OffensiveTweetDetection_with_BERT.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SXV357/Inspirit-AI-Deep-Dive-Designing-DL-Systems-Notebooks/blob/main/OffensiveTweetDetection_with_BERT.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ieqzRuk4S6iT",
        "outputId": "009dc259-08e1-4a61-fe8f-852daa67158f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q colorama\n",
        "!pip install -q sentencepiece\n",
        "!wget --quiet \"https://storage.googleapis.com/inspirit-ai-data-bucket-1/Data/Deep%20Dives/Advanced%20Topics%20in%20AI/Sessions%201%20-%2010%20(Main%20Curriculum)/Session%204_%20State%20of%20the%20Art%20NLP%20-%20Transformers/tokenization.py\"\n",
        "\n",
        "\"\"\"\n",
        "Import libraries\n",
        "\"\"\"\n",
        "\n",
        "import os\n",
        "import gc\n",
        "import io\n",
        "import zipfile\n",
        "import requests\n",
        "import warnings as wrn\n",
        "wrn.filterwarnings('ignore')\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tokenization\n",
        "import seaborn as sns\n",
        "import tensorflow as tf\n",
        "import tensorflow_hub as hub\n",
        "from tqdm.notebook import tqdm\n",
        "import matplotlib.pyplot as plt\n",
        "from colorama import Fore, Back, Style\n",
        "\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.layers import Dense, Input\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
        "\n",
        "\"\"\"\n",
        "Define data-loading function\n",
        "\"\"\"\n",
        "\n",
        "data_link = 'https://storage.googleapis.com/inspirit-ai-data-bucket-1/Data/Deep%20Dives/Advanced%20Topics%20in%20AI/Sessions%201%20-%2010%20(Main%20Curriculum)/Session%204_%20State%20of%20the%20Art%20NLP%20-%20Transformers/labeled_data.csv'\n",
        "\n",
        "def get_dataset():\n",
        "    return pd.read_csv(io.StringIO(requests.get(data_link).text))\n",
        "\n",
        "\"\"\"\n",
        "Load the dataset and create a 'label' column\n",
        "\"\"\"\n",
        "\n",
        "train_df = get_dataset()\n",
        "del train_df['Unnamed: 0']\n",
        "train_df['label'] = np.int32(train_df['class'] != 2)\n",
        "\n",
        "def display_training_curves(training, validation, title, subplot):\n",
        "    if subplot%10==1:\n",
        "        plt.subplots(figsize=(30, 15), facecolor='#e6e6e6')\n",
        "        plt.tight_layout()\n",
        "    ax = plt.subplot(subplot)\n",
        "    ax.set_facecolor('#f8f8f8')\n",
        "    ax.plot(training)\n",
        "    ax.plot(validation)\n",
        "    ax.set_title('Model '+ title)\n",
        "    ax.set_ylabel(title)\n",
        "    ax.set_xlabel('Epoch')\n",
        "    ax.legend(['Training', 'Validation'])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "nFHahirzTMdV",
        "outputId": "6a71a330-bee2-4fbc-c133-fe25aa34e328"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       count  hate_speech  offensive_language  neither  class  \\\n",
              "0          3            0                   0        3      2   \n",
              "1          3            0                   3        0      1   \n",
              "2          3            0                   3        0      1   \n",
              "3          3            0                   2        1      1   \n",
              "4          6            0                   6        0      1   \n",
              "...      ...          ...                 ...      ...    ...   \n",
              "24778      3            0                   2        1      1   \n",
              "24779      3            0                   1        2      2   \n",
              "24780      3            0                   3        0      1   \n",
              "24781      6            0                   6        0      1   \n",
              "24782      3            0                   0        3      2   \n",
              "\n",
              "                                                   tweet  label  \n",
              "0      !!! RT @mayasolovely: As a woman you shouldn't...      0  \n",
              "1      !!!!! RT @mleew17: boy dats cold...tyga dwn ba...      1  \n",
              "2      !!!!!!! RT @UrKindOfBrand Dawg!!!! RT @80sbaby...      1  \n",
              "3      !!!!!!!!! RT @C_G_Anderson: @viva_based she lo...      1  \n",
              "4      !!!!!!!!!!!!! RT @ShenikaRoberts: The shit you...      1  \n",
              "...                                                  ...    ...  \n",
              "24778  you's a muthaf***in lie &#8220;@LifeAsKing: @2...      1  \n",
              "24779  you've gone and broke the wrong heart baby, an...      0  \n",
              "24780  young buck wanna eat!!.. dat nigguh like I ain...      1  \n",
              "24781              youu got wild bitches tellin you lies      1  \n",
              "24782  ~~Ruffled | Ntac Eileen Dahlia - Beautiful col...      0  \n",
              "\n",
              "[24783 rows x 7 columns]"
            ],
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              "\n",
              "  <div id=\"df-e3820ca5-86f2-45a4-9aed-cec61aa5618c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>hate_speech</th>\n",
              "      <th>offensive_language</th>\n",
              "      <th>neither</th>\n",
              "      <th>class</th>\n",
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              "      <th>0</th>\n",
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              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>!!! RT @mayasolovely: As a woman you shouldn't...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>!!!!! RT @mleew17: boy dats cold...tyga dwn ba...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>!!!!!!! RT @UrKindOfBrand Dawg!!!! RT @80sbaby...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>!!!!!!!!! RT @C_G_Anderson: @viva_based she lo...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>!!!!!!!!!!!!! RT @ShenikaRoberts: The shit you...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24778</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>you's a muthaf***in lie &amp;#8220;@LifeAsKing: @2...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24779</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>you've gone and broke the wrong heart baby, an...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24780</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>young buck wanna eat!!.. dat nigguh like I ain...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24781</th>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>youu got wild bitches tellin you lies</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24782</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>~~Ruffled | Ntac Eileen Dahlia - Beautiful col...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>24783 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e3820ca5-86f2-45a4-9aed-cec61aa5618c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e3820ca5-86f2-45a4-9aed-cec61aa5618c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e3820ca5-86f2-45a4-9aed-cec61aa5618c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "Visualize the distribution of the number of characters per tweet\n",
        "\"\"\"\n",
        "\n",
        "sns.set(font_scale=1.5)\n",
        "plt.figure(figsize=(30, 7.5))\n",
        "fig = sns.distplot(sorted(train_df.tweet.apply(len))[:21000], color='b')\n",
        "fig.set(title='Characters per tweet', xlabel='Characters', ylabel='Probability')\n",
        "plt.show('--------------------------------------------------------------------')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "WcNBtyP-TOG6",
        "outputId": "7e9e97fc-cbe8-455e-e1b7-3626f02c9f48"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2160x540 with 1 Axes>"
            ],
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "Visualize the distribution of the number of words per tweet\n",
        "\"\"\"\n",
        "\n",
        "sns.set(font_scale=1.5)\n",
        "plt.figure(figsize=(30, 7.5))\n",
        "words = lambda x: len(x.split())\n",
        "fig = sns.distplot(sorted(train_df.tweet.apply(words)), color='r')\n",
        "fig.set(title='Total words per tweet', xlabel='Words', ylabel='Probability')\n",
        "plt.show('----------------------------------------------------------------')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "C90Hh4lSTTGM",
        "outputId": "c9947c8d-6b98-488f-83aa-e0ce53cd566a"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2160x540 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "Visualize the distribution of the number of characters per word\n",
        "\"\"\"\n",
        "\n",
        "sns.set(font_scale=1.5)\n",
        "plt.figure(figsize=(30, 7.5))\n",
        "ratio = lambda x: len(x)/len(x.split())\n",
        "fig = sns.distplot(sorted(train_df.tweet.apply(ratio))[:21000], color='seagreen')\n",
        "fig.set(title='Average characters per word', xlabel='Ratio', ylabel='Probability')\n",
        "plt.show('----------------------------------------------------------------------')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "AjE6ZQtSTWSb",
        "outputId": "b4650f11-f601-4223-a826-21af601100a5"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2160x540 with 1 Axes>"
            ],
            "image/png": 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zKgAAAAA4jZQz+7Xi0EY19g/Rb9oMlJebp9mRAKfj6eahwdE9NMzeR3lFBfpk59fadmqPDMMwOxoAAIDTcTVr4oyMDIWGhlYYDw4OlqSr3pH26quvKjIyUnfccUeV5AkK8q2S4zir4GA/syMAMAHXPuCcuPaBuqcoq0B+vh7akrZH3x5Nli04Qr+J6SdXl//3ldHP16NK5nJzc6myYznjXHXxnJjrytr5tpC9YbiW7F6vdce36nRuhm5v00ve7hUL7pt5Xj8/rre3u4Lr8fsAUNfxez/gnLj2KzKtSCsoKJCbm1uFcQ+PH345KywsvOK+qampWrx4sWbPni2LxVIleTIzc1Rayv/ZdSOCg/2UkXHZ7BgAqhnXPuCcuPaBuimv2KHNR/do5aFNigxorFua91B+fomkH57N5Ofrocs5V/6Odj2Kikqq7FjOOFddPCfmuhqLhkT1VEOfYG04vk3vJC7UkOieauwfchPmqqiy6z8vz6EMB78PAHUZv/cDzslZr32r1fKLN1uZtrSjp6enioqKKoz/WKD9WKj9nGEYev755zVo0CB17NjxpmYEAAAAAGeQeCJFqw4lqol/mG6z9ZaL1cXsSAB+wmKxqH3DVhrddrBcLC6av3ulkk/uZKlHAACAamBakRYcHFzp8o0ZGRmSpJCQkAqvSdKqVauUmpqqcePGKT09veyPJOXk5Cg9PV0FBQU3LzgAAAAA1CFJ6al6bcMchfk10LCWfeRKiQbUWKG+Qfpt7K2KDmqqTSd2aNHeNcp15JsdCwAAoE4zrUhr2bKljh49qtzc3HLjKSkpZa9X5tSpUyotLdU999yj/v37l/2RpIULF6p///5KTk6+ueEBAAAAoA7Yfnqv/vfb/6ppQCMNb9lP7i4Vl98HULN4uLprSHQPDWieoJPZ5zQ39UulZZ0xOxYAAECdZdoz0gYPHqyZM2fq888/17333itJcjgcWrhwoTp06KDQ0FBJPxRn+fn5ioqKkiT169dP4eHhFY738MMPq2/fvrrzzjvVpk2bajsPAAAAAKiN9pw7rGe++Y8a+4fomb6TlHLqkNmRAFwji8WitqHRCvNtoGUH12nhntXy9nRXoHtAlT1LHgAAAD8wrUiLi4vT4MGDNXXqVGVkZCgiIkKLFi3SqVOn9MILL5Rt99RTTyk5OVn79++XJEVERCgiIqLSYzZp0kQDBgyolvwAAAAAUFsdzDyhKatfV5BXPb008HG5upu2WAmAX6GBT4DGth2iFYc2ava2ZWoV3Fz9myewRCsAAEAVMq1Ik6SXX35Z06ZN0xdffKGsrCzZ7XbNmDFD8fHxZsYCAAAAgDrr+KXTmrzqNfm4e+nlQU8oyLu+soqzzY4F4AZ5uLprmL2P0nJOaeGub3QhP0vDbH3k6+FtdjQAAIA6wdQizcPDQ0899ZSeeuqpK24ze/bsazrWj3esAQAAAAAqdyE/S1NWT5OL1UUvD3pCob5BZkcCUAUsFovujBkgR1GJVhzcqHk7l2movbca+YWYHQ0AAKDWY/0OAAAAAHACBcWF+vua6couzNHz/f+kcP9QsyMBqGItAiM0NmaI3F3cNH/3Ku08e9DsSAAAALUeRRoAAAAA1HGlRqleXP++Dmae0N96TlJ0UFOzIwG4SYK862tszBA18Q/TmiNJ+ubIZpWUlpodCwAAoNaiSAMAAACAOu7drQu04cR2PdRptLpFtDM7DoCbzNPVQ3e06qv4Rm2UevaAvtj3jQqLHWbHAgAAqJUo0gAAAACgDlu6f60+371Sd9j7akSr/mbHAVBNrBarejbtoIFRXZWefUaf716py4W5ZscCAACodSjSAAAAAKCO+v7kLk3f/LE6N47RHzqPkcViMTsSgGrWJqSF7mjZT9mFOfp013Jl5F4wOxIAAECtQpEGAAAAAHXQkYvp+ud37yiyfiP9vfcDcrG6mB0JgEma1m+kUW1ukSR9vnuljl86ZXIiAACA2oMiDQAAAADqmMy8S3p6zXR5uXrquf5/krebp9mRAJgs2CdAY9oOlr+Hrxbv/Ua7zx0yOxIAAECtQJEGAAAAAHVIQXGhnvnmTV0uzNVz/f+oYJ9AsyMBqCH8PHw0qs0gNakXplWHE7XpxA4ZhmF2LAAAgBqNIg0AAAAA6gjDMPTqpo90MPOEnu41SdFBTc2OBKCG8XB11x0t+6lNSAsln9yplYc2qaS01OxYAAAANZar2QEAAAAAAFVj0d41+uZosn7Xfri6NokzOw6AGsrFatWA5gny9/BRYlqKCkscutXWS648SxEAAKAC7kgDAAAAgDog5cx+vb3lc3Vv0k7jYoaYHQdADWexWNQlPFZ9IzvryMV0fbHvGzlKisyOBQAAUONQpAEAAABALZeRe0H//O4dNfIL1l97TJTVwlc9ANcmLsyuQS26KT3rrBbtWaOCYofZkQAAAGoUvl0BAAAAQC3mKCnS/659W4XFDv1v34fl4+5ldiQAtUzr4CjdZuuls7mZmr97pfKK8s2OBAAAUGNQpAEAAABALfaf5E+07/xR/bXHRDWt39DsOABqqRZBEbq9ZV9dKsjW57tW6nJhrtmRAAAAagSKNAAAAACopb46sF7LDqzT2LZD1LNpB7PjAKjlmtVvpBGtBii3KF+f7VqhS/nZZkcCAAAwHUUaAAAAANRC+zKOavrmeYpv2Fq/az/c7DgA6ojG/iG6s/VAFZcW67PdK3Qu54LZkQAAAExFkQYAAAAAtczF/Gz979r/Ksi7nv7Wa5JcrHy1A1B1QnyDNKrNLbLIotlbvlJm3iWzIwEAAJiGb1sAAAAAUIuUlJbo+XUzlFWYo/+vzx9Uz9PX7EgA6qBA73q6s80gWS1WLdizShfys8yOBAAAYAqKNAAAAACoRWanfKkdZ/brsYS7FR0UYXYcAHVYgJe/xne8VZK0YPcqXeSZaQAAwAlRpAEAAABALbH11B7NTV2mW1p016AW3cyOA8AJNPCpr9+0HqhSo1QL9qzSpYLLZkcCAACoVhRpAAAAAFALnM+7pBfWv6em9RvqT13GmR0HgBMJ8q6vka0Hqri0RAt2r9TZnEyzIwEAAFQbijQAAAAAqOFKSkv0r3XvqqC4UM/0/r08XT3MjgTAyQT7BGhk6wFylBTrf1a/TZkGAACcBkUaAAAAANRws1O+VOrZA3ok4S41rd/I7DgAnFSIT6BGth6gHEeenlzxis7lXjA7EgAAwE1HkQYAAAAANVi556JF8Vw0AOYK9Q3SP/r9XlmFOfrLileUmXfJ7EgAAAA3FUUaAAAAANRQPBcNQE0U3SBCLw54VJn5WZq8epouF+aaHQkAAOCmoUgDAAAAgBqI56IBqMlah0Tpf/v+QelZZ/X3NdOVX1RodiQAAICbgiINAAAAAGognosGoKaLb9RaU3rdr73nj+jZ795WUUmx2ZEAAACqHEUaAAAAANQwPBcNQG3Rq2m8Hk24W9+f3KWXN8xUqVFqdiQAAIAq5Wp2AAAAAADA/3MxP1svrn9fEfXCeC4agFrhNlsvXS7M1XvbFsrPw0d/6vJbWSwWs2MBAABUCYo0AAAAAKghSo1SvbjhfeUW5evlQU/wXDQAtcbYmCHKLszVZ7tXyN/DV/e2v8PsSAAAAFWCIg0AAAAAaojPd6/S1lN79FjC3YoMaGx2HAC4LpPif6PLjlzNSf1S/h4+Gtl6gNmRAAAAfjWKNAAAAACoAfZlHNXMbYvUs2kH3WbrZXYcALhuFotFjyXcrcuFuXrr+0/l5+GjgVFdzY4FAADwq1jNDgAAAAAAzi7Xka/n172rIO96eqLrBJ4tBKDWcrG66G+9Jql9WEv9e+OHSk7faXYkAACAX4UiDQAAAABMZBiGpiXN0dncTD3da5L8PHzMjgQAv4q7i5v+p+8f1DwgXM9+97b2ZRw1OxIAAMANo0gDAAAAABOtOLRJ3x5N1j3tblebkBZmxwGAKuHj7qV/DXhE9T399fSaN5SedcbsSAAAADeEIg0AAAAATHIi67TeTJ6ndmF2jW07xOw4AFClAr3q6cWBj0mSJq+epgv5WSYnAgAAuH4UaQAAAABgAkdJkZ7/boY8XNw1uef9crHy9QxA3RPuH6rnBzyiSwWXNWX168p15JsdCQAA4Lq4mh0AAAAAAJzRjC3zdfhiup7r/yc18K5f4XWHHMovLqiWLCUqqZZ5ADinlg0i9Y8+D+mZNW/qf9b+V8/3/5PcXdzMjgUAAHBNKNIAAAAAoJptOrFDi/d9o5GtBighPLbSbfKLC7T+yI5qydO5WatqmQeA8+rcuK3+3H2CXt7wgf694QNN6XW/rBbuxAUAADUfRRoAAAAAVKOM3Av696YPFR0YofvjR5odBwCqzaCobrqQl6X3ti1UgFc9PdRptCwWi9mxAAAAfhFFGgAAAABUk5LSEv1r3XsqLinW070fYGkzAE5nTNvByszP0sK9qxXkXU9j2g42OxIAAMAvokgDAAAAgGoyJ3WZdp47qMk97lO4f6jZcQCg2lksFj3UabQu5mfr3a0L5O/hqyHRPcyOBQAAcEUUaQAAAABQDVLO7Nfc1C81MKqrBkQlmB0HAExjtVj1VI+JuuzI1WuJH8nfw0fdI9qbHQsAAKBSPNUVAAAAAG6yrIIcvbD+PTX0C9YjXX5rdhwAMJ2bi6v+p89DsgU103PfzVDKmf1mRwIAAKgURRoAAAAA3ESGYWjqxg+VVZCjv/f6vbzcPM2OBAA1gpebp57v/4ga+gXrmW/+o4OZJ8yOBAAAUAFLOwIAAADANXLIofziguva56v965WYnqLfxd+hkHr1lVWcfU37lajkRiICQK1Sz9NXLw58TI9+/ZKmrJ6m14dMVmP/ELNjAQAAlKFIAwAAAIBrlF9coPVHdlzz9udyL+jTnV8rMqCx/N38rmvfzs1a3UhEAKh1QnwC9dLAx/TY1y/rqVWv6fUhTynIu77ZsQAAwP/P3n1HR1UnbBx/ZtIr6ZX0DgkJLfSaUAUFEduurrviqqivq+uKrv3VV1bFXV3R11WxoSiKVEEEpPeaBCShhACBAAkBEkghbd4/fJezSEkCSW7K93OO5zh37r2/5x7PmJl55ve7kMTSjgAAAADQKCqqK/XD3jWyt7bT0IjeMplMRkcCgGYruJ2/Xk39L50pP6unlr2ls+dLjI4EAAAgiSINAAAAABrFipzNOlN+ViOi+nFfNACog1ivML00aKJyi47r2eVTVVZ53uhIAAAAFGkAAAAA0NAyCw4os+CAktsnqH07X6PjAECL0TWgg57uN0GZBdl6dvk7Kq+iTAMAAMaiSAMAAACABnS6rFjLD2xSoIuPerRPMDoOALQ4A0K7aVLfe7XzxF4999NUyjQAAGAoijQAAAAAaCBVNdX6Yd8amU1mDY/qK7OJj1wAcC1SwnvoyT5/UNrxPXpu+bs6X1VhdCQAANBG8akOAAAAABrIusM7lF9ySkMje8vFzsnoOADQoqVG9NRf+t6jtGNZep4yDQAAGIQiDQAAAAAaQM7pI9pxLFOJfjGK8AgyOg4AtApDI3rrL33u0fZjmXp+xbuqqK40OhIAAGhjKNIAAAAA4DqdqyjVkv3r5eXorn4hXY2OAwCtytDI3nqiz++0PS9TL6x4jzINAAA0KYo0AAAAALgONZYaLd63VpU1VRoZ3U/WZiujIwFAqzMsso8e7323thzdpRcp0wAAQBOiSAMAAACA67Dl6C4dKT6hQWHJ8nBoZ3QcAGi1RkT11eO97tbmo7v0/PJ3VVpZbnQkAADQBlCkAQAAAMA1Olqcr425GYrxClUH7wij4wBAqzcyup+e6P3LPdMeW/y6TpaeMToSAABo5SjSAAAAAOAalFee1+J9a+Rq56zBYT1kMpmMjgQAbcLwqD56JeUR5RXn65FFk5Vz+qjRkQAAQCtW7yKtvJxp8wAAAADaNovFoiXZ61VSWa4R0X1lZ21rdCQAaFOSA+P1jxFPqqamWo/+8Jq2H8s0OhIAAGil6l2k9e3bVy+88IJ27drVGHkAAAAAoNnbcSxTB04fUd+QLvJz9jI6DgC0SZEewfrnyKfl4+Sup5e+rSXZ642OBAAAWqF6F2ldunTRrFmzNH78eN1000364osvVFxc3BjZAAAAAKDZyTtboLWHtyvCI0id/WKNjgMAbZqvs6feGjFJnXyj9PraTzQ9fYEsFovRsQAAQCtiXd8DPvjgA504cUKzZ8/W7Nmz9corr+iNN95Qamqqxo8fr549ezZGTgAAAAAwXHH5OS3au1rOtk4aEtGb+6IBQDPgbOuoV1Mf1d83fK7P0ubr+NmT+lOvu2RjVfevvSpUobKqprmdiYO1vWzFksAAALQU9S7SJMnX11cPPvigHnzwQW3cuFGzZs3S0qVLtWjRIrVv317jxo3T2LFj5evr29B5AQAAAMAQNZYa/XPDVyqrLNet8cNlz33RAKDZsLGy1pN9fi8/Z09NT/9eB8/k6en+E9TetW7fTZVVlWvNgbRGTvmLfuFJsuVvCAAALUa9l3b8tZ49e2rKlClau3atRo8erdzcXL399ttKSUnRxIkTlZGR0RA5AQAAAMBQM3f9qB15Weof2k2+zp5GxwEA/IrJZNLvkm7S8wMe0NGz+Xpgwcv6Yd9alnoEAADX5ZpmpP2n06dPa968eZo1a5b2798vBwcHjRw5Ura2tpo/f75WrlypF198Ubfeeuslx1ZUVOjtt9/WvHnzVFxcrNjYWD322GPq1avXVcecP3++Zs2apezsbBUVFcnHx0c9evTQww8/rMDAwOu9JAAAAAC4SMbxvfpkxxz1CUlSJ99oo+MAAK6if2hXxXqH6bW1H+vN9Z9py9Fd+lOvu+Rq52R0NAAA0AJdU5FmsVi0Zs0azZo1SytWrFBlZaU6dOigF154QaNHj5azs7Mk6fHHH9cjjzyi995777JF2lNPPaUlS5bo7rvvVkhIiObMmaP77rtP06dPV+fOna84flZWlnx9fTVgwAC1a9dOeXl5+uabb7Ry5UrNnz9f3t7e13JZAAAAAHCJ02XF+p/VH8jfxVsP9BivbblZRkcCmuyxbQAAIABJREFUANTCx8lDrw95XLN2L9HH2+dqd0G2nup7r5L8Y42OBgAAWph6F2lvvfWW5s6dqxMnTsjBwUFjx47Vrbfeqvj4+Ev2dXFx0ZgxY/T0009f8lxGRoYWLlyop59+Wvfcc48kacyYMRo1apSmTJmiL7/88ooZnnzyyUu2paSk6Oabb9b8+fN177331veyAAAAAOAS1TU1mrzmI52tKNWrqY/K0cbe6EgAgDqyMpt1W/xwdfaP06urP9Rflvxdt8YP1T1JY2Rjdd2LNAEAgDai3vdIe//99+Xp6amXXnpJa9eu1X//939ftkT7t44dO+qhhx66ZPvixYtlY2Oj8ePHX9hmZ2enW265Rdu2bVN+fn69cgUEBEiSiouL63UcAAAAAFzJjJ0Ltf1Yph5OvkMRHkFGxwEAXINozxD976jnNDK6n2bu+lEPL3pVO0/sMzoWAABoIer985s5c+YoLi6uzvtHRUUpKirqku2ZmZkKCwuTk9PF61N36tRJFotFmZmZ8vHxueq5z5w5o+rqauXl5endd9+VpFrvrwYAAAAAdbEtb7c+T1ug1PCeGhHV1+g4ANBsmMwWFVU1zQ+ZHaztZSvb6z+PjZ0e63WXkgPj9c+NM/TY4tfVN7iL7us6ToGuV//+CQAAtG31LtImT56sBx988IqF1caNG/Xee+/p888/v+p5CgoK5Ovre8n2f9/frC4z0oYNG6YzZ85Iktzc3PT888+rZ8+etR53OZ6eztd0HH7h7e1idAQABuC1D7RNvPbRFhwrPqnJaz9SuGegXh71gBxtf1nSsbKoXC7Odk2SwcbGqlmN1VBZmtt1tbSxWuM1MVbzH+vX5602VSsjv2lmdPUOTZR3u4Z77zHGu7+GJiRr+raF+mTzfG2cl67bkobqxo4Dmuy/laOjbYNeE9BYeN8PtE289i9V7yJt8+bNFy3H+GunTp3Sli1baj1PeXm5bGxsLtluZ/fLm5bz58/Xeo6pU6eqtLRUOTk5mj9/vkpKSmo95koKC8+ppsZyzce3Zd7eLiooOGt0DABNjNc+0Dbx2kdbUFFdqcd+mKKKqko91+9+lRRVqkSVkqTSqgqdPVf7Z5WGUFlZ3WzGcnG2a7Aszem6WuJYrfGaGKt5j3W5139TXldpaYUKKhr+vcfNkUM1MLCHPt0xT1/tWKy5u1aqi38HJfrFyNps1eDj/afGuiagIfG+H2ib2upr32w2XXWyVYPfWbW4uFi2trVPube3t1dlZeUl2/9doP27ULua7t27S5IGDBiglJQUjR49Wo6Ojvrtb39bz9QAAAAA8Iv3Ns/UnsKDenHgg2rfzs/oOACARuLh0E6P975bY+NS9N6Wr7Xm0DalH9+jXkGJivYMkVUjF2oAAKBlqFORlpWVpaysrAuPt27dqurq6kv2O3PmjL766itFRETUek5vb+/LLt9YUFAgSbXeH+3XgoKC1LFjRy1YsIAiDQAAAMA1WbJ/vb7fu0q3xQ9T35AuRscBADSBMPdAPTv4Pn2xfZHWHNquH/ev09pD25XoF6ME3yg52NgbHREAABioTkXasmXLNHXqVEmSyWTSzJkzNXPmzMvu6+TkpGeeeabWc8bGxmr69OkqKSmRk5PThe3p6ekXnq+v8vJylZWV1fs4AAAAANh/6rDe2viFkvxi9IfOY42OAwBoYiFuAQpu569DZ/K0/Vim1uemadORnYrzDlNn/zh5OroZHREAABigTkXa2LFjlZycLIvFot/97ne6//771adPn4v2MZlMcnR0VGRkZJ2WZRw+fLg+/vhjffvtt7rnnnskSRUVFZo9e7a6dOkiX19fSVJeXp7KysoumuV26tQpeXh4XHS+Xbt2KSsrSyNHjqzLJQEAAADABWfPl+ilFe/L1c5Jz/T/I8t5AUAbZTKZFOoeqFD3QBWWntGOY5nKLMjRrvz9Cm7nry7+cQpxC5DJZDI6KgAAaCJ1KtICAwMVGBgoSZo8ebK6deumoKCg6xo4MTFRw4cP15QpU1RQUKDg4GDNmTNHeXl5mjx58oX9Jk2apM2bN2vPnj0Xtg0aNEgjRoxQdHS0HB0dtX//fn333XdycnLSxIkTrysXAAAAgLalxlKjv62dpoLSU/r7sL/I3cHV6EgAgGbA09FNqRG91Ce4s3ae2Kf043s0N2u5XGyd1ME7XHE+EXKzdzE6JgAAaGR1KtL+09ixDbfEyeuvv6633npL8+bNU1FRkWJiYvTBBx+oa9euVz3uzjvv1IYNG7Rs2TKVl5fL29tbw4cP18SJE6+74AMAAADQslSoQmVV5dd8/KydS7XpyE5N6DZWgR7eKqoqvuK+1br0XtEAgNbNwcZeye0T1DWgg/afOqyf87O16ehObTq6U4EuPurgE6EozxDZWtkYHRUAADSCWou0uXPnSpJuuukmmUymC49rM2bMmFr3sbOz06RJkzRp0qQr7jN9+vRLtl1tfwAAAABtS1lVudYcSLumYw+dydOczJ8U6xUmJ2unWs+THBp3TeMAAFo+K7OVYrzCFOMVprPnS5R58oAy8w9oafYGrczZokjPYHXwjlB7V1+WfgQAoBWptUh76qmnZDKZNHLkSNna2l54bLFYrniMyWSqU5EGAAAAAEYpKj+rH/atkZejm1LCe/ClJwCgzlzsnJQcmKDuAfE6dq5Au/OztbfwkDILDsjVzklx3hHq4B2udiz9CABAi1drkfb5559LkmxtbS96DAAAAAAtVWV1pRbsWSWLRRoVM1A2LMcFALgGJpNJAS4+CnDx0YDQ7so+lavdBdnadCRDm45kKNDVVx29IxTpGczSjwAAtFC1FmnJyclXfQwAAAAALYnFYtHS7A0qLD2jm+IGy43ZAgCABmBjZa1Y7zDFeoep+HyJsgoOaHdBtpZkr9eKnM2K8gxRvE/kVVd5AgAAzU+tRRoAAAAAtCbb8nZrb+Eh9QnurFC3AKPjAABaIVc7JyW3T1D3wHgdO1ug3QW/LP24uyBbm45maGxcqlLCkuVgY290VAAAUItai7QtW7Zc04m7d+9+TccBAAAAQGM5dCZP6w7vUJRniLoFdDQ6DgCglTOZTApw9VGAq48GhHZT1smDyj59WG9tmK4Pts7SsMjeGh0zQMHt/I2OCgAArqDWIu2uu+6q1023LRaLTCaTMjMzrysYAAAAADSkM+VntWjvGnk6ttPQiF71+pwDAMD1srGyUYJvlB7seYuOni7QvKwVWrBnpeZk/qTOfrG6MXaQegclycpsNjoqAAD4D7UWaZMnT26KHAAAAADQaCqrK/X9npWSpNExA2VjZWNsIABAm2UymdTRJ1IdfSL1YNlt+mHfGi3Yu1ovrfxfBbr66Pb4EUoN7ykbK+7IAgBAc1DrX+SxY8c2RQ4AAAAAaBQWi0VLsjeosLRIY+IGq529i9GRAACQJLk7uOrOTjfotvjhWnt4h2bsXKQ313+m6ekLdFv8cA2P7CM7a1ujYwIA0Kbx0xYAAAAArdrWvJ+1r/CQ+gZ3UYhbgNFxAAC4hJXZSgNCu6l/SFdtPrpLMzIW6p1NM/RF+ve6peNQjY4ZIEcbe6NjAgDQJtVapOXl5UmSAgICLnpcm3/vDwAAAABGOXj6qNYd3qFozxB1DehgdBwAAK7KZDKpR/sEJQfGK/3EXs3IWKgPt83S1zsXaVyHIRrXIVUOFGoAADSpWou0wYMHy2w2Ky0tTba2tho8eHCdbsqdmZnZIAEBAAAA4FqcKivSon1r5OXoriERver0OQYAgObAZDIpyS9GSX4xyiw4oBkZi/Rp2jzNy1qhu5NGa0RUX1mbWWgKAICmUOtf3Iceekgmk0nW1tYXPQYAAACA5qq8qkILslbKymTWjbEDZWNlY3QkAACuSZx3uF5OeVi7C7L1wdZZenvjl5q9+ydN6Hqzegcl8T0dAACNrNYi7ZFHHrnqYwAAAABoTmosNfph7xoVnT+rcR2GyNXO2ehIAABctw7eEfrH8Ce1ITddH23/Ti+seE8dvSP0x263qKNPpNHxAABotZgDDgAAAKBVWXtohw4V5SklvKcCXX2NjgMAQIMxmUzqHZykHu0TtHj/On2WNl+P/vCa+gZ30YSuN6s9f/cAAGhw11ykZWRkaOnSpcrNzZUkBQUFKTU1VYmJiQ0WDgAAAADqY3d+trYf261Evxgl+EYZHQcAgEZhZbbSDdH9NTish77bvVQzdy3WpiMZuqXjEN2ZcIMcbOyMjggAQKtR7yKturpazz33nObMmSOLxXLRcx999JHGjBmjV155RVZWVg0WEgAAAABqc+xsgX46sFFBrn7qH9LN6DgAAFyWyWxRUVVxA51MGt2xv/qGJ+mLtIX6aucPWpK9Xr/rPFq9Q5LkaOMgW9k2zFgAALRR9S7S/vd//1ezZ89WamqqJkyYoMjIX9Zg3rdvnz766CPNnTtXgYGBevjhhxs8LAAAAABcztnzJVqwZ5WcbR01Mrq/rMxmoyMBAHBZ5VUV2nwws8HPm+QbJx9HT63I2aK/r/tC3+xcqj/3u0sJXjENPhYAAG1JvYu07777Tn369NHUqVMv2t65c2e9++67+v3vf6/vvvuOIg0AAABAkzhfVakFe1aqsrpS4zqkspwVALQiDTp76yqqVd3oYzSFABcf3ZEwQrvy92v94R3686K/a0zsYN2dNFrOto5GxwMAoEWqd5FWWFioCRMmXPH51NRUvfbaa9cVCgAAAADqwmKx6L2NM5Vfcko3xgyUp6Ob0ZEAAA2osWZv/VpyaFyjj9FUzCazOvlGK8ojRDlncjUn8yctz9mkB7vfqsFhPWQymYyOCABAi1Lv9U5CQ0NVUFBwxefz8/MVGhp6PZkAAAAAoE6+3rVYaw/tUO/gJIV7BBkdBwCAZsPBxk7397hF7456Rn7OXpq8ZpqeWvqW8orzjY4GAECLUu8i7f7779eMGTOUlZV1yXO7d+/WV199pQceeKBBwgEAAADAlWzITdfH2+eob0hndQ+INzoOAADNUrRniN4e8ZQe6XGnMk8e0IT5L2pGxkJVVlcZHQ0AgBah1qUdf30vNElq3769xo0bpz59+ig8PFySlJ2drfXr1ysmJkY5OTkNnxQAAAAA/t/B00f16uoPFeUZrIk9b9Pmwz8bHQkAgGbLymzWTbGD1CcoSe9tmamPd8zVTwc26U+97lKCb5TR8QAAaNauqUj7t9WrV2v16tUXbdu9e7cyMzP10EMPXX86AAAAAPiVovJzem75u3KwsddLgybK1rret34GAKBN8nJy1/MDH9DG3HT9c9MMPbb4dd0Q3V8TutwsFzsno+MBANAs1fqJ86effmqKHAAAAABQq6qaKr2y6l86WXpabw5/Qt5OHiqqKjY6FgAALUrPoEQl+sXo87QF+i5zmdYd3qGJ3W/ToLBkmUwmo+MBANCs1FqkBQYGNkUOAAAAAKjV+1u+1Y7jWXqyz+/VwTvC6DgAALRYDjb2ur/7eA0O76G3NkzXq2s+0pLsDfqvnr9RgIu30fEAAGg2zEYHAAAAAIC6WLh3teZmLdf4DkM1NLK30XEAAGgVojyD9c+RT+uh5Nu1uyBbE+a9oK92/qCqmiqjowEA0Cxc080EqqqqtGzZMqWnp6u4uFg1NTUXPW8ymfTqq682SEAAAAAA2Hlir97ZNEPdA+M1oes4o+MAANAimMyWOi+BPDiquxIDozRt61xN2z5bS7PX6/4etyjWO6xOxztY28tWttcTFwCAZqneRdqZM2d09913a9++fbJYLDKZTLJYLJJ04d8p0gAAAAA0lBPnCvXSyvfl5+ylZ/rfJyszC2sAAFAX5VUV2nwws17H9GqfJB9HT63M2axnlkxVgm+0+gR3lr311UuyfuFJsq1lHwAAWqJ6fwJ96623dODAAb3yyitaunSpLBaLpk2bpkWLFumGG25QQkKCNm3a1BhZAQAAALQxZZXn9fzyqaqsrtLLgx+Ws62j0ZEAAGj1IjyCdFfSjersH6ddJ/bp87T52ld46MKP6QEAaEvqXaStWrVKY8aM0bhx4+Ts7PzLScxmhYeHa8qUKbK3t9ebb77Z4EEBAAAAtBwVqlBRVfF1/XO68oz+Z80HyjlzVI/1/a1cnRwvu1+1qo2+XAAAWh1bKxsNCO2m2xNGyMnWQQv3rtb3e1bp3PlSo6MBANCk6r20Y0FBgRISEn452PqXwysqKi48n5KSomnTpumll15qoIgAAAAAWpqyqnKtOZB2XefYmJuhjUcy1D+kq86VX/l8yaFx1zUOAAC4Ml9nT92RMELb8zK1ITddn6fPV9/gLkrwjZLJZDI6HgAAja7eM9Lc3NxUVlYmSXJycpK1tbWOHTt24XkbGxsVF9ftJqYAAAAAcDn7Cw9r45F0xXmHq7M/RRkAAEYym8zqFthRdyWNlq+zp5bnbNK3Py/RqdIio6MBANDo6l2khYaGav/+/b8cbDarQ4cOmjNnjioqKlRWVqa5c+cqKCiowYMCAAAAaBsKSk5p8f618nP2Ukp4T37tDgBAM+Fm76Kb41I1JKK3CkvP6MuM77UxN0PVNSyzDABovepdpPXp00c//vjjheUc77nnHqWnpys5OVm9e/fWrl279Lvf/a7BgwIAAABo/UoryzV/z0rZW9tpdMwAWZutjI4EAAD+g8lkUkefCN2ddKMiPIK18Ui6ZmQs1J6Cg0ZHAwCgUdT7HmkPPPCA7r33Xtna2kqSRo4cKWtra82fP19ms1nDhw/XyJEjGzwoAAAAgNatuqZaC/esUmlFuW6NHyYnW0ejIwEAgCtwsnXQyOh+ijsdpuUHNumZJVN1Y8xA/aHLWDnZOhgdDwCABlPvIs1kMl0o0f5t6NChGjp0aIOFAgAAANC2WCwWrcjZoqNn8zU8qq98nT2NjgQAAOogzL297kry1cGio5q/Z6XW56bpv3r+Rr2CEo2OBgBAg6j30o6/Vl5ervLy8obIAgAAAKCNyjixV7vy96l7YLxivcKMjgMAAOrB1spG93Ybo7dHTpKTraOeWz5VL6/6l06VFRkdDQCA61bvGWmSVFhYqHfeeUfLli1TYWGhJMnT01Opqal6+OGH5eXl1aAhAQAAALReuUXHtTJni8Ld26t3UJLRcQAAwDXq4B2h/x31rGbuWqwvMxZqW95u3d9tvIZH9pHJZDI6HgAA16TeM9Jyc3M1ZswYff3113JxcVFKSopSUlLk4uKir7/+WmPHjlVubm5jZAUAAADQyhSfP6dFe1fL3cFVw/iSDQCAFs/Gylq/TRylD258QeHu7fXm+s/0lyVv6kjxCaOjAQBwTeo9I+21117TmTNnNHXqVKWmpl703NKlS/X444/rtdde09SpUxssJAAAAIDWp7K6Sgv2rFK1pUajYwbKztq29oMAAECLENTOT1OG/VmL9q3Vh1tn6Y/zX9JdiaM1vuMQWZuvaZEsAAAMUe8ZaRs2bNBvfvObS0o0SRoyZIjuuOMObdiwoUHCAQAAAGidLBaLfjqwUQUlpzQiqp/cHVyNjgQAABqY2WTWqOj+mjbmv5UcmKBp22froe//R3tOHjQ6GgAAdVbvn3+YTCaFhIRc8fnQ0FCWYwEAAABwVTuOZSrrZI56ByUpzD3Q6DgAAOA6mcwWFVUVX/Y5G1uzHuv3G/XO7aQPt8zWI4te1Q0x/XV74jDZW9vVeywHa3vZipnsAICmUe8irXv37tq0aZPuuOOOyz6/efNmJScnX3cwAAAAAK3T4aJjWnNouyI9gtU9MN7oOAAAoAGUV1Vo88HMWve7PX6E1h7eoQVZq7QqZ6sGh/dUqFtAvcbqF54kW5aEBgA0kXov7fjXv/5V6enp+tvf/qbCwsIL2wsLCzV58mRlZGTor3/9a4OGBAAAANA6FJWf06K9a+Th4Kqhkb1ZzQIAgDbGztpWKeE9NL7jUFmZrTQ38yct3rdWpZXlRkcDAOCyap2RlpKScsm28vJyffbZZ/rss8/k6vrLvQyKi3+Zuu3m5qZ77rlHy5Yta+CoAAAAAFqyyuoqfb9npSwWi0bHDJStlY3RkQAAgEECXX31m06jtOXoLm05uksHz+RpQGg3xXqF8UMbAECzUmuRFhBQv6nVAAAAAPBrFotFy7I3qKD0tMbEDpabg6vRkQAAgMGszVbqFZSoaM8QLcveqB/3r1NmwQGlhPdQO3sXo+MBACCpDkXa9OnTmyIHAAAAgFZs+7Hd2lN4UH2COyvUPdDoOAAAoBnxdHTTrfHDlHFir9Ye2q7p6QvUKyhJnf1jZTbV+840AAA0qFqLNAAAAAC4HofO5GntoR2K8gxRt4CORscBAADNkMlkUqJfjMLd22tFzmatObRNe07mKDWil3ycPIyOBwBow665SDt8+LB++ukn5ebmSpKCgoKUkpKi4ODgBgsHAAAAoGUrKj+rH/atkadjOw2J6MU9TwAAwFW52DlpdMxA7Tt1WCtzNuurjEXqEtBBPdt3ko0VcwIAAE3vmv76vPXWW/rwww9VXV190fY33nhD999/vx599NEGCQcAAACg5aqsrtSCPStlsUijYgbK1srG6EgAAKAFMJlMivYMUXA7P605tF3b8n7W/sJDSgnvqWA3f6PjAQDamHoXabNmzdL777+vzp07a8KECYqKipIk7du3T9OmTdP777+voKAg3XzzzQ0eFgAAAEDLYLFYtCR7gwpLi3RT3GC52bsYHQkAALQw9tZ2GhLRS7FeYfrpwEbNzlymOO9wJQVGqZ21q9HxAABtRL3v1jljxgwlJiZq+vTpF5ZyDA4OVkpKij7//HN16tRJX3zxRWNkBQAAANBCzNu9QvsKD6lPcGeFugUYHQcAALRgQe389NtOo9Q9MF57Tubovxa8rp8ObJLFYjE6GgCgDah3kZadna2RI0fK2vrSyWzW1tYaOXKksrOzGyQcAAAAgJZny9Fd+jJ9kaI9Q9Q1oIPRcQAAQCtgbWWtPsGddUfCDfJxdtfkNR/p6WVv60jxCaOjAQBauXoXaTY2NiotLb3i8yUlJbKx4d4HAAAAQFuUV5yv/1n9oYLa+WlIRC+ZTCajIwEAgFbE28ldrw79L03sfpt2F2Trvnkv6tMd81Redd7oaACAVqreRVpCQoJmzpypkydPXvJcYWGhvvnmGyUmJjZIOAAAAAAtR1lluZ5f8Z5MJpOe7P972VjxAzsAANDwrMxm3dwhVZ+MeVn9Q7vqi4zvNWHeC1p/OI3lHgEADe7S9RlrMXHiRN1zzz0aOXKkxo0bp8jISEnS/v37NXv2bJWUlGjKlCkNHhQAAABA82WxWPTGuk91uChPk1P/JD8XT+0ryDU6FgAAaMU8Hd30dL8JGhnVT+9smqHnV7yrHu0T9FD32xXg6mN0PABAK1HvIq179+5655139PLLL+uTTz656LmAgAD97W9/U7du3RosIAAAAIDm76tdP2j1oW36Y9db1DWgg4qqio2OBAAA2ohEvxi9P/o5zclcrs/T5uveeS/o9oQRuj1+uOysbY2OBwBo4epdpEnS4MGDNXDgQO3atUtHjhyRJAUFBaljx44ym+u9WiQAAACAFmzzkZ36ZPtcDQpL1viOQ42OAwAA2iBrs7XGdxyqQaHd9a+t32p6+gIt3r9Ov+98k1LDe8ps4jtLAMC1qddfkJKSEqWmpurTTz+V2WxWp06dNHLkSI0cOVIJCQmUaAAAAEAbc7Q4X6+u+UjhHu315953y2QyGR0JAAC0YV5O7npmwB/15rAn5G7votfXfqIHv39F249lGh0NANBC1av5cnJy0pkzZ+Tk5NRYeQAAAAC0EKWV5Xphxbsym8x6adBE2VvbGR0JAABA0i/LPU694a/6a78JOne+VE8u+bv+uuxt5Zw+anQ0AEALU+8pZImJidq5c2djZAEAAADQQlgsFr2+9hMdLjqmZwf8UX7OXkZHAgAAuIjZZNbg8B76ZOzL+mPXW/RzfrbuX/CS3lz/uQpLzxgdDwDQQtS7SHviiSe0ePFifffdd7JYLI2RCQAAAEAzN2PnIq09vF1/7HqLuvjHGR0HAADgimytbHRr/DB9fvOrGhOboqXZ63X37Gf03uaZOlly2uh4AIBmzrq+B0yePFmurq569tln9cYbbyg4OFj29vYX7WMymfTZZ581WEgAAAAAzcfGIxn6dMc8pYT30LgOQ4yOAwAAUCft7J01Mfk2jYkdpC8yvtfcrOVasGelhkf11W3xw5hhDwC4rHoXaUeOHJEk+fv7S5JOnjzZsIkAAAAANFtHio5r8uqPFOERpMd63SWTyWR0JAAA0MaYzBYVVRVf8/FOjva6v+ctGhM/SHN/XqEf9q3Ror2rNSCsq8Z2TFGAq/eFfR2s7WUr24aIDQBooepVpJ06dUr/+Mc/5O7uruDg4MbKBAAAAKAZKqko0/Mr3pO12UovDZooe2s7oyMBAIA2qLyqQpsPZjbIuTp4RyrI1V/b8nZrVc52rTiwVdGeIeoa2FE+Th7qF54kW2uKNABoy+pUpNXU1OjFF1/UrFmzLtwXLSkpSe+++648PDyuefCKigq9/fbbmjdvnoqLixUbG6vHHntMvXr1uupxS5Ys0aJFi5SRkaHCwkL5+/tr0KBBmjhxolxcXK45DwAAAIDLq7HU6PW1H+tI8Qm9PuQx+Tp7Gh0JAACgQbjYOWlgWHd1D4zXjmOZSj++R3sKD8rf2VvVqtKIiH6ytbIxOiYAwCDmuuz0xRdf6JtvvpGXl5eGDBmi6Oho7dixQ88///x1Df7UU0/ps88+04033qhnnnlGZrNZ9913n3bs2HHV45577jllZ2frpptu0rPPPqu+fftq+vTpuuOOO3T+/PnrygQAAADgUjMyFmldbpoe6DZeSf6xRscBAABocE62Duob0kX3dr1Z/UO7qbzqvN7Z8JVu//ZJ/WvrtzpanG90RACAAeo0I23u3LmKiIjQzJkz5ezsLEmAZBo7AAAgAElEQVR69tlnNWfOHBUXF8vV1bXeA2dkZGjhwoV6+umndc8990iSxowZo1GjRmnKlCn68ssvr3jsP//5T/Xo0eOibfHx8Zo0aZIWLlyom2++ud55AAAAAFzehtx0fZo2T0MiemlsXIrRcQAAABqVvbWduvjHqbNfrNydnLV8/xbN3v2Tvv15iboGdNCNMQPVo32CrM31umsOAKCFqtOMtJycHI0dO/ZCiSZJv/3tb1VdXa2DBw9e08CLFy+WjY2Nxo8ff2GbnZ2dbrnlFm3btk35+Vf+hcevSzRJSk1NlSRlZ2dfUx4AAAAAl8otOq7Ja6Yp2jNEf+r5W5lMJqMjAQAANAmTyaQEvyg9P/ABzbjlb7on6SYdPnNML6x4T+O/eUJvrv9M2/J2q7qm2uioAIBGVKefTZSVlcnHx+eibf9+XFpaek0DZ2ZmKiwsTE5OThdt79SpkywWizIzMy8Z82pOnjwpSXJ3d7+mPAAAAAAuVlJRphdWvCtbK2u9MPBB2VnbGh0JAADAEJ6Obvpt4ijdkTBCW/J+1sqcLVp1cKt+2LdWbvYu6hvcRQPDuivBJ0pW5jrNXTBUhSpUVlV+xecri8pVWlVx3eM4WNvLVryHBNCy1Xn+8a9/efrvxxaL5ZoGLigokK+v7yXbvb29JemqM9Iu58MPP5SVlZWGDh16TXk8PZ1r3wlX5O3tYnQEAAbgtQ+0Tbz224YaS41emfu+jhbn64Nbn1V8UGi9jq8sKpeLs13jhPsVGxsrxmqCsRoqS3O7rpY2Vmu8JsZq/mP9+ryt5bqMGIexWt5YtvZWqjRfXDh1DY9W1/BoPVw1XltzM7X6wHYtPbBB3+9dJXcHV/UNS1T34I7q5B9Vrx8iOdk5qJ1903xHmVdUoLS8zEYfp3doorzb8fkBaEn4zH+pOhdpq1atujDrS/pllprJZNLixYuVlZV10b4mk+nCfc+upLy8XDY2Npdst7P75Y/g+fPn6xpNCxYs0KxZs3T//fcrODi4zsf9p8LCc6qpubZSsK3z9nZRQcFZo2MAaGK89oG2idd+2/FZ2nytzN6mh5JvV4h9UL3/u5dWVejsubq/p78elZXVjNXIY7k42zVYluZ0XS1xrNZ4TYzVvMe63Ou/NVyXUeMwVssb62xpqTYfvHrh1MW3oxK8opVz+qj2Fh7SD1nrtWD3GlmZrNS+na/C3AIV6hYgNwfXq56nX3iSKqyb5vvJ2t6rNdTf/tLSChVU8PkBaCna6md+s9l01clWdS7Svv/+e33//feXbJ85c+Yl2+pSpNnb26uysvKS7f8u0P5dqNVm69ateuaZZzRw4EA9+uijdToGAAAAwJWtO5ym6ekLNDSil8bEDjY6DgAAQLNnY2WjaK9QRXuFqqq6SkfO5uvg6aM6eOaoVh7cIklys3dRqFugQtwCFOjqI1urSycZAACanzoVaZ9//nmDD+zt7X3Z5RsLCgokqU73R8vKytKDDz6omJgY/eMf/5CVlVWD5wQAAADakkNnjum1tdMU4xmqP/W665Il3gEAAHB11lbWCnULUKhbgKTuOlN+9v9LtTztPLFPacezZDaZ5OfsraB2fgpu5y8/Zy+jYwMArqBORVpycnKDDxwbG6vp06erpKRETk5OF7anp6dfeP5qDh8+rAkTJsjDw0P/+te/5Ojo2OAZAQAAgLbkXEWpXljxrmytbPTioAf5lTQAAEADcLN3UZJ/rJL8Y1VVXaW8cwXKLTquw2eOafORndp0JEM2Zmuty92u5IB4dfaPU5h7oMwms9HRAQCqx9KODW348OH6+OOP9e23315YBrKiokKzZ89Wly5d5OvrK0nKy8tTWVmZIiIiLhxbUFCgP/zhDzKZTJo2bZo8PDyMuAQAAACg1aiuqdHf1kzTsbMnNWXY4/J24j02AABAQ7O2slZwO38Ft/NXn+DOKq86ryNFJ5RbdFzHz53U+1u/lfT/5ZtfrLr4x6mzf6z8XbwNTg4AbZdhRVpiYqKGDx+uKVOmqKCgQMHBwZozZ47y8vI0efLkC/tNmjRJmzdv1p49ey5smzBhgnJzczVhwgRt27ZN27Ztu/BccHCwOnfu3KTXAgAAALR0n6bN1cYjGXqkx51K8I02Og4AAECbYG9tp0jPYEV6BqtfeJIqzldpx7EsbT+WqR3HMi/cX83P2Utd/OPUNaCDugV0lJOtg8HJAaDtMKxIk6TXX39db731lubNm6eioiLFxMTogw8+UNeuXa96XFZWliTpo48+uuS5sWPHUqQBAAAA9bAiZ7O+2vmDRkb1040xA42OAwAA0GZ5O3loaGRvDY3sLYvFosNFx7X92G7tOJalVQe3atG+NbIxWyvRL0a9g5LUOyhRXk7uRscGgFbN0CLNzs5OkyZN0qRJk664z/Tp0y/Z9p+z0wAAAADUXYUqVFZVfuHxgVNH9Ma6TxXnHaa7u45ScfXZBhmnWtUNch4AAIC2ymQyKcTNXyFu/hobl6LqmmrtLsjW+sPpWp+bpn9u+lL/3PSloj1DfinVgpMU5hYok8lkdHQAaFUMLdIAAAAANK2yqnKtOZAmSSqpKNNXOxfJzspW/UK6auOhXQ02TnJoXIOdCwAAAJKV2UoJvtFK8I3WH7vdosNFx7QuN03rD6fp07R5+jRtnoLb+WtYZG+lhveUp6Ob0ZEBoFWgSAMAAADaoKqaan2/d5XKq87r1vjhcrThPhsAAAAtxS+z1QIU4hagOxNG6mTpGW3ITdPS7I36cNt3mrZ9jroHdtSwiD7qGdRJtlY2RkcGgBaLIg0AAABoYywWi1bkbNaxswUaGd1fPk4eRkcCAADAdfBydNPomIEaHTNQuUXH9eP+9VqavUGbjrwvFzsnpYT10LDIPoryDDY6KgC0OBRpAAAAQBuTfnyPfs7fr+TABEV7hhgdBwAAAA0oqJ2fJnS9Wb/vPEbbju3Wj/vXaeHe1ZqbtVwJPlG6NX6YYv14DwgAdUWRBgAAALQhGcf3atXBrQp3b69eQYlGxwEAAEAjsTKblRwYr+TAeBWfL9GS/es1O3OZnls+VYGuPorzClesd7iszVZGRwWAZo0iDQAAAGgj8orz9eaaz+Xh4KrhUX1lMpmMjgQAAIAm4GrnpFs6DtGYuEFadXCbvt61SMsObNT63DQl+cWqk1+07K3tjI4JAM0SRRoAAADQBpyrKNWzy6dKMml07CBuOA8AANAGWZutlRLeQ12DYjVjx2JtzftZ63PTtOXoLsX7Rql7YEc52jgYHRMAmhWKNAAAAKCVq66p1ssr/6W8s/l6bvAfdbrknNGRAAAAYCCTyaRgN38Fu/mroOSUtuXtVtqxLO06sU+d/eM0IKqz0REBoNkwGx0AAAAAQOOxWCyauvlrbTu2W3/qeZfifSONjgQAAIBmxNvJQ8Oj+uqupNEKdQ/U5qM79c7amdp69GdVVlcZHQ8ADMeMNAAAAKAVm5P5kxbsWanb4odpeFQfFVUVGx0JAAAAzZCHQzvdEN1f+SWntPloutYe3q7txzLVo32C4n0iZWW2MjoiABiCGWkAAABAK7XxSIbe3/qN+gQl6d4uNxsdBwAAAC2Aj5OH7ugyXOM7DpWbvYtW5GzWZ2nzlVVwQBaLxeh4ANDkKNIAAACAVujAqSP6n1UfKMI9SE/1myCzibf+AAAAqLtAV1+N7zhUY2IHy87aVov3r9NXOxcpt+i40dEAoEmxtCMAAADQypwqK9Izy9+Ro42DXh78sBxs7IyOBAAAgBbIZDIp1D1QIW4ByjqZo/WHd+i73UsV5h6ovsFd5OnoZnREAGh0FGkAAABAK3K+qkLPL39XZ8+f0z+GPykvJ3ejIwEAAKCFM5lMivMOV5RHsHYcz9KWo7v0Rfr3iveNVM/2iXKydTA6IgA0Goo0AAAAoJWosdTotbUfa8/Jg3px0IOK8gwxOhIAAABaEWsra3UPjFe8T6Q2HclQxom9yirIUdeAjuoaECcbKxujIwJAg6NIAwAAAFqJadvnaPWhbbqv6zj1Ce5sdBwAAAC0Ug429hoYlqxEv1itO7xDG4+ka+eJveoVlKgOPhHcnxdAq8L/0QAAAIBWYF7WCs3ctVijovvr1o7DjI4DAACANsDdwVWjYgbo1o7D5GLnpGUHNurL9IXKOX1UFovF6HgA0CCYkQYAAAC0cGsPbdfUTV+pV/tEPdLjTplMJqMjAQAAoA0JcPXRbfHDtf/UYa09tEPzspYruJ2fgtx9lOTjanQ8ALguzEgDAAAAWrCf8/fr1TUfKcYrVM8MuE9WZiujIwEAAKANMplMivIM0d1JozUgtJvyS07pLz/8Q6+t/Vj5JaeMjgcA14wZaQAAAEALlVt0XM8unypvR3e9kvKI7K3tjI4EAACANs7KbKXO/nGK847Q8ZJ8Lcxao5U5WzQssrduTxghP2cvoyMCQL1QpAEAAAAt0KmyIj297C1ZmcyanPqo3OxdjI4EAACA62AyW1RUVdwkY1WrutHHsLe21V2dR2l83DB9tXORFu9fpx/2rVNqRE/dmTBSga4+jZ4BABoCRRoAAADQwpRWluuZZf/UmfKzmjLsCQXwJQQAAECLV15Voc0HM5tkrOTQuCYZR5J8nT31p1536TedbtDMXT9q0b41Wpq9XoPCknVnwg0KcfNvsiwAcC0o0gAAAIAWpKqmSv+98n1lnz6ilwc/rFivMKMjAQAAALXydvLQwz3u0J2dRurbn5dowZ6VWn5gs/qFdNEtHYcozitcJpPJ6JgAcAmKNAAAAKCFsFgs+sf66dqa97P+3Ptu9WifYHQkAAAAoF48HNrp/m7jdXv8cH23e5nmZi3X6kPbFO7eXjdE91dKeA852zoaHRMALqBIAwAAAFoAi8Wif239Vj9mr9ddiaM1Iqqf0ZEAAACAa9bO3kV/6DJWtyeM0PIDm/T93tV6Z9MMfbhtlgaGdtcN0f0V6xXGLDUAhqNIAwAAAFqALzMWatbupRoTO1h3J442Og4AAADQIBxt7DUqZoBGxQzQ3sJD+n7PKi3P2azF+9cp3L29Rkb1U9+QLvJydDM6KoA2iiINAAAAaObmZi7Xp2nzNCSilyYm38avcgEAANAqRXuG6PHed+uB7rdemKU2dfNXmrr5K8V6halXUKJ6ByUp1C2A98QAmgxFGgAAANCMLcveqKmbv1KfoCQ90ft3MpvMRkcCAAAAGtV/zlI7ePqo1uemaX1uuj7ZMVef7Jgrf2dv9Q7+pVSL94mUldnK6MgAWjGKNAAAAKCZWnc4Ta+v+0Sd/WL1zIA/8gUBAAAA2pxQ90CFugfqzk436GTpGW3MTdf63DTNz1qp73Yvk6ONveK8w5XgE6V43yjFeoXK3trO6NgAWhGKNAAAAKAZSjuWpVdW/UvRniF6afBDsrWyMToSAAAAYCgvR7cLM9VKK8u19ejP2nE8S7vy9+mztPmyyCJrs5WiPEMU7xOpeJ8oxXmHycOhndHRAbRgFGkAAABAM5NVkKPnlk9VoKuvXk19VI429kZHAgAAAJoVRxt79Q/tqv6hXSVJZ8+X6OeCbO06sV+78vdpbuZyffvzEkmSt6O7oj1DFOMVqmjPUEV5hqidvbOR8QG0IBRpAAAAQDNy8PRRPf3T23Kzd9VrQ/4kVzsnoyMBAAAAzZ6LnZN6tu+knu07SZIqqiu15+RB7S08pD0nc7S38JDW5aZd2N/f2VvRXiGK8QxVtFeooj1D+AEbgMuiSAMAAACaiUNnjumJJW/K1myj14c+Jk9HN6MjAQAAAC1ChSpUVlV+0bZgT18Fe/oqNTpZklRSUaYDp45of2Gusk/landBtlYd3CpJMsmkAFdvRXoGKcIjSJGeQQrzCLzsEusO1vaylW3jXxSAZoEiDQAAAGgGDhcd0xM/TpHZZNaUYX+Wv4u30ZEAAACAFqOsqlxrDqTVvqMkLwcPeQV6qEdgokory3XiXKHySwp14lyhthzZrVU52yRJViaz/F28FdTOT+1d/eTn7Ckrs5X6RyaqrKa8llEaBqUdYDyKNAAAAMBgR4qO6y8/vilJemPonxXUzs/gRAAAAEDb4GhjrzD3QIW5B17Ydu58qY6XnFRecb5yi09oQ266pHRZm60U4OKj46X5UrVZvs6eMplMjZqvX3iSbK0p0gAjUaQBAAAABjpSfEJ//vFNVVtq9OawJxTi5m90JAAAAKBNc7ZzVKRdsCI9giVJ5ZXndaT4hHKLj+tI0XF9nf6jJMnJxkGRnsGK8gxRgIu3zCazkbEBNBKKNAAAAMAgR4vz9cSPU1RVU6Upw55QiFuA0ZEAAAAA/Iq9jZ0iPYMV6flLsRbj217zdq3WvlOHtevEfqUf3yNHGwdFefx/qeZKqQa0JhRpAAAAwGVc7mblDen42UI9v+xdVVRX6m/D/nTRUjIAAAAAmq92Di6K9Q5XrHe4KqorlXP6qPYVHtLPBfuVfmKPHG3sFeUZok6+0fJ0dDM6LoDrRJEGAAAAXEZ9blZeX0XlZzXr56WqrKnUuA5D5Ofi2SjjAAAAAEYymS0qqipukrGqVd0k4/yarZWNYrxCFeMVqorqSh38/1Jt14l9Sj++R0Ht/JTkF6sw90BmqQEtFEUaAAAA0ISKys9q1u6lqqiu1LiOQ+Tt5GF0JAAAAKBRlFdVaPPBzCYZKzk0rknGuRpbKxtFe4Uq2itUZZXl2pm/TzuP79WCPSvlauekTr4xiveJlL2NndFRAdQDRRoAAADQRE6VFWn27mWqrK7SzR1S5UOJBgAAALRKDjb2Sg5MULeAjso+lau043u09vB2bchNV6x3mLr4x7HsI9BCUKQBAAAATaCg5LTmZC6TJN3Scai8ndwNTgQAAACgsZlNZkV5hijKM0QFJaeVfnyPsk4e0O78bMV5h6tnUKJc7ZyMjgngKijSAAAAgEZ24lyhZu9eJhsra93cIVUeDu2MjgQAAACgiXk7uSs1oqf6BCdpy9GflX48S3tO5qiTX4y6B8bL0cbe6IgALoMiDQAAAGhER4vzNS9rueytbTWuwxC1s3cxOhIAAAAAAznY2Kt/aFf9X3t3Hh5VebB//J6ZzExWspAQ1iSAkBB2omBAQPZdcHtxBWq11VoXsL5C7fuzVatWoYIL1oKt1GpbQTBssqoIyCIihDVAkCWyJGzZk9nO7w8gNSYqYDIny/dzXbmSOdtzP4Ekk7lzzunaJEkbs9K17fhe7co+oG5NktWtaTs5bHazIwL4Foo0AAAAoJocOXdcCzM+UZgjRDclD1QYl2wBAAAAcEGYM0SDWqcqpUmyPj+6TRuztmv7ib3q3ryjOsUmyma1mh0RgCjSAAAAgGrx9dksLc5Yo8igBrqx3UCFOIK+d1uL1VCuJ88vubzy+mUcAAAAAJcmKjhcIxP76kT+Ka07slVrDm3RruxMDW6d6tccLrlU7Cnxy1hBAYFyyOGXsYCfiiINAAAAqGL7Tx/WR/vXKiY4SmPaDVCQ3fmD25d4XNp8aI9fsnVPaOeXcQAAAABcnsZh0bo5eZAyzx7VJwc36187PlKRt1j3db1FzoDqL52KPSVae3BbtY8jSb1bdZHDD3MCqgJFGgAAAFCFdpzcp48PblaTsGiNTurvl194AQAAANQNFotFV0XFqXmDxlp7+Et9uPsTfZG1S4+ljlenxm3NjgfUS1xkFQAAAKgChmFoU1a6Vh/cpPiIprqx3UBKNAAAAABXJDDAoUGtU/XUgF/K6/Np0vKXNGPjuyp0FZsdDah3KNIAAACAn8gwDK059IU2HN2upOiWGpV4vew2Lv4AAAAA4Kfp1LitZt3we92cPFCLM9bo3rSntDlrh9mxgHqFIg0AAAD4Cbw+r5btX6dtJzLUrUk7Dbmql2xWnmYDAAAAqBpBdqceuGasZgx/QsH2QP129Sua/eV8eX1es6MB9QK/4QMAAABXyOV1K23vJ8o4fUjXxXVT7/gUWSwWs2MBAAAAqIOSY1rrjVH/pxFt++jfOz/S5FUzdK4k3+xYQJ1HkQYAAABcgWJ3iT7YvVJHc09oUOtUXd2sPSUaAAAAgGrlsNk1MfVu/abnBO3KPqAHFj2jPTkHzY4F1GkUaQAAAMBlyist1Pu7lutU4TmNTOyr9o2uMjsSAAAAgHpkaJtemjFssmxWmyYue1EL934qwzDMjgXUSRRpAAAAwGXILjyj/+z4SIWuYt2YPECto1qYHQkAAABAPdSmYZxmjvydujVJ1iub3tWL6/+uEk+p2bGAOociDQAAALhEh88d07xdK2SxWPQ/HYaoeYNYsyMBAAAAqMcaOEP07IBfa3yXG7Qqc6MeXvqCjuXnmB0LqFMo0gAAAIBLsCs7U2l7P1YDZ6hu6zBM0cGRZkcCAAAAAFktVt3deZT+OPBh5RSe0cNLn9f+00fMjgXUGRRpAAAAwA8wDEMbj6ZrZebnat4gVre2H6xQZ7DZsQAAAACgnO7NOuiV4VNkt9n12PKp2n4iw+xIQJ1AkQYAAAB8D6/Pp1WZG7Qxa7vaxbTS6KT+cgY4zI4FAAAAAJVqEd5Yrwx7QtHBEZq8crrWH9lmdiSg1qNIAwAAACpR7C7Rwr2faFdOpno066jBrXvKZrWZHQsAAAAAflBMSJReHvq/ah3VQn/4dKaW7V9vdiSgVqNIAwAAAL7jVNE5/d/KmTqSe1wDW12r1LguslgsZscCAAAAgEsSHhiqlwZPUtcm7TT187f1n53LzY4E1FoUaQAAAMC3HDhzRL9e8kcdzz+lG5L6qUNsG7MjAQAAAMBlC7IH6tn+D+n6hGs068t5enPLXBmGYXYsoNYJMDsAAAAAUFNsPLpdz342S2GOYP1x8K919Gy22ZEAAAAA4IrZbQGa0vteNXCGaO6uFcorKdCknuO4bD1wGSjSAAAAUO8ZhqEFe1brL1ve11VRcXqm/68V4LBSpAEAAACo9WxWqx7qcYfCA8P0zvZFMmToN70myGrhgnXApaBIAwAAQL3m9Xk1c/N/lJbxiXq16KLJve9VkN2pXE+e2dEAAAAAoEpYLBaN73KDrBaL5mxbqGB7kB7sfhv3ggYugalFmsvl0owZM5SWlqa8vDwlJSVp4sSJSk1N/cH90tPTNX/+fKWnp2vfvn1yu93KyMjwU2oAAACYxSWXij0lVXa8IneJ/rz2HX11fK9Gt7ted3UdIZelVC5PqbzyVtk4AAAAAFAT3NVppIpcJZq7e4WC7YG6p9uNZkcCajxTi7TJkydrxYoVGjdunOLj47VgwQLdd999euedd9S1a9fv3W/NmjWaO3euEhMT1aJFCx08eNCPqQEAAGCWYk+J1h7cViXHyistUNqeT3SmOFcDWl2rlhEttP7r9LL13RPaVck4AAAAAFBTWCwW/eLqW1ToLtZ7O5Yq2B6o2zoOMzsWUKOZVqSlp6dryZIlmjJliiZMmCBJGjNmjEaOHKmpU6fq3Xff/d59b7/9dt13330KDAzUH//4R4o0AAAAXJZj+TlanPGpPD6vbmw3QHERTcyOBAAAAAAVWKxGtVx2fsLVNyjXla/ZW+fLYrNoaNueXJUD+B6mFWnLli2T3W7XrbfeWrbM6XTqlltu0csvv6zs7Gw1atSo0n2jo6P9FRMAAAB1zO6cTK3O3KhQR4huTh6khsERZkcCAAAAgEqVeFzafGhPtRy7W+P2Op53SrO++EBHzp7Q+GtGVMs4QG1nNWvgPXv2qGXLlgoJCSm3vFOnTjIMQ3v2VM83BwAAANRPPsOntYe3asWBz9U0rJFu6ziMEg0AAABAvWWzWjW8bR81bxCrFQc+1xdZu82OBNRIpp2RlpOTo9jY2ArLY2JiJEnZ2dl+zdOwYahfx6trYmLCzI4AwAR87QP1k5lf++7cEoWFOi97v1KPSwt2fKb9p44qpXk7DUlMlc36w39TZrfbrmisK1EXx6qLc6rvY1VVlpo2r9o2Vl2cE2PV/LG+e9y6Mi8zxmEsxqpNY1VFlpo2J8aq3B0pQ/Tu1o/06vp/aWyXQWrVsHm1jidJwcEOxYTzulJNxOt9FZlWpJWUlMhut1dY7nSe/6ZQWlrq1zynTxfI5zP8OmZdERMTppycfLNjAPAzvvaB+snsr/0ij0v5BZf3PDG3JF8L936iM8V56teyuzo3TlRRkftH93O7vZc91pWqi2PVxTnV57HCQp1VlqUmzas2jlUX58RYNXusyr7+68K8zBqHsRirtoxVVT/7a9KcGOuHjWrTT0sPfKb3t63S/3QYopiQqGodr6jIpRwXryvVNGb/zm8Wq9XygydbmXZpx8DAQLndFV/AuFigXSzUAAAAgCuVlXtS/9rxkQpcxboxeYA6N040OxIAAAAA1DiBdqee6DtBzgCHFu79RIWuYrMjATWGaUVaTExMpZdvzMnJkSQ1atTI35EAAABQh6Sf3Kf5e1YqKMCp2zoOU1x4E7MjAQAAAECNFRncQDckXq9iT6kWZnwij9djdiSgRjCtSEtKStLXX3+twsLCcsu3b99eth4AAAC4XB6fV6syN+rjg5vUIryJxnYcpsigBmbHAgAAAIAar1FoQw1tc51OFpzWiszPZRjcDgkwrUgbOnSo3G635s6dW7bM5XJp/vz56tatm2JjYyVJx44dU2ZmplkxAQAAUIsUuor0wa6V2pm9X1c3ba/RSf0UGOAwOxYAAAAA1BpXRcWpV1xX7Tt9WBuz0s2OA5guwKyBO3furKFDh2rq1KnKyclRXFycFixYoGPHjun5558v2+6JJ57Q5s2blZGRUbbsm2++UVpamiRpx44dkqSZM2dKOn8mW//+/f04EwAAANQEx/NztDhjjUq9Lg1v20dtG8abHQkAAAAAaqWrm7bX2eI8bcpKV2RQAyVFtzQ7EmAa04o0SXrxxRc1ffp0paWlKTc3V4mJifrrX/+qlJSUH9wvKytLM2bMKLfs4uMbb7yRIg0AAKCe2Xlyv1vd0fMAACAASURBVD75erNCHMEa226YYkIizY4EAAAAALWWxWLRgFY9lFuSr5UHPle4M1RNwmLMjgWYwtQizel06oknntATTzzxvdu88847FZb16NGj3BlqAAAAqJ+8Pq/WHNqi9JP7FBfeRMPb9Fag3Wl2LAAAAACo9WxWm0Ym9tW/d3ykRRmf6raOw9TAGWp2LMDvTLtHGgAAAPBTFLqK9MHuVUo/uU8pTZM1pl1/SjQAAAAAqEJB9kCNTuovj8+rhXs/kcvrNjsS4HcUaQAAAKh1svJO6t30JcouPK2hba5T7/gUWS08tQUAAACAqhYVHK4RbfvodFGulu9fL8MwzI4E+BWvNgAAAKDWMAxDXx7bpQ92rZTT5tBtHYdx02sAAAAAqGbxEU3VOyFFmWePasuxXWbHAfzK1HukAQAAAJeqwFWkl9bN0aajO3RVVJwGtU6VM8BhdiwAAAAAqBe6Nk7SifwcfX5km2JDGyouvInZkQC/4Iw0AAAA1HgHz2bpwSV/1BdZu9QnPkUj2vahRAMAAAAAP7JYLBrYOlWRQQ300b61yi8tNDsS4BcUaQAAAKjRVmVu1ENLnleJu1R/GPiAujVNlsViMTsWAAAAANQ7DptdIxP7ymv4tHjfGnl8XrMjAdWOIg0AAAA1UqnHpekb/qkX1r2lxOgEvTHq/5TcqJXZsQAAAACgXosKCtfg1j11suC0Pju0xew4QLXjHmkAAACocY7kHteza/6qg2ezNLbDEN3T9UbZrDblevLMjgYAAAAA9d5VDeOU0rS9vjy2S41Do5XcqLXZkYBqQ5EGAACAGmXFgc/1yqZ35bQ59NyAh9W9eUezIwEAAAAAvqNXXBedLDil1Qc3KTokUo1CosyOBFQLLu0IAACAGqHIXaIX1r6lF9f/XYnRLfXmDU9RogEAAABADWW1WDWsTW8F2Z1anLFGJZ5SsyMB1YIiDQAAAKY7cOaIfrX4WX389SaN73KDXhw0SdHBEWbHAgAAAAD8gBBHkIa37aMCV5GW718vwzDMjgRUOYo0AAAAmMYwDKXt/UQPLXlexe5SvTT4Md3deZRsVp6mAgAAAEBt0DQsRn0TrtbX577RF9/sNDsOUOW4RxoAAABMca4kX9M+n6MNR7ere7OO+t/rfqaIwDCzYwEAAAAALlOn2Lb6Ji9bG45uV9OwRmoeHmt2JKDKUKQBAADA7zZl7dDU9W+rwFWk+6/+H92UPEBWC2ehAQAAAEBtZLFYNLD1tcopPKOl+9fqzk4jFOIIMjsWUCV4tQIAAAB+U+Ip1asb39OTq19RRFCYXh/5pG5pP4gSDQAAAABqOYfNruFt+6jU49KyA+vkM3xmRwKqBGekAQAAwC/2nz6s59fO1pHcE7oleZDu6XajHDa72bEAAAAAAFUkJiRS/Vt118rMDdqUtUOpLTqbHQn4ySjSAAAAUK28Pp/e37Vcb3+VpsigML04eJK6NWlndiwAAAAAQDVIjmmtrLyT2pSVrqZhMYqPaGp2JOAnoUgDAACog1xyqdhTUuXHdeeWqMjjKrcsKCBQDjkq3f5Yfo6mrn9b6Sf3qU98ih5NvVsNnCFVngsAAAAAUDNYLBb1b9ldJwtOa9n+dbqz80iFOoLNjgVcMYo0AACAOqjYU6K1B7dV+XHDQp3KLygtt6x3qy5yBJQv0nyGT4sy1mjWlx/IarHqf3v9TINap8pisVR5JgAAAABAzWK32TWybV+9t2OJPtq3Vjdzb2zUYhRpAAAAqFInCk5p2vo5+urEXqU0TdZjPcerUUiU2bEAAAAAAH4UFRyuAa2u1fID67Xh6Hb1iutqdiTgilCkAQAAoEoYhqEl+9fqzS/elyRNTL1bw9v05iw0AAAAAKin2sW00jd5J/XFNzvVNKyRWkY2MzsScNko0gAAAPCTZRee0bT1c/Tl8d3q2jhJv+k1QbGhDc2OBQAAAAAw2fUJ1+hEwWktP7BOd3QaoQbOULMjAZeFi5ICAADgihmGoVUHNuretN9rV06mHu5xp/40eCIlGgAAAABAkhRgC9DItn3kMwwtyfhMHp/X7EjAZeGMNAAAAFyRc8V5WnVwo7LyTqpTbFv9ptcENQ2LMTsWAAAAAKCGiQhqoMFX9dTijDVac2iL+l2VYnYk4JJRpAEAAOCy+Ayfvjy2WxuPpstmter+HrfqpsSBslq42AEAAAAAoHJXRcUppWl7fXlslz49uEWj2/Y3OxJwSSjSAAAAcMmO551S2s7PlFN4Rq2jWqhfy+4adNW1lGgAAAAAgB/VK66LThSc0pub56ljTFu1imxudiTgR/GKBwAAAH6U2+vR2sNb9dbmNBW6ijWybV+NSrxeoY5gs6MBAAAAAGoJq8Wq4W16K9geqD98+oYKXcVmRwJ+FEUaAAAAftDhc8f0z+2L9eWxXerStK3GdRmlqxrGmR0LAAAAAFALhTiC9FjvcTqef0ovrX9bhmGYHQn4QRRpAAAAqFSBq0hL932mBXtWy2KRbk4epJHJvRUY4DQ7GgAAAACgFktu1Er3pdysdUe2at7ulWbHAX4Q90gDAABAOT7Dp+0nMrTh6HZ5fV5d27yTrm7WQQFWm9nRAAAAAAB1xC3Jg7QrO1OzvvxASdEJ6hjb1uxIQKU4Iw0AAABlTuSf0r92fKQ1h7aocWi07u48Ste26EyJBgAAAACoUhaLRY/3mqAmYdF6Zs1fdaY41+xIQKUo0gAAAKAST6lWH9ykf+/8SEWuYg1v01s3thugiKAGZkcDAAAAANRRIY4gPXX9Ayp0FesPn7whl9dtdiSgAoo0AACAesxn+LTz5H7N+SpNO0/uV5fGSRrX5Qa1jU6QxWIxOx4AAAAAoI5rFdlcT1x3j3blZOrPn/9DhmGYHQkoh3ukAQAA1FPH8rL16aEvlF14Rk3DYnR9y+5qFBJldiwAAAAAQD3TJyFF43Nv0JxtC5UQ0VS3dRxmdiSgDEUaAABAPZNfWqh1h7cq4/QhhTqCNbTNdUpsyBloAAAAAADz3NVppA6fO663ti5QXHgT9YzrYnYkQBJFGgAAQL3h8Xn15bFd+uKbnTIMQ92bddQ1zdrLbrObHQ0AAAAAUM9ZLBY93muCjuVn67m1s/XKsMlqFdXc7FgA90gDAACo6wzD0L7Th/WPbWnacHS7EiKaaVyXG9QzrgslGgAAAACgxnAGOPR0/18rxB6k3338qs4W55kdCaBIAwAAqMuyck/q3zs/0tJ9n8lutevm5IEamdhX4YFhZkcDAAAAAKCC6OAIPd3/QeWWFOj3n74hl9dtdiTUcxRpAAAAddDhs8eVtudjzdu9QoWuYg1qnao7O49Qi/AmZkcDAAAAAOAHJUYn6PHrJmhX9gFN3/CODMMwOxLqMe6RBgAAUIecLDitf2xbqBWZG2S3Bei6uG7q0jhRATae9gEAAAAAao/rE67R4XPH9c72RYqPaKaxHYaYHQn1FK+oAAAA1AG5Jfn6z87lWrBntSRpVLs+ahoSq0C7s9rHtlgN5Xr8c916r7x+GQcAAAAAYL67O4/U4XPHNPvLDxQdHKEBrXqYHQn1EEUaAABALXa2OE/zdq9Q2t5PVepxaWDrazWhy2gFBtq19uA2v2Qo8bi0+dAev4zVPaGdX8YBAAAAAJjParHqievu0bmSfP1p3d8UGOBUr7guZsdCPUORBgAAUAudLjqnubtWaFHGGrl9bvVNuEZ3dhyuhMhmkuS3M8QAAAAAAKhOzgCHnh3wkP53xZ/17Jo39Uz/X+vqZu3NjoV6hCINAACgFskpPKP/7FyupfvXyuPzakCrHrqj43C1CG9sdjQAAAAAAKpFsD1Qzw98RL9ZPlVPfTJTLwx6RB1j25odC/UERRoAAEAtcPjccS3Ys0rLD3wun2Fo8FWpur3DMDVt0MjsaAAAAAAAVLswZ4heGDRRk5a9pCdXv6qXBj+mxOgEs2OhHqBIAwAAqKF8hk9ffLNL8/es0pfHdstuDdCQq3rqto7D1Dg02ux4AAAAAAD4VWRQA704eKImLntRk1dN17Qhv1GryOZmx0IdR5EGAABQwxS5S7T8wHp9uPdjfZOXrYZBEfpZ1zEa0baPIgLDzI4HAAAAAIBpYkKi9OLgxzTxoxf1xIqX9fLQx9Wc2x2gGlGkAQAA1BBHco9rccYaLTuwXkXuErWLaaXxvW9Qn4QUBVh52gYAAAAAgCQ1DYvRi4MnadKyF/X4ypc1fej/Kja0odmxUEfxigwAAICJcksK9OmhL7Qyc4P2nvpaNotNfRNSdFO7gUqKaWl2PAAAAAAAaqT4iCb60+CJ+s3yaZq47EU9N+BhJUQ2MzsW6iCKNAAAAD9zez3a/M1Orcz8XBuz0uXxedUqsrnuv/pW9W/VQ1FB4WZHBAAAAACgxrsqKk4vDZ6kJ1e/qkc++pOe6veAujVpZ3Ys1DEUaQAAAH7g8Xn01cm9+uzQFq07/JXyS4sUERimYW2v0/WtrlZCZNOybXM9eT95PK+8P/kYAAAAAADUdG0axuvV4VP05OpXNGXlDE3qebeGXNXL7FioQyjSAAAAqkmhq1hfHNupDUe3a3PWDuW7imSzWNU6qoX6t2yt+IgmslqsOno2W0fPZlfp2N0T+As8AAAAAED9EBvaUNOHPaGnP/2LXlr/to7l52hCl9GyWCxmR0MdQJEGAABQhU4UnNKmrB36/Og2bT+RIY/Pq3BnqHq26KLOzdqqsKREdpvd7JgAAAAAANQpoY5gPTfwYc3Y+K7eTV+iEwWn9FjP8XLwOzh+Ioo0AACAn+BEwSltP5Fx4W2fThaeliQ1bxCrm9oNVGqLzkqOaS2b1apcT57WHtxmcmIAAAAAAOqmAGuAJqWOU5PQaP3tqw+VU3hWv+/3KzVwhpgdDbUYRRoAAMAl8hk+ZeWd1O7sg9p+8nx5ll14RpLUwBmqzo3b6pb2g5TSNFlx4U1MTgsAAAAAQP1jsVh0R6cRahwarZfWv62Hlz6vp/s/yO/puGIUaQAAAJXwGT4dy8tWxunD2n/6sPZdeF/sKZUkhTtD1blxov6n/RB1bpxYdr8zAAAAAABgvv6teigmJEpPfTJTv1z4tH7WdYxuTh4km5Xf3XF5KNIAAEC9l1uSr8PnjuvQuWM6nHtMh84d0/7TR1TkLpYkOWx2tYpsrsGte6ptdLwSGyYoPqIpNy0GAAAAAKAG6xjbRrNH/17TN/xTf/1yntYd+UqP95qgFuGNzY6GWoQiDQAA1Aten1cnC07rm/xsHcvP0ZFzx8tKs3Ml+WXbBQU41Tw8Vr0TuqpVVHNd1bCFmofHKsBqK3e8PG/+d4f48Qzy/uR5AAAAAACASxcVFK4/9PuVPv56k17b9C/9ctHTuqfrGN3YbiBnp+GSUKQBAIAaySWXij0ll7VPoatYOYVnlVN4RtkFZ3Wi4JSO55/SifxTyi44I6/hK9v2YmHWtVmSPB6fGgaFq2FwhEIdweXONDt6NltHz2ZXyZy6J7SrkuMAAAAAAIBLZ7FYNKDVterSOEnTN/5Tf9kyV2uPfKXHe45Xc85Ow4+gSAMAADVSsadEaw9uK3tsGIYKXcXKcxUqv7RAeaWFyr/wlldaqHxXoVxed7lj2K0BighqoIjAMDVv2lgRgWEKDwxTZGCYgu1Bslgs6p7QTpsP7fH39AAAAAAAgJ81DI7Q0/0e1OqDm/Ta5n/pF4vO3zttdFI/OWx2s+OhhqJIAwAApvP6fDpXkqfTxbk6VXhWp4vP6ZuCk9p18mBZWZbvKpLvW2eUSZLT5lCYM0ThgaFqER6rMGeowpwhauAIUZgzRMH2QO5jBgAAAAAAylgsFg1sfa26NEnS9A3v6M0tc/XB7pUa22GohrfpLWeAw+yIqGEo0gAAQLUxDEOF7mKdLjqnU0Xnzr8vPqfThed0uvi/y04X51YoyawWi4LtQQpzhqhxaLTaOM+XYw2coQpzBquBM5S/FgMAAAAAAFckOjhCz/T/tbYe36N/bl+s1zf/W++lL9Ut7QfrhsS+CrIHmh0RNQRFGgAAuGQuuVToLlJBaZHOlRQot6RAuSX5yi0p0LmSfOVdeJ9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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df['label'].sum() / len(train_df['label']) * 100 # pecentage of bad tweets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F-0iMZfcTZMR",
        "outputId": "b18360bf-acc6-4cd1-f11b-f2a11fb6a5ee"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "83.20219505306056"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sentences = train_df['tweet'].values\n",
        "labels = train_df['label'].values\n",
        "\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "sentences_train, sentences_test, labels_train, labels_test = train_test_split(sentences, labels, test_size=0.2, random_state=1000)\n",
        "\n",
        "#build bag of words encoding for training data\n",
        "vectorizer = CountVectorizer()\n",
        "vectorizer.fit(sentences_train)\n",
        "X_train = vectorizer.transform(sentences_train)\n",
        "X_test  = vectorizer.transform(sentences_test)\n",
        "\n",
        "model = LogisticRegression(max_iter = 5)\n",
        "model.fit(X_train, labels_train)\n",
        "print(model.score(X_train, labels_train) * 100)\n",
        "print(model.score(X_test, labels_test) * 100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u5pp4KcmTfvE",
        "outputId": "07dbf08a-13cc-42cc-cc9d-d0a874fb754d"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "89.39271663472208\n",
            "88.1783336695582\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hub_url = 'https://tfhub.dev/tensorflow/'\n",
        "hub_id = 'bert_en_uncased_L-24_H-1024_A-16/1'\n",
        "bert_layer = hub.KerasLayer(hub_url + hub_id, trainable = True)"
      ],
      "metadata": {
        "id": "FcfWmro5TnN1"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# preprocessing data\n",
        "\n",
        "do_lower_case = bert_layer.resolved_object.do_lower_case.numpy()\n",
        "vocab_file = bert_layer.resolved_object.vocab_file.asset_path.numpy()\n",
        "\n",
        "#Define the tokenizer to convert tweets into token vectors\n",
        "tokenizer = tokenization.FullTokenizer(vocab_file, do_lower_case)\n",
        "tokenizer.tokenize(\"This is a sentence\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ol0AqTZ9T2aa",
        "outputId": "bdd72633-fc88-4e17-cb33-2af22fe0d639"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['this', 'is', 'a', 'sentence']"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# tokenize sequence and truncate it\n",
        "def tokenize_sequence(text, tokenizer, max_length):\n",
        "  sequence = tokenizer.tokenize(text)\n",
        "  true_size = max_length - 2\n",
        "  sequence = sequence[:true_size]\n",
        "  sequence.insert(0, '[CLS]')\n",
        "  sequence.append('[SEP]')\n",
        "\n",
        "  return sequence"
      ],
      "metadata": {
        "id": "rqJMAc9QT8wH"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"Make sure your function works correctly here!\"\n",
        "max_length = 7\n",
        "\n",
        "print(tokenize_sequence(text, tokenizer, max_length))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tUx4hi-7UFPC",
        "outputId": "8d674b08-4c5f-4dc3-ee66-61a33e271031"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['[CLS]', 'make', 'sure', 'your', 'function', 'works', '[SEP]']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_input_tokens(text, tokenizer, max_length):\n",
        "  sequence = tokenize_sequence(text, tokenizer, max_length)\n",
        "\n",
        "  seq_length = len(sequence)\n",
        "  pad_length = max_length - seq_length\n",
        "  input_ids = tokenizer.convert_tokens_to_ids(sequence)\n",
        "\n",
        "  return sequence, input_ids, seq_length, pad_length"
      ],
      "metadata": {
        "id": "SjbkJjXeUHL9"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample_tweet = \"You can replace this with any string!\"\n",
        "sequence, input_tokens, seq_length, pad_length = get_input_tokens(sample_tweet, tokenizer, len(sample_tweet)+2)\n",
        "for a, b in zip(sequence, input_tokens):\n",
        "  print(str(a) + '\\t' + str(b))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B-kvaSE8ULyQ",
