Chatbot_T5_Evaluation Metrics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "gWGLkxdaLRHE",
   "metadata": {
    "id": "gWGLkxdaLRHE"
   },
   "source": [
    "## Install Libraries, Import Libraries, Collect Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8yeMXjFPDbG7",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "8yeMXjFPDbG7",
    "outputId": "35fe286a-58b0-43aa-ddf0-1ca6e71125f1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset URL: https://www.kaggle.com/datasets/rajathmc/cornell-moviedialog-corpus\n",
      "License(s): CC0-1.0\n",
      "cornell-moviedialog-corpus.zip: Skipping, found more recently modified local copy (use --force to force download)\n",
      "Archive:  cornell-moviedialog-corpus.zip\n",
      "replace .DS_Store? [y]es, [n]o, [A]ll, [N]one, [r]ename: A\n",
      "  inflating: .DS_Store               \n",
      "  inflating: README.txt              \n",
      "  inflating: chameleons.pdf          \n",
      "  inflating: movie_characters_metadata.txt  \n",
      "  inflating: movie_conversations.txt  \n",
      "  inflating: movie_lines.txt         \n",
      "  inflating: movie_titles_metadata.txt  \n",
      "  inflating: raw_script_urls.txt     \n"
     ]
    }
   ],
   "source": [
    "# Download directly from Kaggle - Thai is using this 'code block' for file path\n",
    "# !pip install kaggle\n",
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "# Set Kaggle API credentials\n",
    "os.environ['KAGGLE_USERNAME'] = 'outhaixayavongsa'  # Replace with your Kaggle username\n",
    "os.environ['KAGGLE_KEY'] = '013bebdbf0776ed704f846ef0b3b3381'  # Replace with your Kaggle API key\n",
    "\n",
    "# Download the dataset\n",
    "!kaggle datasets download -d rajathmc/cornell-moviedialog-corpus\n",
    "\n",
    "# Unzip the dataset (A for All and Press Enter))\n",
    "!unzip cornell-moviedialog-corpus.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "icQcxa97K9Ay",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "icQcxa97K9Ay",
    "outputId": "5c59ee94-524b-448b-9075-2c123209af9f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Extracted files:',\n",
       " ['.config',\n",
       "  '.DS_Store',\n",
       "  'movie_titles_metadata.txt',\n",
       "  'raw_script_urls.txt',\n",
       "  'chameleons.pdf',\n",
       "  'README.txt',\n",
       "  'movie_conversations.txt',\n",
       "  'cornell-moviedialog-corpus.zip',\n",
       "  'movie_characters_metadata.txt',\n",
       "  'movie_lines.txt',\n",
       "  'sample_data'])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List files to ensure they were extracted from kaggle\n",
    "extracted_files = os.listdir()\n",
    "\"Extracted files:\", extracted_files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "AtAY_EdMLqXx",
   "metadata": {
    "id": "AtAY_EdMLqXx"
   },
   "source": [
    "## Load and Explore Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "51d61db5-d3f9-4dd4-b6ab-e7c801e2f92f",
   "metadata": {
    "id": "51d61db5-d3f9-4dd4-b6ab-e7c801e2f92f"
   },
   "outputs": [],
   "source": [
    "# Individual Team member's file path\n",
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "# Define the folder path\n",
    "# folder_path = 'C:/MS_AAI/CornellMovie/' #Anand data path file\n",
    "folder_path = 'C:/Users/Saad/Desktop/Saad Learnings/Python/School Python/Natural Language Processing/Project/CornellMovie/' #Saad data path file\n",
    "#folder_path = './' # Thai data path file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "-Dgu7cY-HM1D",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "-Dgu7cY-HM1D",
    "outputId": "d98ccf18-79f3-4ed7-83fc-15f66db22eef"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data loaded successfully!\n"
     ]
    }
   ],
   "source": [
    "# Initialize a dictionary to store file content\n",
    "data = {}\n",
    "\n",
    "# Loop through each file in the directory\n",
    "for file_name in os.listdir(folder_path):\n",
    "    if file_name.endswith('.txt'):\n",
    "        file_path = os.path.join(folder_path, file_name)\n",
    "        with open(file_path, 'r', encoding='utf-8', errors='replace') as file:\n",
    "            content = file.readlines()  # Read each line\n",
    "        data[file_name] = content\n",
    "\n",
    "df = pd.DataFrame(dict([(k, pd.Series(v)) for k, v in data.items()]))\n",
    "\n",
    "# Load completed\n",
    "print(\"Data loaded successfully!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f7572640-f552-4870-a7cd-421ad09986b3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1077
    },
    "id": "f7572640-f552-4870-a7cd-421ad09986b3",
    "outputId": "ea460a7b-864d-4311-bbde-822f5e96d5cb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. Basic Information:\n",
      "Number of rows: 304713\n",
      "Number of columns: 6\n",
      "\n",
      "2. Data Types:\n",
      "movie_characters_metadata.txt    object\n",
      "movie_conversations.txt          object\n",
      "movie_lines.txt                  object\n",
      "movie_titles_metadata.txt        object\n",
      "raw_script_urls.txt              object\n",
      "README.txt                       object\n",
      "dtype: object\n",
      "\n",
      "3. Missing Values:\n",
      "movie_characters_metadata.txt    295678\n",
      "movie_conversations.txt          221616\n",
      "movie_lines.txt                       0\n",
      "movie_titles_metadata.txt        304096\n",
      "raw_script_urls.txt              304096\n",
      "README.txt                       304600\n",
      "dtype: int64\n",
      "\n",
      "4. Descriptive Statistics:\n"
     ]
    },
    {
     "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",
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       "    }\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>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>movie_characters_metadata.txt</th>\n",
       "      <td>9035</td>\n",
       "      <td>9035</td>\n",
       "      <td>u0 +++$+++ BIANCA +++$+++ m0 +++$+++ 10 things...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movie_conversations.txt</th>\n",
       "      <td>83097</td>\n",
       "      <td>83097</td>\n",
       "      <td>u0 +++$+++ u2 +++$+++ m0 +++$+++ ['L194', 'L19...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movie_lines.txt</th>\n",
       "      <td>304713</td>\n",
       "      <td>304713</td>\n",
       "      <td>L1045 +++$+++ u0 +++$+++ m0 +++$+++ BIANCA +++...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movie_titles_metadata.txt</th>\n",
       "      <td>617</td>\n",
       "      <td>617</td>\n",
       "      <td>m0 +++$+++ 10 things i hate about you +++$+++ ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>raw_script_urls.txt</th>\n",
       "      <td>617</td>\n",
       "      <td>617</td>\n",
       "      <td>m0 +++$+++ 10 things i hate about you +++$+++ ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>README.txt</th>\n",
       "      <td>113</td>\n",
       "      <td>85</td>\n",
       "      <td>\\n</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                count  unique  \\\n",
       "movie_characters_metadata.txt    9035    9035   \n",
       "movie_conversations.txt         83097   83097   \n",
       "movie_lines.txt                304713  304713   \n",
       "movie_titles_metadata.txt         617     617   \n",
       "raw_script_urls.txt               617     617   \n",
       "README.txt                        113      85   \n",
       "\n",
       "                                                                             top  \\\n",
       "movie_characters_metadata.txt  u0 +++$+++ BIANCA +++$+++ m0 +++$+++ 10 things...   \n",
       "movie_conversations.txt        u0 +++$+++ u2 +++$+++ m0 +++$+++ ['L194', 'L19...   \n",
       "movie_lines.txt                L1045 +++$+++ u0 +++$+++ m0 +++$+++ BIANCA +++...   \n",
       "movie_titles_metadata.txt      m0 +++$+++ 10 things i hate about you +++$+++ ...   \n",
       "raw_script_urls.txt            m0 +++$+++ 10 things i hate about you +++$+++ ...   \n",
       "README.txt                                                                    \\n   \n",
       "\n",
       "                              freq  \n",
       "movie_characters_metadata.txt    1  \n",
       "movie_conversations.txt          1  \n",
       "movie_lines.txt                  1  \n",
       "movie_titles_metadata.txt        1  \n",
       "raw_script_urls.txt              1  \n",
       "README.txt                      25  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5. Sample Data (first 5 rows):\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>movie_characters_metadata.txt</th>\n",
       "      <th>movie_conversations.txt</th>\n",
       "      <th>movie_lines.txt</th>\n",
       "      <th>movie_titles_metadata.txt</th>\n",
       "      <th>raw_script_urls.txt</th>\n",
       "      <th>README.txt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>u0 +++$+++ BIANCA +++$+++ m0 +++$+++ 10 things...</td>\n",
       "      <td>u0 +++$+++ u2 +++$+++ m0 +++$+++ ['L194', 'L19...</td>\n",
       "      <td>L1045 +++$+++ u0 +++$+++ m0 +++$+++ BIANCA +++...</td>\n",
       "      <td>m0 +++$+++ 10 things i hate about you +++$+++ ...</td>\n",
       "      <td>m0 +++$+++ 10 things i hate about you +++$+++ ...</td>\n",
       "      <td>Cornell Movie-Dialogs Corpus\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>u1 +++$+++ BRUCE +++$+++ m0 +++$+++ 10 things ...</td>\n",
       "      <td>u0 +++$+++ u2 +++$+++ m0 +++$+++ ['L198', 'L19...</td>\n",
       "      <td>L1044 +++$+++ u2 +++$+++ m0 +++$+++ CAMERON ++...</td>\n",
       "      <td>m1 +++$+++ 1492: conquest of paradise +++$+++ ...</td>\n",
       "      <td>m1 +++$+++ 1492: conquest of paradise +++$+++ ...</td>\n",
       "      <td>\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>u2 +++$+++ CAMERON +++$+++ m0 +++$+++ 10 thing...</td>\n",
       "      <td>u0 +++$+++ u2 +++$+++ m0 +++$+++ ['L200', 'L20...</td>\n",
       "      <td>L985 +++$+++ u0 +++$+++ m0 +++$+++ BIANCA +++$...</td>\n",
       "      <td>m2 +++$+++ 15 minutes +++$+++ 2001 +++$+++ 6.1...</td>\n",
       "      <td>m2 +++$+++ 15 minutes +++$+++ http://www.daily...</td>\n",
       "      <td>Distributed together with:\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>u3 +++$+++ CHASTITY +++$+++ m0 +++$+++ 10 thin...</td>\n",
       "      <td>u0 +++$+++ u2 +++$+++ m0 +++$+++ ['L204', 'L20...</td>\n",
       "      <td>L984 +++$+++ u2 +++$+++ m0 +++$+++ CAMERON +++...</td>\n",
       "      <td>m3 +++$+++ 2001: a space odyssey +++$+++ 1968 ...</td>\n",
       "      <td>m3 +++$+++ 2001: a space odyssey +++$+++ http:...</td>\n",
       "      <td>\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>u4 +++$+++ JOEY +++$+++ m0 +++$+++ 10 things i...</td>\n",
       "      <td>u0 +++$+++ u2 +++$+++ m0 +++$+++ ['L207', 'L20...</td>\n",
       "      <td>L925 +++$+++ u0 +++$+++ m0 +++$+++ BIANCA +++$...</td>\n",
       "      <td>m4 +++$+++ 48 hrs. +++$+++ 1982 +++$+++ 6.90 +...</td>\n",
       "      <td>m4 +++$+++ 48 hrs. +++$+++ http://www.awesomef...</td>\n",
       "      <td>\"Chameleons in imagined conversations: A new a...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       movie_characters_metadata.txt  \\\n",
       "0  u0 +++$+++ BIANCA +++$+++ m0 +++$+++ 10 things...   \n",
       "1  u1 +++$+++ BRUCE +++$+++ m0 +++$+++ 10 things ...   \n",
       "2  u2 +++$+++ CAMERON +++$+++ m0 +++$+++ 10 thing...   \n",
       "3  u3 +++$+++ CHASTITY +++$+++ m0 +++$+++ 10 thin...   \n",
       "4  u4 +++$+++ JOEY +++$+++ m0 +++$+++ 10 things i...   \n",
       "\n",
       "                             movie_conversations.txt  \\\n",
       "0  u0 +++$+++ u2 +++$+++ m0 +++$+++ ['L194', 'L19...   \n",
       "1  u0 +++$+++ u2 +++$+++ m0 +++$+++ ['L198', 'L19...   \n",
       "2  u0 +++$+++ u2 +++$+++ m0 +++$+++ ['L200', 'L20...   \n",
       "3  u0 +++$+++ u2 +++$+++ m0 +++$+++ ['L204', 'L20...   \n",
       "4  u0 +++$+++ u2 +++$+++ m0 +++$+++ ['L207', 'L20...   \n",
       "\n",
       "                                     movie_lines.txt  \\\n",
       "0  L1045 +++$+++ u0 +++$+++ m0 +++$+++ BIANCA +++...   \n",
       "1  L1044 +++$+++ u2 +++$+++ m0 +++$+++ CAMERON ++...   \n",
       "2  L985 +++$+++ u0 +++$+++ m0 +++$+++ BIANCA +++$...   \n",
       "3  L984 +++$+++ u2 +++$+++ m0 +++$+++ CAMERON +++...   \n",
       "4  L925 +++$+++ u0 +++$+++ m0 +++$+++ BIANCA +++$...   \n",
       "\n",
       "                           movie_titles_metadata.txt  \\\n",
       "0  m0 +++$+++ 10 things i hate about you +++$+++ ...   \n",
       "1  m1 +++$+++ 1492: conquest of paradise +++$+++ ...   \n",
       "2  m2 +++$+++ 15 minutes +++$+++ 2001 +++$+++ 6.1...   \n",
       "3  m3 +++$+++ 2001: a space odyssey +++$+++ 1968 ...   \n",
       "4  m4 +++$+++ 48 hrs. +++$+++ 1982 +++$+++ 6.90 +...   \n",
       "\n",
       "                                 raw_script_urls.txt  \\\n",
       "0  m0 +++$+++ 10 things i hate about you +++$+++ ...   \n",
       "1  m1 +++$+++ 1492: conquest of paradise +++$+++ ...   \n",
       "2  m2 +++$+++ 15 minutes +++$+++ http://www.daily...   \n",
       "3  m3 +++$+++ 2001: a space odyssey +++$+++ http:...   \n",
       "4  m4 +++$+++ 48 hrs. +++$+++ http://www.awesomef...   \n",
       "\n",
       "                                          README.txt  \n",
       "0                     Cornell Movie-Dialogs Corpus\\n  \n",
       "1                                                 \\n  \n",
       "2                       Distributed together with:\\n  \n",
       "3                                                 \\n  \n",
       "4  \"Chameleons in imagined conversations: A new a...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"1. Basic Information:\")\n",
    "print(f\"Number of rows: {df.shape[0]}\")\n",
    "print(f\"Number of columns: {df.shape[1]}\")\n",
    "print(\"\\n2. Data Types:\")\n",
    "print(df.dtypes)\n",
    "print(\"\\n3. Missing Values:\")\n",
    "print(df.isnull().sum())\n",
    "print(\"\\n4. Descriptive Statistics:\")\n",
    "display(df.describe().T)\n",
    "print(\"\\n5. Sample Data (first 5 rows):\")\n",
    "display(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ed2889a-b65a-49b5-b41c-87ca6182bebb",
   "metadata": {
    "id": "4ed2889a-b65a-49b5-b41c-87ca6182bebb"
   },
   "source": [
    "## Data Clean and Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "738b39c9-b079-4d30-a880-ac8dc9835502",
   "metadata": {
    "id": "738b39c9-b079-4d30-a880-ac8dc9835502"
   },
   "outputs": [],
   "source": [
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b6ee5b80-1a53-4d32-9f83-0b2362c3f8f0",
   "metadata": {
    "id": "b6ee5b80-1a53-4d32-9f83-0b2362c3f8f0"
   },
   "outputs": [],
   "source": [
    "# Focusing on Loading movie_lines.txt and movie_conversations.txt where we will parse the files\n",
    "lines_file = './CornellMovie/movie_lines.txt'  #Saad data path file\n",
    "conversations_file = './CornellMovie/movie_conversations.txt' #Saad data path file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8_xDDgnKz9h3",
   "metadata": {
    "id": "8_xDDgnKz9h3"
   },
   "outputs": [],
   "source": [
    "# Thai used this file path for Kaggle download\n",
    "#lines_file = 'movie_lines.txt'\n",
    "#conversations_file = 'movie_conversations.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8127e366-a378-44b6-9355-eab276b9f2d0",
   "metadata": {
    "id": "8127e366-a378-44b6-9355-eab276b9f2d0"
   },
   "outputs": [],
   "source": [
    "# Function to parse movie_lines.txt\n",
    "def parse_lines(lines_file):\n",
    "    lines = {}\n",
    "    character_names = {}\n",
    "    with open(lines_file, 'r', encoding='utf-8', errors='replace') as f:\n",
    "        for line in f:\n",
    "            parts = line.split(\" +++$+++ \")\n",
    "            if len(parts) == 5:\n",
    "                line_id = parts[0]\n",
    "                character_name = parts[3]  # Extract character name\n",
    "                text = parts[4].strip()\n",
    "                lines[line_id] = text\n",
    "                character_names[line_id] = character_name  # Store character names\n",
    "    return lines, character_names\n",
    "\n",
    "lines, character_names = parse_lines(lines_file)\n",
    "\n",
    "# Then, construct the DataFrame\n",
    "lines_df = pd.DataFrame({\n",
    "    'LineID': list(lines.keys()),\n",
    "    'Text': list(lines.values()),\n",
    "    'CharacterName': [character_names[line_id] for line_id in lines.keys()]  # Add CharacterName\n",
    "})\n",
    "\n",
    "# No need to load or process the other metadata or script files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cr-HyzGY6MIb",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 390
    },
    "id": "cr-HyzGY6MIb",
    "outputId": "eaaccc3e-f8c6-489b-bb6a-3ec2844711d0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 304713 entries, 0 to 304712\n",
      "Data columns (total 3 columns):\n",
      " #   Column         Non-Null Count   Dtype \n",
      "---  ------         --------------   ----- \n",
      " 0   LineID         304713 non-null  object\n",
      " 1   Text           304713 non-null  object\n",
      " 2   CharacterName  304713 non-null  object\n",
      "dtypes: object(3)\n",
      "memory usage: 7.0+ MB\n",
      "LineID           0\n",
      "Text             0\n",
      "CharacterName    0\n",
      "dtype: int64\n"
     ]
    },
    {
     "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>LineID</th>\n",
       "      <th>Text</th>\n",
       "      <th>CharacterName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>L1045</td>\n",
       "      <td>They do not!</td>\n",
       "      <td>BIANCA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L1044</td>\n",
       "      <td>They do to!</td>\n",
       "      <td>CAMERON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>L985</td>\n",
       "      <td>I hope so.</td>\n",
       "      <td>BIANCA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L984</td>\n",
       "      <td>She okay?</td>\n",
       "      <td>CAMERON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>L925</td>\n",
       "      <td>Let's go.</td>\n",
       "      <td>BIANCA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  LineID          Text CharacterName\n",
       "0  L1045  They do not!        BIANCA\n",
       "1  L1044   They do to!       CAMERON\n",
       "2   L985    I hope so.        BIANCA\n",
       "3   L984     She okay?       CAMERON\n",
       "4   L925     Let's go.        BIANCA"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines_df.info()  # Check for missing values and data types\n",
    "print(lines_df.isnull().sum())\n",
    "lines_df.head()  # Check if the data looks correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "471e24aa-0b1a-4b13-9d09-c3a4a4417f1d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "471e24aa-0b1a-4b13-9d09-c3a4a4417f1d",
    "outputId": "9fbe8d2b-0449-46db-fab3-6961027076ca"
   },
   "outputs": [],
   "source": [
    "# Function to parse movie_conversations.txt\n",
    "def parse_conversations(conversations_file):\n",
    "    conversations = []\n",
    "    with open(conversations_file, 'r', encoding='utf-8', errors='replace') as f:\n",
    "        for line in f:\n",
    "            parts = line.split(\" +++$+++ \")\n",
    "            if len(parts) == 4:\n",
    "                line_ids = eval(parts[3])  # This is a list of line IDs in a conversation\n",
    "                conversations.append(line_ids)\n",
    "    return conversations\n",
    "\n",
    "# Call the function and store the result in the conversations variable\n",
    "conversations = parse_conversations(conversations_file)\n",
    "\n",
    "# Now you can print the conversations variable\n",
    "#print(conversations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f77018c4-d584-4650-a6c8-75feb0199b40",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "f77018c4-d584-4650-a6c8-75feb0199b40",
    "outputId": "79591cf9-4a28-428b-f413-d5242a5af92d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input: Can we make this quick?  Roxanne Korrine and Andrew Barrett are having an incredibly horrendous public break- up on the quad.  Again.\n",
      "Response: Well, I thought we'd start with pronunciation, if that's okay with you.\n",
      "\n",
      "Input: Well, I thought we'd start with pronunciation, if that's okay with you.\n",
      "Response: Not the hacking and gagging and spitting part.  Please.\n",
      "\n",
      "Input: Not the hacking and gagging and spitting part.  Please.\n",
      "Response: Okay... then how 'bout we try out some French cuisine.  Saturday?  Night?\n",
      "\n",
      "Input: You're asking me out.  That's so cute. What's your name again?\n",
      "Response: Forget it.\n",
      "\n",
      "Input: No, no, it's my fault -- we didn't have a proper introduction ---\n",
      "Response: Cameron.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Function to create dialog pairs\n",
    "def create_dialog_pairs(conversations, lines):\n",
    "    dialog_pairs = []\n",
    "    for conv in conversations:\n",
    "        for i in range(len(conv) - 1):\n",
    "            input_line = lines.get(conv[i], \"\")\n",
    "            response_line = lines.get(conv[i + 1], \"\")\n",
    "            if input_line and response_line:\n",
    "                dialog_pairs.append((input_line, response_line))\n",
    "    return dialog_pairs\n",
    "\n",
    "dialog_pairs = create_dialog_pairs(conversations, lines_df.set_index('LineID')['Text'].to_dict())\n",
