simpleTransformerschat_webApp.ipynb

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  "cells": [
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      "cell_type": "markdown",
      "source": [
        "## install libraries"
      ],
      "metadata": {
        "id": "dUwoJVQtrpG7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade simpletransformers\n",
        "!pip install --upgrade transformers\n",
        "!pip install --upgrade torch\n",
        "!pip install bert-score\n",
        "!pip install nltk                          #يمنح مجموعة واسعة من الأدوات والموارد لمعالجة اللغة الطبيعية\n",
        "!pip install sentence-transformers         #for compute_semantic_similarity"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mpsrQ3u6z6_X",
        "outputId": "60a47882-3fbd-4e5b-e1cf-9e83e919e778",
        "collapsed": true
      },
      "execution_count": 1,
      "outputs": [
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          "name": "stdout",
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            "\u001b[?25hBuilding wheels for collected packages: seqeval\n",
            "  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16161 sha256=9760320294eb7990f27c979a7667d48a1aa2d15818f2bf2cfac959a5fb39a120\n",
            "  Stored in directory: /root/.cache/pip/wheels/1a/67/4a/ad4082dd7dfc30f2abfe4d80a2ed5926a506eb8a972b4767fa\n",
            "Successfully built seqeval\n",
            "Installing collected packages: xxhash, watchdog, tensorboardx, fsspec, dill, pydeck, multiprocess, seqeval, streamlit, datasets, simpletransformers\n",
            "  Attempting uninstall: fsspec\n",
            "    Found existing installation: fsspec 2024.10.0\n",
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            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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            "\u001b[0mSuccessfully installed datasets-3.2.0 dill-0.3.8 fsspec-2024.9.0 multiprocess-0.70.16 pydeck-0.9.1 seqeval-1.2.2 simpletransformers-0.70.1 streamlit-1.41.1 tensorboardx-2.6.2.2 watchdog-6.0.0 xxhash-3.5.0\n",
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            "\u001b[?25hInstalling collected packages: transformers\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.47.1\n",
            "    Uninstalling transformers-4.47.1:\n",
            "      Successfully uninstalled transformers-4.47.1\n",
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            "Collecting bert-score\n",
            "  Downloading bert_score-0.3.13-py3-none-any.whl.metadata (15 kB)\n",
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            "\u001b[?25hInstalling collected packages: bert-score\n",
            "Successfully installed bert-score-0.3.13\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# import libraries"
      ],
      "metadata": {
        "id": "ggfOBVmXsqcT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import json\n",
        "import os\n",
        "import torch\n",
        "import nltk\n",
        "import re\n",
        "import random\n",
        "import logging  #لمنع الاخطاء\n",
        "logging.basicConfig(level=logging.ERROR)\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from simpletransformers.question_answering import QuestionAnsweringModel, QuestionAnsweringArgs\n",
        "from sklearn.model_selection import train_test_split  #for split data\n",
        "from torch.utils.data import DataLoader\n",
        "from torch.optim.lr_scheduler import ReduceLROnPlateau\n",
        "from bert_score import score\n",
        "from nltk.translate.bleu_score import corpus_bleu\n",
        "from nltk.translate.meteor_score import meteor_score\n",
        "from scipy.spatial.distance import cosine  #for compute cosine similarity\n",
        "from sentence_transformers import SentenceTransformer  #for compute_semantic_similarity\n",
        "from google.colab import drive"
      ],
      "metadata": {
        "id": "TaI7nivqso1B"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# connect to Google Drive"
      ],
      "metadata": {
        "id": "W4YroBm6eIQB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dz8ZEbcKKOJy",
        "outputId": "4e4f76c5-0194-49fc-ebf8-6bef41d39076"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# cleaning peocess"
      ],
      "metadata": {
        "id": "DSrHkXKUsLfo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "file_path = '/content/drive/MyDrive/chatBot/Data/data.csv'\n",
        "df = pd.read_csv(file_path)\n",
        "\n",
        "def clean_text(text):\n",
        "    if isinstance(text, str):\n",
        "        text = text.strip()\n",
        "        text = re.sub(r'[،؛:!؟()\\-_\\'\"]', '', text)\n",
        "        text = re.sub(r'[\\u0610-\\u061A\\u064B-\\u0652\\u0670]', '', text)\n",
        "        text = re.sub(r'\\.\\s*$', '', text)\n",
        "    return text\n",
        "\n",
        "for column in df.columns:\n",
        "    df[column] = df[column].astype(str).apply(clean_text)\n",
        "\n",
        "\n",
        "df_cleaned = df.drop_duplicates()\n",
        "\n",
        "# save file after cleaning\n",
        "output_path = '/content/drive/MyDrive/chatBot/Data/cleaned_data_no_full_stop.csv'\n",
        "df_cleaned.to_csv(output_path, index=False)\n",
        "\n",
        "print(\"Data has been cleaned\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yf4QRLyPKWQ9",
        "outputId": "8bce680d-0e1e-411f-db2b-7880ee0786ae"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data has been cleaned\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# convert to json structure"
      ],
      "metadata": {
        "id": "WGozW2C6scDH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "file_path = '/content/drive/MyDrive/chatBot/Data/cleaned_data_no_full_stop.csv'\n",
        "data = pd.read_csv(file_path)\n",
        "\n",
        "def find_answer_position(context, answer):\n",
        "    context_words = context.split()\n",
        "    answer_words = answer.split()\n",
        "\n",
        "    if len(answer_words) >= 2:\n",
        "        first_two_words = answer_words[:2]\n",
        "\n",
        "        for i in range(len(context_words) - len(first_two_words) + 1):\n",
        "            if context_words[i:i+len(first_two_words)] == first_two_words:\n",
        "                return context.find(\" \".join(first_two_words))\n",
        "    return -1\n",
        "\n",
        "def find_end_position(context, answer):\n",
        "    context_words = context.split()\n",
        "    answer_words = answer.split()\n",
        "\n",
        "    if len(answer_words) >= 2:\n",
        "        # تحديد أول كلمتين من الإجابة\n",
        "        last_two_words = answer_words[-2:]\n",
        "\n",
        "        for i in range(len(context_words) - len(last_two_words) + 1):\n",
        "            if context_words[i:i+len(last_two_words)] == last_two_words:\n",
        "                # العثور على موقع بداية الكلمة الثانية\n",
        "                second_word_start = context.find(last_two_words[1], context.find(\" \".join(last_two_words)))\n",
        "\n",
        "                # إرجاع موقع آخر حرف من الكلمة الثانية\n",
        "                return second_word_start + len(last_two_words[1]) - 1\n",
        "    return -1\n",
        "\n",
        "def csv_to_json(data):\n",
        "    json_data = []\n",
        "    id_counter = 1\n",
        "\n",
        "    for _, row in data.iterrows():\n",
        "        context_entry = next((entry for entry in json_data if entry[\"context\"] == row[\"context\"]), None)\n",
        "\n",
        "        if not context_entry:\n",
        "            context_entry = {\n",
        "                \"context\": row[\"context\"],\n",
        "                \"qas\": []\n",
        "            }\n",
        "            json_data.append(context_entry)\n",
        "\n",
        "        # تحديد ما إذا كان السؤال يحتوي على إجابة\n",
        "        is_impossible = pd.isna(row[\"answer\"])\n",
        "\n",
        "        if not is_impossible:\n",
        "            answer_start = find_answer_position(row[\"context\"], row[\"answer\"])\n",
        "            answer_end = find_end_position(row[\"context\"], row[\"answer\"])\n",
        "\n",
        "            # put -1 if not find answer\n",
        "            if answer_start == -1:\n",
        "                answer_start = -1\n",
        "\n",
        "            if answer_end == -1:\n",
        "                answer_end = -1\n",
        "\n",
        "        qa_entry = {\n",
        "            \"id\": f\"{id_counter:05d}\",\n",
        "            \"is_impossible\": False,\n",
        "            \"question\": row[\"question\"],\n",
        "            \"answers\": [\n",
        "                {\n",
        "                    \"text\": row[\"answer\"],\n",
        "                    \"answer_start\": answer_start,\n",
        "                    \"answer_end\": answer_end\n",
        "                }\n",
        "            ] if not is_impossible else []\n",
        "        }\n",
        "        context_entry[\"qas\"].append(qa_entry)\n",
        "        id_counter += 1\n",
        "\n",
        "    return json_data\n",
        "\n",
        "json_output = csv_to_json(data)\n",
        "\n",
        "# save json file\n",
        "output_path = '/content/drive/MyDrive/chatBot/Data/output.json'\n",
        "with open(output_path, 'w', encoding='utf-8') as json_file:\n",
        "    json.dump(json_output, json_file, ensure_ascii=False, indent=4)\n",
        "\n",
        "print(f\"The json file is saved in : {output_path}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wh_dMrJsK4Mn",
        "outputId": "cc2cbdbd-f821-410a-808e-0a47c4bbdfbd"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The json file is saved in : /content/drive/MyDrive/chatBot/Data/output.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# split data into 80% training and 10% testing and 10% evaluate"
      ],
      "metadata": {
        "id": "qcVQ31zyxIW8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the JSON data\n",
        "output_path = '/content/drive/MyDrive/chatBot/Data/output.json'\n",
        "with open(output_path, 'r', encoding='utf-8') as json_file:\n",
        "    data = json.load(json_file)\n",
        "\n",
        "train_data, temp_data = train_test_split(data, test_size=0.2, random_state=42)      # 20% reserved for testing and evaluation\n",
        "test_data, eval_data = train_test_split(temp_data, test_size=0.5, random_state=42)  # Split 20% into 10% test and 10% evaluation\n",
        "\n",
        "# Prepare DataLoader\n",
        "train_loader = DataLoader(train_data, batch_size=16, shuffle=False)\n",
        "test_loader = DataLoader(test_data, batch_size=32, shuffle=False)\n",
        "eval_loader = DataLoader(eval_data, batch_size=32, shuffle=False)\n",
        "\n",
        "# Save the training data in separate file\n",
        "train = '/content/drive/MyDrive/chatBot/Data/train.json'\n",
        "with open(train, 'w', encoding='utf-8') as train_json_file:\n",
        "    json.dump(train_data, train_json_file, ensure_ascii=False, indent=4)\n",
        "\n",
        "# Save the testing data in separate file\n",
        "test = '/content/drive/MyDrive/chatBot/Data/test.json'\n",
        "with open(test, 'w', encoding='utf-8') as test_json_file:\n",
        "    json.dump(test_data, test_json_file, ensure_ascii=False, indent=4)\n",
        "\n",
        "# Save the evaluation data in separate file\n",
        "eval_file = '/content/drive/MyDrive/chatBot/Data/eval.json'\n",
