NLP_Project.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "_In0EH508sVZ",
        "UuzL-1VJgVOI",
        "l6Pt41j0wQW4",
        "uSjoEeWF080B"
      ],
      "authorship_tag": "ABX9TyN9A//uTSf8lW2CdrOrrk5c",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/vdovetzi/NLP_Language_Detection/blob/main/NLP_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Постановка задачи и формулировка задания"
      ],
      "metadata": {
        "id": "_In0EH508sVZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Имеется $dataset$ с предложениями на разных языках. Хочется обучить модель, которая сможеть решать задачу $language$ $detection$.\n",
        "\n",
        "**Формулировка задания:**\n",
        "\n",
        "*  На вход подается предложение $sentence$.\n",
        "*  Вызов функции $predict(sentence)$ выводит на экран, на каком языке написано предложение.\n",
        "\n"
      ],
      "metadata": {
        "id": "29zJ-_lr_AF3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Необходимая теория"
      ],
      "metadata": {
        "id": "yBB_5QXVeVOm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Bag of Words;\n",
        "2. Выборки: тестовая и тренировочная;\n",
        "3. Модель;\n",
        "4. Владение keras;\n",
        "5. Понимание того, как работает нейронная сеть;\n",
        "6. Наивные байесовские классификаторы;\n",
        "7. Классификаторы;\n",
        "8. Оптимизаторы;\n",
        "9. Алгоритм обратного распространения ошибки\n",
        "\n"
      ],
      "metadata": {
        "id": "brDurR6vtEZf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Используемые инструменты"
      ],
      "metadata": {
        "id": "UuzL-1VJgVOI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "В своей работе я буду использовать следующие инструменты:\n",
        "\n",
        "\n",
        "\n",
        "*   Библиотека $pandas$ для обработки и анализа данных\n",
        "*   Библиотека $numpy$ для линала\n",
        "* Библиотека $re$ для регулярных выражений\n",
        "* Библиотека $seaborn$ для красивых визуализаций\n",
        "* Библиотека $matplotlib.pyplot$ для построений графиков\n",
        "* Библиотека $string$, чтобы воспользоваться $string.punctuation$\n",
        "* Библиотека $nltk$ нужна для всего, так как это основной инструмент\n",
        "* Библиотека $sklearn.model$_$selection$ для разбиения на выборки\n",
        "* Библиотека $sklearn.feature$_$extraction.text$ для использования модели $Bag$ $of$ $Words$ и использования $TF-IDF$ метрики\n",
        "* Библиотека $sklearn.naive$_$bayes$ для наивного байесовского классификатора\n",
        "* Библиотека $sklearn.metrics$ для оценки качества модели\n",
        "* Библиотека $sklearn$ для $pipeline$\n",
        "* Библиотека $sklearn.linear$_$model$ для алгоритма обучения $LogisticRegression$\n",
        "* Библиотека $keras$ для использования более умных нейросетей\n",
        "* Библиотека $tensorflow$, чтобы пользоваться нейросетями на более низком уровне\n",
        "\n"
      ],
      "metadata": {
        "id": "k__OSqAajwvN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "nltk.download('punkt')\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import re\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import string\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.stem import SnowballStemmer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn import pipeline\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "import keras\n",
        "import tensorflow as tf\n",
        "from sklearn.pipeline import Pipeline"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "091W_iIRtMoo",
        "outputId": "8d0d0732-8379-4e24-9c6c-857d7655d03c"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Посмотрим поподробнее на имеющийся $dataset$"
      ],
      "metadata": {
        "id": "l6Pt41j0wQW4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv(\"Language Detection.csv\")\n",
        "dataset.head(10) # возращает первые 10 строчек"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "L8TXj1tetHpC",
        "outputId": "547e1232-2e15-4c64-b5cb-50659394dc8c"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                Text Language\n",
              "0   Nature, in the broadest sense, is the natural...  English\n",
              "1  \"Nature\" can refer to the phenomena of the phy...  English\n",
              "2  The study of nature is a large, if not the onl...  English\n",
              "3  Although humans are part of nature, human acti...  English\n",
              "4  [1] The word nature is borrowed from the Old F...  English\n",
              "5  [2] In ancient philosophy, natura is mostly us...  English\n",
              "6  [3][4] \\nThe concept of nature as a whole, the...  English\n",
              "7  During the advent of modern scientific method ...  English\n",
              "8  [5][6] With the Industrial revolution, nature ...  English\n",
              "9  However, a vitalist vision of nature, closer t...  English"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-08ecc12d-6b3e-4e3b-9928-1f1ce9e5aa8d\">\n",
              "    <div 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>Text</th>\n",
              "      <th>Language</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Nature, in the broadest sense, is the natural...</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>\"Nature\" can refer to the phenomena of the phy...</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>The study of nature is a large, if not the onl...</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Although humans are part of nature, human acti...</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>[1] The word nature is borrowed from the Old F...</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>[2] In ancient philosophy, natura is mostly us...</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>[3][4] \\nThe concept of nature as a whole, the...</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>During the advent of modern scientific method ...</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>[5][6] With the Industrial revolution, nature ...</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>However, a vitalist vision of nature, closer t...</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-08ecc12d-6b3e-4e3b-9928-1f1ce9e5aa8d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-9147b39f-c079-44be-891b-dbed586dd19d\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9147b39f-c079-44be-891b-dbed586dd19d')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-9147b39f-c079-44be-891b-dbed586dd19d button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-08ecc12d-6b3e-4e3b-9928-1f1ce9e5aa8d 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-08ecc12d-6b3e-4e3b-9928-1f1ce9e5aa8d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Как можем заметить, мы имеем формат **предложение** - **язык**.\n",
        "\n",
        "Посмотрим теперь на то, какие языки мы вообще имеем в нашем $dataset$. Изобразим это в виде красивой столбчатой и круговой диаграмок."
      ],
      "metadata": {
        "id": "lVaOpyxaurpJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 5))\n",
        "sns.countplot(data= dataset, x=\"Language\", order=dataset[\"Language\"].value_counts().index)\n",
        "plt.xticks(rotation=30)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "R3k-gZpXvASg",
        "outputId": "e06f156b-c688-4183-fd57-ae9f3c1c66f0"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "plt.pie(x = dataset['Language'].value_counts() ,autopct='%1.1f%%' ,labels=dataset['Language'].value_counts().index)\n",
        "plt.title('Languages',fontsize=20)\n",
        "plt.show"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "u_Bmkrst0cGQ",
        "outputId": "9f1a0886-d497-426c-b4eb-addd2021cd13"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show(close=None, block=None)>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[\"Language\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vc09u3ms8OrO",
        "outputId": "31dbef49-282e-4e33-b01e-9f97d12fc041"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "English       1385\n",
              "French        1014\n",
              "Spanish        819\n",
              "Portugeese     739\n",
              "Italian        698\n",
              "Russian        692\n",
              "Sweedish       676\n",
              "Malayalam      594\n",
              "Dutch          546\n",
              "Arabic         536\n",
              "Turkish        474\n",
              "German         470\n",
              "Tamil          469\n",
              "Danish         428\n",
              "Kannada        369\n",
              "Greek          365\n",
              "Hindi           63\n",
              "Name: Language, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Text Preprocessing"
      ],
      "metadata": {
        "id": "uSjoEeWF080B"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Text** **Cleaning**"
      ],
      "metadata": {
        "id": "BWVDBsS71MDc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def Cleaner(text):\n",
        "  # удалим все цифры и иные символы, кроме букв и приведём текст к нижнему регистру\n",
        "  text = re.sub(r'[{}]'.format(string.punctuation), '', text)\n",
        "  text = re.sub(r'\\d+', '', text)\n",
        "  return text.lower()\n"
      ],
      "metadata": {
        "id": "5PE6gpgv1VFK"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Может быть такое, что в нашем $dataset$ в каком-то предложении помимо основного языка, на котором оно написано есть и другие языки, например английский или русский, поэтому напишем функции, которые будут удалять эти побочные языки из предложения.\n",
        "\n",
        "Понятно, что вариантов очень много, но кажется, что основным является английский, так как есть куча аббревиатур или каких-то названий, которые невозмомножно перевести на другой язык, поэтому их оставляют на английском.\n",
        "\n",
        "Так что я считаю, что достаточно написать функцию, удаляющую английские символы."
      ],
      "metadata": {
        "id": "5dY6Am-jFPRb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def RemoveEnglish(text):\n",
        "        text = re.sub(r'[a-zA-Z]+', '', text)\n",
        "        return text.lower()"
      ],
      "metadata": {
        "id": "6yPwzNhbFF1y"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# функция, которая применяет к предложению функцию RemoveEnglish\n",
        "def ApplyToSentence(sentence):\n",
        "  if (sentence.Language != 'English'):\n",
        "    sentence.Text = RemoveEnglish(sentence.Text)\n",
        "  return sentence.Text"
      ],
      "metadata": {
        "id": "18F-xhuIMhC1"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = dataset.copy()\n",
        "X = data.apply(ApplyToSentence, axis = 1) #axis = 1, так как применять функцию надо к столбцу\n",
        "X = X.apply(Cleaner) # применяем Cleaner к столбцу с предложениями\n",
        "\n",
        "y = data[\"Language\"]\n",
        "\n"
      ],
      "metadata": {
        "id": "FQR3b9sOOWuI"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Посмотрим, что получилось:"
      ],
      "metadata": {
        "id": "aWe57YMSVbh1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ELTxXMq5VtvP",
        "outputId": "ce7d1e3f-6601-429e-be70-00132fb942cc"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         nature in the broadest sense is the natural p...\n",
              "1        nature can refer to the phenomena of the physi...\n",
              "2        the study of nature is a large if not the only...\n",
              "3        although humans are part of nature human activ...\n",
              "4         the word nature is borrowed from the old fren...\n",
              "                               ...                        \n",
              "10332    ನಿಮ್ಮ ತಪ್ಪು ಏನು ಬಂದಿದೆಯೆಂದರೆ ಆ ದಿನದಿಂದ ನಿಮಗೆ ಒ...\n",
              "10333    ನಾರ್ಸಿಸಾ ತಾನು ಮೊದಲಿಗೆ ಹೆಣಗಾಡುತ್ತಿದ್ದ ಮಾರ್ಗಗಳನ್...\n",
              "10334    ಹೇಗೆ  ನಾರ್ಸಿಸಿಸಮ್ ಈಗ ಮರಿಯನ್ ಅವರಿಗೆ ಸಂಭವಿಸಿದ ಎಲ...\n",
              "10335    ಅವಳು ಈಗ ಹೆಚ್ಚು ಚಿನ್ನದ ಬ್ರೆಡ್ ಬಯಸುವುದಿಲ್ಲ ಎಂದು ...\n",
              "10336    ಟೆರ್ರಿ ನೀವು ನಿಜವಾಗಿಯೂ ಆ ದೇವದೂತನಂತೆ ಸ್ವಲ್ಪ ಕಾಣು...\n",
              "Length: 10337, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SFOwUwZKVwcy",
        "outputId": "807ad009-f1ad-47b9-80ce-e0fd621de531"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        English\n",
              "1        English\n",
              "2        English\n",
              "3        English\n",
              "4        English\n",
              "          ...   \n",
              "10332    Kannada\n",
              "10333    Kannada\n",
              "10334    Kannada\n",
              "10335    Kannada\n",
              "10336    Kannada\n",
              "Name: Language, Length: 10337, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Выбор модели"
      ],
      "metadata": {
        "id": "n2xmSSds7Rlm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Первая модель - **Bag of Words** + ***MultinomialNB*** из наивных байесовских моделей c минимальной предобработкой текста"
      ],
      "metadata": {
        "id": "Je1UVY2mAQEC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x=np.array(dataset[\"Text\"])\n",
        "y=np.array(dataset[\"Language\"])\n",
        "\n",
        "d={}\n",
        "i=0\n",
        "for word in pd.unique(y):\n",
        "    d[word]=i\n",
        "    i+=1"
      ],
      "metadata": {
        "id": "K7Rh92SmICel"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Bag of Words с помощью CountVectorizer\n",
        "cv=CountVectorizer()\n",
        "x=cv.fit_transform(x)"
      ],
      "metadata": {
        "id": "aCrdUKIwIMWm"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Выборки\n",
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=.2,random_state=42)"
      ],
      "metadata": {
        "id": "t7F3K-AaIqhW"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = MultinomialNB()\n",
        "model.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "YUSPZXO-2Zax",
        "outputId": "1a885c7a-057a-4eee-bc4b-beabb62b6093"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultinomialNB()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(x_test) # делаем предугадывание"
      ],
      "metadata": {
        "id": "-7O0FOAw4d1V"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ac1 = accuracy_score(y_test,y_pred)\n",
        "cr1 = classification_report(y_test, y_pred)"
      ],
      "metadata": {
        "id": "ooAmyHDpxK4V"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy is \", ac1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BwNgY0w3NXbM",
        "outputId": "8d633180-5268-4309-b676-d98f58a52e8a"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy is  0.9825918762088974\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(cr1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NHs8yN8NQ55Z",
        "outputId": "973c5a51-05b4-4cbb-8f11-ce13205550d0"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "      Arabic       1.00      0.98      0.99       106\n",
            "      Danish       0.97      0.96      0.97        73\n",
            "       Dutch       0.98      0.97      0.98       111\n",
            "     English       0.92      1.00      0.96       291\n",
            "      French       0.99      0.99      0.99       219\n",
            "      German       1.00      0.97      0.98        93\n",
            "       Greek       1.00      0.99      0.99        68\n",
            "       Hindi       1.00      1.00      1.00        10\n",
            "     Italian       1.00      0.99      1.00       145\n",
            "     Kannada       1.00      1.00      1.00        66\n",
            "   Malayalam       1.00      0.98      0.99       121\n",
            "  Portugeese       0.99      0.98      0.99       144\n",
            "     Russian       1.00      0.99      1.00       136\n",
            "     Spanish       0.99      0.97      0.98       160\n",
            "    Sweedish       1.00      0.98      0.99       133\n",
            "       Tamil       1.00      0.99      0.99        87\n",
            "     Turkish       1.00      0.93      0.97       105\n",
            "\n",
            "    accuracy                           0.98      2068\n",
            "   macro avg       0.99      0.98      0.99      2068\n",
            "weighted avg       0.98      0.98      0.98      2068\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Анализ ошибок**"
      ],
      "metadata": {
        "id": "ITEykdBzdov_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Как можем заметить по cr1, у нас модель обучилась практически идеально и если и есть ошибки, то это в основном на English.\n",
        "\n",
        "Связано это может быть с тем, что в других предложениях встречаются несколько английских слов (аббревиатуры и тд) и это снижает точность определения английского языка. То есть скорее всего есть такие предложения, написанные на латинских языках, по типу Dutch, French, Portoguese и др, что наша нейросеть путает их с английским или наоборот.\n",
        "\n",
        "Найти пример не из тестовой выборки будет довольно трудно, а в тестовой выборке можно поискать."
      ],
      "metadata": {
        "id": "tfqnafKqdsBx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "На самом деле лучше всего понять где наша нейросеть ошибается можно на Confusion Matrix, поэтому давайте её построим"
      ],
      "metadata": {
        "id": "3IiQqe_vj0vv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cm1 = confusion_matrix(y_test, y_pred)"
      ],
      "metadata": {
        "id": "GuNHAxtSifVg"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,15))\n",
