Ahmaric_preprocessed.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "qt2w71mQGZC5"
      },
      "outputs": [],
      "source": [
        "# Installing required packages\n",
        "!pip install transformers sentencepiece > /dev/null 2>&1"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing packages\n",
        "import pandas as pd\n",
        "import re\n",
        "import glob\n",
        "from collections import Counter\n",
        "from sklearn.model_selection import train_test_split\n",
        "from transformers import MT5Tokenizer\n",
        "from google.colab import drive\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "mnEoio3GGoFV"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Mouting Google Drive to the current Colab session for accessing files stored in the Drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cUUtO9hBGuaT",
        "outputId": "ab1f1473-8cfd-4fbf-b1e1-c3af64467f84"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "na11lUCkHF53",
        "outputId": "807c9cbc-6aad-4691-8e41-8bde53ab5348"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-d2462529-b452-4230-8b91-91f612f1d833\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-d2462529-b452-4230-8b91-91f612f1d833\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving preprocessed.csv to preprocessed (1).csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initializing an empty DataFrame to compile all data entries\n",
        "aggregated_data = pd.DataFrame(columns=[\"text\", \"tags\"])"
      ],
      "metadata": {
        "id": "y_iXXoJJJagQ"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "data = pd.read_csv('/content/preprocessed.csv')\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "X7ujFskjHpAn",
        "outputId": "25417847-8d28-4eea-fa2c-1cba3efba987"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     id                         url    source  title  \\\n",
              "0    59  http://t.me/tikvahethiopia  telegram    NaN   \n",
              "1   130  http://t.me/tikvahethiopia  telegram    NaN   \n",
              "2   241  http://t.me/tikvahethiopia  telegram    NaN   \n",
              "3   897  http://t.me/tikvahethiopia  telegram    NaN   \n",
              "4  1308  http://t.me/tikvahethiopia  telegram    NaN   \n",
              "\n",
              "                                                text catagory  \\\n",
              "0  🇪🇹 የፖለቲካ ፓርቲዎች ምን ይላሉ ? 🇪🇹\\n\\n\" ግጭቶች ከትንሽ ነገር ...     news   \n",
              "1  🇪🇹 የፖለቲካ ፓርቲዎች ምን ይላሉ ? 🇪🇹\\n\\n\" ... አሁንም ቢሆን ለ...     news   \n",
              "2  \" በ5ቱ ኮሪደር ልማት ስራ 16 ሺህ ሰዎች የስራ እድል ተፈጥሮላቸዋል \"...     news   \n",
              "3  #Update\\n\\nኦክሎክ ሞተርስ “ስሜን አጥፍቷል” ላለው ቱሪስት ታክሲ ...     news   \n",
              "4  #ደሴ\\n\\n“ አንድ ቀን ውሳኔ ተሰጥቶባቸው ከአካባቢ ብክለት ነፃ እንዲሆ...     news   \n",
              "\n",
              "                                                tags  word_count language  \n",
              "0  #በነፃነት_ተንቀሳቅሰን,#መካከለኛ_ገቢ,#አባላትን_አፍርቻለሁ,#Tikvah...         576       am  \n",
              "1  #ኦፌኮ,#ከመገዳደል,#መደራደር,#ግድያ,#እስራት,#ብልሹ_አሰራርና,#ሙስና...         462       am  \n",
              "2                                                NaN         145       am  \n",
              "3                          #Update,#ከሄሎ_ታክሲ,#እየሸጠ_ነው         186       am  \n",
              "4  #ደሴ,#ቪዲዮም,#ከህዝብ_መኖሪያ,#ሞዲፋይ,#TikvahFamilyAddisA...         408       am  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dc7171e6-4c5b-482d-b89a-25768c9d6072\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>url</th>\n",
              "      <th>source</th>\n",
              "      <th>title</th>\n",
              "      <th>text</th>\n",
              "      <th>catagory</th>\n",
              "      <th>tags</th>\n",
              "      <th>word_count</th>\n",
              "      <th>language</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>59</td>\n",
              "      <td>http://t.me/tikvahethiopia</td>\n",
              "      <td>telegram</td>\n",
              "      <td>NaN</td>\n",
              "      <td>🇪🇹 የፖለቲካ ፓርቲዎች ምን ይላሉ ? 🇪🇹\\n\\n\" ግጭቶች ከትንሽ ነገር ...</td>\n",
              "      <td>news</td>\n",
              "      <td>#በነፃነት_ተንቀሳቅሰን,#መካከለኛ_ገቢ,#አባላትን_አፍርቻለሁ,#Tikvah...</td>\n",
              "      <td>576</td>\n",
              "      <td>am</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>130</td>\n",
              "      <td>http://t.me/tikvahethiopia</td>\n",
              "      <td>telegram</td>\n",
              "      <td>NaN</td>\n",
              "      <td>🇪🇹 የፖለቲካ ፓርቲዎች ምን ይላሉ ? 🇪🇹\\n\\n\" ... አሁንም ቢሆን ለ...</td>\n",
              "      <td>news</td>\n",
              "      <td>#ኦፌኮ,#ከመገዳደል,#መደራደር,#ግድያ,#እስራት,#ብልሹ_አሰራርና,#ሙስና...</td>\n",
              "      <td>462</td>\n",
              "      <td>am</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>241</td>\n",
              "      <td>http://t.me/tikvahethiopia</td>\n",
              "      <td>telegram</td>\n",
              "      <td>NaN</td>\n",
              "      <td>\" በ5ቱ ኮሪደር ልማት ስራ 16 ሺህ ሰዎች የስራ እድል ተፈጥሮላቸዋል \"...</td>\n",
              "      <td>news</td>\n",
              "      <td>NaN</td>\n",
              "      <td>145</td>\n",
              "      <td>am</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>897</td>\n",
              "      <td>http://t.me/tikvahethiopia</td>\n",
              "      <td>telegram</td>\n",
              "      <td>NaN</td>\n",
              "      <td>#Update\\n\\nኦክሎክ ሞተርስ “ስሜን አጥፍቷል” ላለው ቱሪስት ታክሲ ...</td>\n",
              "      <td>news</td>\n",
              "      <td>#Update,#ከሄሎ_ታክሲ,#እየሸጠ_ነው</td>\n",
              "      <td>186</td>\n",
              "      <td>am</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1308</td>\n",
              "      <td>http://t.me/tikvahethiopia</td>\n",
              "      <td>telegram</td>\n",
              "      <td>NaN</td>\n",
              "      <td>#ደሴ\\n\\n“ አንድ ቀን ውሳኔ ተሰጥቶባቸው ከአካባቢ ብክለት ነፃ እንዲሆ...</td>\n",
              "      <td>news</td>\n",
              "      <td>#ደሴ,#ቪዲዮም,#ከህዝብ_መኖሪያ,#ሞዲፋይ,#TikvahFamilyAddisA...</td>\n",
              "      <td>408</td>\n",
              "      <td>am</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dc7171e6-4c5b-482d-b89a-25768c9d6072')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-dc7171e6-4c5b-482d-b89a-25768c9d6072 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-dc7171e6-4c5b-482d-b89a-25768c9d6072');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-9ccb0ccb-6eed-4281-aa25-b94607def12a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9ccb0ccb-6eed-4281-aa25-b94607def12a')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9ccb0ccb-6eed-4281-aa25-b94607def12a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 12010,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11353,\n        \"min\": 59,\n        \"max\": 67940,\n        \"num_unique_values\": 12010,\n        \"samples\": [\n          65029,\n          61629,\n          50644\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"url\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"http://t.me/tikvahethiopia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"source\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"telegram\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 11889,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"catagory\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tags\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4250,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"word_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 103,\n        \"min\": 1,\n        \"max\": 849,\n        \"num_unique_values\": 584,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"language\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = data[[\"text\", \"tags\"]].rename(columns={\"text\":\"text\", \"tags\":\"tags\"}) # Renaming columns: text--> text, tags --> tags (to have consistent column names)\n",
        "\n",
        "print(df.head())\n",
        "aggregated_data = pd.concat([aggregated_data, df], ignore_index=True) # Adding to the aggregation dataframe"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uBtIa_RgI86Z",
        "outputId": "5dbf25fc-e6f4-430a-9254-1b8f9a8c3161"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                                text  \\\n",
            "0  🇪🇹 የፖለቲካ ፓርቲዎች ምን ይላሉ ? 🇪🇹\\n\\n\" ግጭቶች ከትንሽ ነገር ...   \n",
            "1  🇪🇹 የፖለቲካ ፓርቲዎች ምን ይላሉ ? 🇪🇹\\n\\n\" ... አሁንም ቢሆን ለ...   \n",
            "2  \" በ5ቱ ኮሪደር ልማት ስራ 16 ሺህ ሰዎች የስራ እድል ተፈጥሮላቸዋል \"...   \n",
            "3  #Update\\n\\nኦክሎክ ሞተርስ “ስሜን አጥፍቷል” ላለው ቱሪስት ታክሲ ...   \n",
            "4  #ደሴ\\n\\n“ አንድ ቀን ውሳኔ ተሰጥቶባቸው ከአካባቢ ብክለት ነፃ እንዲሆ...   \n",
            "\n",
            "                                                tags  \n",
            "0  #በነፃነት_ተንቀሳቅሰን,#መካከለኛ_ገቢ,#አባላትን_አፍርቻለሁ,#Tikvah...  \n",
            "1  #ኦፌኮ,#ከመገዳደል,#መደራደር,#ግድያ,#እስራት,#ብልሹ_አሰራርና,#ሙስና...  \n",
            "2                                                NaN  \n",
            "3                          #Update,#ከሄሎ_ታክሲ,#እየሸጠ_ነው  \n",
            "4  #ደሴ,#ቪዲዮም,#ከህዝብ_መኖሪያ,#ሞዲፋይ,#TikvahFamilyAddisA...  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Current Number of Entries: \", aggregated_data.shape[0]) # 12010"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DwZVLP-oMOKp",
        "outputId": "d23af469-d390-449f-e187-b6accecfb375"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Current Number of Entries:  12010\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove duplicates based on 'text' and 'summary' columns\n",
        "aggregated_data.drop_duplicates(subset=['text', 'tags'], inplace=True)\n",
        "\n",
        "duplicate_count = aggregated_data.duplicated(subset=['text', 'tags']).sum()\n",
        "print(f\"Number of duplicate rows: {duplicate_count}\") # There doesn't seem to be any duplicates"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yFUOcDrmMyQT",
        "outputId": "0b4624fb-53d2-49df-db04-9045649b892e"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of duplicate rows: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Removing any NaN (incomplete) entries\n",
        "aggregated_data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "JILd0kL-NqHq",
        "outputId": "bdfd12ab-1055-4282-c65b-5cbdc2fd1871"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                     text     tags\n",
              "count                                               11889     6128\n",
              "unique                                              11889     4250\n",
              "top     🇪🇹 የፖለቲካ ፓርቲዎች ምን ይላሉ ? 🇪🇹\\n\\n\" ግጭቶች ከትንሽ ነገር ...  #update\n",
              "freq                                                    1     1007"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2df51f9b-875e-412e-b42d-9a87f31027a7\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>tags</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>11889</td>\n",
              "      <td>6128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>11889</td>\n",
              "      <td>4250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>🇪🇹 የፖለቲካ ፓርቲዎች ምን ይላሉ ? 🇪🇹\\n\\n\" ግጭቶች ከትንሽ ነገር ...</td>\n",
              "      <td>#update</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>1</td>\n",
              "      <td>1007</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2df51f9b-875e-412e-b42d-9a87f31027a7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2df51f9b-875e-412e-b42d-9a87f31027a7 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-2df51f9b-875e-412e-b42d-9a87f31027a7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-68621e97-d852-43e4-b66b-ba1cc746dc10\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-68621e97-d852-43e4-b66b-ba1cc746dc10')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-68621e97-d852-43e4-b66b-ba1cc746dc10 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"aggregated_data\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"11889\",\n          \"\\ud83c\\uddea\\ud83c\\uddf9 \\u12e8\\u1356\\u1208\\u1272\\u12ab \\u1353\\u122d\\u1272\\u12ce\\u127d \\u121d\\u1295 \\u12ed\\u120b\\u1209 ? \\ud83c\\uddea\\ud83c\\uddf9\\n\\n\\\" \\u130d\\u132d\\u1276\\u127d \\u12a8\\u1275\\u1295\\u123d \\u1290\\u1308\\u122d \\u12a5\\u12e8\\u1270\\u1290\\u1231 \\u12a5\\u12eb\\u12f0\\u1309 \\u1290\\u12cd \\u12a0\\u1201\\u1295 \\u12eb\\u1209\\u1260\\u1275 \\u12f0\\u1228\\u1303 \\u12e8\\u12f0\\u1228\\u1231\\u1275\\u1362 \\u12ed\\u1204 \\u12f0\\u130d\\u121e \\u1260\\u1218\\u1295\\u130d\\u1235\\u1275 \\u1278\\u120d\\u1270\\u129d\\u1290\\u1275 \\u1363 \\u1275\\u12a5\\u130d\\u1235\\u1275 \\u121b\\u1323\\u1275 \\u12e8\\u121a\\u1218\\u1323 \\u1290\\u12cd \\\" - \\u12a2\\u12dc\\u121b\\n\\n\\u1260\\u12a2\\u1275\\u12ee\\u1335\\u12eb \\u1208\\u121a\\u1235\\u1270\\u12cb\\u1209 \\u12d8\\u122d\\u1348 \\u1265\\u12d9 \\u127d\\u130d\\u122e\\u127d \\u12eb\\u1208\\u12cd\\u1295 \\u130d\\u121d\\u1308\\u121b \\u1260\\u1270\\u1218\\u1208\\u12a8\\u1270 \\u12e8\\u12a2\\u1275\\u12ee\\u1335\\u12eb \\u12dc\\u130e\\u127d \\u1208\\u121b\\u1205\\u1260\\u122b\\u12ca \\u134d\\u1275\\u1205 \\u1353\\u122d\\u1272 (\\u12a2\\u12dc\\u121b) \\u12e8\\u12f5\\u122d\\u1305\\u1275 \\u1309\\u12f3\\u12ed \\u1200\\u120b\\u134a \\u12e8\\u1206\\u1291\\u1275 \\u12a0\\u1276 \\u12cb\\u1232\\u1201\\u1295 \\u1270\\u1235\\u134b\\u12ec \\u12a8\\u1272\\u12ad\\u126b\\u1205 \\u12a2\\u1275\\u12ee\\u1335\\u12eb \\u130b\\u122d \\u1246\\u12ed\\u1273 \\u12a0\\u12f5\\u122d\\u1308\\u12cb\\u120d\\u1362\\u00a0\\n\\nQ. \\u1260\\u1270\\u1208\\u12eb\\u12e8 \\u130a\\u12dc \\u12dc\\u130e\\u127d \\u1260\\u1230\\u120b\\u121d \\u12c8\\u1325\\u1270\\u12cd \\u1218\\u130d\\u1263\\u1275 \\u12a5\\u1295\\u12f0\\u121b\\u12ed\\u127d\\u1209 \\u12ed\\u1308\\u1208\\u1340\\u120d\\u1362 \\u12a8\\u1230\\u12cd \\u1205\\u12ed\\u12c8\\u1275 \\u12e8\\u121a\\u1260\\u120d\\u1325 \\u1290\\u1308\\u122d \\u12f0\\u130d\\u121e \\u12e8\\u1208\\u121d\\u1362 \\u12a5\\u1295\\u12f0 \\u1353\\u122d\\u1272 \\u1208\\u1218\\u134d\\u1275\\u1204 \\u121d\\u1295 \\u12a5\\u12e8\\u1230\\u122b\\u127d\\u1201 \\u1290\\u12cd ?\\n\\n\\u12a0\\u1276 \\u12cb\\u1232\\u1201\\u1295 \\u1270\\u1235\\u134b\\u12ec \\u1366\\n\\n\\\" \\u127d\\u130d\\u122e\\u127d\\u1295 \\u1260\\u12e8\\u1275\\u129b\\u12cd\\u121d \\u12c8\\u1308\\u1295 \\u1260\\u1200\\u12ed\\u120d \\u1208\\u121b\\u1235\\u1348\\u1340\\u121d \\u12a8\\u1218\\u121e\\u12a8\\u122d \\u1260\\u134a\\u1275 \\u1260\\u12cd\\u12ed\\u12ed\\u1275 \\u12a5\\u1295\\u12f2\\u1348\\u1271 \\u1260\\u1270\\u12f0\\u130b\\u130b\\u121a \\u1320\\u12ed\\u1240\\u1293\\u120d\\u1362 \\n\\n\\u1290\\u1308\\u122d \\u130d\\u1295 \\u1260\\u12e8\\u1275\\u129b\\u12cd\\u1295 \\u12a0\\u12ab\\u120d \\u1270\\u1230\\u121a\\u1290\\u1275 \\u12a0\\u120b\\u1308\\u1299\\u121d\\u1362 \\u120b\\u1208\\u121b\\u130d\\u1298\\u1273\\u1278\\u12cd \\u121b\\u1235\\u1228\\u1303\\u12ce\\u1279 \\u12a0\\u1201\\u1295 \\u12eb\\u1209\\u1275 \\u130d\\u132d\\u1276\\u127d \\u1293\\u1278\\u12cd\\u1362\\n\\n\\u130d\\u132d\\u1276\\u127d \\u12a8\\u1275\\u1295\\u123d \\u1290\\u1308\\u122d \\u12a5\\u12e8\\u1270\\u1290\\u1231 \\u12a5\\u12eb\\u12f0\\u1309 \\u1202\\u12f0\\u12cd \\u12a0\\u1201\\u1295 \\u12eb\\u1209\\u1260\\u1275 \\u12f0\\u1228\\u1303 \\u120b\\u12ed \\u12e8\\u12f0\\u1228\\u1231\\u1275\\u1362 \\u12ed\\u1204 \\u12f0\\u130d\\u121e \\u1260\\u1218\\u1295\\u130d\\u1235\\u1275 \\u1278\\u120d\\u1270\\u129d\\u1290\\u1275\\u1363 \\u1275\\u12a5\\u130d\\u1235\\u1275 \\u121b\\u1323\\u1275\\u121d \\u132d\\u121d\\u122d \\u12e8\\u121a\\u1218\\u1323 \\u1290\\u12cd\\u1362 \\u12e8\\u130d\\u132d\\u1276\\u1279\\u1295 \\u1218\\u1295\\u1235\\u12a4 \\u1260\\u121b\\u12e8\\u1275 \\u12a5\\u1295\\u12f2\\u1348\\u1271 \\u121b\\u1233\\u1230\\u1262\\u12eb\\u12ce\\u127d\\u1295 \\u12a5\\u12e8\\u1230\\u1320\\u1295 \\u1290\\u12cd\\u1362 \\\"\\n\\nQ. \\u12a0\\u1295\\u12f3\\u1295\\u12f6\\u127d \\\" \\u1218\\u1295\\u130d\\u1235\\u1275 \\u1260\\u12a2\\u1275\\u12ee\\u1335\\u12eb \\u12e8\\u121a\\u1235\\u1270\\u12cb\\u1208\\u12cd\\u1295 \\u130d\\u132d\\u1275 \\u1235\\u1208\\u121a\\u1348\\u120d\\u1308\\u12cd \\u12a5\\u1295\\u1302 \\u121b\\u1235\\u1246\\u121d \\u12a0\\u1245\\u1276\\u1275 \\u12a0\\u12ed\\u12f0\\u1208\\u121d \\\" \\u12e8\\u121a\\u1209 \\u1275\\u127d\\u1276\\u127d \\u1232\\u12eb\\u1240\\u122d\\u1261 \\u12ed\\u1235\\u1270\\u12cb\\u120b\\u120d\\u1362 \\u1260\\u12da\\u1205 \\u1309\\u12f3\\u12ed \\u1353\\u122d\\u1272\\u12ce \\u121d\\u1295 \\u12ed\\u120b\\u120d ?\\n\\n\\u12a0\\u1276 \\u12cb\\u1232\\u1201\\u1295 \\u1270\\u1235\\u134b\\u12ec \\u1366\\n\\n\\\" \\u12a0\\u1295\\u12f5 \\u1218\\u1295\\u130d\\u1235\\u1275 \\u12e8\\u1270\\u1228\\u130b\\u130b \\u1230\\u120b\\u121d \\u1232\\u1296\\u122d \\u1290\\u12cd \\u120a\\u1320\\u1240\\u121d \\u12e8\\u121a\\u127d\\u1208\\u12cd \\u1265\\u1208\\u1295 \\u12a5\\u1293\\u121d\\u1293\\u1208\\u1295\\u1362 \\n\\n\\u127d\\u130d\\u122e\\u127d \\u1263\\u1209\\u1260\\u1275 \\u1218\\u1295\\u130d\\u1235\\u1275 \\u12d8\\u120b\\u1242 \\u12e8\\u1235\\u120d\\u1323\\u1295 \\u121b\\u122b\\u12d8\\u121d\\u1363 \\u12e8\\u120d\\u121b\\u1275 \\u12cd\\u1324\\u1275 \\u121b\\u121d\\u1323\\u1275 \\u12a0\\u12ed\\u127d\\u120d\\u121d\\u1362 \\u1235\\u1208\\u12da\\u1205 \\u130d\\u132d\\u1276\\u1279 \\u12a5\\u1295\\u12f2\\u1263\\u1263\\u1231 \\u134d\\u120b\\u130e\\u1275 \\u12a0\\u1208\\u12cd \\u1265\\u1208\\u1295 \\u12a0\\u1293\\u121d\\u1295\\u121d\\u1362\\n\\n\\u130d\\u1295 \\u12085 \\u12a0\\u1218\\u1273\\u1275 \\u1218\\u1295\\u130d\\u1235\\u1275 \\u1208\\u121d\\u1295 \\u127d\\u130d\\u122e\\u1279\\u1295 \\u121b\\u1235\\u1246\\u121d \\u12a0\\u120d\\u127b\\u1208\\u121d ? \\u1265\\u1208\\u1295 \\u12ab\\u12e8\\u1295 \\u12a0\\u1295\\u12f0\\u129b \\u12e8\\u130d\\u132d\\u1276\\u1279\\u1295 \\u1263\\u1205\\u122a \\u1218\\u12cd\\u1230\\u12f5 \\u12eb\\u1235\\u1348\\u120d\\u130b\\u120d\\u1362 \\n\\n\\u1240\\u12cd\\u1236\\u1279 \\u12cd\\u132b\\u12ca\\u1293 \\u12cd\\u1235\\u1323\\u12ca \\u1265\\u1208\\u1295 \\u1265\\u1295\\u12a8\\u134d\\u120d \\u12a5\\u1295\\u12b3 \\u130d\\u132d\\u1276\\u127d \\u12a5\\u12e8\\u1270\\u1290\\u1231 \\u12eb\\u1209\\u1275 \\u1265\\u120d\\u1345\\u130d\\u1293 \\u12cd\\u1235\\u1325 \\u1263\\u1209 \\u1200\\u12ed\\u120e\\u127d \\u1290\\u12cd\\u1362\\u00a0\\n\\n\\u12e8\\u12ad\\u120d\\u120d \\u120d\\u12e9 \\u1200\\u12ed\\u120d\\u1295 \\u1275\\u1325\\u1245 \\u12a8\\u121b\\u1235\\u1348\\u1273\\u1275\\u1363 \\u1270\\u1348\\u1293\\u1243\\u12ee\\u127d\\u1295 \\u12a8\\u121b\\u1235\\u1218\\u1208\\u1235 \\u130b\\u122d \\u1270\\u12eb\\u12ed\\u12de \\u1218\\u1295\\u130d\\u1235\\u1275 \\u12ed\\u12c8\\u1235\\u12f3\\u1278\\u12cd \\u12e8\\u1290\\u1260\\u1229 \\u12a5\\u122d\\u121d\\u1303\\u12ce\\u127d \\u1353\\u1208\\u1272\\u12ab\\u12ca \\u1309\\u12f3\\u12ee\\u127d \\u1353\\u1208\\u1272\\u12ab\\u12ca \\u12e8\\u1206\\u1290 \\u1218\\u134d\\u1275\\u1204 \\u121b\\u130d\\u1298\\u1275 \\u1232\\u1308\\u1263\\u1278\\u12cd \\u12eb\\u1295\\u1295 \\u120b\\u1208\\u121b\\u12f5\\u1228\\u130d \\u12a5\\u12e8\\u1270\\u1348\\u1320\\u1228 \\u12eb\\u1208 \\u130d\\u132d\\u1275 \\u1290\\u12cd\\u1362 \\\"\\n\\nQ. \\u1353\\u122d\\u1272\\u12eb\\u127d\\u1201 \\u12a2\\u12dc\\u121b \\u201c \\u12e8\\u1218\\u1295\\u130d\\u1235\\u1275 \\u1270\\u1208\\u1323\\u134a \\u201d \\u12e8\\u121a\\u120d \\u1235\\u12eb\\u121c / \\u12a0\\u1235\\u1270\\u12eb\\u12ec\\u1275 \\u1232\\u1230\\u1320\\u12cd \\u12ed\\u1235\\u1270\\u12cb\\u120b\\u120d\\u1362 \\u121d\\u120b\\u123d\\u12ce \\u121d\\u1295\\u12f5\\u1295 \\u1290\\u12cd ?