    "\n",
    "# Print some dialog pairs\n",
    "for pair in dialog_pairs[:5]:\n",
    "    print(f\"Input: {pair[0]}\\nResponse: {pair[1]}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9516311d-79e1-489e-92aa-e3d6a0159c18",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "9516311d-79e1-489e-92aa-e3d6a0159c18",
    "outputId": "b8ecea45-c83e-45c2-edef-89561f56c34a"
   },
   "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>input</th>\n",
       "      <th>response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Can we make this quick?  Roxanne Korrine and A...</td>\n",
       "      <td>Well, I thought we'd start with pronunciation,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Well, I thought we'd start with pronunciation,...</td>\n",
       "      <td>Not the hacking and gagging and spitting part....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Not the hacking and gagging and spitting part....</td>\n",
       "      <td>Okay... then how 'bout we try out some French ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>You're asking me out.  That's so cute. What's ...</td>\n",
       "      <td>Forget it.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No, no, it's my fault -- we didn't have a prop...</td>\n",
       "      <td>Cameron.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               input  \\\n",
       "0  Can we make this quick?  Roxanne Korrine and A...   \n",
       "1  Well, I thought we'd start with pronunciation,...   \n",
       "2  Not the hacking and gagging and spitting part....   \n",
       "3  You're asking me out.  That's so cute. What's ...   \n",
       "4  No, no, it's my fault -- we didn't have a prop...   \n",
       "\n",
       "                                            response  \n",
       "0  Well, I thought we'd start with pronunciation,...  \n",
       "1  Not the hacking and gagging and spitting part....  \n",
       "2  Okay... then how 'bout we try out some French ...  \n",
       "3                                         Forget it.  \n",
       "4                                           Cameron.  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert dialog pairs to DataFrame for easier manipulation\n",
    "cleaned_dialog_df = pd.DataFrame(dialog_pairs, columns=['input', 'response'])\n",
    "\n",
    "# Check the first few rows of the DataFrame\n",
    "cleaned_dialog_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "70a6a24d-7028-4644-a2ce-aa3371854c68",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 410
    },
    "id": "70a6a24d-7028-4644-a2ce-aa3371854c68",
    "outputId": "e9ec335d-7c74-41f5-8529-83beb4f57fed"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Displaying Line length distribution\n",
    "cleaned_dialog_df['input_length'] = cleaned_dialog_df['input'].apply(lambda x: len(x.split()))\n",
    "cleaned_dialog_df['response_length'] = cleaned_dialog_df['response'].apply(lambda x: len(x.split()))\n",
    "\n",
    "plt.figure(figsize=(8, 4)) # Shorten it to 6, 4 to ensure it fits to pdf print\n",
    "plt.hist(cleaned_dialog_df['input_length'], bins=30, alpha=0.7, label='Input Line Length')\n",
    "plt.hist(cleaned_dialog_df['response_length'], bins=30, alpha=0.7, label='Response Line Length')\n",
    "plt.title('Distribution of Line Lengths (Input vs. Response)')\n",
    "plt.xlabel('Line Length (Number of Words)')\n",
    "plt.xlim(0,150)\n",
    "plt.ylabel('Frequency')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1bece27e-bda1-4f48-be2b-21361c2e0ffd",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 412
    },
    "id": "1bece27e-bda1-4f48-be2b-21361c2e0ffd",
    "outputId": "7260b262-b0c0-4050-a39e-95b5f9760a4b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TOwYNGgQR0QnShjifQUFBmD59OoKCgjBu3Dhl2tKmTZt06o4ePYoBAwbg008/RdWqVWFiYoKtW7fi6NGjGDZsmGFOBNGrruiucySi193jq3aIPFzFoU+fPlK+fHkxNjYWR0dHGT58uNy/f1+nDoD0799fZs2aJZUrV5bSpUtL9erVZdmyZf/Z7oIFCwTAU2+PLg8mInLnzh0JCQkRe3t7MTExkTp16vznyg15Hl2141keX7VDROTSpUsSEBAg1tbWUrp0aXFxcZEpU6ZITk6OUrNo0SLx9vYWOzs7MTExEa1WK+3bt5ejR4/qPNeMGTPE2dlZjIyMnniMTxIXFyddunSRihUriomJiZiZmUm9evXk22+/1Vk+LycnRyZNmiTVqlWT0qVLi42NjXTu3FkSEhJ0ns/T01Nq1aqVrx1HR0fx8/PLtz3v3zjP087lk36Ontbe85zTvFU7pkyZ8sQ+jRw5Mt/2J5k1a5YAkHLlyuXbl7cqCQA5c+aMzr6XPZ8iIpcvX5aPP/5Y3njjDTE3N5ePP/5Ydu/erfNvf+3aNQkODpbq1auLmZmZvPHGG1KnTh2ZPn26zmolRPR0KpHHJiMSEZUAKpUK/fv3x8yZM4u6K0RE9JriHGkiIiIiIj0wSBMRERER6YEXGxJRicRZaUREVNQ4Ik1EREREpAcGaSIiIiIiPTBIExERERHpgXOkC1lubi6uXr0Kc3PzJ361KxEREREVLRHBnTt3oNVqUarU08edGaQL2dWrV+Hg4FDU3SAiIiKi/5CQkIAKFSo8dT+DdCEzNzcH8PAfxsLCooh7Q0RERESPS0tLg4ODg5LbnoZBupDlTeewsLBgkCYiIiIqxv5rGi4vNiQiIiIi0gODNBERERGRHhikiYiIiIj0wCBNRERERKQHBmkiIiIiIj0wSBMRERER6YFBmoiIiIhIDwzSRERERER6YJAmIiIiItIDgzQRERERkR4YpImIiIiI9MAgTURERESkB+Oi7gAVPKdhGwrkeS9O9CuQ5yUiIiIqCTgiTURERESkBwZpIiIiIiI9MEgTEREREemBQZqIiIiISA8M0kREREREemCQJiIiIiLSA4M0EREREZEeGKSJiIiIiPTAIE1EREREpIciDdI7d+5E69atodVqoVKpsG7duqfW9u7dGyqVCjNmzNDZnpmZiYEDB8LGxgZmZmZo06YNLl++rFOTkpKCwMBAaDQaaDQaBAYG4vbt2zo18fHxaN26NczMzGBjY4OQkBBkZWXp1Pzzzz/w9PSEqakp3nrrLYwZMwYi8jKngIiIiIhKqCIN0hkZGXj77bcxc+bMZ9atW7cO+/btg1arzbdv0KBBWLt2LVauXIldu3YhPT0d/v7+yMnJUWoCAgIQFxeHqKgoREVFIS4uDoGBgcr+nJwc+Pn5ISMjA7t27cLKlSsRGRmJ0NBQpSYtLQ3NmjWDVqvFgQMHEBERgalTpyI8PNwAZ4KIiIiIShrjomy8ZcuWaNmy5TNrrly5ggEDBmDTpk3w8/PT2Zeamor58+djyZIlaNq0KQBg6dKlcHBwwObNm+Hr64uTJ08iKioKe/fuhbu7OwBg3rx58PDwwKlTp+Di4oLo6GicOHECCQkJSlifNm0agoODMW7cOFhYWGDZsmW4f/8+Fi5cCLVaDVdXV5w+fRrh4eEYMmQIVCpVAZwhIiIiIiquivUc6dzcXAQGBmLo0KGoVatWvv2xsbHIzs5G8+bNlW1arRaurq7YvXs3AGDPnj3QaDRKiAaARo0aQaPR6NS4urrqjHj7+voiMzMTsbGxSo2npyfUarVOzdWrV3Hx4sWnHkNmZibS0tJ0bkRERERU8hXrID1p0iQYGxsjJCTkifuTkpJgYmICS0tLne12dnZISkpSamxtbfM91tbWVqfGzs5OZ7+lpSVMTEyeWZN3P6/mSSZMmKDMzdZoNHBwcHjWIRMRERFRCVFsg3RsbCz+97//YeHChS88bUJEdB7zpMcboibvQsNn9W/48OFITU1VbgkJCc9/IERERERUbBXbIP3XX38hOTkZFStWhLGxMYyNjXHp0iWEhobCyckJAGBvb4+srCykpKToPDY5OVkZLba3t8e1a9fyPf/169d1ah4fVU5JSUF2dvYza5KTkwEg30j1o9RqNSwsLHRuRERERFTyFdsgHRgYiKNHjyIuLk65abVaDB06FJs2bQIAuLm5oXTp0oiJiVEel5iYiGPHjqFx48YAAA8PD6SmpmL//v1Kzb59+5CamqpTc+zYMSQmJio10dHRUKvVcHNzU2p27typsyRedHQ0tFqtEuyJiIiI6PVRpKt2pKen4+zZs8r9CxcuIC4uDlZWVqhYsSKsra116kuXLg17e3u4uLgAADQaDbp3747Q0FBYW1vDysoKYWFhqF27trKKR40aNdCiRQv07NkTc+bMAQD06tUL/v7+yvM0b94cNWvWRGBgIKZMmYJbt24hLCwMPXv2VEaQAwICMHr0aAQHB+Orr77CmTNnMH78eHz77bdcsYOIiIjoNVSkQfrgwYPw9vZW7g8ZMgQA0KVLFyxcuPC5nmP69OkwNjZG+/btce/ePfj4+GDhwoUwMjJSapYtW4aQkBBldY82bdrorF1tZGSEDRs2oF+/fmjSpAlMTU0REBCAqVOnKjUajQYxMTHo378/GjRoAEtLSwwZMkTpMxERERG9XlTCr+YrVGlpadBoNEhNTS20+dJOwzYUyPNenOj330VEREREJczz5rViO0eaiIiIiKg4Y5AmIiIiItIDgzQRERERkR4YpImIiIiI9MAgTURERESkBwZpIiIiIiI9MEgTEREREemBQZqIiIiISA8M0kREREREemCQJiIiIiLSA4M0EREREZEeGKSJiIiIiPTAIE1EREREpAcGaSIiIiIiPTBIExERERHpgUGaiIiIiEgPDNJERERERHpgkCYiIiIi0gODNBERERGRHhikiYiIiIj0wCBNRERERKQHBmkiIiIiIj0wSBMRERER6YFBmoiIiIhID8ZF3QF69TgN21Bgz31xol+BPTcRERHRi+CINBERERGRHhikiYiIiIj0wCBNRERERKQHBmkiIiIiIj0UaZDeuXMnWrduDa1WC5VKhXXr1in7srOz8eWXX6J27dowMzODVqtFUFAQrl69qvMcmZmZGDhwIGxsbGBmZoY2bdrg8uXLOjUpKSkIDAyERqOBRqNBYGAgbt++rVMTHx+P1q1bw8zMDDY2NggJCUFWVpZOzT///ANPT0+YmprirbfewpgxYyAiBj0nRERERFQyFGmQzsjIwNtvv42ZM2fm23f37l0cOnQI33zzDQ4dOoQ1a9bg9OnTaNOmjU7doEGDsHbtWqxcuRK7du1Ceno6/P39kZOTo9QEBAQgLi4OUVFRiIqKQlxcHAIDA5X9OTk58PPzQ0ZGBnbt2oWVK1ciMjISoaGhSk1aWhqaNWsGrVaLAwcOICIiAlOnTkV4eHgBnBkiIiIiKu6KdPm7li1bomXLlk/cp9FoEBMTo7MtIiICDRs2RHx8PCpWrIjU1FTMnz8fS5YsQdOmTQEAS5cuhYODAzZv3gxfX1+cPHkSUVFR2Lt3L9zd3QEA8+bNg4eHB06dOgUXFxdER0fjxIkTSEhIgFarBQBMmzYNwcHBGDduHCwsLLBs2TLcv38fCxcuhFqthqurK06fPo3w8HAMGTIEKpWqAM8UERERERU3JWqOdGpqKlQqFd58800AQGxsLLKzs9G8eXOlRqvVwtXVFbt37wYA7NmzBxqNRgnRANCoUSNoNBqdGldXVyVEA4Cvry8yMzMRGxur1Hh6ekKtVuvUXL16FRcvXnxqnzMzM5GWlqZzIyIiIqKSr8QE6fv372PYsGEICAiAhYUFACApKQkmJiawtLTUqbWzs0NSUpJSY2trm+/5bG1tdWrs7Ox09ltaWsLExOSZNXn382qeZMKECcrcbI1GAwcHhxc5bCIiIiIqpkpEkM7OzkbHjh2Rm5uLWbNm/We9iOhMtXjStAtD1ORdaPisaR3Dhw9HamqqcktISPjP/hMRERFR8Vfsg3R2djbat2+PCxcuICYmRhmNBgB7e3tkZWUhJSVF5zHJycnKaLG9vT2uXbuW73mvX7+uU/P4qHJKSgqys7OfWZOcnAwA+UaqH6VWq2FhYaFzIyIiIqKSr1gH6bwQfebMGWzevBnW1tY6+93c3FC6dGmdixITExNx7NgxNG7cGADg4eGB1NRU7N+/X6nZt28fUlNTdWqOHTuGxMREpSY6OhpqtRpubm5Kzc6dO3WWxIuOjoZWq4WTk5PBj52IiIiIirciDdLp6emIi4tDXFwcAODChQuIi4tDfHw8Hjx4gE8++QQHDx7EsmXLkJOTg6SkJCQlJSlhVqPRoHv37ggNDcWWLVtw+PBhdO7cGbVr11ZW8ahRowZatGiBnj17Yu/evdi7dy969uwJf39/uLi4AACaN2+OmjVrIjAwEIcPH8aWLVsQFhaGnj17KiPIAQEBUKvVCA4OxrFjx7B27VqMHz+eK3YQERERvaaKdPm7gwcPwtvbW7k/ZMgQAECXLl0watQorF+/HgBQt25dncdt27YNXl5eAIDp06fD2NgY7du3x7179+Dj44OFCxfCyMhIqV+2bBlCQkKU1T3atGmjs3a1kZERNmzYgH79+qFJkyYwNTVFQEAApk6dqtTkLcfXv39/NGjQAJaWlhgyZIjSZyIiIiJ6vaiEX81XqNLS0qDRaJCamlpo86Wdhm0okOe9ONGvUNt7VptEREREhvK8ea1Yz5EmIiIiIiquGKSJiIiIiPRQpHOkiQylsKevEBEREXFEmoiIiIhIDwzSRERERER6YJAmIiIiItIDgzQRERERkR4YpImIiIiI9MAgTURERESkBwZpIiIiIiI9cB1pIj3wa9CJiIiII9JERERERHpgkCYiIiIi0gODNBERERGRHhikiYiIiIj0wCBNRERERKQHBmkiIiIiIj0wSBMRERER6YFBmoiIiIhIDwzSRERERER6YJAmIiIiItIDgzQRERERkR4YpImIiIiI9MAgTURERESkBwZpIiIiIiI9MEgTEREREemBQZqIiIiISA8M0kREREREeijSIL1z5060bt0aWq0WKpUK69at09kvIhg1ahS0Wi1MTU3h5eWF48eP69RkZmZi4MCBsLGxgZmZGdq0aYPLly/r1KSkpCAwMBAajQYajQaBgYG4ffu2Tk18fDxat24NMzMz2NjYICQkBFlZWTo1//zzDzw9PWFqaoq33noLY8aMgYgY7HwQERERUclRpEE6IyMDb7/9NmbOnPnE/ZMnT0Z4eDhmzpyJAwcOwN7eHs2aNcOdO3eUmkGDBmHt2rVYuXIldu3ahfT0dPj7+yMnJ0epCQgIQFxcHKKiohAVFYW4uDgEBgYq+3NycuDn54eMjAzs2rULK1euRGRkJEJDQ5WatLQ0NGvWDFqtFgcOHEBERASmTp2K8PDwAjgzRERERFTcGRdl4y1btkTLli2fuE9EMGPGDIwYMQLt2rUDACxatAh2dnZYvnw5evfujdTUVMyfPx9LlixB06ZNAQBLly6Fg4MDNm/eDF9fX5w8eRJRUVHYu3cv3N3dAQDz5s2Dh4cHTp06BRcXF0RHR+PEiRNISEiAVqsFAEybNg3BwcEYN24cLCwssGzZMty/fx8LFy6EWq2Gq6srTp8+jfDwcAwZMgQqlaoQzhgRERERFRfFdo70hQsXkJSUhObNmyvb1Go1PD09sXv3bgBAbGwssrOzdWq0Wi1cXV2Vmj179kCj0SghGgAaNWoEjUajU+Pq6qqEaADw9fVFZmYmYmNjlRpPT0+o1WqdmqtXr+LixYuGPwFEREREVKwV2yCdlJQEALCzs9PZbmdnp+xLSkqCiYkJLC0tn1lja2ub7/ltbW11ah5vx9LSEiYmJs+sybufV/MkmZmZSEtL07kRERERUclXbIN0nsenTIjIf06jeLzmSfWGqMm70PBZ/ZkwYYJykaNGo4GDg8Mz+05EREREJUOxDdL29vYA8o/2JicnKyPB9vb2yMrKQkpKyjNrrl27lu/5r1+/rlPzeDspKSnIzs5+Zk1ycjKA/KPmjxo+fDhSU1OVW0JCwrMPnIiIiIhKhGIbpJ2dnWFvb4+YmBhlW1ZWFnbs2IHGjRsDANzc3FC6dGmdmsTERBw7dkyp8fDwQGpqKvbv36/U7Nu3D6mpqTo1x44dQ2JiolITHR0NtVoNNzc3pWbnzp06S+JFR0dDq9XCycnpqcehVqthYWGhcyMiIiKikq9Ig3R6ejri4uIQFxcH4OEFhnFxcYiPj4dKpcKgQYMwfvx4rF27FseOHUNwcDDKli2LgIAAAIBGo0H37t0RGhqKLVu24PDhw+jcuTNq166trOJRo0YNtGjRAj179sTevXuxd+9e9OzZE/7+/nBxcQEANG/eHDVr1kRgYCAOHz6MLVu2ICwsDD179lSCb0BAANRqNYKDg3Hs2DGsXbsW48eP54odRERERK+pIl3+7uDBg/D29lbuDxkyBADQpUsXLFy4EF988QXu3buHfv36ISUlBe7u7oiOjoa5ubnymOnTp8PY2Bjt27fHvXv34OPjg4ULF8LIyEipWbZsGUJCQpTVPdq0aaOzdrWRkRE2bNiAfv36oUmTJjA1NUVAQACmTp2q1Gg0GsTExKB///5o0KABLC0tMWTIEKXPRERERPR6UQm/mq9QpaWlQaPRIDU1tdCmeTgN21Agz3txol+htlcUbRanYyQiIqLC8bx5rdjOkSYiIiIiKs4YpImIiIiI9MAgTURERESkBwZpIiIiIiI9FOmqHUT0/Ar7gkoiIiJ6No5IExERERHpQa8gfeHCBUP3g4iIiIioRNErSFepUgXe3t5YunQp7t+/b+g+EREREREVe3oF6SNHjqBevXoIDQ2Fvb09evfujf379xu6b0RERERExZZeQdrV1RXh4eG4cuUKFixYgKSkJLz77ruoVasWwsPDcf36dUP3k4iIiIioWHmpiw2NjY3Rtm1b/PLLL5g0aRLOnTuHsLAwVKhQAUFBQUhMTDRUP4mIiIiIipWXCtIHDx5Ev379UL58eYSHhyMsLAznzp3D1q1bceXKFXz44YeG6icRERERUbGi1zrS4eHhWLBgAU6dOoVWrVph8eLFaNWqFUqVepjLnZ2dMWfOHFSvXt2gnSUiIiIiKi70CtI//vgjunXrhq5du8Le3v6JNRUrVsT8+fNfqnNERERERMWVXkH6zJkz/1ljYmKCLl266PP0RERERETFnl5zpBcsWIDVq1fn27569WosWrTopTtFRERERFTc6RWkJ06cCBsbm3zbbW1tMX78+JfuFBERERFRcadXkL506RKcnZ3zbXd0dER8fPxLd4qIiIiIqLjTK0jb2tri6NGj+bYfOXIE1tbWL90pIiIiIqLiTq8g3bFjR4SEhGDbtm3IyclBTk4Otm7dis8//xwdO3Y0dB+JiIiIiIodvVbtGDt2LC5dugQfHx8YGz98itzcXAQFBXGONBERERG9FvQK0iYmJli1ahW+++47HDlyBKampqhduzYcHR0N3T8iIiIiomJJryCdp1q1aqhWrZqh+kJEREREVGLoFaRzcnKwcOFCbNmyBcnJycjNzdXZv3XrVoN0joiIiIiouNIrSH/++edYuHAh/Pz84OrqCpVKZeh+EREREREVa3oF6ZUrV+KXX35Bq1atDN0fIiIiIqISQa/l70xMTFClShVD94WIiIiIqMTQK0iHhobif//7H0TE0P0hIiIiIioR9JrasWvXLmzbtg1//vknatWqhdKlS+vsX7NmjUE6R0RERERUXOk1Iv3mm2+ibdu28PT0hI2NDTQajc7NUB48eICvv/4azs7OMDU1RaVKlTBmzBidVUJEBKNGjYJWq4WpqSm8vLxw/PhxnefJzMzEwIEDYWNjAzMzM7Rp0waXL1/WqUlJSUFgYKByDIGBgbh9+7ZOTXx8PFq3bg0zMzPY2NggJCQEWVlZBjteIiIiIio59BqRXrBggaH78USTJk3C7NmzsWjRItSqVQsHDx5E165dodFo8PnnnwMAJk+ejPDwcCxcuBDVqlXD2LFj0axZM5w6dQrm5uYAgEGDBuH333/HypUrYW1tjdDQUPj7+yM2NhZGRkYAgICAAFy+fBlRUVEAgF69eiEwMBC///47gIdL/vn5+aFcuXLYtWsXbt68iS5dukBEEBERUSjng4iIiIiKD72/kOXBgwfYvn07zp07h4CAAJibm+Pq1auwsLDAG2+8YZDO7dmzBx9++CH8/PwAAE5OTlixYgUOHjwI4OFo9IwZMzBixAi0a9cOALBo0SLY2dlh+fLl6N27N1JTUzF//nwsWbIETZs2BQAsXboUDg4O2Lx5M3x9fXHy5ElERUVh7969cHd3BwDMmzcPHh4eOHXqFFxcXBAdHY0TJ04gISEBWq0WADBt2jQEBwdj3LhxsLCwMMgxExEREVHJoNfUjkuXLqF27dr48MMP0b9/f1y/fh3Aw9HhsLAwg3Xu3XffxZYtW3D69GkAwJEjR7Br1y5l2b0LFy4gKSkJzZs3Vx6jVqvh6emJ3bt3AwBiY2ORnZ2tU6PVauHq6qrU7NmzBxqNRgnRANCoUSNoNBqdGldXVyVEA4Cvry8yMzMRGxtrsGMmIiIiopJB7y9kadCgAY4cOQJra2tle9u2bdGjRw+Dde7LL79EamoqqlevDiMjI+Tk5GDcuHHo1KkTACApKQkAYGdnp/M4Ozs7XLp0SakxMTGBpaVlvpq8xyclJcHW1jZf+7a2tjo1j7djaWkJExMTpeZJMjMzkZmZqdxPS0t7rmMnIiIiouJN71U7/v77b5iYmOhsd3R0xJUrVwzSMQBYtWoVli5diuXLl6NWrVqIi4vDoEGDoNVq0aVLF6Xu8W9WFJH//LbFx2ueVK9PzeMmTJiA0aNHP7MvRERERFTy6DW1Izc3Fzk5Ofm2X758WbnAzxCGDh2KYcOGoWPHjqhduzYCAwMxePBgTJgwAQBgb28PAPlGhJOTk5XRY3t7e2RlZSElJeWZNdeuXcvX/vXr13VqHm8nJSUF2dnZ+UaqHzV8+HCkpqYqt4SEhBc5BURERERUTOkVpJs1a4YZM2Yo91UqFdLT0zFy5EiDfm343bt3UaqUbheNjIyU5e+cnZ1hb2+PmJgYZX9WVhZ27NiBxo0bAwDc3NxQunRpnZrExEQcO3ZMqfHw8EBqair279+v1Ozbtw+pqak6NceOHUNiYqJSEx0dDbVaDTc3t6ceg1qthoWFhc6NiIiIiEo+vaZ2TJ8+Hd7e3qhZsybu37+PgIAAnDlzBjY2NlixYoXBOte6dWuMGzcOFStWRK1atXD48GGEh4ejW7duAB4G+EGDBmH8+PGoWrUqqlativHjx6Ns2bIICAgAAGg0GnTv3h2hoaGwtraGlZUVwsLCULt2bWUVjxo1aqBFixbo2bMn5syZA+Dh8nf+/v5wcXEBADRv3hw1a9ZEYGAgpkyZglu3biEsLAw9e/ZkOCYiIiJ6DekVpLVaLeLi4rBixQocOnQIubm56N69Oz777DOYmpoarHMRERH45ptv0K9fPyQnJ0Or1aJ379749ttvlZovvvgC9+7dQ79+/ZCSkgJ3d3dER0frTDGZPn06jI2N0b59e9y7dw8+Pj5YuHChsoY0ACxbtgwhISHK6h5t2rTBzJkzlf1GRkbYsGED+vXrhyZNmsDU1BQBAQGYOnWqwY6XiIiIiEoOlYhIUXfidZKWlgaNRoPU1NRCG8l2GrahQJ734kS/Qm2vKNp8nY+RiIjodfW8eU2vEenFixc/c39QUJA+T0tEREREVGLovY70o7Kzs3H37l2YmJigbNmyDNJERERE9MrTa9WOlJQUnVt6ejpOnTqFd99916AXGxIRERERFVd6BeknqVq1KiZOnJhvtJqIiIiI6FVksCANPFzZ4urVq4Z8SiIiIiKiYkmvOdLr16/XuS8iSExMxMyZM9GkSRODdIyIiIiIqDjTK0h/9NFHOvdVKhXKlSuHDz74ANOmTTNEv4iIiIiIijW9gnTeV3QTEREREb2uDDpHmoiIiIjodaHXiPSQIUOeuzY8PFyfJoiIiIiIijW9gvThw4dx6NAhPHjwAC4uLgCA06dPw8jICPXr11fqVCqVYXpJRERERFTM6BWkW7duDXNzcyxatAiWlpYAHn5JS9euXfHee+8hNDTUoJ0kIiIiIipu9JojPW3aNEyYMEEJ0QBgaWmJsWPHctUOIiIiInot6BWk09LScO3atXzbk5OTcefOnZfuFBERERFRcadXkG7bti26du2KX3/9FZcvX8bly5fx66+/onv37mjXrp2h+0hEREREVOzoNUd69uzZCAsLQ+fOnZGdnf3wiYyN0b17d0yZMsWgHSQiIiIiKo70CtJly5bFrFmzMGXKFJw7dw4igipVqsDMzMzQ/SMiIiIiKpZe6gtZEhMTkZiYiGrVqsHMzAwiYqh+EREREREVa3oF6Zs3b8LHxwfVqlVDq1atkJiYCADo0aMHl74jIiIioteCXkF68ODBKF26NOLj41G2bFlle4cOHRAVFWWwzhERERERFVd6zZGOjo7Gpk2bUKFCBZ3tVatWxaVLlwzSMSIiIiKi4kyvEemMjAydkeg8N27cgFqtfulOEREREREVd3oF6ffffx+LFy9W7qtUKuTm5mLKlCnw9vY2WOeIiIiIiIorvaZ2TJkyBV5eXjh48CCysrLwxRdf4Pjx47h16xb+/vtvQ/eRiIiIiKjY0WtEumbNmjh69CgaNmyIZs2aISMjA+3atcPhw4dRuXJlQ/eRiIiIiKjYeeER6ezsbDRv3hxz5szB6NGjC6JPRERERETF3guPSJcuXRrHjh2DSqUqiP4QEREREZUIek3tCAoKwvz58w3dFyIiIiKiEkOviw2zsrLw008/ISYmBg0aNICZmZnO/vDwcIN0joiIiIiouHqhEenz588jNzcXx44dQ/369WFhYYHTp0/j8OHDyi0uLs6gHbxy5Qo6d+4Ma2trlC1bFnXr1kVsbKyyX0QwatQoaLVamJqawsvLC8ePH9d5jszMTAwcOBA2NjYwMzNDmzZtcPnyZZ2alJQUBAYGQqPRQKPRIDAwELdv39apiY+PR+vWrWFmZgYbGxuEhIQgKyvLoMdLRERERCXDC41IV61aFYmJidi2bRuAh18J/v3338POzq5AOpeSkoImTZrA29sbf/75J2xtbXHu3Dm8+eabSs3kyZMRHh6OhQsXolq1ahg7diyaNWuGU6dOwdzcHAAwaNAg/P7771i5ciWsra0RGhoKf39/xMbGwsjICAAQEBCAy5cvK19x3qtXLwQGBuL3338HAOTk5MDPzw/lypXDrl27cPPmTXTp0gUigoiIiAI5fiIiIiIqvl4oSIuIzv0///wTGRkZBu3QoyZNmgQHBwcsWLBA2ebk5KTTnxkzZmDEiBFo164dAGDRokWws7PD8uXL0bt3b6SmpmL+/PlYsmQJmjZtCgBYunQpHBwcsHnzZvj6+uLkyZOIiorC3r174e7uDgCYN28ePDw8cOrUKbi4uCA6OhonTpxAQkICtFotAGDatGkIDg7GuHHjYGFhUWDngYiIiIiKH70uNszzeLA2tPXr16NBgwb49NNPYWtri3r16mHevHnK/gsXLiApKQnNmzdXtqnVanh6emL37t0AgNjYWGXJvjxarRaurq5KzZ49e6DRaJQQDQCNGjWCRqPRqXF1dVVCNAD4+voiMzNTZ6oJEREREb0eXihIq1SqfMveFeQyeOfPn8ePP/6IqlWrYtOmTejTpw9CQkKUrydPSkoCgHxTS+zs7JR9SUlJMDExgaWl5TNrbG1t87Vva2urU/N4O5aWljAxMVFqniQzMxNpaWk6NyIiIiIq+V54akdwcDDUajUA4P79++jTp0++VTvWrFljkM7l5uaiQYMGGD9+PACgXr16OH78OH788UcEBQUpdY+HeRH5z4D/eM2T6vWpedyECRP4xTVEREREr6AXGpHu0qULbG1tlZUtOnfuDK1Wq9zPuxlK+fLlUbNmTZ1tNWrUQHx8PADA3t4eAPKNCCcnJyujx/b29sjKykJKSsoza65du5av/evXr+vUPN5OSkoKsrOzn3mx5fDhw5GamqrcEhIS/vO4iYiIiKj4e6ER6Ucv+isMTZo0walTp3S2nT59Go6OjgAAZ2dn2NvbIyYmBvXq1QPwcI3rHTt2YNKkSQAANzc3lC5dGjExMWjfvj0AIDExEceOHcPkyZMBAB4eHkhNTcX+/fvRsGFDAMC+ffuQmpqKxo0bKzXjxo1DYmIiypcvDwCIjo6GWq2Gm5vbU49BrVYrI/hERERE9OrQ6wtZCsvgwYPRuHFjjB8/Hu3bt8f+/fsxd+5czJ07F8DDqRaDBg3C+PHjUbVqVVStWhXjx49H2bJlERAQAADQaDTo3r07QkNDYW1tDSsrK4SFhaF27drKKh41atRAixYt0LNnT8yZMwfAw+Xv/P394eLiAgBo3rw5atasicDAQEyZMgW3bt1CWFgYevbsyRU7iIiIiF5DxTpIv/POO1i7di2GDx+OMWPGwNnZGTNmzMBnn32m1HzxxRe4d+8e+vXrh5SUFLi7uyM6OlpZQxoApk+fDmNjY7Rv3x737t2Dj48PFi5cqKwhDQDLli1DSEiIsrpHmzZtMHPmTGW/kZERNmzYgH79+qFJkyYwNTVFQEAApk6dWghngoiIiIiKm2IdpAHA398f/v7+T92vUqkwatQojBo16qk1ZcqUQURExDO/OMXKygpLly59Zl8qVqyIP/744z/7TERERESvvpdaR5qIiIiI6HXFIE1EREREpAcGaSIiIiIiPTBIExERERHpgUGaiIiIiEgPDNJERERERHpgkCYiIiIi0kOxX0eaiIqG07ANBfbcFyf6FdhzExERFRaOSBMRERER6YFBmoiIiIhIDwzSRERERER6YJAmIiIiItIDgzQRERERkR4YpImIiIiI9MAgTURERESkB64jTUTFRkGtXc11q4mIqCBwRJqIiIiISA8M0kREREREemCQJiIiIiLSA4M0EREREZEeGKSJiIiIiPTAIE1EREREpAcGaSIiIiIiPTBIExERERHpgUGaiIiIiEgPDNJERERERHpgkCYiIiIi0oNxUXeAiKioOA3bUGDPfXGiX4E9NxERFQ8ckSYiIiIi0kOJCtITJkyASqXCoEGDlG0iglGjRkGr1cLU1BReXl44fvy4zuMyMzMxcOBA2NjYwMzMDG3atMHly5d1alJSUhAYGAiNRgONRoPAwEDcvn1bpyY+Ph6tW7eGmZkZbGxsEBISgqysrII6XCIiIiIqxkpMkD5w4ADmzp2LOnXq6GyfPHkywsPDMXPmTBw4cAD29vZo1qwZ7ty5o9QMGjQIa9euxcqVK7Fr1y6kp6fD398fOTk5Sk1AQADi4uIQFRWFqKgoxMXFITAwUNmfk5MDPz8/ZGRkYNeuXVi5ciUiIyMRGhpa8AdPRERERMVOiQjS6enp+OyzzzBv3jxYWloq20UEM2bMwIgRI9CuXTu4urpi0aJFuHv3LpYvXw4ASE1Nxfz58zFt2jQ0bdoU9erVw9KlS/HPP/9g8+bNAICTJ08iKioKP/30Ezw8PODh4YF58+bhjz/+wKlTpwAA0dHROHHiBJYuXYp69eqhadOmmDZtGubNm4e0tLTCPylEREREVKRKRJDu378//Pz80LRpU53tFy5cQFJSEpo3b65sU6vV8PT0xO7duwEAsbGxyM7O1qnRarVwdXVVavbs2QONRgN3d3elplGjRtBoNDo1rq6u0Gq1So2vry8yMzMRGxtr+IMmIiIiomKt2K/asXLlShw6dAgHDhzIty8pKQkAYGdnp7Pdzs4Oly5dUmpMTEx0RrLzavIen5SUBFtb23zPb2trq1PzeDuWlpYwMTFRap4kMzMTmZmZyn2OXhMRERG9Gop1kE5ISMDnn3+O6OholClT5ql1KpVK576I5Nv2uMdrnlSvT83jJkyYgNGjRz+zL0T0euBye0REr5ZiPbUjNjYWycnJcHNzg7GxMYyNjbFjxw58//33MDY2VkaIHx8RTk5OVvbZ29sjKysLKSkpz6y5du1avvavX7+uU/N4OykpKcjOzs43Uv2o4cOHIzU1VbklJCS84FkgIiIiouKoWAdpHx8f/PPPP4iLi1NuDRo0wGeffYa4uDhUqlQJ9vb2iImJUR6TlZWFHTt2oHHjxgAANzc3lC5dWqcmMTERx44dU2o8PDyQmpqK/fv3KzX79u1DamqqTs2xY8eQmJio1ERHR0OtVsPNze2px6BWq2FhYaFzIyIiIqKSr1hP7TA3N4erq6vONjMzM1hbWyvbBw0ahPHjx6Nq1aqoWrUqxo8fj7JlyyIgIAAAoNFo0L17d4SGhsLa2hpWVlYICwtD7dq1lYsXa9SogRYtWqBnz56YM2cOAKBXr17w9/eHi4sLAKB58+aoWbMmAgMDMWXKFNy6dQthYWHo2bMnwzERERHRa6hYB+nn8cUXX+DevXvo168fUlJS4O7ujujoaJibmys106dPh7GxMdq3b4979+7Bx8cHCxcuhJGRkVKzbNkyhISEKKt7tGnTBjNnzlT2GxkZYcOGDejXrx+aNGkCU1NTBAQEYOrUqYV3sERERERUbJS4IL19+3ad+yqVCqNGjcKoUaOe+pgyZcogIiICERERT62xsrLC0qVLn9l2xYoV8ccff7xId4mIiIjoFVWs50gTERERERVXDNJERERERHpgkCYiIiIi0gODNBERERGRHhikiYiIiIj0wCBNRERERKSHErf8HRERPT+nYRsK7LkvTvQrsOcmIioJGKSJiMhgiiK4880CERUVTu0gIiIiItIDR6SJiIheAEfAiSgPR6SJiIiIiPTAIE1EREREpAcGaSIiIiIiPTBIExERERHpgRcbEhERFXO8wJGoeOKINBERERGRHjgiTURERDo4Ak70fBikiYiIqMgVVHhncKeCxKkdRERERER64Ig0ERERvXY4fYUMgUGaiIiIqBAU9vQVvlkoeJzaQURERESkB45IExEREZFBvG4XjXJEmoiIiIhIDwzSRERERER6YJAmIiIiItIDgzQRERERkR4YpImIiIiI9MAgTURERESkh2IdpCdMmIB33nkH5ubmsLW1xUcffYRTp07p1IgIRo0aBa1WC1NTU3h5eeH48eM6NZmZmRg4cCBsbGxgZmaGNm3a4PLlyzo1KSkpCAwMhEajgUajQWBgIG7fvq1TEx8fj9atW8PMzAw2NjYICQlBVlZWgRw7ERERERVvxTpI79ixA/3798fevXsRExODBw8eoHnz5sjIyFBqJk+ejPDwcMycORMHDhyAvb09mjVrhjt37ig1gwYNwtq1a7Fy5Urs2rUL6enp8Pf3R05OjlITEBCAuLg4REVFISoqCnFxcQgMDFT25+TkwM/PDxkZGdi1axdWrlyJyMhIhIaGFs7JICIiIqJipVh/IUtUVJTO/QULFsDW1haxsbF4//33ISKYMWMGRowYgXbt2gEAFi1aBDs7Oyxfvhy9e/dGamoq5s+fjyVLlqBp06YAgKVLl8LBwQGbN2+Gr68vTp48iaioKOzduxfu7u4AgHnz5sHDwwOnTp2Ci4sLoqOjceLECSQkJECr1QIApk2bhuDgYIwbNw4WFhaFeGaIiIiIqKgV6xHpx6WmpgIArKysAAAXLlxAUlISmjdvrtSo1Wp4enpi9+7dAIDY2FhkZ2fr1Gi1Wri6uio1e/bsgUajUUI0ADRq1AgajUanxtXVVQnRAODr64vMzEzExsY+tc+ZmZlIS0vTuRERERFRyVdigrSIYMiQIXj33Xfh6uoKAEhKSgIA2NnZ6dTa2dkp+5KSkmBiYgJLS8tn1tja2uZr09bWVqfm8XYsLS1hYmKi1DzJhAkTlHnXGo0GDg4OL3LYRERERFRMlZggPWDAABw9ehQrVqzIt0+lUuncF5F82x73eM2T6vWpedzw4cORmpqq3BISEp7ZLyIiIiIqGUpEkB44cCDWr1+Pbdu2oUKFCsp2e3t7AMg3IpycnKyMHtvb2yMrKwspKSnPrLl27Vq+dq9fv65T83g7KSkpyM7OzjdS/Si1Wg0LCwudGxERERGVfMU6SIsIBgwYgDVr1mDr1q1wdnbW2e/s7Ax7e3vExMQo27KysrBjxw40btwYAODm5obSpUvr1CQmJuLYsWNKjYeHB1JTU7F//36lZt++fUhNTdWpOXbsGBITE5Wa6OhoqNVquLm5Gf7giYiIiKhYK9ardvTv3x/Lly/Hb7/9BnNzc2VEWKPRwNTUFCqVCoMGDcL48eNRtWpVVK1aFePHj0fZsmUREBCg1Hbv3h2hoaGwtraGlZUVwsLCULt2bWUVjxo1aqBFixbo2bMn5syZAwDo1asX/P394eLiAgBo3rw5atasicDAQEyZMgW3bt1CWFgYevbsyVFmIiIiotdQsQ7SP/74IwDAy8tLZ/uCBQsQHBwMAPjiiy9w79499OvXDykpKXB3d0d0dDTMzc2V+unTp8PY2Bjt27fHvXv34OPjg4ULF8LIyEipWbZsGUJCQpTVPdq0aYOZM2cq+42MjLBhwwb069cPTZo0gampKQICAjB16tQCOnoiIiIiKs6KdZAWkf+sUalUGDVqFEaNGvXUmjJlyiAiIgIRERFPrbGyssLSpUuf2VbFihXxxx9//GefiIiIiOjVV6znSBMRERERFVcM0kREREREemCQJiIiIiLSA4M0EREREZEeGKSJiIiIiPTAIE1EREREpAcGaSIiIiIiPTBIExERERHpgUGaiIiIiEgPDNJERERERHpgkCYiIiIi0gODNBERERGRHhikiYiIiIj0wCBNRERERKQHBmkiIiIiIj0wSBMRERER6YFBmoiIiIhIDwzSRERERER6YJAmIiIiItIDgzQRERERkR4YpImIiIiI9MAgTURERESkBwZpIiIiIiI9MEgTEREREemBQZqIiIiISA8M0kREREREemCQJiIiIiLSA4M0EREREZEeGKSJiIiIiPTAIK2HWbNmwdnZGWXKlIGbmxv++uuvou4SERERERUyBukXtGrVKgwaNAgjRozA4cOH8d5776Fly5aIj48v6q4RERERUSFikH5B4eHh6N69O3r06IEaNWpgxowZcHBwwI8//ljUXSMiIiKiQmRc1B0oSbKyshAbG4thw4bpbG/evDl27979xMdkZmYiMzNTuZ+amgoASEtLK7iOPiY3826BPO/TjqGg2iuKNnmMBeN1PcZX6Zw+rU0eo+HbK4o2X6VjLC7ntCDbfJ2PsaDktScizy4Uem5XrlwRAPL333/rbB83bpxUq1btiY8ZOXKkAOCNN95444033njjrYTdEhISnpkNOSKtB5VKpXNfRPJtyzN8+HAMGTJEuZ+bm4tbt27B2tr6qY8pKmlpaXBwcEBCQgIsLCxeyTZf9faKok0e46vRJo/x1WiTx/hqtPmqt1dUbb4IEcGdO3eg1WqfWccg/QJsbGxgZGSEpKQkne3Jycmws7N74mPUajXUarXOtjfffLOgumgQFhYWhf5DXdhtvurtFUWbPMZXo00e46vRJo/x1WjzVW+vqNp8XhqN5j9reLHhCzAxMYGbmxtiYmJ0tsfExKBx48ZF1CsiIiIiKgockX5BQ4YMQWBgIBo0aAAPDw/MnTsX8fHx6NOnT1F3jYiIiIgKEYP0C+rQoQNu3ryJMWPGIDExEa6urti4cSMcHR2LumsvTa1WY+TIkfmmorxKbb7q7RVFmzzGV6NNHuOr0SaP8dVo81Vvr6jaLAgqkf9a14OIiIiIiB7HOdJERERERHpgkCYiIiIi0gODNBERERGRHhikieilbNy4EdnZ2UXWft5lHrzcg4iIChuDNBHpLSwsDEOGDMH169eLrA/79+8H8PAbRxmmiYioMDFIv+bu379f1F2gEuro0aNYunQpvv/+e2i1WiQnJxd6kN29ezc8PDwwadIkAAzThpabm/vE/y+JREQ5htfhZyTvGOPj45GZmVnEvSlcOTk5Rd2FVxLP65MxSL/Grly5gqCgIGzbtq2ouwKg6D6iL+iA8Kr+0RYRWFtbQ0SwaNEidO/eHcnJyYXah0qVKmHMmDGYNGkSJk+eDODVDdMJCQnIyMgo0DbyfhcyMjKQk5ODUqVKYe/evQCAUqVK1p+LvGPJC5EqlQrnz59X/v9Vp1Kp8Msvv+Ddd9/FuXPnSvwboWfJO7a0tDQAgJGREfbt21fgvy9FoajC7HfffVfk0/iKq5L1ykgGlZmZicuXL2PatGn4+++/i6wfeaEnPT0dOTk5yotfYbzwT5w4ET/++GOBvDjkHdfjf7QLMuSdOnUKBw8exK5duwqsjTxvv/026tSpgz59+qBr167w8/ODnZ1doYZYe3t7DB48GCNGjMCECRMwa9YsAK9emD527Bj8/f0xb968Ag0HpUqVwqVLl9CxY0fExsZi1apVaNy4MXbs2GHQdh7/tymI3/VSpUrh3LlzCAsLw7Vr1/Drr7+iWrVq+Pfffw3e1osqyJ/NvOfOzMzEn3/+iSFDhqBmzZol7o3QiyhVqhSuXLmCwMBA/Pbbb1i9ejU8PDxw6NChou6aQUVHR2PIkCG4fft2oba7Zs0ajBs3DlqtFqVLly7w9kraa/er+5tF/6lSpUpYtGgRcnJy8N133xVJmBYRqFQqbNy4EUFBQWjSpAkCAwMRExNTKC/8169fx8CBA7F06VKDhum849qzZw/Gjx+PKVOmIDIyEkDBjYatW7cOLVq0QFBQEJo3b47u3bsjMTGxQNrKCz4ff/wxLl26BK1WCxcXF2RmZhbaaF9eH44cOYI7d+7gjTfewIABA/D9998DMEyYznv8rVu3cOPGjZfr8EtwdXWFi4sLli9fjkWLFhVomFar1bh48SJ69OiBwMBA/Pzzz/D09DRY2M373di2bRvGjRsHwPCj3atWrcLp06dx6dIlLF68GJ999hk6d+6MhQsXonr16oX6h/rRKRYXLlxARkZGgf6OqFQq7Ny5Ex4eHkhKSoK3t3eBtfUsecd99uxZHD9+XPlkoyDaAICsrCzcu3cP33zzDQIDA7Fw4UK89957Bn2TNnPmTCQlJRns+V5EZGQkOnToAODhz1JhWbVqFVJTUzFjxgy4ubkZ7Hnz/u2OHDmCjRs3YtWqVUhISABQAj8xEnrtnT59Wlq0aCG+vr6ya9euQm9//fr1UqZMGZkwYYIsX75cPvvsM1GpVHLq1KlCaX/kyJFibGwsP/30k2RlZRnseSMjI+WNN96Qpk2bSv369UWtVkuPHj3kwYMHIiKSm5trsLY2bdokb775psyZM0cyMzNl48aNolKppGPHjpKQkGCwdh63atUqWbx4sfj7+0uVKlXk999/l8zMzAJr73Hr1q2TsmXLypgxY+S7774Tf39/MTMzk8mTJys1L3ue16xZI40aNRJHR0cJDQ2VQ4cOvWy3/9PT+hwcHCx169aVH374QTIyMgzebk5OjoiIrFy5UoyMjMTFxUX++usvZfvLnsu8x//6669iY2Mj/fv3lyNHjuTb/zISEhKkSZMmcunSJRERGT9+vKhUKmnSpIlcuHDBoG09rzVr1ki5cuWkatWqUrlyZTlw4ECBtrd//36pWbOmGBkZycGDB0VElNedwpB3btesWSNVqlSRunXrioWFhXTo0EH+/vtvg7Z17do15TVu5cqVYmxsLC4uLrJmzRqlJu/n92UMHTpUatWqJbdv337p53pRhw4dEisrK/npp590tqenpxfoz/GVK1fE0tJSVCqVjBw5UkQM+3sTGRkp5cqVk6ZNm4qDg4N4e3vL999/b7DnLywM0iQiRRemMzIyxN/fX6ZMmSIiD39xHR0dpVevXgXW5vnz5/Nt+/rrr5UwbYggeP78ealQoYJERESIiEhaWpps3LhRLC0tpXfv3i/9/I9KTU2VXr16yejRo5W2K1euLJ988om8+eab8uGHHyqh4mXlvYgeOXJE/vzzT4mMjFT2ffjhh1K5cuVCC9MZGRnSqlUrCQ0NVbYlJCTIqFGjpGzZsvK///0vX7+fx6O1Bw4ckHLlysk333wj48aNE0dHR2nbtq1s3brVMAfxDLt375Zff/0135u74OBgqVatmsyePbtAwrSIyIYNG2TRokXi4eEh77//vkRFRSnn5dHz8zwBZeXKlXLy5Enl/u7du8XCwkLmzZunU2fIP9B3794VEZFjx45JUFCQTJo0SRwdHaVLly5y9OjRJ7ZZUIHk0qVL4uLiIhEREfL7779Lhw4dxNzcXP78888CaU9EJDs7Ww4ePCguLi7SsGFDuX//vogYJlA+rx07dohGo5H58+eLyMM3+yqVSpYtW2awNtLT06V3797SuXNnSUxMlAMHDsiiRYvko48+Ei8vL1m6dKlS+zLHvnjxYmnQoIFcv37dEN1+YStXrhRvb28REbl586YsX75cWrVqJS4uLjJx4kRJTU0tsLZ3794t9erVEzc3N7lz546IGObn6ODBg2Jraytz585V2lGpVEoWKEkYpEnxaJg29KjB06SkpIizs7Ps3btXkpOT5a233tIJ0YsXL5Zz584ZrL0//vhDVCqVbNy4Md++oUOHipmZmSxZskTu3bundxu5ubkSFxcnlSpVytf39evXS9myZZ/Yvr4yMzNl9erVcvbsWbl586bUq1dPunfvLiIiK1asEJVKJa1atZLLly8bpL3Vq1eLlZWV1K1bV0qVKiUNGjSQxYsXi8jDMF2lShXZsGFDgYfpu3fvSq1ateTzzz/X2R4fHy9NmzYVlUolEydOfO7nezzwnT17VqZMmSLfffedsu3AgQPi5uYmH330kWzbtu1lD+GpcnNzxcfHR6pXry5r167NF6bfe+89qV69ukydOlXS09MN0t6TXL16VRo2bCjvvfeebNq0SWdE+XkkJCTIu+++K/Hx8cq28PBw+fDDD0VE5NatW7J+/Xr59NNPxcPDQ+eN2cu6ffu2uLu7S2BgoNy/f1/++usvcXBwkC5dusixY8eUuv379xuszTyPns+bN2/KiBEjdPZ169bNIGE6NzdXaevixYty7NgxOX/+vLItNjZWnJ2d5d1331V+hgorTI8bN06CgoJE5OHflqpVq0qPHj2U/dnZ2QZpZ8WKFRIcHCz9+/dXXuMuXLgg/v7+4uXlJcuXL1dq165d+8JvPocNGyZTpkyRGzduGKS/+tiwYYOoVCqZPHmyNG7cWFq3bi19+/aVoUOHirm5uc6bQ0OYMGGCREREKD8re/fuFUdHR/H29lZ+tl7252jhwoXStGlTEXn4Wuvs7Kzzt//RT4+KOwZp0nH69Gnx9/eXRo0ayZ49ewq8vbt378rHH38sEydOlIoVK0rv3r2VjyCvXbsmQUFBsnz5coONFuXm5kpQUJBYWloqf8TynjsuLk7UarWoVCpZv379cz9nfHy8rF69WkQevqj37NlTTp8+LWXKlJG1a9fq1CYnJ0u1atXyjca9rLzgv2zZMvHw8FA+6lyxYoV4eXmJo6OjQUalDx06JDY2NvLTTz/JrVu3JCkpSbp06SIeHh7KH6xWrVpJuXLlJCoq6qXb+y9Dhw4VX19fOX36tM72YcOGiZOTkzg7O8uNGzf+8+fn8cB369Yteeutt8TU1FQGDhyoU7tv3z6pX7++fPLJJ7Jp0ybDHtAjMjIyxNfXV9zc3CQyMlLnjcnw4cPF2tpamjVrJrdu3XqpdvLOzbZt22T06NESFBQkO3fulMTERBF5GKbd3d3Fy8tLfvjhB/n6669FpVI98ZOdJ8kbHT569KicO3dOfvnlF1GpVLJkyRLx9fWVVq1aSUBAgLRv3140Go1cu3btpY7nUfv375cGDRpIt27d5NatW7Jr1y6pWLGidOnSRaKjo2XMmDGiUqnk+vXrBn2NERHZuHGj9OjRQ9577z3x9vbWOa68MG1lZSW//fbbC7eRlpam01ZkZKQ4OjpK5cqVxcTERLp06SLbt28Xkf8P015eXgaduva4x0f3O3ToIF999ZXk5uYqAyR5NQsWLJBffvnFYO2tWbNGWrVqJatWrVK2XbhwQVq3bi3e3t4yduxY+eabb0SlUr1QQBs8eLA0bNiwyEaiHzVhwgR5++23ZcCAARIbG6tsr1evnuzcudOgbQ0ZMkRUKpXMnz9fJ0xXrFhRfHx8nvjp1IuaOnWqBAYGyt27d6VChQrSq1cvpa2oqCiZNm1agY60GxKDNOVz8uRJ+eSTTww2HUDk4fy8vF+6+/fv64xGDBw4UFQqlfj7+ysfQYo8DEPVq1c3aD/yBAUF5RsROnHihIwYMULmzZv33KMlWVlZ0rFjR2ncuLEMHjxYVCqVzJkzR3JycqRDhw7i7++vM7qfk5MjHh4e8uOPPxr8mERExo4dK66urkq4GjZsmERERBjsD+iyZcukZs2akpqaqvx7JiUlSUBAgDRq1Eipa9u2rZw9e9YgbYr8/wt2cnKyJCUlKdt/++03qVGjhnz55Zc6c+pDQkJk8uTJLzSf8dHAd+vWLdmzZ49UrFhR3n33XTl8+LBO7YEDB8TZ2Vk+++yzl55e8eio4s2bN+Xu3bvKH+6MjAxp2rSpuLm5ya+//qr8fgwbNkwiIyPl6tWrL9V2njVr1oi5ubl07NhRmjZtKjVr1pSvvvpKCcuJiYnSsmVLcXd3l+rVq7/wPPHU1FSpU6eOfPbZZ7Jlyxb56quvxN7eXrp27aqEgOTkZKlTp47OpwKGcOjQIalbt64Spnfv3i2urq5Sq1YtcXR0LJD5ytu3bxe1Wi0ffvihvP/++6JSqWTevHnKz1ieTz/9VBwcHF7oU4WePXtKt27dlNeonTt3ipmZmURERMjJkyfll19+ES8vL2nVqpVybmNjY8XS0lJatmxpuIN8gpiYGGXO+08//SSVK1cWa2trGTBggM4IZrdu3aR37946r/cv69Gfyby2Ll26JMHBwdKoUSOpVavWC/3cbtq0Sby9vSU5OdlgfXxeO3fulHHjxsnnn38uW7duVX5uUlJSdOqGDx8uVatWVd70GlLetUPz5s3TCdPOzs5Sp06d5w7Rubm5yuDYjRs3lOkh+/fvF5VKJWXKlJGwsDCd5+vbt6+0b99eqS3uGKTpiQz1sfyOHTt07v/+++/i6+srfn5+MmHCBGV7u3btRKvVyuDBg2Xs2LHStWtX0Wg0+QKMPhYvXizDhg2Tb775RmeEOCgoSExNTWXGjBmyceNGad26tXz66afK/ucN0ykpKeLu7i4qlUr69u2rc6xeXl7SokULWbZsmcTGxkpYWJhYW1sbdLrKow4fPixqtVqaNGkiPj4+YmFhoXMx18tasWKFVK5cWXnhzjtHFy5ceOqUGUNZs2aNVKtWTVxcXMTb21suXrwoIiJz586VGjVqiJeXl3Tr1k06deoklpaW+Uapn0dqaqrUrl1bOnXqJDdv3pQ9e/aIg4ODBAcH5/v4NDY29rlHZZ/m5s2byv//9ttv8v7770vNmjXl3XffVeYO3r17V1q0aCENGjSQVq1aSWBgoJiZmRnso8+9e/eKg4ODMpf1zp07olarpXLlyjJkyBDlPN+5c0fi4+P1/oj7wIED0qhRI+nVq5ecPXs23/SpL7/8UurUqVMgH6E/GqZv3Lgh169fl9jYWINNdxL5//B29epVGT58uMycOVPZ179/fzE1NZUlS5bkC9Mv8mZoxYoVUq5cOZ3XxXHjxkmzZs106rZv3y5NmjRRrsd48OCBHD58WM6cOfOih/XcsrKyxN/fX3x8fOTevXty/PhxadmypTg7OyvX3aSlpclXX30l5cuXl3///VevdvIC18mTJ+XIkSP5Xt/y/h3y/nvnzh25fv26zu/a87hz506+f6vCEBkZKebm5tKpUydxd3eXxo0byxdffKF8CiHy8G9Lt27dxMbGxmAXPz/r2qFHB5d27twpbdu2/c+LVzds2CBxcXE6x9WwYUOpVKmStGnTRhYsWCCzZs2SMmXKyNKlSyUzM1OuXLkiw4YNE2trazl+/LhBjqswMEhTgYmLixOVSiVfffWViDz86NjU1FR69eolQUFBolarpUuXLkr9sGHDpHXr1uLm5ibdunXTmceor7zg2r59e3F1dZXq1atLcHCwsn/o0KFiZ2cnlSpVksaNG+s1cpuVlSUffPCB1K1bV5o1a6bMFxZ5OCc7KChIypQpI9WrV9drNO9F7d69Wzp37iz9+/c3yDl81NmzZ0WtVuvM+RR5OD+zdu3asnfvXoO29+i0G1tbWxk7dqz8/PPP0qBBA3F2dlY+4ty0aZOMHDlSmjRpIp06dXqpNw8HDhzINx0gL0z/888/BjkuEZHr16+LVquVf//9V6Kjo8XExEQmTpwo06ZNk9DQUFGpVPLtt9+KyMMwPWbMGOnQoYN89NFHBp0TuWbNGmWe+fnz58XJyUn69OkjI0eOFDMzMxk6dKjBPl2IjY2VevXqSY8ePZSfza1bt0qvXr3EysrKIG+cn+bQoUPSoEED6dChg0FXBHo0bB0/flycnZ3Fyckp3/Stfv36SZkyZWTZsmV6B7TJkydL9erVReThijXTp0+X8ePHi4eHh2RmZuqM6i1atEhMTU0N9qnF8/jpp5+kXr16ypvYNWvWiJ+fn1haWoqHh4e89957otVqX/o1cPXq1WJraysODg5SuXJlmTp1qrKvMC+oNLTdu3eLg4ODsjrHxYsXxczMTKpVqyYDBw6UO3fuSHZ2tsybN0/atWtnsLD5X9cOlS1bVhYvXpzv7+PTwnRSUpI4OztL165d5dy5c3L8+HGxsLCQsWPHysSJE6Vfv35iamoq3bt3l2nTpolKpZLKlStLvXr1pHLlyoWyMpIhMUhTgbl//77MnTtXypQpI6NGjZL169fLtGnTROThSGZUVJRYWFhI586dlcdkZ2fL/fv3DbJUU0xMjLz11ls6oyE//fSTVK9eXfr166fUnT59Ws6dO6e8AOtzEcz9+/clMTFR/Pz8xNvbWydMizwcsb1w4UKhXbCSk5NTYKsQLF26VExMTGT48OFy5swZuXbtmowYMUIcHBzkypUrBm/v4MGDsm7dOvnmm2+UbVlZWfLee++Jo6OjznzBrKwsg0xjeXw6wK5du6RSpUry8ccfG+yPV97KLnv37pVevXpJnz59dPYvXLhQVCqVLFy4UEREZ2qUIV29elVOnTolmZmZ0qpVK+nWrZuyr3LlylK+fHkZMWKEwS4OO3TokNSvX1969OghGzZskDlz5kjz5s0N+iblafbv3y+enp4GC5eJiYny+eefS0xMjLItNDRUTExMpHfv3vl+3/OmsT06l/dF7N+/X1xcXOSDDz4QlUol69atk1WrVomxsbEyJzrP7t27pUaNGjoXehrS015fXF1dJSAgQLl/6tQpWblypYSFhcnPP/+s96c4j05/ql69uixYsEC2bt0qEyZMkNKlSyvLs4mUzDB9//59Wb9+vfL7l7f6UnBwsHz55ZdSrlw5+eKLL5TpDoac9vC81w69yHz+2NhYadCggfTv319GjBghYWFhyr7bt2/LrFmzpGzZsrJ8+XL5559/ZNGiRRIVFWXQT4kKC4M0GdSTXsBmz54tZcqUkXLlykl4eLjOvqioKDE3N9f5420oq1evFicnJ50XnNTUVJkyZYo0aNDgiaNsL/sCfO7cOfHz8xMfHx9ZtGiRiDwcaX88JJVkubm5snz5cjE3N5eKFStKtWrVpEKFCjqB1lDu378v1apVE5VKpfOGS+T/w3S1atVk9+7dBn/j8GiYTklJkW3btomrq6tB3yy888478vXXX4u7u7v0799f2Z73RrJfv37SsmVLuXPnzkuvP/6s6xREHl406+rqqlxom5iYKJ9++qkMGzZMmd5hKIcOHZJGjRrJZ599Jtu3b9f52LqgvcyKPI/KysqSy5cvS5s2baRPnz6yefNmZd/w4cPFwcFBpk+fni9Mh4WFvdQ88H79+olKpdK5JiEgIECsra1l69atynUBYWFh4urq+sJTGl7E/v375Z9//tE5p5GRkfL222/LX3/9ZfD2Nm/eLMOGDZMBAwYo0w/v3LkjM2fOFCMjoxIbpg8ePCh9+vSRhIQEOX36tGRmZkqzZs2ka9euIvJwqqWTk5PY2dlJaGhogQ2SGOraoTyxsbHSsGFDcXR01Hl9E3k4JbJr167SsWNHg/S9KDFIk8HFx8crV2SvWrVKAgICZP78+aLRaHSWP8oTHR0tKpUq3y+avn7++WeJiIiQLVu2SKVKlWT37t06+48fPy5GRkYSHR1tkPYed/78eWnbtq24urrKO++8IxYWFgaf8lAcXLx4UaKiomTDhg0F+qUvly5dkiZNmkiVKlWUNz95f0iys7Oldu3aUq9ePYMFpEflTQdo37693L5922BzJvP+yH/88cfy9ddfy+zZs6VevXr5phx888034u7u/lKh4FnXKUyaNEnZfuLECalRo4ZMnTpVzp49K6NGjZL33nuvwK6c37dvn3h7exfq1ANDSUhIkE8++USOHj0qCQkJ8tlnn8nHH3+sE5BDQ0PF0dFRwsPDDfZJ1N27d+WDDz6QHj16SM2aNaVTp04i8vD3oHPnzqJWq8XV1VU8PDzEysqqQD8iz3uTW6dOHfnwww/l5MmTkp2dLdevX5c6derIqFGjRMRwgTYzM1NGjBghRkZG4ubmprMvL0yXKVNGhg4dapD2CtP3338vtWvXVi58PXHihFSvXl353Y2Pj5cPP/xQvvnmG4N9wlDQ1w7lOXLkiDg5OUn16tXzTd366quvpE6dOgW6mkxhYJAmg3p0FYtBgwaJSqWSBQsWSG5ursyfP19Kly4tX3/9db7HbdmyRe+LTx51//59ad26tXz88cdy69YtZU70ox8nxsfHF9iISZ7Lly/L/PnzZfTo0QY5rtdFXkD+999/5cCBA8qqAwkJCcobk7w/JI+GaUOPmD5q//798v7777904Dt37pzMnDlTTp48qRzD0qVLpVmzZrJmzRpxd3eXfv366VwQNmDAAGnTpo3eAf55rlPIW3M8rz0HBwepWLGi2NnZFcinDI8qiDc/heHcuXPi7u4urVq1kuPHj8u5c+eUT6AeHS0MCwuTKlWqyLhx4ww2Mpy3Ssz8+fPFxcVF55OaX3/9Vb7//nuZMWOGQVfNeZq0tDRZunSp+Pv7i4WFhXTv3l3+/vtviYyMFCsrK4O/9p0/f15Gjx4tKpVKZs2apbMvPT1dpkyZItbW1gZdzrAgPbrij5eXl7z77rsi8v9f4jNhwgS5fv26jBw50iBLXeYpjGuHHnX06FGpXbu2dO3aVSdM9+7dW3x8fAyyFn5RYpAmg3vaKhb37t2Tn376SYyNjZ8Ypl9W3gvnoUOHxNzcXPbt2yd79+4VS0tL6dChg8yePVt27twpzZs3Fzc3t0L9ylz6b3n/fmvXrhUnJyepUaOGmJqaSnBwsFy9elXi4+OlVq1a8s477ygj4IX1x/JlA19WVpa0b99eKlasKM7OzmJhYSG+vr5SqVIlqVChgqSkpEhkZKS4u7tLzZo1JSgoSD7++GMxNzd/qQsnn/c6hbwvzhB5+PH5pk2bCvTNyavg9OnT4uvrK82bN9cZ+X3w4IHOa0ufPn2kTp06Bp9icefOHfn555/FxcVFGZkuSI8ud3nz5s18b2gXL14sXbt2FbVaLV5eXqJSqWTatGl6j0jnPW96errO9Lxbt27JsGHDxMzMTGbPnq3zmIyMDIOFzYL2559/SqdOnZT19i9fviyVK1dWvgBq4MCBUrlyZXFwcDDom9rCvHboUYcOHRJXV1dxdnaW4OBg6d27t1hbWxfoBcaFhUGaDO7xVSwe/ZrWu3fvyk8//SSmpqYyePDgAmk/LS1NOnbsKAMGDBCRh6sCtGrVSrRardSpU0eaNm2qvMNmmC5eNm3aJG+++abMmTNHMjMzZePGjaJSqaRDhw6SkJAg8fHxUrduXalSpUqJuyglb/Tp9OnT8ttvv0lERIS0b99eqlevLm3btpU7d+7IkSNHZMyYMdKiRQu9V13R9zqFR0ek6PnkhWlfX998n3A9+tpiyC+ZeVR6err8/PPP4urqKq1bty6QNkT+P9SuX79eGjVqJDVq1BA3Nzed13aRh6/vBw4cEH9/f6lRo4beq6Pktff777+Lp6enuLq6SpMmTSQyMlIyMjIkLS1Nhg8fLubm5gb/cqvCkJubKz179hSVSiWWlpby7bffyrlz52TcuHHy0UcfyalTp+Tu3buyefNmiYyMNOi3/BXFtUN5jh49KlWqVJGKFSvKhAkTXpk36wzSVCAeX8ViyZIlOvvDw8PFzs7OIIvdh4eHy9SpU3Xm6f70009StmxZ5YU8NTVVrl27pvP1uYZahYAMIzU1VXr16iWjR48Wkf+/av2TTz4RjUYjbdq0kYsXL8rFixfFw8PjpddwLmxPGz1fu3atNGrUSPz8/JS5tPfv33+p0faivk7hdXL69Glp0aKF+Pr6KqN8Ig//vQvjgrf09HSZNWuWNGzYsEBWzcnz+++/i5mZmUybNk22bNmifAHVo0E2783DnTt3XujLkB6Vd87+/PNPMTExUS50+/DDD6V69eoyceJE5UuL8r6tcMGCBS99fIVt37590qlTJxk7dqw0bNhQ+vbtKz169JAaNWroXLtgKEV97VCegwcPSrNmzYrki24KCoM0FahHV7HIWxLu22+/lS5duhjko867d+/Kl19+KRqNRj744APp1q2b3Lx5U+7fvy+fffaZ9OnT54lfLlOSruh+XWRmZsrq1avl7NmzcvPmTalXr54yf3f58uWiUqmkZcuWcvny5VfiTdCjXxyxcuVK8fT0FHd395f+OuKivk7hdfRomH70m0wLS0ZGht7B9XnEx8eLj4+PzJgxQ0QeLpno5OQkdevWFZVKpfNNrfq8ti5evFh++OEH5X5GRoa0adMm36eWX3zxhVSrVk02bNggIg8veB47dmyJ+bndsmWLskZ0Tk6ODBgwQIKDgyU1NVVmz56tjFKrVKp8QfdlFJdrh/KU1GsjnoZBmgrco6tYNGjQQDQajcFXsUhISJC5c+dK/fr1pXr16hIYGCh+fn7i5+enfIRVEi4+ed3lvcAuW7ZMPDw8lE8ZVqxYIV5eXuLo6FggXxlfVPJ+JnNzc2XRokXSsmVLgxxfUV2n8Do7ffq0+Pv7S6NGjWTPnj1F3R2Dunr1qnz77beSmJgoV69elRo1akivXr3k1q1b0qFDB1GpVBIREaHXc6enp0vTpk3Fw8NDZ2TZ09NTvvzySxHRXTvd19dXmjZtqtwvKdPzHjx4IOPGjROVSiVBQUGya9cuyc3NlXr16inL9qWlpcnAgQNFq9Xq9c2sT8JrhwoegzQVisJcxWLu3Lny+eefK+/sx44dW6DtkeGNHTtWXF1dlQuHhg0bJhERESV+maQneTRMG2o95aK+TuF1dfLkSfnkk09K9Ju93NxcJUzduHFDWVEhb47/t99+K61atZKUlBQRebhmdoUKFcTKykrvTxmvXr0qn376qXh5ecncuXNFRKRz587SsGFDpSbvk8UJEybIe++9V2I/lTpy5Ig0b95cmjRpIp9//rn8+eef0rp1a52R4Lxza0i8dqjgqEREQPQKEBGoVCrl/oEDBzBr1ixcv34dy5cvh4WFRRH2jl5EXFwcGjVqhAYNGqBMmTI4cOAA/vrrL9SpU6eou1YgHv/ZNYTMzEykpKSgR48euHv3Lrp164bOnTsr+6dPn45Jkybhn3/+Qbly5Qza9ussKysLJiYmRd2NF7Zx40a89dZbePvttwEAa9euxbRp05CcnIyAgAC0adMG9evXxyeffAK1Wo1ly5YBAAYPHoy3334b7dq1e+HXWBHBgwcPULp0aZw4cQJhYWFISUnB0KFDUaNGDbRs2RKNGjXCypUrlcd07doVt27dwurVq0vkeQaAa9euISYmBtOmTcPZs2dRrlw5dOjQARMmTDBYG9OnT0dubi46dOiAChUqAADmz5+PkJAQHD58GNWqVUNaWhru37+PjIwMODk5QaVS4cGDBzA2NjZYP14HDNL0SsoLJvv374enpyc2bdqE999/v6i7RS9gz549mDVrFjQaDfr27YtatWoVdZdKpPPnzyMkJAT3799Hly5dEBgYiJEjR+LSpUsIDw+HlZVVUXeRiti1a9fg4eEBLy8vjBgxAtnZ2fDw8EBoaChu3LiBv/76C05OThgxYgSOHDmC3r17Y9iwYUhISMAff/yB3bt3o2rVqi/cbt7r9C+//ILIyEgkJCQgLi4Ob731Fr788ktYWVlhyJAhsLKygpubG+7evYvffvsNe/bsQe3atQvgTBSunJwcfPHFF5g1axYsLCxw9uxZmJubv/Tz3rt3D6NHj8bs2bPh5uYGJycnTJkyBWZmZujevTvMzc3xv//9L98bkdzcXJQqVeql23/dMEjTKyvvRfqdd97BwIEDERQUVNRdoheUm5sLlUpl8NHa182FCxcQGhqKM2fOoEyZMjhz5gw2bdoEd3f3ou4aFROHDh1C79690ahRI9jZ2QEAvv76awDAhg0bMG3aNGg0GnTq1AmXLl3CkiVLYGNjg/DwcNStW1fvdvft24cPPvgAERERaNKkCYyMjNCjRw8AQGBgIDw9PTFt2jRcu3YNFhYWGDp06CvxpvrRT6G2bt2KypUrw9HR0aBtXL58GX/++Sdmz56Nu3fv4p133sGtW7cAACtXrsQbb7xRIJ+GvW4YpOmVNnfuXPTp0wdnzpxB5cqVi7o7REXmypUr2LRpEy5fvowOHTrAxcWlqLtExcyhQ4fQt29fXLt2DR07dsTEiROVfX/88QemT58Oa2trhISE4N1330VGRgbMzMxeqs25c+di+vTpOHjwoPJcly9fRqdOnZCYmIgpU6agbdu2APDKTTsozBA7b948HD9+HN9//z0A4LvvvsOIESMKpe1XHYM0vdLOnTuHzMxM1KxZs6i7QkRU7B09ehQfffQRtFot5syZozP6u2HDBnz99deoWbMmfv75Z6jV6pdub8mSJfjuu+/w119/wc7ODtnZ2ShdujT++ecfNG7cGI6Ojhg6dCi6dOnC0VM98NqhgsfJMPRKq1y5MkM0EdFzqlOnDtatW4eMjAx8//33OH78uLLPz88PkyZNwvjx4w0SogHAw8MDCQkJ+OGHHwAApUuXBvDwok03NzfUqVMHH3zwAQAwROvh0XMmInjnnXfQt29fbNmyBXFxcUXXsVcIgzQREREp6tSpg59//hkHDx7EjBkzcOLECWVf8+bNDTqXt0qVKpg3bx4mTpyIESNG4MKFC0hJScG6devg5OSE2bNnw8HBwWDtvc5UKhVEBA0bNoSrqysuXrxY1F16JXBqBxEREeVz+PBh9OnTB5UqVcLIkSNRvXr1AmlHRLBixQr07t0bNjY2KFWqFG7fvo2YmBjUr1+/QNp8nfHaIcNikCYiIqInOnDgAIYOHYoVK1agfPnyBdrWxYsXcfToUdy7dw/u7u5wcnIq0PZeV7x2yLAYpImIiOip7t+/jzJlyhR1N4iKJQZpIiIiIiI98GJDIiIiIiI9MEgTEREREemBQZqIiIiISA8M0kREREREemCQJiIiIiLSA4M0EREREZEeGKSJiIiIiPTAIE1EREREpAcGaSIiIiIiPTBIExERERHpgUGaiIiIiEgP/wehsyoccuzFQAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For visialization of the most common words used\n",
    "all_words = ' '.join(cleaned_dialog_df['input'].tolist() + cleaned_dialog_df['response'].tolist()).split()\n",
    "word_counts = Counter(all_words) # Most common words in the cleaned dialog pairs\n",
    "\n",
    "# Top 20 most common words\n",
    "common_words = word_counts.most_common(20)\n",
    "words, counts = zip(*common_words)\n",
    "\n",
    "plt.figure(figsize=(8, 4))\n",
    "plt.bar(words, counts)\n",
    "plt.title('Top 20 Most Common Words')\n",
    "plt.ylabel('Frequency')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "CzTLC0HVOHEZ",
   "metadata": {
    "id": "CzTLC0HVOHEZ"
   },
   "source": [
    "## Text Preprocessing\n",
    "Steps Include: <br>\n",
    "* Lowercasing\n",
    "* Removing Punctuation and Special Characters\n",
    "* Tokenization\n",
    "* Stopwords\n",
    "* Lemmatization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8f8e198f-c784-4e2e-b2fb-108ceaa692be",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\Saad\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\Saad\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\Saad\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n",
      "[nltk_data] Downloading package punkt_tab to\n",
      "[nltk_data]     C:\\Users\\Saad\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt_tab is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('punkt')\n",
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')\n",
    "nltk.download('punkt_tab')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e6335eed-b7bc-4b2b-911a-30778680886b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import WordNetLemmatizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "62b5533f-16ba-4ad4-8195-dcd6a3ced2a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Row 0: ['Can', 'we', 'make', 'this', 'quick', '?', 'Roxanne', 'Korrine', 'and', 'Andrew', 'Barrett', 'are', 'having', 'an', 'incredibly', 'horrendous', 'public', 'break-', 'up', 'on', 'the', 'quad', '.', 'Again', '.']\n",
      "Row 1: ['Well', ',', 'I', 'thought', 'we', \"'d\", 'start', 'with', 'pronunciation', ',', 'if', 'that', \"'s\", 'okay', 'with', 'you', '.']\n",
      "Row 2: ['Not', 'the', 'hacking', 'and', 'gagging', 'and', 'spitting', 'part', '.', 'Please', '.']\n",
      "Row 3: ['You', \"'re\", 'asking', 'me', 'out', '.', 'That', \"'s\", 'so', 'cute', '.', 'What', \"'s\", 'your', 'name', 'again', '?']\n",
      "Row 4: ['No', ',', 'no', ',', 'it', \"'s\", 'my', 'fault', '--', 'we', 'did', \"n't\", 'have', 'a', 'proper', 'introduction', '--', '-']\n",
      "Row 5: ['Cameron', '.']\n",
      "Row 6: ['The', 'thing', 'is', ',', 'Cameron', '--', 'I', \"'m\", 'at', 'the', 'mercy', 'of', 'a', 'particularly', 'hideous', 'breed', 'of', 'loser', '.', 'My', 'sister', '.', 'I', 'ca', \"n't\", 'date', 'until', 'she', 'does', '.']\n",
      "Row 7: ['Why', '?']\n",
      "Row 8: ['Unsolved', 'mystery', '.', 'She', 'used', 'to', 'be', 'really', 'popular', 'when', 'she', 'started', 'high', 'school', ',', 'then', 'it', 'was', 'just', 'like', 'she', 'got', 'sick', 'of', 'it', 'or', 'something', '.']\n",
      "Row 9: ['Gosh', ',', 'if', 'only', 'we', 'could', 'find', 'Kat', 'a', 'boyfriend', '...']\n"
     ]
    }
   ],
   "source": [
    "for i, text in enumerate(cleaned_dialog_df['input'].head(10)):  # Try first 10 rows\n",
    "    try:\n",
    "        tokens = word_tokenize(text)\n",
    "        print(f\"Row {i}: {tokens}\")\n",
    "    except Exception as e:\n",
    "        print(f\"Error in row {i}: {e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a6f72127-6f85-4ea9-8721-2a0098488fba",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "a6f72127-6f85-4ea9-8721-2a0098488fba",
    "outputId": "e283bbf3-5e5a-4ce6-e44a-7dab9aec0423"
   },
   "outputs": [],
   "source": [
    "#import re\n",
    "#import nltk\n",
    "#from nltk.corpus import stopwords\n",
    "#from nltk.tokenize import word_tokenize\n",
    "#from nltk.stem import WordNetLemmatizer\n",
    "\n",
    "# Download required resources from nltk\n",
    "#nltk.download('punkt')\n",
    "#nltk.download('stopwords')\n",
    "#nltk.download('wordnet')\n",
    "\n",
    "# Initialize stopwords and lemmatizer\n",
    "stop_words = set(stopwords.words('english'))\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "\n",
    "# Preprocessing function\n",
    "def preprocess_text(text):\n",
    "    # 1. Lowercasing\n",
    "    text = text.lower()\n",
    "\n",
    "    # 2. Removing Punctuation and Special Characters\n",
    "    text = re.sub(r'[^\\w\\s]', '', text)  # Removes punctuation\n",
    "\n",
    "    # 3. Tokenization\n",
    "    tokens = word_tokenize(text)\n",
    "\n",
    "    # 4. Removing Stopwords\n",
    "    tokens = [word for word in tokens if word not in stop_words]\n",
    "\n",
    "    # 5. Lemmatization (Optional but recommended)\n",
    "    tokens = [lemmatizer.lemmatize(word) for word in tokens]\n",
    "\n",
    "    # Join tokens back into a single string\n",
    "    return ' '.join(tokens)\n",
    "\n",
    "# Applying the preprocessing to both 'input' and 'response' columns\n",
    "cleaned_dialog_df['cleaned_input'] = cleaned_dialog_df['input'].apply(preprocess_text)\n",
    "cleaned_dialog_df['cleaned_response'] = cleaned_dialog_df['response'].apply(preprocess_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "gplZf90QaBnf",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "gplZf90QaBnf",
    "outputId": "a810f064-3f0f-4191-97bc-cc5b7af16edc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                       cleaned_input  \\\n",
      "0  make quick roxanne korrine andrew barrett incr...   \n",
      "1    well thought wed start pronunciation thats okay   \n",
      "2               hacking gagging spitting part please   \n",
      "3                 youre asking thats cute whats name   \n",
      "4                    fault didnt proper introduction   \n",
      "\n",
      "                                  cleaned_response  \n",
      "0  well thought wed start pronunciation thats okay  \n",
      "1             hacking gagging spitting part please  \n",
      "2      okay bout try french cuisine saturday night  \n",
      "3                                           forget  \n",
      "4                                          cameron  \n"
     ]
    }
   ],
   "source": [
    "# Display the first few rows to check the preprocessing\n",
    "print(cleaned_dialog_df[['cleaned_input', 'cleaned_response']].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dqDFX9TaOzEH",
   "metadata": {
    "id": "dqDFX9TaOzEH"
   },
   "source": [
    "## Additional Preprocessing Step - Handling Rare Words\n",
    "Words that are not used often and are insignficant to the training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dZnh0xAfOzlo",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "dZnh0xAfOzlo",
    "outputId": "c9d492b1-2b32-41e2-b757-4821d89f21d2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                               input  \\\n",
      "0  Can we make this quick? <UNK> <UNK> and Andrew...   \n",
      "1  Well, I thought we'd start with <UNK> if that'...   \n",
      "2  Not the hacking and gagging and spitting part....   \n",
      "3  You're asking me out. That's so cute. What's y...   \n",
      "4  No, no, it's my fault -- we didn't have a prop...   \n",
      "\n",
      "                                            response  \n",
      "0  Well, I thought we'd start with <UNK> if that'...  \n",
      "1  Not the hacking and gagging and spitting part....  \n",
      "2  Okay... then how 'bout we try out some French ...  \n",
      "3                                         Forget it.  \n",
      "4                                           Cameron.  \n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "# Step 1: Combine all text (input and response) into a single list of words\n",
