NLP_SQLi.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nSgovRfexxi1"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer,CountVectorizer\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
        "from sklearn.metrics import accuracy_score\n",
        "from scipy.stats import randint\n",
        "from sklearn.metrics import accuracy_score,confusion_matrix\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import classification_report\n",
        "import xgboost as xgb"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "envu_vkZx1kC",
        "outputId": "1b01a01d-20a5-4539-b48b-692bb4e29dee"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ghl-PMyMx3Pj",
        "outputId": "7e598bcd-05cf-4dbb-dd20-735bccf7aa7b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive/Data_SQL\n"
          ]
        }
      ],
      "source": [
        "cd drive/MyDrive/Data_SQL"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EnrXWNkFR2kT"
      },
      "source": [
        "# Importing data"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ls\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D7StWA69MyH6",
        "outputId": "92cd5c78-fd5e-485f-c44a-1793b3defad4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "24_SQLI.csv             metrics_chunk_0.txt              random_forest_model_best_v4.pkl\n",
            "arr_0.csv               metrics_chunk_1.txt              somepatharr_0.csv\n",
            "bert_model_BCELoss.pth  model_chunk_0.pkl                SQLi.npz\n",
            "bert_model.pth          model_chunk_1.pkl                Untitled0.ipynb\n",
            "data_layer2.csv         random_forest_model_best.pkl     wrong_predictions.csv\n",
            "Final_Attacks.csv       random_forest_model_best_v1.pkl  wrong_predictions_final.csv\n",
            "Final_Vaild.csv         random_forest_model_best_v2.pkl\n",
            "Final_Vaild_V2.csv      random_forest_model_best_v3.pkl\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "JKgfxymAx5o9",
        "outputId": "11c7738c-ec43-47da-9217-e6335b792760"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-26-7d8b7e8e0716>:1: DtypeWarning: Columns (2) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  df=pd.read_csv(\"Final_Vaild_V2.csv\")\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-42b1a3be-73bc-4849-8654-bd8961ebef1f\" 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>Unnamed: 0</th>\n",
              "      <th>entry</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>CREATE SCHEMA /*!IF NOT EXISTS*/ schema;</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>DROP TABLE /*! IF EXISTS*/ table0_int_autoinc;</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>CREATE TABLE `table0_int_autoinc` (</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>CREATE INDEX idx_table0_int_autoinc_`col_set_l...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>CREATE INDEX idx_table0_int_autoinc_`col_enum_...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1019481</th>\n",
              "      <td>1019481</td>\n",
              "      <td>DELETE FROM door WHERE grow = 'small'</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1019482</th>\n",
              "      <td>1019482</td>\n",
              "      <td>DELETE FROM tomorrow</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1019483</th>\n",
              "      <td>1019483</td>\n",
              "      <td>SELECT wide ( s )  FROM west</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1019484</th>\n",
              "      <td>1019484</td>\n",
              "      <td>SELECT * FROM  ( SELECT slide FROM breath )</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1019485</th>\n",
              "      <td>1019485</td>\n",
              "      <td>SELECT TOP 3 * FROM race</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1019486 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-42b1a3be-73bc-4849-8654-bd8961ebef1f')\"\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-42b1a3be-73bc-4849-8654-bd8961ebef1f 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-42b1a3be-73bc-4849-8654-bd8961ebef1f');\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-0b022a28-a90b-45d3-ba13-27504f25b155\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0b022a28-a90b-45d3-ba13-27504f25b155')\"\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-0b022a28-a90b-45d3-ba13-27504f25b155 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "         Unnamed: 0                                              entry label\n",
              "0                 0           CREATE SCHEMA /*!IF NOT EXISTS*/ schema;     0\n",
              "1                 1     DROP TABLE /*! IF EXISTS*/ table0_int_autoinc;     0\n",
              "2                 2                CREATE TABLE `table0_int_autoinc` (     0\n",
              "3                 3  CREATE INDEX idx_table0_int_autoinc_`col_set_l...     0\n",
              "4                 4  CREATE INDEX idx_table0_int_autoinc_`col_enum_...     0\n",
              "...             ...                                                ...   ...\n",
              "1019481     1019481              DELETE FROM door WHERE grow = 'small'     0\n",
              "1019482     1019482                               DELETE FROM tomorrow     0\n",
              "1019483     1019483                       SELECT wide ( s )  FROM west     0\n",
              "1019484     1019484       SELECT * FROM  ( SELECT slide FROM breath )      0\n",
              "1019485     1019485                           SELECT TOP 3 * FROM race     0\n",
              "\n",
              "[1019486 rows x 3 columns]"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df=pd.read_csv(\"Final_Vaild_V2.csv\")\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "NpqB1U8fy3CW",
        "outputId": "feaf81b5-0784-43cc-c0f5-3934df959938"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-28-8c900f271fd6>:1: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  df2=pd.read_csv(\"Final_Attacks.csv\")\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df2"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-4606118e-ad7f-4915-b068-8c56d8bf1261\" 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>entry</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>SELECT `col2` FROM `tab` WHERE `col1` LIKE '%'...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>DELETE FROM `tab` WHERE `col2` &lt; \"exec sp_addl...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>INSERT INTO `tab` ( `col2`, `col2` ) VALUES ( ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>--\" );</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>INSERT INTO `tab` ( `col1` ) VALUES ( '\" OR EL...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1298200</th>\n",
              "      <td>â or 1  =  1 --</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1298201</th>\n",
              "      <td>or 'x'  =  'x</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1298202</th>\n",
              "      <td>29%</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1298203</th>\n",
              "      <td>28%</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1298204</th>\n",
              "      <td>or 3  =  3 --</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1298205 rows × 2 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4606118e-ad7f-4915-b068-8c56d8bf1261')\"\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-4606118e-ad7f-4915-b068-8c56d8bf1261 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-4606118e-ad7f-4915-b068-8c56d8bf1261');\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-07367a4c-9e6d-4329-82d0-4e105d82f59d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-07367a4c-9e6d-4329-82d0-4e105d82f59d')\"\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-07367a4c-9e6d-4329-82d0-4e105d82f59d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                                     entry label\n",
              "0        SELECT `col2` FROM `tab` WHERE `col1` LIKE '%'...     1\n",
              "1        DELETE FROM `tab` WHERE `col2` < \"exec sp_addl...     1\n",
              "2        INSERT INTO `tab` ( `col2`, `col2` ) VALUES ( ...     1\n",
              "3                                                   --\" );     1\n",
              "4        INSERT INTO `tab` ( `col1` ) VALUES ( '\" OR EL...     1\n",
              "...                                                    ...   ...\n",
              "1298200                                  â or 1  =  1 --     1\n",
              "1298201                                      or 'x'  =  'x     1\n",
              "1298202                                                29%     1\n",
              "1298203                                                28%     1\n",
              "1298204                                      or 3  =  3 --     1\n",
              "\n",
              "[1298205 rows x 2 columns]"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df2=pd.read_csv(\"Final_Attacks.csv\")\n",
        "df2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b6uV7NR3SUaZ"
      },
      "source": [
        "#Preprocessing for SQL Injection Detection\n",
        "\n",
        "### 1 Converting to Lower Case\n",
        "\n",
        "Standardizing all SQL query text to lower case helps in maintaining uniformity and reduces complexity during data processing.\n",
        "### 2 Converting Label to Numeric\n",
        "\n",
        "Transforming categorical labels into numerical values is essential for machine learning models to perform classifications effectively.\n",
        "### 3 Removing Missing Data\n",
        "\n",
        "Eliminating rows or columns with missing data ensures the robustness and accuracy of the model, leading to more reliable detection outcomes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UGRmspJqzT_Y"
      },
      "outputs": [],
      "source": [
        "df['entry'] = df['entry'].astype(str)\n",
        "df['entry'] = df['entry'].str.lower()\n",
        "df['label'] = pd.to_numeric(df['label'], errors='coerce')\n",
        "df['label'] = df['label'].astype('Int64')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IOdfhya8zVCB"
      },
      "outputs": [],
      "source": [
        "df2['entry'] = df2['entry'].astype(str)\n",
        "df2['entry'] = df2['entry'].str.lower()\n",
        "df2['label'] = pd.to_numeric(df2['label'], errors='coerce')\n",
        "df2['label'] = df2['label'].astype('Int64')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xA4snu1HzbTZ"
      },
      "outputs": [],
      "source": [
        "df.drop_duplicates()\n",
        "df2.drop_duplicates()\n",
        "df2.dropna()\n",
        "df.dropna()\n",
        "df.drop(columns=['Unnamed: 0'], inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fzOzueziSqmU"
      },
      "source": [
        "#Final Training and Testing Data for SQL Injection Detection\n",
        "\n",
        "### Balanced Label Distribution\n",
        "Ensuring an equal distribution of labels (50% label 1 and 50% label 0) promotes model fairness and prevents bias, leading to a more accurate evaluation of SQL injection threats"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "aFqUyr9uyFFH",
        "outputId": "c9814d08-2177-4143-a2a0-7c752f22d9b3"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "Final_df_"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-2da5c311-3954-4dd1-8161-b1447f6fad72\" 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>entry</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>select `col1`, `col3`, `col3` from `tab` where...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>update `tab` set `col1` = 6 where `col3` = 180...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>insert into `tab` ( `col1` ) values ( 52461568...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>update `tab` set `col3` = 9 where `col1` = -13...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>delete from `tab` where `col2` &gt;= -241172480 l...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1999995</th>\n",
              "      <td>delete from `tab` where `col2` like \"%'l'%\" li...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1999996</th>\n",
              "      <td>update `tab` set `col1` = 6 where `col3` &gt;= \"'...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1999997</th>\n",
              "      <td>select `col1`, `col3`, `col3` from `tab` where...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1999998</th>\n",
              "      <td>select * from `tab` where `col2` = '3\")) union...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1999999</th>\n",
              "      <td>select count(*) from domain.domains as t1,doma...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2000000 rows × 2 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2da5c311-3954-4dd1-8161-b1447f6fad72')\"\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-2da5c311-3954-4dd1-8161-b1447f6fad72 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-2da5c311-3954-4dd1-8161-b1447f6fad72');\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-b253a1e8-d9df-4aaf-9d81-65a39deeeb0a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b253a1e8-d9df-4aaf-9d81-65a39deeeb0a')\"\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-b253a1e8-d9df-4aaf-9d81-65a39deeeb0a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                                     entry  label\n",
              "0        select `col1`, `col3`, `col3` from `tab` where...      0\n",
              "1        update `tab` set `col1` = 6 where `col3` = 180...      0\n",
              "2        insert into `tab` ( `col1` ) values ( 52461568...      0\n",
              "3        update `tab` set `col3` = 9 where `col1` = -13...      0\n",
              "4        delete from `tab` where `col2` >= -241172480 l...      0\n",
              "...                                                    ...    ...\n",
              "1999995  delete from `tab` where `col2` like \"%'l'%\" li...      1\n",
              "1999996  update `tab` set `col1` = 6 where `col3` >= \"'...      1\n",
              "1999997  select `col1`, `col3`, `col3` from `tab` where...      1\n",
              "1999998  select * from `tab` where `col2` = '3\")) union...      1\n",
              "1999999  select count(*) from domain.domains as t1,doma...      1\n",
              "\n",
              "[2000000 rows x 2 columns]"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sample_0 = df.sample(n=1000000, random_state=42)\n",
        "smaple_1=df2.sample(n=1000000,random_state=27)\n",
        "Final_df_ = pd.concat([sample_0, smaple_1], ignore_index=True)\n",
        "Final_df_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7A3EIYaOztWZ"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import torch\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "import torch.nn as nn\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kuWe49bYSzqx"
      },
      "source": [
        "#Building Custom Dataset for SQL Injection Detection\n",
        "\n",
        "### 1 Processing Data Using DataLoader\n",
        "\n",
        "Utilizing a DataLoader streamlines the process of feeding data into the BERT model by efficiently managing data loading and transformations.\n",
        "### 2 Converting to Batches\n",
        "\n",
        "Batch processing is essential for training the BERT model, as it helps in handling large datasets effectively and improves the learning process."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cxWxcHL9zy9D"
      },
      "outputs": [],
      "source": [
        "class CustomDataset(Dataset):\n",
        "    def __init__(self, texts, labels, vectorizer):\n",
        "        self.texts = texts\n",
        "        self.labels = labels\n",
        "        self.vectorizer = vectorizer\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.texts)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        text = self.texts[idx]\n",
        "        label = self.labels[idx]\n",
        "        # Convert text to numerical representation\n",
        "        text_vectorized = self.vectorizer.transform([text]).toarray()\n",
        "        return torch.tensor(text_vectorized), torch.tensor(label)\n",
        "\n",
        "\n",
        "\n",
        "texts = Final_df_['entry'].tolist()\n",
        "labels = Final_df_['label'].tolist()\n",
        "\n",
        "# Split data into train and test sets\n",
        "texts_train, texts_test, labels_train, labels_test = train_test_split(texts, labels, test_size=0.2, random_state=42)\n",
        "\n",
        "# Initialize CountVectorizer (you can use other tokenizers/embeddings as needed)\n",
        "vectorizer = CountVectorizer()\n",
        "vectorizer.fit(texts_train)\n",
        "\n",
        "# Create custom datasets\n",
        "train_dataset = CustomDataset(texts_train, labels_train, vectorizer)\n",
        "test_dataset = CustomDataset(texts_test, labels_test, vectorizer)\n",
        "\n",
        "# Create data loaders\n",
        "train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
        "test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)\n",
        "\n",
        "# Now you can use train_loader and test_loader to iterate over batches of data in your neural network model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3seaiZ16TA-4"
      },
      "source": [
        "# Final Formatting of Data for SQL Injection Detection\n",
        "\n",
        "### 1 Converting to Input IDs\n",
        "\n",
        "Transforming text data into input IDs is vital for token representation in BERT, which understands and processes these tokens.\n",
        "### 2 Attention Mask\n",
        "Creating attention masks helps the BERT model focus on relevant tokens, enhancing the effectiveness of the model in detecting SQL injections.\n",
        "\n",
        "### 3 Labels\n",
        "\n",
        "Assigning labels to data points is crucial for supervised learning, allowing the model to learn from examples and make accurate predictions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ndj45nb3IUeI"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from transformers import BertTokenizer\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Load pre-trained BERT tokenizer\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "\n",
        "class CustomDataset(Dataset):\n",
        "    def __init__(self, texts, labels, tokenizer, max_len=128):\n",
        "        self.texts = texts\n",
        "        self.labels = labels\n",
        "        self.tokenizer = tokenizer\n",
        "        self.max_len = max_len\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.texts)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        text = str(self.texts[idx])\n",
        "        label = self.labels[idx]\n",
        "        encoding = self.tokenizer.encode_plus(\n",
        "            text,\n",
        "            add_special_tokens=True,\n",
        "            max_length=self.max_len,\n",
        "            return_token_type_ids=False,\n",
        "            padding='max_length',\n",
        "            truncation=True,\n",
        "            return_attention_mask=True,\n",
        "            return_tensors='pt',\n",
        "        )\n",
        "        return {\n",
        "            'input_ids': encoding['input_ids'].flatten(),\n",
        "            'attention_mask': encoding['attention_mask'].flatten(),\n",
        "            'labels': torch.tensor(label, dtype=torch.long)\n",
        "        }\n",
        "\n",
        "# Split data into train and test sets\n",
        "texts_train, texts_test, labels_train, labels_test = train_test_split(texts, labels, test_size=0.2, random_state=42)\n",
        "\n",
        "# Create custom datasets\n",
        "train_dataset = CustomDataset(texts_train, labels_train, tokenizer)\n",
        "test_dataset = CustomDataset(texts_test, labels_test, tokenizer)\n",
        "\n",
        "# Create data loaders\n",
        "train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
        "test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0N4WFN9ZIbU7",
        "outputId": "338f2c0a-d97b-4caf-ad56-c0ca3c5224e1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<torch.utils.data.dataloader.DataLoader at 0x78b0042b9030>"
            ]
          },
          "execution_count": 83,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_loader"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZQUcvJqOTZs_"
      },
      "source": [
        "# Training Loop for SQL Injection Detection\n",
        "\n",
        "### Training Loop for 10 Epochs\n",
        "Running the training process for 10 epochs allows the model to iteratively learn from the dataset, optimizing its performance on SQL injection detection tasks."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "id": "smPiz_jcIwHe",
        "outputId": "13a3d324-f66b-43e8-88bc-6752ea95d621"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:429: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n",
            "Epoch 1/1:   0%|          | 0/2500 [01:26<?, ?batch/s]\n"
          ]
        },
        {
