source.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Downloading and Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# natural language processing for most of the languages in this dataset (to check the list here: https://spacy.io/models)\n",
    "# !pip install -U spacy\n",
    "\n",
    "# natural language toolkit for arabic and turkish (also supports other languages) \n",
    "# (to see all available stopword languages, you can retrieve the list of fileids using: print(stopwords.fileids()))\n",
    "# !pip install --user -U nltk\n",
    "\n",
    "# indic-nlp-library for Indic languages (need it for lemmatising/stemming)\n",
    "# (check documentation here: http://anoopkunchukuttan.github.io/indic_nlp_library/)\n",
    "# !pip install indic-nlp-library\n",
    "\n",
    "# a useful library for text analysis and pre-processing. Keeping it here for now, but not use it.\n",
    "# (check documentation here: https://advertools.readthedocs.io/en/master/)\n",
    "# !pip install advertools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# download the spacy models for the languages that we need\n",
    "# !python -m spacy download en_core_web_sm # English \n",
    "# !python -m spacy download fr_core_news_sm # French\n",
    "# !python -m spacy download es_core_news_sm # Spanish\n",
    "# !python -m spacy download pt_core_news_sm # Portuguese\n",
    "# !python -m spacy download it_core_news_sm # Italian\n",
    "# !python -m spacy download ru_core_news_sm # Russian\n",
    "# !python -m spacy download sv_core_news_sm # Swedish\n",
    "# !python -m spacy download nl_core_news_sm # Dutch\n",
    "# !python -m spacy download de_core_news_sm # German\n",
    "# !python -m spacy download da_core_news_sm # Danish\n",
    "# !python -m spacy download el_core_news_sm # Greek\n",
    "\n",
    "# nltk.download('stopwords') # download the stopwords that are available in nltk (needed for arabic and turkish)\n",
    "# nltk.download('wordnet') # download the wordnet that is available in nltk for lemmatisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for data manipulation\n",
    "import pandas as pd\n",
    "import re \n",
    "import numpy as np\n",
    "\n",
    "# for visualisation\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# for natural language processing\n",
    "import spacy\n",
    "from spacy.lang.ml import STOP_WORDS as stopwords_ml\n",
    "from spacy.lang.ta import STOP_WORDS as stopwords_ta\n",
    "from spacy.lang.kn import STOP_WORDS as stopwords_kn\n",
    "from spacy.lang.hi import STOP_WORDS as stopwords_hi\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.tokenize.toktok import ToktokTokenizer\n",
    "from nltk.stem.arlstem2 import ARLSTem2\n",
    "from snowballstemmer import TurkishStemmer \n",
    "from indicnlp.normalize.indic_normalize import IndicNormalizerFactory\n",
    "\n",
    "# This library also has functions to remove stopwords for Tamil, Hindi and Arabic, but \n",
    "# the quantity of stopwords is not as much as compared to the other libraries that are used.\n",
    "# However it contains other languages that are not in the used libraries e.g. Japanese.\n",
    "# It might be useful for future work, so I am keeping it here.\n",
    "# import advertools as adv \n",
    "\n",
    "# for machine learning\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import CountVectorizer \n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
    "from sklearn.naive_bayes import MultinomialNB # for the Naive Bayes model (1st model)\n",
    "from sklearn.svm import LinearSVC # for the Linear SVM model (2nd model)\n",
    "from sklearn.model_selection import GridSearchCV # for hyperparameter tuning\n",
    "\n",
    "# for the deep learning models\n",
    "# TextCNN model (3rd model)\n",
    "# GRU model (4th model)\n",
    "# BiLSTM model (5th model)\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from keras_preprocessing.sequence import pad_sequences\n",
    "from keras_preprocessing.text import Tokenizer\n",
    "from keras import layers\n",
    "from keras.layers import Dense, Embedding, Conv1D, Dropout, GlobalAveragePooling1D,Bidirectional, LSTM, GRU\n",
    "from keras.models import Sequential\n",
    "from keras.optimizers import Adam\n",
    "from keras.losses import CategoricalCrossentropy\n",
    "\n",
    "# hide warnings\n",
    "import warnings \n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# for reproducibility\n",
    "import random \n",
    "seed = 42"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing and Exploring the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"LanguageDetection.csv\") # read the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10337, 2)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape # check the shape of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Text</th>\n",
       "      <th>Language</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nature, in the broadest sense, is the natural...</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>\"Nature\" can refer to the phenomena of the phy...</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The study of nature is a large, if not the onl...</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Although humans are part of nature, human acti...</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[1] The word nature is borrowed from the Old F...</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10332</th>\n",
       "      <td>ನಿಮ್ಮ ತಪ್ಪು ಏನು ಬಂದಿದೆಯೆಂದರೆ ಆ ದಿನದಿಂದ ನಿಮಗೆ ಒ...</td>\n",
       "      <td>Kannada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10333</th>\n",
       "      <td>ನಾರ್ಸಿಸಾ ತಾನು ಮೊದಲಿಗೆ ಹೆಣಗಾಡುತ್ತಿದ್ದ ಮಾರ್ಗಗಳನ್...</td>\n",
       "      <td>Kannada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10334</th>\n",
       "      <td>ಹೇಗೆ ' ನಾರ್ಸಿಸಿಸಮ್ ಈಗ ಮರಿಯನ್ ಅವರಿಗೆ ಸಂಭವಿಸಿದ ಎ...</td>\n",
       "      <td>Kannada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10335</th>\n",
       "      <td>ಅವಳು ಈಗ ಹೆಚ್ಚು ಚಿನ್ನದ ಬ್ರೆಡ್ ಬಯಸುವುದಿಲ್ಲ ಎಂದು ...</td>\n",
       "      <td>Kannada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10336</th>\n",
       "      <td>ಟೆರ್ರಿ ನೀವು ನಿಜವಾಗಿಯೂ ಆ ದೇವದೂತನಂತೆ ಸ್ವಲ್ಪ ಕಾಣು...</td>\n",
       "      <td>Kannada</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10337 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Text Language\n",
       "0       Nature, in the broadest sense, is the natural...  English\n",
       "1      \"Nature\" can refer to the phenomena of the phy...  English\n",
       "2      The study of nature is a large, if not the onl...  English\n",
       "3      Although humans are part of nature, human acti...  English\n",
       "4      [1] The word nature is borrowed from the Old F...  English\n",
       "...                                                  ...      ...\n",
       "10332  ನಿಮ್ಮ ತಪ್ಪು ಏನು ಬಂದಿದೆಯೆಂದರೆ ಆ ದಿನದಿಂದ ನಿಮಗೆ ಒ...  Kannada\n",
       "10333  ನಾರ್ಸಿಸಾ ತಾನು ಮೊದಲಿಗೆ ಹೆಣಗಾಡುತ್ತಿದ್ದ ಮಾರ್ಗಗಳನ್...  Kannada\n",
       "10334  ಹೇಗೆ ' ನಾರ್ಸಿಸಿಸಮ್ ಈಗ ಮರಿಯನ್ ಅವರಿಗೆ ಸಂಭವಿಸಿದ ಎ...  Kannada\n",
       "10335  ಅವಳು ಈಗ ಹೆಚ್ಚು ಚಿನ್ನದ ಬ್ರೆಡ್ ಬಯಸುವುದಿಲ್ಲ ಎಂದು ...  Kannada\n",
       "10336  ಟೆರ್ರಿ ನೀವು ನಿಜವಾಗಿಯೂ ಆ ದೇವದೂತನಂತೆ ಸ್ವಲ್ಪ ಕಾಣು...  Kannada\n",
       "\n",
       "[10337 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data # check the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Language'] = data['Language'].str.replace('Sweedish', 'Swedish') # correcting the spelling of Swedish\n",
    "data['Language'] = data['Language'].str.replace('Portugeese', 'Portoguese') # correcting the spelling of Portuguese"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "English       1385\n",
       "French        1014\n",
       "Spanish        819\n",
       "Portoguese     739\n",
       "Italian        698\n",
       "Russian        692\n",
       "Swedish        676\n",
       "Malayalam      594\n",
       "Dutch          546\n",
       "Arabic         536\n",
       "Turkish        474\n",
       "German         470\n",
       "Tamil          469\n",
       "Danish         428\n",
       "Kannada        369\n",
       "Greek          365\n",
       "Hindi           63\n",
       "Name: Language, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Language'].value_counts() # checking the distribution of the target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the distribution of the target variable\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.barplot(x=data['Language'].value_counts().index, y=data['Language'].value_counts().values, palette='magma')\n",
    "plt.title('Languages Distribution')\n",
    "plt.xlabel('Languages')\n",
    "plt.ylabel('Number of Samples')\n",
    "plt.xticks(rotation=50)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Text Pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "norm_data = data.copy() # creating a copy of the original dataset and working on the copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(norm_data[norm_data.duplicated()]) # check for duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "norm_data.drop_duplicates(inplace=True) # drop duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load spaCy models for the languages that we need\n",
    "nlp_en = spacy.load(\"en_core_web_sm\") \n",
    "nlp_fr = spacy.load(\"fr_core_news_sm\")\n",
    "nlp_es = spacy.load(\"es_core_news_sm\")\n",
    "nlp_pt = spacy.load(\"pt_core_news_sm\")\n",
    "nlp_it = spacy.load(\"it_core_news_sm\")\n",
    "nlp_ru = spacy.load(\"ru_core_news_sm\")\n",
    "nlp_sv = spacy.load(\"sv_core_news_sm\")\n",
    "nlp_nl = spacy.load(\"nl_core_news_sm\")\n",
    "nlp_de = spacy.load(\"de_core_news_sm\")\n",
    "nlp_da = spacy.load(\"da_core_news_sm\")\n",
    "nlp_gr = spacy.load(\"el_core_news_sm\")\n",
    "\n",
    "# function to remove stopwords with spaCy for the most of languages of the dataset\n",
    "def remove_stopwords(df, is_lower_case=False):\n",
    "  if df['Language'] == 'English':\n",
    "        df['Text'] = ' '.join([token.text for token in nlp_en(df['Text']) if not token.is_stop])\n",
    "  elif df['Language'] == 'French':\n",
    "        df['Text'] = ' '.join([token.text for token in nlp_fr(df['Text']) if not token.is_stop])\n",
    "  elif df['Language'] == 'Spanish':\n",
    "        df['Text'] = ' '.join([token.text for token in nlp_es(df['Text']) if not token.is_stop])\n",
    "  elif df['Language'] == 'Portuguese':\n",
    "        df['Text'] = ' '.join([token.text for token in nlp_pt(df['Text']) if not token.is_stop])\n",
    "  elif df['Language'] == 'Italian':\n",
    "        df['Text'] = ' '.join([token.text for token in nlp_it(df['Text']) if not token.is_stop])\n",
    "  elif df['Language'] == 'Russian':\n",
    "        df['Text'] = ' '.join([token.text for token in nlp_ru(df['Text']) if not token.is_stop])\n",
    "  elif df['Language'] == 'Swedish':\n",
    "        df['Text'] = ' '.join([token.text for token in nlp_sv(df['Text']) if not token.is_stop])\n",
    "  elif df['Language'] == 'Dutch':\n",
    "        df['Text'] = ' '.join([token.text for token in nlp_nl(df['Text']) if not token.is_stop])\n",
    "  elif df['Language'] == 'German':\n",
    "        df['Text'] = ' '.join([token.text for token in nlp_de(df['Text']) if not token.is_stop])\n",
    "  elif df['Language'] == 'Danish':\n",
    "        df['Text'] = ' '.join([token.text for token in nlp_da(df['Text']) if not token.is_stop])\n",
    "  elif df['Language'] == 'Greek':\n",
    "        df['Text'] = ' '.join([token.text for token in nlp_gr(df['Text']) if not token.is_stop])\n",
    "  return df   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "norm_data = norm_data.apply(remove_stopwords, axis=1) # apply the function to the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = ToktokTokenizer()\n",
    "\n",
    "stopwords_ar = set(stopwords.words('arabic'))\n",
    "\n",
    "# function to remove arabic stopwords\n",
    "def remove_stopwords_ar(df, is_lower_case = False):\n",
    "  if df['Language'] == 'Arabic':\n",
    "    text = df['Text']\n",
    "    tokens = tokenizer.tokenize(text)\n",
    "    tokens = [token.strip() for token in tokens]\n",
    "    # if is_lower_case:\n",
    "    filtered_tokens = [token for token in tokens if token not in stopwords_ar]\n",
    "    # else:\n",
    "    #   filtered_tokens = [token for token in tokens if token.lower() not in stopwords_ar]\n",
    "    filtered_text = ' '.join(filtered_tokens)    \n",
    "    df['Text'] = filtered_text\n",
    "  return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# apply the function to the dataset for Arabic\n",
    "norm_data = norm_data.apply(remove_stopwords_ar, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "stopwords_tr = set(stopwords.words('turkish'))\n",
    "\n",
    "# function to remove turkish stopwords\n",
    "def remove_stopwords_tr(df, is_lower_case = False):\n",
    "  if df['Language'] == 'Turkish':\n",
    "    text = df['Text']\n",
    "    tokens = tokenizer.tokenize(text)\n",
    "    tokens = [token.strip() for token in tokens]\n",
    "    # if is_lower_case:\n",
    "    filtered_tokens = [token for token in tokens if token not in stopwords_tr]\n",
    "    # else:\n",
    "    #   filtered_tokens = [token for token in tokens if token.lower() not in stopwords_tr]\n",
    "    filtered_text = ' '.join(filtered_tokens)    \n",
    "    df['Text'] = filtered_text  \n",
    "  return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# apply the function to the dataset for Turkish\n",
    "norm_data = norm_data.apply(remove_stopwords_tr, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to remove hindi stopwords\n",
    "def remove_stopwords_hi(df, is_lower_case = False):\n",
    "  if df['Language'] == 'Hindi':\n",
    "    text = df['Text']\n",
    "    tokens = tokenizer.tokenize(text)\n",
    "    tokens = [token.strip() for token in tokens]\n",
    "    # if is_lower_case:\n",
    "    filtered_tokens = [token for token in tokens if token not in stopwords_hi]\n",
    "    # else:\n",
    "    #   filtered_tokens = [token for token in tokens if token.lower() not in stopwords_hi]\n",
    "    filtered_text = ' '.join(filtered_tokens)    \n",
    "    df['Text'] = filtered_text\n",
    "  return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# apply the function to the dataset for Hindi\n",
    "norm_data = norm_data.apply(remove_stopwords_hi, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to remove kannada stopwords\n",
    "def remove_stopwords_kn(df, is_lower_case = False):\n",
    "  if df['Language'] == 'Kannada':\n",
    "    tokens = tokenizer.tokenize(df['Text'])\n",
    "    tokens = [token.strip() for token in tokens]\n",
    "    # if is_lower_case:\n",
    "    filtered_tokens = [token for token in tokens if token not in stopwords_kn]\n",
    "    # else:\n",
    "    #   filtered_tokens = [token for token in tokens if token.lower() not in stopwords_kn]\n",
    "    filtered_text = ' '.join(filtered_tokens)    \n",
    "    df['Text'] = filtered_text\n",
    "  return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# apply the function to the dataset for Kannada\n",
    "norm_data = norm_data.apply(remove_stopwords_kn, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to remove malayalam stopwords\n",
    "def remove_stopwords_ml(df, is_lower_case = False):\n",
    "  if df['Language'] == 'Malayalam':\n",
    "    text = df['Text']\n",
    "    tokens = tokenizer.tokenize(text)\n",
    "    tokens = [token.strip() for token in tokens]\n",
    "  # if is_lower_case:\n",
    "    filtered_tokens = [token for token in tokens if token not in stopwords_ml]\n",
    "  # else:\n",
    "  #  filtered_tokens = [token for token in tokens if token.lower() not in stopwords_ml]\n",
    "    filtered_text = ' '.join(filtered_tokens)  \n",
    "    df['Text'] = filtered_text  \n",
    "  return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "norm_data = norm_data.apply(remove_stopwords_ml, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to remove tamil stopwords\n",
    "def remove_stopwords_ta(df, is_lower_case = False):\n",
    "    if df['Language'] == 'Tamil':\n",
    "        text = df['Text']\n",
    "        tokens = tokenizer.tokenize(text)\n",
    "        tokens = [token.strip() for token in tokens]\n",
    "    # if is_lower_case:\n",
    "        filtered_tokens = [token for token in tokens if token not in stopwords_ta]\n",
    "    # else:\n",
    "    #   filtered_tokens = [token for token in tokens if token.lower() not in stopwords_ta]\n",
    "        filtered_text = ' '.join(filtered_tokens)  \n",
    "        df['Text'] = filtered_text  \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "norm_data = norm_data.apply(remove_stopwords_ta, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to lemmatize text with spacy\n",
    "def lemmatisation(df):\n",
    "    if df['Language'] == 'English':\n",
    "        df['Text'] = ' '.join([token.lemma_ for token in nlp_en(df['Text'])])\n",
    "    elif df['Language'] == 'French':\n",
    "        df['Text'] = ' '.join([token.lemma_ for token in nlp_fr(df['Text'])])\n",
    "    elif df['Language'] == 'Spanish':\n",
    "        df['Text'] = ' '.join([token.lemma_ for token in nlp_es(df['Text'])])\n",
    "    elif df['Language'] == 'Portuguese':\n",
    "        df['Text'] = ' '.join([token.lemma_ for token in nlp_pt(df['Text'])])  \n",
