{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style='text-align: center;'>\n",
    "    <img src='https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSQzJzIHdangJTrH2mFXFgsLjuLCjpfXXwbxg&usqp=CAU' width='100'/>\n",
    "    <h1>Sharif University of Technology</h1>\n",
    "    <h2>Natural Language Processing</h2>\n",
    "    <h3>Final Project</h3>\n",
    "    <h4>Spoiler classification and summary generation</h4>\n",
    "    <p><strong>Authors:</strong> Ali Nikkhah, Ramtin Khoshnevis, Sarina Zahedi</p>\n",
    "    <p><strong>(Equal Contribution)</strong></p>\n",
    "</div>\n",
    "<hr/>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T01:08:16.919150Z",
     "iopub.status.busy": "2024-08-16T01:08:16.918649Z",
     "iopub.status.idle": "2024-08-16T01:08:19.653111Z",
     "shell.execute_reply": "2024-08-16T01:08:19.651514Z",
     "shell.execute_reply.started": "2024-08-16T01:08:16.919108Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /usr/share/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from collections import Counter\n",
    "import string\n",
    "\n",
    "# Download NLTK stopwords if not already downloaded\n",
    "nltk.download('stopwords')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------------------------------------------------------------------------------------------------------------------------------------\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **1. Load the Dataset**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T01:08:19.656416Z",
     "iopub.status.busy": "2024-08-16T01:08:19.655928Z",
     "iopub.status.idle": "2024-08-16T01:08:44.624368Z",
     "shell.execute_reply": "2024-08-16T01:08:44.622674Z",
     "shell.execute_reply.started": "2024-08-16T01:08:19.656378Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import json\n",
    "import torch\n",
    "\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "# Load the dataset\n",
    "file_path = '/kaggle/input/imdb-spoiler-dataset/IMDB_reviews.json'\n",
    "data = []\n",
    "with open(file_path, 'r') as file:\n",
    "    for line in file:\n",
    "        data.append(json.loads(line))\n",
    "\n",
    "df = pd.DataFrame(data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **2. Exploratory Data Analysis (EDA)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T01:08:44.626964Z",
     "iopub.status.busy": "2024-08-16T01:08:44.626261Z",
     "iopub.status.idle": "2024-08-16T01:08:45.316125Z",
     "shell.execute_reply": "2024-08-16T01:08:45.314693Z",
     "shell.execute_reply.started": "2024-08-16T01:08:44.626923Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 573913 entries, 0 to 573912\n",
      "Data columns (total 7 columns):\n",
      " #   Column          Non-Null Count   Dtype \n",
      "---  ------          --------------   ----- \n",
      " 0   review_date     573913 non-null  object\n",
      " 1   movie_id        573913 non-null  object\n",
      " 2   user_id         573913 non-null  object\n",
      " 3   is_spoiler      573913 non-null  bool  \n",
      " 4   review_text     573913 non-null  object\n",
      " 5   rating          573913 non-null  object\n",
      " 6   review_summary  573913 non-null  object\n",
      "dtypes: bool(1), object(6)\n",
      "memory usage: 26.8+ MB\n"
     ]
    }
   ],
   "source": [
    "from tabulate import tabulate\n",
    "import numpy as np\n",
    "\n",
    "# Basic info\n",
    "info = df.info()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T01:08:45.319604Z",
     "iopub.status.busy": "2024-08-16T01:08:45.319129Z",
     "iopub.status.idle": "2024-08-16T01:08:51.515072Z",
     "shell.execute_reply": "2024-08-16T01:08:51.513329Z",
     "shell.execute_reply.started": "2024-08-16T01:08:45.319552Z"
    }
   },
   "outputs": [],
   "source": [
    "# Describe the dataset\n",
    "description = df.describe()\n",
    "\n",
    "\n",
    "# Check for missing values\n",
    "missing_values = df.isnull().sum()\n",
    "\n",
    "\n",
    "# Distribution of spoiler vs. non-spoiler\n",
    "spoiler_distribution = df['is_spoiler'].value_counts(normalize=True)\n",
    "\n",
    "# Length of reviews\n",
    "df['review_length'] = df['review_text'].apply(len)\n",
    "review_length_description = df['review_length'].describe()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T01:08:51.517647Z",
     "iopub.status.busy": "2024-08-16T01:08:51.516981Z",
     "iopub.status.idle": "2024-08-16T01:08:51.530667Z",
     "shell.execute_reply": "2024-08-16T01:08:51.529217Z",
     "shell.execute_reply.started": "2024-08-16T01:08:51.517576Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Missing Values:\n",
      "+----------------+----------------+\n",
      "|     Column     | Missing Values |\n",
      "+----------------+----------------+\n",
      "|  review_date   |       0        |\n",
      "|    movie_id    |       0        |\n",
      "|    user_id     |       0        |\n",
      "|   is_spoiler   |       0        |\n",
      "|  review_text   |       0        |\n",
      "|     rating     |       0        |\n",
      "| review_summary |       0        |\n",
      "+----------------+----------------+\n",
      "\n",
      "Spoiler vs. Non-Spoiler Distribution:\n",
      "+------------+---------------------+\n",
      "| Is Spoiler |     Proportion      |\n",
      "+------------+---------------------+\n",
      "|   False    | 0.7370263437141169  |\n",
      "|    True    | 0.26297365628588304 |\n",
      "+------------+---------------------+\n",
      "\n",
      "Review Length Description:\n",
      "+-----------+--------------------+\n",
      "| Statistic |       Value        |\n",
      "+-----------+--------------------+\n",
      "|   count   |      573913.0      |\n",
      "|   mean    | 1460.5535246631457 |\n",
      "|    std    | 1125.577018615146  |\n",
      "|    min    |        18.0        |\n",
      "|    25%    |       719.0        |\n",
      "|    50%    |       1052.0       |\n",