\\n\\n\\u12a0\\u1276 \\u12cb\\u1232\\u1201\\u1295 \\u1270\\u1235\\u134b\\u12ec \\u1366\\n\\n\\\" \\u1218\\u1295\\u130d\\u1235\\u1275 \\u12a5\\u12e8\\u1230\\u122b\\u1278\\u12cd \\u12eb\\u1209 \\u130d\\u12f5\\u1348\\u1276\\u127d\\u1295 \\u12a5\\u12eb\\u1235\\u1240\\u1218\\u1325\\u1295 \\u1290\\u12cd\\u1362 \\u12a5\\u1295\\u12f0\\u12db \\u12e8\\u121a\\u1263\\u1208\\u12cd \\u1260\\u121d\\u1295 \\u1218\\u1235\\u1348\\u122d\\u1275 \\u12a5\\u1295\\u12f0\\u1206\\u1290 \\u12a0\\u12ed\\u1308\\u1263\\u129d\\u121d\\u1362 \\u12a5\\u129b \\u1260\\u12a0\\u130d\\u1263\\u1261 \\u1235\\u122b\\u127d\\u1295\\u1295 \\u12a5\\u12e8\\u1230\\u122b\\u1295 \\u1290\\u12cd\\u1362\\n\\n\\u1208\\u121a\\u1290\\u1231 \\u12a0\\u1209\\u1267\\u120d\\u1273\\u12ce\\u127d \\u121d\\u120b\\u123d \\u1218\\u1235\\u1320\\u1275 \\u12a0\\u1295\\u127d\\u120d\\u121d \\u1364 \\u130a\\u12dc \\u12e8\\u1208\\u1295\\u121d \\u1364 \\u12a5\\u129b \\u1235\\u122b \\u120b\\u12ed \\u1290\\u1295\\u1362 \\n\\n\\u12a5\\u1295\\u12f0\\u12da\\u1205 \\u12e8\\u121a\\u1209\\u1275 \\u1218\\u1235\\u122b\\u1275 \\u12e8\\u121b\\u12ed\\u127d\\u1209 \\u1230\\u1290\\u134e\\u127d \\u1293\\u1278\\u12cd\\u1362 \\\"\\n\\nQ. \\u1260\\u12a2\\u1275\\u12ee\\u1335\\u12eb \\u1235\\u120b\\u1208\\u12cd \\u12e8\\u1340\\u1325\\u1273 \\u12a5\\u1293 \\u12e8\\u12f0\\u1280\\u1295\\u1290\\u1275 \\u1201\\u1294\\u1273 \\u12e8\\u1353\\u122d\\u1272\\u12ce\\u1295 \\u130d\\u121d\\u1308\\u121b \\u1262\\u12eb\\u130b\\u1229 ?\\n\\n\\u12a0\\u1276 \\u12cb\\u1232\\u1201\\u1295 \\u1270\\u1235\\u134b\\u12ec \\u1366\\n\\n\\\" \\u12e8\\u1340\\u1325\\u1273\\u1293 \\u12f0\\u1205\\u1295\\u1290\\u1275 \\u1309\\u12f3\\u12ed \\u1260\\u1270\\u1208\\u12ed \\u1263\\u1208\\u1349\\u1275 3 \\u12a0\\u1218\\u1273\\u1275 \\u12a5\\u12e8\\u1270\\u1263\\u1263\\u1230\\u1293 \\u1218\\u120d\\u12a9\\u1295 \\u12a5\\u12e8\\u1240\\u12e8\\u1228 \\u12eb\\u1208\\u1260\\u1275 \\u1201\\u1294\\u1273 \\u1290\\u12cd \\u12eb\\u1208\\u12cd\\u1362\\n\\n\\u1218\\u1320\\u1291 \\u1260\\u1270\\u1208\\u12eb\\u12e8 \\u12f0\\u1228\\u1303 \\u12ed\\u1201\\u1295 \\u12a5\\u1295\\u1302 \\u1260\\u1270\\u1208\\u12eb\\u12e9 \\u12ad\\u120d\\u120e\\u127d\\u1293 \\u12c8\\u1228\\u12f3\\u12ce\\u127d \\u120b\\u12ed \\u12eb\\u1209 \\u130d\\u132d\\u1276\\u127d \\u12e8\\u1340\\u1325\\u1273 \\u1201\\u1294\\u1273\\u12cd\\u1295 \\u12a5\\u12eb\\u12c8\\u12a9\\u1275 \\u12ed\\u1308\\u129b\\u1209\\u1362\\n\\n\\u12e8\\u121a\\u1273\\u12e9\\u1275 \\u12e8\\u1340\\u1325\\u1273\\u1293 \\u12f0\\u1205\\u1295\\u1290\\u1275 \\u127d\\u130d\\u122e\\u127d \\u12dc\\u130e\\u127d \\u1260\\u1290\\u1343\\u1290\\u1275 \\u1270\\u1295\\u1240\\u1233\\u1245\\u1230\\u12cd \\u1309\\u12f3\\u12eb\\u1278\\u12cd\\u1295 \\u12a5\\u1295\\u12f3\\u12ed\\u1348\\u1345\\u1219 \\u1364 \\u12e8\\u1353\\u1208\\u1272\\u12ab \\u1353\\u122d\\u1272\\u12ce\\u127d\\u121d #\\u1260\\u1290\\u1343\\u1290\\u1275_\\u1270\\u1295\\u1240\\u1233\\u1245\\u1230\\u1295 \\u12e8\\u121d\\u1295\\u1348\\u120d\\u1308\\u12cd\\u1295 \\u12e8\\u1353\\u1208\\u1272\\u12ab \\u1235\\u122b \\u12a5\\u1295\\u12f3\\u1295\\u1230\\u122b \\u12a5\\u1295\\u1245\\u134b\\u1275 \\u12a5\\u12e8\\u1348\\u1320\\u1228 \\u1290\\u12cd\\u1362 \\\"\\n\\nQ. \\u12e8\\u1291\\u122e \\u12c8\\u12f5\\u1290\\u1271 \\u1205\\u12dd\\u1261\\u1295 \\u1260\\u12a5\\u1305\\u1309 \\u1348\\u1275\\u1296\\u1273\\u120d\\u1362 \\u1208\\u12da\\u1205\\u121d\\u00a0 \\u201c \\u1218\\u1295\\u130d\\u1235\\u1275 \\u1275\\u12a9\\u1228\\u1275 \\u12a0\\u120d\\u1230\\u1320\\u121d\\u00a0 \\u201d \\u12e8\\u121a\\u1209 \\u1245\\u122c\\u1273\\u12ce\\u127d \\u1232\\u1230\\u1290\\u12d8\\u1229 \\u12ed\\u1235\\u1270\\u12cb\\u120b\\u120d\\u1362 \\u1218\\u1295\\u130d\\u1235\\u1275 \\u1200\\u120b\\u134a\\u1290\\u1271\\u1295 \\u12a5\\u12e8\\u1270\\u12c8\\u1323 \\u1290\\u12cd ?\\n\\n\\u12a0\\u1276 \\u12cb\\u1232\\u1201\\u1295 \\u1270\\u1235\\u134b\\u12ec \\u1366\\n\\n\\\" \\u1291\\u122e \\u12cd\\u12f5\\u1290\\u1271 \\u1260\\u1270\\u1208\\u12ed\\u121d #\\u1218\\u12ab\\u12a8\\u1208\\u129b_\\u1308\\u1262 \\u12e8\\u121a\\u1296\\u1228\\u12cd \\u1205\\u1265\\u1228\\u1270\\u1230\\u1265 \\u1270\\u1218\\u130d\\u1266 \\u121b\\u12f0\\u122d \\u12e8\\u121b\\u12ed\\u127d\\u120d\\u1260\\u1275\\u1363 \\u120d\\u1306\\u1279\\u1295 \\u12e8\\u121b\\u12eb\\u1235\\u1270\\u121d\\u122d\\u1260\\u1275 \\u12f0\\u1228\\u1303 \\u120b\\u12ed \\u12f0\\u122d\\u1237\\u120d\\u1362 \\n\\n\\u12e8\\u1291\\u122e \\u12cd\\u12f5\\u1290\\u1271 \\u1260\\u12e8\\u1240\\u1291 \\u12a8\\u1241\\u1325\\u1325\\u122d \\u12cd\\u132a \\u12a5\\u12e8\\u12c8\\u1323 \\u1290\\u12cd \\u12eb\\u1208\\u12cd\\u1362\\n\\n\\u1260\\u12da\\u1205 \\u1201\\u1294\\u1273 \\u12a5\\u1295\\u12b3 \\u1230\\u122b\\u1270\\u129e\\u127d \\u12e8\\u12c8\\u122b\\u1275 \\u12f0\\u1218\\u12c8\\u12dd \\u1233\\u12ed\\u12a8\\u1348\\u120b\\u1278\\u12cd \\u12a5\\u12e8\\u1230\\u1229 \\u1290\\u12cd\\u1362 \\n\\n\\u1260\\u1270\\u1243\\u122b\\u1292\\u12cd \\u12f0\\u130d\\u121e \\u1218\\u1295\\u130d\\u1235\\u1275 \\u1208\\u1245\\u1295\\u1321 \\u1355\\u122e\\u1300\\u12ad\\u1276\\u127d \\u12e8\\u121a\\u12eb\\u12c8\\u1323\\u1278\\u12cd \\u1260\\u1300\\u1276\\u127d \\u12a0\\u1209\\u1362 \\u1218\\u1295\\u130d\\u1235\\u1275 \\u12a5\\u12e8\\u1204\\u12f0\\u1260\\u1275 \\u12eb\\u1208\\u12cd \\u1218\\u1295\\u1308\\u12f5 \\u1291\\u122e \\u12cd\\u12f5\\u1290\\u1271\\u1295 \\u12e8\\u12d8\\u1290\\u130b \\u1290\\u12cd\\u1362 \\\"\\n\\nQ. \\u12a5\\u1295\\u12f0 \\u1353\\u122d\\u1272 \\u12e8\\u121a\\u12f0\\u122d\\u1235\\u1263\\u127d\\u1201 \\u132b\\u1293 \\u121d\\u1295\\u12f5\\u1295 \\u1290\\u12cd ? \\u1208\\u1240\\u1323\\u12e9 \\u121d\\u122d\\u132b\\u1235 \\u12dd\\u130d\\u1305\\u1275 \\u12a5\\u12eb\\u12f0\\u1228\\u130b\\u127d\\u1201 \\u1290\\u12cd ?\\n\\n\\u12a0\\u1276 \\u12cb\\u1232\\u1201\\u1295 \\u1270\\u1235\\u134b\\u12ec \\u1366\\n\\n\\\" \\u12e8\\u1340\\u1325\\u1273\\u12cd \\u127d\\u130d\\u122d \\u12e8\\u1353\\u1208\\u1272\\u12ab \\u1353\\u122d\\u1272\\u12ce\\u127d \\u1260\\u121a\\u1348\\u1208\\u1308\\u12cd \\u12f0\\u1228\\u1303 \\u1270\\u1295\\u1243\\u1233\\u1245\\u1230\\u12cd \\u12a0\\u1263\\u120e\\u127b\\u1278\\u12cd\\u1295 \\u12a5\\u1295\\u12f3\\u12ed\\u1240\\u1230\\u1245\\u1231\\u1363 \\u12f0\\u130b\\u134a\\u12ce\\u127b\\u1278\\u12cd\\u1295 \\u12a5\\u1295\\u12f3\\u12eb\\u1308\\u1299 \\u12a0\\u12f5\\u122d\\u1313\\u120d\\u1362 \\u12a5\\u129b\\u121d \\u12eb\\u1295\\u1295 \\u12a5\\u12eb\\u12f0\\u1228\\u130d\\u1295 \\u12a0\\u12ed\\u12f0\\u1208\\u121d\\u1362\\n\\n\\u1270\\u134e\\u12ab\\u12ab\\u122a \\u1353\\u122d\\u1272\\u12ce\\u127d \\u12e8\\u1218\\u1295\\u130d\\u1235\\u1275 \\u121a\\u12f2\\u12eb\\u12ce\\u127d\\u1295 \\u12a5\\u1295\\u12b3 \\u1218\\u1320\\u1240\\u121d \\u12a0\\u12ed\\u127d\\u1209\\u121d\\u1362 \\n\\n\\u12e8\\u1218\\u1295\\u130d\\u1235\\u1275 \\u121a\\u12f2\\u12eb\\u12ce\\u127d \\u12e8\\u1265\\u120d\\u1345\\u130d\\u1293 \\u120d\\u1233\\u1295 \\u1206\\u1290\\u12cb\\u120d\\u1362 \\u12e8\\u1265\\u120d\\u1345\\u130d\\u1293 \\u12a5\\u1295\\u1302 \\u12e8\\u1218\\u1295\\u130d\\u1235\\u1275 \\u121a\\u12f2\\u12eb \\u120a\\u1263\\u1209 \\u12a0\\u12ed\\u127d\\u1209\\u121d\\u1362\\u00a0\\n\\n\\u1235\\u1208\\u12da\\u1205 \\u12e8\\u1353\\u1208\\u1272\\u12ab \\u121d\\u1205\\u12f3\\u1229 \\u12a8\\u1240\\u1295 \\u1240\\u1295 \\u12a5\\u12e8\\u1320\\u1260\\u1260 \\u1290\\u12cd\\u1362 \\u12ed\\u1204 \\u12f0\\u130d\\u121e \\u1218\\u12f5\\u1265\\u1208 \\u1353\\u122d\\u1272 \\u1260\\u1273\\u12c8\\u1300\\u1260\\u1275 \\u12a0\\u1308\\u122d \\u120b\\u12ed \\u12a0\\u12ed\\u1320\\u1245\\u121d\\u121d\\u1362 \\n\\n\\u12e8\\u1218\\u12f5\\u1265\\u1208 \\u1353\\u122d\\u1272 \\u1235\\u122d\\u12a0\\u1275\\u1295 \\u12a5\\u12eb\\u122b\\u1218\\u12f5\\u1295 \\u1290\\u12cd \\u12a5\\u1295\\u120b\\u1208\\u1295\\u1364 \\u1218\\u122c\\u1275 \\u120b\\u12ed \\u12eb\\u1208\\u12cd \\u1200\\u1245 \\u130d\\u1295 \\u12a0\\u1295\\u12f5 \\u12a0\\u12cd\\u122b \\u1353\\u122d\\u1272 \\u1265\\u127b \\u1296\\u122e (\\u1265\\u120d\\u1345\\u130d\\u1293) \\u12a5\\u1231\\u1295 \\u1265\\u127b \\u12a5\\u12eb\\u121e\\u1308\\u1235\\u1295 \\u12a5\\u1295\\u12f5\\u1295\\u1296\\u122d \\u1290\\u12cd\\u1362\\n\\n\\u1265\\u120d\\u1345\\u130d\\u1293 \\u1260\\u1240\\u1325\\u1273 \\u12a5\\u12e8\\u1204\\u12f0 \\u12eb\\u1208\\u12cd \\u122b\\u1231\\u1295 \\u12c8\\u12f0 \\u12a0\\u12cd\\u122b \\u1353\\u122d\\u1272 \\u1208\\u1218\\u1240\\u12e8\\u122d \\u12a5\\u1295\\u1245\\u1235\\u1243\\u1234 \\u12a5\\u12eb\\u12f0\\u1228\\u1308 \\u1290\\u12cd\\u1362 \\n\\n\\u2018 13 \\u121a\\u120a\\u12ee\\u1295 \\u1363 14 \\u121a\\u120a\\u12ee\\u1295 #\\u12a0\\u1263\\u120b\\u1275\\u1295_\\u12a0\\u134d\\u122d\\u127b\\u1208\\u1201 \\u2019 \\u12ed\\u120b\\u120d\\u1364 \\u12a8\\u1218\\u122b\\u1329 \\u1205\\u12dd\\u1265 \\u130d\\u121b\\u1239\\u1295 \\u12a0\\u1263\\u120d \\u12a0\\u12f5\\u122d\\u130c\\u12a0\\u1208\\u1201 \\u12e8\\u121a\\u120d \\u12a8\\u1206\\u1290 \\u121d\\u122d\\u132b \\u1262\\u1218\\u1323\\u121d \\u1275\\u122d\\u1309\\u121d \\u12e8\\u1208\\u12cd\\u121d\\u1362 \\u1208\\u12ed\\u1235\\u1219\\u120b \\u12e8\\u121a\\u12f0\\u1228\\u130d \\u121d\\u122d\\u132b \\u1290\\u12cd\\u1362 \\\"\\n\\n#TikvahEthiopiaFamilyAA\\n#EZEMA #\\u12e8\\u1356\\u1208\\u1272\\u12ab\\u1353\\u122d\\u1272\\u12ce\\u127d\\u121d\\u1295\\u12ed\\u120b\\u1209?\\n\\n@tikvahethiopia\",\n          \"1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tags\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4250,\n          \"1007\",\n          \"6128\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aggregated_data.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EsWuWqwLOk9u",
        "outputId": "cdc61802-bb68-46ad-d5c8-be2415e442bd"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "text              0\n",
              "tags           5761\n",
              "text_length       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Removing any NaN (incomplete) entries\n",
        "aggregated_data = aggregated_data.dropna(subset=['text', 'tags'])"
      ],
      "metadata": {
        "id": "rXd45jHUjyrJ"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "aggregated_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "E0w339kAYPbS",
        "outputId": "c0693158-97f9-4617-e441-bc26ee13d452"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                text  \\\n",
              "0  🇪🇹 የፖለቲካ ፓርቲዎች ምን ይላሉ ? 🇪🇹\\n\\n\" ግጭቶች ከትንሽ ነገር ...   \n",
              "1  🇪🇹 የፖለቲካ ፓርቲዎች ምን ይላሉ ? 🇪🇹\\n\\n\" ... አሁንም ቢሆን ለ...   \n",
              "3  #Update\\n\\nኦክሎክ ሞተርስ “ስሜን አጥፍቷል” ላለው ቱሪስት ታክሲ ...   \n",
              "4  #ደሴ\\n\\n“ አንድ ቀን ውሳኔ ተሰጥቶባቸው ከአካባቢ ብክለት ነፃ እንዲሆ...   \n",
              "5  #Update\\n\\n\" 4 ሚሊዮን ብር ለማስለቀቂያ ከፍለናል ሰነዱ በእጄ አ...   \n",
              "\n",
              "                                                tags  text_length  \n",
              "0  #በነፃነት_ተንቀሳቅሰን,#መካከለኛ_ገቢ,#አባላትን_አፍርቻለሁ,#Tikvah...         2947  \n",
              "1  #ኦፌኮ,#ከመገዳደል,#መደራደር,#ግድያ,#እስራት,#ብልሹ_አሰራርና,#ሙስና...         2295  \n",
              "3                          #Update,#ከሄሎ_ታክሲ,#እየሸጠ_ነው          926  \n",
              "4  #ደሴ,#ቪዲዮም,#ከህዝብ_መኖሪያ,#ሞዲፋይ,#TikvahFamilyAddisA...         1964  \n",
              "5  #Update,#የቀን_ሰራተኞች,#ተቀበሉን,#የከፈልናቸው,#መክፈላቸውንና,#...         2746  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dfa91577-3a89-4278-90ba-8f483b48db9e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>tags</th>\n",
              "      <th>text_length</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>🇪🇹 የፖለቲካ ፓርቲዎች ምን ይላሉ ? 🇪🇹\\n\\n\" ግጭቶች ከትንሽ ነገር ...</td>\n",
              "      <td>#በነፃነት_ተንቀሳቅሰን,#መካከለኛ_ገቢ,#አባላትን_አፍርቻለሁ,#Tikvah...</td>\n",
              "      <td>2947</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>🇪🇹 የፖለቲካ ፓርቲዎች ምን ይላሉ ? 🇪🇹\\n\\n\" ... አሁንም ቢሆን ለ...</td>\n",
              "      <td>#ኦፌኮ,#ከመገዳደል,#መደራደር,#ግድያ,#እስራት,#ብልሹ_አሰራርና,#ሙስና...</td>\n",
              "      <td>2295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>#Update\\n\\nኦክሎክ ሞተርስ “ስሜን አጥፍቷል” ላለው ቱሪስት ታክሲ ...</td>\n",
              "      <td>#Update,#ከሄሎ_ታክሲ,#እየሸጠ_ነው</td>\n",
              "      <td>926</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>#ደሴ\\n\\n“ አንድ ቀን ውሳኔ ተሰጥቶባቸው ከአካባቢ ብክለት ነፃ እንዲሆ...</td>\n",
              "      <td>#ደሴ,#ቪዲዮም,#ከህዝብ_መኖሪያ,#ሞዲፋይ,#TikvahFamilyAddisA...</td>\n",
              "      <td>1964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>#Update\\n\\n\" 4 ሚሊዮን ብር ለማስለቀቂያ ከፍለናል ሰነዱ በእጄ አ...</td>\n",
              "      <td>#Update,#የቀን_ሰራተኞች,#ተቀበሉን,#የከፈልናቸው,#መክፈላቸውንና,#...</td>\n",
              "      <td>2746</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dfa91577-3a89-4278-90ba-8f483b48db9e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-dfa91577-3a89-4278-90ba-8f483b48db9e 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-dfa91577-3a89-4278-90ba-8f483b48db9e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-10059385-3062-452e-a2dc-9ee7e64d94a7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-10059385-3062-452e-a2dc-9ee7e64d94a7')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-10059385-3062-452e-a2dc-9ee7e64d94a7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "aggregated_data",
              "summary": "{\n  \"name\": \"aggregated_data\",\n  \"rows\": 6128,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6128,\n        \"samples\": [\n          \"Nekemte! \\\"Command Post warned it has lost patience to Nekemte protest and also to people who nag MPs to reject SOE in #Ethiopia.\\\"\\n\\n# BefeQadu Z. Hailu\\n@tsegabwolde @tikvahethiopia\",\n          \"\\u1260\\u122d\\u1260\\u122b\\u203c\\ufe0f\\n\\n\\u12e8\\u12a2\\u1275\\u12ee\\u1335\\u12eb \\u1218\\u1295\\u130d\\u1235\\u1275 \\u1260\\u122b\\u1235 \\u1308\\u12df \\u1231\\u121b\\u120c\\u120b\\u1295\\u12f5 \\u12e8\\u121a\\u1308\\u1299\\u1275\\u1295 \\u12f2\\u1355\\u120e\\u121b\\u1272\\u12ad \\u120d\\u12a1\\u12a9\\u1295 #\\u1320\\u122d\\u1277\\u120d\\u1361\\u1361 \\n\\n\\u1260\\u1260\\u122d\\u1260\\u122b \\u12e8\\u12a2\\u1275\\u12ee\\u1335\\u12eb \\u1246\\u1295\\u1235\\u120b \\u1200\\u120b\\u134a\\u12cd \\u1260\\u122d\\u1204 \\u1270\\u1235\\u134b\\u12ed \\u12c8\\u12f0 \\u12a0\\u12f2\\u1235 \\u12a0\\u1260\\u1263 \\u12a5\\u1295\\u12f0\\u1270\\u1218\\u1208\\u1231\\u121d All East Africa \\u12e8\\u1270\\u1230\\u1298\\u12cd \\u12dc\\u1293 \\u12c8\\u12aa\\u120d \\u12db\\u122c \\u1260\\u12f5\\u1228\\u1308\\u1345 \\u12a0\\u1235\\u1290\\u1265\\u1267\\u120d\\u1361\\u1361 \\n\\n\\u12e8\\u1246\\u1295\\u1235\\u1209 \\u1218\\u1320\\u122b\\u1275 \\u1260\\u1240\\u1320\\u1293\\u12cd \\u12e8\\u12a0\\u1230\\u120b\\u1208\\u134d \\u1208\\u12cd\\u1325 \\u1260\\u1218\\u121d\\u1323\\u1271 \\u12a5\\u1293 \\u12a2\\u1275\\u12ee\\u1335\\u12eb \\u12a8\\u1231\\u121b\\u120a\\u12eb \\u130b\\u122d \\u12eb\\u120b\\u1275\\u1295 \\u1201\\u1208\\u1295\\u1270\\u1293\\u12ca \\u130d\\u1295\\u1299\\u1290\\u1275 \\u1260\\u1218\\u1325\\u1260\\u124b \\u120a\\u1206\\u1295 \\u12a5\\u1295\\u12f0\\u121a\\u127d\\u120d \\u1270\\u1308\\u121d\\u1277\\u120d\\u1361\\u1361 \\n\\n\\u201c\\u121d\\u1293\\u120d\\u1263\\u1275 \\u1320\\u1245\\u120b\\u12ed \\u121a\\u1295\\u1235\\u1275\\u122d #\\u12a0\\u1262\\u12ed_\\u12a0\\u1205\\u1218\\u12f5 \\u12a8\\u1231\\u121b\\u120c\\u120b\\u1295\\u12f5 \\u130b\\u122d \\u12eb\\u120b\\u1278\\u12cd\\u1295 \\u12f2\\u1355\\u120e\\u121b\\u1232\\u12eb\\u12ca \\u130d\\u1295\\u1299\\u1290\\u1275 \\u1208\\u121b\\u1240\\u12dd\\u1240\\u12dd \\u12a5\\u1295\\u12f0\\u121a\\u1348\\u120d\\u1309 \\u12a0\\u1295\\u12f5 \\u121d\\u120d\\u12ad\\u1275 \\u120a\\u1206\\u1295 \\u12ed\\u127d\\u120b\\u120d\\u201d \\u1232\\u1209 \\u12e8\\u1231\\u121b\\u120c\\u120b\\u1295\\u12f5 \\u1353\\u122d\\u120b\\u121b \\u12a0\\u1263\\u120d \\u12a2\\u1235\\u121b\\u12a4\\u120d \\u12a0\\u1265\\u12f2\\u122b\\u1205\\u121b\\u1295 \\u1208\\u12dc\\u1293 \\u12a0\\u1308\\u120d\\u130d\\u120e\\u1271 \\u1270\\u1293\\u130d\\u1228\\u12cb\\u120d\\u1361\\u1361 \\n\\n\\u12e8\\u1231\\u121b\\u120a\\u12eb\\u12cd \\u1355\\u122c\\u12dd\\u12f3\\u1295\\u1275 #\\u121e\\u1200\\u1218\\u12f5_\\u134e\\u122d\\u121b\\u1306 \\u1260\\u1245\\u122d\\u1261 \\u12a2\\u1275\\u12ee\\u1335\\u12eb \\u1260\\u12cd\\u1235\\u1325 \\u1309\\u12f3\\u12eb\\u127d\\u1295 \\u12a8\\u1218\\u130d\\u1263\\u1275 \\u1275\\u1246\\u1320\\u1265 \\u1232\\u1209 \\u121b\\u1233\\u1230\\u1263\\u1278\\u12cd \\u12ed\\u1273\\u12c8\\u1233\\u120d\\u1361\\u1361\\n\\n\\u121d\\u1295\\u132d\\u1366 wazemaradio\\n@tsegabwolde @tikvahethiopia\",\n          \"\\u12a0\\u1233\\u12db\\u129d \\u12dc\\u1293\\u203c\\ufe0f\\u1275\\u120b\\u1295\\u1275 \\u1260\\u121d\\u12a5\\u122b\\u1265 \\u12a0\\u122d\\u1232 \\u1260\\u12f0\\u1228\\u1230 \\u12e8\\u1270\\u123d\\u12a8\\u122d\\u12ab\\u122a \\u12a0\\u12f0\\u130b #\\u12e8\\u12a0\\u121d\\u1235\\u1275 \\u1230\\u12ce\\u127d \\u1205\\u12ed\\u12c8\\u1275 \\u12a5\\u1295\\u12f3\\u1208\\u1348 \\u12d8\\u130d\\u1260\\u1295 \\u1290\\u1260\\u122d\\u1362 \\u1260\\u12a0\\u12f0\\u130b\\u12cd \\u12a8\\u134d\\u1270\\u129b \\u1309\\u12f3\\u1275 \\u12eb\\u1235\\u1270\\u1293\\u1308\\u12f0\\u12cd \\u12a0\\u1295\\u12f5 \\u12c8\\u1323\\u1275 \\u1260\\u1205\\u12ad\\u121d\\u1293 \\u1263\\u1208\\u121e\\u12eb\\u12ce\\u127d \\u1205\\u12ed\\u12c8\\u1271\\u1295 \\u1208\\u121b\\u1275\\u1228\\u134d \\u12e8\\u1270\\u12f0\\u1228\\u1308\\u12cd \\u1325\\u1228\\u1275 #\\u1233\\u12ed\\u1233\\u12ab \\u1240\\u122d\\u1277\\u120d\\u1362 \\u1260\\u12da\\u1205\\u121d \\u1260\\u1275\\u120b\\u1295\\u1271 \\u12e8\\u1218\\u12aa\\u1293 \\u12a0\\u12f0\\u130b \\u1260\\u12a0\\u1320\\u1243\\u120b\\u12ed \\u1205\\u12ed\\u12c8\\u1273\\u1278\\u12cd \\u12eb\\u1208\\u1348\\u12cd \\u12dc\\u130e\\u127d \\u1241\\u1325\\u122d #\\u1235\\u12f5\\u1235\\u1275 (6) \\u1206\\u1297\\u120d\\u1362\\n\\n\\u1260TIKVAH-ETH \\u1235\\u121d \\u1208\\u121f\\u127d \\u1264\\u1270\\u1230\\u1266\\u127d\\u1293 \\u12c8\\u12f3\\u1306\\u127d \\u1260\\u1219\\u1209 \\u1218\\u1345\\u1293\\u1293\\u1275\\u1295 \\u12a5\\u1295\\u1218\\u129b\\u1208\\u1295!\\n@tsegabwolde @tikvahethiopia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tags\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4250,\n        \"samples\": [\n          \"#\\u1219\\u1209_\\u1218\\u1228\\u1303\\u12cd,#\\u1218\\u1228\\u12a8\\u1261\\u1295,#\\u1275\\u1295\\u1270\\u1293\\u12cd\",\n          \"#update,#\\u12a8\\u1206\\u122b\\u12ed\\u12d8\\u1295_\\u12a9\\u1263\\u1295\\u12eb,#\\u12f5\\u130b\\u134d\",\n          \"#\\u1260\\u1261\\u12f5\\u1295\\u121d,#\\u1260\\u1270\\u1293\\u1320\\u120d,#\\u1230\\u120b\\u121b\\u12ca,#\\u12e8\\u1270\\u12a8\\u1208\\u12a8\\u1209,#\\u12e8\\u1270\\u12a8\\u1208\\u12a8\\u1208,#\\u12a5\\u122d\\u121d\\u1303,#\\u1232\\u12f3\\u121b\\u122a\\u1348\\u1228\\u1295\\u12f0\\u121d2011,#\\u1200\\u12cb\\u1233\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 598,\n        \"min\": 13,\n        \"max\": 4096,\n        \"num_unique_values\": 1603,\n        \"samples\": [\n          318,\n          1508,\n          98\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Looking at the number of tokens\n",
