{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "d4ad56f5-dd6b-47e2-8b75-bdc3cb0d5acd",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "288100c1-33d1-4e46-abaf-9a5ea4f7eca5",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "workding dir: /home/inflaton/code/projects/courses/llm-finetuning\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "from pathlib import Path\n",
    "\n",
    "workding_dir = str(Path.cwd().parent)\n",
    "os.chdir(workding_dir)\n",
    "sys.path.append(workding_dir)\n",
    "print(\"workding dir:\", workding_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "396e9b1b-b8b6-4281-a574-e9decfd020f7",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading env vars from: /home/inflaton/code/projects/courses/llm-finetuning/.env\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dotenv import find_dotenv, load_dotenv\n",
    "\n",
    "found_dotenv = find_dotenv(\".env\")\n",
    "\n",
    "if len(found_dotenv) == 0:\n",
    "    found_dotenv = find_dotenv(\".env.example\")\n",
    "print(f\"loading env vars from: {found_dotenv}\")\n",
    "load_dotenv(found_dotenv, override=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package wordnet to /home/inflaton/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to /home/inflaton/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package omw-1.4 to /home/inflaton/nltk_data...\n",
      "[nltk_data]   Package omw-1.4 is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "from llm_toolkit.translation_utils import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chinese</th>\n",
       "      <th>english</th>\n",
       "      <th>unsloth/Qwen2-0.5B-Instruct</th>\n",
       "      <th>unsloth/Qwen2-0.5B-Instruct(finetuned)</th>\n",
       "      <th>unsloth/Qwen2-1.5B-Instruct</th>\n",
       "      <th>unsloth/Qwen2-1.5B-Instruct(finetuned)</th>\n",
       "      <th>unsloth/Qwen2-0.5B-Instruct-bnb-4bit</th>\n",
       "      <th>unsloth/Qwen2-0.5B-Instruct-bnb-4bit(finetuned)</th>\n",
       "      <th>unsloth/Qwen2-1.5B-Instruct-bnb-4bit</th>\n",
       "      <th>unsloth/Qwen2-1.5B-Instruct-bnb-4bit(finetuned)</th>\n",
       "      <th>unsloth/Qwen2-7B-Instruct</th>\n",
       "      <th>unsloth/Qwen2-7B-Instruct(finetuned)</th>\n",
       "      <th>unsloth/Qwen2-7B-Instruct-bnb-4bit</th>\n",
       "      <th>unsloth/Qwen2-7B-Instruct-bnb-4bit(finetuned)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>老耿端起枪，眯缝起一只三角眼，一搂扳机响了枪，冰雹般的金麻雀劈哩啪啦往下落，铁砂子在柳枝间飞...</td>\n",
       "      <td>Old Geng picked up his shotgun, squinted, and ...</td>\n",
       "      <td>Old Tang held his gun, squinting his eyes with...</td>\n",
       "      <td>Old Geng lifted his rifle and narrowed his eye...</td>\n",
       "      <td>Old Geng took up his gun, squinted one of its ...</td>\n",
       "      <td>Old Geng raised the rifle, squeezed one tiny t...</td>\n",
       "      <td>Old Teng raised his gun and looked up at a pai...</td>\n",
       "      <td>Old Geng raised his rifle, squinted his eyes, ...</td>\n",
       "      <td>Old耿拿起枪，眯着眼睛一搂扳机就响了枪，金麻雀噼里啪啦的往下掉，铁砂子在柳枝间飞溅，发出“...</td>\n",
       "      <td>Old Geng raised his pistol, squinted, and fire...</td>\n",
       "      <td>Old Aigang raised his rifle, squinting one of ...</td>\n",
       "      <td>Old Geng raised his rifle and squinted into th...</td>\n",
       "      <td>Old Geng raised his gun, squinting one of his ...</td>\n",
       "      <td>Old Geng raised his rifle and squinted into th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>次日天未明时，刘老老便起来梳洗了， 又将板儿教了几句话； 五六岁的孩子，听见带了他进城逛去，...</td>\n",
       "      <td>Next day Grannie Liu was up before dawn. As so...</td>\n",