        "ax= plt.subplot()\n",
        "sns.heatmap(cm1, annot=True, fmt='g', ax=ax,cbar=False,cmap='RdBu')\n",
        "ax.set_xlabel('Predicted labels')\n",
        "ax.set_ylabel('True labels')\n",
        "ax.set_title('Confusion Matrix')\n",
        "ax.xaxis.set_ticklabels(d.keys())\n",
        "ax.yaxis.set_ticklabels(d.keys())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "GgV5jmRukcJ_",
        "outputId": "df132853-29ba-42b7-d546-fd0a84dc2f1d"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0.5, 'English'),\n",
              " Text(0, 1.5, 'Malayalam'),\n",
              " Text(0, 2.5, 'Hindi'),\n",
              " Text(0, 3.5, 'Tamil'),\n",
              " Text(0, 4.5, 'Portugeese'),\n",
              " Text(0, 5.5, 'French'),\n",
              " Text(0, 6.5, 'Dutch'),\n",
              " Text(0, 7.5, 'Spanish'),\n",
              " Text(0, 8.5, 'Greek'),\n",
              " Text(0, 9.5, 'Russian'),\n",
              " Text(0, 10.5, 'Danish'),\n",
              " Text(0, 11.5, 'Italian'),\n",
              " Text(0, 12.5, 'Turkish'),\n",
              " Text(0, 13.5, 'Sweedish'),\n",
              " Text(0, 14.5, 'Arabic'),\n",
              " Text(0, 15.5, 'German'),\n",
              " Text(0, 16.5, 'Kannada')]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABMkAAATYCAYAAAAF2pCqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3RU5b7G8WdSIZUEQi+hCSq9WEBAAVFBEVGx0lE5KEixgAoaEEEUEFAPIkrxqAiIBRXpojRFeu9dIECAQAghJHP/4BrP66YkHsg7yf5+1pq1mHf2zDzz3J3cnJ977/F4vV6vAAAAAAAAABfzsx0AAAAAAAAAsI0hGQAAAAAAAFyPIRkAAAAAAABcjyEZAAAAAAAAXI8hGQAAAAAAAFyPIRkAAAAAAABcjyEZAAAAAAAAXI8hGQAAAAAAAFyPIRkAAAAAAABcjyEZAADAFbJ161Y1adJEkZGR8ng8+vrrr6/o6+/atUsej0fjx4+/oq+bk91666269dZbbccAAAC5AEMyAACQq2zfvl1PPfWUypQpozx58igiIkJ169bViBEjlJycfFXfu23btlq7dq0GDhyoTz75RLVq1bqq75ed2rVrJ4/Ho4iIiAv2uHXrVnk8Hnk8Hr399ttZfv0//vhDr732mlatWnUF0gIAAGRdgO0AAAAAV8r333+vBx98UMHBwWrTpo0qVaqks2fPauHChXr++ee1fv16jRkz5qq8d3JyspYsWaKXX35ZzzzzzFV5j1KlSik5OVmBgYFX5fUvJyAgQKdPn9b06dPVqlUr47FPP/1UefLk0ZkzZ/7Ra//xxx+Ki4tTbGysqlWrlunnzZo16x+9HwAAwN8xJAMAALnCzp079fDDD6tUqVKaN2+eihQpkvHY008/rW3btun777+/au9/+PBhSVK+fPmu2nt4PB7lyZPnqr3+5QQHB6tu3br6/PPPHUOyzz77TM2aNdOXX36ZLVlOnz6tkJAQBQUFZcv7AQCA3I/TLQEAQK4wZMgQnTp1Sh999JExIPtTuXLl9Oyzz2bcP3funAYMGKCyZcsqODhYsbGxeumll5SSkmI8LzY2VnfffbcWLlyoG264QXny5FGZMmU0ceLEjG1ee+01lSpVSpL0/PPPy+PxKDY2VtL50xT//Pd/e+211+TxeIy12bNn65ZbblG+fPkUFhamChUq6KWXXsp4/GLXJJs3b57q1aun0NBQ5cuXT/fee682btx4wffbtm2b2rVrp3z58ikyMlLt27fX6dOnL17s3zz66KOaMWOGjh8/nrG2bNkybd26VY8++qhj+4SEBD333HOqXLmywsLCFBERobvuukurV6/O2Oann35S7dq1JUnt27fPOG3zz8956623qlKlSlq+fLnq16+vkJCQjF7+fk2ytm3bKk+ePI7Pf8cddygqKkp//PFHpj8rAABwF4ZkAAAgV5g+fbrKlCmjOnXqZGr7Tp06qV+/fqpRo4aGDx+uBg0aaNCgQXr44Ycd227btk0PPPCAbr/9dg0dOlRRUVFq166d1q9fL0lq2bKlhg8fLkl65JFH9Mknn+idd97JUv7169fr7rvvVkpKivr376+hQ4eqefPmWrRo0SWfN2fOHN1xxx2Kj4/Xa6+9pp49e2rx4sWqW7eudu3a5di+VatWOnnypAYNGqRWrVpp/PjxiouLy3TOli1byuPxaNq0aRlrn332mSpWrKgaNWo4tt+xY4e+/vpr3X333Ro2bJief/55rV27Vg0aNMgYWF177bXq37+/JOnJJ5/UJ598ok8++UT169fPeJ2jR4/qrrvuUrVq1fTOO+/otttuu2C+ESNGKCYmRm3btlVaWpok6YMPPtCsWbM0atQoFS1aNNOfFQAAuIwXAAAghztx4oRXkvfee+/N1ParVq3ySvJ26tTJWH/uuee8krzz5s3LWCtVqpRXkvfnn3/OWIuPj/cGBwd7e/XqlbG2c+dOryTvW2+9Zbxm27ZtvaVKlXJkePXVV73//afY8OHDvZK8hw8fvmjuP99j3LhxGWvVqlXzFixY0Hv06NGMtdWrV3v9/Py8bdq0cbxfhw4djNe87777vPnz57/oe/735wgNDfV6vV7vAw884G3UqJHX6/V609LSvIULF/bGxcVdsIMzZ85409LSHJ8jODjY279//4y1ZcuWOT7bnxo0aOCV5B09evQFH2vQoIGxNnPmTK8k7+uvv+7dsWOHNywszNuiRYvLfkYAAOBuHEkGAAByvMTERElSeHh4prb/4YcfJEk9e/Y01nv16iVJjmuXXXfddapXr17G/ZiYGFWoUEE7duz4x5n/7s9rmX3zzTdKT0/P1HMOHDigVatWqV27doqOjs5Yr1Klim6//faMz/nfOnfubNyvV6+ejh49mtFhZjz66KP66aefdPDgQc2bN08HDx684KmW0vnrmPn5nf+TMy0tTUePHs04lXTFihWZfs/g4GC1b98+U9s2adJETz31lPr376+WLVsqT548+uCDDzL9XgAAwJ0YkgEAgBwvIiJCknTy5MlMbb979275+fmpXLlyxnrhwoWVL18+7d6921gvWbKk4zWioqJ07Nixf5jY6aGHHlLdunXVqVMnFSpUSA8//LAmT558yYHZnzkrVKjgeOzaa6/VkSNHlJSUZKz//bNERUVJUpY+S9OmTRUeHq4vvvhCn376qWrXru3o8k/p6ekaPny4ypcvr+DgYBUoUEAxMTFas2aNTpw4ken3LFasWJYu0v/2228rOjpaq1at0siRI1WwYMFMPxcAALgTQzIAAJDjRUREqGjRolq3bl2Wnvf3C+dfjL+//wXXvV7vP36PP6+X9ae8efPq559/1pw5c9S6dWutWbNGDz30kG6//XbHtv+L/+Wz/Ck4OFgtW7bUhAkT9NVXX130KDJJeuONN9SzZ0/Vr19f//nPfzRz5kzNnj1b119/faaPmJPO95MVK1euVHx8vCRp7dq1WXouAABwJ4ZkAAAgV7j77ru1fft2LVmy5LLblipVSunp6dq6dauxfujQIR0/fjzjmyqvhKioKOObIP/096PVJMnPz0+NGjXSsGHDtGHDBg0cOFDz5s3T/PnzL/jaf+bcvHmz47FNmzapQIECCg0N/d8+wEU8+uijWrlypU6ePHnBLzv409SpU3Xbbbfpo48+0sMPP6wmTZqocePGjk4yO7DMjKSkJLVv317XXXednnzySQ0ZMkTLli27Yq8PAAByJ4ZkAAAgV3jhhRcUGhqqTp066dChQ47Ht2/frhEjRkg6f7qgJMc3UA4bNkyS1KxZsyuWq2zZsjpx4oTWrFmTsXbgwAF99dVXxnYJCQmO51arVk2SlJKScsHXLlKkiKpVq6YJEyYYQ6d169Zp1qxZGZ/zarjttts0YMAAvfvuuypcuPBFt/P393ccpTZlyhTt37/fWPtzmHehgWJWvfjii9qzZ48mTJigYcOGKTY2Vm3btr1ojwAAAJIUYDsAAADAlVC2bFl99tlneuihh3TttdeqTZs2qlSpks6ePavFixdrypQpateunSSpatWqatu2rcaMGaPjx4+rQYMG+u233zRhwgS1aNFCt9122xXL9fDDD+vFF1/Ufffdp27duun06dP697//rWuuuca4cH3//v31888/q1mzZipVqpTi4+P1/vvvq3jx4rrlllsu+vpvvfWW7rrrLt18883q2LGjkpOTNWrUKEVGRuq11167Yp/j7/z8/PTKK69cdru7775b/fv3V/v27VWnTh2tXbtWn376qcqUKWNsV7ZsWeXLl0+jR49WeHi4QkNDdeONN6p06dJZyjVv3jy9//77evXVV1WjRg1J0rhx43Trrbeqb9++GjJkSJZeDwAAuAdHkgEAgFyjefPmWrNmjR544AF98803evrpp9W7d2/t2rVLQ4cO1ciRIzO2HTt2rOLi4rRs2TJ1795d8+bNU58+fTRp0qQrmil//vz66quvFBISohdeeEETJkzQoEGDdM899ziylyxZUh9//LGefvppvffee6pfv77mzZunyMjIi75+48aN9eOPPyp//vzq16+f3n77bd10001atGhRlgdMV8NLL72kXr16aebMmXr22We1YsUKff/99ypRooSxXWBgoCZMmCB/f3917txZjzzyiBYsWJCl9zp58qQ6dOig6tWr6+WXX85Yr1evnp599lkNHTpUS5cuvSKfCwAA5D4eb1au0goAAAAAAADkQhxJBgAAAAAAANdjSAYAAAAAAADXY0gGAAAAAAAA12NIBgAAAAAAANdjSAYAAAAAAADXY0gGAAAAAAAA12NIBgAAAAAAANcLsB3gaji7aLLtCD6n2y0v2I4AH+bvsZ3A96R5bScAAAAAAFwpo727LrsNR5IBAAAAAADA9RiSAQAAAAAAwPUYkgEAAAAAAMD1GJIBAAAAAADA9RiSAQAAAAAAwPUYkgEAAAAAAMD1GJIBAAAAAADA9RiSAQAAAAAAwPUYkgEAAAAAAMD1GJIBAAAAAADA9RiSAQAAAAAAwPUYkgEAAAAAAMD1AmwHkKSkpCQNHjxYc+fOVXx8vNLT043Hd+zYYSkZAAAAAAAA3MAnhmSdOnXSggUL1Lp1axUpUkQej8d2JAAAAAAAALiITwzJZsyYoe+//15169a1HQUAAAAAAAAu5BPXJIuKilJ0dLTtGAAAAAAAAHApnxiSDRgwQP369dPp06dtRwEAAAAAAIALWTvdsnr16sa1x7Zt26ZChQopNjZWgYGBxrYrVqzI7ngAAAAAAABwEWtDshYtWth6awAAAAAAAMBgbUj26quv2nprAAAAAAAAwOAT1yTbu3ev9u3bl3H/t99+U/fu3TVmzBiLqQAAAAAAAOAWPjEke/TRRzV//nxJ0sGDB9W4cWP99ttvevnll9W/f3/L6QAAAAAAAJDb+cSQbN26dbrhhhskSZMnT1blypW1ePFiffrppxo/frzdcAAAAAAAAMj1fGJIlpqaquDgYEnSnDlz1Lx5c0lSxYoVdeDAAZvRAAAAAAAA4AI+MSS7/vrrNXr0aP3yyy+aPXu27rzzTknSH3/8ofz581tOBwAAAAAAgNzOJ4Zkb775pj744APdeuuteuSRR1S1alVJ0rfffptxGiYAAAAAAABwtQTYDiBJt956q44cOaLExERFRUVlrD/55JMKCQmxmAwAAAAAAABu4BNHkkmSv7+/MSCTpNjYWBUsWNBSogv7ffMuPTPiP2rYY4gqd+iruSs2GI97vV69+9Vc3dbjTdV6Kk6d3hqn3YeOXvC1zqae0wOvvqfKHfpq057cfe21Bl1aa+DOhRqVvFkvLv1asbWr2o5kHZ2c16R3F73w6zcaemKdBh/8XU9OG6OC15SxHcs69g8nOjHRhxOdmOjDRB9OdGKiDyc6MdGHiT6c6MSUE/uwNiSrUaOGjh07JkmqXr26atSocdGbL0lOOatrShTWy4/ffcHHP57xiz6bs1R92zTXp688pbzBQXpq6ASlpKY6th02ZaZi8oVf7cjW1Wx1tx4Y9oq+ixuhN2o0077VG9R15kSFx7j3enN08pfy9W/Uz+9/ordvvk+jmrSWf2CAus6cqKCQvLajWcP+4UQnJvpwohMTfZjow4lOTPThRCcm+jDRhxOdmHJqH9aGZPfee2/GN1q2aNFC995770VvvqRelWvUrWVjNap5neMxr9er/8xeoifvaaCG1a9VhRKF9Uan+3X4+EnNW7HR2PaXNVu0eP02PdfqzuyKbk3jnp206MNJWjJ+ig5s3KbPOr+s1NPJqtOhle1o1tDJX95r2lZLJ0zVgQ1btX/NRn3S/jlFlyqukjUr245mDfuHE52Y6MOJTkz0YaIPJzox0YcTnZjow0QfTnRiyql9WLsm2auvvnrBf+dk+w4f05ETp3TTdWUz1sJD8qhymeJavX2v7rqxiiTpyIlTem3CNxr5zKPKExxoK2628A8MVMmalfTjoPcz1rxerzbOWaQyN/vWUYLZhU4uLW/k+aMrkxKO2w1iCfuHE52Y6MOJTkz0YaIPJzox0YcTnZjow0QfTnRiysl9+Mw1yXKDo4mnJEn5I8KM9fwRoTpy4vxjXq9Xr3w0Ta1ura3rSxfL9ozZLaxAlPwDApR46IixfvLQYUUUjrGUyi46uTiPx6P7h/fT9oXLdGD9FttxrGD/cKITE3040YmJPkz04UQnJvpwohMTfZjow4lOTDm5D2tHkkVFRcnj8WRq24SEhIs+lpKSopSUFGPNczZVwUG+eYTWZ3OW6vSZFHVqVt92FMDnPPTeABWtVEHD6j1gOwoAAAAAwGWsDcneeeedK/I6gwYNUlxcnLH2SvsH1Lfjg1fk9bPizyPIjiaeMi7IfzQxSRVLFpYk/bpph1Zv36uaT5qZH+4/Ws1uqqKBne7PvsDZ4NSRY0o7d04RhQoY6+GFYpR48LClVHbRyYW1GhWnSs0aaniDVjq+/6DtONawfzjRiYk+nOjERB8m+nCiExN9ONGJiT5M9OFEJ6ac3Ie10y3btm2b6dul9OnTRydOnDBuL7RukT0f4m+Kx0SpQGSYft2wI2PtVPIZrd2xT1XLljif99Fmmhr3tKa81kVTXuui97u3liS91bmVurZsbCX31ZSWmqo9y9epYqM6GWsej0cVG9XRjiUrLCazh06cWo2KU9UWd2hEo0d1dNc+23GsYv9wohMTfTjRiYk+TPThRCcm+nCiExN9mOjDiU5MObkPa0eS/bfExMQLrns8HgUHBysoKOiizw0ODs74lsw/nb2Kp1qePpOiPfF/nf65/8hxbdpzQJGheVUkfz49fvvN+uC7n1SyULSKxUTp3a/mKiZfuBrWuFaSVCR/PuP1QvKc/2wlCkarcHTkVctt05xhY9VuwlDt/n2tdv22Sg27d1RQaIgWj5tiO5o1dPKXh94boFqP3KsPWjyhlJNJiih0/hz15BOJSj2Tcpln507sH050YqIPJzox0YeJPpzoxEQfTnRiog8TfTjRiSmn9uETQ7J8+fJd8vpkxYsXV7t27fTqq6/Kz8/udw2s3/WHOgz5OOP+W5NmSJKa162ugR1bqsNd9ZSckqq4Cd/q5Okzql6+pEb3bKPgQN+8Rlp2WD75O4XHROue/j0UUThG+1Zt1Kg72+pk/JHLPzmXopO/1P/X+aMpe/z0hbH+SfvntHTCVBuRrGP/cKITE3040YmJPkz04UQnJvpwohMTfZjow4lOTDm1D4/X6/XaDjFx4kS9/PLLateunW644QZJ0m+//aYJEybolVde0eHDh/X222/r+eef10svvXTZ1zu7aPLVjpzjdLvlBdsR4MP8M/cdGq6SZv03IwAAAADgShnt3XXZbXziSLIJEyZo6NChatWqVcbaPffco8qVK+uDDz7Q3LlzVbJkSQ0cODBTQzIAAAAAAAAgK+yeu/j/Fi9erOrVqzvWq1evriVLlkiSbrnlFu3Zsye7owEAAAAAAMAFfGJIVqJECX300UeO9Y8++kglSpz/VsijR48qKioqu6MBAAAAAADABXzidMu3335bDz74oGbMmKHatWtLkn7//Xdt2rRJU6eev3D3smXL9NBDD9mMCQAAAAAAgFzKJy7cL0k7d+7UBx98oC1btkiSKlSooKeeekqxsbFZfi0u3O/EhftxKVy434kL9wMAAABA7pFjLtwvSaVLl9bgwYNtxwAAAAAAAIAL+cyQ7Pjx4/rtt98UHx+v9PR047E2bdpYSgUAAAAAAAA38Ikh2fTp0/XYY4/p1KlTioiIkMfz17lfHo+HIRkAAAAAAACuKp/4dstevXqpQ4cOOnXqlI4fP65jx45l3BISEmzHAwAAAAAAQC7nE0Oy/fv3q1u3bgoJCbEdBQAAAAAAAC7kE0OyO+64Q7///rvtGAAAAAAAAHApn7gmWbNmzfT8889rw4YNqly5sgIDA43HmzdvbikZAAAAAAAA3MDj9Xq9tkP4+V38gDaPx6O0tLQsvd7ZRZP/10i5TrdbXrAdAT7M33P5bdwmzfpvRgAAAADAlTLau+uy2/jEkWTp6em2IwAAAAAAAMDFrF6TrGnTpjpx4kTG/cGDB+v48eMZ948eParrrrvOQjIAAAAAAAC4idUh2cyZM5WSkpJx/4033lBCQkLG/XPnzmnz5s02ogEAAAAAAMBFrA7J/n45NB+4PBoAAAAAAABcyOqQDAAAAAAAAPAFVodkHo9HHo/HsQYAAAAAAABkJ6vfbun1etWuXTsFBwdLks6cOaPOnTsrNDRUkozrlQEAAAAAAABXi9UhWdu2bY37jz/+uGObNm3aZFccAAAAAAAAuJTVIdm4ceNsvj0AAAAAAAAgiQv3AwAAAAAAAAzJAAAAAAAAAIZkAAAAAAAAcD2GZAAAAAAAAHA9hmQAAAAAAABwPYZkAAAAAAAAcD2GZAAAAAAAAHA9j9fr9doOcebMGY0aNUrz589XfHy80tPTjcdXrFiRpdfr7Im9gulyh5dfbmQ7gk8ZOHCu7Qg+xd9jO4HvSbP+mxEAAAAAcKWM9u667DYBVz/G5XXs2FGzZs3SAw88oBtuuEEeD/+LHQAAAAAAANnHJ4Zk3333nX744QfVrVvXdhQAAAAAAAC4kE9ck6xYsWIKDw+3HQMAAAAAAAAu5RNDsqFDh+rFF1/U7t27bUcBAAAAAACAC/nE6Za1atXSmTNnVKZMGYWEhCgwMNB4PCEhwVIyAAAAAAAAuIFPDMkeeeQR7d+/X2+88YYKFSrEhfsBAAAAAACQrXxiSLZ48WItWbJEVatWtR0FAAAAAAAALuQT1ySrWLGikpOTbccAAAAAAACAS/nEkGzw4MHq1auXfvrpJx09elSJiYnGDQAAAAAAALiafOJ0yzvvvFOS1KhRI2Pd6/XK4/EoLS3NRiwAAAAAAAC4hE8MyebPn287AgAAAAAAAFzMJ4ZkDRo0sB0BAAAAAAAALuYTQ7I/nT59Wnv27NHZs2eN9SpVqlhKBAAAAAAAADfwiSHZ4cOH1b59e82YMeOCj3NNMgAAAAAAAFxNPvHtlt27d9fx48f166+/Km/evPrxxx81YcIElS9fXt9++63teAAAAAAAAMjlfOJIsnnz5umbb75RrVq15Ofnp1KlSun2229XRESEBg0apGbNmtmOCAAAAAAAgFzMJ44kS0pKUsGCBSVJUVFROnz4sCSpcuXKWrFihc1oAAAAAAAAcAGfGJJVqFBBmzdvliRVrVpVH3zwgfbv36/Ro0erSJEiltMBAAAAAAAgt/OJ0y2fffZZHThwQJL06quv6s4779Snn36qoKAgjR8/3m44AAAAAAAA5Ho+MSR7/PHHM/5ds2ZN7d69W5s2bVLJkiVVoEABi8kAAAAAAADgBj4xJPu7kJAQ1ahRw3YMAAAAAAAAuIS1IVnPnj0zve2wYcOuYhIAAAAAAAC4nbUh2cqVKzO1ncfjucpJAAAAAAAA4HbWhmTz58+39dYAAAAAAACAwc92AAAAAAAAAMA2n7lw/++//67Jkydrz549Onv2rPHYtGnTLKUCAAAAAACAG/jEkWSTJk1SnTp1tHHjRn311VdKTU3V+vXrNW/ePEVGRtqOBwAAAAAAgFzOJ4Zkb7zxhoYPH67p06crKChII0aM0KZNm9SqVSuVLFnSdrx/pEGX1hq4c6FGJW/Wi0u/VmztqrYjZYsivd5Uidc/ctzy3f3Y+Q0CApTv7sdU9KURKtb3PeV/pIv8QiPshrbErfvI3zXp3UUv/PqNhp5Yp8EHf9eT08ao4DVlbMeyqly9G9Tl27EavP9XjfbuUtV7m9iO5BP4mTHRhxOdmOjDRB9OdGKiDyc6MdGHiT6c6MSUE/vwiSHZ9u3b1axZM0lSUFCQkpKS5PF41KNHD40ZM8Zyuqyr2epuPTDsFX0XN0Jv1Gimfas3qOvMiQqPyW872lV36N8DtH9wj4xb/Li3JUnJ63+XJEXd9bDyVqyqo5P+rfiPhsg/PJ8KPNrFZmQr3LyP/F35+jfq5/c/0ds336dRTVrLPzBAXWdOVFBIXtvRrAkODdG+1Rs16el+tqP4DH5mTPThRCcm+jDRhxOdmOjDiU5M9GGiDyc6MeXUPnxiSBYVFaWTJ09KkooVK6Z169ZJko4fP67Tp0/bjPaPNO7ZSYs+nKQl46fowMZt+qzzy0o9naw6HVrZjnbVpZ8+pfRTiRm3vBWqKvXoIaXs3CxPcF6F1qyn4zO+UMqOTUr9Y7cSpn2s4FLlFVTcXUcOuXkf+bv3mrbV0glTdWDDVu1fs1GftH9O0aWKq2TNyrajWbP+x5/0bd+hWvX1TNtRfAY/Myb6cKITE32Y6MOJTkz04UQnJvow0YcTnZhyah8+MSSrX7++Zs+eLUl68MEH9eyzz+qJJ57QI488okaNGllOlzX+gYEqWbOSNs5ZlLHm9Xq1cc4ilbm5hsVkFvj7K6TqTUpasVCSFFSslDwBATqzfUPGJueOHNS540cVVLKsrZTZjn3k0vJGhkuSkhKO2w0Cn8HPjIk+nOjERB8m+nCiExN9ONGJiT5M9OFEJ6ac3IdPDMneffddPfzww5Kkl19+WT179tShQ4d0//3366OPPrKcLmvCCkTJPyBAiYeOGOsnDx1WROEYS6nsyHttdfnlCVHSisWSJP+wSHnPpcp7JtnYLu3UCfmHuecLGthHLs7j8ej+4f20feEyHVi/xXYc+Ah+Zkz04UQnJvow0YcTnZjow4lOTPRhog8nOjHl5D4CbAeQpOjo6Ix/+/n5qXfv3pl+bkpKilJSUoy1NHnlL88Vy4d/JrRmPZ3ZulbpJ4/bjoIc4qH3BqhopQoaVu8B21EAAAAAAC7jE0eSNWjQQBMnTlRycvLlN/6bQYMGKTIy0rit1ImrkDJzTh05prRz5xRRqICxHl4oRokHD1tKlf388+VXnrLX6dTvv2SspZ06IU9AoDx5zAuy+4dFKu2Uvf+bZTf2kQtrNSpOlZo11IiGD+v4/oO248CH8DNjog8nOjHRh4k+nOjERB9OdGKiDxN9ONGJKSf34RNDsurVq+u5555T4cKF9cQTT2jp0qWZfm6fPn104sQJ41Zd9k7dS0tN1Z7l61SxUZ2MNY/Ho4qN6mjHkhXWcmW30Bp1lZ6UqDNb1mSsnd2/W95z55SnzHUZawEFCikgX36d3bPdRkwr2EecWo2KU9UWd2hEo0d1dNc+23HgY/iZMdGHE52Y6MNEH050YqIPJzox0YeJPpzoxJST+/CJ0y3feecdvf322/r22281YcIE1a9fX+XKlVOHDh3UunVrFSpU6KLPDQ4OVnBwsLFm+1TLOcPGqt2Eodr9+1rt+m2VGnbvqKDQEC0eN8Vqrmzj8Si0xi1KWrlYSk/PWPamJCtp+S/K1/QhpSefUnrKGUXd/ahS9mzT2X07LAbOfq7fR/7LQ+8NUK1H7tUHLZ5QyskkRRQ6f4568olEpZ5Jucyzc6fg0BDFlIvNuF+gdAkVr3qdkhKO69jeP+wFs4ifGRN9ONGJiT5M9OFEJyb6cKITE32Y6MOJTkw5tQ+fGJJJUkBAgFq2bKmWLVsqPj5eY8aMUd++ffXSSy+padOm6tatmxo2bGg7ZqYsn/ydwmOidU//HoooHKN9qzZq1J1tdTL+yOWfnAsEl71OAfnyK2n5Qsdjx2ZMUj6vV/kfefr8N11uXadj0/9jIaVdbt9H/lv9f7WWJPX46Qtj/ZP2z2nphKk2IllXqlYV9fxpUsb9B4f3lSQtGT9VE9o/ZyuWVfzMmOjDiU5M9GGiDyc6MdGHE52Y6MNEH050YsqpfXi8Xq/Xdoj/9ttvv2ncuHGaNGmSIiIi1K5dO+3fv1+fffaZunTporfffvuyr9HZE3v1g+YwL7/cyHYEnzJw4FzbEXyKP99z4ZDmU78ZAQAAAAD/i9HeXZfdxieOJIuPj9cnn3yicePGaevWrbrnnnv0+eef64477pDHc/5/vbdr10533nlnpoZkAAAAAAAAQFb4xJCsePHiKlu2rDp06KB27dopJibGsU2VKlVUu3ZtC+kAAAAAAACQ2/nEkGzu3LmqV6/eJbeJiIjQ/PnzsykRAAAAAAAA3MTPdgBJlx2QAQAAAAAAAFeTTxxJJklTp07V5MmTtWfPHp09e9Z4bMWKFZZSAQAAAAAAwA184kiykSNHqn379ipUqJBWrlypG264Qfnz59eOHTt011132Y4HAAAAAACAXM4nhmTvv/++xowZo1GjRikoKEgvvPCCZs+erW7duunEiRO24wEAAAAAACCX84kh2Z49e1SnTh1JUt68eXXy5ElJUuvWrfX555/bjAYAAAAAAAAX8IkhWeHChZWQkCBJKlmypJYuXSpJ2rlzp7xer81oAAAAAAAAcAGfGJI1bNhQ3377rSSpffv26tGjh26//XY99NBDuu+++yynAwAAAAAAQG7nE99uOWbMGKWnp0uSnn76aeXPn1+LFy9W8+bN9dRTT1lOBwAAAAAAgNzOJ4Zkfn5+8vP766C2hx9+WA8//LDFRAAAAAAAAHATa0OyNWvWZHrbKlWqXMUkAAAAAAAAcDtrQ7Jq1arJ4/Fc9sL8Ho9HaWlp2ZQKAAAAAAAAbmRtSLZz505bbw0AAAAAAAAYrA3JSpUqZeutAQAAAAAAAINPXLj/Txs2bNCePXt09uxZY7158+aWEgEAAAAAAMANfGJItmPHDt13331au3atcZ0yj8cjSVyTDAAAAAAAAFeVn+0AkvTss8+qdOnSio+PV0hIiNavX6+ff/5ZtWrV0k8//WQ7HgAAAAAAAHI5nziSbMmSJZo3b54KFCggPz8/+fn56ZZbbtGgQYPUrVs3rVy50nZEAAAAAAAA5GI+cSRZWlqawsPDJUkFChTQH3/8Ien8xf03b95sMxoAAAAAAABcwCeOJKtUqZJWr16t0qVL68Ybb9SQIUMUFBSkMWPGqEyZMrbjAQAAAAAAIJfziSHZK6+8oqSkJElSXFyc7rnnHtWrV0/58+fXpEmTLKcDAAAAAABAbucTQ7I77rgj49/ly5fXpk2blJCQoKioqIxvuAQAAAAAAACuFqtDsg4dOmRqu48//vgqJwEAAAAAAICbWR2SjR8/XqVKlVL16tXl9XptRgEAAAAAAICLWR2S/etf/9Lnn3+unTt3qn379nr88ccVHR1tMxIAAAAAAABcyM/mm7/33ns6cOCAXnjhBU2fPl0lSpRQq1atNHPmTI4sAwAAAAAAQLaxOiSTpODgYD3yyCOaPXu2NmzYoOuvv15dunRRbGysTp06ZTseAAAAAAAAXMAnvt3yT35+fvJ4PPJ6vUpLS7MdJ1cZOHCu7Qg+5d3f37Mdwac8W/tp2xEAAAAAALDK+pFkKSkp+vzzz3X77bfrmmuu0dq1a/Xuu+9qz549CgsLsx0PAAAAAAAALmD1SLIuXbpo0qRJKlGihDp06KDPP/9cBQoUsBkJAAAAAAAALmR1SDZ69GiVLFlSZcqU0YIFC7RgwYILbjdt2rRsTgYAAAAAAAA3sToka9OmjTwej80IAAAAAAAAgN0h2fjx422+PQAAAAAAACDJBy7cDwAAAAAAANjGkAwAAAAAAACux5AMAAAAAAAArseQDAAAAAAAAK7HkAwAAAAAAACux5AMAAAAAAAArseQDAAAAAAAAK7HkAwAAAAAAACux5AMAAAAAAAArseQDAAAAAAAAK7HkAwAAAAAAACux5AMAAAAAAAArseQDAAAAAAAAK7HkAwAAAAAAACuF2DrjUeOHKknn3xSefLk0ciRIy+5bbdu3bIpFQAAAAAAANzI2pBs+PDheuyxx5QnTx4NHz78ott5PB6GZAAAAAAAALiqrA3Jdu7cecF/AwAAAAAAANmNa5IBAAAAAADA9awdSdazZ89Mbzts2LCrmAQAAAAAAABuZ21ItnLlSuP+ihUrdO7cOVWoUEGStGXLFvn7+6tmzZo24gEAAAAAAMBFrA3J5s+fn/HvYcOGKTw8XBMmTFBUVJQk6dixY2rfvr3q1atnKyIAAAAAAABcwieuSTZ06FANGjQoY0AmSVFRUXr99dc1dOhQi8kAAAAAAADgBj4xJEtMTNThw4cd64cPH9bJkyctJPrfNejSWgN3LtSo5M16cenXiq1d1XYkq9zUx+8bt6vLW2N1a5fXdP2jPTV32Vrjca/Xq1FTZqhBl1dVo+0L6jjw39p9wNz/dx2I1zNDP1LdJ/vqho599Phro/Tr+q3Z+TGyTZPeXfTCr99o6Il1Gnzwdz05bYwKXlPGdizr3PQzk1l0YqIPJzox0YeJPpzoxEQfTnTyl3L1blCXb8dq8P5fNdq7S1XvbWI7knXsH050YsqJffjEkOy+++5T+/btNW3aNO3bt0/79u3Tl19+qY4dO6ply5a242VZzVZ364Fhr+i7uBF6o0Yz7Vu9QV1nTlR4TH7b0axwWx/JKWdVoVRRvdL+wvvuR9Pn6dOZv+jVDg/q8wHdlTdPkJ4c/IFSzqZmbNPlrY+Ulpauj1/5l6a83lMVShXV029/pMPHE7PrY2Sb8vVv1M/vf6K3b75Po5q0ln9ggLrOnKigkLy2o1njtp+ZzKATE3040YmJPkz04UQnJvpwohNTcGiI9q3eqElP97MdxSewfzjRiSmn9uETQ7LRo0frrrvu0qOPPqpSpUqpVKlSevTRR3XnnXfq/ffftx0vyxr37KRFH07SkvFTdGDjNn3W+WWlnk5WnQ6tbEezwm191Kt2rZ5t1VSNa1dxPOb1evXJjz/rqRa3q2GtSqpQsqgG/etRxR9P1Nzf10mSjiWe0u6Dh9WpeSNVKFlUpYrEqOfDzZScclbb9h7M7o9z1b3XtK2WTpiqAxu2av+ajfqk/XOKLlVcJWtWth3NGrf9zGQGnZjow4lOTPRhog8nOjHRhxOdmNb/+JO+7TtUq76eaTuKT2D/cKITU07twyeGZCEhIXr//fd19OhRrVy5UitXrlRCQoLef/99hYaG2o6XJf6BgSpZs5I2zlmUseb1erVxziKVubmGxWR20IdpX3yCjhw/qZsqXZOxFh6SV1XKltTqrbskSfnCQ1W6SEF988synT6TonNpaZo8d4nyR4TputLFLSXPPnkjwyVJSQnH7QaxhJ8ZJzox0YcTnZjow0QfTnRiog8nOsGlsH840YkpJ/fhE0OyP4WGhqpKlSqqUqVKjhuO/SmsQJT8AwKUeOiIsX7y0GFFFI6xlMoe+jAdOXH+dMkC/z8I+lP+yHAdOXH++nsej0djX+qsTbv264aOL6lG2xc14YcF+qD3k4oMC8n2zNnJ4/Ho/uH9tH3hMh1Yv8V2HCv4mXGiExN9ONGJiT5M9OFEJyb6cKITXAr7hxOdmHJyHwG2A0hSUlKSBg8erLlz5yo+Pl7p6enG4zt27Ljoc1NSUpSSkmKspckrf3muSlbgavN6vXp9/JeKjgjTxH7PKE9QoKbOX6qn3/5IXwzooZioCNsRr5qH3hugopUqaFi9B2xHAQAAAAC4jE8MyTp16qQFCxaodevWKlKkiDyezA+4Bg0apLi4OGOtpiJVS/mucMrMOXXkmNLOnVNEoQLGenihGCUedH6DZ25HH6YCkecHXEdOnDSGXUdPnFTFUsUkSb+u36oFKzZoyYcDFRaSR5LUr/QDWrJ2i77+ZZmeaN4o+4Nng1aj4lSpWUMNb9BKx/fnvmuvZRY/M050YqIPJzox0YeJPpzoxEQfTnSCS2H/cKITU07uwydOt5wxY4amTJmiN998U927d9ezzz5r3C6lT58+OnHihHGrrshsSu6UlpqqPcvXqWKjOhlrHo9HFRvV0Y4lK6zlsoU+TMULRqtAvnD9un5rxtqp02e0ZvseVS0fK0lKTjn/LZceP3NY7OfnkTfdm21Zs1OrUXGq2uIOjWj0qI7u2mc7jlX8zDjRiYk+nOjERB8m+nCiExN9ONEJLoX9w4lOTDm5D584kiwqKkrR0dH/6LnBwcEKDg421myfajln2Fi1mzBUu39fq12/rVLD7h0VFBqixeOmWM1li9v6SDqToj0H/zr3et/hBG3ctV+RYSEqWiBKre+srw++mq2ShQuoeEy0Rk35UQXzRahRrUqSpGrlSykiNEQv/fsz/atlk/OnW85bqn3xCapf/VpbH+uqeei9Aar1yL36oMUTSjmZpIhC589RTz6RqNQzKZd5du7ktp+ZzKATE3040YmJPkz04UQnJvpwohNTcGiIYsrFZtwvULqEile9TkkJx3Vs7x/2glnC/uFEJ6ac2odPDMkGDBigfv36acKECQoJyfkXJl8++TuFx0Trnv49FFE4RvtWbdSoO9vqZPyRyz85F3JbH+t37FX719/PuD/kP99Iku6tX1tvdH5EHe9pqOSUs3pt7BSdPJ2sGteU1ge9n1RwUKAkKSoiTB/0flIjvvhBHQb+W+fS0lSuWGG926tDximZuUn9f7WWJPX46Qtj/ZP2z2nphKk2Ilnntp+ZzKATE3040YmJPkz04UQnJvpwohNTqVpV1POnSRn3HxzeV5K0ZPxUTWj/nK1Y1rB/ONGJKaf24fF6vdbP36pevbq2b98ur9er2NhYBQYGGo+vWJG1w/E6e2KvYDrkRu/+/p7tCD7l2dpP247gc9Ks/2YEAAAAAFwpo727LruNTxxJ1qJFC9sRAAAAAAAA4GI+MSR79dVXbUcAAAAAAACAi/nEt1sCAAAAAAAANlk7kiw6OlpbtmxRgQIFFBUVJY/n4t9ImZCQkI3JAAAAAAAA4DbWhmTDhw9XeHh4xr8vNSQDAAAAAAAAriZrQ7K2bdsqMTFRKSkpatmypa0YAAAAAAAAgN0L9+fLly9TR5ClpaVlQxoAAAAAAAC4ldUh2fz58zP+7fV61bRpU40dO1bFihWzmAoAAAAAAABuY3VI1qBBA+O+v7+/brrpJpUpU8ZSIgAAAAAAALiRn+0AAAAAAAAAgG0MyQAAAAAAAOB6Pjcky8yF/AEAAAAAAIAryeo1yVq2bGncP3PmjDp37qzQ0FBjfdq0adkZCwAAAAAAAC5jdUgWGRlp3H/88cctJQEAAAAAAICbWR2SjRs3zubbAwAAAAAAAJJ88JpkAAAAAAAAQHZjSAYAAAAAAADXY0gGAAAAAAAA12NIBgAAAAAAANdjSAYAAAAAAADXY0gGAAAAAAAA12NIBgAAAAAAANdjSAYAAAAAAADXY0gGAAAAAAAA12NIBgAAAAAAANdjSAYAAAAAAADXY0gGAAAAAAAA12NIBgAAAAAAANdjSAYAAAAAAADXC7AdALDhmVpP247gUz6u1tB2BJ/TYdU82xEAAAAAANmII8kAAAAAAADgegzJAAAAAAAA4HoMyQAAAAAAAOB6DMkAAAAAAADgegzJAAAAAAAA4HoMyQAAAAAAAOB6DMkAAAAAAADgegzJAAAAAAAA4HoMyQAAAAAAAOB6DMkAAAAAAADgegzJAAAAAAAA4HoMyQAAAAAAAOB6DMkAAAAAAADgegzJAAAAAAAA4HoMyQAAAAAAAOB6DMkAAAAAAADgegzJAAAAAAAA4HoBNt60evXq8ng8mdp2xYoVVzkNAAAAAAAA3M7KkKxFixY23hYAAAAAAAC4ICtDsldffdXG2wIAAAAAAAAXxDXJAAAAAAAA4HpWjiSLjo7Wli1bVKBAAUVFRV3y+mQJCQnZmAwAAAAAAABuZGVINnz4cIWHh0uS3nnnHRsRAAAAAAAAgAxWhmRt27a94L8BAAAAAAAAG6wMyS4kPj5e8fHxSk9PN9arVKliKREAAAAAAADcwvqQbPny5Wrbtq02btwor9drPObxeJSWlmYpGQAAAAAAANzC+pCsQ4cOuuaaa/TRRx+pUKFCl7yIPwAAAAAAAHA1+NkOsGPHDg0ZMkQ33nijYmNjVapUKeOWUzXo0loDdy7UqOTNenHp14qtXdV2JKvow8kNnbzQoakW/6evji58X/vmvqOpw57RNaUKG9uUKR6jKUOf0f55I3Tkl/f02Zv/UsHoCGOb3h3v1oLxL+n44n8r/ud3s/MjWOOG/SOr6MREH050YqIPE3040YmJPpzoxEQfJvpwohNTTuzD+pCsUaNGWr16te0YV1TNVnfrgWGv6Lu4EXqjRjPtW71BXWdOVHhMftvRrKAPJ7d0Uq9GBf37i3mq1+Z1Nf3XUAUE+Ov7f/dUSJ4gSVJIniB9/34veb1e3fHkEN3a/g0FBfrrqxHdjKNKgwL99eXsZfpg6k+WPkn2csv+kRV0YqIPJzox0YeJPpzoxEQfTnRiog8TfTjRiSmn9uHx/v1CYNnsyJEjatu2rW644QZVqlRJgYGBxuPNmzfP8mt29sReoXT/zItLv9buZas1qeurks5fW23Q3iWaP2qCZr75b6vZbKAPJ1/r5ONqDbPlfQpEheuPeSPUsONgLVyxRY1vul7T3+2hgg2e0cmkM5KkiLC8il8wSk27DNO8XzcYz299T10Nff4RFaz/zFXP2mHVvKv+Hhfja/uHL6ATE3040YmJPkz04UQnJvpwohMTfZjow4lOTL7Yx2jvrstuY/1IsiVLlmjRokWKi4vTgw8+qBYtWmTc7rvvPtvxssw/MFAla1bSxjmLMta8Xq82zlmkMjfXsJjMDvpwcnMnkWF5JUnHTiRJkoKDAuT1epVy9lzGNmdSUpWe7lXdauWtZLTNzfvHxdCJiT6c6MREHyb6cKITE3040YmJPkz04UQnppzch/UhWdeuXfX444/rwIEDSk9PN2458ZstwwpEyT8gQImHjhjrJw8dVkThGEup7KEPJ7d24vF49PZzj2jRyq1av32/JOnXtTuUlJyiN559UHnzBCkkT5De7NlKAQH+Klwg0nJiO9y6f1wKnZjow4lOTPRhog8nOjHRhxOdmOjDRB9OdGLKyX1YH5IdPXpUPXr0UKFChf7R81NSUpSYmGjc0mT1DFIAFzCyz+O6vlwxPd57dMbakWMn9cgL/1az+lV1bNH7OvLLe8oXFqIVG3Yp3e6Z4AAAAAAAlwmwHaBly5aaP3++ypYt+4+eP2jQIMXFxRlrNRWpWsp3BdJl3akjx5R27pwiChUw1sMLxSjx4GErmWyiDyc3dvLOi4+pab2qatRxsPbHHzMem7N0va5t3lv584Xp3Lk0nTiVrD2zh2vnzN8spbXLjfvH5dCJiT6c6MREHyb6cKITE3040YmJPkz04UQnppzch/Ujya655hr16dNH7dq109ChQzVy5Ejjdjl9+vTRiRMnjFt12TtNKy01VXuWr1PFRnUy1jwejyo2qqMdS1ZYy2ULfTi5rZN3XnxM9zasoTueGqJdfxy56HZHj5/SiVPJurV2RRWMDtd3C1ZlX0gf4rb9IzPoxEQfTnRiog8TfTjRiYk+nOjERB8m+nCiE1NO7sP6kWRjx45VWFiYFixYoAULFhiPeTwedevW7ZLPDw4OVnBwsLHmL88Vz5kVc4aNVbsJQ7X797Xa9dsqNezeUUGhIVo8borVXLbQh5NbOhnZ53E9fNdNur/HSJ1MOqNC+SMkSSdOJetMSqokqU3zW7Rp5x86cuykbqpSVkOff1QjPp2tLbsPZrxOicLRio4IVcki0fL381PVa0pIkrbtjVdSckr2f7CrzC37R1bQiYk+nOjERB8m+nCiExN9ONGJiT5M9OFEJ6ac2of1IdnOnTttR7jilk/+TuEx0bqnfw9FFI7RvlUbNerOtjoZf/GjaHIz+nBySyedWzWUJM0d29tY79jvI30y/fw3nVSILazXu96v6MhQ7f7jiAZ/9J1G/GeWsf2r/2qhNs1vybi/7Ivzp1g37vSmfl6++Wp+BCvcsn9kBZ2Y6MOJTkz0YaIPJzox0YcTnZjow0QfTnRiyql9eLze3Hd17M6eWNsRgBzl42oNbUfwOR1WzbMdAQAAAABwhYz27rrsNtaPJJOkffv26dtvv9WePXt09uxZ47Fhw4ZZSgUAAAAAAAC3sD4kmzt3rpo3b64yZcpo06ZNqlSpknbt2iWv16saNWrYjgcAAAAAAAAXsP7tln369NFzzz2ntWvXKk+ePPryyy+1d+9eNWjQQA8++KDteAAAAAAAAHAB60OyjRs3qk2bNpKkgIAAJScnKywsTP3799ebb75pOR0AAAAAAADcwPqQLDQ0NOM6ZEWKFNH27dszHjtyxLe/9QAAAAAAAAC5g7UhWf/+/ZWUlKSbbrpJCxculCQ1bdpUvXr10sCBA9WhQwfddNNNtuIBAAAAAADARTxer9dr4439/f114MABnTp1SqdOnVKVKlWUlJSkXr16afHixSpfvryGDRumUqVKZfm1O3tir3xgIBf7uFpD2xF8TodV82xHAAAAAABcIaO9uy67jbVvt/xzNlemTJmMtdDQUI0ePdpWJAAAAAAAALiU1WuSeTwem28PAAAAAAAASLJ4JJkkXXPNNZcdlCUkJGRTGgAAAAAAALiV1SFZXFycIiMjbUYAAAAAAAAA7A7JHn74YRUsWNBmBAAAAAAAAMDeNcm4HhkAAAAAAAB8hbUh2Z/fbgkAAAAAAADYZu10y/T0dFtvDQAAAAAAABisHUkGAAAAAAAA+AqGZAAAAAAAAHA9hmQAAAAAAABwPYZkAAAAAAAAcD2GZAAAAAAAAHA9hmQAAAAAAABwPYZkAAAAAAAAcD2GZAAAAAAAAHA9hmQAAAAAAABwPYZkAAAAAAAAcD2GZAAAAAAAAHA9hmQAAAAAAABwPZ8akp09e1abN2/WuXPnbEcBAAAAAACAiwTYDiBJp0+fVteuXTVhwgRJ0pYtW1SmTBl17dpVxYoVU+/evS0nBHK3Dqvm2Y7gc4KnTbcdwaektLzHdgQAAAAAuKp84kiyPn36aPXq1frpp5+UJ0+ejPXGjRvriy++sJgMAAAAAAAAbuATR5J9/fXX+uKLL3TTTTfJ4/FkrF9//fXavn27xWQAAAAAAABwA584kuzw4cMqWLCgYz0pKckYmgEAAAAAAABXg08MyWrVqqXvv/8+4/6fg7GxY8fq5ptvthULAAAAAAAALuETp1u+8cYbuuuuu7RhwwadO3dOI0aM0IYNG7R48WItWLDAdjwAAAAAAADkcj5xJNktt9yiVatW6dy5c6pcubJmzZqlggULasmSJapZs6bteAAAAAAAAMjlfOJIMkkqW7asPvzwQ9sxAAAAAAAA4EI+cSTZihUrtHbt2oz733zzjVq0aKGXXnpJZ8+etZgMAAAAAAAAbuATQ7KnnnpKW7ZskSTt2LFDDz30kEJCQjRlyhS98MILltMBAAAAAAAgt/OJIdmWLVtUrVo1SdKUKVPUoEEDffbZZxo/fry+/PJLu+EAAAAAAACQ6/nEkMzr9So9PV2SNGfOHDVt2lSSVKJECR05csRmNAAAAAAAALiATwzJatWqpddff12ffPKJFixYoGbNmkmSdu7cqUKFCllOBwAAAAAAgNzOJ4Zk77zzjlasWKFnnnlGL7/8ssqVKydJmjp1qurUqWM5HQAAAAAAAHK7ANsBJKlKlSrGt1v+6a233pK/v7+FRAAAAAAAAHATnziSTJKOHz+usWPHqk+fPkpISJAkbdiwQfHx8ZaTAQAAAAAAILfziSPJ1qxZo0aNGilfvnzatWuXnnjiCUVHR2vatGnas2ePJk6caDsiAAAAAAAAcjGfOJKsZ8+eat++vbZu3ao8efJkrDdt2lQ///yzxWQAAAAAAABwA58Yki1btkxPPfWUY71YsWI6ePCghUQAAAAAAABwE58YkgUHBysxMdGxvmXLFsXExFhIBAAAAAAAADfxiSFZ8+bN1b9/f6WmpkqSPB6P9uzZoxdffFH333+/5XQAAAAAAADI7XxiSDZ06FCdOnVKBQsWVHJysho0aKBy5copPDxcAwcOtB0PAAAAAAAAuZxPfLtlZGSkZs+erYULF2rNmjU6deqUatSoocaNG9uOBgAAAAAAABfwiSHZn2655RbVqlVLwcHB8ng8tuMAAAAAAADAJXzidMv09HQNGDBAxYoVU1hYmHbu3ClJ6tu3rz766CPL6QAAAAAAAJDb+cSQ7PXXX9f48eM1ZMgQBQUFZaxXqlRJY8eOtZgMAAAAAAAAbuATQ7KJEydqzJgxeuyxx+Tv75+xXrVqVW3atMliMgAAAAAAALiBTwzJ9u/fr3LlyjnW09PTlZqaaiERAAAAAAAA3MQnhmTXXXedfvnlF8f61KlTVb16dQuJAAAAAAAA4CY+MSTr16+fnnnmGb355ptKT0/XtGnT9MQTT2jgwIHq16+f7Xj/SIMurTVw50KNSt6sF5d+rdjaVW1Hsoo+/lKu3g3q8u1YDd7/q0Z7d6nqvU1sR7LOTZ00viZGvW4tqzfvvk6vN71WHW8sqYJhQcY2N8dG6ZlbSuvNu6/TiPsqK2/ghX9VX1coXD0alNVbza/XoGbXqeONJbPjI1jD7xETfTjRiYk+TPThRCcm+nCik7+46e/VzGL/cKITU07swyeGZPfee6+mT5+uOXPmKDQ0VP369dPGjRs1ffp03X777bbjZVnNVnfrgWGv6Lu4EXqjRjPtW71BXWdOVHhMftvRrKAPU3BoiPat3qhJT+fMAfDV4KZOyhUI1S87jmr4gu16f+FO+ft59K+6pRXk78nYJsjfT5viT2n2lsMXfZ2qRSP0eK3i+m33MQ2Zt1Xv/Lxdy/edyI6PYAW/R0z04UQnJvow0YcTnZjow4lOTG76ezUz2D+c6MSUU/vweL1er+0QV1pnT6zV939x6dfavWy1JnV9VZLk8Xg0aO8SzR81QTPf/LfVbDbQx8WN9u7Sv1s8qdXfzLIdxWf4SifB06Zny/uEBvnrjWbXaeTP27X96GnjsXIFQtW1Xhn1/m69klPTM9b9PNKrd1TUjI2HtHT3sWzJmdLynmx5n4vh94iJPpzoxEQfJvpwohMTfTjRycX5yt+rNrF/ONGJyRf7GO3dddltfOJIstzEPzBQJWtW0sY5izLWvF6vNs5ZpDI317CYzA76AC4tb+D5b/Q9fTYt088pni+v8uUNlNcrPX9bOfW/q6KeujlWRcKDr1ZMq/g9YqIPJzox0YeJPpzoxEQfTnSCS2H/cKITU07uwyeGZFFRUYqOjnbc8ufPr2LFiqlBgwYaN27cBZ+bkpKixMRE45YmewfHhRWIkn9AgBIPHTHWTx46rIjCMZZS2UMfwMV5JLWsUkQ7jibpwMmUTD8vf8j5a5jdeW1BzdocrzFLdik5NU3P1CujkP8fuuUm/B4x0YcTnZjow0QfTnRiog8nOsGlsH840YkpJ/fhE0Oyfv36yc/PT82aNVNcXJzi4uLUrFkz+fn56emnn9Y111yjf/3rX/rwww8dzx00aJAiIyON20rl3uvyAMg9HqhaVIXD82j8b3uy9Dy//7982azN8Vr9R6L2HT+jT1fskyRVKxZ5pWMCAAAAgCsE2A4gSQsXLtTrr7+uzp07G+sffPCBZs2apS+//FJVqlTRyJEj9cQTTxjb9OnTRz179jTWekVWvuqZL+bUkWNKO3dOEYUKGOvhhWKUePDiF+HOregDuLD7qxTV9YXDNfKXHTpx5lyWnvvn9of+6+iztHSvjiSdVVRI4BXN6Qv4PWKiDyc6MdGHiT6c6MREH050gkth/3CiE1NO7sMnjiSbOXOmGjdu7Fhv1KiRZs6cKUlq2rSpduzY4dgmODhYERERxs1fHsd22SUtNVV7lq9TxUZ1MtY8Ho8qNqqjHUtWWMtlC30ATvdXKaoqRSP03sKdSjidmuXn7z2erNS0dBUM++saZH4eKX9IoBJOn72SUX0Cv0dM9OFEJyb6MNGHE52Y6MOJTnAp7B9OdGLKyX34xJFk0dHRmj59unr06GGsT58+XdHR0ZKkpKQkhYeH24iXZXOGjVW7CUO1+/e12vXbKjXs3lFBoSFaPG6K7WhW0IcpODREMeViM+4XKF1Cxatep6SE4zq29w97wSxyUycPVi2qGsXzaezS3TpzLl3hwed/DZ9JTVNq+vnrKYYHBygiT4AKhJ6/9liRiDxKOZeuY6dTdTo1TSnn0rVoZ4LuuraQjiWn6tjps2pY/vy5/av2587Tzfk9YqIPJzox0YeJPpzoxEQfTnRictPfq5nB/uFEJ6ac2odPDMn69u2rf/3rX5o/f75uuOEGSdKyZcv0ww8/aPTo0ZKk2bNnq0GDBjZjZtryyd8pPCZa9/TvoYjCMdq3aqNG3dlWJ+OPXP7JuRB9mErVqqKeP03KuP/g8L6SpCXjp2pC++dsxbLKTZ3cUia/JKlb/TLG+qfL9+q3PcclSXVLR+uuawtlPPZs/bKObb5Zd0DpXq9a1yyuQH8/7T52Wu8u3Knk1PSr/yEs4PeIiT6c6MREHyb6cKITE3040YnJTX+vZgb7hxOdmHJqHx6v12vvqyD/y6JFi/Tuu+9q8+bNkqQKFSqoa9euqlOnzmWe6dTZE3uF0wFwm+Bp021H8CkpLe+xHQEAAAAA/rHR3l2X3cYnjiSTpLp166pu3bq2YwAAAAAAAMCFfGJItmfPnks+XrJkyWxKAgAAAAAAADfyiSFZbGysPJ6LfyNlWlpaNqYBAAAAAACA2/jEkGzlypXG/dTUVK1cuVLDhg3TwIEDLaUCAAAAAACAW/jEkKxq1aqOtVq1aqlo0aJ666231LJlSwupAAAAAAAA4BZ+tgNcSoUKFbRs2TLbMQAAAAAAAJDL+cSRZImJicZ9r9erAwcO6LXXXlP58uUtpQIAAAAAAIBb+MSQLF++fI4L93u9XpUoUUKTJk2ylAoAAAAAAABu4RNDsvnz5xv3/fz8FBMTo3LlyikgwCciAgAAAAAAIBfziQmUx+NRnTp1HAOxc+fO6eeff1b9+vUtJQMAAAAAAIAb+MSF+2+77TYlJCQ41k+cOKHbbrvNQiIAAAAAAAC4iU8Mybxer+OaZJJ09OhRhYaGWkgEAAAAAAAAN7F6umXLli0lnT/dsl27dgoODs54LC0tTWvWrFGdOnVsxQMAAAAAAIBLWB2SRUZGSjp/JFl4eLjy5s2b8VhQUJBuuukmPfHEE7biAQAAAAAAwCWsDsnGjRsnr9crSRo1apTCwsJsxgEAAAAAAIBLWb8mmdfr1aeffqoDBw7YjgIAAAAAAACXsj4k8/PzU/ny5XX06FHbUQAAAAAAAOBS1odkkjR48GA9//zzWrdune0oAAAAAAAAcCGr1yT7U5s2bXT69GlVrVpVQUFBxgX8JSkhIcFSMgAAAAAAALiBTwzJ3nnnHdsRAAAAAAAA4GI+MSRr27at7QgAAAAAAABwMZ8YkklSWlqavv76a23cuFGSdP3116t58+by9/e3nAwAAAAAAAC5nU8MybZt26amTZtq//79qlChgiRp0KBBKlGihL7//nuVLVvWckIAAAAAAADkZj7x7ZbdunVT2bJltXfvXq1YsUIrVqzQnj17VLp0aXXr1s12PAAAAAAAAORyPnEk2YIFC7R06VJFR0dnrOXPn1+DBw9W3bp1LSYDAAAAAACAG/jEkWTBwcE6efKkY/3UqVMKCgqykAgAAAAAAABu4hNDsrvvvltPPvmkfv31V3m9Xnm9Xi1dulSdO3dW8+bNbccDAAAAAABALucTQ7KRI0eqXLlyqlOnjvLkyaM8efKobt26KleunEaMGGE7HgAAAAAAAHI5j9fr9dp68/T0dL311lv69ttvdfbsWZUsWVJt27aVx+PRtddeq3Llyv2j1+3sib2yQYFczt9jO4HvSbP2m9E3DZvcxXYEn9Oz1fu2IwAAAADIpNHeXZfdxuqF+wcOHKjXXntNjRs3Vt68efXDDz8oMjJSH3/8sc1YAAAAAAAAcBmrp1tOnDhR77//vmbOnKmvv/5a06dP16effqr09HSbsQAAAAAAAOAyVodke/bsUdOmTTPuN27cWB6PR3/88YfFVAAAAAAAAHAbq0Oyc+fOKU+ePMZaYGCgUlNTLSUCAAAAAACAG1m9JpnX61W7du0UHBycsXbmzBl17txZoaGhGWvTpk2zEQ8AAAAAAAAuYXVI1rZtW8fa448/biEJAAAAAAAA3MzqkGzcuHE23x4AAAAAAACQZPmaZAAAAAAAAIAvYEgGAAAAAAAA12NIBgAAAAAAANdjSAYAAAAAAADXY0gGAAAAAAAA12NIBgAAAAAAANdjSAYAAAAAAADXY0gGAAAAAAAA1/OJIdmhQ4fUunVrFS1aVAEBAfL39zduAAAAAAAAwNUUYDuAJLVr10579uxR3759VaRIEXk8HtuRAAAAAAAA4CI+MSRbuHChfvnlF1WrVs12FAAAAAAAALiQT5xuWaJECXm9XtsxAAAAAAAA4FI+MSR755131Lt3b+3atct2FAAAAAAAALiQtdMto6KijGuPJSUlqWzZsgoJCVFgYKCxbUJCQnbHAwAAAAAAgItYG5K98847tt4aAAAAAAAAMFgbkrVt29bWWwMAAAAAAAAGn7gm2Q8//KCZM2c61mfNmqUZM2ZYSAQAAAAAAAA38YkhWe/evZWWluZYT09PV+/evS0kAgAAAAAAgJv4xJBs69atuu666xzrFStW1LZt2ywkAgAAAAAAgJv4xJAsMjJSO3bscKxv27ZNoaGhFhIBAAAAAADATXxiSHbvvfeqe/fu2r59e8batm3b1KtXLzVv3txiMgAAAAAAALiBTwzJhgwZotDQUFWsWFGlS5dW6dKlde211yp//vx6++23bcf7Rxp0aa2BOxdqVPJmvbj0a8XWrmo7kjXl6t2gLt+O1eD9v2q0d5eq3tvEdiSfwD5yXpPeXfTCr99o6Il1Gnzwdz05bYwKXlPGdizr3Lx/JKWc1VvfL9Vdb03STa+NV9sPpmv9vsMZj3u9Xr0/Z7luH/yZbnptvJ76eIZ2HzlhMbEdbt5HLoZOTPRhog8nOjHRhxOdmOjDRB9OdGLKiX34xJAsMjJSixcv1vfff68uXbqoV69emjt3rubNm6d8+fLZjpdlNVvdrQeGvaLv4kbojRrNtG/1BnWdOVHhMfltR7MiODRE+1Zv1KSn+9mO4jPYR/5Svv6N+vn9T/T2zfdpVJPW8g8MUNeZExUUktd2NGvcvn/0/2qhlm7fr9cfaKDJXVvq5nLF1HncDMUnJkmSxv+yRp8v3aCX7q2riZ2bK29QgJ6eMFMpqecsJ88+bt9HLoROTPRhog8nOjHRhxOdmOjDRB9OdGLKqX34xJBMkjwej5o0aaLnn39ezzzzjOrXr2870j/WuGcnLfpwkpaMn6IDG7fps84vK/V0sup0aGU7mhXrf/xJ3/YdqlVfz7QdxWewj/zlvaZttXTCVB3YsFX712zUJ+2fU3Sp4ipZs7LtaNa4ef84k3pOczfsUvc7aqtm6SIqmT9CnRvVUIn8EZry60Z5vV59tni9nri1mm67tpSuKRytAQ800OGTpzV/427b8bONm/eRi6ETE32Y6MOJTkz04UQnJvow0YcTnZhyah8+MySbO3euXnrpJXXq1EkdOnQwbjmJf2CgStaspI1zFmWseb1ebZyzSGVurmExGXwF+8il5Y0MlyQlJRy3G8QSt+8faenpSkv3KiggwFgPDgjQyt2HtP/YSR05lawbyxbNeCw8T5AqFY/Rmr3x2R3XCrfvIxdCJyb6MNGHE52Y6MOJTkz0YaIPJzox5eQ+fGJIFhcXpyZNmmju3Lk6cuSIjh07ZtwuJSUlRYmJicYtTd5sSu4UViBK/gEBSjx0xFg/eeiwIgrHWEoFX8I+cnEej0f3D++n7QuX6cD6LbbjWOH2/SM0OEhVShTUh/NXKj4xSWnp6fp+1Tat2RuvI6eSdeRUsiQpOsw8HTd/WF4dPZlsI3K2c/s+ciF0YqIPE3040YmJPpzoxEQfJvpwohNTTu4j4PKbXH2jR4/W+PHj1bp16yw/d9CgQYqLizPWaipStZTvCqUDkF0eem+AilaqoGH1HrAdBRa9/kADvfbVL7pjyCT5+3lUsUh+3VmljDb+ceTyTwYAAACAf8gnhmRnz55VnTp1/tFz+/Tpo549exprvSLtXcvo1JFjSjt3ThGFChjr4YVilHjw8EWeBTdhH7mwVqPiVKlZQw1v0ErH9x+0Hcca9g+pRP4IfdSpmZLPpupUSqpiwkP04qR5KhYVrgL/fwRZwqlkxYSHZDzn6KlkVSgSbStytmIfcaITE32Y6MOJTkz04UQnJvow0YcTnZhych8+cbplp06d9Nlnn/2j5wYHBysiIsK4+ctzhRNmXlpqqvYsX6eKjf4a+nk8HlVsVEc7lqywlgu+g33EqdWoOFVtcYdGNHpUR3ftsx3HKvaPv+QNClRMeIgSk1O0eNt+3XptqYxB2a/b/8jY7tSZs1q377CqlChoMW32YR9xohMTfZjow4lOTPThRCcm+jDRhxOdmHJyHz5xJNmZM2c0ZswYzZkzR1WqVFFgYKDx+LBhwywl+2fmDBurdhOGavfva7Xrt1Vq2L2jgkJDtHjcFNvRrAgODVFMudiM+wVKl1DxqtcpKeG4ju394+JPzMXYR/7y0HsDVOuRe/VBiyeUcjJJEYXOn6OefCJRqWdSLKezw+37x+Kt++T1SrEFIrU3IVHDf/xNpQtEqnmNa+TxePRones19qdVKpk/QsWiwvX+3OWKCQ/RbdeWsh0927h9H7kQOjHRh4k+nOjERB9OdGKiDxN9ONGJKaf24RNDsjVr1qhatWqSpHXr1hmPeTz2jgr7p5ZP/k7hMdG6p38PRRSO0b5VGzXqzrY6Ge/O6+mUqlVFPX+alHH/weF9JUlLxk/VhPbP2YplFfvIX+r/6/y1CHv89IWx/kn757R0wlQbkaxz+/5x6sxZjZr1uw4lJikyb7AaXR+rp2+vpUD/8wc/t6tXRclnz+n1bxbp5JmzqlaykN5re4eCA33i/6VlC7fvIxdCJyb6MNGHE52Y6MOJTkz0YaIPJzox5dQ+PF6v195XQV4lnT2xtiMAOYp/zptFX3Vpue434/9m2OQutiP4nJ6t3rcdAQAAAEAmjfbuuuw2PnFNsj9t27ZNM2fOVHJysiQpF87vAAAAAAAA4IN8Ykh29OhRNWrUSNdcc42aNm2qAwcOSJI6duyoXr16WU4HAAAAAACA3M4nhmQ9evRQYGCg9uzZo5CQkIz1hx56SD/++KPFZAAAAAAAAHADn7jK8axZszRz5kwVL17cWC9fvrx2795tKRUAAAAAAADcwieOJEtKSjKOIPtTQkKCgoODLSQCAAAAAACAm/jEkKxevXqaOHFixn2Px6P09HQNGTJEt912m8VkAAAAAAAAcAOfON1yyJAhatSokX7//XedPXtWL7zwgtavX6+EhAQtWrTIdjwAAAAAAADkcj5xJFmlSpW0ZcsW3XLLLbr33nuVlJSkli1bauXKlSpbtqzteAAAAAAAAMjlrB9JlpqaqjvvvFOjR4/Wyy+/bDsOAAAAAAAAXMj6kWSBgYFas2aN7RgAAAAAAABwMetDMkl6/PHH9dFHH9mOAQAAAAAAAJeyfrqlJJ07d04ff/yx5syZo5o1ayo0NNR4fNiwYZaSAQAAAAAAwA2sDsl27Nih2NhYrVu3TjVq1JAkbdmyxdjG4/HYiAYAAAAAAAAXsTokK1++vA4cOKD58+dLkh566CGNHDlShQoVshkLAAAAAAAALmP1mmRer9e4P2PGDCUlJVlKAwAAAAAAALfyiQv3/+nvQzMAAAAAAAAgO1gdknk8Hsc1x7gGGQAAAAAAALKb1WuSeb1etWvXTsHBwZKkM2fOqHPnzo5vt5w2bZqNeAAAAAAAAHAJq0Oytm3bGvcff/xxS0kAAAAAAADgZlaHZOPGjbP59gAAAAAAAIAkH7twPwAAAAAAAGADQzIAAAAAAAC4HkMyAAAAAAAAuB5DMgAAAAAAALgeQzIAAAAAAAC4HkMyAAAAAAAAuB5DMgAAAAAAALgeQzIAAAAAAAC4nsfr9Xpth7jSOntibUcAAORyL/aqZzuCT3lz6C+2IwAAAAAXNdq767LbcCQZAAAAAAAAXI8hGQAAAAAAAFyPIRkAAAAAAABcjyEZAAAAAAAAXI8hGQAAAAAAAFyPIRkAAAAAAABcjyEZAAAAAAAAXI8hGQAAAAAAAFyPIRkAAAAAAABcjyEZAAAAAAAAXI8hGQAAAAAAAFyPIRkAAAAAAABcjyEZAAAAAAAAXI8hGQAAAAAAAFyPIRkAAAAAAABcjyEZAAAAAAAAXI8hGQAAAAAAAFwvwHaAP6Wnp2vbtm2Kj49Xenq68Vj9+vUtpQIAAAAAAIAb+MSQbOnSpXr00Ue1e/dueb1e4zGPx6O0tDRLyQAAAAAAAOAGPjEk69y5s2rVqqXvv/9eRYoUkcfjsR0JAAAAAAAALuITQ7KtW7dq6tSpKleunO0oAAAAAAAAcCGfuHD/jTfeqG3bttmOAQAAAAAAAJeydiTZmjVrMv7dtWtX9erVSwcPHlTlypUVGBhobFulSpXsjgcAAAAAAAAXsTYkq1atmjwej3Gh/g4dOmT8+8/HuHA/AAAAAAAArjZrQ7KdO3faemsAAAAAAADAYG1IVqpUKVtvDQAAAAAAABh84sL9gwYN0scff+xY//jjj/Xmm29aSAQAAAAAAAA38Ykh2QcffKCKFSs61q+//nqNHj3aQiIAAAAAAAC4iU8MyQ4ePKgiRYo41mNiYnTgwAELif53Dbq01sCdCzUqebNeXPq1YmtXtR3JKvpwopO/lKt3g7p8O1aD9/+q0d5dqnpvE9uRrGP/cHJzJ/4RUYp5pLNKxr2vUoM+UrFebyioeOmMx0u//ckFb5G3NrWYOvu5eR+5EPow0YcTnZjow4lOTPRhog8nOjHlxD58YkhWokQJLVq0yLG+aNEiFS1a1EKi/03NVnfrgWGv6Lu4EXqjRjPtW71BXWdOVHhMftvRrKAPJzoxBYeGaN/qjZr0dD/bUXwC+4eTmzvxyxuiIs/0lTc9TQfHvq39b/VWwvTPlJ6clLHNnrhnjNvhL8bIm56upDXLLCbPXm7eRy6EPkz04UQnJvpwohMTfZjow4lOTDm1D58Ykj3xxBPq3r27xo0bp927d2v37t36+OOP1aNHDz3xxBO242VZ456dtOjDSVoyfooObNymzzq/rNTTyarToZXtaFbQhxOdmNb/+JO+7TtUq76eaTuKT2D/cHJzJ5G33a204wk68sWHOrt3h84lHFbylnU6dzQ+Y5u0kyeMW8j1NXVm+0adSzhsMXn2cvM+ciH0YaIPJzox0YcTnZjow0QfTnRiyql9+MSQ7Pnnn1fHjh3VpUsXlSlTRmXKlFHXrl3VrVs39e7d23a8LPEPDFTJmpW0cc5fR8Z5vV5tnLNIZW6uYTGZHfThRCe4FPYPJ7d3EnJ9DaXs26mCrbuq5GvvqWiPAQq/8daLbu8XFqGQa6vq5G8Lsi+kZW7fR/6OPkz04UQnJvpwohMTfZjow4lOTDm5D58Yknk8Hr355ps6fPiwli5dqtWrVyshIUH9+vWTx+O55HNTUlKUmJho3NLkzabkTmEFouQfEKDEQ0eM9ZOHDiuicIylVPbQhxOd4FLYP5zc3klAdIzCb26o1CMHdXDMEJ1cPE/RLVorrNYtF9w+vFY9paec0em1v2dzUnvcvo/8HX2Y6MOJTkz04UQnJvow0YcTnZhych8+MSTr0KGDTp48qbCwMNWuXVuVKlVScHCwkpKS1KFDh0s+d9CgQYqMjDRuK3Uim5IDAHB1eTx+Ort/t47NmKKzf+zWyV/n6+TSnxR+U8MLbh92Q32dWrFY3nOp2ZwUAAAAyNl8Ykg2YcIEJScnO9aTk5M1ceLESz63T58+OnHihHGrrsirFfWyTh05prRz5xRRqICxHl4oRokH3XNtmD/RhxOd4FLYP5zc3knayeNKPbTfWEuN/0MBUc6LngaXvkZBBYvq1K/uOdVSYh/5O/ow0YcTnZjow4lOTPRhog8nOjHl5D6sDskSExN14sQJeb1enTx50jhl8tixY/rhhx9UsGDBS75GcHCwIiIijJu/Ln2K5tWUlpqqPcvXqWKjOhlrHo9HFRvV0Y4lK6zlsoU+nOgEl8L+4eT2Ts7s3KLAmCLGWmBMYZ07dtSxbfgNtypl7w6dPbAnu+L5BLfvI39HHyb6cKITE3040YmJPkz04UQnppzcR4DNN8+XL588Ho88Ho+uueYax+Mej0dxcXEWkv1v5gwbq3YThmr372u167dVati9o4JCQ7R43BTb0aygDyc6MQWHhiimXGzG/QKlS6h41euUlHBcx/b+YS+YJewfTm7u5MQvP6roM/0U2fAeJa3+VcElyyr8ptt0ZMrHxnae4DwKrXqDEqZ/ZimpXW7eRy6EPkz04UQnJvpwohMTfZjow4lOTDm1D6tDsvnz58vr9aphw4b68ssvFR0dnfFYUFCQSpUqpaJFi1pM+M8sn/ydwmOidU//HoooHKN9qzZq1J1tdTL+yOWfnAvRhxOdmErVqqKeP03KuP/g8L6SpCXjp2pC++dsxbKG/cPJzZ2c3btTh8aPUHTTVsp3ewudSzishG/+o6SVi43twqrdLEk6tXKJjZjWuXkfuRD6MNGHE52Y6MOJTkz0YaIPJzox5dQ+PF6v195XQf6/3bt3q2TJkpf9JsvM6uyJvSKvAwDAxbzYq57tCD7lzaG/2I4AAAAAXNRo767LbmP1SLI/7d69W7t3777o4/Xr18/GNAAAAAAAAHAbnxiS3XrrrY61/z6qLC0tLRvTAAAAAAAAwG2sfrvln44dO2bc4uPj9eOPP6p27dqaNWuW7XgAAAAAAADI5XziSLLIyEjH2u23366goCD17NlTy5cvt5AKAAAAAAAAbuETR5JdTKFChbR582bbMQAAAAAAAJDL+cSRZGvWrDHue71eHThwQIMHD1a1atXshAIAAAAAAIBr+MSQrFq1avJ4PPJ6vcb6TTfdpI8//thSKgAAAAAAALiFTwzJdu7cadz38/NTTEyM8uTJYykRAAAAAAAA3MT6kCw9PV1z587VtGnTtGvXLnk8HpUuXVoPPPCAWrduLY/HYzsiAAAAAAAAcjmrF+73er1q3ry5OnXqpP3796ty5cq6/vrrtXv3brVr10733XefzXgAAAAAAABwCatHko0fP14///yz5s6dq9tuu814bN68eWrRooUmTpyoNm3aWEoIAAAAAAAAN7B6JNnnn3+ul156yTEgk6SGDRuqd+/e+vTTTy0kAwAAAAAAgJtYHZKtWbNGd95550Ufv+uuu7R69epsTAQAAAAAAAA3sjokS0hIUKFChS76eKFChXTs2LFsTAQAAAAAAAA3sjokS0tLU0DAxS+L5u/vr3PnzmVjIgAAAAAAALiR1Qv3e71etWvXTsHBwRd8PCUlJZsTAQAAAAAAwI2sDsnatm172W34ZksAAAAAAABcbVaHZOPGjbP59gAAAAAAAIAky9ckAwAAAAAAAHwBQzIAAAAAAAC4HkMyAAAAAAAAuB5DMgAAAAAAALgeQzIAAAAAAAC4HkMyAAAAAAAAuB5DMgAAAAAAALgeQzIAAAAAAAC4HkMyAAAAAAAAuB5DMgAAAAAAALiex+v1em2HuNI6e2JtRwAAwFUK5wmwHcGnHDxzznYEAAAA/JfR3l2X3YYjyQAAAAAAAOB6DMkAAAAAAADgegzJAAAAAAAA4HoMyQAAAAAAAOB6DMkAAAAAAADgegzJAAAAAAAA4HoMyQAAAAAAAOB6DMkAAAAAAADgegzJAAAAAAAA4HoBtgNI0ty5czV37lzFx8crPT3deOzjjz+2lAoAAAAAAABuYX1IFhcXp/79+6tWrVoqUqSIPB6P7UgAAAAAAABwGetDstGjR2v8+PFq3bq17SgAAAAAAABwKevXJDt79qzq1KljOwYAAAAAAABczPqQrFOnTvrss89sxwAAAAAAAICLWTndsmfPnhn/Tk9P15gxYzRnzhxVqVJFgYGBxrbDhg3L7ngAAAAAAABwGStDspUrVxr3q1WrJklat26dsc5F/AEAAAAAAJAdrAzJ5s+fb+NtAQAAAAAAgAuyfk2yv0tMTNTXX3+tTZs22Y4CAAAAAAAAl7A+JGvVqpXeffddSVJycrJq1aqlVq1aqXLlyvryyy8tpwMAAAAAAIAbWB+S/fzzz6pXr54k6auvvpLX69Xx48c1cuRIvf7665bTAQAAAAAAwA2sD8lOnDih6OhoSdKPP/6o+++/XyEhIWrWrJm2bt1qOR0AAAAAAADcwPqQrESJElqyZImSkpL0448/qkmTJpKkY8eOKU+ePJbTAQAAAAAAwA2sfLvlf+vevbsee+wxhYWFqVSpUrr11lslnT8Ns3LlynbDAQAAAAAAwBWsD8m6dOmiG264QXv37tXtt98uP7/zB7eVKVOGa5IBAAAAAAAgW3i8Xq/XdogrrbMn1nYEAABcpXAe6//dzaccPHPOdgQAAAD8l9HeXZfdxspftD179tSAAQMUGhqqnj17XnLbYcOGZVMqAAAAAAAAuJWVIdnKlSuVmpqa8e+L8Xg82RUJAAAAAAAALsbplgAA4H/G6ZYmTrcEAADwLZk53dLv6sdwpwZdWmvgzoUalbxZLy79WrG1q9qOZBV9ONGJiT5M9OFEJyY391Gqbm09MnWMeu1YpNeSt6niPY0d29zW91n12rFYLyesU5vvJyi6bCkLSe1y8z5yIfThRCcm+nCiExN9mOjDiU5MObEP60OypKQk9e3bV3Xq1FG5cuVUpkwZ45YT1Wx1tx4Y9oq+ixuhN2o0077VG9R15