Bert_Model_Sentiment_Analysis.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/atharvabiyani/Sentiment-Analysis-For-Movie-Reviews/blob/main/Bert_Model_Sentiment_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rvFotJ_1cDQG"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/IMDB Dataset.csv')\n",
        "\n",
        "df.head()"
      ],
      "metadata": {
        "id": "ZFkvrFoRgWnL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "aca7f352-3d9b-4902-eba1-059f810243fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                              review sentiment\n",
              "0  One of the other reviewers has mentioned that ...  positive\n",
              "1  A wonderful little production. <br /><br />The...  positive\n",
              "2  I thought this was a wonderful way to spend ti...  positive\n",
              "3  Basically there's a family where a little boy ...  negative\n",
              "4  Petter Mattei's \"Love in the Time of Money\" is...  positive"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8a84ac2a-7a29-4588-9fd8-f0f3be6f31d5\" class=\"colab-df-container\">\n",
              "    <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>review</th>\n",
              "      <th>sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>One of the other reviewers has mentioned that ...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A wonderful little production. &lt;br /&gt;&lt;br /&gt;The...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>I thought this was a wonderful way to spend ti...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Basically there's a family where a little boy ...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Petter Mattei's \"Love in the Time of Money\" is...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8a84ac2a-7a29-4588-9fd8-f0f3be6f31d5')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-8a84ac2a-7a29-4588-9fd8-f0f3be6f31d5 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-8a84ac2a-7a29-4588-9fd8-f0f3be6f31d5');\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",
              "\n",
              "\n",
              "<div id=\"df-6bf29b59-60d4-4d91-a24b-a166dbf988a7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6bf29b59-60d4-4d91-a24b-a166dbf988a7')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6bf29b59-60d4-4d91-a24b-a166dbf988a7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "\n",
        "# Assuming 'review' is the column name containing the text data\n",
        "df['review'] = df['review'].apply(lambda x: re.sub(r'<.*?>', '', x))  # remove HTML tags\n",
        "df['review'] = df['review'].apply(lambda x: re.sub(r'[^a-zA-Z\\s]', '', x))  # removing non-alphabet characters\n",
        "df['review'] = df['review'].str.lower()  # text to lowercase\n",
        "df['review'] = df['review'].str.replace(r'\\s+', ' ', regex=True)  # remove extra spaces\n",
        "\n",
        "df.head()\n"
      ],
      "metadata": {
        "id": "nnb6OeYkps3Q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "2443eccc-410b-4cd1-aed6-75b3093cd44c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                              review sentiment\n",
              "0  one of the other reviewers has mentioned that ...  positive\n",
              "1  a wonderful little production the filming tech...  positive\n",
              "2  i thought this was a wonderful way to spend ti...  positive\n",
              "3  basically theres a family where a little boy j...  negative\n",
              "4  petter matteis love in the time of money is a ...  positive"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-61da7e82-e095-4c65-9090-1750853ebd99\" class=\"colab-df-container\">\n",
              "    <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>review</th>\n",
              "      <th>sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>one of the other reviewers has mentioned that ...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>a wonderful little production the filming tech...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>i thought this was a wonderful way to spend ti...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>basically theres a family where a little boy j...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>petter matteis love in the time of money is a ...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-61da7e82-e095-4c65-9090-1750853ebd99')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-61da7e82-e095-4c65-9090-1750853ebd99 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-61da7e82-e095-4c65-9090-1750853ebd99');\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",
              "\n",
              "\n",
              "<div id=\"df-eaadeec8-215a-42d1-b4d2-d45d1b31baa5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-eaadeec8-215a-42d1-b4d2-d45d1b31baa5')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-eaadeec8-215a-42d1-b4d2-d45d1b31baa5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q -U tensorflow-text --quiet\n",
        "!pip install -q tf-models-official"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1cAfZHUyumbY",
        "outputId": "43173196-c57e-49da-a1aa-63be6c7c78a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.2/5.2 MB\u001b[0m \u001b[31m26.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m475.2/475.2 MB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.5/5.5 MB\u001b[0m \u001b[31m102.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m442.0/442.0 kB\u001b[0m \u001b[31m40.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m85.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.7/2.7 MB\u001b[0m \u001b[31m15.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m119.7/119.7 kB\u001b[0m \u001b[31m13.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m32.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.6/43.6 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m241.2/241.2 kB\u001b[0m \u001b[31m21.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import shutil\n",
        "\n",
        "import tensorflow as tf\n",
        "import tensorflow_hub as hub\n",
        "import tensorflow_text as text\n",
        "from official.nlp import optimization  # to create AdamW optimizer\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "tf.get_logger().setLevel('ERROR')"
      ],
      "metadata": {
        "id": "8JVXCxO4vDvU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "url = 'https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz'\n",
        "\n",
        "# keras uitlity to download a file\n",
        "dataset = tf.keras.utils.get_file('aclImdb_v1.tar.gz', url, untar=True, cache_dir='.', cache_subdir='')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LUZbOsx1vPb2",
        "outputId": "482b0b0a-f85f-4b67-8a1e-57bc6592c2ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\n",
            "84125825/84125825 [==============================] - 61s 1us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_dir = '/content/aclImdb'\n",
        "\n",
        "train_dir = os.path.join(dataset_dir,'train')\n",
        "\n",
        "# remove unused folders to make it easier to load the data\n",
        "remove_dir = os.path.join(train_dir, 'unsup')\n",
        "shutil.rmtree(remove_dir)"
      ],
      "metadata": {
        "id": "S-PV08DPveiZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "AUTOTUNE = tf.data.AUTOTUNE\n",
        "batch_size = 32\n",
        "seed = 42\n",
        "\n",
        "raw_train_ds = tf.keras.preprocessing.text_dataset_from_directory(\n",
        "    'aclImdb/train',\n",
        "    batch_size=batch_size,\n",
        "    validation_split=0.2,\n",
        "    subset='training',\n",
        "    seed=seed)\n",
        "\n",
        "class_names = raw_train_ds.class_names\n",
        "train_ds = raw_train_ds.cache().prefetch(buffer_size=AUTOTUNE)\n",
        "\n",
        "val_ds = tf.keras.preprocessing.text_dataset_from_directory(\n",
        "    'aclImdb/train',\n",
        "    batch_size=batch_size,\n",
        "    validation_split=0.2,\n",
        "    subset='validation',\n",
        "    seed=seed)\n",
        "\n",
        "val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)\n",
        "\n",
        "test_ds = tf.keras.preprocessing.text_dataset_from_directory(\n",
        "    'aclImdb/test',\n",
        "    batch_size=batch_size)\n",
        "\n",
        "test_ds = test_ds.cache().prefetch(buffer_size=AUTOTUNE)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nUWOopAOwLZ8",
        "outputId": "771152e8-535b-46a1-e492-af24977db0c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 25000 files belonging to 2 classes.\n",
            "Using 20000 files for training.\n",
            "Found 25000 files belonging to 2 classes.\n",
            "Using 5000 files for validation.\n",
            "Found 25000 files belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for text_batch, label_batch in train_ds.take(1):\n",
        "  for i in range(3):\n",
        "    print(f'Review: {text_batch.numpy()[i]}')\n",
        "    label = label_batch.numpy()[i]\n",
        "    print(f'Label : {label} ({class_names[label]})')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CLYTLnWwwU5v",
        "outputId": "2a402086-81f1-41c4-9101-f74b3653b3e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Review: b'\"Pandemonium\" is a horror movie spoof that comes off more stupid than funny. Believe me when I tell you, I love comedies. Especially comedy spoofs. \"Airplane\", \"The Naked Gun\" trilogy, \"Blazing Saddles\", \"High Anxiety\", and \"Spaceballs\" are some of my favorite comedies that spoof a particular genre. \"Pandemonium\" is not up there with those films. Most of the scenes in this movie had me sitting there in stunned silence because the movie wasn\\'t all that funny. There are a few laughs in the film, but when you watch a comedy, you expect to laugh a lot more than a few times and that\\'s all this film has going for it. Geez, \"Scream\" had more laughs than this film and that was more of a horror film. How bizarre is that?<br /><br />*1/2 (out of four)'\n",
            "Label : 0 (neg)\n",
            "Review: b\"David Mamet is a very interesting and a very un-equal director. His first movie 'House of Games' was the one I liked best, and it set a series of films with characters whose perspective of life changes as they get into complicated situations, and so does the perspective of the viewer.<br /><br />So is 'Homicide' which from the title tries to set the mind of the viewer to the usual crime drama. The principal characters are two cops, one Jewish and one Irish who deal with a racially charged area. The murder of an old Jewish shop owner who proves to be an ancient veteran of the Israeli Independence war triggers the Jewish identity in the mind and heart of the Jewish detective.<br /><br />This is were the flaws of the film are the more obvious. The process of awakening is theatrical and hard to believe, the group of Jewish militants is operatic, and the way the detective eventually walks to the final violent confrontation is pathetic. The end of the film itself is Mamet-like smart, but disappoints from a human emotional perspective.<br /><br />Joe Mantegna and William Macy give strong performances, but the flaws of the story are too evident to be easily compensated.\"\n",
            "Label : 0 (neg)\n",
            "Review: b'Great documentary about the lives of NY firefighters during the worst terrorist attack of all time.. That reason alone is why this should be a must see collectors item.. What shocked me was not only the attacks, but the\"High Fat Diet\" and physical appearance of some of these firefighters. I think a lot of Doctors would agree with me that,in the physical shape they were in, some of these firefighters would NOT of made it to the 79th floor carrying over 60 lbs of gear. Having said that i now have a greater respect for firefighters and i realize becoming a firefighter is a life altering job. The French have a history of making great documentary\\'s and that is what this is, a Great Documentary.....'\n",
            "Label : 1 (pos)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Choose a BERT model to fine-tune\n",
        "\n",
