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      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/xcomponent/chatbotNLP-data-augmentation/blob/main/data_augmentation_for_intent_detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data augmentation for intent prediction\n",
        "\n"
      ],
      "metadata": {
        "id": "ysBfNd8GGLL4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In intent detection, user defines some examples (phrases) of intent that he wants to be detected.\n",
        "\n",
        "The NLP model needs as many as possible examples to be able to predict correct intents. \n",
        "\n",
        "Nevertheless, it happens that user provides small intents with very few examples or even just one message; because of the lack of ideas on intent's phrases, the time constraints, etc.. \n",
        "\n",
        "In such cases, it is important to be able to automatically collect more data for small intents before making classification.\n",
        "\n",
        "This notebook studies how to improve the intent detection using data augmentation. \n",
        "\n",
        "Three methodes to enrich the training data will be explored, including:\n",
        "1. Oversampling intent\n",
        "2. Back translation\n",
        "3. Paraphrase generation"
      ],
      "metadata": {
        "id": "kV4t9Lt-VaG0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Set up"
      ],
      "metadata": {
        "id": "5AzxOA8lta_q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/colab-data-drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WFEBm10GFaXB",
        "outputId": "94e2f5c2-e4cb-4429-98d9-6012774a1dca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /colab-data-drive; to attempt to forcibly remount, call drive.mount(\"/colab-data-drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install deep_translator\n",
        "!pip install git+https://github.com/PrithivirajDamodaran/Parrot_Paraphraser.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lNNjkkuXQnxv",
        "outputId": "ab9cff94-f10a-415a-ef16-5d64a9f8b11a"
      },
      "execution_count": null,
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        {
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          "text": [
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            "Collecting deep_translator\n",
            "  Downloading deep_translator-1.9.1-py3-none-any.whl (30 kB)\n",
            "Collecting beautifulsoup4<5.0.0,>=4.9.1\n",
            "  Downloading beautifulsoup4-4.11.1-py3-none-any.whl (128 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m128.2/128.2 KB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: requests<3.0.0,>=2.23.0 in /usr/local/lib/python3.8/dist-packages (from deep_translator) (2.25.1)\n",
            "Collecting soupsieve>1.2\n",
            "  Downloading soupsieve-2.3.2.post1-py3-none-any.whl (37 kB)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests<3.0.0,>=2.23.0->deep_translator) (2022.12.7)\n",
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            "Installing collected packages: soupsieve, beautifulsoup4, deep_translator\n",
            "  Attempting uninstall: beautifulsoup4\n",
            "    Found existing installation: beautifulsoup4 4.6.3\n",
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            "Collecting git+https://github.com/PrithivirajDamodaran/Parrot_Paraphraser.git\n",
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            "  Running command git clone --filter=blob:none --quiet https://github.com/PrithivirajDamodaran/Parrot_Paraphraser.git /tmp/pip-req-build-_ldev6mo\n",
            "  Resolved https://github.com/PrithivirajDamodaran/Parrot_Paraphraser.git to commit 720a87a1ee557d8ed8d9a021adbdd1dd5616c5f9\n",
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            "Installing collected packages: tokenizers, sentencepiece, fuzzywuzzy, rapidfuzz, Levenshtein, huggingface-hub, transformers, python-Levenshtein, sentence-transformers, parrot\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from collections import Counter\n",
        "\n",
        "import random\n",
        "\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "from deep_translator import GoogleTranslator, MyMemoryTranslator\n",
        "\n",
        "from parrot import Parrot\n",
        "\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "\n",
        "from nltk.corpus import stopwords\n",
        "import re\n",
        "\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "from nltk.stem.snowball import FrenchStemmer\n",
        "\n",
        "from sklearn.metrics import accuracy_score, f1_score, precision_recall_curve\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qZN4NQhxF0U5",
        "outputId": "02e69346-23fb-49c0-9086-e787a90db8d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data preparation"
      ],
      "metadata": {
        "id": "HTSzm8zy8VzI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load data"
      ],
      "metadata": {
        "id": "CIbgu15TxF8W"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the following experiments, we use a supervised dataset including users' messages and theirs intent (label). \n"
      ],
      "metadata": {
        "id": "NGky-5KLtt2A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# load data\n",
        "data_fn=r\"/colab-data-drive/MyDrive/Colab Notebooks/data/data_augmentation_intent_messages_data.xlsx\"\n",
        "\n",
        "def load_training_text(testing_data_fn):\n",
        "    messages = []\n",
        "    df = pandas.read_excel(testing_data_fn, header = None)\n",
        "    return df\n",
        "\n",
        "df = load_training_text(data_fn)"
      ],
      "metadata": {
        "id": "STLqpaiE8ZO3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Analyze data"
      ],
      "metadata": {
        "id": "PKKown8OxKUX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# display data examples\n",
        "df.head(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "LluQyvlbyhcI",
        "outputId": "a92b73e3-7dcd-4bb6-a2cb-3bd71b74d978"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     0                         1  \\\n",
              "0   fr  5f86f3bb4edf1d3f90d34c70   \n",
              "1   fr  5f86f3bb4edf1d3f90d34c70   \n",
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              "16  fr  5f86f3b54edf1d3f90d342c2   \n",
              "17  fr  5f86f3b54edf1d3f90d342c2   \n",
              "18  fr  5f86f3b54edf1d3f90d342c2   \n",
              "19  fr  5f86f3b54edf1d3f90d342c2   \n",
              "\n",
              "                                                    2                  3  \n",
              "0          Peux tu m'envoyer mon identifiant client ?      get_id_client  \n",
              "1                   J'ai perdu mon identifiant client      get_id_client  \n",
              "2                  J'ai oublié mon identifiant client      get_id_client  \n",
              "3    Je ne me souviens plus de mon identifiant client      get_id_client  \n",
              "4          Peux-tu me donner mon identifiant client ?      get_id_client  \n",
              "5              Puis-je avoir mon identifiant client ?      get_id_client  \n",
              "6                    Donne moi mon identifiant client      get_id_client  \n",
              "7             J'aimerais avoir mon identifiant client      get_id_client  \n",
              "8                Je veux avoir mon identifiant client      get_id_client  \n",
              "9        Je souhaiterais avoir mon identifiant client      get_id_client  \n",
              "10                  Envoie-moi mon identifiant client      get_id_client  \n",
              "11                  Quel est mon identifiant client ?      get_id_client  \n",
              "12                                Mon solde de congés  get_leave_balance  \n",
              "13                      Quel est mon solde de congé ?  get_leave_balance  \n",
              "14         Combien de jours d'absence me reste-t-il ?  get_leave_balance  \n",
              "15                    j'ai combien de congés restants  get_leave_balance  \n",
              "16               Dis-moi quel est mon solde de congés  get_leave_balance  \n",
              "17  Puis-je obtenir le nombre de jours de congés r...  get_leave_balance  \n",
              "18              Peux tu me donner mon solde de congés  get_leave_balance  \n",
              "19                      Donne moi mon solde de congés  get_leave_balance  "
            ],
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              "      <td>fr</td>\n",
              "      <td>5f86f3bb4edf1d3f90d34c70</td>\n",
              "      <td>Peux tu m'envoyer mon identifiant client ?</td>\n",
              "      <td>get_id_client</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>fr</td>\n",
              "      <td>5f86f3bb4edf1d3f90d34c70</td>\n",
              "      <td>J'ai perdu mon identifiant client</td>\n",
              "      <td>get_id_client</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>fr</td>\n",
              "      <td>5f86f3bb4edf1d3f90d34c70</td>\n",
              "      <td>J'ai oublié mon identifiant client</td>\n",
              "      <td>get_id_client</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>fr</td>\n",
              "      <td>5f86f3bb4edf1d3f90d34c70</td>\n",
              "      <td>Je ne me souviens plus de mon identifiant client</td>\n",
              "      <td>get_id_client</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>fr</td>\n",
              "      <td>5f86f3bb4edf1d3f90d34c70</td>\n",
              "      <td>Peux-tu me donner mon identifiant client ?</td>\n",
              "      <td>get_id_client</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>fr</td>\n",
              "      <td>5f86f3bb4edf1d3f90d34c70</td>\n",
              "      <td>Puis-je avoir mon identifiant client ?</td>\n",
              "      <td>get_id_client</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>fr</td>\n",
              "      <td>5f86f3bb4edf1d3f90d34c70</td>\n",
              "      <td>Donne moi mon identifiant client</td>\n",
              "      <td>get_id_client</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>fr</td>\n",
              "      <td>5f86f3bb4edf1d3f90d34c70</td>\n",
              "      <td>J'aimerais avoir mon identifiant client</td>\n",
              "      <td>get_id_client</td>\n",
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              "      <th>8</th>\n",
              "      <td>fr</td>\n",
              "      <td>5f86f3bb4edf1d3f90d34c70</td>\n",
              "      <td>Je veux avoir mon identifiant client</td>\n",
              "      <td>get_id_client</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>fr</td>\n",
              "      <td>5f86f3bb4edf1d3f90d34c70</td>\n",
              "      <td>Je souhaiterais avoir mon identifiant client</td>\n",
              "      <td>get_id_client</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>fr</td>\n",
              "      <td>5f86f3bb4edf1d3f90d34c70</td>\n",
              "      <td>Envoie-moi mon identifiant client</td>\n",
              "      <td>get_id_client</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>fr</td>\n",
              "      <td>5f86f3bb4edf1d3f90d34c70</td>\n",
              "      <td>Quel est mon identifiant client ?</td>\n",
              "      <td>get_id_client</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>fr</td>\n",
              "      <td>5f86f3b54edf1d3f90d342c2</td>\n",
              "      <td>Mon solde de congés</td>\n",
              "      <td>get_leave_balance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>fr</td>\n",
              "      <td>5f86f3b54edf1d3f90d342c2</td>\n",
              "      <td>Quel est mon solde de congé ?</td>\n",
              "      <td>get_leave_balance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>fr</td>\n",
              "      <td>5f86f3b54edf1d3f90d342c2</td>\n",
              "      <td>Combien de jours d'absence me reste-t-il ?</td>\n",
              "      <td>get_leave_balance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>fr</td>\n",
              "      <td>5f86f3b54edf1d3f90d342c2</td>\n",
              "      <td>j'ai combien de congés restants</td>\n",
              "      <td>get_leave_balance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>fr</td>\n",
              "      <td>5f86f3b54edf1d3f90d342c2</td>\n",
              "      <td>Dis-moi quel est mon solde de congés</td>\n",
              "      <td>get_leave_balance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>fr</td>\n",
              "      <td>5f86f3b54edf1d3f90d342c2</td>\n",
              "      <td>Puis-je obtenir le nombre de jours de congés r...</td>\n",
              "      <td>get_leave_balance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>fr</td>\n",
              "      <td>5f86f3b54edf1d3f90d342c2</td>\n",
              "      <td>Peux tu me donner mon solde de congés</td>\n",
              "      <td>get_leave_balance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>fr</td>\n",
              "      <td>5f86f3b54edf1d3f90d342c2</td>\n",
              "      <td>Donne moi mon solde de congés</td>\n",
              "      <td>get_leave_balance</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a2291f9a-5999-4762-85b6-75dd5e84383d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a2291f9a-5999-4762-85b6-75dd5e84383d 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-a2291f9a-5999-4762-85b6-75dd5e84383d');\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": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To build our intent classifier, we are only interessed in the last two columns which are the message and the intent name. "
      ],
      "metadata": {
        "id": "LcSNujz4y-CO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# get data infos\n",
        "all_messages = []\n",
        "all_intents = []\n",
        "\n",
        "for i, row in df.iterrows():\n",
        "  lang, intent_id, message, intent_name, *_ = row\n",
        "\n",
        "  all_messages.append(message)\n",
        "  all_intents.append(intent_name)\n",
        "\n",
        "print(f\"The loaded data has:\")\n",
        "print(f\"\\t{len(all_messages)} messages\")\n",
        "print(f\"\\t{len(set(all_intents))} intents\")\n",
        "print(f\"\\taverage: {int(len(all_messages)/len(set(all_intents)))} messages per intent\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-G2pd2C7Ftmi",
        "outputId": "a6d64e7e-228a-4249-c984-ed60da58091d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The loaded data has:\n",
            "\t287 messages\n",
            "\t24 intents\n",