        "with open(eval_file, 'w', encoding='utf-8') as eval_json_file:\n",
        "    json.dump(eval_data, eval_json_file, ensure_ascii=False, indent=4)\n",
        "\n",
        "print(\"Data has been split into training, testing, and evaluation sets and saved as separate JSON files.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "erWLJx-6LERY",
        "outputId": "9a0bd6a4-dc02-46ff-b870-dfbe6786236b"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data has been split into training, testing, and evaluation sets and saved as separate JSON files.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open(r\"/content/drive/MyDrive/chatBot/Data/train.json\", \"r\", encoding=\"utf-8\") as read_file:\n",
        "    train = json.load(read_file)"
      ],
      "metadata": {
        "id": "sxpACChuM1BV"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open(r\"/content/drive/MyDrive/chatBot/Data/test.json\", \"r\", encoding=\"utf-8\") as read_file:\n",
        "    test = json.load(read_file)"
      ],
      "metadata": {
        "id": "LZo9t0MDM3hN"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open(r\"/content/drive/MyDrive/chatBot/Data/eval.json\", \"r\", encoding=\"utf-8\") as read_file:\n",
        "    eval = json.load(read_file)"
      ],
      "metadata": {
        "id": "Qkww9Yt_GuOJ"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# fine tune"
      ],
      "metadata": {
        "id": "RfjFSkpxmiMj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# تثبيت العشوائية لضمان نتائج متسقةعند التدريب\n",
        "seed = 42\n",
        "torch.manual_seed(seed)\n",
        "np.random.seed(seed)\n",
        "random.seed(seed)\n",
        "\n",
        "# تثبيت العشوائية على GPU\n",
        "torch.cuda.manual_seed_all(seed)\n",
        "torch.backends.cudnn.deterministic = True\n",
        "torch.backends.cudnn.benchmark = False\n",
        "torch.backends.cudnn.enabled = True       #للتأكد من أن كل شيء يحدث بشكل حتمي in gpu\n",
        "\n",
        "contexts = [entry['context'] for entry in train]\n",
        "vectorizer = TfidfVectorizer()\n",
        "tfidf_matrix = vectorizer.fit_transform(contexts)\n",
        "\n",
        "model_type = \"bert\"\n",
        "model_name = \"aubmindlab/bert-base-arabertv2\"   # arabert\n",
        "\n",
        "train_args = {\n",
        "    \"reprocess_input_data\": True,\n",
        "    \"overwrite_output_dir\": True,\n",
        "    \"use_cached_eval_features\": True,\n",
        "    \"evaluate_during_training\": True,            # Evaluate during training\n",
        "    \"max_seq_length\": 512,\n",
        "    \"num_train_epochs\": 10,\n",
        "    \"evaluate_during_training_steps\": 500,       # Evaluate every 500 steps\n",
        "    \"early_stopping_patience\": 5,                # Stop if no improvement after 50 evaluations/early stop epoch\n",
        "    \"early_stopping_metric\": \"eval_loss\",        # Metric to monitor\n",
        "    \"early_stopping_metric_minimize\": True,      # Minimize eval_loss\n",
        "    \"save_model_every_epoch\": False,\n",
        "    \"save_eval_checkpoints\": False,\n",
        "    \"n_best_size\": 3,                            # Choose top 3 possible answers\n",
        "    \"train_batch_size\": 16,\n",
        "    \"eval_batch_size\": 32,\n",
        "    \"learning_rate\": 5e-5,                       # معدل التعلم الابتدائي\n",
        "    \"scheduler\": \"linear_schedule_with_warmup\",  # استخدام جدولة صحيحة\n",
        "    \"warmup_ratio\": 0.1,                         # نسبة الخطوات ضمن مرحلة warmup\n",
        "}\n",
        "\n",
        "# تسجيل معدل التعلم في بداية التدريب\n",
        "logging.info(f\"Learning Rate at Start: {train_args['learning_rate']}\")\n",
        "\n",
        "saved_model_dir = \"/content/drive/MyDrive/chatBot/Data/saved_model\"\n",
        "\n",
        "# التحقق مما إذا كان المجلد الأول موجودًا (إذا كان هذا هو أول مرة لتشغيل الخلية)\n",
        "if not os.path.exists(saved_model_dir):\n",
        "    print(\"تدريب أول مرة\")\n",
        "    model = QuestionAnsweringModel(model_type, model_name, args=train_args, use_cuda=True)\n",
        "    # حفظ الأوزان\n",
        "    model.model.save_pretrained(saved_model_dir)\n",
        "    model.tokenizer.save_pretrained(saved_model_dir)\n",
        "    print(\"تم حفظ الأوزان \")\n",
        "else:\n",
        "    print(\"تم العثور على الأوزان المحفوظة - سيتم تحديث الأوزان \")\n",
        "    model = QuestionAnsweringModel(model_type, saved_model_dir, args=train_args, use_cuda=True)\n",
        "    model.model.save_pretrained(saved_model_dir)\n",
        "    model.tokenizer.save_pretrained(saved_model_dir)\n",
        "import shutil\n",
        "outputs_dir = '/content/drive/MyDrive/chatBot/Data/outputs/bert'\n",
        "if os.path.exists(outputs_dir) and os.path.isdir(outputs_dir):\n",
        "      shutil.rmtree(outputs_dir)\n",
        "\n",
        "\n",
        "# دالة لحساب BLEU score باستخدام nltk\n",
        "def compute_bleu_score(preds, labels):\n",
        "    # تحويل النصوص إلى قائمة من القوائم لتناسب صيغة BLEU\n",
        "    preds = [pred.split() for pred in preds]\n",
        "    labels = [[label.split()] for label in labels]\n",
        "    return corpus_bleu(labels, preds)\n",
        "\n",
        "# دالة لحساب BERTScore باستخدام bert-score\n",
        "def compute_bertscore(preds, labels):\n",
        "    P, R, F1 = score(preds, labels, lang=\"ar\")  # Use Arabic language setting for AraBERT\n",
        "    return {\"bert_score\": F1.mean().item()}\n",
        "\n",
        "\n",
        "nltk.download('wordnet')\n",
        "# دالة لحساب METEOR score\n",
        "def compute_meteor_score(preds, labels):\n",
        "    # التأكد من أن النصوص مقسمة إلى كلمات\n",
        "    meteor_scores = [\n",
        "        meteor_score([ref.split()], pred.split()) for ref, pred in zip(labels, preds)\n",
        "    ]\n",
        "    return sum(meteor_scores) / len(meteor_scores)\n",
        "\n",
        "# قائمة لتخزين الـ BERTScore لكل Epoch لرسمة learning curve\n",
        "bert_scores = []\n",
        "# قائمة لتخزين المقاييس بعد كل Epoch لرسمة bar chart\n",
        "all_metrics = []\n",
        "\n",
        "def compute_cosine_similarity(preds, labels):\n",
        "    # تحويل النصوص إلى متجهات باستخدام TfidfVectorizer\n",
        "    vectorizer = TfidfVectorizer()\n",
        "    all_texts = preds + labels\n",
        "    tfidf_matrix = vectorizer.fit_transform(all_texts)\n",
        "\n",
        "    # تقسيم المصفوفة إلى مصفوفات (preds_matrix) و (labels_matrix)\n",
        "    preds_matrix = tfidf_matrix[:len(preds)]\n",
        "    labels_matrix = tfidf_matrix[len(preds):]\n",
        "\n",
        "    # حساب التناسق باستخدام cosine_similarity بين النصوص المتنبأة والنصوص الحقيقية\n",
        "    cosine_similarities = cosine_similarity(preds_matrix, labels_matrix)\n",
        "\n",
        "    # حساب المتوسط العام للتناسق\n",
        "    avg_cosine_similarity = cosine_similarities.diagonal().mean()\n",
        "\n",
        "    return avg_cosine_similarity\n",
        "\n",
        "# تحميل نموذج Sentence-BERT بشكل منفصل\n",
        "modelT = SentenceTransformer('all-MiniLM-L6-v2')\n",
        "# دالة لحساب Semantic Similarity باستخدام Sentence-BERT\n",
        "def compute_semantic_similarity(preds, labels):\n",
        "    # تحويل النصوص إلى تمثيلات (Embeddings)\n",
        "    preds_embeddings = modelT.encode(preds)\n",
        "    labels_embeddings = modelT.encode(labels)\n",
        "\n",
        "    # حساب التشابه بين النصوص باستخدام cosine similarity\n",
        "    similarity_scores = []\n",
        "    for pred_emb, label_emb in zip(preds_embeddings, labels_embeddings):\n",
        "        similarity_scores.append(1 - cosine(pred_emb, label_emb))  # 1 - Cosine Distance gives similarity\n",
        "\n",
        "    return sum(similarity_scores) / len(similarity_scores)\n",
        "\n",
        "def compute_contextual_appropriateness(preds, labels):\n",
        "    # تحويل النصوص إلى تمثيلات (Embeddings) باستخدام النموذج\n",
        "    preds_embeddings = modelT.encode(preds)\n",
        "    labels_embeddings = modelT.encode(labels)\n",
        "\n",
        "    # حساب التشابه بين النصوص باستخدام cosine similarity\n",
        "    similarity_scores = []\n",
        "    for pred_emb, label_emb in zip(preds_embeddings, labels_embeddings):\n",
        "        similarity_scores.append(1 - cosine(pred_emb, label_emb))  # 1 - Cosine Distance gives similarity\n",
        "\n",
        "    # حساب المتوسط للتناسب السياقي\n",
        "    avg_contextual_appropriateness = sum(similarity_scores) / len(similarity_scores)\n",
        "\n",
        "    return avg_contextual_appropriateness\n",
        "\n",
        "\n",
        "def compute_metrics(labels, preds):\n",
        "    bleu = compute_bleu_score(preds, labels)\n",
        "    bert_score = compute_bertscore(preds, labels)\n",
        "    meteor = compute_meteor_score(preds, labels)\n",
        "    semantic_similarity_score = compute_semantic_similarity(preds, labels)\n",
        "    cosine_similarity_score = compute_cosine_similarity(preds, labels)\n",
        "    contextual_appropriateness_score = compute_contextual_appropriateness(preds, labels)\n",
        "\n",
        "    # تسجيل BERTScore  عشان رسمة learning curve فقط\n",
        "    bert_scores.append(bert_score['bert_score'])\n",
        "    # داخل دالة compute_metrics\n",
        "\n",
        "\n",
        "    metrics = {\n",
        "        \"bleu_score\": bleu,\n",
        "        \"bert_score\": bert_score['bert_score'],\n",
        "        \"meteor_score\": meteor,\n",
        "        \"semantic_similarity\": semantic_similarity_score,\n",
        "        \"cosine_similarity\": cosine_similarity_score,\n",
        "        \"contextual_appropriateness\": contextual_appropriateness_score\n",
        "    }\n",
        "\n",
        "    all_metrics.append(metrics)\n",
        "    return metrics\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# تدريب النموذج\n",
        "model.train_model(train_data, eval_data=eval_data,metric=compute_metrics)#model.train_model(train_data): تمرر الى النموذج اثناء عملية التدريبtrain_data\n",
        "                                                                          #تُستخدم أثناء عملية التدريب لتقييم أداء النموذج على بيانات غير موجودة في مجموعة التدريب:eval_data\n",
        "# تسجيل معدل التعلم الحالي بعد التدريب\n",
        "current_learning_rate = model.args.learning_rate\n",
        "logging.info(f\"Current Learning Rate: {current_learning_rate}\")\n",
        "\n",
        "# تقييم النموذج\n",
        "result, texts = model.eval_model(test_data,metric=compute_metrics)#تُستخدم لتقييم أداء النموذج بعد الانتهاء من التدريب بشكل كامل:test_data\n",
        "\n",
        "# طباعة النتائج النهائية\n",
        "print(\"Final Evaluation Results : \", result)\n"
      ],
      "metadata": {
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        "id": "UzWBC33CNeIn",
        "outputId": "760d420c-6a72-48cc-e160-c40f912e81f7"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "تم العثور على الأوزان المحفوظة - سيتم تحديث الأوزان \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
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              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
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            "text/plain": [
              "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
            ],
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        {
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          "text": [
