src/classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Arabic Dialect Classifier\n",
    "This notebook contains the training of the classifier model. The goal is to classify the dialects at the country level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "from datasets import DatasetDict, Dataset\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import torch\n",
    "from transformers import AutoModel, AutoTokenizer\n",
    "\n",
    "from utils import evaluate_predictions, plot_confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Exploring the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_csv(\"../data/DA_train_labeled.tsv\", sep=\"\\t\")\n",
    "df_test = pd.read_csv(\"../data/DA_dev_labeled.tsv\", sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>#1_tweetid</th>\n",
       "      <th>#2_tweet</th>\n",
       "      <th>#3_country_label</th>\n",
       "      <th>#4_province_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TRAIN_0</td>\n",
       "      <td>حاجة حلوة اكيد</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>eg_Faiyum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TRAIN_1</td>\n",
       "      <td>عم بشتغلوا للشعب الاميركي اما نحن يكذبوا ويغشو...</td>\n",
       "      <td>Iraq</td>\n",
       "      <td>iq_Dihok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TRAIN_2</td>\n",
       "      <td>ابشر طال عمرك</td>\n",
       "      <td>Saudi_Arabia</td>\n",
       "      <td>sa_Ha'il</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TRAIN_3</td>\n",
       "      <td>منطق 2017: أنا والغريب علي إبن عمي وأنا والغري...</td>\n",
       "      <td>Mauritania</td>\n",
       "      <td>mr_Nouakchott</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TRAIN_4</td>\n",
       "      <td>شهرين وتروح والباقي غير صيف ملينا</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>dz_El-Oued</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  #1_tweetid                                           #2_tweet  \\\n",
       "0    TRAIN_0                                     حاجة حلوة اكيد   \n",
       "1    TRAIN_1  عم بشتغلوا للشعب الاميركي اما نحن يكذبوا ويغشو...   \n",
       "2    TRAIN_2                                      ابشر طال عمرك   \n",
       "3    TRAIN_3  منطق 2017: أنا والغريب علي إبن عمي وأنا والغري...   \n",
       "4    TRAIN_4                  شهرين وتروح والباقي غير صيف ملينا   \n",
       "\n",
       "  #3_country_label #4_province_label  \n",
       "0            Egypt         eg_Faiyum  \n",
       "1             Iraq          iq_Dihok  \n",
       "2     Saudi_Arabia          sa_Ha'il  \n",
       "3       Mauritania     mr_Nouakchott  \n",
       "4          Algeria        dz_El-Oued  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>#1_tweetid</th>\n",
       "      <th>#2_tweet</th>\n",
       "      <th>#3_country_label</th>\n",
       "      <th>#4_province_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DEV_0</td>\n",
       "      <td>قولنا اون لاين لا يا علي اون لاين لا</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>eg_Alexandria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DEV_1</td>\n",
       "      <td>ههههه بايخه ههههه URL  …</td>\n",
       "      <td>Oman</td>\n",
       "      <td>om_Muscat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DEV_2</td>\n",
       "      <td>ربنا يخليك يا دوك ولك المثل :D</td>\n",
       "      <td>Lebanon</td>\n",
       "      <td>lb_South-Lebanon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DEV_3</td>\n",
       "      <td>#اوامر_ملكيه ياشباب اي واحد فيكم عنده شي يذكره...</td>\n",
       "      <td>Syria</td>\n",
       "      <td>sy_Damascus-City</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DEV_4</td>\n",
       "      <td>شد عالخط حتى هيا اكويسه</td>\n",
       "      <td>Libya</td>\n",
       "      <td>ly_Misrata</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  #1_tweetid                                           #2_tweet  \\\n",
       "0      DEV_0               قولنا اون لاين لا يا علي اون لاين لا   \n",
       "1      DEV_1                           ههههه بايخه ههههه URL  …   \n",
       "2      DEV_2                     ربنا يخليك يا دوك ولك المثل :D   \n",
       "3      DEV_3  #اوامر_ملكيه ياشباب اي واحد فيكم عنده شي يذكره...   \n",
       "4      DEV_4                            شد عالخط حتى هيا اكويسه   \n",
       "\n",
       "  #3_country_label #4_province_label  \n",
       "0            Egypt     eg_Alexandria  \n",
       "1             Oman         om_Muscat  \n",
       "2          Lebanon  lb_South-Lebanon  \n",
       "3            Syria  sy_Damascus-City  \n",
       "4            Libya        ly_Misrata  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(#1_tweetid           0\n",
       " #2_tweet             0\n",
       " #3_country_label     0\n",
       " #4_province_label    0\n",
       " dtype: int64,\n",
       " #1_tweetid           0\n",
       " #2_tweet             0\n",
       " #3_country_label     0\n",
       " #4_province_label    0\n",
       " dtype: int64)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.isnull().sum(), df_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the distribution of the labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Value counts of country label in train data')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.barh(y=df_train[\"#3_country_label\"].value_counts().sort_values(ascending=True).index,\n",
    "         width=df_train[\"#3_country_label\"].value_counts().sort_values(ascending=True))\n",