        "# Using mT5's tokenizer to find the number of tokens\n",
        "tokenizer = MT5Tokenizer.from_pretrained('google/mt5-small')\n",
        "\n",
        "def count_tokens(text, tokenizer):\n",
        "    '''\n",
        "    Count the number of tokens in a given text using a specified tokenizer.\n",
        "\n",
        "    Args:\n",
        "        text (str): The text to be tokenized.\n",
        "        tokenizer: The tokenizer to be used for tokenizing the text.\n",
        "\n",
        "    Returns:\n",
        "        int: The total number of tokens in the tokenized text.\n",
        "    '''\n",
        "\n",
        "    # Encoding the text using the tokenizer\n",
        "    tokens = tokenizer.encode(text, add_special_tokens=True)\n",
        "\n",
        "    # Returning the number of tokens\n",
        "    return len(tokens)"
      ],
      "metadata": {
        "id": "U_C9HrCUZxf1"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Statistical analysis of the dataset\n",
        "aggregated_data['text_length'] = aggregated_data['text'].apply(len)\n",
        "aggregated_data['tags_length'] = aggregated_data['tags'].apply(len)\n",
        "\n",
        "# Word counts\n",
        "aggregated_data['text_word_count'] = aggregated_data['text'].apply(lambda x: len(x.split()))\n",
        "aggregated_data['summary_word_count'] = aggregated_data['tags'].apply(lambda x: len(x.split()))\n",
        "\n",
        "# Token counts\n",
        "aggregated_data['text_token_count'] = aggregated_data['text'].apply(lambda x: count_tokens(x, tokenizer))\n",
        "aggregated_data['tags_token_count'] = aggregated_data['tags'].apply(lambda x: count_tokens(x, tokenizer))\n",
        "\n",
        "# Descriptive statistics\n",
        "print(aggregated_data[['text_length', 'tags_length', 'text_word_count', 'text_token_count', 'tags_token_count']].describe())\n",
        "\n",
        "# Counting most common words\n",
        "all_words = ' '.join(aggregated_data['text']).split()\n",
        "word_counts = Counter(all_words)\n",
        "print(\"Most common words: \", word_counts.most_common(10))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cgSpFXLdcCSS",
        "outputId": "125a8cbf-1577-4bf8-e7b7-b412d3780eab"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       text_length  tags_length  text_word_count  text_token_count  \\\n",
            "count  6128.000000  6128.000000      6128.000000       6128.000000   \n",
            "mean    569.178198    18.575881       103.274315        350.450392   \n",
            "std     598.041810    17.621562       116.321960        376.380045   \n",
            "min      13.000000     2.000000         1.000000          8.000000   \n",
            "25%     197.000000     7.000000        32.000000        115.000000   \n",
            "50%     353.000000    13.000000        61.000000        213.000000   \n",
            "75%     685.250000    23.000000       124.000000        427.000000   \n",
            "max    4096.000000   205.000000       849.000000       2634.000000   \n",
            "\n",
            "       tags_token_count  \n",
            "count       6128.000000  \n",
            "mean          12.400131  \n",
            "std           11.897013  \n",
            "min            2.000000  \n",
            "25%            5.000000  \n",
            "50%            9.000000  \n",
            "75%           16.000000  \n",
            "max          155.000000  \n",
            "Most common words:  [('እና', 6560), ('ላይ', 6216), ('@tikvahethiopia', 5753), ('@tsegabwolde', 5585), ('ወደ', 3210), ('ነው', 3111), ('አቶ', 2955), ('ጋር', 2641), ('#update', 2293), ('ውስጥ', 2170)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing the distribution of the token counts for both the texts and summaries\n",
        "\n",
        "# Extracting token counts\n",
        "text_token_counts = aggregated_data['text_token_count']\n",
        "tags_token_counts = aggregated_data['tags_token_count']\n",
        "\n",
        "# Determining the upper limit for the x-axis\n",
        "upper_limit_text = max(text_token_counts) + 5\n",
        "upper_limit_summary = max(tags_token_counts) + 5\n",
        "\n",
        "# Plotting histograms\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "# Histogram for text token counts\n",
        "plt.subplot(1, 2, 1)  # 1 row, 2 columns, first plot\n",
        "plt.hist(text_token_counts, bins=60, color='blue', alpha=0.7)\n",
        "plt.title('Distribution of Text Token Counts')\n",
        "plt.xlabel('Token Count')\n",
        "plt.ylabel('Frequency')\n",
        "plt.xlim(0, upper_limit_text)\n",
        "\n",
        "# Histogram for summary token counts\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.hist(tags_token_counts, bins=60, color='green', alpha=0.7)\n",
        "plt.title('Distribution of tags Token Counts')\n",
        "plt.xlabel('Token Count')\n",
        "plt.ylabel('Frequency')\n",
        "plt.xlim(0, upper_limit_summary)\n",
        "\n",
        "# Show the plots\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "LfRkku_dmeef",
        "outputId": "9ef714aa-f731-42d9-f5ac-5fa7e1d065bf"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABKUAAAJOCAYAAABm7rQwAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB3tUlEQVR4nOzde3zP9f//8ft7Zgdjm9E2C7NQzhTSIinLHD6L6EAjSimNHErlU0QloeSQSJ8+qOigTwnVMocojRhzSqMSim3VbDMysz1/f/Tb6+ttw8z22ul2vVzel4v38/l8vV6P5+v93nsPj73ez5fDGGMEAAAAAAAA2MilpAMAAAAAAABAxUNRCgAAAAAAALajKAUAAAAAAADbUZQCAAAAAACA7ShKAQAAAAAAwHYUpQAAAAAAAGA7ilIAAAAAAACwHUUpAAAAAAAA2I6iFAAAAAAAAGxHUQq4RBMmTJDD4bDlWJ06dVKnTp2s519//bUcDoc+/vhjW44/aNAg1atXz5ZjFVZGRoYefPBBBQYGyuFwaOTIkSUdUonJfW/++eefJR0KAKACIkcqXciR8lq4cKEcDoe2bt1a0qEA+P8oSqFCy/3FlPvw8PBQUFCQwsPDNWvWLB0/frxIjnPkyBFNmDBB8fHxRbK/olSaYyuIl156SQsXLtTQoUP17rvvasCAAXnG5CbJF3ucndxeriVLlmjGjBkXHVcSsdkpKSlJTzzxhBo1aqQqVarIy8tLrVu31osvvqjU1NSSDk9SwV8rAKhIyJFKd2wFUZAc6eyxy5Ytsy+4S3Due/F8j9JeJDyf9PR0TZw4US1btlTVqlXl6empZs2a6amnntKRI0dKOjxJ0hdffKEJEyaUdBgop1xLOgCgNHj++ecVEhKirKwsJSYm6uuvv9bIkSM1ffp0LV++XC1atLDGPvvss3r66acvaf9HjhzRxIkTVa9ePbVq1arA261ateqSjlMYF4rtrbfeUk5OTrHHcDnWrl2rG264Qc8999x5x/Tu3VsNGjSwnmdkZGjo0KG644471Lt3b6s9ICCgyOJasmSJdu/efdG/SpZEbHbZsmWLunfvroyMDPXv31+tW7eWJG3dulUvv/yyNmzYYMt7/GIK+loBQEVEjlS+c6RcL730ku6880716tWr+AO7RB07dtS7777r1Pbggw/q+uuv15AhQ6y2qlWr2h3aZfvll18UFhamQ4cO6a677tKQIUPk5uamnTt36u2339ann36qffv2lXSY+uKLLzRnzhwKUygWFKUASd26dVObNm2s52PHjtXatWv1r3/9S7fffrv27t0rT09PSZKrq6tcXYv3R+fkyZOqUqWK3NzcivU4F1O5cuUSPX5BJCcnq0mTJhcc06JFC6ek+c8//9TQoUPVokUL9e/fv7hDvKDSHNvlSE1N1R133KFKlSpp+/btatSokVP/pEmT9NZbb5VQdACAgiJHyl95yZHKgquuukpXXXWVU9sjjzyiq666qkznSmfOnFHv3r2VlJSkr7/+Wh06dHDqnzRpkqZMmVJC0QH24et7wHnceuutGjdunA4ePKj33nvPas9vvYSYmBh16NBBvr6+qlq1qq655hr9+9//lvTPGgdt27aVJN1///3WJcYLFy6U9M+aCM2aNVNcXJw6duyoKlWqWNueu15CruzsbP373/9WYGCgvLy8dPvtt+vw4cNOY+rVq6dBgwbl2fbsfV4stvzWSzhx4oQef/xx1alTR+7u7rrmmmv0yiuvyBjjNM7hcGjYsGFatmyZmjVrJnd3dzVt2lTR0dH5n/BzJCcna/DgwQoICJCHh4datmypRYsWWf25a0ccOHBAn3/+uRX7r7/+WqD95+fHH3/UnXfeKT8/P3l4eKhNmzZavny5U0xXXHGFOnXq5DTfn376SV5eXrrnnnsk/XOOP//8cx08eLDILilfu3atbrrpJnl5ecnX11c9e/bU3r17L7rdwYMH1aBBAzVr1kxJSUmS/ikYjRw50noNGzRooClTpjj9xffXX3+Vw+HQK6+8ovnz56t+/fpyd3dX27ZttWXLlose980339Tvv/+u6dOn5ylISf9c+fXss886tb3xxhtq2rSp3N3dFRQUpKioqDxf8SvI+1r6v/fHRx99pEmTJql27dry8PBQ586d9dNPPzltd6HXavbs2WratKmqVKmi6tWrq02bNlqyZMlF5w8A5Rk5UvnKkRwOh06cOKFFixZZY3PPz8GDB/Xoo4/qmmuukaenp2rUqKG77ror333t3LlTN998szw9PVW7dm29+OKLWrBgQZ5jb926VeHh4apZs6Y8PT0VEhKiBx54oEBzv5Dt27erW7du8vb2VtWqVdW5c2dt2rTpotsdO3ZM119/vWrXrq2EhARJUmZmpp577jk1aNBA7u7uqlOnjp588kllZmY6bXs5r+X//vc/7dixQ88880yegpQkeXt7a9KkSU5tS5cuVevWreXp6amaNWuqf//++v33353GnO9n49z3bEFzvUGDBmnOnDnWfHMfuT744AO1bt1a1apVk7e3t5o3b66ZM2dedP5ALq6UAi5gwIAB+ve//61Vq1bpoYceynfMnj179K9//UstWrTQ888/L3d3d/3000/auHGjJKlx48Z6/vnnNX78eA0ZMkQ33XSTJOnGG2+09vHXX3+pW7du6tu3r/r373/Rr2pNmjRJDodDTz31lJKTkzVjxgyFhYUpPj7e+mtlQRQktrMZY3T77bdr3bp1Gjx4sFq1aqWvvvpKY8aM0e+//67XXnvNafy3336rTz75RI8++qiqVaumWbNmqU+fPjp06JBq1Khx3rj+/vtvderUST/99JOGDRumkJAQLV26VIMGDVJqaqpGjBihxo0b691339WoUaNUu3ZtPf7445KkK664osDzP9uePXvUvn17XXnllXr66afl5eWljz76SL169dL//vc/3XHHHfL399fcuXN11113afbs2XrssceUk5OjQYMGqVq1anrjjTckSc8884zS0tL022+/Wefkci4pX716tbp166arrrpKEyZM0N9//63Zs2erffv22rZt23kLXj///LNuvfVW+fn5KSYmRjVr1tTJkyd188036/fff9fDDz+sunXr6rvvvtPYsWN19OjRPGsrLVmyRMePH9fDDz8sh8OhqVOnqnfv3vrll18u+Ffi5cuXy9PTU3feeWeB5jhhwgRNnDhRYWFhGjp0qBISEjR37lxt2bJFGzduLPRfpF9++WW5uLjoiSeeUFpamqZOnarIyEht3rxZ0oVfq7feekuPPfaY7rzzTo0YMUKnTp3Szp07tXnzZt17772FigcAygtyJGdlOUd6991383wdrn79+pL++Sr+d999p759+6p27dr69ddfNXfuXHXq1Ek//PCDqlSpIkn6/fffdcstt8jhcGjs2LHy8vLSf/7zH7m7uzsdKzk5WV26dNEVV1yhp59+Wr6+vvr111/1ySefXOwluaA9e/bopptukre3t5588klVrlxZb775pjp16qT169erXbt2+W73559/6rbbblNKSorWr1+v+vXrKycnR7fffru+/fZbDRkyRI0bN9auXbv02muvad++fXnW3irsa5n7h88LrfV1toULF+r+++9X27ZtNXnyZCUlJWnmzJnauHGjtm/fLl9f3wLt51wXy/UefvhhHTlyRDExMXm+RhkTE6N+/fqpc+fO1lVde/fu1caNGzVixIhCxYMKyAAV2IIFC4wks2XLlvOO8fHxMddee631/LnnnjNn/+i89tprRpL5448/zruPLVu2GElmwYIFefpuvvlmI8nMmzcv376bb77Zer5u3TojyVx55ZUmPT3dav/oo4+MJDNz5kyrLTg42AwcOPCi+7xQbAMHDjTBwcHW82XLlhlJ5sUXX3Qad+eddxqHw2F++uknq02ScXNzc2rbsWOHkWRmz56d51hnmzFjhpFk3nvvPavt9OnTJjQ01FStWtVp7sHBwaZHjx4X3N+5/vjjDyPJPPfcc1Zb586dTfPmzc2pU6estpycHHPjjTeahg0bOm3fr18/U6VKFbNv3z4zbdo0I8ksW7bMaUyPHj2czt3lxNaqVSvj7+9v/vrrL6ttx44dxsXFxdx3331WW+57848//jB79+41QUFBpm3btiYlJcUa88ILLxgvLy+zb98+p+M+/fTTplKlSubQoUPGGGMOHDhgJJkaNWo4bf/ZZ58ZSWbFihUXnEf16tVNy5YtCzTn5ORk4+bmZrp06WKys7Ot9tdff91IMv/973+ttoK+r3N/Vho3bmwyMzOt9pkzZxpJZteuXVbb+V6rnj17mqZNmxZoDgBQ3pAjVawcycvLK99zcvLkyTxtsbGxRpJ55513rLbhw4cbh8Nhtm/fbrX99ddfxs/Pz0gyBw4cMMYY8+mnn170fVWYeHv16mXc3NzMzz//bLUdOXLEVKtWzXTs2NFqO/t9ffToUdO0aVNz1VVXmV9//dUa8+677xoXFxfzzTffOB1z3rx5RpLZuHGj1XY5r+W1115rfHx8CjTf06dPG39/f9OsWTPz999/W+0rV640ksz48eOttnPfx7nOfc9eSq4XFRXl9LOda8SIEcbb29ucOXOmQPMA8sPX94CLqFq16gXvMJP7V4nPPvus0Ateuru76/777y/w+Pvuu0/VqlWznt95552qVauWvvjii0Idv6C++OILVapUSY899phT++OPPy5jjL788kun9rCwMOsvbdI/6yd5e3vrl19+uehxAgMD1a9fP6utcuXKeuyxx5SRkaH169cXwWz+T0pKitauXau7775bx48f159//qk///xTf/31l8LDw7V//36nS6Nff/11+fj46M4779S4ceM0YMAA9ezZs0hjynX06FHFx8dr0KBB8vPzs9pbtGih2267Ld/XfPfu3br55ptVr149rV69WtWrV7f6li5dqptuuknVq1e35vnnn38qLCxM2dnZ2rBhg9O+7rnnHqftc/9SfLHXMD093ek9eiGrV6/W6dOnNXLkSLm4/N+vpYceekje3t76/PPPC7Sf/Nx///1O644UNH7pn5/t3377rUBfVwSAiogc6f+U1xzp7KvLsrKy9Ndff6lBgwby9fXVtm3brL7o6GiFhoY6LQjv5+enyMhIp/3lvidWrlyprKysIokxOztbq1atUq9evZzWnqpVq5buvfdeffvtt0pPT3fa5rffftPNN9+srKwsbdiwQcHBwVbf0qVL1bhxYzVq1MgpV7r11lslSevWrXPaV2Ffy0vJlbZu3ark5GQ9+uij8vDwsNp79OihRo0aXVauVNhcT/rn9Txx4oRiYmIKfXyAohRwERkZGRf8hXHPPfeoffv2evDBBxUQEKC+ffvqo48+uqTk68orr7ykBTsbNmzo9NzhcKhBgwaXtZ5SQRw8eFBBQUF5zkfjxo2t/rPVrVs3zz6qV6+uY8eOXfQ4DRs2dCpQXOg4l+unn36SMUbjxo3TFVdc4fTIvWNNcnKyNd7Pz0+zZs3Szp075ePjo1mzZhVpPGfLnes111yTp69x48b6888/deLECaf2iIgIVatWTV999ZW8vb2d+vbv36/o6Og88wwLC5PkPE8p72uYm7Rc7DX09vYu8O3CzzdHNzc3XXXVVZf1ehc2fkl66qmnVLVqVV1//fVq2LChoqKirK+cAADIkc5WXnOkv//+W+PHj7fWyapZs6auuOIKpaamKi0tzSmus+8mnOvctptvvll9+vTRxIkTVbNmTfXs2VMLFizIs1bTpfjjjz908uTJ8+ZKOTk5edYVGzBggJKTk7V+/XpdeeWVTn379+/Xnj178uRKV199taSL50pSwV7LosiVJKlRo0Yllis9+uijuvrqq9WtWzfVrl1bDzzwQIHXRgNysaYUcAG//fab0tLS8v0lm8vT01MbNmzQunXr9Pnnnys6Oloffvihbr31Vq1atUqVKlW66HEuZY2Dgjp3odFc2dnZBYqpKJzvOOacBT9LWm5y/MQTTyg8PDzfMee+B7766itJ//zC/u233wr9Pf7i0KdPHy1atEiLFy/Www8/7NSXk5Oj2267TU8++WS+2+YmXLkK+xo2atRI8fHxOn36dJHeIelS39eX8x5s3LixEhIStHLlSkVHR+t///uf3njjDY0fP14TJ068tMABoJwhR7o8ZSVHGj58uBYsWKCRI0cqNDRUPj4+cjgc6tu3b6GufnM4HPr444+1adMmrVixQl999ZUeeOABvfrqq9q0adNlrcF5KXr37q133nlHM2fO1OTJk536cnJy1Lx5c02fPj3fbevUqeP0/HJype3bt+vw4cN59nk5HA5HvsfOzs7Od/zlvBf9/f0VHx+vr776Sl9++aW+/PJLLViwQPfdd5/T4vvAhXClFHABuYv5na9QkcvFxUWdO3fW9OnT9cMPP2jSpElau3atdXnv+ZKfwtq/f7/Tc2OMfvrpJ6cFr6tXr57nzmVS3r+gXUpswcHBOnLkSJ6/6vz4449Wf1EIDg7W/v378yQ7RX2cXLmXeleuXFlhYWH5Ps7+y2d0dLT+85//6Mknn9QVV1yhgQMH6syZM077LKrXPHeuuXeDOduPP/6omjVrysvLy6l92rRpGjx4sB599NE8d4qrX7++MjIyzjvP/P7aVxgRERH6+++/9b///e+iY883x9OnT+vAgQNOr3dB39eX4kKvVe5dFRcsWKBDhw6pR48emjRpkk6dOlXo4wFAeUCO5Kys50jnm+vHH3+sgQMH6tVXX9Wdd96p2267TR06dMhz/oKDg53ubpsrvzZJuuGGGzRp0iRt3bpVixcv1p49e/TBBx8UKvYrrrhCVapUOW+u5OLikqfoM3z4cD3//PN6+eWX9fLLLzv11a9fXykpKercuXO+uVJ+VysVRkREhCQ53cHyfC6UDyYkJJRoruTm5qaIiAi98cYb+vnnn/Xwww/rnXfeOe9rD5yLohRwHmvXrtULL7ygkJCQPN+HP1tKSkqettzv0+deipxbNMjvF0RhvPPOO05Jz8cff6yjR4+qW7duVlv9+vW1adMmnT592mpbuXJlnsuXLyW27t27Kzs7W6+//rpT+2uvvSaHw+F0/MvRvXt3JSYm6sMPP7Tazpw5o9mzZ6tq1aq6+eabi+Q4ufz9/dWpUye9+eabOnr0aJ7+P/74w/p3amqqdYeal156Sf/5z3+0bds2vfTSS07beHl5OV3WXli1atVSq1attGjRIqfXaPfu3Vq1apW6d++eZxuHw6H58+frzjvv1MCBA627u0jS3XffrdjYWOtKr7OlpqbmKa4V1iOPPKJatWrp8ccf1759+/L0Jycn68UXX5T0z1oMbm5umjVrltNf5d5++22lpaWpR48eVltB39eX4nyv1V9//eX03M3NTU2aNJExpsjWwQCAsogcKa+yniN5eXnlO89KlSrluWJm9uzZea66CQ8PV2xsrOLj4622lJQULV682GncsWPH8uzv3PfEpapUqZK6dOmizz77zOlrmklJSVqyZIk6dOiQZzkDSRo3bpyeeOIJjR07VnPnzrXa7777bv3+++9666238mzz999/51k2obDuvPNONW/eXJMmTVJsbGye/uPHj+uZZ56RJLVp00b+/v6aN2+e03n68ssvtXfv3jy50o8//uiUv+7YseOyliA438/CubmSi4uLWrRoIanwrycqHr6+B+ifD/Qff/xRZ86cUVJSktauXauYmBgFBwdr+fLlTgsKnuv555/Xhg0b1KNHDwUHBys5OVlvvPGGateurQ4dOkj655eDr6+v5s2bp2rVqsnLy0vt2rVTSEhIoeL18/NThw4ddP/99yspKUkzZsxQgwYNnG7J/OCDD+rjjz9W165ddffdd+vnn3/We++957QQ46XGFhERoVtuuUXPPPOMfv31V7Vs2VKrVq3SZ599ppEjR+bZd2ENGTJEb775pgYNGqS4uDjVq1dPH3/8sTZu3KgZM2YUeFHISzFnzhx16NBBzZs310MPPaSrrrpKSUlJio2N1W+//aYdO3ZIkkaMGKG//vpLq1evVqVKldS1a1c9+OCDevHFF9WzZ0+1bNlSktS6dWt9+OGHGj16tNq2bauqVatafxG7VNOmTVO3bt0UGhqqwYMH6++//9bs2bPl4+OjCRMm5LuNi4uL3nvvPfXq1Ut33323vvjiC916660aM2aMli9frn/9618aNGiQWrdurRMnTmjXrl36+OOP9euvv6pmzZqFivNs1atX16effqru3burVatW6t+/v1q3bi1J2rZtm95//32FhoZK+ucvnGPHjtXEiRPVtWtX3X777UpISNAbb7yhtm3bqn///tZ+C/q+vhTne626dOmiwMBAtW/fXgEBAdq7d69ef/119ejRo1jegwBQGpEjVYwcqXXr1lq9erWmT5+uoKAghYSEqF27dvrXv/6ld999Vz4+PmrSpIliY2O1evVq1ahRw2n7J598Uu+9955uu+02DR8+XF5eXvrPf/6junXrKiUlxbrSZtGiRXrjjTd0xx13qH79+jp+/LjeeusteXt75/uHtoJ68cUXFRMTow4dOujRRx+Vq6ur3nzzTWVmZmrq1Knn3W7atGlKS0tTVFSUqlWrpv79+2vAgAH66KOP9Mgjj2jdunVq3769srOz9eOPP+qjjz7SV199pTZt2hQ61lyVK1fWJ598orCwMHXs2FF333232rdvr8qVK2vPnj1asmSJqlevrkmTJqly5cqaMmWK7r//ft18883q16+fkpKSNHPmTNWrV0+jRo2y9vvAAw9o+vTpCg8P1+DBg5WcnKx58+apadOmeRZ8L6jcHO6xxx5TeHi4KlWqpL59++rBBx9USkqKbr31VtWuXVsHDx7U7Nmz1apVK2udM+Ci7L/hH1B65N4WNvfh5uZmAgMDzW233WZmzpzpdFvdXOfe7njNmjWmZ8+eJigoyLi5uZmgoCDTr18/s2/fPqftPvvsM9OkSRPj6urqdHvhm2+++by3nT/f7Y7ff/99M3bsWOPv7288PT1Njx49zMGDB/Ns/+qrr5orr7zSuLu7m/bt25utW7fme5vY88V27q1jjTHm+PHjZtSoUSYoKMhUrlzZNGzY0EybNs3k5OQ4jZNkoqKi8sR0vtswnyspKcncf//9pmbNmsbNzc00b94831syX8rtjnP98ccfRpJ57rnnnNp//vlnc99995nAwEBTuXJlc+WVV5p//etf5uOPPzbG/N8tcl999VWn7dLT001wcLBp2bKlOX36tDHGmIyMDHPvvfcaX19fIynPebzU2FavXm3at29vPD09jbe3t4mIiDA//PCD05jc9+bZt94+efKkufnmm03VqlXNpk2bjDH/vIZjx441DRo0MG5ubqZmzZrmxhtvNK+88ooVf+5tgqdNm5YnxvziO58jR46YUaNGmauvvtp4eHiYKlWqmNatW5tJkyaZtLQ0p7Gvv/66adSokalcubIJCAgwQ4cONceOHcuzz4K8r3N/VpYuXeq0be68zn4vne+1evPNN03Hjh1NjRo1jLu7u6lfv74ZM2ZMnrgBoDwiR7pwbOUtR/rxxx9Nx44djaenp5FkxXHs2DHrWFWrVjXh4eHmxx9/zDfW7du3m5tuusm4u7ub2rVrm8mTJ5tZs2YZSSYxMdEYY8y2bdtMv379TN26dY27u7vx9/c3//rXv8zWrVsLFGcuLy+vPMfftm2bCQ8PN1WrVjVVqlQxt9xyi/nuu++cxuS+r7ds2WK1ZWdnm379+hlXV1ezbNkyY4wxp0+fNlOmTDFNmzY17u7upnr16qZ169Zm4sSJTnnA5b6WxvxzjsePH2+aN29uqlSpYjw8PEyzZs3M2LFjzdGjR53Gfvjhh+baa6817u7uxs/Pz0RGRprffvstzz7fe+89c9VVVxk3NzfTqlUr89VXX+V5z15KrnfmzBkzfPhwc8UVVxiHw2H9nH/88cemS5cuxt/f37i5uZm6deuahx9+OE/cwIU4jCllq+kBAAAAAMq8kSNH6s0331RGRoZti8gDKFtYUwoAAAAAcFn+/vtvp+d//fWX3n33XXXo0IGCFIDzYk0pAAAAAMBlCQ0NVadOndS4cWMlJSXp7bffVnp6usaNG1fSoQEoxShKAQAAAAAuS/fu3fXxxx9r/vz5cjgcuu666/T222+rY8eOJR0agFKMNaUAAAAAAABgO9aUAgAAAAAAgO0oSgEAAAAAAMB2rClVADk5OTpy5IiqVasmh8NR0uEAAIAiZozR8ePHFRQUJBcX/mZ3KciTAAAo34ozT6IoVQBHjhxRnTp1SjoMAABQzA4fPqzatWuXdBhlCnkSAAAVQ3HkSRSlCqBatWqS/nkBvL29SzgaAABQ1NLT01WnTh3rdz4KjjwJAIDyrTjzJIpSBZB7Kbq3tzfJFgAA5RhfP7t05EkAAFQMxZEnsWgCAAAAAAAAbEdRCgAAAAAAALajKAUAAAAAAADbUZQCAAAAAACA7ShKAQAAAAAAwHYUpQAAAAAAAGA7ilIAAAAAAACwXYkWpTZs2KCIiAgFBQXJ4XBo2bJlTv3GGI0fP161atWSp6enwsLCtH//fqcxKSkpioyMlLe3t3x9fTV48GBlZGQ4jdm5c6duuukmeXh4qE6dOpo6dWpxTw0AAAAAAAAXUKJFqRMnTqhly5aaM2dOvv1Tp07VrFmzNG/ePG3evFleXl4KDw/XqVOnrDGRkZHas2ePYmJitHLlSm3YsEFDhgyx+tPT09WlSxcFBwcrLi5O06ZN04QJEzR//vxinx8AAAAAAADy5zDGmJIOQpIcDoc+/fRT9erVS9I/V0kFBQXp8ccf1xNPPCFJSktLU0BAgBYuXKi+fftq7969atKkibZs2aI2bdpIkqKjo9W9e3f99ttvCgoK0ty5c/XMM88oMTFRbm5ukqSnn35ay5Yt048//lig2NLT0+Xj46O0tDR5e3sX/eQBAECJ4nd94XHuAAAo34rzd32pXVPqwIEDSkxMVFhYmNXm4+Ojdu3aKTY2VpIUGxsrX19fqyAlSWFhYXJxcdHmzZutMR07drQKUpIUHh6uhIQEHTt2zKbZAAAAAAAA4GyuJR3A+SQmJkqSAgICnNoDAgKsvsTERPn7+zv1u7q6ys/Pz2lMSEhInn3k9lWvXj3PsTMzM5WZmWk9T09Pv8zZAAAAAAAA4Gyl9kqpkjR58mT5+PhYjzp16pR0SAAAAAAAAOVKqS1KBQYGSpKSkpKc2pOSkqy+wMBAJScnO/WfOXNGKSkpTmPy28fZxzjX2LFjlZaWZj0OHz58+RMCAAAAAACApdQWpUJCQhQYGKg1a9ZYbenp6dq8ebNCQ0MlSaGhoUpNTVVcXJw1Zu3atcrJyVG7du2sMRs2bFBWVpY1JiYmRtdcc02+X92TJHd3d3l7ezs9AAAAAAAAUHRKtCiVkZGh+Ph4xcfHS/pncfP4+HgdOnRIDodDI0eO1Isvvqjly5dr165duu+++xQUFGTdoa9x48bq2rWrHnroIX3//ffauHGjhg0bpr59+yooKEiSdO+998rNzU2DBw/Wnj179OGHH2rmzJkaPXp0Cc0aAAAAAAAAJbrQ+datW3XLLbdYz3MLRQMHDtTChQv15JNP6sSJExoyZIhSU1PVoUMHRUdHy8PDw9pm8eLFGjZsmDp37iwXFxf16dNHs2bNsvp9fHy0atUqRUVFqXXr1qpZs6bGjx+vIUOG2DdRAAAAAAAAOHEYY0xJB1Hapaeny8fHR2lpaXyVDwCAcojf9YXHuQMAoHwrzt/1pXZNKQAAAAAAAJRfFKUAAAAAAABgO4pSAAAAAAAAsF2JLnReHkVEXHzMihXFHwcAAEBpEPH+hZOjFf1IjAAAqKi4UgoAAAAAAAC2oygFAAAAAAAA21GUAgAAAAAAgO0oSgEAAAAAAMB2FKUAAAAAAABgO4pSAAAAAAAAsB1FKQAAAAAAANiOohQAAAAAAABsR1EKAAAAAAAAtqMoBQAAAAAAANtRlAIAAAAAAIDtKEoBAAAAAADAdhSlAAAAAAAAYDuKUgAAAAAAALAdRSkAAAAAAADYjqIUAAAAAAAAbEdRCgAAAAAAALajKAUAAAAAAADbUZQCAAAAAACA7ShKAQAAAAAAwHYUpQAAAAAAAGA7ilIAAAAAAACwHUUpAAAAAAAA2I6iFAAAAAAAAGxHUQoAAAAAAAC2oygFAAAAAAAA21GUAgAAAAAAgO0oSgEAAJRCGzZsUEREhIKCguRwOLRs2bLzjn3kkUfkcDg0Y8YMp/aUlBRFRkbK29tbvr6+Gjx4sDIyMpzG7Ny5UzfddJM8PDxUp04dTZ06tRhmAwAAkBdFKQAAgFLoxIkTatmypebMmXPBcZ9++qk2bdqkoKCgPH2RkZHas2ePYmJitHLlSm3YsEFDhgyx+tPT09WlSxcFBwcrLi5O06ZN04QJEzR//vwinw8AAMC5XEs6AAAAAOTVrVs3devW7YJjfv/9dw0fPlxfffWVevTo4dS3d+9eRUdHa8uWLWrTpo0kafbs2erevbteeeUVBQUFafHixTp9+rT++9//ys3NTU2bNlV8fLymT5/uVLwCAAAoDlwpBQAAUAbl5ORowIABGjNmjJo2bZqnPzY2Vr6+vlZBSpLCwsLk4uKizZs3W2M6duwoNzc3a0x4eLgSEhJ07NixfI+bmZmp9PR0pwcAAEBhUJQCAAAog6ZMmSJXV1c99thj+fYnJibK39/fqc3V1VV+fn5KTEy0xgQEBDiNyX2eO+ZckydPlo+Pj/WoU6fO5U4FAABUUBSlAAAAypi4uDjNnDlTCxculMPhsPXYY8eOVVpamvU4fPiwrccHAADlB0UpAACAMuabb75RcnKy6tatK1dXV7m6uurgwYN6/PHHVa9ePUlSYGCgkpOTnbY7c+aMUlJSFBgYaI1JSkpyGpP7PHfMudzd3eXt7e30AAAAKAyKUgAAAGXMgAEDtHPnTsXHx1uPoKAgjRkzRl999ZUkKTQ0VKmpqYqLi7O2W7t2rXJyctSuXTtrzIYNG5SVlWWNiYmJ0TXXXKPq1avbOykAAFDhcPc9AACAUigjI0M//fST9fzAgQOKj4+Xn5+f6tatqxo1ajiNr1y5sgIDA3XNNddIkho3bqyuXbvqoYce0rx585SVlaVhw4apb9++CgoKkiTde++9mjhxogYPHqynnnpKu3fv1syZM/Xaa6/ZN1EAAFBhUZQCAAAohbZu3apbbrnFej569GhJ0sCBA7Vw4cIC7WPx4sUaNmyYOnfuLBcXF/Xp00ezZs2y+n18fLRq1SpFRUWpdevWqlmzpsaPH68hQ4YU6VwAAADyQ1EKAACgFOrUqZOMMQUe/+uvv+Zp8/Pz05IlSy64XYsWLfTNN99cangAAACXjTWlAAAAAAAAYDuKUgAAAAAAALAdRSkAAAAAAADYjqIUAAAAAAAAbEdRCgAAAAAAALajKAUAAAAAAADbUZQCAAAAAACA7ShKAQAAAAAAwHYUpQAAAAAAAGA7ilIAAAAAAACwHUUpAAAAAAAA2I6iFAAAAAAAAGxHUQoAAAAAAAC2oygFAAAAAAAA21GUAgAAAAAAgO0oSgEAAAAAAMB2FKUAAAAAAABgO4pSAAAAAAAAsJ1rSQdQEUVEXLh/xQp74gAAAAAAACgpXCkFAAAAAAAA21GUAgAAAAAAgO0oSgEAAAAAAMB2FKUAAAAAAABgO4pSAAAAAAAAsB1FKQAAAAAAANiOohQAAAAAAABsR1EKAAAAAAAAtqMoBQAAAAAAANtRlAIAAAAAAIDtKEoBAAAAAADAdhSlAAAAAAAAYDuKUgAAAAAAALAdRSkAAAAAAADYjqIUAAAAAAAAbEdRCgAAAAAAALajKAUAAAAAAADbUZQCAAAAAACA7ShKAQAAAAAAwHYUpQAAAAAAAGA7ilIAAAAAAACwHUUpAAAAAAAA2I6iFAAAAAAAAGxHUQoAAAAAAAC2oygFAAAAAAAA21GUAgAAAAAAgO1KdVEqOztb48aNU0hIiDw9PVW/fn298MILMsZYY4wxGj9+vGrVqiVPT0+FhYVp//79TvtJSUlRZGSkvL295evrq8GDBysjI8Pu6QAAAAAAAOD/K9VFqSlTpmju3Ll6/fXXtXfvXk2ZMkVTp07V7NmzrTFTp07VrFmzNG/ePG3evFleXl4KDw/XqVOnrDGRkZHas2ePYmJitHLlSm3YsEFDhgwpiSkBAAAAAABAkmtJB3Ah3333nXr27KkePXpIkurVq6f3339f33//vaR/rpKaMWOGnn32WfXs2VOS9M477yggIEDLli1T3759tXfvXkVHR2vLli1q06aNJGn27Nnq3r27XnnlFQUFBZXM5AAAAAAAACqwUn2l1I033qg1a9Zo3759kqQdO3bo22+/Vbdu3SRJBw4cUGJiosLCwqxtfHx81K5dO8XGxkqSYmNj5evraxWkJCksLEwuLi7avHmzjbMBAAAAAABArlJ9pdTTTz+t9PR0NWrUSJUqVVJ2drYmTZqkyMhISVJiYqIkKSAgwGm7gIAAqy8xMVH+/v5O/a6urvLz87PGnCszM1OZmZnW8/T09CKbEwAAAAAAAEr5lVIfffSRFi9erCVLlmjbtm1atGiRXnnlFS1atKhYjzt58mT5+PhYjzp16hTr8QAAAAAAACqaUl2UGjNmjJ5++mn17dtXzZs314ABAzRq1ChNnjxZkhQYGChJSkpKctouKSnJ6gsMDFRycrJT/5kzZ5SSkmKNOdfYsWOVlpZmPQ4fPlzUUwMAAAAAAKjQSnVR6uTJk3JxcQ6xUqVKysnJkSSFhIQoMDBQa9assfrT09O1efNmhYaGSpJCQ0OVmpqquLg4a8zatWuVk5Ojdu3a5Xtcd3d3eXt7Oz0AAAAAAABQdEp1USoiIkKTJk3S559/rl9//VWffvqppk+frjvuuEOS5HA4NHLkSL344otavny5du3apfvuu09BQUHq1auXJKlx48bq2rWrHnroIX3//ffauHGjhg0bpr59+3LnPQAAUGpt2LBBERERCgoKksPh0LJly6y+rKwsPfXUU2revLm8vLwUFBSk++67T0eOHHHaR0pKiiIjI+Xt7S1fX18NHjxYGRkZTmN27typm266SR4eHqpTp46mTp1qx/QAAABKd1Fq9uzZuvPOO/Xoo4+qcePGeuKJJ/Twww/rhRdesMY8+eSTGj58uIYMGaK2bdsqIyND0dHR8vDwsMYsXrxYjRo1UufOndW9e3d16NBB8+fPL4kpAQAAFMiJEyfUsmVLzZkzJ0/fyZMntW3bNo0bN07btm3TJ598ooSEBN1+++1O4yIjI7Vnzx7FxMRo5cqV2rBhg4YMGWL1p6enq0uXLgoODlZcXJymTZumCRMmkCcBAABbOIwxpqSDKO3S09Pl4+OjtLS0i36VLyLi8o+3YsXl7wMAABTcpfyuLwkOh0OffvqpdSV4frZs2aLrr79eBw8eVN26dbV37141adJEW7ZsUZs2bSRJ0dHR6t69u3777TcFBQVp7ty5euaZZ5SYmCg3NzdJ/9z9eNmyZfrxxx8LFNvFzl3E+xdOjlb0I/EBAKA0K848qVRfKQUAAICCSUtLk8PhkK+vryQpNjZWvr6+VkFKksLCwuTi4qLNmzdbYzp27GgVpCQpPDxcCQkJOnbsWL7HyczMVHp6utMDAACgMChKAQAAlHGnTp3SU089pX79+ll/wUxMTJS/v7/TOFdXV/n5+SkxMdEaExAQ4DQm93numHNNnjxZPj4+1qNOnTpFPR0AAFBBUJQCAAAow7KysnT33XfLGKO5c+cW+/HGjh2rtLQ063H48OFiPyYAACifXEs6AAAAABRObkHq4MGDWrt2rdM6D4GBgUpOTnYaf+bMGaWkpCgwMNAak5SU5DQm93numHO5u7vL3d29KKcBAAAqKK6UAgAAKINyC1L79+/X6tWrVaNGDaf+0NBQpaamKi4uzmpbu3atcnJy1K5dO2vMhg0blJWVZY2JiYnRNddco+rVq9szEQAAUGFRlAIAACiFMjIyFB8fr/j4eEnSgQMHFB8fr0OHDikrK0t33nmntm7dqsWLFys7O1uJiYlKTEzU6dOnJUmNGzdW165d9dBDD+n777/Xxo0bNWzYMPXt21dBQUGSpHvvvVdubm4aPHiw9uzZow8//FAzZ87U6NGjS2raAACgAuHrewAAAKXQ1q1bdcstt1jPcwtFAwcO1IQJE7R8+XJJUqtWrZy2W7dunTp16iRJWrx4sYYNG6bOnTvLxcVFffr00axZs6yxPj4+WrVqlaKiotS6dWvVrFlT48eP15AhQ4p3cgAAAKIoBQAAUCp16tRJxpjz9l+oL5efn5+WLFlywTEtWrTQN998c8nxAQAAXC6+vgcAAAAAAADbUZQCAAAAAACA7ShKAQAAAAAAwHYUpQAAAAAAAGA7ilIAAAAAAACwHUUpAAAAAAAA2I6iFAAAAAAAAGxHUQoAAAAAAAC2oygFAAAAAAAA21GUAgAAAAAAgO0oSgEAAAAAAMB2FKUAAAAAAABgO4pSAAAAAAAAsB1FKQAAAAAAANiOohQAAAAAAABsR1EKAAAAAAAAtqMoBQAAAAAAANtRlAIAAAAAAIDtKEoBAAAAAADAdhSlAAAAAAAAYDuKUgAAAAAAALAdRSkAAAAAAADYjqIUAAAAAAAAbEdRCgAAAAAAALajKAUAAAAAAADbUZQCAAAAAACA7ShKAQAAAAAAwHYUpQAAAAAAAGA715IOAHlFRFx8zIoVxR8HAAAAAABAcaEoBQAAgBIT8f6F/xq3oh9/iQMAoLzi63sAAAAAAACwHUUpAAAAAAAA2I6iFAAAAAAAAGxHUQoAAAAAAAC2oygFAAAAAAAA21GUAgAAAAAAgO0oSgEAAAAAAMB2FKUAAAAAAABgO4pSAAAAAAAAsB1FKQAAAAAAANiOohQAAAAAAABsR1EKAAAAAAAAtqMoBQAAAAAAANtRlAIAAAAAAIDtKEoBAAAAAADAdhSlAAAAAAAAYDuKUgAAAAAAALAdRSkAAAAAAADYjqIUAAAAAAAAbEdRCgAAAAAAALajKAUAAAAAAADbUZQCAAAAAACA7ShKAQAAAAAAwHYUpQAAAEqhDRs2KCIiQkFBQXI4HFq2bJlTvzFG48ePV61ateTp6amwsDDt37/faUxKSooiIyPl7e0tX19fDR48WBkZGU5jdu7cqZtuukkeHh6qU6eOpk6dWtxTAwAAkERRCgAAoFQ6ceKEWrZsqTlz5uTbP3XqVM2aNUvz5s3T5s2b5eXlpfDwcJ06dcoaExkZqT179igmJkYrV67Uhg0bNGTIEKs/PT1dXbp0UXBwsOLi4jRt2jRNmDBB8+fPL/b5AQAAuJZ0AAAAAMirW7du6tatW759xhjNmDFDzz77rHr27ClJeueddxQQEKBly5apb9++2rt3r6Kjo7Vlyxa1adNGkjR79mx1795dr7zyioKCgrR48WKdPn1a//3vf+Xm5qamTZsqPj5e06dPdypeAQAAFAeulAIAAChjDhw4oMTERIWFhVltPj4+ateunWJjYyVJsbGx8vX1tQpSkhQWFiYXFxdt3rzZGtOxY0e5ublZY8LDw5WQkKBjx47le+zMzEylp6c7PQAAAAqDohQAAEAZk5iYKEkKCAhwag8ICLD6EhMT5e/v79Tv6uoqPz8/pzH57ePsY5xr8uTJ8vHxsR516tS5/AkBAIAKiaIUAAAACmzs2LFKS0uzHocPHy7pkAAAQBlFUQoAAKCMCQwMlCQlJSU5tSclJVl9gYGBSk5Oduo/c+aMUlJSnMbkt4+zj3Eud3d3eXt7Oz0AAAAKg6IUAABAGRMSEqLAwECtWbPGaktPT9fmzZsVGhoqSQoNDVVqaqri4uKsMWvXrlVOTo7atWtnjdmwYYOysrKsMTExMbrmmmtUvXp1m2YDAAAqKopSAAAApVBGRobi4+MVHx8v6Z/FzePj43Xo0CE5HA6NHDlSL774opYvX65du3bpvvvuU1BQkHr16iVJaty4sbp27aqHHnpI33//vTZu3Khhw4apb9++CgoKkiTde++9cnNz0+DBg7Vnzx59+OGHmjlzpkaPHl1CswYAABWJa0kHAAAAgLy2bt2qW265xXqeWygaOHCgFi5cqCeffFInTpzQkCFDlJqaqg4dOig6OloeHh7WNosXL9awYcPUuXNnubi4qE+fPpo1a5bV7+Pjo1WrVikqKkqtW7dWzZo1NX78eA0ZMsS+iQIAgArLYYwxJR1EaZeeni4fHx+lpaVddN2EiAh7Ylqxwp7jAABQEVzK73o4u9i5i3j/8pKjFf1IegAAKEnFmSfx9T0AAAAAAADYjqIUAAAAAAAAbEdRCgAAAAAAALajKAUAAAAAAADbUZQCAAAAAACA7ShKAQAAAAAAwHYUpQAAAAAAAGA7ilIAAAAAAACwHUUpAAAAAAAA2I6iFAAAAAAAAGxHUQoAAAAAAAC2oygFAAAAAAAA21GUAgAAAAAAgO0oSgEAAAAAAMB2FKUAAAAAAABgu1JflPr999/Vv39/1ahRQ56enmrevLm2bt1q9RtjNH78eNWqVUuenp4KCwvT/v37nfaRkpKiyMhIeXt7y9fXV4MHD1ZGRobdUwEAAAAAAMD/V6qLUseOHVP79u1VuXJlffnll/rhhx/06quvqnr16taYqVOnatasWZo3b542b94sLy8vhYeH69SpU9aYyMhI7dmzRzExMVq5cqU2bNigIUOGlMSUAAAAAAAAIMm1pAO4kClTpqhOnTpasGCB1RYSEmL92xijGTNm6Nlnn1XPnj0lSe+8844CAgK0bNky9e3bV3v37lV0dLS2bNmiNm3aSJJmz56t7t2765VXXlFQUJC9kwIAAAAAAEDpvlJq+fLlatOmje666y75+/vr2muv1VtvvWX1HzhwQImJiQoLC7PafHx81K5dO8XGxkqSYmNj5evraxWkJCksLEwuLi7avHmzfZMBAAAAAACApVQXpX755RfNnTtXDRs21FdffaWhQ4fqscce06JFiyRJiYmJkqSAgACn7QICAqy+xMRE+fv7O/W7urrKz8/PGnOuzMxMpaenOz0AAAAAAABQdEr11/dycnLUpk0bvfTSS5Kka6+9Vrt379a8efM0cODAYjvu5MmTNXHixGLbPwAAAAAAQEVXqq+UqlWrlpo0aeLU1rhxYx06dEiSFBgYKElKSkpyGpOUlGT1BQYGKjk52an/zJkzSklJscaca+zYsUpLS7Mehw8fLpL5AAAAAAAA4B+luijVvn17JSQkOLXt27dPwcHBkv5Z9DwwMFBr1qyx+tPT07V582aFhoZKkkJDQ5Wamqq4uDhrzNq1a5WTk6N27drle1x3d3d5e3s7PQAAAAAAAFB0SvXX90aNGqUbb7xRL730ku6++259//33mj9/vubPny9JcjgcGjlypF588UU1bNhQISEhGjdunIKCgtSrVy9J/1xZ1bVrVz300EOaN2+esrKyNGzYMPXt25c77wEAAAAAAJSQUl2Uatu2rT799FONHTtWzz//vEJCQjRjxgxFRkZaY5588kmdOHFCQ4YMUWpqqjp06KDo6Gh5eHhYYxYvXqxhw4apc+fOcnFxUZ8+fTRr1qySmBIAAAAAAAAkOYwxpqSDKO3S09Pl4+OjtLS0i36VLyLCnphWrLDnOAAAVASX8rsezi527iLev7zkaEU/kh4AAEpSceZJpXpNKQAAAAAAAJRPFKUAAAAAAABgu0IVpX755ZeijgMAAKBcIE8CAAAomEIVpRo0aKBbbrlF7733nk6dOlXUMQEAAJRZ5EkAAAAFU6ii1LZt29SiRQuNHj1agYGBevjhh/X9998XdWwAAABlDnkSAABAwRSqKNWqVSvNnDlTR44c0X//+18dPXpUHTp0ULNmzTR9+nT98ccfRR0nAABAmUCeBAAAUDCXtdC5q6urevfuraVLl2rKlCn66aef9MQTT6hOnTq67777dPTo0aKKEwAAoEwhTwIAALiwyypKbd26VY8++qhq1aql6dOn64knntDPP/+smJgYHTlyRD179iyqOAEAAMoU8iQAAIALcy3MRtOnT9eCBQuUkJCg7t2765133lH37t3l4vJPjSskJEQLFy5UvXr1ijJWAACAUo88CQAAoGAKVZSaO3euHnjgAQ0aNEi1atXKd4y/v7/efvvtywoOAACgrCFPAgAAKJhCFaX2799/0TFubm4aOHBgYXYPAABQZpEnAQAAFEyh1pRasGCBli5dmqd96dKlWrRo0WUHBQAAUFaRJwEAABRMoYpSkydPVs2aNfO0+/v766WXXrrsoAAAAMoq8iQAAICCKVRR6tChQwoJCcnTHhwcrEOHDl12UAAAAGUVeRIAAEDBFKoo5e/vr507d+Zp37Fjh2rUqHHZQQEAAJRV5EkAAAAFU6iiVL9+/fTYY49p3bp1ys7OVnZ2ttauXasRI0aob9++RR0jAABAmUGeBAAAUDCFuvveCy+8oF9//VWdO3eWq+s/u8jJydF9993HWgkAAKBCI08CAAAomEIVpdzc3PThhx/qhRde0I4dO+Tp6anmzZsrODi4qOMDAAAoU8iTAAAACqZQRalcV199ta6++uqiigUAAKDcIE8CAAC4sEIVpbKzs7Vw4UKtWbNGycnJysnJcepfu3ZtkQQHAABQ1pAnAQAAFEyhilIjRozQwoUL1aNHDzVr1kwOh6Oo4wIAACiTyJMAAAAKplBFqQ8++EAfffSRunfvXtTxAAAAlGnkSQAAAAXjUpiN3Nzc1KBBg6KOBQAAoMwjTwIAACiYQhWlHn/8cc2cOVPGmKKOBwAAoEwjTwIAACiYQn1979tvv9W6dev05ZdfqmnTpqpcubJT/yeffFIkwQEAAJQ15EkAAAAFU6iilK+vr+64446ijgUAAKDMI08CAAAomEIVpRYsWFDUcQAAAJQL5EkAAAAFU6g1pSTpzJkzWr16td58800dP35cknTkyBFlZGQUWXAAAABlkR15UnZ2tsaNG6eQkBB5enqqfv36euGFF5zWsjLGaPz48apVq5Y8PT0VFham/fv3O+0nJSVFkZGR8vb2lq+vrwYPHkw+BwAAbFGoK6UOHjyorl276tChQ8rMzNRtt92matWqacqUKcrMzNS8efOKOk4AAIAywa48acqUKZo7d64WLVqkpk2bauvWrbr//vvl4+Ojxx57TJI0depUzZo1S4sWLVJISIjGjRun8PBw/fDDD/Lw8JAkRUZG6ujRo4qJiVFWVpbuv/9+DRkyREuWLCmSOAEAAM6nUFdKjRgxQm3atNGxY8fk6elptd9xxx1as2ZNkQUHAABQ1tiVJ3333Xfq2bOnevTooXr16unOO+9Uly5d9P3330v65yqpGTNm6Nlnn1XPnj3VokULvfPOOzpy5IiWLVsmSdq7d6+io6P1n//8R+3atVOHDh00e/ZsffDBBzpy5EiRxQoAAJCfQhWlvvnmGz377LNyc3Nzaq9Xr55+//33IgkMAACgLLIrT7rxxhu1Zs0a7du3T5K0Y8cOffvtt+rWrZsk6cCBA0pMTFRYWJi1jY+Pj9q1a6fY2FhJUmxsrHx9fdWmTRtrTFhYmFxcXLR58+Z8j5uZman09HSnBwAAQGEU6ut7OTk5ys7OztP+22+/qVq1apcdFC4uIuLC/StW2BMHAABwZlee9PTTTys9PV2NGjVSpUqVlJ2drUmTJikyMlKSlJiYKEkKCAhw2i4gIMDqS0xMlL+/v1O/q6ur/Pz8rDHnmjx5siZOnFhk8wAAABVXoa6U6tKli2bMmGE9dzgcysjI0HPPPafu3bsXVWwAAABljl150kcffaTFixdryZIl2rZtmxYtWqRXXnlFixYtKrJj5Gfs2LFKS0uzHocPHy7W4wEAgPKrUFdKvfrqqwoPD1eTJk106tQp3Xvvvdq/f79q1qyp999/v6hjBAAAKDPsypPGjBmjp59+Wn379pUkNW/eXAcPHtTkyZM1cOBABQYGSpKSkpJUq1Yta7ukpCS1atVKkhQYGKjk5GSn/Z45c0YpKSnW9udyd3eXu7t7kc0DAABUXIUqStWuXVs7duzQBx98oJ07dyojI0ODBw9WZGSk04KeAAAAFY1dedLJkyfl4uJ80XulSpWUk5MjSQoJCVFgYKDWrFljFaHS09O1efNmDR06VJIUGhqq1NRUxcXFqXXr1pKktWvXKicnR+3atSuyWAEAAPJTqKKU9M96A/379y/KWAAAAMoFO/KkiIgITZo0SXXr1lXTpk21fft2TZ8+XQ888ICkf742OHLkSL344otq2LChQkJCNG7cOAUFBalXr16SpMaNG6tr16566KGHNG/ePGVlZWnYsGHq27evgoKCijV+AACAQhWl3nnnnQv233fffYUKBgAAoKyzK0+aPXu2xo0bp0cffVTJyckKCgrSww8/rPHjx1tjnnzySZ04cUJDhgxRamqqOnTooOjoaHl4eFhjFi9erGHDhqlz585ycXFRnz59NGvWrCKJEQAA4EIcxhhzqRtVr17d6XlWVpZOnjwpNzc3ValSRSkpKUUWYGmQnp4uHx8fpaWlydvb+4JjL3ZXPLtw9z0AAAruUn7XXwx5krOI9y8vOVrRj6QGAICSVJR50rkKdfe9Y8eOOT0yMjKUkJCgDh06sNA5AACo0MiTAAAACqZQRan8NGzYUC+//LJGjBhRVLsEAAAoF8iTAAAA8iqyopT0z6KeR44cKcpdAgAAlAvkSQAAAM4KtdD58uXLnZ4bY3T06FG9/vrrat++fZEEBgAAUBaRJwEAABRMoYpSubcRzuVwOHTFFVfo1ltv1auvvloUcQEAAJRJ5EkAAAAFU6iiVE5OTlHHAQAAUC6QJ9nrYnf34+59AACUXkW6phQAAAAAAABQEIW6Umr06NEFHjt9+vTCHAIAAKBMIk8CAAAomEIVpbZv367t27crKytL11xzjSRp3759qlSpkq677jprnMPhKJooAQAAygjyJAAAgIIpVFEqIiJC1apV06JFi1S9enVJ0rFjx3T//ffrpptu0uOPP16kQQIAAJQV5EkAAAAFU6g1pV599VVNnjzZSrQkqXr16nrxxRe5qwwAAKjQyJMAAAAKplBFqfT0dP3xxx952v/44w8dP378soMCAAAoq8iTAAAACqZQRak77rhD999/vz755BP99ttv+u233/S///1PgwcPVu/evYs6RgAAgDKDPAkAAKBgCrWm1Lx58/TEE0/o3nvvVVZW1j87cnXV4MGDNW3atCINEAAAoCwhTwIAACiYQhWlqlSpojfeeEPTpk3Tzz//LEmqX7++vLy8ijQ4AACAsoY8CQAAoGAK9fW9XEePHtXRo0fVsGFDeXl5yRhTVHEBAACUaeRJAAAAF1aootRff/2lzp076+qrr1b37t119OhRSdLgwYO5zTEAAKjQyJMAAAAKplBFqVGjRqly5co6dOiQqlSpYrXfc889io6OLrLgAAAAyhryJAAAgIIp1JpSq1at0ldffaXatWs7tTds2FAHDx4sksAAAADKIvIkAACAginUlVInTpxw+stfrpSUFLm7u192UAAAAGUVeRIAAEDBFKooddNNN+mdd96xnjscDuXk5Gjq1Km65ZZbiiw4AACAsoY8CQAAoGAK9fW9qVOnqnPnztq6datOnz6tJ598Unv27FFKSoo2btxY1DECAACUGeRJAAAABVOoK6WaNWumffv2qUOHDurZs6dOnDih3r17a/v27apfv35RxwgAAFBmkCcBAAAUzCVfKZWVlaWuXbtq3rx5euaZZ4ojJgAAgDKJPAkAAKDgLvlKqcqVK2vnzp3FEQsAAECZRp4EAABQcIX6+l7//v319ttvF3UsAAAAZR55EgAAQMEUaqHzM2fO6L///a9Wr16t1q1by8vLy6l/+vTpRRIcAABAWUOeBAAAUDCXVJT65ZdfVK9ePe3evVvXXXedJGnfvn1OYxwOR9FFBwAAUEaQJwEAAFyaSypKNWzYUEePHtW6deskSffcc49mzZqlgICAYgkOAACgrCBPAgAAuDSXtKaUMcbp+ZdffqkTJ04UaUAAAABlEXkSAADApSnUQue5zk2+AAAA8A/yJAAAgAu7pKKUw+HIsxYCayMAAACQJwEAAFyqS1pTyhijQYMGyd3dXZJ06tQpPfLII3nuKvPJJ58UXYQAAABlAHkSAADApbmkotTAgQOdnvfv379IgwEAACiryJMAAAAuzSUVpRYsWFBccQAAAJRp5EkAAACX5rIWOgcAAAAAAAAKg6IUAAAAAAAAbEdRCgAAAAAAALajKAUAAAAAAADbUZQCAAAAAACA7ShKAQAAAAAAwHYUpQAAAAAAAGA7ilIAAAAAAACwHUUpAAAAAAAA2I6iFAAAAAAAAGznWtIBoHhERFy4f8UKe+IAAAC4HBHvXySpAQAAZRZXSgEAAAAAAMB2FKUAAAAAAABgO4pSAAAAAAAAsF2ZKkq9/PLLcjgcGjlypNV26tQpRUVFqUaNGqpatar69OmjpKQkp+0OHTqkHj16qEqVKvL399eYMWN05swZm6MHAAAAAABArjJTlNqyZYvefPNNtWjRwql91KhRWrFihZYuXar169fryJEj6t27t9WfnZ2tHj166PTp0/ruu++0aNEiLVy4UOPHj7d7CgAAAAAAAPj/ykRRKiMjQ5GRkXrrrbdUvXp1qz0tLU1vv/22pk+frltvvVWtW7fWggUL9N1332nTpk2SpFWrVumHH37Qe++9p1atWqlbt2564YUXNGfOHJ0+fbqkpgQAAAAAAFChlYmiVFRUlHr06KGwsDCn9ri4OGVlZTm1N2rUSHXr1lVsbKwkKTY2Vs2bN1dAQIA1Jjw8XOnp6dqzZ489EwAAAAAAAICTUl+U+uCDD7Rt2zZNnjw5T19iYqLc3Nzk6+vr1B4QEKDExERrzNkFqdz+3L78ZGZmKj093ekBAABQ2vz+++/q37+/atSoIU9PTzVv3lxbt261+o0xGj9+vGrVqiVPT0+FhYVp//79TvtISUlRZGSkvL295evrq8GDBysjI8PuqQAAgAqoVBelDh8+rBEjRmjx4sXy8PCw7biTJ0+Wj4+P9ahTp45txwYAACiIY8eOqX379qpcubK+/PJL/fDDD3r11VedljqYOnWqZs2apXnz5mnz5s3y8vJSeHi4Tp06ZY2JjIzUnj17FBMTo5UrV2rDhg0aMmRISUwJAABUMKW6KBUXF6fk5GRdd911cnV1laurq9avX69Zs2bJ1dVVAQEBOn36tFJTU522S0pKUmBgoCQpMDAwz934cp/njjnX2LFjlZaWZj0OHz5c9JMDAAC4DFOmTFGdOnW0YMECXX/99QoJCVGXLl1Uv359Sf9cJTVjxgw9++yz6tmzp1q0aKF33nlHR44c0bJlyyRJe/fuVXR0tP7zn/+oXbt26tChg2bPnq0PPvhAR44cKcHZAQCAiqBUF6U6d+6sXbt2KT4+3nq0adNGkZGR1r8rV66sNWvWWNskJCTo0KFDCg0NlSSFhoZq165dSk5OtsbExMTI29tbTZo0yfe47u7u8vb2dnoAAACUJsuXL1ebNm101113yd/fX9dee63eeustq//AgQNKTEx0WnvTx8dH7dq1c1p709fXV23atLHGhIWFycXFRZs3b873uCxzAAAAioprSQdwIdWqVVOzZs2c2ry8vFSjRg2rffDgwRo9erT8/Pzk7e2t4cOHKzQ0VDfccIMkqUuXLmrSpIkGDBigqVOnKjExUc8++6yioqLk7u5u+5wAAACKwi+//KK5c+dq9OjR+ve//60tW7bosccek5ubmwYOHGitnZnf2ppnr73p7+/v1O/q6io/P7/zrr05efJkTZw4sRhmBAAAKppSXZQqiNdee00uLi7q06ePMjMzFR4erjfeeMPqr1SpklauXKmhQ4cqNDRUXl5eGjhwoJ5//vkSjBoAAODy5OTkqE2bNnrppZckSddee612796tefPmaeDAgcV23LFjx2r06NHW8/T0dNbfBAAAhVLmilJff/2103MPDw/NmTNHc+bMOe82wcHB+uKLL4o5MgAAAPvUqlUrz1IEjRs31v/+9z9J/7d2ZlJSkmrVqmWNSUpKUqtWrawxZy9xIElnzpxRSkrKedfedHd352pzAABQJEr1mlIAAADIX/v27ZWQkODUtm/fPgUHB0uSQkJCFBgY6LT2Znp6ujZv3uy09mZqaqri4uKsMWvXrlVOTo7atWtnwywAAEBFVuaulAIAAIA0atQo3XjjjXrppZd099136/vvv9f8+fM1f/58SZLD4dDIkSP14osvqmHDhgoJCdG4ceMUFBSkXr16SfrnyqquXbvqoYce0rx585SVlaVhw4apb9++CgoKKsHZAQCAioCiFAAAQBnUtm1bffrppxo7dqyef/55hYSEaMaMGYqMjLTGPPnkkzpx4oSGDBmi1NRUdejQQdHR0fLw8LDGLF68WMOGDVPnzp2tdTpnzZpVElMCAAAVjMMYY0o6iNIuPT1dPj4+SktLk7e39wXHRkTYFNRlWrGipCMAAKD0uJTf9XB2sXMX8X7JJkcr+pH0AABwOYozT2JNKQAAAAAAANiOohQAAAAAAABsR1EKAAAAAAAAtqMoBQAAAAAAANtRlAIAAAAAAIDtKEoBAAAAAADAdhSlAAAAAAAAYDuKUgAAAAAAALAdRSkAAAAAAADYjqIUAAAAAAAAbEdRCgAAAAAAALajKAUAAAAAAADbUZQCAAAAAACA7ShKAQAAAAAAwHYUpQAAAAAAAGA7ilIAAAAAAACwHUUpAAAAAAAA2I6iFAAAAAAAAGxHUQoAAAAAAAC2oygFAAAAAAAA21GUAgAAAAAAgO0oSgEAAAAAAMB2FKUAAAAAAABgO4pSAAAAAAAAsB1FKQAAAAAAANiOohQAAAAAAABsR1EKAAAAAAAAtqMoBQAAAAAAANtRlAIAAAAAAIDtKEoBAAAAAADAdhSlAAAAAAAAYDuKUgAAAAAAALCda0kHgJIREXHxMStWFH8cAAAAAACgYuJKKQAAAAAAANiOohQAAAAAAABsR1EKAAAAAAAAtqMoBQAAAAAAANtRlAIAAAAAAIDtKEoBAAAAAADAdhSlAAAAAAAAYDuKUgAAAAAAALCda0kHAAAAAJSUiPcjLti/ot8KmyIBAKDi4UopAAAAAAAA2I6iFAAAAAAAAGxHUQoAAAAAAAC2oygFAAAAAAAA27HQOc4r4sLrfmoF634CAAAAAIBC4kopAAAAAAAA2I6iFAAAAAAAAGxHUQoAAAAAAAC2oygFAAAAAAAA21GUAgAAAAAAgO0oSgEAAAAAAMB2FKUAAADKgZdfflkOh0MjR4602k6dOqWoqCjVqFFDVatWVZ8+fZSUlOS03aFDh9SjRw9VqVJF/v7+GjNmjM6cOWNz9AAAoCKiKAUAAFDGbdmyRW+++aZatGjh1D5q1CitWLFCS5cu1fr163XkyBH17t3b6s/OzlaPHj10+vRpfffdd1q0aJEWLlyo8ePH2z0FAABQAVGUAgAAKMMyMjIUGRmpt956S9WrV7fa09LS9Pbbb2v69Om69dZb1bp1ay1YsEDfffedNm3aJElatWqVfvjhB7333ntq1aqVunXrphdeeEFz5szR6dOnS2pKAACggqAoBQAAUIZFRUWpR48eCgsLc2qPi4tTVlaWU3ujRo1Ut25dxcbGSpJiY2PVvHlzBQQEWGPCw8OVnp6uPXv22DMBAABQYbmWdAAAAAAonA8++EDbtm3Tli1b8vQlJibKzc1Nvr6+Tu0BAQFKTEy0xpxdkMrtz+3LT2ZmpjIzM63n6enplzMFAABQgXGlFAAAQBl0+PBhjRgxQosXL5aHh4dtx508ebJ8fHysR506dWw7NgAAKF8oSgEAAJRBcXFxSk5O1nXXXSdXV1e5urpq/fr1mjVrllxdXRUQEKDTp08rNTXVabukpCQFBgZKkgIDA/PcjS/3ee6Yc40dO1ZpaWnW4/Dhw0U/OQAAUCFQlAIAACiDOnfurF27dik+Pt56tGnTRpGRkda/K1eurDVr1ljbJCQk6NChQwoNDZUkhYaGateuXUpOTrbGxMTEyNvbW02aNMn3uO7u7vL29nZ6AAAAFAZrSgEAAJRB1apVU7NmzZzavLy8VKNGDat98ODBGj16tPz8/OTt7a3hw4crNDRUN9xwgySpS5cuatKkiQYMGKCpU6cqMTFRzz77rKKiouTu7m77nAAAQMVCUQoAAKCceu211+Ti4qI+ffooMzNT4eHheuONN6z+SpUqaeXKlRo6dKhCQ0Pl5eWlgQMH6vnnny/BqAEAQEVBUQoAAKCc+Prrr52ee3h4aM6cOZozZ855twkODtYXX3xRzJEBAADkRVEKhRYRcfExK1YUfxwAAAAAAKDsYaFzAAAAAAAA2I6iFAAAAAAAAGzH1/cAAABQbkW8X4D1BgAAQIngSikAAAAAAADYjqIUAAAAAAAAbEdRCgAAAAAAALajKAUAAAAAAADbUZQCAAAAAACA7ShKAQAAAAAAwHYUpQAAAAAAAGA7ilIAAAAAAACwHUUpAAAAAAAA2I6iFAAAAAAAAGxHUQoAAAAAAAC2oygFAAAAAAAA21GUAgAAAAAAgO0oSgEAAAAAAMB2FKUAAAAAAABgu1JdlJo8ebLatm2ratWqyd/fX7169VJCQoLTmFOnTikqKko1atRQ1apV1adPHyUlJTmNOXTokHr06KEqVarI399fY8aM0ZkzZ+ycCgAAAAAAAM5SqotS69evV1RUlDZt2qSYmBhlZWWpS5cuOnHihDVm1KhRWrFihZYuXar169fryJEj6t27t9WfnZ2tHj166PTp0/ruu++0aNEiLVy4UOPHjy+JKQEAAAAAAECSa0kHcCHR0dFOzxcuXCh/f3/FxcWpY8eOSktL09tvv60lS5bo1ltvlSQtWLBAjRs31qZNm3TDDTdo1apV+uGHH7R69WoFBASoVatWeuGFF/TUU09pwoQJcnNzK4mpAQAAAAAAVGil+kqpc6WlpUmS/Pz8JElxcXHKyspSWFiYNaZRo0aqW7euYmNjJUmxsbFq3ry5AgICrDHh4eFKT0/Xnj17bIweAAAAAAAAuUr1lVJny8nJ0ciRI9W+fXs1a9ZMkpSYmCg3Nzf5+vo6jQ0ICFBiYqI15uyCVG5/bl9+MjMzlZmZaT1PT08vqmkAAAAAAABAZehKqaioKO3evVsffPBBsR9r8uTJ8vHxsR516tQp9mMCAAAAAABUJGWiKDVs2DCtXLlS69atU+3ata32wMBAnT59WqmpqU7jk5KSFBgYaI059258uc9zx5xr7NixSktLsx6HDx8uwtkAAAAAAACgVH99zxij4cOH69NPP9XXX3+tkJAQp/7WrVurcuXKWrNmjfr06SNJSkhI0KFDhxQaGipJCg0N1aRJk5ScnCx/f39JUkxMjLy9vdWkSZN8j+vu7i53d/dinFnFERFx4f4VK+yJAwAAAAAAlC6luigVFRWlJUuW6LPPPlO1atWsNaB8fHzk6ekpHx8fDR48WKNHj5afn5+8vb01fPhwhYaG6oYbbpAkdenSRU2aNNGAAQM0depUJSYm6tlnn1VUVBSFJwAAAAAAgBJSqotSc+fOlSR16tTJqX3BggUaNGiQJOm1116Ti4uL+vTpo8zMTIWHh+uNN96wxlaqVEkrV67U0KFDFRoaKi8vLw0cOFDPP/+8XdMAAAAAAADAOUp1UcoYc9ExHh4emjNnjubMmXPeMcHBwfriiy+KMjQAAAAAAABchlJdlAIAAABKs4j3L7yA5op+LKAJAMD5lIm77wEAAAAAAKB8oSgFAAAAAAAA21GUAgAAAAAAgO0oSgEAAAAAAMB2FKUAAAAAAABgO4pSAAAAAAAAsB1FKQAAAAAAANiOohQAAAAAAABsR1EKAAAAAAAAtqMoBQAAAAAAANtRlAIAAAAAAIDtKEoBAAAAAADAdq4lHQAqtoiIC/evWGFPHAAAAAAAwF5cKQUAAAAAAADbUZQCAAAAAACA7ShKAQAAAAAAwHasKQUAAACcR8T7F1kAEwAAFBpXSgEAAAAAAMB2FKUAAAAAAABgO4pSAAAAAAAAsB1FKQAAAAAAANiOohQAAAAAAABsR1EKAACgDJo8ebLatm2ratWqyd/fX7169VJCQoLTmFOnTikqKko1atRQ1apV1adPHyUlJTmNOXTokHr06KEqVarI399fY8aM0ZkzZ+ycCgAAqKAoSgEAAJRB69evV1RUlDZt2qSYmBhlZWWpS5cuOnHihDVm1KhRWrFihZYuXar169fryJEj6t27t9WfnZ2tHj166PTp0/ruu++0aNEiLVy4UOPHjy+JKQEAgArGtaQDAAAAwKWLjo52er5w4UL5+/srLi5OHTt2VFpamt5++20tWbJEt956qyRpwYIFaty4sTZt2qQbbrhBq1at0g8//KDVq1crICBArVq10gsvvKCnnnpKEyZMkJubW0lMDQAAVBAUpS5RRERJRwAAAJBXWlqaJMnPz0+SFBcXp6ysLIWFhVljGjVqpLp16yo2NlY33HCDYmNj1bx5cwUEBFhjwsPDNXToUO3Zs0fXXnutvZMAAAAVCkUpAACAMi4nJ0cjR45U+/bt1axZM0lSYmKi3Nzc5Ovr6zQ2ICBAiYmJ1pizC1K5/bl9+cnMzFRmZqb1PD09vaimAQAAKhiKUijVCnJl2ooVxR8HAAClWVRUlHbv3q1vv/222I81efJkTZw4sdiPAwAAyj8WOgcAACjDhg0bppUrV2rdunWqXbu21R4YGKjTp08rNTXVaXxSUpICAwOtMefejS/3ee6Yc40dO1ZpaWnW4/Dhw0U4GwAAUJFQlAIAACiDjDEaNmyYPv30U61du1YhISFO/a1bt1blypW1Zs0aqy0hIUGHDh1SaGioJCk0NFS7du1ScnKyNSYmJkbe3t5q0qRJvsd1d3eXt7e30wMAAKAw+PoeAABAGRQVFaUlS5bos88+U7Vq1aw1oHx8fOTp6SkfHx8NHjxYo0ePlp+fn7y9vTV8+HCFhobqhhtukCR16dJFTZo00YABAzR16lQlJibq2WefVVRUlNzd3UtyegAAoAKgKAUAAFAGzZ07V5LUqVMnp/YFCxZo0KBBkqTXXntNLi4u6tOnjzIzMxUeHq433njDGlupUiWtXLlSQ4cOVWhoqLy8vDRw4EA9//zzdk0DAABUYBSlAAAAyiBjzEXHeHh4aM6cOZozZ855xwQHB+uLL74oytAAAAAKhDWlAAAAAAAAYDuKUgAAAAAAALAdRSkAAAAAAADYjqIUAAAAAAAAbMdC5yjzIiIu3L9ihT1xAAAAAACAguNKKQAAAAAAANiOohQAAAAAAABsR1EKAAAAAAAAtqMoBQAAAAAAANtRlAIAAAAAAIDtKEoBAAAAAADAdq4lHQBQ3CIiLj5mxYrijwMAAOBcEe9fOFFZ0Y8kBQBQflGUAgAAAIrJxYpOAABUZHx9DwAAAAAAALajKAUAAAAAAADb8fU9QBdfd4o1pwAAAAAAKFpcKQUAAAAAAADbUZQCAAAAAACA7ShKAQAAAAAAwHYUpQAAAAAAAGA7FjoHisDFFkqXWCwdAAAAAICzUZQCCqAgRSc7jkFhCwCAiiXi/QsnCCv6kRwAAMouvr4HAAAAAAAA23GlFGATO662AgAAAACgrOBKKQAAAAAAANiOohQAAAAAAABsR1EKAAAAAAAAtqMoBQAAAAAAANux0DlQhlxssfQV3BUaAAAAAFBGcKUUAAAAAAAAbEdRCgAAAAAAALajKAUAAAAAAADbsaYUUIFcbE0qiXWpAAAAAAD24EopAAAAAAAA2I6iFAAAAAAAAGzH1/eAcqQgX88DAAAAAKA04EopAAAAAAAA2I6iFAAAAAAAAGzH1/cAAACAciri/Yt/t39FP269CwAoGVwpBQAAAAAAANtxpRQAJxdbLH0Ff0wFAAAAABQBrpQCAAAAAACA7bhSCkCR42orAADsUZA1owAAKK24UgoAAAAAAAC240opALbjSioAAMqOi12Nxd37AACFRVEKwCW5WEHJLhS2AAAoGsX9FUCKWgCA86EoBaDUKS2FLwAAAABA8WFNKQAAAAAAANiOK6UAlEsFudqKr/gBAAAAQMmhKAUA51EU61ax9hUAAAAA5I+v7wEAAAAAAMB2XCkFACWorHzNsKzECQCwH3fvAwAUFkUpABVWebnLX3n5iiCFLwBAcaCoBQClV4UqSs2ZM0fTpk1TYmKiWrZsqdmzZ+v6668v6bAAlFF2FbXKSvGsrMQJIH/kSSgpl3ulVXFfqQUAKD4Vpij14YcfavTo0Zo3b57atWunGTNmKDw8XAkJCfL39y/p8ACgRJWVglJRxMnVVkBe5EkAAKAkOIwxpqSDsEO7du3Utm1bvf7665KknJwc1alTR8OHD9fTTz99wW3T09Pl4+OjtLQ0RUZ62xEuAKAcKy2FsfLy1c+icPbvem/vive7vqjypPzOHVexoKzj630AKrrizJMqxJVSp0+fVlxcnMaOHWu1ubi4KCwsTLGxsSUYGQCgIior62fZcQVdQeZpRxyLFxf/MUor8iTg8pSFwuvFCmusuwWgpFSIotSff/6p7OxsBQQEOLUHBAToxx9/zDM+MzNTmZmZ1vO0tDRJ/1QHs7KKN1YAACSpa9eSjsAepWWe6enpkqQKcgG5k6LMk/KTdZLkCWVb17dLyQfVZbjcOVzu9h/d9dEF++9eevdl7b+4XSz+0qC4z+HlnoOLxVcWzvHFXO5rUJrPcXHmSRWiKHWpJk+erIkTJ+Zpr1OnTglEAwAAilvur/jjx4/Lx8enZIMp5ciTAFwqnwfL9udqWY+/KBT3OeAcl41z/NdffxV5nlQhilI1a9ZUpUqVlJSU5NSelJSkwMDAPOPHjh2r0aNHW89TU1MVHBysQ4cOkaiWgPT0dNWpU0eHDx+ukOt8lDTOf8ni/Jcszn/JsvP8G2N0/PhxBQUFFetxSiPypMtTkT8nmHvFnLtUsefP3Jl7RZu79M9V0XXr1pWfn1+R77tCFKXc3NzUunVrrVmzRr169ZL0zwKea9as0bBhw/KMd3d3l7u7e552Hx+fCvkGLC28vb05/yWI81+yOP8li/Nfsuw6/xWxoCKRJxWVivw5wdwr5tylij1/5s7cKyIXF5ci32eFKEpJ0ujRozVw4EC1adNG119/vWbMmKETJ07o/vvvL+nQAAAAShR5EgAAKAkVpih1zz336I8//tD48eOVmJioVq1aKTo6Os+ingAAABUNeRIAACgJFaYoJUnDhg3L9zL0i3F3d9dzzz2X76XqKH6c/5LF+S9ZnP+SxfkvWZx/e5EnFU5Fnj9zr5hzlyr2/Jk7c6+IinP+DlMR730MAAAAAACAElX0q1QBAAAAAAAAF0FRCgAAAAAAALajKAUAAAAAAADbUZQqgDlz5qhevXry8PBQu3bt9P3335d0SGXehAkT5HA4nB6NGjWy+k+dOqWoqCjVqFFDVatWVZ8+fZSUlOS0j0OHDqlHjx6qUqWK/P39NWbMGJ05c8buqZQJGzZsUEREhIKCguRwOLRs2TKnfmOMxo8fr1q1asnT01NhYWHav3+/05iUlBRFRkbK29tbvr6+Gjx4sDIyMpzG7Ny5UzfddJM8PDxUp04dTZ06tbinViZc7PwPGjQoz89D165dncZw/gtv8uTJatu2rapVqyZ/f3/16tVLCQkJTmOK6jPn66+/1nXXXSd3d3c1aNBACxcuLO7plXoFOf+dOnXK8zPwyCOPOI3h/JdeFSFPKqrPkfLg5ZdflsPh0MiRI6228jz333//Xf3791eNGjXk6emp5s2ba+vWrVZ/QXKosio7O1vjxo1TSEiIPD09Vb9+fb3wwgs6e0ni8jJ/u3Ll0upC88/KytJTTz2l5s2by8vLS0FBQbrvvvt05MgRp32U1flf7LU/2yOPPCKHw6EZM2Y4tZfnue/du1e33367fHx85OXlpbZt2+rQoUNWf1F8/lOUuogPP/xQo0eP1nPPPadt27apZcuWCg8PV3JyckmHVuY1bdpUR48etR7ffvut1Tdq1CitWLFCS5cu1fr163XkyBH17t3b6s/OzlaPHj10+vRpfffdd1q0aJEWLlyo8ePHl8RUSr0TJ06oZcuWmjNnTr79U6dO1axZszRv3jxt3rxZXl5eCg8P16lTp6wxkZGR2rNnj2JiYrRy5Upt2LBBQ4YMsfrT09PVpUsXBQcHKy4uTtOmTdOECRM0f/78Yp9faXex8y9JXbt2dfp5eP/99536Of+Ft379ekVFRWnTpk2KiYlRVlaWunTpohMnTlhjiuIz58CBA+rRo4duueUWxcfHa+TIkXrwwQf11Vdf2Trf0qYg51+SHnroIaefgbOLqpz/0qui5ElF8TlSHmzZskVvvvmmWrRo4dReXud+7NgxtW/fXpUrV9aXX36pH374Qa+++qqqV69ujSlIDlVWTZkyRXPnztXrr7+uvXv3asqUKZo6dapmz55tjSkv87cjVy7NLjT/kydPatu2bRo3bpy2bdumTz75RAkJCbr99tudxpXV+RckT5ekTz/9VJs2bVJQUFCevvI6959//lkdOnRQo0aN9PXXX2vnzp0aN26cPDw8rDFF8vlvcEHXX3+9iYqKsp5nZ2eboKAgM3ny5BKMqux77rnnTMuWLfPtS01NNZUrVzZLly612vbu3WskmdjYWGOMMV988YVxcXExiYmJ1pi5c+cab29vk5mZWayxl3WSzKeffmo9z8nJMYGBgWbatGlWW2pqqnF3dzfvv/++McaYH374wUgyW7ZsscZ8+eWXxuFwmN9//90YY8wbb7xhqlev7nT+n3rqKXPNNdcU84zKlnPPvzHGDBw40PTs2fO823D+i1ZycrKRZNavX2+MKbrPnCeffNI0bdrU6Vj33HOPCQ8PL+4plSnnnn9jjLn55pvNiBEjzrsN57/0qqh5UmE+R8q648ePm4YNG5qYmBinn9nyPPennnrKdOjQ4bz9BcmhyrIePXqYBx54wKmtd+/eJjIy0hhTfudfXLlyWZFfrnqu77//3kgyBw8eNMaUn/mfb+6//fabufLKK83u3btNcHCwee2116y+8jz3e+65x/Tv3/+82xTV5z9XSl3A6dOnFRcXp7CwMKvNxcVFYWFhio2NLcHIyof9+/crKChIV111lSIjI63LAOPi4pSVleV03hs1aqS6deta5z02NlbNmzdXQECANSY8PFzp6enas2ePvRMp4w4cOKDExESn8+3j46N27do5nW9fX1+1adPGGhMWFiYXFxdt3rzZGtOxY0e5ublZY8LDw5WQkKBjx47ZNJuy6+uvv5a/v7+uueYaDR06VH/99ZfVx/kvWmlpaZIkPz8/SUX3mRMbG+u0j9wx/L5wdu75z7V48WLVrFlTzZo109ixY3Xy5Emrj/NfOlXkPKkwnyNlXVRUlHr06JHn56w8z3358uVq06aN7rrrLvn7++vaa6/VW2+9ZfUXJIcqy2688UatWbNG+/btkyTt2LFD3377rbp16yap/M8/V1HlyuVJWlqaHA6HfH19JZXv+efk5GjAgAEaM2aMmjZtmqe/vM49JydHn3/+ua6++mqFh4fL399f7dq1c/qKX1F9/lOUuoA///xT2dnZTkmwJAUEBCgxMbGEoiof2rVrp4ULFyo6Olpz587VgQMHdNNNN+n48eNKTEyUm5ub9SGX6+zznpiYmO/rktuHgss9Xxd6nycmJsrf39+p39XVVX5+frwmRaBr16565513tGbNGk2ZMkXr169Xt27dlJ2dLYnzX5RycnI0cuRItW/fXs2aNZOkIvvMOd+Y9PR0/f3338UxnTInv/MvSffee6/ee+89rVu3TmPHjtW7776r/v37W/2c/9KpouZJhf0cKcs++OADbdu2TZMnT87TV57n/ssvv2ju3Llq2LChvvrqKw0dOlSPPfaYFi1aJKlgOVRZ9vTTT6tv375q1KiRKleurGuvvVYjR45UZGSkpPI//1xFlSuXF6dOndJTTz2lfv36ydvbW1L5nv+UKVPk6uqqxx57LN/+8jr35ORkZWRk6OWXX1bXrl21atUq3XHHHerdu7fWr18vqeg+/12LMnCgoHL/wiJJLVq0ULt27RQcHKyPPvpInp6eJRgZYL++ffta/27evLlatGih+vXr6+uvv1bnzp1LMLLyJyoqSrt373Zaww72Od/5P3vdhebNm6tWrVrq3Lmzfv75Z9WvX9/uMIELqmifI4cPH9aIESMUExPjtI5IRZCTk6M2bdropZdekiRde+212r17t+bNm6eBAweWcHTF76OPPtLixYu1ZMkSNW3a1FqvLygoqELMH3llZWXp7rvvljFGc+fOLelwil1cXJxmzpypbdu2yeFwlHQ4tsrJyZEk9ezZU6NGjZIktWrVSt99953mzZunm2++uciOxZVSF1CzZk1VqlQpz+rxSUlJCgwMLKGoyidfX19dffXV+umnnxQYGKjTp08rNTXVaczZ5z0wMDDf1yW3DwWXe74u9D4PDAzMs2jtmTNnlJKSwmtSDK666irVrFlTP/30kyTOf1EZNmyYVq5cqXXr1ql27dpWe1F95pxvjLe3N8V2nf/856ddu3aS5PQzwPkvfSpinnQ5nyNlVVxcnJKTk3XdddfJ1dVVrq6uWr9+vWbNmiVXV1cFBASU27nXqlVLTZo0cWpr3LixteREQXKosmzMmDHW1VLNmzfXgAEDNGrUKOuKufI+/1xFlSuXdbkFqYMHDyomJsa6Skoqv/P/5ptvlJycrLp161qffwcPHtTjjz+uevXqSSq/c69Zs6ZcXV0v+hlYFJ//FKUuwM3NTa1bt9aaNWustpycHK1Zs0ahoaElGFn5k5GRoZ9//lm1atVS69atVblyZafznpCQoEOHDlnnPTQ0VLt27XL6AMj9cDz3BwcXFhISosDAQKfznZ6ers2bNzud79TUVMXFxVlj1q5dq5ycHOs/j6GhodqwYYOysrKsMTExMbrmmmuc7lKDi/vtt9/0119/qVatWpI4/5fLGKNhw4bp008/1dq1axUSEuLUX1SfOaGhoU77yB1T0X9fXOz85yc+Pl6SnH4GOP+lT0XKk4ric6Ss6ty5s3bt2qX4+Hjr0aZNG0VGRlr/Lq9zb9++vRISEpza9u3bp+DgYEkFy6HKspMnT8rFxfm/i5UqVbKuoCjv889VVLlyWZZbkNq/f79Wr16tGjVqOPWX1/kPGDBAO3fudPr8CwoK0pgxY6y7+5bXubu5ualt27YX/Awsst99l7QkewX0wQcfGHd3d7Nw4ULzww8/mCFDhhhfX1+nOwDh0j3++OPm66+/NgcOHDAbN240YWFhpmbNmiY5OdkYY8wjjzxi6tata9auXWu2bt1qQkNDTWhoqLX9mTNnTLNmzUyXLl1MfHy8iY6ONldccYUZO3ZsSU2pVDt+/LjZvn272b59u5Fkpk+fbrZv327dMePll182vr6+5rPPPjM7d+40PXv2NCEhIebvv/+29tG1a1dz7bXXms2bN5tvv/3WNGzY0PTr18/qT01NNQEBAWbAgAFm9+7d5oMPPjBVqlQxb775pu3zLW0udP6PHz9unnjiCRMbG2sOHDhgVq9eba677jrTsGFDc+rUKWsfnP/CGzp0qPHx8TFff/21OXr0qPU4efKkNaYoPnN++eUXU6VKFTNmzBizd+9eM2fOHFOpUiUTHR1t63xLm4ud/59++sk8//zzZuvWrebAgQPms88+M1dddZXp2LGjtQ/Of+lVUfKkovgcKU/OvWNmeZ37999/b1xdXc2kSZPM/v37zeLFi02VKlXMe++9Z40pSA5VVg0cONBceeWVZuXKlebAgQPmk08+MTVr1jRPPvmkNaa8zN+OXLk0u9D8T58+bW6//XZTu3ZtEx8f7/QZePZdn8vq/C/22p/r3LvvGVN+5/7JJ5+YypUrm/nz55v9+/eb2bNnm0qVKplvvvnG2kdRfP5TlCqA2bNnm7p16xo3Nzdz/fXXm02bNpV0SGXePffcY2rVqmXc3NzMlVdeae655x7z008/Wf1///23efTRR0316tVNlSpVzB133GGOHj3qtI9ff/3VdOvWzXh6epqaNWuaxx9/3GRlZdk9lTJh3bp1RlKex8CBA40x/9zqdty4cSYgIMC4u7ubzp07m4SEBKd9/PXXX6Zfv36matWqxtvb29x///3m+PHjTmN27NhhOnToYNzd3c2VV15pXn75ZbumWKpd6PyfPHnSdOnSxVxxxRWmcuXKJjg42Dz00EN5/kPH+S+8/M69JLNgwQJrTFF95qxbt860atXKuLm5mauuusrpGBXVxc7/oUOHTMeOHY2fn59xd3c3DRo0MGPGjDFpaWlO++H8l14VIU8qqs+R8uLcolR5nvuKFStMs2bNjLu7u2nUqJGZP3++U39BcqiyKj093YwYMcLUrVvXeHh4mKuuuso888wzToWI8jJ/u3Ll0upC8z9w4MB5PwPXrVtn7aOszv9ir/258itKlee5v/3226ZBgwbGw8PDtGzZ0ixbtsxpH0Xx+e8wxpiCX1cFAAAAAAAAXD7WlAIAAAAAAIDtKEoBAAAAAADAdhSlAAAAAAAAYDuKUgAAAAAAALAdRSkAAAAAAADYjqIUAAAAAAAAbEdRCgAAAAAAALajKAUAAAAAAADbUZQCUGb8+uuvcjgcio+PL+lQAAAAShXyJABlEUUpALZyOBwXfEyYMKGkQ8zXTz/9pPvvv1+1a9eWu7u7QkJC1K9fP23dutXWOEg4AQAov8iTLg95ElD2uJZ0AAAqlqNHj1r//vDDDzV+/HglJCRYbVWrVi2JsC5o69at6ty5s5o1a6Y333xTjRo10vHjx/XZZ5/p8ccf1/r160s6RAAAUA6QJwGoaLhSCoCtAgMDrYePj48cDof13N/fX9OnT7f+ytaqVStFR0efd1/Z2dl64IEH1KhRIx06dEiS9Nlnn+m6666Th4eHrrrqKk2cOFFnzpyxtnE4HPrPf/6jO+64Q1WqVFHDhg21fPny8x7DGKNBgwapYcOG+uabb9SjRw/Vr19frVq10nPPPafPPvvMGrtr1y7deuut8vT0VI0aNTRkyBBlZGRY/Z06ddLIkSOd9t+rVy8NGjTIel6vXj299NJLeuCBB1StWjXVrVtX8+fPt/pDQkIkSddee60cDoc6dep0wfMNAADKDvKkkU77J08Cyj+KUgBKjZkzZ+rVV1/VK6+8op07dyo8PFy333679u/fn2dsZmam7rrrLsXHx+ubb75R3bp19c033+i+++7TiBEj9MMPP+jNN9/UwoULNWnSJKdtJ06cqLvvvls7d+5U9+7dFRkZqZSUlHxjio+P1549e/T444/LxSXvR6avr68k6cSJEwoPD1f16tW1ZcsWLV26VKtXr9awYcMu+Ty8+uqratOmjbZv365HH31UQ4cOtf5K+v3330uSVq9eraNHj+qTTz655P0DAICyhzzpH+RJQDljAKCELFiwwPj4+FjPg4KCzKRJk5zGtG3b1jz66KPGGGMOHDhgJJlvvvnGdO7c2XTo0MGkpqZaYzt37mxeeuklp+3fffddU6tWLeu5JPPss89azzMyMowk8+WXX+Yb44cffmgkmW3btl1wLvPnzzfVq1c3GRkZVtvnn39uXFxcTGJiojHGmJtvvtmMGDHCabuePXuagQMHWs+Dg4NN//79rec5OTnG39/fzJ071+kcbN++/YLxAACAso08iTwJqAhYUwpAqZCenq4jR46offv2Tu3t27fXjh07nNr69eun2rVra+3atfL09LTad+zYoY0bNzr9xS87O1unTp3SyZMnVaVKFUlSixYtrH4vLy95e3srOTk537iMMQWKf+/evWrZsqW8vLycYs/JyVFCQoICAgIKtJ9z48u9bP988QEAgPKPPOn/kCcB5Qtf3wNQ5nTv3l07d+5UbGysU3tGRoYmTpyo+Ph467Fr1y7t379fHh4e1rjKlSs7bedwOJSTk5Pvsa6++mpJ0o8//njZcbu4uORJ3rKysvKMu5T4AAAAzkaeBKAsoSgFoFTw9vZWUFCQNm7c6NS+ceNGNWnSxKlt6NChevnll3X77bc73dHluuuuU0JCgho0aJDnkd86BwXRqlUrNWnSRK+++mq+CU9qaqokqXHjxtqxY4dOnDjhFLuLi4uuueYaSdIVV1zhdFed7Oxs7d69+5LicXNzs7YFAAAVA3lSwZAnAWUPRSkApcaYMWM0ZcoUffjhh0pISNDTTz+t+Ph4jRgxIs/Y4cOH68UXX9S//vUvffvtt5Kk8ePH65133tHEiRO1Z88e7d27Vx988IGeffbZQsfkcDi0YMEC7du3TzfddJO++OIL/fLLL9q5c6cmTZqknj17SpIiIyPl4eGhgQMHavfu3Vq3bp2GDx+uAQMGWJek33rrrfr888/1+eef68cff9TQoUOtZK2g/P395enpqejoaCUlJSktLa3QcwMAAGUHedLFkScBZQ9FKQClxmOPPabRo0fr8ccfV/PmzRUdHa3ly5erYcOG+Y4fOXKkJk6cqO7du+u7775TeHi4Vq5cqVWrVqlt27a64YYb9Nprryk4OPiy4rr++uu1detWNWjQQA899JAaN26s22+/XXv27NGMGTMk6f+1cwenFQJhFEZvwJUluLQH1yK24FrBksSNpdiULbzs3jYJhB8SzilgmOXlY5i0bZv7vvM8T4ZhyLIsmec553m+z9n3Pdu2ZV3XjOOYvu8zTdOP7tI0TY7jyHVd6bruPfYAgP/NTvqanQR/z8fru7/TAQAAAMAv8VIKAAAAgHKiFAAAAADlRCkAAAAAyolSAAAAAJQTpQAAAAAoJ0oBAAAAUE6UAgAAAKCcKAUAAABAOVEKAAAAgHKiFAAAAADlRCkAAAAAyolSAAAAAJT7BLOFCD23TMbUAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting boxplots\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "# Boxplot for text token counts\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.boxplot(text_token_counts)\n",
        "plt.title('Boxplot of Text Token Counts')\n",
        "plt.xlabel('Text Token Count')\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "# Boxplot for summary token counts\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.boxplot(tags_token_counts)\n",
        "plt.title('Boxplot of Tags Token Counts')\n",
        "plt.xlabel('Tags Token Count')\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "# Show the plots\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "-Us2qlRenkp_",
        "outputId": "e1339fc1-c2a7-47b8-c639-98a26074551c"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Saving Amharic\n",
        "\n",
        "# Splitting the dataset\n",
        "# 80% training, 10% validation, 10% testing\n",
        "train, temp = train_test_split(aggregated_data, test_size=0.20, random_state=16)\n",
        "valid, test = train_test_split(temp, test_size=0.50, random_state=16)\n",
        "\n",
        "# Save them to different CSVs\n",
        "train.to_csv('/content/drive/MyDrive/Ahmaric_data' + 'amharic_train.csv', index=False)\n",
        "valid.to_csv('/content/drive/MyDrive/Ahmaric_data' + 'amharic_valid.csv', index=False)\n",
        "test.to_csv('/content/drive/MyDrive/Ahmaric_data' + 'amharic_test.csv', index=False)\n",
        "\n",
        "# Also saving everything to one file to make it available together\n",
        "aggregated_data.to_csv('/content/drive/MyDrive/Ahmaric_data' + 'amharic_1_all.csv', index=False)"
      ],
      "metadata": {
        "id": "5SqboyRVn6pg"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load each Amharic dataset from CSV\n",
        "# All of these files were saved to the same directory all the raw data files are saved and made available\n",
        "# The path can be updated to wherever these datafiles are stored if that has changed\n",
        "train_df = pd.read_csv('/content/drive/MyDrive/Ahmaric_data' + 'amharic_train.csv')\n",
        "valid_df = pd.read_csv('/content/drive/MyDrive/Ahmaric_data' + 'amharic_valid.csv')\n",
        "test_df = pd.read_csv('/content/drive/MyDrive/Ahmaric_data' + 'amharic_test.csv')\n",
        "\n",
        "# Concatenate them back into one DataFrame\n",
        "aggregated_data_further_cleaned = pd.concat([train_df, valid_df, test_df], ignore_index=True)"
      ],
      "metadata": {
        "id": "Xf6D8pdjqwco"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating Normalized Data\n",
        "\n",
        "def normalize_char_level_missmatch(input_token):\n",
        "  '''\n",
        "    Normalize variations of Amharic characters in a given token to standardize them.\n",
        "\n",
        "    Args:\n",
        "        input_token (str): The Amharic token to be normalized.\n",
        "\n",
        "    Returns:\n",
        "        str: The normalized Amharic token.\n",
        "  '''\n",
        "\n",
        "  rep1=re.sub('[ሃኅኃሐሓኻ]','ሀ',input_token)\n",
        "  rep2=re.sub('[ሑኁዅ]','ሁ',rep1)\n",
        "  rep3=re.sub('[ኂሒኺ]','ሂ',rep2)\n",
        "  rep4=re.sub('[ኌሔዄ]','ሄ',rep3)\n",
        "  rep5=re.sub('[ሕኅ]','ህ',rep4)\n",
        "  rep6=re.sub('[ኆሖኾ]','ሆ',rep5)\n",
        "  rep7=re.sub('[ሠ]','ሰ',rep6)\n",
        "  rep8=re.sub('[ሡ]','ሱ',rep7)\n",
        "  rep9=re.sub('[ሢ]','ሲ',rep8)\n",
        "  rep10=re.sub('[ሣ]','ሳ',rep9)\n",
        "  rep11=re.sub('[ሤ]','ሴ',rep10)\n",
        "  rep12=re.sub('[ሥ]','ስ',rep11)\n",
        "  rep13=re.sub('[ሦ]','ሶ',rep12)\n",
        "  rep14=re.sub('[ዓኣዐ]','አ',rep13)\n",
        "  rep15=re.sub('[ዑ]','ኡ',rep14)\n",
        "  rep16=re.sub('[ዒ]','ኢ',rep15)\n",
        "  rep17=re.sub('[ዔ]','ኤ',rep16)\n",
        "  rep18=re.sub('[ዕ]','እ',rep17)\n",
        "  rep19=re.sub('[ዖ]','ኦ',rep18)\n",
        "  rep20=re.sub('[ጸ]','ፀ',rep19)\n",
        "  rep21=re.sub('[ጹ]','ፁ',rep20)\n",
        "  rep22=re.sub('[ጺ]','ፂ',rep21)\n",
        "  rep23=re.sub('[ጻ]','ፃ',rep22)\n",
        "  rep24=re.sub('[ጼ]','ፄ',rep23)\n",
        "  rep25=re.sub('[ጽ]','ፅ',rep24)\n",
        "  rep26=re.sub('[ጾ]','ፆ',rep25)\n",
        "\n",
        "  #Normalizing words with Labialized Amharic characters such as በልቱዋል or  በልቱአል to  በልቷል\n",
        "  rep27=re.sub('(ሉ[ዋአ])','ሏ',rep26)\n",
        "  rep28=re.sub('(ሙ[ዋአ])','ሟ',rep27)\n",
        "  rep29=re.sub('(ቱ[ዋአ])','ቷ',rep28)\n",
        "  rep30=re.sub('(ሩ[ዋአ])','ሯ',rep29)\n",
        "  rep31=re.sub('(ሱ[ዋአ])','ሷ',rep30)\n",
        "  rep32=re.sub('(ሹ[ዋአ])','ሿ',rep31)\n",
        "  rep33=re.sub('(ቁ[ዋአ])','ቋ',rep32)\n",
        "  rep34=re.sub('(ቡ[ዋአ])','ቧ',rep33)\n",
        "  rep35=re.sub('(ቹ[ዋአ])','ቿ',rep34)\n",
        "  rep36=re.sub('(ሁ[ዋአ])','ኋ',rep35)\n",
        "  rep37=re.sub('(ኑ[ዋአ])','ኗ',rep36)\n",
        "  rep38=re.sub('(ኙ[ዋአ])','ኟ',rep37)\n",
        "  rep39=re.sub('(ኩ[ዋአ])','ኳ',rep38)\n",
        "  rep40=re.sub('(ዙ[ዋአ])','ዟ',rep39)\n",
        "  rep41=re.sub('(ጉ[ዋአ])','ጓ',rep40)\n",
        "  rep42=re.sub('(ደ[ዋአ])','ዷ',rep41)\n",
        "  rep43=re.sub('(ጡ[ዋአ])','ጧ',rep42)\n",
        "  rep44=re.sub('(ጩ[ዋአ])','ጯ',rep43)\n",
        "  rep45=re.sub('(ጹ[ዋአ])','ጿ',rep44)\n",
        "  rep46=re.sub('(ፉ[ዋአ])','ፏ',rep45)\n",
        "  rep47=re.sub('[ቊ]','ቁ',rep46) #ቁ can be written as ቊ\n",
        "  rep48=re.sub('[ኵ]','ኩ',rep47) #ኩ can be also written as ኵ\n",
        "  return rep48\n",
        "\n",
        "# :: No longer removed\n",
        "def remove_punc_and_special_chars(text):\n",
        "  '''\n",
        "    Remove punctuation and special characters from a given text.\n",
        "\n",
        "    Args:\n",
        "        text (str): The text from which to remove punctuation and special characters.\n",
        "\n",
        "    Returns:\n",
        "        str: The text stripped of punctuation and special characters.\n",
        "  '''\n",
        "\n",
        "  normalized_text = re.sub('[\\!\\@\\#\\$\\%\\^\\«\\»\\&\\*\\(\\)\\…\\[\\]\\{\\}\\;\\“\\”\\›\\’\\‘\\\"\\'\\:\\,\\.\\‹\\/\\<\\>\\?\\\\\\\\|\\`\\´\\~\\-\\=\\+\\፡\\፤\\;\\፦\\፥\\፧\\፨\\፠\\፣]', '',text)\n",
        "  return normalized_text\n",
        "\n",
        "# Remove all ascii characters\n",
        "def remove_ascii_and_numbers(text_input):\n",
        "  '''\n",
        "    Remove ASCII characters and numbers from the given text.\n",
        "\n",
        "    Args:\n",
        "        text_input (str): The text from which to remove ASCII characters and numbers.\n",
        "\n",
        "    Returns:\n",
        "        str: The text without any ASCII characters and numbers.\n",
        "  '''\n",
        "\n",
        "  return re.sub('[A-Za-z]','',text_input)"
      ],
      "metadata": {
        "id": "XQDuPos9rd50"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Normalizing the text and tags columns using the three normalization functions\n",
        "aggregated_data_further_cleaned['text'] = aggregated_data_further_cleaned['text'].apply(\n",
        "    lambda x: remove_ascii_and_numbers(\n",
        "        remove_punc_and_special_chars(\n",
        "            normalize_char_level_missmatch(x))))\n",
        "\n",
