Countries_Autocomplete.ipynb

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  "metadata": {
    "colab": {
      "name": "Countries_Autocomplete.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/babakansari/DeepLearning/blob/master/Countries_Autocomplete.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aA6d99otpjvp",
        "colab_type": "text"
      },
      "source": [
        "https://medium.com/@curiousily/making-a-predictive-keyboard-using-recurrent-neural-networks-tensorflow-for-hackers-part-v-3f238d824218"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SmAV5SrQpjXC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vhxkCs_rjzvH",
        "colab_type": "code",
        "outputId": "283e7d61-ccee-49f3-f15d-854803974f8c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        }
      },
      "source": [
        "import numpy as np\n",
        "np.random.seed(42)\n",
        "import tensorflow as tf\n",
        "tf.set_random_seed(42)\n",
        "from keras.models import Sequential, load_model\n",
        "from keras.layers import Dense, Activation\n",
        "from keras.layers import LSTM, Dropout\n",
        "from keras.layers import TimeDistributed\n",
        "from keras.layers.core import Dense, Activation, Dropout, RepeatVector\n",
        "from keras.optimizers import RMSprop\n",
        "import matplotlib.pyplot as plt\n",
        "import pickle\n",
        "import sys\n",
        "import heapq\n",
        "import seaborn as sns\n",
        "from pylab import rcParams\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "sns.set(style='whitegrid', palette='muted', font_scale=1.5)\n",
        "\n",
        "rcParams['figure.figsize'] = 12, 5"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TIb4O1qwnM2e",
        "colab_type": "code",
        "outputId": "79ce7701-10b0-4dc3-f372-621b326864fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "import pandas as pd\n",
        "import io\n",
        "\n",
        "file_name = 'https://github.com/babakansari/DeepLearning/blob/master/Countries.xlsx?raw=true'\n",
        "\n",
        "df = pd.read_excel(file_name)\n",
        "df"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Ext</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Afghanistan (AF)</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Aland Islands (AX)</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Albania (AL)</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Algeria (DZ)</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>American Samoa (AS)</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>254</th>\n",
              "      <td>Wallis and Futuna (WF)</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>255</th>\n",
              "      <td>Western Sahara (EH)</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>256</th>\n",
              "      <td>Yemen (YE)</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>257</th>\n",
              "      <td>Zambia (ZM)</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>258</th>\n",
              "      <td>Zimbabwe (ZW)</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>259 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                       Name  Ext\n",
              "0          Afghanistan (AF)  NaN\n",
              "1        Aland Islands (AX)  NaN\n",
              "2              Albania (AL)  NaN\n",
              "3              Algeria (DZ)  NaN\n",
              "4       American Samoa (AS)  NaN\n",
              "..                      ...  ...\n",
              "254  Wallis and Futuna (WF)  NaN\n",
              "255     Western Sahara (EH)  NaN\n",
              "256              Yemen (YE)  NaN\n",
              "257             Zambia (ZM)  NaN\n",
              "258           Zimbabwe (ZW)  NaN\n",
              "\n",
              "[259 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5UwY0OGJ8P2l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "text = \"\"\n",
        "for index, row in df.iterrows():\n",
        "  text = text + row['Name'].lower()\n",
        "\n",
        "chars = sorted(list(set(text)))\n",
        "char_indices = dict((c, i) for i, c in enumerate(chars))\n",
        "indices_char = dict((i, c) for i, c in enumerate(chars))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cRjzoq9A-1uQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d0f22c15-808c-4e7c-ad74-cebe43d919f3"
      },
      "source": [
        "print(f'unique chars: {len(chars)}')"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "unique chars: 34\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9g-7_e28Ai-4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "71a77f0b-45cc-4572-a146-a333f4df80c5"
      },
      "source": [
        "SEQUENCE_LENGTH = 3\n",
        "step = 1\n",
        "sentences = []\n",
        "next_chars = []\n",
        "for index, row in df.iterrows():\n",
        "  country = row['Name'].lower()\n",
        "  for i in range(0, len(country) - SEQUENCE_LENGTH, step):\n",
        "    sentences.append(country[i: i + SEQUENCE_LENGTH])\n",
        "    next_chars.append(country[i + SEQUENCE_LENGTH])\n",
        "\n",
        "print(f'num training examples: {len(sentences)}')"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "num training examples: 2913\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZNTXzzzeBU4m",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fdae702c-7d4e-4c11-a99a-f51e2f82c453"
      },
      "source": [
        "print(\"After '\" + sentences[0] + \"' is '\" + next_chars[0] + \"' for one sample\")"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "After 'afg' is 'h' for one sample\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fDbjIk2SClH1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = np.zeros((len(sentences), SEQUENCE_LENGTH, len(chars)), dtype=np.bool)\n",
        "y = np.zeros((len(sentences), len(chars)), dtype=np.bool)\n",