        "outputId": "8d5ec9ac-cb31-4262-b7ef-36b7169c4f42"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CLS]\t101\n",
            "you\t2017\n",
            "can\t2064\n",
            "replace\t5672\n",
            "this\t2023\n",
            "with\t2007\n",
            "any\t2151\n",
            "string\t5164\n",
            "!\t999\n",
            "[SEP]\t102\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# get encodings and masks for all tweets\n",
        "def bert_encode(texts, tokenizer, max_length=32):\n",
        "    all_masks, all_tokens = [], []\n",
        "    \n",
        "    for text in tqdm(texts):\n",
        "        sequence, input_tokens, seq_length, pad_length = get_input_tokens(text, tokenizer, max_length)\n",
        "\n",
        "        # pad the sequences and add the attention masks \n",
        "        # (to pay attention to the relevant tokens in the tweet)\n",
        "        input_tokens += [0] * pad_length\n",
        "        input_pad_masks = [1] * seq_length + [0] * pad_length\n",
        "        all_tokens = all_tokens + [input_tokens]\n",
        "        all_masks = all_masks + [input_pad_masks]\n",
        "    \n",
        "    return np.array(all_tokens), np.array(all_masks), np.zeros_like(all_tokens)"
      ],
      "metadata": {
        "id": "YtkPsBE0UOZT"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# train model\n",
        "train_labels = train_df.label.values.reshape(-1, 1)\n",
        "train_input = bert_encode(train_df['tweet'], tokenizer, max_length = 32)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "99258de651b34598b86eb4239b5df4bc",
            "1ad9a4f8e9214df0be8b3c486fb3a76d",
            "4b78ea6fdbc44f799f18271e9de0ccaa",
            "ae50a2cf9e074c549acabe8aa10de4f8",
            "541c06cfec564bbda916fd1710dc414f",
            "828b63ee4e7343a9b45dfce7eff705d0",
            "1f07076663f94b6090ca4862634e3c1f",
            "33bf0ebeef134e8ba950ef3da1f75bd5",
            "e3af26f5c5e241c9a224c20786c08112",
            "0a3bc0b2b2bf4120bd26f228742aaa8c",
            "771fbd398d8646edb57d4cd86771cf4c"
          ]
        },
        "id": "j4B8ry_OUUKm",
        "outputId": "99540c36-eeca-419f-b82f-1510e2d1796c"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/24783 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "99258de651b34598b86eb4239b5df4bc"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Build a text classification model with a BERT backbone\n",
        "\n",
        "def build_bert_model(bert_layer, max_len):\n",
        "    word_ids = Input(shape=(max_len,), dtype=tf.int32)\n",
        "    input_mask = Input(shape=(max_len,), dtype=tf.int32)\n",
        "    segment_ids = Input(shape=(max_len,), dtype=tf.int32)\n",
        "    _, bert_sequence_output = bert_layer([word_ids, input_mask, segment_ids])\n",
        "\n",
        "    clf_output = bert_sequence_output[:, 0, :]\n",
        "    output_layer = Dense(1, activation='sigmoid')\n",
        "    output = output_layer(clf_output)\n",
        "    return Model(inputs=[word_ids, input_mask, segment_ids], outputs=output)"
      ],
      "metadata": {
        "id": "0tYNzDGkUbkq"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = build_bert_model(bert_layer, max_len = 32)\n",
        "model.compile(loss = 'binary_crossentropy', optimizer = Adam(1e-5), metrics = ['accuracy'])\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e3DNIuMYUd4r",
        "outputId": "09e6b588-0db3-47b4-cd29-92162b50711f"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)           [(None, 32)]         0           []                               \n",
            "                                                                                                  \n",
            " input_2 (InputLayer)           [(None, 32)]         0           []                               \n",
            "                                                                                                  \n",
            " input_3 (InputLayer)           [(None, 32)]         0           []                               \n",
            "                                                                                                  \n",
            " keras_layer (KerasLayer)       [(None, 1024),       335141889   ['input_1[0][0]',                \n",
            "                                 (None, 32, 1024)]                'input_2[0][0]',                \n",
            "                                                                  'input_3[0][0]']                \n",
            "                                                                                                  \n",
            " tf.__operators__.getitem (Slic  (None, 1024)        0           ['keras_layer[0][1]']            \n",
            " ingOpLambda)                                                                                     \n",
            "                                                                                                  \n",
            " dense (Dense)                  (None, 1)            1025        ['tf.__operators__.getitem[0][0]'\n",
            "                                                                 ]                                \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 335,142,914\n",
            "Trainable params: 335,142,913\n",
            "Non-trainable params: 1\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_history = model.fit(train_input, train_labels, \n",
        "                          epochs = 2, \n",
        "                          validation_split = 0.2, \n",
        "                          batch_size = 64, shuffle = False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jmi8xiXnUjU6",
        "outputId": "f8d677bc-625f-49fd-a27d-eec79c5d6c71"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/2\n",
            "310/310 [==============================] - 479s 1s/step - loss: 0.1958 - accuracy: 0.9162 - val_loss: 0.1208 - val_accuracy: 0.9510\n",
            "Epoch 2/2\n",
            "310/310 [==============================] - 446s 1s/step - loss: 0.0917 - accuracy: 0.9631 - val_loss: 0.1274 - val_accuracy: 0.9536\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# visualize model performance\n",
        "display_training_curves([0.43] + train_history.history['loss'], [0.43] + train_history.history['val_loss'], 'Loss', 211)\n",
        "display_training_curves([0.85] + train_history.history['accuracy'], [0.85] + train_history.history['val_accuracy'], 'Accuracy', 212)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 733
        },
        "id": "Pim42IR7yZ7X",
        "outputId": "2e80e68e-e103-42a4-c8ce-ede93f74051c"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2160x1080 with 2 Axes>"
            ],
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NDw/ry1/+8sLXfeMb31BPT4+eeeaZhdc+/vGPa+/evdq0aZOCwaCGhob02GOPKRQK6dOf/vTC1/3nf/6nXnjhBd16662qrq7WyMiIfv3rX2t8fFxf//rXb+YvFwBwDVxen0LR2xSK3qbM2Dkleg8qeew5zQy8JG9ZjUIt+xXa9n55gkWmR12ywgU+3d5So9tbajSXzSk2NL7wiJRXB0b0XUmb15SovSmi9saI1lYXsgADAAAAAAAAAABwE7nOnz/vmPrhqVRKP/zhD/Xkk09qampKdXV1uv/++9XW1rbwNQ888MBbFjW+853v6NChQ7pw4YJSqZQqKiq0c+dOffrTn1ZNTc3C13V1delnP/uZTp48qampKQWDQW3dulWf/OQn1dzcfF2zejweRSIRpVKpvH/duH6BQIDfe8ACN6JVJ5PWzPGXlejpVPr8ccnjU7D+lvlbNlY3sGRwjRzH0Znh6YWljcGzlyVJkbKg2qMRtUcjatxQJq/HbXhS3Cx8tgJ2oFXAHvQK2IFWAXvQK2AHWgXsQa9mBAIBxeNxZbPZK143uqhhExY1zOLgAOxwo1vNjJxRoqdTyb7n5aRn5K1YrXBrh0JNd8hdUHjDfu5yND45q+6BEXX1xXXk9TFl5nIKF3i1o6FK7dGIttdXKlzgMz0mbiA+WwE70CpgD3oF7ECrgD3oFbADrQL2oFczWNTIE4saZnFwAHa4Wa3m0rOaGXhJiZ6nlLl4Ui6vX8GG9ync2iFfzWZu2bhOs+k59Q6OqasvrkP9cU0mM/J6XGraWK72aERt0YiqSoOmx8Qi47MVsAOtAvagV8AOtArYg14BO9AqYA96NYNFjTyxqGEWBwdgBxOtpoeHlOjt1EzfC3Iys/JWrZu/ZSN6u9yB0E2dZTnI5hydeGNCXX3zj0g5P5qQJG2oKVpY2thUW8wyzDLAZytgB1oF7EGvgB1oFbAHvQJ2oFXAHvRqBosaeWJRwywODsAOJlvNpWc0E3tRiZ5OZeJDcvkCCjbepnBrh/yrNhmZaTk4NzKt7v75R6QcPzOunCOVFwcWlja2baqQz+s2PSbeAz5bATvQKmAPegXsQKuAPegVsAOtAvagVzNY1MgTixpmcXAAdlgKrTqOo8zF15XoOaiZ/hflzKXlq96ocGuHgo23ye0vMDqfzS5Pp3X4+Ii6YnH1nBjVbDqrAr9H2+sr1R6NaGdDlYpCftNj4hothV4BXB2tAvagV8AOtArYg14BO9AqYA96NYNFjTyxqGEWBwdgh6XWam42oWTf80r0HtTc6Bty+YMKRW9XuLVDvsh60+NZLZ3J6ujJS+qKxdUdi2t8KiW326XG9aULt23UVIRNj4l3sdR6BfD2aBWwB70CdqBVwB70CtiBVgF70KsZLGrkiUUNszg4ADss1VYdx1H6/Aklep7SzMDLUjYjX83m+Vs2Gt4nty9gekSr5XKOTp6fXFjaOH1xSpK0piqs9qb5pY3Na0rlcbsMT4o/tlR7BXAlWgXsQa+AHWgVsAe9AnagVcAe9GoGixp5YlHDLA4OwA42tJqbmVay73dK9DyluUsX5AqEFNp6p8ItHfJVrjE93rIwfCmp7v64umMj6jt1Sdmco5KwX7saq9TeVK2WugoF/B7TY654NvQKgFYBm9ArYAdaBexBr4AdaBWwB72awaJGnljUMIuDA7CDTa06jqP02X4lejo1c/xlKZeVf3XD/C0b9bfI5fWbHnFZSMxkdPj4iLpicR0eGFUyNSe/162WzRVqb6rWroYqlRZxo4kJNvUKrGS0CtiDXgE70CpgD3oF7ECrgD3o1QwWNfLEooZZHByAHWxtNZucVPLos0r0HlR2YljuYKFCTe9XqHW/fOW1psdbNjJzOcWGLi08ImVkYlYul7RlTYnaohG1R6u1JhKWy8UjUm4GW3sFVhpaBexBr4AdaBWwB70CdqBVwB70agaLGnliUcMsDg7ADra36jg5pc4cU6KnU7ODh+Zv2VjbNH/LxpZ2uTxe0yMuG47j6PTFaXXFhtUdi+v1c5OSpFXlIbVFq9QWjSi6vkwej9vwpMuX7b0CKwWtAvagV8AOtArYg14BO9AqYA96NYNFjTyxqGEWBwdgh+XUajYxoeSRZ5TofVrZyRG5Q8UKbdujcMt+eUurTY+37IxdntWh/ri6YnEdeX1Mc1lHhUGfdjbML23sqK9UMMCizGJaTr0CyxmtAvagV8AOtArYg14BO9AqYA96NYNFjTyxqGEWBwdgh+XYquPklBo6Mn/LxuuvSk5OgQ3NCrd0qKBuJ7ds3AAzqTn1nBhVVyyuVwdGNJXMyOtxadumCrVFI2prrFJladD0mNZbjr0CyxGtAvagV8AOtArYg14BO9AqYA96NYNFjTyxqGEWBwdgh+XeanbqkhJHnlHyyEFlpy7JHS5VuHmvQs375C2pMj3espTN5jRwZkLd/XF19cV1YSwpSdpUWzy/tBGNaGNNkVwul+FJ7bPcewWWC1oF7EGvgB1oFbAHvQJ2oFXAHvRqBosaeWJRwywODsAOK6VVJ5fV7KnXlOg5qNTJ1yRJgY2tCm/vUMHG7XK5PYYnXJ4cx9H5kYS6+uPqjsU1cGZCjiNVlhQsLG1s3Vgun9dtelQrrJReAdvRKmAPegXsQKuAPegVsAOtAvagVzNY1MgTixpmcXAAdliJrc5dHlHyyNNKHHlGucSEPEXlCjXvU7h5nzxF5abHW9YuT6d0aGBE3bG4ek6MKZXJKhjwaEd9ldqiEe2or1RRyG96zCVrJfYK2IhWAXvQK2AHWgXsQa+AHWgVsAe9msGiRp5Y1DCLgwOww0pu1cnOafbkYSV6nlJq6IjkcqugbofCLR0KbGiRy80tDzdSKpPVkdfH1B2Lq7t/RBNTKbndLkXXl6m9KaL2aETV5SHTYy4pK7lXwCa0CtiDXgE70CpgD3oF7ECrgD3o1QwWNfLEooZZHByAHWh13tzEsBK9Tyt55BnlZiblKa5SuGWfQs175QmXmh5v2cvlHA2euzy/tBGL68zwtCRpbaRQbdGI2psi2ry6RG63y/CkZtErYAdaBexBr4AdaBWwB70CdqBVwB70agaLGnliUcMsDg7ADrR6JSc7p5kT3Ur0PKX0G32S26OCzbsUbu1QYN1WuVzcsnEzXBxLqrt/fmmjb2hcuZyj0kK/djXO37TRvLlCAZ/H9Jg3Hb0CdqBVwB70CtiBVgF70CtgB1oF7EGvZrCokScWNczi4ADsQKvvLHPpgpK9B5U89qxyM9PylFYr3LJfoW175AkVmx5vxZieyejwwIi6YnEdPj6imVRWfp9brZsr1RaNqK2xSiWFAdNj3hT0CtiBVgF70CtgB1oF7EGvgB1oFbAHvZrBokaeWNQwi4MDsAOtXp0zl9bM8VeU6D2o9Nl+yeNVcEu7wq13yb+mUS7Xyn4cx82Umcup79Qldf3hESmjl2flckn1a0vnH5ESjWh1VXjZ/jOhV8AOtArYg14BO9AqYA96BexAq4A96NUMFjXyxKKGWRwcgB1o9fpkRs8q0dup5LHfyUkl5S2vnb9lY+v75Q4Wmh5vRXEcR0MXptQdi6srFtfJ85OSpJqK0MLSRsO6Unk8y+dxNfQK2IFWAXvQK2AHWgXsQa+AHWgVsAe9msGiRp5Y1DCLgwOwA62+N7lMSjMDLynR06nMhUHJ41OwYbfCrR3y19Yv2xsdlrKxy7PzSxv9cR19fUxzWUdFIZ92NlSpPRpR65ZKBQNe02PmhV4BO9AqYA96BexAq4A96BWwA60C9qBXM1jUyBOLGmZxcAB2oNX8ZeKnlejpVDL2gpz0jLyVa+dv2Wi6Q+6CsOnxVqTk7Jx6Toyqqz+uV/tHND2TkdfjUnNdhdqiEbU1RlRRUmB6zOtGr4AdaBWwB70CdqBVwB70CtiBVgF70KsZLGrkiUUNszg4ADvQ6uLJpWc10//7+Vs2hk/K5fUr2Hibwq375VtVxy0bhmSzOfWfnlB3f1xdfXFdvJSUJNWtLl54RMr6VUVW/POhV8AOtArYg14BO9AqYA96BexAq4A96NUMFjXyxKKGWRwcgB1o9cZIXzypRG+nZmIvysmk5IusV7j1LgWjt8ntD5oeb8VyHEdn44n5pY1YXCfemJDjSJWlBWpvjKi9KaLohnL5vG7To74tegXsQKuAPegVsAOtAvagV8AOtArYg17NYFEjTyxqmMXBAdiBVm+sXCqpZOwFJXsOKjNyWi5fgYLR2xRu7ZC/eqPp8Va8iamUDg2MqCsWV+/gqNKZnEIBr7bXV+qWpoh21FcpHPSZHnMBvQJ2oFXAHvQK2IFWAXvQK2AHWgXsQa9msKiRJxY1zOLgAOxAqzeH4zjKXBhUoqdTMwMvyZlLy7dqk8KtHQo23Cq3v8D0iCteKp1V7+tj6o7F1R2L63IiLY/bpeiGMrVHI2qLRlRdHjI6I70CdqBVwB70CtiBVgF70CtgB1oF7EGvZrCokScWNczi4ADsQKs3X252Wsm+55Xo6dTc2Dm5/EGFmu5QuLVDvqp1pseDpFzO0YmzE+qOxdUVG9HZ+LQkad2qQrU3zi9t1K0ukdvtuqlz0StgB1oF7EGvgB1oFbAHvQJ2oFXAHvRqBosaeWJRwywODsAOtGqO4zhKnxtQovegZgZelrIZ+WvrFW7dr2D9++Ty+U2PiD+4MJb4w9JGXP1D48o5UllRQG2NVWqLRtRcVyG/z3PD56BXwA60CtiDXgE70CpgD3oF7ECrgD3o1QwWNfLEooZZHByAHWh1acjOTCl57Dklew5qbvyCXAVhhba+X+GW/fJVrDY9Hv7IVDKtwwOjeiU2rNeOj2o2nVXA51Hrlkq1R6u0syGiksIbs2RDr4AdaBWwB70CdqBVwB70CtiBVgF70KsZLGrkiUUNszg4ADvQ6tLiOI7Sb/Qp0dOpmRNdUi4r/5pGhVs7FNxyi1xen+kR8UcyczkdOzmmrj/ctnFpMiW3S6pfV6b2aERt0Sqtrip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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# general testing\n",
        "prediction = bert_encode(\"You're stupid\", tokenizer, max_length = 32)\n",
        "prediction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "e6688048ad4147639eaf7a6aef84e497",
            "dddfedde7f4441bb8a804132e2140397",
            "5c6f40bbd3de4839993d5686566b180e",
            "16efca7a23da4e4e8deaca2a4ee87868",
            "8fc8f4fd365945149a484fa0ffa6ca25",
            "6c5925a5cda441d3b0aecc31e5b96a44",
            "7e497b03a83a4d828fba2b701fba9568",
            "558bbfe187a24b5094dac0ea346516b3",
            "547db938356f4f9b9b5e868d9ebf4292",
            "c2ce288541224aee99faa8c0049bcda0",
            "3b24e4128b654dac852a8dc2e2c5114f"
          ]
        },
        "id": "kSIJnApb5QDo",
        "outputId": "4ccb8902-e35a-4750-fe55-3d187cb47319"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/13 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e6688048ad4147639eaf7a6aef84e497"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[ 101, 1061,  102,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "            0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "            0,    0,    0,    0,    0,    0,    0,    0,    0,    0],\n",
              "        [ 101, 1051,  102,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "            0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "            0,    0,    0,    0,    0,    0,    0,    0,    0,    0],\n",
              "        [ 101, 1057,  102,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "            0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "            0,    0,    0,    0,    0,    0,    0,    0,    0,    0],\n",
              "        [ 101, 1005,  102,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "            0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "            0,    0,    0,    0,    0,    0,    0,    0,    0,    0],\n",
              "        [ 101, 1054,  102,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "            0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "            0,    0,    0,    0,    0,    0,    0,    0,    0,    0],\n",
              "        [ 101, 1041,  102,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "            0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "            0,    0,    0,    0,    0,    0,    0,    0,    0,    0],\n",
              "        [ 101,  102,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "            0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "            0,    0,    0,    0,    0,    0,    0,    0,    0,    0],\n",
              "        [ 101, 1055,  102,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "            0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "            0,    0,    0,    0,    0,    0,    0,    0,    0,    0],\n",
              "        [ 101, 1056,  102,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "            0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "            0,    0,    0,    0,    0,    0,    0,    0,    0,    0],\n",
              "        [ 101, 1057,  102,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "            0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "            0,    0,    0,    0,    0,    0,    0,    0,    0,    0],\n",
              "        [ 101, 1052,  102,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "            0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "            0,    0,    0,    0,    0,    0,    0,    0,    0,    0],\n",
              "        [ 101, 1045,  102,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "            0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "            0,    0,    0,    0,    0,    0,    0,    0,    0,    0],\n",
              "        [ 101, 1040,  102,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "            0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "            0,    0,    0,    0,    0,    0,    0,    0,    0,    0]]),\n",
              " array([[1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "        [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "        [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    }
  ]
}