      "|    75%    |       1815.0       |\n",
      "|    max    |      14963.0       |\n",
      "+-----------+--------------------+\n"
     ]
    }
   ],
   "source": [
    "# Display the results\n",
    "\n",
    "print(\"\\nMissing Values:\")\n",
    "print(tabulate(missing_values.items(), headers=[\"Column\", \"Missing Values\"], tablefmt=\"pretty\"))\n",
    "\n",
    "print(\"\\nSpoiler vs. Non-Spoiler Distribution:\")\n",
    "print(tabulate(spoiler_distribution.items(), headers=[\"Is Spoiler\", \"Proportion\"], tablefmt=\"pretty\"))\n",
    "\n",
    "print(\"\\nReview Length Description:\")\n",
    "print(tabulate(review_length_description.items(), headers=[\"Statistic\", \"Value\"], tablefmt=\"pretty\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T01:08:51.533626Z",
     "iopub.status.busy": "2024-08-16T01:08:51.532623Z",
     "iopub.status.idle": "2024-08-16T01:08:55.001037Z",
     "shell.execute_reply": "2024-08-16T01:08:54.999471Z",
     "shell.execute_reply.started": "2024-08-16T01:08:51.533561Z"
    }
   },
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 4000x2000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Plot some samples of the dataset\n",
    "sample_size = 2\n",
    "\n",
    "spoiler_samples = df[df['is_spoiler'] == True].sample(sample_size)\n",
    "non_spoiler_samples = df[df['is_spoiler'] == False].sample(sample_size)\n",
    "\n",
    "plt.figure(figsize=(40, 20))\n",
    "\n",
    "# Spoiler samples\n",
    "for i, review in enumerate(spoiler_samples['review_text']):\n",
    "    plt.text(0.5, 1.0 - i*0.2, f\"Spoiler Review {i+1}: {review[:150]}...\", ha='center', va='top', wrap=True)\n",
    "\n",
    "# Non-Spoiler samples\n",
    "for i, review in enumerate(non_spoiler_samples['review_text']):\n",
    "    plt.text(0.5, 0.5 - i*0.2, f\"Non-Spoiler Review {i+1}: {review[:150]}...\", ha='center', va='top', wrap=True)\n",
    "\n",
    "plt.axis('off')\n",
    "plt.title('Sample Reviews (Spoiler vs Non-Spoiler)')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T01:08:55.003751Z",
     "iopub.status.busy": "2024-08-16T01:08:55.003226Z",
     "iopub.status.idle": "2024-08-16T01:08:59.079200Z",
     "shell.execute_reply": "2024-08-16T01:08:59.078034Z",
     "shell.execute_reply.started": "2024-08-16T01:08:55.003702Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ysuvaGgCqrYJl2LZt21aodvHiRaNRo0ZGo0aNjAsXLhiGYRi//fab8cgjjxjBwcGGm5ubcf311xt33323sWjRokKPT0pKMiQZkoyNGzcW+9y2S5utXbvWiImJMXx9fQ1PT0+jUaNGxpAhQ4zt27dbjdu3b58hyYiIiLDaPnnyZEOSMX78+BK9BwVLjRV1a9SokWXczp07jfvuu8/w9/c3PDw8jLCwMOPBBx80Vq9ebRlTsCxYwfJkV3qtZ86cMUaMGGHUrVvXqF27ttG3b18jMTHRkGS8+eabVo9//fXXjeuvv95wcXGx2o8kY8SIEYVek+1SZ1eSlZVl1KxZ05Bk/Pvf/y5Unzx5snH77bcbfn5+Rs2aNY2mTZsab7zxhpGXl1fo9X322WdXfK7ff//dmDBhgtG+fXsjMDDQcHV1NQICAoxevXoZa9assRpbsDTfb7/9ZnTr1s2oVauWERQUZEycONG4ePGi1djs7Gxj9OjRRkhIiOHm5maEh4cbb7/9ttUybkW9L7ZLwBUoy/caQPkyGUYFnM0BAHAqu3btUuvWrfXvf/9bgwcPtnc7djVkyBAtWrSoyGkPAKoP5iQDQDVz7ty5Qtvee+89ubi4lMsJhwBQFTAnGQCqmWnTpmnHjh2666675Orqqu+//17ff/+9hg8frtDQUHu3BwAOgZAMANXMHXfcofj4eL3++uvKyclR/fr1FRsbq1deecXerQGAw2BOMgAAAGCDOckAAACADUIyAAAAYIM5yeXEbDbrxIkT8vb2rtBLuQIAAODaGIah7OxshYSEXPVCU4TkcnLixAnOCgcAAHACx48f1w033HDFMYTkcuLt7S3p0pvu4+Nj524AAABgKysrS6GhoZbcdiWE5HJSMMXCx8eHkAwAAODASjI1lhP3AAAAABuEZAAAAMAGIRkAAACwQUgGAAAAbBCSAQAAABuEZAAAAMAGIRkAAACwQUgGAAAAbBCSAQAAABuEZAAAAMAGIRkAAACwQUgGAAAAbBCSAQAAABuEZMDJHDhwQNOmTdOBAwfs3QoAAFUWIRlwInl5eVq6dKkyMjK0dOlS5eXl2bslAACqJEIy4EQSEhKUnZ0tScrOzlZCQoKdOwIAoGoiJANOIj09XQkJCTIMQ5JkGIYSEhKUnp5u584AAKh6CMmAEzAMQ3FxcUXW4uLiLMEZAACUD0Iy4ATS0tKUlJQks9lstd1sNispKUlpaWl26gwAgKqJkAw4gYCAAIWHh8vFxfpH1sXFReHh4QoICLBTZwAAVE2EZMAJmEwm9e7du8ha7969ZTKZKrkjAACqNkIy4CT8/f0VHR1tCcQmk0nR0dHy9/e3c2cAAFQ9hGTAiURHR8vb21uS5O3trejoaDt3BABA1URIBpyIu7u7+vTpIz8/P/Xp00fu7u72bgkAgCrJ1d4NACidiIgIRURE2LsNAACqNI4kAwAAADYIyQAAAIANQjIAAABgg5AMAAAA2CAkAwAAADYIyQAAAIANQjIAALCrAwcOaNq0aTpw4IC9WwEsCMkAAMBu8vLytHTpUmVkZGjp0qXKy8uzd0uAJEIyAACwo4SEBGVnZ0uSsrOzlZCQYOeOgEsIyQAAwC7S09OVkJAgwzAkSYZhKCEhQenp6XbuDCAkAwAAOzAMQ3FxcUXW4uLiLMEZsBdCMgAAqHRpaWlKSkqS2Wy22m42m5WUlKS0tDQ7dQZcQkgGAACVLiAgQOHh4XJxsY4iLi4uCg8PV0BAgJ06Ay4hJAMAgEpnMpnUu3fvImu9e/eWyWSq5I4Aa4RkAABgF/7+/urYsaPVto4dO8rf399OHQH/Q0gGAAAAbNg1JK9fv1733HOPQkJCZDKZtGTJkmLHPvnkkzKZTHrvvfestp86dUqDBw+Wj4+P/Pz8NHToUOXk5FiN2bNnjzp16iRPT0+FhoZq2rRphfb/zTffqGnTpvL09FSLFi303XfflcdLBAAAxUhPT9fGjRuttm3cuJEl4OAQ7BqSz5w5o5YtW2rWrFlXHLd48WL99NNPCgkJKVQbPHiw9u3bp/j4eC1btkzr16/X8OHDLfWsrCx169ZNYWFh2rFjh95++23FxsZq7ty5ljGbNm3SwIEDNXToUO3cuVN9+/ZV3759tXfv3vJ7sQAAwIIl4ODoTIaD/Cs0mUxavHix+vbta7X9zz//VLt27fTDDz+oV69eGjVqlEaNGiXp0rXemzVrpm3btqlt27aSpBUrVqhnz576448/FBISotmzZ+uVV15RcnKy3N3dJUkvvfSSlixZooMHD0qS+vfvrzNnzmjZsmWW523fvr1atWqlOXPmlKj/rKws+fr6KjMzUz4+PmV8NwAAqNpSU1MLfTp8uVGjRikwMLDyGkK1UJq85tBzks1msx5++GGNHTtWN998c6H65s2b5efnZwnIktS1a1e5uLhoy5YtljFRUVGWgCxJMTExSkxM1OnTpy1junbtarXvmJgYbd68udjecnNzlZWVZXUDAAAlwxJwcHQOHZLfeustubq66plnnimynpycXOh/ma6urqpbt66Sk5MtY4KCgqzGFNy/2piCelGmTp0qX19fyy00NLR0Lw4AgGqMJeDg6Bw2JO/YsUMzZ87UvHnzHPIHZdy4ccrMzLTcjh8/bu+WAABwKv7+/oqOjrb8nTeZTIqOjmYJODgEhw3JGzZsUGpqqurXry9XV1e5urrq6NGjeu6559SgQQNJUnBwsFJTU60ed+HCBZ06dUrBwcGWMSkpKVZjCu5fbUxBvSgeHh7y8fGxugEAgNKJjo6Wt7e3JMnb21vR0dF27gi4xGFD8sMPP6w9e/Zo165dlltISIjGjh2rH374QZIUGRmpjIwM7dixw/K4NWvWyGw2q127dpYx69evV35+vmVMfHy8mjRpojp16ljGrF692ur54+PjFRkZWdEvEwCAas3d3V19+vSRn5+f+vTpY3UOEWBPrvZ88pycHB06dMhy//Dhw9q1a5fq1q2r+vXrF/q4xc3NTcHBwWrSpIkkKSIiQt27d9ewYcM0Z84c5efna+TIkRowYIBlubhBgwZp0qRJGjp0qF588UXt3btXM2fO1IwZMyz7ffbZZxUdHa3p06erV69e+uqrr7R9+3arZeIAAEDFiIiIUEREhL3bAKzY9Ujy9u3b1bp1a7Vu3VqSNGbMGLVu3VoTJkwo8T7mz5+vpk2bqkuXLurZs6c6duxoFW59fX21cuVKHT58WG3atNFzzz2nCRMmWK2lfMcdd2jBggWaO3euWrZsqUWLFmnJkiVq3rx5+b1YAAAAOA2HWSfZ2bFOMgAAgGOrMuskAwAAAPZASAYAAABsEJIBAAAAG4RkAAAAwAYhGQAAALBBSAYAAABsEJIBAAAAG4RkAAAAwAYhGQAAALBBSAYAAABsEJIBAAAAG4RkAAAAwAYhGQAAALBBSAYAAABsEJIBAAAAG4RkAAAAwAYhGQAAALBBSAYAAABsEJIBAAAAG4RkAAAAwAYhGQAAALBBSAYAAABsEJIBAAAAG4RkAAAAwAYhGQAAALBBSAYAAABsEJIBJ3PgwAFNmzZNBw4csHcrAABUWYRkwInk5eVp6dKlysjI0NKlS5WXl2fvlgAAqJIIyYATSUhIUHZ2tiQpOztbCQkJdu4IAICqiZAMOIn09