    "all_words = ' '.join(cleaned_dialog_df['input'].tolist() + cleaned_dialog_df['response'].tolist()).split()\n",
    "\n",
    "# Step 2: Count the frequency of each word\n",
    "word_counts = Counter(all_words)\n",
    "\n",
    "# Step 3: Set a threshold (e.g., words that appear fewer than 5 times are considered rare)\n",
    "threshold = 5\n",
    "rare_words = {word for word, count in word_counts.items() if count < threshold}\n",
    "\n",
    "# Step 4: Define a function to replace rare words with '<UNK>'\n",
    "def replace_rare_words(text, rare_words_set):\n",
    "    return ' '.join([word if word not in rare_words_set else '<UNK>' for word in text.split()])\n",
    "\n",
    "# Step 5: Apply the function to both the input and response columns\n",
    "cleaned_dialog_df['input'] = cleaned_dialog_df['input'].apply(lambda x: replace_rare_words(x, rare_words))\n",
    "cleaned_dialog_df['response'] = cleaned_dialog_df['response'].apply(lambda x: replace_rare_words(x, rare_words))\n",
    "\n",
    "# Step 6: Check a few examples\n",
    "print(cleaned_dialog_df[['input', 'response']].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "K98StiwGPWPD",
   "metadata": {
    "id": "K98StiwGPWPD"
   },
   "source": [
    "## Data Exploration and Visualization after Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "G8JIg4IMPbsB",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 410
    },
    "id": "G8JIg4IMPbsB",
    "outputId": "99740b71-3be6-4140-e255-1c48a2a8fb1c"
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from collections import Counter\n",
    "from wordcloud import WordCloud\n",
    "import warnings\n",
    "\n",
    "# Suppress all warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# 1. Distribution of Input and Response Lengths\n",
    "\n",
    "# Calculate the length of each cleaned input and response in terms of number of words\n",
    "cleaned_dialog_df['input_length'] = cleaned_dialog_df['cleaned_input'].apply(lambda x: len(x.split()))\n",
    "cleaned_dialog_df['response_length'] = cleaned_dialog_df['cleaned_response'].apply(lambda x: len(x.split()))\n",
    "\n",
    "# Plot histograms for input and response lengths\n",
    "plt.figure(figsize=(8, 4))\n",
    "plt.hist(cleaned_dialog_df['input_length'], bins=30, alpha=0.7, label='Input Line Length')\n",
    "plt.hist(cleaned_dialog_df['response_length'], bins=30, alpha=0.7, label='Response Line Length')\n",
    "plt.title('Distribution of Line Lengths (Input vs. Response)')\n",
    "plt.xlabel('Line Length (Number of Words)')\n",
    "plt.xlim(0, 50)\n",
    "plt.ylabel('Frequency')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "mrRckkRcQS1_",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 414
    },
    "id": "mrRckkRcQS1_",
    "outputId": "8e5e19e0-5db7-45e4-f2be-bf3253402745"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2. Most Common Words in Cleaned Input and Responses\n",
    "\n",
    "# Combine all words from both input and response\n",
    "all_words = ' '.join(cleaned_dialog_df['cleaned_input'].tolist() + cleaned_dialog_df['cleaned_response'].tolist()).split()\n",
    "\n",
    "# Count the frequency of each word\n",
    "word_counts = Counter(all_words)\n",
    "\n",
    "# Get the 20 most common words\n",
    "common_words = word_counts.most_common(20)\n",
    "words, counts = zip(*common_words)\n",
    "\n",
    "# Create a bar plot for the 20 most common words\n",
    "plt.figure(figsize=(8, 4))\n",
    "sns.barplot(x=list(words), y=list(counts), palette='viridis')\n",
    "plt.title('Top 20 Most Common Words After Preprocessing')\n",
    "plt.ylabel('Frequency')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "rMHyoVnyQVRi",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 367
    },
    "id": "rMHyoVnyQVRi",
    "outputId": "3ca7dbfa-c095-4e53-b9bf-1423e7b009e2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3. Word Cloud of Most Common Words\n",
    "\n",
    "# Generate a word cloud\n",
    "wordcloud = WordCloud(width=800, height=400, background_color='white').generate(' '.join(all_words))\n",
    "\n",
    "# Display the word cloud\n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.imshow(wordcloud, interpolation='bilinear')\n",
    "plt.axis('off')  # Hide axes\n",
    "plt.title(\"Word Cloud of Movie Dialogues After Preprocessing\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9eT4si75QXFY",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 447
    },
    "id": "9eT4si75QXFY",
    "outputId": "bf0aa782-d5eb-4131-f59a-011a55110981"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Input Length (in words): 5.18\n",
      "Average Response Length (in words): 5.38\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 4. Statistics: Average Input and Response Length\n",
    "\n",
    "# Calculate average input and response length\n",
    "avg_input_length = cleaned_dialog_df['input_length'].mean()\n",
    "avg_response_length = cleaned_dialog_df['response_length'].mean()\n",
    "\n",
    "print(f\"Average Input Length (in words): {avg_input_length:.2f}\")\n",
    "print(f\"Average Response Length (in words): {avg_response_length:.2f}\")\n",
    "\n",
    "# Optional: Visualizing Input vs Response Length\n",
    "plt.figure(figsize=(8, 4))\n",
    "sns.scatterplot(x='input_length', y='response_length', data=cleaned_dialog_df, alpha=0.6)\n",
    "plt.title('Input vs. Response Length (in words)')\n",
    "plt.xlabel('Input Length (words)')\n",
    "plt.ylabel('Response Length (words)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5jHI6B2l708_",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 456
    },
    "id": "5jHI6B2l708_",
    "outputId": "baaa2050-7b10-4f72-d183-4b349aca52f7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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N11zEWrRooXO519FceHX9qn7y5In6N120aJHWvLxKWrI6X2WVtLxKUlKSmJubS5kyZbKMKWPSkp6erv5I1fWj4ObNm2qNy7Zt29TpmzdvVmsvHj16lGk5zb7LraTl7t27YmFhIfb29nLjxo1My6SlpYmfn58AkN27d6vTLSwscvzjVRe2aXlDf/31F4AX7T7c3Nwyzf/kk09gYWGBa9eu4dy5c5nmFy1aFO+9916m6c2bN0fx4sXx5MkT7Nu3L/cDz+DZs2cAAHNz8yzLaBr0ahrGvc7jx48BADY2Nm8UW6FChdC6detM0zWNdi9fvqxzuSNHjmDYsGFo2bIlgoKCEBgYiMDAQJw/fx4AcOLECZ3LtWnT5rUxb9++HZ999hneffdd1KtXT113fHw8kpKScOHCBbXsvn378ODBA7i7u2fZZkdf+/fvR2xsLKpXr45q1arpLNOyZUsAwK5du3TOz6rRoab3xNq1a9W2FrmhT58+maa5uLigbdu2AF60zdGws7ND27ZtkZqaip9++klrmQ0bNuD+/fvw9fVFxYoVcxxP6dKlERYWhtTUVLXt1dvk6Oios/t61apVAQDVqlXT+bfUTMvqOG/durXO807v3r0BvDj+NO0rUlJSsHHjRpiammbZNienx82rJCYmYv/+/QCAfv36ZZpvbW2Nnj17Avjf+TSvab7zmvNWdqSnp+O3335Dnz590KxZM9StWxeBgYFo3LgxFEXBhQsXstUuMTo6Gjdu3IClpaW6HzLS9AgFtPfPli1bALw4BnR1yda0jcstGzduRHJyMkJCQlCsWLFM801MTNC8eXMA2sePh4cHHj16pMb7ptjl+Q1pLoIVKlTQOd/Ozg4eHh64ePEizp8/j3LlymnNL1u2rM6xTxRFQdmyZXH9+nWcP38eTZs2zf3g/5+lpSUAvLLho6ahaHZb8dvZ2QFAjhqkZVSqVCmd011cXAC8OCFmJCLo27fvaxuMZtWwrXz58lkuk5KSgg4dOrx20LeM646OjgYA1KxZM0dj3Ohy8uRJAC8a1gYGBuos8+jRIwDArVu3Ms1zdnbO1ItLo1u3bpg2bRoiIyOxadMmNG3aFHXr1kX9+vVRsmTJHMVrZmaG0qVL65yn2d+a75FG9+7dsWTJEixZsgQDBgxQp2saTebG4G+jR4/GsmXLsHTpUowYMSLLYy03ZLXuwoULZ2v+y8e5RlbHa8mSJWFhYYHk5GRcunQJlStXxvnz5/Hs2TOYm5vjnXfe0bmc/H+jX13Hzas+71UuXryI9PR0tcGmLpoE9OXjIK9o9q+9vX22yj969AjvvPOOmoxlJS4uDtbW1q8so9nm4sWLZ/mDSdf+0fw4qly5ss5lPD09YW9vj4SEhFd+fnZpzjsHDhzI8rxz584dANrHz2effYY+ffqgSZMmqFGjBho1aoTAwEAEBQWp1wl9MGl5Q5qDXXMR1cXV1RUXL17UmcW/bjlAv+w/JwoVKgTgxRcsK5p5mrKvU7RoUQDQ2fpcH1l9iTUJgOYkq7F06VLMnz8fNjY2mDZtGho3boyiRYuqyVbnzp2xfPnyLLtuv6qW5csvv8S6devg5uaGqVOnol69enBzc1NroQIDA7Fv3z6tdWtOGAULFszeBmdDfHw8AODevXtZ9qDS0FUz9qptdHd3x/79+zFq1Chs2LBBTRwAoFatWpg5cyYCAgL0itfJySnLhC2rY7xevXooU6YMjh07hpMnT6JSpUq4f/8+NmzYAHNzc61u+TlVokQJdO3aFd9//z3GjRunbufbkNWFS9OD43XzXz7ONbI6fyiKgsKFC+PmzZvqvtUcNykpKa+tvdXUvr4sJzWnmnNk4cKFs+yxklfnuqxoerG96nyc0cCBA7F//36ULVsWkyZNQq1atdSRvgGgWLFiuHXrVraGiMjuNQTQ3j+aH4SvuvDb2dnlWtKiOX5u3LiRqbfXyzKed3r37g07OzvMmDEDR48exdGjRzFlyhRYWloiLCwM06ZN02vwPt4eekO2trYAgLt372ZZRpN96jq4XnXR0awzJ9moPsqUKQPgxRc3q1sCmuppTdnXqV27NoAXyU5Wt2LehuXLlwMAZsyYgU8//RSlS5fWqh163ZctO+uOjIxEWFgYPD09tcbB0bVuzd9OU/ORGzTH3AcffAB50S4ty1dOnnFSvnx5/Prrr3j06BF27NiBiIgIlCtXDgcOHECTJk30TkQfPHiQ5WMgXnWMa2pTNMnEihUrkJqaipYtW8LR0VGvGLIycuRImJmZYfny5a/8lf+65OFNaxRzKqvzh4io8zT7VnPcFC1a9LXHTVbbmROaz713716W633VOTIvaAYMrVmz5mvLPn/+HD///DMA4LffflOHMdAkLM+fP8+y668uOb2GaBLIrGrhgNcngfocz5o4R4wY8dpj5+Uu4mFhYYiKikJMTAxWrlyJHj16oECBAjkaBZ1Jyxvy9vYG8OKZP7o8fvxYvZhpymZ07tw5nSd0EVHbwOhaLjdVq1YNZmZmePbsGf75559M81NTU3H48GEAL579kR3u7u5qFWJOx/bICc0FVZM0ZZSamqrersntdT948EBnlbqmWvfw4cPZfn7Tq8ZPAP53K1LzWIm3xcLCAsHBwRgzZgxOnTqFOnXqIDExEStWrNBrPampqTrHjQH+d/tM1zHetWtXmJqaYvny5Xj+/Ll6IszN5wJ5enqie/fuSEtLw7hx47Isp7lAZJUkXLx4Mddi0kdWx/OVK1eQnJysPjYDePGDw8zMDDExMa8c9yO3lS5dGiYmJkhOTs6ybc7p06cBvP1znS7//POPen573XhSwItj4MmTJ3B0dETZsmUzzT916hTS0tKy/fmabb5+/XqWCYiu/aP5f1Y/Cq9fv55lLcurjuf4+HidY07lxnnHzc0NHTp0wPfff4+DBw/CxMQEf/zxB2JiYrK9DiYtbygkJAQA8Msvv+jMrhcsWIDk5GR4enrqPMBv3ryJ33//PdP0DRs24Nq1a7CxsdH5rKPcZG9vj0aNGgEAFi1alGn+L7/8goSEBDg5Oen1YMiRI0cCeDGI2caNG19Z9vbt22pNxpvQ1KpofplktHjx4tfeTsnpumfMmKHzRFWnTh04Ozvj1q1b2b7Yaz4nq0bPdevWhbOzM44fP55nT4s1NTVVGz/fvn1b7+V1Ja737t3DL7/8AgBo0qRJpvnu7u5o0qQJYmNjMWPGDPzzzz9wc3PL9fZdI0aMgLm5OVasWJFlEqBpixEVFZWpNjI9PR2LFy/O1Ziya/Xq1TqPR83+rlOnjnqBsra2RkhICNLT0zFnzpw8i9HW1lZN9OfOnZtp/tOnT/H9998D+N/5NK88fPgQXbp0AQA0bNgwWzUtmu9nQkKCzu/o1KlT9YqhfPnyKF68OJ49e6buh4xu376N1atXA9DeP40bNwYArFmzRmeNyqueTK05njXJWka6YgBeJHTm5ubYuHGjVmeDnKpQoYJ6W0ifcwqTljfUoEED+Pn5ITk5GR07dtSq4vvrr78wduxYAMCwYcN0/oIuUKAA+vXrpzZyAl7U2vTt2xfAi95HeVFlOmLECCiKgu+//17r4nr8+HEMHDgQADBkyJBX9jB6WUhICAYOHIj09HS0atUKY8eOzZTB37t3DzNmzEClSpV0foH0pandGTlypFaCsnnzZnz++edqo+M3WfegQYPUX0Qigh9//BHTp0/XuW5LS0uMGjUKANCrVy+sWLFCq0o2Li4Os2bN0oo14wlFV+8DS0tLtVagXbt2WLt2baZq3lOnTmHo0KF69zwbMWIEFi1alOl21qlTp9Qq8erVq+u1zgIFCmD+/PlqggK8uFh07twZz549g6+vr/pcr5d1794dwP8S4M6dO8PU1FSvz38dDw8PfPTRR0hPT1efJvyyKlWqwN3dHTExMRgzZoy6v589e4bw8PAsa1rftrS0NHzwwQda36u1a9eqycHnn3+uVX78+PGwsLDAhAkT8OWXX2a66MbExOCrr75SR1TNLUOHDgXwIpnK2CPs8ePH+PDDD3Hv3j14eXkhNDQ0Vz83KwkJCViyZAmqV6+OU6dOwc3N7ZUX+YwKFiyIihUr4vnz5/jss8/UDgyaEY9XrVql13lSURT17zRmzBhs27ZNnXfnzh2EhoYiJSUFtWrV0vqeNGrUCJUrV8b9+/fRqVMnre/sunXrMHnyZJiZmen8zGbNmgF48b3KmPRu3rwZ48aNQ4ECmZu7uru7Izw8HKmpqQgJCcn0g0lEcOjQIXz66adqjVpCQgJCQ0Oxc+dOrZrmtLQ0zJkzB3FxcbCxsdH5gz5LudZ5+l/udSPiagY704xMWbp0abV8WFhYtkbE9fHxkUqVKqkjZfr5+ek9ZsiKFSvEyclJfWlGaLS1tdWarotmUDLgxYiMlStXVkcyfPfdd7M9+ujLxo0bpw5yZGJiIt7e3lKzZk0pXbq0un5ra2tZvny5ukxW415oZBxtNaNr166Jo6OjAC9GiKxatao6umj9+vXlgw8+0DnuQnbGYzhy5IhYWFgI8GLE2Ro1aoi7u7v6N37VaJeaEWEBiLOzs/j5+YmXl5eYmppmGg8hLS1NHRXXyclJAgICJCgoKNMYFxlHGHV0dBQ/Pz+pXr26uv0AZNOmTa/dZxm999576t+pdOnS6t9Js7769etnOWryy3SNiOvp6Sm+vr5iZWWlbt+ZM2eyXEdycrI6hgf0HJslo5fHaXnZzZs31TFjkMV4LEuXLlXnFy5cWHx9fcXe3l5sbW3VQfleNSKuLq8b/yWrcUo02zN8+HBxcHAQS0tLqVGjhtZIur1799a5zjVr1qgjmFpaWkrVqlWlZs2a6lghAGTo0KFay2Q12Jg+Mh6vmpGYNYNvFipUSA4dOpTt7c8Ozfcx42i6L593NMf09evXda4jq/PC+vXr1fO0o6Oj+Pr6qsfpqFGjstxf2R0Rt3Tp0loj4hYvXvy1I+JaW1uLr6+vegz069dPjePl7bt79646CrKFhYXWeXLYsGFZxp+amiqdO3dW43Rzc5OaNWtKlSpVxM7OTp0eHR0tIi8G3tRMs7GxkSpVqmjtK0VR5Lvvvnv9HzMDJi3Z9KqkRUTk3r17MnjwYClTpow6AE+9evVk6dKlmRIWEe2TVUJCgoSHh0vx4sXF3NxcPD09ZdiwYTka5CzjoHWvemXl999/lwYNGoiDg4NYW1tLlSpVZPbs2TlOWDQuX74sQ4YMUS+qBQoUECcnJ6lbt65MnDhRbt++rVU+p0mLyIsRc1u3bq2ezMuVKydjx46V5OTkLE9C2R1E6uDBg9K4cWOxtbUVGxsbqVq1qsyZM0fS09OzPCFpbNiwQZo3by6FCxcWc3NzKVq0qDRo0EDmz58vycnJWmXPnz8vbdu2FRcXFzWx0bUv9u3bJ506dRIPDw8xNzcXR0dHqVy5snTv3l02bNggKSkp2dpnGocPH5Zhw4aJv7+/uLm5qXEGBQXJjz/+mO2E5eXPS09Pl6+++kp8fHzE0tJSnJ2d5YMPPsg0jL4u/fv3FyDzSJv6eF3SkvFzXpVE/Pzzz+pjCBwdHaVVq1Zy+vTpbA3jr8ubJi2LFy+Wc+fOSbt27aRw4cJiYWEhVapUkYULF+o872hcvXpVBgwYIOXKlRMrKyuxtbWVsmXLSqtWrWTJkiUSFxenVT43khaRF+eXxo0bS6FChdRz3SeffJJl0pAbSUvGl7W1tRQpUkTq1q0rgwYN0pkoZfSq88LmzZuldu3aYmVlJXZ2dlKrVi11tF99kxaRF4nLjz/+KHXr1hV7e3uxsLCQMmXKyOeffy7379/PMsaLFy9KaGioODk5iaWlpVSqVEnmzZsnIqImBy//PUVELl26JO3atZNChQqJlZWVVKtWTd3O1/29N2zYIO+//764ubmJmZmZuLi4SI0aNaRv376yc+dOdaTl58+fy9KlSyUsLEzKlSsnDg4OYmVlJd7e3tK5c+dMIy9nhyKSi83EKdsiIyPRrVs3dOnSJdvVkkT/RaGhoVi1ahXmzZunc5A6IsrswYMHcHZ2RsGCBV85nIWxYZsWIsq3Hjx4gN9++w0WFha5MjYL0X+FpnG4rt6OxoxJCxHlWxEREXj27BlCQ0NzbWwWon+LkydPYuHChVpdpUUEy5YtUzsAfPLJJ4YK763giLhElK9ERUUhPDwct2/fxoULF2BlZaWegInofx48eIBevXqhd+/e8PT0hJOTEy5fvowHDx4AeNFjsUWLFgaOMnexpoWI8pVHjx5h165duH79Ovz8/LBx48a3+lwgImNVoUIFDBkyBJUqVUJ8fDyOHTsGEUHDhg2xcuXKXO+6nh+wIS4REREZBda0EBERkVFgmxY9pKen4/bt27Czs3vt82GIiIjof0QEjx8/hru7e5ZPfn8dJi16uH37Njw8PAwdBhERkdG6ceMGihUrlqNlmbToQfMMoBs3bsDe3t7A0RARERmPhIQEeHh4vNHz9Ji06EFzS8je3p5JCxERUQ68SfMKNsQlIiIio8CkhYiIiIwCkxYiIiIyCkxaiIiIyCgwaSEiIiKjwKSFiIiIjAKTFiIiIjIKTFqIiIjIKDBpISIiIqPApIWIiIiMApMWIiIiMgoGT1q++eYbVK5cWX2eT0BAADZt2qTOFxFERETA3d0dVlZWCA4OxunTp7XWkZycjH79+sHZ2Rk2NjZo2bIlbt68qVUmLi4OYWFhcHBwgIODA8LCwvDo0aO82EQiIiLKBYqIiCED+P3332FqaorSpUsDAJYsWYJp06bh2LFjqFixIqZMmYKJEyciMjIS3t7emDBhAnbv3o1z586pT4r89NNP8fvvvyMyMhJOTk4YNGgQHj58iKNHj8LU1BQA0KxZM9y8eRMLFy4EAHz88cfw8vLC77//nu1YExIS4ODggPj4ePWBiZ+OjsrFvZF7vhlX1dAhEBERqXRdQ/Vl8KRFF0dHR0ybNg3du3eHu7s7wsPDMXToUAAvalVcXV0xZcoU9OrVC/Hx8ShcuDCWLl2KDh06AABu374NDw8PbNy4ESEhIYiOjkaFChVw4MAB+Pv7AwAOHDiAgIAAnD17FmXLls1WXExaiIiIciY3khaD3x7KKC0tDStXrsSTJ08QEBCAK1euIDY2Fk2aNFHLWFhYICgoCH///TcA4OjRo0hNTdUq4+7uDh8fH7XM/v374eDgoCYsAFCrVi04ODioZXRJTk5GQkKC1ouIiIgMI18kLSdPnoStrS0sLCzwySefYO3atahQoQJiY2MBAK6urlrlXV1d1XmxsbEwNzdHoUKFXlnGxcUl0+e6uLioZXSZPHmy2gbGwcEBHh4eb7SdRERElHP5ImkpW7YsoqKicODAAXz66afo0qULzpw5o85XFEWrvIhkmvayl8voKv+69QwfPhzx8fHq68aNG9ndJCIiIspl+SJpMTc3R+nSpeHr64vJkyejSpUq+Oqrr+Dm5gYAmWpD7t69q9a+uLm5ISUlBXFxca8sc+fOnUyfe+/evUy1OBlZWFiovZo0LyIiIjKMfJG0vExEkJycjBIlSsDNzQ1btmxR56WkpGDXrl2oXbs2AKBGjRowMzPTKhMTE4NTp06pZQICAhAfH49Dhw6pZQ4ePIj4+Hi1DBEREeVvBQwdwBdffIFmzZrBw8MDjx8/xsqVK7Fz505s3rwZiqIgPDwckyZNQpkyZVCmTBlMmjQJ1tbW6NSpEwDAwcEBPXr0wKBBg+Dk5ARHR0cMHjwYlSpVQqNGjQAA5cuXR9OmTdGzZ08sWLAAwIsuz82bN892zyEiIiIyLIMnLXfu3EFYWBhiYmLg4OCAypUrY/PmzWjcuDEAYMiQIXj69Cl69+6NuLg4+Pv746+//lLHaAGAWbNmoUCBAmjfvj2ePn2Khg0bIjIyUh2jBQCWL1+O/v37q72MWrZsiXnz5uXtxhIREVGO5ctxWvIrjtNCRESUM/+6cVqIiIiIssKkhYiIiIwCkxYiIiIyCkxaiIiIyCgwaSEiIiKjwKSFiIiIjAKTFiIiIjIKTFqIiIjIKDBpISIiIqPApIWIiIiMApMWIiIiMgpMWoiIiMgoMGkhIiIio8CkhYiIiIwCkxYiIiIyCkxaiIiIyCgwaSEiIiKjwKSFiIiIjAKTFiIiIjIKTFqIiIjIKDBpISIiIqPApIWIiIiMApMWIiIiMgpMWoiIiMgoMGkhIiIio8CkhYiIiIwCkxYiIiIyCkxaiIiIyCgwaSEiIiKjwKSFiIiIjAKTFiIiIjIKTFqIiIjIKDBpISIiIqPApIWIiIiMApMWIiIiMgpMWoiIiMgoMGkhIiIio2DwpGXy5Mnw8/ODnZ0dXFxc8P777+PcuXNaZbp27QpFUbRetWrV0iqTnJyMfv36wdnZGTY2NmjZsiVu3rypVSYuLg5hYWFwcHCAg4MDwsLC8OjRo7e9iURERJQLDJ607Nq1C3369MGBAwewZcsWPH/+HE2aNMGTJ0+0yjVt2hQxMTHqa+PGjVrzw8PDsXbtWqxcuRJ79+5FYmIimjdvjrS0NLVMp06dEBUVhc2bN2Pz5s2IiopCWFhYnmwnERERvZkChg5g8+bNWu8XL14MFxcXHD16FPXq1VOnW1hYwM3NTec64uPjsWjRIixduhSNGjUCACxbtgweHh7YunUrQkJCEB0djc2bN+PAgQPw9/cHAHz33XcICAjAuXPnULZs2be0hURERJQbDF7T8rL4+HgAgKOjo9b0nTt3wsXFBd7e3ujZsyfu3r2rzjt69ChSU1PRpEkTdZq7uzt8fHzw999/AwD2798PBwcHNWEBgFq1asHBwUEt87Lk5GQkJCRovYiIiMgw8lXSIiIYOHAgAgMD4ePjo05v1qwZli9fju3bt2PGjBk4fPgwGjRogOTkZABAbGwszM3NUahQIa31ubq6IjY2Vi3j4uKS6TNdXFzUMi+bPHmy2v7FwcEBHh4eubWpREREpKdcuT108OBBHDt2DEFBQShfvnyO19O3b1+cOHECe/fu1ZreoUMH9f8+Pj7w9fWFp6cnNmzYgNatW2e5PhGBoijq+4z/z6pMRsOHD8fAgQPV9wkJCUxciIiIDETvpOWjjz7C8+fPERkZCQBYuXIlPvjgA4gIzM3NsWPHDgQEBOgdSL9+/bB+/Xrs3r0bxYoVe2XZIkWKwNPTExcuXAAAuLm5ISUlBXFxcVq1LXfv3kXt2rXVMnfu3Mm0rnv37sHV1VXn51hYWMDCwkLvbSEiIqLcp/ftoR07dqBBgwbq+4kTJyIkJARRUVGoXbs2Jk2apNf6RAR9+/bFmjVrsH37dpQoUeK1yzx48AA3btxAkSJFAAA1atSAmZkZtmzZopaJiYnBqVOn1KQlICAA8fHxOHTokFrm4MGDiI+PV8sQERFR/qV3TUtsbCw8PT0BALdv38bp06cxf/58VK5cGQMGDMAnn3yi1/r69OmDn376Cb/99hvs7OzU9iUODg6wsrJCYmIiIiIi0KZNGxQpUgRXr17FF198AWdnZ7Rq1Uot26NHDwwaNAhOTk5wdHTE4MGDUalSJbU3Ufny5dG0aVP07NkTCxYsAAB8/PHHaN68OXsOERERGQG9kxYzMzM8e/YMALBv3z5YWlqqA70VKlRI78HavvnmGwBAcHCw1vTFixeja9euMDU1xcmTJ/Hjjz/i0aNHKFKkCOrXr49Vq1bBzs5OLT9r1iwUKFAA7du3x9OnT9GwYUNERkbC1NRULbN8+XL0799f7WXUsmVLzJs3T99dQERERAagd9JSrlw5LF26FLVr18aiRYtQp04dmJmZAQBu3ryJwoUL67U+EXnlfCsrK/z555+vXY+lpSXmzp2LuXPnZlnG0dERy5Yt0ys+IiIiyh/0TloGDRqE0NBQrFixAgCwbt06dd62bdtQuXLlXAuOiIiISEPvpKVdu3bw8PDA33//DT8/P9StW1edV6xYMbRp0yZXAyQiIiICcjhOS61atTI9sBAAxo4d+8YBEREREemS4xFx//zzTwwfPhw9e/bE9evXAQCHDx/GvXv3ci04IiIiIg29a1qSkpLw3nvvYdu2bepIsp9++imKFy+O6dOnw8PDA9OnT8/1QImIiOi/Te+alhEjRuDIkSNYvXo14uPjtXr/NGnSBFu3bs3VAImIiIiAHNS0/PLLLxg/fjxatWqFtLQ0rXnFixdXbxURERER5Sa9a1ru3buHihUr6l6ZiQmePn36xkERERERvUzvpKVo0aI4efKkznknTpzI1rODiIiIiPSld9LSunVrTJw4EceOHVOnKYqCa9euYdasWWjXrl2uBkhEREQE5CBpGTNmDNzd3VGzZk34+vpCURR069YNPj4+cHFxwbBhw95GnERERPQfp3fSYmdnh7///hvjx4+Hra0tSpUqBWtrawwfPhy7d++GlZXV24iTiIiI/uNyNCKulZUVhg0bxloVIiIiyjM5HhGXiIiIKC/pXdPSoEGDV85XFAXbtm3LcUBEREREuuidtKSnp6vD92vcv38f586dg4uLC7y9vXMtOCIiIiINvZOWnTt36px+/vx5vPfeexgzZsybxkRERESUSa61afH29sbnn3+OIUOG5NYqiYiIiFS52hDXy8sLp06dys1VEhEREQHI5aRl9erVcHd3z81VEhEREQHIQZuW7t27Z5qWnJyMEydO4MyZM5g6dWquBEZERESUkd5Jy/bt2zP1HrK0tISXlxeGDx+OTp065VpwRERERBp6Jy1Xr159C2EQERERvRpHxCUiIiKjoHdNy+7du7OcZ2JigoIFC6JcuXIoUCBHjzUiIiIi0knvzCI4ODhTm5aX2draYuDAgRxojoiIiHKN3knL77//jn79+qFs2bLo2LEjXF1dERsbixUrVuDcuXMYN24c9uzZg/Hjx8PR0RH9+vV7G3ETERHRf4zeScvmzZtRr149REZGak3v0qULunTpgr1792LhwoUAgO+++45JCxEREeUKvRvirlq1Ch07dtQ5r1OnTlizZg0AoHnz5rhw4cKbRUdERET0//ROWp48eYJ79+7pnHfnzh0kJSUBAOzs7NgYl4iIiHKN3klLnTp1MGrUKJw7d05r+tmzZzF69GgEBgYCAC5fvoxixYrlTpRERET0n6d3Vcjs2bNRr149VKxYET4+PnB1dcWdO3dw6tQpFCpUCLNnzwYA3L59G126dMnteImIiOg/Su+kpUKFCjh16hRmzpyJ3bt34/Lly3BycsKgQYMQHh6OIkWKAABGjRqV68ESERHRf1eOGp24ubnxwYhERESUp3LcUvbx48fYv38/Hjx4gMKFC8Pf3x92dna5GRsRERGRKkdJy/Tp0zF27FgkJSVBRAAANjY2GDt2LAYOHJirARIREREBOUhafvzxRwwZMgTNmjVD165d4e7ujtu3b2PJkiX4/PPPUbhwYYSFhb2NWImIiOg/TO8uz7NmzUKnTp2wYcMGtGvXDnXq1EG7du3wxx9/oGPHjpg1a5Ze65s8eTL8/PxgZ2cHFxcXvP/++5m6U4sIIiIi4O7uDisrKwQHB+P06dNaZZKTk9GvXz84OzvDxsYGLVu2xM2bN7XKxMXFISwsDA4ODnBwcEBYWBgePXqk7y4gIiIiA9A7aTl79iw6d+6sc17nzp0RHR2t1/p27dqFPn364MCBA9iyZQueP3+OJk2a4MmTJ2qZqVOnYubMmZg3bx4OHz4MNzc3NG7cGI8fP1bLhIeHY+3atVi5ciX27t2LxMRENG/eHGlpaWqZTp06ISoqCps3b8bmzZsRFRXFWiEiIiIjofftISsrKzx8+FDnvIcPH8LKykqv9W3evFnr/eLFi+Hi4oKjR4+iXr16EBHMnj0bI0aMQOvWrQEAS5YsgaurK3766Sf06tUL8fHxWLRoEZYuXYpGjRoBAJYtWwYPDw9s3boVISEhiI6OxubNm3HgwAH4+/sDePFspICAAJw7dw5ly5bVd1cQERFRHtK7pqVu3bqIiIjA7du3tabHxsZi3LhxqFev3hsFFB8fDwBwdHQEAFy5cgWxsbFo0qSJWsbCwgJBQUH4+++/AQBHjx5FamqqVhl3d3f4+PioZfbv3w8HBwc1YQGAWrVqwcHBQS3zsuTkZCQkJGi9iIiIyDD0rmmZNGkSateujdKlS6Nhw4YoUqQIYmJisH37dpiZmakPTMwJEcHAgQMRGBgIHx8fAC+SIQBwdXXVKuvq6opr166pZczNzVGoUKFMZTTLx8bGwsXFJdNnuri4qGVeNnnyZIwdOzbH20NERES5R++alooVK+Lw4cN47733cPjwYSxevBiHDx/G+++/j0OHDqFChQo5DqZv3744ceIEVqxYkWmeoiha70Uk07SXvVxGV/lXrWf48OGIj49XXzdu3MjOZhAREdFbkKNxWry9vXUmFm+iX79+WL9+PXbv3q31oEU3NzcAL2pKNI8IAIC7d++qtS9ubm5ISUlBXFycVm3L3bt3Ubt2bbXMnTt3Mn3uvXv3MtXiaFhYWMDCwuLNN46IiIjemN41LblNRNC3b1+sWbMG27dvR4kSJbTmlyhRAm5ubtiyZYs6LSUlBbt27VITkho1asDMzEyrTExMDE6dOqWWCQgIQHx8PA4dOqSWOXjwIOLj49UyRERElH9lq6Zl3Lhx2V6hoih6PSyxT58++Omnn/Dbb7/Bzs5ObV/i4OAAKysrKIqC8PBwTJo0CWXKlEGZMmUwadIkWFtbo1OnTmrZHj16YNCgQXBycoKjoyMGDx6MSpUqqb2Jypcvj6ZNm6Jnz55YsGABAODjjz9G8+bN2XOIiIjICCiiGYf/FUxMsl8hoyiK1tgo2Smvy+LFi9G1a1cAL2pjxo4diwULFiAuLg7+/v74+uuv1ca6APDs2TN8/vnn+Omnn/D06VM0bNgQ8+fPh4eHh1rm4cOH6N+/P9avXw8AaNmyJebNm4eCBQtmK9aEhAQ4ODggPj4e9vb2AIBPR0dle1vz0jfjqho6BCIiIpWua6i+spW00AtMWoiIiHImN5IWg7dpISIiIsoOJi1ERERkFHLU5Xn37t2YM2cOoqOj8fTpU615iqLg0qVLuRIcERERkYbeScvevXvRsGFDBAcHIzo6Gk2bNsXjx4+xf/9+lCxZEnXq1HkbcdJbMCny9usL5bEvurobOgQiIsqn9L49NGbMGHTr1k190OGECROwZ88e/PPPP0hMTFQfakhERESUm/ROWk6dOoVWrVqpXZU13ZsrV66MUaNG6TWmCxEREVF26Z20JCUlwdbWFiYmJrCwsMD9+/fVeeXKlcOZM2dyNUAiIiIiIAdJS/HixdVn+FSoUAEbNmxQ5+3atQtOTk65Fx0RERHR/9O7IW5wcDB27tyJtm3bomfPnujduzeio6NhYWGBv/76C4MGDXobcRIREdF/nN5Jy9ixY/Hw4UMAwCeffIKkpCQsX74ciqJg5MiRGDFiRK4HSURERKR30uLs7AxnZ2f1/cCBAzFw4MBcDYqIiIjoZRwRl4iIiIxCtmpaxo0bh48++gju7u6v7dKsKApGjRqVK8ERERERaWQraYmIiEDTpk3h7u6OiIiIV5Zl0kJERERvQ7aSlvT0dJ3/JyIiIsorbNNCRERERkHv3kMpKSk4cuQIbt9+8bA9d3d3+Pr6wtzcPNeDIyIiItLIdtKSlpaGkSNHYt68eUhKStKaZ2Njg/DwcERERMDEhJU3RERElPuynbR07NgRv/76KypWrIgWLVrA09MTIoJr165h/fr1mDhxIi5evIiffvrpbcZLhKVbEwwdgk5hjewNHQIR0b9atpKWLVu24Ndff0VERARGjx6daf7kyZMRERGB8ePHo0ePHmjYsGGuB0pERET/bdm6l7Ns2TLUrVtXZ8KiERERgcDAQCxdujTXgiMiIiLSyFbScvjwYYSGhr62XGhoKA4dOvTGQRERERG9LFtJy+3bt1G2bNnXlitbtixu3br1xkERERERvSxbScvjx49ha2v72nI2NjZ48uTJGwdFRERE9LJsJS0iAkVRsrVCEXmjgIiIiIh0yXaX50GDBqFgwYKvLPPo0aM3DIeIiIhIt2wlLcWLF8eNGzdw48aNbJUlIiIiym3ZSlquXr36lsMgIiIiejWOuU9ERERGgUkLERERGQUmLURERGQUmLQQERGRUWDSQkREREYhW0nLwIED1e7O169fR2pq6lsNioiIiOhl2UpaZs+ejZiYGABAiRIlcOzYsbcaFBEREdHLspW0FCpUCHfu3AGg35D+RERERLklW4PL1apVCz169EDNmjUBvHpIf0VR8Ntvv+VagERERERANpOW+fPnIzw8HKdPn4aiKLh48SIsLCx0lmUtDBEREb0N2UpaPD09sXbtWgCAiYkJ1q1bp9a6vKndu3dj2rRpOHr0KGJiYrB27Vq8//776vyuXbtiyZIlWsv4+/vjwIED6vvk5GQMHjwYK1aswNOnT9GwYUPMnz8fxYoVU8vExcWhf//+WL9+PQCgZcuWmDt37msfAkmUm7Yff2zoEDJpUMXO0CEQEWWL3l2ed+zYgQoVKuRaAE+ePEGVKlUwb968LMs0bdoUMTEx6mvjxo1a88PDw7F27VqsXLkSe/fuRWJiIpo3b460tDS1TKdOnRAVFYXNmzdj8+bNiIqKQlhYWK5tBxEREb1d2appySgoKAgAcPHiRWzfvh0PHjyAs7Mz6tevj9KlS+sdQLNmzdCsWbNXlrGwsICbm5vOefHx8Vi0aBGWLl2KRo0aAQCWLVsGDw8PbN26FSEhIYiOjsbmzZtx4MAB+Pv7AwC+++47BAQE4Ny5cyhbtqzecRMREVHe0jtpERH069cP3377LdLT09XpJiYm6N27N+bMmZOrAQLAzp074eLigoIFCyIoKAgTJ06Ei4sLAODo0aNITU1FkyZN1PLu7u7w8fHB33//jZCQEOzfvx8ODg5qwgK8aFzs4OCAv//+O8ukJTk5GcnJyer7hISEXN82IiIiyh69bw/NmjUL8+fPR69evXDw4EHcuHEDBw8exCeffIL58+dj1qxZuRpgs2bNsHz5cmzfvh0zZszA4cOH0aBBAzWZiI2Nhbm5OQoVKqS1nKurK2JjY9UymiQnIxcXF7WMLpMnT4aDg4P68vDwyMUtIyIiIn3oXdPy/fffo1+/fvjqq6/UaUWLFoWfnx9MTU3x3Xff4bPPPsu1ADt06KD+38fHB76+vvD09MSGDRvQunXrLJd7eTwZXb2aXjfmzPDhwzFw4ED1fUJCAhMX+k86fTHG0CHoVLF0EUOHQER5SO+k5fLly2jevLnOec2bN8eCBQveOKhXKVKkCDw9PXHhwgUAgJubG1JSUhAXF6dV23L37l3Url1bLaMZHC+je/fuwdXVNcvPsrCwyLJrNxEZh1tnjxs6hEyKlqvy2jIP/v49DyLRn1PtFoYOgf7D9L495ODggGvXrumcd+3aNdjb279xUK/y4MED3LhxA0WKvPiFVaNGDZiZmWHLli1qmZiYGJw6dUpNWgICAhAfH49Dhw6pZQ4ePIj4+Hi1DBEREeVvete0NG7cGCNHjkS1atVQo0YNdXpUVBTGjBmDkJAQvdaXmJiIixcvqu+vXLmCqKgoODo6wtHREREREWjTpg2KFCmCq1ev4osvvoCzszNatWoF4EUS1aNHDwwaNAhOTk5wdHTE4MGDUalSJbU3Ufny5dG0aVP07NlTrQn6+OOP0bx5c/YcIiIiMhJ6Jy2TJ0/Gzp07UbNmTVSoUAFFihRBTEwMzpw5A3d3d0yePFmv9R05cgT169dX32vakHTp0gXffPMNTp48iR9//BGPHj1CkSJFUL9+faxatQp2dv8bEGvWrFkoUKAA2rdvrw4uFxkZCVNTU7XM8uXL0b9/f7WXUcuWLV85NgwRERHlL3onLR4eHoiKisKMGTOwY8cOXLlyBU5OThg2bBg+++wzODs767W+4OBgiEiW8//888/XrsPS0hJz587F3Llzsyzj6OiIZcuW6RUbERER5R96Jy0A4OzsrHeNChEREdGb0LshLhEREZEh5KimhYiIKCuxq3J/ZPQ35dahv6FDoFzApIWIiAjA+cmDDB2CTt7DZxg6hHyDt4eIiIjIKOidtKSkpLyytw8RERHR26BX0vLs2TNYWVlh3bp1bykcIiIiIt30SlosLS3h5OQEGxubtxUPERERkU563x5q0aIF1q5d+zZiISIiIsqS3r2HQkND0aNHD3Tv3h2tW7dGkSJFoCiKVpnq1avnWoBEREREQA6SFs0DESMjI7FkyRKteSICRVGQlpaWO9ERERER/T+9k5bFixe/jTiIiIiIXknvpKVLly5vIw4iIiKiV3qjweXOnTuHffv24cmTJ7kVDxEREZFOOUpafvzxRxQrVgwVKlRAvXr1cO7cOQBA+/bt8d133+VqgERERERADm4P/fLLL+jatSuaN2+OZs2aoU+fPuq86tWr4+eff0bPnj1zNUgiIiLK2u4P2xo6hEzq/fhrrq9T75qWyZMno1u3bli/fj0+/vhjrXnly5fHmTNnci04IiIiIg29k5bo6GiEhobqnOfo6IgHDx68cVBEREREL9M7abG2tkZ8fLzOebdu3UKhQoXeOCgiIiKil+mdtNSpUwfz5s3T+aTnyMhIBAcH50ZcRERERFr0bog7evRoBAYGombNmujUqRMURcGaNWswZswY7N69G4cOHXobcRIREdF/nN41Lb6+vti0aRMSExMxaNAgiAgmTZqE8+fPY+PGjfDx8XkbcRIREdF/nN41LQBQv359REdH49KlS7hz5w6cnZ3h7e2d27ERERERqXKUtGiUKlUKpUqVyq1YiIiIiLKUoxFxr169il69esHb2xtOTk7w9vZGr169cOXKldyOj4iIiAhADpKWqKgoVKtWDZGRkShatCiaNGmCokWLIjIyEtWqVUNUVNRbCJOIiIj+6/S+PRQeHo7ChQtj69atKF68uDr92rVraNy4MT777DPs2LEjV4MkIiIi0rum5dChQxg7dqxWwgIAnp6eiIiIwMGDB3MtOCIiIiINvZMWBwcHODg46JxXsGBB2Nvbv3FQRERERC/TO2np1KkTvv/+e53zvvvuO3Ts2PGNgyIiIiJ6WbbatKxZs0b9f40aNfDrr7+iZs2a6NixI9zc3BAbG4sVK1bg7t27aNeu3VsLloiIiP67spW0tG3bFoqiQETUf2/cuIEjR45kKhsWFoZOnTrleqBERET035atpIW9gYiIiMjQspW0BAUFve04iIiIiF4pRyPiEhEREeW1HD17aN26dVi+fDmuXbuGZ8+eac1TFAXHjx/PleCIiIiINPROWqZNm4ahQ4eicOHCKF26NGxsbN5GXERERERa9L49NH/+fHTv3h23b9/Gvn37sGPHjkwvfezevRstWrSAu7s7FEXBunXrtOaLCCIiIuDu7g4rKysEBwfj9OnTWmWSk5PRr18/ODs7w8bGBi1btsTNmze1ysTFxSEsLEwdHC8sLAyPHj3Sd/OJiIjIQPROWh48eIBOnTrB1NQ0VwJ48uQJqlSpgnnz5umcP3XqVMycORPz5s3D4cOH4ebmhsaNG+Px48dqmfDwcKxduxYrV67E3r17kZiYiObNmyMtLU0t06lTJ0RFRWHz5s3YvHkzoqKiEBYWlivbQERERG+f3reH6tSpg+joaDRo0CBXAmjWrBmaNWumc56IYPbs2RgxYgRat24NAFiyZAlcXV3x008/oVevXoiPj8eiRYuwdOlSNGrUCACwbNkyeHh4YOvWrQgJCUF0dDQ2b96MAwcOwN/fH8CL0XsDAgJw7tw5lC1bNle2hYiIiN4evWtaZs+eja+//hrr169HSkrK24hJdeXKFcTGxqJJkybqNAsLCwQFBeHvv/8GABw9ehSpqalaZdzd3eHj46OW2b9/PxwcHNSEBQBq1aoFBwcHtYwuycnJSEhI0HoRERGRYeidtJQuXRqNGjVCq1atYG1tDXt7e61XVg9TzInY2FgAgKurq9Z0V1dXdV5sbCzMzc1RqFChV5ZxcXHJtH4XFxe1jC6TJ09W28A4ODjAw8PjjbaHiIiIck7v20NDhgzBvHnzULVqVZQvXx7m5uZvIy4tiqJovdc8TuBVXi6jq/zr1jN8+HAMHDhQfZ+QkMDEhYiIyED0TloiIyMxdOhQTJ48+W3Eo8XNzQ3Ai5qSIkWKqNPv3r2r1r64ubkhJSUFcXFxWrUtd+/eRe3atdUyd+7cybT+e/fuZarFycjCwgIWFha5si1ERET0ZvS+PZSWlobGjRu/jVgyKVGiBNzc3LBlyxZ1WkpKCnbt2qUmJDVq1ICZmZlWmZiYGJw6dUotExAQgPj4eBw6dEgtc/DgQcTHx6tliIiIKH/Tu6alSZMmOHDgQK71HkpMTMTFixfV91euXEFUVBQcHR1RvHhxhIeHY9KkSShTpgzKlCmDSZMmwdraWn2StIODA3r06IFBgwbByckJjo6OGDx4MCpVqqT2JipfvjyaNm2Knj17YsGCBQCAjz/+GM2bN2fPISIiIiOhd9IyatQodOjQATY2Nnj33Xfh6OiYqYyuaVk5cuQI6tevr77XtCHp0qULIiMjMWTIEDx9+hS9e/dGXFwc/P398ddff8HOzk5dZtasWShQoADat2+Pp0+fomHDhoiMjNQaS2b58uXo37+/2suoZcuWWY4NQ0RERPmP3klLlSpVALxILjI2Us0o46BurxMcHAwRyXK+oiiIiIhARERElmUsLS0xd+5czJ07N8syjo6OWLZsWbbjIiIiovxF76Rl9OjRr+25Q0RERJTb9E5aXlXjQURERPS26N17iIiIiMgQ9K5pGTdu3CvnK4qCUaNG5TggIiIiIl1y/fYQkxYiIiJ6G/S+PZSenp7pdf/+fXz//ffw8fHB1atX30KYRERE9F+XK21aHB0d0b17d3Tq1An9+/fPjVUSERERacnVhrg1a9bEtm3bcnOVRERERAByOWk5fvw4bG1tc3OVRERERABy0BD3xx9/zDQtOTkZJ06cwA8//IDOnTvnSmBEREREGemdtHTt2lXndEtLS3Tu3BnTp09/05iIiIiIMtE7ably5UqmaZaWlnB1dc2VgIiIiIh00Ttp8fT0fBtxEBEREb0Sh/EnIiIio5CtmpbKlStne4WKouD48eM5DoiIiIhIl2wlLY6OjlAU5ZVlEhMTcfTo0deWIyIiIsqJbCUtO3fuzHLe8+fPsXDhQowbNw6KoqBTp065FRsRERGR6o3atPzyyy+oUKEC+vXrhypVquDo0aNYunRpbsVGREREpMpR0rJz5074+/ujQ4cOsLe3x19//YU///wTVatWzeXwiIiIiF7QK2k5efIk3nnnHTRs2BAPHjzATz/9hCNHjqBhw4ZvKz4iIiIiANlMWm7cuIEuXbqgevXqOHr0KGbPno3o6GiEhoa+7fiIiIiIAGSzIa63tzdSUlLQtGlTDBkyBHZ2djh58mSW5atXr55rARIREREB2UxakpOTAQCbNm3C5s2bsywnIlAUBWlpabkTHREREdH/y1bSsnjx4rcdBxEREdErZStp6dKly9uOg4iIiOiV+OwhIiIiMgpMWoiIiMgoMGkhIiIio8CkhYiIiIwCkxYiIiIyCkxaiIiIyCgwaSEiIiKjwKSFiIiIjAKTFiIiIjIKTFqIiIjIKDBpISIiIqPApIWIiIiMQr5PWiIiIqAoitbLzc1NnS8iiIiIgLu7O6ysrBAcHIzTp09rrSM5ORn9+vWDs7MzbGxs0LJlS9y8eTOvN4WIiIjeQL5PWgCgYsWKiImJUV8nT55U502dOhUzZ87EvHnzcPjwYbi5uaFx48Z4/PixWiY8PBxr167FypUrsXfvXiQmJqJ58+ZIS0szxOYQERFRDhQwdADZUaBAAa3aFQ0RwezZszFixAi0bt0aALBkyRK4urrip59+Qq9evRAfH49FixZh6dKlaNSoEQBg2bJl8PDwwNatWxESEpKn20JEREQ5YxQ1LRcuXIC7uztKlCiB0NBQXL58GQBw5coVxMbGokmTJmpZCwsLBAUF4e+//wYAHD16FKmpqVpl3N3d4ePjo5YhIiKi/C/f17T4+/vjxx9/hLe3N+7cuYMJEyagdu3aOH36NGJjYwEArq6uWsu4urri2rVrAIDY2FiYm5ujUKFCmcpols9KcnIykpOT1fcJCQm5sUlERESUA/k+aWnWrJn6/0qVKiEgIAClSpXCkiVLUKtWLQCAoihay4hIpmkvy06ZyZMnY+zYsTmMnIiIiHKTUdweysjGxgaVKlXChQsX1HYuL9eY3L17V619cXNzQ0pKCuLi4rIsk5Xhw4cjPj5efd24cSMXt4SIiIj0YXRJS3JyMqKjo1GkSBGUKFECbm5u2LJlizo/JSUFu3btQu3atQEANWrUgJmZmVaZmJgYnDp1Si2TFQsLC9jb22u9iIiIyDDy/e2hwYMHo0WLFihevDju3r2LCRMmICEhAV26dIGiKAgPD8ekSZNQpkwZlClTBpMmTYK1tTU6deoEAHBwcECPHj0waNAgODk5wdHREYMHD0alSpXU3kRERESU/+X7pOXmzZvo2LEj7t+/j8KFC6NWrVo4cOAAPD09AQBDhgzB06dP0bt3b8TFxcHf3x9//fUX7Ozs1HXMmjULBQoUQPv27fH06VM0bNgQkZGRMDU1NdRmERERkZ7yfdKycuXKV85XFAURERGIiIjIsoylpSXmzp2LuXPn5nJ0RERElFeMrk0LERER/TcxaSEiIiKjwKSFiIiIjAKTFiIiIjIKTFqIiIjIKDBpISIiIqPApIWIiIiMApMWIiIiMgpMWoiIiMgoMGkhIiIio8CkhYiIiIwCkxYiIiIyCkxaiIiIyCgwaSEiIiKjwKSFiIiIjAKTFiIiIjIKTFqIiIjIKDBpISIiIqPApIWIiIiMApMWIiIiMgpMWoiIiMgoMGkhIiIio8CkhYiIiIwCkxYiIiIyCkxaiIiIyCgwaSEiIiKjwKSFiIiIjAKTFiIiIjIKTFqIiIjIKDBpISIiIqPApIWIiIiMApMWIiIiMgpMWoiIiMgoMGkhIiIio8CkhYiIiIwCkxYiIiIyCkxaiIiIyCgwaSEiIiKj8J9LWubPn48SJUrA0tISNWrUwJ49ewwdEhEREWXDfyppWbVqFcLDwzFixAgcO3YMdevWRbNmzXD9+nVDh0ZERESv8Z9KWmbOnIkePXrgo48+Qvny5TF79mx4eHjgm2++MXRoRERE9BoFDB1AXklJScHRo0cxbNgwrelNmjTB33//rXOZ5ORkJCcnq+/j4+MBAAkJCf9bb3LiW4j2zWWMMSvPnj7Og0j0k524nz55fRlDyEboeJKYH/e5vLZM4uP8FzcAJCTYvLbM48T89x3NznH++ElSHkSiP7PsxJ70LA8i0Y91NuJOfJb82jKGkJ3j5UlKah5Eop+X49a8F3n9OSdL8h9x69YtASD79u3Tmj5x4kTx9vbWucyYMWMEAF988cUXX3zxlUuvGzdu5Pha/p+padFQFEXrvYhkmqYxfPhwDBw4UH2fnp6Ohw8fwsnJKctlciohIQEeHh64ceMG7O3tc3Xdb5uxxm6scQPGG7uxxg0Yb+zGGjdgvLEba9zA241dRPD48WO4u7vneB3/maTF2dkZpqamiI2N1Zp+9+5duLq66lzGwsICFhYWWtMKFiz4tkIEANjb2xvdQa5hrLEba9yA8cZurHEDxhu7scYNGG/sxho38PZid3BweKPl/zMNcc3NzVGjRg1s2bJFa/qWLVtQu3ZtA0VFRERE2fWfqWkBgIEDByIsLAy+vr4ICAjAwoULcf36dXzyySeGDo2IiIhe4z+VtHTo0AEPHjzAuHHjEBMTAx8fH2zcuBGenp6GDg0WFhYYM2ZMpttRxsBYYzfWuAHjjd1Y4waMN3ZjjRsw3tiNNW4g/8euiLxJ3yMiIiKivPGfadNCRERExo1JCxERERkFJi1ERERkFJi05HNPnz41dAhERET5ApOWfGzJkiUYNWoU0tLSDB1KtqSm5r9nXxAZEvs5vB2J+fB5UpQ3mLTkQyKCtLQ0LFu2DC4uLjA1NTV0SK+1fPlyTJw4Ec+fPzd0KHnm8uXL2Llzp6HD+Fe4fv06zpw5Y+gwck1MTAzu3r2b64/7yC0XL17Ejh07DB1GjmzatAmBgYE4e/asoUMhA2DSkg8pigJTU1M8evQo3570MlqwYAHCwsJQu3ZtFCjw3xj6JyoqClWqVMHVq1cNHcorXbhwAb/++quhw3il+/fvo2bNmrh48aKhQ8kV9+/fR6NGjXDu3DlDh6LT7du34e3tjYYNG2LTpk2GDkdvNWrUwIMHD9CjRw+cP3/e0OHkKtbMvR6TlnwmJSVF/f/z589hY2NjwGheb8GCBejbty/WrFmDJk2aqNP/zTUux48fR2BgIPr06YOuXbsaOpwsPXv2DO+88w4uX75s6FBeycTEBObm5ihVqpShQ8kVzs7OSE1Nzbc1Afb29qhWrRpcXV3RunVrrFmzxtAhvZbmYp6amgoXFxccO3YM9+/fR1hYmNEmLrGxsdi2bRt27NiB27dvA8j8QF/KjElLPvLHH39g+vTpuHHjBoAXCYyLiwuAF0+YBoC0tDS1jYuIGDQzj4yMRO/evfHHH3/g/fffV6cPHz4ce/bsMVhcb9OJEycQEBCA8PBwfPnll+r0nTt35rtaFwsLC5iZmakjPufHX3Hp6el49OgRkpOTYWVlZehw3lhaWhpEBJ6enrhy5Yqhw8lERGBmZoaAgAC8//77GD58ODp06JDvE5f79+8DAMzMzAC8SAz37t2Lhw8fGmXicvLkSQQHByM8PBwNGzZE7969sX//fkOHZRSYtOQju3btwsyZM7Fy5Urcvn0biqLA1tYWwItfowBgamqqtnFRFMVgmXlUVBQ+/fRThIaGIiQkRJ3etm1brFy5EuXLlzdIXG/T1atX0aBBA7z//vuYMGGCOn38+PHo2LFjvmqILCJITU2FoihwdHQ0dDiZxMTE4Pnz5zAxMcGDBw+QkJCQL5MqfZmamkJRFDRu3BhRUVFaPzLyA0VRYGFhgS5dumDFihXw9/fH8OHDERoairVr1xo6PJ1WrVqFqlWron///li0aBHu3LmDpKQkFC5cGPv27UNiYiI6duxoNInLiRMnUKtWLbRq1QqbNm3CkiVLsGHDBmzYsMHQoeXYuXPnEBkZmSef9d9ogGAkpk2bBgsLC3z99dcQEcTFxeG7777Dnj17kJKSgvT0dDVhuXHjBtq1a4fWrVsbJNYKFSogNDQUFy9exJw5c9C/f3+Ehobi/Pnz2LlzJ9zc3CAiWklVenq6mnwZo6tXr8LBwQHm5uY4cOAAatWqhSlTpmDu3Ln48ccfUaZMGUOHiGvXriEmJga1atXCo0ePcPv2bTUZyC9Vz/Hx8ejcuTMsLS2xYcMGuLm5wd7eXk3QNRd5Y2iADgDR0dHYuHEjihUrhrp168La2hpFihTBpUuXABh+O86fP4/Lly+jcuXKcHd3BwD4+fmhW7du2LdvH0aMGIH4+HiEhoZi5cqVaNWqlUHjzejRo0dYvXo1YmJi8PPPP+PKlSsIDw9H3bp1ERwcjPfeew/79u1DQEAA+vbtizlz5qBcuXKGDjtLZ86cQc2aNdGvXz9MnjwZABAWFoaIiAjs2LEDz549g6WlpdYyL59H86ONGzdi0KBBSE5ORq9evd7uhwkZ1K5du+Srr76SkSNHqtOGDRsmhQoVEmtra/Hy8pJWrVpJYGCg+Pr6SqNGjSQkJERCQkIkNTU1T2NNT08XEZG0tDQREXn27Jn06NFDatWqJT4+PlKhQgW5e/euVlkRkRkzZsjDhw/zNNa3Zf369VKzZk3p1q2bfPLJJ+Ls7Cx//fVXpnInT57M89iSkpKkW7du4uHhIX///beIiBQqVEh27dolIiLPnz/P85h0SUpKkjlz5oifn5907txZduzYIXXq1JEzZ86IiEhiYqIkJCRIenq6PH/+XM6ePWvgiF9t0qRJUr58efHw8BAvLy8pUqSIhIaGiqIosm/fPq3vQl67ffu2KIoiiqJIq1atZNCgQfLw4UNJS0uTtWvXiqenp/rdHDBggNjY2MiKFSsMFq8uBw8elJ49e0rhwoXl0KFDsn37dhk2bJgUK1ZMSpYsKYGBgdKjRw9RFEWCgoLk6tWrhg45S998840oiiILFiyQxMREERGZMGGCKIoi/v7+0qVLF5kxY4asW7dORP53rs2vHj9+LM+fP5dnz57JlClTRFEUmT9/fpblc+O7wKTFgCIjI8Xb21v69+8vY8aM0Zo3adIkcXd3l7Fjx8qDBw9ERHQmKXl5IXr69KkkJibKkydP5PHjx+q0Tz/9VFxcXGTEiBGZ4gkJCZFq1arlmwumvh4+fCjR0dFy4MABddr69eulRo0aYmVlJbNmzVKna76Qo0ePlqpVqxokUfvrr7+kQ4cOUqVKFVm5cqU0bNhQ1q1bJ5cvX5aoqCg5e/asXL16VU6fPi1btmyRe/fu5VlsiYmJEhMTI0lJSZKSkiILFy6UgIAAKVmypCiKIiVKlBBXV1dxcXERZ2dnKVKkiDg5OUmJEiXyNM7sevn7mJaWJlFRUbJ48WKZMGGCuLq6SoMGDeTEiRMGilAkPj5emjVrJoqiSEREhPj6+kpwcLB88sknEhMTI02aNFF/MKWkpMjHH38sLi4u6vfbUNasWSMTJkxQ3x85ckTef/99KVasmJrcxsTEyKlTpyQ8PFzCwsJEURSpXLlyvrzQa87hIiITJ04UExMTWbJkiYwbN04KFSokixcvlr/++ktmzJgh7733njg6Okq5cuVk8ODBBoz61Y4ePSr169eXixcvisiLHyOTJ0/WSlwyJinJycny0UcfydKlS9/oc5m0GMiKFSvE0tJSfv75Z0lJSVGnZzwRjhgxQooXLy5ffvmlxMbGZlpHXv6CW716tbRq1Uo8PT3F2dlZQkJCZPXq1SLyosalZ8+e4u/vL9OmTVO3oVmzZuLt7a1uX348mbzKmTNn5J133pH33ntPpk6dqjVvw4YN4uvrKx988IHs27dPnT5q1CgxMzOTI0eO5FmcV69elW+//VZ9v3PnTgkNDRUPDw9RFEWKFi0qNjY2YmFhITY2NmJnZyfW1tZSuHBhuXLlSp7EGB0dLR06dJAGDRrIggULROTFSW7BggVSs2ZNcXV1laVLl8pvv/0mq1atknXr1snKlSvl119/lQsXLuRJjPo4d+6c9O7dWxo3biwRERHyzz//iIj2dzI+Pl6KFSsmderUkdOnTxsqVImPj5cmTZqIj4+PnDp1SlavXi3du3cXNzc3cXFxkYCAAElISBCRF4nLnTt3DBaryIuL29ixY8XU1FSmTZumTv/nn3/kvffekyJFiqj7O6Pz58+rP47y07nm2LFjEhAQIIcOHVKnjRs3ThRFkQIFCsj69eu1yiclJcn58+fl888/l/Pnz+d1uNkSFRUlVlZWMnDgQK3pSUlJMmnSJFEURb7++mt1+rNnz6R///6iKIocPnz4jT6bSYsB3Lp1S2rXri0zZ87UOT/jF27kyJHi5eUlI0eOlLi4uDyKUNv3338vtra2MmbMGFm0aJGMGzdOatasKYqiqCeVp0+fqreKZs6cKY0bN9ZKWPL6VtabOnHihLi6usqQIUPk4MGD6vSjR4+q/1+zZo3UrFlTQkND5fjx4zJx4kSxtLTM04QlNTVVhg4dKl5eXjJnzhx1+vbt26Vjx47i6ekps2fPlrt378qlS5fk7NmzcurUKbly5YrcunUrT2I8ceKEuLi4yLBhw+SPP/7Qmvf06VP59ttvpVatWtK9e3ejOE6ioqKkcOHC0q5dO2ndurVYW1tLq1at5M6dO2rSojnu7969KyVKlBAfHx+Jjo7O0zjT09PVeBISEiQwMFDKlSun1vzs2LFDvvjiC/npp5/yNK7suHPnjkycOFEcHBzkyy+/VKf/888/8v7770uRIkXk+PHjIvLiO5AxWcxPtbpRUVFibm4uw4YNExHtc/usWbPUWglN0iiSv+LXRZOwfPHFF1rTnz17JiK6a1z69OkjVlZWOpNNfTFpMYCzZ89K4cKFZc+ePTrnv1yD0qdPH2nVqpVB7o3v3r1b3N3d5ddff9WafuLECenevbsoiiKRkZEi8uKg7dWrl5iZmUnlypWNNmG5fv26lC5dWvr37691kpkxY4YUK1ZMRo0apU5bu3at1K5dWzw9PfM8YdE4d+6cDBgwQKpVqyazZ89Wp2/btk1CQ0OlSpUqarsWkbz9e9y8eVO8vb1lwIABWtPT0tLU4zkpKUkWLlwo1atXl06dOklycrKI5G1NYnadOHFC7OzsZMyYMWp84eHhYmpqqrZj0kzXbEdsbKxUrFgxT2q1bt26lel2lObvnZCQIMHBwVK8eHG1TH67QGaMJzExUWbMmCEmJiYyd+5cdfo///wjrVq1Eg8PD4N837JLc3EfPny41vQbN26o/x83bpyYmJjIV199ZfBbctlx4sQJcXBwyJSwjBo1Srp27ar+/TSJi7m5uVSpUkVsbW21fvC9CSYtBrB161ZRFEVtMKbr5Hzu3DkZMWKE+l5TJq9P5LNnz5YmTZrIkydPtC40Ii+q/N99910pXbq0XLp0SUReJC6zZs1ST5TGlrCIvGgsFxQUpFUTMWnSJHFwcJD33ntPateurdUGafXq1VK3bl2DNL7VOH/+vPTt2zdT4rJz505p27at1KhRQ7Zt25bncS1fvlzq1q0rly9f1jk/Y6PuH374QUqUKCE9evTIyxCz7cGDB6IoitSvX1/re9CvXz9RFEU2bNggN27ckKdPn2ZaNi9uV9y6dUssLS3F3NxcxowZI7/99lumMvHx8RIcHCyenp4GPV5fdu7cOUlKShKR//1iFxFp2bKlODs7i62trUyePFmdHhUVJUFBQdKyZcs8jzU7oqOjxdraWkaPHi0i/ztvT5gwQXr27KnV3m38+PFiYWEhX375pdo4N79q0qSJKIqidbvzyy+/FEdHx0y1qElJSTJ+/Hhxc3PLlRoWDSYtBnD8+HGxtbWVKVOm6DzBiYj89NNP0qpVK/WLLJL3Ccvz58+lVatWUr9+/SzLLF26VExNTdXGcRkZY8IiIvLBBx9IgwYNRETUHiw9e/aUffv2SUJCggwZMkT8/f1l6NCh6jJPnjzJs/guXLgga9euVX+5aI6Lc+fOSZ8+faRy5cpaDYR37dolISEhEhgYKElJSXl6HA0YMCDLxpGaOBITE+XevXvy7NkzWbJkSZYJTn4wYMAAsbW1lUWLFomIyNSpU8Xc3Fx8fHykW7duYmNjI40aNZIxY8bIpk2b8nRfx8TESLt27WTy5Mnqrdp69erJn3/+qfXrPjExUerXry/29vYGbWujceTIEalcubJMnjxZ6zZJmzZtpFKlSnL06FGZOnWq2Nvba90qOn/+fL5qu5JRhw4dxNnZWetCPnnyZLG1tZXNmzeLiHat0tChQ8XR0THf97JMSkqSihUrStWqVeX69esyefJkcXR0lD///DNT2bS0NElNTc31bWLSkocyHqTNmjUTd3d32bZtW6aLe1JSkrRt29ZgLcf//vtvtZuppmuhpoW4huZkcffuXbGwsFC/iP8GYWFhUrVqVa1pGS8+Dx48kPr160vHjh3zOjS5c+eOmJqaiqIo4uDgIO+8846Eh4dLVFSUJCYmyuPHj6Vv374SEBAg06dPV5fbu3ev3Lx5M8/i1Bzrn332mXh7e6vTdV1kPv/8c/nmm2/yLLacyNhYfuDAgWJpaSmtWrUSFxcX2bFjh1o7sG3bNpk3b564ublJ9erV86zdkMiL21Ht2rVTG0dev35dunXrJu+99574+PjI6tWr1XY1T548kZYtW+aLRs7Pnj2TDz/8UAICAtTbQB06dJCKFSuqtdF3795VL5Av35rIT4nL8ePH5Y8//pArV65Is2bNpHHjxrJjxw6ZOnWqODk5vfI8mR97yGWk+Q4kJSWJt7e32Nvbi5OTk5qwZPw7zJ07VxYvXvxWknYmLW/ZgQMHZPfu3ZmmX7t2TSpXriwlSpSQn376SeLj40VE5PTp09KkSRPx9fVVk5m8/LV25coV8ff3lxYtWsiNGzdky5YtoiiKTJ8+XavqUnOA7tmzRypXrpxvW7nrQ7O/v/zySylYsKD8/PPP6na+3CshLCxMq11DXurSpYs4OzvLgAED5MMPP5R33nlHChUqJN7e3jJ69GgZNWqUfPTRR1K1alWtxrl54fz581oNl3///Xext7fX6r6aMQFITk6Wjh07qu2i8ls7lqdPn0paWlqm9gZffPGFKIoin332mc6L5sOHD/OkF87Ln33x4kUpWbKkrF27Vp0WGBgotra2UqpUKQkMDJQPP/xQHj58aNB9/aoxnypVqqQ15pPG3bt3Zfjw4dK4cWOtRsb5RVRUlJiZmaldyE+fPi2NGzeWcuXKibW1tWzfvl1EtH+8Tps2TSZNmiQi+e/YF8l8fGnaaT19+lT8/PykWLFicvLkSa1yo0ePznQLKTcxaXmLDh48KIqiSMWKFaVFixaye/durWz6zJkz4u/vL2ZmZuLq6iolSpSQKlWqSFBQkHpiN0RDuYULF0r9+vWlQ4cOEhsbK0OHDhVLS0uZO3eu3L59Wy2XkpIi7777rrz77rv56teOPhITE+Xu3btat+ni4uKkRIkSUrFiRdm8ebNWTVhqaqoMHz5cihcvnqn26W3LeCx06tRJ/Pz81NsUhw4dku+//14CAwOlevXq6oBi3t7e8ujRozyL8aOPPhJFUdTB7W7duiUNGzaUkiVLanWBFHmxL0ePHi3e3t75ckCw6OhoCQ0NlYCAAGnTpk2mHx9Dhw4Vc3NzWbRokdZt3Lz6zl65ckUWLlyothdIS0uTpKQk6dGjh9r+o0uXLuLm5iY3btyQ48ePy5w5c6RMmTJ51tU9K/qM+ZRxf8bFxRmsfd+rHD9+XKysrNR2iJrzYXR0tISEhIifn1+mrs2jRo0Sc3NztRdUfnPy5Elp0aKF7Ny5U+v2ouZ8+PTpUylbtqxUrVpVvVUdEREhVlZWb7WBNJOWt2jv3r1Svnx5OXDggPTq1UveeecdqVixolp9qPHjjz/K+PHjZeLEifLHH3+oX1JDjXgrIvLDDz9IYGCgtG/fXnbt2iUDBgwQRVEkODhYJk6cKGPGjJHGjRuLj4+P0Y7Dcvr0aWnUqJH4+PhIqVKlZO3ateo99ZMnT4q7u7uUKlVKRowYIZcvX5affvpJevXqJQ4ODrnasCw7Xu5KK/IicSlTpoxERkaqtWAJCQkSHx8vixcvli+++CLP2ixoko60tDTp1KmTWFtbqxf5kydPStWqVcXNzU06d+4su3btkm+//Va6desmhQoVkmPHjuVJjPqIioqSggULSq9evaR3795SrVo1KVOmjJw7d06r3KBBg8Tc3FwWL16cp+2aTpw4Id7e3tKqVSvZuHGj1rwVK1aIo6OjNGrUSNzd3TONi6H5tWwo2R3zafr06Woy+HIimJ8SljNnzoiTk5N069ZNnZaenq6eD8+cOSONGzeWxo0bq70wx44d+9Yv7m+qVatWoiiKmrSPHTtWEhMTtc7zmltFtWrVkh49euTJNjFpecvatGkj3bt3F5EXWfeMGTOkXLly4uvrK2PHjpXHjx/r/AIaqiviy4lLcHCwtG/fXi5duiRLliyRwMBAcXJykqZNm0p4eLjR9hI6duyYODg4SO/eveWHH36Qxo0bS5EiRbR6VFy7dk0aNGggTk5OoiiKeHp6SvPmzfO018WFCxfkwIEDWhfEjBedzp07S9myZWXRokVajRhF8u7E/uzZM/H395eSJUuqJ+sOHTqItbW12tX67Nmz0r9/f/H09BQ7OzspXbq0tGvXTk6dOpUnMerj9OnTYmVlJePHj1enTZ06VRRFUR/ZkPH7OXjwYFEU5Y1H+syu6OhoKVSokAwbNizLNjMtWrSQokWLag1olh/oM+aTv7+/zJw5M0+TQX1FRUWJtbW1ODg4SNmyZTN1N9dc4DW3ipo3by4tWrQw2PAI+vj9999l6NChsm3bNvn555+laNGi8u6778onn3wit27dUn8oPXnyRB3IMi9+gDBpyWV///231q+xPXv2yDvvvKP1i9fNzU3q168vhQsXlsDAQGnUqFGmC44hZbzYLVq0SOrWrSsdOnRQ7zG/3Bo8v4318DonT54Ua2trGTt2rDpt48aNoiiK1jSRF78krl+/Lrt27ZK7d+/m6d/p7t27oiiKODk5yYABA9R73y/XaHXq1EnKlSsnkZGRBhnrIT09Xfbs2SMVK1aUGjVqZEpcNDUuSUlJ8vjxYzl27JjExcXly4tRXFyc+Pj4SMWKFbXaVIwYMUIURZEvv/xSzpw5k+n2yvDhw3X2oMttmkb6ffr00ZqekpIiN27cUBvaRkZGSqlSpdTvan6oBc3JmE8lSpTId89C0tC0YRk1apSkp6dL/fr1xcvLK8vEJTo6WmrVqiWFCxfOl7WLLzt//ryUKlVK3f/JycmyevVqMTMzUx8/o6nlS0lJybNbvExacpGmEWvGVvn37t2TSpUqqfeYK1euLHXq1JHk5GR58OCBTJo0ScLCwvLdhT+rW0WahCw/3lfOjmfPnqnVnhkNHz5cbVi5YsUKuXHjhsGHMxcR6dixo4SGhsqCBQukbNmyUrduXRkzZoxcv35dq1znzp2lcOHCsmzZMoP8TdLS0mT//v3i7e2tM3HJaiDF/Gj8+PFSs2ZNGTRokCQnJ8usWbPEyspKmjZtKh999JGULVtW3N3dZcCAATJx4sQ8jS0lJUUCAwO1BlvbvHmzhIeHi729vXh6ekqLFi0kLS1Nqlevnq/GvMnJmE9TpkzJd+dGkRe3YevVq6f1oNv4+PjXJi6XLl2Sa9eu5Wms2aWpwU1LS1Pj/frrr6V69epqW8YPPvhAypQpI+PGjZMPPvhAFEWRsLCwPK1pZ9KSyxYuXCgNGjSQdu3aqd2GN23aJGXLlhVXV1epU6eOVmPcjF/I/PblfDlxCQoKkrCwMK1GWcYmPT1dDh8+LD4+PuLn5yciIjNnzhRbW1v57LPPZNCgQRIYGCheXl7i7+8vw4YN03pYYl7RnDTmzJkjH3/8sYi8qDafPXu2fPjhh1KoUCGZMGGCVuO+fv365VkX1piYGNm/f7/WtJSUFDl48KCUKlVKqlevrpW4FCxYUHbu3JknseVUxtqIKVOmSI0aNSQwMFAcHBzk4MGD6vwLFy7ImjVrpH79+lK2bNk8bUQcHx8v5cqVk549e0p0dLRMmjRJypYtK23atJGvvvpKFi1aJF5eXjJ06FAZNmyYBAYGqj0TDelNx3zKT+fGf/75R7755hutGvWMj0vIKnHJzz/wbt68KQ0bNlRvJ2r29/Hjx6Vhw4Zy8uRJ6datm7i6uqoNh589eyY7duzI88dTMGnJJRkPyMWLF0u9evWkXbt2cv78eYmPj5caNWpI3bp1tdoj5OeDWOPlW0X+/v5qjxVjiF8XzdN4y5QpI05OTuLk5CR79+7VKrNhwwaJiIgweE+La9euiZOTk9Yot61btxYHBwdp0aKFuLm5SYMGDTLF/zZdv35dbecTHBwsw4cPl23btqm3zg4dOiRVq1aVKlWqqIPzNWvWTIoWLarVyyY/yvj9nDFjhnh4eEibNm20HliqOaHHx8cb5Lbutm3bpECBAmr7oG+//VZNVlNSUqRJkybSp08fuXr1qsF7Cf3bxnyKiooSRVFkyJAhWtMzngszJi75adThV9m1a5fUq1dPatWqlamTQZcuXURRFLV7s4ahzv9MWnKRrsSlffv2cv/+fVm0aJHY29tLTExMprL5XcZY3333XXnvvfcMF0wOJCYmSkxMjJw5c0ary/bx48elbt26UqJECZ29c0S0hxTPa5oq17lz58oHH3wgaWlp0rVrVylSpIicP39e7t69K9u2bZPatWvnaffrq1evStWqVaVs2bLi6+srXbp0EUtLS6latap07txZVq1aJT///LOUKVNGGjZsKCIvLvR5Obhddp0+fVqWLl2qNc5QxhqXadOmSbVq1eSzzz5Tq/VfvrVhCNevX5cjR45kGpAsLS1N2rZtm2kANkP4t435pHmWUMZbQllJSEiQxo0b55tRh7Njy5Yt0qJFC/H19dXqhn3+/Hnx8/OTr776yoDR/Q+Tllz2cuISGBgoHTp0kP3798v7778vI0eOzHRhNAaa7erTp4+EhoYavNtkdp0+fVpCQkKkQoUKYm9vL/b29jJkyBD114Sm66ifn5/O7pV5dXG6du2aLFiwQGbNmqX1cEORF0/jLVeunPj5+Ymnp2emHiGGuIBeuHBBWrVqJe+9954cOHBArl27JitWrJA6depIzZo1xcrKSnx8fERRFGndunWex5cdCQkJ4uLiIi4uLtK9e3fp2LGjXLt2LVPtyeTJk6VatWoyePBgg9dcvEpycrKMHDlS3N3d882F/98y5tPx48fFzs4uUzI4fvx4rYH8MoqPj5cWLVrki1GHXyVje5Tjx49L+fLlpWbNmmpj4YSEBGnatKmEhoYaKEJtTFreAl3dhtu0aSNlypSRTz/9NF9+KbPj3r17UqdOHaOp8jx58qQ4ODhInz59ZP369bJ+/XoZNGiQFChQQIKDg9VbKsePH5dy5cpJQECAQXq0HD9+XDw9PaVmzZpSrFgxsbOzU8et0OjXr59YWVnJvn37Mi1vqF/9Z8+elZCQEGncuLFWIhUXFyc//vijjBgxQqpXr57nY9roo3///hIUFCQ7duxQBwFr166d7Ny5U6s2YOrUqVK8eHEZMWJEvuzev3TpUunfv7+4urrmi/39bxrzSZPcVqpUSWv6lClTxNzcXDZt2pRpmfzeUeHWrVtaT13W7O9p06aJra2t1KpVS3x9fdUxfg4cOCCKomTq+WUITFrekowH6/fffy/vv/++tGvXTv2lll8P5tfJ6gGP+c3Dhw+lVq1a6nNYMlq5cqVYWVlJy5Yt1W6tx48fF1dXV/VBiXlFM87DsGHDJDExUfbt2ydeXl5Ss2ZNefjwoXoS37ZtmwQEBOh8zochnT9/XkJCQiQkJERnQ9v8eIEX+d9twJ07d0q7du3U4+DPP/+UMWPGiKIo8sEHH8jMmTPVZb7//nu1Z0t+cvbsWQkODpZWrVrlSbfr7Po3jfn0ww8/iIWFhYwbN05ERH0OkmbcHmPy7Nkz8fPzk0aNGmk1pp84caI4OjrK9u3bZdeuXeqtIk1y07FjxzwfBVwXJi1vUcYv7YIFC9RGrPnhnvi/3YkTJ6RixYrqAE4Zu/GJvLh1pyiK/P777+q0kydP5umX8tq1a1KoUCEJCwvTmh4QECClS5eWhIQE9eKanJwsDRo0yPOkKjvOnz8vTZs2lZCQEJ01QfnJlStXtB5r8PjxY6lSpYp88skn6rSuXbuKi4uL9O3bV9zc3KRkyZL5/oGOd+7cydPHNWSXMY/5dP36dVm4cKHMnj1b9uzZI7///ruYmJhI3bp1xcXFRU1YMm7j+vXr1YbH+dnu3bulfPny0qZNG7ly5YpMnTpVHB0dtUZX3rp1q7z//vtSunRpuXz5cr5pEsCk5S0z9kasxuqPP/4QOzs7tTtexuratLQ0ef78uVSpUkX69etnkAQyKSlJNm3aJJUqVZL27dur3SMnT54siqJIpUqVpHnz5tK8eXOZMWOG3Lx5U37//XcpX7683Lt3L98lvefPn5fmzZtLrVq1MnWFzi9SUlKkfv36UqRIEYmLi1Onb9u2TerUqSPXrl1TGzpraixu3bolPXr0yPftEvIzYxzz6fjx4+Ll5SVVq1YVOzs7sbOzk8WLF8vGjRvF1NRUOnfunGmZL774QkxMTPLtOCxxcXESHR2tjqJ86NAhKV26tJQvX14cHBxk69atIqJd07Vx40bp0KGDXL582SAx68KkJQ8YayNWYxMXF6fWTGgeVrly5UoR0X1CNNTgW4cPH5ayZcvKvXv35Oeff5YGDRpIhw4dpG/fvlK4cGF1cLvff/9dpkyZIh4eHlKkSBHx8/NTe5/lR9HR0dK2bdt8e9IWeVGb5ufnJxUqVFB/5Z8/f16CgoKkRIkSUqpUKbV9Tn65BfdvYExjPh0/flysra1l6NCh8vDhQzlw4ICEhYVJ4cKF5eTJk/LDDz+IiYmJTJgwQT1GRo8eLTY2Npme85RfnDlzRho2bKjud01icvToUSlbtqzUqVNHa5TejIlLfhu5mklLHjG2RqzG5p9//pE6depIVFSUiLz4ooWEhIiXl5d6iyjjPfMnT57IO++8IwsWLBCRvPuVFxUVJXZ2dtK3b1912k8//SSBgYFibm6e6UnIIiKxsbGyZcuWfHE/+XXya0Ku+fumpaVJdHS01K5dW6pXr64mLnPmzBFFUXQ2qqTcYQxjPl2/fl2cnZ2lXbt2WtPXrl0rtra2ai3iwoULxcTERGbOnCljxowRCwuLfPssoZMnT4qjo6P6qAnNeVCTcB05ckRKly4tbdq0kYMHD6rL5adbdRkxaclDxtKI1dhERUWJpaWlDB48WGv6Tz/9JCVKlBA/Pz+t3i3p6ekyZswYKVKkSJ42rNT8gtN0m8x4ov7rr7+kXr160rp1a3WMBM1tLMq5jN+5jEMNDBo0SL0N9/DhQ3n8+LE0aNBAZsyYISL55yL6b5Pfb5dfuXJF/Pz8pGXLllqPnti3b586MrLGd999J4qiiKIoWj1x8pM7d+5IjRo1ZMCAAVrTMybxIi9qf0uXLi3t27fP04Eqc4JJCxk1zYBPL4+foBlr45tvvpHy5cur96FDQ0OlY8eOUrhw4Tw90Wh+wbVv315r+vTp02XYsGEiIrJixQpp0KCBvPfee2obF148c+7mzZvSrl072b59u9b0KVOmiJOTk3z//ffi6+srPj4+EhcXJ0OGDBFPT0/eFnrL8vvtck3D8iZNmsiZM2fULs8v/ygSEfn111/zVY+tl+3Zs0fKlSsnR48e1Xkuydgp5ODBg+Lk5CQffvhhvv6BzaSFjNbp06fF3t5evehrjBkzRj744AO1lmL//v0yduxYCQoKkkaNGsmIESO0nhuSFzL+gtP8kpk8ebLY29urDeBEXnTHbtKkidSvX19OnTqVpzH+21y6dEkCAgLknXfe0drnjo6OsmXLFhF5ca+/SpUqUrduXfnnn3/Ez88v08MoKffl99vl58+fl2bNmklQUJAUKlRIwsPD1XnGlNTOmDFDHB0d1fe6EpfExER1tOp//vkn39+GZtJCRik9PV3atGkjlpaWsm3bNvXLOHnyZClYsKBs2LAh0zKaEW8NVXuh+QXXsmVL6dmzp7i4uOgcd2XJkiXSsmXLfNlI0dho9vl7770nPXv2lMKFC6v7XCM6Olq8vLzEz88vX3Yb/rfKz7/mRV4cOw0aNBBPT0+tUaqNqfZz9erVWQ5KqTF69Ghp37690YzUzqSFjNbDhw8lODhY6tSpIwcOHJBJkyaJk5NTpouSiHZ7BkOedM6dOyeNGzcWKysrmT59uta8jImLIR7E92+V1T7PuL/Pnj2br4foJ8O4cOGCOgZRfm/rIfLiuWBjxoxRn5m2f/9+MTc3lwEDBsj9+/fVcppz4PPnz6V///4yZcoUg8SbE0xayKjcvHlTli9fLvPnz5enT5/K/fv3JSAgQIoWLSr29vZq74+MDVhnzZolU6dOzTe/kC5evChNmjSRZs2aaTX2S09Pzzcx/ttktc+NqaqfDMMYxiDSmDZtmpQqVUqGDh2qJi7jx48XU1NTGTNmjFbtbUpKiowYMUJKlSplVOMQMWkho3Hq1CmpWrWqfPDBBzJkyBD1gvPo0SNp2rSpeHt7y19//aWVsIwaNUoURcl3984zjiJrDL/g/g24zymn8vsYRFeuXJFt27bJ8+fPZeLEieLr6yuDBg1SGzl/9tlnWs97GjVqlISGhoqTk1O+eFaVPpi0kFE4deqUFCpUSD7//HN1CHARkTVr1sjevXvlyZMnEhQUJLVq1ZI//vhDRETGjRsnVlZW+Xb8BGP6BfdvwX1OOZXfejlp3Lp1S5ydnaVMmTLy22+/SVpamowbN05q1Kghn3/+uXprfPHixVKnTh1xc3OTatWqSa9evdQRw40JkxbK9x48eCD16tXLNOT+l19+KYqiSL169WT//v2SmJgowcHBEhQUJG3bthVLS8t8m7Bo5PdfcP9G3Of0b7J9+3ZRFEX8/PykefPmsnr1aq3EZfDgweqtosTERPXp5cY6BpQiIgKifCw6OhotWrTAd999h6CgIJiYmODbb79F//79MXv2bKxbtw6KoiAiIgKVK1dGvXr1EB0djb///htVq1Y1dPivlZKSAnNzc0OH8Z/CfU7/Jj169MDRo0dRunRp3L9/H5999hlatGiBiRMnYv369QgKCsKkSZNgbm6OtLQ0mJqaQkSgKIqhQ9cbkxbK95YtW4auXbsiNTVV/ZLdvHkTV65cQd26dXHq1CmEh4fj4cOH2LRpEywsLJCQkIDixYsbOHIiorcnOTkZFhYW2LhxI3755Rd07NgRCxYswJ07dzBkyBA0b94cEydOxMaNG1GtWjXMnj3b6JN1E0MHQPQ6Xl5eKFCgANauXQsAEBEUK1YMdevWRXp6Onx8fNChQwcUKFAAqampKFiwIBMWIvpXunHjBtatWwcAsLCwAAD4+fnhwIEDuHDhAr799lu4urpi2rRp+OOPPzBixAgEBwfj7NmzePTokeECzyVMWijf8/LygoODA5YsWYJr165pVWmamLw4hM+dO6eWIyL6N7px4waqVauG1q1b491338XPP/+M8+fPo3Dhwpg6dSpWrVoFAJgwYQJcXFwwe/ZsrF69GhMnTsTPP/8MFxcXA2/Bm2PSQvlesWLFMH/+fGzevBmjRo3CmTNn1HkJCQkYMmQIfvjhB4wZMwZ2dnYGjJSI6O1JT09HiRIlUKtWLdy5cwdbtmxBkyZNsGDBAjx9+hQODg44cuQIypcvj/Hjx8PU1BSRkZFISkqCs7OzocPPFWzTQkYhLS0N33//Pfr27YvSpUujdu3aMDMzw61bt3DkyBH1ni0R0b/ZhQsXMGzYMKSnp+PDDz+EiYkJZs+ejYIFC+K3336Dn58f9uzZA3Nzc5w7dw42NjYoVqyYocPONUxayKgcPHgQU6dOxaVLl2BnZ4fAwED06NEDpUuXNnRoRER54ty5c/jss8+QlpaGuXPnomjRojh58iQmTpyI9u3bIywszGh7B70OkxYyOpoue0RE/1UXLlxA3759AQCjR49GnTp1DBxR3mCbFjI6msa3wIueRERE/zVlypTBvHnzYGJigvHjx2Pv3r2GDilPMGkho5OxyvPfWP1JRJQdZcqUwZw5c2BmZobPP/8cBw4cMHRIbx2TFiIiIiNVpkwZTJs2DcWKFYO7u7uhw3nr2KaFiIjIyP1XHk3BpIWIiIiMAm8PERERkVFg0kJERERGgUkLERERGQUmLURERGQUmLQQERGRUWDSQvQvceLECXTr1g0lSpSApaUlbG1tUb16dUydOhUPHz5Uy3l5eaF58+YGjDTn5s+fj8jIyDz9zODgYCiKgqZNm2aad/XqVSiKgunTp+dpTET/VUxaiP4FvvvuO9SoUQOHDx/G559/js2bN2Pt2rVo164dvv32W/To0cPQIeYKQyQtGn/++Se2b99ukM8mohcKGDoAInoz+/fvx6efforGjRtj3bp1sLCwUOc1btwYgwYNwubNm/M0prS0NDx//lwrlvxKRPDs2TNYWVllWcbb2xvPnz/HkCFDcPjwYT4+gshAWNNCZOQmTZoERVGwcOFCnUmCubk5WrZsmWn65s2bUb16dVhZWaFcuXL44YcftObfu3cPvXv3RoUKFWBrawsXFxc0aNAAe/bs0SqnuUUydepUTJgwASVKlICFhQV27NiBZ8+eYdCgQahatSocHBzg6OiIgIAA/Pbbb5niSU9Px9y5c1G1alVYWVmhYMGCqFWrFtavXw/gxW2t06dPY9euXVAUBYqiwMvLS10+ISEBgwcPRokSJWBubo6iRYsiPDwcT5480focRVHQt29ffPvttyhfvjwsLCywZMmSV+5jMzMzTJw4EUePHsWqVateWVbf/TZt2jRMmTIFXl5esLKyQnBwMM6fP4/U1FQMGzYM7u7ucHBwQKtWrXD37t1Mn7dq1SoEBATAxsYGtra2CAkJwbFjx14ZI5GxYk0LkRFLS0vD9u3bUaNGDXh4eGR7uePHj2PQoEEYNmwYXF1d8f3336NHjx4oXbo06tWrBwBqO5gxY8bAzc0NiYmJWLt2LYKDg7Ft2zYEBwdrrXPOnDnw9vbG9OnTYW9vjzJlyiA5ORkPHz7E4MGDUbRoUaSkpGDr1q1o3bo1Fi9ejA8//FBdvmvXrli2bBl69OiBcePGwdzcHP/88w+uXr0KAFi7di3atm0LBwcHzJ8/HwDUJC0pKQlBQUG4efMmvvjiC1SuXBmnT5/G6NGjcfLkSWzdulWrdmTdunXYs2cPRo8eDTc3N7i4uLx2n3Xo0AHTp0/HyJEj0aZNG5iZmeksp+9++/rrr1G5cmV8/fXXePToEQYNGoQWLVrA398fZmZm+OGHH3Dt2jUMHjwYH330kZrEAS8S1pEjR6Jbt24YOXIkUlJSMG3aNNStWxeHDh1ChQoVXrtdREZFiMhoxcbGCgAJDQ3N9jKenp5iaWkp165dU6c9ffpUHB0dpVevXlku9/z5c0lNTZWGDRtKq1at1OlXrlwRAFKqVClJSUl55Wdr1tGjRw+pVq2aOn337t0CQEaMGPHK5StWrChBQUGZpk+ePFlMTEzk8OHDWtN//fVXASAbN25UpwEQBwcHefjw4Ss/SyMoKEgqVqwoIiJbt24VADJ37lwR+d+2T5s2LcvlX7ffqlSpImlpaer02bNnCwBp2bKl1nrCw8MFgMTHx4uIyPXr16VAgQLSr18/rXKPHz8WNzc3ad++fba2j8iY8PYQ0X9Q1apVUbx4cfW9paUlvL29ce3aNa1y3377LapXrw5LS0sUKFAAZmZm2LZtG6KjozOts2XLljprH3755RfUqVMHtra26joWLVqktY5NmzYBAPr06ZOj7fnjjz/g4+ODqlWr4vnz5+orJCQEiqJg586dWuUbNGiAQoUK6f05DRs2RJMmTTBu3Dg8fvw4y3L67Ld33nkHJib/OxWXL18eAPDuu+9qldNMv379OoAXDYOfP3+ODz/8UGubLS0tERQUlGmbif4NmLQQGTFnZ2dYW1vjypUrei3n5OSUaZqFhQWePn2qvp85cyY+/fRT+Pv7Y/Xq1Thw4AAOHz6Mpk2bapXTKFKkSKZpa9asQfv27VG0aFEsW7YM+/fvx+HDh9G9e3c8e/ZMLXfv3j2YmprCzc1Nr+3QuHPnDk6cOAEzMzOtl52dHUQE9+/ff22s2TVlyhTcv38/y27O+u43R0dHrfeaJ/VmNV2z3+7cuQMA8PPzy7Tdq1atyrTNRP8GbNNCZMRMTU3RsGFDbNq0CTdv3kSxYsVybd3Lli1DcHAwvvnmG63pWdUw6OpRs2zZMpQoUQKrVq3Smp+cnKxVrnDhwkhLS0NsbGyOEgpnZ2dYWVllakyccf7rYs2uqlWromPHjpg5cybeeeedTPP13W85pdmmX3/9FZ6enrm6bqL8ijUtREZu+PDhEBH07NkTKSkpmeanpqbi999/13u9iqJk6o104sQJ7N+/X691mJubayUJsbGxmXoPNWvWDAAyXehf9nJtkEbz5s1x6dIlODk5wdfXN9MrYy+j3DBhwgSkpKRg7Nixmeblxn7LjpCQEBQoUACXLl3Suc2+vr65+nlE+QFrWoiMXEBAAL755hv07t0bNWrUwKeffoqKFSsiNTUVx44dw8KFC+Hj44MWLVrotd7mzZtj/PjxGDNmDIKCgnDu3DmMGzcOJUqUwPPnz7O9jjVr1qB3795o27Ytbty4gfHjx6NIkSK4cOGCWq5u3boICwvDhAkTcOfOHTRv3hwWFhY4duwYrK2t0a9fPwBApUqVsHLlSqxatQolS5aEpaUlKlWqhPDwcKxevRr16tXDZ599hsqVKyM9PR3Xr1/HX3/9hUGDBsHf31+v7X+VEiVK4NNPP8VXX32lc5vfdL9lh5eXF8aNG4cRI0bg8uXLaNq0KQoVKoQ7d+7g0KFDsLGx0ZlUERkzJi1E/wI9e/ZEzZo1MWvWLEyZMgWxsbEwMzODt7c3OnXqhL59++q9zhEjRiApKQmLFi3C1KlTUaFCBXz77bdYu3Zttht5duvWDXfv3sW3336LH374ASVLlsSwYcNw8+bNTBfUyMhIVK9eHYsWLUJkZCSsrKxQoUIFfPHFF2qZsWPHIiYmBj179sTjx4/h6emJq1evwsbGBnv27MGXX36JhQsX4sqVK7CyskLx4sXRqFGjXK9pAYCRI0di8eLFSEhI0JqeG/stu4YPH44KFSrgq6++wooVK5CcnAw3Nzf4+fnhk08+ydXPIsoPFBERQwdBRERE9Dps00JERERGgUkLERERGQUmLURERGQUmLQQERGRUWDSQkREREaBSQsREREZBSYtREREZBSYtBAREZFRYNJCRERERoFJCxERERkFJi1ERERkFJi0EBERkVH4P+44w0eBAxQ4AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Assuming 'lines_df' is the DataFrame with the 'CharacterName' column\n",