    "    elif df['Language'] == 'Italian':\n",
    "        df['Text'] = ' '.join([token.lemma_ for token in nlp_it(df['Text'])])\n",
    "    elif df['Language'] == 'Russian':\n",
    "        df['Text'] = ' '.join([token.lemma_ for token in nlp_ru(df['Text'])])\n",
    "    elif df['Language'] == 'Swedish':\n",
    "        df['Text'] = ' '.join([token.lemma_ for token in nlp_sv(df['Text'])])\n",
    "    elif df['Language'] == 'Dutch':\n",
    "        df['Text'] = ' '.join([token.lemma_ for token in nlp_nl(df['Text'])])\n",
    "    elif df['Language'] == 'German':\n",
    "        df['Text'] = ' '.join([token.lemma_ for token in nlp_de(df['Text'])])\n",
    "    elif df['Language'] == 'Danish':\n",
    "        df['Text'] = ' '.join([token.lemma_ for token in nlp_da(df['Text'])])\n",
    "    elif df['Language'] == 'Greek':\n",
    "        df['Text'] = ' '.join([token.lemma_ for token in nlp_gr(df['Text'])])\n",
    "    return df       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "norm_data = norm_data.apply(lemmatisation, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "stemmer_ar = ARLSTem2()\n",
    "\n",
    "# function to stem arabic text\n",
    "def stem_ar(df):\n",
    "    if df['Language'] == 'Arabic':\n",
    "        text = df['Text']\n",
    "        tokens = tokenizer.tokenize(text)\n",
    "        tokens = [token.strip() for token in tokens]\n",
    "        stemmed_tokens = [stemmer_ar.stem(token) for token in tokens]\n",
    "        text = ' '.join(stemmed_tokens)\n",
    "        df['Text'] = text\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "norm_data = norm_data.apply(stem_ar, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "stemmer_tr = TurkishStemmer()\n",
    "\n",
    "# function to stem turkish text\n",
    "def stem_tr(df):\n",
    "    if df['Language'] == 'Turkish':\n",
    "        text = df['Text']\n",
    "        tokens = tokenizer.tokenize(text)\n",
    "        tokens = [token.strip() for token in tokens]\n",
    "        stemmed_tokens = [stemmer_tr.stemWord(token) for token in tokens]\n",
    "        text = ' '.join(stemmed_tokens)\n",
    "        df['Text'] = text\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "norm_data = norm_data.apply(stem_tr, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "normalizer_factory = IndicNormalizerFactory()\n",
    "\n",
    "# functions to normalize malayalam, hindi, kannada and tamil text\n",
    "\n",
    "def lemmatise_ml(df, remove_nuktas=False): # nuktas are diacritics marks \n",
    "  if df['Language'] == 'Malayalam':\n",
    "    text = df['Text']\n",
    "    normalizer = normalizer_factory.get_normalizer(\"ml\", remove_nuktas=remove_nuktas)\n",
    "    text = normalizer.normalize(text)\n",
    "    df['Text'] = text\n",
    "  return df\n",
    "\n",
    "def lemmatise_hi(df, remove_nuktas=False):\n",
    "  if df['Language'] == 'Hindi':\n",
    "    text = df['Text']\n",
    "    normalizer = normalizer_factory.get_normalizer(\"hi\", remove_nuktas=remove_nuktas)\n",
    "    text = normalizer.normalize(text)\n",
    "    df['Text'] = text\n",
    "  return df \n",
    "\n",
    "def lemmatise_kn(df, remove_nuktas=False):\n",
    "  if df['Language'] == 'Kannada':\n",
    "    text = df['Text']\n",
    "    normalizer = normalizer_factory.get_normalizer(\"kn\", remove_nuktas=remove_nuktas)\n",
    "    text = normalizer.normalize(text)\n",
    "    df['Text'] = text\n",
    "  return df\n",
    "\n",
    "def lemmatise_ta(df, remove_nuktas=False):\n",
    "  if df['Language'] == 'Tamil':\n",
    "    text = df['Text']\n",
    "    normalizer = normalizer_factory.get_normalizer(\"ta\", remove_nuktas=remove_nuktas)\n",
    "    text = normalizer.normalize(text)\n",
    "    df['Text'] = text\n",
    "  return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "norm_data = norm_data.apply(lemmatise_ml, axis=1)\n",
    "norm_data = norm_data.apply(lemmatise_hi, axis=1)\n",
    "norm_data = norm_data.apply(lemmatise_kn, axis=1)\n",
    "norm_data = norm_data.apply(lemmatise_ta, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to remove numbers, symbols, links and extra spaces\n",
    "def extra_clean(text):\n",
    "  text = re.sub(r'[\\([{})\\]!@#$,/\"%^*?.:-;~`0-9]', ' ', text) # removing the symbols, numbers, punctuations etc.\n",
    "  text = text.lower() # converting the text to lower case\n",
    "  text = re.sub(r' +', ' ', text) # removing extra spaces\n",
    "  text = re.sub(r'(\\w+:\\/\\/\\S+)', ' ', text) # removing links\n",
    "  # removing words of up to 3 characters does not work well with indic languages. It returns weird symbols\n",
    "  # text = re.sub(r'\\b\\w{1,3}\\b', ' ', text) \n",
    "  return text "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "norm_data[\"Text\"] = norm_data[\"Text\"].apply(lambda x: extra_clean(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to remove english characters from non-english text\n",
    "def remove_english_char(text):\n",
    "    text = re.sub(r'[a-zA-Z]+', '', text)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# removing english characters from 7 non-english languages (Greek, Arabic, Hindi, Malayalam, Tamil, Kannada, Russian)\n",
    "# english characthers should be removed from the other languages as well (obviously not English), but the english characters \n",
    "# are similar to the characters of the other languages. It should be considered in the future.\n",
    "norm_data.loc[norm_data['Language'] == 'Greek', 'Text'] = norm_data.loc[norm_data['Language'] == 'Greek', 'Text'].apply(lambda x: remove_english_char(x))\n",
    "norm_data.loc[norm_data['Language'] == 'Arabic', 'Text'] = norm_data.loc[norm_data['Language'] == 'Arabic', 'Text'].apply(lambda x: remove_english_char(x))\n",
    "norm_data.loc[norm_data['Language'] == 'Hindi', 'Text'] = norm_data.loc[norm_data['Language'] == 'Hindi', 'Text'].apply(lambda x: remove_english_char(x))\n",
    "norm_data.loc[norm_data['Language'] == 'Malayalam', 'Text'] = norm_data.loc[norm_data['Language'] == 'Malayalam', 'Text'].apply(lambda x: remove_english_char(x))\n",
    "norm_data.loc[norm_data['Language'] == 'Tamil', 'Text'] = norm_data.loc[norm_data['Language'] == 'Tamil', 'Text'].apply(lambda x: remove_english_char(x))\n",
    "norm_data.loc[norm_data['Language'] == 'Kannada', 'Text'] = norm_data.loc[norm_data['Language'] == 'Kannada', 'Text'].apply(lambda x: remove_english_char(x))\n",
    "norm_data.loc[norm_data['Language'] == 'Russian', 'Text'] = norm_data.loc[norm_data['Language'] == 'Russian', 'Text'].apply(lambda x: remove_english_char(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to remove extra hyphens, after the text is normalized some hyphens are left\n",
    "def remove_extra_hyphens(text):\n",
    "    text = re.sub(r'[-]+', ' ', text)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "norm_data[\"Text\"] = norm_data[\"Text\"].apply(lambda x: remove_extra_hyphens(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to remove empty cells because empty texts were left after the normalization\n",
    "def remove_empty_texts(df):\n",
    "    df = df[df['Text'].str.strip().astype(bool)]\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "norm_data = remove_empty_texts(norm_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(norm_data[norm_data.duplicated()]) # check for duplicates again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "norm_data.drop_duplicates(inplace=True) # drop duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "English       1359\n",
       "French         992\n",
       "Spanish        791\n",
       "Portoguese     736\n",
       "Italian        673\n",
       "Russian        668\n",
       "Swedish        645\n",
       "Malayalam      588\n",
       "Arabic         529\n",
       "Dutch          524\n",
       "Turkish        471\n",
       "Tamil          463\n",
       "German         439\n",
       "Danish         404\n",
       "Kannada        365\n",
       "Greek          349\n",
       "Hindi           62\n",
       "Name: Language, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm_data['Language'].value_counts() # checking the distribution of the target variable in the normalized data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the distribution of the target variable in the normalized data\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.barplot(x=norm_data['Language'].value_counts().index, y=norm_data['Language'].value_counts().values, palette='magma')\n",
    "plt.title('Languages Distribution')\n",
    "plt.xlabel('Languages')\n",
    "plt.ylabel('Number of Samples')\n",
    "plt.xticks(rotation=50)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train and Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting the normalized into train and test set (65% train and 35% test)\n",
    "train_data, test_data = train_test_split(norm_data, test_size=0.35, random_state=19, stratify=norm_data['Language'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BTJIkaWAGMkmSpIEZyCRJkgZmIJMkSRqYgUySJGlgBjJJkqSBzRm6AiuTrTa758TKuuqar0+sLEmS1C9byCRJkgZmIJMkSRqYgUySJGlgBjJJkqSBGcgkSZIGZiCTJEkamIFMkiRpYAYySZKkgRnIJEmSBmYgkyRJGpiBTJIkaWAGMkmSpIEZyCRJkgZmIJMkSRqYgUySJGlgBjJJkqSBGcgkSZIGZiCTJEkamIFMkiRpYAYySZKkgRnIJEmSBmYgkyRJGlivgSwiNoiIj0XE/0bEDyPiXhGxUURcEBE/br837Dz+xIi4IiJ+FBEP6LNukiRJy4u+W8jeBHw+M28P3AX4IXACcGFm7gRc2G4TETsDhwK7APsBp0XEqj3XT5IkaXC9BbKIWA/YA3gPQGb+IzOvBw4CzmwPOxM4uP19EHB2Zt6QmVcCVwC79VU/SZKk5UWfLWS3Aa4F3hsR346Id0fE2sDmmXk1QPu9WXv8lsAvOv9/VVsmSZK0UuszkM0B7ga8PTPvCvyF1j25ELGAZXmLB0U8ISIuiYhLrr322vHUVJIkaUB9BrKrgKsy8xvt9seogPabiNgCoP2+pvP4rTv/vxXwq+lPmpmnZ+a8zJy36aab9lZ5SZKkSektkGXmr4FfRMTt2qJ9gB8A5wFHtGVHAJ9qf58HHBoRq0fE9sBOwMV91U+SJGl5Mafn538q8MGImAv8BHg8FQLPiYijgJ8DhwBk5uURcQ4V2m4Ejs3Mm3qunyRJ0uB6DWSZeRkwbwF37bOQx58CnNJnnSRJkpY3ztQvSZI0MAOZJEnSwAxkkiRJAzOQSZIkDcxAJkmSNDADmSRJ0sAMZJIkSQMzkEmSJA3MQCZJkjQwA5kkSdLADGSSJEkDM5BJkiQNzEAmSZI0MAOZJEnSwAxkkiRJAzOQSZIkDcxAJkmSNDADmSRJ0sAMZJIkSQMzkEmSJA3MQCZJkjQwA5kkSdLADGSSJEkDM5BJkiQNzEAmSZI0MAOZJEnSwAxkkiRJA1tsIIuI4yNivSjviYhvRcT9J1E5SZKk2WAmLWRHZuYfgfsDmwKPB17Va60kSZJmkZkEsmi/9wfem5nf6SyTJEnSMppJILs0Is6nAtkXImJd4OZ+qyVJkjR7zJnBY44CdgV+kpl/jYiNqW5LSZIkjcFMWsgS2Bl4Wru9NrBGbzWSJEmaZWYSyE4D7gUc1m7/CXhbbzWSJEmaZWbSZXmPzLxbRHwbIDOvi4i5PddLkiRp1phJC9k/I2JVquuSiNgUB/VLkiSNzUwC2ZuBTwKbRcQpwEXAK3qtlSRJ0iyy2C7LzPxgRFwK7EPNP3ZwZv6w95pJkiTNEgsNZBGxUefmNcCHu/dl5u/7rJgkSdJssagWskupcWMLmpU/gdv0UiNJkqRZZqGBLDO3n2RFJEmSZquZTHtBRDwUuDfVMvbVzDy3z0pJkiTNJos9yzIiTgOeBHwP+D7wpIhwYlhJkqQxmUkL2Z7AHTNzNA/ZmVQ4kyRJ0hjMZB6yHwHbdG5vDXy3n+pIkiTNPjNpIdsY+GFEXNxu3x34WkScB5CZB/ZVOS25Xbd6xMTKuuyqcyZWliRJK7OZBLIX914LSZKkWWwmM/V/BSAi1us+3olhJUmSxmOxgSwingCcDPyNuqh44MSwkiRJYzOTLsvnALtk5m/7rowkSdJsNJOzLP8f8Ne+KyJJkjRbzaSF7ETgfyLiG8ANo4WZ+bTeaiVJkjSLzCSQvRP4IjUZ7M39VkeSJGn2mUkguzEzn9l7TSRJkmapmYwh+1JEPCEitoiIjUY/vddMkiRplphJC9mj2u8TO8uc9kKSJGlMZjIx7PaTqIgkSdJsNZMWMiLijsDOwBqjZZn5/r4qJUmSNJvMZKb+lwB7UYHss8ADgYsAA5kkSdIYzGRQ/8OBfYBfZ+bjgbsAq/daK0mSpFlkJoHsb5l5M3Bju8D4NTigX5IkaWxmMobskojYAHgXcCnwZ+DiPislSZI0m8zkLMuntD/fERGfB9bLzO/2Wy1JkqTZY6GBLCK2Ba7PzD+023sDBwM/i4j/zcx/TKaKkiRJK7dFjSE7B1gbICJ2BT4K/Jwa1H9a7zWTJEmaJRbVZblmZv6q/f0Y4IzMPDUiVgEu671mkiRJs8SiAll0/r4v7dJJmXlzRCz4PyRg362fsvgHjcF//sKGWknSymFRgeyLEXEOcDWwIfBFgIjYAnD8mCRJ0pgsKpA9HXgksAVw78z8Z1t+K+AFPddLkiRp1lhoIMvMBM5ewPJv91ojSZKkWWYmM/VLkiSpRwYySZKkgS00kEXEhe33qydXHUmSpNlnUYP6t4iIPYEDI+Js5p8Gg8z8Vq81kyRJmiUWFcheDJwAbAW8ftp9Sc1NJkmSpGW0qLMsPwZ8LCJelJknT7BOkiRJs8qiWsgAyMyTI+JAYI+26MuZ+Zl+qyVJkjR7LPYsy4h4JXA88IP2c3xbJkmSpDFYbAsZ8CBg18y8GSAizgS+Tbu2pSRJkpbNTOch26Dz9/o91EOSJGnWmkkL2SuBb0fEl6ipL/bA1jFJkqSxmcmg/g9HxJeBu1OB7HmZ+eu+KyZJkjRbzKSFjMy8Gjiv57pIkiTNSl7LUpIkaWAGMkmSpIEtMpBFxCoR8f1JVUaSJGk2WmQga3OPfScitplQfSRJkmadmQzq3wK4PCIuBv4yWpiZB/ZWK0mSpFlkJoHspb3XQpIkaRabyTxkX4mIbYGdMvM/I2ItYNX+qyZJkjQ7LDaQRcQxwBOAjYAdgC2BdwD79Fs1aekduuOzJlLO2VecOpFyJEkrt5lMe3EssDvwR4DM/DGwWZ+VkiRJmk1mMobshsz8R0QAEBFzgOy1VtJK4Cl3eu5Eyjnte6+ZSDmSpP7MpIXsKxHxfGDNiLgf8FHg0/1WS5IkafaYSSA7AbgW+B7wROCzwAv7rJQkSdJsMpOzLG+OiDOBb1BdlT/KTLssJUmSxmQmZ1k+iDqr8v8BAWwfEU/MzM/1XTlJkqTZYCaD+k8F9s7MKwAiYgfgPwADmSRJ0hjMZAzZNaMw1vwEuKan+kiSJM06C20hi4iHtj8vj4jPAudQY8gOAb45gbpJkiTNCovqsjyg8/dvgD3b39cCG/ZWI0mSpFlmoYEsMx8/yYpIGr8XzTthIuWcfMmrJlKOJK2sZnKW5fbAU4Htuo/PzAP7q5YkSdLsMZOzLM8F3kPNzn9zr7WRJEmahWYSyP6emW/uvSaSJEmz1EwC2Zsi4iXA+cANo4WZ+a3eaiVJkjSLzCSQ3Qk4HLgvU12W2W5LkiRpGc0kkD0EuE1m/qPvykiSJM1GM5mp/zvABktbQESsGhHfjojPtNsbRcQFEfHj9nvDzmNPjIgrIuJHEfGApS1TkiRpRTKTQLY58L8R8YWIOG/0swRlHA/8sHP7BODCzNwJuLDdJiJ2Bg4FdgH2A06LiFWXoBxJkqQV0ky6LF+ytE8eEVsBDwJOAZ7ZFh8E7NX+PhP4MvC8tvzszLwBuDIirgB2A762tOVLkiStCBYbyDLzK8vw/G8Engus21m2eWZe3Z776ojYrC3fEvh653FXtWWSJEkrtcV2WUbEnyLij+3n7xFxU0T8cQb/92Dgmsy8dIZ1iQUsywU87xMi4pKIuOTaa6+d4VNLkiQtv2bSQtZt3SIiDqa6Ehdnd+DAiNgfWANYLyLOAn4TEVu01rEtgGva468Ctu78/1bArxZQn9OB0wHmzZt3i8AmSZK0opnJoP75ZOa5zGAOssw8MTO3ysztqMH6X8zMxwDnAUe0hx0BfKr9fR5waESs3q6fuRNw8ZLWT5IkaUUzk4uLP7RzcxVgHgvoSlwCrwLOiYijgJ8DhwBk5uURcQ7wA+BG4NjMvGkZypEkSVohzOQsywM6f98I/JQ6I3LGMvPL1NmUZObvgH0W8rhTqDMyJUmSZo2ZjCF7/CQqIkmSNFstNJBFxIsX8X+ZmSf3UB9JkqRZZ1EtZH9ZwLK1gaOAjQEDmSRJ0hgsNJBl5qmjvyNiXeoSSI8HzgZOXdj/SZIkackscgxZRGxEXfLo0dRlju6WmddNomKSJEmzxaLGkL0WeCg1CeudMvPPE6uVJEnSLLKoFrJnATcALwReEPGvKxsFNah/vZ7rJmkF96Y9nj+xso7/r1dMrCxJGrdFjSFb4ln8JUmStOQMXZIkSQMzkEmSJA3MQCZJkjQwA5kkSdLADGSSJEkDM5BJkiQNzEAmSZI0MAOZJEnSwAxkkiRJAzOQSZIkDcxAJkmSNDADmSRJ0sAMZJIkSQMzkEmSJA3MQCZJkjQwA5kkSdLADGSSJEkDM5BJkiQNzEAmSZI0MAOZJEnSwOYMXQFJ6tPZ+z1/IuUc+vlXTKQcSSsnW8gkSZIGZguZJPXswoc+byLl7POJV0+kHEnjZwuZJEnSwAxkkiRJAzOQSZIkDcxAJkmSNDADmSRJ0sAMZJIkSQMzkEmSJA3MQCZJkjQwA5kkSdLADGSSJEkDM5BJkiQNzEAmSZI0MAOZJEnSwAxkkiRJAzOQSZIkDcxAJkmSNDADmSRJ0sAMZJIkSQMzkEmSJA1sztAVkCT177LHPWsi5ez6vlMnUo60srGFTJIkaWAGMkmSpIHZZSlJmogrn/WMiZSz/alvmEg50jjZQiZJkjQwA5kkSdLADGSSJEkDM5BJkiQNzEAmSZI0MAOZJEnSwAxkkiRJAzOQSZIkDcyJYSVJs8LVr5zMxLQAW5zo5LRaMraQSZIkDcxAJkmSNDADmSRJ0sAMZJIkSQMzkEmSJA3MQCZJkjQwA5kkSdLADGSSJEkDM5BJkiQNzEAmSZI0MAOZJEnSwAxkkiRJA/Pi4pIkTci1Z7xgYmVteuQpEytLy84WMkmSpIEZyCRJkgZmIJMkSRqYgUySJGlgBjJJkqSBGcgkSZIGZiCTJEkamIFMkiRpYAYySZKkgRnIJEmSBmYgkyRJGpiBTJIkaWAGMkmSpIEZyCRJkgZmIJMkSRrYnKErIEmSJue6z7xmIuVs+ODnTqSclYUtZJIkSQMzkEmSJA3MQCZJkjQwA5kkSdLAHNQvSZIm6g8Xv3si5ay/29ETKWccbCGTJEkamIFMkiRpYAYySZKkgRnIJEmSBmYgkyRJGpiBTJIkaWAGMkmSpIEZyCRJkgZmIJMkSRqYgUySJGlgBjJJkqSBGcgkSZIG1lsgi4itI+JLEfHDiLg8Io5vyzeKiAsi4sft94ad/zkxIq6IiB9FxAP6qpskSdLypM8WshuBZ2XmHYB7AsdGxM7ACcCFmbkTcGG7TbvvUGAXYD/gtIhYtcf6SZIkLRd6C2SZeXVmfqv9/Sfgh8CWwEHAme1hZwIHt78PAs7OzBsy80rgCmC3vuonSZK0vJjIGLKI2A64K/ANYPPMvBoqtAGbtYdtCfyi829XtWXTn+sJEXFJRFxy7bXX9lpvSZKkSeg9kEXEOsDHgadn5h8X9dAFLMtbLMg8PTPnZea8TTfddFzVlCRJGkyvgSwiVqPC2Acz8xNt8W8iYot2/xbANW35VcDWnX/fCvhVn/WTJElaHvR5lmUA7wF+mJmv79x1HnBE+/sI4FOd5YdGxOoRsT2wE3BxX/WTJElaXszp8bl3Bw4HvhcRl7VlzwdeBZwTEUcBPwcOAcjMyyPiHOAH1Bmax2bmTT3WT5IkabnQWyDLzItY8LgwgH0W8j+nAKf0VSdJkqTlkTP1S5IkDcxAJkmSNDADmSRJ0sAMZJIkSQMzkEmSJA3MQCZJkjQwA5kkSdLADGSSJEkDM5BJkiQNzEAmSZI0MAOZJEnSwAxkkiRJAzOQSZIkDcxAJkmSNDADmSRJ0sAMZJIkSQMzkEmSJA3MQCZJkjQwA5kkSdLADGSSJEkDM5BJkiQNzEAmSZI0MAOZJEnSwAxkkiRJAzOQSZIkDcxAJkmSNDADmSRJ0sAMZJIkSQMzkEmSJA3MQCZJkjQwA5kkSdLADGSSJEkDM5BJkiQNzEAmSZI0MAOZJEnSwAxkkiRJAzOQSZIkDcxAJkmSNDADmSRJ0sAMZJIkSQMzkEmSJA3MQCZJkjQwA5kkSdLADGSSJEkDM5BJkiQNzEAmSZI0MAOZJEnSwAxkkiRJAzOQSZIkDcxAJkmSNDADmSRJ0sAMZJIkSQMzkEmSJA3MQCZJkjQwA5kkSdLADGSSJEkDM5BJkiQNzEAmSZI0MAOZJEnSwAxkkiRJAzOQSZIkDcxAJkmSNDADmSRJ0sAMZJIkSQMzkEmSJA3MQCZJkjQwA5kkSdLADGSSJEkDM5BJkiQNzEAmSZI0MAOZJEnSwAxkkiRJAzOQSZIkDcxAJkmSNDADmSRJ0sAMZJIkSQMzkEmSJA3MQCZJkjQwA5kkSdLADGSSJEkDM5BJkiQNzEAmSZI0MAOZJEnSwAxkkiRJAzOQSZIkDcxAJkmSNDADmSRJ0sAMZJIkSQObM3QFJEmSJu1PP/3URMpZd7uDZvQ4W8gkSZIGZiCTJEkamIFMkiRpYAYySZKkgRnIJEmSBmYgkyRJGpiBTJIkaWAGMkmSpIEZyCRJkgZmIJMkSRqYgUySJGlgBjJJkqSBGcgkSZIGZiCTJEkamIFMkiRpYAYySZKkgRnIJEmSBmYgkyRJGpiBTJIkaWDLXSCLiP0i4kcRcUVEnDB0fSRJkvq2XAWyiFgVeBvwQGBn4LCI2HnYWkmSJPVruQpkwG7AFZn5k8z8B3A2cNDAdZIkSerV8hbItgR+0bl9VVsmSZK00orMHLoO/xIRhwAPyMyj2+3Dgd0y86mdxzwBeEK7eTvgR8tY7CbAb5fxOZbV0HUYunzrYB2WtzoMXb51sA7LU/nWYXx12DYzN13QHXOW4Un7cBWwdef2VsCvug/IzNOB08dVYERckpnzxvV8K2Idhi7fOliH5a0OQ5dvHazD8lS+dZhMHZa3LstvAjtFxPYRMRc4FDhv4DpJkiT1arlqIcvMGyPiOOALwKrAGZl5+cDVkiRJ6tVyFcgAMvOzwGcnWOTYuj+XwdB1GLp8sA4j1qEMXYehywfrMGIdhi8frMNIb3VYrgb1S5IkzUbL2xgySZKkWcdAJklaoUSE+y6tdFypl2MREUPXQdL838XZ/r0c6vVHxK4R8QWAzLy5XWpPGkxEbDzO5zOQLcDQG9yIWK39OXfAOkREzJm2bLk5CWQSR8gehS/Y0N+P6fr+nCJi1czMiNgiIrbMgQbeTn+dQ38OEbFDRKwyqWCUmZcBq0fEZRGxWWbe5Hd0+dX3+jla7wY8QFgDeG9EbDau53RlXoDRBjci9o6IQyJit/bm9y4i5mbmPyNiR+D9EXFyRNwrItacRPkda2Xmja1Op0XEO4HnR8QuE67HLUTEKpl5c/v7kIh4TERs2+auG1cZq46OwiPijuN63mWpz7TbE//udg4UNmq3J/KdWJzOunC/iLhdRGwzrudu69pNbd06D9h/XM+9pDqv87YRsUNnOzWRHVJErDsqNyKeBHwc+CTwsIjYosdyV4mIowEycy/gS8D3I+JenfdkqJ3yKBTMjYgNhqhDK3/NiFhnqPKn6xzE9Ladysyb2p8fiIh79FXOIsr/O3AYsH5EvHu0fFnWRQPZNBHx8oiYExFbAu8CHgUcDTw5Im7Vc9kHAB9qO5TXAN+lLtPwaOCgcTePLqIeGwJXRMQ+EXEfYHvgP4EAnhsR+06iHgvT2Qi/Hbg/cDB1IfoFXo5iSUVEdL7sHwPOiIj/jIh/HyAYz1efiDirve63RsR2E6zDdu1AYWvg8xFxNvCUiNh5UnVYlBYQ3g08D3h2RNx3HM87WteoU92/lJnv6pQZ3d99iYgtI+IO7e93Am8DvhQRz2x1zAkFkkOoFoHHAQ8EDgL+A9gbeHhE7NRTuRsD/4iIDVpAfgbwYuALEXEETPQ9+JdOWF8F+DDwHxHxkkkdLEXEuu33A4BPAO+LurTgxHW+C4+LiNcB50bE7dtB7Vg/l3bQ9ZLOohuAn3Xr0bfWUHIUcBPwa2CXiDg3IjZclnXRQNbRjoK3Av4LOAV4fGY+BDifusj50RFxpx6rcEX7OQ34Q2aekplPBr4H7Ak8apxH/wuTmdcBz6E2Mi8HnpeZH6VCz9eAx0bEE/uux6JExO7AJpl5DPAn4POZ+ctxHKV2Wh6eBvwyM3cDvgo8E3hARKy/rGUsZX0eBawHfAi4FnhXRNy77/Ij4u7A1yLiocDTgPe2OmwMHNE+i4kb7fjaZ7418O/AycCPgUMj4pHL8NxbTQu8v6e+D0TEWm3ZrWDq8+lD1DCB5wFHRsRjgc0y835US90zIuIdozpMYGf0eeD9VDD7e2b+rF3K7lPAHYCjxh3KWui5NjPfT22TvxPVbfwO4EDgVRHxVuj3c1iQTlg/Ffh/wFHAA4B39N1aFhEvpHosbkdtq8+kDhqOjYhT+yx7AXVZpa1/ewDHAW+hWtG/GRGH9fC5rA/cMSLe3ELp76