        "bert_model_name = 'small_bert/bert_en_uncased_L-2_H-128_A-2'  #@param [\"bert_en_uncased_L-12_H-768_A-12\", \"bert_en_cased_L-12_H-768_A-12\", \"bert_multi_cased_L-12_H-768_A-12\", \"small_bert/bert_en_uncased_L-2_H-128_A-2\", \"small_bert/bert_en_uncased_L-2_H-256_A-4\", \"small_bert/bert_en_uncased_L-2_H-512_A-8\", \"small_bert/bert_en_uncased_L-2_H-768_A-12\", \"small_bert/bert_en_uncased_L-4_H-128_A-2\", \"small_bert/bert_en_uncased_L-4_H-256_A-4\", \"small_bert/bert_en_uncased_L-4_H-512_A-8\", \"small_bert/bert_en_uncased_L-4_H-768_A-12\", \"small_bert/bert_en_uncased_L-6_H-128_A-2\", \"small_bert/bert_en_uncased_L-6_H-256_A-4\", \"small_bert/bert_en_uncased_L-6_H-512_A-8\", \"small_bert/bert_en_uncased_L-6_H-768_A-12\", \"small_bert/bert_en_uncased_L-8_H-128_A-2\", \"small_bert/bert_en_uncased_L-8_H-256_A-4\", \"small_bert/bert_en_uncased_L-8_H-512_A-8\", \"small_bert/bert_en_uncased_L-8_H-768_A-12\", \"small_bert/bert_en_uncased_L-10_H-128_A-2\", \"small_bert/bert_en_uncased_L-10_H-256_A-4\", \"small_bert/bert_en_uncased_L-10_H-512_A-8\", \"small_bert/bert_en_uncased_L-10_H-768_A-12\", \"small_bert/bert_en_uncased_L-12_H-128_A-2\", \"small_bert/bert_en_uncased_L-12_H-256_A-4\", \"small_bert/bert_en_uncased_L-12_H-512_A-8\", \"small_bert/bert_en_uncased_L-12_H-768_A-12\", \"albert_en_base\", \"electra_small\", \"electra_base\", \"experts_pubmed\", \"experts_wiki_books\", \"talking-heads_base\"]\n",
        "\n",
        "map_name_to_handle = {\n",
        "    'bert_en_uncased_L-12_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/3',\n",
        "    'bert_en_cased_L-12_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_cased_L-12_H-768_A-12/3',\n",
        "    'bert_multi_cased_L-12_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/bert_multi_cased_L-12_H-768_A-12/3',\n",
        "    'small_bert/bert_en_uncased_L-2_H-128_A-2':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-2_H-128_A-2/1',\n",
        "    'small_bert/bert_en_uncased_L-2_H-256_A-4':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-2_H-256_A-4/1',\n",
        "    'small_bert/bert_en_uncased_L-2_H-512_A-8':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-2_H-512_A-8/1',\n",
        "    'small_bert/bert_en_uncased_L-2_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-2_H-768_A-12/1',\n",
        "    'small_bert/bert_en_uncased_L-4_H-128_A-2':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-4_H-128_A-2/1',\n",
        "    'small_bert/bert_en_uncased_L-4_H-256_A-4':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-4_H-256_A-4/1',\n",
        "    'small_bert/bert_en_uncased_L-4_H-512_A-8':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-4_H-512_A-8/1',\n",
        "    'small_bert/bert_en_uncased_L-4_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-4_H-768_A-12/1',\n",
        "    'small_bert/bert_en_uncased_L-6_H-128_A-2':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-6_H-128_A-2/1',\n",
        "    'small_bert/bert_en_uncased_L-6_H-256_A-4':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-6_H-256_A-4/1',\n",
        "    'small_bert/bert_en_uncased_L-6_H-512_A-8':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-6_H-512_A-8/1',\n",
        "    'small_bert/bert_en_uncased_L-6_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-6_H-768_A-12/1',\n",
        "    'small_bert/bert_en_uncased_L-8_H-128_A-2':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-8_H-128_A-2/1',\n",
        "    'small_bert/bert_en_uncased_L-8_H-256_A-4':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-8_H-256_A-4/1',\n",
        "    'small_bert/bert_en_uncased_L-8_H-512_A-8':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-8_H-512_A-8/1',\n",
        "    'small_bert/bert_en_uncased_L-8_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-8_H-768_A-12/1',\n",
        "    'small_bert/bert_en_uncased_L-10_H-128_A-2':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-10_H-128_A-2/1',\n",
        "    'small_bert/bert_en_uncased_L-10_H-256_A-4':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-10_H-256_A-4/1',\n",
        "    'small_bert/bert_en_uncased_L-10_H-512_A-8':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-10_H-512_A-8/1',\n",
        "    'small_bert/bert_en_uncased_L-10_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-10_H-768_A-12/1',\n",
        "    'small_bert/bert_en_uncased_L-12_H-128_A-2':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-12_H-128_A-2/1',\n",
        "    'small_bert/bert_en_uncased_L-12_H-256_A-4':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-12_H-256_A-4/1',\n",
        "    'small_bert/bert_en_uncased_L-12_H-512_A-8':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-12_H-512_A-8/1',\n",
        "    'small_bert/bert_en_uncased_L-12_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-12_H-768_A-12/1',\n",
        "    'albert_en_base':\n",
        "        'https://tfhub.dev/tensorflow/albert_en_base/2',\n",
        "    'electra_small':\n",
        "        'https://tfhub.dev/google/electra_small/2',\n",
        "    'electra_base':\n",
        "        'https://tfhub.dev/google/electra_base/2',\n",
        "    'experts_pubmed':\n",
        "        'https://tfhub.dev/google/experts/bert/pubmed/2',\n",
        "    'experts_wiki_books':\n",
        "        'https://tfhub.dev/google/experts/bert/wiki_books/2',\n",
        "    'talking-heads_base':\n",
        "        'https://tfhub.dev/tensorflow/talkheads_ggelu_bert_en_base/1',\n",
        "}\n",
        "\n",
        "map_model_to_preprocess = {\n",
        "    'bert_en_uncased_L-12_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3',\n",
        "    'bert_en_cased_L-12_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_cased_preprocess/3',\n",
        "    'small_bert/bert_en_uncased_L-2_H-128_A-2':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3',\n",
        "    'small_bert/bert_en_uncased_L-2_H-256_A-4':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3',\n",
        "    'small_bert/bert_en_uncased_L-2_H-512_A-8':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3',\n",
        "    'small_bert/bert_en_uncased_L-2_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3',\n",
        "    'small_bert/bert_en_uncased_L-4_H-128_A-2':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3',\n",
        "    'small_bert/bert_en_uncased_L-4_H-256_A-4':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3',\n",
        "    'small_bert/bert_en_uncased_L-4_H-512_A-8':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3',\n",
        "    'small_bert/bert_en_uncased_L-4_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3',\n",
        "    'small_bert/bert_en_uncased_L-6_H-128_A-2':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3',\n",
        "    'small_bert/bert_en_uncased_L-6_H-256_A-4':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3',\n",
        "    'small_bert/bert_en_uncased_L-6_H-512_A-8':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3',\n",
        "    'small_bert/bert_en_uncased_L-6_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3',\n",
        "    'small_bert/bert_en_uncased_L-8_H-128_A-2':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3',\n",
        "    'small_bert/bert_en_uncased_L-8_H-256_A-4':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3',\n",
        "    'small_bert/bert_en_uncased_L-8_H-512_A-8':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3',\n",
        "    'small_bert/bert_en_uncased_L-8_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3',\n",
        "    'small_bert/bert_en_uncased_L-10_H-128_A-2':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3',\n",
        "    'small_bert/bert_en_uncased_L-10_H-256_A-4':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3',\n",
        "    'small_bert/bert_en_uncased_L-10_H-512_A-8':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3',\n",
        "    'small_bert/bert_en_uncased_L-10_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3',\n",
        "    'small_bert/bert_en_uncased_L-12_H-128_A-2':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3',\n",
        "    'small_bert/bert_en_uncased_L-12_H-256_A-4':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3',\n",
        "    'small_bert/bert_en_uncased_L-12_H-512_A-8':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3',\n",
        "    'small_bert/bert_en_uncased_L-12_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3',\n",
        "    'bert_multi_cased_L-12_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/bert_multi_cased_preprocess/3',\n",
        "    'albert_en_base':\n",
        "        'https://tfhub.dev/tensorflow/albert_en_preprocess/3',\n",
        "    'electra_small':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3',\n",
        "    'electra_base':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3',\n",
        "    'experts_pubmed':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3',\n",
        "    'experts_wiki_books':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3',\n",
        "    'talking-heads_base':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3',\n",
        "}\n",
        "\n",
        "tfhub_handle_encoder = map_name_to_handle[bert_model_name]\n",
        "tfhub_handle_preprocess = map_model_to_preprocess[bert_model_name]\n",
        "\n",
        "print(f'BERT model selected           : {tfhub_handle_encoder}')\n",
        "print(f'Preprocess model auto-selected: {tfhub_handle_preprocess}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vPx6RlfnwlTI",
        "outputId": "90d86cc4-6fed-404a-dcd8-61a8d3195b18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BERT model selected           : https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-2_H-128_A-2/1\n",
            "Preprocess model auto-selected: https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bert_preprocess_model = hub.KerasLayer(tfhub_handle_preprocess)"
      ],
      "metadata": {
        "id": "WGXpGif0yf5B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text_test = ['this is such an amazing movie!. I hate the movie', 'hello world']\n",
        "text_preprocessed = bert_preprocess_model(text_test)\n",
        "\n",
        "print(f'Keys       : {list(text_preprocessed.keys())}')\n",
        "print(f'Shape      : {text_preprocessed[\"input_word_ids\"].shape}')\n",
        "print(f'Word Ids   : {text_preprocessed[\"input_word_ids\"][0, :12]}')\n",
        "print(f'Input Mask : {text_preprocessed[\"input_mask\"]}')\n",
        "print(f'Type Ids   : {text_preprocessed[\"input_type_ids\"]}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4H82HBVYyhPv",
        "outputId": "7f557a70-49d1-4fba-8bcd-523e8f1c741a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Keys       : ['input_mask', 'input_type_ids', 'input_word_ids']\n",
            "Shape      : (2, 128)\n",
            "Word Ids   : [ 101 2023 2003 2107 2019 6429 3185  999 1012 1045 5223 1996]\n",
            "Input Mask : [[1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 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 0 0 0 0 0 0 0 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 0 0 0 0 0 0 0 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 0 0 0 0 0 0 0 0 0 0]\n",
            " [1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 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 0 0 0 0 0 0 0 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 0 0 0 0 0 0 0 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 0 0 0 0 0 0 0 0 0 0]]\n",
            "Type Ids   : [[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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 0 0 0 0 0 0 0 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 0 0 0 0 0 0 0 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 0 0 0 0 0 0 0 0 0 0]\n",
            " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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 0 0 0 0 0 0 0 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 0 0 0 0 0 0 0 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 0 0 0 0 0 0 0 0 0 0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bert_model = hub.KerasLayer(tfhub_handle_encoder)"
      ],
      "metadata": {
        "id": "1MtUPDdBymWC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bert_results = bert_model(text_preprocessed)\n",
        "\n",
        "print(f'Loaded BERT: {tfhub_handle_encoder}')\n",
        "print(f'Pooled Outputs Shape:{bert_results[\"pooled_output\"].shape}')\n",
        "print(f'Pooled Outputs Values:{bert_results[\"pooled_output\"][0, :12]}')\n",
        "print(f'Sequence Outputs Shape:{bert_results[\"sequence_output\"].shape}')\n",
        "print(f'Sequence Outputs Values:{bert_results[\"sequence_output\"][0, :12]}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oSxZNiU-yoto",
        "outputId": "d9349d0e-f964-4f99-8173-488970872e7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loaded BERT: https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-2_H-128_A-2/1\n",
            "Pooled Outputs Shape:(2, 128)\n",
            "Pooled Outputs Values:[-0.9999869   0.14298673 -0.99944335  0.9977272  -0.9996821   0.9681122\n",
            " -0.99642783 -0.9565576   0.09264198 -0.01836786 -0.8612362  -0.04051023]\n",
            "Sequence Outputs Shape:(2, 128, 128)\n",
