FINAL (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification of News Articles \n",
    "\n",
    "It is a notebook for multiclass classification of News articles which are having classes numbered 1 to 4, where 1 is \"World News\", 2 is \"Sports News\", 3 is \"Business News\" and 4 is \"Science-Technology News\".\n",
    "\n",
    "I have used various models for classification of the News articles. The classification algorithms used are:-\n",
    "\n",
    "1. Multinomial Naive Bayes\n",
    "2. Decision Tree \n",
    "3. Gaussian Naive Bayes\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import nltk\n",
    "import string as s\n",
    "import re\n",
    "from nltk.corpus import stopwords\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from wordcloud import WordCloud\n",
    "\n",
    "from sklearn.feature_extraction.text  import TfidfVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.metrics  import f1_score,accuracy_score\n",
    "from sklearn.metrics import  confusion_matrix\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from lightgbm import LGBMClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "train_data=pd.read_csv(\"train.csv\",header=0,names=['classid','title','desc'])\n",
    "test_data=pd.read_csv(\"test.csv\",header=0,names=['classid','title','desc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>classid</th>\n",
       "      <th>title</th>\n",
       "      <th>desc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>Wall St. Bears Claw Back Into the Black (Reuters)</td>\n",
       "      <td>Reuters - Short-sellers, Wall Street's dwindli...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>Carlyle Looks Toward Commercial Aerospace (Reu...</td>\n",
       "      <td>Reuters - Private investment firm Carlyle Grou...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Oil and Economy Cloud Stocks' Outlook (Reuters)</td>\n",
       "      <td>Reuters - Soaring crude prices plus worries\\ab...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Iraq Halts Oil Exports from Main Southern Pipe...</td>\n",
       "      <td>Reuters - Authorities have halted oil export\\f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Oil prices soar to all-time record, posing new...</td>\n",
       "      <td>AFP - Tearaway world oil prices, toppling reco...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   classid                                              title  \\\n",
       "0        3  Wall St. Bears Claw Back Into the Black (Reuters)   \n",
       "1        3  Carlyle Looks Toward Commercial Aerospace (Reu...   \n",
       "2        3    Oil and Economy Cloud Stocks' Outlook (Reuters)   \n",
       "3        3  Iraq Halts Oil Exports from Main Southern Pipe...   \n",
       "4        3  Oil prices soar to all-time record, posing new...   \n",
       "\n",
       "                                                desc  \n",
       "0  Reuters - Short-sellers, Wall Street's dwindli...  \n",
       "1  Reuters - Private investment firm Carlyle Grou...  \n",
       "2  Reuters - Soaring crude prices plus worries\\ab...  \n",
       "3  Reuters - Authorities have halted oil export\\f...  \n",
       "4  AFP - Tearaway world oil prices, toppling reco...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>classid</th>\n",
       "      <th>title</th>\n",
       "      <th>desc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>Fears for T N pension after talks</td>\n",
       "      <td>Unions representing workers at Turner   Newall...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>The Race is On: Second Private Team Sets Launc...</td>\n",
       "      <td>SPACE.com - TORONTO, Canada -- A second\\team o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>Ky. Company Wins Grant to Study Peptides (AP)</td>\n",
       "      <td>AP - A company founded by a chemistry research...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Prediction Unit Helps Forecast Wildfires (AP)</td>\n",
       "      <td>AP - It's barely dawn when Mike Fitzpatrick st...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Calif. Aims to Limit Farm-Related Smog (AP)</td>\n",
       "      <td>AP - Southern California's smog-fighting agenc...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   classid                                              title  \\\n",
       "0        3                  Fears for T N pension after talks   \n",
       "1        4  The Race is On: Second Private Team Sets Launc...   \n",
       "2        4      Ky. Company Wins Grant to Study Peptides (AP)   \n",
       "3        4      Prediction Unit Helps Forecast Wildfires (AP)   \n",
       "4        4        Calif. Aims to Limit Farm-Related Smog (AP)   \n",
       "\n",
       "                                                desc  \n",
       "0  Unions representing workers at Turner   Newall...  \n",
       "1  SPACE.com - TORONTO, Canada -- A second\\team o...  \n",
       "2  AP - A company founded by a chemistry research...  \n",
       "3  AP - It's barely dawn when Mike Fitzpatrick st...  \n",
       "4  AP - Southern California's smog-fighting agenc...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting Data into Input and Label "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x=train_data.desc[:60000]\n",
    "test_x=test_data.desc\n",
    "train_y=train_data.classid[:60000]\n",
    "test_y=test_data.classid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Representation of the distribution of data used for training i.e. 50% of the entire training dataset for the different classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\G5\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=train_data[:60000]\n",
    "sns.countplot(df.classid);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing of Data\n",
    "\n",
    "The data is preprocessed, in NLP it is also known as text normalization. Some of the most common methods of text normalization are \n",
    "* Tokenization\n",
    "* Lemmatization\n",
    "* Stemming\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tokenization of Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tokenization(text):\n",
    "    lst=text.split()\n",
    "    return lst\n",
    "train_x=train_x.apply(tokenization)\n",
    "test_x=test_x.apply(tokenization)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conversion of Data to Lowercase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lowercasing(lst):\n",
    "    new_lst=[]\n",
    "    for  i in  lst:\n",
    "        i=i.lower()\n",
    "        new_lst.append(i) \n",
    "    return new_lst\n",
    "train_x=train_x.apply(lowercasing)\n",
    "test_x=test_x.apply(lowercasing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removal of Punctuation Symbols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_punctuations(lst):\n",
    "    new_lst=[]\n",
    "    for i in lst:\n",
    "        for  j in  s.punctuation:\n",
    "            i=i.replace(j,'')\n",
    "        new_lst.append(i)\n",
    "    return new_lst\n",
    "train_x=train_x.apply(remove_punctuations) \n",
    "test_x=test_x.apply(remove_punctuations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removal of Numbers(digits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_numbers(lst):\n",
    "    nodig_lst=[]\n",
    "    new_lst=[]\n",
    "\n",
    "    for i in  lst:\n",
    "        for j in  s.digits:\n",
    "            i=i.replace(j,'')\n",
    "        nodig_lst.append(i)\n",
    "    for i in  nodig_lst:\n",
    "        if  i!='':\n",