            "convert squad examples to features: 100%|██████████| 1790/1790 [00:08<00:00, 205.05it/s]\n",
            "add example index and unique id: 100%|██████████| 1790/1790 [00:00<00:00, 632875.68it/s]\n"
          ]
        },
        {
          "output_type": "display_data",
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            "text/plain": [
              "Epoch:   0%|          | 0/10 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6200bf2b1558447facfd5d3c2fbd561f"
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/simpletransformers/question_answering/question_answering_model.py:697: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n",
            "  scaler = amp.GradScaler()\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 1 of 10:   0%|          | 0/112 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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        {
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/simpletransformers/question_answering/question_answering_model.py:720: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
            "  with amp.autocast():\n",
            "\n",
            "convert squad examples to features:   0%|          | 0/221 [00:00<?, ?it/s]\u001b[A\n",
            "convert squad examples to features: 100%|██████████| 221/221 [00:00<00:00, 379.80it/s]\n",
            "\n",
            "add example index and unique id: 100%|██████████| 221/221 [00:00<00:00, 467208.26it/s]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Evaluation:   0%|          | 0/7 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f09f5bef34e846bcbff2e2c500feec5c"
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          "metadata": {}
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        {
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          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/simpletransformers/question_answering/question_answering_model.py:1184: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
            "  with amp.autocast():\n"
          ]
        },
        {
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          "metadata": {}
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 2 of 10:   0%|          | 0/112 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/simpletransformers/question_answering/question_answering_model.py:302: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
            "  features = torch.load(cached_features_file)\n"
          ]
        },
        {
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            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
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            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
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            }
          },
          "metadata": {}
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            "application/vnd.jupyter.widget-view+json": {
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            }
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            }
          },
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            "application/vnd.jupyter.widget-view+json": {
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          },
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        {
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            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "convert squad examples to features: 100%|██████████| 226/226 [00:00<00:00, 251.38it/s]\n",
            "add example index and unique id: 100%|██████████| 226/226 [00:00<00:00, 524868.61it/s]\n"
          ]
        },
        {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final Evaluation Results :  {'correct': 155, 'similar': 63, 'incorrect': 8, 'metric': {'bleu_score': 0.825862176105196, 'bert_score': 0.9562832117080688, 'meteor_score': 0.9046357782559985, 'semantic_similarity': 0.9368295907006038, 'cosine_similarity': 0.8961903618584739, 'contextual_appropriateness': 0.9368295907006038}, 'eval_loss': -5.98876953125}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "XKhGILtzIk2k",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7b5aa74b-2273-4fe1-f2be-ceb97621951b"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Learning Curve يظهر تطور الاداء عبر epoch باستخدام BERT score"
      ],
      "metadata": {
        "id": "UZ9tuKb8xwUB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(range(1,len(bert_scores)+1), bert_scores, marker='o', color='#1f77b4', label='BERTScore')\n",
        "plt.title('Learning Curve - BERTScore')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('BERTScore')\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "t4Ji6jILAbXi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "f74d1046-639c-4927-ec4e-1ff3d21a64e6"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Bar chart مقارنة الاداء في كل مقياس من مقاييس الدقة"
      ],
      "metadata": {
        "id": "IJZ597vNx1If"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_metrics_after_training():\n",
        "    if all_metrics:\n",
        "        final_metrics = all_metrics[-1]\n",
        "\n",
        "        # الحصول على أسماء المقاييس والقيم\n",
        "        metric_names = list(final_metrics.keys())\n",
        "        metric_values = list(final_metrics.values())\n",
        "\n",
        "        # إنشاء الرسم البياني\n",
        "        plt.figure(figsize=(10, 6))\n",
        "        plt.bar(metric_names, metric_values, color='skyblue')\n",
        "\n",
        "        # إضافة عنوان وتسميات للمحاور\n",
        "        plt.title(\"Final Evaluation Metrics\", fontsize=14)\n",
        "        plt.xlabel(\"Metrics\", fontsize=12)\n",
        "        plt.ylabel(\"Scores\", fontsize=12)\n",
        "        plt.ylim(0, 1.1)  # نطاق القيم بين 0 و1.1 لتحسين العرض\n",
        "        plt.xticks(rotation=45, fontsize=10)\n",
        "        plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
        "\n",
        "        # عرض القيم فوق الأعمدة\n",
        "        for i, value in enumerate(metric_values):\n",
        "            plt.text(i, value + 0.02, f\"{value:.2f}\", ha='center', fontsize=10)\n",
        "\n",
        "        # عرض الرسم البياني\n",
        "        plt.tight_layout()\n",
        "        plt.show()\n",
        "\n",
        "# رسم المقاييس النهائية بعد اكتمال التدريب\n",
        "plot_metrics_after_training()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "id": "SpN6yLCB_3dL",
        "outputId": "0d710e7d-c1b4-4ac8-b265-2794e285c4cc"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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DBgwwJUqUMFmyZDFeXl7m5ZdfNocPH47X9l7LJaFXat2L4rwy7H4SemVYrI0bN5rWrVub/PnzmyxZspiCBQuaunXrmnHjxpmzZ886tF2zZo2pVauW8fT0tI+LWPd75dq9+nfgwAHTrl074+3tbbJkyWIKFSpkWrRoYVavXm2MMSYyMtJMmDDBNGvWzBQtWtS4ubkZb29v07BhQ7N48WJjs9mS9D0AAFKexRhjUqm+BwAAAADgocLNPQAAAAAAOAlFNwAAAAAATkLRDQAAAACAk1B0AwAAAADgJBTdAAAAAAA4CUU3AAAAAABO4prWCaQ1m82mwMBA5ciRQxaLJa3TAQAAAABkAMYY3bhxQ4ULF5bVmvj57Ie+6A4MDFSxYsXSOg0AAAAAQAZ07tw5FS1aNNHPH/qiO0eOHJLufFE5c+ZM42wAAAAAABnB9evXVaxYMXtNmZiHvuiOvaQ8Z86cFN0AAAAAgAdyv9uUeZAaAAAAAABOQtENAAAAAICTUHQDAAAAAOAkFN0AAAAAADgJRTcAAAAAAE5C0Q0AAAAAgJNQdAMAAAAA4CQU3QAAAAAAOAlFNwAAAAAATkLRDQAAAACAk1B0AwAAAADgJBTdAAAAAAA4CUU3AAAAAABOQtENAAAAAICTUHQDAAAAAOAkFN0AAAAAADgJRTcAAAAAAE5C0Q0AAAAAgJNQdAMAAAAA4CQU3QAAAAAAOAlFNwAAAAAATkLRDQAAAACAk1B0AwAAAADgJBTdAAAAAAA4CUU3AAAAAABOQtENAAAAAICTpKui+9dff1Xr1q1VuHBhWSwWff/99/edJiAgQNWrV5e7u7vKlCmjhQsXOj1PAAAAAACSIl0V3WFhYfLx8dGMGTOS1P706dNq1aqVGjdurIMHD+rNN99Uz549tX79eidnCgAAAADA/bmmdQJxtWjRQi1atEhy+9mzZ6tUqVKaNGmSJKlChQraunWrpkyZoubNmzsrTQAAAAAAkiRdnel+UDt27FCTJk0cYs2bN9eOHTvSKCMAKWXGjBkqWbKkPDw8VKdOHe3evTvRtlFRUXrvvfdUunRpeXh4yMfHR+vWrYvX7sKFC3rllVeUL18+eXp6qnLlytq7d68zuwEAAICHXLo60/2ggoKC5O3t7RDz9vbW9evXdfv2bXl6esabJiIiQhEREfafr1+/LkmKjo5WdHS0JMlqtcpqtcpms8lms9nbxsZjYmJkjLlv3MXFRRaLxT7fuHFJiomJSVLc1dVVxhiHuMVikYuLS7wcE4vTJ/qUkfr03XffafDgwZoxY4Zq166tTz75RM2bN9eff/6pvHnzxst95MiR+vrrrzV79myVK1dOGzZs0Isvvqht27apSpUqkqSQkBDVr19fjRs31tq1a5U3b16dOHFCOXLkUExMDMuJPtEn+kSf6BN9ok/0iT49UJ/uzjMxGbroTg5/f3+NHTs2XvzAgQPKli2bJCl//vwqXbq0Tp8+rcuXL9vbFC1aVEWLFtXx48cVGhpqjz/yyCMqUKCAjhw5otu3b9vj5cuXV+7cuXXgwAGHBV6lShW5ubnFO8NWs2ZNRUZG6tChQ/aYi4uLatWqpdDQUB07dswe9/T0lI+Pj4KDg3Xq1Cl7PFeuXKpQoYICAwN1/vx5e5w+0aeM1KfJkyfrxRdfVKVKlXTr1i29+uqrWrt2rebPn6+nn346Xp8WLVqkTp06KX/+/Lp69apq1qypFi1a6KOPPtKbb74pSZo5c6Zy586tBQsW6Nq1azp27Jjy5MmjkJAQHTlyhOVEn+gTfaJP9Ik+0Sf6RJ8eqE+BgYFKCouJe2ghHbFYLFq5cqVeeOGFRNs0bNhQ1atX19SpU+2xBQsW6M0333RYqHEldKa7WLFiunLlinLmzCnp4T1SQ5/oU3roU3R0tLJnz65vv/1Wzz33nD3+2muv6dq1a1q+fHm83L28vOTv769XX33VHu/evbu2bt2qEydOSLqzsm7atKkCAwO1efNmFS5cWH369FHPnj1ZTvSJPtEn+kSf6BN9ok/06YH7FBoaqnz58ik0NNReSyYkQ5/prlu3rtauXesQ27hxo+rWrZvoNO7u7nJ3d48Xd3V1laur49cR+4XeLXbhJjV+93yTE7dYLAnGE8vxQeP0iT4lFk/tPl26dEkxMTEqVKiQw+/19vbWsWPHEsylefPmmjZtmho3bqzSpUtr06ZNWrFihWJiYuztT506pTlz5mjw4MEaMWKE9uzZo4EDB8rT01PdunVzap/uF8+Iy+l+cfpEn+gTfbpXnD7RJ/pEn+4Vzyh9SiyfePNIUqtUcvPmTR08eFAHDx6UdOeVYAcPHtTZs2clScOHD1fXrl3t7fv06aNTp05p6NChOnbsmGbOnKlvv/1WgwYNSov0AaSRadOmqWzZsipfvrzc3Nzk6+urHj16OKwgbTabqlevrg8//FDVqlVT79691atXL82ePTsNMwcAAEBml66K7r1796patWqqVq2aJGnw4MGqVq2aRo8eLUn6559/7AW4JJUqVUpr1qzRxo0b5ePjo0mTJmnu3Lm8LgzIwLy8vOTi4qKLFy86xC9evKiCBQsmOE3+/Pn1/fffKywsTH///beOHTum7Nmz65FHHrG3KVSokCpWrOgwXYUKFRzWKQDgjDcnxBo/frwsFov9WRMAEIt1T+aWroruJ598UsaYeP8tXLhQkrRw4UIFBATEm+bAgQOKiIjQyZMn1b1791TPG0DKcXNzU40aNbRp0yZ7zGazadOmTfe8dUSSPDw8VKRIEUVHR2v58uV6/vnn7Z/Vr19ff/75p0P748ePq0SJEinbAQAZ1tKlSzV48GD5+flp//798vHxUfPmzXXp0qUE248aNUpz5szRp59+qj/++EN9+vTRiy++qAMHDsRru2fPHs2ZM8f+RgUAiMW65yFgHnKhoaFGkgkNDU3rVAD8vyVLlhh3d3ezcOFC88cff5jevXub3Llzm6CgIGOMMV26dDHDhg2zt9+5c6dZvny5OXnypPn111/NU089ZUqVKmVCQkLsbXbv3m1cXV3NBx98YP766y/z9ddfm6xZs5pFixaldvcApFO1a9c2/fr1s/8cExNjChcubPz9/RNsX6hQITN9+nSH2EsvvWQ6d+7sELtx44YpW7as2bhxo2nUqJEZOHBgiucOIONi3ZNxJbWWTFdnugFAktq3b6+JEydq9OjRqlq1qg4ePKh169bJ29tbknT27Fn9888/9vbh4eEaNWqUKlasqBdffFFFihTR1q1blTt3bnubWrVqaeXKlfrmm29UqVIljRs3TlOnTlXnzp1Tu3sA0qHIyEjt27dPTZo0scesVquaNGmiHTt2JDhNRESEPDw8HGKenp7aunWrQ6xfv35q1aqVw7wBQGLd87DI0E8vB5B5+fr6ytfXN8HP7r7NpFGjRvrjjz/uO89nn31Wzz77bEqkByCTCQ4OVkxMjP3gXqzYNyckpHnz5po8ebIaNmwY780JsZYsWaL9+/drz549Ts0fQMbEuufhwJluAACAZLjfmxPOnTungQMH6uuvv453VgoAkot1T8ZD0Q0AAB56znhzwr59+3Tp0iVVr15drq6ucnV11ebNm/XJJ5/I1dXV4awUgIcT656HA0U3AAB46DnjzQlPP/20Dh8+rIMHD9r/q1mzpjp37qyDBw/KxcXFqX0CkP6x7nk4cE83AACApMGDB6tbt26qWbOmateuralTpyosLEw9evSQJHXt2lVFihSRv7+/JGnXrl26cOGCqlatqgsXLmjMmDGy2WwaOnSoJClHjhyqVKmSw+/Ili2b8uXLFy8O4OHFuifzo+gGAADQnTcnXL58WaNHj1ZQUJCqVq0a780JsfdMSv++OeHUqVPKnj27WrZsqa+++srhzQkAcD+sezI/izHGpHUSaen69evKlSuXQkNDlTNnzrROB3CK8QeC0zoFpJBh1bzSOgUAAAAo6bUk93QDADKVGTNmqGTJkvLw8FCdOnW0e/fuRNtGRUXpvffeU+nSpeXh4SEfHx+tW7fuP80TAAAgLopuAECmsXTpUg0ePFh+fn7av3+/fHx81Lx5c126dCnB9qNGjdKcOXP06aef6o8//lCfPn304osv6sCBA8meJwAAQFxcXs7l5XgIcHl55sHl5fdWp04d1apVS9OnT5d05wmwxYoVU//+/TVs2LB47QsXLqyRI0eqX79+9libNm3k6empRYsWJWueAADg4cDl5QCAh0pkZKT27dunJk2a2GNWq1VNmjTRjh07EpwmIiJCHh4eDjFPT09t3bo12fME8HDi1hYAiaHoBgBkCsHBwYqJibE/7TWWt7e3goKCEpymefPmmjx5sv766y/ZbDZt3LhRK1as0D///JPseQJ4+HBrC4B7oegGADy0pk2bprJly6p8+fJyc3OTr6+vevTo4fBqFgC4n8mTJ6tXr17q0aOHKlasqNmzZytr1qyaP39+gu2/+uorjRgxQi1bttQjjzyivn37qmXLlpo0aVKy5wkg/eI93QCATMHLy0suLi66ePGiQ/zixYsqWLBggtPkz59f33//vcLDw3XlyhUVLlxYw4YN0yOPPJLseeJfPE8i8+B5EomLvQ1l+PDh9lhK3dryIPPEv1j3ZA6Zab3DoXwAQKbg5uamGjVqaNOmTfaYzWbTpk2bVLdu3XtO6+HhoSJFiig6OlrLly/X888//5/nCeDhwK0tAO6HohsAkGkMHjxYn3/+ub744gsdPXpUffv2VVhYmHr06CFJ6tq1q8OZo127dmnFihU6deqUtmzZomeeeUY2m01Dhw5N8jwB4EFxawvwcOHycgBAptG+fXtdvnxZo0ePVlBQkKpWrap169bZzxadPXvWYac2PDxco0aN0qlTp5Q9e3a1bNlSX331lXLnzp3keQJ4uHFrC4D7oegGAGQqvr6+8vX1TfCzgIAAh58bNWqkP/744z/NE8DDLe5tKC+88IKkf29Dud96I/bWlqioKC1fvlzt2rX7z/MEkP5QdAMAAAD/weDBg9WtWzfVrFlTtWvX1tSpU+Pd2lKkSBH5+/tLunNry4ULF1S1alVduHBBY8aMSfDWlnvNE0DGQdENAAAA/Afc2gLgXizGGJPWSaSl69evK1euXAoNDVXOnDnTOh3AKXh1RuaRmV6fgcyPdU/mwboHGQnrnswhI6x3klpL8ohEAAAAAACchKIbAAAAAAAn4Z5uAMA9cZle5pARLtMDACAz4kw3AAAAAABOQtENAAAAAICTcHk5AAAA0hVua8k8uLUF4Ew3AAAAAABOQ9ENAAAAAICTUHQDAAAAAOAkFN0AAAAAADgJRTcAAAAAAE5C0Q0AAAAAgJNQdMOpZsyYoZIlS8rDw0N16tTR7t2779l+6tSpKleunDw9PVWsWDENGjRI4eHh9s9nzZqlKlWqKGfOnMqZM6fq1q2r//3vf87uBgAAAAAkC0U3nGbp0qUaPHiw/Pz8tH//fvn4+Kh58+a6dOlSgu0XL16sYcOGyc/PT0ePHtW8efO0dOlSjRgxwt6maNGiGj9+vPbt26e9e/fqqaee0vPPP6/ff/89tboFAAAAAElG0Q2nmTx5snr16qUePXqoYsWKmj17trJmzar58+cn2H779u2qX7++OnXqpJIlS6pZs2bq2LGjw9nx1q1bq2XLlipbtqweffRRffDBB8qePbt27tyZWt0CAAAAgCSj6IZTREZGat++fWrSpIk9ZrVa1aRJE+3YsSPBaerVq6d9+/bZi+xTp05p7dq1atmyZYLtY2JitGTJEoWFhalu3bop3wkAAAAA+I9c0zoBZE7BwcGKiYmRt7e3Q9zb21vHjh1LcJpOnTopODhYDRo0kDFG0dHR6tOnj8Pl5ZJ0+PBh1a1bV+Hh4cqePbtWrlypihUrOq0vAAAAAJBcnOlGuhEQEKAPP/xQM2fO1P79+7VixQqtWbNG48aNc2hXrlw5HTx4ULt27VLfvn3VrVs3/fHHH2mUNQAAAAAkjjPdcAovLy+5uLjo4sWLDvGLFy+qYMGCCU7z7rvvqkuXLurZs6ckqXLlygoLC1Pv3r01cuRIWa13jhG5ubmpTJkykqQaNWpoz549mjZtmubMmePEHgEAAADAg+NMN5zCzc1NNWrU0KZNm+wxm82mTZs2JXr/9a1bt+yFdSwXFxdJkjEm0d9ls9kUERGRAlkDAAAAQMriTDecZvDgwerWrZtq1qyp2rVra+rUqQoLC1OPHj0kSV27dlWRIkXk7+8v6c6TySdPnqxq1aqpTp06OnHihN599121bt3aXnwPHz5cLVq0UPHixXXjxg0tXrxYAQEBWr9+fZr1EwAAAAASQ9ENp2nfvr0uX76s0aNHKygoSFWrVtW6devsD1c7e/asw5ntUaNGyWKxaNSoUbpw4YLy58+v1q1b64MPPrC3uXTpkrp27ap//vlHuXLlUpUqVbR+/Xo1bdo01fsHAAAAAPdD0Q2n8vX1la+vb4KfBQQEOPzs6uoqPz8/+fn5JTq/efPmpWR6AAAAAOBU3NMNAAAAAICTUHQDAAAAAOAkFN0AAAAAADgJRTcAAAAAAE5C0Q0AAAAAgJNQdAMAAAAA4CS8MiwDGX8gOK1TQAoZVs0rrVMAAAAAkAo40w0AAAAAgJNQdAMAAAAA4CQU3QAAAAAAOEm6K7pnzJihkiVLysPDQ3Xq1NHu3bvv2X7q1KkqV66cPD09VaxYMQ0aNEjh4eGplC0AAAAAAIlLV0X30qVLNXjwYPn5+Wn//v3y8fFR8+bNdenSpQTbL168WMOGDZOfn5+OHj2qefPmaenSpRoxYkQqZw4AAAAAQHzpquiePHmyevXqpR49eqhixYqaPXu2smbNqvnz5yfYfvv27apfv746deqkkiVLqlmzZurYseN9z44DAAAAAJAa0s0rwyIjI7Vv3z4NHz7cHrNarWrSpIl27NiR4DT16tXTokWLtHv3btWuXVunTp3S2rVr1aVLl0R/T0REhCIiIuw/X79+XZIUHR2t6Oho+++1Wq2y2Wyy2WwO+VitVsXExMgYc9+4i4uLLBaLfb5x45IUExOTpLirq6uMMbLY4sQtFhmLVTJGFmNLIG6TJU4uxmKR7hG3GJvkELdKFkvicZtjjsZy5/iNQy73iltd7pF75u9TTEyMXFxcEh1jKT32YvvGcsr4fTLGJLqOiBu3WCwJjrHE4vcae/ZcWE4Zuk9xt0UpvX1KbOwlvN1iOWXEPtlstlTdN7rvdovllGH65MztU2Jjz54LyylD9+nu7ZaUctunlBp7d6/LEpNuiu7g4GDFxMTI29vbIe7t7a1jx44lOE2nTp0UHBysBg0ayBij6Oho9enT556Xl/v7+2vs2LHx4gcOHFC2bNkkSfnz51fp0qV1+vRpXb582d6maNGiKlq0qI4fP67Q0FB7/JFHHlGBAgV05MgR3b592x4vX768cufOrQMHDjgs8CpVqsjNzU179+51yKFmzZqKjIzUoUOH7DEXFxfVqlVLoaGhKhL8pz0e7equoLyllS38mvLc+MceD3fLpuDcJZTz1hXlDPs39zDP3ArJUVh5bgYp2+1r9vj1bPl1PVt+5Qs9J4/IMHs8JEchhXnmkXfIablG/3uQIjh3cYW7ZVfhq3/JEmfwBeUtrRirq0OOknTBq5xcbNEqePWkPWasVl3wKi+PqDB5XTv7UPbp+PGrqlChggIDA3X+/Hl73Fljr0hopNP7lBmXU3rs0+3btxNdR8RdV3p6esrHx0fBwcE6deqUPZ4rV64HHnuSB8spE/Rp795/x0FKb58SG3txvxuWU8bu0+nTN1N13yh2u8Vyyvh9cub2KbGxJ1lZTpmgT3v3nrbHU3r7lFJjLzAwUElhMXEPDaWhwMBAFSlSRNu3b1fdunXt8aFDh2rz5s3atWtXvGkCAgLUoUMHvf/++6pTp45OnDihgQMHqlevXnr33XcT/D0JnekuVqyYrly5opw5c0pKv2e6P9of5972dHD0Ka7MckQttfo0pKpXqp7pnvTbFaf3KTMup/TYp3eqeaX60dyPfrvKcsoEfXqrSl57PLXOdH984N+dFJZTxu7T29Xyp+q+0X23WyynDNOnd6rmS/Uz3ffcbrGcMkyf3vLJZ4+n1zPdoaGhypcvn0JDQ+21ZELSzZluL687RcjFixcd4hcvXlTBggUTnObdd99Vly5d1LNnT0lS5cqVFRYWpt69e2vkyJH2y0vicnd3l7u7e7y4q6urXF0dv47YL/RusQs3qfG755ucuMViufMHEf8DGUtCcauMJYGZJxK/M8AfIJ5QLlLCuSQWTzT3zN+n2LGS2BhL6bF3d99YThm3TxaLJdF1RELxBx1jicVZThm/TwmNj5TaPiU29hLebrGcMmKfYtcLqbVvlKTtFsspQ/TJ2dunxMYYyynj9+m/brdSY+wllk+8eSSpVSpwc3NTjRo1tGnTJnvMZrNp06ZNDme+47p161a8LyD2Dy+dnMAHAAAAADzE0s2ZbkkaPHiwunXrppo1a6p27dqaOnWqwsLC1KNHD0lS165dVaRIEfn7+0uSWrdurcmTJ6tatWr2y8vfffddtW7dOtGjXgAAAAAApJZ0VXS3b99ely9f1ujRoxUUFKSqVatq3bp19oernT171uHM9qhRo2SxWDRq1ChduHBB+fPnV+vWrfXBBx+kVRcAAAAAALBLV0W3JPn6+srX1zfBzwICAhx+dnV1lZ+fn/z8/FIhMwAAAAAAHky6uacbAAAAAIDMhqIbAAAAAAAnoegGAAAAAMBJKLoBAAAAAHASim4AAAAAAJyEohsAAAAAACeh6AYAAAAAwEkougEAAAAAcBKKbgAAAAAAnISiGwAAAAAAJ6HoBgAAAADASSi6AQAAAABwEopuAAAAAACchKIbAAAAAAAnoegGAAAAAMBJKLoBAAAAAHASim4AAAAAAJyEohsAAAAAACeh6AYAAAAAwEkougEAAAAAcBKKbgAAAAAAnISiGwAAAAAAJ6HoBgAAAADASSi6AQAAAABwEopuAAAAAACchKIbAAAAAAAnoegGAAAAAMBJKLoBAAAAAHASim4AAAAAAJyEohsAAAAAACeh6AYAAAAAwEkougEAAAAAcBKKbgAAAAAAnISiGwAAAAAAJ6HoBgAAAADASSi6AQAAAABwEopuAAAAAACchKIbAAAAAAAnoegGAAAAAMBJKLoBAAAAAHASim4AAAAAAJyEohsAAAAAACeh6AYAAAAAwEkougEAAAAAcBKKbgAAAAAAnISiGwAAAAAAJ6HoBgAAAADASSi6AQAAAABwEopuAAAAAACchKIbAAAAAAAnoegGAAAAAMBJKLoBAAAAAHAS15ScmTFGv/zyiyIiItSgQQPlyJEjJWcPAAAAAECGkuwz3SNHjlTjxo3tPxtj1KxZMzVt2lStWrVS5cqVdfLkyRRJEgAAAACAjCjZRffy5ctVu3Zt+8/Lli3Tpk2b9P7772v16tWKiYnRmDFjUiJHAAAAAAAypGQX3RcuXFCZMmXsP69YsUIVK1bU8OHD1bJlS/Xt21cBAQEPPN8ZM2aoZMmS8vDwUJ06dbR79+57tr927Zr69eunQoUKyd3dXY8++qjWrl37wL8XAAAAAICUluyi29XVVREREZLuXFq+adMmPfPMM/bPvb29FRwc/EDzXLp0qQYPHiw/Pz/t379fPj4+at68uS5dupRg+8jISDVt2lRnzpzRsmXL9Oeff+rzzz9XkSJFktstAAAAAABSTLKL7kqVKmnRokUKCQnRggULdOXKFbVq1cr++d9//y0vL68HmufkyZPVq1cv9ejRQxUrVtTs2bOVNWtWzZ8/P8H28+fP19WrV/X999+rfv36KlmypBo1aiQfH5/kdgsAAAAAgBST7KJ79OjROnjwoLy8vNSrVy/Vr1/f4cFqa9asUa1atZI8v8jISO3bt09NmjT5NzmrVU2aNNGOHTsSnGbVqlWqW7eu+vXrJ29vb1WqVEkffvihYmJiktstAAAAAABSTLJfGda0aVPt379fGzduVO7cudW+fXv7ZyEhIWrYsKGef/75JM8vODhYMTEx8vb2doh7e3vr2LFjCU5z6tQp/fzzz+rcubPWrl2rEydO6I033lBUVJT8/PwSnCYiIsJ+WbwkXb9+XZIUHR2t6OhoSXeKfavVKpvNJpvNZm8bG4+JiZEx5r5xFxcXWSwW+3zjxiXFOziQWNzV1VXGGFlsceIWi4zFKhkji7ElELfJEicXY7FI94hbjE1yiFsliyXxuM0xR2O5c/zGIZd7xa0u98g98/cpJiZGLi4uiY6xlB57sX1jOWX8PhljEl1HxI1bLJYEx1hi8XuNPXsuLKcM3ae426KU3j4lNvYS3m6xnDJin2w2W6ruG913u8VyyjB9cub2KbGxZ8+F5ZSh+3T3dktKue1TSo29u9dliflP7+muWLGiKlasGC+eJ08eTZky5b/MOklsNpsKFCigzz77TC4uLqpRo4YuXLigjz/+ONGi29/fX2PHjo0XP3DggLJlyyZJyp8/v0qXLq3Tp0/r8uXL9jZFixZV0aJFdfz4cYWGhtrjjzzyiAoUKKAjR47o9u3b9nj58uWVO3duHThwwGGBV6lSRW5ubtq7d69DDjVr1lRkZKQOHTpkj7m4uKhWrVoKDQ1VkeA/7fFoV3cF5S2tbOHXlOfGP/Z4uFs2BecuoZy3rihn2L+5h3nmVkiOwspzM0jZbl+zx69ny6/r2fIrX+g5eUSG2eMhOQopzDOPvENOyzX634MUwbmLK9wtuwpf/UuWOIMvKG9pxVhdHXKUpAte5eRii1bBq/++Ps5YrbrgVV4eUWHyunb2oezT8eNXVaFCBQUGBur8+fP2uLPGXpHQSKf3KTMup/TYp9u3bye6joh7gNLT01M+Pj4KDg7WqVOn7PFcuXI98NiTPFhOmaBPe/f+Ow5SevuU2NiL+92wnDJ2n06fvpmq+0ax2y2WU8bvkzO3T4mNPcnKcsoEfdq797Q9ntLbp5Qae4GBgUoKi4l7aCgZdu7cqV9++UWXLl3SG2+8obJly+rWrVs6duyYHn30UWXPnj1J84mMjFTWrFm1bNkyvfDCC/Z4t27ddO3aNf3www/xpmnUqJGyZMmin376yR773//+p5YtWyoiIkJubm7xpknoTHexYsV05coV5cyZU1L6PdP90f44D5RLB0ef4sosR9RSq09Dqnql6pnuSb9dcXqfMuNySo99eqeaV6ofzf3ot6ssp0zQp7eq5LXHU+tM98cH/t1JYTll7D69XS1/qu4b3Xe7xXLKMH16p2q+VD/Tfc/tFsspw/TpLZ989nh6PdMdGhqqfPnyKTQ01F5LJiTZZ7ojIyPVoUMH/fDDD3cufbZY1Lp1a5UtW1ZWq1XNmjXToEGDNHLkyCTNz83NTTVq1NCmTZvsRbfNZtOmTZvk6+ub4DT169fX4sWL7Zc8SdLx48dVqFChBAtuSXJ3d5e7u3u8uKurq1xdHb+O2C/0brELN6nxu+ebnLjFYrnzBxH/AxlLQnGrjCWBmScSvzPAHyCeUC5SwrkkFk8098zfp9ixktgYS+mxd3ffWE4Zt08WiyXRdURC8QcdY4nFWU4Zv08JjY+U2j4lNvYS3m6xnDJin2LXC6m1b5Sk7RbLKUP0ydnbp8TGGMsp4/fpv263UmPsJZZPvHkkqVUC3n33Xa1evVqzZs3Sn3/+6XCEycPDQ23btk3w7PS9DB48WJ9//rm++OILHT16VH379lVYWJh69OghSeratauGDx9ub9+3b19dvXpVAwcO1PHjx7VmzRp9+OGH6tevX3K7BQAAAABAikn2me5vvvlGffv2Ve/evXXlypV4n1eoUEHffffdA82zffv2unz5skaPHq2goCBVrVpV69atsz9c7ezZsw5HGYoVK6b169dr0KBBqlKliooUKaKBAwfqnXfeSW63AAAAAABIMckuui9duqTKlSsn+rmLi4tu3br1wPP19fVN9HLygICAeLG6detq586dD/x7AAAAAABwtmRfXl6sWLFEX+UlSdu2bVOZMmWSO3sAAAAAADK8ZBfdnTp10pw5c7Rjxw57zGK5cxf8559/rm+//VZdu3b97xkCAAAAAJBBJfvy8pEjR2rnzp1q2LChKlSoIIvFokGDBunq1as6f/68WrZsqUGDBqVkrgAAAAAAZCjJPtPt5uamdevWacGCBXrkkUdUvnx5RUREqEqVKlq4cKF+/PHHRB/hDwAAAADAwyBZZ7pv376tkSNHqnHjxnrllVf0yiuvpHReAAAAAABkeMk60+3p6ak5c+bo4sWLKZ0PAAAAAACZRrIvL69Ro4aOHDmSkrkAAAAAAJCpJLvonjp1qpYsWaK5c+cqOjo6JXMCAAAAACBTSPbTy7t37y6r1arXX39dAwYMUJEiReTp6enQxmKx6LfffvvPSQIAAAAAkBElu+jOmzev8uXLp3LlyqVkPgAAAAAAZBrJLroDAgJSMA0AAAAAADKfZN/TDQAAAAAA7i3ZZ7olKSYmRosWLdKaNWv0999/S5JKlCihZ599Vp07d5aLi0uKJAkAAAAAQEaU7DPdoaGhql+/vl599VVt2LBBUVFRioqK0saNG9WjRw81aNBA169fT8lcAQAAAADIUJJddI8cOVL79u3Tp59+qsuXL2v//v3av3+/Ll26pOnTp2vv3r0aOXJkSuYKAAAAAECGkuyie+XKlXrjjTf0xhtvKEuWLPZ4lixZ1LdvX/Xt21fLly9PkSQBAAAAAMiIkl10X7ly5Z6vCytfvryuXr2a3NkDAAAAAJDhJbvoLlOmjFatWpXo56tWrVLp0qWTO3sAAAAAADK8ZBfdb7zxhjZs2KCWLVtqw4YNOnPmjM6cOaP169erVatW2rhxo3x9fVMyVwAAAAAAMpRkvzLsjTfe0KVLlzR+/HitX7/e4bMsWbJo9OjR6tu3739OEAAAAACAjOo/vad7zJgx8vX11U8//eTwnu4mTZrIy8srRRIEAAAAACCj+k9FtyR5eXmpQ4cOKZELAAAAAACZSrLv6f7pp580YsSIRD8fOXKkfv755+TOHgAAAACADC/ZRfe4ceN07ty5RD+/cOGC3n///eTOHgAAAACADC/ZRffhw4dVp06dRD+vVauWDh06lNzZAwAAAACQ4SW76I6IiFBkZOQ9P79161ZyZw8AAAAAQIaX7KK7UqVKWrlyZYKfGWO0YsUKVaxYMdmJAQAAAACQ0SW76O7fv7+2bdumtm3b6vDhw4qOjlZ0dLQOHTqktm3baseOHerfv39K5goAAAAAQIaS7FeGvfLKKzp58qTGjRunFStWyGq9U7/bbDZZLBaNGjVK3bp1S7FEAQAAAADIaP7Te7r9/Pz0yiuvaOXKlTp16pQkqXTp0nrhhRdUunTpFEkQAAAAAICMKtmXl8cqXbq0hgwZogEDBqhQoUI6efKk1qxZo+vXr6dEfgAAAAAAZFgPdKZ7+vTp+uSTT7R9+3Z5eXnZ46tXr9bLL7+sqKgoGWMkSZ988ol27tzp0A4AAAAAgIfJA53pXrVqlUqXLu1QSEdHR+u1116Ti4uL5s+fr8OHD2v8+PH6+++/9cEHH6R4wgAAAAAAZBQPVHT/8ccfevzxxx1iv/zyiy5fvqxBgwapW7dueuyxxzR06FC1a9dOa9euTdFkAQAAAADISB6o6L5y5YqKFSvmENu0aZMsFotefPFFh3j9+vV19uzZ/54hAAAAAAAZ1AMV3d7e3goKCnKIbdmyRVmzZpWPj49D3M3NTW5ubv89QwAAAAAAMqgHKrpr1qypL774Qjdu3JAk/f7779q9e7eaN28uV1fHZ7IdO3ZMRYsWTblMAQAAAADIYB7o6eV+fn6qVauWypYtq8cee0z79u2TxWLR8OHD47VduXKlnnrqqRRLFAAAAACAjOaBznRXrlxZP//8s2rUqKHAwEA9/vjjWrt2rWrUqOHQLiAgQFmzZlXbtm1TNFkAAAAAADKSBzrTLUn16tXTmjVr7tnmySef1OHDh5OdFAAAAAAAmcEDnekGAAAAAABJR9ENAAAAAICTUHQDAAAAAOAkFN0AAAAAADgJRTcAAAAAAE5C0Q0AAAAAgJNQdAMAAAAA4CQU3QAAAAAAOAlFNwAAAAAATkLRDQAAAACAk1B0AwAAAADgJBTdAAAAAAA4CUU3AAAAAABOQtENAAAAAICTUHQDAAAAAOAkFN0AAAAAADhJuiy6Z8yYoZIlS8rDw0N16tTR7t27kzTdkiVLZLFY9MILLzg3QQAAAAAAkiDdFd1Lly7V4MGD5efnp/3798vHx0fNmzfXpUuX7jndmTNnNGTIED3xxBOplCkAAAAAAPeW7oruyZMnq1evXurRo4cqVqyo2bNnK2vWrJo/f36i08TExKhz584aO3asHnnkkVTMFgAAAACAxKWrojsyMlL79u1TkyZN7DGr1aomTZpox44diU733nvvqUCBAnrttddSI00AAAAAAJLENa0TiCs4OFgxMTHy9vZ2iHt7e+vYsWMJTrN161bNmzdPBw8eTNLviIiIUEREhP3n69evS5Kio6MVHR0t6U6hb7VaZbPZZLPZ7G1j4zExMTLG3Dfu4uIii8Vin2/cuHTnDH1S4q6urjLGyGKLE7dYZCxWyRhZjC2BuE2WOLkYi0W6R9xibJJD3CpZLInHbY45Gsud4zcOudwrbnW5R+6Zv08xMTFycXFJdIyl9NiL7RvLKeP3yRiT6DoibtxisSQ4xhKL32vs2XNhOWXoPsXdFqX09imxsZfwdovllBH7ZLPZUnXf6L7bLZZThumTM7dPiY09ey4spwzdp7u3W1LKbZ9SauzdvS5LTLoquh/UjRs31KVLF33++efy8vJK0jT+/v4aO3ZsvPiBAweULVs2SVL+/PlVunRpnT59WpcvX7a3KVq0qIoWLarjx48rNDTUHn/kkUdUoEABHTlyRLdv37bHy5cvr9y5c+vAgQMOC7xKlSpyc3PT3r17HXKoWbOmIiMjdejQIXvMxcVFtWrVUmhoqIoE/2mPR7u6KyhvaWULv6Y8N/6xx8Pdsik4dwnlvHVFOcP+zT3MM7dCchRWnptBynb7mj1+PVt+Xc+WX/lCz8kjMsweD8lRSGGeeeQdclqu0f8epAjOXVzhbtlV+OpfssQZfEF5SyvG6uqQoyRd8ConF1u0Cl49aY8Zq1UXvMrLIypMXtfOPpR9On78qipUqKDAwECdP3/eHnfW2CsSGun0PmXG5ZQe+3T79u1E1xFxD056enrKx8dHwcHBOnXqlD2eK1euBx57kgfLKRP0ae/ef8dBSm+fEht7cb8bllPG7tPp0zdTdd8odrvFcsr4fXLm9imxsSdZWU6ZoE979562x1N6+5RSYy8wMFBJYTFxDw2lscjISGXNmlXLli1zeAJ5t27ddO3aNf3www8O7Q8ePKhq1arZj3BIsh+FsFqt+vPPP1W6dGmHaRI6012sWDFduXJFOXPmtE+bHs90f7Q/zsPk0sHRp7gyyxG11OrTkKpeqXqme9JvV5zep8y4nNJjn96p5pXqR3M/+u0qyykT9OmtKnnt8dQ60/3xgX93UlhOGbtPb1fLn6r7RvfdbrGcMkyf3qmaL9XPdN9zu8VyyjB9essnnz2eXs90h4aGKl++fAoNDbXXkglJV2e63dzcVKNGDW3atMledNtsNm3atEm+vr7x2pcvX16HDx92iI0aNUo3btzQtGnTVKxYsXjTuLu7y93dPV7c1dVVrq6OX0fsF3q3uEV+UuJ3zzc5cYvFcucPIv4HMpaE4lYZSwIzTyR+Z4A/QDyhXKSEc0ksnmjumb9PsWMlsTGW0mPv7r6xnDJunywWS6LriITiDzrGEouznDJ+nxIaHym1fUps7CW83WI5ZcQ+xa4XUmvfKEnbLZZThuiTs7dPiY0xllPG79N/3W6lxthLLJ94+SWpVSoaPHiwunXrppo1a6p27dqaOnWqwsLC1KNHD0lS165dVaRIEfn7+8vDw0OVKlVymD537tySFC8OAAAAAEBqS3dFd/v27XX58mWNHj1aQUFBqlq1qtatW2d/uNrZs2cTPNIAAAAAAEB6k+6Kbkny9fVN8HJySQoICLjntAsXLkz5hAAAAAAASAZOGQMAAAAA4CQU3QAAAAAAOAlFNwAAAAAATkLRDQAAAACAk1B0AwAAAADgJBTdAAAAAAA4CUU3AAAAAABOQtENAAAAAICTUHQDAAAAAOAkFN0AAAAAADgJRTcAAAAAAE5C0Q0AAAAAgJNQdAMAAAAA4CQU3QAAAAAAOAlFNwAAAAAATkLRDQAAAACAk1B0AwAAAADgJBTdAAAAAAA4CUU3AAAAAABOQtENAAAAAICTUHQDAAAAAOAkFN0AAAAAADgJRTcAAAAAAE5C0Q0AAAAAgJNQdAMAAAAA4CQU3QAAAAAAOAlFNwAAAAAATkLRDQAAAACAk1B0AwAAAADgJBTdAAAAAAA4CUU3AAAAAABOQtENAAAAAICTUHQDAAAAAOAkFN0AAAAAADgJRTcAAAAAAE5C0Q0AAAAAgJNQdAMAAAAA4CQU3QAAAAAAOAlFNwAAAAAATkLRDQAAAACAk1B0AwAAAADgJBTdAAAAAAA4CUU3AAAAAABOQtENAAAAAICTUHQDAAAAAOAkFN0AAAAAADgJRTcAAAAAAE5C0Q0AAAAAgJNQdAMAAAAA4CQU3QAAAAAAOAlFNwAAAAAATkLRDQAAAACAk1B0AwAAAADgJBTdAAAAAAA4CUU3AAAAAABOQtENAAAAAICTpMuie8aMGSpZsqQ8PDxUp04d7d69O9G2n3/+uZ544gnlyZNHefLkUZMmTe7ZHgAAAACA1JLuiu6lS5dq8ODB8vPz0/79++Xj46PmzZvr0qVLCbYPCAhQx44d9csvv2jHjh0qVqyYmjVrpgsXLqRy5gAAAAAAOEp3RffkyZPVq1cv9ejRQxUrVtTs2bOVNWtWzZ8/P8H2X3/9td544w1VrVpV5cuX19y5c2Wz2bRp06ZUzhwAAAAAAEeuaZ1AXJGRkdq3b5+GDx9uj1mtVjVp0kQ7duxI0jxu3bqlqKgo5c2bN8HPIyIiFBERYf/5+vXrkqTo6GhFR0fbf6fVapXNZpPNZnPIxWq1KiYmRsaY+8ZdXFxksVjs840bl6SYmJgkxV1dXWWMkcUWJ26xyFiskjGyGFsCcZsscXIxFot0j7jF2CSHuFWyWBKP2xxzNJY7x28ccrlX3Opyj9wzf59iYmLk4uKS6BhL6bEX2zeWU8bvkzEm0XVE3LjFYklwjCUWv9fYs+fCcsrQfYq7LUrp7VNiYy/h7RbLKSP2yWazpeq+0X23WyynDNMnZ26fEht79lxYThm6T3dvt6SU2z6l1Ni7e12WmHRVdAcHBysmJkbe3t4OcW9vbx07dixJ83jnnXdUuHBhNWnSJMHP/f39NXbs2HjxAwcOKFu2bJKk/Pnzq3Tp0jp9+rQuX75sb1O0aFEVLVpUx48fV2hoqD3+yCOPqECBAjpy5Ihu375tj5cvX165c+fWgQMHHBZ4lSpV5Obmpr179zrkULNmTUVGRurQoUP2mIuLi2rVqqXQ0FAVCf7THo92dVdQ3tLKFn5NeW78Y4+Hu2VTcO4SynnrinKG/Zt7mGduheQorDw3g5Tt9jV7/Hq2/LqeLb/yhZ6TR2SYPR6So5DCPPPIO+S0XKP/PUgRnLu4wt2yq/DVv2SJM/iC8pZWjNXVIUdJuuBVTi62aBW8etIeM1arLniVl0dUmLyunX0o+3T8+FVVqFBBgYGBOn/+vD3urLFXJDTS6X3KjMspPfbp9u3bia4j4q4nPT095ePjo+DgYJ06dcoez5Ur1wOPPcmD5ZQJ+rR377/jIKW3T4mNvbjfDcspY/fp9OmbqbpvFLvdYjll/D45c/uU2NiTrCynTNCnvXtP2+MpvX1KqbEXGBiopLCYuIeG0lhgYKCKFCmi7du3q27duvb40KFDtXnzZu3ateue048fP14fffSRAgICVKVKlQTbJHSmu1ixYrpy5Ypy5swpKf2e6f5of5z72tPB0ae4MssRtdTq05CqXql6pnvSb1ec3qfMuJzSY5/eqeaV6kdzP/rtKsspE/TprSp57fHUOtP98YF/d1JYThm7T29Xy5+q+0b33W6xnDJMn96pmi/Vz3Tfc7vFcsowfXrLJ589nl7PdIeGhipfvnwKDQ2115IJSVdnur287hQiFy9edIhfvHhRBQsWvOe0EydO1Pjx4/XTTz8lWnBLkru7u9zd3ePFXV1d5erq+HXEfqF3i124SY3fPd/kxC0Wy50/iPgfyFgSiltlLAnMPJH4nQH+APGEcpESziWxeKK5Z/4+xY6VxMZYSo+9u/vGcsq4fbJYLImuIxKKP+gYSyzOcsr4fUpofKTU9imxsZfwdovllBH7FLteSK19oyRtt1hOGaJPzt4+JTbGWE4Zv0//dbuVGmMvsXzizSNJrVKJm5ubatSo4fAQtNiHosU98323jz76SOPGjdO6detUs2bN1EgVAAAAAID7SldnuiVp8ODB6tatm2rWrKnatWtr6tSpCgsLU48ePSRJXbt2VZEiReTv7y9JmjBhgkaPHq3FixerZMmSCgoKkiRlz55d2bNnT7N+AAAAAACQ7oru9u3b6/Llyxo9erSCgoJUtWpVrVu3zv5wtbNnzzqc3p81a5YiIyP18ssvO8zHz89PY8aMSc3UAQAAAABwkO6Kbkny9fWVr69vgp8FBAQ4/HzmzBnnJwQAAAAAQDKkq3u6AQAAAADITCi6AQAAAABwEopuAAAAAACchKIbAAAAAAAnoegGAAAAAMBJKLoBAAAAAHASim4AAAAAAJyEohsAAAAAACeh6AYAAAAAwEkougEAAAAAcBKKbgAAAAAAnISiGwAAAAAAJ6HoBgAAAADASSi6AQAAAABwEopuAAAAAACchKIbAAAAAAAnoegGAAAAAMBJKLoBAAAAAHASim4AAAAAAJyEohsAAAAAACeh6AYAAAAAwEkougEAAAAAcBKKbgAAAAAAnISiGwAAAAAAJ6HoBgAAAADASSi6AQAAAABwEopuAAAAAACchKIbAAAAAAAnoegGAAAAAMBJKLoBAAAAAHASim4AAAAAAJyEohsAAAAAACeh6AYAAAAAwEkougEAAAAAcBKKbgAAAAAAnISiGwAAAAAAJ6HoBgAAAADASSi6AQAAAABwEopuAAAAAACchKIbAAAAAAAnoegGAAAAAMBJKLoBAAAAAHASim4AAAAAAJyEohsAAAAAACeh6AYAAAAAwEkougEAAAAAcBKKbgAAAAAAnISiGwAAAAAAJ6HoBgAAAADASSi6AQAAAABwEopuAAAAAACchKIbAAAAAAAnoegGAAAAAMBJKLoBAAAAAHASim4AAAAAAJwkXRbdM2bMUMmSJeXh4aE6depo9+7d92z/3XffqXz58vLw8FDlypW1du3aVMoUAAAAAIDEpbuie+nSpRo8eLD8/Py0f/9++fj4qHnz5rp06VKC7bdv366OHTvqtdde04EDB/TCCy/ohRde0JEjR1I5cwAAAAAAHKW7onvy5Mnq1auXevTooYoVK2r27NnKmjWr5s+fn2D7adOm6ZlnntHbb7+tChUqaNy4capevbqmT5+eypkDAAAAAOAoXRXdkZGR2rdvn5o0aWKPWa1WNWnSRDt27Ehwmh07dji0l6TmzZsn2h4AAAAAgNTimtYJxBUcHKyYmBh5e3s7xL29vXXs2LEEpwkKCkqwfVBQUILtIyIiFBERYf85NDRUknT16lVFR0dLulPoW61W2Ww22Ww2e9vYeExMjIwx9427uLjIYrHY5xs3LkkxMTFJiru6usoYo4jr1/4NWiwyFqtkjCzGlkDcJkucXIzFIt0jbjE2ySFulSyWxOM2xxyN5c7xG4dc7hW3utwj98zfp5AQF7m4uCQ6xlJ67MWOHZZTxu9TaGiWRNcRceMWiyXBMZZY/F5jL/zmDZZTJujT1av/HmdP6e1TYmMv4e0Wyykj9unaNddU3Te673aL5ZRh+nT3dislt0+Jjb17brdYThmmT3dvt6SU2z6l1NiLrSXjjsGEpKuiOzX4+/tr7Nix8eKlSpVKg2zwsBqT1gkgw4q/9gKSZkxaJ4AMbUxaJ4AMi+0WkmtMWifwAG7cuKFcuXIl+nm6Krq9vLzk4uKiixcvOsQvXryoggULJjhNwYIFH6j98OHDNXjwYPvPNptNV69eVb58+WSxWP5jD/BfXL9+XcWKFdO5c+eUM2fOtE4HGQzjB8nF2EFyMXbwXzB+kFyMnfTDGKMbN26ocOHC92yXropuNzc31ahRQ5s2bdILL7wg6U5RvGnTJvn6+iY4Td26dbVp0ya9+eab9tjGjRtVt27dBNu7u7vL3d3dIZY7d+6USB8pJGfOnKxAkGyMHyQXYwfJxdjBf8H4QXIxdtKHe53hjpWuim5JGjx4sLp166aaNWuqdu3amjp1qsLCwtSjRw9JUteuXVWkSBH5+/tLkgYOHKhGjRpp0qRJatWqlZYsWaK9e/fqs88+S8tuAAAAAACQ/oru9u3b6/Llyxo9erSCgoJUtWpVrVu3zv6wtLNnz8pq/fem+nr16mnx4sUaNWqURowYobJly+r7779XpUqV0qoLAAAAAABISodFtyT5+vomejl5QEBAvFjbtm3Vtm1bJ2cFZ3N3d5efn1+8y/+BpGD8ILkYO0guxg7+C8YPkouxk/FYzP2ebw4AAAAAAJLFev8mAAAAAAAgOSi6AQAAAABwEopuAAAAAACchKIbAAAAAAAnoegGAAAAHnI8WxlwHopuAACATMxms6V1CsgALBaLpH/HC0U40pu467KMtl6j6AaAu7CjAWdhbCEtWK13dve++uor3b59O42zQXo2c+ZMPf3005L+LcKB9MBms9nXZYsXL9ayZcsy1PqMohuZ0t1Hv9jRRVLZbDaHHQ3GDlJK3LEVHBysc+fOKSIiwv45Yw3OFBISotdff12TJ09O61SQjuXPn183b97UkSNHJLFeQvpgjLEX3EOHDtXbb7+tW7du6dq1a2mb2ANwTesEgJQW90jYzJkzdezYMf3+++8aMGCAfHx8VLJkybRNEOlW3LEzZ84cbdu2Tbdv31aNGjU0bNiwNM4OGVncHYYxY8YoICBABw4c0HPPPafq1atr0KBBnFWCU+XJk0cDBgzQ/v37FRYWpqxZszLmHnLGmHhjoG7dugoODtby5ctVqVIlxgjShdhxOHXqVH311Vf6/vvvVadOHfvnERERcnd3d9iPS2/SZ1bAfxD7x/bOO+9o3LhxypUrlypVqqRXX31VkyZN0o0bN9I4Q6RXccfOe++9pyJFiqhBgwYaMWKEfH19FRkZmcYZIqOK3WEYM2aMpk+frrfffls///yzgoOD9fHHH+vo0aNpnCEyk8TudWzdurXWrFmjrVu3UkzBPgbiXnFTtGhRvfvuu/riiy/0xx9/pFVqgANjjKKjo7V161a9+uqrqlOnjk6ePKkVK1aoVatWat++vU6ePJluC26JohuZ1M8//6zvvvtOa9as0bhx49S5c2eFhISofv36ypEjR1qnh3Rs+/btWr58uZYsWSJ/f3+VL19ebm5u8vHxkZubW1qnhwwsMDBQGzdu1FdffaVWrVopNDRUv/76q95//31VqFBBUVFRaZ0iMonYHc9NmzZp586d9nj9+vXVpUsXffrppwoJCUmr9JDG4h6U8ff3V+fOnfX555/bY08++aRy5sypQ4cOSZJiYmJSPUcg7jiNiYmRq6ursmbNqkOHDmnSpEnq27ev5s6dq+zZsys0NFRdunRJ1/d4U3QjU7j7qP6NGzdUvHhxVa9eXd98842aNGmiGTNmqEOHDrp586b27t3LDi4kxR87165dU968efXEE09o5cqVevnllzVt2jT16tVLoaGh2rhxYxpliowm7tiKjIyUm5ubQkNDVblyZf3www96/vnnNWnSJL366qsKDw/X119/rd9//z0NM0ZmcvToUXXu3Fndu3dX9+7dtW/fPkVGRqpjx446ceKELl68KCnjPQEY/01ISIj9oMyBAwfUtGlTubm5acKECapbt64+++wzFS1aVK1atZKfn5/Cw8Pl4uKSxlnjYRP3MvGlS5fqp59+kiS98MILypIliyZOnKgnn3xS7733npYuXarWrVurYMGC8vT0TMu074miG5lC7B+mn5+fgoKCFBUVpZCQEK1bt059+vTR+PHj1bdvX0nS+vXrNWvWLF25ciUtU0Y6ETt2pkyZonXr1qlgwYLy8PDQJ598om7dumnixIl6/fXXJUn79u3TjBkzdPLkybRMGRlE7NgaNGiQ/P39de3aNVmtVk2YMEE9evTQhAkT1KdPH0nSX3/9peXLlyswMDAtU0YGtmPHDu3atUuS1L9/f505c0ZbtmzRjBkzdPDgQQ0YMEAtW7ZUnjx5ZLPZ9NFHH0lSur4cEylr9erVGjBggAIDAzVgwADVqFFD1apV07x58/Trr7+qcuXK+vrrr1WqVCndunVLV69e1Q8//CCJB6oh9cR9Bso777yjQYMGKSgoSFeuXNFLL72kBQsW6ODBgxoxYoRq1qwpSdq4cWP6v5LVABlYTEyM/d9ffvmlsVgsZseOHebmzZvm8ccfNxaLxUyfPt3eJjw83Dz77LOmc+fOxmazpUXKSCfijp3PPvvMFCpUyOzevdv89ddfpmHDhsbd3d34+fnZ29y+fdu0atXKdOrUibGDe4o7Pvbt22cKFy5stm3bZowxZvr06cZisZhevXrZ29y4ccO0atXKNG3a1ERHR6d6vsj4zpw5Y3x8fEynTp3MK6+8YlxcXMzBgwftn9++fdts3LjRdOzY0Tz66KOmcOHCpmDBgubo0aPGGMM6LZOLiooyxhizdu1aU6BAAVO5cmWTL18+c+TIEWOMcVjvXLp0yXzyySemQYMGxs3Nzbz44otpkjPw8ccfm4IFC5pdu3Y5rKMiIiKMMcZcvXrV/PTTT6Z58+amcuXK9nGeXtdnFmM4dIWMb82aNdq/f7/KlCmjjh07yhijFStW6IMPPlCePHk0btw4nTt3TgsXLtT58+d14MABubq6JvjkTjxc9u3bpy+++EI1atRQt27dJEkrV67UgAED1LBhQz399NPKkSOHPvvsM128eFH79++Xq6trun5CJtKHiRMn6ubNm4qIiJC/v7+kO5eZv/vuu/r444/VrVs3RUdH68KFC7p8+bL279+vLFmyMLaQZAsWLFCPHj0k3XmWSZcuXXTp0iUtXLhQnTt3liRFR0fL1fXfl9Vs3rxZv//+u95++20NGzZM7777bprkjtTRu3dvPfnkk+rYsaMsFou6deumr7/+Ws8++6ymTJmiUqVKSYo/Ti5duqSdO3fq1Vdf1cKFC/Xss8+mVRfwEIqKilK7du1UtWpV+fn56cyZMzpy5Ijmzp2rAgUKaODAgXJzc7Ovv7766itlyZIl3jhOT9JnVsAD2LNnj9588039888/WrhwoaQ7T+Rs2bKlsmTJosmTJ+u5555T6dKlVapUKa1atUqurq6KiYnhPqWH3I4dO/TUU0/JarWqcuXK9viLL76oiIgIrVixQoMHD1a1atXk7e2ttWvXMnaQJLdu3dLOnTu1YsUKvfDCC/Z47L2TlStX1rp162S1WvXUU09p2LBhcnV1Tdc7DEhfPv30U+3atUuvvPKKsmTJonz58ilfvnzKkyePNmzYoFKlSqlevXr2g4TSnUvJGzVqpEaNGilLliz65JNP9Oqrr6pIkSJp3Bs4Q3h4uAoWLKi2bdvaTzA0btxYjz/+uCZMmKCxY8fqrbfeUuXKle3rndiTEQUKFFDDhg1VuXJlbqlCqouKilJUVJROnDih6dOna926dYqOjpaLi4tOnjypUaNGaeXKlRo3bpzKlCkji8WS/refaXqeHUgBV69eNZMmTTKFCxc2zz77bIJtTp48aW7cuGG/5CT2EhRg+vTpJkeOHKZdu3bm1KlTDp9FRkaaoKAgc+vWLXuMsYOEJHQ52/nz503fvn2Nh4eH+emnn4wxjrc13H0pOZeW40GEhITYx8z27dvt8TVr1phatWqZjh07OsTvtn37dlO+fHnz999/Oz1XpL646xpjjJkzZ46ZMmWK/dLcH3/80RQvXtx0797dfpm5McasXr3aYbqnnnrK9OzZ0xiTfi/bRcZ291iNHWcrVqww9erVM/nz5zfvvfee2bFjhzHGmNGjR5s2bdrccx7pUTo+HADEd/dllzabTXny5FGvXr3k5uamTz75RL1799Znn30m6d8nBpcsWdI+nTEmfR8Jg1PEHTsmzm0F/fr1U3h4uCZNmqSFCxeqV69eKlq0qCTJxcVF3t7e9nkwdpCQuGPr+vXrunXrlgoWLKgiRYpo/Pjxunbtmp5//nlt3LhRdevWtV8pcfetLVw9gaSKiopS7ty5Jd15LVivXr3UpUsXjR07Vi1btlRERITGjx+vmTNnymazqX79+mrRooW6du2qjh07Srpza83p06eVJUuWNOwJUsvWrVt15MgRWSwWvf7663r22WdlsVjk6+urqKgoPffcc/ryyy+1d+9e/fPPP5KkLVu26PTp05oyZYokcTseUlzc7ee0adP0xx9/6PDhw/Yx2qpVKwUHB6tw4cL2aXbv3m3fT4uVEW7J4p5uZBhx/zDnzp2rI0eOKDg4WO3atVPr1q0VERGhzz//XJ9//rnq1aun2bNnx5sOD6e4Y+Dzzz/Xnj175OrqqvLly2vAgAGSpPHjx2v69Ol67bXX1Lt3by63RJLEPYAzbtw4rV+/Xn/++acaNWqkl19+WR06dFBYWJh69uypNWvWaMOGDXr88cd5ngRSxA8//KCqVatqypQp2rNnj5o2baoxY8ZIuvNsikmTJiksLEzGGIWGhur48ePKkiWLbt26pS+++EL169dXlSpV0rYTSHFx1y/dunWTt7e33nvvPfXr10+///67OnTooD59+sjDw0Pr1q2Tn5+fIiMjlStXLm3cuNF+IOby5cuKiYlRwYIF07I7eAgMGzZM8+fP19ChQxUUFKQVK1aodu3a+vzzz5UjRw6FhoZqz549mjJlis6ePZsxn82UVqfYgeQaMmSIyZ8/v2nbtq1p2bKlsVqtZtCgQebSpUsmLCzMTJs2zVSrVs20a9curVNFOjN06FDj5eVlevXqZZo2bWqKFy9umjVrZv98woQJpnjx4ubNN980ly5dSsNMkdGMGTPG5MuXz8yaNct89dVX5qmnnjL169c3kyZNMsbcuRS4S5cuxmKxmMOHD6dxtsio4l5C+eGHHxqLxWICAwPNpUuXzODBg02dOnUc3roQEBBgPv30UzN69Gj7rTGx/+d2hswp7iXgv/32m6lcubJZt26dMcaYmzdvmu7du5s6deqYKVOmmNu3bxtjjDl79qw5deqUfXxxGxVS05YtW0zZsmXN7t27jTHGbN682bi6uppFixbZ2/z222+mefPm5qWXXjKRkZHGmIw3Tim6kaEEBASYQoUKmT179thjixcvNnnz5jWjRo0yxty5x/v99983Xbt2zRD3eCB17Ny50xQrVswEBAQYY+68cmLjxo2mePHi5vnnn7e38/PzM88//zz3riFJbDabOX/+vKlWrZpZunSpPX7p0iXTr18/U6dOHft9aIGBgWbs2LEZbkcB6c+BAwfMxIkTzf/+9z97LDg4OMHCOy4K7YfH3LlzTbt27UyfPn2MMcZeqISFhZkePXqYxx9/3EydOtVeeMdivwmpbePGjaZmzZrGGGOWLFlicuTIYWbOnGmMuXOgKPaZKCdOnMjQB4a45hYZyq1bt5QtWzYVLVpUMTExkqSOHTvq448/1oQJE3T48GHlyZNHgwYN0sKFC2W1Wu1PbcXD7eLFi4qKirI/pdzNzU2NGjXSlClTdOzYMW3ZskWSNGbMGK1cuVIWi0WGu2+QgLjjwmKxKFu2bLpx44Zu3rwpSYqJiVH+/Pn14Ycf6tKlS1q7dq0kqVChQho9erT9KeVAcmzatEnVq1fXhx9+KE9PT0l3XveUL18+jRgxQvXr19eGDRs0ZMiQeNPy3ICHQ3BwsLZu3aqff/5ZFy5ckCRlyZJFkZGRypo1q6ZPn67HHntMn3zyiVatWuUwLbfjwZnibj9j/x0WFiabzabVq1erd+/e8vf3V9++fSVJAQEB+vrrr3X27FmVLl3avl+fEZ+vw18W0q3EiuUzZ84oJCRELi4uioiIkHTnFU+FChXSX3/9JUnKmjWrvWhiA/LwiTt2YsfIo48+KldXVwUEBNg/y5Ili6pXr66LFy/q4sWL9njs2Mkw9wkh1dhsNvu4uHLlij2eNWtW7d69W5LsOwU5c+bU448/bn8oUVwZcYcB6UPJkiX19ttv69atWzpy5IikO8V03MK7YsWKun79OgcOHxJ3L2cvLy8NGTJE7dq104YNG+wPl3Vzc1NUVJSyZs2qadOm6bXXXlObNm3SImU8hOJuP+PuYz3//POy2Wx67rnnNHnyZPXr10/SnVfezZo1S7dv31axYsXs88mo+/Vs9ZEuxX3w1bx58xQWFqYBAwaoRYsWatGihTp37qwVK1aoZMmSku48pdzd3d1+1D8WRdPD5+6HplmtVjVv3lwFChRQxYoV9c0338jb21v169eXJOXIkUMlS5aM9wRfxg7uFndsffDBB9q1a5f8/f312GOPafz48XruuedUokQJDR8+XBaLxf6O0RYtWqRx5sioEnoQaOnSpfX6668rPDxcgwYNUt68edWxY0f7FRT58uXTpEmTlDNnTg4gPgTijpHw8HC5u7vLYrHoscceU79+/WSM0aRJk+Tm5qbu3bvbz3hny5ZNI0aMkCT7GxUAZ4l7EmzmzJnatm2bypQpo0aNGumpp57SjBkz1KNHD33++ecqUKCArly5om+++UaBgYE6cOCALBZLhn8wMk8vR7r29ttva+nSperfv786dOigYsWKKSAgQP7+/jp58qQ+/PBDSdIXX3yhoKAg7d69mw0HJN0ZO19++aXGjx+vZs2aqUiRItq5c6f69u0rb29v1a1bV1WrVtWnn36qK1euaO/evYwdJMnw4cO1cOFCTZgwQY0aNVKJEiUk3XmrQu/evdWsWTPlzJlTly5d0uXLl/Xbb79xZhsPLO4O5ubNmxUeHi5jjJ555hlJd676mjZtmubPn685c+aoQ4cOkhwLqIy+k4p7i7t8p0+frp9//lnGGFWpUkVjx46VJB06dEifffaZNm3apGHDhqlbt25pmTIeQuaut3xMmTJFLVq00G+//abcuXOrb9++6ty5s/bv36+33npLZ8+eVYECBVSmTBnNnz9fWbJkyRQHhii6kW59/fXXGjx4sFatWqU6deo4fLZnzx7NmjVLq1atUvHixVWoUCF9//33meYPE//NN998oyFDhmjVqlWqUaOGpH9X+nv37tX8+fO1Zs0aFShQQN7e3lq5ciVjB0myd+9etWvXTnPmzFHTpk3jfb5nzx7Nnz9ft27dUoECBeTv728/A0nhjaSKu5M6YsQIfffddzLGKEuWLKpWrZoWL14sSTp9+rQ+/fRTLVy4UBMnTtSrr76almkjFcUdI8OGDdPChQvVr18/RUZGasmSJapevbqWLl0qSTp8+LA+//xzLVq0SIsWLVLLli3TMnU8pPbt26cvvvhCbdu21RNPPKHDhw9r2rRp2rdvnwYPHqwuXbpIks6dO6d8+fLJ09NTFosl02w/KbqRbg0dOlSBgYFatGiRfeNy9x/e2bNnlStXLvtldJnlDxP/zahRo/Tbb79p5cqVcnFxSfCypLCwMN26dUteXl6MHSTZ6tWrNWDAAO3fv1+5c+eW9O/ZpsjISLm5ucU7eMPYQnKNHz9eU6dO1ffff6+aNWvqo48+0qhRo/Tcc8/p+++/l3TnjPe4ceN07tw5bdiwIW0ThtPdvn3b4Va6b7/9Vn5+flq4cKHq1KmjFStWqEuXLnJ3d1etWrW0fv16SdL+/fsVEBCggQMHcnAZqW7FihUaN26cjDFas2aNihQpIkk6cuSIpk6dqoMHD+qNN96Id+AwM90ewzVHSBcSemhaSEiILl++7PAH5+rqqoiICPvTgIsXL65cuXLZiyp2bB8+dx83NMbo+PHjiomJkaurq0PBHR0drV9++UXnz59XtmzZlD9/fvs9j4wd3C2h9VL27Nl17do1HTt2zCFujNHq1at18ODBeDu0jC0kxbfffqtz587Zfz558qT27NmjuXPn6vHHH9f69ev10UcfadCgQdq+fbv9AVglS5bU+++/r3Xr1qVV6kglbdq00QcffKDr16/bY7du3VKbNm1Up04drV69Wr169ZK/v78+/fRT/fLLL2rfvr0kqXr16ho8eLBcXFzsb38BUkuOHDlUqFAhnTx5Ujt37rTHK1WqpEGDBqlGjRoaM2aMff8+VmYpuCWKbqQDcc9AHj582B4vW7asDh06pD179jgUVjdv3tSsWbO0Zs0ah/lw39rDJ+6