    "# Count the number of lines spoken by each character\n",
    "top_characters = lines_df['CharacterName'].value_counts().head(10)\n",
    "\n",
    "# Plot the top 10 characters by the number of lines spoken\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.barplot(x=top_characters.index, y=top_characters.values, palette='coolwarm')\n",
    "plt.title(\"Top 10 Characters by Number of Dialogues\", fontsize=16)\n",
    "plt.xlabel(\"Character Name\", fontsize=12)\n",
    "plt.ylabel(\"Number of Dialogues\", fontsize=12)\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bGB4I8XIQ_Hg",
   "metadata": {
    "id": "bGB4I8XIQ_Hg"
   },
   "source": [
    "The visuals **after preprocessing** provide insights into the cleaned dataset. The first histogram shows the distribution<br> of line lengths for both input and response dialogues, indicating that most conversations are short, with the <br>majority being under 10 words. The bar chart of the top 20 most common words reveals frequent usage of <br>conversational terms like \"don't,\" \"know,\" and \"I'm,\" illustrating common speech patterns in movie dialogues. The word <br>cloud highlights key dialogue words after preprocessing, showcasing prominent terms such as \"know,\" \"one,\" <br>and \"well.\" The scatter plot, comparing input and response lengths, indicates a positive correlation where longer inputs <br>tend to produce longer responses. Lastly, the bar chart displaying the top 10 characters by the number <br>of dialogues reveals \"Jack\" as the most frequent speaker, followed by \"Joe\" and \"George.\" Together, these <br>visuals demonstrate how the dataset has been effectively cleaned and analyzed for key conversational patterns."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "XTezWkFoc_Wr",
   "metadata": {
    "id": "XTezWkFoc_Wr"
   },
   "source": [
    "## Check for Imbalance in the Data\n",
    "Balancing the dataset by addressing **imbalances** in character dialogue count and sentiment is <br>essential to prevent the model from favoring certain characters or dialogue types, ensuring fair <br>representation and diversity in responses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "jglrVMi_dEVY",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 580
    },
    "id": "jglrVMi_dEVY",
    "outputId": "6aa5860e-d023-4b18-ef62-d7d3fd3ad751"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/+1u3blV7b3NycuDq6oopU6aUib916xYAaHx/NX1uDEmXz/vGjRuxaNEi/Pnnn8jJyUFJSYm0rbzPR0XXo+3xzp49i7y8PHTt2hU2NjbaX5wGpZ+JhIQEjYW7iYlJtXhf6OmwCKbnRkZGBgDAwcGhwrjGjRsjJSUFU6ZMwfbt2/Hzzz8DeNij8dlnn+G1117T6bxPGoNXHkdHx3LXX79+/amO+bRKe/bKuxZnZ2cADx9S8ThN4/qMjR/+aHn0f1IVnVtTofCk8xqCtvfQv//+i3bt2uHKlSvo0KEDunbtinr16sHIyAhpaWn49ddfoVKpKjyGrm1eGl9ebk97Hz7q33//BYAn9rzfvXu3wu2luet6H+fl5aFOnToar9HJyQl16tTRy71Q3mwfxsbGWt2z2irvM176XpVeS2m7nz59GqdPny73eJraXdf33dnZGenp6bh+/foTx3Zrou3nffbs2RgzZgwcHBwQEhKCBg0aSH99++qrr8r9fJR3Pbocr/QXmfr16+t8fY8rfW8+//xznfbTx+eRDI9FMD03Sp/A1q5duyfGtmjRAhs2bEBRURGOHj2K7du3Y968eejfvz9cXV3RoUMHrc/7tBPpZ2Vllbv+0f/RlP7Jtri4uEysvorD0j9v3rx5U+P20vWP/hlUX6ytravkvJpoew8tXboUV65cwbRp0/Dxxx+rbZsxY4ZW8xfr2ual/y3tFSwv/lG63jul5/jtt9+k+ZSfRunnJzExEQ8ePFAbdlARa2trPHjwALdu3SpTQGZlZeHBgwdq90JlfDaeRXmf8dL3qvRzXnpNr776KtavX6/TOXT9+dOhQwekp6dj165dT1UEa6O4uBifffYZXF1dkZaWpvZLjRACs2bNKndfTdej6/FKH5yjj86E0vcmLy/viX/VetTz+oCV5w3HBNNz4e+//8ZPP/0EuVyOPn36aL2fiYkJ/P39MWXKFMybNw9CCGzZskXaXjqWTJ+9Q6X27dtXZt2RI0dQUFCg9u3z0j/nafqBXvrn48cZGRnplHPp+fbu3VtmSIAQQspVH7MwPK5169YoKCjAoUOHymxLSkoy2Hk1nWvfvn1wdHREly5dKowtfbhDz549y2zT9L5q8uKLL8LMzAyHDx/GvXv3NOYD/N+1e3p6Qi6X4+jRoygsLFSLFUJIf7Z9lK73jp+fHwAgJSVFq2soT9OmTdGpUydcvXr1iePbH+3Ba926NQBofKS4pnvhaT4bunqWnwGXL1/WOI7+8c+Tl5cXrK2tceTIERQVFT19sloonQN79uzZZYbwPO5Jf80oz+3bt6FUKuHv71+mV7/0Z5whj+fp6Qlra2scPnwYOTk5Tzx+RT8vSz8Tmj5fVPOxCKYab//+/QgNDYVKpcL48eOf+Ceww4cPa+yhKe2defQLc6VfhLh27ZoeM35o9erVan/6LC4uxoQJEwA8/JJOKU9PT9StWxebN2+W/jRXmu+0adM0HtvW1ha3b9/G/fv3tcqlYcOG6Ny5szQl2qOWLVuG06dPo0uXLuUOW3gWpdc6fvx4tQLg+vXrmDNnDoyNjTU+aEGfNm/ejFdffRUAMHPmzCdOHeXu7g7g4b33qHXr1mHbtm1andPU1BSvv/46bt++jbi4OLVtO3fuxPbt29G0aVOpV1Uul+O///0vMjMzMW/ePLX4VatW4cyZM2XO0aZNG8hkMsTHx6vdC+fPn8fXX39dJr5Xr15o2LAh5syZg71795bZXlRUVOaayzNv3jyYm5tjxIgR+PHHHzXG7Nu3T+0XjtJ7YcqUKWpfvsvLy5PGyj762dD1+p7Gs/wMKCkpwccff6z2i2VSUhK2bduGpk2bon379gAeDid4//33cfnyZYwZM0ZjIXzq1Klye5Z10blzZ7z++us4d+4c+vbtq/GYeXl5mDBhAhYvXvxU53B0dIS5uTn+/PNPtV/wcnJytJo68FmPZ2xsjHfffRdKpRIffPBBmQJXqVSqPa67op+Xw4YNg7GxMWJiYjT+QpObm6u3X7io8nE4BNUYFy5ckL6sU1hYiKysLBw8eBCnTp2CkZERPvnkE0ycOPGJx1m7di0WLFiAoKAgNG3aFNbW1vjrr7+wbds22Nvbq32jt0uXLli/fj1ee+01vPLKKzAzM4OPjw+6d+/+zNfTtWtX+Pv7Y8CAAbC1tcW2bdtw6tQphIaGSo+HBh4WSyNGjMCMGTPQpk0b9OrVC3fu3MFvv/2GwMBAjY+c7dKlC44cOYIePXrgP//5jzSfbceOHcvNZ+HChejYsSOGDh2K3377Dc2bN8dff/2FzZs3w8HBAQsXLnzma9YkMjISGzduxK+//ooWLVogIiJCmic4Ozsbs2fP1tufbY8cOSLdQ/fv30dGRgYOHDiAf/75B+bm5vj222+luYeflPPMmTMRExODPXv2wN3dHSdOnMDOnTvRt29fjd/i12TmzJlISkrCtGnTkJycDD8/P2meYAsLCyxfvlxtKEFcXBx27tyJjz76CHv27EGrVq1w7tw5bNmyBWFhYUhISFCLr1+/Pvr374/4+Hi0bdsWYWFhyMrKwqZNmxAWFoYNGzao5SOXy7F+/XqEh4cjMDAQwcHB0py5V65cwb59+2BnZ6fVF/BatmyJ3377Df369cOAAQMwdepUdOrUCba2tvj3339x4MABnDx5Um3u6U6dOiEmJgbffPMNvL298eqrr0IIgY0bN+Lq1asYOXKk2pMCdb2+p/EsPwNatGiBxMRE+Pv7o0uXLrhx4wbi4+NhYmKCJUuWqL1XU6ZMwZ9//ol58+Zh69atCAwMhIODA65fv46TJ0/i+PHjSElJKXecsS6WLl0KIQTi4+PRuHFjhISEoFmzZhBC4Pz589i1axfu3LmD1atXP9Xx69Spg2HDhmH27Nlo2bIlevTogby8PGzfvh3u7u7SLCyGPN7UqVORmpqK1atXIzU1FeHh4ZDL5bh48SISEhKwf/9+qSe+op+X3t7eWLBgAd5//314enrilVdewQsvvIC8vDxcvHgRSUlJGDx4ML777runaiuqYlUzMxuR9jQ91cfc3Fy4uLiIzp07i08//bTcOS81zRWampoq3n33XeHt7S3q1asnzM3NhYeHhxg5cmSZJ6MVFRWJsWPHioYNGwpjY2ONT2er6AlNT3pi3KJFi6QnxjVo0ECMGzdO3Lt3r8xxiouLxcSJE6UneDVr1kx8/fXX4uLFixpzuHPnjhg6dKhwcXERderU0fqJcenp6eKtt94SLi4uwtjYWLi4uIi33npL45PCKpo/tXTOz9In5z1JUVGR+PLLL4WPj4+Qy+XCyspKBAYGanxSlhBPP0/wo4uFhYVo0KCBCA0NFTNmzBA3btzQuG95c9GmpaWJkJAQYWNjI+W7c+fOcuPLy/nWrVti5MiRwt3dXZiYmAh7e3vx3//+V5w8eVJjPhcvXhSvvfaaUCgUwsLCQvznP/8RSUlJYsSIEQKAOHbsmFr83bt3RUxMjHBychJyuVy0aNFCrF27tsL5p69duyY++OAD4eHhIeRyubC2thZeXl7i7bffFrt27SqvmTXKzs4Wn332mfD39xc2NjbC2NhY2NnZiaCgIPH111+rPZ2v1LJly0S7du2EhYWFsLCwEO3atRPLli3TeHxdr6+ie0fT57WinwEVKT3P5cuXxWuvvSZsbGyEubm56NSpk9i/f7/GfYqLi8WiRYtEhw4dhLW1tZDL5aJhw4YiLCxMLFy4UK2tdP2MabJjxw7x+uuvC3d3d2FmZibMzMyEh4eHiI6OFgcPHlSL1fXzXlhYKD7//HPpHmrYsKEYNWqUuHPnjsZ2ftL16Ho8IR4+8e7LL78UrVq1Eubm5qJu3bqiefPmYvTo0SInJ0eKq+jnZalDhw6JAQMGSE/TtLe3F23atBHjxo0TZ86ckeIq+lxR9SMTQoc5gYiIqFrq2LEjUlJSoFQqtZ4DlQxHJpMhMDBQ4/hmIqoeOCaYiKgGKZ3G7VFr167FgQMH0LVrVxbARERa4phgIqIaxNvbG61bt0bz5s2leYkTExNhZWWFL7/8sqrTIyKqMVgEExHVIO+99x5+++03HDlyBHfv3oWDgwMGDhyITz/9VC+PTiYiqi04JpiIiIiIah2OCSYiIiKiWodFMBERERHVOhwTrIMHDx7gxo0bsLKy4nPBiYiIiKohIQTu3LkDV1dXtYfSPI5FsA5u3LhhkMfGEhEREZF+Xb16FQ0aNCh3O4tgHVhZWQF42KjW1tZVnA0RERERPS4vLw9ubm5S3VYeFsE6KB0CYW1tzSKYiIiIqBp70tBVfjGOiIiIiGodFsFEREREVOuwCCYiIiKiWodFMBERERHVOiyCiYiIiKjWYRFMRERERLUOi2AiIiIiqnVYBBMRERFRrcMimIiIiIhqHRbBRERERFTr8LHJ1VxBQQFUKpVWsXK5HObm5gbOiIiIiKjmYxFcjRUUFMDNrTGys29qFW9n54SrVy+xECYiIiJ6AhbB1ZhKpUJ29k34+6fDyEhRYWxJiRKpqY2gUqlYBBMRERE9AYvgGsDISAETk3pVnQYRERHRc4NfjCMiIiKiWodFMBERERHVOiyCiYiIiKjWYRFMRERERLUOi2AiIiIiqnVYBBMRERFRrcMimIiIiIhqHRbBRERERFTrsAgmIiIiolqHRTARERER1TosgomIiIio1mERTERERES1TrUqguPi4iCTyRAbGyutE0Jg8uTJcHV1hbm5OYKCgnD69Gm1/VQqFWJiYmBvbw9LS0v07NkT165dU4vJyclBZGQkFAoFFAoFIiMjkZubWwlXRURERETVTbUpgg8fPozFixejRYsWautnzZqFOXPmYP78+Th8+DCcnZ3RrVs33LlzR4qJjY3Fpk2bEB8fj/379yM/Px8REREoKSmRYgYOHIi0tDQkJCQgISEBaWlpiIyMrLTrIyIiIqLqo1oUwfn5+XjjjTewZMkS2NjYSOuFEPjqq6/w8ccfo2/fvvD29sbKlStx7949rFu3DgCgVCqxdOlSzJ49G127dkXr1q2xZs0anDx5Ejt37gQAnDlzBgkJCfj+++8REBCAgIAALFmyBFu2bMG5c+eq5JqJiIiIqOpUiyJ4+PDh6N69O7p27aq2/tKlS8jMzERISIi0Ti6XIzAwEMnJyQCAo0ePoqioSC3G1dUV3t7eUkxKSgoUCgX8/PykGH9/fygUCilGE5VKhby8PLWFiIiIiGo+46pOID4+Hn/++ScOHz5cZltmZiYAwMnJSW29k5MTLl++LMWYmpqq9SCXxpTun5mZCUdHxzLHd3R0lGI0iYuLw5QpU3S7ICIiIiKq9qq0J/jq1av44IMPsGbNGpiZmZUbJ5PJ1F4LIcqse9zjMZrin3Sc8ePHQ6lUSsvVq1crPCcRERER1QxVWgQfPXoUWVlZaNu2LYyNjWFsbIykpCTMmzcPxsbGUg/w4721WVlZ0jZnZ2cUFhYiJyenwpibN2+WOf+tW7fK9DI/Si6Xw9raWm0hIiIiopqvSovg4OBgnDx5EmlpadLi6+uLN954A2lpaWjSpAmcnZ2xY8cOaZ/CwkIkJSWhffv2AIC2bdvCxMRELSYjIwOnTp2SYgICAqBUKnHo0CEp5uDBg1AqlVIMEREREdUeVTom2MrKCt7e3mrrLC0tYWdnJ62PjY3F9OnT4eHhAQ8PD0yfPh0WFhYYOHAgAEChUCA6OhqjR4+GnZ0dbG1tMWbMGPj4+EhftPPy8kJYWBiGDh2KRYsWAQDeeecdREREwNPTsxKvmIiIiIiqgyr/YtyTjB07FgUFBRg2bBhycnLg5+eHP/74A1ZWVlLM3LlzYWxsjH79+qGgoADBwcFYsWIFjIyMpJi1a9di5MiR0iwSPXv2xPz58yv9eoiIiIio6smEEKKqk6gp8vLyoFAooFQqK2V8cG5uLmxsbNChQw5MTOpVGFtUlIsDB2yQk5ODevUqjiUiIiJ6Xmlbr1WLeYKJiIiIiCoTi2AiIiIiqnVYBBMRERFRrcMimIiIiIhqHRbBRERERFTrsAgmIiIiolqHRTARERER1TosgomIiIio1mERTERERES1DotgIiIiIqp1WAQTERERUa3DIpiIiIiIah0WwURERERU67AIJiIiIqJaR+ci+P79+8jLy1Nb99NPP2HcuHHYtWuX3hIjIiIiIjIUnYvgyMhIjBw5Uno9b948DBgwALNmzUJISAi2bdum1wSJiIiIiPRN5yL40KFDCAsLk17PmzcPgwYNQm5uLvr27Ysvv/xSrwkSEREREembzkXwrVu3UL9+fQDApUuXcPHiRcTExMDa2hrR0dE4deqU3pMkIiIiItInnYtgCwsLKJVKAMC+fftQt25d+Pr6AgDMzMyQn5+v3wyJiIiIiPTMWNcdfHx88O2338Ld3R0LFixA586dIZPJAABXrlyBs7Oz3pMkIiIiItInnYvgTz/9FBEREWjVqhVMTU2xc+dOadvWrVvRpk0bvSZIRERERKRvOhfBXbp0wZkzZ3D06FG0atUKTZo0UdvWqlUrfeZHRERERKR3OhXBBQUFiI6OxrBhw9C3b98y29999129JUZEREREZCg6fTHO3Nwcv/76Kx48eGCofIiIiIiIDE7n2SFatWql12nQFi5ciBYtWsDa2hrW1tYICAjA9u3bpe2DBw+GTCZTW/z9/dWOoVKpEBMTA3t7e1haWqJnz564du2aWkxOTg4iIyOhUCigUCgQGRmJ3NxcvV0HEREREdUcOhfBM2bMwKxZs5CUlKSXBBo0aIAZM2bgyJEjOHLkCLp06YJevXrh9OnTUkxYWBgyMjKk5fGn0sXGxmLTpk2Ij4/H/v37kZ+fj4iICJSUlEgxAwcORFpaGhISEpCQkIC0tDRERkbq5RqIiIiIqGaRCSGELjv4+PggIyMDOTk5sLGxgYuLizRFGgDIZDIcP378mZKytbXFF198gejoaAwePBi5ubn45ZdfNMYqlUo4ODhg9erV6N+/PwDgxo0bcHNzw7Zt2xAaGoozZ86gefPmSE1NhZ+fHwAgNTUVAQEBOHv2LDw9PbXKKy8vDwqFAkqlEtbW1s90jdrIzc2FjY0NOnTIgYlJvQpji4pyceCADXJyclCvXsWxRERERM8rbes1nWeHsLOzg729/TMlV56SkhL8/PPPuHv3LgICAqT1iYmJcHR0RL169RAYGIjPP/8cjo6OAICjR4+iqKgIISEhUryrqyu8vb2RnJyM0NBQpKSkQKFQSAUwAPj7+0OhUCA5ObncIlilUkGlUkmv8/Ly9H3JRERERFQFdC6CExMT9Z7EyZMnERAQgPv376Nu3brYtGkTmjdvDgAIDw/Ha6+9Bnd3d1y6dAmffvopunTpgqNHj0IulyMzMxOmpqawsbFRO6aTkxMyMzMBAJmZmVLR/ChHR0cpRpO4uDhMmTJFj1dKRERERNWBzkWwIXh6eiItLQ25ubnYsGEDoqKikJSUhObNm0tDHADA29sbvr6+cHd3x9atWzVO01ZKCFFmmMaTYh43fvx4jBo1Snqdl5cHNzc3XS+PiIiIiKoZnb8YBwC3bt3C+PHjERAQAA8PD+lLbIsWLcKxY8d0Pp6pqSmaNm0KX19fxMXFoWXLlvj66681xrq4uMDd3R3nz58HADg7O6OwsBA5OTlqcVlZWXBycpJibt68qfE6SmM0kcvl0qwVpQsRERER1Xw6F8GXLl1Cy5YtMW/ePMhkMly8eFEaN3vixAnMmzfvmZMSQqiNxX1UdnY2rl69ChcXFwBA27ZtYWJigh07dkgxGRkZOHXqFNq3bw8ACAgIgFKpxKFDh6SYgwcPQqlUSjFEREREVHvoPBxi7NixqFevHo4cOQJHR0eYmppK2zp27IhJkybpdLwJEyYgPDwcbm5uuHPnDuLj45GYmIiEhATk5+dj8uTJePXVV+Hi4oL09HRMmDAB9vb26NOnDwBAoVAgOjoao0ePhp2dHWxtbTFmzBj4+Piga9euAAAvLy+EhYVh6NChWLRoEQDgnXfeQUREhNYzQxARERHR80PnInjXrl1YuHAhXF1d1ebhBR4OVbhx44ZOx7t58yYiIyORkZEBhUKBFi1aICEhAd26dUNBQQFOnjyJVatWITc3Fy4uLujcuTN+/PFHWFlZSceYO3cujI2N0a9fPxQUFCA4OBgrVqyAkZGRFLN27VqMHDlSmkWiZ8+emD9/vq6XT0RERETPAZ2L4Pv378PW1lbjtrt376JOHd1GWCxdurTcbebm5vj999+feAwzMzN88803+Oabb8qNsbW1xZo1a3TKjYiIiIieTzqPCfb09MTOnTs1btu7dy+8vb2fOSkiIiIiIkPSuSd46NChGDVqFFxdXfHGG28AAAoLC7F+/XosWLCAQwyIiIiIqNrTuQgeNmwY0tLS8OGHH2L06NEAHn4hTgiBoUOHIioqSu9JEhERERHp01M9LGPx4sUYMmQItm7dips3b8Le3h4RERGcboyIiIiIagSdi+ArV67AxcUF/v7+8Pf3V9tWXFyMGzduoGHDhnpLkIiIiIhI33T+Ylzjxo3LfSrc8ePH0bhx42dOioiIiIjIkHQugoUQ5W4rKSmBTCZ7poSIiIiIiAxN5yIYgMZCV6VSYfv27bC3t3/mpIiIiIiIDEmrMcFTpkzB1KlTATwsgB8fC/yot99+Wz+ZEREREREZiFZF8Msvv4xhw4ZBCIEFCxbgv//9L5ycnNRi5HI5fHx8MHDgQIMkSkRERESkL1oVweHh4QgPDwfw8NHIEydO5BfgiIiIiKjG0nmKtOXLlxsiDyIiIiKiSqPzF+NmzpyJmJgYjdtiYmLw5ZdfPnNSRERERESGpHMRvHLlSnh7e2vc1rJlS6xcufKZkyIiIiIiMiSdi+DLly+jWbNmGrc1bdoU6enpz5oTEREREZFB6VwEm5iYICsrS+O2mzdv8mEZRERERFTt6VwE+/r6YsmSJRq3LVmyBL6+vs+cFBERERGRIek8O8SYMWPQvXt3BAUFYdiwYahfvz6uXbuG7777Dnv37sW2bdsMkScRERERkd7oXASHhYVh8eLFGD16NAYMGACZTAYhBBQKBZYsWYLQ0FBD5ElEREREpDc6F8EAEB0djQEDBiA5ORm3bt2Cg4MD2rdvD0tLS33nR0RERESkd09VBAOApaUlunXrps9ciIiIiIgqxVMXwUqlEn///TcKCgrKbOvUqdMzJUVEREREZEg6F8HFxcV47733sGrVKpSUlGiMKW89EREREVF1oPMUaXPnzsVvv/2GZcuWQQiB+fPnY9GiRfD19YWHhwe2b9+u0/EWLlyIFi1awNraGtbW1ggICFA7hhACkydPhqurK8zNzREUFITTp0+rHUOlUiEmJgb29vawtLREz549ce3aNbWYnJwcREZGQqFQQKFQIDIyErm5ubpePhERERE9B3QuglevXo2PP/4Yr7/+OgDAz88Pb7/9Ng4ePAh3d3fs2bNHp+M1aNAAM2bMwJEjR3DkyBF06dIFvXr1kgrdWbNmYc6cOZg/fz4OHz4MZ2dndOvWDXfu3JGOERsbi02bNiE+Ph779+9Hfn4+IiIi1HqkBw4ciLS0NCQkJCAhIQFpaWmIjIzU9fKJiIiI6DkgE0IIXXaoW7cutm7dik6dOsHIyAj79+9H+/btAQCbNm3CBx98gCtXrjxTUra2tvjiiy8wZMgQuLq6IjY2Fv/73/8APOz1dXJywsyZM/Huu+9CqVTCwcEBq1evRv/+/QEAN27cgJubG7Zt24bQ0FCcOXMGzZs3R2pqKvz8/AAAqampCAgIwNmzZ+Hp6alVXnl5eVAoFFAqlbC2tn6ma9RGbm4ubGxs0KFDDkxM6lUYW1SUiwMHbJCTk4N69SqOJSIiInpeaVuv6dwTbGlpicLCQshkMtja2uLy5cvSNnNzc2RnZz9dxng4ljg+Ph53795FQEAALl26hMzMTISEhEgxcrkcgYGBSE5OBgAcPXoURUVFajGurq7w9vaWYlJSUqBQKKQCGAD8/f2hUCikGE1UKhXy8vLUFiIiIiKq+XQugl988UVcunQJANC+fXvMmTMH165dQ1ZWFmbNmqV1r+qjTp48ibp160Iul+O9997Dpk2b0Lx5c2RmZgIAnJyc1OKdnJykbZmZmTA1NYWNjU2FMY6OjmXO6+joKMVoEhcXJ40hVigUcHNz0/naiIiIiKj60Xl2iP79++Pvv/8GAEyZMgWdOnWCu7s7AMDExAQbN27UOQlPT0+kpaUhNzcXGzZsQFRUFJKSkqTtMplMLV4IUWbd4x6P0RT/pOOMHz8eo0aNkl7n5eWxECYiIiJ6DuhcBA8bNkz6d+vWrfHXX3/hl19+gUwmQ7du3Z6qJ9jU1BRNmzYFAPj6+uLw4cP4+uuvpXHAmZmZcHFxkeKzsrKk3mFnZ2cUFhYiJydHrTc4KytLGqvs7OyMmzdvljnvrVu3yvQyP0oul0Mul+t8PURERERUvek0HOL+/fuYMGECjh49Kq1zc3NDTEwMRowY8VQFsCZCCKhUKjRu3BjOzs7YsWOHtK2wsBBJSUlSgdu2bVuYmJioxWRkZODUqVNSTEBAAJRKJQ4dOiTFHDx4EEqlUoohIiIiotpDp55gMzMzzJ07F2FhYXpLYMKECQgPD4ebmxvu3LmD+Ph4JCYmIiEhATKZDLGxsZg+fTo8PDzg4eGB6dOnw8LCAgMHDgQAKBQKREdHY/To0bCzs4OtrS3GjBkDHx8fdO3aFQDg5eWFsLAwDB06FIsWLQIAvPPOO4iIiNBb4U5ERERENYfOwyG8vLxw6dIlvT0a+ebNm4iMjERGRgYUCgVatGiBhIQEdOvWDQAwduxYFBQUYNiwYcjJyYGfnx/++OMPWFlZSceYO3cujI2N0a9fPxQUFCA4OBgrVqyAkZGRFLN27VqMHDlSmkWiZ8+emD9/vl6ugYiIiIhqFp3nCd60aRPGjh2LhIQEvPDCC4bKq1riPMFERERE1Zu29ZrOPcHLly/HvXv34OXlhRYtWsDFxaXMLAy//vrr02VNRERERFQJdC6CT5w4AVNTU9SvXx/Z2dllHo7xpKnLiIiIiIiqms5FcHp6ugHSICIiIiKqPDo/MY6IiIiIqKbTuSf4Ubdu3UJBQUGZ9Q0bNnyWwxIRERERGdRTFcHTpk3DvHnzyowHLlVSUvJMSRERERERGZLOwyGWLVuGGTNmYOTIkRBCYMKECRg/fjwaNGgADw8PfP/994bIk4iIiIhIb3Qugr/99lup8AWAPn36YNq0aTh79iysrKxw+/ZtvSdJRERERKRPOhfBFy5cgL+/P+rUebhrYWEhAMDc3ByjR4/G4sWL9ZshEREREZGe6VwEGxs/HEYsk8lgbW2Na9euSdvs7e1x/fp1/WVHRERERGQAOhfBHh4euHr1KgCgXbt2WLJkCYqKilBSUoLFixejUaNG+s6RiIiIiEivdJ4d4pVXXsHevXsRFRWF8ePHIzQ0FPXq1YOxsTHy8/OxbNkyQ+RJRERERKQ3OhfBEydOlP7dpUsXJCcnIz4+HjKZDN27d0fnzp31miARERERkb4908MygIdDItq1a6ePXIiIiIiIKgUfm0xEREREtY7ORXBRURGmTZuG5s2bw9LSEkZGRmpL6ewRRERERETVlc4V6/jx4zF37lyEh4ejd+/ekMvlhsiLiIiIiMhgdC6Cf/rpJ0ycOBGTJk0yRD5ERERERAan83CInJwcdOrUyRC5EBERERFVCp2L4E6dOiEtLc0AqRARERERVQ6di+B58+Zh6dKl2LhxIwoLCw2RExERERGRQWk1JtjKygoymUx6XVhYiNdeew0ymQwWFhZqsTKZDEqlUr9ZEhERERHpkVZF8KuvvqpWBBMRERER1WRaFcErVqwwWAJxcXHYuHEjzp49C3Nzc7Rv3x4zZ86Ep6enFDN48GCsXLlSbT8/Pz+kpqZKr1UqFcaMGYMffvgBBQUFCA4OxoIFC9CgQQMpJicnByNHjsTmzZsBAD179sQ333yDevXqGez6iIiIiKj6qfInxiUlJWH48OFITU3Fjh07UFxcjJCQENy9e1ctLiwsDBkZGdKybds2te2xsbHYtGkT4uPjsX//fuTn5yMiIgIlJSVSzMCBA5GWloaEhAQkJCQgLS0NkZGRlXKdRERERFR9aNUTfOHCBfj5+WHJkiXo27evxphNmzbh7bffxr59+9C8eXOtE0hISFB7vXz5cjg6OuLo0aNqU7HJ5XI4OztrPIZSqcTSpUuxevVqdO3aFQCwZs0auLm5YefOnQgNDcWZM2eQkJCA1NRU+Pn5AQCWLFmCgIAAnDt3Tq3nmYiIiIieb1r1BM+fPx8vvfRSuQUwAPTp0wctWrTA/Pnznymh0i/V2draqq1PTEyEo6MjmjVrhqFDhyIrK0vadvToURQVFSEkJERa5+rqCm9vbyQnJwMAUlJSoFAopAIYAPz9/aFQKKSYx6lUKuTl5aktRERERFTzaVUEb9u2TathA4MGDcKuXbueOhkhBEaNGoWOHTvC29tbWh8eHo61a9di9+7dmD17Ng4fPowuXbpApVIBADIzM2FqagobGxu14zk5OSEzM1OKcXR0LHNOR0dHKeZxcXFxUCgU0uLm5vbU10ZERERE1YdWwyGuXr2q1XABDw8PXL169amTGTFiBE6cOIH9+/erre/fv7/0b29vb/j6+sLd3R1bt26tsHdaCKE2q4WmGS4ej3nU+PHjMWrUKOl1Xl4eC2EiIiKi54BWPcHGxsZaPRijsLAQRkZGT5VITEwMNm/ejD179qjN6KCJi4sL3N3dcf78eQCAs7MzCgsLkZOToxaXlZUFJycnKebmzZtljnXr1i0p5nFyuRzW1tZqCxERERHVfFoVwY0bN1abjqw8KSkpaNy4sU4JCCEwYsQIbNy4Ebt379Zq/+zsbFy9ehUuLi4AgLZt28LExAQ7duyQYjIyMnDq1Cm0b98eABAQEAClUolDhw5JMQcPHoRSqZRiiIiIiKh20KoIfuWVV/DNN98gIyOj3JgbN25g/vz5iIiI0CmB4cOHY82aNVi3bh2srKyQmZmJzMxMFBQUAADy8/MxZswYpKSkID09HYmJiejRowfs7e3Rp08fAIBCoUB0dDRGjx6NXbt24dixYxg0aBB8fHyk2SK8vLwQFhaGoUOHIjU1FampqRg6dCgiIiI4MwQRERFRLaNVETxq1CgIIdC+fXv8/PPPUoEKAAUFBfjpp5/Qvn17yGQyfPjhhzolsHDhQiiVSgQFBcHFxUVafvzxRwCAkZERTp48iV69eqFZs2aIiopCs2bNkJKSAisrK+k4c+fORe/evdGvXz906NABFhYW+O2339SGZ6xduxY+Pj4ICQlBSEgIWrRogdWrV+uULxERERHVfDIhhNAm8NChQ+jVqxeysrJgZGQEBwcHAA/H1JaUlMDJyQm//vor2rVrZ9CEq1JeXh4UCgWUSmWljA/Ozc2FjY0NOnTIgYlJvQpji4pyceCADXJycvgEPCIiIqq1tK3XtJodAgBefvll/P3331i8eDF27NiBK1euAAB8fHwQGhqK6OhofnGMiIiIiGoErYtgALCyssLo0aMxevRoQ+VDRERERGRwWo0JJiIiIiJ6nrAIJiIiIqJah0UwEREREdU6LIKJiIiIqNbRqgjevHkzlEqloXMhIiIiIqoUWhXBffr0wblz5wAATZo0wfHjxw2aFBERERGRIWlVBJubm+PevXsAgPT0dKhUKoMmRURERERkSFrNE+zl5YWPP/4Yffr0AQCsW7cO+/fv1xj7NI9OJiIiIiKqTFo9NnnXrl3o378//v33X8hkMlS0i0wmQ0lJiV6TrC742GQiIiKi6k2vj00ODg7G7du3cf36dbi5uWHTpk1o1aqVvnIlIiIiIqpUOj02uX79+pg0aRLatWsHV1dXQ+VERERERGRQOhXBADBp0iTp33///Teys7Nhb28PDw8PvSZGRERERGQoT/WwjJ9//hnu7u7w8vJCx44d8eKLL8Ld3R3r16/Xd35ERERERHqncxG8bds2DBgwAAqFAjNmzMCqVasQFxcHhUKBAQMGYPv27YbIk4iIiIhIb7SaHeJRHTp0gLW1NbZu3Yo6df6vhhZCIDw8HHfu3MGBAwf0nmh1wNkhiIiIiKo3bes1nXuC09LSMGzYMLUCGHg4NdqwYcP4NDkiIiIiqvZ0LoKNjIxQWFiocVtRUVGZ4piIiIiIqLrRuWJt164dZs2ahYKCArX1KpUKX375Jfz8/PSWHBERERGRIeg8RdqUKVMQHByMJk2a4LXXXoOzszMyMjKwceNGZGdnY/fu3YbIk4iIiIhIb3Qugjt27Ig//vgD48aNw7fffgshBOrUqQM/Pz/88MMPaN++vSHyJCIiIiLSG52LYAAIDAxESkoK7t27h5ycHNjY2MDCwkLfuRERERERGcRTFcGlLCwsWPwSERERUY1T5VM5xMXFoV27drCysoKjoyN69+6Nc+fOqcUIITB58mS4urrC3NwcQUFBOH36tFqMSqVCTEwM7O3tYWlpiZ49e+LatWtqMTk5OYiMjIRCoYBCoUBkZCRyc3MNfYlEREREVM1UeRGclJSE4cOHIzU1FTt27EBxcTFCQkJw9+5dKWbWrFmYM2cO5s+fj8OHD8PZ2RndunXDnTt3pJjY2Fhs2rQJ8fHx2L9/P/Lz8xEREYGSkhIpZuDAgUhLS0NCQgISEhKQlpaGyMjISr1eIiIiIqp6Oj8xztBu3boFR0dHJCUloVOnThBCwNXVFbGxsfjf//4H4GGvr5OTE2bOnIl3330XSqUSDg4OWL16Nfr37w8AuHHjBtzc3LBt2zaEhobizJkzaN68OVJTU6Vp3FJTUxEQEICzZ8/C09OzTC4qlQoqlUp6nZeXBzc3Nz4xjoiIiKiaMtgT4wxNqVQCAGxtbQEAly5dQmZmJkJCQqQYuVyOwMBAJCcnAwCOHj2KoqIitRhXV1d4e3tLMSkpKVAoFGrzGPv7+0OhUEgxj4uLi5OGTigUCri5uen3YomIiIioSuhcBGdmZhoiDwAPx/6OGjUKHTt2hLe3t9r5nJyc1GKdnJykbZmZmTA1NYWNjU2FMY6OjmXO6ejoWO41jR8/HkqlUlquXr36bBdIRERERNWCzkVww4YN8frrr+PAgQN6T2bEiBE4ceIEfvjhhzLbZDKZ2mshRJl1j3s8RlN8RceRy+WwtrZWW4iIiIio5tO5CP7kk0+wb98+dOrUCa1atcLSpUvLPEL5acTExGDz5s3Ys2cPGjRoIK13dnYGULYHOisrS+oddnZ2RmFhIXJyciqMuXnzZpnz3rp1q0wvMxERERE933QugidOnIjLly/jhx9+gLW1NYYOHYoGDRpgzJgx+Oeff3ROQAiBESNGYOPGjdi9ezcaN26str1x48ZwdnbGjh07pHWFhYVISkqSnk7Xtm1bmJiYqMVkZGTg1KlTUkxAQACUSiUOHTokxRw8eBBKpZJPuSMiIiKqZZ7qi3FGRkbo168f9u7di7S0NLz66qv47rvv4OnpiYiICPz+++9aH2v48OFYs2YN1q1bBysrK2RmZiIzM1PqXZbJZIiNjcX06dOxadMmnDp1CoMHD4aFhQUGDhwIAFAoFIiOjsbo0aOxa9cuHDt2DIMGDYKPjw+6du0KAPDy8kJYWBiGDh2K1NRUpKamYujQoYiIiNA4MwQRERERPb+eeXYIHx8fhIeHw9vbGw8ePMCuXbvwyiuvwNfXF3///fcT91+4cCGUSiWCgoLg4uIiLT/++KMUM3bsWMTGxmLYsGHw9fXF9evX8ccff8DKykqKmTt3Lnr37o1+/fqhQ4cOsLCwwG+//QYjIyMpZu3atfDx8UFISAhCQkLQokULrF69+lmbgIiIiIhqmKeeJ/j27dtYsmQJFi1ahKtXryIgIAAffPABevfujYSEBIwZMwZ2dnblTj9WE2k775y+cJ5gIiIiIt1oW68Z63rggwcP4ttvv8XPP/8MIQT69++PDz74AG3atJFievToAWNjY/Tu3fupkiciIiIiMiSdi+CAgAA4Oztj3LhxeP/99zXOvQsAjRo14hfOiIiIiKha0rkIXrVqFfr37w8TE5MK47y8vLBnz56nToyIiIiIyFB0LoIHDRpkiDyIiIiIiCqNzrNDzJw5EzExMRq3xcTE4Msvv3zmpIiIiIiIDEnnInjlypXw9vbWuK1ly5ZYuXLlMydFRERERGRIOhfBly9fRrNmzTRua9q0KdLT0581JyIiIiIig9K5CDYxMUFWVpbGbTdv3oRMJnvmpIiIiIiIDEnnItjX1xdLlizRuG3JkiXw9fV95qSIiIiIiAxJ59khxowZg+7duyMoKAjDhg1D/fr1ce3aNXz33XfYu3cvtm3bZog8iYiIiIj0RuciOCwsDIsXL8bo0aMxYMAAyGQyCCGgUCiwZMkShIaGGiJPIiIiIiK90bkIBoDo6GgMGDAAycnJuHXrFhwcHNC+fXtYWlrqOz8iIiIiIr17qiIYACwtLdGtWzd95kJEREREVCmeqggWQuDw4cO4fPkyCgoKymx/8803nzkxIiIiIiJD0bkI/vvvv9GzZ0+cP38eQogy22UyGYtgIiIiIqrWdC6Chw8fjvv37+PHH39EixYtIJfLDZEXEREREZHB6FwEHzp0CEuWLMF///tfQ+RDRERERGRwOj8so27durC2tjZELkRERERElULnIvitt97CunXrDJELEREREVGl0Hk4hLe3N3744Qf07NkTPXr0gJ2dXZmYvn376iU5IiIiIiJD0LkIHjhwIADg0qVL2LJlS5ntMpkMJSUlz54ZEREREZGB6FwE79mzxxB5EBERERFVGp2L4MDAQEPkQURERERUaXT+YlwppVKJ33//HWvXrkVOTs5TJ7B371706NEDrq6ukMlk+OWXX9S2Dx48GDKZTG3x9/dXi1GpVIiJiYG9vT0sLS3Rs2dPXLt2TS0mJycHkZGRUCgUUCgUiIyMRG5u7lPnTUREREQ111MVwZ999hlcXV0RHh6ON998E5cuXQIABAcHY8aMGTod6+7du2jZsiXmz59fbkxYWBgyMjKkZdu2bWrbY2NjsWnTJsTHx2P//v3Iz89HRESE2tjkgQMHIi0tDQkJCUhISEBaWhoiIyN1ypWIiIiIng86D4dYsGABpkyZgmHDhiE8PBzdu3eXtkVERGDjxo0YN26c1scLDw9HeHh4hTFyuRzOzs4atymVSixduhSrV69G165dAQBr1qyBm5sbdu7cidDQUJw5cwYJCQlITU2Fn58fAGDJkiUICAjAuXPn4OnpqXW+RERERFTz6dwTPH/+fIwaNQrz5s1DSEiI2jYPDw+cP39eb8mVSkxMhKOjI5o1a4ahQ4ciKytL2nb06FEUFRWp5eLq6gpvb28kJycDAFJSUqBQKKQCGAD8/f2hUCikGE1UKhXy8vLUFiIiIiKq+XQugi9evIjQ0FCN26ysrPQ+zjY8PBxr167F7t27MXv2bBw+fBhdunSBSqUCAGRmZsLU1BQ2NjZq+zk5OSEzM1OKcXR0LHNsR0dHKUaTuLg4aQyxQqGAm5ubHq+MiIiIiKqKzsMhFAoFbt68qXFbenq6xmLzWfTv31/6t7e3N3x9feHu7o6tW7dW+FAOIQRkMpn0+tF/lxfzuPHjx2PUqFHS67y8PBbCRERERM8BnXuCg4ODMWvWLNy9e1daJ5PJUFxcjIULF5bbS6wvLi4ucHd3l4ZdODs7o7CwsMwMFVlZWXBycpJiNBXut27dkmI0kcvlsLa2VluIiIiIqObTuQieOnUqLl++jObNm2P06NGQyWSYP38+Xn75ZVy4cAGffvqpIfKUZGdn4+rVq3BxcQEAtG3bFiYmJtixY4cUk5GRgVOnTqF9+/YAgICAACiVShw6dEiKOXjwIJRKpRRDRERERLWHzkVw06ZNceDAAXh5eWHBggUQQmDVqlWwt7fHvn370LBhQ52Ol5+fj7S0NKSlpQF4+DjmtLQ0XLlyBfn5+RgzZgxSUlKQnp6OxMRE9OjRA/b29ujTpw+Ah8MzoqOjMXr0aOzatQvHjh3DoEGD4OPjI80W4eXlhbCwMAwdOhSpqalITU3F0KFDERERwZkhiIiIiGohnccEA0Dz5s2RkJAAlUqF7Oxs2NjYwNzc/KkSOHLkCDp37iy9Lh2DGxUVhYULF+LkyZNYtWoVcnNz4eLigs6dO+PHH3+ElZWVtM/cuXNhbGyMfv36oaCgAMHBwVixYgWMjIykmLVr12LkyJHSLBI9e/ascG5iIiIiInp+yYQQoqqTqCny8vKgUCigVCorZXxwbm4ubGxs0KFDDkxM6lUYW1SUiwMHbJCTk4N69SqOJSIiInpeaVuv6dwTPHXq1Aq3y2Qyg48LJiIiIiJ6FjoXwZMnT65wO4tgIiIiIqrudP5i3IMHD8ost2/fxvfffw9vb2+kp6cbIE0iIiIiIv3RuQjWxNbWFkOGDMHAgQMxcuRIfRySiIiIiMhg9FIEl3r55Zexa9cufR6SiIiIiEjv9FoEHz9+HHXr1tXnIYmIiIiI9E7nL8atWrWqzDqVSoUTJ05g2bJlGDRokF4SIyIiIiIyFJ2L4MGDB2tcb2ZmhkGDBuHLL7981pyIiIiIiAxK5yL40qVLZdaZmZnByclJLwkRERERERmazkWwu7u7IfIgIiIiIqo0ev1iHBERERFRTaBzT3CdOnUgk8m0ipXJZCguLtY5KSIiIiIiQ9K5CJ44cSJWrFiB/Px89OjRA87OzsjIyMCWLVtQt25dvPXWW4bIk4iIiIhIb3Qugq2srODs7IydO3eqzQl8584ddO3aFRYWFvjoo4/0miQRERERkT7pPCZ4wYIFGDt2bJmHYlhZWWHs2LFYsGCB3pIjIiIiIjIEnYvg69evw9hYcweysbExMjMznzkpIiIiIiJD0rkI9vLywpw5c1BUVKS2vrCwELNnz8aLL76ot+SIiIiIiAxB5zHB06ZNQ+/evdGkSRP07dsXzs7OyMzMxMaNG5GZmYlffvnFAGkSEREREemPzkVw9+7dkZCQgI8//hjffvstHjx4AJlMhpdffhnLly9H165dDZEnEREREZHe6FwEA0BwcDCCg4Nx79495OTkwMbGBhYWFvrOjYiIiIjIIJ7piXGlD80wNTXVSzJERERERJXhqYrgPXv2ICAgAFZWVnB3d8eJEycAAMOHD8fGjRv1miARERERkb7pXATv3r0bISEhuH//PsaMGYMHDx5I2+zt7bFixQp95kdEREREpHc6F8ETJ07EK6+8gmPHjmHatGlq21q2bIm0tDSdjrd371706NEDrq6ukMlkZWaXEEJg8uTJcHV1hbm5OYKCgnD69Gm1GJVKhZiYGNjb28PS0hI9e/bEtWvX1GJycnIQGRkJhUIBhUKByMhI5Obm6pQrERERET0fdC6Cjx07hnfffRfA/40JLuXg4ICsrCydjnf37l20bNkS8+fP17h91qxZmDNnDubPn4/Dhw/D2dkZ3bp1w507d6SY2NhYbNq0CfHx8di/fz/y8/MRERGBkpISKWbgwIFIS0tDQkICEhISkJaWhsjISJ1yJSIiIqLng86zQxgbG5d5UEaprKwsWFlZ6XS88PBwhIeHa9wmhMBXX32Fjz/+GH379gUArFy5Ek5OTli3bh3effddKJVKLF26FKtXr5amZ1uzZg3c3Nywc+dOhIaG4syZM0hISEBqair8/PwAAEuWLEFAQADOnTsHT09PnXImIiIioppN557gdu3aYfXq1Rq3rV+/HgEBAc+cVKlLly4hMzMTISEh0jq5XI7AwEAkJycDAI4ePYqioiK1GFdXV3h7e0sxKSkpUCgUUgEMAP7+/lAoFFKMJiqVCnl5eWoLEREREdV8OhfB48aNw6ZNm9CnTx9s3rwZMpkMBw8exIgRI7B+/XqMHTtWb8llZmYCAJycnNTWOzk5SdsyMzNhamoKGxubCmMcHR3LHN/R0VGK0SQuLk4aQ6xQKODm5vZM10NERERE1YPORXDXrl2xcuVK7Nu3D6+++iqEEBg+fDjWrVuHFStWoGPHjnpP8vGxx0KIMuse93iMpvgnHWf8+PFQKpXScvXqVR0zJyIiIqLqSKcxwSUlJfjnn38QERGBV199FcnJybh58ybs7e3RoUMHWFpa6jU5Z2dnAA97cl1cXKT1WVlZUu+ws7MzCgsLpSfXPRrTvn17KebmzZtljn/r1q0yvcyPksvlkMvlerkWIiIiIqo+dOoJFkKgefPmSElJgbm5OYKDgzFw4ECEhITovQAGgMaNG8PZ2Rk7duyQ1hUWFiIpKUkqcNu2bQsTExO1mIyMDJw6dUqKCQgIgFKpxKFDh6SYgwcPQqlUSjFEREREVHvo1BNsbGwMZ2dntQdkPKv8/HxcuHBBen3p0iWkpaXB1tYWDRs2RGxsLKZPnw4PDw94eHhg+vTpsLCwwMCBAwEACoUC0dHRGD16NOzs7GBra4sxY8bAx8dHmi3Cy8sLYWFhGDp0KBYtWgQAeOeddxAREcGZIYiIiIhqIZ2nSBswYABWrVqF7t276yWBI0eOoHPnztLrUaNGAQCioqKwYsUKjB07FgUFBRg2bBhycnLg5+eHP/74Q20qtrlz58LY2Bj9+vVDQUEBgoODsWLFChgZGUkxa9euxciRI6VZJHr27Fnu3MRERERE9HyTCSGELjusWbMG48ePh4eHB/r27QsXF5cyXy4rndP3eZOXlweFQgGlUglra2uDny83Nxc2Njbo0CEHJib1KowtKsrFgQM2yMnJQb16FccSERERPa+0rdd07gl+8803AQDXr19HYmJime0ymUztSW1ERERERNWNzkXwnj17DJEHEREREVGl0aoIHjt2LEaOHIkGDRogMDAQAPDgwQPUqaPzNMNERERERFVOqyp29uzZuHHjhvS6pKQEJiYm+PPPPw2WGBERERGRoWhVBGv67pyO36cjIiIiIqo2OJ6BiIiIiGodFsFEREREVOtoPTvEuXPnYGz8MLx0CrSzZ89qjG3Tpo0eUiMiIiIiMgyti+DBgweXWRcZGan2WgjBeYKJiIiIqNrTqghevny5ofMgIiIiIqo0WhXBUVFRhs6DiIiIiKjS8ItxRERERFTrsAgmIiIiolqHRTARERER1TosgomIiIio1mERTERERES1DotgIiIiIqp1WAQTERERUa3DIpiIiIiIah0WwURERERU67AIJiIiIqJah0UwEREREdU6LIKJiIiIqNZhEUxEREREtU61L4InT54MmUymtjg7O0vbhRCYPHkyXF1dYW5ujqCgIJw+fVrtGCqVCjExMbC3t4elpSV69uyJa9euVfalEBEREVE1Ue2LYAB46aWXkJGRIS0nT56Uts2aNQtz5szB/PnzcfjwYTg7O6Nbt264c+eOFBMbG4tNmzYhPj4e+/fvR35+PiIiIlBSUlIVl0NEREREVcy4qhPQhrGxsVrvbykhBL766it8/PHH6Nu3LwBg5cqVcHJywrp16/Duu+9CqVRi6dKlWL16Nbp27QoAWLNmDdzc3LBz506EhoaWe16VSgWVSiW9zsvL0/OVEREREVFVqBE9wefPn4erqysaN26MAQMG4OLFiwCAS5cuITMzEyEhIVKsXC5HYGAgkpOTAQBHjx5FUVGRWoyrqyu8vb2lmPLExcVBoVBIi5ubmwGujoiIiIgqW7XvCfbz88OqVavQrFkz3Lx5E9OmTUP79u1x+vRpZGZmAgCcnJzU9nFycsLly5cBAJmZmTA1NYWNjU2ZmNL9yzN+/HiMGjVKep2Xl1ftC2GlUql1rFwuh7m5uQGzISIiIqqeqn0RHB4eLv3bx8cHAQEBeOGFF7By5Ur4+/sDAGQymdo+Qogy6x6nTYxcLodcLn/KzCtXSUkBAGM0atRI633s7Jxw9eolFsJERERU61T7IvhxlpaW8PHxwfnz59G7d28AD3t7XVxcpJisrCypd9jZ2RmFhYXIyclR6w3OyspC+/btKzV3QxKiEEAxfH3PQy63f2J8SYkSqamNoFKpWAQTERFRrVMjxgQ/SqVS4cyZM3BxcUHjxo3h7OyMHTt2SNsLCwuRlJQkFbht27aFiYmJWkxGRgZOnTr1XBXBpYyNFTAxqffExchIUdWpEhEREVWZat8TPGbMGPTo0QMNGzZEVlYWpk2bhry8PERFRUEmkyE2NhbTp0+Hh4cHPDw8MH36dFhYWGDgwIEAAIVCgejoaIwePRp2dnawtbXFmDFj4OPjI80WQURERES1S7Uvgq9du4bXX38dt2/fhoODA/z9/ZGamgp3d3cAwNixY1FQUIBhw4YhJycHfn5++OOPP2BlZSUdY+7cuTA2Nka/fv1QUFCA4OBgrFixAkZGRlV1WURERERUhWRCCFHVSdQUeXl5UCgUUCqVsLa2Nvj5cnNzYWNjgw4dcmBiUq/C2IKCyzh4sBH8/bNgZubwxGMXFeXiwAEb5OTkoF69io9NREREVFNoW6/VuDHBRERERETPikUwEREREdU6LIKJiIiIqNZhEUxEREREtQ6LYCIiIiKqdVgEExEREVGtwyKYiIiIiGodFsFEREREVOuwCCYiIiKiWodFMBERERHVOiyCiYiIiKjWYRFMRERERLWOcVUnQFVLqVRqFSeXy2Fubm7gbIiIiIgqB4vgWqqkpACAMRo1aqRVvJ2dE65evcRCmIiIiJ4LLIJrKSEKARTD1/c85HL7CmNLSpRITW0ElUrFIpiIiIieCyyCazljYwVMTOpVdRpERERElYpfjCMiIiKiWodFMBERERHVOhwOQVrjTBJERET0vGARTE+k60wStraOOHUqTatCmAUzERERVQUWwfREuswkUVR0E4cOecPV1VWrY3PqNSIiIqoKLIJJa9rMJFFcrASnXiMiIqLqjkUwGYQuU69pO9YY4PAJIiIi0o9aNzvEggUL0LhxY5iZmaFt27bYt29fVadUaz061tjGxkarxc2tMQoKCqo6dSIiIqrhalVP8I8//ojY2FgsWLAAHTp0wKJFixAeHo6//voLDRs2rOr0ah1dxhoD/zd8IisrCwqF4onxDx48QJ062v2ep0sse6OJiIhqvlpVBM+ZMwfR0dF4++23AQBfffUVfv/9dyxcuBBxcXFVnF3tpe3QCV1nqZDJTP9/oa3fWF1mvwAMV4zrEgtUj+K9oKAAKpVK63jmTEREhlJriuDCwkIcPXoU48aNU1sfEhKC5ORkjfuoVCq1//mVjl3Ny8szXKKPKD2PSnUNxcUVn7Ow8LoUK8SThwvoEl8dYh/GXwNQjBYtEmFiYvuE2Bs4eTJM77HFxbdw/HiI1rNfPGQCoKiKYwEbG3ukph6AmZmZ1vvo0/379+Hv3xE5Obe03oc5ExE9H8zMzCrt52Jp/SSEqDhQ1BLXr18XAMSBAwfU1n/++eeiWbNmGveZNGmSAMCFCxcuXLhw4cKlhi1Xr16tsDasNT3BpWQymdprIUSZdaXGjx+PUaNGSa8fPHiAf//9F3Z2duXuoy95eXlwc3PD1atXYW1tbdBz0UNs88rHNq8abPfKxzavfGzzyldd2lwIgTt37jzxr7a1pgi2t7eHkZERMjMz1dZnZWXByclJ4z5yuRxyuVxtXb169QyVokbW1tb88FYytnnlY5tXDbZ75WObVz62eeWrDm2uzRfoa80Uaaampmjbti127Nihtn7Hjh1o3759FWVFRERERFWh1vQEA8CoUaMQGRkJX19fBAQEYPHixbhy5Qree++9qk6NiIiIiCpRrSqC+/fvj+zsbEydOhUZGRnw9vbGtm3b4O7uXtWplSGXyzFp0qQywzHIcNjmlY9tXjXY7pWPbV752OaVr6a1uUyIJ80fQURERET0fKk1Y4KJiIiIiEqxCCYiIiKiWodFMBERERHVOiyCiYiIiKjWYRFcTS1YsACNGzeGmZkZ2rZti3379lV1SjXS5MmTIZPJ1BZnZ2dpuxACkydPhqurK8zNzREUFITTp0+rHUOlUiEmJgb29vawtLREz549ce3atcq+lGpr79696NGjB1xdXSGTyfDLL7+obddXG+fk5CAyMhIKhQIKhQKRkZHIzc018NVVT09q88GDB5e57/39/dVi2Oa6iYuLQ7t27WBlZQVHR0f07t0b586dU4vhva5f2rQ573X9WrhwIVq0aCE97CIgIADbt2+Xtj9393iFD1WmKhEfHy9MTEzEkiVLxF9//SU++OADYWlpKS5fvlzVqdU4kyZNEi+99JLIyMiQlqysLGn7jBkzhJWVldiwYYM4efKk6N+/v3BxcRF5eXlSzHvvvSfq168vduzYIf7880/RuXNn0bJlS1FcXFwVl1TtbNu2TXz88cdiw4YNAoDYtGmT2nZ9tXFYWJjw9vYWycnJIjk5WXh7e4uIiIjKusxq5UltHhUVJcLCwtTu++zsbLUYtrluQkNDxfLly8WpU6dEWlqa6N69u2jYsKHIz8+XYniv65c2bc57Xb82b94stm7dKs6dOyfOnTsnJkyYIExMTMSpU6eEEM/fPc4iuBp6+eWXxXvvvae27sUXXxTjxo2rooxqrkmTJomWLVtq3PbgwQPh7OwsZsyYIa27f/++UCgU4rvvvhNCCJGbmytMTExEfHy8FHP9+nVRp04dkZCQYNDca6LHCzJ9tfFff/0lAIjU1FQpJiUlRQAQZ8+eNfBVVW/lFcG9evUqdx+2+bPLysoSAERSUpIQgvd6ZXi8zYXgvV4ZbGxsxPfff/9c3uMcDlHNFBYW4ujRowgJCVFbHxISguTk5CrKqmY7f/48XF1d0bhxYwwYMAAXL14EAFy6dAmZmZlqbS2XyxEYGCi19dGjR1FUVKQW4+rqCm9vb74fWtBXG6ekpEChUMDPz0+K8ff3h0Kh4PtQjsTERDg6OqJZs2YYOnQosrKypG1s82enVCoBALa2tgB4r1eGx9u8FO91wygpKUF8fDzu3r2LgICA5/IeZxFczdy+fRslJSVwcnJSW+/k5ITMzMwqyqrm8vPzw6pVq/D7779jyZIlyMzMRPv27ZGdnS21Z0VtnZmZCVNTU9jY2JQbQ+XTVxtnZmbC0dGxzPEdHR35PmgQHh6OtWvXYvfu3Zg9ezYOHz6MLl26QKVSAWCbPyshBEaNGoWOHTvC29sbAO91Q9PU5gDvdUM4efIk6tatC7lcjvfeew+bNm1C8+bNn8t7vFY9Nrkmkclkaq+FEGXW0ZOFh4dL//bx8UFAQABeeOEFrFy5UvryxNO0Nd8P3eijjTXF833QrH///tK/vb294evrC3d3d2zduhV9+/Ytdz+2uXZGjBiBEydOYP/+/WW28V43jPLanPe6/nl6eiItLQ25ubnYsGEDoqKikJSUJG1/nu5x9gRXM/b29jAyMirz21BWVlaZ375Id5aWlvDx8cH58+elWSIqamtnZ2cUFhYiJyen3Bgqn77a2NnZGTdv3ixz/Fu3bvF90IKLiwvc3d1x/vx5AGzzZxETE4PNmzdjz549aNCggbSe97rhlNfmmvBef3ampqZo2rQpfH19ERcXh5YtW+Lrr79+Lu9xFsHVjKmpKdq2bYsdO3aord+xYwfat29fRVk9P1QqFc6cOQMXFxc0btwYzs7Oam1dWFiIpKQkqa3btm0LExMTtZiMjAycOnWK74cW9NXGAQEBUCqVOHTokBRz8OBBKJVKvg9ayM7OxtWrV+Hi4gKAbf40hBAYMWIENm7ciN27d6Nx48Zq23mv69+T2lwT3uv6J4SASqV6Pu/xSv0aHmmldIq0pUuXir/++kvExsYKS0tLkZ6eXtWp1TijR48WiYmJ4uLFiyI1NVVEREQIKysrqS1nzJghFAqF2Lhxozh58qR4/fXXNU730qBBA7Fz507x559/ii5dunCKtEfcuXNHHDt2TBw7dkwAEHPmzBHHjh2TpvTTVxuHhYWJFi1aiJSUFJGSkiJ8fHxq5RRGQlTc5nfu3BGjR48WycnJ4tKlS2LPnj0iICBA1K9fn23+DN5//32hUChEYmKi2nRc9+7dk2J4r+vXk9qc97r+jR8/Xuzdu1dcunRJnDhxQkyYMEHUqVNH/PHHH0KI5+8eZxFcTX377bfC3d1dmJqaijZt2qhNCUPaK53D0MTERLi6uoq+ffuK06dPS9sfPHggJk2aJJydnYVcLhedOnUSJ0+eVDtGQUGBGDFihLC1tRXm5uYiIiJCXLlypbIvpdras2ePAFBmiYqKEkLor42zs7PFG2+8IaysrISVlZV44403RE5OTiVdZfVSUZvfu3dPhISECAcHB2FiYiIaNmwooqKiyrQn21w3mtobgFi+fLkUw3tdv57U5rzX9W/IkCFS7eHg4CCCg4OlAliI5+8elwkhROX1OxMRERERVT2OCSYiIiKiWodFMBERERHVOiyCiYiIiKjWYRFMRERERLUOi2AiIiIiqnVYBBMRERFRrcMimIiIiIhqHRbBRERERFTrsAgmohphxYoVkMlkMDMzw+XLl8tsDwoKgre3dxVkBiQmJkImk2H9+vVVcn5dpaeno3v37rC1tYVMJkNsbGy5sY0aNYJMJoNMJkOdOnWgUCjg5eWFN998E3/88YfGfWQyGSZPnvxUuT3LvkREujCu6gSIiHShUqnwySefYPXq1VWdSo314Ycf4uDBg1i2bBmcnZ3h4uJSYXyHDh3w5ZdfAgDy8/Nx7tw5xMfHIzQ0FK+++ip++OEHmJiYSPEpKSlo0KCBQa+BiOhZsSeYiGqUsLAwrFu3DsePH6/qVCpdQUEB9PGk+1OnTuHll19G79694e/vD3d39wrj69WrB39/f/j7+6Nr164YPnw49u3bh0mTJmHDhg345JNP1OL9/f1ZBBNRtccimIhqlLFjx8LOzg7/+9//KoxLT0+HTCbDihUrymx7/E/ukydPhkwmw4kTJ/Daa69BoVDA1tYWo0aNQnFxMc6dO4ewsDBYWVmhUaNGmDVrlsZz3r9/H6NGjYKzszPMzc0RGBiIY8eOlYk7cuQIevbsCVtbW5iZmaF169b46aef1GJKh3/88ccfGDJkCBwcHGBhYQGVSlXuNV+5cgWDBg2Co6Mj5HI5vLy8MHv2bDx48ADA/w3buHDhArZv3y4Nc0hPT6+wLcszefJkvPTSS5g/fz7u378vrX+8fW/duoVhw4ahefPmqFu3LhwdHdGlSxfs27dPq/OcOnUKvXr1go2NDczMzNCqVSusXLmyTNzp06cREhICCwsLODg4YPjw4di6dStkMhkSExOluEaNGmHw4MFl9g8KCkJQUJDaury8PIwZMwaNGzeGqakp6tevj9jYWNy9e1ct7ueff4afnx8UCgUsLCzQpEkTDBkyRKvrI6KqwSKYiGoUKysrfPLJJ/j999+xe/duvR67X79+aNmyJTZs2IChQ4di7ty5+PDDD9G7d290794dmzZtQpcuXfC///0PGzduLLP/hAkTcPHiRXz//ff4/vvvcePGDQQFBeHixYtSzJ49e9ChQwfk5ubiu+++w6+//opWrVqhf//+Ggv2IUOGwMTEBKtXr8b69evVhh086tatW2jfvj3++OMPfPbZZ9i8eTO6du2KMWPGYMSIEQCANm3aICUlBc7OzujQoQNSUlKQkpLyxOEQFenRowfu3buHI0eOlBvz77//AgAmTZqErVu3Yvny5WjSpAmCgoLUilNNzp07h/bt2+P06dOYN28eNm7ciObNm2Pw4MFqv4xkZGQgMDAQ586dw8KFC7Fq1SrcuXNHuvance/ePQQGBmLlypUYOXIktm/fjv/9739YsWIFevbsKfXKp6SkoH///mjSpAni4+OxdetWTJw4EcXFxU99biKqBIKIqAZYvny5ACAOHz4sVCqVaNKkifD19RUPHjwQQggRGBgoXnrpJSn+0qVLAoBYvnx5mWMBEJMmTZJeT5o0SQAQs2fPVotr1aqVACA2btworSsqKhIODg6ib9++0ro9e/YIAKJNmzZSPkIIkZ6eLkxMTMTbb78trXvxxRdF69atRVFRkdq5IiIihIuLiygpKVG73jfffFOr9hk3bpwAIA4ePKi2/v333xcymUycO3dOWufu7i66d++u1XGfFLtw4UIBQPz444/Susfb93HFxcWiqKhIBAcHiz59+qhte3zfAQMGCLlcLq5cuaIWFx4eLiwsLERubq4QQoiPPvpIyGQycfr0abW40NBQAUDs2bNH7ZqioqLK5BUYGCgCAwOl13FxcaJOnTri8OHDanHr168XAMS2bduEEEJ8+eWXAoCUCxHVDOwJJqIax9TUFNOmTcORI0fKDCN4FhEREWqvvby8IJPJEB4eLq0zNjZG06ZNNc5QMXDgQMhkMum1u7s72rdvjz179gAALly4gLNnz+KNN94AABQXF0vLK6+8goyMDJw7d07tmK+++qpWue/evRvNmzfHyy+/rLZ+8ODBEELovde8lNByjPJ3332HNm3awMzMDMbGxjAxMcGuXbtw5syZCvfbvXs3goOD4ebmprZ+8ODBuHfvHlJSUgAASUlJ8Pb2RvPmzdXiXn/9dR2uRt2WLVvg7e2NVq1aqb1XoaGhakMs2rVrB+DhXxJ++uknXL9+/anPSUSVh0UwEdVIAwYMQJs2bfDxxx+jqKhIL8e0tbVVe21qagoLCwuYmZmVWf/oGNhSzs7OGtdlZ2cDAG7evAkAGDNmDExMTNSWYcOGAQBu376ttr+2QxWys7M1xrq6ukrbDaH0l4HS82gyZ84cvP/++/Dz88OGDRuQmpqKw4cPIywsDAUFBRUeX9vrys7OhpOTU5k4Teu0dfPmTZw4caLMe2VlZQUhhPRederUCb/88guKi4vx5ptvokGDBvD29sYPP/zw1OcmIsPjFGlEVCPJZDLMnDkT3bp1w+LFi8tsLy1cH/8imaGKQQDIzMzUuM7Ozg4AYG9vDwAYP348+vbtq/EYnp6eaq8f7VmuiJ2dHTIyMsqsv3Hjhtq59UkIgd9++w2Wlpbw9fUtN27NmjUICgrCwoUL1dbfuXPniefQ9rrs7OykXzIepek9MTMz0/gFw9u3b6u1k729PczNzbFs2TKNuT0a26tXL/Tq1QsqlQqpqamIi4vDwIED0ahRIwQEBDzhKomoKrAnmIhqrK5du6Jbt26YOnUq8vPz1bY5OTnBzMwMJ06cUFv/66+/GiyfH374QW14wOXLl5GcnCzNOODp6QkPDw8cP34cvr6+GhcrK6unOndwcDD++usv/Pnnn2rrV61aBZlMhs6dOz/1dZVnypQp+Ouvv/DBBx+U6S1/lEwmg1wuV1t34sQJaShDRYKDg7F7926p6C21atUqWFhYwN/fHwAQGBiIU6dO4a+//lKLi4+PL3PMRo0albkv/v777zJDUSIiIvDPP//Azs5O43vVqFGjMseWy+UIDAzEzJkzAUDj7CBEVD2wJ5iIarSZM2eibdu2yMrKwksvvSStl8lkGDRoEJYtW4YXXngBLVu2xKFDh7Bu3TqD5ZKVlYU+ffpg6NChUCqVmDRpEszMzDB+/HgpZtGiRQgPD0doaCgGDx6M+vXr499//8WZM2fw559/4ueff36qc3/44YdYtWoVunfvjqlTp8Ld3R1bt27FggUL8P7776NZs2ZPfV25ublITU0FANy9e1d6WMa+ffvQr18/TJkypcL9IyIi8Nlnn2HSpEnSDA5Tp05F48aNnziDwqRJk7BlyxZ07twZEydOhK2tLdauXYutW7di1qxZUCgUAIDY2FgsW7YM4eHhmDp1KpycnLBu3TqcPXsWAFCnzv/1+URGRmLQoEEYNmwYXn31VVy+fBmzZs2Cg4OD2rljY2OxYcMGdOrUCR9++CFatGiBBw8e4MqVK/jjjz8wevRo+Pn5YeLEibh27RqCg4PRoEED5Obm4uuvv4aJiQkCAwN1bm8iqiRV+rU8IiItPTo7xOMGDhwoAKjNDiGEEEqlUrz99tvCyclJWFpaih49eoj09PRyZ4e4deuW2v5RUVHC0tKyzPken4midHaI1atXi5EjRwoHBwchl8vFf/7zH3HkyJEy+x8/flz069dPODo6ChMTE+Hs7Cy6dOkivvvuO62utzyXL18WAwcOFHZ2dsLExER4enqKL774QppxopSus0MAEACETCYTdevWFZ6eniIyMlL8/vvvGvd5vH1VKpUYM2aMqF+/vjAzMxNt2rQRv/zyi4iKihLu7u4V7iuEECdPnhQ9evQQCoVCmJqaipYtW2qc9ePUqVOia9euwszMTNja2oro6GixcuVKAUAcP35cinvw4IGYNWuWaNKkiTAzMxO+vr5i9+7dZWaHEEKI/Px88cknnwhPT09hamoqFAqF8PHxER9++KHIzMwUQgixZcsWER4eLurXry9MTU2Fo6OjeOWVV8S+ffu0amMiqhoyIfTw+CEiIqJq6J133sEPP/yA7OxsmJqaVnU6RFSNcDgEERE9F6ZOnQpXV1c0adIE+fn52LJlC77//nt88sknLICJqAwWwURE9FwwMTHBF198gWvXrqG4uBgeHh6YM2cOPvjgg6pOjYiqIQ6HICIiIqJah1OkEREREVGtwyKYiIiIiGodFsFEREREVOuwCCYiIiKiWodFMBERERHVOiyCiYiIiKjWYRFMRERERLUOi2AiIiIiqnX+H/T5kYWDi4z/AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    5356.000000\n",
      "mean       56.891897\n",
      "std       133.209117\n",
      "min         3.000000\n",
      "25%         6.000000\n",
      "50%        15.000000\n",
      "75%        48.000000\n",
      "max      3032.000000\n",
      "Name: count, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Check for imbalance in character dialogue count\n",
    "character_dialogue_counts = lines_df['CharacterName'].value_counts()\n",
    "\n",
    "# Visualize the distribution\n",
    "plt.figure(figsize=(8, 4))\n",
    "sns.histplot(character_dialogue_counts, kde=False, bins=50, color='blue')\n",
    "plt.title('Distribution of Dialogue Count per Character', fontsize=14)\n",
    "plt.xlabel('Number of Dialogues', fontsize=12)\n",
    "plt.ylabel('Frequency of Characters', fontsize=12)\n",
    "plt.show()\n",
    "\n",
    "# Identify any imbalances (characters with significantly more dialogues)\n",
    "print(character_dialogue_counts.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1BY-griEggBT",
   "metadata": {
    "id": "1BY-griEggBT"
   },
   "source": [
    "The **bar chart above** shows the distribution of dialogue counts per character in the dataset, with most characters <br>contributing fewer than 500 lines and a significant majority speaking under 100 lines. Addressing this imbalance could <br>enhance response diversity by including less frequent characters, but it's not essential unless broader character <br>representation is desired."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "NZueUvF0hdWZ",
   "metadata": {
    "id": "NZueUvF0hdWZ"
   },
   "source": [
    "## Split the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ft9L3V2Sh7Tt",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "ft9L3V2Sh7Tt",
    "outputId": "70ec0b89-517c-4536-8345-7df9027c9f65"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set size: 177025\n",
      "Validation set size: 22128\n",
      "Test set size: 22129\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split the data into training, validation, and test sets\n",
    "train_data, temp_data = train_test_split(cleaned_dialog_df, test_size=0.2, random_state=42)  # 80% training\n",
    "val_data, test_data = train_test_split(temp_data, test_size=0.5, random_state=42)  # 10% validation, 10% test\n",
    "\n",
    "# Display the sizes of each set\n",
    "print(f\"Training set size: {len(train_data)}\")\n",
    "print(f\"Validation set size: {len(val_data)}\")\n",
    "print(f\"Test set size: {len(test_data)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "jWBPz0BYGZeT",
   "metadata": {
    "id": "jWBPz0BYGZeT"
   },
   "source": [
    "## Implement T5 Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "448f0f1d-fce9-4bd8-9dac-6954817bf10c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.45.2\n",
      "0.2.0\n"
     ]
    }
   ],
   "source": [
    "import sentencepiece\n",
    "import transformers\n",
    "print(transformers.__version__)\n",
    "print(sentencepiece.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "09fO4yi7Gdg-",
   "metadata": {
    "id": "09fO4yi7Gdg-"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'transformers' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[29], line 4\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# !pip install transformers torch datasets\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;66;03m#!pip install sentencepiece\u001b[39;00m\n\u001b[1;32m----> 4\u001b[0m \u001b[38;5;28mprint\u001b[39m(transformers\u001b[38;5;241m.\u001b[39m__version__)\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28mprint\u001b[39m(sentencepiece\u001b[38;5;241m.\u001b[39m__version__)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'transformers' is not defined"
     ]
    }
   ],
   "source": [
    "# !pip install transformers torch datasets\n",
    "#!pip install sentencepiece\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1YnuuXruGvdi",
   "metadata": {
    "id": "1YnuuXruGvdi"
   },
   "outputs": [],
   "source": [
    "# Import Necessary Libraries\n",
    "import torch\n",
    "import sentencepiece as spm\n",
    "from transformers import T5Tokenizer, T5ForConditionalGeneration\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "xmEeN8j0G147",
   "metadata": {
    "id": "xmEeN8j0G147"
   },
   "source": [
    "## Setup Tokenizer and Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "kB9OaVmkG5PE",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 2744,
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    "id": "kB9OaVmkG5PE",
    "outputId": "9ece6c75-8de4-494f-c515-303d2d417912"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You are using the default legacy behaviour of the <class 'transformers.models.t5.tokenization_t5.T5Tokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "T5ForConditionalGeneration(\n",
       "  (shared): Embedding(32128, 512)\n",
       "  (encoder): T5Stack(\n",
       "    (embed_tokens): Embedding(32128, 512)\n",
       "    (block): ModuleList(\n",
       "      (0): T5Block(\n",
       "        (layer): ModuleList(\n",
       "          (0): T5LayerSelfAttention(\n",
       "            (SelfAttention): T5Attention(\n",
       "              (q): Linear(in_features=512, out_features=512, bias=False)\n",
       "              (k): Linear(in_features=512, out_features=512, bias=False)\n",
       "              (v): Linear(in_features=512, out_features=512, bias=False)\n",
       "              (o): Linear(in_features=512, out_features=512, bias=False)\n",
       "              (relative_attention_bias): Embedding(32, 8)\n",
       "            )\n",
       "            (layer_norm): T5LayerNorm()\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (1): T5LayerFF(\n",
       "            (DenseReluDense): T5DenseActDense(\n",
       "              (wi): Linear(in_features=512, out_features=2048, bias=False)\n",
       "              (wo): Linear(in_features=2048, out_features=512, bias=False)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "              (act): ReLU()\n",
       "            )\n",
       "            (layer_norm): T5LayerNorm()\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (1-5): 5 x T5Block(\n",
       "        (layer): ModuleList(\n",
       "          (0): T5LayerSelfAttention(\n",
       "            (SelfAttention): T5Attention(\n",
       "              (q): Linear(in_features=512, out_features=512, bias=False)\n",
       "              (k): Linear(in_features=512, out_features=512, bias=False)\n",
       "              (v): Linear(in_features=512, out_features=512, bias=False)\n",
       "              (o): Linear(in_features=512, out_features=512, bias=False)\n",
       "            )\n",
       "            (layer_norm): T5LayerNorm()\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (1): T5LayerFF(\n",
       "            (DenseReluDense): T5DenseActDense(\n",
       "              (wi): Linear(in_features=512, out_features=2048, bias=False)\n",
       "              (wo): Linear(in_features=2048, out_features=512, bias=False)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "              (act): ReLU()\n",