    "            new_lst.append(i)\n",
    "    return new_lst\n",
    "train_x=train_x.apply(remove_numbers)\n",
    "test_x=test_x.apply(remove_numbers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removal of Stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_stopwords(lst):\n",
    "    stop=stopwords.words('english')\n",
    "    new_lst=[]\n",
    "    for i in lst:\n",
    "        if i not in stop:\n",
    "            new_lst.append(i)\n",
    "    return new_lst\n",
    "\n",
    "train_x=train_x.apply(remove_stopwords)\n",
    "test_x=test_x.apply(remove_stopwords)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lemmatization of Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "lemmatizer=nltk.stem.WordNetLemmatizer()\n",
    "def lemmatzation(lst):\n",
    "    new_lst=[]\n",
    "    for i in lst:\n",
    "        i=lemmatizer.lemmatize(i)\n",
    "        new_lst.append(i)\n",
    "    return new_lst\n",
    "train_x=train_x.apply(lemmatzation)\n",
    "test_x=test_x.apply(lemmatzation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x=train_x.apply(lambda x: ''.join(i+' ' for i in x))\n",
    "test_x=test_x.apply(lambda x: ''.join(i+' '  for i in x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Extraction\n",
    " \n",
    " Features are extracted from the dataset and TF-IDF(Term Frequency - Inverse Document Frequency) is used for this purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tfidf=TfidfVectorizer(max_features=10000,min_df=6)\n",
    "train_1=tfidf.fit_transform(train_x)\n",
    "test_1=tfidf.transform(test_x)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation of Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Function for evaluation of model**\n",
    "\n",
    "This function finds the F1-score and Accuracy of the trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_model(y,y_pred):\n",
    "    print(\"Accuracy of the model\")\n",
    "    print(accuracy_score(y,y_pred))\n",
    "    print(\"Accuracy of the model in percentage\")\n",
    "    print(round(accuracy_score(y,y_pred)*100,3),\"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Function for Displaying the Confusion Matrix**\n",
    "\n",
    "This function displays the confusion matrix of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def confusion_mat(color):\n",
    "    cof=confusion_matrix(test_y, pred)\n",
    "    cof=pd.DataFrame(cof, index=[i for i in range(1,5)], columns=[i for i in range(1,5)])\n",
    "    sns.set(font_scale=1.5)\n",
    "    plt.figure(figsize=(8,8));\n",
    "\n",
    "    sns.heatmap(cof, cmap=color,linewidths=1, annot=True,square=True, fmt='d', cbar=False,xticklabels=['World','Sports','Business','Science'],yticklabels=['World','Sports','Business','Science']);\n",
    "    plt.xlabel(\"Predicted Classes\");\n",
    "    plt.ylabel(\"Actual Classes\");\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first 20 actual labels\n",
      "[3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]\n",
      "first 20 predicted labels\n",
      "[3, 4, 4, 4, 1, 4, 4, 4, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3]\n"
     ]
    }
   ],
   "source": [
    "DT=DecisionTreeClassifier()\n",
    "DT.fit(train_1,train_y)\n",
    "pred=DT.predict(test_1)\n",
    "\n",
    "print(\"first 20 actual labels\")\n",
    "print(test_y.tolist()[:20])\n",
    "print(\"first 20 predicted labels\")\n",
    "print(pred.tolist()[:20])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation of Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the model\n",
      "0.7748684210526315\n",
      "Accuracy of the model in percentage\n",
      "77.487 %\n"
     ]
    }
   ],
   "source": [
    "eval_model(test_y,pred)\n",
    "    \n",
    "b=round(accuracy_score(test_y,pred)*100,3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_mat('Blues')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Countvec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "vectorizer=CountVectorizer(max_features=10000,min_df=6)\n",
    "train_2=vectorizer.fit_transform(train_x)\n",
    "test_2=vectorizer.transform(test_x)\n",
    "x=train_2.toarray()\n",
    "y=test_2.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DT=DecisionTreeClassifier()\n",
    "DT.fit(x,train_y)\n",
    "pred=DT.predict(y)\n",
    "\n",
    "print(\"first 20 actual labels\")\n",
    "print(test_y.tolist()[:20])\n",
    "print(\"first 20 predicted labels\")\n",
    "print(pred.tolist()[:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the model\n",
      "0.7842105263157895\n",
      "Accuracy of the model in percentage\n",
      "78.421 %\n"
     ]
    }
   ],
   "source": [
    "eval_model(test_y,pred)   \n",
    "c=round(accuracy_score(test_y,pred)*100,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_mat('YlGnBu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Binary vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "vectorizer=CountVectorizer(max_features=1000,min_df=6,binary=True)\n",
    "train_3=vectorizer.fit_transform(train_x)\n",
    "test_3=vectorizer.transform(test_x)\n",
    "train_arr=train_3.toarray()\n",
    "test_arr=test_3.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first 20 actual labels\n",
      "[3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]\n",
      "first 20 predicted labels\n",
      "[3, 4, 4, 2, 4, 4, 3, 3, 1, 4, 4, 3, 4, 4, 4, 4, 4, 4, 4, 3]\n"
     ]
    }
   ],
   "source": [
    "DT=DecisionTreeClassifier()\n",
    "DT.fit(train_arr,train_y)\n",
    "pred=DT.predict(test_arr)\n",
    "\n",
    "print(\"first 20 actual labels\")\n",
    "print(test_y.tolist()[:20])\n",
    "print(\"first 20 predicted labels\")\n",
    "print(pred.tolist()[:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the model\n",
      "0.7627631578947368\n",
      "Accuracy of the model in percentage\n",
      "76.276 %\n"
     ]
    }
   ],
   "source": [
    "eval_model(test_y,pred)   \n",
    "d=round(accuracy_score(test_y,pred)*100,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe8AAAHvCAYAAAB9iVfNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAACDMUlEQVR4nOzddVgU6/vH8feyNIgCBtiKgV2oIBjYYnd3d9c5R0/Y3d2B3XnsxK5jdwdKSDfs7w++rmd/CwoeZFm9X9fldcHMs7P37Mh+Jp55RqFSqVQIIYQQQm8Y6LoAIYQQQiSPhLcQQgihZyS8hRBCCD0j4S2EEELoGQlvIYQQQs9IeAshhBB6xlDXBXxPdoVH67oEkUTedycRHXdd12WIJDIyKAVAVNwVHVciksrYwAmAiNjzOq5EJJWp0iXReXLkLYQQQugZCW8hhBBCz0h4CyGEEHpGwlsIIYTQMxLeQgghhJ6R8BZCCCH0jIS3EEIIoWckvIUQQgg9I+EthBBC6BkJbyGEEELPSHgLIYQQekbCWwghhNAzEt5CCCGEnpHwFkIIIfSMhLcQQgihZyS8hRBCCD0j4S2EEELoGQlvIYQQQs9IeAshhBB6RsJbCCGE0DMS3kIIIYSekfAWQggh9IyEtxBCCKFnJLyFEEIIPSPhLYQQQugZCW8hhBBCz0h4CyGEEHpGwlsIIYTQMxLeQgghhJ6R8BZCCCH0jIS3EEIIoWckvIUQQgg9I+EthBBC6BkJbyGEEELPSHgLIYQQekbCWwghhNAzEt5CCCGEnpHwFkIIIfSMhLcQQgihZyS8hRBCCD0j4S2EEELoGQlvIYQQQs9IeAshhBB6RsJbCCGE0DMS3kIIIYSeMdR1AT+r6X82Jm+ujDTptExj+sFNfShVPIdW+32HbtFt8Ab17zmzWfP7CA8qlMsLwNGT9/lj6gH8PoYm+z3Ft/t9zFJevHjH6rW/a80LDAyhft2hDBnamkaNq2jMi4uLY+WKvWzbcgwfn4/kym1P23a1adqsauoU/hP7Y8wyXrx4z6q1v2lMv3jhDgvmbePB/ZdYWppRs1Z5+g9sjrmFqUa7168/MH2KJ5cv3wOgcuVSDBvZFhsbq1Rbh5/Jn2NX8vLFe1asGa0xvU3LP7lz65lW++o1nZgxu5/699evfZgxdRNXLt0HoFKVEgwd0Urvt5eEtw60buJEu+blOHfpqda8/A6ZOXD0DvuP3NaY/vptgPpn6/TmbF/dHSMjJQuWn0JpaECfzpUoVNCOOi0XEh0dm6z3FN9m+7bjbN92HKeyhbTmRUfFMHTwHPz9AhN87V9/LGfb1uOULVeYDp08uHf3efyOwPN3DBnW9nuX/tPase0k27ed1Npmly7eoUfXSRQukodBQ1ri7e2P57q/uXPnKavXjcHAIP4kZcDHYLp2nEB0dAxdutYnNjaW1Sv38/DhSzZuHoeRsXylpqQd20+xY9tpnMoW1JiuUql49uQd7tVKU72Gk8a8rFlt1T8HBITQvdNkoqNj6dzNg9iYWNasOsijB6/w3Py7Xm8v/a1cDxkYKBjU051hfaslOD9nNmssLUw4dPwu2/feSHQ5PTu5YZ/FCvdGc3j01