j99iTXgUupxpojM/NPmXmvVo/zI+IOS12PzPSn7e9Gv6mLl/8OeHbn/n2ANwIvBOb0UXb7e0tqx38d8MjO8kOA9wHb9fw+zOn8fVvq+qKfnla/I4EnDfUZtb+3o46SPwS8vy1bhdow7TiGsu4KXAS8qLPssdSkxccDa074tR9GdQ/t1m5vCBwDfBk4ZgLlP6y9H//dWTYPeD51AHHghN+PVdrvnYAT2/uwS1u2NfA44CPAAUvx3Ku1detWwIZt2cuoUH6rzmMuBvacwGvdHngtcA5wBjC3LV+31eE77Xb0VH53m7Aa8Agq9L6gs/yewIvGuW0cfcbTlr0WuBz493Z7G2rnuMkk179OfQ6gDthH694a1IHr+cBOPZZ7u7Y9+Cjwhs7yrYALgAsX9P71UI+tO+vF24HdgYdQ3fsPBf4BnDTu9RHYGXgz8AHgXGrffHjbNp/R13sPrNr5++3A1dQ+aIO27HnAb4D7L9Xz9/2BrQg/3RUX2Kb9fhTwI+CZnfvmjTbIPdXj4cAdgQ3aDuVc4LjO/Vv0/D6s2n5vBhze/l4X+DbVXTN63Hqdv3vZCSyibtu0n/WpI8L/7mwUXgl8Ykzl7USF7wuAR3eW7w8cPYnXPK0+DwB+CpzWWbYmtXN8Wk9lxrTb/0Z1Dbyvs2xH4LnAHSf4XowOnlYHvtI+py+1z2rzdt/GVHf2qktZxhrAralhC3u178EJwNepVsJPA6/p+XV2g9AqwNNbuQeNXme774Qe69DdNp4B7EKdbLQntTN8I7B2u3+p3usZ1OFJ7bXfut0+hgqEj5vUOreg96X9PI7qtn09cPvO/acDj+r581idap37OtU6vFpbvkb7rLbu+T24BxVIHtJub9i+d18E5rVl76cC9BpjKG/Vabe3pALQFdS++tD23exlezit7LdRw4ruS4XvDwI7tPsOBe6xVM/bd8VXpJ/2Br8XWL3d3q2tXO/ssczRzmW/tpHZsd1ei9oJnw28tfvYnurRTf5fpbopu/e/i2o13HKAz2XUGrI21SJwTLt9F2pg8fuAz1BHhaPPbol2DkwFvk2BzUevs21w3912CKtP8DWP6rNm5zVtC3yzbWzntGVjba1dyLr5+M5GdxtqvMqnaMF8HBvbpazf86nr3Y5unwZcAtxzQa9jSd73znt/Unu/H0q1PjyIagnqNZR31vl1qFbZf2u3n0jt5B7HGFqCl2AdeDNwTmf5XGBXqnvqo1QIGPv2idrBfpVqHfwgcHBbft+2Pbprn9vFRXwu63eWzaOC6UtYyh3xknwW7e+nAHdrf7+U6k7ejwm13HfehwOB7wBP7Nz3FuAx1DjD9zCGRoxp34cXUy3W9wZ2oHouzgUeOoHXHcAW1IHgTp3l51AHzHsv0/NP4sNbEX6ogfNfZao7YB61A9yG6g55eI9lr0Md4T+g3V61s/zeTCDxd+rycuBd7e87tNvHdO67y4Cf0bnAs9rftwLuR7Vk3YUKz6PupaUNY+tQge9l7feD2vL9gbOAp07odY7qszrVCvEB4KlUy+lcKoReDGzcYx1GgW9f6gj3Dp37NqBaI69oG6eJ7RA7ddiG6r76MXBIZ/nzqG72TZfhudcA7kVr9aO6az9IddWvO+HXeT7TunyoneAn2zrRSyCnWqBHO8EdqdbHtYE7A2+lxtPdF7gNYw4hnXJXo1pfN2i3j23fhaNG6+GEP4vR9/I21D7h5VTL2AZUt/IrqG6seT3X4zmt/E06yw6kxj4/mdZSNqH35NFt2/gT4E1t2eFUEPsu8LAxl3c+1WJ6FjWeeb32/h/a1sn1J/S630CNGVy33V6vvd5HL9PzTuqDWx5/qEH7o7+fRjWFH9q+aFdSzc63poW0nutyBi3sMNUicp++Nzpt4/IRpo6EHwq8mhoD8Ka24r+N1hzbHtP72IQF1HOt9nncixq4/W6qr/4t0x631OGAaol4AtU192MqcJzQ7ps3wA7gY20ndCRwTdv4367d9xpa93oP5XYD4efbxn5N6kj0Q60+awOHTXo9mFbP1dv39p20nXRbvu1SPNcBnb8/1zb8H6O6ozamuoTOpccQtIA6bQGc27m9ZufvnUfrQk9lPxu4W2dd+DjVWn821TpzKvDyHsodhbF5VPfsd4FXde5/ePueHjTQOjcX+EbbTj6T6ir7AnD7tp4c28f60fkcdqC6KFengsix7Tu5CxWQj53ge/H49l7cjjpwG/VWjLpOxzq8h+oxekv7+z+BR7S/t6Rartbv6XWO3vuNqQPBudSZxf9BtQTuTu0zX7LMZU3qw1ueftqHdysq4c4FVqVaWM6iWqr2pYLY2cCDu/83pvIXNFj15Lbx2ardvj/wf/TYCtIp+zbAHtTA3LWpYPqCzor4FZZicHQP9TyMasV8GTUdyFrUjnOpggnV3bF3+3vT9rrnUOPS9qUC8R+A90zo9W1Ozf8GNW7pVKqV4MvAM6hWmotYygGjM6zDw+iMSaLGZ721rQMvBV5FdQ1OtJuSqR31QdTR6TuY6sZ7FBXWT27f59Fjl6Sr8mPAL6gd3MvasvWoEwY+RwXS3ek3BM3t/L1r++wvog4SRgdMm1DDKvpsHQ1qPNCaVKvDDtRO9+FMjdF7EfCOnj7jtdrncXT7vL8BvL7zuLtOct2bVsf7US0061FddXtT3bnfA+7XU5kvZupAbHXqxKXzqYPSt1JjG988wHvxZFpXZVtXdwQuaz+3WpLv3wzL24oKPt8Ant+Wbdm2j7fp6TWO1smNqJ6Cs9u2cCtqf/m2tuyjYylv0h/i8vDT/UJTY0S+BWzUbo+aIA8C/oceuyjaF+0kqktsLnWGyFVUy9R3gL16fh+642WOB26mjRVqy+ZQ41V6G0O3iLp1d8CnUmHkrkwdfY12Fq9bhjL2p454bttZdk/grPb31sDrmEAYbZ//O6gTKkavfU1qJ/iOdvtutAG8E6jLK6iQvgUVePZs9+1KndW22QTXheiU/S2qG/9c6gSDUffVQcATlrGc51FnhZ3ebq9OHaCcAdxpAq/zIVQ37IOoVpd1qJaYV1LdVNtQrUYv67EOo67qVdpn/xJqvOC+ncc8l+ou6qVrrG0XP87UwcltWh0+z+QPBKaf2LJ6+14+i9YiAhxBddHdvac63IUKyY+jgsFtqYPSndv9jwM+NoH3YpVptw+juip37CwbTZM0tvLa92CTtk6+hTpAui01tOATwCsm8NpfAJzY/n42Nc3JXt31Yizl9P1ClqeftlJvRE0k96bO8le2ncx92u29qBS8a7s99rOHqO6frzI17uCF1FHpzlRz/V16fi9GrV+rMLXD25eab+mUdntX4NWd/5loVyU1XuUSKoh9Fvhw574TaNNdLGndqKO50cZsvfYFfy0VQDdpt4+gxmQcP8HXuw41bvEM2hEf1Q3xE6rL7AzgOT2VvSvwhc7tZ1Etto/oLLsH8H3ggRN8T7pd5ae2dXSv9tk8sL03r5r2P0vSMjZ9h/tw4J90un6oEykOndDr/R/gRlprC9VS/yiqVWy+KQ56rse7qVaxDdq26tO0wEt1VY3l5B5qm9ydcmgVaud3IdU1vmm7b4O2/u88qXVvWj0fTPVajPYRj2qfx05tXXxQD2V2z6jciToYeTmdgyFqOqTvANv3/PpH+4s51LCR0fQvx1Lh5GgqqF/AGII6U2Hs7m3du4BqAb8TdYLZh6guw7f39Hq3YWrs4gvojPFuyw4GfsVUS914es/6/BCX1x+qa+gy4DOdZU9qyx7Tbo92iGMPIW2H8vnOB/4AqivoVfR4lk6n/NHKvgo1FunDTJ2xsw3wA9rcY52NZS+ntC+mns+kdsB7Ut2Io7P67sj8UwIs6SD+B1Pjzw5pt/+NGof0bqpF5EDqKP1V43gdM6hPd2O3JtUaeBbtDDJqnNQFwHt7rscFVPjYuN3ejzpQeWlnPe3t5JYF1GcXqsvuUe32ptSO+QJgn7bs3VSr4YbL8L5vSc0zODqzdlfqDL5vUMF/qVthZ1iP7o73qVQXyK/otITS82BlasjGUzq3PwqsMyq7rQufpjMv35jKXafz9xadvx/SvgdPpOe5F2dQx72o2dhfT7XcPbVtO9/b6nhqD2V2z6gctVpu0rYLZ1InXG1MDbPYo+fX363LJ9tr/gQVwG5Fddu+nxpKMLYufepg+b/a9/GpwPdG9Wn39TINFFNT3KxPDWe6AzWm+jQ6XbHUfmO/sZbd5we5vP1wy6Phz1N9/5u02/sDP6f/s2QeRh2Fn9JZthvVPXbEpN4D6ojzNCp8/LBbNnWE2ku//BJ8Po+guiq+MdooUy0YZzJ14sNSHZlQ3ULfok3+S+2UT6XC6cSOwpn/DM+XMHWm6MlUt9X92sa/l2kFWlmj93Lz9p78jNb9QoWi/6Qz5cEE35v1qRaAT9DOrm3L30q1jt29fV6jcZdL0ko6OijZuG0DTm3f/aPb8nWB/wU+O8HXexxTE54eQ41fPJja4X2YHs+eo7qBf0CbW40KhTt37g9qTOXuYyxzPSrwr0O1OJ3ftoHPog5MdqPOqjyFZThrdinr1p125tHAvdvtPamToF7ABE7uaOvEp6gxpKMDhldRAWGvPteJBdTlJbSxatRJb2e1781WYyyje3CyPTVWcTfqQPFObfnBfb1u6qA42u+dqJ6T7agDwQ9SXdO3p6859yb1YQ79w1SqvSfzzzD9ZuroZ7QDGvs8W8zfAjIaA/Xv1DiMUzuP23aC78cBwKc6t/ejdsYvm/a4Ic6oPJQKTKu1Dc+32/K9qZ3nfZZlHejcvhvVAnNauz2ad+rDTODM2ml1+QK3nN7gIW2H9ZgJlH9nqpv+UVRI/yPw2HbfJoxpXMhS1GuttuP5MHUEvhp1Ms657b1ZpqsUUF3hh1Otb7+ijshfy9T0N+st62uYyXpJHYm/h9rZj1r/7kudxXcRLRD0VP4onN6GCmLvpQ56jqe6oo6iWtLHOu8Z1dryzLZ+/bCV/8m2Hp5OtUbsSOcM2gmtc92xS2+hupGfRR0YrUqN6foQ8Mq+1of2e6/2XhxGBdPXMHWljhOWdd1fkvejvRejkxnOog7i70ydif4BxtBlyvyNBa+mWq3fS+eECeoA8Xt0xv2O+bUe377/m1DB643tfb9b+56+ihpq1M+VACbxgQ79w1QgunXb6N0MfLxz/5Pbsu7A1bGfUUl1i72D2unfobMBmthg1bZSbUCNXfsWdQQ2Gjx7h7Zh7OVsoRnWbSPqSOytVNfhWu19+0p7rw5bms9n2pd9Q6Zm99+iPe8nmOpCnuhlWKgxOZ9qf89rO4H3tI3CA5jA3G9tnXxd5/a+wPX0tNNZTF2681CNvru7UDvp09tOYTWWosti+npDhd4NqYOj3agxnH9vO4SJTARM5yCQCiifYupKGWvTZqjvqezR+zuP6nbarL3HN1PjZp5EdU29YMzlvp2aSPNe1Bi13ajW4K9RYxW/QLWYDTJmrNXxnVRL9UupCcIPYKr7cDt67EZu26Uv04YIUENJXt62Db1PgNrKnD6If9RV+NHOsnPonAg2pnIfA3yg/X0cdXbjg6mWyotH340eXu+q7TN+BdXyt2n7PozONL9/e9whfZSfOUsCWXsT51JHOodQAwO/QHWFjbo7dqcFk57Kfze10zuQOpNydO2zjdp9vW54mDYVQHs/jqFaCI9mavDsxGaj79QtptVxC+pU/zdRF2+FOrtpmevWNrDvp1pXjhuV23YQl/e5kV1EnW7b1oGLqXEqL6NC6Vg3dAt6zzu3D2Ha9B5tI3QlE5h6pVPmKCDsRo3XOY02MXLbQL6SasXqzpK9xAdP7T1+Zft7Z+ri9KP7Pskyzri9BPW4I9UVclBn2ZFUGD6xz21Sp7x1qPDxsHZ7LaqL7JvAnXso70Sq1e/F7ecoqov4tcC92mNe176r64y7/EXUq3vQdiTwjc7tw6lhHE+dRJ3aOvkf1GXrdmrL1hi9ZxMov3ut2DdToXlUj3PbturdjOHsTqpnYj/qJJJtqAaK7jQnR7d9walM4LJ11DQvX2iv727UvudJVEtgr2Noe31hy9MPdYT9Oeaf6+dz1HiZ7jXI+jijcnNqvpJ1qObe0Wzzt5/Ezo75Z5h+H3XJmdGA9oe1L9yL6ByJL81ObhnruC2dHW17r46mBmy/gvFcfuMx1FH/qtSllv7B/Gdujm18zGLq0Z3S49HUUdg9mf/yI+fR0ySP03Y8m7X3fm57rz9Ajd26KxO4Ht5C6rcZFZjvT+2UfwN8sN23Ost4jUBqOo8PMnUNxjWoQHIOddDWy5msnfI3at/FjdvreUYr/8jOYz4OPLfHOnTXgaOA6+gMUKZaQ46jumfWHNf2gBqr+dX2/V6dms7jDa0Oo4lF96DOHNxhHGUuRR0PpMYT/4T5g8GeVBC5Ww9lLmhuynWo8XPnMv8UC732pnS2T2u27eUb2vbo1W07tQoVTF/IeM6o/CR1IPpQKgw9jxq3+qTOY3prKKC6htfu3D6irX8nUGd0jq6g80jGOF5uQT+jlomVTkREdl5cRKxL7WDekZkXtmW7UUdlW1LddFeOu/yIeBA14eTB1NioL2fmU9pjvkqdxv6JcZW7iPqsTn25RoMS96fOmjqRGpt1b2o+lxv7rkunTqtm5k2d269pdTmh8xl9HPh6Zr52Gcuay9TlsR5InSFzNNVaeTU1hrD3195ZL+5KhYJjMvO/232rZObNEfEu6ij8sJ7rcgIVvNalNkDPp7rLbqad0ZWZ7+izDp26/Ov7GhGHUxv9r1I76SOo1ro51Jxwv2uPWyUzb17CclahAv6xVBfE19ryO1Pfiy0z8w3jeVULLP811E5nUyoIP5s6Gj+QCiJ/B/5EjSd9TA/lz7ddbMvWpLrDdqNONPp85771MvOPY67DOpn5507Zj6K6AP9IHTR9DfhiZp4zznIXU6fR93IPqjXmAVRoPoY6cHt+Zl4XERtm5nV9lN3+fivwF2oOxMMz86aIOJY6cH5vZn5gnGUvpl6vB/6WmS+IiC2pXouNqP3Ip7rb7mUo4wzg5sw8urNsP6oRYwfgr9Ts/H9Z1rIWUYdDqG76HVu5H6De718zNRP/1zPznX3V4V/6THtD/jD/ZJK3Z2o6g8upFeuB1LiAe1ED9fpomt+BGjR8J2p8xKepI8G7U8l7IjPAt7ocTV0ge3TNrYdRLYQfaMtGYyMm0jLG/GPrXszUxLyPp8LBU6kWpAuYmu5iiU4woFpFR6fuP4EKGetSO/k92vKXAh+a5HpJtdB9nJrQcVUqrJ9FneJ/Z+DpE6jHflSL5DpUq8Q7OvetywS7KafVa6f2XV2HGm856q58LnXUvMRd+1R39POYukD6xlSX8OfoYf6oRdTjmdRYyNEF7B9MHQy8uN0/r22LPtDX+8/8LQGPpnoI1m+3j6JaQp7UR9kLqEt3G3BI+0xOYqBpLqihEhcydUmetdq2+3XUeLat+tw+ttd/dqvHX6grtWzf7nsMbXLmCb0XW7W6/JTWg0S1JD+PGmi/+RjKmAd8ZdqyfVqZ36em+3kV1WjQ636pfc7XUa3xd+4s35Dqru7t6ijz1WNSH/Akf5hqcn0wdebUGVQY2pEKaO+kAtGxVPfIDxnTTNxtBZpHNfd+lfknL308dST6MXqe4HH6CkydsbR+2zk9sy17DdVfP/Ym+JnWjbou3h+ps3VG4+p2p7qOPk6bl42lONuTGqB5HhVAD+8sP5kKgS9s909inM70z+OZVDP9Ra0+p1Ld2r2d1cf8O8D928bvGNrUDlQ4PJIBxtG18u9DnUE1Or3/OW0HsCvVinTnJV0XqJ3qF6ggdFZ7vw9uPw9u38Ujx/1aFlCPw4BLO7dHB0A7Ua20T+3c19cp/etR48K64+9Op0LZXdrtA6jB5HtN6DPvbgseyJgvRr2E5d+eGspwOVNDJ6J9Rr2e6UmNnfpIW1/fRoWQV1CtpftM+D0Z7T/XbvuIT9IZU8mYzralprX4SOf2ulSvwX2p7vKLqEaUXuYbW0B9NqTC4BOnLZ/ItWszV9JA1t7Ezan+93u3D/oJbaPcnW13HWrczOPGVObxVNPm6PZjqMG53Y3tXKo7ptfpJJhqIbwTNVhytJN7GRVE51Ih9d7dx0/4MzqJOr1+ByqcXAUc2O6bw9K3jO1JuxAzNRbpSjpnJlFB5KXUDrqXy50som6vo4LARtSR1/5t+dZUcOx17jdqp3zrtl78Cvh+575nMaZrsi1D/V5GuxRK+5w+SXWjLvXlgqixH5e29/zh1BxSP6MG7Z5JTQjd65m17TO/gak5D1dhamqNxwHvnsB7ey6t9RW4Q2f5cVRX6UPb7YmO3Rpi2zP6DDp/b0odkGxBjac9l86Eq33Usa2P2zN1QtUmVLfxf7Xba1EnnvXek9LZXxxMjSl+D1NTNB1LBdWxtpy2138J889/OZqQfdR1uNGE14m5VM/ZxHqv5it/iEJ7fDMfTI0HW5Vqer608wGvRaXtS5i6IOo6jGkgN3UE/K3Oin0ENVhwb2og6BDTB+xPXdbivVQoeTA1tcV3qa7UiV+jslO3OdTcUvt3lh1LjV963DI+9yHUJaC2oYLHAdS0Fi/uPOZ+DDPH2mPbZ3JMZ9nObV19bE9lTj977HtUMDuMaqI/mjoiv5gep1hYRP02Zip8b0u1FGzWbm/A/LO5L+1EwG9g6pJgH25lnNw2+hPptmzr4u9oZ1Qy1RKxBxUO5y7t65tB2aNpJu5NjZt9CTWOcrS9en777k3kElFD/0z7TryL6kU5v20zt6IO4C+kpzkAqZanj1PDBn7O1DQnW1Jnge9O5/qJPb8Xo/VwB6q36GHUPGtXMNVlegQ1tnfcZT+8rfuHT1v+GTqTQU963aAC6eQnwx7iBff0Jj6JChmjVL891fT+vs5GZy7VnTj2o2HaEXD7+07U6cq3bbd3al/2cyfwPozGTG3evvS7t9v7UjvffWmtJJ3/mUgwmb6zaRuck6YtO4tquVmmbqT2GVzLVAvUnkxNevllxtQqOoN6jDZ2/5ppnzoKvozWbU0F9967zTp1ejpTLSX3bxvE5zJA13V7Xz5HDdp/NzWE4ENUy9U60x+/tOscdfr626jhCv/VuX/SR+AbUwdHJ3aWfZA2Q35PZXanmXgeNWziXVQoG42l3IeaUmSJL0G1Iv9QY1VHZ/Bew9Q4ww2pkw3GflkibjmW8EHUWMJXt/tfQXUlX8aE5kSkGixOol0wvS17cXtPHtxjuaOJns+gDpqfQQ0jee9ysG6sP+kyV4qzLCNib2rs1lGZ+f2IuAc1w/XfqQ85qR3ebzr/s8RnaM2gHneixo39nRoE+N2ImJuZ/4iITajusc+Ns8xp5Z9MdcWcS7UA3JpqBflu1tl7D6fGZD2n8z+3OOuqp7qtmnXG0AbUTndtKnidR5218x/UIP4/Uhui22TmG5exzE2oMTNvzcxTI2Ir6ssfmXnSsjz3EtZjG2rj9lrgx+2z2Il67VdQLSZjXRcXUIfjqI3dE6ku7J2AMzPz29PPdu3bAs6u3ZhqrX4KNdZxVarV6IDM/M64vqsR8UoqkNwxM38w6dfdqcdcKoR+nzoDe/fMfEhPZW1JHYg8kLpo+dOo8a1/p1pE/kKNLd2DOnj5vz7qsbxoZ3XuBVydmZdFxEuocPQgapjDce37umlmXtpD+YdRl2v7t3Z7Tmbe2LYHX6JC0XupA5XVMvP6cddhIfXandpG/YPqSfh2W/5oKsDvnJl/66nsoNbFg6lGk6sy8/19lLW8W+EDWURsBvwSuG9mfjUi7k3NZvyIzPxxe8zbqabou2U7Zb7H+mxItY69MttpsqMvXc/lPpkai/LQzPxl2xB/kNrhPzUz/xYRj6K6px6YmTf0WZ9pdRudUj6ayuBH1BH56dSR+olUF+bm1NQg76G+lC8aQ9lzqUHd/5eZT1zW51uCch8H/Jna4X2eep3rUIP3v9NC+snUgNWjF/pEy1aHfwWZtsE9jWoxXpNqUb6OGr9xWR/lz6BOr6C68G4ahe+I2J46gHoxNe3A3uM8YIiI11Jn8Z6Tmf8Y1/MuRT2C6kZ8ONUt9Kcey5o+zcRjqO7831PB7GfA3zPzi33VYXkREZ+k5hf7emZ+NCIeQXVP/iUzD2qP+Qi1/XlWD+U/mOqq3DIzf9u2iXPa9uBxVIvckeMudwH1WND0J9tSJzr9huoy/Far15p9hTHNb4UPZAARcSo1NupYamd+emaePe0xd8/Mb06oPnOpnfCVmXnUBMrbgZrUcv/M/E1EbEodadyTGjtzORUSt6DmlrpwUi1j0+r5aqoZ+EmtpeyT1Ht0ZLt/DtWKs3dm7j/GckdjAtbLzIf3/doj4tlUsPweNfnle9p7/hLqMxmd2n5P6lJQY9/YtbmLPkG1RO6dme+KiJOoHfEzqe/KccCrMvNN4y5/EfUazbX2Oqo17EPUZ/M/1ISvN3Qe+ylqktaxtdq0VuIHUa3pvbZKzrA+6/YZxqaV1Q3DD6Om37mZOoniz5Oow5AWMufV4VQovgD4JzWG8R6ZuU+P9bgTNWziyMz8VOc7sQc1xvQpwD8n1HNxPPWaf0NtL35DjSfcgDqZ5gLqgGnFDworgFWGrsA4tCOZ91GDpb/XDWMR8eqIOHoUxtoRSd/1+QfVAkRETGJyw1Wo1o5/tub2l1BfpAOpILY2NR7ivBYMVpnQl336e30FNbcOmXl9Zu4NbNpaRUa+2uo9NlmOpLor6TmMHUaFrHlU6Pkf6jRuMvOl1GzXd6TmG3t5T2HsNOqkhb9SoeewiDiT6iLdnBoncwrVmjqRMNZaZmg7nnnUqfOPoebCOo3qKrkgIm7THr8j1Y0x1klJM/NjVIvx4GEMYFJhrJV1czs4ITM/Tp1N9u1ZEsbmUWePdsPYvtQ41ntR38f12l2H9FmXzPwedbm0N0bEiZ118YnA7zPzHxPaPh9OzY7/BeAR1JCKPalA9mtqdvwbDWOTs1K0kI1ExM7UeICjMvO8iHg6NY3CfQes0/qZ+Yeeywhq7ME9qDmbPkyNI/suNVj7POpMvpcDT87Mc/usT6vTqsC/U+PB9qBaitagzvJ7Qmb+pD3u69QOciKtl33rdElsn5m/agH5s9RZlH+nPotfA5dlD+OXIuKJwCO763xErE1NK/L/gLtQn8s+o89gEiLiZdQZZMe17vMtqfX1iZn5gKjZuU+lpt04KSI2ouaHu2pSdZwthmgdH1o76HtVZj6y3V6Xmnj4DKpr/HDqjPibJ/XeTHIsYafM46kTaDagxl2/gAqg+zM1WfXHMvPNfdZDCzZn6AqMU9ZA3dsDX4uIo6gzZR4BtxxIPME69RrGWhkZEW+gQtetWysAAG2w6L0y8/UR8QcqFEzChtTp4y+gZsy/Y2b+odXnwxHxX1Q4+ObKEsYAMvMz7Wj8uxFxBG1CUGq6laBaL1+W/QwYDupEjnPb7ftSLWWj6V62pFqjdqG6tCeitZSeR+303hkRL8zMn0fEdcAPWlf1PYE3Zua7ADLz99QYJ43ZbAtjzR+AHSLiiMw8MzP/FBEvysyfRMT3qbONN8iexxh3tfFZ+1JjCU+gZgboTUTcgbo49psi4vdUIFuDuq7xXhGxFjXUYqM+66GFW6layEbakcd51LxDX40ezqhcEUTEc6izq/ad5OsfHYFHxP2o6Qu+RV2P7Avt/vtQA9zXzHYdz5XtM4o6c/BSaiqU23WW9zpAtr3nz6fGDe5NHfX+HzXj9wHZ0wkEM6zbbagj8DtTU7L8HzWG7FqqZffumfnXlW1d0PKhjR/cj7pczwc6yz8DXJg9XsN0BnXrfSxhRNyWOpv937JdnzTqzPOPUENa7kBduePwzPxrn3XRgq2UgaxrNm7cW3fP/alLzzw4M6+OCZzp2coeDVBdjZr36TKqi24/4PJ2dHYXqmvge93/6btuk9begy8AP8rMJ0+ozLlU98PBVHfI+VkXRd6KmnDyaOCnk3i/F/S5Rl3k/knUxZtfSV0pYxPgH5n5u6FasrXya9/Hx1KTrm5AjVfdG/hdZj5+wKr1KiL2Ai5uBzuXUlMy/a5z4HwS1W35R+AZmfn14Wo7u630gWw2al1XtwJWzcyrhtjJRcTzgDUy86WttWhfasDoFlTX6r6Z+YtJ1mkI7bN4NzXJ6SMHrMe7qNa64yZU3r/GKUVNb/F3qgv7xLYzOIwaT/j5zDx1+v9IfWjfx1k151VEHEJNiHwv4MnU4P3rqUlZ/0mdUHMv4H8z85qBqikMZOpB6zZ7LvBb6gSLv7az7O5AzZj+o9nWlTyJkzsWUu6tqclQb5uZD5xguaOJgJ9JnWV6InVa/V+pkzq+0Qby35yZ50+qXtJsFFOTlq9HTcm0ARXGNqJm6X9BTpsqSpNnINPYtbP6Dmg/XwE+nZlXT3uMrSETEHW1gvtRn8FEpjeIiA0y8/qI2II6c/IYaqLX1akzPZ8JvCgzz5pEfSTV95K6Xu2PqJPdguqx+O0QB4u6JQOZllln3NjuVHfAJsCbqRMK7k/NBP65zLx8wGpqAiJiNBntM9pZz7emrtn3tsy8d+u+Pou6dE3vM5JLmtLG0Z0P/DAznzJ0fTS/lWJiWA2ndU3d3E6pfjcVxvanBrNf3JbdgZriQiu/NamZv18bEQdl5q+okzp+EjXZ66Ops26PhslM1CypZOY/qSEEq8dkJi3XErCFTGMREW+iZpl+abv9aurMynsAOwI/mC3jxWaziLg7Nd/YN6j5z/6nnVn7Fmq8yq7Umb9X2m0tDWeoca1auJVqYlhNTkQcSp01+QvqumwXAbcf3Z+Zz4u6pubmmfn99j/ugFdCbUqNP7WN++XUZJP3pWZCf1JErJaZT23dJetnXVTZ6S2kARnGlj92F2iJtRngnwP8mNr5Ph7YDDgwIh4ZEZtEXTFhd2o8ETBrZwhfqUXErYD/BT7Tzpq8M/BU4EpqqovXAHeOiA8CqwK/AzCMSdL87LLUEomIzYCrgPu0qQu2B95AXUtzLrUD/ilwO+D0zHyvLWMrt4g4mbpE1suAv1Fndf4d+H5mnhB1jdltMvPzA1ZTkpZrBjItsYg4lZpPbL/MvCEiPklNq3BGO4tuTeqySD9ujzeQreQi4gDgXcB9qNbShwE3UZcvu77zONcFSVoAA5mWSkQ8gmoNuwBYOzMftZDHuQOeJSLizsB/Aodl5oWTulyXJK0MDGRaahGxC/Bt4LGZefZoCgPPppy92kS0/wN8KDNPGrg6krTCcFC/llqb6HUL4GURcXJm3mwYm90y87fAHYE9I+I2Q9dHklYUtpBpmUXEXGoi2KMy8ydD10fLD7usJWlmDGQaK3fAkiQtOQOZJEnSwBxDJkmSNDADmSRJ0sAMZJIkSQMzkElaYUTEn4eugyT1wUAmSZI0MAOZpBVaRBwQEd+IiG9HxH9GxOZt+UkRcUZEfDkifhIRT+v8z4si4n8j4oKI+HBEPLst/3JEzGt/bxIRP21/bxcRX42Ib7Wff2/LV4mI0yLi8oj4TER8NiIe3u77t4j4SkRcGhFfiIgt2vKnRcQPIuK7EXH2RN8sScutOUNXQJKW0UXAPTMzI+Jo4LnAs9p9twf2BtYFfhQRbwfuQl38/K7UNvBbwKWLKeMa4H6Z+feI2An4MDAPeCiwHXAn6qLqPwTOiIjVgLcAB2XmtRHxSOAU4EjgBGD7zLwhIjYYw+uXtBIwkEla0W0FfKS1QM0Fruzc9x+ZeQNwQ0RcA2wO3Bv4VGb+DSAiPj2DMlYD3hoRuwI3Abdty+8NfLRdMuzXEfGltvx21CWkLogIgFWBq9t93wU+GBHnAucu8auVtFKyy1LSiu4twFsz807AE4E1Ovfd0Pn7JuogNBbxXDcytV3sPs8zgN9QrWvzqODHIp4rgMszc9f2c6fMvH+770HA24B/Ay6NCA+MJRnIJK3w1gd+2f4+YgaPvwg4ICLWiIh1qIA08lMqKAE8fFoZV7eWsMOpFq/Rcz2sjSXbHNirLf8RsGlE3AsgIlaLiF0iYhVg68z8EtW1ugGwzkxfqKSVl0dmklYka0XEVZ3brwdOAj4aEb8Evg5sv6gnyMxvRsR5wHeAnwGXAH9od78OOCciDge+2Pm304CPR8QhwJeAv7TlHwf2Ab4P/B/wDeAPmfmPNrj/zRGxPrWtfWN7zFltWQBvyMzrl/RNkLTy8VqWkmadiFgnM/8cEWsB/wU8ITO/tYzPtTFwMbB7Zv56nPWVtPKzhUzSbHR6ROxMjRM7c2nDWPOZdrbkXOBkw5ikpWELmSRJ0sAc1C9JkjQwA5kkSdLADGSSJEkDM5BJkiQNzEAmSZI0MAOZJEnSwP4//jashPN3SesAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking the distribution of the target variable in the train set\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.barplot(x=train_data['Language'].value_counts().index, y=train_data['Language'].value_counts().values, palette='magma')\n",
    "plt.title('Languages Distribution in Train Set')\n",
    "plt.xlabel('Languages')\n",
    "plt.ylabel('Number of Samples')\n",
    "plt.xticks(rotation=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# oversampling the train set to balance the target variable\n",
    "languages = train_data['Language'].unique()\n",
    "count_languages = train_data['Language'].value_counts()\n",
    "max_count = count_languages.max()\n",
    "balanced_train = pd.DataFrame(columns=['Text', 'Language'])\n",
    "\n",
    "for i in languages:\n",
    "    random_indices = np.random.choice(train_data[train_data['Language'] == i].index, max_count, replace=True)\n",
    "    balanced_train = balanced_train.append(train_data.loc[random_indices])\n",
    "\n",
    "# checking the distribution of the target variable in the balanced train set\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.barplot(x=balanced_train['Language'].value_counts().index, y=balanced_train['Language'].value_counts().values, palette='magma')\n",
    "plt.title('Languages Distribution in Balanced Train Set')\n",
    "plt.xlabel('Languages')\n",
    "plt.ylabel('Number of Samples')\n",
    "plt.xticks(rotation=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cleaning the balanced train set because commas, equals signs and other symbols in the indic languages are observed in the text after oversampling\n",
    "balanced_train[\"Text\"] = balanced_train[\"Text\"].apply(lambda x: extra_clean(x))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Selection\n",
    "\n",
    "The following models were used in this project:\n",
    "* Naive Bayes with CountVectorizer and TF-IDF Vectorizer\n",
    "* Linear Support Vector Machine with CountVectorizer and TF-IDF Vectorizer\n",
    "* Gated Recurrent Unit (GRU)\n",
    "* BiDirectional Long Short-Term Memory (BiLSTM)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CountVectorizer and TfidfVectorizer are used in natural language processing to vectorise text data. CountVectorizer counts the number of times a word appears in a document (using a bag-of-words approach), while TF-IDF Vectorizer considers how many times a word appears in a document and how important that word is to the whole corpus. Both methods would be compared based on their performance in the classification tasks."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Bayes with CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a pipeline with CountVectorizer and MultinomialNB\n",
    "BayesCountVectorizer = Pipeline([('countVectorizer', CountVectorizer()),('MultinomialNB', MultinomialNB())])\n",
    "\n",
    "BayesCountVectorizer.fit(balanced_train['Text'], balanced_train['Language']) # fitting the pipeline with the balanced train set\n",
    "preds = BayesCountVectorizer.predict(test_data['Text']) # predicting the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "     English       0.59      1.00      0.74       185\n",
