhUg3J37bIRaJIEsSZy/d4I+/+yMmxoeZmVtElmWePHqQeDyOqqnMLC5z7e49kuk4+UoFXdfY1t1Bd1eamfklMWPLMq6s0LCFLIlhWVTqBuVag2rdxHYcypUalWqNhmGQK5TIF8t8dPoCsWiYsaE+fJqC40kgGaYpFnqWje2JZ5crNSzboVqtU67U0HWNarVOsVRhZS2LT1dQVZl7c4scPXqAZ555nN2ZNbEYqNYwTQskBRsJR5Jbyhy6prYWtSDC3KqiUCkLr9jqWoF8qUKtbrSIg33hEEG/D1yHzNIS2XwR23EIBvwEAmLB1N/TyaIiFjeO44LS5Ml7RPhws1frS8vZ2uS1aXektLwwbAQLm9sme/ppYtOP3McG0AUtLxUSG0tDH9420GM09/mpNn+dOHo9hww2yAJt6N9naQ8JtXqeoRZQdR1sRzgIXC+BXtoQsdyM1r5Y++JhxAcBevgMbsMvp7XuoyxRqtaZml9hR76IP+BDVsFBlE5fvjFJIhZhYssQy5kskgS9XUkUVeHi1Tvcm1kimYyzd9cW+ns68Hlu945kjPNXJ+lIJ/H5/EjeJNBoGFRqDRoNi2K5iqoqmJbNxxeucfXWNJFwiIBfp1ytU/WSiWVVYce2UX5bUXjz/VPcvDMjjLjjonixY9t2yBUrFMo1um2XfKlKtW4ws7BCLl8kXyjxx3/9NqPDfRzcs53rd+5x8uw1tm8bYe/OcVRZ4vyVW5w8f52+njSDA91cunaHq9cnGRvuY/vEGJFwiK6OJLfuzrKwvEoiHiEZj/LWh2f5d3/xU44e2o2MSzjop6enk+GBHi5fn+TM5duk0ilW13LkChU6anXq9Qa1eoP5pSxLmRzFcoVsocRavsSb751meLCXg7u3cfLsVXLFCkMDPSxlcsiyTDDoY6RhEpRErprfrzO/tMq//Ys3Of7xZTJrOQ6YFqZqeYs+mWAwQCDg4+0Pz7CwtMKvf/0ZertSFL2y/FDQT7la4/bkLHt2jiPJMmv5smccbNYKZaZml6jVDap1k+/97AOCwRDbtw6TTESw7I38MvMraxz/5BKLmTWCfh/vfHiag3snGOjtRZYlao0G5WqDYqWOOb+MpqoYhsnC0irlSlXkXyG8W9duTbOymkNWZOLREL/x2jN0d6WxXYneng4GZQVNVXE6E0iyiusKItpGw2QtJ/KuAIYGevi1V5/h9uQs9XoDzWPVTqUSjA72ceKTy1QrFUDGH/CRy5XIZssYhks+X+W9X56nUKoiAf29ncSiYWzHoW7UCQUFEA/6dUwHxkYHiH5yhS2jA6RTCUGiulZYn4Q0hYDPx8vPH6Wvp5PMapbv/+wDBno72Tkxxo6JUXq6OlheWePAngk+PneFE2eucOn6JK7r8o1XnubrLx8jHItRrdaEmLosc296ganZJcaG+wmEAqSSUSRFJpMrUK03qBsWC0trrOSK1KoN7s0sMbe8Rr1usjsWRq/bINVbYsvd3R0M9HZw9uIt/tn/8EfYjousKLzw9GG6uruolIp8fPYap8/fIBoJ0dPTiayqhCMRRkcGkWWJldUcoaCf0xduMTrST+/Xu1jNFnnzvU8I+HRKlRo9XUl8fj+2Y7O6lmdqbolKpc7c4grLmRxdnSn2793BvdllLl69zdzSKj5dY+e2ESYmxtdDVZLE1qF+dm0f48yFG3z3h28RCQXRdY0Xnj5CMhVleLCLcDjAzNwyf/b9n3P84wvkCyV8msaxo/vwBX2sFUtUanVWc3mKlSJruTwr2Tyu61BrmFQbBvPLqxTLNdZyJfLlKrIsE49FCQUDnL14k3tTC4yP9vP8U4cZGepHAlZW8+SLZTRNpVav49M1QkE/Pp9GNl/k0rU7+H06+WIZv1/HME0O7NvOrXvzXLp2G+PPfshgfy9D/T1MbB3BME3KlSq1esPzjBZZyuQACAYDKIrCrbvTXL91D7/fx6H9O7FMk3OXbqCpKqGQn3gsLPLGPjrboqiIx8OMjgygKgqWZZMvloXX3XUplKuoHtGyP+BDCehe+sv9Rq1FW/QgDqaHGf524/6ZwEFTPLtdrqbVgzbvzkZA2O6JeRDg2iCbJ30O4LXpCA9MqPosAEqScXHu191s30dr0ybIaT8rF1F1+Wn6h5u63M7d57UNgdqmJ6vpTPZIri1bJBRJbKYdkjbv4Qu1zwy27ncIeuWnzSZcXOtvPZoDNn78uVu7pEp7WKk5viRJploz+OTCDZYzWV58+jAjg70kkzFM2+bitUkKxQoXr9/m9r050qk4Rw7sJJmIsbiS5cTZ68KV7wj3fjQUIBYN09fbRTgUIBoJkUzECIdC+HWdat1gaTVPqVLn4pVJXnu5Sndniut3Zrk7tci28UEM02JmfoVffPAJoWCASrXO5auTRGMR+vu6UGS5bZy6656i2WXOXbpFKhnn6q1pCuUay6t5lteKJGNhfH4fH5+7TiZbwLZsLNtmamaRLaMDLCwt8IM3f8nVW9Oomsxf/817nL98B1lW6OpKCw0oWXhLsvky129PsWPrCAf2bufD05d596MLXLh6l+H+Ll55/jFG/X4eP7SLheVVjn98mbVckVg0RLnWoF43cF0YHujl+WMHsSyL1WyBoN9Hd2eKUCiAz6eDLGNYNh9+fBnt3DVikRA7njpEKpHwkuyFqzadjPPkYztIJ+MM9ndhmhYdF+4RsB2h+SZBJBLiD77zCsdPnccf0PF7Rq5eN9BUDUVR6e/pwLQsKtU60UiIeDREqdog7tOxbJeOdJJINAKyRL5U4b2TF3n3o7MUiiX27BjdMHfcnpzl47PXvbg9TM8tiSTsER1N1xjs7+HZYwoH920nGPCjKDLZXJFSuYKmqaRTCX7j9RfYu3MLs/PLmLZFJBqiqzNJyK/jIOEP+Hn16AF6/+wXqKqK0tvVmhQVRSGZiPCNV55mfGQAn64RCYc4sHeCxw/vRlNVZudXuHbzHolohMGBXv7R3/82jVoVWRIu/OVMlsH+bjRNpacrzWB/N+ev3mF0uI/HD+8ilYxTaZhYpoXq8VbJsoymSDTqDYYHunn+6UMEA34c20bJldZX+4qKoijs3bmNHdvGyKxmWVjKEI2EGezvJhwSBQepVJyXnjvKgb3byOWLSLJEf28nwwO9BII+QashK4yNDvKdb38FVVUJBvxIsoTP56O/r5OXnzvCscf3YtsO2WyBro44E1uGcWyHlcwaNyfnkSSJvR0JQm+ehnxJSAgBOybGeOHpQ/zrP/kb1rJFXnjmMGMj/WwbE0n3Pp/OxPgI3/jqMVKpGHt2bUHTVAb6eujqTOE4DvWGwb7dE6zl8gz19eDT/TzzxAGKpSo3bs+we2KYxw/uJOALUKs1KFeq4l7t2cbwQI9X6KAw2N/Nr736LOlkjLvT8/R2pXnq6H5ilfoGlvquaJhf//oLDPb3sJYtEA4EGB/pZ/v2MXRNZXiwl8cO7OD4x5eYW8iwsLRKLBrihacOs3V8mEDAR2c6ztbRfvp7O/HpGqqqMD7SRyIWIR4N0/DyG/t7u+jr7UTVNHy6zv6928GrGK1W64SCQULBAD6fRqNhsLyWb3m0XFd4m/p7u4hHw9ydXqBcrdPVkRT2XVawXJfxsSEO7J3gnQ/PcO7yTUrlKj1daVRFXBfT0y1tGCaTU7OUylV2bB0mnYyxls1z6dpt3j5+BsOwmFtY4eihXfh0nf7eTjrTSQJBP9/7yXtcuH6XO1ML+Hwah/dPMDQ8QP9AL+eu3uH81TvMLq6KY0zP8xUEcbCuaUj+4IM9W57NeWD14aOA1qO+f0Rr/8XDTWYraYdHGf4vr1pSeuCfn/qrzd6tB7VNEbEHKktsDkV+WhTNpQ1oNUOhm//d/yPbo4hyN+z/Qdt+8fb5ebYkEBn9D6jKcEX4o3VXNrhBv5xeu3ieIO/CpJMxcoUyt+/Ns5YrcnDvdrbpOnt3bCGbKzIzv8Lk7BKzSytsGxvgay8eZXx0AMdx2DExxq17Cxw/dZnF5azITXIhGPCzdXSQ/p4OOjuSxCJR/D4vV8dj2u7sSLCylufjM9f56guPcWT/bgzDYS1bZtf2ccIBnVg0QqVu4tN9TM8tceGtE/T1pnjxmSNoZ261HnAXiVg0zFeeOUQsGkKSJCa2DPFP/uG38Pn9JBJRBvq6+QffeZ2LV28TCPjZOTGK36cTjwQxLZt3j3/CqXNXqRsm125NMzWzhK5rHNm3jZ6ulCAclRVefPowg31dxKIhLNtmz86t/OPf/w0uXr2NJEl0phN0d6YwLZuujhT/4Hdf48kjeygUy7iuyxOHTIYGeujuTBGNhHjq6AH27NqKA9SrdW7emSadirNj6yjFcoWD+7YLFnnbYdfEKPt2bUWWZa/SA0AiGgnze7/xCl1dKaLhEIbhEPzr91HyJaRiBRCM7K+8eIxd28fJ5HMkoiHuTc3R2ZFgfGSIIwf3ICuQjEeIhINEwkF+99svs5ItouuiinCwr4fh4QF8qsyvf/0ZouEgS5ks48f20d2ZhO8db42z3u40337tWWKRIJoq09fdQW9PB0F/EMcGJ+wQGBkg4PcRDPhBEtQTk9Nz1OsNZFlmbLif0aF+kYDsOlTrdRaXV8msrtFkwg7kyy3iSbmvE1kW7OW6ppGOhfj6y08RCQc9AyehazrxaARFVvHpBaLhMJFICFVVmNgyggtUqzWQFSzTYOvoIJquM9DXw1dfeAJV1xke7GZ8bBhV9+FURZjVME1qtTqWZTGzsMq5SzfZsW2EbeND4K2S5bXC+sSnipxDWZGJBkLEYxFGhwdaeW7NnChd0+hKp+jpFJI9TQJfJBfHAslV0FSZifExtm/ZIiZcT5BW1RRGBnsZG+5HVlQkWcFxLGzTEgbQdfm9b7+MYQmPs27baO8IbqlWgrwsYZomW0Z6+f3fe43Hj+zFMi0cByTbxe/zsXfnVia2DIIsoWg6juugqgqqGkCSJOIxme6uJI5jI8tiyhwa6OEPfu91CsUyNe9+NytH+3u7+Id/79u4ssgFqtbqXpWsy9hwP2PDA7iui6YLL6Z24XrLc+JKEj7bYf+eCZF7pKkojoTdaNCwBSjp7kzzd3/7NR47uJt8sYTf5yMSDjI+MkAiHgPguScPsWW4j1g8Tm93B329nfT3drGcWSMSCpKIR/mtX/sqr73yLOGAXxDQmhbjI4OMDg/w+ivPgiSJUKcjCIn9/gBffelxnn9qH4FgkFQ0hk/T2DI6yGtfOcbSyhpbt44yOjZEZmWNlcwqsWiYWDTM1156kq3jgyiywvjIMJ3pJJZt4/Pp/M63XmFpRVSNd6aTHD24C9M0KXgetJ3bRrkzNce129PcnVnk26+/SDqZQNE0Oru7eOWlJOFwkHMXb2CaFru2j/HE4/vpSKcIhUPMzi/ziw9Oc/32NIqi0NOZYry/G/98RvCAaQ/2av3tN5cNQn0Pa03vQutnzZytdWAntYUi7weGX0Zfm/tq2nb3U71bUvv2zd9s2pf7UJArtt+Qp+1+QSzR3Nxt49VivXgGmtrsbnu2mnASuQ4P9Ox9gfY5w4gugl5/Pb4q4eCyLjIsAe6XeHebopcPurw+n85Lzxxm+5ZhHNfhyP4dHDmwi2KpzMhQLx3puGBBtm38fpWBgW4SsQgNL3l3oK+bxw/u5N7MIqGgT6xAZRlNEWzuHak448P9hINBQefvgZF//Pu/wWq2QLVaI5WMIaGwbXyY8ZFBVC+vQJFAUgQxpm0afOOVZ9gyPsLoSB/jo70of/ZW6yrpmkZ/Os5/9b//fVRFQkPmlRc7QdNwJQfXtDAMg86OJEcO7iSdEqXdgrnewajW2L51mL/7Wz4S8SipRIzOjiTJZAxd1ilXqqiaQiDgY//ubYwO91EolAkE/Ph8Oo8f2s2+3dsolsoiwdy2CYcChENBOv0JujvTrGXzFEpl/LpORzpJ1DPworzeLzxzawUCfh99PZ1omkq1WiMWCfPbv/4yHR0pFMA0TfLFsucplcCy8d+4x9h/+//xmKlVgrjIN6eQDFN4KO7Nw/wK/j/4PzLquHRWqpimSdw0CYeChEMhzw3dnITA57g85lUqCuOp4vfpLSmWQ7jstwXxq3t3hXqjjuyt2LEsdv2f/g3bLAtdVVHVdSkHwc/i4vf4iZpACSDoOMQN4SUV3gTxeDWBh2wY9Nfq9DiuWFX//4n77yDLsvu+E/yca5436b2pLG+7qrqq2vtueBIAQRKkaGVHCs1Io4mJlTQjaWMkzmq1OzuxGzuh0e6sNJIoUhQdQBgCaDQabat9eZfls9Lb5921Z/849z6TmdVdDYCaE5H53L3nnHvc7/vzkQiplXWii+sqiv0f/hD54w+R1TqO72FZEWo1m2QiiecJPA903cTQI4F6QGDohooCL0QzBIQK4TCpHBUAXwiiiQQ9vT0MDfQSj8eUwbHWyn1p2Q7rhTLLqznOXbpOvWFxaP9OkokYslJTid9nl5r70NM0yBXp/tu/0xG4skMS3aZKafLioZogANrbl1AtEto0Nk8DOmcZFa8t1JRIiTa7opIOa+oUunHjDh+cvcrJo/s4dngPQvrKyF+qcwsp0XSIxyJIgcqcQCtfhgrG6QIeQlfeiBIVAy0SUTGuavUGjuPQ05VVIUiEhqnpeL5PqVzBatjNQ9123CD1VYqIqWNbNoYnWwbyAmTdQtd1Bfh0TeVANTX80NNbQG9PFyePHcQNEsdrQhn3u65LoVRmeXWDSCzG0GAfyWRCjaVU4x+PRYlGVIDPnq40nudTt1Q4lkjEbLVtGiAllaqKwafrGkODfaTTg2r+XEUDUskYu6fG6Ovt5tChffT099DfkyWRMInHIkSjEfp6u+jKpkkkYsSicTxHNhmOsZFBhgZ61VkSi2JoAttqkE4mcFwXwzQZGx3k3uIqA/3dZLu7qVcrrOfLLK+qbAl7du1gx+QYQ4N99PR2YZgRcD2GBvt58dlHyaSTLK9tkE4lSScTjL5xBnOloNajabatqOYQN9W6P7uynSqqXT3Yim6uMpg0rcXa1rzYBGB+ht27b39DadqnHIvQu1F6W0Fa83e2lRw2vRHDe37iItvMm2QorQFaqXrCvRPmGN6ShNv3VQLrdnXiT7AuPgXY8tsaaMN/4UMQuGy2cGHzzo9N3PkAJfQakIEOMfQe6sqkeeHpU9i2QyRi0tfbTaVaY2FpFc/zVPC6VBLbUYdlNqOyyWM7+L5PqVThzswCuqYxOa7CIsSiUep1m7mFFTKpBBNjg8SCaMlCEwhNZ3ion96eLry2GEe+lMQiEaKxKAKlB3aDxzYNjT27JhkeGSYWiygXcloqUgloQiMqzCBurkDXheK+AampEaw3lApPBduUiCARdSIZ5/mnT2LbjiIMQqjEz4ZBo648ITVNC7ytNBLxOLqmE4tEmnp9J0i70rBssumU8ryLx9CEIGIKerqzpFMJzMDQ3jANwjxttVqdxaVVVtfVYZYMPCBd10PXVTtx00CiPKwK5RoZ2yEWci6Oi54vISJmK6m37QSeWUK92qgEy0CkYWFXa0Q0jZgUCCtMPto6nAAMzydiq4j3pmlgmCqVUHhNuPh96ROxnTYOCrRcEVPKAIDrbftMInyJIZWdgcrTpjapJiEack41i6a9p1T36K5L1HERAqKej9AaGMWKApRSINbziPW8Aoe+JBndIPpP/iWR0UG8niwik4SBXsyxIbzeLAbKI6xuu7hSJVQlAIDhnIbSpVAorWka9WoDd6OIXmuQmFum5/pdqvOrVNfzaAurPLSW5wuxKAdm1jEtR81FMCcyqAtNqATe+VJrwKXKLdk63JobuJO83IfzbjHwrQOy3VpCXbM5jXwwJ+Fb32+qEqSUzM4tsbpeYHCgl1QiyNPZdnZJaM1T29rpbLj9lxAAtoCqlKixlhI0vZm7z1fsskoHEoB8Cc2cmr7jImt19EKxaWqhVeok/u03kYO9aKaJFlFxoAwEphBgaAjTBF3H1DWVV1HXQdeQuo4jJUalSlfDIt2VJlWzMeMxpK6TRhLzJdQdzLqNFo2AqSOlwHdspcqMRdEiJlKgJHO6imEXMYPQDhJlv6eB0CX4Es2QZLMJ4om4ionlejh2nVQyRjqVJGro0PDVPt8oIusbyGoDUWug92RwhvrxgryLvu+jBarkaMQMmIAKpXKDfbsmePGZUypKvUyScjw0IXA9H03XiBsx4pEIppT4rosmIB6PMDk+QjaTomHZKm2S59F3dQYzMqem1WyRQUkrs264prcQ1k9lj3W/cj/g5Xd8ar/yP4v0bcvzbqXnHdc+SAklYO31NgHXx8Ut20aq9XFtiKC+TfZazbqaWsEWjlESaSUU0YP8vFuEjU3g1wZ2P61TIJ8GbAmV7VuIUIrVDrzUySSFCmam5qcletuygO/bxv1Fjp2HrhqkSGA709SzShU7qVprAJJ4NEosFg1iYekYRiQ4PNTBcW9+meW1HAf37eDQvinS6STVWoNzF29w9tINpiZHGezvJRqNBBOhIdDxfUWYwwnygkNV1xWn7KEicrewqY5hClKBisgwdEX0NR3huhizS7C8hvlX/sl995Pp++y2LFxXcbKRiNlE4uEt0ZARaC4pge5LYr6v3gcSmEhAJEQzoStEfJ+M46pYProecMutzsTbzpf21AZSSmKOy6htMyqVtDFimoBkl+MyFRD7MGZW2vfZZTtEZhZUHUYQVLC/GwwTaQTrazWH7ziESYQxDejrUk9lOzg5nVgsip5JBxkBwp62bTTPx29Y2LaDiJgYsSiiGcSxbawk6K6HyJUUsDMM/N4utQZNI5A6tOLciOZYqEwEzY3eZIbaZC+haF/6aJ6P4fmKmzKU6kIvVxTBbBtTpFI/CV3DWFxDLK2hSYIE5aK51/f7PlPJOGKwj8jooEpnEosiTAOBRPN8NNdDr9SRuQLjy2t8dXGVSK1BIhbFNHRiUtLlebiuh207lMpVbMehu0sjWq4qgBBIoUSlBj1ZiMcQgSSiY71Kmp5QnR5Rmw450Xq/hUlvu69VV9s1He1tc6as5JANGzSB7MqQzaTp6U4TMU1lR9rsoxpvNbU6rlSDqqlsqgp4iCD3H+BLDSF8da4JDc/xEA0Ls9ZgqNLALVaIrZSJuaBbFqLWIFGtkylXoNpAqzcQ9YZ637DQ6g2oNRRwqVQRFeW4gO0QOXMFmYipdRo8rw6YWx53K8GOSUmqTZ2kaS2gGNnuHikxpE/U9ZvgFj/wyg2QaFRAJrhWjUcbDJAS05dMBSBXSBC+T7/v0edJhO83vdVlG7MeAlbn8G54/KiKc2QaGPE4WjyCbqhUaYam0720yq75FTLdWfbnqsSu3CKiCeK+jysELqBZNr4QSMfFTSfRolEwDTRTJ64ZxLu7kIbKwyklaIl4k/GSbWCrXW3V3Mu+B5r+U4OrMHBpO2xvtdtxgGxTOnvV3ENCAV65SZX4k3VxM4DYTOPbrntgoBX82wagyuDztlKtEDB9mrbC65u1q7Gh/Uxqela2dbHJMLcJdaQfrIVt5qRdhfop1sRPGdQ0JBDhLhMIobVYVKkFzxYcbg8yaPfjIlCeXbIdXQgBgQ0FAqTvk82mmRwfptGwSKcSJOIxIoaSqkQCY+fu/8u/QhTK7JCS5wN7FP3cHYQQFEsVhpbW+Vu+z9jwPNl3rzZBSgewDCZtC5MuwikKN21nMLpQfaJduY2wHahb6rc6iDNXOiszdOT4kHp2CXEZtNuwm9K+9uEgGPb2z1pwb/vQdkoK1GddKru8Vj87K+4A2KLts5SYUqJLqfJP2k6TukabR2qj1R8piUmJHOpX20GAWM0RDaRsIgQTB3cG0js2iZkDe6Yw9k4H0AqfWG00LQC2bpicOuTMRTv8V20ahkAe2t0cGzNMOyHaAhoGD97CVmLr2STbxikER0gQGoauYeiyualBICdGkIN9EI3g/tqXEOsFFaV9NQdrOcTKOlguwvUUEPR8cD2E52EEf1y5rQhCWzc0gs0thAJzhkHS0EnougK3lqOkEqYKKGkaKqF5JGLg+5J4IobozuJnU2hvnQXXU+uuWIFkPFAdbmKImiCqxQmHkp/W722rafPeaatRdqUVQ1Aod/zQfk1YQXO0JcggsTa+RCuUeOZf/SkPV2pk/uAVIt98C9911UHu+8FY+sG4emoMPb81zm1/Wts9wvO2Ocs6+/KgRYKa21B6CGgrG/hTYxBIUZFtRCPIeYqUgUQ05MR98KUCNrL9d0KDlI462g2GH6TPn3TNZvnEJvLcWUfb0JkXr6MtrRHaBYpQnRPsLQ2Y8n3GHBdd0zA+mm6eBZEA7Pm+JBNIMg09UPmLEMBsgilaIA102xLWmxGa+/Q+Rfp+5zn0cbRsk2RIXek3O9EOsdqvuv8Ytz1B27mstBLQJrL56ct9AddPWtrPz+33zP3v/BQlXM8d3niy8/ewXimQogV1pQzPqTagFtAKsbmezfV9CiD404GtEFThBh+UNCu0s0JIJRVoZiL4GMDVztm3Xxu8Nm09/qv/ES5Mq8Pp7gLa0a+27pGKA8sg7z8GpgGOq/7ojBwmgW4p6QoSx2qBl9NfSGlYTfCtyjbtaBqytwtx7Q6a01KVbQZLYd+3q6mz1hAidP66+ZDZduyahHT7+u9nQni/A1eaBvLATvXdWh49X1L2MZ5Ks2LoOoZov6ftkCHk0u9fwjPI9D1wXbWZDEOpXJr1iC3Xh58+ziSy/fAOwf8W5if4twmqbV9XqQq6hrgwDfmSAjSFsspDqGltudsCgtmOLjy/A2htHQjZXO9CCKWm1bVA9aSDoUE8Cj1ZxEAPseE+GBukMdDN2eU1NkyDn3vtA3RX2TmFQERsFNqftPU8wYNLqQJ61huWshOKxzD0Vo677cas/f6w7yIwym+X1m6/I4Pf3QCYAvqV2yR9SdL3kd5yALRkYJ+htd23VTEJKnOBFwB1LbBP3NLftv+bvty+CKHmVFnnKulKLAJCecRJTSCH+3H+X/8dxv/y+4gL11tjIcM8cbJtkwrFTUlNZSNpJwLN92wlDgEx2WrL0vY+nIuO99td11KnKlOL1vM1z/XgvQzP80qt2beOcW3h9Obz6ZqOHt26I1unmcRYWFHrUmyzPj7hDNf+y38GEQO6s/Av/8l9rroPwd0MvpptyeC/RBFA8TH9kG3/27rd8V1bO9tIg36m5SdQkW1fTdsTBdkwAnS4VSp+v3586kbZniiG67itNNNpSYKMKAFtDIGX2FLJT1V+ogjyzaUhJeCxrd5VEGzE9o30yUCrAzRsB87yRRUOIORMXJftSkg4wwkPuW4ZpC/B9QJ8qAJnekFSSqBp3/QXuJwhyPfVLBET/6G9qq/5kuJge7uw/90/J/KX/3vkRiE431o5tFpEqJOH2w6/NRfSJqjQWeTW9RkA0pbUgqZaqVkeZN90dAxkbxb73/1zQBL57X+Et7pBqVpjyXZIJeMMD/UpSWSzqQdohDYoGTbo+/i2je04+IaBiKiI0QGbu7WDmzvKNs12PHqTzWxVE/4WEqWtnWxVlC9CrQH1BuKd89s8pwgkUzoQbasguC4agUwKErFA5RlkqXddsByo1xWYs+02VUgbEQ4Qv7BsxNwyzC3DB5eJ+j57NgoMlqtoNavVF12HTBL6uhTx3/p0hDnHyoUii/kiQhOMdqXJpJPout7xjFvOxVxRAcr2oglFCLcfzY5vZbmqnhvAdpv1e76P5ap8lVHdRDP0NhK3zekswcfDcWVg16EjDAOpawFYVYBVtgFXEdhPiRDEhlIUXamv0LWWIbzc0qJaC75EZNNok8OIoV7E9Uizf1JKHNfDtm0koGs6RuDAERKv+zFdzXUUi6izJxpBRiPqc7T1J8PPsShEo8H7CER1iEfQ4uH3JsQjyIjJjbllfu/br3JzbplnHjvGX/rKS3Rlkkq95fr4jgeWRLgOvmPjbxQw//7/jFheD85gTYFaXyq7QwImxYem0XhTEhe+D9SWvsp9J2wXpI9MqxT3ruviOm4zOGpIRAlf20Fjvqjmpjn3baO42bTlYzQvHYunCbLCOgIa1357E8S2r7/W+46am8f2XyhV+qlLB8BqquNACmW+IQkA+VaV0KZxp5NJ+HS9IMQnHYb221wVssG+VKnjtlwpwrm8H7v84OXTSbZEi2i31q0ylG0uqLDI1j3IdrXa5jpFxy0yeCjl5UjwftMQhEbUkU3d75hn5UVk2Q6aEMQCb0IRGGoLQJoG3pFdlMpV1jeKOIHNUjIRY3igj9hmQLT5NAufb9uy3QRvVTs2S3cG///599Xj/eV/DIHkoH1RetKnEjNxXS/IayZwXBfpKyPWaCTSOnhbA0G90aBSbah0PfHQFkQ0wdR2ffV9lZi70bCJRSPE4yqxbnvQuU8CQR0zHi6YTcS0CVGkpKAL/u5EHy88+TB/4ze/Sn9vd8ARB3ZW9w3Y1skZ+kEGeul7uJ5DLldgYWkV0zAYGeqnuyuDYZgI2gC1lEHYkpBPUJtP2yxx3VzaVAtboKskIAqbfpEaIdATv/0PYSXXNhoSkgnozkBXWr12Z4PX4LuutLKdymYgEe8Yg1Y9bSBQoNTVazlY2VB/6zlY3Qj+curPaamydE0jEY9RLtfUfpFBl6dGkb/9Zfj65zsZsOAZ/fDV98nPLvL7v/8tpm/O8jd/8ys88chRkoEKUgvVsyIwNA/u0f/yP0ZsFIOuBwCiO0Pj//c/3GcCvI51Yfybb2F8txXCQwBETSxNcG1+hYVyjQNH9zF1YCcybuLHI/jxJDIWw09EIW5CIkLN0Hnn8k3+48unmTowxW/+5pcZHupv9QvQhIFAhXwIPfjCZ2vaNTYZzg4qq/5E57ipZy+o8RcggsC4APLYASqxCJfvzHJ7eY3BsUG6+nuIZ9OM7hgjM9DTAkmxKCJmBu8D8BSPIcxtjvtNjJPYdh1JNcbCa7s2+F4qj9BIV5qFc9NM31uk4mt0ZdLNbaNLwNNagGlkABELZNOOixfE/ApT8jTPp46+3X8PCikDZkSH4T6sdJL5xRVyhRK9XRnGx4aIRCP3r2Oj2OFYsX0jLcD7YFKl9iDJLc5CbKJ3nWeD2PS+jYh+nDSrCVTa1tz/gUX6rUwcrfPVb9L2TyrNSPI/XS/u8171qgXC1J9sA2atWFtim3t/8vLAYEuEfd7MddOKUaL2RvsFobt820LZbiG03RMeUtJXNiFiO3uviWFlLP17/9dOgOUHqg7ps7Fe5H/7D9/i9775Ckf27+Bfr1dINRz8XsUhi40isi/L8v/j7/Jn33sDz/N57skTSF9Q1XQaI4PoidhWEbfU0WiPxyHat0Sr6Ntv2vb6BOqwRnhKYhUQ9xZvExrwBvQyEeO/mehlYXmd5584xvEje7g7s8TisspfODYywMMP7WPf7h10ZZIYmqBUqvBv/9P3ePuDS/zGL32Wzz//JFEzAkIFqvXxkW0RekN1QK3e4PW3P+L9s1d48pGHeP6pk03wqYhJYBujtbzxaIpetwMeKrWS8dv/PawXmt+FREfXddLJJBMjA/T2ZIlGo21Gi0IRrPaguc1h9EF67WQhgOfKTV/TNDKpBF5vFsty0PHRpIcmjQ4g1H7wha1o9zncWtLSzt87IZfasHLz2gUQoc1fAPaMNmeSf/jX4ZkThNJetedFSyUTcl9SIj0f6djK2F5s2mMCOiTOiRhMjsDEcNPmp10F5EsfUSoqQLaWh/UChfPXuPKjd9lxb1WFxQg5zd5uECrMABI0vQVaQ6cNTdPo7U3T25MmlYoSjZlNRqB9XNsN6jtsJu5ThFAMhmXZGLqOGdFbwb0luF96ivqzJ/BiUYxkFJGIoUVMKlWb3/9//y7vfHSFf/BbX2LsuUeRnosvPVyp40vQpI8QHuDjWw711XVq8QjS1JtrpbXXDcKNGfY9TGkjtPCs1oNn6yQeipa4Heuifd83Sxuz5/3ql7hYKfE//atbxMb6+Qf/5a8xMTqM53lEEnG0RAxfSBDhmQLgEtrSIrROoNDBI4XnuLv5h865kBrKUSpk1NSO68qmGR8ZbCZ0blg1pO8hQvu5AASEjLoeUZI+AUHwa4Hs7UIzDCUdDCd0ExDcllBLicgVae7+3i42/qf/ht//D3/Gu2eu8AtfeIZf+drnMHu7uW8ewb/0f4L1PE0QvJlJ7NhXm0Fze2nb6x1YqVXn9mfHJulLa4oekNSHbYpWH38awLVFJbrNbx9TtgCt/+xFfszAtXrlS4kfaLS8IJ+vRM1N1FT2q60lswX8fOryUyWibkINqQfUr/0hWwe9eOBVI5oTrGIAtaoUITHffH0o1dj0W1c2xfNPnuDtDy+pgYRNhtSq/+sbReYW1ti/Z4Lx0UHSqQRIPaBDnehYC8GfDCPZiwAMtvrSkv5IvDaE3j5VTWITHuDSxw9EzrJtgXeudTXrlWqdWrXGQG+Wx08d49Txo6yvq9hIb717jotXb/GFFx/nxSeOk812YehR9k5NMjzQx7EDezD1lnRNw21KF6CVeFkIiEZMThw7wNTkSCAJMlpPKbXg4BUIqbx7RKiaE54CEpv141KiNw+VlvTD81xMKdEExKIRerozKs8ZalyUqkp00ACJxKrblCsVEgmTRDzatAXxfEmYdEHp4n2ihk5/bzeep0JR+D406sqz0zB0DCGwXJUUOvTabLrqB8DF0NXz1+sNqrV6M6K7GY10AAbbtnFcCzOiJI2INuKhadRqdYqFIq7r0Z3NkEZ2qjCC4LHS95WkVfqYpokZhBQBkK5LIV9kdS1Hvd4gnU4yPjFKJLhGSolnu1i2DUIjEo1imKaaWF/iOQ61ep16vabCXphRUsk4ejaJl4xRG1ABez+oV/j23RleXC+RshzQdfx/9U+wMinmbt9jZnaRrmyavTvHSadTzfGyXZXOaXV9IxgrA9MUoHkgVPLnet2iXK3heR6xqGrfMDrtcqSUW4SZaxt5Lly5we27yp5yx8QQRw/vpK+nCwmUExHubOQprK0z3NfF2MgAMV1HOUHoxKMR4hEDEawVx5VUGjUs28EQkljEwIxqzSTOmqZRLFVZ38jjex6+0Mhm0qSTaRXbzXMpWSq8iJRKXdnXnSaTTiM9QbVaIx/k5zMNg2wmTSIRQUqfhmUFNmHKazAh/aYzi4SW9EctPMq1OuVKjWwQ4y4ejxKLRgKmRK16z3cQBBIiIRHSC9awhtBVrkHpSaRUxugK+Kux1nW1j0WQjy6YhIB+h4yPYq6lFEjfoVKrsLC8wdLqeuDJLKhUq9iWTTQI9ut7Ho7lIH2PiKHGNJTaCST6QA/ev/6nCNPAA9wAL4Sq2+Y57+vB8RGcDQETL37778OduebZFTFNursyKlZgEK6mNaqdYUXav//EsskeS722zDranXNaADY488KQAsGl6sjoZC6Ujsjfhq5/AqH/KXDVx5bNoKtdDftxt/ExzgbbPYYIGOlmvYo5aLMF+lTdvl+jW8ySpAqO7nleM4I8KKl+KpUgkYh3AOWftvwU6XpaJZQmKAKs0eqc2iRS3keFuKmOsHSIWqVsgbVtkHaTyw89bJDKE8vQOLB3gqmJQVbWclvuC1FcuVqjUCpTb1j4gUeLED4CPVBFetiOg6FpxEwNz5fk8kWqVZt0KkU6FcOyLRzHJ5FIBIcfCkA5Hg3bxrFVxOtELEIkEsHXNFzHYWM9Ty5XQWgQi5ukkklSmRRJ6DgIwvcR02DXxBClYpl0KoWpG8RMna4dKi1HKhnnD/70ZX78xgfsGBnioYMZ4ok4Tz92jGJJAROhKRfkRsPCcW0ikQhS06jVLVzHJhqNEolEVOwy02Swv5dEQkV7txoWVr1GveFjuSqEQTqVJJ1KEolGVac9qNVr5AtFLMfB0HUy6RTppgdbOK/gOB62XSfueSoWlSbQNY1Spcbs/BKpWISIaZDt6iKRTKAF6uNKtcIHZy7x0YWrnDx2gMdOHiYRj1EsVbh1dw7Ph6GhAZXeJV8gamgM9veQyaQQQLVa5/bdRe7NL6kUQcP9bBTLLK2sIyVU6w26s2kiEZONnAJGgwM9aEJwe2aB+cVVRob7eO6JE+zcMYZuGFiWxdz8Mldv3GJ1PUdvT5ajh/YwPj6MaURB08nlinzrz3/Mlek7xGNRnnn8OC/6Xpu3q0TmS1iWzfLqBleu32Z1Lc/wYC/HjuxjcHgAECzML/PD197jzr0F8oUSkYjJ17/yEo88olzoi4US12/OcG9uCSklPd1Zdu4YY2CgDykFc4vLnL98jdl5FaR0764JHj95hGgswr17C8zcm2dheYMLl29QrTWQbZIGx9S4ODPHt77/BldvzDDc382v/cJnOHXyIXRdJ5/Pc/Xmbe7cW2I9V+D6rVlM02gS9Eq1ytzcGldv3OXy9B0sy2agv5v9u3dw9PBexoI9vZl5klKysrbBt77/Jj966yPqDYtKtc5Afxd//de+yDNPPEyxVOZHb3zIj948w+Jqjr1To/zGL32WIwdV7kFDNzD0ULKmnDHyhRLvX5jmxp05+roz7NkxwshIL7FAPSuBmfkVXj99Dl3XKFUa7Nk1wTOPn6C/p5tSociP3vqIhZUcUirj+59/6XGOHTlAw3a5cv0mP/jxe+SLJXq6sjzxyBEeefggjuNy7tI09+aWEUKQiEf5XLFC2nPRpQqCSzTS1A1g1Rke7mXn5DA37y7wje+9zvNPnODg3imymRSaLinVGuQKZSzLJhZTUeUrlQq5jTy6YTI6OoSmady+s0C5WCaVTiN0jXK5imHo7JwcZWR0AM3Q8ByXQqFEsVRRZ0/EJJNOkkwpuzvHtpibX+LitetcvHaXMxevN9N4lSs1qtUaQggKxTJzCyusrxWp12rs2TnOvt07MOOB1M5X0nzf91leWWd+aZVGw6K3p4uhgT7S2SxCE5QKRcrlOq7jUW80qDdseruz7JgY3eQsI5r5Vk3DIGIaKvyF7+PYjopF6LgBk2EST8SVF3aTJiiJ8/1MB0RTyuFvBURbcviFsEPF0Wt55Yd1hPcRgFhQAG1TPZ+otmwDBD8rNWI70Nos3fsJSiDXC+5XdtKdknipGIYw2bXgJ5SsCUJ7vrabOqsIMIkCWkqy5Xt+U9OiaRrJRJxY9JPcsD5deXCbLamDkC0OJ5SG0CbDuk8yz0+sOnzTnuxzc9lmEXm+T71WI58rUqs3VD65rhTZpEqzYegaiXi0KbGBcN0EkggkteCAunp9hqs37rJ/9ySZdCoIfuczO7/Elek7DPVlObR3CstxeeXND7h2Y4aTRw9w9PAeLly5ydziKicOH+TEsQOYhk65Uubm3Xlm5pawHWU3NjE2xIE9O8hk0uRyBb7zw7d47+w0ruuQSSc4cWQvLzz7KIkQnHZIltVBnkomiEYiRIwInuvhCR/pSqK6zoE9O5gcH+L8pRtcuHKTXZPjJBIxLl65yTsfXeTo4V08/ehRNM3lg3OXmV1Y4enHj5NIJvnw3BXm5xc5dngvO6cmuXZzhguXb2CaBo+feojR4QHu3Jnl3KVrFEt1LMulblkMD/Ty3BMn2b9vN4ZhsJ7L8ea7H3JndgFD17Fsh+HBPk4dP8jUxCix9vnzPOwgGKSC6GrtLC6vce7idWrVKoVShcMH9vDsUydJp1P4nsWtW3c4d3ma2fkVAHZPjTE+OkguX+Kbf/4mtYbF5158Atu2ef30OaKGxpc+8wRHD+/Fth3OXpjm2y+/w807C6RTcZ46eYj+vh5uzaiUT+ev3GJ4sIeB3i7mFtdYzxUZGeploK+bWq3B4soGyetRerszDA/1Y+guZy9e4/V3PqJQLGM7DsVSlfOXb/KVzz/NQ4f3oulRPvjoIr//pz9kaTVHxFDhFp4LAjKG/IS/UWB2fok33j3LjduzFEtVHMelWqvzxc8+g+/7/PitD/nBa++xY3yYibEhFpbWuH7rHkePHqCQK/Lqm+9z7cYMXdk0uiZ4/8xlMpkUjz58iJGhPj68cJXv//h9GpZNf0+GnkySSqXK2YvTnH7vHDsnhjm0b4rllXXmFteQWmtPl1fW+OjcNRzHZWpimOu37nF5+g5HjuzDdT3e/uA8b7xztmlUXihV6cokm6L66Rv3+P0/fYXF5Q2ymSS6pnH20g1eO32OL730BH/dcTHZWsqVGq+9fYYfvPYe3dk0n3vuEeaXVilXKyQSMSzL5rW3z/IH3/wxM/Or+L5PLl/m2JE97N45rpwtAt8BPzhoLdvm6s3b/Omfv8GNO/P096R5+NBuHjt1gN27JmnG40IxAbqmcXtmnis37pGMxXju8YcpFMu8+vZH3FtcY2p8hMmxQSWE8T1yhQLvnrnIj09/hO24GLqG49pMTQyDlPz4rY84/eFldE0jEjE4srLOzmQCEYyVaLcZrVuMjw7y4jMnKFWqvP7uOe7NL/OZZ07xzGNHyXRlWFhe44MzV5i+dY/B/m4eefgwC0urnH7vHP29XXz1515ECMEfffuHTN+4x+6dE3R3pZmZW6JhOXz5c0/wc597hng8xt2Zed567xyFYpmubJqGZdPTleHE0QMqmvvsIt/94dvMLi4hNA3bdnE9D8uyaVgqSf29+WVOv3cez/fJJJK8f+YyV6bv8Fu/kmIiGm0eyFJKpm/N8NrbZ7h5Zw7P8xjo6+bFZ09y9KGD5PJlvv/K28zOLRGNRKnW6qxtFNgzNc5f/Y2vsaN9oYQEM4idFDJ4pXyRm7dnmZlbpFSuUq01mBwb4tknT5LtuF12SNE7S/jtZqC16eewH011oB4ALrZKSmT7jeEpoDKDtC7b3l6raXe0xSbwZ1g2q04fCHQFQpZNvWrqoZqAtu1Hv/2KgF6HcMAXIQe2FYC1mx6E4xFWteVRZPM6PwBanufh+4FxfEB3VWYQDe2nSeq8TfkUBvKBCFn6rfGG+9sst9/ZDpQ2679Dw8PAfmWrHUybDlK0FpXv+0xfv8O5i9e4cXuWclklAD55ZC/PP/kwPd3ZINK6j+eHomP1r8k1SyhXqmzkyqxtFEn/6F1sy+bUw4eIRXVc12VucYVvv/wWvV0qgWtXdxd355Z55a0zGLrGzskRLk/f4ZW3zrK4vMHkxAhd2QTnr9zkG997A8f1GB/pp1CqcObidTYeL/LUI0cplyp8dGGaM5euk07GMXSdZDzG4ycfao6tpD1diXpux/GauexkoH6TUrmop+IxurJpytU6M3OLVGoVJC7vnLnIn3zvDTQDHjl+kFgsypXrd3jv7FWOHNhFKp1ifnGJH731EbFolJ0ToxSKRd796CK6rrNrxxjJRJyLV2/y/dc+oK87y+7JcVbWKly8douergzj4yOYEYM33v2AH77+AYf2TbFrxxjTt2b44evvc/POHL/xy19grwxd2GVgTC2aTyeEsv+JmgbDAz3k8xqvvn2WmfkVHjq0h3Qyjms30ITkyVMPMTk2zJkL06yu5xkdGQjSF0nWNwpYts1gfy/xWIS1tRwNS0Xen745wze+9wYXr94lGjGZXSjzDj7PPn6c/t4ufN+nVrcYGezj8y88xpXpO3zr5bdZXF7j5LG9HDu8j9PvX+T0h5eYmVugVCpTrdd5+bXTlCo1fu6zT9LX08W7H17iOz98B0PX2Tk1RiIuuH13npW1PKlEjLHhfkaG+kDcaDl8SJCFMuVKDelLnn38YRDwnZffZGl1FcdtsLaW48PzVxjs6+I3vv5FxkcHWVxcwXE9BII33jnDH3/nNZ565CG+9nMvoOsa33n5Td4/c5mdO4bYMTGAY9vk8kV2TY3xuWdPsH/PDqxGgzfeOcfGRo5f/sqLPPTwIRzH5cr1Gfw22y+3XCGdSfDQod3MLazw4YVpFtfWcT2bO/cWePPd80SjUX7+c08ipaTesNjIl/ClpFypcfbiNO+dvcaxg7v4tV/8HKah86fffY1X3z7HD9/4gF+t1uje5gyZW1zhjXfPs7iS49nHj/Pzn3sG27EplouMDvezvLrBG++cx7IdHjmmvHoNQ6cnm1bqYl3Hsm08T+L6kobjUSiVeP/8VXL5Ivt3j+M5Dhv5IoViBScIlaFpGl2ZFMcO72ZyfIje3i6++b23efejS+zfOUE6nSAWi5BNJ/jqF59m1/gwk2PDIH2q1Sr1eoODeydJJeOcu3yLlbWcMtzu6ULTdRq2y95d4+wYHyK6oKRjEoknffRopCm0EHWLZCLOM0+cJBKN8trbZ7h+e5Y/f/U0yVSMh48exNANYvEoC8trVOsNTh47iGkYWK6Hphv4vkpb1bAdipUaOyeHOX5Urecfvv4BN+/MYQfSn7feO8e7H13m+JG97NwxxvSNu5z+4CK1eoPnnzrJR+evceX6HR4/dZiHjx/gB6++x8uvfUC9oVSqa+t5/vRbrzIzu8jf+Mu/wK7JUa7fuse5Szc4cmAXoxGVLYMAWJ65cI1X3/pIqQEjBivrG+zaOcLRo/tZXlvjzffOs7K6wUP7dzEy2M/127N8cP4KTzx2nAlfMWuhyUnd9oKsIeB5Po26xc079/jmn7/Oxem71OoNrIbN8SN7OLBvF5kWLqIzGNBmQna/H7Yp7dpEIRCB005TitNRNmuDwu/uY6y/uZ1PUjNud9tmM48HMfp/AEmTECLAihpim/63YNh2fQ6+3/x1IOySfotWNNt+IEleWyYKKZG+ij7guC6O6+F6XtMMoPkMzf787MqnAluq/fZghqEe/2PuCu1e1If2H1R1BAt8O3dQNk9JC4JUq3X++FuvcPrDS+owD8SAc/PLTIwO0tOdxfM8XM+/z5Cpb6ORCMlElHQqzt5d4/T1dimxczCRA309xOMRbs/Ms7ZRYGrHOHt3jauUNYbO6MgATz12hA8vXOPW3TkKpQKacDj94SVu3l3ga198muefPMHswgp//J0f850fvk13V4Id48P09WQZ6O3is8+cYGSon2w6STaT7Oiliv/RGgPLdqjVbYqlCp7vo2m6WkDBkOq6gedLbMfBtm1SyRh9PWl0XSg7Jc9DN3SiURNd1/ClSyppsmdqlHc+uIBlN9AN2LNjiH07R5hdXMdxnQC0+piGzpEDO/mFLz3PvdlF/tW//zMWV1ap1iq4FY/3z14lm07y+RcfY2yknyOHpsgVCnx4fpqHDu5mt6/sUsJ8ge1SUhmsl9HBXo4f3kOlUuP1d8+zkS9RrdWR0kfXNXbvHKfacFha3aBSrVMqV/E8n3g8ykB/N0urG5iGwY6JEU4cO8CHZy7hOC6lcpWzF69z5fo9Rod6OXZoL7GYSSJq0N+bxYjEGejvYWFpjWOH9/L0c48yOTHMzPwid+4tsm/XBMeP7GF5dZ33zl5hI1+iVC2zvlFgeTXH5PgQu6dUfrZEPMrZSzeYXVilUCyTSiYYHFA54+qWzdTEEEcO7kb/wXstsAVoxTITY0MkEip/3b35RaJRE4nEajS4e2+B+aU1jh3aTU9PN13dWbqyGXzpUypVOXfpBpVqg2OH97JjfJh63WJ0qB/TVG3E41H1F43Q35tl/54d9Pd1c/X6XVbWNsgkE0RjcXzHJxqLoutah2TLtBwSsQhXb8zy/tmrlMpVHNelYdksrayzsLzOIw8fYNfUGI7j0tfbRb6o7NDK5SrzS2sgYWpimOGBXmKxKE8/doyL126zup6nYaMyCQRERKDWSi5fZHU9j+241BsWxVKFwf5uslkl7m9YK5SrNaYmhvjazz9PJp3C0FRictM0cFyXSrWO6/l4vku1UWWjWGZ5rcD46ABf/fyzuI5DuVKhty8bhH/RMXQdx5OU6w6xRIJTDx/m0vQMM/eWWF7fINuVJBo16enOcPzIXiZGhjGEkp5lMxmef/IEtbrF3OIK127ew/d9bNcFHSJRg3jc5PiR3Xzps08x9MFVzFIVCbjSx4gHtos+OOUqjYZFPB7lmccfZmJ0kPfOXObMhWnOXr7JxPgIvT1Z9uwcY2piCMv20DTlBb5napwD+6ZUgOeIydhwP3MLKzx0eA+PPnocJFyevt3chytrG1y6dpt4zOTksb3s272T4YFeiuUqM7NL3Lh9j7nFFSIRk707Jzl8aC9rGwU+ujANqBRX80urXLp2m8G+LibGh8l2Zxgb6ePilVu8d+YKz0tfRaUHXMdlbmEVz/cY6u8iEpzJ/b1dGIZBOhUnk4zTSCc5eWwfh/btYm5pmduzS8wtLqkUP027SWUGUK1b+AHYz+ULFIsVJEodWihVqFk2tutje21aD4Kg3O1FhEGqQzr3oAS4afvSpCVNEr4ZoGx7/6cj9OJTAK6W511wr/Zg93U2KDpf26RKLcClxmC72kPbu05gsw3Qgjb8KVQ4kWYX2oFWoKZtk83cr/hSKucYTwEt13VxXDfQgAV2gIJtbLx/uvLTBzUNpFOhYfe2A/sx32+7wLctEuV6rOyzKtUaZy5cZ2WtgKYJohGVq69hOeSLym7BcpRouw0KNyVF4Vd9vVkG+rrpyqY4efwg42PD6LqO5dgUSzXWc3mlBtQFmi7QIzqJZJRoRKWLSWQSTEwM09/bRaFYxfNcltfy3Lw7TywW4dDeKcaGB4hHI+ycHOLHp89xa2aBfbsnGRroJhGPMDE2xAtPnkDXNWWUh2QzWg+lqL6vQlpUanVcz8UwBK4v8X2Pck2lptF0nWjExA+ioCfiMUzTwJMSz/eQuAghVY7GII5INpsiHosGhLNBLGYyNjrAWq6I41p4vt004teDzSk1xa1VanVc32N5dYO5hRUO7Z8ik0riei79fV3s2z3Bh+dvsLC8hueFTgEy4CY6Y5tIKZtea6ZhkEklyRUq2I6L56uYbstreS5cvc2lq7cpV+sUimXKlTqurxLpakEU6UjEJJmIUWs4LK5s0NOdZXk1R8Q0eOzkIT7/wmOkknFcW4HXYtnCdlx2T42RiEeQVp1oRCOdUvY7tuNQqzfU4a6pHGuuqzat5/uUKzUqtQaO66MZJvFEgvVckWpdSdUO7t3BV7/wNG++d57rt+a4dWeOE76vPE4D43CZL4OQLK6scPvuPMurOQWsPZ/V9TwLy2vU6g1mF1Y5f/k6dsOipytNNBqhXq1RLFVwXBfbcWhYdpBEWVKrW+SLZVzXIxbYwnmOAkmlcpVCoazybnqSmblFdF1nYWUdhIbfpt5fn1vmGx9dJF+oUm9YuJ7P2kaR+cUVJZEJ7KAq1TqRiNlUdYTg2nGVMWq5WidfLDMYiaicpUEYE9CD89tv7VhJsB50bNvl3OWbxGIRurMpspkEJ44fJJVO0d/bzdziKjNzyxw+sIv+3m5lTK7rlAPHBl8qSUrDsqnVbWp1i3rdQSKZ2jFGqVzBl27TtEbXdWXMX6nhuArsu65HpVYnXyqxXihStxx8XyoQpfnUbJdGw6XWsKg1bJZW15mZX8J2PSzXpdqoY9k2rucReoKmUgmi0QhCq+NLZU/mRiMYMohZtp7jxvRtCsUyB/ZOcWDfTrKZNOsbRWbmlrg7u4BpQiJuMjTQzeXpu5z+8DyWbbNzcoxdk6PEY1Fcz2vmTXVdFw1IJuKYpoHtuDRsl/nFVRZXNhjoz6IZBtFYhOHBPoYGejh/+SY378yxtLKB7/tYroMUgv7+niC3bIREPEat1lAJ2aMRfM/H0ATDgz1EoyY378yRQyeNoo8ysItNxCPs3DHKrp3jjA32Mjo6hJQqaXs8EcXQlbNHV1cmyL8qKRTLeL7XJGTKScXBdlQg41q9Qb5UI9ud5cTRg9Qsi/VcEU1opJIJZU+4iT5tl7uzAxR8EhFuqga3kbo00921f/9xcOtBKf6nRAYtVU/LAeKnLZukTELTgnA62z2JGvUHDqUhFKCUgWNwGDR/K+DabK+1qYT23bTCPfjSxfe9lv0/EsT9hDQ/efnUcbaEkAG+UovpQQZKBDBx21hbQmxazFtLezrakNN1PY+6ZaEJ2DE2yME9k/T1ZunpytCVSbKWy1Gp1WnYFiCbwKNtRJGAEYkiAnG+40l8BPVGg7szC1yZnmF5dQPbcenOptB1sKyGcvEWAjOiqzx0mt6SzEnYyBepVGsYhqZyDGoaUigC7fsSx/MRukEyGaNWt7hxZ5bHTz1EX3dWLdBwP0uQm+B16GJu2w6O42GaGrbtUypXOX/5Bpen7xCLmIyMDBKJRlXOtyAGUMOyqNkWSS+JFR5GDZtKzUIIjXgsgu/71BsWtXojSPLqUanWsYIk157nUa1b5IoVcvkSruspGxjPp1ypUqnWVZodTVMRxOsW1Vodx3FxA5GtFgAsGaikw1ANoUOCZTuUy4qY+1JJ1ErlqgIGlTJ/8M0fcXd2id7ebvKlKpdv3GN4ZIhkMk614eC4flO1WihWKJaq5AoVKnULhIbj+VQbNmYsRjSwHUln0ni+zuLyGpbj4HoujuOoPrsejuthWQ6OqxJJ68E8eZ4iKNFohPmldW7cXQDNYHZ+iXyhBIDr++QKZaZv3uXAnh0A/Ps/+gFnL13nFz1PxSEyDAUs8kWuXr/DH33rx6yuF8ik4hRKSqKwvJajp6ebkeF+rt+Z59/9wXfZOTHCzokhTh07yNTkKDvGh7k8fZf3PrrM6PAA2XSS9Vwey3bwPeXmkowr4tqwlbTHcRxcz6Mrm+b6zVn++M9eZeeOUSrVGolETEm2AsazvFbg5swyEVOl9/F9ye17i1y/s8D46CDdXRmu3rjHW++eZ2Sol/WNogKgjks0GqOvuxsQXLtxj5NH95OIx/jowjQr6wV2TQ6TyNfB8RTUkjKQWEgG+3vZv2eSO3MrXL05y/zyOromOHV8H7t272BwoJedO8c5c+kW/+mbrzL63gUeP3GQL7zwGN09XVTqNuVqHSHA9VQeyFq9ju/53JxZ4Hf/6AeMj/STzaTYvXOYaCJOI1CpFUoVNvJF5haWuXNvkelbc+iGQb5U5ebdBXL5MpW6xZ3ZJbq7M1TKFRaXc9y8s8jb71/Admx6utNomiBfLHNvfploPEbDVh6w67ki1Vq9eT55nt90UAlBqleps7C0xvdffZcjB2b5/ItP0GhYVOsNKtU6swtLCF0wMtwfPK/F6+9eYHJskFPHDhKLm/hISpUapWBv1QKP3FKlppiWSo18qUKx2kDTdWzHo1CuYrsK6C+tbCisIVTy5/V8mcvX75BIxZm5t0i9YRGNmk1vwEQ8znpeGcgn48prsiubJBaNYeVrTRsZDcikkqpuTZkt9GSTWLY6P5qkQjfQTZNILIbQVRquesD8tGhFwJB6Hr5USdoLpQqO67G4us7qeh7HdRRo9lyKxTLSb7MvehDC/0BCpE0X3E/VdZ/2OsxH7teW7HzbvOwT7Kpakqf27rVJpba0E4rAoFO1qTBAiC3btaOhRkt0RI7f1J/OTm/X0S3tCwgyJrR1RYjgYYI4cFrwcNs+fkuNG+YO1XQN09TRdA0p1O8PlsTq05VPCbYCSEmQwFhrs7P6hEW62R00hE7tgTK3NCdUDqMQGLVLCDVNYJi64gqTcU4e389Dh3Zj6AYbuRwruQKr63nK1TqVagPbdjuSeStir+yGfF/Zk2zkSwwN9WE1Gpy/fJOzF28wOjpIb28Xa6sbfHjxBv1DA+imqdyShcByXBaX11jPlXAcBRSSyTiT44NcuTHDuSs3GRkdYqNUZXZxDU3T6M5m8KXEsh08z+f6nTluzsySSO4nrulEwv6h9MttA4JEYDkOaxsFFhZX6cqmyRVKXJm+zZvvXWBpLccjD+/n+EP7SCaTAfrX8H3J3NIaG7kifYG9SKlS5+rNexiRCCurG9TqFpWaAlaWZVMsVylXapQqNSzLwvN9GrbNynqBjXyBumUp4BIAqoG+HnZNjXFrZpFzV2/R151i5t48Zy/epLc3y+BQD37TrVwiRZBjLkCWrueRK5SIGDo37swyv7TGrbsLFMo1bs/M09+Xodaoc+X6PWbmV5hsOCwsrdNo2IwND3D44G7qDYvF5Q1efeND7s4uce7SDWYXVpiaHCGVSrBjcoT3z13jrfcukkzE2bdrnL6uNL09WTLpJIvL68zOr5BKxCiVq+SLFZZX8+SLVRZWNqhZLkI3sF2P9XyZQqXO8GAv+/dO8e6Hl3n73fPMzi1TrlTp7ckyMTZILBrFcjw+vDBNqVQLcj4KeroyaCKw5TACt/aGTXE1x9JqnoZl092VIZlMsLJRYmZuhV07x3n68eM0LIe7s8usrRdYWlln964dHEgmee7pU6znCkzfusd3Xn6dsZEBllc3mJoYoq+3CyMSoasrzchQH5lsCtuTNCp1UpkUzz91gkw6yez8CjfvzNHX08WOsUG083eaS3AgHuf44b2srucZG+lncnwQgEwmxdjoII+fOsL3XnmH7/3oXQb7u1lZyyN9SaPukk6lefKRo8wtrnLx6m2+88PTDA30cOfeIg/t38nnX3qC1Ddew8+V2s4I9Tow2MfPfeE5Eskk7525wuLKOtl0gkP7dzHQ30M0YvLEqSOsbeT58Ow0q2t5ltfylOsWGV9SKFXIF6vUGjbVegPbcdF1jYcO7aJUrXN3bom1jQJHDkyxY3JQSZZ8H83QEJqgUKzwxjvnuXF7np7uDHumRojHo5SrDYSuU67UuX7rHjt3jFFvWKwXSty+N8eNu3P40idXqlCu1NB1nfmldcbHR0gmE2QyKSq1BrlCiak29brv+3hx5SqgCUFSQHdXhvV8kW//8DS3ZuaJmAZrGwX2754glUpQrtVxXI/u7i40TSNfrHBw3xTJTApHSnzHZm5pjZszi6zlyyyt5VnPlbh26x6rG0U0XWd1fYOpyWEePrqH67dmOXthWjmFLKyzuLLOyNAAg4P9LKzmmFta4/0zV7h9d17Zvrge2UwKw9CZHB/mxNF9vH/mCn/yrVfZOTFIsVQhm05z6thBsudvNteUqWkcO7KXWzMLXLk+Q8Oy6e3Jcmj/Tvr6eljfKFAqVxFChYdxXY9iqYplOUqq2AYqfCkplSvk8kXlfOMrG84LV29y9cYdZXLiK4I6ffMeP3rjA444LpvDV2+hX00JjMYWb8HmReGbj9HUyM7zfLsKZJjeh0+gqx0qMx9l9/wTgIRg3YW3bokBFtTf0S5sAnPalmTYHfUIEQSMDXKSBNLyB7YVa5eYiSC9ltYGqKTX1tet93Q+i2LwlROPhmHoRCImut6SOf6spVrwacCWCBdYW3TkwONDIds20PQxyLpDZbTd0rifwZsIr1dtRyImfb1dLC1vsLy6wdlL16nUariuR19PhrHRQWbnV1hZK1Aq16h7GtI0aa1QiS9V8M5avcHi8jqXrt5iYmyIVCpBb18Pji+ZmV2iUqszM7+K7XqcePgI6XSKeCLG+Wt36P3uawEgqJKMx1grVBgf7efokb3kihXe/vAy+VINy25wd26FvbvH2b1zHMf16Onp4qtfeIpCucK9+SX2750iFot1DFS7bl3XNCbGhohFI1y4ehvTNBjo62Z9I8/C0jq+hBeePslzTx1ncmKIaMykUq0rrlnA8lqeu7OLHNg3xb7dk5y9dINzl26ytl6gVm+wvF4klUxQqVq4riBfqFGrK+6+GnCQZiRCrlhkcXkVT/p0ZRNIoFipMzk+wmdfeIIf/PgdvvfKaSKmMkoeHx/iS4f2MD4yqGLsNFmUznn2XA/TMChX69ydW6ZarTI2MsCQpySTt2fmSCXjPHJ8P8NDfZimiaHr9Pd1MTrcTzIRY6i/h9HhPpZWc3gIorEoe3ZNMDY6SFdPD8ePHWJpNccbp89x7uJ1+nqyjA73E43H0bQIO6Ym2Ld3CoRko1DGcn127BglnowTjUXxPJ/RkUEOH9hFJAhp0NPdzbNPnqC7K8NGrkg0ajLQP8ZjJ7ubYTNisRhPPnaCN06foViq8MtffoEvvPAY+kc3AhGrUiNqQnBkdIhf+YWXKFdqDA70EI9GqdbrCASO47J/zySjw/3cnV3Eajj09fSwa2ocIQQH9u3i178e4cz5i1RrdRLxKA8f3YfjevT2dBPVdHals/zWqaOYtQaJu0tUl1aJ1G26EXymamHbEn8tR+zWMjHLIbVWaDI7/fE4f+uv/CKVSp3+vi4aVp1SuUJfXzeZdIpHTx0hnYxRLFfozmYCdZXkwN6dxGNRdu0Y49e+9ll277jC2kaBseF+njh5hInxEcbGRtC++WbrsG47HXRTsG/POMNDvTz1+EMsLK2STiU4fGAXqWQC13OZmBjm6195kaOH9lCpVBno7yGdUuszlUpw7PBu7s4u4XgSqen09PVw6mSC3bt3sLKSRxcwMtRHd0+aZCrJ1OQILz17ikK+QG93BqFpTE6OMtDXTX9vlkg0Sr1uYbse0zdnmJocoSubIptJEY1GyRUrzC6uYUYMsumUYrS60xzav4vhoX4ef+QhxkYHyaSSpDPpjvNR0zRkNIitpmlEfMmeneN87rlHee/MZSqVGsODvbz49En27J6kbjWoNRpEoxG6uzR6e7L0bBSZHB+iK5smkYgjAM93MQ2dnZMjigFbWGJ5dYOerjSGoZEvFBgZ7mX31BhWYJLx0flrGHqE4aEB5QDUlWH/nikmRofIFfJ4vs/eXRP09XbRlUkzONBLd1eGX/3Fz3Jw3xS37s6D9Onr7mb/rp08dGgPyXurTXqhASePHaZuu7x++ixzC2t0d2UYHuoDoTO7tEbDdojHosRjMVbXc9QaKr5cMpXoOENc1+HazTvMLqwgBMRjUXwpWV7LsRoEU9YNHd/zKVerzC0t43qfDLY6y2ZvwXDiQkP30E75E1RkW1SMbeIaIeA+dszq1nZgwVZ08ADSrS3Jae4rsWvR/k67644OBd9pW9ptxSsMApUHN3Y+m9j6IKEB/P2KEBCKUDbFu9xSQqGFDIFWINkSGkJoQcy58PkEvgxv+dlJuB48gnyo6msCKq2VyiT0Emz/vc1ToN1DoTUQ2xjFt4O1bVBpuBaEEESjEfbvnmR1Lc/aRpFX3jzLm+9fJpmI8jd/+ysMDg6wlishEZgRE6vYwNeNjqHzPI/bdxfwfUk2kyKXL5IvlshmUhw5uIdaw6JUqqBpGgs71+jOpkml4jiOx7NPnqBaq5FIJjn58CGGBnvpyaQZGxnAl5KJ0UF+5RdeYmW1wOLSGrbt8twTxzh+ZC+jIwOYhsGTjx7FtnzKlQpCEyTjyU3RtcMnV/81TfDSs48SMTTyhRL9vd2Y0QhdmRT7du8glU4zMT7E2Ggf8UQcKQQN1yWdyfDlLzyDLz0ymQx1y2Vqxxi/+KXnmnZUsViEkaF+EvEYUhhouuTAvl3smBxjcnyQeMxk/54djA4PYBg6iWSSSMTkN37lSyTicYaHBjFMQ7mGjwxye+Yed2fn2TExwuH9u0ilkjhBCAz1iCrZdLsKIJVK8Fd/7eepVKpoOiTjUV6IREHo6DrMLy4D8PkXHsOWcG92CYlk99Q4/X09lCtV9u3ZwZ7dEyQSSWKJeDPhcCIRI5mIk0jEefbpUwhNp783y9Ej+xga6MHQDQQGI0NRfvHnXyKXL2JGTCZ7evn1X/o85WoNTdPo7+1maHCAwf4+bFsBnYgZY3hgiHgsScOyMAxBJGLg+5JSuYZtq7htp44dZqCvh3rgxt/fnUaBfokw9Oba7Nd1XnjmFKVyVaWaikeRvrKNA4jHY0xGoxzYuwsvV0AvNeheWMW8PY9RKrNvfYPxpXVksUzs6hxauYZfKGNWG0SFAnTDsqWOV553YBqmUnXLNolqENw1PJq1msXOybGA09dwvAaW1d3cuqlkgqOH95CIx0jEEmiailcXCaTBJGB8ZJBoJIKuawwN9JFIJtB1Ax/lZbxZLiBpBdzNpuMcObiL/Xsng9yAag0JVHiGoYE+urMZbMdB13VlB4WSCv3yV16kWKrQ25PFDJ4rEo3S29PFvl070YVASo+Gq1JUZdJJkok4uXyJdDJGOp0iEjGDtFjq/oal7PxMQ6m/EsGaE7rOnl2TmKbJyPAAPUGbpmmQSMSIx2L09nSxa+cEWnCe0XEUCpUgPDzzGir0whdefJyHDu5WzGZPFz1dGWzf597iIkZFJ5mIU6sr0DU82MOOiRG6sxlSyQRCwMH9O+nKZujOZuntyWKYGl/50tO88OwJNN0gm0mysrZBNBrhi595QtlcSbAtn7V1Fa+wv7ebidFhLNvm3vw8miY4fHA3PV0ZQKlmdE1jZLCf3u4uTj18iEq1hlVroAudVCqJjEdbB5wv6erp4vmnHmFqcoyNXI7+vi4G+nrQTYMD+3by5S88j++69Pd2YUZMPvf8Y9Qthz27d2C8c6ljncQSMR49cZhUIs7+vVNITefAvp10d6cxDYNqvc7aeoH+3i4+8/yjRGa+hWhTV7ZNQhux3/yqtanGQolX++8PWDqcx/y2vLMPUM+mHKLb3nE/4QVtIOj+naNDovVJ4Ke9zfZ7CLHDfXv5MV2QrXnYBOBaUGLr79t2K4SXHaY5MnAoU5LsUOMpZZiF+D7g7Scon9pAvilBbPsstxN3bvPwanKD99sBrY+ViLUkUppQEc5PHj9ANGpyefoui8vrCAQnjx/g2EMHiKeSPHLyMDt2jLK2nifxx6+1wFqT+xCMDQ/zX/zWL5BMxBAaZNJJXNcjmYzz6InDysstpgJ2moZyn7Zth6/9/AtEo4pg+L4PJ48gpCC3ofLwRWNRdu2e4uTxhDLYtCzAJx4ziEejaJqgp6sLpMZAXy8IJblqD/yphqJzLKZ2jDEy1IcX5LHzNQ3hS2xXeV4qbzOVnNb1FEcyOjLA0SN7SKdVlG6BwIhE2L1rgv6+bmr1Ol3ZtJIQuD6Vijp4Hn34MNFohIbVoFwt05VJk0ol8CWUqnVMw6C7O0M0GsX3wLZUgM7J8REGB7qZmhwmk0nRGyQRLpRaEetD8O43NyOYps7BfTsplSqsbqxhGjo93VmSiSTFcpnlFeWU0N/fgxQ6nudj6DojQ/3ouk65UqWvt4v+3h6S6SRoihCHtmJ+sJlSyQSPnjrCxMgAQ33dGKaO9CUqfZIifNlMGimVq3w2k6G/18PzJLqho2s68eE4nqcMpvXAGD+bSZP2koGXp0+5XEVIjYgZxTAMdE1jbGSwuaYU9ySD3G4G4cFtXLlNplwllS9BqYpRrSGLVXoKZUSxgl6po1dr6HW7Y7+E3GrU94lI2czTF9ooCU1TefKEaJ57wheEKXRC7k76EmkakE3jp5P4hTLC9cDQMV55h+SHl5oHgLKFDHaWlCQdBz+ICh9mHWgvUSDp+wxJZQrQyiEYKBgKJVo51FS9Wr5Mz9/+F8E5sflg2Gya0PkuNEFASkZkyyhW3dau7lDPbdkOjusQj0eJRkwGHWVMH42YxKIRJXEK9qWUPrbtkqw3eEhKst97T2U/EIK455OsNzjiecpBJRiLLSxk29yJfDlQ+ysVh4jHWm3V6oCgt6eL7q4sEdMgElFqxnypgm0rdVrDsrl9d57F5XV6uzNkUsoI3NB1ENDf20VXNk00ElFORUI0PaBdBCurOW7cukdXJsnI8ADJIGah50gy6STrG3l0XSeZjGMYGsNDfWTSSXq6soENX0CigrmNRkxMM0M8FqVklLDqNo2Ghe55pEKw4vkYhk4mk2T/ngmq9T4VnsF2iMZi7J4YY7R/gFq9jgDqDZsTRw9gRiJ0ZzPq2YKlYRgGJx/ai/nwYaKRGELTqFuOCgUkVbicYqlCrdZgaKCPyfEhjH/351vW6ZaV1LHutpHCbFqYmwNzb/d95w2bXbceoDRprOpLZ57b0Ib4QYBC284JA2cKArMf2dml7QBXM87XJnreJnD5NH3YUra0t426sKOdbVCKEKqfQkPiNZlJz1d7plJvYAe2oq3WQkzynxtstYNVQvADobiT9u+23tIkBJ+oo20a021qXgrCDOIgME2TE0cPs3tqkheeLlOt1RCaUMaVPV3UqnUOHdjFrobFRq5M5gcfoFeVgXQ4eNFylef+1z8idPX0OzgVSVdwILfblYVqPaF15ngD8D2J3mjQ7XrEY1GVSzDQY3cYCYYg/z4bSxTKzZ/aRb0iVyL2V/8xsbCHQX0+LZQfpukAiEgwXZdexw1UbiqQnPQlqcAuZEj6qCBuKlG3FxjfS6k8+wzdwPM8HNdRdnKGktj0egpYmaYZEHQfN4htIzSNiFRtGIaSPkgpidgOWrESBK9VY+K5XtB3RVRTf+t3iHseyWoV1/UwIyamodPt+xyrW8TiURLxOBKINRQojAVpagYtG9+XxGJRTFMnzEsYOhX4UpLyPHqCpLemqQBQ28g33yW22c+bjUhbARDVsyRl22xJSTZIE6XrerOdZNAfEQKeQlmpCwJvWglE/uP3m3WEHZBSce1NcKoF+eXa1kno2SZ92bk+hcBKxPDTCdIjA5h93ZBJ4qWTVHRBETB7u0gOD1CPxFh1bTxDpyuTJhKN0v93/zlGoazGx5MqUXTwrFrbrpdSojsuwvcRkQZCNzYd1OqlPapQ+7A3PYzC9d38XqLlSx0zFAKo5qftzhXZqSjROoDW5ksljuPiNyyE9NHiMUTERFo2Xt3Gj0VUnsUAbIVJ7j3XIyoE0aiJWaiouQSE72PYDgZgWE6TiWrDeB2leYKKIF2UrqFrBkLXka6HrDXwfKVm1yN6R2oj6UsqlTozswu888Elrk7fpd6w2L97EtdTaZ/CNadinzWoVKukU0pypwf70UQSN3Wy6QSe56vsBKZJNBolFlPZJCKmScOycFwH09QYHuwLAi2bwdkThCcOiJQQKgmNlBLLsqg3GoBA19rmulRG/61/gA6YEuJSObdIScDM6CRp7UEp/YC+Bja/wdoAMA2dibHhYH8ZEDA0PiCF35TkGkIjFokSiZhNtdb9Smi+spV2PTgwakqKtfsBrk2Jq8V27bW1HKoBFeEKCcL2/bsf4GniiE2Aomm/dZ/grR0PFv7bsqs79+RPC7jC+raoT9v6rmlticQVLhFhsNTN+Itgz7ueslWuKnvHptVOYCLVauinLz9d6IeghHZULREodByA2xnMfZr6Q/23bE2olivT93f+BX2EQUpDwqS8UZKeR9RVwcqGhSBas1S+RcLDXG0xrVButqNgmAy63Ap+up0RfxPHty1YISXxQJpkuD5ardHsrwjGoYm129f35vdhfwQYBFFeJICP2Ch09AG5yRyzbV0hwfB9dClbUoTgB62N6AjRmkPhSwhc0g1deWgIX6IFMbG0YFObUqqo2gFXqfkS3fOaJ6gIxk3TNBW5W4LheoR+HlIoSV4Y5iEYdESuiO5LIo0GfsPCEyqsiBCCmC6IuT5aXYFB01XgLhxnw1Eu+FpEGS03JRdSrSEh1XMLQrC8PdDazDbIsA46i9j0XjbFKGr9aMHgtm+LrUGAAwbEMMD1WkshBB4hc4UiZEL6yFQC2Z2F7gwym4JMEj+VwE8lcJNx3GQcrTuL2ZvFTyWpabBRKOD7HvR1k0zGgxQpEs22SbkumjAQmoGsNYiUykrFFomgGyayO4uv6W1bu40hans4KX2E56n8frqO1DcrBFujJZsjG3zTptKQANkUNEX5nXPRklbRrCdcw7L9StneQrinae5DEcwPKHtBq2FhR5UdoEjGcIWgWq5RD6RMsWwaETAOnuPiWDaaUCpj3dCDUCiqH77vI11XhWo2jWb4jHaJWtgnCFKSB3tLdGcw9Khan8k4ermKcNxAFduSfodjFouYxKMxbt1d5PT7l9B1jc88+wiPPvwQQ0N9xKKtvA2GbhKPxZqMke9LNE2dNdKHVDLOvt07sSybeCyOrkXQhIGm6cTjejNmmapLQzeECgUjBKCr88H3oe6AZQd/FmalRle+QKrWQHN9YjVLnQ2+csUX68Xm/GrIZo7I9gOttQdlS6CzeWUVyiT+2v/QurdD2kLnggimvx2sbfmx/d6foKg8py36tBlziCYh2PxAbTkpt6mz7cOmPgb1SIkCS6J1kGy6PjTvUd+LVh0CBN72z9zxACEl1T7mmp+ybKcx266NLZIt2fnapjkTCNBUWiTP86g2LKp1qxX8nM0U4GcDtoT8eKVtsxSufkuJhqHlgmua6IYBugFawOOGi0cGR+qm6u8rUt2sdw25UF8ipI+QPvxXvwOvfwC201bh1r62Sxc2H6qYBtI0EMFhs+19wb1+mxRLr9W3D+i7eR42r8X7NeK1ebQY+vb3CQGpBFRqP/MFLJPxbQ+fbS4F7tN8CNLCarbZAC141yJ+QgCmgbd/SgHCDy6B57eelzAju9cikIIghYLWPD/C+kIJjpQSqnUFzoWAdCJsfCug/QmG88FukdtfuA1n1fFzta76rGv4Q314mgBDR4tEEKaBNAykoSN1HRGoB9urDStvOlR0tBeG2gi32aa7gjH2AlVrWH9TvRY0FDIM+vW7zf0jTQNv7w586becH9h6Rga3tnVNoF9XXo7ywE7aOflm/Bs/9FzdfsJagujA6HbTGIfApzUMnZkk2HRP2J4mVEw9JEEsLGWwbgQeyBKaklJNBIL9Jq0SLVoXzlBL/NicC/P6XQjH0DBw900FgFG1Fdq4aBeuq/PONJBH920dVNResSybtY0Cq+s5IhGTkcE+0ok4RmiUHOSOFb5E3JkH11WAeHwIpK+YgDA/nO8Hf4GKO3wN3hPmoQ2uQfqt79uARftcbFsalqonHoPIdkma7lPaz0MhkKkEolxV38U+nam7qgMVWPjhg8jf+7+3VNsiXPv6TxT4M1zHoj3GgmKd1bsmAyxBk5uEFcF7wZb1Q9tLC6htfST1dVsKvTba+gk9h/uBrWYDAoXUtpFotTNjD1y2abNNMr99/cHaa2+vKdkKfg/XqlRrWroevuPjuio48sJqjnNXb/P++eus5Cq4UqLrOk8/dYqv//LPM9jf+0BQS6QOf+I1PxPJFkBL9Bg0fh+gdd94HvdB6wIfZHAI5EuItoi/Hwu0VCNbB8r3kf09dFCk4MWXNNV9fhCF3fd9ohETbWFVAQI6biFMr9FqUrBNq1tLuAiE6Igg3lEEUG+0ckZ+mv3+cWvd0GF0oOPS+1X9sfFGxCf8Ds2xaf6X4IXcfK6oxsowQHhqHIJ+KWmUj+cqwKUHIAMtbLMFNmR4JgHcnQfXU3WODhKGz3BcD9u2kago0hHTUBzZJ4AgOvq/9bqm4WfzIGvd0U7sN4/H5rq0e4sI10NqGpahYVk2UU0Q9X2wbYTt8CArK4QW7edyGP+6A8+3VyTB9zxq1ToSFURSv9+aBEStoWy4AGk7lO7O07Bt4kHyY13XO+BRuxCso/+1hnrdKLSlzJBB9HYlydPaJF7blQ5Llw6ipIoKpOo27euikcjHYuFWBWpeFana+qsK9bN19XueAmyG0emM075+JGoMCcZQ6C5arhDQVWVj53oqxVDEddVYez7iyq0W4JGtP92XxKVk3PcZ83xEzUYUq/dfLbbTPHtE49724yA3f/FTlPBcDZmzsE4zID/ZNPRmAzAc7utwF6mV1CQvAsTdBcWsStRZNjIA88vN8fzkPb25CCUs6Mp0fNtcWT+LMeiodbOmRD1j8KSbLu9A7h09awde4r6rui17cSjMuJ9acfO9m2h6W/f52QKtsGzj5flx2jBJZ1ttAVS3gNAAtAqhI7RQpQ6u69OoO9i21zwzhaYFUmSDn5gz36b87MAWD7gmQ1WBpG0NbELe0OKwQvATqIDk+DBoAtmd2abaTs51uzkXbW9UE63DWGgqajlC4LmuSm/jupCIE1nLt6RhQdRhx3GxbQfP95uHScQMDWkFKpBre8NtoMxxaG7kxGZurDPF0afd62FAT9txlZojquwS3LoK8KobOvR2dd7zMfXdT5QdSkSEUNz4dhujRShlMzhpqVQhEY/R251RRH1mofms9Kl+Sd+nUCyztJEnYppMTgxjhIdzEyQH5Lu92bsLrfd93c1sAxu5AtV6g3rDwjAMdkwMkU2nm/Fe7oemtiX0AaFFqqjh1VqDRsPG0HVSybjygAtUlFJsqvF+A31vEYmSpNwsVRACRnu7MNPtHqqByapUhtz1hoWh68Rj0cA7rm0fBAQu9MDZIs1qe+/7klqtzlqtRiIeI9adQQu87cKr24dBW1xtSmalobOGZLVSo0sT7Ej2kkrGkXSqBbcIPdtUN5btcKukIthHIyaRiIHr+cSikSA4Zkytk03SvPbsA6JjGQT7TqqYSwu5ApVqna5siqn+Hgxd7wSetKBx52ZrZWbdfESF9bcf7Y7jsLqWw3YcRocHlGPN5nuCN3JptQVYIwZ+TxYhBI7tcG9+iXKlRjIRY59pYob2Ptt5zAX1hrG5hBbEMBJbyW/z0do1Ax8DqsO6m6rOjkEWSqQX7ntNgNBa34XOFgLqlk251iCZjJNIJdB0vXWNpkEiDslYZx/bBsxxXIrlCqahHGSipoEIM4NEDGRvFtmr6EFokyWlxDTNptdpx/i3j0m+pMBrXzf+//KPtkbI6rDd+XSlFQopzH0aAq1N+7GDR/iktlorTjTrZOsG66jKYwvg+vies208MRn8JjV1sP0kaX4+ts3w9UHBTduzN8XcbTuy7fxByiZDJgLJse+DZSmbLZVxgOZZGY1G0IJA0z+rp/yZgq2PK03D8rbBkL5oRpjtMDpWbwhFhEqdGBhFIpFdWRr/5neCetSh7gdqh7AO3+9clBqyUwUvVLqV85eu8wfffIX5pTU+++wpPv/Sk2TTKSqVKjfvzrGaK7J31yQP/9/+LUa+hOzNUv3//J85c/Ea3/jOqxzaN0V3Ns13XnmH81fv8OzjR/mHf+e3GBzoD0JdhA36+DLgZIH4o38JahYkotTf/wM0TcNxbG7cvMtH564zfXMG3/fJZjOMDPVx7NBe9u/fQSqZaBEGWsvLR6U61YBSucq//8Pv8rt//DLjQ338s3/4X9DdlaHytb9Hotagb88E0X/7O3xSCedk82Hj+z4buQJvv3+eW3fnOX5kH4+dPEI2naK15ENgABIXiSRfKPFH33yF7/7wNI+fOszf+iu/qMJXHP264kpNA/93/xlCKMJ1/vRZ/ud/9YfousG/+Ed/g4MHdoAPProCM0Io3bsQSKGpvX/4y0pFEjHw/8O/YGZ2gdPvnWNwIMueXePMXL3N9K1ZnnvmFEcO7MMwzOac0BJLBevVb4KW5qYLwEyt1uDy9G2+8/KbrG/kiUcjrOeK9Pf18Atfep5jR/YSjUSaVLo5Jq0mOkr60V8F18XXdf7ZsV2UylW+/tUX+dJnnyKbTSvVllDemxsbZf7kW6/yze+9wb7do/y9v/Vr7J4aV+MQ5LB0XQ98T6ngxf2CB6t9UqnWeOWN91lYWuO5p04S2TGhDIebezJYu4HXUfpv/lOMc9cAcA7u4v1ffJ7f+9OXmZoY5u/8tV9m765JQEl5fClx/NaeDPd39m//DnoAsu1Miv9uuIvl1Rx/9S99kSMHdvPdV97mzfcuMNyf5je//iUeefhw0KdwbWrNtQih6q25cpvMQKVW59XvvMq//8Pv8fjJQ/y3f/s3yWbTHVL2zWtc2zRWm8euCSKb0iUf6XssLq/xv/3unzEzv8x//Te+zqnjh5RDCcrxpL3NzN/6p5jnp9UYHtpN4X/9J+iahmXbfOs/fZc//ParTIwM8K9jSfrnVth21Qih1PGGQa5WY3ZlA0/XmNg5TvdALyIWRZgmMmIoL1TTANMg+i//QKkwIwbW7/xtiEYQpokIVNZETKRpUPd8Tp+/yvfe+pDegR5+5etfZN/hvWixqKorZCg6iHebw4QPjUqNN97+kP/wpy/ztS8+y5c++ySxeBTQWhL7tnOb8EmD976ULK+s8y//9z/m6vUZnnvyOH9bSuKXbqnfj+zF/be/g0RSb9R47a0P+ZPvvoFA44svPs4XX3qKZDK+dewC0GP85X8MG4WPIe/3t516kLJtaKNt+hI0tY1okfYvm781gdbHAacOyVRb5Q8EuAB0kO2AqxVH7NOUdpre7MU2YyFCiZn8GBVmm0S3Y4yaXtHh721tN581AHLN/awFYR9cPK/zLDC2OE/99OUn8kb8xLJpEYRASwbehG3HljKODEP6B/cqPXdrUEMgpTWJoUSTYV49tXCElM3f2w3mA+khTe+vtucRQjDY38vk2CC3780HrsU+uq5UTcl4lHgkMJhtG4ZCscQrr73LyEAvv/LLX0A6LqNDfUzfnmXnjgm6ujJKdSPU0gpzLW01jm4NU7lS5Qc/Os0ffPMVNvJl9u4YZXJskLuzi3z7lXfo783yd/7KV/ncC49jmEYzBkjYrxavLknEo5x4aD/vfngJQ9PQfI9UPArJOIbjNo3l5SZbgo7h2ZY4t8rswgrf/P6b3Lq7SH9fd4trkJ3icEWf1adUMs6RQ7v54NxViiUVnb6/r3tTu+rVNE2Ghvro781SqVmKqEoNgbKTkU3uMHzuTqddAMeyuHnzLvMLyxw7tJPJ8WHGRgZ57pmTGLqBYdK2psJDgAD8Q1OCJdpC2gglCSqUyly8ehNdCH77l7/A7h2jnLt8gz/4s1c5/eEFdu8cV2BLtq1DZPN/2ON2CZBAYJoGn33hUb7x3de5dPUWDz+0j1QygRHYtLiux8LSEmcvTbOWyzPlDDXd/ptHg+zkDzvAQdv3SJUaZnl1g7l5FWV/ZLCPeCSMeRWCAw8pAvG+7Dwywkj42XRSqWY3rRlXtuoJmaKw7bDousbocD+24zLQ38vxI/swDJ3b95aYmVtmPdcihiHjhPTxhRaEtwi+I2Q8Wn2LmAYH9uygpytNJBJp7nuCNFCWZWMYRgtcbkOENks3Q8P00CMslAFomkY8HsF2HKq1Gq6r4m+1ywgUR+1vbiAISyKJRiLs2TVB1DSRwNxXX6B7zw4wdTB1pGkiIqaycTJ0pNBo2B63r9/mj/7sh5QrVX71a5/joUN7iZghOO2ck8i/+VNlmB6N4HzlueZeN5ou/OqZG8Uyty9eZUb69A72Ehvqg2RCaRaAjnwvHa0EG1Qqb+NiqYqUklg0ih5KeZAdIKuTYRbNc0CNicnYUB/vn7nCRr64ZfxakjeDVCoFCOaX1imWq007xM3zR9teVE8dMPbBGhBt8/yxqqyfWdEReG1nQbBPO4jGx/ejaSPWziTL8AnbNA8PBLTCHmyWfLb1YfPY3GfvtNuttbBfaLvWtrM1EQiXQ5DXVt92e7MDeKGu97d7tvtwuG1rfbP69pPo309SfirJljpsgokNv9w0IE1uToa50zfVEQKu5mCGFjltRrK+j5BeG5ptHU5tDbW3ukXC2QRLgXhCBqCqtzfD+GgfiXgUiR/oc32iUYOxkUH6+3pIJeOhvSQIWFle5fzlWzxx4iA0bJLxGCceOsCpU8fwfV8hYmVxSpPLbuPqN09ivlDij779I/79H36fyZF+/tu/+XWef/5JUj1drC8s8/t/+Of84Xd+zKVrt3nuqROYptECAs361HsdiW7q7N45zt6doxQKZRVIMWrS0FVsKt/zQfhoUh1s7WEk7yfN6pxT6OnK8KWXnsS2HU4c3deM+bN5vNUYqMVu6Dr9vd30dGdwPZUsuYMWCBS3LCTCMEgGcb0sy1Uu96ERvQgkG4HqrBUduPNQrdXqzC0sBwe02oimoWMacZXvTwQoKrSLaIlNmgevFALptQ7BMERApVpH+pKHDu7i2OE9JJIJjgvB+Su3qFQb1OsNvIzKh9deQpf2iGmClLgylAG2Rq4rk0IgmL45y5XpO4wEgWQlUKnWuHL9Duu5PLquIaWP49h4nqeMeDVl22bVG+B76IGrueu61GqNIHCfVHGaTDP4zWN8dJCpiRFi0Yjqj+9juz6WZVGplZtqmWwqu2U9hGEm2tkZP4j9Vqyo9E/JeAzDNLYCPkD6knrDAoGKCRWN4HkuCImp66QSUQVagpxnAjVlnqfSYxkaRE0diYYMHShCAoxE+l4z6XJzmaGkp7fuzFKt1dm1c5S+nm50bTOYVHc0Qze07WGlplXBeVX/BL6nAKzntUKOKMlgcA7QeSaqz0EwV6FRadQoV6pomlCpaUb6cSZH0KSvvEkDKUu4rjxf4kmHhlXD9RwSyRiJZASheWo8fJ0wFExzibcNvu9JNE3lsHM88BxbAS9dJxqJ8PxTJ3jo0G6GB/oYGuhDSInn+gHz05JWmqaBphvga4RshFVvMDu/xDtnLjG/vI5l2UgZckoqg4fruE0bNc/zA+eEljOCEBCNRsmmEsQiKsyMbbskaEUil75Ew8fUDQ7u3cXxw3spFs+RSsbRNA2v3da2jWar/ROeHS0pSIdZi1D1t+sXf6ZEuKMuve0sEAhCGhcApvs028lMhXSmHXAFHx8IZHV07uN/DusLB3Ub2s92QEv1ctO5J8EXgaMdCBFknA4kUR31y3aWiuA6fwvQajdhUfWHb9X3Pn6QR7N9nNX1fxHw+lODrbBLZoj8tlt4HSCITqDV9lu7TUi7BCAc2JDbkaF3TKu6po2CDCVagLdZOtO2QH3aOJXQ60FKPN+hYdm4rheYHqgbDF2QTiVVZnqtM4l1sVzBdV3iUbOZn7FQqlBvbJDt7lKbPEDtdduhVK5iGipydMtzrrX4rt64ww9+/B7jw33813/9l3j8kaOYehQsm5GBPn7r619CJQCXOLaDnwDX8/E8Dy8Ad4ahQixoUgShxPxgzMMgg8rF2yPkfmSwmVVuvlCPrYalUxISzpUMJwCYGB9mfM8O8ms55mcXmFtYYXx0iGgAunzfp2E7lMolXM8JAKuG77nomobrOnieu8lTR8Uv2ljf4N78Mteu32V5NYeu6diWzfLqRvNgtzyoB/nturNpMqlk0zA7XCi5fJGZuWXKlRqOo8JOSFQgztxqkfmlVSJGhPGRYVLJBGgalqMSDzuWTb5Qolyu0t/bxchQP0IIGpZNvlDk1t1Zrt+exbEtTEPn0P6dLC6vMTO/gGV7vPHuWfp6uti7a4LR4QGVq65Q4vT7l1hbL7B7apyDe3fS1ZXBMDoPCdMwGB7s4c69Jc5evM6hA7tIJlRsscXlNdbW8+zdNY7jugqECR/fd0BoFPNlzl6+wez8MjtGB5iaGCGdSnBvbonTH1xgcTVHLGKyc3KEo4f2MjTQSyadJBIxWFxZY3ion4hpYtk2a/kSN+/c4/rtGWbnV9g9NcbnX3ia7rZ1sZ1Ng5RQb1hM357jzXfPcfveAof37+T5p0+pvJya1jo8A0lPuVqjYdk4js3K+jo/Pv0RSyvrnDi6l8P7dhI1ddzAK86TPmu5Ih+cv8admQUyqTgnj+5nYmJMRTz3JbZtUyyVsep15hdXlE2dobxZFQjy2MgVOXtxmkvTtxgZ6uXRE4c4cnAPXeksUgo28gXW1nNEoxES8TiWbRGLRunv7VHx5TQN09CplMssrawxfXOGhZX1QJKigIhl25QqdYrVGhHTVCrhNmkmwXpcW8+zsLTKzOwi5y/fwHE9YtEIEdNUtqGWTaVcZi1fot6wSMRj9PV2k8kkkIELu+t5RE0Tw4w0Jde1WoNGwyESMYhEIs0AoOEesSyPUrnI7MIy92ZXKBTKpOJRjhzczb5dk4yPDWNGDEqlCqVKCsdxuDx9u5l1Y25B5QF9+NB+JsfHMA2dYqnExau3uHrjDpen73Duyk1czw+cLhRzY7su84urQV0+Y8MDJBMx0qkkiYRS+zmuRa1mUarUuTe/QrlaZ22jiG3bTQAhpbIF1UQQo0zTSMRjJBMxEvEYmq7Alu041GoNqvU6jYaFaUQYHuyjq2PdttZkexH4LZOQJj/2FwW4toKvVq7EdkBABy1VwL/9LA2kUjKoczug9cDqxE8o2wCtZh+asTE3Nb3t1S1NV1Ma11QPtrUV4gUFBNgs0eowRQquFU2mOmQSfDzPxnZsLNtpmh2F+OL/2ETULbDcUZS0Z7MYqXMStwCtcEHfR9ITNidB2Wq1iwEDDqRSKlGu1ACIRgyiUROha9Qsh0q5hmGY9HZ3KY4rCFmBlAgtDCugFqbretiOg8QLOEA1eZVqjdt3FlleyzE02M1jjhPEaQqDS6pF4fs+H166zo9Pn6VSa/Crv/A5Du7bqZLXFoqcPnOFsxevs3NylGceP66SAbcdeFJKbt2Zozub4re//gUeO3kEU9PUxvHVGA329/DXfv3nyeULRCMRHMtmJV9i+uYMcwsrRCIGo8N9TE2MMNjdpWwifMUJ+76H76nXpog5kO55vke5UuPu3Corazni8SiTY8MM9vdgmgaNhs16rsDaRh47MKoVQjDQ3013NsPl98/zjT9/Hc/1+MKLj9Pb04Vh6Ni2w9Xrdzj9wQVW13Ps2TXKww/tw9B17s0uqNQ3QuI4Fr7vobetiwuXb/D9V97m9t0FSpU6C8sbjA3107AdcoUS0ve5fW+B985NI4Qgly+xY2KEL3/+afbv3dkm9JasrG2wGBA/11exuayGxa07c7z9/gV+/PY5dE3wK195kc+/8BSW7fLK6+/xyhsfoOsay6s5VtbzPHJsP3/9N36edDrJnZl5zlyY5s69RcqVOoViiT97+Q1mF1ZYy+W4dnMOoQk2CmVSyThf/uzTDPT1UKnW+P0/fZnv/PA05Uqd7mya3/765/nCi0+RTnfalEgkDz+0j0QixszsEucv3WB0WEXJvzOzwGB/D7unxpldWAmCSYKUHoViiT/+1mv88M0zLK/mGBno4a//+pc5engPpUqV6VuzfHTxBrqucWTfFP29XfT2ZCmVq7z61hkS8Ri7doyTSqqsB+9/eJEzl6cZHxngyvQMcwurnDp2iN3NHRq+iCYY99BwfFjeyPPuRxf48NwV7i2scvnabTRN8KXPPE08SEPTZLgJVEjS587cItM3Z3j5tQ/Ys2uCX/vFzzI+NoQnHXwpwPNZXl7jz15+m+//+D1yhTKmrjE3v8Svf/1L9PZ0s7SyzvlL15mZW6RYKLG0skGtYRGNhgbrkpX1At/+wRu89f55dE2wvLLO9M17fPGlx3np2UdpWDbfffktTn9wiaGBHrqzaeYWVT7Gr3/lMxzev5dYLMK5S9P84NV3WN9QCcEXVzbIZpII3cDzfSqVKpem7/DOmcsAPPLwYXbvGKU7CHgLUK83+E/ffJnbM/OUSlXW8yUV8V8IDEPHcVzmFld47e2PeO+jyxRKFTLpBMcO7+WlZ0+xa+coZmDuAErSqGs6hVKZ0+9d4OKVOyQTMR5/5CEO7dvZfpRy994Cf/7DN3jnw8tYloNpmkpKKCWTE6Ncu3aD3/2D72CaBn/jN75CNpPi5dfeIZ1KMjk2zI/e/IiLV2/xtc8/w2/+8s+RzaT49g/e5Pe+8TKppEptZtuucnIwI3hSgZ+5hRX+4zd+wJvvXsBxPU4d28+XPvMEsVgU3/eZm1/m/XMXmZ1f5ubdReYX16hbDo7jKgN7WoTRcV1ml1aZW1xldSPP1et3cT1PZeQQgnK1yvSNu3x0/jo3785hOw47J0b57HOP8YjcSlZ9KdFDCX9zpYfrPQDKm8x5PjX4apeif/yFKGvcFtDa/o7O3xRjLJFCR9wPOGyiwz/rssVsZjP93wJqoSWZC79UsQVVxOMQPLWHewg+swlkNd/7QWDTNgYvEOK4rkfDruM4jcAmdVNvRFPH9jOBXg8MtkK7q81R04Mft+hum5O9eTF3oE5ak7150tsGTPqyoy7f9/nw3BXePXsZ23FJJ+NMTQyxc3KYazdnefv9yxw5sItf+vmX6O3OUigUuHZjhnyxrMTXSNJBotlUMq7imxGoeIRP3arx0bmrvPvhFWaX1gHYVyjRj3KgF0KlGkGTuJ7NzPwCpz+8RKlq8dRjx9m9c5Sa6/DOhxd4/Z0L3L63yIUrN4Ngg4+SSsXbHlOyupFnYmyIwwf2IHQjIEI+CJ1Qn5zNpOjKJPF9n7WNAj9+6yN+8OP3MAydgb5uPjp/he7uDJ996hTHj+wDTUPTdWUs7fsq9IHjqjgjvlJ1FEsV3nznAt/50XtUqnVc1+ehg7v4S1/7LP293dy4dY+3PzhPuVLDtl3uzS+TTMT50ktPcPyhfVy/OcMH564xPNirVCi+T7lS5Z0PLvDya+8zM7/M/NI6lmPzyPGDJBJxSpUGG7kiyUSUar2B4/qECkjP8/nGd1+nUq7w1S8+gyfhD//sVfAhlUqya2qMQqHE7/7xD3jnzDTPPX6MlbUcK2s5jh7cxd7dO9DblpAvWyE8nCDg6cLiKq+fPoOPxsH9O3nrnXNcuX6Hl154jLnFJf7kO6/xwflpDuwZZ9+uCXRdcOPOLNN3Ztm3ZwfTt2d54/2L7Jwc4de/+CzSU1HEx0eHuLewzO3ZZXq7M7zw9AnisSj9fd1I6XPzzhznL9/g5NF9uK7Hux9dYXFljUq9RiQRaxFAKckVKySTcY4f2cv6RpH3P7rMwf1TSF8yM7vIof07cV2PcrlGX08XvufjeT7zi6vcmpnj6OE9JO/MMze/jGVb6JpSCdUbFv19XTz96DEO7B5nZGgAQ9eD+GWCUrlKtVpT4NTzcFyLA3t38OyTD3Pt5j1W1/LU6kG4hqDDvu/TcFw8CVJo+BIF4qsVkskoP/+FJ7l2fYbTH1wiXyzi+jYQbTLcbVXRsFRUdtf3qdZtEokY0XicmmU392i1WuG9M5d4892zNCyboX5lL2g7Lq7jki+U+P6rp7l6/S5PnjrC/l0TvPnOWe7OLYNQ/bUdl3c/usib755jaLCHF585ieO4vPnOOd4/c4WJsSF6e7KUK1UsyyYRjzIxPoTreZy5cJ3TH5xncmyYG3c2+P/+uz9FSvi5zz5NrlDk5R+/h2U7SMDx1N5+78xlzl26AQJu3J7j2ceOstN1iQXznS+UOXfpOo+fPMzBfTt578xl3nz3PJ6vLL1sRzEvL7/2HnOLq2rcFyRr6wWGBnqZGBtBSpSEJzgzG5bNB2cu88HZK7iuz6WrNylVygwPdjPcHHXJ9M27vPX+RXzf5wufeYzJ0WEq1QpHDu3GjOrULYtyzSIek9Qdh4jVwJc+0YjO6HAfLzx1grX1PLliibptUVqs8d0fvUMqmeDv/c1fQ9c0/vf/+C3WcgWi0QgSie25zC+usLKa4+DeHays57gzu8jSyhrjIwO4nsdHF67yxjvnOLRvit1TI/i+z/ziOslELEiJ1Nov7310hW9+7zWq1Tq1hs3s4ioDvVlFUD2fmXuLfPvlt3jno6uUKjVAcndumYH+bk5sk4vz44s6nWWbJOVTx+BqAq2wvuaHbdoKX2XHtyKsR7bf3zJAF03pzH20T83KfgqgFQKmB6ljM91vp/+bq5U+rSCrYbD0QO0YAq1mHbIpgGl+JyWgBXm9w+wtPs1wVIFpksqM4gWR4wPFZpOHDG0yNwfN+cnLT2SzpeypaD2caHtP673KN+d3qAa3lG2AWufPapD9tu+llNQaNRaX15i+Pc/SaoHx4V5++eeepliuce32nAIASFZW1/iDb77C+Ss3SSairG4UWFwtcGD3GL/1dWVImkrElETGdZG+ZH5xlbfeO8/KWoFYNEK11lCqgcC40zAMohFTqcwEPP3oMW7emecHb5yjUq1hOzaVUolcociTJw/xxMlDfPP7bzK/sES90SAR7wz1UCqXqdcdypWayuGlaUrNJz2lZiGcdIErJXcXVnnljQ+xHZdf/6XPcXj/Tt47c4Hv/vAdYobBjvFhNEOl51FrSyUNDqUP0vdxHIeFxVX+5Luvc/nGHE89cpiFpXXu3FukWKoQj0a5NH2b5dUcX3jxcXRN5/e/8TJmkMMsEY9z+MAudowPUatbQQBYSb1W4+KVGwz29/D5Fx7n3/3hn1MqVzEMnd6eLEP93Ri6rvJRVerYtk0imHfHdbl5Z44vf+YxXnr+UYqVBu98eJmN9YKy3YhFmF9a5e7cMtl0gnqtzokje9gzNcbBvVNbDs1kPEYiHqXeqDVTqxiGxpEDu+gf6OfClVt8eOaKUm/4HrGowUBfF+lUnF/4wrP83Bef5c3TH/B7f/wy9+aW2bt7Egk0GjbxeIzhoX6G+rswdRVJf35pBcty6O/t5tSxA6SSCRUWxJcM9HXztS89x87JUc5cuMZHF6/jeWoeqtUa/cHBIaWkWK5h6oKdkyPsnBzh/OWb/PnLp4lEDHq6MowM9XNl+jYNS0nrlLu7koQcO7IXwzSZX1wlFjOJxgzAxzQNorEoqWSCA3unOLRnUkU+11UU/0jEoNZoYLsWrmeBcHno8B6kENy4dY+FpXU0TTTd6kEd+p4vyRcr1Bt201ZKSp+e7gwnjx8kly9y5sI0sSBhuqYJEF5oegeo9em6SrU/PNjPjolhLly+xcUrt3nrvbO88PQpurIpNF0jVyxz694cluNw4qF9HNq/k3QywXB/N8ODvVyavsvZC9PsmhzhxadO0NudJR41uTu3jG05uJ5HuVJl+sYMdcvi1PEDPPvEw/hShQj54OxV7s4uMtDXzdBAL309WY4e2sNzT53g1t157s4usbZRoNao8cbpD7kzu8hf/tUv8vwzDwfSsTtcvTGD5zu40sH1XXp6Mjz3xMPkCiXOXLzO+kYe12t5WpcqVYTWw6EDUzz/1Amy2QRXpm8DPkLzsV2LO/cWqFTrTIwOkojHcFyXbDpJLBZB+hLXUTZPpvQRUtJoNNA0jZeePcXwcD//4T99j3yhTL3eoF0q6Xo2tu2wY3yIpx47ytTECJbtEI9FkVKSTsbJpBNKQuW41G0PIQTRWIT+wT4G+vt558NLGKYyqbg9M8/S2gbHj+xjbGSQgYE+Tl67yatvfUDDrtOw6xiGTk93hi++9CSJeJQfvv4B127dJZcvUKlVEBqs5/IMDfTwlS8+QyqV5E++9SO+s/EufT1ZZbMarJ5qvc7v/tH3KZRKfPULz6AZBt//0btYto0rlWf2pWu3uHjtDgLJ2HAfgMoZaWidAtqgbCel6oz79fHXfmwJkVCHyk9s+muBrFYOhfC38PvWp1YflNpNHSVak179hZVPC9QegP53PiE07bGan5tcnvqu3XZb+sFlmpJmSdkEWk173vCzr1Tvyh63Lb8r8DPCVlvKT2SzpbEZkLZPfas0jfM+sdKPuWizRSfK6+f4Q/sCYC0olGpUag0S8RiPPHyIVDLGkQO76OnJ8q3vv8H3XvuAZx45wte//BznLl/nd//kR2TTCcYGe4lEDNKBJ1W9blEsV7hxa5ZcocTYcB9DQwPsnBim+998B0o1NVdCicJrdZtCoUahWKFStXEcl1K5SsOy6MqmeOmZU7iu5L0zV9CDXHxuIGFqL/FYlMvTM5y5eI3+viypVAItiNTpS4nluFQqNTzfI5tOU6nWKVdrHNgzyZEDuxjoz/LQwd2cu3SD5dUNNnIF0tm0suvyAw8fzUTXNPwAcDmuz43bs+QLZZ48dZhD+3bS39vN5PgQiXiMXKGIbTtEoyapZIJsJsXk6CDVWiOI66STTiWJREyqYfwfIYjHY3z1C88RiUZZXFlTSZAdl1qQx7C7K0M2k6JQKlGt1ak3GnQFM+z7Ppbt4ro+xWIVy3bQNR1NNylW6jiukupZtsPuHSOcOLqPZ584Tl9fF4ZhKvF/k9NBqT6lxLZdqtU6juuSyaTp7qoyfXOGsxenqVs2uqa4n0w6wY6JYd7+4BKVwL5DCpX3MV8sY1mBfZ/ntdIfSRUnqljMMX1zlo18Cc8PCJNl0WjY1OsqAffU+AiVao1rN2ZoWE5TEpgvVRn3fLRgrtbXiyAkoyODDPZ3o+s6r751lsdPHuS5p06QSiXIF8rN5NoNy6LesAMnBcGb757n2o0ZurtSVKt1VnIbOL5PxDSp1y3Wcnlq1jCGruH5PuVKjVK1ju061Bo2DcdlYWmd0x9dZnU9T75QZnU9T293hlyhQnt8qxCkVOt1fF85DziuS7lS48r0Hc6cn+b85VtoQtCwLMrlKol4mD5GnRnqGWwEglrDIh6P8tipQ3z3h+/yg1ffJ5mIcTzwyrRsBx9BLBbl+EP7+Ozzj2PoOoYmSCaTisi6Htl0EiMwqh8e7COZiCnbHVvtU8dxMQ0jiAkm8XxJPBZF1zUaDZt8sUypXKXWsGjYNsVylXKlCiij7o1cgXvzy2r+A1uzeDyqktV7Ks5do9Egnohz/Og+lpZzLK1uEI0YxNvi6smAK7dth8XlNdZzeTRNpQByAkmd53usrK1jGDqfefYRnn78OI2GjdAEQwN96IaO5ypQb1kOtXqDof5uHj66n3rD5vL0DKVylf6+rqZzTSgNiEWjREyDhmWzsrJBIhYjGlUJpb2oT6XWwLZdEOo5PU9JyU0zguU4LC1tUChVGBroIRpReRM9X1Jv1MkVcqTTUWzHolprkMsXyeWLaJqG63r4QuPCtdtcvzOv7rFscqUajg+lcgXDUA492Wya3p6sYvTSyY40W+VKjdszdQ7tn2Tn1ASO4zLQ18380iqe51OuW6znSyAlh/fv5NTxA/R0ZYhGTCbGhtC+916TzIQR3e8Hn2QH4KFpkvFAgKspzQqNvjtr7pRQBd81P4YSqvB9oOlpWoSERv3tLmgqJM7P2qNuS/kY2r1FZred9uo+krHWPc3F2rqnKdFqC0sRAC2BFmCzlgd0mKpNMbNKquV7Hm6Qzs+XHS22gN7PuHwqsNW0+Qk2ahhotPnwbF54mwdIvW9OwgMsBLW0RAe4E0LgS6hU6zQsG4CG5XDx2h3mFleJRiOMDPbhez4ra3ksy2Gov4ddO8aJRiP86K2z9HZn6O7KYAhBxDCUbr9SY25hhbmFVRqWw5EDu3j01EOB3ZemJERSHcy1hs3C8gZzC6tcuXGX6VuzeJ7Peq5AuVIlm+ylVq/x4fkb/OD1j1haz3FMaJTKFTzfpS8cMyEYGRzAsi/zypvv09+X4ZGHD5GIJcGX1BoWi8trfHD2CqtrOX7pyy8pKUXEbNqbeZ6PpivC0rBsStU6BLZTrufRcBzlteQTGAb6WLU61+/M40vJS8+c4pHjh1hYViqKUrlCvWEpqV61wYUrN9m1YxShCTLpBKZhNL0rfV95tjXt0ISG6/u8/daHXLx2i/mlNUb8HkqlKrYn0UyTdCqB57t4nke9bnfMdr1hcfnGDANBgtt63VZ2ZbOLHNy3k3jA1c8urnDq2D4iEYPV1Q10Xae3v6+pkpQozz3LsanVG2zki1iWzdLKOr/7R9/HdiX9vV3ouk6uUKZaa6ikuxEDz/eYX15jaXmNXL4ceJf5WI5DqVSlXrdZWy8wO7+M4zjkc3nW1guBxMMmV6hwb2EV33PZyBWJR+PcnV3m9IeXmBgdYGU9T6Nhs7SywezcEp4UeL6n1pbns7C4RsQUFPNFursyjAVhEfbu2cH46BD5QomZ2SUsy6FcqbO0ssFAXw+vvfURf/LdN6nWLWXbognOXLxOzXJIxGPYro3rKeBerdeImAIzorOaK7K6kcdxXNYLJWzX5/qdeb79g9NUaw3GRvoxdJ18ocLcwgpeaEwazL8QAtfxKBTLrOfyDPRnuXz1Ft/47usUihV8X1JtWLx35iqjwwP09XZ37H2VOkOth1y+RKNhBSrUAj968yzfefk0Dcvm4L4pUsn/P3H/HSZZdp53gr9zbfiIjEhvK7O8913V3qEbHgQoUiQlUhQpzUia2V3tzko70oy8VlqNtFrNI620kihS9A4kQACERwPdDbSvtuVdZpn0PsNHXHP2j3PvjYjMrO7qJvTMAaozIq4797jvPZ95vzj9vQXOX5rkyrUp+nsKdHVlyGXSaIYZ5OIULCytsbpeJJtJo+sqcbPjeYFJXM2LZlM5aU/PLbFWrHBreiFwSjfY2CizvlGOqANWVlV/V6t1qrUGU3dmKZWrVOt1rty4zfziKpZl0HAcfCTFcpXrk3dYWl5nYXGFi1ducWt6QSWDb/e/EWAYOqVyjQuXJxkbGWBufplKtY7n+Vy9cYvhoX5KlZoCWAJ2jY8ihFDaTE3geSoYpVZv4kmP5ZV1PNfl5tRdFpbWuHlrgbnFVSbGBlX0YJsXUjqTIpdLMzO/zI/eOE+l3iCTSjDY3002k2F+aY1KrUpXNq3A30aJ1Y0y1XozeK+bLCyvsXfXGELTSCUT5LvSzC6s8Ob561TqTWbmlylVaqxtlChVapQrVb7/oze5eHWKpuOqfkonqTue4j1qSjZKNVbXi8wvrzM0PIjjeEreaO1ZH9R8cQSUqw2KpbKi8NBEEGWmtHCxmCLFdVyXZDLB2EgfyXiCXFeu0wQYAifRLnjbzXFbZdaHAjObgdYWkPF+95IgNsXTSl+pOUTrXmGOW8SHM452PGlTvX4sgO393IU2mUk7S2dwWgS8gndv+WEBm4AWBO447TjE9yM3Gs9rBdlt+3S5OWLyz1Y+nGYrso2qcE4ZJCcGVP9vJgG7F+qVihLhftWcCt91drim61i2TSoZJxG32CjVeO6ld4nZFp/92APEYzZCwM4dQxS60rzxziV27Rik2WxiaIKB7hy2qSum9UaTWr1Jpdqg3mgSs02qtQZvvHOFbDbN0GAfvb6KepRSmTxcz2ejWKFcq1OtN6jWG7iex8qaYqueW1jmy994kduzS6SSShN0+fot4jGbibFB9srWG42PDXH0wG7eePcyv/PH38FxPE4dPUij6TB1e4a3z1/j+VfeRtc0HnvoBJlUku58ltW1IvOLyxTyaZaWV1kvljFtE8d3aThNfCmp1hpslEo4TgPXc3E9RXi5tl6k3nCoNxzeuXCNkaE+pJTUag1q9QZCQE8hRyxmcfHKJPV6g5hlkc9nMAwDL+Q5kyHhrBK8t+/O8Dt//G1ePneB0cEeTFOBmYtXpxgdG8b3fcUknbCI2TYtBa4KN8+kkrx9QZnIuvNdLCyvUq7WmJldpNFwGB4aYGSwjzffu8b3X3qbUqWG67ocPrCbB/L5wKqt6rSwsky1WqdcrTO/tEa5Wmd1vcT03DJDg33E4za+L7l5a5a5hWVGhgdACEzTRAiNSlWBtHrToem4OI6HHbPJZBJcunabtY2v09vdha4JervzaIaBbVtcm5zmi1/9AZ7ncWT/BA8cH+P1d65w6dotFpfXQEpsy+DW3XmW1op0F7ra1gNJpVKhYQiajsOuiRF0XWdosIdD+8YxdI0r129xbfIuzabL6lqJ6zfuMtDXzfT8CstrJRJxi0Q8RrXW4MatObq6cjiOMjkO9Bfw8ak3G/giiYsyyYPasMwvrACSZDLO+OgA03NLuJ4kl8uqbAlSaVvbKRAs08D3JbPzy1y9cYvxsT5GhvsZHx3g0vU7iKZLveGwsrpBuVLF0I02eaa0eYl4nKbjcfvuAgd2F9mza4SnHzvN4tIaN2/NcuHyTXq7uxjs7+Hood3cvDXDjVvTSCEZHx1g3+5x8l1ZuvM5ervzXL55h++9eI4Th4ssr65TbzgsLa9RrlSJ2RbpVBKAN96+Sq2paDHmF1fYu2uM3r4eVjZKrGyUVDqPusPaepG5+WWqtQalSpX19RLDAz3Uag2u37zLV7/5An09edY3yoBgdmGVXDbNjclp3nzvKrV6E9d18T3BRqmifH6ESuGbjMewLZNrN++ife9lHMcNIiod5hZWGB0dZnCgm2s37vLyG+dJp5IqyEDCvt076OvtplSqsL5RRtMFlXKFxcVlvvm9V4nFbJLJFFJK5peUdu1Y27KczWU5sG8ni8vrvHruEjNzyxzZP0G+K4uo1pmdX6becEink6gAkya6rnN7ej7gaFtG13V0w8Dxobe/l5PH9vPWe1d57odv8O6l60zPLqIZOkI3iCcTLK2XeOfiDZZW1hnsL2BbJqWyshBIH7LpNL29PVy6foevfuuHKsPA7VlcT+ILrQUkhOKli9kay6sbXL1xh7GRfjxP+Ry6QURnX1+BeDzGlclpas0mha40fT0FPv7Uw/S3aZAEbboroWhfQOsAZB8NeASAYLts9pESYrMZUaByfsrIfYmAg6zD2iNAyjZlhAxtTx+ttGhJgttrLU65D3uf+ziLiBronsc3Hd4WaAUSxGttAtWHIB9ocI6M6Ep8vMCMqPKhtrd6e47WTuPtn7V8JJ+t0A4qpaIOEOFAovWiyl9rK90DhJUPB2BoJ93a4BHe3aYvstk0+/dOsLxWZHZxjVp9jmKlTk93jv17x1UoP3Dq6D4+9uhJvvn9V/nKt3/IYG+e4wd28fCpw1iGSaleZ22tRKlcI2bHyKWzjJ4epF5zeOfiJN954RxnTh5gj68c6DypOIFSyYTymUHQlcuSzaSp1psUy8rBuFJ3uDO3zEapyu7xYZZWNnjn4k0q1UYno7GUDPR18+lnHsbQNV5/5zK/96Xvce3mNEi4eXtGLVaaxtmTB8mkk8RjMY4c2MUPfvQmL7z8Nssra0zPLWKbJhM7htB1jdm5JZZWiyyvl1lZK1KpVVVKIpSaVNM0do4PMXV3gTffu4ppGowM9ZKMx7Esk1QiTiIRV2STTZd8LqN+TybwpUrk6fsSXRM4rkvTcRUFxkaJi9dusVGsoI/1kUknmZlb4dW3LnPyxEElEIQgn8+RiMcjBndQO/xHzx7h1XMXWdsoo2sG/T0FUokqCAU+8l0ZnnjkJDMLK9y8M0+l1uDYwd10F/Id48b3lVN6w3Xp7cljWhYNx6Onp5uHzhzFcz1iMZux0X4EUKo2aLo+iWSCZDJGqVyh2XTIZVNkUgmlNXQ9hocHePjsceYWlllaWqVUqnJo/zh7d40ph+pAWzK3uIJpGAwO9jO2Y4Tjh/exvLLOwvIauWya40f2kE2n2L9ngljMDqg3FBfZYw8epVgqk82kyHdlKOSz7NwxRH9/D1Iqctjjh/cwPjpEOpWkp5Cn2XR4+MxRllbWWV0rcXDfOCtrG/T15BkfG+TSlUl2T4wwPDJAImYHAjuGaZr09eQ5c/Igpmmwb88OLNPk4L6dZFIZpueW8HwolitUKjXOnD6C+OE7UVubpsFAfw/Hj+xhaXmNxaUViqUKwwO9PPHwCXaMDVGpNVhfL5JLJzlz4hDxWIKWsUYQj8V46tEHWFheZ7Cvm57uAulUklw2zU986jEuXZ2ip5Cjv68byzLZs3OMz33yMV576zK1Wj1IkxRDMyT9fQWefvQ05965xNLqBu9cuonneeS7sqSSceIxm+5CF0cO7KLZdJieW+Lm1AwIGB7o5czpwxTyWVbWigihkUjGqTUdyrUmrueTTMTwfR/TNHny0ZOcOXWQ6zfvslGqcGd2kUKhC9O20DSNQwf3IKXk6s076JqGCOZ9o+m2tPuaRi6b5tSxXSytrKMHXHTDg71Ypsm+PTvo6+vmobPHWF8rc+XGHb7+3ZdIpxKMjw2yd/cOmp5PpVrD0HWlrTdtGk2VDsmybOJxm0JXlo1ihen55cC8RBBZnOcTH3uIbDbFhcs30TWNiYlRhoYGuDOzQK3eYHion6GhAex4nIlsmlwuzfpGCc0wOdCVYcfoILvGxzAM9fxnn3yQRDzG7MISqWScrlyKmG2yd/cwqWSc3u4cxw/v4dbdOTKpBJlUktmFZSqVBp4HhUKOpx59ACklk7dneO3cJRxHcvTgHnaNj6Brb0XCMZNKcubIBJevTvHepRvMzC2yvLJOLGZhmgamaTA63MeBfWNsnCtzfXKa68COkX6OH93bAQpEmzZIBmY5tBDIhOgjFM336SITSjFti1Ftm/PaiZlb8lHIgGw1OnXTvcI6RZqjjwYPIpDVJnTDTQF8BKC5WYPVIePD990MQLexhm26V6czPCBFEHHYfsyLgJaUEnz13ZcyyrDh+4o6ST2yU6P536J8eJ+tdjMgARpsQ5itfvqzqd8kYeer3t+MlG3LpL+vwKH9O1Uky+IKjuezb+cIOydGMW0bH41YIqmcZGMxPAm9vQWO7pugO99Fte7hNH0yqQQH9+zACWy4g/09fOJjD9Pb283M3DKWaaHpWmS+HB7o5fOffIzllXUMw2DnjmE+88xDLK2uo2uSRDxOLpvisQePMT27RCGfIZWKMzzYy2BfDwO93R3vIoRgdKiPn/rc04wM9XF9aprbd+dUzrpanbGRfo4f2sORA7tIBALy1LH9rG+UWFha4cKVm3QXcjz92CkGB3qo1xvcmJpG13X27R4lm0khhFACqZnEjsVwkinGRgZ57KzPwvI6nuezvlEmHgsIJHWN9Y0Si0trFPJZHFep+4VYxfU8sukUmXSSw/snmJ5dCjSJgpGhPh47c4S7o8ucOr6XWMzi2s07dBdy2JbN2voslmmQTadJJBItMtRg8f/EU2cZGuhGoDHQ24thGkzPLkTmKsu0OH3sALNzS6yuFzm0bydnTh5isL8bobXGiC8ljusHO/88+/eMk0jGSSbifPYTj+G5HpqucejATmq1BoMDvUgJuWyGidFhenvyGJrB+OgQn//UYwwPD5DJpKhVa5w+vo9cLovTaGCZBoV8jmZTmSif7cnj+5KFRcUVlsulMU2D0ycP093dxUuvvUssZnHiyD76egqk00mKpVIwzxWh4xOPnGBuYQnTUOZrXdfo6soQj1lYlsXp4wc5tH8XAo163WXq9l00DU6dOIjredycmuaJR05hGDqGrrG+Uebm1DTjO4bYu3cnugBT14KxZDA81M8jZ6Cnu4vengKapmFbcfK5AscO76PueFy9cQvX9dgxOtSx+JqGwcmjBxge7OPilRusFzfwPI9EIs7JY/s5ffIQSKgHpKWFXGhCbC1ssZjNg6cOU6krc38+myIetzEMwZmTRzm0b5eiWkEt/oZtcOzQXiYmxqiUq+iacniO2xa6pvHAyYMcPbSHaq1OSAD6eKWGaegMD/YhhODwgd1M7BhmeWWd5bV1pSEbG2J8xxDNpsve3WPkuzI4rkc+myaZsMmm4uyeGGZuYQXXcynkc5yZGOWBk4dYWVFaO1+qXID1RpNcOslDp48oX8R6M8iZqegc9PduRe+fSiX4iU89TsjGn0jEgrFukkol0A21+fEbHvv3jGNbJpl0ivGxIUaG+lkvKvPZYw8dZ8/OMQb7e7AMnacePUk+3x3Mfw/HadLb2xO1vxBCaQMLXQwP9XH29BFMQ2diVPnzLS7pHDm4i/SZo+S7sooDyzbYrWt4UuB4iow6ZuokYrYyabqSsbFeYrFTrBdLZNLJwKEd5R9mawwNdvOFTz/B7Pwyhq7jS8nU7RkcxyWbSRKzbQ7um2Cgr5u5BUWGGpK4DgX9FywZxOM2P/eTH+etdy8zdXuWSr3BzokRRod72Ld7BE2X5LJJHjl7lK5chpm5JVzXZWSoj+58trX2oExvAtECNpqGEH6kSOgUxiEIaTnZ39OSo213fdv3UIWynTktBFxAp+RFMa4Hl4TRcy0g+OHkb0i8rWxIctOxbep+nyVStmwBWgHYatNwd/hjtZ+6qZ7qQwCSpKayIbQDMFoaLRmYDkPNlh+kM/OCiERQ69gmZeGHbb77Kh+O+oFOdBu9TDRoQs3JB3VM25t1PCS8diu4ajsl+heL2XTnc2TTSUCQSSXYt3tMmXZ0gYaKXrp0/Tazi6vYlsGNqVlm55boyqTYs3sHe3eNceTQbrJdORaX18ikkziOS1dXhscePE6j4at0Kb/73ahThgb76C50cXdmHoDB/h7GRgZY3yixUS6SSMSIx2I8fOYotXoj8PfyaTQ8NKFFzN3trWEYBiODffT3FigWyzSaDk1HOWSbhopCk0gazQaGbtBbyPP0Y6epVGokEha5QEAhYWFphYG+AkMD3QwP9jE80EsiHieVzaD7AhIxSsIgl85waN9OzmYySCnxmk2ajiJhVaYM5Rh+e3qeTDqJ53mRiTSfy7BrYoQHTx3m1t05CvksmhBkM2meefwBGo7L8Gg/lmkEwQwCV6pcmL09ebq78+RyWRLxWEdUTX9fb7Ch1Cl05UEIctk09XqDeFw57+ZzGQ7sHcc0DI4f2UcmnUQIH79N7SyE4NCBXYol2lcpP6wgMW2hKxs9L5/LRCbQ9WIJIQQH9o5z8uh+kokY68UiRw/voa+vh9X1Engepm6zY6gPyzSRSFzXZXZOpSTp6e6ip9DF0EAPqVSCmG3ieU2SiRhduQymqWMaBv19PXQXunCcJr50iVxFBNi2qXiJAlbykONMaRIVSErEY0gpmJldplSuMjRYIJWI01PoYm5+GSl9hgb6qdbqTN6aIRaz6O/J05VOqL72vDZWdB/LMjFNC0M3EWhoZuD6ITSQDfKZFLphELeVE3noSCyEIJmI09udpzQ0QCKuTHSxmBXkFlNzJpVMBDJF6xj34Sz3fR9TV+mYqrV6QBiaIG6bAclqwMofqP51XSOTsLE0xbNkmqYitXVdDEMjm80FiaBlsKlVfDleYEbvLuTo7ckzNjJIqVymVq+TzSSxdQ2pw8hgL7snRjB1PVqktYkRXM/j0pVJ3nznKiurZfbtthjqz9Lf24PnuTiux+raBtOzCziOQz6X4ZknziiQpSt/wIXFlQ7TlK7p7N+9A8uysGwz2oGHa0SlUmFtZZVCPsfhA3vIdylTvqHrNJpN7tydQxOC08cPsm/3OI1mk1qjythIP+OjowGvmYvjuRFGCLA9qZiFEOA2dArZJJl0kmTcplZrkErE2DcxQr4rSzwei8yemi6QQlfJF6TSbhtakKdS14jZFr09ebpyaWxbaZhCFnfTVHxgw0M9FPJdaJpii5/YMcz6Rol8MEcMQ6e3J09Pdx4pfZpNFa1tGAbt4kUIjT27RuntybG4ska5pgIs+gpZ0skEjqPmTn9fNyND/UFQSpWm41DI5wJtVgA+1Q0JDYqRBSb4XW5as5VE9pQ7zLbiKtBo3QuovJ9El9t9CTsu+B5xnbZvXjZpaO4XNQQKkhBoifafP3LZ7urwN68FYjuAVvDLPV2QZMf5ImSCkGH+2jbFT7Cua6FTvAx9tbzAd5FoM9v+zkLT0DXRMc5+HOW+wZYv5fYcW6jm09o+v69TmWhXv4Yq2fCbAKmASZijKlSSbW58KVWKEMsycF2fWt1hZLCbifFh4vEYoo2mIZ1KUqs3ef3dG1y6Mc1If55k3OL6rVmymTS7do0w2C/Id3Vh6Aa+J9AExOw46VSwiAfUCeHOVEqTTDoZRRZapkkiEccM0qBomhJCdiymkkRrAnyB53gRog6LrusBC7wemOrigI/rSuoNlYrFcTzK1TKuq2NbNoZh0J3voq+ngG1bKqIOH8dxScQTDPb3ApKeQp5cNoeu6Qrk6TqeEAjpk04lyXdlAwdSDafpML+4TKOpAF4uk+Lowd0A5HNpXNfj/KXrLCyvs7yyzvjoEIl4nGQ8HvWpaegM9vdQazi4jqMY43WdarXOaqlCLB5nIpehr6+Hvr4eYvFYxxjQNJNUIq12mbqB60t0yyYbi2PH4gjDQugGumFgWCaWZaEbQo2HMDxHqEk0NNBLsVSmWKrg+S0m4yhKVipBp5LY1pmeWWZ5ZYNcNk2+KxvktrNIxlMk7QR1q4llWfiewPcEZtwGAb7XQKC4rOKxGLGYTU+hC8NQhLoSieMqUtauXIadO0bIZdMYuka16lKpNqJ1RiAwDINUMkEsZhOPxdA0B13XsC1lnvJ8T0W+FmvcnZ4jFrMZ6OtDSEmXbdGj66xdnsKYXaS+XkabW+K0bTExvUR6YQ23Wsev1jE8D9FwSK2uY5eqZKwYCU3DfeAwzcdORZsLFRihNBQxU0MXKj8Cno9x4Trpv/aPSHg+2WYTkNh2J1N55ywP3nKtGC0DrutRrzexbZNGo0GlWkXTNFKpOLqhgafmhm7oUZ471/WoNxpUa3VFXplUdfU8j0q1gqK7UGBVChn6xgIi0Pjp6LoeRHI2WN8oU6872LaBxCOVTJCwUli2hesqDi/d0LGkDNYISaVSC9LdGOi6AaZEbzYiegzXVX8T8bgaJ75Peb3CwvwS/Z7X3hwRiAjJlj3Po1ZvsrSyytz8MhsbZcaGlcZVrW/Kl6beaFKqVFT0dLMZIdh4LBGBXrUp0KKUT9FjHZfYX/4HgCTnOCQdlZHANE1MzycW0ImEAUJRH7Y7kMvOrXVokci0bc4Fbfk/26J4MxEOEXTJMLMFHc8Kx05cQtILXBcuT4Lnga4jBMRiBn12F4V8RkUkB+mTkCrAKN+VAQSJRAxDVySxtXoD24ptkmnbybegQaPzQggmW4fCNtkWIGwd/Z2Ht9PihOdr7Y2K0LYBIVKwrY/W+7jnbFsi0LadquQjoI73fWwItLam2IF27dUmbViH+VAGv3qtegdjMbSIqYA2Hym9iHMR3wt+k2hCUfYYuq42P4ppCcs0iCfi6FqQXeXDv/225c+UGzE0/Wwerh/UvRFOD9SnUS4nKVpAJABYUQ4x2eIWkag+8lyX9Y0y68UyAEMDPfT39aAbJj6KOTcRj1HIZxnoKygzyHAvD508wNWbd5ieXcJxXXTdIhEXWKYX1VDtrNWuOnpXQN8ok/8//wuklHQFi2nYWYVg4GhaWy61tnaJ3guJcINnCYFpaBi6QA8XGiFA6GgaUW6vutbA9dQiqwdElLZlBshcRxMCKTV0XZCIJ8lllCNgzE6AZuBrWtturQVC9YAjCgDLJB6LkU4mEZpgbb2IAE4c3cdgfw8bxTL1RhPLtslk1DmWZZDJqDB7UMAxloiDbuL6zYCYMUxEnaCQzykwk01h2TF0vW0H4bgk/to/JuYpHZUWCJSMVBodpVHRyLguD5QrSquSSgQ7EyVQheMCIgr5j8UsspkUhXwO0zJRav/2hTOcwBqNejMiIk2nEoAgm00H7aSTTqYYGRwM8l/qkebBNAzyXTmSyTjJRAxDV4DbtpUDvkCj2ajTa9v0jo/SnUqSmF9BNB0SG0V6ltfQPV85eDoufT94k956A9310B0X6k00x0V3XUTDQdYb+LUGyUqNnkoN0/UxfR/heezzfXY5btDuLedWw9DbxklLcywB21PsyXoQlSsHeqLxC6rtM+mkoljQtWDzL6I+E6sb6FKSCJ4lOiJM2bI+hOM+LGaxzEP/9nfQhRZ1iWmaGIYeyTCJTz40cwgR7VKllGia0oRoAuKeT5ercmmq60U059o1GCGQTPqSdNNRuSVDcBnMj/D6jgwWwIMNh+ONJuaL75D8g++i6S1hZ0nJuOMyEmwyQi0gqPXMclxStQbpqVmE5ys/vfPXyf71f9bZrkDS80k1Ggw2HQxdJ5lIYLYl+5ZA2vM4XalysFon/vWXVFonKclHabxa4zS8Rjhe1M5iZQOJxPB9dF+25bmEWBAAFZlZotaj437tH4LW2rbvWz+IFkgJvmpR5bYfLwIwgj7HURo6PA/tvWvYf/kf3POBpoRstJNpHewKf1ortqofAsfWf1A1C/p/W2AW2lnEVsD1kaR0e9DYpueFVjet1f/hOfd81H1quYQQAU1XC3DJ8NNHeY8tQKBdo7V9LsOO0m5ODD6LtijozecIqQU4oR03+CAdfKlStSkKCMVDp2sikNOipalXTLAkk3EKXVm1Afoxlo98t8jGHdg/NjdbB/PqlkGoEXJkhA3n+0pFLZAICW64vnU0XrhoStZLZa5dn+Lc25eYujOPZRoM9neTy6ajNA0CwdziKm+ev46uC/buGuWBYweZGBng+tQ0o8P99PQU0A2DWGDqA5RzfyCgWv4B4Y5MItY2kFItrOHrgWhNkw4A2t4G7b+1poqpC4xwwQkmbmilV9oRH01T/jHtO01fb9sJhnUTAiOlq0g7NCzLUu2NWjBl0C62ZbYAbPCOuqZ8g9LpJOVyhZtT07z8xnusrW9w9MBumq5HtVZn/+4djA73k4jZGIU86WQS0zLRDBNf09E1g4Ru4ksbKV083yeZUfe3bUuZFnQNTdeivJZh0Vc30EJAGizdRjDhw1yXupSkAw2AFnAAhW0ZNbfrMvZ3/i0jbQBYbBcOHag8Ep7kMcdBui66EGgox1R8H+GrhSHl+/Qo/ozgmFoApO+R9cNzg0nud14rpaSv7bHh+EgC3YCoN1Vdaj7pf/6rW+u5acHTUYJkc9GATp1S6+LNGoiwGG3niXqD+P/nN4j9h9/bevPgOlGpIsLkvq6HHrCaf7gi1Dsjic0sEq81CDUI0rY3vW9IoiyjMdBZodb76UhaLFbbSAldR470R18N1HiSm4Rke1RSh8gTSssSB3CVZnBzlUwpMTqaPhiDdxYwPA8biag1WmO16WC+fWXbVjJB5Wdtr0DbrQ1dIyMladfbsvHdrgiAekONNc1DzCwEEDRcd1olhOPvJ863G1MarWvZctePXjTUBl3NlWD8OS7ijYv3BARbUsZsexKwsNJBHxq9gQSBpgDCFsBFcOY9tB+RD9V9FAnBG37wuYHTegcmvFfZzhr1QaCr43gItj9cH3bIP+EH8l6qDmlnge+o1iYQJQGCtVbSOUdDIlIZjl2P0AiqLvcR0kWGybylD9IN+jOU22pjoTSp4SiX2LYif47S/P2YyofIjah3jlxNIIUWEJwKwtjDD5NJKGR8kX7A16VatLXDCBrYCwEXYV9J3rt4nT/6ynPcmJphebVIOhmnrydPPBFTiDWoT91xKZZr3J1bplZvoGsa127eoem4fPyJM0oTFoAzNuUsbB9gfi59T2kVfWwb/bL1MTqkzKTBuwVpgKSh4zou0jQjIBQ1ccApowkRkKKaQdMHz9BauQ7DuhptE8NrUy8n/vb/G/38dbUrnF0k8z/+s0iAqQqK6GWUOcanv1bjydUN3HPX0I3ncFIJGn/jpzm0bxc9hTyWaWDZFokg3YyPFphb1RvraAhhIDSJH6QP0kNgqAVjRuhgGOqfZUB3nmgXI2UEXkQAXhTA8TF8T4HiMEm576OIxDz12ZEYl6ZaE9snmIAKJNH+NyjRZLgXKrlHufeuctPHbe617bVuS8MarDNqM9u+4IVOXlrwV2itz9FvouM3KQRS01rH2s9xPLSFFdUmQR7QzUUlig18HUxDzXZDx+krRItW69zt3rHzbcX0PHhea8aE43ET0Grx4aA0scE7bC9s2rbULdtsNK+kLpCFXGdTtt9m82tv00HRHA0rsHkfFWwYW4AjWNxnFtXGnqDd2+Zeu+a+fbPWKeO2qUxgcovA7/2UMIOFriOHejsObTfct13PRaulQ849EbTz5mC4+8E74U07L21dGfJmAZjBXG8B0Pe95fZFbnO8TdsbPLRFZyQ1ovx8HRvnTYBqC1iRvH8Fwwd2Aq0P5LoK6/FhywcArQjvqF/unYroA8yUreZVkiFqO9/rPK8DQG2+lx9pszrPD9DGFnNjkEc5zL5Ci9hUk8pny0flNQ6XmU6aNfU/pRUPFQE/Prj1ITRbgjAdwPYoPvrPB5YOO7/00CIo0tKQachOLic6h63v+7iuj2EYjA33sWO4l55CFj8wLYQTfrC/h5/9wsf43gsprk3e5drkNDvHBnnm8TOcOHYQ27KC+rf8CtQGpvNdKv+vv7nlHaQMJpZoIWWEwEfrMMOEAESd6uO5Dqm/8U+RS2uUbJOrN+9yYM8OTNtWGrlopSVS70Yq5DZgFRhEot9DEBam5dGRYbIkxHpR7QLdIAHuyjpeQIrqup5KP2Tb0b0MCRkpScZiKq2BlPiJOLkHjpJKxDE8H7lRQas30JsONByou9BsIptNaDQQTUf9azjgNBGOg9Z0EY0moulA00GrO4HA10KUh9ZoItt3r+87sgRourreACxL7dhB7eDveZkA/ce5b2lVJ7D/gqYhNdH2fdNnIUAPvs8tKeBo6Pijg6AJ6k6T9VIVqQm6urLEk3GErgfXhslp6QT+m3al7QfvucxKiaw10FbWwXFUfUwdGUSRCYJ10vdpNJr4EmKWpfz8BEw3FQFuIZuJzMkdt4/u0lm8Qo6QR0h/6xI4HtLU8U4cIFxQXddjcWmV2YUVkNDfm6e/t4DewdW16b6+x0axzEaxTCIRU7Ql5ZrSQhZyeP/1n3Tu1rc0TJspbNND9PbrwnG7uS1DDblUybURKuycX/yHiJUNNS66MtE5KhK4SLlaDXwxs8Rsu818J9rW1+DZqxsKDHR3qXsvr6mxFgZ/tLV9KKAItRTBCe8jd7eWTet7uK2u1xusrqkI1EJeRX1/dBLMTcAlWPSkhHK5wsLSKkII+vfuIB6zW2vsthqkTsDTCabbzl0rRu0oZOhkHUikgL1caUPUnGttCsJnBtpWKbc2qM8mk9/Wt+18701Q02/JlVa04X/jIiDyAbuXRqt9/MM2AykYbyLQZkU+ytsAp20HYQCofIGiyW/hgAhotW4S3au19rVtc2TYh4F2XEqVdBrZ8uMKME34L5zRnY43f/byoUlNQ9NhWI1QsEe/y9ZEpH231l6CnUE0vNq3QtHC0BK0EoHfdo4QguOH92HoOpevTdGo1dF0jemZBd5++wLHDu+l0FUAAYl4nKcfe4AD+8e4OTVNtVpjZKCbidERErF4x2AKnU7Dem/fAG0dIkK1pR9opVTRgwEhgwka4iWEZG29yBtvX+TY9AJ2qYIrJfv/+a9SyOeIBfw84cLWKSo7GnDbn95vWGjnryNqddXurgfTi2i+j+G6eI4LpoEW0DdExfMx6nXwgj65PUf6Y//9+9vaN7WWaPu7fdVFoMmR4BsKFIaLW6RBeJ9W2NRI7SaiLY1nGkjbBNtCWuovthX9hm0hYhbSsoLvJtK2omPt30UsvFb9Hp1jhGkyNKSgBXahc5GALYESqviEpDGL80t85wevMbuwzNOPnuL0iYPEErHoTqF/gh+ENoeku8rvIvgnW3/Uzs+PmsYLFidNSrTZJfS//s8Rt2aRcRt5YCeN//KPCRfNMNH1f/3drzIzu8z/8n/5S4wO9fPm+au88Mrb5HM5PvPMI/T25BDRewfvLmUgvrS27hLI0FFHQPzMz4HjIUyD5q/+A8IFt9FwuH7+Kr/5+9/k7swiP/u5p/j8Jx8jnUoqTV20QQuEmpTUGjX+9Nsv8mu/902eePgo/93P/wQjn/ufENU6YnYJ85f/QdsV7bCks2z+ZesYbpt0uQz8u/9ly3rXyqnXOiC7Mji/9k9URJRU0Zc/fPkN/uSbL2CaBv+nv/JT7N29I1gPBAidlpNCcJNf+DuwvB6tl57vU4vHmfvHf52+HkXiGTr4Oo7LRrFCpVojm06SyaSCSFE6cs5u99ahOVUEG4T2GL1mvcH3X3yNX/vdP8XzfP6HX/pJHj17HMsy2uZ9+/ra5ncrw98AqUcyoMNHSLiAxEfjvdff5d/8p9/H933+4d/6Kxzct1PVKTg/Wrs3d1H7W0UapJYWSfzi/wwra8FwU7vaKM5GBnQIWjiOgzuLzh6+Z4lAoNzm3LYG6Igm3Fo+Cqnoveuz7QPaTtn8fve4/p6AS8luEbLlR5rAewGre5QAa7RvuKFtDd3mfjL4vZ2KKjoQgLEQYHm+g+e5+EFkoro3bQDzoxPD3qt8eJ+tIPqhs1PUYOmkM1AO24I2ICJbL9Xp0+UHAz0sSijcY2+CRHHTHNi3k7mFZX548QZLyypn3tGDOxke7CefywfWIx9NFwwN9DLQ243n+xjCD7RA7c8NJtQ2fhrquwycONuBmHIglaFJQIAMoxaj15GR7bfpuLx74Srfe/5VDrouKdMgpmvEfImxUUIYRkBu2bnn2dIF7fXd5vh2RYROpYBvmxQzCSzTwPV8lpbXSCZimAWVJDrCOPUGzCyB6wSTBmi693zGlmdufof3RYOaAip93WDbHQAn/OvHrOiYiJlKi2WbYBkReJJtgEhaZvB7cCw019xjzitFxf1NsvdbNloaCQWAfDQ0AvX5JhuL3LzQESizNYFt2ei6ycLCOnMLqzSbLnY8fHoLjIfRXn7bDr/T1LWp6YO1TxdtwtZsaaRCE5imCaVB9jwazSaJmEEyrjTBuq5jmjoH94yza8cItmkrugqEWiBFm1Y1MCGo+a4FbygDMLpN+4U7MQSmodPb3UV3IcP07FKU71ALNzpIvKAlVFsLNGGQSKgIwMhR3nVVBFsTWFln+xm2GWjcZwk6QkrAbwHcVmNrbZvK1l01TQFm2zLp6e5CSljfULQrgrblVYYb2rZady6O+L7PxkaJ3/qDP+XMiUOcPX1UJf7WRdRXcwtLLC2vcmDfzg7KlfYbaZs2BKEWNdTgtQ9foWkM9vXwE594lJ5CF/v3jGPoBhqhRrS1cWjdn0CgdT432hx0/CYDLKXQjyZUQEwmneywHITv7/sy6nP1mDagIwXKmzE0lW1jHJU+QiiBHHVhe0O3A4f7KbK13exggt9yTpv5PdTU+KHSoe206J02/agO3F+d7mWO/KB3EiEIDp1/2lxeIkzZSvgcwct2MLbpOZGiZnN9wgklNo+fe5eOdbTNEtb+e7gx9XwVtd90mjRdF9f1Arke1lWAf5/+cx+ifEQHeXHPr+2RN2q30W4cfL9bBo2qBT42HeNoE7AI1rNkQuVJqzea3JlZYmy4hweO7w9oD8KikDagHLK1gCIg6A4hfKTfKWA7dxHqPD3oChH6xkRbJYHvi5ayR4TCdmtHua5HzDJ47OxRhlerJBoBf1KIwjVNAbfWKhstNLQ1SQi2OlwHPqDI2cXotr6uUwr4quKWSSGnaAhEIo4Mw64FyEodsbCqTEsIZfLKJNvMYO1msvDv9r+hCWUC0zWCsEuEFnwPd4v5DPz7v6c0M5E/VZuGUygruh/4+ehCUqvWaDSa6IZOzLaUfV7T0E1TZYRvC7EPBTQypALwO3aNQtPQLFM1asDnFP4uDAOhKwJD6blI14smsmYYiICPKSTQa40etRJJIfBdh2Kxhu/72JYVmXBjMQVUQkuqJgWeI1nfqHLn7gJziytIX+A5UC1VKVUq+GjE47FoQVFEpCrZuK5JXFeiG4pE0/c8HMdBEz6GEYCdoIkb9Qau52P4Pmb7rpHQT8ZhenaBG1N3iVsGK6tFKrV6FJGZzaQol+uUyjVsy8TQLRASTUC5Wmd9o0TM1MllkoqkNVhvi5Uqnu+TzmZa0bBhaROYUpPYtuJmsi2VDikMCFHTQkVLhqSMftAetVoDz/PUM9uiBRECCrkAZCqTUdtjO+oQbIjRtiG1VCS7IN69Co7SFIuf/zttoEjtsDve7PIk9BaCWwXnaD4xy6S/L086laBab9AebelLSalUZmFxFU1TwDOXy6j7ttQ1aEJD0+DClSlu3Z1HCHjo9BFS6RRCaMRiNlLCzPwS42NDxGyrtZPXNAj46IT0lDk7aMdysUSxXANdR9cNxTemaaRTCWzLZPfEKMODvTSbDslEUq2tQb2kFHhBcvVoOQgjhxEqC0UghDWhUv4Iw1KN77uI0E0DGVFiWKaBnYgjNC1aB9R09XBcLwok0oIxIcI+bl/zCbVgbXnzAJU6JpRXbSDB99W6+GGAVlgk6tlB6p0tmiCpbTlfSn+LzGwXiVsAV+Sj8hHAwQe+U5tCZLvfO+4lNx0WneeFm8EtG8xNlwXvsplW5H5KKLvuJRilVHLYcRycpovjOME4bDuBtr4JlQw/hvLhwVbAj9Fhb+3YyQWnbVLrbrlNRydsOhKiTNHqjJBBOGxNTQh0y+Dg/l3s2TnCxSu3Gezr5uihfWTSaWXak20PCBtNCEzLQrMMNKEhPQ+v4eA7MtrlS0QUvaY0VIrfxWk0mF9YY32jjGmair/J8zh+eG+QAFXDDIRouVxFE4JYzEYTGr5UaHp8ZICDe8bQPvkojqG4rXwPTEMRZIYREpVqjTszC7iuR39vQbG4Oy5Nx6HhqvMSiTi2ZUbgTtMVKMBVeZ/C777jYv7y38d46zIAxYlBfuPRI/yFn3yWkcE+Yo6D06iz5slo0CUSMeLzK4j/679C3J6DRAy5f4L1//3v8uZ7l5m8PYsQgj0TIxzat5NkQplkddPAcX1qjTqu55JKxlu0EIaOFB6u76MZBvVag1rVIRaPkUglqVWqbNyZVbkWfUUmm0mngu6TzC9vcH3yDpZpsHtijETM4u7deX702jusF0v0dufULgWY2DHMof07yWVSAFRrdS5fneL29BwSGB8dZMfIAMlEIuBNk8wvrvD6O5ewTIMnHjqhUhP5PivLayytrdNsuiytrJFKxDi8X7H51+tNbt6eZPLOLKlEnEN7J+jtyUdaqnA8N5sNLl25wZe/8SNK5SqH9+9kaKAH2zKZCISfrhs4jsPVm7d58/xVLt24w8Wrt0glFX1JrdFgdb1OuVrmrQvXmLozr4hk0wl27Rxl18QImXSSxfklnn/5bTRhkA244ErlKhNjfTz64DFSyQRzC0u8eu4iN6amicUszu6d4EHPV5FkkWnK4frkHZ578Q3mF1ep1Wpcn5qhkMth6KrNNkplvvP8G1y9eZc/96kn2bdrB67ncfXmbV587Tx3ZxcYG+rlkQeOMDE2RDxmc/naJF9/7hVW14s8cvY4Tz16ui2CUHFN3Zya5u33rrK4vM7qepEr128Tsy26sikMXVcJ1htNFpfXmF/dIJdOMj7Uh26pO3mexLIskolYJ5gzdSr/6R9w5+4cb71zkWqtgWVYpJIJdo0PMTE2wNpGkeu3pmk0XUZHBgI+OQth6BQ3Spx78wKLy2sM9BV4/H/8l4hAYyaXV2k0HKp1FVlpWyYx245cA4TrbpIBPktLK1y/NcOlq7eYX1ptaWk8j2rN4/bdOb77/DnOX54CKdm/ZwefeOpBDvt+RySvEALbNDFNg7cu3Axyt5Z55OxxursLirbD0KlWaxFhsW4YeL7P8vwit+4oBvedO4bo6+/BNAwa1RrPv/wWr791iR2jA2QyGa7cuI3jujz+4AlOHN2P67p8/4dv8PIb53nyoVM8/fApEvEYtXqdt967woWrkziOy9hQHyeO7GGgP0+94XDlxi3euXCN5dUivqc2zgN9BbKZNI2mg2Xp7N8zxsjIAAQpwZwgRVi5UmPaXUBHkE4nsQyLRtOhVq+rSO2EFoEjEWgbVSCXxHM9pBBout6mG4ZIkyZD2aQEfgjo/2xFENkm20tHPsTWqRG+aatZ+CGsy7aA68dewnZoz8m4TUXbTw+PhqY42aI4UjghiOy8hytKpO0KAbsmEH5rQyxl67li83VShrj23veWiv+u2XTUv4YCW0rz3opJFGGf/Zn7vlXuG2xpWrATEAAa93CT7ywfua4tKBYCidbgEq3fUTudZCKuVPI++J5Eel5kvgmJUtECVaGUXL1+l7szCyTjMeJxm650kpH+PkxNCxVMFCsV5pc3aLoN4jELXUBxo8i7F2/wrefP0Wh4lCo1Du4ZZdf4MN2FLhpNlzcvXOe1c+e5Pb2AHbPYMzHC8UN76OstsLq2wZWrN5hbXIomcqlco5DP8OxjZxga7AEhKZYbfPv51/n6917B9Xx68ln27RrjxOHdCuRV6ySTCXbtGMYO0t2srReZvDPDRrHC6FAfPYUu7szMM7e4Qj6X4WxbtJLretyeXqBcqeJ5HhvFEtMz89y8M8/U9Dyr6yX6e/I8NT7C0XB3Hqhgf/DSG/zh175PsVxlbnGVE4d28zf/6p9nz8QYmq4zdXuG777wKrdn5hCaxlOPnOThs8exTJPVtXVeev1tbt2dwzQMpmeXWFkrMdjbzdjIADPzi1y9fhvTNDh2aA8PnjpMX48yB1+5cYuvP/cq569Mks+m+cKnn+CZxx5ienaJL33jReYWl7FME8d18TyPnWOD/NLPfoqnHz+D53m88PKbvPLGeVKpBKtrRb72nR/x2Wcf4cmHT6msAa7L3MIS3/nBq9y8NUsiZvPkI6fwPI9X33yX3/3Sd8mmUwgBe3eNMtTfg+t6vPbmBb71/Ve4dO0WhqHzMz/xND/5maci8Kkhcd0mb713id/94+8wdXueYrnOO+ev88xjJzh+eC9C+uiawHU9rt24xetvnqdWb1Kt1FhaWSed7I/Y72vA5au3+cZ3XkYCo0O9LC6uUKlUKeTSlDaK/PCVd/ji114kEY/hOC5rxTKJmM1PffZRHj5zhNX1Db73wht894Vz1OoNSuUa83fnOek46IE9TCJ5891LfO07P2JkoIc/97mneO2Ndzl/eYpk3Fas4L7PyuoGr719iZu353j87Al86XN3doE/+eaL3JldJJmMc+7dy8RjFl3ZNBuaxhf/9Hl+9MZ5hvu7effiNXbvHCFDa/G8eXuGL33tB1RrDWzb4sbUDPNL6xzZP05XVmVSmLozy/Mvv827l28yPb/M2FAfP/+Tz7Jv3y7CgBrfkwhhIGSwzAX7w6XlVb741e/xze+/qsx4QUaH8ZE+DuzdweLSKu9cuknTcXn28dP80l/8CRKpXuZmF/jy177P2xeuYQbJtx/xfLWICmik4kyXSlTqdQBi0mcwnyHVcLcIl0azydvvXeT7L77O3NIa03Mr3J1bYmSgB08q94TllTW+/PUX+N6Lb1GtqUTWV2/eoVZvsLfpYBMKFqVlMwydgd48G6Uqt6cX+NXf+zrTc0t89pNPML5jGE3TcINgF9f1uDMzzytvnOf1Ny+wvLKGLyW93V08cGw/Tzxyklw6xfTsEheuTtFdyLFrYowLVyd55Y3zJGI2E2MqD+uFKzf5wUtv09eT5+zxA7iuy9e/9xJ/8NXnqNbrpBJxHMflr8hP8WzXGRZXN/jyN17g+y+9g5QS27KoN5qk4ja5XJpqTRHDPnhyP7/4c59lbHwU13FpNl1m5pf5jd/5CuVKDV3XGRns46HTxxge7GNmdoE7s7NIXxBPKK3ujpF+do2P43uSazemePfiNaq1OiMDPZw8up+BUKijNtuhb61yMWmTcYF2K5Q9H16kBVFOrR82/W0/1DJ1bj20/fnvX9rMLtvW7f20W/cy420GWnLLPTo0cR9WIxhpwZT4Fm2mI0nbK2++b5tiqvN2LdwgJbiepOm41BpqrKk70/r73wC7foh0PV4AELarxfs15PaIqzP2sP30IDzdV7tsT8g2wNW6Y1g8z6PRUEliVT0D+7gvFXN0aEYMVOO37s7yH3/tj3n7/A0KXWkMXWfnWD8/89mnOXF4H0LApes3+d6LbzB5d4HVjZKihgBOHtnNYF+B1fUyl25Ms3O0j2OHJkimLKQmuXBtkv/t3/0O6xtF9u4a4c7sAi++8jYPnjjA5z7xKOVqnedfeZv3Lk/R29PF0YM7uTuzyPOvvIttmfzM559G13XeOn+Z3/jit9GExskju/E8nz/51o84f+UmD586RDqTZqCvh2RSJUiWwNzCMr/9xW9x/vJNPvn0WZ565CRf/sbzvPjqeT7+xGkeaAu5FULtFGv1GkL3aTgNzp2/zNefO0dPdxee5/HtF84xv3ec/U2HeNCutXqdH7xyjoG+Ln7+z32Ml964oHpS+LieywuvvMXvfPlbeJ7H3p0jXJ+a4b/89lep1eo89fhpKtUyr715kR+9foHhAZWz7fb0PK+/fYWRgW52T4wQj1lcuDpF03E5enA35UqN85dv8Pt/8j3eu3KLhuNiaBpS+pw6tJ96o4kA0skEJw/vZXSkj8vXprh9d5aVtQ2aTYer1yd54aVz7Bof5Sc+9SSr60W++/yrxCxLOSHj4zUbDPb18As//Un+3X/5Q869c55Hzh7ANAycZp3ZhWV2jQ/yyacfpKc7R1cuyfJKkeuTdxge7OXQ/p28+Mrb3Jmep1ZrkEzECY2I0ne5cu02i0tr/Pe/8FnW14v85he/Q7lcxdAElqGjI5mbX+JHr75NTyHHz3z+IW7dmcV1XdaLZaTvkUwleP3ti3z5my/Q253j0888RF9PnsXlVd54+zLFjRKe7xOzLX7p5z7Jrh1jfOnrP+DFV9+lvyfHqSN7SCZiXL46ycUrN0FKBnrzrK7f5tLUTKQVJHAi/c7zrzEzt8Qv/8VPsv/oYRIxg5ffuIgI/KCklMTjNt35LLfuzqNMR4pMc++uUZ565CS6rvHrf/ANSuUKhq6zul7k1vQc8ZjFJ586w86xIWyj3WcFLl+d4urNO3z+U4/w+IOneOu9y/zRV76PZRoqAtJxmZlf4rW3LzG3pFJsXbp+mxdfe4fevh5SyQSu61FrNGm4Ph5h4I4q4UbB83w+/sRp9kyMcPnaLV5+8yKLq+s8cHw/Dz1whHPvXuXW9AIrqxsk4zH+6Cvf40vffJHurgyJeIxKtR6FkWMavPO3f4F//R//gAdPHuDIgV1sFMvoh/aS/If/UUUhtpUbk7f54lefY2FxlS986lFq9SZ//PUXsSwDy1DvOTO7xFvvXsW2Tfp78mgCiqUK1Wqt5XrQJmws0+RTzzzCZz+hc+PWNF/79kt84/uv4Un4+Z/5DKmUykUqhGBto8gff+37fO07LzExOsDPfOEZdE3jS19/nt/44reRUvLZZx8hk1Ypp2K2xYHdYzQbDW5O3qXZbOK4LulUlp1jw6RScXzfQwifmfkFvvrdH3Fl8i47hnrJZZJcvnmXxeU1QJDLZhgc6CWZiLFn5zBnjh/gBy+9xfTcEscO7WJksI8XX32Hc+evcfDAFXp7u5GAbmh4TQ/LNMmkNV596xLfefEc84sr/PTnnuLdS1f54td+QKlSI5WMoWs6n3jyAQpdOa7euMvv/PG3ePfiDWr1Bj2FDD/zuaf4ZdfFDOSQlMoMLTwPoWlKyCsvRMAPkjK3s4h1ysL3BWESorDyUIrdK2H0FvxwH8+J/NPC+9+nz9E9QVD7vWiNtdD/eos5dLPZUxI5t39IoNWyiokWfBBtCZS2AaIdvln3uK+mtXCE78kAwDsRdmhdL+99kz9DuX+wBbSc1+51htj600comy9rabZa2DNEy6VyhfnFVRoBuWWLDlQom79oQd1qrc63nnuFl9+4xPGDE3z8qTPMzi/zwktv8fXvvcLwQB++9PmjP/0B16dm+PhTD7Gyts4ffu15bFPnC598mEMHdjE6/DrzS2t8/PETnDlxAMsyqdRqvPHOBUrlMn/l5z7Bs08/yNTtWX7/S9/h6s07nHv3IqPDA8TiMXLZFE8/epKf/clnuDE5zf/v1/6I65PTOL7E9V1efPU9qrUGf/3nP8tDpw+TyaT4j7/+Zb76vVfo6+7is88+zI7h/oCYVA20TDpJKhWnWK6yuLyCZesU8hmGB7s5eWwPxrs329pTCctavR5wFsGd2UVuzy7S31sgl0ky0NOFnYq3qbAllWqdSrXOkw8d59TR/ezcMYzv+/T3FvB8hy9/63mmpuf5h/+3X+TsqcPMzC3yz/733+A//uaXMQx45KETHNizg+nZRT797MM8+cQDvPvOFX7jD77B2FA/v/wLP4mlC37lN77MxWu3qdbquJ4ypcwtrvLA8X2k00mkLzmwe5R4XLHD93Z3ITT4wqef4MTRvXzruZf43T/+Ns1Gg1KlxMUr11haXuXjT5wh35Whr6+b3RPDaIaJLiW+0ySVTpLq6sIDegpZyqUKruNgGzrpZJx8Ls3E2CCnThzE1LVgcmt85tlHsW2L1bUNbk5Nq5xaoJiKUV4gtm3yscdPc/jAToZ6u3jljfNk0wl0Q0Sgpdl0uDszx9TtOfq6uzANna6uLKPD/dRu3EYIjbW1Iq+8cZ7puSWefeI0B/aO4zgu+a4Mr711me88/zoPnDjAyaP72Ld7guuT06yul0jEbB574AiH9+3Ed5rMzi6wtlZifLSf3YH5MZmIof/Wt6O+llJSrtQj36nmRol8V469O0eU9lhCs+mosZdKBGmPQDcEo8P99PcVmFtY4evPvcxGqUImHceO6Qz2F3jwxEF+/6s/4M33rnH25CFGhlp0rxLJnZkFlteK1BsNDENjqL+H/t48M/PL1BtNypUad2cW0HWNnWOD+FIyO7/C9OwSxVKZmG1RqdaifHztO38J6Loyr/b15nnk7HEeOXOMV954h9vT84wM9/ELf/5TkTPtrbvzlCpV5hdXuHbzDqZhMNDXTTweY2yoF+2Na9GNbdMkEbd55c1LDPR288yTZ0glk1tWNc/3efvCFW7dXeCxM4d46tFT1BoON6ammZlfwTRNnKbLwtIK66UKj509yseffgRDE1TLVbq78xhXfoUo3D1aFgWpZJL+vm6OH9lPOpnkd7/0Hb7zwht05TIcObCLZCKO5/tsFIvcvDVNrd7k6cdO8+nPfAwjyH/4n3/zT1hYWsVxXExdZbCo1upUqlVcx8EydXKZFLZl4fktThrPk8wvr/H2e5eZX1rh5KFd7NoxSDaTZOeOIQ7uHUfoGnHLYHSkn3Q6QT6fYefOES5cnWRpZY2J8UGeeOg05UqV5350jlqjSj1Yp2KWRaErw+MPHaevp8Dp4wf497/2x7x3+SZnTx3EsgwScRvD0Hny4ROMDg8wPjpAqVzhhVfOceHKTVzfwzA0llc2eO2tC/zFRkMR0EoZuJ4ELhm+H5i0PEKaYAEts1jHuFImtg5NznZFou4lfBVIcc9zoDNy+cNxV0Z1bVX0Ay56H8C1xfQpt2rCtgVaSu6Ke5xz/1Yv+YHnhprs8Dnh6VsBWKCwibRbys/PD1O5CUC2orx/3OUjOchLAk6nDtPedmg7OH/bnEviHgNj0y2E0oCJDrWWmhhuo8HqyhobxXLUyFpgPJQItUuRgc1ZwuStaV5+/TyNZpOjB8Z5+rFTavf4zhUuXJlk6s4shqEzeXuOof5uPvb4WZpugxtTd9GF5PCBneTyObpyaXKZBOlMilhcOWuub5R5+/x1CvkMxw7vUSlfkgk2Nor8+u9/k5XVDcbHhkgl46RTCXq6syo5cOCnUK7UcF2P9fUiV27epTuf4dSx/crpWcCJI3v41gtvIIRgdKg/IBIN2lUI+nsLPPnwMW5M3WV+aY2NjRIH9+5g1/gQD54+DL/5TUJWdqGpf5WaMneEzqT5XIo9O4c5cmgXn/34QwwmUtgX/gMhmaXrutiWcqy3bYsdIwORMF5eXadYrjA60EN/bwGn2WSgr8ADx/fyK7/7Db7z/OscPbyXerOJ43rEY1aQmzFDMh4jEbeI2xbZdILeQo4b1gwLS6voukbTUb5fP/v5j3Hs6AEa9Sa5VJx0KsnKapFUMs7C8jKuq0wtegCGqrU6lUqFUrmqEvvi4/sujbpDs97EdX1s00RKj9WNMrWmyw9eepMrN+7w8Kl9rKys00gm8KUkHlMCvFQskU4pASo0KFWqfP9H53j97UvcvDXLM4+eBFTkqdtsUm40WdtYZ2Vtg7mFZd546wJXrt+mVKlimQa+5zG3sIzjuiytFplbWuX2zALVhkMskSCeiGPZNkITlMsV1jZKgXO2QBgmhtBIxCw0IdgoKu2RbujcnJzmP/36l7hwZYozx/byqacfRAjBwtIq68UKjusyPjbIs08+gG0pnxfjj14IJ6zyC5awuLzOleu3GR4cIpvNcPjgbiYnZymVK7iex/TcEtPzyzQcB89XJqqFpVVePneeyzfucvXmHaq1RmB2dInHEjz18AnWi2VeePVd/uArz/GXfu4zDIdrm5QsLK1RrzcjPlojIM81DQNf+qysrnP5+m0cT/LgkX0kEjHGhgdIBmSdmmFgWXbgZO5FC3W4YazUG6yXKmiawDBQicA1gS9U9HKtUaPZdKg16jSCVD5Nx6XaaJJI2Bw9tIfDB3YzPNCD8W+/CChfrHg8xgPH9/Olb/yQX//it2g6Dk89fpY+z0OXEi3YkXuex9zCCk3HJZ1KIIWGZVv09OSZWVjBdV3K1Sqr6yXqTZdypcHQ6BDDu8dxikXW5pfVe3k+vqPGuy6h6Ti8/vZFerq7eOj0UR576ASVao0//Or3+cOvPMdrb17k8P4Jdk6MsVosUak3kEjqjQaNcgVDE8RjNom4TbPpKJO8L/ECP6kbUzPcuD2L67tksyl0Q2N6do6bt+/geS6xmMXS6gbvXZ6k3mhy9vRBvvCpJyiXKswtLGGYBnOLK1iWRq1WR9ME8bhNImGTSMbQTSOgulBRpZatfOmaThPdUJkzBnq7KXQXqDsu2VyWvt4CU3dm8X3JQF83A30FVtdLPPzAEU6fPY7fcHjv/BXmF1cYG+njwJ5xkJLV9SJD/d1oM+studYmYH1AC1I4ISRoekci6kA4RTJKhrat0FfpfU17Ovc0z3XIzLZxG67b29VBhCDLj24g2/7bfvP7hWz3Nh/yvpqqFqfb+zkahfRG2zPJby2bAxlatex4hlRBKSHFQ3udInM7m6xzbX7a7RqzVjzjj698ZLB1P9WInNrUt+Bi2XZMpxUV8v73CfMohqi1VKqwvLTM7PwSjuMGVCgSCBJN+iDxFTGdVJNmo1iiVFa+LV3ZNM2mg6YJ+nu7uHL9DvOLywwP9hGLWWwUK9y4MUk2kyAZM/GlT61apzsvSScU0edGqYrj+fhS4Po+5Wod3/OZXVihu7sL0zBIJhMk4lbkpKsijDxcz6HRqFGplmk6DtL3cV2Xer2B63qKsFXX0HVYWl1B1yXppE13IU0sEUahhTt3iR3TOHZ4D089coLvvnCO3/jDbzHU381Tj56kXK6Q83z0aHSqoVSuNvB8H13XyKSSpJNxDh8Y54knH0A2mvjFRqvBpY/n+aysFbl68w5DA70qf6CUFPI5VteKrBcr2JbBzPwisYTJ+nqJ1fUNBSoCZ+pypUat3qRcrrG8sMzkrRnWi2X6+wqBWtdTybJ9ycrqBj2FHJl0AoFkbb1ET283lmni1apBlJParbiuoicIQ3s96VOu1VhYXmWjVGF9o8w7F6/T1d1FpVLjytVJTMPizImDzM4t8a0fvMYjD55QSUhjNpVqgzuzS/T25FndqNBoOiyvFllZL2FaJs1GkzffvcpXvvkSC8trFEsViuUqCFjbKDK3sERpo4gnBFN37vL8S+8wu7DCJ558gL6eAldvTuO4LpN3Z2k4HplMGqFpVGoNXnnzEoVCnkI+x+zcEqVylfWNMiOD/YrQU9OYvDvPg7UGhqFz/uotltdKnD55kFjM5pXXL3L52m3eunCDvp4uzp48xI3bs/z+V75HOhXDk5Jipc7NW7NcvXEHUO2XF2AF00gIQT6fZXmtxB9/7YfU6x49hRw3pqaZW1hjvVxDajoLKxvMLq7g+iq4otZwOPfuZX7rj79LNpNk98Qwl683OaY6AAEAAElEQVTdYmF5g/VSnTfPT3Hh8k0KhRz5riyTd+dZWl5juH1t8X3qdYfb00vU6g0cx2Nto4zQNIrlGsurJeaX13E8n/6+AscO7eX28Cy37sypRVbTMQKtb5g/UQ19tfY0mw7FcpVGo0m90cD13GhV8zyfer3BylqR9Y0yUipKAd0AyzLYKFWZXVjizMnDdOWy0T5TSsmb56+RTqf5zLMP883vv8aXvvUjrHiMzzSa2EHwAYD0Jc2mi+N63Lw9x3SgUfZ9SalSZ71YJpNJ0mw6+L7klTcvIv7Db/PAiYOUShUWl1b5G7UGiYCSw7JMNKkY1ueWVognYxSrNXoLXTz75IN4Er78jRd45a3LKpeoqZNKJ0km4vhScmd2nrWNdaykzXqxTKOpkm4LBE3HRddU5Ovi6gaTd+dYWi1RazRoNGssrq6yuLyOrmsk4iqvaH9fN6lknAtXphgdGsAwdDaKFSbGknie5OK127xz8QaVWgPTsjFsG01XuSgN08D1PcrVGq7nqY2gENi2ja5reBLS6RRXb9zmxuRt1osVkskEhe4uFZBk21SqS6ysbVAvFnE9l0q1Rr3epK87zyeeOks2naJUriqz6ksXoFoHQrJL0KXy9XUj9xUfLcgT2aGvCS0uQVBTRHAbcqfcU8O1nfQM5SKEmrJI/rXJwU2CkdZqLjt+2/qMe6Qq2+LzJNuAUJiiaGtmiC1RhJE5cRMw2vKMyNlK3Tdklr9nm6g8ilILcyMGEeHbuCG1z/H2erWITdXjdc1oy0GsAuHUhum+eBM+cvnIuREjVaEIkpqKrczR0Aa4ZLgL2EaTdQ8NY7ipkG2/h8+uVGs0HVf5HzlqZ+kHjPNewFHiS4HreWwUN7Bsi0I+R1cuxdKyix2LYVsWmUyKfFeWrmyKVDJBTyHH6GAvr719mR+99jaD/d2kkjH6ersQQSJk0zKpNRw2iuUgSsYjFo8zOtzPO+evc2dmgcH+HoQG1yfv0nR9+noL5HMZYjEL35esFyvcmlnkxs07bJQqmIZayLu6cuwYHeCVc5eYvDPLsSO7mV5Y5YevXyAWj7F3zzg+PndmZnBcl0JXllRSJWPOZVMcPrCTV85d4OVzl9m/a4R9u8fIplMMBQNNCTNJo+lQLNdwPIlpmvT2qCihH732HslETAkYT3A2DK/2Fb2A43p8/bnXuHDtNrZlMjrYwxc+9QTr5ZoKW9eg6XmUq3WuTt7lvSu38YFdEyNomhZFf5SrNcqVGosr6xRLVRpNh6bTpFyBar1OqVrFsjR2jPVjx3S+/tzLfPP7r5Dryqidd8Ph9LEDyCANTb3pslGqUqo2qDWUP0mpUmV5ZQPFti74+g/e4PV3r0ckoJ9+9lESqRQzi1e5OjVDLBEnl00Rj1vcvDPPu5ducmCvz8zcEgtL68Ris1ydnCaZTNBsNJm6M8+NW7NYpqKdWNso88Y7VzB0PdBednHkwG4cVwn3UqWmNG6NJpVak1ffvkq55nBw/16yuRz9fRX6evJcn5rhuy+8jq7rzC6s4Dgu07OLPPTAUZ5+9BSLy6vcvjvPN7/3MrquMXlrlh1jgzz84HFuTt7l2tRdXn37Ek3Ho9CV4c7sAn/y7R9ya3qBw/vGGOwv4Lger79zhem5JRoNh57uHA/4kmSwu9M0jeNH9nLh8iR3Zlf46rdfZs/EMHem50knU2TSKdKpJL7vs7ZewrYMdFOn6flohkkqGWd1vYQ3Oc3KeomrN2fYc2OG9y7d4EvfeJGBvgLNphMkuG4tQ0IIDu4b5/W3r3Lunat057OsrRc5f2WK4YFuNsplpBQYpsbs4gqvv30JX0pm5uZZXt5gbGyIer3BwuIK9UYz8mlsX2pMy0DTBdWGw+ziGuulKrWmg+dL6k2Hcr2hjEemgRSCSrPOWGqA0ZEBrk5Oc+XGHd65eBXDEOyTRNFPF65OUSxV2LNzBNtWGywjjISMdtCKo2zf7nGuT05z6fpd/uhrP2BkqJdL127RdFyKpQpIyKaT9HbnmF9c47kfvskr5y7SlU1y6uh+lVS9WldaZ+krZkIpcX2VdFczwJUeyXSaT3/8cXK5DH/yjRfo6e6i0JWhtyfHsSN7OH95knPvXuMPv/oco8P9PP/ymziex87xUTJdORKJGEITnL8yxe2ZRWbmF1laLTI9t0i13gw0Pmp+VRoNDFPn6OG9XLg6xZsXbrC8WmTX+BDxuM3+fTvJd+d448Jl3rsyyUa5Sqla5/bMItPzy5RrDapNh0q9SalaY2WtxOTdeY4dPYxhxXB9yeXrt7l49SaLi6vK7Dq3xJmT+0ln4srCoQmarke52qThOIBE6BqO53Hlxm1ePXeB/bt3YBg6hXw2IEYNRJnvKC4+P8iVJzS8QKPkESYqb9c4BcY+AUILEr2H403Tt8q5tnGwfQmoJzpcZjoubvscarT86GvHNWLzZduwoW8HtKRSTij29/D39hQ7IjLDba3eJsqKe/hztSoot3zsrH8baJVEYKDDp6vjeeHjZNu/9uqoL5quYeg6WjA/2/m+pNzstvTjg18fGmxJ2W4QDLRN95PtM3Tgu8ep7UBKC4yAXtvg2Ez0aVsWua4cN6ZmqNYb2JZJLpMkFrNwfZ9GvUHdh1vTszz/8psc2LeT40f3c+rYfr76rZd4872rDA72sbK6zpUbdxjo62F4sI+uXIajB3YxdWeOydtzGIbO0QM7GRzoxrJtPARduSzJZJxypUbT8Wl4PnbM5vGHT3Drzhzf/sHrXLt5Vzmirpc4vH+CJx4+SSxmY7xzhcWVIi++egFfaszOLbFeVGDjwuVJPv7UWT79zEPcuDXHb3/5u/zo3HnuzixyZ3aRJx86xr7dO1haWef3vvRdJm/P8cRDJ/jEU2fp7y1g6ILe7gLd+RyJuE1vd45EzA5SW6jBE7Zio9Fko1im4bjohsHE2CATY4O89vZ1Ll2fxjB0du8Y5lQYbSUhnU7yuU88zndffJ2L124rnw5Dp9pwyOXSHNizg1t3Z5hfXCGXTXHx6i2m55Y5dXQvj5w5Si6bJpmM4Tge712aBE3n4rXbVOpN5hbXuDZ1l7hlcnt6jnKlRjxhU+jKkMsmefrRk7x7aZI//Mp3sS2DU8cOcuzIfiSSpuOwXqrw7RfewEcwv7RKJpOmp6dAMp1k985h8l0pbtya49U3L6MJjS986hGeeeIUiVicdDrBjtF+iuUqjuMy2NdNo9mkVm/QbDpYlhmAMFuBTt3Aimns2DHCzvFh5hdX6O/OMTLYS7la49rkNLvHhzlycLfKDBCzeOj0If7kmz/i3LtXGR7opjufpVKtMzzQx8hQH7Zt0RvsvM9slMhn0/i+ZG5xhZW1DYaHehFCcPLkYbL5HC+99haTt2ZACA7tn+BjT5whmYgxPbNAOp0kHrepN0rcnl5gfnGZar3Brh0DHNgzRioZpyubZvL2HKvrJdKpBGOjg4gb861JDpw9dZjB/h6WlteoVGtkknEeOH4ApKC/rxspJYm4zcTYIIP93fT15NE0jX17xvmpzz7F5O0ZGg2H00f3MbFjiNHhfpqOw67xITaKZfbsHOFjjz9AT3e+bW4LHjp9hPVihenZReYWlvF9yd6dI3Rl05imjmVZjI30s7C8zstvXuDi9Vs0Gg327BhCC4SeilDWuH13jvX1YpQEXACxmEVvd47llXVu3Z1nen6JjXIFhNK+Lq6skM2k1Niu1ZmeXeT08YN84pmHsWM27164zvkrNykUsuwNV0MBO0YHee7FNyhVaiTjNmdPHuTg3p3oehtRsVRRg598+hFy2STf+N7LXLgyxUaxzMmj+xge6OHY4T0k4jGOHTtAVyHPW+9d5frNaZKJOMcP7+Xpx84Qf28KT1Nm49CUrwlBKpmgVm9Qq9bpymQRQpBOJXn84VPEYjZd2TSxuI0QcOb0UW5OTfPexZu8cu4i16emaTYdnnjkBA+dPU4ik+LMqWPUHS9Yz5TJ1TB0hgd6SWfS5BsOEzsGlXvDcD92wmbv3h184bNPYMdfVhuPTJp6vaFyo1oGQjeoNRx8CTdvzVCt1VlcWcPxPJZWN1gvVdk1Pozr+aSSScrlatBuBndnF/nPv/knmIbO2kaZI4d28tM/+Qy5ri7WNsrUmw2K5Sqzi4tU6g3i8RiJZJLR4QEmb8/xR3/6PJlUgt6eHL/wU59gJBLIMiIS9cN1Uqrkxp4frJxRkNZmDRMI4SF9A90w0CNw8EFCcTspeC+7USj8/chcKaOqbAJn22KE+6xMmF7qnjXclLS9497bAK0tqXrk9udvc8eWJTXU4Glt+qz7BUKBgbWNJV7RPyrAZZpGYHVyIgBnGAaGEUKjHx/g+ggM8m32UBES1GlRtbYrrYWmTa3VZk6UclP66pBMMjp168tWajWee+ENnnvxTWbmV5Uj9fQif/jV7xO3Y0hfUm26vH3xGrMLK/T0FLAti2efPMvKygYXr0xSqnwHTdPIZdOcPXmQrmwKJLiuT3chy8G9O9i/ewzLNHBcj0bDoek4nDlxiHQqSbVWxzIVMtY1jbOnj5BJJjl/6Rora0Vq9QaPPXSMR88cp6+3i4WVdXZNjPBQuab4gRbWaDZdxkf7yWVTdBdymKbJY4+eIhFP8KNX32N5dZ14PMaDJw7yyacfIpNOUq01EehM3p7H997k8L6dDPZ2o6HRm8+zb9c4l6/fZaC3h55CN7lsDk1r0zwGqtNyQAhqWTZjIyP81Oee4Z0L15i8PYttmRzYtxPtpQthL2CaBo8/dIKd40NcvX6bheVV9u4cI9+VRdM0Pvn0Wd69dI1ytc7c0hqO6/Hkw8f53McfZmLHEFJKTh8/QNPxWCsqX6rx0QGOHNhFIZ8lnUqjIXnq0dOcOXGQvbtHsW0L2zL5hT//SZ5cWGF2fpmYbXFw7y7SyQRz88sM9HXz1CMn2DHUT19Pnh1jfRw/sodcNo3nedSrFcZG+ujKZbk7vUg8bvPYg8fIpBMUixVSKZtjB3ezf88EXbk0mq4WvEQ8hpRSCcIjuzl0cA9HDu0mEbfxHIeRgT4ee/A4pibYs3OEVCpJuVzh6o1parUaqWQCKSXJeJxnn3iAYqlMJpXk40+doVytc+v2LNm0IvXUdY14zGb3xCi6Jsjnc1imSa1ep15vEovZ+CgaiT17Rhka6mFmZh7X9Rjs7yGRiFOuVOntyfPw2aPsHBtmYWEF1/NIxG26CxkG+wvouk6j0SSfy/DZTygzsA/Kb+iVS8Haot4/mYhz6MAuANbXizRrNRKxBIauyEVr9QajwwP8+c9/jP6eAoV8DoBCV5bHHzrBQw8codl0QCp/Js/3qNcbQbqdBPt3jzM82Nfp5A1kMymeffw0CIFtm+iajhOY2IUQlKoNdtWbpNIpiuUKGxsV1jaKFApdGIZBPB7jiYdPKWoT36dUqXWM/UIuw6effYSzpw7R15tnaKCXRDxG/CdjmKbGxPgQ+IIHTh5idHiAseF+LNNg/55xunIZeru7VAaL/bsCU4QCOp/+5JPs2z3OyuoalmVyYM846VQSXdNbCX2DHXsyEePJx84yMT7Ezcm7FPJZ9u7eQSqVQEhwPZ+JVIrxsWF27xxldnaZkcF+Bgf7ooT0QogOIKfrGvt2j2HbynG9VnewLR/TNIjZNscP78OyzCCZuKSvO8snP/YQTz92mkQipjaDuk5fb56Bvh40BIMDvXz2449RKpeZW1jk0rUpbMvkwTPH6OnO47oezzxxhlQiTnehC9NSCco/9uQZ9u8dZ3llHUM3MU2LsdEBXN8nmYhz9vQhBvt76OvrJmZbnDyxj+XlNboLXUgpOXPqCI8+eAohhIpidTd49okz7Ns1xvzSCtVajeNH9/CZZx9hfMeQogmxTEYGe/FcSTKewHE8UimNrlyGxx46QW9Pnjt355iZXVTtFlD9KO2gwPMD/2DCKPagjYNxE7ojbU4RBSC0UJEQmBSFTkub1GYWvKdmS6BMfd49BGnw8O1AVrtJT7T9baMb2faWQtCRDidEIh+ALbbeaxsNGXQCLSlbz2q/gULw2z9F6m11DDMYqHYQ0X9F63n3gYsiC1jUdOH8aVneLNuKonb/j9VsBfbp8N8WVP1jKgIi2v3Nxfd9Ll+d4tVzF1nfKNPfmyeZsLFsi3cuTUZm4IbjUm82OXlkDxNjQyBgeLCPn/780xy8OoEemHt681m6cwroOK7LynqRqTvzNBoOd6YXKVWqrBcr9PV08dlPPMauiREG+gssLq2RiMcwhI6m6cQMnYdOHuHYgd0srKxQLFXZvWOEdDqJ5znksxkeffAEp08cVLv2+SVcx2WoL8+uiTF6CtkgHYnBw2dOcPTgPtY2iqyvF6nW6vR1dyPQGerv4y/8uY9jmQZXb96h0Wio6S8llqGTTMQY7u9m3+4dDPb3kEomaTFuqxDY00f3c+TQTuKxOELopNJpjh7KcPTgHkrlCvWGoi8Qv/UN1R9SsVTHYzY7dwyTz2W4dHWSvp6uIPksHNw7wehoP66nIrlisRiFfIad46MYpglSMjExRk9vD8VSBQmkU0my6ZRyaPYk+B7HDu5BNzSkVCSu9UaTXDZFT3eeE4f34vk+wodGwyVmGZw5eYiBvgI7RwfQTRs0RepaqdWZnl3Atm1itglSJx632ShVuHV3jqGBHhzXxbYthgd7GB8dJJdNo5tqB+f5PtMzC1SqdUaH+zi0fyf5riyWLvCEpCef5cFTh4nbys9L13R6C12Yhs2dmdlI3S00jVwmzaF9EyQTcYYGezENg7htMTe/RqlcwTIN1oslypUqha4stmVGufNs20LTNerNBr7bxC9VsJoOg55ENlysqRlwXKximd6VdbprLg/qOlo2h9ZoojkO2sw6zSszlFfXEE2HjGkSEwLNcfGrDbxaHX2jHExyNQN9T/kRrq0XmbozS08uQz7bhWWZuJ6P7/ukknGymRSZVBLd0PE9P2Lq1zQNXdPwAqZ+6UuymTRHD+ymr1fl71MM/p0rSLXqsLpeoq83T3chjycFwldEnLWGj0eJXDZDdyGnskUIePf8NVbXi/hBFOiuiVH+2l/+KWq1BrGY1VpXHJeev/kvKQRUKJapUmSNSckxQg2GWmN2K5UG+ssX0X7lKyAEI77PTzUddEPHML6t0mABOB5Df+tf0e/51Ko1dE3DCpLcy40yodkHBKyV0H7h76J5kp1CskP6GKYREG2q5rckmCgfslHXZULTI5AlAdY2IoGv8JvS0OS7MmTSKSzTptFo4jiu8unSlNZLnarAhGWa7N8zTiaTxLKtIMK7lccVKSkWS9y8NU2pUma1WCKdTnHowC4GB/pAQCaTIptLk0mn0DURJK1X4HN0uJ9cNo3rQiIWR9MkSysbWJbJM0+e5fCB3WSzaUDQdBoUS0Wq1SaWYZJLpiNg2HQcTNMgk0xw8sBumvUGtubT15Ulbhh4s4skGk1Gqg0+MzaMMTpCOhYnPTmPIWfpqTtkqlWOxJM4w4NUMxk036fnwiTa/DKiXEXUGohvv4p85izbsmLKTuASCnsZyEBFjebjeS6aJtB1h1YqY537l5EBeNjudBGaLUX0vd1Jv7O6ne/QUma0ac/a8xdGz9ju3e+z6uHJksANW2/7zdn+PluiFFvgSZn3wlyWLXt9Cxe03l1sA2JbyaeDN5ZBuh7PU3kRfYknfZWspFUBZf43jE4Q+2MoHxpsCYgcywBUugFx/2NpU2ll8t7uWfcKeFULxYOnD5LLpkknE6STdpCCQiB8oRjfPR9hCFLpJD3dXViGgQTGRoYYHR7CMHQ816PpOBgINFMlUB0eHKArl2F1vUQyESOXSbGyWuSVc5fYv3ecHaMDIAUxO4YuFBO9HkySpuMggWwqTS6dwbYslQYDHdvU0HWVbqSQzyr272SM4cF+pdUJkLT0lbretky6u7KKkblaD6LsRKRJazoulmkSt61A4+izUSyxtLxGX0+eXePDFPI5rID4NCymYfD4wyfYNa58S1zXwzBUKg4pIB6ET+thvsKgzUOW+mbdoVSqomka8VgMw9AxDcVQncym0ISgWq+jGzqWaQZpOFQdYobEMCxSyRRCBMcDIaOCeEzFjC19qjWXheUlhBAM9vco3x5fBmZrETDNexhCYgfPQtMUKWQwtkzDoKenm0wqQa3WYO+uUZ578S1ee/Mih/ZNYFkqFUwyaQZJSn2Eq+H5LovLq7x36QblSpXDB3bRW8gFKWckwjCJx00M08L3HJr1BoYvMRyPgucgpMS8MwdLawjHQ1tZY+/cMl0xi/hSGRpNehZWSK0XSWg6wpd01er0S0lS1zF8H1lrkqrVodFENBxEs6nYc4Kceqavoo/CKCXb94l7ig9I+Y8E6vcguMFwXDK+j2kZWKbZAkVB6GG44AgpEWtFkv/dP1Hh0aUKe5tNcpkM8ZgSyprnoTkucd/HMAxMwwjWSRk8M1Dgh3/VEKIQ9I2uKW0ewSYAp5Vz03E91jeqmIZJJptTGghf4npQb3qUylWaTYdUMkM6FUSLxm28FQ/fd5VgERCP2cRsOwAAMsAPEn11A3wFCjCU74aqXrg4t5b4UHMVGuA1KbGCuaBYpqOFDH9xBafh4DUdDMtEWCYCsSXZs/R9xO05xNIqUgYZ+7RgTdW0IKBN7eR1EaTi0dvSXonQbAiG76vxUKmh+z7dM0vEs3WsdAYj4WOYJsLQwTIjmpHQ+dkwdJWBAoHvetG6EkbUyUYTbXIaeXUKf6PIgGkw2J1j4O4S5uwqstEk23CQrocmJdJ10ZuOSl/keeiNJolKFdlwMaQAz6G7XKGr6ZK2bWJfeRHN88HzMB2XhOOC4yAdD831VIJ6x8VuOqSaKohIRoKYSAaF8CEVtfNWqZHePBbVzFFgy/WQ5SrGq+/R+NiZDryhmmN7KRRucNXY0ZH44IPng+5LkCYRmem9OLU23THSbm05FMrcdtXV/QGtlmky5Ajz235rA3YhMmm/DMEHJcruuKBDS9WmzZOGeq7wiFyKwveSsmMedbx2oGeM/LbCz9G596l9ku2JqH0836Ppqihj1/NpRyEfibT2PspHA1v8eHRZHcRn2x1HLW4+dCQH1TWd3TtHGRnuI5VKKL8k04iS1AqMaHH08PBkixhVShCmGaFcAF2TGAI0Xe3MPV+yb/cYp47sYef4MKah8/xLb/GHX3seTeggVR48TRgq8eomx0lNCGzbwjTUAteR21FKHMfH9yCfzdBX6CKdTEX0C7QFEqi0gmpHK2NKYHq+T7PqcOX6LabuzLJv1xj9Qb61SuCUPj23pNJudOcVaWdb/YTnkbxxlxP/6rfahHFgGg5V6qF9WxOYN+4iPB90JQQ8T5mCPOkzPNBLf193lH9O03TsIIeW5fkk43Es28TQTdVuYStovhJymogcFKMiVNCw70Pd8Wk0PNKpGKZhEfHdBL4UvpQ4oRNsIOjVPNZBSnTNIJ9LYwoN0/NZ26gwhMZg3SE2NYf35mWsmE1fuYbpQuLGLIYm0B0PrVYnvbzGkYVlHhAa+cll7D99Fa3pQKMJdQe70cCvNaDeQLhuJIDTQHcgGEJfuaTv0ycVCNI1BXL6A+1Qa6Mq0LRgDAdrkBYt5mqRaZH7ykggt2hRlLZ5U6pPpCRiDjdMA8MwEUGeSWkaSMvAN0ykZSBX1hXw8SViZQPN90k0mySFwC5XEUHklvB8hOtioLjCQkdj0fbQaA0P1f7R5lS2RQMFV4Xrpeuy43/9/zLYbKLpegDiRDRGXdejEPS5bSlWfYkkV63TaDRJffO1KEWWlK21XEiJNHQwTejuCvL/SQjydcqwodq06SKciCJK4BLVU4ayb3EVHBdp6nhdGdyGo9omZoOhuMfC+SSLZWQhp/rScVWu0NaDOhfVgCNMSEUaLBy2FA3QKjV1ruuSAo7/o19p1XvLBdGiApoij1Z5Edt+j0CdDq5HbnaB02HFBKrOQX0FsiPN0uaiS7BCtUJQn0Swim8n0MSmv63fZQSA7iVW7yWPPkgUC7cN2LieSmR+z03++90/HCFKU+L7GnqkoQoBzv0lub9X6dSt0Qb0Np/UDl7azm8/SbCt+a4TKomWMuXDVXBrEcH2JUzX03GsDfBtAWy0HOJDLSKo9SXU9AXjSQK+0NRmOLhOiJaTfDgXfakUMU3HodF0FVfcB73XjwHwfORoxHBxbdXmfi7qTC6pFmHZGo+gyN7aUO4mrA0oM9jQQC+u5yEECihF64EAfIRUbFuGTvA5WB090TJLa+GCbGBoCiTVHYeZ+UUm78wxNtLPjh1QDXysRocGGB8dwTRMBB6pZBzLstA1M+hslTVeBBE6uq7Clv2gzppULMjJeALLtDB0nUTcRgodz+/U74VcrL7Q0A2TGAroBEsVQwM9fPqZh9k9PkK+K4fnqTQpP3z1PcqVOuOjI2TS6ZZQa+sqzXFhrdh6VrCYRVM3GFwCoUAE4XqpwIRhGPR150kmE8QTiUCoiKAPlACxDMikdSzTwDACjaNQuxRNU2dpUiIcB9F0oN5ENJsBkGli/8tfJb5eos9VYRJK+xLk1AoSPqc9n2Ouh/Q9pYWQdJyT9dR5Cn9ppHq7+PRGmWcdl2SxTvrXvx6ZThAqIa8WghkJBSQFGU7arbNNItFlCPK0jgmpa50Lq9BbCaBDbY6h60rYtzqCKPG4EIi4DbYFtgm2hbAtpG0ibUv9bllI28KzTXzTRJo6TU3DMwy0uI0ejyFtC8fQqHo+vmEQ78oQy6Sie0uhtMBhUm797/4btI0S4ctogB2a+gKAJlDtLALiR6G166A7F7d2gRB+N6/dgqBfo/80muqerov93rVthbiUSrgng4W3db0yu4UDNzSZR3WI2lctFMa1W2qV2L8zUhaEXqNbNpJBv4fjv/2gBDg4EU0ZQ0q0mNaqQ3BulNd11ygRz0+4AQSIxwI1lY7cPwGNBtTVPAjltex4IRngwkDbfHs2qrXwPkBwbCpiy4e24ktoOp2H2kyyH3jvNpnf3lfbnxx1RARwW/ZlLcDjbeC84zyUOW+TeU21mwgUM6KzEiIwAQrQ5lcACa7SdvriwwMuhMpY0k6T0NLeQLvf0UcpcuvIbHtEAETukW+w87r3GR/RWqj8zaS/9ZkfCXNs1sjdy0waHhdt/Fvt2ksZtq8MyPgVNO7cVQX92maKRMg2ZZiMFC2O4wTZZ7z2p3eW90u2+CHLRwNb7WuObCMc/fC3Ce6hvskwy3Z0xEWKltYiLPqVW+T+xj9rm8hts7rj7uFC3zloolM77PAK2KU9n5/a2ODRpTW059/BTCcxgc85rvIvmvztIHN9tLwRQryOnupcsTu+RvX+wBUI7KBeoU9GeHUBOBIutsEkGSyW+NnFFWzLov/uCrHf/NNojRFXJiOBhushZhe3tNZ2RdRDISgwLt2k56/+Q/B8ZCJG8xc/j3A9RKMZAKYGoqE0P3ajqX53nEAb5CAaAZgKABVNp3MstT93aga87aNiWtwpoItNC9hmKes40VoUcz36guu0pgsb5XZs2RLgQbnn0hhpCARSdGoKaDMH0W76CTUHou24JpTGMNI2aNG9pdYGzjxfaZSq9c5xFLyj3iZ8zW18FwCybBpv4Z9QeG86X7Rt58SmSFYhlDO2VsjR+Nd/Gy/QUKs6KF6qyHQm/cAhu/XQ9P/wT9FWix2SXkzPK7MR7TO382Vbp3e+Q+un1jzfrhUEqPFfDch8VzfCm7euCP74srU7bj1j60gN20mgtG6O62LoKiotWuC3q0upGo1vmUtD3IbuHP5v/NNgvgsq5Trzt2cx3SYDXVkMx4V6g2a5xszUNOuLy+zo7ab7978FpUq0yRDBhkP6Hr7rIV1F9KypF1Nt4PlRn215q/AHX7bMu5pQWsGxgQ4wJLW2z5tAktJQKzOnpistn+O5uL4klohjWkYnsEJsUh+q//g+SjA2FQehpmnYlhVYDdor3Lo2HEOd00EG/+/sEW15XWk32xKFexHwbq3y9yxRlpLtN2WtG0juz5z4vg+7z9/ai9Litta597l7uLmQqPVIiqjNxJaW2yzmIkTzAfXZPMM39V8HMA7ODTAY0ZUaaDLgcRXBZe2UEK3vIcl5e/F9SdN1aARcdlFttp0McrsDH7rcN9hqb57NdGIdu5f3K+GCGQrLwI8iUlVuemMhNIR0W9/Dw46LWCluqYUfOGaHvkWhlmA73tytgEtlhHddl7jr0R/wpRjVBpqm2Itt10ffKLXuFw6ITePmnosXm9tx00HYdjCHg1wEg15pItRyoLWZ4dKuhxWLYRgGVqWmBHQ4/nsL6rvrqR30YO+WNmnVoVULbXpBAShAVBsYV26pA4aO/h9+b5uX7ByY28jF9xnY9zjQpv2RgOsqO7sQAssSaO2AKwI5AaBZKwG+8oWJ22oCCqEAQpvAEFq7X0wAitqBU/v3TW8jQamnfb+lzRHK/yXShrR3ui/x3CZI0A291UhtZXugJzYPtej5EUPy+6xz77cnuud1YvPHcE6pf2awP1LyPVgG215Ztp2PINKOirBC+SwAfiHb8djQubXdj2rrfioUcGH9Ws/yPBfP9XE9H8dRzvXpdIpYtQ437xJVNHi+RBE4+n6YmqZOLGaRTiZaXEybGrFd+Piez+LSKqulCslEjKHuLJZp0A4AO0q5CuXALB75jAlCdvHFxTW+/+JrvP3eFfbtGuMv/LlPgGXiuh6Li8t87dy7zK+u8jOPniT5O/8ion8Ii+t5zC8s89qbF3jlrYvs2zPOZ559jEI+19KoSYlwle8dzSZaQ22Mok3R8hrG//M/wdKq0r4d2we/9b9tfZe27vDbQHez6VCp1gBJzDa4OzvPN777I+YWVvjZn3yWkycOoxmhy0coA2QUxSalxHclK0trvP3eJS5fv8HNW9NslCr8+c8/y+njB4nHrI4+CVnV1FqpqaCboO+krwgyfel1rHHZv/S/QtNVvlvhOAr62hNgbAOXO10fZGBWFvgIRf0QlFD72KrUB0nJoC2kv2nYvA/Mafd52vb0cAJuOqlNA7SlFqF6X7T1S/vtpURs5wJ0X4DrfsBY+Lfts+YHPruhKTH06FLrsojchVQ97plyJ1hXvMBlqB2g0Vqd7rOu91/uH2xFarpgkQkB04fVanU0gIyAVrigdp4bNLZoDW6BAMdVqnMBBHwYnqeAUpjKxDLNSAN1P8XzfXBcdJRZ0vKlSoMRCEzVgeVtgdsHlc1N9H53EFs+te3UQqHjeUjPV+ZJQ5miBIr924yu3grrAAW0TAO5Sbi1l45uWFqL/EcAqNXVbR0H7dbs+7zJptLmLyLaNENso9VB09SuWkpl6vrXfxtiFtIyWavV+d5r7/BH33mJRC7F//y3/ip7Du5Ct0w800SKNvs9AvvET6s6Gwby0G5836dUqdCoN0kl4hiBT5AIwNS9TIbifSavlJJarUHTdbCtNjOLoUeZA9qL5/usrKzj49OTU07325ZtUPsWaB4qAUJz3TYLb0vD0gZ8NtX/3mXrBWKtuGWuKg1r+JyA2yZ00tLCvXWneU12ZWj+l3/cliBWHfB9pR3zPE9lA/D8yITeok2UIHy0yPSrzJmaQlq8/tYF3jl/nbX1CrMLK0yMDvCLP/sZxv/ev29VOp+F3/lXCmihfDcWFlf48td/wAsvvcPTj5/iJz/9FF25jDITh3ntAlOMprX6w3FcvvEHX+e3v/gt9u8a5e//33+JwYGeAHAagNHRztq/+C+IF15Xb5JKIsqVqL2FFGysrfPya+/y1vlr9HZ3RV3qOC5Xb9zmvcs32TU+RFcug6GLjkVGSuUSkUzEEEJw9YYiIHZdt62vgvXUNCBuI0SqDRQEsL5YgXQSFlfUuhO3O9xACPusza8wBNVI0DWhomqlixak21lcXuPytVssLq3iey66EYUihLVvQ+qSSrnI5K0ppHTZt3sMXdd47a2LNOt1hPTf39wXpIlDEuQqbW2saY0iFQTUdCLNalRE62xBmwzqWB8U9VEYlBGN43tVqn1ZvmfZZv3prHFbrTqq0nb2vY7Jzke8D+BSxQM66RFaRUPK90vLs7moGfz+F4hNf8PPwftqtMylkdlGEDpRqkjFDyOlw9WqNfY7AGMEUj+83N9cPpQZUbRXQoY76vu3bW8ZZTIQANsBraB0Yuc2KOJ6SENHDqoFrVKusrK2Qa3ewDQMenMpxVnzAWBQQJCYtUy1ViedSmAG6SWEJshmUlhtArnlNxBe/z733zRZtxFz21/QdrRdl+F6PuVyNXK8dl0P2zJJJOKEPkP3wuQdE7WQxfuVfxQJLtl2lhamaECF3crf/TpiaTXyGzL+3e8q04KuIbsy99b+tJnMZLACC+1DTAI9mEC6jnz2oaB6kpTnMZZNsvbmBczuLmIjA4hMGim9aNcYLowd4cCmgfPr/5zbd+f4vS99k1w6wec/+QTppOpvAqGtaVok/KNe2TSGOhKcSkmpVOHbz7+K67o88/hZbNvC9310XfmshQ7valMhKZYrfPu5l/E8n0997CG6C10dzwijBDueH6UCaVscpUT6fhRh47hulJ4mGnFCRbMFYYwdYm270m6iVTfQVJYzIUj8rX+FWC8qk1/ThZlFjF/6+1vBWrsWqsOJVw1Q7coUsifP5tJ+n1BwEWhJwgitdq16u29W+BAhVNqaqzfucPn6HXKZNPlcOiKY3W5zqO6pR01crTYolio06y5OUyKEEQkbKVVEsPR90AW6LkHT0G2LdDqpKD1iVst8HNwfualP6w2iPtJbvRLmnB0Y6OHood28ffEGruvhex5CWDSaTa7dvEM8ZvPw6SP0dHehCU2BWRlNOQxDJ5NJMzTQQy6TJBE3sUxN6U1CuRLOxwB86bR8W5AyiESW4S4PkYjhOA7NwLRoGgZ6EOEYWhTaW1fTNJXP0ldOy6lknK5cOgpqiLKQ+JKIX0oC+EgkTqPOpSvXeefiNU4e2cfwYB/7du/g+OE99HbnMU1j69iLqi8UAbH0oyhGISQyojtojTOC1ETCbaPRbvvQkunbzZxwzdEQgaN3NHa3u0a0rmmr7Db31VFM7q1zwr6KLmsfUcGaLSKd+Oa6tqM8SSe/1j0AV/Tsdn+z1nspsb0VPW7/zsHGC9Q6/YGmRlqDGQLM0UpPFwJK2eoc2hq/tRHYpLToeC+2cXPbUvf/ozRbtCH31gE+CPVFkyrUzASTF6m9j1MfEOzyZLjb6UqrHcTsgjpsGcjurAqzT8UQOqzNr2CaOl3ZJDKTQmidJig2LQgSaDYaLJfKkIiR7yug6RoVx8VxXeLpBGYiRqhO7RDC23wKtcSdk/beOZdaocxbJ0crPEAdq5QrzBdLdOezpLJpfDeYjIaJFJq64n3BZWB3z6YDU4/Eka1+BdAR+EHfCATuz35KgTsVtAW/8keqz0wdOTF8j2eoCei6HhulMpVqQxF1dmVIxOObhss2kwGgTWsWRQhpAsuM0d2dw7ZN4nEb27ZAgOcr8Lm6thEQ1aY6tUyA73lM3Znl0tVbPPnQMWxLpVTRAkoNL8g1Z5qm4l9q27m7XpDkVaj7AJHpZmllnavXbzHQn8eyNMVPJlTfqoS+DkKArqno1Lht88iZYwghVHu0t55UKWMUHYeu3g+UwAjGkec6SM9TZqpA4+YHWplq3aFQ6FLvLgJBH0QA+dINNBMCUxi0FL8iSLbsBLQRmtLICYGywgTRhOtFxMqGAlqBycVbXGltOjUNZBA5HJiT1NhSgC8a4200D27gJxNqkDVNRO2q8o+WkNLDtg2EpkdB8ZE5MtBoKUwQRj1Kdu4YZmxYReRatsXoYB/JRGdbh+2tihYAVR1DN7FtW/kZSZXcVkiJh8BxHFbXijSaTUU5k45hxy18z6daqSmKDV1XgCYAi2HdOp5ZrbWmQbuZXCrAk8mkmRgbwjINmo4bBTE0HYdcNs0Dx/czPjaEaRh4UuVarNWV+dO2TTLpFIauYxgCw9BUPsjSBulUClNTYDA0vxhCgVTf83E8FyQYuoYmdJUTEJQPmG2xUSxz+doki4vLFLqyjO8YASG4dvM2Mdtm7+4dZDNKS6Y0lGoT0Kw5lMq1CKBFY9r3lRyIAJDKHiI9j7m5RZ578Q0WllbYOTZEf2+BTDrFwb3B/aWk4TjoAZ8b4TO9gDvJD8xxQTqjDq0ZMgiUEviGoeg3wsTT4fiK1tIASL1v0Wj5OYYrYJusiTZQPqIjtV3bYhiNkbCS25kTt7kyqtoHbKXaNV+ROfODNE1hdbYCLqAVKBfI9cjlRYTvIImcrdqBphBtpKfbPbMNOEX1D+7ZpsEKt0odLGJtmEREQKz91qEmXYsUPi3+MhG8Tjs4+6AGur/y4akfOlSkEiE8ENv7nXQUuSnNo5Tcj7OgQEME+q3mv/5/oCExf/nvqxD1QhbnV/9e4BDqc+ftS/zTf/PbxGyT/+mv/RRHD+whGYtHjagGhuqwsCauL3nv8nX+82//CWODPfzyX/gs9abDr/7Wn1CpNPjln/sME2ODaJqBETB9S6mARFjCiD8pfapNF01oJGwD35eU60p4JxKxSIi4rke90aRSqbJeLNGVy5DPKULTpuNSrTep1ptouiSfS2JqOo7j863v/JAvf+MFfuGnPsmTj55SDra+T8MHT6pIxzBUvlKtsbq6Tr1Wx3Gh0ahjmRqDfSpVjAZsFEtcnZwh15VhdKhfRccJ1eKuJ1leWWNucRlNCAb6u8nlUhihZtU0cH7tnwAq9U+lViMeM0jGLXQ0PE/y3A/P8V//4Bs0Gk127RjiZ7/wDCeOH0CgnuMD1XqDaqWK9H1SyQSJeExNoWM/BaHZQ9PxcalWqpRLVWZm53E9D9PQFXOz8PB8l7szi3z/R+eoN5oc2LODIwf30t82lhqNBtdu3mZtfYNYzAjIBzWaTZe5xWVu353j7swCtm1x9tRhRgb7qDUcLl25wZ2ZBZJpRd65sLDERrHEob0T7N8zTrFUZq1YpKcnS7jA+D5cuX6LN9+9TKlcxjB0zpw8zOH9uymVK1y8cpM7MwucPnaAE0f3AVAqV5i8NcPk7Wlq9SZ7d46xf98uNF1nem6RUmBqWlhcoVgsceLIXkZHhtCFZG5hmT/8ynMUKzXOnDrM0YN7yKRTSClZXttgdn6O5RWVMFtKya7xHezduYNU3GJ1fYM33rrIhauT2KbJ8SN7OXJgD1YsRri4hRumyESoCZq+z0LTQQ8c6HNZBWY2imU8z8OXEs8NEp2nk2QcNwpokEhq9QZf+dYLAPTkcximwdjwAEMDPSwsrfDaWxe5MXWXnTsGOHZ4L4lUilq9EbWDGaTVSCXjJJNxRXshFa3E4f27cRz17FQiTiqVxJfKp7ZjsyVVntDllTXmF5a5PT3L9Zt3sQyDTCqJpmv4nkuz6bCyUWZ+YYmr12/T05NnYscwnu+R9l3QNCq1Or6UWLaFHlC5SB/cpkO5WqZUKlOp1hGaYGK9hCVV3r42+yuNeoONjQ3m5pd4+/xVqrVGMJ6U71mpXKVWq1FvOKxvlOgudOE4DndnFnj13HkuX7/FUH8PTz56kr27xvClAjxTd+b43vOvc2DPOKNDA/T19hBLJjE0gdtsMnV3lslbM6ytF5FSsmfnKLvGR+lqS6Dt2xbFUpk3373MCz/6/xP330GWJfl9L/Y57np/b3lvurrad8+0mx7v12KxCxCeAA0oPvKFyKAYeg9iUBL/EBmMoETxRZBPTxRI8IEA9sGsX8zuzOzsju1x7X2X6/LeXG+OS/2R55qq7p6ZHa5CGd1Vde9xefLkyfzmz3y/F4jHIvz6114gGovyV99/g2QiSioZJ+p5FGzLZjubZ3p2kZnZBZbXNpieXZCLFeG5i22HmlnDMqWFNmBI7q9azeT2xD0uXrtLNByU9+FZcm1XxsfNzC1RLFU4uF8qEaiqSrVaZSdXYGl5A1U16GiL4/PpDemtSrUmdXVNSZacSsZ5Ei+Hup6g8SCrtmc5avnQ+Ni04mqND4I9/UyezOtzDnVQVj+PNNa0WnyaQO+TAFezfEaKhs97/gbgagLF1nZqAKyWcUIu/j15oQYcYs997r1OC8B6qCXxAVaq+td1j4bSBFVua1uzB0jdd+5fGLa6r3xu6gcApd6QSh31PqSWovFjz3fK/d/vKZqi4AoNsOVLL3bHwNiWjWWZCFcGuKsKBPw+LxZHBvEJVHKemxEgHo0QjYbwGQaO47C9k6dQKGOZNqbpsLq+xfLaNqqiMXlvkWAgQEdbCp+hky+UuHZ7ijtTc5Ikc6SfM48cIhIOspMr8NaHV4mFQ5w5cZBiucLbH14jHA5y7tQxIpEAkzPzXLk+yeLyBgLB2L4BHj16kGRSJZsv8rP3LjI1u0S+UCIc9vPyM6c4vH8Iy3LZ2NymUq1i2zLDznEF+Wyee0trrG9lScSijI324zoub71/ifc/vk6xWMZ1XfLFMj6fwVeeP8tvfP0lXMfh3Y+u8aff+QnxWISvf+lpnjxzHMOvs7Gd5YMLt3j/4k1W1rdIxaM8dfY4px85QKj1EQrBTrbAOx9c4uK12/R1Z/jaS4/T3ZlBCMgkI3z1pcfoyKRpzyTp7+uWwcBCoVAocenqba7cmCCbKwKCgb4uHj9znKH+7kbsGYrMYLp4+SrXrt9lbSPHwuo6W9kCQ/2dCGTEzer6Jt//8VtcvHaXUrnKx5dv8xtft/lKi5u7Wq2ysb6JaZoelZBkif/g4nV++u4FKpUqEzOL5IoVdrIFfudvfJH1nQJ//cb7nP/4Ol0dGQ6ND7G4tM7NuzN8/YtPMjzYi2XbDc42SYPgcPvODP/pm39NrlDi4NggU7OLKIrK+OgQswsr/Pn33mBucY1kIsrxI/spVyqc/+gqH1+5TTZXYG1jh5W1Tdo72yhXavz5d15jdn6ZRDzKdjbP+sY2Fy7f4m/+xlfZN9THjTvT/OSdC2QLZRaW11EVhbOnjrG4tMorP3mX1fUN9o/2EwoGuHFnhotXJ/jKi09x8vgBJqfn+P6r7zIzv0KpUuXSjUn+6X//OwwP9jUH0AY3jsDVVVYNg7lKjf8h4iMQkHxXv/615/AZBv/1r14lXywTi4QoV6qUKzVO7O/lX08sEa9TCQhBoVjij/63VzB0jfZMgmgkyFdfehyBzXd/9BYLi+tk8wUWV9Zob08z4PczOT3PW+cvUSpVPGUGh0QsytOPP8LY6ADVao2V5RUmpxcpluQEq6oqxw+PMTY2QsZ1Pf4jGSeaL5a4duMuF6/eRNc1CsUKiyvrZFIJujvbCAcDlMsVPrhwg3c/uMbiyhqWY/Oll55kRFGoVGtYtlRbEMJFVRVCoSCa7sOpuaysbnLhyh3uTM2xvrFFvlQlEgnxB8trtLkuerTJ6l4zTb7/6tvcmZhhcWmdO9NzOK5DMBgARVoB780t8sZ7F4lHQpw8vh8hbBZXVvjrn7zLzTuz+P0+avM1Ou7GGezv8Kw9guW1bc5fvMWNiTmiIT9fePZxzp55BEVXuXTtDt/64U+JR8P0drczdW+Rjy7f4vFzj/IrrosqkKSyHu1DOhmnUjMprGxguZK7bTublyEXhlxkaqpCsVzhwuWbfHTpBj1d7eia7unJOtRMC8uyqZkWdybuMTu/SlsqxUBPO53taaq1GuvbOXLFMpFwAJ8h6XVyhRI3bk9x8dodNrey7OSKLK6s8ytfeQ5N1ZldWORn713ko8t3CAcDPP/EIxw6MMLK2gaXrt1lbmmdfKHUuPaJI6OccV18ILO01d2W/uaMtnd+q0chQp0SRT5IFVz1AfhlN7jyHnpz2y4LzicAoofGSX9Gd9d/E6CDJl/Y/dai+vnr/JlSWscLJxF1ItVPKA1C3YfUu1H3VtzgZcQ6SgvQUnA9T0/DiqUq4MjD69ZPx3YbCR14tp/6U3cVzQPMe2Hz5y+fC2w1mVYFDd6Lh+3ruRQUz4X48xYFGUsguTNaYSxYlsMbb1/g9t0ZSuUyaxs7rG/lyKTjcmWJzDqbnpnje6+/z9TsEo7r0tOZ4cWnTnHs0D5QNfKFMqVyla2dAleuT3Jneo5782tYtsNOrkD5C1W+8OxZLMvmB6+/x/defZfFlU1qpsXRA4N0dWYYHxnAth0uX7+L6ziMDvYQDgZY39xi8qMFYtEwhw8M8dHlG3znlXcRAjraUnR2ZVB16Xv+8PIt/uh/+yHrm1kvjdyhVCrR29VGKBigZpmyo3gv+r2FRV59433uTC0igIG+DgJ+H8lEjMmZBT66fIdMMsoTp48wM7/C+Yu3aE/F+cLz59A1lXc/usrEzAIDfR1sbG+BcKhWHX7y9sf8l7/4Mctr25JEMhEhGg4y1N9Jq+NweyfPux9e4s6UJFg9/9F1OjMJvvLSE9imyfLqGvlCgVrNZOreIpMzCzzzxEmiiTgfXbrJt374U8KhIMcP7aNcqXLx6h3mFlf51a8+x+GWJeLlq7f40796leH+Tg6M9aP5NGbmlqWrS9OoWg5vnr/M9197j0rVxHFddHWHmdnF+/oieNIxniVwO7vNK6+/w3sXbxGLhCgUKwgENbOKrkM4FCAWi7CxnUPVVAbLHZi2RSQcoK+7DcPQd40Puq6xubktwcLSGl/74lM8duoocwsrhIIBXNdt8C7li2UqVRPLtplbWOb1tz5manaRgM9HtlBkJ1fA8eKwZueXWdvY4dHjB3iiI8Mbb33IzYl7rK5vMb5vEMuypaxRLExvV5uny+gyOT3PhxdvsG+kl3OnjxL2JFte+9lHXLx6i+H+DtY2tlnf3KajPUUkFCAY8CMcx0s2aboCWslJq1UTgMG+Dvp7ZL/r7+2iVKqgKArRcJAnTh+hWK7wxruXmFtaxXaaqfUokgxV0zW280WeOHOI0aFe2tuTlMoVVEXh1IlxFlc2uHDlDoV8EZ+usrmdZWs7R3dnBkVRePvCTQIBH/tG+ujv7WRieo4fvfYehUKZc6eOkk7GuH5rmv/y569w5uQRftdLgqlbi67enORbP3iDcMjPV15+CtO0WV6VAeHhcAifz+Da9Sn+4ntvcPH6BI7jEvDrtKVvcGDfEIl4lFK5LLOfFekCDYeDaIaPrZ0Cr7/5Id9/7TzxWIRD+weJlWvcuDtDYXOHZDCIGvBLLUTbZmVtg7/6wRs8dvIIpx5tZydf5N7cMrpu4CpS9mgrn2dxdYvgkB+fT8N2bK5cv8u1W9Mc2j/E42eOYFkO0UgQwzBwvcmmPRPnxJExFlc2eP/ibdrSaU49eoRi1eaNtz9ibnGV3//tX+LwgVE62yf5zitv8f0fvcWXa5Zc+AiBpcv4w+HBXoYHe7k3t+y5sAWJWISD+4dIJGIN38HM7CKvv/URbek4Lz37mJcsInjr/CVy+QLlSpVKqcz5Dy5z5eY0Rw/sI+TXiYR9uC4YhophqNiuS7FaoVAssb2T4y9/8AbxWIznnzxFrlCiWCqRyxdwLJt3P7jMjTszhAI+7kzNEQzopNNxKQ2FYPLeArbtkEpE0TSFSDiA4pPTYKsbEWgkETTnnNbPEqB4nqjmGNPYV/MMAy1WmrpXqL5uaTGUPXxKr3+rSbdbwxUnr9QgdxefARg0Fk57AZd3gkZA+MOOxzvuk40rSp1zrK6ZXHfPtc7/nwML0MAALe7guqteUQDVu653TVVF8RLmGlZFhAe4ZGiI41kzJTypt6nc/3PU8BPL5wRb9Z+tSP3+B73bXLe3ocXu7/Zeox7bpdQ7kbRhCM8k6bqC7Wye//LnP6ZYKtPZlmRjK0euWKWRAq7AyuYWf/nXb/K91z9E11Rc1+XDy3cRQtDdmSGRTMkXxltZ6oZKOhUlFg3i030cOTDMYH8nmq7ywUfX+PZfv8nMwhq6ruIzNLL5ItVaBUWBgN8g6NdZWs1SrdXo7WpnbLibazcnmLk3y/hYH4lYBF1TGRns4YnHTtDelsTvNyhVCnxw8RobW1m6OlL0drWxnS14Kyd5M00meEGpUuTDyzf5wU/O4zjQ05WhVCpSq5VIxLsY7OsmEg4wMtjNb339JW5OzDB5b5GtnRy5fIFkKkKxXCaTivHVl89x9tHD6D6Frc0s125Ps7CySSjgIxYJ0NOZoa+7nVBwN9Xk5s4WS6vr7B/tZ7C/kz/8kx8wt7SCZZtUKlXeeOcSd2eXefLMEXayBT66cpOhgW76NZ2PL99keyfHF545zXNPnCKXy7O5uc2HV+4wNtLDofr7JASvv/k+hVKFF585w+hIP0urG6xvbuHYEoTv5HJcuHaHTDrOk2eOEwwGCPg1Du0fQVH+slFfVWkOjq4rB53F5XUm7i2xf6SHJ88ew+/3EQkFGR7swQj4CCsKRw8N0/tehu1sgcmZBao1k7HhHg7sHyAQkK5MtUGQqzC/tMrVW1O0pZM8cmSc3u42ujvTIFQMQyedjNOWTjTiWqrVCjfuTDN5b4F0Mk5XRwafoXP6xEEyqSQ126GvpxN/wMeZk4c4cnicUqmIrisk4hFUTUc3DAzDYHSoj2eeOMnYaD8g2M7lcFyHfcO99HR3oCgKY6MDXLs1xU62QC5fxvD70XSDre08I4M9PPfkKdKZFI7rUNdPa2Rj1j0oikIo4OO3f/UFhvp7cByHWDTG3ak5Uoko4VCAx88coVAscWdqFkVR0Uo2ituM1/L7fLS3JTEti7Onj3H0wDB+n4FlO3z5pScoFEtMzy2jqKonpuxjfN8gbekkkXCQN9+7hBCCw+PD9Pd24grB5PQC5y/e4pmzR3n8zBHi0SjtmTT/4b98h++88ha/WqkR8IYjx3G4cPkGEzNzvPzsaQb7O6hUTdozcdY3s17MXY07k7NMzy0RDgbIpGJomtpgndY0Fdt2ME0Lx8va9fkMVMVlYWWFdz66Qs00ef6pU7z47Bk2trYofrOM8/FtTF1H9/tQylXKlSqbuQLs6+WlZx+jPZPCdWz+8E9+QKVWkwssReAzfF48oeRiq9RM7i2sYFk2YyN9HBgbaoQ7GH6f7OdAV2cbjxwbRzd0Prx4i62dPKZpk83mmJlfpqMtxchQH6FQkL6eDnp7Ovjw0i2pPAAgXCqqimXZJOJRBvu7uTs1z0eXb9GRSZJJJxgd7m/EOrquy4Urt7hxZ5qvfeEpUqkEmqp6mZWCYrFCtWaysrHN1OwSbekE42MDxGNhamaNqmkhkIoLtuOwtr6JWatiWTazi6v8ja8c4OTxA7hCsLa2SbVSpVgq0d6W4ssvnENVVUrf/Qm27eLzaWTSUQZ624lFgwT8Pr7+pSdpSyfpaEti3FuXg40XttAKshRFeQgwkJN7fUEi6u+H0gRcjWD13TaCptuY+hRYd709LIGojiz2aiy6n2DpekhpBVytRWg0TD+fCXA9rEgwpjRAYeuBD6hH6+efsyiedZpdcVteCo0iLV6qULzkombWshz/FU8tZm/cWFPF4xcZHA8/D9hqsdwp4MU/fXplpC5gi7+2JYgOxeFBcgB1oFXXVmsAMwXqGQmWZZEtl1kUVZ5/4jhf+8ITTM8u8Yd/9iMvQBccx+XGnRl+9v41csUynZkEjusSEBK4WY7UUfP5DPx+H/6Aj97ediLREPHYVbraMnz15ccZGugC1+Gjq7fZ3Mnzd37zJcb3DUj6gHCQgd4OyrUyQnEagMQVDj6fTjoZw/DM7I7jkErGScQjZNJxxvcPNuKU8sUSS6ub9Pd28Nu/+jJDA90gBKGgj1g0jG3ZDYoA07TY3M4xPbuEZTs8eeYoT587QSIeZqC7nUgkQFdHknDIj67phEMBkvEY4WCAUqVGuVImiQwyDQT89HS1k0rFEQI2t7OsbW4z1NvOr3z5afq62ggFg7Sl0sRjkV3PqaMtxZdePEc4FOTWxBR+v+E9NwdVV6naNpFwiGefPEmxWOaP/+LH3FtYItPWRrVmEg4G6MwkCfp1rKCf9kwCEOQKxcar47ouM3NLBPwG0VgUFI1QOIBh6JRKJS8Oo0y+UGKwv5tf+5UvEY2GUIQlxUTr3QfRYJJ2XUGpUsW0HVzhYtoWgwOdfO2rT+PYEgT4DJ1iuUahUKKzPc2RA8O8+rOPmby3TDjk5/GThwj4ZbxTNldoii0LIfW2XJeqabG5naW/r4NQyAdCxokFAgE0XUfTVMq1KsVqmWqtgt/n49FjBxgdGcDvMxgfHSAcCeMWyySTUXbyWWzbQjgmwZB8ltvZHSy7BoqKz2cw0N/N4EAPgaCBaZrYjoXjOCiqBAV+v0Eo6CcUDFAum6xt7ZBOJXnk2Div/uxDPr5yhyMH97FvdJBytUTNtBCKiq5rRBxHptsrMl5KCJn519GeQlMVKlWHYrlGzXKI+wz8oSAuSKFyTUVVy808Ge/BOI4jeex8Bv5QCENT2c7vcOnGFB9cuM61WzN0d6YJBAO4KHR3tZFJJbh8/S5Xb07S293O0+dOSNdTtcby6iaW5dDV1Y4/4ANVMDzSw/59A/zojfepekOeQGYhb+7kMT1iQ8v2uOsUFdtyqNVMyhWTXF66ub/wwlmeefwkhmGQjEfp7Miwk8uhqgqWI6jUPOoZpIu/UKqwuVMgHAkyOtxLNBakUPJh6Aq6aUtqC5+Bk8157jWLSDhInScwk056MYWWTFJRaehgooArFGzhUrUsiuUq61tZ6XYLBVBtFc0nhcQdIBQKEo2ECIeCaJpGuWpSqlTZ2MmzkysSDIUo1Sx85Rr5cpVKzWwx5kgJr5omxzXD0Onv6SAcCvDBxRt0ZJKcPXmERCyKK2QdHdNlYzNLqVQlly/Ke4uEMQydUDDgAVIF07Kp1Cy6O9sY3z9IezpJtVImV6xQLFVkhq3roqiqBFYb25QrNTRDw/DJSTQWi7K4ssb6Vp5IOITPp3Pjzj0UBTKZOOFwQC6GAzJBpWpaBAI+ThwZIxIOofl9eHEZUtViD2fZ/Vat1u+hNStKqRsJWixHrVmDu+Kcdu3X2OETwN3ev1U+kRH+YeVhwEZIjrdm1t+ujS3A5nMw4dfB166sGXX3588CuHZ5uFrO3bB0tVZcEp/KVtIaVE4ITSZQKE0vXSM4XgHDZ2AYGqLOn/bz3+0Dy2cHW61Aa++mhgVq93dSE6w+uoqW09S/dz1A1SIDUj9217nwInNkcV2Xaq0miQfbIgz0dDDY10U6Fee1ty5gWlLjyrQdNrayFMoVjh0Y4jd++TlCQR+lSo2OTAKf39942R1Hxp9pqhQ0VlWVUrVGqWpi2g4+XWWgr4NUMsrIYC/PP/sYihAUC0XW17fI5YtyAPFM5dVaDeFNJq4QOK7w4kcUDF3DFXLVFgr50H2Kt4o2MU0LQ9fo7+lEgNwOmKaJpsmVVKFUYWs7S7VaJRjwMzrUw7kzRwkF/AgBlm1TT8KsmSaFSg3LkStj07IoVWvYjpAEfLpGMBTCdlwKpQqrG9tsbefw+XQGejs5dfwAtZpFoegNwC1FUWBja4d33r/Ch5dusLS6jaIquEJB1WVGl99vUqrUUFQVTdfZykq3oqHr2K6L6QgsV6Fi2pi2jepx87RY2LEsh+1sntX1Ldra0rhoVE2bfKHExsYOht9A0w2mZpe5eWeaxx47RiFbwbYd+loWao6QE5TjuiysrHNvfoG2jiSPnznM3OIa3//RO+QLJfaPDnFw/zCaprG+JV2pB/cP8f6Fm+xki8SiKfp6O2QszOoGS6sbcrJ2BaYLqVSCVCLG/NIGP/7ZBxRKZcJhP44NZ04fx1XlZIeiML+0xuLKFt1d7QQCfu7OLOAP+Onr6cAR9WSKGpZtUanU2NrJkc0XKVUqbG7nWNnYoVSt4uLg8xnE4zEqlsnS+rbM9FKlFNTNu7OcevQoHe1pZhfXWVzZIB6NMD07LwO2B7t44sxRzn98nas373Li2D7uTM5y5eY0oVCAfSO9JMoVQpZNAMmZVC6W+dYP30JVFMZG+rGFysr6FsVShe6ulHSXKgqqJ2q+y5btuZ/qmXbCFRg+lZXVDf7wj3/A5euTDPZ1kkpEsSxbhgS4Asu0uXFnmldeP0+hWOGJs8fpaE9SqVXYyZbY3smjKLLfL65tSpeoF6fhM3SwmnUQAkKhALYjWFndZHs7TyQUwXEFVdOkUCpTrtawJIIgk0qwb2SAaCzKxvomtyZmCAb8aIZKoVQhmytI14SQ8aO6phEOBbAsG8e18QX8OI6LVao0+nZVVzx9PjlOlsoSnAyPDBIISmCk61IsWlHA75eZsqqioGoQi4Xo7W7jw4u3OX/hJigyMD8Y8PPic4/hOJKzTFWl27Y9kySdjLG1k2N1fYtCsUyuUEawwczsIgfGhtncyjJ1bwHTMlsmQIEeCxOKBnEcQV9fByPDPVy6epdINESmLUXNcbEEGKjo/gAdHWmCQT/X78zw/kdX2TcywOZWrjG/qqpCZ3ua3q42JmYWePWN9zl6eJ+MlQuHScQlTYSmqkQiYbq6MszMLVOu1Lh2a5pHjo7R3d5GzTTx+3xMzy7y0ZXbBPwGxVKlEddZrpqEI2F0v4HPb7CzvsPd6QVOHN1PPB4Fj3xWAU8HtjUu+FNcc40on5Zd6xEQQqCIzwpMPu069+/dvL48Vuza+lnO1WJLayQCNJPIWkGXdEa5SIpXZdch95UHfi8tgbtEtj8JYDXq8wDrV8N92Apo6+fRvbClOmu+kMnYwpNUE9ougNW8vOdmVOTYpustcXi/oPK53IgPbp+9Zkn5Q6GZhdikgBAtaZ8uLjI7ZTdg9bKWhILAbt0g41OqJqZlY1q2jH3xyDYVVaqvC8AWYNoy8yORiHLk8D4G+7rIFYosLK5RLFdQVIN8sUzVW906rtvw65bKVcqVilzdC539Y4Mk3rvCX/zgLYSi8viZo6ysbmJbFplknGyhxNzyBivrO9y6e4+ujgz3FlYxdI3e7nZMy2Y7W6BcrVEsValWawgiCFyCQT9dHRkmZ5b469fPEwj42cnmMQyd5x9/BJBZFZbjUqqa+AMB2tukpW5yZpGZ2SVGBnvQdZ1KpcrGZpZ8qcLS2jZzi2uYloWLQtU02ckVsV1p0atUTbazeTZ38ijCRTMMYrEwtycX+d6r7zYyv4qlCsND/QzSjNu5enOS//oXrzK7uEqlWqNm2kzcW2F6bplUIkG5amL4DHyBALZpomky21LRFTo7U1y9NcGdqTnG9g1RMU02szn8foNMOknrIJZMRLk1Mcc3v/0auq4TCAcplassr+/ImKWxIQb7e7hyfYr/9F+/y/mPLqGqCiePH6CX5nBUNS3KlSo102J2YYVLV+/wxGPH+eoXn+RbP3iTV37yAaVSlXyhzPBAL/F4BAWFgN/P6FAv3R1piqWKdNeMjxCNx8mWKtguVEyLlbVNNrey9PZ28dS5E/zF937GOx9d5+bEHJZtc+r4AU6dOcHC8hpr61tomspOtsDm9g69XR309XbyzgdXmbq3SF9PB8VSlWefPMVOocyqF484MbOELxDg9uQC27kiuUKJctWkatlUajUuX7tFNp+lvS3F+L5BMukEHR1pPr58m1rNIhIJcW9uCV3XeezgCNFwiLfPX8Z2HM8i5+APBljdzPKjn37IR5duSVel+hhnLBvXC6QNBQNEXMHd6SX+pz/8NmMjfZw6cYi1jR22cwUK5Ro7hTLZbIFCsYKma1h7SCMtyyZXKFOpWcwvb3BK9ZHNV7h6c4al1S2SiRi247K+meXjy7fp7e2iWKrywcWb3Lw7x9i+ftrbM1y8Pkm5XOb40YMMj/Rzc2KWu1PzjAz1MtjfzZ3JWW5P3GNksIfw0nZjgFJUhR4v7u7mxCyvvH6eUDDIrYl7VCo1SqUKmqqSSSUxDJ0337uEomp0tqeZnl1koL+HI4f247g1VhZXmJ5dplKtsbaRZTtforu7g8dOHeGn71zgp+9cwHZcFpZW2V7akCLaho5pGAQNnVAoiM92mJlf4c+/+zora5tcvz2FKwShoB9dMyiU8kxOL1AolilXauRyeQy/j0cfOczNu7NcvT7N6voOuq7x5JmjuI5DpVKTweA1C5/PR0dbilg0xNZ2gcnpeY4eHuP5p07xozfe55vffo19w31s7WTRNZUvvXCOwJ+/0RjP1VgEn8+PZdskUwmGBnvY2Mry+GPH6e/vQFGbHG2qpnHq5DEuXL3N7Tv3+I9//B2S8Sjb2QLZfJ6J6XnKlSrtmRSnHz3EzNwyf/7dN/j48m1++ctPc3D/UMOdZ1rSupVKJujqbCcYDPD2B9eZW1yjpzPDobEhnn/6LKFwiHuLa/h9Oj7DIFcoc3Nynq7uNs6eOtSwKtUt2+WaSa5YIaCqjRw7YTsoPoPW8mnTrkLdQuJJyzUcia3bH1JUpYX+yANJD7NutcQd7bp4w9LRaikTNKkaHhay06oIuveOoK5m0DBHu2oD5zwcyyncF8fdsF5BIxj/U0vdBVs3zHwK9FGkxU3sva6gbsJCQQcsaX1VPOURpUmLKzzMIi3In3SPn6/83GBLGqlaiA/rPt49YPrh6ZXuroZrcG61mv+U+n7Ixm7ZvyGJ4pVypcbEzCIrGzvouk65YlKp1djcztHb3UFbW5pEPMLs4irvfngV23aYvLfATrbA8cNj2K6gXK5iWjalqslWtihJIoFcvsQ7H12nUK6yf6SXUDhMJBLm46sT/NE3X+Fn717i1IkDPPv4IziuI4FUucrqZo7vv/4+d6YXKVdrjA73MzrcR7lqsZUtUizXmJlbYXV9m0xHCt1RCEUiPP3Eo1y/PcOFK3eZnl3GsiweP3PE42RS5aRaMylXagRDQQ6Oj/Dx5Qne+/gm27kiX3r+DI+fPkahVOHuzDLZXJliscbEzDzJRIxcoYxlOyyubbNvzKKjPcP5C7f5L998hSfPHuWFp08zPNTH6UcPMT23xpsfXufW1DzJWIQzjxzk4MF9ux7y+laOydlltrJ5/H4DTdf46PIERw7c4uihMXKFMo4rMHw+bNshmy+SK5Tx+X0cObSPqzcn+eufnOfm3XtomsLS6jr9vR10drY1OpuiKDx6fJyZ+WUuXZvAcQU9PR2EgiGOHxmju6udVDLBY6ePks0VuXn7Hu9fuMVLz5xkeLC/0WcRkjJhY3OHgN9HPBbBMHSqVZOBvi6+8OI5dMNgY2Obg+NDdHakqdUsDEMnGPATCvrp7Eizur7N/tF+erra8fskkaXf7yPo90uqj2KZ7s42njj3CMWyxVvnLyOEYGSol5eeO4ffMLh89Q7lao32TAoUFcPnp7engxefOcPmdp7bk3OUKzUCAT+qrpPNF2RMkCtYWF6jUq1xd2oOkPxY5UrVs4BoXL4hAexjpw5z/Mh+2ttS7B/tJx4NSyuhbXPy+EH2jw3S1ZGhUq3R093OW+9dolI1eezUEV5+/hy2bdPX0061VmVmdhlNU/D7DXRv8RLw++lNRPm9X3ueu1PzREJBkvE465ECnR0ZYrEI5UoN03bYNzpAJBxEW7uIaASkSuqPzo408ViYqdklZudWGBjo5wsvPMbb56/Q093B6HAfW1s7pJIxKjWb1c1tpmeXyeZLzC2s8oMfvcNOrkAmHefEscM8de5R5hdXuXTlNo77Lj2dbcwurFAqV/jKF58l+J2fUh9kFAWGB3s4cXQ/125M8s4HV/H7fRRLFWLRMACBgJ+jh/cxfvU2H12+zZ9/9ydEo2FOHNnP+P4REokoW9s1pqYX2NiUQGd5ZZOl1Q327xvi9KljLK1tcXtynpn5VUrlCr1evzEMg1pAhh0EA36SmkrA7+O9j29w8dpdzJqJ6ZGIKqpKvlBmeXWToYEuAn4fU7NLjI2PMjDQw0vPP4ZtOWxuF9g/2s/Tjz9CMBigZpoEA34cx8GyLFwhyUUdR2ALQWdHG7/+9ZcxfD7eeu8SN+5Mc+TAEF//8tMcOLgP41tvNt5FN+D34rEE1ZqJadqkUwn27xskGgmjALouvQMKCqNDffy93/tVVpZXWVpek9O/opAvFtg33EcmnSQUDHD4wCgvP1fg+q1p4tEw8WiYUrnKxMwCO7kSpmlyd2KWQ+PDnDi6n5efPcUPXvuAiZllsvkiY6ODGD6DwYEuejoz5IslkvEomqayvpllcmaBgwcGKJYqsk9aDleuT5MvfI9Txw/wW0Lgq48TD9Fj3Tun7eVFVIUUsN7tNKzPkff7onZxT36WsssK1DJntmRPCiH28FY+SE6n9RR1i9gnAa4Wy5zi7afcf/+76tk6j+/CAT8PenkAyGp1u+5tt92ZAg9oVw8EejFbiqqiG5LOadeuu/DIL7Z8fuoH77lq0KigRL2foEnkHddwKXruQtX7vDc5VFEUzxXZ+p0Mlg2HA0QUQFW5eGOG3H/8K3Rd4+60zMy7M73AwfERjh7ex2OnDvP9187zH//kFb753TexbYff+PpzdHS2Y1nSilWpmkzdW+ba7Wn2j/aTTMS4N7/KK298SKFUIRwOEgj4ef6ZM/iDAabuLSFUjUdOHMIWgtsTs9RqJkcODLOyvs3YyABPnzsuA547M4RCAXYKFUaGenj2yUfZyea9+5OmTVA5/ehh/qHjcOHybZZWN4mGQzz35Gn8vhA7uQILSxvkixXuTM7x+OmjHDowxvNPb/Hx5dvE41F8/gCqpuK4GoP9vXzjy0+BgI62NF1dHXzhubOUKhXaMkkq1RpPnjtOpWby1vkrrG3sEA6HSKYSvPzC4/j9QS5cvo2mKhwcG+LRY+O0pVOtD4Lu7k5Gh3tJbOXo7U5jWhbrG1nprkPhyKFRbt2ZZWNjm1g0zOhwHwMDPQT8AYYHB/jdX/8lrt+Y4PbkLFs7Obo72zj96GE62jO7loXHj+wnkYixkysSj0fp7enEZ+gkE3HSqTiO43Lk0BhDff3k8wVUTWGgtwtfiwyOAHp7Ovhbv/UViqUKmbYUQthSMcDQGR8bwu/zsbOT5fD+UXRdp+S5e1xXUK5UsW2bZCJKX7d0+QGEQyHG9w1y9NA+MpkU6aTMxsqkknzh+cc5uH+YWCzC8PAAyUQUu2Zx9Mg4QlEIBgMkEmEGBnrxBYIcO3aAcDTO9VsThMNBDh/ch99nMDrUy//+7/8GpXIFw5Cr7keOjbO4vE4mnQAhOLB/mG989VluT9wjlYjx+NkTdLTLZ9KWSXLm5BGGBnsJ+H34fAamabG6ukMVk8fPHmdsdADXdTl2ZD+ZdJL5hWVOnzzE6EgP/8t//g5b2zk0TcoPKcjA8Gg4zBdfeIrnnjQ9d5VLNBbh2KFRorEgmUyKUCjIM0+cpFozCb1/s6GzCRJg/K3f+iqpZByBIBoJEfD7+OoXn+Hg+AjhUICO9gyOZUouL0Vl3nbIpBPsH+0jGgnhui5+n8HhAyO0t6VIpxL83m9+laG+LjY2tqQqRDjEk+ce4bmnTqJ+72e7xpOhgR7+3t/6BjvZPLWqiab6sB2HYrFEKhHDEdDf38vf/M2v0dnZztr6Fn29HXzlC0/T291JqVzCsm3G9w8xMtSLpmsEfD462tK4rsvgQA//4O/+GvliiXKlSrVSI725Q+T/+WdyxAwHAAVN0+hOJfjn/+i32dzOoikKVq3G+uY2Rw6OoioKnR1p/ru/8ysY4RAg0FU8N6ngxPEDdHe1o6HRkUk0ONaeOicpMXL5IplUAsMf4B/8vV9nZXWTcCiCZvjo7I7xN3/zl3ji3HEq1SrtmSSJeBTdMHa5fW2fjqFAuVxhanqee/PLpJIx0qkEpVLZU18AVZNANhIOcfLEYbRHj2BZZn3wp1Yt4zgOkXAIBWhLJ3j+qdM8++QpAj4DTdMolEqcPH6A9rY00/fmQVHYyeY5ND7C3/7tX+LUIwfZ3snLmNm+LnRDYXSkl1/75ecoVaoM9nVRLJZ5/+PrRCMyw3bfSB+/+kvPMDW9yOraDvl8Wcbj6S1uQ8sj/t0DJu5TVmiZo+T4UrckiV3x741BTEgjQ+MM9RAbaJn0PsGi9QnxTPeRjNdBjtjz3Z4qNSTRaIaXP+DsjVN8ppgqITyz0MNBq8xS+iRC0zpdxAPaYpfl7v563rd7yxZHgO16QFfxAuF1rRlor7T8//9B+Xxgq/4c68BJ4QEd1HMhemh0bzDgLtFer+HVxlF1van7i+IFiQYDATojYb5wdpy7U/OUqyaJWIQTR/YTCvvp7elE0TRSyQTf+KXncIErN6YwdJ3HTh7iqccfJRQKUCxWObB/gG98+SmCwQCPHhsnHApy4ugYHR0pMskEJ47tx2fo5PJFjhwc4cTR/RSLZeKxCBHHZfHV8/RMztNpGKzt60ZVFAb7O3ni7HECfp8XNO0QCcPwYB/7RgbkatMV6LqOriqowiXk9/HUY49w6sQhHBtqpkU4FPC2GTx++hidHUlGh3rp6+0kEYvwpZee4JknThKPRYlGQuiKiq4F+OJzZ/F7UjGqKiVXhgd7sZwaW9tZfD6Dnq52+nu7OXPqCOGAj1QyjqoopFNxXn7uMV566gyxeEQO/N7quvUJHzs0xv/lf/h9Nja22cnmSSTCpNJx/IaffK7EV15+km985VlJKOm6/O5vfJl4LIbfJwleD+wfZXzfEKViUbJy2zbxRJhoJLTrmYeCfg4fGCUYDkvGd0XB79PR67EWikrQHyTSFqS3u026km2barXW7DdAIhbl1IlDXhaXxfbWNqGghmHoaJqgqz1JyO/D8PlwbEfSNAgXIVyWV9ZZXt6gqz1Jd1c7qqJgOw4Bv4+RoT7CoaBke/eCxn1+g/ZMiq6ONgJBP6qq4HrM6SNDfSiqSigYIJWKEAz6EYBh6AwP9BAOB9FVhXQihl/XCMSiZJIx6jImjuPS393G4vI65aq0gIVCQZ5+4iQnjo6TTMaJRcNUqzLA3zB0YtEI7ZkUgYCPcqXK0tI6a+tbktE+aHD44Cgd7WlCoSC1ao1IJEzNqjK/uIptu4SDASkb4wqwbbS5FbSVDXr/6b9pJAVYlk2fKgP1G7qBSl3WQ6Dkis3nocisvQP7hwkFA434LttxCIUCjI0OoCgKhqaiBQ2EkPQIIwNd/N6vf5FgIIDh91MoltjY3PEsnHGEEPR0t/Mrv/wixUKBYqEsAWd7G/F4+D7ru6qqpJJxujravMwzDdO02drOYlqWdDMoKt2dbXzppScIh/20tSUJePGeplXDth32jw6SySRRkCLQlmVj2w5CCGKxCG3tacn14wqUq3flO+m6+BfX0Td3UMtVDOD0ThE9EsUfDuKqkG3L4A+H0BdW0QMakVAAxfBRw8Vy7YZMTigYkBm0iu7NtXL0jEUjRKNhKuUqmq5Ja2wkRFsmBULF59dxsfH7DQb7+7Btk2q1zPraBrFYhI6Wyc00dLZWN3n9Zx/w1psfUtvK8UsvP46uaRSLZQJecpCqNTnZFEVF1TT8moFjm5QKJXZyhQYNiu7VKZOWcZp1ZvhAwEc6Gee043JvbpG5+ZUGsMykEpw7fcxrY1veryLVGM48egjDMAgG/NRMk/a2JJtbWQzdIJWIcvzwGCMDfUSCfrq72oiEggT+/V80+4Rl72o/kIoIyp6Jvz7f1X83gx7kfXs7yb9Fi2uw9TSNv9VmzFfjuD3FAwjN4plvxP1uxYZQcyM06X70IIRoujC9+Vcongjzp7nsPq1I05F3P3tMKNLP+nAw1XqOB5XWbMqWmK1WCZ/72tjDH67X/DJcSHgi1M1TNGP194pR/2LKz+9GFJ4UR0M+pF6tFmuV2wK0WoureJZB0RiAVVFXWpelGebn4j4wuFA2nOO6aLrBc0+f4uXnzqJrKj5/gFAoguKl+Pt9PgSCkcE+/v7f+oYcEDwNM01VsWwHyzZJp+I8cfY47W1JotEIxWKJA2NDHDk0SndnO5qmsrq2STTgJ7ORI7GyiTYxj3r3Hs7SOhHTlHIjqsbtSonNnTylcpVq1ZShaULB8iYq6ZKS2ThSPNtBbayGZBBrLBJFUTSZreQ4KApEQwGeOHOcQweGSSUjhENBXFcQi0RIxpJe5p2EqKGAXw7K3kvmCAVXgN/vp1xVCYWqBAJ+ggE/Ac+M71impwkog/ANn044KleEUp5G7Ho3FMsm8Q/+JQkFhlzXyypTJcO+l9lVn8zq/UIu0FTPVw51UYsQkHZdLNuhUqmgGbqUhAGwbDr/yf8DXdOkXIuXvSaTGBTEHp2t1oFFc10pMEtT+MgVCrYlY1lUTScQCKGqBkLYUk5H83mLUIHiOiBc1tc3efPdi1SrNY4cGKG7M4PjEeMpitIAF8KtCxR7cjc6CEXFdgSqKxpjQSgUZGSwD03XUDX5Lqm46IqCpdiEAzoBLyNSURVUTd6j47hYtolpmhSKJVzXJRaLEo2EMXwy06ou02I7DiurmywtrxMNh0mnE+iejp1pWqyubbK+vkkyEUVRgjiO42XqCErlCjduTXLxyi3WN7f5zW+8wMF9g1S+/Q4B28UnaLBtq9s5KRRdreE6Dn6/D90wdoGa+uRVp9uof6vnirT99/+KVmkU4br4HQ/oqpoUWfaGF4Eg7bVjo297Mjb1RUX9evX+6nh8YRJQa6jZfKM/uq7AsRUs00G4teZzxEVVwaer6KpAVwWGT6G9PUEw4Jfta9s4juQ2sx2ZoVdPLTdaBmvblu+4ZllSoQFQStVG2/gmZtGX1lEcFzVbIPrv/5S6qLdA0O4Njq3C8kIIhGWhuy6G34cS8IOhoxg6wmeAzwBDRxg6GAauoUmSZ5+B4jPQfDopTUMxDCnb5Qh0y8Jfs7ErVYxCnkilRtTvQ1vdRLEcFATd//NfUCyU+PryOt+wbELBAPrkMnoy1pBd0j1LAYBQhBf7IrxxCGwPYIWCgea+Kqhu48GhokjyY01D1+X43eMtcAxNQTg2umOjWDa+Wg1qJrZp4TNtopaDYtkotk3QtIhVTZyajTqzju6uoJo2qm3jEwKDO2DaaDMtfHy2LQXUW/rRwyb+VgNDw2MmlCZYq4OrB87bckRqZPa1eHwecKHdIIPmOyHDbXZbtqQo9ieDhYYHSlU8b6NnFPmFAa66NU27LxRINtAnWL+E+xksW/dteBDGoq7B6bZYJuuAy7YdHEfSVChCQVFa2+2/8f4fUD4X2GoicHbdvKhPKK0V3WXVUqgLsu6ydAnR2FxfJTzoVoUAByH9467AdQShYJCO9nRjoPT7/CiKHOSaz0uQTERJJ+Mo3urZMi0sy8KxJUNzJh0g4hE+Bvx+OttTGNt5Au9fQ707y/DUPKGlDXyCBsswAtDkysx1XCrVKuateziOS61mUa3aqEpdv46GMLHhTaKKkHIxqqo2RGElePAEY7X6QkDDVVQikQj+gB+lRWtKBRRPg0xX65pPcqNMb61bCKXFxRU20UiYQMCPrmtYroxLEXpdiFm6NBxb4AipjaeoGrrR5OypjyzKVhaQruRWWKwAuhCeAKw3PtRNt7uAVksfcRysShWrVkMP+Js9wBWoWzmPOkBFEdKFpWreQC2a/a7Bj+N1IG2Pf99BcmFZti0BbCSMLxBAeKswQzcIh+qTgJw4coUS33vlbd6/cIujB4c5eXycSDgkrTxCeGzEWnMl7D1rVdOwpYoUtqc3WI938PmUBjgO/h/+NcpO3iNPVvA7DjEPPOj1e/TuR5GVB9sm5bpkNA3DMDxdOBqApG4FGiiU6Beeey7waoNMNe24nCuVqZkmikfrEPD78BkGKAqpao1TmzscqlSIhkNkFrZx7LfxrW2juwKlasoB0XFgeR1VQMBxGrqKdQvo3ndYEgyCUqk1nq22na83mxwXHKdhXdA8y2z9IQpvodZaVFcu6lrfoXqfcl0XYdtSZNnna9x/vWjZAp3/+F81novmiXWHBERdyTmkqiqqAhHq86bSAIx+18WomWRsm2DA77Vfo7o06GtofS+BrRzq7JK05AshM+DqbaTspmHyqtZyWnlfjgf0FVegFKsgHBTXlZ8dR1osHFf+d10paeb9jSs8ibP7R1kBJKiDDQUsq7GfbytLXFEIJqJonsi0Ajj5En2Oi1sz0acWUBwXxXZQayaKZcsFj2Xjq5kkazVSloVq2mBZiJopXcuW7f23oGbJY0wLTBvDsgjXP9dMuZ/bXEDVn3fr3ex60g1AtBv57MYj3oeWJA6BQBctgOgBpVXbtkUinSaRpuB+FtPWj+6eF0W5fz95oZb7qNfu4WBkl9HnIcBrd8hPS133MrX+vKUVFAqBJHi1W26rbolqdSfuGTE+FeztHWGUB5xCYhV3726KzEi3HUfybHmPSv0M8oH/LeXzuREVz3JV960CdaSO4AHuQ7fxHOtoE+GFyArRiMtqDS9UdpkGm0WgoaiKF7cVZLC/h4Dfh6KontVEdnRdc2glmVNQGn53CWRUaR2JqkQVBcNy8N+ZRb07R3RyDmNyvun2aPh462K6Xp18OmK0D8VnoF64SWgjy/+4IVjXNWJ/+TNib16VPC5aXcQaaB10ReOH/N77pAiluXnXAFG3LomWw5sve/0au4aWxiQsS5i6xIT87BcQ8p5PfbIKteBgVd398iulipxkVQdlab35festAfWww73vjLLrj6ZVVBcQchyCrkCrmCimHFAVBYJbuV0gUmk5dq9gaGtvEQg5kAsXpVQh9vv/otlmLdYUubNLqNE/5HkSwiVeqdG2vM7vVmt0ufO0/9//rCFQ7HrWEdnfPMtK3YQvoAUyNtsnGaP27/4ZmibrruWKKB5dgQKewGp98G4dQCRfjOo46I6DokjrmaJW7xuahRCorkvCcWScVamKUm66VDUg6goiAgQOiu2i1KxGewRcQbeuQzSKrqko+bJ8L3vaZZ1ml2Uf0DREd7u0bDmObFNN40GDe8OyBRhbWdx0onl/3rMTQlKlOJYtLcWGtjtD2Xs+QrS0p+ceoL7QoNlkjuMgHAdN11F0vTkWLK6DKy2Jyk6+8XRa41LUxnPY/fxaiyIEfsfBEAK9at7Hz/SQKQ4cB9GZ8USY5aJEcQUi4Mf+6jOoxRJKsQKlCkq5glKpIio1qFTl/3IVvVLDcF1UpyVbzBtz62Pv55kwW6fz5jfyROrCGqqmYiiKZzEGFtdRf++fY9DS1x80uc+vtACZZsWU1o+7X96HFqGpuL3te+Za5X5Pyifc164atvBk1YlNQXixxJ71/AEV2qsNqCoCRwhUtGa3FiDBVmvPbL22aDlDvTwoFe7+NlVojSNruYfWeUUgWdX3jnetFW+9RP3YT3PzfVppAYfynHs5wervvrbnwJZ9Hnr9h9StfjlF2cVH2tovVS8swHEFlkeLIueU5qJIbZ3jf4HlcwbIKxL9NlCzBFoNZH9fQwjvn9JYeSu4cmJpgZ27Gapp+p53XVmmeKqqhmL4SMaTu6/kXVv1NBSFcBt1bNbPxb+ySWhiHnViDm1iDnVhtQmBW6wU8mTeSr2nA3f/EO74EGL/IO5gj+RkmVvGuHwXVVHpMzSSsQiKAv5SFd1yvEm49YXffVet2SAPfMSteKL1GEHDJSurWwdbLfrzDbC5+/jW62h7Xtj76tBad58Bjgq6hujpeFBtW6FgAxwKbzJomN1bX3wPV2uiSSJXh9yC3c5qOWG2gNFdj6oJ1ht3ZC7JQV4B/eKtB9Z3d713A8cYcNCLL1SX1lGWNnY9vd0r2wecYG/xGai/9weNm1SvT8iJS1HA0O4bK/cCSB1pNUTZm/l0/3PTP2HA2DvEtVZY/ZRjURQJtAydgt/HxtYOmqbR3pZCaw2qZnff07N5qWF6YJjSf/qXDbe1F64FQuA6Lq7roGgqVmPlLRpgzXVdXOE02l3ilbrlqLnYEAIif/Bv0bMF7zpevxOAYaDYtnSRPfwuP0MRqIaOODAsPzqunKwdV/Y5x5F6e95vbLfxHY6DWt+nbtnSNZQfvtVsZu8a8tXxgKaiYPl92LqK0A0Mn74btO6u3h4ABmgqIhhABH0QCIBfuiCbrkcDDOnCQ1NR37mIuropT9eeboLP2WV5r27TMnH/aN3y2bZ3WY1ad6mPYfWxqxWANwYxbwIUqoLw+7DHBxG6jmsYCM9l6uhe3Q0DoWsIn4Fi6FKOx6+jGIZcIPs8l6phIAyf3N/bRiIGQnjWegXhPjjhqznv7b4ZCR69uUYI6uBBEd6T9MYpb0DcPVc2xo0HWcL2WJsUhSaAqYMzd/cLt+cx7HUv3n9f3kAsPOvWw0DNfeUBaLnRUeq/PTqIXefb229bKv+ZLFs0rXMPxsP3jY+apnrGDy9uvGGQac59Csru8fwXVD6/NqLS0ih7XH/1DlffG4EXr+UhcfcBN7KnVRpuxj1oXNnzYW/n2RW0WO+Q2Tzq5Cza3Tm0yVm0qXloWenfVwEFRCiAOdyLGOtHPTyCsn8YJRZDBszWH4l3ZF8najgoY4kcl3gihkhEG4CxWcdm59rLByJ2rQLuL3v7Xt06WK2Z1DyyUVVVCXhs+M0smYec7xPO/SCwJep3vLQGJmAYkEl8wslbVlZ1l453bhkX02LhU2S8k9T3k7xA9ZgyTXsILKiDrF0/HmBYb7G+fVrZe9+N6j3AYvGJQ8GDNtZTyk1QtnaaZzethpXI6sxQLlcwLRvhChk87Ln3NG2PVfUB9/qAKaHx64Fz8a4DPtvgJv+S13YSUV799Rf59//5W3RkkvyT/+43ODg+goJ0P9MyTqiqQuR/9y9QtrOgKPjqbOiNCUjurmoKaHrLXdbBlABVgm5VqA2roqbuiSX0jtQ0xbMa5qgTggohrV1KJiG1DD/DXd9XimWUQlmCDEegKC5Ua9ASh7WryPQ8MO7ftHtZ0HxtlD37OLbjuTykqxZDR4nGcOMRnGQMJRpGREOIcBARCUE4BJEghAPyc0R+r8RC4PPjivr4pTYWtGrzRWppSxlLKq5NSiDSiAnzQa4ordyGjnj6FPh9kp/Kp4PfaAI4vxdD9u/+BAol2RbxCELFI4+1sBzHaycFv99HIBhoLFCbnhBgO49wXdxUjOK/+PsIUY/ZlPu5dbdty7TRtFTsXow2Xc73vxmK66Ioqnf+Bz/WuoW16ZoWzfmn9bkqDnUwpDSMDt4C7WEnb1RJtPzyQNUewKV4524uk+qAZs/c+YC5tPF980Pz+uKzuhObgPKheokKu+vzeS1miiL7SV0jsmVBunuFWv9azr2inv3oead03ZDW7gcJXzdOtUf55hdQPh/YEnjxOLIzKt7AuhtkydJsk+Zf9zV1fWCuI/U62tyD0qVftW6hEqg7eYK//39ubvfqRaWKWqrKwaBcQamZn9xnFCAQQITlfzcUpOzY5C7fRrl8m/RrcfwBv7zHB1jbAJTFVajU5KpoYQU3FWuupr0f9fiNZmvVO3f97094tI3dm61Zs2yy2TzVag1Vk7plAb+fjrYkfr+/ee3PVB7cQq1gVkFAXye4LiKTwP1f/2XL0S3tIrwcFA9k2bZDpVrDcRx0XcdvSFZoTQNNUSiWKtydusfM7BIT0wvML61y5MAI3/jKc/R0tbdM2DKpYW5hhZ1cgfZMku72tCcG7bmhhJCuZA9hiH/8r1Gy+Qbmb7R3y6An67z7/luTOGrVGvliGV3XSMSi6J4CgCtk3CCArtcDtJUHjmnK1bsSWPkMRDrR3G9pDSzAMKiEAyxm85geN5KqgGJZBFSVZFTKOtUnjt2J061oo3Uh00QxDwxHqA/iD1kRts6/D1ocKfEIh8aHObR/kLnFNQrFsoxuUaQbWb6l8ky7wDHeu7x3UkG6YnbVXgG8ttYA4Y21AoGrSsC1+97q4Q0t51AV3GScze0dNnJ5YpEQnek2dO1hsTh74U7LFsuGjZ0meHa0XQHvDy4PaGBNg6gERiISxI2EIBKCcBARDSLCYUQ4yEI2x/XFFbpH+iDsh2iAVGc7sXgKw9ARWl3HzfUSNNTd11Vcr60VEBoIRbpPGwDkE+opwPknv7urSeoTmPZ//Lfw+nmomSg3JnefqPF3y/kW12T/DwVx3vpPlCtVpmfmyeULjA73EQ2HpOi6rkEoKHkoPUCt/uN/AzsFlMU1FMtCXd4g9g/+dbOaexYNuxb9rW/DnpV6/bMbj1L+1/8YkASmyieArL1FsvMDitfT5aAhXcOa482Pu2H9zz2Be+/ArrLH+9Js8U8HWLs4Mh+2ZKsDLlV9iCJQ/Th3z997LGIP6gsPdVHu7UOCT6aI2HOe+lrUy/5UlFa84YUYeDGqhqE3AFdrm9XBvWyZT6LE+PnL5wBbSkMVW2YUCJq+n9aHSNM65WUV1jMDGiN541nXgVYr4FJQFK0Zo9R42eu1AOE40sRdNVFqNe+3+cCxbVeD6Roi4AO/D+GXv2lkMgmUUgVRLGPu5HBdF8u0CUbC8iwPsRI07lsIWY/t3H3X3WXPq6P9+87x4FPvwVoIBFaphMiXSAYDhP0+SqaNmS/K5+P3P3yi3HOZ3VVpGar2VnfPMbuH9eYZ5UBZjxmTQfd+XcVRBJqmoKugKAIVqY12+fod7kzOcnh8hBNHxllZWcMwdCKh0K7rqorC/MIy/+E//SULS2u8/OwZfusbL0sZFq9oeyeR/+kPHjyceD92ZanUXTWiaWK2LJsfvXGeP/7LH3PkwBB/+ze/ymBfN0IIbBdqtoOqSFFmn641Vka7wCeg/+4/g60sIp3A/l//VWOy03/v/wSbWUQ6xuQf/B7/rz/6NltbO/z6156jv7ebN9+9xKXrE3z5hXP88hefaehTOvU6A4rionpWHyl9JUcdFwVF1WSf3jPmyYmhnlXcvPfWojaoPu7vSPX9Y2ubRCNhOTF7AKRVqksBFFVBNByXCurtGQJ/+59/Yv96UGmt3+6/9+zodV31+iSKZSNCAcrf/nd8879+iz/5q9c588gh/sd/9Lfo6kiD6+I2LNUym7fVlSWEJF+tZ6ipP3oH43/+Jsq9JQm4dB162iEchGgYIiHPmtTy3/ueSBDhWZ2UYMCbHFzqrnKZuSy8ekA2W+Rbf/oDtipl/tHZY/gMFVUV+Hx+VD3QaDUVGmOs09IeqqKgK832EICzp6U/kROxOXvd38ihQNNVur4NqirVBWgCsvr5hRByXLYdMC1qpsnyyhofX7zO/tF+2lNxdL9vl0tSuG7DbazuFGAzK4Pnbak2omxl749B2ruA8p7qrn57HwST7SdBVv2Yn5c+XADS+ODdNCAQwkYRWsNV9YuatJuX3QtomgCzMTq0rnLc+pTduvKs1/+BF5AHKdoDgFkLyGqMLd7+ewEXIK1ke6R6Pg1wgXftPfQRdeuW1zdbA/2Vxrm9d9rri67iLUiUekKativGtn52OfbXx8LdIPm/tfzcYKtulm246MSeafc+BC5vXAi35cWuv8S0HKu0/K4/rPpI4TV2pYoyOY+SL8JOAbVckZkprWWv20lVEKGAXDGGg3JQbMgxPMAf7yF6LRzAtC1KlRqJaAg3FfcsJt5xextGU2Ftu3FN0on7z+sdLZR6fMNDOtt9wIf7vrNtm51qlVLAIN6ZRg+HENk8VrmCk4xCKNgcjB4CPlu/3tVZGzs9YFVU/yMRe/hA3XpdIaSVQ1VQlXoGYR3cKGxubvHRhWtEI2GG+rvp6Wpj/3APrhCouu++AbVSrYIQBAJ+kvEoPr9/18u263eLlaeeOSi8LCZVURq++3pxXddjapfZXj6fQTAkudfisTDdnW309XR6lBNgKN7Y70qBZgW1AajvH/Trz7s+EO/eVgc89QVdPBbm4P5Bpu8t8s6HV7k9MctjJ7eJREK74gkkUJZZmo1re0BSRXhJBkodlYGQFBK2IxMQ6s9KIAFS0xVff9dVNFVH05XGmkcgs1/LlRpr61tsbWepmZYnqyIHeNOyqHg8Z/5AAH8g2Lxh24btbKMP1mMP6zf0sBikxld7Jou9Ld54Sy0ZJ6TYNj7Dx8H9I3S2XcRv6Oi6htYCRFyQ+mk0n4G0Yu9aRkAw0PyUScJIH+7/+/9638TXWn23AT7rZ5EJDFK/1PXWaM3knbprdWltjSu3Jjk0Nkg8EUZXaVivbFdS8Mj8FUXqMdZ5gxQa50IVMlsVhXK1SqEiKS4CfpmN3JrtKalFZBamrku6CIRM3RfCpVSuUK5U8Rk6aUPD1/I8zFiYze0sti0pIYJBP5qqUjNNHNcl7orGSG9ZNjP3FpiYnmewv0suXGpSF1ZCXclAr6pyMaRKWVfZT1QVR1WoRYLout4yFz247HoLW0NOADVbaJCLKo31/IPDFh5eBFJltt4LZXu4wkFDpWGQ8N75T6Nk+LT7uO/oBwCu+n7NFvdAlyK8+92z8nrQeVvP37Au7eXM2rOvUq9PS21bDTBKc7z5TKbDhsennrUodoO0hvtwzzEeuEWhEe6nKAq2olKX8oMWTtfWJnnQwuIXVH5+y5Z0erfApU9pvF2K6C0BzEL9hOyR+jldxMoW+nd/gnJ3BqU1iD0ZRSSjLdfxfne3I8YGvP/9MNi9myGYOl5X6qGLu24NxcVxbdZWN/nTP/shtybm+Lu/81Wef+IMAV2T4E1RPS645lCsZAvoT/6eXOnFo9T+8F/gOBJgSrFd1+O1CqKoKrZjYts16U7TNWo1E8uy8RtB/IGAnICFA14sk+3KeJY6lpybX+Y/f/OHVCo1/s5vf5Wu7k5e/+n73J24xxeff4wjh0bx+wxMU2CaNq4jV9B+n49QwCfjFV3hyb2YCAQBnyGzz+oTsKLgCEWKV5cqVL3YsGDATywaRvU4jOoTRZ02QigaLhLc1EyTWrmM4zoNoK7rOn6fjobGxvomi8vr7BvqRUMKiCqairAFpUKVQrmEZVskYlGi0TCjg338/d/7OhubO4yN9uEzdOq8a4VimdX1TfKFEromNewy6SSFYon5xRVPU05qI2ZSCQ6NjzQIVCvVGrfvznDjzjRb2zkEcOzgKE89fpLB/m4yqQTlShXLsvB7HGmbWzvcmrhHoVhibGSA0aE+LzN2T1ak2BPILlw5PCtNsKcgJ2XHcamaJtlckVy+QKkiSTmX1zZY39xiaKC7MUE6joNpSgmWkMdvpSiS9mQnV8D2MqsURSEWlXQf1ZrJ9k6Ojc0dTNMiGPQTDgURQLFYolyuoSgaqiJdtrZtE49FGBsZbOxXqVWYnJnn8vUJbt2dYXpuBdd1qXjt4zguS6vrXLk+wfrmNt1d7Rw7coBDojn8402cdixCtVqjWq16YsmSGsXvM3aDYUW+bzXTpFqzQAgMXcfwGWie6rrtyPesPtUEW95uTREkYlECftnHdW+F7wqBZVlkCyXKpTKBgI9UIo7fZ8h+5biUq5IzT9M1Ir6WIdOzCm5u51jf2JTgPBBA0yRhbcgDZusb26ysbyIE6JpKsVQhmYgwOtyDpmuUy1XypQoKkIiGpDqBgO1cidXNLKNDJkI4KEgR3ULZYn1rA9d1CYdCRCMhKtUaK6sbVGumpHSxbGo1k+62BIN93eQKJc5f8OS3ElH2DfcxPNBDT2c7iqpi1kyW1zaZnJ6nWqvR19PB2MgQkVAE4bosr63z0eWbzMwu0dGW4pdNi/Z6O0RDFP7DH/Cd77zGa299jGU7vPzMSc4+eph3P7qG7Tj805klad1SwDZNFpfWWNvYoVI1MU2HxeU1btyZxjQdouEwmqrg8/lIpeIccmx0BMLQqaRi5P0GP/6VZ0DA8EA3qUScTEqGetjC07QtVxBCEPT7ZEa4UU8kaAKN+D/8l6h16hHZS3bNB59eBE2uqNbZsNXCU8cgLUDjs5x5z9zY4BOsT6d747Hkly1H7M3MRgKW+9HaJ9+xaLXy1d9H5+HN5BkrmkaTevkseogPOaHiXbsVcD3MMubRIjVSqhRXkri58lxufVECXiiUaMGD8g9d17wFyi+2fH65ns9a6qtEFElyh+K1m+rBnU9GkYrloL723oM3hoPYI71YI73Yw704I/0QDWMYks+qTtVQH1BtW2YGSp6r3aZiVVFk3JNtsbOTZydXwLRtGTfT2MvFtgX5Yol8oYLjOBg+H4lEjEA8Ki1mFQdKFW7cnGRhdQNd19jY3KFQLLNvWIr1JhMxZueWuDU5zUBvJz1d7dyeuMf0vSUOjo1w7PB+/AEd17JZXFrjztQcxUqNgf4uxkZ78ft8FEpldnJFEC6FQgllbZOpe4tMzizSlrlDNBoiEYty4/Y9Fpc3cV2XQqnMvoFunn38EUKhIOVShXvzS9yauIff7+fYwVG6OjIYnlvOthwW17e4dO0Od6bmKJWqDPR2cPbkEQyjG8uyWd/cIZcvSDZsv49EIk4sHiMY8FGpVbh89Q537s6gqVIcVtc1+ns6ODo+hB4NUyiWqVRNapYt03CFoFyscntinrvTCyxvbJLNFzh8YIQXnjpNIh7FdV1m5pYoV0ucOxUgFAyxvrHNz967wPmPb7C1kyMUDPDMuRM89dgj3J6Y4QevvkOpUkNBajp2tCX4B7/3DY4f2Q/ArbvT/Om3XiWbK6BrKqvr21SrVU49egSzVuPVNz9gcnqBM48e4oWnTrOxsc2b5y/x7kfXWF3f5sSRMX77V15m33D/ri5ap4hohftyGPKWVA13oMCxXU9YvcqtyXlKFZPL1ydJJaM8emyMTCYurW+WTalYYnNji/WtHRCCtnSS0eE+/MEAS8sr/PD182xl8wR8PiKRIM89eYq+7g6WVzd498MrXLo2QblSJZWMMTbch2HorKxtcW9+hUrFJJ2Mo6oqa5vbdGRS/P7vfJ3x0UFKlSrnL1zhp+9eIJsrks0XyRZKxCIhLE8zL5cv8vHlW/zw9fPML