        "for i, sentence in enumerate(sentences):\n",
        "    for t, char in enumerate(sentence):\n",
        "        X[i, t, char_indices[char]] = 1\n",
        "    y[i, char_indices[next_chars[i]]] = 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lLLjQKAZEnxy",
        "colab_type": "text"
      },
      "source": [
        "# Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TF5CY6e_DqJn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "263434c2-a487-43b2-d043-6d856d1cd140"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(LSTM(128, input_shape=(SEQUENCE_LENGTH, len(chars))))\n",
        "model.add(Dense(len(chars)))\n",
        "model.add(Activation('softmax'))\n",
        "model.summary()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_2 (LSTM)                (None, 128)               83456     \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 34)                4386      \n",
            "_________________________________________________________________\n",
            "activation_2 (Activation)    (None, 34)                0         \n",
            "=================================================================\n",
            "Total params: 87,842\n",
            "Trainable params: 87,842\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KiQ1DMVfEttx",
        "colab_type": "text"
      },
      "source": [
        "# Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zHRIkQyYD5L9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "81206c85-18a5-45dd-d356-4c376da7b5f7"
      },
      "source": [
        "optimizer = RMSprop(lr=0.01)\n",
        "model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n",
        "history = model.fit(X, y, validation_split=0.05, batch_size=128, epochs=20, shuffle=True).history"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3576: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3005: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "Train on 2767 samples, validate on 146 samples\n",
            "Epoch 1/20\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
            "\n",
            "2767/2767 [==============================] - 5s 2ms/step - loss: 2.7305 - acc: 0.2548 - val_loss: 2.5685 - val_acc: 0.3630\n",
            "Epoch 2/20\n",
            "2767/2767 [==============================] - 0s 100us/step - loss: 2.3256 - acc: 0.3354 - val_loss: 2.4995 - val_acc: 0.3562\n",
            "Epoch 3/20\n",
            "2767/2767 [==============================] - 0s 97us/step - loss: 2.2171 - acc: 0.3759 - val_loss: 2.4862 - val_acc: 0.3699\n",
            "Epoch 4/20\n",
            "2767/2767 [==============================] - 0s 105us/step - loss: 2.1241 - acc: 0.3921 - val_loss: 2.4875 - val_acc: 0.3562\n",
            "Epoch 5/20\n",
            "2767/2767 [==============================] - 0s 98us/step - loss: 2.0639 - acc: 0.4095 - val_loss: 2.4162 - val_acc: 0.3767\n",
            "Epoch 6/20\n",
            "2767/2767 [==============================] - 0s 101us/step - loss: 1.9991 - acc: 0.4221 - val_loss: 2.4359 - val_acc: 0.3973\n",
            "Epoch 7/20\n",
            "2767/2767 [==============================] - 0s 99us/step - loss: 1.9318 - acc: 0.4384 - val_loss: 2.4144 - val_acc: 0.3904\n",
            "Epoch 8/20\n",
            "2767/2767 [==============================] - 0s 93us/step - loss: 1.8634 - acc: 0.4554 - val_loss: 2.4552 - val_acc: 0.4110\n",
            "Epoch 9/20\n",
            "2767/2767 [==============================] - 0s 96us/step - loss: 1.7973 - acc: 0.4687 - val_loss: 2.6184 - val_acc: 0.4110\n",
            "Epoch 10/20\n",
            "2767/2767 [==============================] - 0s 102us/step - loss: 1.7267 - acc: 0.4857 - val_loss: 2.4653 - val_acc: 0.4041\n",
            "Epoch 11/20\n",
            "2767/2767 [==============================] - 0s 113us/step - loss: 1.6614 - acc: 0.4919 - val_loss: 2.5337 - val_acc: 0.3904\n",
            "Epoch 12/20\n",
            "2767/2767 [==============================] - 0s 99us/step - loss: 1.5958 - acc: 0.5183 - val_loss: 2.7153 - val_acc: 0.4041\n",
            "Epoch 13/20\n",
            "2767/2767 [==============================] - 0s 97us/step - loss: 1.5327 - acc: 0.5208 - val_loss: 2.6842 - val_acc: 0.4041\n",
            "Epoch 14/20\n",
            "2767/2767 [==============================] - 0s 99us/step - loss: 1.4643 - acc: 0.5479 - val_loss: 2.6748 - val_acc: 0.3973\n",
            "Epoch 15/20\n",
            "2767/2767 [==============================] - 0s 106us/step - loss: 1.4058 - acc: 0.5595 - val_loss: 2.8349 - val_acc: 0.3904\n",
            "Epoch 16/20\n",
            "2767/2767 [==============================] - 0s 96us/step - loss: 1.3504 - acc: 0.5725 - val_loss: 2.8673 - val_acc: 0.3699\n",
            "Epoch 17/20\n",
            "2767/2767 [==============================] - 0s 97us/step - loss: 1.2989 - acc: 0.5866 - val_loss: 3.0192 - val_acc: 0.3562\n",
            "Epoch 18/20\n",
            "2767/2767 [==============================] - 0s 104us/step - loss: 1.2632 - acc: 0.5996 - val_loss: 2.8917 - val_acc: 0.3493\n",
            "Epoch 19/20\n",
            "2767/2767 [==============================] - 0s 99us/step - loss: 1.2187 - acc: 0.6191 - val_loss: 3.0317 - val_acc: 0.3562\n",
            "Epoch 20/20\n",
            "2767/2767 [==============================] - 0s 99us/step - loss: 1.1883 - acc: 0.6202 - val_loss: 3.1281 - val_acc: 0.3493\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5L2Ays9qE-iG",
        "colab_type": "text"
      },
      "source": [
        "## Saving the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CsbeljHnD89N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.save('keras_model.h5')\n",
        "pickle.dump(history, open(\"history.p\", \"wb\"))\n",
        "model = load_model('keras_model.h5')\n",