      "  Portoguese       0.87      0.87      0.87       141\n",
      "       Dutch       0.97      0.89      0.93       183\n",
      "      Arabic       0.97      0.95      0.96       476\n",
      "     Turkish       1.00      0.95      0.97       347\n",
      "      French       0.94      0.84      0.89       154\n",
      "     Swedish       1.00      0.92      0.96       122\n",
      "     Italian       1.00      0.95      0.98        22\n",
      "   Malayalam       0.97      0.91      0.94       235\n",
      "     Kannada       1.00      0.95      0.97       128\n",
      "       Greek       1.00      0.99      0.99       206\n",
      "     Russian       0.96      0.99      0.97       258\n",
      "       Tamil       1.00      0.94      0.97       234\n",
      "     Spanish       0.97      0.91      0.94       277\n",
      "      German       0.96      0.94      0.95       226\n",
      "      Danish       1.00      0.96      0.98       162\n",
      "       Hindi       0.98      0.97      0.98       165\n",
      "\n",
      "    accuracy                           0.94      3521\n",
      "   macro avg       0.95      0.94      0.94      3521\n",
      "weighted avg       0.95      0.94      0.94      3521\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# printing the classification report\n",
    "print(classification_report(test_data['Language'], preds, target_names=languages)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot confusion matrix\n",
    "cm = confusion_matrix(test_data['Language'], preds, labels=languages)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=languages)\n",
    "\n",
    "disp.plot(cmap=plt.cm.Blues)\n",
    "disp.figure_.set_size_inches(12,12)\n",
    "plt.xticks(rotation=50)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grid Search for Hyperparameter Tuning for Naive Bayes with CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {\n",
    "    'countVectorizer__binary': (True, False),\n",
    "    'countVectorizer__ngram_range': [(1, 1), (1, 2)],\n",
    "    'MultinomialNB__alpha': (0.1, 0.2, 0.5, 0.8, 1.0, 1.2, 1.5)\n",
    "    }\n",
    "\n",
    "GridSearch = GridSearchCV(BayesCountVectorizer, parameters, cv=5)\n",
    "#GridSearch.get_params().keys()\n",
    "GridSearch.fit(balanced_train['Text'], balanced_train['Language'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(GridSearch.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Naive Bayes with CountVectorizer and best parameters.\n",
    "BayesCountVectorizer = Pipeline([('countVectorizer', CountVectorizer(binary=True, ngram_range=(1, 2))),('MultinomialNB', MultinomialNB(alpha=0.1))])\n",
    "\n",
    "# fitting the pipeline with the balanced train set\n",
    "BayesCountVectorizer.fit(balanced_train['Text'], balanced_train['Language'])\n",
    "preds = BayesCountVectorizer.predict(test_data['Text']) # predicting the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# printing the classification report\n",
    "print(classification_report(test_data['Language'], preds, target_names=languages)) # classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot confusion matrix\n",
    "cm = confusion_matrix(test_data['Language'], preds, labels=languages)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=languages)\n",
    "\n",
    "disp.plot(cmap=plt.cm.Blues)\n",
    "disp.figure_.set_size_inches(12,12)\n",
    "plt.xticks(rotation=50)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Bayes with TF-IDF Vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a pipeline with TfidfVectorizer and MultinomialNB\n",
    "BayesTfid = Pipeline([('tfidfVectorizer', TfidfVectorizer()),('MultinomialNB', MultinomialNB())])\n",
    "\n",
    "BayesTfid.fit(balanced_train['Text'], balanced_train['Language']) # fitting the pipeline with the balanced train set\n",
    "preds = BayesTfid.predict(test_data['Text']) # predicting the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# printing the classification report\n",
    "print(classification_report(test_data['Language'], preds, target_names=languages)) # classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot confusion matrix\n",
    "cm = confusion_matrix(test_data['Language'], preds, labels=languages)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=languages)\n",
    "\n",
    "disp.plot(cmap=plt.cm.Blues)\n",
    "disp.figure_.set_size_inches(12,12)\n",
    "plt.xticks(rotation=50)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grid Search for Hyperparameter Tuning for Naive Bayes with TF-IDF Vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {\n",
    "    'tfidfVectorizer__binary': (True, False),\n",
    "    'tfidfVectorizer__ngram_range': [(1, 1), (1, 2)],\n",
    "    'MultinomialNB__alpha': (0.1, 0.2, 0.5, 0.8, 1.0, 1.2, 1.5)\n",
    "    }\n",
    "\n",
    "GridSearch = GridSearchCV(BayesTfid, parameters, cv=5)\n",
    "#GridSearch.get_params().keys()\n",
    "GridSearch.fit(balanced_train['Text'], balanced_train['Language'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(GridSearch.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Naive Bayes with TfidfVectorizer and best parameters.\n",
    "BayesTfid = Pipeline([('tfidfVectorizer', TfidfVectorizer(binary=True, ngram_range=(1, 2))),('MultinomialNB', MultinomialNB(alpha=0.1))])\n",
    "\n",
    "# fitting the pipeline with the balanced train set\n",
    "BayesTfid.fit(balanced_train['Text'], balanced_train['Language'])\n",
    "preds = BayesTfid.predict(test_data['Text']) # predicting the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# printing the classification report\n",
    "print(classification_report(test_data['Language'], preds, target_names=languages)) # classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot confusion matrix\n",
    "cm = confusion_matrix(test_data['Language'], preds, labels=languages)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=languages)\n",
    "\n",
    "disp.plot(cmap=plt.cm.Blues)\n",
    "disp.figure_.set_size_inches(12,12)\n",
    "plt.xticks(rotation=50)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear SVC with CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a pipeline with CountVectorizer and LinearSVC\n",
    "LinearSVC_CountVectorizer = Pipeline([('countVectorizer', CountVectorizer()),('LinearSVC', LinearSVC())])\n",
    "\n",
    "LinearSVC_CountVectorizer.fit(balanced_train['Text'], balanced_train['Language']) # fitting the pipeline with the balanced train set\n",
    "preds = LinearSVC_CountVectorizer.predict(test_data['Text']) # predicting the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# printing the classification report\n",
    "print(classification_report(test_data['Language'], preds, target_names=languages)) # classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot confusion matrix\n",
    "cm = confusion_matrix(test_data['Language'], preds, labels=languages)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=languages)\n",
    "\n",
    "disp.plot(cmap=plt.cm.Blues)\n",
    "disp.figure_.set_size_inches(12,12)\n",
    "plt.xticks(rotation=50)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grid Search for Hyperparameter Tuning for Linear SVC with CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {\n",
    "    'countVectorizer__binary': (True, False),\n",
    "    'countVectorizer__ngram_range': [(1, 1), (1, 2)],\n",
    "    'LinearSVC__C': (0.1,1,5,10,20,50,70,100),\n",
    "    }\n",
    "\n",
    "GridSearch = GridSearchCV(LinearSVC_CountVectorizer, parameters, cv=5)\n",
    "# GridSearch.get_params().keys()\n",
    "GridSearch.fit(balanced_train['Text'], balanced_train['Language'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(GridSearch.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SVM with CountVectorizer and best parameters.\n",
    "LinearSVC_CountVectorizer = Pipeline([('countVectorizer', CountVectorizer(binary=False, ngram_range=(1, 1))),\n",
    "('LinearSVC', LinearSVC(C=1))])\n",
    "\n",
    "# fitting the pipeline with the balanced train set\n",
    "LinearSVC_CountVectorizer.fit(balanced_train['Text'], balanced_train['Language'])\n",
    "preds = LinearSVC_CountVectorizer.predict(test_data['Text']) # predicting the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# printing the classification report\n",
    "print(classification_report(test_data['Language'], preds, target_names=languages)) # classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot confusion matrix\n",
    "cm = confusion_matrix(test_data['Language'], preds, labels=languages)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=languages)\n",
    "\n",
    "disp.plot(cmap=plt.cm.Blues)\n",
    "disp.figure_.set_size_inches(12,12)\n",
    "plt.xticks(rotation=50)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear SVC with TF-IDF Vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a pipeline with TfidfVectorizer and LinearSVC\n",
    "LinearSVC_Tidf = Pipeline([('tfidfVectorizer', TfidfVectorizer()),('LinearSVC', LinearSVC())])\n",
    "\n",
    "LinearSVC_Tidf.fit(balanced_train['Text'], balanced_train['Language']) # fitting the pipeline with the balanced train set\n",
    "preds = LinearSVC_Tidf.predict(test_data['Text']) # predicting the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# printing the classification report\n",
    "print(classification_report(test_data['Language'], preds, target_names=languages)) # classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot confusion matrix\n",
    "cm = confusion_matrix(test_data['Language'], preds, labels=languages)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=languages)\n",
    "\n",
    "disp.plot(cmap=plt.cm.Blues)\n",
    "disp.figure_.set_size_inches(12,12)\n",
    "plt.xticks(rotation=50)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grid Search for Hyperparameter Tuning for Linear SVC with TF-IDF Vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {\n",
    "    'tfidfVectorizer__binary': (True, False),\n",
    "    'tfidfVectorizer__ngram_range': [(1, 1), (1, 2)],\n",
    "    'LinearSVC__C': (0.1,1,5,10,20,50,70,100),\n",
    "    }\n",
    "\n",
    "GridSearch = GridSearchCV(LinearSVC_Tidf, parameters, cv=5)\n",
    "#GridSearch.get_params().keys()\n",
    "GridSearch.fit(balanced_train['Text'], balanced_train['Language'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print best parameters\n",
    "print(GridSearch.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SVM with TfidfVectorizer and best parameters.\n",
    "LinearSVC_Tidf = Pipeline([('tfidfVectorizer', TfidfVectorizer(binary=True, ngram_range=(1, 2))),\n",
    "('LinearSVC', LinearSVC(C=5))])\n",
    "\n",
    "# fitting the pipeline with the balanced train set\n",
    "LinearSVC_Tidf.fit(balanced_train['Text'], balanced_train['Language'])\n",
    "preds = LinearSVC_Tidf.predict(test_data['Text']) # predicting the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# printing the classification report\n",
    "print(classification_report(test_data['Language'], preds, target_names=languages)) # classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot confusion matrix\n",
    "cm = confusion_matrix(test_data['Language'], preds, labels=languages)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=languages)\n",
    "\n",
    "disp.plot(cmap=plt.cm.Blues)\n",
    "disp.figure_.set_size_inches(12,12)\n",
    "plt.xticks(rotation=50)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gated Recurrent Unit (GRU)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(15011, 200) (15011, 17)\n",
      "(3521, 200) (3521, 17)\n"
     ]
    }
   ],
   "source": [
    "# pre-processing the data for deep learning models\n",
    "\n",
    "max_num_words = 15000\n",
    "\n",
    "# create a tokenizer\n",
    "tokenizer = Tokenizer(num_words=max_num_words)\n",
    "\n",
    "# fit the tokenizer on the training data\n",
    "tokenizer.fit_on_texts(balanced_train['Text'].values)\n",
    "\n",
    "# transform the training and test data\n",
    "X_train = tokenizer.texts_to_sequences(balanced_train['Text'].values)\n",
    "X_test = tokenizer.texts_to_sequences(test_data['Text'].values)\n",
    "\n",
    "# pad the sequences\n",
    "max_seq_length = 200\n",
    "X_train = pad_sequences(X_train, maxlen = max_seq_length)\n",
    "X_train = np.array(X_train)\n",
    "X_test = pad_sequences(X_test, maxlen = max_seq_length)\n",
    "X_test = np.array(X_test)\n",
    "\n",
    "# one-hot encode the labels\n",
    "y_train = pd.get_dummies(balanced_train['Language']).values\n",
    "y_test = pd.get_dummies(test_data['Language']).values\n",
    "\n",
    "# print the shape of the training and test data\n",
    "print(X_train.shape, y_train.shape)\n",
    "print(X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding_1 (Embedding)     (None, 200, 128)          3344640   \n",
      "                                                                 \n",
      " gru (GRU)                   (None, 64)                37248     \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 17)                1105      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 3,382,993\n",
      "Trainable params: 3,382,993\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# create the model\n",
    "embedding_dim = 128\n",
    "input_len = X_train.shape[1]\n",
    "vocab_size = len(tokenizer.word_index) + 1 \n",
    "\n",
    "def build_GRU():\n",
    "\n",
    "    # create a sequential model\n",
    "    model = Sequential()\n",
    "\n",
    "    # add one embedding layer, one GRU layer and one dense layer\n",
    "    model.add(Embedding(vocab_size, embedding_dim, input_length = input_len))\n",
    "    model.add(GRU(64))\n",
    "    model.add(Dense(17, activation='softmax'))\n",
    "\n",
    "    # compile the model\n",
    "    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "    \n",
    "    return model\n",
    "\n",
    "model = build_GRU()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/7\n",
      "118/118 [==============================] - 24s 178ms/step - loss: 2.0922 - accuracy: 0.5128 - val_loss: 1.1332 - val_accuracy: 0.7893\n",
      "Epoch 2/7\n",
      "118/118 [==============================] - 19s 164ms/step - loss: 0.3425 - accuracy: 0.9601 - val_loss: 0.4747 - val_accuracy: 0.8855\n",
      "Epoch 3/7\n",
      "118/118 [==============================] - 19s 164ms/step - loss: 0.1043 - accuracy: 0.9814 - val_loss: 0.3978 - val_accuracy: 0.8949\n",
      "Epoch 4/7\n",
      "118/118 [==============================] - 19s 165ms/step - loss: 0.0716 - accuracy: 0.9829 - val_loss: 0.4043 - val_accuracy: 0.8898\n",
      "Epoch 5/7\n",
      "118/118 [==============================] - 23s 192ms/step - loss: 0.0608 - accuracy: 0.9833 - val_loss: 0.3705 - val_accuracy: 0.8995\n",
      "Epoch 6/7\n",
      "118/118 [==============================] - 19s 161ms/step - loss: 0.0564 - accuracy: 0.9824 - val_loss: 0.3647 - val_accuracy: 0.8972\n",
      "Epoch 7/7\n",
      "118/118 [==============================] - 19s 161ms/step - loss: 0.0537 - accuracy: 0.9839 - val_loss: 0.3635 - val_accuracy: 0.9012\n"
     ]
    }
   ],
   "source": [
    "# train the model\n",
    "history = model.fit(X_train, y_train, validation_data=(X_test,y_test), batch_size=128, epochs=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_results(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "111/111 [==============================] - 2s 15ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     English       1.00      0.95      0.97       185\n",
      "  Portoguese       0.83      0.79      0.81       141\n",
      "       Dutch       0.94      0.84      0.89       183\n",
      "      Arabic       0.93      0.93      0.93       476\n",
      "     Turkish       0.98      0.93      0.96       347\n",
      "      French       0.39      0.91      0.55       154\n",
      "     Swedish       0.99      0.87      0.93       122\n",
      "     Italian       1.00      1.00      1.00        22\n",
      "   Malayalam       0.96      0.84      0.90       235\n",
      "     Kannada       0.96      0.87      0.91       128\n",
      "       Greek       0.96      0.92      0.94       206\n",
      "     Russian       1.00      0.97      0.98       258\n",
      "       Tamil       1.00      0.88      0.94       234\n",
      "     Spanish       0.93      0.89      0.91       277\n",
      "      German       0.93      0.88      0.91       226\n",
      "      Danish       0.99      0.84      0.91       162\n",
      "       Hindi       0.98      0.96      0.97       165\n",
      "\n",
      "    accuracy                           0.90      3521\n",
      "   macro avg       0.93      0.90      0.91      3521\n",
      "weighted avg       0.93      0.90      0.91      3521\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# predict target values on the test data\n",
    "preds = model.predict(X_test)\n",
    "\n",
    "# convert the predicted values to the original labels\n",
    "preds = np.argmax(preds, axis=1)\n",
    "\n",
    "# convert the test labels to the original labels\n",
    "y_test = np.argmax(y_test, axis=1)\n",
    "\n",
    "# print the classification report\n",
    "print(classification_report(y_test, preds, target_names=languages))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the confusion matrix\n",