TMPfv369r167JYrGoTZs2+vnnn7Vy5UpJ/46Ny5cva86cOTp69KjDfDLTSh0pI+56acWKFdqzZ48iIiJUvHhxlSpVSgsXLrSPI6vVqpiYGE2fPl3ffvttWqaNDGr58uXq1KmTFi5cqMDAQElSqVKl1Lp1az3++OPasWOH+vbtq/Hjx2vSpEnq3r27Vq5cqSeeeELSndfR8YrMzK9q1ary9/fXrFmzdO3aNUlS9+7d1atXL127dk3vvfeehgwZogEDBqhhw4YqWrSovvvuO/Xv399hPpzphjMltB5q2rSpxowZoyeeeEJTp0512Id/7LHH1LdvX/Xr10/NmzdPzVRTl7NfBA7cS+xL7o0xZvTo0aZixYrmf//7nz3WoEEDU7p0abNu3Tpz+vRpc+rUKdOiRQtTt25dEx0dnRYpI52IO3ZGjhxpnnjiCbNo0SITGRlpzp8/b3r06GFKlSplFi1aZEJCQsyff/5pWrVqZWrWrMnYwT3ZbDb7v4cNG2YKFy5sFi5caEJCQowxxixYsMCULVvWtGnTxsyePdt8++235qmnnjJVqlQxUVFRaZQ1Mrrx48eb4sWLm7Fjx5qzZ88aY/4di2PGjDHt27c3N2/eNMYYM2XKFNOhQwfTtm1b1mcPgd27d9v/PW3aNGOxWIy/v7+5evWqPb5v3z5TvHhxc+TIEWOMMadOnTIdO3Y0mzZtYowg1cTdN/v666/NRx99ZPr372/++usvY4wxe/bsMc8//7x58sknzZo1axKcR2YdrxTdSBeGDRtmvL29zdq1a825c+fs8ejoaNOqVStTvHhxkzNnTlOtWjVTu3ZtExkZaf8cD7cRI0YYLy8vs3HjRnPlyhV7/NSpU2bQoEHGzc3NFClSxDz66KOmbt26jB0kmb+/vylQoIDZtWuXiYiIcPhs/fr1pn379iZHjhymTp065rnnnmNs4YG9+eabZt26dfafP/zwQ1O0aFEzduxYc/78eXu8Xbt2pk6dOsYYY27dumVeeOEF8+mnn9o/j7uji8yle/fupnfv3g7rlUmTJtkL72vXrhljjDl//rwpWbKk6dmzpzlw4IBp1qyZad26tX1ssF5CahoyZIgpUqSI6dChg3n66adNzpw5zeeff26MMebXX381L7zwgnn66afN8uXL0zjT1EPRjTR3+PBhU758ebNp0yZjjDE3btwwp0+fNvPnz7cX4Lt27TIrV640P/30k33DwRkl7Nu3z5QrV85s27bNGGPMtWvXzB9//GFmzZpl/vzzT2OMMQcPHjQ//PCD+eWXXxg7SLKwsDDTsmVLM2nSJGOMMWfPnjUbN240bdu2NWPHjrWvmy5fvmxCQkLsZyQZW0iqU6dOmd69e8cbMx988IG98I4dZz///LPJkSOHqVKliqlSpYqpVKkSY+0hERgYaD+gd+LECXs8buEdGhpqYmJizMyZM02JEiVMyZIlTb169ezTcVAGqem7774zRYoUMYcOHTLGGLNt2zZjsVgcCuytW7eaJ554wvj6+qZVmqmOp7sgzd24cUOXLl1SuXLltHfvXi1atEgbNmzQ2bNnVbp0aS1atEi1a9d2mCb2IVl4uGXLlk0RERG6fv26Dh8+rDlz5mjjxo2Kjo7WgAEDtHv3blWtWlU+Pj72aRg7SIro6GhdvHhRf//9txYtWqQVK1YoJCREMTEx+vvvv3Xx4kVNnjxZXl5e9mkMD+RDEhljVKpUKc2ZM0eStHjxYtlsNr3yyisaMWKEJGnWrFmSpN69e6tx48ZavXq1li1bpnz58mnkyJFydXXlNYeZnM1mU6FChSRJc+fO1ezZs/XBBx+oefPmGjx4sCRpyJAhMsZoyJAhev311/Xyyy/r3Llzqlq1qv0BoqyX4Ex3vx3m4sWLaty4sSpXrqzFixerT58+mjFjhl566SVdv35d4eHhql+/vj755BNVqVIlDTNPXfwVIs09/vjjeuSRR1SrVi3dvHlTXbp0sW9Uihcvrh07dqhy5coO07CTAUny8PBQtWrV9NZbb+n06dPq3r273n//fTVt2lSNGjXSpk2bVLVqVYdpGDtIipw5c6p///4aO3asFi9erL59+6pp06Z64okn1K9fP4WGhsrd3d1hGh7Ih6SKHSvGGF25ckVTp05Vjhw55OHhoZdfftleeM+cOVPSncK7YcOGatiwoX0eFFOZX9xC5qmnntK0adM0ZcoUWa1WNW3a1KHwtlqt6t27t/Lnz6/8+fNLEm91QaqIHadXrlxRvnz5dP78eYWGhmrr1q3q06ePJkyYoL59+0qSFi1apNOnT+uDDz6w75/dXbRnVvwlIk2Z/38d2IYNG7RixQo98sgjqlevntzd3WWMUcWKFZU9e/a0ThPpVIkSJfTBBx/ozJkzypYtm+rXry8XFxeFh4fL3d1dBQoUSOsUkQHFrpe6deumxo0bS7rzesJYJ0+e1KOPPppW6SGDi7uDabFY5OXlpXnz5mno0KH67LPPZLPZ1K5dO40YMUJWq1WzZs3S9evXNXToUId1GsVU5pVQEfLII4/oxx9/1IsvvqgJEyZIkr3wtlqtGjx4sIoUKaJXXnnFPs3DUMgg7WzatEnHjx9X37595evrK5vNppkzZ6pTp05q3769GjZsqNmzZ6t3796S7rze7n//+5+KFSumLFmy2OfzsIxTizF3veQWSGO3b99WcHCw+vbtq8DAQO3Zs4ezk4jHxHl/e6zw8HAFBgaqf//+unjxonbt2sXYQbLcPb5CQ0O1f/9+TZo0SWfOnNHBgwfl6uqa4DgEEhO3mDpz5oy8vLxktVqVNWtWHT58WG+99ZYkqWfPnmrXrp0kacSIETp69KhWrFjBWHsIxF2nLFmyRH///beqV6+uSpUqqVChQjp9+rReeukl5cuXT++8846aNm0qSVq6dKnatGnDwRikiuvXr6tfv346fvy48ubNq61bt2rnzp167LHHdP36dU2aNEnLli1T69atNWjQIP3111/68MMPFRgYqL179z6U20+KbqQrMTEx+vbbbzV9+nRZrVb9/PPPypIlC/etPaQeZIUcHR2tefPmadWqVQoNDdUvv/zC2EGiHvRytt27d+utt95S7ty5tWLFCsYW/pN3331X33zzjbJnz65GjRpp6NChKlKkiA4dOqQhQ4bIYrGoZ8+eatu2raR/14UP207qw2zEiBH67LPPVKBAAUVGRurJJ5/U22+/rXLlyun06dNq06aN8ufPL19fX7Vu3do+HbcdILUEBQWpefPmOnz4sEaNGqX33nvP/llgYKA+++wzLVq0SEFBQSpTpowKFiyoH3/88aHdflJ0w6nu3rG93x+ZzWbT8ePHtWfPHnXq1EkuLi5sQKCwsDBly5btvu0OHDigP//8U23btmXsIElWrFihxx9/XIULF75v26NHj6pcuXI8nAj/ycqVK/Xmm29q8uTJ2rZtmw4ePCir1aoFCxaoWLFiOnz4sIYOHapLly7p448/1lNPPSXpwQ5CIuOJ3V8yxig0NFTdu3eXn5+fqlWrpjlz5mjx4sUqWLCgxo4dq/Lly+v06dNq0KCB2rZtq6lTp6Z1+njI2Gw2XbhwQcOGDVNYWJiuXLmiTp062e/dlqTIyEhFRUXp4MGDKly4sEqUKPFQbz8pupEqtm3bpvr160u6/xmmuDsWD+ORMEi//PKLwsLC9Oyzz2rgwIEqVKiQhg4d+kBnJs2dVyI+NPcKIWnirn/GjRunDz/8UL/99pvKli17z4Im7nQUP3gQd2/zli5dqvPnz9svJV++fLmmT58ui8WiL774QsWKFdO+ffu0aNEiTZo0iXXYQyDuGDl9+rSsVqsGDBigefPm2d+QsGDBAi1cuFCFChXS2LFjVa5cOf3zzz8qUKAA+0lIFYntv585c0Zjx47Vn3/+qa5du6pPnz6S7mwrr127pjx58tx3Hg8Dim443S+//KJXX33VftRWSvyPLm784sWL8vb2TtVckfYuX76s7t27KywsTF5eXlqzZo127dp139dKxB07R48eVYUKFVIjXWRQJ06c0LJly1SlShW1bNnynm3jFtm//vqrvLy8VLFixdRIExlc3LEze/ZsBQUF6dChQ6pdu7aGDRtmb7d8+XLNmDFDLi4u+uyzz1SqVCn7Zw/zTurDZvjw4fryyy/l6uqqqKgobd++XSVLlrR/vmDBAn311VdycXHRvHnz7A945AQFnC3uemjnzp26ePGiihcvrjJlyihHjhw6evSoPv74Y/31119q166d+vfvr6ZNm6pmzZry9/dP4+zTB9bicLqyZcvq5Zdf1urVqzVu3DhJd55UaLPZHNrFPSs5bdo0NWzYUCEhIameL9KOzWZT/vz5NW7cOJ0/f14rV67UxIkT7QV3YscI446dOXPmqG3btjpz5kxqpY0MZv369Xr00Uc1ceJEubm53bNt3KJp5syZatGihcLDw1MjTWRwNpvNPnZGjBihESNGaP369dqzZ4+mT5+uy5cv29u2adNG/fv3V1BQkKZMmSLp3/UdBXfmFXc/KCAgQF988YVmzpyp1157Td7e3urYsaNOnDhhb9OjRw+1adNG5cqVU9GiRe1xCm44U9x9rGHDhqlbt27q06ePBgwYIF9fX125ckUVKlTQ22+/rUqVKmnSpEkqXbq0/vnnH4f7vB96BkhBNpstwXhgYKAZOnSoqVatmnnvvffs8ZiYmHjTzZ492+TNm9csXrzYuckiXYk7Br766ivTqlUr8+STT5omTZqYVatW2T+LHTMJ/Tx79myTPXt2s2zZMucnjAzr8uXLZtiwYcbFxcXMmjXLGBN/XBkTf72UJ08e8+2336ZansgcLl++bPr162f27dtnoqOjTUBAgKlbt66pWLGiuXTpkkPbgIAAEx0dnUaZIq3MmjXLjB8/3kydOtUeW7VqlWnWrJlp0KCBOXHihEP72HVTQustwFnGjx9vChUqZH799VdjjDEDBw40np6eplWrVvZ12dmzZ83mzZvNggUL7OuyqKioNMs5PaHohlN88cUX5pNPPnGIXbhwwQwdOtSUL1/efPzxx/Z43D/G2bNnm5w5c1I0PWTi7jgMGTLE5M6d21y6dMns3bvXvPDCC+bJJ590KLyNMSY0NNTh59ixs3z58lTJGRlDYjul165dM76+vsbV1dX8+OOPxhjHIvvugpv1EpLiiy++cBg7X3zxhbFaraZGjRrmzz//NMbcGVvbtm0zDRo0MI899pi5fPlyvPlQeGde9evXN/PmzbP/fOHCBVOvXj1jsVjMu+++69A2tvBu1KiROXbsmMNniZ3kAFLC/PnzHcbYyZMnTcOGDc33339vjDFm3bp1Jnv27KZHjx6mUqVK5vnnnzdXrlyJNx/WZf+i6EaKu3LlinnppZdMrVq1zNy5cx0+u3z5snn88ceNt7e3eeeddxw+mzNnjsmVKxc7tg+xCxcumH79+plffvnFHtu6dat54YUXTJMmTczKlSuNMca0aNHC4YzAzJkzTe7cuRk7cBC34P7222/N1KlTzYQJE8zRo0dNdHS0iYqKMn369DFZsmQxa9asMcbE35GdPXs26yUkyffff298fHwcxt2ff/5pWrdubdzd3c3+/fvt8djCu1GjRsbLy8uEhISkQcZIbZGRkeabb74x4eHhDvFt27aZli1bmgIFCpgzZ844fLZ69WpTrVo107dv39RMFQ+x1atXm6pVq8Y7aL18+XJz/vx5s337dlO4cGEzZ84cY4wxPXv2NBaLxdSpU4d12T1QdOM/S+hM0u+//25effVVU6dOHfPZZ585fNavXz9Tq1Yt88Ybb9h3cJcsWWIsFgtnKR9iX331lcmaNaupXLmyOXHihEPxs3XrVtOuXTtTrFgx89hjj5nSpUubyMhIY8ydjUPWrFnNd999l1apI5176623TL58+UyTJk1Mvnz5jI+Pj/nwww9NZGSkiYyMNG+88Ybx8PCIV1jv2bOHghsPJHZ7uHXrVnvsxIkTplGjRqZEiRLm/Pnz9rjNZjO//PKL6du3L2eDHkLvv/++GTlypP3n3bt3m8aNG5tHHnkkXuG9detWLiVHqoodb5s3b7bvb8UaNmyY6datm4mIiDDGGPPRRx+Z5s2bm6FDhzJO74GiG/9J3D+u3377zWzZssWcOnXKGGPMuXPnTLdu3Uy9evXsR8Nu375tunTpYr766iuHouqPP/4w69evT93kka78/PPP5plnnjHZsmWzX0YXu0I35s4Y+e6778zEiRPttyRER0eb9evXmy1btqRJzkj/Vq5caQoXLmz27dtnjLlzO8vAgQNN/fr1zbRp04wxxoSEhJhXXnnFNGzYMN70f/zxR6rmi4xv3759xmKxODy/5OTJk6ZBgwamZMmSDoV3XBTemdvdxcj48eONxWIx/v7+9tjOnTvN008/bcqUKWP+/vvv+84DSGlxx9ixY8eMxWIxo0ePdoj36NHD1KpVy16Mt2nTxr49vXse+BevDEOymThP9R05cqS+/fZbWa1W3bp1S88//7xGjhyp27dva8KECfr555+VN29eWa1W3bx5UwcPHpSLiwuvQnlIJbTcjTHas2eP3njjDV29elW7du1S/vz5FRUVpSxZssSbR3R0tFxdXVMrZWRQ06dP19y5c7Vt2zZ5enrKarXq+vXr8vX11YkTJ7R9+3ZJ0s2bN5U1a1b7uGTdhKSKO1Zi/z1z5kwNGjRIo0eP1siRIyVJp06dUvfu3RUYGKiff/7Z/ronZH5xx8i5c+dUsGBBZcmSRTNnzlT//v313nvv2cfJ7t27NXr0aG3dulUnTpxQwYIF0zJ1PETMXa84rFevng4cOKDevXtr1KhRGj58uFxdXbV48WJNmTJFkZGRcnd3182bN3Xo0CG5uro6zAN3ScuKHxlX3LPUkydPNt7e3iYgIMAYY0zv3r1N7ty57ZfXBQYGmmXLlpnXXnvNDB8+3H6WkiNhD6e4y33FihVm+vTp5pNPPjFHjx41xhizf/9+U69ePVOxYkVz8eJFY4yJd2kTcD+xZw1nzJhhypcvb65evWqM+Xcs/fnnn8ZisZht27Y5TMd6CQ8i7nhZsmSJWb58uf1+3Tlz5hir1Wref/99e5tTp06Z8uXLmzZt2qR6rkgbccfImDFjzMsvv2x+/fVXY7PZzO3bt8306dPjjZMtW7aYgQMHcvUDUk3ccTpt2jRTuHBhs3fvXmOMMfPmzTNWq9WMGTPGGGNMeHi4Wbx4sXnrrbfM22+/7XD1IRJH0Y0HcvLkSfu/o6OjTUxMjGnTpo2ZOHGiMebOg2Ry5cplfw3P7du3EyyYeH0A3n77bVOoUCHz8ssvm6pVq5pq1arZn+i6bds288QTT5hKlSqZf/75J40zRUZwd7Ece2DwzJkzJmvWrKZ///4On+/fv9889thjXD6OZIt78Hno0KGmSJEiZv78+SYwMNAenzVrlrFareaDDz6wxy5cuMDO6UPonXfeMQUKFDDfffed/YCyMXfWXdOmTTMuLi7mww8/jDcdYwWp6dChQ+b111+P93rM2MLbz88vwenYr78/im4kWZ8+fUyzZs3s90YaY8ytW7dMgwYNzO7du83WrVtN9uzZzezZs40xd+7HnTZtGvfbIp7FixebokWLmj179hhj7ryaws3NzeFBert27TLlypUznTt3Tqs0kUHELbjnzp1rBg4caNq3b28fT6tWrTJZs2Y1PXr0MJs2bTL79u0zLVu2NPXq1ePMNv6zSZMmmYIFC5odO3Y4xGMPOE+fPt1kyZLFDBs2zOFziqmHx8aNG03RokXtZw6joqLMhQsXzK+//mqCgoKMMcZ88sknxmKxmIULF6ZlqniIrV271uTMmdPkz5/f/PDDD8YYx4OLCxYsMG5ubmbw4MEU2cnADZFIsrZt26p3796aOHGi3nrrLdWoUUOenp4qXbq02rRpoytXrmj27Nnq0qWLJOn69etauXKlXF1d1aBBgzTOHunJiRMn1LBhQ9WsWVPfffed3nzzTU2bNk0vvfSSbt68qUuXLql27dpatmyZKlSokNbpIp2LvVdy6NCh+uabb9SqVSuVLFlSL7/8svz9/TVw4ED9+OOP6tmzpzZu3ChPT08VKlRIAQEBslqt3L+NZIuMjNTmzZvVp08fPf744zp16pQOHTqkhQsXys3NTVOmTFG/fv0UHh6u77//3uF+RxcXlzTOHqnFGKOCBQvKy8tLhw8f1jfffKNvvvlGVqtVuXPn1sqVK+Xr66siRYroueeeS+t08ZBq0aKFevXqpWnTpumnn35S/fr1lS9fPvvn3bt3V1hYmJYsWcL6Kxl4kBqSJHandNu2beratatq166tQYMGqXbt2jp8+LD69u2ra9eu6fDhw5KkkJAQde7cWdevX9evv/7KH+dDLKGCZtiwYXJxcVHr1q3VtGlTffzxx+rTp4+MMVq4cKGuXr2qAQMG2B+gFhMTwxjCPW3cuFE9e/bUsmXLVKtWLW3fvl0NGjTQl19+qVdeeUWSdPXqVV28eFHR0dF67LHHZLVaeSAf/pOYmBh16dJF4eHhaty4sdasWSNJypYtmwIDA+Xu7q7169fL3d3dXnAbHjT00NmyZYs6deqkihUras+ePXrxxRfVsGFDFSxYUL6+vpoxY4aaNWtmb896Cc52r4PN/fv316pVqzR06FB17txZuXPndvicdVny8BeN+4r9w7TZbKpfv74WLlyo7t27a+LEiRo5cqR8fHzk6+urDz/8UIUKFVLp0qUVFRWlmJgY7dy5Uy4uLhRND6m4y/3kyZPy9PRU/vz59dxzz6lBgwby9/fX0qVL1bZtW0nSrVu3tGTJElWqVMnhieWMHdzt7nVKcHCwqlSpolq1aunbb7/Va6+9ppkzZ+qVV17RtWvXFBQUpPLlyytv3rz2aWw2Gzu2+E9cXFzUsmVLffvtt3rvvfc0cOBANW/eXLVq1dL48eO1b98+ubu7SxI7qQ+xJ554Qp9//rn9JMWTTz6p3LlzKzQ0VNmyZdPd579YL8GZjDH2gnvu3Lnat2+fChQooAoVKqhDhw769NNPFRkZqcmTJ8tisahTp04OhTfrsuThTDfuKe6O7V9//SVPT08VLVpUf/zxh1q3bq1q1arpvffeU8WKFRUYGKhFixZJkgoWLKjOnTvLxcWFI7YPoVmzZunxxx9XtWrVJEnvvPOOVq1apcuXL+uxxx5Tx44d5ebmpjfeeEPz589X/fr1df36db399tu6dOmSdu/ezZhBoiIiIuyFTHBwsLy8vOyvMHnzzTf1xhtvaPz48erbt68kacmSJVq6dKnmzp3rcKkc8F/E3ekMCQlRRESEw+udnnnmGRUuXFjz589PqxSRDsQdJ7H/joqK0o0bN9SlSxeFhIRoy5YtHFxGqog7Ht99911NnTpVTZs21cWLF3X8+HG1a9dOM2bMkCT17dtXmzZtUs+ePfXGG28oe/bsaZl6hkfRjQTdq2iqUKGChgwZosqVK6tp06aqVq2aRo4caW8bF2e4Hz6nT59Ww4YN1aJFC73zzjs6dOiQ3njjDc2ePVvXrl3T77//rk8++UQ9evRQhQoV9M477yhPnjzy9vZWnjx5tH79emXJkoWxgwRt3LhRO3fu1Lvvvqu+ffvq8OHD2rRpk86ePavXX39dW7du1Xvvvadhw4ZJkm7fvq0OHToob968mj9/PkfmkaLuPttz48YN7d+/X/7+/goMDNT+/ft5dy3sjDGKiorShAkTtHnzZl2/fl3btm1jm4dUt3fvXr3//vt666239MQTTygkJESrV69W37591bt3b02ePFmS1LlzZ0VFRWnp0qWsw/4jim7Ec7+i6ciRI5o8ebIWLFigBg0aqFmzZqpVq5b69++vevXqpXX6SAcOHjyonj17qkGDBoqIiNCjjz6qQYMGSbrzgL1FixZp2LBh+uabb1ShQgWdO3dOOXPmlI+PD/fZIlHGGPn6+mr79u3KmTOnjhw5om3btql8+fKSpE8//VRz5sxR9erV1bNnT4WGhmrmzJkKDAzUvn37KH7gdFu2bNH06dNljNHXX3+tLFmysD5DPJs2bdKuXbs0dOhQubq6MkaQqr788kt9+eWXCg0N1f/+9z95eXlJksLDwzV//nxNmzZNixcvVo0aNST9e5sp28//hr9wxFOqVCn7k34//fRTRUREaOjQoXr++ecl3TmSX6xYMb3++uvatGmTvvvuOzVo0ECPPvooRTckSVWrVtVnn32m119/XSdPntTgwYPtn+XMmVPt27fXhg0btG7dOrVq1UqPPPKI/XPus0ViLBaLZsyYoSeeeEJbtmyRr6+vypUrZ/+8f//+io6O1vr169WkSRPVrFlT3t7e2rt3r1xdXTmThAcS90FDkZGRcnNzu+80TzzxhAoUKKCyZcvKarUqKirK4fkUyFyS++aDp59+Wk8//bR9HqyXkJo8PT0VFBSkEydOaPfu3WrZsqUkycPDQ/Xq1dPIkSMVEhJib89bPlIGZ7qRqP379zsUTaNGjbJ/FhISou7du6tYsWKaPn26Dh48qMqVK7PhgIPDhw/rueeeU968eTV37lyHWxB69uypwMBArV27Ng0zREYQe3Q9MjJSYWFhGjFihG7cuKG//vpLrVu3Vv/+/ZUrVy57e5vNpr/++kuFChVSjhw5ZLFYOJOEBxJ3B3P+/PmKjIxUmzZtlD9//kSn4SzQwyXuGNm7d6+Cg4NVokQJeXt7Ozyw8W5xD/4FBgaqcOHCqZIvHk6JFcvr1q3TsGHDVKxYMb399ttq2LChJOny5cuqV6+eJk6caD/ZhpRB0Y17ul/RdP78ea1bt84e40wS7nbo0CF17dpVPj4+GjRokKpWraobN278X3v3HVd1vfhx/HU4HETce6cmuXOkXk0rwZWZWs4M1BwgmNuumpjmyr0XuFHc4EJTMy23uTXLzEUO3FsUEM75/v7wx7mQ1rVucEDfz8fjPh7X7+Jz4NP3fN/fz6J+/fqUKVOGWbNmObqIkor92dv1nj17snPnTpo0aZIkeEdGRlKgQIHnuobIn+nTpw8hISGMHDmSunXrUrBgwT88NnHoXr9+PRkzZsTDwyOFSiopKfHfun///qxYsQKLxYKzszOlSpUiICDgmfPcJD5v5syZ7Ny5k8mTJ2uCR0kWib/7du7cSVRUFM7OztStWxeA8PBwhg8fjpOTE97e3uTNm5eFCxdy9uxZjh8/ruf5f5ieQuRPvf7666xduxar1cqkSZM4evQo8KSL+S+//MIrr7yS5Hj9Byq/V65cOebPn8/Bgwdp0KABjRs3pkOHDkRHR9tnyNS7P3mWxA8M06dPx8vLizp16jB8+HAMw2DSpEm88847rFu3jnHjxhEREUHt2rXts5YnUOCW55X4XrRkyRKWLFlCeHg47du3f+7AHRgYSNu2bdWz4gWW8LeeNm0awcHBBAcHc/LkSerVq8fmzZuTdM1NkLiOzJo1i549e9KkSRMFbkk2Cd99ffr0oW3btnTs2JGOHTtSoUIFLl26ROPGjRk4cCCPHj2iZ8+ehISEULFiRXvgtlqtDv4ELxhD5DkcPnzYKF26tJEvXz6jUaNGRvPmzY2KFSsajx8/NgzDMGw2m4NLKKnd8ePHDXd3d6Ns2bLGggULjPj4eMMwDCMuLs7BJZPUrl+/fkb+/PmNL774wpgzZ45hMpmMTp062ff37dvXKF++vFGwYEGjSpUqRmxsrANLK2nRqlWrDKvVmmRb3759jQ8//DDJtoTvusTfeYnPCwoKMrJmzWqsWLEiGUsrjmaz2QybzWZ4eXkZw4YNMwzDMNauXWtkzpzZCAoKMgzDMKKjo41bt27Zj08QFBRkZM6c2Vi5cmXKF1xeOjNnzjSyZ89u7Nu3zzh37pxx4MAB48033zTc3d2Nu3fvGoZhGBs3bjQqV65s+Pr6Gnv27DEMw3jqfij/O73+l+dSsWJFli9fToYMGYiIiKBRo0YcOHDAPjOrxrHJf1O2bFmWLFlC9erVadOmDWazWZOmyX+1b98+Vq5cyfLlyxk2bBglSpTA2dmZqlWr2o8ZPXo0s2fPZt68eezduxcXFxfi4+MdWGpJSwYPHsyaNWuSbDMMgytXrmCz2YAnvS6M/2+ptFqtbN68mRs3bgD/aU2aNWsWffv2Zc6cObRo0SJFP4OkPJPJRHR0NNWqVWPbtm14e3szZswY/Pz8iIuLY+HChezevRur1Wp/RgoKCqJfv37MmzePpk2bOvgTyIso4Z6V4Pjx4zRp0oR//etfFC1alMqVK7NmzRoyZMiAt7c3APXr12fAgAEcO3aMadOmsWPHDvUQSw6OTv2Stuzfv9/o1KmT/a2t3oTJX6W6I3/Fpk2bjGrVqhmGYRhhYWFGxowZ7S1Jd+7cMb777runzknoRSHyPO7fv2/vcXPo0CF7/Zk5c6ZhMpnsLT8Jrl27Znz88cfGpk2b7NtmzJhhZMyYUa2XL6g/+r7y8fExsmfPbmTIkMEICQmxb79x44bh6elpTJw40b5twYIFRqZMmYywsLDkLq68pBL3qAgNDTXi4uKM1q1bG1WrVrVvT7i/zZgxwyhXrpxx48YN+75169YZr732muHj42NER0enXMFfEnqNIX9JlSpVCAoKwmQyaXIi+VtMJhOGYajuyFN+/4Yeniwx9/DhQyZOnEiHDh0YO3Ysfn5+ABw4cIDhw4dz9uzZJOdobgl5XnFxcWTKlAlnZ2fCw8P5+OOPCQwMxGq10qlTJ5o2bcp7773Hpk2buHTpEhEREbRr147Tp09Tp04d4ElL0ty5c5k/f75aL19AiZ919u7dy8GDB4mIiABg7NixVKhQgWzZstGsWTOioqK4fv06bdq0ISYmhm7dutmvc/v2bZYuXUqzZs0c8jnkxWYkmjNg5MiRdO/enXPnztGiRQsePHjAnDlzgP98P+bKlQubzZbke7dhw4ZMmTKFgIAAXF1dU/5DvOA0e7n8LYaWRhGRf1DiB9tNmzZx7949ypYtS4ECBejYsSMbN26kZ8+ejBgxAoCYmBhatmyJm5sbS5Ys0Usc+Z/8+uuvFCpUiA4dOnD58mW8vLzw8/Pj2rVrfPnllyxYsIBcuXKRKVMmsmTJwo4dO5Ksv3327FmKFSvmwE8gySHxs86///1vli9fzt27d6levTotW7akY8eO7Ny5E39/f27cuEGePHlwc3PDZrOxZ88eLBaL1mqXFHXo0CEmTpxIu3btqFOnDteuXeOzzz7j2rVrNGjQgK5du3Lt2jU6deqExWJhzZo19sYQPdcnL4VuERFJNfr378/UqVPJnz8/v/32GzNnzsRkMhEYGEj27Nlp0aIFZrOZJUuWcOXKFQ4fPoyzs7N63shfEhoayvHjxxk6dCi9evXi8OHDbN++nTt37tC1a1fOnTtH+/bt8fHxwcnJiV27dnH//n0sFgu1atXCbDYTHx+Pk5OT6t0LyGazYTKZ7CFkz549dOrUidmzZ3P37l1WrlzJ4cOH6dSpE/7+/litVmbNmoXJZCJXrlx8+OGH9jqieUskpSxZsoQpU6YQFRXFunXrKFq0KAARERGMHDmSrVu3cuPGDQoXLoyLiws//PADFotF358pRKFbREQcJuHtumEYnD9/njZt2jBmzBhKlCjB3LlzCQgIYPLkybi4uLBv3z7CwsKoUKECBQoUYP78+VgsFqxWq7qUy3OzWq3MmTOHzp078/bbb3PkyBF27dpFuXLlAJIE77Zt2+Lr6/tUcFKde3H9/m8bFhbG119/TYECBRg+fDgAZ86cYfLkyezatYsOHTok6Ub+R9cRSW4HDx7k3//+N/v27WPSpEn2oVgA9+/f5/79+2zbto08efIkeXmoF0MpQ6FbREQcIvHb9du3b3Pr1i3mzZvH8OHD7Q+rEydOpG/fvowdOxYfHx/i4uLInDmzfb8eGOTvevvtt9m9ezedO3dm+vTpGIaB1WrF2dmZu3fv0qVLFy5evEjjxo3p3bu3WoJeAh06dCB37tyMGjUKm83GlStX8PX15YcffuCDDz5g/vz59mPPnj3L5MmT2bt3L02aNCEgIMCBJZeXzR+1Th8/fpxevXoRHx9Pr169+OCDD/7weL0YSln6BhEREYdIeAAYMGAA9evXp0qVKmzYsIEzZ87Yj+nVqxdjx46lT58+fPXVV1gsFvtDgmEYCtzy3H7fxuDh4cHnn3/OzJkzGTx4MCaTCWdnZ2JjY8maNSvTp08nS5Ys/Prrrxrr+BJ4/Pgx77//PsOGDbNvK1CgAEOGDKFOnTps3bqVpUuX2vcVK1aMHj16ULJkSU6fPv1U/RJJLokno125ciUzZsxg0aJFXL9+nddff53x48djNpsJCgoiPDwcePJ9+/s6qsCdstTSLSIiKSrxG/dly5bRu3dv+vfvz7lz55g1axaffvopXbt2pXDhwvZzRowYwddff82uXbsUgOQvS1znfj+xVWBgIN26deOLL75g8ODB9u2//PIL7u7umM1m+wOr6t7LYc6cOaxatYq1a9disVg4ePAgY8aM4dq1a3Tp0oWWLVvaj42MjCRfvnyqI5IiEtex3r172yd5tNls3Lp1i5UrV+Lh4cHhw4fp27cv6dKlo23btnz00UcOLrmopVtERFJUQvjZvn07O3fuZNSoUXTr1o2JEycyduxYli1bRmBgIOfPn7efExAQYA/celcsf0XiVqEJEybg6+tLq1atOHnyJDabjc6dOzNt2jRGjBhBQEAAly5dolGjRgQEBGCxWHBycrJPrCUvpsTLJsXHx/Po0SMiIyNp164dcXFxVK5cmd69e5M3b16mT59OWFiY/fgCBQqojkiKSahjhw8f5ujRo2zevJkDBw6wefNmGjZsSOPGjTl06BBvvPEGY8eOJTIykv379zu41AJq6RYREQe4evUqb731FteuXWP48OH06NHDvm/69OmMGjWKtm3b0rFjR1599VX7PrUkyV+RuIV7xIgRjB49Gm9vb7Zu3UpUVBSTJ0+mcePGuLi4sGDBAnx8fHjttddwcXHhwIEDWurpJZC4jpw5cwZ3d3diYmJYvHgxgYGBuLu7ExISgsVisU9QdezYMaZNm0atWrUcXHp5GS1btoy5c+diNptZs2aNfU3t2NhYvLy8OHXqFLt37yZz5sycOXOGV199VXNSpAL6C4iISIrLmzcvq1atIn/+/Hz99dccP37cvq9Lly4EBAQwevRoNm/enOQ8BW75KxIeNC9cuMCZM2fYsGEDM2bM4Ndff6V69ep89tlnrF27ltjYWD755BNOnjzJ5MmTOXToEBaLhfj4eAd/AklOiQP34MGDadOmDbt27cLV1RVvb2/8/Pw4c+YMbdq04fHjx1StWpVPP/2U5s2bU7NmTQeXXl5GVquVQ4cOce7cOU6ePGl/MRgfH2/vSh4VFcWNGzcAcHd3x8nJCavV6shiCwrdIiLiIOXKlWPFihXcvHmTqVOn8vPPP9v3de7cmRUrVuDr6+vAEsqLYP78+ZQsWZKDBw+SOXNm+/bQ0FCqVq1Knz59WLduHVFRURQrVoy6detiNpvtM5nLiyshcPfv35+ZM2fSp08fihUrBoCrqytt2rTh008/5fTp07Rr147Y2Fjefvtthg4daq8jIskp8dAHeDL52dChQ+nUqRNxcXH4+/vz4MED+70qX758WK1WoqKinjpPHEvdy0VExKGOHDmCj48PlSpVomfPnpQuXTrJfi1rIv+revXqsWXLFpYvX07Tpk2T1KdWrVqxevVqNm7cqO7CL6GDBw/SokULgoODqVmzJtHR0dy+fZtDhw5Rrlw5ihQpQnBwMIMGDaJdu3YMHTpUw1wkRSTuiXH06FHSp08PQIkSJYiNjWXs2LGsXr2aV199laFDh3L//n0GDx7M3bt32b17t7qUpzIK3SIi4nBHjhzBz8+PwoULM2bMGIoWLeroIkka9Edr1wK89dZbREZGEhISQvXq1ZMcN3DgQAYPHqyXOy+h77//no4dO7Jnzx5+++03wsLCWL9+PVevXqVixYpMnToVd3d3Nm/ezPvvv686IimuT58+LFq0CCcnJ+Li4ujWrRsDBgwgPj6ecePGMWHCBOLi4qhTpw65c+dmwoQJpE+f/k/vh5LyFLpFRCRV2L9/P0FBQcyZM0cPCvKXJX7APHz4MFFRUbzyyisUKlTIHpTefPNNrl+/zoIFC54K3qBeFS+6Z4WQhHpSsGBBIiIi8Pb2pm7duhQvXpzatWszbdq0JEuEqY5Ickvck2Lbtm20bt2akJAQnJycOHnyJF27dsXHx4fAwEBiY2MZP3484eHhVKpUidGjR5MxY0ZiYmLsE6xJ6qDQLSIiqUbCw4be0MtfkfghdcCAASxatAiz2czNmzcZOHAgTZs2tY/VrV69Ojdv3iQwMJBatWqpm/BLIvE95eeff7ZPlFe6dGmuXr3KsmXLKFmyJO+88w5ubm4YhkH16tXp1asXLVu2VJdySXELFixg3759ZMuWja+++sq+fdOmTTRo0ICgoCA6depETEwMo0aN4ptvvqFGjRp8+eWXZMqUyYEll2fRE42IiKQaCetwK3DLX5EQhkaMGEFwcDDz58/n3LlzfPTRR4wcOZI5c+Zw9uxZAPbs2YPNZmPmzJkKUS+JxPeUgIAAmjRpgqenJx4eHgwcOJAcOXLQs2dP6tevj8lk4saNG7z//vvExcXRrFkzQCsnSMo6f/48S5cuZeHChdy9exd48uIoLi6O+vXr061bN5YsWcKDBw9wdXWlf//+NGzYkPXr1zNy5EjHFl6eSdNyiohIqqKHW/k7zp49y65du5gyZQq1atUiPDycsLAw6tSpw+TJk7Farfj6+vLaa69x5swZzTz9Ekm4p4wbN45Zs2YRGhqKyWQiIiICf39/rl+/zvTp04Ens90vXLgQs9nM3r177bOUq0u5pKTChQvTp08fzGYzixYtwtvbm+rVq9vrctasWbHZbGTMmBGbzUa6dOn47LPPsFgsSYZDSOqh0C0iIiJpXrZs2ejYsSPvvfcee/fupXPnzgwbNoyuXbvi6+tLcHAwjx49ol+/fvZx3gpTL7bEXcJtNhu7d+/G398fT09PADw8PChSpAi1a9emYsWK+Pv7U7t2bQzDwN/fH7PZTHx8vJaOk2T1R8OpateujdlsxmQy4ePjw5w5c6hatSrR0dHs3LmT3LlzA0+WvrPZbLi6utK3b9+ULr48J91FREREJE151kNq9uzZqV27Nm5ubixduhQPDw86deoEQJYsWShYsCBXr16lYMGC9nMUuF9cievIzZs3yZkzJ6dOneK1114DngTy+Ph4PD096dGjB6GhoXzyySeUKFGCEiVKAGitdkl2ievp/Pnz2bt3L+nTp6dSpUq0bdsWDw8PrFYrY8aM4Z133qFUqVJUqlSJe/fusWnTJg3JSkN0JxEREZE0I/EDZmhoKLGxsWTJkoVGjRqRNWtW4uPjuXHjBs7OzsTGxuLi4kJERAQTJkygZs2a9odUDWN4cSUOMhMmTODMmTMMGDAAb29v5syZQ8uWLalcubI9UGfMmBGTyWRfBzmBXspIckuop/369WPRokU0aNCAqKgoBgwYwMWLFxkwYIC9xTt9+vQcP36c2rVrExwcDEBcXBwWi8WBn0Cel0K3iIiIpBkJYfnzzz9n1qxZZMuWDbPZTHh4OLNnz8bZ2Zny5cvz1VdfcefOHS5evEhcXBxvvfWWAvdLInGQmT9/vn1Mf/369fnhhx8YOHAgw4YNo3Llyjx8+JD9+/dTqFAhB5daXlbBwcGEhYWxatUqqlatypIlS1i0aBHDhw/n3r17jBkzBg8PDx4/fszs2bOZNGkSZcuWpWLFiuqJkYboLyUiIiKpXkLrpWEY3L59m2PHjrFjxw6yZMnCd999x6BBg/joo49Yvnw5n3/+ORaLhfPnz+Pu7s64ceNwdnbWGO6XyNatWwkNDWX16tXUqFEDgFdeeQUfHx/mzp2Lh4cHpUqVIjY2FsMwWL9+PYBeykiKslqtXLp0CV9fX6pWrcq6devo0qULI0aMIDo6mkGDBpElSxYGDBhAvXr1cHFxYdq0aTRr1oywsDDeeOMNR38EeU4K3SIiIpKqJe4ufP36da5fv066dOnIly8fOXLkoEWLFqRLl46+ffvSsmVLVqxYwWeffZbkPE2I9XK5cOECbm5ulClTBvhPHWrcuDFly5bl1KlTHDhwgFy5cuHj44Ozs7PqiKQ4s9lMr169uHr1KpGRkfTv358vvviCzz77jH379jFmzBgGDhxIxowZ6dGjh73Fe+HChWTLls3RxZe/QHcWERERSdUSgvOAAQNYtmwZuXLl4tatW/btbm5uNG7cGCcnJ/r160fdunX59ttvk0wupDD1ckhoqY6Ojk6yLJzJZLL3dDh06BBvvPEG9evXt+/XpGniCIZhkCFDBooVK8aWLVswDANvb28AXF1d+eCDD2jZsiUNGjSwn1OvXj3eeust3NzcHFVs+Rs01Z2IiIikSjabzf7/Fy9eTEhICP369aN+/frcv3+fTz75xL7fzc2NRo0a8eWXX5IpU6Yk58rLI6FruKenJ6dPn2bSpEn27WazmaioKBYtWsSmTZuSnKdhB+IIiYcyZM6cmQsXLhAaGsrly5cJCAjAMAwaNmxoX+LQMAwABe40yGQk/PVEREREUqE1a9Zw8eJFMmTIQIcOHYiLi2PXrl18/PHHVKtWjTVr1tiPjY2NJV26dMAfr38rL4dZs2bRtWtXOnfuTMOGDXFxcWHEiBFcvXqVQ4cOqWVbkt1fmV387t27jB49mqlTp5I7d26yZs3Kvn37sFgsmmvgBaDQLSIiIqnWzZs3KVKkCI8ePWLIkCEMHDgQeBKod+zYwccff8ybb77JqlWrHFxSSW0MwyA8PJzu3btjtVrJmjUrBQoUYP369VgsFk2sJ8nqo48+ombNmrRv3/6p5ej+yN27d7l06RKRkZHUqVMHs9msuQZeEArdIiIikmo8q0XnxIkTNGvWjOzZs7Ny5Ury5s1rP3bHjh14enrSt29fRo0a5YgiSyp38+ZN7t27h81mo1ixYjg5OSnISLJr164dK1asIDAwkJYtWz538E5M9fTFodAtIiIiqULi7uC3bt3C2dkZk8lE5syZ+emnn6hbty4VKlQgJCSEnDlz2s85duwY5cqVU6ulPBcNO5DklPjFYa9evQgMDGTmzJnPFbzVjfzFpdAtIiIiDpf4YXP48OHs2LGDs2fPUq1aNVq0aMGHH37Izz//TL169ShfvjwLFy60B+8E6i4sIqlB4ntRz549CQoK+q/BO/E9cNGiRTx8+BA/P78UK7MkL73mExEREYdLeNgcOHAgkyZNonPnzkyZMoUrV67QoUMHLl26RJkyZdi8eTM//fQTDRo04N69e0muocAtIo6U0JaZMBYbYNKkSfj5+eHn58eKFSuIjo5+5nkJ98CgoCC6dOlCoUKFUq7gkuwUukVERCRVuHDhAlu2bGHFihU0adIEZ2dnDh06xOjRoylYsCBxcXGUKVOG8PBw8uXLR6ZMmRxdZBER4MmwhYTgHBsby6NHj+z7Jk+eTKdOnZ4ZvBOfN3PmTD7//HPmzZuXZG1uSfs0Ml9EREQc4vdja2NiYjh//jxlypRh3bp1eHl5MXbsWHx9fYmOjmbhwoXUq1ePChUqsHbt2mdeQ0QkpSW+D40ZM4Zt27Zx6tQp2rRpw0cffUTJkiWZMmUKAP7+/phMJpo3b46bm5v9vFmzZtG3b1/mzZtHs2bNHPZZJHnoW0pERERSXOKH1NWrV3P+/HmyZ89OqVKlCAoKok2bNowdOxZ/f38ATp06xbfffsvFixeTXEeBW0QcLeE+NGDAACZOnIinpydDhgxh/PjxjB49mr179wIwZcoU/Pz8aNeuHdu3b7efHxQURM+ePZk/f74C9wtK31QiIiKSogzDsD+kBgQE0K1bN8LDw8mZMyfu7u4MGTKETp062QP3w4cPCQgI4OHDh7z11luOLLqIyDOtX7+e0NBQVq9eTZ8+fShZsiSPHj1i3bp1jBo1iv379wNPxniPHz+eunXrAhAZGcl3333HwoULadq0qSM/giQjzV4uIiIiDjFs2DCmTJnChg0bKF68OFmyZAGgdevWbNmyhRYtWuDs7MzRo0e5efMmhw8fxmKxqEu5iDhcwn3IMAxsNhu7du3ip59+okuXLmzYsAFvb2+mT59O8eLFqVatGl5eXrRv3x5PT8+nrnHjxg1y5crlwE8jyU1jukVERCTF3b59mx07djBp0iSqVKlCZGQkhw8fZunSpdSvXx+TycStW7eIjo6mevXqDBkyBGdnZ+Lj43F21uOLiDhWwou/x48fky5dOsqUKUPx4sW5e/cuo0aNom/fvnh5efH48WPc3d1ZtGgRRYsWTRK6EyZQU+B+8elbS0RERFKcyWTixIkT/PLLL+zYsYMZM2YQERGBzWZjw4YNfPHFF/j7+ydZSsdqtSpwi0iqsWDBAsaNG8eOHTvImTMnAJcvX+bOnTu89tprADx69IjatWszdepUatWqleT8hHubvPjUN0tERERSXLZs2Rg6dCgzZsygUaNGFC5cmK+++ooDBw7g4eFhn3go8UOp1uEWkdQkU6ZMuLm50aRJE+7cuQM8mYPi3r17fPvtt4SEhODl5cXRo0epU6cOZrMZq9Xq4FKLI2hMt4iIiDjMhQsXiI2NtbcK2Ww26tWrR7Vq1Rg+fLiDSyci8sSz5pKw2Wxs2rSJ4cOHYzabWbNmDTly5GD16tX07t2bTJkykSNHDjZv3ozFYknSc0deLgrdIiIi4nBRUVEcPXqU0aNHc/78eQ4fPqyu5CKS6qxZs4b33nuPdOnSAU+C98aNGxkxYgROTk6sXbuW7Nmzc/HiRSwWC7lz58bJyUnzUbzk1L1cREREHMowDA4ePMjo0aOJi4vj0KFDODs7qxumiDhc4vbJEydO0KNHD7y9vXn8+DHwZEK1d999l65du3LixAm8vb25desWhQoVIm/evDg5OWGz2RS4X3Jq6RYRERGHi42N5cSJE5QvX16tQiKSKly9epW8efMCsGrVKpo2bcqcOXOYO3cur7zyCiEhIbi4uABw9+5d3nnnHc6dO4e3tzczZ850ZNEllVFLt4iIiDhcunTpqFixolqFRCRV2Lx5M61ateLIkSP06tWL5s2bc+vWLVq3bo2Pjw/nzp2jbdu29uPj4+OpUKECoaGhBAYGOrDkkhqppVtERERERCSR8+fP8/777/Po0SPu3LnD9u3bKVeuHPCkZ86iRYuYMWMGTk5O+Pj4sHTpUlxdXdmwYQNOTk5YrVatuCB2aukWERERERHhycRoNpuNwoUL07BhQy5evEjJkiWJjo62j+9Oly4dbdq0YfTo0eTIkYPZs2eTNWtW1q1bZ++to8AtiamlW0REREREJJFr165x5swZYmJi6Nu3L9mzZycgIABPT8+njr1y5Qp58+bFZDJpPgp5JrV0i4iIiIiI/L+FCxfywQcfUK1aNWrXrk1oaCg3b95kxIgRbN++3X7clClTAMiXLx8mkwnDMBS45ZnU0i0iIiIiIvL/xo8fz+zZszl58qR9W0REBM2aNSNbtmy8//77bNu2jZ07d3Lr1i2cnNSOKX9ONURERERERF5Kz2p/rFevHiaTiQsXLgDw+PFjihYtyurVq3Fzc2Pt2rU8fvyY69ev28dwi/wZ9X8QEREREZGXkslkAmDChAmkT5/evi739evXuXTpEq+88op9Le7ChQuzdOlSYmJiyJEjh8Zwy3NTDRERERERkZfWhQsX2LlzJ1euXOH06dOULl2aO3fu0Lp1a1q2bEmWLFl49913iYmJoXr16mTMmBF4MtO5Arc8D43pFhERERGRl4bNZnvmOGyr1UpMTAwXL16kY8eOuLi4kCFDBiIiIoiKiqJo0aJ8//339tZxkeelVzMiIiIiIvJSSBy4v/nmG27evElMTAzt2rXDbDaTIUMGSpYsSf78+cmbNy9Tp04FnrSGFyxYUIFb/ha1dIuIiIiIyEulb9++rF69mmzZsgFw+fJlvvnmG0qXLo3JZGLu3LksXLiQrVu3Yjab7WH7j1rJRf6MaoyIiIiIiLw0Zs+eTXBwMMuXL2f//v307NmTy5cvc/78eXu4zpMnD0ePHuX+/ftJWrcVuOXvUK0REREREZEX1u879p49e5Zu3brxxhtvEBYWhr+/P0FBQTRo0IAHDx5gGAb58+enbt26ZM2a1TGFlheKupeLiIiIiMgLyTCMp8ZhN2/enMKFC/Puu+/SvHlzRo8eTefOnTEMg/Hjx2M2m+nVq5f9XKvVitlsdtAnkBeBWrpFREREROSFs3v3bg4ePAhAp06dGDFiBACNGjVi9+7dfPDBB/bADXDv3j22bdvG7du3gSdreBuGocAt/zPNXi4iIiIiIi8MwzC4desWrVq1olq1ari6urJmzRp27twJQO3atVm8eDHFihUjT548REdHc+HCBXr16sWNGzf48ssv7dfSbOXyT1D3chEREREReeGcPn2aGjVqcOfOHRYsWICXl5d935kzZ/D19eXatWtcvnyZ4sWL4+Liwvfff4/FYlGXcvlHqaVbREREREReKPHx8Tx69IicOXOSPn161q1bx6uvvkq1atUAcHd3JzQ0lMuXL3Ps2DFKlChBpUqVMJvNxMfH4+ysmCT/HLV0i4iIiIhImvdHa2j/+OOPNGvWjPLly9OnTx+qVq36h9dQC7ckB02kJiIiIiIiaZphGPbAvWrVKqZMmcKWLVu4ffs25cqVIyQkhGPHjjFx4kR27doFgIeHB1OnTk1yHQVuSQ5q6RYRERERkTQr8bJgffr0YeHChWTIkAFXV1eqVKnCiBEjKFCgAPv27aNjx464uroSExOD1Wrl2LFjuLi4OPgTyItOLd0iIiIiIpJmJQTuH3/8kZMnT7Jp0yaOHz9Ot27diIiIoFu3bkRGRlK1alWWLl1Ku3btaNu2LcePH8fFxYX4+HgHfwJ50amlW0RERERE0rRly5Yxd+5csmbNypIlS7BYLADMnz+f+fPnkytXLqZMmUKBAgWSjP3WGG5JCWrpFhERERGRNMtms/Hjjz8SERHB8ePHk0ym1r59e9q3b8/t27dp3bo1t27dSrJfgVtSgkK3iIiIiIikGTabLcm/nZycGDJkCJ07dyY+Pp4uXbpw//59+/727dvTrFkzSpcuTbZs2VK6uCLqXi4iIiIiImlD4q7hP//8MxaLBavVSqlSpYiPj2fcuHGsWbOGypUrM3LkSDJlymQ/N2HCtT9aWkwkuSh0i4iIiIhIqpd4lvKAgADCwsJ4+PAh8fHx+Pr6MnjwYADGjBnD+vXrqVy5MsOGDSNLlizPvIZISnF2dAFERERERET+m4SwPG7cOGbNmkVoaCgmk4mIiAj8/f25evUqc+bMoU+fPgDMmzePIkWK0Lt376euIZKSFLpFRERERCTVStw6bbPZ2L17N/7+/nh6egLg4eFBkSJFqF27NhUqVKBr1678+9//pmDBgnh7ezuy6CKAJlITEREREZFUymaz2