       "            )\n",
       "            (layer_norm): T5LayerNorm()\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (final_layer_norm): T5LayerNorm()\n",
       "    (dropout): Dropout(p=0.1, inplace=False)\n",
       "  )\n",
       "  (decoder): T5Stack(\n",
       "    (embed_tokens): Embedding(32128, 512)\n",
       "    (block): ModuleList(\n",
       "      (0): T5Block(\n",
       "        (layer): ModuleList(\n",
       "          (0): T5LayerSelfAttention(\n",
       "            (SelfAttention): T5Attention(\n",
       "              (q): Linear(in_features=512, out_features=512, bias=False)\n",
       "              (k): Linear(in_features=512, out_features=512, bias=False)\n",
       "              (v): Linear(in_features=512, out_features=512, bias=False)\n",
       "              (o): Linear(in_features=512, out_features=512, bias=False)\n",
       "              (relative_attention_bias): Embedding(32, 8)\n",
       "            )\n",
       "            (layer_norm): T5LayerNorm()\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (1): T5LayerCrossAttention(\n",
       "            (EncDecAttention): T5Attention(\n",
       "              (q): Linear(in_features=512, out_features=512, bias=False)\n",
       "              (k): Linear(in_features=512, out_features=512, bias=False)\n",
       "              (v): Linear(in_features=512, out_features=512, bias=False)\n",
       "              (o): Linear(in_features=512, out_features=512, bias=False)\n",
       "            )\n",
       "            (layer_norm): T5LayerNorm()\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (2): T5LayerFF(\n",
       "            (DenseReluDense): T5DenseActDense(\n",
       "              (wi): Linear(in_features=512, out_features=2048, bias=False)\n",
       "              (wo): Linear(in_features=2048, out_features=512, bias=False)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "              (act): ReLU()\n",
       "            )\n",
       "            (layer_norm): T5LayerNorm()\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (1-5): 5 x T5Block(\n",
       "        (layer): ModuleList(\n",
       "          (0): T5LayerSelfAttention(\n",
       "            (SelfAttention): T5Attention(\n",
       "              (q): Linear(in_features=512, out_features=512, bias=False)\n",
       "              (k): Linear(in_features=512, out_features=512, bias=False)\n",
       "              (v): Linear(in_features=512, out_features=512, bias=False)\n",
       "              (o): Linear(in_features=512, out_features=512, bias=False)\n",
       "            )\n",
       "            (layer_norm): T5LayerNorm()\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (1): T5LayerCrossAttention(\n",
       "            (EncDecAttention): T5Attention(\n",
       "              (q): Linear(in_features=512, out_features=512, bias=False)\n",
       "              (k): Linear(in_features=512, out_features=512, bias=False)\n",
       "              (v): Linear(in_features=512, out_features=512, bias=False)\n",
       "              (o): Linear(in_features=512, out_features=512, bias=False)\n",
       "            )\n",
       "            (layer_norm): T5LayerNorm()\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (2): T5LayerFF(\n",
       "            (DenseReluDense): T5DenseActDense(\n",
       "              (wi): Linear(in_features=512, out_features=2048, bias=False)\n",
       "              (wo): Linear(in_features=2048, out_features=512, bias=False)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "              (act): ReLU()\n",
       "            )\n",
       "            (layer_norm): T5LayerNorm()\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (final_layer_norm): T5LayerNorm()\n",
       "    (dropout): Dropout(p=0.1, inplace=False)\n",
       "  )\n",
       "  (lm_head): Linear(in_features=512, out_features=32128, bias=False)\n",
       ")"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load T5 tokenizer and model\n",
    "tokenizer = T5Tokenizer.from_pretrained('t5-small')  # You can also use 't5-base' or 't5-large'\n",
    "model = T5ForConditionalGeneration.from_pretrained('t5-small')\n",
    "\n",
    "# Move model to GPU if available\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "model.to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9XgDveEJG8mA",
   "metadata": {
    "id": "9XgDveEJG8mA"
   },
   "source": [
    "## Modify Dataset Class for T5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "QL9-LIRMG_9G",
   "metadata": {
    "id": "QL9-LIRMG_9G"
   },
   "outputs": [],
   "source": [
    "class DialogDataset(Dataset):\n",
    "    def __init__(self, data, tokenizer, max_length=512):\n",
    "        self.data = data\n",
    "        self.tokenizer = tokenizer\n",
    "        self.max_length = max_length\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        input_text = self.data.iloc[idx]['cleaned_input']\n",
    "        response_text = self.data.iloc[idx]['cleaned_response']\n",
    "\n",
    "        # Prepare the text-to-text task in T5 format (input: \"dialogue: <input> </s>\" and target: \"<response> </s>\")\n",
    "        input_text = \"dialogue: \" + input_text + \" </s>\"\n",
    "        response_text = response_text + \" </s>\"\n",
    "\n",
    "        # Tokenize inputs and responses\n",
    "        input_ids = self.tokenizer.encode(input_text, return_tensors='pt', max_length=self.max_length, padding='max_length', truncation=True)\n",
    "        target_ids = self.tokenizer.encode(response_text, return_tensors='pt', max_length=self.max_length, padding='max_length', truncation=True)\n",
    "\n",
    "        return input_ids.squeeze(), target_ids.squeeze()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3WQHJAkCHBjD",
   "metadata": {
    "id": "3WQHJAkCHBjD"
   },
   "source": [
    "## Create DataLoader for Training and Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "pGkvHmDSHFSF",
   "metadata": {
    "id": "pGkvHmDSHFSF"
   },
   "outputs": [],
   "source": [
    "# Split dataset into training and validation\n",
    "train_size = int(0.8 * len(cleaned_dialog_df))\n",
    "val_size = len(cleaned_dialog_df) - train_size\n",
    "\n",
    "train_dataset = DialogDataset(train_data, tokenizer=tokenizer, max_length=50)\n",
    "val_dataset = DialogDataset(val_data, tokenizer=tokenizer, max_length=50)\n",
    "\n",
    "# DataLoader\n",
    "train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True)\n",
    "val_loader = DataLoader(val_dataset, batch_size=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "YsGnA7dzHIIb",
   "metadata": {
    "id": "YsGnA7dzHIIb"
   },
   "source": [
    "## Model Training and Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "PglxwCI1HK3u",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "PglxwCI1HK3u",
    "outputId": "bf911cc2-f501-48bf-bca7-1423b569f034"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Epoch 1/3: 100%|██████████| 22129/22129 [3:31:00<00:00,  1.75it/s]  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3, Average Training Loss: 0.8786\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Validating: 100%|██████████| 2766/2766 [06:57<00:00,  6.63it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1, Average Validation Loss: 0.8150\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Epoch 2/3: 100%|██████████| 22129/22129 [3:27:08<00:00,  1.78it/s]  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/3, Average Training Loss: 0.8337\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Validating: 100%|██████████| 2766/2766 [07:07<00:00,  6.47it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2, Average Validation Loss: 0.8034\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Epoch 3/3: 100%|██████████| 22129/22129 [5:31:41<00:00,  1.11it/s]  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/3, Average Training Loss: 0.8188\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Validating: 100%|██████████| 2766/2766 [38:38<00:00,  1.19it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3, Average Validation Loss: 0.7984\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Define optimizer\n",
    "optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4)\n",
    "\n",
    "# Training loop\n",
    "num_epochs = 3\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()\n",
    "    total_loss = 0\n",
    "\n",
    "    for batch in tqdm(train_loader, desc=f\"Training Epoch {epoch+1}/{num_epochs}\"):\n",
    "        input_ids, target_ids = batch\n",
    "        input_ids = input_ids.to(device)\n",
    "        target_ids = target_ids.to(device)\n",
    "\n",
    "        # Forward pass\n",
    "        outputs = model(input_ids=input_ids, labels=target_ids)\n",
    "        loss = outputs.loss\n",
    "\n",
    "        # Backward pass and optimization\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        total_loss += loss.item()\n",
    "\n",
    "    avg_train_loss = total_loss / len(train_loader)\n",
    "    print(f\"Epoch {epoch+1}/{num_epochs}, Average Training Loss: {avg_train_loss:.4f}\")\n",
    "\n",
    "    # Validation loop (optional)\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        total_val_loss = 0\n",
    "        for batch in tqdm(val_loader, desc=\"Validating\"):\n",
    "            input_ids, target_ids = batch\n",
    "            input_ids = input_ids.to(device)\n",
    "            target_ids = target_ids.to(device)\n",
    "\n",
    "            outputs = model(input_ids=input_ids, labels=target_ids)\n",
    "            val_loss = outputs.loss\n",
    "            total_val_loss += val_loss.item()\n",
    "\n",
    "        avg_val_loss = total_val_loss / len(val_loader)\n",
    "        print(f\"Epoch {epoch+1}, Average Validation Loss: {avg_val_loss:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "jVFXY-NgXQ07",
   "metadata": {
    "id": "jVFXY-NgXQ07"
   },
   "source": [
    "The model's performance is improving across the three epochs. The decrease in both training and validation losses suggests that the model is learning effectively and generalizing well. The small gap between training and validation losses indicates good generalization, meaning the model is not just memorizing the training data but is likely to perform well on new, unseen data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "xLdiIDYdHsLZ",
   "metadata": {
    "id": "xLdiIDYdHsLZ"
   },
   "source": [
    "## T5 Model Responses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "YCwz1f0KHwe4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YCwz1f0KHwe4",
    "outputId": "f9b64842-ba1a-4ad0-ebfa-5ce3661efe29"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input: Hey, how's everything going with you?\n",
      "Response: oh well thats great thats great\n"
     ]
    }
   ],
   "source": [
    "def generate_response(input_text):\n",
    "    model.eval()\n",
    "    input_text = \"dialogue: \" + input_text + \" </s>\"\n",
    "    input_ids = tokenizer.encode(input_text, return_tensors='pt', max_length=512, truncation=True).to(device)\n",
    "\n",
    "    # Generate output\n",
    "    #output_ids = model.generate(input_ids, max_length=100, num_beams=5, early_stopping=True)\n",
    "    output_ids = model.generate(input_ids, max_length=100, min_length=10, do_sample=True, top_k=50, top_p=0.65, temperature=0.9)\n",
    "    response = tokenizer.decode(output_ids[0], skip_special_tokens=True)\n",
    "\n",
    "    return response\n",
    "\n",
    "# Example\n",
    "input_text = \"Hey, how's everything going with you?\" #\"How are you?\"\n",
    "response = generate_response(input_text)\n",
    "print(f\"Input: {input_text}\")\n",
    "print(f\"Response: {response}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0a1fd8e-46d6-46eb-99b7-b939b4b5df8b",
   "metadata": {},
   "source": [
    "## T5 Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "86f3e7e4-e04b-4f42-a5ff-6762a60187d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluating: 100%|██████████| 2766/2766 [07:39<00:00,  6.03it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.7984\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Function to evaluate model and print metrics\n",
    "def evaluate_model(model, dataloader, device):\n",
    "    model.eval()  # Set model to evaluation mode\n",
    "    total_loss = 0\n",
    "    all_predictions = []\n",
    "    all_targets = []\n",
    "    with torch.no_grad():  # Disable gradient calculation for evaluation\n",
    "        for batch in tqdm(dataloader, desc=\"Evaluating\"):\n",
    "            input_ids, target_ids = batch\n",
    "            input_ids = input_ids.to(device)\n",
    "            target_ids = target_ids.to(device)\n",
    "            # Forward pass\n",
    "            outputs = model(input_ids=input_ids, labels=target_ids)\n",
    "            val_loss = outputs.loss\n",
    "            total_loss += val_loss.item()\n",
    "            # Collect predictions and targets for metrics calculation\n",
    "            predicted_ids = torch.argmax(outputs.logits, dim=-1).cpu().numpy().flatten()\n",
    "            all_predictions.extend(predicted_ids)\n",
    "            all_targets.extend(target_ids.cpu().numpy().flatten())\n",
    "    avg_loss = total_loss / len(dataloader)\n",
    "    return avg_loss, all_predictions, all_targets\n",
    "    \n",
    "# Evaluate model on validation data after training\n",
    "val_loss, val_predictions, val_targets = evaluate_model(model, val_loader, device)\n",
    "print(f\"Validation Loss: {val_loss:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d09a026-a656-46bf-914e-b9a61d02ab02",
   "metadata": {},
   "source": [
    "## Evaluation Metrics of T5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "abd1ea67-b067-4d52-9171-b1c92d8a651b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_score,f1_score, recall_score, accuracy_score\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "927e1a59-6bde-4bdd-be7a-805e1e0551e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_accuracy(predictions, references):\n",
    "    # Comparing each of the prediction with its reference\n",
    "    matches = [pred == ref for pred, ref in zip(predictions, references)]\n",
    "    \n",
    "    # Calculate accuracy\n",
    "    accuracy = accuracy_score(matches, [True] * len(matches))  # All references are correct\n",
    "    print(f\"Exact Match Accuracy: {accuracy:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "62609e96-27a4-44d7-a27c-7e918781843a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_metrics(predictions, references):\n",
    "    all_preds = []\n",
    "    all_refs = []\n",
    "\n",
    "    for pred, ref in zip(predictions, references):\n",
    "        # Splitting into tokens\n",
    "        pred_tokens = pred.split()\n",
    "        ref_tokens = ref.split()\n",
    "        \n",
    "        # Align the lengths by padding with an empty string\n",
    "        max_len = max(len(pred_tokens), len(ref_tokens))\n",
    "        pred_tokens.extend([''] * (max_len - len(pred_tokens)))\n",
    "        ref_tokens.extend([''] * (max_len - len(ref_tokens)))\n",
    "        \n",
    "        all_preds.extend(pred_tokens)\n",
    "        all_refs.extend(ref_tokens)\n",
    "\n",
    "    precision = precision_score(all_refs, all_preds, average='macro', zero_division=1)\n",
    "    f1 = f1_score(all_refs, all_preds, average='macro', zero_division=1)\n",
    "    recall = recall_score(all_refs, all_preds, average='macro', zero_division=1)\n",
    "\n",
    "    print(f\"Model Precision is: {precision:.4f}\")\n",
    "    print(f\"Model F1-Score is: {f1:.4f}\")    \n",
    "    print(f\"Model Recall is: {recall:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "97bb6a6c-cff3-4fa9-8b57-6cea8024b61e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exact Match Accuracy: 0.0000\n",
      "Model Precision is: 0.5358\n",
      "Model F1-Score is: 0.0038\n",
      "Model Recall is: 0.1612\n"
     ]
    }
   ],
   "source": [
    "# Evaluating T5 predictions\n",
    "predictions = []\n",
    "references = []\n",
    "\n",
    "# Generate predictions for the validation set\n",
    "for idx, row in val_data.iterrows():\n",
    "    input_text = row['cleaned_input']\n",
    "    target_text = row['cleaned_response']\n",
    "    \n",
    "    # This refers to the model's generate method\n",
    "    prediction = generate_response(input_text)\n",
    "    \n",
    "    # Store the prediction and the reference (target)\n",
    "    predictions.append(prediction)\n",
    "    references.append(target_text)\n",
    "\n",
    "# Evaluate exact match accuracy\n",
    "evaluate_accuracy(predictions, references)\n",
    "\n",
    "# Evaluate token-level precision, recall, and F1\n",
    "evaluate_metrics(predictions, references)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a62de56-bec3-4a38-b9f5-00a4d1222496",
   "metadata": {},
   "source": [
    "These metrics evaluate how many words in the generated response match the reference. Since conversational responses can vary widely in phrasing, it's normal for the scores to be low, especially when measuring them at the token level. For the precision, 0.5358, suggests that some words in the generated responses are correct, however, F1 are much lower, indicating that many words in the reference responses are missing from the generated ones.\n",
    "\n",
    "By looking this up, better Evaluation is using BLEU Score (Bilingual Evaluation Understudy) and ROUGE Scores (Recall-Oriented Understudy for Gisting Evaluation).\n",
    "\n",
    "BLEU Score is a metric that evaluates the quality of generated text by comparing it to a set of reference responses. This is done by calculating on how many sequences of words in the generated text match those in the reference text.\n",
    "\n",
    "For ROUGE Score, it measures the overlap between the generated text and reference text. It’s often used in summarization tasks but works well in dialogue generation as well. Also, ROUGE-L is commonly used for measuring the longest common subsequence between the generated and reference responses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "75f09431-0401-4583-9065-f4e121d23522",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Collecting absl-py\n",
      "  Using cached absl_py-2.1.0-py3-none-any.whl.metadata (2.3 kB)\n",
      "Using cached absl_py-2.1.0-py3-none-any.whl (133 kB)\n",
      "Installing collected packages: absl-py\n",
      "Successfully installed absl-py-2.1.0\n",
      "Collecting rouge-score\n",
      "  Downloading rouge_score-0.1.2.tar.gz (17 kB)\n",
      "  Preparing metadata (setup.py): started\n",
      "  Preparing metadata (setup.py): finished with status 'done'\n",
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      "Building wheels for collected packages: rouge-score\n",
      "  Building wheel for rouge-score (setup.py): started\n",
      "  Building wheel for rouge-score (setup.py): finished with status 'done'\n",
      "  Created wheel for rouge-score: filename=rouge_score-0.1.2-py3-none-any.whl size=24971 sha256=3d97ecab34079d4c8b6c57cee7c417ee551d7b19a0deb38ab2cfcb58cb920e1a\n",
      "  Stored in directory: c:\\users\\saad\\appdata\\local\\pip\\cache\\wheels\\85\\9d\\af\\01feefbe7d55ef5468796f0c68225b6788e85d9d0a281e7a70\n",
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      "Installing collected packages: rouge-score\n",
      "Successfully installed rouge-score-0.1.2\n"
     ]
    }
   ],
   "source": [
    "!pip install datasets\n",
    "!pip install absl-py\n",
    "!pip install rouge-score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "c939f075-4f07-40ff-b13a-dacadddbd08e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aec33948-f246-4e86-a80f-b4fed874b88a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to evaluate BLEU and ROUGE scores\n",
    "def evaluate_bleu_rouge(predictions, references):\n",
    "    tokenized_preds = [pred.split() for pred in predictions]\n",
    "    tokenized_refs = [[ref.split()] for ref in references]  # BLEU expects list of lists\n",
    "    \n",
    "    # Compute BLEU\n",
    "    bleu_score = bleu.compute(predictions=tokenized_preds, references=tokenized_refs)\n",
    "    print(f\"BLEU Score: {bleu_score['bleu']:.4f}\")\n",
    "    \n",
    "    # Compute ROUGE\n",
    "    rouge_score = rouge.compute(predictions=predictions, references=references)\n",
    "    rouge_l_precision = rouge_score['rougeL'].mid.precision\n",
    "    rouge_l_recall = rouge_score['rougeL'].mid.recall\n",
    "    rouge_l_f1 = rouge_score['rougeL'].mid.fmeasure\n",
    "    \n",
    "    print(f\"ROUGE-L Precision: {rouge_l_precision:.4f}\")\n",
    "    print(f\"ROUGE-L Recall: {rouge_l_recall:.4f}\")\n",
    "    print(f\"ROUGE-L F1-Score: {rouge_l_f1:.4f}\")\n",
    "\n",
    "# Generate model predictions for the validation set\n",
    "predictions = []\n",
    "references = []\n",
    "\n",
    "for idx, row in val_data.iterrows():\n",
    "    input_text = row['cleaned_input']  # Input from validation set\n",
    "    reference_text = row['cleaned_response']  # True reference response\n",
    "    \n",
    "    # Generate a prediction using the model\n",
    "    prediction = generate_response(input_text)\n",
    "    \n",
    "    # Append prediction and reference\n",
    "    predictions.append(prediction)\n",
    "    references.append(reference_text)\n",
    "\n",
    "# Evaluate the model's performance\n",
    "evaluate_bleu_rouge(predictions, references)"
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