    "cm = confusion_matrix(y_test, preds)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=languages)\n",
    "\n",
    "disp.plot(cmap=plt.cm.Blues)\n",
    "disp.figure_.set_size_inches(12,12)\n",
    "plt.xticks(rotation=50)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BiDirectional Long Short-Term Memory (BiLSTM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(15011, 200) (15011, 17)\n",
      "(3521, 200) (3521, 17)\n"
     ]
    }
   ],
   "source": [
    "# pre-processing the data for deep learning models\n",
    "\n",
    "max_num_words = 15000\n",
    "\n",
    "# create a tokenizer\n",
    "tokenizer = Tokenizer(num_words=max_num_words)\n",
    "\n",
    "# fit the tokenizer on the training data\n",
    "tokenizer.fit_on_texts(balanced_train['Text'].values)\n",
    "\n",
    "# transform the training and test data\n",
    "X_train = tokenizer.texts_to_sequences(balanced_train['Text'].values)\n",
    "X_test = tokenizer.texts_to_sequences(test_data['Text'].values)\n",
    "\n",
    "# pad the sequences\n",
    "max_seq_length = 200\n",
    "X_train = pad_sequences(X_train, maxlen = max_seq_length)\n",
    "X_train = np.array(X_train)\n",
    "X_test = pad_sequences(X_test, maxlen = max_seq_length)\n",
    "X_test = np.array(X_test)\n",
    "\n",
    "# one-hot encode the labels\n",
    "y_train = pd.get_dummies(balanced_train['Language']).values\n",
    "y_test = pd.get_dummies(test_data['Language']).values\n",
    "\n",
    "# print the shape of the training and test data\n",
    "print(X_train.shape, y_train.shape)\n",
    "print(X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding_3 (Embedding)     (None, 200, 128)          3344640   \n",
      "                                                                 \n",
      " bidirectional_1 (Bidirectio  (None, 64)               41216     \n",
      " nal)                                                            \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 17)                1105      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 3,386,961\n",
      "Trainable params: 3,386,961\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "embedding_dim = 128  # Embedding dimension\n",
    "input_len = X_train.shape[1]  # Length of input sequences\n",
    "vocab_size = len(tokenizer.word_index) + 1\n",
    "\n",
    "# create the model\n",
    "def build_RNN():\n",
    "\n",
    "    # create a sequential model\n",
    "    model = Sequential()\n",
    "\n",
    "    # add one embedding layer, one bidirectional LSTM layer and one dense layer\n",
    "    model.add(Embedding(vocab_size, embedding_dim, input_length= input_len)) \n",
    "    model.add(Bidirectional(LSTM(32)))\n",
    "    model.add(Dense(17, activation='softmax'))\n",
    "\n",
    "    # compile the model\n",
    "    model.compile(loss=CategoricalCrossentropy(), optimizer=Adam(), metrics=['accuracy'])\n",
    "    \n",
    "    return model\n",
    "\n",
    "# print the summary of the model\n",
    "model = build_RNN()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/7\n",
      "118/118 [==============================] - 28s 215ms/step - loss: 2.3743 - accuracy: 0.4753 - val_loss: 1.8760 - val_accuracy: 0.5291\n",
      "Epoch 2/7\n",
      "118/118 [==============================] - 23s 194ms/step - loss: 1.0089 - accuracy: 0.8961 - val_loss: 0.8471 - val_accuracy: 0.8659\n",
      "Epoch 3/7\n",
      "118/118 [==============================] - 23s 195ms/step - loss: 0.3409 - accuracy: 0.9680 - val_loss: 0.5541 - val_accuracy: 0.8901\n",
      "Epoch 4/7\n",
      "118/118 [==============================] - 24s 199ms/step - loss: 0.1723 - accuracy: 0.9785 - val_loss: 0.4706 - val_accuracy: 0.8969\n",
      "Epoch 5/7\n",
      "118/118 [==============================] - 23s 198ms/step - loss: 0.1124 - accuracy: 0.9806 - val_loss: 0.4339 - val_accuracy: 0.8938\n",
      "Epoch 6/7\n",
      "118/118 [==============================] - 23s 195ms/step - loss: 0.0882 - accuracy: 0.9819 - val_loss: 0.4328 - val_accuracy: 0.8884\n",
      "Epoch 7/7\n",
      "118/118 [==============================] - 23s 194ms/step - loss: 0.0755 - accuracy: 0.9832 - val_loss: 0.3915 - val_accuracy: 0.9012\n"
     ]
    }
   ],
   "source": [
    "# train the model\n",
    "history = model.fit(X_train, y_train, validation_data=(X_test,y_test), batch_size=128, epochs=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_results(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "111/111 [==============================] - 3s 22ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     English       0.99      0.96      0.98       185\n",
      "  Portoguese       0.84      0.79      0.81       141\n",
      "       Dutch       0.94      0.83      0.88       183\n",
      "      Arabic       0.96      0.91      0.94       476\n",
      "     Turkish       0.98      0.93      0.95       347\n",
      "      French       0.39      0.93      0.55       154\n",
      "     Swedish       0.93      0.89      0.91       122\n",
      "     Italian       1.00      0.95      0.98        22\n",
      "   Malayalam       0.94      0.86      0.90       235\n",
      "     Kannada       0.97      0.88      0.92       128\n",
      "       Greek       0.95      0.92      0.94       206\n",
      "     Russian       1.00      0.97      0.99       258\n",
      "       Tamil       0.98      0.90      0.94       234\n",
      "     Spanish       0.94      0.91      0.92       277\n",
      "      German       0.94      0.88      0.91       226\n",
      "      Danish       1.00      0.83      0.91       162\n",
      "       Hindi       0.99      0.94      0.96       165\n",
      "\n",
      "    accuracy                           0.90      3521\n",
      "   macro avg       0.93      0.90      0.90      3521\n",
      "weighted avg       0.94      0.90      0.91      3521\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# predict target values on the test data\n",
    "preds = model.predict(X_test)\n",
    "\n",
    "# convert the predicted values to the original labels\n",
    "preds = np.argmax(preds, axis=1)\n",
    "\n",
    "# convert the test labels to the original labels\n",
    "y_test = np.argmax(y_test, axis=1)\n",
    "\n",
    "# print the classification report\n",
    "print(classification_report(y_test, preds, target_names=languages))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Dkh47ndtdEiNZc54/Alqa2T5m9rKZzTSzT82sE4CZjTazB8xsIvAEcD1wemTk+vRS1qttZm+b2XQzu9/MFplZrpk1NbPZJcHN7DIzGx153sLM3jSzfDP7yMzaRsp/Hxkl/9LMJkXKMs3sVjP7PBL7gkTsnGUFa2lYN2fb6wZ1c5LagUwVPyxdycyvF9O9Q5OyF46ThUtXsm9ONn+54SkOP+efXHLj02zYtCVp8UsEkXs6C/Izl86f96BzDzp+Oht5+4uMHjqADEv+qVZqd4m3hB/FkZHk44BZwHXAF+7eCbiKcEe5RHdggLufBVwDPBcZuX6ulPWuBd5z927AS0AskzUfAP7i7t2By4B7I+XXAMe4e2egf6TsXGCtu/cAegDnm9lOk2HNbIiZTTOzaQUrCmKowvbcfaeydDuZdf3GLQwa/jA3/e1kauwwOp5IRaFiZn69mD+efCjvP3kl1SpX5K7H305afAgu93QW5GcunT/vQecedPx09dbHs8ndJztp51PsSO0eP4ZhFuwjFSTyb/JVzGxG5PlHwMPAVOAUAHd/z8z2NbOakWVedfdNu9nWIbtZ7xDgpEj5m2a2urQKmVk2cDDwfFQDVIr8nAw8ZmbjgBcjZX2ATmZ2auR1TaAV8H30dt39AcKdcrp3z9v5U1qGBnVqsWT5r1Vfunw19XJrlrJG+VJYFGLQlQ9x6jF5nHB4l6TGblCnFg3q1KJ7x6YAnHBEF+58Inmd5yBzT2dBfubS+fMedO5Bx09XU79cwJuTZvPOJ3PZsqWQdRs2c8G1j3P/dYOSEl/tLvGWjDnPXdz9L+6+lV1P9S7pbG4oZVu7W293X0GK2D63ypGfGcCaqHp1cfd2AO5+ITAKaAzMMLN9I9v/S9Syzdx9Yin1/E26tW/Cdz8UsGjJCrYWFvHi29M5rleneIdJSe7OsBvG0rpZPS4eeETS49fdtwYN69Ti20XLAZg07RvaNKuflNhB557OgvzMpfPnPejcg46frq65uD+zJ9zAjJev48F//JFD81onreMMavd408hz8i9VNwkYCNxgZr2BFe7+yy52xjqgegzrfQycBvzTzPoAJZOalgN1Ih3g9UA/4M3IOt+b2e/d/XkLB+7k7l+aWQt3nwpMNbMTCHei3wL+bGbvuXuhmbUGlrh7aR39PZaVlcktV5zGKcPuIRRyBvY/kHYt4t+Bu/GOcXw593vWrtvIGRfeyqDTjqB6dhX+88hrrP1lAyPHPEmLpvX558jwL7WBF9/Gxo1bKCwKMfnzefxz1CCaNKoT1zpN/XIBz73xOe1bNqDXwDEAXH3RCRzds0Nc45Tm5st+z4XXPE5hUYgmDfbl7qvPTkrcIHK/4JrHmDx9PqvWrKdz/6u54ry+1KpRlav+PZ6Va9Zz1t/vp2Prhoy746KE1aHEuSMfZXL+t6xcs54Ox49i+JC+nDPg4ITHheR95lItNqTvfk+F+EHu+yBjBy2d210Sw3Y1FyguGzZb7+7ZO5TtAzwKNAM2AkPcfWbkZL717v6vqOXeAioANwNv72a9OsAzhDvNHwKnA83cfYuZDQOGEZ5isQRY6O6jI3OW7wPqR7b/rLtfb2YvEp6SYcC7wF8jz/8BnBB5XgCc6O67PdOge/c8nzx12m/fcf+j/O9LnbmSUN2a1gosNkBxYg7lmGQE/GW4KBRc8hWydK8lkXRRHOQvWiAj6F+2AapSwfLdPS/IOmTt29yrH3dDkFVgzdizg98Pidrwjh3nSNkqYMAuykfvYrkeOyy203rAWsIn+RWZ2UHA4e6+JbKNu4C7dhHre+DYXZSfvKs0CJ+geNUu3hMRERFJK6kydSJIe/sdBvcDxplZBuFrSZ8fcH1EREREpBzbqzvP7v4t0DXoeoiIiIikA408J+8mKSIiIiIiez11nkVEREREYrRXT9sQERERkSQxdn+HjTSikWcRERERkRhp5FlEREREYqITBjXyLCIiIiISM3WeRURERERipM6ziIiIiJTJMMyCfZRZR7PKZvaZmX1pZnPM7LpI+WgzW2JmMyKPvlHrjDCz+Wb2tZkdU1YMzXkuZzrvVzOw2C/NWhJYbICT9m8YWOyg54CZeaDxRSR53PV5FynFFuAId19vZhWAj83sjch7t7v7v6IXNrP2wBlAB6AB8I6ZtXb30O4CqPMsIiIiIjEJerCoLB7+drk+8rJC5FHaN84BwLPuvgX43szmAwcAU3a3gqZtiIiIiMjeItfMpkU9huy4gJllmtkM4GfgbXefGnlrqJnNNLNHzCwnUtYQ+DFq9cWRst1S51lERERE9hYr3D0v6vHAjgu4e8jduwCNgAPMrCNwH9AC6AIsA26LLL6rofRS50ap8ywiIiIisbGAH3vA3dcAHwDHuvvySKe6GHiQ8NQMCI80N45arRGwtLTtqvMsIiIiIuWCmdU2s1qR51WAo4CvzKx+1GInAbMjz18FzjCzSmbWDGgFfFZaDJ0wKCIiIiJls9Q/YRCoDzxuZpmEB4nHufsEM3vSzLoQnpKxELgAwN3nmNk4YC5QBFxc2pU2QJ1nERERESkn3H0m0HUX5eeUss6NwI2xxtC0DRERERGRGGnkWURERERishdM20g4jTyLiIiIiMRII88pYuj1T/HWx7PJzanOlOdGJj3+/z3zPk+9OgUzo12L+tw1aiCVK1WI2/YfeeQ1Zs78jurVq3LDDecB8MorHzFp0pdUr14VgJNPPoxOnVowZ873vPDCBxQVFZOVlcHvf3847do1jVtdoi1evpqLRj/J8pW/kGHGoJN6cuEZvRMSa1fe+WQuI24bT6i4mHMGHMylg/skNN6wf4zl7clzyM2pzkdPjwBg9doNnD/qMX5Ytor96u/DQzf+kVo1qia0HpD83FMpfpCf96B/16jdg9n3m7cU0u+CO9iytYiiUDH9j+zCiCHHJ7UOoVAxRw6+lfq1a/LMvy9Mauwg2z3oY17ib68feTazkJnNMLM5Zvalmf3NzMrMy8yuimGZx8zs1PjUtHRn9juQ8XddnIxQO1n28xoeHPchbz96GR89PYJQcTEvvT09rjF69tyfSy89bafyo4/uwejRf2L06D/RqVMLALKzq/CXv5zK9defy5/+1I+HHpoQ17pEy8rM4IZLTmLquFFMfOTvPPz8JL5asCxh8aKFQsVcfss4nr/zIj4dN4oXJuYnPPYZx/+OZ2//83Zldz3xDof2aM1n46/m0B6tueuJtxNaBwgm91SKH+TnPcjYQe/3oOMHue8rVczi5XuH8dHTI5g0djjvTpnH57O+T2od7n/uA1o3rZvUmBBsuwd9zCWCmQX6SAV7fecZ2OTuXdy9A3A00Be4Nob1yuw8J1PPbi3JScJo3+4UhYrZvKWQoqIQmzYXUq92jbhuv02b/ahWrXJMyzZpUo+cnOoANGyYS2FhEYWFRXGtT4l6uTXp3DZ8bfTq1SrTulk9lhWsTUisHeXPWUjzxrk0bZRLxQpZnHx0N17/cGZCYx7cdefj7I2PZnF63/C14k/vewCvT5qV0DpAMLmnUvwgP+9Bxg56vwcdP8h9b2ZkV60EQGFRiKKiUFI7IkuWr2bi5DmcPeCgpMUsEWS7B33MSWKUh87zNu7+MzCE8L3LzcwGm9l/St43swlm1tvMxgBVIiPWYyPv/SFyv/MvzezJqM32MrNPzGxBskahk61+nVpcNPAIupx4LR37jaJGtcoc/rt2SYn93nv5XHvtwzzyyGts2LB5p/fz879mv/3qUqFC4mcY/bB0JTO/Xkz3Dk0SHgtgWcFaGtbN2fa6Qd2cpHXcoxWsWke93JpA+MvEitXrEh4z6NyDjp+ugt7vQccPWihUTK+BY2hzzAh6H9CWvI5NkxZ75O0vMnroADLK/sNw3AXZ7uXtmDOCHXXWyHOCuPsCwnnVKWWZ4fw6Yj3QzDoAI4Ej3L0zcEnU4vWBQ4B+wJjE1Tw4a37ZyJuTZpH/4rXMmvAPNm7eyvNvfJ7wuL17d2PMmAu59to/UatWNs899+527y9ZUsD48R/whz8cm/C6rN+4hUHDH+amv51MjewqCY8H4O47laXI74WECzr3oOOnq6D3e9Dxg5aZmcGkscOZPeEGps9dxNzvSr0Dcdy89fFscvfJpku7/ZISb0dBtnu6H3PlVbnrPEfs6aF5BDDe3VcAuPuqqPdedvdid58L7HKylpkNMbNpZjatYEXBb6txgD78/Gv2a7AvuTnVqZCVyfG9OydlLlzNmtXIyMggI8Po1asz33//6zywVat+4Z57XuTcc/tRp05OKVv53xUWhRh05UOcekweJxzeJaGxojWoU4sly1dve710+eptI8DJVHuf6vy0IjwS8tOKteRGpswkUtC5Bx0/XQW934OOnypqVq9Kz24teXfKvKTEm/rlAt6cNJsuJ17L+aMe5aNp33DBtY8nJTYE2+465sqnctd5NrPmQAj4mfBtFqNz3N2kWyN8u8Zd2bLDcjtx9wfcPc/d82rn1t7DGgevUd0c8mcvZOPmrbg7k6Z9Q6sknNSxZs36bc+nT/+Ghg3D+27jxs3ceefznHzyYbRq1SihdXB3ht0wltbN6nHxwCMSGmtH3do34bsfCli0ZAVbC4t48e3pHNerU1LrAHDsoR157vXPAHju9c847tD9Ex4z6NyDjp+ugt7vQccP0orV61i7biMAmzZv5cPPvqZ1k+ScvHfNxf2ZPeEGZrx8HQ/+448cmtea+68blJTYEGy7l8tjzgJ+pIBydak6M6sN/B/wH3d3M1sIXBS5+kZD4ICoxQvNrIK7FwLvAi+Z2e3uvtLM9tlh9Dnhzh35KJPzv2XlmvV0OH4Uw4f05ZwBBycldveOTTnhiC4cOegWsjIz2b91Q/5wYnxj33//K3z99Q+sX7+Jyy67hwEDDuGrr37gxx9/xgz23bfmtukZ776bz88/r2HChE+YMOETAP72t9OpUaNaXOsE4RGR5974nPYtG9BrYHhWztUXncDRPTvEPdaOsrIyueWK0zhl2D2EQs7A/gfSrkX9hMYccvVjTJ4+n1Vr1tPphKu54vy+DPvD0Zw38lHGvvopjerl8PCNf0xoHSCY3FMpfpCf9yBjB73fg44f5L5fvuIXLrruKULFxRQXOyce1ZVjDu2YlNhBC7Ldgz7mJDFsV/Nx9iZmFgJmARUIjzQ/Cfzb3YstPLP8KaALMJvwtIvR7v6Bmf0T6A9Mj8x7HgRcTnjU+gt3H2xmjwET3H18JNZ6d88urT7du+f55KnTEpFqTIpCxYHFfnVOcubP7c5J+zcMLHbQJzEE2e5ZmeXuD1giKS3I/7eD7jJkZKTI0GMAqlSwfHfPC7IOFeu09Nqn3BpkFVj6fycHvh/2+pFnd88s5T0HBu7mvSuBK6NePw48vsMyg3d4XWrHWURERETKNw0ZiYiIiIjEaK8feRYRERGR5Ah6mmIq0MiziIiIiEiMNPIsIiIiIjHRyLNGnkVEREREYqbOs4iIiIhIjDRtQ0RERERio1kbGnkWEREREYmVRp5FREREJCY6YVAjzyIiIiIiMVPnWUREREQkRpq2Uc5kZgT355STOzUKLDZATo+hgcVe9dndgcUG2FJUHFjsjID/hJcR4DHv7oHFhmD/fFpcHGzuavegBJu7BMvMNG0DjTyLiIiIiMRMnWcRERERkRhp2oaIiIiIxETTNjTyLCIiIiISM408i4iIiEhMNPKskWcRERERkZip8ywiIiIiEiNN2xARERGR2GjWhkaeRURERERipZFnEREREYmJThhU5zllvPPJXEbcNp5QcTHnDDiYSwf3SVrsxctXc9HoJ1m+8hcyzBh0Uk8uPKN30uInK/eMDOP9J65g2c9rOeNv/8dVFx5P316dKHanYNU6Lr7uKX5asZbeB7Tl2qH9qVghi62FRVxz18t8NO2buNcniP2+dPlqLvnHWApW/UKGZXBW/4M477TDuOGeV3hn8hwqVMikSYNc/n3VmdSsXjWhdQEIhYo5cvCt1K9dk2f+fWHC40UL6jMX9OcNgv19A9DlxGvJrlqJzIwMMjMzeO/xK5IWO11/1w69/ine+ng2uTnVmfLcyKTEjLZ5SyH9LriDLVuLKAoV0//ILowYcnzS4gfZ7kHve4m/vbrzbGYnAS8C7dz9qz1cd727Z++i/Hpgkru/E6dqlikUKubyW8bx0n+G0qBuLY4YdCvH9dqfts3rJyV+VmYGN1xyEp3bNmbdhs0c8Ydb6H1Am6TET2buF55xON98v5zq1SoDcPeT73LT/70GwJDTD+OK847jb2OeZeWa9Zz5t/v5acVa2rWoz/i7LqbD8aPiXp8g9ntmZgbXDB3A/m0as37jZo7702306tGGXj3aMOKCfmRlZXLjva/ynyffYeRF/RNWjxL3P/cBrZvWZd2GzQmPFS3Iz1yQnzcI/vdNiVfuHca+tXb6FZxQQeceZNuf2e9Azj/tMC689omEx9qVShWzePneYWRXrURhUYjjzr+dow5qT4/9myU8dtDtHvS+l/jb2+c8nwl8DJyx4xtmlvlbNuju1ySz4wyQP2chzRvn0rRRLhUrZHHy0d14/cOZSYtfL7cmnds2BqB6tcq0blaPZQVrkxI7Wbk3qFOLPod04IlXPtlWFt1hq1alEu4OwKxvFvPTinD+875bRuWKFahYIf7fM4PY73Vza7J/m3DM7KqVadW0Lj+tWMthB7QlKyv8kenWoWlS2n/J8tVMnDyHswcclPBYOwryMxfk5w2C/30TpKBzD7Lte3ZrSU6NxP81aXfMjOyqlQAoLApRVBRK2p//g273oPd9XFm4LYN8pIK9tvNsZtlAT+BcIp1nM+ttZu+b2dPArEjZy2aWb2ZzzGzIDtu4zcymm9m7ZlY7UvaYmZ0aed7DzD4xsy/N7DMzq56IXJYVrKVh3ZxtrxvUzUnqf6bRfli6kplfL6Z7hyZJiZes3G/62ylce9fLFBf7duWj/nwCsyfcwO+PzeOm+1/bab3+R3Rh5jc/srWwKO51ipbs/Q7w47KVzP5mMV3bbx/zudemcviB7RIef+TtLzJ66AAyLPm/hlLlMxdEu6dC7gacOuwejvjDLTz+0uSkxU2F3EsE0fZBC4WK6TVwDG2OGUHvA9qS17FpUuKmUrtL+bDXdp6BE4E33f0bYJWZdYuUHwCMdPf2kdd/cvfuQB4wzMz2jZRXA6a7ezfgQ+Da6I2bWUXgOeASd+8MHAVs2lVFzGyImU0zs2kFKwr2OJGSEc/tt7nHm/mfrd+4hUHDH+amv51MjewqSYmZjNyPOaQjK1av48uvftzpvX/c91869rua59+cxvmn9druvbbN6zH6LwO49KZn41uhHQSx3zds3MKQkY8y+pKTtk1jAbjr8YlkZmZwcp/uCY3/1sezyd0nmy7t9ktonN1Jhc9cEO0OqZH76w/+jfefuJLn7vgzD4+fxCdfzE9K3FTIHYJr+6BlZmYwaexwZk+4gelzFzH3u6VJiZsq7V4eGOF9F+QjFezNneczgZJezbOR1wCfufv3UcsNM7MvgU+BxkCrSHkx4c4xwFPAITtsvw2wzN0/B3D3X9x9l8OP7v6Au+e5e17t3Np7nEiDOrVYsnz1ttdLl6+mXm7NPd7O/6KwKMSgKx/i1GPyOOHwLkmLm4zcf9e5Occeuj9fvnIdD9/0Rw7t0Zr7r//DdsuMf/Nz+h/RZbt6PXnLEP587ZMsXLIirvWJFsR+LywKMWTUI5zUpzt9D+u8rfz5Nz7jnU/m8J9rz0n4n8amfrmANyfNpsuJ13L+qEf5aNo3XHDt4wmNGS3oz1xQnzcIPneA+rXD8WrvU53je3dm+pxFSYmbCrkH2fapomb1qvTs1pJ3p8xLSrxUaHcpX/bKznNk9PgI4CEzWwhcDpxO+EvRhqjlehMeMT4oMnr8BVCZXdvxq6ntoiwhurVvwnc/FLBoyQq2Fhbx4tvTOa5Xp2SEBsLfyofdMJbWzepx8cAjkhYXkpP79fe8Ssd+V9N5wLWce9WjfPT5N1xwzRM0b/zrF51je3Xim4XLAaiRXYXnbr+Q6+95lakzF8S1LtGC2O/uzmU3P0PLJnUZcsbh28rf/3Qe9459l0fHnE+VyhUTXo9rLu7P7Ak3MOPl63jwH3/k0LzW3H/doITHLRHkZy7IzxsE//tmw6Yt28432LBpC+9P/Yp2LZJz4lbQuQfd9kFasXoda9dtBGDT5q18+NnXtG5SNymxg253KX/21qttnAo84e4XlBSY2YfsPHpcE1jt7hvNrC1wYNR7GZHtPAucRfjEw2hfAQ3MrIe7fx6Z77xpd6PP/4usrExuueI0Thl2D6GQM7D/gUn7zwTCo4DPvfE57Vs2oNfAMQBcfdEJHN2zQ8JjB5n7tUMH0KpJHYqLnR9/WsXfbg7/IeP803rRrHFtLj/vWC4/71gATh76H1asXh/X+EHs989nfs8Lb02jbYv69Bl8CwBXXtCPa+54ka2FRZx56b1A+KTBMZeflrB6BC3I4y7IzxsE//umYNU6/nDFgwAUhYo55Zg8jjyofRlrxUfQuQfZ9ueOfJTJ+d+ycs16Ohw/iuFD+nLOgIMTHrfE8hW/cNF1TxEqLqa42DnxqK4cc2jHpMQOut2D3vfxlTon7QXJdjUXKNWZ2QfAGHd/M6psGPBn4Dt37xcpqwS8DDQEvgZqA6Pd/QMzWw/cDvQF1gKnu3uBmT0GTHD38WbWA7gbqEJ4vvNR7l5qD6p79zyfPHVaPNPdI0G2Z9AfqJweQwOLveqzuwOLDbBxayiw2FUq/KYL28RNRkZwx13Qvz+D/MztePJtsqndg5HOuQetSgXLd/e8IOtQuV5rb3zOXUFWgfn/Oi7w/bBXjjy7e+9dlN0F3LVD2RbguN1so+QCo1fvUD446vnnbD9aLSIiIpK20vj7yzZ75ZxnEREREZEgqPMsIiIiIhKjvXLahoiIiIgkXzrPOy+hkWcRERERkRhp5FlEREREypZCd/kLkkaeRURERERipM6ziIiIiEiMNG1DRERERMpkBHuDolShkWcRERERkRip8ywiIiIiEiNN25ByY9VndwcWu+PwNwKLDfDFjccGFlt/wktP6dzuQV/n1t0Dix107hI8HQIaeRYRERERiZlGnkVEREQkJvrrg0aeRURERERips6ziIiIiEiMNG1DRERERMqm23MDGnkWEREREYmZRp5FREREpEyGThgEjTyLiIiIiMRMnWcRERERkRhp2oaIiIiIxMA0bQN1nlPGO5/MZcRt4wkVF3POgIO5dHCfpMVevHw1F41+kuUrfyHDjEEn9eTCM3onLX55z71iVgZPXHggFTMzyMw0Js76iXve/pa/921L73Z1KAwV8+PKjYx6fibrNhdxUKtcLj22DRUyMygMFXPb618x9buVcanLJf8Yy9ufzCE3pzqTxo4AYMz9r/HmR7PIyDByc7K5a9TZ1KtdMy7xSjP0+qd46+PZ5OZUZ8pzIxMeb0dBHXdBf97Sdb+nQvwgY2/eUki/C+5gy9YiikLF9D+yCyOGHJ+0+Ol83AV9zEv87TXTNsxsXzObEXn8ZGZLol5XLGPdpmY2exfleWZ2Vynr9TazCfGof2lCoWIuv2Ucz995EZ+OG8ULE/P5asGyRIfdJiszgxsuOYmp40Yx8ZG/8/Dzk5IWPx1y31pUzJ8emMrJd37MKXd8zCGta9Npv1pM+XYFJ97+ESff8TGLVmzg/MNbALB6w1YufmwaJ93xEVeN+5KbT+8ct7qccfzvePb2P29XdvHZR/DBU8N574krObpnR2575M24xSvNmf0OZPxdFycl1o6CPO6C/LxB+u73oOMHnXulilm8fO8wPnp6BJPGDufdKfP4fNb3SYufrsdd0O2eCGbBPsqun1U2s8/M7Eszm2Nm10XK9zGzt83s28jPnKh1RpjZfDP72syOKSvGXtN5dveV7t7F3bsA/wfcXvLa3bfubj0zyyxlm9PcfVgCqrtH8ucspHnjXJo2yqVihSxOProbr384M2nx6+XWpHPbxgBUr1aZ1s3qsaxgbVJip0vuG7eGAMjKNLIyDXfnk29XECp2AL78YQ11a1YG4Kulv1CwbgsA85evp1JWBhUy4/NRPahrS2rVqLpdWfVqVX6t56YtSbuGZ89uLcnZoS7JEuRxF+TnDdJ3vwcdP+jczYzsqpUAKCwKUVQUSuqf39P1uAu63dPUFuAId+8MdAGONbMDgeHAu+7eCng38hozaw+cAXQAjgXuLa3vCHtR53lXzOwxMzs16vX6yM/eZva+mT0NzNphneZm9oWZ9YgeWTazw6JGsr8ws+qRVbLNbLyZfWVmYy0Bv22WFaylYd1tX4BoUDcnqf+ZRvth6Upmfr2Y7h2aJCVeuuSeYfDCJYfw0dVHMeXbFcz6cfscT85rxEdfF+y0Xp/96zFv6S8UhorjXqdoN/3fBLoOuIYXJuZzxfl9ExorFaTKcZfsz1vQgt7vQcYPOncIj4L2GjiGNseMoPcBbcnr2DSp8YOS7u2ebjxsfeRlhcjDgQHA45Hyx4ETI88HAM+6+xZ3/x6YDxxQWoy9uvNchgOAke7evqTAzNoALwB/dPfPd1j+MuDiyMj2ocCmSHlX4K9Ae6A50DPeFXX3ncqCmI+/fuMWBg1/mJv+djI1squUvUIcpEvuxQ6n3PkxR9z0Hvs3rkXLutnb3htyeAuKip0JXyzdbp0WdbO59Lg2XPfiTjOO4u6qC/vxxSvXc0qf7jwy/qOExwtaKhx3QXzeghb0fg8yftC5A2RmZjBp7HBmT7iB6XMXMfe7pWWvVA6ke7vHm5kF+gByzWxa1GPILuqYaWYzgJ+Bt919KlDX3ZcBRH7WiSzeEPgxavXFkbLdKs+d588i3yBK1AZeAc529xm7WH4y8G8zGwbUcveiqO0sdvdiYAbQdMcVzWxISSMWrNh59LAsDerUYsny1dteL12+mnq5iT9hK1phUYhBVz7EqcfkccLhXZIWN91yX7e5iM8WrOSQNrUBGNCtIYe1q8OVz87Ybrm6NStz1zndueq5mfy4amNC6xTt5D55TPjgy6TFC0rQx11Qn7egBb3fg4wfdO7RalavSs9uLXl