        "history = pickle.load(open(\"history.p\", \"rb\"))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tIDXSQoLFJkO",
        "colab_type": "text"
      },
      "source": [
        "## Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a3DAhvb5FGp2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "outputId": "b7039747-729f-443a-a98c-7d33559994da"
      },
      "source": [
        "plt.plot(history['acc'])\n",
        "plt.plot(history['val_acc'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left');"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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aoS4tLSUwMLABK6o/t9uNxWLhlVdeOe8DEs4unWgymXjuuefYsmULq1at4quv\nvmLq1KnMnTuXt99++7I7LxERubydyHecGQkvZkt2CSXlLkwmSIgL4Pb+LemREExSGy1XKE2XQnkz\ndr7Q3bZtW1wuF+3atfPMM/8xqamppKam8vDDD/Pxxx97/hw3bpyeeiYiIhdUVuEic1/JmeUKSzhy\nqgKAyFArfZND6dE5mLSOQXp4jzQb+k5vxgICAgAoLi72tA0ePJinn36a559/nhkzZlTr73a7KSoq\nIiwsjMLCQkJCQqoF77MPCHI4qub6+fv7A1BUVNSo5yEiIpc/e4WLHYfKyNpfwtb9pew8XEaly8DX\naialQyA3X1u1XGGbKD3KXponhfJmrGvXrgDMmjWL4cOHY7VaGTBgAL/97W959tlnOXz4MAMGDMDf\n35/Dhw/z6aefcv/99zNu3DgWLVrE22+/zcCBA2nbti12u50FCxYQFBTEDTfcAEBcXBxhYWG88847\nBAYGEhAQQEpKCm3atPHmaYuIyCVQYnex/WApWftLyNpfSvaRMlxuMJuhUyt/RveNJD0hmC7tArH5\naLEAEYXyZqxLly488sgjzJ8/nzVr1uB2u1m5ciX3338/7dq1480332T27NmYTCZatWrFoEGD6NOn\nD1B1o2dWVhaffPIJp06dIjg4mJSUFJ566ilP6Pbx8eHJJ59k5syZ/OUvf6GyspIZM2YolIuINEGF\npZVsPVBK1r4Sth4oZd+xqvXCfSwmElr7M/aGlnRrH8hV7QIJ8NUqKSI/pFDezN13333cd999NdqH\nDx/O8OHDL7hfly5dePrpp3/y+P3796d///71KVFERC5DeUVOsg6UkLWvlK0HSjl4ohwAm4+Jq9oG\n8vMbo0luH0RS2wB8rRoJF/kpCuUiIiLyk07kO9h6ZirK1v0lHDl95v4hm5ku7QIZkBZGcnwQCa39\nsWo6ikidKZSLiIhINYZhcOy0wzMfPGt/CScLnEDV4+uT4wMZdk0LurUPomOsPxYtUyhSbwrlIiIi\nzZxhGBw6WeFZGSVrfwl5xZUAhAb60K19IGOuDySlfRDtov0w6/H1Ig1OoVxERKSZcbkNDhwv/z6E\nHyihqNQFQIsQKykdgujWPohu7QNprSUKRS4JhXIREZFmwjAMvswqYO4nxzzTUWLCbVyTFEJyfBDd\nOgQSE25TCBfxAoVyERGRZmB3ThkvLTnC9oNldIj1466bYunWPpCoMJu3SxMRFMprzTAMjRx4kWEY\n3i5BROSKdLrIyRvLj7EiI5+wIB8eGtOaQekRWDQvXOSyolBeCzabDbvd7nksvVx6drsdq9Xq7TJE\nRK4YFU4373+Vy39Wn6TSZYuxiPYAACAASURBVDCuXxS3948m0E8P7hG5HCmU10JkZCQ5OTlERkYS\nHByMj4+PRs0vEcMwsNvtHDlyhOjoaG+XIyJy2TMMgzVZhbz6yVFOFjjp2zWUycNiadXC19uliciP\nUCivhdDQUHx9fcnNzeX06dNUVlY2yHEdDgc2m+by/RSr1Up0dDQhISHeLkVE5LK250gZLy05yrYD\npXSI9eP349qS0iHI22WJSC0olNeSn58fbdq0adBjZmRkkJqa2qDHFBGR5ievyMnry4/x2cZ8QgJ8\n+O2trbmpp+aNi1xJFMpFRESuUI4z88bfPTNv/Lbro5gwQPPGRa5ECuUiIiJXGMMw2JpjYvbKXZzI\nd9Cnayj3aN64yBVNoVxEROQKkn1m3vjWAxbax5iZcW8H0joGe7ssEaknhXIREZErQF6xkzeWH2dF\nRh4hAT6M6uFiym0Jmjcu0kQolIuIiFzGHE43i9bm8u7nJ3G6DMZcF8XEG6PZuW2zArlIE6JQLiIi\nchkyDIO12wp59eNjHM930LtLCPcOa0WrSM0bF2mKFMpFREQuM9lHz8wb319KfIwff7unA907ad64\nSFOmUC4iInKZyCt28uby4yzPyCM4wMKDo1szpGcEFoumqYg0dQrlIiIiXuZwuvng61O88/kJnJUG\nt14XxcQB0QT5a71xkeZCoVxERMRLDMPg622F/OvMvPFrrwrh3uGtiNO8cZFmR6FcRETEC/YetfPS\nkiNk7S8lPlrzxkWaO4VyERGRejAMA2elQVmFm7IKF2UVLuwVbsrK3d9/XeGirNzl6VNQUsmG3cUE\nB1j4zeg4hvZsoXnjIs2cQrmIiDRLLpdBmcNVPTyfE5x/+LrszGu74/t2+5n2Spfxk+9nMkGArxl/\nXwsBvmbNGxeRahTKRUSk2XC7DVZvKWDeiuMcz3fUah8/mxl/XzMBZ8J0gJ+FmAib57W/r4UAv6rt\nnn5+ZvxtFgLPafezmTGZNBouIuenUC4iIs1C5r4S/vXxUfYcsdOplT93pEcT6HdOkD4ndJ8N2/6+\nZj01U0QuCYVyERFp0nJyy5m77BjfbC8iKtTKo+Pb0j81DLPCtohcRhTKRUSkSSooqeTfq47z8frT\n2Kxm7h4Sw+i+Ufhazd4uTUSkBoVyERFpUhxON4vPPIin3OlmWK8W3DEomrAgq7dLExG5IIVyERFp\nEgzD4IstBbz26TFOFji5OimEyUNjaRft5+3SRER+kkK5iIhc8bYeKOFfS4+xK6eMDrF+PHRbGz2I\nR0SuKArlIiJyxTpyqoLXlh1j7bZCWoRYeWRsGwZ2D9dNnCJyxfFqKHc4HPzjH/9g8eLFFBUVkZSU\nxMMPP0zv3r1/dL/Zs2czZ86cGu2RkZGsXbu2scoVEZHLRFFpJf9edYIl605h9TEzaXAMt14XhZ9N\nN3GKyJXJq6H8scceY/ny5UyaNIl27dqxaNEipkyZwrx58+jevftP7j99+nT8/L6fK3ju1yIi0vQ4\nKt189M0p3ll1krIKF0N6RXDHoBgignUTp4hc2bwWyjMzM1m6dCnTpk3j7rvvBmD06NHcfPPNzJw5\nk/nz5//kMYYNG0ZISEgjVyoiIt5mGAZrsgp5bdkxjuc76JkQzD3DYomP8fd2aSIiDcJroXzZsmVY\nrVbGjRvnafP19WXs2LHMmjWLkydP0rJlyx89hmEYlJSUEBgYqEcXi4g0UTsOlvLy0qPsPFxGfIwf\nj/+iA+kJuolTRJoWr4XyHTt20L59ewIDA6u1p6SkYBgGO3bs+MlQ3r9/f8rKyggMDGTIkCFMnTqV\nsLCwxixbREQukWN5VTdxrskqJCLYh4dua82gHhF67L2INEleC+W5ublER0fXaI+KigLg5MmTF9w3\nJCSEO++8k9TUVKxWK+vWrePdd99l+/btLFiwAJvNVud6tm7dWud9GkJGRoZX3rep0PWrP13D+tH1\nq5/zXT+7A1bvMLMu24TZDAOuMrgusRxf0wE2bzpw6Yu8jOn7r350/epH169heS2Ul5eXY7XWvDHH\n19cXgIqKigvue9ddd1V7PXToUDp37sz06dP54IMPGD9+fJ3rSU5O9rz3pZKRkUF6evolfc+mRNev\n/nQN60fXr35+eP2clW6WrDvNv1edoLTcxeD0CCYNjqFFiG7iPB99/9WPrl/96PpdnIqKigsOBHtt\n7Sg/Pz+cTmeN9rNhvK4BeeLEifj7+/PNN980SH0iInJpGIbBV1sLuG/WLl5eepTOcf48/2ACD9/W\nRoFcRJoNr42UR0VFnXeKSm5uLsBPzif/IbPZTHR0NIWFhQ1Sn4iINL6dh0p55eOjbD9YRrtoP/7v\n7vakJwTr5n0RaXa8FsqTkpKYN28epaWl1W723LJli2d7XTidTo4dO0ZycnKD1ikiIg3L7TY4cqqC\nd9ebyTqcTXiQDw/e2poh6RFYLArjItI8eS2UDx06lLlz57JgwQLPOuUOh4P333+fHj16eG4CPXr0\nKHa7nY4dO3r2zcvLIyIiotrxXn31VSoqKrj++usv2TmIiEhNhmFQYndxPN/B8bwz/+U7OHH2z3wH\nlS4Dq8XExAEtGduvJQG+Fm+XLSLiVXUK5X/84x8ZP348qamp9X7j1NRUhg4dysyZM8nNzaVt27Ys\nWrSIo0ePMmPGDE+/qVOn8u2337Jr1y5P24ABAxg+fDgJCQnYbDbWr1/Pp59+Snp6OjfffHO9axMR\nkR9X7nBz4kzAPp5X9eexvArP67IKd7X+wf4WYiJsdIj1o3eXEGIibPg7DnLj9bFeOgMRkctLnUL5\nokWLeP/99+nYsSNjx45l1KhRhIeHX/SbP/XUUzz77LMsXryYwsJCEhMTefnll3/ybt6RI0eyceNG\nli1bhtPpJC4ujl//+tfcd999+Ph4bfBfRKTJcLkMThU5z4x0V3hGvU/kV4125xdXVuvvazURHW4j\nJtxG1/hAYsJ9iYmwERNhIzrcRqBfzZHwjIyDl+p0REQue3VKsF988QWLFi1i4cKFPPHEEzzzzDMM\nHDiQsWPH0rdv3zq/ua+vL1OnTmXq1KkX7DNv3rwabY8//nid30tERL5nGAYFJZXVppWcO83kZKED\n9zmD3WYzRIVWhe6rE0OIjqj6OibcRnSEjfAgH92cKSJSD3UK5VFRUfzyl7/kl7/8Jd999x3vvfce\ny5cvZ9myZcTGxnLbbbcxZswYYmP1caSIyOXGMAx2HCpj1aZ81mQVUFTmqrY9PMiH6HAbSW0D6Bce\nRmyEzRO+o0JtuglTRKQRXfRcj169etGrVy/+53/+h6VLl/Lee+8xZ84cXnjhBfr06cPtt9/OwIED\nNXIiIuJlh0+W8/nmfD7fXMDxfAe+VhO9u4SS1DbAM80kOtyKn003W4qIeEu9J2A7HA5KSkooKSnB\nMAz8/f3JzMzkq6++olOnTjz77LPVVk4REZHGl1/s5IvMAlZtymfPETtmE6R1CuKOQdH07hqq1U5E\nRC4zFxXKDcPgiy++4L333mP16tVUVlaSkpLC448/zogRI7BYLHz44YfMnDmT//7v/+btt99u6LpF\nROQH7BUuvtlexKpN+WzKLsZtQOc4f345ohX9UsKI0NMxRUQuW3UK5YcOHWLhwoUsWrSI3NxcgoKC\nuP322xk/fjyJiYnV+o4dO5by8nKefPLJBi1YRES+53IZbMwu5vPN+Xy9rYgKp5vocBvj+7dkQFo4\nbVv6ebtEERGphTqF8ptuugmAtLQ0Hn74YYYPH46vr+8F+8fFxREVFVW/CkVEpBrDMNidY+fzzfl8\nkVlAQUklQf4WBvYIZ0BaGF3aBmI2634eEZErSZ1C+Z133sn48ePp3LlzrfoPGDCAAQMGXFRhIiJS\n3dHTFVU3bG7K58hpB1YfE9ckhXBj93DSE4Kx+Zi9XaKIiFykOj/RU0RELp2CkkrWZFXdsLnzcBkm\nE6S0D2Jcv5b0TQ4jyF83bIqINAV1CuUff/wxq1ev5qmnnjrv9qlTpzJgwACGDh3aIMWJiDRH5Q43\n63cWsmpTARm7i3C5IT7Gj8lDY+mfGkZUmM3bJYqISAOrUyh/6623aNu27QW3m81m3nrrLYVyEZE6\ncrkNMveVsGpTPmu3FmJ3uGkRYuXW66K4MS2c9rH+3i5RREQaUZ1C+d69exkyZMgFt3fp0oXPP/+8\n3kWJiDQHhmGw95idzzcXsHpzPnnFlQT4mrk+JYwb08JJbh+IRTdsiog0C3UK5Xa7HYvlwvMXTSYT\npaWl9S5KRKSpMgyDfcfK+XZXEas353PoZAU+FhO9EoMZkBbO1Ukh+Fp1w6aISHNTp1DeunVrMjIy\nuOOOO867PSMjg1atWjVIYSIiTUVxWSUbs4vZsKuYjD3F5BdXAtA1PpDfjI7k+uQwQgLr/YBlERG5\ngtXpp8DgwYN5+eWX6dOnD+PGjau27b333mPZsmXcc889DVqgiMiVxuU2yD5iZ8OuIjbsLmZ3Thlu\nA4L8LfToHEzPhGDSOwfrCZsiIuJRp1A+ZcoUVq5cyZ/+9CfeeOMNkpKSANi1axfZ2dm0b9+eX/3q\nV41SqIjI5Sy/2MnGPcVs2F3Mxj3FFJW5MJkgIS6AiTdGk54QTELrAM0RFxGR86pTKA8KCuLtt9/m\n6aef5pNPPiE7OxuA0NBQJk6cyEMPPURQUFCjFCoicjlxuQwOnIKsT4+RsbuY7KN2AMKCfOiVGELP\nhGC6dw4mVNNSRESkFur80yI4OJi//OUv/PnPfyY/Px+A8PBwTCaN/ohI05Zb6CBjd9Vo+ObsYkrL\nfTCbT9KlbSB33RRDz4RgOsT66xH3IiJSZxc9hGMymYiIiGjIWkRELiuOSjfbDpSSsbuYjN3FHDhR\nDkCLECvXdwsj3JLLmJtS9VRNERGpt4sK5S6Xi3379lFYWIhhGDW29+rVq96FiYh4w7G8CjJ2FbNh\nTzFb9pZQ7nDjYzGRHB/IPT1i6ZkQTLtoP0wmExkZJxXIRUSkQdQ5lL/88su88sorlJSUXLDPjh07\n6lWUiMilUu5wk7W/xDMt5cipCgBiwm0M6hFOekIwqR2C8PdV+BYRkcZTp1C+YMECnnnmGXr16sV1\n113HrFmzuPvuu/Hx8eG9996jTZs2/OxnP2usWkVEGkRekZM1WQV8t6uYrP0lOCoNbD4mUjoEMfLa\nFvRMDKFVC5vulRERkUumTqH87bffJi0tjXnz5pGfn8+sWbPo168fvXv3ZtKkSYwePRqXy9VYtYqI\nXLSyChdfbytk1aZ8tuwtwW1Amyhfhl/Tgp4JISS3D9STNEVExGvqFMr37dvHQw89BOAZQXK73QC0\nbNmS8ePH8+abbzJ27NgGLlNEpO4qXQYb9xSzalM+63YUUuE0iAm3cXv/lgxIC6dNSz9vlygiIgLU\nMZSbzWb8/f0BCAgIAKCgoMCzPS4ujoMHDzZgeSIidWMYBrsOl7Fqcz5fZBZQVOoi2N/CoB4R3Ng9\nnKvaBmhaioiIXHbqFMpbtWpFTk4OADabjdjYWDZs2MCIESMAyMrKIjQ0tOGrFBH5CUdOVfD55nxW\nbcrnWJ4Dm4+Ja68KYUD3cNI7B2P10dQUERG5fNUplPfs2ZPVq1fz+9//HoChQ4fyxhtvUF5ejmEY\nfPjhh9x2222NUqiIyA8VlDj5IrOAzzcXsOtwGSYTpHQIYsKAaPomhxLopxVTRETkylCnUD5p0iSS\nkpIoLy/Hz8+PBx98kP379/PBBx8A0LdvX09gFxFpDOUON+u2F7Jqcz4Ze4pxu6FDrB/3DIulf2o4\nkaFWb5coIiJSZ3UK5R06dKBDhw6e1wEBAfzzn/+kuLgYs9lMYGBggxcoIuJyG2zZW8KqTfl8va0Q\nu8NNVKiVsde3ZEBaGPEx/t4uUUREpF5qHcpLS0t5/PHHueGGGxg2bFi1bcHBwQ1emIg0b4ZhsPeo\nnVWb8lmdWUB+cSWBfmb6pYYxIC2c5PhAzGbdsCkiIk1DrUN5YGAgH3/8MT169GjMekSkmTuR72DV\n5nw+35zP4ZMV+FhMXJ0Uwo1pYfRKDMGmtcRFRKQJqtP0lY4dO3LkyJHGqkVEmqniskrWZFXNE992\noBSA5PhARt8axfXJoQQH1OmfKhERkStOnX7S3Xvvvfzv//4vo0aNon379o1Vk4g0Aw6nm/U7i/h8\ncz7f7Sqm0mXQpqUvdw+JoX9qONHhNm+XKCIicsnU+YmesbGxjBw5kgEDBtCuXTv8/Ko/Ec9kMvHA\nAw80aJEi0jRUugw2ZxfzZVYBa7cWUlbhJiLYh1t6RzKgexgdY/31YB8REWmW6hTK58yZ4/l6xYoV\n5+2jUC4i53K5DbYdKOWLLQV8tbWAojIXgX5m+iaHMiAtnJQOQVh0w6aIiDRzdQrlK1eubKw6RKQJ\nMQyDnYfL+GJLAWuyCsgrrsTXaqZ3lxD6pYTRIyEYm56wKSIi4lGnUB4XF9dYdYjIFc4wDPYes/Nl\nZgFfbCngZIETq4+JXolVQfzqpGD8bHrCpoiIyPloSQMRqZdDJ8v5YksBX2QWcORUBRYz9OgczKTB\nMVzbRY+6FxERqY06hfJp06b9ZB+TycTf/va3iy5IRC5/x/IqPCPi+4+XYzJBSocgbrs+ir5dQwkJ\n1O/7IiIidVGnn5yLFi36yT4K5SJNU26hgzWZhXyRmc/uHDsAXdoF8KuRrbg+OYyIEKuXKxQREbly\n1SmU79y5s0aby+Xi8OHDzJ07l927d/Ovf/2rwYoTEe8qKHHyVVYhX2QWsPXMQ306x/lzz7BYbkgJ\no2WY1hIXERFpCPX+jNlisRAfH8/06dP51a9+xd///nf+93//tyFqExEvKLZX8vW2Qr7YUsCWvSW4\nDWjb0pdJg2O4ISWMuEhfb5coIiLS5DToxM/rr7+eOXPmKJSLXGHKKlys217EF5kFbNxT9XTN2Agb\n4/u3pF9KGPEx/t4uUUREpElr0FBeUFBAWVlZQx5SRBpJhdPNthwTy3Ye4NudRTgqDaJCrYzqE0m/\n1DA6tdLTNUVERC6VBgnlRUVFfP3117zxxht07dq1IQ4pIg3MMAwOnaxg455iNmcXk7m/lHKHhbCg\nUob0iqBfSjhXtQ3ArKdrioiIXHJ1CuVJSUkXHDkzDIPQ0FAee+yxBilMROrvVKGTzXuL2bSnhE17\ni8kvrgQgLtKXQT3CibKe5LYhKVgsCuIiIiLeVKdQPnr06POG8rCwMOLj4xkxYgRBQUENVpyI1E1p\nuYus/SVszi5hY3Yxh09WABAa6EP3TkGkdQqie6dgz6opGRknFMhFREQuA3UK5U888URj1SEiF6HS\nZbDrcBmbsovZlF3MzsNluN3gazWRHB/EkJ4RdO8UTHy0n6aliIiIXMb02D2RK8jZeeGbs4vZmF1C\n1r4S7A43ZhN0jgtg3A0t6d4pmKvaBWDzMXu7XBEREamlOoXy+fPns2LFCl5//fXzbp88eTI33XQT\nEyZMaIjaRAQ4XeRkc3Yxm7JL2Ly3mNNFVfPCW7WwcWP3cLp3CiKlYxDB/vodW0RE5EpVp5/i77//\nPsnJyRfcHh8fz8KFCxXKReqhrMJF1r5SNmUXs3lvCQdPlAMQEmghrWMw3c/MC48O19M0RUREmoo6\nhfKDBw8yZsyYC27v1KkTS5YsqXdRIs1Jpctgd04Zm/YUs2lvCTsPleJyg83HRHL7QAb1iCWtUxAd\nYvw1L7yBGBVlOPaswyiv33MVQg4fotw4dnE7m8Ac1AJLRCvM4bGYfPRLlohIc1anUF5ZWYnD4bjg\ndofDQUVFRb2LEmnq8oqdfJVVyMbsYjL3lWCvcGMyQadW/oy9oSVpnYLo0jYQm1XzwhuSK/8YFRlL\nqNiyHCrq/6CzSMC+o/51gQlzSCTmiFaYw+OqgnpEKyzhrTCHx2CyWBviTURE5DJWp1AeHx/P2rVr\n+cUvfnHe7V999RVt27ZtkMJEmppyh5t12wtZuSmfjXuKcRsQG2FjQGo4aZ2CSOsYRHCA5oU3NMMw\nqDyYScWGD3HuXg9mM9ak6/DrORJzeKt6HXtL5hZSU1IvsjAX7qJTuPOP4so7ijvvCK78ozh3fImj\nvOT7fiYz5tCo78N6eCtPaDeHRmOy6HtGRKQpqNO/5iNGjOCZZ57h2Wef5de//jU2W9XHrU6nkxdf\nfJG1a9fy0EMP1fp4DoeDf/zjHyxevJiioiKSkpJ4+OGH6d27d51OYsqUKXz55ZdMmjSJP/7xj3Xa\nV6Qxud0GWw+U8tnGPL7aWoi9wk1UqJVx/VpyY/dw2rb083aJTZbhrMCxbTUVGz7CdXI/Jv8Q/PqM\nxzd9OObgyAZ5D7ctEHNg6EXvbw6KgFYJNY9bVlQjrLvzjlKxdWf1EX6zBXNo9Pej6hGtPMHdHNoS\nk9ly0bWJiMilVadQfvfdd/Pll1/yz3/+k7fffpsOHToAsG/fPgoLC+nZs+cFR9HP57HHHmP58uVM\nmjSJdu3asWjRIqZMmcK8efPo3r17rY6xevVqNmzYUJfTEGl0ObnlrNyUz6pN+ZwscOJvM3Ndt1AG\n9YggOT5Qc8Mbkbv4FBUZS6nYtAzDXoQlKp6AEb/D1qUfJquvt8urFXNACOaAEHzikqq1G4aBUVaI\nO+8orvwjVX/mHcWdf5SKQ1vBWX7OQXwwh8VUnwoT0QpLRBymkEhMJk2NEhG5nNQplFutVubOncvr\nr7/OkiVL2LGjajJlfHw8v/zlL5k0aRJWa+3mPmZmZrJ06VKmTZvG3XffDVQ9MfTmm29m5syZzJ8/\n/yeP4XA4mDFjBvfccw+zZ8+uy6mINLjC0kq+zCxg5cZ8duWUYTZBj87B3D0klt5dQvGzKQQ1psoj\nOyn/bjHOnWvB7caacC2+vW7Bp2238z6J+EpkMpkwBYZhDgzDp02XatsMw8Aoza8xuu7OO4rzwBao\nPOd+H4sVc3grfNp0wdqxJ9b4VEw2/0t8NpeeYRhV12Pvdzj3bcQoL/ZqPeaA0KpPNc78smQOb4U5\nJFKfcIg0U3WejGi1WpkyZQpTpkyp1xsvW7YMq9XKuHHjPG2+vr6MHTuWWbNmcfLkSVq2bPmjx3jz\nzTcpLy9XKBevcVS6+W5nESs35fPdrmIqXQbtY/y4d3gsA1LDiQjRDXqNyXBV4tz5FeXfLcZ1dDf4\nBuDb8xZ8e96MJSzG2+VdUiaTCVNQRNWUmLbVl641DDdGcR6uvCPfT4s5fRjHttU4Nn0CFh982nar\nCugde2GOaNVkfpExnBVUHsrCuXcDzr0bcOdXrZZjbtEGc+iP/4xp5MpwF53CeTATnD/4hanaJxxx\n33/CERyhTzhEmjCv3SG0Y8cO2rdvT2BgYLX2lJQUDMNgx44dPxrKc3NzeeGFF/jTn/6Ev3/TH+GR\ny4dhGOw8VMbKTfl8kVlAid1FeLAPt/SJZGD3cDrE6vuxsblLC6nY9AkVGz/GKDmNOaIV/jf9Ct9u\nAzH5Bni7vMuOyWTGFBKJOSQS4r+/MdVwOak8vN0TWO2fvYL9s1cwh8eeCeg9qz5puEKm/ZzlKjiO\nc+8GKvduwHkgs+pTAh9frPGp+F19Kz4de2IJi/Z2mcCZTzhKTp/5hOOo5xMOV95RnPs2gsv5fWcf\nXyzhsVU3+Ua0IrjIiTPKF0tEK0yB4U3mFymR5qpOofy5555j+fLlF1yLfOTIkQwbNoxf//rXP