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-18-75b85931dde7>\u001b[0m in \u001b[0;36m<cell line: 19>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     37\u001b[0m         \u001b[0mtotal_preds\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m         \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m         \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    520\u001b[0m                 \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    521\u001b[0m             )\n\u001b[0;32m--> 522\u001b[0;31m         torch.autograd.backward(\n\u001b[0m\u001b[1;32m    523\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    524\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    264\u001b[0m     \u001b[0;31m# some Python versions print out the first line of a multi-line function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    265\u001b[0m     \u001b[0;31m# calls in the traceback and some print out the last line\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 266\u001b[0;31m     Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n\u001b[0m\u001b[1;32m    267\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    268\u001b[0m         \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "from transformers import BertForSequenceClassification, AdamW\n",
        "from tqdm import tqdm\n",
        "\n",
        "# Define BERT model for sequence classification\n",
        "model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)  # Adjust num_labels according to your classification task\n",
        "\n",
        "# Set device\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "model.to(device)\n",
        "\n",
        "# Define optimizer and loss function\n",
        "optimizer = AdamW(model.parameters(), lr=2e-5)  # You can experiment with different learning rates\n",
        "criterion = nn.BCELoss()\n",
        "num_epochs=10\n",
        "# Training loop\n",
        "model.train()\n",
        "for epoch in range(num_epochs):\n",
        "    epoch_loss = 0.0\n",
        "    correct_preds = 0\n",
        "    total_preds = 0\n",
        "\n",
        "    for batch in tqdm(train_loader, desc=f'Epoch {epoch + 1}/{num_epochs}', unit='batch'):\n",
        "        input_ids = batch['input_ids'].to(device)\n",
        "        attention_mask = batch['attention_mask'].to(device)\n",
        "        labels = batch['labels'].to(device)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n",
        "        loss = outputs.loss\n",
        "        logits = outputs.logits\n",
        "\n",
        "        _, predicted = torch.max(logits, 1)\n",
        "        correct_preds += (predicted == labels).sum().item()\n",
        "        total_preds += labels.size(0)\n",
        "\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        epoch_loss += loss.item()\n",
        "\n",
        "    epoch_accuracy = correct_preds / total_preds\n",
        "    print(f'Epoch {epoch + 1}/{num_epochs}, Loss: {epoch_loss}, Accuracy: {epoch_accuracy}')\n",
        "\n",
        "# Evaluation\n",
        "model.eval()\n",
        "eval_loss = 0.0\n",
        "correct_preds = 0\n",
        "total_preds = 0\n",
        "\n",
        "with torch.no_grad():\n",
        "    for batch in tqdm(test_loader, desc='Evaluation', unit='batch'):\n",
        "        input_ids = batch['input_ids'].to(device)\n",
        "        attention_mask = batch['attention_mask'].to(device)\n",
        "        labels = batch['labels'].to(device)\n",
        "\n",
        "        outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n",
        "        loss = outputs.loss\n",
        "        logits = outputs.logits\n",
        "\n",
        "        _, predicted = torch.max(logits, 1)\n",
        "        correct_preds += (predicted == labels).sum().item()\n",
        "        total_preds += labels.size(0)\n",
        "\n",
        "        eval_loss += loss.item()\n",
        "\n",
        "eval_accuracy = correct_preds / total_preds\n",
        "print(f'Evaluation Loss: {eval_loss}, Evaluation Accuracy: {eval_accuracy}')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T8CdDvC0TuZf"
      },
      "source": [
        "#Importing Model for SQL Injection Detection Testing\n",
        "\n",
        "### Importing Model and Tokenizer\n",
        "Loading the pre-trained model along with its tokenizer is essential for preparing the test data in a format compatible with the model for effective SQL injection testing."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227,
          "referenced_widgets": [
            "bad16a09002b40edaf666bf83bcda006",
            "de213231db594cd49f9a34abf6eac6fa",
            "32e75e0e5b1b42088cd46b0d88634733",
            "bc521a03adf34a1885fd98603e0300c8",
            "ef6fbf534d3f45c88e9b02d66efd1072",
            "96f5ccd2dbd440c4940c3bb800472f7c",
            "7bf70df58fc6407891e1edae3da85ce7",
            "f440af68f8ad4871afbf35c2a2dfdd9a",
            "f9257070fd5f4c69bd3e03a0f6351e58",
            "9c8bb33e850a495ba14d9e8213bcafca",
            "f25bbe6fd9c94ef68497486ea6ddd1c5",
            "4750d96985904f1183f4dcf41c43415b",
            "1b57b37be9ca4fb7aaf17b7f7ce6c57b",
            "dac85df23dbc44dc99cb2daa7c40c1d2",
            "7a6cedce400f4e7c9f5a1254f8d9caec",
            "c949b6949c2947bfa7e2f7bb9d5efd9e",
            "3904164a0cfc4727aaf77c7f3e1912fa",
            "a78d57b4d4a2462ea1cae3e82e6f82b6",
            "4762f0281f3e4dbab8d536c6350f41a1",
            "3123351a5f4743aa87a740e843196e80",
            "f4ed4a5272a2441692217b1034015d58",
            "d6bc7d0fa12c482e83411b27fcbd7f5d"
          ]
        },
        "id": "9b1FZfUoQshn",
        "outputId": "21bb7fe3-15dc-4822-95c2-c8de98bb64b1"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bad16a09002b40edaf666bf83bcda006",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4750d96985904f1183f4dcf41c43415b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "from transformers import BertForSequenceClassification, BertTokenizer\n",
        "\n",
        "# Load the fine-tuned BERT model\n",
        "model_path = 'bert_model_BCELoss.pth'\n",
        "# model = torch.load(model_path,map_location ='cpu')\n",
        "model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)\n",
        "model.load_state_dict(torch.load(model_path))\n",
        "\n",
        "# Load the tokenizer\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cLNkF6L2lxPo"
      },
      "outputs": [],
      "source": [
        "model.to('cuda')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7-APIXcGT1wf"
      },
      "source": [
        "#Plotting Confusion Matrix for SQL Injection Detection\n",
        "\n",
        "### Building Confusion Matrix\n",
        "Constructing a confusion matrix for a dataset of 200,000 entries provides a clear visualization of the model's performance, highlighting its precision in detecting and classifying SQL injection attacks."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "97LIZH2BZqQo",
        "outputId": "29d369cf-def3-4c6a-a581-6f0ffe1700ba"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "# Initialize lists to store true labels and predicted labels\n",
        "true_labels = []\n",
        "predicted_labels = []\n",
        "\n",
        "# Predict labels for the test dataset\n",
        "with torch.no_grad():\n",
        "    for batch in test_loader:\n",
        "        input_ids = batch['input_ids'].to(device)\n",
        "        attention_mask = batch['attention_mask'].to(device)\n",
        "        labels = batch['labels'].to(device)\n",
        "\n",
        "        outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n",
        "        logits = outputs.logits\n",
        "\n",
        "        _, predicted = torch.max(logits, 1)\n",
        "\n",
        "        true_labels.extend(labels.cpu().numpy())\n",
        "        predicted_labels.extend(predicted.cpu().numpy())\n",
        "\n",
        "# Build confusion matrix\n",
        "conf_matrix = confusion_matrix(true_labels, predicted_labels)\n",
        "\n",
        "# Plot confusion matrix\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['Class 0', 'Class 1'], yticklabels=['Class 0', 'Class 1'])\n",
        "plt.xlabel('Predicted Labels')\n",
        "plt.ylabel('True Labels')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M_0zOO6DUACT"
      },
      "source": [
        "# Exploratory Data Analysis for Incorrect Predictions in SQL Injection Detection\n",
        "\n",
        "Exploratory Data Analysis (EDA) for Incorrect Predictions\n",
        "Conducting EDA on incorrect predictions helps identify patterns or common characteristics in misclassified data, enabling improvements in model accuracy."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oh8rU7uK1nsj",
        "outputId": "bcb45fe3-ece2-4f29-8c83-788819908868"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Wrong predictions saved to wrong_predictions_final.csv\n"
          ]
        }
      ],
      "source": [
        "import csv\n",
        "\n",
        "# Define the file name for the CSV\n",
        "csv_file = \"wrong_predictions_final.csv\"\n",
        "\n",
        "# Write wrong predictions to CSV\n",
        "with open(csv_file, mode='w', newline='', encoding='utf-8') as file:\n",
        "    writer = csv.writer(file)\n",
        "    writer.writerow(['Index', 'Text', 'True Label', 'Predicted Label'])\n",
        "\n",
        "    for i, (text, true_label, predicted_label) in enumerate(wrong_predictions_text):\n",
        "        writer.writerow([i, text, true_label, predicted_label])\n",
        "\n",
        "print(f\"Wrong predictions saved to {csv_file}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "StCTZfgSskhA",
        "outputId": "8ee16ba9-1d6f-4e46-c25c-8f1d56679850"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"worng\",\n  \"rows\": 7055,\n  \"fields\": [\n    {\n      \"column\": \"entry\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3455,\n        \"samples\": [\n          \"update `tab` set `col1` = 0 where `col2` like \\\"%'w'%\\\";\",\n          \"update `tab` set `col3` = 9 where `col1` = 'w';\",\n          \"update `tab` set `col1` = 6 where `col3` = 'y';\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "worng"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-56672ba8-16c2-4e62-9c8f-c834c4a7f3af\" 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>entry</th>\n",
              "      <th>Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>insert into `tab` ( `col2` ) values ( 'l' );</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>insert into `tab` ( `col2` ) values ( 'f' );</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>update `tab` set `col3` = 5 where `col1` like ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>update `tab` set `col1` = 1 where `col3` = 'r';</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>update `tab` set `col2` = 6 where `col1` like ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7050</th>\n",
              "      <td>delete from `tab` where `col3` like '%'x'%';</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7051</th>\n",
              "      <td>select `col3` from `tab` where `col3` like '%'...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7052</th>\n",
              "      <td>select `col2`, `col3`, `col2`, `col1` from `ta...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7053</th>\n",
              "      <td>update `tab` set `col1` = 9 where `col1` like ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7054</th>\n",
              "      <td>update `tab` set `col1` = 2 where `col3` like ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7055 rows × 2 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-56672ba8-16c2-4e62-9c8f-c834c4a7f3af')\"\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-56672ba8-16c2-4e62-9c8f-c834c4a7f3af 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-56672ba8-16c2-4e62-9c8f-c834c4a7f3af');\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-14c85ae0-5c10-4e08-9464-1fe58f8ffcd2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-14c85ae0-5c10-4e08-9464-1fe58f8ffcd2')\"\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-14c85ae0-5c10-4e08-9464-1fe58f8ffcd2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                                  entry  Label\n",
              "0          insert into `tab` ( `col2` ) values ( 'l' );      0\n",
              "1          insert into `tab` ( `col2` ) values ( 'f' );      0\n",
              "2     update `tab` set `col3` = 5 where `col1` like ...      0\n",
              "3       update `tab` set `col1` = 1 where `col3` = 'r';      0\n",
              "4     update `tab` set `col2` = 6 where `col1` like ...      0\n",
              "...                                                 ...    ...\n",
              "7050       delete from `tab` where `col3` like '%'x'%';      0\n",
              "7051  select `col3` from `tab` where `col3` like '%'...      0\n",
              "7052  select `col2`, `col3`, `col2`, `col1` from `ta...      0\n",
              "7053  update `tab` set `col1` = 9 where `col1` like ...      0\n",
              "7054  update `tab` set `col1` = 2 where `col3` like ...      0\n",
              "\n",
              "[7055 rows x 2 columns]"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "worng=pd.read_csv(\"data_layer2.csv\")\n",
        "worng"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "zlOKojEdtdqH",
        "outputId": "e6e4578f-570a-4b3a-e9a5-fd6cc807dc68"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"select `col1`, `col1`, `col2` from `tab` where `col3` like '%'q'%';\""
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "worng.iloc[0].Text"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zrUu4EzhULwL"
      },
      "source": [
        "# Creating a CSV for Incorrect Predictions in SQL Injection Detection\n",
        "\n",
        "### Generating CSV for Incorrect Predictions\n",
        "Creating a CSV file with detailed columns like 'Total_Words', 'Total_Chars', 'Unique_Chars', and 'Size_of_Text' to analyze misclassified cases thoroughly and refine detection strategies."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "j0tY4YIKtvHE",
        "outputId": "bcc94cd7-decf-482e-f60e-359fd1479932"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"worng\",\n  \"rows\": 741,\n  \"fields\": [\n    {\n      \"column\": \"Index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 214,\n        \"min\": 0,\n        \"max\": 740,\n        \"num_unique_values\": 741,\n        \"samples\": [\n          120,\n          196,\n          335\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 583,\n        \"samples\": [\n          \"update `tab` set `col1` = 7 where `col3` like '%'m'%' and `col2` like '%'c'%';\",\n          \"update `tab` set `col1` = 2 where `col1` = 'h';\",\n          \"update `tab` set `col1` = 2 where `col3` = 'w';\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"True Label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted Label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total_Words\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 23,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          10,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total_Chars\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 6,\n        \"max\": 169,\n        \"num_unique_values\": 46,\n        \"samples\": [\n          73,\n          28\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Unique_Chars\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 6,\n        \"max\": 30,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          25,\n          21\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Size_of_Text\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 6,\n        \"max\": 169,\n        \"num_unique_values\": 46,\n        \"samples\": [\n          73,\n          28\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "worng"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-70a09cb6-52f2-49cf-b563-a594cf1d35b0\" 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>Index</th>\n",
              "      <th>Text</th>\n",
              "      <th>True Label</th>\n",
              "      <th>Predicted Label</th>\n",
              "      <th>Total_Words</th>\n",
              "      <th>Total_Chars</th>\n",
              "      <th>Unique_Chars</th>\n",
              "      <th>Size_of_Text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>select `col1`, `col1`, `col2` from `tab` where...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>67</td>\n",
              "      <td>25</td>\n",
              "      <td>67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>select `col3`, `col3`, `col2` from `tab` where...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>60</td>\n",
              "      <td>21</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>select * from `tab` where `col3` like '%'j'%';</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>46</td>\n",
              "      <td>23</td>\n",
              "      <td>46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>insert into `tab` ( `col3` ) values ( 'o' );</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>44</td>\n",
              "      <td>20</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>select `col2` from `tab` where `col1` like \"%'...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>51</td>\n",
              "      <td>23</td>\n",
              "      <td>51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>736</th>\n",
              "      <td>736</td>\n",
              "      <td>select `col1`, `col1`, `col1`, `col2` from `ta...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>23</td>\n",
              "      <td>139</td>\n",
              "      <td>25</td>\n",
              "      <td>139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>737</th>\n",
              "      <td>737</td>\n",
              "      <td>delete from `tab` where `col3` like '%'r'%';</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>44</td>\n",
              "      <td>21</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>738</th>\n",
              "      <td>738</td>\n",
              "      <td>delete from `tab` where `col2` like '%'i'%' an...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>71</td>\n",
              "      <td>23</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>739</th>\n",
              "      <td>739</td>\n",
              "      <td>update `tab` set `col3` = 9 where `col1` like ...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>54</td>\n",
              "      <td>26</td>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>740</th>\n",
              "      <td>740</td>\n",
              "      <td>update `tab` set `col1` = 3 where `col2` like ...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>54</td>\n",
              "      <td>26</td>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>741 rows × 8 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-70a09cb6-52f2-49cf-b563-a594cf1d35b0')\"\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-70a09cb6-52f2-49cf-b563-a594cf1d35b0 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-70a09cb6-52f2-49cf-b563-a594cf1d35b0');\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-e79f4ad6-b4d3-4d5a-a3b2-929ff335e198\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e79f4ad6-b4d3-4d5a-a3b2-929ff335e198')\"\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-e79f4ad6-b4d3-4d5a-a3b2-929ff335e198 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "     Index                                               Text  True Label  \\\n",
              "0        0  select `col1`, `col1`, `col2` from `tab` where...           0   \n",
              "1        1  select `col3`, `col3`, `col2` from `tab` where...           0   \n",
              "2        2     select * from `tab` where `col3` like '%'j'%';           0   \n",
              "3        3       insert into `tab` ( `col3` ) values ( 'o' );           0   \n",
              "4        4  select `col2` from `tab` where `col1` like \"%'...           0   \n",
              "..     ...                                                ...         ...   \n",
              "736    736  select `col1`, `col1`, `col1`, `col2` from `ta...           0   \n",
              "737    737       delete from `tab` where `col3` like '%'r'%';           0   \n",
              "738    738  delete from `tab` where `col2` like '%'i'%' an...           0   \n",
              "739    739  update `tab` set `col3` = 9 where `col1` like ...           0   \n",
              "740    740  update `tab` set `col1` = 3 where `col2` like ...           0   \n",
              "\n",
              "     Predicted Label  Total_Words  Total_Chars  Unique_Chars  Size_of_Text  \n",
              "0                  1           10           67            25            67  \n",
              "1                  1           10           60            21            60  \n",
              "2                  1            8           46            23            46  \n",
              "3                  1           10           44            20            44  \n",
              "4                  1            8           51            23            51  \n",
              "..               ...          ...          ...           ...           ...  \n",
              "736                1           23          139            25           139  \n",
              "737                1            7           44            21            44  \n",
              "738                1           11           71            23            71  \n",
              "739                1           10           54            26            54  \n",
              "740                1           10           54            26            54  \n",
              "\n",
              "[741 rows x 8 columns]"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def calculate_metrics(text):\n",
        "    total_words = len(text.split())\n",
        "    total_chars = len(text)\n",
        "    unique_chars = len(set(text))\n",
        "    size_of_text = len(text.encode('utf-8'))\n",
        "    return total_words, total_chars, unique_chars, size_of_text\n",
        "\n",