    "plt.title(\"Value counts of country label in train data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Value counts of country label in test data')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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o0aLINlT5kW5Vq1aFs7Mzli1bVuT2gv8wAcCIESMwYsQIZGZm4o8//sCiRYvQvXt33Lhxo9R/+1WqVIFEIim2f18VHByMzMxMHDhwQOk4sbGxJT7e7t27oa2tjZCQEKX/kPLRZh8eR06JKpmRI0ciLy8P33zzDQ4fPozPP/8c+vr64naJRFJodOny5cs4d+7cW9suuDP88uXLSuX/+9//lNY9PDygpaWFhIQEuLi4FLm8SdeuXXHq1Kk3Xsru2LEj4uLicPHiRaXygju23dzc3hhzwdSHd1HSEUArKytMnDgR7u7uheJ8lYGBAZo3b44DBw4otZmfn48dO3agZs2aqFOnTqnj7Nq1K27cuPHGKQ4dO3ZERkZGoS/kn376SdwOvEymdHV1C/Xjr7/+Wuq4XvWuo3yWlpbo378/Nm7ciM2bN6NHjx7iTS2lIZFIIAhCob+JH3/8EXl5eUXus3PnTqX1iIgI3LlzR7yZsHXr1jA2NkZcXFyxn38dHZ1Sx1rWiuv77t274+rVq6hVq1aRsb+anBYwMDBA165dMW/ePGRnZ+Ovv/4SjwGUbBTSwMAAzZo1w4EDB5SuVDx9+rTQvzEF/2l59fcmCAK2bNlS4vOUSCTQ0tKCpqamWPb8+XNs3779rbHS++HIKVEl4+LiAmdnZ6xZswaCIBR6tmn37t3h6+uLRYsWoX379rh+/TqWLl0KW1vbQo+JeV23bt1gYmKCkSNHYunSpdDS0kJgYCCSk5OV6tnY2GDp0qWYN28ebt26hS5duqBKlSq4d+8eLly4AAMDAyxZsqTY4yxduhRHjhxBu3btMHfuXDRo0ABPnjzB0aNHMW3aNNSrVw9ffvklfvrpJ3h6emLp0qWwtrbGb7/9ho0bN2L8+PFiMmdhYYFOnTrBz88PVapUgbW1NUJDQ3HgwIF37OGXc+Csra3x66+/omPHjjAxMUHVqlVRpUoVuLm5YdCgQahXrx4MDQ0RFRWFo0ePok+fPm9s08/PD+7u7nBzc8OMGTOgo6ODjRs34urVq9i1a9c7Pdtz6tSp2LNnD3r27Ik5c+agWbNmeP78OcLDw9G9e3e4ublh2LBh2LBhA4YPH47ExEQ0aNAAZ86cwddff41u3bqJ81UlEgmGDBmCgIAA1KpVCw0bNsSFCxeKffFDSTVo0ABhYWH43//+B0tLSxgaGqJu3bol2nfKlClo3rw5AIhTWUrLyMgI7dq1wzfffIOqVavCxsYG4eHh2Lp1q3jn/euio6MxatQo9O/fH8nJyZg3bx5q1KghPmNYJpNh/fr1GD58OB49eoR+/frB3NwcDx48wJ9//okHDx4UunJRHorr+6VLl+LEiRNo1aoVJk+ejLp16+LFixdITEzE4cOHsXnzZtSsWROjR4+Gnp4eWrduDUtLS9y9exd+fn6Qy+XiExOcnJwAAD/88AMMDQ2hq6sLW1vbIqcjAICvry+6dOkCd3d3TJ8+HXl5eVi+fDkMDAyURrLd3d2ho6ODgQMHYtasWXjx4gU2bdqEx48fF3meBw4cwKZNm/DJJ59AQ0MDLi4u8PT0xKpVqzBo0CCMGTMGqamp+Pbbb99pagiVUjnejEVE5WTt2rUCAMHR0bHQtqysLGHGjBlCjRo1BF1dXaFJkyZCcHCwMHz48EJ3tKKIO9IvXLggtGrVSjAwMBBq1KghLFq0SPjxxx+LvOM4ODhYcHNzE4yMjASpVCpYW1sL/fr1E37//fe3nkNycrLg7e0tWFhYCNra2kL16tWFAQMGCPfu3RPr3LlzRxg0aJBgamoqaGtrC3Xr1hW++eYbIS8vT6mtlJQUoV+/foKJiYkgl8uFIUOGCNHR0UXerW9gYFAolqKeUvD7778LjRs3FqRSqQBAGD58uPDixQth3LhxgrOzs2BkZCTo6ekJdevWFRYtWiRkZma+9ZxPnz4tdOjQQTAwMBD09PSEFi1aCP/73/+U6pTmbn1BEITHjx8LU6ZMEaysrARtbW3B3Nxc8PT0FK5duybWSU1NFcaNGydYWloKWlpagrW1teDj4yO8ePFCqa20tDRh1KhRQrVq1QQDAwOhR48eQmJiYrF36z948EBp/6LuOI+NjRVat24t6OvrCwCE9u3bK9WNiop64/nZ2NgIDg4OJeoLQSj6bv1//vlH6Nu3r1ClShXB0NBQ6NKli3D16lXB2tpa6W72gpiOHz8uDB06VDA2Nhb09PSEbt26CX///XehY4WHhwuenp6CiYmJoK2tLdSoUUPw9PQU9u3b98Y+KUpxd+vXr1+/UN2i/paLUlzfC8LLJ1JMnjxZsLW1FbS1tQUTExPhk08+EebNmydkZGQIgiAIQUFBgpubm1CtWjVBR0dH/Bu9fPmy0nHWrFkj2NraCpqamsU+8eFVhw4dEpydnQUdHR3ByspK8Pf3L/Jv8H//+5/QsGFDQVdXV6hRo4Ywc+ZM4ciRI4V+v48ePRL69esnGBsbCxKJRKmdgIAAoW7duoJUKhXs7OwEPz8/YevWrSX6ndC7kwgCnyRLREQVz+XLl9GwYUNs2LChyDejEZFqYnJKREQVSkJCAu7cuYO5c+ciKSkJN2/eVJpXTUSqjTdEERFRheLr6wt3d3dkZGRg3759TEyJ1AxHTomIiIhIZXDklIiIiIhUBpNTIiIiIlIZTE6JiIiISGXwIfykdvLz8/Hff//B0NDwnR48TkRERB+fIAh4+vQpqlevDg2N4sdHmZyS2vnvv/+gUCjKOwwiIiJ6B8nJyahZs2ax25mcktoxNDQE8PLDbWRkVM7REBERUUmkp6dDoVCI3+PFYXJKaqfgUr6RkRGTUyIiIjXztil5vCGKiIiIiFQGk1MiIiIiUhlMTomIiIhIZTA5JSIiIiKVweSUiIiIiFQGk1MiIiIiUhlMTomIiIhIZTA5JSIiIiKVweSUiIiIiFQGk1MiIiIiUhlMTomIiIhIZTA5JSIiIiKVweSUiIiIiFSGVnkHQPSunBYdg4ZUv7zDICIiqjAS/T3LOwSOnBIRERGR6mBySkREREQqg8lpJeTl5QWJRFJo6dKly0eNQyKRIDg4+KMek4iIiFQb55xWUl26dMG2bduUyqRSaTlFQ0RERPQSR04rKalUCgsLC6WlSpUqAIBr166hTZs20NXVhaOjI37//XelUc4OHTpg4sSJSu2lpqZCKpXi5MmTAAAbGxv4+vpi0KBBkMlkqF69OtavXy/Wt7GxAQD07t0bEolEXCciIqLKjckpKcnPz0evXr2gr6+PyMhI/PDDD5g3b55SnVGjRuHnn39GVlaWWLZz505Ur14dbm5uYtk333wDZ2dnXLx4ET4+Pvjyyy9x4sQJAEBUVBQAYNu2bUhJSRHXi5KVlYX09HSlhYiIiComJqeVVEhICGQymdLi6+uL48ePIyEhAT/99BMaNmyINm3aYNmyZUr79u3bFxKJBL/++qtYtm3bNnEua4HWrVtjzpw5qFOnDiZNmoR+/fph9erVAAAzMzMAgLGxMSwsLMT1ovj5+UEul4uLQqEoy64gIiIiFcLktJJyc3NDbGys0vLFF1/g+vXrUCgUsLCwEOs2a9ZMaV+pVIohQ4YgICAAABAbG4s///wTXl5eSvVatmxZaD0+Pr7Usfr4+CAtLU1ckpOTS90GERERqQfeEFVJGRgYwN7evlC5IAhKo5/FGTVqFBo1aoR//vkHAQEB6NixI6ytrd+6X0nafp1UKuXNWkRERJUER05JSb169ZCUlIR79+6JZUXNB23QoAFcXFywZcsW/Pzzz/D29i5U5/z584XW69WrJ65ra2sjLy+vDKMnIiIidceR00oqKysLd+/eVSrT0tKCu7s7atWqheHDh2PFihV4+vSpeEPU66Oeo0aNwsSJE6Gvr4/evXsXOsbZs2exYsUK9OrVCydOnMC+ffvw22+/idttbGwQGhqK1q1bQyqVik8LICIiosqLI6eV1NGjR2Fpaam0tGnTBpqamggODkZGRgaaNm2KUaNGYf78+QAAXV1dpTYGDhwILS0tDBo0qNA2AJg+fTpiYmLQuHFj+Pr6YuXKlfDw8BC3r1y5EidOnIBCoUDjxo0/7AkTERGRWpAIgiCUdxCk2s6ePYs2bdrg5s2bqFWrllienJwMGxsbREVFoUmTJkr72NjYYOrUqZg6dWqZx5Oeng65XI60tDQYGRmVeftERERU9kr6/c3L+lTIwYMHIZPJULt2bdy8eRNTpkxB69atxcQ0JycHKSkpmDNnDlq0aFEoMSUiIiJ6V0xOqZCnT59i1qxZSE5ORtWqVdGpUyesXLlS3H727Fm4ubmhTp06+OWXX8oxUiIiIqpoeFmf1A4v6xMREakfXtanCs9p0TFoSPXLOwwiordK9Pcs7xCI1Abv1iciIiIilcHklErEy8sLvXr1Ku8wiIiIqIJjckrvLTs7u7xDICIiogqCySmVmqurKyZOnIhp06ahatWqcHd3BwCsWrUKDRo0gIGBARQKBSZMmICMjAylfQMDA2FlZSW+VWrlypUwNjYuh7MgIiIiVcTklN5JUFAQtLS0cPbsWXz//fcAAA0NDaxbtw5Xr15FUFAQTp48iVmzZon7REZGwtvbGxMmTEBsbCzc3Nzw1VdfvfVYWVlZSE9PV1qIiIioYuLd+vRO7O3tsWLFCqWyV98GZWtrC19fX4wfPx4bN24EAKxduxYeHh6YM2cOAKBOnTqIiIjA0aNH33gsPz8/LFmypGxPgIiIiFQSR07pnbi4uBQqO3XqFNzd3VGjRg0YGhpi2LBhSE1NRWZmJgAgPj4eLVu2VNrn9fWi+Pj4IC0tTVySk5PL5iSIiIhI5TA5pXdiYGCgtH7nzh1069YNTk5O2L9/P2JiYrBhwwYAL193CgDv+r4HqVQKIyMjpYWIiIgqJl7WpzIRHR2N3NxcrFy5EhoaL//Ps3fvXqU6jo6OOH/+vFLZ6+tERERUuTE5pTJRq1Yt5ObmYv369ejRowfOnj2LzZs3K9WZPHkyWrVqhRUrVqBXr144fvz4W+ebEhERUeXCy/pUJho1aoRVq1Zh+fLlcHJyws6dO+Hn56dUp0WLFvjxxx+xfv16NGrUCMePH8f8+fPLKWIiIiJSRRLhXScCEpWBwMBATJ06FU+ePCnxPunp6ZDL5VBM3QsNqf6HC46IqIwk+nuWdwhE5a7g+zstLe2N94/wsj6pratLPHhzFBERUQXDy/pEREREpDKYnFK58vLyKtUlfSIiIqrYeFmf1JbTomOcc0qkBjjfkohKgyOnRERERKQymJyqAS8vL/Tq1Utcd3V1VXqPfXmSSCQIDg4udntiYiIkEgliY2M/WkxERESkvpiclsL9+/cxduxYWFlZQSqVwsLCAh4eHjh37txHjePAgQPw9fUt1T4///wzNDU1MW7cuA8UVdEUCgVSUlLg5OT0UY9LRERE6onJaSn07dsXf/75J4KCgnDjxg0cOnQIrq6uePTo0UeNw8TEBIaGhqXaJyAgALNmzcLu3bvx7NmzN9bNy8tDfn7++4Qo0tTUhIWFBbS0OL2ZiIiI3o7JaQk9efIEZ86cwfLly+Hm5gZra2s0a9YMPj4+8PR8Odl/1apVaNCgAQwMDKBQKDBhwgRkZGSIbSxevBiNGjVSanfNmjWwsbER1/Py8jBt2jQYGxvD1NQUs2bNwuvvSSjtZf3ExERERERgzpw5qFevHn755Rel7YGBgTA2NkZISAgcHR0hlUpx584dREVFwd3dHVWrVoVcLkf79u1x8eLFQu2npKSga9eu0NPTg62tLfbt26d07Fcv6+fl5WHkyJGwtbWFnp4e6tati7Vr174x/qysLKSnpystREREVDExOS0hmUwGmUyG4OBgZGVlFVlHQ0MD69atw9WrVxEUFISTJ09i1qxZpTrOypUrERAQgK1bt+LMmTN49OgRDh48+F6xBwQEwNPTE3K5HEOGDMHWrVsL1Xn27Bn8/Pzw448/4q+//oK5uTmePn2K4cOH4/Tp0zh//jxq166Nbt264enTp0r7LliwQBxVHjJkCAYOHIj4+PgiY8nPz0fNmjWxd+9exMXFYeHChZg7dy727t1bbPx+fn6Qy+XiolAo3qs/iIiISHUxOS0hLS0tBAYGIigoCMbGxmjdujXmzp2Ly5cvi3WmTp0KNzc32NraokOHDvD19X1j0lWUNWvWwMfHB3379oWDgwM2b94MuVz+znHn5+cjMDAQQ4YMAQB8/vnnOHfuHG7evKlULycnBxs3bkSrVq1Qt25dGBgYoEOHDhgyZAgcHBzg4OCA77//Hs+ePUN4eLjSvv3798eoUaNQp04d+Pr6wsXFBevXry8yHm1tbSxZsgRNmzaFra0tBg8eDC8vrzf2k4+PD9LS0sQlOTn5nfuDiIiIVBuT01Lo27cv/vvvPxw6dAgeHh4ICwtDkyZNEBgYCAA4deoU3N3dUaNGDRgaGmLYsGFITU1FZmZmidpPS0tDSkoKWrZsKZZpaWnBxcXlnWM+fvw4MjMz0bVrVwBA1apV0blzZwQEBCjV09HRgbOzs1LZ/fv3MW7cONSpU0cctczIyEBSUpJSvVfjLVgvbuQUADZv3gwXFxeYmZlBJpNhy5Ythdp8lVQqhZGRkdJCREREFROT01LS1dWFu7s7Fi5ciIiICHh5eWHRokW4c+cOunXrBicnJ+zfvx8xMTHYsGEDgJejksDLy/6vzx8t2PahBAQE4NGjR9DX14eWlha0tLRw+PBhBAUFIS8vT6ynp6cHiUSitK+XlxdiYmKwZs0aREREIDY2FqampsjOzn7rcV9vq8DevXvx5ZdfwtvbG8ePH0dsbCxGjBhRojaJiIio4mNy+p4cHR2RmZmJ6Oho5ObmYuXKlWjRogXq1KmD//77T6mumZkZ7t69q5Sgvvr8T7