62TiEd46rElhssVwp6lE8BNRvnp73+Db37rNVbXN2lLx0jEI4QCAR47dZizpw6TSsUB2M7muXl7mivXJ5hfXKVSqdLZ3sa5U0c5dmiMUqXCW+9dYm5xpUFY+H+7NbMrLr31ja8D4Eq1xtTMAh9evM780hqdbSmee+o0YyP9uK5gcXmNazcnmFtcI+A3eCIU5qDryufpWZfeOn+Jn7z9Ial4jLZMAsuy6WhLcezQGO1tKa7fnuKvXz/PdjbvSQw5nHn0AOl0jFqtxq2JOeaW16lVaxwZH+L0I4fw+wwvYcRkJ1/GtF10TadmWVy8dpd3PryCcAVd7WmOHRxBKCo/euM884urJBMxLMtmYyvLI0dG+K1ffpHNnQKvvfkhl2/OEAoFSMTDfPXFs3zjyy+iairXb0/xg9fe5dL1Cao1k/0jffzOr3yR0yeOUqlV+PjyTd4+f5mqaZIvlMg5ThNsOS7JRIwXnzrFzbv3ePfjmxQKRRKREIam0tWRaVjXAGqWRaFYlvqKNZtq1eLu5Dx/+f2fYdsuj586ius63JmaJ5NO8M9MC61uqVMUypUaf/Ktn1Cpmewf6qGvp51f/sJT9Pf1sLSywZvvXWRheQ3HcelsT3Hu9FH2jwygGzoKUttTfSBaaJo46oHvrvuw+akJtOqWyeb77UIL/UPDrVhfmH8K6Nptpa8ThDcD9hX1U0DbLrO5NIs0oKBnYfp8tps9lqZPBFz19tnjQ/m8ViPvPu47fhfo8kBuw2TuNE1XLbGgrca3ZrazfNaBgE4sFsXwGQ/yX/03lc8MthS16UaAumFUdqGHJUnKTACxG3B9xhtQPH4n0dchQYxpgabivviYpF0YG6ASDzE9v8TWjsxScSamsRyHRCzC2Eg/6UQcy7aZXVhhYnqeordy7e/pZGSwj2BExr+oqiQc3cnmuHrrDgtLa5imxfrGTstd2tQsh8mpRV5/+wJTs0toqkp7W4onH3uEE0fG8YWDUKkiHIdbr73Ht25NMjrYg6IqTM8uc2dqjkwqQSgYYHJmgb/6/lu8+PQpMsk0M7Or/NUP3+KpM1kGe9rJpBNMzyzwZ999nTtT89iWTWdXhr/zm19ifP8QCjKFNZstsLGTpWLZbGztkCuUWFhaY3Mrh2XZ/PinH3D+wi1CAT+OY3P84AhHD46gqirvfnSVKzcmmF1cJZsrUiiWePmZsyTjURzXZX5pje+++g4Xr02gqiqFYpliqcyJo+M4jsutiXv87N0LWJZNWyZBuVxFUVSOHh1laKCHUrnCt3/4M67fnmGkv5tQMMDS6iaDPW1Egz4O7h/GdlxcIXma6paE25P3+NNvv45lC1LpGKvrW2iaypOPnaBaM3n3w6t887s/4cThEQ6PjwAK7390hT/9q9dYXN0EBUIBg1Q8zP7hPhYWVpidX+HQ/gEO7h/h2q0pbtyd4c7kFONjfZRKFV55/W3mF1f4nb/xEoP93bx9/jLtqRiGT2N9Pc8771/m1uQ8fd3t2LbJ2voaplnh+OFRLl69y9TsAqsbWwwP9jZWRa1B9vf1b0B4K7V6BpUjXBzblVa6UoXltS3aMkmeOXecUycO0pZOs7GVZXp2kZ3tHUrFErFoBEPX+Om7F7AdhyOHx9ja2uG1Nz8kV6xwaP8gfd3tVKs18oUSH168wbdfeZv1zZ3GoL+TKzAy0I1pWiwsb7C5k+eFJ9o5cmCUC1duc/H6XZ6enmN0qI8L1+7w//mT75NKxvi1X36RnVyBb37rx5TKFfw+H6ZpMjE1x9sfXGF2UYpmlyo13v3oGv+wbBJqyXBSvYXQ5MwCrnB46uwRNE3jzfOXmZpdJFcs8KWXnsIVgtff/Jg337tEIh5m/75B7s0u8u5H18gXyrSlk+xk8/z4Zx8weW+JoN8vXWu2I/mfxIOfgeu6LCyt8PpbH7CwtE61ZjK7sEpPdztD/d3UTJMPLl7nJ299jM9vkMsX0Xu72e/UwZZUv8iXSly7NUM6GeXEkVHZzpducHd6li88+xg102R1Y4u70wsM9nVw9OAwnR1pisUSb79/hQvXJkBRmJtfYXJ6loG+dnq7pXutLvpsWTau38/C6iavvHGea7emiYaD3Lp7D7NW5fCBUTa3skzMLHLq+DjjY4PYt6a4OzXP7OIS/b2dDPa3c2dmgeGBLtoycUIBP7oiyOaL/OC1d3n1rY/RVQ1XuMwtrrG8to7l1KjWaiytbmLaNo8c2c/wQDfh69PNicCRFvj2TJJ9Q728d+EWEzNLXL01RaVSZd9QL3WqBFeIRnKB6wpsx8Kya1TNKtVqjfZMkkePjwOC21OzrK5vetZKr8+oCjiwupnF0FUcTzJLVVSy2Syvv/kB3/7R2xRLVSzbIZ2MYhgq7Zk4qWSsYcnQ1frU13S9tk7kDwdZ9ZtuEkvjHdqMJ24FXk0XnoKLEGqLq75lv1YXZwvIqp+qHmQvWi96X7U+DdSoEoDsPcOneaV2lRYrl9jdBg/c7RcFWFrdmaLF1Sf2xJqK+o+6IacJxBo8fJ5bsUHfIeph8IKA30cqlcDnM1q8d7+Y8nNZthR1d6aViosi1IfXSMi9dt/0Z72OIlOUVQUx3Ity5x44Ls7f/AoiEsKsmVy/dpvvvvoO2VyBcCjI1k6OfLFCV3uK3/jl5zl59AAfX73N9378DkurmyRiYXRNQ9VUnjh9lCfPnaQtk0TVBFs7W7z25odcuT5BV2e6EYhcd3HLgXmVb37vJ7z30U1KlRpCyBTzaCTE+L4hYuEgysYOAvDPLqHrOk+cPc5Qfzd//fq7XL81zcraJvtG+mVGmzfoBIMB+ns6cR2XtfVNqrUa2VyBb/3wp7z21gVinkDvzL1FFhaW2T86QDweprMjRT5fwHVtQiEfmVQUy2zn3OkjHBwfoliskElGMWsm4yO9PH7yMD2dGRLxGJVqjdWNTbo6Uxw+MMSffecNrt2e5rGTh4lFw5TKFT66fJO3zl9mfN8ALz17llKxhHBdOtIJ1ja2+OGr7zAzv8yXnj/Lo8cPMLewwrd++DM2drb52pef8Rj7IRkL85UXH6erI8O3fvhTZmaXWFheY3SoD0OX4saW7WALyOaLvPfRNRZX1vnaF5/h1CMHWd/cRtM04jFp1fIZBpWKSaVqgqKQyxe5cPU2G9tZujvSJBNhQgGD0cEOkvEQfr9OLBrgkaOjvPTUWdpSUe7NL7K6toltm8zOLXD+wk32Dfdy7swxOrvaGejrRDguobCPdDpKf2870/PLhMI+DEOltztDOhWjYtqsrm9yb34Fy6ohsHDRsCwX12m6Vne9Fnviyxr93uv70XCQwweGOf3IYQJ+H8lYiIDPR820uXztDt955S062xL88stPMj42hOU4vH/pFj947V16ejpJt2XQDYO+7ii/9kvP05ZOEovKmJqL1+9Sq5k8cmSMaCQkgXI6Tmd7mlAwwMLKBpZl88SZ4zz26FH8Ph8fX7tNoVSmUKnxzoeXuTM1z2/9ykscObCPUqXCrbtTXL5+l6DfoFiqcOn6XabuLZFORGhL92L4DPp62wl8dFeS1Xr3rgCd7WkS8TAgePHZs/T1deO6Lt9/7T3eeOsCB8ZHyKSTXL05yfTsEl//8lO89Pxj3Lo1ydTsMvfmV1he3SCZiHruRx8vPn2ans52tH/zR03Zpz3LRBnu4VCzqiQSYUZGTlIolnnr3YsUymVMYSNUQbYgFzNnTh7m0cw4vYaO8u5VeUZXPt8zJw/x16+/w1BfJ7/1qy9hWTbf/sHPmLy3yObONv09HRza38/WTpaD+wf5pS88yWBfF/fml7h8fYJw0E9PTweGrhINS00/x3EaGa+2LTN6Tcvi7tQ8N+7MEI9F2T/Si66q9Ha1055O0N/dxsLSGmNDvXzh2bMkIwF+8u4F1ja3OTA2yMhgN5nkBI+dPMSXXzpHUFcJh4JMzy8zv7jGUF8nT54+Ckg+ooHeTmm913U621Ncvn6X25P3aMvEMA2tGRfmyFCHUrlKOhkjFgkyM7/C9197l/HRfvp6OmgABW9yrL8TtiNdUn6fQTDoJxYLkYiHsG0bv09vbG+dU3yGTiLoZ7Cvk9/9G18knYijCMF7H13jw4s3GB3sobsjw9LqBtl8QY6n+TylSoV8vki1ZuH3BThrWgRajSIPmMjqE/PuIhdI9xmQHvShGUXj2c3qgGEv4NlzSH272O2BUfDi/9RPcCV+EugSCqh1a0/Lvg8CXOJhcKPhnAecVoPg/x/LHoyxx6Ami+Tc1FSZtKV6BqTWJtA0nYDf3xLL+Isrn92y1cjyqrM0i2a82yf6oz0NpMZxDyaJ+6TSAFuAcm8R9/A+FpbXeeWnH/D+xVucPTHOyWPjvPvxda7cuie5dFDI5gt8+5W3ePP9qzx+8hDPPfEICPjxmx/ynR+9A6rOS8+eIRjUuX5rkrfOXyYcCnD6xEE629MsLm9w6+49hCsDfq/enOLDS7c5PD7EobFBNraybGxnGR7sJRDwIcJB7wURJNe2ifW309vVTl9PJ+2ZFK47Qb5QxHGk7pumqo14p4ZunWWjaRpr21ne/fgGoYCfcycPkU7FCQR9DPR1oWoq4aCUF1I96Zp4LEIqFSefK9OeTqJrGoaukUnHCfh9DPZ387UvPU0s4kfXdCpVhyfPHkHXNDa2soRCPkyrRrFUolKtsr2TY+reEjXTYnzfII8cPYCGi2s7+Pw+bn18nZsTs/R0pTl+ZJTR4W56uzN8dOUmN27PUi5XObB/mPZ0gmKxzPBQN8MD/Vy+fof5hVVK5SqO6+AzdHRNw7QdypUapVKZuaU1QqEAA32d9HZ30NUhtQhVwHFdopEghqHjChnnVK1V2NzOEQz4OXfqMI+dOkgo6KctGSURj6Dp0kXsugKhq4QjQXyGQblqUq3ZbGULVCo1DEOX1jkhyLRnZLarphII+kkmYoSDAbmK9mJytnNFrt6eYX5prcGD5DjSzbewuEq+UCWZiJNJJUiIJgmt60o5J9OySDhuC1u54pHuyec50NeFqqrYtk3FssgXCtxbWGRqdonuzgz9A71EYjFK5QqW7XD5xhRfXF6noz0jV2aKQkdbmu7ODDWzxvZOlu2dHIP9XXz9y8/S39uJWauxvZMjXyzLmClNwzA0ImE/Pr9OKORHVRTKVek+2tjcwXZcTwtPEA4FiUejCAFVy2ZzJ8/C8hoCl5PH9/PMk482RNLDN+ZQzLoYtfAGN2mhVRWpd5lMJOju6kDTNda3cmxt50gnEziOg89nyOcQChGLRUkloqysbVOtmQS9GCm/T+fogVGefeIU+r/9Y8ADd4qKoyi4wqVaq1KplohG/HRkUpw7c5yN7Rx3p2bZyRcpV2tUayY+w6Crqw2/38fFq3d46dnT9A4PN904XnyYrstYqoDfRzIZJxIMcOjAMNNzy1RqFrFYlK7ODD6/DxSVcCxKMBxiY3ubYqXCkSOjjO8bYHS4m0jQh6apzC2usLS+je1IpuuaJcMTFpfXKVdqHD00xovPnCURDZGIhrFsh0AggM+neyANDJ+ObTuUSlWKpTLZXJGaaaEokErG8GsqilComTaO65BOxDh35jg93R3ksjkAiqUKqqExtn+Qlc0t3vvgKn/9k/NE/UH6PJewsB1qNZNsrkA6GWd8pI/LN6ZZ38ry1ZceJ5mMN1w3QoDtZa8LId29KBr+kHwnTdOmZppyUayquJ7Gan3GcByXim3h+AJEQgES8Qj5Yon1zS2u3poiX6pw8sRBXn72LGubW0zPzpNMxMjmCly9McXM3DKVmomh+di3naNTU9kdOXn/vLcbbNXBuvpQizU8WEXhM+OROvC7D2jV8YOgqT2499jPMLe6HuB6GNBqGEk+qcafDrgeNs9/viSBlro2OpP7kF3u12WW8kmedq2XIa+pLRxs4Okj1glNf/Ho8eeP2RJ1H7LMI/1UHgrF2YWg6+ysD1FA2GWWlX3ORYz01S+OMrOAc3CQmflFbk3NUjNN+nvbefzMYVwcrtycIh4L05ZOsLWTY2ZuhVAwwPHDoxzeP0wkHGRlbZPvvPoeH1+5xalHxhGEmZxdZnVjh4GedipVE8cVhD0xZ8t2KHluHdOy6etq46lzjxKPBykUy2QySTnIBXzUmTnaNrKU0lHWNrfp7moDRZL41bxsN8UbbKRgda0hmFsv5WKZQqlKezrOwbEhurrbSSUjpFNxr/0UD5w5mJaD4ZOI3HFd8sUSpVJFSsmoqsxUchxUXcEWAssyyRbL3FtcY2FhlaW1TTa38xhtKbayOSLhCFs7ORlk7biUylVy+SKGrqK4LpbrspPNU6nWGjI1risIBGX8VKFUoVo1MQwDRVVxXZdqtSaDVpGEgvUJQSgqmq5j2162U7VKsVylalrs5AqsrG1SKlcolMr0dLYRCgaoVGvS9ei62J5otIvU5QuHA7KNgJrtUq5Z1CwH23bZ2MmTyxdlewhBqVJjbWMby3HxB/zMzK3w4zfe5znTxHZcalWTQwdGvCw9F8PQ0DSVQrHCxcu3+OjyHQrlCjXTwrJs1jZ2yBdKuMLl4tU7zC1vsG9kgH0DvUQcB93r8KVyhbX1LSpVk7Bl4fMAd13cumaabG3nKFWqBHw+iqUy+UKRQrFMoVzFcmwq1Qq2K4XKi6UyO/kSqAqVSpVaper1rQqlSgVUB9uxyOZzVKo1BAqW4xIKh9E1le2dnMwEUxSqpiUF0h0bRXGpmVVs26FQki7kaCSIKwQTM4ssLK8TDvrZ2smTL1RY29hp6DSqqgSpqWSCSDiIqjUXFoL6eCnbQ1VVbMehWKnJTEnPbRSOhAgGAyiKdPP7fTrBgNQ+dbz28vl0QkGpS2joOq4rKFUquO4etnIvw9IVSBft6ibVqsnC0ioz82tsbmeZX5Juz83tHCvr24QCPkLBACNDPbz/8U1+/JMPUEybEdNuxGwBmKaFZTksrGwyPbvEvqE+KWHkCilfpOu4iortuGTzBfL5IlZniqplky9KANWWThAM+gkE/GRzBW5NLnHt5oyM2zItcqUKfk2hUq1KYkZDp7MjQzoeRQjBxuY25ZopXXOuoFqrUfEIgk3Lplyusrq+Ta5QlnxjW1k603F0VSMYDGDoOrNL69ydWUDVDYrFotRgRCFfKrCTzbN/3wCbW1lu373HutMymbqSdNV2HPp7Onj5mTPMzK/S2Zbm0PgomqZ7o7f8X7MsKlX5jpmWg+MKdN1A0TS2dvLMLqyQTsZwHJdiubZrXBRIt2q5Kvn5ZIiBRldnGwO9nVy+NcPU7CKHV9fobE/h8w2iKirlSpWp2SUuX5/CtB00VSNfrJKJhJtZlZ9YGnCvpUs9YOZTFE8Z5QFZjXu9XeAF4bQ6B+sgq/nNrtM3WuHh4TufXLyJ11WbFq49daOFpuWznU+jlR8Tmq7Q3YCobqbxPn1W0NXqNqx/bq20t31vjJUCXuZ/0wFbNxnqnranpjVVO2TtmiCzNXP+F1E+J6lpMwNN8fzRD3wwSj0uzeWBVX4A4GoI7zaC60CM9Da2q9OLgMDnU/H7DRzXZXVji5ppkk7GCAZ9tKVjxGNhpmYXKVerhEM+ErEgwZBBwC8D2mWmkMB2bBwhQYnluMyvbHLpxpScqKsmpmWzk81TKlfRdB2B4NLNSaKJCMcP76NaM/EH/XIl5roohg62S6ZUpZotML+4yshgrxyQbQfLW6kZnu7b1nZWxlrli5iWjeUIarZLMBAglYiysZ3j1uQcQlVY39pibLiPQMCPbTtUayb5QpmtbTlZGj6dbL7E1L0lujrbicUiXvaFQ82sYVoWupeZOT27xB/+6SskYmEG+jqwbJfNbIH17TxtbRnKpo1u6NiOywcXb1Lzspx8usaB/cNYHr/UVlYmEwgh2NrOsbGdIxaLEAj4JKGp41EEbGwRCoYoFsuYlk3VtKmYNqbtoukSxFQsywMHsLqxw3deeYtL1+9SrdVQFZW/8UvP4zguaxtZb6CWq+BIOMzIYDd3phd5/+JNFlY2qFar9HW3c/TwPpbXttnKFblwdUIC3ppFNl9iYXmD2fkV0uk4Q/1d3JqY44evv8+tiXk0FUYGexge7MG0LHZyRSzLpuhZFz64eJv3L96ivS2JbTusbWa5cOUORw+O4LqCjy7fZuLeMqsb29iWyZgtg3yFEOQLJbazeXw+KW3jutI1UCiUyRdL5PIlLl69y9BQP8NDvVTL5Ub7RyIhDEPn+p17vH3+AgfHhpm6t8zC8jrjo/2EAgFyhSKmaWPZNvlShWrN8iaoGqlUnMl7i/zxX/yQ/p4OmeUZkRlwPsOH4wgKpSrTc8sMDvYzeW+JSq1GqVLG59d45Oh+rtycYGpmgf/lj/6KaCTI7YnZRjZtJBKmp7udW5PzfHDxNoWizArr7W7nH1p2Q7S8zi0FCrqhs7aV5dqtKcKhELMLy+i6xvFDowz0dgKQiEWwbYfbE7MYhs6NW9Msrmwxvq+P3p5OXCGtzzXTolytUrGtXeOM67psbWUxLRfLhorpsL6d48c/+5jJe4vs3zdIIBTEclzmltZZWt0kGDS4cXcGRdVob0uyurbFRzcm+XXbJigjakEImXemKkzPrvDDV8/z5GPHuHxzkkw6wVB/N47rsr2Tp1CssLq+w/LaOiPDXaSScTRN48MLt1nb2KEtneDU8f10d2YolSvUTBNXCHZ28iwsrdLX3UFvTyfdXRnmFpZ5/a0POHZwmO6ONkqVGtvZArlihc3tLOtbm2xnCxTLVSqmjWYY6LpOtWrx8ZW7uK7g8TPHOHX8MN1d7Rw5tI97r77HX37/DTKpj+npauPsycMEQwarGxWuXJ+gVjNxXZezp45wJBhAfesaOC7CkxsyTYtAwM/IYA+ZVJwjB0fp6e7Edd1GUgRAuVJlO1ugZtlUTAsX4XHlKcwubfD9V98nHg0zv7xBMBTYNd9pmlRVMC2HqbkVXn/7Y0YHe3n89FFGhvqIhoNcuHqXrZ0co0M9jA52s3/fIIlEjPF9A5TKVZlY0NFG+sPb6G5L3NGD5iI5A3mbnF0Gpb0zXpNbTBoV6iSyu92gzcm8SfTRCr0edOZfdKkjIm+CbjSwcv8+n1B2Wa4acjwtsVJ13rX6/t73UhHiAW66B4GbRjS72G3Vaolna+7b+NH42HisnjtYWrQ0DJ+B3+dD99Qq6nWU0mE/L/3HZyufC2zVXWW7y8MBV8Pf+KDOrHoPbdc2gdPqju7rlJpXlg3TC6iayvBAD4+fPkI2W+Dm3Tm+/+o7FAplhvs7OfvIAQyfgq5DMOAjmy9i2Q6KqlA1a5TKFeKxMIP9HQQCfjTDx+EDo9yemOXStQmu3ZyiVjOZW1xjfTPH7MIapx9xGBnsIZWMM3Fvia1ckbffv4rP0Pl7v/tLBINBAkIg/H5Up4KuqbRliywsrTHvpTnni2Wq1RpCCCLhEJ1tKVxXMHVvkas3JymWq2RzRbZ38gz0dvLC06f41itv8+7H15mcXUTTFL74/FkSXrZRuWySy5dZWt7CtgTpZBLbcXnzvcsIReWpc49guzLofGV9m9X1bfp7O1AVlYpps76VpVKtousa5UqNQqnM9Owyh8ZHSKUiHNw/SL5Y4e7MIvOvvQcCRod62Dc6yOhQH4fHh7i3sMzVG5O4rsvVW9Osrm9x9tGDDPR141g2QkChWObqrSlMS7C2uUOlJsFLLl/CcR1SiRiBUFDGIWgqfb0drG9muTU9z82pOXRV4ZlzJ2jPpNjJFVhYWadaq7GTLbCdzdPRluapJ04xv7LFpWsTLCxvkIxHaG9LUSxV8Qd8dLSnyZcq3JyYJxDwEQwF5WBvOwwP9fOl58+iqgpV0yadjjM62MNgbweW4zI1t8LkzALFUpWF5Q0G+zpJJGTmma7rZNpSdHSkGR8bJJ5MsrSyzvpWjvXNHYIBHxuDvdhOkzzUdQWhYIBEPIqu6Q0XfFs6yZlHDrO4vE53Vxs+vx9N0wmFgkTDIVRV5cjBfcwtrHLz7izf+dE7vP3BNapVi1QyxuNnjtPW3oZpWRw9NErVlOA1X6qgqhqDg334gyHart1l6t4ia5vbhEMBgsEgwaAfVVHp7WnH8OlsbOeZX1xlYzvH8GAv+0cHSCdjnDg2zt9VFe5MzmI7Drqm4o4OIIRgaKCb7q42zp0+SjZX5OqtaT64eIt4NExXZ6aFJLU5bgohLVv5Qon3PrzK8so6K+ubPPvEI7z03FkS8ShbOzkG+jqZnlvi5t05ZhdXyeeL9Pd18MIzZ+jq7uDazSm2dgqYtsPs4ipb2zn6GiOSwsrqBh9cuE4uX6KjLUU8GiOVjJBIRKXVcyuHYWjoukapVMF2LKLROOVKldmFVcqVGqFQgI62JCLol6NdPYUcyUrtuA4TM/Pki3kc1+X5p0/T09tDpVIhHA7S39tOOp2UllzHYWSolyfPHuG1n33M3akFANrb08QSMcKhoLRgaSrFcpWJqXnaMykG+nv4+pef5s7kLAtLqxiGhj8QIFesUKrUcIVgZX2T2YUVKlWTeFzGsoUjYfaNDpBK3WB9M8eFqxMM9nXz6FFBJBLhKy8/ja7r3Lw7QyQSoL+3nb6eDlLJeCOze2Nrh2Q8xvBgD13r2w0XDI7LxPQC7350BdOyqdQkP+HwQA/BBi9ZE80E/X5Gh/soV2oE/D4Jxns6eOnZ02TScRRUopEwsViU/t5OAt97uxHrJ2lMInRHfCwsr/P+pTtUaxZHD4+RSiU4dniEjZ0cy+s7VE2bUCjE2JiKLaC7q4NYLEJ3e5ruznaSdxfRckXPEqtQj1HaFeze+O3ieJOwi2hRMmmZy+p9u0H9UufZqv+ut8OuyXFX2/w8pR5o/0nb63VrJRPfxb/40Lis3efY/WX9V3PernPzodRx1/0er9a7vK/un2TlarWO7bFqNfn5XFC97ENVLl5bxajrtEOapjZCbKQ1vM792LKvoPm8foHWrc8PtnY1pUx1ffgBAkURnkbRA3yprSdW6nawlu8NHTHQhTK1gLK8jlIxiUZCDPbJVV6pXPZS1BN85dhjHNo/hN/no6sjw8mj+3j1rUucv3SbmBcYfv7iLTRdZ3RkkHAkgq7rDPR18dRjJ9jYyrK6to3jSItLMhnDHwhg+AMcGBvipWdO8cPX35cDu2lz9uRB0pk0quZDUTUI+KBSxeczeDaTYjUVx6drdHdmOHVsnOHBXnyGQVsqyRdfeBzDkJQIpVKZMycO0NWRxu9xJj331Gks2+HO9DyuK/AHDAxDx6xZ+Pw+jh7ah2laDPR2EgmFGd83xFdeOsfE5LwkFdRUOjJJjh0Yob+3oxF4aTo26VSCpx47xvr6Fv29HSRScVZWNglHQhh+P4ahsW+0n1giSld3G3cm5yiXKvT3dNDb3U57Js2vf/1FJmdmyRdK3Lwzg3BdnnrsOAfGBkkmoqAoPHH2KO3pGKqu4whBT3cHiViE3p4OUFWi8RhHj+xvJBqoChwcH2F4sI/p2UXmFlbx+3188fnHSKfi7OTyaJpGZ1uatnQc27LRdYMDY6P8zq9+mb6eTkrlMj1dbQz1d5FKxRkb7efxM8cxPY4sRVFZXt1gazvLQG8n4XCIo4dG2dzOgqrxyPEDdHdkCPp0GS9VrjDY28mBfYOMDveRiEd5+txxHj22n3LNAkUlFg3T1ZEhmYyhahovPXeGkaFe2jNJenvad0VeBAKS8DEUCqCokghVKAr9vT1848svUKlWCYaCJNIxfD4DDYGhKTiOy8hQL1//yjM8/bh0iSqoREJhgkE//b1dxGIRDF3nd379K1SqNQIBg2BQOkq6OjK0ZzKMjw5T9lyNPp9BoVihUqkSjYQY6OtCURQMXScei3D25GGOHBjh+OExfD4f8ViUY4fGGN83JC1zQpDLF1hd2yQcDoGAA/tGiceiHD00TaFYpj2TYGx0AP29m3sGBkHNtTyZKYPxfX2cOLofRVEY7O+ioz0DiiRW7ezI8KUXzqEbBggX23FoT6cYGxkmHAzRlk7y3JMnWV7bwtA1Njazu640fW+BOc89NdjXRTqZIJkM88LTpxka6MEfDKLrGiurGwQCfsbHRggGDfr7utjYzNPVkaa7I834vn60y9PyeXqWLU2RE0dne4ovPv8YXR1pYrEQA/09+PwGuip44anTnDp+gEAwQDoZR9d8BP0BXnj6LIFAkOXVDY4eGmZkqJdKtUYyEaO3q42XnztNd0eGg+NDCCF5xg6ODzM82EO1WiMcChAJBjFrDo+fOsyTpw/TlokRj0UYHujmsVNH6GhLEgwEOLBvgN/5lRewbIf2TJoD+4bxGT5UVWG4v5uvf/k5Th0YRSuXSRoaHZsFIut5IuUS7dkCbr6Ifm8d3r2Ks7QhCUcBx7L4wU/O88HFm7hCUKmZdHdkSCZie8Z4+TsSCfH4mWMk4lF8npB2JpXgqccfYWy0n0K+iuO46IZOV3sG45X3GwsVx3EIREL85tef58adGRCCsZF+DJ+BqiqcODxGZ0cGV0Aw6Kctk8Af8LG9naNmWfT2dDI21OPJbnkyX7RkpvGgGC23Qe3kBcLIUJo93FFKk0Gz5Rx1S9Z9OYotc9zn0OH7xDjpvbt64KPO/fUZjmu6iOuWqmZd62XXWeoeREGDKPZhN3SfCM5eZLsLIzwI6NxvnGlkHyp4QG+3mLcCaIoMV9M1FUPT8Bk+uZDwsqJ3Wbc+JwD+pPL5wJay53l5rsKHP0hFblfqZkVlF2Jsug4l0BL3BSWCGOlDmVqQ7Tm7xEbA4IOLt1jd2OLYwWEOjQ+iaxpVy2Z1K0ckFqW9o41f+/qLhKMRrtyY5PuvvouiKIRCAZ44+wgH9g/j9/tQVZVIOMiRQ6O4wmVzK0cqKV2NqqqSSScIR0L4dY2Xnj1DT3cHs4trpFNxThwZo7uzA9v20nkDfhQgGg7zxd5usl95nlQ8yoGxYc/lJTMLNVXl4NhwA2m3Z5L09XaSjEfpbM+gAO2ZFL/x9Zep1mpULYedfA6Eg6pqBHxBDo+PEAkHyaQTRCIh/D6D5544yRMnT6D7dFBhdLCPv/vbX6Wnu4NMKort2KhC0NmR4de//hI4NsFwCNN2qdZqhENB/H4fm5s7VEyL7q52xsfk5Ly9nSURCdOeSeEzdEYH++jtSmM7jifwqbKTK6EZ0kSraipnTx7h5LEDmLZLsVjjyIEakXBIAkpdx3VdujozaLqCbcv4I8PQOTQ+zBNnj7O2sY0CjA72Umc0Pzw+zKkTBzk4PkxHWxqf4cfQ4cjBMXq62ymWCrjCRdM0gn4/wVCAYMAvgYuq4DrQmYmzsLJBJhnD8IBeIh5FoNDd2UYyEUPDxTItDu8fYd9QP/6AD1XT8Ot15XgFy4sZ0z1WcoB4NMyLz5zh9CMyzkpqcRkoyOzJYMAv9SG9pAjhMdv7fX66OjMIgeQ601VvoQI6Ase1UXQf3d2djA714/cbqIqKEBqWaaGoqmxXTaOvu9Oj0wDbke41hIbPUIiEI7RrGromU+cXl1ZxbJuOtrR0bXmvnu0KDiv7qFZrpFMJDF3DdWVWnN9v0NGWRgiB32ewvZNjZyeHa1tEQ0F62jOM9HfjImPrFFVtGRqanEOVSpViuUwkLIPKTz1yiHyhSCgYQlNk+rWu63S0pejr6WhmCbsufl+QUCCIoioMDfTQlpEUEPlCiVQyvmusj8civPD0afp6Ohns76YtkwTFYWx0gMGhflRNw3VciqUymqYRi4UxzSpjIwN0d3bQlkkSDQWxLRPNI8JVPFJTx3awPSWLUDDAvpEB4rEIuuZl7Gka7W0pOjsyKEorrYBGe1uGR48eYGxkgP7edsLhEKbpkIjHeOGZ03R1tmFoOtVajYnpeTY2d2jPJAkFA0QiIVKxMI5lE48EOXZwH+2ZOCEdrO08NcshYNoEV7ZRJhZQ17d4OV8k6CgEZjbR376GWqmiFMv4CmVGCiUGPQJmVVXRWp9ZyzxYrdawqjVc15WwwZEhF+Wahd+nc2R8mMdPH6W/t1MmQHhxfIoig5NDwQABvx/DMHBdh3A4KBe28RhR7/4ty5XSXkazDortEFjZJLCZ5Te//SaOK9n31Vvz8IN3EQIOIDNEpUyV7Gt1tQPLtgmHg4QCAXlv2UIjjsyzkzSO2TW5KRIcSP5xrzGUZqM0+pkiZWE0VfWsft4WVSAULyxG0eszYEtRPILc+2OO9tZGblDvmx8/rTSzIJuyQq2SSveVBsji/jZ50P67MiMVdrMQNMxfLfFRYrd16wH3U5f7a2xpqdP9dbj/+FZQp3iArBEcr2n4DK3BKSnbxPVAmcJuxcRfTPncQtS7PtSB00NrV++cdTV1+d1ex6MQnht5D9gSQuAO9zaEa9XpRbSjIwgh2M4WeefDm3x46S6O46CpKulEjJefPcVXXn6S3p4OfuPrL3Lu1BEWVzfQFIW+7m4GBvqJRoOomqybrhsk43GOHhpDCOnm0T0TY32lr6oKqWSco0fGGR4aJBoJykm5bPL/Ze6/oyRJ8vtO8GPuHlpHpJZVWVp0dVdrPS2nZ6YHgxGQBLEgCFAtebt7t+QKkASxXPLIt7fHPYLkIx8BLAcgMRhgAIzA6J7WarqrqktrkZVaZ4YWruz+MHePiKzM6uqZwe3Ze1WZGeFubmZu9vt9f1q7Mo2+vIa+XlKbShMkZxZx02n0cJhkJEraCz11UQUp4ppf+kgjmYgzPNjngQIjmL8eUU66puOiGQLHMomEI2iawHVUf5FwmJAX1ReOx0nGNCzXpWE2KeRyDPb1eup83z9BEImECYdDaEIxMwnU600q1RqLS6tsFMu4rqS/N0Z/bx7HdUnEo4Q1laqhs6RLOpUgEgljWTbFUhVDSHQvNFkLhQiFQkRdjZAR8bQYbaAlpURoIWwk1XqJUqmiwIZheIBMMSzX02ZUvZIhe3aNs2NshGgk7E+JSDhEIZclFFKOxOFwmEQsGgA7XRPoQpXSCBk6qXiEqJdPRUq/tp8IkuEKNEJGiFwmi0+uHLzSPAIQWuBc66crATBCBulUkmgkQj6XUVnRPSkaIdANA13XvOeq0jfaeonk3/wnHVG//hlRUXvSdQg5Dj2uApEhQ1cE3VOBO95e8Mcewg9N98yWXh4zJXV7srZ3b6TZxLRsEvEokXDbXVhKSdxWEZahkBqzbjv0lioYhkE8lUAIQdiyiFdqCnQLEeQ+UtGyaj6ulIhiFT/Pke8WvLK6QbXWJBmPkIgnMbQwq6sVknGb2GAEIxQmGomTy+gqOjIWDcinJvTgjOqaMuXFolH6emz0TVUjdowN09OTJ5NKBGfBtBxcCZFIREW3um6Q8FgPGUhc+vt7yDRM4vEYkXAIu6VBItq2ibgu9YZaP1eq4IZypUoirjRlytWk/S6EJ/5rKFdcV7rU6nXm5pbIpJP09hRotWzWF1eJp5PkonHcUpWFM5conb1MyrQJnblGwpFEbZuU4yIrddLrJfR6i6hto3tVHVyPmfmOvhFXRWnrur8Xu6m0JgF/73cK1KL9QyLRBO0zIgSG7fC/nJukKXV0SxCdXiG8+Cb6N98OkoOqsmoC4Thk/rYqYJ/tNHN5DCBgLUFeKYlx7pq633HQJUjHxShV0TywG5gz8ddYCWUBmECi2SppcshtoDesNoBTd+CieUXQtyjxLtr1Rjue0lWkR9UL1ryUAppKmi0EQZ0h73ufoXdpvJQt3T90+H5J3ZDPpws+cN2kjNjO3Ndhggt0Na6j5iQ7Lrwlsq9b97bVd+2ORfdnvllOCtVvuxbSthihs8dux/QOyNT5+aZrVJpox1tRccv78rVhwTAEao8EY3I7rvzJAy34kTPI/6jqtTai7gRc/laSAJtyeQVt11gAzsSNOfJP3su+3WMcO32JVstkbLiXwb4crZbNsVNX+N5rx7jnrn3s37uTZCLGyHA/mXyWWCRMLp33opx8cU31Gw4JenI6UrhdZWv8GQtA2JCbXqH3xizhm/MY12fQFlaQUqItrKq6bwiMqXmYgsyv/WO0aOTWw0F3eGrYdUl5Ge7beZna38ekJOUqYq1pyhw73rIYtG0i4RCRSIggZFVCGKmceGWbGCkbu1cdytPECh/cIkg4DumWSb/HNMPhkAJkXkRh2rIRKOCJgIitcgGFvBprrusSbrU84BPxCI6ahutKoq5aaF9iDlz1vDElLYt+y0bXNSKRCFK6hOqqFmIykUDXNfZW6wzVG6RTb5OIx9Eu3VA18Px1QpL2GJw/t/bOa+/DUSkZ9qQmIQQJKfmC9+5Cod/DT3Hi78+ujXBrhx/ShBqjlMj5ZeJ/838Jdr64cANMSz1vZnHLuz0+HTgZd5UDMnTc0QHPh0S0mahPWFbW0Jum2jOahtTVTzShKt7rOiHHRkiJVgtDOIIv1QgJuuuqfFK6xzwsG1GtqfIn3rhDrkvSUlGM0iseHLZdDMvBry8ofIeOYM+p6NzJqTkq1TqhkE6xWKFcqXnlrRx0IdANnVg04kUOaSoZpTc/TesI1RZCEbMwgYbRJyTCssn93X/WwdwV+Iu4LmGU07Vf79IXGn0/l6StIj41TUP3zA3GjVmwPeLsOKo+5EaZWDRMq6Uc83VNQ7Mc3HINrVRDVJsYjSZ6vYFeq6HV6+j1Jk6pyu7ZJcbXS+RCr5NwJWOlCoO1hnIxCIdwHJeJWoNR2yZkGITD0+iarsp3eeljdNtLVqmrkmLCA3OK9ajo76A26ea9G4tCShXQdhMx7EgEmYyhZxOQSiCTcUQyhpaKQyKKHQphGyHkb/wbZLGiNPktixRC1eKst6Bhdu5e/xiofblWCv5UV3jMTXZ+1vG3rwk0DPWhYWBlEjRbJlIS+JT5eb80T2vrAxMATeWJQeoarqd58jEB2ZQ6C8GYfF+89ig30+/NvAEIgJYqcOz/o2u9g9sCcOPzGRFcEACnDmDR+fTNCVH9QQS1VfF+75oB3b/L7qC19ufbaY42Pa7TKrUl0OsklH4ZpE2FqG9pPu3aDMI77pGi4363TaOlhipavVUpIS/tTveTun/3OlL49EfNsH/7dsdgK8hH0bExgqR2Qdusq9rcOr4Xfm37trqwy6Nt8507htWBcF3ktRmuTc7ywZnLREIhvvDikzz95H2k0mlWl1ZZ3fgjqrUGhmeqiXr1B9OZDIYRIqT72pDOAyO9Uj5h/FMuTBNtag7t6jT6tRm0azOI2WXaURbt+9tdKU4iDQMZCaEVy4hwCLFpXTavki5VviVEu5RE5yvXpFdpyqeVEsUsJGhNE0337/L/bxOw7oVUpWPaIFcEfYYAo2OjC8tB1JrqbykRXuFizTvsuteXD6o0CVFXma7QWl2MXwuATXvdOgmr5vUX8Z9dV5FYcdtWmgtLqYDjjkPUBaPWRDRMRK2hqkH7ayP9R8r2G7pVwAFkV34dgSTsA1Db+TBB7PbtNjcJE7T1UkAIcd1tCE+7HyHxgpq2AI6aBoVsABqDBffbyjrCdvAJ3eYnCdQ+CkmpGJCmIXQNdB2p6+iGYh7CMMDTpsWjEfRwCMIhpKGDrmN4qnfHddW+MAykrjQCvpbFn6e2WlRKoZV1PvV/foMnKjW01QqZf/F7ZHvzDETC6B7D9O8JFOe3vFA6GAwdGj3aIFxKtPUSnQ69voOzQAQmTl/7BG2GFpIS3S9W7AEYrQPc66eu8sI//Pc8VqqgA4lLXySi6+osb5OFvHs7Sob98bZMkBJDCCKeO4J/fbqTuW3Rj6YJVQNW19s/dU0BFF1DGDrC8L4z1LsV/nUK+SJKVfRiFeG6yh9LCKWM2SRsxIUgKTTElSlPY7X9FvbvwatnieMi5pZuczHdGohao72OmkAmYmr83l41RKdwCr4jNL5Gj+BWxY+lD6M61rFYIfo//CvM//3vd62t4vttU5fYYu/5n/uA39dqCU33OnGDe7rNZv5c2zUUbwF2t13U7vWS0jPp+ntpy5FuGndw/9YAqxPQ+UJxAPx87ZsyRbX73BJ8+QdUafb8Mxz4uN0ysHYfW8/Bo3FdpE6goj/9NVBgLLigc44+L9KkMhl6ygYh/nI0Wn77yElN1e/bXHRbUyIojuztP1+tEnzlS5RbPhyiYdzRAcTUPHJyntffPM5b758nHovgupBMpdFCIc5enKRUqfPME/cGpVMU7zBw0FXG+03zAcB20Kfm0a5OIa5Po1+dRpueR0wtIBxni0F5TdNUdFIipoibrzlAEXuxsuHtrVsBRntVZHv5Ovbqh734ruDZrZiQ/6Q7hem3Awly0/O2G1PIwNm/03tyN1tpw8A7YUIiAIBAIOXotEMxJCDnl4P1lSFP8vW+lJs6la7Esm2VfsPL/h0KKRPf5jHI7Tf5Np9vzQRvuTtkQE+2/Xchi492laPu7fqWAS0RGyUVct+Txf79f6Gu0m4NQNH/xm8i55fbRFgoLWOnEd92VPZvIQSJeKy7APSm+RhSEQ2JB2YsldxUA9B13EQMNxFFJuLYqTh2JEwrEsJNJhCZFCKVIP07X0FUm4R0jZ3RCE44rMAYEK3WCZv2Ldt5C5i5zSp1zF/XFAEOh6An2yHweITcWwJf4Ai+Eh3sWEo0L89asLS1BjRNdU2zRcJ18dIZo1kOwnbZ9KiupkzDfpkREWjoAlAhQIQN0BRgEp4mUtUV04KfUhMd9EZ09L9Nk9J7Z9sXEhZ4WiDpglQZ3luWiesok57rShXcEYsS6svfSkdv82y/uZ2MNFAvcStdkCCm5gNhSho67lCf8hMs1wh5SWU1Xwst/eTAnomoK/pVguOoZ2g6ostyIUBoHQJr97C72JQHwsUmgKH++Vpj34SoBhNA9S2BBVsCrjturp9z63bghFsB0B1qrnwQ1wZm7WsU1unmL8pAvomR+1rtrYTBrgW+FfT5swqE0y4wp/pqa/oEUmrt520xZukBME2DkK7y1d36Wu6UYX609mMVov7ITnqirXFQzZfWOiIJ2GyDVy/eERrsHkNMLaABB8NRRgZ7uXJjlv/8le9z/tIkuq4xM7fEvUf28OmPP04sGlPZ2oUKv9X8F+66iOkFtGvTaNemEFen0W7OBVJa15gdp0tzQjiMjIYhEoZoGNnp4xKPBZuvffBF13x8YtC1Xbo2Qxt4bW6d97QHeMsnW7StAZfs7FFs1Uv7YHTd3n1bdytksX7vn6rs7tyq7u4sVeO6vk+JL3AofxBdeJK6EPhBF93TbD818tf/EWKtiCxkafze/4rjODRayu+s2Wox0NejStWsbfDqm8colSogNd5+/xw3phd44akH+Ad/96+SiMcCoik7/D02RyY5rvd+fQLkfa97Zggg0JRsKTfQ9iaQHURD+sR60xyF5xvjO9LjMbzQr/wGrBWBtg/WZtMAgCh5Gdv7CzR/6+8wf2WKG2cukUayt6+HqGmzcH2KCyfOEzVt7h3tJwOIagOqdc8pNZh+xzy2aI6DVq6ilaoKULgS4TqEpDLtBH6I88ueACMI+Uuo6UhDg0YLDB2h64HWTGlqvIABQ/O0M6E20NhM172BuoWs+jCXwfnX/3OwltCuy6gm065u0TVBT2Pg2jauowrZS9fF+INvEPr6q0oanppH2I4CqLquxmN0a5ek/9PQsSXUTZOaaVJutGhYNvmeLINDfUTOXVO0xjCQ9+zr1lJ0HOHuM7UlVdi2BVffjnZLF9u0KVUqVBstLNuh0Wxh26qiguO4pF2HkbRy6Edotzy+i+K1B07LsihXahi6TjIRw/BMg+0pdQtLMtgrqKj0QsYzgrjKqVy6nu+UwHFVjVVNaIGPoe+g4jiuKnxtmoTDIWJRzyd3owxS+VxpRuiOBMpbmhDeGmwCVF0g7TY9B4DrzvI7BZagTXmsfOHgNjfeef8A0gloVdfnXl9KmPW/9R3iXS/rwFZ7bHtIuN1u9KNFu0CZx8PVY7Vu60AAxkTHmNs+jK4XQdyZBmKzSfi2ZTF/jPZjga1b2u3Ob4BONyFZ0e2Yp23esOARQwG7R+Hl99B0naf7+xj4b3+FY6cucPbiNZotk/17xviZTz/Nwf17yKaSOI6rrCdLKxhXp5HXZxTAuj4LLZPbNk3gjvRDpaaijmIRGOgN/Fk2I2cfkvgv1/WcVIWmDmKn75DsvrVrM7dME+kSlFsJrvOYSCdQ88fh5827XbjqLaCH9qX+Y1x/nTddeQsD39SPAEW0PIdVXROKObgdnW8ai8BPIEcQ5aP52k1vLupOpVHQfeldKsfUWyGgAiPlcpV3T5zklTeOk0nF+amPP8rYyACXLl9nZWWdpx+7l327d/Dis49wbXKWaCSM1WpBNOz5bWh0ur76vh2di9a5Im1S45kLvPlsXqetWuf71T2O2mlmkF5/bYdNJcFqwvdHU9/pHc/rWhHbgVpd/Z5LUc8keXV1lT/74Cx37d/Jrz7yDIN9PVx47xRfXF4mlYyT+9Wf4eCesQDguU0LUalDrYGo1BC1OlTqiGrD+7yGqNTU91X1HdU6NFvBCgmtbdoBcLxaeQJ/bX2wf+sZ6VpD10Warmce9PrXdWQqAck4JGOIpPI9IpXATcTUd6kk4tg5SCchk4REHNIJNF1v799u9QW+W4MyN3r7Go9gP3w31tggMhUn8q/+s5p/bx73i/8MIhGlNZGSWq1Oo9EkHlcZ2kvlKsdOnicWizKxY4TLXp3LHaP9/I1f/ilGPvF/U2ArbCD/4J+q4sNSw/WBh+zUiG3TOjVIW/iebPZz67gRzQM9TdPm3MUrfPWbP+DQ/gmeePQe3njlPd774CJjowNcnZzn5swif/uvPMdnX3yGRCLJrVSh8+0pzaBj2Rw/dob/8Adf5eCeHfzaX/kpegpZNTdX4vg0zrvdAcK//lvoH1xU87lrD83f+SdIHC+psYVlqkSrKpK0hWnaZNIp8rkMkbDSi5umxdTsAu+fPE+pVOXgvp3cc9c+evJZQr/2T2CtqExKhr8VvdxYiDvCJ50z3w7EflhOrI/ctjKz+SDsR+4yQMVdQK7Lh2w7DVlQcxGUqbADOAZnv70vbrsW/nfBHAPxdBOoCrrrFgRFt5B/y5Ok652NW8+SilS8VcP5k2g/WbC1TVPwQ62IQqpuYBbqkjC3utdDqQKQu8YBtXiphVXu+9xzHD6wh0azhSMdYuEwyXINzlzFvTKFcXUa7foMNJodZHyb5wz14u4ZR+4exZkYUfUYYxHCv/ZPYK0EhQzOF/8ZeHFEmtBxvQ3pa2WUNsvGcWxalk3TctB1XaV60HXPB6JzoylG6RP7G1Oz/Pv/9OeUy3X+6udf4JH7Dgd7yJUCV4CK6OzYSN5G1gICumnlfULthUmrMXb4tXRqcALtSjsnTFApANlNVDp+1zRB6K//ZuD46mu11PkKdHVE//7/C7FRgU19bde61PzbXXPWK248v0T8b/wmotFkYm2d/yMR4tEHDlGp1tnYKLOyskbY0MgmoySiBnt2DLNzZMCrRamhglKVZOZ6EqamKT+kQCYVIDSBI/3D7O3sYA39Md+ulEbnXvcAHR3h4KKDntANtNqQHsTFGwrgzC0jfuU3NvXoNcuGyTn1+UaZ9D/8bfp/+mMkYhFcz5HdMHQGe3Nk0wlU/EKH5k0IiEZwoxFEb07tO6lG7O8IKV2QypTopzRwXRfXsrHKNaxiFb3WwGiZGPUmstrAKVcRtQbhpvpMqzfQqnVEta58dBx3m9XbgmE5DqJYhmI5AMOdsnZ7YbbYvfEowgdq6aT3M+GBsSQc2Q17FM3RpIsfWSX3jMDeUaXNSMaV4BYywIvulVIyO7fIy2+8jxDw3JMP0VPIMT27wOTULA/ddxeD/T0UnnyQg3t3IoSgkMu3xyVBuHqXQNTpa9IFoTq1X/7cvW50DRWfJbxgiA9hxgIHKSSm1WJufgnLcRgb6aevt8DYcB9LqxscObyXoYFe+nuyDPQVMHQB2Jv2u79PvQzqPvOWjsqntnuc0eG+jmhNZbLUOrTe6iTeugt8gV3XIBLS0UUI23FxHUhrKhVKPB4lHDIIaep6V8DC4grvnTiPlJKB/oLqS/NSq/grqPma7Tb3Fq4S8D4y8xVtd4ltTYi3uTdot31fm675MYBW+3mbUz54zXW3utq7B4L6iMFdrhJcO81+Irg4ADnd32+zRrdqGDo0WF7HHq/5sCalR58cB8dWvJqAlkM4FCIcDm8Chz+Zdsdgq2txtm23U5UKpPS1W75DmkZXraltbnWlqwj7zmGMmUWwHcQf/AXi7VOE6g2S9abyo+hwlr6tQjYSQsZjkIhBPKp+b5lo567Cuatd94qLN6Cv4K0BdKiRPPOkYs/qAn+qAl0IDF8LJr2bN62dQOX0UHOUzM0ve8lBYWllVZW5CKJk/HW7dQMosLa5e1975AEu0SbRrjcNt9NFPDCvbNIW0S62tPn9tMFFd/OJZBB67WuyNiqI9eLt97DY/Ot2bNe7xrSDdy7WikQch5FohF//pU9yaN9OUsk4zVaLcrlKNBwimUyo/Fau62VA19vh6WqwIJXkbBgaoXBIAUZPMtU1EJquTGS2pdJBSOFZEXxNptef66U58ICw6yrTh64p4u0KEBgIoXsaU4HAoXOBfMlS+qpyn6B4ofBYwGqpQ7rrWFzTUtd4v4tSlUgkTCQc8qQ3QUhAOhEjGol4Qos3XpRY4aD8dgQQ0dWndBRsRehIqSGljRROsE5aOESkkEHk0qpMlXRpoRi+KwS6HkKGdBy9nc9J0zTlmtQyMWp1RKWJrNeg2oBKQ2npqnWlUavUlAatUvO0aTVlgtxqb21Hs+pN9W9prf3Z9EJ7zUb6EYM9CFcipOsJ7t1aG+3cVXAksifnAR3B7NwCf/K1l1haWeenPv4EuWyalmlyY2qOUqlCwivvZcSi7N45qiIINzEzXzCyTBPbsgh7ZjEhNNqO0DIwx/tpHjTNp62eEKbptGybRqOJDsSjkcCJW9N1bNvGMi0MTaAZSrtUb9SZW1rBshzCkYgCC5qOadkk4zEe//Q9mLZNPBEjnIh5+9rZFEQFPgO2LQukjZAaB/buZPf+XUjLJqxpuL6WQbbnItRm2Mb0pvag7ZiosmchBfikhtBUdKZf2FwTKjtWPBbm0P4Jzl26wes/PMXJs1e45/Be+vuzbZAohDJlB2jVo12aREjFu24ZhveibiMKIregsdtcqsB7sL1Em2dsCSI2Aaxt3DU+9Lkd/YkOoPWRWhfO9vmcAthdgMvHRD74vp1p9Y6eu2mNPHOulE7wfTvzQfcQXddV+fFsu41tEIRDhkp8rf1fCLbUIH3JspMjfviL9Be7k4W0+/FfDJ5U3+4viGJSf+FGIyq6pmVCuYo4eXHTALd4uKGyusuI8rOSkbCKtvLGgWkjzEpw8y0A2rYDCaXRbGI2XaLRCLFoJMiRpSCQBKG0BS3TpNVShVY1XSesC4RmKJOE1HBdcDzbMbrA0FV84J6JMf7nv/dXyGSzjA8PeeHMYNoWQmhIP4J204GUAG47VYtvmmvjJhm8A1e6wcHUkB0ExpOag+AZGRD8jhep/hZ0aNK2ev+3gsrAo0kqs6ebSwWStv8IP3RaPao747IaUlsqcr37hJeUjpAqe6PrOr2FNJ//zDNYrRZCaEzPLlIs18imEywsr7G0us76RplatcXBPROMDvej6woMWZbNytoGr717ilbL5Ohdezm8f4J4NIrtOmyUypRqTdLJONlkXNn8NZeWadFotZCOS63RpN5okozH6S304DgOxXKVRr1BMhpW5YmiYTQEUthU6zU2ilXQBJl0QuWn8tZBAmtrG1y/MQOaYHx4gFw2TZR2a6bigZbK8ExjUkq0ah1N1xV40JUWoa1UlZ7pU3pMWQT3ORJCuqBSb3FzZpFmq0U+m6Y3myQSDtNqOdS90jC6V0TadR2yGZWwt1ZvUq7WiEYixJNJDEPgmjYC3fOpaxKNukT0WBAmJjTPNKppEItiR8M4WRtd70c3wkgELdvCcVzCfpJYL12DOnMWdssk1GgRbrbQArOmMnH65k3hg7RqoxuwOe1UDip9C1Bv4i6ve75yBGAqaBIw7a4/K+UK33vlXVZWi/zyz73IocO7Ea5kfm6Zyal5arUGhiawbZPlpVU2ynUGB3rJZdJdQp5t2zRqNW5MzXH1xjT9vXl2jg2Ty6aIRhTt8WnN6kaZYqlCyFDJkTPplMoTJjSuXJ/itbdPcH1ylkwqzosff5y7jxwAw2BmdpGTpy5QKlXo780yOlQgV8hRrTdYXitSrjWwHZdqrcHCygbzS+ssrW4ghGBgoIdGy+LypRtcvHQD3dC5/579DPT1IIFiuUKj0aTZbHHt+gzDfTl2jA0xM7fCeycukEknefSBI6STCSq1GqtrRVbWipSrNeLRKDvGh+nt7SGyibKo3IoVLl27QbPVYnigl+GBQWKxaJBrzRdsHCkxdKXVG+zv4bknH2RmYZlrN2c5e/EKEzsHiXews82+mt2mPwFuW+Rta1J8B/VtmhRsdpW59Rp/dwVM0HukP7At0MJWv3eskX+Wfbp/5xqkLk9e72OB9H2j7qS57iZfSk9z5K31jwRjNoPOYG3aInmwjJumJ/HcGAKFUVsA9gVLH2Mbuu5Ft94JQP1o7c5TP0inS627bdjmFi1IsBdIZEEv4EUIyk3msVv68H/Go8pvBBQD6WwhHeIxZDwK8SjEY4oJe2NwvCy9uubl1emYj7sp4kgvVYIanf7GnZ5dZGWlyHB/L/lMknA4TCwaQRghEAJXOqysrvPKW8e5NjmLEIKDe8d55IHDFPIFkJJGrc7C0gqtluXlkwoz1JtX/jLZFHt2jeEA0WgYoQlOnrnEqfNX2LVzhF0ToySTccKhkDr7rotpWrRaJuFwmHQqoYjRRpHJyRlq9SbhkIHjuiQTMQb7CpiWxfTcEusbZeKxKHcd2MXwYJ//orAdh0q9SbnWUJJtWCXojHs1zuqNJjNzS8zMLSKl5OC+CYaH+ryN5APWdrjz5rfoug4bIYM/f+Ehms0WxXIVx7YZGezl7iP72bd7B+GQKgNTr9eZn1+iVm8w0NvDUF8fmq6zurbBO8fPMDW7yM8ur5CzHSKDvWiahr28xtryGn/25W9x9Mgu9u9RWfpN0+LNH57lvZOXsR2HpeUNopEwf/uXf5qhgR5sx2F6doGmabGyusE3X3qHi9dnObxvnH/wt3+BIwd3c/bidb723Tc4e/kme3cO85mPP8Z9d+/Fch1+8OYHvP/BBQDWNsosLm9wcN84Dx49xOzcCqcvXMPQNT719IM8+fA9CKFqRt6cXeDt989y4cpNwmGDvbvGeerx+1U5nEiYcqnMX3z3Db7/+ntUqg0+8fRDfP7TTzPs71td598+eTcXr96kvzfPC08/jG07nD5/lUOrRZ5pNAnNLCF1RY4c11GAW0iV2RqfMHv6Zk2ZUVY2ynzzpbf57mvvg4Sf+vhjPH7/IcKhEJPTC7z0+nHmllaJRyOYpoUUkrsP7iKdjDM1u8jMwgqjg3188vnHVVSwoeNaLqfOX+G7r7xLNBLm8Yfu4ZEHjpBKquCEaq1BtVanVm9yY3KapeVVxUyHB1lYXuPilUmapsVDRw9xcJ+qalCp1lheWefcpRtslCrksykOH9jFvUf2k0klFeOVklq9wfLKOrbtkMumyGUzhCJhgqKzjZbSjn3+v4H5ZQCsVJxl26LVVE7VibhKkNv0nMUTpkWctslYui7XJme4en2G5z72IAcOTqBJE8d2qNSqLHmmbE0TzC8s86U/+y5rG1V+4XMfJ3Uo3gW21lbXefWt4/zgzePMLKxQyKaZGB/kwaMHeerRoyTjcUzL4ubsAi+/eYLTF28Qj4R49on7ePKxe0lHUly8fI0v/tG3KJarJONRLl9fZu+NYQ7dvZczx8/zu//560zPr5CIRVkvltm9c5Bf+YVPkc+mabZMGk2TRqPF2lqR2fllVlaLVCoNzJZLvdrg3fdP8/7Ji8zOrzE9t0irZfKJZx6m3mjxB3/ybS5fmyGdjHFzdokXnryPWDLBsTOX+OO/eIWjh3dx14GdtFom731wnrePnWVxZR3TVMLtvUf288lnH+WolB3rIllaXeeVN4/zzrHTlCo1+npyPP7gPTz12P2kUwmqtQaXrk4yM7dELpviyP5d9PfmEJrBjvEhjh7ew+T0HGcvXufxh+8m7nftaVY7W5cgKaWXv0zrGI13o3R8mNT1XcBfNhejZtOFfoLTzZ1s1+4EqfhAUPrzUDYK1TrnKbp/Ci8x0OYkp3AbLdvWrWsqQQqMDu3nj4NntjWJd4LlW1P/+Bp0Xdc6kk17CaZvGU+35e3HbXee+gGlnfhxFGsBBpWdL9jPb+FPTN5yT9cnccX0ZThE9dmHWC1kqA7nKQ0UWDBbRCJRjhzYw0BfIUj4ZzsOy2sbzM4v4zgOE+PD9OSzGKEQpiO5fnOWU+cuUyxVsWybof5ePvOl7xCtNHwlD67rcn1yhmuTM5y/qBINRiMRBvsLHDm4j/7BAkiXi5ev8/XvvsnaRhkpJaVKmcMHJyjk8piWzcmzl3j7vdMc2LuDvp48q+tFXMtk365xVlY3+L3/8jUWV9b59V/8NKPD/fzxX7zEm++dY3CgwN2HdvPxpx7iwJ6dIKBaq7O8ss7U7ALxWJQHjh7CsmxeeeN9/vSbr7FRqpJOxmm2FLO459AudE3jyo0Z5hbXkBJ+6XPP8ss//yKRcJhiscypc1e4cGWS9VKVSq1JLpPi8Yfv5uhd+zAMgzMXrvLqW8dZWy+xtLrBxx45ys/81DMMBC9Igl+aAk9rQVuDZdk2SxtFfvcP/4KB3hyRcIjltSKDfXmGhvrYMzFGrV7n0tWb3Lw5y7WbsyytrJNJJfncJ57irgN7WC+W+aOvvcTpizd4sFhHRsIUelRfjWaLuXKVP/3m6+RzCfZMjGEYBpl0gp5Cmv17J+jrzfHqG8e5MjlHrV5D4nBjeo6X3zjGjtFhJsZHeOGpB4hFwwz0ZEkl42xslPjeq+9y7PRlYtEI569M0ltIsW/PMM2WybvHzvLK26fp9/yfytU6L795kuOnL6MJXfmOlWpI1+XuQ3sxQiH+7Fuv8o2X3iWXTnDX/gkkkvdOnGNheZ1f/tlPsm/vTqZmFzhx+iKDfQXGho0AYAfnQwgymSQXr05z+sIN7j60h3QqwRvvnSJUqfMxx1HpM3QVLeS6NuAq06kmcITE1SQIV2Ua0CVNs8Hbx0/znVffpV5v0WxZvPHuSe49vJtEPMbpC1f41qs/RADjI32EQyGm5pY4d3mSQjZFIh6jXK2zvFpkYucoYyMDhHWNa5M3+fYP3uLq5CwzC6tMziwwMtTDkUO7uTY5y3d+8DYLy2sIAZevTbO0skFvPs3O8WHWixWmZhdpNE3MlsnwUC9TMwt895V3uXxthmqtTioRQ0rJa++c5HOf+hif/vjjZNMpisUy333lXd56/zTNlsn48ACf+cST3HVwN6FIVDHFWARiHcmHdZ3SP/obfO3ydb75vbdYXC3yU48e4rkn7uer33qNizdm+U8bLokOSt5smVy4fINcNsXdR/ZjCBdsW6XowMUwBPF4mEhUaZMbLZO1Yklpubua5Nyla/zel7/N9Pwy6UQMIWDu/VWm55YYG+7jyKFdFCtFXn/3BN98+V3WSzV0AdFYiAMHdmJLl+++8i5nLt7gs594jOefepDVlTUKhSyteoMfvP5D3jlxnqGBgvfeBaVKlWarSTTaSyIWVVUqpEM6naC/N0c0FqVhWjTNJtaG2hePPnAXQuj80Z9/D9O0PABcZ2Z+hQ/OXefAnlHuO7KXvr4CLdMmEgkTi4aJRsM0zRZXb8zx/TeOUa01ePCeA4yPDHD95hwnL1wDIThkWkH6F8d1eeOdD/jWS+8ozV/LZGZuleWVEsMDvdx1cA+Xrt7ki1/+NovLa+zaMYxl2Tz+0FGSSZ1wOMTYaB+5TJLpuWXmFpYZkXfI8zs0WV0MHakAyi3ldjqiiwVszkzvk8r2BbdRXnSAHN+ycIu58ZZb2iFGqluHIJu97ARdHVaeQFu01RDaWf4/WpMdPzfD0U1gtnM+Xc/ZBljJzr43P6+zdTvfCCHQvEhEwzA8rZev7bqjSf1I7SOZEX+ccbQ1iuKWDdJG3sIXsTs+9zRjvrW3kEFKl1I8yr8oxDlz4RLigiSdjFEs12iZFj/34lP87GeeI5OKY1k2J89d4QdvnmB2YRld1zl6eA8fe+Qoo8P91JsWb757kj/88+9TqzdJJGI8et8hPmV3h9Fbls3x05d459hZLMuir5AjFo0wPbfMi88+zM9/4QV6c2lm5hap1Rsc3DtGOhVnaKCHWDSMEC61SoXTZy+zslbk2YFehgZ6ScSUSdJ1XTaKZU6dv65MVdU6yVKVy9dnsGwbTYP1YplSpYLtKDtzvVGjXC0zPbeAZTrsmRgjGo2wXqwwt7hGOhnjkfsPYeg6337lPb7x/Xe559BOPvHM/cwtrPG1773D8TMXefbpBxno6+G1H57k9//4OyQTMe67+wC1Rot3T5zDsm3GRwZJpxKcOneFWr3BJ559hJvTCyTiMSyrG/1rQnoRVJ5pqINQOI6DZdv0FjL80uefY2Sgj69881Wm5hZomU0sp8WZ81f5/T/6Nn2FLJ969lFChsE3vv8Wf/Cn3+KXPvcC+UzG8zMyVPkVTfPqoans9tGIKtMTDofA9xNyXMZHBvnkc49QKGSZn19icmYRy7YxbZOLVyZ55e2TPPmwy/337OfzL36Mjz18CKRgZLiPtbVVkokwLz77AMODvXznlfeo1ptYtuPlrYJMKs7nX3yC++/ez9e/+yZ//u23iMeifPYTT7C4tM6ffftNVtaKVGt1IpEQ7xw/x/JqkV/9+U/w4k89i9Vs8aUvf5PvvXGCH544y/BQD41GA9u1ySVS3HdkH+Mjg0F9QHVG4IGjh/jWS+8wu7hKNpfk4MHdjA73sGuphF71om4982LneKXrh0MrIUcxVwWI640m9x89wNjIIG+9d5qz56+zXi7T05uj0TRxHIfBvjy/9LnnGOzv4Y++9gNOnL3Kob07eO7J+7l8bZrX3zvD9PwS9UYTPR5lcWWV3kKGB48e4E+/9Rq2beG4qhzQ+UvX+PPvvEkkbPDp5x9h/55xjp+6xKVr04QMg0899whr6yVef+cklWod6UqiEXX+HMfhk089wMP3H6bVNPn9P/0ev//H36GvJ8czTz7I9NwSf/rNV2k0W0yMD3F9ao6Z+SUO7d+lnLOFF/EnJUZH+pfUcD/P9RWYX1jme68dI59NMzY8QDKVZKA3T8LcCK6VUrK2UeLK9RkO7t1BJp0A28Z1JLbnkxSLRUgmYoTDYYRhkE7Fg2oQm3lJMh4jFg2jCcGusQE+6Z231987zbsnzrBv3zhTs0u8c/w8zZbJob1jGIaqwSiBufllLl+bASCbSTHQ38Pe/TuRrmTyxiyXrs8SChk88dBdjA33U6s3SKdi7BwfIBYLkUzGEBrUmw1saZPJJQlHDEqVKsVqjcHeHPfds5+1YpWXX3+fpdUNdZYsGyMcJp/PEo0oX6lPPfMQpmWxvLqOrmtEoxEcFxaX13j3xFluTC1w9PBuHnnwLg7u28HeuVFuzi9x8uwVmq1WoD20LZu33j/N/NIaI4O9qipFJBxoiSvVGtOzi8zMLyuzfrPF+kaZal3VW7Udk4inoVxa2WB1rXR7E+AtbWtQ01luRwbAWXT4tPqCJ21GGHTVnSPtFm6/lQbnQwDP5ijTQInhq3mCMSghoA26Osehq7Oxec53ak70tRTY7eduNYc7XvwfRc2jBH/pbg5wUOWcNM337fPVQLdzjfnx2/9PohHbg9eC4JQtrkK98FsrqQfvDVeZBUMGtuMwu7DM7PwKA70ZHn/oAWLRCF//ztu8f/ICj91/iNyBXVy8dI3/8Adf4/LkPLvGBonFwnz7lfe4ObPIT338MYYH+8ikE2RTcXaPD/L4w0fZtWOE0IVpaKjs6RJlhvjg7BVKlRrPPXaUTzzzEOlUkn/9O1/h3Q/O8chDR+jJp+ntyZBJx1kvlujryTAxPkQyEUMDKpUq5UqVdDLm1UHLM9CTx9CkKrbbV2Dv7hFOn7sGmkY2n6G/L4tpWfz1X/w0B/aNq+LDusSyLCIRg2wmheO6LK6ssbZRZGJ8hEIhSyYdZ+foAJ/95JOkknGmZxc4d+UmLz73EJ/7uU9x4fQlTp2/xtJqkeXVdeLxCMfPXKTebPHXf+kzfPKTH2N5fpH/8pVvMzW7zMzCIgeTE0TCIYqlKhev3OTg3h3sHBsmkYipdzWzhJhfJvLXfgvvbbXPdSblBTooNW4yHqWvt8DI6BD9fXkmZ+ZptEzq9Qanz12lXK3zhRef4IGj+4mGI0SjYf7xv/wdXnr9PX7lZz7Jnp2DXJ2aZSgdImPaKpeRlIRChsfQvDhuqYDyRrmG40rKlRrhUAjXc/I2LZXQs9mymF1c5/SFG3zq2QrpVIyBgV4cx8GRFql0kmeefIBms8Xk9ALVehNda4cYG4ZBLptiqL+H/r4C/b154rEIe3YO88wT9zI9u8Lbx8/RbFk0Gk1sO0koFGKoP89Ab56IFwwxNNCLrmtMzy9QqpTpKaTZvWOIl9/8gIWlVX7pC8+DGFAHwjNxDw72c+/de7k2Ncc3vvcWVyZncVyYyGXR59bV+gf+fy4ty6FlWrRME8uyqVTq1JsmrhTUWiauENxzz15G1/uZnl2mUq2TSqri4Y7jYHiJYFPJGMMDvYwO93No7w5uTM+TycTJ5NKgC2rNJo1mQ6VgERp3HdrHXYf2Y9k27544R6PZIhoJI4Hx0SF2jQ8ipcvzTz3AQF8BgWRpZYO7D+/h088/wbWbM1y8OkXLsoKcSpZlMzrYy/Mfe5DD+3fRaLY4c+kGf/wXrzE5NU+z0fJqZqZpmRZPPnwPfT05JnaMKFcCx0HiYrlKENAtO/B11LNpeqVk78Qo731wgVq9ydLKOq7r8qlnHyH+lVcDOuVKydTMAgtLqzx03yGk7dCQFq1Wi2Klzo3peSrVBoVcBik0XMfF8ioVWLZDs9ki2kEYR4f7mRgb5MrkHKPD/Tx07yEKuTRvnzjH3OIqS6sbXJ9eZHG1yK4dQ/zcTz/DQF+eaCRCKhFnZXWDptkilYxSKGSwbJuN1RJCE6wXy9RqDTRNY3iwjwfuPcjC0iqxWIhsJkXTtNB1HduyKdeaNFqWSrVgWqytlyhXaqTiUc5fnuS1d05x7OQlqtU6zZaJ5Upapk2t3sCybRACTdcprxe5eOUmjWYL03JUtLZp0Wg20XRBoZAmnUkgPb/LWDSMRNUN9Zvj1aFMxMMUcilcmeCpXWMcObiX0eF+ypUalm0Ri4ZZXFlnaWVDuXe4Lo1WHcuyvL0ogsL0H4mtKgmFzcCkKxBIaN1+rsGNuqKIshNxbQI4m9u22pxNAPG25r0Of6mt5gN0OOoCbbDRdu5X2rAAWH7Y83xNWuAkup0j/GbvMG7TL93XbNJqBXfJjj88jZW6pR3kJDT1naAzJZPnH+7jjL+EdufRiEgv+s7TVrDZJnv7yIIgKkA43rW+ynKrjbnVgsuuK2LRCPt2jXLl+ixDAwUeOnqQbCbJ8VOXmZ5dpliq0DItXnnnJKcu3OCZx+7hMy88RiIe4wdvnOC1d09iWiY/95lnSERVbcGBvjwP3XuI3t6857SumutKqs0m68ImHA7R318gn8vSm8+wa3yQH568yPqG0jrt3jXGM0/cy0uvHef46SsMDigHaaTKXm6alnJAXVmnkMsSDRtEQzqappNJp9gxNsjJs9eoN5oYRrs+YSweIhwJEQoZWLZFsaTMKucu3+TdDy4S0gTLa2uMjfQRChlomq6IU72GECqkW0pJpdZAWg5CaNiupGU5FEsVllciLK8WiUTCZNIxNMckFgsxMtzPzbll1ssVwlGdo0d2M7e4wqvvfMCpc1f4qRceIxqNkHEdNMdBOhJ3ZUMJcf5GkeA6DrbjeGdRJSBstFq40kVoYNoOjUZLFcOu1XGlpGHatGyHcAiS8ThGyGCjVGZhdZVaowm0i2ErpWj7pFmWTbNp40pVYLrRbFGpNZnzpN5qTYEA37ysaYJMKsHyWpF3T5znSKOJ6Tk/79s9hmlaXLk+zdTsEtNzSyytFtk7ocwUtopcD9KYmJYqMWToOvVGk7nFFcrVCoauUWu2sGybWDRCNp2kWKq0neE9h2fbUftkYWmVdDLB4w/fjW4YfO/V93n5zRP09uTIKZ2+0lJpgkcfOsL7py7xzrFznDx3jU88/SB9a9XAWRYvKMSyHUzLpt60KNealMpVpuaXWVkrE4mEaDZbmLbJ1MwiP3j9GGcv3mRjo8rO8UHC4TASSTgS8sYskJqOpoeIxxXgrtSbVOt1ao0Gtu0Evp2O66r92DQ5f3mSm3PL9PfmcIFGy2ZgsJ99eyc4e+EK6+Uamq5TqdWxLJtSuUqlpph5yzS7NKS27eACrtAQuk4kFiWTSWEYBk3TpNlqEYnq7Bzv53uvHefbL7/NZz/5BNHoOM1Wg1bLwXZVuhLTthg1LTxajIxHidgO46OD9OQynDx3hYWlVXRD59CB3ej66wGNME2Ti9duUq7WEUCtUadULrO6XmR6dpEPzlxhZn6Z0eF+ALXXG00c12VlbYOllTUynpUHFCj2AxAMw8Avxi40ge1KiqUqaxslXFfSNC3qTQ8g2Q7rxTItyyERi7K2Vub4yYuUSlWikTCjw/1Ylk08Fgl8+8JhlQMsl00yMNiH4wgsS50d23EC7YzjujSaLVqmxfkrN/nil7/D+ctTSCkJhw2qDRPTcqhUqxRLZQW2cElnEwgdLl6bZnmtRNM0QUJ/fy8H909w9eYCjuti6Dqm935z2RT9jQahYovOpgIiJNlMnN6eHE8/fi+7d+6g2bRZXFlDIujrzbO0WqTlmdvDYQXewmHDSzchSafi5HIZtuBkW7c78FfyzYpbJfNUbStfqS2alN0/N41DdmosxCatzS333AF4EYB02AwH2iASVJSfG5znLkAZAGLPhcSP7m5H47D1fKVn6eqwat1p2ypIoAOISv+x0gO5XdP1olb9OpYdZkY/1dRfRvsImi01HAHBggMdG+s2Nuf2Fd51KlvurWvroeCOYpL+UztAamDqsGwL4YGIRrNFLBoB4UmKps3S8gaTM0tomsboUC/5XJJ8Ls3hA+McO32RmYVlKtUqhqGi4GzbwXXdIKWDz7p9vy/H1QnpGpZtYbkO6+UqtUbLswELFpZWuDE1x56JUVotk5deP8Hs/DKNpkkuobQfkXCYq5OzvPLmMeYXV0nGo4wN9bF/zw5CsTjJRAxXutRbLSzXxbRtqo0GF65M0bRc+vtyhAyD4x+c59s/+CGVWoNG0ySZiLKyVqRlWahkDRJHSkzXRTZMGk2LetOkWK1hW01s18KVLkZI0LQtVosKnLquShbouC7FakNphBwXXdewHEk4GuHee/bjSMlrb5/k2y+/SzKZZMi2MTyHzEqjSS0SIhIJk7YcdMD2CvpKqcZmu6oMiEQ5lzquS7XepNEyMXSDZstkeXWDerNFLBpncWUNx5UkU0lWNsrUmiam7dC0XRy8JKqoTNG2bVOttyiWKpimiRAC24XF5Q1On7/G6HAfC8vr1FsWl6/PMTI8SG8hz75do/zwg0t8+euvceLMFRoNk327Rxgd6efilZv88ddfo1pvoBsGrZbFRqnKykaFeDyG47o0TYtStUGp2qDesjBth/VSjY1SjXK1QblW9+pbKiCWTsao1BpcvHqT0eEBmqbF1ZvzaJpGLBbhwuVJVtZK9PbkGB7qJ5/PsFqssFaqBcTEcRxW14oc3L+Hn/7Ek/zhn32PWr1JIZ/BmF7BsmzC0sX1GIHjONi2Q6NlMr+0RqNlMbe4SrlaJ6cn1d6rN3jv+Hne/+AKzZYJQr2/pfUSaQ/I6IaOIyWW42LaDtWGSdO02ShVmZlfpliuY9kOLcvBciVzS+v8+TdfodZoEo1GPEYoWF4r4aJhWzYLS6usFytslCqEwyGano9PvWVRaZhYTtvc5riqgHQkEmZto8zk9AK9PXmKpQo3puYJhw3C4TCzCwusrG2QTCUo5DOcvzqNy5sMD/cxNjLIybNXaDQcBgd6ABixLECq2qZCYBg6Q4P97Bwf5r2TF1hcLfHC0/fR25cOqJNE0Ye5pRUarRaOtLFtBfTKlRqLy+usrZc9egmmF/G6tl6m1TJZXl1nbLi/iyWVa2ofSZTWzJYSW/q58pSmMpmKk8kkmJpb5ls/eJfR4V4KuQz7do/T25vn0ME9LK+Vef/UZc5fvsnYcB+fzKTo6c1z+NBu6i2L2YU16s0zhAyN3ROjmKaipWulGqblYpreP0sBWylB15SvSzIRoyefJhGPYtkOa+slarWGAvHVBpbtMD2/TK3eIJNNMTjQw5Xrs2i6zuBgDwP9PUgpOXHmCtduzvPDE+fp7ckAsGvHMPv27CA68z38hDearpPNprlwdZqrNxdA07k6OYfQI4yNDBONRVlaLVKq1unry1HIZkilkhiGTihkILxQf8t2yKSTZLLpbv51uxZoPT5cqXCbb2//jM3P264XTUO6Pmd024Ltln1so9XaamjbAI12+gQRmF07tXm3dORrv/wxbPV78HdnqaLbje9DwNim77bWLra70jTh+SpqHXoib706tWYf2Udt+3bn0YjegLavGXcHzbdf33YCt/bvCuEVAxAeYbNpNB3WShVcqSKsHCkxPandclwaLYtao0WtZSrwrIPQVFinyl2lfFSi0YgqlYKKhqrVm7e8KCEEkbBy7Gw0TSLRMMlklNn5Za5NzZFKxRkaLBAOGbz61knSqTjRaIRoRPkHaJpKApnLpNm/Z5zr0/O8fewcJ89fZ7i/wIvPPMLuXTvRpaTeUJqPar3BRqlKy7Qpluu89s4prt2cY8+uYfbu2sHyWom5pbUgXUKzZbFeqmI6Ko+RKz11ftNC02xKVWUqWt0oU2u0WC9VqNSaGCGdWr2Brmtks0mWV0ucvTRJJpvhxs05Tp29Qjgcpq+3h3qjyQ+Pn6dSadCTz5LLpbBdqDct2nloJKvS5W9lY6SScf7d7Bp9CM8viCCpa6tlUyzXqDZNpFA5fxwJRjjCnj07uHh1msvXZnj32BlymSyvvnmcZCLK4f0T9BVy5DIpADaKFWKGQTSdwDB0ryyHFZh8KtU6ISPMnh2jWJalTJiJODvHBry1U1La7olRnnz0HmYW15hbXMWVc6QTMXoKWYxQmPVyjUqjBajCt9lsikq1yez8CqMj/UQiYTRdZ22jxMLSKkvLnvlOSqKxKMVKjVqtydBAgXAkSt20KRRy7J0YZb1U4+T5aziOQyis8+Qjd9PfV2Blrcjk7BIzCyvksmmSiRi7d46Qz6WDtZaemTSdSPLko/fz2jsfsLS8ztBAD1r1LKZlobttFblpWRiGrqJSZ5V/IUB/bw7pSkzTJhwOc8+RvTRMm5vTCzSbFpl0gqXlNcaG+7Edl0w6gaHrVKs1SpUKU7PKDLy4WuT0+eusrJcwbYdytUaj2aRcq3P5+jSrGyV27xolHo+yUSrz/snz7Nm9g2bTZHp+mWK5xvzSGrlcBtt10QyNYqnCRrGMaVkqVaK3V9KZDPv27OD0uSv84I33uTY5S6lc4ebsEo88cIT+vh5uzCxw9oIaTz6bZr1YodmycKRgo1zjB28c5+b0Eg8ePcih/bu8nG1CFTv2yEAhn+Wug7u4MT2PpgnuuWsviXi8Axwp01chlyEUMqjUWxihEL2FHCBY2yizd9cIkUiYsZF+Wq0WlUqNnkKGfC5DIZchnUq2rToSpmYXWV4tBv2ZtkXTA/CNlomuG+zft5P55TVOn7uuBJNGk3sOR4knYvT15nnq8fvoyWdZXS8S0mDf7nEOH9hNy3Z46vH72L9nB1euT7OwtEYkHGJsdJBkMkG9oQJj9nu+Z6Zl4ji2CjLJZ9B1jcGBHl549mHmF1ZIxKNIKUl5OexsR9WiHRroQQiNYlnld4tGI+Rzafr78jz28FHS6TSapvHko0d574OLXJmcY3ltjXw2yd7dE0zsHCf0Jy8HdDhkGNx/9ABXJ+dYWtnAtGzK1TrhSISx0QFCIZ1ao8H03CI7Rgc4sG+cwaEe0HVatmR9o8LVm/OUqw3Gx4a8UkMfxrSCo8a29Qs/YpTe7Z9zewYfpKQIcjh6Pn++WuIWn69u5cU2nap/t0mzpbR2ft3B7cxsAq/ad9dHXc/5kZqvfbydqfRDehDe2kiBuCVdrgJZ3bazO+v3o7SPFI3Yzi3tf3iHUsEtPflqvg+fjOz4zd9otu3QlC7rxQqW47JeqrOwUqRcbbK6obRNG+Ua4ViU/t4CrnuV2cV1Flc3qLdMzl25SaNpcnDfTvI9OdbWSxhhg1rTpNFssdmXUQhIpxIcOjDGB6cvc+biJAiNhcVVKvUmTz9+L/0DfWhIdu0Y5tipS9RbFr35NPt37yAejSM0jUw6wcP33YXQQ7z3wQVM0+L+e/Zz96H9RCMxlpZXuHh1BolgYWmNtfUioyN9LK0UmV1YYaNcJRTW2Tk+wu6JUR65/yDNlgUSNkpVbMcNcnslEjEcKVkvlhWgRJBMxpAIak2LUqVBKGQQjYbYqNSJxmPs2zNOJBzh8rUZFpbWKVWqmLbDQw8eYGh4AOG6aJrOzPwyUsLO0UHuPrRPhfZ7ZiqBIJmI01vIcPz0VdZtyCcT3joKNN0glYxz6OAEsXgcKWF4qI97j+xjx9gg6XSSI4f20GqZnDh1kbfeP0ssEmajVOHeI/vZt3eCwf4eDi6u8vbx87Tm12lJSVgG5IZwSEVejg334zguiViSpx67j0MHdqBpgqGBXnZPjLK8ss7i0ho9hSzJZJyD+3by4D37WFrt4+5Duxkd6mPPxCiRSIS9E2N84cUnA62TYzvUGk0i0TCRcJjHH7qbiZ2jJBMxNoplegoZXnjmQXKZlKrNuF5i5/gAB3aPEY/HqNWbpFJJPvmc8hu0bEtFyu4cJJ5IYDkus/NLCux5hbL37hpjdKSfbDYdnAohVISbZgiWVtYxWyb3HJzg0N4dhCp1araD4+eSEYKxkQE+/vSDJOJRhgb6SKcSmJZFIZ/l3IXrtFoW8XiURx66m/7BPs6cu0a10mDPzhFGh/sxDINYNMSj9x+ir5CntyeD0CQjQ708fP8hNE0jZOjkc2nyuTR7J8aIxMKMZZN87PGjnL10g727xzm0f4LllXXSmSThsM56scE9d+3BOrCT0ZEhEskkiWRSaegMg4WlVVLJKGPDfSTiCQxDmYQeuPcQKa/25XqpQjIR5xPPPRY4vy+vrhIKGSwtryM0jVQywdEjexno76HZbJFOqdxrr7x1gpgmENIzQ3hrLoQqVp7NpEgl4+zfM8o9h/eih0Ltd4DKzzOxY4gzF65xY2qOhnmUeDSMn/ZmbHSQIwd3kc+m1bmLROjvK2CaNrFIjFSqHfQgAUPXuPeuPQwP9hAOGZiWTSGf4aF7DzA42KuiL1NJnn3yAe695zD1egtDE+SyKXryWSKhEOMjg/T3Fmg2WxhCkk6pdbNrTeUvOdTPwX0TTM8tUq3Wmdg5QiIeY6Cvh09/4gkcxyEWU7ULR4f7eeLhIxTyeTLpJIlEjHsO72XvrjFaLdMTYrx56CHuuWsf9UaDQ/smSMaTVCotdE3ngaMHGR0ZYGSwzyuwrbN37056egue87xOKhGlJ5cn7pUB8mmxpms8+uAR5T97+jIt0+LA3h0c2LeTSCxKTMKePeNcujbFyFAv+/eM09+XB6GxUa5w/vINTp27huNKdowNke5Y8w9nQL7ryxa8brO25k7aVgDtDu/tykXZ2Vcn6Aou9lI5iE0gblOaC6XuQZn1brFEbgZyGkE5H+Fl3A/6VtYpdWVHVPpmoCW2+Xz7SX8k7OOnfiDwQfOT/cq2pcr75/qRpLLb5ev/MrAFW+BZf6HEhyxaZ+gq0HY0vP1Cq4lrbZ8c7+W5rqu0Ww2TXCaNEQoxOTWvnG0lZFJJWpZDKBTi4fvvYm5phUvXZ2lZNrFImOm5ZfbtHuPpx+8jn83QaJjcc2gPsWiUgf4CQtOhI3QWBJFohGc/9iCO41Kr1bh6Y5Z4PMqD9x3k4QfuIh6Loes6P/2pj7Fn1zgz86sIIRgfGSYajSqtmqbR29vDxx59gAN7doGEgf4eUgklJTcaTcZHBigU0owO96EbBs8++SB7d40zPbeE0AW9+TSJeIzx0SFGhvoCADozt0QiESMajzE8PMhzTz+MpgkGhgYAwX33HuQe1+aeI3sJRcKMjA7yM597BsdxGR3px7Icevt6OLh/D8WNMsur69SbJr09BY7efZB4NIl0HB6+72568nnq9aZn3uojl0l7pldlf0/Eoxzav5OzFyfBsrpesyYgm03xqecfo5BXaRUO7Z9gZLCPvr480UiYUMjg3nsO0N+bp1SuIjQNx7bIphLksikikTDDwwMc2LuD/FKZNKhM70AopNObSfALn32e3kKWRCxOyNDJZdMYhkbLahEydOKxtFdvUQFpJMRiUUaH+8hmkzz24BH6enMB0BnoL5BOJwmHDMIhA9txqdXruK6S6PP5LAf27VE5tpZWGLy3V9Ve85zJa/UGzz55P4f2jtPXk2N+cYWWaZLPpdkzMYrjuIHE6qDRMps4rkM+l1Z73NBpNFtBf/6+FAJisQj1epPX3jqOY7s8/tBRevMF8EzAjiYwPEflI4f2cmDvBIahQuE1TWDbLv19BRqNJrFoBF0YJJJRxkYG2dio0qo3eeDeg8RjUWbmlojHY3z6+ccZ6O8hHg+paMDnHqVSb1Gu1lhbL1Kt1unvKzA+PkI+n0PTNB64/wi9fQV2T4yRyaSwTItQyGB1vYiUkqNH9pHPZUgm40gpeeKRo+wcGWR5ZQPLsshme3jxhceJhJXGsVprMDzYR39fAddxldN+KqGSempCaWwNRXjT6RRzS2sYhs6jD99LMp0mnnB45KG7uXxjlqmZJVprxfZGNXQcBAaSarXOtRszGIbG8GAPtaZJyrRol6BXQR9Dg33sGB3k6o0ZLl6ZZN/uHSyuFilXG0zsGKankCPlBZNEIxEymRSmadMyJVL4aX8V1Tt8aB8jo8NcvTbJ0vIa8ViUwYEehocGcIFKtUaj0aInn2dkRGmUdKmCBnQvpF3XdeKxGBEjjIYCcBIXTdOIRsJEImFCoRB9pkkiHiWdTBAKGYRDKVJezj7bMWk01DrnsmmikTDRSBShSaJRnXA4SbOp3A/SqSSpdJp4KsVzTz3E6lpRncF4BMuyGRsdIplKkE7EMHSV4FbTNDLpFD35HPF4FMNQCW4NtFusKAIYHOjl2SdVMFS1Vuf5px5ifHQQzTBIxGMcOrCbcrlKIZti98QomXSSWq3BpSs3eef9s9ycXmB8tJ+9u8eJxaNs15QFzgdXm8pzITycsMk89qHO4/5ENvk0/RhasbYzu+pPui5+oaB25nZdmes257HcimcLB+XMv41ZcivQc8uct1DIdGKEW/DCRwFcW69VOwm22EYJ5ptTNVypTOItSwUJOV4NUd81o/OWn2T7iNGI2y3KNp9vs6mkhzADLdcWZrtgph0ZxUGFaYZCIRKRMI89eIR4PEYukyKTjNFsmHzy2STpRNxLpGhw9+F9xBMxjp++wNLKOppu8LFH7uH+u/exY3wITRjEonGefORecpk0hXwGoevdMxKKmO7buxNN0zBbLaKREJGwclgfGxkgEomiCZ3BgSGymQKzC8tMTs0SiURURlohcIWGBqSTIVKJtrbHdzjsKeT4zCefQjc0NE0GvlJ7do1jOpJGq0G9WiURi5FNp+jN5xCacmbN5zIYoRCJeJwd4yPk81kEkMmksG2HVDKO0KC3J0M0GmFsdJB8Nk25UqdebzI5tUAoZDCxc4zEgRCWaVE3HXQ9TDqZVKDDMBgdHaJ/oBdc6dm+/XIr/jtWPilr6yXCYYO41JU/nASkiytVmaN8Nk0mrTKSF3IaqUSCmJfnSCBIxGPs3T2OrutIKVlYXKFWq6EbGo6jMr1LiWIa3j5RJW00opEIO0aHQSrCqGmap30I0WpZOI4gEjYwtDDxmDL5CqGYUShk4LoqqjEWjbTV9rFokExWSdsW8VjU0/RElMbAljTqBrlsklw2STaTxpWSaq1BOBRmbHiA0cFBdF2Zbl1HOZxqnqOmcvxWc6vWWjTqJtFYiGQyrkCWl+y1fT7UGXEcl3ffP8s7x84yMdrP+MgAuuWAaeI4LiHLJjS9iFgt0fNb/xFScWQygRuP0IqFqLoupWKZbLnCgG4QWdpAZFO06k3q1TqRUIh4LIrrSjY2SjQayiesJ5/BxVYh+NEIOVuytl5ko1jGdlx6ClkGenPo4TCOIynkMzRbLXRdaUFC2TTNpiqx09/by8jgMKlUXO0T22a0vx9pK2fwVCrB8EA/2WwCTYS9AIhWUHdU13VChk4sFiUWjShHailBKIDQ199LsVxF1zV2TYyiGwYSgWmpoI18LsWwl1wVZGBGbFgOl65NcebiDQb68zRMm7feO839Rw9yl0/CJAhN0FvI8cC9B5hfXOXbL71DvdHCcRx6ClmGh/oJRSI4aOhCIjRwbVS1CUtgGN679cheNpdCNwwuo/wNay2LsVyWvv4wjUaTuYVlSuUq1VqDSCxOPBImHFJVAhzXUeYS4dVx1RUo14SGg0sopGix4dVrzWXSZNMpksl4IIAgtWB/6oZOIhElHDbU+RRtTUdI19C1KAhBLBYlEtaJaDoTO0cYHupHSAfDcx1QySS9hNKuRNM0ImEP9Hl+gCoVhou+VVJNIBQyGOzv5f6jB7Fth5GhfsKe8KGFNHYM95F48n4lYOUyGLrO1PoCx05e4MyFa4RCBkeP7GPn+LCiyx2sp82HPIDl+lzAryDg8y+tTRc6QcOPArh+QubHzkjBdpeSwIFeah7gul0nbEIcHfy7c5xCaa+6Ep/613RqzMR22sBOi9hHtYp569uVgmIbE6PwgZfiVb7J1fVKtZmWSgjemefOq3bMbW2qP2L7CNGId9B8xOq/mG20XRK83EttwOV2qCUJXCLxIiC0wIQpPLDV47r816+cVErLwFmPLublH4QR1+UJ28KybIQmiJybQn/5A/UUCVm/HIdXVwwBolhRn3n/9GKFPb/xb5hwHC+XigjAhmHo6EIP9k3UdYk2Wgy3WiRiUS8XjNfTZs1x8IskhEta+usmvbBnGYzCryklhMoT4ke/OI5LQSoTn+ZFUfb7ql/vvgHfoVJTgFXilTlpmdRqDcabLeKxKLnsS0oClv7Ytsk90uFEKCVoxXL7q7Uiv/IXb/PrIYP+RAJNaG3HXumXYNIwtDCGHiES1jH0MLqhBT4GYUNghAx0TaNlWpQqNWbnl8gX8iRjFpeuTXH5+jSWZasSTh12dn9POB37UPfMQeFwGIEaj6ZrhI0QAh3pauh6iGQijmW7CD2MxHunnlbIEA7SlTiORAiXSDjkOWKrEiGOYxEKCQrZBJl0kpChU220ME2LaCRGNp0lk8lgmSYt0yKfy5D1fM/UksqACDSbLQwjTDKeImSE1R7U22Hl0n9BUnLq3GX++Ksvs7pe4q59OzAtG0rVtr+rECqqpNlCuzLVXidXErJtEs0me0yb/bpG4ocXCIW+jZSS4WaTlGUTyqXJfO0N7GiY8XodNko0zkziHNiFkUuiJWI48RgtCXNLK0xNzZMZ6CGTTmPohpKTBSTiMfLZDE3TpFxR5XxqtToCjZ4eT7MBNJotNoplVtY2mJyaRdc1do4NU8jlCYV1QMN1ZaDBCRlG4LuomLki+KFQCNeNInQDI+qQSCjQGgmHPY2eTbFcZaC/wJHePexPpQMtOoZKL3P95iw/ePM4a6UK9x3dTzgcplSu0mi0Ok+vAi3ZNIf276bRNPnm997iuy+/y0P3HeLoXfvo780rgCcEtneuXLxoTsNVgMgjOMJySPyN3yJkWjy2oYBr8huvk0kl0HSduJSkLRvTthEoAOIXug8gQRedkV2nI9rFTCVJ2a1skK6LNG1VW9MySQiBm4pv6XSsaIPKV+T7YwogdsuVm4e0OQUobWBAewLi7FWE46jULgg0oROJRNi9c9Q7v0ZAmzSJyls4olIsCyFomSYrK+tMzy6STiZ48L6DPP7wUXK5TAdN80xwAdETbM5R5Ncbkd6bk65X3BuCPIKdc7ttuxP3mTYB7tai3aZ1abkkHSNWnSiM7HZlSr+lV6GuUVF8XZ1vGrvo+OrWpKGqKU/rrbVaHwVkbTnSbcGt7ABZ4FuovPkI5TvseoFUllcb8dbYAMktH/2Y7Y7BluuCFG1pervmqzO7P/N9RggWQDmlq0iEzkPs5ypRiygRUpXRCSgRirCFdZ1Qrdl+ZsBXb10eHQghiXr+GDSt4F2oe+UWuLB7gwgpMTYq+GzdfxXCe2bn/Zoridg2hutiWA5Cb7Bd3fj2OGTgL9ImHl45lY63HmxT7xohZXDYRQfAlP6ieGrfti2/PVZNgnAcaJlEHYeIaWIUK8H2DMa4LWj2+txENcOGwWg4QiwWIRpWEruuqwgaIQSRSoO9v/nvMXRDOXrK7o0d/O4RX8d1OVqusLfWIJX6PuGQwfPrJR4sVxmoNTGEQMwugRetFZpbYeTv/DNwXEJzy4pYK67U/ZyOQ+YD9p2uiu7RtT/cRPw7yFatgVAqOmQi1vV9QUq1dbw7Cx1MxWdEEujzAP7mEiEJIAsMdxLJTc8H0Cp1kBLNdTn0T3+X/3a9RCIeI/3GKbKnrpEIGchqnaxlE25Z3nkC4WXmJhpRIEhKQh0+GaJYDcYYc12V/2u1hHZ9DgPY4bqMOg7i1DUM/bWuvRFzXfpdlyekAoZaOKRKahnqX1LT6dM1XA1cTQPDQGoaImSghcNoIQOha0SkJGaaZBtNdtoO67tr1wABAABJREFUyUSMRPxVzy9QPS/uugw5jlcGLFjggOkjIeZJsdKVQbF4IbSg+HhCSj7bMvm046BrGtFqHdFoqmMzv0zkV/8R2aUVPrtW5LO5NJVf30E6HUPzau1t1mpEwiHy2TQPHFWJhK/fnGXn2BBDg73EY1H80kG+4GR4pi9F//RAqwugrZcIOS45W43baJholhM8MyTB8ARWXzvuj6YNCjZtXqXuC+o/CsdRAQG2g/B+4n/WcZvuuki/BummngOA+BHZkjR05Gj/lt8FPUlUoXmPTmlnrxD5a7/JpmUPRqV+6eZPhpQ8UKmyr1wjGg2TXqkSfe10u/j8eltIFG7nHDanC9g0NiS4DnguJx9ZQ3UngEsR7jsCWsE4N/Ne6b8x7715qRBUBaEfR3PTfe/W5fs6AFbQfozi0/5m8wEWECDHAFeK4A9fcGqfjw6cgUp07QZFq9U4DcPACOl8xO18R+0jOMirLMtqX3UOul1hXgb4v/NG4WlXXAUMfIYjBVK4qoJAABDauby6EHnni7w2g6g1vG/uvG21rW6t1bjFNY0m2vzK9t9v029IQsj//sMOi/TJ7639bT3u2w24faP0H+1PM2TgHNgZgF9fKos4ar01Tz0rb+lv+yfKzjVcWUcCkaE+wj7A8tTbejqJ2GjnlBLrJQVYABwX4Rfm9hmCq/YLrovuuhRsh7zjIlY20CQkXRfpOCrjd8eA/eGGr6vs2cK02pjT6Y5C2QwSgY46bLeyj+Bv2/HGKRCNVsc6eInzFHrD3/lyU18ftm+3+74LdHlHRLguOxbXGJdSMeNyDTG/GlyrQ5fQgAeA5Ei/yp/gOIrpOlK9A9dV5WUcxXh1x1VFxB31PnTawFQ4mxmSxPDX1JWIphl8J+kuDrLF0gdN1wQhTSMhBOgqf5YwdGU60DUF4LxrpP+ZpnWYMLyacFKiuTIQZEAiOrTeuoB4596p1NqgxLKJnL7EqOMw7LoIx8X97S8rnyJvLcVGB6NeLxH6lX+MAUSlpN9RecX0l0+i6Z75QraZ55a5mCQKmIYNKOTQJIQDQVAxDina13aDKRdpOZ42ylL73rIRlqWKZXs/xZ1k/zb0LraE6Qb0wn+q/7crZWAS1Laic1tJK6BqcBayWz6+DUcV6A1ucyRiregvBZ0nYrszoyFJOS4Jw0CXAr3WQHhCetcgpeigBx3MfJsWrIfrKoHhQ67v5JkfRUv1ozD9rryDshNU+czAK6EWaChuO/Atfu/kz9sNgluv8csX/WUgGaEhhNveowGD8/GDRPrCmBCBm0GXEU4TxBMRIrEwKjnVT7Z9RDOi938XpewUM9R/vmTZyR06UaXAV/UJkDZCKH2R8gWwu99Hh8+WlBJ2jyPXNm43yC2/kB96TeeX4pZPbsGQ3v8unrlPovwdNkk5LmLTZt96dH7yyWDunXt8i/F19bT9H13TdfMZqr/7z1BYQPWheZzb3USEfVDcyRiCcGPaB7ltFlS/62+eQH//DFq9Ac0WsmEimi2MYhXj0g1F+H+EtnnjCyGU+dBx2SwlSgSu56cV+OAIgYxFu66SrheVYivfuFDY6FrZ7baKsFSOKATIWAQpVcJalQhSMfeIppLQBkWKg3F3DcH/tONPect1vvk1mDd4gE9lw9Y90/FW20B0/6eYeSgEg71t7af3w9+HzZYqc+K4LumkCkqQPgju0H5I2wkAG7YNttvWljgO0lEaE2k7wR7x/eduGe/mNUaBJV/zQqv7pq4+OhdX15C6jtQ1penVdaShQJmrCQgpsCbDBug6wjCQundum2Z7rQylaTJQAggSZLm6/ZhdCavFAExqKOLqg3DXlbiOg+MqB3VDN9r+UX6noyrXlixkMb/4z9u8sNlCrJXQ14qwXoTVDVgrKeCxVlQ/i5VuIOVpE4lFArrSft9i68UXAplL4xYyuLkU+qlL0DQR86u4gz24AhyvVJS/DUvlKksrGySTqjRZuCNKM6CSwneBALFRAld6c/yn3mW+k0gHaJIAOvx3/xtyvaTAum9+6mQvHeatbgDfJqK6IDBli+12Xy69tbZpK2DUcRYDIavr+y1u8a4Tm6MAb9M+ikZru/tviU70V8BfX+HRz60e5Qu/W7U71eR1zUGw9YPupMlbxxNgCO/7zucEpkTPiiZcFZ3VMR4/UbC/RpomiMejhCPKB9GVP1nA9ZHAlovY4uFqApuX3mfM0vWl/S1agMDbAKsTMwcaAZ/Z+2pBUBqYfKZ7gFvscrn5c9nZ/63Xd0tM2wk4ahO7UlIqVykVayAEPfksqUSCzce+S2fViU1l58i2OLQd3225t/1F+5D9KzbKgUO7jkTrJDkeNvRBSadeS/N9EvDe5yZu7juIahAwU31uidBbJ2+dgXRxBjaZXrp1NduNvvtPTSAjYWQsAtEwMhrx/oVxImHKlsXJq1PMrBcZ2jXCp946g9YyIRxi/bXfV/1JC9d1KJVrvPT6e/zeH32LA3t38Ft//9fIZzOKqHvmKF8S8rUhLhB/6BcR9RYyHqH01v/J+x+c4633zpCIRRGa4PT56zxy3yF+9jPPk4jH22B1s0ZDwq36nnbUkOu63Jye570TZ5meXQQhOHJwNw/ed5jBf/67CM/kG2yBLaVhAacvKZDbMmHnCLKQw/79/2dAbJAgpNLW2bbDtckZ/s3v/jHnr9zkr37+CX7tr35G+flJz7ey0+nDi9jaDAh9jOu4LsvLq7z/5nH0WoOHD+6mYITRag1EtYmo1aBaR1TraNU6VBuIWg1RrqvPbbt7Ltvs9c5z7rrKj9HXKfp012q1kKZJTAg01w3otIbaU04qDomY0tgd3IVIJhBnLiltUSgEPXmPObWfxfI6IBE9GbhwAyzfI8sbl+viuiqnn4ZAD0BnOyQd120LDa6LmJon/I9+OwBTeNrTH5VNCVBFtgsZKGShkEX2ZCGfgUIG8hncfBo3k8ZG5cRzpEXs//7/RptaVO9ASshnKP+bf6DohWf+n19c5ff+8C8IhQz+61/9POOjgwih4XqURg+0GWrJjAd/Xpnh55YJ/7Xf9Ma3rVjj/dgkAgX0ygPkneCr8zoUf5WBpmobZp/LwL/9R92fdZqqOj/r/On1JvHORCBob8dXtun3L6nJbcbSNSLpW5I2aTwl2wCq2wCwzms6l/rHnq8PtDaPcYt1DrCIRrtajbfmQvlAu65UCZdNC9s3mQvlg6cbRuD3/JNudwy2nC4A4skNokN9KkTwd7cvg4elu/CHL3doSE0gZDvPhW+OBILQ3y1Bdz6D+5//ZccHSvKWUnbxAjyBvNOxWHq+PYG07RFjX/JRvNYfgxZ83ilAIyWNRpNvfutl/vBPv4/jSP6bX/9ZPvns48GBE34mernJfkx7U/g1m9ReUiU6XPwxeg7ywgOt3hppoj1GITRP+ddFjgCB4w049uv/GO3UZZhfIvHrv9m1jp13BQLOpgW/xQ8gl8b+P/4HFZDiaXIEKqsKXmRfsFi6hh02WK03cSNh8kO9GKkERKPIaFRpm6IhZDyspPBoFKIRZDyCjEQgFsGNRSASUQArHFJ7RLrBMwIVvWPTKJW5+r03+fI3XuVIPMwLho7WQjEx1wFNOdTqmiCViDExPkwiFqVeq+PaNrqQ7XItPtDqIPabq5mtlapcvTFDbz7DU4/fRyGf4anHlsmls8Rjvj+Xx/RpM5bN8mbH6uKzjhtTc3z9269y+vxVErEojZbJ+Us3KJWrvPhbf4dsJtU23wQAsUNb4r+3X/qfYLUIc4vBd7oApOvl728/VTd0egs5JsaHuHR9mnDI6BYIwAODXiACEqSOROB6elzNvwQF8MORMFomyTo2zbE+GBj0Dp0W1M+U+PyqQ5OKRDNtqCpApn7WoNaAcg2qTag0EJUaolxFVuvg/a5V616wkhqz7TgUy1Vs26G3kAnejfDfT6uF7krsoV6EEDj/5h8SGR1E/PL/CKsbyJ6cR29cNNcK+lXTVLtC/Mo/VOvs0QfLdihXqliWQzaTJBwKKdFLqr0VaPeLFcTqRnsLuBJx+vIWe2OLJgTk0sokl89AT1aBqUIWejKQzyL6chCLBPtKEUVlLu4ELZqUaI6LlA64Lm7aS7TaoVFQgkf7HfX35nnswXtIxKLkc9mAN/g1OPTOCqkChGUrTaiJApObp9Omsh2fdeJbbw4+VmDrU9TZQ5cg8qO0LUBW599qfDIQQrYDj9Ib9y1RjB9pKB1A707MkWxvYm3PR9DtEL8NoPLB2zaRov690nvnH9WPb7v+tgSw/p78EEAZvAuhBWDLL9dWqtRpNC3F472rgzsDjdlPrt0x2NJRWa7UQFBEvUvi2DpqzVdlStl2zBYdB1cRWf9Fdx+d270qsUkLE0xHSNCcoDspVeSexEVKCyk8s5fHcAKQJTYtNL7a2j/ct658LBLiiYeO8v4HF7h8bSbQ4GnCxZXtTL+bNRtd2zCQvBT6drw6ba7UvDy3XmV2n7iIdu45BQQUqBPeEKV3aKRw2yWqwPPLAW0LAnfrenfMdQvAKwC9Q0Xtonin5oIWjwQXmX/rZ7Gff4RypcZLr/8Q6Tg898T95LMZkEYA4DXhKDUvai8I4QcHEADGzjG0I3XaxE5DgpdSYGx0kGQihuPKrlBk0WwhEsq0p+sGkXic8fFhCrkkjusgcNGFklQFGoYATRdoQo1DbtonAIsr69ycXWFibIB0KkE6mWD/7h1oIhSYzIR0Ag22D9xdKXEDZOs5Tov2nrZch5vT81RrdV587lHuu2sfaxslvvLNVzl26iL33n2ATDp5yxmR3ju7lQ53SuOKQLVZb/s8ClTUYCadIhIOEY1G0EOG8rf0TGGda+4LHqKjb0EnCFd1yJotlTBYRQx2pgrpfrbmHUihaQhciIUgmlUgQnNA69DLSoGfE0h6WiFVM9HBtW0aqxvUVlZxSxWunL7MB++cYiAW4WNH9jOcSqLXGoFWjaoCbaFKXTmPF7JtBj29gJhbQvvl/8mTfTqAfufePHPF04LpyLv30qjWWSyW0UM6yWwSPRz2hCMfEHv9axpslBUI6XhdMmwgC1m0nhz0ZJCFDPTmIJ+FfF6tST6J1HRVdaLWwLJUSpJYLIKmQ6lUY3l2ESldQoaOpmuYpoVlWkRDIYYH+4hGwiBUJYLV9SKO4xKPhwmn4+3xOO1ocSHVPpVCw7ZdHMcln8sQj8aDHSAQqnSW6xKKhJXJtNPMKYRaY38dgg3byUTpxoeeUKr2CArWezxReqZaKV00TxMtu85BG6AFbb2kiO2dtNtpc4L9/yF9eQKOR61+dMDlymAud9LH7QDph83Lv98XrrYHWn4/Ln45vq5+fF7qKVQ+ctvsO9YFtG4DuHwG73hjEKrEWKVWZ6NUoeVpov0IRv9Ntv//ySGuj6jZEkGINaIDMghx+xfa2TYx78AXq+vTrZu7aWMIn1kFmMgfhVId+ofehywSvKRv7Wf7P7vAFnjEvnvMm383DI2B0UFGBnu5Ob2gniBUrhxNSNAM5Tfhmwd85uRvOtQGULllJNJ1MXRlknGEKjirQKoMNAW+tgDNj15qM2h/OVSCRA1HWrcuoqb8MvyZtplkN+OQot1f8Ol6SRFM4c3vv/vfEMVyt9S5XkLcnAcg8u++TPgHP0T/3/8+n37+CYSrCjD7cq8RMHzvHUlViikaNtAMHddxEFKAbiA9RqobujJTODaWqaLsbFeCY3uJOsNEolE0XTn5yi6wZUJSBQLUGzXWFha5ObfoEWoXcBAeUNd1Q6WcWC9iWzbpVJJYItGtYpawuLTK3OIKQwM9OGiepklDCN0LO18jFjIo5LOEQiG0sIHwwIsuBNLWwHGV74B0EVKBO9OW1OqqLNJD9x5iz74JKqUK75+8wDsnLgRMUQjls+Wv38bqOtV6g3whRzKVCDQM3U2AYeA6NmajiaBbW6F5easMXaVJWJhbolwqk80kyeVy6h3INmCTrotlKX+kcFjHECEP8LkIoWNbJjen53GlEyRk1QyDVrPFRrFEKplQ+bI0QEpW10sUy1UyyRiFQlYVD3ZdHNsFXSJCChy4pg2OyheF8IqA2zYrqxtUqnWl/UxGMbIJtGiI/Yd3s2fXKH1jQyq1g7cumqajhQwl/drK3GpEO/z7HM83bXXDi250A6HJ9TTTmibQTUtdGzYw/9M/4d13TvEff//r7Bzr56984ePoQkNKjXw2SzwexbaVj19mYZXEb/w2TM5BJIzcPUrl3/0G1xZXmJlfVgBVKCf0ZDLOUH8Pg719RCIRhObSbDQ4ffE6b7x7kmqtwf337OfBe/eTzaQ4d+kqf/qNV5lfWPWKMYcoV+pEIyGefPhuPv+pZ4hEYtRqVV5/5wO+//r7uFLy+INH+GQsSpBjvcOcW6vXMVstpBScvXiT773yQ5567F4O7d9FwojiOi7Tswucu3SdZsskHA6xd9c4e3aOtlNChAzc3//nmJbN9Nwi0/NLhDTByGAvI0N9hIxQsG+WVzeIxaIUcmluTi+wvLbBwd272LNrHD0ksGyHyRmVS6u4UWR4sJfHH7ybnr5eladPCGzHwTItRVOEULm9fvUfKzMtP6K2qUur0q3P6vxd8fuOqOIfA3AFpsE7BC2yU7vz0R5EW4slaZsZt/M5CzhsNxfvNJtKF3zFipIKg1F+ZCVYF9Dy/+bW3/0mfH2GonOO49A0Lc/dYGtF0U+6fYSkpm22HEQHeqjxTl7l7a6600mKTX9Zjku1VsdxHCJhI0CjkVhU5bRxpRJcHIl0XFVIFXXYDD+6yXfA95Cy0DRVW7BrXIoxdnkOCAVAW3YL13ZwbBvHNDskMslGscjC8jq5fJL+3iyGF5VkmzaVSoN6vanGEo2QSiWIxmJYlkW1VkdDEI9EkJoMzKx+keiVYpVavUE6laCvJ08s7DNbl2q1ysp6EdO0yaQSZLIJOl1WZS6N9Z/+V2q1Bssra1hNm55chp58Fk3XqNYazMwtslEpYZomzZZJJBxm964xxn7j36KtlwCJI21YXYe1UrtvKXErNUKWpdaqXMVdK3Lsg3NMzS7Ql0tz75H9JBOqRIxl2QihETI0Gq06x89cYnJmieH+PBPjQ4wPD1Cs1JheWKG/kCWdSrK6XqTVMhFAIZfFcWxaloUmoFiqcv7qFJeuT1Gp1hWQD2qIgVkqYSejzM7M8dIbP+T61ByLy0Wm5lY4uGdUkRQpMS2bczcmefmNk0zOLICU3Hf3fp576hF6+nqI0ganG8UK5UrNy9eiNIu27bC8usr5Szf4/ivvMjE2zC/89McZGOhlaXmdyalZarUGrisplWtEI2HuP3qAQiGHDtTrTU5euM4PT5xjamaRRqPJx8tVGs0WF65OcXN2ia99+3UWl9Z4/OF7GOrvwQHOXLjCn33zFRaXNzi4dwef//Qz7Nwx2kUepQTbsjh5/AyXrkxSqzc4etc+Du7bSSQcRp1vFezhuC4fnLrElSs3uXpjhkI+zYsvPMZ99xwkFjZoNlvcmJpncmqRUqVOPpvk7sO7iEfjbBSrlKsVGg2L61PzXL85x66dQ+i6IjnrGyW+84O3WVxa5XOfekoVYpZw4coN/vw7b9JomvQX0kyMD/PwfYdxXMnxU+dZWl2l0JNH0zTK5Sq7xoc5cvgAyXgc23aYmlngz7/5KpeuTYGQPP+x+/n40w+R6ylw5foUF6/cZH2jzF0Hd5OMRZCuy0axxNXrMziuZGJ8mL7+HkI4HdF1baGyEgmztl5EE6quajhsUKmovTbiuuje8bdslzWvDqnjSlotk2bTZG29im1Psbi8xsqaAswHevP8gmkR8d9RNEJT17k+NceXv/Yyi8trCJQpNBIOcXDPOJ994UkeuvcuYjGDcxeu8Mdfe5n3T12mUmtwc3aedDrKfUf2Yzs20/PLLC9vcOTABL2FLOXKNKcu3GDn2BCa0HEdl3MXr/PGe6dYWi0yObNIuVLnkV078LPACVcx+HKlxqtvHadcqrJ75zjHTl/m6s05jhzaQ63eIBIOMzUzz+/8l29QLFXI51J8cPYqu3cM89//nV9iX8deNC2Li1cm+eq3XmN+eRVdaPQUsnzh00+zf+9OLNPh0rVp/vjrLxOPRfmVn32BqdlFvvWDH/L3frUXISTSdrh2fYr//Gff5/L1GVrNFslkjFg0wtNPZqnUSly+dpMbN+dYXS/SbFnEohGO3rWPJ2ynTRuFDyh8Yb3jwGzRlHa9y7nGJzOBdtf1xcjN1h7//h+BuQd+fh9yq++eclsz4oc1qawqgg6N67Z1EekeUyew6u4UX5vfvc7b1W/sUKZsP9BN6FZ4IM/p/s7TdOq6Kkxu+AFtHe+h/bqV4iSILv8JtI+UQb4zQs0fT+c4pCuD9etM5yDdO3zhm0wL/iP83/xXBGCZFq+//h7vf3CeRqNBIh7FNG0Mw2DfrlFGhvtZXiuxtFIiFo1weP9uBgcK6DpEwiHqdYvjpy6wsLzGyFAfRw7tIZdLs7FR4tTZqyyvrgemkEhY5/CBCXbtHMMwQjiOy+TULO+fuMCVa9O8c+wstXoDy7aUCUqTbGwU+crXX+GVd06yd2KYz3/qY9x9ZD+NRpMPTl3g5NlrLK0UaZkmuiHYu3OUIwf2cvzMJa7cmCYRj3HPoT088dARctkkrutSqtR4/+R5XnvnFEurGwz0FfjEMw/zyNGDxONRlpfXePXtD/jBWycpVWo8ct9hfvqFx9i9iWBcuHSdV988xrlLN2g1Le47vJfPfOJjjI0McvzUBb70te8zv7yCLgT1pommCZ546Aj/faNJwnsjjWaL2noRZ2UdR4KZjKkCx/UGI7ZDWBNojkOz2eKLX/4m75+6zBMPHmLXzhGqtTpvvneGm9OLSOC+u/aQSET402++xnunLhMNh3jg7r38P/7WL3Dx6k2++CffZdf4IJ//1Mf45ktvc+LsFcKGwcc/9jD7d49Sq9eZXVjm5bdPcmNmiWq9obQdCBVp5rX12UVO3pzh9XdO0Gg0ePSBQ1y6Mcf16UV0Q8MVGq6UTE3P8++/+FVe++F5bFv59529PEU4EuHTn3iyYyUlpicduZ7Z03Xh+uQM33/tPTLpJLbj8vaxMzx6/2GEcPmDr3ybb73yHi3TIhGL4LiSZCLKrzQ+ySc//gTCdfjh8dPBNaMjvVy+MU0iHkXXdRaW17Edl7eOnWN5rciu8UGGerKcvzLJb//unzA7v0yt3uTU+WtkUnH+Sl+ezgpwtmWxsLjC/+c//BGZdJyJHUPMLy2STkcYGRxQSV8FCE0QMjR0Q2fXziFsx+LVd05TrNTIp5PcdXAXM3PLfOUvXuX85Sls2+Hhew8Qj4e5dHWaMxcn6S2kmRgbYnJ2hbmlVUaGe5U/EHDt2hRf+rPvk0rG+cTTD6MhWVxe40tffYnvv/EBD96zn+mZeb772jEiYQMtZPA7X/omM/PLDA/2EAmHKVeqPPnI3fT1F9gxOsTC0gp/9NXv8sGZKxw9vBvD0CmVK1TrDerNEr/3X77OleuzPPbgXQwP9ZFO9LG4vM43v/82PzxxnpZp8cwT9/HpTz5Fb08OuZlR5TNM/o+/yr/811/k3OWbPH7XPn7+p5/n1LkrfP17b/GdWUnMu8d2XBrNFqZl4UhomDb1RpOFpRVOnr2GRHLX/l2UKlW+e+IcH6/UGJDSCwiQmFaL1bUilWqdaq1BPpPkgbv3srpe4viZK8SiYfp6MqRSCV564zjnLt0gGlE57Wq1BuVyDYDeQo4do/0M9eb5m3/1M+zcMcqrbx7jt3/vz2g0WliWg2UprfBTjxwll0nxlW++xuzCMrWQ0VYgOA6udDl/6QYvv/EB46NDPPPkEJFolMWVdWLRMKZps7y6xle/8yrvnTzP3Qd2KWd7x2Zyep65lRX2dqzp4vIaL73+HsdOXWRifAgpJWcvXmfXjmH6evPEohHGRocYGujl2OlLvHPiPOlknIfvO8j+fWPohmBtrcJXv/06N6bm+dSzj5CMRTh57gqO7bAwv8grbx3nG99/m41yjXw25QU11Th57iqHiiV6XN/g5TH7zck8N/GjzrO/+e9O/gSe7krcJi2E9OwvHwF0+b6/d9Q+TAuyBb8NbhR4YOtDiljf7tG+ttDvX/jZ7dvP60hCdAcD9jvvuG7b2yS+b2lb+6Vu0HWNkBHCCBkdrkIqcbrnvOIBNVWJ4UdagC3aRwNb0BX+D7QXNDApqGsC06BUrrPdHW16yT4w85zSOrFcO2eZi/DsxVJKKrUaf/TV73P24g0E0FvIEDJ0VtbK/ODNE/TkU0gJjabFRrnGA3cf4K984eMMDhQoliq88uYxvvvqezSbLQr5DD/7U8/wxKNHOHfxCn/059/l4tVpQiG/PAU8+/hRfu2Xf5psPsMbbx7ntTdPEI/F0IQklYxRbzQ9RmIDgkq5jGWbDA8UuHZzjmOnLrB31xhLK+t87btvcuzMFXaNDbFzdICL16Z55/h5Rgc/IJNWjrTHT13k3RPniUYMnn3ifiq1Ol/77ht8++Uf0pvPMD7cx6nz11haXsO1bR685wDnLl7lpTeOgRSEDZ1zl67zyH0HusBWvd7gX//HP+bN98/i2A5SwvkrN0km4/z8Z1/g+tQsJ89fZcdIH888di/Laxu89u4pzl26gWmaxKXKWWQYqkxPrWWyYDv88Sfv5/CBCd768kv8w0aL3VJJw4ah05NPB35xhq6zXizzje+/xdnLU+weH2R4IEc/eWzbYXSwwN6JUY4e3k0ukySTSlCtNzhzcZJf/NzzPP34fZy7NMmxs9fZNzHGgT0jvPX+Gd547yz7do3yyz/zHNem5vngzFVlSuwwI4Ysm4vXbnLlxgy/9ouf4LOffY7LVye5dPWmirITLqbV4oNzlzhx9hqpRJSMlzm7rydLOhlF1zokJY9YKiuxMgNKYYNwObhvJ/fec4hkPMYPXn8P27GpNxtMzS2xtFrkoXv28tgDd7GwvMYHZ69w8dpNHn7oMJomOHd1kr6eLB9/5iF68lnmF1eIx+KYpsPVm/NEImFeePphDh+YYHS4D9O0WN/YYO/ECPffvZe33z/HjekF6s0mLbNB0ic4UtKyTIqVGuGwzi9+/jnuO3qAmZlF3vvgPNVagwN7d3p+hi7JRIznnnqARx+7l/XVNWzb4ZV3TvPym8cZGujhnWNnOXtxknQqwfhIH4f27yBkGEzPLTG7sMLD9x7gxRee4OrkLNNzi7heBKAegsHBHOOjfWwUK6q8jHC4cOU6p85fZe/EMPt3j3H52qSqk2cIhC4Ih3UG+/O8+PyjDA308IPXj1Gu1KlW6zSaTV5+8xg/PH6eJx+5m1//r34aXRdMzcyC5pJKRenryXLq3HUazQautHGEw/zSMg3T5MVPPMmVq5Ncn5xlfmGZfC6DZrjtF+1pKPZMjPLUo0e5fnOeWDTKnolRLNvm8vVp9Muz7YSgXqkSgQqw8EtYXbkxzdpGkU88/TA/9/kXqNZqfPFLf8HS6ev0ui6G5ZXB0iGTTbB31wiVWo2H7z/I3/2bP8ulSzf47f/4pyytbDA9v0RvPsvc4gr5XJodo0Pkckn2Toxw5NAeIl4pI8dxVcmdeIxkOkm/B2SqjSZNs04qFebgvp3Y7jjnLt2gUq+j6RpWqsNny3awLZszF25QrjbYMaJ8FJstk2w6yUapwuVrkyyvrfH6u6cY6s/z9MfuRUpBNKbKJ/kVEJRvqeTq9WlOnL6MaTtEoxHAZWSoB02XlCpl6vUQlmPzyIOHuDE7zzdeepdnHj/Kz33mOcKRCKYjmVta4+T5a+wcH+bh+w8TCylhO5NOMj27wLsnzrG6UeboXXv52CNHqdabvPzWCW7OLrC8skEiEScuXUQXw9+kRegEDD7f6vqzm9vfGXhS/mW+39n//zRv/sJRPpI/ggWyq58OrZJqndHMygkmcK2509b5PrZ6F9LTUnraPZWz0Y+69J+LB65ufV/Sxys/YbPiR6yN2LZ5bj8M2V0yKagr1XmJ5BY0LQJU1XG1JKjnJAnstFIqM8dAb4Grk3NoAp5/8n4evu8w33v1Pb72vbcJGTpf+NQTjI0M8pW/eI33T13g4L5xegsPMju/xKlzV9i9Y5iJHUO8e+wcF67c4NCBcSzLwrIs0skYzzx5H4Vchh+8cZxrN2bY2ChRbbT40p+9BNLlN/7+rzE8OsJL336N3/3SN2k0zcCvrLeQ5Rc+93FW1op86c++i9lsYTWbhHRNlcVJJ3nxuYf5+DOP8Na7J/lX//FPSCZj/L2/+fP0FXL8wZe+wR997WVm5paQhk6xWuOVt04Qi4b5O3/tcwwNFvjqt1/jq995m9ffPcXOkX7KlRrrxTJ7doyyf9cY8Xic/t4CnQklSpUax0+vEDJ0+vLKdysR8wrACsnwYA9D/Xnu2j/BL/7sCywsLrGwvMLaRgU/AlAChiHIZpO4KzH0Wp1EIkpfb4bekR6ySyX0agOkSyhkcGDPTt744dmg2HImnaSvkGXXWItf/sLzPHz/YWbnlwmFdI7sn+Cv/fyn2LljgJBu0N+bJ52IUW8o5+p77z7A3Qd38sH5G+iaZG5+kauTM0TCBr/8s89z372HOH1xknKl5mmq22Ar4tiUqzVCIYNCIYWQDolYmHwurUrcCCjXm5y+MIntSP6rn3mGe4/sAyT9vXnGxoZVXTd/n6OyEPs1DR3HxWzZGEaIfDbDyTOX+N5r7+PYNlK6JBNx9k6M8u4HF9g9McQnn3+MKzemmVlYZmFljY1iSRXFBvbuHmXnzlF0TSOfS6NrIZZXisRiEXoLOR57+AgTO4awLRPTMTmwfyeH79rLH3z5W6xtlAkZqmC2oetdZ82xHWzHZWggTzaTVPXvdJ0rN2ap1hr09uRIp5LYjkMiHiWZiIFl05PL8dB9B3nr2HmuTy0wOT3Hm++fob83xy994XmGB3qJxyIsr27gonxienrypNJJ8rk0yWQ7BYYmIJdNMTRQ4ObsIhvlEo6ASr2OZdu88PT9fPy5x1iYnceVkrGhPmYW17x6mpKH7z/E2MgA12/OMDO3jOM6lEoVzl+exHEd9kyMEAqHiEU09u4aw3QkjpQMDfYQDns+YxpI12WgP8fP/PSzxBJJlpdXWVxcpVqpegWdNfxCYT7disei3Hf3fr79g3eZX1zlnWNneP/kBXaODWLoOsL2S3kJz1FbBQg4jsvqRoWZhVUQ0NebQQgbXZNEIjrLQrYdnx2XRDzOyMgg2atTqg9DJxwJ09dXIJdNUi7XKZZU6o+KF3mpaYLenjx7do+RzSRBqAAdV0oc26VpKlO3n/XctB02KnUkMDO3SMu2qdRr1BpNLMelGY3iuiqprfRyhK1tlDC9AuK6rlOqVFleW6dcrSNdl/ViifVihXA4RDqdor+3QD6fwXEcotFwl5KhVKli2TaFbIrD+yfoKaRpmS36e/PEY2FqtQYLS8tk0gkeuvcAf/6tN7kxPc8HZy9jWjY7x4Yp1+pU6k0q1Tqnz18lFY+yZ8cw6VSClfUiS6tF0qkEA30FjJBBPBEjk47jzijNYytkEOsI8FBIcHug5ftM+XwtYO7+Ndvl0fIVCu0PEGIz0NoMOH5MZn8nyqJOQNFWYxJoPW7nQP8hj+4yJaoN2qWYAQuEwR1lld9yLB2Ai07QK7u/87CDMq2KgF6r+rqbgPVfYvvIZkToRvLdXg2divdbr/nQ/rv+8oBW8Gd7IaV0kFInn0sSCmmkE1EO7t3B3Uf2MbewzMtvf8BAX45D+3ayZ/c4J85c4uzlG1RqNUzbpK+Q5uc/8wwN02ZqdkFFhjkOUkqSiTjZTBJd13jo3oOMDPdz+fpNrt2Yo1JtsD67xPpGmVQiyqWL1+jL5+jryWLoOuVqDcdW6QU0XaNUKnP2wjWKpSp9+Qy2bRMNhzyNTZxcJkVI00gl4uQzKQ7v28GO8QF0TRWPbdk2pm2BpmHZqqTI3XftZseOQZotk6GhPtKJGIsra2yUq4yPDDA8UODtE+coVWq8+NxjytnecYPtbDsOluNweN8OfvbTT5NMxkjHY+zfPU40apDJJMmkk9QaLUrFMkiXSDhMkJ7Cc+RvtlrENZ14PErEtnj3+AWuXJ9lVz6jignXmkGkT6Gg6t55m4iQFzWYScXp782TTaeYnl3Eth10wyAUDnmmLJSqV9OIRMLKSd6ycFyXSNigUquzVqyi6zrZdJxYPAqaRiadJJlMYFqmSmTptdrqBsur61TrTVY3ytiO7WW/FtTqTZZX1slmUuiGTjQaZmLnKE8+/QgaLq1Gk3K5jmk7pAPpHJqmjSvdoM7k9ZvzvP72Cd49fo6VtSIzi2vsHuvn5swCRihEs9VCCDh2+goP3X+D8ZFBCvkMU3NLLC4p7dHi8gbLq0UGhwYYHe4Hx8G2HGbnVylXajjSZX5pFctqEY9FlDZpdolL16Y4f2kSy7axHYf1Ypnrk7McbJmEHVWtQTl0K4f2ldUNUqkklXqTesNkZb3M0lqJSt1kYXkD25uTT5TKtZZXi1DHtm1SySgDvT3smBijN5/BAFaLFf6/vP13lCVZft8Hfm7Y5/OleeldZVWW99VVXe17pnv8DGYADACCXIiglpBW5JHI1a64qyVWS1DUEQ/37B4eitIeLkURACXsEHYwwMz0mO6emfa2qrq8z6xKb14+b8Ld/eNGvPfSVHfVzID3VFa+jIgXceOa3+/78/WGQ7FcY71Qotl0MAxTFeoOJJ4fqChRIbBMk0KhwtLKOoFURcWDQFKu1unu62ZgsAdNgNdwWFwrYFkWnu8T+AGO4wCy9a4Nx8EPVK2zhZU18sUS3dlUuI40vKbKZm8aOkEgWVsrIKSkXqvRcMssXrzFlRszeGGtNN/38T2BCIJ2cErIVCdGh9k/PcFb71/id//9dzBNg2fOHg8DhyJyFSX7VRoxPwio1BpUaw0F+DRBpVxjaXWdtfUiK6KDYjourh+wspZnaSVPo+kyc3+Rjy5cx7JM6k2XWNwm05VBGAZ1R/llCU3DjtuMDPfR3ZXGipvK51MTaq8Ui/jSx/OVY36z6VJrNNF1nW/94C3uLSxx6ug0vh9QKFW4srbGIddFlyD8AN0wSGdS1JsOV2/OcuzwPoaH+zl96hCrq0WEbvDYyUMIXeO1ty/yr3//Wxw5OMXE2JCqI2qaoYiimKFlWSQTSpDKdqU5tH83G4UCqVSCZCqFFDqaYXB/cQ3HVSD6+u37CAED/d2MjfZjWTrJhM2dewuYhs7wYB+GrpFMJqg3PZqOh22bJBI2DddhcXmNjWJZ0RpdlW4SIV1qER3ZMRk7KQbkA4BWxLdamtCd6wu26/yyw/cinvfT53qK/LUeGii1gExkQmudIEyi+FP3JYxVZyt3F5vObzv98G0b/ormJDwppQqyklKlhgoUDfR9Hz/k+W30grJOdGrNfo5A7OHL9WxFwB/bj0hB99N2NQJaHTbF0MQYMfwgCPACD9/30TSBoWv4TVXFOwgdUgulMqureYrlKn4QhP4DLjLwqdcbvHP+Gucu3cR1XWLmFEIKTMPGskxcz6dSrbO+XqDeaNJwHFzPVeYiYHmtyEdX73Do4B4anoMXak2cZpNmo8o7713g1Tc/YnZ+lY1iicFchkq1SibTpUKtgWbTodl0qDVdHNej0WhSLZSJJ2I4jjoWyVHVWpNitU6t0WRxZR3f91lYWqdYqRGLWZSrFaYnx/n8p85Sbbicu3JbLWMhGXIcDBm0TBqGodPfl+Xoob2Mjgxgmyq6rNZwKFXr1JsO5VqdfKlMrVanXKkpJhflAfMDarUGMRkQi9lkpKTRdLl0/R57PnM69JdC5eqREssyicdtDEMP/eAU8ymUKly4cosgCLg1M0++UKanJ6uiC1F526TQEbqOiYqSs8P8SFJCT7aLowf3cOnGDDdn5qlU6yQSccV8G01lRuyQKA3Xozub4crNWd7+4CoTY0OtfCvFcp2Z+RWmdJ2uTIp6w+G7L7+t9qjnMjO3xMjwAAf2TzHRsRkbjovnK/DihpqBO7Nz3JpdIJ1M0JtNs7Je5KPrd8j2ZEBXWp/5pXUWltbZu3sCgEq1wdzyOlLTKFdrXLh8h7rj8+wTx3Fdh1q1iWVaNByP9Y0it+7eR8oR+rozBFLyR3/xKh9evMWJI3uwbYtytcH5y7cplqv8vXyBbtcnJlWmfF03WN8osbiyjhmLMb+4ytJqHt3QmV9cpdFwmLm/xL35Vd549yK9PV3ous77F65RrTfRDQPLsjlz8iA//PE5/s3vf4snTh9mamIYwzDJdqVwXI+rN2Y5fnSZeDyOYZgUSlXypSq5gRxNLwBNxwsCVtcKrKyss14o43o+r71zkempMcaGc3SlEqTSKYSmYRgajuvQcDwcX7YiAV1PpRfo683iej7vnb/G1K5RujJJAhkwNTlKMhEjnUxgmiZLKwU+/OgGg/1ZLnx0ndWNMgf27sa2LSxDxzANSuUq6/ki45Ua6UBl6g98lX8qm01z+vgB3jt/jYXldb70wlmOHpxuM08BjaZDuVxVJlJQ1QrC6EPX9dkoVrk9O8+1mzPcubfIhG213Sccj+W1DS5fvUOpXMXxfO4vrvGt779BMhFjfaPEk48d5tD+PQBMTw0zv7RKqVpleS3P0soGU7vGSDg+N2/PcXtmkWKpyrXb9zl+/ACrhSJrhRJC03H9gEQiRtN1uXDlDnNLaziOR7XW4AcfXeMXmg4JFNgyDIOD+3Zx4fJtfvTWBQrlGi88d4psJk2j4dLf28vjp44yOTHCzP0Vrt68R63e4Mat+xw9tIeJsaEOGi/oynaR683y7vlrfPvlt1jJF8n1ptk9Nd7SHdUaDu+cu4plmjz/zCmWVjcoVxv4QCA0+vp72T89wUuvvod/c5ZCqYqmafT29eK4Xhh5aDI5Ocz46CB3ZudZ3yhhx2xSyQSWbSneEiVwFiFxierzdnKlDgCzM9ACwsx1oQGZnThgq1ROoACeem4ItB7BdWmnFvlIbzrW+n8nVUjYQ00qEPmz4KqdOxSpuVrarc0os0ML9VOhhS3zsdV8GWqz1LFQqyZVsmU/UJkBO8MIWkC1feufW3uE2ojt9iCctV1R+ghNyu2TsPUGoXOgEBqxmEU6lQChohId3wddJwgLWwcSdMNEajq1hhNqeJQq/K33L/HSy++S6+tm79QYN+7cY2ltg3yhgmEapFMJllcLqsitrre0Op7vsWtimJHhXj66fJe5hVUWFle5M7tIpd6kUKlSadRYWVnjT196g5m5FbKZFK7vMzO/wszCEoe7uujKpAgCydJagZrjI5E0XZeFlTy1hoMZiyGFwDINhB6WMtI0dF3nzuwi12/fY2J8hEqtQa3pkskk8aXk/YtXaTY9nn/yBMVylabrYMVUhJkMkb5lmRyc6mejWOLPvvsKu8ZHCDyffXsmGB8boel6OK5HqVJjdm6JSkWp6ZtOmL9IKv8kx5H4vsTUQi1Vl02p2qA71weWApMEKrLv3twStXqTYrnOWqFCb3cXmqbjBZLVjRJXb93jxp37rBcryLvz/OjND6mfOsC+6V3ohollWayu5bl2+z7FcpW5pTWVmTxfwrJMhgf6+PDSLf74L3/M8nqRO/cWuXZrjr6eLhqeRzJckt22zfPPPMbC0irvnb9Jrd4kFrMpV+rs3jXM1K5RbMtE13V6sik+uHiL67fnsEyD4cE+9uyeJBaLb1mmEqfpspYvgISpyRGmpyeYmV+mrzeL5/ncmVmg3mgyOTGCbhq8+sZ55pfWWcmXKNebxOMxBvp7yGYz6IbJ+Ngwt2eX+PGb5/noym3iMYuTR/Zz5uRhkokE7390nT/7zmu88Mwpvvy5p0kkYiRTSQIpuTO7hOt7BIHk6q37dHWlMQwDt+5g+gGmYdLbbeEHkvOX7zC3lGfm3iKuq6JXi6UKuqaxa2KY27PKQX1lvUhXJsnM/SV2Twxx8tg+hkeGMCybt96/zhvvXWKjWOZzn36cPbvG6O/vo6+vm9mFVf78pdcBuH77HqlknKWVdfZMjbO0vM712/doOh5Xbt5j1+Qdcrkenn/qBD968wL/n9/9JiODfezdPcbnX3gCoRlohkmhvEG+WGFwqB8vEPhS/WQyXZw8dpDzl+9w5fp9/tc//AGWqbFv7zgjQ/34HtyfW6PRcFhbL9BseiSTCW7fW2YtX2Kovx/H8RBC4gU+pXKVv/jea3xhdp4Dnoeh64oeSJVqYt+eCYb6ezAMnU89c4Zcf65N8SSUK1Xu3lug0XDwPA/P88lmUuwaH2J+cY3X3/mIj67cZnFlHd0wGDy4G7G4ofi341CuVEETpNIpfD+g6bjcmpmjrzfLC8+d5jPPnWZkeICm0+CpM0dZWNlgcSVPpVIPBUuJ70oyqTSffuYUtVqT3t4spXINPwg4e+ogQ/05LNvGti2eOnOIpuvSn+smFrOZX1gh15uFu6uqUkGg8lcdO7yXcqXG++eu09vTpczafd2Ylon0lSbk8ME9/PJXnud7r75Dtd5kcDDH2dNHyKSTm7BJVybN6VNHMG2bmXuLfP9H77J3zyhDQ/1IoVGu1rl8/S75jTKnTxzg8IE97J68yvJqnvmFFY4emqarK8NzT5/i5t157t1f5u79RZKJGKdPNLBsi3QmSb5Q4tXXP2R4MMetuwuYpsmJY/vJvXMVs+mGgEe5UeyoKaHTmtNRJ3Ib0ApzIrbVm1sY52Zn+KiED2iqTvDP3ZoVqjxCxYUqLhc9W3ReEV3+8wdbEJoTQ9tKNGadftsi0n7tBLoiM+C2m7ZPd5oPpcoHGCllIq1W21dQXRcJapGetX3Pv4oBUO2n0my11K6dvzvPbW0R6oxUtdvUsjv8HdYyUvMvIFTvyZDZlyt13j1/g1KljtA07i+ts5wvsVGu4XgB5VqDYrVBslymWFGarbrj0vQC1jZKzC2tkUqn0Kp1llY2qNaaHDu6nxNHDzAyPMitmUUWVzaw43EcT5k+1osVDuzfy+dffJJa3eXqrfv8mz/4ditFwlpe+SsgNOLxGKZpkEjYZNwEs/OrXL4xy9TUJIND/QwO9uF4Pj6SSrNJ0/W5MTPPmx9cYvfkKNdu3wdN4Po+zaZLf66P0ycO8cob7/PN777B5PgQ12/do6+ni9OnDtPdk+Xtc1e5fG2GTDpNqdJgoL+PwYF+DNNo7aN43Oapx4/x8k/e53s/fh/k+/T1ZPit3/gaI2MC3TAxTIuNUoW3P7zKRqHMynqRwJfUGw5+mCfJk21NYyIRo7cnHYZUH0D86JKax0CZed7/6AaFUhXT0Ll68w5nTh0inlCALB6Lkcv1Kim26XD5xj3+3Z98n9v35vmt/+gXse04Q4N93F9Y5js/fJPR4RyuH5BOxWk4LoZl8dTZ45QbdeYWVnn93Yukkgn27RknZll4M8tEG0hvupw+cRDPc/nJWx8yMpxjamKEp588zkCuh+mpceqNJqcfO0pPTw93ZhdpNJoM9vfy7BPH2b9vis5qAAjByGAvzz15nN27RkmnEqSSCU4c2UsQBEzvHqOvJ8uFyzexDJ1sT5bF1Q0QGolEnFjMQjcMDh+aZs/uCXbvGkNKyfTuMRAwv7CK5wcM9vdw5sRhpqcmqdbq1Bo1ltcKmJaBZZnEbKVlAhjo72VooJfZe4ucv3STQ/unSM2uoTc9VXJH1+jt7uLzLz7J1Rt3Wd8oMj46wOdfeIL+XDe1eoNMOsmp4weYGBvk1t05DF2nP9fLr//yZ9E1weToINlMCpCcOLIXPwh46vGj7J4aB02nP9fDVz//DMlkHMdxKZQqvPjsacbHBpgcH8YIQ68nx4ZwHR/H8ckXarz43GOMjY2QTCV579xVbt9bYmS4HysWR7guQwN9uK6HbVvYtsXYyBC2ZdOVTiKEYO+eSb744pPcvjtPrd7ANE3GhgbwfdjY2GB5Lc9ArptkIk6uN8vE6BDHj0zzwfnrLK+tU6k2sSydIAgwdJ1MKo7vKbMlmo4WRqt6nkcqmaArk2R4KMeB/XuUhqSThAWC6d3jdIU+a4ahMzyUo3+gj7X1AuvrG/iBGr+D+3axP19GvPIByADheMRiNoP9vTSaHrqukUrGmJoc5umzR3nq8ZP09/UCgkqzzsBgP0+ePkypUqM7m2EoLIul6zr7picZGxlEoBGPmViG4OThPRzYPYZhmqQSKWIxg7Onj7BnepJMOkUiHqOYL2KVKiTeuYaYX1WFyIUgk07y+KkjHDmwj6GBXrqySdxAUn/3ItdvzFIolhke7ONzLzzJgQPTVOsNurMZEjGL+2EalaiZpsHU5CgH9k6RD7W1nucqc6ymoxkmuVwv4+PDHNi7i+6eLF/5wjNsFEqMDCm6Fo/ZHD+yj//4b3yZH7/2PovLeU4cnaYv1w264FNPn2B2bomF5TyLKxvEYhbPPHGc555+jNSlWXS3TORZvInD7aC1CmTwMd5FIc+K8jVFZqyopPE2ZUIHttkhYbbym4o0Y4+KwjoUHsJjq+LigbkBdgI0D2GK/MSs9lLSyoEZjWunhktG4yS2K1wktHN4hde2kuN29E0qh3jRwgkR0AKVvzDy1wrC5LfbraMREPsZkmV8bHtkB3mglT1+U34KtqPlBwKvB7XOc1JH+L7yOYBwsCS+51NrNFlrNrl5R7aKuS4s57l49Ta3ZuaJxWyars97F66QnU2zUSwTi1nKT8Fx2b1rjAN7JyhVati2zdSuUWK2RSKZJJVKMb1nF47nk8t1I4SgP9eDH0gMw0Q3DB4/dYxEPMGP3ziHpmuMDuU4fbJBJp2gpydLJhnnN37189yZXcSyLebml7l07Q6ZrgyxeIxD+6ZIxONYsRixeIy+3iwvPvcY6xtFltcLYW6hQ8RiFqapU65UyWTSfO6Fp/ADnzuz88zcW2R0uJ8zJw9yaP8UsZjF2VOHqdYaXLo6S39flicfO0Kur7u1AQQoSfzZx7Btkw8u3MDzPJ56/BiHDu7GMA0mx4f5la9+mlK5rJxXe7PsmRwhmUjQ9Sc/Qms6Kr966J8ipcS2LL7+lRdwHJddEyPIKIt8EBCzLV781BkO7J/AMg0Gh/sxbYvHTx2iO5tmYmyI0eF+xkf72Ds9xum5VYrlClOTI8TjMYTQOHPqMM2mCzJg//QEp0/u59kn8hzcu4vx0WH8wGfXxBDlWg1fCpKJOJ4nWc+XsD+6hVLqg9ZwsG2Lk8cO0NfbxchIjpGhHFrowIzU8H3B8GA/I4MDfOHFeFicNE4iHsf3wJdOa2ULAbsnRzm4b4rhwRzxmCpK3d2dYXpqjF3jIwwP5dA0wfrGBq7rkOvN8vkXztBoOjx24iB9PVlOnzyEpmkk4jbVWoN6o0FXJsULz54mHrPxgwBdGMTjNo8/doju7iTr+SLHjuxlINdLw3GZ2j3B8OgwUxMjZJIxFpdWGRroIxaLIaWKCtV0lVdOMwyef/oU+6YnWFsvMNjfS3+uB8dxWN8okU4l6O3J0p/rYW2tgG5o9Pf1gBBcv3kXKTR8BIZlMzzczylN57lnzpBMJpibXyIRt9m/d5KpiVFM02gB1M6C2bvGR/it3/hFbt6Z4/rNGXK93cRjKbJd3fz1r3+RMyePcn9+gT1To/T0dFGrNXjq8aOcPLaPPbvGSMRjnDy6j0ajqfan52OZJmcfO8KnnzlNEDqbR87cgwMJ/qNf+yL5jSL5jRJDgzlS6STPPHGcVDJOOpWkr6cHIQST46NkUimeOXucVPYvEeV6iw84jku90WRlLY/jejx9VCUPdV0PS8oW3xzs7+dXfuEzNJwGxVKFxaU11vIFRkeHefz0MczQQV/XdRBQv3K7TfscN8zinyIW28DQdVLJOMcO7+GJM0cZ7O8DCY7nUa5U2SiWyHalyYVRhl0ZBZhM08A0DWK2pTRyYV2yPjNLUKmjrxfQ51bQ1gokV/L0rxXR1gpoawUGN0qhOUqBBOEHECjNaK63F2vQwo4ZCA2cposds7BjFo4f4AeQSiXYs2cCp+mwkS+wsLCC5wQI0a4AYJkm8ZhNd7aLgf5estk0K2vrqu+GRk93F2ceO6LAcV83zWadTCZOMhFj//Qu0qkkutBIp5IcPTiN02iwsLTKU2eO0JfrxrINMhklOKxtlJlbXEUIOH5kP1O7RlXewxbrCZn9DqkfIuatheBjZ63Wg1haxxh2fqeVcgCiQtwtjtmy9W3JxP4ITWxyxdn5iofS4nzCJZv9m7aYJ3c0F4pN128GXNH5jlqx0fEtzwHZKs/XBsadNYTbx1u4V7Y1Xn7o4qLwnLpGEwLDNFuJq6O06D+v9ghgK1L1tYdkM9RqOyzI1v8/a9NVMr2O1BFBOIDJRJyvfv45hAapZJy9U5Mkk3EeCyVyx3EJpE+z2eTMyYPkcspk2JVJk0jEiMfjNOouPd1dKudRMs7+6V2kknFMcxdTE6PEYibFUjnM1A37pidV5J6uMz2lpNaxkUF6uruoN5ogJalEDAKfwwen2Tu9i1K5iud5+EHAscN7VakadDLpNK4EhODgvilGhvpZWd0gZtnsnRzl4N4pnnr8OE0vIGbHMHXB7vFBvvrFT7FRKJJKxcn1ZTENg0BKdF3j4EGVK+zg3ll6u7s4tG83hmXTKTEYxSr7/5t/xS7H5Wu1uirtsfI62rffRAbQFQTsDReqH0oQuqYrcBXa3CWaIkqh5KAXK3z5975LEPiIP/g+5o17ShIGDMPgCy8+Qb3eoN5oKu1azObMY0c4fnQ/TdejVCrRCHwmJ0Y4ceygShZpW/heQKFQY6CvhzMnD6nQ9YRNJm1z9pQyqdVqLk7dJduVZWxkAClUTUjfDeiybCwjTHbr+yT//EdY56/T5fuMBMoHxTD01soNAonpusQcF9s220k+ae1VJIGq7xa2XF83iXiMmG2h6Rqe52PoOslUHGHo1J0GtUaNcqVKuVphZDTHp545weLyOr3dXSQScVIpFWLvewHloIaUkEom6A+Tdy4ur+H5DTKZNPFYjN6eLoRAOfcKgdAMEokEqZTAMA1qTQ/X9YjFYuiajucHeL7fKoEkBNiWisKMWSbpdArTNGg0m4BE18P5DgmRZSoH91q9gef5+CLA9ZWzuxSCRNwmCHPpBYH6fsy20Q297b+HIr6qSoDyY+rtzRKPJTANHV03EJrAMAx6u3uw99n09WZawSq2ZTI8mGtpV4TQSKcSxGIqQEDTtRCwxsJko6ZypHc9hICYbdN3aC+FYok7M3MkE3Es0ySTTtLTnaEnm+X44QOqzI1QARPFUhU79PvRwr4vr64zO7fEW+9dpF5v0teTpdFoki8UmQqCsHgQCqAnYkg8utIpCmGwgO95dKUSJBM21WqdYrFMrdHAT8aJstLjepimiaHr1Kt1fF+l4dg9OUKut7dtOpFqTA3DoLc3SzqVIJ1O0ZWMk3EDjFv3YWUda2kdsZpHW91ArBUQqwWo1kMLxVaa22b4mwAAoK0X6fm7/73aE6Lt4G1LyTOex1nXw/jOG9imCnCxUME5CddjKEw/ofnKAVt4PhP/9T8PAaeKUuvxAg4EPpquYegaXQgGQu6pCYHne8QrVYQQpJIJVY1ACCwpifk+n200CaQk9oMP0XWd/iCkYVKGGkmfAEnsW28obX+hFL6ZAskqPUB0KAQPncw9Ov5JCgPRCRgUz1Lla0X7fGhiFMJXGhehPYCt7+xg/nFNaBqylSS0bZ7bzpN/el69FWR1nAhvLdpWAFBqJC26dot1a1tEZGcS1Y57Bu0s9TLUZKmf8HgQgqtQw9XqZ6jGCqTAlx0mxI4+SAmaroV04afSQX1iezSwtUmTFZYB0AKkDBlwqEKVYQHYFgTr1II9pETQCd7aKFwl34vHY8QQ/Oc/PqfOahqaeAcpJUd9H9/zWw7xgZRYpio7omnvtOb5YGsPqWgdBSjUs5LhG0oJ2SBgLPBVGZYwsk3zfHKuy4AmsC0LoWlYHe8mUKVorEAiqzUeL1exDIOeVy+oiBwJupTEApXRIxEEdAU+YwjFeMIQ2SNS+Z6JsD8JCZnQH0PTROjDGRaQDtNx9EvJYS8IfT7DXD+FcseoCkS+hOF5pHwfyw/QG031hECGxQhVrh8zIqpROSEpWw7nUSJPDSCQ6PlSSwYTnkckTkg/wHH8VkShEALTMFRiOctC1Bus55U0nECQqDlohSqy1lSaqNUNemfnyZQrxCWUV/NQb2D2dmFpBlq5il2qYgUBMd9HOC6i1oCGQ5cfoM0utN+9VkdbL4IMMBCbavQpramH77joQmDFbDStsWk1qpxabWIrHJc9f+efgq6p8k6aKqyckQG7AN00EZogWa/TaDokkglVJqTeYMT1SCQSxON2a3X7vk+/49LrB2HpIZVAN1VvIJHEY3F0TSPeaOC4KhO2bcdIBJKU66JpAtM0kYHErNZIV6sYhkFqfgXheqr4cRAl3fRxmspkY4bExfN8QAWbRODC8321r8PceUpjpMaqUlVVEIjGBqVBSybjaq7DvRu9XxCoEkQbxRK6ZtDbrfyDxkeHqdbqrXHVNKUFy3ZlSSat1t4TYdFtiVABMl4oDBgGlmkSxCT1eii1+kqLYhpGOC4qDYZlWSQScSzLRBMGgQ+eJ9E0BbxNwyAKwFlcXqW3Awz4QcAb71zglTc+5O69RQ5MT5BNJygWi2xsFDdTrRat1LHtGAP9fVSqDbrSCXRNgQfXdSmWK0gpyfb3InRVlkg4LoO/8694ZmaB6dU8XyxW6K40mVwoEU/+uSoi7bgYTY9djSbjjSbCddFcH+F6iLCM1aM0qWswNqhYWTaNzHUj+3qQuW6MP/shsu6oNZ4vtb8k2noIGwW62lgtnP2Q1kXgrJODmIUybV2IwOrkGWHrNNuZMqDLV3xIcyvh89XqMlDPARBNVcFCC49FmigzFB41rRlaZ9ozpskObcrWsZFhwNcDgdYnjfUWHYmUYSH2qObtA74veOR5jFIrCMUYiaLtosCk9rMeBMAe8AY79eMhrFSSjr3Q6bsVMeJOAPqg+3dorhSo2gK0OrRbLaAVfg6kRARBeKmiX0EQhGW3Qp1WKLjouk4ikcAy9Z3f92dsDw+2OvFStHBaOEi0gJYQAjSQwQ41mT5JKmhdFi6EzvAegG+/gX7xFnqYE4nF1bBT4S4WArMj+kyGx1SSxsj0SRv8bfkRW8+hCi7rm46DFkhsGRYp1tsMectbICTEHBfDk5hCYharRFGVUXkgLVog0Cqg2nKHo0MYCO9HCHA6iVYnqNVkqHhuSWbhaOga6Dqyp0sBC9dV0oFlIQ1dreGopJGULYYnWuZC2hJBJgVC4GWSiBC0yI7NohXLCDeMytkoE/+3fw71JtQbiGYTvemgNZrQcEhUamTLVYTjhYxxs1eEGQQkXbf1Hp7rh8DDxtB1DCmxpWyNXSelFkIgjLZmRRoGXlgXUte0Tdnl/SCgWVcZv2O2DTE7LBTdGuZQuyXRFtcQjpLOzdv3o6ep+yJbJUCiHWBFcxyuRTucqwigRWtSA4yQSKp9JNBpMytN10ETJAJJTAZohtGqi2hLVWpKM1RNSLvpIDwP3TDQXE+Z5PW2Fk8Ganxs28I0DITU0DWDZDyJbcUR6Ah0YlYc24whpK7OJ5VpFSHwPOU/lc2kMHQDGUA8FifX20cilkRggNRbc+K6LguLeeYXVxgezNGdyap8bV1ppRnUNEUApVQgMZnANk00NDQRYBl2uAUNAt9HEwaGaWCZcaXBlAbS13BcD9cDU9cwdCNMwGuowBo7Rl9PD7quEuzqukFXOk13Nq1KeIXNMHT6e7rIpBJohfa+XVkvcO3WPQb7e5Qf2sig8s2M2cqloSVThvONjmkIxoaH6evpQRKQjMfQdY1MOtWicalkAmGb4LrguJjLebryJfRKneFAkijWiFdnEYHf0hq39huf0DpJrkDtdyOs22qq37I3i/sv/i/Q1w2R/1mUb+rKbVUou3W7tlbrQdRchs9CdoAMAazkUZNjqGdtuUNEvdtkT24693E6nnZlrla8nxKmCWlXCLakpm3iB6K7C7kV5REKsgrab4IpLaYfHW1lKqcDNLRL/2hipxnq+E70t+z8/s/SHjwrHwuu5Nb+RDzk4fj2w3VLsmlBBB1KnK0ark3PDcdrUyHsUDHQoalSQKsjoWnIM6VUwqNK+xDg+R1mRBnSdSEwdFOtjVb7ecxHeCf5kBCucvObCKFh6Ba6aSE0HaHLULuiI4WxyUYro8LLnTd5wKRtBVfbwVbYvvFt+J3/qVWBfnuTO6+zTxyvj7tgK2Dc+rUQhGkdIC38LDuBm9ZmoK1zoGr3ta7TVIb+nb4vUJoJRAtEokXHO++tqXtqgpa5Ieyn7M5Q+af/JUEgaThNkJBMxDAtAyS4rh+aXwMMXfl7RAWKgyBcoKFU4QcKmPX9P/4lRqm6aa70mXlEB3H+uBHfNpwPGP1NausI1G/9nq6pjPG6crCVSoWgTJ+ahrRN/FxPq8j4pvUaajMktDSL0dO3Lln9+l2lQag3wPORscg5+kHrKNI+bh+Fncbhk9sOhHkLs+jc1lqj2X6AQI1HIq7WTqgxQmjtdaUrZiSFWmsi9PVChKkcNYHQNaQMA901PQTmAj9cn7quUnaIUCskbs0iPR/P95GBVCYlrV1vVO70Ettee+dR2jqPna8a/bcFN7fOt8wJQnTUQlRXBIFEOi56mKNLxmN4mgKZuq60YKL9EChWWuuTrtTOi7zV6R2Ol6uKtnV8LzB1glCzp0VJMD/ufoYOlgmWiQx/Y5lI00CaBjXPo95oKg1pIkas2lB97s3i/JvfaQvMYTP+y//nJqDVepx4yJq4DwQPShTZMlut945ycYmrdwGJPDDVPsnmj+05EBEmBjaXhpEyrPKARBNaR+F10ZLV6c4g/4ff3sSHpGzzMWW68tg2AZ2v2Dl+EkAHYW4GGEhUhnaxeQ8LfTuRiO6x7UEf1yRRvqzN5rKPqZWoBqjj++rvB5oLW19rz19Lh/2g8YhaaEFq/0THO0DONif4Nvhq+2mpqjIKW4V/R5njQwsQMkAEkiAQKqrX9VnJF/ng0i3euXCdpXwFT6rqj+lMmq986QU+/7nniMdNkAGabPsYflwTqcOfeM0jRSO2AF9I0ESHOND5OVqgm9rHYLoHgqutzfVhfDjsuYrwkH6HfXfHR3wSO1fHH8wmt9xj64WfRHF2uLHs+CA2Hd1MndUG7zgkgx0DV8SW3/Vf+RzOL7/QHlfU2AZCMThN00gZibapMZRgDUPfZFrb9AyhzEsyNE2aUiMwA4xyDW2jtPk1my7C87fdY1unNQUupVCgSGptcBQB1AhAtgBoB6AUWsf3NtsoNreOcTYK5c10r6PpHdd3fhRb5kbmepCAtrQGzSbBxBDO555EmgbC8RCep/y6XA/heOCFph3XR3jhb8dV510PHHeTANFac3JLJx+ARltSXWhm2zbOd+ch2ifRbcNi3mLLpVu/uukP2aFFCf/eSkA6jASbPzRdhK5KPEkp0TxP7Tqx5fqP208PwWuE3KHfH3ObrWPQ2bRIWIkkfNdFBwyE0jC5HWs82qzRhq0/SOP9Ma31/XaPNNNEmxoJ/xDQnYFcL/QrEx/9PZDrgYE+yGUhEW+ZT0AJvYqR+jSbDj96433+5Ns/ptpo8rXPPc1f++ZraBulELur5wZBRI+BjRKio9h85/vuuBxDM42m77AWd/iy2IEvtFxUAFGtq4Prhc4L8Hz1PpF2Vu/Q2nb+7oRySgNCy6S5dfHJ6PpN2rRWBiY2VTN5mCZAFViWKOoS+U9Fpi92ksB2uEfbX/rh2uYOCiHaptCH/W4ITNWhNmDrnK7WuIT1A1vpMwgevKkg9N/a1MPWM7e/w2awtS1LfABI5eai/O5AEihLY6CiENXnLakeIsDXysEVzrUQqM5pbCsz+DO2hwdbWmt3dLAePexkWMBRBi3m9ED0vNO9tyy4B4Iu1219DP7b/wJ5fD/SD5R2wXXxmy54itEL10N6iuFp4d94nqpdFn12PPCUD5b0w7/98Lzrtq93XPBd5Yvk+jSrNZxaHVMIbKGFx13wAvWdiIlG99r6vh0f2kOysxj8sLKMNrfUYtjxP/hL7B++9QCCGB3cgYlvEvm39mUrKVOn9Uu3VPLS8UFkVpnopG0iG1mladI15YsSapxk+Fm0pJttvdsyLh0Pa0m8WwnpxxD1DulZShlq7lS+MT0y2XaAGrll/NvS8ZY1Wih1dFLg/upnIJvZZjLQtBDChXgw2NbV8IAfIB0f2fDAbSCbDsIJ1JpyXITbXtc44VpsuvhNh8JanoV7C/QkEwz3dmNKqa7xfQVyag1lxg0C5Zfm+mhCmcgVgYoY8sNykc1t07c6NEuIThOSqoSAYSCjdfFQJpPORbmlParg83FP6TT1dmg2fN9XTt2AF2ZANwxdaf5CyV+EWifhh+MoBMRjD+7ng/qqOYqOROoZTcDUCPyzv6eAVW8XUjcgUGtMKWYkanFpm5iVYrCBcoFAIjWVzHhsZIBYzObDS7dZPrmx6fFBoN43SmCsaRpGxKQ1Ddmd+QSZRtAMgwW64nGSycQWUvMxNKdjQFrprjY6QJ6mQXe6danXdFhbzato1HSCbFeaTeVv5A5DLDvL8nT0ZaPUASYiHibCN+rs8man6odqInpWR/qCB174oPZTLOyIXQuxqc8P5MudQCdoC2adhFgFwXQkLJfQFlElcscUFg9o2wDXtk61f2/pRwuoBhGIjY5FqaEEMtDCaMWgJQBEflpCCHSht11WQsDV2f0HJaT9WdpP4XbfRrvIkHuIiD5sr3z+c20dkqSwTLUpdAG6AaaOHt+c66azH53LQGu9gwaoiC7EZv1F+4vhtZp6dr3W5Lsvv8HbH1zkyccO8/nPPE3MNNgMA6IHhZPpB22A53YAPtcjcBRApOmB7yEdJTErsBgCvfBa6bngNtVn11dgMgSA1h99D1GoIJF4hTKO62JZJoZu7CA8fQzF3PTunQMpdgBjICIA3N1F4//737QkaoisnOpBUboQxUO21NcUhFJwu1OSzZmQI+ds2WGz10S0Jdobo80ooxeQIFTAxPzSGt979W3qTYcvf+ZpJkYG0MNQ9DCwGyk9ZOijoYSpIExVuFkuTPwn/xjmltt9NmNo6GFPOmBgKHxpUknT2qZ+KUk3kCCFEZp7ApBxkDKMQwqZAKEGMHycFhIHx/X44J0P+XfnPuLEyD7+d7/0Arne7g7/NYH7f/3bIANq1Ro/efs8r7z2PqeOHeCzzz9ONpMMlYYK4QZNF833lWbSdZUA4obaOicSUlxk00U66oemg3Q9ZNPBrdYobxTB8cjEbOL5EvrlW4i785BNw+4xSv/jP8Q2TVW2ZsvilKEzqxp/PdQmyNbc6pGfW8f7tWByRJQDVdtNhtFJAh1VOqVz3QegqWsKxTLvnb/MzTv3OXVkL8cP78W2LJZX87z6+nvU6g32To1hWDb35pcZGcpxYO8U9VqNWrVKLB4nl+vFNM1WPcT2RGmqEkLHO2ohc9tGMTQBwkf8xm/De5fVsb4s8sg0BKjo6rpDve4Qs01ithmm8+jYNy2zT4AWAiUZLjdN0+jryarSSoaObRlo1+8iak2YX0H/zd9G+j56GLlq6DrajVlFixIxvG/+c/SWFqDD0hG+hQRWl9f51//uzxgfGeQXv/QpurOZtpjW6ZvTMrXJFo1tAZ7ws/6bvw3Mqeu6M3j/9ncQ0keg4debXHzjA/78pdd49uwxfvlLnyaRjIc0V4T8uE1DonuqMi0q5YaKuhXof+u3Q+2dbPOG7dS83feHBVyha0g0Pjue/w/UOrwitrdHBJBtsbv9qS1gfYJWq7PtqOF6MMjaDHRDtY5kk9auJTSFNDYI2o7yrZ9A+XC1nxel5dgOrn+eaOaRayPurH9BEbW/6sXjdWiJTB2tnRK2RZA7JevWUEUMLuxfe2l0htVGG22z5ib8QvtDIFldWWd9vUjTcZWaXhASdW2bEkQIwNDAsEHYm15HypDw+oF6emtxiZYzeqTe1PAh8JB0mAjCywUgX3kHUSgTBJLlZpN84DOQ66a3O4Wh6R0dCv/bJHJ2Sg3t453y0E5KBNEpeYbf0kNApYsIeLfV8AEh6IJQ3bypU5vdF2SkBYo2kfqO1pqr9jxGm0KEPmytOQiT2EU3NwT0ZtP4HsQNC0PoIVCLplAS7CC9aoAuoo3cMYa0fSA001CfIyfccM1JoSE1XWXNoO0nEoX5yxA0qb/DsGRka2raLhNBi2C2Hi/A1AWZRJx9u8fZu2uMVMxCR6nP/Y5Qdg2Jrgmy6QQH9+9i965R4rYZAt9o0AXCtlq4ur1E2sJIa/5lByELNTyBlDQaDvkVlVNqINfL5N154pdute6vCUE6nQpNOsE2Qq/GIUxCKMKkL1KqwJH2wzv2/Q60KKQLWuhPGuVOEiEBb7HRAJAB62t53n7vEmsbRfZNjiB9n8D3WF5Z5e7sApMTw/T29WJZFnfvLXJvfpm9uyfIZBJ0pS0MI6bMWFKiEYBUQQoRgFWZu0M/wcinJKrBJqXygdM0ZOAT4G8nyr5GEATUa00+unyD67dmGRnMMT01xtBgDjsOvq/G3vU8LF3DDoGsREUON5sOpUqVheU1ytWaijjVNCXAej5Iibu0huOqSEZdVz6P0nGVxs71lGOx67JRKNFouti2SiqaDKM7A1SOu76eLJeu3eLg/gmOHdoPEhzXRROaCmwxDRXsIVVNWi+MHvfCOpeWaWDZOhrtQAApgUC09ms8FmNybIhEPKY0coFPpKGJXNq10KcnkEpY8n2fSqWG47it1ByaZrT929vb7gFNY1tags2Ldwug/Big9R+wKe3WDiceBLIeAMwiLVmAoFN8kKi9ihZs/cInY4IwunkbGnyQlr1TS9epRQ/PtemlbJmoW4FdoXbLD3y8wG+ZyzuZ9mYqp7PjQPyU7dHAFmziiG0wI/lZUPpD40enbUYURjt/yFbmuDWpqiRCulueIgBcWvnDIvV9AL7rhWHuKtpOMyQIk5ht8eUXn+K5J07S3dMVSucyzNGiI3VBID1kKFm2TCmhdCsRSvqSIfxQao2ORRL2OFKLImk6TTy3gW0aGIbR8oVQysV20leAQBN888A49w9O8bnnTnPq2AHiMbsFlCVKwlVB/oooCSQ6UWCwAKm1suwqc58WAp/N42f/7d+Gu3OtURahI6+UEnwFjPUw0hGU33oElxXZbAMh2X751kzpYXkJKRXh3BRlonWYoWQQAq0A3/dR7jQejuOg6xpx20bXNfp7u/n0048jpAjrU4rwn8D1PArFMvliXjn+93SR7UqjaWosiuUylWqdZCJOOp2k1dGw1X2f+zfn8FyHeDxGpVqj2XTI9fXSle0KI+Q8Usk43WEJHYnyo5OaxKnXqdVqSFC53EyVxywIglbdTD9Ug9u2STJloesGhq6xf3qSgVwPyUS8VXezXm+qQtCuTyqZIBkzsS2TQ/v3MDo2QuD7FEtlXNfFMk38wG+Np+f5mIZBImZh6GqNRMWf/cAHoVGvN9E0QTxmb4og1TRBKpWgGdYSdWoN4tFACcLUbIJCqUw+nycZj5OIx/B8H9syFQBEpcFwXA83XIeGpvKzYZj4QUC5UiVfKKMJQXc23Yrsq9XquM0G8ZiNaZnU6w5u0ycWs7FjNp7n4zpK62tZFgKdTDLNmWMHMSyDA3tUIlbH9VpFiw+nUmSzGarVOovLa2TSSZV+JVx+lXKF9UIZz/Pozqjkoiq/XZs+ik3AQeK4Lvn8OqVSKcwPFidfKLC6XuB0qUKmJTAA0sdtNrl09Sbf+PMfcvXmPbKZFKeO7uVrn3+WPVNjLCyu8u65y8wtrjA5Nsjp4/vp7+vFdX2WVvNcvnabG7fvM7e0yqXrs/iBVKaW1u6Fkm2yVq2iaxrpeIxE3CYm14iCDmq1BnfuzvLGOxdYXFnHtiwGct08+8QJ9k6Ns7xe4OrNWVzPRRJQLJXZKGxw7eZ95hZW0HWdseEBDu2fore7i0bT4dbd+9yemaNUrlILc/Ht3T3CqeMHGNzC5wJfgTHHcfF8n2KxQqFYotF0QITFvis1XNcjnkgQ+B7VWh3f80mkUxiaoJDfoOm4xCwDXcQ6mOvDMNVH8eHZYobaEmW9rf1VKio6ND/bWrDTO4WcoIMuRwFDUioBMeIbAqkMRJ1C28O0rYCuE0M8pDuDbG2o1n9tzVa4ZqO/W9qtIMD3A3xf5eHskJ6JNFybzaShixQ7qRwerT26GTGqoB2+yCON8ANUsA92pNzSWv5PkkDXcUInVE3T0PRI8g2JYGS2CoJWmDtA4PnU64pxxWNhIsrQPGlZFq7rcn9umfmFNRzHxfU8TFNjanKYsdERTDtOT283pUqVDy9c4siBKUbHh5E6eK7L4v0lLl69SalcZXxkgAP7psj2ZBHCp5gvcvfePPWGg+sHVKt1bMtkcnyUgVw/lhklDQhABARI1tbzvPH2OeYXV5gYHWTX+DC7J0YQQrC+UcTzfRKxGIN+oIL1dY2vful5nKeOk820Q+pdzyO/UaTeaNJ0XBzHJZ1KMDQ0QMy2ESGzrdcd7s4u0mg6DPb30pPNYFkWOiKU6XaeKyEE1Vqd2fuL3L4zy0ahRH9fN6dPHGQo14sQgkq1xt17i+iGwfhIP+lkAikltZpinNWwjpzruGRSCYYH+xR4CAIcx2WjWOL2zDxr+QK53iyTY0P09XZj6jq+73J/fpnbM3N4vk+hWGZxZZ2xkQFOHzvA0EAfAbC8vsGdu3OMD+fYOzWObVusFyq89vY53r9wjfnlFQxD5zPPnOIXPvcpHMfljfcu8Po7H1EoV5ieHOH5px/jMG3tSmDovHf+Ct/+/muUKzXiMZv1jRKu53Fk/y66u7uZX1yl3nQYGujl008/xvHDezEMg3yhwIXL1/nosloz6VSCU8f2c+zwHpLxJCtrG7z74UVuzcxTrTUIgoDenjRnTh7g0L4p5RcjfG7dnWFpZYPnnjzJ6PAgN+/e4wc/eofF1Q2GB/p4/MQBjh/Zh+d5XLh0nctXbjExOsCRA3tIJmJsFMsUimUsS4Gyaq3OSH8Pk2NDmKYZJvK8QK1RR9Mt5pfW6MmmOXFkP3umxkgnk7iex9r6Bjfv3KdUqdCTzdBfKpOWUsmIoWRcqVX5wY/f4uq1O5w4so+YbXHj1j0OTY9z4shekok49xeWOX/pJo4fYJkG6YTN0YPTdHdnuTu3xE/eOsela3cwdMG+qRGeOXuS7p5uPjh/hdt37jGY68E0De7MzuO5PrvHRxka6OP+4gor6wV2jQ1z9tQR+nO9ZLvS7J+eZHF1lWbTVWCo6bCwtEa+WG5F3t6Znef2zDwH9k4CEhn4LK2s8b1X3+PClbs0my57do3wy198jr17p9B1Y9P+aH+WeG6d67fu8KM3zuE4blj2yOcvf/A246t50h0Mzvc8Zu8t8OcvvcaFK3fI9WTIZpJ4rke90aBcqnDp2i3u3p9jLV9keXWN3p4U6XSC2XvLfPuHb3Hz7jwTowOkUwmarkvDcal3CK+BrvM/fuokV27O0mg2Gcz18IUXz/K1f/A/qLQhMuD67Vn+8ns/4d7cEn09XSyu5Ll2+x5TEyPsmRrn1sw8f/rtV+nKpBgd6iedSnDj9izf+LOXidkWhq7zxrsf8fWvfJqzp48xe3+RP/rWy5y7dJOYbRKPWaxvFPnwYoZA0/iC79N2DJGUq3Wu37zL8moex3G5fG2GjUIFQ9cwYzYbxTKvvXUOTQgeO36AqzdmuHT9Dj3ZDGceO0p/XzcNx8H3fUxdUz6LkTUpfMamRJwdz96EDD7JlBillei8vvP3Tu1nVFhs6UCHdj/su9wCqkIt4JaDbHvXLU0J0+FtO6Ipt43Zx2m1Wv7fnY96SA1Sa+wjQVupDTrI8ab3UPYUlS5IdoCtSCuPABGmJ+mMIt0euPHQKqEHtkcEW+HKDEf7kc2Gj2gb3ta8dthtoVLlg9t3WV5dU+U2kjaBF6DrJsODfaRSSfIbRVbXNhDA9OQI/QM5rt28y2tvncMwDB47foBKrc7tmXlGB3M8dvwAa/kCf/itV7h5Z45EPI7neziOy5kTB/jFLzzL0Mgo167e5Pf/5CWW1tb5O3/zFxkZG8JzPV5/4wP+4vtvcG9+mURc1Ww7fmgPLzxzmr7eLFeu3ebPvvsTSpUqyXiM+eV1XNfjuSdP8LUvKl8bXY98UZQz95vvXeR3//AlVtYKdHelOHVkmr/5q5/HcTz+9Ds/plCqMJDr5u/XGyTDGSo3HN595wLDA72cOrYXXdd57e3zvPneJRzXo+mohJSZVIIvvfgkLzx3Bk0TrKzm+eFP3uOHr58DCaeP7eO5J04xNTGKZRrogg7AtXlTep7PSy+/yfdefZtSqUI9jBRa3yjy9S9/GqTk+z95l+//6D1iMYvPPneaZx4/jhCC194+z2vvfkSt3kAIQalcZTDXw9e//CmOHNiNlJKrN+/ynZff5srNGdLJGJlUEtM0efrxo5w9eYiVtQ3+5z/4C27cmWOovxvbsrh5d55MOokmBM89dZpaw+G7r7zJ6+9c4CsvnmV8Yphqsc43X/ox33n5bWzLJF8s4/kKeBq2zfUrs3zjmy8zt7RG03G4eO0utm1xIAgwwvXsCcGFSzd4/8IN/CBgYqQf0zC4v7DK/YU1errTpJIJypUab31wuVXIuFLN88d/+TJvvneJXG8XE6ODrOU3+PYPXsPzHZ44fZTl1RV+8JN3uXV3np5smkBKavUGV2/M8Dd/7QucOLoP3/d57/xV3vnwKgO5LCNDPVSrZd49dxUpJelEjEbTIQgCGo0GV6/f4dU3PuDsqUPs2TWKH/i8++ElPrpyi717xnjqzFEKxSIfnr/I0UN7ePr0Ua7euMUffPOH+EHAvt1j1BoO71+4yq2Z+3z9Ky9w6ugB5hcX+d6r73D+0k0kkr6eDBnNpM/3O4qOSOqNOlev3+a1dz5ieLCHsZEBXnvnHJev3ySZiHH04DQfXLjGH3zzh2iazvSuUY4f2o3n+czem+cvXvoJ7124TqFcwfd97s8v0teT4fiRQ1y+eptX3viQXG8Xu8aGmLm3wN37y6STCUYH+qg3myytFUglExiGzovPnaFQLPHya+/wwcUbPPP4EXq7M/hBQLXepF53cFwlnJQrNdY3SpQrNXxfJXO+dvMe3/zem/R0panVm/z4rVUmRgbYNTGCntBDk39kTozMpD6GpvbMlZuzNJsuJ45Mt7LtpxIxEKXWFquUla/duUu3GBvK8ZlnTzE9NU42k2JwoBuJT09PmrNnjrK0kuet9y6yuJJneLjEu+cv8/IbHzA5PsxnPnUWwzDYKNV4/d2L+NJvSe0SiWVZPP/Uca7enGEhLPbemrVA8tb7F7h84y4nDu3hC596gmKpwvzSCiNDvRi2Su6syj0lmRgdJpVI8IP33uXm3TlOHN6LZRlUanXqjQa1hkOxUmNxeY2VtQ0eO7aPY0emuXTtDucu3uTDC9f5VNPBDKlNICW3Zub49g/fZHrXKKPD/ZiWzq6JYQ4f2I2dSLAwv8J7568y1N/Lk6eP0mg2ee/CNZKJOLt2jdHXl8WXUG24NH1J0jSVD2XLfPggfiaJUikIRFvJ8EB+th14fHLU4V+BZktGIOtR+e7OdsTNAsP2Y5tOfGy/Ovq36fjH93Ors786KFv3U8MsWioviQ8iQDmHhD6SoTlRyqg4d3Sfzn7sNGZRzrWffp5+utqIQkmpDw22On0sfpbWodlayK/xymvvcvnGDKahE7MMGk0HQzcY7u8jFrNYWy+SL5XRkDx56jBf+/yz3Lu/yA9f+wDbUqU6avUGP377ArvGh9g1MUyhWOHWzDyzc4u88MwppneN8ZN3LnD15gzPnjnC0PAwcduk3miwuJKnVm9AEDB3f5Hf/6OXuHTtLl//4jMc2r+bazdn+Mlb56jX6rz47Gmq1RrLK2vk+pTq/drNWb77o3e5emOGTz9bpjubRmjKfBSNra4Lao2m0mDFLWUqkx6Fcolzl25QqtZ5MnGow7QoeffKTf5gfomzp/axb88IUsLr737Ey6+fZ3rXMGdO7OfW3Xne/PAKyUScx04ewtA1/uzbr/DSj97D8wPKlRr1epVdowNMjvSjmUYrTZ+MbK0drVqr8yfffhXX8fj6l56lVm/y3vmrJGIxDEPn0pVb/OUP3iSbSeO6Lj968wMmRnMM5Hq5dvsur737EUP9PTx9+gjlap33L1zjzfc+YniwD9sy+fFb5/juq+/wzJkjPPfEcar1Bn/y7R8xv7RKVzpJKplgbnGV1XyBk4enOXFsH/FEjA8+usGt2UVOHK+HJkFViLxQqeP6AbfuzvGt779BIm7za197kUq1SrVa5+ypQwg8arUa3Vllsrk1M8+rb11geXWj5XwNoFkGgwM9ZNJxkvEYX//SswwN5PjT7/6YV964wNH9U3z+xSf54KOr/P4f/YA79+ao1CrcuDPLd19+m+6uNL/0pefYPz3J5Wt3+LPv/JhXXvuAwf6eliNzX3eKz37qNIP9ffz4zXNcunaXtbV1Al+ZyDLpJKvrRTYKJUxNkE5Y6Boc3LeHL3/2Kfr7utENQTxuM9yvagBuFMtUqhVW723w47fOUas32LdnjHQqzuEDU7z21ofcnJlnfKSf8dF+bNvEcT0++9xj9PZk+MY3X2Hm3iIbxSKO1+TdDy/x8mvvky9WSCZiLC6vc1I3Oe56KtFrKNGmEjF6uzN4no/QNE4e28flKzf53o/f50dvf8CuyUEymQRBIInFTJ594jgnD0+TSsS5ePUWl67fYWIsxy8dfQrX9dA0wf7pCQZyaQZyGQIZkOvt4qufe5qZ2Xn+l3//EgsreY7un+TE4RNcuTHLD944z7U7Mzz35DH8wGM1v8G12/cY6u9mo1QllUoo86lUeZlitkW2K42ua/ihpkfXlQnzmTOHePrMUT68eJ3vvPwu6xt5HLdBQtqE3pYtqVqTAZqmUiOMDfcz0NfN7NwKvh9QqtSYHBsgu15H3F9t7efV/AbnL98iX6ywd2qU7q404yP9DAx0I3Qdx/UYGR3i/vwSt2fmWFhZY255lfjNu1y6MUuhUieX66Z/oJv8RlnlONPDgIEWoxIcO7qbxeV1avUGtmUSi1mbTJ+LK+tIYGrXCAPDfYyO5ti/d5REMolmCJIJVSrJMAx6e7LousnM/SW6u9IcO7SHWCxG4PsM5Hqo1WvKfJpMKEd1QyOdTtLb04WmadydXaBaa5AKhXrf93nz/QvMzC1y9uRBYrbFrvEh+vuyjAz2I0J3i3yxTDxuU6pU6e5Kk80kWS+UqNQqBNKj2qjx0dXb3Lx7n1xvD7ZtcrZaU8+hAzyIyAzohdqR8DAPYPoP0zq/sk159vPUbEHbv+ynAFotjRibTImtKx7Uzwdpuba2nwcW2PRcZQlqDaqQICKzfeezAgXAkKGvqtZy8+lMGRO5/nZ0mEcfx+3tEX22wgSFYofs8J/45U9QvT5Mc532RwHVep21fJFMKsHB6WmklFy8OsPb564yMdrP/t3j2LbJ+cu3ePfcVU4d2Uu2K0VXOoHr+cTjNkMDfdy4O0e5UmuZ9bLpBP29WU4f3cehA9Ncu32PW3fnWF3bwK3V6OvJMjE6wLU79ylXajRqDT66dINbd+eYHO3nyy+cZWi4n3TS5PL127z/0TUO7ZtEyAAhJL3daY4fmqYrE+ed85ep1KrU6zUC6RMEGkKE2W2Fz2B/N93daTQNfuUrz3Jgepz+viwykEyND7KaL/Hs2eNYr5wPh1kwPjFCplxCCA1d13GDACk04jGL5588zl/7a1/gjdc/5MrNexTKZVzPh0ABrJNHpjl5eJqXfvQuC8vrGLrA0JR/VRCqWwPRlobVbyiVK8w1Snz9i8/y5c8+TbPp8OzZ4/T2dGEaOt2ZBF/73NMcO7yX197+kNfeU2a5sZF+cn1ddGdTHD86zVe++AzFUpU79+eZW1qlUq1RLPu8c/4K2a4kX/nMUxzaP4Xv+6zl8/zJd17jxp17fO75sxzaN06tXmd6zyiPnz5E03W4PTtP03HwfZd4PMlgfw/JREz5rXk+M3OLLK5u8JlnT3Fw/25K5QrVchkQ+K7LwX2T/J3cL1EolrlxZy6s+xc6kUcEyTIZH+0nnU6QiFlMTQ4xMTbI6PleDENjcnyAo4em8AOXb33vTcrVOk2nyfpGkXKlzpkT+9k/PUFXJsnk+CC92TTLq3lW1jbo7+smETPJZlLsHh/i8IHdrK6ucfXGDEhlgg8g9LsK07KEzs9SytAcmyOVjKFpYFk62a4MsZhFfqPA7Zk5FpfXWcsX6cmm0TRBvd5kcnyI7myam+fmWVkrsGf3OImYTcwyOXRwD0Ojg3xw4QZ37y/TaDgUimWu376P43o8/8TR1hwduXgHY+Nqmwagxs3zfCzLIJWI0Z3N8NSZI7zx/mXePX+dr37+aU4e38fBvZPUmx5HD+1laLCXZrOOxMNxXbrSCc6eOkQ2o3y1bNvCjMdIpZOAJNeTYfeuIfKFPLat05WO8/jJg3z200+QSn/AT96/zNJKnkqtTjqTZnrPJG99eJWl9QIblSqxZKLVZ9MwVIkf01C8KKTGXhAwOpzj2bPHWFnb4Nqt+zRdD9fzWVxexfd97HgMTTcJgoBq1aFeraLrAclEjL6eLIf37eLu/SVeeeMcMdvk6MHdmJeiqgQqEefySp6V9Q1qtQbzS2tcu32f0dEBYqkYpmmyurbBm+99xMLSCjNzy1SrDeYX19goVrhx9z6u6+F4AXXHY3E1z/zyGo7n423xjXnjvcuc++g6zabL6HAuTKLaNoEZuo4MAhaX17l+a5be7oyqJRsLCDxPBbCEfpuJeAwhBOVqXfkhplIMD/eTjNmYhk69WccwdGIxW+XKA0xTVw70hk5+o4QTmjkl0HRcLly5Ta3eIF8skC9WyG+UmLm/wPSuMQaHctRqDdbyRcqVGj966xyJmImuC7oyKdKpBL4fsLiyzocXr7O4nEcla5b8i5V1Yom4EghaZsVQmwW0fHq3aUM6eFrIF6MRbV0T5QAM/X+Ug1OHsPpQ+OpRQViomWppeR6W73Zc2/lxB8C1+WsPNiWGDsvtm3Xe4+eJuQTKpKipgulCyI6pieZJmRU1TSVdNgyV+kfILXnXdmz/gcFWZ2ceafofBWg98Fq5KWdVT38PAzlVqDebTXJ4/xTFcpWrt+bwfI/RwT4mxwYplqsEUlJvOlTrDVKpVKssRzxm05PNkErEWV5dx/GapJMpkgnlHKq6EhCzLcrVOpdv3GXXmPKXcl0Xz/Op1uo06k2VkV1IutIJTNOg3qzjuA66prX8YeIxO3SOdfB8h0CqEGSVnd0jkJ7yDZSSputSLteoNRpousD1fHqyGfbuHse2bep1h4FcL44b0J/rbbv4CcHorhEGlpdpOC6LawUFLIIA2zbp6+ki3ddLT18PiUQszKvj0pft4W/+2hfxg4APzl1ldV2B2HQqjmEKHK9JodIgEGDH4+i6psrQoNS4juvSlJJMOk48bpFJJRkc6AMhkH7Arl1jdHV38e65K7xz7qpyZA9U3T3LjpFKxULinaBSq4f+a6psUL5YZnmtwKHpcUaH+xUh1yQDuW4EUCiWicVj9PZ2oxsGfqDcN1OpBPGYjW4I0CSaDlIP/WB8D9dvUCyXcX0f1/NJJGIkEzYblqaSu2o6rutx4cotfvTmOd7/6IYC4v3heIcBFE0kxXIV11M1IA1Da5VdCqRSXxuGAmmmqSumubjK6toGjutRbzg4ripXFBWXjhzzfV9FVxmGhu8H3F9aY2mtSL3p4ofremllnWbTwdB1XE9Fd2ma0sAsr+ZZzxcRQhKPm4rwhLUcG45LvdHEts1wzToUShXqjYbKkG6apFMJUukUngShaZi6CmvQTBPdMAgCSblaYy1foFKrYVk6+/dO8KXPPUMibiKqL2F8eLPtRSEla+sbzM4tIYQqKm2lklhhQWllRtcxbVuZfXxIJpWfkeO62Jbq6917S9yZXeDkkb3E4zE0TdBsOKzmSxTLdcrVOuVqDaFpxOM2sbpDPBlnYWmVKzdmqTWa1OoNqk2XTNakrydLJp3E8z2azTpBoBxohdZOhOiHuZJ0XcN1HeYWi1y8fIO33r/M7PwKa2Hyz6bjcv7STRJhZF4smaAnm6HRcFheWsV1HYYG+xgZ7ufQ/l28/u5Frt26z/49Yxw9sBteer9F54JA4noeuydGmJlb4ebMAqZl4UmlITp8YIo7swv8ybd/zOT4ILphIHSdVDrJ8FCO2flV7s6tcunaHb73yts0HZdKtYbve2yUVH4pgcR1PS5fu0tPdxf5DeV0XqnVI6qCEIL+/h7ev3CdN9+/hKYJ9k1PUC5X6e3NcubUEaq1Gq6ngg9Mw8C2dRJxm2u35vjJW+d5/ulTHNw3RTxu4fsumu5iWxa2ZdDTnSHX18P84poqGC0lpmm0tBMyCKjVmpQqNdKpJKdPn2Bubpnzl29y7dY9Hj9znIbj0mw62LZJd3caz3Npuh6ZTIpUOoUrJfcXVrlzb4nV9SKBlKQT8bCObphiRAt9gDbxupbOow24IsFhM4fauW0CWpErjgDhd1wSOWHv1MQDz3xie2QlR6dmb4tFqhNMbdHEbQJaIrq2s9+dWic6XKDaZsDNr9jSqYaWsYfp+04XdQDPcB1rusC0jLAChGhfsr0TP9f20GArStbYUqOKDuvlz9vevMMCkaASOYa96OrtIdeXxbYM4jELoRNGfPkYhk5PdwrL0qjV6zihqSEIIwsUQxSqKHRIaCK7rrLxghem9tc0jUTMxvcDZuaWWVpdJ5NO4bgqMqZSreN6HsmkjWmaLK7mOX/lJgf2TVJrONSbDrWGS7FcA00jkFAs19golWk6HiKUkB3XxfO8MArLpVis4DguAjB0nabjsloo4wQKvAtdJWVzXI9qvUmnA6SwVD269Y0yV2/exXU9FpbWFJLXBNJVxbqF0HC8gEq1Rj0VY3ltg1de/5BX3zzH0uoGh/eOg4BGs8ns/Arf/dG7rBfKnDi6n717xtjnOsRCbYqua2ia5MbdBVZLFQYGcly/dodypcZwfy+3Z+/xw9c+YKNYZnZ+lVTKplxvUKo1aTgOnh/gy0BlhnY8PD+g4TgUqzUClHQaRZNUa3XK9SrlWhOEIJ1OYFiqXEzTcckXK5TrTcLiDbiBxEcjCAuzSiFoOA6OL0kkE5iGweUbd3nzvfM8dnw/2Z4uMuk0Td/jL19+k3/9v32bYrmKHwRo3To37t6j3myGYBMcCavrBWr1Jl3pBIHQ8YVGteGqwqcSrHi8FUHZdF1W1jZIpZL09WS4eWeO989f5ciB3Vy4fJPbs4sIIcgXSjQdh0KpSuD7rBXKVBoOSyv50OdFaXbv3lukVK4Si5nML6ywslag6foEAdy9t8j7F66SiFvs2TXCwEAflVoVx3XRNY3VjTKZdIJMJsni0jr35pZZXF7HDyTXb8/Rnc0wONRPpVxVe6LpcG9umZGxYVzPp9F0WF0vUKnX6etJc+3mPd7+8ArDI/0cObibrNMMNYAyzLSgzFEr6wVqtQYXLt9i3/4prt28x1q+xMF9ExiWyXq+SKFYxg9gfnGFTNqmsFGgUKqQzaS4dus+3/r+m6zni0xODDM9NU6l3mR+cZVAStYKZeaX1xkZHmBkKMdGscrFqze5t7BEsaLMnI7v4foOAR6aDratwJ4XBDRcJZz5gaTuNtVPs6kAarNJsVLFc10uXpvhjQ+ukojHVEoD0VZkvPPhFe7Nr6IbBqeO7ePZs8eIJWzcUrTeJcOjA+yeGqVSdzhxdB/jE6N0EnwvCEimU/zK1z5DtruLb770OjNzSzSaDZ5/6jiZbBfMreC4PvfmV0nEbRzXo1Cs8tjJQ/zSL3yKRCLBhx/d5A/+9GUs06DRaBIEAfOLKy3trB8ErOeLxGMWtVqDQrHCpSu3+ZXQvKZpgtMnD3H33gIfXb7DO+evc29hjYbTZM/UCLlcH+eu3OH+4ir9vVlc3yHTNcBTZ49z6fo93v7wMgvLa+zdPc4XP3OWsbEhPFnD9fywQHmTtUKFuuPS29tFT1eaWMlpDYVtWZw+sZ/v/PBt3nj/CqmuLEvLeVbzRUqVukplGI6ppmtU63VWVjdY2yijmzrVZo0Bq4fJiUFOHtuLJnRGhnNMTYww/D/9MUa1wSeBGqXhUYCrFbHWwftCp4L2F8KcX1EqkG3sXGqh+etjWgTUHql1unmIHfnpx7e2NlPl4Am2aa0kWwGW2AEHbB3PB0DTFjjrPNwB0oRkm4N/9MzOCHYhwpQxHcBKi3BhWAhdqGA609BVOTqhtR6vcIHx8AF7j9gePoM8Kmvy9hM7DfIDzNMPM+lbQVZn5GPTJULCDiq5dit3kxDYMbMV9RKP22S6MqQzSTRNwwuZnmlaJBIJ6o0mEtgolCmUKviBDPO9+Hi+p5w3V/LUmy5SCBzXwzBNTDtG0/Op1pt4ns9qvoDjuuyaGGX35BDvfHid/+2br3LyyG4q1Tr3F9fp7krh+gG1poMXBMwv57lxdwFd12i6Ho7rUSxXKFdrvHfuMrNzS+zeNcr+PRP09XXTlUlSazhcvHqHbFeag/umwvQAJpVag/VCaVO4bN3zWqH5yaRNtUboHihoOB5uoxmCRJ+m41Kq1Ngolvj9/993WFjOs2dyBMs0mZ1f5eXXz2GYJl4A9+aXefODq8wvr/EL+pPsDotWSyCZiDOcjfP6e5cxzT8m19vNhcu3OHJgN2dPHeab33uT9y5c59mzR+mq1Fld3+DWzAJ9fX2Uaw2ark+t3qTacClVGzQcpfFZXNlg755RDu/fxXvnrvHyGx8wOjzItdszvP3BZXq6uzh8aBpPSvLFKpV6g+W1gpof36NSbZDfKFOrN0jEY8rs23RY2yhTbziMjgzQn+vmzuwC/8s3vstr717kubNH+fwLT1CtN7hya4Z7i2t0pVUCg0KpwuUbMzSaDplwYweGxnqhQr5UpeE4vP3BZYbmlrl49S7lapPbs4tslCqUKnWqtYZKNmtZHJvexUapynd/+Dbf+OYrvPrGORaX1gDYv2ec5ZU8i8vr3J5ZRNM07s+v0Nebpd5oUm84zC2sEvgBA33dlCo1iqUqd+8tUShVMa04w0P9XLt1jz/8i1dJp2I8/+Rxjhzay/zCCvV6E9fzWV7dYO/0KE+fPcIrr33I3bll/uKHb9GVTmJaOk89fpRkIsGN2/fJF8qsF8q89e5FJseGmJ1fplCqMTO3wpnHDnPq5CFm59e4eFUB/KWVdT6zVqAvCJQjckgrlMZO4vkBr755jpn7ixRKVYaHcnzqmcdIJBPM3FtkJV9iYTnPxet3Obh3jOt35lR/90zgA7dnl5idX+Xxkwfo689RqTVYyZfQdJ2NYpWltSJSaOiGgR2zuXTjHmdOHmDP7lGu3LqPE2r2Go7DRlkxet0wcHzJ8lqR+aV1ag2H9Y0SjWZTmbZcj3vzK1TqDmPDOZWv7J2LeJ5PNpOkWmuwUarSn+tFu3mfmflVSpUa9UaT/dPjjAzmlNkzbqEZkErFSKfjZLuS7J+exLKsNs0Dmk2XmftLPHH6KH/tFz/L8GAfN+/cZ2Ksn+efPkX/QD8g0P66pFqtITTB3MIKIEhn0kxOjjAw2M/BD65yf26Zaq1Bo17HNAz2TI3Cjz8CwDIMnnniOLV6nYP7JjF1ndHhHO2SSoL9e6f49V/+HEMDHzK/uMrQUI7R4T7q9Qar6wXiMZvD+3fh+QHFSh3NMPj0s6dpNDy++/LbxGyrZcJ3/QDX9ch0JdgzNYphGqysrhOPWxw/sodMMol5b61F0wxT5xe/9ByNRpOXX/+Qm3cXGMz1MD46yMTEYEtzm0knWF7d4Dsvv0OpXKNcrTE01KuUBVIyOtLPZz99honREQYH+hRI/r3vIBqRi8rHM9pIKSMfdOWOvsySMA0xUSKBlqrigTfiZwBaPw/73BZN1ZZbbtdk7ZTa4gF9l1uvkTuArK19UNhhc03OjmoPof9hK8WRUHkMhZAIXdEfXdPQdImuaeiGhmHoCg+H/vCGaRCLqwwFfxXtEcyIHWgX9XKf5AgX+e5LtkQuPqLvlqrU7aM7rioqaejcnpnn7r1FpATX86k2XNAMNMNoRZwkkgnsmJI4XS+gUncYTSbo7e3mjfcu8vt/8gMEcPPuHMlEjJszSxzYO4WmWxQrNS7fmGX//t1ouoFlWwwO9DE+Okh+o6D2iKaxtlGmVGsyMjzMr331M/Rlu/no6m2+/5NzWKbB4f27eO6J4+yeGMWXkjOnity4fZ/1Qpnu7i6GhvpwXA8n8Lkzt8B3X3mXc5dus2/PKP/F3/5lJsdGOHPqENduzvHqmxcoVesMDQ8wkOvl9MnDWLZFIhHvyJQsWdoos7S6QSoRw3MhFkuQTCSZm1/j8vUZnllep1Jv4rg+G6UqtaZH3DZYXi+ytlEmky6wUapyZ24V44MrHD+6jyceO8KXXnyS9VKFZNJmINejTD7hkkgmYnzxxcf59g/f5kdvXQAp6O/LsmdqHD8IWF4rUKo0uHzjHggoVhpcuj7LxPgYrquk25t3Frh1d471jSLVepPA86nVmnR39/C1L32K9XyJb3zrR2RSCSzTIJGI8dUvPM2Bvbuo1B38QGIYZmiuAk0zyWbTylwqlXnHDySZVJJSpUqpUiWTTnL6xH7SqQSrYZ6g0aFeipU6iUSCE0f3sbxWYM/UGKahs54vqESS33m7pXrWYjEymST794wrsG9ZVOtNJscHlebLcVlb26Cnp4uDeycYGe5neDDHQH9vK8v2Sy+/zdLKBkcP7eHZJ46za3yI8xdvcPPOfQb7e+jJppXTtpRk0knGhnMEQUAsbhNPxGi6HrZtMTE+TE9PNz1dXfzd//2v8MFH17m/sEKjWcf1ApZX1nFcj4mxIRzXZWiwm33T4wz299Lbk+F7r7xL03HZNTnEi8+dYXxkuCUQnD19hEajQU9PllK1weToIP5ZiW1bJBMJjh7aixA6P/zx++iahu9BtVAm6/kqqiyUODVdaRhNU+f08f3s2z1OuVLlyIHd7N+/B93QkUJj355d7N09yfTuSeyYjW4Y9GQzTE9P8sTjx/iLl17j7v0lpndP0NXVRTyR4MXnzoSVEzSSqSTVhkOmK8NTZ48gpWR0dJD+XC/PVhokk3H6cn2Ua03uzC7QbLqkU0maTYfR4QG+/tVPs7ZepLcnQzIR5/FThxkc6KXpuAwP5shmu/jU04/RdH3OXbzBmRP7yaQSZNJJRkcG+OoXn+XY4Wn+9Ns/xnE8arUGmqYRi9mYtgW6ST5fZmZuhcGBHMPDA+F+atNK1/OYvb/IkQO7GRsZ5LPPn+X44WkymTi9PVkCKenOZnji9FEkAY1Gk/V8AR+N4aF+TN1kdHiIXG8f5XKNW3fu4TQdhgd6sEwDwR8DitH86tdeYG5uiaGBXsZGBkKNwL8JGaHE0DUOTk8yMTJAvelhWQbFcpVbt+/RnU1zaP9unjt7knc/uIjj+GF6mSS/8IXnOXZ4P1IGZNIpdENpwkeGB/gbv/IFfN9DCo1iqcKt27OkYjGmJkYwX/5gEx8YHujjV7/2AqlkHM/zOXPyECNDfcTjNgifibFBvvyZJ7l2+z67p8ao1Rqsrq9z7Mg0g0P9uFJQb7roukYiEW/nH/xEf53NTQhN+Qd9/EUdSggVuapGsdPf+WH44KOUjYnu15nFXe7Mbx+FB3/SpY+UvHWLWfFjP3cca5k1g03H5bZropJZyorkCx0I0DUNdIEeqLVnGHrLZUOZhWm5eqDpH4uBf9r2yNGI7aH65MkSmqDF+VoHH16l2anVklIiXQ+kxJGSn7x5jvfPX2ejUMHzfN5P38S2DBaW1ylV61y7PUdPXw/3FlYpVRtINBZXNzh4cJpjR/ZRKFW4v7BCzLY4feIAuydGmJ6aJNfbw5Onj5LtSjM5NsTIYD/dXRkmx4bYvWuUrq4UQkgeP3mIeCLOp586Ra6vB8MwOHPqGAf27OLGnXvcubfI8EAf+6ZGQ+IQgG7wtYEeNgpFNN0gmU5y+NAuGk2HeMymWq3x7FPHqDccbt6d587MPPv3TPHM2ZOYukm+UGL/9CSD/X2Yps2Jo/vZvWs0RPUyoonU/YBsJonjeMwtrDA6MsCnnznFc2ePkojb1BtNRocH+M1f/yKGoTM40ItA8pnnTrOeLzM1MYJtGczMztPX08XB6UnMULvlui62pZiP6CAohm7wi196nqefOEmhpJxiu1JJbNtieWWdz336cZ54/ChDg330dGdYXF7BNHR27xpldKif3ECOwHUZHOhlz9QYwwM55uaWQ1BncuTAXv7eb/06r797gWbT4eC+XezeM0GuJ430JemkzS984VkO7N3FyFAfo0M5cr1ZBnLdaEIw1N+Does8/9RJjh+eJh6zyfV147oeLz5/hq9+4Tm8IKBQKJK0TWKWQSJm8guff4bnnzmt/IKAer1Gte5ifv+9lgSVbDj8Oia/9PgJzJhNLJ1A6Bpfevw4zccg0A2yZQctZvFPfvVLVFyXZACpch0rEeOzjx2mN2bhSjh2fD+DgzkIfPIbJU4e3UeuN8uBvZN4fkChUOLYkX2kUknSyRhd6STVusP+vZPk+rr52pefp7e3D11o7Nk9ydjYCNVanbX8Gvfnl8hmM+zdO4XQdIrlMssry5iGQU93F8eP7iO/USLX18fTT5ygv68bz/Wp1huMDPXzq7/4IpZpEovZpFMJfu3rX1CJYAslRkcGyKRTPPPEKQ7u290qhSLeuar8YcK9LwgzO4dE8/D+Kb7y+We4fed+uKaUr1oiFmPv7nGGh/oZGxmkWq2R6+9FE4Lenm5lYnV9Du2f4uTxg8TjMSSS40f2YhjKvD45NkRXV5o9U6OYls7dmXmKpQqplM0Lz50mHovR092F53t87tNPcPzwXsrVGtlsmkw6ydFDeymXK+i6TiIRpyuTYmgwh+d5aJqGbuj0D+Z48fnH6e/v4dSx/QznulnLF/nJW+eoVuv0dmda+ySWiKNbFqamoxs2Ap3786ukUymeefIk2WxG1V3cUn6nVm+0cqwhJKapzB2BVGBso1CiXKmS7UoTs+P09enopoVl2YCOJsC2BEVZod5okohZZLtSJBOJFgAWrsehf/Sv2Ou66LqOZZlKM+CHKQ9cn/Rv/WMQkAoVEb7vk2k6DPsB8XhMuWVIyUitgQTsmK1AL9AbfkcJhUpK0aKakuHr+n7AdENppGO2jV6ubuM8g/29fPqZU0gJ/X3duKE7hJQS3/MZHe7HjtlMTo7iuh6VapXJiUHSqSR1J7JSyNCUKqP80psZ647sKTIB6oovRWBLbL2iNXFtvyYBAg8pjY5rJNurptPO8/XIWq1Io7W1mPPPQcvVAo07+E61fLMe6kY/3XOj9CkCogLRLd3vJlOiUu5oQuCHWemF1FU6YSERGmiaRNN1Bb62dF0LTYkgfi7DtrX9VKkfHrr9HDocRD4DQqB5HgiBZhoc7s6SefYUmmUhDB1hmxQqNZ4+cwTLNEmllLbn4L5dnD5xiEQszkCuh0Q8zuOnj3P4wF5WVtdxPZfurjS5ni5syyKQkuOH9zI5Pkw6lSCbSTOQ62FibEj5TvkBi2sFmq7H4ycOcur4QeIxWzllC0hl0hw6MM3E+CjpVAJTE9RqNWoNF9OGZCJBKhXHk6rOW3c2SSBVqYpslyLmt+/Oc/32HKZp4nseXakEn37mFJaltFhCaGhCwzJNerJdqkBoB4A9cmQ/I6cOUiiVMHSd/lwPmUwKO3Q6R2gkkykmRgeJWQYBGtVKhTMnD5OMJxgeyiFQEYaapmEaBldv3eOlV98mXyjzxGOHSKXirecJQC9VGPo//3NGNL1N/Hzlg7VbSp7Wwkz8YSblQAbh+vgJQSA5HvoF6N96A4FgT6B8yUCNma5r7JWSXa6LDCTmy+8r83GEMIXgqIDDQbCJeuai+nhhvbqeKF1DyNCCQLILia4pKcf1VAkmyzRV9nkhyETKfynpksqfT5ZrLXcIY71A9ze+F5oQaG3+JKqMCoAeOvPEJXSWEBLAkJR8pukAKteRFhZrPhIE7JcBVjyGlYjhAZ6mYSZimIkYwjRA17GBr64V8DXB0Ddfw059ALqONFT9Nz2QuNUSzY0i3X09DI4NI2yLtUKJYGEBe3EDsVElni8yul5lIJ6ha7WI0XDx/QCjXicjBJZlI3RDpaMwTVLJBOl0kqGBvtAhP8A0DAb7+1pBJE6oxerwQEXIAD8066uxUakVfKmuUX/b9Od66O3OKoasCbq7FBgJpOTWnfvcujtHJp3gw4+u8cTpo8Rsi1QyzrHDe7FMk0wmhWUq7WatXgtBSY0gCMgXCvi+0hL2dHcxNTlKf18Pa/kNNNFep5ZlYVmmSuYYrndd19EWVzH/+GUCDeLFMpP5Ivb5W4i+LEa1Ru+1O2zcW+SW43EoCNh3YIrD91ZJbNSQuoYrJZfvLXDxo+vsz/VwqOmRnFlUIKfWbCWcTJSq/Mffep3sjy8QT8SIIckGgfLZNHQ8z4Nqg14ZkEzEMQxF0rcCCAl0ex67HOWrZ5qGCuLw2hoaq1hRhaeF2OSjG91B5IsdN5QIP8DyVRoL0/XRhEp4mYwEv0aTDs/eFo1qOYNvMbnpQDx0VtfqzqZz2kaJ2G/9t8SAw616d0EImNR8GY7L4XpD7duY3eq9YehougLxvWFgk2UamJalBIJ8iYhZSRmVBOoAGNtAggAsEC6bmdzHgIkQcImWk/x2IKRKckU0TfDwWq0OoLXt8h1sgD9123IvQQcQ26GfkTC+Y5b6h2jblDJiSxdCANyytoVzFQFWDQQaIgCphWdDECaRBNurhXU+fPuzf8b2yLURww8P+4Wd3+YhtFtRen2BAjFSCIQXgBBYDYfP/e63iQpfB0GA6ymnanQdTJNAF6oQj20RT6cwbBtf19BsC8My6TMNRnUNXxOKcFkG0jAINA1DBiQ1gRGzsRIxBehMA2noSAn2Wp4Tvs9oV5bU1TsEmoZuGgS68h0zBKQ1HRoODQGu56ryBpbZ2sgidMQPAhXrpGkauoR33v+I8xdvs3tyiP7+HlzPI51KkE7E0HULGRZOFiHzipwAW5mCfZ+xf/a7jJsmUX4YEaICEc5hFNwRBWIEQMoPyPkqICAyD8Zk2xB8oNHkH5SraEKQ+d57mK+cQ+ZLLUlMSNDWS+2FL1XNPyOU7gRK0xntFa1jnrUIaImO8Gk/wGw0lQTccFV0ngzQ/aC9FkP1f3s5ClUiqS33oIdSUXRNVFeylcds699RZJKhq7XElm0nJZpsa103iUcd2kUBeL5yIBcIYjGrFWEYRPXAUMBCCKXa9jxPBS4A0gPDV3XydC9Aq9QxgrbvgdAijYDAkgFDjgsSrDsLHeVzwpJLQUBfs0m372PbFoZh4vk+3c0mac8nFlOAwmw6dNeb2JZJIhHH0DSMIMD2/ZaUHviqNJJpGAjTxDR0MDSkYYBpqHEzQhBo6gT3l/F0tW98KSEIWN8oUqs1QkdygRQ6hmkihVA1BoXAMEylLbHMkMaKViSipuuYlomUcPPuPFeu3+XY4b0k4jHisRjJRDzUrIXlowKYm1/lzswiqVSCWr3JzMwylmXi7w+UJBwEoY9jAl3TsExD0aAQlNAx3UJqiHwJ45V3CYKAHscl7XrYV2bQLZNu3+dJ1+VE6BcXsy0S1+9j3F5sOd96vo9WKDFSrZPtSpP54EarCoa4uwANBwEkl9dJSmBpbRNwiZoBWFKCJjYVH6djf0S/LQEJoo0falxcF8IgF63WUP3b+qNpIDTlJNtxX03TEbr6UQW3N+9JKUSrzFdnEXUplMCnaRpBVFg8erUgUDXsCCuLLK4RcUWxXtw0BjJa3+HfVhCoItqahu542/iVgUAPI6hF3UGIenuMCKucdta1bO3vzU0pc3RAA+nQTuMg2pdv8WVuPWZryZ8QWImOv6WmhUd+VqDVesD29ggWph01WtE9OvsZ0Z1WpGLH8Z8WcD1yCx3jUfQmWs8SlP+mroX+e7SEQLWOhAoQfSC2ETvuv0dpf2WarU654OG+sH3yFWMWrdMUK1CoKObUQTB0oTZRIFGO4o4blhyUaE0PvdZUYesiAh0hcZFghAtJPUt9tjoZsBAdGwUsCdMRcg7tvdFLyvaH9nt3TGoUdi8NxZSkqavfhg6mjgvsmZnnHwnB8PggyT96VZkebAvNMsG0FCMz9Bb4E2HBWBrtHGR6saIKEW5dHqL9qzNyV2VRlmhSpVqI1NgtbZmApIREaMumXMeXAb7nIzVBoOv43V1optG6qYzKIaDmRRCqaDWxaZyisGtd09oEW0qk5+PVG4pZxmKqKK4fEPhemyC1aGkLfoZgpzWhm95dhv2KFlQnsY6AoKpaGxDoQiV+7Jz8cGJl1L+YjUgncP7TryP8QGkJfB/hBgRNl8JanlK+QFc8hh6PowtBs1qnuF7AbzqkLJOUbSrA12jiV+v4nqeAuR8gXA8tUBoEfB8cF83xEIHffiloaTmJxrjVXdEC5JalUploehieE65h27YwjdBkJFSErmkabdArwkhkoebKDwJ0ESbE9P2WZuRBZEgHJayEIL/ZaHL77jylap1KrcFqXoXgxxIqr5Wu6Uip3kcBfz00nwkM01S+FqbBwX27+Mrnn2I9X+Tksf309WYxDaMz7ZHau0KgaYJcbzePHT+EbVvEY7YKjOjrobe7q6VhMU2TjCbQQ/AbSKXFajM+gYaugnLcSOOuhekLrNazNE3DME3i8QSqVlgk8LR/6+gk4jFMwyCZjIeFtjsWbTSgRvT8zS1a2lEduE6TPtEekJ1X7jxHMioRpuuQSmw/Pz4cPm87bW47eUuE7+94/61lTzZvyY4x0QTooQlVSnxNIC0TwzLB8xGVBnJiaEd9Q0S3tfB5bUGM1v0VsxU7jIMCWUIA2TRS+uH1kT/qxzFYAcICnFB4egiA1MkYpb7pYGv2fYnUOtZCB7HeuT8PAFohXf+ZWlt2/eTWCW4j4hH6qin10iMmWe1c051gbQtWiNyMWsUSWzRA0XjRGjvZ2qOBECotT0s4j/hVh4as/UC2D/ADAOjHtIePRtwy2Z+E8jqGYvOVkS172xe2bGYRZeltE30mhhELK+1FFA2UlOhSokdgKzy26brWwlNSVUSMESKcI7HpWOePjBix1s4425b6wjcMCbZi6tFngbayvgXVh4Rgh01jITkWhJqi+fXo6odrjWZrDPXl9R3v/6AmaWuaPulbEZjRpcSQEik0pfFrXRESUE3RH5UIdRMHJIK7sjtN9Z/9V0gpsU0dU9Naz3AcF6euHIr1MI+S5/rU6yqdgmHqaCLKgKQ2XNNxaTRVWLumaSrqRFchvjHbIvkP/l+IjVJHTzcXGVWgCyWxd0pmW5oABf4ySYTQsP7nP2sx5GjYpYSs65IJTZJR3i1dgu2rPEJ6hxbRlGBfvQ2uhzQN/H27aDnvhkBUM02FesN+ydDETqCAMp0/kY48PKdFe0BJJCqnl6Yp5iQluD52LEbw9WdVEEogkZ6H8Hz0l95A1BvogcQMVGZm0dpTodk2el74TNGxzzWARhNrcQ1raZ3//OZ9/g+up0yU/+9vYP/LPyGzZf31dK65lvDTPjYi4WDLJKx1nOtE2W0qNQKMIKHWgESMo9HZ8HITiH0M9dx2xvURUR4qy4RWXVOUdm9iOByDMDdTS3gJwFfzkTRNpK6j+QH4TliUPsCLWzgCagLOTwyQy2YY7M2SiceUwOj74PlINyBwXKTnqzkOAggBP776HAkA24r7tgQvsenvnV46AjPRoUj+EJ3Xie1jtFXe2fyYLQ8MJAReuBZdAs/DXt1oC8Jxu4MnbL1nxz5ubcDo2W3wqvDQ1h6E57MZ+Bf/dZS+FCl9lPbqYZoBeJsPRev/4+iwsm9teZdwvEOBquVQL0ULLHZeHFWs7RyX9lUfo02KNJYPo3F6WC3YpveNgFY0H9F7ejt88QHPjH4+8dkiBFqCtgO9bD1LaELhUaEEIRHxGSnDaQpxhpTIQG7bCm0hqGMTIB9ZW/fIYKvz/Vqg40FNfqxR9KGe2UaeIJ8+gbhzf8drtwhxH9M2gzA1wAGBr9TXGqH6ulMylBFj2wrgpCqZJTsYTURUI+1B09k+KZqGjNkdYFHdU0SA8eOI/hazV6sZRguRB8O5EBTuPDcPM0ydkqwMVVOd94siRN3QWdjKF5XvXAtQtbWHnS26h1q+grhthZXW1VOjXCl2WBC587113SAeNzo2tQcyIAh8mk2H4kaBQqmMrmnYtqXMN/E4mmUqn79iZRPYArllebaBl0o+6yIh9NkJJV0ZyrxB25zZeueO4RaAFWr3NCFaTs+RR8jW0QYQtUaoGfNgUz/b99w8pWIz09vhrpsYYvR9gSI4mtY6IQGttwvjP/1VIhIuEGrcLt6C9cIO8l2b+Mgt52TnCSRidiF0AwjNo1Iq+uj7iHqw7R0iphMRQBmC007n8bZeYDttemBzPag3t18iO0CF2H7PbdcHQRvAeD4ykC3/UhFqY9rfFe1xR7Qkm5364Pk+G40mjSCgmU7wv+4ZZm5xjem4zq989RkeO7YfXddDmqS3hB+ln5E0HI9qrY4QgkRcmVNbjMGXLTCmBQEiCNB89Vv4EtEB1qSrQJx0PfXj+UjP6/i+Eri0IGhrXv1AOdR74d+eh+/6BJ4LnhdeF12jNLYi8NVv3w+P+8haA7dWI75WVGZOAaLhIMM12CZNctOy7jyuUvmovIXAJo3tzi1csaFmTABSBsiwasqDm+j46Tz8CWKregCbN2bHqdbn6FMIuCJFAiC0IARg0T4M2s/dKS/VTk1ra7of2M9owXZGYGoPFkbb87DlvPwYYrXjszsk153OySCk1REdi6iQCLuoERC051Rsfn5EXzp7K4n8n7eICpv6ILcLLw/Rfmoz4kOVZIyYqHxQpx++aUhw2qg4+IVPw2AfOK4ioI6LdKPPnmJYngdNRxEI123/dsJzjot0PZxKjUqhiGy6JOI2MdtqOYgC2xlcxygoYie2/N3R76bTMrXIMHO9TCXwp8c3v+BWSTGc0EjzIIOAUqnKRr6IbRr09XRhheUz1E8HEOzNbrkhD5QKt7bOK4KwhE+lWidmW2QyyVaxXAHg+wRhHrLAtpCm0ZIaNo2U3Hxf8sVwo4ARbloZgVOkUvcSbZ/2S2hagCl0pC+hVkPWK8hqHVmpERTLZNY2SFdr2L7EdD0Mx0VvuuiOi9500G7MIGoNZDaF3DXaXosyAl3hc4XAcV3m5pdxXI/hwT6yXZmOtSuRvmwRcl1XGrStBFaEQEFJkR0gcwtqip7NzEI0WdCX3cQ8Wsdb31HaJ5URuc0Qts3wxxxovfNGCREE4Z7eTkRbj9UE9GQ6ar+KVl92fFTHEbFWUHtvh7aTfCGlypzuup6SzwUYuoYdAt9ObUvbxNLu07bmhkXsPV9pnhBg6pvewfO8sF6jwIp8tR7QgqYbJlkWYBpIy1T0RKICF3qzHV2J9mJbE9vZWtRDSmrlKmtF6B3O0T8xxN/7T36F85duUK7UwsoJCrgrCVzNWFgUgUq1wbmL1/jgo+tMTY7wxGNH6OnKgAAfTSkXTD2sC0dLc6Ac4kUHc0PtiUCNh6pi0BYsNCFAV0mCAyFC/8Ht4x5pDrwgwAv8lgZBiKhKgrqfHuJP9TcEno/faCJ/63fgoxvqZolYSNfYmY51HPN8n1KpQqHm0HRU1YFcdwYjldy+RzdKbWE4CJQgLCPT0yPwqq1AYqtZd6cmQKUq2KxBi/ZWK3G4DEsga9EeD2sBRn3sKJy81Wz78c8X7d8dtHAzj46ukYBO5GvaftkHAa4dwKcAxE/hv7VVu7V1alp/by6X1BIARQRSBTIQgIYmdOUOEYLezRSsk+t08FUptz/7EdojO8hHPgKfeG30oXPyHsG0tfmZYfJSz2sTpmdOweE9retagIPNPg6KYYeLUbT7rYQKn8DzuX7zLr/377/N3dkFvvypx/nSp54gbVtK6nOVKUWEGgcRAbYQ1EnXRToesumC6yMdrwXshONi/eF3oVpH2hYykwIk3vF91P9vv9XWdgjlnCsFSpLqkBp0DXQkjabDN/74B/zeH36PQ3sn+K/+7m8wMdzHwuIKs3NLdPf2MDzYj22ZqpZh5HsV+tuIzvdm83qRmw7qoV4jYGk1z7/7w5f44WsfcGB6gr/117/M/ukJxZI1Qb3eYGl1HRAMD+bo6c4iLHsT4RVSSfzS96HWQNYbaP/Z7yDWCshSGe2ND6FWVyaZal2ZeWoNRKPZ+kw9/F2rI+tNBaBb/VUbxZKSdCTxdbxwp1+IKFeVxG1bBL/7T5Chds73g5AxBOgoTZTr+Xzz9/6U7//kfX7rb3yZX/rqZ9B8v7UWm45HuVRifaNILBYj199HPBYLaWybgQdBgAxNmuFkb1qb0ZwEMkA7+atKCDAN/H/7T1pXtIl/ew/euD3LW+9dJJtJ8eKnztKV7eq4b9tRVQYRgFLrXwZ+tPjV/f7+P0V8dAMcFzG/gvWb/5DOJqB1nmQc+Rf/MszkHxLf8EWkDDrooQyZAVuaVEITkaJFFY+VqMLOoDSmtZrDd3/4Bn/+0mvkert44vQRZu4vsrJa5Nd/6bMcPTCN66moMiE04raqW2hoKt0ELVOB3wJl2m/+Q6Wdm19BjvQje7tw/u0/jsJVqFRrfHDuIt/45g/JZlL82i+8yP49kwRBQKXhEI/HSMcttLAk0uv/9F+z95s/oiebIfb3fwP5lU+h/63fVqCyL4v8vf8uHDzRdhwmJONhCSeEWm+RtqLZdPje917ne6++w//pP/t1ctMTHA0C9k6NUa7WMHQNU4AIfDzCahjRHpCSfKHI+xeu8d6F68RjNoHvt0zNmgwdwEXQTq8ZqPWJprXqaYa8Bxlo6sd38VxX+erpKjJZ03X1DkEIwnQdTTc2rfvW+hFKoNKEwJcBgdTVmIcWJg0PIRWdklKZhC3LxLLMMIIX5U/2iy/g/x9/o3X/9g8tQU0PtRh3Zud56ZW3WFrboCeb5vC+KdLHD5Dq60UKsYmVGr/527BeaC/RDsAVaU8+Xrsl2dFnapNJ7RNay5zYQRtCYKtwRhTcFD0rAq1ty8+jc9atfRCbP3eCL3UwQi4Peb+gA3C1RWc16Z+gTYueG53fCs5aXwt9sjYfasumHeBLNS1cJzqWYSmBSmz29ZWhdao1rlv7+VMCLXjkQtS0fJF+qvaIWq1WAckQcHXWRtQsM5y6SMLtUCmHAxUuyVZR581MOFzQusboUD97p8aYub9EoVbHMXREJhX6aKGkv0gDs/WVAokfBK3NHwQSGbQ3jvHWOcR6Ufn3lKpKYkfSdBwV5q8JNGGE+gRViy16G6HpLeZkGgbjo/30dacxdEEQeJQqFV55/QPeOXeFTz11kvGhPmxdtN3JRLhBNbF5i0hlUfBR46sJgdIVKHWsJpRTe19XisN7x3nv/GUajRqB5yopOlyghmGQTqoCr4auIr+EssOq9w9Vrdo/+pfw1oX2w+/OK7OCoSP+u3+1qV+PtJrVJKpZaUmB4a1aYs12GiECGRL/zc/SQxOmlJJ4IsbBfbt484PL+H6gAhv8yMFT+VpZpto+DcfFcVys0IEbaGkKos28tW3SBu1IeEIw1Alcw8++73Pl+h3e+fAyuydHOF2q0JXNdPirBESq/0gWVhRFQKCFBCpA4ivJ3nFDB3wQ6wU6hk616Lzrtda5GnsFllS+ILlld2xmvAowRuQ2UOs83Cub1PlSMrewyMuvv49tm/z6L32GsZEBFpfXcFw1zjoqIlI3NJA6mlQmMGX+0VoCfyBVBKsCGFvHHxoNDwIH3dAIpIcfeBSKZVXeakXl4KvVGyyu5BkbHWDv1DCJRJxqrcb92XkO6jq2ZaHbBpgdtFGE+xfRWpeRObTZbFKt1mg4TUxTI5WMEw/9kVbWNjh38UZYJ1PDdx02CiXW8kWaTZdcbxcJW5nEA9+n7gRoAVih+d62LB47tp99e8aZHBsmnUoqmiQh8AN8HySqdJmuaVRrDVbW8ti2RTaTUmknWmtIIIRGIH0836debyClJBGPq8haoVGqVKlUayQTMZLJJLqhAmQMXVeADKUdDwKJ0AJFj/AATUV0IlFR2QIZBEQ+UlGKmE6hXoZ7rRNodf6ttBPh3o3bnDy6n650kv6+Hrq6UiqBaQT0t+yybeJPBEDp5Co7tVCo2OmahwZahDyu038rouEhw+8kbKHg+FfRJLTpaAfvBR5NG9VSK/m0C2/TIYBFhDlaaw8AXtueKdu/O6wM2wBgRGh2GCdNRIFARktL3i44TksQVb+2uzf8LO3hwVbnOz0saPok9PoQLQJcEhTRj44bWmvztIl1oEB/S8MRMV2ltRBs8WOKNA9APB7D0ATS9xGBjyFA68jJ4Tsuji/RDUVgI+SrC2XuqDaaAGFtJQ1BaKKIAESgai82mk1uzM5z87V32Te9i+GBHpLxGJquikV7fsRkQdcC5YgNCE3Q29NFKhUDAvzABXxiMYOpiWEmx4awDCPEOe23DCJzg9YRnShAFxLPUykzLE1Jp4pPqHfWhSCRiDE9NcbESD8SsE0dQ6jxDKQKHIjHbKVhCAlmCwALFL8PBNjW1oXxgMmGHavUaxok4xCzlTkhHkPGbWTMIgiPybgNMZsgZhHYJk3DwLMtrGyGWFcKY3kd/W/931vzDhI/cKnUqnhegK6ZmLqGqUuE0AmkIJlKEbctZCDxHJdmpapSChiWkmeFTiqTAgTFUoViqUJfb5ZUIg5C4Pk+q2sbbBRL5Hq76evJtqTR/z9z/xlsWZam52HPtsf7c713edO7yszypqu7q7tnusdjhkMAgwBAkFQMqRAl/RIlkEFQFIM/qFCEIJABgYKRwBGmB9Mz0z3dNVXd1eVNem9uXu/vPfd4u93Sj7XPuedmZrnuGlArKuses8/ea6+9zLve7/ver1ZvsJMrUG80CQZM+nozdLlY02pZ2I7TMdvohkycqipSHuHcqaNMT0jF+lgk7N+WHG/NZotKuUYgYBKLRFFUHYRLs16nVKkjEAQDAUIhjUD3+DR1vFQc15PjpX0tpW3ehM7C22xZGIaB6Sd07eineR7NpkyyrutSPsF1HBRNYBp6JyDAsh0q1RrVagtNhVgs7Cdqh+W1LVbXdzl1bJLR4X4azRaFYgXT0DA0DddnbDzPw3Xk9YTnEQ4FiUbDaIDtyN9UGy1isfB+yiC//wnhsbubYy+/RzgcIBwMUqnUsWyHaq7AR5dusZcv0ZNJU67WWFpZpzcTJRAwKBQLFPMlHNdn5DQFFBchXIQno3QLe0U0TSEWjWKYBrbtsLO7x50H86xubFGu1nBdjyMzY5w/c5RYLMLDxTVu3FkgGglh2Q5rG7v86I33mVtcI5NK8I2XnpLZEByP9e0c9+bXcSyH/myK4cFeErGoD0z3sB2HVstGUWTf3NjaZWeviPCkHlcqGaPZtLh2Zw7X9cim4nIeDAZwXZnGa6i/h5nJUUCwvZunVKmRiEXo780QjYRZ39zh9v0FopEwPZlkx4UgHAqQzqTJZtI0mk3mFlaIhE0mxgZxXddPnRUhGDT8OdqjWmtQLEoNtGgkTCaVJCb2GRuha1i2TaPRAoRMMK9peMLDdT1/EyjXi3QiTsA0cBwpyNpmrtu+WNqB+aV7d9a1yIm2LtMTltv2Z+0O1WZwflEiogO4XB8jaDyZGX7yT7/ctZRP+ZU/7/qgtQO42qXNzn5R0NUhtT7l+Efb6XNxQjfQevSzL1BE130r0iXBMCR72uHHuk4thHiCb+0vV74Es+U7Wj4BSP47K47vd4ECpibNbq5Hs9Gk2WyiCo+gGcI0zH1wK8DzbMnb6NK8IJDJZmuNGts7OVY3tllaXafZbMnNCr6ysPAoFKtsbObJFwvsFUrEEnHOnT5KMhGTC6ptceXGXeaWNwgGTKbHhxkfGcQ0za6dkej4VjVbFnOrW/zLP/4Jx2bH+NbXznHm+CGCgQC7u3keLm8gFKljFAiYjI8MkExEZULsUIBI0MRxHDzbIhoJ8crzZ7Asj3AoRNsjRAWaLYvNnRz5QhlNUxns76Uv24Oua7iuR7laZmltk+3dPGNDfYwM9slUBf7O3HU9Gs0mlmV1BBBNo73z9bAtmUx5e7eA5bhkszJPWUJVUX3nf6dlUanUMYUg7En9G84chnIN0WwiomHqv/caREJo0TBmPIYaCfvgyUSEAjRVjUpLSnfEohFZR6BUqbG2sY0CWI5NLBpmsD+LEIJKtc784jp7hSJDSoTxRIR4y+44VCM8HM9ifWubG7cf4jgukXCQZCLGUG+SeCxBQDP8vqKwvr3HR5/cYHNzh3gswvTUOD2ZFPlSkdWNLXJ7RVbXdzENnde+9gyTY8M0LYu7DxZ58+2LbG7nuHD2KK+98gw9mRQty+Ly9bv85K2P2NktMDE2wO987+udiDwhBBev3WFuYRVNlaklsukE05PDjAz1EQwEyWaSFMsVbtydY6re4HQihqap5HbzXL1xj6u3HjA82Ms3XrpAbyaF1Wxx7+E8H1++S6lSY2y4n3OnDzHZ3r1pGtaRKa7+7/4mC8vrFIplYtEIz50/zdSv/687Y69arXPl5j3uPliiN5uSiap7M4wM9RONhNgrlLh45Tb1ZouZiREEHvNLq0TCQY4cGqevN4Nl2cwtrHDvwTIr6zuYhs6Lz5xgamKEQCBA1c9f6XmCcqXG3MIqiyub6LrO/PI6fZkEpqGztrlLbq9MvlihVm/w1MnDnDp2CNt2eLi4ynufXGdjO8fhmVF+t94g4puJ8Tdkufwel6/fxVAVwqEADxfXqdSafi5Vj2QiSiYdZXF1kwcLSwz0pUgl4mzv7tGsNXAcVzJnujQ7NRtN7GoNx1B54+2PsFstzp48zNj4CFvbOT64eJ3FlTWmJ0fozaZ4/+MbPFxYIxoJc+r4DIoiGe9gwMRxPeotm9tzK3x0+S7nz8xi2S7lSo3F5Q1+9v5Vltd3UVAIB0xeee4s508f4d7DZf7s9ffpzST5G997lVg0zCdXb/Phpds0Wy3GR/rRNY1ytU4sGsZ2PErlKu98fIPdvSIzE0PomsbCyiZjQz387d/+NuOjg2zt5rl47S6W7fDsU8d49twJmq0W9x6uUKrUGOrPousaeT/X7PnTR/nut1+m2Wrxgx//nEw6zt/5vV+hWKrw/ic3GB7o5cJTxwgFAzRbcqxcuf6AZtMiGAzw9NnjvOC4nTFrC8Hla3e5dusB8XiIb7x8nnQqTrUmk6on4lF0xcC2bJbXNlla3SRfLFOtNRge7OXsiVkG+3v8yNUnL2D7SYw5yOo8kT3xwdkvY+k5cL723/0UaF/ux18AeCj+DvjT7h9lf73qcjWQP/WZrs8yAX4Z0+mj9XrUL+vxkz/y90nfddXDXw27uBef7d4XSdd1FU1pkxC+OdHz5YCUL9CeX7J8qdyIwlNA/SqotfaNfJkzeTIypW2eMDRsy2FpaZUbd+YoFEqEAwaTY6McnhonEY/gOS47+SI7eznCoSB9vRk/JYhLuVzj8q273Lm/wOb2HgvLGzQtS7o4KQqtls3C8jofXblFvlAlnYySy5fYyhWwbIeXnz1LPB5hfnmdP/nhW7T87PVr61v8zq++Qk8m3e62EqP6PmWe62FpGo1mkw8u3aYnE+fQxDCmaXDl5n3+9MfvMjzYS29PmqWVLc6cmOIbL5yjt6+HgGkQCgaoVGu0bKeTy+nh4iKFYoXjhycYHuylWrO4fO0ub31wle1cAUVReOXZs/zat75GIh5lczvHz967yNXb91nb3OWZs0f5je+8xMhAH5qmUK01eDC/wtLqBmubu2zvFhge8CcqFSq1GlduPOCjS/fY2t2j2bJIJeO8+MxZXnj2NJlsBqtlcfX6HT65eofj9x5yrlonGg6h/t538BbXqa9tUVDgL2mht1wShmAmnmVqbIhIOIQnHLZzOa7dfMiD+VVsy+HZ8yc4f/Y4qqJy5948f/LDt1AUsByLMydm+M1feYVavcmHl27yyZU77OQKDA/28J2vP8epeIw2vyY8wfbOHj9580M+uHgLFLmjR8DpoxO89PxTjI+OgBDYrsvy+hbhcJDllQ3ypQrHDq/z9ZfOs7mzwx//+c9YXNlC11TOnjyE6zq4wub2/Yf8mx/8jJW1HYrlKutbuwwPZXkxe4ZytczPP7jEz9+/SjaTIBgwKJer+6NDCN754ApvvntZMpyaRjgU4Omzh/md777Moekpllc3+ed/9CNu3J3nd7/3KrOzkwQDBlev3ebP3/iA9a1dvEuQjkf55svnKZXLvP3hVX781sc0mhYTI/3EY0HG3H0TW73Z4Ac//jk7uwW2cwVfcNdjUtBxd9zezfP9v3iLW/cWmBwdIBoN09+b5rvffJHjR6bI7RV5+8OrrG/u8ju/9grRSJCfvnuJeqPJd197jm+8dIGVtU2+/2c/ZXl9h1K5TrlaR7g22VScnt5sh1FwXY98scS9uWXml7dIJSIsrW4yOzmM57j86M0PuP1gGUVR6MummJkYwbFdbt59yPd/+FMqtQaFUpmLN+7ytXxdsiH+/COEoF5vcP/hKp5jMzSQZX1rj1qjxdT4IM8/fYqnTh6mWquzvVfk0vU5js1OMDM9TrXekhF7yInc03U8x6VSrePUGwSTMeqNJldv3KZUrfCrsTAPF5f58NINhgZ6ePb8cQKmSaFY4c13LnHt1hxHZkeJRYNSTBXpNjA02MPpE4d4/9Id0sk4oXCYtY1dfvzTj7k7v8bzF06QTad4OL+C67pyI+UJtnbztFoW9UaTcrXKm+9cZnVzh9deOsc3XjpPs9XgX/yb1/nwyh2eO3+cyYlBlta2KFfrnD42RSQcZnF1i/vzayyvbTIxNkQqEaNSrXN/YY3eTJKzJ2apN1usbeVQFYUjM+MM9mf55Ood3rt4m0g4zHe++QKBgMnWTr4TuazrGsurWzx4uMrocB8TY0Pk9opcvHqX67fm8ISgXG3ieYKnbWcfbHmCa7fu88d/8XOymRinjk0TDJgsrawTi0VIJeNYns3y2ho/evNDKlXJ4N6ZWyYaDtBo1XnluXOkkglUReusQbrYN38/trx2/LbawKrrc1V8ueXrM0sbsHWZ8L7ET6Uv0ufkasQ/7nNO3faRPlCHto8hXYEVB/yYxC8OtPYv/EtYwZ7EAB1k8ERHg6sdhIUEWl1Mn4IqGdK/pvKFwVbbYbC7Lgcsi48c/9l1/gXAliL86B9ZWsCdew/50U/e4eqtOWzLQlHg6PQ40d8KYmj90q/l2m2WN3ZIp+J86+WnOXZ4Es/zuHbzLu99eIWe3gzHZicQnscDa422QGa+WOInb33E0uoWLz97mnOnDlOp1vn+j37Oz9+/TH9vhjNPHeHewwU2dnZ5+fmzRCMhWvVm54F2OqGQzJaigGFojIz18Y1zs3x4+Q62ayE8G12FcqVCLl/khQvHuXD2OG84l3j7gysETYPvvvYipq4SMHUKrodjS3HPnVyBt96/zO5eiWwmTiaT4Pa9OX7w43eoNZpMjQ6yVyxTKVdwHRvXcbh24y4/fPN9BvszCCH45NpdnnnqGMP9vdQaTT64eJ1LN+4SCJjsFcqUa1UUNYuietSbdT65coe/eOMDmk2b2alRhBDcuLvA9//ip6RTMZ47H2VrfZM/+fOfsrS2TTxf4VijKWUeKjVs26FWa5BzbOYX10klY9y8M8/d+wv82rde4sjhQzSbVd557zK37i+hGxo37y5SqdeYmRkhEYuzurnFe5du0t+T5MyJadKpOJ7wmFtY4f7cEj3ZpPRterDMoalRDh2fJdbuPK7LJ1flPZQrNZ556ghDA1mu3Zrnx299Qr1p8Xf/Zi+a6mGoMDUywLdeucD2zh5vvnORy9fv0t+Xpq9X5mbLFytcOD3Li8+epq8vjedJs4uqwq+89jRz82u8f/EWe/liJ9KxWm/geC6moREMmv6Gdp/Hdn2tsPGRPgb7eljZ2OLy9fscmR5mZGiQaDSE53nsFWSfsSxL+vm4LqePH+L5Cyf5N3/+M5bXNmhZLerNBqsbO1SqDfqySWanRkglYgedmYGxkX5OH5/h48u3ef/ibW7ceeib4GXV8qUi9x4uk0xE+M43nqbRaHHnwSIr62scPjRKy2pRq9dpWS2CAZ2RoT56Mkk+uHSblbVtWq0WKysb3Ly7CMBAb5pkPIxl25KdVhWZH1ABTzjouko0GiRgavRkEpw6Ns3k2ABr69vUm032imXOn5zlGy8+xfEj46gaVGpVQmGTb796gbnFNf7Vn7zhR44qndnHE4KN7Ry5vSLnT8/y3IWTpG/Nsb6dJ5WM0debQTd0QuEQmVQc1/MoVms4noML6MI3OwiBp8pcjLbjoGsq0WiEF589RatZY3evSKFYZDcvWeAjh8aIRSIIBAO9GYIBk7XNHWr1BkJVUTUVzZ/xPU8Qj4alRlw4iK5r3H2wwdU784wO9/PCM6cImCYp37SnGyaDg/0M9mXxXBfP89jezbO4tkk6GeepU4cZH5PJ0U8cm+aTG3Ps5os8fe4omUyCXKHE1NQIkVCYbDrB0mqTQrlGpVqlryfJa6+c49jhUabGhtF0lVy+SLlS58jMKIdnxhgZ7CMRj/JgYY2+npR0qdDBNDSf+WvhuC6mabCwvMHy2hbpVIIH88ss+wmw+3szVGoNwiET13E648ESHq6rUK03MEyN7VwBx3VZ2djh/JmjtE2d7128yY17Dzl9/JAcn65Ds2VRqbd4sLRGPFZEUw1p7rVsDjWahDxxwMPx4Eol9t0iDpiyOnbLL76GfVrpqOv/Iuf6MqzWF7tGN/MngZfh+986TzYxPun9Z9aBx4FVO5DkU12PnnSf++ijnSKucy4hn5EitC64sW+aFb7Zq+3tIhQp6BwIhKULkOjaYX5F5UuALZe2Y7SCgtT0aFdKdry2+vfnJ6n+4p1KiDYZ6HWkHwSCxfUtfvzOJ7z78XUMXefQ5BDlSg3LsSmWS3x0eZuf/PxjgkETy7Z5/+JNMqk4UxND1Op1Prx8k1yhxMsvPMXE2BCaqrCzW+hUL18ss7CySSYV59jsBD2ZFJFImKGBHn781icsrqxy+txhzGAAT8DSyiYvPnuKwUOjRCImruf4Oo/+rsi3dQcCJiNjg6RTcdq+Zq5w8fAwTZ1Q0KQnG2d2epS61eTOvXlu3JnnpWdOS38DTcNxPRqOjau4mEGdcDiIs5OnXm9Sqzd4uLTO2vYuL104ya98/Tls2yEYMAgGdOmjFdR5+qkjPH32GK+/9Qn351cImiaKqnDz7hw/eesjRkb6efXFcyyubLCTK0rHftfl4dIar7/9Cctr23zvm8/yra89DQJCQYM/ff0DHszN8/TpwxRLZeaX1+nJppka7CeUk6l83HIVT0hnZl3Xee78CWZnxvnw4g0uXrnNwvIq4xNDNBtNGs0WR2fHGR3uZ2l1m929Iq1WCy0lSCWjGLrGscOT/N5vvkZvJkEgoNHTE+eFZ08QCYf4+Mod1rdyklXU98OrXcflo8u32drJMzs9zLdePc+RQ+NMjg/wL/7oda7fXaBp2+imTjhsMjLcy+j4CIlEglK1xtL6Ftu7OSbHBkjEIpimwcjIAFNTY0QiURQEh6ZGSSTiaJrKw4X1jpSAEB6BgM6xwxNcvT3Pg6VNstkUqrk/FAWgGwYDvWlee/lpzp85ynufXOFP//IdVjf3qDXqJJMRstk4uq5i+6H5KIKpqRH6B/pklGRAxxUejueg6wrxWJCAqeN6gmDAJBg0ka4K0pk7FAryza89S6FU4cpNyTJs7xZl/jkfCOq6gmlojI/08/LzZ9nczrG0usFeoUy92cTxHJlWSZfJjGXuwSimoaHrCqoGmqFimhpjI4N85+vPEAiYpOMh0ul4h0TwfMdnTYNoJIBp6iTiUYaHskTjYfRdDU3X0HWdwcFeDh2aJJaIA4LJsQFCQYNAwJQMhxB++p79BnZdl/mlDSzbYXiol9GRPmqNBr09CVAE5VqVar2Gpulo/rPz/B2+qgp0fx7yPIGjgGW5OK6HjjRB6Ib0tazUmr4P2jaVWoNavUmlVqfVsiiWq7ieh2VbNB0Xx23ni1Ro2C6lWpNStSrrr+sYho5hGDQaFrqm0Wy22M0VaNoOKCr1lo1pGoTCQWq1Bg3bplyrYtk2rutQbzWptywCAZPenhS6rlGuNjACAQzTwAyY6IZOOBIgkYiirG1TqlRYXF1DVRXisTDjoyfIppOoqkq9XqfWaFCp1qlU6zSaLUzTZKA/TTgcQDfwIxBlRHLbrGfbDo7jsbW9x4PwMrfuLlKp1Hn+/AmePneccrlKpVpH5a3OmGh5Hp6ATCpGrdnig4u3SCSipJNxotEotVqDheV17s2vUmtaxBNRMukks9NjmAGDnkyS5bUtNrZyuI7AcT1KxTJ/uJtn2DQxhAwaUuk2IfkBIG3njG45oG5A8EsBLuWXAFrdp/k8Zkj5hS7R5nykor0G4gmipF8UaH2en1a3OfEx37BPoXUUhS4tmseK6JJl0hBSqgTpKtTNfSmKQiQcIh6NoCgaX1h89UuUL25GVDwfdKoHdsMHDlEOwMbPOtkXvmz7aEVRZDQUYNsO71y6ycWrdylVaxyZHuFrL54lEQ3hOR7BYIAHD5cIBQO8+MwpHq5scntuic3dHC2rSbXRYGevSKnaoN5oyYhATe/46DhCwXIEqp/Q1BOCYqXKbr7MVq7gq5RLAb7DhyY4dWyaa7cfUq01eP7CcaLhEPGoS63RwrBtdNcFV+qLtFoWq7t55kyVetNmZ7dIrlAhEo3QsqSkQcuyEKpHIh5B01WaLQvbcaVjMFK3ptlq4QqIJ6IMDPSwvLpNtd6gWq2zV6wAkEknSadThMMh8Dw/xY7FzOQIZsBkdTPH0vo2oaCJGdSxXIfr9+ZZXNvm7OkjDPRnsWyHnmwSy3apt2zWN7eZW1zH9TwG+7IkYlFUVWF4UCavLpQquJ5DLBJmsC/D+naevV7dz2mnYpUqIKSmjo5KKhljoC/D+OgAN24/ZDdfplypoesax45OoyoKG9s5CsUK0cGg73AtTSamaRCNRUhnE0TiERAeQ4M9qKrCzTvz3JtbxrKlo3mtixUVrtzxq5rK6FAvE2ODpJJxerNpAqaJ4zqgCHRdJqsVKqApBAIG8USEYEgmuVVUOWRdT9CypCI6qiqjHDWDje09rt54wI078zieoFJv0GjUaFgtevvSPH/hBD999wr5UoVGo3WAKXZcD8t2EHgYAR1UaVZrWTYty8FyG1RqTbkRUQUeLru5HHceLLCyvstOvshuviQBroCm4xKOhBjoz5DLV/jk+gOGhno5KvYV8yzb4drtOR4urDK/vEnLkn1YtANUAKFomAGTSCSIh0LDcsgVygSCAYqVukyfp0pmyhUCD88H16Co0k8ilYoTCgcJhQLMTo8RDgVpWg2EIiU3LMdBCkqCUFSCwQCGH43mup6fiktGGXsIhCJwPEdGhbZslta2ufdwiUgkRKVWx9A1REvKF6gAinSuzxfK1BtNcvkC9WaTeCJKLBah3mixs1cinUkRDBg0LUuK+AoFBwUXBdWXuvCEQNF0Gs0mtu2g2Q6WZXF/bpkHC+sEgzKVj25I8PdwaZ1Prt1BUTQJ6lsO/f1ZdF2Xzt6ewHY8Gi0LgUK51sDxZICLGQjQ35clFA6ytpnj1r0FQsEAtuNibGvopgRgjuPgei6O6xCOhInHohTKNW7cmWegv4dg0GRpdROBDPhptWwpAuq61Js2A/0hwuEAjueSK8h+1LJsmk2L/t40p45O09+bJhYLo+sa61s51rZ2icYjLK1usLi6xWB/Vs5XqorjSL2+heUNXFcmrDZNHc/z2M0V2dzOsZsvUarUCAZMWgGTaq1xYB1RDI2hgQznzhzh4rW7XLx2h0w6we//9mtouk65XEHXNBLxKK2Wza27C1RrDZm7MhkDFMqVGldvzrG7V8I0DBkMUWtgq9r+QqhAJ8KwDYIUn1x4lMnqNjN+FQxXpw5f0qT2BcmtX7x4XZfYl1v4wuVJQOtLmw0FT75JhceCqh45b7eGYzu6/0CaHmQEfjBkEo6YyCjK/0V9tuRS0Kn4Y3ZaH112PNO/mtIWlxOKgmc7eJ5HvdHk0u059grlziMIBkyOHJ7EarYol2ucODrDiaMztCyb9c1dHEdGCgk/L5LrQaXaYG1jh8H+LI1mi0bLomk7OJ5HIBggGAiytVfk4coW0+M6e8USCyvrRKIhkskEtmVTKFZ46vQRwpEwP3rjQzzPwzB0rJbN3MIqf7CzR8YTmKqK4slJ9Ob8GveaEujNL29x7+EKMT9BrmU7zC2ss7VboNpo0Wo5ZEfjaLpGsVLDsmVy42K5iuM4mKZBJBzE8QTFSoNStUGzZdG0HDZ28mzt7qFpmh8pJ9m5yzcf8N7Fm/RkUgQDJpVajVrLwnIdao0mjZZFvlRhdWsPV0AoZFKplqjV67ieiyeg0bRY25IaX4GAyW6+hEAhHAmjBUwisQjffOkCP//oOtfuL/FctS6TLRcr/u5RwfM8/z6kicdxPcrVOhtbu9i2w537ixTLVR4urrOxtUd/X5LdvT0URaXqg5NqrU6z1SIuwjRbNtduz/P+RzdYXt1kZ7dApdrgwcIqQ+kk42I/rY3qC5EKRcERUKjWuXFvkd18iZnJIQxDhr1X603WN3NYdguhemzt5ClV6gTDISwXWraDZTuUKjUK5RpDrkelUuWtdy/y5tuX2Njao95oEo2EuDu3xKnVabZ387z74TXiiTijI/0UihWKlXrXuJECm9t7Bd775DqFSpVb9xap1pvE41E006RYqlJvtGjDs0qlzvsfXeHdD67iolCrNcmXqpQqNVqWTS5fYnVjl8H+LOlUnPvza6xt7XTmFIEgXyjzP//JG7JPVOvYjkujaR2Ydzz/2dmOR6XWwLId6k1Lbl7KVQxDJxaLsLa5w/Zenr5immKlTtNyKFWblOsWPT1p+vvSPJhf5V99/3UMTQXF46nTs4yODlOu1fFQsF2B7XqouoFp6Ozmily9+YBAIEC9aeEIQbNlU6k3yJeraIZOqVTlnU+ucfPuAt/5+tNEY2GisRDV3Qo1D5L+zaiaSjYT59Z9m4+u3CUYlsBhr1ih1fRZJ9dFYOD66ZUkE+0zj+1WEwJP1ylWGlJbr2WztZPn9bc+od5s8fzTpzhyZJpAOISqaaxubPLx5duyr1fqHD86ydlTR2SkpqrS15PEdeSGAgSu6zE23EMmnQRVo6cnw5GZMd756CZvvHOF4cEeHNsmm04SDAepVOts7OyhKAqW7TA+OsjTZ4/w3ie3eP/SLZqWjaLClZtz9PWmmJoYpFJv0rJsCsUaN+8uEAwGqDUsGXkdDHY03C5evcv1u4vYrsc3XjrP2NgQh6ZGuH5ngTfevcjyxhZbO3nuPVwlEAxg2Q6ReJR4LMKt+4uYH90gFg1TLJZxPY9gMEgoHCQWk8m/3/v4BpVqDQWFaDTEhS52I5aOc/bMMbI9Waq1OldvzdHfl2FksBdPeNSasr7HZidYWd/m/vwqO7kCkXCIidF+ejIpdE0jHouQzSQYHxkgFg2T+Zc/QWtL1CgKygGg5Y8NP5JR+kU94mDeruMTdPS6TnDw9QHw0V43u7Zan+co/tjXn2Z6e+SYX5jaEp2X4gv7h3Vf+isEBE+6d9QOeNrPRnKwHFBXE20Atv+BooCuKX60tPiFmurzyhcGW5oQtFWGpLCpr+VzwE7q0hZg65TPQ7BfEOEKOKDzoxgmui4FBovlGqubO4wM9VGqVGk1GtRqDdY2cliWS8u2ZZgwCi3LJRwKMtCfZWFli0s3HlBptFhb3yGXL1NvNLAdh1QqyaHpUd796BoXr96h0WyytrlDoVzj2OFJ+gd6sRyPa3fm2fJDrVVNJRoPsbmzy9Wb89y6t8yv5EuETBM9YGJqGqFAgEQmgQJEoyE0Q6fasEA3CIXD2K7LR5fv4XhyARwa7OGlZ0+h6Qa5fJnNnTzFUo3l1S1K5SrRSNhnO2yalk3DsgkEAmiayvuXbrO6mUN4gngszHe/9RJZVC5ev88n1+7zzVcuEAwF2djJUazUUHRDgkjX4+L1+4QjEQb7M9RbFk3LxvFgYLCPifFBrlx/wOVbD8lmMzRbNu9dvIMAhof7EZrKpRv3yZerfOcbz3GrVKN+d5lQKIBbrhJALtiO43Lr7gL9vVmWVzflbhaFRstmZzfPW+9fJZcvU2s0sRyXpZVtrt58wNPnIoCCoWusrG2zvpkjlUqQL5R58+cX+fDiHXRdw3M9ytUmtx+sEAmFeK2d1w84eWyGlfVdHsyv8f4n1+nrSfHBJzepNSyGh/vQ/Z1vpVrno8t3GBjooWk5fPjJDSKRMNOTI3hCRpdatsvqxi5rmzkmJ8fIl2rce7jK8voOADPTo4wM9WK7gqX1HfKFMldvzaPrOi3LZnSon0wm3j32cYRHtd7kxt1FNrbzVGp1JsYGOTw7STgUYmVti1JFOtUXSlVy+SLL6zusbedJxCOYAcmkFMo1mrYLqjQZWXZFRraGg1KKQt03+mu+VlqpUiMUDNDXk6SdzLVdbNvBdlzWNndZWdsiFAwQjYawbJu9UpVMOsngYJZb9x7y7oc3WFndllF+1QaLy5tsbu8xOT7Ea68+w4/f/JifvnsFw9A5eXQcVyhsbO2yvbtHJBSgXKnxcH4Ny3HwBKxu7fHDNz9it1Dm2OwkA70ZpieGMHSdrZ09QqEAuqmTTMUxTYOV9W0MXSZ8brYsbF3v3ImuabzywnlcAffmlnnnw2uEQ0FaLYtEMkY4FMIwDFzXo95soWkqUndKugL0ZxKEKvJzT1Op1OpEPAniPUVB03XOnTnK2TPHSCbiTI4rZDNJytUy5XKNRsNG13R6elKkk3E8T5BOJfi1b79Eq2WTSsro0nNnDjMxNkBfj4z2CwRifONrz1BtWty+t8h2rkgiFqa3N0s4HKJh2aRTCRLxCPFkgoGBPr71zefp6c1w6dpdbj9YIpGIcPrENNlMkkAwRCqV5LmnT9Pf10M6kwJF5dkLJzh+dJLp8SHGRwaoVuvcvDvPXqFMvlilZTv09WR49cWz9PWmKZYqPFxYlwxVtUGl2qRhuWRDIb7zzedoWTbLa9uMDvfR05NhMhqmrz+LpmlMT44xPjpIvWlRqdTwXJdYNLqfNB4wI2EGB3qJRMJs7uyRK5Q4eewQyUQSx/FoNFtU6xYjIwN871sv8smVO+TyRUYGejh76gi9PSkKpSrTE8McOzzB4ekxopEQqb/8GL1Ykf1c9UHEAcfkR1irdi5QSdXSWbE915e2a2ssdgGnJ/khPXpO1IOA6/HVz//j0wuP+if9OyqS7GsrE3wJ7a0vW8cD97XPQiG6U+wIaIv1dvvTAY9a3/bz88rHpqhtLbOD7d1J6fPX0KRf3GfLk7o7nu+I1gFcXVEUPve1/6PPcohrf/95x8gDJYjz8/AFYxGee/Y0LdtmYXGdYrnG1VvzFEpVWs0WmVSc9c0drtx8yJGZCVTNAEVhbSvH7bklTh2b5cK549QaLdY2d7l1b1FOsokYyVQcVIVgOMCZ00fIFUtsbOW4t7CCYeg8//RJZqfHSCbiqJrO5PgIC8ubbGzlOHf6MM9dOEq1VmOkUGEnV8JZyUmziOehGTqhUIDTZ47gTg8jFEjEgkxPDJJMxEin4qCoWK5Hb0+Kvp4UQwM9jA8PoSgq9UaTWqMJwPLqLmsbOSbHhqhUZBi6rkml8onxIaqNFvfm1ljf3iMcDHBoZpxUOo1umkxOjrKdL+G4HsFwkN6eNK6QOQCPzE4wMzXKwuI6H126RU82QbFUo78vSyIRZ3Aww4vPnqbZssjtlXjzncvUGi1yeyXOnTnC9PQ4lu3xYGGVi1fvceLoNGXLRtVUKfDZaIEiRRWlY/0iLcejUqmRTScYHxuhv68XxxO4HpQqdQJBk2hEZa9YY3Ujz9ejYcZGBzl7+gjlap2tXJHhagPLFehmgEgkRDgUIBoJ0dtsYRoGZijobxRAEYJXXjhP07K5fmuO9z68RSwaQlFUzp+ZZXigF9v2SKdSnDw6zdzCGh98chMzYDI63M9Tp49w5NAku/kiU5Oj5EtVGeXq9+FQKMiRw5Nouk5fb4YzJ2fJZlPs7RVQNUE4HOJXv/U86xs5NFXj/FPHGB8b2h8GgO24mIYUsj06O0E6GefQ9BiHD02gGDq1pk0oHGZiYoj+/h4y2TTPXTiJoevE4xHisSi9N+77ZroQvT1ZTp2Y5fY9qeP0zLnjnDg+i/qD9zpjLJ2M8w/+4NfZK1bRDYPdXJ6NrRzKlYftQ+jr7+VXX3ueza1d8oUSE+PDHD00QalSQ9d10ukkT58/iSJcdvdKaLrK6FAfliX9Bm3HwTB1jh2Zoliqo2s6U+NDHD82weBAD+sbu2RSSb799QvEoxHi8QgbmzmGB3sZGeonEY/Qk0kRjYZ54dkzPHvhpGTffPHMbDrBKy88xczUKLqmoeCRSsbpWc4RUTWUpo1AJqSdmRojHAlxaHqRQrFMIh4BVbKdmXQSIxjE0DVOnpglk01x5MgUwXCI6ekJlGOzpEstDEOnYeiUSmUiyKhWPR7l8KFxZqZGScSjtBPgxmNRspkkwpOMnO04OD4zGhZBkvEkZ08doVZrEA4HCRgmM1OjDA40UNClqrqmcXR2kmDQ5NLVuyyubNDXk+bpp47Rk02RzaRI/24c0zB8k2OI0eEBKZsSDLC2scWxw+NMTY6wV6ywtpEjkYgxe2iC55897Ue+BjlxdBrPc6VAqariCEinUxw7PMGRQ+NEo2Gsls3E+AiDg320mi0eLKxy58Eyq5t7VGp1LMcmFAry4nNnCQYCvPnORYYHejl+dJJIWOZtzBdK9PVmGBnuJxwKUm9IvzY8DV17ozMmXFWl3mhhOy7Dw4M8c/4Ux45MEQ6HaLVa2LZDy7YxAwGOH50hEAzwYH6Fwf4eDs1M4LiulK5UVcxgkGQyTjwalubptt/Pl2Ez2ily1EdNWD771f73qQ7fB07WBbjaM0D31+1zPOE8n0dkdP52eyj9EqXjKoRELV8GcH3pazxyb935CP18xIrXpaIvvI4oNcgAk31iSCCFzaUzvaa0k1IrnUt9BTr8n1m+ZG5E4SuNt9mtNgXKZ9utP8+X69MA16Oo3XeQV02T08dnAdmgi8vr3JuTES2Hp8cYGOilUKpTLNe4fX+JUCiIYZisrOe4cXuBw4emOH3iMBNjw+SLFRqNBpZlY9s2vT1pUqk4gaDO8FAvz144QbFUYbC/h2Qi5vuLqATCYTxFZfbwNNF4jGKpQiIeIWQq7O7l6cmmpVPu1XmZ2sTz85hpGv1DfTz91HFUXSMc0gkH9E6If7Np09eT4msvPEU6FUdTFTTVwPVgdLif77z6NPlCidGhfnozGXTNYGZqlFAoyPBAD+FwiKnxYSZGh/nWKy6uJzADJulUgng8RrPZ4vjRaSYnRkgkE1RrVer1OodnxvCEYHJ8hN/99a9zf34FTdPJpGMETI1QIEhvNk0sEuHcGblTXFreYm19l1qjyfPPnObsyVn6ezMowMS4NC88XFzjxPggqbtLmKaJ3bL8xMzSjDc02MuZk7PEomGChkEykSQSCTEy1M/5s0cZGe5jemIYkLIDE6MDqIrC4EAff+9v/ybrm9uoKtiWTTIZ57vffoFnLpwgaGiEgyaeEOiqSTgURP3HfwqeDULQ19/H9371a5w+dRir1cIMmERDJq2WTaXSwGq59Pek+f3f+ialcg1FMwmYAZLJKPF4CE1VaVlhnjl/gonxITKpBLMz4wSCJqqqcvb0EU4cm2FooJdYLIrwPNLJBJVqlWAwwJFDM1SrPouajGMY3Um3oVqVytxPP3WM73zjedKpBIGAia4b2J4gHo/x8vNnGR7sY2J8iGQiRiwcZHiwD03XqdYa6JrKxNggZkA6wp88OsVgX4aJ8SF6e9JEw2EUZT8OyzANzp4+huV6VCp1Hi6sEI2E/TQucgc5NNDL7//2tyiWyuT2igQMnSOHJqhU6wz4JspUPMLYYJZ6o4ntOGxv51ha3SQRjzI00IOuaYSCYQb7evj6S1FOHp8hGgkjPEGjZjMxOsRAfw8D/VmaTYtoeIWZyTGGfC0423EQniAcDmKaJuVylXKl2oliHB7sY3iwH0UBx3E5ND1O/8+uEijXuzxiVUKhIAP9vaAIqlVpZg+GQxRKVZrNFqZpkEokuHD2JOVKlWg0RCgYJhKKoaeSmH6i9FKtwcrqJiOaStDQQNNkfw6Y0mfO87BsGwREIxEUBcrVOvl8CdtxiEXDBAIBQqEgmi/a6nkeruehCA3X8dA0aebSVJVIJMShqTES8Sjzi2uEQ0ESiVhHguVodtJPQC2LpqrE41EOz4wzPtrP2Gg/8VgEV0C1ZhEJhyS41HVQfHMaIFybZqNFqVylUCgTDJicOXWEmelxgsEQuqaRzaZBUXBsGzSdpuXwcHFd1t+VC3E8GuHZCydJJhOUy3XiMdk21Vod0zQY6M8y2N8jtQKF8F0+VAxN62AEF1hd22RjO4dlOxw9NM7UaD+GrmFZcl41fCX9UDhIKhknGg51AqzaQriWZUuJDtdPFSXay9f+68fW3C6fxf2lqq0cf1A+oPOd8JAO5U9YE5+4Tgr/nF0ArWNp/AVplm6fsnZd4RHmpnNnXe+Vro8+Y00X7LNc3b//6wBgjwHNLjNgl1tTdwqn/VRG8q8QMtWRgp9tQlG6rLn+sV+l790j5Us4yOObFD7FJq0oB5/hE8/xRR3oOch6CQAVLBvPdWmpCs5WjmP9vRjPP8XFeBShCI7OTjAzOYLrSRNMMhUnnUwRCYdotiwajSZPP3WCRDyGYWgEQiGyvT24joPn2AghME2jI+MficD46CC2bZNIxDHMAI2Whet6HdXsQMBkdGSQoUGpJu3ZLVzXZW1jh8WVLemUq2v7vpYomJEQyWQcVdNQVYGm0kn22pNNMTrcRzgWxQwGwZPq7LqikE4lOHl0ikKxwszUiAQ2isKpYzMcm52gZTuUqw1MTSURj6HpQZ8zlQPO89MKZVIJhgdDmKEAlp3EdlwS0RCuUAiEQszMTDA4PEAwEMA0TRyrhes4RGMRNM0kk06TjCc4MjNDy7JQFCnCapoGhi98enR2kmhE6t/0xyKE374qB0W9iYeUNvAEjA0PcPr4YYLBII4ttcpUFaLRMN969Rl/AYmh6zqlskylYvhK5Jl0gmDQpF5vYBgmgYDJ9OQoE+Oev6+UE75tSaFTf/pDadn0/OF/Q0YIpoTw0xrJNqr4Pk7plFShTvgbCk8onWwDmq83FnRcspZkSgxD91kAaXZL+KrVmqp1NoAR16FXSKdkBZWeR0aL4idLFsJjL1/C8zwCpkk8FiEaCe1PDopKf29WKnf3pAiHgniewDAMwn4kWrlcpq8nLSUMdA3blv0gFoswNNBHLBrujOvuoqoKzZrFwtIqqyubDPamZeohBIplE/mD/yMR4ZH2PIYtm2bLwnUcNN80qWsaimEQ12WS9HqjSaJUYVpIU3YkJH2XhCe44AeEBPxIWOEJzEaTvnpDspPBIALBqJ/IvZ0KybYdHFeyLrqukbUdHJ/1VlWZSklr50n0pw+11uoonAsU2CsR+Lv/ENV2CPr1lxIcKr2+M33ANDENA4FgwE/xpPhpXJSFNcgV8IQg9H/6x7xqO0Q8gRk0cFQZWKOq0olfKBqGGUBTVTTTRFMkAyajC3UyqSTRSBhT11GQUVGtloVtO9TrTZpNh0jY6HpGMmggm0l1wINl2UTDIULBIOFQEBQF1w+qQVEImAYjw/0HnlNfOkMsFCUWj8kAFmU/w0abARBArd6g3mgyPjbI2MgAPekEpq6hqQFSuoFAwXVsBGAaBvceLLO5s+f3GxlEEAoGOTY7TblSx7KbCKTiv2HoRKNhQsFgJzejoQdQFJVuhxQ9FCAej1KtNQgGAwz0ZUklY6ia1BrsyaYIBgPEo2E5buNRBgd60DQNTdOIRAwmx4fpzaYZ6OvBMELIBND7a5rw1E/XkeyQCW1Q5BfPA9UHHN0LtUSr7RnnSWd88jU65xafs5g+6bdd5YlrreuvBf59fCYrpsrjPtW02XWNAw5uSADWlRz7FyqKIv3gHolKbPuMt92q9m9h35jYnc5pH2h6bQMjiuoBLh6P+J4JGTX711W+MNhSVd+P30eE7fLoo/jUbtX98A/Qm0845gm/VdCkA6qqYrYsjv2X/wMAx4Tg1zxPKm1ffgiGjg2cVwDDAMOQebUMHS1gYq7mUH78Lp6uIwwdoWkI00Ax5fdqQJd5Fw0DRVXICk9Gq4RDKP5OVug6RjiIFgqi6iqqquHqKp4q/Td0oXH12hwra7sk4lHiho6hdw0600TTDD9Z9H64dzab5dWXztLbkyYSCfn5uQQqKuafvk3w8j1CmgKxCJGFTdRkDKIR9EgIEQtjhYKSFjUNDMNE0XTwgxpc4WE5Fq7nEAzqhMM6mqEiMJDBdgq6KnPLRcIhgsGg1MlRFVq6KlPy6CaeoqJqJorqYQZUYmpURmW5rh/VKdmFdCqGaZjEohHMgFzEhOsiqg1cx/EXE5typUGt1sI0gti23H1quuaHmkeIRsMETBNVU6nXG7RaFjKVggQlpqGjx2MEAgamqckJ3vPA8XDrDVo7BWpbOSobu3JnI2QePXUnL3MkdlH0tmWjNFtENQ3DslEUjXbuTeG6uJaN63qohoGmqxhCoHmeVEHxgZC0KshotU5/B4R/LUXpMroLOYI649uywfNQPY9fvbWAazuc/MmHRB5uoAdM6fzpCTQP+m25e1bb1Lnj4lgWXqUK9SbTAmKREEFDRxEC03Y4VSyDJ4hFwnLgewL1zjxKvQkI9PeukXzq94m7HsOe8Oc7FbXRkvdhOaiXbnWGZUAIDJ+qlzk+pRlE9GVQYhFAgmu1XEFXVQxXoDbszlwS9MGrqjTk71wP0WhiOi4Bx0NttBACdH+TgCJTQnm2gyIEmm9Wc20HtQO2fEDkm78OziP7Lz3XxdspoAqPkM+yak0bEAR8RkbTrM45tEdoD6XRlM9KQKRlo7keDceROTJ1rcM267qsRyAeRVM1dN1AQSGbyhKLxFEVhUBQxzDk/KBpOtFIBNOQJtf2eGhv7hSEtHgpCuFQkKGBXuqNJrquEY2ECYWCnToLrQ0uhczlaRpoqib9z1S5eUulBKqqoSlq19KoyqgtRSEcCjE82E9/rwQuQVOmTFIVDVWTOEPggqljmAHp5+bJVGDdnIkQAsM0yKQSuF5UJsBWREdEWukyvamKTBfWcUlxXAL/t3/NVCrOhOf5a9D+OAoIZBL6tnO0opDyPKb9gdVWjR9pL9KK0smRR77UuWtprVEQPuB5nOWQIr+Pfey5XcnGH//NlzLffZHF/gCQ+JTfqp+iFL9P5X3ONdouQl8yaU3nmm0T42fU8bHffFZ9Hn3Tnj8VZJq+fVarDbxkbkPfpCj28xfLDes+49V99U9TWvgqyhf32ULg0ZVf77ED/Ep+XsN92e/994oAHBdNVVENo4Nw28q2qqpIuQ3LRhOCoAAa9qec+/EGfRJ9qAGGEE8eLm0m78CCKY9NuR7/Sb3BP0CQrTVRPQF6A2+4V/7WCHSST4MrE0WrKrpucvvuMgvLmxyaHmVkqA9PUfGEirq4iX53EQOBqqh+ouOu+ggIANH27Rk6Ihru/HPDAYLhALFICOJRtGQUEYuhhE1s00TJJlGSMfRwEKFJlqfta6LrBprq58DTVDzXJ2cVKSvgCZk0G02VPi+AZhiEQvL3qJrMbVipodRliLzt+6rML62xu1cgGgnTbLXA8zBdjdQ/+h8QpSqK46ILAbZLvN7AbVkENU3uFx1XJiD2BLguiutJoOUJfJEzYkLQ6w9C1ZE2e1wPzXdebz874YMwQ0hHca3aOMCMq0Jg+rssVVU7u3a1q/272ffHe9j+0nPw+33pBcWVIceqUHil0kAIQWppk3CuLM1CXYu99oRJXRUC1XEJOa40q5TrB2jyiC1zLWqWu9/ffdaoDfywXWRCK0Aosk07tKwCXXplCKSSuhAyUbfiJ4GORyGTkIMj3KTuOnieQI+G0KNhdF3fr73nb7QRsl8YugTb4ZDMC+oJqcrvM4WW7WDbNqZpSj88VcVpNmm1LFTflCTFNPUO2NhvNaBYkXXzPDT/3lTFT+GitttWlnYy8QOPr+s8SkPmQw1U6hhCEHBcVLuGviaY/id/IvO3ajpoOoomx4eia9JvSFOJqCpoGmgKiqYiVK3DzBlKW9LCb1t/jkDTELqK4id9DmsqGU1D0XVUQ8rXYGig6GgaCEVBtK+tSRCKrsl6aBItKZoB7eTRmuqPY3ktTdUJh+QmUxEC1TfBiE5aGaXdbDiuzb0Hi2iawtBAVvpoum2tKt8UrfjzV7dUd3vpEApaG3ypCgfMUZUaiufR1fs6/VpDMoj7axDoKI89MuUJr9pvFQSaEP4lhVxw1Pbz7yYKHq0AXd89ft79Dv4lANdnlQ6z8yRQ0PWZn0x7vy7d7f1lAcWXBIx0z3ZPKN1s1acRL8KfwzvAaj/hOP6GQ7736DbztsGSiq96L4TcDDxy6k8rf51mREV8QSjXWvohCrpP77K/iKrafgW7J6evugiB+Cd/BPU6imUhLBth2WDbKLYLtiPzt1n+P8cF25ZsgeUgPCkvcEAx+wAl17UIfom+2J193v9kf/FWQF/Z9Bc0BXQJVt2JYbxkrE1e+9cHy7LZ3SugqdJE1tYWQihoD1dQKjU6B/N4R/5i1X7SMJCDqb0rFJq/COjyn9A0KQqq7b9Hbx8j2UG5iOybIToDQ/En5JtzKC1LSm+YBnbLouF5rIz2M5JKEgaoN1Ca0qdLX954jEJuOzxq6n7aCQXlM+eBNghuzxeKXF3xpkb8Z+2H73vSzCrFVjXaSWvbxXU9HLdtztI7Jiq67vfTBuq+74B83fL9AxVF6bAWiqKir2yAI+/Z88/fXvC/6KjqbPY67fIpf32wrlTq++2sKIhkvOv7rvGsIBN9v3QOxe8fQlHIlysUyhX6HY/YRzdRXBcxOYTIJP12c6lUajSaLcKhYIftaUsLyjrL5+N5UgBTa+ev83cznvA6z6ftB2QYun8eaZJuK8Sr/gZBssaPz0fKnXkU20EYOu7hif3P4cnHt5/cI9Ok8nAFxdez63wjOp3ssXN82SLw28Pvk5KdefKxX7x3POE6ugYj/Z99kKqirGxCG3grEsh4/v2quuq7mPhitMhj2mZdoSgoPqsKSPCYTnT1MXkXKIoM8Gu7Pqxvy/kb5Gat2zrQNdt5Qvp50WHF1S/fIoYOhyfl6VMx+L/+byVGUtVO3WRbdCvK+0VR9uv8aBHQNld2t+cXXie7wZXS3Q8fne27P/dNo+2PtLYbyy/QT9r39mXBVgcUeY9+8bhg6aMMdPu4tgmwDag6beGhtNluzwdenpzD5TXlddv+esIXKBVCSqmU602W1ne5emeB63cXKZSbKKrO6VNH+IO/9dv09PfJTe8jrNdnNlPi9Oce86XMiHJ67JrElC9JMf6SRfmPf4/uTqb4E3HH5r4Pbw+AKiGkYrBrOXgtH6A5LrrnoHp+zkXb9f9K3xQsG2HvH6tYPpizHR/kOdCy8Gwb0bLkpODIz7Dl90qrhdjIobj+TkkgO0q1huq22YX9egaBYUMmg1bK9YP3Xm+iOHKycwZ7EI6LcF00FGkKa7M6HXZHvlbaTM8jpa0Z1FlkoL2yoHQTggqfn7Wg3SNVuSMWnZ2x/z6TkIDTdVFc0CyZ8yyga0RRUfzQa0ACuc4u0T9x2xyi+APIB0MHwYD/2tDB0BC67r/WEb7JWDEMRMAA00QcnpS7flVBqJI5UP3dvKfIBUT4fgNCVff9XH3g6fkMhaL6rxVVMoKKPJ+/RQdFsgue4lGsVvno0i0+vHqXfMlGNTRefPY0X3vlGSLRMLH/+n9EKdf825G/9fzx1l7A2ou50vVa8Oiu+lNA3xMmDzGwf7yXSdD4f/6jTp9oMwz7INIH5YpCpVJjbmGZza1dyZpt5Ii/fw1cF8q1Tp/RgYTnEUVBs2zfL617u6B0wJaC6DAX+wBCsmztDUwbfGgdMCXZxe6J7LPAh1Jryn5oOahtE5IP9ASgtM2hj9TywOImkOO823TVMe186qWfUJknfLaP3PB8nytFUVB0vRM59fhPvzgU72yrDg75zy6eJ+c7d58FFcLD84GvqrU3WjzOPLVr17K7mCcFJVd88rW6bqU91oUQVAIGaiQkgwh0rcMoCyGoN5rs5oqAQk8mIX3WHmmTT2NaDrzfKx74QPHvXajdYEOFbqbks4DWL1seZbEeA1ptMMITv28L3aiuKzfG/w5LJ3H1FymPqcV/VvHvef/H8Aj47ZZ5EMJ5hJiToMyybVotC8dt44dHSZOvvnypaERFBUUo+8hc6ZqIf4nO9pmU4+O16Py/TRjun8j71MZSVRUvaKIYBm7bHGRIel/x9VLaz6T9sBTaNt6uhd/zUMo1RL6I2CtArigHaaEM+SJKrYFSqqIUyiiNlkT2moqXSeCNDh64zy442AW8RPuTA8eom7sIXQVdp5aMsrWTRwjBYH9W+nc9Ug60QhuEOXLCdFoWhVwBq9EiFjBJRMJonsCzbaxGC9UTGIoizVpPKJ/5vIRAcVy6JyQxMw6Fij9Zdy0Yuopu6IhQEIImIhxEBAN4wQBquSon6EiQxt/+HpamsV4qcXNplaJj4wYMQpkkT7/wFGNHpgmmk6gBA1XV6CSMUv0UG6LdlkrX/CX8RaO9Y3IR7YWdAy6wtCe07q2F6Ox01Y4ZZf+8+5OjJzyE59Fs1fmf/z9/zsfrGzz97WfxtnJcuv6AIwM96CdmwTTxBvtQwhXaDFxnSLVN6V0r5GdOCT6b9ySWtvP2SQ8vGdvv+0I6SHdluEHBDzqwXa7fuM3/+C9/gBCCV184i+ccXIBEJtH5Xa3eIJ8vYaKTSsYIBEwcXzAVIBwKoWqq9LvwT9E2PnW3Y7VaZ69QJhwOkE5IeQOlzYp0l8+ai5bW9+8/k8TzPCrVGuVyA8f1fOfqmJ+w3n8G4mAzCiEgk9hnRh9r4PbGSjzxtdL1YJTuhdQ/rp0loVKpsrmVQ1EUhvuzhIKBzqzQBrPKgflOdDqu0tW/ER6O7dJoNFEUhUgoiNYGi9WGz9B11bNdl+7PfH9CFAUSMTxHRioqAtSAIdnmR+7jwGKr2AcnDv2zF3/Xk0yy58m5saBArdUiqmskQkFi0RC65o/teoNqtYFAkE7GfH9BfBJFshmKgs+IKwe6R3frKYV99k0a99vPtmuNE/7f9gbn3xXQevwAOppfTyhyrAofivwy3OdXeG+feT+f87tP+0pRuqQd2mOpPXu3B8M+iFIUKVzaljTZDwiReOYxP8+vsHwJZsvvpDLfBQeUdH8JwCWADkPWRTl+OQAmHvv/fpGAUEVgAo7a1bD+PwXJZqnFEuRLKAX5l0IJ8mWUfAkKZfl5oSz9VDqX2vfXkv91TzD7IO3gjv7RGh7cQ+/fSXvXTMcUKUIB7v7Df8A//Zd/Tq3R5A//7m9z6vgsmqo8csb9M0nw4KEpHopQqDUb/Mt//Rf88K8+5Jsvn+M/+1/9+5imzptvfcw/+9d/yVOnj/Ef/p3fJhkNo1ZreKUqotKAahPKVajWoVJDrdVRqjX5vlpHqdRQqnWoVPfZNEXF/hf/HcrcEmrTQo2EIByU/4IB3IDSGQzSTCRwBGh/+F+jbuUQ8QjO73+HaqXKB29+wJXNLU6cOYQK/NlbH7Ns6vxHR6cIm+3oMwXVjwAST/CVkL5mogOm1HYLKcoBZrQddYbvgP7Yg/Ov1e0Z0kksr8jIOkWROfRc1WVne5dLV+9yaHKEv/Pvf5dqpcbc/ApD/f3EQqZUFP+v/1Nc1+2YyNrmtPY/maD4oHmszUJ1omgECH/T4UmdEh53cm3DyfZ97d+c1nU/KrK/tMdL+0hd15iZHGV8ZIDb9xYImCaG00WHxiI4//y/QlEEwhMszi3yz/7ff85eoczf+p1v8eIzp/ng4g3+xR+9Tiwa4T/6g9/g6Oxku+npTg+k4CGQWRMuX7/Lv/ijH5NJJ/j7f/O7TE+MdEy+7fppqp+jVWlvm3w3WL8/Gqd+V44lQ6f1z/4hV2/c40dvfEAqEWVmcoRavcFgfy9nTx6T0gF+Pdq5GrtTfwhPIDy3s3kTbXNLpzm7xuEj7gtt06Dmt393xhGBjLhcerjMf/Xf/080Wi3+L//5fyhlPRThz1sSPKi+2KrQ/HbwVDkCVAVPgOO6PJhf5gc/fpt7D5f45svn+Z3vfZPEH/43sFdEcXYQyRiP1/hgb1FqUiGfcBDn0h9x5/4C/4//1/fRdY0//Ht/g0NTY53fK4rSGc8IgeK6aH//v4ArdwGBODWL+9//7xHtTaDrymAVJMN4+9YD3vz5R7RaFs+eOcLsxAj61DBLS+u89f41DN3g17/9HOdOHUHXTQobO/zT//u/olav8Z/+/d/ixJFJEALLctjYzLO5s0c8GmGgP0MsGkRTjCea/LX/4L9E2Sv5k6YPtR7D8e21z/tfCGiJ/b8dnN51XMcqgFzjusZupzxhvf3U8lXf3lfCHB0UWm7nZG4vlkJpb2wOXqvdToauEwoGCAbNAxIpf53+WvClHOTbTNb+NPhVsFrdv3/SmvboMU+mUvHtq95BsCMEotpE5EvSZyBfIlAoS+BUrEj2qQ2sKt1mu0/rEMrBV+0JUmlv6PzfhYKIZAwvFUdd3pBMT62Bmi8+cqaDi/XBK3TdoRDSjOnfU282yfjIAPceLEpHX+HRJtZdv1KPtqPUz5d26FQqxfEjU/zVzy9JGQUfqMViMmzasloowpHMczyCEgvLRVuo/qLlE68d3yllfxL6z/7P0n/r5gNpajF0An////Ap7SkX0/Y9du5b+FFyTQuhqUT/43+E3mpxcnOXte8+z3e/+Ty6qXHn3ryUSHBdVK3dN/0H8oTdnNyUCt/RV3QYorYZ6+AkJY89uDv3H7Sqdt4++uxEe63VFL/NXITjsb65w+5ekRNHJgiZJrG+EKl4BMeWjuGu50k/LrUrUqpzWaXjI6bi0TboyzG5r5PlQ0lkRkIVmdRR9jHtsT4to65kv/U6QKFjzvXBmuo7LHMALCjEohH6sikWQgF6s2kiufIjZ5e/URRIJ+OkknE2tvY65xaeRzYdZ2RogFAo2GlP2Yb7dRB+nXRdIxIOEQoFfO059eDk2GGgD44hxTffu8pBf02ASrXGhxdvsrWzx3e+/gxnTh2m0WjiuQLTN1/Jc3s+uJI5K13bQlMkW95qWWzt7BEOBUklY1IvrQ026LLgC4Hnub5pUAYqBEwDzTAO7KZdD2xX+q7JrBAW0UiIQCCAKtrzhYeiGKiqgapJ8OV2+q6fDBsFD4VavcGHl25w/c4cLz17km9//WkiEZMnFSce7QBBCZikrxyKgtFpVHlDAVMCFsf25SXakV+K6j8zOiBYUekEH4ACuoaXjOG4nsxaoPhgU9fZ3Njmzx4usR0M8Du/+x2effY0gZAJqsrA4RmOHp7lZ29/zMLSOieOTqEbcsEUQmqZea7UYJNjW8gcqoaO69lUqmU0TSEc1Lr6z5PneolZPuV7xWfMO/TiIxPBZwGKz1srHzMZ7rf5ftl3DTnApD/h+sqjwOPAmq0e/O4rL0+41y9jXnzib5RHPvM31Mp+wJpQVJQnpBRqzw+apsrE64aMZlbgEXeJv57yxcGWqtE2G7YBgmSlDlbwseo+ulh9ZjmY5LJtS33sV64DhQpKvigBTL4oGai9ImqhJE1W+RLky2DbXV1V6fz5nFo/XlQFEjFIxSGdkA6eqTgkY9ixMPVQkC3LohYwGJ4cIRgMoCgKxk8/kjtCx0Wkk51rd6JnFDkxPmm8dkyYtrMP7HSdgGkSCZuYhiZ9V7oGTCfIp6tzuv5F21Fmqq4Ri0U6Ku5CCHRNJZtJkkpFEcJBUVw5SXqyUwvPxXM9NETH+Vgu14oPbxRQhASve0VotOTOSVVhr/Cpzdreo/ibEtrPW7FlkIPieij5EqLewKjWScTCRIMBLE8CFMPQUdD9yQ8Oeo3sP28UZV+Et9NE7czv8jl0K+x06vTo5KBIva32V4p/bpDMWMu2sYVAVxQChoKqyAS/O7uFjtnMcRwUZL5MPaDgeiqOK82Yuiojtdpt3HYIV1ARwpWX8lkMz3WoNFoIVSMUCmKqii+JIZ+H4wksx0YFgoavAeZJLZlWq0m+VCSXLxIKBshmMzKvpX/utrik53mdfIihgIGpyUlaJjp2O/pUgYC533DQYQZs28aybHRNIxIJSf0pTeX5585y/umzBIJhAoqC6rmyjykKrisoV2oUS2VMUyOZjGIaOqahEzQNIuEQ4XDI17GS0V4dTadOUgvh62r55qBH1yNgZ6/kJzkvc29umcGBHgb6spiGjuqBEC5Wy6JWl5GhpimFOOeX1rBsh4mRAYQQvP3RVVRVZXZqlEwqQb3RxNB1Rof7MUIh34zvsrOT4/b9RVY3dhBCMDE6wJnjs/RmU7T9z8rlKveX1tna2ePh4hqlco1MOiEjJoVACJn4vFSvSKFU1SMSCZNIJTB1HeG5CCFFQRGC1Y0N7s8vcfTQGN959Vl6s2naW4x906agFQvzP33jLHuFMoN9GSbHhvA8j+t3HhKJhPkPbs6h+a4BApdYLEo2nWB7Ny9lTfzzCOFiey54rj+XS/atm1t1XY87DxbZ3Nkjm04wPT5MJByk2Wxx+fo9bj9Y4vlzxzl9dIaAGehsenRFYWJ8iN+Ov0q+sEcgFADVw/UsbMf2g1hkUIFly7y1mzt5Nrd38TybRDTEQH8fw/1Srb4t6NsWvfj8VaC9sKuyCdX9wJLHyqcBim7m6UnfHQBP3X/3V8XPBFpPqvJj1/H250vRnsSecK4uy8yXL+2N76Pn40uArU85roO32puyrnAbZd+FRPETz3v+OnpgQ/8oAlCkVuL+xucXSLj9OeULg60DqK+zSXmSyOkTKvhZHaz7MMWXb+hiJzoN/t/+U+lvkS9Bsdx5YPsbrscfjHjkhVCk9fqATptpdoCTkk4g0nFJq6cTKOkEbiJK1dDIex6OkPnL0pmkjLLxBFarRb5Q4sadh7z13iU81+O1rz3DiaMzBIOm1PpCpqhp/uP/nBaCldUN5heWKZZkbrvebJJzx2foyaYAj2q9xs37Syyu7DA5NsRRQyfzd/4LCdp0GZVkGFJYs2U1KJSK6JpOIBCQApE+ILAsW0Z3GQaq3h1tKaS6vabuM3KKQjBgEjANaR4R+w3nOg57hTKNRpN4KEg8FvVzxUlTnYfMB6fr/m5P+ArKmibNhX5k2v6j8uHZPsLq+twvO3kpOwCgyKS6rusx0JclHI3SKpekkKeuo6kauCpC9aQ4pvAQrsB2pLlX1zQ0U+8MOOF5OI6D67qoioKptyddaLVaNJryeXmuTIEUCgUwdB3Pc9jO5ZhfWMcTHvFYhN6eJL09GQAezq/x3kc32N7Nk07HePrsEY7OTuI4LnuFEo7jdGhr6fSsoXiwvrHF/NI6hWIZw9A4f+Yofdle5hZWuH7rAYauEQkHqVZr9GSSnDg8RV9fluWVTf789XfZzRcZGerj1NFpzpyYJWCaFIsVrt+d5/aDRTRV5djMGIenx7Ftm/WtXZbW1rl+Z47786v09aR46dmzTE+NkM3EScRiaJpOoVTh3oNFPr5yG03X+NqzZ3jqxCEM05AaXYoE2N1SGO1iWTa5/B7La5vcvr/A9dsP96NrAV1VWVnd4uHCGlPD/RyeHUdRwGlZzC1t8M5HV7l5d55QyOTZc8d48dnTaLqG6QsO7+4V2CsUQfHIplMkYnF0XfZn23bYyeXZ3N5DU1WGB3vp680SCAa7JgPJbGVSMRaWBT968wMWlzd47twxTh2bpjeTwvMU7j5Y5OcfXqVcqfHyc2dxXY/v//BnFIoV/tZvv8bwQC9Lq1vcuLtAKGjSl02zVyxh6Dq/8a0XePXlpzENg5XNHf74hz/j2q05opEQluXw0/cv88pza/z2r77MQF8vO7ki3/+Ln3Hn4TLRSJhcvkS13pAmbUXFQ8GyWly9eZ/3Pr6OJ3yRSkXl5NFpXnruDAM9cg4Bl5ZlcX9+Gcuy+fqL5xge6uuMs+6FRPHHiOcJ3nz3MrFomN/73tcY6MvITBK6cWBsep6DqnoEAgaRcEBOcYqH8AT5YoWPr9ymXK76DGiYI4enmRD71ytXarz+9scSqAdMHMfm5NFJSpU6dx8udgCmYRigiv1KqlJnLplJksxG5b07Lo7jpz5yPSxHCumube7wzofXuHjtPju5Io7jcPrIBN948SzpeIRwUGqFtdeZR1cnuZn0Gbc2QycpEPkbtb0+fYop7rMYnEdZpvb7jqN4N8h6BAR1iK/PAVptdrwNmDqvQaq+wz7rILpeP/Fkn36dTysCvhol+U+7T18PrYOyVDrbZZ8Rb/vdHTibb95up/dquxdomkowEDgwR33V5cs5yD8GuHztF9FeKD/j4T/awQ6e6IBJUnhex2dGeD67tbwBC6tPuITwr+/59LHfyWJRvGQUOxahFQlRC5q04jHSE0PEhvsgm6IeDLJRLlNrtMikEoSCJpub25TKVT+jfD+5vTzvfniVm/cWsGyH58+f4JuvPk0kFgZF0LIafHDpKv/2h+9QKJaJhIK8+fbHhMNBDs9MyCg4/y7r23tcWt/kZ+98zPLqJqah07IsAoaOcB2+9vxZFMXlrfev8M//+A1alkNvNsnfPzbDy/45hC7Vij0B9UaTm3fmuf9wGU8IpsaGOXHkEIlYlEq1yuqGNF3VGy2S6RiHJgfJJOMY7DMzEnzIf7qmYegGChoIDTyVRr3GnXvzXLk1RyQU4PSxGQIBHcd12cuXaFkOzZZDuVJhZmpYCge6EmhZQ31Ujk6S/09+n3g8ggIsLK9TrdXp60kzPTFMMGgiULBcQbVWp1ypomkaA//dP8eYW5ZGk2QMN1dACEEiFsHQNPK5Mo2GTSgId+7PUypXmBztxwhrWE2LpZV1FpbW8YRgdmqU8YkRNF2jXCiztrnD9m6eYqlC0DQ4eWyagb4ePCG4fO0Ol67fJhqWi104bPL02SMcmZ3Esm1++s4n/OiND3Ecl3QyxsvPnua7336BYrHKH//5m/zV25epN1oETJ0H88v8g7/96/T3Zmg2W7iewO0W6ERhZX2TH/z4bXb3ihi6Rr5YIR6LYBgmf/XWR7z+849Jx6Nk0nFWNnaJR8P8zd/8Bq998wU2d3J8cOkmm7tFgoF7LK1sMjzQSzgS5sc//ZA/f+N9Nrf3CAVNPurN8L1vvoCua3x0+SZ355ep1ZvEoiFyhRLvfnKN5fUNJsYGOHl0hlQiwYcXb/CjN95neW2LSq3J1naO4f4sA4N9KL6/EIJOhGB7OAsheDC/xPsXr7Gyvs3y6hZrmzmGB7JU6w3q9SaO4/LjN9/jr966xO9850Wmp0fxbIcbt+f409ffZWltm2w6QS5X4JPLt5mdHvMV0T3Wt3b40U/fZ8cPEvnGy+e4cOYEzWaLrZ0cDxfXuXLjPuubu7iux+T4ML/27Rdk7sjOlCGIBExeffEppiYG+fjyHe7cX/RTfy3y2ssXGB7sZWl9nTfevUihVGN0dIC+3gwbO3tMjvYxNtILaFi2w85ekYHeNIenR/GExxvvXOLDy7c5ffIIqUSEjy7f4MadOc6enOZbr1ygWmvw/R++zU/fu8xAX5pXXjjHG29/xOvvXOQbL5/jtVef4cHcEourmziOI1k21+Xewgr/9ifv4jo23/3GM8SiEX701sf8yQ9/TqPZ4je+8xLJmPQ1y5dqLCyt09eT5sihcUxD7zKNHphB0TSVF585zZVbcyytbhIKBRgZ6mOgL0s0GjkQCSmAZqtFtVaTZm8VXNdhN5fng4u3+Ms3P5Zaaj5jUixXGXFcDGTfqNZq1OsNvvnSBQQemq7Qsm2q9RqlSpV6q0XLsXGE41/N3xR64Ni2vwnaj5D3fJO3bTuUq3X2ihXuPVjm46v32CuUURSFaq3JwuoWW7ki480WMdfB9fGQqijoqvFYJKWUT9BAuP6irnaBls8AW22gpSht888jJ+4iH4R4JBpP7P8TfAkW6AnFX6MPAq19149HDvx3V1T1EQbvc0o3cG23qQJoslNI31AFPE3mR/TZLaGqclMIHZ9WAR1tyG7fUM3PyqDr+mfCmF+mfDmwBQcmVclO+HSbonQE4bqJI3nsk1F+u1vtE2WSeVC6fBg6v82mYH5FfjY1BNk0XjJKK2SSE4Ity6KgqRi9KTJTY/QN9eO6LrfuLXD15n0eLq7h7e7wrZEs35ocIhaNsrGwzB/94A1W1rd57eULjAz18ZOffcTG1i6/+Ssv05tOcv/BAm+8/TGOKwgGTZbXN2i0GkTiYcDDEx4ra9t4wmNmcoh8XrJcM5MjTI0PS/kB/z6378zx40vXWN/c5dXnTvHUqVlWV7f5s9ff56fvXWFqvJ9Q0OTHb13k4fIWv/Ha07Rsl/pWbr89NM2feCQyd4Wkzu/OLTO3uE40EuLY4UmqjSrXbt/j46v32N0rAYLf/81X+dYrTxPsejhCSId0AWi6jmm2Q8w9Gq06H1++yV/81fvcnVthoC9NbyZFf2+GB/Mr/OmP32FrN0+jaVGu1PjtX3mJv+e66L7QZaFU5r1b93j9n/wrTh+bZnigh3/743fZ3NnjxQsn+Vu//S3GRgexLZsHS+t88MkNtnf26O1J81vVGlnP8xlErzM42n1kZ2cP1/XYyRX4s5+8y+HpUXp/81VSoSDrm9v8/L1LLCyvUyrXqL7wFH29GVRV5WfvfsJf/NV7bO3kqVTrhEIB/u7v/Qq/9d1XsWyHKzfu8frPP+GZs8fYzRVZXFlnaWmVv/O7v0IqFWN+YZXF5U0y6QQjA1kGe1OYmsrt+wtcvn6faq1BKGgihGB5ZZONzR1GhvrIpBLSd6QTtSewmy3e/egqn1y9y8zkMJlUnEajRblSo9Fo0LRaeMLj+JEJvv7COd79+Do/ffcK88sb2C2LdrTskekRnj57lKH+LMGAyfLKOj94/V3mVzYZ6c9KZmVjh/mlNWamhrAci3qjiaqqjAz1MTbah21LB3fTMAiFArSsFtduP8B2HH7rV17mz15/j5W1LXL5IoMDPSiKnKAE0izUPeBd1+Xdj66xtrnNiSOTDPZlpKla16lUauzuFUnEIvRkktSbLfbKNRzHo1Qo8/rbn3Dp+n3+vd/4Jl9/+Sm2d3YpV2okE1E2NndxXJdUKsbxI1PcVVU+vnyHheV1nnnqOKom+PkHl7l45R5Dg1leevYEc5excJsAAMc0SURBVAtrXLp2D9PQmJ0ZYz/dN8STCXoDKWZnRjh5dIpL1+/zyaV7XLrxkHQqQU+vzCM5PtJPsbJAtV5nQE37TOBpRkYG2NopMjTYRyIW4cj0GL/2Ky/heS6rG1tUajXqzSaOZ3NnbgmAI4fGGR8fRlEUtnNFFv7N69xfWCObTfPuxVtouspTp48wNNiPpipMjg5QrtawHZtSucSHl26ysLzJ7/76K3zt5afRNMiVS8wvrXP5xj3OnDxEdHZSjpFcgUKpwokjU2R9drk9jtoTf/c8PT7Sz1MnDvFwcZ0bd+aJhIPEYyGmp4b3TTb+sY6vlm87Ls1mi1qtzs8/uMof/8XbTI8P8bvf+zrNpsVP37vExWt3+bWWhZ9/A1VVWdva4eKNO5w/c5hYNEylVqdca2K5HvWmxeZOkXKlTiwRRTUUPFdQKFRY29ymUChjagrDQ/309GSkF6OqUm+2WFzZIBQ0eeiP/ZmJIUYGeiiUKhSKFYqVOoVSFdtxKVfr1OtNRof7GOrroyMY0WazOq3zCEDpgBcJBD8dcMETcyPKiffg38865rGPvyCr1TF7dnOYv4xp8CsoBwibLsD5qffTrv++k8e+0UWj7TenqNKcKGhnXQFXCP/9vpJ927IhfA070RGqllGKX8QC94uWX4wz881HbafHDkjyPz9A3X2OQ9yjTmmPvu/sxLpMUeJ/8wc4owMsr67z/odXuHl3nlq9Sa3RlDnzrt/lG688zfTUMO9+dIUrN+cIBQKUKlWu357j3Okjvs1emopye0VKlSr9dgZNVZgYHWBspA/PtVjf3KLZbHFoaoSZ6RF6e9IETBMQeL5/wAtPn6SvJ8UnV++ysVPww9qbEk130ZJ7CyusbeWYGOnnay+dY3p8kP7eNJdv3uPi9Tnml9c5cWSSVDJKKCidQr/z9XPM3l3Zn+V0DZmRRyMei3D2xCEmRgdJJqJ8dPk227k9DtkjMq2MqvDCheNomsZfvX2JcqUmfYPaTu6Kiic8HM/BRTIuqg+0BC7FUpk7DxdJJiI8f+4Y9+ZXWFrb4PiRCVzPYWVjh8XVbWbGB5mZHCYYDMgNmS8ZYRg6BSG4cW+Rgd4M4yMDfgoS6TSt6xqe55LLF3jrvYu8/cE1BnrT2I5NvlIl7XnSVwWlY7ZssyijIwOkkzGatstrrzzNscPjxJNRPOHQajUZGepjZnKEdz68yl6+SL3ewDRNNrdzzC+t43mCwb4MA30ZBnoSMoxcMUjEIowM9vD7v/UtWi2bf/0nf8n1Ww+4/3CV82dnpQlX1zh9fJrf+/VXOXJ0EtvxWN/cxXZcpscHmJ0eJREP05NNMDbaRyAYIJ2KY3ZT1ALq9Qara9v096b5zqvPkE7FKVdqvqO1RiQSIBYN0dub5tjRQ1RrDS7fvE+xXKaQL+B5Ml3R+Eg/v/6dVwiaOq7rkS+U2SuUyKbiPHfuBJqmUW80OH1imrGxQXLFCtt7JTa397g/v0IwZDAxMkA2k+TwoXF6Milcz+PZc0eZnRplZKifdz6+QaPRpFZrdPz8VFXtMArdQ9xxXPLFMkP9WWamRplVFCzLYW5xHTMYwBXQdDyisSiBgInt2NiuTbVVp1Askk0nOXV8hr7eNH29KTzXw/E85le2QFEZGxni6XOnCQUDPFyQfk2WbeEJj1q9ydHD4/x7v/l1Zo/N8ODePP/kn/2AheVNdnIFhroWHtcT1FsOhmEyNNRHT0+a6fER/uL192lYDo4nyGTSHJmd4P7COjfvLrC6vk02k+DI4Sl00yQcDjI+MiATO3sujVYTIWR6nJZl07JbuEKj2bKk3ppv3jBNk0QyRigUoNm0uP9whb1CCQS0LEf6maBimiaNpgQe1VqdO3PLqKpKJpUE308yk5G5FcuVKju5XSbH+gGNtY0dGk2LnmxK5nj0/fBsx8XwhCQGfGZSuDIX5NNnj/Lh5Vt8eOU2tUaNsdEB+nrT+4Y234qhaxpmwKBab7KysYvnuly7/ZBiqcrz549x+uQstWqDzd09vv+jd6g3WsT8dg+Hg0xPjnLxxhwb23ucOTbF1MQwiqYTj0XQNY17cytcvPEAMxQkmYzTbLZYWt3g/twSt+8tUiiUeOXFp3j1pQu4nkw51GhaLK1skUklyBUqqKrKuVOHefWFs1Srde7MLTG/uM5fvX2RSrVOLl/CshxefvYUv/Gdlwk9akrrMFkqSlvK5sD6pNBJHO37fbbbCAD3MxbuJwKMrvdP0Eb8QuUAmFEPAq0nRU8+yVn4ly2fZ+V60nUV5Ql6W10N2gXMOrqDwuciFffg5z4AVhVFuhz7p2hbcVzX7biRtBmvX0Yc44uWr9RAuc9pfdEHpnSh8M8pvm4PgNjNs2Gq/OWb7/P2+5fp701x6tgkdx8uc+f+MpVqjVqjDkKQiIc5fWwKIRSu3Z6j3mhSb7QQQpBKxDg0NcqDhTVW17ZlKLWqMjM1TDIRoVSp0LItAgGduaU1XOHR15NBeB52o0WlWmNtM8fC6jZzC2ssr25TbbQwdY16qyUjmwzdT30iUPJFbMdF0zVcT2A50lHRAyzboeV4BMJBXn7uNFu5EpdvPCQYMJlodfk1aTJQQdN0hJD+CaqmEouGpTpupY7tOJiGzpFD4yRiUbbzZcZG+kmmYliejdVq4dpSLNERAle4Mnei7fgq3mC7gnA4xHMXTmJbFrfvLzO3vEbTamI7Fj3ZBMMDaaq1Br/22vOcO3WEaCSI+sMPfKVpqefTO9ZLqFDA8RyCQYNYJERvNsn5s0cJR0NYrs36zi5Lqxtk0zEmxwcwTYNwLIxSqtHWvmonefakzYO47zenaVJrbKA/K31LPI+BvjSmYfBgfpW9QplAwKTZahKNhhkfGSSViKGqCr/zvVc5emiU4YEUCAdVMQgETIqlCuubOxyfnWJksIeLV++Qy5ekqVXVUHxfk3A4hCrAc1wcxyUSDvLUqUN87aULDPanCZgy9YrwHN8/TibErjea6JpG03ZQVJV4NMxAf5ahgV6azVbH56jZlEm+NU1F0yEUCWDoKuVqjc2dHXbzBZqWTalc4+79BYIBk2gkRKVWRwhBJh3n6y8/w9hIP65rEw4HEcDhmXH2CiVi8TBrG7vcebBMPB7j7JmjDAz0oeoGjVqVvp40+WKFn717iXyxjKHr1JstLNuVQMJPRF2u1nA9Fd0fxo6/gdkrFEmn4pw4OkUyFceZXwVF9fPn+SKiioLt2Diuje16NG2HRrNJqVymWq1hGLpUj0eh5fsg6oYhx5MftFNrWDRsF1V4vl9fmr6eNJrrkvQBXbFco+Gn18Efj2ub26yubRGLS8AUiYSotSwJRgzN1+hVGR7sIZOKM7+0QToV43e+9wp9PWkQAt1Q8TyPerNF07KwbAdNU+X4th0arRZRLUTATxZeLFVoNFt4nsfObgFVVenNphjoy9CXTXFnbpU7DxYZGuwlv1eiXm/SshxWNncpl6u4nswxmCuU2NzOEYkGqNZb2K6HaRq0LIdao45te6xubFNrtDqmI8lCShmNiBC0pTql74r0Y5wcG+KZs0f5Nz/8OfcXN5ieGiUWCR+c531zuOcK1jdzzC2synv2owtzhTKFSplarUW10aLRsjrPu+0f+u2vP4uua1y58YCx4V5ORCJkMknOnjrC/fl1Hq6s8/2/+BmO63DiyDSWY7OxtcuD+RWu3nyAEIJztRoCl3KlSq3RpGU7nQ23aRpU6k2W1rZZ39pFVRQMXUPTVZZWt9jJFbFsGbm4vrmLbR9kZx9bp9ou/t0muc53fiJq4e4fKz7FxPippT3He11g5RcDAIoPEA+YEB8FWp/1+ouwbp9Xvoy/Vvv63VptB67fzShKkLqPMnTZ1qrr4919dkvxjxdtnUV/k+i6Ho4jA3yE2Pf7UuAxDPhVll8YbO2TykrXZ9Kk2N0Ukn3taiy/iEfed33qF6/rnYBssvO9s7XDXKvKlZt3qdRqvDBxjENTw+wVitzTFLLZJKNDfWiayuGZcdY2drhxZ4FmS6rG1uoNPE+CiYmxITKpOHfmlljd2CGdihEJBxBAw3LQdYOhgV42d/PcnVtBAH29aTLpBLVak1K1zvuf3OTGnYd+ShFB07K5/3CF+ZU1RtrifQokbJdg0GRjJ8e9+QXCUZONrV2290qkUnH6+jOUazUy2Th/62+8xo/e+Ij3Lt7h5UCICb8thQ/UbEcueLV6E9uycV35vlip07Ak2Go2W9yZW2Ftc5cHC6uEwyZHZscxzQDlWh2BNAfYloPjuJSqDZotG8cTlKp1TENGpq1tSQbEsiVosF0XTdcJBgNYjg2KoKcngaKC57kojiOTLqsKfdPD9Cx6zK9sYn5yg0q9ztEj42iGSqlaRdFi1BstHMfh7KlZXn3hHLqukZnbRFnPHehhAqQzvqohPHy5BId8oUy1VvOT+UKxXOHi9dvcnVulUm/QtJrUm3UUJUkiHiYSDlJvNAkGdWKxkNztuw6245AvFimUquzu7dG0h7D8tDqBgIlhGqh+TjqBjDK0bBmRF42GURSFQqnK6sY21XoV27IIhwJMTozQsixQFLZ282zv5AkFg6iaTjwe4e7lFX7y1idcOHOEYMBA1zTKtSZNy8FxBY1mC8d1aPt51RstipUapWqNRqvF/OoGb39yjcHeNL3ZJLlCCaEo7OwVuTe/QCIRkk7smkokEkEoCtVGg3a6oGKpimma9PpO5I7jMr+8xf/3z37G7l6Bwb4smqrSsm0W17c5lC/6G40tqrU6G1u7NBNJAv4a4bkeu/kixXIVQ9cwAiY7eyWqtaY/9jxs3/9K0xRattsxR9UbLTZ38rz+9ifkyxVMXcMwdCYmRmm1LFyfTbYdF6tl02jZVOtyAxULBzFNnXK1znaugKIqXL15n42tPUaHemVe0I7vhmBtY4e3P7hGuVJhfLSfvp4Uu3tlKrUm2WwS3e9PqXSMocFeCuUagwO9zB6aRNMNaaYQMq9hLBqhWKmRy0uAKRRFaqeVa2SSCSbHBtnc3mNucY1U8g6u63H7/iITowOcPXOMyfEhmo7L+k6et969wl6+RCgQpNG0UDWV9c1d0qk4F84e5eadBe49XEXXdcIRk/mldYIBg9HhQdKpBKqqUiwXKVerCAG1WgPXcVENf2MrZBL3/bbwU954EAiFOHniEO9cvEGzZZNJJx4TTfaEIF8oUSxXaTRlsvBELMroyACrG7tcv7OAZhjYtsPte4sEA6YfNSwvWanWmV9aJx6LkErG6OnJ0pNNE41FOX70EC9v72H9/CKrG7v84C/f4dbdeXRNo1pv8nBxjWqtyZmT04yP9VNvNJhfWqFWb5BORZmcGGFkZJC9Qpnrt+e5cVf62kb9vLADfT0M9PdSq9Wx/FRAE6ODvur8/somF/buu1b5bDAGHDDOfhmg0mbGZBTn/k+74v7bn32aWbKbuOj+91nl077/HGvUL3zsFzlX9zkPmBkfOU4BRbj+pTXfD8YBDxTPB1zKQVkUzxfKdV2pjddJ7+ObXH9ZF7nPK18Zs9X23Wo3SrcCbzfgevReHme1uvUx5PKKIhDZeEd5WeTyaNmoHMS6TrNlycidWgND15mZGGJosJed3QJzC6vcnVtiYyuP7TiUKlW2tnPMTo8RDgUZ7O9hdmqUxZVN8oUykYgpH4brohs6jifzbQ0O9FCq1JlbWGd1fRsUhWgsRn9vllq9SalSJxSQmi+247C8scvS2haupvqNrNCnasxOj3Ht1j3eePsKS2s7bO8U2M4Vefqpo4xPjnD1+j0uXrnDkZkJYrEIlVoTrM6eEHSNZrNFoVShVGlQrDSoWy7lapNCuc52rki13iQRj7CyucsnV++iahqFco2rtxd44dnTpLNplte3qNWbbO8WWNvYZkI3ye3JBVJRVTa3c6ytb/Kztz+hUm3QbLWwbJtqvUHTsdB1HU+RoK/abNJyXVzLRXNdDEeGfQshiA/2MtJq8vGVe2zvFhkZzJJOJljb2KFcqWKaBrFomEg4RLlcpVypogcCRCybgPBQUBGO01GTrtbqOP7i7HpQb7SYX9lAM+DooXFCoSAfXLrF629dpCeTJB4Pdxacaq2G49ooiqBlWdQbUjXcdqRP2PrWNvfnV6QZrFDi4cIKG1s5BvoyTE2OYgRC6Ibu59/TABXbkYzC9NQYPT13ebCwRqlaIxoNEQwYzEyNEIqE2StVEECp0mBnr8TQYC+RSJijh6e4dH2Odz+6Qa3R5MTRCfr7Mqi6TiweJRIJUqzU2clXcHxWo1pvgqqRzaaJRiOsbuyiaZJhSaUToCkEAgbbu0X+/K/e4+7DFWKREC88c4aTJw7hKR5buwUWVjZpNi0S8QhDg73EYhFf40uqvq9v71FvNMlmZLL0WqXBR5dvMjKURVEUNrZztCybxZU1dodc2tyzrmuMj/Rz6/4i88ubuChUaw1sx6HRaKHpGrVKnZW1LeoNS+aK9KDesolEwsQTUS7feMDC8gaxSIgTR6dIZ9OUKzVsx8F1pBp6syXD/UvlGoVimXgsQjwRZ3F5gzffuUI4HOTOvUUMw+DlF56it793fz4SkEzEODI7wcP5VVbWdtneKZJJJ7jw1DEOzUyhGiae5xEMBUmn4iTiEWanx8ikUx3JE0coBENhLpw9xoOHizx4uMrUxBDZVIKAYXQSxh+aGsGyLXb2ityeW0RVFPr7sxw/MsPY6CCGaXLm5BH28mUezK/SaFoEzCDPXjiJ47gM9KUZGuglkYgxPNDLzbvz3Hu4gqIoRCJBXn7uKY7MTjIzOUwgoBIKNRgfHyJfqrGdy1NvtEiYhmR9DL0T9d1uD0WRaak8pE/c8EAPtXqTSDhEs2V3ttAokgkr1xoMD/TQk05w7PAk42ODaLqOYRhUaw2u35lnN1dEUVUunDtK5KdX/Alf+vrtFsqoms6hqVHGRvoJhUN4KKj+BhlNZ3Mrx06uwOrGrjTDCulXev7MYc6fnSUYMCmVK0QiQZ6/cJxoJMy5M8fIphP09fUwMTaE8B2mm7aLV2uSSqdkf0rGUVUFXdcxNY2Q2QZbfiU7UYfdem6ftRJ3rWOPOb13r2dPKl1A68CpxEFz46e6NflAq2MqfBKr9fjxn1m+iHnxUXD0i5Yn/f5JZkbh99aO2VZD6ZAymmTT1C5mUPFAcTvQV4IpP3ext5/S7yCj1UG1X7lh8auPc1SgLYf8uGLW47fwxBtS9im/jrBkOt4Bc2KvTF9vhpNHp6nVGqysbWNZNpVag8mxfs6fOUIqEefmnYd8cPEWuXwJVdMRKDJCZ2WTkydqhEJBkokYhw+Nc/3OQ9Z3ckSiYTwUbA+CoRCGabK5W5TpIzzB8FAPgVAID5VQKEgwKDh8aAxFkaauYNBE1zSi0TCJRAxHVQn4DzBuu7z84nli8RDbO3usrO9gtWxOnZjmay+fI5FKEgqHqTVt3rt4i2q1xdjoAKntkgT6SGar2mjRsl1p9mg0qdbq2K5HJp0gYBq0LBsUFU03cHwNnGwmSTAomRlPQKPRYmp8kIBpsri0RU+mh2qtTrMlmZzdXB6QTryVSh1N1yhVG6xs5ihXG6TTSRRVw3JctnbybOcKBAImMSHDs9s5NEU8wszkCPcX1sgXKowM9ZHNJKk3WpK58WBgoJcTx2a4fP0eP/rpx+iGxndyeRKeQNd8Z1zbQUFGM5aLZQxNI5NOsLqV48rNB+zs7ZFKp+jJqmzvFrFsm3g8QigYIBmP0nI8tnJF1rdylGsN6i2bfKmGJyAUCeMJwdbOHnl/0XY9jwfzK5gBk1eeP8fE2DCuKwgGAySTcVKpBEYwgIeKKxQmJkb4+itPc+XGXWzHIZWMMT0+SDaT9M1FCsdmJwiHA7hCUGvaJOIBDs1M8NzTJ9nY3GVidJDR4QHC4QClisX01DjRWIhsMobjusTjMY4dnSK3l8cMmfQPZDhzcpqW7eChEo3HOHx4mnyhzMztBYYGJLu7vpVjZKiXcDiEaeqMjQzw0gvnCF29w+raFkMDPYyODWOagY4v1szUKL/13a+xvLpJwDTo7c2wsZXDsW3WtnIM9Wc5MjNGKhElHAqyubXHpJC6NsGgyW/9+jcYvXaHpZVNYtEwk2NDPugYJRmPYVsezaZNf2+anoz0y1I1jcmJYQ7PTtBoNLEsh55MgqOHJwkFA0QjYU4dn2FibAhN04hGQr6KvEokHMHQDcZHB9A1mbLJslwmxkY4diTImZOHiXSbwxQYGR5gcnKMYr7Ig4VVGi2LQ5PDTI4OEo2GpRq9sPE8aDQtMukE2Uyyve3p+BCCwqHpMUaHstiO02nv0WHpwN1oWQQjYc6cPixNrq5HNBIinUqQiicxTMNPpA2zM2McPzJFJp0mFAih6xqWbVMsF9B1naH+HnoySSbGh9jNFdB0nWxPloHeNMlYBMPQaFkW4UiEqYlR9vJVNrf32NjOkYhHUPBTkvigSfUnFrnYuFi2x16+BAr0ZJNUag3uza8y5bbhJSi6ztTkGJlMCkNVyGbTBINBevoEp05BKBiiXKmysLhOuVrj9IlDmO/c9NtMJZNJ8dJzZ6XDerFEMBSi3rJptWps7eSwHIfTxw/x0vNP0Wi0KJRKlEoliqUK9YbNUH+WcFCnUq0TiYQ5ffwwh6enO8ndc3k5Xx87MsX46ACDfVlKxRJzCyvUG01aLYtIJNwRZlVQOKgE5neQA/5O/BJ2JvHI365rtIHWE0Q4HyufTWrJc6ltgqJd3315C3nsl6h/N7P0pO8+5ftHjH9f4Bp++34aYHsSqGs7YikqHTFzRdk3KXrevoSYKtMatd2EOu6H7WjE7or+/x2z1V75n/hd+8XBO9i/hy6z46c8+E5ajDZTJgReOr4vSpkromgaqZRUpq7WahRKVTKpGKeOH2J2ZgJFUxGKRiAYwAyYBANBwmGBY7vkitJUgKISDgUZ6M2SSSeot1pkMylC4RCqphEMhZiaHGV1Y4fNnTznTx/m+WdOMTY8AIqCoesYhs6v/+rXKBZK1BsNdE0jEDBRNY2W7UoK079Po1Rldmac/r4UtbqksW3bkovE+DCaYXDi+CFSyRhb2wVqNRkin/mjN/w+qYCmEYlGeOb8CQ7PjJFORAmHQ5w8Os346ACRaJT+gX4UBXr7ennm/EnSqTjhUABUwUB/D6qm8cIzp3nm3Cks2yYWDWPoOsmkBJ6eC9lMmmw6gee6LC6vkyuUMUyDWDQCioZhhOjNZujvzVBv2Syv79CbTTHoiy+2Ke2KrpNMJRgfHcR21piaGmV0bJi9fAld1zBMk3AoyIVzJ/AE3LzzkHrTAj90XFGkLlrANMiEg0yMDYIQBA2Tw9NjLKxu0WxZ9GTTpFJxGbiaTXJoapTZQxOMjwwQjYRwXZe9fJGdfIlIJIyqaWxs5yhV6wz2Z3Ach1bLQlUU0skYx2YnKZXrRMIRZqfGiEcjNFsW05NjoOicPHZIBlmocqIIhUOce+oEk5OjqIpKOBLAMBQePFxmZSPH9Pggx2ZnKFdqnQUURSqxXzh7jHqjyfjoANFIGMuyEV6dmYkhZiYG8VyXoA82njt/kr1CnmQiRjqT5LlnThEIhjBNk5eePcNAXxYhBNMTw4wODzI2MojjOMRjUYYH+zANg/7eDK+++BTDg71cuX6HUCBAbzqOoe0L1GbSKb716rPU6jKzgufJ6NKd3TyO49Lbk+bXB3ppNFpUqnV446P9naIi2Yn+vgxr6zuYhk46nUDXNMLhIAEzQDqlcu70MYYGshIg6RqO4xKLhnn6qeOkknFs2yFo6piG9v9j7r+DLcny/D7sc9Jeb573trzt7upq3z3TM7Njdnd2dgGssFiAACFAgASCJBgSGQyQgAiGIogApZCgIBHBECSCcIvdBXaxs7Njdnpse1td3ttXVc+/d71Lc/THycyb9773qqt7ZiSeiKp7X97MkydPnvP7fX+erUqduZlxjhyYVUDIMJmaGOWVF54kl0kzMzmBRDI7NcXi3CyZdCrwnfLptB3GRgcxDSPQaikBTpOSXCpBJjGCbmg02w4zE0p7FCb87XRarG+U2dgqUchlcF2XWq1BNp2OGJymgWkICqMDbJaqVCp1JidGmJkax5HQKFXxpUc2myafyyCEIJkMcuKh4zoujuNQqzdIJROMDg8yUBiIcrI1Wy3K1ZLSthsGqVSSTDrFwvwMhmVhWRZ6kNAW31c5KzHI57Ic3DfLw+V1Pjx7hfGRQQr5rAo26aGzirZ0HIeVtQ3OXLjGVqlGLpej2mhTqjbwfB9NgtZxSf/1/5aUlEzFaLbv+Yw4DsMSLNNAN3SecVyazRbWt99Bv35PleXRVUb3ifERNE2jVm9QrjbQdKV9q1TrpJJJxseGGRwsousartemVK6y9GCF7e06mWQSQ/PxpUc6lSSfy5HPQq3eZGu7RLPVJp1KMnywyMT4MIVclnQqQalSZXOrTL3ZVumFQrObVJU39NBNOsJY3WLnESD4zE3uwtQD7Utwrx5z4eO0HdqfWLkoIZFBSaEdw/55OSb9rDY3hYQizPlIwNVzTfDpd+dOBNUwQuQpRJgDNOAhGqBpCnDFNH2xdKihIfFne6ZHtM8ejai+7HiR8XwsUZMaUSLL3X7vaTqg8qtEUY1SQjaNsExkx0EvVfF8ycraFhtbZRK2iWmqzOq1RourN+4xOjoEQnD4wBwnDJNMOo3juNQbTWamxsnnMmi6gRaAYc/3GSjmGB8dZKCYJ5NOKuf02UmEJqhWa8xOTzA+OhyZbHRDxzQMJgN1eqvVRteVj4kTmLh0Pahb5nmY755n/D/9R4zHwGo4L3qQOTwlJSNSIr0gasLzSF69g/CUD5S+UWL80h2KtkVrcgKZTWEP5NHHTDwJumEGzNzlwOIMRw4tKICkqZqA4X0PHMwpHzPPQxcqSmlkaIAXTp9UfmoTg6RTSYqFHCeOHWB9Y5uVtQ2Gh/JMT06gaRonjx1gYCDP5NgI+XwmSh8gfD+iT1UNnI5yxh8cyLF/YYbx0aFIqkwmE5imydjIEF/43DMsLsywVakx/tr76A9Uygtd18llM6QKWb786gvks2k6HcVATz9xhPm5SZ48sY+BYp5Ox+XIoX3MzE4zPTVGMZ/DdTpsrG+g6zrHj+xj38I095aWubu0QqlcxfeUY3Wn49LpuCSSFtMT42RSVc5fvokQAstSZVgW52YZGhxSoKjtYxoOuqZhGCo6NJVKIgDHdVleWeXs+etslypKCFicp1yqsLym8j8JlMlteKiIAAq5LAhBo9HCCYIVWq0OjUYTXdcZHCiQyaRptltREr7R0UEmx4fRdYPh4UEMwwh8vDyGBgocPbQPI6ifFwJDy7IYLOSYHB3g/kAeXwb19gJC5EvQ0EjYOoZuIfHQdI18PsPo8CDtdgfLMlVyUymp15vUz1ylK6mDZZmBBks5tRcLYeFopUewLUkum6bVzjIxPoJhGJEPjW1Z5HOZgBpIHKdD2jbJpRKkkzaartPxIZ/PomkqOCSVtGk0W2QzKbIZBWoMw4gAhWEYiNAl3HXRay6Lf+u/Q0sl8BMWWU1DJm3sfBYjm4akjWsaeM0mxu0HnNzYYnJhipEbD0j7GvpEGZI2KSSThqTiKKfxXDrJ6MgQk6ODpNNJGi2HdCqJxCOdslUyYaHMV0LT8V0C3z8HXVdrIZ/PxfJUKSd907QIKwgoWmciNQ0plLQehrSrSCvlU5nPZVmYM7h3f4Xzl25QyGX4wstPY9umMhtG9BmQPtdu3uXt989z/c5D8vksz1QazN8+h5ew0B1XJRkWIO48RGgaUhMBXVHaIQuJCAv8CrAkGL6P6Liq3FifNSzMw9XuKFOybZtMZkYoFnIUCjnlMycEmm5FvmhDAyovoY5E4pFMpNCEhkSSsC3yOVXoXNM0kgmbdEr5Kw4MFNi/f4GhcoVCLoOm92fV+qT2mEx4R5Thbgxc0i31Ex7TusdjkYjdCLudrTfvpdj5r19b9zjmw8dt/T5Vn7bFgVak2RLdeaF/HvuvJcjX5aM0eSLw1Qr7Cf8RmMw1tDABsyZUlLsmehBM99X9/E2I8HM2I+4ZVRhT/z068jBIqS91uiVkFfLUdB0xXEQ83EBsV8jnsuTzWYQmKFVreJ7PdqnKvYfrfHD2KvsXppmaHOX0k0cYHx0lmUjQcZQTdC6TZnhoAN0waLfb3Hu4TrnaYHpqhPGxYcZGBsmkUxi6Tj6XYf/CDK7rks6klTYDie+pxaahxip1iZ4K6i0ZGq4UWJaNYZiqwr3rIdoO+kZJpcwQsYzu4VOKnlePLiWmL9HaTiSRa0urpP7xvyYV+FyIagNRqYFlIlMJZDqJzKVJZVLkChnEUBExVMAbGUCOFPBGB5CDA8hUCl8HTAPh+yA7JBI2I8MFbMsiYVvK8TudJJVKkE4nA2fZFMkgbca+hWnmYs6lpXJVDT3I/C59SQXB/YdrLD1cY//CNDNTYxi6Ki8TAlPD0BHoFPM5hGEyWGuQfPdSF9T7KuO9bllKMtcEnucxOFDgmVPHmJoYJZO20DWdVMZmfn4G3/exAx+6TqeDL2FooECxkCOVTHAul2HpwSrNdieKBq3U6tSbLTzfp9l2SCSTGLqKPpRCRzc1UqkUrqcit5pNVXvNtkETGpqu8uz5vk+j0eLe0irLK1uMjgxQLA5g2gnSWZ98q6WAh6aMF+lgLkLA0Wi2qNTqSAmdTkeVxdF1pemQkkajQ6vjgWZgGMqU6TgeLcfD6Lhsl6q4nhuVn9CN7jaXUkKtjfkP/gmD9QZPbpXAMhm8cBdjsIAwTXW+ZeLpOr4QSF3DTCWwbANLA0/X0GwL3baRlkECyKMrk5SUiFIV+y/+XQwJo52OYoTJRJSXCwnC9ThQb7Ifn2wmjaYJTtUaHOs45P/oDSzTjBhR0vfJBIBQVQgAy/exHZdBz8eyDAzDIO16FD1PJec1zV7BLnQsdtxIitYR0GyjN9skg9Bx7eFGlA1fui6JSo2D1TonLJN8qYF14Xa0JgDSUjIou862WBakk5C0kckEGdtiwDLwbQtSNiRtRMJCJm1kwkbaFq5pIBMWIpnEdCS6r0PKRSSTYBqYhsnIwBBCA9O00YQemEMEvggrZQZFnwPBwDQNhFBr65lTx1jf2OKdDy4wPzvOwcXZSKujRq3e2YH/5v/FaKWGYRjKDWK7irlVUf27geO24yI2SxHNCglYxNY1pX1H17qJbzUN3KBmoeOjf3SF3Df+DgnfZ8zzELqGlUxgWBaaoSMMAxHkPArpowGMBlaOkD7uaBLyXa+8LmENPnIBDUWoQt4ifgJClRrbtQlUlCCPB1Z2G2N0WWDmC81ePSa+4D8tOC/cT49zzx0JV0FEJkR+vkArbD+TZiumMdz17xCZd3Nk9d08mDpvJ54NtJXqrNgcB/6ooSnxk9I9/LwB12cHW59yJI+zYEKVtqpZFwYmA2gIzYPhIixvIBptcqbBwf1z3H+4yuZGiaHBPLlsmo7jcub8deA+T508zP7FBdLpdCDlAshAE2EggYcr65w5dxXfl8xNTTI1McZAMa80GSjzlWkaeAg03VBEBDA0lVNMBsxV6Ba60BCGodSVgG3q6ENFyGbAVRtZ5tKQsHuAFuHa2MUpUXg+4u5y95ihx4QAgXBcRLMNzTYiKP/TO/Hqw+x9GaBpSDsg+kkbmU6STlq46SR+PoscyCOGCojRQcTYIKKYxcqkMBI2uqbSMGSzgfYhKF2hGzqGroOnnBbbusbS2iaXrt6m0Wxz7PAChXwWz1c5vgzdRBOGWvQChK6TSNjY75wncW8ZrdpAagLp+eD5iHYbvVRBJG0M02BkaECFvbsu7Y4gmUpgWhamZcXCfFVJk3Q2SzZpkwki0uJSr9R0Wq5Ps+Ph+pLVzTLX7zzk8IF5pqcnyOVyCF2902RC+TU5joOmadiWjaFbCAw04SvFbKB9GBwo8PILTzEzNc7oyJByTrZtCsUihqFjGKo0km2peVN7xA38kVKqQK9MY5oGxUIWyzLJ57IMDLRJJDJomkUqnWVycgzXcZUpzpd4CGanJhgaKEYFrHuWleujnbtOstlkvNVRPoYPN9HDiLFw10nQZTenlhACI/K87PZrIRGlGqLtqDW7solY30ZISToU4INajzIg+pqUmMFxTddBQD6womgrmwHA7RJgLTAFyLCenS8xfB9DU+tOAVEQUuW4EiEg6pH2CUwPEtDQbRtvpAitDnqzhWh3iJxwUY60ruuhaRrpVIqEbUf53oKZDLaTAtoSqQSNSl39C84wgr3ctyWj30W4J6G7F8IzdY1EMkHOtpDpBFoiAZkkIpFApBLIZEKBuISNn7TQEjZG0oSEjUxYGLbFvG7w9aePc+neAzQZYoZucXPflxhCUvB9MpYSUHTXR2930MKEtZYZPIwOcxNAr8ZF7sYQQ7MOElGuKcAG4Lhodx5iS7CkDF7PLimAgjUnwznXNAXmgk8p+v6O/S40XYGWcA10p3gP1vUI/iRQ6hHR79f1Sa1fexUHWo8YQ6TtCc2KocplFzOjgF4HeIFyFg8+f1aAtUNT93NqPcBqjxOE2qc7/dlCdOX1nt93fZSId5c5ULyhvy7vL7Z9ZjMiYq8Fs3fr+nTs/vBqTYVVjaC7+CRSaojBYnR+Z2WD+w/XqNZbHDowxzNPHlalNJbXuP9wjenJMQ7MzzGQzyJ0HYROv9q1Vq3z5rsfc+7SDY4cnOfgvjnGR0ZJ2LYyCUvQdFMVXybctIHOLehKBhKyFtQvi9eI0zUN7//5X8M//T30P3gNKcD9u38T98RBBN3iuTJGbLpXq2hO4bm4q5tKe1Wuqc/YP+0nHyCaLUXkPeUY2KMu2/1FKP+JRhPRaEbn64CtToiNQbUMKLOBroFtKWKeUoRfZtOQS2MXsuhLK4hOByk02s02Kz/+gObqJkcOzPDEgXlMw8JxfCxDYBkGQhhEpFoILFNgf+cNjHcvILYr6uaaUGNeXkf/K/8VAJYQjFkGnmXhJ220dAojl0FkUoh0MgKRWsIibxpkEzZmJqHMRkmbqZbDlw4vUiwWMDyfdDLJ4QNzLD1Y5dzFm2xtV8hlMxQCs0SolUkkbCzLJEzia+iaYvQCDKnjBwwim0lz8MA8B1GAPfS/MU2DXC6LQK1HDR/dVCZiJBi6xUC+iMRH09X8a5pQBcd9yWA2TVrXMTQds9nC9HyODA+B72G3OkivxbFsHi2dI++4aLfvqzfq+aq2pu8jKjW0Vhvb9TAATfoYrTa0O+F2i96JCnaIyZ8y3Jsyov1CSkSr3SWggek80mYH7zbehBDoAc0MmXDEyoK/YzJv77XBPfWAUob72giXrohd039xsx2YaTy0pWXk3Ditf/L3A57mQ9tR4KvVpl2usnL1Fk6lhlVQwSfS7SCbLWS9yeb9ZTqlGiOZFLYvVd/NFqLVUcJPq41w3C7m6BtOmGQUwNCVydP1lMndNAyVdsjz0WoNqDdgq9tJDJpFTY9xjDCYyECB4eNScjR8l8kE4uEa/kAeaRg4+QwYuhIGQjokBKJaB0PlJutMj+C4HiKVQP/Lv4rluMhGG9loIZttRL0JjRay0VL0qN5CNFrBXCvA1NtUgkk/cDnQdH1XvgAgvDDirJfpij2+7+wgoFu6psYRAvFwnQbHZCGHSNlQyPWMs/++e7YdUYiB6KI2CRHQio590shjCznU1ux2etRPUGYtpt36/197nELOez1Qt4+dKCicx0eUSQq0WOExETrCh5GIUuK54Lp+VBtR7RdfRa/SL0r8fNqnB1s9G6LvweBnQsF71VeM4Ffg2yKB++eu8doH51h6sIapaxw5MMewlNx7sErCtnn65FEK+WzMNh9I4yHACbQTSMmRA/O89NyTLC5Mk0mnUCwwKBAgFBHze55Tqori5SqiXIXtCpSq6l+5AtsVRKmKLAXHN7eja/XtMiCjOqYCgmRuwTOquNRu9XFdh/FhGBuKZrw7VyAabWSzjUSy/Ld/m5VyhWLbZVSAVa6jbZXVGMtVZLWuCGhDEULR6oDjEG7+QJ5X/cY3QbhufamkDLcJ9SZis/c96aCYVQBECmvb/K1KDen75OoOmf/8H6PZFnY6BRn1TwafZDNo6SRGJo1+dwU6TncteaGE6CsCHjy/JmXEoEONQFeYlAiptDNGpJJXM6hLyaiUjKqOAEhpgud1naeBtu9j3v0D7H/1HSXJ6zqYwaemgxGYRlSBNvVP1yIJU2pCbWLpgy8RvqeYjucrYuwrjaX67seO+4ppe546FhLvvjDyXB+RzkRPrI5GrKJ/q4bN8xAP17AkyKSNP1yMzpHBXIlASxQmEVQ8Qna7Cv/21e/SMGCwgJCS9hMHqeZSyFaHhKGTEBqa5yMcV5mhXA8ZfnYcpOtFA5SA56pkg6F/k+xBT+E4g0fcBVXJnj/7QN6tJQjWkKjUMd78mNRf//s99w+vSgAnCQXAWOF0oRzC3eU1qpU6hckRkrlM19lcAqaBNE1FwL3e9yyCjO1ex6FSqYHrkUsl8RyXZr2JBmQStjLBeep8da0M0gN8MiPt+rt2z47DHVWzVeAcnOPW3/8bpJI2xWKaRCLmV/YP/ie0j64gpeS9/9Nv8803z7A4N8E3Xj3F8NCA4m1B0XoPTWn6g7/9UCPScaHZRDTryHoLmi30Zhu3XGH9/gq3L9+ivV1l/9gQU8U8luspTX29CQ2lsafRUp8x/VmXPsnu0z5iWvqXg9zxm8D/z/4DeOGJ+CwpAvK44GVX3hdeF6xxgcIJj4WFYi9P8mgtlQjAVkSsxWPeY7fb/uxAY0fqp5+lidA3Kwa04vMCEe0Pq9n0jEUqvhrW0HRdieuqnFs+XWGtK9z9YtRcn1mzpaI0VLRDHL+KCF2y46U90pQoQYQFkne/aVCyR81q3vVZnJ+i3e6wsVXh/Y8vc/POA0rlKs+eOsqJI/tUUcnwviGDCt++EOTzWX75yy/TbnfIZdKkNA2xugkBSNJL1eB7BWNbARZKVUSpojRL/SUV7i0rRtkdMdENg7kw/uE/xfif/7Bn4wgCVWfExfrmS+6qpFfnXLsD9SZyaoyznsP3L13lwOIMv/61zzM8WERoGl60fHykCCqiB7wfz0Xb2kYsb9C4tcyFn76HvlXh6PAAGcdDr9QQ1RrUAoDVbEO73QUIezUpMdodJv3AqbZTRmxW9niI7mwBirj6AdgI50DXwPfxtyt4jov0PHQh0MP1FvrM9M0+0TvY5Za7HLNlqN0r/WxUIm726DF3hI7F4W+x77ucH5rd+m0gfRCi99gedLYLJ2LzIQQdqUruaJqImG1vz+qbjB3ctX8pcVyX1S+d5u7+aVZXN5gYG+XowUUStgWA47jcu7+C47rsW5zFti28dgfheciOS6NS5Uc/eoeb125z4sA8Jw8ukLdtFXThOOD44DgIV4E14brKlO56yh/LdaHjIaPjLtLxlFDheMjVDaWFC9eL40Q+SOG8xJ9N7PimGKDwfZL1Fp16E32rinC8rtVnx7W9PCH8dCU4YfUB00AaOtV6U/noJRMYGRXxKOPzHdIIX0bAPdor8b+jz9hvnh+AFlSwkQBDF+SzaRxX5bETdJelCDPuS6hLWFndYDCfxndVrrowvQtSC3Koy4C+xIC6rYOdgWIqYL3B756PXN/i/mCWH795hhP7xvm1r77C5PgwIoiqk54ItA0S6Xn4jVbgMtFCa7Yj9wma6nj4m2i0Ea0WNNpKy9ZsKQGz2YJGUwk6O1cvImV135RA8bcdKSE+QxPezkUVLYhd4IgI5g56lp3iI3sRpb56wp9Wu9UPsOK091O2yHwnNeJpzqOxfbrOgvEIFGB9VKb/XVrwXGFNUN/38YLgMz8QaEMrhW7oUXHzX0T7GRzk1QuVhAsyhBayC7jiwOtTtH7CFB0fGkDcW0a4HnP/7I/5T4pZWq22iuy6soQmBJZhkkhcQPudH+C5KoJG8321wZwuQRaOi+G6DDmuMls4Tg94+rSxKoACCTKQRgKfr0jrYRpKfd3qQETcwwcVfcR058P3/N7FYEoCdJWTaWYoT6vdplyp4vsuQvMQCHQIkjAGiz4Ab5oGntAQw0W8gTz3Uza/98EZyo7F3/6LX+Pk8UOB30T3tutbZT46c5GsLzk5OUa62UKsbSM3thAbZbTf/54yKXieisJM2l3i36/N2Wtt9DtFaiKav1ouzeZ2GdeHQi7NwEA+8tfpZUJ+IAR5fcd88FQGbbErU4oxr5+lyVBDFby9iMuG4eWxFyzi58Q5qwiYrVBrp95QzzmQ6xJTEesrLlRosWPRrWMLx5egCZzFKT6aGeXslVtohsHnXjrF4sIMhm1GppZIe2coE4UyveigqezpUhMqT9n6JveWlynsn2J2coStzW3u3H3I7OQ4yYSN63pcuHKTf/XvvgsS/upvf50Txw4qZ2hdQ1gmhmVQTVh85+pt7vg+Q08eJr9vVqUAoMtAhBBKmRis6TBzttKyiGDZdE1iUbu/Au+cU3tV05Q5PFYKbDdg3msM6Wq2mu022/Um+WySzEC+q3eRvcEvvX11m9/uUG826XQcBoo5TNPAdV02S1Vy2RTZgXz0XN11Qs/62LN3GfsSgsCOgzh3TR1NJWAgjyhkKOTStB1HBarE56oVgC3LwNcUmPaCvSECK4Tma5Hrh5RSbZ9A76QhIxOO9MEXmsp5JEAPSowNDxbwPI/t7TKtZhPpucoa4euIAGiFNFVL2Yi0CscQsTUvUYECYSkWhMp5qH5WYzE0EMJDCInsOIhWR9Hif/Ft+EmQcDVhKGAUTK2IfLQek/3G31XoGyh2AQeSLp/YQehFpBOIiiwTYA25R2k7CfT5OO/oN07P9gI8uwGsz0gHu+PsFznimsLHhTXhvH6W8keBpisoPK1IvSpAHa0tKdCEjmVanyFK9fHbz2hGDI/5IPte8KcFXN0VhQzDYPvJ1eCAAkaui7ZVJtVxSLou0vUQgelF+D7S9fA7TuSoLTXtMVJOPGYLGF9kXgrs/UH2TcXEDB2ePKIAwq7PL2OfjxjXXj+FQs92JahDCCKXJpVKYJrK6Inwg1B/iZACXfr4+IQmWQXBPAwBUqiCwkLIwDImME0DU4/7oClCdvnKTb75p68zPTHC1KE5sofnI6IhEUq7Vyr3jVXEh038iPQ8cAPwFfidiWt3FCj2PDWHyQQMFvCTNvdO7OPdjy+zWanx/NNHee7Zk2jJRBCYYCgzn06viU8EPnaCqP6W1AS+CECJpoCDjAELAWiuq8yZ0T9X+TW1Owq4t9vQUn+LZhvZbkOzoxytWy1oBp+NFtILWVD/TBCT3nbRYMYP3HkAvkTGirJ35zLG3qP+wv/CfRW0wC8IXcfdN835ozP861u3GSxkOPr5p5l/8mh37YYtlkm7hwQIZR5pVeu888NlrtUb/Hohx0BRMcTbSw85srnA8FARXRM0m21c16OYz6FrWrQ3FLNWPnGTk6NYpkG7rWpD+kILAJRiNlrobC/7pGYpabQdWm1HrV/TwDQMdZ+w/ce/DSsbiJtLyGIO+dJTuP/w7xCmTnA7HZyOQ6vdRtM0lULCTijna9dF4oEG9UaLP/rdb/Nvv/UT/g//4Tf42qvPqEARoYPUVG5JiAI1fN+PyGHoIvBgZZ1/8W+/y+Z2mb/zv/vzjIwM883f+Sbfeu0d/o9/8y/w5c89p/wDhabccTSBCMt/BYwi7o8CKFokhDLRBkxGBiYUllbQ/9b/BSR4r56m87f/ApqQWEJiWEGElhZj6IFmS9qWWkHSp9VSNWEz5SqaZmIaqryUFCgfLOlTrTdptzsMDhQUDfIluqkrv0ddQ9N8PNclmVC1PAE2tyvcvvuATrvN0GCBQrYQAaawRFO11qBcrZHNpBkayAcBJDLwr1X+b9WaCkzI5TKYuo4eCG6eK5Eo7a1m6PgpGz9hYRmxdKZJa3dNU7jodwU6wbzHcj6pl+yzsz5guEfjm3q3+4W8NC6kSfAFUvZaf9TtdURUu3EvTVy/2jV2Tyl3HvuMWq142wkMP4HfPbqzCB88VgsfV2pI6QV7kMCk2O0yHI+maZiG1eNz/fNujw224hKi4p99krNOVOW8W9phlxf6yJtosS4Dv5e4qmeo0D23WlcqY3a+Pt/z6YRqcYECDXp/ZEbsxRu6Yi6mAYah/HOM8Hvwt2mqv/VYErb+tl1RgCthwe/9Y0KHPKfTCRiHxAqYgPIFAOl7tBpNOh1VAieZTChfjWDzdByXcrlKqOXQNEEqmcRO2Gh/+e+qTa4JyGUCBZBOLpsimUoi9BDoBegdUMnuNPxQdye96BzHcZFSkkxapFMJdNPsnoPa0qODeb7w4lPMTo0yOlhA6ELl93FddMOCf/xfKrYufLWwAzFth8Kuf2Nrgc+TL+GX/6YCN6ubMD0GX3gW7+/8Jdx2C/3uAzb/dIDvv/4hYy+e4Jk/82VEKqkAU0Rw+t6LVD52MtDo+J4XMcG4z09ciyBiBUqjDiWR5InvKzW0prQB7Y6q2xfm+An91qQm8P0OXttF1tuYHRet1Q5MG02aGyWa22VodbBdl6SPkrijc5S/irilHN0jM+NAnjDaUkqVrysiFEINNi6odi37Eja2e2Bfx1NAYHgwRyGfpO20kNLCMk31LFqgYdOEqkfZclSOOctECKUdrNTq3Lh1X6X/ECp/TaVW5+GKysovpY9lmzx58hBT0+NkkjaFXAoNiSYkvq+qCTgdh3KlihuY47Ug67MeaNXanQ7tRhvf9dBNg4RtYxo6BqqU05vvX+DW0jL5XIZ989Psm58ml01H74RKLdDKQJCNlHa7w83bS1y4fJ1KRSWdNXSNybFhTh47gGm1ub+ySdLSGRrIYqcS6LpGIZ9B1zWuXL+LgcoXNVDIsTg7w2CxgGUaeJ7L3fsrXLh6G9+XDA8WGBrIk04l2SpXcYO5d5XzSOQo3k28qNbQwwcPuXjlBs1Wm3w+y76FWcbGhjFMA89xWVvb4M79FTRNY3pqnJHhAQwtpKeBpi3QVIn7K+jf+gnaBxeU36oQGIaOlKp4Nqg8aeaN+zA2CEk7ysF2485Dfv+bPyadSjI0UOTw/jkWZifJpJJUag3OX7nBh+eusr5R4qVnT/DqS0+TyqZ5cH+FK9fu4Houvu9SKldZmJug3VbFu6/dekCj8RN0XWN4sMDzp47z1PFD5LMZHNfl5p37vPPhRa7fvk82k+LLnzvNUycOYRpGJFR4nket0UQA6XQKS9eR0mdlfYv7D1dptZsUC6riwer6Nmsb2zx/5yETfuAjG9VH7KNPn9Ti7hRx36LdupAxYhgRxYAXhdrZ6P5hHiglMKvjOj3ReUKg2PheQCnkoeG4BF3frhj3/Dn4aT26RQQoGGM02J2n7oUbxG6RiX13iZ/va0jp4gdgPbp1mHpJhAJkoH6IRa7+ItpnNCOK3s+fFwreBeD3CAcDxe53Q4f5SfovklLScBzWPY+6aSDzacYPzDO0bw5RzCsTTCEHA1n1mUuDZihgoMVu5onIQT5w/4wApPR8GvUG9x+uUmu0GB4aZGxsGOvJP6PAlhoIvuezsrLGux+c5dbd++iaxrGDC5x+8ii5QpZqrcL5i9e4cPkG26UKqVSSZ586yvEj+0kmE2xtl/n2a2/x8cXripkGea+OHd7P86dPMBKavzQdmU2CkCq3ktBYuveAu3d8cpkUo6PDJJOpwA9f0Go2OXfxIqVajQP7ZpkcG8JptVhdWaNUrpLNptjaLnPr9j1MXadYyJBOJRG6xqGj+1g8OEu7WccwBJ7X4fbth5y/dJPZ6XEWF2ZJJGzMkFELgTQ0Bbg8LwIh0cYXKh2ALxVA09ptNc++T2i/lEkbUIkppyZHmZ4cVWHrPQKijw9RNm7p+1iGgWGq5JCe67K1XebhwzVa7Q75bJqRoQEymRSapuF5LtvlKqtrmwghVMmWdAqkpN1p02y11TvQddrtNuXtMo2mqjjg+5L3z16h3mgyOz3OxNgw46NDaLrOzVv3uHD5OqVyDRAcObDAyWMHSaUS3L/3gG+dvcjFa3fwPJ99cxP8ypdeZHFxTqU+CB3SpcT8i/9lsCc0KOap/A9/j7MXr3Hm/FVc12VuapSnjh1kamKUSr3O2YtX2CqVSScTIAS6rjE+NsLUxBi5v/nfqn4BX/q4ntKCbpdr/NF3f4plmUxPjPHcUyeYnhhGN3WkblDaLvPOe+e4dfcBg8Ucxw7vY/+BKUxdo1Susra5jfTB6fhsbFZ5uLylSrKUq3iOg0hYZJI2ZUPj9u07TE4MMjM7g9t0WF5d5crN+1y+scS7H11mu1RhdnIYofnohmRza52LV+5w9fpdtksVOo7DxNgIz5w6zsLsJAlT46NzV/iDP/khd+6vIiU8dfwAv/mNL3Hk4KIScABRbXTXja7hapIPz13g299/k83NMtV6E8/32D83xdBAnpu3lzh3+SYfX7xBOmnz+RdP8uzTx0hl0gwNFMikErTbHVbWtzh38SYPVjc5cWiRX/7i80yODLC1Xeb3v/UTfvzueTzPZ2J0gGdOHuDAvjk0w8RxVZ1PJwCXIiylI1BF13XB3TtL/Mt/+y3OXrqp6vnpOgcXp/n6l1/m2NH91Ko1fvrmB3z/9Q9pdRyOHZznN375VQ7sn+uh1LKlok1xPWi1aT9co1SpIX1VhkoIlYhZIslnMySC6FQSNkLTMC0Dv+1TqtS4c3+FlbUzzEyM8md+5XM8//Rxzl++zh997w2GinluLy1z6dodBgs5nn7uSV5/+wy/980f0Wp3SKdsTFPny59/mkw2i+9LJseHOXl0Eafj8OH5a9xZWqXddvjc80/RaDT43g/e4ubSKqmkzQ/f+phSpcb0xChTE6OBUKEiwo0gTYQeJFtttFyu3LrPt773U7ZLFY4cmGFidJCPL97gzv1VZrYajLgulmVBKhHRpBhnChkUj24hJ98jtYME8Hv9fENBmD4BXgjF+ERo0oqJRnGGKFHnRGa53cbk9v0UHEOL/ftFt0jNtBPUxYXZT2waOyJD97Ic+RpSKnOhH5kMA0FaiKgGbE9X2h5m2p9Te3zNVoSCNZAqxDQoEPHIuQqNBIq/7oKeP8nMGF+EpgHFHLTaeJkkpV97Fa2YQyvmEMUc1lABJ2VRaTtkzQRJz6XRrGMPD0Axr8J7hcB3PSrlGs1aDWo1hK/y6eimRjqdwnE8HjzYYHl1nY7jUiymWZibYmRkEMM0qNfqvPaTd3nj3bNslSqcPLKPb/zyq8zFn1tKlpdX+IM/fo0fv/1RoHWQVKs15mcmkNLnT3/yFt/83hv4vsdgIcvdB2vcvH2f33JdTj11nHv3V/i9P/4RG5slpieHyWVTfHh2jYtXb5NJp3i143S3Si6NEALHcXnvzGUeLq9SrtQwDYNf+txpnn/2CXLZNI7T4cMzF/nRm2e4+2CVE0f28Vd+65dxPZdm4P92/+EGv/MH36PZbGOZBs+fOsrnX3yKyYlhHKfDB2cucfbiDZ558jD7Fqb5ydsf8kffe1vVrju8yNe++ByHDu5DNwxW17e4cXuJre0ynuswNjrMof0zFAoZNE2n3VJS67WbS0gpOTExwj6p6isqvyOJn7Aol6vcW3rI8uoGF6/ejkLmAaTv02m2uHHrPmcv3mBja5tsJsWxQ4vMz01jWSY3by/x/Z+8x7lLN1QJpMEcX3jxFK88fwpNE5y/dIPX3/mYW3cfYOo6X/3cc3z51efxXI833/uYD89dppBP40mfUqWOqevMzYzz9MlD3Huwyr/95mvcX9mkkM/ytS8+y5/7tVfZLlX557/7LV776Ue0Ow6WZfDMyYOkUzaH9s/x/pkLfPNP3wyAgOD2vWUOLMywOD/dQ9yFEJFJByHwfI8fv/E+v//Hr3H73jKu55GwLX71Sy/wV37zV/jo/GX+x//l37G+WSaTTuD5Prqmc+LwAr/9577GE5G0q0wvtboqyN1stbl+6wGb22Uazfe5c2+ZP//1LzA3N0W9XOVPvv1DfvePf8TS8gb5TIqnTxzgf/vbX2P/vlmajRbNRgvLNFhd3eDchWucOX+Nja0yW1vbytdHCFZX1/l///M/5MLVW/zmr77C5Owk9x4s8+++9SNu3l3BsEyazRaaUDUWfd/n/oMVfvCTd3ntpx+SStocP7yApkneO3OBzVKFr3/lJYaKad7+8BxLy2scOTBLIZdmaCBHvVahWqtQKOQQaCp1SsiwNI1qq82f/uhdNOCv/tZX8XyfW/ceIhCk0xb3V1boOG1efOYoP3zzI9547xzHDs+TTicwNEk6afOFF5/g2VPHOHP2Cr//rR/zxvsXmBwZIPHkYT48f5UPzl9DCOi4LksP15kcKzI8XMBOJJGA4/k4jquk7dDMLXzQfUqlbf7w2z/g3OWbfOHFJzlyYJ7L1+/w03fO0mr/AF0T5HMZbt19gPR9jh6YZ3ioiOd7O7TvWuiDFTy7D6x4Ph3HIaUrQN7wXIZEAFr8oFR1SoWM6LrOySOLfPHlU3Q6Dr//xz/mjfcvMTZa5Imj+0laJi89fZSXXn6G3/uD7/Gv/uA1bt+5z9NPHmF+ahTT0NiudPj8Cyc4sDjF5PgwD9e2SacSHD04x9d+6UVy6SQzUyP8u2/9lHOXr/PksX2srW2xvlVG1zXmpse5cvM+lWoDT6qcap5SCwamZcV1hKaqfDQC64fjely//ZBW22GrVOP+8iaphM1wWqI7wXpIWD18SQotwDH9ACyiPOqf8LrfQ54VMi4ZO68/oCr8KRI8u5erw36gzem9X+g7L9FR9mV24cGSR6asiDRwXVP+L6xJdmqk4kAT9tZmRU0o3BFUl3nkuaExR4Z+Wn5gTo9r/6L/or9F7B38ItqnAFsG6sUExD96wYEYJvpt0T0X7/5TpM3aHXB1oxq6v8l9s7C+RSNp8YfZBPXSNka1jH5fI5OyObxvhvHhIZaWHnL2yk2W19d59skjvPj8k9jpFK1Gk7fePsP7H1+mGWQLlz5k0imePL6ffC7Dh+eucOPWQzzPU5m8kZw8fpCvf/llFuanuX33Ad/94dtcv60k/HOXbnDq5CHmYo9Qr9X4yRvv8fq7H3Pq2AE+/+IpGvU6tmlQLGS4cuMWf/KDt5BIfvOXX+HUyUP8+O0zfPuH7/L9n77P4sIMg4MFFmbH0HX4S3/uSxw5tMA//Rff5OylW9xeesArIdiSQDalimV7HuMjA3ztS8+ztrbJt77/Nm9/eJGD+2fJZhJKI1Ov82e/8QXe//ASb39wnuWVdSYnRhkoZCkWspQqNdIpm+GBHDfvPAjG6fH1L7+IBM5cuM5rP/2QYj7L/Mw4zVabWqNJu+Ny9dY9XnruBEbS5NrVO/yL3/8OFy/fJJ2y6TjKl+YLLz/Nlz//LKPDA1y9doPf/+Mf89GFW5QrDX77+SeYcz3MgDlLCau1Or/zb/6Y1dUNHNdjZWMLPWAO+D5Os8WZjy/xL37/e5y9dJu245DPpnn+1H2++PLTdFyX7//0fc5evMnQYIFUymJtfYtSpQICrl6/zT//3T/hxu375LNptst1KuUah/fNkEmn+M4P3+L9s1c4uDDF6OgAD1Y2qdWbFAppUkkT8Gl3OuQySRbmxhgbKSIEfHjuMu98dBkhIJdJUa7WuXztDnfv3WN2cpAHK6uUq3WeeeIQxw8t0Go7zE2PKbFGeoQZXwiDOAA0QavV5ns/foePL92g0VR+TbquceXGXSq1Outb22xslTEMjReePobv+7zx3gUuXb/L+uZ2d5FKFR24uVVC1zS++MpT/NLnn2VlbZP/5d98h+/88B3Gh4pMzkxw9eotvvX9t9gqVRgqZtGEYHltnQcPV9m3MEXSNsikbDodF4HHUDHDYDGD53sMFnLK/0YIZf7WBCtrW9x7uEatVOGDs1c4c+EGX/rcaQ4fmOfqzXt8+7W30QVUa3XuLD3krQ8uYRg6v/lrr/K5V05T2irxL//tn3Lp2l0O3Z5hcOAoiWRCJXWVkueeOc6TR/dxb2mZUrlEJpvENMxAsxVoDIVgs1bnfqfBy8+e4MiR/Zi6YHZqjLWNbVUeZrDA8cOL1BoN3v3oIrVGA8dtI3BxnBbtTotKpcbG+haDxRxffuVplh6us7q5iS89SpUK89PDPH3yEOVak3anw+zkMOl0imbbQdMEruvhBBpxXVOZ1wVq7d9/uMo7H11kcXacP/ONzzEyPsLs7Dg37z7g0rU7XL99j6eOH6DT6VBrNBkeLPDKc08yNTmmAg8Cvy0hhMqHFdJWTcMcG+SfPLnI2Uu3eObJAxiWwYXLd/ifVirYYYCPEMqXKZCqE7ZFPpumWMxx6uRBzl+5Q6Vao9luceLIPgYHCpz5+DIXrtxG15TZt1mrMTo8wEAxT7XR5OiRRZ556igakmqzg2Xp1Bp1SpUyyYTJ+Ogww4N5Gq0WS8sr3Lh1n7bjsLld4SfvnKXRbJFJJzBNg3bHoVSqKhcPBI1mC0PX8D2ftuOwvrZBs6Xqjt6+t8yd+6usrG+TTtp88eUnGfn4Jlq1FQQxmYF5qVtXb6emK7Z58OgCmtBUB8p/OeL4nwxmImf5bhNhn2GqDyGUa4MMFB6x8e3uGxUCwUfeOABB/7/Qbu01hABQPhbKkXsAS/rwhQZ0Iw1DrVaUITCOJ+hquH7RhtRPYUaMKaR3LI5QRSd7znx0dyHQ2i3C4JN76HQc3nhbaWc0wLYMBgpZMgmLQibND954n9feOoPjuowMFXn2qWNAgx//9H1+75s/ZHpihGwmxZsfXKDT6fDVzz/D5NgwF67c5K33L3D04BzPnzrG5naZ1989y9vvn2ewmGNkJE+pss3Wdhnf9zl98hD5XFrVH4xNx+bWJh9duIJtGXzh5dM8/dQxNOEh8Wi3Oly5cYeN7Qq/8oXTPHv6KEMDRZ4/dYyfvvsxl67d4eHqOtMLU4G/tsbU5ChjI6oO3ofnrlOpVJCuE60QP5MKsogLDh+c4cmnj7G9tsWH566xtV2hUW/iez6ZTJrnnz2Jpxs03viISrXGVqnM3OwkQwMFsukkE6MD/IXf+CKLB+b46P3z/O4f/ZDzl2/y1IkDLMxOMlDIoBuKkIYld4r5NF//pReYnRplYnyAWrXMH3zrNX7w+gecPrmfL7/6LNulCn/0nTf4zg/fZXS4yIunj3Pl+l3WNrY4dWIfrbbPYNJGIAPzqEa70+GHH17gDc3jt77xBRbnp7h17wH/5t+9hhAS13EoVyv86U/e460PL9FxPXRNUKsL1rdK3F9e496DFS5dvc2hfZP81p/9CgMDecqlMpMTI+QLKe4/WObO0jIzU6OMDw+wvlmikEljWSbZfJaRgTy5dJJfeuUUv/SlF3nnvXP84Xd/SqPVAqF8kpMJk2eeOsSf+8aXKOaztDsO128u0ek4/NpXXmBhdpJbd+8jfY+BYg7p+xxYnGF+ZoxL1+8wNTHEVz7/DPMzk4RW1lCmoUcjIWi229xZekgyYXNk/yzFQgZTN3jh1GGK+SSzU6MkEzbZTJJf+/KL2JbFdrnKjTsPqUdJbNX+8wLpX9c1BgcKTE+PsrBvmqvX73Lr7jIXr92i4zncvb/M6sYWn3/hJC88e5J6tYr0JbPT46rAdNImYdvUGm0KhQwLc5NcunGHdsehWMhgWAbokBvIsjA7hmWZWKZBo95UPk+GwfzMOKlUgsnxYQYH8lQbbeqNFiuBZmPf/CRzs1MkLItUOs3wUJHOxZtUaw3SqSS/8uWX2K7UePejK3z3B+8yPjqEbppslWqMjDhYpt2N6ASkJmh0XJq+DMK+dZV3xxCqkLhhIpCcv3yLH791lovX7lIspHmwvMH05GigSfY4e+karVYbCZSrdaSUVBttHLdDpdZASshm00xNjjA8WCCVTFCu1lnfrqIbSlPpBL5ShqEcyVvtNs1Gk0azpcBFJkHCMsF18dwOtqUCYVrtFtlskgP7pvjw4k3+5LW3SCcTDBRyUQ3EUFsm+tZRMmHzzJOHuHt/Fcu2ME2dibFBCk0Pw5ORtC+TiaBUlU02nyGVS6NbJsmkTS6bJJm0qdZqPHiwxtsfXaRaa7C6vo1lmei6Rsf1A8EBXM/H832SyQRSSmxbR9PA9z18z6NSr7FZLuN6PrZl0Wi2ub20wmapQr3ZZHO7hut5FPIpLFNjfWOTW/eW0QyTdDqF67rK/I/Ecx0EktGBPMmj+1hd3eDdj6/QbLWZnx7j9MlDJD++pUaWTHSDQIRGlJNR6wciIUv2urwrzEXnx3/7tE0GIG0P4CGh19fqk5r3mIw4TPr1i1LpPEIJ82n7kaEmbrdnC3/vKm40YikaYxcocKpqJIYGN5UfTv5CEddn8tmKTMtSYUUZcgf5mPk0+h34RLjQfDVDUt/x0DKIulEFSCWWaXLs8AKtVovtcpXjhxc4ffIQ+xdnyWfTjI0Mkk4l2CpVVAJCz+PKxTv83h//EMsy+M2vv0qxmKHerLN0f52TR/YxWMyxsV0mm1a+UyeO7McJtEX/9ts/5dK1m3zuxZMU8xnGRgpsbJdZWl7h0IHT5HMp4mrjVrtN23Hw8Wk06zTqFVIJC00XdDoOzXYL09BIp5O4vk+91cSRLkITNFotHK+DlB6tVieIlFIJ2cJwdtfzVERcMFFeOoFERT01m03a5SpCqvM7joPjOPie8g+5cPkGH1+4wU/eOY/jOrieiyYEqWSCTNKiHDgIp22LwwfmObAwxY3b99ncKjM7PR5EFgmk8JCajxa4DYyMDHL0yH4yKZsbt5b44OMrFHIpvvjy03z+paeoN+rcX1nljXcvcPPuEk8dXwhUvZJMKsHnnj/EER/0195Xal8B9UaTS0sPOf7V5/ncy6cYKBawEjqGqdPqtKk361y5eY/zV2+Rz6U4emiBmalRCvk08zPjjA4V2a6UyWQSPHvqCMeP7COdy4BQzvFOx6NUq6MbOl/5wjOcPLofQwrSts3ExBiO4zI6VCCdTjAyVGRoZJCpiRFSSZNypU6z4yqJSQjqjTZrG1u0Ow5ekD/JtkzmZsb5wsunOP3UQYSQ2LZFvdNibnaU3/jll/jD77zJd3/0PuVqja9/6SXmZ6YwTAPDtJQTeoxJSiHwXOUIPDs5wv/+r3yDw/vnSNgWqZSNLjSy6SSWaaAJERVnHh0qcv32A2r1JvHUkJoQKlO9EHg+KgpP+Ni2Ig2NjoPUoO12MEyDhfkpXv78s+jtFkL6CBFE+wjwNei4Ho2WQ73Voe0ojY3juUjcIELLi6wytmVh6BqlSo1Wu02pUqM4OBDkSQ3WcqtNKyisrRxdwzJMal+ahk4mnaRca2CYBl/8/NOsrpd484NLHDk4z8lj+3i4usHK+haH9s9xIOYgLzWBNA08r8OV6/f44NwVcpkU9UYdAaSSCT6+cIXv/fhDlle3abcdmu02r73+EZNT47iBln9to8Tc9AS6bnDzzgPq9RbpVAIpdNodlyu3HrBRqjE6XGBuaozJ8SEKBZW+w/d8Oo5Lo9nCcdU+9HyP1fUNtsrbVOo10ARb5TqbpRqlSoP3zlxiJdC8ub5Ps+MwMz3Oqy8+wU/eOsdrr3/A6HCRqSm1fkeGBxkcLKK1nS5B1QS6rvPUE4d4/d3zXL1xH9PUOH5kgcx6Da3ULTdE0saTygxaqdWoNxuUKlWWltcwLYOx0UEerKzz7dfe5vbSKl979Rnmpsf4+OINytUmuqFqdPq+j+N6VGtNfN9XlREMHYRGpdbkwco67XsPuXL9HrVGm8MHBhCaRr3ZYnO7SttxMS0dt+lxe2mVjy5eZ2xkkNWtbTa3KyAlczMT5HMZhNDIplPYMxMIoFSpcmdpmZt3HtJstTl+aJ7F+Sm0jgoQIJWIgFa8zmA3oW7Xb0rQ9cuNMai9+d2jWgiiZAikgvs/9vVB3rIdTvHhzzL6jJ/TX3LqF9NCzd6nSdmwd1d7q7Xi5yksEkUBhxG7KlxWzQMq0CwsQda99her2/rsebakVNXeZW/oNX3fd88L0iX2kRgf/QtMkn3J2yTgeS5acL9EwuKrX3yWVrvFux9cZGy4yOknDjMyNIBp6uzfN83Ae1k2tkq02h3WNra4fP0OG5slFqZHcYPEfMnArNGoN1hZ36RUriiBRggcz8dD4mvguA7lap2O4zIxMcqvffVlDNPk/OVbgCSXTTMbKPykVGr6RCJBubrMmYvXSaUSFHIZspkUpmliJyw8X3Lt9gOmJ0eZnZmg1mhTrbVU3IgIiwu7+L6k2XZwPInj+Wi6Rsdx8R0nWIMSJ6UkxXbHoVpr0mx1IhrhdFya7Q6u6/Hehxf4Z7/7XUaGB0inEtxfqbK5pbR0hqHjS6jVm2xslem02ui6KibbbHdod1o4blslYvRVPh1PClwJzbZS87cdl5yZpu10cFwPyzQxbR1fqtp9mUwKUCDK831mpofJpBP8+M2PWV3fYuTAAuMQJEVUPmitjEHC67C+sUHCNlle26JcbVCuNVjdLHN7aYVavcnE2CB//htf5OnnTiKkj++4bJcq6KaJ4/pslevcvL1EIZ/FMjRlKtF0GvUGnY5DwrKZnhhFOh7C92nWG5RrFbaD6DjH9/Clg4dP2/FotDrqOX1Jo+Vwe2mFs+evMzCQJ5dJYRg6juvy4zfP0Gq18aWLZRnsW5imVmvyk7fOMD0xygunj/HHf/oWH56/yfDQADeXVtjarjI0NMCpE4eY8mL7Sig/SaFpNDsOK+vbDA0OMDKQAylJ2LaKxJWStuNSb7bpOK6KinU9ao1WzzY1DINUQvl1rW1us1Wu4XopVjdKyl+xkEM3TFLpNBLB9374HtKXnDiySDqVIJm0GBzM0XYdpPRptlosPVzF8302t0uUKnVu31vm5HaZ4VQCx1GpGVpth+1yDV3TKOazvHvmCm++fx5HCiqVGuVKHdsy2QhyqqWSCTa2K9y695BcPsO9+6tslmocOrjA/gMLrG1V+OlbH2EYyjHaNA0GijmSCZuPzl7lvTNXOHxwln+wWqUgfUVQDJ3sQJ4Br8N7H1/lxt1lCvkMuWySY4cWyGXTvP3hFe492MDzfFWD04G3P7rKyeMHqNYabFfqNFsdhgYHyOeybFXqmJbB4vwUxYECQ8NF6q0OlaVVVjbK3F5aY//CJIf2q2LQD1c3qVTrPFhZZ2pzm61yhXqjxa2lZZY3SmxsVzBti8s3lvjmd9/A8zwuX7/L/ZUtNCFwfbh1b4V3P7yIaZkcPjCNZZrYCZOlBw956/3z5LJZvvLq8xxqtrovPig/tbAwy8EDs/zojY/wMZibncC+sASyW2fVsy1q9Rb3HqzzcHWT9a0Kruuztr7F7PQYp548RKPeYWWjTLna4PrdZRrtDkLTOHv5Fl/ZruK4LvVWh1KlzgfnrnHk8D7mZ8axkwkM0+Tc5ds8WNlSe8ZxmBwdYXpqDBdB2/Wp1JUW2bZMbNvi6q0H/M4f/oA/96ufY3iowPnLt7hw5Rb756fIZlJMjA2jGRYpXcf1fYTRolBQ9XMTyQT7FqfJD+RV5C9A0g4imgOQFQKvPjNiVLmjx2+Iz8aoQw1zXLPUb3Z8hNJCcU4/wDSxPoSneGisFI2IFAFA4Gv9i3QG/7kCrbC/qMWiEsP5igCViABoqPyQsqusiQAYBClkVDdCiMe3Zn7G9jOBrSi2/FEL7VHnKFRCTFcG8UUTnoYK6+102mi+hy5VYdxUEKXWdhwqtRqtditYQIJCPksunwEhKFVr1OoNZiZHWJwbZ3llnbMXrrC4ME2r1WZ8pIhlGmyXK0h8hAbblTKVepXtSpXrt+7TaDlYtk2t0aJya4lUKsmLzxznwfI69x6sUW+2eoadymSYnhrno/PXeP39C9xf2SBpW8xMjnL61FGKgwOk0inOXLxFs+1wYHGahyub3F/dZqCYoe34EWiqNdvcuLvMyOgwW6Ua7Y7L6noJr92JlGleKkm52mS73GBto8LGdhVD16jVW2yVa6yslzimKZNfo9liZX07cIrucO/BBu2Oi9A0HM9nZW2b9z66BIEG4e7SKo6rtG5t16XtODiuG+QNlXi+pNnucPn6XQrFPPOzIzi+Ty6XYX2zRKVWx3VVbiff8wM7uUaz7SCFxqGDc1QbHc5fucP5RofDrocVpLQQEjzLZHmtxMUrt0gkbKr1Ji3H5eHaBvdXVkilLAq5DI1mm/OXbpDKJDEMjVqtQdtx8STohsF7Zy6zsr7N8GAex3EZGx7g2VNHAZXL58dvfMTWVhnf88in05w6eZBGu8PD9W0cz6fterTbLqVanVqzTTqbxvEl6Ko88+r6NrfuPVTgq91BM3Usy+bspdusbZZwPSUR//qvvIJp6rz70TU+unCLkaECjVZbFZu2LVY3t/jhTz+i7XhsbpX4jQMLjIYeB5rAskyGBvMsr23wnR+9x/krt7Etg8WZCb7w8mk2yzXlD+R43FvewLRtaq0OCBEBsWC3oeuqyLLn+bzzwUVarSapVIJ3P7qCZVscPDCHQGN2ZpJiIcvSw3X+/Xff4KPz15gaH+bzLz5BLpdic7PMVqnKVrnOhat3aLTaOJ5Pq+Pw3sdXmJsZ56V8nq2tKqsbJRKWydLDNZZWNnnm6WN8eOE6r793kRt3V8mkkqxtlJgcH0KiMT01Rrla5+MLN/iTH7zDw7UtEgmb/QszzC/MMDE5wfZ2iY7rc3dpjaGBAn/+N77I8eMH0TWNgcGieqYPL7Pp6+R8qWpNahqDI0N85fAM6dTHrK5t0Wi0yOcypDNpMtk0o6ODrG1WSNgWpUqN1fVttst1llc3mZ0aZd/8JHeWVnn7o8tk00k0TbA4P87MzDC5QopnTx/lzoM1Ll69h6EbDA8VWZybJJNJc+bCdW7eW8HzfK7fWUIzdT6+dIOO6/LxxVsUCjlOnTzElz//LN/6/pt88/tvKQ2kp1LIHNg3zcLCDL6Aq7cfsLldYWSowBdeeJJ9i1NcuX6XpZUNHpy5AkIw3fHJiJDMKht1Lpfm2JF53v7gApZtMT8/jdDe72HUrm1RazbxPI/N7Qr1xhWGBvIcWJzihWdPMDc3xfraNs88dRjH9Th+eJFKtc7kmArqMSyDUrVGNptidmaMRrPFrbsPmZwYYbCY55mnDtNstWk224yOFJmeGGFhdpqhwSIPV9YZKBY4eniBaq0BwNhwgWwmheO4XLlxl+eePsYLp4/xYHmdi1fv8MTx/Rw9tIhhaLQ9F6fjUm+2aHVUypKRYoaJiSFV1aDVUROS6EYiykiz1dUwhXndxA5u3Gsm6y8V80hAIwSqWHRf8tR4zq54/pZdru8674Tj8Ol3Ro9zVHWuD+jB8/yC/LUknwJofRqEE2KE6CZ0EZVK3x31KESQ5iE4O9R2+aEVTiCE7O3nF9g+HdiKaas+1bAeBcb8wA4bnbv75Z7n0Ww1McJiulJSqjbYLtfoOCrr8lalyoTXBmFTbbZpd1w6rke5WsOydY5Oz3Hv4TL//sEqr715hgdrW0yMDXF0/wJDg0Ucz+PQgXlK1QZvfniRm/dWWN8qc+HqHQaKOU4eO0CpUudHr3/A+sYWpmmyVaqStG2y2SzxRZNJp3j21HHuPVzh/KWbfHj+GkjJ4VKFmbkJZmcm+PxLp3j97Y85d/k2H1+6he/7DA7mmZ9WBOnazftUag08z+farfvsW5jGcZWG6MHqBm6ro6QWAW4qwZ3b9wJTVonL1+8Ags1Sje1KnY8uXOeF55/k0KFFXnruCa7euo/vS1Y2tlle26bleFh2gkwmTbnW5Ns/+pC3P7wCAZh6+on9DA0Psr5V5cHKFqVKg/srm5SqTSzbwpPw1oeXuHH3IV/90jO89Mxxjh1e4Ls/epe337+Iaarizecu30HTNEZGBqk2Wrz1wUXW1ktk0kk8X9KqqIAEfAmmQcIwsPJZHq5u8oPXz3Dp2j3uBw7q1289ZN/iDLNTY8zOjfHWe5f49o/e5b1zVzBNg0I+w1NPHGJqcozBwQJnzl3n8o372KZBJp3g17/6EslkgpmZCfK5NO+dvcLF63cwdJ1DC9PsPzDLzbvLnL92l3qzxY27D5m5cY9zl29xf2WLdDpFu+NTyOfI5TI8WN7g44u3ELrJy3MnGR4axOn4vPXBBVbWS1imzsH908xMjdLuOIyMFKlU6lRqDYaHCxzaP8PCwgzNZpuBCzf46NwNfvLOxzxtmoyEC0sTpNNJXn3lSb75ndc5d+UmV27eQxfw6199mY7rceHKLbbKNSzT4MLlmxi6xsraJq2Oy8raJl6spJSuKw2fOmeLH71RCZLamrz64pOcfuoovuMyNTHK5158ih+/+REbWxXOXbpNOpVgaHAA39e4eWeFh8tbNFsdHqxscGBxmtnpcW7dXebG7Yd8ePYqx47s5+7SMhev3cXxJVdvPeD1d8/xW3/my/zHf93gtdc/5OHqJguzEzx/6jCppM309ATFYp65mQkO7pvj/sN1bMviyMEFJsaGSCSVBnF8bJhf+ernuHnjNoPFPHMz4+RzWaq1Oi8+dxKhCX7nD36AX6mrmoVBioBELsOLzz3Jof1zbJXKuI6HpgnshEE+l+Ebv/wKp544TC6bYm2jxJvvnGO7VCGXz3Lo0AJ/tZDl3oNVKtUG0lflilJJlfrEl5L9izP85d/6OtdvLQEwPjrEyECOW0srJBI2J44sMjiQZ2F2glK5SjqVYH5mnK1SlVt3H/KFl5/m6199hUIhw3sfXWBrq4Jt2xxYnOHFZ09y5NAC5UqNp04c5KNz1xgfHWJhYYZcLks2l6WQz3Ll+j3uPVylZSRIB9p3gsLQruNhWwmSiQTzs6PMTI/RU3INgWsaaLrO/n0zDBVzDA8VGBrMMzs9zuBAnlqjiS99Xnn+CQaKBYqFLBubJcZHBxkcyFPIp6jVa7z47HFyuSxTk6MMFPIkEwlMS/Dc6WMMDxVpttoU81mymQwJW6XU8H1JLpdmfGwI35fUG00O75/lwOIMqxvbnD13Gd/zOXnsAO99dImtUjUwWXr4UiU5XVnbZOn+KneWHtJ2Okznh1Ux+HhgW8ru+mcJlN9WH8iJHOelgQI1u5gTw7+l2qv95ruepsVAQ+TzvItZKw64wmN7mgQfDXAicCa9rpb8563OkZ8CuET55D6hv/hc+DELGARYS49puYjur2kCNHCFH5Xq8X1fabcCCxZCIlFFqPvB8s+z/QzlenYOKj7Qn4eKshtJoDITe0G+jNCh7eHqBpulKr6ErXKN1c0yi45Hs9Pk/bOXuHTjDq1Oh1K1rhI3agZO4FPiSUgmbQ4fmCOTSdFqO+SLOZ44fhDDMHj3zGXOXLpJs9VhfHSIz718iqefPEqnqcDF0vKmyknjS548Ps/YyGjP2E3T4NDBef6D5Nc4c/4K127eY7tUZWF2gonxYaanxhgZKnJw/yzXby5RrTfIplMMDxVUHhDDwDbgV7/yEvlchvHRIaYmx/jKF55jdHSIB8sbiA9vEqXkKOQ4uE/5q1m6YHR4ANuy+JVfep4Pz17FMk0812V4qMhvfuOLrG6WWV7d4NLV2yzOT5NM2CDg+LEDrG2WWFnZYKtUI5tO8LnTx3jp+RMUi1mu3XpAs9VheKioNEztDiePHeDe/TUuXLlNqVzDtiyGhof40qvPUq7VuHZzid//5k9AgGkYfPkLz/DCcyfxXI9Kvc2Fq3fxfTBNk+mBAvr9rSiCKp1O8sKrT7N87ip37q+xsV0hn8swUMgqKXW4yPT0GM+cOsqN28vce7DGeqnCQDHL5NQIY+OjTIwMKS1X22G7XMU0DE6f3M/nXnqKQrHAqaeOsbK+zXd/9B5bWxWEJpmdm2RwcJBzV25TLGQZGirgA5VaA03TmRwbVIXONY3R4QFOnTzE5pbKvZXLppmaGGVwoMD+hRmOHJrn9bc/JpOx+dqXnmduZpzL1+7w5PF97FtQSTertQbpZIKhwTylUo2hwSKarrGxXaa2ud3dW5qGYZp87SuvYNsWP33zDFulCuOjQzz3zAkyuQz5Qp5981MADA0NMDQ0wNFDi+imSaulzLvROjVMjh5aQNM1EralEnoagvHRIY4c2s/k2DBSStLpJL/567/EkUMLLD1YxXFcDu+fYWx0GOl7HNw3y5/9tS/iuB6JhMXh/XO0HWXSvnvvIcViDiGgWmuQStqcPnmQYiGr1mnC4pWXn2ZkZIgzF66xf36KowfmqVYbbJerGFaC0eFBjh48wHapovzQsklSSUuZuYVER7IwPcZQXgWqJBO2ApK2RTabwfclCcsgKwMCG6ZYsEzsZJKpKZuZ6XE8z2Nru8zWdhldM5ifnWJuZgJfSra3q+iaoFRW+elyuSzpdJLBgTzlSp3tSp3N7Qq6rmElEqCZ+AgmxkeYmhxD1zQ836dSqQIwMzXG4twkczMTaJpGrd7giWMH2Ngq0Wy1GRkeZGJ8hHQ6yRdffYYTJ/aztr6FqetMjI5QLOSxLJ2J8SK/8auv8uTJQ6SSCWanxul4Uq3HoOh2KpnAasUSbQqVoPbhygbXby7heT4H980yVMyrn2OJOl1TJcw9sH+WJ47tZ35mTKVVaLSo1Rp4jSY2GoOjw+RyGYSuk0ynyWbqeJ5Po9kETWNkZIjJ8RHmZycxTQPP99B9SSGfY3FeUK02cBwfy7RIp5JIKUnYFoW8ipIu5LJkMikmxkcYGizg+lLlIyxVeO+jiyyvbTFQyGJbZhSh6zgOSw9WuXbzHg9W1ug4LvlchmQ6hdYJTIgCSCYC32NA6Hvyr4jeyiAFkggyxcdMdNF3GbrExM2CRP5gRP5gksipXgRO8v2YaVdA16UJqp+Y0/4jMEOo9VHZ6GOWpR7ftMdp/WMKv/wcyp1FffZrr+JWMJWIMyw0HT8vrKygRb53AdjyPTzXxQ+1biL2xOLnDjt72qcAW7LnYy/VpvSDzNl0tWCfpUXMRRmX0TUd2zIjpzZPStY2tqnWFPhJJJNIoeMLjXqtxla5SrGQJZdJk81m8DzJdqnCtVv3KVXrTKYH2dgs8+O3ztBsdhgaKPDcMydYnJ/k2VPHmJqe4M7SCo7jMj05yuT0BIlEArJpvvrFF1icm2R9o4TnwbOnjjM8NBCNXbguib/29wE4Jn0O+8rfw/M89BsrWK+fRxMaRQHjUvKKVMklQ8m403HQdY2EbfMFwwgyyiuQOel5nO44tNsdkmGpHiAxVOTZA7NqfqSPkCor8rHDCxw/vI9mqx0k6YTBgQKGZZPJpHnixGGGBgvKkb7j8MTxgyzOT7GxscX9+8vksxkO7Jslk0lSbzTIZjO89NwJTj1xkGwmxeBAnkw6xeBAjhs3H3Dr7n0O7JtBCJieHOPXf+VVzl+8FkRUpVicn+Lg/lly2TTbpQrHjyyyvLJBpdrk+OF9HLYT6Bfuqs0TONB+7osvkDw0x7mL11mcn1Jg5eod0qkEzz19nEw6hWkYvPDMKvkrdxgbHeLI4QUO7ptlbGRI5UJ65TSL89Osrm+SSibYPzfB8GABHclQIcuf+ZXPsX9hinOXb1Ov1vnSK88wNTnGL33+OQ7sm6XldBgZHmB8bJipiVFOnTyEbVtMT41h6DqnnzoCKEnqieMH6Tgua+tbTIwN89zTx0kmbRK2zsLcZLS2hweL7F+YZnRkkGq1TttxkJ6g47i0OiqlQzqZwIr7bAW1KoeGBvjKL73MvsVZ7t9fwXWVpkwzBM+dPkEqmUDTNPbNTzNQzDM9NcETJw6xXapiXLgddWf4Pi8sznJqfop0PouRsBCGgTSMyB/PEKocSj6X4ZUXTuF6nopu9X2k0NAMjaNHDnD85BGQ0Gm3EUIBi6Rt8+DhKvPTYxTyWY4cnOe3DJ2hgXzgN5gkZVv4nkcyYZNJJcH30YRBMpGiXGmo6ktCgOdiaQLP0BG+wHX9KGhGIPClHwGasOKF63qsrW+yvlnii6ePk//hGVUn1fdhq4I8ew1RyGHmUhjpBB1DR9TqeI0m3lARLZ3CN3Rq1QabmxUs02JxforJiRHS6RSe51Eq15V/1VYZy1AO4wPFPJZt4nQ8Om1lwjV05cNXrTdxXS+IGE0zMFBQwo6UShhsNHERWJaNZRlIXce2NMbHRxgaGkAIgWUYmFoolQtGR4eUE7wATWjcunOfD85eZnV9WwHng/Mkz96MtBieVBn3f/T6+3x07iqZdJLDBxZJ2yllWvK79LftKBeA0ZFBBos5ivksnu+rIAXXZfDsTUb/1Z+i2RYM5JCFLEY2RSph42STaIMFMqZBptNRQRjFHHpQzknXdWzbIpPO0Gn7+F4b0zSiguhzMxNMTY6Sy6bJZVVy5UTCQhMqenZqapxOq02pXOHowTmK+Syjw0XMIAm0bVnomka13qAUaHsnxobJZtJozU6X06YSweN+UiHimIYl/FPq9PDHWAdd9h0Q6rCOUz9fjE4LgdencCDyw2oXny4Ksjs2peXqrXi+V+vDAf3mt08NsvombNd7xRO5xkBVKBCEvmExkNXTiyTw1VJr1vO8oDpNF5+ICGj94uDWpwBbHvHyzAJUMcdYaKwMJiLMa/Fp6hH2q+/iYZjKt0QnmUxgGAZCEzhSYlkmC3MTmIbB0UMLHD44h2XZ1ESdo4cX2b84QzqVpFjIMjiY5/6DVTa3ytimQTJh4QUJz7bLFR6ubjA8UmR2eox0OsnM1Dgjw4MAJGwbDJV3RugW++anmZ+ZpFxp0mg0FXgxjFigAIhYsWldKsm7O3nNwF7cFWw0qYq8ao6DLSWmaWK23WiPhkvSCM61fT8IFlB3Tf7V/zPYJmRSkElCKoHMptDTSfYlbbx0EnH5HmRTkLBJWAZ2NoNh2lBt4CYtECjVflAGZWSwQDqVIJ1KKgd63aSQU+AKAdl0imwmjWUajI2OkM8UmBgfIZdLousmpqkxOjxE4fmsAhsJFS5umQZSqpxC05MjPP3kYXTN4PSTRxn98IraAL6MtA+ZoUGO51MkExZTk6OMDA8wMzWmMkYbOu1OB5Aszk9y5OCiKtGSy2AaOkLT0HWNfD7LkVSS+dmJIPLSxtKFKlwrJdlMiudOHePA4iwbG9tMTYwiBIyNDDIwkKPjeei6hq7rWKahos0k2KaB5/ukU0mOHJzHTlhYpsG9pWWVkyiXwXGDCvMYKvTdc2m3O+i6FkTzKbO870Gt3uLjize4cn2JRNJm3+I0Q5lMV2gJ95RUJoN8LkUln8VzOkqbo2kUCymOHppH0wylIUDZjjLpFMVCLsqmDqCdv87wf/KPFKESYY4hBa6kpqlafJaJ0DVVf9I0sHQNQxP4uobUNHwh8DSJSNgYCRtT1/F1DReY83ymhCCRvoGZTjIuBNlOB08TZLNprGQCYWh4UmKVqowsr1McHsDYauL5EmO7hJVKoI9tqwLYzRbC8xCZFNI0aelg2DYynUDqGu1WB0f4mIaOrqu14TguLz5zgiOFPIk3L0G9oYq3b5ax3zyDdfFmIMSB6ftYrsuY56PrGoZhIIUgbxqMGTq+ZWLkUljZNFomhW8bzGiCTLNFU4CVy5BueyRKDUgmaWvKJ0cmTEgmcHUN6bhomkYiYWHbFrZlkrCtCCialknb8aP1bRo6uiHRfBmUzRFBdQZUkmahoWsmhm6iCZCeim4s5rO88NxxJsZGeeH0cYwPr0W8xPUlG5slfvj6h5TKNZ57+iiLc0rjJARdsCVg+Hdf4xuei0jaGNkUeiaFTCexbQvfNtBvPsDYrqj9urENQoXVW8pOA0KQlZIhx0U3dOxkAj2TgmIOWcxiFLKY2TR20qadSWIMD5AY66AN5inMjKObqv6ipndNexLJyPAALz33JM1WG9fpYAhVdsiyTGxL1XO0A23tdqlKs9XmwMI0+/fNqooY29Uuv0kmFNASn5BFfE8wETLt/sOBFkvGiL3we0+MmwkjP61d6g3vPSh68311x7OrKTK6R9x9J6zY8QlmvchEJx8xF5+mPcJnTLI30AqOd4tJh8fVMREbW8hnRU8u0N5oRCEUxvhfRQZ5GdRWEmEGeVCb3Y/ZpKWMRWlIpNxp91anxZEwEZqQUgbf4y9RXa9rGkIYygYbSIknjuxn3/w0tm0xWMyTy2XQNY18Ps/TJw9j2SaWqVTKzWaDcrVOs+0wMzXKF185xbFDCxia4E9//B4fnL3K5rYqA5IWSQxDx0ZlFNa0br0qQ9ewTQOBTsJWTpqGrmOE9RUD5sRQsed5ewqI9qiS1bP7Uir1puOi6Rq6ZUZAVk2RDNeSWkhSIta2VPHmsDht24F2GTbLgbJbXSlQMDmcVV1KbMLbhxswIDjpBF4qoYIP0kn0XAYtm8JLJdAsk4Rt0rZMHNsiOVLEFCZ6PqOihjodLNMiYaUwNAOpo4hkIhGE+RtowgR0PM+lVutQq7UZG1Zm1cX5aax3L6nx+DGClEzgNZtBxXaJrumkUkm2tys8XF2nHiSLzOVSLM5NU8gVAIK6gbG6V4YRlT/TdB1PCJWPF4mmKw1NNqc2nZ0wlQ0fiWYILMOMnGQ7jku5UlP957OYpollmRQLOaSESrVGq9UhmUzgS0mz2aLeaGEYOm5HUm+0aDbbZDMZUokUoON6OvVWi61ynfXNMrlchhPH9/PqS08zurLddfQM1oQvfcqVGneWVtjYKLE4N0E2m8YwddLpJCDwPPX2250O0lfCydBAQSWDDbelRGVYhhjxUVmqZVjcvROkDAjBWEDMtMDMLz0PzffRAt+eYIBonofl+SQCHzBNE+gSMq6qV2YYepB4E4TrUex0yDguyYSt1ovnMeWq/WUaOlKC6bpKKAnKGflSRmHcmhCkpVrzuq6Aoi0EI4Bumxi+RLv7MKIvAlQC0XgIuNDQdQNNC+haIPGaHQej4yAaLSjVIgKuAwaSjB8WxRaxTR5jBjFyN+/5OEIFfhi5DHY+i5ZOIlI2ejKBnrBJ2iYyYSPSKbRUApG20RKm0jwmUoh0AhIWMpVES6cQKaJ8WugmU7NTfKmQVvQwlyeXzSLaTjQuH0kjyGE2MTbEs6eOMTw0EAgARMxKINANDV23VCWacgNRbqg5CZ9oZRNRrQcbSwt8aulpQkoMqXI6aZpA6HpQBF5D1wSGppEQQRSgpimfOk1XvmXZlCqtVszBYB45mIfRAYyRQbJDedLFHKKQgXRCseJg7KEZcmigwPTECIV8ltOnjjI7M4Zla4hOLO9YMvHJQCvYAz2mrK4X1M6m9RWUDmm+6Cuhs9s9hRKmdgdc/ZMbZIOP1prSeO6IbJQEEZdKGRLx3ogWBKBN0h1zCK5ivGrP5w372AuExXle9De7PF98wDGwGPUrdx2CCHNldQcafWq6FiTCVtpUw9CiIvVCKBeWZDLxv45C1L70I0IiQhWnBBEvOBy0Ryy/qPUUmQ6cA0XfJAZZvKL1rQVFfxECwzRY3DcX5JLqqgHDhHmJhN3tR0p0zaDTdmi2OgwUc+TzWZLJJKVyha1SlXQmzfBgAcu00DVV1kdKTUUuBKtS03R03QRd1R/UNB9D1xFBzhp5dB9slJBDRdx/+d9Hz+n5XuSUHNKSeNipyo+lTIiu62GZBo5pRAwlPkdaULjW8yXaP/8j9I1tRKOlkjXWmlBvIGrNoA5a78IRsS873pEE0Wgi6qqQa7gwwjHqUhHXdGDOlCGTC6QjXdcwbZOhVAKjkEPLpTGTCQzbwEsm0HIZ9GwaLZ9B5NJotoVer5N2fIYmRpibnSKTTkJYjy3QbImgQLhhGAwUiuQyOQzdVPPl+CyvbLFVqjI6MsDU9ATZfA6h64FgHjC8gKFqiEg3KzT1TehdpkuggvYJatNpIfvUo+s8KXEch0azhet5pNMpUskk+VyWVDKN63gYukrmmEwmVPCC64KUaJoyI21tVxHCYGZynHQ6hfQlrY5DtdYgl8vwlS88T6vdYXRkkLmZCdJ/8npX5a8JxFaF7F/7b1hoO4y3lE9OOqVyawkgKSU5PwT5au/6gWnNNAy0ch2ZTUPSpvHMUWrlGpamk0nYisl6HrLjIDuO+u764LlovlQmONcDx0G4ntIQ+TJITRQv5NolfCGIE4gIkIRRw6DqYnqej+cqM6DKNq/2tKGrJJ+gfEwc142KJXcdXdV5iYSFYaj8YkITCF9iBCXXRSMIkZ8YVmsrKOHh//IryGP7EU1V8JtmC9mIfW81VSHwZhvRUp+0O32lV4L79WvnwxXYR8B1Q0eXElwfsV1FK9cDuqbmZi9Zfy9iHRVQTwT1/ZIJBpMWg0kbUjYimUAmk7C0okYVFPbOZJMcOTjLE8cO8uSJg1HWeIFQWvKhIvLFJ5Q2sdFSz15vdgukh4AsXjprZlzhA88HzwPXR7ouTquN13EUHRFCPb/nRZURIhlb+gGtF13gurbZ+7y9D4+udQEaCaXlJJ1CZlOYuQwn0gnmLINOPkNmq0r6ym20sUG497DbY9LmU7UIdIkIYEbHo88QaMVAhoCo6O9efD22h3qBXbzFwYT3WGAEUOrzEJSHmiPRN8bd+vgkLVb4e7wo927nxIs9h9eIaKfs3ueO4+HP8TmQO07otY4FApmuklDrutJ8h7QpnUowUMwrQfQX1B4bbGnRAwnUy1VgQUYk9PFamKBT9HgAdiXNvrOD/0Pi3K1ijxAkkkn1u68iCeJOnaGEE2o2NF1HMxQwWl3b4kdvfMTZizeo11tUa02eO3WMUyePkUln0YSOIQTCUOANQGo6umEoaTxIfCd0HaEHPmpaVwIRKMc8qSYOQzPQtS4DCZtAIqWHEB66LjB0E2EpNb6yFkmk0JDoESESwcbWhI/7l76OKxXQkRI6joMQQjmIej6irsAXtQZ+tY7WaCFqDUUwq3X1Gfyu1ZtQa0C9gVZrgOf3WtNF7Ol2SGWKCZv1FqLeQmyUgzWjzJ7huonvZQuY831mpDKN6OkkpJPwYA3Wt6DjQKmKdH30f/cDBjSNvG1irZXQUkm0hMWA73MsU6CdH6AwNkQyl0Vg9Ox3oekITUdqOggFeKKVJSW6IEoU6/s+rXaHcqVOMpnAtq1gzaorPN+n03FwHA/TsEjYGkk7hWUm0ISGtJSmJZNOUyxkVWLbtku742AYRuR8Xa02GA38vwxDp93uUCmVWd/Y4vDBBfYvzmDoyhximgYidOQN5ltIib5ZJiEldkDwNbfes4d0qUB8RDNjZkIEyNFBvEKWi3/2VVbXtti3f56Z6Qn0QMKXXmj6DKJ3fKlMWkFUaQjepO9HjDTKTeQ4OI0m9UoNHJe0aaHrBtL18V0Xr9nC7zgRMfXaHWpbZSrbZRK6wE6nMH/nO2i1RgDwXHzfp9Nu02q08Bw3YC7hHpJIXUdLJlQm+OB5pWkgnzjUBYjRPzf6Lp86jP/F57o7MqBPxOiHjGhf8E9KcJwIhIXgQzbbKtt/swnNjtKCtToKqLXbiEYb0e5As43W7qgcT62OAm+B9vBxBFW1DPqopZQBQGwDZSW4xpUI8U51DdM0mRSCf3RtGfvOJtb33+8C+q0ypFMwVIC//zcITVAyuK+UEt/zkK02st5E/8/+r4ilVdAE7n/1V/GbbWi0cCs16uvb1De2qW+Wccs1srrOYDJBGoHe7kCtoebQ85COR6vWoN1qkzJNLE1DBNpVgiL2O5qUKk2454ODmu9StUcZkwAS/fxcE4rrGoYCaW+cQb7wBByY231+1c32eDsi0Bj1HesBWd3+ZFhOpnvm7k2EGqo9zvDD+oZ7A7ddg9aCtDpqDIFD/2fV6MTNlI9jVowAVni/QHu3G9YKxx2/brfvkr4C37GLdx2TjF5NSPsTCZtsNh2A6E9+jM/SPkVtxPgYusCr93hoRAzB06PUg49YZH33jIO5uBtb149FQ/pBiUbNR0MjTGIWLmnTMpmdmeTpJ49y8/Z9KrUmjWab4cECX3j5KU4/cYzhQeWjpTRhAmHo6IEdX9M0pVnT9CAPS/f+Xee6riQbaaVioCuSV6SvfLgkdENfVUELGeUdkyGOQYGuWAYRTTlxuu0O9UYTwzAwdJ16o4mUMijVYUFQoDs0+8i+DeXLmPYhrkKXEtlqK/DVaKLV6lBrQaD5otZUoK3RRFTryFoDUWsEv6nj4WvuMVX2vX9N1wIdqegyiq1yt+hytY5ottH+P/+euNwZrocckIuBCs/3kbqOSNpKyk8qKV9LJhCppJJeExYkTPyEBbaNSJhKG2Db+IZOu1SmVq3hHJiH0SFk0sbVTVxNx/U8Op0OngeZVJpkMkE6lVJ+PYE5XYNATa1jGBblShWkrvzepE+12iCXSzM5MUoyEWi+PI+trRKVSg3fV2VKErYZAaMog7yuw0Be+eShfG376UKEbWOfoTm5u0CVObZuGdy5u0I+n2Iwn4kCUEAlO9V1GZwbZF6Oreu4v4MRADAI+YPEkHlsTxFSTagSA6G4ZMfWncRHxyPn+iQ7LroAw9AQ3/ppZL70fZ9Ws8V2q0Oz43TNnrqGFoToZzIppa0zze46Hyrg/Y9/DyE0Qius9GNamGBf6HRphRBEUfjd/QDd1M1SMUBLA8tCFjI9fiNe7Huc/otYD0JTApSmKZOlrusYArROB63dgmYHv+UEwK2ttG6NANQFGjjZbCmw1mip9dFoqfPDz3pTaS1CKhrfgroyudqmid1xoe1ClV1bGM218wdN7S/bRtRbal+ND+G/cDIytX907grn3TbGcJZ6Q+VA3Dc/xfGj+7BHh0BX9QyF6yKbLTqlCu++foY3fvwen3viCE/vnyMp6T5/uYrYLEO5CqUqVGqBgKi0j7TaCkh7u2hX+h/Bl+C7qu4oIM5cQa5sIINks3u2Hcx7j3OjNBJ9vwtlxgsBvNgrVUOoOVNMIaZJ6z8HVAb6R5TnkXLnOCREDvUyVoz6cUHXrpqsR6GUmDYrfg8pY5JA/1z1P3MXRfVrtQRx5Yo6FglLshd0IkVXoAp5kdCUQuQXBLTgZ0r9sLMJTcQArnjs9wb0ImSIpIK4PCB7XkZv55F/hRBooguyQqWgaejMz07yG7/6Kg+W16jW6piGweT4MFPjI2RT6aAkjh+sBxE5MIuYTwhC4BEbW/DZ1RrEAJgIE+H1PmekA4v+64bfiuh7+ARheG5vE0L54tx/sAoCRkcG0TW1YHRd7/rOBPeMSwchCOxJJxvXuGkaIpVEJhO90ln4XmLX7crsfT/QmjWhVoeq0pjJar0LyhpNRLUWaN8CkFZrwspGb4dBUd7eO3SPKL9TZWISAJ6ntHT1FkJUInNVP+jTep5AMSThS0aaLfIdh3QqgWWa0TVSCKRl4tsmJGxI2grEJRPIpI20TbBtZcZI2pi2iWVZpEyDAQEtIXBNDalrJEybhOuhtztI26TTcahU67idDl6noxL3YkTTrrXagETOjOH/P/5zmJvovkfZ/VSaGRGVu4m/oAi4BWuyVKrw8YWrGI0mC3PTFIoFjFgh3lATJlH+knGiFzdvxwWO7hpR49ExdkjlEeAIzg9DsHVDmQKVKU7GT6SVTvKwWmPLcfCQSE2VGTJMQ/n7GAbZoQJ6Pqe0FNuVmKQrgrJCouf+8TcfNlVtUPUdjiuUfpF+lAw2ehIJyuwVaACl8mOLwGD/vEhA6gipaIkgRHYCoRuQtiGbRQhlopeRKWqPFtuLu2q6XAdaARhptKGpwBn//T/rk+B3Qe0AA7m+LgPwHTfPNJuRcCUH8tF4Vtc2ee3H7+P7Pl985Wk2Nkt4vs/E2BD5bCYwDaMYn6FDNoVvGdRHCjzIp1mbHaP9zFHsdEo5xmt6N6uCVGs4jMILTbjSd5HtNn61hlgrIda2EOsl2CzDVgW2K4hyDcp1pdmvNRVA830wdUQuTZfe7jHvEQgSMXOc7P09/tlzbVfoECEkkOG3Pe4Fu9ynV3B6LMAFkcAU9RUI+V2NyR4AcffO9gBCsbW0Q9DeC3AFc96zOaUqvN1/r900XP0gNMbnJL1AS8Tere/7UU3Ex7fNffb2qcHWo5K0Sf/RAw6Zwo7F9aiJ7CGSsbe4VUb7C/9Fd2KhW/MseOEy3pcQJIC8lOyLHYtrpSLGRYx47QB9MS1W/zxslaLhiV0S2u0IDOhpkTdR+DR0MwGH4CjoM2B2uqbTbLVottoMDxYZKhYwA/+m+H0j2UP0jUnsrXsUQkQlc3Yc3+OacPToOjKXUZoGhsKH7z52dH/1T8T/KFcVs9yuqPlstqGQVaaWQKKn1Vbml1CabbW7fiUtZapRJprAbLPruux/CgEirLunfOykYQZ0LnjmjoPecRR43NF2X/s2kI6Wd7i2eq+ypORLrRZtTSPzJ++QGB5Az6URiUATd/VOd7S21WV0aIpX9wnHmmDHe1P37UYbarrO8GCRxflppqZGgrxQsbD3fnASZzLQQ7T794Ha5royl4jufo8Af0xi7XarwEdXCxcw9mKW7/35L/JP//WfsJJUOighVL62QiGDJjQGiln+2l/6BqdPHccyDcy/8vdgowSA/p/+d4hSJbbHZQAW5E6tTTGH+3//LwCJJiT923W3ksTKX1XDl25kXvX9+HuO6+KVG0P4HiL3aRHfAeFdel/qbjS3h0bFJz88Zttga5BNB6MPtCq/938LaJ0fpInpZ5w7W9ekGj67RPgecm2re85gAaRKHHn77gOuXr/LqRP7ObZ/htrkCB3XpVgsAFCp1DANE9sw0E0Bmq72ndvB9RzqtQr3lh6STCTIppNkMikSiUQkKKxvbuN5PpZlMjk+RDabwPU8au025VYbJ2WSOTpHsZBXWn5NV/8EXfCi6fAP/2f4wbtqygrZ4FnDKhe7Ubru2nwksIpOD+n67hqsx4A1MZYgdr6nQIhS3QeJPaMC2bHzdtNu+TJIRSGJtFwiDKX6hJHtBbT6zXla/0D26i8EXEqw2cv891imyhA6yXCccbClB5G8gTk8hj2iIKRfEO769JqtPRbWTr8tGZs/0TUbBFqWHb081iR2+8YH1ksQJ+RBPyJ+Xp8wEH6N6UYiaSmu6lcnil0vftzUZ3uro/uJ4y6MP0IiQRRoqEqOjSGTSTA3M0HHcRgaKJBKJaMNGYVI983r44a27mk+eNw++plwbA30+q31nkMhp/7Nd/vpufeO23Tf/45RBcBCOK4yv7TaKnCg2f0UnRai2VC/1RuUbz9gdWmZoWSC0VwWW0rlUxKadVod5UAd87PZfWTxYcg+fVw0ekX7XZ9Ex8PyHRKrm+iVukq1sNuTJzPEt60AJfEHDHNXgTqWniVk8sV8jlwmjWEaaLbRBVpiDzk7JPZ9/ff8Gb0LBSyk6BN6AqFB9J2vNEmB5lpoXYlWKM3X0soaG6UKPmGEoOqz3XbwPJ9MOhkq9nY+e6mC2NgGlOm00+nQbLXxfUkqaZOwrcCs132e6HtffzsF6FAoAqSOlG5UKBtU8IgIoslEzPUg/k8L6GOkUey78a6aw+6Pvd93M9FET6P10ILQhPo4QCu6pudaXwHjMMWNBDFYUBGqbofV1Q3KVeX4rxs6o8NFHNdlc6vC9dtLVKp1sqk0k+NDTE4Mk0ylwVfRw47jcvn6HZYerOE4PqPDRRZnJ5mZGiOVTPDR+au89cF5Go0WA8Usf/7Xv8DBxRnWN7c5e/EG127eo9ZoMVjM8cSx/Zw4sp9UJovy3A/nJJivSp1o4eSzxEvByD3nXaOfibPjrz6wJmVgnpb9m+bxZl7EtE4yvqmCZ+kBCUHZmk96r6HmNBqPF3QSF/zZofjYo7PgI85zY3xsx30/S4s9ZziXeAowenE+GiZs7ZvdMBAviEBEyl7atHOkP9f2KVI/QG/Ctzha7H2orudWYBsN1vhnSoUfl6YBggzHew4yunf/OPvvLXqPylCmDol+/8SHB0T88t1bMb8L0SPSKuyZPG6HOlrQlYzizFQVGrUsjcmJQQgImoCu40nUTVdWVkD/k9/BY4VAP0Yfj3NsB8PYrT1izI8apwyYmLQtpREihDddE69So/sgJBqSQqWGs7mNYZmIgYJK4xEROA0ptUga8gMn6Ui71uoo/5kwYq2pHKRpqU8Z+t+0u1o50e4g6g3sah2abUxfornuns9EwmbHygyY+F7vtkdzEsyJoWkYQSFuBYBkhBt6+99rvSuKIPs1nQHQCLU64Z6Rfi9hU4eVf4WUAeAipKPxvQNtx8VxPRWmHeYZExLX95U2o9mmXK1H5rvdmhSCiq2xVmlSb7bxfZ+sJpgcyJFuOT3JEB8lqPQ7HIeaYiEF8eSWykSoRSAqHLdyc1DftTBKtn+/icA8JmIBQf1tN61KtE57qTHEqGBPvTrZw7gflYi6v0chA83fZrn7w2AeBLTbHVbXt3CCdawF7g13lpd5673zrG5sMzJYYHu7yuXrtzm0f5aTxw6QTNgIEZRYAZIJk3K1xI/fuc2H567y7JOHefbJI5y5cI1zl28yPjLA2GgRx3GoN5qcu3SD7//kfR6sbNJqt9GEoFSuMjE2zEw2QxTt7nfnhVJVzYGmQQTIUDR6hzVRxOb7MXhZ/zqSHhHIEz57abt6+4jdagf6D7/48ZOC+2kgvC7PYbf32g8YBciwL5Pu+pC7r7cdgwzmJzpFi/3+OO1x8IEgel4tzNwffhdBurEQA8hICaQwbhc8yhCPSBTvD0/aVfnx82mPn/ohjtSDuY1PTa8+qXewjzQvPor595kcAPgf/uvuPfoBc5zQxFWEe77EvSdVE8pnIiK8QovUz3sSv/DceO/xhRqOR9MiR91HgprdgEhMohJCJfHr3jvuqdYLuMLxfAa4u/fYdlNpx9vjgusYQehGt4YAYKcm5FFNRuOI+byI7kbrnVKBkuICwoQkncuQyqQINXM7fG6IMVnDgIyOTCejcUaEtQ+ExM0wUSHoaL146PgIdIRm4IFyDg9MoaGjNK22An97TqPo+YyOxx68R3sSRtAKjW5SRNTxLnfe6269k9JzLNTmdJ9daCKWn1AGPr8SpVEmAi2Rtig2XsWEVdUIM9DA6bqGZRkIoVGpNlhd28J1PYLUeH3jAYo5Lv5Hv8HHF68B8Pb7FylVGvxHf/U3+Oo//w5iq0zPlX2MUqUhCMSx/vcX0RzQ0CNZKgwoiDRawbEo4EbXIEhnE5UV6WNqO+jDo4STXbRUEj0m6MbyIEpU9HZgBlWJdd3HUjp02ZGMzLUADBaCuUKlRnE9BbiERqPl8s77F3j3zGWeOr6f008cwnNdfvLOWX781hmSCYtjhxcRKJq2MDPOs6eOUm80+e4P3+XtDy8zXMzxyjPHaHfaCCF48fRRnn/6GKPDRWqVCpev3MLzfF557gSdjsN2qcJAIYtlGiB9qpUG1WqdjqPyjVm2ychGSTHBXFrtAxnQyeA/uYe2eLeDe09dXITp8qfH4unyUT3HwFL/No3xzj15jITIR61fOSBdegznn0h7Y4Brx/H4n4966McFOX4EosLrAvFX/RnSWikj33shFKUXwW1EdGXIUj9lvefP0D5FNGKXWAT61YBJBgyt//wYwt0TbD2OCrsfcEUEuU8G3mPx+339f5LGJvJJikCWej6haTvvsQvh6wWgsfvJGHCQUqUi8P2uNLnHM+wcYJhUdpfSDI+Byj+Vxmo3Yv5Jmqi+5/2k1o2Q7BpxIyAdOCTt0J7s1VfYT7A2e8yVe44lnLMQlOkq4SKS3ppnevfx+tTqmgjH3AtA48w4dDpHSvQd68gAPIQIorOEUBqshA1kH+PJu8w9HNej9oUCWnrf75rSeoTjjN7jY90+xhTiYRfhLUJfw3A+uuCux3FdykCbEwuL1zWOHN7H4vwEF6/exfN8bNtQb0NCmHur1Wojfb/rdxEC7XA+NMHJ4wc4uF8V+r59b4Vao0UiESeBe9EpNUIhNRASIWLZq+P3ECLQyoiubxZEEZ7BaepvTQEtTev6kKgfiaoE7OqTFfu+G8zdbexxoLXzao3wnQnUOtxtHkTf0VARoMXAlgzquiaTCYr5LEIIavUmnvRpNRpcunabra0KY4N5RgdyWJbJ0tQoV27e497DZQ4dmI7GmUolGB4sMDYyyDNPVjh3+TaNVhOhg2Ua+NInm00zNzOOoetsbm6zulkmmbT5/AtPUshnqDdamKZBsZij03I5e/4aH52/SrXewPd9ErbF33i4Ss6yELkMUfRmz9rvf/Kd76F3vh93w4T77BGnSNgzw3rEA+UePqnhtZ+uhE84NJXBXvSCvcek6bFOdjksdu+jZ7+y9zxqsefd7ZSe4tQQarF69oFUJ8ThR4hvfpHt8fNs9YSyCpTz6+6V0eNNxhaUoFcN/9gtuCb0S/GlVIRViCj7s+g5XfZ8l0E2ck10798DhILvoXo/NLVEeYPCBfIIoNHHXqKj0d1Efyyl6GaI98Ps/J/QIkaoB3tQ9v4W3jru3/VZ5rv/nrt9/4QWAqRHk+3eee0BDITvW4s2Sfy86Jz++9JdE4/XwvHodFXowf8yMA8REp2dGg0teK9+qI2Lr6noS3dd7ea3ptpOlfujnuCR+65HUNjt4sBnoW9FRi3ipI8xh9ELeEQEV8/4/BhB7FZmEFJ28+QJree6E8cO8Bf/N1/hhz/9gFK5iqZprK5t0Wi2gzx4IqgfGvhGBYSzJxBSCDK5LOlMkps3l3BdjxdOH+XA4nTfst5FYBGovDIE60FIQm1VHHCFZXTCudW03ncSAXIRmA9D2hWALNmXtV5dw459t/OvT97juwYlxZJRd9/P7gw6dEeIa2WFJrpmRAEiAFuGaVAsZDFNg0azjeN6CE3gOi4d1+H20jIDhSzjo4MqGbKh43kqmtgwdDxP0m47uK6H76uySdlMCts2EboeRIkLSpV6cD+dZMYmmbS4fvshb394gbnpcXKZFCPDA+i6juv4rG1u8dGFa2xtV1XBdCH5D2sNZNFC5DPE3/0jZ3UvwBCf277Z39l6hYpdr4/ATi/9i1s3+oHWp+avYVbn3cao+eD1gb1H9r8HwIoJG3vPm+gjR6JXqI2E1V6erabH774vqXReIVYINVzqNKkSGQd1Pf0gQbh6LBlZ1MVuNODn0D6dg3w0aYFCrk97sFvboV2QPJapur+FJhjPdXEcF9dT9mhVO0yVCQm1B76UUW4gx1Hna5qGZZkIZJRfSgQEUdN0NEPrEjlNAFqU7TYkfMFD9Kgve4BDdFj2oXO1OZSGo7tgwukIE5X2J7eIPXzvZ9il2OWcx6O9n9w+CazExxQjPt0njs1T/xzuuNXex+OgOKw6sOPRRVcu+RR4MH6n4FPrPoPfu7EfNamh3iAcc3jVbr4/e2npIq1ecG4P8H+sR4jNfygk7H7i3sS177THvDFdX87HWXzBHgiuk6GjtVL5xPL2hIwFctkMr770NIcPztJotLh6/S7f/M4bNIL8Y6q4rPJz0kSYKmTnA/hSsra2xQ/f+JBytc7Lz51QdT73erI48A+SmilZM5aqpe89Rc7v6o/YU2uRxjX00Qr/ydCSHQfrobC3y3PExAEed7PHk09HY4s7M0d9fTJxFuG4JIjNknrrmgYDOYQIU2SoElnttkOr1SabTpLNZqjUmrz54SXWNstMjw9TqtZxXBfbMtEDWi6BW/eWuXTtDlJK3jtzBcd1Obh/jkw6jSfB9T1u3ltmo1xncqzI4ECeg/tnOHf1Dj96+yypM1cp5NK8/NxJhocHSSQTHDu8j1KlztmL17m/vM6UYWJbJTUhucyus7bLw3/S5ASXPs57MUC6fbwi9t0P77UL/X/UPSLT9+O2Pc6Ukh01Gj+Fa0icZz72ueF45G7rMODLYZqL/t+kSvPSzR8ZA6XhrpES1/PwXIUhwoC9CJQ9Konsz9geH2xpccn70w1mN78D6fs7ylt8UvN9VWW+2WrjOA5CU/WNPNPDskx0w0DKoMagVJPvdCTNposZ5OQBScfp0E3MqJJQWphqo2saCL0rccYJZv+CiH8KiCq6hy9YxM4NTWISRBhuK0MWqwr6Kmi9yyLbMU+SSAUd6kKjTR4f4iM20eO0x2H0u5wTFnTtwZ9iD2D6CfeJs6sgp8GOfgS7rLHP1LoE5XFcKpSmVIsAYT/IIv53yIz3mPtwwz+e6bO3yah/0SVcYreEiXEAFoLkEL7KWB883ruXgtC8usO5es81JtT87vWbpvVI677v02y2SCQSzE1PsrVd4oMzlylVanQ6Lq7rR/tYQW493lvPt/X1LX7nD17juz98F8d1efuDi0xPjnI62IdhH71bKMbwRPwZe+enx8k99nsExANfs7h/FsT52F5AS0TvaW9zEXsLarHx9jIgoQBe3Lc6nKpPfIdhaTVdJRkFGMjjB3X3HqxscuXWfXwpqdYaLC+vMXxkH8ePLnLu6i2W17ZptV3u3F/DcV2Gh/IMjwxi2DZoAtf3uXrrAbVGG8/3uP9wnYP7pjl96hhmIoHvS2r1NldvLHHu0g1GR06TSKU5efwAH1+6xflLt3m4usn4yKDaAZoqIL+4b5rR8SGeO32C1ZV1BjbLpJb+QO3jQjb2xgIaE6ffvRO617QQ0ebw0J6uMiEdCwBXvyD9yCLUXf7xqPaJ+pk9BbL+XmK9xXli/P67zckngqz++wdzsoPd9T2rCH1Kw5qQwd9SglQAypcqYja8Kq5H9H2V3sdx3SioZrc9/fNujw+2Iim99+/P1D6D5iVcOLoAQ8ggrNxHug6ekHi6Us1LX+K53YkTUmIIHz043/VUHhhNU7zb830cx8XzPEQyiW7p9IOsHai75zMYnRZ3pNwNgEi1SPygDE6P/TuoO4mGDOpmRT3stqGECIBHr1Yp+u1RgOtx7e6f5v3GtCqRKU30g4WQWUmi0GIR+63bWY/kLoDQvCcib8fuuaDARldm785p9wwR4zGxcz61yl10pcY4KIp+3mPOwnfUP/d99//UgFGEpinomrshiuvr7y4O5COmGgMIfSkiehOj7sZw+gnU48ynQAaSqQwcXSGUHQRRkfvgF8d1Wd/YYmJ8BE1q3H+wzgcfX2V5dRsBDBTzvHj6GC8+e5JUKtF1+t+lNVptfN9jcnyQB8ubfHThBof2zfCU78dqge7u6hB/N7sVqxUBQJIidn8Rrb6d2i4hlHAWvIf/b3tf0mtZcpz3RZ77hqpXb6ih2dXVbDbRJEVSzQEEaVuQBUGSAQkmBNiw4YUX/hNeeeX/IK+88co7LTxA0IIwYVmUZdKkBRA2DXoGaYpqq5tdw+uuqjfck+FFRkRG5slz7rmv6lGEeAJ47957hszIKfKLyMhIL3MGFi39qsuSsZ5Gx+t94DeooEuWvzlw8kVLQqktL8oLCJyAVjpS68PEy73bAIDzizX+8D98F9/93v/Gs7ML/N93foLf/zffwv2P3MVv/OqXsdoJ+Po3/gTv/PlD9BzxkXsn+Gt/5W188hNvYHd3F5eREboVIp/j2fMz3Ly5j7c/83H8xq98BW++8RqYga986bO4jIxV1+HWzXToehd28NbHP4a//dVfx97+DfzwR+/gF976GD711sewt7sryjXh6OgAR8cH+Myn3wC+/f0U/JkIdHwAUDqGDkXXHgckbdWxtQw7BoRFJrBELid18PZ9eBzwbqYNzwwsSmjIfUY6nq+bfm7r+SLUFYcMtColsbksK2moEcOHuuD69QzWkhFFANc6xcRTZfm6aavQDwlMJ5s3YRu/mGFqc9/08bkCGNQF3NjfTU6SApRi3wP9GggdYsyxVAiEjhhdSMfCIPaIfWrQlRypwus1zs4vcHZ+DlBI57hp5q3OmGtj4veQTIgLGFJLpYEuM6U4sDQGtMh9b1I2/Q5ScBPAJqoV3tk0AFpwAozSwJjMNJeRPchS4UQuTKbWWZGADET4YzG4enb6aIai5iZA2STAqr+36kRZvgLwyzvXkuZAfgt4iy1/Tfp2rkfpF9JPkxGEYFvh6wS3ZNcDN5YBQKj6svhFaTtxBJ49P8f/e/d93H/1FXSBcOf2bfzVL72NNx7cx6rr8OC1e/ilL38eb3z0Pvb298o0K55ff+0V/IO/91t4cvoU//5b38Xv/t4f4t33H7uQEfN7+aDdgyhKFcDSsinQMtxKbAEfDWgN+lKDH7NuzW0AnWTq9HSnXZqo0v3NYCv1BnEjeXQqaTLoJ4/Q/bN/ie5gD0ff/i/4hUcf4PnNG9h/9TaAiHcfPcabbz3AV//mr+Jzb38KP37nPcS+x/HxLTy4/wru3U0Bmb/4uU/i5PgIFxdr3Lyxn4KZ7q1wcnSEg4O0S/jXfuUr+MLnP4v1eo17J4foQjo7dnd3D1/+0tt48PpreO+9hzi+dQMPXr1nZ8d6axAR0ukWWu6TA5iCQjEDgtEazfWZrYoh1+HUeK6XDJmRgUMrtwrWjVrL9DHtg3MOu66ujSqDNcAfpqGyJCsVtVx2aVh/dOUzOTgle+Udf0yMvMLyvLYHoW0MToCr9OcKW/n5Xo22AFva8XS7uPycspxsO4GMUJQG0MpUdNrHaLFc4mo3gWXuAfHnCkRpG71qdzEmy5JvcyKs1z0++PBDoFvhxsGttFtsVsVz1k5TapNPm0OfvApInCK/JAjdM1QesdNIDb5Tcn0LGgGb3LNuEM4g80PC0E9qvH7GBrEE9Rsf/fJRCRaqoDk13hl15CYZbXHoLM49iLpm/er5WlReHMmjzrIBsur09b9qsnBHEs3Jx1tBkGJPkWrTc7pta1JX6wonfrJyvqUAakwEJtR0wjNKIQfSTl8FHxks9jHi2bMLPHx0CkKyRr9+/x7+zm//Oi4u1+j7tKPs8PAA3c5OqkPvu2isp2s7OyvcvXOCsLPC7u6OHSye3Q95sHu5pmZteOALNOJmZQskWwytDG7rSa4c2cgydTBzGFSeILFM2K7D2s+2E+WnhhCNYgKmxIEonWOqY/r9J6B//QfYB/DbZ+f4zYtL8Ok5uvc+QPf9P8XqP/8Qq4/cwerOET5z+xCfPrkFPr4F7gMoPAL1DNw+wuuvv4IHr9+XOTckNSlGBFWYQDg8OsTB4SHUDSPoxNlHrLqAj772Ch7cv4fAheco+j7i6bPnICLcOriBcPo0F+54uOs34YVqPNvvUq6y8dfuPwP5OYgJVwMbbihh85T6jc8SwSzhxbXqPVMW5LpuqBiRH25SHcu4/b0CwgMauc7u/6gLToODQLntFJjt7qTzha+TtnOQr9eRW0i41uLnOvVNUIowLWIjRpyfnePpszM8ffYcl5drHN66iZv74tzMjL6PaSdQSBqN5UgBafGiB3MHPV7j7Owcj08/xN7NA+ERaEkcbR672amWtOWEZJOaZqYfeSBrR2qlbFcLXFINBILspKssEwywRtKdx6yJk3lUVZ6BAsejXh/1ZYADUJu5HKt/ZtWAqsjRypHWW+Vj0fSrmmrj1tKs+92sucAABWgsmHZw0IkskVowlb06nmNs7A0AgLwUMtBi+PqaS8LNCHAFu3POirdyfyAEB7TtZXQh4O7xAXY7QoeIQIxbBztg2gNRh0Bd2n1q5fdacvn74vkZ/uydd/FH3/wuvvbvvoNnz85xdHhQhGaYU9JBTzDhTU2gJQUEUw5/UfhnuRSbvUwzHaAwzOgz+vLwnoXkILfj196p0/fCRq5++CwFCz6/KNLd39/D7u6OLV1SoHRY9Q/eSX9lKiV1IR2bc/sQfHIMvn0E3D4C307X4skR6M4xwslhOgibEtAJ0nd6tQ4xyTmXnA4hZ8blOuJ//Z8f4f1Hp3j7M2/htUeneRXj+HCMowmaB364kjEA8pJ98yBnnvTPm01+PNlc7QGUv5eU0txVqtAwDCTlsLOfGbhXfbrNTPuyFXnL8qpSJ4A0K3QpnYCGXygBGsHA509E2NvbRbdabd0DtqErHNczUaljE84LWLg0L3Vwi9ThbM04ffpMDpPewWon/aFboY+XWMuW/cCUziljlgCCASFE5HBCjLOzCzx6corzi0vs7u7KFnJpxBEzaWJoe6BlSylibZGLUDMyS8cxy1NdfwW4TRF+/TJbwQsjaS+oy6AayubJfFsaqwk2kzybBpUEPUbsvFcAsC1+yPvfePFeCSG3VK1m7CL3TbwUgHJIyV8vt2Fa6tIdfJ3Tiqu8Zo6bJnc1mJ2y1kbOUQBieKHxWgNX1ThH4PCkbSZ0ATf3d3H/1btyGHHyZ0xNGbLg7DqRuXXUv8xL3/f44Y/exb/9o/+IP/jj7+KHP34Pr9+/iy/84qfRffO/G/CrI8S3uW6BnAbgzgk5YFwFySwsJXW9S06bFFif1uDetLZeltHVHMvSou4QlQ1HBf3SF4F/9U/SSQkPnwCPHwFPPgC//wR4+AHo8SnCk1M5BPqDFK39cuJ0BADoYwon8f4TEP5UFMZcpmKLx84OcPswgbGTQ/DtI4Q7CZzR7aN0MPbtQ/DxLWB/B5eXl3j4+BTf+k/fw5+/+xB/68fv4lgTO5ZlxEFfdNe8HK7aKoF97e/uXmvJrwbjxTgdJD2famWLat/YwQ+Tx0xRNnG4/iiyINkBfDkIxfFBVyYZNzMsUuU7jTSQAFZEljvFhhC3fJxC2fkdwcD+3i5WO6ur1/0M2g5sUSOq9tZ0tXc1zsuq63Djxj76eIwbNw+wv7eLg4Ob6HZWYA4giWFjcikEBFqh61YpiGC3tqk0MuP52RkuLy9x5+QER3Ia/cbyyZr+lerBAS0yZB6y9sPesRuDwVhaNHSTuwNv+tALYPSstYgTI+tZU1NTo3u7qf4LXK6BVKAScJk5u0F1fW+ztNeyuiibBeC6Ws01IJqkHmXzRL6nsprRFZN7TqwhoKu8LH3SfFqgtV1uz53pfyaXGGV6Lwh6W5s9dAlNM2Z1Ui15JyKs+x7/7X/+AAcHN3H7zgm63R2AQnKt73ucffgMT56c4v2HjxFCwCc//qYd/J15YJydX+Bf/P438PVvfAc/eXSKo1s38Wu//BV84XOfBeH33KOblSg2fvMkrOdfNh4uosKPW4hHgJakofXBOr6s9kbGpO1ungnYB+UVYFsoJqqolRMa7e8Br38E9LF7iRN23bJz/nDMyRr28IPkWP/wFPzwcQJjjz9Inw+fAI8+TP5geqD3SB+ky0vg3YfpDyg8EAZv7O9idWMPn7q4wM6HT3F+8H3EsLK6xZELAWJ1JpYcnxhzlhkzabTNt2ifkYRR7ugrGEV2Uxh73ymJqphbn5bvFKr6IKD21/ZWfH/ZW/RGyzlR/tH64WG3dO+k3y5oKad38tJ9OoGi68h0UArBddrroS3AlgpKCe9/VaYI2y1jFQKN0AnY2pOz7joXC4uZEXcIu6Qxt+S4DLCrTI13lWJs3bixj48+uI/j4yMc3Lwxr1gmcK4C7EWAmNaf4FKac0bi8Q8y0U7jr4eq3w5TyhP7jAHuLVCQpYboNIXR0tFEnegBqchpayXaRDyDr1SY6r02sLAJihyo42Ytp/oMAMdx9bLVa+08TZHypeO39L/a6ijtpdMv5xvzuiA82NfyjAAu/+lZKN7Vizq+fV8pUNAM7vJkLD+Gn54vjshngGqe6f1nz8/wz3/3a/jmd76HL/ziJ3D3zjHOLtZ4+vwCT589x8OHT/Dewyd4/PgUb7z+Kv7+3/0q3mrUw8XFJb7/P34AZuBLb38Sv/yVz+E3/8Zfx907xyXP7vtsZSqE4mgvI/Ln0jWWZj3orOtmJG8iSt4cpqBk0FWE3Zg4J3IuFS1PjkdVVmPE+cVF2my0twc9hdVYD+onZswDh8fp702F+u2xyOse/PgUeHSK7vEpSIHY4w8SEHt4Cjx6kr4/eZotS2N0doG9Z2d4cLnG/RiBs1Ps7OwAK+l3FmdLlUWty8ZxNgNmp+TpNQEtAOXKRc3fhrQLuZDBvL1aBbxNcloDBjeAlsnZan4gYDxO14jc8mOheD4rhuzusyps7M88bJefADmrM2C16hACgdf1vHg9gGuL43rSUkMyL8/0cRgt9MzCNCYKIkJgzkdc+NSYsbvTIa6EP5E5qWFCWmJcr3FxeYn9vV3s7uzgzslRiua82kHQcwarPJv89wA6ng24iiWqgUVLgdY0AOViOaLiZ+4kGGOZT2VtsMsuRfPtCDmftsPzMJ0CROlhvdSXGfg+EkUtblVqMXlROaFMWXAGoC5pZ6MCiaiRXDY5l8VjMOdwF8ZbfmKkg2RLBRHZyUB2LmQxubXezkqDXinMcvUS4kgqg5+sB+TWJwCMCfWxa1l5GPCjv6nOQ0lDfRBu3byBL37+02lcM+Hp8wsLVAwAd++e4ONvPsCtg5u4e+cE9+4qeFLAln4dXqzxOz9+iBgJu3/2GPtf+za6r/9JuvnoSSqFm2woUNvKSdo9vZxI/cmuVzJE27NZS/WSk32OvSDkd2O1xuBI35mzTDogcn0rsOUbQjCnYjKrx4DRqYRd+1vHTfKpo3Sw9Z2j0heQys0IYAZdroFHp2AFYo/UQiYg7XECZ+HRKfaePh+ycXMf2F2hEEqF8lcpHC2Z+aLAaVuyfgeMdpRaTyreRcV/MOjbmk/y5pk20BrmwW564Pz+Bmu7YYYpS5g3Wck85IOZ6oau0riQI8Tr6TM7qxVWYt2ac1jGi9IWYAtImgjDzKtXoCSMGp16+GDZiBXgStyUz5tClWo0fY+6lZ2x7iOePX+Oi8s1bh3cxGq1SunKOWVWzgnNEvkpAVxAWyo3yFob0rnTdwVA49n5xJMQfmnbVMfQYjVQ6/yyPxQ5i9fIs9D5g4FI6VxI6tPF6l2AMFYVdrk4QmcelUI9Dq4OeG/USavOp9uhLpvnpnpfVm2SPNKDuDWZuo0cXFYBpumqELIJcshfrkdGaaXWfpx9yOq3B+CLXd6D6mxDqXZ6nP+0CQLhuI/4h3/8X6GW7Tz2NRBpQAjihK0vPjx1iaccVoFwciH+Qut0sPcUlEUvx+24+lMNujT0KfryYI3LI0qKSdHVyWyLCAlu9NarqVc31LnU5SbrHYHybmnHCgKDYrq3s7ubxnUYm9k3k06O+h1oj8M8dTowQAzsBeD+PdCrd3OihWXP+ceeXwggc8DsYp37ilp63LhiOTPxajKX2gDD9xX/7FzQRoMvYw80btXymeRYyG48XQNarl0a6aR6i1nPsfocqYcWn2NLh7U8VcuWAix2wUzN0pWfYUT0MW2Ki5w2TqRh6o4Qu0ba3kEeul3y6oPr6u95LlRjdI3uOzBLwwYgOBPjs+dnOD+/wPrkGIzkhFtsgx0ZUO2phxJ4kJ0c0/OucgyYWZUZHDdbtGaTL3/VsbMlauQdfS5dnCVYvMVLfRyb73FyCk5pxxRPKUjwv1Y3amWtmhz7pYmrAC6fYXV8CVeC/oWJLZZS+jnNrFpTqtUaayOdpnWy8dY0mQVRBFQsrAC18NTn/fXe2mrAb2VFtunRLqh/yxYThqUFELmYQLY8kham9j54OvruIKH6wslR87mmOD85BGRjDVFZO8nWpk7BY0eJbOCNpM653/g4scBJcgyzYltfACdXprKXNvGfk0BLrcjKt1mEOU1MnaoDdWcVcisPk+XULIYwvgJX+W7t1J/PTnVcDI5+koG1uwO8ejf91cRJjpsf1IjVf/DOVH8nlVmbDoTerJhYeqTPD5iZ936TGOAIrv2xCv66kXyr5zQAKl3hEGygaruKx3pDFQvfDliBOW2MA+DBVpRzEdfrFMTcW75e2hw8QfPBlk5EAIAeTGHr43aMijG6nXAuBiC5P5+8CgoiUMwDJjnFdSBQiqejFq3CJItRAILiU29mLYiLKPIVCdq4KkSdVUOtJYnidhl3xvPmr9qyx0zABWB6tcDzo89HQMyCKBxxCWX9W4UFwALqAmYGegmUY51tDxSapBqxCsU8o0jbD2d7X9e2mlTzCTcpSZdtLynzCMiqv9fTmqRvg6zqT966DB2DnsseoBwslgJJoNKRPif9jjo3YWse5kvleRgmU9y0+9KOJ4eIv/OP2tMYVzG1dGz2NBC8NcC1NhRAkc+GzGVrcmj5lZPv2DjLFh8/HrIELD/RnvhZ3meIhaZMbopPbu4iHdOM2rJ4Ew0hlgPyIORduy7HUTCgPEtfrcMrzNk04AGX709m9aqe3SgvBAi+DHJuHNP5bT/DiNQYLjNr/3HnIcvNjakRSwy3bc4a9Bb5ahlQiQVgGSbRscwAobRqMacTDxjp8T6usV735ooQo2542FSmF6f5YCvKydqmnaUjc7Zlb2Bh2dTxN5i5R++J1cXiFzFjd2cHr9y9jfPDS9y4eQMIK8zfwuo0u7HbPNyRNNxaffWB1/bXmk+pTgjebF+a6WU7ve94Y/lV7TYLmLEsQ9sysv4pUkvtTfI1XZLIyrYtWQbeFUDRcOefAxsDgF2XZ4sgp+QmDKvH/Jpu3hh9PYi23njGlAiq+4Lm1wJaU23j42P1mOyfgzZ3aVvX7q0NM2isduGZdh7LKOr+/j/9x7AlUTgB2y6ChAliRE6HW6tZRgNipik05dFJmil+X5ZHhA5EUeRuyRMDCLI7N4lAtdyE8tkm4MhnbaZPKZMeWF/U6Vg6DoBUQKvZulbdakVH6k+DwxCr17zlS/u8By4tC3lrdWEs/cHvDGLHaJNssaVlZpPn1uKqVEczvafOMrWBwPW79E4DMM2SP1JXW4U2UGCHsskbYXya+dmnytbt5GQCKwEamocMaNVZTc/NGXD5VYiZcntM6a1xg1mzIjiWS4bpEV0+ZPPnUgtX3/foY3TPYmabXp3mR5B36nayHEVxnckDbdOZYqnvsduJx+331NLQoFL8tE3hhfO2+ATpoDs6Okxod7XC3HXaUuRKV3b8mcjzWryEh7CnYkLi2vkGOwMbddDk5ApYq86rFaIg+bxU7UXu7D3N376m9lMgO5fyzr1Cksh3AVatQkaZUV90PIycJj8ARw0eLGseVzIy0FFhVM+mPInby/qvMk/aDpoWLQJAUeo2NBIYKY11jZHzFLchezdmOUFaUA86k6I2faLAIFGwbmrg6j0BpWkeTf416kWA0An4YnQOAdoSOLQKulRzI3Je89Njn8jGxtCVod1F83ghf8nbcQpZmYFUAalMZjXAf8G49oP8HslvHgHVpUVrgzxSEDN2v+K+eLVq87wlf9vO52XIcK4p+DIrjdzchEPcDtkXGRIbBVZruVNPCC/k7ohcLMiPNRJZUSk5s94WaxR1GCwd+hWKTXK/0Z9fiAyDCPhllv08aSxHZlBMS4iRM8DKQAvQ48dIQJrv59cLtbYAW1HOLSM9QJLYGsUmmBYYHUSFBiCHRmq8GgNHJhc2oeaUEAHgGAb9kD3A4ggP5oJqaUFABI/tohknqixYpAVjOL5FwybVsgmEzqFxEWqiQRadsdmRCUQxhSWgHHyxjN9UlqPQUqWDjpZUD2D1POTC6UNjbw+TGwPeXEKtQqA40N72/WrnMUdIGz+k8YM8A67z+evkOPXBFckdz1G3m4CJ8clHx4QI1SGjQ9CbX7VP1vxAMB+i0Zg7Y5SF1uDw1zlUl73gV9INkMPXpf6MV6BpFRohs15EYHCSBZwaIUtFOkbT9WCPE0Wb3IqNNobjyapVR3mKRA6AxNdKHiDx9bSDo116ece2U/zKEtl/FrNXnse0kVMfIQ7CT+IGhFIZrceagdFKNhST98i44T6/OZbHVLvbdb/dv/TdGQA5dn5SL4kGXBUylSS/cVeErJZuMzc02mHKqjUKWDyoUMA0lw+qvrt2n7P6INmnLhvG3UPmgKeXainKc5lPmzit1sQYgNgX99WqlfmQOJw6FoouXSsrL5/mg60eADiZ2UWghTkayYD/JDU4pkrSHW1Wekq7BjSA6lgFpLwTMmXZ6aCiTi1nzGkZgYXHLGvEl0st41rxrbw8mKlAlu3qKcorEy17gZvrIitX0cDggFpABbpDjeGXl1jysKWnij+deJpAyyYHxiCkh5RjFPhWnb7IU8tqswfMkqD3iDRn1/Y24Wcn+Mnur2By6pn8MAbLA1NEBASZJFrRs/1zdT4+DwsMq/w61ESMwkTPgC1ZzNEaCSiP6tliYjBFw/G3nc6RaEoxIrkfNCiu53G4dJQBZIMVVViok6ppjdV0mVw6/knb4+msCUSUIk+L0tUTo3PAs5d2IpVHQTfUOKClG2wq3m0pRsRYwlSZb9u9BQCI8KOUzDKVC5HKpdtWp1BCCbTqB2hDXynuXDlel1NWuLja4EjkIG27w31Th61mVJtPFHmrpbSSYf59oqJtx8mD6rZs3Jo0Pa/0bTVICWmKn146HuYphgEzIGyR5WzicTDaqjeu7jMEP0QbWxyDzTt+rklTS/bBo0I5Sp9hY/u+OM0GW0G0E2Yyx9LSN0SXgOZRsTXTUkjfI3GedFQom0lUnhENNJ+LlsVYlCU7NR9qhyGp1MDiyxW9Njrk3Z9QWPg5pYuloih5FI779t29F2MWlvI3e2+TN+HKJ3GyGiRrnn+LhcckyKY0PXKDubAq1RPpQINuAy2L5eVkTl6OlBPXvdZdCxKKbhC2wMzwawHYi2v6IA+uFmXwVg4A4D61FZFbHs4CdWqZm8k7hTa0Z/sdyotFh8KwvgdU95xG3YzPudVNUjRwNRqrD12ua1guBhYtcm3o+yADkMO68+gf5lezrsra4EnHq1rXvWyp91CRpmV/FdCaOHXC+xO1FxhTyIrkS5k+J3fyRSD3mzjSXlP+PTMByk9B0/c5zrG6XCHRcnx71Gvjg5K8qXe5DXx5p/jLcH47/6xWOpzFWHDjolAwZrRtARA7bPbH1C+q6Hr5NW9n6WZyAGhTXbGrU+mPPlYlmMGRzDlelwmJkzJSztVyRrIAM4DFBzgguQ9waYy5JtpiGdFVDEVE3yg1op8g2/HAgJn4GAiIZVg8M8/ndPMEncBYWoLoi2cMYMlj0awZgl5JxBtlQaiCdNSU6JY6BjoGuQ3Q9o8M3JXnTcGAFrcmgRFS1F3DiHIJMRRjrUD3aLQMlZYFAsB9D1BIYFfTZ6BY9x/ROliACDMLaHb+XM7aBwHOeemlBHSDCXOilnL5nAN2UVdcpjkFFtWSpz9JhIGLI2UAa2oSYvHLo74U7sab/8s8DvjaMNGlt9X615fXi7bXevbpy3Umd0B02lU3OKJF+SHPr+93tJHXgu9RK5hL39czQ/jK4Gtym3axBAexXCHvPCyavA5+WH5v+RIV4WbU0lXkzc0mTaAzgFBOBLkkqZ1odnRFNcvXE9bEcpwpflUeRftlr8pJmpp8t1wS1FHqZX+bt23I16tLwwP7qH1d6qWqh+EGlDmA6yVRPa6qYkxSqIEikNxacsiRJCd84jncSkEvooDlREqMUADHme9XPGUfrIwn0ioXwAq4bEUiBWpOuXNq9ijyTpSdVbcj5yJfH813kNcxLQ7Mae5kZ8yanhS1zTLAknhdUvF9NaimhTL56TylqrtsBFxpcmotYwDriam72NFSXd+0U0bLVcsFBURlEFb5r/xt0BiIGgJYUHoNmOz2hIDyR8kEmTDGJhUgRYnW7zWAc+zYZNY1+oNOaqY9kEZPp7RMI/eUn6owzXKkcRZllYqShdMHtqyerX8xVxMr4OrZCWStrxiL9ttMHXI0dpemCLYhfPYsKo8j97UebZ5wfmim+0iwPzsnVEGIPiQ7il1+qV8l30CIQpN5GmM1t1nCZK4PFdqpUtAiDNIegGIFWpELUDFo4REFaQiuNdHSX3QMbCnZAWUjFt9cZnZPt9LTHjQ25tmOn2tVdzvMR4f8dNV/y5fdvQYAYecSwe75EeUq3Q/5fhpAOW/PQqMtyl8VYJ1rWZ9DrX5QXJO6cFVCY3Jo1HfKXRv4hE7wPSb79Xk9TcPymAK4Fc8Da7G0DalsoYrzsfl2AwBReQIBNcW4ZlilvojFrxhCWRkrxVNdx0Ga1LkLqBxkSiJF0lmtOhzcvIludYWwo1vQFmCr9j9ycGcTYDBlT0EWHODS5KrBOAa+qglSUpXvOrnlZ2Kjo3th52VFq79VXEwWtQQlFd8NsvGAsgqLJbaGeVMniTr9nO+UUCIrqwKvKR5rMNiqT5/jGqhAU+Yp5VfWoR4epP0oqEWTHYDVsrqcGECMGcSFkM7BDGEYl2eM3wy4GIEyKJlvXSjJ6lN4SlbWKPy5PkoR6jfQrHrDGinorZ8W1CLLalUxYZfOnSTZfGKW6AHgECuRCa3yM4E4Jzx1IirKKYxAHVBjLj/D2i8/74QuZAL1kefzLFfVAQnQmhF2Y6xfFvKCE2jmciu4f84bIf2SegQQ5JgPlsN5/fJ7RkhZTnk550f15GkRFraCyomW3GK7WuFdyapE8p1ablraXDzn66B4vpmsxLyzTTWc/2xCg2vaCtxZcjoGRhTagXWnAZw20axJXvKu41gVSvIk1IHV6Vik9Npa7UNptCi69koa5TgXTUsWYD5wKtesHcg1y0TgUVM2p0quFnH97eJWOdkwSa26krQMM1TyymR3wwCQQ+vUWCEgYJVCuMj7DEbXdbh16wA73c8I2BqbZGdNbAOBNhRwU9YYoA3ayzwSN8yyy0SEyQDxcinsqLpXZtoqzASPaqVxj+Yz7xrpOdk0ajlwqbcWHtNYzBMCHLD12ZEbZCOlb5dpiHFnvWNOiODBuzpJxYGfRNJHdJzUfW5QfQbWBcSFbB1jzlsK4ErtLXse7HugVE6NOe96ktxk9TQ+AYBCssApH/Juy7Kp4QVUIOT8hNNAEvNJrXmShwo2jui5h4ZGIbNCBGeJBajhy9daWtPyq0WSXDlyAgwbd77TsOtvlBzck3IpPkqo3oeb8JjNQbvZ/UYsrWb5k3JEEcrEabdSAukZbHklxYMtBcNELBZeiIUzbxJKfagewHkndC1vWrwWA6SYaLIkYQMwIZd506A0UOaBmyapykWE37dZ+5MaKwByGAAFlux+E1K4gii383OqABQ8m5U7X87A0N/Qm66nbBRGMsamwOMArI6AvTrZyeGuAiQI8HKxvXyeRcwy395T1MhcldomUFXtrpwF9HcSwRvyZN5QXuE6ajkiEFxA2G2sWWMbMQoeM7jiSPCRBlQ+16nkWSiVOSnlXaGEhI6wu7NzZSV7Lr0wlEtjbvNM3DLXzwFZ/vn8fdOzimyjOCvPyqKR0Iu/UAzjRnoKLMqhXwpHdv+LdythWOg+9ng9abqJyL/kHrcpeEqRnKib7GvXuFf1gRnJ6cODZ1rWvQxeGgWbSaP5pMyKe3OHZzl1pv+Eeol5ZrpiHczWuLqdSoDrlxEzEN7A76C60u5jPS/Q+qgVTHRHknGXuPBIS/IPYoHMPMT6bE2nTbPuGgwK8vL8yyMCWpeTs59pAj8BAHMwBUhBGCPKbkTkMjhAGWSnb6SAwMn1occqLcwwJ4ABtdBq62ZwkYfioFGaQCuFgnA+gvJsmh8r/7wJUgXUgHs98YYEwlmBofZJzsuDqQ3JsZnaqrWkSmoR1bMS2bVr1Va6y5kNqAX7Tk5qsgohTkA+72p1YxJ+fHmZyO36rX63iIhsg4x275Y/X01qKbdNSTVw1booQLYDuvVkYO9InfpSjwEt64svATywHABcWVPNuhph5cjz3BYgq66byed8XiZhwIAFLq6JNHAwO2zapTHRm2tIQxm5Brpeu9kGmgu0XihtRTM/hcrcpjylFlelMzbgZj4z7XxfDhiqd5AySXjHijd3HpxtcihSzekkq9Lm/AeK8+DROp5ZG5zl8CEY+P6NUdu3aJB9I7fJVDc+65eY/yIoW2xa2jxnq5pQyOKo+AzOMTwJPN0WUaUYS+syAQOLIKN6sTFWg1nVxmUzIYNJVe7ytEMAl64QBieYE4ihBCh7cptpiBBZpn9Z/qXQg9cRoZNlayqB4FWJAQtV48ucJ3waPL+R3JLuoFoHfjtw+ESgi81xZW5FSBin9JiiF3UCi7n9tf5VljDDlqy1O+oJCG4rc7kRhuT9Crx5vtjZ6qpKKstCQCH/cg+2lTlmFyM4Chaa8GMywNQGmfk5P1oagGwsbR8eI8bmzlUtfWEtvCJV8A4W7sgBVwO52xzmPGV1HDxa1xMV9eclme1+9nyTD1odwdxjve7z+YjWyX9GHOSvg66yBD8/bdXGfCyV657lpBNu6N8mkGZQ7mYVuBmYxeelV6bR2tc1wnwhlIbPzEqF/dB0VofNyLL4klPRvLIWPLsavJbWvOXArAN9o87N3gdmExM0XOJlTpYW70eX2KietMlnqLdmuFP1dbM8qZNsWU8E1YWdUFNHWqvzMre+9wde53ZhJvjFyazp51Scl14GPJJ7cJzlkCskwMO1vK8XyrVh/y1LqauwYcmXGQiMKIAqkgIs51Mk7UKRAET0fVrK9fEGR5ciDMAoJPPTWD3hJ4o+X7hyjJeiommNfao+6p3ZXt4EMOoF6EG8QeE9rTJYKpkvuGsUc/uSginL2CxELOlCgbMdPZRhbnQTpu16H0wybYtQnjNSEO+keJD4fXNaHSMCQZe229txKCdS8lK/ZTH1pHxlBVrRipqdEeCUNL8NQKvVGpvIb6ZiseomjoYuO7PT3QAEhj6KWge6zE9gQcdBASgA5h6RCSFyGjd9SqOPPdb9GpfrmPAqcz6G8Brppwa26t1+Y7v/XmZe8isL/Gsm1ayvSgXY12nI1LMX01DGaW66854b27E4pGATSCptuTyQJ0p/1Wfk+HJH/Fy5/mtsUgCGOe/Pz7hU6jJwqi2KQ0sJF7y1Ie/wrUHlmUO7vlZPGrXGv4FIeUsgrQPbbp9gqMcva4RcB2bUqEV/jqVljvcDYOLaqCxgxSAD/fhEYBMIQ1a0YjGpJN863UWpfnASGJWAtVjcQAD1g9RRtsuQN9IJ2pYLxQtMl9kULHstrdiKRY02TM+UOyVbIHxYDwZsGh0/qzVArKBxmbZLfNMAIo1Z1t4MNMy/wbNy5mIsed+21BeFd1evfgywWkwIyZopIEDbm3oA1tYpwHdRXpeft8iVQKsQ8vZp4U2Ko3ty3ZCFosEswGVylEdO49iCWpvXimuGp4etN5pzzdOouTqgduJvpWr2a5I97iFKvEfxI5SaZ2b0kbHuL9FzzKOiWAlrFOUl0F+oZeu6yUyKTmN5GTQFJl4EPE5g+yun+dOioWCZQyogAfahCFoHvxaZmV2/+px+bVsacxZ/ubRZS33ZaSplg8P8mG/N3BzwVWfx7MGUdypFN+l4Jtp5j54TUgGIq5F/03oRq8wQjTkI6AzkJl9Fh06zRtKc0zWu0k19eVaLsPtwE32hWDQnTgd5qLIQuudTIGiVhZxXGBvjrc7FlpPNclkBI1NoE+BmYGD90nS0RjK8phQ3WFO15vWbgri4P6c+ddwGcAkLGQk0jSo1AIsPmVrsktUyn5yS+2BjU4jL34PR7M8n+XLiDjpGYhFpskgv8eLDfMwr/5RvmV9u25DSjPnT12KrRje9Thhm4tLx44CTddE/HRBBlHy0iWGRidO6VgoNxaxgK+Ji3ePi8gKX60t05JQ/yAa7a6C/1GBL6WVOlprW2O7Mn2e6cj2brFeBtVkEvBTnzwmyXWrXmstfDmrbP/yS50vO75rGntoL1MfLjk2NSWCrg22AKnHuZdkKnBz/h/xty7H3jfHgcqrfC4YSQ5j6T+WcQw1UeB5oSZShI6pv9TM6foMAr/r50pKZbxsI43xcF6CbDsgmTUunuTTMCJysQL4lBtz6ZbaGL5yOfY2vpsuquU2UvwAgn8k3Xp91ifV5mein+rTxSs6yhTKgbvtFS7YNuub1ynIf/HZyd6MNzlsDldxO3yEzeTe03rVYmm4znO70DkjL8UyMGNJy9Wq1AhHh4nKN88tL7IQVuq5LYPoa6ecCbL1sWoDWy6UyFtLPjhVvaeWfD2qBtxSaNIt0P1lFzufC1ju0BuFMrkgZB9T+KmMTZ0ktRYEaOJAsvZc17qj8bwiw35gFQfn2fn+6ZKlgY046ZWy6MRqz/BSx/yxRanxPFZoMKYSutg4O2q72lfQW3m36jbOijQAEhcZzbVcbchqk1eqXZW2X25kmuag3EYwuKfrvuTNrsNKoy+kkGhIRAuczdkPHWK0IJ0cH+PLnP41PffJNPD2PoJ0b+NQn3rz28xGJr3eNZKGFFlpooYUWWujnmq7XbrbQQgsttNBCCy30c04L2FpooYUWWmihhRa6RlrA1kILLbTQQgsttNA10gK2FlpooYUWWmihha6RFrC10EILLbTQQgstdI20gK2FFlpooYUWWmiha6QFbC200EILLbTQQgtdIy1ga6GFFlpooYUWWugaaQFbCy200EILLbTQQtdI/x8BNs5Kt3/tDAAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 1200x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from src.data_utils import plot_bounding_boxes\n",
-    "book = 2\n",
-    "page = 3\n",
-    "\n",
-    "plot_bounding_boxes(image_path= f\"data/GAN-DATA/3_processed/books/book_{book}_{page}.png\",\n",
-    "                    text_file_path=f\"data/GAN-DATA/4_bounding_boxes/book_{book}_{page}.txt\",\n",
-    "                    save_path = None)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d8f4c9a7",
-   "metadata": {},
-   "source": [
-    "## Mapping word images with corresponding transcript word"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "03bb9512",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Processing 98 files...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Mapping bounding boxes: 100%|██████████| 98/98 [51:52<00:00, 31.76s/file]  "
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "=== MAPPING SUMMARY ===\n",
-      "1. Total number of words in transcript files: 17528\n",
-      "2. Total number of bounding boxes detected: 18010\n",
-      "3. Total number of image-word pairs successfully mapped: 11239\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from src.data_utils import mapping_bounding_boxes\n",
-    "\n",
-    "total_words, total_bboxes, total_mapped = mapping_bounding_boxes(\n",
-    "    image_dir=\"data/GAN-DATA/3_processed/books\",\n",
-    "    bbox_dir=\"data/GAN-DATA/4_bounding_boxes\", \n",
-    "    transcript_dir=\"data/GAN-DATA/3_processed/transcripts\",\n",
-    "    output_dir=\"data/GAN-DATA/5_mapped\",\n",
-    "    tesseract_model=1,\n",
-    "    similarity_threshold=0.8\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2dfe9cb5",
-   "metadata": {},
-   "source": [
-    "## Creating Dataset of words images and labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "3ed01768",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Creating Word Dataset: 100%|██████████| 98/98 [00:28<00:00,  3.46image/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Processed 11239 regions from 98 images\n",
-      "Images saved to: data/GAN-DATA/6_word_data/images\n",
-      "CSV saved to: data/GAN-DATA/6_word_data/words.csv\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from src.data_utils import extract_and_process_all_regions\n",
-    "\n",
-    "df = extract_and_process_all_regions(\n",
-    "    image_root = \"data/GAN-DATA/3_processed/books\",\n",
-    "    aligned_root = \"data/GAN-DATA/5_mapped\",\n",
-    "    output_root = \"data/GAN-DATA/6_word_data/images\",\n",
-    "    csv_output_path = \"data/GAN-DATA/6_word_data/words.csv\"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "ffba16a1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Image</th>\n",
-       "      <th>label</th>\n",
-       "      <th>book</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>image0.png</td>\n",
-       "      <td>fue</td>\n",
-       "      <td>book_10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>image1.png</td>\n",
-       "      <td>de</td>\n",
-       "      <td>book_10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>image2.png</td>\n",
-       "      <td>Trajano</td>\n",
-       "      <td>book_10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>image3.png</td>\n",
-       "      <td>escriviendole</td>\n",
-       "      <td>book_10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>image4.png</td>\n",
-       "      <td>la</td>\n",
-       "      <td>book_10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11234</th>\n",
-       "      <td>image11234.png</td>\n",
-       "      <td>ha</td>\n",
-       "      <td>book_9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11235</th>\n",
-       "      <td>image11235.png</td>\n",
-       "      <td>la</td>\n",
-       "      <td>book_9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11236</th>\n",
-       "      <td>image11236.png</td>\n",
-       "      <td>de</td>\n",
-       "      <td>book_9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11237</th>\n",
-       "      <td>image11237.png</td>\n",
-       "      <td>virtudes</td>\n",
-       "      <td>book_9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11238</th>\n",
-       "      <td>image11238.png</td>\n",
-       "      <td>porque</td>\n",
-       "      <td>book_9</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>11239 rows × 3 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                Image          label     book\n",
-       "0          image0.png            fue  book_10\n",
-       "1          image1.png             de  book_10\n",
-       "2          image2.png        Trajano  book_10\n",
-       "3          image3.png  escriviendole  book_10\n",
-       "4          image4.png             la  book_10\n",
-       "...               ...            ...      ...\n",
-       "11234  image11234.png             ha   book_9\n",
-       "11235  image11235.png             la   book_9\n",
-       "11236  image11236.png             de   book_9\n",
-       "11237  image11237.png       virtudes   book_9\n",
-       "11238  image11238.png         porque   book_9\n",
-       "\n",
-       "[11239 rows x 3 columns]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9661c4e8",
-   "metadata": {},
-   "source": [
-    "## Resizing and Padding"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "a787fcf2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Analyzed 11239 images in data/GAN-DATA/6_word_data/images\n",
-      "Dimensions (width x height):\n",
-      "  Minimum: 17 x 22 pixels\n",
-      "  Maximum: 1671 x 578 pixels\n",
-      "  Average: 211.0 x 92.1 pixels\n"
-     ]
-    }
-   ],
-   "source": [
-    "from src.data_utils import analyze_image_sizes\n",
-    "analyze_image_sizes(\"data/GAN-DATA/6_word_data/images\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "2f52040b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Resizing and Padding Images: 100%|██████████| 11239/11239 [00:36<00:00, 305.54img/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "from src.data_utils import resize_and_pad\n",
-    "\n",
-    "target_height, target_width = 64,128\n",
-    "\n",
-    "resize_and_pad(\n",
-    "    input_dir=\"data/GAN-DATA/6_word_data/images\",\n",
-    "    output_dir=\"data/GAN-DATA/final_dataset/target\",\n",
-    "    target_height=target_height,\n",
-    "    target_width=target_width\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dfdd46f0",
-   "metadata": {},
-   "source": [
-    "## Rendering Latex Source Images"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "7337ff19",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Successfully loaded custom font: fonts/RomanAntique.ttf\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Rendering text images: 100%|██████████| 11239/11239 [07:39<00:00, 24.48it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset generated successfully!\n"
-     ]
-    }
-   ],
-   "source": [
-    "from src.data_utils import generate_text_image_dataset\n",
-    "\n",
-    "config = {\n",
-    "            'text.usetex': False,\n",
-    "            'mathtext.fontset': 'cm',\n",
-    "            'font.family': 'serif',\n",
-    "            'font.serif': ['Times New Roman'],\n",
-    "            'font.weight': 'bold',\n",
-    "            'mathtext.bf': 'bold',\n",
-    "        }\n",
-    "\n",
-    "df = generate_text_image_dataset(\n",
-    "csv_path='data/GAN-DATA/6_word_data/words.csv',\n",
-    "output_dir='data/GAN-DATA/final_dataset',\n",
-    "target_dir_prefix='data/GAN-DATA/final_dataset/target/',\n",
-    "image_width=target_width,\n",
-    "image_height=target_height,\n",
-    "base_fontsize=70,\n",
-    "matplotlib_config=config,\n",
-    "custom_font_path='fonts/RomanAntique.ttf', \n",
-    "progress_bar=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "adcaf47a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "from src.data_utils import *\n",
-    "df = pd.read_csv(\"data/GAN-DATA/final_dataset/data.csv\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ee8c6626",
-   "metadata": {},
-   "source": [
-    "## Grid Generation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ee8319af",
-   "metadata": {},
-   "source": [
-    "I manually cleaned the final dataset to improve the data quality. Rerunning the below cell block will change the cleaned data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "8c54ada2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Creating 3000 image grids...\n",
-      "Grid size: 4x2 = 8 images per grid\n",
-      "Target image size: (128, 64)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Creating grids: 100%|██████████| 3000/3000 [03:02<00:00, 16.46it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Successfully created 3000 image grids!\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# grid_df = create_image_grids(\n",
-    "#     df=df[df['book'].isin([\"book_1\", \"book_5\"])],\n",
-    "#     output_directory=\"data/GAN-DATA/grid_dataset\",\n",
-    "#     num_grids=3000,\n",
-    "#     grid_size=(4,2),\n",
-    "#     target_image_size=(128,64),  # (width, height)\n",
-    "#     random_seed=42\n",
-    "# )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ff5fa797",
-   "metadata": {},
-   "source": [
-    "# Model Building"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1c8eb41b",
-   "metadata": {},
-   "source": [
-    "## Dataset Class\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "14e40106",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-07-27T04:19:44.005094Z",
-     "iopub.status.busy": "2025-07-27T04:19:44.004270Z",
-     "iopub.status.idle": "2025-07-27T04:19:52.090568Z",
-     "shell.execute_reply": "2025-07-27T04:19:52.089797Z",
-     "shell.execute_reply.started": "2025-07-27T04:19:44.005038Z"
-    },
-    "trusted": true
-   },
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import Dataset, DataLoader\n",
-    "from torchvision import transforms\n",
-    "from PIL import Image\n",
-    "import pandas as pd\n",
-    "\n",
-    "class WordImageDataset(Dataset):\n",
-    "    def __init__(self, csv_file):\n",
-    "        self.data = pd.read_csv(csv_file)\n",
-    "        self.transform = transforms.Compose([\n",
-    "            transforms.Grayscale(num_output_channels=1),\n",
-    "            transforms.Resize((256, 256)),\n",
-    "            transforms.ToTensor(),\n",
-    "            transforms.Normalize((0.5,), (0.5,))\n",
-    "        ])\n",
-    "\n",
-    "    def __len__(self):\n",
-    "        return len(self.data)\n",
-    "\n",
-    "    def __getitem__(self, idx):\n",
-    "        source = Image.open(self.data.iloc[idx]['source_path']).convert('RGB')\n",
-    "        target = Image.open(self.data.iloc[idx]['target_path']).convert('RGB')\n",
-    "        return self.transform(source), self.transform(target)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f9b2c05d",
-   "metadata": {},
-   "source": [
-    "## Generator\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "40e2cd84",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-07-27T04:19:52.092159Z",
-     "iopub.status.busy": "2025-07-27T04:19:52.091798Z",
-     "iopub.status.idle": "2025-07-27T04:19:52.102939Z",
-     "shell.execute_reply": "2025-07-27T04:19:52.102060Z",
-     "shell.execute_reply.started": "2025-07-27T04:19:52.092139Z"
-    },
-    "trusted": true
-   },
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "\n",
-    "class UNetGenerator(nn.Module):\n",
-    "    def __init__(self, in_channels=1, out_channels=1, features=64):\n",
-    "        super().__init__()\n",
-    "\n",
-    "        def down(in_f, out_f, norm=True):\n",
-    "            layers = [nn.Conv2d(in_f, out_f, 4, 2, 1, bias=False)]\n",
-    "            if norm:\n",
-    "                layers.append(nn.BatchNorm2d(out_f))\n",
-    "            layers.append(nn.LeakyReLU(0.2))\n",
-    "            return nn.Sequential(*layers)\n",
-    "\n",
-    "        def up(in_f, out_f, dropout=False):\n",
-    "            layers = [nn.ConvTranspose2d(in_f, out_f, 4, 2, 1, bias=False),\n",
-    "                      nn.BatchNorm2d(out_f), nn.ReLU()]\n",
-    "            if dropout:\n",
-    "                layers.append(nn.Dropout(0.5))\n",
-    "            return nn.Sequential(*layers)\n",
-    "\n",
-    "        self.down1 = down(in_channels, features, norm=False)\n",
-    "        self.down2 = down(features, features*2)\n",
-    "        self.down3 = down(features*2, features*4)\n",
-    "        self.down4 = down(features*4, features*8)\n",
-    "        self.down5 = down(features*8, features*8)\n",
-    "        self.down6 = down(features*8, features*8)\n",
-    "        self.down7 = down(features*8, features*8)\n",
-    "        self.down8 = down(features*8, features*8, norm=False)\n",
-    "\n",
-    "        self.up1 = up(features*8, features*8, dropout=True)\n",
-    "        self.up2 = up(features*16, features*8, dropout=True)\n",
-    "        self.up3 = up(features*16, features*8, dropout=True)\n",
-    "        self.up4 = up(features*16, features*8)\n",
-    "        self.up5 = up(features*16, features*4)\n",
-    "        self.up6 = up(features*8, features*2)\n",
-    "        self.up7 = up(features*4, features)\n",
-    "        self.final = nn.Sequential(\n",
-    "            nn.ConvTranspose2d(features*2, out_channels, 4, 2, 1),\n",
-    "            nn.Tanh()\n",
-    "        )\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        d1 = self.down1(x); d2 = self.down2(d1)\n",
-    "        d3 = self.down3(d2); d4 = self.down4(d3)\n",
-    "        d5 = self.down5(d4); d6 = self.down6(d5)\n",
-    "        d7 = self.down7(d6); d8 = self.down8(d7)\n",
-    "\n",
-    "        u1 = self.up1(d8); u2 = self.up2(torch.cat([u1, d7], 1))\n",
-    "        u3 = self.up3(torch.cat([u2, d6], 1))\n",
-    "        u4 = self.up4(torch.cat([u3, d5], 1))\n",
-    "        u5 = self.up5(torch.cat([u4, d4], 1))\n",
-    "        u6 = self.up6(torch.cat([u5, d3], 1))\n",
-    "        u7 = self.up7(torch.cat([u6, d2], 1))\n",
-    "        return self.final(torch.cat([u7, d1], 1))\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "103fa4f0",
-   "metadata": {},
-   "source": [
-    "## Discriminator"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "76a984e0",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-07-27T04:19:52.104068Z",
-     "iopub.status.busy": "2025-07-27T04:19:52.103816Z",
-     "iopub.status.idle": "2025-07-27T04:19:52.255829Z",
-     "shell.execute_reply": "2025-07-27T04:19:52.255009Z",
-     "shell.execute_reply.started": "2025-07-27T04:19:52.104044Z"
-    },
-    "trusted": true
-   },
-   "outputs": [],
-   "source": [
-    "class PatchDiscriminator(nn.Module):\n",
-    "    def __init__(self, in_channels=2, features=64):\n",
-    "        super().__init__()\n",
-    "        self.model = nn.Sequential(\n",
-    "            nn.Conv2d(in_channels, features, 4, 2, 1),\n",
-    "            nn.LeakyReLU(0.2),\n",
-    "            nn.Conv2d(features, features*2, 4, 2, 1),\n",
-    "            nn.BatchNorm2d(features*2),\n",
-    "            nn.LeakyReLU(0.2),\n",
-    "            nn.Conv2d(features*2, features*4, 4, 2, 1),\n",
-    "            nn.BatchNorm2d(features*4),\n",
-    "            nn.LeakyReLU(0.2),\n",
-    "            nn.Conv2d(features*4, features*8, 4, 1, 1),\n",
-    "            nn.BatchNorm2d(features*8),\n",
-    "            nn.LeakyReLU(0.2),\n",
-    "            nn.Conv2d(features*8, 1, 4, 1, 1),\n",
-    "            nn.Sigmoid()\n",
-    "        )\n",
-    "\n",
-    "    def forward(self, x, y):\n",
-    "        return self.model(torch.cat([x, y], dim=1))\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "562f0eaa",
-   "metadata": {},
-   "source": [
-    "## Training Loop"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "9c1f64b9",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-07-27T04:19:52.327105Z",
-     "iopub.status.busy": "2025-07-27T04:19:52.326838Z",
-     "iopub.status.idle": "2025-07-27T04:19:52.341873Z",
-     "shell.execute_reply": "2025-07-27T04:19:52.341273Z",
-     "shell.execute_reply.started": "2025-07-27T04:19:52.327084Z"
-    },
-    "trusted": true
-   },
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import torch.optim as optim\n",
-    "from torch.utils.data import DataLoader, random_split\n",
-    "from tqdm import tqdm\n",
-    "import os\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "\n",
-    "def train_pix2pix(csv_file, epochs=100, batch_size=8, lr=2e-4, save_dir=\"models\", val_interval=10):\n",
-    "    \n",
-    "    os.makedirs(save_dir, exist_ok=True)\n",
-    "    device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
-    "    print(f\"Using device: {device}\")\n",
-    "\n",
-    "    # ---- Dataset Splitting ----\n",
-    "    full_dataset = WordImageDataset(csv_file)\n",
-    "    total_size = len(full_dataset)\n",
-    "    train_size = int(0.9 * total_size)\n",
-    "    val_size = int(0.05 * total_size)\n",
-    "    test_size = total_size - train_size - val_size\n",
-    "\n",
-    "    train_set, val_set, test_set = random_split(full_dataset, [train_size, val_size, test_size], generator=torch.Generator().manual_seed(42))\n",
-    "\n",
-    "    train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)\n",
-    "    val_loader = DataLoader(val_set, batch_size=batch_size, shuffle=False)\n",
-    "    test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=False)\n",
-    "\n",
-    "    # ---- Models ----\n",
-    "    G = UNetGenerator().to(device)\n",
-    "    D = PatchDiscriminator().to(device)\n",
-    "\n",
-    "    # ---- Optimizers and Loss Functions ----\n",
-    "    opt_G = optim.Adam(G.parameters(), lr=lr, betas=(0.5, 0.999))\n",
-    "    opt_D = optim.Adam(D.parameters(), lr=lr, betas=(0.5, 0.999))\n",
-    "    criterion_GAN = nn.BCELoss()\n",
-    "    criterion_L1 = nn.L1Loss()\n",
-    "\n",
-    "    # ---- History ----\n",
-    "    history = {\n",
-    "        \"epoch\": [],\n",
-    "        \"loss_G\": [],\n",
-    "        \"loss_D\": [],\n",
-    "        \"val_loss_L1\": [],\n",
-    "        \"val_loss_GAN\": []\n",
-    "    }\n",
-    "\n",
-    "    for epoch in range(epochs):\n",
-    "        G.train()\n",
-    "        D.train()\n",
-    "\n",
-    "        loop = tqdm(train_loader, desc=f\"Epoch {epoch+1}/{epochs}\")\n",
-    "        running_loss_G = 0.0\n",
-    "        running_loss_D = 0.0\n",
-    "\n",
-    "        for x, y in loop:\n",
-    "            x, y = x.to(device), y.to(device)\n",
-    "            valid = torch.ones((x.size(0), 1, 30, 30), device=device)\n",
-    "            fake = torch.zeros((x.size(0), 1, 30, 30), device=device)\n",
-    "\n",
-    "            # ---- Train Generator ----\n",
-    "            opt_G.zero_grad()\n",
-    "            y_fake = G(x)\n",
-    "            pred_fake = D(y_fake, x)\n",
-    "            loss_GAN = criterion_GAN(pred_fake, valid)\n",
-    "            loss_L1 = criterion_L1(y_fake, y) * 100\n",
-    "            loss_G = loss_GAN + loss_L1\n",
-    "            loss_G.backward()\n",
-    "            opt_G.step()\n",
-    "\n",
-    "            # ---- Train Discriminator ----\n",
-    "            opt_D.zero_grad()\n",
-    "            pred_real = D(y, x)\n",
-    "            loss_real = criterion_GAN(pred_real, valid)\n",
-    "            pred_fake = D(y_fake.detach(), x)\n",
-    "            loss_fake = criterion_GAN(pred_fake, fake)\n",
-    "            loss_D = (loss_real + loss_fake) * 0.5\n",
-    "            loss_D.backward()\n",
-    "            opt_D.step()\n",
-    "\n",
-    "            running_loss_G += loss_G.item()\n",
-    "            running_loss_D += loss_D.item()\n",
-    "\n",
-    "            loop.set_postfix(loss_G=loss_G.item(), loss_D=loss_D.item())\n",
-    "\n",
-    "        avg_loss_G = running_loss_G / len(train_loader)\n",
-    "        avg_loss_D = running_loss_D / len(train_loader)\n",
-    "\n",
-    "        history[\"epoch\"].append(epoch + 1)\n",
-    "        history[\"loss_G\"].append(avg_loss_G)\n",
-    "        history[\"loss_D\"].append(avg_loss_D)\n",
-    "\n",
-    "        # ---- Validation ----\n",
-    "        if (epoch + 1) % val_interval == 0 or epoch == 0:\n",
-    "            G.eval()\n",
-    "            total_L1 = 0.0\n",
-    "            total_GAN = 0.0\n",
-    "\n",
-    "            with torch.no_grad():\n",
-    "                for x_val, y_val in val_loader:\n",
-    "                    x_val, y_val = x_val.to(device), y_val.to(device)\n",
-    "                    y_pred = G(x_val)\n",
-    "                    pred = D(y_pred, x_val)\n",
-    "\n",
-    "                    loss_l1 = criterion_L1(y_pred, y_val)\n",
-    "                    loss_gan = criterion_GAN(pred, torch.ones_like(pred))\n",
-    "\n",
-    "                    total_L1 += loss_l1.item()\n",
-    "                    total_GAN += loss_gan.item()\n",
-    "\n",
-    "            val_L1_avg = total_L1 / len(val_loader)\n",
-    "            val_GAN_avg = total_GAN / len(val_loader)\n",
-    "\n",
-    "            history[\"val_loss_L1\"].append(val_L1_avg)\n",
-    "            history[\"val_loss_GAN\"].append(val_GAN_avg)\n",
-    "\n",
-    "            print(f\"\\n[Validation @ Epoch {epoch+1}] L1 Loss: {val_L1_avg:.4f}, GAN Loss: {val_GAN_avg:.4f}\\n\")\n",
-    "        else:\n",
-    "            history[\"val_loss_L1\"].append(np.nan)\n",
-    "            history[\"val_loss_GAN\"].append(np.nan)\n",
-    "\n",
-    "    # ---- Save Final Models ----\n",
-    "    torch.save(G.state_dict(), os.path.join(save_dir, \"generator_final.pth\"))\n",
-    "    torch.save(D.state_dict(), os.path.join(save_dir, \"discriminator_final.pth\"))\n",
-    "\n",
-    "    # ---- Save Training History ----\n",
-    "    pd.DataFrame(history).to_csv(os.path.join(save_dir, \"training_history.csv\"), index=False)\n",
-    "    print(\"Training complete. Models and history saved.\")\n",
-    "\n",
-    "    return G, D, train_loader, val_loader, test_loader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "1aba5554",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-07-27T04:20:17.263085Z",
-     "iopub.status.busy": "2025-07-27T04:20:17.262808Z",
-     "iopub.status.idle": "2025-07-27T05:58:40.499564Z",
-     "shell.execute_reply": "2025-07-27T05:58:40.498939Z",
-     "shell.execute_reply.started": "2025-07-27T04:20:17.263062Z"
-    },
-    "trusted": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using device: cuda\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/100: 100%|██████████| 85/85 [01:18<00:00,  1.09it/s, loss_D=0.00815, loss_G=38.6]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 1] L1 Loss: 0.3402, GAN Loss: 4.9725\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 2/100: 100%|██████████| 85/85 [00:57<00:00,  1.48it/s, loss_D=0.692, loss_G=23.7] \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 2] L1 Loss: 0.2195, GAN Loss: 0.7120\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 3/100: 100%|██████████| 85/85 [00:57<00:00,  1.48it/s, loss_D=0.84, loss_G=20.9] \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 3] L1 Loss: 0.1917, GAN Loss: 0.6744\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 4/100: 100%|██████████| 85/85 [00:57<00:00,  1.49it/s, loss_D=0.579, loss_G=17.1]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 4] L1 Loss: 0.1754, GAN Loss: 0.8116\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 5/100: 100%|██████████| 85/85 [00:57<00:00,  1.48it/s, loss_D=0.569, loss_G=17.7]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 5] L1 Loss: 0.1673, GAN Loss: 0.9723\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 6/100: 100%|██████████| 85/85 [00:57<00:00,  1.48it/s, loss_D=0.509, loss_G=16.4]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 6] L1 Loss: 0.1623, GAN Loss: 1.4706\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 7/100: 100%|██████████| 85/85 [00:59<00:00,  1.43it/s, loss_D=0.617, loss_G=17.9]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 7] L1 Loss: 0.1569, GAN Loss: 0.6012\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 8/100: 100%|██████████| 85/85 [01:00<00:00,  1.40it/s, loss_D=0.642, loss_G=17]  \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 8] L1 Loss: 0.1608, GAN Loss: 0.5612\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 9/100: 100%|██████████| 85/85 [01:01<00:00,  1.38it/s, loss_D=0.55, loss_G=15.5] \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 9] L1 Loss: 0.1583, GAN Loss: 0.7738\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 10/100: 100%|██████████| 85/85 [00:58<00:00,  1.46it/s, loss_D=0.5, loss_G=15.8]  \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 10] L1 Loss: 0.1605, GAN Loss: 1.3104\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 11/100: 100%|██████████| 85/85 [00:56<00:00,  1.50it/s, loss_D=0.577, loss_G=17.9]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 11] L1 Loss: 0.1546, GAN Loss: 0.6327\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 12/100: 100%|██████████| 85/85 [00:56<00:00,  1.50it/s, loss_D=0.484, loss_G=14.3]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 12] L1 Loss: 0.1537, GAN Loss: 1.0667\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 13/100: 100%|██████████| 85/85 [00:57<00:00,  1.49it/s, loss_D=0.502, loss_G=12.2]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 13] L1 Loss: 0.1586, GAN Loss: 1.1486\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 14/100: 100%|██████████| 85/85 [00:56<00:00,  1.50it/s, loss_D=0.446, loss_G=14.7]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 14] L1 Loss: 0.1520, GAN Loss: 0.8762\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 15/100: 100%|██████████| 85/85 [00:56<00:00,  1.49it/s, loss_D=0.574, loss_G=14.3]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 15] L1 Loss: 0.1524, GAN Loss: 0.5796\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 16/100: 100%|██████████| 85/85 [00:56<00:00,  1.50it/s, loss_D=0.758, loss_G=12.6]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 16] L1 Loss: 0.1480, GAN Loss: 0.4574\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 17/100: 100%|██████████| 85/85 [00:56<00:00,  1.50it/s, loss_D=0.514, loss_G=11.9]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 17] L1 Loss: 0.1507, GAN Loss: 1.1372\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 18/100: 100%|██████████| 85/85 [00:56<00:00,  1.49it/s, loss_D=0.554, loss_G=12.1]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 18] L1 Loss: 0.1470, GAN Loss: 1.3690\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 19/100: 100%|██████████| 85/85 [00:56<00:00,  1.50it/s, loss_D=0.525, loss_G=12.3]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 19] L1 Loss: 0.1454, GAN Loss: 0.8760\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 20/100: 100%|██████████| 85/85 [00:56<00:00,  1.50it/s, loss_D=0.494, loss_G=11.8]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 20] L1 Loss: 0.1514, GAN Loss: 1.4122\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 21/100: 100%|██████████| 85/85 [00:57<00:00,  1.48it/s, loss_D=0.468, loss_G=11.2]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 21] L1 Loss: 0.1443, GAN Loss: 1.3258\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 22/100: 100%|██████████| 85/85 [00:59<00:00,  1.43it/s, loss_D=0.563, loss_G=10.5]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 22] L1 Loss: 0.1435, GAN Loss: 1.2896\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 23/100: 100%|██████████| 85/85 [00:56<00:00,  1.50it/s, loss_D=0.465, loss_G=10.6]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 23] L1 Loss: 0.1457, GAN Loss: 1.5493\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 24/100: 100%|██████████| 85/85 [00:56<00:00,  1.50it/s, loss_D=0.483, loss_G=11.4]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 24] L1 Loss: 0.1471, GAN Loss: 0.8889\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 25/100: 100%|██████████| 85/85 [00:56<00:00,  1.51it/s, loss_D=0.607, loss_G=9.51]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 25] L1 Loss: 0.1429, GAN Loss: 1.6186\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 26/100: 100%|██████████| 85/85 [00:57<00:00,  1.49it/s, loss_D=0.495, loss_G=10.3]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 26] L1 Loss: 0.1475, GAN Loss: 1.2238\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 27/100: 100%|██████████| 85/85 [00:56<00:00,  1.50it/s, loss_D=0.44, loss_G=11.7] \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 27] L1 Loss: 0.1456, GAN Loss: 0.5902\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 28/100: 100%|██████████| 85/85 [00:56<00:00,  1.51it/s, loss_D=0.26, loss_G=13.1] \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 28] L1 Loss: 0.1462, GAN Loss: 0.5533\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 29/100: 100%|██████████| 85/85 [00:56<00:00,  1.50it/s, loss_D=0.627, loss_G=10.2]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 29] L1 Loss: 0.1509, GAN Loss: 3.3013\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 30/100: 100%|██████████| 85/85 [00:56<00:00,  1.50it/s, loss_D=0.307, loss_G=9.33] \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 30] L1 Loss: 0.1443, GAN Loss: 0.7578\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
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-     "text": [
-      "Epoch 73/100: 100%|██████████| 85/85 [00:58<00:00,  1.46it/s, loss_D=0.146, loss_G=7.57] \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 73] L1 Loss: 0.1435, GAN Loss: 3.1803\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 74/100: 100%|██████████| 85/85 [00:57<00:00,  1.47it/s, loss_D=0.403, loss_G=6.33] \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 74] L1 Loss: 0.1447, GAN Loss: 4.5987\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 75/100: 100%|██████████| 85/85 [00:57<00:00,  1.49it/s, loss_D=0.0504, loss_G=9.17]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 75] L1 Loss: 0.1487, GAN Loss: 2.1783\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 76/100: 100%|██████████| 85/85 [00:57<00:00,  1.49it/s, loss_D=0.463, loss_G=9.28] \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 76] L1 Loss: 0.1453, GAN Loss: 1.8279\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 77/100: 100%|██████████| 85/85 [00:56<00:00,  1.49it/s, loss_D=1.74, loss_G=12]    \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 77] L1 Loss: 0.1469, GAN Loss: 3.9024\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 78/100: 100%|██████████| 85/85 [00:56<00:00,  1.50it/s, loss_D=0.293, loss_G=7.34]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 78] L1 Loss: 0.1429, GAN Loss: 3.9114\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 79/100: 100%|██████████| 85/85 [00:57<00:00,  1.48it/s, loss_D=0.511, loss_G=5.94] \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 79] L1 Loss: 0.1453, GAN Loss: 4.7674\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 80/100: 100%|██████████| 85/85 [00:59<00:00,  1.44it/s, loss_D=0.118, loss_G=8.02] \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 80] L1 Loss: 0.1433, GAN Loss: 2.0119\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 81/100: 100%|██████████| 85/85 [00:56<00:00,  1.50it/s, loss_D=0.122, loss_G=9.25] \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 81] L1 Loss: 0.1464, GAN Loss: 4.7321\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 82/100: 100%|██████████| 85/85 [00:59<00:00,  1.43it/s, loss_D=0.174, loss_G=7.32] \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 82] L1 Loss: 0.1447, GAN Loss: 1.8274\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 83/100: 100%|██████████| 85/85 [00:57<00:00,  1.48it/s, loss_D=0.707, loss_G=6.48] \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 83] L1 Loss: 0.1449, GAN Loss: 1.3086\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 84/100: 100%|██████████| 85/85 [00:56<00:00,  1.51it/s, loss_D=0.568, loss_G=7.5] \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 84] L1 Loss: 0.1423, GAN Loss: 4.8921\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 85/100: 100%|██████████| 85/85 [00:57<00:00,  1.48it/s, loss_D=0.483, loss_G=6.18] \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 85] L1 Loss: 0.1422, GAN Loss: 3.8358\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 86/100: 100%|██████████| 85/85 [00:58<00:00,  1.44it/s, loss_D=0.138, loss_G=7.93] \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 86] L1 Loss: 0.1420, GAN Loss: 3.0824\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 87/100: 100%|██████████| 85/85 [00:56<00:00,  1.50it/s, loss_D=0.151, loss_G=6.96] \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 87] L1 Loss: 0.1434, GAN Loss: 2.3327\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 88/100: 100%|██████████| 85/85 [00:56<00:00,  1.50it/s, loss_D=0.679, loss_G=6.41] \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 88] L1 Loss: 0.1409, GAN Loss: 1.3068\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 89/100: 100%|██████████| 85/85 [00:58<00:00,  1.45it/s, loss_D=0.43, loss_G=6.15]  \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 89] L1 Loss: 0.1426, GAN Loss: 2.5921\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 90/100: 100%|██████████| 85/85 [00:56<00:00,  1.50it/s, loss_D=0.0586, loss_G=9.19]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 90] L1 Loss: 0.1446, GAN Loss: 2.0331\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 91/100: 100%|██████████| 85/85 [00:56<00:00,  1.50it/s, loss_D=0.0761, loss_G=8.22]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 91] L1 Loss: 0.1449, GAN Loss: 1.5616\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 92/100: 100%|██████████| 85/85 [00:56<00:00,  1.51it/s, loss_D=0.647, loss_G=5.45]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 92] L1 Loss: 0.1423, GAN Loss: 3.9825\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 93/100: 100%|██████████| 85/85 [00:56<00:00,  1.50it/s, loss_D=0.0752, loss_G=7.72]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 93] L1 Loss: 0.1462, GAN Loss: 2.2039\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 94/100: 100%|██████████| 85/85 [00:56<00:00,  1.50it/s, loss_D=0.169, loss_G=7.09]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 94] L1 Loss: 0.1419, GAN Loss: 1.8300\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 95/100: 100%|██████████| 85/85 [00:56<00:00,  1.50it/s, loss_D=0.113, loss_G=10.4] \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 95] L1 Loss: 0.1437, GAN Loss: 1.7164\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 96/100: 100%|██████████| 85/85 [00:56<00:00,  1.50it/s, loss_D=0.197, loss_G=8.8]  \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 96] L1 Loss: 0.1435, GAN Loss: 4.9744\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 97/100: 100%|██████████| 85/85 [00:56<00:00,  1.50it/s, loss_D=0.0526, loss_G=8.72]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 97] L1 Loss: 0.1447, GAN Loss: 4.0935\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 98/100: 100%|██████████| 85/85 [00:56<00:00,  1.50it/s, loss_D=0.0386, loss_G=11.9]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 98] L1 Loss: 0.1454, GAN Loss: 2.6717\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 99/100: 100%|██████████| 85/85 [00:56<00:00,  1.50it/s, loss_D=0.359, loss_G=8.66] \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 99] L1 Loss: 0.1426, GAN Loss: 2.8546\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 100/100: 100%|██████████| 85/85 [00:56<00:00,  1.50it/s, loss_D=0.111, loss_G=7.03] \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[Validation @ Epoch 100] L1 Loss: 0.1440, GAN Loss: 2.8443\n",
-      "\n",
-      "Training complete. Models and history saved.\n"
-     ]
-    }
-   ],
-   "source": [
-    "generator, discriminator,train_loader, val_loader, test_loader = train_pix2pix(\"data/GAN-DATA/grid_dataset/grid_info.csv\",\n",
-    "                                                                               epochs=100,\n",
-    "                                                                               batch_size=32,\n",
-    "                                                                               val_interval=1,\n",
-    "                                                                               save_dir=\"output/GAN_OUTPUT\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "bc9d09f8",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-07-27T06:01:14.270357Z",
-     "iopub.status.busy": "2025-07-27T06:01:14.269571Z",
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-     "shell.execute_reply": "2025-07-27T06:01:14.275285Z",
-     "shell.execute_reply.started": "2025-07-27T06:01:14.270330Z"
-    },
-    "trusted": true
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "def plot_gan_history(csv_path):\n",
-    "    # Load the history CSV\n",
-    "    df = pd.read_csv(csv_path)\n",
-    "\n",
-    "    # Plot generator and discriminator losses\n",
-    "    plt.figure(figsize=(10, 5))\n",
-    "    plt.plot(df['epoch'], df['loss_G'], label='Generator Loss', linewidth=2)\n",
-    "    plt.plot(df['epoch'], df['loss_D'], label='Discriminator Loss', linewidth=2)\n",
-    "\n",
-    "    # Plot validation losses if present\n",
-    "    if 'val_loss_L1' in df.columns:\n",
-    "        plt.plot(df['epoch'], df['val_loss_L1'], label='Val L1 Loss', linestyle='--', color='green')\n",
-    "    if 'val_loss_GAN' in df.columns:\n",
-    "        plt.plot(df['epoch'], df['val_loss_GAN'], label='Val GAN Loss', linestyle='--', color='orange')\n",
-    "\n",
-    "    # Labels and legend\n",
-    "    plt.xlabel('Epoch')\n",
-    "    plt.ylabel('Loss')\n",
-    "    plt.title('GAN Training History')\n",
-    "    plt.legend()\n",
-    "    plt.grid(True)\n",
-    "    plt.tight_layout()\n",
-    "\n",
-    "    # Save the plot\n",
-    "    save_path = csv_path.replace('.csv', '_history.png')\n",
-    "    plt.savefig(save_path)\n",
-    "    plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "aeba2272",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-07-27T06:01:16.595364Z",
-     "iopub.status.busy": "2025-07-27T06:01:16.594688Z",
-     "iopub.status.idle": "2025-07-27T06:01:17.036100Z",
-     "shell.execute_reply": "2025-07-27T06:01:17.035368Z",
-     "shell.execute_reply.started": "2025-07-27T06:01:16.595337Z"
-    },
-    "trusted": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_gan_history(\"output/GAN_OUTPUT/training_history.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "93b33418",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-07-27T06:01:26.192779Z",
-     "iopub.status.busy": "2025-07-27T06:01:26.192478Z",
-     "iopub.status.idle": "2025-07-27T06:01:26.204406Z",
-     "shell.execute_reply": "2025-07-27T06:01:26.203719Z",
-     "shell.execute_reply.started": "2025-07-27T06:01:26.192758Z"
-    },
-    "trusted": true
-   },
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import torchvision.transforms as T\n",
-    "import torch\n",
-    "import random\n",
-    "\n",
-    "def visualize_pix2pix_results(generator_path, dataloader, num_samples=5, figsize=(12, 4)):\n",
-    "    \"\"\"\n",
-    "    Visualize Pix2Pix outputs given a test dataloader.\n",
-    "\n",
-    "    Args:\n",
-    "        generator_path (str): Path to the trained generator model.\n",
-    "        dataloader (DataLoader): PyTorch DataLoader for test data.\n",
-    "        num_samples (int): Number of samples to visualize.\n",
-    "        figsize (tuple): Width x Height of each row in the plot.\n",
-    "    \"\"\"\n",
-    "    device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
-    "    print(f\"Using device: {device}\")\n",
-    "\n",
-    "    # Load trained generator\n",
-    "    generator = UNetGenerator(in_channels=1, out_channels=1)\n",
-    "    generator.load_state_dict(torch.load(generator_path, map_location=device))\n",
-    "    generator.eval().to(device)\n",
-    "\n",
-    "    # Image un-normalization for display\n",
-    "    to_pil = T.Compose([\n",
-    "        T.Normalize((0.0,), (1 / 0.5,)),  # std\n",
-    "        T.Normalize((-0.5,), (1.0,)),     # mean\n",
-    "        T.ToPILImage()\n",
-    "    ])\n",
-    "\n",
-    "    # Gather samples from dataloader\n",
-    "    samples = []\n",
-    "    for batch in dataloader:\n",
-    "        inputs, targets = batch\n",
-    "        for i in range(inputs.size(0)):\n",
-    "            samples.append((inputs[i], targets[i]))\n",
-    "            if len(samples) >= num_samples:\n",
-    "                break\n",
-    "        if len(samples) >= num_samples:\n",
-    "            break\n",
-    "\n",
-    "    # Plotting\n",
-    "    fig, axs = plt.subplots(len(samples), 3, figsize=(figsize[0], figsize[1] * len(samples)))\n",
-    "    if len(samples) == 1:\n",
-    "        axs = [axs]  # For consistent 2D indexing\n",
-    "\n",
-    "    fig.suptitle('Pix2Pix GAN Results: Font → Handwriting Translation', fontsize=16, fontweight='bold', y=0.98)\n",
-    "\n",
-    "    for row_idx, (input_img, target_img) in enumerate(samples):\n",
-    "        input_tensor = input_img.unsqueeze(0).to(device)\n",
-    "        with torch.no_grad():\n",
-    "            output_tensor = generator(input_tensor)\n",
-    "\n",
-    "        input_pil = to_pil(input_img.cpu())\n",
-    "        output_pil = to_pil(output_tensor.squeeze(0).cpu())\n",
-    "        target_pil = to_pil(target_img.cpu())\n",
-    "\n",
-    "        images = [input_pil, output_pil, target_pil]\n",
-    "        for col_idx, image in enumerate(images):\n",
-    "            axs[row_idx][col_idx].imshow(image, cmap='gray')\n",
-    "            axs[row_idx][col_idx].axis('off')\n",
-    "\n",
-    "            # Column titles only on the first row\n",
-    "            if row_idx == 0:\n",
-    "                if col_idx == 0:\n",
-    "                    axs[row_idx][col_idx].set_title(\"Input Font\", fontsize=12, fontweight='bold')\n",
-    "                elif col_idx == 1:\n",
-    "                    axs[row_idx][col_idx].set_title(\"Generated Handwriting\", fontsize=12, fontweight='bold', color='blue')\n",
-    "                elif col_idx == 2:\n",
-    "                    axs[row_idx][col_idx].set_title(\"Target Handwriting\", fontsize=12, fontweight='bold', color='green')\n",
-    "\n",
-    "    plt.tight_layout()\n",
-    "    plt.subplots_adjust(top=0.92, hspace=0.3, wspace=0.2)\n",
-    "    save_path = generator_path.replace('generator_final.pth', 'test_visualizations.png')\n",
-    "    plt.savefig(save_path)\n",
-    "    plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "ffab8617",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-07-27T06:01:45.406416Z",
-     "iopub.status.busy": "2025-07-27T06:01:45.406172Z",
-     "iopub.status.idle": "2025-07-27T06:01:47.364941Z",
-     "shell.execute_reply": "2025-07-27T06:01:47.364146Z",
-     "shell.execute_reply.started": "2025-07-27T06:01:45.406398Z"
-    },
-    "trusted": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using device: cuda\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x1200 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Assuming you already have your trained model and test_loader:\n",
-    "visualize_pix2pix_results(\"output/GAN_OUTPUT/generator_final.pth\", test_loader, num_samples=3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2d344156",
-   "metadata": {},
-   "source": [
-    "## Inference"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "16e4aeaa",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-07-27T06:02:04.775964Z",
-     "iopub.status.busy": "2025-07-27T06:02:04.775376Z",
-     "iopub.status.idle": "2025-07-27T06:02:04.799618Z",
-     "shell.execute_reply": "2025-07-27T06:02:04.798905Z",
-     "shell.execute_reply.started": "2025-07-27T06:02:04.775936Z"
-    },
-    "trusted": true
-   },
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "from PIL import Image, ImageDraw, ImageFont\n",
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib.font_manager as fm\n",
-    "import numpy as np\n",
-    "import io\n",
-    "from pathlib import Path\n",
-    "import os\n",
-    "from typing import List, Optional, Tuple\n",
-    "import matplotlib\n",
-    "\n",
-    "class GANInferencePipeline:\n",
-    "    def __init__(self, \n",
-    "                 generator_path: str,\n",
-    "                 custom_font_path: Optional[str] = None,\n",
-    "                 device: str = 'cuda' if torch.cuda.is_available() else 'cpu',\n",
-    "                 single_image_size: Tuple[int, int] = (128, 64),\n",
-    "                 grid_size: Tuple[int, int] = (4, 2),\n",
-    "                 base_fontsize: int = 45):\n",
-    "        \"\"\"\n",
-    "        Initialize the GAN inference pipeline.\n",
-    "        \n",
-    "        Args:\n",
-    "            generator_path: Path to the trained generator model\n",
-    "            custom_font_path: Path to custom font file (optional)\n",
-    "            device: Device to run inference on\n",
-    "            single_image_size: Size of individual word images (width, height)\n",
-    "            grid_size: Grid arrangement (rows, cols)\n",
-    "            base_fontsize: Base font size for rendering\n",
-    "        \"\"\"\n",
-    "        self.device = device\n",
-    "        self.single_image_size = single_image_size\n",
-    "        self.grid_size = grid_size\n",
-    "        self.base_fontsize = base_fontsize\n",
-    "        self.custom_font_path = custom_font_path\n",
-    "        \n",
-    "        # Load the generator model\n",
-    "        self.generator = self.load_generator(generator_path)\n",
-    "        \n",
-    "        # Setup matplotlib configuration\n",
-    "        self.setup_matplotlib_config()\n",
-    "        \n",
-    "        # Load custom font if provided\n",
-    "        self.custom_font_prop = self.load_custom_font()\n",
-    "        \n",
-    "        print(f\"GAN Inference Pipeline initialized on {device}\")\n",
-    "        print(f\"Single image size: {single_image_size}\")\n",
-    "        print(f\"Grid size: {grid_size[0]}x{grid_size[1]} = {grid_size[0]*grid_size[1]} images\")\n",
-    "        \n",
-    "    def load_generator(self, generator_path: str):\n",
-    "        \"\"\"Load the trained generator model.\"\"\"\n",
-    "        # You'll need to replace this with your actual UNetGenerator class\n",
-    "        # For now, I'll create a placeholder\n",
-    "        generator = UNetGenerator()  # Replace with your actual generator class\n",
-    "        generator.load_state_dict(torch.load(generator_path, map_location=self.device))\n",
-    "        generator.to(self.device)\n",
-    "        generator.eval()\n",
-    "        return generator\n",
-    "    \n",
-    "    def setup_matplotlib_config(self):\n",
-    "        \"\"\"Setup matplotlib configuration for text rendering.\"\"\"\n",
-    "        matplotlib_config = {\n",
-    "            'text.usetex': False,\n",
-    "            'mathtext.fontset': 'cm',\n",
-    "            'font.family': 'serif',\n",
-    "            'font.serif': ['Times New Roman'],\n",
-    "            'font.weight': 'bold',\n",
-    "            'mathtext.bf': 'bold',\n",
-    "        }\n",
-    "        matplotlib.rcParams.update(matplotlib_config)\n",
-    "    \n",
-    "    def load_custom_font(self):\n",
-    "        \"\"\"Load custom font if provided.\"\"\"\n",
-    "        if self.custom_font_path and os.path.exists(self.custom_font_path):\n",
-    "            try:\n",
-    "                custom_font_prop = fm.FontProperties(fname=self.custom_font_path)\n",
-    "                print(f\"Successfully loaded custom font: {self.custom_font_path}\")\n",
-    "                return custom_font_prop\n",
-    "            except Exception as e:\n",
-    "                print(f\"Warning: Could not load custom font {self.custom_font_path}: {str(e)}\")\n",
-    "                print(\"Falling back to default font configuration\")\n",
-    "        return None\n",
-    "    \n",
-    "    def render_single_word(self, word: str) -> Image.Image:\n",
-    "        \"\"\"\n",
-    "        Render a single word as an image.\n",
-    "        \n",
-    "        Args:\n",
-    "            word: The word to render\n",
-    "            \n",
-    "        Returns:\n",
-    "            PIL Image of the rendered word (grayscale)\n",
-    "        \"\"\"\n",
-    "        width, height = self.single_image_size\n",
-    "        \n",
-    "        # Create figure with exact pixel dimensions\n",
-    "        dpi = 150\n",
-    "        figsize = (width/dpi, height/dpi)\n",
-    "        fig = plt.figure(figsize=figsize, dpi=dpi, facecolor='white')\n",
-    "        \n",
-    "        # Create axis that fills the figure completely\n",
-    "        ax = fig.add_axes([0, 0, 1, 1])\n",
-    "        ax.set_xlim(0, 1)\n",
-    "        ax.set_ylim(0, 1)\n",
-    "        \n",
-    "        # Adjust fontsize based on word length\n",
-    "        length_adjustment = max(1, min(15, len(word))) / 7.0\n",
-    "        fontsize = int(self.base_fontsize / length_adjustment)\n",
-    "        \n",
-    "        # Prepare text formatting arguments\n",
-    "        text_kwargs = {\n",
-    "            'fontsize': fontsize,\n",
-    "            'ha': 'center',\n",
-    "            'va': 'center',\n",
-    "            'transform': ax.transAxes,\n",
-    "            'color': 'black'  # Ensure black text for good contrast\n",
-    "        }\n",
-    "        \n",
-    "        # Use custom font if available\n",
-    "        if self.custom_font_prop:\n",
-    "            text_kwargs['fontproperties'] = self.custom_font_prop\n",
-    "            display_text = word\n",
-    "        else:\n",
-    "            display_text = f\"$\\\\mathbf{{{word}}}$\"\n",
-    "            text_kwargs['fontweight'] = 'bold'\n",
-    "        \n",
-    "        # Render text at exact center\n",
-    "        ax.text(0.5, 0.5, display_text, **text_kwargs)\n",
-    "        \n",
-    "        # Remove all axes and ensure clean background\n",
-    "        ax.axis('off')\n",
-    "        ax.set_facecolor('white')\n",
-    "        \n",
-    "        # Save to buffer\n",
-    "        buf = io.BytesIO()\n",
-    "        fig.savefig(buf, format='png', bbox_inches='tight', \n",
-    "                    pad_inches=0, facecolor='white', edgecolor='none',\n",
-    "                    dpi=dpi)\n",
-    "        plt.close(fig)\n",
-    "        buf.seek(0)\n",
-    "        \n",
-    "        # Load and process the image\n",
-    "        rendered_img = Image.open(buf)\n",
-    "        rendered_img = rendered_img.convert('L')  # Convert to grayscale\n",
-    "        \n",
-    "        # Create final canvas with exact target dimensions (grayscale)\n",
-    "        final_img = Image.new('L', (width, height), 255)  # White background\n",
-    "        \n",
-    "        # Calculate scaling to fit the rendered text optimally\n",
-    "        rendered_width, rendered_height = rendered_img.size\n",
-    "        \n",
-    "        # Scale to use 90% of the canvas for better visual appearance\n",
-    "        scale_w = (width * 0.9) / rendered_width\n",
-    "        scale_h = (height * 0.9) / rendered_height\n",
-    "        scale = min(scale_w, scale_h)\n",
-    "        \n",
-    "        new_width = int(rendered_width * scale)\n",
-    "        new_height = int(rendered_height * scale)\n",
-    "        \n",
-    "        # Resize with high quality resampling\n",
-    "        resample_method = Image.LANCZOS if hasattr(Image, 'LANCZOS') else Image.ANTIALIAS\n",
-    "        resized_img = rendered_img.resize((new_width, new_height), resample_method)\n",
-    "        \n",
-    "        # Calculate exact center position\n",
-    "        x_offset = (width - new_width) // 2\n",
-    "        y_offset = (height - new_height) // 2\n",
-    "        \n",
-    "        # Paste the resized image at the center\n",
-    "        final_img.paste(resized_img, (x_offset, y_offset))\n",
-    "        \n",
-    "        return final_img\n",
-    "    \n",
-    "    def create_grid_from_words(self, words: List[str]) -> Image.Image:\n",
-    "        \"\"\"\n",
-    "        Create a grid image from a list of words.\n",
-    "        \n",
-    "        Args:\n",
-    "            words: List of words to render and arrange in grid\n",
-    "            \n",
-    "        Returns:\n",
-    "            PIL Image of the grid (grayscale)\n",
-    "        \"\"\"\n",
-    "        rows, cols = self.grid_size\n",
-    "        expected_words = rows * cols\n",
-    "        \n",
-    "        # Pad or truncate words list to match grid size\n",
-    "        if len(words) < expected_words:\n",
-    "            # Pad with empty strings\n",
-    "            words = words + [''] * (expected_words - len(words))\n",
-    "        elif len(words) > expected_words:\n",
-    "            # Truncate to fit grid\n",
-    "            words = words[:expected_words]\n",
-    "            print(f\"Warning: Truncated word list to {expected_words} words to fit {rows}x{cols} grid\")\n",
-    "        \n",
-    "        # Render individual word images\n",
-    "        word_images = []\n",
-    "        for word in words:\n",
-    "            if word.strip():  # Only render non-empty words\n",
-    "                img = self.render_single_word(word)\n",
-    "            else:\n",
-    "                # Create empty image for empty words (grayscale)\n",
-    "                img = Image.new('L', self.single_image_size, 255)  # White background\n",
-    "            word_images.append(img)\n",
-    "        \n",
-    "        # Create grid\n",
-    "        single_width, single_height = self.single_image_size\n",
-    "        grid_width = cols * single_width\n",
-    "        grid_height = rows * single_height\n",
-    "        \n",
-    "        grid_image = Image.new('L', (grid_width, grid_height), 255)  # Grayscale grid\n",
-    "        \n",
-    "        # Paste images into grid\n",
-    "        for i, img in enumerate(word_images):\n",
-    "            row = i // cols\n",
-    "            col = i % cols\n",
-    "            \n",
-    "            x = col * single_width\n",
-    "            y = row * single_height\n",
-    "            \n",
-    "            grid_image.paste(img, (x, y))\n",
-    "        \n",
-    "        return grid_image\n",
-    "    \n",
-    "    def preprocess_image(self, image: Image.Image) -> torch.Tensor:\n",
-    "        \"\"\"\n",
-    "        Preprocess image for GAN input.\n",
-    "        \n",
-    "        Args:\n",
-    "            image: PIL Image to preprocess (grayscale)\n",
-    "            \n",
-    "        Returns:\n",
-    "            Preprocessed tensor ready for GAN (1-channel)\n",
-    "        \"\"\"\n",
-    "        # Convert to numpy array\n",
-    "        image_array = np.array(image)\n",
-    "        \n",
-    "        # Convert to tensor\n",
-    "        image_tensor = torch.from_numpy(image_array).float()\n",
-    "        \n",
-    "        # Add channel dimension (HW -> CHW) for grayscale\n",
-    "        image_tensor = image_tensor.unsqueeze(0)\n",
-    "        \n",
-    "        # Normalize to [-1, 1] range (assuming input images are in [0, 255])\n",
-    "        image_tensor = (image_tensor / 127.5) - 1.0\n",
-    "        \n",
-    "        # Add batch dimension\n",
-    "        image_tensor = image_tensor.unsqueeze(0)\n",
-    "        \n",
-    "        return image_tensor.to(self.device)\n",
-    "    \n",
-    "    def postprocess_output(self, output_tensor: torch.Tensor) -> Image.Image:\n",
-    "        \"\"\"\n",
-    "        Convert GAN output tensor back to PIL Image.\n",
-    "        \n",
-    "        Args:\n",
-    "            output_tensor: Output tensor from GAN (1-channel or 3-channel)\n",
-    "            \n",
-    "        Returns:\n",
-    "            PIL Image\n",
-    "        \"\"\"\n",
-    "        # Remove batch dimension and move to CPU\n",
-    "        output_tensor = output_tensor.squeeze(0).cpu()\n",
-    "        \n",
-    "        # Denormalize from [-1, 1] to [0, 255]\n",
-    "        output_tensor = (output_tensor + 1.0) * 127.5\n",
-    "        output_tensor = torch.clamp(output_tensor, 0, 255)\n",
-    "        \n",
-    "        # Convert to numpy\n",
-    "        output_array = output_tensor.numpy().astype(np.uint8)\n",
-    "        \n",
-    "        # Handle different channel configurations\n",
-    "        if output_tensor.shape[0] == 1:  # Single channel (grayscale)\n",
-    "            output_array = output_array.squeeze(0)  # Remove channel dimension\n",
-    "            return Image.fromarray(output_array, mode='L')\n",
-    "        elif output_tensor.shape[0] == 3:  # Three channels (RGB)\n",
-    "            output_array = output_array.transpose(1, 2, 0)  # CHW -> HWC\n",
-    "            return Image.fromarray(output_array, mode='RGB')\n",
-    "        else:\n",
-    "            raise ValueError(f\"Unsupported number of channels: {output_tensor.shape[0]}\")\n",
-    "    \n",
-    "    def generate_handwriting(self, words: List[str]) -> Tuple[Image.Image, Image.Image]:\n",
-    "        \"\"\"\n",
-    "        Generate handwriting from a list of words.\n",
-    "        \n",
-    "        Args:\n",
-    "            words: List of words to convert to handwriting\n",
-    "            \n",
-    "        Returns:\n",
-    "            Tuple of (input_grid, output_handwriting) PIL Images\n",
-    "        \"\"\"\n",
-    "        # Create grid from words (grayscale)\n",
-    "        input_grid = self.create_grid_from_words(words)\n",
-    "        \n",
-    "        # Preprocess for GAN\n",
-    "        input_tensor = self.preprocess_image(input_grid)\n",
-    "        \n",
-    "        # Generate handwriting using GAN\n",
-    "        with torch.no_grad():\n",
-    "            output_tensor = self.generator(input_tensor)\n",
-    "        \n",
-    "        # Postprocess output\n",
-    "        output_handwriting = self.postprocess_output(output_tensor)\n",
-    "        \n",
-    "        return input_grid, output_handwriting\n",
-    "    \n",
-    "    def save_results(self, \n",
-    "                    input_grid: Image.Image, \n",
-    "                    output_handwriting: Image.Image,\n",
-    "                    output_dir: str = \"inference_results\",\n",
-    "                    filename_prefix: str = \"result\"):\n",
-    "        \"\"\"\n",
-    "        Save the input and output images.\n",
-    "        \n",
-    "        Args:\n",
-    "            input_grid: Input grid image\n",
-    "            output_handwriting: Generated handwriting image\n",
-    "            output_dir: Directory to save results\n",
-    "            filename_prefix: Prefix for saved filenames\n",
-    "        \"\"\"\n",
-    "        os.makedirs(output_dir, exist_ok=True)\n",
-    "        \n",
-    "        input_path = os.path.join(output_dir, f\"{filename_prefix}_input.png\")\n",
-    "        output_path = os.path.join(output_dir, f\"{filename_prefix}_output.png\")\n",
-    "        \n",
-    "        input_grid.save(input_path)\n",
-    "        output_handwriting.save(output_path)\n",
-    "        \n",
-    "        print(f\"Results saved:\")\n",
-    "        print(f\"  Input: {input_path}\")\n",
-    "        print(f\"  Output: {output_path}\")\n",
-    "        \n",
-    "        return input_path, output_path\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "9931db7b",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-07-27T06:02:09.073908Z",
-     "iopub.status.busy": "2025-07-27T06:02:09.073596Z",
-     "iopub.status.idle": "2025-07-27T06:02:10.279096Z",
-     "shell.execute_reply": "2025-07-27T06:02:10.278380Z",
-     "shell.execute_reply.started": "2025-07-27T06:02:09.073887Z"
-    },
-    "trusted": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Successfully loaded custom font: fonts/RomanAntique.ttf\n",
-      "GAN Inference Pipeline initialized on cuda\n",
-      "Single image size: (128, 64)\n",
-      "Grid size: 4x2 = 8 images\n",
-      "Results saved:\n",
-      "  Input: output/GAN_OUTPUT/inference_results\\sample_input.png\n",
-      "  Output: output/GAN_OUTPUT/inference_results\\sample_output.png\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "('output/GAN_OUTPUT/inference_results\\\\sample_input.png',\n",
-       " 'output/GAN_OUTPUT/inference_results\\\\sample_output.png')"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Initialize the pipeline\n",
-    "pipeline = GANInferencePipeline(\n",
-    "    generator_path=\"output/GAN_OUTPUT/generator_final.pth\",  \n",
-    "    custom_font_path=\"fonts/RomanAntique.ttf\", \n",
-    "    device='cuda' if torch.cuda.is_available() else 'cpu'\n",
-    ")\n",
-    "\n",
-    "# Example words to convert\n",
-    "words = [\"confieso\", \"los\", \"a\",\"y\", \"noticias\",\"mucho\",'la','todo']\n",
-    "\n",
-    "# Generate handwriting\n",
-    "input_grid, output_handwriting = pipeline.generate_handwriting(words)\n",
-    "\n",
-    "# Save results\n",
-    "pipeline.save_results(input_grid, output_handwriting, \n",
-    "                        output_dir=\"output/GAN_OUTPUT/inference_results\", \n",
-    "                        filename_prefix=\"sample\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "4ced859b",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-07-27T06:02:12.379417Z",
-     "iopub.status.busy": "2025-07-27T06:02:12.379152Z",
-     "iopub.status.idle": "2025-07-27T06:02:12.734980Z",
-     "shell.execute_reply": "2025-07-27T06:02:12.734218Z",
-     "shell.execute_reply.started": "2025-07-27T06:02:12.379396Z"
-    },
-    "trusted": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1500x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axes = plt.subplots(1, 2, figsize=(15, 6))\n",
-    "\n",
-    "axes[0].imshow(input_grid, cmap='gray')\n",
-    "axes[0].set_title(\"Input Grid (Font)\")\n",
-    "axes[0].axis('off')\n",
-    "\n",
-    "axes[1].imshow(output_handwriting, cmap='gray')  # Added cmap='gray'\n",
-    "axes[1].set_title(\"Generated Handwriting\")\n",
-    "axes[1].axis('off')\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5f2b339b",
-   "metadata": {},
-   "source": [
-    "# Degrading Pages using algorithmic approach"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3eafe496",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import random\n",
-    "from PIL import Image, ImageDraw, ImageFont, ImageFilter, ImageEnhance\n",
-    "import numpy as np\n",
-    "import cv2\n",
-    "import textwrap\n",
-    "from tqdm import tqdm\n",
-    "\n",
-    "class SyntheticPageGenerator:\n",
-    "    def __init__(self, font_path, background_dir, text_dir, output_dir, bbox_dir,\n",
-    "            font_size=None, line_spacing_ratio=0.4, margin_ratio=0.08):\n",
-    "            self.font_path = font_path\n",
-    "            self.background_dir = background_dir\n",
-    "            self.text_dir = text_dir\n",
-    "            self.output_dir = output_dir\n",
-    "            self.bbox_dir = bbox_dir\n",
-    "            self.font_size = font_size\n",
-    "            self.line_spacing_ratio = line_spacing_ratio\n",
-    "            self.margin_ratio = margin_ratio\n",
-    "            os.makedirs(output_dir, exist_ok=True)\n",
-    "            os.makedirs(bbox_dir, exist_ok=True)\n",
-    "\n",
-    "    def get_random_background(self):\n",
-    "        bg_files = [os.path.join(self.background_dir, f) for f in os.listdir(self.background_dir) if f.lower().endswith(('.jpg','.jpeg', '.png'))]\n",
-    "        return Image.open(random.choice(bg_files)).convert(\"RGB\")\n",
-    "\n",
-    "    def get_random_text_file(self):\n",
-    "        \"\"\"Get a random text file for bleed-through effect\"\"\"\n",
-    "        text_files = [f for f in os.listdir(self.text_dir) if f.lower().endswith('.txt')]\n",
-    "        if not text_files:\n",
-    "            return None\n",
-    "        \n",
-    "        selected_file = random.choice(text_files)\n",
-    "        try:\n",
-    "            with open(os.path.join(self.text_dir, selected_file), 'r', encoding='utf-8') as f:\n",
-    "                return f.read()\n",
-    "        except:\n",
-    "            return None\n",
-    "\n",
-    "    def create_bleed_text_image(self, text, image_size, font_size, line_spacing, padding):\n",
-    "        \"\"\"Create a text image for bleed-through effect with improved realism\"\"\"\n",
-    "        if not text:\n",
-    "            return None\n",
-    "            \n",
-    "        # Create a blank image for the bleed text (same size as cropped background)\n",
-    "        bleed_img = Image.new('L', image_size, 255)  # White background\n",
-    "        cropped_width, cropped_height = image_size\n",
-    "            \n",
-    "        try:\n",
-    "            font = ImageFont.truetype(self.font_path, font_size)\n",
-    "        except:\n",
-    "            font = ImageFont.load_default()\n",
-    "        \n",
-    "        # Prepare text lines (same as main text processing)\n",
-    "        lines = text.splitlines()\n",
-    "        \n",
-    "        # Calculate line widths (same as main text)\n",
-    "        line_widths = []\n",
-    "        draw_temp = ImageDraw.Draw(bleed_img)\n",
-    "        \n",
-    "        for line in lines:\n",
-    "            if not line.strip():\n",
-    "                line_widths.append(0)\n",
-    "                continue\n",
-    "            try:\n",
-    "                bbox = draw_temp.textbbox((0, 0), line, font=font)\n",
-    "                line_width = bbox[2] - bbox[0]\n",
-    "            except AttributeError:\n",
-    "                line_width, _ = draw_temp.textsize(line, font=font)\n",
-    "            \n",
-    "            line_widths.append(line_width)\n",
-    "        \n",
-    "        # Draw text with EXACT same layout as main text but with variations\n",
-    "        draw = ImageDraw.Draw(bleed_img)\n",
-    "        current_y = padding  # Start from the same padding as main text\n",
-    "\n",
-    "        for i, line in enumerate(lines):\n",
-    "            if not line.strip():\n",
-    "                current_y += line_spacing\n",
-    "                continue\n",
-    "\n",
-    "            # Center each line horizontally within the cropped area (SAME as main text)\n",
-    "            line_width = line_widths[i]\n",
-    "            # start_x = (cropped_width - line_width) // 2\n",
-    "            start_x = padding\n",
-    "\n",
-    "\n",
-    "            # Enhanced random offset for bleed text to create misalignment\n",
-    "            x_offset = random.randint(-3, 3)\n",
-    "            y_offset = random.randint(-2, 2)\n",
-    "\n",
-    "            # Vary the intensity of different words/characters for more realistic bleed\n",
-    "            words = line.split()\n",
-    "            current_x = start_x + x_offset\n",
-    "            \n",
-    "            for word_idx, word in enumerate(words):\n",
-    "                # Different bleed intensity for each word\n",
-    "                # word_intensity = random.randint(60, 120)  # Varying gray levels\n",
-    "                word_intensity = random.randint(30, 90)\n",
-    "                \n",
-    "                # Draw word with random micro-offsets\n",
-    "                word_x_offset = random.randint(-1, 1)\n",
-    "                word_y_offset = random.randint(-1, 1)\n",
-    "                \n",
-    "                draw.text((current_x + word_x_offset, current_y + y_offset + word_y_offset), \n",
-    "                         word, font=font, fill=word_intensity)\n",
-    "                \n",
-    "                # Calculate word width for next word positioning\n",
-    "                try:\n",
-    "                    word_bbox = draw_temp.textbbox((0, 0), word + \" \", font=font)\n",
-    "                    word_width = word_bbox[2] - word_bbox[0]\n",
-    "                except AttributeError:\n",
-    "                    word_width, _ = draw_temp.textsize(word + \" \", font=font)\n",
-    "                \n",
-    "                current_x += word_width\n",
-    "            \n",
-    "            current_y += font_size + line_spacing\n",
-    "            \n",
-    "        return bleed_img\n",
-    "\n",
-    "    def create_recto_verso_bleed_map(self, recto_image, verso_text_image, bleed_intensity):\n",
-    "        \"\"\"Create enhanced bleed intensity map with more realistic patterns\"\"\"\n",
-    "        if verso_text_image is None:\n",
-    "            return np.ones(recto_image.size[::-1], dtype=np.float32) * 255, np.zeros(recto_image.size[::-1], dtype=np.float32)\n",
-    "        \n",
-    "        # Convert PIL to numpy\n",
-    "        verso_array = np.array(verso_text_image)\n",
-    "        \n",
-    "        # Invert so text areas are high intensity\n",
-    "        verso_inverted = 255 - verso_array\n",
-    "        \n",
-    "        # Create base bleed intensity map\n",
-    "        bleed_intensity_map = verso_inverted.astype(np.float32) / 255.0\n",
-    "        \n",
-    "        # Add multiple layers of noise for more realistic texture\n",
-    "        # Fine grain noise\n",
-    "        fine_noise = np.random.normal(0, 0.05, bleed_intensity_map.shape)\n",
-    "        # Medium grain noise\n",
-    "        medium_noise = np.random.normal(0, 0.08, bleed_intensity_map.shape)\n",
-    "        # Coarse noise for paper fiber variations\n",
-    "        coarse_noise = np.random.normal(0, 0.12, bleed_intensity_map.shape)\n",
-    "        \n",
-    "        # Combine noise layers\n",
-    "        combined_noise = fine_noise * 0.4 + medium_noise * 0.4 + coarse_noise * 0.2\n",
-    "        bleed_intensity_map = np.clip(bleed_intensity_map + combined_noise, 0, 1)\n",
-    "        \n",
-    "        # Apply multiple blur stages to simulate ink spreading through paper fibers\n",
-    "        # Initial blur for immediate ink spread\n",
-    "        bleed_intensity_map = cv2.GaussianBlur(bleed_intensity_map, (5, 5), 1.2)\n",
-    "        \n",
-    "        # Secondary blur for deeper penetration\n",
-    "        secondary_blur = cv2.GaussianBlur(bleed_intensity_map, (9, 9), 2.5)\n",
-    "        bleed_intensity_map = bleed_intensity_map * 0.7 + secondary_blur * 0.3\n",
-    "        \n",
-    "        # Add directional bias to simulate paper grain\n",
-    "        kernel_x = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]) * 0.1\n",
-    "        kernel_y = np.array([[-1, -2, -1], [0, 0, 0], [1, 2, 1]]) * 0.1\n",
-    "        \n",
-    "        grad_x = cv2.filter2D(bleed_intensity_map, -1, kernel_x)\n",
-    "        grad_y = cv2.filter2D(bleed_intensity_map, -1, kernel_y)\n",
-    "        \n",
-    "        # Apply subtle directional bleeding\n",
-    "        bleed_intensity_map += (grad_x * 0.05 + grad_y * 0.03)\n",
-    "        bleed_intensity_map = np.clip(bleed_intensity_map, 0, 1)\n",
-    "        \n",
-    "        # Scale by overall bleed intensity\n",
-    "        bleed_intensity_map *= bleed_intensity\n",
-    "        \n",
-    "        # Create enhanced blurred verso for bleeding with multiple passes\n",
-    "        verso_blurred = verso_inverted.astype(np.float32)\n",
-    "        verso_blurred = cv2.GaussianBlur(verso_blurred, (3, 3), 0.8)\n",
-    "        verso_blurred = cv2.GaussianBlur(verso_blurred, (7, 7), 1.8)\n",
-    "        \n",
-    "        return verso_blurred, bleed_intensity_map\n",
-    "   \n",
-    "    def apply_recto_verso_bleed(self, recto_image, verso_text_image, bleed_intensity_range=(0.7, 0.9)):\n",
-    "        \"\"\"Apply enhanced ink bleed without converting to grayscale (preserve color)\"\"\"\n",
-    "        bleed_intensity = random.uniform(bleed_intensity_range[0], bleed_intensity_range[1])\n",
-    "\n",
-    "        verso_blurred, bleed_intensity_map = self.create_recto_verso_bleed_map(\n",
-    "            recto_image, verso_text_image, bleed_intensity\n",
-    "        )\n",
-    "\n",
-    "        # Convert PIL to numpy RGB array\n",
-    "        recto_array = np.array(recto_image.convert(\"RGB\"))\n",
-    "        verso_blurred = verso_blurred.astype(np.float32)\n",
-    "        bleed_intensity_map = bleed_intensity_map.astype(np.float32)\n",
-    "\n",
-    "        result = recto_array.astype(np.float32)\n",
-    "\n",
-    "        # Prepare different contribution layers (grayscale, but to be applied per-channel)\n",
-    "        strong_bleed_areas = bleed_intensity_map > 0.15\n",
-    "        medium_bleed_areas = (bleed_intensity_map > 0.05) & (bleed_intensity_map <= 0.15)\n",
-    "        weak_bleed_areas = (bleed_intensity_map > 0.02) & (bleed_intensity_map <= 0.05)\n",
-    "\n",
-    "        if np.any(strong_bleed_areas):\n",
-    "            strong_contribution = verso_blurred[strong_bleed_areas] * bleed_intensity_map[strong_bleed_areas]\n",
-    "            strong_contribution = strong_contribution * 0.5 + 180 * 0.5\n",
-    "            alpha_strong = bleed_intensity_map[strong_bleed_areas] * 0.9\n",
-    "            for c in range(3):\n",
-    "                result[..., c][strong_bleed_areas] = (\n",
-    "                    result[..., c][strong_bleed_areas] * (1 - alpha_strong) +\n",
-    "                    strong_contribution * alpha_strong\n",
-    "                )\n",
-    "\n",
-    "        if np.any(medium_bleed_areas):\n",
-    "            medium_contribution = verso_blurred[medium_bleed_areas] * bleed_intensity_map[medium_bleed_areas]\n",
-    "            medium_contribution = medium_contribution * 0.4 + 200 * 0.6\n",
-    "            alpha_medium = bleed_intensity_map[medium_bleed_areas] * 0.6\n",
-    "            for c in range(3):\n",
-    "                result[..., c][medium_bleed_areas] = (\n",
-    "                    result[..., c][medium_bleed_areas] * (1 - alpha_medium) +\n",
-    "                    medium_contribution * alpha_medium\n",
-    "                )\n",
-    "\n",
-    "        if np.any(weak_bleed_areas):\n",
-    "            weak_contribution = verso_blurred[weak_bleed_areas] * bleed_intensity_map[weak_bleed_areas]\n",
-    "            weak_contribution = weak_contribution * 0.3 + 220 * 0.7\n",
-    "            alpha_weak = bleed_intensity_map[weak_bleed_areas] * 0.3\n",
-    "            for c in range(3):\n",
-    "                result[..., c][weak_bleed_areas] = (\n",
-    "                    result[..., c][weak_bleed_areas] * (1 - alpha_weak) +\n",
-    "                    weak_contribution * alpha_weak\n",
-    "                )\n",
-    "\n",
-    "        # Clip and convert back to uint8\n",
-    "        result = np.clip(result, 0, 255).astype(np.uint8)\n",
-    "\n",
-    "        return Image.fromarray(result), bleed_intensity\n",
-    "\n",
-    "    def add_stains_and_spots(self, image):\n",
-    "        \"\"\"Add random stains and age spots to the image with improved variety\"\"\"\n",
-    "        draw = ImageDraw.Draw(image)\n",
-    "        width, height = image.size\n",
-    "        \n",
-    "        # Add random stains with varying types\n",
-    "        num_stains = random.randint(2, 6)\n",
-    "        for _ in range(num_stains):\n",
-    "            # Random stain position and size\n",
-    "            x = random.randint(0, width)\n",
-    "            y = random.randint(0, height)\n",
-    "            radius = random.randint(15, 60)\n",
-    "            \n",
-    "            # Enhanced brownish stain colors\n",
-    "            brown_variations = [\n",
-    "                (139, 117, 93),   # Dark brown\n",
-    "                (160, 142, 116),  # Medium brown  \n",
-    "                (101, 84, 63),    # Very dark brown\n",
-    "                (121, 101, 75),   # Another brown variant\n",
-    "                (145, 125, 95),   # Light brown\n",
-    "                (85, 70, 50),     # Very dark coffee\n",
-    "            ]\n",
-    "            \n",
-    "            stain_color = random.choice(brown_variations)\n",
-    "            # Variable transparency\n",
-    "            alpha = random.randint(15, 45)\n",
-    "            \n",
-    "            # Create different stain shapes\n",
-    "            stain_img = Image.new('RGBA', image.size, (0, 0, 0, 0))\n",
-    "            stain_draw = ImageDraw.Draw(stain_img)\n",
-    "            \n",
-    "            # Randomly choose stain shape\n",
-    "            if random.random() < 0.7:  # Circular stains\n",
-    "                stain_draw.ellipse([x-radius, y-radius, x+radius, y+radius], \n",
-    "                                 fill=(*stain_color, alpha))\n",
-    "            else:  # Irregular polygon stains\n",
-    "                points = []\n",
-    "                for i in range(6):\n",
-    "                    angle = i * 60 + random.randint(-15, 15)\n",
-    "                    r = radius + random.randint(-radius//3, radius//3)\n",
-    "                    px = x + r * np.cos(np.radians(angle))\n",
-    "                    py = y + r * np.sin(np.radians(angle))\n",
-    "                    points.append((px, py))\n",
-    "                stain_draw.polygon(points, fill=(*stain_color, alpha))\n",
-    "            \n",
-    "            # Variable blur for natural look\n",
-    "            blur_radius = random.uniform(1.5, 4.0)\n",
-    "            stain_img = stain_img.filter(ImageFilter.GaussianBlur(radius=blur_radius))\n",
-    "            \n",
-    "            # Composite the stain onto the main image\n",
-    "            image = Image.alpha_composite(image.convert('RGBA'), stain_img).convert('RGB')\n",
-    "        \n",
-    "        return image\n",
-    "\n",
-    "    def add_noise_and_grain(self, image):\n",
-    "        \"\"\"Add enhanced paper grain and noise for aged texture\"\"\"\n",
-    "        img_array = np.array(image)\n",
-    "        \n",
-    "        # Multi-scale noise for realistic paper texture\n",
-    "        # Fine grain\n",
-    "        fine_noise = np.random.normal(0, 4, img_array.shape).astype(np.int16)\n",
-    "        # Medium grain\n",
-    "        medium_noise = np.random.normal(0, 6, img_array.shape).astype(np.int16)\n",
-    "        # Coarse grain\n",
-    "        coarse_noise = np.random.normal(0, 8, img_array.shape).astype(np.int16)\n",
-    "        \n",
-    "        # Combine noise layers\n",
-    "        combined_noise = (fine_noise * 0.5 + medium_noise * 0.3 + coarse_noise * 0.2)\n",
-    "        noisy_img = np.clip(img_array.astype(np.int16) + combined_noise, 0, 255).astype(np.uint8)\n",
-    "        \n",
-    "        return Image.fromarray(noisy_img)\n",
-    "\n",
-    "    def fade_edges(self, image):\n",
-    "        \"\"\"Add enhanced edge fading/vignetting effect\"\"\"\n",
-    "        width, height = image.size\n",
-    "        \n",
-    "        # Create a more sophisticated gradient mask\n",
-    "        mask = Image.new('L', (width, height), 255)\n",
-    "        mask_array = np.array(mask)\n",
-    "        \n",
-    "        # Create radial gradient from center\n",
-    "        center_x, center_y = width // 2, height // 2\n",
-    "        max_distance = np.sqrt(center_x**2 + center_y**2)\n",
-    "        \n",
-    "        for y in range(height):\n",
-    "            for x in range(width):\n",
-    "                distance = np.sqrt((x - center_x)**2 + (y - center_y)**2)\n",
-    "                # Non-linear falloff for more realistic aging\n",
-    "                falloff = 1 - (distance / max_distance) ** 1.5\n",
-    "                falloff = max(0.7, falloff)  # Don't fade too much\n",
-    "                mask_array[y, x] = int(255 * falloff)\n",
-    "        \n",
-    "        mask = Image.fromarray(mask_array)\n",
-    "        \n",
-    "        # Apply gaussian blur to smooth the mask\n",
-    "        mask = mask.filter(ImageFilter.GaussianBlur(radius=25))\n",
-    "        \n",
-    "        # Apply the mask with aged paper color\n",
-    "        aged_paper_colors = [(240, 230, 210), (235, 225, 200), (245, 235, 220)]\n",
-    "        faded = Image.new('RGB', image.size, random.choice(aged_paper_colors))\n",
-    "        return Image.composite(image, faded, mask)\n",
-    "\n",
-    "    def add_crumple_effect_to_page(self,pil_img, strength=5, noise_scale=1):\n",
-    "\n",
-    "        img = np.array(pil_img.convert(\"RGB\"))\n",
-    "        h, w = img.shape[:2]\n",
-    "\n",
-    "        noise = cv2.resize(\n",
-    "            np.random.randn(h // noise_scale, w // noise_scale).astype(np.float32),\n",
-    "            (w, h),\n",
-    "            interpolation=cv2.INTER_CUBIC\n",
-    "        )\n",
-    "        noise = cv2.GaussianBlur(noise, (0, 0), sigmaX=10, sigmaY=10)\n",
-    "\n",
-    "        # Normalize to [-1, 1]\n",
-    "        disp = (noise - noise.min()) / (noise.max() - noise.min())\n",
-    "        disp = (disp - 0.5) * 2.0\n",
-    "\n",
-    "        # Create remap coordinates\n",
-    "        map_x, map_y = np.meshgrid(np.arange(w), np.arange(h))\n",
-    "        map_x = (map_x + strength * disp).astype(np.float32)\n",
-    "        map_y = (map_y + strength * disp).astype(np.float32)\n",
-    "\n",
-    "        # Warp image using the displacement map\n",
-    "        warped = cv2.remap(img, map_x, map_y, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT)\n",
-    "\n",
-    "        # Convert back to PIL and return\n",
-    "        return Image.fromarray(warped)\n",
-    "  \n",
-    "    def get_aged_text_color(self):\n",
-    "        \"\"\"Return enhanced variety of aged text colors\"\"\"\n",
-    "        aged_colors = [\n",
-    "            (40, 35, 30),    # Dark brownish\n",
-    "            (55, 45, 35),    # Medium brown\n",
-    "            (45, 40, 35),    # Dark grayish brown\n",
-    "            (50, 40, 30),    # Another brown variant\n",
-    "            (35, 30, 25),    # Very dark brown\n",
-    "            (60, 50, 40),    # Lighter brown\n",
-    "            (48, 42, 38),    # Balanced brown-gray\n",
-    "        ]\n",
-    "        return random.choice(aged_colors)\n",
-    "\n",
-    "    def age_background(self, image):\n",
-    "        \"\"\"Apply aging effects to make the background look old and weathered\"\"\"\n",
-    "        # Convert to numpy array for easier manipulation\n",
-    "        img_array = np.array(image)\n",
-    "        \n",
-    "        # Enhanced sepia tone with slight variations\n",
-    "        sepia_weights = np.array([\n",
-    "            [0.393, 0.769, 0.189],  # Red channel\n",
-    "            [0.349, 0.686, 0.168],  # Green channel  \n",
-    "            [0.272, 0.534, 0.131]   # Blue channel\n",
-    "        ])\n",
-    "        \n",
-    "        # Add slight random variation to sepia\n",
-    "        variation = random.uniform(0.95, 1.05)\n",
-    "        sepia_weights *= variation\n",
-    "        \n",
-    "        sepia_img = img_array @ sepia_weights.T\n",
-    "        sepia_img = np.clip(sepia_img, 0, 255).astype(np.uint8)\n",
-    "        aged_image = Image.fromarray(sepia_img)\n",
-    "        \n",
-    "        # Variable saturation reduction\n",
-    "        enhancer = ImageEnhance.Color(aged_image)\n",
-    "        saturation_factor = random.uniform(0.5, 0.7)\n",
-    "        aged_image = enhancer.enhance(saturation_factor)\n",
-    "        \n",
-    "        # Variable brightness reduction\n",
-    "        enhancer = ImageEnhance.Brightness(aged_image)\n",
-    "        brightness_factor = random.uniform(0.8, 0.9)\n",
-    "        aged_image = enhancer.enhance(brightness_factor)\n",
-    "        \n",
-    "        # Variable contrast reduction\n",
-    "        enhancer = ImageEnhance.Contrast(aged_image)\n",
-    "        contrast_factor = random.uniform(0.75, 0.85)\n",
-    "        aged_image = enhancer.enhance(contrast_factor)\n",
-    "        \n",
-    "        return aged_image\n",
-    "\n",
-    "    def render_degraded_text(self, text, font, text_color):\n",
-    "        \"\"\"Render text with realistic faded/flaked ink degradation (minimal effect)\"\"\"\n",
-    "        # Get text size and prepare RGBA canvas\n",
-    "        bbox = font.getbbox(text)\n",
-    "        width = bbox[2] - bbox[0]\n",
-    "        height = bbox[3] - bbox[1]\n",
-    "        text_img = Image.new(\"RGBA\", (width + 10, height + 10), (0, 0, 0, 0))\n",
-    "        \n",
-    "        draw = ImageDraw.Draw(text_img)\n",
-    "        draw.text((5, 5), text, font=font, fill=text_color)\n",
-    "\n",
-    "        # Convert to numpy\n",
-    "        text_np = np.array(text_img)\n",
-    "        alpha = text_np[..., 3].astype(np.float32)\n",
-    "\n",
-    "        # (1) Random light ink fade (multiplicative noise)\n",
-    "        fade_strength = np.random.uniform(0.85, 1.0, alpha.shape)\n",
-    "        alpha *= fade_strength\n",
-    "\n",
-    "        # (2) Tiny dropout spots (ink flaking)\n",
-    "        flake_mask = np.random.rand(*alpha.shape) < 0.01 \n",
-    "        alpha[flake_mask] *= np.random.uniform(0.1, 0.3)  \n",
-    "\n",
-    "        # Reapply modified alpha\n",
-    "        text_np[..., 3] = np.clip(alpha, 0, 255).astype(np.uint8)\n",
-    "        return Image.fromarray(text_np)\n",
-    "\n",
-    "    def draw_centered_text_on_image(self, background, text, progress_bar=None):\n",
-    "        page_width, page_height = background.size\n",
-    "        dynamic_font_size = int(page_height / 40) if self.font_size is None else self.font_size\n",
-    "        font = ImageFont.truetype(self.font_path, dynamic_font_size)\n",
-    "        line_spacing = int(dynamic_font_size * self.line_spacing_ratio)\n",
-    "        lines = text.splitlines()\n",
-    "        text_color = self.get_aged_text_color()\n",
-    "\n",
-    "        # Calculate line widths and total text block dimensions\n",
-    "        line_widths = []\n",
-    "        max_width = 0\n",
-    "        total_height = 0\n",
-    "        for line in lines:\n",
-    "            if not line.strip():\n",
-    "                line_widths.append(0)\n",
-    "                total_height += line_spacing\n",
-    "                continue\n",
-    "            try:\n",
-    "                bbox = ImageDraw.Draw(background).textbbox((0, 0), line, font=font)\n",
-    "                line_width = bbox[2] - bbox[0]\n",
-    "            except AttributeError:\n",
-    "                line_width, _ = ImageDraw.Draw(background).textsize(line, font=font)\n",
-    "            line_widths.append(line_width)\n",
-    "            max_width = max(max_width, line_width)\n",
-    "            total_height += dynamic_font_size + line_spacing\n",
-    "\n",
-    "        if lines:\n",
-    "            total_height -= line_spacing\n",
-    "\n",
-    "        padding = int(0.05 * max(page_width, page_height))\n",
-    "        cropped_width = max_width + 2 * padding\n",
-    "        cropped_height = total_height + 2 * padding\n",
-    "        bg_crop = background.resize((cropped_width, cropped_height))\n",
-    "\n",
-    "        if progress_bar:\n",
-    "            progress_bar.set_description(\"Applying aging effects\")\n",
-    "\n",
-    "        aged_bg = self.add_stains_and_spots(bg_crop)\n",
-    "        aged_bg = self.add_noise_and_grain(aged_bg)\n",
-    "        aged_bg = self.fade_edges(aged_bg)\n",
-    "        aged_bg = self.add_crumple_effect_to_page(aged_bg, strength=3, noise_scale=2)\n",
-    "\n",
-    "        if progress_bar:\n",
-    "            progress_bar.set_description(\"Creating ink bleed effect\")\n",
-    "\n",
-    "        bleed_text = self.get_random_text_file()\n",
-    "        bleed_text_img = self.create_bleed_text_image(\n",
-    "            bleed_text, \n",
-    "            aged_bg.size, \n",
-    "            dynamic_font_size, \n",
-    "            line_spacing, \n",
-    "            padding\n",
-    "        )\n",
-    "        aged_bg, bleed_intensity = self.apply_recto_verso_bleed(aged_bg, bleed_text_img)\n",
-    "        if progress_bar:\n",
-    "            progress_bar.set_description(f\"Applied ink bleed (intensity: {bleed_intensity:.2f})\")\n",
-    "\n",
-    "        if progress_bar:\n",
-    "            progress_bar.set_description(\"Drawing text\")\n",
-    "\n",
-    "        draw = ImageDraw.Draw(aged_bg)\n",
-    "        current_y = padding\n",
-    "\n",
-    "        line_bboxes = []\n",
-    "        word_bboxes = []\n",
-    "\n",
-    "        for i, line in enumerate(lines):\n",
-    "            if not line.strip():\n",
-    "                current_y += line_spacing\n",
-    "                continue\n",
-    "\n",
-    "            line_width = line_widths[i]\n",
-    "            # start_x = (cropped_width - line_width) // 2\n",
-    "            start_x = padding\n",
-    "            x_offset = random.randint(-3, 3)\n",
-    "            y_offset = random.randint(-2, 2)\n",
-    "            line_x = start_x + x_offset\n",
-    "            line_y = current_y + y_offset\n",
-    "\n",
-    "            # Line bbox\n",
-    "            try:\n",
-    "                line_bbox = draw.textbbox((line_x, line_y), line, font=font)\n",
-    "            except AttributeError:\n",
-    "                size = draw.textsize(line, font=font)\n",
-    "                line_bbox = (line_x, line_y, line_x + size[0], line_y + size[1])\n",
-    "            line_bboxes.append(line_bbox)\n",
-    "\n",
-    "            # Word bboxes\n",
-    "            words = line.split()\n",
-    "            current_word_x = line_x\n",
-    "            space_width = font.getlength(\" \") if hasattr(font, \"getlength\") else font.getbbox(' ')[2]\n",
-    "\n",
-    "            for word in words:\n",
-    "                # Get accurate bbox by rendering at the actual location\n",
-    "                try:\n",
-    "                    word_bbox = draw.textbbox((current_word_x, line_y), word, font=font)\n",
-    "                except AttributeError:\n",
-    "                    size = draw.textsize(word, font=font)\n",
-    "                    word_bbox = (current_word_x, line_y, current_word_x + size[0], line_y + size[1])\n",
-    "                \n",
-    "                word_bboxes.append(word_bbox)\n",
-    "\n",
-    "                word_width = word_bbox[2] - word_bbox[0]\n",
-    "                current_word_x += word_width + space_width\n",
-    "\n",
-    "            # draw.text((line_x, line_y), line, font=font, fill=text_color)\n",
-    "            ##################################################################\n",
-    "            degraded_line = self.render_degraded_text(line, font, text_color)\n",
-    "            aged_bg.paste(degraded_line, (line_x, line_y), degraded_line)\n",
-    "            ##################################################################\n",
-    "            current_y += dynamic_font_size + line_spacing\n",
-    "\n",
-    "\n",
-    "        blur_radius = random.uniform(0.3, 0.)\n",
-    "        aged_bg = aged_bg.filter(ImageFilter.GaussianBlur(radius=blur_radius))\n",
-    "\n",
-    "        return aged_bg, line_bboxes, word_bboxes\n",
-    "\n",
-    "    def generate_all_pages(self):\n",
-    "        text_files = [f for f in os.listdir(self.text_dir) if f.lower().endswith('.txt')]\n",
-    "        if not text_files:\n",
-    "            print(\"No text files found in the specified directory.\")\n",
-    "            return\n",
-    "        print(f\"Found {len(text_files)} text files to process.\")\n",
-    "\n",
-    "        with tqdm(total=len(text_files), desc=\"Generating pages\", unit=\"page\") as pbar:\n",
-    "            successful_generations = 0\n",
-    "            failed_generations = 0\n",
-    "\n",
-    "            for fname in text_files:\n",
-    "                try:\n",
-    "                    pbar.set_description(f\"Processing {fname}\")\n",
-    "                    with open(os.path.join(self.text_dir, fname), 'r', encoding='utf-8') as f:\n",
-    "                        text = f.read()\n",
-    "                    background = self.get_random_background()\n",
-    "                    composed_page, line_bboxes, word_bboxes = self.draw_centered_text_on_image(background, text, pbar)\n",
-    "                    out_path = os.path.join(self.output_dir, fname.replace('.txt', '.png'))\n",
-    "                    composed_page.save(out_path)\n",
-    "\n",
-    "                    bbox_filename = fname.replace('.txt', '')\n",
-    "                    bbox_path = os.path.join(self.bbox_dir, bbox_filename)\n",
-    "                    with open(bbox_path + \"_lines.txt\", \"w\", encoding=\"utf-8\") as f:\n",
-    "                        for bbox in line_bboxes:\n",
-    "                            x0, y0, x1, y1 = bbox\n",
-    "                            f.write(f\"{x0},{y0},{x1},{y1}\\n\")\n",
-    "                    with open(bbox_path + \"_words.txt\", \"w\", encoding=\"utf-8\") as f:\n",
-    "                        for bbox in word_bboxes:\n",
-    "                            x0, y0, x1, y1 = bbox\n",
-    "                            f.write(f\"{x0},{y0},{x1},{y1}\\n\")\n",
-    "\n",
-    "                    successful_generations += 1\n",
-    "                    pbar.set_description(f\"✓ Generated {fname}\")\n",
-    "                except Exception as e:\n",
-    "                    failed_generations += 1\n",
-    "                    pbar.set_description(f\"✗ Failed {fname}: {str(e)}\")\n",
-    "                    print(f\"Error processing {fname}: {str(e)}\")\n",
-    "                finally:\n",
-    "                    pbar.update(1)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "ebb8341b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 29 text files to process.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "✓ Generated Borbón_ROMAN_page_029.txt: 100%|██████████| 29/29 [03:33<00:00,  7.38s/page]\n"
-     ]
-    }
-   ],
-   "source": [
-    "generator = SyntheticPageGenerator(\n",
-    "    font_path='fonts/RomanAntique.ttf',\n",
-    "    background_dir='data/ALGO-DATA/page_background',\n",
-    "    text_dir='data/ALGO-DATA/synthetic_page_input',\n",
-    "    output_dir='output/ALGO-OUTPUT/output_pages',\n",
-    "    bbox_dir='output/ALGO-OUTPUT/output_bboxes',\n",
-    "    line_spacing_ratio=0.005,  \n",
-    "    margin_ratio=0.08  \n",
-    ")\n",
-    "\n",
-    "# Generate all pages\n",
-    "generator.generate_all_pages()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "3ceeeb51",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[INFO] Image shape: (2112, 1510, 3)\n",
-      "[INFO] Loaded 256 bounding boxes.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x1000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[INFO] Image shape: (2112, 1510, 3)\n",
-      "[INFO] Loaded 29 bounding boxes.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x1000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import cv2\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "def plot_bounding_boxes(image_path, bbox_txt_path, box_color=(0, 0, 255), thickness=2):\n",
-    "    def imread_unicode(path):\n",
-    "        with open(path, \"rb\") as f:\n",
-    "            bytes_array = bytearray(f.read())\n",
-    "        np_array = np.asarray(bytes_array, dtype=np.uint8)\n",
-    "        img = cv2.imdecode(np_array, cv2.IMREAD_COLOR)\n",
-    "        return img\n",
-    "\n",
-    "    # Load image\n",
-    "    image = imread_unicode(image_path)\n",
-    "    if image is None:\n",
-    "        raise FileNotFoundError(f\"Image not found or failed to load: {image_path}\")\n",
-    "    \n",
-    "    print(f\"[INFO] Image shape: {image.shape}\")\n",
-    "    \n",
-    "    # Read bounding boxes\n",
-    "    with open(bbox_txt_path, 'r') as f:\n",
-    "        lines = f.readlines()\n",
-    "\n",
-    "    print(f\"[INFO] Loaded {len(lines)} bounding boxes.\")\n",
-    "    \n",
-    "    for idx, line in enumerate(lines):\n",
-    "        line = line.strip()\n",
-    "        if not line:\n",
-    "            continue\n",
-    "\n",
-    "        # Try splitting by comma\n",
-    "        parts = line.split(',')\n",
-    "        if len(parts) < 4:\n",
-    "            print(f\"[WARNING] Skipping invalid line {idx}: '{line}'\")\n",
-    "            continue\n",
-    "\n",
-    "        try:\n",
-    "            x_min, y_min, x_max, y_max = [int(float(p.strip())) for p in parts[:4]]\n",
-    "        except ValueError as e:\n",
-    "            print(f\"[ERROR] Line {idx} has invalid coordinates: {e}\")\n",
-    "            continue\n",
-    "\n",
-    "\n",
-    "        # Draw bounding box\n",
-    "        cv2.rectangle(image, (x_min, y_min), (x_max, y_max), box_color, thickness)\n",
-    "\n",
-    "    # Convert to RGB for matplotlib\n",
-    "    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
-    "\n",
-    "    # Plot the image\n",
-    "    plt.figure(figsize=(12, 10))\n",
-    "    plt.imshow(image_rgb)\n",
-    "    plt.axis('off')\n",
-    "    plt.title(\"Bounding Boxes\")\n",
-    "    plt.show()\n",
-    "\n",
-    "page_number = \"021\"  # Example page number\n",
-    "image_path = rf\"output/ALGO-OUTPUT/output_pages/Borbón_ROMAN_page_{page_number}.png\"\n",
-    "bbox_path_line = rf\"output/ALGO-OUTPUT/output_bboxes/Borbón_ROMAN_page_{page_number}_words.txt\"\n",
-    "plot_bounding_boxes(image_path, bbox_path_line)\n",
-    "\n",
-    "page_number = \"021\"  # Example page number\n",
-    "image_path = rf\"output/ALGO-OUTPUT/output_pages/Borbón_ROMAN_page_{page_number}.png\"\n",
-    "bbox_path_line = rf\"output/ALGO-OUTPUT/output_bboxes/Borbón_ROMAN_page_{page_number}_lines.txt\"\n",
-    "plot_bounding_boxes(image_path, bbox_path_line)\n"
-   ]
-  }
- ],
- "metadata": {
-  "kaggle": {
-   "accelerator": "gpu",
-   "dataSources": [
-    {
-     "datasetId": 7874539,
-     "sourceId": 12480055,
-     "sourceType": "datasetVersion"
-    },
-    {
-     "datasetId": 7949368,
-     "sourceId": 12586361,
-     "sourceType": "datasetVersion"
-    }
-   ],
-   "dockerImageVersionId": 31090,
-   "isGpuEnabled": true,
-   "isInternetEnabled": true,
-   "language": "python",
-   "sourceType": "notebook"
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
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diff --git a/RenAIssance_SyntheticImageGeneration_Saarthak_Gupta/readme.md b/RenAIssance_SyntheticImageGeneration_Saarthak_Gupta/readme.md
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--- a/RenAIssance_SyntheticImageGeneration_Saarthak_Gupta/readme.md
+++ /dev/null
@@ -1,199 +0,0 @@
-# Synthetic text generation with Generative Models
-
-For Historical Manuscripts, limited availability of data a limiting factor in training OCR or Layout Detection models. This project propose a solution to this bottleneck by using a GAN architecture to generate synthetic renaissance style Image Data which can be used to improve OCR model performance on historical Spanish texts.This project is part of the HumanAI Foundation initiative and was developed during Google Summer of Code 2025.