kSFx+S3Hc0K+nCiExN9mOjDiU5Mbu8jMDSvDq3dqO+7v3bBx+v2elI3dmmr77r109j69+tsUrJaTx+ngOCg7A1qkdv3kb+jDyc6MdGHE52Y6MNEH050YsqpfVg/3fKRRx7RggUL1Lp1axUpUsRxHbJnn302y69p+3TLF5d+rd3LVmtS11clnb+22qC9SzR/1ATNfPPfVrPZQB9OdGKiDxN9ONGJyRf7sHW65WvJ2zSpVWdtmj4nY63XjsVaMvIjLX7nI0lScESYnt/9q75+8gWtm/J9tuSyfbqlL+4jNtGHE52Y6MOJTkz0YaIPJzox+WIfOeJ0yxkzZmjKlCl688031b17dz377LPGLafxDwxUyZqVtHHOoow1r9erjXMWqczNNSwms4M+nOjERB8m+nCiExN9XFpUbAmFFymoHfMWZ6ylJJ7SvmWrVfzG6haTZR/2ERN9ONGJiT6c6MREHyb6cKITU07uw/qQLCoqStHR0f/4+SkpKUpMTDRuabJ3cFxYgSj5BwQo8dARY/3kocOKKBxjKZU99OFEJyb6MNGHE52Y6OPSwgoXkCSdijf7SYo/orBC7uiHfcREH050YqIPJzox0YeJPpzoxJST+7A+JBswYID69eun06dP/6PnDxo0SJGRkcZtpU5c4ZQAAAAAAADIzax/X/vQoUO1fft2FSpUSLGxsQoMDDQeX7FixSWf36dPH/Xs2dNY6xVZ+YrnzKxTR44p7dw5RRQqYKyHF4pR4sHDllLZQx9OdGKiDxN9ONGJiT4u7dTB8//FMqxgAZ36rz5CCxbQwTUbbMXKVuwjJvpwohMTfTjRiYk+TPThRCemnNyH9SPJWrRooV69eum5557TAw88oHvvvde4XU5wcLAiIiKMm788l33e1ZKWmqo9y9epYqM6GWsej0cVG9XRjiWXHvjlRvThRCcm+jDRhxOdmOjj0o7t2quTB+JV+ra/+gkOD1Px2lW179eVFpNlH/YRE3040YmJPpzoxEQfJvpwohNTTu7D+pFkr776qu0IV9ycYWPVbsJQ7f59rXb9tkoNu3dUUGiIFo+bYjuaFfThRCcm+jDRhxOdmNzeR1BoiKLLlsq4ny+2hApXuVbJx47rxN4DWvreeNV/sYsStu3SsV171fDVHjp54JA2fTvbYurs5fZ95O/ow4lOTPThRCcm+jDRhxOdmHJqH9aHZLnR8snfKTwmWvf076GIwjHat2qjRt3ZVif/dhFht6APJzox0YeJPpzoxOT2PorWqKx2sz7NuH/nkJclSas++VJfP/miFg0do6CQvLrn3deVJ1+E9iz+Xf9p3kHnUs7aipzt3L6P/B19ONGJiT6c6MREHyb6cKITU07tw+P1eu19FaSktLQ0DR8+XJMnT9aePXt09qz5B2xCQkKWX7OzJ/YKpQMAAJlROA//3e2/HTxzznYEAAAA/JfR3l2X3cb6Ncni4uI0bNgwPfTQQzpx4oR69uypli1bys/PT6+99prteAAAAAAAAHAB60OyTz/9VB9++KF69eqlgIAAPfLIIxo7dqz69eunpUuX2o4HAAAAAAAAF7A+JDt48KAqV64sSQoLC9OJEyckSXfffbe+//57m9EAAAAAAADgEtaHZMWLF9eBAwckSWXLltWsWbMkScuWLVNwcLDNaAAAAAAAAHAJ60Oy++67T3PnzpUkde3aVX379lX58uXVpk0bdejQwXI6AAAAAAAAuIH1b7f8uyVLlmjJkiUqX7687rnnnn/0Gny7JQAA2YtvtzTx7ZYAAAC+JTPfbulzf9HefPPNuvnmm23HAAAAAAAAgIv4xJBs8+bNGjVqlDZu3ChJuvbaa9W1a1dVqFDBcjIAAAAAAAC4gfVrkn355ZeqVKmSli9frqpVq6pq1apasWKFKlWqpC+//NJ2PAAAAAAAALiA9WuSlS1bVo899pj69+9vrL/66qv6z3/+o+3bt2f5NbkmGQAA2Ytrkpm4JhkAAIBvycw1yawfSXbgwAG1adPGsf7444/rwIEDFhIBAAAAAADAbawPyW699Vb98ssvjvWFCxeqXr16FhIBAAAAAADAbayfG9G8eXO9+OKLWr58uW666SZJ0tKlSzVlyhTFxcXp22+/NbYFAAAAAAAArjTr1yTz88vcwWwej0dpaWmZ2pZrkgEAkL24JpmJa5IBAAD4lsxck8z6X7Tp6em2IwAAAAAAAMDlrF2TbMmSJfruu++MtYkTJ6p06dIqWLCgnnzySaWkpFhKBwAAAAAAADexNiTr37+/1q9fn3F/7dq16tixoxo3bqzevXtr+vTpGjRokK14AAAAAAAAcBFrQ7JVq1apUaNGGfcnTZqkG2+8UR9++KF69uypkSNHavLkybbiAQAAAAAAwEWsDcmOHTumQoUKZdxfsGCB7rrrroz7tWvX1t69e21EAwAAAAAAgMtYG5IVKlRIO3fulCSdPXtWK1as0E033ZTx+MmTJxUYGGgrHgAAAAAAAFzE2pCsadOm6t27t3755Rf16dNHISEhqlevXsbja9asUdmyZW3FAwAAAAAAgIsE2HrjAQMGqGXLlmrQoIHCwsI0YcIEBQUFZTz+8ccfq0mTJrbiAQAAAAAAwEU8Xq/XazPAiRMnFBYWJn9/f2M9ISFBYWFhxuAsszp7Yq9QOgAAkBmF81j7724+6eCZc7YjAAAA4L+M9u667DbW/6KNjIy84Hp0dHQ2JwEAAAAAAIBbWbsmGQAAAAAAAOArGJIBAAAAAADA9a7IkOz48eNX4mUAAAAAAAAAK7I8JHvzzTf1xRdfZNxv1aqV8ufPr2LFimn16tVXNBwAAAAAAACQHbJ84f7Ro0fr008/lSTNnj1bs2fP1owZMzR58mQ9//zzmjVr1hUPCQAAfBvf5mh6J2m97Qg+p3vo9bYjAAAAXFKWh2QHDx5UiRIlJEnfffedWrVqpSZNmig2NlY33njjFQ8IAAAAAAAAXG1ZPt0yKipKe/fulST9+OOPaty4sSTJ6/UqLS3tyqYDAAAAAAAAskGWjyRr2bKlHn30UZUvX15Hjx7VXXfdJUlauXKlypUrd8UDAgAAAAAAAFdblodkw4cPV2xsrPbu3ashQ4YoLCxMknTgwAF16dLligcEAAAAAAAArjaP1+v12g5xpXX2xNqOAAAAXIwL9ztx4X4AAGDTaO+uy26TqSPJvv3220y/afPmzTO9LQAAAAAAAOALMjUka9GiRaZezOPxcPF+AAAAAAAA5DiZGpKlp6df7RwAAAAAAACANX7/y5PPnDlzpXIAAAAAAAAA1mR5SJaWlqYBAwaoWLFiCgsL044dOyRJffv21UcffXTFAwIAAAAAAABXW5aHZAMHDtT48eM1ZMgQBQUFZaxXqlRJY8eOvaLhAAAAAAAAgOyQ5SHZxIkTNWbMGD322GPy9/fPWK9atao2bdp0RcMBAAAAAAAA2SHLQ7L9+/erXLlyjvX09HSlpqZekVAAAAAAAABAdsrykOy6667TL7/84lifOnWqqlevfkVCAQAAAAAAANkpIKtP6Nevn9q2bav9+/crPT1d06ZN0+bNmzVx4kR99913VyMjAAAAAAAAcFVl+Uiye++9V9OnT9ecOXMUGhqqfv36aePGjZo+fbpuv/32KxouOTn5ir4eAAAAAAAAcCFZHpJJUr169TR79mzFx8fr9OnTWrhwoZo0afKPAnTr1u2C60lJSWratOk/ek0AAAAAAAAgK7J8uuWffv/9d23cuFHS+euU1axZ8x+9zvfff6+oqCjFxcVlrCUlJenOO+/8p9EAAAAAAACALMnykGzfvn165JFHtGjRIuXLl0+SdPz4cdWpU0eTJk1S8eLFs/R6s2bNUr169RQVFaXu3bvr5MmTuuOOOxQQEKAZM2ZkNR4AAAAAAACQZVk+3bJTp05KTU3Vxo0blZCQoISEBG3cuFHp6enq1KlTlgOULVtWP/74owYMGKCRI0eqSZMmCgoK0owZMxQaGprl1wMAAAAAAACyKstHki1YsECLFy9WhQoVMtYqVKigUaNGqV69ev8oRJUqVfTdd9/p9ttv14033qjvvvtOefPm/UevBQAAAAAAAGRVlodkJUqUUGpqqmM9LS1NRYsWzdRrVK9eXR6Px7EeHBysP/74Q3Xr1s1YW7FiRVYjAgAAAAAAAFmS5SHZW2+9pa5du+q9995TrVq1JJ2/iP+zzz6rt99+O1Ov0aJFi6y+LQAAAAAAAHDVeLxer/dyG0VFRRlHfiUlJencuXMKCDg/Y/vz36GhoUpISLh6aTOpsyfWdgQAAOBi7ySttx3B53QPvd52BAAA4GKjvbsuu02mjiR75513/scol3b8+HFNnTpV27dv1/PPP6/o6GitWLFChQoVUrFixa7qewMAAAAAAACZGpK1bdv2qgVYs2aNGjdurMjISO3atUtPPPGEoqOjNW3aNO3Zs0cTJ068au8NAAAAAAAASJLf//LkM2fOKDEx0bhlVc+ePdWuXTtt3bpVefLkyVhv2rSpfv755/8lHgAAAAAAAJApWR6SJSUl6ZlnnlHBggUVGhqqqKgo45ZVy5Yt01NPPeVYL1asmA4ePJjl1wMAAAAAAACyKstDshdeeEHz5s3Tv//9bwUHB2vs2LGKi4tT0aJF/9GpkcHBwRc8Am3Lli2KiYnJ8uv5igZdWmvgzoUalbxZLy79WrG1q9qOZBV9ONHJX8rVu0Fdvh2rwft/1WjvLlW9t4ntSNaxfzjRiYk+nNzSyfLly9X12WfV+PYmqlq9hubNn3/RbQe8PlBVq9fQfz799IKPnz17Vq0eelhVq9fQps2br1Zkn+CW/SMr6MREH050YqIPE3040YkpJ/aR5SHZ9OnT9f777+v+++9XQECA6tWrp1deeUVvvPGGPr3IH2CX0rx5c/Xv31+pqamSJI/Hoz179ujFF1/U/fffn+XX8wU1W92tB4a9ou/iRuiNGs20b/UGdZ05UeEx+W1Hs4I+nOjEFBwaon2rN2rS0/1sR/EJ7B9OdGKiDyc3dZKcfEYVrrlGffr0vuR2c+fN09q1ay/5Hx2HvzMiR/9Hycxy0/6RWXRiog8nOjHRh4k+nOjElFP7yPKQLCEhQWXKlJEkRUREKCEhQZJ0yy23/KNriA0dOlSnTp1SwYIFlZycrAYNGqhcuXIKDw/XwIEDs/x6vqBxz05a9OEkLRk/RQc2btNnnV9W6ulk1enQynY0K+jDiU5M63/8Sd/2HapVX8+0HcUnsH840YmJPpzc1Mktt9TVM08/rUYNG150m0Px8Rr85hC98cZABQZc+HuaFi5cpCVLl6hnjx5XK6rPcNP+kVl0YqIPJzox0YeJPpzoxJRT+8jykKxMmTLauXOnJKlixYqaPHmypPNHmOXLly/LASIjIzV79mxNnz5dI0eO1DPPPKMffvhBCxYsUGhoaJZfzzb/wECVrFlJG+csyljzer3aOGeRytxcw2IyO+jDiU5wKewfTnRiog8nOjGlp6fr5VdeUbu2bVSubNkLbnP06FHFDRiggQNeV568eS64TW7B/uFEJyb6cKITE32Y6MOJTkw5uY8L/6fFS2jfvr1Wr16tBg0aqHfv3rrnnnv07rvvKjU1VcOGDfvHQW655RbVqlVLwcHB8ng8mX5eSkqKUlJSjLU0eeWvzL/GlRRWIEr+AQFKPHTEWD956LAKV7zwH6q5GX040Qkuhf3DiU5M9OFEJ6Zx48bL3z9Ajz7yyAUf93q96tvvVT34wAO6/vrrtP+PP7I5YfZi/3CiExN9ONGJiT5M9OFEJ6ac3EeWh2Q9/uuQ/MaNG2vTpk1avny5ypUrpypVqmQ5QHp6ugYOHKjRo0fr0KFD2rJli8qUKaO+ffsqNjZWHTt2vOTzBw0apLi4OGOtpiJVS/mynAUAACAn27Bhgz79/HNN+uyzi/5Hx88+n6Sk06fVsUP7bE4HAADg27J8uuXflSpVSi1btvxHAzJJev311zV+/HgNGTJEQUFBGeuVKlXS2LFjL/v8Pn366MSJE8atuiL/UZYr4dSRY0o7d04RhQoY6+GFYpR48LClVPbQhxOd4FLYP5zoxEQfTnTylxUrVyohIUF3Nm2qGrVqq0at2vrjwAENHTZcdzVtJklatmyZ1qxZo9o33qQatWrrnub3SpIefexxvdI3932BCvuHE52Y6MOJTkz0YaIPJzox5eQ+MjUkGzlyZKZvWTVx4kSNGTNGjz32mPz9/TPWq1atqk2bNl32+cHBwYqIiDButk61lKS01FTtWb5OFRvVyVjzeDyq2KiOdixZYS2XLfThRCe4FPYPJzox0YcTnfzl7mbNNGXyF/pi0ucZt5iYGLVt00b/fv89SdKLLzyvyV9Mynj83VHn/34bMniwuj7ztM34VwX7hxOdmOjDiU5M9GGiDyc6MeXkPjJ1uuXw4cMz9WIej0fdunXLUoD9+/erXLlyjvX09HSlpqZm6bV8xZxhY9VuwlDt/n2tdv22Sg27d1RQaIgWj5tiO5oV9OFEJ6bg0BDFlIvNuF+gdAkVr3qdkhKO69je3H2tnAth/3CiExN9OLmpk9OnT2vP3r0Z9/fv369NmzcrMiJCRYoUcXyRUmBAgAoUyK/Y2FhJUpEiRYzHQ0JCJEnFSxRXoUKFrmp2W9y0f2QWnZjow4lOTPRhog8nOjHl1D4yNST789ssr4brrrtOv/zyi0qVKmWsT506VdWrV79q73s1LZ/8ncJjonVP//9j787jbKz//48/zyyGWW1j7HuhLMWkUvYl+1pakKVNlC0JRY2KVEakQj62vkmSkDKVLVlSdrKE7Fljxj7GzPn94depd5e1mPcZ1+N+u53bbc51rnPOc57ezoyX61ynuyJzR2vP6o16t25bHT94+PJ3vgHRhxOdmArFllWPBZN91x8Y2k+StHT8VE1o39NWLGtYH050YqIPJzd18suGDXr8iSd9198ecv6Dkxo3aqRXB8Rd7G6u5qb1caXoxEQfTnRiog8TfTjRiSmj9uHxer1emwFmzJihtm3bqk+fPhowYIDi4uK0efNmTZw4UbNmzVLt2rWv+jE7egpf+6AAAABX6J2Tv9iO4He6hd1qOwIAAHCxkd4dl93nP5+4/79q0qSJvvzyS82ZM0dhYWHq37+/Nm7cqC+//PJfDcgAAAAAAACAq3VFb7e8Xs6dO6eBAweqQ4cO+u6772xGAQAAAAAAgItZPZIsKChIb775ps6dO2czBgAAAAAAAFzO+tsta9asqe+//952DAAAAAAAALjYv3q75Q8//KBRo0Zp27Ztmjp1qvLly6ePPvpIRYoU0b333ntVj1WvXj317t1b69atU4UKFRQWFmbc3rhx438TEQAAAAAAALhiVz0k+/zzz9WmTRu1atVKq1atUnJysiQpKSlJAwcO1Ndff31Vj9epUydJUnx8vOM2j8ej1NTUq40IAAAAAAAAXJWrfrvla6+9ppEjR+rDDz9UcHCwb/s999yjlStXXnWAtLS0i14YkAEAAAAAACA9XPWRZJs3b1aVKlUc26OiopSYmHjFj3P69GnNnTtXDRs2lCT16dPHd1SadP6k/gMGDFDmzJmvNiIAAAAAAABwVa56SJY7d25t3bpVhQsXNrYvWrRIRYsWveLHmTBhgr766ivfkGzEiBG69dZblSVLFknSpk2blDt3bvXo0eNqIwIAAAAAAABX5arfbvnEE0+oa9euWrZsmTwej37//Xd9/PHH6tmzp55++ukrfpyPP/5YTz75pLFt0qRJmj9/vubPn6+33npLn3322dXGAwAAAAAAAK7aVR9J1rt3b6WlpalmzZo6deqUqlSpopCQEPXs2VPPPvvsFT/O1q1bVaZMGd/1zJkzKyDgr5ldxYoV1blz56uNBwAAAAAAAFy1qx6SeTwevfjii3r++ee1detWnThxQrfccovCw8Ov6nESExONc5AdOnTIuD0tLc24HQAAAAAAALhernpI9qdMmTLplltu+ddPnD9/fq1fv14lSpS44O1r165V/vz5//XjAwAAAAAAAFfqqodk1atXl8fjuejt8+bNu6LHqV+/vvr3768GDRo4PsHy9OnTiouLU4MGDa42HgAAAAAAAHDVrnpIdttttxnXU1JStHr1aq1fv15t27a94sfp27evpkyZohIlSuiZZ57RzTffLEnavHmzRowYoXPnzqlv375XGw8AAAAAAAC4alc9JBs6dOgFt7/yyis6ceLEFT9OTEyMlixZoqefflq9e/eW1+uVdP6cZ7Vr19b777+vmJiYq40HAAAAAAAAXDWP98/p1H+0detWVaxYUUeOHLnq+x45ckRbt26VJBUvXlzZs2f/T1k6egr/p/sDAAD8F++c/MV2BL/TLexW2xEAAICLjfTuuOw+//rE/f+0dOlSx7nFrlT27NlVsWLFaxUFAAAAAAAAuCpXPSRr3ry5cd3r9Wrfvn1avny5+vXrd82CAQAAAAAAAOnlqodkUVFRxvWAgACVKFFCAwYMUJ06da5ZMAAAAAAAACC9XNWQLDU1Ve3bt1eZMmWULVu265UJAAAAAAAASFcBV7NzYGCg6tSpo8TExOsUBwAAAAAAAEh/VzUkk6TSpUvrt99+ux5ZAAAAAAAAACuuekj22muvqWfPnpo1a5b27dunY8eOGRcAAAAAAAAgo7nic5INGDBAzz33nOrXry9Jaty4sTwej+92r9crj8ej1NTUa58SAAAAAAAAuI6ueEgWFxenjh07av78+dczDwAAAAAAAJDurnhI5vV6JUlVq1a9bmEAAAAAAAAAG67qnGR/f3slAAAAAAAAcKO44iPJJOnmm2++7KDsyJEj/ykQAABARtct7FbbEfzOC89Vth3Brwwe8oPtCAAA4B+uakgWFxenqKio65UFAAAAAAAAsOKqhmQPPfSQcuXKdb2yAAAAAAAAAFZc8TnJOB8ZAAAAAAAAblRXPCT789MtAQAAAAAAgBvNFb/dMi0t7XrmAAAAAAAAAKy54iPJAAAAAAAAgBsVQzIAAAAAAAC4HkMyAAAAAAAAuB5DMgAAAAAAALgeQzIAAAAAAAC43hV/umV6OHv2rA4ePOj4JM2CBQtaSgQAAAAAAAA38Ish2ZYtW9ShQwctWbLE2O71euXxeJSammopGQAAAAAAANzAL4Zk7dq1U1BQkGbNmqU8efLI4/HYjgQAAAAAAAAX8Ysh2erVq7VixQqVLFnSdhQAAAAAAAC4kF+cuP+WW27R4cOHbccAAAAAAACAS/nFkGzw4MHq1auXFixYoD/++EPHjh0zLgAAAAAAAMD15Bdvt6xVq5YkqWbNmsZ2TtwPAAAAAACA9OAXQ7L58+fbjgAAAAAAAAAX84shWdWqVW1HAAAAAAAAgIv5xZDsT6dOndKuXbt09uxZY3vZsmUtJQIAAAAAAIAb+MWQ7NChQ2rfvr1mz559wds5JxkAAAAAAACuJ7/4dMtu3bopMTFRy5YtU5YsWZSQkKAJEybopptu0syZM23HAwAAAAAAwA3OL44kmzdvnmbMmKHY2FgFBASoUKFCql27tiIjIzVo0CA1aNDAdkQAAAAAAADcwPziSLKTJ08qV65ckqRs2bLp0KFDkqQyZcpo5cqVNqMBAAAAAADABfxiSFaiRAlt3rxZklSuXDmNGjVKe/fu1ciRI5UnTx7L6QAAAAAAAHCj84u3W3bt2lX79u2TJL388suqW7euPv74Y2XKlEnjx4+3Gw4AAAAAAAA3PL8YkrVu3dr3dYUKFbRz505t2rRJBQsWVM6cOS0mAwAAAAAAgBv4xZDsn0JDQ1W+fHnbMQAAAAAAAOAS1oZkPXr00KuvvqqwsDD16NHjkvvGx8enU6prp2qnNqrz/FOKzB2tPWs26tNnX9aOn9fYjmUNfTjRiYk+TPThRCcm+nCiE5Ob+wiMzKbsDR5UlpJl5ckUonOHD+jQpx/q7J7tvn2Cc+VVtgYPKkvRklJgoFIO7NWBCcOVmviHxeTpy81r5ELow4lOTPRhog8nOjFlxD6snbh/1apVSklJ8X19scvq1attRfzXKrRsqPvjX9KsuGEaWL6B9qzZoGe/maiI6By2o1lBH050YqIPE3040YmJPpzoxOTmPgKyhCrPM/3kTUvV/jFva+9bvXXky0lKO33St09QjlzK0/klpRzcp30fDNTeIX2V+N10ec+lWEyevty8Ri6EPpzoxEQfJvpwohNTRu3D4/V6vbZDXGsdPYWtPv8LP07Xzp/XaPKzL0uSPB6PBu1eqvnvTtA3gz+wms0G+nCiExN9mOjDiU5M9OFEJyZ/7OOF5yqny/Nkq99SmQvfrH3vv3bRfaJbdZbSzunQJ6PSJdOFDB7yg7XnlvxzjdhEH050YqIPE3040YnJH/sY6d1x2X2sHUl2KceOHdP06dO1adMm21GuWmBwsApWKK2Ncxb7tnm9Xm2cs1hF73bfedbow4lOTPRhog8nOjHRhxOdmNzeR+it5ZW8Z7tytXlWBV95T3m7v6qIO6v9tYPHo9BS5ZRyaL9innheBV95T3m6vKLQWytYy5ze3L5G/ok+nOjERB8m+nCiE1NG7sMvhmQtW7bUiBEjJEmnT59WbGysWrZsqTJlyujzzz+/5H2Tk5N17Ngx45IqewfHhefMpsCgIB07cNjYfvzAIUXmjraUyh76cKITE32Y6MOJTkz04UQnJrf3EZQ9WhF311DK4f3aP/pNHV8yT9mbtlF47L2SpMDwSAVkzqKoGo10etM67R89WKfWLVeutl2UuWhJy+nTh9vXyD/RhxOdmOjDRB9OdGLKyH34xZBs4cKFqlz5/CH4X3zxhbxerxITEzV8+HC99trFD5WXpEGDBikqKsq4rFJSesQGAACAn/F4AnR2704dnf2Zzv6+U8eXzdfxHxco4q4af+4gSTq1foWO/ZCgs7/vUtL8WTq1cbUi7q5hMTkAALDNL4ZkSUlJyp49uyQpISFBLVq0UGhoqBo0aKAtW7Zc8r59+vRRUlKScbldUekR+4JOHD6q1HPnFBmT09geEROtY/sPWUplD3040YmJPkz04UQnJvpwohOT2/tIPZ6olAN7jW0pB39XULbzJwpOPXlc3tRzSjnwu3OfrP59MuFrxe1r5J/ow4lOTPRhog8nOjFl5D78YkhWoEABLV26VCdPnlRCQoLq1KkjSTp69KgyZ858yfuGhIQoMjLSuATKkx6xLyg1JUW7VqxXyZqVfNs8Ho9K1qyk35autJbLFvpwohMTfZjow4lOTPThRCcmt/dxZvuvCo7OY2wLjs6tc0f/OH8lNVXJu7crOFduc5+cuXXuqPm2kBuV29fIP9GHE52Y6MNEH050YsrIfQTZDiBJ3bp1U6tWrRQeHq5ChQqpWrVqks6/DbNMmTJ2w/0Lc+LHqN2EIdq5fJ12/LRaNbo9pkxhoVoy7jPb0aygDyc6MdGHiT6c6MREH050YnJzH0k/JCjvM/0VVaORTq5ZppCCxRRxV3Ud/mzsX/ss+Eq5Wj+jM79t1umtG5SlZFmF3nK79n0w0GLy9OXmNXIh9OFEJyb6MNGHE52YMmoffjEk69SpkypWrKjdu3erdu3aCgg4f4Bb0aJFL3tOMn+0YsosRURnV6MB3RWZO1p7Vm/Uu3Xb6vhBd/zv5D/RhxOdmOjDRB9OdGKiDyc6Mbm5j7O7t+vA+GHKXr+lstZuqnNHDunIjP/TyVVLfPucWr9Chz8fp6w1Gil70zZKObhPBycOV/KOXy0mT19uXiMXQh9OdGKiDxN9ONGJKaP24fF6vfY+CvIiUlNTtW7dOhUqVEjZsmW76vt39BS+9qEAAADwr73wXGXbEfzK4CE/2I4AAICrjPTuuOw+fnFOsm7duul///ufpPMDsqpVq6p8+fIqUKCAFixYYDccAAAAAAAAbnh+MSSbOnWqypUrJ0n68ssvtX37dm3atEndu3fXiy++aDkdAAAAAAAAbnR+MSQ7fPiwcuc+/wlDX3/9tR544AHdfPPN6tChg9atW2c5HQAAAAAAAG50fjEki4mJ0YYNG5SamqqEhATVrl1bknTq1CkFBgZaTgcAAAAAAIAbnV98umX79u3VsmVL5cmTRx6PR7Vq1ZIkLVu2TCVLlrScDgAAAAAAADc6vxiSvfLKKypdurR2796tBx54QCEhIZKkwMBA9e7d23I6AAAAAAAA3Oj8YkgmSffff79jW9u2bS0kAQAAAAAAgNv4xZBswIABl7y9f//+6ZQEAAAAAAAAbuQXQ7IvvvjCuJ6SkqLt27crKChIxYoVY0gGAAAAAACA68ovhmSrVq1ybDt27JjatWunZs2aWUgEAAAAAAAANwmwHeBiIiMjFRcXp379+tmOAgAAAAAAgBuc3w7JJCkpKUlJSUm2YwAAAAAAAOAG5xdvtxw+fLhx3ev1at++ffroo49Ut25dS6kAAAAAAADgFn4xJBs6dKhxPSAgQNHR0Wrbtq369OljKRUAAAAAAADcwi+GZNu3b3dsO3PmjN577z3ddNNN2r9/v4VUAAAAAAAAcAur5yRLTk5Wnz59FBsbq3vuuUfTp0+XJI0bN07FihXTsGHD1L17d5sRAQAAAAAA4AJWjyTr37+/Ro0apVq1amnJkiV64IEH1L59e/34448aMmSIHnjgAQUGBtqMCAAAAAAAABewOiT77LPPNHHiRDVu3Fjr169X2bJlde7cOa1Zs0Yej8dmNAAAAAAAALiI1bdb7tmzRxUqVJAklS5dWiEhIerevTsDMgAAAAAAAKQrq0Oy1NRUZcqUyXc9KChI4eHhFhMBAAAAAADAjay+3dLr9apdu3YKCQmRdP4TLTt27KiwsDBjv2nTptmIBwAAAAAAAJewOiRr27atcb1169aWkgAAAAAAAMDNrA7Jxo0bZ/PpAQAAAAAAAEmWz0kGAAAAAAAA+AOGZAAAAAAAAHA9hmQAAAAAAABwPYZkAAAAAAAAcD2GZAAAAAAAAHA9j9fr9doOca119BS2HQHIUAI9thP4n9Qb7pURAOBP3t821XYEv9Kp2P22IwAAbnAjvTsuuw9HkgEAAAAAAMD1GJIBAAAAAADA9RiSAQAAAAAAwPUYkgEAAAAAAMD1GJIBAAAAAADA9RiSAQAAAAAAwPUYkgEAAAAAAMD1GJIBAAAAAADA9RiSAQAAAAAAwPUYkgEAAAAAAMD1GJIBAAAAAADA9RiSAQAAAAAAwPUYkgEAAAAAAMD1GJIBAAAAAADA9YJsB5Ck1NRUjR8/XnPnztXBgweVlpZm3D5v3jxLyQAAAAAAAOAGfjEk69q1q8aPH68GDRqodOnS8ng8tiMBAAAAAADARfxiSDZ58mRNmTJF9evXtx0FAAAAAAAALuQX5yTLlCmTihcvbjsGAAAAAAAAXMovhmTPPfechg0bJq/XazsKAAAAAAAAXMja2y2bN29uXJ83b55mz56tW2+9VcHBwcZt06ZNS89oAAAAAAAAcBlrQ7KoqCjjerNmzSwlAQAAAAAAgNtZG5KNGzfO1lMDAAAAAAAABr84J9np06d16tQp3/WdO3fqnXfe0bfffmsxFQAAAAAAANzCL4ZkTZo00cSJEyVJiYmJqlixooYMGaImTZrogw8+sJwOAAAAAAAANzq/GJKtXLlSlStXliRNnTpVuXPn1s6dOzVx4kQNHz7ccjoAAAAAAADc6PxiSHbq1ClFRERIkr799ls1b95cAQEBuuuuu7Rz507L6QAAAAAAAHCj84shWfHixTV9+nTt3r1b33zzjerUqSNJOnjwoCIjIy2nAwAAAAAAwI3OL4Zk/fv3V8+ePVW4cGHdeeeduvvuuyWdP6rs9ttvt5wOAAAAAAAANzq/GJLdf//92rVrl5YvX66EhATf9po1a2ro0KEWk/17VTu10evbF+nd05v1wo/TVfiOcrYjWUUfTnRyXp3endRr2QwNSVqvN/Yv15PTRivXzUVtx7KO9eFEJyb6cKITE32Y3NTHz+s26umX31aVVp1Vql4rzVmy3Lj928U/67G+g3RXy6dUql4rbdy2w/EYu34/oGcGDFWlBzsqtvlj6j5wuA4fTUqn78AON62RK0UnJvow0YcTnZgyYh9+MSSTpNy5c+v2229XQMBfkSpWrKiSJUtaTPXvVGjZUPfHv6RZccM0sHwD7VmzQc9+M1ER0TlsR7OCPpzo5C83VblTC9//SG/f3Uzv1mmjwOAgPfvNRGUKzWI7mjWsDyc6MdGHE52Y6MPktj5On0lWiaIF1a9Tu4vcfkblby2h5zo8dMHbT505o8dffEMejzT+jb6aNORlpZw7p06vvK20tLTrmNwet62RK0EnJvow0YcTnZgyah8er9frtfHEzZs31/jx4xUZGanmzZtfct9p06Zd1WN39BT+D8n+uxd+nK6dP6/R5GdfliR5PB4N2r1U89+doG8Gf2A1mw304eRvnQR60v0pLyo8Z3YNPrhSQ6u21NYffrKWI9XKK+N5/rY+/AGdmOjDiU5M9GHyxz7e3zY1XZ6nVL1Werdfd9WqFOu4be+BQ6rVrpumjXhdpYoV9m1fvGKtnuz/ppZNGa3wsFBJ0vGTp3TnA09qzOu9Ven20tc8Z6di91/zx7wa/rhGbKMTE32Y6MOJTkz+2MdI747L7mPtSLKoqCh5PB7f15e6ZCSBwcEqWKG0Ns5Z7Nvm9Xq1cc5iFb27vMVkdtCHE51cWpao8590e/JIot0glrA+nOjERB9OdGKiDxN9XL2zKefkkUeZgoN920KCgxXg8WjlL5stJrs+WCNOdGKiDxN9ONGJKSP3EWTriceNG3fBr69WcnKykpOTjW2p8ipQdg6NCc+ZTYFBQTp24LCx/fiBQ8pdspiVTDbRhxOdXJzH41GLof21bdHP2vfLr7bjWMH6cKITE3040YmJPkz0cfXKlSyuLJlD9PbYyererqW88ip+7KdKTUvToRvwP7FYI050YqIPE3040YkpI/fhN+ck+7cGDRrkOPJslW7sk4oCN6oH33tVeUuX0NiHn7UdBQAA18qeNVLv9O2iBctWqkLzx1SxxRM6dvKkbile2PdOEAAAbkTWjiT7uwMHDqhnz56aO3euDh48qH+eJi01NfWi9+3Tp4969OhhbHsuqsx1yXklThw+qtRz5xQZk9PYHhETrWP7D1lKZQ99ONHJhbV8N06lG9TQ0Kotlbh3v+041rA+nOjERB9OdGKiDxN9/Dv3VCirb8cN1dGk4woMDFBkeJgqP9JJBfLksh3tmmONONGJiT5M9OFEJ6aM3IdfHEnWrl07rVy5Uv369dPUqVM1bdo043IpISEhioyMNC623mopSakpKdq1Yr1K1qzk2+bxeFSyZiX9tnSltVy20IcTnTi1fDdO5Zrep2E1H9EfO/bYjmMV68OJTkz04UQnJvow0cd/ky0qQpHhYfpx9S/6I/GYatzl3+eS+TdYI050YqIPE3040YkpI/fhF0eSLVq0SD/88INuu+0221GuiTnxY9RuwhDtXL5OO35arRrdHlOmsFAtGfeZ7WhW0IcTnfzlwfdeVezDTTSq6RNKPn5SkTHRkqTTSceUcib5Mve+MbE+nOjERB9OdGKiD5Pb+jh5+ox2/f7XUdl7DhzSxm07FBURrry5cirx+AntO3hYB/9IlCRt37NPkpQzW1ZFZ88qSZr27fcqWiCvskdFavWmLRo48iO1bVZXRfLnTe9vJ124bY1cCTox0YeJPpzoxJRR+/CLIVmBAgUcb7HMyFZMmaWI6OxqNKC7InNHa8/qjXq3blsdP3j48ne+AdGHE538pcrTbSRJ3Rd8amz/qH1P/Thhqo1I1rE+nOjERB9OdGKiD5Pb+vhly29q+8LrvuuDR/+fJKlprcoa9FxHzf9xhfrGj/bd/twbIyRJnVs11zOtW0g6PzgbOv5TJR0/obwx0er4UBO1bVYvHb+L9OW2NXIl6MREHyb6cKITU0btw+P1g+nUt99+qyFDhmjUqFEqXLjwf368jp7//hiAmwRyDl6HVOuvjACAG9n729z5H0EX06nY/bYjAABucCO9Oy67j18cSfbggw/q1KlTKlasmEJDQxUcHGzcfuTIEUvJAAAAAAAA4AZ+MSR75513bEcAAAAAAACAi/nFkKxt27a2IwAAAAAAAMDF/GJI9ndnzpzR2bNnjW2RkZGW0gAAAAAAAMANAmwHkKSTJ0/qmWeeUa5cuRQWFqZs2bIZFwAAAAAAAOB68oshWa9evTRv3jx98MEHCgkJ0ZgxYxQXF6e8efNq4sSJtuMBAAAAAADgBucXb7f88ssvNXHiRFWrVk3t27dX5cqVVbx4cRUqVEgff/yxWrVqZTsiAAAAAAAAbmB+cSTZkSNHVLRoUUnnzz925MgRSdK9996rhQsX2owGAAAAAAAAF/CLIVnRokW1fft2SVLJkiU1ZcoUSeePMMuaNavFZAAAAAAAAHADvxiStW/fXmvWrJEk9e7dW++9954yZ86s7t276/nnn7ecDgAAAAAAADc6vzgnWffu3X1f16pVS5s2bdKKFStUvHhxlS1b1mIyAAAAAAAAuIH1IVlaWprGjx+vadOmaceOHfJ4PCpSpIjuv/9+lSlTxnY8AAAAAAAAuIDVt1t6vV41btxYjz/+uPbu3asyZcro1ltv1c6dO9WuXTs1a9bMZjwAAAAAAAC4hNUjycaPH6+FCxdq7ty5ql69unHbvHnz1LRpU02cOFGPPvqopYQAAAAAAABwA6tHkn3yySfq27evY0AmSTVq1FDv3r318ccfW0gGAAAAAAAAN7E6JFu7dq3q1q170dvr1avn+9RLAAAAAAAA4HqxOiQ7cuSIYmJiLnp7TEyMjh49mo6JAAAAAAAA4EZWh2SpqakKCrr4adECAwN17ty5dEwEAAAAAAAAN7J64n6v16t27dopJCTkgrcnJyencyIAAAAAAAC4kdUhWdu2bS+7D59sCQAAAAAAgOvN6pBs3LhxNp8eAAAAAAAAkGT5nGQAAAAAAACAP2BIBgAAAAAAANdjSAYAAAAAAADXY0gGAAAAAAAA12NIBgAAAAAAANdjSAYAAAAAAADXY0gGAAAAAAAA1wuyHQCAfYEej+0IfifV67UdAQBwA+tU7H7bEfzKsONrbEfwO10jytmOAACuw5FkAAAAAAAAcD2GZAAAAAAAAHA9hmQAAAAAAABwPYZkAAAAAAAAcD2GZAAAAAAAAHA9hmQAAAAAAABwPYZkAAAAAAAAcD2GZAAAAAAAAHA9hmQAAAAAAABwPYZkAAAAAAAAcD2GZAAAAAAAAHA9hmQAAAAAAABwPYZkAAAAAAAAcD2GZAAAAAAAAHA9hmQAAAAAAABwvSDbAf7u7NmzOnjwoNLS0oztBQsWtJQIAAAAAAAAbuAXQ7ItW7aoQ4cOWrJkibHd6/XK4/EoNTXVUjIAAAAAAAC4gV8Mydq1a6egoCDNmjVLefLkkcfjsR0JAAAAAAAALuIXQ7LVq1drxYoVKlmypO0oAAAAAAAAcCG/OHH/LbfcosOHD9uOAQAAAAAAAJfyiyHZ4MGD1atXLy1YsEB//PGHjh07ZlwAAAAAAACA68kv3m5Zq1YtSVLNmjWN7Zy4HwAAAAAAAOnBL4Zk8+fPtx0BAAAAAAAALuYXQ7KqVavajgAAAAAAAAAX84sh2Z9OnTqlXbt26ezZs8b2smXLWkoEAAAAAAAAN/CLIdmhQ4fUvn17zZ49+4K3c04yAAAAAAAAXE9+8emW3bp1U2JiopYtW6YsWbIoISFBEyZM0E033aSZM2fajgcAAAAAAIAbnF8cSTZv3jzNmDFDsbGxCggIUKFChVS7dm1FRkZq0KBBatCgge2IAAAAAAAAuIH5xZFkJ0+eVK5cuSRJ2bJl06FDhyRJZcqU0cqVK21G+9eqdmqj17cv0runN+uFH6er8B3lbEeyij6c6OS8ezu20gurZuvNo2v15tG16r7oc5Wqy4d5sD6c6MREH050YqIPE304uaWT5StX6pluPVTzvvoqW6Gi5s1fcNF9Xx04SGUrVNRHkz4xto/+31i1af+YKlaqrHuq1rjOif2HW9bIlaIPE3040YkpI/bhF0OyEiVKaPPmzZKkcuXKadSoUdq7d69GjhypPHnyWE539Sq0bKj741/SrLhhGli+gfas2aBnv5moiOgctqNZQR9OdPKXxD379WXfwXrrjsZ6q2IT/Tp/qZ74YrRy33KT7WjWsD6c6MREH050YqIPE304uamT06fPqMTNN6nvC89fcr+58+Zr7br1yhUd7bgtJeWc6tSqqZb3t7heMf2Om9bIlaAPE3040Ykpo/bhF0Oyrl27at++fZKkl19+WbNnz1bBggU1fPhwDRw40HK6q1erx+Na/OFkLR3/mfZt3KpJHV9UyqnTqtShpe1oVtCHE538Zf2sudowe4EObd2hQ1u266t+byv5xCkVvut229GsYX040YmJPpzoxEQfJvpwclMnle+ppGc7Pa2aNapfdJ8DBw9q0FtDNOi1AQoKcp6RpnPHJ9Wm1SO6qXjx6xnVr7hpjVwJ+jDRhxOdmDJqH34xJGvdurXatWsnSapQoYJ27typn3/+Wbt379aDDz5oN9xVCgwOVsEKpbVxzmLfNq/Xq41zFqvo3eUtJrODPpzo5OI8AQEq/2BDhYRl0Y6lGfOt1v8V68OJTkz04UQnJvow0YcTnZjS0tLUt9/LatemtYoXK2Y7jl9gjZjow0QfTnRiysh9+MWQ7J9CQ0NVvnx55cyZ87L7Jicn69ixY8YlVd50SHlh4TmzKTAoSMcOHDa2Hz9wSJG5nYdu3+jow4lOnPKULqG3ktYr/vRmtXz/dY1p0VH7N261HcsK1ocTnZjow4lOTPRhog8nOjGNHT9RQYFBavVwxvrP+euJNWKiDxN9ONGJKSP3Ye3TLXv06KFXX31VYWFh6tGjxyX3jY+Pv+htgwYNUlxcnLGtgqIUq6zXIiaAdHBw828aXL6BskRF6LYW9dR63NsaXv0h1w7KAABA+tiwcaM+njxZn378kTwej+04AADLrA3JVq1apZSUFN/XF3O5H1Z9+vRxDNmeiyrz3wP+SycOH1XquXOKjDGPgouIidax/YcspbKHPpzoxCk1JUWHt+2UJO1euV4FY8uqapf2+vTpFy0nS3+sDyc6MdGHE52Y6MNEH0508pcVq1bryJGjuq9BY9+21NRUDRk6TB9PmqyEWTMsprOHNWKiDxN9ONGJKSP3Ye3tlvPnz1fWrFl9X1/sMm/evEs+TkhIiCIjI41LoOz9L1BqSop2rVivkjUr+bZ5PB6VrFlJv7nwHEv04UQnl+cJCFBQSCbbMaxgfTjRiYk+nOjERB8m+nCik780ql9PUydP0pRJ/+e75IqOVrs2rfXBiOG241nDGjHRh4k+nOjElJH7sHYk2Y1sTvwYtZswRDuXr9OOn1arRrfHlCksVEvGfWY7mhX04UQnf2n0+vPakPC9ju7aq5CIcMU+3FjFq92lD+q1tR3NGtaHE52Y6MOJTkz0YaIPJzd1curUKe3avcd3fe/vv2vT5l8VFRmpPHly+/7j/k9BQUHKkTOHihQu5Nu2b99+JR07pn379ys1LU2bNv8qSSpYIL9CQ0PT5ftIb25aI1eCPkz04UQnpozah7UhWfPmza9432nTpl3HJNfeiimzFBGdXY0GdFdk7mjtWb1R79Ztq+MHD1/+zjcg+nCik7+E58qh1uOHKCpPtE4nHdfvazfpg3pttXnOItvRrGF9ONGJiT6c6MREHyb6cHJTJ79s2KjHnnrad/2t+HckSY0bNtBrcS9f0WO8N3KUZs76yne95SOtJUn/G/WB7oitcO3C+hE3rZErQR8m+nCiE1NG7cPj9XqtfBRk+/btr3jfcePGXdVjd/QUvso0gLtlCuBEtf90Ns3ep+QCAOA2w46vsR3B73SNKGc7AgDcUEZ6d1x2H2tHkl3t4AsAAAAAAAC4XqyduB8AAAAAAADwF35z4v6pU6dqypQp2rVrl86ePWvctnKlf3/6AQAAAAAAADI2vziSbPjw4Wrfvr1iYmK0atUqVaxYUTly5NBvv/2mevXq2Y4HAAAAAACAG5xfDMnef/99jR49Wu+++64yZcqkXr166bvvvlOXLl2UlJRkOx4AAAAAAABucH4xJNu1a5cqVaokScqSJYuOHz8uSWrTpo0++eQTm9EAAAAAAADgAn4xJMudO7eOHDkiSSpYsKB+/PFHSdL27dvl9XptRgMAAAAAAIAL+MWQrEaNGpo5c6YkqX379urevbtq166tBx98UM2aNbOcDgAAAAAAADc6v/h0y9GjRystLU2S1LlzZ+XIkUNLlixR48aN9dRTT1lOBwAAAAAAgBudXwzJ9uzZowIFCviuP/TQQ3rooYfk9Xq1e/duFSxY0GI6AAAAAAAA3Oj84u2WRYoU0aFDhxzbjxw5oiJFilhIBAAAAAAAADfxiyGZ1+uVx+NxbD9x4oQyZ85sIREAAAAAAADcxOrbLXv06CFJ8ng86tevn0JDQ323paamatmyZbrtttsspQMAAAAAAIBbWB2SrVq1StL5I8nWrVunTJky+W7LlCmTypUrp549e9qKBwAAAAAAAJewOiSbP3++JKl9+/YaPny4IiIibMYBAAAAAACAS1kdkjVv3tz3ddu2bS+637Rp09IjDgAAAAAAAFzK6pAsKirK5tMDAAAAAAAAkiwPycaNG2fz6QEAAAAAAABJUoDtAAAAAAAAAIBtDMkAAAAAAADgegzJAAAAAAAA4HoMyQAAAAAAAOB6DMkAAAAAAADgegzJAAAAAAAA4HoMyQAAAAAAAOB6DMkAAAAAAADgegzJAAAAAAAA4HoMyQAAAAAAAOB6QbYDALDvbJrXdgQAAOBiXSPK2Y7gd4b/scR2BL/SJUcl2xEAuABHkgEAAAAAAMD1GJIBAAAAAADA9RiSAQAAAAAAwPUYkgEAAAAAAMD1GJIBAAAAAADA9RiSAQAAAAAAwPUYkgEAAAAAAMD1GJIBAAAAAADA9RiSAQAAAAAAwPUYkgEAAAAAAMD1GJIBAAAAAADA9YJsB5CkuXPnau7cuTp48KDS0tKM28aOHWspFQAAAAAAANzC+pAsLi5OAwYMUGxsrPLkySOPx2M7EgAAAAAAAFzG+pBs5MiRGj9+vNq0aWM7CgAAAAAAAFzK+jnJzp49q0qVKtmOAQAAAAAAABezPiR7/PHHNWnSJNsxAAAAAAAA4GJW3m7Zo0cP39dpaWkaPXq05syZo7Jlyyo4ONjYNz4+Pr3jAQAAAAAAwGWsDMlWrVplXL/tttskSevXrze2cxJ/AAAAAAAApAcrQ7L58+fbeFoAAAAAAADggqyfk+yfjh07punTp2vTpk22owAAAAAAAMAlrA/JWrZsqREjRkiSTp8+rdjYWLVs2VJlypTR559/bjkdAAAAAAAA3MD6kGzhwoWqXLmyJOmLL76Q1+tVYmKihg8frtdee81yOgAAAAAAALiB9SFZUlKSsmfPLklKSEhQixYtFBoaqgYNGmjLli2W0wEAAAAAAMANrA/JChQooKVLl+rkyZNKSEhQnTp1JElHjx5V5syZLacDAAAAAACAG1j5dMu/69atm1q1aqXw8HAVKlRI1apVk3T+bZhlypSxGw4AAAAAAACuYH1I1qlTJ1WsWFG7d+9W7dq1FRBw/uC2okWLck4yAAAAAAAApAvrQzJJio2NVWxsrLGtQYMGltIAAAAAAADAbawMyXr06KFXX31VYWFh6tGjxyX3jY+PT6dUAAAAAAAAcCsrJ+5ftWqVUlJSfF9f6pJRVe3URq9vX6R3T2/WCz9OV+E7ytmOZBV9ONHJX4pXrqhOM8fojb3LNNK7Q+Wa1LEdyTrWhxOdmOjDiU5M9GGiDyc6Mbmlj+Wr1uiZnn1Vo9H9KnN3dc39fpFx+/tjxqvRg4+qYvV6qlSnkR5/9jmt/WWDsc99zR5SmburG5cxEyel57dhhVvWyJWiDyc6MWXEPqwMyebPn6+sWbP6vr7UJSOq0LKh7o9/SbPihmlg+Qbas2aDnv1moiKic9iOZgV9ONGJKSQsVHvWbNTkzv1tR/ELrA8nOjHRhxOdmOjDRB9OdGJyUx+nz5zRzTcV04vPdb3g7YUK5Fff57rq8//7nyaOHK58eXLrqa69dORoorFf5yfaa/6sz32XRx5olg7p7XHTGrkS9OFEJ6aM2oeVIdnfXWoQ9t5776VjkmunVo/HtfjDyVo6/jPt27hVkzq+qJRTp1WpQ0vb0aygDyc6Mf2SsEAz+w3R6unf2I7iF1gfTnRiog8nOjHRh4k+nOjE5KY+Kt99p7o89ZhqVqt8wdsb3FdLd1esoAL58qp40SJ6vmsnnTh5Ur9u3WbsFxYaqpw5svsuoVmypEd8a9y0Rq4EfTjRiSmj9mF9SNa8eXOtWLHCsX3YsGHq06ePhUT/TWBwsApWKK2Ncxb7tnm9Xm2cs1hF7y5vMZkd9OFEJ7gU1ocTnZjow4lOTPRhog8nOjHRx8WlpKRo6vRZiggPU4mbihu3/e+jSbr3viZ64NEnNO7/JuvcuVRLKa8/1oiJPpzoxJSR+7D+6ZZvvfWW6tWrp4ULF6pkyZKSpCFDhmjAgAH66quvLnv/5ORkJScnG9tS5VWgPNcl7+WE58ymwKAgHTtw2Nh+/MAh5S5ZzEomm+jDiU5wKawPJzox0YcTnZjow0QfTnRiog+n7xct1fP9B+jMmWRF58ih0cPeVrasUb7bH2nZXLeUuFmRkRFas/YXvTPyQx364w/16trZYurrhzViog8nOjFl5D6sD8kef/xxHTlyRLVq1dKiRYv06aefauDAgfr66691zz33XPb+gwYNUlxcnLGtgqIUq6zXKTEAAAAA3LjuqHCbpk4Yo6NJSfp8xiz1fClOH495XzmyZ5MktX34r7dLlSheTMHBQRowOF7dnn5CmTJlshUbAP4z62+3lKRevXqpVatWio2N1RtvvKFvvvnmigZkktSnTx8lJSUZl9sVdfk7XicnDh9V6rlziozJaWyPiInWsf2HLKWyhz6c6ASXwvpwohMTfTjRiYk+TPThRCcm+nAKzZJFBQvkU7nSt2jAi70UGBioL778+qL7l7m1lM6lpmrvvv3pmDL9sEZM9OFEJ6aM3IeVIdnw4cMdl3z58ik0NFQNGjTQTz/95Nt+OSEhIYqMjDQutt5qKUmpKSnatWK9Stas5Nvm8XhUsmYl/bZ0pbVcttCHE53gUlgfTnRiog8nOjHRh4k+nOjERB+Xl+b16mxKykVv37RlqwICApQ9W7Z0TJV+WCMm+nCiE1NG7sPK2y2HDh16we2BgYFavHixFi8+f3I3j8ejLl26pGe0a2JO/Bi1mzBEO5ev046fVqtGt8eUKSxUS8Z9ZjuaFfThRCemkLBQRRcv7Lues0gB5S93i04eSdTR3b/bC2YJ68OJTkz04UQnJvow0YcTnZjc1MepU6e1a89e3/W9v+/Tpl+3KioyQlFRkfpw/P+pWuV7FJ0ju44mJWny1Ok6eOiQ6tSoKklave4XrftloypWuE2hoaFas/4XvTXsfTW8r5aiIiNsfVvXnZvWyJWgDyc6MWXUPqwMybZv327jadPNiimzFBGdXY0GdFdk7mjtWb1R79Ztq+MHD1/+zjcg+nCiE1Oh2LLqsWCy7/oDQ/tJkpaOn6oJ7XvaimUN68OJTkz04UQnJvow0YcTnZjc1McvmzarQ+fuvutvDX9fktS4/n3q36uHtu/crZlfv6yjSUnKGhWpW0uV0IQPhqt40SKSpEyZgpUwZ54++N94nT2bonx586jNg/fr0YcfsPL9pBc3rZErQR9OdGLKqH14vF6v19aTp6SkqGTJkpo1a5ZKlSp1zR63o6fwNXssAAAAAEhvw/9YYjuCX+mSo9LldwKASxjp3XHZfayeuD84OFhnzpyxGQEAAAAAAACw/+mWnTt31uDBg3Xu3DnbUQAAAAAAAOBSVs5J9nc///yz5s6dq2+//VZlypRRWFiYcfu0adMsJQMAAAAAAIBbWB+SZc2aVS1atLAdAwAAAAAAAC5mfUg2btw42xEAAAAAAADgctbPSQYAAAAAAADYZv1IsiJFisjj8Vz09t9++y0d0wAAAAAAAMCNrA/JunXrZlxPSUnRqlWrlJCQoOeff95OKAAAAAAAALiK9SFZ165dL7j9vffe0/Lly9M5DQAAAAAAANzIb89JVq9ePX3++ee2YwAAAAAAAMAF/HZINnXqVGXPnt12DAAAAAAAALiAtbdbDhgwQM8995zuvfde48T9Xq9X+/fv16FDh/T+++/bigcAAAAAAAAXsTYki4uLU8eOHdWkSRNjSBYQEKDo6GhVq1ZNJUuWtBUPAAAAAAAALmJtSOb1eiVJr7zyiq0IAAAAAAAAgCTL5yT7+xFkAAAAAAAAgC3WjiSTpJtvvvmyg7IjR46kUxoAAAAAAAC4ldUhWVxcnKKiomxGAAAAAAAAAOwOyR566CHlypXLZgQAAAAAAADA3jnJOB8ZAAAAAAAA/IW1Idmfn24JAAAAAAAA2Gbt7ZZpaWm2nhoAAAAAAAAwWDuSDAAAAAAAAPAXDMkAAAAAAADgegzJAAAAAAAA4HoMyQAAAAAAAOB6DMkAAAAAAADgegzJAAAAAAAA4HoMyQAAAAAAAOB6QbYDALAvU4DHdgS/czbNazsCAABwsS45KtmO4FcGJP5iO4Lf6Z/1VtsRgBsOR5IBAAAAAADA9RiSAQAAAAAAwPUYkgEAAAAAAMD1/OKcZHPnztXcuXN18OBBpaWlGbeNHTvWUioAAAAAAAC4hfUhWVxcnAYMGKDY2FjlyZNHHg8nEAcAAAAAAED6sj4kGzlypMaPH682bdrYjgIAAAAAAACXsn5OsrNnz6pSJT7eGAAAAAAAAPZYH5I9/vjjmjRpku0YAAAAAAAAcDErb7fs0aOH7+u0tDSNHj1ac+bMUdmyZRUcHGzsGx8fn97xAAAAAAAA4DJWhmSrVq0yrt92222SpPXr1xvbOYk/AAAAAAAA0oOVIdn8+fNtPC0AAAAAAABwQdbPSfZPx44d0/Tp07Vp0ybbUQAAAAAAAOAS1odkLVu21IgRIyRJp0+fVmxsrFq2bKkyZcro888/t5wOAAAAAAAAbmB9SLZw4UJVrlxZkvTFF1/I6/UqMTFRw4cP12uvvWY5HQAAAAAAANzA+pAsKSlJ2bNnlyQlJCSoRYsWCg0NVYMGDbRlyxbL6QAAAAAAAOAG1odkBQoU0NKlS3Xy5EklJCSoTp06kqSjR48qc+bMltMBAAAAAADADax8uuXfdevWTa1atVJ4eLgKFSqkatWqSTr/NswyZcrYDQcAAAAAAABXsD4k69SpkypWrKjdu3erdu3aCgg4f3Bb0aJFOScZAAAAAAAA0oX1IZkkxcbGKjY21tjWoEEDS2kAAAAAAADgNlaGZD169NCrr76qsLAw9ejR45L7xsfHp1MqAAAAAAAAuJWVIdmqVauUkpLi+/piPB5PekUCAAAAAACAi1kZks2fP/+CXwMAAAAAAAA2BNgOAAAAAAAAANhm5Uiy5s2bX/G+06ZNu45JAAAAAAAAAEtHkkVFRfkukZGRmjt3rpYvX+67fcWKFZo7d66ioqJsxAMAAAAAAIDLWDmSbNy4cb6vX3jhBbVs2VIjR45UYGCgJCk1NVWdOnVSZGSkjXgAAAAAAABwGevnJBs7dqx69uzpG5BJUmBgoHr06KGxY8daTAYAAAAAAAC3sD4kO3funDZt2uTYvmnTJqWlpVlIBAAAAAAAALexPiRr3769HnvsMcXHx2vRokVatGiRhgwZoscff1zt27e3He9fq9qpjV7fvkjvnt6sF36crsJ3lLMdySr6cKKT8+7t2EovrJqtN4+u1ZtH16r7os9Vqm5V27GsY3040YmJPpzoxEQfJvpwopO/FK9cUZ1mjtEbe5dppHeHyjWpYzuSX3DLGlm9coVe6NFVTevXUeWK5bVwwXzj9tfjXlbliuWNy3NdOjseZ8miH/Rk+0dVs/Ldqlezqvr07JFe34IVblkfV4NOTBmxD+tDsrffflu9evXSkCFDVKVKFVWpUkXx8fF6/vnn9dZbb9mO969UaNlQ98e/pFlxwzSwfAPtWbNBz34zURHROWxHs4I+nOjkL4l79uvLvoP11h2N9VbFJvp1/lI98cVo5b7lJtvRrGF9ONGJiT6c6MREHyb6cKITU0hYqPas2ajJnfvbjuI33LRGzpw5o+I33awez/e+6D533l1J07/+1nd55bVBxu0L5s3Va6/0U/2GjTXu/ybr/Q/HqfZ9da93dGvctD6uFJ2YMmofHq/X67Ud4k/Hjh2TpP98wv6OnsLXIM2/98KP07Xz5zWa/OzLkiSPx6NBu5dq/rsT9M3gD6xms4E+nPytk0wBnnR/zksZdGiVZrwwSD+OnWItw9k0ey+N/rY+/AGdmOjDiU5M9GGiDyc6ubiR3h36oOmTWjPjW9tRrPK3NTIg8Zd0eZ7KFcvr9TeHqEq16r5tr8e9rBPHj2vQ2/EXvM+5c+fUsmlDdXiioxo2aZouOSWpf9Zb0+25/snf1oc/oBOTP/Yx0rvjsvtYP5JMOv+iMmfOHH3yySfyeM7/Y/3333/XiRMnLCe7eoHBwSpYobQ2zlns2+b1erVxzmIVvbu8xWR20IcTnVycJyBA5R9sqJCwLNqxdKXtOFawPpzoxEQfTnRiog8TfTjRCS6HNeK0euVyNbqvph65v5nefmOgkhITfbf9unmTDh08KE+ARx1aP6wm9eqoZ9dn9Nu2rfYCX0esDyc6MWXkPoJsB9i5c6fq1q2rXbt2KTk5WbVr11ZERIQGDx6s5ORkjRw58pL3T05OVnJysrEtVV4Fys6RMeE5sykwKEjHDhw2th8/cEi5Sxazkskm+nCiE6c8pUuox+LPFZQ5RMknTmlMi47av/HG/KXiclgfTnRiog8nOjHRh4k+nOgEl8MaMd15dyVVrV5DefLm1d49ezT6gxF6vtuz+uB/4xUYGKjf9+6VJI37cJSe6fac8uTJo8kf/5+6dHxSk6Z+ocioKMvfwbXF+nCiE1NG7sP6kWRdu3ZVbGysjh49qixZsvi2N2vWTHPnzr3s/QcNGqSoqCjjskpJ1zMygGvs4ObfNLh8A8Xf3UyLR/6fWo97W7lLFbcdCwAAAFCtOvfp3ipVVaz4TapSrbrejB+mjRt+0aoVyyVJ3rQ0SdKj7R9TtRo1VaLULerT/xXJI82f+53F5ACulvUh2Q8//KCXXnpJmTJlMrYXLlxYe///RP5S+vTpo6SkJONyu+xN6k8cPqrUc+cUGZPT2B4RE61j+w9ZSmUPfTjRiVNqSooOb9up3SvX68sX39LeNRtVtUvG/XTb/4L14UQnJvpwohMTfZjow4lOcDmskUvLmy+/orJm1d49uyVJOXKe76lwkaK+fTJlyqS8+fLrwP79VjJeT6wPJzoxZeQ+rA/J0tLSlJqa6ti+Z88eRUREXPb+ISEhioyMNC623mopnf/H/q4V61WyZiXfNo/Ho5I1K+k3F55jiT6c6OTyPAEBCgrJdPkdb0CsDyc6MdGHE52Y6MNEH050gsthjVzawQMHdCwpSTlyRkuSSpQspUyZMmnXzp2+fc6dS9H+fb8rd548tmJeN6wPJzoxZeQ+rJ+TrE6dOnrnnXc0evRoSeeLO3HihF5++WXVr1/fcrp/Z078GLWbMEQ7l6/Tjp9Wq0a3x5QpLFRLxn1mO5oV9OFEJ39p9Prz2pDwvY7u2quQiHDFPtxYxavdpQ/qtbUdzRrWhxOdmOjDiU5M9GGiDyc6MYWEhSq6eGHf9ZxFCih/uVt08kiiju7+3V4wi9y0Rk6dOuU7KkyS9v2+V1t+3azIyEhFREZp3JhRqla9prLnyKm9e3brgxHDlC9/AVW8625JUlh4uJo0b6GxH45UrpgY5c6TR5M+mihJql6ztpXv6Xpz0/q4UnRiyqh9WB+SDRkyRPfdd59uueUWnTlzRo888oi2bNminDlz6pNPPrEd719ZMWWWIqKzq9GA7orMHa09qzfq3bptdfzg4cvf+QZEH0508pfwXDnUevwQReWJ1umk4/p97SZ9UK+tNs9ZZDuaNawPJzox0YcTnZjow0QfTnRiKhRbVj0WTPZdf2BoP0nS0vFTNaF9T1uxrHLTGtm8cYO6PP2k7/qId+IlSXUbNFLPF/po25YtSvhqlk4cP66c0dG648679PhTnYxTBnXq0k2BgUF67ZV+Sk5O1i23ltaw90YpIjIy3b+f9OCm9XGl6MSUUfvweL1er+0Q586d0+TJk7V27VqdOHFC5cuXV6tWrYwT+V+Njp7C1zYgcIPLFGDvLcr+6mya9ZdGAAAA/H8DEn+xHcHv9M96q+0IQIYy0rvjsvtYP5JMkoKCgtS6dWvbMQAAAAAAAOBS1k/cL0kfffSR7r33XuXNm1c7///JDocOHaoZM2ZYTgYAAAAAAAA3sD4k++CDD9SjRw/Vq1dPR48e9X3SZbZs2fTOO+/YDQcAAAAAAABXsD4ke/fdd/Xhhx/qxRdfVFDQX+/+jI2N1bp16ywmAwAAAAAAgFtYH5Jt375dt99+u2N7SEiITp48aSERAAAAAAAA3Mb6kKxIkSJavXq1Y3tCQoJKlSqV/oEAAAAAAADgOtY/3bJHjx7q3Lmzzpw5I6/Xq59++kmffPKJBg0apDFjxtiOBwAAAAAAABewPiR7/PHHlSVLFr300ks6deqUHnnkEeXNm1fDhg3TQw89ZDseAAAAAAAAXMD6kEySWrVqpVatWunUqVM6ceKEcuXKZTsSAAAAAAAAXMT6OcnGjh2r7du3S5JCQ0MZkAEAAAAAACDdWR+SDRo0SMWLF1fBggXVpk0bjRkzRlu3brUdCwAAAAAAAC5ifUi2ZcsW7dq1S4MGDVJoaKjefvttlShRQvnz51fr1q1txwMAAAAAAIALeLxer9d2iD+dOnVKP/zwgz755BN9/PHH8nq9Onfu3FU/TkdP4WsfDriBZQrw2I7gd86m+c1LIwAAgOsNSPzFdgS/0z/rrbYjABnKSO+Oy+5j/cT93377rRYsWKAFCxZo1apVKlWqlKpWraqpU6eqSpUqtuMBAAAAAADABawPyerWravo6Gg999xz+vrrr5U1a1bbkQAAAAAAAOAy1s9JFh8fr3vuuUdvvvmmbr31Vj3yyCMaPXq0fv31V9vRAAAAAAAA4BLWh2TdunXTtGnTdPjwYSUkJKhSpUpKSEhQ6dKllT9/ftvxAAAAAAAA4ALW324pSV6vV6tWrdKCBQs0f/58LVq0SGlpaYqOjrYdDQAAAAAAAC5gfUjWqFEjLV68WMeOHVO5cuVUrVo1PfHEE6pSpQrnJwMAAAAAAEC6sD4kK1mypJ566ilVrlxZUVFRtuMAAAAAAADAhaydk2zp0qWaNWuW3nrrLTVs2FBRUVGaOHGiihQpoly5cunJJ59UcnKyrXgAAAAAAABwEWtDsgEDBuiXX37xXV+3bp0ee+wx1apVS71799aXX36pQYMG2YoHAAAAAAAAF7E2JFu9erVq1qzpuz558mTdeeed+vDDD9WjRw8NHz5cU6ZMsRUPAAAAAAAALmJtSHb06FHFxMT4rn///feqV6+e7/odd9yh3bt324gGAAAAAAAAl7E2JIuJidH27dslSWfPntXKlSt11113+W4/fvy4goODbcUDAAAAAACAi1gbktWvX1+9e/fWDz/8oD59+ig0NFSVK1f23b527VoVK1bMVjwAAAAAAAC4SJCtJ3711VfVvHlzVa1aVeHh4ZowYYIyZcrku33s2LGqU6eOrXgAAAAAAABwEWtDspw5c2rhwoVKSkpSeHi4AgMDjds/++wzhYeHW0oHAAAAAAAAN/F4vV6v7RDXWkdPYdsRAAAAAAC4bt49sMB2BL/ybEw12xHg50Z6d1x2H2vnJAMAAAAAAAD8BUMyAAAAAAAAuB5DMgAAAAAAALgeQzIAAAAAAAC4HkMyAAAAAAAAuB5DMgAAAAAAALgeQzIAAAAAAAC4HkMyAAAAAAAAuB5DMgAAAAAAALgeQzIAAAAAAAC4HkMyAAAAAAAAuB5DMgAAAAAAALgeQzIAAAAAAAC4HkMyAAAAAAAAuJ71IdnPP/+sZcuWObYvW7ZMy5cvt5AIAAAAAAAAbmN9SNa5c2ft3r3bsX3v3r3q3LmzhUQAAAAAAABwG+tDsg0bNqh8+fKO7bfffrs2bNhgIREAAAAAAADcxvqQLCQkRAcOHHBs37dvn4KCgiwkAgAAAAAAgNtYH5LVqVNHffr0UVJSkm9bYmKi+vbtq9q1a1tMBgAAAAAAALewfqjW22+/rSpVqqhQoUK6/fbbJUmrV69WTEyMPvroI8vpAAAAAAAA4AbWh2T58uXT2rVr9fHHH2vNmjXKkiWL2rdvr4cffljBwcG24wEAAAAAAMAFrA/JJCksLExPPvmk7RgAAAAAAABwKStDspkzZ6pevXoKDg7WzJkzL7lv48aN0ykVAAAAAAAA3MrKkKxp06bav3+/cuXKpaZNm150P4/Ho9TU1PQLBgAAAAAAAFeyMiRLS0u74NcAAAAAAACADQG2AwAAAAAAAAC2+cWQbO7cuWrYsKGKFSumYsWKqWHDhpozZ47tWAAAAAAAAHAJ60Oy999/X3Xr1lVERIS6du2qrl27KjIyUvXr19d7771nO96/VrVTG72+fZHePb1ZL/w4XYXvKGc7kjXFK1dUp5lj9MbeZRrp3aFyTerYjuQXWCN/YY04sT6c6MREH050YqIPE3040YmJPpzoxOSWPpavXqfOL/RX9aYPq3Tl+zR34RLj9vfGfqRGrR7THbUbq1K9Fnq82wta+8smY59REyep1dPdFFurse6u1zw941vlljVypTJiH9aHZAMHDtTQoUP1ySefqEuXLurSpYsmTZqkoUOHauDAgbbj/SsVWjbU/fEvaVbcMA0s30B71mzQs99MVER0DtvRrAgJC9WeNRs1uXN/21H8BmvExBoxsT6c6MREH050YqIPE3040YmJPpzoxOSmPk6fOaMSxYvqxR7PXPD2wgXyqW/3zpo2YZQmvj9EeXPn1pPP9dGRo4m+fVJSzum+alX0YNMG6ZTaPjetkSuRUfuwPiRLTExU3bp1Hdvr1KmjpKQkC4n+u1o9HtfiDydr6fjPtG/jVk3q+KJSTp1WpQ4tbUez4peEBZrZb4hWT//GdhS/wRoxsUZMrA8nOjHRhxOdmOjDRB9OdGKiDyc6Mbmpj8p33aEuT7RTrSr3XPD2BrVr6O7Y8iqQN4+KFymsXs8+qRMnT+nXbdt9+zzz2KN69MHmuqlokfSKbZ2b1siVyKh9WB+SNW7cWF988YVj+4wZM9SwYUMLif6bwOBgFaxQWhvnLPZt83q92jhnsYreXd5iMvgL1gguhfXhRCcm+nCiExN9mOjDiU5M9OFEJyb6uLiUlBR9NvNrRYSHqUTxorbjWMMaMWXkPoJsPOnw4cN9X99yyy16/fXXtWDBAt19992SpB9//FGLFy/Wc889ZyPefxKeM5sCg4J07MBhY/vxA4eUu2QxS6ngT1gjuBTWhxOdmOjDiU5M9GGiDyc6MdGHE52Y6MNpweIf9XzcIJ05k6zoHNk1On6QsmWNsh3LGtaIKSP3YWVINnToUON6tmzZtGHDBm3YsMG3LWvWrBo7dqxeeumlSz5WcnKykpOTjW2p8ipQnmsXGAAAAAAASJIqlr9Nn499X0eTjmnql7PV8+XXNWnUcOXIltV2NOA/sTIk2759++V3ukKDBg1SXFycsa2CohSrrNfsOa7GicNHlXrunCJjchrbI2KidWz/ISuZ4F9YI7gU1ocTnZjow4lOTPRhog8nOjHRhxOdmOjDKTRLZhXMn08F8+dTuVtLqf7D7TVtVoKeaPOQ7WhWsEZMGbkP6+ck+6/69OmjpKQk43K77B3mmZqSol0r1qtkzUq+bR6PRyVrVtJvS1daywX/wRrBpbA+nOjERB9OdGKiDxN9ONGJiT6c6MREH5eXlubV2ZQU2zGsYY2YMnIfVo4k+6c9e/Zo5syZ2rVrl86ePWvcFh8ff8n7hoSEKCQkxNhm+62Wc+LHqN2EIdq5fJ12/LRaNbo9pkxhoVoy7jOruWwJCQtVdPHCvus5ixRQ/nK36OSRRB3d/bu9YBaxRkysERPrw4lOTPThRCcm+jDRhxOdmOjDiU5Mburj1KnT2rX3r9/B9+7br01btikqMkJRkZEaPXGSqt97t6JzZNfRpGP6ZNpMHTx8WPdVr+y7z74DB5V07Lj2HTio1NQ0bdqyTZJUMF9ehYZmSffvKT24aY1ciYzah/Uh2dy5c9W4cWMVLVpUmzZtUunSpbVjxw55vV6VL+/fn3pwMSumzFJEdHY1GtBdkbmjtWf1Rr1bt62OHzx8+TvfgArFllWPBZN91x8Y2k+StHT8VE1o39NWLKtYIybWiIn14UQnJvpwohMTfZjow4lOTPThRCcmN/WxfvOv6tCll+/6myNGSZKa1K2t/j27aPuuPZr50qs6mnRMWSMjVLrUzZowYoiKFynsu8+IMRM1I+E73/X7O3SSJI0d/qYq3l4ufb6RdOamNXIlMmofHq/X67UZoGLFiqpXr57i4uIUERGhNWvWKFeuXGrVqpXq1q2rp59++qofs6On8LUPCgAAAACAn3j3wALbEfzKszHVbEeAnxvp3XHZfayfk2zjxo169NFHJUlBQUE6ffq0wsPDNWDAAA0ePNhyOgAAAAAAALiB9SFZWFiY7zxkefLk0bZt23y3HT7s34fhAQAAAAAA4MZg/Zxkd911lxYtWqRSpUqpfv36eu6557Ru3TpNmzZNd911l+14AAAAAAAAcAHrQ7L4+HidOHFCkhQXF6cTJ07o008/1U033XTZT7YEAAAAAAAArgWrQ7LU1FTt2bNHZcuWlXT+rZcjR460GQkAAAAAAAAuZPWcZIGBgapTp46OHj1qMwYAAAAAAABczvqJ+0uXLq3ffvvNdgwAAAAAAAC4mPUh2WuvvaaePXtq1qxZ2rdvn44dO2ZcAAAAAAAAgOvN+on769evL0lq3LixPB6Pb7vX65XH41FqaqqtaAAAAAAAAHAJ60Oy+fPnX/S2devWpWMSAAAAAAAAuJX1IVnVqlWN68ePH9cnn3yiMWPGaMWKFXrmmWcsJQMAAAAAAIBbWD8n2Z8WLlyotm3bKk+ePHr77bdVo0YN/fjjj7ZjAQAAAAAAwAWsHkm2f/9+jR8/Xv/73/907NgxtWzZUsnJyZo+fbpuueUWm9EAAAAAAADgItaOJGvUqJFKlCihtWvX6p133tHvv/+ud99911YcAAAAAAAAuJi1I8lmz56tLl266Omnn9ZNN91kKwYAAAAAAABg70iyRYsW6fjx46pQoYLuvPNOjRgxQocPH7YVBwAAAAAAAC5mbUh211136cMPP9S+ffv01FNPafLkycqbN6/S0tL03Xff6fjx47aiAQAAAAAAwGWsf7plWFiYOnTooEWLFmndunV67rnn9MYbbyhXrlxq3Lix7XgAAAAAAABwAetDsr8rUaKE3nzzTe3Zs0effPKJ7TgAAAAAAABwCb8akv0pMDBQTZs21cyZM21HAQAAAAAAgAv45ZAMAAAAAAAASE8MyQAAAAAAAOB6DMkAAAAAAADgegzJAAAAAAAA4HoMyQAAAAAAAOB6DMkAAAAAAADgegzJAAAAAAAA4HoMyQAAAAAAAOB6DMkAAAAAAADgekG2AwAAAAAAgKvzbEw12xH8ylsT2tmO4FeebzvedoQMiSPJAAAAAAAA4HoMyQAAAAAAAOB6DMkAAAAAAADgegzJAAAAAAAA4HoMyQAAAAAAAOB6DMkAAAAAAADgegzJAAAAAAAA4HoMyQAAAAAAAOB6QbYDSNKWLVs0f/58HTx4UGlpacZt/fv3t5QKAAAAAAAAbmF9SPbhhx/q6aefVs6cOZU7d255PB7fbR6PhyEZAAAAAAAArjvrQ7LXXntNr7/+ul544QXbUQAAAAAAAOBS1s9JdvToUT3wwAO2YwAAAAAAAMDFrA/JHnjgAX377be2YwAAAAAAAMDFrL/dsnjx4urXr59+/PFHlSlTRsHBwcbtXbp0sZQMAAAAAAAAbmF9SDZ69GiFh4fr+++/1/fff2/c5vF4GJIBAAAAAADgurM+JNu+fbvtCAAAAAAAAHA56+ckAwAAAAAAAGyzfiSZJO3Zs0czZ87Url27dPbsWeO2+Ph4S6kAAAAAAADgFtaHZHPnzlXjxo1VtGhRbdq0SaVLl9aOHTvk9XpVvnx52/EAAAAAAADgAtbfbtmnTx/17NlT69atU+bMmfX5559r9+7dqlq1qh544AHb8QAAAAAAAOAC1odkGzdu1KOPPipJCgoK0unTpxUeHq4BAwZo8ODBltMBAAAAAADADawPycLCwnznIcuTJ4+2bdvmu+3w4cO2YgEAAAAAAMBFrJ+T7K677tKiRYtUqlQp1a9fX88995zWrVunadOm6a677rIdDwAAAAAAAC5gfUgWHx+vEydOSJLi4uJ04sQJffrpp7rpppv4ZEsAAAAAAACkC+tDsqJFi/q+DgsL08iRIy2mAQAAAAAAgBtZH5L93YkTJ5SWlmZsi4yMtJQGAAAAAAAAbmH9xP3bt29XgwYNFBYWpqioKGXLlk3ZsmVT1qxZlS1bNtvxAAAAAAAA4ALWh2StW7fW0aNHNXbsWM2dO1fz5s3TvHnzNH/+fM2bN892vH+taqc2en37Ir17erNe+HG6Ct9RznYkq+jDiU7+UrxyRXWaOUZv7F2mkd4dKtekju1I1rE+nOjERB9OdGKiDxN9ONGJiT6c6MREHya39pGalqYPFqxV4xEzdc/gKWry3pca88N6eb1eY7/th5PUfcpCVX17qu59c4oeHfuN9iedtJTajoy4RqwPydasWaNx48bpwQcfVLVq1VS1alXjkhFVaNlQ98e/pFlxwzSwfAPtWbNBz34zURHROWxHs4I+nOjEFBIWqj1rNmpy5/62o/gF1ocTnZjow4lOTPRhog8nOjHRhxOdmOjD5OY+JizdqKkrt6jXfRX02VP19WyNcpr440Z9uvxX3z57jh7X4xPnqHCOCI1qXUOTn6inx+69VZmCAi0mT18ZdY1YH5Ldcccd2r17t+0Y11StHo9r8YeTtXT8Z9q3casmdXxRKadOq1KHlrajWUEfTnRi+iVhgWb2G6LV07+xHcUvsD6c6MREH050YqIPE3040YmJPpzoxEQfJjf3sXbPYVW9Ob/uvSmf8mYNV61SBXVnkdz65fc/fPu8t2CtKhXLq641b1fJ3NmVP1uEqt6cX9nDMltMnr4y6hqxPiQbM2aMBg8erAkTJmjFihVau3atccloAoODVbBCaW2cs9i3zev1auOcxSp6d3mLyeygDyc6waWwPpzoxEQfTnRiog8TfTjRiYk+nOjERB8mt/dRNn9O/bzjgHb+cUyS9OuBo1qz55AqFcsrSUrzerV46+8qlD1Cz3wyX7WHTlPbcd9qweY9NmOnq4y8Rqx/uuWhQ4e0bds2tW/f3rfN4/HI6/XK4/EoNTXVYrqrF54zmwKDgnTswGFj+/EDh5S7ZDFLqeyhDyc6waWwPpzoxEQfTnRiog8TfTjRiYk+nOjERB8mt/fRrtItOpmcovtHfqWAAI/S0rzqVK2s6pUuLEk6cvKMTp09p/FLN+jpqmX1bPXbtPS3fXp+6g8a2bqmKhTKZfcbSAcZeY1YH5J16NBBt99+uz755BPFxMTI4/Fc1f2Tk5OVnJxsbEuVV4G6uscBAAAAAAC4lO827FLC+p16rWklFYuO0uYDRxX/3UpFR2RRw7JFfSfwr3pzfrW6s6QkqUTubFqz57A+X7nFFUOyjMz6kGznzp2aOXOmihcv/q/uP2jQIMXFxRnbKihKscp6DdJdvROHjyr13DlFxuQ0tkfEROvY/kNWMtlEH050gkthfTjRiYk+nOjERB8m+nCiExN9ONGJiT5Mbu9j+NzValuplO67tZAkqXiurNqXdFLjlmxQw7JFlTU0RIEBHhXJGWncr0jOSK3efeP3I2XsNWL9nGQ1atTQmjVr/vX9+/Tpo6SkJONyu6KuYcKrk5qSol0r1qtkzUq+bR6PRyVrVtJvS1day2ULfTjRCS6F9eFEJyb6cKITE32Y6MOJTkz04UQnJvowub2PM+fOKeAf74AL9Hj0/w8gU3BgoG7Nk0M7/zhu7LPrj+PKExWWXjGtyshrxPqRZI0aNVL37t21bt06lSlTRsHBwcbtjRs3vuT9Q0JCFBISYmyz/VbLOfFj1G7CEO1cvk47flqtGt0eU6awUC0Z95nVXLbQhxOdmELCQhVdvLDves4iBZS/3C06eSRRR3f/bi+YJawPJzox0YcTnZjow0QfTnRiog8nOjHRh8nNfVS+KZ/GLv5FuSNDVTQ6Spv3H9XHP21W43JFffu0uauk+nyxROULRiu2UIyWbNunH7bs1ag2NS0mT18ZdY1YH5J17NhRkjRgwADHbRnxxP2StGLKLEVEZ1ejAd0VmTtae1Zv1Lt12+r4wcOXv/MNiD6c6MRUKLaseiyY7Lv+wNB+kqSl46dqQvuetmJZw/pwohMTfTjRiYk+TPThRCcm+nCiExN9mNzcx/N1Kmjk92v1RsJyHT2VrJzhWdT89uJ6ovKtvn2qlyygPvViNX7JBr397UoVyh6hwS3u1W0Foi0mT18ZdY14vH+eVe4G0tFT2HYEAAAAAACQTt6a0M52BL/yfNvxtiP4nZHeHZfdx+o5yVJSUhQUFKT169fbjAEAAAAAAACXszokCw4OVsGCBTPkWyoBAAAAAABw47D+6ZYvvvii+vbtqyNHjtiOAgAAAAAAAJeyfuL+ESNGaOvWrcqbN68KFSqksDDzI1FXrvTvjwcFAAAAAABAxmd9SNa0aVPbEQAAAAAAAOBy1odkL7/8su0IAAAAAAAAcDnr5ySTpMTERI0ZM0Z9+vTxnZts5cqV2rt3r+VkAAAAAAAAcAPrR5KtXbtWtWrVUlRUlHbs2KEnnnhC2bNn17Rp07Rr1y5NnDjRdkQAAAAAAADc4KwfSdajRw+1a9dOW7ZsUebMmX3b69evr4ULF1pMBgAAAAAAALewPiT7+eef9dRTTzm258uXT/v377eQCAAAAAAAAG5jfUgWEhKiY8eOObb/+uuvio6OtpAIAAAAAAAAbmN9SNa4cWMNGDBAKSkpkiSPx6Ndu3bphRdeUIsWLSynAwAAAAAAgBtYH5INGTJEJ06cUK5cuXT69GlVrVpVxYsXV3h4uF5//XXb8QAAAAAAAOAC1j/dMioqSt99950WL16sNWvW6MSJEypfvrxq1aplOxoAAAAAAABcwtqRZKdPn9asWbN812fNmqWtW7dq//79+vrrr9WrVy+dOXPGVjwAAAAAAAC4iLUjySZMmKCvvvpKDRs2lCSNGDFCt956q7JkySJJ2rRpk/LkyaPu3bvbiggAAAAAAACXsHYk2ccff6wnn3zS2DZp0iTNnz9f8+fP11tvvaUpU6ZYSgcAAAAAAAA3sTYk27p1q8qUKeO7njlzZgUE/BWnYsWK2rBhg41oAAAAAAAAcBlrb7dMTExUcnKy7/qhQ4eM29PS0ozbAQAAAAAAgOvF2pFk+fPn1/r16y96+9q1a5U/f/50TAQAAAAAAAC3sjYkq1+/vvr373/BT7A8ffq04uLi1KBBAwvJAAAAAAAA4DbW3m7Zt29fTZkyRSVKlNAzzzyjm2++WZK0efNmjRgxQufOnVPfvn1txQMAAAAAAICLWBuSxcTEaMmSJXr66afVu3dveb1eSZLH41Ht2rX1/vvvKyYmxlY8AAAAAAAAuIi1IZkkFSlSRAkJCTpy5Ii2bt0qSSpevLiyZ89uMxYAAAAAAABcxuqQ7E/Zs2dXxYoVbccAAAAAAACAS1k7cT8AAAAAAADgLxiSAQAAAAAAwPUYkgEAAAAAAMD1GJIBAAAAAADA9fzixP0A7MoS6LEdwe+cTfPajuBXUqkDAAAAfuz5tuNtR/Ar73zTz3aEDIkjyQAAAAAAAOB6DMkAAAAAAADgegzJAAAAAAAA4HoMyQAAAAAAAOB6DMkAAAAAAADgegzJAAAAAAAA4HoMyQAAAAAAAOB6QbYD/Gn58uWaMmWKdu3apbNnzxq3TZs2zVIqAAAAAAAAuIFfHEk2efJkVapUSRs3btQXX3yhlJQU/fLLL5o3b56ioqJsxwMAAAAAAMANzi+GZAMHDtTQoUP15ZdfKlOmTBo2bJg2bdqkli1bqmDBgrbjAQAAAAAA4AbnF0Oybdu2qUGDBpKkTJky6eTJk/J4POrevbtGjx5tOR0AAAAAAABudH4xJMuWLZuOHz8uScqXL5/Wr18vSUpMTNSpU6dsRgMAAAAAAIAL+MWJ+6tUqaLvvvtOZcqU0QMPPKCuXbtq3rx5+u6771SzZk3b8QAAAAAAAHCD84sh2YgRI3TmzBlJ0osvvqjg4GAtWbJELVq00EsvvWQ5HQAAAAAAAG50fjEky549u+/rgIAA9e7d22IaAAAAAAAAuI21IdmxY8eueN/IyMjrmAQAAAAAAABuZ21IljVrVnk8nivaNzU19TqnAQAAAAAAgJtZG5LNnz/f9/WOHTvUu3dvtWvXTnfffbckaenSpZowYYIGDRpkKyIAAAAAAABcwtqQrGrVqr6vBwwYoPj4eD388MO+bY0bN1aZMmU0evRotW3b1kZEAAAAAAAAuESA7QDS+aPGYmNjHdtjY2P1008/WUgEAAAAAAAAN/GLIVmBAgX04YcfOraPGTNGBQoUsJAIAAAAAAAAbmLt7ZZ/N3ToULVo0UKzZ8/WnXfeKUn66aeftGXLFn3++eeW0wEAAAAAAOBG5xdHktWvX19btmxR48aNdeTIER05ckSNGjXSr7/+qvr169uOBwAAAAAAgBucXxxJJkn58+fX66+/bjsGAAAAAAAAXMhvhmSSdOrUKe3atUtnz541tpctW9ZSIgAAAAAAALiBXwzJDh06pPbt22v27NkXvD01NTWdEwEAAAAAAMBN/OKcZN26dVNiYqKWLVumLFmyKCEhQRMmTNBNN92kmTNn2o4HAAAAAACAG5xfDMnmzZun+Ph4xcbGKiAgQIUKFVLr1q315ptvatCgQbbj/StVO7XR69sX6d3Tm/XCj9NV+I5ytiNZRR9/KV65ojrNHKM39i7TSO8OlWtSx3Ykv1KzV0cNPbddTYf0sx3Fmjq9O6nXshkakrReb+xfrienjVaum4vajmUdryMm+nCiExN9mOjDiU5M9OFEJyb6MNGHk5s7OXkmWW9+Pld1+49UxR7xejT+/7R+5z7f7aeSz2rglO9Uu9/7qtgjXs1e/5+mLFplMfGF+cWQ7OTJk8qVK5ckKVu2bDp06JAkqUyZMlq5cqXNaP9KhZYNdX/8S5oVN0wDyzfQnjUb9Ow3ExURncN2NCvowxQSFqo9azZqcuf+tqP4nQKxZXX3E49o75qNtqNYdVOVO7Xw/Y/09t3N9G6dNgoMDtKz30xUptAstqNZw+uIiT6c6MREHyb6cKITE3040YmJPkz04eT2Tl6ZlKClm3bo9UcbaGqf9rq7ZGE9NeJTHUg8Lkl6e9o8Ldm4XQMfbagvXnxMrapV0BufzdGCdVssJzf5xZCsRIkS2rx5sySpXLlyGjVqlPbu3auRI0cqT548ltNdvVo9HtfiDydr6fjPtG/jVk3q+KJSTp1WpQ4tbUezgj5MvyQs0Mx+Q7R6+je2o/iVTGGhaj3xHU3p2EenE5Nsx7Hqvfpt9eOEqdq3YYv2rt2oj9r3VPZC+VWwQhnb0azhdcREH050YqIPE3040YmJPpzoxEQfJvpwcnMnZ86maO6aX9W9STVVKF5ABaOz6en696pAdDZ9tmi1JGn19t/V6M7SuuOmgsqXI0r333Obbs6XS+t37rcb/h/8YkjWtWtX7dt3/jC8l19+WbNnz1bBggU1fPhwDRw40HK6qxMYHKyCFUpr45zFvm1er1cb5yxW0bvLW0xmB33gSt3/7gBtnD1Pv85dfPmdXSZLVIQk6eSRRLtBLOF1xEQfTnRiog8TfTjRiYk+nOjERB8m+nByeyepaWlKTfMqJNj8bMiQ4CCt2rZHknRbkbz6ft1WHUg8Lq/Xq59+3amdB4/o7pKFLSS+OL/4dMvWrVv7vq5QoYJ27typTZs2qWDBgsqZM6fFZFcvPGc2BQYF6diBw8b24wcOKXfJYpZS2UMfuBK3t2yofLffqqF3NbEdxe94PB61GNpf2xb9rH2//Go7jhW8jpjow4lOTPRhog8nOjHRhxOdmOjDRB9Obu8kLHOIyhXJq9EJS1Qkd3bliAjT7BUbtXb77yoQnVWS1Pv+Whow+RvV6feBggIC5Anw6OWH7lOF4gXshv8HvxiS/VNoaKjKl7+yaWtycrKSk5ONbanyKlCe6xENwDWWNX8eNRv6sj6o20bnks/ajuN3HnzvVeUtXULxle+3HQUAAAAALuj1Ng308qTZqv3SBwoM8Khk/hjVrVBKG3effzvlJwtXau2O3zXsyebKmz1SK7bu0cDPvlN0VLju8qOjyfxiSJaamqrx48dr7ty5OnjwoNLS0ozb582bd9H7Dho0SHFxcca2CopSrLJej6iXdeLwUaWeO6fIGPMIuIiYaB3bf8hKJpvoA5eTv3xpRcTk1HM/f+nbFhgUpKKVK+rezo/q+dAS8v7jNcEtWr4bp9INamho1ZZK3Otf79VPT7yOmOjDiU5M9GGiDyc6MdGHE52Y6MNEH050IhWIzqaxXR/RqeSzOnnmrKKjwvX82BnKnyOrzpxN0fAvF2ro481UpfT5I+tuzpdLm/ce0IR5P/vVkMxvzknWtWtXpaamqnTp0ipXrpxxuZQ+ffooKSnJuNyuqHRK7pSakqJdK9arZM1Kvm0ej0cla1bSb0sz3id1/lf0gcvZMm+JBpe7T29XaOC77Pp5jVZOmqG3KzRw9YCsXNP7NKzmI/pjxx7bcazidcREH050YqIPE3040YmJPpzoxEQfJvpwopO/hIZkUnRUuI6dOqOlm3aoWtniOpeapnOpaQrwmO/4CwgIUJrXaynphfnFkWSTJ0/WlClTVL9+/au+b0hIiEJCQoxttt9qOSd+jNpNGKKdy9dpx0+rVaPbY8oUFqol4z6zmssW+jCFhIUqunhh3/WcRQoof7lbdPJIoo7u/t1eMEuST5zU/n+ca+vsqdM6+cdRx3a3ePC9VxX7cBONavqEko+fVGRMtCTpdNIxpZxJvsy9b0y8jpjow4lOTPRhog8nOjHRhxOdmOjDRB9Obu9k8cbtkterQrmya/fhRA2dvkCFY7KryV1lFBwYqNjiBRQ/Y4FCMgUpT7ZIrdi6W7N++kU9m1W3Hd3gF0OyTJkyqXjx4rZjXDMrpsxSRHR2NRrQXZG5o7Vn9Ua9W7etjh88fPk734Dow1Qotqx6LJjsu/7A0H6SpKXjp2pC+562YsGPVHm6jSSp+4JPje0fte+pHydMtRHJOl5HTPThRCcm+jDRhxOdmOjDiU5M9GGiDye3d3LidLKGf7lQBxKPKyo0s2qWu1nPNqqi4MBASdLg9o00bOZC9ZkwS8dOnVGebJF6pmFlPXDvbXaD/4PH67V/bNuQIUP022+/acSIEfJ4/vtRYB09hf97KMBFsgTyQRf/dDbN+kujX0mlDgAAACDDeOebfrYj+J3MdR677D5+cSTZokWLNH/+fM2ePVu33nqrgoODjdunTZtmKRkAAAAAAADcwC+GZFmzZlWzZs1sxwAAAAAAAIBL+cWQbNy4cbYjAAAAAAAAwMUCbAcAAAAAAAAAbPOLIdmBAwfUpk0b5c2bV0FBQQoMDDQuAAAAAAAAwPXkF2+3bNeunXbt2qV+/fopT5481+QTLgEAAAAAAIAr5RdDskWLFumHH37QbbfdZjsKAAAAAAAAXMgv3m5ZoEABeb1e2zEAAAAAAADgUn4xJHvnnXfUu3dv7dixw3YUAAAAAAAAuJBfvN3ywQcf1KlTp1SsWDGFhoYqODjYuP3IkSOWkgEAAAAAAMAN/GJI9s4779iOAAAAAAAAABfziyFZ27ZtbUcAAAAAAACAi/nFkOzvzpw5o7NnzxrbIiMjLaUBAAAAAACAG/jFiftPnjypZ555Rrly5VJYWJiyZctmXAAAAAAAAIDryS+GZL169dK8efP0wQcfKCQkRGPGjFFcXJzy5s2riRMn2o4HAAAAAACAG5xfvN3yyy+/1MSJE1WtWjW1b99elStXVvHixVWoUCF9/PHHatWqle2IAAAAAAAAuIH5xZFkR44cUdGiRSWdP//YkSNHJEn33nuvFi5caDMaAAAAAAAAXMAvhmRFixbV9u3bJUklS5bUlClTJJ0/wiwqKspmNAAAAAAAALiAXwzJ2rdvrzVr1kiSevfurffee0+ZM2dWt27d1LFjR8vpAAAAAAAAcKOzOiQbOnSoJKl79+7q0qWLJKlWrVratGmTJk2apNWrVyshIcFmRAAAAAAAALiA1RP39+3bVzly5NCjjz5qbC9UqJBy5MihunXr6o8//rCUDgAAAAAAAG5h9Uiyjz76SE899ZRmzpxpbD9x4oTq1aungwcPat68eZbSAQAAAAAAwC2sHkl2//33KzExUQ8//LC++uorVatWTSdPnlS9evW0f/9+LViwQHnz5rUZEQAAAAAAAC5gdUgmSY8//riOHDmiJk2aaMaMGerfv79+//13ff/998qXL5/teAAAAAAAAHAB60MySerVq5eOHDmimjVrqnDhwlqwYIHy589vOxYAAAAAAABcwuqQrHnz5sb14OBg5cyZU127djW2T5s2LT1jAQAAAAAAwGWsDsmioqKM6w8//LClJAAAAAAAAHAzq0OycePG2Xx6AAAAAAAAQJIUYDsAAAAAAAAAYJ0X18WZM2e8L7/8svfMmTO2o/gNOjHRhxOdmOjDRB9OdGKiDyc6MdGHiT6c6MREHyb6cKITE304ZbROPF6v12t7UHcjOnbsmKKiopSUlKTIyEjbcfwCnZjow4lOTPRhog8nOjHRhxOdmOjDRB9OdGKiDxN9ONGJiT6cMlonvN0SAAAAAAAArseQDAAAAAAAAK7HkAwAAAAAAACux5DsOgkJCdHLL7+skJAQ21H8Bp2Y6MOJTkz0YaIPJzox0YcTnZjow0QfTnRiog8TfTjRiYk+nDJaJ5y4HwAAAAAAAK7HkWQAAAAAAABwPYZkAAAAAAAAcD2GZAAAAAAAAHA9hmTpYPz48cqaNavv+iuvvKLbbrvtiu57NfteTzt27JDH49Hq1avT9XmrVaumbt26petzXmv//PP/t/7ZReHChfXOO+/858fNyP7L3y24z7X6u5iR2Xot9zdX87PlRvg59G+48fXUrX/WF9KuXTs1bdr0mu/rr/75PbAWzrvc75q8ll7af1lXGbWvK/n3icfj0fTp09MlD24sCxYskMfjUWJi4nV9HtcPydq1ayePx+O41K1b97o9Z8+ePTV37tzr9vh/+vN769ixo+O2zp07y+PxqF27dtc9x43sYr8Y/v0v8IMPPqhff/31mj/3zz//rCeffPKaP+6/daG/R3+/vPLKK9f8Oa9lt39/LciUKZOKFy+uAQMG6Ny5c//pMTP6Pxz+i4u9vm7dutV2NKv+3ktwcLBiYmJUu3ZtjR07VmlpaVf8OOn1i8K1dOjQIT399NMqWLCgQkJClDt3bt13331avHhxuuaYNm2aXn311XR9zn9j//796tq1q4oXL67MmTMrJiZG99xzjz744AOdOnXKdrzr4p9/P4oUKaJevXrpzJkz1/25/X1dXKvXjisxbNgwjR8//po+5vXy95+112qw4O9r4Z9s/A4m2e3JX36eXA1/W1dLly5VYGCgGjRokK7Pu2/fPtWrVy9dn/Pv3Piz9e8u9O+TqVOnKnPmzBoyZIidUH4myHYAf1C3bl2NGzfO2HY9P540PDxc4eHh1+3x/65AgQKaPHmyhg4dqixZskiSzpw5o0mTJqlgwYLpksHtsmTJ4uv+WoqOjr7mj/lf7Nu3z/f1p59+qv79+2vz5s2+bddjzV/rbv98LUhOTtbXX3+tzp07Kzg4WH369Lmqx0lNTZXH47lmuTKyC72+/nPtnj17VpkyZUrPWNb92UtqaqoOHDighIQEde3aVVOnTtXMmTMVFHRj/nhu0aKFzp49qwkTJqho0aI6cOCA5s6dqz/++CNdc2TPnj1dn+/f+O2333TPPfcoa9asGjhwoMqUKaOQkBCtW7dOo0ePVr58+dS4cWPH/VJSUhQcHGwh8bXz59+PlJQUrVixQm3btpXH49HgwYOv6/NmhHWRXq8dUVFR1+RxMqqMsBb+7lr/DnalP5dt9uQvP0+uhr+tq//973969tln9b///U+///678ubNe8H9vF6vUlNTr9nrS+7cua/J4/wb//Zn6+Vk5N9lx4wZo86dO2vkyJFq37697Th+wfVHkkny/e/D3y/ZsmWTdP5/ZsaMGaNmzZopNDRUN910k2bOnGncf+bMmbrpppuUOXNmVa9eXRMmTLjk/+7/8y0MCxYsUMWKFRUWFqasWbPqnnvu0c6dO437fPTRRypcuLCioqL00EMP6fjx41f0vZUvX14FChTQtGnTfNumTZumggUL6vbbb/dtS0hI0L333qusWbMqR44catiwobZt23bRx01NTdVjjz2mIkWKKEuWLCpRooSGDRvmu33hwoUKDg7W/v37jft169ZNlStXliT98ccfevjhh5UvXz6FhoaqTJky+uSTTy75/Xz00UeKjY1VRESEcufOrUceeUQHDx703f7nkRXffPONbr/9dmXJkkU1atTQwYMHNXv2bJUqVUqRkZF65JFH0u1/Ci72lsBL/ZmePHlSjz76qMLDw5UnT54LTvX97e2Wf//7ExUVJY/H47t+8uRJtWrVSjExMQoPD9cdd9yhOXPmGPcvXLiwXnvtNd/3XahQIc2cOVOHDh1SkyZNFB4errJly2r58uW++1zrt8/9+VpQqFAhPf3006pVq5Zmzpypo0eP6tFHH1W2bNkUGhqqevXqacuWLY4cM2fO1C233KKQkBB16NBBEyZM0IwZM3z/k7tgwYILHv2zevVqeTwe7dixw7ftww8/VIECBRQaGqpmzZopPj7e8b3OmDFD5cuXV+bMmVW0aFHFxcUZR74lJibq8ccfV3R0tCIjI1WjRg2tWbPGd/uaNWtUvXp1RUREKDIyUhUqVDD6XbRokSpXrqwsWbKoQIEC6tKli06ePPmvOv37pWbNmnrmmWfUrVs35cyZU/fdd58kaf369apXr57Cw8MVExOjNm3a6PDhw77Hqlatmrp06aJevXope/bsyp07t+N/xxMTE/XUU08pJiZGmTNnVunSpTVr1ixjn2+++UalSpVSeHi46tata/zjIr382Uu+fPlUvnx59e3bVzNmzNDs2bM1fvz4C74tMjEx0beOduzYoerVq0uSsmXLZhwZnJaWpjfffFPFixdXSEiIChYsqNdff914/t9++03Vq1dXaGioypUrp6VLl1737zkxMVE//PCDBg8erOrVq6tQoUKqWLGi+vTp4/uF1OPx6IMPPlC9evWUJUsWFS1aVFOnTjUe54UXXtDNN9+s0NBQFS1aVP369VNKSorv9it5jf3n0Sbvv/++7+d4TEyM7r//fuM509LSLrnurodOnTopKChIy5cvV8uWLVWqVCkVLVpUTZo00VdffaVGjRpJ+quzxo0bKywszPdn/V9fH/5p27ZtKlq0qJ555hl5vd7r+r3/+fejQIECatq0qWrVqqXvvvtO0oV/9t12222+PxOv16tXXnnFd3RJ3rx51aVLF9++l/qz/ue6uNLfOebOnavY2FiFhoaqUqVKxnDiWrvca4ckxcfHq0yZMgoLC1OBAgXUqVMnnThxwvcYf/7MutRr4T+PNJg6darKlCmjLFmyKEeOHKpVq5bj58Hbb7+tPHnyKEeOHOrcubPx9zI9tGvXTt9//72GDRvm+7m7Y8eOy/7OeiEZYS383aV+Bxs5cqTuvfdeY/933nlHhQsX9l3/88/79ddfV968eVWiRIkLPs+YMWOUNWtW3ztibL2WXu7nSc+ePdWwYUPj+/V4PEpISPBtK168uMaMGWN8b6VKlVLmzJlVsmRJvf/++8Zz7t69Wy1btlTWrFmVPXt2NWnSxPi9LTU1VT169PD9W6pXr16O10p/+tlz4sQJffrpp3r66afVoEED48jRP9fz7NmzVaFCBYWEhGjRokXatm2bmjRpcsnf5yXp+PHjevjhhxUWFqZ8+fLpvffeM27/59st9+zZo4cffljZs2dXWFiYYmNjtWzZsmv2vf7dlf5svdzPyD9/1xgzZoyKFCmizJkz+763UaNGqWHDhgoNDVWpUqW0dOlSbd26VdWqVVNYWJgqVapk/Dv7SnotXLiwBg4cqA4dOigiIkIFCxbU6NGj/3Mfb775pp599llNnjzZNyC7lj9DLvVz4XKvq5L09ddf6+abb1aWLFlUvXp14++c9O/mCVeCIdkViIuLU8uWLbV27VrVr19frVq10pEjRyRJ27dv1/3336+mTZtqzZo1euqpp/Tiiy9e8WOfO3dOTZs2VdWqVbV27VotXbpUTz75pHEUyrZt2zR9+nTNmjVLs2bN0vfff6833njjip+jQ4cOxpEcY8eOdUyJT548qR49emj58uWaO3euAgIC1KxZs4sevp+Wlqb8+fPrs88+04YNG9S/f3/17dtXU6ZMkSRVqVJFRYsW1UcffeS7T0pKij7++GN16NBB0vkj2ipUqKCvvvpK69ev15NPPqk2bdrop59+uuj3kpKSoldffVVr1qzR9OnTtWPHjgu+ZfSVV17RiBEjtGTJEt8PtXfeeUeTJk3SV199pW+//VbvvvvuFXd4rV3uz/T555/X999/rxkzZujbb7/VggULtHLlSmt5/6sTJ06ofv36mjt3rlatWqW6deuqUaNG2rVrl7Hf0KFDdc8992jVqlVq0KCB2rRpo0cffVStW7fWypUrVaxYMT366KPX/R9of8qSJYvOnj2rdu3aafny5Zo5c6aWLl0qr9er+vXrGy/0p06d0uDBgzVmzBj98ssvGj58uFq2bOn7obFv3z5VqlTpip538eLF6tixo7p27arVq1erdu3ajiHHDz/8oEcffVRdu3bVhg0bNGrUKI0fP97Y74EHHvANiFesWKHy5curZs2avtevVq1aKX/+/Pr555+1YsUK9e7d23cEyrZt21S3bl21aNFCa9eu1aeffqpFixbpmWee+a+1SpImTJigTJkyafHixRo5cqQSExNVo0YN3X777Vq+fLkSEhJ04MABtWzZ0nG/sLAwLVu2TG+++aYGDBjg+8dzWlqa6tWrp8WLF+v//u//tGHDBr3xxhsKDAz03f/UqVN6++239dFHH2nhwoXatWuXevbseU2+p/+qRo0aKleunPGfGhdToEABff7555KkzZs3a9++fb5/9PXp00dvvPGG+vXrpw0bNmjSpEmKiYkx7v/iiy+qZ8+eWr16tW6++WY9/PDD/+mtxVfiz6Oop0+fruTk5Ivu169fP7Vo0UJr1qxRq1at9NBDD2njxo2+2yMiIjR+/Hht2LBBw4YN04cffqihQ4caj3E1PzeXL1+uLl26aMCAAdq8ebMSEhJUpUoVY59Lrbvr4Y8//tC3336rzp07Kyws7IL7/P33hFdeeUXNmjXTunXr1KFDh2vy+vB3a9eu1b333qtHHnlEI0aMSNcjZdevX68lS5Zc8f/Qf/755xo6dKhGjRqlLVu2aPr06SpTpoykK/uz/rsr/Z3jxRdf1JAhQ7R8+XIFBQX5fs9JL/987QgICNDw4cP1yy+/aMKECZo3b5569epl3OdqXgv37dunhx9+WB06dNDGjRu1YMECNW/e3PhZPH/+fG3btk3z58/XhAkTNH78+HR/u+awYcN0991364knnvD93C1QoMBlf2e9EhllLfwXc+fO1ebNm/Xdd985/nNJOv+P6d69e+vbb79VzZo1Hben52vp5X6eVK1aVYsWLVJqaqok6fvvv1fOnDm1YMECSdLevXu1bds2VatWTZL08ccfq3///nr99de1ceNGDRw4UP369dOECRMknf/zv++++xQREaEffvhBixcv9g0Gzp49K0kaMmSIxo8fr7Fjx2rRokU6cuSIvvjii4t+D7Z/9kyZMkUlS5ZUiRIl1Lp1a40dO9bx+3Xv3r31xhtvaOPGjSpbtuwV/z7/1v9r787jesr+P4C/WlQfLVKSalTS4hP1mZIxMcoS2ZpMDX2tZZIlZjAhfRWaKFvMZJCxFGPJEsZEY49UllIJ7QozwwyNwWfGls7vjx6f+/vcPp/q00J8vZ+PR49H995z7z13O+fc8znn3JUrIRKJkJ2djfnz52PmzJm1xlssFsPV1RW//fYbDh8+jNzcXMybN6/Zu5ADDctbFckjS0pKkJiYiAMHDvB+1IyIiMCECROQk5ODLl26YMyYMZgyZQpCQkKQmZkJxhivPK3oeY2OjoaTkxOys7MRGBiIadOmNakiPjg4GBEREUhKSsJnn33GzW+uPKS+fKG+dPXOnTvw8vKCh4cHcnJyMGnSJMyfP5+3j8bUJyiEved8fX2ZiooK09TU5P0tXbqUMcYYABYaGsqFF4vFDABLTk5mjDEWHBzMunXrxtvmggULGAD28OFDxhhjcXFxrE2bNtzyRYsWMZFIxBhjrKKiggFgKSkpcuO3aNEi1rp1a/b48WNu3ty5c1nPnj0VOjZPT0/2559/MnV1dVZeXs7Ky8uZhoYGu3//PvP09GS+vr5y171//z4DwPLy8hhjjJWVlTEALDs7u9b9TZ8+nXl7e3PTy5cvZ0KhkJtOTExkWlpaTCwW17qNYcOGsaCgIG7a1dWVzZw5s9bwly9fZgDYkydPGGOMnTlzhgFgJ0+e5MJERUUxAKy0tJSbN2XKFObu7l7rdhVV2/2joaHB3QPyrn9d1/TJkydMTU2N7d27l1teUVHBBAIB71yYmZmxNWvWNPkYXoeaxyxP165d2dq1a7lpMzMzNm7cOG767t27DAALCwvj5mVkZDAA7O7du3L3I/1sNZTkeWGMsaqqKnbixAmmrq7ORowYwQCwtLQ0LuyDBw+YQCDgrlFcXBwDwHJycmrdpoTkHpWkD4wxlp2dzQCwsrIyxhhjPj4+bNiwYbz1xo4dyzvWAQMGsMjISF6YH3/8kRkZGTHGGEtNTWU6Ojrs2bNnvDCdO3dmGzduZIwxpq2tzeLj4+WeD39/fzZ58mTevNTUVKasrMyePn0qd52a5D0fn3/+OXN1dWUODg68sBEREWzQoEG8eXfu3GEAWGFhIWOsOj345JNPeGF69OjBgoODGWOMHTt2jCkrK3Pha5Jcp5KSEm7eunXrmKGhoULH01zk3RcSPj4+TCgUyk1zHz58yACwM2fOMMbk30uPHz9m6urqbNOmTXK3L9nu5s2buXnXr19nAFh+fn5TD61e+/fvZ23btmUaGhqsV69eLCQkhOXm5nLLAbCpU6fy1unZsyebNm1ardtcuXIl6969OzetSL4pnbckJiYyHR0dXnhp9d13r8OFCxcYAHbgwAHefH19fe5ZmjdvHmOs+pzNmjWLF6450gdJepqWlsbatm3LVq1a1azHWBvpdENdXZ0BYMrKymz//v2MMfl5n0gkYosWLWKMMRYdHc2sra3ZixcvZLatyLVuapnjyJEjDIDC6WRDKJJ2yLNv3z6mr6/PTSuSFkrvKysriwFg5eXltcbLzMyMVVZWcvNGjhzJfHx8FD20JpGOa33XUKJmmbXmuX3b74W6KFI2WrNmDTMzM+OmfX19maGhIXv+/DkvnOR5mzdvHjMyMmLXrl3jLW/JtLSu/OThw4dMWVmZXb58mVVVVTE9PT0WFRXF5QM7duxgJiYm3LY6d+7Mdu3axdt+REQEc3Z2ZoxVp582NjasqqqKW/78+XMmEAjYsWPHGGOMGRkZsRUrVnDLX758yT744INa76uWznt69erFvv32Wy6u7dq1kylfHDp0qN7tyCvPDx48mBfGx8eHDRkyhJsGwA4ePMgYY2zjxo1MW1ubVVRUNPGI6qdo3qpoHtmqVSv2559/8sLUrDuQvLts2bKFm7d7926moaFRZ1zre0+qqqpi7du3Zxs2bFDw6P+fr68vU1NTYwDYqVOn6g3f2DykoflCzXQ1JCSE2dra8sIEBwfLlH1rqlmf0BjUkgxAv379kJOTw/uTHuze3t6e+19TUxM6OjpcU8DCwkL06NGDt72PPvpI4X3r6enBz88P7u7u8PDwwHfffSfT9cfc3Bza2trctJGRkUxTxLoYGBhwzWjj4uIwbNgwtGvXjhemuLgYo0ePhoWFBXR0dLgm2DVrsKWtW7cO3bt3h4GBAbS0tPDDDz/wwvv5+aGkpAQXLlwAUN00c9SoUVzN/atXrxAREQE7Ozvo6elBS0sLx44dq3OfWVlZ8PDwgKmpKbS1teHq6io3ntLXzNDQkOuWIz2vIeewLvLuH+nm2/LUdU1LS0vx4sUL9OzZk1uup6dXa9P3d4FYLMacOXMgFAqhq6sLLS0t5Ofn13vdAHAtAKTnNde1qykpKQlaWlrQ0NDAkCFD4OPjAz8/P6iqqvKuh76+PmxsbHitW9TU1Hjxb4rCwkKZdKTmdG5uLr755hvu11QtLS3u1/N///0Xubm5EIvF0NfX54UpKyvjmnh//fXXmDRpEtzc3LBs2TJe0+/c3FzEx8fz1nV3d0dVVRXKysoUPpaaz0dMTAwAoHv37jLHc+bMGd7+unTpAgC8eNU8x9LPTk5ODj744ANYW1vXGp/WrVujc+fOctd/GzDGmtRKJz8/H8+fP5f7K7806fNoZGQE4PU9V9K8vb3x+++/4/Dhwxg8eDBSUlLg6OjI+2XR2dmZt46zszPvWduzZw969+6NDh06QEtLC6GhoTJpSUPyzYEDB8LMzAwWFhYYP348du7cKdMdv6777k26dOkScnJy0LVrV17rCScnJ1645kgfgOq8deDAgVi4cCGCgoLe2HFK0o2LFy/C19cXEydOhLe3t0Lrjhw5Ek+fPoWFhQUCAgJw8OBBrpWkItdaWmPKHG/yeZImnXacPHkSAwYMgImJCbS1tTF+/HhUVFTwjrUhaaFIJMKAAQNgZ2eHkSNHYtOmTXj48CEvTNeuXXmtdt+2tLW+Mmt93qV7obHs7OzkttiMjo7Gpk2bcP78eXTt2rXW9d90WlpXfqKrqwuRSISUlBTk5eVBTU0NkydPRnZ2NsRiMc6ePctdw3/++QelpaXw9/fnpYdLlizh0sPc3FyUlJRAW1ubW66np4dnz56htLQUjx49wt27d3llRVVVVZm0uSXPl7TCwkJcunQJo0eP5uLq4+ODLVu28MLVjL+i5fn68nFpOTk5cHBwaNHx2mrmrYrmkWZmZnLHiFbkfebZs2d4/PgxgMa9J0m6VDf2frC3t4e5uTkWLVrE60oJNF8eUl++UF+6mp+fz3umANl7qzH1CYqgSjJUV3xZWlry/qQf1JoD4CopKTVrE9C4uDhkZGSgV69e2LNnD6ytrbmKpeba/xdffIH4+Hhs27ZNbtNvDw8P/PXXX9i0aRMuXrzI9QOXNCGuKSEhAXPmzIG/vz+OHz+OnJwcTJw4kRe+ffv28PDwQFxcHP744w8kJyfz9r1y5Up89913CA4OxpkzZ5CTkwN3d/da9/nPP//A3d0dOjo62LlzJy5fvsw1Y665jvQ5k3wFSlpzXkN594+JiUmd67zue+ptM2fOHBw8eBCRkZFITU1FTk4O7Ozs6r1utc17XedK8mJWXFyMp0+fcuMLKkIgECgUVlm5OtllUk3aGzNui1gsRnh4OK8CKi8vD8XFxdDQ0IBYLIaRkZFMBW5hYSHmzp0LoLqL1vXr1zFs2DCcPn0atra23DMlFosxZcoU3rq5ubkoLi7mZYr1qfl8SF4aajZzF4vFXHNq6b/i4mJe94O6nh1FPuIgb332hrrvKiI/Px+dOnVq9H2i6Ics3uRzVZOGhgYGDhyIsLAwpKenw8/PD4sWLVJo3YyMDIwdOxZDhw5FUlISsrOzsWDBgjrTEqDuNFZbWxtXrlzB7t27YWRkhIULF0IkEvHGDXzTabalpSWUlJRkulFYWFjA0tJS5jrLe56amj4A1T+yffTRR9i9ezdXmH8TJOmGSCTC1q1bcfHiRe7lTVlZWeaZlX42OnbsiMLCQqxfvx4CgQCBgYFwcXHBy5cvFbrWEk0pcwBv7nmSkKQd5eXlGD58OOzt7ZGYmIisrCxuPCDpeDckLVRRUcGJEyeQnJwMW1tbrF27FjY2NrwfTN7mco0iZda6vGv3Qk31PTMStXU/69OnD169elVv99SWSEvryk/69u2LlJQUrkJMT08PQqEQ58+f51WSSSoINm3axEsPr127xr2PicVidO/eXSbNLCoqwpgxYxoV95bMe7Zs2YLKykoYGxtDVVUVqqqq2LBhAxITE/Ho0SMuXM17QtHyfEO8jo+b1UbRvFXRPLK2Z6ah7zONeU+SbKex94OJiQlSUlLw22+/YfDgwdy4rc2Zh9QV34akq3VpaH2CoqiSrIlsbGx4A10DwOXLlxu8HQcHB4SEhCA9PR3dunXDrl27miuKAMD1mZf0qZdWUVGBwsJChIaGYsCAARAKhTK/ENaUlpaGXr16ITAwEA4ODrC0tJQ7CwRfewAAFZJJREFU0P+kSZOwZ88e/PDDD+jcuTN69+7N24anpyfGjRsHkUgECwsLFBUV1brPgoICVFRUYNmyZejTpw+6dOnyzvw61xCdO3dGq1ateANWPnz4sM5z87ZLS0uDn58fPvvsM9jZ2aFDhw4yAy++DSQvZqamptwXfIRCISorK3nXQ/LM2Nra1rk9NTU1bjwMCckvTtItRqXHMQCq05Wa6UjNaUdHRxQWFspU0FpaWkJZWRmOjo64d+8eVFVVZZZLtyS1trbG7Nmzcfz4cXh5eXHjFzo6OuLGjRtyt/86vt7j6OiI69evw9zcXGZ/tRVCarK3t8evv/76zj4rp0+fRl5eHry9vRW6TyTXQfoes7KygkAg4AZVfhfY2tryBgCX/pFIMi0UCgEA6enpMDMzw4IFC+Dk5AQrKyuZD900hqqqKtzc3LBixQpcvXoV5eXlOH36dJO321j6+voYOHAgvv/++wZ/LANovvRBIBAgKSkJGhoacHd3V/ijQc1JWVkZ//3vfxEaGoqnT5/CwMCA91w8fvxYpnWrQCCAh4cHYmJikJKSgoyMDOTl5QFQ/Fq/S2UO6bQjKysLVVVViI6Oxscffwxra2v8/vvvTd6HkpISevfujfDwcGRnZ0NNTa3O8ZZairx8V9Eya23epXtBHgMDA9y7d4/3AlszP6nLRx99hOTkZERGRmLVqlV1hm3ptFQ6P5GMS3bq1Clu7LG+ffti9+7dKCoq4uYZGhrC2NgYN2/elEkPO3XqBKA6TS0uLkb79u1lwrRp0wZt2rSBkZERr6xYWVmJrKysOuPbEuersrIS27dvR3R0tMwPocbGxnUOeq5oeb6ufLwme3t75OTkyB0Ps7kpmrcqmkc2l5Z6TzIzM8PZs2dx7949rqLsdeUhNSmSrgqFQpmxxWreWw2tT1AUVZIBeP78Oe7du8f7k/6iWl2mTJmCgoICBAcHo6ioCHv37uW6jSjSqqSsrAwhISHIyMjArVu3cPz4cRQXF9eakDSWiooK8vPzcePGDV6zR6D6y2j6+vr44YcfUFJSgtOnT+Prr7+uc3tWVlbIzMzEsWPHUFRUhLCwMLmVg5Ia