            "\taverage: 11 messages per intent\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We visualize the distribution of data over the intents."
      ],
      "metadata": {
        "id": "XJqrPHKXwSJX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# get number of training phrases in intents\n",
        "plt.figure(figsize=(30,5))\n",
        "plt.hist(all_intents, bins=len(all_intents))\n",
        "plt.xticks(rotation='vertical')\n",
        "plt.title('Number of training phrases in intents',{'fontsize': 20})\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "YaVXD_c2HiWE",
        "outputId": "f7c3ce3d-9a48-4650-cbe9-466931242ba1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2160x360 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The data is quite balanced. "
      ],
      "metadata": {
        "id": "u83CKXWvwwK1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Split data"
      ],
      "metadata": {
        "id": "38gowb_axTuo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We now split the data into training and testing sets, respectively used for building and predicting intents.\n",
        "\n",
        "For the sake of this study, we need to have some minor intents in our data. \n",
        "To do this, we randomly select 5 intents and make them become minor intents. \n",
        "For these 5 intents, half of phrases will be used for training and the others for testing. For the other intents, we will use 70% phrases for training and 30% for testing. \n"
      ],
      "metadata": {
        "id": "D7-o_5lzJJsF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# uncomment this code if you want to try with other minor intents\n",
        "# random.seed(42)\n",
        "# minor_intents = random.sample(list(set(all_intents)), 5)\n",
        "# print(\"5 selected minor intents name :\")\n",
        "# for mi in minor_intents:\n",
        "#   print(f\"\\t{mi}\")\n",
        "\n",
        "minor_intents = [\n",
        "    'greeting',\n",
        "    'how_are_you',\n",
        "    'get_id_client',\n",
        "    'cancel_ticket',\n",
        "    'i_do_it_now'\n",
        "]"
      ],
      "metadata": {
        "id": "FIBi-AXJOhvP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# get info of the minor intents and other intents\n",
        "minor_intents_messages = []\n",
        "minor_intents_labels = []\n",
        "\n",
        "other_messages = []\n",
        "other_labels = []\n",
        "\n",
        "for i in range(len(all_intents)):\n",
        "  if all_intents[i] in minor_intents:\n",
        "    minor_intents_messages.append(all_messages[i])\n",
        "    minor_intents_labels.append(all_intents[i])\n",
        "  else:\n",
        "    other_messages.append(all_messages[i])\n",
        "    other_labels.append(all_intents[i])\n",
        "\n",
        "print(f\"Nb messages in total: {len(all_messages)}\")\n",
        "print(f\"Nb messages in {len(minor_intents)} minor intents:  {len(minor_intents_messages)}\")\n",
        "print(f\"Nb messages in {len(set(all_intents)) - len(minor_intents)} other intents:  {len(other_messages)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dyB4eD-WPku2",
        "outputId": "bfbe73a0-9593-4b12-89e9-246fbd534258"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nb messages in total: 287\n",
            "Nb messages in 5 minor intents:  50\n",
            "Nb messages in 19 other intents:  237\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# split data of minor intents\n",
        "minor_intents_train_messages, minor_intents_test_messages, minor_intents_train_labels, minor_intents_test_labels = train_test_split(minor_intents_messages, minor_intents_labels, test_size=0.7, stratify=minor_intents_labels, random_state=42)\n",
        "\n",
        "# split data of other intents\n",
        "other_train_messages, other_test_messages, other_train_labels, other_test_labels = train_test_split(other_messages, other_labels, test_size=0.2, stratify=other_labels, random_state=42)\n"
      ],
      "metadata": {
        "id": "UgXVK0mhRHco"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# collect training data of all intents\n",
        "train_messages = minor_intents_train_messages + other_train_messages\n",
        "train_labels = minor_intents_train_labels + other_train_labels\n",
        "\n",
        "# collect testing data of all intents\n",
        "test_messages = minor_intents_test_messages + other_test_messages\n",
        "test_labels = minor_intents_test_labels + other_test_labels"
      ],
      "metadata": {
        "id": "83CjTYlxSGgV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Training - Nb of messages: {len(train_messages)}')\n",
        "print(f'Training - Nb of intents: {len(set(train_labels))}')\n",
        "\n",
        "print(f'Testing - Nb of messages: {len(test_messages)}')\n",
        "print(f'Testing - Nb of intents: {len(set(test_labels))}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DFx9jwG3vW8m",
        "outputId": "43200ff3-7bb6-4e03-ebad-ba3b6745a4ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training - Nb of messages: 204\n",
            "Training - Nb of intents: 24\n",
            "Testing - Nb of messages: 83\n",
            "Testing - Nb of intents: 24\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# distribution of data over intents\n",
        "train_counter = Counter(train_labels)\n",
        "test_counter = Counter(test_labels)\n",
        "\n",
        "for intent_ in train_counter:\n",
        "  print(f\"Intent '{intent_}' : {train_counter[intent_]} trainings, {test_counter[intent_]} testings\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RWBLBi-UVrLl",
        "outputId": "f9635539-4b11-4223-8fc3-2f7cb46a10fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intent 'cancel_ticket' : 3 trainings, 6 testings\n",
            "Intent 'greeting' : 4 trainings, 9 testings\n",
            "Intent 'i_do_it_now' : 3 trainings, 6 testings\n",
            "Intent 'how_are_you' : 2 trainings, 5 testings\n",
            "Intent 'get_id_client' : 3 trainings, 9 testings\n",
            "Intent 'where_do_you_live' : 16 trainings, 4 testings\n",
            "Intent 'problem_access_application' : 6 trainings, 1 testings\n",
            "Intent 'listing_incidents' : 9 trainings, 3 testings\n",
            "Intent 'i_am_busy' : 9 trainings, 3 testings\n",
            "Intent 'i_am_angry' : 16 trainings, 4 testings\n",
            "Intent 'get_info_activity' : 9 trainings, 3 testings\n",
            "Intent 'update_ticket' : 7 trainings, 2 testings\n",
            "Intent 'get_info_incident' : 10 trainings, 3 testings\n",
            "Intent 'i_am_tired' : 11 trainings, 3 testings\n",
            "Intent 'anotate_ticket' : 9 trainings, 2 testings\n",
            "Intent 'get_link_dashboard' : 9 trainings, 2 testings\n",
            "Intent 'where_are_your_from' : 8 trainings, 2 testings\n",
            "Intent 'are_you_real' : 17 trainings, 4 testings\n",
            "Intent 'it_is_done' : 8 trainings, 2 testings\n",
            "Intent 'close_ticket' : 6 trainings, 2 testings\n",
            "Intent 'get_leave_balance' : 10 trainings, 2 testings\n",
            "Intent 'yes' : 10 trainings, 2 testings\n",
            "Intent 'i_have_a_question' : 10 trainings, 2 testings\n",
            "Intent 'say_i_miss_you' : 9 trainings, 2 testings\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Baseline"
      ],
      "metadata": {
        "id": "1DJ4XeZtVdhC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The baseline consists in building the intent predictor using the original data. No data augmentation technique will be used in this experiment. \n",
        "\n",
        "The pipeline is the following:\n",
        "\n",
        "\n",
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "XUnPRhyl8OEB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We start with some text preprocessing, such as tokenization and stemming before extracting the TF-IDF vectors of the training data. "
      ],
      "metadata": {
        "id": "sCqAS4ow_ROZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize_text(text):\n",
        "    alphanumeric_characters = \"a-zA-ZàâäôéèëêïîçùûüÿæœÀÂÄÔÉÈËÊÏÎŸÇÙÛÜÆŒ0-9\"\n",
        "    delimiter_pattern = '[^' + alphanumeric_characters + ']'\n",
        "\n",
        "    words = text.split()\n",
        "\n",
        "    tokens = []\n",
        "    for word in words:\n",
        "        tokens = tokens + re.split(delimiter_pattern, word)\n",
        "\n",
        "    tokens = filter(lambda x: len(x) > 1, tokens)\n",
        "    tokens = [w.lower() for w in tokens]\n",
        "\n",
        "    return tokens"
      ],
      "metadata": {
        "id": "NdAgehezWtAJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Stemming(object):\n",
        "    def __init__(self):\n",
        "        self.fr_stm = FrenchStemmer(ignore_stopwords=True)\n",
        "\n",
        "    def __call__(self, text):\n",
        "        return [self.fr_stm.stem(t) for t in tokenize_text(text)]"
      ],
      "metadata": {
        "id": "y4nldamOuH5M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train_tfidf_vectorizer(messages):\n",
        "    vectorizer = TfidfVectorizer(lowercase=True, \n",
        "                                  stop_words=list(set(stopwords.words('french'))), \n",
        "                                  use_idf=True,\n",
        "                                  tokenizer=Stemming(), \n",
        "                                  ngram_range=(1, 1))\n",
        "    vectorizer.fit(messages)\n",
        "\n",
        "    print(f\"{len(vectorizer.get_feature_names_out())} words in vocabulary used for extracting TF-IDF\")\n",
        "    print(vectorizer.get_feature_names_out())\n",
        "\n",
        "    return vectorizer"
      ],
      "metadata": {
        "id": "mdDD56EfuXvU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def transform_tfidf(vectorizer, messages):\n",
        "    return vectorizer.transform(messages)"
      ],
      "metadata": {
        "id": "5o1ugy3p_2ii"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# build TFIDF vectorizer\n",
        "vectorizer = train_tfidf_vectorizer(train_messages)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WXsbdxwR_-rL",
        "outputId": "fd080518-d4fc-468a-df1f-f4e330cd8040"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "151 words in vocabulary used for extracting TF-IDF\n",
            "['absenc' 'acced' 'accompl' 'activ' 'affirm' 'agent' 'aim' 'air' 'ajout'\n",
            " 'allez' 'annul' 'appli' 'appliqu' 'arriv' 'aujourd' 'avoir' 'beaucoup'\n",
            " 'besoin' 'bien' 'bonjour' 'bot' 'bug' 'chang' 'chat' 'chatbot' 'client'\n",
            " 'coler' 'combien' 'comment' 'commentair' 'complet' 'cong' 'connect'\n",
            " 'conversationnel' 'coucou' 'crev' 'dashboard' 'dat' 'dis' 'don' 'déjà'\n",
            " 'détail' 'ennui' 'entrepris' 'envoi' 'fach' 'fair' 'fais' 'fait' 'fatigu'\n",
            " 'faut' 'ferm' 'fourn' 'franc' 'furieux' 'fâch' 'habit' 'hey' 'horair'\n",
            " 'hui' 'humain' 'identifi' 'incident' 'indident' 'inform' 'infos' 'joint'\n",
            " 'jour' 'journ' 'lien' 'list' 'mainten' 'manqu' 'march' 'merc' 'met'\n",
            " 'mettr' 'modif' 'modifi' 'mou' 'nombr' 'non' 'obten' 'occup' 'origin'\n",
            " 'ouai' 'ouaiii' 'ouaip' 'ouais' 'ouii' 'ouvert' 'ouvr' 'où' 'pay'\n",
            " 'person' 'peux' 'piec' 'pj' 'pris' 'problem' 'prévu' 'puis' 'quand'\n",
            " 'quel' 'question' 'quoi' 're' 'rempl' 'rend' 'resalut' 'rest' 'robot'\n",
            " 'récuper' 'réel' 'répond' 'répondr' 'répons' 'san' 'sold' 'sort'\n",
            " 'souhait' 'souhaiter' 'statut' 'suit' 'sup' 'surcharg' 'tel' 'temp'\n",
            " 'termin' 'ticket' 'tout' 'tres' 'trop' 'va' 'vais' 'ver' 'veux' 'vien'\n",
            " 'vill' 'vis' 'voir' 'vou' 'voudr' 'vrai' 'vraisembl' 'web' 'wé' 'ça'\n",
            " 'énerv' 'énorm' 'épuis']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/feature_extraction/text.py:516: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# extract TF-IDF vectors of training messages\n",
        "train_tfidf = transform_tfidf(vectorizer, train_messages)\n",
        "\n",
        "# extract TF-IDF vectors of test messages\n",
        "test_tfidf = transform_tfidf(vectorizer, test_messages)"
      ],
      "metadata": {
        "id": "d-c-L9i1uw5-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Shape of tfidf of training data :  {train_tfidf.shape}\")\n",
        "print(f\"\\t{train_tfidf.shape[0]} vectors TF-IDF\")\n",
        "print(f\"\\t{train_tfidf.shape[1]} dimensions in each vector\")\n",
        "\n",
        "print(f\"Shape of tfidf of testing data :  {test_tfidf.shape}\")\n",
        "print(f\"\\t{test_tfidf.shape[0]} vectors TF-IDF\")\n",
        "print(f\"\\t{test_tfidf.shape[1]} dimensions in each vector\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OxcggP8dvB_F",
        "outputId": "362840e6-3586-41d7-ed2c-b202e244d394"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of tfidf of training data :  (204, 151)\n",
            "\t204 vectors TF-IDF\n",
            "\t151 dimensions in each vector\n",
            "Shape of tfidf of testing data :  (83, 151)\n",
            "\t83 vectors TF-IDF\n",
            "\t151 dimensions in each vector\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def build_classifier(features, labels):\n",
        "\n",
        "    classifier=SVC(random_state=42, probability=True, class_weight='balanced')\n",
        "\n",
        "    c_values = [1.0,10.0,100.0,500.0,1000.0]\n",
        "    g_values = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]\n",
        "    d_values = [2,3,4]    \n",
        "    parameters=[\n",
        "        {'C': c_values,'kernel': ['linear']},\n",
        "        {'C': c_values,'kernel': ['rbf'],'gamma': g_values} ,\n",
        "        {'C': c_values,'kernel': ['poly'],'gamma': g_values,'degree': d_values}\n",
        "    ]\n",
        "\n",
        "    # Grid search\n",
        "    grid_search = GridSearchCV(classifier, parameters, cv=5)\n",
        "    grid_search.fit(features, labels)\n",
        "\n",
        "    print('Best parameters : ', grid_search.best_params_)\n",
        "    print('Best score : ', grid_search.best_score_)\n",
        "\n",
        "    classifier=SVC(**grid_search.best_params_, random_state=42, probability=True, class_weight='balanced')\n",
        "\n",
        "    classifier.fit(features, labels)\n",
        "\n",
        "    return classifier"
      ],
      "metadata": {
        "id": "u8jW5viYwwz5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "baseline_classifier = build_classifier(train_tfidf, train_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uP85_mmQw6Oh",