            "Sequence Outputs Values:[[-0.57619894 -1.4529747  -2.6356785  ... -0.7881536  -2.3346107\n",
            "   0.10347117]\n",
            " [-1.1001804  -0.5877319   0.44278538 ... -2.1196792  -1.84661\n",
            "   0.1279445 ]\n",
            " [-0.8815449  -0.44954476  0.22649878 ... -1.4267336  -1.9974498\n",
            "   0.08139417]\n",
            " ...\n",
            " [-0.26786923 -0.87213975  0.7460682  ... -1.3083895  -1.3133665\n",
            "   1.4343864 ]\n",
            " [-0.23225716 -0.5403942  -0.19768512 ... -0.26064777 -1.9160799\n",
            "   1.5167717 ]\n",
            " [ 0.34898585 -0.44532913 -0.19966501 ... -1.0288537  -2.6912522\n",
            "   0.72709894]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def build_classifier_model():\n",
        "  text_input = tf.keras.layers.Input(shape=(), dtype=tf.string, name='text')\n",
        "  preprocessing_layer = hub.KerasLayer(tfhub_handle_preprocess, name='preprocessing')\n",
        "  encoder_inputs = preprocessing_layer(text_input)\n",
        "  encoder = hub.KerasLayer(tfhub_handle_encoder, trainable=True, name='BERT_encoder')\n",
        "  outputs = encoder(encoder_inputs)\n",
        "  net = outputs['pooled_output']\n",
        "  net = tf.keras.layers.Dropout(0.1)(net)\n",
        "  net = tf.keras.layers.Dense(1, activation=None, name='classifier')(net)\n",
        "  return tf.keras.Model(text_input, net)"
      ],
      "metadata": {
        "id": "3saqhNCLywUG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classifier_model = build_classifier_model()\n",
        "bert_raw_result = classifier_model(tf.constant(text_test))\n",
        "print(tf.sigmoid(bert_raw_result))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uHuHfauoyzCn",
        "outputId": "534e7e1b-33f1-408a-e3f4-e3f643edcc9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor(\n",
            "[[0.09972695]\n",
            " [0.11154438]], shape=(2, 1), dtype=float32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.keras.utils.plot_model(classifier_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "_vs2RFiey_3p",
        "outputId": "466a2393-1e50-4d5b-9c4b-1f98f141e2e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss = tf.keras.losses.BinaryCrossentropy(from_logits=True)\n",
        "metrics = tf.metrics.BinaryAccuracy()"
      ],
      "metadata": {
        "id": "oWSh462RzEBB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "epochs = 3\n",
        "steps_per_epoch = tf.data.experimental.cardinality(train_ds).numpy()\n",
        "num_train_steps = steps_per_epoch * epochs\n",
        "num_warmup_steps = int(0.1*num_train_steps)\n",
        "\n",
        "init_lr = 3e-5\n",
        "optimizer = optimization.create_optimizer(init_lr=init_lr,\n",
        "                                          num_train_steps=num_train_steps,\n",
        "                                          num_warmup_steps=num_warmup_steps,\n",
        "                                          optimizer_type='adamw')"
      ],
      "metadata": {
        "id": "dCp6P1zMzIO3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classifier_model.compile(optimizer=optimizer,\n",
        "                         loss=loss,\n",
        "                         metrics=metrics)"
      ],
      "metadata": {
        "id": "k5ovOQgozKmX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Training model with {tfhub_handle_encoder}')\n",
        "history = classifier_model.fit(x=train_ds,\n",
        "                               validation_data=val_ds,\n",
        "                               epochs=epochs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tMdHS8a6zMVw",
        "outputId": "d08e4551-d42f-4682-cfb7-a21c5240b42f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training model with https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-2_H-128_A-2/1\n",
            "Epoch 1/3\n",
            "625/625 [==============================] - 438s 691ms/step - loss: 0.6447 - binary_accuracy: 0.6191 - val_loss: 0.5112 - val_binary_accuracy: 0.7444\n",
            "Epoch 2/3\n",
            "625/625 [==============================] - 435s 696ms/step - loss: 0.4907 - binary_accuracy: 0.7508 - val_loss: 0.4570 - val_binary_accuracy: 0.7746\n",
            "Epoch 3/3\n",
            "625/625 [==============================] - 435s 696ms/step - loss: 0.4521 - binary_accuracy: 0.7808 - val_loss: 0.4485 - val_binary_accuracy: 0.7782\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss, accuracy = classifier_model.evaluate(test_ds)\n",
        "\n",
        "print(f'Loss: {loss}')\n",
        "print(f'Accuracy: {accuracy}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gg8wgLVI40P5",
        "outputId": "494bf95e-b0fc-469b-dd4b-0e13f65e2e50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "782/782 [==============================] - 150s 192ms/step - loss: 0.4536 - binary_accuracy: 0.7786\n",
            "Loss: 0.45358219742774963\n",
            "Accuracy: 0.7786399722099304\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history_dict = history.history\n",
        "print(history_dict.keys())\n",
        "\n",
        "acc = history_dict['binary_accuracy']\n",
        "val_acc = history_dict['val_binary_accuracy']\n",
        "loss = history_dict['loss']\n",
        "val_loss = history_dict['val_loss']\n",
        "\n",
        "epochs = range(1, len(acc) + 1)\n",
        "fig = plt.figure(figsize=(10, 6))\n",
        "fig.tight_layout()\n",
        "\n",
        "plt.subplot(2, 1, 1)\n",
        "# r is for \"solid red line\"\n",
        "plt.plot(epochs, loss, 'r', label='Training loss')\n",
        "# b is for \"solid blue line\"\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "# plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(2, 1, 2)\n",
        "plt.plot(epochs, acc, 'r', label='Training acc')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend(loc='lower right')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "o1BoAW0a5hiz",
        "outputId": "c98ecb83-a59e-41e6-ec73-89e9987e2929"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['loss', 'binary_accuracy', 'val_loss', 'val_binary_accuracy'])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x79fbbd2841c0>"
            ]
          },
          "metadata": {},
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1cAAAIjCAYAAADvBuGTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAC7vklEQVR4nOzdd1xT1/sH8E9YAWQKyFBkuXCA1kHVOlpRHHVX0W+tolZbt3VUrdbZ1tY9q9WqqLWKu1q3VqxVHHXUjQvcoKBsZCT398f9EYgkkEAgjM/79cqryeHem+derjRPzjnPkQiCIICIiIiIiIgKxUDfARAREREREZUFTK6IiIiIiIh0gMkVERERERGRDjC5IiIiIiIi0gEmV0RERERERDrA5IqIiIiIiEgHmFwRERERERHpAJMrIiIiIiIiHWByRUREREREpANMroiISrmgoCC4u7sXaN+ZM2dCIpHoNqASJjIyEhKJBMHBwcX6vqGhoZBIJAgNDVW0afq7KqqY3d3dERQUpNNjaiI4OBgSiQSRkZHF/t5ERMWJyRURURGRSCQaPXJ++CYqrLNnz2LmzJmIi4vTdyhEROWOkb4DICIqqzZv3qz0etOmTTh27Fiudm9v70K9z9q1ayGXywu077Rp0zB58uRCvT9prjC/K02dPXsWs2bNQlBQEGxsbJR+Fh4eDgMDfq9KRFRUmFwRERWRfv36Kb0+d+4cjh07lqv9XSkpKTA3N9f4fYyNjQsUHwAYGRnByIj/Kyguhfld6YJUKtXr+xMRlXX8+oqISI9at26NunXr4tKlS2jZsiXMzc3xzTffAAD++OMPdOrUCS4uLpBKpfDy8sKcOXMgk8mUjvHuPJ6s+ToLFizAmjVr4OXlBalUisaNG+PixYtK+6qacyWRSDBy5Ejs3bsXdevWhVQqRZ06dXD48OFc8YeGhqJRo0YwNTWFl5cXfvnlF43ncZ0+fRq9evVC1apVIZVK4erqiq+++gqpqam5zs/CwgLPnj1Dt27dYGFhAQcHB0yYMCHXtYiLi0NQUBCsra1hY2ODAQMGaDQ87t9//4VEIsHGjRtz/ezIkSOQSCT4888/AQCPHj3C8OHDUbNmTZiZmcHOzg69evXSaD6RqjlXmsZ87do1BAUFwdPTE6ampnBycsKgQYMQGxur2GbmzJmYOHEiAMDDw0Mx9DQrNlVzrh4+fIhevXqhYsWKMDc3x/vvv48DBw4obZM1f2z79u34/vvvUaVKFZiamqJNmza4f/9+vuetzs8//4w6depAKpXCxcUFI0aMyHXu9+7dQ8+ePeHk5ARTU1NUqVIFffr0QXx8vGKbY8eO4YMPPoCNjQ0sLCxQs2ZNxb8jIqLixK8riYj0LDY2Fh06dECfPn3Qr18/ODo6AhCLAFhYWGDcuHGwsLDAX3/9henTpyMhIQHz58/P97i///47EhMT8cUXX0AikWDevHno0aMHHj58mG8Pyj///IPdu3dj+PDhsLS0xLJly9CzZ088fvwYdnZ2AIArV66gffv2cHZ2xqxZsyCTyTB79mw4ODhodN47duxASkoKhg0bBjs7O1y4cAHLly/H06dPsWPHDqVtZTIZAgIC4OfnhwULFuD48eNYuHAhvLy8MGzYMACAIAjo2rUr/vnnH3z55Zfw9vbGnj17MGDAgHxjadSoETw9PbF9+/Zc24eEhMDW1hYBAQEAgIsXL+Ls2bPo06cPqlSpgsjISKxatQqtW7fGrVu3tOp11CbmY8eO4eHDhxg4cCCcnJxw8+ZNrFmzBjdv3sS5c+cgkUjQo0cP3L17F1u3bsXixYthb28PAGp/J9HR0WjWrBlSUlIwevRo2NnZYePGjejSpQt27tyJ7t27K23/448/wsDAABMmTEB8fDzmzZuHTz/9FOfPn9f4nLPMnDkTs2bNgr+/P4YNG4bw8HCsWrUKFy9exJkzZ2BsbIz09HQEBAQgLS0No0aNgpOTE549e4Y///wTcXFxsLa2xs2bN/Hxxx/Dx8cHs2fPhlQqxf3793HmzBmtYyIiKjSBiIiKxYgRI4R3/+y2atVKACCsXr061/YpKSm52r744gvB3NxcePv2raJtwIABgpubm+J1RESEAECws7MTXr9+rWj/448/BADC/v37FW0zZszIFRMAwcTERLh//76i7b///hMACMuXL1e0de7cWTA3NxeePXumaLt3755gZGSU65iqqDq/uXPnChKJRHj06JHS+QEQZs+erbRtgwYNhIYNGype7927VwAgzJs3T9GWmZkptGjRQgAgbNiwIc94pkyZIhgbGytds7S0NMHGxkYYNGhQnnGHhYUJAIRNmzYp2k6ePCkAEE6ePKl0Ljl/V9rErOp9t27dKgAQ/v77b0Xb/PnzBQBCREREru3d3NyEAQMGKF6PHTtWACCcPn1a0ZaYmCh4eHgI7u7ugkwmUzoXb29vIS0tTbHt0qVLBQDC9evXc71XThs2bFCK6eXLl4KJiYnQrl07xXsIgiCsWLFCACCsX79eEARBuHLligBA2LFjh9pjL168WAAgvHr1Ks8YiIiKA4cFEhHpmVQqxcCBA3O1m5mZKZ4nJiYiJiYGLVq0QEpKCu7cuZPvcQMDA2Fra6t43aJFCwDiMLD8+Pv7w8vLS/Hax8cHVlZWin1lMhmOHz+Obt26wcXFRbFdtWrV0KFDh3yPDyifX3JyMmJiYtCsWTMIgoArV67k2v7LL79Uet2iRQulczl48CCMjIwUPVkAYGhoiFGjRmkUT2BgIDIyMrB7925F29GjRxEXF4fAwECVcWdkZCA2NhbVqlWDjY0NLl++rNF7FSTmnO/79u1bxMTE4P333wcArd835/s3adIEH3zwgaLNwsICQ4cORWRkJG7duqW0/cCBA2FiYqJ4rc09ldPx48eRnp6OsWPHKhXYGDJkCKysrBTDEq2trQGIQzNTUlJUHiuraMcff/xR5MVCiIjyw+SKiEjPKleurPSBNcvNmzfRvXt3WFtbw8rKCg4ODopiGDnnm6hTtWpVpddZidabN2+03jdr/6x9X758idTUVFSrVi3XdqraVHn8+DGCgoJQsWJFxTyqVq1aAch9fqamprmGtuWMBxDnQjk7O8PCwkJpu5o1a2oUj6+vL2rVqoWQkBBFW0hICOzt7fHRRx8p2lJTUzF9+nS4urpCKpXC3t4eDg4OiIuL0+j3kpM2Mb9+/RpjxoyBo6MjzMzM4ODgAA8PDwCa3Q/q3l/Ve2VVsHz06JFSe2HuqXffF8h9niYmJvD09FT83MPDA+PGjcOvv/4Ke3t7BAQEYOXKlUrnGxgYiObNm+Pzzz+Ho6Mj+vTpg+3btzPRIiK94JwrIiI9y9kjkSUuLg6tWrWClZUVZs+eDS8vL5iamuLy5cuYNGmSRh8cDQ0NVbYLglCk+2pCJpOhbdu2eP36NSZNmoRatWqhQoUKePbsGYKCgnKdn7p4dC0wMBDff/89YmJiYGlpiX379qFv375KFRVHjRqFDRs2YOzYsWjatCmsra0hkUjQp0+fIv1A37t3b5w9exYTJ05E/fr1YWFhAblcjvbt2xdbIlHU94UqCxcuRFBQEP744w8cPXoUo0ePxty5c3Hu3DlUqVIFZmZm+Pvvv3Hy5EkcOHAAhw8fRkhICD766CMcPXq02O4dIiKAyRURUYkUGhqK2NhY7N69Gy1btlS0R0RE6DGqbJUqVYKpqanKSnGaVI+7fv067t69i40bN6J///6K9mPHjhU4Jjc3N5w4cQJJSUlKPUHh4eEaHyMwMBCzZs3Crl274OjoiISEBPTp00dpm507d2LAgAFYuHChou3t27cFWrRX05jfvHmDEydOYNasWZg+fbqi/d69e7mOqUmlxpzvr+r6ZA07dXNz0/hY2sg6bnh4ODw9PRXt6enpiIiIgL+/v9L29erVQ7169TBt2jScPXsWzZs3x+rVq/Hdd98BAAwMDNCmTRu0adMGixYtwg8//ICpU6fi5MmTuY5FRFSUOCyQiKgEyvq2PWePQHp6On7++Wd9haTE0NAQ/v7+2Lt3L54/f65ov3//Pg4dOqTR/oDy+QmCgKVLlxY4po4dOyIzMxOrVq1StMlkMixfvlzjY3h7e6NevXoICQlBSEgInJ2dlZLbrNjf7alZvnx5rrLwuoxZ1fUCgCVLluQ6ZoUKFQBAo2SvY8eOuHDhAsLCwhRtycnJWLNmDdzd3VG7dm1NT0Ur/v7+MDExwbJly5TOad26dYiPj0enTp0AAAkJCcjMzFTat169ejAwMEBaWhoAcbjku+rXrw8Aim2IiIoLe66IiEqgZs2awdbWFgMGDMDo0aMhkUiwefPmIh1+pa2ZM2fi6NGjaN68OYYNGwaZTIYVK1agbt26uHr1ap771qpVC15eXpgwYQKePXsGKysr7Nq1S+u5Ozl17twZzZs3x+TJkxEZGYnatWtj9+7dWs9HCgwMxPTp02FqaorBgwcrFVwAgI8//hibN2+GtbU1ateujbCwMBw/flxRor4oYrayskLLli0xb948ZGRkoHLlyjh69KjKnsyGDRsCAKZOnYo+ffrA2NgYnTt3ViRdOU2ePBlbt25Fhw4dMHr0aFSsWBEbN25EREQEdu3alevcdcXBwQFTpkzBrFmz0L59e3Tp0gXh4eH4+eef0bhxY8Xcwr/++gsjR45Er169UKNGDWRmZmLz5s0wNDREz549AQCzZ8/G33//jU6dOsHNzQ0vX77Ezz//jCpVqigV6iAiKg5MroiISiA7Ozv8+eefGD9+PKZNmwZbW1v069cPbdq0Uay3pG8NGzbEoUOHMGHCBHz77bdwdXXF7Nmzcfv27XyrGRobG2P//v2K+TOmpqbo3r07Ro4cCV9f3wLFY2BggH379mHs2LH47bffIJFI0KVLFyxcuBANGjTQ+DiBgYGYNm0aUlJSlKoEZlm6dCkMDQ2xZcsWvH37Fs2bN8fx48cL9HvRJubff/8do0aNwsqVKyEIAtq1a4dDhw4pVWsEgMaNG2POnDlYvXo1Dh8+DLlcjoiICJXJlaOjI86ePYtJkyZh+fLlePv2LXx8fLB//35F71FRmTlzJhwcHLBixQp89dVXqFixIoYOHYoffvhBsQ6br68vAgICsH//fjx79gzm5ubw9fXFoUOHFJUSu3TpgsjISKxfvx4xMTGwt7dHq1atMGvWLEW1QSKi4iIRStLXoEREVOp169YNN2/eVDkfiIiIqCzjnCsiIiqw1NRUpdf37t3DwYMH0bp1a/0EREREpEfsuSIiogJzdnZGUFCQYm2iVatWIS0tDVeuXEH16tX1HR4REVGx4pwrIiIqsPbt22Pr1q2IioqCVCpF06ZN8cMPPzCxIiKicok9V0RERERERDrAOVdEREREREQ6wOSKiIiIiIhIBzjnSgW5XI7nz5/D0tISEolE3+EQEREREZGeCIKAxMREuLi45Lu4OpMrFZ4/fw5XV1d9h0FERERERCXEkydPUKVKlTy3YXKlgqWlJQDxAlpZWek5GiIiIiIi0peEhAS4uroqcoS8MLlSIWsooJWVFZMrIiIiIiLSaLoQC1oQERERERHpAJMrIiIiIiIiHWByRUREREREpAOcc0VEREREpY4gCMjMzIRMJtN3KFTKGRoawsjISCdLMDG5KukyMgBjY31HQURERFRipKen48WLF0hJSdF3KFRGmJubw9nZGSYmJoU6DpOrkm7KFODcOWD8eKBLF8DQUN8REREREemNXC5HREQEDA0N4eLiAhMTE530OFD5JAgC0tPT8erVK0RERKB69er5LhScFyZXJVl6OrBpE/DqFXDmDODlBXz1FRAUBFSooO/oiIiIiIpdeno65HI5XF1dYW5uru9wqAwwMzODsbExHj16hPT0dJiamhb4WHovaLFy5Uq4u7vD1NQUfn5+uHDhQp7bx8XFYcSIEXB2doZUKkWNGjVw8OBBxc9nzpwJiUSi9KhVq1ZRn0bRMDEB/vsP+OYbwNYWePAAGDkScHUV21680HeERERERHpRmN4Fonfp6n7S610ZEhKCcePGYcaMGbh8+TJ8fX0REBCAly9fqtw+PT0dbdu2RWRkJHbu3Inw8HCsXbsWlStXVtquTp06ePHiheLxzz//FMfpFA1nZ+D774EnT4AVK8TeqzdvgLlzATc3sRfr2jV9R0lEREREVO7pNblatGgRhgwZgoEDB6J27dpYvXo1zM3NsX79epXbr1+/Hq9fv8bevXvRvHlzuLu7o1WrVvD19VXazsjICE5OToqHvb19cZxO0apQARgxAggPB3bvBpo3F4tdbNwI+PoC7doBR44AgqDvSImIiIiIyiW9JVfp6em4dOkS/P39s4MxMIC/vz/CwsJU7rNv3z40bdoUI0aMgKOjI+rWrYsffvghVwnOe/fuwcXFBZ6envj000/x+PHjPGNJS0tDQkKC0qPEMjQEuncH/vlHLHTRqxdgYAAcOwa0bw/4+AAbNgBpafqOlIiIiIiKmLu7O5YsWaLx9qGhoZBIJIiLiyuymAAgODgYNjY2RfoeJZHekquYmBjIZDI4OjoqtTs6OiIqKkrlPg8fPsTOnTshk8lw8OBBfPvtt1i4cCG+++47xTZ+fn4IDg7G4cOHsWrVKkRERKBFixZITExUG8vcuXNhbW2teLi6uurmJIuanx+wfTtw/z4wZgxgYQHcuAEMGgS4u4vDCWNj9R0lERERUbn3bk2Adx8zZ84s0HEvXryIoUOHarx9s2bN8OLFC1hbWxfo/ShvpWomoFwuR6VKlbBmzRo0bNgQgYGBmDp1KlavXq3YpkOHDujVqxd8fHwQEBCAgwcPIi4uDtu3b1d73ClTpiA+Pl7xePLkSXGcju54eABLlojzsn76CahcGYiKAqZNE4tfDB8O3Lun7yiJiIiIyq2c9QCWLFkCKysrpbYJEyYots1aIFkTDg4OWlVNNDExgZOTE8vXFxG9JVf29vYwNDREdHS0Unt0dDScnJxU7uPs7IwaNWrAMMdaT97e3oiKikJ6errKfWxsbFCjRg3cv39fbSxSqRRWVlZKj1LJxgb4+mvg4UNg82agfn0gNRVYtQqoWRPo1g04fZrzsoiIiKhsEQQgOVk/Dw0/V+WsB2BtbQ2JRKJ4fefOHVhaWuLQoUNo2LAhpFIp/vnnHzx48ABdu3aFo6MjLCws0LhxYxw/flzpuO8OC5RIJPj111/RvXt3mJubo3r16ti3b5/i5+8OC8wavnfkyBF4e3vDwsIC7du3x4scVakzMzMxevRo2NjYwM7ODpMmTcKAAQPQrVs3rX5Nq1atgpeXF0xMTFCzZk1s3rw5x69QwMyZM1G1alVIpVK4uLhg9OjRip///PPPqF69OkxNTeHo6IhPPvlEq/cuLnpLrkxMTNCwYUOcOHFC0SaXy3HixAk0bdpU5T7NmzfH/fv3IZfLFW13797NczXlpKQkPHjwAM7Ozro9gZLMxATo1w+4fBn46y+gUyfxH/4ffwAtW4rDCUNCAA2/ESEiIiIq0VJSxOkR+nikpOjsNCZPnowff/wRt2/fho+PD5KSktCxY0ecOHECV65cQfv27dG5c+d86wnMmjULvXv3xrVr19CxY0d8+umneP36dR6XLwULFizA5s2b8ffff+Px48dKPWk//fQTtmzZgg0bNuDMmTNISEjA3r17tTq3PXv2YMyYMRg/fjxu3LiBL774AgMHDsTJkycBALt27cLixYvxyy+/4N69e9i7dy/q1asHAPj3338xevRozJ49G+Hh4Th8+DBatmyp1fsXG0GPtm3bJkilUiE4OFi4deuWMHToUMHGxkaIiooSBEEQPvvsM2Hy5MmK7R8/fixYWloKI0eOFMLDw4U///xTqFSpkvDdd98pthk/frwQGhoqRERECGfOnBH8/f0Fe3t74eXLlxrHFR8fLwAQ4uPjdXey+nbrliAMGSIIUqkgiKmWILi5CcKiRYJQls6TiIiIyrTU1FTh1q1bQmpqanZjUlL255vifiQlaX0OGzZsEKytrRWvT548KQAQ9u7dm+++derUEZYvX6547ebmJixevFjxGoAwbdq0HJcmSQAgHDp0SOm93rx5o4gFgHD//n3FPitXrhQcHR0Vrx0dHYX58+crXmdmZgpVq1YVunbtqvE5NmvWTBgyZIjSNr169RI6duwoCIIgLFy4UKhRo4aQnp6e61i7du0SrKyshISEBLXvV1gq76v/p01uoNc5V4GBgViwYAGmT5+O+vXr4+rVqzh8+LCiyMXjx4+VuiRdXV1x5MgRXLx4ET4+Phg9ejTGjBmDyZMnK7Z5+vQp+vbti5o1a6J3796ws7PDuXPn4ODgUOznV6J4ewNr1gCPHwMzZgD29sCjR8C4ceK8rIkTxTlbRERERKWNuTmQlKSfhxbznfLTqFEjpddJSUmYMGECvL29YWNjAwsLC9y+fTvfnisfHx/F8woVKsDKykrtOrIAYG5uDi8vL8VrZ2dnxfbx8fGIjo5GkyZNFD83NDREw4YNtTq327dvo3nz5kptzZs3x+3btwEAvXr1QmpqKjw9PTFkyBDs2bNHMe+sbdu2cHNzg6enJz777DNs2bIFKTrsMdQlI30HMHLkSIwcOVLlz0JDQ3O1NW3aFOfOnVN7vG3btukqtLKpUiVg5kxg0iRxXtaiReLaWQsWiEUxevcGxo8H3ntP35ESERERaUYiEdcELeUqvHMOEyZMwLFjx7BgwQJUq1YNZmZm+OSTT9TWGshibGys9FoikShNq9Fke6GY5+i7uroiPDwcx48fx7FjxzB8+HDMnz8fp06dgqWlJS5fvozQ0FAcPXoU06dPx8yZM3Hx4sUSV+69VFULJB0yMwOGDgVu3QL27wc+/FCcg/X770DDhuLr/fuBPP4hEhEREVHROXPmDIKCgtC9e3fUq1cPTk5OiIyMLNYYrK2t4ejoiIsXLyraZDIZLl++rNVxvL29cebMGaW2M2fOoHbt2orXZmZm6Ny5M5YtW4bQ0FCEhYXh+vXrAAAjIyP4+/tj3rx5uHbtGiIjI/HXX38V4syKht57rkjPDAyAjz8WH5cviz1ZISFAaKj4qFkT+OoroH9/MSEjIiIiomJRvXp17N69G507d4ZEIsG3336bZw9UURk1ahTmzp2LatWqoVatWli+fDnevHmjVTn3iRMnonfv3mjQoAH8/f2xf/9+7N69W1H9MDg4GDKZDH5+fjA3N8dvv/0GMzMzuLm54c8//8TDhw/RsmVL2Nra4uDBg5DL5ahZs2ZRnXKBseeKsr33HvDbb2Ip94kTASsrccjgl18CVauKc7XyGK9LRERERLqzaNEi2NraolmzZujcuTMCAgLwnh6mbkyaNAl9+/ZF//790bRpU1hYWCAgIACmpqYaH6Nbt25YunQpFixYgDp16uCXX37Bhg0b0Lp1awDi8klr165F8+bN4ePjg+PHj2P//v2ws7ODjY0Ndu/ejY8++gje3t5YvXo1tm7dijp16hTRGRecRCjuAZWlQEJCAqytrREfH19617zShcREYN06cS7Wo0dim1QKfPaZWAjD21uv4REREVH58/btW0RERMDDw0OrD/ekO3K5HN7e3ujduzfmzJmj73B0Iq/7SpvcgD1XpJ6lJTB2LHD/vjhUsEkTIC0N+PVXoHZtcf2sv/7iosREREREZdijR4+wdu1a3L17F9evX8ewYcMQERGB//3vf/oOrcRhckX5MzISqwieOwecPg106yZW5Tl4EGjTRhxOuHkzkE/lGiIiIiIqfQwMDBAcHIzGjRujefPmuH79Oo4fPw5vjmLKhcMCVeCwQA3cuycOF9ywAUhNFdsqVwZGjQK++AIoYWUxiYiIqGzgsEAqChwWSPpVvTqwcqW48PD33wNOTsCzZ8DkyUCVKsCYMUBEhL6jJCIiIiIqNkyuqHDs7IBvvgEiI8VerLp1geRkYNkyoFo1oFcvcTghEREREVEZx+SKdEMqBYKCgGvXgCNHgHbtxAWId+4EmjYFmjcHdu8GZDJ9R0pEREREVCSYXJFuSSRiYnXkiJhoBQUBxsbA2bNAz57iosQrVoi9W0REREREZQiTKyo69eqJQwUfPRKHDtraAg8eiEUvXF3FtufP9R0lEREREZFOMLmioufsLBa9ePJELIJRrRrw5g0wdy7g7g4MGCD2chERERERlWJMrqj4VKgADB8O3LkD7NkDfPABkJEBbNoE+PoCbdsChw9zUWIiIiIiNVq3bo2xY8cqXru7u2PJkiV57iORSLB3795Cv7eujpOXmTNnon79+kX6HkWJyRUVP0NDcSHi06eB8+fFBYoNDIDjx4EOHcThhOvXA2lp+o6UiIiISCc6d+6M9u3bq/zZ6dOnIZFIcK0AI3kuXryIoUOHFjY8JeoSnBcvXqBDhw46fa+yhskV6VeTJkBIiDgXa+xYwMICuHkTGDwYcHMDvvsOiI3Vd5REREREhTJ48GAcO3YMT58+zfWzDRs2oFGjRvDx8dH6uA4ODjA3N9dFiPlycnKCVCotlvcqrZhcUcng7g4sXizOy5o3T1yIODoa+PZbsfjFsGHA3bv6jpKIiIhKIEEQCxHr46HpbIaPP/4YDg4OCA4OVmpPSkrCjh07MHjwYMTGxqJv376oXLkyzM3NUa9ePWzdujXP4747LPDevXto2bIlTE1NUbt2bRw7dizXPpMmTUKNGjVgbm4OT09PfPvtt8jIyAAABAcHY9asWfjvv/8gkUggkUgUMb87LPD69ev46KOPYGZmBjs7OwwdOhRJSUmKnwcFBaFbt25YsGABnJ2dYWdnhxEjRijeSxNyuRyzZ89GlSpVIJVKUb9+fRw+fFjx8/T0dIwcORLOzs4wNTWFm5sb5s6dCwAQBAEzZ85E1apVIZVK4eLigtGjR2v83gVhVKRHJ9KWjQ0wcaLYi7V9O7BwIXDlCrB6NfDLL0DnzsD48UCLFmLZdyIiIir3UlLEwS/6kJQkTivPj5GREfr374/g4GBMnToVkv//HLNjxw7IZDL07dsXSUlJaNiwISZNmgQrKyscOHAAn332Gby8vNCkSZN830Mul6NHjx5wdHTE+fPnER8frzQ/K4ulpSWCg4Ph4uKC69evY8iQIbC0tMTXX3+NwMBA3LhxA4cPH8bx48cBANbW1rmOkZycjICAADRt2hQXL17Ey5cv8fnnn2PkyJFKCeTJkyfh7OyMkydP4v79+wgMDET9+vUxZMiQ/C8agKVLl2LhwoX45Zdf0KBBA6xfvx5dunTBzZs3Ub16dSxbtgz79u3D9u3bUbVqVTx58gRPnjwBAOzatQuLFy/Gtm3bUKdOHURFReG///7T6H0LTKBc4uPjBQBCfHy8vkMhuVwQTp4UhI8/FgTxyyHx0aiRIGzdKggZGfqOkIiIiIpRamqqcOvWLSE1NVXRlpSk/DGhOB9JSZrHfvv2bQGAcPLkSUVbixYthH79+qndp1OnTsL48eMVr1u1aiWMGTNG8drNzU1YvHixIAiCcOTIEcHIyEh49uyZ4ueHDh0SAAh79uxR+x7z588XGjZsqHg9Y8YMwdfXN9d2OY+zZs0awdbWVkjKcQEOHDggGBgYCFFRUYIgCMKAAQMENzc3ITMzU7FNr169hMDAQLWxvPveLi4uwvfff6+0TePGjYXhw4cLgiAIo0aNEj766CNBLpfnOtbChQuFGjVqCOnp6WrfL4uq+yqLNrkBhwVSySaRAK1bA/v3A7dvA0OHAqamwL//An37Al5ewKJFQEKCviMlIiIiPTE3F3uQ9PHQZrpTrVq10KxZM6xfvx4AcP/+fZw+fRqDBw8GAMhkMsyZMwf16tVDxYoVYWFhgSNHjuDx48caHf/27dtwdXWFi4uLoq1p06a5tgsJCUHz5s3h5OQECwsLTJs2TeP3yPlevr6+qJCj26558+aQy+UIDw9XtNWpUweGhoaK187Oznj58qVG75GQkIDnz5+jefPmSu3NmzfH7du3AYhDD69evYqaNWti9OjROHr0qGK7Xr16ITU1FZ6enhgyZAj27NmDzMxMrc5TW0yuqPSoVUscGvj4MTBzJuDgID4fP16clzVhgviaiIiIyhWJRByap4+HtrMUBg8ejF27diExMREbNmyAl5cXWrVqBQCYP38+li5dikmTJuHkyZO4evUqAgICkJ6errNrFRYWhk8//RQdO3bEn3/+iStXrmDq1Kk6fY+cjI2NlV5LJBLI5XKdHf+9995DREQE5syZg9TUVPTu3RuffPIJAMDV1RXh4eH4+eefYWZmhuHDh6Nly5ZazfnSFpMrKn0cHIAZM4BHj4A1a8SkKyFBnJ/l6Qn873/ApUv6jpKIiIgol969e8PAwAC///47Nm3ahEGDBinmX505cwZdu3ZFv3794OvrC09PT9zVoqCXt7c3njx5ghcvXijazp07p7TN2bNn4ebmhqlTp6JRo0aoXr06Hj16pLSNiYkJZDJZvu/133//ITk5WdF25swZGBgYoGbNmhrHnBcrKyu4uLjgzJkzSu1nzpxB7dq1lbYLDAzE2rVrERISgl27duH169cAADMzM3Tu3BnLli1DaGgowsLCcP36dZ3EpwqTKyq9zMyAIUPE0u1//gl8+CEgkwFbtwKNGmUPJ9ThtyNEREREhWFhYYHAwEBMmTIFL168QFBQkOJn1atXx7Fjx3D27Fncvn0bX3zxBaKjozU+tr+/P2rUqIEBAwbgv//+w+nTpzF16lSlbapXr47Hjx9j27ZtePDgAZYtW4Y9e/YobePu7o6IiAhcvXoVMTExSFOx9uinn34KU1NTDBgwADdu3MDJkycxatQofPbZZ3B0dNTuouRh4sSJ+OmnnxASEoLw8HBMnjwZV69exZgxYwAAixYtwtatW3Hnzh3cvXsXO3bsgJOTE2xsbBAcHIx169bhxo0bePjwIX777TeYmZnBzc1NZ/G9i8kVlX4GBkCnTsBffwGXLwP9+gFGRsCpU0CXLoC3t1htMCVF35ESERERYfDgwXjz5g0CAgKU5kdNmzYN7733HgICAtC6dWs4OTmhW7duGh/XwMAAe/bsQWpqKpo0aYLPP/8c33//vdI2Xbp0wVdffYWRI0eifv36OHv2LL799lulbXr27In27dvjww8/hIODg8py8Obm5jhy5Ahev36Nxo0b45NPPkGbNm2wYsUK7S5GPkaPHo1x48Zh/PjxqFevHg4fPox9+/ahevXqAMTKh/PmzUOjRo3QuHFjREZG4uDBgzAwMICNjQ3Wrl2L5s2bw8fHB8ePH8f+/fthZ2en0xhzkgiCptX5y4+EhARYW1sjPj4eVlZW+g6HCuLpU2D5cnGOVny82GZnBwwfDowYAejwGxUiIiIqPm/fvkVERAQ8PDxgamqq73CojMjrvtImN2DPFZVNVaoAP/0kLkq8ZIm4SHFsLDBnDuDmBnz+OXDrlr6jJCIiIqIyhMkVlW2WlsCYMcC9e+KixH5+QFoasG4dUKcO0LEjcOKE5surExERERGpweSKygcjI6BXLyAsDPjnH6B7d7F26qFDgL8/0KABsGkTUERlSImIiIio7GNyReWLRAI0bw7s3g3cvSvOvzI3B/77DxgwAPDwAH78EXjzRt+REhEREVEpw+SKyq9q1YAVK8R5Wd9/Dzg5Ac+fA1OmiIsSjx4NPHyo7yiJiIhIBdZkI13S1f3E5IqoYkXgm2+AyEggOBioVw9ITharDVavDnzyiTickIiIiPTO2NgYAJDCJVZIh7Lup6z7q6BYil0FlmIv5wQBOH4cWLgQOHIku71pU2D8eKBbN8DQUG/hERERlXcvXrxAXFwcKlWqBHNzc0gkEn2HRKWUIAhISUnBy5cvYWNjA2dn51zbaJMbMLlSgckVKdy4ASxaBGzZkl3swtMTGDsWGDgQsLDQa3hERETlkSAIiIqKQlxcnL5DoTLCxsYGTk5OKhN1JleFxOSKcomKEudnrVoFvH4tttnYAF9+CYwaBeRYXZ2IiIiKh0wmQ0ZGhr7DoFLO2NgYhnmMSmJyVUhMrkit5GRg40Zg8WLg/n2xzdgY6NsXGDcO8PXVb3xEREREpFPa5AYsaEGkjQoVgOHDgTt3gL17gRYtgIwMcY2s+vXFNbMOHeKixERERETlkN6Tq5UrV8Ld3R2mpqbw8/PDhQsX8tw+Li4OI0aMgLOzM6RSKWrUqIGDBw8W6phEWjM0BLp2Bf7+Gzh/HggMFNtOnAA6dgTq1gXWrQPevtV3pERERERUTPSaXIWEhGDcuHGYMWMGLl++DF9fXwQEBODly5cqt09PT0fbtm0RGRmJnTt3Ijw8HGvXrkXlypULfEyiQmvSBNi2TRwm+NVXgKUlcOsW8PnngJsbMGcOEBOj7yiJiIiIqIjpdc6Vn58fGjdujBUrVgAA5HI5XF1dMWrUKEyePDnX9qtXr8b8+fNx584dtTXotT2mKpxzRYUSHw+sXQssXQo8fSq2mZkBAwaIyVeNGvqNj4iIiIg0VirmXKWnp+PSpUvw9/fPDsbAAP7+/ghTs2Drvn370LRpU4wYMQKOjo6oW7cufvjhB8hksgIfEwDS0tKQkJCg9CAqMGtrYMIE4OFDsYT7e+8BqanA6tVArVricMJTpzgvi4iIiKiM0VtyFRMTA5lMBkdHR6V2R0dHREVFqdzn4cOH2LlzJ2QyGQ4ePIhvv/0WCxcuxHfffVfgYwLA3LlzYW1trXi4uroW8uyIIFYR/N//gH//BU6eBD7+WEyo9u0DWrcWhxNu3SoWxCAiIiKiUk/vBS20IZfLUalSJaxZswYNGzZEYGAgpk6ditWrVxfquFOmTEF8fLzi8eTJEx1FTARAIhGTqf37gdu3gS++AExNxaTrf/8DqlUDFi4UhxMSERERUamlt+TK3t4ehoaGiI6OVmqPjo6Gk5OTyn2cnZ1Ro0YNpUW+vL29ERUVhfT09AIdEwCkUimsrKyUHkRFolYtcXjg48fArFmAg4P4fMIEwNUVGD9efE1EREREpY7ekisTExM0bNgQJ06cULTJ5XKcOHECTZs2VblP8+bNcf/+fcjlckXb3bt34ezsDBMTkwIdk0gvHByA6dPFRGrtWsDbG0hMBBYtAjw9xUWJ//1X31ESERERkRb0Oixw3LhxWLt2LTZu3Ijbt29j2LBhSE5OxsCBAwEA/fv3x5QpUxTbDxs2DK9fv8aYMWNw9+5dHDhwAD/88ANGjBih8TGJShRTU7Fk+40bwIEDwEcfATKZWNq9cWOgVStxjlaOLxSIiIiIqGQy0uebBwYG4tWrV5g+fTqioqJQv359HD58WFGQ4vHjxzAwyM7/XF1dceTIEXz11Vfw8fFB5cqVMWbMGEyaNEnjYxKVSAYG4uLDHTsCV66IPVjbtomLFP/9t1i+/auvgP79AXNzfUdLRERERCrodZ2rkorrXFGJ8PQpsHw58Msv2cUu7OyAYcOAkSMBfmFAREREVORKxTpXRJSPKlWAn34Sk6ylSwEPDyA2FvjuO6BqVWDwYODmTX1HSURERET/j8kVUUlnYQGMHg3cvQvs2AG8/z6Qng6sXw/UrQt06AAcP85FiYmIiIj0jMkVUWlhZAR88gkQFgacOQP06CGuoXX4MNC2LdCgAbBpk5h4EREREVGxY3JFVBo1awbs2gXcuyfOvzI3B/77DxgwQBw++OOPwJs3+o6SiIiIqFxhckVUmnl5iUUvnjwBfvgBcHYGnj8HpkwR52yNGgU8eKDvKImIiIjKBSZXRGVBxYpiQhUZCWzcCPj4ACkpwIoVYhn3nj2Bs2f1HSURERFRmcbkiqgsMTER18K6ehU4dgxo315cgHj3bqB5c6BpU2DnTnGhYiIiIiLSKSZXRGWRRAL4+wOHDgE3bgCDBomJ17lzQK9eQPXqwLJlQFKSviMlIiIiKjOYXBGVdXXqAOvWAY8eAdOmiUMIIyKAMWMAV1dg8mTg2TN9R0lERERU6jG5IiovnJyAOXPE4hc//yz2XsXFiQsVu7tnDyckIiIiogJhckVU3pibA8OGAXfuAHv3Ai1aAJmZwObN4lpZbdoABw+Kc7WIiIiISGNMrojKKwMDoGtX4O+/gQsXgMBAwNAQ+OsvoFMnoG5d4Ndfgbdv9R0pERERUanA5IqIgMaNgW3bxDWxxo0DLC2B27eBIUMANzdg9mwgJkbfURIRERGVaEyuiCibmxuwcKE4L2vBArHgxcuXwIwZ4vMvvwTCw/UdJREREVGJxOSKiHKztgbGjxd7sn7/HWjYUBwe+MsvQK1aQJcuwKlTgCDoO1IiIiKiEoPJFRGpZ2wM9O0LXLwIhIYCnTuL7fv3A61bi8MJt24FMjL0GSURERFRicDkiojyJ5EArVoB+/aJVQa/+AIwNQUuXQL+9z/Ay0scRhgfr+9IiYiIiPSGyRURaadmTWD1auDxY7HQRaVK4hytiRPFeVnjxokLFhMRERGVM0yuiKhgHByAb78VE6lffwVq1wYSE4HFi8WerD59xOGEREREROUEkysiKhxTU2DwYOD6dXHx4TZtAJkMCAkBmjQBWrYE/viDixITERFRmcfkioh0w8AA6NABOH4cuHIF+OwzwMgIOH0a6NZNrDK4ahWQkqLvSImIiIiKBJMrItK9+vWBTZuAyEhg0iTAxga4dw8YPhyoWlUcThgVpecgiYiIiHSLyRURFZ3KlYEffxQLXixdCnh4ALGxwHffiQsWDxoE3Lih7yiJiIiIdILJFREVPQsLYPRosfdqxw7g/feB9HRgwwagXj2gfXvg2DEuSkxERESlGpMrIio+hobAJ58AYWHA2bNAz57iXK0jR4B27cThhBs3iokXERERUSlToOTqyZMnePr0qeL1hQsXMHbsWKxZs0ZngRFRGde0KbBzp9ibNWoUUKECcO0aEBQEuLsDc+cCr1/rO0oiIiIijRUoufrf//6HkydPAgCioqLQtm1bXLhwAVOnTsXs2bN1GiARlXGensCyZeK8rLlzARcX4MUL4JtvxEWJR40CHjzQd5RERERE+SpQcnXjxg00adIEALB9+3bUrVsXZ8+exZYtWxAcHKzL+IiovLC1BSZPBiIixKGBPj5i2fYVK4Dq1cUhhGfP6jtKIiIiIrUKlFxlZGRAKpUCAI4fP44uXboAAGrVqoUXL17oLjoiKn9MTID+/YGrV8UiFx06iIUudu8GmjcXhxPu2AFkZuo7UiIiIiIlBUqu6tSpg9WrV+P06dM4duwY2rdvDwB4/vw57OzsdBogEZVTEgng7w8cPCiWax88WEy8zp0DevcWe7OWLgUSE/UdKRERERGAAiZXP/30E3755Re0bt0affv2ha+vLwBg3759iuGCREQ6U6cO8OuvwOPH4gLEdnbiAsVjx4rzsiZNAnIU2SEiIiLSB4kgFGxhGZlMhoSEBNja2iraIiMjYW5ujkqVKuksQH1ISEiAtbU14uPjYWVlpe9wiOhdKSnApk3AokVitUEAMDIC+vQBxo8XS7oTERER6YA2uUGBeq5SU1ORlpamSKwePXqEJUuWIDw8vNQnVkRUCpibA19+Cdy5A/zxB9CypTgH67ffgAYNgDZtxOGEcrm+IyUiIqJypEDJVdeuXbFp0yYAQFxcHPz8/LBw4UJ069YNq1at0mmARERqGRgAXboAp04BFy+KPVeGhsBffwGdOgF16wJr1wJv3+o7UiIiIioHCpRcXb58GS1atAAA7Ny5E46Ojnj06BE2bdqEZcuW6TRAIiKNNGoEbN0KPHwIjBsHWFoCt28DQ4cCVasCs2YBr17pO0oiIiIqwwqUXKWkpMDS0hIAcPToUfTo0QMGBgZ4//338ejRI62Pt3LlSri7u8PU1BR+fn64cOGC2m2Dg4MhkUiUHqampkrbBAUF5domq6IhEZVxVasCCxeKBS4WLhRfv3oFzJwpPv/iCyA8XN9REhERURlUoOSqWrVq2Lt3L548eYIjR46gXbt2AICXL19qXQAiJCQE48aNw4wZM3D58mX4+voiICAAL1++VLuPlZUVXrx4oXioSujat2+vtM3WrVu1O0kiKt2srMQerAcPxB6tRo3E4YFr1gC1agGdOwOhoeIaWkREREQ6UKDkavr06ZgwYQLc3d3RpEkTNG3aFIDYi9WgQQOtjrVo0SIMGTIEAwcORO3atbF69WqYm5tj/fr1aveRSCRwcnJSPBwdHXNtI5VKlbbJWdWQiMqRrCqCFy6Ic7O6dBHX0PrzT+DDD8Wk6/ffgYwMfUdKREREpVyBkqtPPvkEjx8/xr///osjR44o2tu0aYPFixdrfJz09HRcunQJ/v7+2QEZGMDf3x9hYWFq90tKSoKbmxtcXV3RtWtX3Lx5M9c2oaGhqFSpEmrWrIlhw4YhNjZW7fHS0tKQkJCg9CCiMkYiEasK/vGHWGXwyy8BU1Pg8mXg008BT09gwQIgPl7fkRIREVEpVaDkCgCcnJzQoEEDPH/+HE//f/HOJk2aoFatWhofIyYmBjKZLFfPk6OjI6KiolTuU7NmTaxfvx5//PEHfvvtN8jlcjRr1kwRAyAOCdy0aRNOnDiBn376CadOnUKHDh0gk8lUHnPu3LmwtrZWPFxdXTU+ByIqhWrUAFatAp48AWbPBipVEudoTZwIVKkCfPWVuEgxERERkRYKtIiwXC7Hd999h4ULFyIpKQkAYGlpifHjx2Pq1KkwMNAsZ3v+/DkqV66Ms2fPKoYWAsDXX3+NU6dO4fz58/keIyMjA97e3ujbty/mzJmjcpuHDx/Cy8sLx48fR5s2bXL9PC0tDWlpaYrXCQkJcHV15SLCROXF27fi0MCFC4Fbt8Q2AwPgk0/ERYmbNNFvfERERKQ3Rb6I8NSpU7FixQr8+OOPuHLlCq5cuYIffvgBy5cvx7fffqvxcezt7WFoaIjo6Gil9ujoaDg5OWl0DGNjYzRo0AD3799Xu42npyfs7e3VbiOVSmFlZaX0IKJyxNQUGDQIuHEDOHQI8PcXFyDevh3w8wNatAD27gXU9H4TERERAQVMrjZu3Ihff/0Vw4YNg4+PD3x8fDB8+HCsXbsWwcHBGh/HxMQEDRs2xIkTJxRtcrkcJ06cUOrJyotMJsP169fh7OysdpunT58iNjY2z22IiCCRAO3bA8eOAVevAv37A8bGwD//AN27i1UGf/4ZSEnRd6RERERUAhUouXr9+rXKuVW1atXC69evtTrWuHHjsHbtWmzcuBG3b9/GsGHDkJycjIEDBwIA+vfvjylTpii2nz17No4ePYqHDx/i8uXL6NevHx49eoTPP/8cgFjsYuLEiTh37hwiIyNx4sQJdO3aFdWqVUNAQEBBTpeIyiNfX2DjRiAiApg8GbCxAe7fB0aMAFxdgWnTADVzQ4mIiKh8KlBy5evrixUrVuRqX7FiBXx8fLQ6VmBgIBYsWIDp06ejfv36uHr1Kg4fPqwocvH48WO8ePFCsf2bN28wZMgQeHt7o2PHjkhISMDZs2dRu3ZtAIChoSGuXbuGLl26oEaNGhg8eDAaNmyI06dPQyqVFuR0iag8q