4iVLlsh8LMDKygonTpxAeno68vPzMWXKFPzxxx+17tPU1BRqampYu3Ytbt68icOHDyMiIqIBZ+HdoKWlBX9/f8ydOxenT5/GtWvX4Ofnx7UseRdZWVlxg1rm5uZizJgxLf7LqqKsrKzg6emJgIAAnD9/Hrm5uRg3bhxMTEzg6elZ57rm5ua4evUqCgsL8eDBA7x8+RKWlpbo2LEjFi9ejOLiYhw5ckTm66Vffvkljh49itWrV6O4uBgbN25EcnIyL01ZuHAhtm/fjvDwcFy/fh35+flISEhAaGgoAMDNzQ3Ozs4YMWIEjh8/jvLycqSnp2PBggXIzMzE06dPMWPGDKSkpODWrVtIS0vD5cuXubQnODgY6enpmDFjBtei66effmq2gftrmj59Ov766y+MHj0aly9fRmlpKY4dO4aJEyfKvPDUxtXVFS4uLvD29saJEydQVlaG5ORk3tes3haSfOe3337DlStXEBkZCU9PTwwfPhwTJkyAQCDAxx9/zA2Ye/bsWe7aSpiZmUFJSQlJSUm4f/8+xGIxNDQ0EBwcjHnz5mH79u0oLS3FhQsXZLpQtISKigr0798fO3bswNWrV1FWVoZ9+/ZhxYoVvGdp37592Lp1K4qKirBo0SJcunSJu++srKxw+/ZtJCQkoLS0FDExMU1+UU9KSkJMTAxycnJw69YtbN++HVVVVS3exX39+vWorKyEk5MT9uzZg/z8fBQWFmLHjh0oKCiQyculNTV9kKapqYkjR45AVVUVQ4YMkemW8SaMHDkSKioqWLduHfr3748ff/wRqampyMvLg6+vL+9cxMfHY8uWLbh27Rpu3ryJHTt2QCAQwMzMrEHX+m0tc9SXdlhaWuLly5dcvH/88UfExsY2aZ8XL15EZGQkMjMzcfv2bRw4cAD3799v9rJqczA3N8fFixdRXl6OBw8eoKqqSuEya23e1ntBUX379sX9+/exYsUKlJaWYt26dUhOTm7QNnr16oWjR48iPDy81i+rv8m0VJH8xMXFBU+ePEFSUhKvkmznzp0wMjLiDc0QHh6OqKgoxMTEoKioCHl5eYiLi8Pq1asBVH/oqF27dvD09ERqairKysqQkpKCr776Cr/++isAYObMmVi2bBkOHTqEgoICBAYGym2l2hLnq+Z+Hz58CH9/f3Tr1o335+3tXWd5QdHyfFpaGlasWIGioiKsW7cO+/btw8yZM+Vuc/To0ejQoQNGjBiBtLQ03Lx5E4mJia/tq9uK5K0NySObQ0u+J3Xs2BEpKSn4888/4e7u/lryEHkUSVenTp2K4uJizJ07F4WFhdi1a5fMB2EaWp+gqHf3rbsZ/fLLLzAyMuL91fxUcm06deqE/fv348CBA7C3t8eGDRu4r1uqq6vXu37r1q1RUFAAb29vWFtbY/LkyZg+fTqmTJnSpGOSR0dHBzo6OjLzlZWVkZCQgKysLHTr1g2zZ8/GypUr69zWlClT4OXlBR8fH/Ts2RMVFRUIDAyUu20/Pz+8evUKEyZM4C0LDQ2Fo6Mj3N3d0bdvXy6BrI2BgQHi4+Oxb98+2NraYtmyZfX+ovWuWrlyJfr06QMPDw+4ubnhk08+kRnD6V2yevVqtG3bFr169YKHhwfc3d3h6OjY0tFSWFxcHLp3747hw4fD2dkZjDEcPXpUphlxTQEBAbCxsYGTkxMMDAyQlpaGVq1aYffu3SgoKIC9vT2WL1+OJUuW8Nbr3bs3YmNjsXr1aohEIvzyyy+YPXs293lpoLoCOikpCcePH0ePHj3w8ccfY82aNTAzMwNQXUl/9OhRuLi4YOLEibC2tsZ//vMf3Lp1C4aGhlBRUUFFRQUmTJgAa2trjBo1CkOGDEF4eDiA6l/2zp49i6KiIvTp0wcODg5YuHAhjI2Nm/nsVjM2NkZaWhpevXqFQYMGwc7ODrNmzYKurm6DKogTExPRo0cPjB49Gra2tpg3b57ClWxvkiTfMTc3x+DBg3HmzBnExMTgp59+4l74t27disrKSnTv3h2zZs2SuU9MTEwQHh6O+fPnw9DQkKtICgsLQ1BQEBYuXAihUAgfH5+3otWDlpYWevbsiTVr1sDFxQXdunVDWFgYAgIC8P3333PhwsPDkZCQAHt7e2zfvh27d+/mWm1++umnmD17NmbMmIEPP/wQ6enpCAsLa1K8dHV1ceDAAfTv3x9CoRCxsbHYvXt3nWPvvAmdO3dGdnY23NzcEBISApFIBCcnJ6xduxZz5syp8yW9qelDTVpaWkhOTgZjDMOGDWtU67amUFVVxYwZM7iv67m6umL48OEYNmwYRowYwesCrquri02bNqF3796wt7fHyZMn8fPPP0NfX79B1/ptLXPUl3aIRCKsXr0ay5cvR7du3bBz505ERUU1aZ86Ojo4d+4chg4dCmtra4SGhiI6OhpDhgxppqNqPnPmzIGKigpsbW1hYGCA27dvK1xmrc3bei8oSigUYv369Vi3bh1EIhEuXbrUqK86f/LJJzhy5AhCQ0PlfiH+TaaliuQnbdu2hZ2dHQwMDLgxTl1cXFBVVcV1tZSYNGkSNm/ejLi4ONjZ2cHV1RXx8fFcS7LWrVvj3LlzMDU1hZeXF4RCIfz9/fHs2TPu3SooKAjjx4+Hr68vnJ2doa2tzftiYEueL2lbtmyBm5sb2rRpI7PM29sbmZmZuHr1qtx1FS3PBwUFITMzEw4ODliyZAlWr14t05NJQk1NDcePH0f79u0xdOhQ2NnZyXyZvDkpkrc2NI9sqpZ+T/rggw+QkpKCBw8eYOrUqVi8eHGz5iHyKJKumpqaIjExEYcOHYJIJEJsbCwiIyN5YRpan6AoJfY2DcbyP2Lp0qWIjY3FnTt3WjoqbwV/f3/cv38fhw8fbumoEPLOCggIQEFBAVJTU1s6KoS8NkpKSjh48GCzFHAIIYQQQghpKNWWjsD/gvXr16NHjx7Q19dHWloaVq5c+dq6JL1LHj16hLy8POzatYsqyAhpoFWrVmHgwIHQ1NREcnIytm3bhvXr17d0tAghhBBCCCHkfxZVkjWD4uJiLFmyBH/99RdMTU0RFBSEkJCQlo5Wi/P09MSlS5cwdepUDBw4sKWjQ8g75dKlS1ixYgWePHkCCwsLxMTEYNKkSS0dLUIIIYQQQgj5n0XdLQkhhBBCCCGEEELIe48G7ieEEEIIIYQQQggh7z2qJCOEEEIIIYQQQggh7z2qJCOEEEIIIYQQQggh7z2qJCOEEEIIIYQQQggh7z2qJCOEEEIIIYQQQggh7z2qJCOEEEIIeYP8/PwwYsQIbrpv376YNWvWG49HSkoKlJSU8Pfff9caRklJCYcOHVJ4m4sXL8aHH37YpHiVl5dDSUkJOTk5TdoOIYQQQkhDUSUZIYQQQt57fn5+UFJSgpKSEtTU1GBpaYlvvvkGlZWVr33fBw4cQEREhEJhFanYIoQQQgghjaPa0hEghBBCCHkbDB48GHFxcXj+/DmOHj2K6dOno1WrVggJCZEJ++LFC6ipqTXLfvX09JplO4QQQgghpGmoJRkhhBBCCAB1dXV06NABZmZmmDZtGtzc3HD48GEA/99FcunSpTA2NoaNjQ0A4M6dOxg1ahR0dXWhp6cHT09PlJeXc9t89eoVvv76a+jq6kJfXx/z5s0DY4y335rdLZ8/f47g4GB07NgR6urqsLS0xJYtW1BeXo5+/foBANq2bQslJSX4+fkBAKqqqhAVFYVOnTpBIBBAJBJh//79vP0cPXoU1tbWEAgE6NevHy+eigoODoa1tTVat24NCwsLhIWF4eXLlzLhNm7ciI4dO6J169YYNWoUHj16xFu+efNmCIVCaGhooEuXLli/fn2t+3z48CHGjh0LAwMDCAQCWFlZIS4ursFxJ4QQQgipD7UkI4QQQgiRQyAQoKKigps+deoUdHR0cOLECQDAy5cv4e7uDmdnZ6SmpkJVVRVLlizB4MGDcfXqVaipqSE6Ohrx8fHYunUrhEIhoqOjcfDgQfTv37/W/U6YMAEZGRmIiYmBSCRCWVkZHjx4gI4dOyIxMRHe3t4oLCyEjo4OBAIBACAqKgo7duxAbGwsrKyscO7cOYwbNw4GBgZwdXXFnTt34OXlhenTp2Py5MnIzMxEUFBQg8+JtrY24uPjYWxsjLy8PAQEBEBbWxvz5s3jwpSUlGDv3r34+eef8fjxY/j7+yMwMBA7d+4EAOzcuRMLFy7E999/DwcHB2RnZyMgIACamprw9fWV2WdYWBhu3LiB5ORktGvXDiUlJXj69GmD404IIYQQUh+qJCOEEEIIkcIYw6lTp3Ds2DF8+eWX3HxNTU1s3ryZ62a5Y8cOVFVVYfPmzVBSUgIAxMXFQVdXFykpKRg0aBC+/fZbhISEwMvLCwAQGxuLY8eO1brvoqIi7N27FydOnICbmxsAwMLCglsu6ZrZvn176OrqAqhueRYZGYmTJ0/C2dmZW+f8+fPYuHEjXF1dsWHDBnTu3BnR0dEAABsbG+Tl5WH58uUNOjehoaHc/+bm5pgzZw4SEhJ4lWTPnj3D9u3bYWJiAgBYu3Ythg0bhujoaHTo0AGLFi1CdHQ0d046deqEGzduYOPGjXIryW7fvg0HBwc4OTlx+yWEEEIIeR2okowQQgghBEBSUhK0tLTw8uVLVFVVYcyYMVi8eDG33M7OjjcOWW5uLkpKSqCtrc3bzrNnz1BaWopHjx7h7t276NmzJ7dMVVUVTk5OMl0uJXJycqCiogJXV1eF411SUoJ///0XAwcO5M1/8eIFHBwcAAD5+fm8eADgKtQaYs+ePYiJiUFpaSnEYjEqKyuho6PDC2NqaspVkEn2U1VVhcLCQmhra6O0tBT+/v4ICAjgwlRWVqJNmzZy9zlt2jR4e3vjypUrGDRoEEaMGIFevXo1OO6EEEIIIfWhSjJCCCGEEAD9+vXDhg0boKamBmNjY6iq8otJmpqavGmxWIzu3btz3QilGRgYNCoOku6TDSEWiwEAR44c4VVOAdXjrDWXjIwMjB07FuHh4XB3d0ebNm2QkJDAtU5rSFw3bdokU2mnoqIid50hQ4bg1q1bOHr0KE6cOIEBAwZg+vTpWLVqVeMPhhBCCCFEDqokI4QQQghBdSWYpaWlwuEdHR2xZ88etG/fXqY1lYSRkREuXrwIFxcXANUtprKysuDo6Cg3vJ2dHaqqqnD27Fmuu6U0SUu2V69ecfNsbW2hrq6O27dv19oCTSgUch8hkLhw4UL9ByklPT0dZmZmWLBgATfv1q1bMuFu376N33//HcbGxtx+lJWVYWNjA0NDQxgbG+PmzZsYO3aswvs2MDCAr68vfH190adPH8ydO5cqyQghhBDS7OjrloQQQgghjTB27Fi0a9cOnp6eSE1NRVlZGVJSUvDVV1/h119/BQDMnDkTy5Ytw6FDh1BQUIDAwED8/ffftW7T3Nwcvr6++OKLL3Do0CFum3v37gUAmJmZQUlJCUlJSbh//z7EYjG0tbUxZ84czJ49G9u2bUNpaSmuXLmCtWvXYtu2bQCAqVOnori4GHPnzkVhYSF27dqF+Pj4Bh2vlZUVbt++jYSEBJSWliImJgYHDx6UCaehoQFfX1/k5uYiNTUVX331FUaNGoUOHToAAMLDwxEVFYWYmBgUFRUhLy8PcXFxWL16tdz9Lly4ED/99BNKSkpw/fp1JCUlQSgUNijuhBBCCCGKoEoyQgghhJBGaN26Nc6dOwdTU1N4eXlBKBTC398fz54941qWBQUFYfz48fD19YWzszO0tbXx2Wef1bndDRs24PPPP0dgYCC6dOmCgIAA/PPPPwAAExMThIeHY/78+TA0NMSMGTMAABEREQgLC0NUVBSEQiEGDx6MI0eOoFOnTgCqxwlLTEzEoUOHIBKJEBsbi8jIyAYd76efforZs2djxowZ+PDDD5Geno6wsDCZcJaWlvDy8sLQoUMxaNAg2NvbY/369dzySZMmYfPmzYiLi4OdnR1cXV0RHx/PxbUmNTU1hISEwN7eHi4uLlBRUUFCQkKD4k4IIYQQogglVtvIsYQQQgghhBBCCCGEvCeoJRkhhBBCCCGEEEIIee9RJRkhhBBCCCGEEEIIee9RJRkhhBBCCCGEEEIIee9RJRkhhBBCCCGEEEIIee9RJRkhhBBCCCGEEEIIee9RJRkhhBBCCCGEEEIIee9RJRkhhBBCCCGEEEIIee9RJRkhhBBCCCGEEEIIee9RJRkhhBBCCCGEEEIIee9RJRkhhBBCCCGEEEIIee9RJRkhhBBCCCGEEEIIee/9H6REVV3cMORRAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Вторая модель - это ***TF-IDF + LogisticRegression***"
      ],
      "metadata": {
        "id": "0kpYCIUIBBAK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Подправим немного тестовую и тренировочную выборки\n",
        "x_train, x_test, y_train, y_test = train_test_split(X,y, random_state=42)"
      ],
      "metadata": {
        "id": "N3IlMNgkBv9b"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Воспользуемся TF-IDF метрикой\n",
        "vectorizer = TfidfVectorizer(ngram_range=(1,3), analyzer='char')"
      ],
      "metadata": {
        "id": "19xC2qQuCqOu"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# используем pipeline, так как она позволяем последовательно применить несколько transforms, чтобы получить в итоге один final estimator\n",
        "model = pipeline.Pipeline([\n",
        "    ('vectorizer', vectorizer),\n",
        "    ('clf', LogisticRegression())\n",
        "])"
      ],
      "metadata": {
        "id": "LtZfyrWeCzrT"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# обучаем модель\n",
        "model.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "RZef-BAdDRw8",
        "outputId": "e39bffb2-296a-45b6-b48a-3eed79e30270"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('vectorizer',\n",
              "                 TfidfVectorizer(analyzer='char', ngram_range=(1, 3))),\n",
              "                ('clf', LogisticRegression())])"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;vectorizer&#x27;,\n",
              "                 TfidfVectorizer(analyzer=&#x27;char&#x27;, ngram_range=(1, 3))),\n",
              "                (&#x27;clf&#x27;, LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;vectorizer&#x27;,\n",
              "                 TfidfVectorizer(analyzer=&#x27;char&#x27;, ngram_range=(1, 3))),\n",
              "                (&#x27;clf&#x27;, LogisticRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer(analyzer=&#x27;char&#x27;, ngram_range=(1, 3))</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# предсказываем\n",
        "y_pred = model.predict(x_test)"
      ],
      "metadata": {
        "id": "rTVDu13CDVbE"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ac2 = accuracy_score(y_test, y_pred)\n",
        "cr2 = classification_report(y_test, y_pred)"
      ],
      "metadata": {
        "id": "sQ-4a23FOKJl"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy is \", ac2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FCpCHrSfOHn9",
        "outputId": "1a634bd3-eaa7-48d6-bbd6-a14e6c19ee89"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy is  0.8499032882011606\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(cr2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "abwCb1OPRAay",
        "outputId": "4f5d4389-325d-4ca5-9d77-d41ca043b0af"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "      Arabic       1.00      0.99      1.00       134\n",
            "      Danish       0.90      0.68      0.78        97\n",
            "       Dutch       0.32      0.96      0.48       139\n",
            "     English       1.00      1.00      1.00       364\n",
            "      French       0.88      0.79      0.83       269\n",
            "      German       0.81      0.41      0.54       116\n",
            "       Greek       1.00      1.00      1.00        86\n",
            "       Hindi       1.00      1.00      1.00        13\n",
            "     Italian       0.88      0.43      0.58       180\n",
            "     Kannada       1.00      1.00      1.00        78\n",
            "   Malayalam       1.00      1.00      1.00       158\n",
            "  Portugeese       0.96      0.77      0.85       170\n",
            "     Russian       1.00      1.00      1.00       171\n",
            "     Spanish       0.88      0.83      0.86       206\n",
            "    Sweedish       0.87      0.85      0.86       162\n",
            "       Tamil       1.00      1.00      1.00       114\n",
            "     Turkish       1.00      0.82      0.90       128\n",
            "\n",
            "    accuracy                           0.85      2585\n",
            "   macro avg       0.91      0.85      0.86      2585\n",
            "weighted avg       0.91      0.85      0.86      2585\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Анализ ошибок**"
      ],
      "metadata": {
        "id": "p2g7UZpgNG5b"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Давайте посмотрим на Confusion Matrix в этом случае"
      ],
      "metadata": {
        "id": "ApgCQGf_NMIT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cm2 = confusion_matrix(y_test, y_pred)"
      ],
      "metadata": {
        "id": "RSA6bLjuNS7p"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,15))\n",
        "ax= plt.subplot()\n",
        "sns.heatmap(cm2, annot=True, fmt='g', ax=ax,cbar=False,cmap='RdBu')\n",
        "ax.set_xlabel('Predicted labels')\n",
        "ax.set_ylabel('True labels')\n",
        "ax.set_title('Confusion Matrix')\n",
        "ax.xaxis.set_ticklabels(d.keys())\n",
        "ax.yaxis.set_ticklabels(d.keys())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "o0SSM3NlrfxL",
        "outputId": "f631d8c3-0de6-4087-b9f0-667251e702de"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0.5, 'English'),\n",
              " Text(0, 1.5, 'Malayalam'),\n",
              " Text(0, 2.5, 'Hindi'),\n",
              " Text(0, 3.5, 'Tamil'),\n",
              " Text(0, 4.5, 'Portugeese'),\n",
              " Text(0, 5.5, 'French'),\n",
              " Text(0, 6.5, 'Dutch'),\n",
              " Text(0, 7.5, 'Spanish'),\n",
              " Text(0, 8.5, 'Greek'),\n",
              " Text(0, 9.5, 'Russian'),\n",
              " Text(0, 10.5, 'Danish'),\n",
              " Text(0, 11.5, 'Italian'),\n",
              " Text(0, 12.5, 'Turkish'),\n",
              " Text(0, 13.5, 'Sweedish'),\n",
              " Text(0, 14.5, 'Arabic'),\n",
              " Text(0, 15.5, 'German'),\n",
              " Text(0, 16.5, 'Kannada')]"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Тут мы наблюдаем совершенно другую картину и понятно, что путаница возникает из-за предобработки текста.\n",
        "\n",
        "\n",
        "Не могу до конца понять, почему так, но предобработка текста портит точность предсказаний и это факт."
      ],
      "metadata": {
        "id": "prqzOkpir7OW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Третья модель - **keras-model** с алгоритмом **back propagation**."
      ],
      "metadata": {
        "id": "8YUiKuY0D1wa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Эта модель будет самой умной из всех, что были до этого. Здесь уже действительно надо будет придумывать архитектуру нейросети именно с нуля, то есть думать насчет того, сколько скрытых слоёв будет и какими они будут.\n",
        "\n",
        "Мы так же будет пользоваться эпохами, чтобы улучшить качество нашей нейросети за счет алгоритма обратного распространения ошибки (back propagation)"
      ],
      "metadata": {
        "id": "OMxWiNSRPpIY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Архитектура"
      ],
      "metadata": {
        "id": "jCbsOIhhQfMB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ну тут я не стал долго думать, потому что задача, которую мы будем решать не настолько сложна, чтобы тратить на неё очень много слоёв. Думаю, что хватит и 4 последовательных слоёв. (так как они последовательные, то буду использовать Sequential model)\n",
        "\n",
        "Какими будут эти слои? Я решил попробовать сделать их полносвязными с прямым распространением, то есть обычный densely-connected NN layer.\n",
        "\n",
        "Помимо всего этого, применим сглаживание входных данных, чтбобы не париться с тем, что у нас входные данные могут не поместиться по размерности во входной слой. (вот картинка)"
      ],
      "metadata": {
        "id": "vu9PVgfRQk8Z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![1_OAWmMNK9_UjcDi-RaOO1dw.webp](data:image/webp;base64,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)"
      ],
      "metadata": {
        "id": "HkSbWPAof13r"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Функция активации нейрона**"
      ],
      "metadata": {
        "id": "xZRojvOPUWiY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Тут самый простой вариант - это функция вида $f(x) = max(feature, 0)$.\n",
        "\n",
        "Благо такая функция уже вшита в tensorflow и называется tf.nn.relu."
      ],
      "metadata": {
        "id": "tSMmr6HoUd1B"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Размерности слоёв**"
      ],
      "metadata": {
        "id": "8RLQccrWU4WS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "На этом моменте я должен был перебирать размерности слоёв, пока не получил бы лучшую оценку качества, но, к сожалению, я перебирал очень долго, чтобы указывать все мои попытки, поэтому приведу финальный вариант, к которому я пришел.\n",
        "\n",
        "\n",
        "Размерности слоёв:\n",
        "\n",
        "\n",
        "1.   512\n",
        "2.   256\n",
        "3. 128\n",
        "4. 17\n",
        "\n"
      ],
      "metadata": {
        "id": "ibHg8PHGU79C"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Итого имеем:"
      ],
      "metadata": {
        "id": "67-JsTrMcL2o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.models.Sequential([tf.keras.layers.Flatten(),\n",
        "                                    tf.keras.layers.Dense(512, activation=tf.nn.relu),\n",
        "                                    tf.keras.layers.Dense(256, activation=tf.nn.relu),\n",
        "                                    tf.keras.layers.Dense(128, activation=tf.nn.relu),\n",
        "                                    tf.keras.layers.Dense(17, activation=tf.nn.softmax)\n",
        "                                   ])"
      ],
      "metadata": {
        "id": "pG5QyBI7Tb8_"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Выборки"
      ],
      "metadata": {
        "id": "ck5Osc9Rrksg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Тут воспользуемся метрикой $TF-IDF$"
      ],
      "metadata": {
        "id": "J778wpgbrolU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tfid=TfidfVectorizer()\n",
        "PipelineModel = Pipeline([('tfid',tfid)])\n",
        "X1 = PipelineModel.fit_transform(dataset[\"Text\"])\n",
        "X1 = pd.DataFrame.sparse.from_spmatrix(X1)"
      ],
      "metadata": {
        "id": "WeLA2_6FrwK2"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "1qid8h5msUYu",
        "outputId": "df69b881-1a4c-44f6-8ead-d32d6db143cd"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       0      1      2      3      4      5      6      7      8      9      \\\n",
              "0        0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "1        0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "2        0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "3        0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "4        0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "...      ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
              "10332    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "10333    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "10334    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "10335    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "10336    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "\n",
              "       ...  39918  39919  39920  39921  39922  39923  39924  39925  39926  \\\n",
              "0      ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "1      ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "2      ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "3      ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "4      ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "...    ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
              "10332  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "10333  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "10334  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "10335  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "10336  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "\n",
              "       39927  \n",
              "0        0.0  \n",
              "1        0.0  \n",
              "2        0.0  \n",
              "3        0.0  \n",
              "4        0.0  \n",
              "...      ...  \n",
              "10332    0.0  \n",
              "10333    0.0  \n",
              "10334    0.0  \n",
              "10335    0.0  \n",
              "10336    0.0  \n",
              "\n",
              "[10337 rows x 39928 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-79d60186-da5d-436d-9e7b-64c3fe0faec2\">\n",
              "    <div 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>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>...</th>\n",
              "      <th>39918</th>\n",
              "      <th>39919</th>\n",
              "      <th>39920</th>\n",
              "      <th>39921</th>\n",
              "      <th>39922</th>\n",
              "      <th>39923</th>\n",
              "      <th>39924</th>\n",
              "      <th>39925</th>\n",
              "      <th>39926</th>\n",
              "      <th>39927</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</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",
              "      <td>...</td>\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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10332</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10333</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10334</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10335</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10336</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10337 rows × 39928 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-79d60186-da5d-436d-9e7b-64c3fe0faec2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-661cad3d-8952-4095-8c24-a21d27439276\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-661cad3d-8952-4095-8c24-a21d27439276')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-661cad3d-8952-4095-8c24-a21d27439276 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-79d60186-da5d-436d-9e7b-64c3fe0faec2 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-79d60186-da5d-436d-9e7b-64c3fe0faec2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Это мы сделали X выборку, а теперь надо подготовить y выборку. Тут можно воспользоваться LabelEncoder, но мне слишком лень читать к нему мануал, поэтому сделаем что-то свое."
      ],
      "metadata": {
        "id": "bm47drzt5BIi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "d={}\n",
        "i=0\n",
        "for word in pd.unique(y):\n",
        "    d[word]=i\n",
        "    i+=1\n",
        "d"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tY51GbiuswSq",
        "outputId": "83b24aaf-50fd-44e0-c006-344c031ce7ff"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'English': 0,\n",
              " 'Malayalam': 1,\n",
              " 'Hindi': 2,\n",
              " 'Tamil': 3,\n",
              " 'Portugeese': 4,\n",
              " 'French': 5,\n",
              " 'Dutch': 6,\n",
              " 'Spanish': 7,\n",
              " 'Greek': 8,\n",
              " 'Russian': 9,\n",
              " 'Danish': 10,\n",
              " 'Italian': 11,\n",
              " 'Turkish': 12,\n",
              " 'Sweedish': 13,\n",
              " 'Arabic': 14,\n",
              " 'German': 15,\n",
              " 'Kannada': 16}"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Мы написали функцию для кодирования языка, потому что fit в keras не умеет работать со строковыми данными, однако, когда мы будем угадывать язык, то надо выводить уже строчку - название языка.\n",
        "\n",
        "Так что надо написать функцию декодер."
      ],
      "metadata": {
        "id": "Mox8l-dGGfLv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_lang(n):\n",
        "  for lang,num in d.items():\n",
        "    if (num == n):\n",
        "      return lang;"
      ],
      "metadata": {
        "id": "T1xuON5QGeJL"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "label=[]\n",
        "for word in y:\n",
        "    label.append(d[word])\n",
        "label=pd.DataFrame(label,columns=['langauge'])\n",
        "label"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "9cc3SrpFtbw9",
        "outputId": "248d6dbd-0901-4ba0-f6b0-c3735da84dbd"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       langauge\n",
              "0             0\n",
              "1             0\n",
              "2             0\n",
              "3             0\n",
              "4             0\n",
              "...         ...\n",
              "10332        16\n",
              "10333        16\n",
              "10334        16\n",
              "10335        16\n",
              "10336        16\n",
              "\n",
              "[10337 rows x 1 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-11c9c9a8-3f3e-4209-ac24-d0060325990b\">\n",
              "    <div 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>langauge</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10332</th>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10333</th>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10334</th>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10335</th>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10336</th>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10337 rows × 1 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-11c9c9a8-3f3e-4209-ac24-d0060325990b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-2e10ee6e-d3c5-4cb1-b3da-60125ed68d68\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2e10ee6e-d3c5-4cb1-b3da-60125ed68d68')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-2e10ee6e-d3c5-4cb1-b3da-60125ed68d68 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-11c9c9a8-3f3e-4209-ac24-d0060325990b 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-11c9c9a8-3f3e-4209-ac24-d0060325990b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X1, label, test_size=0.1, random_state=33)"
      ],
      "metadata": {
        "id": "U85T0YEussm2"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Обучаем"
      ],
      "metadata": {
        "id": "WtDSYGNEglpg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Чтобы обучить модель с помощью метода fit, в keras надо задать несколько параметров в функции compile().\n",
        "\n",
        "А именно:\n",
        "\n",
        "\n",
        "\n",
        "1.   Оптимизатор - воспользуемся алгоритмом Адама\n",
        "2.   Функция потерь - воспользуемся кросс-энтропией, только будем возвращать индекс самой подходящей категории а не вектор из энтропий каждой категории, то есть это будет sparse_categorical_crossentropy\n",
        "3.   Метрика для мониторинга (опционально) - я буду использовать accuracy\n",
        "\n"
      ],
      "metadata": {
        "id": "Ry54cqhkgq_K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "UgWNH8lPqiO3"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train, y_train, epochs=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hXZXSBm8qnqR",
        "outputId": "4a7fca2a-42a5-4e72-9b1c-079071d6c1d1"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/2\n",
            "291/291 [==============================] - 56s 189ms/step - loss: 0.5843 - accuracy: 0.8463\n",
            "Epoch 2/2\n",
            "291/291 [==============================] - 56s 191ms/step - loss: 0.0229 - accuracy: 0.9949\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction = model.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iI7DwZbQtLgg",
        "outputId": "7cb847a4-f64d-43bc-becd-a2fd3104ba44"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "33/33 [==============================] - 2s 47ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ac3 = model.evaluate(X_test, y_test)[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5fdUm8un6OBQ",
        "outputId": "7d9f180d-b87a-416d-cd9e-ca89659f2bac"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "33/33 [==============================] - 1s 28ms/step - loss: 0.0891 - accuracy: 0.9739\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# хотим построить y_pred для confusion matrix и classification report\n",
        "y_pred = []\n",
        "for row in prediction:\n",
        "  y_pred.append(np.argmax(row))"
      ],
      "metadata": {
        "id": "P48-r6bWvNun"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cr3 = classification_report(y_test,y_pred)"
      ],
      "metadata": {
        "id": "QK43y5z3vpcD"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy is \", ac3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YaZWNEAQFJWs",
        "outputId": "b6509527-9bb4-49b6-af4b-6ce101d1fec2"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy is  0.9738878011703491\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(cr3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wnpVBE4OtJAp",
        "outputId": "a21468ee-0786-424c-a7c5-2e5973121b0e"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.99      0.99       138\n",
            "           1       1.00      0.95      0.98        62\n",
            "           2       1.00      1.00      1.00         6\n",
            "           3       0.79      1.00      0.89        58\n",
            "           4       0.95      0.96      0.96        79\n",
            "           5       1.00      1.00      1.00       104\n",
            "           6       1.00      1.00      1.00        42\n",
            "           7       0.99      0.93      0.96        75\n",
            "           8       1.00      0.97      0.99        36\n",
            "           9       1.00      0.97      0.98        66\n",
            "          10       0.93      0.95      0.94        42\n",
            "          11       0.97      0.99      0.98        67\n",
            "          12       1.00      0.93      0.97        61\n",
            "          13       1.00      0.95      0.98        64\n",
            "          14       1.00      1.00      1.00        43\n",
            "          15       1.00      0.96      0.98        55\n",
            "          16       1.00      1.00      1.00        36\n",
            "\n",
            "    accuracy                           0.97      1034\n",
            "   macro avg       0.98      0.97      0.98      1034\n",
            "weighted avg       0.98      0.97      0.97      1034\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Анализ ошибок**"
      ],
      "metadata": {
        "id": "tAor4Tftv7WJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Так же постром Confusion Matrix"
      ],
      "metadata": {
        "id": "2c3Q8TLwwAAd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cm3 = confusion_matrix(y_test, y_pred)"
      ],
      "metadata": {
        "id": "EQTORNEywH50"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,15))\n",
        "ax= plt.subplot()\n",
        "sns.heatmap(cm3, annot=True, fmt='g', ax=ax,cbar=False,cmap='RdBu')\n",
        "ax.set_xlabel('Predicted labels')\n",
        "ax.set_ylabel('True labels')\n",
        "ax.set_title('Confusion Matrix')\n",
        "ax.xaxis.set_ticklabels(d.keys())\n",
        "ax.yaxis.set_ticklabels(d.keys())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "RBG9ABMTwDx1",
        "outputId": "77ed4cbe-cf61-4a50-eca3-ccf9df184e08"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0.5, 'English'),\n",
              " Text(0, 1.5, 'Malayalam'),\n",
              " Text(0, 2.5, 'Hindi'),\n",
              " Text(0, 3.5, 'Tamil'),\n",
              " Text(0, 4.5, 'Portugeese'),\n",
              " Text(0, 5.5, 'French'),\n",
              " Text(0, 6.5, 'Dutch'),\n",
              " Text(0, 7.5, 'Spanish'),\n",
              " Text(0, 8.5, 'Greek'),\n",
              " Text(0, 9.5, 'Russian'),\n",
              " Text(0, 10.5, 'Danish'),\n",
              " Text(0, 11.5, 'Italian'),\n",
              " Text(0, 12.5, 'Turkish'),\n",
              " Text(0, 13.5, 'Sweedish'),\n",
              " Text(0, 14.5, 'Arabic'),\n",
              " Text(0, 15.5, 'German'),\n",
              " Text(0, 16.5, 'Kannada')]"
            ]
          },
          "metadata": {},
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Тут мы видим, что большинство языков нейросеть распознала правильно, но иногда путает некоторые языки\n",
        "\n",
        "\n",
        "Решить эту проблеу можно по-разному, но самый простой вариант - это заняться оптимизацией гиперпараметров."
      ],
      "metadata": {
        "id": "622jndD0wpjU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Результаты"
      ],
      "metadata": {
        "id": "qtKk52DBJnJ3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results = pd.DataFrame([ac1, ac2, ac3], index = [\"Model 1\",\n",
        "                                \"Model 2\",\n",
        "                                \"Model 3\"],\n",
        "                       columns=[\"Accuracy\"])\n",
        "results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "jlOhNJTJ64gK",
        "outputId": "af794a38-d24b-4aa8-863e-d5f435e9f5af"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Accuracy\n",
              "Model 1  0.982592\n",
              "Model 2  0.849903\n",
              "Model 3  0.973888"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-8cf686ba-fd22-4725-90d4-1072c56d3d3f\">\n",
              "    <div 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>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Model 1</th>\n",
              "      <td>0.982592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model 2</th>\n",
              "      <td>0.849903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model 3</th>\n",
              "      <td>0.973888</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8cf686ba-fd22-4725-90d4-1072c56d3d3f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-34707010-bba8-428d-8945-fd1d11da3d90\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-34707010-bba8-428d-8945-fd1d11da3d90')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-34707010-bba8-428d-8945-fd1d11da3d90 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8cf686ba-fd22-4725-90d4-1072c56d3d3f 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-8cf686ba-fd22-4725-90d4-1072c56d3d3f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Несмотря на то, что у первой модели точность лучше, я воспользуюсь третьей моделью, так как она была обучена более грамотно и имеет возможность дальнейшей оптимизации за счет изменения гиперпараметров."
      ],
      "metadata": {
        "id": "P68kyuQvI7Vs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def predict(sentence):\n",
        "  sentence = PipelineModel.transform([sentence])\n",
        "  sentence = pd.DataFrame.sparse.from_spmatrix(sentence)\n",
        "  print(get_lang(np.argmax(model.predict(sentence))))"
      ],
      "metadata": {
        "id": "fqTApp5YFjih"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# English\n",
        "predict(\"LANGUAGE DETECTION MODEL CHECK\")\n",
        "# French\n",
        "predict(\"VÉRIFICATION DU MODÈLE DE DÉTECTION DE LA LANGUE\")\n",
        "# Arabic\n",
        "predict(\"توففحص نموذج الكشف عن اللغة\")\n",
        "# Spanish\n",
        "predict(\"VERIFICACIÓN DEL MODELO DE DETECCIÓN DE IDIOMAS\")\n",
        "# Malayalam\n",
        "predict(\"ലാംഗ്വേജ് ഡിറ്റക്ഷൻ മോഡൽ ചെക്ക്\")\n",
        "# Russian\n",
        "predict(\"ПРОВЕРКА МОДЕЛИ ОПРЕДЕЛЕНИЯ ЯЗЫКА\")\n",
        "# Hindi\n",
        "predict('भाषा का पता लगाने वाले मॉडल की जांच')\n",
        "# Hindi\n",
        "predict(' boyit9h एनालिटिक्स alhgserog 90980879809 bguytfivb ahgseporiga प्रदान करता है')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GpCO-sI7xbp2",
        "outputId": "7e8827a1-d124-4d27-8153-590943dcd07d"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 51ms/step\n",
            "English\n",
            "1/1 [==============================] - 0s 112ms/step\n",
            "French\n",
            "1/1 [==============================] - 0s 43ms/step\n",
            "Arabic\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "Spanish\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "Malayalam\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "Russian\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "Hindi\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "Turkish\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Последнее предложение иногда определяет как Турецкий, это можно поправить засчет оптимизации гиперпараметров."
      ],
      "metadata": {
        "id": "7peeACIB8XtN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Оптимизация гиперпараметров"
      ],
      "metadata": {
        "id": "p40ddRhEqaCA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Это больше относится к последней модели конечно, но тут можно попробовать поменять следующие гиперпараметры:\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "*   Оптимизатор\n",
        "*   Функцию активации нейрона\n",
        "*   Количество эпох обучения\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "m9FFmBqMqtDE"
      }
    }
  ]
}