        "outputId": "ba793823-151a-4c4c-84fc-64d41f979b61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_split.py:676: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=5.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best parameters :  {'C': 1.0, 'kernel': 'linear'}\n",
            "Best score :  0.8675609756097561\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate(labels, predicitions):\n",
        "\n",
        "    acc=accuracy_score(labels, predicitions)\n",
        "    print('Accuracy : ', round(acc,2))\n",
        "\n",
        "    f1_sc = f1_score(labels, predicitions, average='macro')\n",
        "    print('F1 Score (macro) : ', round(f1_sc, 2))\n",
        "\n",
        "    nb_incorrect_detection = 0\n",
        "    incorrect_preds = [i for i in range(len(predicitions)) if predicitions[i] != labels[i]]\n",
        "\n",
        "    incorrect_preds_rate = round(len(incorrect_preds) / len(labels), 2)\n",
        "\n",
        "    print(f'Incorrect detection rate : {len(incorrect_preds)}/{len(labels)} ({incorrect_preds_rate})')\n",
        "\n",
        "    # print(\"\\nIncorrect predictions:\")\n",
        "    # for i_pred in incorrect_preds: \n",
        "    #   print(f'{test_messages[i_pred]}')\n",
        "    #   if labels[i_pred] in minor_intents:\n",
        "    #     print(f'\\tlabel : {labels[i_pred]} (minor intent)')\n",
        "    #   else:\n",
        "    #     print(f'\\tlabel : {labels[i_pred]}')\n",
        "    #   print(f'\\tpredicted : {predicitions[i_pred]}')\n",
        "\n",
        "    return acc, f1_sc, incorrect_preds_rate\n"
      ],
      "metadata": {
        "id": "fdZgBWtOxDba"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# predictions on test messages\n",
        "baseline_predictions = baseline_classifier.predict(test_tfidf)\n",
        "\n",
        "# display metrics\n",
        "baseline_acc, baseline_f1, baseline_incorrect_pred_rate = evaluate(test_labels, baseline_predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ySR1AMxRKfRy",
        "outputId": "38182351-bc6a-40fd-a9db-be5c599c5ba0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy :  0.88\n",
            "F1 Score (macro) :  0.89\n",
            "Incorrect detection rate : 10/83 (0.12)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Oversampling"
      ],
      "metadata": {
        "id": "OaypgIGhGdhQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This method consists in duplicating phrases in all intents so that every intent has the same quantity of phrases of the largest intent. \n",
        "\n",
        "The pipeline is the same as in baseline. However, a larger volumn of messages are used for training the model. \n",
        "\n",
        "![image.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAh4AAADECAYAAADQ+SgqAAAgAElEQVR4nO3de3RU5b038O/ek8mdIEEICYQEkup7EOEUGtRcgFPThCUKhSJyk1NvaNL2rViEYtFSi1pjLLanTURBPXLVIhxQXAXxvAIJVCKuQiOnchjIhYRrgNwvM7P3+8dk78wkk2Qmc9lz+X7WyiIze8+eZ5KHzHee37OfLciyLIOIiIjIC0StG0BERETBg8GDiIiIvIbBg4iIiLyGwYOIiIi8hsGDiIiIvIbBg4iIiLyGwYOIiIi8hsGDiIiIvCZE6wb4A0k2w2jugEkywiyZIclmmGXL9yapA5IsAQDMklndvzeioAMA6EQdREGEKOggCjroRB0EiAgRQ6HX6Tv/DfX8iyMiIvIigSuXdrEOGO2mVrSZmiHJEozmjj7DhCeFiKEICwmHXhcKvRiOsJBwhIVEaNIWIiIiVwV98Gg1NqHV2Iw2UzPaTW2aBQxnRYXGIFQXjqjQGAYRIiLyG0EZPNpNrWjuaECLsRHtplatm+MyUdAhJnwIYsKGsjxDREQ+LaiCR6uxCfVtdWg1NvvNyIazokJjMDQyngGEiIh8UtAEj5ut13Cj9UrABo7uhkQMR2xknNbNICIishEUp9PebL2GupaLQRM6AOBG6xXcbL2mdTOIiIhsBEXwqG+r07oJmrjRekXrJhAREdkIknU8JK0bQHa0tJvR2GJCe4cZRrMMSQqKqp9X6ENEhOlFRIXrEBOl17o5A8L+ob1A6Efke4IieLQYmxCqC9e6GV5nlk1aN8GudqOEy9fb0G5kIPQUo0mC0SShqdWEm01GxMWGI0zvHwOc7B++w5/7EfmuoAgeN1qvYEjEcISHRAXFPA9R0MEsm3ClqRqpQydo3RwbTa0mXL7Rzk+vXtRulFB1uQUjh0UgMkyndXP6xP7hu/ypH2lOMkM2GyGbjYDJ2Hmf5XtZNgOSBHSudC3L9gO2IHQGPFEHiCIEQQeIOshiCIQQPQSdHggJtfzrZ4IieABd8x3iByWrS5wHGlHQQRAEXG2ugdHcrnVzemg3SnxT0VDN1VaMjov02U+s7B/+wdf7kVdZBQy5ow0wtgCSZAkckmsfcmV0Pt5s7Lxtvc2WoNND0IdDDgmzhBJ9OAS9747yB03wUFxsrAAA3BqVgPCQSBjNHRq3yDWW67yEoMPchmsttT4ZOBRXb/JNRWsX69qQPCJS62bYxf7hP3y5H3mUZIZsbIPU3gIYWyAb210OGO6gjq6g0SaUiOGDIIeEQQyLhBAWpVXzegi64KG41lyrfn9rVAJCdeEQBZ1Pv3EDXUFDhoyWjgY0ddT7fJsByxB6a7v2/0GDndEkoaHZ6HMTBdk//Iuv9iOPkMyQ2hqBtiZIHS0+ETQcJbU1AmiEuclyWwwfBIRGQowcbCnhaCRog4c16xACAIPChiBCH40QUd95NVnZ6yMjXVesFSBBhiSZ0NRRjzZTi18Eje4aW3xzomswam4z+9wbBvuH//HFfuQ2nWFDbr7hlrKJr7AEqEZIDZctISQ8GmLkLV5vB4OHHY3tN9DYfsPutkFhQxAWEgFR0CFEtPynEwQRAgABQo/aW3dC578yZHVfSTLBLJthNLfDJBn9Nlz0hWco+I4WHxxZYP/wP77Yj1wltzdDbm+C1FIfMGGjN2oIuXkRYvggCFFDvFaOYfBwUl+hhHpnZu3eZ/jiPAr2D//ji/1oQJR5Gw1XIBvbtG6NJpQQIujDIUQN8fgoCIMHeUXA/JEij2D/IC3I7c1BHTi6k41tkG9ehNx8w6MBhMGDiIiCCgNH36wDiBgz3O0lGAYPIiIKDpIZUtM1SE3XtW6JX5CNbTDXVUGMvAXiLfFuOy6DBxERBTy5vRnSzYud612QM6SWm5DaGiHeEm85G8ZFXHqOAl525gTcljjI7teJsmN4u/iNHve/XfxGr8dTHgcAJ8qO9XhsdmbPZertPTf5n+qqCpvf4b69OwEAjy2Zg7XPLe+xv9I/AGDf3p299g/rfXvbTgMnNV2H+UaNx0JH6fGvoR85zuH9C4s2YebiZW57/g/2fIrb03Pddjy7JDOk6xdgbrjq8qEYPCjgHSw5hTPVjThT3QgA2L7rgHp7cto9AICsadnqfWeqG/FE3tNOPYf1YzOn3ovbEgehuqrCZh/r51XaQv7l3ow7sf7P76q/w10fbgUAzJ2/GCWHP++x/8e7P8Sihx9Xb49OGgMAamCxVvSHAmRNy0ZV5XkPtT4ISWZINy9Cargc8KfHeovcdA3mukqXjsHgQeRma19ej0UPP273EzD5LyVIjogfqd63actuAMDMWfNQVXleHQlTbNu8EQ/MmW9z30OLH1UDi/Wxjxw6iPyfr/RE04OTEjpabnr9qX+6+kUUFm3C7em50I8cB/3IcSg9/rW6bfVLr+PAFyXQjxynjnycr6xW99WPHIfCok3q8QqLNuGnq1/EzMXL1O0f7PlU3bYkfwXOdT7e4yMfAOT2FpfCB4MHkQek3Z2BI4cOat0McqPE0cnImpaNhXNzegQMAFj08OP4ePeH6u19e3didNIYdVRNMWPmD3Hk0EGbEbFNb/7BZmSEXCc1XOlcMlwbq196He+88QqMNafxyq9+gUefXg0A+NMrL+CVX/0COdMzYaw5jX1b3wIA3Jaeiy1FhTDWnIax5jRWv/S6GlYAYMP7O7B0/g9hrDmNLUWFWJK/AgCwIv8xbCkqxNikRBhrTuPbo/u98vpcCR9BHzyeWfoCMpLvt/t18OPDTh1r65sfISP5fof2ra26NKDnIM84cuig3dr9QFl/KlYsnJvT5zwQ8n2btuzGs8/9Vv1dWgeQB+bMx7bNG9XbZX8rxUOLH+1xjMTRyVj08OPY9OYf1Pu2bd6Ix576uWcbH0TMDVc1Gemw9uTSBciYMgkA8KOZOThXWd3rvh/s+RRjkxLx0Oz7bB6/ffcn6u2c6ZnqduXf830c0xvk9hZINy86/bigDx6/f/9FlFZ8gtKKTzAyKR6/+Y+V6u3sB6Y6dazFT/0IpRWf9L8jgITRIwb0HOQZ3ed4zJw1T50MaD0R1VGXLtb0uM96jsfBklPubD550RN5T+NMdSPW//ldm9GPyWn3YHTSGDW0btu8ETNm/tDuMaxDytvFbyBrWjYSRyd75wUEOKmtEXLTNa2bYVdfQeFct1LLhvd39Hu82suuT/R0lXLGizN4Om0ftr75ES5euIzaqkv48vDX+M1/rET2A1PxzNIX8OVhyxDYXVMn4ffvvwgAOPjxYbxVuBkfHnobADB/2hNYtuJh/PpnBeoxrYNJRvL9KP5LASakjUPhmiLEj4rDnu1/RU2lJUEq2wDLCMmDU22HYkcmxavPRe43c9Y8zJw1b0CPLftbKbKmZbu5ReRLZs6ah/UFL9qETOv5G32FCSWkvF38Bj7Y+g6Wr3zBK20OeJIZcqNvho7+jE1K9FqZxN2kmxchjnD8TL2gH/Hoz+4tn+K+B7PV0YnCNUXq7dKKT/Dl4a/7LJf8+mcF6r53TZ2EwjVFve5b9Lt3saZwOUorPkH+Lx/BuhXr1W1PP/w88n/5CEorPsFv/sMyAY2hwze9XfwGtm3eiLUvr+9/Z/Ib+/butJkwfKLsGKoqz9uU1ZT5G+sLXsTc+Yv7PN5Dix/Fay8/DwADDrhky9x03S9WI00cGY+z57vmRzw0+z6cq6xWJ4w6a1T8iD5LOR4nmZ06zZbBox93TZ1kUw5ZsS7f5vZdUyfhck3vP3AlJADAfQ9mo7bqUq/7zllynzrC8W/3ZagjHwBQU3kRd07+FwDAuIm3AUCfxyLvsi7JfLD1HZypbuTQeYCZOWuezToeC+fmYPuuAzaTR5UJqFWV5/sNE8op2/bmgdAASGagtV7rVjhECRr6kePw09WWEfMzR/djSf4Km3KLo0EkY8okjE1KtDlLxtvkFscvnspSywB0n0A6OX2iw4+9UOncRJzaqktIGD0CI5Pi8Y8T/4MJaeNw+uQZAJZ5IuQce+tnPJH3tFPrdlgfY3LaPQ6tycF1OwKDcvrsQPaxV7rr3i8c7U/Uk9TRotmqpBlTJsFYc1q9/adXbEtnYzrPOLHW/ba9fRQr8h/DivzH+ny85mUayQyp5aZDF5Zj8HDS/GlPIP+Xj2DxUz8CYDkrxluKfvcuin73LgDL/A8iIurU1qR1C6itCWDw8Iy4kcMAQJ106syIx0DUVl1CTeVFh8+Y8QXFe85jbEIkctPitG4K+ZjS8jqcMjRgTlY8RsSGa90c8kP2/r7I5g4NW0SAZdTJkfkbDB5OWlO4HHkPrlTPVJmz5L5+HuG6hNEjkP/LR3qUeP5yeKPPllsMNc3YdbgWu49cxJyseIyNd+9llcm/7Tpci12HazF3agLmZLnvqpcUHLr/fclNiwNMvPib5hxcll6QZVn2cFM0d7Ryn9ZNcMmpstPIe3ClzYjH1jc/womjJ9VTeXtz9p93YvMBbReZAYDxY2Kw4PsjERsTqnVTCMCxb677RL8AgOiIEDxxfxK+Mypa66aQk3ylH2WMH4rl0yVEhQlaNyXohST8S//7eKEd5KIrl65hZJLtp8KLFy47NNqxNHc0luaO9lTTerWiqBwnDfWIiw3DzLtH4Pj/3MDfDfX4/neHeb0t1JNW/QKwlFrWvvtPREeEYGLKYDyckwgp8D//BCRf+PsyNysBOWnDEV5/jheC8xMMHn4g+4Gp+PuX5TalFuuFy3xRXGwY8saPQcadQ1H8X+dRfr4BSSMitG4W+YDo8BBkjB+Kh3MSkTLSUoL73wucGEiOU/6+5KQNR3SE5W3M3KSHzOChKUHv2JwtlloCXHrSTM2eu6nVhOI953Gg7AoAYGLKYDw5i2tb+AJfK2swePgnX+pHUsNlSE3XtW5GUBPDB0GMHdX/fl5oCwUpQ00zjpZ3/SE4wzcX0sCJsmO4LdHx5Zxdsfa55eoCY9ZXnyUvCI3U5GmtL3nvSYVFm9SFxbzxfAMSOdih3Rg8yGMmpg7G7nV3IToiBN//7jBEhuk0a4u73nweWzIHjy2ZM+DHKytfWr8pvV38htfeGMm9bkschLeL3wBg+d1u27wRn5f+Q1251nr7QGVnTrC5WrLSl525aGEwEEMjIej0WjfDbW5Pz1VXNQWA1S+9ji92b4Gx5jQypkzqsV1zog6ig+GPczzIoww1zYiLDcO86QmYNz1B6+a4zJGVK53l7Mqp5DusVxm9ctmyKrH1UvmeWIWUq5v2QtQBEYMBH70yrbOsVyJVrmqbEDfM7nZfIEQOsfwOHMARD/Ko0vI6pN8R69RjHlsyp8enROtPfcoIQW9D2tbb9u3diX17d2Lh3BybbdbP1duxHlsyB/v27rTZtva55eqFwrq3Iztzgt3jKiMkJ8qO4d6MOwEA92bcqR5z396dNo/t/hrsbbNuV/ftwcz65259QbfurH++1n1NGU1QvpRRBeuft3U/Ufpl9z6m/M67j1ZYl2KsR8669yXrdlZVnsfynzyi/t7tjZr11Y+VNtg7fqDRRcd6bNTDutRxe3pur/vdnp6r7td9RML6OiyFRZsAAKXHv7a5XymjzFy8DIVFm1B6/Gvc1vl8t6Xnqs+tbFd8sOdTu8dR9rXeft7dF5QTddBFO/53nsHDRc8sfQFb3/xI62b4rANfXcHEFMfqfoq7M6bhg63vqLeVq4DOnDUP+/buxGsvP48z1Y04U92I9X9+F48smqXum505Ac8+99uu7QUvYuasedi+6wAAqPcDUP/wK/c9+9xv1WCgWP6TR2yGzq09kfe0+tisadnInHovAMuby9z5i9VtRw4dxL69OzE57R58XvoPAOj1mIDlzcb6NSQlp/Qo76wveNHmtbg6nB8IlKCh/FyqqyrszrPIzpyg/vy37zqA115+Xt1v1fInsf7P76rHUC4At/wnj2D7rgPq/d1/b937mL2RsbeL30DJ4c9tjnGi7Bj27d2JmupKm75k/VpGJ41R22TvwnOO9mN7xw84og7CYPevlvzBnk+x+qXXYaw5DWPNaTyxZL7dC7jNXLwM77zxCow1p3Hm6H5seH+HGgB+uvpFPLl0gXoM5dorjz69GluKCtX7M6ZMsjlmxpRJONM5unHm6H67Ix2lx7/GkvwVOHN0P4w1p/HF7i2YPmeJzT7W28ckJbrl56IQo291eLQDYPDwqozk+3GqzP5FgAKRoabZslZDqnPBY8bMH6Kq8rz6afPj3R9i0cOPAwB2fbgVzz73W3XfmbPmqfsqAcW6bHGw5JTd56iuqsCRQwdtLl2vPM66dv7sc7/t9yqz+/butDnW2pfX27xBZE3LRm3NBYde+769OzE6aYzNa8j/+UocOXTQZr9X129Qv39o8aOoqa5EsNu2eSPyf951NehNW3bb/d0dLDml3q8EC6VMAgBlfyu1e/yvv/rSpfZ9sPUdLF/ZdW2ntS+vx+S0ezBz1jybfnh3xjSHJ6Y62o/X//ld9fu58xcH9MRXMXyQQxcqc8b7H/4XXvnVL9TbK/Ifw0Oze65avW/rW2pwGJOUiLFJibhwsesq4p8dst+3jvztK5fat333J3hy6QI1UChXq7UOR6/86hduDxwAIEbHQnRitANg8CAPKi2vc3q0A+i6tLjyh37b5o14YM58dftrLz/f67Dx6KQxTj9X98dfuljj1DGW/+QRmz/sgO1QfvfQ0J+k5BSb28PjLIvH9fVmEchvJM5QflZ96V5OsXaw5BRKDn/eoxRyprpR7XeujBaMiB9p937rUslrLz/v9HGd7ceVFQann8OfiLfEQwhz71kuiSP771vnOy91r3ydsypp/OmVF/CDaRnqNsW3R/fjs0OlLl/SfsP7O3p9bk8R9OGW0Q4nMXg46eDHh5GRfD8yku+3e2XarW9+pG5XFvyqrbqkfp/34EqbkY/CNUXqvvOnPeG11+ENpwwNmJASM6DHzp2/GB9sfQcnyo5hdNIY9ZMpAJsyRPch8arK8049T/c37KrK872+Odiz9rnlyJqWbTPC0b3ckzUt26k2dX9TsDdpkeyzHrmwp7qqAgvn5tiUTbo7WHIKZ6obUVlhsClhKftv27zRZt6GM+yFge4lIusRPUe52o8DkW5oklvDR3VN330LsMzBsC6bjO02wvCnV16AseY0nly6wCZkfNtZAjl7vtJm3oYzrMs4ype9URl3EfTh0A0d7VSJRcHg4YTaqkv49c8KUPyXApRWfIL7HszGl4e7JvAc/PgwLl64jNKKT1Ba8QnumjoJhWuKkDB6hHqdFeWxE9LGYeubHyF+VJy6P4CAmS9iqGmGobYZGeOHDujxSgll1fIn8dDiR9X7585f3OsnQiV8WL9ZKBMvu48aKKMq1p9e3y5+o0fI6cuJsmPYtnmjzTC3ImHkKPX5rEc8lPDQ2xuk8rqtX0PRHwrUUhP1LmtaNor+UKDefmzJnF5HgpT+0FeA6D7ypHB2VE2ROfVerC/ommy49rnlajnEOlRaz29S2tFbqc4d/TiQ6YYmQQx3fTLtv2XchdUvva7eLizaZHeOBwCMirdcyqL0+Ne9jjokJ9oPhaljkgbUvoVz7seG93e4f9JoL4SwyAGHDoCn0zrl/31airumTsKENMswWfYDU/HpX7reVLIfmIrsB6aqtyenT8SJoyd7Pd7ip35kc3tK1ndx8cJlN7daGycN9chJG+7SMRY9/Di2bd6IGTN/qN43c9Y81NZc6DFErnxy/bz0H7g34041nCgT/pQ/0Pdm3InRSWNwsOQUNm3ZjezMCeqxlPsd9fHuDwHAZiLf56X/wKvrN2Dh3Bws/8kj6uvo/rqUMyDsfeLu/hoWPfy43XBDtrr/PpX5OdYhL3F0ss3ky6xp2WqQqK6qsPldZk3LxhN5T+NE2TH19wVYfh/2Jnn2Z+3L69WSinKcyWn3YHLaPbgtcRC2bd6o3m8dmObOX4zlP3kEr738PLbvOtCjnORqPw50YuwowMVVTVfkP4aK6hq1RJIzPRP7tr7VY78tRYXqpM6xSYnImZ6pbrs9PdcmiBhrTuN8ZbV6xopyXGXSqTMypkzClqJCm2MBlsmo7p9IGuv0ZNLuuGS6E7a++REuXriMFevy1fueWfoCJqdPVEPEM0tfsBkFsb6mSkby/Sj+S4EaXABg/rQnUFPZ9YdxzpL7bI7vKq2WTF9RVI45U+PVEQ8uie1bfGmpa4D9w1/5Wj/qi9zeDPONGl5IbqBEHcSY4W6ZuMsRDyfVVl3qdVvhmiIAsCmb9DXi8czSFzAl67tYcehtm8f7u6ZWEwy1zQOaWEpE5AlCWBRChqdAargCqeWm1s3xK0JYJMRbEty2RgrneDjhzsn/gi8Pf61ODN365kc2oxsAbC5Vv2f7X222jUyKx5VLtqvqxY/qOud89xb7NUN/c6DMsnaHctVIIiKfIOog3hIP3dDRAbW8uqcI+nCIsaMsE3Xd+PNi8HDChLRxyP/lI+qZKRcvXMacJV2zhlesy8fuLZ+qZ6lMyfquzeNnL5yBX/+sABnJ96O26hJWrMtH0e/eVfe3PpY/O2VowA/ShvW/IxGRBoSwKOjiUiHGxDGA2CPqIMbEQTd0tFsm53bHOR4BzttzPJpaTXjq93/Hm8/8q82Ih6G2GZIU8F3Nb/habZ79wz/5Wj8aKKnpOuTm65DNRq2boilBp4cQFQsxcrBLk0f7w7FwcquThnqkJkT3KLPoRIFvLD5CH+J7A53sH/7HF/vRQInRsUB0LKS2RqCtKbjmgIg6CPowiNG3QtCHezRwKBg8yK0+K7tqt8wSEaaD0SRp0CLqLkzve28Y7B/+xxf7kavE8EFA+CCIt8RbRkFa6yEb27RulkcIYZGAPhJi1C1eLzcxeJDbNLWacLa2CU8l9FxhMyJUREOzBo2iHqLCPf+JxlnsH/7HF/uROymjIJDMkFrqIbc3Qm5v0bpZA9c5sgF9JMSIQZbRDY0weJDbKGWWEbE9O3RMlB7XG438VKsxfYjok2cbsX/4F1/tRx4h6rpCCACprRFyRxtgbPHtINIZNAR9OISwaK+VURwRJD2HvOGzsqt9Xpslfmg4qi778H/UACeKAm4dHApRFLRuil3sH/7B1/uRpynlGIXU1mhZlMzUDtnYpk0YsQoZEPUQwiIt5RMfCRrdMXiQW6hlltm9X8gsTC9i5LAI1Fxt9WLLCLC8WQwbHOrTn1LZP3yfP/Qjb+vtdFOpzXI5BLnDMkdEkEyQzR2AJAGSGbIs9b2KamdoEATREiyUeRghekDUA6JoGc3w4YDRG/YecouThnpER4TYLbNYiwzTYXRcJC7WtXFY3UvC9CJiY/zjzYL9w3f5Uz/yBWog8cA6GP6OPYjc4pShAel3xDq0b5heRPKISDQ0G9HQYkK7UeKplG4migLC9CKiwnUYHKX3q2Fx9g/f4c/9iHxXUASPEFEPkxR8C8OEiN47Raq0vA6FeeOdekxMlB4xUVw1kOxj/yAKTIF3IrYdIwb1Pu8gkA2PHuWV5zl51rEyCxERUVAEj9G33IaEmDFaN8OrEmLGYNTg73jluU4a6h0usxARUXALiuABAMlDxmFifBZiI0f0v7MfGxw+FHfE3Y3kIeO8Vmo58NUVZIwf6pXnIiIi/xYUczwUUaEx+D/DJqO5owFXmy/gStOFgJj7ESLqERsZh/hBYxAV2vs6Gp5gqGlGdEQIUkZGefV5iYjIPwVV8FBEhcYgKnQckoeMw/WWS2jqaEBj+3XUt9Vp3TSHhIj6ztcQg2FRoxAWEuHViaTWSsvrMDFlsCbPTURE/icog4e12MgRNuWX6y2X0GZqRYe5Fc0dDZqHkRBRj7CQCESFxiBSH4PB4UM1DRrdHfjqCp59yDtzSYiIyP8FffDorrc5IPVtdTBLRrSZWmGSjOgwt6LdZFlh0SQZYZKMMEumfks3SmDQiSHq92EhkQjpvB2qs4QMJXD4SsCwRymzTEzliAcRETmGwcNBg8MHNnny/f1VWJo72s2t8Q0ssxARkbOC5qwWrWw+UK11EzzmlKGhz4vCERERdcfgQQNiqGmGobaZp9ESEZFTGDxoQE4a6pGTNlzrZhARkZ9h8KABYZmFiIgGgsGDnNbUasJJQz0nlhIRkdMYPMhpB8quYGLKYERH8KQoIiJyDoMHOe2UoQE/SBumdTOIiMgPMXiQU5paTThb28QyCxERDQiDBznlpKEeqQnRLLMQEdGAMHiQUz4ru8oyCxERDRiDBzlMOZslJSFK66YQEZGfYvAghymn0I6IDde6KURE5KfcFjzy8/MhCAJKSkrccjxBECAIAgBgx44dEAQB+fn5bjl2b89Dffus7CrGJkRq3QwiIvJjfjXikZyc7NLjU1NT7YaMlJQUl44bLM7WNnGZdCIicolfnJqwYMECLFiwwCPHlmXZI8cNNKXldYiOCGGZhYiIXDLgEY9z586pZQp7owiCICA1NVW93b0Uo5ROlPsFQUBBQYHd57JXaikoKFAfZ/081vcLgoAdO3aoz2cwGNTvZ8yYoX5v3f7ur8t6m9KOkpISm+3nzp1z+ufnb04ZGpB+R6zWzSAiIj834OCRk5MDwDJicOTIERQXFzt9DOUxsiwjNzcXq1atcmiOSEFBAVatWoXt27dDlmUsW7YMO3bsQEFBASoqKiDLMmRZRkpKChYuXKg+h1JSkWUZf/3rX3sc99y5c0hJSUFubq56DAA2wQYAsrKyIMuyGmQ8MffE15SW17HMQkRELhtQ8CgpKYHBYEBeXh4AIDMzU/3eWUVFRQCAH//4xwCAo0eP9vuYt956CwDU8svKlSuxYMECrFy5Uj0e0BUYHB2RKCwsBACsWbNGvS8vLw8Gg8EmECmvdezYsQCAs2fPOnR8f2WoaWaZhYiI3MLn5nhUVFS49HgtzlBRRj4CVWl5HcssRETkFj53VosrZ64o8zaUMklubq67mhXUDnx1BRnjh2rdDCIiCgADCh6ZmZkAuuZolJpZ4SgAAAsKSURBVJSU9JjjkZKSAoPBgHPnztndrlAmlL733nsAgPT09H6ff9myZQCgThzdsWMHduzY0aPksX//fpvb/ZVeFi1aBADYtm2bul9xcTFyc3PV1xxslDJLykiuVkpERK4b8IjHkSNHAFhKG1lZWdi+fbvNdiVIpKSk2N2uqKiogCAI2L9/P7Zv3+7QG/zKlSuRl5eHhQsXQhAEvPfee1iwYIH6nMrZJt1HPJS5GykpKeroiLXMzExs374dxcXFEARBnWhqbyJqsCgtr+OVaImIyG0EWaOFLJS5GIG+jsYPflGKz17P0LoZA/bU7/+OvFljMDGV4YOIiFznc3M8yHcYaprR1Gpi6CAiIrdh8KBenTTUc1IpERG5lWbBw3qBLvJNR8uvY0JKjNbNICKiAMIRD7Lr0vU2GGqbObGUiIjcisGD7Dpafh3p42MRHeFza8wREZEfY/Agu04ZGpA+nquVEhGRezF4UA9NrSacrW1imYWIiNyOwYN6KC2vQ2pCNMssRETkdgwe1MOx8hv4QdowrZtBREQBiMGDbLDMQkREnsTgQTZOGupZZiEiIo/hu4uHFO85j8vX2wEAa9/9J+Jiw5A3e4zGrerfZ2VXWWYhIiKP4YiHh4xNiERpeR0Ay2TNqHCdxi3qX1OrCScN9UhJiNK6KUREFKAYPDwkNy0OcbFhAIC42DDMnZqgcYv6d9JQj4kpgzEiNlzrphARUYBi8PCgh3MSAQA53xvuF3MmPiu7irEJkVo3g4iIAhiDhwflpsVhYspgvxjtAICztU3ISRuudTOIiCiA+f7HcF8gmwCpDZDaLd/LJkBu77pfNnftZ/0vgLXzgGhjGWDsvEMI6fpX0HX+2/kFHSCGA2JY57/eK3mUltchOiKEZRYiIvIoBg9r1gHD3ASY6y2hQmqzCRPOiA4DIHd7DmeI4YAuqjOIRFm+10UPqC19MdQ0I/0OXpuFiIg8i8HDdBMw1VtChrl5wAHDY6Q2y1d3+qGWIKIf6pYgcuCrKyjMG+/ycYiIiPoSnMHD3AQY6wDTdcv3/shYB6AOaK+ylGlC44DQhAGVZww1zSyzEBGRVwRX8DDdBDpqLSMcvjay4QrZBLTXWL70Q4HwsU4FkNLyOpZZiIjIK4LnrJb2GqDlfywjBYEUOroz1gGNZUBbpcMPOfDVFV6bhYiIvCI4gkd7DdB2LrADR3ftVZbX3Q+lzDIxlcGDiIg8LziCR0et1i3QRntVv7uUltdxtIOIiLwmOIIHJK0boBG53z1OGRo4v4OIiLwmOIKH8YbWLdBGP6UlQ00zDLXNLLMQEZHXBEfwaK+yrNEh+P4VYt1C0AGQgZZ/9rnbSUM9l0gnIiKvCo7gAVjCR3M5IATwSxZ0gKAH2s72GzoA4Gj5dUxIifFCw4iIiCyCax0PAGj+xvJvRAqgGwxIrdq2x1WCDhBCAanFEjjMdlY5taOp1WQps3BiKREReVHwBQ9Fq6Hr+4gUy/LjQojvBxElaEACjNcB0xWHw4a1A2WWtTuiI4K3CxARkffxXQewDSGAZfnxkCGWN3ghBIBs/3opnqSUTSAAkADZCBivdV5TxvW2nDI04Adpw1w+DhERkTMYPOzpuGz5sic0znJRNkHfOfKAznkjA5k70nmar2y0XAXX3ALI7W4LF71pajXhbG0TVqSkeuw5iIiI7GHwcFbHZQC9hBI/cdJQj9SEaJZZiIjI6wL4FA/qzWdlV1lmISIiTTB4BBmlzMKzWYiISAsMHkGGZRYiItISg0eQ+azsKu4ZP0TrZhARUZBi8AgiLLMQEZHWGDyCiFJmGREbrnVTiIgoSDF4BJFThgaMTYjUuhlERBTEGDyCSGl5Ha9GS0REmmLwcEJB0X4ICU8if/W2HvfPWPRHjVrlmNLyOkRHhLDMQkREmmLwcFJK8jAU/+chlBw/q3VTnGKoaUb6HbFaN4OIiIIcg4eTUpOHI+/fp2HdG5/2uZ+Q8KT6pYyQnKu8BiHhSZyrvGaz3449Zert/NXbeoyouMOBr66wzEJERJpj8BiAFU/lYP8X39gEBmup6Wvw6pq5kGs3QK7dgAOHTmPHnjKMTboVKcnDcPzv5wEAJcfPIiV5GA7/7X/Vxx44dBpT7/6OW9trqGlmmYWIiHwCg8cAjE26Fa+umYs1r+7psa3k+FkYKq5iZX6uet+yJVl474Nj6vdK0Dj6lQHrVs3GgUOnAVhGRAwVV7Fgdppb21taXscyCxER+QQGjwFSgkVB0X67261LLavW7VLvT/9eiho0/rvkW0z51zFITR6OkuNncfzv55E7/Q63t/XAV1e4aBgREfkEXrDDBetWzcbCvI14dc3cHtvk2g12H5M5JRWGiqsoOX4WZyuuYGzSrfh+5u04+pUBFdV1+PFD97i1jUqZZWIqgwcREWmPIx4uWDA7DbnT77AZ0cickoqU5GG9joQAUCen5kwbB8AyCvLfJd/iwKHTmPKvY9zaxtLyOo52EBGRz2DwcFHRK4sAWOZ9KM4eXYdV63bZlFusg8jUu7+D/V98o04izZySiv1ffIPU5OE2x3GHU4YGzu8gIiKfIciyLGvdCI+7+JbWLdBM0y2PIjqCFTUiIvINHPEIcAwdRETkSxg8iIiIyGsYPIiIiMhrGDyIiIjIaxg8iIiIyGsYPIiIiMhrGDyIiIjIaxg8XDRj0R/7XKWUiIiIujB4eJGQ8CRKjp/VuhlERESaYfAgIiIir2HwcNKOPWXq9VdmLPpjj+0FRfttrtECAOcqr6nfZ/3wNZuRj/zV29R9U9PXeO+FEBERaYDBwwnnKq9hYd5GHPmvZyHXbsCPH7oH+7/4Rt2+Y08ZKqrrINdugFy7AbnT70D+6m0Ym3Qr5NoNAKA+NnNKKgqK9iM5cai6PwDOFyEiooDGC3k4Yee+E8idfgcyp6QCABbMTsN7HxxTty+YnYYFs9PU29/PvB3/XfJtr8dbmZ9rcztn2jhUVNe5udVERES+g8HDSf1dtn7Goj/ajILkTr+jz/1T09fAUHFVvZ3379NcayAREZEPY6nFSecqr/W6LX/1NgBQSyevrpnb57FmLPojcqaNU/dn6CAiokDH4OGE9O+lYP8X36gTQwuK9tuMbgC2IyJvbTlisy0leRguXLxhc19y4lD1++L/POTuJhMREfkUQZZlWetGeNzFt9x2qIKi/Vi1bheArrJIcuJQdb6GcvaKsv1c5TX8ddv/7fFYw7GXAAAp9/zKZn8AKHplkdvai/hl7jsWERGRixg8Ah2DBxER+RCWWoiIiMhrGDyIiIjIaxg8iIiIyGsYPIiIiMhrGDyIiIjIaxg8iIiIyGsYPIiIiMhrgiN4iKFat0Abwfq6iYjIZwVH8Igcr3ULtBFxu9YtICIishEcwWPQ94CoO7VuhXdF3QlET9a6FURERDaCY8l0hbEOaDoBtFVo3RLPCU0AoicBYQlat4SIiKiH4AoeCmMd0HoGaP0WkDq0bo3rxFAgfIylpKQf2v/+REREGgnO4GGtrQIwXgM6LgEdtVq3xjFiKBByqyVkRNwG6AZxIikREfkFBo/u2ioAcyNgbrKMjGgdRsRQS7DQ3wqExFpKKQwaRETkpxg8HNVeC8gdllAitQNSM2BqtGyT2y0lG+XfviiBQQjr+l43CBDDACEU0EUDIUMttxkwiIgowDB4EBERkdcEx+m0RERE5BMYPIiIiMhrGDyIiIjIaxg8iIiIyGsYPIiIiMhrGDyIiIjIaxg8iIiIyGsYPIiIiMhrGDyIiIjIa/4/wmE1DJV5L64AAAAASUVORK5CYII=)"
      ],
      "metadata": {
        "id": "UEvchPo9KueL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "max_size_intent = max(train_counter.values())\n",
        "print(f'Number of training messages in the biggest intent : {max_size_intent}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E0B8mcIL13KV",
        "outputId": "c7ecfa59-4be2-4051-d68e-721aecebfc46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of training messages in the biggest intent : 17\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# duplicate phrases in order to have max_size_intent messages per intent\n",
        "oversampled_train_labels = []\n",
        "oversampled_train_messages = []\n",
        "\n",
        "for intent_, count_ in train_counter.items():\n",
        "\n",
        "  intent_phrases = []\n",
        "  intent_features = []\n",
        "\n",
        "  for i in range(len(train_labels)):\n",
        "    if train_labels[i] == intent_:\n",
        "      intent_phrases.append(train_messages[i])\n",
        "\n",
        "  if count_ < max_size_intent:\n",
        "    nb_dupplicate = max_size_intent // count_\n",
        "    remainder = max_size_intent % count_\n",
        "    \n",
        "    new_intent_phrases = intent_phrases * nb_dupplicate + intent_phrases[:remainder]\n",
        "\n",
        "    oversampled_train_labels = oversampled_train_labels + [intent_] * max_size_intent\n",
        "    oversampled_train_messages = oversampled_train_messages + new_intent_phrases\n",
        "\n",
        "  else:\n",
        "    oversampled_train_labels = oversampled_train_labels + [intent_] * max_size_intent\n",
        "    oversampled_train_messages = oversampled_train_messages + intent_phrases"
      ],
      "metadata": {
        "id": "AfwtyIE42Ab7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Before oversampling - Nb of phrases in training data: {len(train_messages)}\")\n",
        "print(f\"After oversampling - Nb of phrases in training data: {len(oversampled_train_messages)}\")\n",
        "print(f\"Nb of duplicated phrases in training data: {len(oversampled_train_messages) - len(train_messages)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5H8xJnX5EIp8",
        "outputId": "f5604a26-978f-4716-e82f-8c8922658b6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Before oversampling - Nb of phrases in training data: 204\n",
            "After oversampling - Nb of phrases in training data: 408\n",
            "Nb of duplicated phrases in training data: 204\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# extract TF-IDF of training data\n",
        "oversampling_train_tfidf = transform_tfidf(vectorizer, oversampled_train_messages)"
      ],
      "metadata": {
        "id": "ZSm1wLvyIp3_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# classfication\n",
        "oversampling_classifier = build_classifier(oversampling_train_tfidf, oversampled_train_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AD6QaLNiEp-8",
        "outputId": "26bd78da-c958-4da8-9d93-2f0756528500"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best parameters :  {'C': 10.0, 'kernel': 'linear'}\n",
            "Best score :  0.9730502860584161\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prediction\n",
        "oversampling_predictions = oversampling_classifier.predict(test_tfidf)\n",
        "\n",
        "# evaluate\n",
        "oversampling_acc,oversampling_f1, oversampling_incorrect_pred_rate = evaluate(test_labels, oversampling_predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F2E_06PlKZdN",
        "outputId": "192f093f-79ca-4d37-bf1b-9fac044ed083"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy :  0.88\n",
            "F1 Score (macro) :  0.89\n",
            "Incorrect detection rate : 10/83 (0.12)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Back translation"
      ],
      "metadata": {
        "id": "2HNP--xgGi34"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "VcRUpzvXHNXk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this experiment, we explore the back translation technique to generate new messages. \n",
        "\n",
        "It consists of three steps:\n",
        "1.   **Temporary translation**: translate each of the original training labeled data into a different language. In our case, it will be from French to English.\n",
        "2.   **Back translation**: translate back each of those translated data into the original language, meaning a translation from English to French.\n",
        "3.   **Duplicate removal**: at the end of the process, we will have for each original text data, its corresponding back-translation. We keep only one occurrence of each duplicate.\n",
        "\n",
        "An example of the back translation for an original english text:\n",
        "\n",
        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+BfoI0qSQFFkFEXuN7DRdfpdfsp+FAUJA1K/j0MJyaDQbzuVMGyJwOYcvLdlG4frj2BQlAtdFEEYcpqms2DebJKTEj985XPQ16xz5MgRLBZrb9ARTazwer04nU5CwSCaptHR0YGua7HJEPv+f+zYMbxeL2PHjmXbtm309HjRNB1N0wkGg4RCIVQ1gqZpdHd3EwwGCQZD1NbWkpyczOG6OgwGA36//4TARpal3kTjwxQWFqEoChUVFbGmsYyMjEG9NoIgnEoENgP03pbt/PYPjzJ2zOjYjVcQPiokJFrb29m8dTuP/Ph7g/o3YDQamTJ1Kg3HjoEE2dnZmM3RhNBp06ZRW1tLdnY2FouFUChEWlpabCLEjIwM4uLiSE1NpaWlhcOHD1NSUkJxcTGHD9dhsZjJzMykoKCAnp4eysvLqa2tRVFkCosK8Xg8FBQUoOs6dXV1JCcnk5CQ8P51kCRKS0upr6/HYrVw+WWXs3t3BTqwcOFCEhLiB+26CILQPxHYDJDP5yN91Cj+53vfvtBFEYQLYvPWHTz1j2eHZCyYosJCigoLAchIT48tz8vLIy8vD4Dc3NxTtps5c2bsdXl5+QnvFRSMjb2eMWNG7HVmZiYAycnJsWWFhYUU9h7/ZAUFBSfk0cyfP//DT0gQhEEjqhoGqK89X1XVC1wSQbgwVFUVtZWCIAw74q4kCIIgCMJFQwQ2giBcVILBIJWVlRw6eDDaNeY86OnpYc+ePbEB/QRBGL5Ejs0wFYlE8Hh7Tpu/4HDYMRn7GVDqA1Ts2cuBqmrKp00lJzvzfBTzI0vXdSKRCABGgyHa9/RD1t++s4K2tnZmzSzHYbcPRTE/kg4fPsyWLVuYOmUKgd7B8lRVjQ2U5/f7MRgMmM1mwuEwmqYhSRKqqmKxWGLNzD6fD5Cw2ax4vV4qKipwOBwYDAZMJhOK6A0pCMOSCGyGqZrDdTzyq9/3jmKqYDRGBxgLhyOYTEa++Ln7mTq57Kz2uXnrDp578T8kJyWJwOYcuT1enlnyHLIic8/HP4bT4fjA9Tu7unj0b09Rezjae2be7JkfuL4wMKFQiLq6OjRNQ1EMbNu2DbfbjcPhoLi4mIMHD+H3+wCYdMkldHZ0UFlZSXJyMh0dHeTn51NSUkJlZSVHjx4FJHJzc0hNTUWSJA4dOsTu3btJSkpi2rRpsXFvBEEYPkZcU1R3d3fvN6lTaZpGV1fXGU1c1ycUCtHd3R0b72K4sNusjC8tpmzieDLS0zhy9BhWi4VJZeMZP66EOJfzw3dyEqPRgNViEd80zwNVVdm5ew+7dleeUQK5zWajtKiQcaVFpB7X20Y4v0wmE6NHj8bhcFBYVIjP5yMQCFJeXk5rSwvV1VUUFBSgqhrbt20j2Pv3X1hYSH5+PpWVlTQ0NLBz507Gjx/P1GlTCQQCRCIRVFUlLS2NKVOmUFVVhdvtvtCnKwhCP0Zcjc3+/ftJTk5mzJgxp7wXCoXYu3cvOTk5ZGVlndH+GhoaqKysZN68eTg+5Fv3UMrIyOC/H3wASZLYvnM3u/bsZfasS7nr9lvQdf2EUVbPlizLhEIhPF4viqzgcjlP6d3i9ngIBUNIsoTBYMBht58SEHk8XgLBIHEuJybTicORu90eQuEwcS7nCd9qQ+EwPd4eNF3HarVgs1oHdA6qqtLZ1Y3NasFqteJ2e4ioEWxWG1Zr/5Pe+fx+/P4AsizjsNsxGs/+4x+ORPC4PbR1dADRifba2juIRFRAj13Pvmvl9fYQCASQJIkbr7+WcDhMXm72CfuMRCJ0uz1YLNGmklAoTEJ8XO/vqAenw37K9dU0DY/HS0SNYDKZPrTG6KNKlmWSk5MwmUz0+P1omsbRo0cxm024XC4i4TB2ux273Y7NZkPXdXx+PwB2u4PExAQSEhJoa23FYDCQmJiIy+XCaDQOuy9DgiBEDavAJhKJcPDgQdxuN1arlTFjxqDrOjU1NciyjNPpxOVy9c4XE6Gquhp/Tw92hwNJlhmdn09cXBwWi4UjR47g8XjQ9ejIomPGjMHpdFJXVxebtK6goBBVVfH5fMPuJiVBLG+jL+jom+jtXIIaRZapqz/CijdWsXn7DlwOJ/PmzOSu228lMTE68Jiu6/zkkV+zafM2zBYziQnxlI0fx/XXXsm40pLYvl5/4y0e/dtT3HfPHXzi47fHlre1tfOdH/yEUDjM//74+6SmJKPrOu9t3cby196kYk8loVCIsaPzuP6aq1gwf85ZV+kfOXqML3/9O8yddSmj83NZ8twLeHt6KCks5L6772RS2fjYuuFwmDVr32XZays4eKgWi8VC2YRx3HrTYsYfdz5nYv+BKr77w5/R5fZg7Q1EPvfg19DRiagqWRnp/OyHD5ObEw1eHn38KZa+ugKTwRB9MCbE851vfpUJ494/bk3tYR7+4c/Iz8shElGpravnU5/4OBV7Kln37iYWzJ3NFz77KRwOe2z9l5e9xsbNW3G7PYxKS2PhvFksvvYqEo8bPG440jQNt8dDfFzcoB6nLzdN1/XY6/j4eIwmE+mj0glHwsTFxdHW1nZCHpumaTgcDoxGI0eO1NPa2kp7ezu5uTn97lMQhOFnWAU2u3bt4siRI0yaNImKigo6OzspLCxk85YtFBcVkZmZyfbt28nLyyMSibBl82ZmzJhBXV0dPT09ZGdlsW/fvtisvjU1NcyYMYNDhw7h9XoZN24cBw4cID8/n+rq6thN7FwChaFw/A31XEiShKpprHr7HYoKCnjwc/fz1ttr+c+y10hKTODjt98aO87li+ZTNnE8uq5TVX2I9Rvfo6JyLz/+3rcZMzofgCmTy0hOSmTnrt3cdtP1WHtrX2rr6mlobGT+3NmkpkSbXXbv3cdv/u9RgsEQV1+xCKfTwarV7/CbP/4FJIkrLltw1uejKAq7K/fS2NTE5Qvn09TcwoZNm/ndn/7Cz370XUalpQKwcfNWfvuHv2CzWVl87ZV0dnbxzroNHGto5Hvf+QbZmWc+7H1+bg4//O5DtLW38/Q/n0OSZO6963biXK5YgmpqSkps/Ruvu5qZ5dPQdZ1lr71B9cFD/fTUiX7+Oru6uev2W3j2+f/w9JLneOD++2hpaWP1O+u4YtF8JpVNoL2jg9/836NUHTzE3NkzKRw7hve2buOZZ5+nx+fjC5/9FMN5XpuW1lZ+/fs/c0nZRG66/hoslv5r186Foiix/ZrN5ljQnJ+Xh8/nY/uO7bhcLsrLyzEYDLGE4b7tkhITmTVrFtu3bwckpkyejMUSrRmUZRlJkmKvBUEYfoZNYKNpGkeOHCEtLY28vDy6urqorq4mLy8Ps8nExIkTcTqjVfyyLNPW1k5KSgpFRUVomkZ1dTUQvan1zdOSkJBAfn4+7e3tNDc3YzQaSUpKxufzEQqF6OjoGFbNT4NNkqI9QiaXzefBz9+PBBQWjOHhH/6UPXv3o2kasiwjyzKXLZh3wrZvr13Pj//3N7yzbkMssMnJyqS0pIj9VdUcPdZAwdho8+D+A9WEQmEmTYjWmui6zrr1G/H5fDz80NcpnzoZgAVz5/Cd7/8Pa9a+y/y5szGZzq7WRtM0AoEgn/nkPRQXRUeF/dkvf8trK1dRW3uYUWmp+AMBnn9pKaDz0Fe/xNQpl6BpOmmpqfzj2X/z1pp3uPeuO844uHU4HJRNGEe328PzL72CoihMnTzptE1BY8eMZuyY0QBs3b6LquqD/Z+LrpORPoqZM6az4b2tdLvdLJw/hz1791GxZy+BYBCALVt3UHO4jvvuvoM7P3YLANdedRk/+vmv2LBpK7ffchNJgzx307lITkri2quvYOWbq9m+cxeLFsxj3pyZWM9jgJOfn09eXh6SJDF79uzYcoPBQNnEiUwYPz52j3A4HBQUFCBJEjk5OWRnZyNJEtnZ2bHm7L7Pxg033BB7ffPNNw/7L0QfBbr+/k9/753ZTvr+098Gp9m5MKwNm68ckiRhNBoJ9k5m5w8EMBgMyLKMwWA45SZiMCiEQiHC4TCBQKDfvBNFUWLLJUmipqaGQ4cOkp6ejslk+shVJ+uajslkYlxpUew7fUJ8PA67HZ/fj3bc9YioKh0dnTQ0NtLQ2ERycjLxcXE0tbTG1jEajUwcP46urm5qDtfFlldU7sPldFJaUgSAt6eH6upDpCQnkZuTTXNLK80trehEH+bt7e34/P0nhH8QTdNITk6kqPD94exH5+WhKAo9vXkS7e0d1B6uZ8zoPKZcEu1FJstSb62RkwMHDsa6bZ+NcDgca5IIhz98BmdV0z6kuVNHliQiqhr9PfXWMuhEA9K+z3ZtXT0Wi4W83Fza2jtobmklGAqRmZGOx+OhubX1A45x4RkMBubNnsl3vvEV5s6eyUtLl/M/P/slO3btPq+jeEvHNduefF/oq3Xp7/2TX3/Qe8KFJUlgMMgosn7aH9CIqBEikdP8qCqqBKqsoCr9/MgKqiJ/5J4VI92wqbGRJIlJl1zCli1bWLFiBYFAgLKyMkwmU+whAsR6J+Tl5VFZWclbb73V27VTiY0tomkaqqrGbpR9/3Y4nLHaHYPBQEtLCzabrXe24Iv/g6sTDfYMyom/9pNv0s3NrTz97HNs27GLjo5OQMdisRIKhWKzKvcpnzaZJ55ewntbtnPlZQs5XHeEffsPsHDenFhTUCgYIhAK0tDYzGe/+JUTHvA+v5+crEw0dWA5TmaT6QMfONH8KZ2E+Hik45oOEuLjMZtNeHq8qJrGSOm0GwyG8Hg8/OyXv4kGonr0Bh8IhDCbjYSCoQtdxDPicNhZfM2VzJwxnTVvr+Pxp/9JcmICN11/LWUTxl3o4gkjQGqKg29+uZy6uqP93r91HVKS7b3d9vunazrB6xchTR0H/TUt6jqK0YA1PwdJ00AWPUpHgmET2ABkZmRgnzePjo4O4uLiSEpKIhgMsmDBglj+xowZM7DZbFitViZOnIjdbqe1tZXm5mbMZjOzZs0iLi6O5ORkwuEwsixTWFhIbm4uKSkpOBx2dF3H5XLR2dmJ2WIhMzMTuxgwDUmS0HV45bUVvLVmLddefTkTx43DYDTQ0trGU8+cOuFhRvooLp0+lc3bdtDZ1c3b69YTiajMnDEtloNgMpuwWqw4HQ4+d/+92KzW3v1EgxC73YbTOThNghaLBVmK9vI6nsfrIRQKY7PaUAaYKyFJnHAeQ8FiMWMyGbntphsYOyYfTeurQpcwGg2MGZ03ZGU5H5ISE7j15uuZUT6Vl5Yu54+P/o3Jl5Rx3dWXk5UpxloSTs9qMbJgbhHhcH7/rUhEm3hV9YO/tEqFBf0HNX10HQWQpWHTwCF8iGEV2EC050J8fHzs32az+YSu2+m9M/u63W7q6+tRFAWv10tJSQmKopCREU0EPT5QSTiup8ioUaNir0dKMKOc1CtqsMiSRE9PDxW7KxmVmsKDn/10rJZj155KIpHIKTUikiRx2aJ5bHhvC8tfW8G+/VXk5WZTfFzzkNPhoLSkkH0HqvH7A6fk7wymlORkcnOzOXiolh27KrikbCKapvH6G6txezwUFxUMaJA1s8mIzWrl6LFGWlpbSUyI/8D1ld7cJWBA4wj1BZQFY0aj6TqdXd2UT59y0dxsszIz+PIXPsuBqmpeXbGKHz/yG6ZNvoQbFl9N8jDOGRIuNAmj0XyhCyEMM8MusDlTLpeLmTNn0dHRjs1mI+W4nigXg7a2dt5ZvwEdOHr0GLqmUVG5F8PLRtB1ZpRPJSvjzHvzQPTh2F+zW1+uiK7rWCxmsjLTWbO2hsefXsKY0fk0NbWwdccuUpKTqD5Yw1P/+BdXLJxPRkY0SMzNziYjPZ3X3lhFOBRh/txZxMW5TjjGvDmzWb9hM48+/hR791dROHY0sizT2dXFlEvKmDCu9Kyv0enO5fjlVquFm66/ll/99k/8+v8e5fKF8+judrNy1Rpyc7JYOG92f7v+UHa7nfGlxeys2MPv//QY5dOmYDIa6fH1sGj+PPLzcvB4vWzfWUEkFEbTdY4cayAcibB56zaampsxGOd9sB8AACAASURBVIzMmD4FSZJOKLOu67F8p5PPZ9rUS5hcNpGlr75GY3MTk8aPx2qz4vX2MGpUKgvnzRnQ+QwXRYUFFBUWsHHzFl5b+RY/eeTXzJ8zi4UL5oixegRBOCMjNrABcLmcuAYwAu9I0NreznMvvkwgEESSJCwWC3v3HWB35X7MZiNZmRlnHdj0dW09vsZAkiRMJhMmkwlN1zEYDNxy4/W0tnXw+sq3QIpe51tuuI49e/ezdv1Gnn9pKeNLimKBTXJSIqUlhax66x0MRgNTJ086pWancOxovvftr/HMs/9m567dbNq8FV3vzX2yO846sOm7JicPXGcwnDq68oK5s3F3u3nltZW8tPRVZFkiPy+HT97zcfJyc87quH1kWeZjt96Ezx9gw+atvLR0ee856xQXFpKfl0NbewdP/H0J3d1uZDmagKgoCktfXYmu6yTEuSgtKcRgiHYz7qs5MhqNWHrPy2g0nnA+LqeTr3758/z7hXTe3bSFqqpDqJqGGokwZ/alzJ89C1kZ+bU4l06fxtTJl7Bhw3u8vPw1Vr+zlltuup4Z06aede85QRA+WiT9o5A1OwjeXvsur7y6gl/89AeDMkWB19tD9aGaaFKtBBISOr3JorLE6Lxc4uPPbpCzhsYmmptbycvNJqG36SQUClF1sAaDQaGoYGwsIPF4vNQdOUooFCIjPY1RaWnUHK7rTSaONokcXyvT0NhEQ2MTsixTWlx42vFJVFXlyNFjdHZ2o2oaBoNCVmY6yUlJZ3Uu/kCA/QeqsFltFBWOjS1vam7hyNFjjM7PIynxxMHqmppbaGpuwWgwkJOddV7yenQdjh47Fh2JWNexmC1kZWXgdDjw+f1UVR86oQmv7/eo6zpGo5GSogIiqkp19SEcTgd5uTnU1R+hp8fHhHEl1B05Smtr2ynXG6CxsZnW9jZC4QiyJJGSnExWZsaHzcd53mx8bytLnnuB3/3iJ4M6TUdXdzdvrn6bdes3kZqSwlVXLGTq5EmDdjxBEEY2EdgM0GAHNoIw3A1VYNPH7fbwwsuv8O7GzZQUFXDLjYvJy80RXa8FQTjByK+zFgThI8HlcvKpT9zFw9/6GmazhZ/84jf8+bEnaGxq/tBt+3KVBEG4+InARhCEESU/L4cHH/g0//3FB2hta+fHj/yKF19eTlt7x2m38QcC/OHRx3lvy/YhLKkgCBfCiE4evpB0XUdVVTEhnvCR1Tfw5YUgSRLjS4spKhzD7j17eeE/y3ht5Spuuv4arr3q8lOapyxmM8VFY1ny3Aukj0ojJ1uMkSMIFyuRYzNAh+uP8OfHnkRiYOOSCMJI5/X5KBtXyqfuvetCFwW/38+ate+yavU72GxWrrpiEdOmXIL5pF5zLy5dxpZtO/ny5z9DRvqo0+xNEISRTAQ258Db48Pn8w1ZLxRBGF4kkhIThtUs152dXby5+h1WrlpNdlYmd9520wlziYXDYZ74+xIam1p46Bv/hcUsBncThIuNCGwEQbjoHD3WwMo3V1OxZy+5uTksvuZyCsZEZ58PBIM8+tiThCMRvvjZT2OzWS9waQVBOJ9EYCMIwkWrrv4I//jX8xyqOcyihfNYNH8Oo1JT8Xi9/OyXv6WwYCz3fvz2AXUZb+/o7K2xFVW2gnC+6bpOQkI8jgFMfSQCG0EQLmqRSISKPXt55dUVtHd0Mn/2TK6+chGBQJBf/v6PXFI2kVtvXHxWAcq+/VX85fG/YzabhlVTnCBcLMLhMEaTka88+HlSU5LPalvRK0oQhIuawWBg8qSJTJ40kXXvbuI/r7zKxs1buHHxNdxyw2Keff5FkhITzmqerYM1hzGZTDzy4+8PYskF4aOr2+3hq996mLa2dhHYCIIgHDx0iLb2DrKzsrDbbFitFsxmM3NmzWDK5DI2vbeF5158mYT4eEqLi/jPK6+SEBfHJZMmntH+ZVkWvSEFYZApijKgpl4R2AiCcNE51tDMmnfW4/P7sNmsmEwmHHY7CQnxjEpNJTUlmfvuvpN1725ix67dtLa28+jjf+f/ffMr5GRnXejiC4JwDkRgIwjCRWfOrBnMmD6VYDCIt8eL2+2lo6uLxsYmKvZU0tjYjKrrKIqMx+MlEAzScbiOH/70F/z0hw+TlppyoU9BEIQBEoGNIAgXHVmWMZtNmM0mXC4nGeknvh+JROjs7KKlrZ32jg66utw0t7TQ2tZGOBK+MIUWBOG8EIGNIAgfOQaDgZSUZFJOSkrUNE30chKEEU78BQuCIPQSQY0gjHyixkYQhIuLrkHEC6FW8DeBMR5smWBwgCRueYJwsRN/5YIgXBxC7dCwHBpehe4dIOuACsigAc5SSJ0PWbeALe8CF1YQhMEiAhtBEEa+hqWw/xcQrAUHkGIDkwPQAQnCAfBvg5rtcPjvMPozMPZLQ1pEVVU/cmPffBTOORKJcPwA/pIkYTCIR+uFJK6+IAgjlx6B/Y9AzR8g3gYpdpAjoAdBD7y/nlkCsxniFfCpUPVT6NoNZY+AMWHQi7luwyaW/OsFbrtpMYsWzBv0452t2rp6mptbKCwYS2JC/DnvT9M0Xly6jDXvvMsD99/HxPGl56GUw4+qqnz/x4+wd38VRqMBWZIpLS7i4Ye+OmT5Wqqq0tTcAkD6qDSRJ4ZIHhYEYSQ78Euo+SOkxUOcBgRAU0GXiN7een90KZp7o4fB5oGMeGhfDru+Abo66MXcvGUbOyv2sG7De4N+rIF4edlrfOM732fvvgPnZX/hcIQNG7ewa3clO3ftPi/7HI4kSaJswnjmzZ7J1MmT8Hi9dLm7h7QMHq+XP/7lb/zuT3/F5/cP6bGHK1FjIwjCyNT0Ghz6XTSosfREg5e+4df7m9q3b2R2XQJDT3S7huVQ9Wso+sagFvX6a69ClmUuWzj8amsAbBYLNpsNo8l4XvZnNpu47ZYbyMvNZv7c2edln8ORLMt87JYbAGjr6GDr9p1DXgaT0UhXt5tIRMVkMg358YcjEdgIgjDyhDrhwC+izU/WAGgSyBAJSwTDEnabFk0Y7iOD3y+jyDomsx5d3+CBZAfU/g2ybgb7mPNWPE1VeevttdTWHUVRovNKJSUm4vcH+l2/2+1m43tbaG5pJTkpifJpU0hOShzQsVe+uQaP18tVVyykct8BDhyoIjExkdkzy4mPi+t3G8Vg+MD5r/YfqGbdhvcwGBTiXE4mjC+lYMzoU9bbXbmP97ZsQ5IkjEYjSYmJaOrAa8SOHD3G2vUbmTK5jLSUFNa8s45AMMglZRMoKS466dh72bx1B7Ik4XI5KS0uPGWd41VVH6Lq4CE6OjvRNQ2zxcL40hLGlxYPqKxnM6fRrt2VVO7djyRJjCstYuL4cWd9vLb2DlauWk1nZxedXV1oms7jf/8HJqMJTdOx22xcdcUCEhPeb2oNh8Ns21FB9aFDmM1mpl5Sxuj89xPpm5pbWP3OOooKx9Ld7aanx8ei+XPZX1XFvv3VzLp0Onm5OQDs3XeA7TsrmD93FpIksW7DJiQkpk+bTH7vOsfrdrvZXbmPYw2N+P1+FEUhOSmJubMuxW63nfX5fxAR2AiCMPK0vAk9+yHbGc2zUSSONhl4+K8ZqKrEz77QQFZqsDfg0dlXY+H7j2cS71D56QPHSE5QQVfApkO3L5pQPO5H5614mqaxs6KSzdt2YDIZCYVCtLV1cMPiq5k+dfIJ627fuYs//fVJGhubsFgsBIJBXlq6nAc/9+kznpTzeG+ueYe6unoCgQCvvLaCgD+ALxBg3bsb+eZXvkRSPwGTsS+wOU1+xq7dlSxd/jpGo0IkoiLLMpcvmMc9d91OnMsZW6+uvp43V7+NLMv4evz4/D7S01LJyzv1QXcmjhxr4PmXXqGxuZmGhiaOHD1KKBTmuRdf5rabb+DjH7slllNSuXc/S5e/jiLLqJqGpmksnD+HT997F3EuV2yfbW3tPP70EtZv2ISm6RgMBiQJVFXjlhsXDziwOT6B+HQ8Xi9/e+ofvLVmLUaDAR0d9TmV666+gnvvvhOLxXzGx/N4PKx/dxMdXV34fH5AZ+36jciSjKqqJCbEM3vm9Fhg09rWzu//9Fe27ajAYjYRUVX+9fxLfOzmG7nj1ptAguaWFp574WWyMzNQDEos+GtuaWVnxR7e27KNn/7w/+FwONhXVc1zL74cC1jaOzoIBIK8uHQZ9911J9dcdRkQ/VtY9+4mnnh6CS1t7RgNCrIko+k6aampXDJxgghsBEEQOLYM7BrIKvQEIRKi8lA6B49ZMCg6R5qNZI0KRmttZNhXb6XDrdDQbuTgMQvJphYIEq3xcejQvAaK/aBYz0vxFIOBT37i49xx203Issy+/VX84rd/OCWxs9vt5rEnnqG7282DD9xPTnYWjc3NPPnMs/zxr0/y4+9/m1FpqWd1bJPRgNvjYceuCh783P3YbVaWr3iT1W+vY9nrK7nv7jtP2cZoNGA0Gk7bm2furBmUFheCBF5vD6vWrOXlV18nI2MUN99w3XHrzWTCuFIkSWL562+wdPnrSOeQzBqtRZLZtn0Xi6+5gs986h683h7++sTTvPDSK0ydPIniwgIA5s+dzbiSYiRJoqenh3fWb+T1latwOR188hN3xYK2d9Zv4K01a7m0fBqXLZyHw25HkiU0VSUt9eyu9dlauWoNr77+BpcvnM8Vly1A13XeWLWGF19eTmpaCjctvvaM95WZmcH3vv0NOjo7+d2f/kJE1fjmV7+Ew2ZD13UURSElOQmIBhd/e/IZNm3eyu233sT0qZMJBoM8+++XePb5F5k4voTS3msnSRAfH8fnPn0v//vr31NVfYiv//cXeeaf/6ZidyVHjjZQUlyIIkeDk81bd3DzDddSXFRAU1Mzf/zLEyx57nkmT5rAqFFptLS28Y9/PU+Pz8+nP3EXuTlZGI1GdKIBdWLiuSern0wENoIgjCy6Cl07IDEeCEQDG6+fmSUevn5nM7MnenHZVYj0Ng1EJK6e4ebScV627rdTNsYPnUEIqeA0gy0euo+B/yg4Cs5LESVJOqEpqbOzs9+miv0HqtlffZBv/NeDXH1l9BvuuNJivD0+HnviaQ7VHj7rwEaSJIKhEFdevoi5sy8FwGa3sX1nBTW1dUQikVMCmCsuW8iUyWVkZ/U/s3l6+ijS00fF/l04dgwVe/ZSsWfvCYGNy+XE1VuDk5Bw7r3NJKLdqcePK+H2W2+KNZV1dnXzs1/+ljVvr48FNqPSUk+4VtOmTqbmcB3bd1Rw2803xGpturq70TSNcaXFlE+dPGR5KaFQiOWvvUFxUQFf/fLnMRqj+UxFBWM4UH2Q7TsquPbKy8+4PCajkfT0NOx2GyaTCTmikpOVic16anBeV3+EbTt3ceVlC7j/vrtjyxMTEvjmwz9g1Zq1lJYUE73iEsnJSWRnZeKwO5AVmYIxo0lNSUbVNCJ9TYuSRCQcpnzaZG647urecxnLscYmHnvyGd5ev5E7br2Rnh4fXq+XtJRkyqdNJjsr85yu45kQgY0gCCNLoAkIg9EKmg8S7OC04rRI3LigM9oRSgNUKTaMjdWmYnVEWJwWji5PcoGugyJHV5Aj4Dt/gc3JVFXrd/mBqmpkWeZw/RGeff4ldF1HkiRqD9cTDofxuD1nfSxdB0VRKC4YG1vmdDiw2axEIiqqpp1y409OSvzAnB6P18uOnRUcqj1MJBzBaDQiSdDT4zvtNprW/zmfDR2QZJmM9LQT8n/Gjs4nMSGe2rr62DJ/IMDOij1UVR0kEAzicNiJRMLoOoRD709sWlpcRHJyEs/88znWb9hEcWEhE8YVM2niBJxOxzmX+XTa2jvo7OokMd7Fi0uXx66PqqoEgyG6u90EQ6GzDrRUVUXXdXRdRz1NPlNLaxuqqtLZ1c2/nn8JrbfZLBgIEolEaGlrB97Pve9Lvu9rXtN1/dTfpx5txss4LuAFKC4qxGI2c7iuDogGnONKS1j37kYe+u6PKC4spLhoLFMnl5Gbk4ssn3lu0pkSgY0gCCNLsBUIg9Sbj6AoYDAAOt3dBjbusTM+P0BWWggsGoQkGptNbDtgZ3qpl9TEMJh6H5K6DpIOkgqhjiE/lXAkgixJbNm2PfYNHqK1LmPHjCYuzvUBW5+eLEnv9wKj9wH14Skg/aqtreMPf3mcw3V1OBwOFEVB13W8Xt8JxxhMknRic5bBaMBsMqOqEQBaWlv53R//yt59B3A6HdFkaEmita2N1JSUE7YtnzaFHz78LbZt38mefftZt2EjK1e9RVZGBnff+TEuLZ86KOcQUVVkWaH+aANe37oT3nM47OTmZA/aYIaapqFrOgdramntDWL6pCQnk5OVAQzsI3JyTaTJaMBoNBKJRIMsu93Glz5/PzPLp1Gxp5K9B6rZtrOCF/6zjJkzpnH/fXdjt9sHdF6nIwIbQRBGFlMC0VuX0vsVU4oGKAYIhmX++FIKmi4xY4KXUYlh2roNbN5jxx+Q+dPX/aSmRHqbqfTo9roEmgzmpCE/lZzsLELhMLffeiNXXb5oyI//YTRN4801b3Og+iCf/eQ9XH3lZRiNRjo7u3jwq9+KXvchcHL85HZ76Orupqi3Vuqd9RtZt2ETX3rgfhZfexUWsxld0/jKQ9+lu9t9Qm6TLMsUFYyNbdvV3c2KN1fz1DP/ZNnrK5k6ueyEIPNsyihJErqmI/UT8SUlxGMyGhmVlsovfvqD89YEFs2LkVHVILrW/+/D5XKBJDF96mS+9uUvnH5nA/h9nhzYdHV14/P5Y02SAHEuFwvnz2Hh/DkA7D1QxaN/fZLlr7/JogVzGV9actbH/SBigD5BEEYWazbIFmhrgYYOcPeAqoE3QKItSPk4H42tRravS+C5l9LY/HYSdY1mphT7yE4NgicIwTD4Q9DYDs1t0d5T9lO7Lw+2sgnjyMrIYMm/XmDrtp34/H40TSMYDOL3+8+op81g0nWd7m4PFouZsWNHYzQaUVWVij17CYZChCMRPN6efptA+h54Z9MN+pR9RAvBodo6Wlpa0TQNv9/PW2vWEggGmT4t2sOsqakZRVEoKY42g2iaxvZdFbS1taPrOh6Pl0gkWrvj8/no6u7G7/cTDAYxmUzkZGdhNJqIhCMDvuYGgwG7zUZbezvVNbUEAgG63W58vmhznd1uZ+H82VRU7uXJp/9Je3sHkUiESCSCz+ePle9sWa1WEuLjOdbQxLoNm/B6ewgEg3i9PYTD0Sa4saPzGDM6j7fWrOXfLy3F7fagqirhcPTYAzpnSUJVVaoOHsLt8aBpOp1d3axctQaDQaG8t/dfJBKhq6sbj9dLIBgkGAySMSqNhPh4NF09bTPtuRA1NoIgjCyBBpCM4G6CpHjo8EKnFzQdg0Xh45eZkI9a+URHHE2SRgYyLzi8XL24HaOnC7qC0W+msgQOKwTcEHGBvxFsA+uWfKZOfsSnJCdzz5238dhTz/DwD3/C+HElJCcl0dXtJjkxgQc+cx8Ox9nlffQleB7/sOrLv1DPMu9FURTGlxaz+p11/PYPj1I2fhxd3W7aOzspGz+O9Rs389B3f8Sn772L4sICXl7+Gu3tHSiKwv6qajRd54231rB3fxUGReHqKxeRk91/gvLpGIxGGpqb+J+f/4qMjHQ6OjrZsauCS8omMGdmOQBTJk1k5ao1PPKr31M+fSrubjf1R4+SmZnB7j2V/OiRX3LrDddz9ZWLeO6FpaxZt560lGRMJhMej5eDtYeRpWgz1UBqayDa5DK+tITlr6/khz95hJysTFra21k4dzZ33XEbADcuvpbauiM89+JSNry3hYKxY0DX6ejs5JYbr2fWpdPP+rhms4nLFs6lcu8+fvuHR1nx5lvY7Xbcbg8fu+UG5s+Zhdls5guf/RQ//9Xv+Mvjf+etNWvJzckiHAoTCAb59tf/C5fLFfucxPJ/NC0a9BOtvVNP+lyZTCYq9x3ge//zc9LT0jja0EDlvgMsmjeHKZPLAKg5XMdv/+8vIEF8nBNN0znW2MSxhkYmlJaQlZE+oOv9QURgIwjCyOE/Cts+BT17QVLAbgaLCVo6ISsZGjoYHd/Jf39BpmmXG63Rgjc1yGcmurHGe+BoKLpecyeYDZAcB0faQXPDtruh7E+Qdvl5L7aqqqDrGI0n3nIlSeKKyxYwZnQeq99Zz86K3TQ2NQOQkBA/oATc5MQEMjPST3hAG40GRqWlkpyYcNY1KIsWzMVssfDqijfYsHkr+Xm5fO5T91K5bz9791edUPuxY+du6uqPoBgUDIqBrIx0jhxtoOZwHVaLhRnlU88qsNGJDnY4Y+pUCsbk8fLyFfgDfu742M1cf82VsaBv+rSpPPS1/2L56yvZsPE9cnKy+OJnP83B2sM0NDTh9fro6uqKXp/kRFwOB82tbWiaisvh5PKF81gwZzalJYUDrmEyKAbuu/sOEhLieXfTezS1tpKRPooxo9+vCUxLTeGhr/8X723extvr3qXq4EEiYRW7zXpOtXNzZ11KXJyLN1atYX9VNd1uN3GuOMzHNXeNyc/jpz/4f6x79z3Wb9zEvv3VRNQI6WmpBIMhIBokpY9KIz4+OpBjclIiihJt2ImPiyM9fdT7++xNKL7q8kWYTQZeXbEKSZL43Kfu5arLF2JQop91i8VMZkY6NYcPU3/Eg9EYTTi+efG1zJpZ3u+4SudK0i90XacgCMKZ6twGm26ETBd428AbgBQneP1gNkafhK1dkJMERgVdA/ryaI92gtEAiU5o74YEJ7hDoIYhNRcaWyD361D41Q8txqsrVrF+wyZ+9qOHz6jYLy1dzp/++gRf/Nynuen6Mx+r5KNu05Zt/PyXv+Paqy7nM5+850IXRzjOy8te47Enn+GBT9/L4muvOu/773Z7+MZ3vs9XHnyAkuLCs9pW1NgIgjByBBoh2AntFkhIB1M7eEPRWpueACQ4wGKGxm5IT4x++5Yk6PBARIXUePD4wGqGQCR6B4zPgK4IuFvBXx+dLFM6t/TDcDiMPxBAUzX2V1fz0tJXsdvtFBcNTnfyi534/j18acPwdyMCG0EQRg7vIYhLhvTsaCCiOsDmAyPR4Aaik1sea4Mub3S8Gl8QuntgVAIYFIizR2t2IjoEJfApEG8HJQ86d4IWPOcRiDdu2sLTz/4bn89HS2sb8XEuPnnPnRSOPX/zUX0UnJzzIQwfmqYTUVUR2AiCIJwTgwMCPvD5wZoEaicQiI5D05eaK0nRQKWrBxI18AejuThW8/vdWSUdjEaQTGBKhFAANDuYU0A+9xmuDUYDdrud9FFpXLFoAZfOmBbrXiycuTiXk4njS8k8aRA44cJLSU5i0oTxpCQN/TAJH0YENoIgjByKNdpUpPijAYiqg0GLjkXTRyK6XLaAnBANYkKdJ43RIQFhMDkh4geLATqOgGsy58PMGdO5tHw659DTWSA6SvCPv/+dc+oyLgyOObNmMHvmjGH5GReBjSAII0f6YujeA8eWgLEREtJAVaI1NpIB0KKJxO1ekB1wrDOaW+MPQ4cbEuJAVqLBkdY7Gq+nJZqn45gFJQ/17ufcDccb/kgkgprha7j+akRgIwjCyGF0woSfQu49cOTf0LEReqrA7ADVDZIFwnFQ/DVIuxL8TWBJhZ5aqPoVtLSA3gOKDUJhMFkhfiYU3ARplzFkcwQIgjBoRGAjCMLI4yqBcd8H1R9NKA62QqgdDC5wjAZHbz5L3MTo/+MnQeqCaG2P/xgYnNF8GnsumM9u9mxBEIY3EdgIgjByKVaIG39m6xrjIXn24JZHEITz4lyaIEVgIwiCcJY0TSMcjg5HLwjC+RcIBAiHwwMaw0gENoIgCGcpLzebN98K8s3/9wOR3Cr8//buOz6us0z0+O+UKZJmRr3LsiTLkiW5d1tusVOc7oRAgOyGXpa+CxfYu2zhwnJZdmHhElgIJCSELGUhkEoSUuy4yb3KseQi2ZLVrTaSppx2/xhpYmGT2LItOfbz/XwcKdKZc95zNDPnmfd93ucVl4Fl2mRmpJOWlnrBj5UlFYQQYgy6TnczNDQkgY0Ql4Ft26SnpeHzJV3wYyWwEUIIIcRV4+IWRBFCiKtIb18f23fuJho1JropQogxkhwbIcQ1zXEc6uqPsn7jZurqjlJWNoUZVRXEFqASQrzdyFCUEOKaZBgGta/X8dKrGzhcV0/51KncdP1qqiqnoWnSmS3E25UENkKIa0pvXz/bduxi05athMMRqiqnsWzJIkqKi1BVCWiEeLuToSghxDWhpaWVnXv3sWHjVkzToHrxQm5Ys4rUlJSJbpoQ4hKSHhshxFXLwaGxsYk//uklDh48RFZWVmy4qaKMQCAw0c0TQlwGEticp8P1R/jTKxuwTPPKXdJUiAlmWxb5+XncfvNNJCYmTFg7oobB/gO1bN22ncN1R5k8uZA1q5Yzd/ZMqTsjxFVOhqLO02ubtpKdlUnVtPKJbooQV6yIEeXBn/6civIyZk6vvOj9maaJqqrnnfsyMDjInn0H+MPTzzEQHGTZ0oV85Ut/R1ZWJpqmXXR7hBBXPglszpNlWUwtKaaqctpEN0WIK5btOHi9Hgzj4uvAtLa289snn+buO24lPy/3Tbdtbmlhy5btbNu1G5dLZ9nihSxdsojsrMyLbocQ4u1FApvzpCgKpmVNdDOEuKKNBDQXO9zT0HiCBx9+jIppU0lPSzvnNpZlcaqllZfXv8bOXXtJ8iVx9x23MmvG9DGVYRdCXB0ksBFCXFGONTTyvR/8mAVzZ3Pfu9951jCU4zhsqdnOy+s30t7ewfSqSj724fdTOa0MXZe3NCGudfIuIIS4YhxraOTBh3/OquXVrLv9llFBTV9fP3v27Wfjlm10dHZSVTGNv3r3PZQUF01gi4UQVxoJbIQQV4RDh+t56JHHWLm8mjtuXRv/eWdXFy+/+hqba3aQmOBl2dLFLF44n8yM9AlsrRDiSiWBjRBiwu3ed4AHH3qUG9dcxx23rsVxbZngxQAAIABJREFUHOqPHuO1TVs5cvQ4CQkebrv5RpYsmk/A75/o5gohrmAS2IwDx3EIRyI4tgN/nlPpgNvtuiS5AZZlsf/gIYZCIebPmYXH47nofY4n27HBiSWeXork0xMnm6isKCcr8+qYGWPbNgCKqp71NDqX9o4OjjecoKK8jJSU5MvbuItwsPZ1Hnnsv3nPO++meslCDtQe4pnnXuRYQyMV08p4/1+9m2nlU2W5AyHEeZHAZhyEQiH+7Tvfp+t0N7quoes6lmVhWRaqqvHue+5iyaL5F32caNTg4cf+m6amUzz4wHfIynx7BTavrN9I/ZFj3HHrWgry8y5qXy9v2Mgvfvkb/unLX2D1qrd/YGPbNr954kn6+4Pce886kt+iaq5hGPzssV/y3PMvcf9738WHP/DX49TSC7P/wCF+8ev/YfWq5Zimyf/9j+/S3dPLzKpK7r7zVsrLpk50E4UQbzMS2IwLBY/HQ2KCl4HBQQ7Uvs6k/DyyszNRFBVNv3SfRD1uN16v521ZXfXAwUO8+tpmli1ZdNGBjUvXSfB6r6KibAqbt+6gra2NO2+7meS3WA1A1TRSU5LJyckmJfnK7K3ZvHUb3/vhT5hWVsrWbTtxbIdVK5Zyw+pVuFwuIBagmaaJYZiYloljOyQnJ6PrV8vfVQhxqUlgQ+zT8KUY/vhLEhK8fOFzn0RRYN+BWv7X3/8Lt9x0A3evuw3Hti9qGMpxHEzLQlO1t7yJ27aNZdlo2vlXcj0Xy7JwHCfebsM0URXlTY9vWRa246Br2lnX2TRNRlb20DQN07TOKPCmoOtnP+Z8jZynaZoof6GNsetiAQqqeu5tRto4csM969xsG03TzrqupmWB46Cd47zPh+M4WJaFaVqoqoKqqsM3+jcK4Om6Ht/3yN8Y4J13r2N59VKKJheec5+qqqIoCpZlxf+Wpmn+xeejbdtYtv2Wf+vz8drmLXz/hz+hPzhA86kWSktLmJSXy8DgEI/+4leEwhEMI0o4EsUwokQiEcLhKD5fEh/90PsoeIuCfUKIa9dVG9i0trYyMDjI1NLSN90uFArz/PN/ZNGiReTlXVwvwV+iKAru4RviyFdd13FdZECzdftOnnjyGRoaTzApP593rLsd/Rw3nIHBQf708npeWb+RU61t5OXmcP3qldy4etUFr+fjOA7/9ZOfsXvvfr74d5/hiSefZteefaSmpnDr2hu45cbrR+X2dPf08vSzz7NxSw29fX0UTZ7EzTdcz/LqxbjdboZCIb78la9Sf7QBTVNxHId/+vo3URQlHkj845c/z9zZMy/4GqmqRntHJ//x3QfYun0nyYFAPDn1zPN+8OGf8/RzL+DxuElNSWHurBncdMMaSqcUx7d5/Ne/5Yknn+UTH/kAN92wOv7znt5evv7N7xAOh/nHL3+enJxsAE6cPMkfX3yZzVt3EAqFmFJSxC033cDy6sUXFFR2nT7Nl/7xa7S3d8Sv/8c/84X47y3L4tvf/BoV5bEhmy01O/jO//sBUcPE43ajqgof+9D7uX71yvhjOrtO8y//+m9kZmTg0l28XlfHXXfcSjgS4alnn2dGZQWf/sRH4j09AwODvPjyK7y8fiOt7R1kpqezesUybrpxDSlv1XV0DqZlcehwPYGAn4L8PHw+HzgOLa3tKGrstRIIBPD7MklNTSHB6yU1JRm/308gEMCXlHjBxxRCXDsueWBj2zb19Ufo6GinsLCQoqIijh07huM4lJaWcuTIUUzToKKigpMnT3Ly5EkmT55MR0cHFRUVHD16FL/fT1dXFympqSQlJdHY0EBlZSUpKSkEg0EOHjyIx+NhxowZhMNhamtryczMoq2tldLSUlwuF1u3biUUCuHzJZGamkbtwYMYhkFlZRWBgJ/Ozk4OHz5MenoGkUgknph5uY30TFzs2qO79+7j29/7AZZlccPqVYRCIR59/Jc4DijK6BvnL3/zBL/67e/Jy81hycJ57D94iB/86Kd0nT7NB//6vRfcexOORuno7OKXv/kdp093M2/OLHbu3suPf/ooWRkZVC9ZBEB/f5Dv/9dP2LBxM6VTSpg/ZzY79+zl29/7AeFIhFvX3oDb5eKeu+6kt6+PDRu3cOTYcW5dewOTCvLjvQqTCsYScCrgwEuvbCAlJYW1N6xhS812Hnr0F7jcOu+48/b4lkVFhdywZhUATc2neOq5F9i6fRdf++e/p3i4t2POrBn89omn2bp9B9etXIbb7QbgZNMpGhpPMH/ebDKGpx+3d3Ty7//5AHVHj7Fk4QIy0tPZum0H//7dBwiFQqy9cc15n4Uvycd973oHA4OD/P7p5+jv7+eeu+4gORDAsR0cxxm1bEDR5Encf9+7URQ4WHuYP72ygUgkMmqfjuMwOBiip/cY77zrDk53d/P4r3/Lu+6+k7LSUl7esJGVy5eyYtlSIpEID/38cZ5+9nmKiiazZuUK6o8e48c/e4yu7m4++sH7z9mL9WZ0TeO+e+/hrttvxe1y4XK50HUNl8t1FQ0dCiEmyiUPbNrb22lpOYXP56OmpoaEhATaOzowDYPS0lJaWlowTYPs7GxqamrIzc2lvr6ejo4OSkqm0NTUhOM4+Hw+DtbWMq28nO6ebrZs2cLKFSt49dX1ZGVlMjQ0xO7duykqKqK2tpayMhPbtti4cSPXXXcdlmWTlJSEruls2rgRTdPwer3U1NSwePEiampq8Hq99PR2x4cp3i6i0ShPPfcCoVCYL3/hs6yoXoLjOGzaUsO3/vP7JAfeyKmoPVzHs398keLJk/mHL36OyYWTONXSxle/8S2e+MMzrFpWPapn4nxoisLg0BB+v4//9blPkZiYwMbNW/nXb/0nm7duY8miBaiqyq49+1j/2maWVS/i85/5JAG/j/0Ha/k/3/h3nnrmeZYuWkBqagorli0B4GRTM8cbT7CiegkV08ou6hopCoQjEYqLJ/O5T34cj8fNwvlz+No3v82efQe4de2NeId7ltZev5q118d6YQzD4LXNW/nWdx7giSef4fOf+QQAM6oqmTm9ktcPH6G5pZWSoskA1B89xlAoxNzZM+NDOK9u2MyRYw186H338c677kTTNO6+8xa++o3/4Nnn/0T10sX4z7Pkf0KClzXXrQBgw6YtRMJhbr7xelL/wiyngvy8eH6S1+PlpVdfO/dzW4H0tDRuu+Umunt66ejsZNXK5eTkZLN5aw39wQEAXj98hBdfXs/1q1fy6Y9/hKSkRAYHh/jOAz/ipVc3cPNN18evxYXw+3z4fb4LfpwQQryVSz5/0uv14vF4CIfDRCIRBgYG0FQt/qY/8smsu7sb27ZZvnw5ZeXluFyu4TwXlZycXGbNmoXb5aKkpISyqWWEQmGaW1ro6+shFAoRiURobm5maCiEx+OhtHQKs2bNxrJsVFXF5/ORmZlJYmIibW1tGIZBNBqlv7+P9vZ2+vv7Wb5sOTNmxG5IF9uDMp66Tvdw5MgxyqZOYUV1LChQFIX5c+cwqaAAy7JQhicE19cfpbunh6WL5lNcNDneAzLSy3PocP0FH992HDxuN6tXLsPnS0JVVWbPmkFCQgIdnacxzdiaWg2NJ1BVldUrlpOSHIhtN3MGU6aUcPT4cdo7Okft1xpei+tSLKAYywHSmD1zOh5PrHclNyeHgN/P4EAI0zBHbW9EowwMDBCJRplWNpXcnGxONp16Y4q1ojBnzkx6+/o4eqwh3t7de/bhdruYPXNG/GeHXj9MTnYWy5YuIWoYDIXCpKamUl5WSndPDz09PRd8PpZlY9sOjhMLbM/HyN/hL9F1Dcu0sC07nqcTf0MYDoaONzaC47Bk0UJ0XWdoKISu61y3vJrevn7a2tsv+FyEEOJyuuQ9Nq+//jo9PT1UVU2nsbERVVVRNZWhoVh3eCgcHh77j+VTOI6DETWw7TcCi5EgR9f1+DCJoijDyY4q6enpBAIBTNOMDwmoqjb8VcW2bRzHjv+/qqokJyeTm5tLbm4uCQmxMXrTMkclrr5dRCMRBgaHmJaactbvEhMT6D7jxjkwMIiuu8jKyhi1XXZWJg7Q398/pjZomkbA/0Z+xUivgMMb13JgaAi320Xyn+Vh5OfmsH3HboLB4JiOfb5UVYn3yjDculg732hjKBzm908+y+at22jv6MCyHbweD8FgkNTUlFiy7PBzcNb0KlwuF/v2H+TGNatoajrF3v0HuW7lsvhwUCQaJRwJ09HZxZe+8tVRQ5y9vX0kJiZimKODqivJn78ShkIhDNPkvx58iB//9Gc4xEoxGaZJJBxhaHBoAlp5/izLorOzk8zMzEs2zGUYBvv376egoIDs7Oyzfh8Khejp6SE3N/dNe4JN06S5uZmCggJZ40qIS+iSv5o0TSMSidDTfZqEhARaWlrIzs7mSH0927Zvp7enh8zMTNLTM/B6vKxfvz7WwzD8+o8FO7GbwchNITbzxyQzI5P09HQ6u7ro7+vDHwjg9wdG3TxGvk9KSqKlpYWSkhIKCgroOn063hMwe/Yc0tPT2bDhNZKTAxiG8bYailI1DV3XiEZH92yMntkVu0W53S4sy2Twz25AQ6EQQLw3Yyycs26Do7l0HdO0zmpnfzCIoijnKCConPHfy2vkMv3xhZd49L9/xewZM1i2dDEul04wGOSZ51+KFVQ8Q+GkfCrKy9h74ACRcJit23diOzYrly2NX3e3y43L5cLtdrFm5XICyQGIB84KvqTEsS0FMPL6wEFVx++56na5wYG5c2ZTWlI0qgfLcRwqppWPW1sulOM4tLa2smvXLhYtWkRycnL8g4zX40XVVPr7+3G5XPh8PizLYnBwELfbTTgcxufz4XK5sCyLYDCI4zj4/X5M04znAmZnZzM4OEgkEsHn86HrOnV1dTQ3N7Nq1Sp8Ph/BYBDDMAgEAqMCmObmZmpqali1ahU5OTnxHm63241fqisLMWaXPLCZMWMGWVlZJCYmUlRczODgINlZ2Xg8HhzHoWhybDze50ti5qyZOI5DJBKhr68PXdeYO3cuCQlekpKSqK6uJhAI4PV6SUxMJBDws2LFCtra2lBVldzcXFRVZcWKFSQnB9A0jaVLl+D3+5k/fz6tra0kJiaycOFC2trasCyLzMxMfL4klixZQkdHB4FAgMLCQlJTUy/1pTinkTc2TRv7KGAg4CcnO5vGEyfp6OwiKzPWG2M7NsHgQOymM7ztpIICPG4Pe/Yf5M7bbsbr9WKYJpu21KBpGsUll2MBwdjR8/NyMcwoe/fvZ/HCeUBs3Z/6o8fJyc4kM3N0L1JiopdIJELn6e7L0KbRFEXBtm127NpDZnoGn//s38QrFLe1d/Dciy+jKIyauebxeLhu5TK+/6Of8NyLL3G4rp5J+XlMLpwU30bXNaaUFLNz917S0lJZd/stl6S9qhILioLBATo6OsnMyHjLx4z0UGgXMbW/cFIBmqri0jXuvvO2Me9nIliWRUNDA93d3bS2ttLS0sKRI0dIT09n6tSpNDQ0YJomkUiEmTNn4vV6eOWVV8nLz6e3p4eUlBSWLl3Kzp07h9+fdAKBAOXl5cNT+zWampo4cOAgHk+sF3rBggU0NTXR29tLd3c3HR0d1NXV4Xa7cbvdLF68OB4sNTc3MzAQpL29A5fLxbZt21BVlUgkwrRp0ygvv3KDRiGuZJc8sElMTKS4+I1k1LS0NAAmTZo0artQKMTxhgYSvF6CweDwEFFCbOrnsMLC2IwUj8cT/wSTlJTElClTRu2rqOiNm/PIcdxuN6VnTPU+cxuA5ORkksepcJlhmOzYtYdoNErjySZUTaGu/igbNm7Btm3Ky0rJy8057/2lJAdYtbyaHz/0CN994EexWTZOLFG463R3fEo5wKwZVSxaOI8tNTt44McPMWfmDA7UHmL7zt3MnzubGRXTLvh8RoYQ3+rnc2fPpHhyIc889yIet4eCgnw2b62hra2de+66Ix6QjSgrnUI0avDr3/2eYHAAny+JgcEhZk6voPgCE1Tfqo2OExuWSk1JYd+BWl58aT3Tq6bR3x/ktc1bcbtcnO7p4cln/sjihfPjQ02zZ04nKyOD3z/1HIZhMnf2zPhsqBFrVi3ntU1b+Okjv6Dr9GmmTpmCrmsMDAxSUJBH1RiuuaIozJ09ky012/nhgw+z9sbrSUxMYGgwRGVFGVNKimM9CccbGBwYRNU0Gk6cQFEUjp84yZ69+3GAKSVF8d6WM2fonXmtzvx+5vQKliyez7PP/4m+/n6WLl5EUmICoXCE5ICPuXNmX1TgdDnpuk5ZWRkdHR1UVVVx4MABotEoixctwuVy09bWRkpKCsePN9DU1ERxcTGhUIjCwkJKp5SyefMmevv66OjowO/3U1JSgmlao66foihkZmaQlJTEvn37ME2TjIwMPB4P+fn5PPPMM6SlpZGdnc22bdsoLS0lNzcXTdPIzs6mubmZ8vIydu3ahaZpXH/99ezes4eDBw8yZcoUGaISYgwm7FWTkJDAksWL6evrQ9M0UtPSrtqpnuFImId//jitbe1oqorX7WXz1u1sqdmOqml88qMfuqDABuC2m2+krb2dl9dv5NDhOjxuNwvmz2XW9EpqX6+Lb5eUlMiH3/9XhCMRNm6u4bWNW3BwWDBvLp/6+IfGtJ6UOpzvdNbP1dGF/wry8/jCZz/JD3/yCL978pl4ntSq5dXce8+6s/7eixfO59577uSFl17lJ488hqqqGIbBB+9/zwUHNm/VRmd4Taq7193GiaZm/uf3T/KHZ/6IrmusqF7K/HlzeP5Pr/DDBx8mNSU5HthkZ2UyvbKCF19ej9fjYeGCOWfVDppcOIkv/u2neeTxX/H0cy+iqAqKA1HD4Jab1owpsAFYe8NqWtrbeeXVjfz4oUfRVBXLtrn/ve9iSkkx4XCYRx77JYfrjqDrOqZl4fV6eOGlV3nplQ3ous7nP/sJSqeUxK7DcFkARR19rc78PjExkU//zUcIBAJs3FTD3v21KMRyiebNmUVVZQWJCRdWC2k8jQRpI0OFPp+fhMQkuro6aWtri+foxYoaxgoP+nw+3C4XiqKiKgqLFy+mtraWXbt2kZKSQlpaajyXr6WlhZ6eHtweT7zY4ZnHjkajDIVCBINBSktL8Xq9Z7VRURSCwSDJycnDuWt+DMMYtxIUQlxtJvTjgM/nG9VDc7Xyejzcf9+9DA2F4tVez/zEVzmGqc2JiQl88mMf5roVy2jv7CQjPY3pVZXUHznK8uolo6bSTi6cxNf/6e+pPVxH9+keUtNSmF4xbcyLZN50/WpmTq8i+4zFJRO83tgN0O9DO+NTZlVlBd/46ld4/XA9g4MDZGdnU1VRfs6cJo/Hw0c+cD83rFlF44kmDMPE63FTUnzhw2VLFy8iNydn1FpDfr+fD/z1e3C5XHi8sXMvLSnm6//8v3m9rp6BgQHycnOomjaNuqNHqZw2DcexmVr6Rg+hpmm8Y93tzJxeidvtZuG8uec8fmVFOd/4l3/gcN0R2ru6sAwTTdcoKpx0zu3PR2JiIp/66Ae5cfV1tLS0YhgGCQne+PXxeDysu/0Wepf3xRbKHM65chwHZzgJumjyJJKSEvnQ++7DM5wPtHL5UirKy0j2+ykvm8oX//bTVFVVxI+bmpLC5z75Me667RZONjcTCoVxcMjLzRnVO3glUlWVUChER0dHvEK0oij09PTQ19c3nFtzioGBIINDQ7Fgwnkj1y8ajdLV2UlxcTE5OTns3buXcDgcy/szTVpbW/H5fHjcbgzDoLu7BwcIBoP09/eTlpaGqmlkZGTQ09MzKrBRFIVoNEpHRyc5OXk0NByjsbGRxsZGUlPTpLdGiDFSnLfblKAJ8qOfPsK8ObNYMG/ORDdFiCtWJBrlS//wVe6/794xVYu+5O2JRNi4cSM+n4+EhAS6u7tZuXIlkUiEbdu2oSgKkyZN4tixo2Rn59DU1MSyZcvQNJ2amq0sXLiQrq7T1NfXoaoqU6ZMIS8vj02bNlFeXo7jONTW1jJ58mSGhkJEImGKiovZs3s35dOmkZ2Vxe7duxkaGoqVrigri/dUDgwOsnnTJpJTUpg9axaHDh2iubmZ1NQ0Zs2eRUASiIUYEwlszpMENkK8tSstsBFCXHuuzKw/IYQQQogxkMBGCCGEEFcNCWwuwNupiJ8QE0FeI0KIiSZp9+fJtu34FEyZhinEuSgYhoFlWW+7ZUqEEFcPCWzO0+RJBTzx1LP86ZX18qYtxDnFAhuPx0NuztlrKAkhxHiQWVHnybZtggOD2Pabr5gsxLUuwes9ZyG6q5VlWZimicvlIhqNouv6OWvQGIaB4zjxooBCiMtDAhshhLgILS0tHDx4kIULF1JXV0deXt5ZS8gAHDp0iP7+fhYvXjwBrRTi2iFDUUKIa97AwADt7e04jkNOTg6JiYmcOnUKl8tFcGAgtpJ3VhbhcJjW1lYURUHXdfx+P9FolNOnT8cX2U1KSsIwDFpbWwmHwyQkJpKXm8fQ0BDd3d00NzcTiUTIzc0lMTFxok9diKuOzIoSQlzTIpEIW7Zs4eTJk3R2drFz505CoRCbNm2itraW7tPdrH/1VYLBILW1tezYsYNgMMhrGzdy7NgxdF2Prx21a9cu2traOHHiBHv27MEwDOrr6+nt7UHTNHr7+mhra2fbtm0cPHhQ8vWEuAwksBFCXNOCwSCdnZ243W4SErz09PQwMDiIrmnk5+cza9YsHMeJ9+qUlZUxc+ZM0lJSzprermlarDdnON+mr6+P/Lw8/H4/lmUR8PuZP38ehYWF9PX1SWAjxGUgQ1FCiGvayEKhHo+XtLQ0srKySPB6UVR1eKFYB03T4kHIyFd7eCHbP2fbNvn5+SSsWEFXVxf79+8HYgtyeoaTql0uF6FQaHxOUIhrjAQ2Qohrmt/vJz8/n46OdiKRMElJPpKTkzFNE8uKzYI0DANd18nPz+fw4cOYpkkwGCQvLy++0vfIV4CW5mYaGk+QnBzA7Xaj6zqWZWEN/96yrPi+hRCXlsyKEuMiFAoxODg4qrih4zj4fD6SkpIueH+2bTM4OEhSUhKRSIRQKERaWtqbbpuQkHDOabhChMNhmpubsSwr1tuSkMCJEyfIyMggKSmJxsZG8vPzMQyDEydOkJKSwv79+8nNzaWsrIy2tjby8/NpbW0lJSUFn8/HqVOnGBoaIjk5mby8PLq6uohEIhQUFNDR0UE0GiU/P1+qNQtxiUlgI8bF888/z86dO8nPz4//zLZt5s+fz6xZsy54f52dnfzmN7/hvvvuo6Wlhfr6etatW3fObfv6+njsscdYt+4uCgryz7mNEOejoaGRurrD+P1+Ojo6WLp0KdnZUoxQiCuJfHwV4yIUCjF9+vRzBh+madLZ2Ynf76enpyc+5XakkFlvby/d3d2kDCdrKqqK7TgMDQ1hWRZFkyeTnp4BxLr4W1tbiUaj+P1+MjMzcRyHUChEOBLh1KlTmKZJXl4eLpdrXK+BePsrLCxEUaC/v5+pU6eSlZU10U0SQvwZCWzEuHiz7vbBwUEeffRRJk+eTFZWFseOHcPn83Hvu99Nc3Mzv/vtb8nKykLXdZqbm5k+fToLFixAVVU0TWPvvn3s2LmTT3/qUzz99NN0dHSQnJxMe3s7S5Ysoby8HIAdO7ajqSqtra2kp6fznve8B03TxusSiKuApqkUFRVNdDOEEG9CAhsxLlRV5cCBA1iWFc+z0XWd5cuX4/V66evrIz0jgzVr1lBaWsrPfvYzenp62L1rF/n5+dx777309vbyrW99i2g0OmrfhmEQCYcJRyIcPHiQe++9l6lTp9Lc3ExPby+2bRONRvElJXH77bdz5MgRHnroYbq7u8nMzJyIyyGEEOIykcBGjAvHccjLy2PevHnxwEZRFPx+P5FIBF3XKSwsBGKzVBRFIRqJ0N19mvnzFwCQkpJCSUnJWfse6Q3yer1Mnz6dX//615SWlrJgwQIqKyoYGBjA4/FQVVUFQGJiIh6Pm0gkMh6nLoQQYhxJYCPGheM4pKWlnbMbPxwOA3CuwSrLHp3brqp/uaakqiisW7eO6dOnc/z4cZ566inKysqorq4GFHRdcmqEEOJqJ5WHxbgxDAPDMIhGo/F/I3U/zkXTNAL+AE1NTZimyenhdXbOla+jKArB4ADbtm2joKCAG2+8kerqahoaGjAMAxSkyqsQQlwDpMdGjAvHcaipqaGlpSUeYJimSWVlJYsWLcKyrFGVXQ0jFvBUVy/lV7/6FadOnSIzKys+U+rMomi2bWOaJrquceLECXbs2EFCQgKhUIj58+fjcumYhjFq/yOPFUIIcXWROjZiXIxM2T7z6WbbNoFAgPT0dNrb20lPT8fr9WKaJu3tHWRlZWIYBu3t7ei6TmpqKo8//jj5+QWsXXsTXV1dZGZmMjQ0xMDAALm5uRiGQVtb26jp3rZt09nZSVpaGm63m2g0SmdnJ5mZmfFASQghxNVBAhtxRTt06BAvvPACS5cuJRgMsn37dt773vfKlFshhBDnJIGNuKJFIhF2797NiRMnAJg5cyaVlZUT3CohhBBXKglshBBCCHHVkFlRQgghhLhqyKwoMe5M0+Lll3ZzsqkDx3ZQ1dj0bXv4e9u2URQV27ZQdReFvW3kD3binLPSzVtzTIvctavJrF54KU9DCCHEFUgCGzHuTNNi06YD7NlzBFXVUJRYcT0HMAwTj8dFKBQhIdFDJGKyLNvDpLLUMR/P1i0UWRNKCCGuCRLYiPGnKOi6RlJSApqmoqCACpqqMTAwRFq6n9NdkJLip79vEF9+DmnzYgtZjiUjzLFtvNmyJpQQQlwLJLAR404htlSCaVrDVYRtFFRwLGzbwTQsHMfGGd6m98DrNDbXjvl4tmVR9N678RUXXrJzEEIIcWWSwEaMO8dx0HUNl1tHARRFI7YElIOuq9i2g6ZpWJaNy62TPmsWU1ZWDC+LMKYD4iuRujdCCHGSfD8/AAACFklEQVQtkMBGTAjHtnGc2KKWsaWflPi/2EKXsURiB+jctI1DW18ce/KwYVD60feReM9tl6z9QgghrkwS2IgJYVk20YiByxVL6lUUBVVVMIfXjDIMC8uyMaImaYvmUbV2Ngpjz7FJkmEoIYS4JkhgIyaEy+3C5daxLRtNV8EBw7TQNY1wOIqqaQwOhnG5dYJ1R2lor0cZnjl1oRzDpPBdd5CYn3vJz0MIIcSVRQIbMe5cLp2bbl7IrDmlOI4TmxWlMFzTRo3VsVEVbMtG1XUyBrtJH+wZ8/Ec28abm30Jz0AIIcSVSgIbMe5UHEqGusjtO4WiqvFeGAVwzvw6nCzsKApOgnfMx3NsG1WXOjZCCHEtkMBGjDvHtuk9fJSevQdQ9Mv/FHRMC292JoGKsst+LCGEEBNLAhsx7hRVI33x/NgUbHUcliuzbQJTSy7/cYQQQkw4CWzEuHNMk1N/eI7OTdtRXZf/KWgbBlM+dj+FhQWX/VhCCCEmluI4Yyp5JsSYObbN4PETRHv7QB1bbZoLYjskFOSRkJN1+Y8lhBBiQkmPjRh3tmly8nfPcLpm5zj12JgUv/89FNy59rIfSwghxMSSwEaMO1XTyF5dTfK0qePSY+PYDslV0y77cYQQQkw8GYoSQgghxFVjHKakCCGEEEKMDwlshBBCCHHVkMBGCCGEEFcNCWyEEEIIcdWQwEYIIYQQV43/D1B1gGl+ofG5AAAAAElFTkSuQmCC)"
      ],
      "metadata": {
        "id": "hlrBxOeCHjRb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We use GoogleTranslator for the translation. \n",
        "Note that for a message, GoogleTranslator returns only one translation. "
      ],
      "metadata": {
        "id": "EHrYhnPEIwK8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# initialize two translators\n",
        "google_translator_fr_en = GoogleTranslator(source='fr', target='en')\n",
        "google_translator_en_fr = GoogleTranslator(source='en', target='fr')"
      ],
      "metadata": {
        "id": "zUq-ce1oS0t0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# example back translation\n",
        "original_text = 'il me faut une réponse'\n",
        "translation_en = google_translator_fr_en.translate(original_text)\n",
        "translation_fr = google_translator_en_fr.translate(translation_en)\n",
        "\n",
        "print(f'Original text : {original_text}')\n",
        "print(f'Temporary translation : {translation_en}')\n",
        "print(f'Back translation : {translation_fr}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4-Vo10qkJOYr",
        "outputId": "da5a11bc-c99a-4b43-ef5f-4464f3aa17ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original text : il me faut une réponse\n",
            "Temporary translation : I need an answer\n",
            "Back translation : j'ai besoin d'une réponse\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def _translate_original_message(phrase):\n",
        "    gg_translation_en = google_translator_fr_en.translate(phrase)\n",
        "    gg_translation_fr = google_translator_en_fr.translate(gg_translation_en)\n",
        "    return list(set([phrase.lower(), gg_translation_fr.lower()]))"
      ],
      "metadata": {
        "id": "uUmX8jpIOVfW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def back_translation(train_messages, train_labels):\n",
        "\n",
        "  backtranslation_train_messages = []\n",
        "  backtranslation_train_labels = []\n",
        "\n",
        "  counter = 0\n",
        "\n",
        "  for phrase_, label_ in zip(train_messages, train_labels):\n",
        "\n",
        "    counter+=1\n",
        "    if counter % 20 == 0:\n",
        "      print(f\"Translation in progress : {round(counter/len(train_messages), 2) * 100} %\")\n",
        "\n",
        "    translations = _translate_original_message(phrase_)\n",
        "\n",
        "    # print(f'{phrase_}')\n",
        "    # print(f'\\t{translations}')\n",
        "    \n",
        "    for translation_ in translations:\n",
        "      backtranslation_train_messages.append(translation_)\n",
        "      backtranslation_train_labels.append(label_)\n",
        "\n",
        "  return backtranslation_train_messages, backtranslation_train_labels\n",
        "      "
      ],
      "metadata": {
        "id": "x_yICEraSXXJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "backtranslation_train_messages, backtranslation_train_labels = back_translation(train_messages, train_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IiRzGpDtUrMQ",
        "outputId": "1e174c7e-1b7e-4b52-b230-931fbfdc0d99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Translation in progress : 10.0 %\n",
            "Translation in progress : 20.0 %\n",
            "Translation in progress : 28.999999999999996 %\n",
            "Translation in progress : 39.0 %\n",
            "Translation in progress : 49.0 %\n",
            "Translation in progress : 59.0 %\n",
            "Translation in progress : 69.0 %\n",
            "Translation in progress : 78.0 %\n",
            "Translation in progress : 88.0 %\n",
            "Translation in progress : 98.0 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Nb of phrases in training data (initial): {len(train_messages)}\")\n",
        "print(f\"Nb of phrases in training data with translations: {len(backtranslation_train_messages)}\")\n",
        "print(f\"Nb of new phrases (translations) added in training data: {len(backtranslation_train_messages) - len(train_messages)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UQmwhQSiWdJ6",
        "outputId": "3fb3c6b4-ec0f-4bb5-ec05-fee7a1ebb1f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nb of phrases in training data (initial): 204\n",
            "Nb of phrases in training data with translations: 363\n",
            "Nb of new phrases (translations) added in training data: 159\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vectorizer = train_tfidf_vectorizer(backtranslation_train_messages)\n",
        "\n",
        "backtranslation_train_tfidf = transform_tfidf(vectorizer, backtranslation_train_messages)\n",
        "backtranslation_test_tfidf = transform_tfidf(vectorizer, test_messages)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NfdjgV01fv-e",
        "outputId": "13ca7262-9b5c-4180-ff62-ddf03003fe3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "179 words in vocabulary used for extracting TF-IDF\n",
            "['absenc' 'acced' 'accompl' 'activ' 'affirm' 'agent' 'aim' 'air' 'ajout'\n",
            " 'allez' 'annul' 'appli' 'appliqu' 'arriv' 'aujourd' 'avoir' 'beaucoup'\n",
            " 'besoin' 'bien' 'billet' 'bonjour' 'bord' 'bot' 'bug' 'cel' 'chang'\n",
            " 'chat' 'chatbot' 'client' 'clôtur' 'coler' 'combien' 'comment'\n",
            " 'commentair' 'complet' 'cong' 'connaîtr' 'connect' 'conversationnel'\n",
            " 'coucou' 'crev' 'crédibl' 'dashboard' 'dat' 'dis' 'dit' 'dois' 'don'\n",
            " 'déjà' 'détail' 'ennui' 'entrepris' 'envoi' 'ete' 'fach' 'fair' 'fais'\n",
            " 'fait' 'fatigu' 'faut' 'ferm' 'fin' 'fonction' 'fourn' 'franc' 'furieux'\n",
            " 'fâch' 'habit' 'hey' 'horair' 'hui' 'humain' 'hyp' 'hé' 'identifi'\n",
            " 'incident' 'indemn' 'indident' 'indépend' 'inform' 'infos' 'joint' 'jour'\n",
            " 'journ' 'lien' 'list' 'mainten' 'manqu' 'march' 'merc' 'met' 'mettr'\n",
            " 'modif' 'modifi' 'mou' 'natal' 'nombr' 'non' 'obten' 'occup' 'origin'\n",
            " 'ouai' 'ouaiii' 'ouaip' 'ouais' 'oui' 'ouii' 'ouvert' 'ouvr' 'où' 'pai'\n",
            " 'pay' 'person' 'peux' 'piec' 'pj' 'plus' 'pouv' 'pris' 'problem' 'prévu'\n",
            " 'puis' 'quand' 'quel' 'question' 'quoi' 're' 'rebonjour' 'rempl' 'rend'\n",
            " 'repons' 'repos' 'resalut' 'rest' 'robot' 'récuper' 'réel' 'répond'\n",
            " 'répondr' 'répons' 'salut' 'san' 'si' 'sold' 'sort' 'souhait' 'souhaiter'\n",
            " 'statut' 'suit' 'sup' 'surcharg' 'tableau' 'tel' 'temp' 'termin' 'ticket'\n",
            " 'tout' 'tres' 'trop' 'va' 'vacanc' 'vais' 'vas' 'ver' 'veux' 'vien'\n",
            " 'vill' 'vis' 'voir' 'vou' 'voudr' 'vrai' 'vraisembl' 'web' 'wé' 'ça'\n",
            " 'énerv' 'énorm' 'épuis']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/feature_extraction/text.py:516: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "backtranslation_classifier = build_classifier(backtranslation_train_tfidf, backtranslation_train_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1WMsSlJVYZgK",
        "outputId": "f71d9cbb-26f1-425c-a766-f3e2e16da55f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_split.py:676: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best parameters :  {'C': 1.0, 'kernel': 'linear'}\n",
            "Best score :  0.9174277016742771\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "backtranslation_predictions=backtranslation_classifier.predict(backtranslation_test_tfidf)\n",
        "\n",
        "backtranslation_acc, backtranslation_f1, backtranslation_incorrect_pred_rate = evaluate(test_labels, backtranslation_predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UGNagG0GYn_p",
        "outputId": "b251c90b-0750-4dea-b13d-b9efa09ca086"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy :  0.96\n",
            "F1 Score (macro) :  0.97\n",
            "Incorrect detection rate : 3/83 (0.04)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Notes:\n",
        "\n",
        "\n",
        "*   The translator may return different results over executions. This is normal to get slightly different performance of prediction.\n",
        "*   You can observe sometime a surprising translation: your back translation can be the same as the original text. This will not help you a lot to improve your data. Try to use at least 2 translators : one for temporary translation and another for back translation. This can help you having a back translation different from the original message. \n",
        "\n"
      ],
      "metadata": {
        "id": "xHF7RKvkMfzi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Paraphrasing"
      ],
      "metadata": {
        "id": "gnIerLeDGn-f"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To have more variations of a message, we want to generate more examples that preserve the initial meaning of message with different choices of word and grammar. Paraphrase is one of the techniques allowing doing this. "