lcDktLS5w/f14sy83NRUxMzDvFlpqail9//RW7d+9GbGys0pKRkYEjR468cf/Tp09j8uTJ6NatG+rXrw+pVIqHDx8WqvdqvAXr9erVK7bNVq1aYcKECWjcuDHs7e2RkJDwTudHREREFQ+f71NCqamp6N+/P7y9veHs7AxDQ0NER0djxYoV6NmzJ2rVqoXc3FysX78ePXr0wNmzZ7F582alNlxdXfHgwQOsWLEC/fr1w9GjR3HkyBGly9RTpkyBv78/ateuDQcHB6xateqd3z2/fft2mJqaon///tDQUP5/SPfu3bF161Z079692P3t7e2xfft2uLi4ID09HTNnzoSenl6hevv27YOLiwvatGmDnTt34sKFC0XedFXQ5k8//YRjx47B1tYW27dvR1RUFGxtbd/pHImIiKhi4chpCclkMjRv3hyrV69Gu3bt4OTkhAULFmD06NH47rvv0KhRI6xatQrLly+Hk5MTdu7cCT8/P6U2HBwcsHHjRmzYsAENGzbEhQsXMGPGDKU606dPx7Bhw+Dl5YWWLVvC0NAQvXv3fqeYAwIC0Lt370KJKfBy/mxISAju3bv3xv0fP36Mxo0bY+jQoZg8eTLMzc0L1VuyZAl2794NZ2dnBAUFYefOnXB0dCyyzXHjxqFPnz747LPP0Lx5c6SmpmLChAnvdH5ERERU8UiE1ydBEqm49PR0yOVypKWl8eYoIiIiNVHS72+OnBIRERGRymByqsZOnz4tvhygqIWIiIhI3fCGKDXm4uKidLd/ZeO06Bg0pPrlHQZRuUr09yzvEIiIyhSTUzWmp6cHe3v78g6DiIiIqMzwsn4lExYWBolE8s6Pp1LVYxEREVHFwOS0goqIiICmpia6dOlSbjG0atUKKSkpkMvl5RYDERERqRcmpxVUQEAAJk2ahDNnzrzxvfUfSk5ODnR0dGBhYVHsq0yJiIiIXsfktALKzMzE3r17MX78eHTv3h2BgYFvrL9lyxYoFAro6+ujd+/eWLVqFYyNjZXq/O9//8Mnn3wCXV1d2NnZYcmSJcjNzRW3SyQSbN68GT179oSBgQG++uqrQpf1U1NTMXDgQNSsWRP6+vpo0KABdu3a9dbzycrKQnp6utJCREREFROT0wpoz549qFu3LurWrYshQ4Zg27ZtKO5dC2fPnsW4ceMwZcoUxMbGwt3dHcuWLVOqc+zYMQwZMgSTJ09GXFwcvv/+ewQGBhaqt2jRIvTs2RNXrlyBt7d3oWO9ePECn3zyCUJCQnD16lWMGTMGQ4cORWRk5BvPx8/PD3K5XFwUCkUpe4SIiIjUBd8QVQG1bt0aAwYMwJQpU5CbmwtLS0vs2rULnTp1QlhYGNzc3PD48WMYGxvj888/R0ZGBkJCQsT9hwwZgpCQEHHEs127dujatSt8fHzEOjt27MCsWbPw33//AXg5cjp16lSsXr1arPP6sYri6ekJBwcHfPvtt8WeT1ZWFrKyssT19PR0KBQKKKbu5aOkqNLjo6SISF2U9A1RfJRUBXP9+nVcuHABBw4cAABoaWnhs88+Q0BAADp16lRk/d69eyuVNWvWTClZjYmJQVRUlNJIaV5eHl68eIFnz55BX/9lguji4vLG2PLy8uDv7489e/bg33//FZNOAwODN+4nlUohlUrffOJERERUITA5rWC2bt2K3Nxc1KhRQywTBAHa2tp4/PhxofqCIBS6Yen1wfT8/HwsWbIEffr0KbS/rq6u+PPbksyVK1di9erVWLNmDRo0aAADAwNMnToV2dnZJTo3IiIiqviYnFYgubm5+Omnn7By5Up07txZaVvfvn2xc+dOODk5KZXXq1cPFy5cUCqLjo5WWm/SpAmuX7/+3g/8P336NHr27IkhQ4YAeJn0/v3333BwcHivdomIiKjiYHJagYSEhODx48cYOXJkoWeL9uvXD1u3blWaEwoAkyZNQrt27bBq1Sr06NEDJ0+exJEjR5RGUxcuXIju3btDoVCgf//+0NDQwOXLl3HlyhV89dVXJY7P3t4e+/fvR0REBKpUqYJVq1bh7t27TE6JiIhIxLv1K5CtW7eiU6dORT70vm/fvoiNjcXFixeVylu3bo3Nmzdj1apVaNiwIY4ePYovv/xS6XK9h4cHQkJCcOLECTRt2hQtWrTAqlWrYG1tXar4FixYgCZNmsDDwwOurq6wsLBAr1693ulciYiIqGLi3fpUyOjRo3Ht2jWcPn26vEMpUknv9iMiIiLVwbv1qcS+/fZbuLu7w8DAAEeOHEFQUBA2btxY3mERERFRJcTklHDhwgWsWLECT58+hZ2dHdatW4dRo0aVd1hERERUCfGyPqmdgssCfAg/lSc+/J6IqHRKelmfN0QRERERkcpgckpEREREKoPJaSWUnJyMkSNHonr16tDR0YG1tTWmTJmC1NTU8g6NiIiIKjkmp5XMrVu34OLighs3bmDXrl24efMmNm/ejNDQULRs2RKPHj0q7xCJiIioEmNyWsl88cUX0NHRwfHjx9G+fXtYWVmha9eu+P333/Hvv/9i3rx5AAAbGxt89dVXGDZsGGQyGaytrfHrr7/iwYMH6NmzJ2QyGRo0aKD0qtPU1FQMHDgQNWvWhL6+Pho0aIBdu3YpHd/V1RWTJ0/GrFmzYGJiAgsLCyxevPhjdgERERGpMCanlcijR49w7NgxTJgwAXp6ekrbLCwsMHjwYOzZswcFD3BYvXo1WrdujUuXLsHT0xNDhw7FsGHDMGTIEFy8eBH29vYYNmyYWP/Fixf45JNPEBISgqtXr2LMmDEYOnQoIiMjlY4VFBQEAwMDREZGYsWKFVi6dClOnDhRbNxZWVlIT09XWoiIiKhiYnJaifz9998QBKHYd9k7ODjg8ePHePDgAQCgW7duGDt2LGrXro2FCxfi6dOnaNq0Kfr37486depg9uzZiI+Px7179wAANWrUwIwZM9CoUSPY2dlh0qRJ8PDwwL59+5SO4+zsjEWLFqF27doYNmwYXFxcEBoaWmzcfn5+kMvl4qJQKMqoR4iIiEjVMDklUcEIqEQiAfAyiSxQrVo1AECDBg0Kld2/fx8AkJeXh2XLlsHZ2RmmpqaQyWQ4fvw4kpKSlI7zarsAYGlpKbZRFB8fH6SlpYlLcnLyu54iERERqTgmp5WIvb09JBIJ4uLiitx+7do1VKlSBVWrVgUAaGtri9sKEtaiyvLz8wEAK1euxOrVqzFr1iycPHkSsbGx8PDwQHZ2ttJxXm2joJ2CNooilUphZGSktBAREVHFxOS0EjE1NYW7uzs2btyI58+fK227e/cudu7cic8++0xMOkvr9OnT6NmzJ4YMGYKGDRvCzs4Of//9d1mETkRERJUEk9NK5rvvvkNWVhY8PDzwxx9/IDk5GUePHoW7uztq1KiBZcuWvXPb9vb2OHHiBCIiIhAfH4+xY8fi7t27ZRg9ERERVXRMTiuZ2rVrIzo6GrVq1cJnn32GWrVqYcyYMXBzc8O5c+dgYmLyzm0vWLAATZo0gYeHB1xdXWFhYYFevXqVXfBERERU4UmEgrtgiNREeno65HI50tLSOP+UiIhITZT0+5sjp0RERESkMpicEhEREZHK0CrvAIjeldOiY9CQ6pd3GKRGEv09yzsEIiJ6C46cEhEREZHKYHJKxZJIJAgODgYAJCYmQiKRIDY2tlxjIiIiooqNl/UrOS8vLzx58kRMQl+VkpKCKlWqfPygiIiIqNJickrFsrCwKO8QiIiIqJLhZX0q1quX9Qtcu3YNrVq1gq6uLurXr4+wsDAAgCAIsLe3x7fffqtU/+rVq9DQ0EBCQgIAYNWqVWjQoAEMDAygUCgwYcIEZGRkfIzTISIiIjXA5JRKZebMmZg+fTouXbqEVq1a4dNPP0VqaiokEgm8vb2xbds2pfoBAQFo27YtatWqBQDQ0NDAunXrcPXqVQQFBeHkyZOYNWvWG4+ZlZWF9PR0pYWIiIgqJianVCoTJ05E37594eDggE2bNkEul2Pr1q0AgBEjRuD69eu4cOECACAnJwc7duyAt7e3uP/UqVPh5uYGW1tbdOjQAb6+vti7d+8bj+nn5we5XC4uCoXiw50gERERlSsmp1QqLVu2FH/W0tKCi4sL4uPjAQCWlpbw9PREQEAAACAkJAQvXrxA//79xX1OnToFd3d31KhRA4aGhhg2bBhSU1ORmZlZ7DF9fHyQlpYmLsnJyR/o7IiIiKi8MTml9yaRSMSfR40ahd27d+P58+fYtm0bPvvsM+jrv3xQ/p07d9CtWzc4OTlh//79iImJwYYNGwC8HGUtjlQqhZGRkdJCREREFROTUyqV8+fPiz/n5uYiJiYG9erVE8u6desGAwMDbNq0CUeOHFG6pB8dHY3c3FysXLkSLVq0QJ06dfDff/991PiJiIhItfFRUoS0tLRCD9c3MTEpsu6GDRtQu3ZtODg4YPXq1Xj8+LFSAqqpqQkvLy/4+PjA3t5eaRpArVq1kJubi/Xr16NHjx44e/YsNm/e/EHOiYiIiNQTR04JYWFhaNy4sdKycOHCIuv6+/tj+fLlaNiwIU6fPo1ff/0VVatWVaozcuRIZGdnKyWtANCoUSOsWrUKy5cvh5OTE3bu3Ak/P78Pdl5ERESkfiSCIAjlHQRVLGfPnoWrqyv++ecfVKtWrczbT09Ph1wuR1paGuefEhERqYmSfn/zsj6VmaysLCQnJ2PBggUYMGDAB0lMiYiIqGLjZX0qM7t27ULdunWRlpaGFStWlHc4REREpIZ4WZ/UTsFlAcXUvdCQ6pd3OPQRJfp7lncIRET0jkp6WZ8jp0RERESkMpic0gcnkUgQHBxc3mEQERGRGmBySqL79+9j7NixsLKyglQqhYWFBTw8PHDu3Ln3ajclJQVdu3YtoyiJiIioIuPd+iTq27cvcnJyEBQUBDs7O9y7dw+hoaF49OjRO7WXnZ0NHR0dWFhYlHGkREREVFFx5JQAAE+ePMGZM2ewfPlyuLm5wdraGs2aNYOPjw88PT3h7e2N7t27K+2Tm5sLCwsLBAQEAABcXV0xceJETJs2DVWrVoW7uzuAwpf1Z8+ejTp16kBfXx92dnZYsGABcnJyPtq5EhERkeriyCkBAGQyGWQyGYKDg9GiRQtIpVKl7aNGjUK7du2QkpICS0tLAMDhw4eRkZGBAQMGiPWCgoIwfvx4nD17FsU9CMLQ0BCBgYGoXr06rly5gtGjR8PQ0BCzZs0qsn5WVhaysrLE9fT09Pc9XSIiIlJRHDklAICWlhYCAwMRFBQEY2NjtG7dGnPnzsXly5cBAK1atULdunWxfft2cZ9t27ahf//+kMlkYpm9vT1WrFiBunXrol69ekUea/78+WjVqhVsbGzQo0cPTJ8+HXv37i02Nj8/P8jlcnFRKBRldNZERESkapickqhv377477//cOjQIXh4eCAsLAxNmjRBYGAggJejp9u2bQPw8uap3377Dd7e3kptuLi4vPU4v/zyC9q0aQMLCwvIZDIsWLAASUlJxdb38fFBWlqauCQnJ7/7SRIREZFKY3JKSnR1deHu7o6FCxciIiICXl5eWLRoEQBg2LBhuHXrFs6dO4cdO3bAxsYGbdu2VdrfwMDgje2fP38en3/+Obp27YqQkBBcunQJ8+bNQ3Z2drH7SKVSGBkZKS1ERERUMXHOKb2Ro6OjeDOTqakpevXqhW3btuHcuXMYMWJEqds7e/YsrK2tMW/ePLHszp07ZRUuERERqTkmpwQASE1NRf/+/eHt7Q1nZ2cYGhoiOjoaK1asQM+ePcV6o0aNQvfu3ZGXl4fhw4eX+jj29vZISkrC7t270bRpU/z22284ePBgWZ4KERERqTEmpwTg5d36zZs3x+rVq5GQkICcnBwoFAqMHj0ac+fOFet16tQJlpaWqF+/PqpXr17q4/Ts2RNffvklJk6ciKysLHh6emLBggVYvHhxGZ4NERERqSuJUNzzfoiK8OzZM1SvXh0BAQHo06dPucSQnp4OuVyOtLQ0zj8lIiJSEyX9/ubIKZVIfn4+7t69i5UrV0Iul+PTTz8t75CIiIioAmJySiWSlJQEW1tb1KxZE4GBgdDS4keHiIiIyh4zDCoRGxubYt/4RERERFRWmJyS2nJadAwaUv3yDqNSSfT3LO8QiIioguND+ImIiIhIZTA5VSNeXl6QSCQYN25coW0TJkyARCKBl5fXxw+MiIiIqIwwOVUzCoUCu3fvxvPnz8WyFy9eYNeuXbCysnrndgVBQG5ublmESERERPTOmJyqmSZNmsDKygoHDhwQyw4cOACFQoHGjRuLZVlZWZg8eTLMzc2hq6uLNm3aICoqStweFhYGiUSCY8eOwcXFBVKpFKdPn37rfgDw119/wdPTE0ZGRjA0NETbtm2RkJAgbg8ICED9+vUhlUphaWmJiRMnituSkpLQs2dPyGQyGBkZYcCAAbh3796H6CoiIiJSQ0xO1dCIESOwbds2cT0gIADe3t5KdWbNmoX9+/cjKCgIFy9ehL29PTw8PPDo0aNC9fz8/BAfHw9nZ+e37vfvv/+iXbt20NXVxcmTJxETEwNvb29x1HXTpk344osvMGbMGFy5cgWHDh2Cvb09gJejs7169cKjR48QHh6OEydOICEhAZ999tkbzzcrKwvp6elKCxEREVVMfEOUGvHy8sKTJ0/w448/ombNmrh27RokEgnq1auH5ORkjBo1CsbGxtiwYQOqVKmCwMBADBo0CACQk5MDGxsbTJ06FTNnzkRYWBjc3NwQHByMnj17AgAyMzPfut/cuXOxe/duXL9+Hdra2oVirFGjBkaMGIGvvvqq0LYTJ06ga9euuH37NhQKBQAgLi4O9evXx4ULF9C0adMiz3vx4sVYsmRJoXLF1L28W/8j4936RET0rkr6hiiOnKqhqlWrwtPTE0FBQdi2bRs8PT1RtWpVcXtCQgJycnLQunVrsUxbWxvNmjVDfHy8UlsuLi6l2i82NhZt27YtMjG9f/8+/vvvP3Ts2LHIuOPj46FQKMTEFAAcHR1hbGxcKK5X+fj4IC0tTVySk5OLrUtERETqjc85VVPe3t7iXM4NGzYobSsYDJdIJIXKXy8zMDAo1X56enrFxvSmbcUd/03lBaRSKaRS6RvbJiIiooqBI6dqqkuXLsjOzkZ2djY8PDyUttnb20NHRwdnzpwRy3JychAdHQ0HB4di2yzJfs7Ozjh9+jRycnIK7W9oaAgbGxuEhoYW2b6joyOSkpKURj7j4uKQlpb2xriIiIio8mByqqY0NTURHx+P+Ph4aGpqKm0zMDDA+PHjMXPmTBw9ehRxcXEYPXo0nj17hpEjRxbbZkn2mzhxItLT0/H5558jOjoaf//9N7Zv347r168DeDk/dOXKlVi3bh3+/vtvXLx4EevXrwcAdOrUCc7Ozhg8eDAuXryICxcuYNiwYWjfvr3S9AIiIiKqvHhZX429aTKxv78/8vPzMXToUDx9+hQuLi44duwYqlSp8sY237afqakpTp48iZkzZ6J9+/bQ1NREo0aNxHmqw4cPx4sXL7B69WrMmDEDVatWRb9+/QC8nC4QHByMSZMmoV27dtDQ0ECXLl3E5JWIiIiId+uT2inp3X5ERESkOni3PhERERGpHSanRERERKQymJwSERERkcrgDVGktpwWHeMbokqJb3giIiJVx5FTeieurq6YOnVqmdclIiKiyo0jpxXQm962BLx83FNgYOB7HePAgQNFvsL0fesSERFR5cbktAJKSUkRf96zZw8WLlwoPiQfePtrRkvCxMTkg9QlIiKiyo2X9SsgCwsLcZHL5ZBIJOL60aNHYW1trVQ/ODhYabR18eLFaNSoEbZv3w4bGxvI5XJ8/vnnePr0qVjn9Uv1GzduRO3ataGrq4tq1aqJD94vqu6OHTvg4uICQ0NDWFhYYNCgQbh//37ZdwQRERGpHSanVKSEhAQEBwcjJCQEISEhCA8Ph7+/f5F1o6OjMXnyZCxduhTXr1/H0aNH0a5du2Lbzs7Ohq+vL/78808EBwfj9u3b8PLyKrZ+VlYW0tPTlRYiIiKqmHhZn4qUn5+PwMBAGBoaAgCGDh2K0NBQLFu2rFDdpKQkGBgYoHv37jA0NIS1tTUaN25cbNve3t7iz3Z2dli3bh2aNWuGjIwMyGSyQvX9/PywZMmSMjgrIiIiUnUcOaUi2djYiIkpAFhaWhZ76d3d3R3W1taws7PD0KFDsXPnTjx79qzYti9duoSePXvC2toahoaGcHV1BfAyyS2Kj48P0tLSxCU5OfndT4yIiIhUGpPTSkZDQwOCICiV5eTkFKr3+t31EokE+fn5RbZpaGiIixcvYteuXbC0tMTChQvRsGFDPHnypFDdzMxMdO7cGTKZDDt27EBUVBQOHjwI4OXl/qJIpVIYGRkpLURERFQxMTmtZMzMzPD06VNkZmaKZbGxse/drpaWFjp16oQVK1bg8uXLSExMxMmTJwvVu3btGh4+fAh/f3+0bdsW9erV481QREREJOKc00qmefPm0NfXx9y5czFp0iRcuHDhvZ95GhISglu3bqFdu3aoUqUKDh8+jPz8fNStW7dQXSsrK+jo6GD9+vUYN24crl69Cl9f3/c6PhEREVUcHDmtZExMTLBjxw4cPnwYDRo0wK5du7B48eL3atPY2BgHDhxAhw4d4ODggM2bN2PXrl2oX79+obpmZmYIDAzEvn374OjoCH9/f3z77bfvdXwiIiKqOCTC6xMQiVRceno65HI5FFP3QkOqX97hqJVEf8/yDoGIiCqpgu/vtLS0N94/wsv6pLauLvHgzVFEREQVDC/rExEREZHKYHJKRERERCqDl/VJbTktOsY5p0XgvFIiIlJnHDklIiIiIpVRLsmpq6srpk6d+kHatrGxwZo1az5I26WRmJgIiURSJg+4L2sSiQTBwcHlHQYRERFRIaVKTotLKoODgyGRSErczoEDB5QevF7eCeXXX38NTU1N+Pv7l1sMrytIbotazp8//15tp6SkoGvXru8d44f8TwYRERFVTuUycmpiYgJDQ8PyOHSRtm3bhlmzZiEgIOCtdYt6D/2H9PvvvyMlJUVp+eSTT96rTQsLC0il0mK3f+xzJCIiIipQ5snp4sWL0ahRI2zfvh02NjaQy+X4/PPP8fTpU7HOqyNurq6uuHPnDr788ktxZLBAREQE2rVrBz09PSgUCkyePFnpnfD3799Hjx49oKenB1tbW+zcubPU8YaHh+P58+dYunQpMjMz8ccffxR5PgEBAbCzs4NUKoUgCDh69CjatGkDY2NjmJqaonv37khISCjU/rVr19CqVSvo6uqifv36CAsLK1V8pqamsLCwUFq0tbULxWZlZQWZTIbx48cjLy8PK1asgIWFBczNzbFs2TKlNl+9rF8wQrt37164urpCV1cXO3bsQGpqKgYOHIiaNWtCX19ffJtUAS8vL4SHh2Pt2rXi7y0xMREAEBcXh27dukEmk6FatWoYOnQoHj58KO77yy+/oEGDBtDT04OpqSk6deqk9Ht9XVZWFtLT05UWIiIiqpg+yMhpQkICgoODERISgpCQEISHhxd7yfzAgQOoWbMmli5dKo4MAsCVK1fg4eGBPn364PLly9izZw/OnDmDiRMnivt6eXkhMTERJ0+exC+//IKNGzfi/v37pYp169atGDhwILS1tTFw4EBs3bq1UJ2bN29i79692L9/vziHNDMzE9OmTUNUVBRCQ0OhoaGB3r17Iz8/X2nfmTNnYvr06bh06RJatWqFTz/9FKmpqaWK8U0SEhJw5MgRHD16FLt27UJAQAA8PT3xzz//IDw8HMuXL8f8+fPfOhVg9uzZmDx5MuLj4+Hh4YEXL17gk08+QUhICK5evYoxY8Zg6NChiIyMBACsXbsWLVu2xOjRo8Xfm0KhQEpKCtq3b49GjRohOjoaR48exb179zBgwAAAL6cUDBw4EN7e3oiPj0dYWBj69OmDN72ozM/PD3K5XFwUCkWZ9R8RERGplg/yKKn8/HwEBgaKl+6HDh2K0NDQQiN4wMtL/JqamjA0NISFhYVY/s0332DQoEHiCGvt2rWxbt06tG/fHps2bUJSUhKOHDmC8+fPo3nz5gBeJpoODg4ljjM9PR379+9HREQEAGDIkCFo3bo11q9fr/TmoezsbGzfvh1mZmZiWd++fZXa2rp1K8zNzREXFwcnJyexfOLEiWLdTZs24ejRo9i6dStmzZpVohhbtWoFDQ3l/0OkpaVBU1MTwMu+DggIgKGhIRwdHeHm5obr16/j8OHD0NDQQN26dbF8+XKEhYWhRYsWxR5n6tSp6NOnj1LZjBkzxJ8nTZqEo0ePYt++fWjevDnkcjl0dHSgr6+v9HvbtGkTmjRpgq+//losCwgIgEKhwI0bN5CRkYHc3Fz06dMH1tbWAIAGDRq8sQ98fHwwbdo0cT09PZ0JKhERUQX1QZJTGxsbpTmllpaWpR7RjImJwc2bN5Uu1QuCgPz8fNy+fRs3btyAlpYWXFxcxO316tWDsbFxiY/x888/w87ODg0bNgQANGrUCHZ2dti9ezfGjBkj1rO2tlZKTIGXI5YLFizA+fPn8fDhQ3HENCkpSSk5bdmypfhzQbzx8fEljnHPnj2FEu6CxBQo3NfVqlWDpqamUkJbrVq1t/b/q/0IAHl5efD398eePXvw77//IisrC1lZWTAwMHhjOzExMTh16hRkMlmhbQkJCejcuTM6duyIBg0awMPDA507d0a/fv1QpUqVYtuUSqVvnCNLREREFUepklMjIyOkpaUVKn/y5InSSGPBnMgCEomk0OXut8nPz8fYsWMxefLkQtusrKxw/fp1se13FRAQgL/++gtaWv/fDfn5+di6datSclpUQtajRw8oFAps2bIF1atXR35+PpycnJCdnf3W45YmZoVCAXt7+2K3F9XX79L/r5/jypUrsXr1aqxZswYNGjSAgYEBpk6d+tbzy8/PR48ePbB8+fJC2ywtLaGpqYkTJ04gIiICx48fx/r16zFv3jxERkbC1tb2jW0TERFRxVeq5LRevXo4cuRIofKoqCjUrVv3nYPQ0dFBXl6eUlmTJk3w119/FZuYOTg4IDc3F9HR0WjWrBkA4Pr163jy5EmJjnnlyhVER0cjLCwMJiYmYvmTJ0/Qrl07XL16VWkE9FWpqamIj4/H999/j7Zt2wIAzpw5U2Td8+fPo127dgCA3NxcxMTEKM2bVVWnT59Gz549MWTIEAAvk86///5baRS3uN/b/v37YWNjo5T0v0oikaB169Zo3bo1Fi5cCGtraxw8eFDp0j0RERFVTqW6IWrChAlISEjAF198gT///BM3btzAhg0bsHXrVsycOfOdg7CxscEff/yBf//9V7yre/bs2Th37hy++OILxMbG4u+//8ahQ4cwadIkAEDdunXRpUsXjB49GpGRkYiJicGoUaOgp6dXomNu3boVzZo1Q7t27eDk5CQubdq0QcuWLYu8MapAlSpVYGpqih9++AE3b97EyZMni02sNmzYgIMHD+LatWv44osv8PjxY3h7e5e4b1JTU3H37l2l5cWLFyXe/13Z29uLI5zx8fEYO3Ys7t69q1THxsYGkZGRSExMFKc2fPHFF3j06BEGDhyICxcu4NatWzh+/Di8vb2Rl5eHyMhIfP3114iOjkZSUhIOHDiABw8elGquMBEREVVcpUpObWxscPr0aXHuYNOmTREYGIjAwED079//nYNYunQpEhMTUatWLXFup7OzM8LDw/H333+jbdu2aNy4MRYsWABLS0txv23btkGhUKB9+/bo06cPxowZA3Nz87ceLzs7Gzt27Ch0U1OBvn37YseOHcVewtbQ0MDu3bsRExMDJycnfPnll/jmm2+KrOvv74/ly5ejYcOGOH36NH799VdUrVr1rTEW6NSpEywtLZWWj/F2pwULFqBJkybw8PCAq6srLCws0KtXL6U6M2bMgKamJhwdHWFmZoakpCRUr14dZ8+eRV5eHjw8PODk5IQpU6ZALpdDQ0MDRkZG+OOPP9CtWzfUqVMH8+fPx8qVK8vkpQBERESk/iTCm57hQ6SC0tPTIZfLkZaWpjTXmYiIiFRXSb+/y+UNUURERERERamwyenOnTshk8mKXOrXr1/e4WHcuHHFxjdu3LjyDo+IiIioXFTYy/pPnz7FvXv3itymra0tPgC+vNy/f7/Y13AaGRmVaO5sZVVwWUAxdS80pPrlHY7KSfT3LO8QiIiICinpZf0P8hB+VWBoaKj0cHpVY25uzgSUiIiI6DUV9rI+/T9XV1fxNbBEREREqozJqZrw8vIq9CgnIiIiooqGySkRERERqQwmpxVAXFwcunXrBplMhmrVqmHo0KHim7YK5ObmYuLEiTA2NoapqSnmz5+PV++F27FjB1xcXGBoaAgLCwsMGjQI9+/fF7eHhYVBIpEgNDQULi4u0NfXR6tWrXD9+nWl42zatAm1atWCjo4O6tati+3btyttl0gk+PHHH9G7d2/o6+ujdu3aOHTo0BvPLysrC+np6UoLERERVUxMTtVcSkoK2rdvj0aNGiE6OhpHjx7FvXv3MGDAAKV6QUFB0NLSQmRkJNatW4fVq1fjxx9/FLdnZ2fD19cXf/75J4KDg3H79m14eXkVOt68efOwcuVKREdHQ0tLS+lVrAcPHsSUKVMwffp0XL16FWPHjsWIESNw6tQppTaWLFmCAQMG4PLly+jWrRsGDx6MR48eFXuOfn5+kMvl4qJQKN6xt4iIiEjVVdhHSVU0Xl5eePLkSaFXly5cuBCRkZE4duyYWPbPP/9AoVDg+vXrqFOnDlxdXXH//n389ddfkEgkAIA5c+bg0KFDiIuLK/J4UVFRaNasGZ4+fQqZTIawsDC4ubnh999/R8eOHQEAhw8fhqenJ54/fw5dXV20bt0a9evXxw8//CC2M2DAAGRmZuK3334D8HLkdP78+fD19QUAZGZmwtDQEIcPH0aXLl2KjCUrKwtZWVnienp6OhQKBR8lVQw+SoqIiFQR3xBVScTExODUqVNKD/GvV68eACAhIUGs16JFCzExBYCWLVvi77//Rl5eHgDg0qVL6NmzJ6ytrWFoaAhXV1cAQFJSktLxnJ2dxZ8tLS0BQLz8Hx8fj9atWyvVb926NeLj44ttw8DAAIaGhkpTCF4nlUphZGSktBAREVHFVGGfc1pZ5Ofno0ePHli+fHmhbQXJ49tkZmaic+fO6Ny5M3bs2AEzMzMkJSXBw8MD2dnZSnW1tbXFnwuS3fz8/EJlBQRBKFT2ahsF+7zaBhEREVVeTE7VXJMmTbB//37Y2NhAS6v4X+f58+cLrdeuXRuampq4du0aHj58CH9/f3E+Z3R0dKljcXBwwJkzZzBs2DCxLCIiAg4ODqVui4iIiConJqdqJC0tDbGxsUplY8eOxZYtWzBw4EDMnDkTVatWxc2bN7F7925s2bIFmpqaAIDk5GRMmzYNY8eOxcWLF7F+/XqsXLkSAGBlZQUdHR2sX78e48aNw9WrV8U5oaUxc+ZMDBgwAE2aNEHHjh3xv//9DwcOHMDvv//+3udORERElQOTUzUSFhaGxo0bK5UNHz4cZ8+exezZs+Hh4YGsrCxYW1ujS5cu0ND4/ynFw4YNw/Pnz9GsWTNoampi0qRJGDNmDADAzMwMgYGBmDt3LtatW4cmTZrg22+/xaefflqq+Hr16oW1a9fim2++weTJk2Fra4tt27aJ81eJiIiI3oZ365PaKendfkRERKQ6eLc+EREREakdJqdEREREpDI455TUltOiY2r5EH4+JJ+IiKh4HDklIiIiIpXB5JSIiIiIVAaT0wpKEAR06tQJHh4ehbZt3LgRcrm80KtJiYiIiMobk9MKSiKRYNu2bYiMjMT3338vlt++fRuzZ8/G2rVrYWVlVY4REhERERXG5LQCUygUWLt2LWbMmIHbt29DEASMHDkSHTt2RLNmzdCtWzfIZDJUq1YNQ4cOxcOHD8V9XV1dMWnSJEydOhVVqlRBtWrV8MMPPyAzMxMjRoyAoaEhatWqhSNHjigdMy4u7q3tTp48GbNmzYKJiQksLCywePHij9UlREREpOKYnFZww4cPR8eOHTFixAh89913uHr1KtauXYv27dujUaNGiI6OxtGjR3Hv3j0MGDBAad+goCBUrVoVFy5cwKRJkzB+/Hj0798frVq1wsWLF+Hh4YGhQ4fi2bNnAICUlJQSt2tgYIDIyEisWLECS5cuxYkTJ4o9h6ysLKSnpystREREVDHxDVGVwP379+Hk5ITU1FT88ssvuHTpEiIjI3Hs2DGxzj///AOFQoHr16+jTp06cHV1RV5eHk6fPg0AyMvLg1wuR58+ffDTTz8BAO7evQtLS0ucO3cOLVq0wMKFC0vdLgA0a9YMHTp0gL+/f5HxL168GEuWLClUrpi6l4+SIiIiUhN8QxSJzM3NMWbMGDg4OKB3796IiYnBqVOnIJPJxKVevXoAgISEBHE/Z2dn8WdNTU2YmpqiQYMGYlm1atUAvEx+AbxTuwBgaWkptlEUHx8fpKWliUtycvK7dgURERGpOD6Ev5LQ0tKCltbLX3d+fj569OiB5cuXF6pnaWkp/qytra20TSKRKJVJJBKxvfdtt6CNokilUkil0mK3ExERUcXB5LQSatKkCfbv3w8bGxsxYVXldomIiKjy4GX9SuiLL77Ao0ePMHDgQFy4cAG3bt3C8ePH4e3tjby8PJVrl4iIiCoPJqeVUPXq1XH27Fnk5eXBw8MDTk5OmDJlCuRyOTQ03v0j8aHaJSIiosqDd+uT2inp3X5ERESkOni3PhERERGpHSanRERERKQyeEs1qS2nRcf4EH4iIqIKhiOnRERERKQymJzSR+Pl5YVevXqVdxhERESkwpicVmBFJYO//PILdHV1sWLFio8ez9q1axEYGCiuu7q6YurUqR89DiIiIlJdnHNaifz444/44osvsGHDBowaNeqjH18ul3/0YxIREZF64chpJbFixQpMnDgRP//8M0aNGoXFixejUaNGSnXWrFkDGxsbAMCVK1egoaGBhw8fAgAeP34MDQ0N9O/fX6zv5+eHli1bAgDy8vIwcuRI2NraQk9PD3Xr1sXatWuV2n91JNfLywvh4eFYu3YtJBIJJBIJEhMTP8i5ExERkfrgyGklMGfOHGzYsAEhISHo1KlTifZxcnKCqakpwsPD0bdvX/zxxx8wNTXFH3/8IdYJCwtD+/btAQD5+fmoWbMm9u7di6pVqyIiIgJjxoyBpaUlBgwYUKj9tWvX4saNG3BycsLSpUsBAGZmZkXGkpWVhaysLHE9PT29xOdORERE6oUjpxXckSNHsHz5cvz6668lTkwBQCKRoF27dggLCwPwMhEdPnw48vPzERcXh9zcXERERMDV1RUAoK2tjSVLlqBp06awtbXF4MGD4eXlhb179xbZvlwuh46ODvT19WFhYQELCwtoamoWWdfPzw9yuVxcFApFqfqAiIiI1AeT0wrO2dkZNjY2WLhwIZ4+fVqqfV1dXcXkNDw8HG5ubmjXrh3Cw8MRFRWF58+fo3Xr1mL9zZs3w8XFBWZmZpDJZNiyZQuSkpLe+xx8fHyQlpYmLsnJye/dJhEREakmJqcVXI0aNRAeHo6UlBR06dJFTFA1NDQgCIJS3ZycHKV1V1dX/PXXX7h58yauXr2Ktm3bon379ggPD0dYWBg++eQTGBoaAgD27t2LL7/8Et7e3jh+/DhiY2MxYsQIZGdnv/c5SKVSGBkZKS1ERERUMTE5rQSsrKwQHh6O+/fvo3PnzkhPT4eZmRnu3r2rlKDGxsYq7Vcw7/Srr75Cw4YNYWRkpJScFsw3BYDTp0+jVatWmDBhAho3bgx7e3skJCS8MS4dHR3k5eWV6bkSERGRemNyWknUrFkTYWFhSE1NRefOndGqVSs8ePAAK1asQEJCAjZs2IAjR44o7VMw73THjh3i3FJnZ2dkZ2cjNDRULAMAe3t7REdH49ixY7hx4wYWLFiAqKioN8ZkY2ODyMhIJCYm4uHDh8jPzy/r0yYiIiI1w+S0Eim4xP/kyROMHj0aX3/9NTZs2ICGDRviwoULmDFjRqF93NzckJeXJyaiEokEbdu2BQC0adNGrDdu3Dj06dMHn332GZo3b47U1FRMmDDhjfHMmDEDmpqacHR0hJmZWZnMTyUiIiL1JhFen3hIpOLS09Mhl8uRlpbG+adERERqoqTf3xw5JSIiIiKVweSUiIiIiFQG3xBFastp0TFoSPXLO4xSS/T3LO8QiIiIVBZHTomIiIhIZTA5raDCwsIgkUjw5MkTtT4GERERVS5MTlWYl5cXJBIJJBIJtLW1YWdnhxkzZiAzM/Ojx+Lq6oqpU6cqlbVq1QopKSmQy+UfPR4iIiKqmDjnVMV16dIF27ZtQ05ODk6fPo1Ro0YhMzMTmzZtKu/QoKOjAwsLi/IOg4iIiCoQjpyqOKlUCgsLCygUCgwaNAiDBw9GcHAwduzYARcXFxgaGsLCwgKDBg3C/fv339hWREQE2rVrBz09PSgUCkyePFlpFHbjxo2oXbs2dHV1Ua1aNfTr1w/AyxHc8PBwrF27VhzJTUxMLHRZPzAwEMbGxjh27BgcHBwgk8nQpUsXpKSkKMWxbds2ODg4QFdXF/Xq1cPGjRvLttOIiIhIbTE5VTN6enrIyclBdnY2fH198eeffyI4OBi3b9+Gl5dXsftduXIFHh4e6NOnDy5fvow9e/bgzJkzmDhxIgAgOjoakydPxtKlS3H9+nUcPXoU7dq1AwCsXbsWLVu2xOjRo5GSkoKUlBQoFIoij/Ps2TN8++232L59O/744w8kJSUpvXlqy5YtmDdvHpYtW4b4+Hh8/fXXWLBgAYKCgoqNPSsrC+np6UoLERERVUy8rK9GLly4gJ9//hkdO3aEt7e3WG5nZ4d169ahWbNmyMjIgEwmK7TvN998g0GDBonzRmvXro1169ahffv22LRpE5KSkmBgYIDu3bvD0NAQ1tbWaNy4MQBALpdDR0cH+vr6b72Mn5OTg82bN6NWrVoAgIkTJ2Lp0qXidl9fX6xcuRJ9+vQBANja2iIuLg7ff/89hg8fXmSbfn5+WLJkSck7ioiIiNQWk1MVFxISAplMhtzcXOTk5KBnz55Yv349Ll26hMWLFyM2NhaPHj1Cfn4+ACApKQmOjo6F2omJicHNmzexc+dOsUwQBOTn5+P27dtwd3eHtbU17Ozs0KVLF3Tp0gW9e/eGvn7pniOqr68vJqYAYGlpKU43ePDgAZKTkzFy5EiMHj1arJObm/vGm6p8fHwwbdo0cT09Pb3YkVsiIiJSb0xOVZybmxs2bdoEbW1tVK9eHdra2sjMzETnzp3RuXNn7NixA2ZmZkhKSoKHhweys7OLbCc/Px9jx47F5MmTC22zsrKCjo4OLl68iLCwMBw/fhwLFy7E4sWLERUVBWNj4xLHq62trbQukUggCIIYA/Dy0n7z5s2V6mlqahbbplQqhVQqLXEMREREpL6YnKo4AwMD2NvbK5Vdu3YNDx8+hL+/vziCGB0d/cZ2mjRpgr/++qtQW6/S0tJCp06d0KlTJyxatAjGxsY4efIk+vTpAx0dHeTl5b3XuVSrVg01atTArVu3MHjw4Pdqi4iIiComJqdqqGCkc/369Rg3bhyuXr0KX1/fN+4ze/ZstGjRAl988QVGjx4NAwMDxMfH48SJE1i/fj1CQkJw69YttGvXDlWqVMHhw4eRn5+PunXrAgBsbGwQGRmJxMREyGQymJiYvFPsixcvxuTJk2FkZISuXbsiKysL0dHRePz4sdKleyIiIqqceLe+GjIzM0NgYCD27dsHR0dH+Pv749tvv33jPs7OzggPD8fff/+Ntm3bonHjxliwYAEsLS0BAMbGxjhw4AA6dOgABwcHbN68Gbt27UL9+vUBADNmzICmpiYcHR3FaQTvYtSoUfjxxx8RGBiIBg0aoH379ggMDIStre07tUdEREQVi0QomBBIpCbS09Mhl8uRlpYGIyOj8g6HiIiISqCk398cOSUiIiIilcHklIiIiIhUBpNTIiIiIlIZvFuf1JbTomPQkJbuJQGqINHfs7xDICIiUlkcOaV35urqKr4OlYiIiKgsMDmtBLy8vNCrV6/yDoOIiIjorZic0hsJgoDc3NzyDoOIiIgqCSanlUxWVhYmT54Mc3Nz6Orqok2bNoiKihK3h4WFQSKR4NixY3BxcYFUKsXp06eRmZmJYcOGQSaTwdLSEitXrizU9o4dO+Di4gJDQ0NYWFhg0KBBuH//fqG2Q0ND4eLiAn19fbRq1QrXr1//KOdOREREqo/JaSUza9Ys7N+/H0FBQbh48SLs7e3h4eGBR48eFarn5+eH+Ph4ODs7Y+bMmTh16hQOHjyI48ePIywsDDExMUr7ZGdnw9fXF3/++SeCg4Nx+/ZteHl5FYph3rx5WLlyJaKjo6GlpQVvb+83xpyVlYX09HSlhYiIiCom3q1fiWRmZmLTpk0IDAxE165dAQBbtmzBiRMnsHXrVsycOVOsu3TpUri7uwMAMjIysHXrVvz0009iWVBQEGrWrKnU/qtJpp2dHdatW4dmzZohIyMDMplM3LZs2TK0b98eADBnzhx4enrixYsX0NXVLTJuPz8/LFmypAx6gIiIiFQdR04rkYSEBOTk5KB169Zimba2Npo1a4b4+Hilui4uLkr7ZWdno2XLlmKZiYkJ6tatq7TPpUuX0LNnT1hbW8PQ0BCurq4AgKSkJKV6zs7O4s+WlpYAoHT5/3U+Pj5IS0sTl+Tk5BKeMREREakbjpxWIoIgAAAkEkmh8tfLDAwMCu33JpmZmejcuTM6d+6MHTt2wMzMDElJSfDw8EB2drZSXW1tbfHnguPm5+cX27ZUKoVUKn1rDERERKT+OHJaidjb20NHRwdnzpwRy3JychAdHQ0HB4c37qetrY3z58+LZY8fP8aNGzfE9WvXruHhw4fw9/dH27ZtUa9evTeOhhIREREVhSOnlYiBgQHGjx+PmTNnwsTEBFZWVlixYgWePXuGkSNHFrufTCbDyJEjMXPmTJiamqJatWqYN28eNDT+//82VlZW0NHRwfr16zFu3DhcvXoVvr6+H+O0iIiIqAJhcloJ5OfnQ0vr5a/a398f+fn5GDp0KJ4+fQoXFxccO3YMVapUeWMb33zzDTIyMvDpp5/C0NAQ06dPR1pamrjdzMwMgYGBmDt3LtatW4cmTZrg22+/xaeffvpBz42IiIgqFolQkgmFpNa6dOkCe3t7fPfdd+UdSplIT0+HXC6HYupeaEj1yzucUkv09yzvEIiIiD66gu/vtLQ0GBkZFVuPI6cV2OPHjxEREYGwsDCMGzeuvMMpc1eXeLzxw01ERETqh8lpBebt7Y2oqChMnz4dPXv2LO9wiIiIiN6KyWkFdvDgwfIOgYiIiKhUmJyS2nJadOyDzTnlvFAiIqLyweec0jsLCwuDRCLBkydPyjsUIiIiqiCYnFYS9+/fx9ixY2FlZQWpVAoLCwt4eHjg3Llz5R0aERERkYiX9SuJvn37IicnB0FBQbCzs8O9e/cQGhqKR48elXdoRERERCKOnFYCT548wZkzZ7B8+XK4ubnB2toazZo1g4+PDzw9PZGYmAiJRILY2FilfSQSCcLCwsSyw4cPo06dOtDT04ObmxsSExOVjpOamoqBAweiZs2a0NfXR4MGDbBr1y6lOq6urpg8eTJmzZoFExMTWFhYYPHixR/u5ImIiEitMDmtBGQyGWQyGYKDg5GVlfVObSQnJ6NPnz7o1q0bYmNjMWrUKMyZM0epzosXL/DJJ58gJCQEV69exZgxYzB06FBERkYq1QsKCoKBgQEiIyOxYsUKLF26FCdOnCj22FlZWUhPT1daiIiIqGJicloJaGlpITAwEEFBQTA2Nkbr1q0xd+5cXL58ucRtbNq0CXZ2dli9ejXq1q2LwYMHw8vLS6lOjRo1MGPGDDRq1Ah2dnaYNGkSPDw8sG/fPqV6zs7OWLRoEWrXro1hw4bBxcUFoaGhxR7bz88PcrlcXBQKRanOn4iIiNQHk9NKom/fvvjvv/9w6NAheHh4ICwsDE2aNEFgYGCJ9o+Pj0eLFi0gkUjEspYtWyrVycvLw7Jly+Ds7AxTU1PIZDIcP34cSUlJSvWcnZ2V1i0tLXH//v1ij+3j44O0tDRxSU5OLlHMREREpH6YnFYiurq6cHd3x8KFCxEREQEvLy8sWrQIGhovPwaCIIh1c3JylPZ9dVtxVq5cidWrV2PWrFk4efIkYmNj4eHhgezsbKV62traSusSiQT5+fnFtiuVSmFkZKS0EBERUcXE5LQSc3R0RGZmJszMzAAAKSkp4rZXb44qqHv+/HmlstfXT58+jZ49e2LIkCFo2LAh7Ozs8Pfff3+Y4ImIiKhCYnJaCaSmpqJDhw7YsWMHLl++jNu3b2Pfvn1YsWIFevbsCT09PbRo0QL+/v6Ii4vDH3/8gfnz5yu1MW7cOCQkJGDatGm4fv06fv7550JTAuzt7XHixAlEREQgPj4eY8eOxd27dz/imRIREZG6Y3JaCchkMjRv3hyrV69Gu3bt4OTkhAULFmD06NH47rvvAAABAQHIycmBi4sLpkyZgq+++kqpDSsrK+zfvx//+9//0LBhQ2zevBlff/21Up0FCxagSZMm8PDwgKurKywsLNCrV6+PdZpERERUAUiEkkwmJFIh6enpL+/an7oXGlL9D3KMRH/PD9IuERFRZVXw/Z2WlvbG+0f4hihSW1eXePDmKCIiogqGl/WJiIiISGUwOSUiIiIilcHL+qS2nBYd45xTIiKiCoYjp0RERESkMpicVkJeXl4f7RFPNjY2WLNmzUc5FhEREak/JqcfkZeXFyQSCcaNG1do24QJEyCRSODl5fXB41i7dq3SA/RdXV0xderUD3KsqKgojBkz5oO0TURERBUPk9OPTKFQYPfu3Xj+/LlY9uLFC+zatQtWVlYf9Nh5eXnIz8+HXC6HsbHxBz1WATMzM+jrf5h5oURERFTxMDn9yJo0aQIrKyscOHBALDtw4AAUCgUaN24slh09ehRt2rSBsbExTE1N0b17dyQkJIjbw8LCIJFI8OTJE7EsNjYWEokEiYmJAIDAwEAYGxsjJCQEjo6OkEqluHPnjtJlfS8vL4SHh2Pt2rWQSCTi/nl5eRg5ciRsbW2hp6eHunXrYu3atUrnUtDOt99+C0tLS5iamuKLL75ATk6OWOf1y/qrVq1CgwYNYGBgAIVCgQkTJiAjI+ONfZaVlYX09HSlhYiIiComJqflYMSIEdi2bZu4HhAQAG9vb6U6mZmZmDZtGqKiohAaGgoNDQ307t0b+fn5pTrWs2fP4Ofnhx9//BF//fUXzM3NlbavXbsWLVu2xOjRo5GSkoKUlBQoFArk5+ejZs2a2Lt3L+Li4rBw4ULMnTsXe/fuVdr/1KlTSEhIwKlTpxAUFITAwEClKQOv09DQwLp163D16lUEBQXh5MmTmDVr1hvPwc/PD3K5XFwUCkWp+oCIiIjUBx8lVQ6GDh0KHx8fJCYmQiKR4OzZs9i9ezfCwsLEOn379lXaZ+vWrTA3N0dcXBycnJxKfKycnBxs3LgRDRs2LHK7XC6Hjo4O9PX1YWFhIZZrampiyZIl4rqtrS0iIiKwd+9eDBgwQCyvUqUKvvvuO2hqaqJevXrw9PREaGgoRo8eXeTxXp3bamtrC19fX4wfPx4bN24s9hx8fHwwbdo0cT09PZ0JKhERUQXF5LQcVK1aFZ6enggKCoIgCPD09ETVqlWV6iQkJGDBggU4f/48Hj58KI6YJiUllSo51dHRgbOz8zvFuXnzZvz444+4c+cOnj9/juzsbDRq1EipTv369aGpqSmuW1pa4sqVK8W2eerUKXz99deIi4tDeno6cnNz8eLFC2RmZsLAwKDIfaRSKaRS6TudAxEREakXXtYvJ97e3ggMDERQUFChS/oA0KNHD6SmpmLLli2IjIxEZGQkACA7OxvAy8vjACAIgrjPq3M9C+jp6UEikZQ6vr179+LLL7+Et7c3jh8/jtjYWIwYMUI8fgFtbW2ldYlEUuzUgzt37qBbt25wcnLC/v37ERMTgw0bNhQbOxEREVU+HDktJ126dBETPQ8PD6VtqampiI+Px/fff4+2bdsCAM6cOaNUx8zMDACQkpKCKlWqAHh5Q9S70NHRQV5enlLZ6dOn0apVK0yYMEEse/WGrHcRHR2N3NxcrFy5UkyuX5/DSkRERJUbR07LiaamJuLj4xEfH690WRx4OY/T1NQUP/zwA27evImTJ08qzbkEAHt7eygUCixevBg3btzAb7/9hpUrV75TLDY2NoiMjERiYqI4hcDe3h7R0dE4duwYbty4gQULFiAqKuqdzxcAatWqhdzcXKxfvx63bt3C9u3bsXnz5vdqk4iIiCoWJqflyMjICEZGRoXKNTQ0sHv3bsTExMDJyQlffvklvvnmG6U62tra2LVrF65du4aGDRti+fLl+Oqrr94pjhkzZkBTUxOOjo4wMzNDUlISxo0bhz59+uCzzz5D8+bNkZqaqjSK+i4aNWqEVatWYfny5XBycsLOnTvh5+f3Xm0SERFRxSIRXp20SKQG0tPTIZfLkZaWVmRyT0RERKqnpN/fHDklIiIiIpXB5JSIiIiIVAbv1ie15bToGDSk+mXebqK/Z5m3SURERCXDkVMiIiIiUhlMTgmLFy8u9OanshIYGAhjY+MP0jYRERFVPExO1ZyXlxckEom4mJqaokuXLrh8+XJ5hwYA+Oyzz3Djxo3yDoOIiIjUBJPTCqBLly5ISUlBSkoKQkNDoaWlhe7du3/QY77+GtPi6Onpwdzc/IPGQkRERBUHk9MKQCqVwsLCAhYWFmjUqBFmz56N5ORkPHjwAAAwe/Zs1KlTB/r6+rCzs8OCBQuKfJf99u3bYWNjA7lcjs8//xxPnz4Vt7m6umLixImYNm0aqlatCnd3dwDAqlWr0KBBAxgYGEChUGDChAnIyMgQ93v9sn7BFII3HYuIiIgqLyanFUxGRgZ27twJe3t7mJqaAgAMDQ0RGBiIuLg4rF27Flu2bMHq1auV9ktISEBwcDBCQkIQEhKC8PBw+Pv7K9UJCgqClpYWzp49i++//x7Ay7dZrVu3DlevXkVQUBBOnjyJWbNmvTHGkhzrVVlZWUhPT1daiIiIqGLio6QqgJCQEMhkMgBAZmYmLC0tERISAg2Nl//3mD9/vljXxsYG06dPx549e5SSyPz8fAQGBsLQ0BAAMHToUISGhmLZsmViHXt7e6xYsULp2FOnThV/trW1ha+vL8aPH4+NGzcWG29JjvUqPz8/LFmypCRdQURERGqOI6cVgJubG2JjYxEbG4vIyEh07twZXbt2xZ07dwAAv/zyC9q0aQMLCwvIZDIsWLAASUlJSm3Y2NiIySIAWFpa4v79+0p1XFxcCh371KlTcHd3R40aNWBoaIhhw4YhNTUVmZmZxcZbkmO9ysfHB2lpaeKSnJz85g4hIiIitcXktAIwMDCAvb097O3t0axZM2zduhWZmZnYsmULzp8/j88//xxdu3ZFSEgILl26hHnz5hW6oUlbW1tpXSKRID8/v9BxXnXnzh1069YNTk5O2L9/P2JiYrBhwwYAKHJOa2mO9SqpVAojIyOlhYiIiComXtavgCQSCTQ0NPD8+XOcPXsW1tbWmDdvnri9YET1fUVHRyM3NxcrV64UpxDs3bu3TNomIiKiyonJaQWQlZWFu3fvAgAeP36M7777DhkZGejRowfS0tKQlJSE3bt3o2nTpvjtt99w8ODBMjlurVq1kJubi/Xr16NHjx44e/YsNm/eXCZtExERUeXEy/oVwNGjR2FpaQlLS0s0b94cUVFR2LdvH1xdXdGzZ098+eWXmDhxIho1aoSIiAgsWLCgTI7bqFEjrFq1CsuXL4eTkxN27twJPz+/MmmbiIiIKieJIAhCeQdBVBrp6emQy+VIS0vj/FMiIiI1UdLvb46cEhEREZHKYHJKRERERCqDN0SR2nJadAwaUv0ybzfR37PM2yQiIqKS4cgpEREREakMJqdEREREpDKYnFZyycnJGDlyJKpXrw4dHR1YW1tjypQpSE1NLXEbYWFhkEgkePLkyYcLlIiIiCoFJqeV2K1bt+Di4oIbN25g165duHnzJjZv3ozQ0FC0bNkSjx49+ugxvem1p0RERFTxMTmtxL744gvo6Ojg+PHjaN++PaysrNC1a1f8/vvv+Pfff8VXnu7YsQMuLi4wNDSEhYUFBg0ahPv37wMAEhMT4ebmBgCoUqUKJBIJvLy8ALx8OUCbNm1gbGwMU1NTdO/eHQkJCeLxExMTIZFIsHfvXri6ukJXVxc7duz4uJ1AREREKoXJaSX16NEjHDt2DBMmTICenp7SNgsLCwwePBh79uyBIAjIzs6Gr68v/vzzTwQHB+P27dtiAqpQKLB//34AwPXr15GSkoK1a9cCADIzMzFt2jRERUUhNDQUGhoa6N27N/Lz85WON3v2bEyePBnx8fHw8PAoFGtWVhbS09OVFiIiIqqY+CipSurvv/+GIAhwcHAocruDgwMeP36MBw8ewNvbWyy3s7PDunXr0KxZM2RkZEAmk8HExAQAYG5uDmNjY7Fu3759ldrcunUrzM3NERcXBycnJ7F86tSp6NOnT7Gx+vn5YcmSJe9ymkRERKRmOHJKRSp4q62Ojg4uXbqEnj17wtraGoaGhnB1dQUAJCUlvbGNhIQEDBo0CHZ2djAyMoKtrW2R+7m4uLyxHR8fH6SlpYlLcnLyO54VERERqTomp5WUvb09JBIJ4uLiitx+7do1mJmZQVtbG507d4ZMJsOOHTsQFRWFgwcPAgCys7PfeIwePXogNTUVW7ZsQWRkJCIjI4vcz8DA4I3tSKVSGBkZKS1ERERUMTE5raRMTU3h7u6OjRs34vnz50rb7t69i507d8LLywvXrl3Dw4cP4e/vj7Zt26JevXrizVAFdHR0AAB5eXliWWpqKuLj4zF//nx07NhRnCZARERE9CZMTiux7777DllZWfDw8MAff/yB5ORkHD16FO7u7qhTpw4WLlwIKysr6OjoYP369bh16xYOHToEX19fpXasra0hkUgQEhKCBw8eICMjA1WqVIGpqSl++OEH3Lx5EydPnsS0adPK6UyJiIhIXTA5rcRq166NqKgo2NnZYcCAAbC2tkbXrl1Rp04dnD17FjKZDGZmZggMDMS+ffvg6OgIf39/fPvtt0rt1KhRA0uWLMGcOXNQrVo1TJw4ERoaGti9ezdiYmLg5OSEL7/8Et988005nSkRERGpC4lQcOcLEYBFixZh1apVOH78OFq2bFne4RQpPT0dcrkcaWlpnH9KRESkJkr6/c1HSZGSJUuWwMbGBpGRkWjevDk0NDi4TkRERB8Pk1MqZMSIEeUdAhEREVVSTE5JbTktOgYNqX6Ztpno71mm7REREVHp8JotEREREakMJqdUpgIDA5VeYbp48WI0atSo3OIhIiIi9cLkVE3dv38fY8eOhZWVFaRSKSwsLODh4YFz586Vd2hKZsyYgdDQ0PIOg4iIiNQE55yqqb59+yInJwdBQUGws7PDvXv3EBoaikePHpV3aEpkMhlkMll5h0FERERqgiOnaujJkyc4c+YMli9fDjc3N1hbW6NZs2bw8fGBp+fLG3qSkpLQs2dPyGQyGBkZYcCAAbh3757YRsHl9oCAAFhZWUEmk2H8+PHIy8vDihUrYGFhAXNzcyxbtkzp2KtWrUKDBg1gYGAAhUKBCRMmICMjo9hYX7+sHxUVBXd3d1StWhVyuRzt27fHxYsXy7aDiIiISG0xOVVDBaORwcHByMrKKrRdEAT06tULjx49Qnh4OE6cOIGEhAR89tlnSvUSEhJw5MgRHD16FLt27UJAQAA8PT3xzz//IDw8HMuXL8f8+fNx/vx5cR8NDQ2sW7cOV69eRVBQEE6ePIlZs2aVOPanT59i+PDhOH36NM6fP4/atWujW7duePr0abH7ZGVlIT09XWkhIiKiiomX9dWQlpYWAgMDMXr0aGzevBlNmjRB+/bt8fnnn8PZ2Rm///47Ll++jNu3b0OhUAAAtm/fjvr16yMqKgpNmzYFAOTn5yMgIACGhoZwdHSEm5sbrl+/jsOHD0NDQwN169bF8uXLERYWhhYtWgAApk6dKsZha2sLX19fjB8/Hhs3bixR7B06dFBa//7771GlShWEh4eje/fuRe7j5+eHJUuWlLabiIiISA1x5FRN9e3bF//99x8OHToEDw8PhIWFoUmTJggMDER8fDwUCoWYmAKAo6MjjI2NER8fL5bZ2NjA0NBQXK9WrRocHR2V3gpVrVo13L9/X1w/deoU3N3dUaNGDRgaGmLYsGFITU1FZmZmieK+f/8+xo0bhzp16kAul0MulyMjIwNJSUnF7uPj44O0tDRxSU5OLtGxiIiISP0wOVVjurq6cHd3x8KFCxEREQEvLy8sWrQIgiBAIpEUqv96uba2ttJ2iURSZFl+fj4A4M6dO+jWrRucnJywf/9+xMTEYMOGDQCAnJycEsXs5eWFmJgYrFmzBhEREYiNjYWpqSmys7OL3UcqlcLIyEhpISIiooqJyWkF4ujoiMzMTDg6OiIpKUlphDEuLg5paWlwcHB45/ajo6ORm5uLlStXokWLFqhTpw7++++/UrVx+vRpTJ48Gd26dUP9+vUhlUrx8OHDd46JiIiIKhYmp2ooNTUVHTp0wI4dO8S5pfv27cOKFSvQs2dPdOrUCc7Ozhg8eDAuXryICxcuYNiwYWjfvj1cXFze+bi1atVCbm4u1q9fj1u3bmH79u3YvHlzqdqwt7fH9u3bER8fj8jISAwePBh6enrvHBMRERFVLExO1ZBMJkPz5s2xevVqtGvXDk5OTliwYAFGjx6N7777DhKJBMHBwahSpQratWuHTp06wc7ODnv27Hmv4zZq1AirVq3C8uXL4eTkhJ07d8LPz69UbQQEBODx48do3Lgxhg4dismTJ8Pc3Py94iIiIqKKQyIIglDeQRCVRnp6OuRyOdLS0jj/lIiISE2U9PubI6dEREREpDKYnBIRERGRymBySkREREQqg2+IIrXltOgYNKT6Jaqb6O/5gaMhIiKissCR00rAxsYGa9asEdcL7uYHgMTEREgkEsTGxpZLbIsXL0ajRo3K5dhERESkepicqjEvLy9IJBLxzU7VqlWDu7s7AgICxLc6AUBUVBTGjBkjrqekpKBr164fPd5Xk+ICM2bMQGho6EePhYiIiFQTk1M116VLF6SkpCAxMRFHjhyBm5sbpkyZgu7duyM3NxcAYGZmBn39/7/8bWFhAalUWl4hK5HJZDA1NS3vMIiIiEhFMDlVc1KpFBYWFqhRowaaNGmCuXPn4tdff8WRI0cQGBgI4M2X9Qtcu3YNrVq1gq6uLurXr4+wsDCl7eHh4WjWrBmkUiksLS0xZ84cMfkt6hjAy4f2L168WNwOAL1794ZEIhHXeVmfiIiIXsXktALq0KEDGjZsiAMHDpR4n5kzZ2L69Om4dOkSWrVqhU8//RSpqakAgH///RfdunVD06ZN8eeff2LTpk3YunUrvvrqqxK3HxUVBQDYtm0bUlJSxPWSyMrKQnp6utJCREREFROT0wqqXr16SExMLHH9iRMnom/fvnBwcMCmTZsgl8uxdetWAMDGjRuhUCjw3XffoV69eujVqxeWLFmClStXKs1tfRMzMzMAgLGxMSwsLMT1kvDz84NcLhcXhUJR4n2JiIhIvTA5raAEQYBEIilx/ZYtW4o/a2lpwcXFBfHx8QCA+Ph4tGzZUqm91q1bIyMjA//880/ZBV0MHx8fpKWliUtycvIHPyYRERGVDz7ntIKKj4+Hra3te7VRkIwWlegKgqBUR0NDQywrkJOT817HLyCVSlXmBi4iIiL6sDhyWgGdPHkSV65cQd++fUu8z/nz58Wfc3NzERMTg3r16gEAHB0dERERoZR8RkREwNDQEDVq1ADw8rJ9SkqKuD09PR23b99WOoa2tjby8vLe6ZyIiIiocmByquaysrJw9+5d/Pvvv7h48SK+/vpr9OzZE927d8ewYcNK3M6GDRtw8OBBXLt2DV988QUeP34Mb29vAMCECROQnJyMSZMm4dq1a/j111+xaNEiTJs2DRoaLz9CHTp0wPbt23H69GlcvXoVw4cPh6amptIxbGxsEBoairt37+Lx48dl1wlERERUYfCyvpo7evQoLC0toaWlhSpVqqBhw4ZYt24dhg8fLiaOJeHv74/ly5fj0qVLqFWrFn799VdUrVoVAFCjRg0cPnwYM2fORMOGDWFiYoKRI0di/vz54v4+Pj64desWunfvDrlcDl9f30IjpytXrsS0adOwZcsW1KhRo1Q3bBEREVHlIBFenyhIFY6lpSV8fX0xatSo8g6lTKSnp0MulyMtLQ1GRkblHQ4RERGVQEm/vzlyWoE9e/YMZ8+exb1791C/fv3yDoeIiIjorTjntAL74Ycf8Pnnn2Pq1KlKj4oiIiIiUlW8rE9qh5f1iYiI1E9Jv785ckpEREREKoPJKRERERGpDCanRERERKQymJwSERERkcpgckpEREREKoPJKRERERGpDCanRERERKQymJwSERERkcpgckpEREREKoPJKRERERGpDCanRERERKQymJwSERERkcrQKu8AiEpLEAQAQHp6ejlHQkRERCVV8L1d8D1eHCanpHZSU1MBAAqFopwjISIiotJ6+vQp5HJ5sduZnJLaMTExAQAkJSW98cNN7y89PR0KhQLJyckwMjIq73AqLPbzx8O+/njY1x+PuvS1IAh4+vQpqlev/sZ6TE5J7WhovJwqLZfLVfqPsCIxMjJiX38E7OePh3398bCvPx516OuSDCrxhigiIiIiUhlMTomIiIhIZTA5JbUjlUqxaNEiSKXS8g6lwmNffxzs54+Hff3xsK8/norW1xLhbffzExERERF9JBw5JSIiIiKVweSUiIiIiFQGk1MiIiIiUhlMTomIiIhIZTA5JSIiIiKVweSU1MrGjRtha2sLXV1dfPLJJzh9+nR5h6RW/Pz80LRpUxgaGsLc3By9evXC9evXleoIgoDFixejevXq0NPTg6urK/766y+lOllZWZg0aRKqVq0KAwMDfPrpp/jnn38+5qmoHT8/P0gkEkydOlUsY1+XnX///RdDhgyBqakp9PX10ahRI8TExIjb2ddlIzc3F/Pnz4etrS309PRgZ2eHpUuXIj8/X6zDvi69P/74Az169ED16tUhkUgQHBystL2s+vTx48cYOnQo5HI55HI5hg4diidPnnzgs3sHApGa2L17t6CtrS1s2bJFiIuLE6ZMmSIYGBgId+7cKe/Q1IaHh4ewbds24erVq0JsbKzg6ekpWFlZCRkZGWIdf39/wdDQUNi/f79w5coV4bPPPhMsLS2F9PR0sc64ceOEGjVqCCdOnBAuXrwouLm5CQ0bNhRyc3PL47RU3oULFwQbGxvB2dlZmDJliljOvi4bjx49EqytrQUvLy8hMjJSuH37tvD7778LN2/eFOuwr8vGV199JZiamgohISHC7du3hX379gkymUxYs2aNWId9XXqHDx8W5s2bJ+zfv18AIBw8eFBpe1n1aZcuXQQnJychIiJCiIiIEJycnITu3bt/rNMsMSanpDaaNWsmjBs3TqmsXr16wpw5c8opIvV3//59AYAQHh4uCIIg5OfnCxYWFoK/v79Y58WLF4JcLhc2b94sCIIgPHnyRNDW1hZ2794t1vn3338FDQ0N4ejRox/3BNTA06dPhdq1awsnTpwQ2rdvLyan7OuyM3v2bKFNmzbFbmdflx1PT0/B29tbqaxPnz7CkCFDBEFgX5eF15PTsurTuLg4AYBw/vx5sc65c+cEAMK1a9c+8FmVDi/rk1rIzs5GTEwMOnfurFTeuXNnRERElFNU6i8tLQ0AYGJiAgC4ffs27t69q9TPUqkU7du3F/s5JiYGOTk5SnWqV68OJycn/i6K8MUXX8DT0xOdOnVSKmdfl51Dhw7BxcUF/fv3h7m5ORo3bowtW7aI29nXZadNmzYIDQ3FjRs3AAB//vknzpw5g27dugFgX38IZdWn586dg1wuR/PmzcU6LVq0gFwuV7l+1yrvAIhK4uHDh8jLy0O1atWUyqtVq4a7d++WU1TqTRAETJs2DW3atIGTkxMAiH1ZVD/fuXNHrKOjo4MqVaoUqsPfhbLdu3fj4sWLiIqKKrSNfV12bt26hU2bNmHatGmYO3cuLly4gMmTJ0MqlWLYsGHs6zI0e/ZspKWloV69etDU1EReXh6WLVuGgQMHAuDn+kMoqz69e/cuzM3NC7Vvbm6ucv3O5JTUikQiUVoXBKFQGZXMxIkTcfnyZZw5c6bQtnfpZ/4ulCUnJ2PKlCk4fvw4dHV1i63Hvn5/+fn5cHFxwddffw0AaNy4Mf766y9s2rQJw4YNE+uxr9/fnj17sGPHDvz888+oX78+YmNjMXXqVFSvXh3Dhw8X67Gvy15Z9GlR9VWx33lZn9RC1apVoampWeh/d/fv3y/0v0l6u0mTJuHQoUM4deoUatasKZZbWFgAwBv72cLCAtnZ2Xj8+HGxdejlZbb79+/jk08+gZaWFrS0tBAeHo5169ZBS0tL7Cv29fuztLSEo6OjUpmDgwOSkpIA8HNdlmbOnIk5c+bg888/R4MGDTB06FB8+eWX8PPzA8C+/hDKqk8tLCxw7969Qu0/ePBA5fqdySmpBR0dHXzyySc4ceKEUvmJEyfQqlWrcopK/QiCgIkTJ+LAgQM4efIkbG1tlbbb2trCwsJCqZ+zs7MRHh4u9vMnn3wCbW1tpTopKSm4evUqfxev6NixI65cuYLY2FhxcXFxweDBgxEbGws7Ozv2dRlp3bp1oUei3bhxA9bW1gD4uS5Lz549g4aGcuqgqakpPkqKfV32yqpPW7ZsibS0NFy4cEGsExkZibS0NNXr9/K4C4voXRQ8Smrr1q1CXFycMHXqVMHAwEBITEws79DUxvjx4wW5XC6EhYUJKSkp4vLs2TOxjr+/vyCXy4UDBw4IV65cEQYOHFjkI0tq1qwp/P7778LFixeFDh06VOrHwJTUq3frCwL7uqxcuHBB0NLSEpYtWyb8/fffws6dOwV9fX1hx44dYh32ddkYPny4UKNGDfFRUgcOHBCqVq0qzJo1S6zDvi69p0+fCpcuXRIuXbokABBWrVolXLp0SXxUYln1aZcuXQRnZ2fh3Llzwrlz54QGDRrwUVJE72vDhg2CtbW1oKOjIzRp0kR8BBKVDIAil23btol18vPzhUWLFgkWFhaCVCoV2rVrJ1y5ckWpnefPnwsTJ04UTExMBD09PaF79+5CUlLSRz4b9fN6csq+Ljv/+9//BCcnJ0EqlQr16tUTfvjhB6Xt7OuykZ6eLkyZMkWwsrISdHV1BTs7O2HevHlCVlaWWId9XXqnTp0q8t/m4cOHC4JQdn2ampoqDB48WDA0NBQMDQ2FwYMHC48fP/5IZ1lyEkEQhPIZsyUiIiIiUsY5p0RERESkMpicEhEREZHKYHJKRERERCqDySkRERERqQwmp0RERESkMpicEhEREZHKYHJKRERERCqDySkRERERqQwmp0RERESkMpicEhEREZHKYHJKRERERCrj/wAgoIXO+AN3gwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.barh(y=df_test[\"#3_country_label\"].value_counts().sort_values(ascending=True).index,\n",