HQlJCTIMAxJkmEYSkhIUHp6up07AwCg6iEkA07AMAzFxcUVWYuLi7MEZwBwRpxrAUdESAacQFpampKSkmQ2m622m81mJSUlKS0tzU6dAUDZ5OXladGiRcrIyNCiRYs41wIOg5AMOIGAgACFh4fLxcX6R9bFxUXh4eEKCAiwU2cAUDZr1qzRuXPnJEnnzp3TmjVr7NwRcAkhGXACJpNJvXv3LrLWu3dvmUymSu4IAMouPT1dGzZssNq2YcMGzrWAQyAkA07C399f0dHRlkBsMpkUHR0tf39/O3cGAKVnGIYWLVpU6JyK4rYDlY2QDDiR6OhoeXt7S5K8vb0VHR1t544A4Nqkpqbq6NGjRdaOHj2q1NTUSu4IsEZIBpyIu7u7+vTpIz8/P/Xp00fu7u72bgkAgCrJ1d4NACidiIgIRURE2LsNACiTwMBAhYWFFXk0OSwsTIGBgXboCvgfjiQDAIBKZzKZ1K9fvyJr/fr144Rk2B0hGQAA2IW/v7+ioqKstkVFRXFCMhwCIRkAANhN586dVbNmTUlSzZo11blzZzt3BFxCSAYAAHbj7u6ufv36yc/PT/369eOEZDgMTtwDAAB2xQnJcEQcSQYAAABsEJIBAAAAG3YNyevXr9c999yjkJAQmUwmLVmyxFLLz8/Xiy++qBYtWsjLy0shISF65JFHdOLECat9nDp1SoMHD5aPj4/8/Pw0dOhQ5eTkWI3Zs2ePOnXqJE9PT4WGhmratGmFevnmm2/UtGlTeXp6qkWLFvruu+8q5DUDAADA8dk1JJ85c0YtW7bUrFmzCtXOnj2rn3/+WePHj9fPP/+s//73v0pMTFTv3r2txg0ePFj79u1TfHy8li1bpvXr12v48OGWelZWlrp166awsDDt2LFDb7/9tmJjYzV37lzLmE2bNmngwIEaOnSodu7cqb59+6pv377au3dvxb14AAAgSTpw4ICmTZumAwcO2LsVwMJkGIZh7yakS4uKL168WH379i12zLZt23T77bfr6NGjql+/vg4cOKBmzZpp27Ztatu2rSRpxYoV6tmzp/744w+FhIRo9uzZeuWVV5ScnGw5Y/all17SkiVLdPDgQUlS//79debMGS1btszyXO3bt1erVq00Z86cEvWflZUlX19fZWZmysfH5xrfBQAAqpe8vDy99dZbOnfunGrWrKkXX3yRFS5QYUqT15xqTnJmZqZMJpP8/PwkSZs3b5afn58lIEtS165d5eLioi1btljGREVFWf3AxcTEKDExUadPn7aM6dq1q9VzxcTEaPPmzcX2kpubq6ysLKsbUBk44gKgKlmzZo3OnTsnSTp37pzWrFlj546AS5wmJJ8/f14vvviiBg4caEn+ycnJha7t7urqqrp16yo5OdkyJigoyGpMwf2rjSmoF2Xq1Kny9fW13EJDQ8v2AoESyMvL09KlS5WRkaGlS5cqLy/P3i0BwDVLT0/X+vXrrbatX79e6enpduoI+B+nCMn5+fl68MEHZRiGZs+ebe92JEnjxo1TZmam5Xb8+HF7t4RqICEhQdnZ2ZKk7OxsJSQk2LkjALg2hmFo0aJFRdYWLVokB5kNimrM4UNyQUA+evSo4uPjreaPBAcHKzU11Wr8hQsXdOrUKQUHB1vGpKSkWI0puH+1MQX1onh4eMjHx8fqBlSk9PR0JSQkWP5wGIahhIQEjrgAcEqpqak6evRokbWjR48W+vsOVDaHDskFATkpKUmrVq2Sv7+/VT0yMlIZGRnasWOHZduaNWtkNpvVrl07y5j169crPz/fMiY+Pl5NmjRRnTp1LGNWr15tte/4+HhFRkZW1EsDSsUwDMXFxRVZi4uL44gLAADlzK4hOScnR7t27dKuXbskSYcPH9auXbt07Ngx5efnq1+/ftq+fbvmz5+vixcvKjk5WcnJyZZ5mBEREerevbuGDRumrVu36scff9TIkSM1YMAAhYSESJIGDRokd3d3DR06VPv27dPChQs1c+ZMjRkzxtLHs88+qxUrVmj69Ok6ePCgYmNjtX37do0cObLS3xOgKGlpaUpKSpLZbLbabjablZSUpLS0NDt1BgDXJjAwUGFhYUXWGjRoUOicI6Cy2TUkb9++Xa1bt1br1q0lSWPGjFHr1q01YcIE/fnnn4qLi9Mff/yhVq1a6brrrrPcNm3aZNnH/Pnz1bRpU3Xp0kU9e/ZUx44drdZA9vX11cqVK3X48GG1adNGzz33nCZMmGC1lvIdd9yhBQsWaO7cuWrZsqUWLVqkJUuWqHnz5pX3ZgBXEBAQoPDwcLm4WP/Iuri4KDw8XAEBAXbqDACujclkUpcuXYqsde7cWSaTqZI7Aqw5zDrJzo51klHR0tPTNWPGDKujyS4uLho9enShqUgA4OgMw9C8efN06NChQlPGwsPDNWTIEIIyyl2VXScZqM78/f0VHR1t+aNhMpkUHR1NQAbglAqmkRV1rI5pZHAEhGTAiURHR8vb21uS5O3trejoaDt3BADXhmlkcHSEZMCJuLu7q0+fPvLz81OfPn24dCsAp2UymdS7d+8ia71792aqBeyOkAwAAOyCaWRwZIRkwIlwWWoAVQ3TyOCoCMmAE+Gy1ACqGqaRwVG52rsBACVT3GWpb731Vj6aBODUIiIiFBERYe82ACscSQacQMFlqW2XSipuOwAAKBtCMuAEiltP1DAM1hMFAKACEJIBJxAQEKCwsLAia2FhYawnCgBAOSMkAwAAuzpw4ICmTZumAwcO2LsVwIKQDDiBtLQ0HT16tMja0aNHmW4BwGmxtCUcFSEZcAIFl2+1vQIVl28F4OxY2hKOipAMOIGCy7cWdZlWLt8KwFkVt7Rlenq6nTsDCMmA0+DyrQCqkoIlLIvC0pZwBIRkwIlw+VYAVUXB0pZms9lqu9lsZmlLOARCMuBEuHwrgKqi4FwLFxfrKMK5FnAUXJYacDJcvhVAVVBwrsWMGTMK1TjXAo6AI8kAAMAu/P391bFjR6ttHTt25FwLOARCMgAAAGCDkAwAAOwiPT1dGzdutNq2ceNGloCDQyAkAwCASscScHB0hGQAAFDpWAIOjo6QDAAAKh1LwMHREZIBAEClK1gCrigsAQdHQEgGAAB24e/vr+joaEsgNplMio6OZgk4OARCMgAAsJvo6Gh5enpKkjw9PRUdHW3njoBLCMkAAACADUIyAACwm4SEBJ07d06SdO7cOSUkJNi5I+ASQjIAALCL9PT0QqE4ISGBi4nAIRCSAQBApSu4mIjtRUPMZjMXE4FDICQDAIBKV3AxkaLCMBcTgSMgJAMAgEpXr1491apVq8harVq1VK9evUruCLBGSAYAAJXu5MmTOnv2bJG1s2fP6uTJk5XcEWCNkAwAACpdwWWpi8JlqeEICMkAAKDSFVyW2vby08VtByobIRlwMitXrtQrr7yilStX2rsVACgTf39/1a9f32pb/fr1uSw1HAIhGXAiOTk5WrdunQzD0Lp165STk2PvlgDgmqWnp+vYsWNW244dO8Y6yXAIhGTAifz73/++4n0AcBbFrZNc3HagshGSASdx6NChIo+4HDp0yE4dAcC1K1gnuSiskwxHQEgGnIDZbNaCBQuKrC1YsEBms7mSOwKAsgkICFBISEiRtZCQEFa3gN0RkgEnkJiYqPPnzxdZO3/+vBITEyu5IwAoG8MwdOrUqSJrp06dYroF7I6QDDiBOnXqlKkOAI7m119/veJ//n/99ddK7giwRkgGnEBQUNAVP5YMCgqq5I4AoGwaN24sT0/PImuenp5q3LhxJXcEWCMkA07AZDJp4MCBRdYGDhzIovsAnI7JZFLdunWLrNWtW5ffa7A7u4bk9evX65577lFISIhMJpOWLFliVTcMQxMmTNB1112nmjVrqmvXroXOhD116pQGDx4sHx8f+fn5aejQoYXWjt2zZ486deokT09PhYaGatq0aYV6+eabb9S0aVN5enqqRYsW+u6778r99QJl4e/vrw4dOlht69ChA4vuA3BKaWlpOnHiRJG1EydOsLoF7M6uIfnMmTNq2bKlZs2aVWR92rRpev/99zVnzhxt2bJFXl5eiomJsZrDNHjwYO3bt0/x8fFatmyZ1q9fr+HDh1vqWVlZ6tatm8LCwrRjxw69/fbbio2N1dy5cy1jNm3apIEDB2ro0KHauXOn+vbtq759+2rv3r0V9+KBa/C3v/1N7u7ukiR3d3f97W9/s3NHAHBtAgICFBYWVmStQYMGrG4BuzMZDnL6qMlk0uLFi9W3b19Jl44ih4SE6LnnntPzzz8vScrMzFRQUJDmzZunAQMG6MCBA2rWrJm2bdumtm3bSpJWrFihnj176o8//lBISIhmz56tV155RcnJyZZw8dJLL2nJkiU6ePCgJKl///46c+aMli1bZumnffv2atWqlebMmVOi/rOysuTr66vMzEz5+PiU19sCFHLgwAF9++23uueeexQREWHvdgDgmhiGoVmzZhV5NDkkJEQjRoxgygXKXWnymsPOST58+LCSk5PVtWtXyzZfX1+1a9dOmzdvliRt3rxZfn5+loAsSV27dpWLi4u2bNliGRMVFWUJyJIUExOjxMREnT592jLm8ucpGFPwPEXJzc1VVlaW1Q0AAJRMamrqFadbpKamVnJHgDWHDcnJycmSVOis/aCgIEstOTlZgYGBVnVXV1fVrVvXakxR+7j8OYobU1AvytSpU+Xr62u5hYaGlvYlAqWWl5enpUuXKiMjQ0uXLlVeXp69WwKAa3K1D7Id5INuVGMOG5Id3bhx45SZmWm5HT9+3N4toRpISEhQdna2JCk7O1sJCQl27ggAgKrJYUNycHCwJCklJcVqe0pKiqUWHBxc6OOYCxcu6NSpU1ZjitrH5c9R3JiCelE8PDzk4+NjdQMqUnp6uhISEixHVwzDUEJCgtLT0+3cGQAAVY/DhuSGDRsqODhYq1evtmzLysrSli1bFBkZKUmKjIxURkaGduzYYRmzZs0amc1mtWvXzjJm/fr1ys/Pt4yJj49XkyZNLFcpi4yMtHqegjEFzwPYm2EYiouLK7IWFxfHx5IAAJQzu4bknJwc7dq1S7t27ZJ06WS9Xbt26dixYzKZTBo1apQmT56suLg4/fLLL3rkkUcUEhJiWQEjIiJC3bt317Bhw7R161b9+OOPGjlypAYMGGC5OtmgQYPk7u6uoUOHat++fVq4cKFmzpypMWPGWPp49tlntWLFCk2fPl0HDx5UbGystm/frpEjR1b2WwIUKS0tTUlJSTKbzVbbzWazkpKSWE8UgNO52soVrGwBe7NrSN6+fbtat26t1q1bS5LGjBmj1q1ba8KECZKkF154QU8//bSGDx+u2267TTk5OVqxYoXVZSznz5+vpk2bqkuXLurZs6c6duxotQayr6+vVq5cqcOHD6tNmzZ67rnnNGHCBKu1lO+44w4tWLBAc+fOVcuWLbVo0SItWbJEzZs3r6R3AriygIAAhYeHy8XF+kfWxcVF4eHhrCcKwOkEBgYWOmm+QFBQUKET84HK5jDrJDs71klGRUtPT9eMGTOsjia7uLho9OjRXHUPgNMxm816/fXXlZubW6jm4eGh8ePHFzowAJRVlVgnGYA1f39/RUdHW22Ljo4mIANwSomJiUUGZOnStQgSExMruSPAGiEZcCKRkZGWeXomk4mTSwE4LT8/vzLVgYpGSAacyObNm62WgLvSVSEBwJFdbSoFUy1gb/wLBJxEwTrJl2OdZADOKjAwUGFhYUXWGjRowIl7sDtCMuAEWCcZQFVjMpnUr1+/Imv3338/S8DB7gjJgBNgnWQAVZG/v7/q169vta1+/fqckAyHQEgGnEDBOslFYZ1kAM4qPT1dx48ft9p2/PhxppHBIRCSASdgMpnUqVOnImtRUVF8LAnA6TCNDI6OkAw4AcMwtHr16iJrq1at4o8JAKdTMI3M9veXYRhMI4NDICQDTiA1NVVHjx4tsnb06FGlpqZWckcAUDYBAQHFrm4RFhbGNDLYHSEZAADYRX5+fqm2A5WJkAw4gYCAAHl6ehZZ8/T05IgLAKeTmpqqEydOFFk7ceIEn5DB7gjJgBM4efKkzp8/X2Tt/PnzOnnyZCV3BABA1UZIBpxAvXr1VKtWrSJrtWrVUr169Sq5IwAoG664B0dHSAacwMmTJ3X27Nkia2fPnuVIMgCnU3DFPdslLE0mE1fcg0NwvZYHrV69WqtXr1ZqamqhK4B9+umn5dIYgP8pOAu8qBUuOAscgLPy9/eXt7e3srKyLNu8vb254h4cQqmPJE+aNEndunXT6tWrdfLkSZ0+fdrqBgAAUBKHDh2yCsiSlJWVpUOHDtmpI+B/Sn0kec6cOZo3b54efvjhiugHQBHS0tKuuE5yWloa8/cAOBWz2ayvvvqqyNpXX32ll19+WS4uzAqF/ZT6X19eXp7uuOOOiugFQDH8/f2LnZ9nMpn4aBKA0/n111+veK7Fr7/+WskdAdZKHZKfeOIJLViwoCJ6AVCMxMTEYi89bRiGEhMTK7kjACibxo0bX3HVnsaNG1dyR4C1Ek23GDNmjOVrs9msuXPnatWqVbrlllvk5uZmNfbdd98t3w4BFDpBtrR1AHA0Li4uGjBgQJEn/A8cOJCpFrC7EoXknTt3Wt1v1aqVJGnv3r3l3hCAwq72x4I/JgCcUZ06dYrc7ufnV7mNAEUoUUheu3ZtRfcB4AqaNm0qV1dXXbhwoVDN1dVVTZs2tUNXAHDtDMNQXFxckbW4uDgNGTKEtZJhV6U+/PT4448rOzu70PYzZ87o8ccfL5emABRW3NFijiIDcEZpaWlKSkoqspaUlKS0tLRK7giwVuq/rp9//rnOnTtXaPu5c+f0xRdflEtTAKwlJiYqLy+vyFpeXh4n7gFwOgEBAQoJCSmyFhISwkWSYHclXic5KytLhmHIMAxlZ2fL09PTUrt48aK+++471mkFKkhx8/ZKWgcAR2MYhk6ePFlk7eTJkzIMg+kWsKsSh2Q/Pz+ZTCaZTKYil2UxmUyaNGlSuTYH4JKgoCAFBQUpJSWl2BoAOJODBw9e8ROygwcPqlmzZpXcFfA/JQ7Ja9eulWEY6ty5s/7zn/+obt26lpq7u7vCwsKK/dgEAADgcsWt/V7SOlDRShySo6OjJUmHDx9W/fr1+QgEqEQpKSlFHkW+vBYcHFzJXQHAtbvalUK5kijsrcQhuUBmZqZ++eWXQttNJpM8PT1Vv359eXh4lEtzAC45ffr0VeuEZADOJDAwUB4eHsrNzS1U8/Dw4Dwn2F2pQ3KrVq2ueBTZzc1N/fv310cffWR1ch+Aa9e4cWO5uLgUeWU9FxcXLt8KwOmcPHmyyIAsSbm5uTp58iRBGXZV6iXgFi9erPDwcM2dO1e7du3Srl27NHfuXDVp0kQLFizQv/71L61Zs0avvvpqRfQLVEvp6enFXnrabDYrPT29kjsCgLKpV69esQfTPD09Va9evUruCLBW6iPJb7zxhmbOnKmYmBjLthYtWuiGG27Q+PHjtXXrVnl5eem5557TO++8U67NAtVVQECAwsPDi1x4Pzw8nPVEATidtLQ0nT9/vsja+fPnlZaWxso9sKtSH0n+5ZdfFBYWVmh7WFiYZa5yq1at9Ndff5W9OwCSLs3579SpU5G1qKgoTqQFAKCclTokN23aVG+++abV2ob5+fl688031bRpU0nSn3/+yf/+gHJkGIZWr15dZG3VqlUslQTA6QQEBFxxugWfkMHeSj3dYtasWerdu7duuOEG3XLLLZIuHV2+ePGili1bJkn6/fff9dRTT5Vvp0A1lpqaqqNHjxZZO3r0qFJTU/mPKQCncvLkyStOt+DEPdhbqUPyHXfcocOHD2v+/Pn69ddfJUkPPPCABg0aJG9vb0nSww8/XL5dAgCAKqVevXqqVauWzp49W6hWq1YtTtyD3ZU6JEuSt7e3nnzyyfLuBUAxAgMDFRgYqNTU1EK1oKAgjrYAcDonT54sMiBL0tmzZzmSDLu7ppCclJSktWvXKjU1tdCyVBMmTCiXxgD8j2EYysrKKrKWmZkpwzA4eQ+AUwkICFBYWFiRU8nCwsKYkwy7K3VI/vjjj/WPf/xD9erVU3BwsNUfZpPJREgGKsCvv/56xbl7v/76q+XEWQAAUHalDsmTJ0/WG2+8oRdffLEi+gFQhMaNG8vT07PIoOzp6ckV9wA4nbS0tCuekJyWlsZ0C9hVqZeAO336tB544IGK6AVAMUwmk7y8vIqseXl5MdUCgNPhintwdKUOyQ888IBWrlxZEb0AKEZKSkqxl55OT09XSkpKJXcEAGVTkivuAfZU6ukWN910k8aPH6+ffvpJLVq0kJubm1X9mWeeKbfmAFxSXEC+vB4cHFxJ3QBA2dme+F/aOlDRSh2S586dq9q1ayshIUEJCQlWNZPJREgGKsDVrqjHFfcAOJuMjIyr1q+77rrKaQYoQqlD8uHDhyuiDwBX4OJy5ZlRV6sDgKMJDw8vUx2oaNf8lzUvL0+JiYm6cOFCefZj5eLFixo/frwaNmyomjVrqlGjRnr99detjpoZhqEJEybouuuuU82aNdW1a1clJSVZ7efUqVMaPHiwfHx85Ofnp6FDhyonJ8dqzJ49e9SpUyd5enoqNDRU06ZNq7DXBZTW1VavYHULAM7G9m91aetARSt1SD579qyGDh2qWrVq6eabb9axY8ckSU8//bTefPPNcm3urbfe0uzZs/Xhhx/qwIEDeuuttzRt2jR98MEHljHTpk3T+++/rzlz5mjLli3y8vJSTEyM1ckAgwcP1r59+xQfH69ly5Zp/fr1Gj58uKWelZWlbt26KSwsTDt27NDbb7+t2NhYzZ07t1xfD3CtCi4Bf611AHA0vr6+ZaoDFa3UIXncuHHavXu31q1bZ7V0S9euXbVw4cJybW7Tpk3q06ePevXqpQYNGqhfv37q1q2btm7dKunSUeT33ntPr776qvr06aNbbrlFX3zxhU6cOKElS5ZIkg4cOKAVK1bok08+Ubt27dSxY0d98MEH+uqrr3TixAlJ0vz585WXl6dPP/1UN998swYMGKBnnnlG7777brG95ebmKisry+oGAABK5siRI2WqAxWt1CF5yZIl+vDDD9WxY0ertVlvvvlm/fbbb+Xa3B133KHVq1dbjpLt3r1bGzduVI8ePSRdmh+dnJysrl27Wh7j6+urdu3aafPmzZKkzZs3y8/PT23btrWM6dq1q1xcXLRlyxbLmKioKLm7u1vGxMTEKDExUadPny6yt6lTp8rX19dyCw0NLdfXDlyuadOm8vDwKLLm4eHB1fYAOB0fH58y1YGKVuqQXNwVcM6cOVPuFzR46aWXNGDAADVt2lRubm5q3bq1Ro0apcGDB0uSkpOTJUlBQUFWjwsKCrLUkpOTC/Xr6uqqunXrWo0pah+XP4etcePGKTMz03I7fvx4GV8tUDwXFxfdddddRdY6d+7MiXsAnI6/v3+Z6kBFK/Vf1rZt22r58uWW+wXB+JNPPlFkZGT5dSbp66+/1vz587VgwQL9/PPP+vzzz/XOO+/o888/L9fnuRYeHh7y8fGxugEVxWw2a926dUXW1q5dy3qiAJxOSZaAA+yp1EvATZkyRT169ND+/ft14cIFzZw5U/v379emTZsKrZtcVmPHjrUcTZakFi1a6OjRo5o6daoeffRRy8UTUlJSrNZSTElJUatWrSRJwcHBSk1NtdrvhQsXdOrUKcvjg4ODC12xrOA+F2iAI0hMTLzilakSExMVERFRyV0BwLVr3LixTCZTkeu8m0wmVu2B3ZX6SHLHjh21a9cuXbhwQS1atNDKlSsVGBiozZs3q02bNuXa3NmzZwt9jFyjRg3LUbOGDRsqODhYq1evttSzsrK0ZcsWy1HtyMhIZWRkaMeOHZYxa9askdlsVrt27Sxj1q9fr/z8fMuY+Ph4NWnSRHXq1CnX1wRci6v9O+TfKQBnk56eXuyFkAzDuOqVRoGKdk0TGRs1aqSPP/5YW7du1f79+/Xvf/9bQUFBmjJlSrk2d8899+iNN97Q8uXLdeTIES1evFjvvvuu7r33XkmX/qc5atQoTZ48WXFxcfrll1/0yCOPKCQkRH379pUkRUREqHv37ho2bJi2bt2qH3/8USNHjtSAAQMUEhIiSRo0aJDc3d01dOhQ7du3TwsXLtTMmTM1ZsyYcn09wLUKCgqy/Hu1df311xeaUw8Ajs7f37/Yc5lMJhNzkmF35Xa2z19//aXx48eX1+4kSR988IH69eunp556ShEREXr++ef197//Xa+//rplzAsvvKCnn35aw4cP12233aacnBytWLHCanm6+fPnq2nTpurSpYt69uypjh07Wq2B7Ovrq5UrV+rw4cNq06aNnnvuOU2YMMFqLWXAnkwmk7p3715krXv37uV+0iwAVLTExMQrHklOTEys5I4AayajuH+hpbR7927deuutunjxYnnszulkZWXJ19dXmZmZnMSHcmcYhubNm1fkFajCw8M1ZMgQgjIAp1LwSXRxHnroITVr1qwSO0J1UJq8xrpRgBNIS0sr9hKtSUlJSktLq+SOAKBsGjVqVKY6UNEIyYATqFu3bpnqAOBovv/++zLVgYpW4iXgrnYSG0eygIqzbdu2q9bLe51yAKhIt912m7Zu3XrFOmBPJQ7JO3fuvOqYqKioMjUDoGhhYWFlqgOAo7nuuutUo0aNIs9lcnV1tbr+AWAPJQ7Ja9eurcg+AFzB1U7K46Q9AM7m5MmTxZ7sf+HCBZ08eVKBgYGV3BXwP8xJBpzA6dOny1QHAEcTEBCg8PDwImvh4eEKCAio5I4Aa4RkwAlwxT0AVY3JZFKnTp2KrEVFRfEJGeyOkAw4gastZ15Oy50DQKUxDEPLli0rsvbtt9/yew12R0gGnMDRo0fLVAcAR5OcnKzU1NQia6mpqUpOTq7kjgBrhGTACdSvX79MdQBwNEeOHClTHahopQ7JUVFRmjBhglavXq3z589XRE8AbGRmZpapDgCO5vrrry9THahopQ7J3bp1008//aQ+ffrIz89PHTt21Kuvvqr4+HidPXu2InoEqj1O3ANQ1WzYsKFMdaCilTokv/rqq1q5cqUyMjK0du1a3X333dq+fbt69erFpXGBCpKenl6mOgA4mubNm5epDlS0El9MxNbvv/+uX375Rbt379aePXvk7e3NFfeACpKVlVWmOgA4mqCgoDLVgYpW6pA8aNAgJSQkKDc3V1FRUYqOjtZLL72kW265hTUNgQrSoEGDMtUBwNEEBgbKw8NDubm5hWoeHh5cbQ92V+qQ/NVXX6levXp64okn1LlzZ3Xs2FG1atWqiN4A/D8XlyvPjLpaHQAczcmTJ4sMyJKUm5vLZalhd6X+y5qenq5PPvlEeXl5GjdunOrVq6c77rhDL7/8slauXFkRPQLVHhcTAVDVXO08Js5zgr2VOiTXqVNHvXv31rvvvqsdO3Zoz549aty4sd5++2316NGjInoEqr3Tp0+XqQ4Ajmbbtm1lqgMVrdTTLdLT05WQkKB169Zp3bp12r9/v/z8/HTPPfcoOjq6InoEqj0/P78y1QHA0fj4+JSpDlS0UofkwMBA1atXT506ddKwYcN05513qkWLFhXRG4D/l5GRcdV6SEhI5TQDAOWgSZMmZaoDFa3UIXnPnj26+eabK6IXAMXgYiIAqppDhw5dtd60adNK6gYorNRzkm+++WZduHBBq1at0kcffaTs7GxJ0okTJ5STk1PuDQJgdQsAVQ9LW8LRlfov69GjR9WiRQv16dNHI0aMUFpamiTprbfe0vPPP1/uDQLgLHAAVc/ixYvLVAcqWqlD8rPPPqu2bdvq9OnTqlmzpmX7vffeq9WrV5drcwAuWbt2bZnqAOBoOnToUKY6UNFKPSd5w4YN2rRpk9zd3a22N2jQQH/++We5NQbgf4KDg8tUBwBHUzBd81rrQEUr9ZFks9msixcvFtr+xx9/yNvbu1yaAmDtapd855LwAACUr1KH5G7duum9996z3DeZTMrJydHEiRPVs2fP8uwNwP/LysoqUx0AHA3rJMPRlTokT58+XT/++KOaNWum8+fPa9CgQZapFm+99VZF9AhUe7Vr1y5THQAcDVfcg6Mr9ZzkG264Qbt379ZXX32lPXv2KCcnR0OHDtXgwYOtTuQDUH5Ksp5oy5YtK6kbACi7hg0bavv27VesA/ZU6pAsSa6urnrooYfKuxcAxWjUqJF27NhxxToAOJOUlJQy1YGKVqKQHBcXpx49esjNzU1xcXFXHNu7d+9yaQzA/yQlJV213qpVq8ppBgDKQa1atcpUBypaiUJy3759lZycrMDAQPXt27fYcSaTqciVLwCUjdlsLlMdABxNRkZGmepARStRSL78DzB/jIHKR0gGUNWcOHGiTHWgopV6dYvjx49XRB8AriA9Pb1MdQBwNGfPni1THahopQ7JDRo0UHR0tD7++GOdPn26InoCYKN+/fplqgOAo7na6hWsbgF7K3VI3r59u26//Xa99tpruu6669S3b18tWrRIubm5FdEfAElubm5lqgOAo/nzzz/LVAcqWqlDcuvWrfX222/r2LFj+v777xUQEKDhw4crKChIjz/+eEX0CFR7AQEBZaoDgKPhxD04ulKH5AImk0l33XWXPv74Y61atUoNGzbU559/Xp69Afh/q1atKlMdABzN1T6B5hNq2Ns1h+Q//vhD06ZNU6tWrXT77berdu3amjVrVnn2BuD/ZWVllakOAI7makvGsqQs7K3UV9z76KOPtGDBAv34449q2rSpBg8erKVLlyosLKwi+gOgS9Mp0tLSrlgHAADlp9QhefLkyRo4cKDef/99tWzZsiJ6AmAjLy+vTHUAAFA6pQ7Jx44dk8lkqoheABQjMzOzTHUAAFA6pZ6TbDKZtGHDBj300EOKjIy0LNHy5ZdfauPGjeXeIAAAAFDZSh2S//Of/ygmJkY1a9bUzp07LWefZmZmasqUKeXeIAAAAFDZSh2SJ0+erDlz5ujjjz+2uoBBhw4d9PPPP5drcwAAAIA9lDokJyYmKioqqtB2X1/fCln4+88//9RDDz0kf39/1axZUy1atND27dstdcMwNGHCBF133XWqWbOmunbtqqSkJKt9nDp1SoMHD5aPj4/8/Pw0dOhQ5eTkWI3Zs2ePOnXqJE9PT4WGhmratGnl/loAAADgHEodkoODg3Xo0KFC2zdu3Kgbb7yxXJoqcPr0aXXo0EFubm76/vvvtX//fk2fPl116tSxjJk2bZref/99zZkzR1u2bJGXl5diYmJ0/vx5y5jBgwdr3759io+P17Jly7R+/XoNHz7cUs/KylK3bt0UFhamHTt26O2331ZsbKzmzp1brq8HAAAAzqHUq1sMGzZMzz77rD799FOZTCadOHFCmzdv1vPPP6/x48eXa3NvvfWWQkND9dlnn1m2NWzY0PK1YRh677339Oqrr6pPnz6SpC+++EJBQUFasmSJBgwYoAMHDmjFihXatm2b2rZtK0n64IMP1LNnT73zzjsKCQnR/PnzlZeXp08//VTu7u66+eabtWvXLr377rtWYRoAAADVQ6mPJL/00ksaNGiQunTpopycHEVFRemJJ57Q3//+dz399NPl2lxcXJzatm2rBx54QIGBgWrdurU+/vhjS/3w4cNKTk5W165dLdt8fX3Vrl07bd68WZK0efNm+fn5WQKyJHXt2lUuLi7asmWLZUxUVJTc3d0tY2JiYpSYmKjTp08X2Vtubq6ysrKsbgAAAKgarmkJuFdeeUWnTp3S3r179dNPPyktLU2vv/66zp07V67N/f7775o9e7bCw8P1ww8/6B//+IeeeeYZff7555Kk5ORkSVJQUJDV44KCgiy15ORkBQYGWtVdXV1Vt25dqzFF7ePy57A1depU+fr6Wm6hoaFlfLUAAABwFKUOyQXc3d3VrFkz3X777XJzc9O7775rNRWiPJjNZt16662aMmWKWrdureHDh2vYsGGaM2dOuT7PtRg3bpwyMzMtt+PHj9u7JQAAAJSTEofk3NxcjRs3Tm3bttUdd9yhJUuWSJI+++wzNWzYUDNmzNDo0aPLtbnrrrtOzZo1s9oWERGhY8eOSbp0EqEkpaSkWI1JSUmx1IKDg5WammpVv3Dhgk6dOmU1pqh9XP4ctjw8POTj42N1AwAAQNVQ4pA8YcIEzZ49Ww0aNNCRI0f0wAMPaPjw4ZoxY4beffddHTlyRC+++GK5NtehQwclJiZabfv1118VFhYm6dJJfMHBwVq9erWlnpWVpS1btigyMlKSFBkZqYyMDO3YscMyZs2aNTKbzWrXrp1lzPr165Wfn28ZEx8fryZNmlitpAEAAIDqocQh+ZtvvtEXX3yhRYsWaeXKlbp48aIuXLig3bt3a8CAAapRo0a5Nzd69Gj99NNPmjJlig4dOqQFCxZo7ty5GjFihKRL86NHjRqlyZMnKy4uTr/88oseeeQRhYSEqG/fvpIuHXnu3r27hg0bpq1bt+rHH3/UyJEjNWDAAIWEhEiSBg0aJHd3dw0dOlT79u3TwoULNXPmTI0ZM6bcXxMAAAAcX4mXgPvjjz/Upk0bSVLz5s3l4eGh0aNHy2QyVVhzt912mxYvXqxx48bptddeU8OGDfXee+9p8ODBljEvvPCCzpw5o+HDhysjI0MdO3bUihUr5OnpaRkzf/58jRw5Ul26dJGLi4vuv/9+vf/++5a6r6+vVq5cqREjRqhNmzaqV6+eJkyYwPJvAAAA1VSJQ/LFixetlkhzdXVV7dq1K6Spy9199926++67i62bTCa99tpreu2114odU7duXS1YsOCKz3PLLbdow4YN19wnAAAAqo4Sh2TDMDRkyBB5eHhIks6fP68nn3xSXl5eVuP++9//lm+HAAAAQCUrcUh+9NFHre4/9NBD5d4MAAAA4AhKHJIvvzQ0AAAAUJVd88VEAAAAgKqKkAwAAADYICQDAAAANgjJAAAAgA1CMgAAAGCDkAwAAADYICQDAAAANgjJAAAAgA1CMgAAAGCDkAwAAADYICQDAAAANgjJAAAAgA1CMgAAAGCDkAwAAADYICQDAAAANgjJAAAAgA1CMgAAAGCDkAwAAADYICQDAAAANgjJAAAAgA1CMgAAAGCDkAwAAADYcLV3AwAAQDIMQ/n5+fZuw6Hk5eXZu4VK4ebmJpPJZO82YIOQDACAA8jPz1dsbKy923Ao1eX9iI2Nlbu7u73bgA2mWwAAAAA2OJIMAIADcHNzqzZHTqWSHSWuLu+Hm5ubvVtAEQjJAAA4AJPJVK0+cp8yZYpefvnlK9YBe2K6BQAAsIvigjABGY6AkAwAAADYICQDAAC7sZ13zFFkOApCMgAAcAjV5UQ9OAdCMgAAAGCDkAwAAADYICQDAAAANgjJAAAAgA1CMgAAAGCDkAwAAADYICQDAAAANgjJAAAAgA1CMgAAAGCDkAwAAADYICQDAAAANpwqJL/55psymUwaNWqUZdv58+c1YsQI+fv7q3bt2rr//vuVkpJi9bhjx46pV69eqlWrlgIDAzV27FhduHDBasy6det06623ysPDQzfddJPmzZtXCa8IAAAAjshpQvK2bdv00Ucf6ZZbbrHaPnr0aH377bf65ptvlJCQoBMnTui+++6z1C9evKhevXopLy9PmzZt0ueff6558+ZpwoQJljGHDx9Wr169dNddd2nXrl0aNWqUnnjiCf3www+V9voAAADgOJwiJOfk5Gjw4MH6+OOPVadOHcv2zMxM/etf/9K7776rzp07q02bNvrss8+0adMm/fTTT5KklStXav/+/fr3v/+tVq1aqUePHnr99dc1a9Ys5eXlSZLmzJmjhg0bavr06YqIiNDIkSPVr18/zZgxwy6vFwAAAPblFCF5xIgR6tWrl7p27Wq1fceOHcrPz7fa3rRpU9WvX1+bN2+WJG3evFktWrRQUFCQZUxMTIyysrK0b98+yxjbfcfExFj2UZTc3FxlZWVZ3QAAAFA1uNq7gav56quv9PPPP2vbtm2FasnJyXJ3d5efn5/V9qCgICUnJ1vGXB6QC+oFtSuNycrK0rlz51SzZs1Czz116lRNmjTpml8XAAAAHJdDH0k+fvy4nn32Wc2fP1+enp72bsfKuHHjlJmZabkdP37c3i0BAACgnDh0SN6xY4dSU1N16623ytXVVa6urkpISND7778vV1dXBQUFKS8vTxkZGVaPS0lJUXBwsCQpODi40GoXBfevNsbHx6fIo8iS5OHhIR8fH6sbAAAAqgaHDsldunTRL7/8ol27dllubdu21eDBgy1fu7m5afXq1ZbHJCYm6tixY4qMjJQkRUZG6pdfflFqaqplTHx8vHx8fNSsWTPLmMv3UTCmYB8AAACoXhx6TrK3t7eaN29utc3Ly0v+/v6W7UOHDtWYMWNUt25d+fj46Omnn1ZkZKTat28vSerWrZuaNWumhx9+WNOmTVNycrJeffVVjRgxQh4eHpKkJ598Uh9++KFeeOEFPf7441qzZo2+/vprLV++vHJfMAAAAByCQ4fkkpgxY4ZcXFx0//33Kzc3VzExMfrnP/9pqdeoUUPLli3TP/7xD0VGRsrLy0uPPvqoXnvtNcuYhg0bavny5Ro9erRmzpypG264QZ988oliYmLs8ZIAAABgZ04XktetW2d139PTU7NmzdKsWbOKfUxYWJi+++67K+73zjvv1M6dO8ujRQAAADg5h56TDAAAANgDIRkAAACwQUgGAAAAbBCSAQAAABuEZAAAAMAGIRkAAACwQUgGAAAAbBCSAQAAABuEZAAAAMAGIRkAAACwQUgGAAAAbBCSAQAAABuEZAAAAMAGIRkAAACwQUgGAAAAbBCSAQAAABuEZAAAAMAGIRkAAACwQUgGAAAAbBCSAQAAABuEZAAAAMCGq70bAK6FYRjKz8+3dxsOJS8vz94tVAo3NzeZTCZ7twEAqOIIyXBK+fn5io2NtXcbDqW6vB+xsbFyd3e3dxsAgCqO6RYAAACADY4kwym5ublVmyOnUsmOEleX98PNzc3eLQAAqgFCMpySyWSqVh+5T5kyRS+//PIV6wAAoPww3QJwEsUFYQIyAADlj5AMAAAA2CAkA07Edt4xR5EBAKgYhGTASVWXE/UAALAHQjIAAABgg5AMAAAA2CAkAwAAADYIyQAAAIANLiYCAHAohmEoPz/f3m2gkuTl5RX5Nao+Nzc3mUwme7dRLEIyAMCh5Ofns3pLNcWyltVLbGysQ189l+kWAAAAgA2OJAMAHNY/OjWTWw2O51RlhmHogtmQJLm6mBz643eUXf5Fs2Zv2G/vNkqEkAwAcFhuNVwIydWA437gjuqM3zwAAACADUIyAAAAYIOQDAAAANggJAMAAAA2CMkAAACADUIyAAAAYMOhQ/LUqVN12223ydvbW4GBgerbt68SExOtxpw/f14jRoyQv7+/ateurfvvv18pKSlWY44dO6ZevXqpVq1aCgwM1NixY3XhwgWrMevWrdOtt94qDw8P3XTTTZo3b15FvzwAAAA4KIcOyQkJCRoxYoR++uknxcfHKz8/X926ddOZM2csY0aPHq1vv/1W33zzjRISEnTixAndd999lvrFixfVq1cv5eXladOmTfr88881b948TZgwwTLm8OHD6tWrl+666y7t2rVLo0aN0hNPPKEffvihUl8vAAAAHINDX0xkxYoVVvfnzZunwMBA7dixQ1FRUcrMzNS//vUvLViwQJ07d5YkffbZZ4qIiNBPP/2k9u3ba+XKldq/f79WrVqloKAgtWrVSq+//rpefPFFyzXD58yZo4YNG2r69OmSpIiICG3cuFEzZsxQTExMpb9uAAAA2JdDH0m2lZmZKUmqW7euJGnHjh3Kz89X165dLWOaNm2q+vXra/PmzZKkzZs3q0WLFgoKCrKMiYmJUVZWlvbt22cZc/k+CsYU7KMoubm5ysrKsroBAACganCakGw2mzVq1Ch16NBBzZs3lyQlJyfL3d1dfn5+VmODgoKUnJxsGXN5