1wZmDtXLH6xbJlYVfD1a+D77wE3N2DgQHE4IREREZV7BSpocerUKXTq1AlVq1ZVDN8LCwvDkydPcPDgQbRo0ULngRYnbSatEVE5I5OJ868WLgRyLhnRrp1Y/KJtW3F4IREREZUJRV7QolWrVrh79y66d++OuLg4xMXFoUePHrh58yY2b95coKCJiEoFQ0OgZ0/g7Fnx0bOnWFnw6FEgIEAcThgcDOSoQEpERETlQ4F6rtT577//8N5776ldT6q0YM8VEWnl4UNg6VJg3TogOVlsc3YGRo4UFyuuWFG/8REREVGBFXnPFRER5eDpKSZXT54AP/4IuLgAL14AU6eKxS9GjhSLYRAREVGZxuSKiEhXbG2BSZPECoObNolDBFNSgJUrgRo1gB49gDNnAN0NGCAiIqIShMkVEZGumZgAn30GXLkCHD8OdOwoJlR79gAffAA0bQrs2AFkZuo7UiIiItIhI2027tGjR54/j4uLK0wsRERli0QCtGkjPm7dAhYvBjZvBs6fB3r3BtzdgTFjgMGDAUtLfUdLREREhaRVQYushX3zs2HDhgIHVBKwoAURFZnoaODnn8VHTIzYZm0NDB0KjB4NVKmi3/iIiIhIiTa5gU6rBZYVTK6IqMilporzshYtAu7eFduMjIDAQHG9rAYN9BsfERERAWC1QCKiks/MDPjiC+D2bWDfPqBVK3EO1pYtwHvvAR99BBw4AMjl+o6UiIiINMTkiohInwwMgM6dgdBQ4N9/gb59xYWKT54EPv4YqFMHWLNG7OkiIiKiEo3JFRFRSdGwIfD77+KixOPHA1ZWwJ07Yg+Xmxswcybw8qW+oyQiIiI1mFwREZU0VasCCxaIixIvXCi+fvUKmDVLfD50qJh0ERERUYnC5IqIqKSysgLGjQMePAC2bQMaNwbS0oC1awFvb3HY4MmTXJSYiIiohGByRURU0mVVETx/Hvj7b6BrV3ENrQMHxMIXDRuKhTAyMvQdKRERUbnG5IqIqLSQSIAWLYC9e8VhgcOGiVUHr1wB+vUDPD2B+fMBLuhORESkF0yuiIhKoxo1xIWIHz8G5swBHB2Bp0+Br78GXF2Br74CIiP1HSUREVG5wuSKiKg0s7cHpk0TE6l168TS7UlJwJIlgJdX9nBCIiIiKnJMroiIygJTU2DQIOD6deDwYaBtW3EB4u3bgfffBz74ANizB5DJ9B0pERFRmcXkioioLJFIgIAA4OhR4L//gAEDAGNj4MwZoEcPoFYtYOVKIDlZ35ESERGVOUyuiIjKKh8fIDhYHDI4ZQpgawvcvw+MHCmulzV1KvDihb6jJCIiKjOYXBERlXUuLsAPP4iLEi9fLlYVfP1abHN3BwYOFIcTEhERUaEwuSIiKi8qVBB7re7eBXbtApo1A9LTxd4tHx+gXTvgyBEuSkxERFRATK6IiMobQ0Nx/tWZM0BYGPDJJ4CBAXDsGNC+vZhobdgApKXpO1IiIqJShckVEVF59v77wI4d4lysMWPE3q0bN8TKg+7uwPffA7Gx+o6SiIioVGByRUREgIeHuDbW06fATz8BlSsDUVHiGlqursCIEWICRkRERGoxuSIiomw2NsDXXwMPHwKbNwP16wOpqcDPPwM1agDduwP//MN5WURERCowuSIiotxMTIB+/YDLl4ETJ4COHcWEau9eoEULcTjh9u1AZqa+IyUiIioxmFwREZF6Egnw0UfAgQPAzZvA558DUilw4QIQGAhUqwYsXgwkJuo7UiIiIr1jckVERJqpXRtYuxZ4/BiYMQOwtwcePQLGjQOqVAEmThTX0iIiIiqnmFwREZF2KlUCZs4Uk6xffgFq1gQSEoAFC8QFij/9VBxOSEREVM4wuSIiooIxMwOGDgVu3QL27wdatxbnYP3+O9CwIfDhh8CffwJyub4jJSIiKhZMroiIqHAMDICPPwZOngT+/Rf43//EhYpDQ4HOnYE6dYA1a8Sqg0RERGUYkysiItKdhg2BLVuAiAhgwgTAygq4cwf44gugalVxrtbLl/qOkoiIqEgwuSIiIt1zdQXmzxcLXCxaBLi5ATExwOzZYpI1ZAhw+7a+oyQiItIpJldERFR0rKyAr74C7t8HQkKAJk2AtDTg11/F6oOdOgF//cVFiYmIqEwoEcnVypUr4e7uDlNTU/j5+eHChQtqtw0ODoZEIlF6mJqaKm0jCAKmT58OZ2dnmJmZwd/fH/fu3Svq0yAiInWMjIDevYFz54DTp4Fu3cQ1tA4eBNq0EYcT/vYbkJGh70iJiIgKTO/JVUhICMaNG4cZM2bg8uXL8PX1RUBAAF7mMSbfysoKL168UDwePXqk9PN58+Zh2bJlWL16Nc6fP48KFSogICAAb9++LerTISKivEgkwAcfAHv2AOHhwPDhYtXBK1eAzz4DPDyAefOAuDh9R0pERKQ1iSDodyyGn58fGjdujBUrVgAA5HI5XF1dMWrUKEyePDnX9sHBwRg7dizi1PyPVxAEuLi4YPz48ZgwYQIAID4+Ho6OjggODkafPn3yjSkhIQHW1taIj4+HlZVVwU+OiIjyFxsLrF4NLF8OREeLbRYWwODBwJgxYsJFRESkJ9rkBnrtuUpPT8elS5fg7++vaDMwMIC/vz/CwsLU7peUlAQ3Nze4urqia9euuHnzpuJnERERiIqKUjqmtbU1/Pz81B4zLS0NCQkJSo+SYu5cYPRocT74nj3il7v8QpeIyhQ7O2DqVODRI2D9eqBuXSApCVi6FKhWLXs4IRERUQlnpM83j4mJgUwmg6Ojo1K7o6Mj7ty5o3KfmjVrYv369fDx8UF8fDwWLFiAZs2a4ebNm6hSpQqioqIUx3j3mFk/e9fcuXMxa9YsHZyR7u3YISZU77KxAdzdxS90Vf3XwqJYwyQiKjypFBg4EAgKAo4eFb9VOnpU/EO4YwfQrBkwfjzQtau4jhYREVEJo9fkqiCaNm2Kpk2bKl43a9YM3t7e+OWXXzBnzpwCHXPKlCkYN26c4nVCQgJcXV0LHasujB0L3LoFREaKy8ZERACvXom9V1evig9V7O3VJ17u7sA7NUCIiEoOiQQICBAf16+LSdaWLcDZs+LDy0v84zhwIFChgr6jJSIiUtBrcmVvbw9DQ0NEZ42x/3/R0dFwcnLS6BjGxsZo0KAB7t+/DwCK/aKjo+Hs7Kx0zPr166s8hlQqhVQqLcAZFL3+/XO3JSeLyVZWwvXuf9+8EZeTiYkBLl5UfVxnZ/U9X1WrAsbGRXRCRETaqFcP2LAB+OEHYMUKYNUq4MEDYNQoYPp04Msvxec5/t4TERHpS4koaNGkSRMsX74cgFjQomrVqhg5cqTKghbvkslkqFOnDjp27IhFixYpClpMmDAB48ePByD2RFWqVKncFLSIj1efeEVEiFMZ8mJgAFSurDrx8vAQf8YROUSkF8nJQHAwsHixmGQB4rdB//sfMG4c4OOj1/CIiKjs0SY30HtyFRISggEDBuCXX35BkyZNsGTJEmzfvh137tyBo6Mj+vfvj8qVK2Pu3LkAgNmzZ+P9999HtWrVEBcXh/nz52Pv3r24dOkSateuDQD46aef8OOPP2Ljxo3w8PDAt99+i2vXruHWrVu51sRSpbQnV3kRBOD1a/WJV2QkkF/FeiMjsXdLXc+Xk5OYoBERFRmZDNi3D1i4EDhzJru9bVtxXla7duLwQiIiokLSJjfQ+5yrwMBAvHr1CtOnT0dUVBTq16+Pw4cPKwpSPH78GAY5Pqm/efMGQ4YMQVRUFGxtbdGwYUOcPXtWkVgBwNdff43k5GQMHToUcXFx+OCDD3D48GGNEquyTiIRC3PZ2QGNGuX+uSCIlZDV9Xw9eiSu8fnwofhQRSoF3NzU93zZ2/MzDxEVkqEh0L27+Dh/Xkyydu0Cjh0TH3XqiD1Zn34q/lEiIiIqBnrvuSqJynLPVWHJZMCLF+p7vp48AeTyvI9RoUJ2YY13Ey93d8DWtqjPgojKpIgIsXz7unXZ458dHYGRI4Fhw8RvlYiIiLRUqoYFlkRMrgouIwN4+lR9z9fz52LvWF6srdUnXh4eLDNPRPmIiwPWrhUTrWfPxDYzM7HE+1dfAdWr6zM6IiIqZZhcFRKTq6KTlgY8fqy+5+vly/yPYWenfsihm5v4GYqICBkZwPbt4pDBrAUDJRKgSxdxXtYHH3CMMhER5YvJVSExudKflBTVZeaznr9+nf8xnJzU93xVrQqYmBThCRBRySMIQGiomGQdOJDd3rixmGT17ClW6iEiIlKByVUhMbkquRIS1CdeERFAYmLe+0skucvM53xeuTI/YxGVabdvi2XcN20Su9IBsct7zBhg8GCAf/OJiOgdTK4KiclV6SQI4gLK6hKvyEggNTXvYxgZAa6uqhMvd3dxnVKWmScqA16+BH7+GVi5UlxxHRATq6FDgdGjxT8EREREYHJVaEyuyiZBED9PqUu8Hj0C0tPzPoaJSXaZeVXzvhwcOIWDqFRJTQV++w1YtAi4c0dsMzQEevcWhww2bKjf+IiISO+YXBUSk6vySS7PXWY+5/MnT8RS9HkxN1cuM/9uAmZry+SLqESSy4FDh8R5WSdPZre3aCHOzXJyyn44Oor/tbdnVzYRUTnA5KqQmFyRKpmZymXm3+35evYs/zLzVlbqEy8PD8DSsqjPgojydfmy2JMVEiL+w1fH0BCoVCl30qUqEbO25jcrRESlFJOrQmJyRQWRlib2bqlKvCIigOjo/I9RsaL6xMvNTewZI6Ji8vQpsHOn+M1JVFT2IzoaePVKu2NJpZolYU5O/IdORFTCMLkqJCZXVBRSUsR5XaoSr8hIIDY2/2M4Oqrv+apaVfz8RkTFICNDTLByJlw5E7Cc7fHx2h3b0lKzRKxSJa4tQURUDJhcFRKTK9KHhITs5EtVApaQkPf+Egng4qK+56tKFZaZJ9KL1FTl5EtVIhYdLU76fPtWu2Pb2WmWiHF+GBFRgTG5KiQmV1TSCAIQF6c+8YqMFHvG8mJoKFaXVpV4ubuLiRk/exHpkSCIi/Vp0hsWHZ33fLB35ZwfpioJ4/wwIiK1mFwVEpMrKm0EQRyhpC7xiozUvMy8qsTLw0P8XMbPW0QlhFwOvH6df29YVBTnhxERFRKTq0JickVlTVaZeVWJV0QE8Phx/mXmzczUJ14eHiwzT1RiaTI/LKutoPPD8usN4/wwIirFmFwVEpMrKm8yM8WCaOp6vp4+zb/MvKWl+iGHHh5iGXoiKuFyzg/La1hiYeaH5dcbxvlhRFTCMLkqJCZXRMrS08XeLXU9X1FR+R+jYkX1iZe7O0cXEZUqmswPy5mk6XJ+WM42zg8jomLA5KqQmFwRaSc1VX2Z+YgIzcrMV6qkfsghy8wTlWKcH0ZEpRyTq0JickWkW4mJ2YU1VCVg+U3zyCozr67ny9WVZeaJyoSc88PyGpbI+WFEVIyYXBUSkyui4vXmjfrEKyJCszLzVaqo7/lydha3IaIyJL/5YVltnB9GRIXE5KqQmFwRlRyCAMTEqE+8Hj0C0tLyPoaxseoy81nPHR05bYOozMo5Pyy/3rCCzg/LrzeM88OISjUmV4XE5Iqo9JDLxc9EqhKvyEixEEd+n5VMTdUnXh4eYjEOfiYiKgdyzg/LqzcsKkr81kebj1BZ88Py6w3j/DCiEofJVSExuSIqOzIzgefP1fd8aVJm3sJCfeLl7i5+IU1E5Ux+88NytulyfljONs4PIyoWTK4KickVUfmRng48eaK+5+vFi/yPYWurvufL3R2oUKEIT4CISr6s+WH5DUvk/DCiEonJVSExuSKiLKmp4tBCVYlXRIQ4Mig/Dg7qe76qVhWHJRIR5Ts/LGebLueHvdvG+WFESphcFRKTKyLSVFKScpn5d4cfxsXlf4ycZebfHXLo6ioW5CAiUsL5YUTFhslVITG5IiJdiYtTn3hFRADJyXnvb2CQXWZeVal5FxeWmSeifGTND8tvWGJB54dpUi2R88OoFGNyVUhMroioOAgCEBurfshhZKRmZearVlXf8+XkxNE9RKSFvOaHvduWmqrdse3sNOsNs7Pjt0ZUojC5KiQmV0RUEsjl4mcZdYnXo0ealZl3c1Pf82Vnx+SLiAoga36YJr1hnB9GpRyTq0JickVEpYFMll1mXtWQw6dPxQQtLxYWuROunP+1sSnikyCisi9rfpgmvWEFmR+mybBEzg+jQmByVUhMroioLMjIUC4z/24C9vx5/sewsVE/5NDdXUzOiIh0Jq/5Ye+26Wp+2LttnB9G72ByVUhMroioPHj7NrvMvKqer1ev8j+Gvb36IYdubiwzT0RF6O1bzYYlcn4YFRKTq0JickVEJFYyfDfhyvnfN2/yP4azs/ohh1Wrssw8ERWDvOaHvdtWkPlhDg6aDUvk/LBSi8lVITG5IiLKX3y8+sQrIkJcAywvWWXm1c35qlyZXwgTUTHTx/wwVW2cH1aiMLkqJCZXRESFk1VmPq+er7dv8z6GkZHYu6Uq8fLwED+PGBgU9ZkQEamRmSmOn9ZkWCLnh5VqTK4KickVEVHREgTxs4e6xOvRI3Fee16k0uzCGqoSMHt7jsAhohJC3fwwVW3azg+rWFGz3jDODyuwUpdcrVy5EvPnz0dUVBR8fX2xfPlyNGnSJN/9tm3bhr59+6Jr167Yu3evoj0oKAgbN25U2jYgIACHDx/WKB4mV0RE+pVVZl5dz9eTJ/mXma9QQX3i5eHBMvNEVAIJgjimWpPeMF3ND1OViHF+mJJSlVyFhISgf//+WL16Nfz8/LBkyRLs2LED4eHhqFSpktr9IiMj8cEHH8DT0xMVK1bMlVxFR0djw4YNijapVApbW1uNYmJyRURUsmVkiOt4qUq8IiI0KzNvba0+8WKZeSIq8eRysbKQJr1hRTU/zNFR/CarjCtVyZWfnx8aN26MFStWAADkcjlcXV0xatQoTJ48WeU+MpkMLVu2xKBBg3D69GnExcXlSq7ebdMGkysiotItLU0cWqiu5+vly/yPYW8PuLrmPZ1Bk/+D5reNvn9eEmIoDTGWhBgYY9mJofhjFMQGuVzxEOT//zpnuyAodhSQf8+VYhsDA7Gn653/CpJ32t85Zn7nUKMG8O+/+YZR5LTJDYyKKSaV0tPTcenSJUyZMkXRZmBgAH9/f4SFhandb/bs2ahUqRIGDx6M06dPq9wmNDQUlSpVgq2tLT766CN89913sLOzU7ltWloa0tLSFK8TEhIKeEZERFQSSKXi/5Rr1FD98+RkMflS1/P15o34RW9MTHFGTURUVLISmyKqApQ1TFum28MmJ+v2eMVBr8lVTEwMZDIZHB0dldodHR1x584dlfv8888/WLduHa5evar2uO3bt0ePHj3g4eGBBw8e4JtvvkGHDh0QFhYGQxUT+ebOnYtZs2YV6lyIiKj0qFABqF1bfKgSHy8mW0+f5j+3K79pCZpMWyjsMcpCDIyx7MRQGmIsCTGU+hgFAUhOhiQ2RqyaGPP//42NVXotifn/5zLV88MkUNF9ZWAIVKwIY3tPAL/nHWQJo9fkSluJiYn47LPPsHbtWtjb26vdrk+fPorn9erVg4+PD7y8vBAaGoo2bdrk2n7KlCkYN26c4nVCQgJcXV11GzwREZUa1taAr6/4ICIiVSQALP7/4Z73ptrOD5MLQAyABC0rJ5YAek2u7O3tYWhoiOjoaKX26OhoODk55dr+wYMHiIyMROfOnRVt8v//StHIyAjh4eHw8vLKtZ+npyfs7e1x//59lcmVVCqFVCot7OkQEREREdG7DAzEUvB2dkCdOnlvm3P9sPzW5CiB9JpcmZiYoGHDhjhx4gS6desGQEyWTpw4gZEjR+bavlatWrh+/bpS27Rp05CYmIilS5eq7W16+vQpYmNj4ezsrPNzICIiIiIiHTEyApydxUcppPdhgePGjcOAAQPQqFEjNGnSBEuWLEFycjIGDhwIAOjfvz8qV66MuXPnwtTUFHXr1lXa3+b/FyrJak9KSsKsWbPQs2dPODk54cGDB/j6669RrVo1BAQEFOu5ERERERFR+aH35CowMBCvXr3C9OnTERUVhfr16+Pw4cOKIhePHz+GgYHmlU0MDQ1x7do1bNy4EXFxcXBxcUG7du0wZ84cDv0jIiIiIqIio/d1rkoirnNFRERERESAdrlBERW7JyIiIiIiKl+YXBEREREREekAkysiIiIiIiId0HtBi5IoaxpaQkKCniMhIiIiIiJ9ysoJNClVweRKhcTERABQu24WERERERGVL4mJibC2ts5zG1YLVEEul+P58+ewtLSERCLRaywJCQlwdXXFkydPWLmwCPD6Fi1e36LF61v0eI2LFq9v0eL1LVq8vkWrJF1fQRCQmJgIFxeXfJeIYs+VCgYGBqhSpYq+w1BiZWWl9xurLOP1LVq8vkWL17fo8RoXLV7fosXrW7R4fYtWSbm++fVYZWFBCyIiIiIiIh1gckVERERERKQDTK5KOKlUihkzZkAqleo7lDKJ17do8foWLV7fosdrXLR4fYsWr2/R4vUtWqX1+rKgBRERERERkQ6w54qIiIiIiEgHmFwRERERERHpAJMrIiIiIiIiHWByRUREREREpANMrorR33//jc6dO8PFxQUSiQR79+7Nd5/Q0FC89957kEqlqFatGoKDg3Nts3LlSri7u8PU1BR+fn64cOGC7oMvJbS9xrt370bbtm3h4OAAKysrNG3aFEeOHFHaZubMmZBIJEqPWrVqFeFZlFzaXt/Q0NBc104ikSAqKkppO97DIm2vb1BQkMrrW6dOHcU2vH9Fc+fORePGjWFpaYlKlSqhW7duCA8Pz3e/HTt2oFatWjA1NUW9evVw8OBBpZ8LgoDp06fD2dkZZmZm8Pf3x71794rqNEqsglzftWvXokWLFrC1tYWtrS38/f1z/dtXdY+3b9++KE+lRCrI9Q0ODs517UxNTZW24f0rKsj1bd26tcq/v506dVJsw/s326pVq+Dj46NYELhp06Y4dOhQnvuU1r+/TK6KUXJyMnx9fbFy5UqNto+IiECnTp3w4Ycf4urVqxg7diw+//xzpQ//ISEhGDduHGbMmIHLly/D19cXAQEBePnyZVGdRomm7TX++++/0bZtWxw8eBCXLl3Chx9+iM6dO+PKlStK29WpUwcvXrxQPP7555+iCL/E0/b6ZgkPD1e6fpUqVVL8jPdwNm2v79KlS5Wu65MnT1CxYkX06tVLaTvev8CpU6cwYsQInDt3DseOHUNGRgbatWuH5ORktfucPXsWffv2xeDBg3HlyhV069YN3bp1w40bNxTbzJs3D8uWLcPq1atx/vx5VKhQAQEBAXj79m1xnFaJUZDrGxoair59++LkyZMICwuDq6sr2rVrh2fPnilt1759e6X7d+vWrUV9OiVOQa4vAFhZWSldu0ePHin9nPevqCDXd/fu3UrX9saNGzA0NMz195f3r6hKlSr48ccfcenSJfz777/46KOP0LVrV9y8eVPl9qX6769AegFA2LNnT57bfP3110KdOnWU2gIDA4WAgADF6yZNmggjRoxQvJbJZIKLi4swd+5cncZbGmlyjVWpXbu2MGvWLMXrGTNmCL6+vroLrIzQ5PqePHlSACC8efNG7Ta8h1UryP27Z88eQSKRCJGRkYo23r+qvXz5UgAgnDp1Su02vXv3Fjp16qTU5ufnJ3zxxReCIAiCXC4XnJychPnz5yt+HhcXJ0ilUmHr1q1FE3gpocn1fVdmZqZgaWkpbNy4UdE2YMAAoWvXrkUQYemmyfXdsGGDYG1trfbnvH/VK8j9u3jxYsHS0lJISkpStPH+zZutra3w66+/qvxZaf77y56rEiwsLAz+/v5KbQEBAQgLCwMApKen49KlS0rbGBgYwN/fX7ENaUculyMxMREVK1ZUar937x5cXFzg6emJTz/9FI8fP9ZThKVT/fr14ezsjLZt2+LMmTOKdt7DurVu3Tr4+/vDzc1NqZ33b27x8fEAkOvfek75/Q2OiIhAVFSU0jbW1tbw8/Mr9/evJtf3XSkpKcjIyMi1T2hoKCpVqoSaNWti2LBhiI2N1WmspZGm1zcpKQlubm5wdXXN1UvA+1e9gty/69atQ58+fVChQgWldt6/uclkMmzbtg3Jyclo2rSpym1K899fJlclWFRUFBwdHZXaHB0dkZCQgNTUVMTExEAmk6nc5t05LaSZBQsWICkpCb1791a0+fn5ITg4GIcPH8aqVasQERGBFi1aIDExUY+Rlg7Ozs5YvXo1du3ahV27dsHV1RWtW7fG5cuXAYD3sA49f/4chw4dwueff67Uzvs3N7lcjrFjx6J58+aoW7eu2u3U/Q3Oujez/sv7V5mm1/ddkyZNgouLi9KHpfbt22PTpk04ceIEfvrpJ5w6dQodOnSATCYritBLBU2vb82aNbF+/Xr88ccf+O233yCXy9GsWTM8ffoUAO9fdQpy/164cAE3btzI9feX96+y69evw8LCAlKpFF9++SX27NmD2rVrq9y2NP/9NdLruxOVIL///jtmzZqFP/74Q2lOUIcOHRTPfXx84OfnBzc3N2zfvh2DBw/WR6ilRs2aNVGzZk3F62bNmuHBgwdYvHgxNm/erMfIyp6NGzfCxsYG3bp1U2rn/ZvbiBEjcOPGjXI596w4FOT6/vjjj9i2bRtCQ0OVii706dNH8bxevXrw8fGBl5cXQkND0aZNG53GXVpoen2bNm2q1CvQrFkzeHt745dffsGcOXOKOsxSqyD377p161CvXj00adJEqZ33r7KaNWvi6tWriI+Px86dOzFgwACcOnVKbYJVWrHnqgRzcnJCdHS0Ult0dDSsrKxgZmYGe3t7GBoaqtzGycmpOEMt9bZt24bPP/8c27dvz9UN/S4bGxvUqFED9+/fL6boypYmTZoorh3vYd0QBAHr16/HZ599BhMTkzy3Le/378iRI/Hnn3/i5MmTqFKlSp7bqvsbnHVvZv2X9282ba5vlgULFuDHH3/E0aNH4ePjk+e2np6esLe35/2rxfXNYmxsjAYNGiiuHe/f3ApyfZOTk7Ft2zaNvqwq7/eviYkJqlWrhoYNG2Lu3Lnw9fXF0qVLVW5bmv/+MrkqwZo2bYoTJ04otR07dkzxTZSJiQkaNmyotI1cLseJEyfUjmGl3LZu3YqBAwdi69atSiVU1UlKSsKDBw/g7OxcDNGVPVevXlVcO97DunHq1Cncv39fo/+5l9f7VxAEjBw5Env27MFff/0FDw+PfPfJ72+wh4cHnJyclLZJSEjA+fPny939W5DrC4jVvubMmYPDhw+jUaNG+W7/9OlTxMbG8v7V8PrmJJPJcP36dcW14/2brTDXd8eOHUhLS0O/fv3y3ba83r/qyOVypKWlqfxZqf77q9dyGuVMYmKicOXKFeHKlSsCAGHRokXClStXhEePHgmCIAiTJ08WPvvsM8X2Dx8+FMzNzYWJEycKt2/fFlauXCkYGhoKhw8fVmyzbds2QSqVCsHBwcKtW7eEoUOHCjY2NkJUVFSxn19JoO013rJli2BkZCSsXLlSePHiheIRFxen2Gb8+PFCaGioEBERIZw5c0bw9/cX7O3thZcvXxb7+embttd38eLFwt69e4V79+4J169fF8aMGSMYGBgIx48fV2zDezibttc3S79+/QQ/Pz+Vx+T9Kxo2bJhgbW0thIaGKv1bT0lJUWzz2WefCZMnT1a8PnPmjGBkZCQsWLBAuH37tjBjxgzB2NhYuH79umKbH3/8UbCxsRH++OMP4dq1a0LXrl0FDw8PITU1tVjPT98Kcn1//PFHwcTERNi5c6fSPomJiYIgiP8eJkyYIISFhQkRERHC8ePHhffee0+oXr268Pbt22I/R30qyPWdNWuWcOTIEeHBgwfCpUuXhD59+gimpqbCzZs3Fdvw/hUV5Ppm+eCDD4TAwMBc7bx/lU2ePFk4deqUEBERIVy7dk2YPHmyIJFIhKNHjwqCULb+/jK5KkZZZanffQwYMEAQBLFkZ6tWrXLtU79+fcHExETw9PQUNmzYkOu4y5cvF6pWrSqYmJgITZo0Ec6dO1f0J1NCaXuNW7Vqlef2giCWv3d2dhZMTEyEypUrC4GBgcL9+/eL98RKCG2v708//SR4eXkJpqamQsWKFYXWrVsLf/31V67jlqd7eMCAAYKbm5vKn+V3fX19fYV3vxOLi4sTzMzMhDVr1qg8Zmm7fyMiIgQAKv/WFYaq65rzfbKufc6lLgYMGCA4ODgINWrUEExMTIQ6deoIBw4cUDquXC4XRo0aJQAQjIyMhDZt2gjh4eE6idnNzU3pb1FJlt/1FQTx723O83Fzc1O5z4wZMwRBEISUlBShXbt2goODg2BsbCy4ubkJQ4YMKZdfvBTk+o4dO1bxd9XR0VHo2LGjcPnyZaXjyuVy4dtvvxUcHR0FqVSq0/u3NCnI9RUEQbhz544AQJEg5MT7V9mgQYMENzc3wcTERHBwcBDatGmjdN1UXd/t27fn+/e3JN6/EkEQBB10gBERlWoSiUSj7U6ePInWrVsX+H2CgoIQGhqKyMhIrfedOXMmZs2ahbL8ZzsyMhIeHh7YsGEDgoKCiu19Q0ND8eGHHyr9fjX9XRUm5rNnz+Lo0aMYO3YsbGxslH7m7u6O1q1bIzg4WKtjEhGR/rBaIBERkKt64aZNm3Ds2LFc7d7e3oV6n7Vr10Iulxdo32nTpmHy5MmFen/SXGF+V5o6e/YsZs2ahaCgoFzJVXh4OAwMODWaiKg0YXJFRATkmox87tw5HDt2LN9JyikpKTA3N9f4fYyNjQsUHwAYGRnByIh/totLYX5XuiCVSvX6/qVFcnJyroVbiYj0hV+JERFpqHXr1qhbty4uXbqEli1bwtzcHN988w0A4I8//kCnTp3g4uICqVQKLy8vzJkzJ9dikUFBQXB3d1e