    "         width=df_test[\"#3_country_label\"].value_counts().sort_values(ascending=True))\n",
    "plt.title(\"Value counts of country label in test data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some countries don't have a lot of observations, which means that it might be harder to detect their dialects. We need to take this into consideration when training and evaluating the model (by assigning weights/oversampling, and by choosing appropriate evaluation metrics)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Training the Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this classifier, we will convert the tweets into vector embeddings using the AraBART model. We will use the last hidden layer of the model to extract the features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Data Preparation\n",
    "The first step is to prepare our data by tokenizing it to use it with the model AraBART."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we load the model and its tokenizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.device"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device(\"cuda\")\n",
    "type(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = AutoModel.from_pretrained(\"moussaKam/AraBART\").to(device)\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"moussaKam/AraBART\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we convert the datasets into a DatasetDict object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapper = {\"#2_tweet\": \"tweet\", \"#3_country_label\": \"label\"}\n",
    "columns_to_keep = [\"tweet\", \"label\"]\n",
    "\n",
    "df_train = df_train.rename(columns=mapper)[columns_to_keep]\n",
    "df_test = df_test.rename(columns=mapper)[columns_to_keep]\n",
    "\n",
    "train_dataset = Dataset.from_pandas(df_train)\n",
    "test_dataset = Dataset.from_pandas(df_test)\n",
    "data = DatasetDict({'train': train_dataset, 'test': test_dataset})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we tokenkize the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                   \r"
     ]
    }
   ],
   "source": [
    "def tokenize(batch):\n",
    "  return tokenizer(batch[\"tweet\"], padding=True)\n",
    "\n",
    "data_encoded = data.map(tokenize, batched=True, batch_size=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tweet', 'label', 'input_ids', 'attention_mask']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_encoded[\"train\"].column_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Feature Extraction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will extract the output of the last hidden layer of AraBART, and use those embeddings as the features of our classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_hidden_states(batch):\n",
    "  inputs = {k:v.to(device) for k,v in batch.items()\n",
    "            if k in tokenizer.model_input_names}\n",
    "  with torch.no_grad():\n",
    "    last_hidden_state = model(**inputs).last_hidden_state\n",
    "\n",
    "  return{\"hidden_state\": last_hidden_state[:,0].cpu().numpy()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                   \r"
     ]
    }
   ],
   "source": [
    "data_encoded.set_format(\"torch\", columns=[\"input_ids\", \"attention_mask\", \"label\"])\n",
    "data_hidden = data_encoded.map(extract_hidden_states, batched=True, batch_size=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"../data/data_hidden.pkl\", \"wb\") as f:\n",
    "    pickle.dump(data_hidden, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"../data/data_hidden.pkl\", \"rb\") as f:\n",
    "    data_hidden = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we only need to convert the data into numpy arrays, and we are ready to train the models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((21000, 768), (21000,))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = np.array(data_hidden[\"train\"][\"hidden_state\"])\n",
    "X_test = np.array(data_hidden[\"test\"][\"hidden_state\"])\n",
    "y_train = np.array(data_hidden[\"train\"][\"label\"])\n",
    "y_test = np.array(data_hidden[\"test\"][\"label\"])\n",
    "\n",
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will start simple, and use a logistic regression to see what we can achieve with those embeddings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_model = LogisticRegression(multi_class='multinomial', \n",
    "                              class_weight=\"balanced\", \n",
    "                              max_iter=1000, \n",
    "                              random_state=2024)\n",
    "lr_model.fit(X_train, y_train)\n",
    "\n",
    "with open('../models/logistic_regression.pkl', 'wb') as file:\n",
    "    pickle.dump(lr_model, file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Evaluating the Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's have a look at some metrics for our models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression\n",
      "\n",
      "Train set:\n",
      "Accuracy: 0.3448095238095238\n",
      "F1 macro average: 0.30283202516650803\n",
      "F1 weighted average: 0.35980803167526537\n",
      "--------------------------------------------------\n",
      "Test set:\n",
      "Accuracy: 0.2324\n",
      "F1 macro average: 0.15894661492139023\n",
      "F1 weighted average: 0.2680459740545796\n"
     ]
    }
   ],
   "source": [
    "lr_train_preds = lr_model.predict(X_train)\n",
    "lr_test_preds = lr_model.predict(X_test)\n",
    "\n",
    "evaluate_predictions(model=\"Logistic Regression\", \n",
    "                     train_preds= lr_train_preds, y_train=y_train,\n",
    "                     test_preds=lr_test_preds, y_test=y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the model is struggling to correctly classify the different dialects, (which makes sense because everything is in arabic at the end of the day). Let's have a look at the confusion matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, lr_test_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the confusion matrix, we see that the model is only really able to detect Egyptian arabic, and to a lesser extent Iraqi and Algerian."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "adc",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}