      ],
      "metadata": {
        "id": "Q_5hafLKLYik"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAjMAAAC/CAYAAAALv11IAAAgAElEQVR4nO3de3hU1b038O+eyeQOaJBLAiQhk+p7UOEUGtRcwKNphkcUC0UkgBy8oaTtW7GIxaKlFrWJsdieNhEF7ZGrFuGg4mMi+gokUIl4Co2cymFCLiRcA+R+mct+/xj2ZvZkkswkM7P3JN/P8+QhM/sya8iC+Wb91t5LEEVRBBEREVGQ0qndACIiIqL+YJghIiKioMYwQ0REREGNYYaIiIiCWojaDRgM7KINFlsnrHYLbHYb7KINNtHxvdXeCbtoBwDY7DZ5/+7oBD0AQK/TQyfooBP00Al66HV6CNAhRBcKg95w9c9Q/785IiIilQm8msl3nENLh7UN7dYW2EU7LLbOHgOKP4XoQhEWEg6DPhQGXTjCQsIRFhKhSluIiIj8gWGmn9oszWiztKDd2oIOa7tqocVbUaFDEaoPR1ToUIYbIiIKagwzfdBhbUNLZyNaLU3osLap3Zx+0wl6DA2/HkPDhrM0RUREQYdhxgttlmY0tNejzdISNCMw3ooKHYrhkbEMNUREFDQYZjx0pe0iLredH7AhxtX1ESMREzlK7WYQERH1ipdme+BK20XUt54ZNEEGAC63nceVtotqN4OIiKhXDDMeaGivV7sJqrjcdl7tJhAREfWK95nxiF3tBpAbrR02NLVa0dFpg8Umwm5nxdRXDCE6hBl0iArXY2iUQe3m9An7h/oGQj+i4MAw44FWSzNC9eFqNyPgbKJV7Sa41WGx49yldnRYGDL9xWK1w2K1o7nNiivNFoyKCUeYITgGctk/tCOY+xEFF4YZD1xuO4/rI0YiPCRqUMyb0Ql62EQrzjfXIHn4RLWbo9DcZsW5yx38LTuAOix2VJ9rxZgREYgM06vdnB6xf2hXMPUj1dltEG0WiDYLYLVcfc7xvSjaALsduHrHeFF0H9oF4Wpo1OkBnQ6CoAd0eoi6EAghBgh6AxAS6vhzAGCY8ZA0fyR2SKK8/MBAoxP0EAQBF1pqYbF1qN2cLjosdn5Qqaj2QhviR0Vq9jdr9o/goPV+FFBOoUXsbAcsrYDd7ggx9v794izi6vE2y9XHztuUBL0BgiEcYkiYI+gYwiEYgqsawTDjpTNNlQCAG6LiEB4SCYutU+UW9Y9jXacQdNracbG1TpMhRnLhCj+o1Hamvh2JoyPVboZb7B/BQ8v9yK/sNoiWdtg7WgFLK0RLR79Diy/Io0BoUgQdXfgQiCFh0IVFQgiLUqt5HmGY6aOLLXXy9zdExSFUHw6doNd0GACuhRcRIlo7G9Hc2aD5NgOO8kFbh/r/6Ac7i9WOxhaL5iZzsn8EF632I7+w22BvbwLam2HvbNVEePGUvb0JQBNszY7HuvAhQGgkdJHDHOUrDWGY8QHnYAMAQ8KuR4QhGiE6w9VVrsWAj+BcW0lbgB0i7HYrmjsb0G5tDYrw4qqpVZuTkQejlnab5j6E2D+Cjxb7kc9cDTBiy2WflIy0whHKmmBvPOcINuHR0EVep3azADDM+EVTx2U0dVx2u21I2PUIC4mATtAjROf4hywIOggABAhdapmuhKt/ihDlfe12K2yiDRZbB6x2S9AGlp7wyhTtaNXgCAj7R/DRYj/qL7GjBWJHM+ytDQMmwHRHDjZXzkAXPgRC1PWqlqIYZgKsp6BD3bNxLoRmaHFeCvtH8NFiP+oTaR5M43mIlna1W6MKKdgIhnAIUderMlrDMENBYcD8x0d+wf5BahA7WgZ1iHElWtohXjkDseVywEMNwwwREZEXGGJ65hxqdENHBqT8xDBDRETkCbsN9uaLsDdfUrslQUG0tMNWXw1d5HXQXRfr19dimCEiIuqF2NEC+5UzV+/HQt6wt16Bvb0JuutiHVdB+QFvwUjUi8z0ibhx3BC3X0fKDuGtwte7PP9W4evdnk86DgCOlB3qcmxmetclJNy9NgWfmupKxc9wz4c7AACPLpqNNc8t77K/1D8AYM+HO7rtH877dred+s7efAm2y7V+CzKlh7+BYcwEj/fPL9iImQuX+uz139v9CW5KNfnsfG7ZbbBfOg1b4wW/nJ5hhqgXe0uO4URNE07UNAEAtu0slh9PSbkDAJAxPVN+7kRNEx5f9pRXr+F8bPq0u3HjuCGoqa5U7OP8ulJbKLjcnXYr1v35HflnuPP9LQCAOfMWomT/5132/2jX+1jw0GPy4/iE8QAghyBnBX/IQ8b0TFRXnfJT6wchuw32K2dgbzw34C+1DhSx+SJs9VU+Py/DDJHGrHl5HRY89Jjb39QpeEnhdHTsGPm5jZt3AQBmzpqL6qpT8oidZOumDbhv9jzFcw8ufEQOQc7nPrBvL3J+vtIfTR+cpCDTeiXgL/3TVS8iv2Ajbko1wTBmAgxjJqD08DfytlUvvYbiL0tgGDNBHqE5VVUj72sYMwH5BRvl8+UXbMRPV72ImQuXytvf2/2JvG1RzgpUXD3e7yM0AMSOVp8HGoYZIg1KuT0NB/btVbsZ5EPj4hORMT0T2XOyuoQWAFjw0GP4aNf78uM9H+5AfMJ4efRPMmPmj3Bg317FyN3GN/6gGMGh/rM3nr96O391rHrpNbz9+iuw1B7HK7/6BR55ahUA4E+vvIBXfvULZN2ZDkvtcezZ8iYA4MZUEzYX5MNSexyW2uNY9dJrcgACgPXvbsfieT+CpfY4NhfkY1HOCgDAipxHsbkgH0kJ42CpPY7vDhYF5P35OtAwzPTT04tfQFrivW6/9n6036tzbXnjA6Ql3uvRvnXVZ/v0GuQfB/btdTsXoq+cf3uXZM/J6nFeDWnfxs278Mxzv5V/ls6h5r7Z87B10wb5cdnfSvHgwke6nGNcfCIWPPQYNr7xB/m5rZs24NEnf+7fxg8itsYLqozIOHti8XykTZ0MAPjxzCxUVNV0u+97uz9BUsI4PHj/PYrjt+36WH6cdWe6vF3681QP5wwEsaMV9itnfHIuhpl++v27L6K08mOUVn6MMQmx+M1/rJQfZ943zatzLXzyxyit/Lj3HQHExY/u02uQf7jOmZk5a648YdN5srCnzp6p7fKc85yZvSXHfNl8CqDHlz2FEzVNWPfndxSjNFNS7kB8wng5CG/dtAEzZv7I7Tmcg89bha8jY3omxsUnBuYNDHD29iaIzRfVboZbPYWPCpcy0/p3t/d6vrpz/pmM6w3pSqf+4qXZfrTljQ9w5vQ51FWfxVf7v8Fv/mMlMu+bhqcXv4Cv9juG/26bNhm/f/dFAMDej/bjzfxNeH/fWwCAedMfx9IVD+HXP8uTz+kcdtIS70XhX/MwMWUC8lcXIHbsKOze9ilqqxxJV9oGOEZyHpimHIYekxArvxb53sxZczFz1tw+HVv2t1JkTM/0cYtIS2bOmot1eS8qgqvzfJieAooUfN4qfB3vbXkby1e+EJA2D3h2G8QmbQaZ3iQljAtYicjX7FfOQDe6f1docmTGz3Zt/gT3PJApj6Lkry6QH5dWfoyv9n/TY6no1z/Lk/e9bdpk5K8u6Hbfgt+9g9X5y1Fa+TFyfvkw1q5YJ2976qHnkfPLh1Fa+TF+8x+OSYIMMtr0VuHr2LppA9a8vK73nSlo7Plwh2JS95GyQ6iuOqUoKUrzYdblvYg58xb2eL4HFz6CV19+HgD6HJpJydZ8KSju6jtuTCxOnro23+TB++9BRVWNPKnXW2NjR/dYxvI7u63fl2wzzPjZbdMmK0pBK9bmKB7fNm0yztV2/0OUggcA3PNAJuqqz3a77+xF98gjMf92T5o8QgMAtVVncOuUfwEATJh0IwD0eC4KLOdy1Htb3saJmiaWDQaYmbPmKu4zkz0nC9t2Fism+EqThKurTvUaUKTL/93Nq6E+sNuAtga1W+ERKbwYxkzAT1c5RvZPHCzCopwVilKTp+EmbepkJCWMU1wdFWhia/8WYGaZSQWuk3ynpE7y+NjTVd5NlqqrPou4+NEYkxCLfxz5H0xMmYDjR08AcMy7Ie+4u7/L48ue8uq+Ms7nmJJyh0f3jOF9ZQYG6VLsvuzjrmzp2i887U/Ulb2zVbW7+6ZNnQxL7XH58Z9eUZYNx1+90siZ62N3+0hW5DyKFTmP9ni86iUquw321it9XpySYSbA5k1/HDm/fBgLn/wxAMfVUIFS8Lt3UPC7dwA45tMQEdFV7c1qt4DamwGGmeAxaswIAJAnBnszMtMXddVnUVt1xuMrpbSgcPcpJMVFwpQySu2mkMaUltfjmLkRszNiMTomXO3mUBBy9/+LaOtUsUUEOEbH+jr3hWEmwFbnL8eyB1bKVyjNXnRPL0f0X1z8aOT88uEu5a2/7t+g2VKTubYFO/fXYdeBM5idEYukWP8vIU/BY+f+OuzcX4c50+IwO8O/q/HSwOP6/4spZRRg5QKSquvHkhGCKIqiD5syIB2s2qN2E/rlWNlxLHtgpWJkZssbH+DIwaPyZeHdOfnPW7GpWN0bKwHALeOHYv5dYxAzNFTtphCAQ99e0kS/AIDoiBA8fm8Cvjc2Wu2mkJe00o/SbhmO5XfaERUmqN2UQS8k7l/6dpyP20EadP7sRYxJUP72eub0OY9GZRab4rHYFO+vpnVrRUE5jpobMComDDNvH43D/3MZfzc34K7vjwh4W6grtfoF4CgzrXnnn4iOCMEk4zA8lDUOdv5OFpS08P/LnIw4ZKWMRHhDBReTDGIMM4NA5n3T8PevyhVlJueb9WnRqJgwLLtlPNJuHY7C/zqF8lONSBgdoXazSAOiw0OQdstwPJQ1DsYxjvLj/57m5E3ynPT/S1bKSERHOD4Gbc0GiAwzqhIMfZ8DxzKTB4K9zNQfqQkzVXvt5jYrCnefQnHZeQDAJOMwPDGL917RAq2VdBhmgpOW+pG98RzszZfUbsagpgsfAl3M2L4d6+O2EPmMubYFB8uv/edygh9YpIIjZYdw47j+3WrdU2ueWy7fVM95VWwKgNBIVV7WMGaCYnVrf8kv2CjfTC8Qr9cnkcP6fCjDDGnWpORh2LX2NkRHhOCu749AZJhetbb46gPt0UWz8eii2X0+XrqDrPMH3VuFrwfsw5Z868ZxQ/BW4esAHD/brZs24PPSf8h3gHbe3leZ6RMVq7hLfdmbhU8HA11oJAS9Qe1m+MxNqSb57sAAsOql1/Dlrs2w1B5H2tTJXbarTqeHrh+BknNmSNPMtS0YFROGuXfGYe6dcWo3p988uQOst7y9AzFph/Pdes+fc9zd23kZC3/czZd3Ce6GTg9EDAM0umK2t5zv6Cutth03aoTb7VogRF7v+Bn0EUdmSNNKy+uRenOMV8c8umh2l99mnX87lUYyuhvOd96258Md2PPhDmTPyVJsc36t7s716KLZ2PPhDsW2Nc8tlxcbdG1HZvpEt+eVRnKOlB3C3Wm3AgDuTrtVPueeD3cojnV9D+62ObfLdftg5vz37rwopCvnv1/nviaNekhf0uiH89+3cz+R+qVrH5N+5q6jKs5lKOcRPte+5NzO6qpTWP6Th+Wfu7vRvZ76sdQGd+cfaPTRMX4bnXEu89yUaup2v5tSTfJ+riMnzusu5RdsBACUHv5G8bxUQpq5cCnyCzai9PA3uPHq692YapJfW9oueW/3J27PI+3rvP2Urxel1Omhj/bu//kup/BRU6iPnl78Ara88YHazdCs4q/PY5LRuzrq7WnT8d6Wt+XH0urEM2fNxZ4Pd+DVl5/HiZomnKhpwro/v4OHF8yS981Mn4hnnvvtte15L2LmrLnYtrMYAOTnAcgfJtJzzzz3WzlsSJb/5GFF2cDZ48ueko/NmJ6J9Gl3A3B8YM2Zt1DedmDfXuz5cAempNyBz0v/AQDdnhNwfIA5v4eERGOX0ta6vBcV76W/pYyBQAov0t9LTXWl23krmekT5b//bTuL8erLz8v7Pbv8Caz78zvyOaRFJJf/5GFs21ksP+/6c3PtY+5G8N4qfB0l+z9XnONI2SHs+XAHamuqFH3J+b3EJ4yX2+Ru8UpP+7G78w84Oj2EYb6/6/h7uz/Bqpdeg6X2OCy1x/H4onluF4GcuXAp3n79FVhqj+PEwSKsf3e7HCp+uupFPLF4vnwOaa2lR55ahc0F+fLzaVMnK86ZNnUyTlwdhTlxsMjtiEzp4W+wKGcFThwsgqX2OL7ctRl3zl6k2Md5+/iEcT75e5Hoom/o16gMwDATVNIS78WxMvcLiQ1E5toWx71Ekr0LMzNm/gjVVafk34o/2vU+Fjz0GABg5/tb8Mxzv5X3nTlrrryvFHqcSzZ7S465fY2a6koc2LcXa15eJz8nHec8F+GZ537b6+rXez7coTjXmpfXKT50MqZnoq72tEfvfc+HOxCfMF7xHnJ+vhIH9u1V7Je7br38/YMLH0FtTZVH5x/Itm7agJyfX1ulfuPmXW5/dntLjsnPS2FFKhEBQNnfSt2e/5uvv+pX+97b8jaWr7y2ltual9dhSsodmDlrrqIf3p423ePJw57243V/fkf+fs68hQN6crIufEifFzvszrvv/xde+dUv5Mcrch7Fg/d3vfv7ni1vymFkfMI4JCWMw+kzZ+Xtn+1z37cO/O3rfrVv266P8cTi+XJIkVbRdg5cr/zqFz4PMQCgi46Brp+jMgDDDGlYaXm916MygGPOQcb0TPnDY+umDbhv9jx5+6svP9/tkHl8wnivX8v1+LNnar06x/KfPKz4sACUZQzXINKbhESj4vHIUY4bJvb0ATSQP5y8If1d9cS1lORsb8kxlOz/vEsZ6ERNk9zv+jOqMTp2jNvnnctEr778vNfn9bYfV1WavX6NYKK7LhZCmG+vbho3pve+daqqRlHqqXAq5/zplRfww+lp8jbJdweL8Nm+UhjGTMDMhUv73L71727v9rX9RTCEO0ZlfIBhJsD2frQfaYn3Ii3xXrcrZm954wN5u3STu7rqs/L3yx5YqRihyV9dIO87b/rjAXsfgXDM3IiJxqF9OnbOvIV4b8vbOFJ2CPEJ4+XfoAEoSjCu5YDqqlNevY5rCKiuOtXtB447a55bjozpmYqRGNdSV8b0TK/a5PpB425iKbnnPMLiTk11JbLnZClKRq72lhzDiZomVFWaFeU7af+tmzYo5sF4w13AcC2POY88eqq//Xgg0g9P8GmgqantuW8BjjktziWjJJeRkD+98gIstcfxxOL5iuDy3dXyz8lTVYp5MN5wLmFJX+5Gj3xFMIRDPzy+3+UlCcNMANVVn8Wvf5aHwr/mobTyY9zzQCa+2n9tktXej/bjzOlzKK38GKWVH+O2aZORv7oAcfGj5XWVpGMnpkzAljc+QOzYUfL+AAbM/BtzbQvMdS1Iu2V4n46XykfPLn8CDy58RH5+zryF3f7mKgUa5w8gaXKs6+iGNPrj/Fv2W4WvdwlOPTlSdghbN21QDPFL4saMlV/PeWRGCiTdfehK79v5PRT8IU8us1H3MqZnouAPefLjRxfN7nbESuoPPYUS1xEyibejf5L0aXdjXd61CaFrnlsul4Kcg6rzfDGpHd2VKX3Rjwcy/fAE6ML7P+H539Juw6qXXpMf5xdsdDtnBgDGxjqWmSk9/E23oyOJ49wHzeTxCX1qX/bse7H+3e2+n9jbDSEs0qdBBuCl2QH1/z4pxW3TJmNiimOIMPO+afjkr9c+qDLvm4bM+6bJj6ekTsKRg0e7Pd/CJ3+seDw14/s4c/qcj1utjqPmBmSljOzXORY89Bi2btqAGTN/JD83c9Zc1NWe7lIekH7D/rz0H7g77VY58EiTMqX/9O9OuxXxCeOxt+QYNm7ehcz0ifK5pOc99dGu9wFAMdny89J/IHfdemTPycLynzwsvw/X9yVd+eJuZMD1PSx46DG3gYmUXH+e0nwn5+A4Lj5RMUE2Y3qmHE5qqisVP8uM6Zl4fNlTOFJ2SP55AY6fh7uJuL1Z8/I6uZwknWdKyh2YknIHbhw3BFs3bZCfdw5hc+YtxPKfPIxXX34e23YWdyml9bcfD3S6mLFAP+8OvCLnUVTW1Mrloaw707Fny5td9ttckC9PvE1KGIesO9PlbTelmhThxlJ7HKeqauQrlaTzShODvZE2dTI2F+QrzgU4Jgz7frJvjE8m/LricgYe8NVyBlve+ABnTp/DirU58nNPL34BU1InycHk6cUvKEZrnNdQSku8F4V/zZPDEADMm/44aquu/Wc7e9E9ivP3l1rLGawoKMfsabHyyAxvV68tWroNPcD+Eay01o96Ina0wHa5lotR9pVOD93QkT6fXC3hyEyA1VWf7XZb/uoCAFCUjHoamXl68QuYmvF9rNj3luL4YNfcZoW5rqVPk3+JiPxBCItCyEgj7I3nYW+9onZzgooQFgnddXF+vcMy58wE0K1T/gVf7f9Gnry75Y0PFKMwABAXP1r+fve2TxXbxiTE4vxZ5d0pY8deuyfCrs3ua7DBprjMcW8ZaTVbIiJN0Omhuy4W+uHxA2rpA38RDOHQxYx1TKb2898Xw0wATUyZgJxfPixfkXTm9DnMXnRttviKtTnYtfkT+eqkqRnfVxx/f/YM/PpneUhLvBd11WexYm0OCn73jry/87mC2TFzI36YMqL3HYmIVCCERUE/Khm6oaMYatzR6aEbOgr64fE+mUDtCc6Z8YCv5swEo0DPmWlus+LJ3/8dbzz9r4qRGXNdC+x2dlWt0NpcB/aP4KS1ftRX9uZLEFsuQbRZ1G6KqgS9AUJUDHSRw3w+wbc3HMcnTTlqbkByXHSXEpNeJ/DDSiMMIdob0GX/CD5a7Ed9pYuOAaJjYG9vAtqbB9ecGp0egiEMuugbIBjCAx5iJAwzpCmflV1wW2KKCNPDYrWr0CJyFWbQ3ocQ+0fw0WI/6i9d+BAgfAh018U6RmvaGiBa2tVull8IYZGAIRK6qOs0UWpjmCHNaG6z4mRdM56M63qn2ohQHRpbVGgUdREVrs5vXj1h/wg+WuxHviSN1sBug721AWJHE8SOVrWb1XdXR2BgiIQuYohjFEZDGGZIM6QS0+iYrv9IhkYZcKnJwt++VWYI0WnyKjP2j+Ci1X7kFzr9tWADwN7eBLGzHbC0ajvcXA0vgiEcQli0qiUkTwyS3kTB4LOyCz2uxRQ7PBzV5zT8j3+A0+kE3DAsFDqdoHZT3GL/CA5a70f+JpWiJPb2JseN+KwdEC3t6gQcp+ACnQFCWKSjdKTh8OKKYYY0QS4x3d/9YohhBh3GjIhA7YW2ALaMAMcH0IhhoZr+bZr9Q/uCoR8FWneXLtvbHUuViJ2OOTeC3QrR1gnY7YDdBlG093w34qtBRBB0jrAizWsJMQA6A6DTOUZdgiy0dIc9ijThqLkB0REhbktMziLD9IgfFYkz9e0sKQRImEGHmKHB8QHE/qFdwdSPtEAOOQG6T0uwY68iTThmbkTqzTEe7Rtm0CFxdCQaWyxobLWiw2LnZbk+ptMJCDPoEBWux7AoQ1CVBNg/tCOY+xEFF4YZD4ToDLDaB9/NkEJ0gbvcrrS8HvnLbvHqmKFRBgyNUv+SQNIm9g+iwWPgXejvB6OHdD+PYyAbGT02IK9z9KRnJSYiIiJ3GGY8EH/djYgbOl7tZgRU3NDxGDvsewF5raPmBo9LTERERK4YZjyUeP0ETIrNQEzk6N53DmLDwofj5lG3I/H6CQErMxV/fR5ptwwPyGsREdHAwzkzXogKHYr/M2IKWjobcaHlNM43nx4Qc2lCdAbERI5C7JDxiArt/j4v/mCubUF0RAiMY6IC+rpERDRwMMz0QVToUESFTkDi9RNwqfUsmjsb0dRxCQ3t9Wo3zSMhOsPV9zAUI6LGIiwkIqCTfZ2VltdjknGYKq9NREQDA8NMP8VEjlaUni61nkW7tQ2dtja0dDaqHnBCdAaEhUQgKnQoIg1DMSx8uKrhxVXx1+fxzIOBmZtDREQDE8OMj3U3p6ahvR42uwXt1jZY7RZ02trQYXXcqdRqt8Bqt8Bmt/ZatpJCiF4XIn8fFhKJkKuPQ/WO4CKFGK2EFnekEtOkZI7MEBFR3zHMBMiw8L5NcH23qBqLTfE+bo02sMRERES+wKuZNG5TcY3aTfCbY+bGHheWJCIi8gTDDKnCXNsCc10LL8kmIqJ+Y5ghVRw1NyArZaTazSAiogGAYYZUwRITERH5CsMMBVxzmxVHzQ2c/EtERD7BMEMBV1x2HpOMwxAdwYvpiIio/xhmKOCOmRvxw5QRajeDiIgGCIYZCqjmNitO1jWzxERERD7DMEMBddTcgOS4aJaYiIjIZxhmKKA+K7vAEhMREfkUwwwFjHQVkzEuSu2mEBHRAMIwQwEjXY49OiZc7aYQEdEAEjRhpqKiAoIgIDk52e+vlZycDEEQUFFR4ffXGkw+K7uApLhItZtBREQDTI9hJi8vD4IgKL5ycnIC1TbVJSUlqd2EAeVkXTOXMCAiIp/rMcxUVlYCALZt2wZRFJGbm4vCwsIBH2hOnjwJURTVbsaAUlpej+iIEJaYiIjI57wqM82dOxcAUFxcLD/nPGrjXALKycmBIAjy6I4UgKTnpa+SkhIAQElJCQRBwPbt2+Uyj/N2ZzNmzJC3b9++HcC1MpTzaFJFRYX8vPQ1Y8YM+TzSa0pfEucykyft2r59e5cRrECUw4LJMXMjUm+OUbsZREQ0APVrzowgCDCbzRBFEdu2bYPZbEZeXp5in2effRaiKKKgoAA5OTlITEyEKIryyMeSJUsU+2dnZ2Pt2rXy9oyMDMV2s9mM1atXQxRFGI1GZGdnd3m93Nxc+Xij0Si/Xm5uLoqKiuQAtGTJEnn7gQMHehxxys7ORnFxcZd2V1RUIDs7W35Nk8kEwDG6Q9eUltezxERERH7hVZjJz88HACxduhQAIIqiPK9k7NixAK6VpiTbtm2Tvy8oKMDKlSvlx0ajEWazWbG/yWTC/PnzAQDLli0DAMUoiNFoRHp6OgDIox/OE3WNRqP8GklJSYpyUXx8PACguroagCMYmc1mlJSUIFZizSEAAA38SURBVD09HQUFBd2+d5PJJL9X53YfPnwYAJCamgoAuOuuu7q0abAz17awxERERH7jUZjJzs6GIAgoLCxEbm6uHBacSzquIyjuuJZ8XINMd06fPt3j9rq6um63OZe1XEdxDhw4AMAx+uNcsvJURUWFHOIOHjwIAPjiiy9gNBo5edhJaXk9S0xEROQ3HoUZaQKwKIpykCkpKVGUdKRg0JOsrCxF2cdoNHrUSCkweGv79u0oLCyU2+88SgQA6enpEEVRDlWrV6/2+jXi4uIAOMpbgiCgqKhIMaeIgOKvzyPtluFqN4OIiAaoPs+ZcR0tkUYmeuI8ElNRUeF2ZKaoqEguKxUWFgKAXFbyllROkuzfv1/xWCpTJSUleRysXO3YsQMA5LlDzqU3ulZiMo7hXX+JiMg/+hxm5s+fD5PJJI9IfPHFF70eI00SFgQBRqNRnizrzGQyYcmSJfLVRZ6WotxZuXKlPEnY3U3wli5dqih59WVERRqpMhqNbq+MGuxKy+u5QjYREfmVIGrkhiolJSXIyMiAyWTCp59+qnZzPJaTk4PCwkKYzWYkJSX5/H388Bel+Oy1NB+0VB1P/v7vWDZrPCYlM9AQEZF/BM1yBlrlOtojld9YanKUmJrbrAwyRETkVyFqNyDYffrpp3LZTLJs2bIeL/MeLI6aGzjxl4iI/E4zYUa6sigYBWu7/e1g+SXMnhardjOIiGiAY5mJ/OLspXaY61o4+ZeIiPyOYYb84mD5JaTeEoPoCM0M/hER0QDFMEN+cczciNRbeNdfIiLyP4YZ8rnmNitO1jWzxERERAHBMEM+V1pej+S4aJaYiIgoIHoMM84LSUpfOTk53e7jvLo14FgbyfV41zvxutuel5fXpS3u9utpm/Tl7eKR1H+Hyi/jhykj1G4GERENEj2GmcrKSgDXFprMzc1FYWGhItBIq0QDwNatWxXHS2sjSYtRSksTSPtLocZ58UlpiQQphJSUlMj3cZH2kc4jBSPpeWmxS5PJJD83f/78fvz1kLdYYiIiokDzqsw0d+5cAJDXMKqoqEBRURGWLl0Kk8kkLwzZHecFHV3vnCu56667AFwLQmvXrgUA/OUvf1GcJzc3FwCQn5/vzVsgPztqbmCJiYiIAqpfc2akFaPnzp2LJUuWAECPZR1ppWyj0djt7f6lBStTU1MBOFbRBrqunC1t78vikMGgcPcprHnnnwCANe/8E4W7T6ncIs98VnaBJSYiIgoor8KMNAqydOlSAMCbb74Jk8mEpKQkTJ06FYByBEUiraxtNBqxbNkynDx5UrFdWklbEAQUFRVBFEWkp6d3O3ozGCTFRaK0vB6AY0JtVLhe5Rb1rrnNiqPmBhjjotRuChERDSIehZns7GwIgoDCwkLk5uZi5cqVKCkpgdlslstCSUlJMJlMKCoq6hJCpDkzRqMRhYWFXSYKS/NhpNKR6yTjniQnJ3u8bzAxpYzCqJgwAMComDDMmRancot6d9TcgEnGYRgdE652U4iIaBDxKMxIE4BFUcTKlSsBXJvsK426SKMqwLXykytp1EaaB+Nq5cqVcuCpqKhQzLFxDUAHDx4EcG2OzUD0UNY4AEDWD0YGxRyUz8ouICkuUu1mEBHRINPnOTOFhYWKK4ycrzJ688033R6Tnp4Oo9HodvRGIpWwpEAkBR/nAFRRUYFnn30WAORwNRCZUkZhknFYUIzKAMDJumZkpYxUuxlERDTI9CnMSJN8peAhkUpNZrO5y0iKRDqmu6uQpCumpLAyf/58HDhwAEVFRfIIkNFolC+/DgqiFbA1A5Z6oPMc0FELtFcAbSeAlmNA8387vprKHF+Nh+SvNXObEG1xek7ap/m/Hce2Hnecp70CaK9ynN96BbC3B/QtlpbXIzoihCUmIiIKOEEMmkQQBESrI0TYOxzhxdYAiDbHc6JVnTbpwgF9lONPXZTje320z1/m3SLHpfSLTfE+PzcREVFPtD8RQ+usVwBrgyO42FrUCy3dsbe7H6UxDHeEG8Nwn4Sb4q/PI3/ZLf0+DxERkbcYZvpCKhlZLzm+D0aWegD1QEc1IIQAoaOA0DjHCI6XzLUtLDEREZFqGGa8Yb0CdNY5RmK0NgLTH6LVMY+no9YxUhOe5FWoKS2vR+rNMX5sIBERUfe4aranOmqB1v9xjGgMpCDjylLvmGDcXuXxIcVfn+daTEREpBqGGU9IVx8N5BDjqqPa8b57IZWYJiUzzBARkToYZjzRWad2C9TRUd3rLqXl9RyVISIiVTHMeMSudgNU0vtV+8fMjZwvQ0REqmKY8YTlstotUEcvZTVzbQvMdS0sMRERkaoYZjzRUe24h4yg/ZWrfULQAxCB1n/2uNtRcwOXLyAiItUxzHiqoxpoKQeEAfxXJugBwQC0n+w1yADAwfJLmGgcGoCGERERdY/3mfFWy7eOPyOMgH4YYG9Ttz39JegBIRSwtzpCjM2zNZ2a26yOEhMn/xIRkcoYZvqqzXzt+wijY2kAIUT74UYKL7ADlkuA9bzHAcZZcZnj3jLREexCRESkLn4S+YJzsAEcSwOEXO8IDUIIADHgq1jLJSMIAOyAaAEsF6+uIdX/thwzN+KHKSP6fR4iIqL+Ypjxh85zji93Qkc5FnYUDFdHSHB1Hk5f5uJcvWRctDhW57a1AmKHzwJLd5rbrDhZ14wVxmS/vQYREZGnGGYCrfMcgG6CTpA4am5Aclw0S0xERKQJA/jSHPKXz8ousMRERESawTBDXpFKTLyKiYiItIJhhrzCEhMREWkNwwx55bOyC7jjluvVbgYREZGMYYY8xhITERFpEcMMeUwqMY2OCVe7KURERDKGGfLYMXMjkuIi1W4GERGRAsMMeay0vJ6rZBMRkeYwzARQXkERhLgnkLNqa5fnZyz4o0qt8kxpeT2iI0JYYiIiIs1hmAkwY+IIFP7nPpQcPql2U7xirm1B6s0xajeDiIioC4aZAEtOHIll/z4da1//pMf9hLgn5C9pJKei6iKEuCdQUXVRsd/23WXy45xVW7uM/PhC8dfnWWIiIiJNYphRwYons1D05beKEOIsOXU1clfPgVi3HmLdehTvO47tu8uQlHADjIkjcPjvpwAAJYdPwpg4Avv/9r/yscX7jmPa7d/zaXvNtS0sMRERkWYxzKggKeEG5K6eg9W5u7tsKzl8EubKC1iZY5KfW7ooA39575D8vRReDn5txtpn70fxvuMAHCM35soLmH9/ik/bW1pezxITERFpFsOMSqSwkldQ5Ha7c5np2bU75edTf2CUw8sXJd9h6r+OR3LiSJQcPonDfz8F0503+7ytxV+f543yiIhIs7jAjorWPns/spdtQO7qOV22iXXr3R6TPjUZ5soLKDl8EicrzyMp4QbclX4TDn5tRmVNPZY8eIdP2yiVmCYlM8wQEZE2cWRGRfPvT4HpzpsVIy/pU5NhTBzR7YgNAHkCcdb0CQAcozVflHyH4n3HMfVfx/u0jaXl9RyVISIiTWOYUVnBKwsAOObRSE4eXItn1+5UlJqcw82027+Hoi+/lSf6pk9NRtGX3yI5caTiPL5wzNzI+TJERKRpgiiKotqN0Lwzb6rdAtU0X/cIoiNYjSQiIu3iyAz1iEGGiIi0jmGGiIiIghrDDBEREQU1hhkiIiIKagwzREREFNQYZoiIiCioMcwQERFRUGOYUdmMBX/s8W6/RERE1DOGmSAixD2BksMn1W4GERGRpjDMEBERUVBjmAmw7bvL5PWWZiz4Y5fteQVFijWZAKCi6qL8fcaPXlWM0OSs2irvm5y6OnBvhIiISCMYZgKoouoispdtwIH/egZi3XosefAOFH35rbx9++4yVNbUQ6xbD7FuPUx33oycVVuRlHADxLr1ACAfmz41GXkFRUgcN1zeHwDn3xAR0aDDhXcCaMeeIzDdeTPSpyYDAObfn4K/vHdI3j7//hTMvz9FfnxX+k34ouS7bs+3MsekeJw1fQIqa+p93GoiIiJtY5gJsKSEG3rcPmPBHxWjNaY7b+5x/+TU1TBXXpAfL/v36f1rIBERUZBhmSnAKqoudrstZ9VWAJDLRrmr5/R4rhkL/ois6RPk/RlkiIhoMGKYCaDUHxhR9OW38uTdvIIixSgMoBy5eXPzAcU2Y+IInD5zWfFc4rjh8veF/7nP100mIiLSPEEURVHtRmjemTd9dqq8giI8u3YngGslocRxw+X5L9JVS9L2iqqL+HTr/+1yrPnQSwAA4x2/UuwPAAWvLPBZexG71HfnIiIi8gOGGU/4MMwEHYYZIiLSOJaZiIiIKKgxzBAREVFQY5ghIiKioMYwQ0REREGNYYaIiIiCGsMMERERBTWGGSIiIgpqDDOe0IWq3QJ1DNb3TUREQYVhxhORt6jdAnVE3KR2C4iIiHrFMOOJIT8Aom5VuxWBFXUrED1F7VYQERH1issZeMNSDzQfAdor1W6J/4TGAdGTgbA4tVtCRETkEYaZvrDUA20ngLbvAHun2q3pP10oED7eUU4zDO99fyIiIg1hmOmv9krAchHoPAt01qndGs/oQoGQGxzBJeJGQD+Ek32JiChoMcz4WnslYGsCbM2OERy1A44u1BFWDDcAITGOMhLDCxERDSAMM4HSUQeInY6gY+8A7C2AtcmxTexwlKukP3sihRAh7Nr3+iGALgwQQgF9NBAy3PGYoYWIiAYBhhkiIiIKarw0m4iIiIIawwwREREFNYYZIiIiCmoMM0RERBTUGGaIiIgoqDHMEBERUVBjmCEiIqKgxjBDREREQe3/AziGAXsOH3zcAAAAAElFTkSuQmCC)"