8joyMhEQiwYIFC7BmzRp4eXlBKpWicePGuHjxotK+M2fOzDV8USKRYOTIkdi7dy/q1q0LqVSKOnXq4PDhw7niDw0NRaNGjWBqagovLy/88ssvKo+pyunTp9GrVy9UrVoVUqkUrq6u+Oqrr5Camprr/CwsLPDs2TN069YNFhYWcHBwwIQJE3Jdi7i4OAQFBcHa2ho2NjYYMGAA4uLi8o3l33//hUQiwcaNG3P97MiRI5BIJPjzzz8BAI8ePcLw4cNRs2ZNmJmZwc7ODr169dJoWOa7vyttYr527RqCgoLg6ekJU1NTODk5YdCgQYiNjVVsM3PmTEycOBGAWPlKIpFAIpEoYnN3d881zPDhw4fo1asXKlasCHNzc7z//vs4cOCA0jahoaGQSCTYvn07vv/+e1SpUgWmpqZo06aNRiWgtblmcXFx+Oqrr+Du7g6pVIoqVaqgf//+iImJUWzz9u1bzJw5EzVq1ICpqSmcnZ3Ro0cPPHjwQCne0NBQpWNn/dvIOSwy6/568OABOnbsCEtLS3z66acANL9HAeDOnTvo3bs3HBwcYGZmhpo1a2Lq1KkAxKG/EokEe/bsybXf77//DolEgrCwsHyvIxGVT/wKlIhIC7GxsejQoQP69OmDfv36KVaHDw4OhoWFBcaNGwcLCwv89ddfmD59OhISEjB//vx8j/v7778jMTERX3zxBSQSCebNm4cePXrg4cOH+fag/PPPP9i9ezeGDx8OS0tLLFu2DD179sTjx49hZ2cHALhy5Qrat28PZ2dnzJo1CzKZDLNnz4aDg4NG571jxw6kpKRg2LBhsLOzw4ULF7B8+XI8ffoUO3bsUNpWJpMhICAAfn5+WLBgAY4fP46FCxfCy8sLw4YNAyCWPu7atSv++ecffPnll/D29saePXswYMCAfGNp1KgRPD09sX379lzbh4SEwNbWFgEBAQCAixcv4uzZs+jTpw+qVKmCyMhIrFq1Cq1bt8atW7e06nXUJuZjx47h4cOHGDhwIJycnHDz5k2sWbMGN2/exLlz5yCRSNCjRw/cvXsXW7duxeLFi2Fvbw8Aan8n0dHRaNasGVJSUjB69GjY2dlh48aN6NKlC3bu3Inu3bsrbf/jjz/CwMAAEyZMQHx8PObNm4dPP/0U58+fz/M8Nb1mSUlJaNGiBW7fvo1BgwbhvffeQ0xMDPbt24enT5/C3t4eMpkMH3/8MU6cOIE+ffpgzJgxSExMxLFjx3Djxg14eXlpfP2zZGZmIiAgAB988AEWLFigiEfTe/TatWto0aIFjI2NMXToULi7u+PBgwfYv38/vv/+e7Ru3Rqurq7YsmVLrmu6ZcsWeHl56b/UMxGVXHotp0FEVEKNGDEiV2W+rOqSq1evzrV9zpK9Wb744gvB3Nxcqezuu9UCs6rj2dnZCa9fv1a0//HHHwIAYf/+/Yq2GTNm5IoJgGBiYqJUAfC///4TAAjLly9XtHXu3FkwNzcXnj17pmi7d++eYGRklOuYqqg6v7lz5woSiURRKj7r/AAIs2fPVtq2QYMGQsOGDRWv9+7dKwAQ5s2bp2jLzMwUWrRooVG1wClTpgjGxsZK1ywtLU2wsbERBg0alGfcYWFhAgBh06ZNirasaoEnT55UOpecvyttYlb1vlu3bhUACH///beibf78+QIAISIiItf271YLHDt2rABAOH36tKItMTFR8PDwENzd3QWZTKZ0Lt7e3kJaWppi26VLlwoAlEoZq6LpNZs+fboAQNi9e3eu7eVyuSAIgrB+/XrFsgLqtlF17QVBdeXIrPtLVUlsTe/Rli1bCpaWlkptOeMRBPH+kkqlSstyvHz5UjAyMlJUMyQiUoXDAomItCCVSjFw4MBc7WZmZorniYmJiImJQYsWLZCSkoI7d+7ke9zAwEDY2toqXrdo0QKAOAwsP/7+/ko9AD4+PrCyslLsK5PJcPz4cXTr1g0uLi6K7apVq4YOHTrke3xA+fySk5MRExODZs2aQRAEXLlyJdf2X375pdLrFi1aKJ3LwYMHYWRkpOjJAgBDQ0OMGjVKo3gCAwORkZGB3bt3K9qOHj2KuLg4BAYGqow7IyMDsbGxqFatGmxsbHD58mWN3qsgMed837dv3yImJgbvv/8+AGj9vjnfv0mTJvjggw8UbRYWFhg6dCgiIyNx69Ytpe0HDhwIExMTxWtN7ylNr9muXbvg6+ubq3cHyK6+uWvXLtjb26u8RppW6FQl5+9AVdzq7tFXr17h77//xqBBg1C1alW18fTv3x9paWnYuXOnoi0kJASZmZkaLRZLROUXkysiIi1UrlxZ6QNrlps3b6J79+6wtraGlZUVHBwcFB/C4uPj8z3uux/0shKtN2/eaL1v1v5Z+758+RKpqamoVq1aru1Utany+PFjBAUFoWLFiop5VK1atQKQ+/xMTU1zDW3LGQ8gzutxdnaGhYWF0nY1a9bUKB5fX1/UqlULISEhiraQkBDY29vjo48+UrSlpqZi+vTpcHV1hVQqhb29PRwcHBAXF6fR7yUnbWJ+/fo1xowZA0dHR5iZmcHBwQEeHh4ANLsf1L2/qvfKqmD56NEjpfaC3lOaXrMHDx6gbt26eR7rwYMHqFmzpk4LsRgZGaFKlSq52jW5R7MSy/zirlWrFho3bowtW7Yo2rZs2YL3339f438zRFQ+cc4VEZEWcn47niUuLg6tWrWClZUVZs+eDS8vL5iamuLy5cuYNGmSRuW8DQ0NVbYLGqxpVZh9NSGTydC2bVu8fv0akyZNQq1atVChQgU8e/YMQUFBuc5PXTy6FhgYiO+//x4xMTGwtLTEvn370LdvX6UP8qNGjcKGDRswduxYNG3aFNbW1pBIJOjTp0+Rllnv3bs3zp49i4kTJ6J+/fqwsLCAXC5H+/bti7y8e5aC3hfFfc3U9WC9WwAli1QqzVWiXtt7VBP9+/fHmDFj8PTpU6SlpeHcuXNYsWKF1schovKFyRURUSGFhoYiNjYWu3fvRsuWLRXtEREReowqW6VKlWBqaqqyUpwm1eOuX7+Ou3fvYuPGjejfv7+i/dixYwWOyc3NDSdOnEBSUpJST1B4eLjGxwgMDMSsWbOwa9cuODo6IiEhAX369FHaZufOnRgwYAAWLlyoaHv79q1GVQkLGvObN29w4sQJzJo1C9OnT1e037t3L9cxtRka5+bmpvL6ZA07dXNz0/hYedH0mnl5eeHGjRt5HsvLywvnz59HRkaG2sIsWT1q7x7/3Z64vGh6j3p6egJAvnEDQJ8+fTBu3Dhs3boVqampMDY2VhpySkSkCocFEhEVUlYPQc4egfT0dPz888/6CkmJoaEh/P39sXfvXjx//lzRfv/+fRw6dEij/QHl8xMEAUuXLi1wTB07dkRmZiZWrVqlaJPJZFi+fLnGx/D29ka9evUQEhKCkJAQODs7KyW3WbG/21OzfPlytb0iuohZ1fUCgCVLluQ6Ztb6TJokex07dsSFCxeUyoAnJydjzZo1cHd3R+3atTU9lTxpes169uyJ//77T2XJ8qz9e/bsiZiYGJU9PlnbuLm5wdDQEH///bfSz7X596PpPerg4ICWLVti/fr1ePz4scp4stjb26NDhw747bffsGXLFrRv315R0ZGISB32XBERFVKzZs1ga2uLAQMGYPTo0ZBIJNi8ebPOhuXpwsyZM3H06FE0b94cw4YNg0wmw4oVK1C3bl1cvXo1z31r1aoFLy8vTJgwAc+ePYOVlRV27dql0XwwdTp37ozmzZtj8uTJiIyMRO3atbF7926t5yMFBgZi+vTpMDU1xeDBg3MNF/v444+xefNmWFtbo3bt2ggLC8Px48cVJeqLImYrKyu0bNkS8+bNQ0ZGBipXroyjR4+q7Mls2LAhAGDq1Kno06cPjI2N0blzZ5WL4k6ePBlbt25Fhw4dMHr0aFSsWBEbN25EREQEdu3alevcC0rTazZx4kTs3LkTvXr1wqBBg9CwYUO8fv0a+/btw+rVq+Hr64v+/ftj06ZNGDduHC5cuIAWLVogOTkZx48fx/Dhw9G1a1dYW1ujV69eWL58OSQSCby8vPDnn3/i5cuXGseszT26bNkyfPDBB3jvvfcwdOhQeHh4IDIyEgcOHMj1b6F///745JNPAABz5szR/mISUbnD5IqIqJDs7Ozw559/Yvz48Zg2bRpsbW3Rr18/tGnTRrHekr41bNgQhw4dwoQJE/Dtt9/C1dUVs2fPxu3bt/OtZmhsbIz9+/dj9OjRmDt3LkxNTdG9e3eMHDkSvr6+BYrHwMAA+/btw9ixY/Hbb79BIpGgS5cuWLhwIRo0aKDxcQIDAzFt2jSkpKSoHLK1dOlSGBoaYsuWLXj79i2aN2+O48ePF+j3ok3Mv//+O0aNGoWVK1dCEAS0a9cOhw4dUqrWCACNGzfGnDlzsHr1ahw+fBhyuRwREREqkytHR0ecPXsWkyZNwvLly/H27Vv4+Phg//796NSpk9bno46m18zCwgKnT5/GjBkzsGfPHmzcuBGVKlVCmzZtFAUnDA0NcfDgQXz//ff4/fffsWvXLtjZ2eGDDz5AvXr1FMdavnw5MjIysHr1akilUvTu3Rvz58/Pt/BEFm3uUV9fX5w7dw7ffvstVq1ahbdv38LNzQ29e/fOddzOnTvD1tYWcrkcXbp00fZSElE5JBFK0lerRERUrLp164abN2+qnA9EVN5lZmbCxcUFnTt3xrp16/QdDhGVApxzRURUTqSmpiq9vnfvHg4ePIjWrVvrJyCiEm7v3r149eqVUpEMIqK8sOeKiKiccHZ2RlBQEDw9PfHo0SOsWrUKaWlpuHLlCqpXr67v8IhKjPPnz+PatWuYM2cO7O3tC7zwMxGVP5xzRURUTrRv3x5bt25FVFQUpFIpmjZtih9++IGJFdE7Vq1ahd9++w3169dHcHCwvsMholKEPVdEREREREQ6wDlXREREREREOsDkioiIiIiISAc450oFuVyO58+fw9LSEhKJRN/hEBERERGRngiCgMTERLi4uOS7YDuTKxWeP38OV1dXfYdBREREREQlxJMnTxSLpKvD5EoFS0tLAOIFtLKy0nM0RERERESkLwkJCXB1dVXkCHlhcqVC1lBAKysrJldERERERKTRdCEWtCAiIiIiItIBJldEREREREQ6wOSKiIiIiIhIB5hcERERERER6QCTKyIiIiIiIh1gtUAiIiIiIio+b98CMTHAq1fiI+fznK8rVwa2btV3tFphckVERERERAUjCEBCgupESV3SlJSk2bE9PYs29iLA5IqIiIiIiESZmUBsrPrESNXzjAzt38fICLC3Bxwcsv/77nNnZ92fXxFjckVEREREVFalpGjWm5T1/PXrgr1PhQrKiVFeSZO9PWBjA2iwKG9pw+SKiIiIiKg0kMuB+HjthuClpBTsvSpWVJ0YqUuazMx0e66lFJMrIiIiIiJ9yMjIToLyGnaX87lMpv37GBvn35uU83XFiuKwPdIarxoRERERUWEJApCcrN1cpbi4gr2XpWX+w+5yJk2WlmVyCF5JxOSKiIiIiOhdcjnw5o12Q/DevtX+fSQSwM5O87lK9vaAqanuz5d0gskVEREREZV9aWnaDcGLjRUTLG1JpdrNVbK1BQwNdX++pBdMroiIiIiodBEEIDFRuyF4CQkFey9ra+2G4FWowCF45RiTKyIiIiLSL5lMLAGuabnwV6+A9HTt38fQUPsheCYmuj9fKrOYXBERERGRbr19q91cpdevxd4obZmZaTcEz8YGMDDQ+ekSZWFyRURE9P9SU4HwcOD2bfHx8KE45cLISPzC29BQN891fTxNnvPzJBWYIGSvraTJXKVXr8SqeQVha6t5uXAHB8DcXLfnSlRITK6IiKjcef0auHMnO4nKekRGFuzL89KiLCSJRf08Z1uZnTaTmSkWa9B0rlJMjLgek7aMjLSbq1SxorgeE1EpxuSKiIjKJEEAnj3LnUDdvg28fKl+v4oVAW9v8VG9uvhZTyYTH5mZyv8tac/zK2yWmSk+SDMGBiUn6cvzuTwdhqlJMExJhNHbJBgmJ8AoOR6GyQkwTIqHUVIcDBPjYJj4Bkbxr2GYHA9DyGCETBhCls9zcxiisvjczASG9rYwtLOBkYOt+NyhIowqVYRhJTsYVLKHpFKOpMnaugxnqESqMbkiIqJSLTMTePAgO3HK6pG6c0csJqaOq2t2ElWrVvZzB4fS+3lQELITLn0lePpOMLV9nldPpVwuPgrSaVO8TABU/P9HEUoF8OT/H2pkJXxF3atYVp5zuG7Zw+SKiIhKhZQU5flQWY9799R/+DU0BKpVy06cciZTFhbFG39xkEjED21G/L+7xuTyIk5I0zKQ+SYJsjcJkMUnis8TkpEZnwxZQjJkSSnITEiFLCkVsuS3yExOg0yQQAZDZMLo//uPNHhuYIxMkwqQmZhBZmKGTGMzyIylkBlJkWloCpmhCWSGxsiUmEBmYASZxAiZmZICnVNesrYlzUgkJSfRK4mJr7Fx6ftbzT+/RERUosTGqh7K9+iR+n3MzZV7n7ISqGrVWEWZ8mZgID40muojCGKhBm3KhcfHFywwS0vNy4U7OIjbF1OXq1zOXlFtnuc1XFcQsofrpqUVy6+vVKlRQ/xSrTRhckVERMVOEIAnT1QXlXj1Sv1+dna5e6G8vcUhfhxeQ1qTy7PXVtKkXPirVwX7BGxgkL22kiblwu3tAalU9+erIwYG/NJCG4JQvAlpSUkqC/M8a7iuoaF+f3cFweSKiIiKTEaG8nyonPOi8qrUXLWq6iTK3r74YqdSKC1Ns96krMfr1/lXAVHF1FS7cuG2tsz+y7GcQ/9IMznnj5Y2TK6IiKjQkpNz90LduSPOh1JXnc7ISKzG925RiZo1S98YeyoCgiBWJNFmCF5eFUzyYmOjeblwe3ugQoXSW/WEqBQozfNHS2HIRESkLzExqudDPX6sfp8KFXLPh/L2Bry8uKRNuSKTZa+tpMkQvJgYID1d+/cxNNQsUco5BI83IhHpCJMrIiJSIpeL86FUJVGxser3c3BQPZSvcmWOiCqTUlPzX3w2Z9L05k3BVmg2N9d8rlLW2kq84YhIT5hcERGVU+npwP37ymtDZT1PSVG/n7t77rWhvL3F+fpUSgmCWNVOk7lKWc/zmjSXl4oVNZ+rZG8vJldERKUEkysiojIuKUl1Vb4HD9TPhzI2Vp4PlfWoWZOfdUuFjAzth+CpuxnyYmys3RA8O7vSOYmCiEhD/AtHRFQGCIL4GVlVVb4nT9TvZ2GRe20ob2/A05PTUEqUrLWVNB2CFxdXsPexsNBuCJ6VFQs7EBHlwOSKiKgUkcvF4hGq5kO9fq1+P0dH1UUlKlfmZ+NiJ5eLyY82Q/BSU7V/H4lE7CnSZgieqanOT5eIqDxhckVEVAKlp4tlzN9NoMLD1X/Olkiy50O92xtVsWKxhl++pKcrD6/LL2mKjS3Y4i0mJpr1JmW9rliRC+sQERUzJldERHqUkJA9HyrnvKgHD9R//jYxAWrUyF1UokYNzofSqbQ04NQp4PnzvIfgJSQU7PhWVprPVXJwEIfssZuRiKhEY3JFRFTEBAF4+VL1UL5nz9TvZ2mpurS5hwdrAhSp+Hjgl1+AJUuAFy8028fAQEyENJ2rZGcHSKVFehpERFT8+L9nIiIdkcuByEjVRSXevFG/n5OT6iTK2ZkdFcXq2TNg6VJg9WogMVFsc3ICfH3zT5psbbm2EhERMbkiItJWWpr6+VBv36reRyIRK/C9W1SiVi3xcznp0a1bwIIFwG+/iSXMAaB2beDrr4G+fcVxmERERBpgckVEpEZ8vOr1oR4+FHupVJFKs+dD5XzUqMFCbCWKIABnzgDz5gH792e3t2wJTJwIdOzInigiItIakysiKtcEAYiKUh7Cl/X8+XP1+1lbq67K5+HBAm0lmlwO7NsnJlVhYWKbRAJ07y4mVe+/r9/4iIioVGNyRUTlgkymej7U7dtiD5U6zs6q50M5OXE+VKmSlgZs3iwO/wsPF9tMTIABA4Dx44GaNfUbHxERlQlMroioTHn7Frh7N3dBifBw8fO1KgYG4nwoVT1R1tbFGz/pWFycWKBi6VKxixIQf6nDhwOjR4tZMhERkY4wuSKiUikuLvcwvtu3gYgI9fOhTE3FDop314eqXp3zocqcp0/FUupr1mRX/qtSBfjqK2DIELHOPRERkY6ViORq5cqVmD9/PqKiouDr64vly5ejSZMmKrdt3bo1Tp06lau9Y8eOOHDgAAAgKCgIGzduVPp5QEAADh8+rPvgiajICIK4zJCqoXxZnRCq2NioHsrn5sb5UGXezZvA/PnAli1AZqbYVqeOWPmvTx9W/iMioiKl9+QqJCQE48aNw+rVq+Hn54clS5YgICAA4eHhqFSpUq7td+/ejfT0dMXr2NhY+Pr6olevXkrbtW/fHhs2bFC8lnKxRqISSyYTK/CpKiqRkKB+v8qVcw/j8/YGHB05H6pcEQTgn3+An34C/v9LNgBAq1ZiUtWhA28IIiIqFnpPrhYtWoQhQ4Zg4MCBAIDVq1fjwIEDWL9+PSZPnpxr+4oVKyq93rZtG8zNzXMlV1KpFE4cS09UoqSm5p4Pdfu22JbjOxMlhoaAl5fq9aGsrIo3fiphZLLsyn/nzoltEgnQo4dY+c/PT7/xERFRuaPX5Co9PR2XLl3ClClTFG0GBgbw9/dHWFaJ3HysW7cOffr0QYUKFZTaQ0NDUalSJdja2uKjjz7Cd999Bzs7O5XHSEtLQ1qOme4JeX1VTkT5evMmdwJ15444H0oQVO9jZpY9Hyrno1o1ce0oIoW3b7Mr/929K7ZJpUBQkFj5r3p1vYZHRETll16Tq5iYGMhkMjg6Oiq1Ozo64s6dO/nuf+HCBdy4cQPr1q1Tam/fvj169OgBDw8PPHjwAN988w06dOiAsLAwGKqYcDF37lzMmjWrcCdDVM4IAvDsmeqiEtHR6verWDH3ML6s+VBcs5Xy9OZNduW/rJvMxgYYMQIYNUocD0pERKRHeh8WWBjr1q1DvXr1chW/6NOnj+J5vXr14OPjAy8vL4SGhqJNmza5jjNlyhSMGzdO8TohIQGurq5FFzhRKZKZqTwfKmdPVFYRNlWqVFFdVMLBgdNfSEtPnmRX/ktKEttcXYFx44DBg1n5j4iISgy9Jlf29vYwNDRE9Dtfc0dHR+c7Xyo5ORnbtm3D7Nmz830fT09P2Nvb4/79+yqTK6lUyoIXVO6lpIhrQb1bVOLevbznQ1WrljuBqlmTn3dJB27cECv//f57duW/evXEIhWBgYCxsX7jIyIieodekysTExM0bNgQJ06cQLdu3QAAcrkcJ06cwMiRI/Pcd8eOHUhLS0O/fv3yfZ+nT58iNjYWzs7OugibqFSLjc09jO/2beDRI/XzoczNxSF87xaVqFaNla1JxwQB+PtvsUjFwYPZ7a1bA5MmAQEB7PokIqISS+/DAseNG4cBAwagUaNGaNKkCZYsWYLk5GRF9cD+/fujcuXKmDt3rtJ+69atQ7du3XIVqUhKSsKsWbPQs2dPODk54cGDB/j6669RrVo1BAQEFNt5EemTIIhrqKpaH+rVK/X72dmpHsrn6sr5UFTEZDJg714xqbpwQWwzMAB69hQr/zVurNfwiIiINKH35CowMBCvXr3C9OnTERUVhfr16+Pw4cOKIhePHz+GwTuf6sLDw/HPP//g6NGjuY5naGiIa9euYePGjYiLi4OLiwvatWuHOXPmcOgflTkZGcCDB7mLSty5kz01RZWqVVWvD+XgUHyxEwEQ6/Nv2iRW/rt/X2wzNQUGDhTnVFWrpt/4iIiItCARBHUDgcqvhIQEWFtbIz4+HlZcSIdKgORk5flQWY/798UESxUjI/XzoSwsijd+olxevwZWrQKWLQNevhTbbG2zK/+pWESeiIhIH7TJDfTec0VE2WJiclfky5oPpU6FCrnnQnl7iwvvcr4/lTiPHwOLFwNr14rfGgBiV2pW5T9m/kREVIoxuSIqZnK5+vlQMTHq93NwyL02lLe3WPKc86GoxLt2Taz8t21bduU/Hx+x8l/v3vwmgIiIygQmV0RFJCNDHLb3bgIVHp79hb0qbm6qi0q8U7uFqOQTBCA0VCxScfhwdvtHH4lJVbt2rPxHRERlCpMrokJKSsoevpezqMT9+9lf0L/L2BioXj13AlWjhjjMj6hUk8mAPXvEpOriRbHNwAD45BOx8l+jRvqNj4iIqIgwuSLS0KtXqofyPXmifh8LC9XzoTw9OQqKyqDUVGDjRrHy34MHYpupKTBokDinystLv/EREREVMSZXRDnI5eJ8e1VFJWJj1e9XqZLqoXyVK3PUE5UDr18DP/8sVv7LWkitYkVg5EjxwRr/RERUTjC5onIpPR24d095GF/WfKiUFNX7SCSAu3vutaG8vcXPkUTlzqNHYuW/X3/Nnkjo5gaMHy/2VnGMKxERlTNMrqhMS0zMnUDdvi2OWJLJVO9jbCzOfVI1H8rcvHjjJyqR/vsvu/Jf1j+k+vXFIhW9eomLrBEREZVD/D8glXqCIK5B+u4wvtu3xZLn6lha5k6gatUS50PxsyHROwQBOHlSLFJx5Eh2u7+/mFT5+3MMLBERlXv8CEmlhlwujkJSVVTizRv1+zk5qV4fysWFnwWJ8pWZCezeLSZVly6JbQYG4tpUEycC772n3/iIiIhKECZXVOKkpYnzoVStD/X2rep9JBLAw0N1T5StbfHGT1QmpKQAwcHAwoXAw4dim5kZMHgw8NVXYhcvERERKWFyRXqTkKB6PtTDh+rnQ0mlyvOhsnqjatQQP/cRUSHFxgIrVwLLlwMxMWKbnR0wahQwYgRgb6/f+IiIiEowJldUpAQBiI5WPZTv+XP1+1lZqS5t7uEBGBoWX/xE5UZkJLBoEbBuXXbJTHd3YMIEYOBAVnMhIiLSAJMr0gmZTPxspqqoRFyc+v2cnVUnUU5OnA9FVCyuXBEr/23fnt1l3KCBWKTik09Y3YWIiEgL/L8maeXtW9Xzoe7eVT8fysBAnJ7xblGJWrUAG5tiDZ+IALFL+cQJsUjFsWPZ7W3biklVmzb8doOIiKgAmFyRSvHxqofyRUSIVftUkUqBmjVz90JVrw6YmhZv/ESkQmYmsHOnmFRduSK2GRpmV/5r0EC/8REREZVyTK7KMUEAXrxQXVTixQv1+9nYqB7K5+bG+VBEJVJKCrB+vVj5LzJSbDMzAz7/XKz85+Gh1/CIiIjKCiZX5YBMJvY4vZtA3bkj9lCpU7ly7rWhvL0BR0eOGCIqFWJisiv/xcaKbfb2YuW/4cNZ+Y+IiEjHmFyVIamp4tynd4tK3L0rrh2lioEB4OWlen0oK6vijZ+IdCQiIrvyX2qq2ObhIVb+Cwpi5T8iIqIiwuSqFIqLUz8fShBU72NmpjwfKqtHqnp1ca4UEZUBly9nV/7LmhzZsKFYpKJHD1b+IyIiKmJa/5/W3d0dgwYNQlBQEKpWrVoUMVEOoaHA9evKSVR0tPrtbW3Vz4cyMCi2sImouAgCcPy4WKTi+PHs9oAAMan68EOO4yUiIiomEkFQ19eh2pIlSxAcHIwbN27gww8/xODBg9G9e3dIy1D3R0JCAqytrREfHw8rPY+Nq1MHuHUrd3uVKqqTKAcHfo4iKhcyM4EdO8Sk6upVsc3QEOjTR6z85+ur1/CIiIjKCm1yA62TqyyXL19GcHAwtm7dCplMhv/9738YNGgQ3nvvvQIFXZKUpOTqq6/E4X7vzoeytNRrWESkL8nJYuW/RYuyK/+ZmwNDhoh/MNzc9BoeERFRWVMsyVWWjIwM/Pzzz5g0aRIyMjJQr149jB49GgMHDoSklHahlKTkiogIAPDqFbBihfh4/Vpsc3AARo8Ghg0D7Oz0Gx8REVEZpU1uUODZzRkZGdizZw82bNiAY8eO4f3338fgwYPx9OlTfPPNNzh+/Dh+//33gh6eiIgA4MEDsZdq/Xrg7VuxzctLrPw3YIBYrYaIiIhKBK2Tq8uXL2PDhg3YunUrDAwM0L9/fyxevBi1atVSbNO9e3c0btxYp4ESEZUrly6J86l27syu/NeoETBpEtC9O1fsJiIiKoG0Tq4aN26Mtm3bYtWqVejWrRuMjY1zbePh4YE+ffroJEAionJDEICjR8Wk6q+/stvbtxcr/7VuzYo1REREJZjWydXDhw/hls+E6QoVKmDDhg0FDoqIqFzJyMiu/Pfff2KboSHQt69Y+c/HR7/xERERkUa0Tq5evnyJqKgo+Pn5KbWfP38ehoaGaNSokc6CIyIq05KSgHXrgMWLgUePxLYKFcTKf2PHsvIfERFRKaP1srIjRozAkydPcrU/e/YMI0aM0ElQRERl2suXwLffAlWriknUo0dApUrAd98Bjx+LyRYTKyIiolJH656rW7duqVzLqkGDBrilarVbIiIS3b8vVv7bsCG78l+1amLlv/79WfmPiIiolNM6uZJKpYiOjoanp6dS+4sXL2BkVODK7kREZdfFi8D8+cCuXdmV/xo3Fiv/devGyn9ERERlhNbDAtu1a4cpU6YgPj5e0RYXF4dvvvkGbdu21WlwRESlliAAhw8DH30ENGkiFqyQy4GOHYHQUOD8eaBnTyZWREREZYjWXU0LFixAy5Yt4ebmhgYNGgAArl69CkdHR2zevFnnARIRlSoZGUBIiFj57/p1sc3ICPjf/8Thf/Xq6Tc+IiIiKjJaJ1eVK1fGtWvXsGXLFvz3338wMzPDwIED0bdvX5VrXhERlQtJScCvv4pzqrKK/lhYAEOHikUrXF31Gh4REREVvQJNkqpQoQKGDh2q61iIiEqf6Ghg+XLg55+BN2/ENkdHYMwY4MsvAVtb/cZHRERExabAFShu3bqFx48fIz09Xam9S5cuhQ6KiKjEu3cPWLgQCA4G0tLEturVxUV/P/sMMDXVa3hERERU/LROrh4+fIju3bvj+vXrkEgkEAQBACCRSAAAMplMtxESEZUkFy6I86l27xaLVgCAn59Y+a9LFxaoICIiKse0rhY4ZswYeHh44OXLlzA3N8fNmzfx999/o1GjRggNDS2CEImI9EwQgIMHgdatxURq1y6x7eOPgb//BsLCgO7dmVgRERGVc1r3XIWFheGvv/6Cvb09DAwMYGBggA8++ABz587F6NGjceXKlaKIk4io+KWnA9u2iWtU3bghthkZAZ9+Klb+q1tXv/ERERFRiaJ1ciWTyWBpaQkAsLe3x/Pnz1GzZk24ubkhPDxc5wESERW7xERg7Vpg8WLg6VOxzcIC+OILsfJflSp6DY+IiIhKJq2Tq7p16+K///6Dh4cH/Pz8MG/ePJiYmGDNmjXw9PQsihiJiIpHVBSwbBmwahUQFye2OTllV/6zsdFndERERFTCaZ1cTZs2DcnJyQCA2bNn4+OPP0aLFi1gZ2eHkJAQnQdIRFTk7t4FFiwANm4UhwICQI0aYuW/fv1Y+Y+IiIg0IhGyyv0VwuvXr2Fra6uoGFjaJSQkwNraGvHx8bCystJ3OERUVM6fB376Cdi7N7vy3/vvZ1f+M9C65g8RERGVMdrkBlp9csjIyICRkRFuZE3s/n8VK1YsM4kVEZVxcjlw4ADQqpWYSO3ZIyZWnTsDp08DZ88C3boxsSIiIiKtaTUs0NjYGFWrVuVaVkRU+qSnA1u3ipX/bt4U24yNxWF/EyYAtWvrNz4iIiIq9bT+anbq1Kn45ptv8Pr1a50FsXLlSri7u8PU1BR+fn64cOGC2m1bt24NiUSS69GpUyfFNoIgYPr06XB2doaZmRn8/f1x7949ncVLRKVIQgKwcCHg6QkEBYmJlaWlOJ8qIgJYv56JFREREemE1gUtVqxYgfv378PFxQVubm6oUKGC0s8vX76s1fFCQkIwbtw4rF69Gn5+fliyZAkCAgIQHh6OSpUq5dp+9+7dSM+acA4gNjYWvr6+6NWrl6Jt3rx5WLZsGTZu3AgPDw98++23CAgIwK1bt2DKielE5cOLF9mV/+LjxTZnZ7GU+hdfANbWeg2PiIiIyh6tk6tu3brpNIBFixZhyJAhGDhwIABg9erVOHDgANavX4/Jkyfn2r5ixYpKr7dt2wZzc3NFciUIApYsWYJp06aha9euAIBNmzbB0dERe/fuRZ8+fXQaPxGVMOHhYuW/TZuyK//VqiX2VH36KSCV6jc+IiIiKrO0Tq5mzJihszdPT0/HpUuXMGXKFEWbgYEB/P39ERYWptEx1q1bhz59+ih60CIiIhAVFQV/f3/FNtbW1vDz80NYWJjK5CotLQ1paWmK1wkJCQU9JSLSl7AwYN484I8/siv/NW8OfP018PHHLFBBRERERU6vnzZiYmIgk8ng6Oio1O7o6IioqKh8979w4QJu3LiBzz//XNGWtZ82x5w7dy6sra0VD1dXV21PhYj0QS4H9u8HWrQAmjXLLqnetSvwzz/igyXViYiIqJho/YnDwMAAhoaGah/Fad26dahXrx6aNGlSqONMmTIF8fHxiseTJ090FCERFYm0NGDDBqBuXTF5+ucfsfLf4MHArVtiktW8ub6jJCIionJG62GBe/bsUXqdkZGBK1euYOPGjZg1a5ZWx7K3t4ehoSGio6OV2qOjo+Hk5JTnvsnJydi2bRtmz56t1J61X3R0NJydnZWOWb9+fZXHkkqlkHIeBlHJFx8PrFkDLFkCPH8utllZAV9+CYwZA7i46DU8IiKi/2vvzuOiqtc/gH8GZJdFZVdEVEQlwEQl3BcK1ExMc8kFc0HNNTWxW26/yj3XvJpelFxxScmbK6JoIqapICaRIkoqSG7AAKIw398f5zI4AsoyMCyf9+s1r5jnfM/MM9/7ZS6P55znUM1W4uIqr0nEywYMGABnZ2fs3r0bo0ePLvZr6erqwt3dHWFhYcpGGQqFAmFhYZg0adJr9927dy+ys7MxbNgwlbiDgwOsra0RFhamLKbS0tLw22+/YcKECcXOjYgqkfv3gdWrgQ0bpNbqgFRITZsG+Puz8x8RERFVCiUuroryzjvvwN/fv8T7TZ8+HX5+fmjTpg3atWuHVatWISMjQ9k9cMSIEahfvz4WLVqksl9gYCB8fX1Rr149lbhMJsO0adPwzTffwNHRUdmK3dbWVu2dDomonMXGSp3/tm0DXryQYi1aSJ3/Pv6Ynf+IiIioUlFLcZWVlYU1a9agfv36Jd530KBB+OeffzB37lwkJyejVatWOHr0qLIhRWJiIrReuRg9Li4OZ8+exfHjxwt9zVmzZiEjIwP+/v54+vQpOnbsiKNHj/IeV0RVRUSE1Pnv4MH8WMeOUue/3r3ZoIKIiIgqJZkQeT2Li6dOnTqQyWTK50IIpKenw9DQENu3b8cHH3yg9iQrWlpaGkxNTZGamgoTExNNp0NUMygUwC+/AEuWAOfOSTGZTOr89/nnUjdAIiIiogpWktqgxEeuVq5cqVJcaWlpwcLCAh4eHqhTp07JsyWimi07G9ixA1i2DPjzTymmqwuMGAHMmCHdAJiIiIioCihxcTVy5MhySIOIapzUVOCHH6TOf0lJUszUFJgwAZgyBXip2ycRERFRVVDi4mrLli2oXbs2PvroI5X43r17kZmZCT8/P7UlR0TV0L17+Z3/0tOlWP36wGefAWPHSq3ViYiIiKqgEl8VvmjRIpibmxeIW1paYuHChWpJioiqoevXgVGjAAcH6RTA9HSgZUsgKAi4dUs6BZCFFREREVVhJT5ylZiYCAcHhwJxe3t7JCYmqiUpIqomhMjv/Pff/+bHO3eWOv/17MnOf0RERFRtlLi4srS0xNWrV9GoUSOVeHR0dIF7ThFRDaVQSG3Uly4FIiOlmEwG9Osndf575x3N5kdERERUDkpcXA0ZMgRTpkyBsbExOnfuDAA4ffo0pk6disGDB6s9QSKqQp49A7Zvl278GxcnxfT0AD8/6bS/Zs00mx8RERFROSpxcfX111/j9u3b6NGjB2rVknZXKBQYMWIEr7kiqqmePpUaVKxeDSQnSzEzM+DTT4HJkwFra01mR0RERFQhSnwT4Tw3btxAVFQUDAwM4OLiAnt7e3XnpjG8iTBRMd29K7VS/+EHQC6XYg0aANOnA2PGAMbGGk2PiIiIqKzK9SbCeRwdHeHo6Fja3YmoKvvjD6nj344dQE6OFHN2lppUDB4s3QSYiIiIqIYpcZuu/v37Y8mSJQXiS5cuLXDvKyKqRoQAzpwB3n8feOst4McfpcKqSxfg0CEgJgYYMYKFFREREdVYJS6uzpw5g169ehWI9+zZE2fOnFFLUkRUieTmAvv3A56e+YWUTAb07w+cPw+EhwO9ekkxIiIiohqsxKcFyuVy6BbyL9M6OjpIS0tTS1JEVAk8ewZs3Sp1/rtxQ4rp6QEjR0qd/3haMBEREZGKEh+5cnFxwe7duwvEg4OD0bJlS7UkRUQa9OQJsGgR0KgRMG6cVFiZmQFffgncuSN1BWRhRURERFRAiY9czZkzBx9++CHi4+PRvXt3AEBYWBh27tyJffv2qT1BIqogf/8tdf7buDG/85+dndT5b/Rodv4jIiIieoMSF1d9+vRBSEgIFi5ciH379sHAwABubm44efIk6tatWx45ElF5iomRTv3buTO/85+Li9T5b9AgQEdHs/kRERERVRGlvs9VnrS0NOzatQuBgYG4dOkScnNz1ZWbxvA+V1Tt5XX+W7oUOHw4P96tm1RUeXuzQQURERERSlYblPiaqzxnzpyBn58fbG1t8d1336F79+44f/58aV+OiCpCbi7w00/AO+8AXbtKhZWWFvDRR8CFC8DJk4CPDwsrIiIiolIo0WmBycnJCAoKQmBgINLS0jBw4EBkZ2cjJCSEzSyIKrOsrPzOfzdvSjF9feCTT6Rrqpo21Wx+RERERNVAsY9c9enTB05OTrh69SpWrVqF+/fvY+3ateWZGxGV1ePHwLffSp3/xo+XCqs6dYA5c6TOf//+NwsrIiIiIjUp9pGrI0eOYMqUKZgwYQIc2YaZqHJLTARWrgQ2bQIyMqRYw4bS/alGjQJq19ZsfkRERETVULGPXJ09exbp6elwd3eHh4cHvv/+ezx8+LA8cyOikrp6FRg+HGjcWGqrnpEBuLkBO3ZIR62mTGFhRURERFROil1cvfPOO9i0aROSkpIwbtw4BAcHw9bWFgqFAqGhoUhPTy/PPImoKEIAp04BPXtKhdT27VLjiu7dgaNHgStXgI8/Zkt1IiIionJWplbscXFxCAwMxLZt2/D06VO8++67OHjwoDrz0wi2YqcqITcX2L9faqf+++9STEsLGDAA+PxzoE0bzeZHREREVA1USCt2AHBycsLSpUtx9+5d7Nq1qywvRUTFlZUFrF8PODkBAwdKhZW+PvDpp8CNG8Du3SysiIiIiDSgzDcRro545IoqpUePpO5+a9cC//wjxerWBSZNkh4WFprNj4iIiKgaKkltUKL7XBGRBty5k9/5LzNTitnb53f+MzLSbH5EREREBIDFFVHlFR0NLFsGBAdL11cBQKtWwKxZwEcfAbX460tERERUmfCvM6LKJK/z39KlwLFj+XEvL6mo8vICZDLN5UdERERERWJxRVQZ5OTkd/67dEmKaWlJDSs+/xxo3Vqz+RERERHRG7G4ItKkzEwgKAj47jvg1i0pZmAAjB4NTJ8OODhoND0iIiIiKj4WV0Sa8OgRsG6d1Pnv4UMpVq8eMHkyMHEiYG6u2fyIiIiIqMRYXBFVpNu3gRUrgMDA/M5/Dg5S579PPgEMDTWaHhERERGVHosroopw5YrU+W/PnvzOf2+/DQQEAP37s/MfERERUTXAv+iIyosQQFiY1KQiNDQ//t57Uue/7t3Z+Y+IiIioGmFxRaRuOTnAvn1SUXXlihTT1s7v/Pf225rNj4iIiIjKBYsrInXJzAQ2b5Y6/92+LcUMDaXOf599xs5/RERERNUciyuisnr4EPj+e+nx6JEUMzcHpkwBPv1U6gJIRERERNUeiyui0rp1S+r8t3kzkJUlxRo3ljr/jRzJzn9ERERENQyLK6KSunxZup5q715AoZBi7u5Sk4oPP2TnPyIiIqIain8FEhWHEMCJE1JRdeJEftzbWyqqunVj5z8iIiKiGo7FFdHr5ORIR6iWLgWioqSYtjYweLDU+c/NTaPpEREREVHlweKKqDAZGfmd/+7ckWKGhsDYsVLnP3t7zeZHRERERJUOiyuil/3zT37nv8ePpZiFhdT5b8IEdv4jIiKqpHJzc/HixQtNp0FVkI6ODrS1tdXyWiyuiAAgPj6/89+zZ1KsSRNg5kzAzw8wMNBsfkRERFQoIQSSk5Px9OlTTadCVZiZmRmsra0hK+M19CyuqGb7/Xdg2TJg3778zn9t2gABAUC/ftL1VURERFRp5RVWlpaWMDQ0LPMfx1SzCCGQmZmJlJQUAICNjU2ZXo/FFdU8QgDHj0tNKk6ezI/37Cl1/uvShZ3/iIiIqoDc3FxlYVWPp+5TKRn87wyllJQUWFpalukUQS11JVVa69atQ6NGjaCvrw8PDw9cuHDhteOfPn2KiRMnwsbGBnp6emjWrBkOHz6s3D5//nzIZDKVR/Pmzcv7Y1BV8OIFsGMH0KoV4OMjFVa1agHDhwPR0cDhw0DXriysiIiIqoi8a6wMDQ01nAlVdXlrqKzX7Wn0yNXu3bsxffp0bNiwAR4eHli1ahW8vb0RFxcHS0vLAuOfP3+Od999F5aWlti3bx/q16+PO3fuwMzMTGWcs7MzTrx0L6JavKlrzSaXA4GB0jVViYlSzMgov/Nfw4aazY+IiIjKhKcCUlmpaw1ptOpYsWIFxo4di08++QQAsGHDBhw6dAibN2/G7NmzC4zfvHkzHj9+jHPnzkFHRwcA0KhRowLjatWqBWtr63LNnaqAlBRg7Vpg3TrgyRMpZmmZ3/mvbl3N5kdERERE1YrGTgt8/vw5Ll26BC8vr/xktLTg5eWFyMjIQvc5ePAgPD09MXHiRFhZWeGtt97CwoULkZubqzLuxo0bsLW1RePGjTF06FAk5h2tKEJ2djbS0tJUHlSF3bwpFU/29sA330iFVdOmwA8/SPes+vJLFlZERERUrTRq1AirVq0q9vjw8HDIZDJ2WVQzjRVXDx8+RG5uLqysrFTiVlZWSE5OLnSfW7duYd++fcjNzcXhw4cxZ84cfPfdd/jmm2+UYzw8PBAUFISjR49i/fr1SEhIQKdOnZCenl5kLosWLYKpqanyYWdnp54PSRXr4kXgo4+AZs2ADRuklupt20qdAP/8E/D3B/T1NZ0lERER1WCv9gZ49TF//vxSve7Fixfh7+9f7PHt27dHUlISTE1NS/V+VLgqdTGSQqGApaUlNm7cCG1tbbi7u+PevXtYtmwZ5s2bBwDo2bOncryrqys8PDxgb2+PPXv2YPTo0YW+7hdffIHp06crn6elpbHAqiqEAI4dA5YsAcLD8+O9ekmd/zp3ZoMKIiIiqjSSkpKUP+/evRtz585FXFycMla7dm3lz0II5ObmFqt/gIWFRYny0NXV5WU05UBjR67Mzc2hra2NBw8eqMQfPHhQ5P/QNjY2aNasmUp7xBYtWiA5ORnPnz8vdB8zMzM0a9YMN2/eLDIXPT09mJiYqDyoknvxAti+HXBzk1qoh4dLnf9GjACuXgUOHWJLdSIioppICCAjo+IfQhQrPWtra+XD1NQUMplM+fzPP/+EsbExjhw5And3d+jp6eHs2bOIj49H3759YWVlhdq1a6Nt27YqzduAgqcFymQy/Oc//0G/fv1gaGgIR0dHHDx4ULn91dMCg4KCYGZmhmPHjqFFixaoXbs2fHx8VIrBnJwcTJkyBWZmZqhXrx4CAgLg5+cHX1/fIj/vo0ePMGTIENSvXx+GhoZwcXHBrl27VMYoFAosXboUTZs2hZ6eHho2bIhvv/1Wuf3u3bsYMmQI6tatCyMjI7Rp0wa//fZbsea7ommsuNLV1YW7uzvCwsKUMYVCgbCwMHh6eha6T4cOHXDz5k0o8m72CuCvv/6CjY0NdHV1C91HLpcjPj6+zDcEo0pCLgdWrQKaNJFaqMfEALVrA9OnA7duAT/+CLi4aDpLIiIi0pTMTOlvg4p+ZGaq7SPMnj0bixcvRmxsLFxdXSGXy9GrVy+EhYXhypUr8PHxQZ8+fd7YV2DBggUYOHAgrl69il69emHo0KF4/Pjxa6YuE8uXL8e2bdtw5swZJCYmYubMmcrtS5YswY4dO7BlyxZEREQgLS0NISEhr83h2bNncHd3x6FDh3Dt2jX4+/tj+PDhKrdf+uKLL7B48WLMmTMH169fx86dO5WXDsnlcnTp0gX37t3DwYMHER0djVmzZqnUA5WK0KDg4GChp6cngoKCxPXr14W/v78wMzMTycnJQgghhg8fLmbPnq0cn5iYKIyNjcWkSZNEXFyc+OWXX4SlpaX45ptvlGNmzJghwsPDRUJCgoiIiBBeXl7C3NxcpKSkFDuv1NRUAUCkpqaq78NS2SQnC/Hll0KYmQkh/duQEFZWQixcKMTjx5rOjoiIiDQgKytLXL9+XWRlZeUH5fL8vxUq8iGXlzj/LVu2CFNTU+XzU6dOCQAiJCTkjfs6OzuLtWvXKp/b29uLlStXKp8DEF999dVL0yIXAMSRI0dU3uvJkyfKXACImzdvKvdZt26dsLKyUj63srISy5YtUz7PyckRDRs2FH379i3uRxZCCNG7d28xY8YMIYQQaWlpQk9PT2zatKnQsT/88IMwNjYWjx49KtF7lFSha+l/SlIbaPSaq0GDBuGff/7B3LlzkZycjFatWuHo0aPKSjUxMRFaWvkH1+zs7HDs2DF89tlncHV1Rf369TF16lQEBAQox+QdNnz06BEsLCzQsWNHnD9/vsTnoVIlceMG8N13QFAQkJ0txRwdgc8/l45csUEFERERvczQUDrTRRPvqyZt2rRReS6XyzF//nwcOnQISUlJyMnJQVZW1huPXLm6uip/NjIygomJCVJSUoocb2hoiCZNmiif29jYKMenpqbiwYMHaNeunXJ7Xg+E1x1Fys3NxcKFC7Fnzx7cu3cPz58/R3Z2tvKmvbGxscjOzkaPHj0K3T8qKgpvv/026laRTs8ab2gxadIkTJo0qdBt4S83KPgfT09PnD9/vsjXCw4OVldqpEkXLgBLlwL79+efw+zhAQQEAB98ALx03R0RERGRkkwGGBlpOosyMXol/5kzZyI0NBTLly9H06ZNYWBggAEDBhTZcyBP3n1h88hkstcWQoWNF8W8lqwoy5Ytw+rVq7Fq1Sq4uLjAyMgI06ZNU+ZuYGDw2v3ftL2y0dg1V0QFCAEcPgx07SoVUj/9JMXefx84cwaIjAT69WNhRURERDVKREQERo4ciX79+sHFxQXW1ta4fft2heZgamoKKysrXLx4URnLzc3F5cuXX7tfREQE+vbti2HDhsHNzQ2NGzfGX3/9pdzu6OgIAwMDlT4ML3N1dUVUVNRrrxWrTFhckeY9fw5s3Qq4ugK9ewOnTwM6OsDIkcC1a8B//wt06sTOf0RERFQjOTo6Yv/+/YiKikJ0dDQ+/vhjjTR0mDx5MhYtWoSff/4ZcXFxmDp1Kp48eQLZa/5Gc3R0RGhoKM6dO4fY2FiMGzdOpVu4vr4+AgICMGvWLGzduhXx8fE4f/48AgMDAQBDhgyBtbU1fH19ERERgVu3buGnn35CZGRkuX/e0tD4aYFUg6WnA5s2AStXAnfvSjFjY2DcOGDqVKBBA83mR0RERFQJrFixAqNGjUL79u1hbm6OgIAApKWlVXgeAQEBSE5OxogRI6CtrQ1/f394e3ur3CbpVV999RVu3boFb29vGBoawt/fH76+vkhNTVWOmTNnDmrVqoW5c+fi/v37sLGxwfjx4wFIHcaPHz+OGTNmoFevXsjJyUHLli2xbt26cv+8pSETZT2RshpKS0uDqakpUlNTec+r8pCcDKxZA/z730DeL5a1NTBtmlRYmZlpMjsiIiKqIp49e4aEhAQ4ODhAn02uKpxCoUCLFi0wcOBAfP3115pOp0xet5ZKUhvwyBVVnL/+ApYvl+5FlXcBppOT1Plv2DBAT0+z+RERERFRke7cuYPjx4+jS5cuyM7Oxvfff4+EhAR8/PHHmk6t0mBxReXv/Hmp819ISH7nP09PqfNfnz6AFi/9IyIiIqrstLS0EBQUhJkzZ0IIgbfeegsnTpxAixYtNJ1apcHiisqHQiF1/lu6FPj11/x4nz7ArFlAx46ay42IiIiISszOzg4RERGaTqNSY3FF6vX8ObBzJ7BsGXD9uhTT0ZFO+5s5E2jZUrP5ERERERGVExZXpB5pafmd/+7dk2LGxsD48VLnv/r1NZsfEREREVE5Y3FFZZOUJHX+W78+v/OfjU1+5z9TU42mR0RERERUUVhcUenExUmn/m3blt/5r3lzqfPf0KHs/EdERERENQ6LKyqZyEipScXPP+d3/uvQQWpS8f777PxHRERERDUWiyt6M4UCOHRIKqrOns2P9+0rHanq0EFzuRERERERVRI8zEBFy84GtmwB3noL+OADqbDS1QVGj5Y6AYaEsLAiIiIi0oCuXbti2rRpyueNGjXCqlWrXruPTCZDSEhImd9bXa9THfHIFRWUmgps3AisWgXcvy/FTEyACROAKVMAW1uNpkdERERUVfXp0wcvXrzA0aNHC2z79ddf0blzZ0RHR8PV1bVEr3vx4kUYGRmpK00AwPz58xESEoKoqCiVeFJSEurUqaPW96ouWFxRvvv3gdWrgQ0bpNbqgFRIffYZ4O8vFVhEREREVGqjR49G//79cffuXTRo0EBl25YtW9CmTZsSF1YAYGFhoa4U38ja2rrC3quq4WmBBMTGSqf6NWokXVeVlga0aCGdEpiQIN38l4UVERERVQFCABkZFf/I6/P1Ju+//z4sLCwQFBSkEpfL5di7dy9Gjx6NR48eYciQIahfvz4MDQ3h4uKCXbt2vfZ1Xz0t8MaNG+jcuTP09fXRsmVLhIaGFtgnICAAzZo1g6GhIRo3bow5c+bgxYsXAICgoCAsWLAA0dHRkMlkkMlkypxfPS0wJiYG3bt3h4GBAerVqwd/f3/I5XLl9pEjR8LX1xfLly+HjY0N6tWrh4kTJyrfqzDx8fHo27cvrKysULt2bbRt2xYnTpxQGZOdnY2AgADY2dlBT08PTZs2RWBgoHL7H3/8gffffx8mJiYwNjZGp06dEB8f/9p5LCseuarJIiKkYurgwfxYp05S579evdj5j4iIiKqczEygdu2Kf1+5HCjOWXm1atXCiBEjEBQUhC+//BIymQwAsHfvXuTm5mLIkCGQy+Vwd3dHQEAATExMcOjQIQwfPhxNmjRBu3bt3vgeCoUCH374IaysrPDbb78hNTVV5fqsPMbGxggKCoKtrS1iYmIwduxYGBsbY9asWRg0aBCuXbuGo0ePKosa00LuX5qRkQFvb294enri4sWLSElJwZgxYzBp0iSVAvLUqVOwsbHBqVOncPPmTQwaNAitWrXC2LFji5hPOXr16oVvv/0Wenp62Lp1K/r06YO4uDg0bNgQADBixAhERkZizZo1cHNzQ0JCAh4+fAgAuHfvHjp37oyuXbvi5MmTMDExQUREBHJyct44f2UiqIDU1FQBQKSmpmo6FfXLzRUiJESI9u2FkP6RRQiZTIh+/YQ4d07T2REREREVW1ZWlrh+/brIyspSxuTy/D9xKvIhlxc/79jYWAFAnDp1Shnr1KmTGDZsWJH79O7dW8yYMUP5vEuXLmLq1KnK5/b29mLlypVCCCGOHTsmatWqJe7du6fcfuTIEQFAHDhwoMj3WLZsmXB3d1c+nzdvnnBzcysw7uXX2bhxo6hTp46QvzQBhw4dElpaWiI5OVkIIYSfn5+wt7cXOTk5yjEfffSRGDRoUJG5FMbZ2VmsXbtWCCFEXFycACBCQ0MLHfvFF18IBwcH8fz582K9dmFrKU9JagMeuaopsrOB7dulG//GxUkxXV1gxAjptD8nJ83mR0RERKQGhobSUSRNvG9xNW/eHO3bt8fmzZvRtWtX3Lx5E7/++iv+7//+DwCQm5uLhQsXYs+ePbh37x6eP3+O7OxsGBbzTWJjY2FnZwfbl5qQeXp6Fhi3e/durFmzBvHx8ZDL5cjJyYFJCS8FiY2NhZubm0ozjQ4dOkChUCAuLg5WVlYAAGdnZ2hrayvH2NjYICYmpsjXlcvlmD9/Pg4dOoSkpCTk5OQgKysLiYmJAICoqChoa2ujS5cuhe4fFRWFTp06QUdHp0Sfp6xYXFV3qalSg4rVq4GkJClmaprf+c/GRrP5EREREamRTFa80/M0bfTo0Zg8eTLWrVuHLVu2oEmTJspCYdmyZVi9ejVWrVoFFxcXGBkZYdq0aXj+/Lna3j8yMhJDhw7FggUL4O3tDVNTUwQHB+O7775T23u87NUiRyaTQaFQFDl+5syZCA0NxfLly9G0aVMYGBhgwIAByjkwMDB47fu9aXt54UU11dW9e9K1U3Z2wOzZUmFVvz6wfDmQmAgsWsTCioiIiEhDBg4cCC0tLezcuRNbt27FqFGjlNdfRUREoG/fvhg2bBjc3NzQuHFj/PXXX8V+7RYtWuDvv/9GUt4/rAM4f/68yphz587B3t4eX375Jdq0aQNHR0fcuXNHZYyuri5yc3Pf+F7R0dHIyMhQxiIiIqClpQWnMpwZFRERgZEjR6Jfv35wcXGBtbU1bt++rdzu4uIChUKB06dPF7q/q6srfv3119c2zSgPLK6qm+vXgVGjAAcH6RTA9HTA2RkICgJu3QJmzGDnPyIiIiINq127NgYNGoQvvvgCSUlJGDlypHKbo6MjQkNDce7cOcTGxmLcuHF48OBBsV/by8sLzZo1g5+fH6Kjo/Hrr7/iyy+/VBnj6OiIxMREBAcHIz4+HmvWrMGBAwdUxjRq1AgJCQmIiorCw4cPkZ2dXeC9hg4dCn19ffj5+eHatWs4deoUJk+ejOHDhytPCSwNR0dH7N+/H1FRUYiOjsbHH3+scqSrUaNG8PPzw6hRoxASEoKEhASEh4djz549AIBJkyYhLS0NgwcPxu+//44bN25g27ZtiMu7PKacsLiqDoQAzp4FPvhAKqS2bAFevAA6dwZ++QW4ehXw85OusSIiIiKiSmH06NF48uQJvL29Va6P+uqrr9C6dWt4e3uja9eusLa2hq+vb7FfV0tLCwcOHEBWVhbatWuHMWPG4Ntvv1UZ88EHH+Czzz7DpEmT0KpVK5w7dw5z5sxRGdO/f3/4+PigW7dusLCwKLQdvKGhIY4dO4bHjx+jbdu2GDBgAHr06IHvv/++ZJPxihUrVqBOnTpo3749+vTpA29vb7Ru3VplzPr16zFgwAB8+umnaN68OcaOHas8glavXj2cPHkScrkcXbp0gbu7OzZt2lTu12DJhChuV/6aIy0tDaampkhNTS3xRX0VSqGQ2qgvXQpERkoxmQzo1w/4/HPgnXc0mx8RERFROXr27BkSEhLg4OAAfX19TadDVdjr1lJJagM2tKiKnj3L7/yXd/6tnp50dGrGDKBZM83mR0RERERUA7G4qkqePs3v/JecLMXMzIBPPwUmTwasrTWZHRERERFRjcbiqiq4exdYtQr44Yf8Gzc0aABMnw6MGQMYG2s0PSIiIiIiYnFVuT1/Dvj7Azt2ADk5Uuytt6QW64MHAxV8UzQiIiIiIioai6vKTFcXuHNHKqy6dpWKKh8fqWkFEREREQEA2J+Nykpda4jFVWW3bBmQmwt4eGg6EyIiIqJKJa+tdmZmJgwMDDScDVVlmZmZAFDmVu0sriq7Nm00nQERERFRpaStrQ0zMzOkpKQAkO65JOMZPlQCQghkZmYiJSUFZmZm0NbWLtPrsbgiIiIioirL+n/dkvMKLKLSMDMzU66lsmBxRURERERVlkwmg42NDSwtLfHixQtNp0NVkI6OTpmPWOVhcUVEREREVZ62trba/kAmKi0tTSdARERERERUHbC4IiIiIiIiUgMWV0RERERERGrAa64KkXcTsbS0NA1nQkREREREmpRXExTnRsMsrgqRnp4OALCzs9NwJkREREREVBmkp6fD1NT0tWNkojglWA2jUChw//59GBsba/xGdGlpabCzs8Pff/8NExMTjeZSHXF+yxfnt3xxfssf57h8cX7LF+e3fHF+y1dlml8hBNLT02FrawstrddfVcUjV4XQ0tJCgwYNNJ2GChMTE40vrOqM81u+OL/li/Nb/jjH5YvzW744v+WL81u+Ksv8vumIVR42tCAiIiIiIlIDFldERERERERqwOKqktPT08O8efOgp6en6VSqJc5v+eL8li/Ob/njHJcvzm/54vyWL85v+aqq88uGFkRERERERGrAI1dERERERERqwOKKiIiIiIhIDVhcERERERERqQGLKyIiIiIiIjVgcVWBzpw5gz59+sDW1hYymQwhISFv3Cc8PBytW7eGnp4emjZtiqCgoAJj1q1bh0aNGkFfXx8eHh64cOGC+pOvIko6x/v378e7774LCwsLmJiYwNPTE8eOHVMZM3/+fMhkMpVH8+bNy/FTVF4lnd/w8PACcyeTyZCcnKwyjmtYUtL5HTlyZKHz6+zsrBzD9StZtGgR2rZtC2NjY1haWsLX1xdxcXFv3G/v3r1o3rw59PX14eLigsOHD6tsF0Jg7ty5sLGxgYGBAby8vHDjxo3y+hiVVmnmd9OmTejUqRPq1KmDOnXqwMvLq8DvfmFr3MfHpzw/SqVUmvkNCgoqMHf6+voqY7h+JaWZ365duxb6/du7d2/lGK7ffOvXr4erq6vyhsCenp44cuTIa/epqt+/LK4qUEZGBtzc3LBu3bpijU9ISEDv3r3RrVs3REVFYdq0aRgzZozKH/+7d+/G9OnTMW/ePFy+fBlubm7w9vZGSkpKeX2MSq2kc3zmzBm8++67OHz4MC5duoRu3bqhT58+uHLliso4Z2dnJCUlKR9nz54tj/QrvZLOb564uDiV+bO0tFRu4xrOV9L5Xb16tcq8/v3336hbty4++ugjlXFcv8Dp06cxceJEnD9/HqGhoXjx4gXee+89ZGRkFLnPuXPnMGTIEIwePRpXrlyBr68vfH19ce3aNeWYpUuXYs2aNdiwYQN+++03GBkZwdvbG8+ePauIj1VplGZ+w8PDMWTIEJw6dQqRkZGws7PDe++9h3v37qmM8/HxUVm/u3btKu+PU+mUZn4BwMTERGXu7ty5o7Kd61dSmvndv3+/ytxeu3YN2traBb5/uX4lDRo0wOLFi3Hp0iX8/vvv6N69O/r27Ys//vij0PFV+vtXkEYAEAcOHHjtmFmzZglnZ2eV2KBBg4S3t7fyebt27cTEiROVz3Nzc4Wtra1YtGiRWvOtioozx4Vp2bKlWLBggfL5vHnzhJubm/oSqyaKM7+nTp0SAMSTJ0+KHMM1XLjSrN8DBw4ImUwmbt++rYxx/RYuJSVFABCnT58ucszAgQNF7969VWIeHh5i3LhxQgghFAqFsLa2FsuWLVNuf/r0qdDT0xO7du0qn8SriOLM76tycnKEsbGx+PHHH5UxPz8/0bdv33LIsGorzvxu2bJFmJqaFrmd67dopVm/K1euFMbGxkIulytjXL+vV6dOHfGf//yn0G1V+fuXR64qscjISHh5eanEvL29ERkZCQB4/vw5Ll26pDJGS0sLXl5eyjFUMgqFAunp6ahbt65K/MaNG7C1tUXjxo0xdOhQJCYmaijDqqlVq1awsbHBu+++i4iICGWca1i9AgMD4eXlBXt7e5U4129BqampAFDgd/1lb/oOTkhIQHJyssoYU1NTeHh41Pj1W5z5fVVmZiZevHhRYJ/w8HBYWlrCyckJEyZMwKNHj9Saa1VU3PmVy+Wwt7eHnZ1dgaMEXL9FK836DQwMxODBg2FkZKQS5/otKDc3F8HBwcjIyICnp2ehY6ry9y+Lq0osOTkZVlZWKjErKyukpaUhKysLDx8+RG5ubqFjXr2mhYpn+fLlkMvlGDhwoDLm4eGBoKAgHD16FOvXr0dCQgI6deqE9PR0DWZaNdjY2GDDhg346aef8NNPP8HOzg5du3bF5cuXAYBrWI3u37+PI0eOYMyYMSpxrt+CFAoFpk2bhg4dOuCtt94qclxR38F5azPvv1y/qoo7v68KCAiAra2tyh9LPj4+2Lp1K8LCwrBkyRKcPn0aPXv2RG5ubnmkXiUUd36dnJywefNm/Pzzz9i+fTsUCgXat2+Pu3fvAuD6LUpp1u+FCxdw7dq1At+/XL+qYmJiULt2bejp6WH8+PE4cOAAWrZsWejYqvz9W0uj705UiezcuRMLFizAzz//rHJNUM+ePZU/u7q6wsPDA/b29tizZw9Gjx6tiVSrDCcnJzg5OSmft2/fHvHx8Vi5ciW2bdumwcyqnx9//BFmZmbw9fVViXP9FjRx4kRcu3atRl57VhFKM7+LFy9GcHAwwsPDVZouDB48WPmzi4sLXF1d0aRJE4SHh6NHjx5qzbuqKO78enp6qhwVaN++PVq0aIEffvgBX3/9dXmnWWWVZv0GBgbCxcUF7dq1U4lz/apycnJCVFQUUlNTsW/fPvj5+eH06dNFFlhVFY9cVWLW1tZ48OCBSuzBgwcwMTGBgYEBzM3Noa2tXegYa2vriky1ygsODsaYMWOwZ8+eAoehX2VmZoZmzZrh5s2bFZRd9dKuXTvl3HENq4cQAps3b8bw4cOhq6v72rE1ff1OmjQJv/zyC06dOoUGDRq8dmxR38F5azPvv1y/+Uoyv3mWL1+OxYsX4/jx43B1dX3t2MaNG8Pc3JzrtwTzm0dHRwdvv/22cu64fgsqzfxmZGQgODi4WP9YVdPXr66uLpo2bQp3d3csWrQIbm5uWL16daFjq/L3L4urSszT0xNhYWEqsdDQUOW/ROnq6sLd3V1ljEKhQFhYWJHnsFJBu3btwieffIJdu3aptFAtilwuR3x8PGxsbCogu+onKipKOXdcw+px+vRp3Lx5s1j/515T168QApMmTcKBAwdw8uRJODg4vHGfN30HOzg4wNraWmVMWloafvvttxq3fkszv4DU7evrr7/G0aNH0aZNmzeOv3v3Lh49esT1W8z5fVlubi5iYmKUc8f1m68s87t3715kZ2dj2LBhbxxbU9dvURQKBbKzswvdVqW/fzXaTqOGSU9PF1euXBFXrlwRAMSKFSvElStXxJ07d4QQQsyePVsMHz5cOf7WrVvC0NBQfP755yI2NlasW7dOaGtri6NHjyrHBAcHCz09PREUFCSuX78u/P39hZmZmUhOTq7wz1cZlHSOd+zYIWrVqiXWrVsnkpKSlI+nT58qx8yYMUOEh4eLhIQEERERIby8vIS5ublISUmp8M+naSWd35UrV4qQkBBx48YNERMTI6ZOnSq0tLTEiRMnlGO4hvOVdH7zDBs2THh4eBT6mly/kgkTJghTU1MRHh6u8ruemZmpHDN8+HAxe/Zs5fOIiAhRq1YtsXz5chEbGyvmzZsndHR0RExMjHLM4sWLhZmZmfj555/F1atXRd++fYWDg4PIysqq0M+naaWZ38WLFwtdXV2xb98+lX3S09OFENLvw8yZM0VkZKRISEgQJ06cEK1btxaOjo7i2bNnFf4ZNak087tgwQJx7NgxER8fLy5duiQGDx4s9PX1xR9//KEcw/UrKc385unYsaMYNGhQgTjXr6rZs2eL06dPi4SEBHH16lUxe/ZsIZPJxPHjx4UQ1ev7l8VVBcprS/3qw8/PTwghtezs0qVLgX1atWoldHV1RePGjcWWLVsKvO7atWtFw4YNha6urmjXrp04f/58+X+YSqqkc9ylS5fXjhdCan9vY2MjdHV1Rf369cWgQYPEzZs3K/aDVRIlnd8lS5aIJk2aCH19fVG3bl3RtWtXcfLkyQKvyzUsKc13xNOnT4WBgYHYuHFjoa/J9SspbF4BqHyndunSReV3Xwgh9uzZI5o1ayZ0dXWFs7OzOHTokMp2hUIh5syZI6ysrISenp7o0aOHiIuLq4BPVLmUZn7t7e0L3WfevHlCCCEyMzPFe++9JywsLISOjo6wt7cXY8eOrZH/8FKa+Z02bZrye9XKykr06tVLXL58WeV1uX4lpf1++PPPPwUAZYHwMq5fVaNGjRL29vZCV1dXWFhYiB49eqjMW3X6/pUJIYSaDoIRERERERHVWLzmioiIiIiISA1YXBEREREREakBiysiIiIiIiI1YHFFRERERESkBiyuiIiIiIiI1IDFFRERERERkRqwuCIiIiIiIlIDFldERERERERqwOKKiIiojGQyGUJCQjSdBhERaRiLKyIiqtJGjhwJmUxW4OHj46Pp1IiIqIappekEiIiIysrHxwdbtmxRienp6WkoGyIiqql45IqIiKo8PT09WFtbqzzq1KkDQDplb/369ejZsycMDAzQuHFj7Nu3T2X/mJgYdO/eHQYGBqhXrx78/f0hl8tVxmzevBnOzs7Q09ODjY0NJk2apLL94cOH6NevHwwNDeHo6IiDBw8qtz158gRDhw6FhYUFDAwM4OjoWKAYJCKiqo/FFRERVXtz5sxB//79ER0djaFDh2Lw4MGIjY0FAGRkZMDb2xt16tTBxYsXsXfvXpw4cUKleFq/fj0mTpwIf39/xMTE4ODBg2jatKnKeyxYsAADBw7E1atX0atXLwwdOhSPHz9Wvv/169dx5MgRxMbGYv369TA3N6+4CSAiogohE0IITSdBRERUWiNHjsT27duhr6+vEv/Xv/6Ff/3rX5DJZBg/fjzWr1+v3PbOO++gdevW+Pe//41NmzYhICAAf//9N4yMjAAAhw8fRp8+fXD//n1YWVmhfv36+OSTT/DNN98UmoNMJsNXX32Fr7/+GoBUsNWuXRtHjhyBj48PPvjgA5ibm2Pz5s3lNAtERFQZ8JorIiKq8rp166ZSPAFA3bp1lT97enqqbPP09ERUVBQAIDY2Fm5ubsrCCgA6dOgAhUKBuLg4yGQy3L9/Hz169HhtDq6ursqfjYyMYGJigpSUFADAhAkT0L9/f1y+fBnvvfcefH190b59+1J9ViIiqrxYXBERUZVnZGRU4DQ9dTEwMCjWOB0dHZXnMpkMCoUCANCzZ0/cuXMHhw8fRmhoKHr06IGJEydi+fLlas+XiIg0h9dcERFRtXf+/PkCz1u0aAEAaNGiBaKjo5GRkaHcHhERAS0tLTg5OcHY2BiNGjVCWFhYmXKwsLCAn58ftm/fjlWrVmHjxo1lej0iIqp8eOSKiIiqvOzsbCQnJ6vEatWqpWwasXfvXrRp0wYdO3bEjh07cOHCBQQGBgIAhg4dinnz5sHPzw/z58/HP//8g8mTJ2P48OGwsrICAMyfPx/jx4+HpaUlevbsifT0dERERGDy5MnFym/u3Llwd3eHs7MzsrOz8csvvyiLOyIiqj5YXBERUZV39OhR2NjYqMScnJzw559/ApA6+QUHB+PTTz+FjY0Ndu3ahZYtWwIADA0NcezYMUydOhVt27aFoaEh+vfvjxUrVihfy8/PD8+ePcPKlSsxc+ZMmJubY8CAAcXOT1dXF1988QVu374NAwMDdOrUCcHBwWr45EREVJmwWyAREVVrMpkMBw4cgK+vr6ZTISKiao7XXBEREREREakBiysiIiIiIiI14DVXRERUrfHsdyIiqig8ckVERERERKQGLK6IiIiIiIjUgMUVERERERGRGrC4IiIiIiIiUgMWV0RERERERGrA4oqIiIiIiEgNWFwRERERERGpAYsrIiIiIiIiNfh/La5YCY/FhBYAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_name = 'imdb'\n",
        "saved_model_path = './{}_bert'.format(dataset_name.replace('/', '_'))\n",
        "\n",
        "classifier_model.save(saved_model_path, include_optimizer=False)"
      ],
      "metadata": {
        "id": "utyrs-6B5pOe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reloaded_model = tf.saved_model.load(saved_model_path)"
      ],
      "metadata": {
        "id": "mgjFyoGz5x6x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def print_my_examples(inputs, results):\n",
        "  result_for_printing = \\\n",
        "    [f'input: {inputs[i]:<30} : score: {results[i][0]:.6f}'\n",
        "                         for i in range(len(inputs))]\n",
        "  print(*result_for_printing, sep='\\n')\n",
        "  print()\n",
        "\n",
        "\n",
        "examples = [\n",
        "    'this is such an amazing movie!',  # this is the same sentence tried earlier\n",
        "    'The movie was great!',\n",
        "    'The movie was meh.',\n",
        "    'The movie was okish.',\n",
        "    'The movie was terrible...',\n",
        "    'This was the worst movie I have ever seen!'\n",
        "]\n",
        "\n",
        "reloaded_results = tf.sigmoid(reloaded_model(tf.constant(examples)))\n",
        "original_results = tf.sigmoid(classifier_model(tf.constant(examples)))\n",
        "\n",
        "print('Results from the saved model:')\n",
        "print_my_examples(examples, reloaded_results)\n",
        "print('Results from the model in memory:')\n",
        "print_my_examples(examples, original_results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "udxyPmn157FE",
        "outputId": "8d3ddbca-43cb-4cf1-b49f-280c69ebeb86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Results from the saved model:\n",
            "input: this is such an amazing movie! : score: 0.928142\n",
            "input: The movie was great!           : score: 0.923835\n",
            "input: The movie was meh.             : score: 0.791181\n",
            "input: The movie was okish.           : score: 0.661395\n",
            "input: The movie was terrible...      : score: 0.046050\n",
            "input: This was the worst movie I have ever seen! : score: 0.045286\n",
            "\n",
            "Results from the model in memory:\n",
            "input: this is such an amazing movie! : score: 0.928142\n",
            "input: The movie was great!           : score: 0.923835\n",
            "input: The movie was meh.             : score: 0.791181\n",
            "input: The movie was okish.           : score: 0.661395\n",
            "input: The movie was terrible...      : score: 0.046050\n",
            "input: This was the worst movie I have ever seen! : score: 0.045286\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "serving_results = reloaded_model \\\n",
        "            .signatures['serving_default'](tf.constant(examples))\n",
        "\n",
        "serving_results = tf.sigmoid(serving_results['classifier'])\n",
        "\n",
        "print_my_examples(examples, serving_results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ojR2XQS56JHs",
        "outputId": "05024cc8-6f42-49e9-89aa-473005d6aad6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input: this is such an amazing movie! : score: 0.928142\n",
            "input: The movie was great!           : score: 0.923835\n",
            "input: The movie was meh.             : score: 0.791181\n",
            "input: The movie was okish.           : score: 0.661395\n",
            "input: The movie was terrible...      : score: 0.046050\n",
            "input: This was the worst movie I have ever seen! : score: 0.045286\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install sklearn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "neOQhzjxHjkM",
        "outputId": "3329abb0-0981-455e-b41e-4bc3357e4447"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting sklearn\n",
            "  Using cached sklearn-0.0.post11.tar.gz (3.6 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: sklearn\n",
            "  Building wheel for sklearn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sklearn: filename=sklearn-0.0.post11-py3-none-any.whl size=2959 sha256=fe89ca54cf5f04bd0b4638a8a3ac9c05d0bd3369a2c513e603a9ba144aeb3444\n",
            "  Stored in directory: /root/.cache/pip/wheels/aa/9c/60/f67813603a52fc35057868f1aba0003cc75b72583dcaa2c341\n",
            "Successfully built sklearn\n",
            "Installing collected packages: sklearn\n",
            "Successfully installed sklearn-0.0.post11\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "from sklearn.metrics import ConfusionMatrixDisplay,  accuracy_score, f1_score\n",
        "\n",
        "# Evaluate the model on the test dataset\n",
        "test_results = classifier_model.predict(test_ds)\n",
        "test_predictions = tf.sigmoid(test_results).numpy()\n",
        "test_labels = np.concatenate([y for x, y in test_ds], axis=0)\n",
        "\n",
        "# Convert probabilities to binary predictions using a threshold (0.5)\n",
        "threshold = 0.5\n",
        "binary_predictions = (test_predictions > threshold).astype(int)\n",
        "\n",
        "# Create a confusion matrix using TensorFlow\n",
        "conf_mat = tf.math.confusion_matrix(test_labels, binary_predictions)\n",
        "\n",
        "# Display the confusion matrix\n",
        "class_names = [\"0\", \"1\"]  # Replace with your actual class labels\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=conf_mat.numpy(), display_labels=class_names)\n",
        "disp.plot(cmap=plt.cm.Blues, values_format='d')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "tWTltWSh-6BD",
        "outputId": "ce8c79f7-c235-4bd9-e211-63f6dc92f881"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "782/782 [==============================] - 146s 184ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import roc_curve, auc\n",
        "import tensorflow as tf\n",
        "\n",
        "# Evaluate the model on the test dataset\n",
        "test_results = classifier_model.predict(test_ds)\n",
        "test_predictions = tf.sigmoid(test_results).numpy()\n",
        "test_labels = np.concatenate([y for x, y in test_ds], axis=0)\n",
        "\n",
        "# Calculate ROC curve\n",
        "fpr, tpr, thresholds = roc_curve(test_labels, test_predictions)\n",
        "\n",
        "# Calculate AUC (Area Under the Curve)\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "# Plot ROC curve\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'AUC = {roc_auc:.2f}')\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "id": "3Pq5nq7QDqSb",
        "outputId": "10d7ec86-e045-4fd9-f9b0-216b722b959b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "782/782 [==============================] - 151s 192ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}