3ms\n3NxcoqNr/qMYFRUFwMmTJ390/2eeeYb27dszatSoWlQuUn/H8ypYtamAlZvyOHraga/VRO8uoQzs\nHk73TsFYLPqB2NgqT+yj4rsPcWxbDS4nPu174Df8QXw6pmsE8SKYLFas8alY41Nh4D24Ck5UBdm9\nG6jYvJyKDR+dCbMp+JwZRb9cwuy5vv/l4juc2Rtwnz4MgDk8Ft+0IVg7nfnl4jJcC95kMmEKjqy6\n+bhdSrVthuHGKDrluW+g6s8juE4dwrnnW6LclZRsW1zV2eZfFdjPWZ3n7Ci7KSBUgV3kClCnUL5i\nxQr69Olzwe19+/bl008/rVUoLy8vP+/8c1/fqhGZH1vvPDMzkw8++IB58+Y12D80W7dubZDj1FVG\nRoZX3repaOzrV+6ErTkmNh00c/BU1fda+yg3t/Y06Bpn4Gc9BSWn2Ly5UctoFJaKYkw+fpf/96Dh\nJuDkLkIPfoN//gHcFislrdIobHstzqAoKAQ2bvJaeZf99auzGOh4M6b4IfjlHyAgdw8BR3Zjzf4O\nOy/iCIyiLCqBssjOlIe3BXP9PnC92OtnKS8i4NQeAnJ34396L2aXA8NkwR4RT1nSMMoiE6gMbFHV\nOR/Iz6pXnd4XDeHREN4dOgBuFz7lhVjLTmMtzav6s+w01kPb8dn1NSbD7dnT7eOLM6DFmf8icAa2\n8Lx225r3J0tN7+/vpaXr17Dq9K9pTk6OZ8WV82nfvj0LFiyo1bH8/PxwOp012s+G8bPh/IcMw+Cv\nf/0rN910Ez179qzVe9VGcnLyBd+zsWRkZJCenn5J37MpaazrV+ky2LinmJWb8lm3vRBHpUHrKF/u\nuimcAWnhV/Tj7Q23C+fudVR8t5jKw9sAMAVH1lj/2hIed+Ypk96bE+8uL8GxZQUVGR/hLjiBOSQK\n3xsnY0u9iRb+wV6r61xN/+/wtZ6vXHlHcGZvwGfvd9gOrSfswFqw+WONT8PaqRfWjj0xB7eo09Hr\ncv0MtwvXkZ1VU22yv8N1cj8AppAorCmDsHbqibVdChHN4IbVszIyMki9fnCNdsNVibvwRLXVeWxn\nRtndJ7bBOYHd5Bd0zuo836+Fb45ohdkv6FKeziXX9P/+Ni5dv4tTUVFxwYHgOg9xFBUVXXBbYWEh\nbrf7gtvPFRUVdd4pKrm5uQAXnE++YsUKMjMzefjhh8nJyam2raSkhJycHCIjI/Hz0/rPUnuGYbD3\nmJ2VG/NZvaWAgpJKQgIsDOkVwcDuESS09r+iP/5124txbFlORcYS3IUnMYdG49fvTo4eOUKMH/+/\nvTsPj6LM14d/V6/ZianikwAAIABJREFUyEb2kJ0sGAhkQQiymBAgBgLKgIwZJ4AOCIpz5B1HYM7R\n41FfcRzmKEfcUBTFDVEwCRiRRVGQJQmQgCySELaEJZCFbL3X748mDTELCVkq6dyf6/ISqquqv1U8\n3X330089Zb5pzclfoKu/9fUt3LhpzS03q2m4gY2zZ5fdZdJ4rQTa3ExoC7YDeg0UfpGwTXwUyrCR\nnJVCQnJXX8jv9oXN3dMg6uqhP5NvGYuu/22veR3PYMvFonLf8A7/e5lqK6E/nQd9YS4MxXkQNbWA\nTA7FgLtgm/iI+YuAm3+vfm12BUGuMP97ufri969S0aCHqfJSo7HrpooSGM7/CtOvuwCIN/dj69ho\nZphbp9fktRtEna9doTw0NBQ7d+7E/PnzmzwmiiJ27tyJoKCgNu0rIiIC69atQ21tbaOLPfPz8y2P\nN6e0tBQmkwmzZ89u8tjGjRuxceNGvPfeexg7dmyb6qC+7WqVHj8crsCOQxU4e1kDhVzAiEGOGB/t\ngriwflAqevc4ZePV8+aAe2QHoNdC4T8EtknzoAwdAUEmR2VeHkJu6ekw1Vc3vmnNjV423a+7IGpr\nb+5YkEHm5NF8D5uzZ7vDmCiKMBQfhCYnE4aiXECugOquceYpDb0GdtbpoE4iqGyhChsJVdhI8zSD\nZWctAV2z9ytofvkSgo0DFMExUIYMhzI4BjJ759vuVxRNMF48BX2heV/Gi6cAiBDsXaAMH2XeV+Aw\nCDb2t90XNU9QKCF384Pcza/JY6JBB1PFxVvGsJfcmFLzMMQjOxrvx97ZMma90Tz4Lj4QVOwUI7oT\n7QrlM2bMwHPPPYelS5fimWeegaurKwCgvLwc//rXv5Cfn49nn322TftKTk7GBx98gA0bNljmKdfp\ndNi4cSNiYmIsF4GWlpaivr4eISEhAIDExEQMGDCgyf6eeOIJJCQkYMaMGYiMjGzPYVEfU1NvxC+/\nVuHH/AocLqqBKAKD/O2w6H5fjB3i3OtvdS+KJhhOH4QmJwOG0wcBuRKqyHuhHp4KhWdIq9vKbPtB\nZhsOhU/47/YpQqy7br7I7HezRGgvHAd09bfsRA6Zs+fNeZdbmYNZ1GmgO7IDmtxMmK5dgGDvApsx\nf4I6+j7IHFw69bxQ1xAEAXKPQMg9AmETPwOiphb64kM3e9GP/QRAgNwn1ByqQ+Ig9x5ouTDXVF8N\nw+mD5nVP50GsqwIEGeS+4bAZ97B5fc9gXsjbDQSFCnL3AMjdA5o8Juo0MFZevDH3/c158PVFuRAL\nKhrvx6F/05tWufiYh8T1spl8iLpTu9LHgw8+iAMHDuCbb75BRkaGZaaUsrIyiKKIlJQUpKWltWlf\nQ4cORXJyMlasWIGysjL4+/tj06ZNKC0txfLlyy3rLVmyBAcOHMDJkycBAP7+/vD39292n35+fkhK\nSmrPIVEfUa81Yv/x69hVUInc38zziXu5qPBQgicSo13g69b7PyhEXT20BTugzc2EqbwEgoMrbMb+\n2RxwO3AreKDhpjVOkNk7QTGglZvWNPpJvLSZOZgVkDl7m6dws3WE/rdfIGpqIfcOhd3Uv0E1aEyX\nDYuh7iHY2EM1aDRUg0abe74vn77R850Dzc+fQfPzpxDsnKAIHAaf0mJUfX8BEE0QbB2hDI6BcuBw\nKIJiILNzlPpQ6BaCygYKjyDAo+mv4aK2DsaKi43vNFtRCv1v+6Crq7p1LxAc3SxBXRkwFMrweAjy\n3t0RQtRZ2v1KWLFiBRITE5GVlYVz584BAIYMGYLU1FQkJye3a1+vvvoqXn/9dWRkZKCqqgrh4eFY\nvXo1LxygTqHVm5B70hzED5y4Dq1eRH9HJabGu2FslHOvHyfewFh5CdrczdDlfw9RWwu5Txjsp/0d\nyoh7uiXgtn7TmpbnYBbPHYUiKBo2w6dBPmCQVfxbUGOCIIPCayAUXgNhO/qPMNVVwVB8CPrCHOjP\nHIYgt4PNqAehHDgccu9QXjPQSwlqOyi8QgCvpr/EiZraG6/7kkZTO+qP/wzdoWwI/dxgEzsZqmHJ\n/CJGfd4dfT1NSUlBSkpKh59crVZjyZIlWLJkSYvrrFu3rk37auhJp75NbzDhUGENfiqoxC/HqlCv\nNcHJXoEJsa4YF+WMuwLsIZP1/vAniiIM545Am5MJ/an9gCBAGXEPbIZPg8K3+esxpNDaHMzU98js\nnKCKvBeqyHsBmGdv8GInjFUTbOyh8A4FvEMbLRdFE/SFudDmZKD+x49Qv/tzqAYnwCZuKuQegdIU\nSySxOwrlR44cQUFBQbOzrQiCgCeeeKJTiiNqC6NJRMFpcxDffbQKNfVGONjIMXaIM8ZGOWNosIPV\n3NhHNOig+/VHaHMyYbxSDMHWETbxM6COnWwOvkREvYAgyKAKvRuq0LthvHIGmtxM6I7+AN3hrVAE\nDoV6+DQoBw7ntQTUp7QrlGs0GixatAh79uyBKIoQBAGiaJ4+qeHPDOXUHUwmEWevAvszL2D3kSpU\n1Bhgq5Jh5F2OGBflgphQh14/c8qtTNXXoM3bAu2hbIj11yF3D4Rdyl+hiryXF04RUa8m9wiEfcpf\nYXvvHOjyt0KTuxm1G14w35E1NhXqoRM4BSP1Ce0K5W+++Sb27NmDBQsWID4+Hunp6XjllVfQv39/\nrF69GhqNBv/85z+7qlbq40RRRGFpPXblV+KngkqUVSmgUpRjeIQjxkU5Y3i4I2xU1hPEAcBQcgKa\nnEzoT+wGTCYow0ZAHTcVioAojsEmIqsis3OETfxMqEdMh/7kL9DkZKB++2rU/7QO6qgJUMelQu7q\nI3WZRF2mXaF869atSE5Oxn/8x3+gosI8BZKnpyfi4+MRHx+PGTNmYNOmTfjb3/7WJcVS33TmUj12\nFVRiV34lLpbroJALiAnth3HhGjyUMhR2auu6OEw0GqA/sRuanEwYS08Cajuo41Khjp0CuYu31OUR\nEXUpQSaHatAYqAaNgeHiKWhzMqA9+C20uVlQDhxuvn9B4DB2TJDVaVcov3jxomVOcbncHIT0er15\nRwoFJk+ejM8//5yhnDqs5KoWPxVUYldBJc5e1kAmAENDHDDrXg+MinRCPzsF8vLKrSqQm2qroD2c\nDW3etxBrrkHm6gPbiQugHjKeP90SUZ+k8A6FYurTsE18xBzMD2ZD//l/QeYWAJvhqVANToCg5M2K\nyDq0K5Tb29vDaDRa/iyTyXDlyhXL4/369cPVq1c7t0LqM65U6vBTgXloyqkS881oBgfa4/Gpvhg9\n2Aku/axz/mrDlWJoczKgO/ojYNRDERQDm5QnoQiJ5UVOREQAZA6usB37MGxGzYLu2C5oczJRl70K\n9T9+BNWwSbCJnQKZo7vUZRJ1SLtCub+/P86cOQPA3FM+cOBAbN26FTNmzIAoiti2bRu8vfnzOrVd\nebUePx8xB/FjZ+sAAGEDbDEvxQdjhjjB3VklcYVdQzQZoT91ANqcDBjOHQGUaqiiJsAmLhVy9+Zv\njkVE1NcJCiXUUUlQDRkPw/lfoc3JhHbfRmj3bYQyfBTve0C9WrtCeXx8PL7++mv84x//gFwux6xZ\ns/Diiy8iKSkJgiDgwoULWLx4cVfVSlbk3BUN3s4sQcHpGphEINDLBrMnemFslDN8+lvvbCKiphba\n/O+hzcuCqfIyBEd32CbMhWrYJMhs+0ldHhFRryAIApT+g6H0Hwxj1RVo8zZDd/g7VJ/YDbnXQKiH\nTzPfIVhhnb+wknVqVyifP38+pk2bZpkG8U9/+hN0Oh0yMzMhk8mwePFizJs3r0sKJetxqLAa//+n\nZ6CQy/DHBE+MjXJGgKd1jwk0XiuBNjcT2oLtgF4DhV8kbBMeMd9imncxJCK6Y3InD9glPgLb0WnQ\nHd0JTU4m6rL+jfqda6COmQx19H2QObhIXSbRbbV7THlwcHCjZXPnzsXcuXM7tSiyXt/lXMOqby7A\nz90Gz88OgqeLdQ5PAW7cdbP4IDQ5mTAU5QJyBVR3jTPPHOA1UOryiIisiqCygTomBaro+2AoPgRN\nTgY0P38KzS/r+d5LvcId3dGTqL1MJhEfbr2Ir34qQ2xoPyxLC4C9jXX2EIs6DXRHdkCTmwnTtQsQ\n7J1hM+ZP7K0hIuoGgiBAGRwDZXBMo18pdUd2QDEgEurhU6EMj5e6TKImGMqpy2l0JqzYcA57jlZh\n8oj+eCzBFqaTO1F3uQhyn3Aog2Mgs3OSuswOM1ZdgTY3C7r8rRA1tZB7DYRd6t84rpGISCLy/r6w\nm7QQtuPSoS34HtrcLNRuWg6Zoztc3CNh8HaA3DuUM11Rj8BQTl2qvFqPFz4qgunSKbwcWYKQ8mOo\nebPI/KBcAeRmARAg9w2HMiQOypA4yL1Ces0bpCiKlhkA9L/tBQDOAEBE1MMINvawufsBqOOmQl94\nANqcLDgX7UJ10Y8Q7JygDI6FMiQOiuAYXnRPkmEopy5hqqvCxUP7cOynn/E300k42NUB52UQfAfB\n5t7Z5vDtHgDj5dPQF+ZAX5QLzU+fQvPTJxDsnS0BXREUDZmNg9SH04Ro0FvmyjVeLoJg4wD1yOmw\niZkMmZOH1OUREVEzBJkcqrB4qMLicWjvz4h0NEJflAt9US50R3cCggxy3wjzZ9DAOMg9gtm5Qt2G\noZw6hSiaYLxUZHlzM5SchB1EhMMeioHDYT9kpDlg/64HQuEdCoV3KGzHpMFUWwV9cR70hbnQ/7YP\nuoLtgCCDYsBdUA6MgzJkOGTuAZK+QZpqyi13lRPrKiFz84fdfYt4Vzkiol7GpLKDKjIWqsh7IZqM\nMF48ZfkM0+z6GJpdH0NwcLV0EimDonl3ZepSDOV0x0yaGhiKD1nexMTaSgACqh2D8L0uEVechmDe\n3DFwd25bWJXZO0E9OBHqwYnmN8jSk+aAXpSL+h/Wov6HtRD6uVl6MASD2LUHeAvDxVPmu24e+xkw\nGaEcONx8JX/gMPaiEBH1coJMDoVvBBS+EbAd+zBMNRXQn84zf76d2ANd/veATA6FX+SNkD4cMjc/\nvv9Tp2Iol4jhUhECdr6CqhxXyF19IHP1gdzlxv9dfSE4uvW4cdWiKMJUdhb6ohzoC3NhuHAMEE0Q\nbBygCI6BPDgOX572wRf7dbg7vB+WPBQAO/WdzbAiyORQDLgLigF3wfbedJiqr5nfIAtzoDu2C7rD\n3yFQkKO6aMjNXnRX3059gxRNRuhP/AJNbgaMF44DKluoY1KgjkuF3NWn056HiIh6FpmDC9RRSVBH\nJUE0GmAoOQFDUS70hTmo3/kB6nd+AJmTh3mYZchwKAOiIKj4ayl1DEO5ROQu3rjuNxweSj1M5aXQ\nn8kHDNqbKyhUkDl7NxPYfSA49O+2b+eirh76M/k3e8Ovl5nr9wyGTfwMKEOGQ+4bDq0B+OcX57Dv\n+HVMG+WGeZN9IJd1Xo2yfv2hHjoR6qETIRr1MJw/hgu/bIFrzXnUb38f9dvfh8zZ60Yv+nAo/IdA\nUN7ZnUFNddehO/wdNHlbIFZfhczZC7ZJ86EeOoE/XRIR9TGCXGG5e6htwhyYrpdZPhO1R3ZCe/Bb\nQK6EImAIlCHDzddMseOG7gBDuUQEtR0qQscjODYWgHlMtlhdDmN5CUwVpTCWl974fwn0RbmAUX9z\nY6XaHNJdfCyhveHPgr1LhwK7KIrmLwlF5osvDeeOAEYDoLKFMigaytEPQRkSC1k/N8s2167r8fxH\nxTh9sR4LU30xdZRbK8/QcYJcCWXgUJRfMyAoNhbGysvmHoyiXGjzt0GbtxlQqKAIiDK/QQ6Mg9zZ\n67b7NV45A01uJnRHfwQMWigChkI9aSGUA4fzrptERAQAkDm6Qx19H9TR90E06GE4f9Qc0gtzUb/t\nXdRvexcyVx9LQFf4D4agsN4b5VHnYSjvIQRBBsHRDTJHNyBwaKPHRJMRputXLSHd1BDYy85Af2of\nYDLeXFlle7NX/ZbedZmLLwQ7x2YDu6jXwnDuiOWbv6niIgBA5uYPddxU85uK310Q5E3n2i4qrcd/\nf1SMOo0R/50ehLsjHDv3xLSB3NkT8tjJUMdOhmjQmY+lMBf6ohzUF+Wi/ntA1n/ALW+QkZZjEUUT\n9IU50OZkwHAmH1CooBqcAJu4qZB7BHb7sRARUe8hKJTmDqugaCBpHowVF2/2oh/KhjYnA1CqoQwc\nCmXIcChC4iDnDF3UAobyXkCQyc3B09nT/MK/hWgywlR1Babyklt610thvFQI/Yk9gGi6uR+1feNe\ndbU9DGfzoT9TYB46ozC/cdjc/YD5jcPZs9W6Dpy4juWfn4WDrRwrFgxEsLdtlxx/ewgKlXm+2eBY\nAI+Zf2m4cbGoNi8L2gObzL3+gcMg9wiE7tcfYaq4CKFff9jcOxvqYZOs4kZGRETU/eQu3pDHpcIm\nLhWiXgPD2RsdXoU50J86AACQuQVAOTAOCr/IG796ezXb6WWNRKMBpqrLMJWXQjQZbx4/f0kAwFDe\n6wkyuflNwMUbypDGj4lGA0yVl2GquBHYy0thrCiFseQE9Md+AiBC5uIN9bBJ5jcI/yFtfmFk/FKG\n1ZtLEexti+dnB6G/Y898Q5G7+kJ+ty9s7p4GUaeB/my++SLVohzof9sLuW8E7MelQxk+CoKcLwci\nIuocgtIGyoHDoRw4HOLEBTBdu3CjFz0H2gMZ0O77+saKMsic3CFz8b3xy/YtQ1OdPHvdZ5Ols7Bh\nKO6N7GEqL4Wp6nLjX/cBAMKN4791SO6Nc+Hs2We+sAAM5VZNkCsg7+8LeX9f/L5JiwY9RE01ZA6u\n7dqn0Sji3S0lyNp7DfF3OeKZWf6wUfWO8daCygaq0BFQhY6AKIoQ66shs+v+4TZERNS3CIIAuZsf\n5G5+sBnxAERtHYxlZy1h1fxLdwm0R44DuvpbNpRB5uzVzJBUH8icPCS73kkUTVDUV0JffKhR6DaW\nl8JUeQkwGW6urLSB3NUHcs9gqAaNvnEsvoBcbgntDQFed2wXRE3tzW0FGWROHrcc/80vLjJnT6u7\n3ouhvI8SFEoI7QzkdVojXvn8LHJOVmP6GHc8kuzdqTOsdCdBECAwkBMRkQQEtR0UAwZBMWBQo+Wi\nKEKsrTSH1EaBvRTac0cBvebmyjIFZM5eTWZpk7n6QtYJ0yq3PAFFKUwVF+Fv1KOmYWWF2vyrvbs/\nlOEjGwXo1iagUPiENz3++utNg35FCbQXfveFRSaHzNnzRg+7b+Oedkf3XhnYGcqpTcoqdXj+42Kc\nuazBovt9MXlE186wQkRE1NcIggDBwQUyBxco/CIbPWYO7BU3epdLbg4JaW5aZbkSMhfvxsNhboRX\noZ+rJbC3tk9jxcUW96kMicOFagOCho1sss8OH7+dE2R2Tj36C0tXYSin2zpVUofnPypGvc6EF2YH\nITaMPcxERETdyRzYXc3DTv0HN3qstV5tfVFe42mVb/RqQyYzB+9Gvc83w6wiaFjjMNuvf6Pe5+q8\nPCh/N1tcV+rsLyzqoRNgM2J6t9XfFgzl1Kq9x6rwzy/Owclejv9dMBCBXtLPsEJEREQ33XZa5epr\nMN2YUrkhsIqiCWq/wT1mnHpH3MkXFoiiRNW2jKGcmiWKIjbtvor3s0sR5muH59ID4dqv71wBTURE\nZA0EmRxyJw/z/Oi/m1a5L2jtC0tPw1BOTRiNIt7KLMG3B67hnsFOeHqmP2xUPXP8FREREZE1YCin\nRmo1Rrz82RkcPFWDB8d5YPZEL8h66QwrRERERL0FQzlZXK7Q4b8/KsaFMg2emj4Ak4b3l7okIiIi\noj6BoZwAACfP1+F/Pi6GzmDCi3ODET2wn9QlEREREfUZDOWEn49UYsWX5+DaT4lX5oXA38NG6pKI\niIiI+hSG8j5MFEVs+KkMH353EYP87fDcn4Pg7MAmQURERNTdmMD6KINRxKpvLmBrbjnGRTnj/5vh\nB5WSM6wQERERSYGhvI/asOsKtuaW46EEDzycxBlWiIiIiKTEUN4HlVXqsP7Hy7gn0gnpE72lLoeI\niIioz+N4hT7o/W9LIYrAvMk+UpdCRERERGAo73MKTtfgpyNVeHCcBzxdVFKXQ0RERERgKO9TjEYR\nb2eVwMNZiRnjPKQuh4iIiIhuYCjvQ7bsv4YzlzSYP9kHas60QkRERNRjMJn1EVW1BqzbdgnDQhww\nKtJJ6nKIiIiI6BYM5X3ER99fRJ3OiAWpvhAETn9IRERE1JMwlPcBhSV1+C6nHFPj3RDgaSN1OURE\nRET0OwzlVk4UzRd3Otkr8HCSl9TlEBEREVEzGMqt3A+HK3HsbB3mTPKCvY1c6nKIiIiIqBkM5Vas\nTmvEmuxShA2wxYQYV6nLISIiIqIWMJRbsc93XkZ5tQELU30hk/HiTiIiIqKeiqHcSl0o0+CbPVcx\nIdYFEf72UpdDRERERK1gKLdCoiji3c2lUCkEzJ3kLXU5RERERHQbDOVWaP+J68j9rRoPJ3nBpZ9S\n6nKIiIiI6DYYyq2MTm/Cu5tL4eehRmq8m9TlEBEREVEbMJRbmY27y3CpXIcFU3yhkPPiTiIiIqLe\ngKHcipRV6fDFD1cwKtIJMaH9pC6HiIiIiNpIIeWT63Q6rFy5EhkZGbh+/ToiIiKwePFixMfHt7pd\nZmYmvvrqKxQVFaGqqgoeHh4YMWIEFi1aBF9f326qvudZ8+1FiKKI+ZN9pC6FiIiIiNpB0lC+dOlS\nfP/990hPT0dAQAA2bdqEefPmYd26dYiOjm5xuxMnTsDT0xPjxo2Dk5MTSktL8eWXX+LHH39EZmYm\n3N3du/EoeoYjxTXYVVCJtERPeLqopC6HiIiIiNpBslBeUFCALVu2YNmyZZgzZw4A4P7778eUKVOw\nYsUKfPrppy1u+8wzzzRZNn78eEyfPh2ZmZl49NFHu6rsHsloFPF2Zgk8nJWYOc5D6nKIiIiIqJ0k\nG1P+3XffQalUYubMmZZlarUaM2bMQF5eHq5cudKu/fn4mIdsXL9+vVPr7A2yD1xD8SUN/pLiAxsV\nLxMgIiIi6m0k6yk/fvw4goKCYG/f+G6TUVFREEURx48fh4dH672+lZWVMBqNKC0txZtvvgkAtx2P\nbm2qag34aNslDA1xwOjBTlKXQ0RERER3QLJQXlZWBk9PzybLG8aDt6WnfNKkSaisrAQAODs747nn\nnsPIkSPvqJ6jR4/e0XYdlZeX16HtMw/KUKcRMDa4EgcPHuykqnqPjp4/4jnsKJ6/juH56xiev47h\n+esYnr/OJVko12g0UCqb3m1SrVYDALRa7W33sWrVKtTV1aG4uBiZmZmora2943oGDx5see7ukpeX\nh9jY2DvevrC0DjnFpzA13g0piX1v1pmOnj/iOewonr+O4fnrGJ6/juH56xievzuj1Wpb7AiWLJTb\n2NhAr9c3Wd4QxtsSkIcPHw4AGDduHMaPH4/U1FTY2dnh4Ycf7txieyBRNF/c6WinwMNJXlKXQ0RE\nREQdINlVge7u7s0OUSkrKwOA244n/z0/Pz9ERkYiKyurU+rr6X44XIljZ+swd5IXHGzlUpdDRERE\nRB0gWSiPiIhAcXFxkyEn+fn5lsfbS6PRoLq6ulPq68nqtEasyS5FqK8tJsS6Sl0OEREREXWQZKE8\nOTkZer0eGzZssCzT6XTYuHEjYmJiLBeBlpaWoqioqNG25eXlTfZ39OhRnDhxApGRkV1beA/wxQ+X\nUV5twMKpvpDJBKnLISIiIqIOkmxM+dChQ5GcnIwVK1agrKwM/v7+2LRpE0pLS7F8+XLLekuWLMGB\nAwdw8uRJy7KEhATcd999CAsLg52dHQoLC/H111/D3t4ejz/+uBSH021KrmqxafdVJMW4YJC//e03\nICIiIqIeT7JQDgCvvvoqXn/9dWRkZKCqqgrh4eFYvXr1ba/mTUtLw969e7F9+3ZoNBq4u7sjOTkZ\njz/+OPz8/Lqpemm8u7kESoWAucneUpdCRERERJ1E0lCuVquxZMkSLFmypMV11q1b12RZa+tbswMn\nriPnZDX+kuIN135Np5MkIiIiot6J92TvJXQGE97dXAI/dzWmxrtJXQ4RERERdSKG8l7im91lKL2m\nw2NTfKFU8J+NiIiIyJow3fUCV6v0+PyHK4i/yxGxYf2kLoeIiIiIOhlDeS+wJrsURpOIeZN9pC6F\niIiIiLoAQ3kPd7S4Bj/mV2LGWA94u6qlLoeIiIiIugBDeQ9mNIl4O6sE7k5KPDjOQ+pyiIiIiKiL\nMJT3YN/lXMPpixrMm+wDGxX/qYiIiIisFZNeD3W91oCPtl7C0GAHjB7sJHU5RERERNSFGMp7qHXb\nL6FWa8RjqT4QBEHqcoiIiIioCzGU90CnL9bj2/3XMGWEG4K8bKUuh4iIiIi6GEN5DyOKIt7OLIGD\nnRwPT/CUuhwiIiIi6gYM5T3MrvxKHD1TizkTvdHPViF1OURERETUDRjKe5B6rRHvZ19EqK8tJsa5\nSl0OEREREXUThvIeZP2PV3Dtuh4LU30hl/HiTiIiIqK+gqG8hyi9qsXXP5dhfLQLBgXYS10OERER\nEXUjhvIe4t0tpVAqBDyS7C11KURERETUzRjKe4ADJ67jwInrSEv0hKujUupyiIiIiKibMZRLTGcw\nYfXmEgxwV2PaKDepyyEiIiIiCTCUSyxjz1WUXNPhsck+UCr4z0FERETUFzEFSuh6PfD5zssYOcgR\nceGOUpdDRERERBJhKJfQ1iMyGEwi5k/xkboUIiIiIpIQQ7lEii/VI/+cDH8Y4w5vV7XU5RARERGR\nhBjKJdLPVoHRYSbMutdD6lKIiIiISGIM5RJxc1IiOcoEG5Vc6lKIiIiISGIM5UREREREEmMoJyIi\nIiKSGEM5EREREZHEGMqJiIiIiCTGUE5EREREJDGGciIiIiIiiTGUExERERFJjKGciIiIiEhiDOVE\nRERERBJTSF2A1ERRBADodDpJnl+r1UryvNaC56/jeA47huevY3j+Oobnr2N4/jqG56/9GvJmQ/68\nlSA2t7QPqa40DZ/bAAAT60lEQVSuxm+//SZ1GURERETUR4SFhaFfv36NlvX5UG4ymVBbWwulUglB\nEKQuh4iIiIislCiK0Ov1sLe3h0zWeBR5nw/lRERERERS44WeREREREQSYygnIiIiIpIYQzkRERER\nkcQYyomIiIiIJMZQTkREREQkMYZyIiIiIiKJMZQTEREREUmMoZyIiIiISGIKqQuwNjqdDitXrkRG\nRgauX7+OiIgILF68GPHx8bfd9vLly3j55ZexZ88emEwmjBw5EsuWLYOfn183VN4zFBQUYNOmTdi/\nfz9KS0vh7OyM6OhoPPXUUwgICGh12zfeeAOrVq1qstzNzQ179uzpqpJ7lP379yM9Pb3Zx7799luE\nhIS0un1fb4NLly7Fpk2bWnz8p59+gqenZ7OP9bX2d+XKFXz88cfIz8/H0aNHUVdXh48//hgjRoxo\nsu6OHTuwatUqFBYWon///pgxYwYWLFgAheL2H0Emkwlr1qzB559/jrKyMgQGBmLhwoVISUnpisPq\nNm05fxUVFfj666+xc+dOnD59GgaDASEhIZgzZw7uu+++2z7HhQsXMH78+GYfe++99zB27NhOO57u\n1tb2l5iYiJKSkibbz5s3D08//fRtn6cvt7/WPk8A4KmnnsLChQtbfNya219XYSjvZEuXLsX333+P\n9PR0BAQEYNOmTZg3bx7WrVuH6OjoFrerra1Feno6amtrLR9Wa9euRXp6Or755hs4OTl141FI5/33\n38fBgweRnJyM8PBwlJWV4dNPP8X999+Pr7766rahEgBeeOEF2NjYWP5+65/7itmzZyMyMrLRspbC\nZAO2QWDWrFlNvkCLoojnn38evr6+tz2HQN9pf8XFxXjvvfcQEBCA8PBwHDp0qNn1du3ahSeeeAIj\nR47Es88+i99++w1vvvkmKioq8Oyzz972eV577TWsXr0as2bNwuDBg7Fjxw4sXrwYMpkMycnJnX1Y\n3aYt5+/w4cN4/fXXMXbsWCxcuBAKhQJbt27FU089hdOnT+OJJ55o03NNnToVo0ePbrQsIiKiU45D\nKm1tfwAQGRmJ2bNnN1oWFhbWpufpy+0vJCQEr776apPlmZmZ2L17N+655542PZc1tr8uI1Knyc/P\nF8PCwsQPP/zQskyj0YhJSUliWlpaq9uuXr1aDA8PF3/99VfLssLCQnHQoEHi66+/3lUl9zh5eXmi\nVqtttKy4uFgcPHiwuGTJkla3/b//+z8xLCxMrKqq6soSe7R9+/aJYWFh4rZt29q9Ldtg83JycsSw\nsDDx7bffbnW9vtb+qqurxfLyclEURXHbtm1iWFiYuG/fvibrpaSkiA888IBoMBgsy/73f/9XjIiI\nEIuLi1t9jkuXLomRkZHiSy+9ZFlmMpnEtLQ0MSEhQTQajZ1zMBJoy/k7d+6ceOHChUbLTCaTmJ6e\nLkZFRYn19fWtPsf58+ebfCZZi7a2v4SEBHHhwoV39Bx9vf21ZMKECeLEiRNvu541t7+uwjHlnei7\n776DUqnEzJkzLcvUajVmzJiBvLw8XLlypcVtt27dimHDhuGuu+6yLAsJCUF8fDyys7O7tO6eJCYm\nBiqVqtGywMBAhIaGoqioqE37EEURNTU1EEWxK0rsNWpqamAwGNq8Pttg8zZv3gxBEDBlypQ2rd9X\n2p+DgwNcXFxaXaewsBCFhYWYNWsW5HK5ZXlaWhpMJhO+//77Vrffvn079Ho90tLSLMsEQcBDDz2E\nkpISFBQUdOwgJNSW8+fn5wdfX99GywRBQFJSEjQaTbPDMlpSV1cHnU53R7X2RG05f7fS6XSor69v\n13P09fbXnIKCApw9exapqant2s7a2l9XYSjvRMePH0dQUBDs7e0bLY+KioIoijh+/Hiz25lMJpw8\neRKDBw9u8tiQIUNw5syZdr+ZWBNRFHH16tU2v4Hce++9iI2NRWxsLJYtW4bKysourrDn+fvf/47Y\n2FgMHToUjzzyCE6ePNnq+myDzdPr9cjOzkZ0dDQGDBjQpm3Y/m46duwYADRpV56envDy8rI83pLj\nx4/DwcEBQUFBjZZHRUU12n9fc/XqVQBo83viypUrER0djaioKMyaNQs5OTldWV6Ps2fPHgwbNgzD\nhg1DUlIS1q9f36bt2P6ayszMBIB2hfK+3v7ag2PKO1FZWVmzY07d3d0BoMWe8srKSuh0Ost6v99W\nFEWUlZXB39+/cwvuJTIzM3H58mUsXry41fUcHR3x5z//GUOHDoVSqcS+ffuwfv16HDt2DBs2bGjS\nA2+NlEolJk2ahLFjx8LFxQUnT57EBx98gLS0NHz11VdNPlwasA02b/fu3aisrGzTBxDbX1NlZWUA\n0GK7au3Xw4bt3dzcmt0WaPk91ZpVVlZiw4YNuPvuu+Hq6trqujKZDKNHj8aECRPg4eGBs2fPYs2a\nNZg7dy7Wrl2LuLi4bqpaOmFhYYiLi0NgYCAqKirw5Zdf4rnnnkNVVRXmz5/f6rZsf40ZjUZkZ2cj\nKirqthMvAGx/d4KhvBNpNBoolcomy9VqNQBAq9U2u13D8uY+tBu21Wg0nVVmr1JUVIQXXngBsbGx\nmDZtWqvr/v5CnuTkZISGhuKFF17AN998gwcffLArS+0RYmJiEBMTY/n7+PHjkZiYiD/84Q9YtWoV\n/v3vfze7Hdtg8zZv3gylUtmmmS7Y/ppqaDMttavb/fqi0WhabZMtvadaK5PJhKeffhrV1dX4r//6\nr9uu7+PjgzVr1jRalpKSgsmTJ2PFihX44osvuqrUHuOdd95p9Pfp06cjLS0Nb731Fh566CH069ev\nxW3Z/hrbu3cvrl69iscee6xN67P9tR+Hr3QiGxsb6PX6JssbXrgNL+Tfa1je3Hirhm2tdQaH1pSV\nleGxxx6Dk5MTVq5cCZms/c31oYcegq2tLfbu3dsFFfYOERERiI+Px759+1pch22wqdraWuzYsQOj\nR4++o7GXANtfQ5tpqV3drk3Z2Ni02iZbek+1Vi+++CJ2796N5cuXIzw8/I724enpicmTJyM/P79P\nDkmTy+WYPXs26uvrW52xBWD7+72srCzI5fIOTQfZ19vf7TCUd6KWfo5t+AnXw8Oj2e2cnZ2hUqks\n6/1+W0EQmv3515pVV1dj3rx5qK6uxvvvv3/Hxy+TyeDp6YmqqqpOrrB38fb2bvUcsA02tX37dtTX\n17f7gqZb9fX219BmWmpXLb0n3rp9w/jp328LtPyeao1WrVqFzz77DH//+9/bfNFxS7y9vWEymXD9\n+vVOqq538fLyAoDbvi7Z/m7SaDTYtm0b4uPjmx3S0x59vf21hqG8E0VERKC4uBi1tbWNlufn51se\nb45MJkNYWBiOHj3a5LGCggIEBATA1ta28wvuobRaLRYsWIAzZ87g3XffRXBw8B3vS6/X4+LFi3fc\n02ktzp8/3+o5YBtsKisrC3Z2dkhMTLzjffT19jdo0CAAaNKuLl++jEuXLlkeb237mpoaFBcXN1re\n8J56u+2txaeffoo33ngDc+bMwaOPPtrh/Z0/fx5yubxP3HugOefPnweA247JZ/u7aefOnaitre1Q\nJ0WDvt7+WsNQ3omSk5Oh1+uxYcMGyzKdToeNGzciJibGchFoaWlpk+n9Jk2ahMOHDze6mvv06dPY\nt29fr75BQXsZjUY89dRTOHz4MFauXIlhw4Y1u15z57C8vLzJemvWrIFWq8WYMWO6pN6eprlzkJub\ni/379ze6eQPbYOvKy8uxd+9eTJgwodkvI2x/bRMaGorg4GCsX78eRqPRsvzzzz+HTCbDxIkTLcuq\nq6tRVFSE6upqy7Lx48dDqVTis88+sywTRRFffPEFfHx8MHTo0O45EAl9++23eOmll5CamoqlS5e2\nuF5z56+5Nnn27Fls2bIFcXFxVj8krbKyEiaTqdEyrVaLNWvWwN7evtHnC9tf67KysmBra4sJEyY0\n+zjbX+fghZ6daOjQoUhOTsaKFSssM1Vs2rQJpaWlWL58uWW9JUuW4MCBA42mqUtLS8OGDRswf/58\nzJ07F3K5HGvXroW7uzvmzJkjwdFI45VXXsHOnTuRkJCAyspKZGRkWB6zt7dHUlISgObPYUJCAlJS\nUhAWFgaVSoX9+/dj69atiI2N7fDPvb3FU089BVtbW0RHR8PFxQWnTp3C+vXr4eLigieffNKyHttg\n67799lsYDIYWe4XY/szeeustALB8QcnIyEBeXh4cHR3x8MMPAwCeeeYZLFy4EI8++ihSUlLw22+/\n4dNPP8WsWbMazQa0bds2LFu2DMuXL8f06dMBmIcZpKen44MPPoBWq8WQIUOwfft25Obm4rXXXruj\n60x6ktudv4KCAjzzzDNwdnZGfHy8ZTq6Bvfcc49lKEFz5+9f//oXzp8/j5EjR8LDwwPnzp2zXFy3\nZMmS7jrMLnO787dz50688847mDRpEnx9fVFZWYlNmzbhzJkzeP755xtNX8z21/zrFzB/ufn5558x\nceLEJlM+N+iL7a8rMJR3sldffRWvv/46MjIyUFVVhfDwcKxevRqxsbGtbufg4IB169bh5Zdfxltv\nvQWTyYQRI0bgP//zP/vUT98nTpwAAPzwww/44YcfGj3m6+trCeXNSU1NxcGDB/Hdd99Br9fD19cX\njz/+OB577DEoFH2jqSclJSErKwsffvghampq4OrqiilTpuDJJ5+Ej49Pq9uyDd6UlZWF/v37Y9So\nUW3epi+2v5UrVzb6+9dffw3A/Fpt+FBPSEjAqlWrsGrVKrz44otwdXXFwoUL8fjjj7fpOZ5++mk4\nOTlh/fr12LhxI4KCgvDvf/+7Qxeb9RS3O3+FhYXQ6/UoLy/HP/7xjybbf/zxx62O773nnnvwxRdf\n4JNPPkF1dTUcHR1xzz33YNGiRQgNDe3cg5HA7c5fWFgYgoODkZGRgfLycqhUKkRGRmLp0qVISEho\n03P05fbXoOE9rb2dC9be/rqCIFr7beeIiIiIiHq43v3bCxERERGRFWAoJyIiIiKSGEM5EREREZHE\nGMqJiIiIiCTGUE5EREREJDGGciIiIiIiiTGUExERERFJjKGciIgkk5iYiD//+c9Sl0FEJDmGciIi\nIiIiiTGUExERERFJjKGciIiIiEhiDOVERFZGp9