AeAazdfsXVFN1o0LI3ntstJfk+RfLGxcSxdvJOFC7YlOP/9e3+GDp7NjesPE5x/585Ttm09TsWKJZm/aARKZXwwZMliw9IlO6lb342CBXN9t/p/RvHbbBeLFuxIcP70qRuwt8/IqrVjMDU1BsDe3pYJ41bjdfYmFSuVBGDtmoO8f+/Pjt2TyeuQDYBixfPRo+skdu86TbMWcuYkJcTGxrFsyV4WL9iV4Pw3b3wJC4vAvWop6jWokOhy1q3+m/fvP7Jt13jyOmQFoFhxB3p2m8ae3Wdp2rzKd6g+dcg171RiYmzIkW39GdG/Btv23OCtt/YRWcF8WQB4+L9ATkyjOsU5d/mZOrgBzpx/wqOnH2jkUTxZ7ymSJzIyiuZNR7Fg/lbqN6hIliw2GvO9vP6hvscQHjx4Qdv2tRNcxskTVwHo2buJOrgBOnWpR1ycij27T3+/FfgJRUZG0aLpryycv536DdzInMVaa76NjRVNm7urgxtQH50/fPBKPe3ggfM4lS2kDm4AlwpFyZ3Hnr8PXvjOa/JziIyMolWz31k0fyf1GlTQ2l4ATx6/ASBP3qxfXNbfBy/iVNZRHdwAzhWKkDuPHX8fuJiyhacyCe9UYmJiSDpLE3oM2cCAX7YSGxun1aZgvswAPHryAQBzMyOtNumtTMmd05abd95ozbt19y0limRP1nuK5ImMjCY0JJzpMwcycXIfjfAFePrkDWXLFWb7zilUq142wWV8eP8RgAIFcmpMt7Q0x8Y2PffvPf8utf+sPm2zaTP7M2FyLwyVSo35JibGLF42ku49G2pMv3//BQD2/zsNGxgYyutXHyhcJI/WexQqnJs7t+WSVEqIjIwmJCScqTP6MH5SdwyV2jH1Kbw/hXJYWKRWm6DAUF6/8qFQkdxa8woVzs3dO89TtO7UJqfNU0lwSCQudWZ8MUAL5rcjOCSCP0fWpWGd4lhamPD8pR+T5hxm98GbANhnTg/Auw9BWq//4BuMVTpT0lmaEBwSmaT3FMljaWnG/r9nY2ioTHB+q1Y1ad/BAwBvb78E25iZmQAQGhqu0RkqLi6O4KBQfH3kDElKsrQ0Y9/fMxLdZv/f2zc+XLp0l+lTNpAvf3aqVY+/pvrhvT8AWRI4EsyUKQMhIeEEB4eRLp15yhX/E7K0NGPvwSlf3F6PH73GwsKU6VM2cujgJcLCIsieIxP9BjaljoczAO8/xO8kZ86svb0yZkyv99sr1cO7Zs2aKBSKJLc/dOjQd6wm9ahUKmJjVV9sUzBfZtJZmpI+nSn9R20hvZUZ3dpXYMmM1hgZKtm29zoWFvFf/OHhUVqvD4+IBsDczJjgkMgkvadIHgMDAwy+cL4qKR1gipfID+sOcvjwRdq2+3xq/fSp60RHxxAZpb1txbf72jb7t8CAEGpVHwTE72T98ltHTEziT6WHhkYAYGpqovW6T23CwyP1NgzSiqRsryeP3xIaGkFwcBjjJ3cnOCiMDesOM2rYYmJiYqnfwJWw/20vMzNjrdd/ujyiz9sr1cO7dOnS6vCOi4tj//79pEuXjsqVK5MpUyYCAgLw8vLC39+fli1bpnZ5OrV+6yWUBgas2vj52tmuA/9wcvcgxg6vw479NzAwiP/sVF/I5LgvzRQ6V6NGOfLkycrsmRsxMjKkgmtx7t97zvi/VmJlZaF1WlekIoWCaTP6ER0dg+f6w3TvMompM/pRs1Z5VKq4/zVJ/ODDIBkHJuLbNW1embi4OFq1qa6eVtujPE0b/sqsaZvxqOtCXNz/vge/sEn0eXulenhPnjxZ/fP06dMpXrw4K1aswMzMTD09KiqK3r17ExYWltrl6dTazZe0pkVExrBt73WG9a1OAYfMhITGX9sxM9W+Hv5pWmio9vUfkXYYGRuyaOkohgycxV9/LAfAzNyEgYNacfDAOZB9L51Jn96C2h4uANSoVZ7GDUYybYonNWuVx9wi/jsqIlL7zEjk/6b9/3vCxffRopV2r35TU2Pq1a/A4oW7efrkDRb/2xaR/zsj+W8REZ+2l5nWPH2h0w5rW7dupXv37hrBDWBsbEyHDh04cOCAjipLW3z94gdesTA34c27AAAyZ0qn1S5LJisCAsMJC9f+zyrSluzZM7Nl+yR27p7G+g1/cfL0Ytq1r8O7t75kz5FZ1+UJ4sOgcpVSeL/z4+PHYOzt4zuu+foEaLX1+RBAOitzzM0lvHXJxjZ+4JWwsEjs7OPvBElwe/l82l7al0D0hc57mwcGJtw5x9vbGxMT/f1gk8susxWn9gxiSG/tPcp8eTMB8PKNP0HBEbx45U/xwtq3SBQtZM8/d15/91rFf/PunS/bth7H1zeA/AVyULJUASwszHj50psPHz5SqnTBry9EpJinT99Sq9pANm04ojUvNDQChUKBsbEhVlYWZMueibt3tUf1unfvOUWK5E2Ncn96799/pHH9X1i8cLfWvGdP3wGQLVtG9fa6d/eFVrv7915QJIG7BvSJTsO7atWqTJ8+nXPnzmlMP378ODNnzsTDw0NHlaU+7w9BpLM0pW2zslhafN5pyWqXnpaNSnP2whN8fEMA2H/kNhWd85EvTyZ1u4ouDuTPm5ldB26meu0iecJCI/hj7FJ27jipnqZSqZg7ezNWVhbUq++mu+J+QjlzZiE4JIwtm48RHRWjnv72jQ9HD1/CqawjFv87vVqjZjkunr/D06dv1e3On7vN82fvqPO/0+3i+8qSxZqQkHB2bDtFSEi4err3Oz/27DpL2fKFyJgpAwDVa5Th4oW7PPvX9rpw7g7Pn3lTy6NcapeeonR6q9jo0aN5/PgxXbp0wdTUFGtra/z9/YmKisLV1ZXhw4frsrxU98uEPaye1559G3qxfutlLC1M6NLWhZjYOEaP/7yXuWDFaZo3KMXWlV1ZvPosJiaG9O1SiX9uv2b73us6XAORFA75slO5SmkWLdhOcFAoOXPacezYFc6eucGESb2xtNTP3q/6ytBQyehfO/LLyEV06jCOevVdCQgIYdOGIygUCkb/2lHdtnPXeuzZfYbunSfSobMHUZHRrFqxj8JF8lCvgasO1+LnMvrXdgweMI8ObcbTtHllQkMj2OR5FKVSyS+/tVe369TVg717ztG9y1Q6dKpNVGQ0q1ceoHCR3NSrn/jIbPpAp+FtZWXFli1bOHXqFFeuXCEoKAhra2ucnZ1xcfn59mL/PnaXjv3WMrCHO78NrU1ERAznLj9l4qxDPH72eTQ1v4+hNOqwlL9G1WNE/+qEh0fz97G7/DX9IFEJjGsu0p4p0/oxb+4W9u/3IigolAIFcjJvwTCquJfRdWk/pfoN3DAyMmTl8r1Mm+KJmbkJ5Z2LMGBgC3LnsVe3s7GxYvW6MUydtJ4F87ZhZmqCe7UyDB3eBmNj7U6k4vuoWr0Ms+cNYPnSfcyesQUTU2OcyjoycHAzjVHXbGysWLV2NNMmb2ThvJ2YmhnjXq00g4e11PvtpVCpftz7iuwKj/56I5EmeN+dRHScnDXQF0YGpQCIirui40pEUhkbxA82ExF7XseViKQyVSZ+EJvqR95jxoxJcluFQsFff/31HasRQggh9E+qh7eXl1eS2yZnJDYhhBDiZ5Hq4X38+HH1z69evSJHjhypXYIQQgih13R6q1jHjh3ZvVv7Xj0hhBBCJE6n4R0ZGYm1tfYTX4QQQgiROJ3eKjZgwADGjx9Pnz59KFCgALa2tlptsmTJooPKhBBCiLRLp+E9YcIEoqOjGT068Vu67t27l4oVCSGEEGmfTsP7zz//1OXbCyGEEHpJp+HduHFjXb69EEIIoZd0Gt4A/v7+rFixgkuXLhEcHIy1tTVOTk507NiRjBkz6ro8IYQQIs3RaW/zN2/e0LBhQ9atW0e6dOkoVqwYJiYmrFmzhkaNGvHu3TtdlieEEEKkSTo98p42bRrm5uZs2bIFe/vPg/+/e/eOzp07M336dGbMmKHDCoUQQoi0R6dH3ufOnWPAgAEawQ1gb29Pv379kjWUqhBCCPGz0Gl4A1hYWCQ43dLSkoiIiFSuRgghhEj7dBreRYsWZdOmTQnO27hxI4ULF07lioQQQoi0T+cjrLVt25aGDRvi4eFBxowZ8fX15cCBAzx+/Jjly5frsjwhhBAiTUr18L58+TKlSpXC0NCQkiVLsmzZMmbMmMHs2bNRqVQoFAqKFCnC0qVLcXFJ/EHkQgghxM8q1cO7ffv2mJmZUa5cOVxdXalQoQLbt28nPDycoKAg0qVLh7m5eWqXJYQQQuiNVA/v+fPnc/XqVa5evcrUqVOJjY0lY8aMuLq6qsNcwlsIIYRIXKqHd/Xq1alevToA4eHh3LhxgytXrnDlyhXGjh1LREQE+fPnx9XVFRcXFypVqpTaJQohhBBpmk47rJmZmeHi4qK+th0TE8Ply5fZvHkz69atY/Xq1fJUMSGEEOL/0fnY5pGRkVy8eJHz589z8eJFHjx4gEKhoFixYri6uuq6PCGEECLN0Ul4P3z4kLNnz3L27FmuXr1KZGQkOXPmxNXVlT59+uDs7IylpaUuShNCCCHSvFQP70qVKuHj44OVlRXly5fnl19+wdXVlezZs6d2KUIIIYReSvXw/vDhA9bW1jRr1owKFSrg5OSEkZFRapchhBBC6K1UD+9Vq1Zx9uxZTp8+zfLlyzE1NaVs2bK4urri5uaGg4NDapckhBBC6BWFSqVS6erNfX19OXv2LF5eXpw7dw4/Pz/s7OyoUKECbm5uVKhQgQwZMnzz8u0Kj065YsV35X13EtFx13VdhkgiI4NSAETFXdFxJSKpjA2cAIiIPa/jSkRSmSoTH2VUp73NM2bMSKNGjWjUqBEA9+7dw8vLiytXrjBq1ChiY2O5c+eOLksUQggh0hyd3yoGEBQUxPXr17l+/To3b97k9u3bxMbGylPFhBBCiAToJLyfP3/O9evXuXbtGtevX+fp06fExcXh4OCAi4sLbdq0oXz58qRLl04X5QkhhBBpWqqHt4uLCwEBAahUKrJmzUr58uXp0aMHLi4uZMqUKbXLEUIIIfROqod32bJlqVChAi4uLuTKlSu1314IIYTQe6ke3nPnzk3ttxRCCCF+KAa6LkAIIYQQySPhLYQQQugZCW8hhBBCz0h4CyGEEHpGwlsIIYTQMxLeQgghhJ6R8BZCCCH0jIS3EEIIoWckvIUQQgg9I+EthBBC6BkJbyGEEELPSHgLIYQQekbCWwghhNAzCpVKpdJ1EUIIIYRIOjnyFkIIIfRMqj/POzVFxl7SdQkiiUyU5TDL2VrXZYgkCn+58X8/PdRpHSI5CgAQq7qt4zpEUikVRROdJ0feQgghhJ6R8BZCCCH0jIS3EEIIoWckvIUQQgg9I+EthBBC6BkJbyGEEELPSHgLIYQQekbCWwghhNAzEt5CCCGEnpHwFkIIIfSMhLcQQgihZyS8hRBCCD0j4S2EEELoGQlvIYQQQs9IeAshhBB6RsJbCCGE0DMS3kIIIYSekfAWQggh9IyEtxBCCKFnJLyFEEIIPSPhLYQQQugZCW8hhBBCz0h4CyGEEHpGwlsIIYTQMxLeQgghhJ6R8BZCCCH0jIS3EEIIoWckvIUQQgg9I+EthBBC6BkJbyGEEELPSHgLIYQQekbCWwghhNAzEt5CCCGEnpHwFkIIIfSMhLcQQgihZyS8hRBCCD0j4S2EEELoGQlvIYQQQs9IeAshhBB6RsJbCCGE0DMS3kIIIYSekfAWQggh9IyEtxBCCKFnJLyFEEIIPWOo6wLEZ3+MXcHLF96sXPNrom0ePnhJq+Zj6dajAX36NdGYd/HCHRbO38GD+y+xtDSjRq1y9B/QDHML0+9d+k9hwZTu5MttR62W4zSmZ7RJx58jW1G3ehnMTI24cfs5YyZv5NL1xxrtcuXIxJTf2lHRpTAAB49dY9S49fj6BwOQM3tGHpyb98Uaarb4izMX7qXgWv2cmjUbwq1bj7Sm16pVgblzRwNw5codZs1ax+3bj7CysqR6dWf692+DjU361C5XAFev3mP2LE/u3H6ClZUl1aqXo1//llhbWyXYfuyYRbx4/o416/5K5UpTh4R3GrFj+0l2bDuJU1nHRNvExMTy2y9LiYmJ1Zp36eJdenabQuEieRg0pAXe3v54rjvE3TvPWLX2VwwM5CTLf9GxZRW6tK7K6fN3NaZbWphyZOvv2GexZt6KAwQEhtKrY00ObvqNivV/4+7D1wDYZLDk0KYxGBsbMnPRHpRKJYN71qOoY04qNviN6OhYfP2C6TxwgdZ7m5kaM/PPjvj4BXHr3stUWd8fmUql4smT11Sv7kzNmhU05mXLlhmAixdv0bXrWKysLOnZswVKpQFr1uzhwoWbbNo0jfTpLXVR+k/r0sXbdO82DisrC3r0bIqBgQHr1u7j4oVbeG6cqLU9tm87yratRylbtoiOKv7+JLx1LDY2jmVLdrNowc6vtl2xbC9PHr9JcN6MaRuxs7dl5ZpfMTU1BsDO3paJ49bgdfYWFSuVSNG6fxYGBgpG9m/Mb4ObJjh/WJ8GFHCwp2aLcXhdug/Atr3nuXt2DkN616fb4EUADOjuQTZ7G5xqjuDB47cAXL7xmAMbfqVds8qs2nicsPBINu08q/Ue037vgJGRIZ0HzicgMPQ7renP4/Xr94SFhVOtWnkaNnRPsM348UtQKg3YtGkqOXPaA1C9ujMNGw5g8eLNjBzZNTVL/ulNGL8CpdIAz40TyZnTDoDqNcrRqOFQlizezoiRHQGIjY1lyeLtLJi/RZflpgo5HNOhyMgoWjb7jYXzd1CvgSuZs1gn2vbhw1csXbybnr0bJrgca5t0NG3mrg5uACen+KP4Rw9fpXzxPwETEyPOH5jE2KHN2bDjLG/e+Wm1adesEgePX1cHN8B7n0BGj/fE69ID9bTmDSpw+sJddXADnDh7mweP39C8gUuiNRQpmIPenWqxbuspjeWJb/f4cfzZCweHHAnOf/36PQ8fvqBhQ3d1cH9q7+5elp07j6dKnSLem9cfePToJfUbVFYHN0DevNlxr1KG3btOAvHfg82aDGf+vM00aFCZLFlsdFRx6pDw1qHIyGhCQsKZNqMfEyb1xFCpTLBdTEwsY39dhrNLEerWd9Wab2JizOKlI+jes4HG9Af3XwDxR+Ai+UxNjLCyNKNdnzl0H7KImJg4jfm5cmQim70tx8/cUk+zMDcBYOm6I6zaGP8lnyG9BXlzZeH6rWda73Hj9nNKF8ubaA1/jGhJeEQUf07/8Y8kUsujR5rhHRYWoTH//fv4nbQCBXJrvTZnzqx8/BjEu3c+37dIofb+gz8ABQrk1JqXM5f9/7aHr/r7dMasIUya0h9lIt+nP4pknTYPCQnh0aNHlCpVCoArV66wdu1alEolbdu2xcnJKUWK8vf358OHDxQoUOCHvlZraWnGvoPTMTT88n+ylcv38fKFN7PnDSI2Vvt69//39o0vly/dZfrUjeTLn51q1cukVMk/laDgcIpWHkxsbFyC8/Pljj8K8PENYuIvbejSphrprcx58tybEX+t48DRawBk/d8Zlbfe/lrL8P4QQHorc6zSmREUHK4xr6hjTurVKMPsJfvw