        "# Apply the function to the \"Text\" column and store results in new columns\n",
        "worng[['Total_Words', 'Total_Chars', 'Unique_Chars', 'Size_of_Text']] = worng['Text'].apply(lambda x: pd.Series(calculate_metrics(x)))\n",
        "\n",
        "# Print the updated DataFrame\n",
        "worng"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vEl-TC7oUy65"
      },
      "source": [
        "\n",
        "#Plotting Graphs for Analysis of SQL Injection Detection Data\n",
        "\n",
        "### Graphical Analysis of Text Features\n",
        "Plotting graphs for features like 'Total_Words', 'Size_of_Text', 'Unique_Chars', and 'Total_Chars' to visually examine the distribution and impact of these metrics on SQL injection detection outcomes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "AprfVWbbvFxn",
        "outputId": "d36e895b-bd5c-4fe4-87fe-fdbad002564c"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1000x600 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "# Total Words\n",
        "plt.subplot(2, 2, 1)\n",
        "plt.hist(worng['Total_Words'], bins=20, color='skyblue', edgecolor='black')\n",
        "plt.title('Total Words')\n",
        "plt.xlabel('Number of Words')\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "# Total Chars\n",
        "plt.subplot(2, 2, 2)\n",
        "plt.hist(worng['Total_Chars'], bins=20, color='salmon', edgecolor='black')\n",
        "plt.title('Total Characters')\n",
        "plt.xlabel('Number of Characters')\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "# Unique Chars\n",
        "plt.subplot(2, 2, 3)\n",
        "plt.hist(worng['Unique_Chars'], bins=20, color='lightgreen', edgecolor='black')\n",
        "plt.title('Unique Characters')\n",
        "plt.xlabel('Number of Unique Characters')\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "# Size of Text\n",
        "plt.subplot(2, 2, 4)\n",
        "plt.hist(worng['Size_of_Text'], bins=20, color='gold', edgecolor='black')\n",
        "plt.title('Size of Text')\n",
        "plt.xlabel('Size of Text (bytes)')\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Uqja_kDbv-fg",
        "outputId": "1cce387a-6e57-4d87-d0ad-bda1b3c1a3a9"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "Final_df_"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-d5e8676d-1ddd-4358-adcd-3b1589d3e378\" 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>entry</th>\n",
              "      <th>label</th>\n",
              "      <th>Total_Words</th>\n",
              "      <th>Total_Chars</th>\n",
              "      <th>Unique_Chars</th>\n",
              "      <th>Size_of_Text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>select `col1`, `col3`, `col3` from `tab` where...</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>87</td>\n",
              "      <td>29</td>\n",
              "      <td>87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>update `tab` set `col1` = 6 where `col3` = 180...</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>71</td>\n",
              "      <td>29</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>insert into `tab` ( `col1` ) values ( 52461568...</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>50</td>\n",
              "      <td>25</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>update `tab` set `col3` = 9 where `col1` = -13...</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>72</td>\n",
              "      <td>31</td>\n",
              "      <td>72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>delete from `tab` where `col2` &gt;= -241172480 l...</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>61</td>\n",
              "      <td>29</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199995</th>\n",
              "      <td>delete from `tab` where `col3` = 'v';</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>37</td>\n",
              "      <td>20</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199996</th>\n",
              "      <td>' or `col2` = '\") as veow where 2949=2949;</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>42</td>\n",
              "      <td>20</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199997</th>\n",
              "      <td>update `tab` set `col2` = 2 where `col1` like ...</td>\n",
              "      <td>1</td>\n",
              "      <td>18</td>\n",
              "      <td>185</td>\n",
              "      <td>40</td>\n",
              "      <td>185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199998</th>\n",
              "      <td>insert into `tab` ( `col1` ) values ( \"' or 21...</td>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "      <td>112</td>\n",
              "      <td>35</td>\n",
              "      <td>112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199999</th>\n",
              "      <td>delete from `tab` where `col3` like '%'b'%' li...</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>108</td>\n",
              "      <td>31</td>\n",
              "      <td>108</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200000 rows × 6 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d5e8676d-1ddd-4358-adcd-3b1589d3e378')\"\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-d5e8676d-1ddd-4358-adcd-3b1589d3e378 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-d5e8676d-1ddd-4358-adcd-3b1589d3e378');\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-326d619a-363d-4b90-8b9a-c2ecd9639279\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-326d619a-363d-4b90-8b9a-c2ecd9639279')\"\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-326d619a-363d-4b90-8b9a-c2ecd9639279 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                                    entry  label  Total_Words  \\\n",
              "0       select `col1`, `col3`, `col3` from `tab` where...      0           12   \n",
              "1       update `tab` set `col1` = 6 where `col3` = 180...      0           12   \n",
              "2       insert into `tab` ( `col1` ) values ( 52461568...      0           10   \n",
              "3       update `tab` set `col3` = 9 where `col1` = -13...      0           12   \n",
              "4       delete from `tab` where `col2` >= -241172480 l...      0            9   \n",
              "...                                                   ...    ...          ...   \n",
              "199995              delete from `tab` where `col3` = 'v';      1            7   \n",
              "199996         ' or `col2` = '\") as veow where 2949=2949;      1            9   \n",
              "199997  update `tab` set `col2` = 2 where `col1` like ...      1           18   \n",
              "199998  insert into `tab` ( `col1` ) values ( \"' or 21...      1           17   \n",
              "199999  delete from `tab` where `col3` like '%'b'%' li...      1           13   \n",
              "\n",
              "        Total_Chars  Unique_Chars  Size_of_Text  \n",
              "0                87            29            87  \n",
              "1                71            29            71  \n",
              "2                50            25            50  \n",
              "3                72            31            72  \n",
              "4                61            29            61  \n",
              "...             ...           ...           ...  \n",
              "199995           37            20            37  \n",
              "199996           42            20            42  \n",
              "199997          185            40           185  \n",
              "199998          112            35           112  \n",
              "199999          108            31           108  \n",
              "\n",
              "[200000 rows x 6 columns]"
            ]
          },
          "execution_count": 51,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def calculate_metrics(text):\n",
        "    total_words = len(text.split())\n",
        "    total_chars = len(text)\n",
        "    unique_chars = len(set(text))\n",
        "    size_of_text = len(text.encode('utf-8'))\n",
        "    return total_words, total_chars, unique_chars, size_of_text\n",
        "\n",
        "# Apply the function to the \"Text\" column and store results in new columns\n",
        "Final_df_[['Total_Words', 'Total_Chars', 'Unique_Chars', 'Size_of_Text']] = Final_df_['entry'].apply(lambda x: pd.Series(calculate_metrics(x)))\n",
        "\n",
        "# Print the updated DataFrame\n",
        "Final_df_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "k8Y3ioh4vVsQ",
        "outputId": "78cec77c-89c6-4da3-c47e-a550e336b3c0"
      },
      "outputs": [
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA90AAAJOCAYAAACqS2TfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAADdbElEQVR4nOzdeVxU1f8/8NeAMCwyICIgIYhLAoqomIj7Qo5KpolbuSCipYGpmJZ9TU0rTXMrSbJSzCyXMisXFFk0BTcUd8hMxVRQVED27fz+6Mf9MLIIyDADvp6Pxzxi7n3Pue9zp/HMe+6958qEEAJEREREREREVON0NJ0AERERERERUX3FopuIiIiIiIhITVh0ExEREREREakJi24iIiIiIiIiNWHRTURERERERKQmLLqJiIiIiIiI1IRFNxEREREREZGasOgmIiIiIiIiUhMW3URERERERERqwqKbiKokKioKMpkMUVFRmk6lWup6/kREVHepawySyWQICAio0TaJqOaw6CaqA2QyWaUelRnEP/30U+zevVut+e7YsQMymQy//vprqXWurq6QyWSIjIwstc7Ozg7dunVTa25ERPR8qWtjaEnXrl3DW2+9hRYtWsDAwAAKhQLdu3fH2rVrkZ2dXWt5qMudO3ewaNEixMXFaToVIrVqoOkEiOjptmzZovL8+++/R1hYWKnlTk5OT23r008/xYgRIzBs2LCaTFFFjx49AABHjx7Fa6+9Ji1PT0/HxYsX0aBBAxw7dgx9+/aV1t26dQu3bt3CmDFj1JYXERE9f+raGFps7969GDlyJORyOSZMmIB27dohLy8PR48exZw5c3Dp0iVs2LBB7Xmo0507d/DRRx+hefPm6NChg6bTIVIbFt1EdcC4ceNUnh8/fhxhYWGllmsLGxsbODg44OjRoyrLY2JiIITAyJEjS60rfl5csFeXEAI5OTkwNDR8pnaIiKh+qGtjKABcv34dY8aMgb29PSIiItC0aVNpnb+/P/7++2/s3bu3VnPKzMyEsbFxrW6zuupSrvR84OnlRPVEZmYmZs+ejWbNmkEul6NNmzb4/PPPIYSQYmQyGTIzM7F582bpdLqJEycCAG7evIm3334bbdq0gaGhIRo3boyRI0fixo0b1cqnR48eOHv2rMrpb8eOHUPbtm0xaNAgHD9+HEVFRSrrZDIZunfvDgAoKCjAkiVL0LJlS8jlcjRv3hwffPABcnNzVbbTvHlzvPLKKzhw4AA6d+4MQ0NDfP311wCAf//9F8OGDYOxsTEsLS0xa9asUq8HgKtXr8Lb2xvW1tYwMDCAra0txowZg7S0tGr1nYiI6hZtG0OXL1+OjIwMfPfddyoFd7FWrVphxowZpZbv3r0b7dq1g1wuR9u2bREaGqqyvrJ5hoSEQCaT4fDhw3j77bdhaWkJW1vbKvc1NTUVs2bNQvPmzSGXy2Fra4sJEyYgJSUFUVFReOmllwAAvr6+0j4NCQmRXn/ixAkMHDgQpqamMDIyQu/evXHs2DGVbSxatAgymQyXL1/GG2+8gUaNGkk/4CclJcHX1xe2traQy+Vo2rQphg4dWu33hai6eKSbqB4QQuDVV19FZGQk/Pz80KFDBxw4cABz5szB7du3sXr1agD/nWI3efJkdOnSBW+++SYAoGXLlgCAU6dOITo6GmPGjIGtrS1u3LiB9evXo0+fPrh8+TKMjIyqlFOPHj2wZcsWnDhxAn369AHwX2HdrVs3dOvWDWlpabh48SLat28vrXN0dETjxo0BAJMnT8bmzZsxYsQIzJ49GydOnMDSpUtx5cqVUteKJyQk4PXXX8dbb72FKVOmoE2bNsjOzkb//v2RmJiId955BzY2NtiyZQsiIiJUXpuXlwelUonc3FxMnz4d1tbWuH37Nvbs2YPU1FSYmppW7c0gIqI6RRvH0D/++AMtWrSo0jwnR48exa5du/D222/DxMQEX3zxBby9vZGYmCiNrVXN8+2330aTJk2wYMECZGZmVqmNjIwM9OzZE1euXMGkSZPQqVMnpKSk4Pfff8e///4LJycnLF68GAsWLMCbb76Jnj17AoDU54iICAwaNAhubm5YuHAhdHR0sGnTJvTr1w9//vknunTpopLryJEj0bp1a3z66afSjyXe3t64dOkSpk+fjubNm+PevXsICwtDYmIimjdvXqX3hOiZCCKqc/z9/UXJj+/u3bsFAPHxxx+rxI0YMULIZDLx999/S8uMjY2Fj49PqTazsrJKLYuJiREAxPfffy8ti4yMFABEZGRkhTleunRJABBLliwRQgiRn58vjI2NxebNm4UQQlhZWYmgoCAhhBDp6elCV1dXTJkyRQghRFxcnAAgJk+erNLmu+++KwCIiIgIaZm9vb0AIEJDQ1Vi16xZIwCIHTt2SMsyMzNFq1atVPI/e/asACB27txZYX+IiKh+0PYxNC0tTQAQQ4cOrXSfAAh9fX2VXM+dOycAiC+//LLKeW7atEkAED169BAFBQUq8ZVtY8GCBQKA2LVrV6n4oqIiIYQQp06dEgDEpk2bSq1v3bq1UCqVUmzxth0cHMTLL78sLVu4cKEAIF5//XWVNh49eiQAiBUrVpTaPlFt4+nlRPXAvn37oKuri3feeUdl+ezZsyGEwP79+5/aRslroPPz8/HgwQO0atUKZmZmOHPmTJVzcnJyQuPGjaVrtc+dO4fMzEzpF+xu3bpJp4jFxMSgsLBQOh1s3759AIDAwMBS/QFQ6jo2BwcHKJVKlWX79u1D06ZNMWLECGmZkZGRdHSiWPGR7AMHDiArK6vK/SQiorpN28bQ9PR0AICJiUmVXufp6SkdeQeA9u3bQ6FQ4J9//ql2nlOmTIGurq7Kssq28csvv8DV1VVlQtViMpmswr7ExcXh6tWreOONN/DgwQOkpKQgJSUFmZmZ6N+/P44cOaJyiRoATJ06tVSe+vr6iIqKwqNHjyrcHpG6segmqgdu3rwJGxubUgN08UysN2/efGob2dnZWLBggXQ9m4WFBZo0aYLU1NRqXdssk8nQrVs36drtY8eOwdLSEq1atQKgWnQX/7e46L558yZ0dHSk2GLW1tYwMzMr1R8HB4dS27958yZatWpVamBv06ZNqdcGBgbi22+/hYWFBZRKJYKCgng9NxHRc0LbxlCFQgEAePz4cZVeZ2dnV2pZo0aNVArOquZZ1vha2TauXbuGdu3aVakPxa5evQoA8PHxQZMmTVQe3377LXJzc0vl+2Sucrkcn332Gfbv3w8rKyv06tULy5cvR1JSUrVyInoWvKabiAAA06dPx6ZNmzBz5kx4eHjA1NQUMpkMY8aMKfVrcmX16NEDf/zxBy5cuCBdz12sW7du0vVyR48ehY2NDVq0aKHy+qf9El7sWWcqX7lyJSZOnIjffvsNBw8exDvvvIOlS5fi+PHj0sQxRERE5anJMVShUMDGxgYXL16s0uuePCJdTJSYDK6qeZY1vqrj+8KTittZsWJFubcSa9iw4VNznTlzJoYMGYLdu3fjwIED+PDDD7F06VJERESgY8eONZIrUWWw6CaqB+zt7XHo0CE8fvxY5Zf6+Ph4aX2x8grZn3/+GT4+Pli5cqW0LCcnB6mpqdXOq+T9uo8dO4aZM2dK69zc3CCXyxEVFYUTJ05g8ODBKv0pKirC1atXVe6bmpycjNTUVJX+lMfe3h4XL16EEEKlzwkJCWXGu7i4wMXFBfPnz0d0dDS6d++O4OBgfPzxx1XtNhER1SHaOIa+8sor2LBhA2JiYuDh4VGtNspSE3lWto2WLVs+9YeD8vZn8WnyCoUCnp6elc6tvLZmz56N2bNn4+rVq+jQoQNWrlyJH3744ZnaJaoKnl5OVA8MHjwYhYWFWLduncry1atXQyaTYdCgQdIyY2PjMgdXXV1dlV/DAeDLL79EYWFhtfPq3LkzDAwMsHXrVty+fVvlSLdcLkenTp0QFBSEzMxMlftzFxfga9asUWlv1apVAAAvL6+nbnvw4MG4c+cOfv75Z2lZVlYWNmzYoBKXnp6OgoIClWUuLi7Q0dEp8/ZiRERUv2jjGDp37lwYGxtj8uTJSE5OLrX+2rVrWLt2bZXbrYk8K9uGt7c3zp07V+qOI8D/jr4X30v7yX3q5uaGli1b4vPPP0dGRkap19+/f/+peWZlZSEnJ0dlWcuWLWFiYsLxnWodj3QT1QNDhgxB37598X//93+4ceMGXF1dcfDgQfz222+YOXOmysQqbm5uOHToEFatWgUbGxs4ODjA3d0dr7zyCrZs2QJTU1M4OzsjJiYGhw4dkm4zUh36+vp46aWX8Oeff0Iul8PNzU1lfbdu3aRfyksW3a6urvDx8cGGDRuQmpqK3r174+TJk9i8eTOGDRuGvn37PnXbU6ZMwbp16zBhwgTExsaiadOm2LJlS6nboURERCAgIAAjR47Eiy++iIKCAmzZsgW6urrw9vaudt+JiKhu0MYxtGXLlvjxxx8xevRoODk5YcKECWjXrh3y8vIQHR2NnTt3SvcIr4qayLOybcyZMwc///wzRo4ciUmTJsHNzQ0PHz7E77//juDgYLi6uqJly5YwMzNDcHAwTExMYGxsDHd3dzg4OODbb7/FoEGD0LZtW/j6+uKFF17A7du3ERkZCYVCgT/++KPCPP/66y/0798fo0aNgrOzMxo0aIBff/0VycnJGDNmTJX3HdEz0di86URUbU/e7kQIIR4/fixmzZolbGxshJ6enmjdurVYsWKFyq02hBAiPj5e9OrVSxgaGgoA0q1PHj16JHx9fYWFhYVo2LChUCqVIj4+Xtjb26vcHqWytwwrNm/ePAFAdOvWrdS6Xbt2CQDCxMSk1C1J8vPzxUcffSQcHByEnp6eaNasmZg3b57IyclRibO3txdeXl5lbvvmzZvi1VdfFUZGRsLCwkLMmDFDhIaGquT/zz//iEmTJomWLVsKAwMDYW5uLvr27SsOHTpUqf4REVHdUpfG0L/++ktMmTJFNG/eXOjr6wsTExPRvXt38eWXX6qMhwCEv79/qdc/uf3K5ll8y7BTp06VarOybQghxIMHD0RAQIB44YUXhL6+vrC1tRU+Pj4iJSVFivntt9+Es7OzaNCgQanbh509e1YMHz5cNG7cWMjlcmFvby9GjRolwsPDpZjiW4bdv39fZdspKSnC399fODo6CmNjY2Fqairc3d1VbiVKVFtkQjxxfggRERERERER1Qhe001ERERERESkJiy6iYiIiIiIiNSERTcRERERERGRmrDoJiIiIiIiIlITFt1EREREREREasKim4iIiIiIiEhNGmg6gfqiqKgId+7cgYmJCWQymabTISIiLSaEwOPHj2FjYwMdHf7+XdM4JhMRUWXVxpjMoruG3LlzB82aNdN0GkREVIfcunULtra2mk6j3uGYTEREVaXOMZlFdw0xMTEB8N+bpVAoNJwNERFps/T0dDRr1kwaO6hmcUwmIqLKqo0xmUV3DSk+fU2hUHCAJyKiSuGpz+rBMZmIiKpKnWMyLyQjIiIiIiIiUhMW3URERERERERqwqKbiIiIiIiISE1YdBMRERERERGpCYtuIiIiIiIiIjVh0U1ERERERESkJhotuhctWgSZTKbycHR0lNbn5OTA398fjRs3RsOGDeHt7Y3k5GSVNhITE+Hl5QUjIyNYWlpizpw5KCgoUImJiopCp06dIJfL0apVK4SEhJTKJSgoCM2bN4eBgQHc3d1x8uRJtfRZ3RITE3HmzBmcOXMGiYmJmk6HiIjouVFyDOY4TERExTR+n+62bdvi0KFD0vMGDf6X0qxZs7B3717s3LkTpqamCAgIwPDhw3Hs2DEAQGFhIby8vGBtbY3o6GjcvXsXEyZMgJ6eHj799FMAwPXr1+Hl5YWpU6di69atCA8Px+TJk9G0aVMolUoAwPbt2xEYGIjg4GC4u7tjzZo1UCqVSEhIgKWlZS3ujWeTmJgIRycnZGdlAQAMjYwQf+UK7OzsNJwZERFR/ZaYmAgnR0dkZWdLy4wMDXElPp7jMBHRc07jp5c3aNAA1tbW0sPCwgIAkJaWhu+++w6rVq1Cv3794Obmhk2bNiE6OhrHjx8HABw8eBCXL1/GDz/8gA4dOmDQoEFYsmQJgoKCkJeXBwAIDg6Gg4MDVq5cCScnJwQEBGDEiBFYvXq1lMOqVaswZcoU+Pr6wtnZGcHBwTAyMsLGjRtrf4c8g5SUFGRnZWHUx+sx6uP1yM7KQkpKiqbTIiIiqvdSUlKQlZ2NkOGDcOLNsQgZPghZ2dkch4mISPNF99WrV2FjY4MWLVpg7Nix0qlYsbGxyM/Ph6enpxTr6OgIOzs7xMTEAABiYmLg4uICKysrKUapVCI9PR2XLl2SYkq2URxT3EZeXh5iY2NVYnR0dODp6SnFlCU3Nxfp6ekqD21h6dAalg6tNZ0GERHRc8fJwhwdbazgZGGu6VSIiEhLaLTodnd3R0hICEJDQ7F+/Xpcv34dPXv2xOPHj5GUlAR9fX2YmZmpvMbKygpJSUkAgKSkJJWCu3h98bqKYtLT05H9/3+BLiwsLDOmuI2yLF26FKamptKjWbNm1doHREREREREVH9p9JruQYMGSX+3b98e7u7usLe3x44dO2BoaKjBzJ5u3rx5CAwMlJ6np6ez8CYiIiIiIiIVGj+9vCQzMzO8+OKL+Pvvv2FtbY28vDykpqaqxCQnJ8Pa2hoAYG1tXWo28+LnT4tRKBQwNDSEhYUFdHV1y4wpbqMscrkcCoVC5UFERERERERUklYV3RkZGbh27RqaNm0KNzc36OnpITw8XFqfkJCAxMREeHh4AAA8PDxw4cIF3Lt3T4oJCwuDQqGAs7OzFFOyjeKY4jb09fXh5uamElNUVITw8HAphoiIiIiIiKg6NFp0v/vuuzh8+DBu3LiB6OhovPbaa9DV1cXrr78OU1NT+Pn5ITAwEJGRkYiNjYWvry88PDzQtWtXAMCAAQPg7OyM8ePH49y5czhw4ADmz58Pf39/yOVyAMDUqVPxzz//YO7cuYiPj8dXX32FHTt2YNasWVIegYGB+Oabb7B582ZcuXIF06ZNQ2ZmJnx9fTWyX4iIiIiIiKh+0Og13f/++y9ef/11PHjwAE2aNEGPHj1w/PhxNGnSBACwevVq6OjowNvbG7m5uVAqlfjqq6+k1+vq6mLPnj2YNm0aPDw8YGxsDB8fHyxevFiKcXBwwN69ezFr1iysXbsWtra2+Pbbb6V7dAPA6NGjcf/+fSxYsABJSUno0KEDQkNDS02uRkRERERERFQVGi26t23bVuF6AwMDBAUFISgoqNwYe3t77Nu3r8J2+vTpg7Nnz1YYExAQgICAgApjiIiIiIiIiKpCq67pJiIiIiIiIqpPWHQTERERERERqQmLbiIiIiIiIiI1YdFNREREKpYtWwaZTIaZM2dKy3JycuDv74/GjRujYcOG8Pb2RnJyssrrEhMT4eXlBSMjI1haWmLOnDkoKChQiYmKikKnTp0gl8vRqlUrhISElNp+UFAQmjdvDgMDA7i7u+PkyZPq6CYREVGtYNFNREREklOnTuHrr79G+/btVZbPmjULf/zxB3bu3InDhw/jzp07GD58uLS+sLAQXl5eyMvLQ3R0NDZv3oyQkBAsWLBAirl+/Tq8vLzQt29fxMXFYebMmZg8eTIOHDggxWzfvh2BgYFYuHAhzpw5A1dXVyiVSty7d0/9nSciIlIDFt1EREQEAMjIyMDYsWPxzTffoFGjRtLytLQ0fPfdd1i1ahX69esHNzc3bNq0CdHR0Th+/DgA4ODBg7h8+TJ++OEHdOjQAYMGDcKSJUsQFBSEvLw8AEBwcDAcHBywcuVKODk5ISAgACNGjMDq1aulba1atQpTpkyBr68vnJ2dERwcDCMjI2zcuLF2dwYREVENYdFNREREAAB/f394eXnB09NTZXlsbCzy8/NVljs6OsLOzg4xMTEAgJiYGLi4uMDKykqKUSqVSE9Px6VLl6SYJ9tWKpVSG3l5eYiNjVWJ0dHRgaenpxRDRERU12j0Pt1ERESkHbZt24YzZ87g1KlTpdYlJSVBX18fZmZmKsutrKyQlJQkxZQsuIvXF6+rKCY9PR3Z2dl49OgRCgsLy4yJj48vN/fc3Fzk5uZKz9PT05/SWyIiotrDI91ERETPuVu3bmHGjBnYunUrDAwMNJ1OlS1duhSmpqbSo1mzZppOiYiISMKim4iI6DkXGxuLe/fuoVOnTmjQoAEaNGiAw4cP44svvkCDBg1gZWWFvLw8pKamqrwuOTkZ1tbWAABra+tSs5kXP39ajEKhgKGhISwsLKCrq1tmTHEbZZk3bx7S0tKkx61bt6q1H4iIiNSBRTcREdFzrn///rhw4QLi4uKkR+fOnTF27Fjpbz09PYSHh0uvSUhIQGJiIjw8PAAAHh4euHDhgsos42FhYVAoFHB2dpZiSrZRHFPchr6+Ptzc3FRiioqKEB4eLsWURS6XQ6FQqDyIiIi0Ba/pJiIies6ZmJigXbt2KsuMjY3RuHFjabmfnx8CAwNhbm4OhUKB6dOnw8PDA127dgUADBgwAM7Ozhg/fjyWL1+OpKQkzJ8/H/7+/pDL5QCAqVOnYt26dZg7dy4mTZqEiIgI7NixA3v37pW2GxgYCB8fH3Tu3BldunTBmjVrkJmZCV9f31raG0RERDWLRTcRERE91erVq6GjowNvb2/k5uZCqVTiq6++ktbr6upiz549mDZtGjw8PGBsbAwfHx8sXrxYinFwcMDevXsxa9YsrF27Fra2tvj222+hVCqlmNGjR+P+/ftYsGABkpKS0KFDB4SGhpaaXI2IiKiuYNFNREREpURFRak8NzAwQFBQEIKCgsp9jb29Pfbt21dhu3369MHZs2crjAkICEBAQEClcyUiItJmvKabiIiIiIiISE1YdBMRERERERGpCYtuIiIiIiIiIjVh0U1ERERERESkJiy6iYiIiIiIiNSEs5cTERERVUNiYiJSUlIAAFeuXNFwNkREpK1YdBMRERFVUWJiIpwcHZGVna3pVIiISMux6CYiIiKqopSUFGRlZyNk+CA4WZhj/9XrWBQZrem0iIhIC/GabiIiIqJqcrIwR0cbKzg0MtV0KkREpKVYdBMRERERERGpCYtuIiIiIiIiIjVh0U1ERERERESkJiy6iYiIiIiIiNSERTcRERERERGRmrDoJiIiIiIiIlITrSm6ly1bBplMhpkzZ0rLcnJy4O/vj8aNG6Nhw4bw9vZGcnKyyusSExPh5eUFIyMjWFpaYs6cOSgoKFCJiYqKQqdOnSCXy9GqVSuEhISU2n5QUBCaN28OAwMDuLu74+TJk+roJhERERERET1HtKLoPnXqFL7++mu0b99eZfmsWbPwxx9/YOfOnTh8+DDu3LmD4cOHS+sLCwvh5eWFvLw8REdHY/PmzQgJCcGCBQukmOvXr8PLywt9+/ZFXFwcZs6cicmTJ+PAgQNSzPbt2xEYGIiFCxfizJkzcHV1hVKpxL1799TfeSIiIiIiIqq3NF50Z2RkYOzYsfjmm2/QqFEjaXlaWhq+++47rFq1Cv369YObmxs2bdqE6OhoHD9+HABw8OBBXL58GT/88AM6dOiAQYMGYcmSJQgKCkJeXh4AIDg4GA4ODli5ciWcnJwQEBCAESNGYPXq1dK2Vq1ahSlTpsDX1xfOzs4IDg6GkZERNm7cWLs7g4iIiIiIiOoVjRfd/v7+8PLygqenp8ry2NhY5Ofnqyx3dHSEnZ0dYmJiAAAxMTFwcXGBlZWVFKNUKpGeno5Lly5JMU+2rVQqpTby8vIQGxurEqOjowNPT08ppiy5ublIT09XeRARERERERGV1ECTG9+2bRvOnDmDU6dOlVqXlJQEfX19mJmZqSy3srJCUlKSFFOy4C5eX7yuopj09HRkZ2fj0aNHKCwsLDMmPj6+3NyXLl2Kjz76qHIdJSIiIiIioueSxo5037p1CzNmzMDWrVthYGCgqTSqbd68eUhLS5Met27d0nRKREREREREpGU0VnTHxsbi3r176NSpExo0aIAGDRrg8OHD+OKLL9CgQQNYWVkhLy8PqampKq9LTk6GtbU1AMDa2rrUbObFz58Wo1AoYGhoCAsLC+jq6pYZU9xGWeRyORQKhcqDiIiIiIiIqCSNFd39+/fHhQsXEBcXJz06d+6MsWPHSn/r6ekhPDxcek1CQgISExPh4eEBAPDw8MCFCxdUZhkPCwuDQqGAs7OzFFOyjeKY4jb09fXh5uamElNUVITw8HAphoiIiIiIiKg6NHZNt4mJCdq1a6eyzNjYGI0bN5aW+/n5ITAwEObm5lAoFJg+fTo8PDzQtWtXAMCAAQPg7OyM8ePHY/ny5UhKSsL8+fPh7+8PuVwOAJg6dSrWrVuHuXPnYtKkSYiIiMCOHTuwd+9eabuBgYHw8fFB586d0aVLF6xZswaZmZnw9fWtpb1BRERERERE9ZFGJ1J7mtWrV0NHRwfe3t7Izc2FUqnEV199Ja3X1dXFnj17MG3aNHh4eMDY2Bg+Pj5YvHixFOPg4IC9e/di1qxZWLt2LWxtbfHtt99CqVRKMaNHj8b9+/exYMECJCUloUOHDggNDS01uRoRERERERFRVWhV0R0VFaXy3MDAAEFBQQgKCir3Nfb29ti3b1+F7fbp0wdnz56tMCYgIAABAQGVzpWIiIjoaa5cuQIAsLCwgJ2dnYazISIiTdCqopuIiIioPkjKyISOTIZx48YBAIwMDXElPp6FNxHRc0hjE6kRERER1VepObkoEgIhwwchZPggZGVnIyUlRdNpERGRBvBINxEREZGaOFmYazoFIiLSMB7pJiIiIiIiIlITFt1EREREREREasKim4iIiIiIiEhNWHQTERERERERqQmLbiIiIiIiIiI1YdFNREREREREpCYsuomIiAjr169H+/btoVAooFAo4OHhgf3790vrc3Jy4O/vj8aNG6Nhw4bw9vZGcnKyShuJiYnw8vKCkZERLC0tMWfOHBQUFKjEREVFoVOnTpDL5WjVqhVCQkJK5RIUFITmzZvDwMAA7u7uOHnypFr6TEREVBtYdNdzV65cwZkzZ3DmzBkkJiZqOh0iItJStra2WLZsGWJjY3H69Gn069cPQ4cOxaVLlwAAs2bNwh9//IGdO3fi8OHDuHPnDoYPHy69vrCwEF5eXsjLy0N0dDQ2b96MkJAQLFiwQIq5fv06vLy80LdvX8TFxWHmzJmYPHkyDhw4IMVs374dgYGBWLhwIc6cOQNXV1colUrcu3ev9nYGERFRDWqg6QRIPR6nJEOmo4Nx48ZJywyNjBB/5Qrs7Ow0mBkREWmjIUOGqDz/5JNPsH79ehw/fhy2trb47rvv8OOPP6Jfv34AgE2bNsHJyQnHjx9H165dcfDgQVy+fBmHDh2ClZUVOnTogCVLluC9997DokWLoK+vj+DgYDg4OGDlypUAACcnJxw9ehSrV6+GUqkEAKxatQpTpkyBr68vACA4OBh79+7Fxo0b8f7779fiHiEiIqoZPNJdT2U/TocoKsKoj9cjYOshjPp4PbKzspCSkqLp1IiISMsVFhZi27ZtyMzMhIeHB2JjY5Gfnw9PT08pxtHREXZ2doiJiQEAxMTEwMXFBVZWVlKMUqlEenq6dLQ8JiZGpY3imOI28vLyEBsbqxKjo6MDT09PKYaIiKiuqdaR7n/++QctWrSo6VxIDSwdWuMFJ1dNp0FERGpSk2PyhQsX4OHhgZycHDRs2BC//vornJ2dERcXB319fZiZmanEW1lZISkpCQCQlJSkUnAXry9eV1FMeno6srOz8ejRIxQWFpYZEx8fX27eubm5yM3NlZ6np6dXreNERERqVK0j3a1atULfvn3xww8/ICcnp6ZzIiIiokqqyTG5TZs2iIuLw4kTJzBt2jT4+Pjg8uXLNZSp+ixduhSmpqbSo1mzZppOiYiISFKtovvMmTNo3749AgMDYW1tjbfeeoszixIREWlATY7J+vr6aNWqFdzc3LB06VK4urpi7dq1sLa2Rl5eHlJTU1Xik5OTYW1tDQCwtrYuNZt58fOnxSgUChgaGsLCwgK6urplxhS3UZZ58+YhLS1Nety6data/SciIlKHahXdHTp0wNq1a3Hnzh1s3LgRd+/eRY8ePdCuXTusWrUK9+/fr+k8iYiIqAzqHJOLioqQm5sLNzc36OnpITw8XFqXkJCAxMREeHh4AAA8PDxw4cIFlVnGw8LCoFAo4OzsLMWUbKM4prgNfX19uLm5qcQUFRUhPDxciimLXC6XbnVW/CAiItIWzzSRWoMGDTB8+HDs3LkTn332Gf7++2+8++67aNasGSZMmIC7d+/WVJ5ERERUgWcdk+fNm4cjR47gxo0buHDhAubNm4eoqCiMHTsWpqam8PPzQ2BgICIjIxEbGwtfX194eHiga9euAIABAwbA2dkZ48ePx7lz53DgwAHMnz8f/v7+kMvlAICpU6fin3/+wdy5cxEfH4+vvvoKO3bswKxZs6Q8AgMD8c0332Dz5s24cuUKpk2bhszMTGk2cyIiorrmmYru06dP4+2330bTpk2xatUqvPvuu7h27RrCwsJw584dDB06tKbyJCIiogo865h87949TJgwAW3atEH//v1x6tQpHDhwAC+//DIAYPXq1XjllVfg7e2NXr16wdraGrt27ZJer6uriz179kBXVxceHh4YN24cJkyYgMWLF0sxDg4O2Lt3L8LCwuDq6oqVK1fi22+/lW4XBgCjR4/G559/jgULFqBDhw6Ii4tDaGhoqcnViIiI6opqzV6+atUqbNq0CQkJCRg8eDC+//57DB48GDo6/9XwDg4OCAkJQfPmzWsyVyIiInpCTY3J3333XYXrDQwMEBQUhKCgoHJj7O3tsW/fvgrb6dOnD86ePVthTEBAAAICAiqMISIiqiuqVXSvX78ekyZNwsSJE9G0adMyYywtLZ86gBMREdGz4ZhMRESk3apVdF+9evWpMfr6+vDx8alO80RERFRJHJOJiIi0W7Wu6d60aRN27txZavnOnTuxefPmZ06KiIiIKodjMhERkXarVtG9dOlSWFhYlFpuaWmJTz/99JmTIiIiosrhmExERKTdqlV0JyYmwsHBodRye3t7JCYmPnNSREREVDkck4mIiLRbtYpuS0tLnD9/vtTyc+fOoXHjxs+cFBEREVUOx2QiIiLtVq2i+/XXX8c777yDyMhIFBYWorCwEBEREZgxYwbGjBlT0zkSERFROTgmExERabdqzV6+ZMkS3LhxA/3790eDBv81UVRUhAkTJvD6MSIiolrEMZmIiEi7VetIt76+PrZv3474+Hhs3boVu3btwrVr17Bx40bo6+tXup3169ejffv2UCgUUCgU8PDwwP79+6X1OTk58Pf3R+PGjdGwYUN4e3sjOTlZpY3ExER4eXnByMgIlpaWmDNnDgoKClRioqKi0KlTJ8jlcrRq1QohISGlcgkKCkLz5s1hYGAAd3d3nDx5smo7hYiISANqakwmIiIi9ajWke5iL774Il588cVqv97W1hbLli1D69atIYTA5s2bMXToUJw9exZt27bFrFmzsHfvXuzcuROmpqYICAjA8OHDcezYMQBAYWEhvLy8YG1tjejoaNy9excTJkyAnp6e9Ov+9evX4eXlhalTp2Lr1q0IDw/H5MmT0bRpUyiVSgDA9u3bERgYiODgYLi7u2PNmjVQKpVISEiApaXls+wiIiKiWvGsYzIRERGpR7WK7sLCQoSEhCA8PBz37t1DUVGRyvqIiIhKtTNkyBCV55988gnWr1+P48ePw9bWFt999x1+/PFH9OvXD8B/9yJ1cnLC8ePH0bVrVxw8eBCXL1/GoUOHYGVlhQ4dOmDJkiV47733sGjRIujr6yM4OBgODg5YuXIlAMDJyQlHjx7F6tWrpaJ71apVmDJlCnx9fQEAwcHB2Lt3LzZu3Ij333+/OruIiIioVtTUmExERETqUa3Ty2fMmIEZM2agsLAQ7dq1g6urq8qjOgoLC7Ft2zZkZmbCw8MDsbGxyM/Ph6enpxTj6OgIOzs7xMTEAABiYmLg4uICKysrKUapVCI9PR2XLl2SYkq2URxT3EZeXh5iY2NVYnR0dODp6SnFEBERaSt1jMlERERUc6p1pHvbtm3YsWMHBg8e/MwJXLhwAR4eHsjJyUHDhg3x66+/wtnZGXFxcdDX14eZmZlKvJWVFZKSkgAASUlJKgV38fridRXFpKenIzs7G48ePUJhYWGZMfHx8eXmnZubi9zcXOl5enp61TpORERUA2pyTCYiIqKaV+2J1Fq1alUjCbRp0wZxcXE4ceIEpk2bBh8fH1y+fLlG2lanpUuXwtTUVHo0a9ZM0ykREdFzqCbHZCIiIqp51Sq6Z8+ejbVr10II8cwJFH9ZcHNzw9KlS+Hq6oq1a9fC2toaeXl5SE1NVYlPTk6GtbU1AMDa2rrUbObFz58Wo1AoYGhoCAsLC+jq6pYZU9xGWebNm4e0tDTpcevWrWr1n4iI6FnU5JhMRERENa9ap5cfPXoUkZGR2L9/P9q2bQs9PT2V9bt27ap2QkVFRcjNzYWbmxv09PQQHh4Ob29vAEBCQgISExPh4eEBAPDw8MAnn3yCe/fuSbOMh4WFQaFQwNnZWYrZt2+fyjbCwsKkNvT19eHm5obw8HAMGzZMyiE8PBwBAQHl5imXyyGXy6vdTyIiopqgzjGZiIiInl21im4zMzO89tprz7zxefPmYdCgQbCzs8Pjx4/x448/IioqCgcOHICpqSn8/PwQGBgIc3NzKBQKTJ8+HR4eHujatSsAYMCAAXB2dsb48eOxfPlyJCUlYf78+fD395cK4qlTp2LdunWYO3cuJk2ahIiICOzYsQN79+6V8ggMDISPjw86d+6MLl26YM2aNcjMzJRmMyciItJWNTUmExERkXpUq+jetGlTjWz83r17mDBhAu7evQtTU1O0b98eBw4cwMsvvwwAWL16NXR0dODt7Y3c3FwolUp89dVX0ut1dXWxZ88eTJs2DR4eHjA2NoaPjw8WL14sxTg4OGDv3r2YNWsW1q5dC1tbW3z77bfS7cIAYPTo0bh//z4WLFiApKQkdOjQAaGhoaUmVyMiItI2NTUmExERkXpUq+gGgIKCAkRFReHatWt44403YGJigjt37kChUKBhw4aVauO7776rcL2BgQGCgoIQFBRUboy9vX2p08ef1KdPH5w9e7bCmICAgApPJyciItJWNTEmExERkXpUq+i+efMmBg4ciMTEROTm5uLll1+GiYkJPvvsM+Tm5iI4OLim8yQiIqIycEwmIiLSbtWavXzGjBno3LkzHj16BENDQ2n5a6+9hvDw8BpLjoiIiCrGMZmIiEi7VetI959//ono6Gjo6+urLG/evDlu375dI4kRERHR03FMJiIi0m7VOtJdVFSEwsLCUsv//fdfmJiYPHNSREREVDkck4mIiLRbtYruAQMGYM2aNdJzmUyGjIwMLFy4EIMHD66p3IiIiOgpOCYTERFpt2qdXr5y5UoolUo4OzsjJycHb7zxBq5evQoLCwv89NNPNZ0jERERlYNjMhERkXarVtFta2uLc+fOYdu2bTh//jwyMjLg5+eHsWPHqkziQkREROrFMZmIiEi7Vfs+3Q0aNMC4ceNqMhciIiKqBo7JRERE2qtaRff3339f4foJEyZUKxkiIiKqGo7JRERE2q1aRfeMGTNUnufn5yMrKwv6+vowMjLiAE9ERFRLOCYTERFpt2rNXv7o0SOVR0ZGBhISEtCjRw9O2kJERFSLOCYTERFpt2oV3WVp3bo1li1bVuoXdyIiIqpdHJOJiIi0R40V3cB/E7ncuXOnJpskIiKiauCYTEREpB2qdU3377//rvJcCIG7d+9i3bp16N69e40kRkRERE/HMZmIiEi7VavoHjZsmMpzmUyGJk2aoF+/fli5cmVN5EVERESVwDGZiIhIu1Wr6C4qKqrpPIiIiKgaOCYTERFptxq9ppuIiIjqpqVLl+Kll16CiYkJLC0tMWzYMCQkJKjE5OTkwN/fH40bN0bDhg3h7e2N5ORklZjExER4eXnByMgIlpaWmDNnDgoKClRioqKi0KlTJ8jlcrRq1QohISGl8gkKCkLz5s1hYGAAd3d3nDx5ssb7TEREVBuqdaQ7MDCw0rGrVq2qziaIiIioEmpqTD58+DD8/f3x0ksvoaCgAB988AEGDBiAy5cvw9jYGAAwa9Ys7N27Fzt37oSpqSkCAgIwfPhwHDt2DABQWFgILy8vWFtbIzo6Gnfv3sWECROgp6eHTz/9FABw/fp1eHl5YerUqdi6dSvCw8MxefJkNG3aFEqlEgCwfft2BAYGIjg4GO7u7lizZg2USiUSEhJgaWlZ3V1FRESkEdUqus+ePYuzZ88iPz8fbdq0AQD89ddf0NXVRadOnaQ4mUxWM1kSERFRmWpqTA4NDVV5HhISAktLS8TGxqJXr15IS0vDd999hx9//BH9+vUDAGzatAlOTk44fvw4unbtioMHD+Ly5cs4dOgQrKys0KFDByxZsgTvvfceFi1aBH19fQQHB8PBwUG63tzJyQlHjx7F6tWrpaJ71apVmDJlCnx9fQEAwcHB2Lt3LzZu3Ij333+/ZnYcERFRLalW0T1kyBCYmJhg8+bNaNSoEQDg0aNH8PX1Rc+ePTF79uwaTZKIiIjKpq4xOS0tDQBgbm4OAIiNjUV+fj48PT2lGEdHR9jZ2SEmJgZdu3ZFTEwMXFxcYGVlJcUolUpMmzYNly5dQseOHRETE6PSRnHMzJkzAQB5eXmIjY3FvHnzpPU6Ojrw9PRETExMtfqiLa5cuSL9bWFhATs7Ow1mQ0REtaVaRffKlStx8OBBaXAHgEaNGuHjjz/GgAEDWHQTERHVEnWMyUVFRZg5cya6d++Odu3aAQCSkpKgr68PMzMzlVgrKyskJSVJMSUL7uL1xesqiklPT0d2djYePXqEwsLCMmPi4+PLzDc3Nxe5ubnS8/T09Cr2WL2SMjKhI5Nh3Lhx0jIjQ0NciY9n4U1E9Byo1kRq6enpuH//fqnl9+/fx+PHj585KSIiIqocdYzJ/v7+uHjxIrZt2/as6dWKpUuXwtTUVHo0a9ZM0ympSM3JRZEQCBk+CCfeHIuQ4YOQlZ2NlJQUTadGRES1oFpF92uvvQZfX1/s2rUL//77L/7991/88ssv8PPzw/Dhw2s6RyIiIipHTY/JAQEB2LNnDyIjI2Fraystt7a2Rl5eHlJTU1Xik5OTYW1tLcU8OZt58fOnxSgUChgaGsLCwgK6urplxhS38aR58+YhLS1Nety6davK/a4NThbm6GhjBScLc02nQkREtahaRXdwcDAGDRqEN954A/b29rC3t8cbb7yBgQMH4quvvqrpHImIiKgcNTUmCyEQEBCAX3/9FREREXBwcFBZ7+bmBj09PYSHh0vLEhISkJiYCA8PDwCAh4cHLly4gHv37kkxYWFhUCgUcHZ2lmJKtlEcU9yGvr4+3NzcVGKKiooQHh4uxTxJLpdDoVCoPIiIiLRFta7pNjIywldffYUVK1bg2rVrAICWLVtKtxQhIiKi2lFTY7K/vz9+/PFH/PbbbzAxMZGuwTY1NYWhoSFMTU3h5+eHwMBAmJubQ6FQYPr06fDw8EDXrl0BAAMGDICzszPGjx+P5cuXIykpCfPnz4e/vz/kcjkAYOrUqVi3bh3mzp2LSZMmISIiAjt27MDevXulXAIDA+Hj44POnTujS5cuWLNmDTIzM6XZzImIiOqSahXdxe7evYu7d++iV69eMDQ0hBCCtwkjIiLSgGcdk9evXw8A6NOnj8ryTZs2YeLEiQCA1atXQ0dHB97e3sjNzYVSqVQ5mq6rq4s9e/Zg2rRp8PDwgLGxMXx8fLB48WIpxsHBAXv37sWsWbOwdu1a2Nra4ttvv5VuFwYAo0ePxv3797FgwQIkJSWhQ4cOCA0NLTW5GhERUV1QraL7wYMHGDVqFCIjIyGTyXD16lW0aNECfn5+aNSokXTvTSIiIlKvmhqThRBPjTEwMEBQUBCCgoLKjbG3t8e+ffsqbKdPnz44e/ZshTEBAQEICAh4ak5ERETarlrXdM+aNQt6enpITEyEkZGRtHz06NEIDQ2tseSIiIioYhyTiYiItFu1jnQfPHgQBw4cUJnVFABat26Nmzdv1khiRERE9HQck4mIiLRbtY50Z2ZmqvyaXuzhw4fSRCmVsXTpUrz00kswMTGBpaUlhg0bhoSEBJWYnJwc+Pv7o3HjxmjYsCG8vb1L3UYkMTERXl5eMDIygqWlJebMmYOCggKVmKioKHTq1AlyuRytWrVCSEhIqXyCgoLQvHlzGBgYwN3dHSdPnqx0X4iIiDShpsZkIiIiUo9qFd09e/bE999/Lz2XyWQoKirC8uXL0bdv30q3c/jwYfj7++P48eMICwtDfn4+BgwYgMzMTClm1qxZ+OOPP7Bz504cPnwYd+7cUbnvaGFhIby8vJCXl4fo6Ghs3rwZISEhWLBggRRz/fp1eHl5oW/fvoiLi8PMmTMxefJkHDhwQIrZvn07AgMDsXDhQpw5cwaurq5QKpUqtz0hIiLSNjU1JhMREZF6VOv08uXLl6N///44ffo08vLyMHfuXFy6dAkPHz7EsWPHKt3Ok9eahYSEwNLSErGxsejVqxfS0tLw3Xff4ccff0S/fv0A/DeLqpOTE44fP46uXbvi4MGDuHz5Mg4dOgQrKyt06NABS5YswXvvvYdFixZBX18fwcHBcHBwkCaTcXJywtGjR7F69WppttRVq1ZhypQp0u1IgoODsXfvXmzcuBHvv/9+dXZTrUhMTERKSgoA4MqVKxrOhoiIaltNjclERESkHtU60t2uXTv89ddf6NGjB4YOHYrMzEwMHz4cZ8+eRcuWLaudTFpaGgDA3NwcABAbG4v8/Hx4enpKMY6OjrCzs0NMTAwAICYmBi4uLiq3EVEqlUhPT8elS5ekmJJtFMcUt5GXl4fY2FiVGB0dHXh6ekox2igxMRGOTk5wc3ODm5sbxo0bp+mUiIiolqlrTCYiIqKaUeUj3fn5+Rg4cCCCg4Pxf//3fzWWSFFREWbOnInu3bujXbt2AICkpCTo6+vDzMxMJdbKygpJSUlSzJP37Sx+/rSY9PR0ZGdn49GjRygsLCwzJj4+vsx8c3NzkZubKz1PT0+vYo+fXUpKCrKzsjDq4/WwdGiNhGPhCPtqaa3nQUREmqGuMZmIiIhqTpWPdOvp6eH8+fM1noi/vz8uXryIbdu21Xjb6rB06VKYmppKj2bNmmksF0uH1njByRWNbOw0lgMREdU+dY3JREREVHOqdXr5uHHj8N1339VYEgEBAdizZw8iIyNVbnlibW2NvLw8pKamqsQnJyfD2tpainlyNvPi50+LUSgUMDQ0hIWFBXR1dcuMKW7jSfPmzUNaWpr0uHXrVtU7TkRE9IxqekwmIiKimlWtidQKCgqwceNGHDp0CG5ubjA2NlZZv2rVqkq1I4TA9OnT8euvvyIqKgoODg4q693c3KCnp4fw8HB4e3sDABISEpCYmAgPDw8AgIeHBz755BPcu3cPlpaWAICwsDAoFAo4OztLMfv27VNpOywsTGpDX18fbm5uCA8Px7BhwwD8d7p7eHg4AgICysxdLpfzVixERKRxNTUmExERkXpUqej+559/0Lx5c1y8eBGdOnUCAPz1118qMTKZrNLt+fv748cff8Rvv/0GExMT6RpsU1NTGBoawtTUFH5+fggMDIS5uTkUCgWmT58ODw8PdO3aFQAwYMAAODs7Y/z48Vi+fDmSkpIwf/58+Pv7S0Xx1KlTsW7dOsydOxeTJk1CREQEduzYgb1790q5BAYGwsfHB507d0aXLl2wZs0aZGZmSrOZExERaZOaHpOJiIhIPapUdLdu3Rp3795FZGQkAGD06NH44osvSk1AVlnr168HAPTp00dl+aZNmzBx4kQAwOrVq6GjowNvb2/k5uZCqVTiq6++kmJ1dXWxZ88eTJs2DR4eHjA2NoaPjw8WL14sxTg4OGDv3r2YNWsW1q5dC1tbW3z77bfS7cKK+3L//n0sWLAASUlJ6NChA0JDQ6vdNyIiInWq6TGZiIiI1KNKRbcQQuX5/v37kZmZWe2NP9leWQwMDBAUFISgoKByY+zt7UudPv6kPn364OzZsxXGBAQElHs6ORERkTap6TGZiIiI1KNaE6kVq0zRTEREROrHMZmIiEg7Vanolslkpa4P4/ViREREtY9jMhERUd1Q5dPLJ06cKE1QlpOTg6lTp5aaKXXXrl01lyERERGVwjGZiIiobqhS0e3j46PyfNy4cTWaDBEREVUOx2QiIqK6oUpF96ZNm9SVBxEREVUBx2QiIqK64ZkmUiMiIiIiIiKi8rHoJiIiIiIiIlITFt1EREREREREasKim4iIiIiIiEhNWHQTERERERERqQmLbiIiIiIiIiI1qdItw0jzEhMTkZKSAgC4cuWKhrMhIiIiIiKiirDorkMSExPh6OSE7KwsTadCRERERERElcCiuw5JSUlBdlYWRn28HpYOrZFwLBxhXy3VdFpERERERERUDl7TXQdZOrTGC06uaGRjp+lUiIiIiIiIqAIsuomIiIiIiIjUhEU3ERERERERkZqw6CYiIiIiIiJSExbdRERERERERGrCopuIiIiIiIhITVh0ExEREREREakJ79P9nLly5QoAwMLCAnZ2vOUYERGRpnBMJiJ6PrDofk48TkmGTEcH48aNAwAYGhkh/soVDvJERES1LCkjEzoymTQmGxka4kp8PMdkIqJ6iqeXPyeyH6dDFBVh1MfrMerj9cjOykJKSoqm0yIiIi1x5MgRDBkyBDY2NpDJZNi9e7fKeiEEFixYgKZNm8LQ0BCenp64evWqSszDhw8xduxYKBQKmJmZwc/PDxkZGSox58+fR8+ePWFgYIBmzZph+fLlpXLZuXMnHB0dYWBgABcXF+zbt6/G+6tJqTm5KBICIcMHIWT4IGRlZ3NMJiKqx1h0P2csHVrD0qG1ptMgIiItk5mZCVdXVwQFBZW5fvny5fjiiy8QHByMEydOwNjYGEqlEjk5OVLM2LFjcenSJYSFhWHPnj04cuQI3nzzTWl9eno6BgwYAHt7e8TGxmLFihVYtGgRNmzYIMVER0fj9ddfh5+fH86ePYthw4Zh2LBhuHjxovo6ryFOFuZwsjDXdBpERKRmPL2ciIiIMGjQIAwaNKjMdUIIrFmzBvPnz8fQoUMBAN9//z2srKywe/dujBkzBleuXEFoaChOnTqFzp07AwC+/PJLDB48GJ9//jlsbGywdetW5OXlYePGjdDX10fbtm0RFxeHVatWScX52rVrMXDgQMyZMwcAsGTJEoSFhWHdunUIDg6uhT1BRERUs3ikm4iIiCp0/fp1JCUlwdPTU1pmamoKd3d3xMTEAABiYmJgZmYmFdwA4OnpCR0dHZw4cUKK6dWrF/T19aUYpVKJhIQEPHr0SIopuZ3imOLtlCU3Nxfp6ekqDyIiIm3BopuIiIgqlJSUBACwsrJSWW5lZSWtS0pKgqWlpcr6Bg0awNzcXCWmrDZKbqO8mOL1ZVm6dClMTU2lR7NmzaraRSIiIrVh0U1ERER12rx585CWliY9bt26pemUiIiIJBotujlTKhERkfaztrYGACQnJ6ssT05OltZZW1vj3r17KusLCgrw8OFDlZiy2ii5jfJiiteXRS6XQ6FQqDyIiIi0hUaLbs6USkREpP0cHBxgbW2N8PBwaVl6ejpOnDgBDw8PAICHhwdSU1MRGxsrxURERKCoqAju7u5SzJEjR5Cfny/FhIWFoU2bNmjUqJEUU3I7xTHF2yEiIqprNDp7OWdK1awrV65If1tYWMDOzk6D2RARkSZlZGTg77//lp5fv34dcXFxMDc3h52dHWbOnImPP/4YrVu3hoODAz788EPY2Nhg2LBhAAAnJycMHDgQU6ZMQXBwMPLz8xEQEIAxY8bAxsYGAPDGG2/go48+gp+fH9577z1cvHgRa9euxerVq6XtzpgxA71798bKlSvh5eWFbdu24fTp0yo/lhMREdUlWntNN2dKVZ/HKcmQ6ehg3LhxcHNzg5ubGxydnJCYmKjp1IiISENOnz6Njh07omPHjgCAwMBAdOzYEQsWLAAAzJ07F9OnT8ebb76Jl156CRkZGQgNDYWBgYHUxtatW+Ho6Ij+/ftj8ODB6NGjh0qxbGpqioMHD+L69etwc3PD7NmzsWDBApUz1Lp164Yff/wRGzZsgKurK37++Wfs3r0b7dq1q6U9QUREVLO09j7dNTlTqoODQ6k2itc1atSo2jOlfvTRR9XomeZlP06HKCrCqI/Xw9KhNe5dv4od86chJSWFR7uJiJ5Tffr0gRCi3PUymQyLFy/G4sWLy40xNzfHjz/+WOF22rdvjz///LPCmJEjR2LkyJEVJ0xERFRHaO2Rbm1XH2ZKtXRojRecXGHp0FrTqRAREREREdVLWlt0c6ZUIiIiIiIiquu0tujmTKlERERERERU12m06M7IyEBcXBzi4uIA/G+m1MTERMhkMmmm1N9//x0XLlzAhAkTyp0p9eTJkzh27FiZM6Xq6+vDz88Ply5dwvbt27F27VoEBgZKecyYMQOhoaFYuXIl4uPjsWjRIpw+fRoBAQG1vUuIiIiIiIioHtHoRGqnT59G3759pefFhbCPjw9CQkIwd+5cZGZm4s0330Rqaip69OhR5kypAQEB6N+/P3R0dODt7Y0vvvhCWl88U6q/vz/c3NxgYWFR7kyp8+fPxwcffIDWrVtzplQiIiIiIiJ6ZhotujlTKhEREREREdVnWntNNxEREREREVFdp7X36SbtlZiYiJSUFACAhYUF7+1NRET0jK5cuSL9zbGViKh+YdFNVZKYmAhHJydkZ2UBAAyNjBB/5Qq/HBAREVVDUkYmdGQyjBs3TlpmZGiIK/HxHFuJiOoJnl5OVZKSkoLsrCyM+ng9Rn28HtlZWdJRbyIiIqqa1JxcFAmBkOGDcOLNsQgZPghZ2dkcW4mI6hEe6aZqsXRorekUiIiI6g0nC3N0tLHSdBpERKQGPNJNREREREREpCY80k0STuJCRERERERUs1h0a6GSs4MD6i+AH6ckQ6ajozKJCydIIyIiIiIienYsurXMk7ODA/8rgNUl+3E6RFERRn28HpYOrXHv+lXsmD8NKSkpLLqJiIg0gGefERHVHyy6tUzJ2cGfLIDVzdKhNV5wclX7doiIiKhsvIUYEVH9w6JbS2lDAVz8Kzt/YSciIqodJW8h5mRhjispDzFx136efUZEVIex6KZSnrzGm9d3ExER1S7eQoyIqP7gLcOolJLXeI/6eD2ys7Jq5fR2IiIiIiKi+oZHuqlclg6tKxXHyV6IiIi0Q8k7oHBMJiLSDiy6qVKKC+uSBTZvNUZERPVdbd/GszyVmWclMTERTo6OyMrOBsAJ2IiItAWLbqpQWYV1Md5qjIiI6rMni1jgf4VsbXlyNvMnC+mSPwpcuXIFWdnZCBk+CAA4ARsRkZZg0U0VerKwTjgWjrCvlqrEaMNM60RERDUtJSVFKmJLziT+559/1loOJWczByBt38nJCXfv3sXIESOQnZOj8honC/Nay4+IiJ6ORTdVSnFhfe/6VU2nQkREVKuKZxIv6x7atZlDedsv/lFg/9XrWBQZrbKO864QEWkei24iIiKiSih51PnGo7RSBW5tbr9kkV38o0B8ykMptqwCndd4ExFpBotuIiIioipwsjCHTMPbf7LIftKTBXrJU+OdnJx41JuIqBax6K4jSp4epu14KhsREZF2KO/UeB71JiKqPSy6tVxFs4drG95CjIiISDtVNCEbwB/JiYjUiUW3lis5e/ijO4mlZg7XJuXdQoynshEREWmH8iZk45FvIiL1YdFdR1g6tNZ0CpVWPNP5k0e+edSbiIhI8552vTfAI99ERDWJRTepTckj3wCwY/40pKSkcBAnIiLSAhXdCo1HvomIag6LblK7kkfpOckaERGRduFM50RE6sWim2oFJ1kjIiLSbpzpnIhIPVh0U6142iRrAI98ExERaQPOdE5EVLNYdFOtKm+SNQCQGxjgl59/RtOmTZGbmwu5XC6tK/mcgz0REZH6lTfTuYFcjp9/+aXM8ZpjNBFRaSy6nxAUFIQVK1YgKSkJrq6u+PLLL9GlSxdNp1XvPHnk+/rZE9i36kO88sorAACZjg5EUZEUX/J5yeIc4ABPRFRfcUzWvCev9z6aeBtzDhyWxmsdmQxFQkjxJQtygD+aExEBLLpVbN++HYGBgQgODoa7uzvWrFkDpVKJhIQEWFpaajq9eqn4yPe961dL3Y+8uCBPOBYuPc9MfahSnAOli3AO8EREdR/HZO1SfL13fMpDqQi/8SgNiyKjyy3IAdWivKKCnEfMiag+Y9FdwqpVqzBlyhT4+voCAIKDg7F3715s3LgR77//voazez6UnOm8ZEFe/LxkcV7WEXKg4qPiFQ3wHPCJiLQHx2Tt5mRhDlmJv58syJ0szLH/6nWpKH+QlV1hQV6VI+Ycr4mormHR/f/l5eUhNjYW8+bNk5bp6OjA09MTMTExpeJzc3ORm5srPU9LSwMApKenP1MeGRkZAIDbV84jLysT929clZ6nJv1b7rqqxGpbO9XZZn5ONvKyMpGV+gCiqAg9J/jDzPoF/HspDmf37kDPCf7IyXyMU7u2qAzwkMmA4kG95N9lPJcbGGDL99/DysoKOjo6KCpxunvJ5xWtq0psTbWjiW3W13bqcu71tR1tyN3a2hrW1tZ4FsVjhSj5bxBJtG1MPnM3GRl5+bhy/4H0/GZqernrqhJbU+1oYpvlrcvOz0dGXj5yCgqk5/czs1AkBAK7uaGZQoHTd5Kw9fwVBHZzQ3JGlvR3M4UCl+6n4LvYC6o/qAMQZfwN/Fegf79ly1PH6yefa/u/NfWlnbqcu7a1U5dzf+7HZEFCCCFu374tAIjo6GiV5XPmzBFdunQpFb9w4UKB//7N54MPPvjgg49qPW7dulVbw1ydwjGZDz744IOP2n6oc0zmke5qmjdvHgIDA6XnRUVFePjwIRo3bgyZTFbBK8uWnp6OZs2a4datW1AoFDWZ6nOP+1Y9uF/Vg/tVfbRp3woh8PjxY9jY2Gg0j/qipsfkYtr0/0xNqo/9qo99AtivuqY+9qs+9glQ7ZeJiYnax2QW3f+fhYUFdHV1kZycrLI8OTm5zFMW5HK5yvVEAGBmZvbMeSgUinr1P7Q24b5VD+5X9eB+VR9t2bempqaaTkFracuYXExb/p+pafWxX/WxTwD7VdfUx37Vxz4B/+uXusdkHbW2Xofo6+vDzc0N4eHh0rKioiKEh4fDw8NDg5kRERE9XzgmExFRfcIj3SUEBgbCx8cHnTt3RpcuXbBmzRpkZmZKM6cSERFR7eCYTERE9QWL7hJGjx6N+/fvY8GCBUhKSkKHDh0QGhoKKysrtW9bLpdj4cKFpU6Po2fHfase3K/qwf2qPty3dYsmx+Ri9fX/mfrYr/rYJ4D9qmvqY7/qY5+A2u+XTAjer4SIiIiIiIhIHXhNNxEREREREZGasOgmIiIiIiIiUhMW3URERERERERqwqKbiIiIiIiISE1YdGuJoKAgNG/eHAYGBnB3d8fJkyc1nVKdsmjRIshkMpWHo6OjtD4nJwf+/v5o3LgxGjZsCG9vbyQnJ2swY+105MgRDBkyBDY2NpDJZNi9e7fKeiEEFixYgKZNm8LQ0BCenp64evWqSszDhw8xduxYKBQKmJmZwc/PDxkZGbXYC+30tH07ceLEUv8PDxw4UCWG+7a0pUuX4qWXXoKJiQksLS0xbNgwJCQkqMRU5vOfmJgILy8vGBkZwdLSEnPmzEFBQUFtdoW0UF0amyvzWejTp0+pf2emTp2qEqNNn4WaGNu1qT/FmjdvXqpfMpkM/v7+AOrO+1Rb3xnOnz+Pnj17wsDAAM2aNcPy5cs11q/8/Hy89957cHFxgbGxMWxsbDBhwgTcuXNHpY2y3uNly5ZprF+19R1Em94rAGV+zmQyGVasWCHF1NZ7xaJbC2zfvh2BgYFYuHAhzpw5A1dXVyiVSty7d0/TqdUpbdu2xd27d6XH0aNHpXWzZs3CH3/8gZ07d+Lw4cO4c+cOhg8frsFstVNmZiZcXV0RFBRU5vrly5fjiy++QHBwME6cOAFjY2MolUrk5ORIMWPHjsWlS5cQFhaGPXv24MiRI3jzzTdrqwta62n7FgAGDhyo8v/wTz/9pLKe+7a0w4cPw9/fH8ePH0dYWBjy8/MxYMAAZGZmSjFP+/wXFhbCy8sLeXl5iI6OxubNmxESEoIFCxZookukJera2FyZzwIATJkyReXfmZJfHrXxs/AsY7s29gcATp06pdKnsLAwAMDIkSOlmLrwPtXGd4b09HQMGDAA9vb2iI2NxYoVK7Bo0SJs2LBBI/3KysrCmTNn8OGHH+LMmTPYtWsXEhIS8Oqrr5aKXbx4scp7OH36dI31qza+g2jbewVApT93797Fxo0bIZPJ4O3trRJXK++VII3r0qWL8Pf3l54XFhYKGxsbsXTpUg1mVbcsXLhQuLq6lrkuNTVV6OnpiZ07d0rLrly5IgCImJiYWsqw7gEgfv31V+l5UVGRsLa2FitWrJCWpaamCrlcLn766SchhBCXL18WAMSpU6ekmP379wuZTCZu375da7lruyf3rRBC+Pj4iKFDh5b7Gu7byrl3754AIA4fPiyEqNznf9++fUJHR0ckJSVJMevXrxcKhULk5ubWbgdIa9T1sfnJz4IQQvTu3VvMmDGj3Ndo22fhWcd2betPeWbMmCFatmwpioqKhBB1730SQn3fGb766ivRqFEjlX699957ok2bNmru0X/KGq+fdPLkSQFA3Lx5U1pmb28vVq9eXe5rNNkvdX0HqQvv1dChQ0W/fv1UltXWe8Uj3RqWl5eH2NhYeHp6Sst0dHTg6emJmJgYDWZW91y9ehU2NjZo0aIFxo4di8TERABAbGws8vPzVfaxo6Mj7OzsuI+r4Pr160hKSlLZj6ampnB3d5f2Y0xMDMzMzNC5c2cpxtPTEzo6Ojhx4kSt51zXREVFwdLSEm3atMG0adPw4MEDaR33beWkpaUBAMzNzQFU7vMfExMDFxcXWFlZSTFKpRLp6em4dOlSLWZP2qI+jM1PfhaKbd26FRYWFmjXrh3mzZuHrKwsaZ02fhaeZWzXxv48KS8vDz/88AMmTZoEmUwmLa9r79OTauo7Q0xMDHr16gV9fX0pRqlUIiEhAY8ePaql3lQsLS0NMpkMZmZmKsuXLVuGxo0bo2PHjlixYoXK6f/a2K9n/Q6ijX0qKTk5GXv37oWfn1+pdbXxXjV4tvTpWaWkpKCwsFDlH04AsLKyQnx8vIayqnvc3d0REhKCNm3a4O7du/joo4/Qs2dPXLx4EUlJSdDX1y/1j6GVlRWSkpI0k3AdVLyvyvp/tXhdUlISLC0tVdY3aNAA5ubm3NdPMXDgQAwfPhwODg64du0aPvjgAwwaNAgxMTHQ1dXlvq2EoqIizJw5E927d0e7du0AoFKf/6SkpDL/vy5eR8+fuj42l/VZAIA33ngD9vb2sLGxwfnz5/Hee+8hISEBu3btAqB9n4VnHdu1rT9l2b17N1JTUzFx4kRpWV17n8pSU98ZkpKS4ODgUKqN4nWNGjVSS/6VlZOTg/feew+vv/46FAqFtPydd95Bp06dYG5ujujoaMybNw93797FqlWrAGhfv2riO4i29elJmzdvhomJSanLS2vrvWLRTfXCoEGDpL/bt28Pd3d32NvbY8eOHTA0NNRgZkSVM2bMGOlvFxcXtG/fHi1btkRUVBT69++vwczqDn9/f1y8eFHlmk+i51F5n4WS11+6uLigadOm6N+/P65du4aWLVvWdppP9TyM7d999x0GDRoEGxsbaVlde5+eV/n5+Rg1ahSEEFi/fr3KusDAQOnv9u3bQ19fH2+99RaWLl0KuVxe26k+1fPwHWTjxo0YO3YsDAwMVJbX1nvF08s1zMLCArq6uqVm20xOToa1tbWGsqr7zMzM8OKLL+Lvv/+GtbU18vLykJqaqhLDfVw1xfuqov9Xra2tS00yVFBQgIcPH3JfV1GLFi1gYWGBv//+GwD37dMEBARgz549iIyMhK2trbS8Mp9/a2vrMv+/Ll5Hz5+6PDaX91koi7u7OwCo/DujzZ+Fqo7t2t6fmzdv4tChQ5g8eXKFcXXtfQJq7juDtva1uOC+efMmwsLCVI5yl8Xd3R0FBQW4ceMGAO3tV7HqfAfR5j79+eefSEhIeOpnDVDfe8WiW8P09fXh5uaG8PBwaVlRURHCw8Ph4eGhwczqtoyMDFy7dg1NmzaFm5sb9PT0VPZxQkICEhMTuY+rwMHBAdbW1ir7MT09HSdOnJD2o4eHB1JTUxEbGyvFREREoKioSPrSQJXz77//4sGDB2jatCkA7tvyCCEQEBCAX3/9FREREaVOAavM59/DwwMXLlxQ+UJR/CXK2dm5djpCWqUujs1P+yyUJS4uDgBU/p3R5s9CVcd2be/Ppk2bYGlpCS8vrwrj6tr7BNTcdwYPDw8cOXIE+fn5UkxYWBjatGmjsdOViwvuq1ev4tChQ2jcuPFTXxMXFwcdHR3pFG1t7FdJ1fkOos19+u677+Dm5gZXV9enxqrtvarStGukFtu2bRNyuVyEhISIy5cvizfffFOYmZmpzEpJFZs9e7aIiooS169fF8eOHROenp7CwsJC3Lt3TwghxNSpU4WdnZ2IiIgQp0+fFh4eHsLDw0PDWWufx48fi7Nnz4qzZ88KAGLVqlXi7Nmz0oycy5YtE2ZmZuK3334T58+fF0OHDhUODg4iOztbamPgwIGiY8eO4sSJE+Lo0aOidevW4vXXX9dUl7RGRfv28ePH4t133xUxMTHi+vXr4tChQ6JTp06idevWIicnR2qD+7a0adOmCVNTUxEVFSXu3r0rPbKysqSYp33+CwoKRLt27cSAAQNEXFycCA0NFU2aNBHz5s3TRJdIS9S1sflpn4W///5bLF68WJw+fVpcv35d/Pbbb6JFixaiV69eUhva9ll41rFd2/pTUmFhobCzsxPvvfeeyvK69D7VxneG1NRUYWVlJcaPHy8uXrwotm3bJoyMjMTXX3+tkX7l5eWJV199Vdja2oq4uDiVz1rx7NbR0dFi9erVIi4uTly7dk388MMPokmTJmLChAka61dtfAfRtveqWFpamjAyMhLr168v9frafK9YdGuJL7/8UtjZ2Ql9fX3RpUsXcfz4cU2nVKeMHj1aNG3aVOjr64sXXnhBjB49Wvz999/S+uzsbPH222+LRo0aCSMjI/Haa6+Ju3fvajBj7RQZGSkAlHr4+PgIIf67BciHH34orKyshFwuF/379xcJCQkqbTx48EC8/vrromHDhkKhUAhfX1/x+PFjDfRGu1S0b7OyssSAAQNEkyZNhJ6enrC3txdTpkwp9eWe+7a0svYpALFp0yYppjKf/xs3bohBgwYJQ0NDYWFhIWbPni3y8/NruTekberS2Py0z0JiYqLo1auXMDc3F3K5XLRq1UrMmTNHpKWlqbSjTZ+Fmhjbtak/JR04cEAAKDWG1qX3qba+M5w7d0706NFDyOVy8cILL4hly5ZprF/Xr18v97MWGRkphBAiNjZWuLu7C1NTU2FgYCCcnJzEp59+qlLA1na/aus7iDa9V8W+/vprYWhoKFJTU0u9vjbfK5kQQlT+uDgRERERERERVRav6SYiIiIiIiJSExbdRERERERERGrCopuIiIiIiIhITVh0ExEREREREakJi24iIiIiIiIiNWHRTURERERERKQmLLqJiIiIiIiI1IRFNxEREREREZGasOgmeg7duHEDMpkMcXFxmk5FEh8fj65du8LAwAAdOnTQdDpl6tOnD2bOnKnpNIiISIvU9zFVJpNh9+7dNZIX0fOKRTeRBkycOBEymQzLli1TWb57927IZDINZaVZCxcuhLGxMRISEhAeHl5qfXBwMExMTFBQUCAty8jIgJ6eHvr06aMSGxUVBZlMhmvXrqk7bSIi0jCOqaU9bUwtlpSUhOnTp6NFixaQy+Vo1qwZhgwZUuFrtMnEiRMxbNgwTadB9FQsuok0xMDAAJ999hkePXqk6VRqTF5eXrVfe+3aNfTo0QP29vZo3LhxqfV9+/ZFRkYGTp8+LS37888/YW1tjRMnTiAnJ0daHhkZCTs7O7Rs2bLKeQghVAp7IiLSfhxTVT1tTAX+O0Lv5uaGiIgIrFixAhcuXEBoaCj69u0Lf3//am+7Mp6lb+qgbflQ/cOim0hDPD09YW1tjaVLl5Ybs2jRolKnha1ZswbNmzeXnhf/yvvpp5/CysoKZmZmWLx4MQoKCjBnzhyYm5vD1tYWmzZtKtV+fHw8unXrBgMDA7Rr1w6HDx9WWX/x4kUMGjQIDRs2hJWVFcaPH4+UlBRpfZ8+fRAQEICZM2fCwsICSqWyzH4UFRVh8eLFsLW1hVwuR4cOHRAaGiqtl8lkiI2NxeLFiyGTybBo0aJSbbRp0wZNmzZFVFSUtCwqKgpDhw6Fg4MDjh8/rrK8b9++AIDc3Fy88847sLS0hIGBAXr06IFTp06pxMpkMuzfvx9ubm6Qy+U4evQoMjMzMWHCBDRs2BBNmzbFypUrS+X01VdfoXXr1jAwMICVlRVGjBhRZv+JiEi9OKZWbUwFgLfffhsymQwnT56Et7c3XnzxRbRt2xaBgYEqYyoApKSk4LXXXoORkRFat26N33//XVpXWFgIPz8/ODg4wNDQEG3atMHatWtVXl+8Xz/55BPY2NigTZs2AIAtW7agc+fOMDExgbW1Nd544w3cu3dP5bWXLl3CK6+8AoVCARMTE/Ts2RPXrl3DokWLsHnzZvz222+QyWSQyWTSd4Rbt25h1KhRMDMzg7m5OYYOHYobN248NR+O66QuLLqJNERXVxeffvopvvzyS/z777/P1FZERATu3LmDI0eOYNWqVVi4cCFeeeUVNGrUCCdOnMDUqVPx1ltvldrOnDlzMHv2bJw9exYeHh4YMmQIHjx4AABITU1Fv3790LFjR5w+fRqhoaFITk7GqFGjVNrYvHkz9PX1cezYMQQHB5eZ39q1a7Fy5Up8/vnnOH/+PJRKJV599VVcvXoVAHD37l20bdsWs2fPxt27d/Huu++W2U7fvn0RGRkpPY+MjESfPn3Qu3dvaXl2djZOnDghFd1z587FL7/8gs2bN+PMmTNo1aoVlEolHj58qNL2+++/j2XLluHKlSto37495syZg8OHD+O3337DwYMHERUVhTNnzkjxp0+fxjvvvIPFixcjISEBoaGh6NWr11PfKyIiqnkcU6s2pj58+BChoaHw9/eHsbFxqfVmZmYqzz/66COMGjUK58+fx+DBgzF27FhpHC0qKoKtrS127tyJy5cvY8GCBfjggw+wY8cOlTbCw8ORkJCAsLAw7NmzBwCQn5+PJUuW4Ny5c9i9ezdu3LiBiRMnSq+5ffs2evXqBblcjoiICMTGxmLSpEkoKCjAu+++i1GjRmHgwIG4e/cu7t69i27duiE/Px9KpRImJib4888/cezYMTRs2BADBw5UOaL9ZD4c10mtBBHVOh8fHzF06FAhhBBdu3YVkyZNEkII8euvv4qSH8uFCxcKV1dXldeuXr1a2Nvbq7Rlb28vCgsLpWVt2rQRPXv2lJ4XFBQIY2Nj8dNPPwkhhLh+/boAIJYtWybF5OfnC1tbW/HZZ58JIYRYsmSJGDBggMq2b926JQCIhIQEIYQQvXv3Fh07dnxqf21sbMQnn3yisuyll14Sb7/9tvTc1dVVLFy4sMJ2vvnmG2FsbCzy8/NFenq6aNCggbh375748ccfRa9evYQQQoSHhwsA4ubNmyIjI0Po6emJrVu3Sm3k5eUJGxsbsXz5ciGEEJGRkQKA2L17txTz+PFjoa+vL3bs2CEte/DggTA0NBQzZswQQgjxyy+/CIVCIdLT05/afyIiUh+OqVUfU0+cOCEAiF27dj11ewDE/PnzpecZGRkCgNi/f3+5r/H39xfe3t7Scx8fH2FlZSVyc3Mr3NapU6cEAPH48WMhhBDz5s0TDg4OIi8vr8z4ku99sS1btog2bdqIoqIiaVlubq4wNDQUBw4cKDcfjuukTjzSTaRhn332GTZv3owrV65Uu422bdtCR+d/H2crKyu4uLhIz3V1ddG4ceNSp2x5eHhIfzdo0ACdO3eW8jh37hwiIyPRsGFD6eHo6AgAKhOUubm5VZhbeno67ty5g+7du6ss7969e5X73KdPH2RmZuLUqVP4888/8eKLL6JJkybo3bu3dF13VFQUWrRoATs7O1y7dg35+fkq29bT00OXLl1Kbbtz587S39euXUNeXh7c3d2lZebm5tLpZwDw8ssvw97eHi1atMD48eOxdetWZGVlVak/RERUszimVo4QotKxANC+fXvpb2NjYygUCpX+BwUFwc3NDU2aNEHDhg2xYcMGJCYmqrTh4uICfX19lWWxsbEYMmQI7OzsYGJigt69ewOA9Nq4uDj07NkTenp6lc713Llz+Pvvv2FiYiLta3Nzc+Tk5Kjs6yfz4bhO6sSim0jDevXqBaVSiXnz5pVap6OjU2pgzM/PLxX35GAkk8nKXFZUVFTpvDIyMjBkyBDExcWpPK5evapyulVZp6WpS6tWrWBra4vIyEhERkZKg7ONjQ2aNWuG6OhoREZGol+/flVuu6r9MDExwZkzZ/DTTz+hadOmWLBgAVxdXZGamlrlbRMRUc3gmFo5rVu3hkwmQ3x8fKXiK+r/tm3b8O6778LPzw8HDx5EXFwcfH19S01O9mTfMjMzoVQqoVAosHXrVpw6dQq//vorgP9NbGZoaFjlvmVkZMDNza3Uvv7rr7/wxhtvlJsPx3VSJxbdRFpg2bJl+OOPPxATE6OyvEmTJkhKSlL5klCT9wEtOVFKQUEBYmNj4eTkBADo1KkTLl26hObNm6NVq1Yqj6p8KVAoFLCxscGxY8dUlh87dgzOzs5Vzrlv376IiopCVFSUyq3CevXqhf379+PkyZPS9dwtW7aUro0rlp+fj1OnTlW47ZYtW0JPTw8nTpyQlj169Ah//fWXSlyDBg3g6emJ5cuX4/z587hx4wYiIiKq3CciIqo5HFOfztzcHEqlEkFBQcjMzCy1viqF5rFjx9CtWze8/fbb6NixI1q1alWpW3bGx8fjwYMHWLZsGXr27AlHR8dSZw+0b98ef/75Z5k/jgCAvr4+CgsLVZZ16tQJV69ehaWlZal9bWpqWmFOHNdJXVh0E2kBFxcXjB07Fl988YXK8j59+uD+/ftYvnw5rl27hqCgIOzfv7/GthsUFIRff/0V8fHx8Pf3x6NHjzBp0iQAgL+/Px4+fIjXX38dp06dwrVr13DgwAH4+vqWGuCeZs6cOfjss8+wfft2JCQk4P3330dcXBxmzJhR5Zz79u2Lo0ePIi4uTjrSDQC9e/fG119/jby8PKnoNjY2xrRp0zBnzhyEhobi8uXLmDJlCrKysuDn51fuNho2bAg/Pz/MmTMHERERuHjxIiZOnKhyuuGePXvwxRdfIC4uDjdv3sT333+PoqIilVPQiYio9nFMrXy+hYWF6NKlC3755RdcvXoVV65cwRdffKFyqvzTtG7dGqdPn8aBAwfw119/4cMPP1S5S0h57OzsoK+vjy+//BL//PMPfv/9dyxZskQlJiAgAOnp6RgzZgxOnz6Nq1evYsuWLUhISAAANG/eHOfPn0dCQgJSUlKQn5+PsWPHwsLCAkOHDsWff/6J69evIyoqCu+8806Fk+xxXCd1YtFNpCUWL15c6lQ1JycnfPXVVwgKCoKrqytOnjxZ7sze1bFs2TIsW7YMrq6uOHr0KH7//XdYWFgAgPRLemFhIQYMGAAXFxfMnDkTZmZmKsVnZbzzzjsIDAzE7Nmz4eLigtDQUPz+++9o3bp1lXPu27cvsrOz0apVK1hZWUnLe/fujcePH0u3FivZR29vb4wfPx6dOnXC33//jQMHDqBRo0YVbmfFihXo2bMnhgwZAk9PT/To0UPlWjszMzPs2rUL/fr1g5OTE4KDg/HTTz+hbdu2Ve4TERHVLI6pT9eiRQucOXMGffv2xezZs9GuXTu8/PLLCA8Px/r16yvdzltvvYXhw4dj9OjRcHd3x4MHD/D2228/9XVNmjRBSEgIdu7cCWdnZyxbtgyff/65Skzjxo0RERGBjIwM9O7dG25ubvjmm2+k092nTJmCNm3aoHPnzmjSpAmOHTsGIyMjHDlyBHZ2dhg+fDicnJzg5+eHnJwcKBSKcvPhuE7qJBNVnUmBiIiIiIiIiCqFR7qJiIiIiIiI1IRFNxEREREREZGasOgmIiIiIiIiUhMW3URERERERERqwqKbiIiIiIiISE1YdBMRERERERGpCYtuIiIiIiIiIjVh0U1ERERERESkJiy6iYiIiIiIiNSERTcRERERERGRmrDoJiIiIiIiIlITFt1EREREREREasKim4iIiIiIiEhNWHQTERERERERqQmLbiIiIiIiIiI1YdFNREREREREpCYsuom0yMSJE9G8eXNNp1Fjbty4AZlMhs8//1zTqRAREWlE8+bNMXHiRE2nUaaCggLMnTsXzZo1g46ODoYNG6bplIjqJRbdRFW0aNEiyGQypKSklLm+Xbt26NOnT+0mpQFxcXEYN24cmjVrBrlcDnNzc3h6emLTpk0oLCzUdHrP7PLly1i0aBFu3Lih6VSIiEgLXbhwASNGjIC9vT0MDAzwwgsv4OWXX8aXX36p6dQqbePGjVixYgVGjBiBzZs3Y9asWaViQkJCIJPJnvqoyYMG+/btw6JFi2qsPSJNa6DpBIjof7755hsUFRVpOo2n+vbbbzF16lRYWVlh/PjxaN26NR4/fozw8HD4+fnh7t27+OCDDzSd5jO5fPkyPvroI/Tp06denX1ARETPLjo6Gn379oWdnR2mTJkCa2tr3Lp1C8ePH8fatWsxffp0KTYhIQE6Otp5nCsiIgIvvPACVq9eXW5Mr169sGXLFpVlkydPRpcuXfDmm29Kyxo2bFhjee3btw9BQUEsvKneYNFNpEX09PQ0ncJTHT9+HFOnToWHhwf27dsHExMTad3MmTNx+vRpXLx4sVZzyszMhLGxca1us7rqUq5ERFS2Tz75BKampjh16hTMzMxU1t27d0/luVwur8XMqubevXul8n9SixYt0KJFC5VlU6dORYsWLTBu3Dg1ZkdUf2jnz25E9UhUVBRkMhl27NiBTz75BLa2tjAwMED//v3x999/q8SWdU13amoqJk6cCFNTU5iZmcHHxwdxcXGQyWQICQmR4vr06VPmae1ltVlUVIQ1a9agbdu2MDAwgJWVFd566y08evToqf356KOPIJPJsHXrVpWCu1jnzp3LvHZtw4YNaNmyJeRyOV566SWcOnVKZf358+cxceJEtGjRAgYGBrC2tsakSZPw4MEDlbji0/svX76MN954A40aNUKPHj2q1AYA3L59G35+frCxsYFcLoeDgwOmTZuGvLw8hISEYOTIkQCAvn37SqfORUVFSa/fv38/evbsCWNjY5iYmMDLywuXLl1S2cbEiRPRsGFDXLt2DYMHD4aJiQnGjh0LALh69Sq8vb1hbW0NAwMD2NraYsyYMUhLS3vqe0BERJp17do1tG3btsyC1dLSUuX5k9d0V3SKdslLmuLj4zFixAiYm5vDwMAAnTt3xu+//16p/DIzMzF79mzpErA2bdrg888/hxACwP/mXImMjMSlS5fKHOeq6vbt25g0aRKsrKwgl8vRtm1bbNy4UVqfnZ0NR0dHODo6Ijs7W1r+8OFDNG3aFN26dUNhYSEmTpyIoKCgUvuKqC7jkW6iWrJs2TLo6Ojg3XffRVpaGpYvX46xY8fixIkT5b5GCIGhQ4fi6NGjmDp1KpycnPDrr7/Cx8fnmXJ56623EBISAl9fX7zzzju4fv061q1bh7Nnz+LYsWPlHnHPyspCeHg4evXqBTs7u0pv78cff8Tjx4/x1ltvQSaTYfny5Rg+fDj++ecfaVthYWH4559/4OvrC2tra1y6dAkbNmzApUuXcPz48VID7siRI9G6dWt8+umn0peIyrZx584ddOnSBampqXjzzTfh6OiI27dv4+eff0ZWVhZ69eqFd955B1988QU++OADODk5AYD03y1btsDHxwdKpRKfffYZsrKysH79evTo0QNnz55V+ZGjoKAASqUSPXr0wOeffw4jIyPk5eVBqVQiNzcX06dPh7W1NW7fvo09e/YgNTUVpqamld63RERU++zt7RETE4OLFy+iXbt2VXrtk6dqA8D8+fNx79496RTtS5cuoXv37njhhRfw/vvvw9jYGDt27MCwYcPwyy+/4LXXXiu3fSEEXn31VURGRsLPzw8dOnTAgQMHMGfOHNy+fRurV69GkyZNsGXLFnzyySfIyMjA0qVLAfxvnKuq5ORkdO3aFTKZDAEBAWjSpAn2798PPz8/pKenY+bMmTA0NMTmzZvRvXt3/N///R9WrVoFAPD390daWhpCQkKgq6uLt956C3fu3EFYWFiZ+4qoThJEVCULFy4UAMT9+/fLXN+2bVvRu3dv6XlkZKQAIJycnERubq60fO3atQKAuHDhgrTMx8dH2NvbS893794tAIjly5dLywoKCkTPnj0FALFp0yZpee/evVW2W16bf/75pwAgtm7dqhIXGhpa5vKSzp07JwCIGTNmlBtT0vXr1wUA0bhxY/Hw4UNp+W+//SYAiD/++ENalpWVVer1P/30kwAgjhw5Ii0r3v+vv/56qfjKtjFhwgSho6MjTp06VSq+qKhICCHEzp07BQARGRmpsv7x48fCzMxMTJkyRWV5UlKSMDU1VVnu4+MjAIj3339fJfbs2bMCgNi5c2ep7RMRkfY7ePCg0NXVFbq6usLDw0PMnTtXHDhwQOTl5ZWKtbe3Fz4+PuW2tXz5cgFAfP/999Ky/v37CxcXF5GTkyMtKyoqEt26dROtW7euMLfi7w4ff/yxyvIRI0YImUwm/v77b2lZ7969Rdu2bZ/W3VKMjY1V+uTn5yeaNm0qUlJSVOLGjBkjTE1NVcbnefPmCR0dHXHkyBFprF2zZo3K6/z9/QXLFKpPeHo5US3x9fWFvr6+9Lxnz54AgH/++afc1+zbtw8NGjTAtGnTpGW6uroqE7RU1c6dO2FqaoqXX34ZKSkp0sPNzQ0NGzZEZGRkua9NT08HgDJPK6/I6NGj0ahRI+l5WX03NDSU/s7JyUFKSgq6du0KADhz5kypNqdOnVpqWWXaKCoqwu7duzFkyBB07ty5VBtPO4UtLCwMqampeP3111X2n66uLtzd3cvcfyXfPwDSkewDBw4gKyurwu0REZH2efnllxETE4NXX30V586dw/Lly6FUKvHCCy9U+hRwAIiMjMS8efMwffp0jB8/HsB/p1tHRERg1KhRePz4sTTOPHjwAEqlElevXsXt27fLbXPfvn3Q1dXFO++8o7J89uzZEEJg//791et0OYQQ+OWXXzBkyBAIIVTGRqVSibS0NJVxfNGiRWjbti18fHzw9ttvo3fv3qVyJapveHo5kRqUVbg9eTp2cRFa0XXUN2/eRNOmTUvNCNqmTZtq53b16lWkpaWVuuas2JMTwJSkUCgAAI8fP67SNivT94cPH+Kjjz7Ctm3bSuVQ1nXODg4OpZZVpo379+8jPT29yqcDFrt69SoAoF+/fmWuL95HxRo0aABbW9tSuQcGBmLVqlXYunUrevbsiVdffRXjxo3jqeVERHXESy+9hF27diEvLw/nzp3Dr7/+itWrV2PEiBGIi4uDs7Nzha//999/MXr0aHTv3l061RoA/v77bwgh8OGHH+LDDz8s87X37t3DCy+8UOa6mzdvwsbGptQP5MWnjt+8ebMq3Xyq+/fvIzU1FRs2bMCGDRvKzbeYvr4+Nm7ciJdeegkGBgbYtGkTr9mmeo9FN1EVGRgYAIDKJCAlZWVlSTEl6erqlhkv/v/1yM9KJpOV2daT98wuKiqCpaUltm7dWmY7TZo0KXcbrVq1QoMGDXDhwoUq5VaZvo8aNQrR0dGYM2cOOnTogIYNG6KoqAgDBw4s8zZqJY9qV7eN6ihuZ8uWLbC2ti61vkED1X9W5XJ5mbeKWblyJSZOnIjffvsNBw8exDvvvIOlS5fi+PHjpYp0IiLSXvr6+njppZfw0ksv4cUXX4Svry927tyJhQsXlvuavLw8jBgxAnK5HDt27FAZO4rHmXfffRdKpbLM17dq1apmO/EMivMdN25cuXPOtG/fXuX5gQMHAPx3VtrVq1fL/CGdqD5h0U1URfb29gD+u+9ms2bNVNZlZWXh1q1bGDBgQI1tKzw8HBkZGSpHuxMSEkrFNmrUqMxT1Z/8Rbtly5Y4dOgQunfvXmbhWhEjIyP069cPERERuHXrVqn+V9ejR48QHh6Ojz76CAsWLJCWFx9Vrsk2mjRpAoVC8dTbmpX3q3vLli0B/Dc7raenZ6XzK4uLiwtcXFwwf/58REdHo3v37ggODsbHH3/8TO0SEZFmFF+2dPfu3Qrj3nnnHcTFxeHIkSOwsrJSWVd8ey49Pb1qjTP29vY4dOgQHj9+rHK0Oz4+Xlpfk5o0aQITExMUFhZWKt/z589j8eLF8PX1RVxcHCZPnowLFy6onOnFI99U3/CabqIq6t+/P/T19bF+/fpSR083bNiAgoICDBo0qEa2NXjwYBQUFGD9+vXSssLCQnz55ZelYlu2bIn4+Hjcv39fWnbu3DkcO3ZMJW7UqFEoLCzEkiVLSrVRUFCA1NTUCnNauHAhhBAYP348MjIySq2PjY3F5s2bn9Y1FcVHwp88Ur9mzZoab0NHRwfDhg3DH3/8gdOnT5dqp/j1xffSfnJ/KJVKKBQKfPrpp8jPzy/1+pL7vzzp6ekoKChQWebi4gIdHR3k5uY+9fVERKRZkZGRZZ5dtm/fPgAVXwa2adMmfP311wgKCkKXLl1Krbe0tESfPn3w9ddfl1m8P22cGTx4MAoLC7Fu3TqV5atXr4ZMJqux7yjFdHV14e3tjV9++aXMH7RL5pufn4+JEyfCxsYGa9euRUhICJKTkzFr1iyV15Q3BhPVVTzSTVRFlpaWWLBgAebPn49evXrh1VdfhZGREaKjo/HTTz9hwIABGDJkSI1sa8iQIejevTvef/993LhxA87Ozti1a1eZ1zhPmjQJq1atglKphJ+fH+7du4fg4GC0bdtWmgANAHr37o233noLS5cuRVxcHAYMGAA9PT1cvXoVO3fuxNq1azFixIhyc+rWrRuCgoLw9ttvw9HREePHj0fr1q3x+PFjREVF4ffff6/ykVqFQoFevXph+fLlyM/PxwsvvICDBw/i+vXramnj008/xcGDB9G7d2+8+eabcHJywt27d7Fz504cPXoUZmZm6NChA3R1dfHZZ58hLS0Ncrkc/fr1g6WlJdavX4/x48ejU6dOGDNmDJo0aYLExETs3bsX3bt3L/VF50kREREICAjAyJEj8eKLL6KgoABbtmyRvrgQEZF2mz59OrKysvDaa6/B0dEReXl5iI6Oxvbt29G8eXP4+vqW+bqUlBS8/fbbcHZ2hlwuxw8//KCy/rXXXoOxsTGCgoLQo0cPuLi4YMqUKWjRogWSk5MRExODf//9F+fOnSs3tyFDhqBv3774v//7P9y4cQOurq44ePAgfvvtN8ycOVM6Y6smLVu2DJGRkXB3d8eUKVPg7OyMhw8f4syZMzh06BAePnwIAPj4448RFxeH8PBwmJiYoH379tJ3qhEjRmDw4MEAADc3NwD/nRGgVCqhq6uLMWPG1HjeRLVGM5OmE9V9P/zwg+jataswNjYWcrlcODo6io8++kjl9h5C/O+WYU/eHqr4dlolb/v15O29hBDiwYMHYvz48UKhUAhTU1Mxfvx46ZZTJV9bnFOLFi2Evr6+6NChgzhw4ECZbQohxIYNG4Sbm5swNDQUJiYmwsXFRcydO1fcuXOnUv2PjY0Vb7zxhrCxsRF6enqiUaNGon///mLz5s2isLBQpY8rVqwo9XoAYuHChdLzf//9V7z22mvCzMxMmJqaipEjR4o7d+6Uiqvolm2VbUMIIW7evCkmTJggmjRpIuRyuWjRooXw9/dXua3bN998I1q0aCF0dXVL3T4sMjJSKJVKYWpqKgwMDETLli3FxIkTxenTp6UYHx8fYWxsXCrPf/75R0yaNEm0bNlSGBgYCHNzc9G3b19x6NChp+12IiLSAvv37xeTJk0Sjo6OomHDhkJfX1+0atVKTJ8+XSQnJ6vElrxlWPG4WN7j+vXr0uuuXbsmJkyYIKytrYWenp544YUXxCuvvCJ+/vnnp+b3+PFjMWvWLGmMbt26tVixYoV0W8xiNXXLMCGESE5OFv7+/qJZs2ZCT09PWFtbi/79+4sNGzYIIf773tCgQQMxffp0ldcVFBSIl156SdjY2IhHjx5Jy6ZPny6aNGkiZDIZbx9GdZ5MiBqaxYmIas2NGzfg4OCATZs2YeLEiZpOh4iIiIiIysFruomIiIiIiIjUhEU3ERERERERkZqw6CYiIiIiIiJSE17TTURERERERKQmPNJNREREREREpCYsuomIiIiIiIjUhEU3ERERERERkZo00HQC9UVRURHu3LkDExMTyGQyTadDRERaTAiBx48fw8bGBjo6/P27pnFMJiKiyqqNMZlFdw25c+cOmjVrpuk0iIioDrl16xZsbW01nUa9wzGZiIiqSp1jMovuGmJiYgLgvzdLoVBoOBsiItJm6enpaNasmTR2UM3imExERJVVG2Myi+4aUnz6mkKh4ABPRESVwlOf1YNjMhERVZU6x2ReSEZERERERESkJiy6iYiIiIiIiNSERTcRERERERGRmrDoJiIiIiIiIlITFt1EREREREREasKim4iIiIiIiEhNeMswoudMYmIiUlJSAAAWFhaws7PTcEZERPVDyX9fAf4bS0RE/2HRTfQcSUxMhKOTI7KzsgEAhkaGiL8SX+NfCvnFk4ieN4mJiXByaoOsrBxpmZGRAa5cSeC/f0REzzkW3UTPkZSUFGRnZWPc1+MAAD+89QNSUlKkL4Q1USw/WdgD6ivuiYi0RUpKCrKycvDDcsCpBXDlH2Dc3ByVf2OJiOj5xKKb6Dlk9aJVqWU1VSyXLOytXrRC8l/JpYp7IqL6yqkF0KmtprMgIiJtwqKbiAA8W7Fc8gj5lStXAPxX2Ddzbab2vImIiIiItBmLbiJSUdViuawj5ERERERE9B8W3UT0TJ48Qn750GXs/2S/ptMiIiIiItIKGr1P95EjRzBkyBDY2NhAJpNh9+7d0rr8/Hy89957cHFxgbGxMWxsbDBhwgTcuXNHpY2HDx9i7NixUCgUMDMzg5+fHzIyMlRizp8/j549e8LAwADNmjXD8uXLS+Wyc+dOODo6wsDAAC4uLti3b59a+kxUXxUfIW9s11jTqRARERERaQ2NFt2ZmZlwdXVFUFBQqXVZWVk4c+YMPvzwQ5w5cwa7du1CQkICXn31VZW4sWPH4tKlSwgLC8OePXtw5MgRvPnmm9L69PR0DBgwAPb29oiNjcWKFSuwaNEibNiwQYqJjo7G66+/Dj8/P5w9exbDhg3DsGHDcPHiRfV1noiIiIiIiOo9jZ5ePmjQIAwaNKjMdaampggLC1NZtm7dOnTp0gWJiYmws7PDlStXEBoailOnTqFz584AgC+//BKDBw/G559/DhsbG2zduhV5eXnYuHEj9PX10bZtW8TFxWHVqlVScb527VoMHDgQc+bMAQAsWbIEYWFhWLduHYKDg9W4B4iIiIiIiKg+0+iR7qpKS0uDTCaDmZkZACAmJgZmZmZSwQ0Anp6e0NHRwYkTJ6SYXr16QV9fX4pRKpVISEjAo0ePpBhPT0+VbSmVSsTExKi5R0RERERERFSf1ZmJ1HJycvDee+/h9ddfh0KhAAAkJSXB0tJSJa5BgwYwNzdHUlKSFOPg4KASY2VlJa1r1KgRkpKSpGUlY4rbKEtubi5yc3Ol5+np6dXvHBEREREREdVLdaLozs/Px6hRoyCEwPr16zWdDgBg6dKl+OijjzSdBlGdU/Ke3gBgYWHx1PuAExERERHVVVpfdBcX3Ddv3kRERIR0lBsArK2tce/ePZX4goICPHz4ENbW1lJMcnKySkzx86fFFK8vy7x58xAYGCg9T09PR7Nmlb+3MdHzqKx7ehsaGSL+SjwLbyIiIiKql7T6mu7igvvq1as4dOgQGjdWvRWRh4cHUlNTERsbKy2LiIhAUVER3N3dpZgjR44gPz9figkLC0ObNm3QqFEjKSY8PFyl7bCwMHh4eJSbm1wuh0KhUHkQUcVK3tN7duRsjPt6HLKzslWOfBOR5i1btgwymQwzZ86UluXk5MDf3x+NGzdGw4YN4e3tXeoH68TERHh5ecHIyAiWlpaYM2cOCgoKVGKioqLQqVMnyOVytGrVCiEhIaW2HxQUhObNm8PAwADu7u44efKkOrpJRERUKzRadGdkZCAuLg5xcXEAgOvXryMuLg6JiYnIz8/HiBEjcPr0aWzduhWFhYVISkpCUlIS8vLyAABOTk4YOHAgpkyZgpMnT+LYsWMICAjAmDFjYGNjAwB44403oK+vDz8/P1y6dAnbt2/H2rVrVY5Sz5gxA6GhoVi5ciXi4+OxaNEinD59GgEBAbW+T4ieB8X39LZ60erpwURUq06dOoWvv/4a7du3V1k+a9Ys/PHHH9i5cycOHz6MO3fuYPjw4dL6wsJCeHl5IS8vD9HR0di8eTNCQkKwYMECKeb69evw8vJC3759ERcXh5kzZ2Ly5Mk4cOCAFLN9+3YEBgZi4cKFOHPmDFxdXaFUKkud2UZERFRXaLToPn36NDp27IiOHTsCAAIDA9GxY0csWLAAt2/fxu+//45///0XHTp0QNOmTaVHdHS01MbWrVvh6OiI/v37Y/DgwejRo4fKPbhNTU1x8OBBXL9+HW5ubpg9ezYWLFigci/vbt264ccff8SGDRvg6uqKn3/+Gbt370a7du1qb2cQERFpWEZGBsaOHYtvvvlGOhsM+O/uId999x1WrVqFfv36wc3NDZs2bUJ0dDSOHz8OADh48CAuX76MH374AR06dMCgQYOwZMkSBAUFST+WBwcHw8HBAStXroSTkxMCAgIwYsQIrF69WtrWqlWrMGXKFPj6+sLZ2RnBwcEwMjLCxo0ba3dnEBER1RCNXtPdp08fCCHKXV/RumLm5ub48ccfK4xp3749/vzzzwpjRo4ciZEjRz51e0RERPWVv78/vLy84OnpiY8//lhaHhsbi/z8fJXbazo6OsLOzg4xMTHo2rUrYmJi4OLionI3EKVSiWnTpuHSpUvo2LFjubfoLD6NPS8vD7GxsZg3b560XkdHB56enhXexpN3FCEiIm2m9ROpEZF24KzjRPXbtm3bcObMGZw6darUuqSkJOjr68PMzExlecnba5Z3+83idRXFpKenIzs7G48ePUJhYWGZMfHx8eXmzjuKEBGRNmPRTURPVdGs40RU9926dQszZsxAWFgYDAwMNJ1OlfGOIkREpM1YdBPRU5WcddzqRSsk/5WMH976gbOOE9UTsbGxuHfvHjp16iQtKywsxJEjR7Bu3TocOHAAeXl5SE1NVTnaXfL2mtbW1qVmGa/sLToVCgUMDQ2hq6sLXV3dKt/GUy6XQy6XV73jREREtUCrbxlGRNqFs44T1U/9+/fHhQsXpDuKxMXFoXPnzhg7dqz0t56ensrtNRMSEpCYmCjdXtPDwwMXLlxQmWU8LCwMCoUCzs7OUkxFt+jU19eHm5ubSkxRURHCw8MrvI0nERGRNuORbiIioueciYlJqTt2GBsbo3HjxtJyPz8/BAYGwtzcHAqFAtOnT4eHhwe6du0KABgwYACcnZ0xfvx4LF++HElJSZg/fz78/f2lo9BTp07FunXrMHfuXEyaNAkRERHYsWMH9u7dK203MDAQPj4+6Ny5M7p06YI1a9YgMzMTvr6+tbQ3iIiIahaLbiIiInqq1atXQ0dHB97e3sjNzYVSqcRXX30lrdfV1cWePXswbdo0eHh4wNjYGD4+Pli8eLEU4+DggL1792LWrFlYu3YtbG1t8e2330KpVEoxo0ePxv3797FgwQIkJSWhQ4cOCA0NLTW5GhERUV3BopuIiIhKiYqKUnluYGCAoKAgBAUFlfsae3t77Nu3r8J2+/Tpg7Nnz1YYExAQgICAgErnSkREpM14TTcRERERERGRmrDoJiIiIiIiIvp/7d15XFTV/z/w14AOi7KIyACJiCmIGyomkksuKC6Zlp8+maCmqGmQW6nR19Q0QzFxi6RFQVPT7JNWmijhrrihuCIuoWPJQKiA7Nv5/eFvbgybgsAM8Ho+Hvchc8+Ze9/nXuTMe86951YTJt1ERERERERE1YRJNxEREREREVE1YdJNREREREREVE04ezkRERFRJSiVSiQnJwMAYmNjtRwNERHpKibdRERERBWkVCrh7OyEzMxsbYdCREQ6jpeXExEREVVQcnIyMjOzsSUQiP4JWDJd2xEREZGuYtJNREREVEnOrYCu7QGH5tqOhIiIdBWTbiIiIiIiIqJqwqSbiIiIiIiIqJow6SYiIiIiIiKqJky6iYiIiIiIiKoJk24iIiIiIiKiaqLVpPvo0aMYPnw4bG1tIZPJsHv3bo1yIQQWLFgAGxsbGBkZwcPDAzdv3tSo8/DhQ3h5ecHU1BTm5ubw8fFBenq6Rp1Lly6hd+/eMDQ0hJ2dHQIDA0vEsnPnTrRt2xaGhobo2LEjfv/99ypvLxEREREREdUvWk26MzIy4OLiguDg4FLLAwMDsXbtWoSEhOD06dNo1KgRPD09kZ2dLdXx8vLC1atXERERgT179uDo0aOYMmWKVJ6WloZBgwbB3t4e0dHRWLFiBRYtWoRvvvlGqnPy5Em8/fbb8PHxwYULFzBy5EiMHDkSV65cqb7GExERERERUZ3XQJs7HzJkCIYMGVJqmRACq1evxvz58zFixAgAwObNm6FQKLB7926MHj0asbGxCA8Px9mzZ9GtWzcAwLp16zB06FB88cUXsLW1xdatW5Gbm4uNGzdCLpejffv2iImJQVBQkJScr1mzBoMHD8acOXMAAEuWLEFERAS+/PJLhISE1MCRIKo+SqUSycnJAIDY2FgtR0NEREREVL/o7D3d8fHxUKlU8PDwkNaZmZnBzc0NUVFRAICoqCiYm5tLCTcAeHh4QE9PD6dPn5bq9OnTB3K5XKrj6emJuLg4PHr0SKpTdD/qOur9lCYnJwdpaWkaC5GuUSqVaOvcFq6urnB1dYW3t7e2QyIiIiIiqle0OtJdHpVKBQBQKBQa6xUKhVSmUqlgZWWlUd6gQQNYWFho1HFwcCixDXVZkyZNoFKpyt1PaQICAvDpp59WomVENSc5ORlZmVnw/tobCkcFrv1xDfuW7tN2WGUqOioPAJaWlmjRooUWIyIiIiIiej46m3TrOn9/f8yePVt6nZaWBjs7Oy1GRFQ2haMCdi52SLyRqO1QyqQelc/KzJLWGRkb4XrsdSbeRERERFRr6WzSbW1tDQBITEyEjY2NtD4xMRGdO3eW6iQlJWm8Lz8/Hw8fPpTeb21tjcREzURD/fppddTlpTEwMICBgUElWkZEpSk+Kp94IxFb3t2C5ORkJt1EREREVGvp7D3dDg4OsLa2RmRkpLQuLS0Np0+fhru7OwDA3d0dKSkpiI6OluocPHgQhYWFcHNzk+ocPXoUeXl5Up2IiAg4OTmhSZMmUp2i+1HXUe+HiGqOelRe4ah4emUiIiIiIh2n1aQ7PT0dMTExiImJAfBk8rSYmBgolUrIZDLMnDkTn332GX799VdcvnwZ48aNg62tLUaOHAkAcHZ2xuDBgzF58mScOXMGJ06cgJ+fH0aPHg1bW1sAwJgxYyCXy+Hj44OrV69ix44dWLNmjcal4TNmzEB4eDhWrlyJ69evY9GiRTh37hz8/Pxq+pAQERERERFRHaLVy8vPnTuHfv36Sa/VifD48eMRFhaGuXPnIiMjA1OmTEFKSgp69eqF8PBwGBoaSu/ZunUr/Pz8MGDAAOjp6WHUqFFYu3atVG5mZoYDBw7A19cXrq6usLS0xIIFCzSe5f3yyy9j27ZtmD9/Pj7++GO0adMGu3fvRocOHWrgKBAREREREVFdpdWku2/fvhBClFkuk8mwePFiLF68uMw6FhYW2LZtW7n76dSpE44dO1ZunTfffBNvvvlm+QETERERVUBsbCwAPo2BiKg+09mJ1IiIiIhqq4R/AD09wNvbGwBgbGyI2Ng4Jt5ERPWQzk6kRkRERFRbpTwGCguBLYFPlszMbCQnJ2s7LCIi0gKOdBMRERFVE+dW2o6AiIi0jSPdRERERERERNWESTcRERERERFRNWHSTURERERERFRNmHQTERERERERVRMm3URERERERETVhEk3ERERERERUTVh0k1ERERERERUTZh0ExEREdavX49OnTrB1NQUpqamcHd3x759+6Ty7Oxs+Pr6omnTpmjcuDFGjRqFxMREjW0olUoMGzYMxsbGsLKywpw5c5Cfn69R5/Dhw+jatSsMDAzQunVrhIWFlYglODgYLVu2hKGhIdzc3HDmzJlqaTMREVFNYNJNREREaN68OZYtW4bo6GicO3cO/fv3x4gRI3D16lUAwKxZs/Dbb79h586dOHLkCO7fv4833nhDen9BQQGGDRuG3NxcnDx5Eps2bUJYWBgWLFgg1YmPj8ewYcPQr18/xMTEYObMmZg0aRL2798v1dmxYwdmz56NhQsX4vz583BxcYGnpyeSkpJq7mAQERFVISbdREREhOHDh2Po0KFo06YNHB0dsXTpUjRu3BinTp1CamoqNmzYgKCgIPTv3x+urq4IDQ3FyZMncerUKQDAgQMHcO3aNWzZsgWdO3fGkCFDsGTJEgQHByM3NxcAEBISAgcHB6xcuRLOzs7w8/PDf/7zH6xatUqKIygoCJMnT8aECRPQrl07hISEwNjYGBs3btTKcSEiInpeTLqJiIhIQ0FBAbZv346MjAy4u7sjOjoaeXl58PDwkOq0bdsWLVq0QFRUFAAgKioKHTt2hEKhkOp4enoiLS1NGi2PiorS2Ia6jnobubm5iI6O1qijp6cHDw8PqU5pcnJykJaWprEQERHpikol3X/++WdVx0FERESVUJV98uXLl9G4cWMYGBhg6tSp2LVrF9q1aweVSgW5XA5zc3ON+gqFAiqVCgCgUqk0Em51ubqsvDppaWnIyspCcnIyCgoKSq2j3kZpAgICYGZmJi12dnaVaj8REVF1qFTS3bp1a/Tr1w9btmxBdnZ2VcdEREREz6gq+2QnJyfExMTg9OnTmDZtGsaPH49r165VUaTVx9/fH6mpqdJy7949bYdEREQkqVTSff78eXTq1AmzZ8+GtbU13n33Xc4sSkREpAVV2SfL5XK0bt0arq6uCAgIgIuLC9asWQNra2vk5uYiJSVFo35iYiKsra0BANbW1iVmM1e/flodU1NTGBkZwdLSEvr6+qXWUW+jNAYGBtKs6+qFiIhIV1Qq6e7cuTPWrFmD+/fvY+PGjUhISECvXr3QoUMHBAUF4Z9//qnqOImIiKgU1dknFxYWIicnB66urmjYsCEiIyOlsri4OCiVSri7uwMA3N3dcfnyZY1ZxiMiImBqaop27dpJdYpuQ11HvQ25XA5XV1eNOoWFhYiMjJTqEBER1TbPNZFagwYN8MYbb2Dnzp1Yvnw5bt26hQ8//BB2dnYYN24cEhISqipOIiIiKsfz9sn+/v44evQo7ty5g8uXL8Pf3x+HDx+Gl5cXzMzM4OPjg9mzZ+PQoUOIjo7GhAkT4O7ujh49egAABg0ahHbt2mHs2LG4ePEi9u/fj/nz58PX1xcGBgYAgKlTp+LPP//E3Llzcf36dXz11Vf48ccfMWvWLCmO2bNn49tvv8WmTZsQGxuLadOmISMjAxMmTKi+g0dERFSNnivpPnfuHN577z3Y2NggKCgIH374IW7fvo2IiAjcv38fI0aMqKo4iYiIqBzP2ycnJSVh3LhxcHJywoABA3D27Fns378fAwcOBACsWrUKr776KkaNGoU+ffrA2toaP//8s/R+fX197NmzB/r6+nB3d4e3tzfGjRuHxYsXS3UcHBywd+9eREREwMXFBStXrsR3330HT09Pqc5bb72FL774AgsWLEDnzp0RExOD8PDwEpOrERER1RYNKvOmoKAghIaGIi4uDkOHDsXmzZsxdOhQ6Ok9yeEdHBwQFhaGli1bVmWsREREVExV9ckbNmwot9zQ0BDBwcEIDg4us469vT1+//33crfTt29fXLhwodw6fn5+8PPzK7cOERFRbVGpke7169djzJgxuHv3Lnbv3o1XX31V6tzVrKysntqBP01BQQE++eQTODg4wMjICC+++CKWLFkCIYRURwiBBQsWwMbGBkZGRvDw8MDNmzc1tvPw4UN4eXnB1NQU5ubm8PHxQXp6ukadS5cuoXfv3jA0NISdnR0CAwOfK3YiIqKaUFN9MhEREVVOpUa6iye1pZHL5Rg/fnxlNi9Zvnw51q9fj02bNqF9+/Y4d+4cJkyYADMzM0yfPh0AEBgYiLVr12LTpk1wcHDAJ598Ak9PT1y7dg2GhoYAAC8vLyQkJCAiIgJ5eXmYMGECpkyZgm3btgEA0tLSMGjQIHh4eCAkJASXL1/GxIkTYW5ujilTpjxXG4iIiKpTTfXJREREVDmVSrpDQ0PRuHFjvPnmmxrrd+7ciczMzCrr2E+ePIkRI0Zg2LBhAICWLVvihx9+kB6FIoTA6tWrMX/+fOletc2bN0OhUGD37t0YPXo0YmNjER4ejrNnz6Jbt24AgHXr1mHo0KH44osvYGtri61btyI3NxcbN26EXC5H+/btERMTg6CgICbdRESk02qqTyYiIqLKqdTl5QEBAbC0tCyx3srKCp9//vlzB6X28ssvIzIyEjdu3AAAXLx4EcePH8eQIUMAAPHx8VCpVPDw8JDeY2ZmBjc3N0RFRQEAoqKiYG5uLiXcAODh4QE9PT2cPn1aqtOnTx/I5XKpjqenJ+Li4vDo0aNSY8vJyUFaWprGQkTVS6lU4vz58zh//jyUSqW2wyHSCTXVJxMREVHlVGqkW6lUwsHBocR6e3v7Kv0g/NFHHyEtLQ1t27aFvr4+CgoKsHTpUnh5eQEAVCoVAJSY0VShUEhlKpUKVlZWGuUNGjSAhYWFRp3i7VFvU6VSoUmTJiViCwgIwKeffloFrSR6fkqlEsnJydJrS0tLtGjRQosRVT2lUom2zm2RlZkFADAyNsL12Ot1rp1EFVVTfTIRERFVTqWSbisrK1y6dKnETKgXL15E06ZNqyIuAMCPP/6IrVu3Ytu2bdIl3zNnzoStra3WL5fz9/fH7NmzpddpaWmws7PTYkRUXxVPRoF/E9K6JDk5GVmZWfD+2hsAsOXdLUhOTmbSTfVeTfXJREREVDmVSrrffvttTJ8+HSYmJujTpw8A4MiRI5gxYwZGjx5dZcHNmTMHH330kbTNjh074u7duwgICMD48eNhbW0NAEhMTISNjY30vsTERHTu3BkAYG1tjaSkJI3t5ufn4+HDh9L7ra2tkZiYqFFH/VpdpzgDAwMYGBg8fyOJnlPRZFThqEDijUQpIa2LFI58Vi9RUTXVJxMREVHlVOqe7iVLlsDNzQ0DBgyAkZERjIyMMGjQIPTv379K7x/LzMws8dgTfX19FBYWAnjy7FFra2tERkZK5WlpaTh9+jTc3d0BAO7u7khJSUF0dLRU5+DBgygsLISbm5tU5+jRo8jLy5PqREREwMnJqdRLy4l0kcJRATsXOyalRPVMTfXJREREVDmVGumWy+XYsWMHlixZgosXL8LIyAgdO3aEvb19lQY3fPhwLF26FC1atED79u1x4cIFBAUFYeLEiQAAmUyGmTNn4rPPPkObNm2kR4bZ2tpi5MiRAABnZ2cMHjwYkydPRkhICPLy8uDn54fRo0fD1tYWADBmzBh8+umn8PHxwbx583DlyhWsWbMGq1atqtL2EFHNqA/3uBOp1VSfTERERJVTqaRbzdHREY6OjlUVSwnr1q3DJ598gvfeew9JSUmwtbXFu+++iwULFkh15s6di4yMDEyZMgUpKSno1asXwsPDpWd0A8DWrVvh5+eHAQMGQE9PD6NGjcLatWulcjMzMxw4cAC+vr5wdXWFpaUlFixYwMeFEdVC5d3jzsSb6rLq7pOJiIiociqVdBcUFCAsLAyRkZFISkqSLvdWO3jwYJUEZ2JigtWrV2P16tVl1pHJZFi8eDEWL15cZh0LCwts27at3H116tQJx44dq2yoRKQjyrvHnUk31UU11ScTERFR5VQq6Z4xYwbCwsIwbNgwdOjQATKZrKrjIiJ6Lup73InqOvbJREREuq1SSff27dvx448/YujQoVUdDxEREVUA+2QiIiLdVqnZy+VyOVq3bl3VsRAREVEFsU8mIiLSbZVKuj/44AOsWbMGQoiqjoeIiIgqgH0yERGRbqvU5eXHjx/HoUOHsG/fPrRv3x4NGzbUKP/555+rJDgi+lfRx2DxEVhEpMY+mYiISLdVKuk2NzfH66+/XtWxEFEZij8Gi4/AIiI19slERES6rVJJd2hoaFXHQUTlKPoYLAB8BFYp1FcCxMbGajsUohrFPpmIiEi3VSrpBoD8/HwcPnwYt2/fxpgxY2BiYoL79+/D1NQUjRs3rsoYiej/UzgqtB2CTip+JQBRfcM+mYiISHdVKum+e/cuBg8eDKVSiZycHAwcOBAmJiZYvnw5cnJyEBISUtVxEtV5Re/ZBnjfdkUUvRLggfIB9i3dp+2QiGoM+2QiIiLdVqmke8aMGejWrRsuXryIpk2bSutff/11TJ48ucqCI6ovShup5X3bFfesVwJwUjqqS9gnExER6bZKJd3Hjh3DyZMnIZfLNda3bNkSf//9d5UERlSfFB2pVTgqkHgjkfdtVxNOSkd1DftkIiIi3VappLuwsBAFBQUl1v/1118wMTF57qCI6iuFowJ2LnbaDqNO46R0VNewTyYiItJtepV506BBg7B69WrptUwmQ3p6OhYuXIihQ4dWVWxERNVG4ajgxHRUJ7BPJiIi0m2VGuleuXIlPD090a5dO2RnZ2PMmDG4efMmLC0t8cMPP1R1jERERFQG9slERES6rVJJd/PmzXHx4kVs374dly5dQnp6Onx8fODl5QUjI6OqjpGIiIjKwD6ZiIhIt1X6Od0NGjSAt7d3VcZCRERElcA+mYiISHdVKunevHlzueXjxo2rVDBERERUMeyTiYiIdFuln9NdVF5eHjIzMyGXy2FsbMwOnoiIqIawTyYiItJtlZq9/NGjRxpLeno64uLi0KtXL07aQkREVIPYJxMREem2SiXdpWnTpg2WLVtW4ht3IiIiqlnsk4mIiHRHpSdSK3VjDRrg/v37VblJIqIapVQqkZycLL22tLREixYttBgRUeWwTyYiItINlUq6f/31V43XQggkJCTgyy+/RM+ePaskMLW///4b8+bNw759+5CZmYnWrVsjNDQU3bp1k/a9cOFCfPvtt0hJSUHPnj2xfv16tGnTRtrGw4cP8f777+O3336Dnp4eRo0ahTVr1qBx48ZSnUuXLsHX1xdnz55Fs2bN8P7772Pu3LlV2hYi0m1KpRJtndsiKzNLWmdkbITrsdeZeJPOqsk+mYiIiCquUkn3yJEjNV7LZDI0a9YM/fv3x8qVK6siLgBP7lPr2bMn+vXrh3379qFZs2a4efMmmjRpItUJDAzE2rVrsWnTJjg4OOCTTz6Bp6cnrl27BkNDQwCAl5cXEhISEBERgby8PEyYMAFTpkzBtm3bAABpaWkYNGgQPDw8EBISgsuXL2PixIkwNzfHlClTqqw9RKTbkpOTkZWZBe+vvaFwVCDxRiK2vLsFycnJTLpJZ9VUn0xERESVU6mku7CwsKrjKNXy5cthZ2eH0NBQaZ2Dg4P0sxACq1evxvz58zFixAgATx6dolAosHv3bowePRqxsbEIDw/H2bNnpdHxdevWYejQofjiiy9ga2uLrVu3Ijc3Fxs3boRcLkf79u0RExODoKAgJt1E9ZDCUQE7Fztth0H0TGqqTyYiIqLKqbKJ1KrDr7/+im7duuHNN9+ElZUVunTpgm+//VYqj4+Ph0qlgoeHh7TOzMwMbm5uiIqKAgBERUXB3NxcSrgBwMPDA3p6ejh9+rRUp0+fPpDL5VIdT09PxMXF4dGjR6XGlpOTg7S0NI2FiIiotgoICMBLL70EExMTWFlZYeTIkYiLi9Ook52dDV9fXzRt2hSNGzfGqFGjkJiYqFFHqVRi2LBhMDY2hpWVFebMmYP8/HyNOocPH0bXrl1hYGCA1q1bIywsrEQ8wcHBaNmyJQwNDeHm5oYzZ85UeZuJiIhqQqVGumfPnv3MdYOCgiqzCwDAn3/+ifXr12P27Nn4+OOPcfbsWUyfPh1yuRzjx4+HSqUCACgUCo33KRQKqUylUsHKykqjvEGDBrCwsNCoU3QEveg2VSqVxuXsagEBAfj0008r3TYiIqKqUFV98pEjR+Dr64uXXnoJ+fn5+PjjjzFo0CBcu3YNjRo1AgDMmjULe/fuxc6dO2FmZgY/Pz+88cYbOHHiBACgoKAAw4YNg7W1NU6ePImEhASMGzcODRs2xOeffw7gyRfmw4YNw9SpU7F161ZERkZi0qRJsLGxgaenJwBgx44dmD17NkJCQuDm5obVq1dLX4YX79OJiIh0XaWS7gsXLuDChQvIy8uDk5MTAODGjRvQ19dH165dpXoymey5gissLES3bt2kjrpLly64cuUKQkJCMH78+Ofa9vPy9/fX+KCTlpYGOztejkpERDWrqvrk8PBwjddhYWGwsrJCdHQ0+vTpg9TUVGzYsAHbtm1D//79AQChoaFwdnbGqVOn0KNHDxw4cADXrl3DH3/8AYVCgc6dO2PJkiWYN28eFi1aBLlcjpCQEDg4OEj3mzs7O+P48eNYtWqVlHQHBQVh8uTJmDBhAgAgJCQEe/fuxcaNG/HRRx9VzYEjIiKqIZVKuocPHw4TExNs2rRJGgV+9OgRJkyYgN69e+ODDz6okuBsbGzQrl07jXXOzs743//+BwCwtrYGACQmJsLGxkaqk5iYiM6dO0t1kpKSNLaRn5+Phw8fSu+3trYucXmc+rW6TnEGBgYwMDCoZMuIiIiqRnX1yampqQAACwsLAEB0dDTy8vI0bulq27YtWrRogaioKPTo0QNRUVHo2LGjxhVonp6emDZtGq5evYouXbogKipKYxvqOjNnzgQA5ObmIjo6Gv7+/lK5np4ePDw8pFvHisvJyUFOTo70Wldv+YqNjZV+5uMIiYjqj0rd071y5UoEBARoXHbdpEkTfPbZZ1U6U2rPnj1L3E9248YN2NvbA3gyqZq1tTUiIyOl8rS0NJw+fRru7u4AAHd3d6SkpCA6Olqqc/DgQRQWFsLNzU2qc/ToUeTl5Ul1IiIi4OTkVOql5URERLqiOvrkwsJCzJw5Ez179kSHDh0APLndSi6Xw9zcXKNu8Vu6SrvlS11WXp20tDRkZWUhOTkZBQUF5d46VlxAQADMzMykRdeuPEv4B9DTA7y9veHq6gpXV1c4OztBqVRqOzQiIqoBlUq609LS8M8//5RY/88//+Dx48fPHZTarFmzcOrUKXz++ee4desWtm3bhm+++Qa+vr4AnlwqN3PmTHz22Wf49ddfcfnyZYwbNw62trbSI1ScnZ0xePBgTJ48GWfOnMGJEyfg5+eH0aNHw9bWFgAwZswYyOVy+Pj44OrVq9ixYwfWrFlTofvkiIiItKE6+mRfX19cuXIF27dvf97waoS/vz9SU1Ol5d69e9oOSUPKY6CwENgSCET/9OTfzMxsJCcnazs0IiKqAZW6vPz111/HhAkTsHLlSnTv3h0AcPr0acyZMwdvvPFGlQX30ksvYdeuXfD398fixYvh4OCA1atXw8vLS6ozd+5cZGRkYMqUKUhJSUGvXr0QHh4uPaMbALZu3Qo/Pz8MGDAAenp6GDVqFNauXSuVm5mZ4cCBA/D19YWrqyssLS2xYMECPi6MqpxSqdT4kMXLC4noeVV1n+zn54c9e/bg6NGjaN68ubTe2toaubm5SElJ0RjtTkxM1Lhdq/gs48Vv1yrrli5TU1MYGRlBX18f+vr6pdap7bd8ObcCurbXdhRERFTTKpV0h4SE4MMPP8SYMWOkS7IbNGgAHx8frFixokoDfPXVV/Hqq6+WWS6TybB48WIsXry4zDoWFhbYtm1bufvp1KkTjh07Vuk4iZ5GqVSirXNbZGVmSeuMjI1wPfa6FqOiiuCXJqSLqqpPFkLg/fffx65du3D48OEST/VwdXVFw4YNERkZiVGjRgEA4uLioFQqNW7pWrp0KZKSkqRZxiMiImBqairN0eLu7o7ff/9dY9sRERHSNuRyOVxdXREZGSldtVZYWIjIyEj4+flV8OgQERFpX6WSbmNjY3z11VdYsWIFbt++DQB48cUXpUeKEFFJycnJyMrMgvfX3lA4KpB4IxFb3t3CywtrifK+NGHiTdpUVX2yr68vtm3bhl9++QUmJibS/dNmZmYwMjKCmZkZfHx8MHv2bFhYWMDU1BTvv/8+3N3d0aNHDwDAoEGD0K5dO4wdOxaBgYFQqVSYP38+fH19pZHoqVOn4ssvv8TcuXMxceJEHDx4ED/++CP27t0rxTJ79myMHz8e3bp1Q/fu3bF69WpkZGRIs5kTERHVJpVKutUSEhKQkJCAPn36wMjICEKI535MGFFdp3BUwM5Ftyb5oacr70sTJt2kC563T16/fj0AoG/fvhrrQ0ND8c477wAAVq1aJd2mlZOTA09PT3z11VdSXX19fezZswfTpk2Du7s7GjVqhPHjx2tcjebg4IC9e/di1qxZWLNmDZo3b47vvvtOelwYALz11lv4559/sGDBAqhUKnTu3Bnh4eElJlcjIiKqDSqVdD948AD//e9/cejQIchkMty8eROtWrWCj48PmjRpUqUzmBMR6RJ+aUK6pqr6ZCHEU+sYGhoiODgYwcHBZdaxt7cvcfl4cX379sWFCxfKrePn58fLyYmIqE6o1Ozls2bNQsOGDaFUKmFsbCytf+uttxAeHl5lwREREVH52CcTERHptkqNdB84cAD79+/XmNUUANq0aYO7d+9WSWBERET0dOyTiYiIdFulRrozMjI0vk1Xe/jwYa14ZAcREVFdwT6ZiIhIt1Uq6e7duzc2b94svZbJZCgsLERgYCD69etXZcERERFR+dgnExER6bZKXV4eGBiIAQMG4Ny5c8jNzcXcuXNx9epVPHz4ECdOnKjqGImIiKgM7JOJiIh0W6VGujt06IAbN26gV69eGDFiBDIyMvDGG2/gwoULePHFF6s6RiIiIioD+2QiIiLdVuGR7ry8PAwePBghISH4v//7v+qIiYiIiJ4B+2QiIiLdV+GR7oYNG+LSpUvVEQsRERFVAPtkIiIi3Vepy8u9vb2xYcOGqo6FiKjWUiqVOH/+vLQolUpth0T1BPtkIiIi3VapidTy8/OxceNG/PHHH3B1dUWjRo00yoOCgqokOCKi2kCpVKKtc1tkZWZJ64yMjXA99jpatGihxcioPmCfTEREpNsqlHT/+eefaNmyJa5cuYKuXbsCAG7cuKFRRyaTVV10RES1QHJyMrIys+D9tTcUjgok3kjElne3IDk5mUk3VRv2yURERLVDhZLuNm3aICEhAYcOHQIAvPXWW1i7di0UCkW1BEdEVJsoHBWwc7HTdhhUT7BPJiIiqh0qdE+3EELj9b59+5CRkVGlAREREdHTsU8mIiKqHSo1kZpa8Q6fiIiItIN9MhERkW6qUNItk8lK3B/G+8WIiIhqHvtkIiKi2qFC93QLIfDOO+/AwMAAAJCdnY2pU6eWmCn1559/rroIiYiIqAT2yURERLVDhZLu8ePHa7z29vau0mCIiIjo2bBPJiIiqh0qlHSHhoZWVxxERHVObGys9LOlpSUfH0ZVin0yERFR7fBcE6nVtGXLlkEmk2HmzJnSuuzsbPj6+qJp06Zo3LgxRo0ahcTERI33KZVKDBs2DMbGxrCyssKcOXOQn5+vUefw4cPo2rUrDAwM0Lp1a4SFhdVAi4ioLkpLTINMTwZvb2+4urrC1dUVbZ3bQqlUajs0IiIiIqphtSbpPnv2LL7++mt06tRJY/2sWbPw22+/YefOnThy5Aju37+PN954QyovKCjAsGHDkJubi5MnT2LTpk0ICwvDggULpDrx8fEYNmwY+vXrh5iYGMycOROTJk3C/v37a6x9RFR3ZKVmQRQKeH/tjQ8OfQDvr72RlZmF5ORkbYdGRERERDWsViTd6enp8PLywrfffosmTZpI61NTU7FhwwYEBQWhf//+cHV1RWhoKE6ePIlTp04BAA4cOIBr165hy5Yt6Ny5M4YMGYIlS5YgODgYubm5AICQkBA4ODhg5cqVcHZ2hp+fH/7zn/9g1apVWmkvEdUNCkcF7FzsoHBUaDsUIiIiItKSWpF0+/r6YtiwYfDw8NBYHx0djby8PI31bdu2RYsWLRAVFQUAiIqKQseOHaFQ/Puh19PTE2lpabh69apUp/i2PT09pW2UJicnB2lpaRoLERERERERUVEVmkhNG7Zv347z58/j7NmzJcpUKhXkcjnMzc011isUCqhUKqlO0YRbXa4uK69OWloasrKyYGRkVGLfAQEB+PTTTyvdLiIiIiIiIqr7dHqk+969e5gxYwa2bt0KQ0NDbYejwd/fH6mpqdJy7949bYdEREREREREOkank+7o6GgkJSWha9euaNCgARo0aIAjR45g7dq1aNCgARQKBXJzc5GSkqLxvsTERFhbWwMArK2tS8xmrn79tDqmpqaljnIDgIGBAUxNTTUWIiIiIiIioqJ0OukeMGAALl++jJiYGGnp1q0bvLy8pJ8bNmyIyMhI6T1xcXFQKpVwd3cHALi7u+Py5ctISkqS6kRERMDU1BTt2rWT6hTdhrqOehtERERERERElaHT93SbmJigQ4cOGusaNWqEpk2bSut9fHwwe/ZsWFhYwNTUFO+//z7c3d3Ro0cPAMCgQYPQrl07jB07FoGBgVCpVJg/fz58fX1hYGAAAJg6dSq+/PJLzJ07FxMnTsTBgwfx448/Yu/evTXbYCKqF5RKpcbjwywtLdGiRQstRkRERERE1UWnk+5nsWrVKujp6WHUqFHIycmBp6cnvvrqK6lcX18fe/bswbRp0+Du7o5GjRph/PjxWLx4sVTHwcEBe/fuxaxZs7BmzRo0b94c3333HTw9PbXRJCKqw5RKJdo6t0VWZpa0zsjYCNdjrzPxJiIiIqqDal3SffjwYY3XhoaGCA4ORnBwcJnvsbe3x++//17udvv27YsLFy5URYhERGVKTk5GVmYWvL/2hsJRgcQbidjy7hYkJycz6SYiIiKqg2pd0k1EVBcoHBWwc7HTdhhEREREVM10eiI1IiIiIiIiotqMSTcRERERERFRNeHl5UREOoQzmxMRERHVLUy6iYh0BGc2JyIiIqp7mHQTEekIzmxOVL/ExsYC4BUtRER1He/pJiLSMeqZzRWOCm2HQvXI0aNHMXz4cNja2kImk2H37t0a5UIILFiwADY2NjAyMoKHhwdu3rypUefhw4fw8vKCqakpzM3N4ePjg/T0dI06ly5dQu/evWFoaAg7OzsEBgaWiGXnzp1o27YtDA0N0bFjx6c+9rO2SfgH0NMDvL294erqCmdnJyiVSm2HRURE1YRJNxERESEjIwMuLi4IDg4utTwwMBBr165FSEgITp8+jUaNGsHT0xPZ2dlSHS8vL1y9ehURERHYs2cPjh49iilTpkjlaWlpGDRoEOzt7REdHY0VK1Zg0aJF+Oabb6Q6J0+exNtvvw0fHx9cuHABI0eOxMiRI3HlypXqa3wNS3kMFBYCWwKfLJmZ2RpzORARUd3Cy8uJiIgIQ4YMwZAhQ0otE0Jg9erVmD9/PkaMGAEA2Lx5MxQKBXbv3o3Ro0cjNjYW4eHhOHv2LLp16wYAWLduHYYOHYovvvgCtra22Lp1K3Jzc7Fx40bI5XK0b98eMTExCAoKkpLzNWvWYPDgwZgzZw4AYMmSJYiIiMCXX36JkJCQGjgSNce5lbYjICKimsCRbiIiIipXfHw8VCoVPDw8pHVmZmZwc3NDVFQUACAqKgrm5uZSwg0AHh4e0NPTw+nTp6U6ffr0gVwul+p4enoiLi4Ojx49kuoU3Y+6jno/REREtQ1HuomIapGijxTj5EtUU1QqFQBAodCcZ0ChUEhlKpUKVlZWGuUNGjSAhYWFRh0HB4cS21CXNWnSBCqVqtz9lCYnJwc5OTnS67S0tIo0j4iIqFox6SYiqiWKP1KMjxMjeiIgIACffvqptsMgIiIqFS8vJyKqJYo+Usz7a29kZWZx8iWqEdbW1gCAxMREjfWJiYlSmbW1NZKSkjTK8/Pz8fDhQ406pW2j6D7KqqMuL42/vz9SU1Ol5d69exVtIhERUbVh0k1EVMsoHBV8nBjVKAcHB1hbWyMyMlJal5aWhtOnT8Pd3R0A4O7ujpSUFERHR0t1Dh48iMLCQri5uUl1jh49iry8PKlOREQEnJyc0KRJE6lO0f2o66j3UxoDAwOYmppqLERERLqCSTcREREhPT0dMTExiImJAfBk8rSYmBgolUrIZDLMnDkTn332GX799VdcvnwZ48aNg62tLUaOHAkAcHZ2xuDBgzF58mScOXMGJ06cgJ+fH0aPHg1bW1sAwJgxYyCXy+Hj44OrV69ix44dWLNmDWbPni3FMWPGDISHh2PlypW4fv06Fi1ahHPnzsHPz6+mDwkREVGV4D3dREREhHPnzqFfv37Sa3UiPH78eISFhWHu3LnIyMjAlClTkJKSgl69eiE8PByGhobSe7Zu3Qo/Pz8MGDAAenp6GDVqFNauXSuVm5mZ4cCBA/D19YWrqyssLS2xYMECjWd5v/zyy9i2bRvmz5+Pjz/+GG3atMHu3bvRoUOHGjgKREREVY9JNxEREaFv374QQpRZLpPJsHjxYixevLjMOhYWFti2bVu5++nUqROOHTtWbp0333wTb775ZvkBExER1RK8vJyIiIiIiIiomnCkm4ioFouNjZV+5nO7iYiIiHQPk24iolooLTENMj0ZvL29pXV8bjcRERGR7tHpy8sDAgLw0ksvwcTEBFZWVhg5ciTi4uI06mRnZ8PX1xdNmzZF48aNMWrUqBLP91QqlRg2bBiMjY1hZWWFOXPmID8/X6PO4cOH0bVrVxgYGKB169YICwur7uYREVVaVmoWRKGA99fe+ODQB3xuNxEREZGO0umk+8iRI/D19cWpU6cQERGBvLw8DBo0CBkZGVKdWbNm4bfffsPOnTtx5MgR3L9/H2+88YZUXlBQgGHDhiE3NxcnT57Epk2bEBYWhgULFkh14uPjMWzYMPTr1w8xMTGYOXMmJk2ahP3799doe4mIKkrhqICdix2f201ERESko3T68vLw8HCN12FhYbCyskJ0dDT69OmD1NRUbNiwAdu2bUP//v0BAKGhoXB2dsapU6fQo0cPHDhwANeuXcMff/wBhUKBzp07Y8mSJZg3bx4WLVoEuVyOkJAQODg4YOXKlQCePGv0+PHjWLVqFTw9PWu83UREz0upVGqMevN+byIiIiLt0OmR7uJSU1MBPHkkCQBER0cjLy8PHh4eUp22bduiRYsWiIqKAgBERUWhY8eOUCj+HQXy9PREWloarl69KtUpug11HfU2iIhqE6VSibbObeHq6iotbZ3bQqlUajs0IiIionpHp0e6iyosLMTMmTPRs2dPdOjQAQCgUqkgl8thbm6uUVehUEClUkl1iibc6nJ1WXl10tLSkJWVBSMjoxLx5OTkICcnR3qdlpb2fA0kIqoiycnJyMrMgvfX3lA4KpB4IxFb3t2C5ORkjnYTERER1bBak3T7+vriypUrOH78uLZDAfBkkrdPP/1U22EQEZVJfb83Eek+Pv6PiKjuqhWXl/v5+WHPnj04dOgQmjdvLq23trZGbm4uUlJSNOonJibC2tpaqlN8NnP166fVMTU1LXWUGwD8/f2RmpoqLffu3XuuNhIREVH9k/APoKcHeHt7S7eDODs78XYQIqI6RKeTbiEE/Pz8sGvXLhw8eBAODg4a5a6urmjYsCEiIyOldXFxcVAqlXB3dwcAuLu74/Lly0hKSpLqREREwNTUFO3atZPqFN2Guo56G6UxMDCAqampxkJERERUESmPgcJCYEsgEP3Tk38zM7P5+D8iojpEpy8v9/X1xbZt2/DLL7/AxMREugfbzMwMRkZGMDMzg4+PD2bPng0LCwuYmpri/fffh7u7O3r06AEAGDRoENq1a4exY8ciMDAQKpUK8+fPh6+vLwwMDAAAU6dOxZdffom5c+di4sSJOHjwIH788Ufs3btXa20nIiKi+sO5FdC1vbajICKi6qDTI93r169Hamoq+vbtCxsbG2nZsWOHVGfVqlV49dVXMWrUKPTp0wfW1tb4+eefpXJ9fX3s2bMH+vr6cHd3h7e3N8aNG4fFixdLdRwcHLB3715ERETAxcUFK1euxHfffcfHhREREREREdFz0emRbiHEU+sYGhoiODgYwcHBZdaxt7fH77//Xu52+vbtiwsXLlQ4RiKi2qjoc7w5aRMRERFR9dHppJuIiKqe+jneWZlZAAAjYyNcj73OxJuIiIioGjDpJqpm6hHFoo+DIdKmos/xBsBneBPpID5CjIio7mDSTVSNio8oEukShaNC2yEQUTFFHyGmZmxsiNjYOCbeRES1lE5PpEZU2xUdURzyf0O0HQ4REek4PkKMiKju4Ug3UQ3giCIREVUEHyFGRFR3MOkmIiIiqiP4ZAIiIt3DpJuIiDTwQzvRv4r+fwB0+/+EUqmEs7MTMjOzAfBecCIiXcGkm4iIJHycGNG/iiexwL+JbE1Tz2ZePOkv+qVAbGwsMjOzsSXwSZn33Gw+mYCISAcw6SYiIgkfJ0b0r+TkZCmJdW4FxP75JJE9duxYjcVQfDZzQ0MD/PTT/2BjY4OEhAS8+eYoZGXlaLzHuVWNhUdERM+ASXctwEs9iaimcfI/on+pJzUr7XFe1a3obObJj4DZy3Pw6quvatRRfynw+1Hgk7U1FhoRET0jJt06jpd6Pl1tut+OiIhqr6IJcPxfNZvgqkfa1fsvmmSrvxSI/bPk+9SXpQPsH4mItIVJt47jpZ7lK/6lBMAvJoiqE7/kItL+5dvlJdlqpY3KF700nf93iYhqDpPuWqKsSz3r+wfgol9KKBwVSLyRyC8miKoJv+Qiqj2Kjso7twKORWtems6ZzYmIag6T7lqMH4D/pXBUwM7FTtthENVp/JKLqPYpOiquTsKBfyeEc3Z2BlD/vrQnIqpJTLprMV34AFzfR9qJ6iN+yUVUezm3Kv3Sc458ExFVHybddYC2PgBzpJ2IiKj2KX7pedFHoXHkm4io6jHppkrThZF2ItI+9ezI/JBOVLuU9yg0jnwTEVUdJt303HT5UlNe/k5UfdIS0yDTk0kf1HmlC1HtVNbIN79EJyKqGky6qc7i5e9E1SsrNQuiUPCRhkR1hHrkW41XsRARVQ0m3XVYeaO8xctycnJgYGBQat3aqqzL39X3rFWkjRwxJyrbszzSkP9niGqP4pebF32+N8D/z0REFcWku44qb5QXQIkymZ4MolCUqFsXOlX15e+VvRT2aSPmTMiJSir+/6Yu/U0hquuKXm6e/Ejz+d6AZhLOPo+I6OmYdBcTHByMFStWQKVSwcXFBevWrUP37t21HVaFlTfJGQCNsmt/XMO+pfvq/IRoT7sUtqzk+WnHkpewE5VU9P8NwEvPqXLqSp9cW6nv7y56v/exaM0kvPgoeF28co6I6Hkx6S5ix44dmD17NkJCQuDm5obVq1fD09MTcXFxsLKy0nZ4lVLeJGfqssQbieXWramR3Jq6FLW0S2GfdmWA+n3Fjw9ncCcq37Nceg7wgzmVVBf75NpMfb930SS8tFFwfT2goPDf93FUnIiISbeGoKAgTJ48GRMmTAAAhISEYO/evdi4cSM++ugjLUenHc+SjD7t/WUl0kXLEhIS8J83/4PsrGyNfZQ2Cl0dnfbTRrOfRpdncCfSNc97ywbvFa8f2CfrttJGwX8/CnyytnKj4sVHyDliTkR1CZPu/y83NxfR0dHw9/eX1unp6cHDwwNRUVFajEy7nicZLe+eztI+dAMo9VLUmrw3lMkzUfV7nls2iv89MDA0wP/KmOCJo+m1F/vk2qXoKHjx1886Kl58hLy8EfPyEvSnJe9VVZd/T4ioIph0/3/JyckoKCiAQqF5KaRCocD16yVHdXNycpCTkyO9Tk1NBQCkpaU9dywqlQoqlQoAEBcXBwC4d/GeVB4dHY309HSNspyMHCTdSpLK1Z6lTHVT9dS6uVm5yMnIQW5W7jPtQx1fVmYW+vv1BwAc/PIg9u/fDycnJ40y8+bmUJ5X4tyP56TtV2Q7lWlndR3L0o6BuvzhXw+rZDvPE3tNHINn+X2qzmNQ3nGuymOp7WNQE79PTzs+z/r7VFZZWX9Xiv5tSPkrpcy/HVmPsxC1OUrjQ7yBoQG+3/w9AGDsuLHIyc4pUab+O6+np4fCwsISPz+tzNraGtbW1nge6r5CCPGUmvWTrvTJ6enpAIDoa0B6JhB7G9Lru3+jzLKK1K2q7Whjn8+6ncxs4J+HTxLwORMBO2vg7BXg+1+fvFYl//tz8TI7a+DqLeCbnf8m7HoyoMjcrxqvyyuryrqGhnJs3rwFCoWi3L8fxV9Xtqw2b6c2x65r26nNsVfXdmpNnyxICCHE33//LQCIkydPaqyfM2eO6N69e4n6CxcuFAC4cOHChQuXSi/37t2rqW6uVmGfzIULFy5canqpzj6ZI93/n6WlJfT19ZGYmKixPjExsdRvT/z9/TF79mzpdWFhIR4+fIimTZtCJpM9dX9paWmws7PDvXv3YGpq+vwNqGXqe/sBHgO2v363H6jfx0AIgcePH8PW1lbboeikmu6Ty1JXf0frYrvqYpsAtqu2qYvtqottAjTbZWJiUu19MpPu/08ul8PV1RWRkZEYOXIkgCeddmRkJPz8/ErUNzAw0Li3BwDMzc0rvF9TU9M69QtcUfW9/QCPAdtfv9sP1N9jYGZmpu0QdJa2+uSy1NXf0brYrrrYJoDtqm3qYrvqYpuAf9tV3X0yk+4iZs+ejfHjx6Nbt27o3r07Vq9ejYyMDGnmVCIiIqoZ7JOJiKiuYNJdxFtvvYV//vkHCxYsgEqlQufOnREeHl5iIhciIiKqXuyTiYiormDSXYyfn1+pl65VNQMDAyxcuLDE5XD1RX1vP8BjwPbX7/YDPAb0dDXVJ5elrv6O1sV21cU2AWxXbVMX21UX2wTUfLtkQvB5JURERERERETVQU/bARARERERERHVVUy6iYiIiIiIiKoJk24iIiIiIiKiasKkWwuCg4PRsmVLGBoaws3NDWfOnNF2SNXm6NGjGD58OGxtbSGTybB7926NciEEFixYABsbGxgZGcHDwwM3b97UTrDVICAgAC+99BJMTExgZWWFkSNHIi4uTqNOdnY2fH190bRpUzRu3BijRo1CYmKiliKuWuvXr0enTp2kZyC6u7tj3759Unldbntpli1bBplMhpkzZ0rr6voxWLRoEWQymcbStm1bqbyut59qt9rUXz9Lf9O3b98S/x+nTp2qUUepVGLYsGEwNjaGlZUV5syZg/z8/JpsiqQq/n7oUnvUWrZsWaJdMpkMvr6+AGrPeaqKz3gPHz6El5cXTE1NYW5uDh8fH6Snp2vUuXTpEnr37g1DQ0PY2dkhMDBQa+3Ky8vDvHnz0LFjRzRq1Ai2trYYN24c7t+/r7GN0s7xsmXLtNaup52rd955p0S8gwcP1qhT284VgFL/n8lkMqxYsUKqU1Pnikl3DduxYwdmz56NhQsX4vz583BxcYGnpyeSkpK0HVq1yMjIgIuLC4KDg0stDwwMxNq1axESEoLTp0+jUaNG8PT0RHZ2dg1HWj2OHDkCX19fnDp1ChEREcjLy8OgQYOQkZEh1Zk1axZ+++037Ny5E0eOHMH9+/fxxhtvaDHqqtO8eXMsW7YM0dHROHfuHPr3748RI0bg6tWrAOp224s7e/Ysvv76a3Tq1EljfX04Bu3bt0dCQoK0HD9+XCqrD+2n2qm29dfP0t8AwOTJkzX+Pxb98FhQUIBhw4YhNzcXJ0+exKZNmxAWFoYFCxbUdHMkz/P3QxfbAzzpD4q2KSIiAgDw5ptvSnVqw3mqis94Xl5euHr1KiIiIrBnzx4cPXoUU6ZMkcrT0tIwaNAg2NvbIzo6GitWrMCiRYvwzTffaKVdmZmZOH/+PD755BOcP38eP//8M+Li4vDaa6+VqLt48WKNc/j+++9rrV1PO1cAMHjwYI14f/jhB43y2nauAGi0JyEhARs3boRMJsOoUaM06tXIuRJUo7p37y58fX2l1wUFBcLW1lYEBARoMaqaAUDs2rVLel1YWCisra3FihUrpHUpKSnCwMBA/PDDD1qIsPolJSUJAOLIkSNCiCftbdiwodi5c6dUJzY2VgAQUVFR2gqzWjVp0kR899139artjx8/Fm3atBERERHilVdeETNmzBBC1I/zv3DhQuHi4lJqWX1oP9Vetb2/Lt7fCCE0/v6U5vfffxd6enpCpVJJ69avXy9MTU1FTk5OdYZbquf9+6Fr7SnLjBkzxIsvvigKCwuFELXvPAlRuc94165dEwDE2bNnpTr79u0TMplM/P3330IIIb766ivRpEkTjXbNmzdPODk5VXOLnijertKcOXNGABB3796V1tnb24tVq1aV+R5ttqu0No0fP16MGDGizPfUlXM1YsQI0b9/f411NXWuONJdg3JzcxEdHQ0PDw9pnZ6eHjw8PBAVFaXFyLQjPj4eKpVK43iYmZnBzc2tzh6P1NRUAICFhQUAIDo6Gnl5eRrHoG3btmjRokWdOwYFBQXYvn07MjIy4O7uXq/a7uvri2HDhmm0Fag/5//mzZuwtbVFq1at4OXlBaVSCaD+tJ9qn7rQXxfvb9S2bt0KS0tLdOjQAf7+/sjMzJTKoqKi0LFjRygUCmmdp6cn0tLSpCuUatrz/P3QxfYUl5ubiy1btmDixImQyWTS+tp2nop7ls94UVFRMDc3R7du3aQ6Hh4e0NPTw+nTp6U6ffr0gVwul+p4enoiLi4Ojx49qqHWlC81NRUymQzm5uYa65ctW4amTZuiS5cuWLFihcbl/7rYrsOHD8PKygpOTk6YNm0aHjx4IJXVhXOVmJiIvXv3wsfHp0RZTZyrBs8XPlVEcnIyCgoKNP5IAoBCocD169e1FJX2qFQqACj1eKjL6pLCwkLMnDkTPXv2RIcOHQA8OQZyubzEH+q6dAwuX74Md3d3ZGdno3Hjxti1axfatWuHmJiYOt92ANi+fTvOnz+Ps2fPliirD+ffzc0NYWFhcHJyQkJCAj799FP07t0bV65cqRftp9qptvfXpfU3ADBmzBjY29vD1tYWly5dwrx58xAXF4eff/4ZwJO/SaW1WV1W057374eutac0u3fvRkpKCt555x1pXW07T6V5ls94KpUKVlZWGuUNGjSAhYWFRh0HB4cS21CXNWnSpFrif1bZ2dmYN28e3n77bZiamkrrp0+fjq5du8LCwgInT56Ev78/EhISEBQUBED32jV48GC88cYbcHBwwO3bt/Hxxx9jyJAhiIqKgr6+fp04V5s2bYKJiUmJW9hq6lwx6SaqIb6+vrhy5YrG/Wj1gZOTE2JiYpCamoqffvoJ48ePx5EjR7QdVo24d+8eZsyYgYiICBgaGmo7HK0YMmSI9HOnTp3g5uYGe3t7/PjjjzAyMtJiZER1V1n9TdH7Lzt27AgbGxsMGDAAt2/fxosvvljTYT5Vffj7sWHDBgwZMgS2trbSutp2nuqrvLw8/Pe//4UQAuvXr9comz17tvRzp06dIJfL8e677yIgIAAGBgY1HepTjR49Wvq5Y8eO6NSpE1588UUcPnwYAwYM0GJkVWfjxo3w8vIq8Xmsps4VLy+vQZaWltDX1y8xs2ZiYiKsra21FJX2qNtcH46Hn58f9uzZg0OHDqF58+bSemtra+Tm5iIlJUWjfl06BnK5HK1bt4arqysCAgLg4uKCNWvW1Iu2R0dHIykpCV27dkWDBg3QoEEDHDlyBGvXrkWDBg2gUCjq/DEoztzcHI6Ojrh161a9+B2g2qk299dl9TelcXNzAwDcunULwJM+qbQ2q8u0raJ/P3S9PXfv3sUff/yBSZMmlVuvtp0n4Nk+41lbW5eYmDA/Px8PHz7U+XOoTrjv3r2LiIgIjVHu0ri5uSE/Px937twBoLvtUmvVqhUsLS01fudq67kCgGPHjiEuLu6p/9eA6jtXTLprkFwuh6urKyIjI6V1hYWFiIyMhLu7uxYj0w4HBwdYW1trHI+0tDScPn26zhwPIQT8/Pywa9cuHDx4sMTlKa6urmjYsKHGMYiLi4NSqawzx6C4wsJC5OTk1Iu2DxgwAJcvX0ZMTIy0dOvWDV5eXtLPdf0YFJeeno7bt2/DxsamXvwOUO1UG/vrp/U3pYmJiQEA2NjYAADc3d1x+fJljQ/X6oSiXbt21RJ3RVT074eutyc0NBRWVlYYNmxYufVq23kCnu0znru7O1JSUhAdHS3VOXjwIAoLC6UvGtzd3XH06FHk5eVJdSIiIuDk5KS1y5XVCffNmzfxxx9/oGnTpk99T0xMDPT09KRLtHWxXUX99ddfePDggcbvXG08V2obNmyAq6srXFxcnlq32s5VhaZdo+e2fft2YWBgIMLCwsS1a9fElClThLm5ucYMlHXJ48ePxYULF8SFCxcEABEUFCQuXLggzfC4bNkyYW5uLn755Rdx6dIlMWLECOHg4CCysrK0HHnVmDZtmjAzMxOHDx8WCQkJ0pKZmSnVmTp1qmjRooU4ePCgOHfunHB3dxfu7u5ajLrqfPTRR+LIkSMiPj5eXLp0SXz00UdCJpOJAwcOCCHqdtvLUnxW2rp+DD744ANx+PBhER8fL06cOCE8PDyEpaWlSEpKEkLU/fZT7VXb+uun9Te3bt0SixcvFufOnRPx8fHil19+Ea1atRJ9+vSRtpGfny86dOggBg0aJGJiYkR4eLho1qyZ8Pf310qbnvfvh661p6iCggLRokULMW/ePI31tek8VcVnvMGDB4suXbqI06dPi+PHj4s2bdqIt99+WypPSUkRCoVCjB07Vly5ckVs375dGBsbi6+//lor7crNzRWvvfaaaN68uYiJidH4v6ae3frkyZNi1apVIiYmRty+fVts2bJFNGvWTIwbN05r7SqvTY8fPxYffvihiIqKEvHx8eKPP/4QXbt2FW3atBHZ2dnSNmrbuVJLTU0VxsbGYv369SXeX5Pnikm3Fqxbt060aNFCyOVy0b17d3Hq1Clth1RtDh06JACUWMaPHy+EePJIiU8++UQoFAphYGAgBgwYIOLi4rQbdBUqre0ARGhoqFQnKytLvPfee6JJkybC2NhYvP766yIhIUF7QVehiRMnCnt7eyGXy0WzZs3EgAEDpIRbiLrd9rIUT7rr+jF46623hI2NjZDL5eKFF14Qb731lrh165ZUXtfbT7Vbbeqvn9bfKJVK0adPH2FhYSEMDAxE69atxZw5c0RqaqrGdu7cuSOGDBkijIyMhKWlpfjggw9EXl6eFlpUNX8/dKk9Re3fv18AKPGZpzadp6r4jPfgwQPx9ttvi8aNGwtTU1MxYcIE8fjxY406Fy9eFL169RIGBgbihRdeEMuWLdNau+Lj48v8v3bo0CEhhBDR0dHCzc1NmJmZCUNDQ+Hs7Cw+//xzjQS2pttVXpsyMzPFoEGDRLNmzUTDhg2Fvb29mDx5cokvGGvbuVL7+uuvhZGRkUhJSSnx/po8VzIhhHj2cXEiIiIiIiIiela8p5uIiIiIiIiomjDpJiIiIiIiIqomTLqJiIiIiIiIqgmTbiIiIiIiIqJqwqSbiIiIiIiIqJow6SYiIiIiIiKqJky6iYiIiIiIiKoJk24iIiIiIiKiasKkm6iK3LlzBzKZDDExMdoORXL9+nX06NEDhoaG6Ny5c7Xvr2XLlli9enW176eq9O3bFzNnztR2GEREpCNkMhl2796t1Rh2796N1q1bQ19fX+f7qD59+mDbtm3Sa104fqUZPXo0Vq5cqe0wqB5j0k11xjvvvAOZTIZly5ZprN+9ezdkMpmWotKuhQsXolGjRoiLi0NkZGSpdcpKPMPCwmBubl6h/Z09exZTpkypRKRVLzc3F4GBgXBxcYGxsTEsLS3Rs2dPhIaGIi8vT9vhPVVljj8REZXtn3/+wbRp09CiRQsYGBjA2toanp6eOHHihFQnISEBQ4YM0WKUwLvvvov//Oc/uHfvHpYsWaJRdvjwYchksnKXw4cPV3rfFel7fv31VyQmJmL06NGV3l9pquML/Pnz52Pp0qVITU2t0u0SPasG2g6AqCoZGhpi+fLlePfdd9GkSRNth1MlcnNzIZfLK/Xe27dvY9iwYbC3t6/iqErXrFmzGtnP0+Tm5sLT0xMXL17EkiVL0LNnT5iamuLUqVP44osv0KVLl2ob+S8oKIBMJoOenm58p6lr8RARacuoUaOQm5uLTZs2oVWrVkhMTERkZCQePHgg1bG2ttZihEB6ejqSkpLg6ekJW1vbEuUvv/wyEhISpNczZsxAWloaQkNDpXUWFhY1EuvatWsxYcKEWtG/dOjQAS+++CK2bNkCX19fbYdD9ZDu/y8hqgAPDw9YW1sjICCgzDqLFi0qkXCtXr0aLVu2lF6/8847GDlyJD7//HMoFAqYm5tj8eLFyM/Px5w5c2BhYYHmzZtrdHJq169fx8svvwxDQ0N06NABR44c0Si/cuUKhgwZgsaNG0OhUGDs2LFITk6Wyvv27Qs/Pz/MnDkTlpaW8PT0LLUdhYWFWLx4MZo3bw4DAwN07twZ4eHhUrlMJkN0dDQWL14MmUyGRYsWlXPknk59TL744gvY2NigadOm8PX11Rg1Lv7t9M2bN9GnTx8YGhqiXbt2iIiI0Lj0TP2NfUpKivSemJgYyGQy3LlzR1p3/Phx9O7dG0ZGRrCzs8P06dORkZFRZqyrV6/G0aNHERkZCV9fX3Tu3BmtWrXCmDFjcPr0abRp00aqW1hYiLlz58LCwgLW1tYljlNQUBA6duyIRo0awc7ODu+99x7S09OlcvWowK+//op27drBwMAASqUSZ8+excCBA2FpaQkzMzO88sorOH/+vMa2U1JS8O6770KhUEi/L3v27MHhw4cxYcIEpKamSiMX6rhycnLw4Ycf4oUXXkCjRo3g5uamMapRVjyHDx9G9+7d0ahRI5ibm6Nnz564e/dumceQiKguSUlJwbFjx7B8+XL069cP9vb26N69O/z9/fHaa69J9Yr2UYsWLSp1JDksLAzAk/4jICAADg4OMDIygouLC3766ady43j06BHGjRuHJk2awNjYGEOGDMHNmzcBPOkTTUxMAAD9+/cvddRaLpfD2tpaWoyMjKRRe2trazRp0gQff/xxqX1EdnY22rdvr3FF2u3bt2FiYoKNGzeW2/cU988//+DgwYMYPnx4iTL11QJGRkZo1aqVxjHp378//Pz8SmxLLpcjMjISffv2xd27dzFr1iwpBrWnfRb46quv0KZNGxgaGkKhUOA///mPxn6GDx+O7du3l3FmiKqZIKojxo8fL0aMGCF+/vlnYWhoKO7duyeEEGLXrl2i6K/6woULhYuLi8Z7V61aJezt7TW2ZWJiInx9fcX169fFhg0bBADh6ekpli5dKm7cuCGWLFkiGjZsKO0nPj5eABDNmzcXP/30k7h27ZqYNGmSMDExEcnJyUIIIR49eiSaNWsm/P39RWxsrDh//rwYOHCg6Nevn7TvV155RTRu3FjMmTNHXL9+XVy/fr3U9gYFBQlTU1Pxww8/iOvXr4u5c+eKhg0bihs3bgghhEhISBDt27cXH3zwgUhISBCPHz8udTuvvPKKmDFjRon1oaGhwszMTOOYmJqaiqlTp4rY2Fjx22+/CWNjY/HNN99Idezt7cWqVauEEEIUFBSIDh06iAEDBoiYmBhx5MgR0aVLFwFA7Nq1SwghxKFDhwQA8ejRI2kbFy5cEABEfHy8EEKIW7duiUaNGolVq1aJGzduiBMnToguXbqId955p9T2CCFEp06dxKBBg8osL9p2U1NTsWjRInHjxg2xadMmIZPJxIEDB6Q6q1atEgcPHhTx8fEiMjJSODk5iWnTpmkcp4YNG4qXX35ZnDhxQly/fl1kZGSIyMhI8f3334vY2Fhx7do14ePjIxQKhUhLS5OOT48ePUT79u3FgQMHxO3bt8Vvv/0mfv/9d5GTkyNWr14tTE1NRUJCgsb5mzRpknj55ZfF0aNHxa1bt8SKFSuEgYGBdN5Liyc1NVWYmZmJDz/8UNy6dUtcu3ZNhIWFibt37z71GBER1QV5eXmicePGYubMmSI7O7vMekX7qMePH0t/gxMSEsQXX3whjI2NxeXLl4UQQnz22Weibdu2Ijw8XNy+fVuEhoYKAwMDcfjw4TK3/9prrwlnZ2dx9OhRERMTIzw9PUXr1q1Fbm6uyMnJEXFxcQKA+N///icSEhJETk5Oue1Sf/ZRe1ofceHCBSGXy8Xu3btFfn6+6NGjh3j99deFEKLcvqe4n3/+WTRq1EgUFBSUOH5NmzYV3377rYiLixPz588X+vr64tq1a0IIIbZu3SqaNGmicQ6CgoJEy5YtRWFhoXjw4IFo3ry5WLx4sRSDEE//LHD27Fmhr68vtm3bJu7cuSPOnz8v1qxZoxHbvn37hFwuL/f8E1UXJt1UZxTteHr06CEmTpwohKh80m1vb6/RmTg5OYnevXtLr/Pz80WjRo3EDz/8IIT4N+letmyZVCcvL080b95cLF++XAghxJIlS0okg/fu3RMARFxcnBDiSSLYpUuXp7bX1tZWLF26VGPdSy+9JN577z3ptYuLi1i4cGG526lI0m1vby/y8/OldW+++aZ46623pNdFk+79+/eLBg0aiL///lsq37dvX4WTbh8fHzFlyhSN2I4dOyb09PREVlZWqW0yMjIS06dPL7fd6rb36tVLY91LL70k5s2bV+Z7du7cKZo2bSq9Dg0NFQBETExMufsqKCgQJiYm4rfffhNCPDk+enp60nkvrvjxF0KIu3fvCn19fY1jKoQQAwYMEP7+/mXG8+DBAwGg3A+CRER13U8//SSaNGkiDA0Nxcsvvyz8/f3FxYsXNeoU7aOKioqKEoaGhmLHjh1CCCGys7OFsbGxOHnypEY9Hx8f8fbbb5e6/xs3bggA4sSJE9K65ORkYWRkJH788UchxJMv5wGIQ4cOPVObin72eZY+QgghAgMDhaWlpfDz8xM2NjbSwIAQpfc9pVm1apVo1apVifUAxNSpUzXWubm5SV9WZ2VliSZNmkjHUYgnX5QvWrRIel30s4Ta0z4L/O9//xOmpqbSF9uluXjxogAg7ty589T2EVU1Xl5OddLy5cuxadMmxMbGVnob7du317hPSaFQoGPHjtJrfX19NG3aFElJSRrvc3d3l35u0KABunXrJsVx8eJFHDp0CI0bN5aWtm3bAnhyiZeaq6trubGlpaXh/v376Nmzp8b6nj17Plebn6Z9+/bQ19eXXtvY2JRov1psbCzs7Ow07kkremye1cWLFxEWFqZxzDw9PVFYWIj4+PhS3yOEeObtd+rUSeN18Tb98ccfGDBgAF544QWYmJhg7NixePDgATIzM6U6crm8xHYSExMxefJktGnTBmZmZjA1NUV6ejqUSiWAJ5fRN2/eHI6Ojs8c6+XLl1FQUABHR0eN43HkyBGN35/i8VhYWOCdd96Bp6cnhg8fjjVr1mjcE0hEVB+MGjUK9+/fx6+//orBgwfj8OHD6Nq1q3S5eFmUSiVGjhyJDz/8EP/9738BALdu3UJmZiYGDhyo8fd48+bNGn+Pi4qNjUWDBg3g5uYmrWvatCmcnJyqpO9+1j7igw8+gKOjI7788kts3LgRTZs2rfC+srKyYGhoWGpZ8b7e3d1dap+hoSHGjh2LjRs3AgDOnz+PK1eu4J133il3f0/7LDBw4EDY29ujVatWGDt2LLZu3arRTwOAkZERAJRYT1QTOJEa1Ul9+vSBp6cn/P39S/wh19PTK5GUlTabdcOGDTVey2SyUtcVFhY+c1zp6ekYPnw4li9fXqLMxsZG+rlRo0bPvM3nZWpqWupsnikpKTAzM9NY97ztL079pUbR81H8XKSnp+Pdd9/F9OnTS7y/RYsWpW7X0dER169ff6YYymvTnTt38Oqrr2LatGlYunQpLCwscPz4cfj4+CA3NxfGxsYAnnTkxWfIHz9+PB48eIA1a9bA3t4eBgYGcHd3R25urvSeikpPT4e+vj6io6M1vvwAgMaNG0s/lxZPaGgopk+fjvDwcOzYsQPz589HREQEevToUeE4iIhqK0NDQwwcOBADBw7EJ598gkmTJmHhwoVlJn0ZGRl47bXX4O7ujsWLF0vr1XN77N27Fy+88ILGewwMDKot/vI8ax+RlJSEGzduQF9fHzdv3sTgwYMrvC9LS0s8evSoUnFOmjQJnTt3xl9//YXQ0FD079//qRO+Pu2zgFwux/nz53H48GEcOHAACxYswKJFi3D27FlpNvaHDx8C0J1JX6l+4Ug31VnLli3Db7/9hqioKI31zZo1g0ql0kj0qvLZ2qdOnZJ+zs/PR3R0NJydnQEAXbt2xdWrV9GyZUu0bt1aY6lIom1qagpbW1uNx5wAwIkTJ9CuXbsKxevk5FRigi/gybfPFRmFLc7Z2Rn37t3TGFEtemyAfzu+onWKn4uuXbvi2rVrJY5X69aty5zVfcyYMfjjjz9w4cKFEmV5eXnlTsJWVHR0NAoLC7Fy5Ur06NEDjo6OuH///jO998SJE5g+fTqGDh2K9u3bw8DAQGPCvE6dOuGvv/7CjRs3Sn2/XC5HQUGBxrouXbqgoKAASUlJJY7Fs8y426VLF/j7++PkyZPo0KGDxrNViYjqo3bt2pXZJwgh4O3tjcLCQnz//fcaX2YWnaiy+N9jOzu7Urfn7OyM/Px8nD59Wlr34MEDxMXFVbjvLs2z9hETJ05Ex44dsWnTJsybN09jlL20vqesfalUqlIT7+J9/alTp6TPQQDQsWNHdOvWDd9++y22bduGiRMnatQvLYZn+SzQoEEDeHh4IDAwEJcuXcKdO3dw8OBBaRtXrlxB8+bNYWlp+dT2EVU1Jt1UZ3Xs2BFeXl5Yu3atxvq+ffvin3/+QWBgIG7fvo3g4GDs27evyvYbHByMXbt24fr16/D19cWjR4+kDsXX1xcPHz7E22+/jbNnz+L27dvYv38/JkyY8EydXFFz5szB8uXLsWPHDsTFxeGjjz5CTEwMZsyYUaHtTJs2DTdu3MD06dNx6dIlxMXFISgoCD/88AM++OCDCm2rKA8PDzg6OmL8+PG4ePEijh07hv/7v//TqKP+cLJo0SLcvHkTe/fuxcqVKzXqzJs3DydPnoSfnx9iYmJw8+ZN/PLLLyVmPy1q5syZ6NmzJwYMGIDg4GBcvHgRf/75J3788Uf06NFDmin2aVq3bo28vDysW7cOf/75J77//nuEhIQ803vbtGmD77//HrGxsTh9+jS8vLw0RrdfeeUV9OnTB6NGjUJERATi4+Oxb98+aQb6li1bIj09HZGRkUhOTkZmZiYcHR3h5eWFcePG4eeff0Z8fDzOnDmDgIAA7N27t8xY4uPj4e/vj6ioKNy9excHDhzAzZs3NT4EERHVZQ8ePED//v2xZcsWXLp0CfHx8di5cycCAwMxYsSIUt+zaNEi/PHHH/j666+Rnp4OlUoFlUqFrKwsmJiY4MMPP8SsWbOwadMm3L59G+fPn8e6deuwadOmUrfXpk0bjBgxApMnT8bx48dx8eJFeHt744UXXigzhop4lj4iODgYUVFR2LRpE7y8vDBy5Eh4eXlJV2GV1veUpkuXLrC0tCzx5T8A7Ny5Exs3bsSNGzewcOFCnDlzpkSfPWnSJCxbtgxCCLz++usaZS1btsTRo0fx999/S19WP+2zwJ49e7B27VrExMTg7t272Lx5MwoLC+Hk5CRt99ixYxg0aFAljy7Rc9LmDeVEVan4DJ5CPJncTC6Xi+K/6uvXrxd2dnaiUaNGYty4cWLp0qUlJlIrvq3SJhwrOtmHeiK1bdu2ie7duwu5XC7atWsnDh48qPGeGzduiNdff12Ym5sLIyMj0bZtWzFz5kxRWFhY5n5KU1BQIBYtWiReeOEF0bBhQ+Hi4iL27dunUedZJlITQogzZ86IgQMHimbNmgkzMzPh5uZWYiKZ0o7JjBkzxCuvvFLq8RBCiLi4ONGrVy8hl8uFo6OjCA8PLzFJzfHjx0XHjh2FoaGh6N27t9i5c6fGRGpF42vcuLFo1KiR6NSpU4lJ5IrLzs4WAQEB0rYtLCxEz549RVhYmMjLyxNClH6sR4wYIcaPHy+9DgoKEjY2NsLIyEh4enqKzZs3a0z+VtakM+fPnxfdunUThoaGok2bNmLnzp0ljs+DBw/EhAkTRNOmTYWhoaHo0KGD2LNnj1Q+depU0bRpUwFAOo+5ubliwYIFomXLlqJhw4bCxsZGvP766+LSpUtlxqNSqcTIkSOFjY2NkMvlwt7eXixYsKDErLNERHVVdna2+Oijj0TXrl2FmZmZMDY2Fk5OTmL+/PkiMzNTqle0j3rllVcEgBJLaGioEEKIwsJCsXr1auHk5CQaNmwomjVrJjw9PcWRI0fKjOPhw4di7NixwszMTOpX1DOLC/F8E6kJUX4fERsbK4yMjMS2bds09mdnZyfmzp0rrSut7ynN3LlzxejRozXWARDBwcFi4MCBwsDAQLRs2VJj0jS1x48fC2NjY43JX9WioqJEp06dhIGBgcbnt/I+Cxw7dky88sorokmTJsLIyEh06tRJY79ZWVnCzMxMREVFlX0wiaqRTIgKzDhERPScZDIZdu3ahZEjR2o7FCIiIqoklUqF9u3b4/z580+9J7u4O3fu4MUXX8TZs2fRtWvXaorwX+vXr8euXbtw4MCBat8XUWl4eTkREREREVWItbU1NmzYID2V41nk5eVBpVJh/vz56NGjR40k3MCTSVPXrVtXI/siKg1nLyciIiIiogqr6FVrJ06cQL9+/eDo6IiffvqpeoIqxaRJk2psX0Sl4eXlRERERERERNWEl5cTERERERERVRMm3URERERERETVhEk3ERERERERUTVh0k1ERERERERUTZh0ExEREREREVUTJt1ERERERERE1YRJNxEREREREVE1YdJNREREREREVE2YdBMRERERERFVk/8HQNC9/SPVsfMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1000x600 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "# Total Words\n",
        "plt.subplot(2, 2, 1)\n",
        "plt.hist(Final_df_['Total_Words'], bins=100, color='skyblue', edgecolor='black')\n",
        "plt.title('Total Words')\n",
        "plt.xlabel('Number of Words')\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "# Total Chars\n",
        "plt.subplot(2, 2, 2)\n",
        "plt.hist(Final_df_['Total_Chars'], bins=100, color='salmon', edgecolor='black')\n",
        "plt.title('Total Characters')\n",
        "plt.xlabel('Number of Characters')\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "# Unique Chars\n",
        "plt.subplot(2, 2, 3)\n",
        "plt.hist(Final_df_['Unique_Chars'], bins=100, color='lightgreen', edgecolor='black')\n",
        "plt.title('Unique Characters')\n",
        "plt.xlabel('Number of Unique Characters')\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "# Size of Text\n",
        "plt.subplot(2, 2, 4)\n",
        "plt.hist(Final_df_['Size_of_Text'], bins=100, color='gold', edgecolor='black')\n",
        "plt.title('Size of Text')\n",
        "plt.xlabel('Size of Text (bytes)')\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fPad73KNVCBS"
      },
      "source": [
        "#Special Character Analysis for SQL Injection Detection Training\n",
        "\n",
        "### Analyzing Special Characters in Training Data\n",
        "Conducting an analysis of special characters within the training dataset helps identify potentially malicious patterns unique to SQL injection attacks, improving the detection capabilities of the model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "xeXKT4pev8dq",
        "outputId": "4374ecd4-99ff-416e-9bc6-fa702c2b06d9"
      },
      "outputs": [