QC6oF9SuNCYrK0vnzp0rsp+pU6fK19fXcgsNDS3zawQAAIBjcJqQPGLECO3du1dfffWVvVuRJI0bN06ZmZmW2/Hjx+3dEgAAAMqJQ89JLjBy5EgtW7ZM69ev1w033GDZHhwcrLy8PGVkZFgdTU5JSVFwcLBlzNatW632V7D6xeVjbFfESElJkY+Pj2rWrFlkTx4eHvLw8CjzawMAAIDjcegjyYZhaOTIkVq8eLHWrFmjhg0bWtXbtGkjNzc3rV692rItMTFRx44dU2RkpCQpMjJSv/zyi1JTUy1j4uPj5ePjo2bNmlnGXL6PgjEF+wAAAED14tBHkkeMGKEFCxZo6dKl8vb2tswh9vX1Vc2aNeXr66uhQ4dqzJgxqlu3rnx8fPT0008rMjJS7du3lyR169ZNzZo108MPP6xp06YpOTlZr776qkaMGGE5Evzkk0/qww8/1AsvvKDHH39ca9as0ddff63ly5fb7bUDAADAfhz6SPLs2bOVmZmpO++8U9ddd53ltnDhQsuYGTNm6O6779b999+vqKgoBQcH67///a+lXqNGDS1btkw1atRQZGSkHnroIT3yyCN67bXXLGMaNmyo5cuXKz4+Xi1bttT06dP1ySefsPwbAABANeXQR5INw7jqGE9PT82aNUuzZs0qdkxYWJi+++67K+7nzjvv1M6dO0vdIwAAAKoehz6SDAAAANgDIRkAAACwQUgGAAAAbDj0nGRcnWEYys/Pt3cbqCR5eXlFfo2qz83NTSaTyd5tAEC1QUh2cvn5+YqNjbV3G7CDKVOm2LsFVKLY2Fi5u7vbuw0AqDaYbgEAAADY4EhyFXJ7z2dVo4abvdtABTIMQ+aLl6bXuNTg4/eq7uLFfG39bqa926h0ly//mX/RbMdOAJS3y3+mS7LUrz0RkquQGjXcVMOVj2OrPDcPe3cAVKjLz7OYvWG/HTsBUJHy8/MtVz92REy3AAAAAGxwJBkA4FDc3P43bewfnZrJrQbHc4CqIv+i2fIJ0eU/646IkAwAcCiXz7V3q+FCSAaqKEc/r4bfPAAAAIANQjIAAABgg5AMAAAA2CAkAwAAADYIyQAAAIANQjIAAABgg5AMAAAA2CAkAwAAADYIyQAAAIANQjIAAABgg8tSOznDMCxfX7yQZ8dOAJS3y3+mL/9Zr07yL5rt3QIqmGEYumC+9O/b1cXk8JcqRtk40880IdnJ5efnW77e+v37duwEQEXKz8+Xh4eHvduodLM37Ld3CwCqKaZbAAAAADY4kuzk3NzcLF/f3uMZ1XB1t2M3AMrTxQt5lk+ILv9Zr+rc3NwUGxtr7zZQSfLy8jRlyhRJ0ssvvyx3d/6OVReO/nuNkOzkLp+7VcPVnZAMVFHVaZ6myWQiKFVT7u7ufO/hMJhuAQAAANggJAMAAAA2mG5RhVy8mH/1QXBqhmHI/P/fZ5cabtXqI/jqiJ9pALAfQnIVsvW7mfZuAQAAoEpgugUAAABggyPJTo6lkqoXlkqqvhx9qSQAqGoIyU6OpZKqL5ZKAgCg4jDdAgAAALBBSAYAAABsEJIBAAAAG4RkAAAAwAYhGQAAALBBSAYAAABsEJIBAAAAG4RkAAAAwAYhGQAAALBBSAYAAABsEJIBAAAAG4RkAAAAwAYh2casWbPUoEEDeXp6ql27dtq6dau9WwIAAEAlc7V3A45k4cKFGjNmjObMmaN27drpvffeU0xMjBITExUYGGjv9gAAVZhhGMrPz7d3G5UuLy+vyK+rEzc3N5lMJnu3ARuE5Mu8++67GjZsmB577DFJ0pw5c7R8+XJ9+umneumll+zcHS7HHxP+mABVTX5+vmJjY+3dhl1NmTLF3i3YRWxsrNzd3e3dBmwQkv9fXl6eduzYoXHjxlm2ubi4qGvXrtq8eXOh8bm5ucrNzbXcz8rKqpQ+cQl/TPhjAgBARSIk/7+TJ0/q4sWLCgoKstoeFBSkgwcPFho/depUTZo0qbLaAwBUcW5ubtXyP/+XfzJYXT8pcnNzs3cLKAIh+RqNGzdOY8aMsdzPyspSaGioHTuqXvhjwh8ToKoxmUzV9lMSDw8Pe7cAFEJI/n/16tVTjRo1lJKSYrU9JSVFwcHBhcZ7eHjwQ21H/DEBAAAViSXg/p+7u7vatGmj1atXW7aZzWatXr1akZGRduwMAAAAlY0jyZcZM2aMHn30UbVt21a333673nvvPZ05c8ay2gUAAACqB0LyZfr376+0tDRNmDBBycnJatWqlVasWFHoZD4AAABUbSbDMAx7N1EVZGVlydfXV5mZmfLx8bF3OwAAALBRmrzGnGQAAADABiEZAAAAsEFIBgAAAGwQkgEAAAAbhGQAAADABiEZAAAAsEFIBgAAAGwQkgEAAAAbhGQAAADABiEZAAAAsEFIBgAAAGwQkgEAAAAbhGQAAADAhqu9G6gqDMOQJGVlZdm5EwAAABSlIKcV5LYrISSXk+zsbElSaGionTsBAADAlWRnZ8vX1/eKY0xGSaI0rspsNuvEiRPy9vaWyWSydzuowrKyshQaGqrjx4/Lx8fH3u0AQJnxew2VxTAMZWdnKyQkRC4uV551zJHkcuLi4qIbbrjB3m2gGvHx8eGPCYAqhd9rqAxXO4JcgBP3AAAAABuEZAAAAMAGIRlwMh4eHpo4caI8PDzs3QoAlAt+r8ERceIeAAAAYIMjyQAAAIANQjIAAABgg5AMAAAA2CAkA05k3rx58vPzs3cbAABUeYRkwA6GDBkik8lU6Hbo0CF7twYA16yo32uX32JjY+3dIlBiXHEPsJPu3bvrs88+s9oWEBBgp24AoOz++usvy9cLFy7UhAkTlJiYaNlWu3Zty9eGYejixYtydSWKwDFxJBmwEw8PDwUHB1vdZs6cqRYtWsjLy0uhoaF66qmnlJOTU+w+du/erbvuukve3t7y8fFRmzZttH37dkt948aN6tSpk2rWrKnQ0FA988wzOnPmTGW8PADV0OW/z3x9fWUymSz3Dx48KG9vb33//fdq06aNPDw8tHHjRg0ZMkR9+/a12s+oUaN05513Wu6bzWZNnTpVDRs2VM2aNdWyZUstWrSocl8cqh1CMuBAXFxc9P7772vfvn36/PPPtWbNGr3wwgvFjh88eLBuuOEGbdu2TTt27NBLL70kNzc3SdJvv/2m7t276/7779eePXu0cOFCbdy4USNHjqyslwMAhbz00kt68803deDAAd1yyy0leszUqVP1xRdfaM6cOdq3b59Gjx6thx56SAkJCRXcLaozPuMA7GTZsmVWHz326NFD33zzjeV+gwYNNHnyZD355JP65z//WeQ+jh07prFjx6pp06aSpPDwcEtt6tSpGjx4sEaNGmWpvf/++4qOjtbs2bPl6elZAa8KAK7stdde09/+9rcSj8/NzdWUKVO0atUqRUZGSpJuvPFGbdy4UR999JGio6MrqlVUc4RkwE7uuusuzZ4923Lfy8tLq1at0tSpU3Xw4EFlZWXpwoULOn/+vM6ePatatWoV2seYMWP0xBNP6Msvv1TXrl31wAMPqFGjRpIuTcXYs2eP5s+fbxlvGIbMZrMOHz6siIiIin+RAGCjbdu2pRp/6NAhnT17tlCwzsvLU+vWrcuzNcAKIRmwEy8vL910002W+0eOHNHdd9+tf/zjH3rjjTdUt25dbdy4UUOHDlVeXl6RITk2NlaDBg3S8uXL9f3332vixIn66quvdO+99yonJ0d///vf9cwzzxR6XP369Sv0tQFAcby8vKzuu7i4yDAMq235+fmWrwvOy1i+fLmuv/56q3EeHh4V1CVASAYcxo4dO2Q2mzV9+nS5uFw6XeDrr7++6uMaN26sxo0ba/To0Ro4cKA+++wz3Xvvvbr11lu1f/9+qyAOAI4mICBAe/futdq2a9cuy/kVzZo1k4eHh44dO8bUClQqTtwDHMRNN92k/Px8ffDBB/r999/15Zdfas6cOcWOP3funEaOHKl169bp6NGj+vHHH7Vt2zbLNIoXX3xRmzZt0siRI7Vr1y4lJSVp6dKlnLgHwKF07txZ27dv1xdffKGkpCRNnDjRKjR7e3vr+eef1+jRo/X555/rt99+088//6wPPvhAn3/+uR07R1VHSAYcRMuWLfXuu+/qrbfeUvPmzTV//nxNnTq12PE1atRQenq6HnnkETVu3FgPPvigevTooUmTJkmSbrnlFiUkJOjXX39Vp06d1Lp1a02YMEEhISGV9ZIA4KpiYmI0fvx4vfDCC7rtttuUnZ2tRx55xGrM66+/rvHjx2vq1KmKiIhQ9+7dtXz5cjVs2NBOXaM6MBm2E4EAAACAao4jyQAAAIANQjIAAABgg5AMAAAA2CAkAwAAADYIyQAAAIANQjIAAABgg5AMAAAA2CAkAwAAADYIyQCAMjty5IhMJpN27dolSVq3bp1MJpMyMjLs2hcAXCtCMgA4uCFDhqhv377X/PjFixerffv28vX1lbe3t26++WaNGjWq3PqTpNDQUP31119q3rx5ue4XAOzF1d4NAAAqzurVq9W/f3+98cYb6t27t0wmk/bv36/4+PhyfZ4aNWooODi4XPeZl5cnd3f3ct0nAJQUR5IBwMksWrRILVq0UM2aNeXv76+uXbvqzJkzRY799ttv1aFDB40dO1ZNmjRR48aN1bdvX82aNcsyJjY2Vq1atdJHH32k0NBQ1apVSw8++KAyMzMtY8xms1577TXdcMMN8vDwUKtWrbRixQpL3Xa6RVE2btyoTp06qWbNmgoNDdUzzzxj1XeDBg30+uuv65FHHpGPj4+GDx9ehncJAMqGkAwATuSvv/7SwIED9fjjj+vAgQNat26d7rvvPhmGUeT44OBg7du3T3v37r3ifg8dOqSvv/5a3377rVasWKGdO3fqqaeestRnzpyp6dOn65133tGePXsUExOj3r17KykpqUR9//bbb+revbvuv/9+7dmzRwsXLtTGjRs1cuRIq3HvvPOOWrZsqZ07d2r8+PEl2jcAVASTUdxvVgCAQxgyZIgyMjK0ZMkS/fzzz2rTpo2OHDmisLCwqz72zJkzevDBB/Xdd98pLCxM7du3V7du3TR48GB5eHhIunQkefLkyTp69Kiuv/56SdKKFSvUq1cv/fnnnwoODtb111+vESNG6OWXX7bs+/bbb9dtt92mWbNm6ciRI2rYsKF27typVq1aad26dbrrrrt0+vRp+fn56YknnlCNGjX00UcfWR6/ceNGRUdH68yZM/L09FSDBg3UunVrLV68uJzfQQAoPY4kA4ATadmypbp06aIWLVrogQce0Mcff6zTp08XO97Ly0vLly/XoUOH9Oqrr6p27dp67rnndPvtt+vs2bOWcfXr17cEZEmKjIyU2WxWYmKisrKydOLECXXo0MFq3x06dNCBAwdK1Pfu3bs1b9481a5d23KLiYmR2WzW4cOHLePatm1b0rcCACoUIRkAnEiNGjUUHx+v77//Xs2aNdMHH3ygJk2aWAXNojRq1EhPPPGEPvnkE/3888/av3+/Fi5cWEldSzk5Ofr73/+uXbt2WW67d+9WUlKSGjVqZBnn5eVVaT0BwJUQkgHAyZhMJnXo0EGTJk3Szp075e7uXqopCg0aNFCtWrWsTpo7duyYTpw4Ybn/008/ycXFRU2aNJGPj49CQkL0448/Wu3nxx9/VLNmzUr0nLfeeqv279+vm266qdCNFSwAOCKWgAMAJ7JlyxatXr1a3bp1U2BgoLZs2aK0tDRFREQUOT42NlZnz55Vz549FRYWpoyMDL3//vvKz8/X3/72N8s4T09PPfroo3rnnXeUlZWlZ555Rg8++KBlWbexY8dq4sSJatSokVq1aqXPPvtMu3bt0vz580vU94svvqj27dtr5MiReuKJJ+Tl5WVZiu7DDz8s+xsDAOWMkAwATsTHx0fr16/Xe++9p6ysLIWFhWn69Onq0aNHkeOjo6M1a9YsPfLII0pJSVGdOnXUunVrrVy5Uk2aNLGMu+mmm3TfffepZ8+eOnXqlO6++27985//tNSfeeYZZWZm6rnnnlNqaqqaNWumuLg4hYeHl6jvW265RQkJCXrllVfUqVMnGYahRo0aqX///mV7QwCggrC6BQBUc7GxsVqyZMkV1zgGgOqGOckAAACADUIyAAAAYIPpFgAAAIANjiQDAAAANgjJAAAAgA1CMgAAAGCDkAwAAADYICQDAAAANgjJAAAAgA1CMgAAAGCDkAwAAADY+D8MJlIoeJIOKgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Plot distribution of spoilers vs. non-spoilers\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.countplot(x='is_spoiler', data=df, palette='coolwarm')\n",