        "aggregated_data_further_cleaned['tags'] = aggregated_data_further_cleaned['tags'].apply(\n",
        "    lambda x: remove_ascii_and_numbers(\n",
        "        remove_punc_and_special_chars(\n",
        "            normalize_char_level_missmatch(x))))\n",
        "\n",
        "# Updating the statistical columns following the normalization steps\n",
        "# Length of the text and tags\n",
        "aggregated_data_further_cleaned['text_length'] = aggregated_data_further_cleaned['text'].apply(len)\n",
        "aggregated_data_further_cleaned['tags_length'] = aggregated_data_further_cleaned['tags'].apply(len)\n",
        "\n",
        "# Word count in the text and tags\n",
        "aggregated_data_further_cleaned['text_word_count'] = aggregated_data_further_cleaned['text'].apply(lambda x: len(x.split()))\n",
        "aggregated_data_further_cleaned['tags_word_count'] = aggregated_data_further_cleaned['tags'].apply(lambda x: len(x.split()))\n",
        "\n",
        "# Token count in the text and tags\n",
        "aggregated_data_further_cleaned['text_token_count'] = aggregated_data_further_cleaned['text'].apply(lambda x: count_tokens(x, tokenizer))\n",
        "aggregated_data_further_cleaned['tags_token_count'] = aggregated_data_further_cleaned['tags'].apply(lambda x: count_tokens(x, tokenizer))\n",
        "\n",
        "# Showing descriptive statistics of the token counts\n",
        "aggregated_data_further_cleaned[['text_token_count', 'tags_token_count']].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "IqFTzB-rryi0",
        "outputId": "4805501c-a4a0-4a0a-d0e1-13c0a522ab99"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       text_token_count  tags_token_count\n",
              "count       6128.000000       6128.000000\n",
              "mean         325.665307          9.074739\n",
              "std          363.851236          9.919027\n",
              "min            1.000000          1.000000\n",
              "25%           97.000000          1.000000\n",
              "50%          194.000000          7.000000\n",
              "75%          403.000000         12.000000\n",
              "max         2570.000000        135.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-68eda7b7-a57b-4d34-bb2a-86bd82a30dc7\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text_token_count</th>\n",
              "      <th>tags_token_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6128.000000</td>\n",
              "      <td>6128.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>325.665307</td>\n",
              "      <td>9.074739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>363.851236</td>\n",
              "      <td>9.919027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>97.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>194.000000</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>403.000000</td>\n",
              "      <td>12.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2570.000000</td>\n",
              "      <td>135.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-68eda7b7-a57b-4d34-bb2a-86bd82a30dc7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-68eda7b7-a57b-4d34-bb2a-86bd82a30dc7 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-68eda7b7-a57b-4d34-bb2a-86bd82a30dc7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-3cb3cef8-08b1-40ba-b129-6d0a36b133a3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3cb3cef8-08b1-40ba-b129-6d0a36b133a3')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3cb3cef8-08b1-40ba-b129-6d0a36b133a3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"aggregated_data_further_cleaned[['text_token_count', 'tags_token_count']]\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"text_token_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2134.6810445470337,\n        \"min\": 1.0,\n        \"max\": 6128.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          325.66530678851177,\n          194.0,\n          6128.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tags_token_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2158.2075378818477,\n        \"min\": 1.0,\n        \"max\": 6128.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          6128.0,\n          9.074738903394255,\n          12.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Setting the thresholds for Amharic\n",
        "\n",
        "max_text_token_count = 512\n",
        "max_summary_word_count = 128\n",
        "\n",
        "# Applying the filters\n",
        "bounded_token_length_aggregated_data_further_cleaned = aggregated_data_further_cleaned[\n",
        "    (aggregated_data_further_cleaned['text_token_count'] <= max_text_token_count) &\n",
        "    (aggregated_data_further_cleaned['tags_token_count'] <= max_summary_word_count)\n",
        "]\n",
        "\n",
        "# Displaying the first few rows of the filtered DataFrame\n",
        "print(bounded_token_length_aggregated_data_further_cleaned.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I0t1hQf1t5mA",
        "outputId": "a65dc7d2-5151-4a1b-c9af-8af2efe2caa7"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                                text  \\\n",
            "0   የጣሊያን መከላከያ ሚኒስትር ኤልዛቤታ ቴራንታ በኢትዮጵያ ይፋዊ የስራ ጉ...   \n",
            "1   የጠቅላይ ሚኒስትር ቃል አቀባይ ፅቤት ሀላፊ የነበሩትን አቶ ፍፁም_አረጋ...   \n",
            "2  አልሸባብ‼️\\n\\nአልሸባብ በሶማሊያ አወዛጋቢው ምሁር የሚባለዉን ግለሰብና...   \n",
            "4  በአፍሪካ ህብረት አዳራሽ እንገናኝ\\nከምሽቱ 1200 ጀምሮ\\n\\nውድ የሀዋ...   \n",
            "5   ሱዳንና ኤርትራ ግንኙነታቸውን ሊያድሱ ነው። ባለፈው አመት ሱዳን በኳታር...   \n",
            "\n",
            "                                tags  text_length  tags_length  \\\n",
            "0                                             351            0   \n",
            "1           ፍፁም_አረጋንሽመልስ_አብዲሳህይወታቸውን          218           24   \n",
            "2                   ገደለአብዲያሲስ_አቡ_ሙሳብ          507           16   \n",
            "4  የሀዋሳ_ዩኒቨርሲቲየጥላቻ_ንግግርአመራሮችተሳታፊአባላት          322           33   \n",
            "5                                             234            0   \n",
            "\n",
            "   text_word_count  summary_word_count  text_token_count  tags_token_count  \\\n",
            "0               62                   1               231                 1   \n",
            "1               42                   1               142                19   \n",
            "2               91                   1               333                15   \n",
            "4               58                   1               216                27   \n",
            "5               46                   1               143                 1   \n",
            "\n",
            "   tags_word_count  \n",
            "0                0  \n",
            "1                1  \n",
            "2                1  \n",
            "4                1  \n",
            "5                0  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Extracting token counts\n",
        "text_token_counts = bounded_token_length_aggregated_data_further_cleaned['text_token_count']\n",
        "tags_token_counts = bounded_token_length_aggregated_data_further_cleaned['tags_token_count']\n",
        "\n",
        "# Plotting histograms\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "# Histogram for text token counts\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.hist(text_token_counts, bins=60, color='blue', alpha=0.7)\n",
        "plt.title('Distribution of Text Token Counts')\n",
        "plt.xlabel('Token Count')\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "# Histogram for summary token counts\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.hist(tags_token_counts, bins=60, color='green', alpha=0.7)\n",
        "plt.title('Distribution of tags Token Counts')\n",
        "plt.xlabel('Token Count')\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "# Show the plots\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "iotOrt_8u1gr",
        "outputId": "c51b48fd-b32a-42cc-e0b6-4861b011ebc9"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting boxplots\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "# Boxplot for text token counts\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.boxplot(text_token_counts)\n",
        "plt.title('Boxplot of Text Token Counts')\n",
        "plt.xlabel('Text Token Count')\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "# Boxplot for summary token counts\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.boxplot(tags_token_counts)\n",
        "plt.title('Boxplot of Tags Token Counts')\n",
        "plt.xlabel('Tags Token Count')\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "# Show the plots\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "8cPzXP7mviqv",
        "outputId": "ec9ee5c3-f750-4a6a-e1d4-4107679014d1"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABKUAAAJOCAYAAABm7rQwAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACD2UlEQVR4nOzdfXzO9f////uxY+d2oomd5GRrpjmZhGKyQrKErzU6JfQmvTOV02pSIhk6ITnpTChRbz7SO5VEaNWoJr0tJ80yI9v0JtsY22yv3x9+O94dzBxmjsOx3a6Xy3HJ6/l6Hq/j8dqOeO6+5/F8mgzDMAQAAAAAAADYkYujCwAAAAAAAEDtQygFAAAAAAAAuyOUAgAAAAAAgN0RSgEAAAAAAMDuCKUAAAAAAABgd4RSAAAAAAAAsDtCKQAAAAAAANgdoRQAAAAAAADsjlAKAAAAAAAAdkcoBdQiJpNJzz//vKPLsPLjjz+qU6dOqlOnjkwmk7Zv3+7okhwmNDRUvXv3dnQZAADUWoyVnEeXLl3UqlUrR5cB4BIRSgHVYPHixTKZTFaPBg0aqGvXrvriiy8cXd4l27lzp55//nllZmZW63VLSkp099136+jRo5o1a5bef/99NWnS5Jx+oaGh53x9K3osXry4WuoqLCzU888/r02bNl2wr71rs7dNmzYpPj5eQUFBcnd3V4MGDdSnTx+tWrXK0aVJurjvFQDAcRgrVc2VOlaqqi5duthU55UWDNpq+/btGjhwoBo1aiQPDw8FBASoe/fuWrRokUpLSx1dniRp2rRpWr16taPLACxcHV0AUJNMmTJFYWFhMgxDubm5Wrx4se688059+umnTj0DZufOnZo8ebK6dOmi0NDQartuRkaG9u/fr7ffflvDhg07b7/Zs2fr+PHjluPPP/9cy5cv16xZs3T11Vdb2jt16lQtdRUWFmry5MmSzgyeKmPv2uxp0qRJmjJliiIiIvTII4+oSZMmOnLkiD7//HP169dPH3zwgR544AGH1ngx3ysAgOMxVro4V+pYqaqeeeYZq/v48ccfNWfOHE2YMEHNmze3tLdu3doR5V2Sd955R//85z8VGBioBx98UBERESooKNCGDRs0dOhQZWdna8KECY4uU9OmTVP//v0VFxfn6FIASYRSQLXq2bOn2rdvbzkeOnSoAgMDtXz5cqceaF0uhw8fliTVrVu30n5n/6OZk5Oj5cuXKy4urloHflVxJdd2KVauXKkpU6aof//+WrZsmdzc3Cznxo8fry+//FIlJSUOrBAA4IwYK12cmjBW+rvbb7/d6tjT01Nz5szR7bff7tS/XNqyZYv++c9/Kjo6Wp9//rl8fX0t50aNGqWffvpJaWlpDqwQuHLx8T3gMqpbt668vLzk6mqd/544cUJjx461TO297rrr9PLLL8swDEnSyZMnFRkZqcjISJ08edLyvKNHjyo4OFidOnWyTAEeMmSIfHx89Pvvvys2NlZ16tRRSEiIpkyZYrleZX7++Wf17NlTfn5+8vHx0W233aYtW7ZYzi9evFh33323JKlr166WadUX+rjU119/rZiYGNWpU0d169ZV3759tWvXLsv5IUOG6NZbb5Uk3X333TKZTJc8GFm6dKnatWsnLy8vBQQE6L777tOBAwcs5xctWiSTyaR3333X6nnTpk2TyWTS559/rszMTNWvX1+SNHny5GqZRn769Gm98MILCg8Pl4eHh0JDQzVhwgQVFRVd8LlLliyRq6urxo8fb2nbunWr7rjjDvn7+8vb21u33nqrvvvuO6vnPf/88zKZTNq7d6+GDBmiunXryt/fXw899JAKCwsv+LrPPvusAgIC9O6771oFUuViY2Otfng4fPiw5QcLT09PXX/99VqyZInVczZt2lTheyczM/OcjxSUv6//+OMPxcXFycfHR/Xr19e4ceMs7/0Lfa9ycnL00EMPqWHDhvLw8FBwcLD69u1b7R+tAABUHWMl+42VPvnkE/Xq1UshISHy8PBQeHi4XnjhhQo/VjZv3jxde+218vLy0k033aTk5GR16dLlnNd//fXX1bJlS3l7e+uqq65S+/bttWzZsirXWG7+/Plq2bKlPDw8FBISooSEBB07duyCz1u3bp28vb11//336/Tp05Kk3bt3q3///goICJCnp6fat2+vf//731bPK/946XfffacxY8aofv36qlOnju666y79+eefF3zd8nHIBx98YBVIlWvfvr2GDBliOb7Q+1uqeHxU7uyxqa3jPpPJpBMnTmjJkiWW92l5XQUFBRo1apRCQ0Pl4eGhBg0a6Pbbb9e2bdsueP/AJTEAXLJFixYZkoz169cbf/75p3H48GEjLS3NeOSRRwwXFxdj3bp1lr5lZWVGt27dDJPJZAwbNsyYO3eu0adPH0OSMWrUKEu/LVu2GGaz2Rg9erSl7b777jO8vLyMPXv2WNoGDx5seHp6GhEREcaDDz5ozJ071+jdu7chyXj22Wet6pRkTJo0yXKclpZm1KlTxwgODjZeeOEFY/r06UZYWJjh4eFhbNmyxTAMw8jIyDAef/xxQ5IxYcIE4/333zfef/99Iycn57xfj6+++spwdXU1mjVrZsycOdOYPHmycfXVVxtXXXWVsW/fPsMwDOP77783JkyYYEgyHn/8ceP999+3+jpV5qWXXjIkWa5lGIYxdepUw2QyGffee68xf/58y2uGhoYaf/31l6Vf7969DX9/fyMrK8swDMP4z3/+Y7i7uxtDhw41DMMwjh8/bixYsMCQZNx1112W+/3ll1+qXNvgwYMNSUb//v2NefPmGYMGDTIkGXFxcVbPbdKkidGrVy/L8ZtvvmmYTCbjmWeesbRt2LDBcHd3N6Kjo41XXnnFmDVrltG6dWvD3d3d2Lp1q6XfpEmTDEnGDTfcYMTHxxvz5883hg0bZkgynnzyyUrv4bfffjMkGf/4xz9suufCwkKjefPmhpubmzF69Ghjzpw5RkxMjCHJmD17tqXfxo0bDUnGxo0brZ6/b98+Q5KxaNEiq6+Zp6en0bJlS+Mf//iHsWDBAqNfv36GJGP+/PmGYVz4e9WpUyfD39/fmDhxovHOO+8Y06ZNM7p27Wps3rzZpvsCAFQfxkrWHDFWiouLM+655x7jpZdeMhYsWGDcfffdhiRj3LhxVs+dP3++IcmIiYkx5syZY4wZM8YICAgwwsPDjVtvvdXS76233rKMb958803jtddeM4YOHWo8/vjjNtVoGIaxYsWKc8YG5WOY7t27G6+//roxcuRIw2w2GzfeeKNRXFxs6XfrrbcaLVu2tBx/+umnhoeHhzFo0CDj9OnThmGc+f75+/sbLVq0MGbMmGHMnTvXuOWWWwyTyWSsWrXK8tzy9+cNN9xgdOvWzXj99deNsWPHGmaz2bjnnnsqvYcTJ04Ybm5uRrdu3Wy6Z1vf3xWNj8qd/T61ddz3/vvvGx4eHkZMTIzlffr9998bhmEYDzzwgOHu7m6MGTPGeOedd4wZM2YYffr0MZYuXWrTfQFVRSgFVIPyf8jOfnh4eBiLFy+26rt69WpDkjF16lSr9v79+xsmk8nYu3evpS0xMdFwcXExvvnmG8s/2n//Id8w/hd4PPbYY5a2srIyo1evXoa7u7vx559/WtrP/gcsLi7OcHd3NzIyMixthw4dMnx9fY1bbrnF0lbRgKEybdq0MRo0aGAcOXLE0vbLL78YLi4uxqBBgyxt5SHFihUrbLpuubMHWpmZmYbZbDZefPFFq347duwwXF1drdqzs7ONgIAA4/bbbzeKioqMG264wWjcuLGRl5dn6fPnn3+e87Wqam3bt283JBnDhg2z6jdu3DhDkvH1119b2v4eSr322muGyWQyXnjhBcv5srIyIyIiwoiNjTXKysos7YWFhUZYWJhx++23W9rKBydnB0t33XWXUa9evUrv4ZNPPjEkGbNmzbLpnmfPnm1Ishq0FBcXG9HR0YaPj4+Rn59vGMbFh1KSjClTplj1veGGG4x27dpZjs/3vfrrr78MScZLL71k0z0AAC4vxkrW7D1WMowz44WzPfLII4a3t7dx6tQpwzAMo6ioyKhXr55x4403GiUlJZZ+ixcvNiRZhVJ9+/a1CoWq4uyv2+HDhw13d3ejR48eRmlpqaXf3LlzDUnGu+++a2n7eyj1f//3f4abm5vx8MMPWz3vtttuM6Kioiz3ZxhnvvedOnUyIiIiLG3l78/u3btbjbFGjx5tmM1m49ixY+e9h19++cWQZDzxxBM23bOt7++qhFK2jPvq1KljDB48+Jxr+vv7GwkJCTbdA1Cd+PgeUI3mzZunr776Sl999ZWWLl2qrl27atiwYVY7lX3++ecym816/PHHrZ47duxYGYZhtQPN888/r5YtW2rw4MEaMWKEbr311nOeV27kyJGWP5tMJo0cOVLFxcVav359hf1LS0u1bt06xcXF6dprr7W0BwcH64EHHtC3336r/Pz8i/4aZGdna/v27RoyZIgCAgIs7a1bt9btt9+uzz///KKveSGrVq1SWVmZ7rnnHv33v/+1PIKCghQREaGNGzda+gYFBVm+TzExMdq+fbveffdd+fn5VXtdkiz3O2bMGKv2sWPHSpI+++yzc54zc+ZMPfHEE5oxY4YmTpxoad++fbvS09P1wAMP6MiRI5b7PHHihG677TZ98803Kisrs7rWP//5T6vjmJgYHTlypNLvbfm5iqafn+8eg4KCdP/991va3Nzc9Pjjj+v48ePavHmzTdepSEX1//777xd8npeXl9zd3bVp0yb99ddfVX59AED1YqzkmLGSdObfxnIFBQX673//q5iYGBUWFmr37t2SpJ9++klHjhzRww8/bPWRygEDBuiqq66yul7dunV18OBB/fjjj9VW4/r161VcXKxRo0bJxeV/P6o+/PDD8vPzq3DctHz5ct1777165JFH9Oabb1qed/ToUX399de65557LPf73//+V0eOHFFsbKzS09P1xx9/WF1r+PDhMplMluOYmBiVlpZq//795625KuMmW9/fF6sq475ydevW1datW3Xo0KEqvz5QFSx0DlSjm266yWrxzvvvv1833HCDRo4cqd69e8vd3V379+9XSEjIOf9wle848vd/9Nzd3fXuu+/qxhtvlKenp2VNpLO5uLhYDZYkqVmzZpJ03vVz/vzzTxUWFuq6664751zz5s1VVlamAwcOqGXLlrbd/P+vvP7zXffLL7/UiRMnVKdOnYu6bmXS09NlGIYiIiIqPH/2mkj33Xefli5dqs8++0zDhw/XbbfdVm21nG3//v1ycXFR06ZNrdqDgoJUt27dcwY5mzdv1meffaannnrKah0p6cx9StLgwYPP+3p5eXlWg8bGjRtbnS8/99dff503iCtvLygoqOzWLPbv36+IiAirwaNU8Xv6Ynh6elrWjCp31VVX2RQyeXh4aMaMGRo7dqwCAwPVsWNH9e7dW4MGDVJQUFCV6gEAXDrGSo4ZK0nSr7/+qokTJ+rrr78+J6TIy8uzqu3scYurq+s5C6Y/9dRTWr9+vW666SY1bdpUPXr00AMPPKCbb765yjWe72vj7u6ua6+99pwxxb59+zRw4EDdfffdev31163O7d27V4Zh6Nlnn9Wzzz5b4esdPnxY11xzjeW4snHT+VRl3GTr+/tiVWXcV27mzJkaPHiwGjVqpHbt2unOO+/UoEGDzvn/BqhuhFLAZeTi4qKuXbvqtddeU3p6+kUPWiTpyy+/lCSdOnVK6enpCgsLq+4ynV5ZWZlMJpO++OILmc3mc877+PhYHR85ckQ//fSTpDNbOJeVlZ0TqFS3igbIFWnZsqWOHTum999/X4888ojV97t8FtRLL72kNm3aVPj8s++1oq+HpEoXdo2MjJQk7dixw6aabXW+r0FFC6xK56/dVqNGjVKfPn20evVqffnll3r22WeVlJSkr7/+WjfccMMlXRsAUD0YK9nHsWPHdOutt8rPz09TpkxReHi4PD09tW3bNj311FPnzLS2RfPmzbVnzx6tWbNGa9eu1f/93/9p/vz5eu655zR58uTLcBfnCg4OVnBwsD7//HP99NNPVoFn+T2NGzdOsbGxFT7/7PCtKuOmpk2bytXV1eHjJqlq9Ze75557FBMTo48//ljr1q3TSy+9pBkzZmjVqlXq2bOnbUUDVcDH94DLrHznj+PHj0uSmjRpokOHDp3z25TyadNNmjSxtP3nP//RlClT9NBDD+mGG27QsGHDLL/J+ruysrJzPtL022+/SdJ5twGuX7++vL29tWfPnnPO7d69Wy4uLmrUqJEk2wOVv9d/vuteffXV1f6bv/DwcBmGobCwMHXv3v2cR8eOHa36JyQkqKCgQElJSfr22281e/Zsq/MXc78X0qRJE5WVlVlmOZXLzc3VsWPHrL7fknT11Vdr/fr1cnNz02233WY1hTo8PFzSmd/IVXSf3bt3r3CnvIvVrFkzXXfddfrkk08s79sL3WN6evo5A9qz39Plv607e/ecS/mN4IW+V+Hh4Ro7dqzWrVuntLQ0FRcX65VXXqny6wEAqh9jJevrXo6x0qZNm3TkyBEtXrxYTzzxhHr37q3u3buf85G88tr27t1r1X769OkKZ5TVqVNH9957rxYtWqSsrCz16tVLL774ok6dOlWlOs/3tSkuLta+ffvOGTd5enpqzZo1ioiI0B133KFff/3Vcq58ho+bm9t5x022fuSuMt7e3urWrZu++eYbq12fz8fW9/flGDdJlb9Xg4ODNWLECK1evVr79u1TvXr19OKLL17S6wEXQigFXEYlJSVat26d3N3dLVNy77zzTpWWlmru3LlWfWfNmiWTyWT5TURJSYmGDBmikJAQvfbaa1q8eLFyc3M1evToCl/r79czDENz5861BBsVMZvN6tGjhz755BOrQUZubq6WLVumzp07W6b5lg+MbNmKNzg4WG3atNGSJUus+qelpWndunW68847L3iNixUfHy+z2azJkyef85sgwzB05MgRy/HKlSv10Ucfafr06Xr66ad13333aeLEiZaBqXRmcCHZdr8XUn6/Zwdfr776qiSpV69e5zynYcOGWr9+vU6ePKnbb7/dUn+7du0UHh6ul19+ucKwyJYti201efJkHTlyRMOGDbP8sPB369at05o1aySducecnBx99NFHlvOnT5/W66+/Lh8fH8t21k2aNJHZbNY333xjda358+dXuc7zfa8KCwvPGRCHh4fL19dXRUVFVX49AED1Yqz0v/6Xc6xUPoPm7+Ok4uLic/4Nbt++verVq6e3337b6t//Dz744JyPsP19fCWd+YhdixYtZBiGSkpKqlRn9+7d5e7urjlz5ljVunDhQuXl5VU4bvL399eXX36pBg0a6Pbbb1dGRoYkqUGDBurSpYvefPNNZWdnn/O86hw3TZo0SYZh6MEHH6xwjJaamqolS5ZIsv397efnp6uvvrpax03Smffq2e/T0tLSc8LcBg0aKCQkhHETLjs+vgdUoy+++MLyW47Dhw9r2bJlSk9P19NPP20ZtPTp00ddu3bVM888o8zMTF1//fVat26dPvnkE40aNcoyG2bq1Knavn27NmzYIF9fX7Vu3VrPPfecJk6cqP79+1sNWDw9PbV27VoNHjxYHTp00BdffKHPPvtMEyZMOGdNnr+bOnWqvvrqK3Xu3FkjRoyQq6ur3nzzTRUVFWnmzJmWfm3atJHZbNaMGTOUl5cnDw8PdevWTQ0aNKjwui+99JJ69uyp6OhoDR06VCdPntTrr78uf39/Pf/885f6ZT5HeHi4pk6dqsTERGVmZiouLk6+vr7at2+fPv74Yw0fPlzjxo3T4cOH9eijj6pr166WxU7nzp2rjRs3asiQIfr222/l4uIiLy8vtWjRQh999JGaNWumgIAAtWrVSq1atbro2q6//noNHjxYb731lmXq/A8//KAlS5YoLi5OXbt2rfB5TZs21bp169SlSxfFxsbq66+/lp+fn9555x317NlTLVu21EMPPaRrrrlGf/zxhzZu3Cg/Pz99+umnl/S1LHfvvfdqx44devHFF/Xzzz/r/vvvV5MmTXTkyBGtXbtWGzZs0LJlyySdWRT0zTff1JAhQ5SamqrQ0FCtXLlS3333nWbPnm35LaS/v79lzQeTyaTw8HCtWbNGhw8frnKd5/tenT59WrfddpvuuecetWjRQq6urvr444+Vm5ur++67r1q+RgCAi8dY6Qx7j5U6deqkq666SoMHD9bjjz8uk8mk999//5xf5rm7u+v555/XY489pm7duumee+5RZmamFi9erPDwcKtZNj169FBQUJBuvvlmBQYGateuXZo7d6569epV5RlI9evXV2JioiZPnqw77rhD/+///T/t2bNH8+fP14033qiBAwdW+Lyrr77a8n3q3r27vv32W11zzTWaN2+eOnfurKioKD388MO69tprlZubq5SUFB08eFC//PJLleo8W6dOnTRv3jyNGDFCkZGRevDBBxUREaGCggJt2rRJ//73vzV16lRJtr+/JWnYsGGaPn26hg0bpvbt2+ubb76x+kVqVbRr107r16/Xq6++qpCQEIWFhem6665Tw4YN1b9/f11//fXy8fHR+vXr9eOPPzLDHJefvbf7A2qiirY59vT0NNq0aWMsWLDAamtZwzCMgoICY/To0UZISIjh5uZmREREGC+99JKlX2pqquHq6mq1dbFhGMbp06eNG2+80QgJCTH++usvwzDObHNcp04dIyMjw+jRo4fh7e1tBAYGGpMmTbLaEtcwzt0+1jAMY9u2bUZsbKzh4+NjeHt7G127djW+//77c+7x7bffNq699lrDbDbbtOXx+vXrjZtvvtnw8vIy/Pz8jD59+hg7d+606lOd2xwbxpntgDt37mzUqVPHqFOnjhEZGWkkJCQYe/bsMQzDMOLj4w1fX18jMzPT6nmffPKJIcmYMWOGpe3777832rVrZ7i7u1f4dbuY2kpKSozJkycbYWFhhpubm9GoUSMjMTHRantiwzCMJk2aGL169bJq27p1q2Xb6fKtnH/++WcjPj7eqFevnuHh4WE0adLEuOeee4wNGzZYnle+NfDft7k2jP+9V8/+2p3Phg0bjL59+xoNGjQwXF1djfr16xt9+vQxPvnkE6t+ubm5xkMPPWRcffXVhru7uxEVFVXhFsZ//vmn0a9fP8Pb29u46qqrjEceecRIS0s7Z8vj8vf12crv6+8q+l7997//NRISEozIyEijTp06hr+/v9GhQwfjX//6l033DQCoXoyVzmXvsdJ3331ndOzY0fDy8jJCQkKMJ5980vjyyy8rrHXOnDlGkyZNDA8PD+Omm24yvvvuO6Ndu3bGHXfcYenz5ptvGrfccotlPBIeHm6MHz/eyMvLs7nOFStWVPj6c+fONSIjIw03NzcjMDDQePTRRy3fz3K33nqr0bJlS6u2vXv3GsHBwUbz5s0tY6CMjAxj0KBBRlBQkOHm5mZcc801Ru/evY2VK1danlf+/vzxxx