QP3zZs3cXJy4tSpUzx+/Bh4Esjj4uLw9PSkR48erF69mgcPHuDi4kLbtm0xm81YrVZHfgQRhW4REREREUl9Ek94NmHCBAYNGkRkZCTe3t6EhYVx8OBBTCYTzs5POu9mzJgRJyenJJOngZYFE8dT6BYRERERkVQnIXD369ePUaNG8fbbb2O1Wqlfvz5ly5Zl4MCB9uD98OFD9u/fT8GCBR1capGnafZyERERERFJlbZu3Yqvry8hISHUqFHDvj08PJy5c+eydetWSpUqRWxsLIZhcPjwYSwWi2Ypl1RFE6mJiIiIiEiqdOHCBdzc3ChTpgzwny7njRs3pmzZspw6dYoDBw6QK1cufHx8cHZ2Jj4+3t7lXCQ1UG0UEREREZFUJaGlOjo6OslEaCaTCavVitls5tChQ7zxxhvUr1/fvt9qtSpwS6qjMd0iIiIiIpKqJHQN9/T05PTp00yaNMm+3Ww2ExUVxaJFi9i0aVOS8zRpmqRGGtMtIiIiIiKp1qxZs+jatSudO3emYcOGuLi4MGLECK5evcqhQ4fUsi2pnkK3iIiIiIikWoZhEB4eTvfu3bFarWTNmpUCBQqwfv16LBaLvbu5SGql0C0iIiIiIqnezZs3uXfvHjabjWLFiuHk5KRJ0yRNUOgWEREREZE0J2Emc5HUTqFbREREREREJJno1ZCIiIiIiIhIMlHoFhEREREREUkmCt0iIiIiIiIiyUShW0RERERERCSZKHSLiIiIiIiIJBOFbhEREREREZFkotAtIiIiIiIikkwUukVEROR/EhwcjMlk4rfffnN0UURERFIdhW4REZE0JCHgmkwmdu3a9dR+wzAoVKgQJpOJhg0b/uXrz5gxg+Dg4H+gpCIiIgIK3SIiImmSq6srS5YseWr79u3buXTpEunSpftb1/07obtNmzZER0dTuHDhv/UzRUREXmQK3SIiImlQgwYNCA0NJT4+Psn2JUuWUKlSJfLmzZvsZXj48CEAZrMZV1dXTCZTsv9MERGRtEahW0REJA36+OOPuXXrFt9++6192+PHjwkLC8PLy+up4202G5MmTaJMmTK4urqSJ08e/Pz8uHPnjv2YIkWK8PPPP7N9+3Z7F3YPDw/gP93at2/fzqeffkru3LkpWLBgkn2/H9O9ceNGatasSaZMmcicOTNVqlRJ0jp/+vRpmjVrRt68eXF1daVgwYK0atWKe/fu/YO/KREREcdydnQBRERE5K8rUqQIb775JkuXLuW9994DnoTce/fu0apVK6ZMmZLkeD8/P4KDg2nfvj3du3cnIiKCadOmceTIEXbv3o3FYmHSpEl069aNjBkzMmDAAADy5MmT5DqffvopuXLlYtCgQfaW7mcJDg6mQ4cOlClThv79+5M1a1aOHDnCpk2b8PLy4vHjx7z77rvExsbSrVs38ubNS2RkJOvXr+fu3btkyZLlH/6NiYiIOIZCt4iISBrl5eVF//79iY6OJn369CxevJiaNWuSP3/+JMft2rWLOXPmsHjx4iSt4J6entSvX5/Q0FC8vLz48MMP+eKLL8iZMyetW7d+5s/Mnj07W7duxWw2/2G57t27R/fu3fnXv/7Ftm3bcHV1te8zDAOAEydOEBERQWhoKM2bN7fvHzRo0N/6XYiIiKRW6l4uIiKSRrVs2ZLo6GjWr1/PgwcPWL9+/TO7loeGhpIlSxbq1q3LzZs37f+rVKkSGTNm5Pvvv3/un+nr6/ungRvg22+/5cGDB3z++edJAjdgH/ed0JL9zTff8OjRo+f++SIiImmNWrpFRETSqFy5clGnTh2WLFnCo0ePsFqtSVqNE5w+fZp79+6RO3fuZ17n+vXrz/0zixYt+l+POXv2LABly5b90+v07t2bCRMmsHjxYt5++20aN25M69at1bVcREReKArdIiIiaZiXlxe+vr5cvXqV9957j6xZsz51jM1mI3fu3CxevPiZ18iVK9dz/7z06dP/3aI+Zfz48bRr1461a9eyefNmunfvzsiRI/nhhx/sk7SJiIikdQrdIiIiaViTJk3w8/Pjhx9+YPny5c88plixYmzZsoUaNWr819D8Tyz7VaxYMQB++ukn3N3d//TY119/nddff50vvviCPXv2UKNGDYKCghg+fPj/XA4REZHUQGO6RURE0rCMGTMSGBjI4MGDadSo0TOPadmyJVarlWHDhj21Lz4+nrt379r/nSFDhiT//jvq1atHpkyZGDlyJDExMUn2JUykdv/+/afWGH/99ddxcnIiNjb2f/r5IiIiqYlaukVERNK4Tz755E/316xZEz8/P0aOHMnRo0epV68eFouF06dPExoayuTJk+1jwStVqkRgYCDDhw/H3d2d3LlzU6tWrb9UnsyZMzNx4kR8fHyoUqUKXl5eZMuWjWPHjvHo0SMWLFjAd999R9euXWnRogXFixcnPj6ekJAQzGYzzZo1+9u/CxERkdRGoVtEROQlEBQURKVKlZg5cyYBAQE4OztTpEgRWrduTY0aNezHDRo0iPPnzzNmzBgePHhAzZo1/3LoBujYsSO5c+dm1KhRDBs2DIvFQsmSJenVqxcA5cuX591332XdunVERkbi5uZG+fLl2bhxI9WqVfvHPreIiIijmYyEfl4iIiIiIiIi8o/SmG4RERERERGRZKLQLSIiIiIiIpJMFLpFREREREREkolCt4iIiIiIiEgyUegWERERERERSSYK3SIiIiIiIiLJRKFbREREREREJJkodIuIiIiIiIgkE4VuERERERERkWSi0C0iIiIiIiKSTBS6RURERERERJKJQreIiIiIiIhIMlHoFhEREREREUkm/weGJ7mMu/bLawAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Part of Answering Question"
      ],
      "metadata": {
        "id": "o1njghuOoeWd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def find_best_context(question, contexts, tfidf_matrix, vectorizer):\n",
        "\n",
        "    question_vec = vectorizer.transform([question])\n",
        "\n",
        "    similarities = cosine_similarity(question_vec, tfidf_matrix)\n",
        "\n",
        "    best_idx = similarities.argmax()\n",
        "\n",
        "    return contexts[best_idx]\n",
        "\n",
        "all_answers = []\n",
        "\n",
        "while True:\n",
        "    user_question = input(\"اكتب سؤالك (أو اكتب 'خروج' للخروج): \").strip()\n",
        "    if user_question.lower() == \"خروج\":\n",
        "        print(\"تم إنهاء البرنامج.\")\n",
        "        break\n",
        "\n",
        "    best_context = find_best_context(user_question, contexts, tfidf_matrix, vectorizer)\n",
        "\n",
        "    predictions = model.predict([\n",
        "        {\n",
        "            \"context\": best_context,\n",
        "            \"qas\": [\n",
        "                {\n",
        "                    \"question\": user_question,\n",
        "                    \"id\": \"0\",\n",
        "                    \"answers\": [{\"text\": \"\", \"answer_start\": 0}]\n",
        "                }\n",
        "            ]\n",
        "        }\n",
        "    ])\n",
        "\n",
        "    if predictions:\n",
        "        answers = predictions[0][0][\"answer\"]\n",
        "        probabilities = predictions[1][0][\"probability\"]\n",
        "\n",
        "        best_idx = probabilities.index(max(probabilities))\n",
        "        best_answer = answers[best_idx]\n",
        "        print(\"\\nالإجابة هي : \", best_answer)\n",
        "    else:\n",
        "        print(\"لم يتم العثور على إجابة لهذا السؤال.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dlSzxvW3FLkO",
        "outputId": "8c59f8af-6f41-4b48-e92c-520bf1402c77"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "اكتب سؤالك (أو اكتب 'خروج' للخروج): خروج\n",
            "تم إنهاء البرنامج.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install flask flask-ngrok"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cigvpe9uK6Bq",
        "outputId": "c9166079-f5c6-4ad1-c2f3-6ac6049c33f3"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: flask in /usr/local/lib/python3.10/dist-packages (3.1.0)\n",
            "Collecting flask-ngrok\n",
            "  Downloading flask_ngrok-0.0.25-py3-none-any.whl.metadata (1.8 kB)\n",
            "Requirement already satisfied: Werkzeug>=3.1 in /usr/local/lib/python3.10/dist-packages (from flask) (3.1.3)\n",
            "Requirement already satisfied: Jinja2>=3.1.2 in /usr/local/lib/python3.10/dist-packages (from flask) (3.1.5)\n",
            "Requirement already satisfied: itsdangerous>=2.2 in /usr/local/lib/python3.10/dist-packages (from flask) (2.2.0)\n",
            "Requirement already satisfied: click>=8.1.3 in /usr/local/lib/python3.10/dist-packages (from flask) (8.1.8)\n",
            "Requirement already satisfied: blinker>=1.9 in /usr/local/lib/python3.10/dist-packages (from flask) (1.9.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from flask-ngrok) (2.32.3)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from Jinja2>=3.1.2->flask) (3.0.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->flask-ngrok) (3.4.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->flask-ngrok) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->flask-ngrok) (2.3.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->flask-ngrok) (2024.12.14)\n",
            "Downloading flask_ngrok-0.0.25-py3-none-any.whl (3.1 kB)\n",
            "Installing collected packages: flask-ngrok\n",
            "Successfully installed flask-ngrok-0.0.25\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab.output import eval_js\n",
        "print(eval_js(\"google.colab.kernel.proxyPort(5000)\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "DE9EuY-DKxA5",
        "outputId": "3dcca784-5d36-4bf5-be22-47a473da9261"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "https://0ypa0jxt1mzn-496ff2e9c6d22116-5000-colab.googleusercontent.com/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from flask import Flask, render_template, request, jsonify\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "import random\n",
        "from flask import send_file\n",
        "\n",
        "\n",
        "# Initialize Flask app\n",
        "app = Flask(__name__, template_folder='/content/drive/MyDrive/chatBot')\n",
        "@app.route('/logo')\n",
        "def serve_logo():\n",
        "    return send_file('/content/drive/MyDrive/chatBot/logo-removebg-preview.png', mimetype='image/png')\n",
        "vectorizer = TfidfVectorizer()\n",
        "tfidf_matrix = vectorizer.fit_transform(contexts)\n",
        "\n",
        "def find_best_context(question, contexts, tfidf_matrix, vectorizer):\n",
        "    question_vec = vectorizer.transform([question])\n",
        "    similarities = cosine_similarity(question_vec, tfidf_matrix)\n",
        "    best_idx = similarities.argmax()  # Get the index of the best match\n",
        "    print(f\"الاجابة : { contexts[best_idx]}\")\n",
        "    return contexts[best_idx]  # Return the most relevant context\n",
        "\n",
        "@app.route('/')\n",
        "def home():\n",
        "    return render_template('index.html')\n",
        "@app.route('/consult')\n",
        "def main():\n",
        "    return render_template('bot.html')\n",
        "\n",
        "@app.route('/get_answer', methods=['POST'])\n",
        "def get_answer():\n",
        "    try:\n",
        "        data = request.get_json()\n",
        "        if not data or 'question' not in data:\n",
        "            return jsonify({'error': 'Missing question'}), 400\n",
        "\n",
        "        user_question = data.get('question')\n",
        "        print(f\"Received question: {user_question}\")\n",
        "\n",
        "        best_context = find_best_context(user_question, contexts, tfidf_matrix, vectorizer)\n",
        "\n",
        "        predictions = model.predict([\n",
        "            {\n",
        "                \"context\": best_context,\n",
        "                \"qas\": [\n",
        "                    {\n",
        "                        \"question\": user_question,\n",
        "                        \"id\": \"0\",\n",
        "                        \"answers\": [{\"text\": \"\", \"answer_start\": 0}]\n",
        "                    }\n",
        "                ]\n",
        "            }\n",
        "        ])\n",
        "\n",
        "        if predictions:\n",
        "            answers = predictions[0][0][\"answer\"]  # Get the answer\n",
        "            probabilities = predictions[1][0][\"probability\"]\n",
        "\n",
        "            # Ensure we're handling both string and list cases for answers\n",
        "            if isinstance(answers, list):\n",
        "                best_idx = probabilities.index(max(probabilities))\n",
        "                best_answer = answers[best_idx]\n",
        "            else:\n",
        "                best_answer = answers  # Use directly if it's a string\n",
        "\n",
        "            print(f\"answer: {best_answer}\")\n",
        "            return jsonify({'answer': best_answer})\n",
        "        else:\n",
        "            return jsonify({'answer': \"No answer found for this question.\"})\n",
        "\n",
        "    except Exception as e:\n",
        "        print(f\"Error occurred: {str(e)}\")\n",
        "        return jsonify({'error': 'Internal Server Error'}), 500\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    app.run(port=5000)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qph7G2k9K0Wy",
        "outputId": "423c1500-b7ec-4376-bc75-4494654c01ed"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " * Serving Flask app '__main__'\n",
            " * Debug mode: off\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:werkzeug:\u001b[31m\u001b[1mWARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.\u001b[0m\n",
            " * Running on http://127.0.0.1:5000\n",
            "INFO:werkzeug:\u001b[33mPress CTRL+C to quit\u001b[0m\n",
            "INFO:werkzeug:127.0.0.1 - - [13/Jan/2025 09:45:38] \"GET /?authuser=3 HTTP/1.1\" 200 -\n",
            "INFO:werkzeug:127.0.0.1 - - [13/Jan/2025 09:45:39] \"GET /logo?authuser=3 HTTP/1.1\" 200 -\n",
            "INFO:werkzeug:127.0.0.1 - - [13/Jan/2025 09:45:40] \"GET /logo?authuser=3 HTTP/1.1\" 200 -\n",
            "INFO:werkzeug:127.0.0.1 - - [13/Jan/2025 09:45:43] \"GET /consult?authuser=3 HTTP/1.1\" 200 -\n"
          ]
        }
      ]
    }
  ]
}