3yrxA4ieb2r3cWeHueVGPB3ZcwN1DkcHQRsABZtaxlO3t6uvMzt96ouztneciIjlEplNEnzi4YYdl1xL+ZrHLkWN3HwOcB1QBPjWztpG3tkQtFmIXVyhx9wdKGrF2bu09TqJb+yZ890MBi5asYGthES++PZ3jenXa4+38Vu7OsBvG0rpZPS4eeETS4kJ65J5TrSLVK4cPm0pZGRzUMpfvf97AIa1zObd3c4Y+ns/mwl+nZVSvnMV9g/O4482v+WLR6t1tNm4W/PjztudvfTyLVk3qlLJ0+RDkcRfk5y1oQX/eg4wfdO4rVq9j7brwF/FNm7fy4Wdf07pJ3aTFD1I6t3vcBXyy4J6O2rv7GuADwnOZl5tZfYDIz5L//BYDjaNWawSU+meZvf1SdQuB7sA4wnNWKpSy7FbC81veMrP17v509Jtm1sLdZwGzzOwgoC2wJgF13klWVia3XHEapwy7h1DIGdj/QNq1qJ+M0ABM/XIBz73xOe1bNqDXwDEAXH3RCRzds0PCY6dD7rWrV+Km0zqRkWFkmPHWzGV8+NXPvHH5YVTIyuCh88JTq778YQ3XvzSbsw5uQuPcqlx4ZEsuPLIlAOc/9BmrNuz2vNiYXXDNY3wyfT6r1qynS/+rufy8vrw7ZS7zf/iZDDMa1cvh1itO/5/jxOLckY8yOf9bVq5ZT4fjRzF8SF/OGXBwUmIHedwF+XmD9N3vQccPOvflK37houueIlRcTHGxc+JRXTnm0NIG4+IrXY+7oNs9HZlZbaDQ3deYWRXgKOCfwKvAIGBM5OcrkVVeBZ42s38DDYBWwGelxtjVfJxUZ2ajgfXAk4STzyB85uRf3D3bzHoDl7l7v8jyTYEJ7t7RzGoBbwP/IDwafZm79zOzu4HDCY8uzwUGAwftsJ3/ANPc/bHd1a179zyfPHVafBPeA0G2Z9AXTg8y947D3wgsNsAXNx4bWOyKWXv7H7B+u6B/fwb9mZNgpPPv+XRWpYLlu3tekHWo2rCNt73gviCrwBfXHlnqfjCzToRPCMwk3D8c5+7Xm9m+hAdb9wN+AH7v7qsi64wE/kR4RsNf3b3U/9T3ypFndx8d9fLAqOcjIu9/QHiYvmT5hUDHyPM1QI+odT6IlP9lF6F23M7Q31pnERERkb2ZkfpfoNx9JuGLPexYvhI4cjfr3AjcGGuM9B0yEhERERHZQ3vlyLOIiIiIJF+KDzwnhUaeRURERERipM6ziIiIiEiMNG1DRERERGKS6icMJoNGnkVEREREYqTOs4iIiIhIjDRtQ0RERERiolkbGnkWEREREYmZRp5FREREpGymEwZBI88iIiIiIjHTyLPETXGxBxo/IyO4b8Nz/tk3sNgAr81ZFljs4zvUDyx20DQCIyKSftR5FhEREZEyGTphEDRtQ0REREQkZhp5FhEREZEYmKaroZFnEREREZGYqfMsIiIiIhIjTdsQERERkZho1oZGnkVEREREYqaRZxERERGJiU4Y1MiziIiIiEjM1HkWEREREYmRpm2kiHc+mcuI28YTKi7mnAEHc+ngPkmLvXlLIf0uuIMtW4soChXT/8gujBhyfNLiA4RCxRw5+Fbq167JM/++MKmxg9z3yYj9wMP/ZcaM+dSoUY0xNw7Z7r3X3viUZ557l/vuvpTq1avy3YIlPPzo69veP+nEQ+nRvW3c6wQw9PqneOvj2eTmVGfKcyMTEqM05b3dUzW+2j2Y2IuXr+ai0U+yfOUvZJgx6KSeXHhG76TFT+djPujc48p0wiDspSPPZhYysxlRj6YJitPbzCYkYtvRQqFiLr9lHM/feRGfjhvFCxPz+WrBskSH3aZSxSxevncYHz09gkljh/PulHl8Puv7pMUHuP+5D2jdtG5SY0Kw+z5ZsXsd0pnL/37GTuUrV/7C7Dnfs+++NbaVNWpYhxtGn8tNN5zP5X8/g0cfe4NQqDjudQI4s9+BjL/r4oRsuyzp0O6pGl/tHsx+z8rM4IZLTmLquFFMfOTvPPz8pLTJPZ3bXRJjr+w8A5vcvUvUY2HJGxa2V+WVP2chzRvn0rRRLhUrZHHy0d14/cOZSYtvZmRXrQRAYVGIoqJQUk8IWLJ8NRMnz+HsAQclLWaJIPd9smK3bbMf2dWq7FT+1DNvc8ZpR2D82taVKlUgMzP88SksDCV0iKFnt5bk1KiasO2XJh3aPVXjq92D2e/1cmvSuW1jAKpXq0zrZvVYVrA2KbGDzj2d2z3ejHCfIchHKtirOpm7Y2ZNzWyemd0LTAcam9nlZva5mc00s+t2WO5BM5tjZhPNrErkvZZm9o6ZfWlm082sRWTz2WY23sy+MrOxloCWW1awloZ1c7a9blA3J2m/1EqEQsX0GjiGNseMoPcBbcnr2DRpsUfe/iKjhw4gI4DvPEHu+yBj53/xDTk51Wmy386j/fO/W8KVV93PiFEP8MdBx27rTJcn6druqRA/SOnc7tF+WLqSmV8vpnuHJkmJF3TuaneJt731f8UqUVM2XoqUtQGecPeukeetgAOALkB3M+sVWa4VcI+7dwDWAKdEysdGyjsDBwMlf1fpCvwVaA80B3ruWBkzG2Jm08xsWsGKgj1Oxt13Kkv2l6vMzAwmjR3O7Ak3MH3uIuZ+tzQpcd/6eDa5+2TTpd1+SYm3oyD3fVCxt2wp5NX/TubUk3rt8v2WLRryz5su4Ppr/8R/J3zC1q1Fia9UkqVju6dK/CClc7uXWL9xC4OGP8xNfzuZGtk7/0UqEYLOXe0u8ba3njC4yd27lLyIzHle5O6fRor6RB5fRF5nE+40/wB87+4zIuX5QFMzqw40dPeXANx9c2S7AJ+5++LI6xlAU+Dj6Mq4+wPAAwDdu+ft/EkpQ4M6tViyfPW210uXr6Zebs093Uxc1KxelZ7dWvLulHm0b9Eg4fGmfrmANyfN5p1P5rJlSyHrNmzmgmsf5/7rBiU8NgS774OK/fPPqykoWMNVVz8EwKrVvzDq2oe57po/UqtW9rblGjbIpVKliixe8jPNmyX+WEimdGz3VIkfpHRudwhPyxt05UOcekweJxzeJWlxg8493ds93lJl6kSQ9taR513ZEPXcgJuj5kS3dPeHI+9tiVouRPgLRGlHwq6Wj6tu7Zvw3Q8FLFqygq2FRbz49nSO69Up3mF2a8XqdaxdtxGATZu38uFnX9O6SXJO3rvm4v7MnnADM16+jgf/8UcOzWudtI4zBLvvg4rduHEd7r37Uu64bSh33DaUfXJq8I/rzqVWrWx+Lliz7QTBFSvWsuynldTOrZXwOiVbOrZ7qsQPUjq3u7sz7IaxtG5Wj4sHHpG0uBB87unc7pIYe+vIc1neAm4ws7Huvt7MGgKFu1vY3X8xs8VmdqK7v2xmlYDMZFU2KyuTW644jVOG3UMo5AzsfyDtWtRPVniWr/iFi657ilBxMcXFzolHdeWYQzsmLX6Qgtz3yYr9n/teYt5Xi1i/fhN/ufQuTjmxF70P67LLZb/55kf++9onZGZmYBnG4HOOpXr1xJzcde7IR5mc/y0r16ynw/GjGD6kL+cMODghsXaUDu2eqvHV7sHs96lfLuC5Nz6nfcsG9Bo4BoCrLzqBo3t2SHjsoHNP53ZPBA08g+1qPk6qM7P17p4d9bopMMHdO0aVXQKcF3m5Hjib8MjxtuXM7DIg291Hm1kr4H4gl3BH+/fAfsBl7t4vsvx/gGnu/tju6ta9e55PnjotXqnusSDbM+hDKSMjfT/Rr80J7tJHx3fYu/8jENnbBPl7Xn+yD06VCpbv7nlB1qF647be7W8Pl71gAk362yGB74e9cuQ5uuMceb0Q6LhD2Z3AnbtYvWPUMv+Kev4tsOPfshYAH0QtM/S31llERERE9n57ZedZRERERJJPf30oXycMioiIiIgklDrPIiIiIiIx0rQNERERESmb6WoboJFnEREREZGYaeRZRERERMpkmE4YRCPPIiIiIiIxU+dZRERERCRGmrYhIiIiIjHRrA2NPIuIiIiIxEwjz+VMkBP59W00OEe3qRtY7Onfrw4sNkC3ZjmBxhdJNp2wJUHK0PGnkWcRERERkVip8ywiIiIiEiNN2xARERGRmGjWhkaeRURERERippFnERERESmTmU5YBY08i4iIiIjETJ1nEREREZEYadqGiIiIiMQkQ7M2NPIsIiIiIhIrjTyLiIiISEx0wqA6zynjnU/mMuK28YSKizlnwMFcOrhP2sRX7smLfck/xvL2J3PIzanOpLEjABhz/2u8+dEsMjKM3Jxs7hp1NvVq14xLvFvvfYlPp39NrZrVePi2vwDwy/qN3HD7OJYXrKZu7RyuufR0qmdXoagoxL/+72Xmf7+UUHExR/fqwlknHRaXeuxKOrV7KsUfev1TvPXxbHJzqjPluZFJi1tC7a7c0y13ib+UnLZhZiPNbI6ZzTSzGWb2uzhue33kZwMzG1/Kck3NbHa84pYmFCrm8lvG8fydF/HpuFG8MDGfrxYsS0bowOMr9+TGPuP43/Hs7X/eruzis4/gg6eG894TV3J0z47c9sibcYt3TO+u3HzVH7Yre+blj+i2f3OeuOtSuu3fnGdengTAh5/OprCoiIdu+wv3jfkzE96Zxk8/r45bXaKlW7unUvwz+x3I+LsuTlq8aGp35Z5uuUtipFzn2cwOAvoB3dy9E3AU8GO847j7Unc/Nd7b/S3y5yykeeNcmjbKpWKFLE4+uhuvfzgzLeIr9+TGPqhrS2rVqLpdWfVqVbY937hpS1zvHtWpfVNqZFfZruyTz+fR57CuAPQ5rCuTP58HgGFs3lxIKBRiy9YisrIyqVq1UvwqEyXd2j2V4vfs1pKcHY7BZFG7K/d0yz0Rwtd6Du6RClKu8wzUB1a4+xYAd18BNDKzFwHMbICZbTKzimZW2cwWRMpbmNmbZpZvZh+ZWdtIeTMzm2Jmn5vZDSVBokeWzayDmX0WGeWeaWatIotlmtmDkVHwiWa2fS8gTpYVrKVh3ZxtrxvUzWFZwdpEhEq5+Mo9uNyj3fR/E+g64BpemJjPFef3TWis1Ws3sG9OdQD2zanOml82ANDrwA5UrlyB3w+5hbMu+henndCTGtmJ6WSlc7sHHT9Ianflnuz4QecuiZGKneeJQGMz+8bM7jWzw4DpQNfI+4cCs4EewO+AqZHyB4C/uHt34DLg3kj5ncB97t4D+Gk3MS8E7nT3LkAesDhS3gq4x907AGuAU+KS4Q7cfaeyZH67CjK+cg8m9o6uurAfX7xyPaf06c4j4z8KpA5fzV9MZkYG4+6/gqf+8zee/+9kli5flZBY6dzuQccPkto9mPjKPZjYkjgp13l29/VAd2AIUAA8B5wNzDezdsABwL+BXoQ70h+ZWTZwMPC8mc0A7ic8gg3QE3gm8vzJ3YSdAlxlZlcCTdx9U6T8e3efEXmeDzTd1cpmNsTMppnZtIIVBXucc4M6tViy/Ne5nUuXr6ZebnxO2Er1+Mo9uNx35eQ+eUz44MuExsipWY2Vq9cBsHL1OmrVqAbAux/PpEeXVmRlZZJTM5uObZrwzXdLElKHdG73oOMHSe2u3JMdP+jc480IT7EL8l8qSLnOM4C7h9z9A3e/FhhKeMT3I+A4oBB4Bzgk8phEOI817t4l6tEuepNlxHsa6A9sAt4ysyMib22JWizEbq5O4u4PuHueu+fVzq29p+nSrX0TvvuhgEVLVrC1sIgX357Ocb067fF2fqsg4yv34HIvseDHn7c9f+vjWbRqUieh8Q7Oa8vED78AYOKHX3Bwj/BHtU5uTb6YvQB3Z9Pmrcz99kcaN9zzz1Ms0rndg44fJLW7ck+33CUxUu5SdWbWBih2928jRV2ARYQ7yU8AT7h7gZntC9QD5ri7m9n3ZvZ7d3/ewhch7OTuXwKTgTOAp4CBu4nZHFjg7ndFnncCFiQwze1kZWVyyxWnccqwewiFnIH9D6Rdi/plr1gO4iv35Ma+4JrH+GT6fFatWU+X/ldz+Xl9eXfKXOb/8DMZZjSql8OtV5wet3j/uGMcX879nrXrNnL6hbcy6LQjOOPEXtxw+3O88V4+dXJrcc3fwvFOPPZ33HLvS5z797txh2MP70aLJvXiVpdo6dbuqRT/3JGPMjn/W1auWU+H40cxfEhfzhlwcFJiq92Ve7rlngi6wyDYrubjBMnMugN3A7WAImA+4SkcGwjPOz7B3Sea2QNAPXfvH1mvGXAf4ekaFYBn3f36SPnThL8ovACMcvdsM2sKTHD3jmY2gvDUkELC86LPAmqUvB/Z/mVAtruPLq3+3bvn+eSp0+K0N0Ris7WoOLDYs38M9uSXbs1yyl5IRGQvV6WC5bt7XpB1qNWknfca+USQVeC/FxwQ+H5IuZFnd88nPH95VypFLTdkh/W+B47dxfa+Bw6KKhoTKV8IdIw8vxm4eYdVV5W8H1nmX7HmICIiIiLlU8p1nkVEREQkBZnp9tyk6AmDIiIiIiKpSCPPIiIiIhITDTxr5FlEREREygkza2xm75vZvMgdoi+JlI82syWRu0nPMLO+UeuMMLP5Zva1mR1TVgyNPIuIiIhIeVEE/N3dp5tZdSDfzN6OvHf7jheAMLP2hC9p3AFoALxjZq3dPbS7AOo8i4iIiEiZDMhI8Xkb7r4MWBZ5vs7M5gENS1llAOHLG28Bvjez+YTvZj1ldyto2oaIiIiI7C1yzWxa1GPI7haM3NOjKzA1UjTUzGaa2SNmVnKTgIbAj1GrLab0zrZGnkVEREQkNikw8LwilpukmFk24Zvj/dXdfzGz+4AbAI/8vA34E+EB9R2VegdBjTyLiIiISLlhZhUId5zHuvuLAO6+3N1D7l4MPEh4agaER5obR63eCFha2vbVeRYRERGRcsHCd3F5GJjn7v+OKq8ftdhJwOzI81eBM8yskpk1A1oBn5UWQ9M2RERERCQme8EdBnsC5wCzzGxGpOwq4Ewz60J4SsZC4AIAd59jZuOAuYSv1HFxaVfaAHWeRcqFilnB/RGpW7OcshdKoI1bigKLXbWSfoWKiKQSd/+YXc9jfr2UdW4Ebow1hn7zi4iIiEiZzFLihMHAac6ziIiIiEiM1HkWEREREYmRpm2IiIiISExS/Q6DyaCRZxERERGRGGnkWURERERionFnjTyLiIiIiMRMnWcRERERkRhp2oaIiIiIxGQvuMNgwmnkWUREREQkRuo8p4h3PplLj1Oup9tJo7n9sYlpFV+5K/dkxF6yfDWn/uU/HDbwJg4/ewwPjfsQgP++N4PDzx5Do0Mv5cuvfkh4PQCGXv8UrfoM56DTY74bbFylU7unUnzlrtyDyF3ib6/uPJvZ+sjPpmZ2VgzLNzWz2ZHneWZ2V6LrGItQqJjLbxnH83dexKfjRvHCxHy+WrAsLeIrd+WerNhZmRlcO3QAH469iv8+8Fcee/Fjvvn+J9o2r8eDN/2RAzs3T2j8aGf2O5Dxd12ctHjR0q3dUyW+clfuQeQebwZkWLCPVLBXd56jNAXK7DxHc/dp7j4sMdXZM/lzFtK8cS5NG+VSsUIWJx/djdc/nJkW8ZW7ck9W7Lq5Ndm/TWMAsqtWplXTuvy0Yi2tmtaj5X51Exp7Rz27tSSnRtWkxiyRbu2eKvGVu3IPIndJjPLSeR4DHGpmM8zs0sgI80dmNj3yOHjHFcyst5lNiDw/wMw+MbMvIj/bRMoHm9mLZvammX1rZrckovLLCtbSsG7OttcN6uawrGBtIkKlXHzlrtyTHRvgx2Urmf3NYrq2b5K0mKkindtduSv3ZMcPOve4M8MCfqSC8nK1jeHAZe7eD8DMqgJHu/tmM2sFPAPklbL+V0Avdy8ys6OAm4BTIu91AboCW4Cvzexud/8xemUzGwIMAWi83357XHl336ksmcdHkPGVezCxg44fZOwNG7dw/shHue6Sk6herXJygqaQdG33oOMr92BiBx0/6NwlMcpL53lHFYD/mFkXIAS0LmP5msDjkY62R9Yv8a67rwUws7lAE2C7zrO7PwA8ANC9e97On5QyNKhTiyXLV297vXT5aurl1tzTzfxmQcZX7so9mbELi0KcP+oRTurTnb6HdU54vFSUju2eCvGVu3JPdmxJnPIybWNHlwLLgc6ER5wrlrH8DcD77t4ROAGIHo7aEvU8RAK+cHRr34Tvfihg0ZIVbC0s4sW3p3Ncr07xDpOS8ZW7ck9WbHfn7zc/Q8smdbngjMMTGiuVpVu7p0p85a7cg8g9EcyCfaSC8jLyvA6oHvW6JrDY3YvNbBCQWcb6NYElkeeD41+90mVlZXLLFadxyrB7CIWcgf0PpF2L+mkRX7kr92TF/nzm97zw1jTatajP0YPDpy8Mv6AfW7cWMeqOF1i1Zj1/uPwBOrRqyNP//nNC63LuyEeZnP8tK9esp8Pxoxg+pC/nDNjp1IyESLd2T5X4yl25B5G7JIbtaj7O3sLM1rt7tplVAN4EcoHHgAnAC8BG4H3gL5HlmgIT3L2jmfUmMk/azA4CHgcKgPeAc9y9qZkNBvLcfWgk3gTgX+7+we7q1L17nk+eOi0R6YrILmzcUhRY7KqVysv4g4ikuioVLN/dSzt/K+H2bd7Bj//HM0FWgScHdg58P+zVv/ndPTvysxA4coe3o/8uMiKy3EKgY+T5B8AHkedT2H5e9NWR8scId8ZL4vWLW+VFREREZK9TXuc8i4iIiIjE3V498iwiIiIiyVFyh8F0p5FnEREREZEYaeRZRERERGKSKnf5C9JuO89mdjfhG4bskrsPS0iNRERERERSVGkjz7remoiIiIhIlN12nt398ejXZlbN3TckvkoiIiIikoo0aSOGEwbN7CAzmwvMi7zubGb3JrxmIiIiIiIpJpYTBu8AjgFeBXD3L82sVyIrJSIiIiKpxQwydMJgbJeqc/cfdygKJaAuIiIiIiIpLZaR5x/N7GDAzawiMIzIFA6RVOK+24vDlHsbtwb3fbZapWCveFk1wPjXTfw6sNgA1/ZpE2h8CUZxcXC/6zJ0hwyRmDrPFwJ3Ag2BJcBbwMWJrJSIiIiIpB7N2oih8+zuK4CBSaiLiIiIiEhKi+VqG83N7L9mVmBmP5vZK2bWPBmVExEREZHUYWaBPlJBLCcMPg2MA+oDDYDngWcSWSkRERERkVQUS+fZ3P1Jdy+KPJ6ilNt2i4iIiIiUV7ud82xm+0Sevm9mw4FnCXeaTwdeS0LdRERERCSFpMjMiUCVdsJgPuHOcsluuiDqPQduSFSlRERERERS0W47z+7eLJkVERERERFJdTHdXcDMOgLtgcolZe7+RKIqJSIiIiKpxTDdnpsYOs9mdi3Qm3Dn+XXgOOBjQJ1nEREREUkrsYw8nwp0Br5w9z+aWV3gocRWK/2888lcRtw2nlBxMecMOJhLB/dJm/hBxt68pZB+F9zBlq1FFIWK6X9kF0YMOT4psRcvX81Fo59k+cpfyDBj0Ek9ufCM3gmNuXT5ai75x1gKVv1ChmVwVv+DOO+0w7j1wdd56+NZZJiRm1Odf488i3q5NRNal/J+zK9bs463xr/NhvUbMTP279GBrgd3YfPGzbz+7Jv8suYXatSqQd8zj6Vylcps2riJ155+g+VLfqZ917Yc3r93XOtTYuj1T/HWx7PJzanOlOdGJiRGacp7u6dqbIAuJ15LdtVKZGZkkJmZwXuPX5G02EHnns7tHlemEwYhts7zJncvNrMiM6sB/AzEfJMUM3PgKXc/J/I6C1gGTHX3fqWs1xu4rLRl9lQithkPoVAxl98yjpf+M5QGdWtxxKBbOa7X/rRtXr/cxw8690oVs3j53mFkV61EYVGI486/naMOak+P/RM/5T8rM4MbLjmJzm0bs27DZo74wy30PqBNQnPPzMzgmqED2L9NY9Zv3Mxxf7qNXj3acOFZR3D5+X0BePj5D7nj0bcYc/lpCatH0O2ejPgZGRn0Ou4Q6jSsw9YtW3n6nufYr+V+zJ0+j8YtGtHjsDw+/3Aan3+Yz6HH9iQrK4uDjjqQlctXsnL5yrjVY0dn9juQ8087jAuvTf4fD9Oh3VMxdrRX7h3GvrWykxoz6NzV7hJvsVzneZqZ1QIeJHwFjunAZ3sQYwPQ0cyqRF4fDSzZk0qWd/lzFtK8cS5NG+VSsUIWJx/djdc/nJkW8YPO3czIrloJgMKiEEVFoaTdwahebk06t20MQPVqlWndrB7LCtYmNGbd3Jrs3yYcM7tqZVo1rctPK9ZSvdq20xnYtHlrwkcWgm73ZMSvVqMadRrWAaBipYrsUzuH9b+sZ8G8BbTv2g6A9l3bsWDeAgAqVKxAw6YNyKwQ06kov1nPbi3JqVE1oTF2Jx3aPRVjBy3o3NXuEm9ldp7d/SJ3X+Pu/0e44zvI3f+4h3HeAEr+Fn4mUXcoNLMDzOwTM/si8rPNjivvbhkz+8jMukQtN9nMOv2P2xxsZi9Hbkn+vZkNNbO/RZb7NOr613GzrGAtDevmbHvdoG5OwjtRqRI/6NwhPDLQa+AY2hwzgt4HtCWvY9Okxgf4YelKZn69mO4dmiQt5o/LVjL7m8V0bR+O+c/7X6PHyaN5aWI+l53bN6Gxg273ZMdfu/oXCpYVUK9RPTas30i1GtWAcAd74/pNCYubatKt3VMldgkDTh12D0f84RYef2ly0uIGnXu6t3u86fbcpXSezazbjg9gHyAr8nxPPAucYWaVgU7A1Kj3vgJ6uXtX4Brgpl2sv7tlHgIGR+rbGqjk7jP/x20CdATOAg4AbgQ2RpabAvxhz1Ivm/vON2xM5vERZPygc4fwVIZJY4cze8INTJ+7iLnfLU1q/PUbtzBo+MPc9LeTqZFdpewV4mDDxi0MGfkooy85aduo85UXHM/nL47mpD7defTFjxIaP+h2T2b8rVu28trTr3PY8YdSqXLFxATZS6RTu6dS7BKvP/g33n/iSp674888PH4Sn3wxPylxg8493dtd4q+0vw/eVsp7DhwRaxB3n2lmTQmPOr++w9s1gcfNrFVkuxV2sYndLfM8cLWZXQ78CXgsDtsEeN/d1wHrzGwt8N9I+SzCnf/tmNkQYAhA4/3229UuKFWDOrVYsnz1ttdLl69O+MlaqRI/6Nyj1axelZ7dWvLulHm0b9EgKTELi0IMuvIhTj0mjxMO75K0mENGPcJJfbrT97DOO71/4tHdGXT5A1x27nEJq0PQ7Z6s+KFQiAlPv0Hbzm1o2aElANWyq7Lhlw1Uq1GNDb9soGqSvjClgnRp91SLXaJ+7XC82vtU5/jenZk+ZxEHd22Z8LhB557u7R5vscz3Le92uw/c/fBSHjF3nKO8CvyLqCkbETcQ7qx2BE4g6lrSZS3j7huBt4EBwGnA0//rNiO2RD0vjnpdzC6+cLj7A+6e5+55tXNr7yJU6bq1b8J3PxSwaMkKthYW8eLb0zmu10599IQJMn7Qua9YvY616zYC4bm+H372Na2b1E1KbHdn2A1jad2sHhcP/C0fqd8W87Kbn6Flk7oMOePwbeULfizY9nzix7NpkeB9EHS7JyO+u/POi++yT50cuh3SdVt587bNmPvFPADmfjGP5u1iPv96r5cO7Z6KsQE2bNrCug2btz1/f+pXtGuRnJPWgs49ndtdEiOxZ6Zs7xFgrbvPilz1okRNfj2BcPBu1i1tmYcIjwx/5O6r4rTNpMrKyuSWK07jlGH3EAo5A/sfmLRfakHHDzr35St+4aLrniJUXExxsXPiUV055tCOSYk99csFPPfG57Rv2YBeA8cAcPVFJ3B0zw4Ji/n5zO954a1ptG1Rnz6DbwHgygv68eyET1nww89YhtGo7j7cfPnvE1YHCL7dkxF/6aJlzJvxNbl19+Wpu8NjBj37HETeYd15/Zk3mZM/l+o1q3P8mb+O8D9862Ns3bKV4lAx381bwEl/PJF968T3NItzRz7K5PxvWblmPR2OH8XwIX05Z8DBcY2xO+nQ7qkYG6Bg1Tr+cMWDABSFijnlmDyOPKh9UmIHnXs6t7skhu1qPk5cA5itd/fsHcp6E7lknJkdBDwOFADvAee4e9NYlona3lfAX939zcjr37xNMxsM5Ln70Mi2FkZer9jxvV3p3j3PJ0+d9hv3lvwvEn0sp7KNW0OBxa5WKZnfwVPLdRO/DjT+tX12Ohda0kBxcXC/6zIyNGE3KFUqWL675wVZh7otO/rp/xofZBW4+6R2ge+HhP+vt2PHOVL2AfBB5PkUoHXU21fHugyAmTUgPP1kYtT2f/M23f0xfp07TXQnfcf3RERERCS9lDnv28LONrNrIq/3M7MDEl+1spnZHwhfuWOkuxcHXR8RERGR8izDgn2kglhOmrwXOIjwlTIA1gH3JKxGe8Ddn3D3xu7+fNB1EREREZHyL5ZpG79z925m9gWAu682s/S+WKmIiIiIpKVYOs+FZpZJ+FrImFltwpdsExEREZE0kipTJ4IUy7SNu4CXgDpmdiPwMbu+Y5+IiIiISLlW5sizu481s3zgSMCAE919XsJrJiIiIiIpwwxM9xcvu/NsZvsBG/n1FtWY2X7u/kMiKyYiIiIikmpimfP8GuH5zkb4FtbNgK+BxN0GTUREREQkBcUybWP/6Ndm1g24IGE1EhEREZGUpBMGYzthcDvuPh3okYC6iIiIiIiktFjmPP8t6mUG0A0oSFiNRERERERSVCxznqtHPS8iPAf6hcRUR+S3S+czgDP1d7RAXNunTaDxpy1YHVjsvOY5gcVOdxn6vEuA0vi/2m1K7TxHbo6S7e6XJ6k+IiIiIiIpa7edZzPLcveiyAmCIiIiIpLGDMjQ0HOpI8+fEZ7fPMPMXgWeBzaUvOnuLya4biIiIiIiKSWWOc/7ACuBI/j1es8OqPMsIiIiImmltM5znciVNmbza6e5hCe0ViIiIiKScvb4GsdJZmaNgSeAekAx8IC732lm+wDPAU2BhcBp7r46ss4I4FwgBAxz97dKi1HaPsgEsiOP6lHPSx4iIiIiIqmkCPi7u7cDDgQuNrP2wHDgXXdvBbwbeU3kvTMI3zn7WODeyAUzdqu0kedl7n79/56DiIiIiJQHqX6+oLsvA5ZFnq8zs3lAQ2AA0Duy2OPAB8CVkfJn3X0L8L2ZzQcOAKbsLkZpI88pvntEREREJM3kmtm0qMeQ3S1oZk2BrsBUoG6kY13Swa4TWawh8GPUaosjZbtV2sjzkWXXX0REREQkaVa4e15ZC5lZNuGb+v3V3X8p5UZqu3qj1HP7dtt5dvdVZVVMRERERNKDme0V13k2swqEO85joy6tvNzM6rv7MjOrD/wcKV8MNI5avRGwtLTtp/pJk2njnU/m0uOU6+l20mhuf2xiWsVX7smLfemNT9Ox70h6D7x5u/KHn5/EIWfcyGEDb+aGe15JeD1A7Z7o2P+67yV+f/4Yzv/73dvKPpwym/P+fhd9zriGr79bsq08f+Z8Lhp+H+dfdjcXDb+PL2YvSEidQO2u3JW7JJaFh5gfBua5+7+j3noVGBR5Pgh4Jar8DDOrZGbNgFaE73WyWynTeTaz9VHP+5rZt2a2XwD1aGpms5MZMxQq5vJbxvH8nRfx6bhRvDAxn68WLEuL+Mo9ubFP63sAT99+4XZlk/O/5a2PZvHuE1fy4dgR/PnMIxJaB1C7JyN2n8O6ctOIP2xX1rRxHa79+5ns367JduU1q1fl+isG8uC//sLlF53MP/8zPu71AbW7clfuyc49EcyCfcSgJ3AOcISZzYg8+gJjgKPN7Fvg6Mhr3H0OMA6YC7wJXOzuodICpEznuYSZHQncDRzr7j8EXZ9kyJ+zkOaNc2naKJeKFbI4+ehuvP7hzLSIr9yTG/ugri3JqVF1u7LHX/qYoeccRaWK4VlcuftUT2gdQO2ejNid2jelenaV7cqaNKpD4wa1d1q2ZbMG5O5TAwh3sLcWFrG1sCjudVK7K3flntzc05G7f+zu5u6d3L1L5PG6u6909yPdvVXk56qodW509xbu3sbd3ygrRkp1ns3sUOBB4Hh3/y5Sdr6ZfW5mX5rZC2ZWNVL+mJndZWafmNkCMzs1Ut7bzD4ws/Fm9pWZjY0M4WNm10S2NdvMHogq7x7Z/hTg4qj6NDWzj8xseuRxcCLyXlawloZ1c7a9blA3h2UFaxMRKuXiK/fgci+x4McCpn75HX3P+zcnXXQXM+YuSnjMoHNXu+/eR1Pn0LJpfSpWiOUGtHsm6NzTud2Ve3rmLomRSp3nSoTnn5zo7l9Flb/o7j3cvTMwj/AdYErUBw4B+hEZfo/oCvwVaA80JzyED/CfyLY6AlUi6wE8SviOMgftUKefgaPdvRtwOnDXripuZkNKLplSsKJgT3IGwH3nkzqTOR8/yPjKPZjY0YqKQqz9ZROvPXgp1wwdwJCrH9tl3eIp6NzV7ru28MflPPT0RP56/oCEbD/o3NO53ZV7MPGDzj0RMizYRypIpc5zIfAJ23eOATpGRn9nAQMJ3wGmxMvuXuzuc4G6UeWfuftidy8GZhC+FSPA4WY2NbKtI4AOZlYTqOXuH0aWeTJqOxWAByPLP0+4M74Td3/A3fPcPa927s5/Ei1Lgzq1WLJ89bbXS5evpl5uzT3ezm8VZHzlHlzuJerXqUXf3p0wM7q2b0KGGSvXbEhozKBzV7vvrGDlWkbf9gxXXHQKDertk5AYQeeezu2u3NMzd0mMVOo8FwOnAT3M7Kqo8seAoe6+P3AdUDnqvS1Rz2035SEgy8wqA/cCp0a29WBkW8bur+d3KbAc6AzkARX3MKeYdGvfhO9+KGDRkhVsLSzixbenc1yvTokIlXLxlXtwuZc4ttf+fJz/LQDf/fAzhUUh9q1VLaExg85d7b699Rs2MWrMk5x75tF0bNuk7BV+o6BzT+d2V+7pmXu8GZARuVxdUI9UEP9Jbf8Dd99oZv2Aj8xsubs/DFQHlkWu2TcQWFLqRnavpNO9InLh7FOB8e6+xszWmtkh7v5xJEaJmsBidy82s0FAqfc6/62ysjK55YrTOGXYPYRCzsD+B9KuRf1EhEq5+Mo9ubH/fM3jfPLFfFatWU+3Addw2XnHcWa/A7n0xqfpPfBmKlTI4s5RAynlYvJxoXZPfOwb7xzHzLnfs3bdRs7886384fdHUD27Cvc8+hprf9nAqH8+SYsm9RkzchCvvDmVpctX8dQLH/DUCx8AMGbkIHJqZse1Tmp35a7ck5u7JIYlem5jrMxsvbtnR543BiYRnrfcALgCWATMAqq7+2AzewyY4O7jo9c3s97AZe7eL1L+H2Cauz9mZv8AzgAWEr4V4yJ3H21m3YFHgI3AW4RHpzuaWSvCF9neCLwP/KWkjrvTvXueT546LU57RSQ2mwtLvapOQlWukJDvlBKDaQtWl71QguQ1zyl7IRGJmyoVLD+WO+slUsPW+/sF97wUZBW4tk+rwPdDyow8R3dK3f1HoFnU2/ftYvnBu1rf3T8APogqHxr1fBQwahfbyic8NaPE6Ej5t0D031dGlJ2JiIiISPmUIjMnApVKc55FRERERFJayow8i4iIiEgKS6HLxQVJI88iIiIiIjFS51lEREREJEaatiEiIiIiMTE0b0MjzyIiIiIiMVLnWUREREQkRpq2ISIiIiJlCt+eO+haBE8jzyIiIiIiMdLIs4iIiIjERCPP6jyLiOy18prnBBb7Xx/MDyw2wGW9WwYaX0TSl6ZtiIiIiIjESCPPIiIiIhITM83b0MiziIiIiEiMNPIsIiIiImXSperCNPIsIiIiIhIjdZ5FRERERGKkaRsiIiIiUjYDnS+okWcRERERkZhp5FlEREREYpKhoWeNPIuIiIiIxEojzyninU/mMuK28YSKizlnwMFcOrhP2sRX7smLfemNT/P25Dnk5mTzwdgR28offn4Sj77wEZmZGRx1cHuuvnhAQusBavfynPt/x01k/tzvqZZdlSGXnQPA8qUFvPHCu2zdWkjNnBqceNaxVKpcCYDJ733Gl5/NwTIy6DPgMFq0aRrX+kRTuyv3dMtd4q9cjDybWV0ze9rMFphZvplNMbOT4rDd3mY2IR51LE0oVMzlt4zj+Tsv4tNxo3hhYj5fLViW6LApEV+5Jzf2aX0P4OnbL9yubHL+t7z10SzefeJKPhw7gj+feURC6wBq9/Kee+e89pxx3va/gl97/h0O73sIQ/5+Dm06tmTKB/kAFCxfydwZ3zDksnM487wTefPF9ykuLo5rfUqo3ZV7uuUebyXXeQ7ykQr2+s6zhe8T+TIwyd2bu3t34Ayg0Q7Lpewoe/6chTRvnEvTRrlUrJDFyUd34/UPZ6ZFfOWe3NgHdW1JTo2q25U9/tLHDD3nKCpVDH9EcvepntA6gNq9vOe+X/NGVKlaabuylQWr2a95QwCat96Pr2fNB+CbOd/RvktrsrKyqLVPTfbJrcnSH36Ka31KqN2Ve7rlLomx13eegSOAre7+fyUF7r7I3e82s8Fm9ryZ/ReYaGbVzOwRM/vczL4wswEAZpZpZrdGymea2QU7BjGzHpF1msc7gWUFa2lYN2fb6wZ1c1hWsDbeYVIyvnIPLvcSC34sYOqX39H3vH9z0kV3MWPuooTHDDr3dG73oOLXrrcv38xZAMC8L7/ll7XrAFi3dgM1av76ha16zeqs+2VDQuqgdlfuyY4fdO6JYBbsIxWk7GjsHugATC/l/YOATu6+ysxuAt5z9z+ZWS3gMzN7BxgIrHX3HmZWCZhsZhNLNmBmBwN3AwPc/Yd4J+DuO5Ul8wAJMr5yDyZ2tKKiEGt/2cRrD17KjHk/MOTqx5g6/hosgZUJOvd0bveg4vc77WgmvvwBH78zlVbtm5OZmVlSoZ3rk6A6qN2Dia/cg4ktiVMeOs/bMbN7gEOArcA9wNvuvirydh+gv5ldFnldGdgvUt7JzE6NlNcEWkW20Q54AOjj7kt3E3MIMASg8X777XGdG9SpxZLlq7e9Xrp8NfVya+7xdn6rIOMr9+ByL1G/Ti369u6EmdG1fRMyzFi5ZgO5OdkJixl07unc7kHFz62zD2cNORkIT+GY/9X3AFSvlb1tFBpg3dp1ZNeolpA6qN2Ve7LjB527JEZ5mLYxB+hW8sLdLwaOBGpHiqL//mfAKe7eJfLYz93nRcr/ElXezN1LRp6XAZuBrrurgLs/4O557p5XO7f27hbbrW7tm/DdDwUsWrKCrYVFvPj2dI7r1WmPt/NbBRlfuQeXe4lje+3Px/nfAvDdDz9TWBRi31qJ6byUCDr3dG73oOJvWL8RAC92Jr/zGd0ODMds3b4Fc2d8Q1FREWtWrWXVijU02K9eQuqgdlfu6ZZ7/BkZAT9SQXkYeX4PuMnM/uzu90XKqu5m2beAv5jZX9zdzayru38RKf+zmb3n7oVm1hpYEllnDXAu4TnTG9z9g3gnkJWVyS1XnMYpw+4hFHIG9j+Qdi3qxztMSsZX7smN/edrHueTL+azas16ug24hsvOO44z+x3IpTc+Te+BN1OhQhZ3jhqY0CkboHYv77m/NPZ1Fn23mE0bNnPXPx6iV58D2bqlkPxPvgSgzf4t6dyjPRCeC92uc2