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlUAAAHHCAYAAACWQK1nAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABUR0lEQVR4nO3dfVzN9/8/8McpzinVOUW6ImmEIpmQ5mI2TS4/LmIiYzQ+pgy5GB9WzcWQD4aZvmyTbRljmMsmuRoS5ZrkYohxctWFogv1+v2xX++Po1B5UyeP++12bjfn/X6+X+/n+9VpPfY+7/M+CiGEABERERG9FIPyboCIiIioMmCoIiIiIpIBQxURERGRDBiqiIiIiGTAUEVEREQkA4YqIiIiIhkwVBERERHJgKGKiIiISAYMVUREREQyYKgiojJTKBQIDQ0t9XZ79+6FQqHA3r17Ze8nMDBQ1jHp1XhVr4FCZX1tEr0MhiqiCuz06dPo27cvHBwcYGRkhFq1auGDDz7AkiVLyru11+ry5cv497//jbfeegtGRkZQq9Vo06YNFi1ahEePHpV3ey/t5s2bCA0NxYkTJ8pl/9u3b4dCoYCdnR0KCgrKpQeiyqBKeTdARMU7dOgQ3nvvPdSpUwfDhw+HjY0Nrl+/jsOHD2PRokUYPXp0ebeIR48eoUqVV/ufkW3btqFfv35QqVQYPHgwmjRpgtzcXBw4cAATJ07E2bNnsXz58lfaw6t28+ZNfPnll6hbty6aNWv22vcfGRmJunXr4urVq9i9eze8vLxeew9yex2vTaKn8RVHVEHNmjULGo0GR48ehbm5uc6627dvl09TTzEyMnql41+5cgW+vr5wcHDA7t27YWtrK60LCAjApUuXsG3btlfaw9OysrJgYmLyWvdZViXpNSsrC7///jtmz56NlStXIjIyslKEqlf92iQqDt/+I6qgLl++jMaNGxcJVABgZWWl87zwWqLIyEg0bNgQRkZGcHd3x/79+4ts+/fff2PYsGGwtraGSqVC48aN8cMPPxSpy87ORmhoKBo0aAAjIyPY2tqiT58+uHz5ss5+n7xu5dq1axg1ahQaNmwIY2Nj1KhRA/369cPVq1fLNAdhYWHIzMzE999/rxOoCtWvXx9jxowpsnzTpk1o0qSJdHxRUVE660vaZ0REBBQKBfbt24dRo0bBysoKtWvXLvWxpqWlYdy4cahbty5UKhVq166NwYMH4+7du9i7dy9atmwJABg6dCgUCgUUCgUiIiKk7ePi4tC5c2doNBpUq1YN7777Lg4ePKizj9DQUCgUCpw7dw4DBw6EhYUF2rZt+8I53rhxIx49eoR+/frB19cXGzZsQHZ2dpG6wteYXHP7tJCQEFStWhV37twpsm7EiBEwNzeX+oqPj4e3tzcsLS1hbGwMR0dHDBs2rEi/T742Hzx4gLFjx0o/AysrK3zwwQc4duzYC+eIqKR4poqognJwcEBsbCzOnDmDJk2avLB+3759WLt2LT777DOoVCp8++236Ny5M44cOSJtn5KSgtatW0t/IGvWrIkdO3bA398fGRkZGDt2LAAgPz8f3bt3R0xMDHx9fTFmzBg8ePAA0dHROHPmDOrVq1dsD0ePHsWhQ4fg6+uL2rVr4+rVq1i2bBk6dOiAc+fOoVq1aqWagy1btuCtt97CO++8U+JtDhw4gA0bNmDUqFEwMzPD4sWL4ePjg+TkZNSoUaNMfY4aNQo1a9ZEcHAwsrKySjVGZmYm2rVrh8TERAwbNgzNmzfH3bt3sXnzZty4cQPOzs6YPn06goODMWLECLRr1w4ApGPevXs3unTpAnd3d4SEhMDAwAArV67E+++/jz///BOtWrXS6bVfv35wcnLCV199BSHEC+crMjIS7733HmxsbODr64vJkydjy5Yt6Nev32uZ20IfffQRpk+fjrVr1+p82CA3Nxfr16+Hj48PjIyMcPv2bXTq1Ak1a9bE5MmTYW5ujqtXr2LDhg3PPc6RI0di/fr1CAwMhIuLC+7du4cDBw4gMTERzZs3f+E8EZWIIKIKaefOncLQ0FAYGhoKT09PMWnSJPHHH3+I3NzcIrUABAARHx8vLbt27ZowMjISvXv3lpb5+/sLW1tbcffuXZ3tfX19hUajEQ8fPhRCCPHDDz8IAGLBggVF9lVQUKCz35CQEOl54fZPio2NFQDEjz/+KC3bs2ePACD27NnzzONPT08XAETPnj2fWfM0AEKpVIpLly5Jy06ePCkAiCVLlpS6z5UrVwoAom3btuLx48c69SUdIzg4WAAQGzZsKFJfOJdHjx4VAMTKlSuLrHdychLe3t468/7w4UPh6OgoPvjgA2lZSEiIACAGDBhQZD/PkpKSIqpUqSJWrFghLXvnnXeKnXO557a414Cnp6fw8PDQ2XbDhg06dRs3bhQAxNGjR597bE+/NjUajQgICHjuNkQvi2//EVVQH3zwAWJjY/Gvf/0LJ0+eRFhYGLy9vVGrVi1s3ry5SL2npyfc3d2l53Xq1EHPnj3xxx9/ID8/H0II/Pbbb+jRoweEELh796708Pb2Rnp6uvRWyG+//QZLS8tiL4ZXKBTP7NnY2Fj6d15eHu7du4f69evD3Ny81G+zZGRkAADMzMxKtZ2Xl5fOmbSmTZtCrVbjr7/+KnOfw4cPh6Ghoc6yko7x22+/wc3NDb179y4y7vPmEgBOnDiBixcvYuDAgbh3757088rKykLHjh2xf//+Ip/WGzly5HPHfNKaNWtgYGAAHx8fadmAAQOwY8cOpKamFql/FXP7pMGDByMuLk7nLebIyEjY29vj3XffBQDp7fCtW7ciLy+vxMdqbm6OuLg43Lx5s8TbEJUWQxVRBdayZUts2LABqampOHLkCKZMmYIHDx6gb9++OHfunE6tk5NTke0bNGiAhw8f4s6dO7hz5w7S0tKwfPly1KxZU+cxdOhQAP+7AP7y5cto2LBhqT899ejRIwQHB8Pe3h4qlQqWlpaoWbMm0tLSkJ6eXqqx1Go1gH+uhSmNOnXqFFlmYWGhExJK26ejo2ORZSUd4/LlyyV6+7Y4Fy9eBAAMGTKkyM/su+++Q05OTpF+i+v1WX7++We0atUK9+7dw6VLl3Dp0iW8/fbbyM3Nxbp164rUv4q5fVL//v2hUqkQGRkJAEhPT8fWrVvh5+cnBdB3330XPj4++PLLL2FpaYmePXti5cqVyMnJee7YYWFhOHPmDOzt7dGqVSuEhobqhEEiOfCaKiI9oFQq0bJlS7Rs2RINGjTA0KFDsW7dOoSEhJR4jMIzGoMGDcKQIUOKrWnatOlL9Tl69GisXLkSY8eOhaenJzQaDRQKBXx9fUt9/yO1Wg07OzucOXOmVNs9fUapkHji+qLS9vnk2ZeyjlEWhePMmzfvmbdaMDU1fWGvxbl48SKOHj0KoPhAHhkZiREjRugsexVz+yQLCwt0794dkZGRCA4Oxvr165GTk4NBgwZJNQqFAuvXr8fhw4exZcsW/PHHHxg2bBjmz5+Pw4cPF5mPQh9++CHatWuHjRs3YufOnZg3bx7mzp2LDRs2oEuXLs/ti6ikGKqI9EyLFi0AALdu3dJZXnhW40kXLlxAtWrVULNmTQD/vJWWn5//wo/M16tXD3FxccjLy0PVqlVL3Nv69esxZMgQzJ8/X1qWnZ2NtLS0Eo/xpO7du2P58uWIjY2Fp6dnmcYojhx9lnSMevXqvTAYPuttwMK32tRqtey3OYiMjETVqlXx008/FQlLBw4cwOLFi5GcnFzs2annedm5HTx4MHr27ImjR48iMjISb7/9Nho3blykrnXr1mjdujVmzZqF1atXw8/PD2vWrMEnn3zyzLFtbW0xatQojBo1Crdv30bz5s0xa9YshiqSDd/+I6qg9uzZU+ynt7Zv3w4AaNiwoc7y2NhYnWtWrl+/jt9//x2dOnWCoaEhDA0N4ePjg99++63YP/JPfpTdx8cHd+/exTfffFOkrrieChkaGhZZv2TJEuTn5z9zm+eZNGkSTExM8MknnyAlJaXI+suXL2PRokWlHleOPks6ho+PD06ePImNGzcWGaNw+8J7ST0dPNzd3VGvXj3897//RWZmZpHti7v9QElFRkaiXbt26N+/P/r27avzmDhxIgDgl19+KfW4Lzu3Xbp0gaWlJebOnYt9+/bpnKUCgNTU1CLjF57Fe9ZbgPn5+UXeerSysoKdnd0L3zYkKg2eqSKqoEaPHo2HDx+id+/eaNSoEXJzc3Ho0CGsXbsWdevWla6DKtSkSRN4e3vr3FIBAL788kupZs6cOdizZw88PDwwfPhwuLi44P79+zh27Bh27dqF+/fvA/jnbMGPP/6IoKAgHDlyBO3atUNWVhZ27dqFUaNGoWfPnsX23L17d/z000/QaDRwcXFBbGwsdu3aJX3cvrTq1auH1atXo3///nB2dta5o/qhQ4ewbt06fPzxx6UeV44+SzrGxIkTsX79evTr1w/Dhg2Du7s77t+/j82bNyM8PBxubm6oV68ezM3NER4eDjMzM5iYmMDDwwOOjo747rvv0KVLFzRu3BhDhw5FrVq18Pfff2PPnj1Qq9XYsmVLqY8/Li4Oly5deub3JNaqVQvNmzdHZGQkPv/881KN/bJzW7VqVfj6+uKbb76BoaEhBgwYoLN+1apV+Pbbb9G7d2/Uq1cPDx48wIoVK6BWq9G1a9dix3zw4AFq166Nvn37ws3NDaampti1axeOHj2qc0aN6KWVz4cOiehFduzYIYYNGyYaNWokTE1NhVKpFPXr1xejR48WKSkpOrUAREBAgPj555+Fk5OTUKlU4u233y72lgUpKSkiICBA2Nvbi6pVqwobGxvRsWNHsXz5cp26hw8fiqlTpwpHR0eprm/fvuLy5cs6+33yY+upqali6NChwtLSUpiamgpvb29x/vx54eDgIIYMGSLVleSWCk+6cOGCGD58uKhbt65QKpXCzMxMtGnTRixZskRkZ2cXmYenPb3/kvZZeEuF4j6+X9IxhBDi3r17IjAwUNSqVUsolUpRu3ZtMWTIEJ1bW/z+++/CxcVFVKlSpcjtFY4fPy769OkjatSoIVQqlXBwcBAffvihiImJkWoKb6lw586dF87n6NGjBQCdn+XTQkNDBQBx8uRJIYT8c/u818CRI0cEANGpU6ci644dOyYGDBgg6tSpI1QqlbCyshLdu3fXuZ1IYb+Fr82cnBwxceJE4ebmJszMzISJiYlwc3MT33777XNmiaj0FEKU4O5wRFShKRQKBAQEFPt2HZG+OXnyJJo1a4Yff/wRH330UXm3Q1RivKaKiIgqlBUrVsDU1BR9+vQp71aISoXXVBERUYWwZcsWnDt3DsuXL0dgYKDefHE1USGGKiIiqhBGjx6NlJQUdO3aVecDFkT6gtdUEREREcmA11QRERERyYChioiIiEgGvKbqNSooKMDNmzdhZmb2wm+nJyIioopBCIEHDx7Azs4OBgbPPh/FUPUa3bx5E/b29uXdBhEREZXB9evXUbt27WeuZ6h6jczMzAD880NRq9Xl3A0RERGVREZGBuzt7aW/48/CUPUaFb7lp1arGaqIiIj0zIsu3eGF6kREREQyYKgiIiIikgFDFREREZEMGKqIiIiIZMBQRURERCQDhioiIiIiGTBUEREREcmAoYqIiIhIBgxVRERERDJgqCIiIiKSAUMVERERkQwYqoiIiIhkwFBFREREJAOGKiIiIiIZVCnvBkgeycnJuHv37isZ29LSEnXq1HklYxMREVUWDFWVQHJyMho5O+PRw4evZHzjatVwPjGRwYqIiOg5GKoqgbt37+LRw4f4cOYyWDk6yTr27SsX8eu0T3H37l2GKiIioudgqKpErBydUMvZrbzbICIieiPxQnUiIiIiGTBUEREREcmAoYqIiIhIBgxVRERERDJgqCIiIiKSAUMVERERkQwYqoiIiIhkwFBFREREJAOGKiIiIiIZMFQRERERyYChioiIiEgGDFVEREREMmCoIiIiIpIBQxURERGRDMo1VO3fvx89evSAnZ0dFAoFNm3aJK3Ly8vD559/DldXV5iYmMDOzg6DBw/GzZs3dca4f/8+/Pz8oFarYW5uDn9/f2RmZurUnDp1Cu3atYORkRHs7e0RFhZWpJd169ahUaNGMDIygqurK7Zv366zXgiB4OBg2NrawtjYGF5eXrh48aJ8k0FERER6rVxDVVZWFtzc3LB06dIi6x4+fIhjx47hiy++wLFjx7BhwwYkJSXhX//6l06dn58fzp49i+joaGzduhX79+/HiBEjpPUZGRno1KkTHBwckJCQgHnz5iE0NBTLly+Xag4dOoQBAwbA398fx48fR69evdCrVy+cOXNGqgkLC8PixYsRHh6OuLg4mJiYwNvbG9nZ2a9gZoiIiEjfKIQQorybAACFQoGNGzeiV69ez6w5evQoWrVqhWvXrqFOnTpITEyEi4sLjh49ihYtWgAAoqKi0LVrV9y4cQN2dnZYtmwZpk6dCq1WC6VSCQCYPHkyNm3ahPPnzwMA+vfvj6ysLGzdulXaV+vWrdGsWTOEh4dDCAE7OzuMHz8eEyZMAACkp6fD2toaERER8PX1LdExZmRkQKPRID09HWq1uizTVKxjx47B3d0dgZG7UMvZTbZxAeDvxJP4xs8LCQkJaN68uaxjExER6YOS/v3Wq2uq0tPToVAoYG5uDgCIjY2Fubm5FKgAwMvLCwYGBoiLi5Nq2rdvLwUqAPD29kZSUhJSU1OlGi8vL519eXt7IzY2FgBw5coVaLVanRqNRgMPDw+phoiIiN5sVcq7gZLKzs7G559/jgEDBkgpUavVwsrKSqeuSpUqqF69OrRarVTj6OioU2NtbS2ts7CwgFarlZY9WfPkGE9uV1xNcXJycpCTkyM9z8jIKPHxEhERkX7RizNVeXl5+PDDDyGEwLJly8q7nRKbPXs2NBqN9LC3ty/vloiIiOgVqfChqjBQXbt2DdHR0TrvZdrY2OD27ds69Y8fP8b9+/dhY2Mj1aSkpOjUFD5/Uc2T65/crria4kyZMgXp6enS4/r16yU+biIiItIvFTpUFQaqixcvYteuXahRo4bOek9PT6SlpSEhIUFatnv3bhQUFMDDw0Oq2b9/P/Ly8qSa6OhoNGzYEBYWFlJNTEyMztjR0dHw9PQEADg6OsLGxkanJiMjA3FxcVJNcVQqFdRqtc6DiIiIKqdyDVWZmZk4ceIETpw4AeCfC8JPnDiB5ORk5OXloW/fvoiPj0dkZCTy8/Oh1Wqh1WqRm5sLAHB2dkbnzp0xfPhwHDlyBAcPHkRgYCB8fX1hZ2cHABg4cCCUSiX8/f1x9uxZrF27FosWLUJQUJDUx5gxYxAVFYX58+fj/PnzCA0NRXx8PAIDAwH888nEsWPHYubMmdi8eTNOnz6NwYMHw87O7rmfViQiIqI3R7leqB4fH4/33ntPel4YdIYMGYLQ0FBs3rwZANCsWTOd7fbs2YMOHToAACIjIxEYGIiOHTvCwMAAPj4+WLx4sVSr0Wiwc+dOBAQEwN3dHZaWlggODta5l9U777yD1atXY9q0afjPf/4DJycnbNq0CU2aNJFqJk2ahKysLIwYMQJpaWlo27YtoqKiYGRkJPe0EBERkR6qMPepehPwPlVERET6p1Lep4qIiIioomKoIiIiIpIBQxURERGRDBiqiIiIiGTAUEVEREQkA4YqIiIiIhkwVBERERHJgKGKiIiISAYMVUREREQyYKgiIiIikgFDFREREZEMGKqIiIiIZMBQRURERCQDhioiIiIiGTBUEREREcmAoYqIiIhIBgxVRERERDJgqCIiIiKSAUMVERERkQwYqoiIiIhkwFBFREREJAOGKiIiIiIZMFQRERERyYChioiIiEgGDFVEREREMmCoIiIiIpIBQxURERGRDBiqiIiIiGTAUEVEREQkA4YqIiIiIhkwVBERERHJgKGKiIiISAYMVUREREQyYKgiIiIikgFDFREREZEMGKqIiIiIZMBQRURERCQDhioiIiIiGTBUEREREcmAoYqIiIhIBgxVRERERDJgqCIiIiKSAUMVERERkQwYqoiIiIhkUK6hav/+/ejRowfs7OygUCiwadMmnfVCCAQHB8PW1hbGxsbw8vLCxYsXdWru378PPz8/qNVqmJubw9/fH5mZmTo1p06dQrt27WBkZAR7e3uEhYUV6WXdunVo1KgRjIyM4Orqiu3bt5e6FyIiInpzlWuoysrKgpubG5YuXVrs+rCwMCxevBjh4eGIi4uDiYkJvL29kZ2dLdX4+fnh7NmziI6OxtatW7F//36MGDFCWp+RkYFOnTrBwcEBCQkJmDdvHkJDQ7F8+XKp5tChQxgwYAD8/f1x/Phx9OrVC7169cKZM2dK1QsRERG9uRRCCFHeTQCAQqHAxo0b0atXLwD/nBmys7PD+PHjMWHCBABAeno6rK2tERERAV9fXyQmJsLFxQVHjx5FixYtAABRUVHo2rUrbty4ATs7OyxbtgxTp06FVquFUqkEAEyePBmbNm3C+fPnAQD9+/dHVlYWtm7dKvXTunVrNGvWDOHh4SXqpSQyMjKg0WiQnp4OtVoty7wBwLFjx+Du7o7AyF2o5ewm27gA8HfiSXzj54WEhAQ0b95c1rGJiIj0QUn/flfYa6quXLkCrVYLLy8vaZlGo4GHhwdiY2MBALGxsTA3N5cCFQB4eXnBwMAAcXFxUk379u2lQAUA3t7eSEpKQmpqqlTz5H4Kawr3U5JeiIiI6M1WpbwbeBatVgsAsLa21llubW0trdNqtbCystJZX6VKFVSvXl2nxtHRscgYhessLCyg1WpfuJ8X9VKcnJwc5OTkSM8zMjKec8RERESkzyrsmarKYPbs2dBoNNLD3t6+vFsiIiKiV6TChiobGxsAQEpKis7ylJQUaZ2NjQ1u376ts/7x48e4f/++Tk1xYzy5j2fVPLn+Rb0UZ8qUKUhPT5ce169ff8FRExERkb6qsKHK0dERNjY2iImJkZZlZGQgLi4Onp6eAABPT0+kpaUhISFBqtm9ezcKCgrg4eEh1ezfvx95eXlSTXR0NBo2bAgLCwup5sn9FNYU7qckvRRHpVJBrVbrPIiIiKhyKtdQlZmZiRMnTuDEiRMA/rkg/MSJE0hOToZCocDYsWMxc+ZMbN68GadPn8bgwYNhZ2cnfULQ2dkZnTt3xvDhw3HkyBEcPHgQgYGB8PX1hZ2dHQBg4MCBUCqV8Pf3x9mzZ7F27VosWrQIQUFBUh9jxoxBVFQU5s+fj/PnzyM0NBTx8fEIDAwEgBL1QkRERG+2cr1QPT4+Hu+99570vDDoDBkyBBEREZg0aRKysrIwYsQIpKWloW3btoiKioKRkZG0TWRkJAIDA9GxY0cYGBjAx8cHixcvltZrNBrs3LkTAQEBcHd3h6WlJYKDg3XuZfXOO+9g9erVmDZtGv7zn//AyckJmzZtQpMmTaSakvRCREREb64Kc5+qNwHvU0VERKR/9P4+VURERET6hKGKiIiISAYMVUREREQyYKgiIiIikgFDFREREZEMGKqIiIiIZMBQRURERCQDhioiIiIiGTBUEREREcmAoYqIiIhIBgxVRERERDJgqCIiIiKSAUMVERERkQwYqoiIiIhkwFBFREREJAOGKiIiIiIZMFQRERERyYChioiIiEgGDFVEREREMmCoIiIiIpIBQxURERGRDBiqiIiIiGTAUEVEREQkA4YqIiIiIhkwVBERERHJgKGKiIiISAYMVUREREQyYKgiIiIikgFDFREREZEMGKqIiIiIZMBQRURERCQDhioiIiIiGTBUEREREcmAoYqIiIhIBgxVRERERDJgqCIiIiKSAUMVERERkQwYqoiIiIhkwFBFREREJAOGKiIiIiIZMFQRERERyYChioiIiEgGDFVEREREMmCoIiIiIpJBhQ5V+fn5+OKLL+Do6AhjY2PUq1cPM2bMgBBCqhFCIDg4GLa2tjA2NoaXlxcuXryoM879+/fh5+cHtVoNc3Nz+Pv7IzMzU6fm1KlTaNeuHYyMjGBvb4+wsLAi/axbtw6NGjWCkZERXF1dsX379ldz4ERERKR3KnSomjt3LpYtW4ZvvvkGiYmJmDt3LsLCwrBkyRKpJiwsDIsXL0Z4eDji4uJgYmICb29vZGdnSzV+fn44e/YsoqOjsXXrVuzfvx8jRoyQ1mdkZKBTp05wcHBAQkIC5s2bh9DQUCxfvlyqOXToEAYMGAB/f38cP34cvXr1Qq9evXDmzJnXMxlERERUoSnEk6d9Kpju3bvD2toa33//vbTMx8cHxsbG+PnnnyGEgJ2dHcaPH48JEyYAANLT02FtbY2IiAj4+voiMTERLi4uOHr0KFq0aAEAiIqKQteuXXHjxg3Y2dlh2bJlmDp1KrRaLZRKJQBg8uTJ2LRpE86fPw8A6N+/P7KysrB161apl9atW6NZs2YIDw8v0fFkZGRAo9EgPT0darValjkCgGPHjsHd3R2BkbtQy9lNtnEB4O/Ek/jGzwsJCQlo3ry5rGMTERHpg5L+/a7QZ6reeecdxMTE4MKFCwCAkydP4sCBA+jSpQsA4MqVK9BqtfDy8pK20Wg08PDwQGxsLAAgNjYW5ubmUqACAC8vLxgYGCAuLk6qad++vRSoAMDb2xtJSUlITU2Vap7cT2FN4X6IiIjozValvBt4nsmTJyMjIwONGjWCoaEh8vPzMWvWLPj5+QEAtFotAMDa2lpnO2tra2mdVquFlZWVzvoqVaqgevXqOjWOjo5FxihcZ2FhAa1W+9z9FCcnJwc5OTnS84yMjBIfOxEREemXCn2m6tdff0VkZCRWr16NY8eOYdWqVfjvf/+LVatWlXdrJTJ79mxoNBrpYW9vX94tERER0StSoUPVxIkTMXnyZPj6+sLV1RUfffQRxo0bh9mzZwMAbGxsAAApKSk626WkpEjrbGxscPv2bZ31jx8/xv3793VqihvjyX08q6ZwfXGmTJmC9PR06XH9+vVSHT8RERHpjzKFqr/++kvuPor18OFDGBjotmhoaIiCggIAgKOjI2xsbBATEyOtz8jIQFxcHDw9PQEAnp6eSEtLQ0JCglSze/duFBQUwMPDQ6rZv38/8vLypJro6Gg0bNgQFhYWUs2T+ymsKdxPcVQqFdRqtc6DiIiIKqcyhar69evjvffew88//6xz6wK59ejRA7NmzcK2bdtw9epVbNy4EQsWLEDv3r0BAAqFAmPHjsXMmTOxefNmnD59GoMHD4adnR169eoFAHB2dkbnzp0xfPhwHDlyBAcPHkRgYCB8fX1hZ2cHABg4cCCUSiX8/f1x9uxZrF27FosWLUJQUJDUy5gxYxAVFYX58+fj/PnzCA0NRXx8PAIDA1/Z8RMREZH+KFOoOnbsGJo2bYqgoCDY2Njg3//+N44cOSJ3b1iyZAn69u2LUaNGwdnZGRMmTMC///1vzJgxQ6qZNGkSRo8ejREjRqBly5bIzMxEVFQUjIyMpJrIyEg0atQIHTt2RNeuXdG2bVude1BpNBrs3LkTV65cgbu7O8aPH4/g4GCde1m98847WL16NZYvXw43NzesX78emzZtQpMmTWQ/biIiItI/L3WfqsePH2Pz5s2IiIhAVFQUGjRogGHDhuGjjz5CzZo15eyzUuB9qoiIiPTPa7lPVZUqVdCnTx+sW7cOc+fOxaVLlzBhwgTY29tj8ODBuHXr1ssMT0RERKQ3XipUxcfHY9SoUbC1tcWCBQswYcIEXL58GdHR0bh58yZ69uwpV59EREREFVqZbv65YMECrFy5EklJSejatSt+/PFHdO3aVfqknqOjIyIiIlC3bl05eyUiIiKqsMoUqpYtW4Zhw4bh448/hq2tbbE1VlZWOt/ZR0RERFSZlSlUXbx48YU1SqUSQ4YMKcvwRERERHqnTNdUrVy5EuvWrSuyfN26dXrzFTJEREREcipTqJo9ezYsLS2LLLeyssJXX3310k0RERER6Zsyhark5GQ4OjoWWe7g4IDk5OSXboqIiIhI35QpVFlZWeHUqVNFlp88eRI1atR46aaIiIiI9E2ZQtWAAQPw2WefYc+ePcjPz0d+fj52796NMWPGwNfXV+4eiYiIiCq8Mn36b8aMGbh69So6duyIKlX+GaKgoACDBw/mNVVERET0RipTqFIqlVi7di1mzJiBkydPwtjYGK6urnBwcJC7PyIiIiK9UKZQVahBgwZo0KCBXL0QERER6a0yhar8/HxEREQgJiYGt2/fRkFBgc763bt3y9IcERERkb4oU6gaM2YMIiIi0K1bNzRp0gQKhULuvoiIiIj0SplC1Zo1a/Drr7+ia9eucvdDREREpJfKdEsFpVKJ+vXry90LERERkd4qU6gaP348Fi1aBCGE3P0QERER6aUyvf134MAB7NmzBzt27EDjxo1RtWpVnfUbNmyQpTkiIiIifVGmUGVubo7evXvL3QsRERGR3ipTqFq5cqXcfRARERHptTJdUwUAjx8/xq5du/B///d/ePDgAQDg5s2byMzMlK05IiIiIn1RpjNV165dQ+fOnZGcnIycnBx88MEHMDMzw9y5c5GTk4Pw8HC5+yQiIiKq0Mp0pmrMmDFo0aIFUlNTYWxsLC3v3bs3YmJiZGuOiIiISF+U6UzVn3/+iUOHDkGpVOosr1u3Lv7++29ZGiMiIiLSJ2U6U1VQUID8/Pwiy2/cuAEzM7OXboqIiIhI35QpVHXq1Alff/219FyhUCAzMxMhISH86hoiIiJ6I5Xp7b/58+fD29sbLi4uyM7OxsCBA3Hx4kVYWlril19+kbtHIiIiogqvTKGqdu3aOHnyJNasWYNTp04hMzMT/v7+8PPz07lwnYiIiOhNUaZQBQBVqlTBoEGD5OyFiIiISG+VKVT9+OOPz10/ePDgMjVDREREpK/KFKrGjBmj8zwvLw8PHz6EUqlEtWrVGKqIiIjojVOmT/+lpqbqPDIzM5GUlIS2bdvyQnUiIiJ6I5X5u/+e5uTkhDlz5hQ5i0VERET0JpAtVAH/XLx+8+ZNOYckIiIi0gtluqZq8+bNOs+FELh16xa++eYbtGnTRpbGiIiIiPRJmUJVr169dJ4rFArUrFkT77//PubPny9HX0RERER6pUyhqqCgQO4+iIiIiPSarNdUEREREb2pynSmKigoqMS1CxYsKMsuiIiIiPRKmULV8ePHcfz4ceTl5aFhw4YAgAsXLsDQ0BDNmzeX6hQKhTxdEhEREVVwZQpVPXr0gJmZGVatWgULCwsA/9wQdOjQoWjXrh3Gjx8va5NEREREFV2ZrqmaP38+Zs+eLQUqALCwsMDMmTP56T8iIiJ6I5UpVGVkZODOnTtFlt+5cwcPHjx46aaIiIiI9E2ZQlXv3r0xdOhQbNiwATdu3MCNGzfw22+/wd/fH3369JG1wb///huDBg1CjRo1YGxsDFdXV8THx0vrhRAIDg6Gra0tjI2N4eXlhYsXL+qMcf/+ffj5+UGtVsPc3Bz+/v7IzMzUqTl16hTatWsHIyMj2NvbIywsrEgv69atQ6NGjWBkZARXV1ds375d1mMlIiIi/VWmUBUeHo4uXbpg4MCBcHBwgIODAwYOHIjOnTvj22+/la251NRUtGnTBlWrVsWOHTtw7tw5zJ8/X+dtx7CwMCxevBjh4eGIi4uDiYkJvL29kZ2dLdX4+fnh7NmziI6OxtatW7F//36MGDFCWp+RkYFOnTrBwcEBCQkJmDdvHkJDQ7F8+XKp5tChQxgwYAD8/f1x/Phx9OrVC7169cKZM2dkO14iIiLSXwohhCjrxllZWbh8+TIAoF69ejAxMZGtMQCYPHkyDh48iD///LPY9UII2NnZYfz48ZgwYQIAID09HdbW1oiIiICvry8SExPh4uKCo0ePokWLFgCAqKgodO3aFTdu3ICdnR2WLVuGqVOnQqvVQqlUSvvetGkTzp8/DwDo378/srKysHXrVmn/rVu3RrNmzRAeHl6i48nIyIBGo0F6ejrUanWZ5+Vpx44dg7u7OwIjd6GWs5ts4wLA34kn8Y2fFxISEnQ+2UlERPSmKOnf75e6+eetW7dw69YtODk5wcTEBC+Rz4q1efNmtGjRAv369YOVlRXefvttrFixQlp/5coVaLVaeHl5Scs0Gg08PDwQGxsLAIiNjYW5ubkUqADAy8sLBgYGiIuLk2rat28vBSoA8Pb2RlJSElJTU6WaJ/dTWFO4HyIiInqzlSlU3bt3Dx07dkSDBg3QtWtX3Lp1CwDg7+8v6+0U/vrrLyxbtgxOTk74448/8Omnn+Kzzz7DqlWrAABarRYAYG1trbOdtbW1tE6r1cLKykpnfZUqVVC9enWdmuLGeHIfz6opXF+cnJwcZGRk6DyIiIiocipTqBo3bhyqVq2K5ORkVKtWTVrev39/REVFydZcQUEBmjdvjq+++gpvv/02RowYgeHDh5f47bbyNnv2bGg0Gulhb29f3i0RERHRK1KmULVz507MnTsXtWvX1lnu5OSEa9euydIYANja2sLFxUVnmbOzM5KTkwEANjY2AICUlBSdmpSUFGmdjY0Nbt++rbP+8ePHuH//vk5NcWM8uY9n1RSuL86UKVOQnp4uPa5fv/7igyYiIiK9VKZQlZWVpXOGqtD9+/ehUqleuqlCbdq0QVJSks6yCxcuwMHBAQDg6OgIGxsbxMTESOszMjIQFxcHT09PAICnpyfS0tKQkJAg1ezevRsFBQXw8PCQavbv34+8vDypJjo6Gg0bNpQ+aejp6amzn8Kawv0UR6VSQa1W6zyIiIiocipTqGrXrh1+/PFH6blCoUBBQQHCwsLw3nvvydbcuHHjcPjwYXz11Ve4dOkSVq9ejeXLlyMgIEDa79ixYzFz5kxs3rwZp0+fxuDBg2FnZ4devXoB+OfMVufOnTF8+HAcOXIEBw8eRGBgIHx9fWFnZwcAGDhwIJRKJfz9/XH27FmsXbsWixYt0vni6DFjxiAqKgrz58/H+fPnERoaivj4eAQGBsp2vERERKS/yvTdf2FhYejYsSPi4+ORm5uLSZMm4ezZs7h//z4OHjwoW3MtW7bExo0bMWXKFEyfPh2Ojo74+uuv4efnJ9VMmjQJWVlZGDFiBNLS0tC2bVtERUXByMhIqomMjERgYCA6duwIAwMD+Pj4YPHixdJ6jUaDnTt3IiAgAO7u7rC0tERwcLDOvazeeecdrF69GtOmTcN//vMfODk5YdOmTWjSpIlsx0tERET6q8z3qUpPT8c333yDkydPIjMzE82bN0dAQABsbW3l7rHS4H2qiIiI9E9J/36X+kxVXl4eOnfujPDwcEydOvWlmiQiIiKqLEp9TVXVqlVx6tSpV9ELERERkd4q04XqgwYNwvfffy93L0RERER6q0wXqj9+/Bg//PADdu3aBXd39yLf+bdgwQJZmiMiIiLSF6UKVX/99Rfq1q2LM2fOSBctX7hwQadGoVDI1x0RERGRnihVqHJycsKtW7ewZ88eAP98Lc3ixYuLfCceERER0ZumVNdUPX33hR07diArK0vWhoiIiIj0UZkuVC9UxltcEREREVU6pQpVCoWiyDVTvIaKiIiIqJTXVAkh8PHHH0tfmpydnY2RI0cW+fTfhg0b5OuQiIiISA+UKlQNGTJE5/mgQYNkbYaIiIhIX5UqVK1cufJV9UFERESk117qQnUiIiIi+gdDFREREZEMGKqIiIiIZMBQRURERCQDhioiIiIiGTBUEREREcmAoYqIiIhIBgxVRERERDJgqCIiIiKSAUMVERERkQwYqoiIiIhkwFBFREREJAOGKiIiIiIZMFQRERERyYChioiIiEgGDFVEREREMmCoIiIiIpIBQxURERGRDBiqiIiIiGTAUEVEREQkA4YqIiIiIhkwVBERERHJoEp5N0D6ITEx8ZWMa2lpiTp16rySsYmIiF4nhip6rgd3U6AwMMCgQYNeyfjG1arhfGIigxUREek9hip6rkcPMiAKCvDhzGWwcnSSdezbVy7i12mf4u7duwxVRESk9xiqqESsHJ1Qy9mtvNsgIiKqsHihOhEREZEMGKqIiIiIZMBQRURERCQDhioiIiIiGTBUEREREcmAoYqIiIhIBnoVqubMmQOFQoGxY8dKy7KzsxEQEIAaNWrA1NQUPj4+SElJ0dkuOTkZ3bp1Q7Vq1WBlZYWJEyfi8ePHOjV79+5F8+bNoVKpUL9+fURERBTZ/9KlS1G3bl0YGRnBw8MDR44ceRWHSURERHpIb0LV0aNH8X//939o2rSpzvJx48Zhy5YtWLduHfbt24ebN2+iT58+0vr8/Hx069YNubm5OHToEFatWoWIiAgEBwdLNVeuXEG3bt3w3nvv4cSJExg7diw++eQT/PHHH1LN2rVrERQUhJCQEBw7dgxubm7w9vbG7du3X/3BExERUYWnF6EqMzMTfn5+WLFiBSwsLKTl6enp+P7777FgwQK8//77cHd3x8qVK3Ho0CEcPnwYALBz506cO3cOP//8M5o1a4YuXbpgxowZWLp0KXJzcwEA4eHhcHR0xPz58+Hs7IzAwED07dsXCxculPa1YMECDB8+HEOHDoWLiwvCw8NRrVo1/PDDD693MoiIiKhC0otQFRAQgG7dusHLy0tneUJCAvLy8nSWN2rUCHXq1EFsbCwAIDY2Fq6urrC2tpZqvL29kZGRgbNnz0o1T4/t7e0tjZGbm4uEhASdGgMDA3h5eUk1RERE9Gar8F9Ts2bNGhw7dgxHjx4tsk6r1UKpVMLc3FxnubW1NbRarVTzZKAqXF+47nk1GRkZePToEVJTU5Gfn19szfnz55/Ze05ODnJycqTnGRkZLzhaIiIi0lcV+kzV9evXMWbMGERGRsLIyKi82ym12bNnQ6PRSA97e/vybomIiIhekQodqhISEnD79m00b94cVapUQZUqVbBv3z4sXrwYVapUgbW1NXJzc5GWlqazXUpKCmxsbAAANjY2RT4NWPj8RTVqtRrGxsawtLSEoaFhsTWFYxRnypQpSE9Plx7Xr18v0zwQERFRxVehQ1XHjh1x+vRpnDhxQnq0aNECfn5+0r+rVq2KmJgYaZukpCQkJyfD09MTAODp6YnTp0/rfEovOjoaarUaLi4uUs2TYxTWFI6hVCrh7u6uU1NQUICYmBippjgqlQpqtVrnQURERJVThb6myszMDE2aNNFZZmJigho1akjL/f39ERQUhOrVq0OtVmP06NHw9PRE69atAQCdOnWCi4sLPvroI4SFhUGr1WLatGkICAiASqUCAIwcORLffPMNJk2ahGHDhmH37t349ddfsW3bNmm/QUFBGDJkCFq0aIFWrVrh66+/RlZWFoYOHfqaZoOIiIgqsgodqkpi4cKFMDAwgI+PD3JycuDt7Y1vv/1WWm9oaIitW7fi008/haenJ0xMTDBkyBBMnz5dqnF0dMS2bdswbtw4LFq0CLVr18Z3330Hb29vqaZ///64c+cOgoODodVq0axZM0RFRRW5eJ2IiIjeTHoXqvbu3avz3MjICEuXLsXSpUufuY2DgwO2b9/+3HE7dOiA48ePP7cmMDAQgYGBJe6ViIiI3hwV+poqIiIiIn3BUEVEREQkA4YqIiIiIhkwVBERERHJgKGKiIiISAYMVUREREQyYKgiIiIikgFDFREREZEMGKqIiIiIZMBQRURERCQDhioiIiIiGTBUEREREcmAoYqIiIhIBgxVRERERDJgqCIiIiKSAUMVERERkQwYqoiIiIhkwFBFREREJAOGKiIiIiIZMFQRERERyYChioiIiEgGDFVEREREMmCoIiIiIpIBQxURERGRDBiqiIiIiGTAUEVEREQkA4YqIiIiIhkwVBERERHJgKGKiIiISAYMVUREREQyYKgiIiIikgFDFREREZEMGKqIiIiIZMBQRURERCQDhioiIiIiGTBUEREREcmAoYqIiIhIBgxVRERERDJgqCIiIiKSAUMVERERkQwYqoiIiIhkwFBFREREJAOGKiIiIiIZMFQRERERyaBCh6rZs2ejZcuWMDMzg5WVFXr16oWkpCSdmuzsbAQEBKBGjRowNTWFj48PUlJSdGqSk5PRrVs3VKtWDVZWVpg4cSIeP36sU7N37140b94cKpUK9evXR0RERJF+li5dirp168LIyAgeHh44cuSI7MdMRERE+qlCh6p9+/YhICAAhw8fRnR0NPLy8tCpUydkZWVJNePGjcOWLVuwbt067Nu3Dzdv3kSfPn2k9fn5+ejWrRtyc3Nx6NAhrFq1ChEREQgODpZqrly5gm7duuG9997DiRMnMHbsWHzyySf4448/pJq1a9ciKCgIISEhOHbsGNzc3ODt7Y3bt2+/nskgIiKiCq1KeTfwPFFRUTrPIyIiYGVlhYSEBLRv3x7p6en4/vvvsXr1arz//vsAgJUrV8LZ2RmHDx9G69atsXPnTpw7dw67du2CtbU1mjVrhhkzZuDzzz9HaGgolEolwsPD4ejoiPnz5wMAnJ2dceDAASxcuBDe3t4AgAULFmD48OEYOnQoACA8PBzbtm3DDz/8gMmTJ7/GWSEiIqKKqEKfqXpaeno6AKB69eoAgISEBOTl5cHLy0uqadSoEerUqYPY2FgAQGxsLFxdXWFtbS3VeHt7IyMjA2fPnpVqnhyjsKZwjNzcXCQkJOjUGBgYwMvLS6ohIiKiN1uFPlP1pIKCAowdOxZt2rRBkyZNAABarRZKpRLm5uY6tdbW1tBqtVLNk4GqcH3huufVZGRk4NGjR0hNTUV+fn6xNefPn39mzzk5OcjJyZGeZ2RklOKIiYiISJ/ozZmqgIAAnDlzBmvWrCnvVkps9uzZ0Gg00sPe3r68WyIiIqJXRC9CVWBgILZu3Yo9e/agdu3a0nIbGxvk5uYiLS1Npz4lJQU2NjZSzdOfBix8/qIatVoNY2NjWFpawtDQsNiawjGKM2XKFKSnp0uP69evl+7AiYiISG9U6FAlhEBgYCA2btyI3bt3w9HRUWe9u7s7qlatipiYGGlZUlISkpOT4enpCQDw9PTE6dOndT6lFx0dDbVaDRcXF6nmyTEKawrHUCqVcHd316kpKChATEyMVFMclUoFtVqt8yAiIqLKqUJfUxUQEIDVq1fj999/h5mZmXQNlEajgbGxMTQaDfz9/REUFITq1atDrVZj9OjR8PT0ROvWrQEAnTp1gouLCz766COEhYVBq9Vi2rRpCAgIgEqlAgCMHDkS33zzDSZNmoRhw4Zh9+7d+PXXX7Ft2zapl6CgIAwZMgQtWrRAq1at8PXXXyMrK0v6NCARERG92Sp0qFq2bBkAoEOHDjrLV65ciY8//hgAsHDhQhgYGMDHxwc5OTnw9vbGt99+K9UaGhpi69at+PTTT+Hp6QkTExMMGTIE06dPl2ocHR2xbds2jBs3DosWLULt2rXx3XffSbdTAID+/fvjzp07CA4OhlarRbNmzRAVFVXk4nUiIiJ6M1XoUCWEeGGNkZERli5diqVLlz6zxsHBAdu3b3/uOB06dMDx48efWxMYGIjAwMAX9kRERERvngp9TRURERGRvmCoIiIiIpIBQxURERGRDBiqiIiIiGTAUEVEREQkA4YqIiIiIhlU6Fsq0JshMTFR9jEtLS1Rp04d2cclIiJ6FoYqKjcP7qZAYWCAQYMGyT62cbVqOJ+YyGBFRESvDUMVlZtHDzIgCgrw4cxlsHJ0km3c21cu4tdpn+Lu3bsMVURE9NowVFG5s3J0Qi1nt/Jug4iI6KXwQnUiIiIiGTBUEREREcmAoYqIiIhIBgxVRERERDJgqCIiIiKSAUMVERERkQwYqoiIiIhkwFBFREREJAOGKiIiIiIZMFQRERERyYChioiIiEgGDFVEREREMmCoIiIiIpIBQxURERGRDBiqiIiIiGTAUEVEREQkA4YqIiIiIhkwVBERERHJgKGKiIiISAYMVUREREQyYKgiIiIikkGV8m6A6FVJTEx8JeNaWlqiTp06r2RsIiLSXwxVVOk8uJsChYEBBg0a9ErGN65WDecTExmsiIhIB0MVVTqPHmRAFBTgw5nLYOXoJOvYt69cxK/TPsXdu3cZqoiISAdDFVVaVo5OqOXsVt5tEBHRG4IXqhMRERHJgKGKiIiISAYMVUREREQyYKgiIiIikgEvVCcqg1dxDyze/4qISL8xVBGVwqu8Bxbvf0VEpN8YqohK4VXdA4v3vyIi0n8MVURlwHtgERHR03ihOhEREZEMeKaqlJYuXYp58+ZBq9XCzc0NS5YsQatWrcq7Laok+CXQRET6i6GqFNauXYugoCCEh4fDw8MDX3/9Nby9vZGUlAQrK6vybo/0GL8EmohI/zFUlcKCBQswfPhwDB06FAAQHh6Obdu24YcffsDkyZPLuTvSZ6/jS6D//PNPODs7yzo2z4AREf0PQ1UJ5ebmIiEhAVOmTJGWGRgYwMvLC7GxseXYGVUmr+IC+Fd5FkxlZITf1q+Hra2t7GPn5ORApVLJPi6DIBG9KgxVJXT37l3k5+fD2tpaZ7m1tTXOnz9f7DY5OTnIycmRnqenpwMAMjIyZO0tMzMTAPB34inkPsySdew7Vy/q3djsWVfy6QSIggK0GxwAc5taso2rvXweRzf8hO7du8s2pg6FAhBC9mFVRkb46ccfi/wuy8HAwAAFBQV6M66+js2eX8/Y+tizjY0NbGxsZB+38O+2eNF/kwSVyN9//y0AiEOHDuksnzhxomjVqlWx24SEhAgAfPDBBx988MFHJXhcv379uVmBZ6pKyNLSEoaGhkhJSdFZnpKS8sxUPGXKFAQFBUnPCwoKcP/+fdSoUQMKhUK23jIyMmBvb4/r169DrVbLNm5lwjl6Ps7Pi3GOno/z82Kco+eryPMjhMCDBw9gZ2f33DqGqhJSKpVwd3dHTEwMevXqBeCfkBQTE4PAwMBit1GpVEWuCTE3N39lParV6gr3QqxoOEfPx/l5Mc7R83F+Xoxz9HwVdX40Gs0LaxiqSiEoKAhDhgxBixYt0KpVK3z99dfIysqSPg1IREREby6GqlLo378/7ty5g+DgYGi1WjRr1gxRUVGv5IJXIiIi0i8MVaUUGBj4zLf7yotKpUJISMgr+fh5ZcE5ej7Oz4txjp6P8/NinKPnqwzzoxDiFXxmmYiIiOgNwy9UJiIiIpIBQxURERGRDBiqiIiIiGTAUEVEREQkA4aqSmDp0qWoW7cujIyM4OHhgSNHjpR3S+UiNDQUCoVC59GoUSNpfXZ2NgICAlCjRg2YmprCx8enyB3yK5v9+/ejR48esLOzg0KhwKZNm3TWCyEQHBwMW1tbGBsbw8vLCxcvXtSpuX//Pvz8/KBWq2Fubg5/f3/p+yb13Yvm5+OPPy7ymurcubNOTWWen9mzZ6Nly5YwMzODlZUVevXqhaSkJJ2akvxeJScno1u3bqhWrRqsrKwwceJEPH78+HUeyitTkjnq0KFDkdfRyJEjdWoq6xwtW7YMTZs2lW7o6enpiR07dkjrK9vrh6FKz61duxZBQUEICQnBsWPH4ObmBm9vb9y+fbu8WysXjRs3xq1bt6THgQMHpHXjxo3Dli1bsG7dOuzbtw83b95Enz59yrHbVy8rKwtubm5YunRpsevDwsKwePFihIeHIy4uDiYmJvD29kZ2drZU4+fnh7NnzyI6Ohpbt27F/v37MWLEiNd1CK/Ui+YHADp37qzzmvrll1901lfm+dm3bx8CAgJw+PBhREdHIy8vD506dUJW1v++9PtFv1f5+fno1q0bcnNzcejQIaxatQoREREIDg4uj0OSXUnmCACGDx+u8zoKCwuT1lXmOapduzbmzJmDhIQExMfH4/3330fPnj1x9uxZAJXw9SPLtw1TuWnVqpUICAiQnufn5ws7Ozsxe/bscuyqfISEhAg3N7di16WlpYmqVauKdevWScsSExMFABEbG/uaOixfAMTGjRul5wUFBcLGxkbMmzdPWpaWliZUKpX45ZdfhBBCnDt3TgAQR48elWp27NghFAqF+Pvvv19b76/D0/MjhBBDhgwRPXv2fOY2b9L8CCHE7du3BQCxb98+IUTJfq+2b98uDAwMhFarlWqWLVsm1Gq1yMnJeb0H8Bo8PUdCCPHuu++KMWPGPHObN22OLCwsxHfffVcpXz88U6XHcnNzkZCQAC8vL2mZgYEBvLy8EBsbW46dlZ+LFy/Czs4Ob731Fvz8/JCcnAwASEhIQF5ens5cNWrUCHXq1Hlj5+rKlSvQarU6c6LRaODh4SHNSWxsLMzNzdGiRQupxsvLCwYGBoiLi3vtPZeHvXv3wsrKCg0bNsSnn36Ke/fuSevetPlJT08HAFSvXh1AyX6vYmNj4erqqvPNE97e3sjIyJDOVlQmT89RocjISFhaWqJJkyaYMmUKHj58KK17U+YoPz8fa9asQVZWFjw9PSvl64d3VNdjd+/eRX5+fpGvybG2tsb58+fLqavy4+HhgYiICDRs2BC3bt3Cl19+iXbt2uHMmTPQarVQKpVFvtDa2toaWq22fBouZ4XHXdzrp3CdVquFlZWVzvoqVaqgevXqb8S8de7cGX369IGjoyMuX76M//znP+jSpQtiY2NhaGj4Rs1PQUEBxo4dizZt2qBJkyYAUKLfK61WW+xrrHBdZVLcHAHAwIED4eDgADs7O5w6dQqff/45kpKSsGHDBgCVf45Onz4NT09PZGdnw9TUFBs3boSLiwtOnDhR6V4/DFVUaXTp0kX6d9OmTeHh4QEHBwf8+uuvMDY2LsfOSF/5+vpK/3Z1dUXTpk1Rr1497N27Fx07dizHzl6/gIAAnDlzRuc6RdL1rDl68ho7V1dX2NraomPHjrh8+TLq1av3utt87Ro2bIgTJ04gPT0d69evx5AhQ7Bv377ybuuV4Nt/eszS0hKGhoZFPimRkpICGxubcuqq4jA3N0eDBg1w6dIl2NjYIDc3F2lpaTo1b/JcFR73814/NjY2RT708PjxY9y/f/+NnLe33noLlpaWuHTpEoA3Z34CAwOxdetW7NmzB7Vr15aWl+T3ysbGptjXWOG6yuJZc1QcDw8PANB5HVXmOVIqlahfvz7c3d0xe/ZsuLm5YdGiRZXy9cNQpceUSiXc3d0RExMjLSsoKEBMTAw8PT3LsbOKITMzE5cvX4atrS3c3d1RtWpVnblKSkpCcnLyGztXjo6OsLGx0ZmTjIwMxMXFSXPi6emJtLQ0JCQkSDW7d+9GQUGB9IfhTXLjxg3cu3cPtra2ACr//AghEBgYiI0bN2L37t1wdHTUWV+S3ytPT0+cPn1aJ3xGR0dDrVbDxcXl9RzIK/SiOSrOiRMnAEDndVSZ5+hpBQUFyMnJqZyvn/K+Up5ezpo1a4RKpRIRERHi3LlzYsSIEcLc3FznkxJvivHjx4u9e/eKK1euiIMHDwovLy9haWkpbt++LYQQYuTIkaJOnTpi9+7dIj4+Xnh6egpPT89y7vrVevDggTh+/Lg4fvy4ACAWLFggjh8/Lq5duyaEEGLOnDnC3Nxc/P777+LUqVOiZ8+ewtHRUTx69Egao3PnzuLtt98WcXFx4sCBA8LJyUkMGDCgvA5JVs+bnwcPHogJEyaI2NhYceXKFbFr1y7RvHlz4eTkJLKzs6UxKvP8fPrpp0Kj0Yi9e/eKW7duSY+HDx9KNS/6vXr8+LFo0qSJ6NSpkzhx4oSIiooSNWvWFFOmTCmPQ5Ldi+bo0qVLYvr06SI+Pl5cuXJF/P777+Ktt94S7du3l8aozHM0efJksW/fPnHlyhVx6tQpMXnyZKFQKMTOnTuFEJXv9cNQVQksWbJE1KlTRyiVStGqVStx+PDh8m6pXPTv31/Y2toKpVIpatWqJfr37y8uXbokrX/06JEYNWqUsLCwENWqVRO9e/cWt27dKseOX709e/YIAEUeQ4YMEUL8c1uFL774QlhbWwuVSiU6duwokpKSdMa4d++eGDBggDA1NRVqtVoMHTpUPHjwoByORn7Pm5+HDx+KTp06iZo1a4qqVasKBwcHMXz48CL/w1KZ56e4uQEgVq5cKdWU5Pfq6tWrokuXLsLY2FhYWlqK8ePHi7y8vNd8NK/Gi+YoOTlZtG/fXlSvXl2oVCpRv359MXHiRJGenq4zTmWdo2HDhgkHBwehVCpFzZo1RceOHaVAJUTle/0ohBDi9Z0XIyIiIqqceE0VERERkQwYqoiIiIhkwFBFREREJAOGKiIiIiIZMFQRERERyYChioiIiEgGDFVEREREMmCoIqJSuXr1KhQKhfRVGxXB+fPn0bp1axgZGaFZs2bl3c5zKRQKbNq0qcT1oaGhshyTXOMQ0bMxVBHpmY8//hgKhQJz5szRWb5p0yYoFIpy6qp8hYSEwMTEBElJSTrfI/akO3fu4NNPP0WdOnWgUqlgY2MDb29vHDx48LX2euvWLXTp0kX2cX/77Td06NABGo0GpqamaNq0KaZPn4779+/Lvi+5VcSgTlQWDFVEesjIyAhz585Fampqebcim9zc3DJve/nyZbRt2xYODg6oUaNGsTU+Pj44fvw4Vq1ahQsXLmDz5s3o0KED7t27V+b9loWNjQ1UKpWsY06dOhX9+/dHy5YtsWPHDpw5cwbz58/HyZMn8dNPP8m6r6fl5eW90vFLq6L1Q2+Y8v6eHCIqnSFDhoju3buLRo0aiYkTJ0rLN27cKJ78lQ4JCRFubm462y5cuFA4ODjojNWzZ08xa9YsYWVlJTQajfjyyy9FXl6emDBhgrCwsBC1atUSP/zwg7TNlStXBADxyy+/CE9PT6FSqUTjxo3F3r17dfZ1+vRp0blzZ2FiYiKsrKzEoEGDxJ07d6T17777rggICBBjxowRNWrUEB06dCj2ePPz88WXX34patWqJZRKpXBzcxM7duyQ1uOp71wLCQkpMkZqaqoAUKTHpwEQ3377rejcubMwMjISjo6OYt26dTo1ycnJol+/fkKj0QgLCwvxr3/9S1y5ckWn5vvvvxcuLi5CqVQKGxsbERAQoLOPjRs3Ss8nTZoknJychLGxsXB0dBTTpk0Tubm50vrifo5PiouLEwDE119/Xez61NRUnXF+/PFH4eDgINRqtejfv7/IyMiQanfs2CHatGkjNBqNqF69uujWrZvO92cW/uzXrFkj2rdvL1QqlVi5cqW4e/eu8PX1FXZ2dsLY2Fg0adJErF69WqeP/Px8MXfuXFGvXj2hVCqFvb29mDlzpjQnTz7effddabsVK1aIRo0aCZVKJRo2bCiWLl36wn6uXr0qunfvLszNzUW1atWEi4uL2LZt2zPnkEguPFNFpIcMDQ3x1VdfYcmSJbhx48ZLjbV7927cvHkT+/fvx4IFCxASEoLu3bvDwsICcXFxGDlyJP79738X2c/EiRMxfvx4HD9+HJ6enujRo4d01ictLQ3vv/8+3n77bcTHxyMqKgopKSn48MMPdcZYtWoVlEolDh48iPDw8GL7W7RoEebPn4///ve/OHXqFLy9vfGvf/0LFy9eBPDP22mNGzfG+PHjcevWLUyYMKHIGKampjA1NcWmTZuQk5Pz3Pn44osv4OPjg5MnT8LPzw++vr5ITEwE8M9ZEG9vb5iZmeHPP//EwYMHYWpqis6dO0tn2pYtW4aAgACMGDECp0+fxubNm1G/fv1n7s/MzAwRERE4d+4cFi1ahBUrVmDhwoXP7fFJkZGRMDU1xahRo4pdb25uLv378uXL2LRpE7Zu3YqtW7di3759Om8jZ2VlISgoCPHx8YiJiYGBgQF69+6NgoICnTEnT56MMWPGIDExEd7e3sjOzoa7uzu2bduGM2fOYMSIEfjoo49w5MgRaZspU6Zgzpw5+OKLL3Du3DmsXr0a1tbWACDV7dq1C7du3cKGDRukYwsODsasWbOQmJiIr776Cl988QVWrVr13H4CAgKQk5OD/fv34/Tp05g7dy5MTU1LPKdEZVbeqY6ISqfw7JIQQrRu3VoMGzZMCFH2M1UODg4iPz9fWtawYUPRrl076fnjx4+FiYmJ+OWXX4QQ/zs7MGfOHKkmLy9P1K5dW8ydO1cIIcSMGTNEp06ddPZ9/fp1AUAkJSUJIf45U/X222+/8Hjt7OzErFmzdJa1bNlSjBo1Snru5uZW7BmqJ61fv15YWFgIIyMj8c4774gpU6aIkydP6tQAECNHjtRZ5uHhIT799FMhhBA//fSTaNiwoSgoKJDW5+TkCGNjY/HHH39I/U6dOvWZfeCpM1VPmzdvnnB3d5eev+hMVZcuXUTTpk2fuf7JcapVq6ZzZmrixInCw8PjmdvcuXNHABCnT58WQvzvZ/+ss2JP6tatmxg/frwQQoiMjAyhUqnEihUriq0tHPf48eM6y+vVq1fkjNeMGTOEp6fnc/txdXUVoaGhL+yRSG48U0Wkx+bOnYtVq1ZJZ1LKonHjxjAw+N9/CqytreHq6io9NzQ0RI0aNXD79m2d7Tw9PaV/V6lSBS1atJD6OHnyJPbs2SOdITI1NUWjRo0A/HO2pJC7u/tze8vIyMDNmzfRpk0bneVt2rQp9TH7+Pjg5s2b2Lx5Mzp37oy9e/eiefPmiIiIeOZxFT5/8rguXboEMzMz6biqV6+O7OxsXL58Gbdv38bNmzfRsWPHEve1du1atGnTBjY2NjA1NcW0adOQnJxc4u2FECWurVu3LszMzKTntra2Oj/XixcvYsCAAXjrrbegVqtRt25dACjST4sWLXSe5+fnY8aMGXB1dUX16tVhamqKP/74Q9ouMTEROTk5pZqXrKwsXL58Gf7+/jqvo5kzZ+q8horr57PPPsPMmTPRpk0bhISE4NSpUyXeL9HLqFLeDRBR2bVv3x7e3t6YMmUKPv74Y511BgYGRf7gFncRb9WqVXWeKxSKYpc9/RbQ82RmZqJHjx6YO3dukXW2trbSv01MTEo8phyMjIzwwQcf4IMPPsAXX3yBTz75BCEhIUXm7lkyMzPh7u6OyMjIIutq1qypE05LIjY2Fn5+fvjyyy/h7e0NjUaDNWvWYP78+SUeo0GDBjhw4ADy8vKK/Nye9qKfa48ePeDg4IAVK1bAzs4OBQUFaNKkSZEPETz9c5s3bx4WLVqEr7/+Gq6urjAxMcHYsWOl7YyNjUt8PIUyMzMBACtWrICHh4fOOkNDw+f288knn8Db2xvbtm3Dzp07MXv2bMyfPx+jR48udR9EpcEzVUR6bs6cOdiyZQtiY2N1ltesWRNarVYnWMn5kfXDhw9L/378+DESEhLg7OwMAGjevDnOnj2LunXron79+jqP0gQptVoNOzu7Irc9OHjwIFxcXF76GFxcXJCVlaWz7MnjKnz+5HFdvHgRVlZWRY5Lo9HAzMwMdevWfeZtHZ526NAhODg4YOrUqWjRogWcnJxw7dq1Uh3DwIEDkZmZiW+//bbY9WlpaSUa5969e0hKSsK0adPQsWNHODs7l/jTpQcPHkTPnj0xaNAguLm54a233sKFCxek9U5OTjA2Nn7mvCiVSgD/nPEqZG1tDTs7O/z1119F5trR0fGFPdnb22PkyJHYsGEDxo8fjxUrVpToWIheBs9UEek5V1dX+Pn5YfHixTrLO3TogDt37iAsLAx9+/ZFVFQUduzYAbVaLct+ly5dCicnJzg7O2PhwoVITU3FsGHDAAABAQFYsWIFBgwYgEmTJqF69eq4dOkS1qxZg++++67ImYbnmThxIkJCQlCvXj00a9YMK1euxIkTJ4o9W/Qs9+7dQ79+/TBs2DA0bdoUZmZmiI+PR1hYGHr27KlTu27dOrRo0QJt27ZFZGQkjhw5gu+//x4A4Ofnh3nz5qFnz56YPn06ateujWvXrmHDhg2YNGkSateujdDQUIwcORJWVlbo0qULHjx4gIMHDxZ7lsTJyQnJyclYs2YNWrZsiW3btmHjxo0lPi4A8PDwwKRJkzB+/Hj8/fff6N27N+zs7HDp0iWEh4ejbdu2GDNmzAvHsbCwQI0aNbB8+XLY2toiOTkZkydPLlEPTk5OWL9+PQ4dOgQLCwssWLAAKSkpUvA1MjLC559/jkmTJkGpVKJNmza4c+cOzp49C39/f1hZWcHY2BhRUVGoXbs2jIyMoNFo8OWXX+Kzzz6DRqNB586dkZOTg/j4eKSmpiIoKOiZ/YwdOxZdunRBgwYNkJqaij179kjBmOiVKudruoiolJ68UL3QlStXhFKpFE//Si9btkzY29sLExMTMXjwYDFr1qxib6nwpHfffVeMGTNGZ5mDg4NYuHChtC8AYvXq1aJVq1ZCqVQKFxcXsXv3bp1tLly4IHr37i3Mzc2FsbGxaNSokRg7dqx0kXdx+ylOfn6+CA0NFbVq1RJVq1YtcksFIV58oXp2draYPHmyaN68udBoNKJatWqiYcOGYtq0aeLhw4dSHQCxdOlS8cEHHwiVSiXq1q0r1q5dqzPWrVu3xODBg4WlpaVQqVTirbfeEsOHDxfp6elSTXh4uGjYsKGoWrWqsLW1FaNHj9bZx5MXqk+cOFHUqFFDmJqaiv79+4uFCxcKjUYjrX/RheqF1q5dK9q3by/MzMyEiYmJaNq0qZg+fXqRWyo86ekPLkRHRwtnZ2ehUqlE06ZNxd69e3X6fdYF5ffu3RM9e/YUpqamwsrKSkybNk0MHjxY57WVn58vZs6cKRwcHETVqlVFnTp1xFdffSWtX7FihbC3txcGBgY6t1SIjIwUzZo1E0qlUlhYWIj27duLDRs2PLefwMBAUa9ePaFSqUTNmjXFRx99JO7evfvCOSR6WQohSnGVIxFRJaZQKLBx40b06tWrvFshIj3Ea6qIiIiIZMBQRURERCQDXqhORPT/8WoIInoZPFNFREREJAOGKiIiIiIZMFQRERERyYChioiIiEgGDFVEREREMmCoIiIiIpIBQxURERGRDBiqiIiIiGTAUEVEREQkg/8HeH0Ys8osevAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "special_chars_count = Final_df_['entry'].apply(lambda x: sum(c in \"!@#$%^&*()_+{}[]|\\;:'<>,.?/~`\" for c in x))\n",
        "\n",
        "# Plot histogram of special character counts\n",
        "plt.hist(special_chars_count, bins=20, color='skyblue', edgecolor='black')\n",
        "plt.title('Special Character Analysis')\n",
        "plt.xlabel('Number of Special Characters')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8TZrvIxmVLqe"
      },
      "source": [
        "#Special Character Analysis for Incorrect Predictions in SQL Injection Detection\n",
        "\n",
        "### Analyzing Special Characters in Incorrect Predictions\n",
        "Examining the role of special characters in the instances where the model incorrectly predicts outcomes can reveal insights into why these errors occur, aiding in model refinement."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "tDDosHk1yv9w",
        "outputId": "137961cd-eccc-412f-afd6-9510ac7717a8"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "special_chars_count = worng['Text'].apply(lambda x: sum(c in \"!@#$%^&*()_+{}[]|\\;:'<>,.?/~`\" for c in x))\n",
        "\n",
        "# Plot histogram of special character counts\n",
        "plt.hist(special_chars_count, bins=20, color='skyblue', edgecolor='black')\n",
        "plt.title('Special Character Analysis')\n",
        "plt.xlabel('Number of Special Characters')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mHW4vC3nVWdw"
      },
      "source": [
        "#Analysis of SQL Query Types for Injection Detection\n",
        "\n",
        "### Queries Attempting to Bypass Access Controls\n",
        "\n",
        "Examining queries that try to circumvent security measures provides insights into methods attackers use to gain unauthorized access.\n",
        "### Queries with Input Validation Failures\n",
        "\n",
        "Analyzing queries that fail input validation highlights vulnerabilities that can be exploited by SQL injection attacks.\n",
        "### Time-Delayed Queries\n",
        "\n",
        "Reviewing queries that introduce delays can indicate attempts to probe database vulnerabilities through time-based SQL injection techniques.\n",
        "### Queries with UNION Keyword\n",
        "\n",
        "Investigating the use of the UNION keyword helps identify SQL injections aiming to retrieve additional data from the database.\n",
        "### Queries with Error Messages\n",
        "\n",
        "Analyzing queries that result in error messages can uncover potential points of failure and exploitation in the system.\n",
        "### Percentage of Parameterized Queries\n",
        "\n",
        "Calculating the percentage of parameterized queries provides a measure of how many queries are safeguarded against SQL injection by using parameters instead of concatenating strings.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vnqcnw1ty4NS",
        "outputId": "8fc11f90-c74d-4ecb-e350-5488eec45e30"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Percentage of parameterized queries: 0.00%\n"
          ]
        }
      ],
      "source": [
        "parameterized_count = worng['Text'].str.contains(r'\\?').sum()\n",
        "total_queries = len(df)\n",
        "\n",
        "print(f\"Percentage of parameterized queries: {(parameterized_count / total_queries) * 100:.2f}%\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5L5DC4rLzEX6",
        "outputId": "ee990275-31ad-4580-f872-48ea00c1231d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Percentage of parameterized queries: 0.03%\n"
          ]
        }
      ],
      "source": [
        "parameterized_count = Final_df_['entry'].str.contains(r'\\?').sum()\n",
        "total_queries = len(df)\n",
        "\n",
        "print(f\"Percentage of parameterized queries: {(parameterized_count / total_queries) * 100:.2f}%\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lp-1-wZ9zHgi",
        "outputId": "7c7ec660-6785-4f16-e688-aa6fdbb04e65"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Queries with error messages:\n",
            "Empty DataFrame\n",
            "Columns: [entry, label, Total_Words, Total_Chars, Unique_Chars, Size_of_Text]\n",
            "Index: []\n"
          ]
        }
      ],
      "source": [
        "error_queries = Final_df_[Final_df_['entry'].str.contains('error', case=False, na=False)]\n",
        "print(\"Queries with error messages:\")\n",
        "print(error_queries)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ici37rujzUgL",
        "outputId": "3b14d4e7-71a4-4670-cc23-ff1913528dc3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Queries with UNION keyword:\n",
            "11793\n"
          ]
        }
      ],
      "source": [
        "union_queries = Final_df_[Final_df_['entry'].str.contains('UNION', case=False, na=False)]\n",
        "print(\"Queries with UNION keyword:\")\n",
        "print(len(union_queries))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OJ3OB1VnzdPu",
        "outputId": "71ae506c-998f-439a-fe58-af4785982f35"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Queries with UNION keyword:\n",
            "0\n"
          ]
        }
      ],
      "source": [
        "union_queries = worng[worng['Text'].str.contains('UNION', case=False, na=False)]\n",
        "print(\"Queries with UNION keyword:\")\n",
        "print(len(union_queries))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7WTbgfOTzm-U",
        "outputId": "5178ede9-7653-4db1-a67a-7a69330381aa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Time-delayed queries:\n",
            "0\n"
          ]
        }
      ],
      "source": [
        "time_delayed_queries = worng[worng['Text'].str.contains('WAITFOR DELAY', case=False, na=False)]\n",
        "print(\"Time-delayed queries:\")\n",
        "print(len(time_delayed_queries))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hi8YzmQPzvZh",
        "outputId": "cd4278f3-1545-4ebe-ff74-0e3f21858ab4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Time-delayed queries:\n",
            "5041\n"
          ]
        }
      ],
      "source": [
        "time_delayed_queries = Final_df_[Final_df_['entry'].str.contains('WAITFOR DELAY', case=False, na=False)]\n",
        "print(\"Time-delayed queries:\")\n",
        "print(len(time_delayed_queries))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ONdjjShE0AMW",
        "outputId": "25f7385c-527c-46bd-d5fa-c05045721126"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Queries with input validation failures:\n",
            "2457\n"
          ]
        }
      ],
      "source": [
        "# Check for input validation failures\n",
        "validation_failures = Final_df_[Final_df_['entry'].str.contains(r'\\bEXEC\\b|\\bEXECUTE\\b', case=False, na=False)]\n",
        "print(\"Queries with input validation failures:\")\n",
        "print(len(validation_failures))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eA1qWZrZ0qOn",
        "outputId": "0153148c-be76-483a-8c31-5f810228aad2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Queries with input validation failures:\n",
            "0\n"
          ]
        }
      ],
      "source": [
        "# Check for input validation failures\n",
        "validation_failures = worng[worng['Text'].str.contains(r'\\bEXEC\\b|\\bEXECUTE\\b', case=False, na=False)]\n",
        "print(\"Queries with input validation failures:\")\n",
        "print(len(validation_failures))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FszL5Z2B0zDX",
        "outputId": "e2991682-3ee4-4164-9f6b-b41203ae077b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Queries attempting to bypass access controls:\n",
            "Empty DataFrame\n",
            "Columns: [Index, Text, True Label, Predicted Label, Total_Words, Total_Chars, Unique_Chars, Size_of_Text]\n",
            "Index: []\n"
          ]
        }
      ],
      "source": [
        "# Identify access control bypass attempts\n",
        "access_control_bypass = worng[worng['Text'].str.contains('GRANT|REVOKE', case=False, na=False)]\n",
        "print(\"Queries attempting to bypass access controls:\")\n",
        "print(access_control_bypass)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A6Njpg1RVxbt"
      },
      "source": [
        "\n",
        "#Plotting Word Map for Incorrect Predictions in SQL Injection Detection\n",
        "\n",
        "### Creating Word Maps for Incorrect Predictions\n",
        "Developing word maps for incorrectly predicted data points allows for visual identification of common words or patterns that may lead to errors, providing insights for model enhancement."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 836
        },
        "id": "07pnoVtZElPt",
        "outputId": "e938b975-5ab8-4e4f-b18d-f8e80008ee11"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from wordcloud import WordCloud\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "text_data = \" \".join(worng['entry'])\n",
        "\n",
        "\n",
        "wordcloud = WordCloud(width = 800, height = 800,\n",
        "                background_color ='white',\n",
        "                stopwords = None,\n",
        "                min_font_size = 10).generate(text_data)\n",
        "\n",
        "\n",
        "plt.figure(figsize = (8, 8), facecolor = None)\n",
        "plt.imshow(wordcloud)\n",
        "plt.axis(\"off\")\n",
        "plt.tight_layout(pad = 0)\n",
        "\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "k6y-p615JlwX"
      },
      "outputs": [],
      "source": [
        "import re"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "y1nSGxYq-1zO"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "def check(query):\n",
        "    patterns = [\n",
        "      r\"(?i)(?:union\\s+select|select\\s+.*?from\\s+.*?where\\s+.*?=\\s*['\\\"].*?--)\",\n",
        "      r\"(?i)(?:or\\s+['\\\"].*?['\\\"]\\s*=\\s*['\\\"])\",\n",
        "      r\"(?i)(?:;|--)\",\n",
        "      r\"(?i)(?:exec\\s*\\(|xp_)\",\n",
        "      r\"(?i)(?:drop\\s+table|drop\\s+database)\",\n",
        "      r\"1'1\",\n",
        "      r\"1 exec sp_ \\(or exec xp_\\)\",\n",
        "      r\"1 and 1=1\",\n",
        "      r\"1' and 1=\\(select count\\(\\*\\) from tablenames\\); --\",\n",
        "      r\"1 or 1=1\",\n",
        "      r\"1' or '1'='1\",\n",
        "      r\"1or1=1\",\n",
        "      r\"1'or'1'='1\",\n",
        "      r\"'-'\",\n",
        "      r\"' '\",\n",
        "      r\"'&'\",\n",
        "      r\"'\\^'\",\n",
        "      r\"'\\*'\",\n",
        "      r\"' or ''-'\",\n",
        "      r\"' or '' '\",\n",
        "      r\"' or ''&'\",\n",
        "      r\"' or ''\\^'\",\n",
        "      r\"' or ''\\*'\",\n",
        "      r\"-'\",\n",
        "      r\"' '\",\n",
        "      r\"'&'\",\n",
        "      r\"'\\^'\",\n",
        "      r\"'\\*'\",\n",
        "      r'\" or \"\"-\"',\n",
        "      r'\" or \"\" \"',\n",
        "      r'\" or \"\"&\"',\n",
        "      r'\" or \"\"\\^\"',\n",
        "      r'\" or \"\"\\*\"',\n",
        "      r\"or true--\",\n",
        "      r'\" or true--',\n",
        "      r\"' or true--\",\n",
        "      r'\") or true--',\n",
        "      r\"') or true--\",\n",
        "      r\"' or 'x'='x\",\n",
        "      r\"') or ('x')=('x\",\n",
        "      r\"')) or (('x'))=(('x\",\n",
        "      r'\" or \"x\"=\"x',\n",
        "      r'\") or (\"x\")=(\"x',\n",
        "      r'\")) or ((\"x\"))=((\"x',\n",
        "      r\"or 1=1\",\n",
        "      r\"or 1=1--\",\n",
        "      r\"or 1=1#\"\n",
        "    ]\n",
        "\n",
        "    for pattern in patterns:\n",
        "        if re.search(pattern, query):\n",
        "            return True\n",
        "\n",
        "    return False\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MCrvFrlcJCIL"
      },
      "outputs": [],
      "source": [
        "def predict_label(model, tokenizer, text, device=torch.device('cuda' if torch.cuda.is_available() else 'cpu')):\n",
        "\n",
        "    if(check(text)):\n",
        "      return true\n",
        "\n",
        "\n",
        "    inputs = tokenizer(text, return_tensors=\"pt\", truncation=True, padding=True).to(device)\n",
        "    with torch.no_grad():\n",
        "        outputs = model(**inputs)\n",
        "    logits = outputs.logits\n",
        "    predicted_label = torch.argmax(logits, dim=1).cpu().numpy()[0]\n",
        "\n",
        "    return predicted_label\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EVL-sG8o9ZHo",
        "outputId": "55eb7f67-6466-41e4-f27a-675d7eadff3b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "String: dad\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "input_=str(input(\"String: \"))\n",
        "predict_label(model,tokenizer,input_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MftNOba19h1n"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "1b57b37be9ca4fb7aaf17b7f7ce6c57b": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_3904164a0cfc4727aaf77c7f3e1912fa",
            "placeholder": "​",
            "style": "IPY_MODEL_a78d57b4d4a2462ea1cae3e82e6f82b6",
            "value": "model.safetensors: 100%"
          }
        },
        "3123351a5f4743aa87a740e843196e80": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "32e75e0e5b1b42088cd46b0d88634733": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_f440af68f8ad4871afbf35c2a2dfdd9a",
            "max": 570,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_f9257070fd5f4c69bd3e03a0f6351e58",
            "value": 570
          }
        },
        "3904164a0cfc4727aaf77c7f3e1912fa": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "4750d96985904f1183f4dcf41c43415b": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_1b57b37be9ca4fb7aaf17b7f7ce6c57b",
              "IPY_MODEL_dac85df23dbc44dc99cb2daa7c40c1d2",
              "IPY_MODEL_7a6cedce400f4e7c9f5a1254f8d9caec"
            ],
            "layout": "IPY_MODEL_c949b6949c2947bfa7e2f7bb9d5efd9e"
          }
        },
        "4762f0281f3e4dbab8d536c6350f41a1": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "7a6cedce400f4e7c9f5a1254f8d9caec": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_f4ed4a5272a2441692217b1034015d58",
            "placeholder": "​",
            "style": "IPY_MODEL_d6bc7d0fa12c482e83411b27fcbd7f5d",
            "value": " 440M/440M [00:06&lt;00:00, 70.5MB/s]"
          }
        },
        "7bf70df58fc6407891e1edae3da85ce7": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "96f5ccd2dbd440c4940c3bb800472f7c": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "9c8bb33e850a495ba14d9e8213bcafca": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "a78d57b4d4a2462ea1cae3e82e6f82b6": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "bad16a09002b40edaf666bf83bcda006": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_de213231db594cd49f9a34abf6eac6fa",
              "IPY_MODEL_32e75e0e5b1b42088cd46b0d88634733",
              "IPY_MODEL_bc521a03adf34a1885fd98603e0300c8"
            ],
            "layout": "IPY_MODEL_ef6fbf534d3f45c88e9b02d66efd1072"
          }
        },
        "bc521a03adf34a1885fd98603e0300c8": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_9c8bb33e850a495ba14d9e8213bcafca",
            "placeholder": "​",
            "style": "IPY_MODEL_f25bbe6fd9c94ef68497486ea6ddd1c5",
            "value": " 570/570 [00:00&lt;00:00, 38.0kB/s]"
          }
        },
        "c949b6949c2947bfa7e2f7bb9d5efd9e": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "d6bc7d0fa12c482e83411b27fcbd7f5d": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "dac85df23dbc44dc99cb2daa7c40c1d2": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_4762f0281f3e4dbab8d536c6350f41a1",
            "max": 440449768,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_3123351a5f4743aa87a740e843196e80",
            "value": 440449768
          }
        },
        "de213231db594cd49f9a34abf6eac6fa": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_96f5ccd2dbd440c4940c3bb800472f7c",
            "placeholder": "​",
            "style": "IPY_MODEL_7bf70df58fc6407891e1edae3da85ce7",
            "value": "config.json: 100%"
          }
        },
        "ef6fbf534d3f45c88e9b02d66efd1072": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "f25bbe6fd9c94ef68497486ea6ddd1c5": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "f440af68f8ad4871afbf35c2a2dfdd9a": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "f4ed4a5272a2441692217b1034015d58": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "f9257070fd5f4c69bd3e03a0f6351e58": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        }
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}