PhnXfeweTJkzFkyBDExcVhwYIFOHbsWKP19u/f\nj/DwcGzcuBHr1q3DpEmTMGTIEEyaNAnr1q1rdt85OTmYO3cuYmNjERUVhQceeAAbNmxodt2zZ89i\n2bJlGDt2LAYPHozRo0dj4cKFOHr0aJN1i4qKMH/+fERHRyM2NhZ//etfUVZW1vGTQUTUSwiiKIpS\nF0FERJ1Dr9fjkUcewaFDhzBt2jRERkaipqYGX375JcrKyvDJJ59gyJAhAMyhPD09HZGRkSgrK8Os\nWbPg4OCAzZs348iRI3jyySexaNEiy7537tyJRYsWwc3NDQ8++CAcHBywZcsWFBQUYMGCBVi8eLFl\n3SNHjmDOnDkwGAyYMWMGQkNDUVVVhQMHDuDee++1XNyZmJgIhUKB2tpaJCUlISIiAidOnMD69esx\natQofPDBB917AomIJMJQTkRkRdauXYvly5fj/fffx5gxYyzLa2pqMGXKFPj5+Vl6wRtCuZ2dHbKz\ns+Hl5QXA3NOelpaG48ePY8eOHfDy8oLRaERSUhKqq6uxZcsWeHp6WtZNT09Hfn4+srOzERgYCFEU\nkZqairNnz2LDhg2IiIhoVKPJZIJMZv6hNjExESUlJXjttdeQkpJiWed//ud/8NlnnyE7OxvBwcFd\nes6IiHoCDl8hIrIimZmZCA4ORmRkJMrLyy3/6XQ6jBo1Cnl5edBoNI22SU1NtQRyAFCpVJZe7p07\ndwIAfv31V5SWluIPf/iDJZA3rPuXv/wFJpMJO3bsAAAcP34cp06dwvTp05sEcgCWQN7Aw8OjUSAH\ngJEjRwIwD4EhIuoLFFIXQEREnaeoqAgajQbx8fEtrlNRUQFvb2/L30NCQpqsM3DgQADA+fPnAQAX\nLlxotPxWoaGhjdY9c+YMAOCuu+5qU81+fn5Nljk7OwMAKisr27QPIqLejqGciMiKiKKIsLAwLFu2\nrMV1XF1du7Gi25PL5S0+xhGWRNRXMJQTEVmRgIAAVFRUYOTIkU2GibSkqKioybLCwkIAN3uxBwwY\n0Gh5a+sGBQUBMA9jISKituGYciIiK3L//fejrKwMH374YbOPX716tcmyrKwsXLp0yfJ3nU6HtWvX\nQi6XIyEhAQAQGRkJHx8fbNy4sdFUhXq9HmvWrIEgCBg/fjwAICIiAqGhofj6669x6tSpJs/H3m8i\noqbYU05EZEXS09Pxyy+/4NVXX8W+ffswcuRIODg4oLS0FPv27YNKpWoyB3lQUBBmzpyJP/7xj7C3\nt7dMifj4449bxp7L5XI8++yzWLRoEWbMmIEHH3wQ9vb2yM7OxuHDh7FgwQIEBgYCAARBwMsvv4w5\nc+Zg5syZlikRr1+/jpycHIwZM8YyJSIREZkxlBMRWRGlUol3330Xn332GTIyMvDGG28AMM9wMmTI\nEDzwwANNtnn44YdRU1ODTz75BKWlpfDx8cE//vEPzJ49u9F6iYmJWLt2Ld5++22sWbMGer0eISEh\neOmllzBz5sxG60ZFReGrr77CW2+9hezsbHzxxRdwdnZGVFQUYmJiuu4EEBH1UpynnIioj2qYp3z5\n8uWYPn261OUQEfVpHFNORERERCQxhnIiIiIiIokxlBMRERERSYxjyomIiIiIJMaeciIiIiIiiTGU\nExERERFJjKGciIiIiEhiDOVERERERBJjKCciIiIikhhDORERERGRxP4fVD65/5j4wCkAAAAASUVO\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s-ELdyYOFL_J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "outputId": "f29c0301-ebbb-4b9b-8d1a-24d6575acdcf"
      },
      "source": [
        "plt.plot(history['loss'])\n",
        "plt.plot(history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left');"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6ixdCCFGreWzFWBe+S8n0seDx4D/8H/j1HyOBXIjLkJ7yBqxt27YAvPPOO/Tr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\nQzkRERERkZsxlBMRERERudn/A0D3dksZmcBxAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "141gS1mFFfMr",
        "colab_type": "text"
      },
      "source": [
        "## Test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KODgxbfEFThV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def prepare_input(text):\n",
        "    x = np.zeros((1, SEQUENCE_LENGTH, len(chars)))\n",
        "    for t, char in enumerate(text):\n",
        "        x[0, t, char_indices[char]] = 1.\n",
        "        \n",
        "    return x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2IYlEObyFka6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "35aecd4e-8221-4914-ceee-86fb120414b5"
      },
      "source": [
        "prepare_input(\"fgh\")"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
              "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "         0., 0.],\n",
              "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.,\n",
              "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "         0., 0.],\n",
              "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.,\n",
              "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "         0., 0.]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "62mzZLYSFm15",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def sample(preds, top_n=3):\n",
        "    preds = np.asarray(preds).astype('float64')\n",
        "    preds = np.log(preds)\n",
        "    exp_preds = np.exp(preds)\n",
        "    preds = exp_preds / np.sum(exp_preds)\n",
        "    \n",
        "    return heapq.nlargest(top_n, range(len(preds)), preds.take)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gRK6DqXoFx5S",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def predict_completion(text):\n",
        "    original_text = text\n",
        "    generated = text\n",
        "    completion = ''\n",
        "    while True:\n",
        "        x = prepare_input(text)\n",
        "        preds = model.predict(x, verbose=0)[0]\n",
        "        next_index = sample(preds, top_n=1)[0]\n",
        "        next_char = indices_char[next_index]\n",
        "        text = text[1:] + next_char\n",
        "        completion += next_char\n",
        "        \n",
        "        if len(original_text + completion) + 2 > len(original_text) and next_char == ' ':\n",
        "            return completion"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ASz0_IZIF7SY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def predict_completions(text, n=3):\n",
        "    x = prepare_input(text)\n",
        "    preds = model.predict(x, verbose=0)[0]\n",
        "    next_indices = sample(preds, n)\n",
        "    return [indices_char[idx] + predict_completion(text[1:] + indices_char[idx]) for idx in next_indices]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UPbVFbQGGCZa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "quotes = [\n",
        "    \"afg\",\n",
        "    \"ira\"\n",
        "]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UvtvtVMUGOoc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "5970ac75-6bff-4761-9525-1f3b33a1b499"
      },
      "source": [
        "for q in quotes:\n",
        "    seq = q[:SEQUENCE_LENGTH].lower()\n",
        "    print(seq)\n",
        "    print(predict_completions(seq, 2))\n",
        "    print()"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "afg\n",
            "['hania ', 'inmaria ']\n",
            "\n",
            "ira\n",
            "['t ', 'n ']\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g5Cuyf_2GWlk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}