vuv/VJMjIzOOakw8nISMy4jtpduadb7pIYtqv5OHsbM6sP3A78DiggPNr8f0AVIM/dh0aWqwLcARxMeLR5obv3M7MM4B/ACZHyAuBEwqPNl0WW2Q94A/iTu0/dXV26d8/zyVOnJSJNkd3aXBgKLHblCpmBxZbg/OuD+YHGv6x3y0DjiyRblQqW7+55QdahSdtOfuUjrwZZBS7u2Szw/VAeRp5x92WEL0+3K49FLbcJ2OlKGu5eDFwVeUT7IPIgcqJgh/+5siIiIiKy1yoPc55FRERERJKiXIw8i4iIiEiCpdBd/oKkkWcRERERkRip8ywiIiIiEiNN2xARERGRmGToFokaeRYRERERiZVGnkVERESkTAZo4FkjzyIiIiIiMVPnWUREREQkRpq2ISIiIiIx0QmDGnkWEREREYmZRp7LGXcPLLbp22hgKlfIDLoKkmYu690y0PhnPjYtsNjPDM4LLLZI0PRfvUaeRURERERips6ziIiIiEiMNG1DRERERMpkaNQVtA9ERERERGKmkWcRERERKZvp4gCgkWcRERERkZip8ywiIiIiEiNN2xARERGRmGjShkaeRURERERippFnERERESmTARk6YVCd51TxzidzGXHbeELFxZwz4GAuHdwnabE3bymk3wV3sGVrEUWhYvof2YURQ45PWvwgcx96/VO89fFscnOqM+W5kUmLW0K5B5N70PHTed8nI/Y+VStw0aHNqFWlAu7w7jcFvDnvZ07p0oAjWuXyy5YiAJ7LX8KMJWvJrpTJX3u3oEVuNT6cv5LHpv4Q9zqBjrl0/bwHnbvEX0pP2zCzkJnNMLPZZvZfM6sVx20/ZGbt47W9/0UoVMzlt4zj+Tsv4tNxo3hhYj5fLViWtPiVKmbx8r3D+OjpEUwaO5x3p8zj81nfJyV20Lmf2e9Axt91cdLiRVPuweUedPx03ffJil3s8NTni7ns5Tlc/do8+rStQ8OalQF4fe5yRrw6lxGvzmXGkrUAFIac579Yythpi+NelxI65tLz8x507pIYKd15Bja5exd37wisAuL2yXf389x9bry297/In7OQ5o1zadool4oVsjj56G68/uHMpMU3M7KrVgKgsChEUVEoaddxDDr3nt1aklOjatLiRVPuweUedPx03ffJir1mUyELV20EYHNRMUvWbmKfqhV3u/yWomK+/nk9W0PFca9LCR1z6fl5Dzr3RLCAH6kg1TvP0aYADQHM7AMzy4s8zzWzhZHnHczss8ho9Uwza2Vm1czsNTP7MjKCffoutnGfmU0zszlmdl1JQDNbaGbXmdl0M5tlZm0TkdiygrU0rJuz7XWDujksK1ibiFC7FQoV02vgGNocM4LeB7Qlr2PTpMRNhdyDotyDyz3o+EEKMvcgYudmV6TpPlWZv2I9AMe0q8M/+7fngp5NqVYxM6Gxo+mYS8/Pe9C5S2LsFZ1nM8sEjgReLWPRC4E73b0LkAcsBo4Flrp758gI9pu7WG+ku+cBnYDDzKxT1Hsr3L0bcB9w2W7qNyTS+Z5WsKJgT1IDwN13sc093sz/JDMzg0ljhzN7wg1Mn7uIud8tTUrcVMg9KMp9e8nMPej4QQoy92THrpSVwaW9W/DEZz+yqbCYd776mUtemMXwV+eyemMhZ/donLjgO9Axt710+bwHnbskRqp3nquY2QxgJbAP8HYZy08BrjKzK4Em7r4JmAUcZWb/NLND3X1XX/lOM7PpwBdAByB6LvSLkZ/5QNNdBXX3B9w9z93zaufWjjG1XzWoU4sly1dve710+Wrq5dbc4+3EQ83qVenZrSXvTpmXlHiplHuyKffgcg86fpCCzD2ZsTPNuPTwFkxesIrPf1gDwNrNRbiDA+99W0CL3GoJib0rOubS8/MedO6JYBbsIxWkeud5U2QUuQlQkV/nPBfxa90rlyzs7k8D/YFNwFtmdoS7fwN0J9yJvtnMrokOYGbNCI8oH+nunYDXorcJbIn8DJGgq5N0a9+E734oYNGSFWwtLOLFt6dzXK9OZa8YJytWr2PtuvD8wE2bt/LhZ1/TukndpMQOOvcgKffgcg86fpCCzD2ZsYf0bMLStZt5fe7ybWW1qlTY9rzHfjn8uGZTQmLvio659Py8B517OjKzR8zsZzObHVU22syWRKb1zjCzvlHvjTCz+Wb2tZkdE0uMveJSde6+1syGAa+Y2X3AQsId4s+AU0uWM7PmwAJ3vyvyvJOZfQWscvenzGw9MHiHzdcANgBrzawucBzwQYJT2k5WVia3XHEapwy7h1DIGdj/QNq1qJ+0+MtX/MJF1z1FqLiY4mLnxKO6csyhHZMSO+jczx35KJPzv2XlmvV0OH4Uw4f05ZwBBycltnIPLveg46frvk9W7DZ1sunVMpcfVm3k5v7hPyQ+l7+Eg5vvQ5N9qoBDwfqtPDRl0bZ17jp1f6pUyCQrw8jbrxY3T/yGJWs3x61OOubS8/MedO7xZ0m7oMD/4DHgP8ATO5Tf7u7/ii6IXHXtDMKzDhoA75hZa3cPlRbAdjUfJ1WY2Xp3z456/V9gHPB55Od64D3gbHdvamYjgLOBQuAn4CygB3ArUBwp/7O7TzOzD4DLIs8fA34HLCA80vyquz8WORExz91XRE4u/Je79y6tzt275/nkqdPitQv2WJDtuRd8oESknDjzseB+zz4zOC+w2JK+qlSw/Mj5WYFp3r6z3zj29SCrwFndGpW5H8ysKTAhcq4bZjYaWL+LzvMIAHe/OfL6LWC0u08pbfspPfIc3XGOvD4h6mX03z1GRd6/Gbh5h828FXnsuO3eUc8H7yZ+06jn04Deu1pORERERJIi18yivz0/4O4PxLDeUDP7AzAN+Lu7ryZ8FbdPo5ZZHCkrVUp3nkVEREQkNRgpcbLcit8wAn8fcAPh84VvAG4D/sSuLx1d5p/wU2AfiIiIiIgkhrsvd/eQuxcDDwIHRN5aDERfs7IRUOa1etV5FhEREZGYmFmgj99Y5+izNE8CSq7E8SpwhplVilx9rRXhi1GUStM2RERERKRcMLNnCJ+jlmtmi4Frgd5m1oXwlIyFwAUA7j7HzMYBcwlfBvnisq60Aeo8i4iIiEg54e5n7qL44VKWvxG4cU9iqPMsIiIiIjHRRWk151lEREREJGYaeRYRERGRspluiAYaeRYRERERiZk6zyIiIiIiMdK0jXJGf05JTxu2FAUWu1ol/RqR5Htm8J7eYCx+hr82L7DYAGOObxdofElfKXKHwcBpH4iIiIiIxEhDRiIiIiISE/2FWyPPIiIiIiIxU+dZRERERCRGmrYhIiIiIjHRpA2NPIuIiIiIxEwjzyIiIiISE50vqJFnEREREZGYqfMsIiIiIhIjTdsQERERkTKF7zCoeRvqPKeIdz6Zy4jbxhMqLuacAQdz6eA+aRNfuScv9tLlq7nkH2MpWPULGZbBWf0P4rzTDuPWB1/nrY9nkWFGbk51/j3yLOrl1kxoXdTuyr085r5+7Tref+ldNq7fiBm0696B/Q/szHdz5pP/wWesLljNyef/ntoN6wCweeNm3h73Jj8vWU6bLu045Pheca1PNLV7euYu8Veupm2Y2b5mNiPy+MnMlkS9rvgbt/m6mdWKPF8f1wpHhELFXH7LOJ6/8yI+HTeKFybm89WCZYkIlXLxlXtyY2dmZnDN0AF8MPYqXn3grzz+4sd88/1PXHjWEbzz+JVMfOwKjjy4PXc8+lZC66F2V+7lNXfLyODAPj05fehZnHjeqcz5bBarf17FPnX2oc/px1G/SYPtls/MyiTv8AM4qE/PuNZjR2r39MxdEqNcdZ7dfaW7d3H3LsD/AbeXvHb3rb9xm33dfU0867mj/DkLad44l6aNcqlYIYuTj+7G6x/OTGTIlImv3JMbu25uTfZv0xiA7KqVadW0Lj+tWEv1apW3LbNp89aEn02tdlfu5TX3atWrUbtBbQAqVqpIrdo5bFi3gZza+1ArN2en5StUrED9Jg3IzMqMaz12pHZPz9wTwSzYRyooV53nXTGz883sczP70sxeMLOqkfLHzOw+M3vfzBaY2WFm9oiZzTOzx6LWX2hmuYms47KCtTSs++sv1QZ1c1hWsDaRIVMmvnIPLvcfl61k9jeL6dq+CQD/vP81epw8mpcm5nPZuX0TGjvo3NO53ZV78uKvW/0LK5etoE7DugmLESu1e3rmLolR7jvPwIvu3sPdOwPzgHOj3ssBjgAuBf4L3A50APY3sy7JqqC771SWzG9XQcZX7sHE3rBxC0NGPsroS07aNup85QXH8/mLozmpT3ceffGjhMZXuwcTO+j46ZR74ZatTBz3JgcdewgVK/+mWYNxpXYPJn7QucefBf4vFaRD57mjmX1kZrOAgYQ7xyX+6+Ejexaw3N1nuXsxMAdoGmsAMxtiZtPMbFrBioI9rmCDOrVYsnz1ttdLl69O+MlaqRJfuSc/dmFRiCGjHuGkPt3pe1jnnd4/8ejuvPHBlwmtg9pduSc7djLjh0IhJo57k1b7t6Z5+xZx3/5voXZPz9wlMdKh8/wYMNTd9weuAypHvbcl8rM46nnJ65ivROLuD7h7nrvn1c6tvccV7Na+Cd/9UMCiJSvYWljEi29P57henfZ4O79VkPGVe3JjuzuX3fwMLZvUZcgZh28rX/Djr1/6Jn48mxZNEvtnZrW7ci+vubs7H77yPrVyc+h0cJe4bvt/oXZPz9wlMdLhUnXVgWVmVoHwyPOSgOuzk6ysTG654jROGXYPoZAzsP+BtGtRPy3iK/fkxv585ve88NY02raoT5/BtwBw5QX9eHbCpyz44Wcsw2hUdx9uvvz3Ca2H2l25l9fcf/phGd/O/Jp96uzL+PueBeCAIw8kFCpm8uuT2LRxE288PYF96+Vy/Dn9ARh7+xMUbtlKKBRi4VcLOP6c/uTU2Seu9VK7p2fuibB3TzuJD9vVfJzywMxGA+uBDcAVwCLC0zOqu/vgyEmBE9x9vJk1jTzvGFk3+r2FQJ67rzCz9e6eXVrc7t3zfPLUaQnKSmTXNmwpCix2tUrp8B1c5FfDX5sXaPwxx7cLNL4Eo0oFy3f3vCDr0KpDF79z3MQgq8DxHesGvh/K7f967j466uV9u3h/cNTzhUDH3bzXNOp5qR1nERERkfJKdxgMS4c5zyIiIiIicaHOs4iIiIhIjMrttA0RERERiaMUustfkDTyLCIiIiISI408i4iIiEhMNPKskWcRERERkZip8ywiIiIiEiNN2xARERGRmJiu86yRZxERERGRWGnkWURERETKZECGBp418iwiIiIiEiuNPIuUAxoIEEmeMce3CzT+GY9NCyz2s4PzAostkirUeRYRERGRmOiEQU3bEBERERGJmUaeRURERCQmusOgRp5FRERERGKmzrOIiIiISIw0bUNEREREYqITBjXyLCIiIiISM3WeRURERERipGkbIiIiIlIm3Z47TCPPIiIiIiIx0shzinjnk7mMuG08oeJizhlwMJcO7pM28Yde/xRvfTyb3JzqTHluZNLiBh0bkr/flyxfzSX/GEvBql/IsAwG9j+I8047jP++N4N/P/Im3y5azmsPXkrntvsltB6gYz6djrtUiR10/GTE3rdqBS46tBm1qlSg2OG9bwp4Y97PnNqlAUe0yuWXLUUAPJu/hBlL1rJ//Rqc2b0hWZlGUcgZO20xc35aF/d66ZgP7piPL9MJg6T4yLOZjTSzOWY208xmmNnv4rz9T8p4f3084+1OKFTM5beM4/k7L+LTcaN4YWI+Xy1YlozQKRH/zH4HMv6ui5MWL1ViB7HfszIzuHboAD4cexX/feCvPPbix3zz/U+0bV6PB2/6Iwd2bp7Q+CWCPuaCjp9ux10qxA46frJihxye/Hwxf395Dle/No8+bevQsGZlAF6fu5zhr85l+KtzmbFkLQDrthRy67vzueKVudz78fdcfGizuNcJdMwHdcxLYqRs59nMDgL6Ad3cvRNwFPBjPGO4+8Hx3N5vlT9nIc0b59K0US4VK2Rx8tHdeP3DmWkTv2e3luTUqJq0eKkSO4j9Xje3Jvu3aQxAdtXKtGpal59WrKVV03q03K9uQmNHC/qYCzp+uh13qRA76PjJir1mUyELV20EYHNRMUvWbmKfqhV3u/zCVZtYvakQgMVrNlMhM4OsBExq1TEfzDEviZGynWegPrDC3bcAuPsKd19qZgvN7J9m9lnk0RLAzE4ws6lm9oWZvWNmdSPlo83sETP7wMwWmNmwkgAlI8tmVt/MJkVGt2eb2aFRy9xoZl+a2acl24y3ZQVraVg3Z9vrBnVzWFawNhGhUjJ+ugp6v/+4bCWzv1lM1/ZNkhazRNC5Bx0/SEHmHvR+T7fca2dXpOk+VZm/IvxH1GPa1eGf/dtzQc+mVKuYudPyv2uSw8JVGykq9oTWK9nSrd0TysK35w7ykQpSufM8EWhsZt+Y2b1mdljUe7+4+wHAf4A7ImUfAwe6e1fgWeCKqOXbAscABwDXmlmFHWKdBbzl7l2AzsCMSHk14FN37wxMAs7fVUXNbIiZTTOzaQUrCvY4Ufedf1El8wAJOn66CnK/b9i4hfNHPsp1l5xE9WqVkxM0StDHXNDxgxRk7kHv93TKvVJWBpf2bsHjn/3IpsJi3v7qZ4a9MIvhr85lzcZCzu7ReLvlG9WqzFndG/LQlEWJq1RA0qndJTlStvPs7uuB7sAQoAB4zswGR95+JurnQZHnjYC3zGwWcDnQIWpzr7n7FndfAfwM7DiC/DnwRzMbDezv7iVnS2wFJkSe5wNNd1PXB9w9z93zaufW3tNUaVCnFkuWr972euny1dTLrbnH2/mtgo6froLa74VFIc4f9Qgn9elO38M6JzzergR9zAUdP0hB5h70fk+X3DPN+NvhLfh4wSo+/2ENAGs3F+EODrz3bQEtc6ttW36fqhX4++EtuefjhSxftyUhdQpSurR7sljAj1SQsp1nAHcPufsH7n4tMBQ4peSt6MUiP+8G/uPu+wMXANHDadG/DULscJURd58E9AKWAE+a2R8ibxX6r18bd1ovXrq1b8J3PxSwaMkKthYW8eLb0zmuV6dEhErJ+OkqiP3u7vz95mdo2aQuF5xxeEJjlSboYy7o+EEKMveg93u65H5BzyYsWbuZ1+cu31ZWq8qvf3DtsV8OP67ZBEDViplceVQrnpm+mG9+Tso58kmXLu0uyZOyl6ozszZAsbt/GynqAiwC9gdOB8ZEfk6JvF+TcOcXYNAexmoCLHH3B82sGtANeOJ/SmAPZGVlcssVp3HKsHsIhZyB/Q+kXYv6yQofePxzRz7K5PxvWblmPR2OH8XwIX05Z0ByzuUMMnYQ+/3zmd/zwlvTaNeiPkcPvgWA4Rf0Y+vWIkbd8QKr1qznD5c/QIdWDXn6339OWD2CPuaCjp9ux10qxA46frJit6mTTa+WuSxatZEx/dsD4cvS9Wy+D032qYI7FKzfum16xjFt61C3eiVO7tyAkzs3AOCmid/wy+aiuNZLx3wwx7wkhu1qPk4qMLPuhEeTawFFwHzCUzimAY8CfQmPnJ/p7vPNbABwO+EO9KdAD3fvHZmKsd7d/xXZ7mygn7svNLP17p5tZoMIT/UoBNYDf3D370vej6x3amS9waXVu3v3PJ88dVo8d4VImTZuie9/dHuiaqWU/Q4uUi6d8Vhw/8c8OzgvsNjprkoFy3f3QBug3f5d/ZGX3g+yChzcKifw/ZCy/+u5ez6w09dSC8+0v8fdr9th+VeAV3axndE7vO4Y9Tw78vNx4PFdrJsd9Xw8MH4P0xARERGRciRlO88iIiIiklpS5aS9IO11nWd3bxp0HUREREQkPaX01TZERERERFLJXjfyLCIiIiIB0bwNjTyLiIiIiMRKI88iIiIiEhPT0LNGnkVEREREYqXOs4iIiIhIjDRtQ0RERERiYpq1oZFnERERESkfzOwRM/vZzGZHle1jZm+b2beRnzlR740ws/lm9rWZHRNLDI08lzPuHnQVAmNp/HU4MyN9cw9S0J+3II/5dM49aM8Ozgss9j/e+Saw2ACjjmodaHzZK65U9xjwH+CJqLLhwLvuPsbMhkdeX2lm7YEzgA5AA+AdM2vt7qHSAmjkWURERETKBXefBKzaoXgA8Hjk+ePAiVHlz7r7Fnf/HpgPHFBWDHWeRURERGRvkWtm06IeQ2JYp667LwOI/KwTKW8I/Bi13OJIWak0bUNEREREYhP8vI0V7h6vuUu7yqbMOWkaeRYRERGR8my5mdUHiPz8OVK+GGgctVwjYGlZG1PnWURERETKs1eBQZHng4BXosrPMLNKZtYMaAV8VtbGNG1DRERERMpkpP7tuc3sGaA34bnRi4FrgTHAODM7F/gB+D2Au88xs3HAXKAIuLisK22AOs8iIiIiUk64+5m7eevI3Sx/I3DjnsRQ51lEREREyma6wyBozrOIiIiISMzUeRYRERERiZGmbaSIdz6Zy4jbxhMqLuacAQdz6eA+SYu9eUsh/S64gy1biygKFdP/yC6MGHJ8uY8NMPT6p3jr49nk5lRnynMjkxY3qPh/vfFp3p48h9ycbD4cOwKAWx96g7GvTmHfnGwARlxwPEcd3CHhdQnymA8y/uLlq7lo9JMsX/kLGWYMOqknF57ROymxSyj3YI67dDjm3xz/Dgu++p6q2VUY/NezAfh5WQHvvPQ+hVsLqZFTnb6nH0OlypUIFYV4++X3WL74Z8yMw0/oRePmjeJeJ0jvdo83zdpI8ZFnM6trZk+b2QIzyzezKWZ2UtD1irdQqJjLbxnH83dexKfjRvHCxHy+WrAsafErVczi5XuH8dHTI5g0djjvTpnH57O+L/exAc7sdyDj77o4afGCjn963wN45vYLdyofckZv3n38Ct59/IqkdJyDPuaDjJ+VmcENl5zE1HGjmPjI33n4+UnKPUmCzD1djvmO3dtxyh8HbFc28YV3OfTYgxn014G07NCCaZOmAzDz89kADPrrQE4990Q+eO0jvLjM+1PssXRud0mMlO08m5kBLwOT3L25u3cHziB8AetY1s9MYPXiKn/OQpo3zqVpo1wqVsji5KO78fqHM5MW38zIrloJgMKiEEVFISxJZwQEGRugZ7eW5NSomrR4Qcc/qGtLagWYb4mgj/kg49fLrUnntuFr8levVpnWzeqxrGBtUmKDcg8q93Q55hs1a0jlqpW3K1u9YjWNmoXveNyk5X58M2c+ACt/XsV+LcLHQ9XsqlSuUomfliyPe53Sud0TwgJ+pICU7TwDRwBb3f3/SgrcfZG7321mmWZ2q5l9bmYzzewCADPrbWbvm9nTwKzI6w/NbJyZfWNmY8xsoJl9ZmazzKxFZL0TzGyqmX1hZu+YWd1I+Wgze8TMPoiMfg9LRKLLCtbSsG7OttcN6uYk9T8UCH877jVwDG2OGUHvA9qS17FpWsSWsEfGf8Th54zhrzc+zZpfNiY8XtDHfNDxS/ywdCUzv15M9w5NkhZTuQeTe9D7Pcj4+9bdl+/mLQDgm1nfsm7NegDq1K/Nd3MXUBwqZu2qtSxf8jPr1q6Pe/x0bndJjFTuPHcApu/mvXOBte7eA+gBnB+5MwzAAcBId28fed0ZuATYHzgHaO3uBwAPAX+JLPMxcKC7dwWeBa6IitUWOCay3WvNrEI8kovmvvOfqZJ9KZjMzAwmjR3O7Ak3MH3uIuZ+V+bdKctFbIHBJ/dk6vNX8+7jV1B33xqMvvvlhMcM+pgPOj7A+o1bGDT8YW7628nUyK6StLjKfXvJyj3o/R5k/GNOOYoZU2by5N3PsHVLIZmZ4T8Md+zenuya2Tx1z7O8P2ESDfarT0ZG/CuVzu0uibHXnDBoZvcAhwBbgUVAJzM7NfJ2TcK3VNwKfObu0ZNmP3f3ZZFtfAdMjJTPAg6PPG/E/7d33mFSVFkffn8DqAgISjYiGBERAXVNiK4Rcw6YxZzWnBVzzmIWwxoxu2vOcRUBFXNOoCIoqAgowvn+OLehnW/QYaaqe5g5L888dFd317m3q7rq3BPhrtTvfC6g+PMPm9lvwG+Svgfa473Qi8e2D7APwCKLLjrbc1uwXStGjxk/4/k3Y8bToU3L2d5PFrRsMS+r91yCp//3Pl27LNhgZDdk2i4w34zH/TdflV2OvDZ3meU+58stf+of09jtmOvZZoPebLp2j5LJhZh7ueZe7u+9nPJbt1uAbfbydKUfx47n8w+/AKCiUQVrb9Jnxvtuv2oI87dulbn8hnzcs0d1vsNgKajLlud3gZ6FJ2Z2IN4dpi0e9XKwmfVIf4ubWUEp/rXSfn4rejy96Pl0Zi4eLgeuMLPlgX2B4oCt4s9Po4oFh5lda2a9zax32zZtZ2eOAPTsuhiffjWWL0eP4/epf3DfkyPYqE/32d5PTRk3/hd++sVd9ZOn/M7zQz9kqcXa13vZgTNm3EwX4qPPj2SZzh1zl1nuc76c8s2MQ06/jaUW78CB/dcpicxiYu7lmXtDPucnTfRrvE03Xnv2dbqv0g2Aqb9PZervUwH44uOvqKiooHX71pnLb8jHPciHumx5fgY4S9L+ZnZV2lbIdHoc2F/SM2Y2VdJSwOhayGpZ9PndarGfGtG4cSPOO3o7tj5kENOmGf03+wfLdslfgSkwZtzPHHDqrUybPp3p040t1l2RDdbsVu9lA+x1wo28PPxjfpgwkeU2PpFj9+nHLpuvVm/l73fyzbzyxif8OGEiK25+MkcN2IhXRnzCOx+PRoJFOrbm/KO3y01+gXKf8+WU/9pbn3HXo6/TdYkF6dP/HABOOmBT1ls9/yonEHMv19wbyjn/3zseY9Tno5j86xSuOfsGVlv3H0z9fSpv/s+T5Jbo1oVuvTyqctKvk7l38AOeOD5fc/ptl08Jt4Z83PMgwk5AVcXj1BVSGMXFwCrAWNyqfDVwN3AGsCluhR4LbAGsCBxpZpukz/et9Py59HxY8WuSNk9yRgOvAiuZWV9JA4GJZnZB+vw7wCZm9sWsxtyrV297+bVhWX0Fs01dPp55U8oqHXWN36ZOK5vsuZvMMYVtMqfcv7dynvMNee4NmTOe+qis8k9cd6myyi8nTZtouJn1LucYunbvabf/9/lyDoEVF5uv7N9DXbY8k2KVd5jFy8env2KeS3+Fz1d+3req18zsQeDBKuQPrPS8dCbRIAiCIAiCoM5Rp5XnIAiCIAiCoG5Qh0otl5W6nDAYBEEQBEEQBHWKsDwHQRAEQRAE1SNMz2F5DoIgCIIgCILqEspzEARBEARBEFSTCNsIgiAIgiAIqkV0GAzLcxAEQRAEQRBUm1CegyAIgiAIgqCaRNhGEARBEARBUC2iuWdYnoMgCIIgCIKg2oTlOQiCIAiCIKgWYXgO5bneofCnNEj+mGZlkz13k7KJDspIXGsaJieuu1RZ5T/x/ndlk73+sh3KJjuoW0TYRhAEQRAEQRBUk7A8B0EQBEEQBH+PiLgNwvIcBEEQBEEQBNUmLM9BEARBEARBtYgOg2F5DoIgCIIgCIJqE8pzEARBEARBEFSTCNsIgiAIgiAI/hYRHQYhLM9BEARBEARBUG3C8hwEQRAEQRBUizA8h+U5CIIgCIIgCKpNWJ7rCE+98h7HXXgP06ZPZ5fNV+Ow3ddvMPJj7qWT/c2Y8Rx65m2M/fFnKlTBTputyoBt1+LCwY9y+39epXWrZgAcs88m/HPVrrmOpaEe91FjxnPAwH8z5oefqZDYbcvV2W+HviWRXaAhnfN1SX7MPV/ZV133H0a8+THzzdeMC8/eF4C77nmOYW98hCRazjcv+++9GQvM34Lvx07g8GOvZsGOrQFYsstC7L1Hv8zHBOU/7kH2zFHKs6RpwNtAE+AP4GbgEjObXsP9XQ9cZGbvzeL154AjzWxYzUZcPaZNm85R5w3h/isOYsH2rVhnt/PZqM/yLNO5Y55i64T8mHtpZTdqVMHJB27O8ksvwsRJU9horwvp03tpAPbebi3223Gd3GQX05CPe+NGFZx+6JassMwi/PLrFNbZ9Tz6rrx0g5h7Qz7uMff8Za+1Znc2WK83g655aMa2TTdele236QvAo08M5d4HXpyhJLdvNz/nnbF3pmOoTLmPey5E3MYcF7Yx2cx6mNlywHpAP+CUmu7MzAbMSnEuJcPf/YLOi7Sh08JtmKtJY7ZaryePPD+yQciPuZdWdvs2LVl+6UUAaD7vPCzZqT3fjfspV5lV0ZCPe4c2LVlhGT8GLZrNw1KLd+DbsaU7Bg3tnK8r8mPu+cvuusxiNG/W9E/b5m0694zHU36bWvJKEeU+7kE+zGnK8wzM7HtgH+AgOZ0kvShpRPpbDUBSX0nPSbpH0geSbpP855O295bUSNJNkt6R9Lakw4pEbStpqKSPJK2Zx1y+HfsTC7Wff8bzBdvPX9KbaTnlx9zLN/evv/2Bdz4axYpdFwPgpvteZN3dzuWIs29nwi+TcpVd7rmXW36Br775gZEfjqLXcouVTGZDPudj7g1z7nfe/SwH/OtSXnrlHbbbaq0Z28eOncAxJ17HwDNv4f0Pv8pFdrnnngcq87+6wByrPAOY2Wf4HNoB3wPrmVlPYHvgsqK3rgj8C+gKdAZWr7SrHsBCZtbNzJYHbix6rbGZrZw+X6WVW9I+koZJGjZ23NiazKOKfc72bmpMOeXH3Msj+9dJv7HPiTcy8JAtadFsHnbdYg1evvMknrjxKNq1bsnpVzyQq/yGfNwLTJz0G7sdewNnHb4V8zVv+vcfyIiGes6XW37MvTyyAXbYdm2uvORQ1litG4895VGY87dqzqCLD+bcM/Zm153W4/Kr7mfS5N8yl13uuQf5MEcrz4nCadgEuE7S28DduKJcYKiZjUqx0W8CnSrt4zOgs6TLJW0I/Fz02n3p/+FVfA4AM7vWzHqbWe+2bdrO9gQWbNeK0WPGz3j+zZjxdGjTcrb3U1PKKT/mXnrZU/+Yxj4nDmbL9XrRb60VAGi7QAsaNaqgoqKCnTb9B2++n48VpkBDPu7gx2C3Y65nmw16s+naPUomFxrmOV8X5Mfcyzf3Amusuhyvvf4BAE2aNKZFi3kB6Lx4R9q3m59vv/0hc5l1Ze5BtszRyrOkzsA03Op8GDAGWAHoDcxV9Nbi5eQ0KiVKmtn49LnngAOB66v47P/7XFb07LoYn341li9Hj+P3qX9w35Mj2KhP9zxE1Tn5MffSyjYzjjznDpbo1J59dlh7xvYxRXHPj73wNksvnm8yS0M+7mbGIaffxlKLd+DA/qVJ0CymoZ3zdUV+zL08sr/97scZj4eN+JiFFvTqGj///CvTp3utgTHfj+fbMeNp327+KvdRG8p93PNAKu9fXWCOqrZRjKS2wNXAFWZmkloCo8xsuqTdgEazsa82wO9mdq+kT4Gbchn0LGjcuBHnHb0dWx8yiGnTjP6b/YNlu5QuE7ec8mPupZX9+tufc+/jw1imc0fW3+M8wMvSPfjUCN79ZDQCFum4AOccuV2u42jIx/21tz7jrkdfp+sSC9Kn/zkAnHTApqy3+nIlkd/Qzvm6Ij/mnr/sS6+8j/fe/4pfJk5i/0MvZdut+vDGW5/yzbc/UFEh2rRuyd67bwTA+x9+xZD7nqeiwj1ue+++Ec1zCJ8q93EP8kFVxePUVaooVfdvvNTcdElLAvcCk4BngYPNrLmkvni5uU3SPq4AhpnZTYVSdMBUPM65YIk/zsweLS5VlxTsYWbW6a/G2KtXb3v5tVwr2wXB/+PXKX+UTXazeebYNXitKff1U3XFDBMEJeKJ978rm+z1l+1QNtkATZtouJn1LucYuq3Q0+57/KVyDoGlOzYr+/cwR931zGyW1mQz+xgo9oUcl7Y/h4djFN53UNHjvkXv71nFPvsWPR7HLGKegyAIgiAIgobBHB3zHARBEARBEASlZI6yPAdBEARBEARlJKLFwvIcBEEQBEEQBNUllOcgCIIgCIIgqCYRthEEQRAEQRD8LYI60yK7nITlOQiCIAiCIAiqSViegyAIgiAIgr+nDnX5KydheQ6CIAiCIAiCahLKcxAEQRAEQRBUkwjbCIIgCIIgCKpFRG2E8hwE9YK5m4QTqRwogv+CoKSsv2yHssm+561RZZMd1C1CeQ6CIAiCIAiqR9gMIuY5CIIgCIIgCKpLKM9BEARBEARBUE0ibCMIgiAIgiCoBooOg4TlOQiCIAiCIAiqTViegyAIgiAIgmoRRYbC8hwEQRAEQRAE1SaU5yAIgiAIgiCoJhG2EQRBEARBEPwtIso8Q1iegyAIgiAIgqDahOW5jvDUK+9x3IX3MG36dHbZfDUO2339BiM/5l462YeccRtPvvwubeZvwYu3HwfA+J9+Ze8Tb+Krb39k0Y4LcP2Ze9BqvnlzHQfEcY+5x9xj7qWjFPJvuvFhRo78lBYt5uXU0wYA8NCDL/Lii2/RvIVfU7faci2W796FceMmcPJJ19O+wwIAdO68ILvssmHmY8qFOcD0LOkL4BdgGvCHmfWWtABwF9AJ+ALYzszG12T/9c7yLGlipee7S7oiPd5P0q6zub/nJPVOjx+R1CqzwSamTZvOUecN4e5LD+DVISdy7xPD+eCzb7MWUyflx9xLK3uHjVfhzov3/9O2y255ijVXWoqh95zEmistxWW3PJnrGCCOe8w95h5zr39zX2315Tn0X9v9v+3rrrcSp5yyJ6ecsifLd+8yY3vbtq1mbJ9jFOc5i7XNrIeZ9U7PjwWeNrMlgafT8xpR75Tnv8LMrjazW2rx+X5mNiHDIQEw/N0v6LxIGzot3Ia5mjRmq/V68sjzI7MWUyflx9xLK3u1FZdg/kpW5UdffJvt+60MwPb9VuaRF97OdQwQxz3mHnOPude/uS+11KI0azZP5vsNMmNz4Ob0+GZgi5ruqEEpz5IGSjoyPX5O0rmShkr6SNKaaXtTSXdKGinpLqBp0ee/kNQm63F9O/YnFmo//4znC7afn2/H/pS1mDopP+ZevrkXGPvjL3Ro0xKADm1aMm78L7nLLPfcG/Jxj7nH3EstvyHPHeDZZ4Yz8JQbuOnGh/n11ykzto8b9xOnnTqY88+7jY8++rpk46ktKvM/oI2kYUV/+1QxTAOekDS86PX2ZvYtQPq/XU2/g/oY89xU0ptFzxcAHprFexub2cqS+gGnAOsC+wOTzKy7pO7AiFxHC5jZ/9tWyiLk5ZQfcy+P7HJT7rk35OMecy+P/Jh7eWSXW37fvj3ZZNPVAfHgAy9w95Cn2X2PjWnZsjnnnncAzZs35csvvmPQoHs59bQBNG06d2kGNmczrigUY1asbmbfSGoHPCnpgywHUB8tz5NTjEsPM+sBnPwX770v/T8cDyAH6APcCmBmI4G/9e1I2qewAho7buxsD3jBdq0YPWZmzPo3Y8bPsASWgnLKj7mXb+4F2i7Qgu/GuRXmu3E/0Wb+FrnLLPfcG/Jxj7nH3EstvyHPfb6WzaioqKCiQqzZZwU+/9xjrZs0aUzz5u7YXqxTB9q2bcWYMT+WZEy1RSrvX3Uws2/S/98D9wMrA2MkdfQ5qCPwfU2/g/qoPM8Ov6X/p/FnK/z/X6b+BWZ2rZn1NrPebdu0ne1B9Oy6GJ9+NZYvR4/j96l/cN+TI9ioT/fZ3k9NKaf8mHv55l5gwzW7cdcjQwG465GhbLTm8rnLLPfcG/Jxj7nH3GPupZM/YcLMGgZvjPiIhRZyHeGXXyYxffp0AMaOncD334+nbZtWJRlTfUdSM0ktCo+B9YF38CiE3dLbdgMerKmM+hi2UVteAPoDz0rqBuT+C2vcuBHnHb0dWx8yiGnTjP6b/YNlu3TMW2ydkB9zL63sfU66iZdHfMKPEybSfdOTOHrvfhyy63oMOOFGbnvoVRbuMD83nLlHrmOAOO4x95h7zL3+zf3aax/kow+/YuLEyRx11CA222wNPvrwK77+2g2cbdq0ZOdUVeOjj77iwQdfolGFqKioYOedN6BZ86Z/tfug+rQH7pebqRsDt5vZY5JeB4ZI2gv4Cti2pgJUVSzQnIykiWbWvOj57kBvMztI0kBgopldIOk54EgzG5aSAIeZWSdJTYEbga7Am8ASwCHpfV+kfY2blfxevXrby68Ny2l2QVA1f0ybXjbZjRs1dAdWEAQNgXveGlVW+bv0XmR4NWJ9c6V7j1728DOvlHMILNp6nrJ/D/XO8lysOKfnNwE3pccDi7b3LXo8jhTzbGaTgR1mse9OmQ42CIIgCIIgmKMIk1EQBEEQBEEQVJN6Z3kOgiAIgiAIcmA2Kl7UZ8LyHARBEARBEATVJCzPQRAEQRAEQTUJ03NYnoMgCIIgCIKgmoTyHARBEARBEATVJMI2giAIgiAIgr9FRMIghOU5CIIgCIIgCKpNWJ6DIAiCIAiCahGG57A8B0EQBEEQBEG1CeU5CIIgCIIgCKpJhG1kzIgRw8c1baIva7GLNsC4rMYzB8kut/yYe/mIuTc82eWWH3MvHzH3mrNYVgOpDZEwGMpz5phZ29p8XtIwM+ud1XjmFNnllh9zj7k3NPkx95h7Q5PfkOceZEsoz0EQBEEQBEG1UKQMRsxzEARBEARBEFSXUJ7rHtc2UNnllh9zb5jyY+4NU37MvWHKb8hzDzJEZlbuMQRBEARBEAR1nBVW7GWPP/9qWcfQseVcw8sdOx6W5yAIgiAIgiCoJpEwGARBEARlRlKFmU0v9ziC4O+IdMGwPAdBnUWKappBUJ+R1EPS4wBmNl1So3KPKSgNklqXewxBzQnleQ6gripRknI9fyrvv9TfQ7G8ch4DSV0kVZTjxlpXzr1SjiPv87qm1IVx5XUcCud2XTnfSoWZvQnMLelNSe3MbFpdOM5BvkiaB7hRUrtyjyWoGfEjnQOwlNUpaW1J20paOf34ykaxizGNaWdJi0maKysZRftfSlKXou8h9xuspEZmZpI6SlrISphZK6lFYb6S9gPuBe4HtpbUsURjaJIeZnY8azmOBdLzXM/7dNynS2okqVuesmaXot/DepKWlrRoKeUX/SYyv2+Y2bT08N+SVsl6/9Wl8gI1L0U2LYYHAJhZX+BZ4B1JqxYd51IuGCWpcaVtJQ3rLFpAzSWpVSllF42hqaTmecsxsynAjkBLSdcXya/zi0ep/H91gVCe6ziSzpDUWNJCwHXATsAAYH9JHco1rqIL/FXA+sAWwJ1ArTospn0uJGnZ9PgaYBDwrKTDk2zL8yKTFgbT0kLgIaBfXrJmwba4VWJ3YCNgc+BhYG1gG0lL5ilc0lxmNlXSEsAtkk6XtKqkpnnKrWIcndI4FgEek3QncICkrjnJU5ESdw8wWNJTklYr9dxnRVpMXQ8cAxwpaZ0cZSn9v7ukC4AHJC2TFheZ/P7SIuCUok2/AV8Wyy8Vxcdf0q3p2naFpE45iGsN/C6pVbreHAacDDwuaTfI/zpXiXnN7A8ASVem6+7xkpYrhfCia24FcAfwsKRTSmGFl9Qi/b8BcB9wk6R9cpR3uqS9gGnAd8Bykh6QNH+Jj3lQC0J5rsMk5W1h4AXgTGAPM9sSeAJYCBggafkyjm