/hCQgmv2c3v06AUWFmZMmrScAwfOEhYWTo4cdgwe3J66dSthbh7fwTM0NFzrtQEBQQD4+HzE3j5Tqtb9szIzi98hDg2N0JoXEBDf2dPXN4AsWWw4eGj+V79PfxRJTsbHjx9Ts2ZNxo4dC8CrV6/o3Lkzx48f5+TJk3Tq1Inz588nu4CQkBB++eUXPD09ATh48CCVKlWicePG1K9fH29v72QvU18YGBh89T/a40evWbJoF0OGt8bO7uungQIDQqhdYzBjfl1GZFQUo3/tgImJ8VdfJ7SpVKpEgxvij6gBxg5rTu1qpRj2xxq6DFpAWHgUW5YNxd2tKADpLM0ACAuP0lpGeET8NAtz7TsCurevTkxMLItWH/rP6yI+e/z4JaGh4QQHhzJ16mAmThyIhYUZQ4ZMY9eu4zg45MDS0pxDh86hUqnUr4uMjOLs2esAREVF66r8n46DQ3YsLc05cviC1vbwOntD/XNSvk9/JEkO79mzZwMwfPhwALZu3UpMTAzr1q3j3LlzFCpUiEWLFiW7gBkzZvD333+TPn387RfTp0/H0dGR+fPnY2BgwLRp05K9zB9FbGwcY39bRqnSBWjWPOGONf+fQqFg6oy+TJjcEweHbPToOpnDhy5950p/TsbG8Seu0ltZULXJH6zfdpqNO85So/mfBASFMm5kKyC+0xug8cXz/8XFae4kmJoY0bqxG/uPXOXlG9/vswI/qRYtajN2bC/mzh1NjRouNG1anc2bp5Ejhx3Tpq1CqTSgc+dG3LnzmGHDpvPgwXPu3XvKgAGTCA+PP/r70U/JpiXGxkZ07FSfO3eeMGLYbB4+eMG9e88YNGA6YeGRAIlecvyRJTm8L1++TKdOnahUqRIAx48fJ1euXJQqVQozMzMaNWrE7du3k13AsWPHGDVqFPXq1eP27du8efOG7t27U61aNfr164eXl1eyl/mjWL1yPw/uv2TQkJZ8/BjMx4/BBP2vt3FERCQfPwZrfelbpbegdh1n6jdwY9Xa37DPmpHpUzboovwfXlhY/BfH7r8vafQCDwwKY/+Rq5QqlgcLcxOCQ+K/8M1Mtc+AfJr2qc0nlSsUIZ2lGTv2X/xe5f+0WreuQ9u2dTWmmZqa0LChO76+ATx+/Io+fVrSsWMDDhw4S4MG/WnUaCAKhQHdusXfdSC3iqWu3n2a0aFjPQ4ePEejhkNo2ngYBgYKunVrBPyc2yPJ17wjIyOxto6/dvfmzRseP35M+/btNdp8y95oQEAAefPGd9g5deoUhoaGuLrGd8pKnz49kZGRyV7mj8Lr7E2io2No0/J3rXmrVx5g9coDHDwyk2zZEr72ZmpqTKXKJdmw/jAfPwZjbZ3ue5f8U/l0DdvHN0hrno9fEAYGBlhamPLqbfyRs13mDFrt7LNY8zEwRH0E8Ult95JERkZz8Pj1lC9cJOjT4CthYREYGBjwyy/d6dGjGc+fv8XePhPZsmVm1qy1KJUG6vvBReowMDBg1OjOdOvemBcv3mFnZ0u2bJmZPcsTpdKArIl8B/7IkhzeOXPm5Nq1azRv3pydO3eiUCioVq0aEH868O+//yZXrlzJLiBbtmw8ePAAJycnjh49SsmSJbG0jN+LOnXqFNmzZ0/2Mn8Uw0a0UR9pf+LnF8jokYup18CVBg3cyJgxPc+evqV3j2l06lqXVq2ra7QPC41AoVCoT/GKlHPnwWsiIqIoXED7/2juHJkJj4jCxy+IuDgVz16+p2TRPFrtShTJxbWb2r3QnZ0KcvXmE4JDtDtNiW/3/r0fXbqMoU6divTr11pj3rNn8QPqZM+ehX37TpEpkw3lyxcjY8bPt3BevnyHIkXyST+SVLZ/3xkyZbKmXPmiZMyYQT39ypV7FC7i8FNujySfNm/dujU7d+6kfv36LFq0iPz58+Ps7MzDhw9p0qQJV65c0ToST4pWrVoxefJkPDw8uHfvHm3atAGgf//+rF69mtatW39lCT+uwkXy4FyhqMa/kqULAJA9e2acKxTFxMSYHDmzEBwSxtbNx4mOilG//u0bX44euYxTWUcsLMx0tRo/rLDwSPYfvUadaqUp9K8Az5UjE3Wrl2bf4SvExcVf59514BJV3YpSwCGrup27W1EK5svG1j3nNJZraKikUP5s/HP7ReqsyE8kSxZbgoPD2Lr1MCEhYerp7975sGPHMcqXL06mTNasXr2bceMWa4xmePLkZa5evat1yl18f2vX7GP8+OUa2+PUyatcu3qPNm1q67Ay3Uny4Vjr1q2xsLBg3759lCpVir59+6rnRUREMG7cOBo21B5A5Gs6duyIra0tly9fpl+/fnh4eABgbGzMuHHjaNasWbKX+bMxNFQy+pcO/DJqMZ07TqBe/QoEBISwccMRFAoFo35N/k6VSJpfJnpS0bkQf2/6jQUr/yY6Ooa+XWoTHhnF2Kmb1e1mLt5Lm6aVOLjxV+Ys3Y+JiRFDetXn6s2nbPx/o6rlyGaLiYmR+nS7SFljx/akb9+JtGo1nObNaxEaGo6n5z4MDZX8/nsvALp3b8qAAZPp2fMvatZ04c2bD6xatQs3t9LUr19Zx2vw8+narRGDBk6nT6+JVK9RnjdvfFizei9ubiWpV7+irsvTCYXqS11gU8Hly5cpXLgwFhYWWvOCgoLw8vKiTp0637TsyFj96mVdu/pgsmbL+MUHk7x540OdGkPo1aex1oNJDh28yMoV+3j86DVmZiaUdy5M/0HNyZ3bPpGlpR0mynKY5UzbZ1nue83lxWsfrQeT5M6ZmQmjW1PVrRgKhQKvy/f5ZYKnxmhqAPnz2jN1bHvcyhciLDySQydu8MsET/WDST5xKuHAmb3j6Td6OSs8j3339foW4S83/u+nhzqt41sdPXqBJUu2cv/+M0xNjSlXrhhDhnTQGHVt//7TLF26jRcv3mJrm4H69avQs2czzMz09UE/8WftYlXJ71icFhzYf5Zly3by8sU7bG3TU69+Jbr3aKK+D/z/q161F9myZdbrB5MoFUUTnZfs8H7w4AEnT57k7du3dOjQAXNzcx4+fEjlyt+2N1qoUCE2b95M8eLFteZduHCBHj16cPPmzW9atr6F989MH8JbfKbv4f1z0u/w/hl9KbyT1Ytp3LhxbNiwAZVKhUKhoHbt2gQFBTFw4ECqVKnCnDlzMDFJeC/o30aOHMm7d++A+M5uf/zxh7qT2r89f/6cjBkzJqdEIYQQ4oeX5A5ra9euxdPTkx49erBlyxb1gBMuLi506tSJkydPsmzZsiQtq06dOiiVSvWtZZ9+/vc/IyMjypQpox4cRgghhBDxknzkvWnTJmrXrs3gwYP5+PGjerqVlRWjRo3C39+fffv20a9fv68uq0qVKlSpUgWApk2bMnnyZBwcHJJfvRBCCPETSvKR96tXr3B2dk50vpOTk/pUeHKEhITw+PHjZL9OCCGE+FklObytra2/+JCQR48eqccnT46AgACsrKyS/TohhBDiZ5Xk8K5RowYbNmzQOEpWKOIfuHDq1Ck2b96Mu3vSHp7xb+3atWPatGlcuXKFwMDAZL9eCCGE+Nkk+VaxoKAg2rZty4sXL8ifPz93796lTJkyhIaGcv/+fbJly8aWLVuwsfn6Yyv/rU6dOrx69Ur9nOqExkf/lgeegNwqpk/kVjH9IreK6SO5VUzfpMitYlZWVmzZsoXly5dz+PBhTExM+Oeff8iWLRudO3emZ8+e33TavG5dGWpQCCGESA6dj7D2PcmRt/6QI2/9Ikfe+kiOvPVNig3SolKpeP36NTlyxA8h+OzZM7Zs2YKhoSFNmjQhTx7tpyYlRWRkJNu3b+fSpUsEBwdjbW2Nk5MTjRo1wtRUX4ciFEIIIb6PJB95e3t707VrV4yNjdm5cye+vr54eHgQFBT/LGMzMzM8PT0pXLhwsgoICAigQ4cOPHz4kFy5cmFra4uPjw+vXr3CwcGBDRs2fNPpeJAjb30iR976RY689ZEceeubLx15J7m3+cyZM3n37p36EZ1btmwhKCiI2bNnc+zYMezt7Zk7d26yi5sxYwb+/v5s2bKFQ4cOsWHDBo4cOcKWLVsIDAxk1qxZyV6mEEII8SNLcnh7eXnRsWNHWrRoAcDx48ext7endu3aZMuWjRYtWnDt2rVkF3Ds2DEGDhyo9WCS4sWLM3DgQI4ePZrsZQohhBA/siSHd3BwMNmzZwfAz8+PO3fuULHi5+eompmZERMTk+wCIiIisLdP+JGVdnZ26tPyQgghhIiX5PDOmjUrDx/GX9/av38/gMagLGfOnFGHe3IUKFBAvbz/b9++feTLly/ZyxRCCCF+ZEnubV6vXj0WLlzIixcvuHjxIvb29lSsWJGXL18yceJETp06xahRo5JdQO/evenZsycBAQHUrVuXjBkz4uvry759+zh16hQzZsxI9jKFEEKIH1mSw7tfv34olUr27dtH6dKlGTFiBIaGhoSEhHDlyhV69epFx44dk11A5cqVmTBhArNmzeLEiRPq6RkzZmTcuHF4eHgke5lCCCHEj+w/D9KiUqmIiYnByMjoPxWiUql4+vQpgYGBpE+fnrx586rHTv9WcquY/pBbxfSL3Cqmj+RWMX2TYoO0AISHh2NmZgbAx48fOXDgAEqlktq1a5MhQ4ZvLvL06dNcvXqVwMBAbG1tcXFxoWzZst+8PCGEEOJHlawHkwwePJigoCC2bt1KSEgIDRo04N27d6hUKjJlysSGDRvUo68l1cePH+nevTu3b9/G2NgYGxsb/Pz8iImJwdXVlQULFmBiYvJNKydH3vpDjrz1ixx56yM58tY3KTJIy+zZs7l48aL69rBt27bx9u1bhg8fztq1azEwMGD27NnJLm78+PG8fv2axYsXc/PmTU6ePMmtW7eYP38+t2/fZvr06clephBCCPEjS3J4Hz9+nHbt2jFgwAAAjh49iq2tLV26dKFcuXK0bduWc+fOJbuAM2fOMHLkSKpUqaIxvVq1agwdOjTR28iEEEKIn1WSw9vPz4/8+fMD8QO23LhxA1dXV/V8a2trwsPDk12AUqkkXbp0Cc7LlCkT0dHRyV6mEEII8SNLcnhnyZKFV69eAfFH3bGxsRpHy9euXUt0pLQvadeuHbNmzeL9+/ca00NCQli6dCnt2rVL9jKFEEKIH1mSe5u7u7uzZs0aQkJC2L9/P+nTp6dq1aq8f/+eZcuWsXv3bvr06ZPsAt69e4ePjw81atSgTJkyZM6cmYCAAK5du0ZoaCjGxsZ06dIFAIVCwYoVK5L9HkIIIcSPJMnhPXz4cMLDw9m2bRtZsmThjz/+wNTUlIcPH+Lp6UmDBg3o0aNHsgt48eIFBQsWBCAmJoa3b98C4OjoCEBsbCyxsbHJXq4QQgjxo/rPg7RERUURGBhIpkyZUqSgoKAg3rx5Q44cObC0tPxPy5JbxfSH3CqmX+RWMX0kt4rpmxS5VSwxxsbG6uC+e/dukl938+ZNevXqxa5du9TTNmzYQKVKlWjSpAkVK1Zk9erV/7U8IYQQ4oeT5NPm0dHRLF26lMOHDxMWFkZcXJx6XmxsLKGhoYSEhHDv3r2vLuv+/fu0b9+eDBky0KRJEwBu3brFuHHjyJcvHwMHDuTp06dMnz6dHDlyUK1atW9YNSGEEOLHlOTwnj17NitWrMDOzg4rKysePnyIk5MTPj4+vHnzBlNTU3799dckLWvJkiUUKFCANWvWYG5uDsC6desAmDZtmvp6t6+vL2vXrpXwFkIIIf4lyafN//77b8qVK8fx48dZtmwZAGPHjuXQoUMsWbIkWQ8nuXz5Mh07dlQHN8DZs2fJkSOHOrgB3NzcknUqXgghhPgZJDm8379/T82aNTEwMCBLlizY2tpy/fp1IP6xno0bN2bLli1JWlZAQAB2dnbq358+fYq/vz/ly5fXaGdmZkZkZGRSSxRCCCF+CkkOb1NTU40j65w5c/Lw4eeepsWLF1cP4vI1GTJkwN/fX/37hQsXUCgUuLi4aLR78uQJtra2SS1RCCGE+CkkObwLFSrE6dOn1b/nzZtXfeQN8UfmSX3+drly5dRH6bGxsezYsQMTExMqVaqkbhMVFYWnpyelS5dOaolCCCHETyHJ4d22bVuOHTtGmzZtCAkJoW7duty9e5fRo0ezbNkyVq9eTbFixZK0rF69enH16lVq1qxJ7dq1uX37Np07d1bf1719+3ZatWrFs2fP6Nq167etmRBCCPGDStYgLVu3bmXVqlXs3bsXpVLJjBkz1J3XsmbNyrJly3BwcEjSsh48eMDq1avx9/enUqVKtG3bVj2vYsWKKJVKxo4dS9WqVZO5Sp/JIC36QwZp0S8ySIs+kkFa9M2XBmn5zyOsvX37lsDAQBwcHDA2Nv4vi1J7//49mTJlwsDgv40hI+GtPyS89YuEtz6S8NY3XwrvJN/nnZisWbOSNWvW/7oYDVmyZEnR5QkhhBA/kkTD+1sGRlEoFBw9evQ/FSSEEEKIL0s0vFP6aFoIIYQQKSPR8P40XKkQQggh0pYk9QiLiopKcPqnkdGEEEIIkXq+GN7R0dFMnjyZSpUqJThM6cyZM6lcuTLjx48nIiLiuxUphBBCiM8SDe+oqCi6d+/O6tWrsbGxwc/PT6uNi4sLDg4OrF+/nu7duxMbG/tdixVCCCHEF8J7zZo1XLhwgeHDh3PgwIEEO7C1bduWnTt30rt3by5fvsz69eu/a7FCCCGE+EJ47927F3d3968OT6pQKBg4cCBly5Zl9+7dKV6gEEIIITQlGt7Pnz/Hzc0tyQuqUqUKT58+TZGihBBCCJG4RG8VMzc3T9Y1bFNT0xQbHjWlmCjL6boEkQyfh9wU+qOArgsQyfSlITeF/kj0yDtPnjxcu3YtyQu6evUq2bJlS5GihBBCCJG4RI+8GzduzB9//MGFCxdwdnb+4kIuXbrE4cOH6devX4oX+F+ExpzSdQkiiSwMK6Pinq7LEEmkoBAA+eus1HElIqkeHewCwMuQvTquRCRVTsv6ic5L9Mi7cePGFC1alF69erFo0SJ8fX212vj6+rJkyRJ69epFtmzZaNOmTcpULIQQQohEJXrkbWRkxMKFCxk8eDBz5sxh7ty5ZM2alUyZMhEXF4efnx9v375FpVJRsmRJZs6ciZWVVWrWLoQQQvyUvvhI0IwZM7Ju3ToOHz7M/v37uXv3Lg8ePMDAwICMGTPSqFEjatSoQdWqVVOrXiGEEOKnl6TnedesWZOaNWt+71qEEEIIkQRJejCJEEIIIdIOCW8hhBBCz0h4CyGEEHpGwlsIIYTQMxLeQgghhJ6R8BZCCCH0TKK3ilWrVi3ZC1MoFBw9evQ/FSSEEEKIL0s0vLNmzZqadQghhBAiiRIN73Xr1qVmHUIIIYRIohS95n337t2UXJwQQgghEpCk4VEBoqOjWbp0KYcPHyYsLIy4uDj1vNjYWEJDQwkJCeHePXmsoxBCCPE9JfnIe/bs2cybN4/AwEDMzMx48+YN9vb2GBoa4u3tTXR0NL/++uv3rFUIIYQQJCO8//77b8qVK8fx48dZtmwZAGPHjuXQoUMsWbKEmJgYjIyMvluhQgghhIiX5PB+//49NWvWxMDAgCxZsmBra8v169cBqFy5Mo0bN2bLli3frVAhhBBCxEtyeJuammocWefMmZOHDx+qfy9evDivXr1K2eqEEEIIoSXJ4V2oUCFOnz6t/j1v3rzqI2+IPzJXKBQpW50QQgghtCQ5vNu2bcuxY8do06YNISEh1K1bl7t37zJ69GiWLVvG6tWrKVas2PesVQghhBAk41ax2rVrM27cOFatWoWZmRkVKlSge/fu6s5rWbNmZfTo0d+tUCGEEELEU6hUKtV/WcDbt28JDAzEwcEBY2PjlKorRYTGnNJ1CSKJLAwro0LGCNAXCgoBkL/OSh1XIpLq0cEuALwM2avjSkRS5bSsn+i8JB95JyZr1qwyDroQQgiRipIc3kl9ytixY8e+uRghhBBCfF2Swzuho+u4uDh8fX158eIFuXPnxtXVNUWLE0IIIYS2JIf3l54ydvv2bbp160a5cuVSpCghhBBCJC5FnipWtGhR2rVrx4IFC1JicUIIIYT4ghR7JGjGjBl5/vx5Si1OCCGEEIlIkfD28fFh48aN0utcCCGESAX/ubd5VFQU/v7+xMbG8vvvv6dYYUIIIYRI2H/qbQ6gVCopX7489erVo0qVKilVlxBCCCESkSK9zYUQQgiRepJ8zbtDhw6cP38+0fnHjx+nbt26KVKUEEIIIRKX6JF3eHg4Hz9+VP9+6dIlatSoQa5cubTaxsXFcfr0aV6/fv19qhRCCCGE2hfDu1GjRgQHBwOgUCiYOHEiEydOTLC9SqWSEdaEEEKIVJBoeNvY2DBt2jRu3bqFSqViwYIF1KhRg4IFC2q1NTAwwMbGRk6bCyGEEKngix3WKleuTOXKlYH4R3+2atWKEiVKpEphQgghhEhYkjusTZo0iUyZMjF9+nQCAwPV05ctW8aUKVPw8/P7LgUKIYQQQlOSw/vhw4c0btyYVatW8e7dO/X0wMBAPD09adSoEa9evfouRQohhBDisySH94wZM7CwsGD//v04Ojqqpw8bNoz9+/djZGTE9OnTv0uRQgghhPgsyeF948YNOnbsSO7cubXm5ciRg3bt2nH58uWUrE0IIYQQCUhyeKtUKiIjI784PyIiIkWKEkIIIUTikhzeJUqUYPPmzQQFBWnNCw0NZevWrdITXQghhEgFSR7bvF+/frRr14569epRv359cuXKhUKh4OXLl+zfv58PHz4wadKk71nrD2/c2LW8fPmBZauHaUy/e+cF82bt4J8bT1AaGFDaqQCDhzcjdx47jXbtW07kzu3nWsutVqM002b3+p6l/7Rev35P9Wo9v9hmzdpxlC9fDH//QGbNXM/x45eIiIiicJG8DB3agZIltcdOEClj/ABXcmezot3IgxrTnUvYM7B9aRzz2BASFsXfZ58za81VwiJiNNq5lc5Gn9YlKJovI3EqFTfu+zB77VVu3PfRaJc9iyWju5ejXHF7AE5cfMXk5ZfwD5Szkd/i+qVHrFl8iKeP3mFuYUKl6iXo3Kc2ZuYmCbafOW4rb175MGNpH615t68/Y+WCAzy8+xpLKzMqVClKx561SG9t8b1X47tKcniXKFGCVatWMWXKFFauXIlKpVLPc3R0ZPLkyZQqVeq7FPkz2LX9LDu3n6VM2QIa058/86ZHp+mYmhnTo1c9ANatOUKX9lPZvGMsmTJnAOIvWzx7+g73aiWpWqO0xjLss9qmyjr8jGxs0jN16iCt6RGRUYwftwxb2/Q4OuYhJCScdm1/5cMHfzp2qk96K0s8PQ/QqeMYtmydRoEC2sMOi/+mWc38tKxTkIs332lML1/cjtUTanH7sR/TV13BPpMFHRsWpmj+jLQZvp9PX23litmxYlxNHr34yMw1VzFUKmhTrxCeUz1oPWw/Nx/6ApAhnQnrptTByFDJsq03USoN6Na0KI55rGk6aC/RMXGpvep67cblx4zqu5T8hbLTtb8HPt4B7Nx0hod3XzFzeR8MDDRPGB/cdZGDuy5SvExerWX9c+Uxo/stwyKdGa27VMPAQMGOjWe4cfkxc1b1I52VeWqtVopLcngDODk5sXXrVvz9/Xnz5g1xcXHY28fvae7Zs4e//vqLffv2fZdCf1SxsXGsWHKAJQv3Jjh/w7pjhIVFsnztcBwL5QSgbHlH2reayPq1Rxk8rBkAb9/4ERYWSWX3ktSt75xq9f/szM1NadCwitb0iROWExMTy7Tpg0mf3pJZs9bz7Nkb1q4bT9myRQCo4+FGjeo9WbF8J1MS2AEQ38bAQEGfViXo3zbhg4lR3crx1ieUtiMOEBkVC8DbDyH82a8CFctk5/SV+Gc0/NqzPO98Qmk2eC8RkfHtdh57zN9LmzKkYxk6/XoIgM5NimKX0YJ6vXfy5FX8GBj/3PdhzaTaNK6ejy1/P/zeq/xDWTp7L5ntMjBjaR9MTI0AyGyXgXlTdnLl/APKuRYC4r87N6w4yrqlRxJd1vypuzAwMGDOyn5kzZERAFf3YvRsNYMNK47Rc3D9779C30mywvsTGxsb0qVLx7Fjx1i4cCFeXl7ExMSgVCpTur4fWmRkNB1aTeLRw9fUa+DCpYv3tNq8fuVDBmtLdXADFCmWmwwZLHj88I162pPHbwHI42CntQyRuh48eM769Qdo3NgdJ6ciqFQqdu08QeUqZdTBDZApkzUjRnTC0Oib/gxFAoyNlGybXZ9CeW3YcfQRFUpm1ZrvHxjBIa/n6uAGuHTLGwDHPNacvvIaK0tjHPPYsHLHbXVwA/gFRHDpljdupT8vt17lPFy86a0OboBzN97y5FUAdSvnlfBOhqjIaNJbW+JWrbg6uAGKl3EA4Omjd5RzLURUZDT9O87l6aN31KhbhuuXH2sty/utP8+feFO3ibM6uAFy5smMc6XCHNl35ecK79u3b7Nz50727dtHUFAQKpWKjBkz0rRpU1q2bPk9avxhRUVGExoazuQZPahZ24m6NUZrtcmZKzOXLtzjo38w1jbpAAgMCCU4OJyMmdKr2z39FN5548+EhIdFJnp9SHxfs2d5YmpqzMBBbQF48/oD79/70bVbYyD+EkdYWAQWFma0aeuhy1J/OCbGSizNjRgw8QQHzzzjxOrmGvOjomPpOuaw1usKO9gA8PZDKAAhYdHU7L6d8IhorbbWVibExsafW7eyNCanvRV/n32u1e7uYz+qlMvxX1fpp2JsYsSk+d21pj95EP/9ltnOGoCoqBjCQiP4bVI7KtcsSbt6E7Re4/shfmcqdz7tA5qsOWw5e/wWH7wDyGyXIQXXIPUkKbz9/PzYvXs3O3fu5PHjx6hUKhQKBQD9+/enZ8+eGBrK0UNyWViasuvAeAwNEz9j0bFLLU6fvMno4csYMqIFCgXMmrYNIyMlrdtVVbd7/PgNFhamzJy6lcMHLxMWFkn2HBnpO6ARtTzKpcbqCODB/eecOHGZzl0akjlzfCA8fxH/xWNrm56pU1azZcthQkLCyJnTjlGju1C1qmyflBISFkWNrtuIjVN9vTGQNbMFzsXtGdW9HA+e+XP43AsA4uJUvHirfWdNwdzWlCmchTNX4896ZbGNv2b63jdMq+0H/3DSWRhjaW5ESJj2ToD4uvfv/Llx+QlLZu8lt4Mdbu5FATC3MGH1zlEov/DdaWpmDEB4qPYtzkEB8dvro1/QjxfeMTExHD9+nB07dnD27FliYmIwNjamcuXK6qeLNWvWDEdHxxQL7rCwMMzN4/8Yjhw5wrt373B3dydHjh9z79XAwACDr9ysZ5/Vli496jB1wkZaNfkLAKXSgKmzemqcSn/6+C2hoREEB4Xx16QuBAeHsXHdMUYPX050TCz1Grh8z1UR/7Nx498olQa0a/f5CXvBQfFHc3PnbMDQUMkvv3ZFaWDAihW76Nd3MstX/E6FCnKbZUpQqSBWlbTgTm9pzKk18WcLwyKi+WvRBaKiYxNtb25qyLRhlQBYuvUmABZm8ad2wyNjtNpHRsWoXyfhnXxBgWG0qxf/CGpTUyP6jWiEsUn8521gYPDVG51z5c2CuYUpZ47folXnquoDzqjIaK5eeBD/c5T2dtMXiaZuxYoVCQgIwNLSkho1alCjRg0qV66MhUV89/o3b94k9tJke/r0Kb169cLDw4NBgwYxe/ZslixZgkqlYubMmaxcuZLSpUt/fUE/oIVzd7N8yX7KlC1Ak2YViY2LY9vmU4wcspRps3tR2T3+S79J80rExsbRso27+rW16pSlRaM/mDN9O3XqlkepTPJt/eIbREREsmfPSapWLUe2bJnV06Oi4r+4g4JC+fvQQtKntwTAvWpZatTozcwZ6yS8dUAFDJx0AiNDAzo0LMyaSbUZPPlkgqfATU2ULP69OoUcbFm06R/1NXIDg/hAUH1hhyGJJwHE/6NQwK+T2hEdHcuuTWcY0Wcpv05sS6XqSftbMTIypFm7SqxdcphJv26gdeeqxMbFsXrh30SERwHo9XdiopV//PgRMzMz6tevT+3atXF2dlYHd0qbMWMGSqWSatWqERUVxYYNG6hTpw5XrlzBzc2NWbNmfZf3TeuCg8JYu+oQhYvkYvGKIdSpV556DVxYtnoYeR3sGf/7OnUwNGtZWSO4AUxNjfGo74yfXxBPn7zVxSr8VC5evEVYWAS1alfQmG5mbgpAjZrO6uAGsLKypGrVsty584TQ0PBUrVVAUEgUB04/Y/fxJ7QZfoC3H0IY3V37EkY6C2NWTaiNS8msbD30kJlrrqrnhf7viNrURPs4yMTYUKONSJ50VuZUqVmSGnXLMHNZX7LYWbN4ZsJ35SSmbbfqNGlTkVNHbtCj1Qx6t5mFwkBBi47u6vfQV4mG9+rVq/Hw8GDfvn0MGjQINzc32rRpw+rVq1P0qBvg8uXLDBkyhGLFinHp0iWCg4Np2bIllpaWtGrVitu3b6fo++mLly8+EBUVQ22Pchp7iEZGhtSpVx4/vyCeP/X+4jJsbKwACAtLfGhbkTJOnbqGkZEhVao4aUzPkiX+Pntbm/Rar7G1Sa/uwCZ0JzIqlhMXX5E1syXWVp87etqkN2X9lDo4FcnCpgP3+WX2WY3XvfWJvySSyUY7BLLYmhMYHJngKXWRPCamRpSvWAif9wEEfgxN8usMDAzoPbQhm/4ey6zlfVm/7xfGzepCWGgkBkoDsthbf8eqv69Ew9vZ2Znx48dz9uxZ5s6dS/Xq1blz5w6TJ0+mevXqdOvWDYVCQViYdkeN5IqOjiZ9+vgvttOnT2NmZkaZMmUAiI2N/Wk7wxn9b889Nk57kIe42PhpcSoVH95/pFmD31m6UPse++fP4sM9W7aMWvNEyrp+7R7FiuXH0lLzizx//pwYGxvx+LH2I3Nfv36PiYmxeidLfF95s6fnxOrmtKnrqDXPwtyIuDiV+rq3hZkhqybUorCDLSt33GbMvHNarwkOjeLVu2CKOGgPhFTIwYbbj3xTfiV+YC+ffaBdvQns2eKlNS88LBKFQoGRcdJvST7+93X+ufIYa9t0FC2Vhyz28Z1Ib117SgHHbOpr6Proqyf8jY2NqVGjBnPnzsXLy4sJEyZQvnx5Xr58iUqlYuTIkXTu3Jn9+/cTFRX1TUUUKFCAw4cP4+Pjw99//42bmxuGhoZER0fj6elJgQIFvr6QH5BDvqxkypyBvbvOERn5+dRbZGQ0+/ZcIIO1JQ75spI5izUhIeHs3H6GkJDPp1+93/mzd9c5nMoV1LitTKS86OgYHj9+RaHCebTmmZubUrVqWU6evMKjRy/V01+/es/x45epVq2cjJGQSl68DSKduTGt6zpiZPj56y9rZgtquebm0i1vQsPjj5R/71OBwg62rN51h0nLLiW6zENez6lQKit5s3/+G6tQMisOOTKw79TT77cyP6BsOWwJDYlg3/YLREd/PmPx/p0/Z47donjpvJhbmCZ5eTs2nGb+1J3ExnzuiHjxzF1u33hGgxauKVp7akvWIa2lpSVNmzaladOm+Pj4sH//fvbu3cv58+e5cOECVlZWXLx4MdlFDBgwgL59++Lp6YmxsTHdu8ff51erVi38/PxYvHhxspf5I1AqDRj5a2tGDF5M+1YTadTEjbjYOHbv9OL5M2/GTe6C0f8G+Bj5a2uGDlhE57ZTaNysImGhEWzecAKl0oBRv7XR8Zr8+N698yE6Ooas9pkSnD9seEcuXbpDxw5j6NChHkZGhqxduw9TU2MGD2mXytX+vGLjVPy16AIzRlTGc6oHu48/wdrKhHb1C6FSqfhr0XkAHHKkp3H1fASFRHLviR8N3B20lrXnxBMAlm27RaNq+VgzqTYrd9zGxFhJ92bFuPXQV91GJI3SUEnfEY2YMmYjQ7svoppHaYICQtmzxQuFAvqOaJSs5bXs6M5fI9by26CVuFUthvdbf7Z7nsbJpSBV6+h3J+hvPh+dKVMmOnXqRKdOnXjx4gV79uz55qFRXV1d2bt3L7du3aJEiRJky5YNgC5duuDs7Ey+fPm+tUy9V7V6KRYuG8yyxfuYP2cnAI6FcjJ3UX9cKxZVt3OvVoqZ8/qwcukB5s7cjompMU5lC9BvUGP1wC3i+wkICAbAwjLhDjDZs2dh85YpTJ+2lhUrdqJSQRmnwowY0ZEcOWRUvNS058QTomPi6NG8GL/0KEdYRAznb7xl5pqrPH8Tf293uWLx28TK0oQpQysluhwA/8AI2gzfzy89yzOwfWkiImM4ev4lU1ZcJipaxjVPruoeZTAyVLJ5zQmWzNyDqZkxJcvmp0vfOmTPlfDOcWIqVivOLxPbsmn1CRbP2E0G23S0aF+FVp2r6nVPcwCF6kv3OOiQv78/Hz58oECBAloD0SdVaMypFK5KfC8WhpVRoT08rEibFMSPL52/zkodVyKS6tHBLgC8DElej22hOzktEx++NU3seoSEhPDLL7/g6ekJwMGDB6lUqRKNGzemfv36eHt/uUe1EEII8TNJE+E9Y8YM/v77b3WP8+nTp+Po6Mj8+fMxMDBg2rRpOq5QCCGESDvSxD1Yx44dY9SoUdSrV4/bt2/z5s0bRowYQbVq1YiJieH333/XdYlCCCFEmpEmjrwDAgLImzf+QeqnTp3C0NAQV9f4bvzp06cnMlIGGBFCCCE+SRPhnS1bNh48iB8o/ujRo5QsWRJLy/hhJE+dOkX27Nl1WZ4QQgiRpqSJ8G7VqhWTJ0/Gw8ODe/fu0aZN/H3J/fv3Z/Xq1bRu3VrHFQohhBBpR5q45t2xY0dsbW25fPky/fr1w8PDA4gf3W3cuHE0a9ZMxxUKIYQQaUeaCG+AevXqUa9ePY1pM2bM0FE1QgghRNqVZsI7JCQET09PvLy88PHxYe7cuZw+fZoiRYrg7Oys6/KEEEKINCNNXPN+//49jRo1YtGiRQA8f/6cqKgobty4Qbdu3Th//ryOKxRCCCHSjjQR3pMmTcLY2Jhjx46xcuVKPo3YOmfOHFxcXFiwYIGOKxRCCCHSjjQR3mfPnqV///7Y2tqiUCjU0w0MDGjXrh3379/XYXVCCCFE2pImwjs2NhYTE5NE56XRZ6cIIYQQOpEmwtvJyYmlS5cSERGhnvbpCHzLli2ULq3fz10VQgghUlKa6G0+bNgw2rRpQ82aNXF2dkahULBmzRoeP37MkydP1E8bE0IIIUQaOfIuWLAg27dvp2zZspw7dw6lUsmpU6fIli0bmzZtokiRIrouUQghhEgz0sSRN0Du3LllUBYhhBAiCdJMeAM8ePCA8PBw4uLitObJdW8hhBAiXpoI79u3bzNw4EDevn0LoO5drlAoUKlUKBQK7t27p8sShRBCiDQjTYT3hAkTMDAwYNKkSdjZ2WFgkCYuxQshhBBpUpoI7zt37jBz5kyqV6+u61KEEEKINC9NHOLa2NigVCp1XYYQQgihF9JEeLdp04alS5cSHh6u61KEEEKINC9NnDZ//fo1jx8/xs3NjQIFCmBmZqYxX6FQsGLFCh1VJ4QQQqQtaSK8nz17hqOjo/r36OhoHVYjhBBCpG1pIrzXrVun6xKEEEIIvZEmrnkLIYQQIul0duRdtGhRNmzYQPHixSlSpIjGc7wTcvv27VSqTAghhEjbdBbevXr1IkuWLOqfvxbeQgghhIins/Du16+f+uf+/fvrqgwhhBBC76SJDmsAr169IioqCgcHB4KDg5kzZw7e3t7Url2bevXq6bo8IYQQIs1IEx3WTp06RZ06ddi2bRsAY8eOZePGjbx584bhw4erpwshhBAijYT3okWLcHNzo2/fvgQFBXHkyBF69OjBzp076dGjB2vWrNF1iUIIIUSakSbC+/79+3Ts2BFLS0tOnz5NbGwstWrVAsDV1ZUXL17ouEIhhBAi7UgT4W1iYkJsbCwAZ8+exdbWVj3imq+vL1ZWVrosTwghhEhT0kSHtdKlS7NixQoCAwM5dOgQjRs3BuLv7Z4/fz5lypTRcYVCCCFE2pEmjrx/+eUXvL29GTp0KNmyZaN3794A9OzZk5iYGIYNG6bjCoUQQoi0I00ceefIkYMDBw7g5+dHxowZ1dMXLVpEoUKFMDIy0mF1QgghRNqSJsIb4h/7+e/gBihevLiOqhFCCCHSrjQR3jK2uRBCCJF0aSK8ExrbPDQ0lGvXrvHy5Uu55i2EEEL8S5oI7y+NbT5ixAhu375N06ZNU7EiIYQQIu1KE73Nv6Rx48YcOHBA12UIIYQQaUaaD++XL18SExOj6zKEEEKINCNNnDZfvHix1rTY2Fi8vb3Zu3cv7u7uOqhKCCGESJvSRHjPnj07wemWlpZUr16d0aNHp25BQgghRBqWJsL7/v37Gr/HxcXh7++Pra3tV28hE0IIIX42CpVKpdLVmz958oQdO3ZgYGBA06ZNyZ07N+vXr2fOnDmEhIRgZWVF165d6dGjh65KFEIIIdIcnYX35cuX6dq1K0qlEjMzM8LDw+nfvz9Tp06lQoUKFCpUiJs3b3LlyhXGjBlDmzZtkv0e4THnvkPl4nswM6xArOqmrssQSaRUxI9++CFij44rEUmV2bQBAPkrLdFxJSKpHp3umeg8nZ02nz9/Ps7OzsybNw8TExNmzpzJtGnTaNq0KRMmTFC3+/XXX9m2bds3hbcQQgjxI9LZrWJ3796lZcuWmJiYANCxY0dUKhW1a9fWaNegQQOePXumixKFEEKINEln4R0cHIyNjY369/Tp0wOQIUMGjXampqZERESkZmlCCCFEmqbTQVqUSqX650+9yg0M0vy4MUIIIYROpbmklFvDhBBCiC/T6X3e48ePx9LSEoBPnd7//PNPLCws1G1CQkJ0UpsQQgiRVuksvMuWLQtAdHT0F6eZmJjg5OSUusUJIYQQaZjOwnvdunW6emshhBBCr6W5a95CCCGE+DIJbyGEEELPSHgLIYQQekbCWwghhNAzEt5CCCGEnpHwFkIIIfSMhLcQQgihZyS8hRBCCD0j4S2EEELoGQlvIYQQQs9IeAshhBB6RsJbCCGE0DMS3kIIIYSekfAWQggh9IyEtxBCCKFnJLyFEEIIPSPhLYQQQugZCW8hhBBCz0h4CyGEEHpGwlsIIYTQMxLeQgghhJ6R8BZCCCH0jIS3EEIIoWckvIUQQgg9I+EthBBC6BkJbyGEEELPSHgLIYQQekbCWwghhNAzEt5CCCGEnpHwFkIIIfSMhLcQQgihZyS8hRBCCD0j4S2EEELoGQlvIYQQQs9IeAshhBB6RsJbCCGE0DMS3kIIIYSeMdR1AeKzP8eu4uXL96xYPUpjur9/EPNmb+fUyRtERkThWDgXAwc3p3gJB412b177MGPaJq5cegBApSolGDK8JTY2Vqm2Dj+rq1fvMXvWRu7cfoKVlQXVqpejX/8WWFt//uzv3H7CzJme3Lj+EAMDA8qWLczwEe3JkzebDiv/8V30esDaZcd4cO81BgoFhYvnpHu/2hQpnot3b/xp4THpi6+fu7wXpcrG/61dvfiYFQsP8fjhOywsTHCvWZxu/Wpjbm6SGqvywxs/vBK5s6en3cC9GtOdS2dlYBcnHPPZEhIazd8nnzJr+SXCwmM02tmkN2VIj3JUc82FqYkhdx76Mn3JRW7c/aDRrkwxO4b2KEfRghkJDI7i6NnnzF15hY+BEd99HVOKhHcasXP7aXZuP02ZsgU1poeGhtO142R8PgTQtkNNrKzM2bThGD26TGX9pjHky58dgICAELp1nkJMdCydu9YhJjaONasO8vDBKzw3jcXIWDb193Lp4h26dxuPlZUFPXo2wcDAgHVr93Pxwm08N44nfXpLnj19Q8cOf2BmZkLvPs0AWL1qL+3ajmHnrulkzmKj47X4MV2/8oThfVeQxyEL3fvVJjY2jl2bz9G/yyLmr+pDHgc7fpvQSut1kZExzJm8iww2luQraA/AtUuPGdJrKQULZafXwDp88A5k64Yz3L/zmvmremNgICcy/4tmdQvSsn4hLl5/qzG9fKmsrJ5Rl9sPfZm+5BL2mS3o2KwYRQtmpE3/PahU8e0szIzYML8BmW3NWbX1FkHBkbRrUpS1s+vRtOdOHj37CEC5kvasml6XoJBIFq+/Tmysik7Ni+FSOisteu8iKCQqtVf9m8g3uo7FxsaxfMleFi/cneD8lcsP8PyZN8tXj6SMU3yw16pdnnq1R7B65UHGT+oOwLo1h/jw/iNbd44jr0NWAIoVz0uvbtPZs9uLps0rp84K/YQmjF+BUmmA58bx5MxpB0D1GuVo1HAoSxbvYMTIDqxdu5+wsAjWrv+LwoXzAFDeuSgtm49mzZp9DB/RQZer8MOaN3UPme3Ss2R9f0zNjAGoXb8M7RpNY9n8v5m1pAe16pXRet3cqbuJiYll7KTWpLMyB2DBzH1kscvAvJW9MTE1AiCLfQZmTtzJpXMPcXZzTL0V+4EYGCjo074U/Ts7JTh/VB9n3n4IoW3/PURGxQLw9n0Ifw6pSMVyOTh98RUAPdqWJE+ODLQbuJfL/7wDYP/xJxzf1JrurUsyYuIJAMYOciU2Lo6WfXbz8m0QAEfOPGfvqmb07lCaKQsvfO9VThGyq6hDkZHRtG72B4sW7KJufRcyZ7HWmK9Sqdi724uKlYqrgxsgY6b0DB7WklKlC6inHTp4EaeyjurgBnB2KULuPHYcOnjx+6/MT+rN6w88evSK+g0qqYMbIG/ebLhXcWL3rpMAvH71AWvrdOrgBihWLB8ZMqTj0cOXqV32TyE4KIzHD9/hXrOEOrgBbGzTUbJMXm7feJ7g6548esf2jV7UaeBEidJ5gfi/1QzWFtRrWl4d3AAly8TPf/Lw3fdbkR+YsbGSXcubMrBrWXYdfoj3hxCt+f4BEWzZe18d3ACXbsR/3o4On89YNaldgJPnX6qDG8DXP5zJCy9w5Wb8tGx2lhTMa8vuw4/UwQ3w9GUAJ869oEntz9+paZ0ceetQZGQ0IaHhTJnRm1q1y1GnxjCN+W/f+PLh/Uc6dakDxId5eFgk5hamtGxdVd0uKDCU1698qF5De8/VsVAuzp6++X1X5Cf2/oM/AAUK5NSalzOXHUeOXOTdO19y5bLj/Pmb+PsHYmOTHoCAgGCCg0PJmMla67XivzO3MMVz93DM/hXcnwQGhKE0VCb4umXz/sbExIhu/Wqrp5mYGDFjUXetto8exJ/izWKfIWWK/smYGCuxtDBiwO9HOHjiKSc2t9GYHxUVS9fhB7ReVzi/LRB/BA6Q3T4ddpktWbbxH3UbczNDwsJj2LDrrnpalowWADx46q+1zBdvgqhVOS92mS3w/hD631fuO0sz4R0ZGcnNmzf58OEDbm5uhIeHY2dn9/UX6jFLS1P2HJiMYSJfIi9evAfAxsaKmdM3s2PrKUJCwsmRIzPDRramsntJAD58iL+W8/+P3AEyZcpASEg4wcFhpEtn/n1W5CdmZhbfUSk0VLujS0BAMAC+vgF06daQEyevMmzoHEaO6ogCmDZ1HUZGhrRr75GaJf80lEoDcuTKpDX98cO33LrxnHIVtI+yHj98i9epu7TqUImMmRLv6On99iPXLj9mwYx95M1nR8WqRVO09p9FSGgUNdpsIjZWlaT2WbNY4lwqK6P6uvDgqR+HzzwHIHf2+B1iv4/hjOztTMv6jqSzNOHF60Amzj/P8XMvAAiPiO/gZmFmpLXsDFbxf8uZbMwlvJPK09OTOXPmEBQUhEKhYNu2bcyZM4eoqCgWLlyIufmPGToGBgZ8qY9LcHAYAAvm7cDI0JDho9qgVBqwZtVBBg+Yy8KlQ3F2KaIODlNT7SMME5P4/6Th4ZES3t+Bg0N2LC3NOHL4At17NEKhUAAQGRmF19l//vdzNFmzZqJHjyZMGL+Cxg3jz7AolQbMmjNU41S6+L7CwiKZ8NsmANp2cdeav2vLeZRKA5q2dkt0GUGBYTSvMxEAU1MjBo1qpP47E8mjUpHk4E6fzoRTW9sCEBYezV+zvYj636n0dJbx332DupUlJiaO8XPPERunolvrEiycUJMuww5w7uobHj//SHBIJLUq52WJ5w31so2NlVQslwOIPxugD3R+zXvbtm2MHz+exo0bs3r1alT/6zrYrFkzbt26xbx583Rcoe5ER8XvJYYEh7F6/S80bOxGvQYVWLFmNOnSmTN31jYA4uLiP7NPwZEQA4XON/UPydjYiI6d6nPnzlNGDJvDwwcvuHfvGYMGzCAsPH6nylBpwNw5m/jzj6WUKlWQqdMGMHlKP4oVy8eQwTM5cfyKjtfi5xARHsXoAat4/OAdbbu4U8pJ81bLyIhoDu+/jmvlwthlTfxShkIBf0xpy6/jW5HbIQuDey7lxBG5NPW9qVQqBv5xlGHjj/P4+UfWzKxH7crxO77GRvGBa2VpTMs+u9jx90N2H35Em/57CAqJYmiPcgBEx8SxcsstijlmYuaYqhTIa0OhfLbM/6sGZqbxx7IxSdyZ0DWdf6OvWLGCzp07M3r0aMqWLaueXrNmTQYPHsyhQ4d0WJ1ufbpWV7V6GazSW6inW1mZU9m9FPfuviAsNAILC1MAIiO0b3GIjIwGULcRKa93n6Z06FiXgwfP0ajhMJo2HoGBgYJu3RoBoDAwYOWKPRQt6sDK1WOpV78iDRpWZs26P3FwyM7YsYuJiorW7Ur84IKDwhnSaxnXLj+hbqOy9OhfW6vNtcuPCQ+LxL1m8S8uK52VOdVql6R2/TLMX9mHLPYZmD997xdfI/67oJAoDhx/og7lt++DGd3PBfh8Ovzw6Wcat3oFh0Rx3OsFRQtmwtwsPpwXrLnKqi038ajqwP7VzdmzshlxKhXLNtwAIDBIP+711nl4v379Gje3hE9R5c+fHx8fn1SuKO34dA07oUFWbGzSoVKpCAuLxM4+vvOGj2+gVjufDwGkszLHTAaR+G4MDAwYNboTJ08vZZ3nXxw5tpAFi0YREhKGUmlAdHQMUVHReNR1Ran8fErOyMiQevUr4ucbyNOnb3S4Bj+2j34hDOy2mFs3ntOgaXlG/tE8wbNUF87cx8hIiUvFQkletompERUqFeaDdwABH9P+ddIfRWRULCfOvyRrlnRYpzflvU/8Z+/3MVyrrV9AOAYGCsz/d51bpYKJ88/j2mQ9rfrupnJzT3qNPoSFuTExMXG8eR+itYy0SOfhbWdnx82bCZ9yunfv3g/fae1L8uXLjrGxIU+eaH+xv3nji4mJEdY26bCyMidb9kzcv/tCq939+y8oXCR3KlT789q/7yyXLt4hY8YMlClTiGzZ4jtJXblyj8JF8qr7GsTGxWm9Ni42ftqnSx8iZYWFRjC09zIePXhLi3YVGT62WaKXl27deI5jkRxYWGqfpXrx7APN60xk5+ZzCbxHJAqFAmM9uVaqT/LmzMCJzW1o06iw1jwLMyPi4lRERcXy8Jk/kZEx5M+jPdhRdrt0RETG4B8Qf0Rdr5oD5Ura4/cxnKu3vNU91suWsOfOQx/1dfS0Tufh3bRpUxYuXMjq1at5/fo1ABERERw7doxFixbRsGFDHVeoO2bmJlR2L8Xpk//w+PHnAH/z2odTJ65TpWoplMr4TVitRhkuXrjLs6ef73G8cP4Oz595U7tO+VSv/Weyds0+xo9fQUzM5z/6Uyevcu3qfdq0qU2+fNnJnNmaXTtPEhn5+ZReZGQUu3efwto6Hfnz59BF6T+8mRN38ujBW5q3daP/8AaJtouJjuX50/fkd0x4qNpsOWwJCY5g19bzREd/HpLT++1HTh27RckyeTGXS1Mp7sWbQNJZGtO6YWGMDD/HVdYsltSqnIdLN94SGh5NeEQMx7xeUMUlJ/lyf+6vkN0+HdVcc3H07HP1DnKnFsX5fZAbSuXnnbgqzjkpW8Ke9TvvpN7K/Uc6723es2dP3r59y5QpU5gyZQoA7dq1Q6VSUbduXXr37q3jCnVr0NAWXLl8n+6dp9CmXQ2MjAzZsP4IJqbG9B/YVN2uU5c67Ntzjh5dp9KhY20io6JZvfIAhYvkpm59Fx2uwY+va7dGDBo4gz69JlG9RnnevPFhzep9uLmVoF59N5RKJb+N6cqggTNo2Xw0TZtVJTY2jh07TvDs6VsmT+2PkZHO/xR/OM+fvufQvmtYpjMlX8GsHNp3VavNp9HV3nt/JDo6NtH7tQ0NlQwa1ZDxv26if5dF1KxbmsCAMHZs8kKhgEGjGn3HNfl5xcaq+Gu2FzPGVMVzbgN2H3mEtZUJ7ZoURaWCv+Z4qdtOXXyB8qWysn5OfdZsu0V0dBwdmxUlIiqWmUsvqdst23CD+eNqsnRyHQ6ffkY2u3R0aVGM0xdfsefIY12s5jdRqD5179ax58+fc+HCBQICAkiXLh1OTk4ULFjw6y/8gvAY7VNcaVmdGsPImi2j1oNJXr/6wOyZW7lw/g6ooFSZAgwe2kJjNDWA58/eMW3KRq5deYipmTFuFYszeFgLvXgwiZlhBWJV+ttj98B+L5Yt28nLF97Y2qanXv2KdO/RWH0fOMCFC7dYtGAbt28/AaBQ4Tz07NWEihVL6arsb6ZUxHfq+hCxR8eVJG7XlvPMmLDji23O/DMNgLu3XtKz3TyG/daEhs0T39k9fugfPFed4Nljb0zNjClTPj/d+9UmZ27t+8nTmsym8Wce8ldaouNKEndicxveeAdrPZikjnteerQpSYE8NoRFRHP+6htmLrvM89ea/Xxy2KdjeK/yuDplR6GAKze9mbLoAk9eBGi0q1vVgZ5tS5Ire3r8Poaz58gjFq+/QUSk5oNOdO3R6Z6JzksT4f3+/XuuXbtGnTrxI4m9ePGCAwcO0KJFC2xtbb95ufoW3j8zfQ/vn40+hLfQpA/hLTR9Kbx1fs377t27NGjQgFmzZqmn+fj4sHLlSho2bMjTp091WJ0QQgiR9ug8vKdOnUrBggXZsePz6S0nJydOnjyJg4MDU6dO1WF1QgghRNqj8/C+desWPXv2xNLSUmO6hYUFXbt25dq1azqqTAghhEibdB7exsbG+Pn5JTjv01jnQgghhPhM5+FdsWJF5syZw5MnTzSmP3v2jHnz5iU6+poQQgjxs9L5zaXDhw+nVatW1K9fn1y5cmFra4u/vz8vXrzA3t6eESNG6LpEIYQQIk3ReXhnypSJvXv3smPHDq5du0ZAQAD58uWjZcuWNG3aVOtauBBCCPGz03l4A5ibm9OuXTvatWun61KEEEKINC9NhPfLly85deoU4eHhxP2/hzcoFAp69kz8RnUhhBDiZ6Pz8N6zZw+jRo3SCu1PJLyFEEIITToP74ULF+Li4sL48eOxs7OTW8OEEEKIr9D5rWJv3ryhW7du2NvbS3ALIYQQSaDz8M6dOzfe3t66LkMIIYTQGzoP78GDBzN//nwuX75MTEzaehybEEIIkRbp/Jr3tGnT8Pf3p0OHDgAolUqtNrdv307tsoQQQog0S+fhXbduXV2XIIQQQugVnYd3v379dF2CEEIIoVd0Ht6fXL9+HS8vL3x8fOjZsydPnjyhcOHC2Nra6ro0IYQQIk3ReXhHRUUxbNgwDh8+jJGRETExMbRo0YIVK1bw+PFjNmzYQM6cOXVdphBCCJFm6Ly3+ezZs/Hy8mLhwoVcuXIFlUoFwPjx40mXLh2zZs3ScYVCCCFE2qLz8N67dy9DhgyhatWqGBp+PhGQPXt2+vfvz6VLl3RYnRBCCJH26Dy8AwMDyZUrV4LzMmTIQEhISCpXJIQQQqRtOg/vfPnysX///gTnnT59GgcHh1SuSAghhEjbdN5hrXfv3vTv35/AwEDc3d1RKBRcu3aNPXv24OnpydSpU3VdohBCCJGm6Dy8a9SowbRp05gxYwbHjx8HYMKECdjY2DB27Fg8PDx0XKEQQgiRtug8vAHq169P/fr1efr0KQEBAaRLlw4HBwcMDHR+Vl8IIYRIc9JEeH+SN29eXZcghBBCpHk6Ce+iRYuyYcMGihcvTpEiRb76HG95MIkQQgjxmU7Cu1evXmTJkkX989fCWwghhBCf6SS8//0wkv79+2vNj4qKAsDY2DjVahJCCCH0hc57hKlUKqZPn07btm3V065evYqzszPz58/XYWVCCCFE2qTz8J4/fz5r166lQoUK6mkFChSga9euLF++nNWrV+uuOCGEECIN0nlv8507dzJixAjatWunnmZra0vfvn2xsLBg48aNdOrUSXcFCiGEEGmMzo+8/f39E71FrGDBgrx79y6VKxJCCCHSNp2Hd548eThy5EiC844fPy7P8hZCCCH+H52fNu/YsSOjRo0iMDCQ6tWrY2Njg7+/PydOnGD//v2MHz9e1yUKIYQQaYrOw7tRo0aEhoaycOFCDhw4oJ6eIUMGfvnlF5o0aaLD6oQQQoi0R+fhDdC2bVvatGnDs2fPCAgIIC4uDkdHRywtLXVdmhBCCJHm6Oya982bN+nVqxe7du0CQKFQcOHCBbp06UL79u2pWLGi3CYmhBBCJEAn4X3//n3at2/PvXv3MDc3B+DWrVuMGzeOHDlyMG/ePHr37s306dM5duyYLkoUQggh0iydnDZfsmQJBQoUYM2aNerwXrduHQDTpk3D0dERAF9fX9auXUu1atV0UaYQQgiRJunkyPvy5ct07NhRHdwAZ8+eJUeOHOrgBnBzc+Pu3bu6KFEIIYRIs3QS3gEBAdjZ2al/f/r0Kf7+/pQvX16jnZmZGZGRkaldnhBCCJGm6SS8M2TIgL+/v/r3CxcuoFAocHFx0Wj35MkTbG1tU7s8IYQQIk3TSXiXK1eOLVu2ABAbG8uOHTswMTGhUqVK6jZRUVF4enpSunRpXZQohBBCpFk66bDWq1cvWrZsSc2aNVGpVLx69YrevXur7+vevn07np6ePHv2jClTpuiiRCGEECLNUqhUKpUu3vjBgwesXr0af39/KlWqpPE874oVK6JUKhk7dixVq1bVRXlCCCFEmqWz8P6S9+/fkylTJgwMdP7cFCGEECLNSZPhLYQQQojEyaGtEEIIoWckvIUQQgg9I+EthBBC6BkJbyGEEELPSHgLIYQQekbCWwghhNAzEt5CCCGEnpHwFkIIIfSMhLcQaZCMnSSE+BIJ7+9owIABODs7a00/d+4cBQsWxM3NTWve0aNHKViwIBcvXvzm961atSq//vrrF9uMGjWKGjVqfPN7/KgePHjA4MGDcXV1pWjRori5uTFo0CDu37+fajWcOHGCkSNHptr7pTXt27enYMGCGv+cnJzo0KEDly5dStH3mjdvHoULF07RZYrPUuLvSbZRwiS8vyMXFxc+fvzI06dPNaafPXuWDBky4OPjo/Wf+OrVq5iZmVGqVKnULFUA9+/fp1WrVgQFBTFmzBhWrlzJiBEjeP36NS1atODGjRupUseaNWt49+5dqrxXWlWsWDE2b97M5s2b2bBhA5MnT8bExISuXbvy6NGjFHuf5s2bs3HjxhRbnvgspf6eZBslTCePBP1ZuLi4AHD9+nXy5s2rnn727FmaNWvG1q1bOXv2LI6Ojup5V65coWzZshgbG6d6vT+7NWvWYGtry9KlS1Eqlerp1apVo06dOixcuJClS5fqsMKfh6WlJSVLltSY5ubmhouLCzt27EixMxN2dnbY2dmlyLKEppT6e5JtlDA58v6OcufOTdasWbl27Zp6mo+PDw8fPsTV1ZXy5ctz9uxZ9bywsDDu3r2Lq6srADdu3KBz586ULVuWsmXLMnDgQF6/fq1uv2PHDooVK8amTZuoUKECVapU4dWrV1p1BAYGMnr0aMqXL0/ZsmWZNm0acXFx33HN9ZOfnx8qlUrrs7GwsOCXX36hTp06QPxp3V9//ZX58+fj7OxM2bJlGTJkCP7+/hqvO3XqFK1ataJUqVK4uLjw22+/8fHjR/X8efPmUbt2bebOnUv58uWpVasWTZo04fz581y6dEnj8smaNWuoXbs2xYoVo2LFivzxxx+EhIR8508kbTExMcHU1BSFQgFAwYIFWbhwoUab/3+K9eXLl/Tq1Yvy5ctTokQJWrZsyalTpxJt3759e8aOHcuSJUuoXLkyxYoVo1WrVty6dUvjfR48eED37t0pVaoUZcqUYeDAgXh7e2u0+do28/LyokWLFpQqVYqyZcvSp08fnjx58t8/qDQiqX9PALt27aJRo0aUKFGCqlWrMnfuXGJjY4GET5sfOXKEJk2aUKxYMdzc3JgyZQpRUVHq+Z/+to4dO0b9+vUpWrQotWrVYvfu3RrL+fDhAyNGjMDZ2ZnSpUvTsWNH7ty5o54fFxfH4sWLqV69OkWLFqV27dps3bo1xT6j/0LC+ztzdnbm+vXr6t+9vLwwNTXFyckJV1dXrl69Snh4OAD//PMPMTExVKhQgXPnztGmTRsMDQ2ZMmUKY8eO5d69e7Rq1QpfX1/18qKjo1m+fDmTJk1i0KBB5MiRQ+P94+Li6NatG6dOnWLEiBFMnjyZa9euceDAgdT5APRIpUqVeP36Na1atcLT01Pji7R27do0btxY/fvhw4fZv38/f/31F6NGjcLLy4tu3bqpv6i2b99Ojx49yJkzJ3PmzGHw4MGcOHGCDh06qLc3wKtXrzhx4gQzZ85k0KBBTJ06lWLFilG4cGE2b95MkSJF2LdvH9OmTaNt27asWLGCvn37snv3biZMmJB6H04qU6lUxMTEEBMTQ3R0NL6+vsyaNYvw8HCaNm2apGXExcXRs2dPwsPDmTp1KgsXLiRDhgz07t2bly9fJvq6AwcOcOLECcaMGcPMmTPx9fVl4MCB6m377NkzWrduTWBgINOmTWPcuHE8fPiQtm3bEhwcDPDVbfbq1Sv69OlD0aJFWbRoEePHj+fp06f07Nnzh+msmNS/J09PT0aOHEnx4sVZsGABnTp1YtmyZcyYMSPB5e7du5d+/fqRP39+FixYQK9evdi8eTNDhw7VaPf+/XsmTJhAx44dWbp0KdmzZ2fkyJE8f/4cgNDQUFq3bs2VK1cYNWoUc+bMIS4ujs6dO6t3xP744w/mz59P48aNWbx4Me7u7owZM4Z169Z9h08seeS0+Xfm7OzMzp07CQgIIEOGDJw9exYnJyeMjY1xdXUlKiqKS5cuUblyZa5cuUKmTJkoUKAAo0ePxsHBgSVLlqifa16mTBlq1arFihUr1KcNVSoVffr0oXLlygm+/+nTp7l58ybLly+nYsWKQPzp/KpVq6bOB6BH2rZti4+PD6tWreKvv/4CwMbGBjc3N9q3b0/x4sXVbcPDw1m5ciX29vbqdr169eL06dNUqlSJmTNnUqVKFaZOnap+jaOjI82bN2fHjh20bdsWgJiYGEaNGkX58uXV7SwtLYmNjVWfNr506RLZs2enXbt2KBQKypUrh7m5OYGBgd/7I9GZCxcuUKRIEa3pw4cPx8HBIUnL8PPz4+nTpxp/H8WLF2f+/PlERkYm+rrY2FiWL1+OpaUlEP8lP3LkSB4+fIijoyPz58/H3NycVatWYWFhAUDZsmWpXr0669evp3fv3l/dZjdv3iQiIoKePXuSJUsWAOzt7Tl27BihoaHq99ZnSfl7iouLY8GCBdSuXVvdxs3NjaCgILy8vLR2ZFQqFdOnT8fd3Z0pU6aop9vZ2dG3b1+uXr1KmTJlgPgzmYsWLVJ3Gs6dOzfu7u6cOnWK3Llzs3PnTt68ecOePXsoUKAAACVLlqRx48Zcu3aNQoUKsWXLFkaMGEGXLl3UtcXGxjJnzhyaNWuGmZnZ9/0Qv0COvL8zFxcXVCoVN27cQKVSce7cOXUv8xw5cpAzZ07OnTsHwOXLl6lQoQJhYWHcuXMHDw8PdXADZM2aFScnJ60et5/+4yXkypUrmJiYqIMbwNzcPNGw/5kpFAoGDx7M2bNnmTlzJs2aNcPCwoI9e/bQokULPD091W3LlCmjDm6AKlWqYGxszJUrV3j27Bm+vr7UrVtXY/nFixcnV65cWncSfGn7QfwO4LNnz2jcuDHz58/n1q1b1K9fn/bt26fAWqdNxYsXZ9u2bWzbto2tW7eydOlSmjRpwrRp01i7dm2SlpExY0by5cvHmDFjGDlyJHv37iUuLo7Ro0eTP3/+RF9XsGBBjfD8FK5hYWFA/I6Fs7MzJiYm6rMD1tbWFC9eXP23/LVtVqJECUxMTGjWrBkTJkzgzJkzODo6Mnjw4B8iuCFpf0/Pnj3Dz89P686Xfv36sXHjRvUlkk+ePn2Kt7c3VatWVX/2MTExVKxYESMjI/Xn/0np0qXVP3+6bv7pzNfVq1fJlSuXxt9funTpOHr0KB4eHly4cAGVSoW7u7vGe1WtWpXg4GBu3ryZop9XcsmR93eWOXNmHBwcuH79OpkzZ8bPz099TRvA1dWVixcvEh0dzc2bN2nSpAnBwcGoVCoyZsyotTxbW1vevn2rMS2hdp8EBgZibW2tNT1Tpkz/Ya1+bFZWVtStW1cdvnfv3mXEiBFMmTKFevXqAfHb9d8UCgU2NjYEBQUREBAAJPwZ29raalz3VCqVCW6ff/Pw8CAuLo4NGzawcOFC5s2bR7Zs2Rg2bBgeHh7/ZVXTLAsLC4oVK6YxrXLlynh7ezNnzhz1mYsvUSgUrFy5kkWLFnHkyBF27dqFkZER1atX588//yR9+vQJvs7U1FTj90870J9OmwcEBLB371727t2r9drcuXMDX99m2bNnZ/369SxdupRt27axdu1arKysaNOmDYMGDdIKLX32pb+nVatWAfF/F0nx6W9rzJgxjBkzRmv+hw8f1D8rlUqNjr8Jbccvve+n96pdu3aC8//9Xrog4Z0KXFxc+Oeff7C0tCRLliwae/1ubm5s3ryZy5cvEx4eToUKFbCwsEChUGhc2/7Ex8fnq1/2/2ZtbY2/vz8qlUrjC+HTf0wRz9vbm2bNmjFw4ECaN2+uMa9w4cIMGjSIvn37qjsM/v/PT6VS4efnh42NjToUfHx8tN7Hx8eHEiVKJLu+evXqUa9ePYKDgzl79izLli1j+PDhlCtX7os7bz+aQoUKce7cOXXnwP/fGerT0fEnWbJk4Y8//uD333/n/v37/P333yxbtgxbW9sEv/yTwtLSkkqVKtGhQwetef8Oi69ts0+n8KOiorh69SqbN29m8eLFFC5cmFq1an1TbWlFUv+ePm2//9/Z09fXl0ePHmkcOUP8kTHA6NGj1afH/y05343p0qVL8JbMK1eukDFjRvV7rV+/XmuHDiB79uxJfq/vQU6bpwIXFxfu3LnDlStXNI66If70moGBAZs3b6ZAgQJkzpwZCwsLihQpwoEDBzS+nN69e8e1a9e0/kN/7b2joqI4duyYelpUVBReXl7/fcV+IJkyZUKpVLJhw4YEr4c+ffoUU1NTcubMCcC1a9cICgpSzz9+/DjR0dE4OzuTN29eMmbMyP79+zWWcfPmTV69evXV7ffv22oAhg4dSr9+/YD4L5w6derQp08fYmJiEtxB+JHdunWL9OnTY2Njg6WlpdaX77/v7Lh58yYVKlTg5s2bKBQKChUqxODBgylQoMB/uo++XLlyPHnyhCJFilCsWDF1B8OlS5dy+vRp4OvbbN26dVStWpWoqCiMjY1xcXFh3LhxAD/EPf5J/XtydHQkQ4YMHD9+XGP+5s2b6dOnj9brHBwcsLGx4c2bN+rPvlixYlhbWzN9+vRk9dYvXbo0L1680HhNaGgovXr1Yv/+/Tg5OQHxZy///V7v3r1j7ty5Gh1PdUGOvFNBuXLlCA0NxcvLi8mTJ2vMs7S0pFixYhw7dkzjVODgwYPp3r07vXr1onXr1oSGhjJv3jwsLS3p1KlTkt/bxcUFNzc3fvnlF3x9fbG3t2ft2rX4+/trnfr9mSmVSsaOHUv//v1p2rQpbdu2xcHBgfDwcLy8vPD09GTIkCHqvfHQ0FB69OhBz5498fX1Zfr06bi6uqo7xwwaNIjffvuNESNGUK9ePd6/f8+cOXPIkyePRq/1hKRLl44rV65w/vx5ChcujLOzM7/99htTpkyhUqVKBAUFMX/+fPLkyfPFa7f6LCQkRGMQj4iICPbu3culS5cYPHgwSqWSKlWqsHfvXooVK0auXLnYsWMHL168UL/G0dERc3NzRowYQf/+/cmYMSPnzp3j3r17dO7c+Ztr69u3Ly1atKB37960aNECQ0ND1q9fz7lz52jdujXAV7eZoaEhU6dOpW/fvrRr1w6lUsmmTZswMTHB3d39m2tLK5Lz99SvXz8mTJiAtbU1VatW5eHDhyxdupSuXbtiYmKitdxBgwbx559/YmBgQKVKlQgMDGTu3LkEBwcnayS2Zs2asW7dOnr37k3//v1Jnz49K1aswMTEhObNm5M5c2bq1avHL7/8wqtXryhUqBCPHz9m5syZFClShKxZs6b0x5YsEt6pwMrKisKFC3P79m0qVKigNd/NzY3r169rzHNzc2PFihXMnTuXgQMHYmZmRoUKFRg2bFiyQ3f+/PlMnz6d2bNnExkZiYeHBy1atODkyZP/ddV+KNWqVWPLli2sWLGCxYsX4+fnh4mJCYULF2b27NkanWrKlStHqVKlGD58OIaGhtSrV49hw4ap5zdv3hxzc3OWLVtGnz59SJ8+PdWqVWPw4MGYm5t/sY7OnTvzzz//0L17d6ZOnUrz5s2Jiopiw4YNbNiwAVNTU1xcXBgxYgSGhj/mn/CtW7do2bKl+nczMzPy5MnDmDFj1Du5o0ePJiYmhilTpmBoaIiHhwdDhw7l999/B+JPYa9YsYIZM2YwYcIEgoKCyJ07N+PGjaNhw4bfXJujoyOenp7Mnj2bYcOGoVAocHR0ZOnSpeq/4a9ts/z587NkyRLmzZvHkCFDiI2NpWjRoqxcuZJcuXL9h08u7Ujq31P79u0xMzNj5cqVbNq0iaxZszJgwIBEd7BatmyJpaUly5cvZ8OGDVhaWqrHWkhOXx5LS0s8PT2ZMmUKf/31FyqVitKlS7N27Vr1d+zkyZNZvHgx69ev5/3792TMmJFmzZoxYMCA//4B/UcK1Y9yU6EQqaR9+/YolUpWr16t61KEED8pueYthBBC6BkJbyGEEELPyGlzIYQQQs/IkbcQQgihZyS8hRBCCD0j4S2EEELoGQlvIRIxatQoChYsqPGvUKFClC5dmubNm7Nz585UqaNq1aoaDyFp3779Nz0VLiQkRGsYyv/i0+eTFCqViv3799OpUydcXV3Vz1eeOXOm1nPJk7NcIX5WP+YID0KkoNGjR6vHTFapVISEhLBnzx5GjRrFx48f1Y8LTC29evVK9tCMt2/fpnfv3kyfPl3j8aOpISQkhGHDhnHixAnc3Nzo3Lkz5ubmXL16lWXLlnH8+HHWr19PhgwZUrUuIfSZhLcQX1G9enWthxA0a9YMDw8PFixYQLt27TQeSPG9/f/x8ZPi4cOHOnsK0rhx4zh16hRTpkyhUaNG6ult2rShWrVqDBkyhN9++4358+frpD4h9JGcNhfiG5iamlK1alVCQkJ49OiRrstJs+7cucOuXbto1KiRRnB/4uHhgaurKydPnsTb2zv1CxRCT8mRtxDf6NMjVmNjY4H4a9MVKlQgLi6OvXv3Ym1tza5du7CxseH69evMnTtX/bCNUqVKMWjQIIoXL66xzAMHDrBkyRKePXtGzpw5+e2337Tet3379rx580bjSUxPnjxh7ty5XLhwgZiYGAoXLszAgQNxcnJi3rx56qPaDh06kC1bNvVrvb29mTlzJqdPnyY0NBQHBwe6dOlCgwYNNN7z9u3bzJw5k+vXr2NpaUn37t2T9Bl9erLav8cp//8mTpyImZkZVlZWibY5f/48K1as4NatW4SEhGBjY4O7uzvDhg1Tv06lUrFgwQL27t3L27dvSZcuHa6urgwZMgR7e3v1sjZu3MiGDRt4+fIlpqamODk5MWjQII2HvERGRrJw4UL27t3Lhw8fyJIlCw0aNKB3794aZ1kOHTrE0qVLefr0KQYGBhQvXpx+/fol+LhKIVKShLcQ3yAuLo5Lly5hbGyMg4ODevr+/fvJkycPv/76K76+vtjY2ODl5UXPnj1xdHRk4MCBREVFsWPHDtq2bcuqVavUjx7csWMHo0ePVj/w5MWLF/Tq1Yu4uDiyZcuWaC3Pnz9XP92qXbt22NjYsGnTJjp37oynpyc1atTAx8eHzZs306tXL4oVKwbA+/fvad68OSqVivbt25M+fXqOHTvG8OHD+fDhA926dQPg0aNHtG/fHisrK/r06UN0dDQLFixQ77R8yZ07dzAyMqJIkSKJtsmSJcsXl3H27Fm6d+9O6dKlGTBgAAqFAi8vLzZv3kx0dDSTJk0CYPHixSxYsIC2bdtSsGBBXr9+zdq1a7l9+zb79u1DqVSyZ88e/vjjDxo1akT79u3x9/dnzZo1tG/fniNHjpAuXTpiY2Pp2bMn165do0WLFjg4OHD79m0WL17MvXv3WLRoEQqFQv2Es0qVKtG8eXPCw8NZv349nTt3Zv/+/eTIkeOrn48Q30wlhEjQyJEjVQUKFFDduXNH5efnp/Lz81N9+PBBdf36ddXAgQNVBQoUUE2cOFHd3t3dXeXo6Kh68eKFelpsbKyqWrVqqlatWqliYmLU00NDQ1U1atRQNWzYUKVSqVQxMTEqFxcXVdOmTVVRUVHqdtu3b1cVKFBA1a5dO/W0du3aqdzd3dW/Dxw4UFW8eHHV8+fP1dP8/f1VZcqUUQ0YMEBjORcuXNBYv3Llyqnev3+vsd5DhgxRFS1aVOXr66tSqVSq/v37q0qWLKl6+/atus3jx49VRYsWVRUoUOCLn6GHh4fK1dX1i23+v0+f+yddu3ZVubu7qyIjIzXatWjRQlWqVCn173Xq1FH16NFDo83GjRtVDRo0UG+Tbt26qerWravR5uTJkyoPDw/VlStXVCrV58/q9OnTGu02bdqkKlCggOrIkSMqlUql+v3331WlSpVSxcXFqdvcv39fVbNmTdXBgweTtc5CJJcceQvxFQk9f9vY2Jj27dszdOhQjek5c+YkZ86c6t/v3r3Lq1evaN26NYGBgRpt3d3dWb16Nd7e3nz48AE/Pz/69euHkZGRuk3Dhg21ngH/b3FxcZw6dYrKlStrPErS2tqaDRs2qHvJJ/S6o0ePUr58eQwNDTVuIatZsyb79u3Dy8uLevXqcebMGSpXrqxx6tnBwQE3NzeNU/cJMTAwSNIR+pcsWbKEoKAgjdPVHz9+xNLSkrCwMPU0Ozs7Ll68yJo1a6hbty4ZM2akVatWtGrVSqONl5cX8+fPp1GjRmTPnp3KlStTuXJldZvDhw9jY2NDkSJFND6XypUro1QqOXnyJNWrV8fOzo7Q0FDGjx9PmzZtcHBwoGDBghw6dOg/ra8QSSHhLcRXTJs2jYwZMwLxYWRlZYWDgwMmJiZabW1tbTV+f/nyJQBTp05l6tSpCS7/3bt36s5a/w5+AKVS+cXnOwcEBBAWFpZgmwIFCiT6uo8fPxIcHMzRo0c5evRoonV9Wv7/rwsgb968Xw3vTJky8fTpU6KjozV2SpJDqVTy6tUr5syZw+PHj3n58iXv37/XajdixAh69+7NxIkTmTRpEkWKFKFq1aq0aNFC/Zznvn37cuPGDebNm8e8efPIly8fVatWpXnz5up1fPnyJf7+/ri4uCRYz7t37wBo164dZ8+eZf369axfv57s2bPj7u5Os2bNcHR0/KZ1FSKpJLyF+IrSpUtr3SqWGKVSqfF7XFwcAAMHDqRkyZIJviZv3rzqMIqMjNSa/2kZCfl0VGtgkLwbRz69rlatWhpHpv/272u2ya3rk1KlSuHl5cWdO3cSXf+jR4+yceNG+vTpk2BHr02bNvH777+TJ08enJycqFmzJiVKlGDdunXs3btX3c7R0ZFDhw5x5swZTpw4wZkzZ5g7dy6rV69m06ZNODg4YGdnx+7du7l48SLHjh3jzJkzLF26lFWrVrFy5UrKlStHbGwsuXPn5vfff0+w3k8d5CwtLVm/fj03btzg6NGjnD59mnXr1uHp6cnUqVOpX7/+Vz8fIb6VhLcQ39Gnjmbm5uZUqFBBY97NmzcJDAzE1NRUHZTPnz/XaKNSqXjz5o1GT+h/s7a2xtTUlBcvXmjNW7FiBb6+vowcOVJrno2NDWZmZsTExGjV9fbtW+7evYuZmRnW1tZYWlpq1QXw+vXrRNf7kxo1ajB//ny2bduWaHhv27aNs2fPal2CgPidhsmTJ1O+fHlWrlyJoeHnr6w5c+aof46NjeX+/ftYWlpSrVo1qlWrBsT33h88eDBbt25l1KhRPHjwAAAXFxf1kfXVq1fp2LEj69ato1y5cmTPnp3bt2/j7OyssVMUHR3NkSNHsLOzA+DZs2cEBwdTsmRJSpYsybBhw3j8+LG6I6KEt/ie5D5vIb6jokWLkilTJtatW0doaKh6ekhICIMGDWL06NEolUoKFy5MtmzZ2Lhxo8boafv37+fjx4+JLt/Q0BBXV1dOnTqlPp0LEBgYyIoVK9Sn7T+F0KejZUNDQypVqsSpU6e4f/++xjInT55M3759+fjxIwqFgho1anDmzBkePnyobvP69WtOnjz51fV3dHTEw8ODHTt2qG8b+7etW7dy4sQJqlSpQuHChbXmR0REEB4eTu7cuTWC+969e1y6dAmAmJgYYmNj6dChAxMnTtR4fYkSJTTWf+DAgYwYMULjOnzhwoUxMjJSt6latSoBAQFs3LhRY1mbNm1i8ODBnD9/HoDx48fTp08fje2aN29erKyskn0mRIjkkiNvIb4jIyMjxowZw6BBg2jSpAnNmjXDxMSErVu38vbtW6ZPn64OpTFjxtC3b19atmxJ06ZNef/+PZ6enl8dNnTo0KE0b96c5s2b07ZtWywtLdmyZQthYWEMGjQIiD/Shvh7nH19falfvz7Dhg3j4sWLtG3blrZt25I1a1ZOnjzJiRMnaNmypfpof+DAgZw8eZL27dvTqVMnlEol69atw8LCgqioqK9+Br///juvXr1iyJAh7N69Wz1C3Pnz5zlx4gQODg5MmDAhwdemT5+eEiVKsGPHDiwtLcmTJw+PHj1i69at6oAMDQ0lffr0tG/fnkWLFtG3b18qVqxIREQEmzdvxszMjKZNmwLQtWtXfvvtNzp16kTt2rVRqVTs3r2byMhI2rRpA6Aet37cuHHcuXOH4sWL8/DhQzZv3kyRIkVo0qQJAJ07d6Z79+60bduWRo0aYWJiwtGjR3n58iVTpkz56ucixH+hUKlUKl0XIURaNGrUKHbu3MmxY8eSdM27atWqZMuWjXXr1mnNO3/+PIsWLeLWrVsYGBiQP39+evbsibu7u0a7M2fOMG/ePB48eECWLFkYMGAAnp6eGBoaqpeb0CAtjx49YubMmVy6dEk9WMjQoUPVR7PR0dEMHz6cEydOYGJiwpkzZzAxMeHFixfMnTsXLy8vwsLCyJEjB82bN6d9+/Ya1++fPXvG1KlT1fe2N2/eHIjvCf7pVPSXhIeHs3XrVvbs2cOrV68IDQ0lR44c1KlTh65du2JhYaH1uX9a7rt375g0aRIXL14kKiqKbNmyUbduXRwc/q+dO8SBEAbCKPwn3IELbHA9BQbRU+BICJchQdTWk9TiuAiCBIXGYJp1JKgVqyZ53wFG1DzRaT8ahkHjOKppGuWcFWPUPM86jkNFUTxvw51zz/yUkmKM2vddOWc559R13evb2eu6NE2TlmXReZ4qy1J1Xavv+9cG/7quCiFo2zbd962qqtS2rbz3P88E+AfxBgDAGC5mAAAwhngDAGAM8QYAwBjiDQCAMcQbAABjiDcAAMYQbwAAjCHeAAAYQ7wBADDmC3+8JiSOlMeNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_mat('YlGnBu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "so the best way to get feature is :\n",
    "    TF-IDF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gaussian Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "NB=GaussianNB()\n",
    "NB.fit(train_arr,train_y)\n",
    "pred=NB.predict(test_arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation of Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the model\n",
      "0.7948684210526316\n",
      "Accuracy of the model in percentage\n",
      "79.487 %\n"
     ]
    }
   ],
   "source": [
    "eval_model(test_y,pred)\n",
    "    \n",
    "x=round(accuracy_score(test_y,pred)*100,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_mat('Greens')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multinomial Naive Bayes (NB_MN) Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first 20 actual labels\n",
      "[3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]\n",
      "first 20 predicted labels\n",
      "[3, 4, 4, 2, 1, 4, 4, 4, 1, 4, 4, 4, 4, 2, 4, 3, 4, 4, 1, 3]\n"
     ]
    }
   ],
   "source": [
    "NB_MN=MultinomialNB()\n",
    "NB_MN.fit(train_arr,train_y)\n",
    "pred=NB_MN.predict(test_arr)\n",
    "\n",
    "print(\"first 20 actual labels\")\n",
    "print(test_y.tolist()[:20])\n",
    "print(\"first 20 predicted labels\")\n",
    "print(pred.tolist()[:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the model\n",
      "0.8310526315789474\n",
      "Accuracy of the model in percentage\n",
      "83.105 %\n"
     ]
    }
   ],
   "source": [
    "eval_model(test_y,pred)\n",
    "    \n",
    "d=round(accuracy_score(test_y,pred)*100,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_mat('YlGnBu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Alogrithm Results Table with TD-IDF :\n",
    "\n",
    "|Sr. No.|ML Algorithm|Accuracy of the model|\n",
    "|-|-|-|\n",
    "|1|Decision Tree Classifier|76.961 %|\n",
    "|2|Gaussian Naive Bayes|79.658 %|\n",
    "|3|(NB_MN) Classifier|83.105 %|\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}