      ],
      "metadata": {
        "id": "fxH-Q74wLEDb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To generate paraphrases, we use Parrot\n",
        "https://github.com/PrithivirajDamodaran/Parrot_Paraphraser\n",
        "\n",
        "Note that this framework works only for english text. For other language, we can translate our text in english, apply Parrot generator and then retranslate generated phrases in initial language. For sure, the quality of translation has an important impact on the final data. \n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "lbtjxE5qkYRG"
      }
    },
    {
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        },
        "outputId": "e6328306-a43e-4814-9d5a-9449470a1777"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.89k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "aa096b3cffd247a4b8423485f5b1c794"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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      "source": [
        "import torch\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "''' \n",
        "uncomment to get reproducable paraphrase generations\n",
        "def random_state(seed):\n",
        "  torch.manual_seed(seed)\n",
        "  if torch.cuda.is_available():\n",
        "    torch.cuda.manual_seed_all(seed)\n",
        "\n",
        "random_state(1234)\n",
        "'''\n",
        "\n",
        "#Init models (make sure you init ONLY once if you integrate this to your code)\n",
        "parrot = Parrot(model_tag=\"prithivida/parrot_paraphraser_on_T5\")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_paraphrases(train_messages, train_labels):\n",
        "\n",
        "  paraphrases_train_messages = []\n",
        "  paraphrases_train_labels = []\n",
        "\n",
        "  counter = 0\n",
        "\n",
        "  for phrase_, label_ in zip(train_messages, train_labels):\n",
        "\n",
        "    # add original phrase\n",
        "    paraphrases_train_messages.append(phrase_)\n",
        "    paraphrases_train_labels.append(label_)\n",
        "\n",
        "    counter+=1\n",
        "    if counter % 20 == 0:\n",
        "      print(f\"Paraphrasing in progress : {round(counter/len(train_messages), 2) * 100} %\")\n",
        "\n",
        "    # translate phrase in en before generating paraphrases\n",
        "    google_translator = GoogleTranslator(source='fr', target='en')\n",
        "    phrase_en = google_translator.translate(phrase_)\n",
        "\n",
        "    # paraphrases\n",
        "    para_phrases_en = parrot.augment(input_phrase=phrase_en, use_gpu=False)\n",
        "\n",
        "    if not para_phrases_en:\n",
        "      continue\n",
        "\n",
        "    google_translator = GoogleTranslator(source='en', target='fr')\n",
        "\n",
        "    # translate paraphrases from en to fr\n",
        "    para_phrases_fr = [google_translator.translate(pp_en).lower() for pp_en, _ in para_phrases_en]\n",
        "\n",
        "    for pp_fr in list(set(para_phrases_fr)):\n",
        "      paraphrases_train_messages.append(pp_fr)\n",
        "      paraphrases_train_labels.append(label_)\n",
        "\n",
        "  return paraphrases_train_messages, paraphrases_train_labels"
      ],
      "metadata": {
        "id": "R05ZlAtfYhdk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "It takes about 20 minutes to generate and translate all paraphrases. You can uncomment the function and directly download the result from file. "
      ],
      "metadata": {
        "id": "lhBhRO9YtbxL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# uncomment the following code if you want to generate paraphrases; it takes about 20 minutes\n",
        "# paraphrases_train_messages, paraphrases_train_labels = generate_paraphrases(train_messages, train_labels)\n"
      ],
      "metadata": {
        "id": "XNxxHXHRmYvW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# load generated paraphrases from file\n",
        "# comment this code if you already called the generate_paraphrases() function\n",
        "paraphrases_train_messages = []\n",
        "paraphrases_train_labels = []\n",
        "\n",
        "with open(r\"/colab-data-drive/MyDrive/Colab Notebooks/data/data_augmentation_paraphrases_train_messages_labels.txt\", 'r') as f:\n",
        "  for l in f.readlines():\n",
        "    m, i = l.strip().split('###')\n",
        "    paraphrases_train_messages.append(m)\n",
        "    paraphrases_train_labels.append(i)"
      ],
      "metadata": {
        "id": "NT0g3CqEmuNe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# display paraphrases\n",
        "def display_paraphrases_intent(intent_name):\n",
        "  print(f\"Intent '{intent_name}'\")\n",
        "  org_phrases = \"\\n\\t\\t\".join([m for m, i in zip(train_messages, train_labels) if i == intent_name])\n",
        "  test_phrases = \"\\n\\t\\t\".join([m for m, i in zip(test_messages, test_labels) if i == intent_name])\n",
        "  all_phrases = \"\\n\\t\\t\".join([m for m, i in zip(paraphrases_train_messages, paraphrases_train_labels) if i == intent_name])\n",
        "  print(f'\\tOriginal phrases : \\n\\t\\t{org_phrases}')\n",
        "  print(f'\\tOriginal phrases + paraphrases : \\n\\t\\t{all_phrases}')\n",
        "  print(f'\\tTesting phrases : \\n\\t\\t{test_phrases}')\n",
        "\n",
        "display_paraphrases_intent('how_are_you')\n",
        "display_paraphrases_intent('greeting')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YRYly2IduPNX",
        "outputId": "7d15b9bd-4e7d-458b-dddd-866d0343352a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intent 'how_are_you'\n",
            "\tOriginal phrases : \n",
            "\t\tVous allez bien ?\n",
            "\t\tComment va\n",
            "\tOriginal phrases + paraphrases : \n",
            "\t\tVous allez bien ?\n",
            "\t\tcomment est votre état de santé?\n",
            "\t\tComment va\n",
            "\t\tcomment allez vous?\n",
            "\t\tcomment vas-tu?\n",
            "\tTesting phrases : \n",
            "\t\tComment allez-vous\n",
            "\t\tTu vas bien?\n",
            "\t\tbien ou bien\n",
            "\t\tça va ?\n",
            "\t\tComment vas-tu\n",
            "Intent 'greeting'\n",
            "\tOriginal phrases : \n",
            "\t\they\n",
            "\t\tcoucou\n",
            "\t\tre bonjour\n",
            "\t\tresalut\n",
            "\tOriginal phrases + paraphrases : \n",
            "\t\they\n",
            "\t\thé\n",
            "\t\tcoucou\n",
            "\t\tbonjour\n",
            "\t\tre bonjour\n",
            "\t\trebonjour\n",
            "\t\tresalut\n",
            "\t\tsalut\n",
            "\tTesting phrases : \n",
            "\t\tsalut\n",
            "\t\tallo\n",
            "\t\tbonjour à toi\n",
            "\t\tbonjour\n",
            "\t\tsalutations\n",
            "\t\tsalut à tous\n",
            "\t\tre salut\n",
            "\t\trebonjour\n",
            "\t\thé\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Nb of phrases in training data (initial): {len(train_messages)}\")\n",
        "print(f\"Nb of phrases in training data with paraphrases: {len(paraphrases_train_messages)}\")\n",
        "print(f\"Nb of new phrases (paraphrases) added in training data: {len(paraphrases_train_messages) - len(train_messages)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qw8HEtqwbHZ7",
        "outputId": "2981a498-1a89-4708-9afc-71e8fc08cc73"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nb of phrases in training data (initial): 204\n",
            "Nb of phrases in training data with paraphrases: 565\n",
            "Nb of new phrases (paraphrases) added in training data: 361\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# build TF-IDF vectorizer\n",
        "paraphrase_vectorizer = train_tfidf_vectorizer(paraphrases_train_messages)\n",
        "\n",
        "# extract TF-IDF vectors\n",
        "paraphrase_train_tfidf = transform_tfidf(paraphrase_vectorizer, paraphrases_train_messages)\n",
        "paraphrase_test_tfidf = transform_tfidf(paraphrase_vectorizer, test_messages)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hADLfHJzjGp5",
        "outputId": "dc4188ca-e9ab-4ccb-ff33-380be7b32b52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "223 words in vocabulary used for extracting TF-IDF\n",
            "['absenc' 'acced' 'accompl' 'activ' 'affirm' 'agent' 'aim' 'air' 'ajout'\n",
            " 'allez' 'annul' 'appli' 'appliqu' 'arriv' 'assez' 'aujourd' 'avoir'\n",
            " 'beaucoup' 'besoin' 'bien' 'billet' 'bonjour' 'bord' 'bot' 'bourr' 'bug'\n",
            " 'calendri' 'cas' 'cel' 'cet' 'chang' 'chat' 'chatbot' 'cher' 'chos'\n",
            " 'client' 'clôtur' 'coler' 'combien' 'comment' 'commentair' 'complet'\n",
            " 'cong' 'connaitr' 'connaîtr' 'connect' 'contrari' 'conversationnel'\n",
            " 'coucou' 'crev' 'crédibl' 'dashboard' 'dat' 'dir' 'dis' 'discuss' 'dit'\n",
            " 'dois' 'don' 'déjà' 'dépass' 'dérang' 'détail' 'encor' 'ennui'\n",
            " 'entrepris' 'envoi' 'ete' 'fach' 'fair' 'fais' 'fait' 'fatigu' 'faut'\n",
            " 'ferm' 'fin' 'fou' 'fourn' 'franc' 'frustr' 'furieux' 'fâch' 'habit'\n",
            " 'heureux' 'hey' 'horair' 'hui' 'humain' 'hé' 'identifi' 'incident'\n",
            " 'inclur' 'incroi' 'indident' 'indépend' 'inform' 'infos' 'joindr' 'joint'\n",
            " 'jour' 'journ' 'laiss' 'lien' 'lieu' 'list' 'mainten' 'manqu' 'march'\n",
            " 'merc' 'met' 'mettr' 'modif' 'modifi' 'montr' 'mou' 'nombr' 'non' 'obten'\n",
            " 'occup' 'origin' 'ouai' 'ouaiii' 'ouaip' 'ouais' 'ouii' 'ouvert' 'ouvr'\n",
            " 'où' 'pai' 'parcour' 'parl' 'pay' 'person' 'peu' 'peut' 'peux' 'phras'\n",
            " 'piec' 'pj' 'plait' 'plaît' 'plus' 'possibl' 'pouv' 'pris' 'problem'\n",
            " 'programm' 'prévu' 'puis' 'pécul' 'quand' 'quel' 'quelqu' 'question'\n",
            " 'quoi' 're' 'rebonjour' 'rempl' 'rend' 'rendu' 'repons' 'repos' 'resalut'\n",
            " 'rest' 'robot' 'récuper' 'réel' 'répond' 'répondr' 'répondu' 'répons'\n",
            " 'réserv' 'salut' 'san' 'sant' 'savoir' 'si' 'sold' 'sort' 'souhait'\n",
            " 'souhaiter' 'statut' 'suit' 'sujet' 'sup' 'surcharg' 'tableau' 'tel'\n",
            " 'temp' 'termin' 'terr' 'terribl' 'ticket' 'tout' 'tres' 'trop' 'truc'\n",
            " 'va' 'vacanc' 'vais' 'vas' 'ver' 'veuill' 'veux' 'vi' 'vien' 'vill' 'vis'\n",
            " 'visit' 'voir' 'vol' 'vou' 'voudr' 'voyag' 'vrai' 'vraisembl' 'web' 'wé'\n",
            " 'ça' 'énerv' 'énorm' 'épuis' 'état']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# classification\n",
        "paraphrase_classifier = build_classifier(paraphrase_train_tfidf, paraphrases_train_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r3I8gZO-jYjW",
        "outputId": "2bf51e0a-9391-409d-ff22-1f457b888b72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best parameters :  {'C': 10.0, 'gamma': 0.3, 'kernel': 'rbf'}\n",
            "Best score :  0.9132743362831859\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prediction\n",
        "paraphase_predictions = paraphrase_classifier.predict(paraphrase_test_tfidf)\n",
        "\n",
        "# evaluate\n",
        "paraphrase_acc, paraphrase_f1, paraphrase_incorrect_pred_rate = evaluate(test_labels, paraphase_predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fqfs0Ih6jdoR",
        "outputId": "39cc5bc7-baad-47d2-8450-ba211b9298c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy :  0.93\n",
            "F1 Score (macro) :  0.91\n",
            "Incorrect detection rate : 6/83 (0.07)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Conclusion"
      ],
      "metadata": {
        "id": "lWl0D0eYoDUh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here we compare the intent prediction performance using three augmentation data techniques : oversampling, back translation and paraphrasing."
      ],
      "metadata": {
        "id": "mCaGvfi8nN01"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy import *\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.bar(['Baseline', 'Oversampling', 'Back-translation', 'Paraphrase'], \n",
        "        height=[baseline_acc, oversampling_acc, backtranslation_acc, paraphrase_acc])\n",
        "plt.title(f'Model performance comparison on {len(test_messages)} testing data')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "5Jnd6lOMqfDm",
        "outputId": "1de8bae7-2e45-4b70-df32-04b5607cb6db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To our dataset, the oversampling did not improve the performance. The back translation and paraphrasing yield better results than the baseline. \n",
        "\n",
        "We observe that the messages in the test set are almost different to the training. By translating or paraphrasing, we succeeded to generate new training phrases that are closer to those in the test set. Training model accounting these new phrases brings improvement on prediction. \n",
        "\n",
        "We have some observations during these experiments: \n",
        "\n",
        "\n",
        "*   It is important to control and validate the generated data while using back-translation and paraphrasing. More data is good, but more bad data is not. \n",
        "As paraphrasing is itself a model learnt from data, it can have some bias and generate phrases with a wrong context with respect to the original meaning. Having these data in our training can harm the intent model. \n",
        "*   In this study, we explored the pipeline with the augmentation techniques one by one. It can be interessting to combine the generated data from all methods on which the model will be retrained.\n",
        "*   On our data, the back translation outperforms the other methods. However, it is not always true on other dataset. You need some tests to figure out the most appropriate augmentation method for your data.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "-q7Nrr12tBQ5"
      }
    }
  ]
}