-
-## Table of Contents
-
-- [Overview](#overview)  
-- [Project Structure](#project-structure)  
-- [Requirements](#Requirements)  
-- [Data Generation Pipeline](#data-generation-pipeline)  
-  - Splitting Pages  
-  - Image Preprocessing  
-  - Text Detection  
-  - Aligning Detection with Transcript  
-  - Dataset Creation  
-  - Grid Construction  
-- [Model Building & Training](#model-building--training)  
-  - Generator & Discriminator  
-  - Training Loop  
-- [Inference & Synthetic Page Generation](#inference--synthetic-page-generation)  
-- [Usage](#usage)  
-
----
-
-## Overview
-
-Historical OCR is challenged by scarce data and variable degradation in old manuscripts. This project:
-
-1. Builds a **data‑generation pipeline** to split, enhance, detect, and align word images from scanned PDFs and transcripts.
-2. Implements a **Pix2Pix‑style GAN** (U‑Net generator + PatchGAN discriminator) to translate clean synthetic text into realistic Renaissance‑era handwriting.
-3. Provides a **procedural synthetic page generator** that simulates paper aging, ink bleed, and layout irregularities.
-
----
-
-## Project Structure
-
-```
-
-.
-├── experimentation.ipynb       # Jupyter notebook demonstrating end-to-end pipeline
-├── data/                       # (not committed) raw PDFs and transcripts
-├── outputs/                    # generated datasets, models, and figures
-└── src/                        # core modules (imported in the notebook)
-├── data_utils.py          # data-splitting, preprocessing, detection, mapping, dataset creation
-└── model_utils.py         # GAN model classes and training/inference helpers
-
-````
-
----
-
-## Requirements
-
-- Python 3.8+  
-- [PyTorch](https://pytorch.org/)  
-- torchvision  
-- Pillow  
-- numpy, pandas, matplotlib  
-- CRAFT text‑detection model weights (download per instructions in `src/data_utils.py`)  
-
-Downlaod the data and required model files from : [LINK](https://iitbhu365-my.sharepoint.com/:f:/g/personal/saarthak_gupta_mec22_iitbhu365_onmicrosoft_com/EtFan2TQidhNhTXXK45qTGwBAvyxOfpaJNxhSPWy16N0EA?e=fbdyuR)
-
-Install with:
-
-```bash
-pip install torch torchvision pillow numpy pandas matplotlib
-````
-
----
-
-## Data Generation Pipeline
-
-All functions below are implemented in **`src/data_utils.py`** and demonstrated in the notebook.
-
-### 1. Splitting Pages
-
-* **`copy_all_images(csv_file)`** / **`copy_all_transcripts(csv_file)`**
-  Splits multi‑page PDFs into per‑page images and aligns each with its transcript.
-
-  ![alt text](assets/image2.png)
-
-### 2. Image Preprocessing
-
-* **`analyze_image_sizes(...)`**, **`resize_and_pad(...)`**
-* **`extract_and_process_all_regions(...)`**
-  Skew correction, DPI adjustment, denoising, binarization, morphological operations.
-
-  ![alt text](assets/preprocessing.png)
-
-### 3. Text Detection
-
-* **`text_detection(image_path, craft_model)`**
-  Uses CRAFT to find word‑level bounding boxes on each page.
-  ![alt text](assets/plots/plot7.png)
-### 4. Aligning Detection with Transcript
-
-* **`mapping_bounding_boxes(...)`**
-  Fine‑tuned PyTesseract + string similarity (threshold 0.8) to match each box to its correct transcript word.
-  ![alt text](assets/mapping.png)
-
-### 5. Dataset Creation
-
-* **`generate_text_image_dataset(...)`**
-  Renders each matched word in RomanAntique font (64×128), stacks 8 word images into a 256×256 grid, cleans and saves \~4,800 paired samples.
-
-### 6. Grid Construction
-
-* **`plot_bounding_boxes(...)`**, **`create_grid_from_words(...)`**
-  Utility functions to visualize detections and composite word grids for GAN input.
-
-![alt text](assets/final_data.png)
-
----
-
-## Model Building & Training
-
-All model definitions and training helpers live in **`src/model_utils.py`**.
-
-### Generator & Discriminator
-
-* **`class UNetGenerator(nn.Module)`**
-  U‑Net with encoder/decoder and skip connections for image‑to‑image translation.
-* **`class PatchDiscriminator(nn.Module)`**
-  70×70 PatchGAN discriminator for local realism.
-
- ![alt text](assets/model.png)
-### Training Loop
-
-* **`def train_pix2pix(csv_file, epochs=100, batch_size=32, lr=2e-4, save_dir="models")`**
-
-  * **Generator loss:** BCE GAN loss + 100× L1 loss
-  * **Discriminator loss:** 0.5 × (Loss\_real + Loss\_fake)
-* **`def plot_gan_history(csv_path)`**, **`def visualize_pix2pix_results(...)`**
-  Scripts to track losses and sample outputs.
-
-![alt text](assets/training_history_history.png)
----
-
-## Inference & Synthetic Page Generation
-
-* **`class GANInferencePipeline:`**
-
-  * `load_generator(generator_path)`
-  * `visualize_pix2pix_results(...)`
-* **`class SyntheticPageGenerator:`**
-
-  * `render_single_word(word) → Image`
-  * `create_grid_from_words(words) → Image`
-  * `preprocess_image(...)`, `postprocess_output(...)`
-
-![alt text](assets/test_samples.png)
-These classes let you generate new word samples and full synthetic pages.
-
-![alt text](assets/pages.png)
-
----
-
-## Usage
-
-1. **Prepare data**
-   Place your PDFs and transcripts under `data/raw/`, then run the “Data Generation” cells in `experimentation.ipynb`.
-
-2. **Generate training set**
-   Execute the notebook cells up through `generate_text_image_dataset(...)`.
-
-3. **Train GAN**
-   In the notebook (or via command line):
-
-   ```python
-   from src.model_utils import train_pix2pix
-   train_pix2pix(
-       csv_file="outputs/dataset/mapping.csv",
-       epochs=100,
-       batch_size=32,
-       lr=2e-4,
-       save_dir="outputs/models"
-   )
-   ```
-
-4. **Visualize results**
-
-   ```python
-   from src.model_utils import visualize_pix2pix_results
-   visualize_pix2pix_results(
-       "outputs/models/generator.pth",
-       dataloader, 
-       num_samples=5
-   )
-   ```
-
-5. **Generate synthetic pages**
-
-   ```python
-   from src.model_utils import SyntheticPageGenerator
-   spg = SyntheticPageGenerator()
-   page_img = spg.create_synthetic_page_from_text("To be, or not to be…")
-   page_img.save("outputs/pages/page1.png")
-   ```
-
----
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diff --git a/RenAIssance_SyntheticImageGeneration_Saarthak_Gupta/src/data_generation.py b/RenAIssance_SyntheticImageGeneration_Saarthak_Gupta/src/data_generation.py
deleted file mode 100644
index dc94af2e..00000000
--- a/RenAIssance_SyntheticImageGeneration_Saarthak_Gupta/src/data_generation.py
+++ /dev/null
@@ -1,177 +0,0 @@
-import warnings
-warnings.filterwarnings("ignore")
-import os
-from data_utils import *
-
-def data_generation_pipeline():
-    
-    # 1. Split books and transcripts into individual files
-    process_books_with_transcripts(
-        input_books_folder="data/GAN-DATA/1_raw/books/",
-        input_transcripts_folder="data/GAN-DATA/1_raw/transcripts/",
-        output_books_folder="data/GAN-DATA/2_splitted/books/",
-        output_transcripts_folder="data/GAN-DATA/2_splitted/transcripts/"
-    )
-
-    # 2. Preprocessing images
-    copy_all_transcripts()
-    copy_all_images()
-
-    book_transformations = {
-        'book1': {
-            'denoise_image': {'method': 'bilateral'},
-            'denoise_image': {'method': 'nlm'}
-        },
-        'book2': {
-            'ensure_300ppi': {'target_dpi': 150},
-            'remove_bleed_dual_layer': {},
-            'denoise_image': {'method': 'bilateral'},
-            'denoise_image': {'method': 'nlm'},
-            'denoise_image': {'method': 'nlm'}
-        },
-        'book3': {
-            'ensure_300ppi': {'target_dpi': 300},
-            'remove_bleed_dual_layer': {},
-            'denoise_image': {'method': 'bilateral'}
-            },
-        'book4': {
-            'ensure_300ppi': {'target_dpi': 300},
-            'remove_bleed_dual_layer': {},
-            'denoise_image': {'method': 'bilateral'}
-        },
-        'book5': {
-            'ensure_300ppi': {'target_dpi': 300}
-        },
-        'book6': {
-            'ensure_300ppi': {'target_dpi': 300},
-            'remove_bleed_dual_layer': {},
-            'denoise_image': {'method': 'bilateral'}
-        },
-        'book7': {
-            'remove_bleed_dual_layer': {},
-            'denoise_image': {'method': 'nlm'},
-            'denoise_image': {'method': 'bilateral'}
-        },
-        'book8': {
-            'remove_bleed_dual_layer': {},
-            'denoise_image': {'method': 'nlm'},
-            'denoise_image': {'method': 'bilateral'}
-        },
-        'book9': {
-            'ensure_300ppi': {'target_dpi': 300},
-            'remove_bleed_dual_layer': {},
-            'denoise_image': {'method': 'wiener'},
-            'sharpen_image': {'method': 'laplacian'}
-        },
-        'book10': {
-            'ensure_300ppi': {'target_dpi': 300},
-            'remove_bleed_dual_layer': {},
-            'denoise_image': {'method': 'wiener'},
-            'sharpen_image': {'method': 'laplacian'}
-        },
-        'book11': {
-            'ensure_300ppi': {'target_dpi': 300},
-            'remove_bleed_dual_layer': {},
-            'denoise_image': {'method': 'bilateral'},
-        },
-        'book12': {
-            'ensure_300ppi': {'target_dpi': 300},
-            'remove_bleed_dual_layer': {},
-            'denoise_image': {'method': 'bilateral'},
-            'sharpen_image': {'method': 'laplacian'},
-        },
-    }
-
-    stats = process_multiple_books(
-        books_base_dir="data/GAN-DATA/2_splitted/books", 
-        output_dir="data/GAN-DATA/3_processed/books",
-        book_transformations=book_transformations
-    )
-    
-    # 3. Generate bounding boxes for each book page
-    input_root = "data/GAN-DATA/3_processed/books"
-    output_root = "data/GAN-DATA/4_bounding_boxes//"
-    model_path = "CRAFT-pytorch/weights/craft_mlt_25k.pth"
-
-    text_detection(
-        input_root=input_root,
-        output_root=output_root,
-        model_path=model_path
-    )
-
-    plot_random_pages(
-        images_dir="data/GAN-DATA/3_processed/books",
-        bbox_dir="data/GAN-DATA/4_bounding_boxes",
-        save_dir="assets/plots/"
-    )
-    # 4. Mapping bounding boxes to transcripts
-    # This function will map the bounding boxes generated in the previous step to the corresponding words in the transcripts.
-    similarity_threshold = 0.8
-    
-    total_words, total_bboxes, total_mapped = mapping_bounding_boxes(
-        image_dir="data/GAN-DATA/3_processed/books",
-        bbox_dir="data/GAN-DATA/4_bounding_boxes", 
-        transcript_dir="data/GAN-DATA/3_processed/transcripts",
-        output_dir="data/GAN-DATA/5_mapped",
-        tesseract_model=1,
-        similarity_threshold=0.8
-    )
-
-    # 5. Extract and process all regions to create word data
-    # This will extract words from the mapped bounding boxes and save them in the specified output directory.
-    df = extract_and_process_all_regions(
-        image_root = "data/GAN-DATA/3_processed/books",
-        aligned_root = "data/GAN-DATA/5_mapped",
-        output_root = "data/GAN-DATA/6_word_data/images",
-        csv_output_path = "data/GAN-DATA/6_word_data/words.csv"
-    )
-        
-    # 6. Resize and pad images to a fixed size
-
-    target_height, target_width = 64 , 128
-
-    resize_and_pad(
-        input_dir="data/GAN-DATA/6_word_data/images",
-        output_dir="data/GAN-DATA/final_dataset/target",
-        target_height=target_height,
-        target_width=target_width
-    )
-
-    # 7. render source text images using matplotlib
-    config = {
-                'text.usetex': False,
-                'mathtext.fontset': 'cm',
-                'font.family': 'serif',
-                'font.serif': ['Times New Roman'],
-                'font.weight': 'bold',
-                'mathtext.bf': 'bold',
-            }
-
-    
-    df = generate_text_image_dataset(
-    csv_path='data/GAN-DATA/6_word_data/words.csv',
-    output_dir='data/GAN-DATA/final_dataset',
-    target_dir_prefix='data/GAN-DATA/final_dataset/target/',
-    image_width=target_width,
-    image_height=target_height,
-    base_fontsize=70,
-    matplotlib_config=config,
-    custom_font_path='fonts/RomanAntique.ttf', 
-    progress_bar=True
-    )
-    
-    
-    df = pd.read_csv("data/GAN-DATA/final_dataset/data.csv")
-    grid_df = create_image_grids(
-        df=df[df['book'].isin(["book_1", "book_5"])],
-        output_directory="data/GAN-DATA/grid_dataset",
-        num_grids=3000,
-        grid_size=(4,2),
-        target_image_size=(128,64),  # (width, height)
-        random_seed=42
-    )
-    
-    
-if __name__ == "__main__":        
-        data_generation_pipeline()
-        
\ No newline at end of file
diff --git a/RenAIssance_SyntheticImageGeneration_Saarthak_Gupta/src/data_utils.py b/RenAIssance_SyntheticImageGeneration_Saarthak_Gupta/src/data_utils.py
deleted file mode 100644
index ce9bc73c..00000000
--- a/RenAIssance_SyntheticImageGeneration_Saarthak_Gupta/src/data_utils.py
+++ /dev/null
@@ -1,1889 +0,0 @@
-# Standard library imports
-import gc
-import glob
-import io
-import os
-import random
-import re
-import shutil
-import pandas as pd
-import numpy as np
-from PIL import Image
-import os
-import random
-from pathlib import Path
-
-from tqdm import tqdm
-import string
-import subprocess
-from collections import defaultdict
-from difflib import SequenceMatcher
-from pathlib import Path
-
-# Third-party data processing
-import numpy as np
-import pandas as pd
-import torch
-from tqdm import tqdm
-
-# Image processing and computer vision
-import cv2
-from PIL import Image
-import pytesseract
-from skimage import restoration, exposure
-
-# Deep learning and AI models
-from basicsr.archs.rrdbnet_arch import RRDBNet
-from realesrgan import RealESRGANer
-
-# Document processing
-import fitz  # PyMuPDF
-from docx import Document
-import time
-
-# Visualization
-import matplotlib
-import matplotlib.font_manager as fm
-import matplotlib.pyplot as plt
-from matplotlib.patches import Polygon
-
-
-#################################################################################################################################
-                                        # Splitting PDF Pages into Images
-#################################################################################################################################
-
-def display_sample(dir, sample_number=5):
-
-    sample_path = os.path.join(dir, f"{sample_number}.png")
-
-    if os.path.exists(sample_path):
-        img = Image.open(sample_path)
-        plt.figure(figsize=(4, 5))
-        plt.imshow(img)
-        plt.axis("off")
-        plt.title(f"Sample Image: {sample_number}.png")
-        plt.show()
-    else:
-        print(f"Image {sample_number}.png not found in '{dir}'. \n")
-        
-def pdf_to_images(pdf_path, output_dir, page_numbers=None, book_number=1, progress_bar=None):
-    # Create book-specific folder
-    book_folder = os.path.join(output_dir, f"book{book_number}")
-    os.makedirs(book_folder, exist_ok=True)
-    
-    pdf_doc = fitz.open(pdf_path)
-    image_counter = 1 
-    processed_pages = 0
-    book_name = os.path.splitext(os.path.basename(pdf_path))[0]
-
-    # If page_numbers is provided, convert to 0-indexed and filter
-    if page_numbers:
-        # Convert 1-indexed page numbers to 0-indexed for fitz
-        pages_to_process = [p - 1 for p in page_numbers if 1 <= p <= len(pdf_doc)]
-        if progress_bar:
-            progress_bar.set_postfix({"Status": f"Processing {len(pages_to_process)} pages from '{book_name}'"})
-    else:
-        # Process all pages
-        pages_to_process = range(len(pdf_doc))
-        if progress_bar:
-            progress_bar.set_postfix({"Status": f"Processing all {len(pdf_doc)} pages from '{book_name}'"})
-
-    for page_num in pages_to_process:
-        page = pdf_doc.load_page(page_num)
-        pix = page.get_pixmap()  # Render page as image
-        img = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
-
-        # If the page is significantly wider than tall, assume it's a double-page
-        if img.width > img.height * 1.19:
-            left = img.crop((0, 0, img.width // 2, img.height))  # Left half
-            right = img.crop((img.width // 2, 0, img.width, img.height))  # Right half
-
-            # Save with page number in filename in book-specific folder
-            left_filename = f"page{image_counter}.png"
-            right_filename = f"page{image_counter + 1}.png"
-            
-            left.save(os.path.join(book_folder, left_filename), format="PNG")
-            image_counter += 1
-            right.save(os.path.join(book_folder, right_filename), format="PNG")
-            image_counter += 1
-        else:
-            # Save single page with page number in filename in book-specific folder
-            filename = f"page{image_counter}.png"
-            img.save(os.path.join(book_folder, filename), format="PNG")
-            image_counter += 1
-
-        processed_pages += 1
-
-    pdf_doc.close()
-    if progress_bar:
-        progress_bar.set_postfix({"Status": f"Completed book {book_number}: {image_counter - 1} images from {processed_pages} pages"})
-        
-def remove_punctuation(text):
-    return text.translate(str.maketrans("", "", string.punctuation))
-
-def extract_text_by_page(docx_file, output_dir, book_number=1, progress_bar=None):
-    # Create book-specific folder
-    book_folder = os.path.join(output_dir, f"book{book_number}")
-    os.makedirs(book_folder, exist_ok=True)
-    
-    document = Document(docx_file)
-    pages = []
-    page_numbers = []  # List to store original page numbers only
-    current_page = []
-    start_reading = False  # Flag to track when to start reading
-    consecutive_empty_lines = 0
-    found_pdf_page = False
-    book_name = os.path.splitext(os.path.basename(docx_file))[0]
-    
-    if progress_bar:
-        progress_bar.set_postfix({"Status": f"Extracting text from '{book_name}'"})
-    
-    for paragraph in document.paragraphs:
-        text = paragraph.text.strip()
-
-        if not text:  # Empty line found
-            consecutive_empty_lines += 1
-            
-            # Check if we have two consecutive empty lines after any PDF page marker
-            if found_pdf_page and consecutive_empty_lines >= 2:
-                # Create a blank page
-                pages.append("")  # Empty page
-                found_pdf_page = False  # Reset flag
-                consecutive_empty_lines = 0
-            
-            if start_reading and current_page:
-                # Save current page and start a new one due to empty line
-                pages.append("\n".join(current_page))
-                current_page = []
-            continue
-        else:
-            consecutive_empty_lines = 0  # Reset counter when non-empty line is found
-
-        # Check for page number pattern BEFORE checking start_reading flag
-        page_match = re.search(r'PDF\s+p(\d+)', text, re.IGNORECASE)
-        if page_match:
-            page_num = int(page_match.group(1))
-            page_numbers.append(page_num)  # This maintains original page numbering
-            
-            # Set flag for any PDF page marker
-            found_pdf_page = True
-            consecutive_empty_lines = 0  # Reset counter
-
-        if (text == "****" or "pdf" in text.lower()) and not start_reading:
-            start_reading = True  # Start reading after the first "****"
-            continue
-
-        if not start_reading:
-            continue  # Skip lines before the first "****"
-
-        if text == "****" or "pdf" in text.lower() or "end of extract" in text.lower() or "--this part intentionally left blank to check after test--" in text.lower():
-            # Page separator found
-            if current_page:
-                pages.append("\n".join(current_page))  # Save current page
-                current_page = []  # Start a new page
-        else:
-            text = remove_punctuation(text)  # Remove punctuation
-            current_page.append(text)
-
-    # Append the last page if it's not empty
-    if current_page:
-        pages.append("\n".join(current_page))
-
-    # Save each page separately in its own text file in book-specific folder
-    for idx, page_text in enumerate(pages, start=1):
-        page_file = os.path.join(book_folder, f"page{idx}.txt")
-        with open(page_file, "w", encoding="utf-8") as f:
-            f.write(page_text)
-    
-    return page_numbers
-
-def process_books_with_transcripts(input_books_folder, input_transcripts_folder, output_books_folder, output_transcripts_folder):
-    book_counter = 1
-    
-    # Create main output directories
-    os.makedirs(output_books_folder, exist_ok=True)
-    os.makedirs(output_transcripts_folder, exist_ok=True)
-    
-    # Get file lists
-    pdf_files = sorted(os.listdir(input_books_folder))
-    transcript_files = sorted(os.listdir(input_transcripts_folder))
-    
-    # Create a single progress bar
-    with tqdm(total=len(pdf_files), desc="Splitting Images and transcripts", unit="book") as pbar:
-        for pdf_file, transcript_file in zip(pdf_files, transcript_files):
-            pdf_input_path = os.path.join(input_books_folder, pdf_file)
-            transcript_input_path = os.path.join(input_transcripts_folder, transcript_file)
-            
-            # Extract text first to get page numbers
-            page_numbers = extract_text_by_page(transcript_input_path, output_transcripts_folder, book_counter, pbar)
-            
-            # Process PDF with the book number
-            pdf_to_images(pdf_input_path, output_books_folder, page_numbers, book_counter, pbar)
-            
-            book_counter += 1
-            
-            # Update progress bar
-            pbar.set_postfix({"Status": f"Completed book {book_counter-1}"})
-            pbar.update(1)
-#################################################################################################################################
-                                        # Image Preprocessing
-#################################################################################################################################
-
-def load_image(image_path):
-    # Convert Path object to string for cv2.imread
-    image = cv2.imread(str(image_path))  # Load image (default is BGR)
-    if image is None:
-        raise ValueError(f"Could not load image from {image_path}")
-    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)  # Convert BGR to RGB
-    return image
-
-def convert_to_grayscale(image):
-    """Converts an image to grayscale if it's not already."""
-    if len(image.shape) == 3:  # Check if the image is in color (BGR)
-        return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-    return image  # Already grayscale
-
-def correct_skew(image):
-    """Corrects skew in an image, supporting both grayscale and RGB inputs."""
-    # Store original color state
-    is_color = len(image.shape) == 3
-    
-    # Create grayscale copy for skew detection
-    if is_color:
-        gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
-    else:
-        gray = image.copy()
-    
-    # Apply thresholding
-    _, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_OTSU)
-
-    # Find contours
-    contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-
-    if not contours:
-        print("No text found, skipping skew correction.")
-        return image  # Return original image with 0° skew angle
-
-    # Find the largest contour (assumed to be text block)
-    largest_contour = max(contours, key=cv2.contourArea)
-    
-    # Get the minimum area bounding box
-    rect = cv2.minAreaRect(largest_contour)
-    angle = rect[-1]
-
-    # Normalize the angle to be between -45° and +45°
-    if angle < -45:
-        angle += 90
-    elif angle > 45:
-        angle -= 90
-
-    # Get image dimensions and center
-    (h, w) = image.shape[:2]
-    center = (w // 2, h // 2)
-
-    # Compute rotation matrix
-    rotation_matrix = cv2.getRotationMatrix2D(center, angle, 1.0)
-    
-    # Perform the rotation with border replication
-    deskewed = cv2.warpAffine(image, rotation_matrix, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
-    
-    return deskewed
-
-def normalize_image(image):
-    # Check if the image is RGB
-    if len(image.shape) == 3:
-        r, g, b = cv2.split(image)
-        r_norm = cv2.normalize(r, None, 0, 255, cv2.NORM_MINMAX)
-        g_norm = cv2.normalize(g, None, 0, 255, cv2.NORM_MINMAX)
-        b_norm = cv2.normalize(b, None, 0, 255, cv2.NORM_MINMAX)
-        normalized = cv2.merge([r_norm, g_norm, b_norm])
-        return normalized
-    else:
-        return cv2.normalize(image, None, 0, 255, cv2.NORM_MINMAX)
-    
-def ensure_300ppi(image, target_dpi=300):
-    
-    height, width = image.shape[:2]
-
-    # Assume A4 document size in inches (common for scanned books)
-    a4_width = 8.27  # inches
-    a4_height = 11.69  # inches
-
-    dpi_x = width / a4_width
-    dpi_y = height / a4_height
-    
-    # Convert to PIL image, preserving color if needed
-    image_pil = Image.fromarray(image)
-
-    if dpi_x < target_dpi or dpi_y < target_dpi:
-        # Calculate upscale factor
-        scale_factor = target_dpi / min(dpi_x, dpi_y)  # Scale based on the lower DPI
-
-        # Compute new size
-        new_size = (int(image_pil.width * scale_factor), int(image_pil.height * scale_factor))
-
-        # Resize using high-quality Lanczos resampling
-        image_pil = image_pil.resize(new_size, Image.Resampling.LANCZOS)
-
-        # Set the new DPI metadata
-        image_pil.info['dpi'] = (target_dpi, target_dpi)
-
-    # Convert back to OpenCV format
-    image_upscaled = np.array(image_pil)
-
-    return image_upscaled
-
-def remove_bleed_dual_layer(image):
-    """Removes bleed-through, supporting both grayscale and RGB inputs."""
-    # Check if image is color
-    is_color = len(image.shape) == 3
-    
-    if is_color:
-        # Convert to LAB color space which separates luminance from color
-        lab_image = cv2.cvtColor(image, cv2.COLOR_RGB2LAB)
-        l, a, b = cv2.split(lab_image)
-        
-        # Apply processing to luminance channel
-        kernel = np.ones((51, 51), np.uint8)
-        background = cv2.dilate(l, kernel, iterations=2)
-        background = cv2.medianBlur(background, 21)
-        
-        # Subtract background to get foreground
-        l_processed = 255 - cv2.absdiff(l, background)
-        
-        # Normalize to improve contrast
-        l_processed = cv2.normalize(l_processed, None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX)
-        
-        # Merge channels back
-        result_lab = cv2.merge([l_processed, a, b])
-        
-        # Convert back to RGB
-        result = cv2.cvtColor(result_lab, cv2.COLOR_LAB2RGB)
-    else:
-        # Original grayscale processing
-        kernel = np.ones((51, 51), np.uint8)
-        background = cv2.dilate(image, kernel, iterations=2)
-        background = cv2.medianBlur(background, 21)
-        
-        # Subtract background to get foreground
-        diff = 255 - cv2.absdiff(image, background)
-        
-        # Normalize to improve contrast
-        result = cv2.normalize(diff, None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX)
-    
-    return result
-
-def denoise_image(image, method="nlm"):
-
-    # Check if input is valid
-    if image is None or image.size == 0:
-        print("Error: Empty input image")
-        return None
-    
-    # Ensure image is in uint8 format for OpenCV functions
-    if image.dtype != np.uint8:
-        image = np.clip(image, 0, 255).astype(np.uint8)
-    
-    # Make a copy to avoid modifying the original
-    image_copy = image.copy()
-    is_color = len(image.shape) == 3
- 
-    if method == "bilateral":
-        # Bilateral filter preserves edges while removing noise
-        if is_color:
-            return cv2.bilateralFilter(image_copy, 9, 75, 75)
-        else:
-            return cv2.bilateralFilter(image_copy, 9, 75, 75)
-    
-    elif method == "nlm":
-        # Non-local means denoising
-        if is_color:
-            return cv2.fastNlMeansDenoisingColored(image_copy, None, 10, 10, 7, 21)
-        else:
-            return cv2.fastNlMeansDenoising(image_copy, None, 10, 7, 21)
-    
-    elif method == "wiener":
-        # Wiener filter (needs float conversion)
-        if is_color:
-            # Process each channel separately
-            channels = cv2.split(image_copy.astype(np.float32) / 255.0)
-            restored_channels = []
-            
-            for channel in channels:
-                restored = restoration.wiener(channel, psf=np.ones((3, 3)) / 9, balance=0.3)
-                restored = np.clip(restored * 255, 0, 255).astype(np.uint8)
-                restored_channels.append(restored)
-            
-            return cv2.merge(restored_channels)
-        else:
-            float_img = image_copy.astype(np.float32) / 255.0
-            restored = restoration.wiener(float_img, psf=np.ones((3, 3)) / 9, balance=0.3)
-            return np.clip(restored * 255, 0, 255).astype(np.uint8)
-        
-def sharpen_image(image, method="laplacian"):
-    is_color = len(image.shape) == 3
-    
-    if method == "laplacian":
-        if is_color:
-            # Process each channel separately
-            r, g, b = cv2.split(image)
-            r_sharp = cv2.Laplacian(r, cv2.CV_8U)
-            g_sharp = cv2.Laplacian(g, cv2.CV_8U)
-            b_sharp = cv2.Laplacian(b, cv2.CV_8U)
-            
-            r_result = cv2.add(r, r_sharp)
-            g_result = cv2.add(g, g_sharp)
-            b_result = cv2.add(b, b_sharp)
-            
-            return cv2.merge([r_result, g_result, b_result])
-        else:
-            laplacian = cv2.Laplacian(image, cv2.CV_8U)
-            return cv2.add(image, laplacian)
-    
-    elif method == "custom":
-        # Custom sharpening kernel
-        kernel = np.array([[0, -2, 0], 
-                          [-2, 9, -2],
-                          [0, -2, 0]])
-        
-        if is_color:
-            r, g, b = cv2.split(image)
-            r_sharp = cv2.filter2D(r, -1, kernel)
-            g_sharp = cv2.filter2D(g, -1, kernel)
-            b_sharp = cv2.filter2D(b, -1, kernel)
-            return cv2.merge([r_sharp, g_sharp, b_sharp])
-        else:
-            return cv2.filter2D(image, -1, kernel)
-    
-    elif method == "unsharp_mask":
-        # Unsharp masking - better for color images
-        if is_color:
-            # Convert to LAB to separate luminance
-            lab = cv2.cvtColor(image, cv2.COLOR_RGB2LAB)
-            l, a, b_channel = cv2.split(lab)
-            
-            # Apply unsharp mask to luminance only
-            gaussian = cv2.GaussianBlur(l, (0, 0), 3.0)
-            unsharp_mask = cv2.addWeighted(l, 1.5, gaussian, -0.5, 0)
-            
-            # Merge back
-            result = cv2.merge([unsharp_mask, a, b_channel])
-            return cv2.cvtColor(result, cv2.COLOR_LAB2RGB)
-        else:
-            # For grayscale
-            gaussian = cv2.GaussianBlur(image, (0, 0), 3.0)
-            return cv2.addWeighted(image, 1.5, gaussian, -0.5, 0)
-
-    return image
-
-def enhance_contrast(image, method="clahe"):
-    """Enhances contrast in an image, supporting both grayscale and RGB inputs."""
-    is_color = len(image.shape) == 3
-    
-    if method == "clahe":
-        clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
-        
-        if is_color:
-            # Convert to LAB color space
-            lab = cv2.cvtColor(image, cv2.COLOR_RGB2LAB)
-            l, a, b = cv2.split(lab)
-            
-            # Apply CLAHE to L-channel only
-            l_enhanced = clahe.apply(l)
-            
-            # Merge channels and convert back to RGB
-            enhanced_lab = cv2.merge([l_enhanced, a, b])
-            return cv2.cvtColor(enhanced_lab, cv2.COLOR_LAB2RGB)
-        else:
-            # For grayscale
-            return clahe.apply(image)
-    
-    elif method == "adaptive_eq":
-        if is_color:
-            # Convert to HSV to separate value from hue
-            hsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
-            h, s, v = cv2.split(hsv)
-            
-            # Apply adaptive eq to value channel
-            v_enhanced = exposure.equalize_adapthist(v, clip_limit=0.03) * 255
-            v_enhanced = v_enhanced.astype(np.uint8)
-            
-            # Merge channels and convert back to RGB
-            enhanced_hsv = cv2.merge([h, s, v_enhanced])
-            return cv2.cvtColor(enhanced_hsv, cv2.COLOR_HSV2RGB)
-        else:
-            # For grayscale
-            result = exposure.equalize_adapthist(image, clip_limit=0.03) * 255
-            return result.astype(np.uint8)
-    
-    elif method == "stretch":
-        if is_color:
-            # Apply to each channel with care to preserve color relationships
-            r, g, b = cv2.split(image)
-            
-            # Get global percentiles for consistent scaling
-            low = np.min([np.percentile(r, 2), np.percentile(g, 2), np.percentile(b, 2)])
-            high = np.max([np.percentile(r, 98), np.percentile(g, 98), np.percentile(b, 98)])
-            
-            # Scale each channel with the same limits
-            r_stretched = np.clip((r - low) * 255.0 / (high - low), 0, 255).astype(np.uint8)
-            g_stretched = np.clip((g - low) * 255.0 / (high - low), 0, 255).astype(np.uint8)
-            b_stretched = np.clip((b - low) * 255.0 / (high - low), 0, 255).astype(np.uint8)
-            
-            return cv2.merge([r_stretched, g_stretched, b_stretched])
-        else:
-            # For grayscale
-            p2, p98 = np.percentile(image, (2, 98))
-            result = exposure.rescale_intensity(image, in_range=(p2, p98))
-            return result.astype(np.uint8)
-    
-    return image
-
-def morphological_operations(image, operation, kernel_size=(2, 2), iterations=1):
-    is_color = len(image.shape) == 3
-    kernel = np.ones(kernel_size, np.uint8)
-    
-    if is_color:
-        r, g, b = cv2.split(image)
-        
-        if operation == "open":
-            r_processed = cv2.morphologyEx(r, cv2.MORPH_OPEN, kernel, iterations=iterations)
-            g_processed = cv2.morphologyEx(g, cv2.MORPH_OPEN, kernel, iterations=iterations)
-            b_processed = cv2.morphologyEx(b, cv2.MORPH_OPEN, kernel, iterations=iterations)
-        elif operation == "close":
-            r_processed = cv2.morphologyEx(r, cv2.MORPH_CLOSE, kernel, iterations=iterations)
-            g_processed = cv2.morphologyEx(g, cv2.MORPH_CLOSE, kernel, iterations=iterations)
-            b_processed = cv2.morphologyEx(b, cv2.MORPH_CLOSE, kernel, iterations=iterations)
-        elif operation == "dilate":
-            r_processed = cv2.dilate(r, kernel, iterations=iterations)
-            g_processed = cv2.dilate(g, kernel, iterations=iterations)
-            b_processed = cv2.dilate(b, kernel, iterations=iterations)
-        elif operation == "erode":
-            r_processed = cv2.erode(r, kernel, iterations=iterations)
-            g_processed = cv2.erode(g, kernel, iterations=iterations)
-            b_processed = cv2.erode(b, kernel, iterations=iterations)
-        else:
-            return image
-        
-        return cv2.merge([r_processed, g_processed, b_processed])
-    else:
-        if operation == "open":
-            return cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel, iterations=iterations)
-        elif operation == "close":
-            return cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel, iterations=iterations)
-        elif operation == "dilate":
-            return cv2.dilate(image, kernel, iterations=iterations)
-        elif operation == "erode":
-            return cv2.erode(image, kernel, iterations=iterations)
-    
-    return image
-
-def binarize_image(image, method="otsu"):
-    if len(image.shape) == 3:
-        gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
-    else:
-        gray = image
-
-    if method == "otsu":
-        # Otsu's method for global thresholding
-        _, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
-    
-    elif method == "adaptive":
-        # Adaptive thresholding - good for uneven illumination
-        binary = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, 
-                                       cv2.THRESH_BINARY, 15, 8)
-    
-    return binary
-
-def apply_binary_mask(color_image, binary_mask):
-    # Ensure binary mask is properly formatted
-    if len(binary_mask.shape) == 3:
-        binary_mask = cv2.cvtColor(binary_mask, cv2.COLOR_RGB2GRAY)
-    
-    # Create a 3-channel mask for direct multiplication
-    mask_3channel = cv2.merge([binary_mask, binary_mask, binary_mask])
-    
-    # Normalize mask to 0-1 range for multiplication
-    mask_normalized = mask_3channel / 255.0
-    
-    # Apply mask to color image
-    masked_image = (color_image * mask_normalized).astype(np.uint8)
-    
-    return masked_image
-
-def upscale(image, ppi_threshold=150, assumed_width_inches=8.5, 
-                             model_path="models/RealESRGAN_x4plus.pth", 
-                             scale=4, force_upscale=False):
-
-    # Calculate current PPI
-    height, width = image.shape[:2]
-    current_ppi = width / assumed_width_inches
-    
-    # Check if upscaling is needed
-    if not force_upscale and current_ppi >= ppi_threshold:
-        print(f"Image PPI ({current_ppi:.1f}) is above threshold ({ppi_threshold}). Skipping upscale.")
-        return image
-    
-    try:
-        # Initialize upscaler (cache this in real implementation)
-        if not hasattr(upscale, '_upsampler'):
-            
-            # Load model state dict
-            device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-            state_dict = torch.load(model_path, map_location=device)
-            if 'params_ema' in state_dict:
-                state_dict = state_dict['params_ema']
-            
-            # Initialize model
-            model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=scale)
-            model.load_state_dict(state_dict, strict=True)
-            model.eval()
-            
-            # Create upsampler
-            upscale._upsampler = RealESRGANer(
-                scale=scale,
-                model_path=model_path,
-                model=model,
-                tile=0,
-                pre_pad=0,
-                half=False
-            )
-            
-        output_array, _ = upscale._upsampler.enhance(image, outscale=scale)
-        
-        return output_array
-        
-    except Exception as e:
-        print(f"Error during upscaling: {e}")
-        return image
-
-def process_book_with_transformations(book_directory, output_dir, transformations=None, pbar=None):
-
-    # Default transformation sequence if none provided
-    if transformations is None:
-        transformations = ['correct_skew', 'ensure_300ppi']
-    
-    def safe_cuda_cleanup():
-        """Safely clear CUDA memory with proper synchronization"""
-        try:
-            if torch.cuda.is_available():
-                # Wait for all CUDA operations to complete
-                torch.cuda.synchronize()
-                # Small delay to ensure operations are truly finished
-                time.sleep(0.1)
-                # Clear the cache
-                torch.cuda.empty_cache()
-        except Exception as e:
-            # Don't crash if CUDA cleanup fails
-            print(f"Warning: CUDA cleanup failed: {e}")
-
-    def safe_delete_image(image_var):
-        """Safely delete image variable with proper checks"""
-        try:
-            if image_var is not None:
-                # If it's a torch tensor, move to CPU first
-                if hasattr(image_var, 'cpu'):
-                    image_var = image_var.cpu()
-                del image_var
-        except Exception as e:
-            print(f"Warning: Failed to delete image variable: {e}")
-
-    
-    # Available transformation functions
-    transform_functions = {
-        'correct_skew': correct_skew,
-        'normalize_image': normalize_image,
-        'ensure_300ppi': ensure_300ppi,
-        'remove_bleed_dual_layer': remove_bleed_dual_layer,
-        'denoise_image': denoise_image,
-        'sharpen_image': sharpen_image,
-        'enhance_contrast': enhance_contrast,
-        'morphological_operations': morphological_operations,
-        'binarize_image': binarize_image,
-        'apply_binary_mask': apply_binary_mask,
-        'upscale': upscale,
-    }
-    
-    # Handle both list and dictionary input formats
-    if isinstance(transformations, dict):
-        transform_config = transformations
-        transform_order = list(transformations.keys())
-    else:
-        # Convert list to dictionary with empty parameters
-        transform_config = {t: {} for t in transformations}
-        transform_order = transformations
-    
-    # Validate transformations
-    invalid_transforms = [t for t in transform_order if t not in transform_functions]
-    if invalid_transforms:
-        raise ValueError(f"Invalid transformations: {invalid_transforms}")
-    
-    # Extract book number from directory path
-    book_name = os.path.basename(book_directory.rstrip('/\\'))
-    book_number_match = re.search(r'(\d+)', book_name)
-    if book_number_match:
-        book_number = book_number_match.group(1)
-    else:
-        # Fallback: use the entire directory name if no number found
-        book_number = book_name
-    
-    # Create output directory
-    os.makedirs(output_dir, exist_ok=True)
-    
-    # Get list of page image files and sort them numerically
-    image_files = []
-    for f in os.listdir(book_directory):
-        if f.lower().endswith(('.png', '.jpg', '.jpeg', '.tiff', '.bmp')):
-            # Extract page number for sorting
-            page_match = re.search(r'page(\d+)', f.lower())
-            if page_match:
-                page_num = int(page_match.group(1))
-                image_files.append((page_num, f))
-    
-    # Sort by page number
-    image_files.sort(key=lambda x: x[0])
-    
-    if not image_files:
-        print(f"No page image files found in {book_directory}.")
-        return {'processed': 0, 'errors': 0, 'total': 0, 'book_number': book_number}
-    
-    # Statistics
-    stats = {
-        'processed': 0, 
-        'errors': 0, 
-        'total': len(image_files), 
-        'book_number': book_number
-    }
-    error_log = []
-    
-    # Process images
-    for page_num, image_name in image_files:
-        image_path = os.path.join(book_directory, image_name)
-        current_image = None
-        previous_image = None
-        
-        try:
-            # Load original image
-            current_image = load_image(image_path)
-            
-            # Apply transformations sequentially
-            for i, transform_name in enumerate(transform_order):
-                transform_func = transform_functions[transform_name]
-                transform_params = transform_config[transform_name]
-                
-                # Store reference to previous image for cleanup
-                if i > 0:
-                    previous_image = current_image
-                
-                # Apply transformation with parameters
-                try:
-                    if transform_params:
-                        current_image = transform_func(current_image, **transform_params)
-                    else:
-                        current_image = transform_func(current_image)
-                except Exception as transform_error:
-                    print(f"Warning: Transformation {transform_name} failed: {transform_error}")
-                    # Continue with the previous image if transformation fails
-                    if previous_image is not None:
-                        current_image = previous_image
-                
-                # Safely clean up previous image
-                if i > 0 and previous_image is not None:
-                    safe_delete_image(previous_image)
-                    previous_image = None
-                
-                # Memory cleanup after intensive operations
-                if transform_name in ['upscale', 'denoise_image', 'remove_bleed_dual_layer']:
-                    # Wait a moment for operations to complete
-                    time.sleep(0.05)
-                    gc.collect()
-                    safe_cuda_cleanup()
-                
-                # Update progress bar description if provided
-                if pbar:
-                    pbar.set_postfix({
-                        'book': book_number,
-                        'page': f"{page_num}",
-                        'step': f"{i+1}/{len(transform_order)}",
-                        'transform': transform_name
-                    })
-            
-            # Generate output filename: book_{book_number}_{page_number}.png
-            output_filename = f"book_{book_number}_{page_num}.png"
-            output_path = os.path.join(output_dir, output_filename)
-            
-            # Ensure we have a valid image to save
-            if current_image is not None:
-                # Save final processed image
-                cv2.imwrite(output_path, cv2.cvtColor(current_image, cv2.COLOR_RGB2BGR))
-                
-                # Safely clean up final image
-                safe_delete_image(current_image)
-                current_image = None
-                
-                stats['processed'] += 1
-            else:
-                raise ValueError("No valid image to save after transformations")
-            
-            # Gentle memory cleanup after each page
-            gc.collect()
-            safe_cuda_cleanup()
-            
-        except Exception as e:
-            error_msg = f"Error processing {image_name} (page {page_num}): {str(e)}"
-            error_log.append(error_msg)
-            stats['errors'] += 1
-            
-            if pbar:
-                pbar.set_postfix({'book': book_number, 'page': f"{page_num}", 'status': 'ERROR'})
-        
-        finally:
-            # Always clean up any remaining references
-            if current_image is not None:
-                safe_delete_image(current_image)
-            if previous_image is not None:
-                safe_delete_image(previous_image)
-            
-            # Force cleanup in finally block
-            gc.collect()
-            safe_cuda_cleanup()
-        
-        # Update progress bar if provided
-        if pbar:
-            pbar.update(1)
-    
-    # Clean up upscaler cache safely
-    try:
-        if hasattr(upscale, '_upsampler'):
-            del upscale._upsampler
-    except Exception as e:
-        print(f"Warning: Failed to clean upscaler cache: {e}")
-    
-    # Final comprehensive cleanup
-    gc.collect()
-    safe_cuda_cleanup()
-    
-    if error_log:
-        print(f"\nErrors encountered in Book {book_number}:")
-        for error in error_log:
-            print(f"  - {error}")
-    
-    return stats
-
-def process_multiple_books(books_base_dir, output_dir, book_transformations=None):
-    
-    if not os.path.exists(books_base_dir):
-        raise ValueError(f"Books base directory does not exist: {books_base_dir}")
-    
-    # Only process books that have transformations specified
-    if not book_transformations:
-        print("No book transformations specified. Nothing to process.")
-        return {'books_processed': 0, 'total_pages': 0, 'total_errors': 0, 'book_results': {}}
-    
-    # Get all subdirectories and filter only those with transformations
-    all_book_dirs = [d for d in os.listdir(books_base_dir) 
-                     if os.path.isdir(os.path.join(books_base_dir, d))]
-    
-    # Filter to only include books that have transformations specified
-    books_to_process = [book_dir for book_dir in all_book_dirs 
-                       if book_dir in book_transformations]
-    
-    if not books_to_process:
-        print(f"No books found in {books_base_dir} that match the transformation specifications.")
-        print(f"Available books: {all_book_dirs}")
-        print(f"Books with transformations: {list(book_transformations.keys())}")
-        return {'books_processed': 0, 'total_pages': 0, 'total_errors': 0, 'book_results': {}}
-    
-    # Calculate total pages across all books to be processed
-    total_pages = 0
-    book_page_counts = {}
-    
-    for book_dir_name in books_to_process:
-        book_path = os.path.join(books_base_dir, book_dir_name)
-        # Count image files in this book
-        image_files = []
-        for f in os.listdir(book_path):
-            if f.lower().endswith(('.png', '.jpg', '.jpeg', '.tiff', '.bmp')):
-                page_match = re.search(r'page(\d+)', f.lower())
-                if page_match:
-                    image_files.append(f)
-        
-        book_page_counts[book_dir_name] = len(image_files)
-        total_pages += len(image_files)
-    
-    print(f"Processing {len(books_to_process)} books with {total_pages} total pages:")
-    for book_name in books_to_process:
-        transformations = book_transformations[book_name]
-    overall_stats = {
-        'books_processed': 0,
-        'total_pages': 0,
-        'total_errors': 0,
-        'book_results': {}
-    }
-    
-    # Create single progress bar for all books
-    with tqdm(total=total_pages, desc="Transforming page Images", unit="page") as pbar:
-        for book_dir_name in sorted(books_to_process):
-            book_path = os.path.join(books_base_dir, book_dir_name)
-            transformations = book_transformations[book_dir_name]
-            
-            try:
-                book_stats = process_book_with_transformations(
-                    book_path, 
-                    output_dir, 
-                    transformations,
-                    pbar=pbar  # Pass the progress bar
-                )
-                
-                overall_stats['books_processed'] += 1
-                overall_stats['total_pages'] += book_stats['processed']
-                overall_stats['total_errors'] += book_stats['errors']
-                overall_stats['book_results'][book_dir_name] = book_stats
-                
-            except Exception as e:
-                print(f"Failed to process book {book_dir_name}: {str(e)}")
-                overall_stats['total_errors'] += 1
-                # Update progress bar for skipped pages
-                if book_dir_name in book_page_counts:
-                    pbar.update(book_page_counts[book_dir_name])
-    
-    return overall_stats
-
-def copy_all_transcripts():
-    
-    """
-    Copy all transcript files from book folders to destination with Book_{book_number}_{page_number} naming.
-    """
-    source_base = "data/GAN-DATA/2_splitted/transcripts"
-    dest_folder = "data/GAN-DATA/3_processed/transcripts"
-    
-    # Create destination folder if it doesn't exist
-    os.makedirs(dest_folder, exist_ok=True)
-    
-    base_path = Path(source_base)
-    
-    if not base_path.exists():
-        print(f"Error: Source folder '{source_base}' does not exist.")
-        return
-    
-    # Find all book folders
-    book_folders = [f for f in base_path.iterdir() if f.is_dir() and f.name.startswith('book')]
-    
-    if not book_folders:
-        print(f"No book folders found in '{source_base}'")
-        return
-    
-    total_copied = 0
-    
-    for book_folder in sorted(book_folders):
-        # Extract book number from folder name
-        book_match = re.search(r'book(\d+)', book_folder.name, re.IGNORECASE)
-        
-        if not book_match:
-            print(f"Warning: Could not extract book number from folder '{book_folder.name}', skipping...")
-            continue
-            
-        book_number = book_match.group(1)
-        
-        # Get all files in the book folder
-        files = [f for f in book_folder.iterdir() if f.is_file()]
-        
-        if not files:
-            print(f"No files found in '{book_folder}'")
-            continue
-        
-        book_copied = 0
-        
-        for file_path in files:
-            filename = file_path.name
-            
-            # Extract page number from filename
-            page_match = re.search(r'(?:page_?|transcript_page_?|^)(\d+)', filename, re.IGNORECASE)
-            
-            if page_match:
-                page_number = page_match.group(1)
-            else:
-                # Use sequential numbering if no page number found
-                page_number = str(book_copied + 1).zfill(3)
-                print(f"Warning: Could not extract page number from '{filename}', using {page_number}")
-            
-            # Create new filename
-            file_extension = file_path.suffix
-            new_filename = f"Book_{book_number}_{page_number}{file_extension}"
-            dest_file_path = Path(dest_folder) / new_filename
-            
-            try:
-                shutil.copy2(file_path, dest_file_path)
-                book_copied += 1
-                total_copied += 1
-            except Exception as e:
-                print(f"Error copying {filename}: {str(e)}")
-                
-def copy_all_images():
-    import gc
-    
-    source_base = "data/GAN-DATA/2_splitted/books"
-    dest_folder = "data/GAN-DATA/2_splitted/images"
-    
-    os.makedirs(dest_folder, exist_ok=True)
-    base_path = Path(source_base)
-    book_folders = [f for f in base_path.iterdir() if f.is_dir() and f.name.startswith('book')]
-    
-    for book_folder in sorted(book_folders):
-        book_match = re.search(r'book(\d+)', book_folder.name, re.IGNORECASE)
-        book_number = book_match.group(1)
-        files = [f for f in book_folder.iterdir() if f.is_file()]
-        
-        book_copied = 0
-        for file_path in files:
-            filename = file_path.name
-            page_match = re.search(r'(?:page_?|image_page_?|^)(\d+)', filename, re.IGNORECASE)
-            
-            if page_match:
-                page_number = page_match.group(1)
-            else:
-                page_number = str(book_copied + 1).zfill(3)
-            
-            file_extension = file_path.suffix
-            new_filename = f"Book_{book_number}_{page_number}{file_extension}"
-            dest_file_path = Path(dest_folder) / new_filename
-            ################################################################
-            img = load_image(file_path)  
-            cv2.imwrite(str(dest_file_path), cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
-
-            # Free memory after each image
-            del img
-            gc.collect()
-            
-            # Clear GPU memory if using CUDA
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
-            ###############################################################    
-            # shutil.copy2(file_path, dest_file_path)
-            book_copied += 1
-#################################################################################################################################
-                                        # Text Detection and Bounding Boxes
-#################################################################################################################################
-
-def plot_bounding_boxes(image_path, text_file_path, save_path=None, figsize=(12, 8), line_width=2,show_image=True):
-
-    # Read the image
-    image = cv2.imread(image_path)
-    if image is None:
-        print(f"Error: Could not read image from {image_path}")
-        return
-    
-    # Convert BGR to RGB for matplotlib
-    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-    
-    # Read bounding boxes from text file
-    bounding_boxes = []
-    try:
-        with open(text_file_path, 'r') as f:
-            for line in f:
-                line = line.strip()
-                if line:  # Skip empty lines
-                    coords = [float(c) for c in line.split(',')]
-                    if len(coords) >= 8:  # Ensure we have at least 8 coordinates
-                        bounding_boxes.append(coords[:8])  # Take first 8 coordinates
-                    else:
-                        print(f"Warning: Invalid bounding box format: {line}")
-    except FileNotFoundError:
-        print(f"Error: Could not read text file from {text_file_path}")
-        return
-    except Exception as e:
-        print(f"Error reading bounding boxes: {e}")
-        return
-    
-    if not bounding_boxes:
-        print("No valid bounding boxes found in the text file")
-        return
-    
-    # Create the plot
-    fig, ax = plt.subplots(1, 1, figsize=figsize)
-    ax.imshow(image)
-    
-    # Plot each bounding box
-    for bbox in bounding_boxes:
-        # Extract coordinates (x1,y1,x2,y2,x3,y3,x4,y4)
-        x_coords = [bbox[0], bbox[2], bbox[4], bbox[6]]
-        y_coords = [bbox[1], bbox[3], bbox[5], bbox[7]]
-        
-        # Create polygon points (connecting the four corners)
-        polygon_points = list(zip(x_coords, y_coords))
-        
-        # Create and add the polygon patch
-        polygon = Polygon(polygon_points, 
-                         fill=False, 
-                         edgecolor='red', 
-                         linewidth=line_width,
-                         alpha=0.8)
-        ax.add_patch(polygon)
-    
-    # Set title and remove axes
-    ax.axis('off')
-    
-    # Adjust layout
-    plt.tight_layout()
-    
-    # Save or show the image
-    if save_path:
-        plt.savefig(save_path, dpi=300, bbox_inches='tight')
-    if show_image:  
-        plt.show()
-
-def text_detection(input_root, output_root,model_path):
-    print("Generating bounding boxes for each book page...")
-    os.makedirs(output_root, exist_ok=True)
-
-    command = (
-        f"python CRAFT-pytorch/test.py "
-        f"--trained_model={model_path} "
-        f"--result_folder={output_root} "
-        f"--test_folder={input_root}"
-    )
-
-    subprocess.run(command, shell=True, check=True)
-    
-def plot_random_pages(images_dir="data/3_processed/books", 
-                              bbox_dir="data/4_bounding_boxes", 
-                              save_dir="assets"):
-    # Create assets directory if it doesn't exist
-    os.makedirs(save_dir, exist_ok=True)
-    
-    # Get all image files
-    image_files = glob.glob(os.path.join(images_dir, "book_*.png"))
-    
-    # Group files by book number
-    books = defaultdict(list)
-    for img_path in image_files:
-        filename = os.path.basename(img_path)
-        # Extract book number from filename (e.g., "book_2_2.png" -> book_number = 2)
-        parts = filename.replace('.png', '').split('_')
-        if len(parts) >= 2:
-            try:
-                book_number = int(parts[1])
-                books[book_number].append(filename.replace('.png', ''))
-            except ValueError:
-                continue
-    
-    # For each book, randomly select one page and plot
-    plot_counter = 1
-    for book_number, pages in sorted(books.items()):
-        # Randomly select one page from this book
-        selected_page = random.choice(pages)
-        
-        image_path = os.path.join(images_dir, f"{selected_page}.png")
-        bbox_path = os.path.join(bbox_dir, f"{selected_page}.txt")
-        save_path = os.path.join(save_dir, f"plot{plot_counter}.png")
-        
-        # Check if both image and bbox files exist
-        if os.path.exists(image_path) and os.path.exists(bbox_path):
-            
-            try:
-                plot_bounding_boxes(
-                    image_path=image_path,
-                    text_file_path=bbox_path,
-                    save_path=save_path,
-                    show_image=False,  # Set to False to avoid showing the image immediately
-                )
-                plot_counter += 1
-            except Exception as e:
-                print(f"Error plotting {selected_page}: {e}")
-        else:
-            print(f"Missing files for {selected_page}")
-            if not os.path.exists(image_path):
-                print(f"  Missing image: {image_path}")
-            if not os.path.exists(bbox_path):
-                print(f"  Missing bbox: {bbox_path}")
-
-
-#################################################################################################################################
-                                        # Mapping Bounding Boxes to Transcript
-#################################################################################################################################
-pytesseract.pytesseract.tesseract_cmd = r".\models\Tesseract-OCR\tesseract.exe"
-
-def similarity_score(a, b):
-    """Calculate string similarity between two strings"""
-    return SequenceMatcher(None, a.lower(), b.lower()).ratio()
-
-def map_bounding_boxes_to_transcript(image_path, bbox_path, transcript_path, output_path, tesseract_model=1, similarity_threshold=0.5):
-    """Maps bounding boxes to transcript for a single image file."""
-    # Read transcript
-    with open(transcript_path, 'r') as f:
-        transcript = f.read().strip()
-
-    # Split transcript into words
-    transcript_lines = transcript.split('\n')
-    transcript_words = []
-    for line in transcript_lines:
-        transcript_words.extend(line.strip().split())
-
-    # Read the image
-    image = cv2.imread(image_path)
-    if image is None:
-        print(f"Warning: Could not read image from {image_path}")
-        return 0, 0, 0
-
-    # Read bounding boxes
-    with open(bbox_path, 'r') as f:
-        bounding_boxes = [line.strip() for line in f.readlines() if line.strip()]
-
-    # Result mappings
-    mappings = []
-    total_bbox_count = len(bounding_boxes)
-
-    # Process each bounding box
-    for bbox in bounding_boxes:
-        try:
-            # Parse the bounding box coordinates
-            coords = [float(c) for c in bbox.split(',')]
-
-            # Make sure we have enough coordinates
-            if len(coords) < 8:
-                print(f"Warning: Invalid bounding box format: {bbox}")
-                continue
-
-            x_coords = [coords[0], coords[2], coords[4], coords[6]]
-            y_coords = [coords[1], coords[3], coords[5], coords[7]]
-
-            # Get the rectangular region (min/max coordinates)
-            x_min, y_min = int(min(x_coords)), int(min(y_coords))
-            x_max, y_max = int(max(x_coords)), int(max(y_coords))
-
-            # Ensure coordinates are within image boundaries
-            x_min = max(0, x_min)
-            y_min = max(0, y_min)
-            x_max = min(image.shape[1], x_max)
-            y_max = min(image.shape[0], y_max)
-
-            # Skip if region is too small
-            if x_max - x_min < 5 or y_max - y_min < 5:
-                continue
-
-            # Extract the region from the image
-            roi = image[y_min:y_max, x_min:x_max]
-
-            # Skip if ROI is empty
-            if roi.size == 0:
-                continue
-
-            # Use pytesseract to get the text in this bounding box
-            detected_text = pytesseract.image_to_string(roi,
-                                                       config=f'--psm 7 --oem {tesseract_model} -l spa').strip()
-
-            # Skip if no text detected
-            if not detected_text:
-                continue
-            
-            detected_text = detected_text.lower() 
-            # Find the closest matching word in the transcript
-            max_similarity = 0
-            best_match = None
-
-            for word in transcript_words:
-                sim = similarity_score(detected_text, word)
-                if sim > max_similarity:
-                    max_similarity = sim
-                    best_match = word
-
-            # Only consider it a match if similarity is above threshold
-            if max_similarity > similarity_threshold and best_match:
-                # Add to mappings and remove matched word from transcript to avoid duplicates
-                mappings.append((best_match, bbox, max_similarity))
-                transcript_words.remove(best_match)
-        except Exception as e:
-            print(f"Error processing bounding box {bbox}: {e}")
-
-    # Write results to output file
-    os.makedirs(os.path.dirname(output_path), exist_ok=True)
-    with open(output_path, 'w') as f:
-        for word, bbox, _ in mappings:
-            f.write(f"{word}\t{bbox}\n")
-
-    return len(transcript_words) + len(mappings), len(mappings), total_bbox_count
-
-def mapping_bounding_boxes(image_dir, bbox_dir, transcript_dir, output_dir, tesseract_model=1, similarity_threshold=0.5):
-    """Processes all files for which transcripts are available and returns summary statistics."""
-    from tqdm import tqdm
-    total_words = 0
-    total_mapped = 0
-    total_bb = 0
-    files_processed = 0
-    no_transcript_files = 0
-
-    # Make sure the output directory exists
-    os.makedirs(output_dir, exist_ok=True)
-
-    # Check if directories exist
-    for dir_path in [image_dir, bbox_dir, transcript_dir]:
-        if not os.path.exists(dir_path):
-            print(f"Error: Directory not found: {dir_path}")
-            return
-
-    # Get list of image files
-    image_files = [f for f in os.listdir(image_dir) if os.path.isfile(os.path.join(image_dir, f))]
-
-    print(f"Processing {len(image_files)} files...")
-    
-    # Use tqdm for progress bar
-    for image_file in tqdm(image_files, desc="Mapping bounding boxes", unit="file"):
-        base_name = os.path.splitext(image_file)[0]
-
-        image_path = os.path.join(image_dir, image_file)
-        transcript_path = os.path.join(transcript_dir, f"{base_name}.txt")
-        bbox_path = os.path.join(bbox_dir, f"{base_name}.txt")
-        output_path = os.path.join(output_dir, f"{base_name}_mapped.txt")
-
-        # Check if transcript exists
-        if not os.path.exists(transcript_path):
-            no_transcript_files += 1
-            continue
-
-        # Check if bbox file exists
-        if not os.path.exists(bbox_path):
-            continue
-
-        # Process the files
-        try:
-            words_count, mapped_count, bbox_count = map_bounding_boxes_to_transcript(
-                image_path, bbox_path, transcript_path, output_path,
-                tesseract_model, similarity_threshold
-            )
-
-            total_words += words_count
-            total_mapped += mapped_count
-            total_bb += bbox_count
-            files_processed += 1
-
-        except Exception as e:
-            print(f"Error processing file {base_name}: {e}")
-
-    # Print summary statistics
-    print(f"\n=== MAPPING SUMMARY ===")
-    print(f"1. Total number of words in transcript files: {total_words}")
-    print(f"2. Total number of bounding boxes detected: {total_bb}")
-    print(f"3. Total number of image-word pairs successfully mapped: {total_mapped}")
-    
-    return total_words, total_bb, total_mapped
-
-#################################################################################################################################
-                                        # building dataset from mapped files
-#################################################################################################################################
-
-def extract_and_process_all_regions(image_root, aligned_root, output_root, csv_output_path):
-
-    # Create output directory
-    os.makedirs(output_root, exist_ok=True)
-    
-    df_records = []
-    global_index = 0
-    
-    # Get all alignment files
-    alignment_files = [f for f in os.listdir(aligned_root) if f.endswith('_mapped.txt')]
-    
-    for alignment_file in tqdm(alignment_files, desc="Creating Word Dataset", unit="image"):
-        # Construct paths
-        image_file = alignment_file.replace("_mapped.txt", ".png")
-        image_path = os.path.join(image_root, image_file)
-        aligned_path = os.path.join(aligned_root, alignment_file)
-        
-        # Extract book name from image filename
-        # Format: {book_name}_{page_number}.png
-        book_name = "_".join(image_file.split("_")[:-1])  # Everything except the last part (page number)
-        
-        # Load image
-        image = cv2.imread(image_path)
-        if image is None:
-            print(f"Warning: Could not read image {image_path}")
-            continue
-        
-        # Check if alignment file exists
-        if not os.path.exists(aligned_path):
-            print(f"Warning: Alignment file not found {aligned_path}")
-            continue
-        
-        # Process alignment file
-        with open(aligned_path, "r", encoding="utf-8") as f:
-            lines = [line.strip().split("\t") for line in f if "\t" in line]
-        
-        # Process each region in the current image
-        for text, bbox_str in lines:
-            output_filename = f"image{global_index}.png"
-            output_path = os.path.join(output_root, output_filename)
-            
-            # Parse bounding box
-            try:
-                bbox = list(map(float, bbox_str.split(',')))
-                pts = np.array(bbox, dtype=np.int32).reshape((4, 2))
-                
-                x_min, y_min = np.min(pts, axis=0)
-                x_max, y_max = np.max(pts, axis=0)
-            except (ValueError, IndexError):
-                print(f"Warning: Invalid bounding box format: {bbox_str} - skipping")
-                continue
-            
-            # Check if bounding box has valid dimensions
-            if x_min >= x_max or y_min >= y_max:
-                print(f"Warning: Invalid bounding box dimensions: {x_min},{y_min},{x_max},{y_max} - skipping")
-                continue
-            
-            # Ensure box is within image boundaries
-            height, width = image.shape[:2]
-            x_min = max(0, x_min)
-            y_min = max(0, y_min)
-            x_max = min(width, x_max)
-            y_max = min(height, y_max)
-            
-            # Check again after clamping to image boundaries
-            if x_max - x_min < 2 or y_max - y_min < 2:
-                continue
-            
-            # Crop region
-            cropped_region = image[y_min:y_max, x_min:x_max]
-            
-            # Validate cropping
-            if cropped_region.size == 0 or cropped_region.shape[0] == 0 or cropped_region.shape[1] == 0:
-                print(f"Warning: Empty cropped region - skipping")
-                continue
-            
-            # Save the cropped image
-            cv2.imwrite(output_path, cropped_region)
-            
-            # Add to records with book name
-            df_records.append((output_filename, text, book_name))
-            global_index += 1
-    
-    # Create DataFrame and save CSV with book column
-    df = pd.DataFrame(df_records, columns=["Image", "label", "book"])
-    df.to_csv(csv_output_path, index=False)
-    
-    print(f"Processed {len(df_records)} regions from {len(alignment_files)} images")
-    print(f"Images saved to: {output_root}")
-    print(f"CSV saved to: {csv_output_path}")
-    
-    return df
-
-def analyze_image_sizes(directory):
-    """Analyze the minimum, maximum, and average sizes of all images in a directory."""
-    # Lists to store dimensions and file sizes
-    widths = []
-    heights = []
-
-    # Count valid images
-    total_images = 0
-
-    # Supported image extensions
-    valid_extensions = ['.jpg', '.jpeg', '.png', '.bmp', '.tiff', '.gif']
-
-    # Process each file in the directory
-    for filename in os.listdir(directory):
-        file_path = os.path.join(directory, filename)
-
-        # Skip if not a file or not an image file
-        if not os.path.isfile(file_path):
-            continue
-
-        ext = os.path.splitext(filename)[1].lower()
-        if ext not in valid_extensions:
-            continue
-
-        try:
-            # Get image dimensions using PIL
-            with Image.open(file_path) as img:
-                width, height = img.size
-                widths.append(width)
-                heights.append(height)
-            total_images += 1
-
-        except Exception as e:
-            print(f"Error processing {filename}: {e}")
-
-    # Calculate statistics
-    if total_images > 0:
-        # Dimensions
-        min_width, max_width = min(widths), max(widths)
-        min_height, max_height = min(heights), max(heights)
-        avg_width = sum(widths) / total_images
-        avg_height = sum(heights) / total_images
-
-        # Print results
-        print(f"Analyzed {total_images} images in {directory}")
-        print(f"Dimensions (width x height):")
-        print(f"  Minimum: {min_width} x {min_height} pixels")
-        print(f"  Maximum: {max_width} x {max_height} pixels")
-        print(f"  Average: {avg_width:.1f} x {avg_height:.1f} pixels")
-    else:
-        print(f"No valid images found in {directory}")
-
-def resize_and_pad(input_dir, output_dir, target_height, target_width):
-
-    # Create output directory if it doesn't exist
-    os.makedirs(output_dir, exist_ok=True)
-    
-    # Get list of image files
-    image_files = [f for f in os.listdir(input_dir) 
-                   if f.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp', '.tiff'))]
-    
-    # Process images with progress bar
-    for image_file in tqdm(image_files, desc="Resizing and Padding Images", unit="img"):
-        try:
-            # Load image
-            img_path = os.path.join(input_dir, image_file)
-            img = Image.open(img_path)
-            
-            # Calculate aspect ratio
-            aspect = img.width / img.height
-
-            # Determine new dimensions that fit within target size while preserving aspect ratio
-            if aspect > (target_width / target_height):  # wider than target
-                new_width = target_width
-                new_height = int(new_width / aspect)
-            else:  # taller than target
-                new_height = target_height
-                new_width = int(new_height * aspect)
-
-            # Resize using LANCZOS for high quality
-            resized_img = img.resize((new_width, new_height), Image.LANCZOS)
-
-            # Create new white image of target size
-            padded_img = Image.new("RGB", (target_width, target_height), color="white")
-
-            # Center the resized image
-            paste_x = (target_width - new_width) // 2
-            paste_y = (target_height - new_height) // 2
-            padded_img.paste(resized_img, (paste_x, paste_y))
-
-            # Save the padded image
-            output_path = os.path.join(output_dir, image_file)
-            padded_img.save(output_path)
-            
-            # Close images to free memory
-            img.close()
-            padded_img.close()
-            
-        except Exception as e:
-            print(f"Error processing {image_file}: {str(e)}")
-            continue
-#################################################################################################################################
-                                        # Rendering Latex Source Images
-#################################################################################################################################
-
-def generate_text_image_dataset(
-    csv_path='data/6_word_data/words.csv',
-    output_dir='data/final_dataset',
-    target_dir_prefix='data/final_dataset/target/',
-    image_width=384,
-    image_height=128,
-    base_fontsize=70,
-    matplotlib_config=None,
-    custom_font_path=None,  # New parameter for custom font
-    progress_bar=True
-): 
-    # Configure matplotlib styling
-    if matplotlib_config is None:
-        matplotlib_config = {
-            'text.usetex': False,
-            'mathtext.fontset': 'cm',
-            'font.family': 'serif',
-            'font.serif': ['Times New Roman'],
-            'font.weight': 'bold',
-            'mathtext.bf': 'bold',
-        }
-    
-    # Handle custom font registration
-    custom_font_prop = None
-    if custom_font_path:
-        if os.path.exists(custom_font_path):
-            try:
-                # Register the custom font with matplotlib
-                custom_font_prop = fm.FontProperties(fname=custom_font_path)
-                if progress_bar:
-                    print(f"Successfully loaded custom font: {custom_font_path}")
-            except Exception as e:
-                if progress_bar:
-                    print(f"Warning: Could not load custom font {custom_font_path}: {str(e)}")
-                    print("Falling back to default font configuration")
-                custom_font_prop = None
-        else:
-            if progress_bar:
-                print(f"Warning: Custom font file not found at {custom_font_path}")
-                print("Falling back to default font configuration")
-    
-    # Apply matplotlib configuration (only if not using custom font)
-    if not custom_font_prop:
-        matplotlib.rcParams.update(matplotlib_config)
-    
-    # Load dataframe
-    df = pd.read_csv(csv_path)
-    
-    # Create output directories
-    source_dir = Path(output_dir) / 'source'
-    os.makedirs(source_dir, exist_ok=True)
-    
-    def render_text_image(word, output_path):
-        """Render a single word as an image with perfect centering"""
-        # Create figure with exact pixel dimensions
-        dpi = 150
-        figsize = (image_width/dpi, image_height/dpi)
-        fig = plt.figure(figsize=figsize, dpi=dpi, facecolor='white')
-        
-        # Create axis that fills the figure completely
-        ax = fig.add_axes([0, 0, 1, 1])
-        ax.set_xlim(0, 1)
-        ax.set_ylim(0, 1)
-        
-        # Adjust fontsize based on word length
-        length_adjustment = max(1, min(15, len(word))) / 7.0
-        fontsize = int(base_fontsize / length_adjustment)
-        
-        # Prepare text formatting arguments
-        text_kwargs = {
-            'fontsize': fontsize,
-            'ha': 'center',
-            'va': 'center',
-            'transform': ax.transAxes
-        }
-        
-        # Use custom font if available, otherwise use matplotlib config
-        if custom_font_prop:
-            text_kwargs['fontproperties'] = custom_font_prop
-            display_text = word  # Use plain text with custom font
-        else:
-            # Determine if we should use mathtext formatting
-            use_mathtext = matplotlib_config.get('mathtext.bf') == 'bold' or \
-                          matplotlib_config.get('font.weight') == 'bold'
-            
-            # Format text based on configuration
-            if use_mathtext and 'mathtext' in str(matplotlib_config):
-                display_text = f"$\\mathbf{{{word}}}$"
-            else:
-                display_text = word
-                text_kwargs['fontweight'] = 'bold'
-        
-        # Render text at exact center with precise positioning
-        text_obj = ax.text(0.5, 0.5, display_text, **text_kwargs)
-        
-        # Remove all axes and ensure clean background
-        ax.axis('off')
-        ax.set_facecolor('white')
-        
-        # Save with tight layout to maintain centering
-        buf = io.BytesIO()
-        fig.savefig(buf, format='png', bbox_inches='tight', 
-                    pad_inches=0, facecolor='white', edgecolor='none',
-                    dpi=dpi)
-        plt.close(fig)
-        buf.seek(0)
-        
-        # Load and process the image
-        rendered_img = Image.open(buf)
-        rendered_img = rendered_img.convert('RGB')
-        
-        # Create final canvas with exact target dimensions
-        final_img = Image.new('RGB', (image_width, image_height), (255, 255, 255))
-        
-        # Calculate scaling to fit the rendered text optimally while maintaining aspect ratio
-        rendered_width, rendered_height = rendered_img.size
-        
-        # Scale to use 90% of the canvas for better visual appearance
-        scale_w = (image_width * 0.9) / rendered_width
-        scale_h = (image_height * 0.9) / rendered_height
-        scale = min(scale_w, scale_h)
-        
-        new_width = int(rendered_width * scale)
-        new_height = int(rendered_height * scale)
-        
-        # Resize with high quality resampling
-        resample_method = Image.LANCZOS if hasattr(Image, 'LANCZOS') else Image.ANTIALIAS
-        resized_img = rendered_img.resize((new_width, new_height), resample_method)
-        
-        # Calculate exact center position
-        x_offset = (image_width - new_width) // 2
-        y_offset = (image_height - new_height) // 2
-        
-        # Paste the resized image at the center
-        final_img.paste(resized_img, (x_offset, y_offset))
-        
-        # Save the final perfectly centered image
-        final_img.save(output_path, quality=95, optimize=True)
-        return str(output_path)
-    
-    # Process all words
-    source_paths = []
-    iterator = tqdm(df.iterrows(), total=len(df), desc="Rendering text images") if progress_bar else df.iterrows()
-    
-    for index, row in iterator:
-        word = row['label']
-        
-        # Determine output filename
-        if 'Image' in df.columns:
-            output_filename = row['Image']
-        else:
-            # Create safe filename from word
-            safe_filename = "".join([c if c.isalnum() or c in "_-" else "_" for c in word])
-            output_filename = f"{safe_filename}.png"
-        
-        output_path = source_dir / output_filename
-        
-        try:
-            source_path = render_text_image(word, output_path)
-            source_paths.append(source_path)
-        except Exception as e:
-            if progress_bar:
-                print(f"Error processing {word}: {str(e)}")
-            # Create fallback empty image
-            empty_img = Image.new('RGB', (image_width, image_height), (255, 255, 255))
-            empty_img.save(output_path)
-            source_paths.append(str(output_path))
-    
-    # Update dataframe
-    df['source_path'] = source_paths
-    
-    # Handle target paths
-    if 'Image' in df.columns:
-        df['target_path'] = target_dir_prefix + df['Image']
-        df = df.drop('Image', axis=1)
-    else:
-        # Create target paths based on source paths
-        df['target_path'] = df['source_path'].apply(
-            lambda x: target_dir_prefix + Path(x).name
-        )
-    
-    # Clean up paths and reorder columns
-    df['source_path'] = df['source_path'].apply(lambda x: x.replace("\\", "/"))
-    df = df[["source_path", "target_path","book","label"]]
-    
-    # Save final CSV
-    output_csv_path = Path(output_dir) / 'data.csv'
-    df.to_csv(output_csv_path, index=False)
-    
-    if progress_bar:
-        print(f"Dataset generated successfully!")
-    
-    return df
-
-
-#################################################################################################################################
-                                        # Creating Grid
-#################################################################################################################################
-
-def create_image_grids(df, output_directory, num_grids=1000, grid_size=(3,1), 
-                      target_image_size=(384,128), random_seed=42):
-
-    # Set random seed for reproducibility
-    random.seed(random_seed)
-    np.random.seed(random_seed)
-    
-    # Create output directories
-    output_path = Path(output_directory)
-    output_path.mkdir(parents=True, exist_ok=True)
-    source_dir = output_path / "source"
-    target_dir = output_path / "target"
-    
-    source_dir.mkdir(parents=True, exist_ok=True)
-    target_dir.mkdir(parents=True, exist_ok=True)
-    
-    # Calculate grid dimensions
-    rows, cols = grid_size
-    images_per_grid = rows * cols
-    
-    # Storage for grid information
-    grid_info = []
-    
-    print(f"Creating {num_grids} image grids...")
-    print(f"Grid size: {rows}x{cols} = {images_per_grid} images per grid")
-    print(f"Target image size: {target_image_size}")
-    
-    for grid_idx in tqdm(range(num_grids), desc="Creating grids"):
-        try:
-            # Randomly sample images for this grid
-            sampled_images = df.sample(n=images_per_grid, replace=False)
-            
-            # Create source and target grids
-            source_grid = create_single_grid(sampled_images, 'source_path', 
-                                           grid_size, target_image_size)
-            target_grid = create_single_grid(sampled_images, 'target_path', 
-                                           grid_size, target_image_size)
-            
-            # Save grids
-            source_filename = f"grid_{grid_idx:05d}.png"
-            target_filename = f"grid_{grid_idx:05d}.png"
-            
-            source_path = source_dir / source_filename
-            target_path = target_dir / target_filename
-            
-            source_grid.save(source_path)
-            target_grid.save(target_path)
-            
-            # Store grid information
-            grid_info.append({
-                'grid_id': grid_idx,
-                'source_path': str(source_path),
-                'target_path': str(target_path),
-                'books_in_grid': list(sampled_images['book'].unique()),
-                'labels_in_grid': list(sampled_images['label'].values),
-                'num_unique_books': sampled_images['book'].nunique(),
-                'num_unique_labels': sampled_images['label'].nunique()
-            })
-                
-        except Exception as e:
-            print(f"Error creating grid {grid_idx}: {e}")
-            continue
-    
-    # Create DataFrame with grid information
-    grid_df = pd.DataFrame(grid_info)
-    
-    # Save grid information to CSV
-    grid_df.to_csv(output_path / "grid_info.csv", index=False)
-    
-    print(f"\nSuccessfully created {len(grid_df)} image grids!")
-    
-    return grid_df
-
-def create_single_grid(sampled_images, image_path_column, grid_size, target_image_size):
-    """
-    Create a single grid from sampled images
-    
-    Parameters:
-    sampled_images: DataFrame with sampled images
-    image_path_column: str, column name for image paths ('source_path' or 'target_path')
-    grid_size: tuple, (rows, cols)
-    target_image_size: tuple, (width, height)
-    
-    Returns:
-    PIL Image object of the stacked grid
-    """
-    rows, cols = grid_size
-    img_width, img_height = target_image_size
-    
-    # Create blank canvas for the grid
-    grid_width = cols * img_width
-    grid_height = rows * img_height
-    grid_image = Image.new('RGB', (grid_width, grid_height), color='white')
-    
-    # Place images in grid
-    for idx, (_, row) in enumerate(sampled_images.iterrows()):
-        try:
-            # Load and resize image
-            img = Image.open(row[image_path_column])
-            img = img.convert('RGB')  # Ensure RGB format
-            img = img.resize(target_image_size, Image.Resampling.LANCZOS)
-            
-            # Calculate position in grid
-            grid_row = idx // cols
-            grid_col = idx % cols
-            
-            x = grid_col * img_width
-            y = grid_row * img_height
-            
-            # Paste image into grid
-            grid_image.paste(img, (x, y))
-            
-        except Exception as e:
-            print(f"Error loading image {row[image_path_column]}: {e}")
-            # Create a placeholder image in case of error
-            placeholder = Image.new('RGB', target_image_size, color='gray')
-            grid_image.paste(placeholder, (x, y))
-    
-    return grid_image
\ No newline at end of file
diff --git a/RenAIssance_SyntheticImageGeneration_Saarthak_Gupta/src/model.py b/RenAIssance_SyntheticImageGeneration_Saarthak_Gupta/src/model.py
deleted file mode 100644
index e0d1875a..00000000
--- a/RenAIssance_SyntheticImageGeneration_Saarthak_Gupta/src/model.py
+++ /dev/null
@@ -1,35 +0,0 @@
-from model_utils import *
-
-def train_model_pipeline():
-    generator, discriminator,train_loader, val_loader, test_loader = train_pix2pix("data/GAN-DATA/grid_dataset/grid_info.csv",
-                                                                               epochs=100,
-                                                                               batch_size=32,
-                                                                               val_interval=1,
-                                                                               save_dir="output/GAN_OUTPUT")
-    
-    plot_gan_history("output/GAN_OUTPUT/training_history.csv")
-    
-    # Assuming you already have your trained model and test_loader:
-    visualize_pix2pix_results("output/GAN_OUTPUT/generator_final.pth", test_loader, num_samples=4)
-    
-    # Initialize the pipeline
-    pipeline = GANInferencePipeline(
-        generator_path="output/GAN_OUTPUT/generator_final.pth",  
-        custom_font_path="fonts/RomanAntique.ttf", 
-        device='cuda' if torch.cuda.is_available() else 'cpu'
-    )
-
-    # Example words to convert
-    words = ["confieso", "los", "a","y", "noticias","mucho",'la','todo']
-
-    # Generate handwriting
-    input_grid, output_handwriting = pipeline.generate_handwriting(words)
-
-    # Save results
-    pipeline.save_results(input_grid, output_handwriting, 
-                            output_dir="output/GAN_OUTPUT/inference_results", 
-                            filename_prefix="sample")
-    
-
-if __name__ == "__main__":        
-        train_model_pipeline()
\ No newline at end of file
diff --git a/RenAIssance_SyntheticImageGeneration_Saarthak_Gupta/src/model_utils.py b/RenAIssance_SyntheticImageGeneration_Saarthak_Gupta/src/model_utils.py
deleted file mode 100644
index 16733feb..00000000
--- a/RenAIssance_SyntheticImageGeneration_Saarthak_Gupta/src/model_utils.py
+++ /dev/null
@@ -1,682 +0,0 @@
-from torch.utils.data import Dataset, DataLoader
-from torchvision import transforms
-from PIL import Image
-import pandas as pd
-import torch
-import torch.nn as nn
-import torch
-import torch.nn as nn
-import torch.optim as optim
-from torch.utils.data import DataLoader, random_split
-from tqdm import tqdm
-import os
-import matplotlib.pyplot as plt
-import torchvision.transforms as T
-import torch
-import random
-import pandas as pd
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-import torch
-import torch.nn as nn
-from PIL import Image, ImageDraw, ImageFont
-import matplotlib.pyplot as plt
-import matplotlib.font_manager as fm
-import numpy as np
-import io
-from pathlib import Path
-import os
-from typing import List, Optional, Tuple
-import matplotlib
-
-
-#########################################################################################################################
-# Dataset class for loading word-image pairs
-#########################################################################################################################
-class WordImageDataset(Dataset):
-    def __init__(self, csv_file):
-        self.data = pd.read_csv(csv_file)
-        self.transform = transforms.Compose([
-            transforms.Grayscale(num_output_channels=1),
-            transforms.Resize((256, 256)),
-            transforms.ToTensor(),
-            transforms.Normalize((0.5,), (0.5,))
-        ])
-
-    def __len__(self):
-        return len(self.data)
-
-    def __getitem__(self, idx):
-        source = Image.open(self.data.iloc[idx]['source_path']).convert('RGB')
-        target = Image.open(self.data.iloc[idx]['target_path']).convert('RGB')
-        return self.transform(source), self.transform(target)
-#########################################################################################################################
-# Model
-#########################################################################################################################
-# Generator
-class UNetGenerator(nn.Module):
-    def __init__(self, in_channels=1, out_channels=1, features=64):
-        super().__init__()
-
-        def down(in_f, out_f, norm=True):
-            layers = [nn.Conv2d(in_f, out_f, 4, 2, 1, bias=False)]
-            if norm:
-                layers.append(nn.BatchNorm2d(out_f))
-            layers.append(nn.LeakyReLU(0.2))
-            return nn.Sequential(*layers)
-
-        def up(in_f, out_f, dropout=False):
-            layers = [nn.ConvTranspose2d(in_f, out_f, 4, 2, 1, bias=False),
-                      nn.BatchNorm2d(out_f), nn.ReLU()]
-            if dropout:
-                layers.append(nn.Dropout(0.5))
-            return nn.Sequential(*layers)
-
-        self.down1 = down(in_channels, features, norm=False)
-        self.down2 = down(features, features*2)
-        self.down3 = down(features*2, features*4)
-        self.down4 = down(features*4, features*8)
-        self.down5 = down(features*8, features*8)
-        self.down6 = down(features*8, features*8)
-        self.down7 = down(features*8, features*8)
-        self.down8 = down(features*8, features*8, norm=False)
-
-        self.up1 = up(features*8, features*8, dropout=True)
-        self.up2 = up(features*16, features*8, dropout=True)
-        self.up3 = up(features*16, features*8, dropout=True)
-        self.up4 = up(features*16, features*8)
-        self.up5 = up(features*16, features*4)
-        self.up6 = up(features*8, features*2)
-        self.up7 = up(features*4, features)
-        self.final = nn.Sequential(
-            nn.ConvTranspose2d(features*2, out_channels, 4, 2, 1),
-            nn.Tanh()
-        )
-
-    def forward(self, x):
-        d1 = self.down1(x); d2 = self.down2(d1)
-        d3 = self.down3(d2); d4 = self.down4(d3)
-        d5 = self.down5(d4); d6 = self.down6(d5)
-        d7 = self.down7(d6); d8 = self.down8(d7)
-
-        u1 = self.up1(d8); u2 = self.up2(torch.cat([u1, d7], 1))
-        u3 = self.up3(torch.cat([u2, d6], 1))
-        u4 = self.up4(torch.cat([u3, d5], 1))
-        u5 = self.up5(torch.cat([u4, d4], 1))
-        u6 = self.up6(torch.cat([u5, d3], 1))
-        u7 = self.up7(torch.cat([u6, d2], 1))
-        return self.final(torch.cat([u7, d1], 1))
-
-# Discriminator
-class PatchDiscriminator(nn.Module):
-    def __init__(self, in_channels=2, features=64):
-        super().__init__()
-        self.model = nn.Sequential(
-            nn.Conv2d(in_channels, features, 4, 2, 1),
-            nn.LeakyReLU(0.2),
-            nn.Conv2d(features, features*2, 4, 2, 1),
-            nn.BatchNorm2d(features*2),
-            nn.LeakyReLU(0.2),
-            nn.Conv2d(features*2, features*4, 4, 2, 1),
-            nn.BatchNorm2d(features*4),
-            nn.LeakyReLU(0.2),
-            nn.Conv2d(features*4, features*8, 4, 1, 1),
-            nn.BatchNorm2d(features*8),
-            nn.LeakyReLU(0.2),
-            nn.Conv2d(features*8, 1, 4, 1, 1),
-            nn.Sigmoid()
-        )
-
-    def forward(self, x, y):
-        return self.model(torch.cat([x, y], dim=1))
-#########################################################################################################################
-# Training function for Pix2Pix model
-#########################################################################################################################
-
-def train_pix2pix(csv_file, epochs=100, batch_size=8, lr=2e-4, save_dir="models", val_interval=10):
-    device = 'cuda' if torch.cuda.is_available() else 'cpu'
-    print(f"Using device: {device}")
-
-    # ---- Dataset Splitting ----
-    full_dataset = WordImageDataset(csv_file)
-    total_size = len(full_dataset)
-    train_size = int(0.9 * total_size)
-    val_size = int(0.05 * total_size)
-    test_size = total_size - train_size - val_size
-
-    train_set, val_set, test_set = random_split(full_dataset, [train_size, val_size, test_size], generator=torch.Generator().manual_seed(42))
-
-    train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)
-    val_loader = DataLoader(val_set, batch_size=batch_size, shuffle=False)
-    test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=False)
-
-    # ---- Models ----
-    G = UNetGenerator().to(device)
-    D = PatchDiscriminator().to(device)
-
-    # ---- Optimizers and Loss Functions ----
-    opt_G = optim.Adam(G.parameters(), lr=lr, betas=(0.5, 0.999))
-    opt_D = optim.Adam(D.parameters(), lr=lr, betas=(0.5, 0.999))
-    criterion_GAN = nn.BCELoss()
-    criterion_L1 = nn.L1Loss()
-
-    # ---- History ----
-    history = {
-        "epoch": [],
-        "loss_G": [],
-        "loss_D": [],
-        "val_loss_L1": [],
-        "val_loss_GAN": []
-    }
-
-    for epoch in range(epochs):
-        G.train()
-        D.train()
-
-        loop = tqdm(train_loader, desc=f"Epoch {epoch+1}/{epochs}")
-        running_loss_G = 0.0
-        running_loss_D = 0.0
-
-        for x, y in loop:
-            x, y = x.to(device), y.to(device)
-            valid = torch.ones((x.size(0), 1, 30, 30), device=device)
-            fake = torch.zeros((x.size(0), 1, 30, 30), device=device)
-
-            # ---- Train Generator ----
-            opt_G.zero_grad()
-            y_fake = G(x)
-            pred_fake = D(y_fake, x)
-            loss_GAN = criterion_GAN(pred_fake, valid)
-            loss_L1 = criterion_L1(y_fake, y) * 100
-            loss_G = loss_GAN + loss_L1
-            loss_G.backward()
-            opt_G.step()
-
-            # ---- Train Discriminator ----
-            opt_D.zero_grad()
-            pred_real = D(y, x)
-            loss_real = criterion_GAN(pred_real, valid)
-            pred_fake = D(y_fake.detach(), x)
-            loss_fake = criterion_GAN(pred_fake, fake)
-            loss_D = (loss_real + loss_fake) * 0.5
-            loss_D.backward()
-            opt_D.step()
-
-            running_loss_G += loss_G.item()
-            running_loss_D += loss_D.item()
-
-            loop.set_postfix(loss_G=loss_G.item(), loss_D=loss_D.item())
-
-        avg_loss_G = running_loss_G / len(train_loader)
-        avg_loss_D = running_loss_D / len(train_loader)
-
-        history["epoch"].append(epoch + 1)
-        history["loss_G"].append(avg_loss_G)
-        history["loss_D"].append(avg_loss_D)
-
-        # ---- Validation ----
-        if (epoch + 1) % val_interval == 0 or epoch == 0:
-            G.eval()
-            total_L1 = 0.0
-            total_GAN = 0.0
-
-            with torch.no_grad():
-                for x_val, y_val in val_loader:
-                    x_val, y_val = x_val.to(device), y_val.to(device)
-                    y_pred = G(x_val)
-                    pred = D(y_pred, x_val)
-
-                    loss_l1 = criterion_L1(y_pred, y_val)
-                    loss_gan = criterion_GAN(pred, torch.ones_like(pred))
-
-                    total_L1 += loss_l1.item()
-                    total_GAN += loss_gan.item()
-
-            val_L1_avg = total_L1 / len(val_loader)
-            val_GAN_avg = total_GAN / len(val_loader)
-
-            history["val_loss_L1"].append(val_L1_avg)
-            history["val_loss_GAN"].append(val_GAN_avg)
-
-            print(f"\n[Validation @ Epoch {epoch+1}] L1 Loss: {val_L1_avg:.4f}, GAN Loss: {val_GAN_avg:.4f}\n")
-        else:
-            history["val_loss_L1"].append(np.nan)
-            history["val_loss_GAN"].append(np.nan)
-
-    # ---- Save Final Models ----
-    os.makedirs(save_dir, exist_ok=True)
-    torch.save(G.state_dict(), os.path.join(save_dir, "generator_final.pth"))
-    torch.save(D.state_dict(), os.path.join(save_dir, "discriminator_final.pth"))
-
-    # ---- Save Training History ----
-    pd.DataFrame(history).to_csv(os.path.join(save_dir, "training_history.csv"), index=False)
-    print("Training complete. Models and history saved.")
-
-    return G, D, train_loader, val_loader, test_loader
-
-
-#########################################################################################################################
-def plot_gan_history(csv_path):
-    # Load the history CSV
-    df = pd.read_csv(csv_path)
-
-    # Plot generator and discriminator losses
-    plt.figure(figsize=(10, 5))
-    plt.plot(df['epoch'], df['loss_G'], label='Generator Loss', linewidth=2)
-    plt.plot(df['epoch'], df['loss_D'], label='Discriminator Loss', linewidth=2)
-
-    # Plot validation losses if present
-    if 'val_loss_L1' in df.columns:
-        plt.plot(df['epoch'], df['val_loss_L1'], label='Val L1 Loss', linestyle='--', color='green')
-    if 'val_loss_GAN' in df.columns:
-        plt.plot(df['epoch'], df['val_loss_GAN'], label='Val GAN Loss', linestyle='--', color='orange')
-
-    # Labels and legend
-    plt.xlabel('Epoch')
-    plt.ylabel('Loss')
-    plt.title('GAN Training History')
-    plt.legend()
-    plt.grid(True)
-    plt.tight_layout()
-
-    # Save the plot
-    save_path = csv_path.replace('.csv', '_history.png')
-    plt.savefig(save_path)
-
-
-def visualize_pix2pix_results(generator_path, dataloader, num_samples=5, figsize=(12, 4)):
-    """
-    Visualize Pix2Pix outputs given a test dataloader.
-
-    Args:
-        generator_path (str): Path to the trained generator model.
-        dataloader (DataLoader): PyTorch DataLoader for test data.
-        num_samples (int): Number of samples to visualize.
-        figsize (tuple): Width x Height of each row in the plot.
-    """
-    device = 'cuda' if torch.cuda.is_available() else 'cpu'
-    print(f"Using device: {device}")
-
-    # Load trained generator
-    generator = UNetGenerator(in_channels=1, out_channels=1)
-    generator.load_state_dict(torch.load(generator_path, map_location=device))
-    generator.eval().to(device)
-
-    # Image un-normalization for display
-    to_pil = T.Compose([
-        T.Normalize((0.0,), (1 / 0.5,)),  # std
-        T.Normalize((-0.5,), (1.0,)),     # mean
-        T.ToPILImage()
-    ])
-
-    # Gather samples from dataloader
-    samples = []
-    for batch in dataloader:
-        inputs, targets = batch
-        for i in range(inputs.size(0)):
-            samples.append((inputs[i], targets[i]))
-            if len(samples) >= num_samples:
-                break
-        if len(samples) >= num_samples:
-            break
-
-    # Plotting
-    fig, axs = plt.subplots(len(samples), 3, figsize=(figsize[0], figsize[1] * len(samples)))
-    if len(samples) == 1:
-        axs = [axs]  # For consistent 2D indexing
-
-    fig.suptitle('Pix2Pix GAN Results: Font → Handwriting Translation', fontsize=16, fontweight='bold', y=0.98)
-
-    for row_idx, (input_img, target_img) in enumerate(samples):
-        input_tensor = input_img.unsqueeze(0).to(device)
-        with torch.no_grad():
-            output_tensor = generator(input_tensor)
-
-        input_pil = to_pil(input_img.cpu())
-        output_pil = to_pil(output_tensor.squeeze(0).cpu())
-        target_pil = to_pil(target_img.cpu())
-
-        images = [input_pil, output_pil, target_pil]
-        for col_idx, image in enumerate(images):
-            axs[row_idx][col_idx].imshow(image, cmap='gray')
-            axs[row_idx][col_idx].axis('off')
-
-            # Column titles only on the first row
-            if row_idx == 0:
-                if col_idx == 0:
-                    axs[row_idx][col_idx].set_title("Input Font", fontsize=12, fontweight='bold')
-                elif col_idx == 1:
-                    axs[row_idx][col_idx].set_title("Generated Handwriting", fontsize=12, fontweight='bold', color='blue')
-                elif col_idx == 2:
-                    axs[row_idx][col_idx].set_title("Target Handwriting", fontsize=12, fontweight='bold', color='green')
-
-    plt.tight_layout()
-    plt.subplots_adjust(top=0.92, hspace=0.3, wspace=0.2)
-    save_path = generator_path.replace('generator_final.pth', 'test_visualizations.png')
-    plt.savefig(save_path)
-
-#############################################################################################################################
-# Inference Class
-#############################################################################################################################
-class GANInferencePipeline:
-    def __init__(self, 
-                 generator_path: str,
-                 custom_font_path: Optional[str] = None,
-                 device: str = 'cuda' if torch.cuda.is_available() else 'cpu',
-                 single_image_size: Tuple[int, int] = (128, 64),
-                 grid_size: Tuple[int, int] = (4, 2),
-                 base_fontsize: int = 45):
-        """
-        Initialize the GAN inference pipeline.
-        
-        Args:
-            generator_path: Path to the trained generator model
-            custom_font_path: Path to custom font file (optional)
-            device: Device to run inference on
-            single_image_size: Size of individual word images (width, height)
-            grid_size: Grid arrangement (rows, cols)
-            base_fontsize: Base font size for rendering
-        """
-        self.device = device
-        self.single_image_size = single_image_size
-        self.grid_size = grid_size
-        self.base_fontsize = base_fontsize
-        self.custom_font_path = custom_font_path
-        
-        # Load the generator model
-        self.generator = self.load_generator(generator_path)
-        
-        # Setup matplotlib configuration
-        self.setup_matplotlib_config()
-        
-        # Load custom font if provided
-        self.custom_font_prop = self.load_custom_font()
-        
-        print(f"GAN Inference Pipeline initialized on {device}")
-        print(f"Single image size: {single_image_size}")
-        print(f"Grid size: {grid_size[0]}x{grid_size[1]} = {grid_size[0]*grid_size[1]} images")
-        
-    def load_generator(self, generator_path: str):
-        """Load the trained generator model."""
-        # You'll need to replace this with your actual UNetGenerator class
-        # For now, I'll create a placeholder
-        generator = UNetGenerator()  # Replace with your actual generator class
-        generator.load_state_dict(torch.load(generator_path, map_location=self.device))
-        generator.to(self.device)
-        generator.eval()
-        return generator
-    
-    def setup_matplotlib_config(self):
-        """Setup matplotlib configuration for text rendering."""
-        matplotlib_config = {
-            'text.usetex': False,
-            'mathtext.fontset': 'cm',
-            'font.family': 'serif',
-            'font.serif': ['Times New Roman'],
-            'font.weight': 'bold',
-            'mathtext.bf': 'bold',
-        }
-        matplotlib.rcParams.update(matplotlib_config)
-    
-    def load_custom_font(self):
-        """Load custom font if provided."""
-        if self.custom_font_path and os.path.exists(self.custom_font_path):
-            try:
-                custom_font_prop = fm.FontProperties(fname=self.custom_font_path)
-                print(f"Successfully loaded custom font: {self.custom_font_path}")
-                return custom_font_prop
-            except Exception as e:
-                print(f"Warning: Could not load custom font {self.custom_font_path}: {str(e)}")
-                print("Falling back to default font configuration")
-        return None
-    
-    def render_single_word(self, word: str) -> Image.Image:
-        """
-        Render a single word as an image.
-        
-        Args:
-            word: The word to render
-            
-        Returns:
-            PIL Image of the rendered word (grayscale)
-        """
-        width, height = self.single_image_size
-        
-        # Create figure with exact pixel dimensions
-        dpi = 150
-        figsize = (width/dpi, height/dpi)
-        fig = plt.figure(figsize=figsize, dpi=dpi, facecolor='white')
-        
-        # Create axis that fills the figure completely
-        ax = fig.add_axes([0, 0, 1, 1])
-        ax.set_xlim(0, 1)
-        ax.set_ylim(0, 1)
-        
-        # Adjust fontsize based on word length
-        length_adjustment = max(1, min(15, len(word))) / 7.0
-        fontsize = int(self.base_fontsize / length_adjustment)
-        
-        # Prepare text formatting arguments
-        text_kwargs = {
-            'fontsize': fontsize,
-            'ha': 'center',
-            'va': 'center',
-            'transform': ax.transAxes,
-            'color': 'black'  # Ensure black text for good contrast
-        }
-        
-        # Use custom font if available
-        if self.custom_font_prop:
-            text_kwargs['fontproperties'] = self.custom_font_prop
-            display_text = word
-        else:
-            display_text = f"$\\mathbf{{{word}}}$"
-            text_kwargs['fontweight'] = 'bold'
-        
-        # Render text at exact center
-        ax.text(0.5, 0.5, display_text, **text_kwargs)
-        
-        # Remove all axes and ensure clean background
-        ax.axis('off')
-        ax.set_facecolor('white')
-        
-        # Save to buffer
-        buf = io.BytesIO()
-        fig.savefig(buf, format='png', bbox_inches='tight', 
-                    pad_inches=0, facecolor='white', edgecolor='none',
-                    dpi=dpi)
-        plt.close(fig)
-        buf.seek(0)
-        
-        # Load and process the image
-        rendered_img = Image.open(buf)
-        rendered_img = rendered_img.convert('L')  # Convert to grayscale
-        
-        # Create final canvas with exact target dimensions (grayscale)
-        final_img = Image.new('L', (width, height), 255)  # White background
-        
-        # Calculate scaling to fit the rendered text optimally
-        rendered_width, rendered_height = rendered_img.size
-        
-        # Scale to use 90% of the canvas for better visual appearance
-        scale_w = (width * 0.9) / rendered_width
-        scale_h = (height * 0.9) / rendered_height
-        scale = min(scale_w, scale_h)
-        
-        new_width = int(rendered_width * scale)
-        new_height = int(rendered_height * scale)
-        
-        # Resize with high quality resampling
-        resample_method = Image.LANCZOS if hasattr(Image, 'LANCZOS') else Image.ANTIALIAS
-        resized_img = rendered_img.resize((new_width, new_height), resample_method)
-        
-        # Calculate exact center position
-        x_offset = (width - new_width) // 2
-        y_offset = (height - new_height) // 2
-        
-        # Paste the resized image at the center
-        final_img.paste(resized_img, (x_offset, y_offset))
-        
-        return final_img
-    
-    def create_grid_from_words(self, words: List[str]) -> Image.Image:
-        """
-        Create a grid image from a list of words.
-        
-        Args:
-            words: List of words to render and arrange in grid
-            
-        Returns:
-            PIL Image of the grid (grayscale)
-        """
-        rows, cols = self.grid_size
-        expected_words = rows * cols
-        
-        # Pad or truncate words list to match grid size
-        if len(words) < expected_words:
-            # Pad with empty strings
-            words = words + [''] * (expected_words - len(words))
-        elif len(words) > expected_words:
-            # Truncate to fit grid
-            words = words[:expected_words]
-            print(f"Warning: Truncated word list to {expected_words} words to fit {rows}x{cols} grid")
-        
-        # Render individual word images
-        word_images = []
-        for word in words:
-            if word.strip():  # Only render non-empty words
-                img = self.render_single_word(word)
-            else:
-                # Create empty image for empty words (grayscale)
-                img = Image.new('L', self.single_image_size, 255)  # White background
-            word_images.append(img)
-        
-        # Create grid
-        single_width, single_height = self.single_image_size
-        grid_width = cols * single_width
-        grid_height = rows * single_height
-        
-        grid_image = Image.new('L', (grid_width, grid_height), 255)  # Grayscale grid
-        
-        # Paste images into grid
-        for i, img in enumerate(word_images):
-            row = i // cols
-            col = i % cols
-            
-            x = col * single_width
-            y = row * single_height
-            
-            grid_image.paste(img, (x, y))
-        
-        return grid_image
-    
-    def preprocess_image(self, image: Image.Image) -> torch.Tensor:
-        """
-        Preprocess image for GAN input.
-        
-        Args:
-            image: PIL Image to preprocess (grayscale)
-            
-        Returns:
-            Preprocessed tensor ready for GAN (1-channel)
-        """
-        # Convert to numpy array
-        image_array = np.array(image)
-        
-        # Convert to tensor
-        image_tensor = torch.from_numpy(image_array).float()
-        
-        # Add channel dimension (HW -> CHW) for grayscale
-        image_tensor = image_tensor.unsqueeze(0)
-        
-        # Normalize to [-1, 1] range (assuming input images are in [0, 255])
-        image_tensor = (image_tensor / 127.5) - 1.0
-        
-        # Add batch dimension
-        image_tensor = image_tensor.unsqueeze(0)
-        
-        return image_tensor.to(self.device)
-    
-    def postprocess_output(self, output_tensor: torch.Tensor) -> Image.Image:
-        """
-        Convert GAN output tensor back to PIL Image.
-        
-        Args:
-            output_tensor: Output tensor from GAN (1-channel or 3-channel)
-            
-        Returns:
-            PIL Image
-        """
-        # Remove batch dimension and move to CPU
-        output_tensor = output_tensor.squeeze(0).cpu()
-        
-        # Denormalize from [-1, 1] to [0, 255]
-        output_tensor = (output_tensor + 1.0) * 127.5
-        output_tensor = torch.clamp(output_tensor, 0, 255)
-        
-        # Convert to numpy
-        output_array = output_tensor.numpy().astype(np.uint8)
-        
-        # Handle different channel configurations
-        if output_tensor.shape[0] == 1:  # Single channel (grayscale)
-            output_array = output_array.squeeze(0)  # Remove channel dimension
-            return Image.fromarray(output_array, mode='L')
-        elif output_tensor.shape[0] == 3:  # Three channels (RGB)
-            output_array = output_array.transpose(1, 2, 0)  # CHW -> HWC
-            return Image.fromarray(output_array, mode='RGB')
-        else:
-            raise ValueError(f"Unsupported number of channels: {output_tensor.shape[0]}")
-    
-    def generate_handwriting(self, words: List[str]) -> Tuple[Image.Image, Image.Image]:
-        """
-        Generate handwriting from a list of words.
-        
-        Args:
-            words: List of words to convert to handwriting
-            
-        Returns:
-            Tuple of (input_grid, output_handwriting) PIL Images
-        """
-        # Create grid from words (grayscale)
-        input_grid = self.create_grid_from_words(words)
-        
-        # Preprocess for GAN
-        input_tensor = self.preprocess_image(input_grid)
-        
-        # Generate handwriting using GAN
-        with torch.no_grad():
-            output_tensor = self.generator(input_tensor)
-        
-        # Postprocess output
-        output_handwriting = self.postprocess_output(output_tensor)
-        
-        return input_grid, output_handwriting
-    
-    def save_results(self, 
-                    input_grid: Image.Image, 
-                    output_handwriting: Image.Image,
-                    output_dir: str = "inference_results",
-                    filename_prefix: str = "result"):
-        """
-        Save the input and output images.
-        
-        Args:
-            input_grid: Input grid image
-            output_handwriting: Generated handwriting image
-            output_dir: Directory to save results
-            filename_prefix: Prefix for saved filenames
-        """
-        os.makedirs(output_dir, exist_ok=True)
-        
-        input_path = os.path.join(output_dir, f"{filename_prefix}_input.png")
-        output_path = os.path.join(output_dir, f"{filename_prefix}_output.png")
-        
-        input_grid.save(input_path)
-        output_handwriting.save(output_path)
-        
-        print(f"Results saved:")
-        print(f"  Input: {input_path}")
-        print(f"  Output: {output_path}")
-        
-        return input_path, output_path