    "plt.title('Distribution of Spoiler vs. Non-Spoiler Reviews')\n",
    "plt.xlabel('Is Spoiler')\n",
    "plt.ylabel('Count')\n",
    "plt.show()\n",
    "\n",
    "# Plot the distribution of review lengths\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(df['review_length'], kde=True, bins=30, color='purple')\n",
    "plt.title('Distribution of Review Lengths')\n",
    "plt.xlabel('Review Length')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()\n",
    "\n",
    "# Correlation between review length and is_spoiler\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.boxplot(x='is_spoiler', y='review_length', data=df, palette='coolwarm')\n",
    "plt.title('Review Length vs. Spoiler')\n",
    "plt.xlabel('Is Spoiler')\n",
    "plt.ylabel('Review Length')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **2.B. Rule based methods, spoiler contining frequent words and phrases**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T01:09:35.791266Z",
     "iopub.status.busy": "2024-08-16T01:09:35.790820Z",
     "iopub.status.idle": "2024-08-16T01:12:44.625097Z",
     "shell.execute_reply": "2024-08-16T01:12:44.623460Z",
     "shell.execute_reply.started": "2024-08-16T01:09:35.791231Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a0a63e49ea8b41ecb3fa9337609e86e3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Cleaning Text:   0%|          | 0/573913 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import re\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import PorterStemmer\n",
    "from tqdm.notebook import tqdm\n",
    "# Set of English stopwords\n",
    "stop_words = set(stopwords.words('english'))\n",
    "\n",
    "def clean_text(text):\n",
    "    # Remove URLs\n",
    "    text = re.sub(r'http\\S+', '', text)\n",
    "    text = re.sub(r'www\\S+', '', text)\n",
    "    \n",
    "    # Remove emails\n",
    "    text = re.sub(r'\\S*@\\S*\\s?', '', text)\n",
    "    \n",
    "    # Remove all non-word characters and digits\n",
    "    text = re.sub(r'[^a-zA-Z\\s]', '', text)\n",
    "    \n",
    "    # Normalize whitespaces\n",
    "    text = re.sub(r'\\s+', ' ', text)\n",
    "    \n",
    "    # Convert text to lowercase\n",
    "    text = text.lower()\n",
    "    \n",
    "    return text\n",
    "# Set up tqdm for pandas apply\n",
    "tqdm.pandas(desc=\"Cleaning Text\")\n",
    "\n",
    "# Apply the cleaning function with a progress bar\n",
    "df['cleaned_review_text'] = df['review_text'].progress_apply(clean_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T01:14:28.105839Z",
     "iopub.status.busy": "2024-08-16T01:14:28.105299Z",
     "iopub.status.idle": "2024-08-16T01:15:04.378970Z",
     "shell.execute_reply": "2024-08-16T01:15:04.377506Z",
     "shell.execute_reply.started": "2024-08-16T01:14:28.105799Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame saved successfully to /kaggle/working/preprocessed.json.\n"
     ]
    }
   ],
   "source": [
    "# Specify the path where you want to save the JSON file\n",
    "json_file_path = '/kaggle/working/preprocessed.json'  \n",
    "\n",
    "# Save the DataFrame to a JSON file\n",
    "df.to_json(json_file_path, orient='records', lines=True)\n",
    "\n",
    "print(f\"DataFrame saved successfully to {json_file_path}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **3. Data Preprocessing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T01:15:04.381694Z",
     "iopub.status.busy": "2024-08-16T01:15:04.381269Z",
     "iopub.status.idle": "2024-08-16T01:15:08.002371Z",
     "shell.execute_reply": "2024-08-16T01:15:08.000938Z",
     "shell.execute_reply.started": "2024-08-16T01:15:04.381661Z"
    }
   },
   "outputs": [],
   "source": [
    "import re\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from imblearn.over_sampling import SMOTE\n",
    "import torch\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "from transformers import BertTokenizer\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Encode the target label\n",
    "label_encoder = LabelEncoder()\n",
    "df['label'] = label_encoder.fit_transform(df['is_spoiler'])\n",
    "\n",
    "\n",
    "train_df, temp_df = train_test_split(df, test_size=0.2, random_state=42, stratify=df['label'])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Re-split the validation and test sets (no need to apply SMOTE here)\n",
    "val_df, test_df = train_test_split(temp_df, test_size=0.5, random_state=42, stratify=temp_df['label'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T01:15:08.004860Z",
     "iopub.status.busy": "2024-08-16T01:15:08.004021Z",
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   },
   "outputs": [],
   "source": [
    "\n",
    "val_df, test_df = train_test_split(temp_df, test_size=0.5, random_state=42, stratify=temp_df['label'])\n",
    "\n"
   ]
  },
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   "cell_type": "code",
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    "execution": {
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   "source": [
    "\n",
    "class SpoilerDataset(Dataset):\n",
    "    def __init__(self, texts, labels):\n",
    "        self.texts = texts\n",
    "        self.labels = labels\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.texts)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        text = self.texts[idx]\n",
    "        label = self.labels[idx]\n",
    "\n",
    "        encoding = tokenizer.encode_plus(\n",
    "            text,\n",
    "            max_length=5000,\n",