+trlf+9b/Q97Jcamqq8cADD1jet1dddZVx2223GUuWLLF6r13o/V2usLDQGDp0qOHv72/4+voa99xzj3H48OFz3qcXM+7bvXu3ccsttxheXl6GJGPw4MFGUVGRMX78eOP66683fH19jTp16hjXX3+9MX/+fJvuG7gUJsOwYdUzAFesIUOGaOXKlTat/QMAAFDbMFa6dGVlZapfv77i4+P19ttvO7ocADUIa0oBAAAAACSd2cXw7HkL7733no4ePaouXbo4pigANRZrSgEAAAAAJElbtmzR6NGjdffdd6tevXratm2bFi5cqFatWunuu+92dHkAahhCKQAAAACAJCk0NFSNGjXSnDlzdPToUQUEBGjQoEGaPn263N3dHV0egBqGNaUAAAAAAABgd6wpBQAAAAAAALsjlAIAAAAAAIDdsaaUzmxxeujQIfn6+spkMjm6HAAAcAUzDEMFBQUKCQmRi0vt+f0e4yUAAGArW8dLhFKSDh06pEaNGjm6DAAA4EQOHDighg0bOroMu2G8BAAALtaFxkuEUpJ8fX0lnfli+fn5ObgaAABwJcvPz1ejRo0s44fagvESAACwla3jJUIpyTIF3c/Pj0EWAACwSW37CBvjJQAAcLEuNF6qPQshAAAAAAAA4IpBKAUAAAAAAAC7I5QCAAAAAACA3RFKAQAAAAAAwO4IpQAAAJxYaWmpnn32WYWFhcnLy0vh4eF64YUXZBiGpY9hGHruuecUHBwsLy8vde/eXenp6Q6sGgAAgFAKAADAqc2YMUMLFizQ3LlztWvXLs2YMUMzZ87U66+/bukzc+ZMzZkzR2+88Ya2bt2qOnXqKDY2VqdOnXJg5QAAoLZzdXQBAAAAqLrvv/9effv2Va9evSRJoaGhWr58uX744QdJZ2ZJzZ49WxMnTlTfvn0lSe+9954CAwO1evVq3XfffQ6rHQAA1G7MlAIAAHBinTp10oYNG/Tbb79Jkn755Rd9++236tmzpyRp3759ysnJUffu3S3P8ff3V4cOHZSSknLe6xYVFSk/P9/qAQAAUJ2YKQUAAODEnn76aeXn5ysyMlJms1mlpaV68cUXNWDAAElSTk6OJCkwMNDqeYGBgZZzFUlKStLkyZMvX+EAAKDWY6YUAACAE/vXv/6lDz74QMuWLdO2bdu0ZMkSvfzyy1qyZMklXTcxMVF5eXmWx4EDB6qpYgAAgDOYKQUAAODExo8fr6efftqyNlRUVJT279+vpKQkDR48WEFBQZKk3NxcBQcHW56Xm5urNm3anPe6Hh4e8vDwuKy1AwCA2o2ZUgAAAE6ssLBQLi7WQzqz2ayysjJJUlhYmIKCgrRhwwbL+fz8fG3dulXR0dF2rRUAAODvmCkFAADgxPr06aMXX3xRjRs3VsuWLfXzzz/r1Vdf1T/+8Q9Jkslk0qhRozR16lRFREQoLCxMzz77rEJCQhQXF+fY4gEAQK1GKAUAAODEXn/9dT377LMaMWKEDh8+rJCQED3yyCN67rnnLH2efPJJnThxQsOHD9exY8fUuXNnrV27Vp6eng6sHAAA1HYmwzAMRxfhaPn5+fL391deXp78/PwcXQ4AALiC1dZxQ229bwAAcPFsHTcwUwoAAAAAcNFKS0uVnJys7OxsBQcHKyYmRmaz2dFlAXAiLHQOAAAAALgoq1atUtOmTdW1a1c98MAD6tq1q5o2bapVq1Y5ujQAToRQCgAAAABgs1WrVql///6KiopSSkqKCgoKlJKSoqioKPXv359gCoDNWFNKrJEAAABsV1vHDbX1vgFYKy0tVdOmTRUVFaXVq1fLxeV/8xzKysoUFxentLQ0paen81E+oBZjTSkAtU5hYaF2795dbdc7efKkMjMzFRoaKi8vr2q5ZmRkpLy9vavlWgAAAPaWnJyszMxMLV++3CqQkiQXFxclJiaqU6dOSk5OVpcuXRxTJACnQSgFoMbYvXu32rVr5+gyKpWamqq2bds6ugwAAIAqyc7OliS1atWqwvPl7eX9AKAyhFIAaozIyEilpqZW2/V27dqlgQMHaunSpWrevHm1XDMyMrJargMAAOAIwcHBkqS0tDR17NjxnPNpaWlW/QCgMoRSAGoMb2/vyzILqXnz5sxuAgAAkBQTE6PQ0FBNmzatwjWlkpKSFBYWppiYGAdWCcBZsPseAAAAAMAmZrNZr7zyitasWaO4uDir3ffi4uK0Zs0avfzyyyxyDsAmzJQCAAAAANgsPj5eK1eu1NixY9WpUydLe1hYmFauXKn4+HgHVgfAmRBKAQAAAAAuSnx8vPr27avk5GRlZ2crODhYMTExzJACcFEIpQAAAAAAF81sNqtLly6OLgOAE3PomlLPP/+8TCaT1ePvO1OdOnVKCQkJqlevnnx8fNSvXz/l5uZaXSMrK0u9evWSt7e3GjRooPHjx+v06dP2vhUAAAAAAABcBIfPlGrZsqXWr19vOXZ1/V9Jo0eP1meffaYVK1bI399fI0eOVHx8vL777jtJUmlpqXr16qWgoCB9//33ys7O1qBBg+Tm5qZp06bZ/V4AAAAAAABgG4eHUq6urgoKCjqnPS8vTwsXLtSyZcvUrVs3SdKiRYvUvHlzbdmyRR07dtS6deu0c+dOrV+/XoGBgWrTpo1eeOEFPfXUU3r++efl7u5u79sBAAAAAACADRz68T1JSk9PV0hIiK699loNGDBAWVlZkqTU1FSVlJSoe/fulr6RkZFq3LixUlJSJEkpKSmKiopSYGCgpU9sbKzy8/P166+/nvc1i4qKlJ+fb/UAAAAAAACA/Tg0lOrQoYMWL16stWvXasGCBdq3b59iYmJUUFCgnJwcubu7q27dulbPCQwMVE5OjiQpJyfHKpAqP19+7nySkpLk7+9veTRq1Kh6bwwAAAAAAACVcujH93r27Gn5c+vWrdWhQwc1adJE//rXv+Tl5XXZXjcxMVFjxoyxHOfn5xNMAQAAAAAA2JHDP773d3Xr1lWzZs20d+9eBQUFqbi4WMeOHbPqk5uba1mDKigo6Jzd+MqPK1qnqpyHh4f8/PysHgAAAAAAALCfKyqUOn78uDIyMhQcHKx27drJzc1NGzZssJzfs2ePsrKyFB0dLUmKjo7Wjh07dPjwYUufr776Sn5+fmrRooXd6wcAAAAAAIBtHPrxvXHjxqlPnz5q0qSJDh06pEmTJslsNuv++++Xv7+/hg4dqjFjxiggIEB+fn567LHHFB0drY4dO0qSevTooRYtWujBBx/UzJkzlZOTo4kTJyohIUEeHh6OvDUAAAAAAABUwqGh1MGDB3X//ffryJEjql+/vjp37qwtW7aofv36kqRZs2bJxcVF/fr1U1FRkWJjYzV//nzL881ms9asWaNHH31U0dHRqlOnjgYPHqwpU6Y46pYAAAAAAABgA4eGUh9++GGl5z09PTVv3jzNmzfvvH2aNGmizz//vLpLAwAAAAAAwGV0Ra0pBQAAAAAAgNqBUAoAAAAAAAB2RygFAAAAAAAAuyOUAgAAAAAAgN0RSgEAAAAAAMDuCKUAAAAAAABgd4RSAAAAAAAAsDtCKQAAAAAAANgdoRQAAAAAAADsjlAKAAAAAAAAdkcoBQAAAAAAALsjlAIAAAAAAIDdEUoBAAAAAADA7gilAAAAAAAAYHeEUgAAAAAAALA7QikAAAAAAADYHaEUAAAAAAAA7I5QCgAAAAAAAHZHKAUAAAAAAAC7I5QCAAAAAACA3RFKAQAAAAAAwO4IpQAAAAAAAGB3hFIAAAAAAACwO0IpAAAAAAAA2B2hFAAAAAAAAOyOUAoAAMDJhYaGymQynfNISEiQJJ06dUoJCQmqV6+efHx81K9fP+Xm5jq4agAAUNsRSgEAADi5H3/8UdnZ2ZbHV199JUm6++67JUmjR4/Wp59+qhUrVmjz5s06dOiQ4uPjHVkyAACAXB1dAAAAAC5N/fr1rY6nT5+u8PBw3XrrrcrLy9PChQu1bNkydevWTZK0aNEiNW/eXFu2bFHHjh0dUTIAAAAzpQAAAGqS4uJiLV26VP/4xz9kMpmUmpqqkpISde/e3dInMjJSjRs3VkpKigMrBQAAtR0zpQAAAGqQ1atX69ixYxoyZIgkKScnR+7u7qpbt65Vv8DAQOXk5Jz3OkVFRSoqKrIc5+fnX45yAQBALcZMKQAAgBpk4cKF6tmzp0JCQi7pOklJSfL397c8GjVqVE0VAgAAnEEoBQAAUEPs379f69ev17BhwyxtQUFBKi4u1rFjx6z65ubmKigo6LzXSkxMVF5enuVx4MCBy1U2AACopQilAAAAaohFixapQYMG6tWrl6WtXbt2cnNz04YNGyxte/bsUVZWlqKjo897LQ8PD/n5+Vk9AAAAqhNrSgEAANQAZWVlWrRokQYPHixX1/8N8fz9/TV06FCNGTNGAQEB8vPz02OPPabo6Gh23gMAAA5FKAUAAFADrF+/XllZWfrHP/5xzrlZs2bJxcVF/fr1U1FRkWJjYzV//nwHVAkAAPA/hFIAAAA1QI8ePWQYRoXnPD09NW/ePM2bN8/OVQEAAJwfa0oBAAAAAADA7pgpBQAAAAC4aKWlpUpOTlZ2draCg4MVExMjs9ns6LIAOBFmSgEAAAAALsqqVavUtGlTde3aVQ888IC6du2qpk2batWqVY4uDYATIZQCAAAAANhs1apV6t+/v6KiopSSkqKCggKlpKQoKipK/fv3J5gCYDNCKQAAAACATUpLSzV27Fj17t1bq1evVseOHeXj46OOHTtq9erV6t27t8aNG6fS0lJHlwrACRBKAQAAAABskpycrMzMTE2YMEEuLtY/Trq4uCgxMVH79u1TcnKygyoE4EwIpQAAAAAANsnOzpYktWrVqsLz5e3l/QCgMoRSAAAAAACbBAcHS5LS0tIqPF/eXt4PACpDKAUAAAAAsElMTIxCQ0M1bdo0lZWVWZ0rKytTUlKSwsLCFBMT46AKATgTQikAAAAAgE3MZrNeeeUVrVmzRnFxcVa778XFxWnNmjV6+eWXZTabHV0qACfg6ugCAAAAAADOIz4+XitXrtTYsWPVqVMnS3tYWJhWrlyp+Ph4B1YHwJkQSgEAAAAALkp8fLz69u2r5ORkZWdnKzg4WDExMcyQAnBRCKUAAAAAABfNbDarS5cuji4DgBNjTSkAAAAAAADYHaEUAAAAAAAA7I5QCgAAAAAAAHZHKAUAAAAAAAC7I5QCAAAAAACA3RFKAQAAAAAAwO4IpQAAAAAAAGB3hFIAAAAAAACwO0IpAAAAAAAA2B2hFAAAAAAAAOyOUAoAAAAAAAB2RygFAAAAAAAAuyOUAgAAAAAAgN0RSgEAAAAAAMDuCKUAAAAAAABgd4RSAAAAAAAAsDtCKQAAAAAAANgdoRQAAAAAAADsztXRBQAAAAAAnE9xcbHmz5+vjIwMhYeHa8SIEXJ3d3d0WQCcCKEUAAAAAOCiPPnkk5o1a5ZOnz5taRs/frxGjx6tmTNnOrAyAM6Ej+8BAAAAAGz25JNP6qWXXlK9evX09ttvKzs7W2+//bbq1aunl156SU8++aSjSwTgJAilAAAAAAA2KS4u1qxZsxQYGKiDBw9q2LBhCgoK0rBhw3Tw4EEFBgZq1qxZKi4udnSpAJwAoRQAAAAAwCbz58/X6dOnNXXqVLm6Wq8G4+rqqilTpuj06dOaP3++gyoE4EwIpQAAAAAANsnIyJAk9e7du8Lz5e3l/QCgMoRSAAAAAACbhIeHS5LWrFmj0tJSbdq0ScuXL9emTZtUWlqqNWvWWPUDgMqYDMMwHF2Eo+Xn58vf3195eXny8/NzdDkArhDbtm1Tu3btlJqaqrZt2zq6HABXiNo6bqit9w3AWnFxserUqaM6deroqquuUmZmpuVcaGio/vrrL504cUInTpyQu7u74woF4FC2jhuYKQUAAAAAsIm7u7t69eqlvLw8HTp0SE899ZR+++03PfXUUzp06JDy8vLUq1cvAikANnG9cBcAAAAAAKTS0lL98ssvCg8PV2ZmpmbMmKEZM2ZIksxms8LDw/Wf//xHpaWlMpvNDq4WwJWOUAoAAAAAYJPk5GRlZmYqJSVFbdu21fz585WRkaHw8HCNGDFCqamp6tSpk5KTk9WlSxdHlwvgCnfFfHxv+vTpMplMGjVqlKXt1KlTSkhIUL169eTj46N+/fopNzfX6nlZWVnq1auXvL291aBBA40fP16nT5+2c/UAAAAAUPNlZ2dLklq1aiWz2aw2bdqoU6dOatOmjcxms1q1amXVDwAqc0XMlPrxxx/15ptvqnXr1lbto0eP1meffaYVK1bI399fI0eOVHx8vL777jtJZ6aO9urVS0FBQfr++++VnZ2tQYMGyc3NTdOmTXPErQAAAABAjRUcHCxJmjt3rt58881zFjofPny4VT8AqIzDZ0odP35cAwYM0Ntvv62rrrrK0p6Xl6eFCxfq1VdfVbdu3dSuXTstWrRI33//vbZs2SJJWrdunXbu3KmlS5eqTZs26tmzp1544QXNmzdPxcXFjrolAAAAAKiRYmJi1KBBAyUmJqpVq1ZKSUlRQUGBUlJS1KpVK02YMEENGjRQTEyMo0sF4AQcHkolJCSoV69e6t69u1V7amqqSkpKrNojIyPVuHFjpaSkSJJSUlIUFRWlwMBAS5/Y2Fjl5+fr119/tc8NAAAAAEAtYhiG1Z/LHwBwsRwaSn344Yfatm2bkpKSzjmXk5Mjd3d31a1b16o9MDBQOTk5lj5/D6TKz5efO5+ioiLl5+dbPQAAAAAAlUtOTtaff/6ppKQkpaWlqVOnTvLz81OnTp3066+/atq0aTp8+LCSk5MdXSoAJ+CwUOrAgQN64okn9MEHH8jT09Our52UlCR/f3/Lo1GjRnZ9fQAAAABwRuULmI8cOVJ79+7Vxo0btWzZMm3cuFHp6ekaOXKkVT8AqIzDQqnU1FQdPnxYbdu2laurq1xdXbV582bNmTNHrq6uCgwMVHFxsY4dO2b1vNzcXAUFBUmSgoKCztmNr/y4vE9FEhMTlZeXZ3kcOHCgem8OAAAAAGqg8gXM09LSZDab1aVLF91///3q0qWLzGaz0tLSrPoBQGUcFkrddttt2rFjh7Zv3255tG/fXgMGDLD82c3NTRs2bLA8Z8+ePcrKylJ0dLQkKTo6Wjt27NDhw4ctfb766iv5+fmpRYsW531tDw8P+fn5WT0AAACc1R9//KGBAweqXr168vLyUlRUlH766SfLecMw9Nxzzyk4OFheXl7q3r270tPTHVgxAGcVExOj0NBQTZs2TWVlZVbnysrKlJSUpLCwMBY6B2ATV0e9sK+vr1q1amXVVqdOHdWrV8/SPnToUI0ZM0YBAQHy8/PTY489pujoaHXs2FGS1KNHD7Vo0UIPPvigZs6cqZycHE2cOFEJCQny8PCw+z0BAADY219//aWbb75ZXbt21RdffKH69esrPT3dalfjmTNnas6cOVqyZInCwsL07LPPKjY2Vjt37rT7MgoAnJvZbNYrr7yi/v37Ky4uzrILX1pampKSkrRmzRqtXLlSZrPZ0aUCcAIOC6VsMWvWLLm4uKhfv34qKipSbGys5s+fbzlvNpu1Zs0aPfroo4qOjladOnU0ePBgTZkyxYFVAwAA2M+MGTPUqFEjLVq0yNIWFhZm+bNhGJo9e7YmTpyovn37SpLee+89BQYGavXq1brvvvvsXjMA5xYfH6+VK1dq7Nix6tSpk6U9LCxMK1euVHx8vAOrA+BMTAZ7dyo/P1/+/v7Ky8vjo3wALLZt26Z27dopNTVVbdu2dXQ5AK4QV9q4oUWLFoqNjdXBgwe1efNmXXPNNRoxYoQefvhhSdLvv/+u8PBw/fzzz2rTpo3lebfeeqvatGmj1157rcLrFhUVqaioyHKcn5+vRo0aXTH3DcDxSktLlZycrOzsbAUHBysmJoYZUgAk2T5euqJnSgEAAKByv//+uxYsWKAxY8ZowoQJ+vHHH/X444/L3d1dgwcPVk5OjiQpMDDQ6nmBgYGWcxVJSkrS5MmTL2vtAJxb+ULnAFBVDlvoHAAAAJeurKxMbdu21bRp03TDDTdo+PDhevjhh/XGG29c0nXZrRgAAFxuhFIAAABOLDg4+Jxdh5s3b66srCxJUlBQkCQpNzfXqk9ubq7lXEXYrRgAAFxuhFIAAABO7Oabb9aePXus2n777Tc1adJE0pmFh4OCgrRhwwbL+fz8fG3dulXR0dF2rRUAAODvWFMKAADAiY0ePVqdOnXStGnTdM899+iHH37QW2+9pbfeekuSZDKZNGrUKE2dOlUREREKCwvTs88+q5CQEMXFxTm2eAAAUKsRSgEAADixG2+8UR9//LESExM1ZcoUhYWFafbs2RowYIClz5NPPqkTJ05o+PDhOnbsmDp37qy1a9fK09PTgZUDAIDajlAKAADAyfXu3Vu9e/c+73mTyaQpU6ZoypQpdqwKAACgcqwpBQAAAAAAALsjlAIAAAAAAIDdEUoBAAAAAADA7gilAAAAAAAAYHeEUgAAAAAAALA7QikAAAAAAADYHaEUAAAAAAAA7I5QCgAAAAAAAHZHKAUAAAAAAAC7I5QCAAAAAACA3RFKAQAAAAAAwO4IpQAAAAAAAGB3ro4uAAAAAADgfEpLS5WcnKzs7GwFBwcrJiZGZrPZ0WUBcCLMlAIAAAAAXJRVq1apadOm6tq1qx544AF17dpVTZs21apVqxxdGgAnQigFAAAAALDZqlWr1L9/f0VFRSklJUUFBQVKSUlRVFSU+vfvTzAFwGaEUgAAAAAAm5SWlmrs2LHq3bu3Vq9erY4dO8rHx0cdO3bU6tWr1bt3b40bN06lpaWOLhWAEyCUAgAAAADYJDk5WZmZmZowYYJcXKx/nHRxcVFiYqL27dun5ORkB1UIwJkQSgEAAAAAbJKdnS1JatWqVYXny9vL+wFAZQilAAAAAAA2CQ4OliSlpaWptLRUmzZt0vLly7Vp0yaVlpYqLS3Nqh8AVMbV0QUAAAAAAJxDTEyMQkND9dhjj+nPP//U/v37LeeaNGmi+vXrKywsTDExMQ6sEoCzYKYUAAAAAMAmZrNZd999t3766SedOnVKb731lg4dOqS33npLp06d0k8//aT+/fvLbDY7ulQAToBQCgAAAABgk9LSUq1YsULt27eXl5eXhg8frpCQEA0fPlze3t5q3769Vq5cye57AGzCx/cAAAAAADYp331v+fLluvHGG5WcnKzs7GwFBwcrJiZGP/zwgzp16qTk5GR16dLF0eUCuMIRSgEAAAAAbPL33ffMZvM5wRO77wG4GHx8DwAAAABgk7/vvlcRdt8DcDEIpQAAAAAANinffW/atGkqKyuzOldWVqakpCR23wNgM0IpAAAAAIBNzGazXnnlFa1Zs0ZxcXFKSUlRQUGBUlJSFBcXpzVr1ujll19m9z0ANmFNKQAAAACAzeLj47Vy5UqNHTtWnTp1srSHhYVp5cqVio+Pd2B1AJwJoRQAAAAA4KLEx8erb9++5+y+xwwpABeDUAoAAAAAcNEq2n0PAC4Ga0oBAAAAAADA7gilAAAAAAAAYHeEUgAAAAAAALA7QikAAAAAAADYHaEUAAAAAAAA7I5QCgAAAAAAAHZHKAUAAAAAAAC7I5QCAAAAAACA3RFKAQAAAAAAwO4IpQAAAAAAAGB3hFIAAAAAAACwO0IpAAAAAAAA2B2hFAAAAAAAAOyOUAoAAAAAAAB2RygFAAAAAAAAuyOUAgAAAAAAgN0RSgEAAAAAAMDuCKUAAAAAAABgd4RSAAAAAAAAsDtCKQAAACf3/PPPy2QyWT0iIyMt50+dOqWEhATVq1dPPj4+6tevn3Jzcx1YMQAAAKEUAABAjdCyZUtlZ2dbHt9++63l3OjRo/Xpp59qxYoV2rx5sw4dOqT4+HgHVgsAACC5OroAAAAAXDpXV1cFBQWd056Xl6eFCxdq2bJl6tatmyRp0aJFat68ubZs2aKOHTvau1QAAABJzJQCAACoEdLT0xUSEqJrr71WAwYMUFZWliQpNTVVJSUl6t69u6VvZGSkGjdurJSUlPNer6ioSPn5+VYPAACA6kQoBQAA4OQ6dOigxYsXa+3atVqwYIH27dunmJgYFRQUKCcnR+7u7qpbt67VcwIDA5WTk3PeayYlJcnf39/yaNSo0WW+CwAAUNvw8T0AAAAn17NnT8ufW7durQ4dOqhJkyb617/+JS8vrypdMzExUWPGjLEc5+fnE0wBsFJaWqrk5GRlZ2crODhYMTExMpvNji4LgBNhphQAAEANU7duXTVr1kx79+5VUFCQiouLdezYMas+ubm5Fa5BVc7Dw0N+fn5WDwAot2rVKjVt2lRdu3bVAw88oK5du6pp06ZatWqVo0sD4EQIpQAAAGqY48ePKyMjQ8HBwWrXrp3c3Ny0YcMGy/k9e/YoKytL0dHRDqwSgLNatWqV+vfvr6ioKKWkpKigoEApKSmKiopS//79CaYA2IyP7wEAADi5cePGqU+fPmrSpIkOHTqkSZMmyWw26/7775e/v7+GDh2qMWPGKCAgQH5+fnrssccUHR3NznsALlppaanGjh2r3r17a/Xq1XJxOTPPoWPHjlq9erXi4uI0btw49e3bl4/yAbggQikAAAAnd/DgQd1///06cuSI6tevr86dO2vLli2qX7++JGnWrFlycXFRv379VFRUpNjYWM2fP9/BVQNwRsnJycrMzNTy5cstgVQ5FxcXJSYmqlOnTkpOTlaXLl0cUyQAp0EoBQAA4OQ+/PDDSs97enpq3rx5mjdvnp0qAlBTZWdnS5JatWpV4fny9vJ+AFAZ1pQCAAAAANgkODhYkpSWlqbjx4/rrrvuUuvWrXXXXXfp+PHjSktLs+oHAJVhphQAAAAAwCYxMTEKDQ3VHXfcoby8PEv7jh075OvrK39/f4WFhSkmJsaBVQJwFsyUAgAAAADYxGw2y2w2WwKpO+64Q99//73uuOMOSVJeXp5cXFxY5ByATZgpBQAAAACwyfHjx5WRkSGTyaRGjRpp7dq1Wrt2rSSpSZMmysrKUkZGho4fPy4fHx8HVwvgSsdMKQAAAACATR588EFJ0sCBA/X7779r48aNWrZsmTZu3KiMjAw98MADVv0AoDLMlAIAAAAA2CQjI0OSNG7cOJnNZnXp0sXq/JgxY/TBBx9Y+gFAZZgpBQAAAACwSXh4uCTp5ZdfVmlpqTZt2qTly5dr06ZNKi0t1auvvmrVDwAqw0wpAAAAAIBN3n//ffn6+mrp0qXavHmzsrKyLOcaN26sAwcOWPoBwIUwUwoAAAAAYBMfHx+Fh4fLMAxlZWUpNjZWycnJio2NVVZWlgzDUHh4OIucA7AJM6UAAAAAADYpLS1VaWmp6tatq2PHjunLL7/Ul19+aTlft25dlZWVqbS0VGaz2YGVAnAGzJQCAAAAANgkOTlZmZmZ+uKLL1RQUKC4uDhFRUUpLi5OBQUF+vzzz7Vv3z4lJyc7ulQAToCZUgAAAAAAm2RnZ0uSWrVqJR8fH3388cdW51u1amXVDwAq49CZUgsWLFDr1q3l5+cnPz8/RUdH64svvrCcP3XqlBISElSvXj35+PioX79+ys3NtbpGVlaWevXqJW9vbzVo0EDjx4/X6dOn7X0rAAAAAFDjBQcHS5LS0tJUXFys2bNn67HHHtPs2bNVXFystLQ0q34AUBmHzpRq2LChpk+froiICBmGoSVLlqhv3776+eef1bJlS40ePVqfffaZVqxYIX9/f40cOVLx8fH67rvvJJ35PHOvXr0UFBSk77//XtnZ2Ro0aJDc3Nw0bdo0R94aAAAAANQ4MTExCg0N1cCBA5WZmanS0lLLuXHjxik0NFRhYWGKiYlxYJUAnIVDZ0r16dNHd955pyIiItSsWTO9+OKL8vHx0ZYtW5SXl6eFCxfq1VdfVbdu3dSuXTstWrRI33//vbZs2SJJWrdunXbu3KmlS5eqTZs26tmzp1544QXNmzdPxcXFjrw1AAAAAKhxzGazrr/+emVkZMhsNuvpp59Wenq6nn76aZnNZmVkZKh169Yscg7AJlfMQuelpaX68MMPdeLECUVHRys1NVUlJSXq3r27pU9kZKQaN26slJQUSVJKSoqioqIUGBho6RMbG6v8/Hz9+uuv532toqIi5efnWz0AAAAAAJUrLi7WZ599Jn9/fwUHB1s++TJ9+nSFhITI399fn332GZMEANjE4aHUjh075OPjIw8PD/3zn//Uxx9/rBYtWignJ0fu7u6qW7euVf/AwEDl5ORIknJycqwCqfLz5efOJykpSf7+/pZHo0aNqvemAAAAAKAGmj9/vk6fPq2XX35ZGRkZ2rhxo5YtW6aNGzdq7969mjlzpk6fPq358+c7ulQATsDhu+9dd9112r59u/Ly8rRy5UoNHjxYmzdvvqyvmZiYqDFjxliO8/PzCaYAAAAA4AIyMjIkSb1795bZbFaXLl2szvfu3duqHwBUxuEzpdzd3dW0aVO1a9dOSUlJuv766/Xaa68pKChIxcXFOnbsmFX/3NxcBQUFSZKCgoLO2Y2v/Li8T0U8PDwsO/6VPwAAAAAAlQsPD5ckrVmzpsLz5e3l/QCgMg4Ppc5WVlamoqIitWvXTm5ubtqwYYPl3J49e5SVlaXo6GhJUnR0tHbs2KHDhw9b+nz11Vfy8/NTixYt7F47AAAAANRkI0aMkKurqyZOnKjTp09bnTt9+rSee+45ubq6asSIEQ6qEIAzcWgolZiYqG+++UaZmZnasWOHEhMTtWnTJg0YMED+/v4aOnSoxowZo40bNyo1NVUPPfSQoqOj1bFjR0lSjx491KJFCz344IP65Zdf9OWXX2rixIlKSEiQh4eHI28NAAAAAGocd3d3jR49Wrm5uWrYsKHeeustHTp0SG+99ZYaNmyo3NxcjR49Wu7u7o4uFYATcOiaUocPH9agQYOUnZ0tf39/tW7dWl9++aVuv/12SdKsWbPk4uKifv36qaioSLGxsVYL5pnNZq1Zs0aPPvqooqOjVadOHQ0ePFhTpkxx1C0BAAAAQI02c+ZMSWd+XnvkkUcs7a6urho/frzlPABciMkwDONin/T777/r2muvvRz1OER+fr78/f2Vl5fH+lIALLZt26Z27dopNTVVbdu2dXQ5AK4Q1TlucKYxFeMlAGcrLi7W/PnzlZGRofDwcI0YMYIZUgAk2T5uqNJMqaZNm+rWW2/V0KFD1b9/f3l6ela5UAAAgNqKMRUAZ+bu7q5Ro0Y5ugwATqxKa0pt27ZNrVu31pgxYxQUFKRHHnlEP/zwQ3XXBgAAUKMxpgLgzEpLS7Vp0yYtX75cmzZtUmlpqaNLAuBkqhRKtWnTRq+99poOHTqkd999V9nZ2ercubNatWqlV199VX/++Wd11wkAAFDjMKYC4KxWrVqlpk2bqmvXrnrggQfUtWtXNW3aVKtWrXJ0aQCcyCXtvufq6qr4+HitWLFCM2bM0N69ezVu3Dg1atTIsoA5AAAAKseYCoAzWbVqlfr376+oqCilpKSooKBAKSkpioqKUv/+/QmmANjskkKpn376SSNGjFBwcLBeffVVjRs3ThkZGfrqq6906NAh9e3bt7rqBAAAqLEYUwFwFqWlpRo7dqx69+6t1atXq2PHjvLx8VHHjh21evVq9e7dW+PGjeOjfABsUqWFzl999VUtWrRIe/bs0Z133qn33ntPd955p1xczmRcYWFhWrx4sUJDQ6uzVgAAgBqFMRUAZ5OcnKzMzEwtX77c8ndVORcXFyUmJqpTp05KTk5Wly5dHFMkAKdRpVBqwYIF+sc//qEhQ4YoODi4wj4NGjTQwoULL6k4AACAmowxFQBnU/5x4latWlV4vrydjx0DsEWVQqn09PQL9nF3d9fgwYOrcnkAAIBagTEVAGdTHqCnpaXpxhtvVHJysrKzsxUcHKyYmBilpaVZ9QOAylQplFq0aJF8fHx09913W7WvWLFChYWFDJwAAABswJgKgLOJiYlRaGioHnvsMf33v/9VZmam5VxoaKiuvvpqhYWFKSYmxnFFAnAaVVroPCkpSVdfffU57Q0aNNC0adMuuSgAAIDagDEVAGdjNpt1991366efftLJkyf11ltv6dChQ3rrrbd08uRJ/fTTT+rfv7/MZrOjSwXgBKoUSmVlZSksLOyc9iZNmigrK+uSiwIAAKgNGFMBcDalpaVasWKF2rdvL09PTw0fPlwhISEaPny4vLy81L59e61cuZLd9wDYpEqhVIMGDfSf//znnPZffvlF9erVu+SiAAAAagPGVACcTfnue6+//royMjK0ceNGLVu2TBs3btTevXs1Z84c7du3T8nJyY4uFYATqNKaUvfff78ef/xx+fr66pZbbpEkbd68WU888YTuu+++ai0QAACgpmJMBcDZ/H33PbPZrC5dulidZ/c9ABejSqHUCy+8oMzMTN12221ydT1zibKyMg0aNIj1DwAAAGzEmAqAs/n77nsdO3Y85zy77wG4GCbDMIyqPvm3337TL7/8Ii8vL0VFRalJkybVWZvd5Ofny9/fX3l5efLz83N0OQCuENu2bVO7du2Umpqqtm3bOrocAFeIyzFucIYxFeMlANKZNaWaNm2qqKgorV69Wi4u/1sRpqysTHFxcUpLS1N6ejqLnQO1mK3jhirNlCrXrFkzNWvW7FIuAQAAUOsxpgLgLMxms1555RX1799fcXFxSkxMVKtWrZSWlqakpCStWbNGK1euJJACYJMqhVKlpaVavHixNmzYoMOHD6usrMzq/Ndff10txQEAANRkjKkAOKP4+HitXLlSY8eOVadOnSztYWFhWrlypeLj4x1YHQBnUqVQ6oknntDixYvVq1cvtWrVSiaTqbrrAgAAqPEYUwFwVvHx8erbt6+Sk5OVnZ2t4OBgxcTEMEMKwEWpUij14Ycf6l//+pfuvPPO6q4HAACg1mBMBcCZVbT7HgBcDJcLdzmXu7u7mjZtWt21AAAA1CqMqQA4s7y8PHXu3FmNGzdW586dlZeX5+iSADiZKoVSY8eO1WuvvaZL2LgPAACg1mNMBcBZNW3aVHXr1tV3332nAwcO6LvvvlPdunUJ2gFclCp9fO/bb7/Vxo0b9cUXX6hly5Zyc3OzOr9q1apqKQ4AAKAmY0wFwBk1bdpUGRkZkqQ77rhDkyZN0uTJk7V27VplZGSoadOm2rt3r4OrBOAMqhRK1a1bV3fddVd11wIAAFCrMKYC4Gzy8vIsgdSJEyfk7e0tSfriiy9UWFioOnXqKCMjQ3l5efL393dkqQCcQJVCqUWLFlV3HQAAALUOYyoAzqZXr16SzsyQKg+kynl7e6tHjx5at26devXqpW+//dYRJQJwIlVaU0qSTp8+rfXr1+vNN99UQUGBJOnQoUM6fvx4tRUHAABQ0zGmAuBMsrKyJEmTJk2q8PzEiROt+gFAZaoUSu3fv19RUVHq27evEhIS9Oeff0qSZsyYoXHjxlVrgQAAADXV5RhTTZ8+XSaTSaNGjbK0nTp1SgkJCapXr558fHzUr18/5ebmVsctAKhlGjduLEmaPHmyTp48qZEjRyo2NlYjR47UyZMnNXXqVKt+AFCZKoVSTzzxhNq3b6+//vpLXl5elva77rpLGzZsqLbiAAAAarLqHlP9+OOPevPNN9W6dWur9tGjR+vTTz/VihUrtHnzZh06dEjx8fGXXD+A2uezzz6TJK1du1be3t6aN2+e1q1bp3nz5snb21vr1q2z6gcAlanSmlLJycn6/vvv5e7ubtUeGhqqP/74o1oKA1A7pKenWz6ucqXZtWuX1X+vNL6+voqIiHB0GQAuQXWOqY4fP64BAwbo7bfftsxUkM4sSrxw4UItW7ZM3bp1k3RmLavmzZtry5Yt6tix46XfCIBaw9/fX97e3iosLJR05u+rpKQkJSYmKjMzU9KZtaVY5ByALaoUSpWVlam0tPSc9oMHD8rX1/eSiwJQO6Snp6tZs2aOLuOCBg4c6OgSzuu3334jmAKcWHWOqRISEtSrVy91797dKpRKTU1VSUmJunfvbmmLjIxU48aNlZKSQigF4KKcPHnSEkhJUmZmpu6//36rPoWFhTp58qTVDFAAqEiVQqkePXpo9uzZeuuttyRJJpNJx48f16RJk3TnnXdWa4EAaq7yGVJLly5V8+bNHVzNuU6ePKnMzEyFhoZecYOqXbt2aeDAgVfsLDMAtqmuMdWHH36obdu26ccffzznXE5Ojtzd3VW3bl2r9sDAQOXk5Jz3mkVFRSoqKrIc5+fn21wPgJpr/PjxkqSnn35aTz/9tHr16qWsrCw1btxYn332maZNm6aZM2dq/Pjxmjt3roOrBXClq1Io9corryg2NlYtWrTQqVOn9MADDyg9PV1XX321li9fXt01AqjhmjdvrrZt2zq6jArdfPPNji4BQA1WHWOqAwcO6IknntBXX30lT0/PaqstKSlJkydPrrbrAagZ0tPTJUnDhg2Tv7+/vv32W6vzQ4cO1cyZMy39AKAyVVrovGHDhvrll180YcIEjR49WjfccIOmT5+un3/+WQ0aNKjuGgEAAGqk6hhTpaam6vDhw2rbtq1cXV3l6uqqzZs3a86cOXJ1dVVgYKCKi4t17Ngxq+fl5uYqKCjovNdNTExUXl6e5XHgwIFLuVUANUT5sgHvvPOOjh8/rrvuukutW7fWXXfdpePHj2vhwoVW/QCgMibDMAxHF+Fo+fn58vf3V15envz8/BxdDlBrbNu2Te3atVNqauoVO1PqSsXXDnCcK23cUFBQoP3791u1PfTQQ4qMjNRTTz2lRo0aqX79+lq+fLn69esnSdqzZ48iIyMvak2pK+2+ATjGyZMn5e3tfcF+hYWFV9zyBwDsx9ZxQ5U+vvfee+9Ven7QoEFVuSwAAECtUh1jKl9fX7Vq1cqqrU6dOqpXr56lfejQoRozZowCAgLk5+enxx57TNHR0SxyDuCieXl5WX7QlKQWLVpYdt/buXOnpDM79BFIAbBFlUKpJ554wuq4pKREhYWFcnd3l7e3N6EUAACADew1ppo1a5ZcXFzUr18/FRUVKTY2VvPnz6+WawOoXY4fP24JpCRp586d6tu3r1WfvLw8HT9+XD4+PvYuD4CTqdKaUn/99ZfV4/jx49qzZ486d+7MQucAAAA2ulxjqk2bNmn27NmWY09PT82bN09Hjx7ViRMntGrVqkrXkwKA83nwwQct/y0sLFRCQoJ69OihhIQEFRYWasCAAVb9AKAyVQqlKhIREaHp06ef8xs/AAAA2I4xFYArWUZGhiRp3LhxFZ4fM2aMVT8AqEyVPr533ou5uurQoUPVeUkAAIBahzEVgCtVeHi4duzYoV69eungwYOW9nXr1mnevHm65pprLP0A4EKqFEr9+9//tjo2DEPZ2dmaO3eubr755mopDAAAoKZjTAXA2bz//vvy9fXVwYMHZTabNX78eA0bNkzvvPOOXnrpJf3xxx+WfgBwIVUKpeLi4qyOTSaT6tevr27duumVV16pjroAAABqPMZUAJyN2Wy2/LmsrEwHDhxQXl6eDhw4oLKysgr7AcD5VCmU+vtfNgAAAKgaxlQAnM348eMlSUFBQcrJydEHH3ygDz74wHI+MDBQubm5Gj9+vObOneuoMgE4iWpb6BwAAAAAULOlp6dLkr799lsVFBQoLi5OUVFRiouLU0FBgb755hurfgBQmSrNlCrfUcEWr776alVeAgAAoMZjTAXA2URERGjdunV65513NH78eO3du1eHDh2SYRgqLi7WwoULLf0A4EKqFEr9/PPP+vnnn1VSUqLrrrtOkvTbb7/JbDarbdu2ln4mk6l6qgQAAKiBGFMBcDYvvfSS5s2bp+nTp2v69OmW9qNHj6pevXpW/QDgQqoUSvXp00e+vr5asmSJrrrqKknSX3/9pYceekgxMTEaO3ZstRYJAABQEzGmAuBsvLy85O7uruLiYkln1paaPn26nn76aeXk5EiS3N3d5eXl5cgyATiJKq0p9corrygpKckyeJKkq666SlOnTmWnGAAAABsxpgLgbI4ePWoJpCQpJydHQ4YMsQRSklRcXKyjR486ojwATqZKoVR+fr7+/PPPc9r//PNPFRQUXHJRAAAAtQFjKgDO5tZbb5UkdezYUYWFhUpISFCPHj2UkJCgwsJC3XTTTVb9AKAyVfr43l133aWHHnpIr7zyiuUvna1bt2r8+PGKj4+v1gIBAABqKsZUAJzNoUOHJEkvvviivLy8NHfuXKvzU6ZM0R133GHpBwCVqVIo9cYbb2jcuHF64IEHVFJScuZCrq4aOnQoC9oBAADYiDEVAGcTEhKio0eP6plnntHmzZs1f/58ZWRkKDw8XCNGjNBzzz1n6QcAF2IyDMOo6pNPnDihjIwMSVJ4eLjq1KlTbYXZU35+vvz9/ZWXlyc/Pz9HlwPUGtu2bVO7du2UmppqtcsULoyvHeA4l2Pc4AxjKsZLAKRzd9k7nyNHjiggIMAOFQG4Etk6bqjSmlLlsrOzlZ2drYiICNWpU0eXkG8BAADUWoypADiLgIAAeXt7W45DQ0O1fPlyhYaGWtq8vb0JpADYpEqh1JEjR3TbbbepWbNmuvPOO5WdnS1JGjp0KFsXAwAA2IgxFQBnU1xcrOLiYplMJklSZmam7r//fmVmZkqSTCaTpQ8AXEiVQqnRo0fLzc1NWVlZVin5vffeq7Vr11ZbcQAAADUZYyoAzmb+/Pk6ffq03nrrLR05ckStWrVSQECAWrVqpSNHjuiNN97Q6dOnNX/+fEeXCsAJVGmh83Xr1unLL79Uw4YNrdojIiK0f//+aikMAACgpmNMBcDZlK9/17t3bwUEBGjHjh1W53v37m3VDwAqU6WZUidOnLD6bV65o0ePysPD45KLAgAAqA0YUwFwNuHh4ZKkNWvWKCcnR0FBQfL09FRQUJBycnK0Zs0aq34AUJkqhVIxMTF67733LMcmk0llZWWaOXOmunbtWm3FAQAA1GSMqQA4mxEjRsjV1VUPP/ywgoODlZubq6KiIuXm5io4OFiPPPKIXF1dNWLECEeXCsAJVOnjezNnztRtt92mn376ScXFxXryySf166+/6ujRo/ruu++qu0YAAIAaiTEVAGfj7u5udRwQEKCkpCQlJibq6NGj5+0HABWp0kypVq1a6bffflPnzp3Vt29fnThxQvHx8fr555+ZpgkAAGAjxlQAnE1OTo5Onz5tOT569KgeeeQRq0Dq9OnTysnJcUR5AJzMRc+UKikp0R133KE33nhDzzzzzOWoCQAAoMZjTAXAGbVp00aSFBYWpt27d2v+/PnKyMhQeHi4RowYoWbNmmn//v1q06YNwRSAC7roUMrNzU3/+c9/LkctAAAAtQZjKgDO6NixY5LOfPzY3d1do0aNsjo/bdo0DRgwwNIPACpTpY/vDRw4UAsXLqzuWgAAAGoVxlQAnE3dunUlSU8++aT++OMPBQQEyM3NTQEBAfrjjz80YcIEq34AUJkqLXR++vRpvfvuu1q/fr3atWunOnXqWJ1/9dVXq6U4AACAmowxFQBns337dgUHB2vfvn1q2LChpf2vv/6yOt6+fbsDqgPgbC4qlPr9998VGhqqtLQ0tW3bVpL022+/WfUxmUzVVx0AAEANxJgKgLMKCgqyOjaZTHrkkUf05ptvyjCM8/YDgIpcVCgVERGh7Oxsbdy4UZJ07733as6cOQoMDLwsxQEAANREjKkAOKs//vjD6tgwDL3xxhsV9rvmmmvsVRYAJ3VRa0r9PfmWpC+++EInTpyo1oIAAABqOsZUAJxVVFSUJCk4OFjZ2dkKDAyUh4eHAgMDlZ2dbZkhVd4PACpTpYXOy509oAIAAMDFY0wFwFkUFBRIkmbMmKGgoCDl5OTo1KlTysnJUVBQkKZOnWrVDwAqc1GhlMlkOmd9A9Y7AAAAuDiMqQA4K19fX0nSU089paysLPn6+spsNsvX11dZWVmaOHGiVT8AqMxFrSllGIaGDBkiDw8PSdKpU6f0z3/+85ydYlatWlV9FQIAANQwjKkAOKsdO3aoYcOGys7OVpMmTSztx48ftzresWOHI8oD4GQuKpQaPHiw1fHAgQOrtRgAAIDagDEVAGdV0eLlAwcO1NKlSy/YDwDOdlGh1KJFiy5XHQAAALUGYyoAziorK+uctrMDqfJ+jRs3tkdJAJzYJS10DgAAAACoPVq2bClJqlevng4ePKirrrpKrq6uuuqqq3Tw4EEFBARY9QOAyhBKAQAAAABsUlhYKElKSkrSNddco6NHj6qkpERHjx7VNddcoylTplj1A4DKEEoBAAAAAGzi7e0tSUpMTNTevXvl7u4uk8kkd3d37d27V88995xVPwCoDKEUAAAAAMAmv/76qyTpyJEjioiIUElJiSSppKREEREROnr0qFU/AKgMoRQAAAAAwCYVLV7+//7f/7OpHwCcjVAKAAAAAGCTvXv3ntP273//26Z+AHA2QikAAAAAgE1atGgh6cyaUfv375ePj49cXFzk4+Oj/fv3W9aSKu8HAJUhlAIAAAAA2KR8DanJkyercePGKigoUGlpqQoKCtS4cWNNmDDBqh8AVIZQCgAAAABgEzc3N0nSpEmTdPToUUVFRalevXqKiorS0aNHNW3aNKt+AFAZh4ZSSUlJuvHGG+Xr66sGDRooLi5Oe/bssepz6tQpJSQkqF69evLx8VG/fv2Um5tr1ScrK0u9evWSt7e3GjRooPHjx+v06dP2vBUAAAAAqPF27twpSSosLFS9evWUlpamo0ePKi0tTfXq1VNhYaFVPwCojENDqc2bNyshIUFbtmzRV199pZKSEvXo0UMnTpyw9Bk9erQ+/fRTrVixQps3b9ahQ4cUHx9vOV9aWqpevXqpuLhY33//vZYsWaLFixfrueeec8QtAQAAAECN1bRp03PaBg8ebFM/ADibqyNffO3atVbHixcvVoMGDZSamqpbbrlFeXl5WrhwoZYtW6Zu3bpJkhYtWqTmzZtry5Yt6tixo9atW6edO3dq/fr1CgwMVJs2bfTCCy/oqaee0vPPPy93d3dH3BoAAAAA1DhHjx49p23JkiUV9gsICLBHSQCc2BW1plReXp4kWf7ySk1NVUlJibp3727pExkZqcaNGyslJUWSlJKSoqioKAUGBlr6xMbGKj8/X7/++muFr1NUVKT8/HyrBwAAAACgcrfeeqskqWPHjkpPT7esHeXm5qb09HTddNNNVv0AoDJXTChVVlamUaNG6eabb1arVq0kSTk5OXJ3d1fdunWt+gYGBionJ8fS5++BVPn58nMVSUpKkr+/v+XRqFGjar4bAAAAAKh5Dh06JEl68cUX1bRpUxUXF8swDBUXF6tp06aaMmWKVT8AqMwVE0olJCQoLS1NH3744WV/rcTEROXl5VkeBw4cuOyvCQAAAADOLiQkRJL0zDPP6I8//lBAQIDc3NwUEBCgP/74w7K2b3k/AKjMFRFKjRw5UmvWrNHGjRvVsGFDS3tQUJCKi4t17Ngxq/65ubkKCgqy9Dl7N77y4/I+Z/Pw8JCfn5/VAwAAwFktWLBArVu3toxroqOj9cUXX1jO27KbMQDYYvPmzZKkLVu2qGHDhvrrr790+vRp/fXXX2rYsKF++OEHq34AUBmHhlKGYWjkyJH6+OOP9fXXXyssLMzqfLt27eTm5qYNGzZY2vbs2aOsrCxFR0dLkqKjo7Vjxw4dPnzY0uerr76Sn5+fWrRoYZ8bAQAAcKCGDRtq+vTpSk1N1U8//aRu3bqpb9++lvU1L7SbMQDY6uzFy81ms8aNGyez2VxpPwCoiEN330tISNCyZcv0ySefyNfX17IGlL+/v7y8vOTv76+hQ4dqzJgxCggIkJ+fnx577DFFR0erY8eOkqQePXqoRYsWevDBBzVz5kzl5ORo4sSJSkhIkIeHhyNvDwAAwC769Oljdfziiy9qwYIFlpkMF9rNGABs9ccff1gdl5aW6uWXX66w3zXXXGOvsgA4KYfOlFqwYIHy8vLUpUsXBQcHWx4fffSRpc+sWbPUu3dv9evXT7fccouCgoK0atUqy3mz2aw1a9bIbDYrOjpaAwcO1KBBgywL7AEAANQmpaWl+vDDD3XixAlFR0fbtJtxRditGEBFoqKiJEnBwcE6cuSIWrVqpYCAALVq1UpHjhyxLKFS3g8AKuPQmVKGYVywj6enp+bNm6d58+adt0+TJk30+eefV2dpAAAATmXHjh2Kjo7WqVOn5OPjo48//lgtWrTQ9u3bL7ibcUWSkpI0efLky1w1AGdTUFAgSZoxY4YCAgK0Y8cOq/NTp07VsGHDLP0AoDJXxELnAAAAuDTXXXedtm/frq1bt+rRRx/V4MGDtXPnzipfj92KAVTE19dXkvTUU09p7969cnd3l8lkkru7u/bu3auJEyda9QOAyjh0phQAAACqh7u7u5o2bSrpzGYxP/74o1577TXde++9lt2M/z5b6u+7GVfEw8OD9TkBnGPHjh1q2LChsrOzFRERYWkvKSmxOj57BhUAVISZUgAAADVQWVmZioqKbNrNGABsVdHi5f369bOpHwCcjZlSAAAATi4xMVE9e/ZU48aNVVBQoGXLlmnTpk368ssvbdrNGABstXfv3nPa/u///q/CfuWzNwHgfAilAAAAnNzhw4c1aNAgZWdny9/fX61bt9aXX36p22+/XdKZ3YxdXFzUr18/FRUVKTY2VvPnz3dw1QCcUYsWLSRJ3t7e+u233xQVFaWCggL5+vpqx44datasmQoLC9WiRQsVFxc7uFoAVzpCKQAAACe3cOHCSs/bspsxANiipKREkjR58mRdc801Onr0qNX5CRMmaOLEiZZ+AFAZ1pQCAAAAANjEzc1NkjRp0qQKd9+bNm2aVT8AqAyhFAAAAADAJjt37pQkFRYWKiIiwjIjqnz3vcLCQqt+AFAZQikAAAAAgE0qWry8e/fuNvUDgLMRSgEAAAAAbFLR7nvr16+3qR8AnI1QCgAAAABgk7/vvpeenm5ZO8rNzU3p6eny9va26gcAlSGUAgAAAADY5O+77zVt2lTFxcUyDEPFxcVq2rSpJkyYYNUPACpDKAUAAAAAsMnfd9/buXOnzGazTCaTzGazdu7cye57AC4KoRQAAAAAwCZ/332vZcuWKisrkySVlZWpZcuW7L4H4KIQSgEAAAAAbFLRrnq33HKLTf0A4GyEUgAAAAAAm1Q0A+qbb76xqR8AnI1QCgAAAABgk6ioKEn/223v7N33yo/L+wFAZQilAAAAAAA2KV9D6umnn65w973Ro0db9QOAyhBKAQAAAABs4uJy5kfI6dOnV3h+1qxZVv0AoDL8TQEAAAAAsMmOHTskSSUlJTp48KDVuYMHD6qkpMSqHwBUhlAKAAAAAGCTFi1aWP7cqFEjubu766mnnpK7u7saNWpUYT8AOB9XRxcAAAAAAHAehmHIZDJJOjNjaubMmeecBwBbEEoBAAAAQC1RWFio3bt3X/J1UlNTtXv3bg0cONASUi1dulSRkZHatm3bJV8/MjJS3t7el3wdAFc2QikAAAAAqCV2796tdu3aVft1DcPQgAEDqu16qampatu2bbVdD8CViVAKAAAAAGqJyMhIpaamVtv1du3apYEDB2rp0qVq3rx5tV03MjKy2q4F4MpFKAUAAAAAtYS3t/dlmYHUvHlzZjYBuGjsvgcAAAAAAAC7I5QCAAAAAACA3RFKAQAAAAAAwO4IpQAAAAAAAGB3hFIAAAAAAACwO0IpAAAAAAAA2B2hFAAAAAAAAOyOUAoAAAAAAAB2RygFAAAAAAAAu3N1dAEAai/T6VO6IchFXsd+kw6RkV8Mr2O/6YYgF5lOn3J0KQAAAABQJYRSABzG83iWtj3iI33ziPSNo6txLs0lbXvER7uOZ0nq5OhyAAAAAOCiEUoBcJhTPo3V9s3j+uCDD9Q8MtLR5TiVXbt3a8CAAVp4Z2NHlwIAAAAAVUIoBcBhDFdP/ZxTppN1m0khbRxdjlM5mVOmn3PKZLh6OroUAAAAAKgSFnEBAAAAAACA3RFKAQAAAAAAwO4IpQAAAAAAAGB3hFIAAAAAAACwO0IpAAAAAAAA2B2hFAAAAAAAAOyOUAoAAAAAAAB2RygFAAAAAAAAuyOUAgAAAAAAgN0RSgEAAAAAAMDuCKUAAAAAAABgd4RSAAAAAAAAsDtCKQAAAAAAANgdoRQAAAAAAADsjlAKAAAAAAAAdkcoBQAA4OSSkpJ04403ytfXVw0aNFBcXJz27Nlj1efUqVNKSEhQvXr15OPjo379+ik3N9dBFQMAABBKAQAAOL3NmzcrISFBW7Zs0VdffaWSkhL16NFDJ06csPQZPXq0Pv30U61YsUKbN2/WoUOHFB8f78CqAQBAbefq6AIAAABwadauXWt1vHjxYjVo0ECpqam65ZZblJeXp4ULF2rZsmXq1q2bJGnRokVq3ry5tmzZoo4dOzqibAAAUMsxUwoAAKCGycvLkyQFBARIklJTU1VSUqLu3btb+kRGRqpx48ZKSUlxSI0AAADMlAIAAKhBysrKNGrUKN18881q1aqVJCknJ0fu7u6qW7euVd/AwEDl5ORUeJ2ioiIVFRVZjvPz8y9bzQAAoHZiphQAAEANkpCQoLS0NH344YeXdJ2kpCT5+/tbHo0aNaqmCgEAAM4glAIAAKghRo4cqTVr1mjjxo1q2LChpT0oKEjFxcU6duyYVf/c3FwFBQVVeK3ExETl5eVZHgcOHLicpQMAgFqIUAoAAMDJGYahkSNH6uOPP9bXX3+tsLAwq/Pt2rWTm5ubNmzYYGnbs2ePsrKyFB0dXeE1PTw85OfnZ/UAAACoTqwpBQAA4OQSEhK0bNkyffLJJ/L19bWsE+Xv7y8vLy/5+/tr6NChGjNmjAICAuTn56fHHntM0dHR7LwHAAAchlAKAADAyS1YsECS1KVLF6v2RYsWaciQIZKkWbNmycXFRf369VNRUZFiY2M1f/58O1cKAADwP4RSAAAATs4wjAv28fT01Lx58zRv3jw7VAQAAHBhrCkFAAAAAAAAuyOUAgAAAAAAgN0RSgEAAAAAAMDuCKUAAAAAAABgd4RSAAAAAAAAsDtCKQAAAAAAANgdoRQAAAAAAADsjlAKAAAAAAAAdkcoBQAAAAAAALsjlAIAAAAAAIDdEUoBAAAAAADA7gilAAAAAAAAYHeEUgAAAAAAALA7h4ZS33zzjfr06aOQkBCZTCatXr3a6rxhGHruuecUHBwsLy8vde/eXenp6VZ9jh49qgEDBsjPz09169bV0KFDdfz4cTveBQAAAAAAAC6WQ0OpEydO6Prrr9e8efMqPD9z5kzNmTNHb7zxhrZu3ao6deooNjZWp06dsvQZMGCAfv31V3311Vdas2aNvvnmGw0fPtxetwAAAAAAAIAqcHXki/fs2VM9e/as8JxhGJo9e7YmTpyovn37SpLee+89BQYGavXq1brvvvu0a9curV27Vj/++KPat28vSXr99dd155136uWXX1ZISIjd7gUAAAAAAAC2u2LXlNq3b59ycnLUvXt3S5u/v786dOiglJQUSVJKSorq1q1rCaQkqXv37nJxcdHWrVvtXjMAAAAAAABs49CZUpXJycmRJAUGBlq1BwYGWs7l5OSoQYMGVuddXV0VEBBg6VORoqIiFRUVWY7z8/Orq2wAAAAAAADY4IqdKXU5JSUlyd/f3/Jo1KiRo0sCAAAAAACoVa7YUCooKEiSlJuba9Wem5trORcUFKTDhw9bnT99+rSOHj1q6VORxMRE5eXlWR4HDhyo5uoBAAAAAABQmSs2lAoLC1NQUJA2bNhgacvPz9fWrVsVHR0tSYqOjtaxY8eUmppq6fP111+rrKxMHTp0OO+1PTw85OfnZ/UAAAAAAACA/Th0Tanjx49r7969luN9+/Zp+/btCggIUOPGjTVq1ChNnTpVERERCgsL07PPPquQkBDFxcVJkpo3b6477rhDDz/8sN544w2VlJRo5MiRuu+++9h5DwAAAAAA4Arm0FDqp59+UteuXS3HY8aMkSQNHjxYixcv1pNPPqkTJ05o+PDhOnbsmDp37qy1a9fK09PT8pwPPvhAI0eO1G233SYXFxf169dPc+bMsfu9AAAAAAAAwHYODaW6dOkiwzDOe95kMmnKlCmaMmXKefsEBARo2bJll6M8AAAAAAAAXCZX7JpSAAAAAAAAqLkIpQAAAAAAAGB3hFIAAAAAAACwO0IpAAAAAAAA2B2hFAAAAAAAAOyOUAoAAAAAAAB2RygFAAAAAAAAu3N1dAEAaq/CwkJJ0rZt2xxcScVOnjypzMxMhYaGysvLy9HlWNm1a5ejSwAAAACAS0IoBcBhdu/eLUl6+OGHHVyJ8/L19XV0CQAAAABQJYRSABwmLi5OkhQZGSlvb2/HFlOBXbt2aeDAgVq6dKmaN2/u6HLO4evrq4iICEeXAQAAAABVQigFwGGuvvpqDRs2zNFlXFDz5s3Vtm1bR5cBAAAAADUKC50DAAAAAADA7gilAAAAAAAAYHeEUgAAAAAAALA7QikAAAAAAADYHaEUAAAAAAAA7I5QCgAAAAAAAHZHKAUAAAAAAAC7I5QCAAAAAACA3RFKAQAAAAAAwO4IpQAAAAAAAGB3hFIAAAAAAACwO1dHFwAAAAAAOL/09HQVFBQ4uowK7dq1y+q/VxpfX19FREQ4ugwA50EoBQAAAABXqPT0dDVr1szRZVzQwIEDHV3Cef32228EU8AVilAKAAAAAK5Q5TOkli5dqubNmzu4mnOdPHlSmZmZCg0NlZeXl6PLsbJr1y4NHDjwip1lBoBQCgAAwOl98803eumll5Samqrs7Gx9/PHHiouLs5w3DEOTJk3S22+/rWPHjunmm2/WggULmDkAOJHmzZurbdu2ji6jQjfffLOjSwDgpFjoHAAAwMmdOHFC119/vebNm1fh+ZkzZ2rOnDl64403tHXrVtWpU0exsbE6deqUnSsFAAD4H2ZKAQAAOLmePXuqZ8+eFZ4zDEOzZ8/WxIkT1bdvX0nSe++9p8DAQK1evVr33XefPUsFAACwYKYUAABADbZv3z7l5OSoe/fuljZ/f3916NBBKSkp531eUVGR8vPzrR4AAADViVAKAACgBsvJyZEkBQYGWrUHBgZazlUkKSlJ/v7+lkejRo0ua50AAKD2IZQCAADAORITE5WXl2d5HDhwwNElAQCAGoZQCgAAoAYLCgqSJOXm5lq15+bmWs5VxMPDQ35+flYPAACA6kQoBQAAUIOFhYUpKChIGzZssLTl5+dr69atio6OdmBlAACgtmP3PQAAACd3/Phx7d2713K8b98+bd++XQEBAWrcuLFGjRqlqVOnKiIiQmFhYXr22WcVEhKiuLg4xxUNAABqPUIpAAAAJ/fTTz+pa9euluMxY8ZIkgYPHqzFixfrySef1IkTJzR8+HAdO3ZMnTt31tq1a+Xp6emokgEAAAilAAAAnF2XLl1kGMZ5z5tMJk2ZMkVTpkyxY1UAAACVY00pAAAAAAAA2B2hFAAAAAAAAOyOUAoAAAAAAAB2RygFAAAAAAAAuyOUAgAAAAAAgN0RSgEAAAAAAMDuCKUAAAAAAABgd4RSAAAAAAAAsDtCKQAAAAAAANgdoRQAAAAAAADsjlAKAAAAAAAAdkcoBQAAAAAAALsjlAIAAAAAAIDdEUoBAAAAAADA7gilAAAAAAAAYHeEUgAAAAAAALA7V0cXAAAAAAComOn0Kd0Q5CKvY79Jh5hTcDG8jv2mG4JcZDp9ytGlADgPQikAAAAAuEJ5Hs/Stkd8pG8ekb5xdDXOpbmkbY/4aNfxLEmdHF0OgAoQSgEAAADAFeqUT2O1ffO4PvjgAzWPjHR0OU5l1+7dGjBggBbe2djRpQA4D0IpAAAAALhCGa6e+jmnTCfrNpNC2ji6HKdyMqdMP+eUyXD1dHQpAM6DDyUDAAAAAADA7gilAAAAAAAAYHeEUgAAAAAAALA7QikAAAAAAADYHaEUAAAAAAAA7I5QCgAAAAAAAHZHKAUAAAAAAAC7I5QCAAAAAACA3RFKAQAAAAAAwO4IpQAAAAAAAGB3hFIAAAAAAACwO0IpAAAAAAAA2B2hFAAAAAAAAOyOUAoAAAAAAAB2RygFAAAAAAAAuyOUAgAAAAAAgN0RSgEAAAAAAMDuCKUAAAAAAABgd4RSAAAAAAAAsDtCKQAAAAAAANgdoRQAAAAAAADsjlAKAAAAAAAAdkcoBQAAAAAAALurMaHUvHnzFBoaKk9PT3Xo0EE//PCDo0sCAAAAAADAedSIUOqjjz7SmDFjNGnSJG3btk3XX3+9YmNjdfjwYUeXBgAAAAAAgArUiFDq1Vdf1cMPP6yHHnpILVq00BtvvCFvb2+9++67ji4NAAAAAAAAFXB1dAGXqri4WKmpqUpMTLS0ubi4qHv37kpJSXFgZQDsrbCwULt376626+3atcvqv9UhMjJS3t7e1XY9AABQsxUWFkqStm3bVi3XO3nypDIzM6vlWpdTaGiovLy8Luka1TmGA3B5OH0o9d///lelpaUKDAy0ag8MDDzvD6dFRUUqKiqyHOfn51/WGgHYx+7du9WuXbtqv+7AgQOr7Vqpqalq27ZttV0PAADUbOU/0zz88MMOrsR5+fr6OroEAOfh9KFUVSQlJWny5MmOLgNANYuMjFRqamq1Xa/8N4nV8Zu6cpGRkdVyHQCoinnz5umll15STk6Orr/+er3++uu66aabHF0WgErExcVJqr7Z1rVpppR0JpCKiIiohooAXA4mwzAMRxdxKYqLi+Xt7a2VK1da/sKWpMGDB+vYsWP65JNPznlORTOlGjVqpLy8PPn5+dmjbAAA4KTy8/Pl7+/vdOOGjz76SIMGDdIbb7yhDh06aPbs2VqxYoX27NmjBg0aXPD5znrfAADA/mwdNzj9Qufu7u5q166dNmzYYGkrKyvThg0bFB0dXeFzPDw85OfnZ/UAAACoydgYBgAAXGmcPpSSpDFjxujtt9/WkiVLtGvXLj366KM6ceKEHnroIUeXBgAA4HDlG8N0797d0sbGMAAAwNFqxJpS9957r/78808999xzysnJUZs2bbR27dpzFj8HAACojdgYBgAAXIlqxEwpSRo5cqT279+voqIibd26VR06dHB0SQAAAE4rKSlJ/v7+lkejRo0cXRIAAKhhakwoBQAAgIpdffXVMpvNys3NtWrPzc1VUFBQhc9JTExUXl6e5XHgwAF7lAoAAGoRQikAAIAajo1hAADAlahGrCkFAACAyo0ZM0aDBw9W+/btddNNN2n27NlsDAMAAByKUAoAAKAWYGMYAABwpSGUAgAAqCVGjhypkSNHOroMAAAASawpBQAAAAAAAAcglAIAAAAAAIDdEUoBAAAAAADA7gilAAAAAAAAYHeEUgAAAAAAALA7QikAAAAAAADYHaEUAAAAAAAA7I5QCgAAAAAAAHZHKAUAAAAAAAC7I5QCAAAAAACA3bk6uoArgWEYkqT8/HwHVwIAAK505eOF8vFDbcF4CQAA2MrW8RKhlKSCggJJUqNGjRxcCQAAcBYFBQXy9/d3dBl2w3gJAABcrAuNl0xGbfs1XwXKysp06NAh+fr6ymQyObocAFeI/Px8NWrUSAcOHJCfn5+jywFwhTAMQwUFBQoJCZGLS+1ZCYHxEoCKMF4CUBFbx0uEUgBwHvn5+fL391deXh6DLAAAgAowXgJwKWrPr/cAAAAAAABwxSCUAgAAAAAAgN0RSgHAeXh4eGjSpEny8PBwdCkAAABXJMZLAC4Fa0oBAAAAAADA7pgpBQAAAAAAALsjlAIAAAAAAIDdEUoBAAAAAADA7gilAOAs33zzjfr06aOQkBCZTCatXr3a0SUBAABcURgvAagOhFIAcJYTJ07o+uuv17x58xxdCgAAwBWJ8RKA6uDq6AIA4ErTs2dP9ezZ09FlAAAAXLEYLwGoDsyUAgAAAAAAgN0RSgEAAAAAAMDuCKUAAAAAAABgd4RSAAAAAAAAsDtCKQAAAAAAANgdu+8BwFmOHz+uvXv3Wo737dun7du3KyAgQI0bN3ZgZQAAAFcGxksAqoPJMAzD0UUAwJVk06ZN6tq16zntgwcP1uLFi+1fEAAAwBWG8RKA6kAoBQAAAAAAALtjTSkAAAAAAADYHaEUAAAAAAAA7I5QCgAAAAAAAHZHKAUAAAAAAAC7I5QCAAAAAACA3RFKAQAAAAAAwO4IpQAAAAAAAGB3hFIAAAAAAACwO0IpALXOkCFDFBcX5+gyAAAArnihoaGaPXu2o8sAUEMRSgG4aCaTqdLH888/X+VrZ2ZmymQyafv27eft8/zzz1+whiuRYRh666231KFDB/n4+Khu3bpq3769Zs+ercLCQrvWQjAHAMDlcznHSherS5culdbSpUsXu9VyMfLz8/XMM88oMjJSnp6eCgoKUvfu3bVq1SoZhmHXWgjmgMvH1dEFAHA+2dnZlj9/9NFHeu6557Rnzx5Lm4+Pz2V9/XHjxumf//yn5fjGG2/U8OHD9fDDD1/W171UDz74oFatWqWJEydq7ty5ql+/vn755RfNnj1boaGhhEQAANQQjh4r/d2qVatUXFwsSTpw4IBuuun/a+9eg6qq3j+Af0G8HDwIeESEJI6h4JFAocjh4g1UvITkjCCKCpbwQksrER1NQy6WCjjhZcZLAhpKNJKaKOqRAyoaggMIxR3RUrylaIcyRdb/BcMeT6KBEfX7z/czc2bYa6199rM3L3hm7cWz3oJarYa9vT0AoEePHl0WS3s1NDTAw8MD9+/fR3R0NFxcXGBgYICcnByEh4fD09MTJiYm/3aYRNQJuFKKiDpswIAB0sfY2Bh6eno6bampqVCpVOjVqxeGDh2Kbdu2See+++67cHR0xB9//AEAePToEZycnDBv3jwAwKBBgwAATk5Oz317J5fLda7XrVs3GBkZSce3b9+Gp6cnZDIZFAoFQkNDodVqn3s/+fn5MDMzw/r16wG0JEILFiyAmZkZ+vTpA09PTxQXF0vjIyIiMGLECOzduxdKpRLGxsYICAjAr7/++txrpKWlISUlBfv378fKlSvh4uICpVIJX19fZGVlYdy4cQCA5uZmREZGYuDAgejZsydGjBiBzMxM6Xuys7Ohp6eHhoYGqa2oqAh6enqoq6sDACQlJcHExATHjx+HSqWCXC7HpEmTpAQ5IiICycnJOHTokPSWNDs7+7mxExERUce8KFdqbGxEYGAgzM3NIZfL4eLiArVarXN+fX09pk6dCplMhkGDBmHfvn06q3WEEIiIiMCrr76Knj17wtLSEosXL24zlr59+0rXNjMzAwAoFAqpTaPRwN7eHj179oRSqURcXNwL723Xrl0wMTHBqVOnAAClpaWYPHky5HI5zM3NMXfuXNy5c0caP3bsWCxevBjh4eFSLH+1UmzlypWoq6tDXl4egoKCMGzYMNja2iIkJARFRUXSpN69e/cwb948mJqawtDQEJMnT0ZVVZX0Pa0529NaXwa2al09HhsbCwsLCygUCixatAiPHz+W4r9y5Qo++uij//SKfKL/VZyUIqJOlZKSgjVr1iAmJgZlZWVYt24dVq9ejeTkZABAQkICGhsbsWLFCgDAqlWr0NDQgC1btgAALly4AABQq9Wor69Henp6h67f2NgIb29vmJqaIj8/H9988w3UajXef//9NsdnZWVhwoQJiImJwfLlywEAfn5+uHXrFo4dO4aLFy/C2dkZXl5euHv3rnReTU0NDh48iCNHjuDIkSPIycnB559//sLnYmdnB19f32f69PT0YGxsDAD44osvEBcXh9jYWFy6dAne3t6YNm2aToLVHr/99htiY2Oxd+9enD59GlevXkVYWBiAlpVm/v7+0kRVfX093NzcOvT9RERE9HK0Wi2mTJmCU6dOobCwEJMmTYKPjw+uXr0qjZk3bx6uX7+O7OxsHDhwADt27MCtW7ek/gMHDmDTpk3Yvn07qqqqcPDgQTg4OHQ4losXL8Lf3x8BAQEoKSlBREQEVq9ejaSkpDbHb9iwAStWrMCJEyfg5eWFhoYGeHp6wsnJCQUFBcjMzMTNmzfh7++vc15ycjJ69+6NvLw8bNiwAZGRkTh58mSb12hubkZqaioCAwNhaWn5TL9cLoeBQcs//AQHB6OgoACHDx/G+fPnIYTAlClTpAml9tJoNKipqYFGo0FycjKSkpKkZ5Ceno6BAwciMjJSypuIqBMJIqK/ITExURgbG0vHNjY2Yt++fTpjoqKihKurq3R87tw50b17d7F69WphYGAgzpw5I/VdvnxZABCFhYXtjsHa2lps2rRJCCHEjh07hKmpqdBqtVJ/RkaG0NfXFzdu3BBCCBEUFCR8fX1Fenq6kMvlIjU1VRp75swZ0adPH/Hw4UOda9jY2Ijt27cLIYT49NNPhaGhoXjw4IHUv2zZMjFy5MjnxqhSqcS0adP+8l4sLS1FTEyMTpuLi4tYuHChEEIIjUYjAIh79+5J/YWFhQKAuHz5shCi5XcCQFRXV0tjtm7dKszNzaXj1mdARERE/6w/50ptsbe3F5s3bxZCCFFWViYAiPz8fKm/qqpKAJDynbi4OGFraysePXrUoVj+nGfNnj1bTJgwQWfMsmXLxLBhw6Tj1jwrPDxcWFhYiNLSUqkvKipKTJw4Uef8n376SQAQFRUVQgghxowZIzw8PHTGuLi4iOXLl7cZ482bNwUAER8f/8J7qaysFABEbm6u1Hbnzh0hk8lEWlqaEKIlZxs+fLjOeZs2bRLW1tbScVBQkLC2thZNTU1Sm5+fn5g5c+Yzz4CIOh9XShFRp2lsbERNTQ3ee+89yOVy6RMdHY2amhppnKurK8LCwhAVFYWlS5fCw8Oj02IoKyvD8OHD0bt3b6nN3d0dzc3NOrUc8vLy4Ofnh71792LmzJlSe3FxMbRaLRQKhc49XL58WecelEoljIyMpGMLCwudN5h/JtpRkPPBgwe4fv063N3dddrd3d1RVlb2l+c/zdDQEDY2Nu2Oj4iIiLqGVqtFWFgYVCoVTExMIJfLUVZWJq2UqqiogIGBAZydnaVzBg8eDFNTU+nYz88Pv//+O1577TWEhITg22+/RVNTU4djKSsrazPvqKqqwpMnT6S2uLg47Ny5E2fPnpVqUQEteZNGo9HJmYYOHQoAOnmTo6OjzjVelJe0J2dqjd3AwAAjR46U2hQKBezs7DqcN9nb26Nbt27tio+IOhcLnRNRp2mt27Rz506dBAGAzh/65uZm5Obmolu3bqiuru7SGFvZ2NhAoVBg9+7dmDp1Krp37w6g5R4sLCzarLH0dEHN1vGt9PT00Nzc/Nzr2draory8/G/Hra/f8i7h6YStrSXqbcXX3iSPiIiI/jlhYWE4efIkYmNjMXjwYMhkMsyYMUMqRt4eVlZWqKiogFqtxsmTJ7Fw4UJs3LgROTk5z+QAnWHUqFHIyMhAWlqaVIIBaMmbfHx8pLqcT7OwsJB+7kjeZGZmBhMTk07Lm/6c/7Q3b3pRXkdEnYcrpYio05ibm8PS0hK1tbUYPHiwzqe1gDkAbNy4EeXl5cjJyUFmZiYSExOlvtYdYJ5+O9cRKpUKxcXFaGxslNpyc3Ohr68POzs7qa1fv37IyspCdXU1/P39pQTF2dkZN27cgIGBwTP30K9fv5eKCQBmz56NyspKHDp06Jk+IQTu37+PPn36wNLSErm5uTr9ubm5GDZsGABIBUqfrmdQVFTU4Xh69Ojx0s+YiIiIXl5ubi6Cg4Mxffp0ODg4YMCAAdJmJQBgZ2eHpqYmFBYWSm3V1dW4d++ezvfIZDL4+PggISEB2dnZOH/+PEpKSjoUi0qlajPvsLW11Xmh+NZbb+HYsWNYt24dYmNjpXZnZ2f88MMPUCqVz+RNT69a7wh9fX0EBAQgJSUF169ff6Zfq9WiqakJKpUKTU1NyMvLk/p++eUXVFRU6ORNN27c0JmYYt5E9N/CSSki6lRr167FZ599hoSEBFRWVqKkpASJiYmIj48HABQWFmLNmjXYtWsX3N3dER8fjyVLlqC2thYA0L9/f8hkMqlQ5v379zt0/cDAQPTq1QtBQUEoLS2FRqPBBx98gLlz58Lc3FxnbP/+/ZGVlYXy8nLMmjULTU1NGD9+PFxdXfHOO+/gxIkTqKurw7lz57Bq1SoUFBS89HPx9/fHzJkzMWvWLKxbtw4FBQW4cuUKjhw5gvHjx0Oj0QAAli1bhvXr1+Prr79GRUUFVqxYgaKiIixZsgRAy/J9KysrREREoKqqChkZGX+5S05blEolLl26hIqKCty5c6fDBUGJiIjo5QwZMgTp6ekoKipCcXExZs+erbMqZ+jQoRg/fjxCQ0Nx4cIFFBYWIjQ0FDKZTNr5LSkpCV9++SVKS0tRW1uLr776CjKZDNbW1h2KZenSpTh16hSioqJQWVmJ5ORkbNmyRdoc5Wlubm44evQo1q5dK+0CuGjRIty9exezZs1Cfn4+ampqcPz4ccyfP/9vTeLExMTAysoKI0eOxJ49e/Djjz+iqqoKu3fvhpOTE7RaLYYMGQJfX1+EhITg7NmzKC4uxpw5c/DKK69IG8uMHTsWt2/fxoYNG1BTU4OtW7fi2LFjHY5HqVTi9OnTuHbtms7OgkT093FSiog61YIFC7Br1y4kJibCwcEBY8aMQVJSEgYNGoSHDx9izpw5CA4Oho+PDwAgNDQU48aNw9y5c/HkyRMYGBggISEB27dvh6WlZZu71b2IoaEhjh8/jrt378LFxQUzZsyAl5eXtLvfnw0YMABZWVkoKSlBYGAgmpubcfToUYwePRrz58+Hra0tAgICcOXKlWcmtTpCT08P+/btQ3x8PA4ePIgxY8bA0dERERER8PX1hbe3NwBg8eLF+Pjjj7F06VI4ODggMzMThw8fxpAhQwC0LC/fv38/ysvL4ejoiPXr1yM6OrrD8YSEhMDOzg5vvvkmzMzMnnlLSkRERP+M+Ph4mJqaws3NDT4+PvD29tapHwUAe/bsgbm5OUaPHo3p06cjJCQERkZG6NWrF4CWkgI7d+6Eu7s7HB0doVar8d1330GhUHQoFmdnZ6SlpSE1NRWvv/461qxZg8jISAQHB7c53sPDAxkZGfjkk0+wefNmaYX3kydPMHHiRDg4OODDDz+EiYmJVHLgZfTt2xfff/895syZg+joaDg5OWHUqFHYv38/Nm7cKO1anJiYiDfeeANvv/02XF1dIYTA0aNHpX/HU6lU2LZtG7Zu3Yrhw4fjwoULbU64/ZXIyEjU1dXBxsZGWrVORJ1DT7DICBERERER0X/Wzz//DCsrK6jVanh5ef3b4RARdRpOShEREREREf2HZGVlQavVwsHBAfX19QgPD8e1a9dQWVn5jxQyJyL6t3D3PSIiIiIiov+Qx48fY+XKlaitrYWRkRHc3NyQkpLCCSki+n+HK6WIiIiIiIiIiKjLsdA5ERERERERERF1OU5KERERERERERFRl+OkFBERERERERERdTlOShERERERERERUZfjpBQREREREREREXU5TkoREREREREREVGX46QUERERERERERF1OU5KERERERERERFRl+OkFBERERERERERdbn/AxLIE1W2pPdXAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Saving Amharic\n",
        "\n",
        "# Splitting the dataset\n",
        "# 80% training, 10% validation, 10% testing\n",
        "train, temp = train_test_split(bounded_token_length_aggregated_data_further_cleaned, test_size=0.20, random_state=16)\n",
        "valid, test = train_test_split(temp, test_size=0.50, random_state=16)\n",
        "\n",
        "# Save them to different CSVs\n",
        "train.to_csv('/content/drive/MyDrive/Ahmaric_data' + 'amharic_train.csv', index=False)\n",
        "valid.to_csv('/content/drive/MyDrive/Ahmaric_data' + 'amharic_valid.csv', index=False)\n",
        "test.to_csv('/content/drive/MyDrive/Ahmaric_data' + 'amharic_test.csv', index=False)\n",
        "\n",
        "# All of it in the same file\n",
        "bounded_token_length_aggregated_data_further_cleaned.to_csv('/content/drive/MyDrive/Ahmaric_data' + 'amharic_3_all.csv', index=False)"
      ],
      "metadata": {
        "id": "FZFp_sgSv2VS"
      },
      "execution_count": 52,
      "outputs": []
    }
  ]
}