91oI2Z7Q38AjxmZqNrYzVI1o5jgD0l7Qq0M7P1cAX2MElXQ743lqKknWuBZ83suqLxqfj/nHgMuAVXoqeY2Zdmdi3wILAssFdeCrSkTYHbk1XzPGAk0AboD2yuEsXpSVoJ+J+krYBDgBuB23GlY7d07mVKkWfjEGC0ma0MvAgcDmwgqWXWMqtDQYFIv6tFgNWA04GPgR0kbZ+HzPQb6wMcBFyOW/9fl7Rjhp6YlkA3SZclJeYH/JpHhjKqRdHx3wmYDz/fxgLXSVojKznpux1rZrfg1/W35N6tq4HNgHMkXVE8pjyRND/wiaR/SloTWBx4Cs8LO1rSunmPoeiaeyHwKbAXsAFwdZ5WaEkn4ouEpYGjgJvx6/6Bki7MSexw3Ai2p5n9Ymar4uf9E5KWLfV5H9SMUJ7rKMkK8juwB3ATsCmwCoCZ3QM8iis1m5fSvVZJaRwNvC3pdqCRmZ2abvSXJqvlbJOsHxenp5sA45Ml9B2gK9BT0lu1mMIskbRwJSvTj7gVBEnzpm0d0jgzv8AVjqOZfYNfxG/G53tC2l5Qqn8FPs9afuKT9Hcl8JOZnWlm+wNvA2sBO6kE4QJm9jqutB0OrGZmV5rZQ3j4yk9Af0mbZS1X0orAdsCYNI5Tce/DPsDuZbC+VyRL75LA/sDqQCsz+xx4AHgF2CoterKSuUiS2QR3LR8K9MRv8LsBN0samJG4EUDB8nwlsBSws6RdJB0qaXBeC8WqkLQjvmg9w8xeBC4BhgBnSNo7g/3/qaKGmR2IL5afkLSamT2HX+dXldSmtvKqg5mNxxXHO4AzgGPM7G7ck/g/YFdJ++Y9jnQOLwfcbGYfAOvgi5ghOZ4DdwPL4PN+28zuNLMngI2B7pKezsr6rRSWYmYPAB8BJ0m6XVIrM9sL93Y9J2n9LOTlicr8ry4QynMdpGD1SU8XSVbHg4G9NTN04WngVuD6grutBOMqxDwumhSo8biVaDHghPS2M4EWZvZJDfZfUB4/x63Pr+BhIBtJap9W6SuTFNosFdikKOwJTEmWGICJwGWSOpjZpPSeByWtlZXcIvkVRW7TwbgS8QAeorOMpEskNTOzV4Gzsj7mhUWRmb2PWxmfAbYoWDXN7Jq0rRc5XjeKF2dmdi+uuC0s6aa0bRiuzHwBfJbDECbiykwfSf2TzFuAK4BfzWxyDjKrJC2gp0uaGw/VaAQYcEn6PXwN/Ae4AXgkI5mrAEMlbWlmU4HjgQ/w689pZnYfrlRtq1rEnxcpEn+Y2XvAucA7QDc8VGkqPtc3zezj2sxpNvkRWBHYPY1vPH6dvRKo9cKpKNxtP0n/krSgmR2FK+k3S9rdzL4ys15mNq628v6OomvurcAaQBf8Go6ZfYifX89BfhqLPP67AvcqTcU9a8uY2RQz2wH/ra+UtUyYMccdgK/wBctqkpqY2SjcYPUl7uWtNUUhQYNwT21/3AA2SJ7jci5+vfspC3lBvkSHwTqMpPNw5XE/M/tN0srAOcDHZpa7JaDSWAoWsGZ4cst1ZnadpBXwWL1f8AtBU6BfGm+johjS6u6/Oa4cnWRmw5PFY3VccXupJkp5dUnKwALAqcBtuHvtQDye+3ZgPeB9Mzs6B9lKC5PLgA5mtl3aPhducd8Lt3rvAvyWl2tP0ja4sjQKn/cWwFNmdkV6vaOZfZuH7LT/wvewBzDBzO5PC7VLcOVxFzP7WdI85kk3tZXXyDzWsi2+KGicQo92x5WJd4CrzOy32sqqxRiPB5Ywsz3T8yuBlYGD0mKq8D7V5rwo+g1uhoeFXJkWTUi6HHgNtz5vA5xgZt/VUk5z3LPQGA9F+xb/rW8F3JIU9VwpOv5NgenpurUYbgV8G9jHzP6Q1DirBas8LGhbfL5TgbvN7AF5/PrdwLr4oiHXm3PR3NsBG5jZv+WhMy/gv7210/vmM7Of0+NanWOV5BfOg5Zm9lPa1hvYGTfMPGZmr2Uhq5LcGXOQdADwqpmNkHQqbvW/BHg+64VyMgx0wBefAwqLQklD8N/zHmb2bJYy86BHz1725AuZH5bZol2LJtFhMKiaZPVaFdg3XdB7467k3YGlk5JTMopcjbcBdyXFuQPQDjgWD7U4DdhmdhXnSvu/DxhqZsPT9mvwG9nmuAU68xCVIivYFPyiPRrYFVeWb8UViZbAg1krzpJaamZ86RJ4TPMekrrL4x5vxhX6i4ELkjUm05tqwdoraUPgbDzOegK+iLkKWCONBTzBJReSgmLyGMsjcSUeM/sK9wq8B4yQVxyptTJbpDw0xxMyDwTul7Sxmd2En4u98ZCNspAWDvMDa0raFsDMDsCVrHuS0k/aXqvzoug32AJXHI+RdGnaNhRPWD0XeKSminMlOfcB3wOdgfPx+OKHmGnZzjXGvOj4z43HuV4v6WDc8rc6/pt/RVLr2irOmhm33gSYB9g0LZBfwavo7GVmzwBdzOyNUinO6em9wIIA5t69FfEY6B/ksdg/Fz6XoeJcqGrTGffmnSHpIjxk7FJgbjxMKnMFqUhxPgoP0foqbT8F9zAcn2Q3meVOaig3GR5G4J6tFumlAcDPpGMQzBlEh8E6hKQ9zOzG9LQ1Ht+6lbxkVn/gSWAgsL55PHSpxzcvfrN7RdLpQEfctTXEzA4uel9x1YLZ2X9HYJKZDUzPm5rZZDN7SNInwLSsrD/FpBvoPLi79hczGyhpa2BroBVuGXo4a7mJvYFnJL1lZp9I+hl3w4NbgLoA65jZieQTplBIvmyOh8oclMbRyDxU5WU8xrpn4b15jCHJ+yMpMkcCxwFfyJNGN8QTmM4ARmZl+S46R4fgYRHD8RjfSyUtb2bnSPoev6GXBTP7Sp7U9DWwbrIC3mBm50q608zGZikvWfz3wxePi+BVfW4C9k6WyQ61UZyL5GwAfGhmVydDwMXJo7AQcBfwaMEamRdFx/823Js2GffsLQ5cY2Zby71/zXCLe40osrD2xuO7F8MXxMea2SBJY/AE6XFm9mAtplRtbGYIwRnAB+l8Wha/z3xpZnuncbXBjQmZy09etTvwBVkn3Bq/HB66cCEeTvFmlnKLFkxd8Ov7WkBTSQfiC6Yz8XvssuZhS1nJa42fR9/hC8Qjgd8kfY4nid5rZrfVVl5QOsLyXAeQ0wGokNe4bAS8iv+w98VjzlbHkyd6FhTngsWwVJjZJPwmcx4ej3gsfqP5U83Z6ipYKqoJLakHMA5oI2mfpIBPltRG0o3AGPP4tMzQn5Os7sfDNQZKehj/zgfhF7bdc7J4C1eU3wduTRf043FL+6FmdiUwAb+B5YqZTcSTEAuKUWG+KwLvmNlleclOC5Wj0jh+A17Cyx8+hi8evsItwNPM7I4M5K0oqeCSbosf+8HAZXhS3h7AcZJuMLNhyQpfEooslJtLulheXaZb+v6fB1aRl7qaC1eos74OzAMMTr+15/EFVQ+8ykYHUiJlBrwLTJL0GvCMmQ1JivNtwOJ5Ks6S2idDAJL64nGt1+ILhrOB9sANktY3s6OT56OmsgqK87z49fJB4CRg7WRpLSSAn1AKxVlSZ0l3FZ0zI4AfJT2EL5o6AT3kMbgnmtlbyq9c3Fp4FZ2n8ETU/YAPca9KTzMblKWxRNLJQCGRfVSS9R/gAtzjNxL39D5jZoMykFcov7cAbgg5DzeAfYx7M1fFcwk6mycmzzGozH91gVCe6wY9zOw7M7sBV55eBz4xs52BzczsKTxhYlH8hgbkX8ao0o38Qkm34a70dczsZNwieQNuDazJDWZjSedL2hi3PswNXIQr5EcmhfxG4Gszq7Hl5y/YTdLXyerwupmtj4cHvISHa7yBu5OfyNrirRSigLuIW+Hf60V4gug9ZjZG0tF4ib6DZ72nGsuv6rc/Gm9SsbB56M36+PHNtbOheWLgRZLOkpdGuwF3Z5+cXKl34rGI82UksiPwqaSlzEuGXYcr55+n39oXeE31BzKSVy3SgnF6WkiegrvTOwD3yd36t+PhJV+b2e+F8IfaXAeqOA8m4OEaS5jZVPMcg/8Cd6RrVI1kFV1LmsurSHwDzIsn6N2TPD+XA6+YWS4eliR/LnyR3DwpN88BJ+JhYR+Y2cW41bMxnjxaK2xmiMqR+H3/9qQk7wh0kfSYPH7/jdrKquZ4PsO9OmtK+gfwOO5ZeQ04PN1zuuF5FpXnUCuqWOS9gIel7Q3cZx7vOxwPEZqQhcxKPAh8JM9naIZbmV8FLjKzg3DDQWahE0Xf2/7ArebJj//B7+HzmNmBadvOWckMSoiZxV+Z/vCL6QJ4/OalRdvPxi8ia6bnffEfXI/0vFEJx9gdGIZbIB/Bb6KF147FE3sKzytqsP9XgD+A9dLzBfEamDfiFoiLc57fMcDvwLXp+dz4hXUwsHwJvt/rgaVxBXpX/OK6T3ptD2ChnOWfjLsp++HdBA/FrTKXAm8BfXOU3QN4vOj5Ebj1Z7uibavgSXsbZSCvCdA1PZ4PL/d4Pq4otUnPd8Nv6ofmfewrja1L0eML8aSxvmksG+EhO+dU+oxqKbNR+r8xbgXrkJ4fiNfaHQAcjVvLmtRCTkX6f6V0fj+JW96WxxcphUXBVSX6rpvjoRODcasfeFm0z/Aa2oOBo2qxfxWOTXpcgVcjehpfnLdNr7VKsrqWaN6Nih4fCkwHtiza1hgPFbwm53FsgnuWCve3nfBr/ZLpfN84Y3kVRY+XxK3tZ+A9BArbt03Xu8UzkLcoXk6SdNyfxRMyC69vgS8ejy+cI6U4/ln9rbBiTxs3cWpZ/4Bh5f4eotpGHUBSe9wCMMrMNknb9sPdWBeY2a2SOpvZZ6pUJ7QEYzscV2imAmfhSszP8jjsD2xmebXZrqyRHh+Mh6T0wZMNX0nbW1pOrttk4bOi59vgsXf/suSuk/Q6cKGZ3Zmx7JWB3uYhGUi6G8+ynihPkFoVV16GmtnpWcquYiy74lafF/GktNF4qEpH3Co41cxyqaldNIYncSViQzP7QZ60eCbwXzM7RR4b28LctV1bWZvgVu2DzOxuSb3wZMBGuDLxT1yhn9fMjq2tvNkY13LANXh1i9tTKMlUXKE4x8yelrfw7Yb//sZnILO44sD9wCS8Us6ruAK1LL54+wG42moZMiVpPtyCfQiwJr5AXD5ZI1sAzSzHKi5pDIX408b4Qup6PPzsQjy29mA8h2OUme1RCznNzcOg/lSdRtKWeKWSF/BF4xe1mM7sjqkw9wpS7po8MXcIvmg5IXk8djSzY9JnMr/XpDCZO/EF02LMDI+7AV+8f2dmR2Qor/g8b2yeV9EGr6gxDY9x/x6v8PKhmb1QS3kt8Ov3VbjnYincqzoKT6gfk777Xvgi6rHayCsHPXr2sqdfLG+1jTbNy19to+yrmIb8R6UVJx7j+TbetQ/cGvgVrmyVa0zb4e6u14BOads2uLtt7qo+MxuyDsIbYIArcT/hq/K1cWW2xtauv5BZsLYthCtLC6XnPXBF4TXcon5BTt/vGnjViPPS8zspsjzhlqo1gdVzPs590/nWKj3fAL+RnAOsUoLzrHDutMctQV8CK6Vty+FxkENykLtxkndk0XlwYTrfSmIBrGJMLXHL133AEUXbr8Ctziul8S2cts+2h+cvZJ8CXJYef46HK11YkFXLfRdb/BbHY31XxsPSlk/bt8jjd17FWAq/++ZpzvOn56fjhov1cAvxPDW9nqX9zYd7DZvjFtUn8JjaI/DFycrAv/EFYtsSnV8Fy38FHnd7Bx5TDG4lfQ/4T3peMKhl5t0s+u6b4gmJa6Tna+HJoSfgJSLz/A4Owu9jhzHzmn8OnrzXN4tzELfcK/2/JO7V6oQbB27DFwjLZPndluMvLM/+FzHPZaKwIpb0D83sILch7uJ5R9JKZvYIsKp5Y4iSYOkqIGmHFIt8P35BmMvMvkiJVqfgzVl+K/5MdZHTCA8FOVTSP83jTrfEV+mnA4Msg2znSnILCRytccWxH94CeoCZvYlf6FoCfczsyCxlF8l/CXdbLipPhPwNWE/SAEl74fP/1sxezlp+JVrji4dCot7juPLWGL/A54p5THV33PJ1Ae62f1rSrmb2Lp5pPzwLWcWxluZVUwbgDWCuNLPReMzrh3jHr7lmsZvcMPewPIwnLPaWJwo2wWPu9wWuxpPqRqX319oaKG9M0RxPADxR0q14qNR5ePzv2ZIWr8X+ZTO9S+fi5eg64wrE8Wb2drK4n44r1rlify7LJkvWezM7Cf9+zwF2stqXgrwZV5TmwRcL++FJr5vhoVBf4THXn1nGlVKqovg44Jb25niC8m2SdjNvyNIVmDd5Nw3+9H3VVn7hmtscP7cOxJNeK/DckrPwxXLmXrbC7z5Zu7fFDRU98XvOyubepVeAJTO61xyIz7EV7s1qAhyAn/c7k1q9p+dzMOXuL1g3UgYjbKMMFLnQFsQvGnsA95vZ1un1/XFX1vrmCUyZFqf/m7EJd+EfjDdoeQK3Al6MK1U/4hbBO2o6Jnnt0NHp8eG4BeIe81JYzYCW5i2qc0HSI7j15TE8zq2QMHOCmf2uoqYAGcosHPPeuOv6SDzubgAez34XbrVqYmZnZim7kvzG+O9+qqTVcCvjK5ZcpZIWM7Mvs5Y/izENBJoXFirJjXwP7kY+LiMZxW7b+ZO8r+VlEa/E3fZ7mtkESW2sBF3disZWqMTQBG/QMS0plIemtxyJl09rYxmFNFR2xReFTdxgZtumbUPw3Ib7M5C3Mx7vuYukg/DkqWPwReqhwOVm9u/ayqnmWPYAtjCzzdPvcDc8POkYvHPmd1aLMCVJV+Gegh1xy+MH+DxPA/6V/hceHvZeLaZSk7Ftijfm2Dw93xBXXP9rnvxdeF8uYYGSrsHDI6bjnrWL8XKEf0jqBIy3HML00u/8DuAKM7tHnoS+D35cnrWMGvEkY1A/POxubnwxpiSrA/CQmT0haVvz1udzLD169rZnyhy20bp547KHbUSd5zJgM2tc3oNfRC4BLpCXbdrazK6SNBK3PBU+k6viXKRkyMx+lHQtHv/3T/zmva+8Bi81tTgnOd3wMmBDzOxBM7tI0gTgckkL44mTmSrOVSj51+Gxdo/gbuOJuCt/uqSTs1acYcYxb46HpzxoZt9L+hduBdoJV2BHZi0XZlp/0tNBgEn6DlfY+wMXSnoMVyxyU5yrOA7v4jWcATCzp5IFdAdJF1gGFVaKFOfT8RjL5STdaGZXyEvkDQJelrRaiRXnwmJmZVx5GyPpAzO7LHmiDsdjhPeymZ3IsuoguCS+OB4G/M/MPpbURNJQPL+hoqaKs7xT31p4CMhk3OL2HkD6zqfgIRJ/4Em6JVGcEy8Dq6d5voQ3RBqHJ67VaqEg6Tg8AXIwPr/R+G/7ZLyKxWuS3sa/kxqXvqvBuIQriqsCi6QFzGAze0zSl3gllxfN7EnItrJG0W9vTzzZfZX0fBd8MdEp/Ra/yELmLJgfrwp1gryW/sfy2tZH4zkEmSjP6fr6H0nv4Yvys9P/5+PGsV2SUWaOVpwhZcPWDeNvWYmwjfKxJB7j+6CZvW1mG+BW3ZclLWNmL5s3qci1TFgBMzN5W9rnJC2ZLF23k1rUSjoLjxOc7c5ukhaQ1xdtjde4HAb0SxdVzGwwnok+zbyWdKYUXcRPk3R2ulF2BH4ys6HJCvQo3g420zbMBddhYns8hvzXNK5J+MLpZmCQpKaV3p8JNtN9fj3eEvgR3OJdYV66ai88oSU3d2Klm2m7dK49iCuz/5Z3WlwRtwT2yUJxLpK9Mx5rvgc+/4sk3WFm081sfzx5Lbe6wlVhM9siX4UnC/6A3+BvM3fnD8QT9T4u+kwWinNTvKbxNDx3YYC8ZNlWeCzuF7jltKbcjldSWA53Wz8LdJcnQGNm1wNHm9kR6XFu6M+lNvvj5c+ux63shyeLay9qWZ5MXp+6H74QPBdXkLvhv/XOwL7yEozrATdZSibMExWVIDSvUz4QP9eWAnaS1NbM3seV2iezll/0W98MLwHXVjPrWv8bt8LvksaTGapUejFd27fHr3nnS+prHppzGh5iUVt5feXe0gJr4JbmT3AvS18zuxpfCL9aW3lB3SHCNkpEZauRPCt3MH6DfDptWxlfqS6El277vATj+lOVDHlHrbXx7leFcd0LvGpm59dg/+fhMX9tccvfkXiCzmZ4hY0pwC/AYuY1RnMh3bz2xRWlX+V1ZS/FLRML42Ezsz2/v5D3/6yESXE5A08aOtOKMq2VQ6hIJdntcSvYMXiM5xtmdqGkZYCxWSqrfzOOY/FY9xZ4yMzxuBI9HS8Xd2u62WQlby7cuv4i7lLvhS8cRuGK9EqWQ9fKvxhP8SJiF9yA8SIei7sbniTYGG/f/EN6X2au9KTATDavrrAQ7lZeAFdyH7RaxLpKGoyHnwwo2rYhnhTaBa/ocbmZ/VqbOVRzLIWckhXxGOS9LeURFC0krsPDeGqzWCjIK66w0RT3JnXC2y7vDPyP1AymtrKqMZaCV6Mz/pv/CPjYvMJMoaveWHwR8U36TGZhgUXffR/8GrsBvojYGy8LeryZjZc0v2VQOaay3PT4CtxIsQiwS/o+DsS7Ct6YlcdD0rb4YnQJ/Dz/d5LxHX7cV8fvnddkIa8usGLP3vbMS+UN21igWfnDNsLyXGIk9UgKy3T8h3aZvKPeRnic1LF4OEeLv9hNVmOZ4c6XdLKkBczsaNzddJGkgyVtjsfiXlP4zGzs/3C8Tu9+eMLGgXhS1L/M7Fb8ojMJz/g+dFb7qS1pzP1whb07gJlNwV329+DttzNTnBPzFsnvn1ykc5nHFt8EHFCwxqXxZK44F6zY8sTP9nhy2FDgZzO7ML3tOvxmmjtJkeqHW7q/xz0ZZmab4VaozbNQnCUtJw+RAdgdry08Bs+qH5yU5WuA90upOMOfLHJL4i7je3EX8o3mYTv/xX8T7Ys+k5XivDBuZe2fvFujcffyKDxEq8adLOUxxF0qKc7/xBdqR+HhEa2AQ/LwrsxiTI3wZNDzgFclbSEPC9pbnqz6bhaKM8zo0Fm4pk42b3g1Er8O3I+XHMxdcU5jmSYPsbuVmWUoB0o6Bz/nHsC9798XfSYzK1pSnDviieVnm9n3+HdxCa7Q3pXOxQlZySzIBZB0Cn4uX4Inv74vaXHzMqSDyTBsxjwMow9uaX4WLzv6Rbq/3As8g4cwBfWMUJ5LQLqgmrzO7CP4zfJOPBawP24N2wr/sX2K/+BLEeNccOcfgFuEh0pazsxuxLOEd8Qbd5xoXtu52hYwSTsC/c1sreSG/sHM/otfaPaRdLB56+Nj8YStTC2fkq6SdIykLdOYz8erOpyclEnMbKSZDTHvKpal7PlI4S9Jzm24tX2kpBXSjfU6PLa3b5ayi0nnXBd80WK49etTYLiklZLl7SPLKGmmKiottirwSgM74g0K9pPUSB6+U5HhOdAZuF2eNzDZzMaZ2S/4762vpBNx6/eAv9pJXkhaE1diWiWl61OgqbzO7j/xGNn3ZmehWg2ZFebVOvbCK5ycLWnt5MI+F/eE1Kb19g/MbO1e8Kztmf6uxkMYXsHDFnK7thUU87Qom4bHOXfHk3J74Yuo7sAXZnZJ1vKTRbswhrvxxerbVsKazold8ON8N275PRlYAa/hPQw4yzxZL7OFTKV9tcTDV06RhwFOwpsdXQPcZWaj8jgP5AmBXfHz7kTc03cPfu39p5ndambP/9U+Zhczexu/5kzGY8sL28fjibdPZCkvqBtE2EaJSG7zQ/DY2rdwBaI/fhF7PL2nOV7Z4mozu6lE4xqIl4o6DVeytgMOMLOH5JUZ5p1dxTntdxN8MbCQmY1LikBj82oWu+O1PnNRXiQVrD3zAF/j7tML0st/4NbIR8xjrfOQ/wDwnJldImlZ89hC5Mk6F+Alse6T1MXMPs1B/jn4DeNdvFrK52a2a3ptD9yFvgze5vmwrOVXMZ758BJZrfFFxI9m1i29dgTwD0uVHmopZy28ucxkScPxcIQjCouDZAntg8//UjN7vbYyazHW0/Dfw/FpXAfhi4u3rKj6QS1lFNznW+Ad9JoB+5lXWjkQD2P5b0bW/gXwc+1yM7s5bSs0dmqPn/eHmtmPtZVVzfFcgCcFv4LX9v7BzB6RtAheFnALy7cNeEmqI81KnqQOuDJ3Dh6ycZE8hK47HjYxImP5xY2v2uL5O+3wBWovvAX2C1WNNQPZC+DK+kQzGytvgtIZr9XfJ90PngXeMbO9spJbxTjmwis4fZ6nnHKzYs/e9uzL5Q3bmH/eCNuo10jaRNJCyX14O55Q8k2yghWaEZwpad+ijx1RQsW5Md4a+i4z+9TMDsfduA9I2t3M/rAUTjA7inN6/3+B3sCHkjZPny+4yD9L8ufKw4WbrByDcYXtENx9txwef7cF7jo8JF1kM0VerqoHMEzS+cB2knqlG8YV+CLlHkk75KQ4H4onqQwzs8m4pWczeSdHklfhNHyRlFknryrGUXxct8GV5i+TzLbyutbH4ck8WYXstANGJ+vT7vix31nSyQDmMfwvAbuWQ3GW1DotJMDrHXeR1C6Naw/cU3Nyem+tfhdF3q4u+G/6ebxJRbEL+248lKLWJKX4HGBNeSw3RcrpDcCbpVKcEyPx3/vWZvbvpDh3xUMWLshTcYb8qyPNCknLS1oab8TxEx7b/FtS7JYFzjCzEVlbnIsU5+vwpMlHce/Ojbi39RR54m6m301aEFyHJ10Pl7SLedWc0cAXklbHG6M8kLdCa2a/454j5OUeg3pMlKrLCXk862Z4HOc0SQPwC8nJkvYwr6TxGN7j/guYETeXa3OM4lV/ctu9gyewPZK2DZK0KnBWugHX2Dpr3ghhKVyR7GpmZ6eX9gVGp4tNLpjZXfIqAkeYJ0fdgWc7f4tbok+wjEuT6c/lqlbHG6AsjDdFaYa35X0Nv7k8nqXsJH9HPOmsV3q+G66wbgmcK2lBMzsuz++9QPEN0swGJ6Vxz2SN/wFX3j/CLaGZlCY0T4j6AG+uspuZ/UfSz8D+8oY0i+NhAyVrbw8zFOFCDOqn8mTV4/GKF+dLOtC8IsKM99dWwTAPH5gXd9/faWb3AvemhcRrkvYsWIgz5EHcAri2vBX1i3jy8VibGWOfC5qZBDgP8JuZ3ZLOhWvTtecwfHE1yMxuyXMs5SAtlPoBl+PXmb5pwXwHXpJyY9zT9FLh/VnKBkjy5jWz/pK+x7uIjpJ0N14OdFRWMpO8Qk7NNrghsDdwvaRuZnaMpFH49XBlYN0sZc+K9F3sJallKeQF5SPCNnJA3oXvHLxG6zuSVsE7mE3BYx0LjRnGFH0ml+L0lcZVyMJuhd9ImuHK+0O4W+thPN76Z+BNoLNlEBeYrB6P4jFvX+Otp7es7X7/Ql7BXd0Tj/FsDCxrZn3S6wtkbQWTVy64E3eF/4FbPZvix7wLbu1uiYcN9DOzj7KUn8awCXCvmc0taXk8vnF7M/tIHn89CJhkZltkLXsW4zkIt/rsi3s4lgRuNrM3VKnKS8Zy2+AtoK8wryiyMH4eyMwG5iFzFuOoXMmmNe4NOQAvZ9UIPx82NbO3sr4GJKvbyXiFg5PN7I20vT8eC9o1eScyIy0UuuAenrmAUaVSVpPH4WQ8v+Fjm1nT+iE8oWvzvK+xpUapyoc8NOYIvGLKy/KGQ7fhoYFD8aoihcoamZxn8qoiffGOqG/KE/WexxX1pmZ2UDombc0sk26hRbJ3BI40s4KhoHEyBi2J38sG4saqefDGUxOylN+QWbFnb3vu5aFlHUOreRuVPWwjlOeMkdduHQ2sY2YvSloDtwZsZzObHVyFVx3oaaUrEVZQKAulsT7EXUzX4m6v43Alsz3eGvkG/MZ3Ulby8Tqj2wCLm4eu5I6ks/HybN3Mk7DyVNoql6vaGa8k8iOuRH8JTDGzZ/KQn+Qujx/fKXiHypGS5jKPNW+Dl2Z7NEf5xbGPq+OVW27CFxL74Y0pdjNvh54bacH2OJ4Que/fvT8H+cXfw1l4Qt20wmJU3vracGWvM7B2ba2BVVms5fW0T8QT5f4LjEjnQtOsFedyIM+fmIgvTh/Dr2XN8ZC4t9JcTwc6Wk45FuUizetLPBTlLrySyo7AyLRw2AZYxcyOKvpMliXp7sdD8F5NXp/t8NKHv9rMToZ34feRTEPE9Pc5NX3MbM8sZQZOKM9OxDxnjHlZnsvwLnqFRLxz7c/NDvYHtimV4pxkFi6YZ+PZ33vi2dfr4mWUDjOzg/H6pEcA7bNSnAvyzWw/Sqg4J7nH4TfSnkmJzEVxTrIql6u6Drfgt8frjb6Qp+KcxvA2Hp4whZT5nW4ojc2rTuSpOB8ItJe0hKRCXd378VCWK/H463aUoDSeeWjKOkATSfek8ZW8L5Y8cW1RvNvcMZLulTS3mX1uXtJqT7xZ0pK1lVXkPj9U0kWSjsGtv0fgC+PdgX/Kcx2m1FZeuZF0JJ5ouTEenrJOUpDfx6+7O8rrii+Pd1SsN0jaH29G86h5+Nmu+ALpIDxECPzY91LqDAvZhWvIa3r/YN7sptA1b258EfOkpH3TorFN1oozVCunZrpyyqlp8AhU5r+6QCjPOWAza/l+iiuqdxZek3SupAGWEpaUYTmqqqhi/5/gsaaY2QQzWxtP4Fq86D0v4vHamVNKxbmI13Are0lq+tqfy1UVan2+YSXoLJZkjsc7d20v6Ya0Lde5S7oS76A2CQ9J2FHSzbjLvD2wpZmdCWxlZpfmOZYCacG2Jx6yUZIkruRxKJwDvYElzJv/9McXEL/jykXn9P4l8DCHTOp8y5P1tsKt7tvhIQxr4THW3+FxqH+U4rvIk+S23zFZnw7Eq2qsA2Bmp+J5Bd3w6hJn1AcrewF5EugAYDMzGy2vbgHu4Vwfb7l9Fv57PNPMfstSiVTVNb3XBU7AF+zd8d4A4PX9cyEZCpYCLpF0XFEoyr54RZ/f5/TzPKi7RMJgTpjZEHky3vOSnjYv/fYv3G1+TNH7covBk1f5WE3Sm3hsZcEysL2kB2xmxnlbvKj850nJqldtRM3sHkmP5fldVyHTCi5SK0Odz2Rt/ieeQDPEzLbLS5a8WswyZrZO2vSSpE2Bi3DPxgQ8UfZ5M/tfXuOYFVba1tvHyePfDzKzYZIOlLQVboE7Xd4o5kLcUjgQD+nZ0My+m/Uu/xp5hZUr8MoZfXCFZVtcWX4Ib1KyuJUw3rsE/AJ0kyfBfiPpIeAReXz7FHzed+JVPnLzNpWJCjz8aWqKKT4aXzi8DryBN0XZCTjMzJ7OKsa5iKpqeu+BL2I6416A/fFuk7kqr2b2g7y6yKPy8nxf40mLR+cpNwjC8pwjZvYeXk/3AkkP4hahnWCGYps3hdbTd+ONCp42s9txy/Idks6X9ATwupWx5m0pKJXVt5LMslo9kuK+F94WNxeSRWtBPO4SSeukOPOheLLOQrjF9SfcjVxvSV6eh/AY3GskLWrexW888F4Kl/gHcElBkTWzH82bl9RU5rJ4CNg0XBE/B//etzWzjXEFcgpe87reUOS2HylvenQWXmVlGF7f/BQ8zrm+Kc7g3sOX8fKnQ4GpuLL6L7wR0DHA6cCVkrbIwWjwE15mcTeY4U08ybzk4kN4+EirUl3/UojWurhx6Fh8YRrkhOrAX10gLM85k1bG3fCLyglm9p2K2mLnRbJ6jpM0DnejjcBv3I+b2amSnsETa/5nM5tI5F7xIyg9eVpek4X9JeB4eVnCtfFEnrNx6+CmKfa5R15jqCuk384wST/i8cWXyevQvo0n5N6Mfw8rQWa/t2lAB0nzmddk/zRZX5ukZMFl8eNwXi3l1DnMS2EujSvNv5nZ0oXXJF1Xn0I1ikm/uYvxTnoLmtk9hdfk1SZWNW+K8hM5xLab2Y/yRkwbSppuXke7uKb301bCfJ40JgP2k3RUmUIDgwZGVNsoMaVQUDWz5mkTvDzZm7ibbUPgXTO7VNIKuFvt7VKNK6ifyCtb9MPLkz0KPGFm45MSdwsen/lFfT2/qvrtpCSt/fDWyGfjoVBtgN/TgrpWVV8k9cW7KU6Sd1NcP+23UFVnIB668TPuvq9XoVjFpOvc48CH5snYDRJJR+GlMtctwT2mCW7hXR0PFyrU9P7BzPbIU3ZQXnr26m3Pl7naxnxNy19tIyzPJaYUCkSRjMPxZiRXymvMNgHWkpcY6kpR4fj6qtgE+ZPcpg+kv2JOAd6znLu5lZOkrBaXpJuCh0odlxap3+Pl6B6z1Cgkfaa2nqe2wCh5Q6MXgXkkNcMtzlOBM/FE1Q/MKwDVW8zbjRfi++8ys+3LPaZSIm9PvT5eYnSTZDhpbDkmCafvfDBe13kLvBTlPVYPG9AEVVBXYifKSCjP9RRJ6+HK8ThJ8yar1EPAx0BP4CIz+zoszkHWSFoQj7tc2Mw2Kvd4cqYCmCbvdtYdD8+4D3hG0j5mdoek8cCM31gWsaA2s5via3hlg6VwC+BUPL55XjxM7IXaypoTSN9pQ+3sNh5XYl8ys2+TVyP3ykLpO/8EuCBvWUFQ1wjluf7yCh5/timws6T/mNm3eOzzCPiz1SwIMuR3PEzhhHIPJE8ktTKzCZI64slr2+NW5ofxMpV3SjrJzG7NQ36K+e2EJ41Nw8sxCugIjCtxlZE6QQOdswHfFj2vj0mSQR1CYXoO5bm+UBTnvDpeN7YN3qzlV9ylN5+kR83s3cJnyl0NIqifmDdtuKPc48gTSfcBzSQdZt658khgCbz1/BopTKofXkIsF+UZvFa7pOWAJ4ALzewAXHEPgiAIciJK1dUDkptueipbdT2uOPfDk2iGpm3L4h0FgyCoPU2BxYDz5R3OvsGTcj+TNz7pj3t4BkC+zZDMbCqupM8taUhecoIgCAInLM/1gCI33X7AnWZ2EXCRpHNxi9QqwMV4DdAgCGrPyXgziNeAnSR1SgmCPwGn4iXpCslbuYdHNfCY3yAISkhdaZFdTkJ5noORtAPeevdr4DngJbwpCwBmdoy8dWt7M3snfUYRrhEEs08qvfdLiqt9F29Gsg7egGg/SU3M7OBUxqtlqrNeq5J0s0tDjPkNgiAoNRG2MYciaR287e7H+E18D6AdsJmk7SW1kbQMXoezWeFzoTgHweyTWv9+APxX3mK7O3Aw8Dlenu48oLuk24BGeAvjSN4KgqDeER0Gw/I8RyKpHfAYsKaZvSZpcTws42XgdfxGvhGwNHBOSmgKi3MQ1JDUGfRivILIP4DJwBm44tzczI5NXdcWNbPMu7oFQRAEdYdQnudAzOx7SZcD50ja0Mw+l2RATzMbLGlrPKGpqZl9XN7RBkH9wMxOkjQUuA5YEw+T2hpolMrWvUfKK4jFahAEQf0llOc5FDM7QtJ2wIeSngQmm9ng9NoPxe+NG3kQZIOZ/UfS+riXZ0czO7yqbm7xewuCoN5SV2Inykgoz3MwZjZE0rvAG8CuMLMkVnF2f9zIgyA7zGykpK7AK5JuN7OB5R5TEARBUDpCeZ7DMbN3U4ez/0lazsxOKveYgqC+kyppdAMel9TZzD4r95iCIAhKQXQYjGob9YIUptENWENS53KPJwgaAmb2u5mtbWafSVH5NAiCoKEQynM9IW7kQVA+IjQqCIKgbiBpQ0kfSvpE0rF5yIiwjXpI3MiDIAiCIMgaUbc7DEpqBAwC1gNGAa9LeihVQ8qMsDwHQRAEQRAE9YGVgU/M7DMz+x24E9g8ayFheQ6CIAiCIAj+lhEjhj/etInalHkY80gaVvT8WjO7Nj1eCPi66LVRwCpZDyCU5yAIgiAIguBvMbMNyz2Gv6GqoJLMQ1kjbCMIggaDpGmS3pT0jqS7Jc1bi33dJGmb9Pj6VPt5Vu/tK2m1Gsj4Qvr/Vp5Zba/0nomzKWugpCNnd4xBEAR1iFHAIkXPFwa+yVpIKM9BEDQkJptZDzPrBvwO7Ff8Yko2mW3MbMDfJKT0BWZbeQ6CIAhmi9eBJSUtLmkuYAfgoayFhPIcBEFD5UVgiWQVflbS7cDbkhpJOl/S65JGStoXvM29pCskvSfpYaBdYUeSnpPUOz3eUNIISW9JelpSJ1xJPyxZvdeU1FbSvUnG65JWT59tLekJSW9IuoZqNMKV9ICk4ZLelbRPpdcuTGN5WlLbtK2LpMfSZ16UtEwm32YQBEGZMbM/gIOAx4H3gSFm9m7WciLmOQiCBoekxsBGwGNp08pANzP7PCmgP5nZSpLmBl6W9ASwIrA0sDzQHngPGFxpv22B64A+aV8LmNmPkq4GJprZBel9twMXm9lLkhbFL/TLAqcAL5nZaZI2Bv6kDM+CPZOMpnhZpntT46RmwAgzO0LSyWnfBwHXAvuZ2ceSVgGuBNapwdcYBEFQ5zCzR4BH8pQRynMQBA2JppLeTI9fBG7AwymGmtnnafv6QPdCPDPQElgS6APcYWbTgG8kPVPF/v8BvFDYl5n9OItxrAt0LepnNJ+kFknGVumzD0saX405HSJpy/R4kTTWH4DpwF1p+63AfZKap/neXSR77mrICIIgCBKhPAdB0JCYbGY9ijckJfLX4k3AwWb2eKX39ePvs7ZVjfeAh8ytamaTqxhLtTPDJfXFFfFVzWySpOeAeWbxdktyJ1T+DoIgCILqEzHPQRAEf+ZxYH9JTQAkLSWpGfACsEOKie4IrF3FZ/8HrCVp8fTZBdL2X4AWRe97Ag+hIL2vR3r4AtA/bdsImP9vxtoSGJ8U52Vwy3eBCqBgPd8JDwf5Gfhc0rZJhiSt8DcygiAIgiJCeQ6CIPgz1+PxzCMkvQNcg3vp7gc+Bt4GrgKer/xBMxuLxynfJ+ktZoZN/AfYspAwCBwC9E4Jie8xs+rHqUAfSSPw8JGv/masjwGNJY0ETgdeLXrtV2A5ScPxmObT0vb+wF5pfO+SQ/etIAiC+ozMMq8dHQRBEARBEAT1krA8B0EQBEEQBEE1CeU5CIIgCIIgCKpJKM9BEARBEARBUE1CeQ6CIAiCIAiCahLKcxAEQRAEQRBUk1CegyAIgiAIgqCahPIcBEEQBEEQBNUklOcgCIIgCIIgqCb/B6bXuuRTNWUTAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the confusion matrix\n",
    "cm = confusion_matrix(y_test, preds)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=languages)\n",
    "\n",
    "disp.plot(cmap=plt.cm.Blues)\n",
    "disp.figure_.set_size_inches(12,12)\n",
    "plt.xticks(rotation=50)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.8.8"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "40d3a090f54c6569ab1632332b64b2c03c39dcf918b08424e98f38b5ae0af88f"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}