    "            add_special_tokens=True,\n",
    "            padding='max_length',\n",
    "            truncation=True,\n",
    "            return_attention_mask=True,\n",
    "            return_tensors='pt',\n",
    "        )\n",
    "\n",
    "        return {\n",
    "            'input_ids': encoding['input_ids'].flatten(),\n",
    "            'attention_mask': encoding['attention_mask'].flatten(),\n",
    "            'label': torch.tensor(label, dtype=torch.long)\n",
    "        }\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "\n",
    "# Create datasets with the resampled training data\n",
    "train_dataset = SpoilerDataset(train_df['cleaned_review_text'].tolist(), train_df['label'].tolist())\n",
    "val_dataset = SpoilerDataset(val_df['cleaned_review_text'].tolist(), val_df['label'].tolist())\n",
    "test_dataset = SpoilerDataset(test_df['cleaned_review_text'].tolist(), test_df['label'].tolist())\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
    "val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False)\n",
    "test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **4. Modeling**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **5. Training and Evaluation**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LSTM model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "\n",
    "# Training function\n",
    "def train_lstm(model, dataloader, optimizer, criterion):\n",
    "    model.train()\n",
    "    total_loss = 0\n",
    "    total_acc = 0\n",
    "\n",
    "    # Wrap the dataloader with tqdm for progress tracking\n",
    "    for batch in tqdm(dataloader, desc=\"Training\"):\n",
    "        optimizer.zero_grad()\n",
    "        input_ids = batch['input_ids'].to(device)\n",
    "        labels = batch['label'].to(device)\n",
    "\n",
    "        # Forward pass\n",
    "        outputs = model(input_ids)\n",
    "        loss = criterion(outputs, labels)\n",
    "        preds = torch.argmax(outputs, dim=1)\n",
    "\n",
    "        # Backward pass and optimization\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        total_loss += loss.item()\n",
    "        total_acc += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\n",
    "\n",
    "    return total_loss / len(dataloader), total_acc / len(dataloader)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
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    }
   },
   "outputs": [],
   "source": [
    "# Evaluation function\n",
    "def evaluate_lstm(model, dataloader):\n",
    "    model.eval()\n",
    "    total_acc = 0\n",
    "\n",
    "    with torch.no_grad():\n",
    "        # Wrap the dataloader with tqdm for progress tracking\n",
    "        for batch in tqdm(dataloader, desc=\"Evaluating\"):\n",
    "            input_ids = batch['input_ids'].to(device)\n",
    "            labels = batch['label'].to(device)\n",
    "            outputs = model(input_ids)\n",
    "            preds = torch.argmax(outputs, dim=1)\n",
    "\n",
    "            total_acc += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\n",
    "\n",
    "    return total_acc / len(dataloader)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2024-08-16T01:15:10.558904Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "# Lists to store loss and accuracy for plotting\n",
    "train_losses = []\n",
    "train_accuracies = []\n",
    "val_accuracies = []\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
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   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "n_epochs = 2\n",
    "# Initialize tqdm progress bar\n",
    "for epoch in tqdm(range(n_epochs), desc='Training Epochs'):\n",
    "    train_loss, train_acc = train_lstm(lstm_model, train_loader, optimizer, criterion)\n",
    "    val_acc = evaluate_lstm(lstm_model, val_loader)\n",
    "    \n",
    "    # Append metrics to lists\n",
    "    train_losses.append(train_loss)\n",
    "    train_accuracies.append(train_acc)\n",
    "    val_accuracies.append(val_acc)\n",
    "    \n",
    "    # Print epoch information\n",
    "    tqdm.write(f'Epoch {epoch+1}: Train Loss {train_loss:.4f}, Train Acc {train_acc:.4f}, Val Acc {val_acc:.4f}')\n",
    "    \n",
    "    # Save model checkpoint every 10 epochs\n",
    "    if (epoch + 1) % 10 == 0:\n",
    "        checkpoint_path = f'lstm_model_epoch_{epoch+1}.pth'\n",
    "        torch.save(lstm_model.state_dict(), checkpoint_path)\n",
    "        print(f'Model checkpoint saved to {checkpoint_path}')\n",
    "\n",
    "# Evaluate on test set\n",
    "test_acc = evaluate(lstm_model, test_loader)\n",
    "print(f'Test Accuracy: {test_acc:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2024-08-16T01:15:10.564212Z"
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   },
   "outputs": [],
   "source": [
    "# Plot training and validation loss\n",
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "# Plot training loss\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(range(n_epochs), train_losses, label='Train Loss', color='blue')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training Loss of LSTM model')\n",
    "plt.legend()\n",
    "\n",
    "# Plot training and validation accuracy\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(range(n_epochs), train_accuracies, label='Train Accuracy', color='blue')\n",
    "plt.plot(range(n_epochs), val_accuracies, label='Validation Accuracy', color='orange')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Training and Validation Accuracy of LSTM model')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Saving the final model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the final model completely after training\n",
    "final_model_path = 'final_lstm_model.pth'\n",
    "torch.save(lstm_model, final_model_path)\n",
    "print(f'Final model saved to {final_model_path}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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