       "      <td>The next morning when it was still dark, Liu G...</td>\n",
       "      <td>It was still not light when this little update...</td>\n",
       "      <td>By the time the next day dawned, Liu Lao got u...</td>\n",
       "      <td>Having been woken just before daybreak, Granni...</td>\n",
       "      <td>The next day at dawn, Liu Geowon got up early ...</td>\n",
       "      <td>Three or four hours before this, Grannie Liu h...</td>\n",
       "      <td>At dawn the next day, Liu Langlang got up to b...</td>\n",
       "      <td>But by some miracle of preparation, Grannie Li...</td>\n",
       "      <td>The next morning, before dawn, Old Liu rose to...</td>\n",
       "      <td>First thing next morning Grannie Liu rose befo...</td>\n",
       "      <td>The next morning, before dawn, Old Liu rose to...</td>\n",
       "      <td>First thing in the morning Grannie Liu rose an...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             chinese  \\\n",
       "0  老耿端起枪，眯缝起一只三角眼，一搂扳机响了枪，冰雹般的金麻雀劈哩啪啦往下落，铁砂子在柳枝间飞...   \n",
       "1  次日天未明时，刘老老便起来梳洗了， 又将板儿教了几句话； 五六岁的孩子，听见带了他进城逛去，...   \n",
       "\n",
       "                                             english  \\\n",
       "0  Old Geng picked up his shotgun, squinted, and ...   \n",
       "1  Next day Grannie Liu was up before dawn. As so...   \n",
       "\n",
       "                         unsloth/Qwen2-0.5B-Instruct  \\\n",
       "0  Old Tang held his gun, squinting his eyes with...   \n",
       "1  The next morning when it was still dark, Liu G...   \n",
       "\n",
       "              unsloth/Qwen2-0.5B-Instruct(finetuned)  \\\n",
       "0  Old Geng lifted his rifle and narrowed his eye...   \n",
       "1  It was still not light when this little update...   \n",
       "\n",
       "                         unsloth/Qwen2-1.5B-Instruct  \\\n",
       "0  Old Geng took up his gun, squinted one of its ...   \n",
       "1  By the time the next day dawned, Liu Lao got u...   \n",
       "\n",
       "              unsloth/Qwen2-1.5B-Instruct(finetuned)  \\\n",
       "0  Old Geng raised the rifle, squeezed one tiny t...   \n",
       "1  Having been woken just before daybreak, Granni...   \n",
       "\n",
       "                unsloth/Qwen2-0.5B-Instruct-bnb-4bit  \\\n",
       "0  Old Teng raised his gun and looked up at a pai...   \n",
       "1  The next day at dawn, Liu Geowon got up early ...   \n",
       "\n",
       "     unsloth/Qwen2-0.5B-Instruct-bnb-4bit(finetuned)  \\\n",
       "0  Old Geng raised his rifle, squinted his eyes, ...   \n",
       "1  Three or four hours before this, Grannie Liu h...   \n",
       "\n",
       "                unsloth/Qwen2-1.5B-Instruct-bnb-4bit  \\\n",
       "0  Old耿拿起枪，眯着眼睛一搂扳机就响了枪，金麻雀噼里啪啦的往下掉，铁砂子在柳枝间飞溅，发出“...   \n",
       "1  At dawn the next day, Liu Langlang got up to b...   \n",
       "\n",
       "     unsloth/Qwen2-1.5B-Instruct-bnb-4bit(finetuned)  \\\n",
       "0  Old Geng raised his pistol, squinted, and fire...   \n",
       "1  But by some miracle of preparation, Grannie Li...   \n",
       "\n",
       "                           unsloth/Qwen2-7B-Instruct  \\\n",
       "0  Old Aigang raised his rifle, squinting one of ...   \n",
       "1  The next morning, before dawn, Old Liu rose to...   \n",
       "\n",
       "                unsloth/Qwen2-7B-Instruct(finetuned)  \\\n",
       "0  Old Geng raised his rifle and squinted into th...   \n",
       "1  First thing next morning Grannie Liu rose befo...   \n",
       "\n",
       "                  unsloth/Qwen2-7B-Instruct-bnb-4bit  \\\n",
       "0  Old Geng raised his gun, squinting one of his ...   \n",
       "1  The next morning, before dawn, Old Liu rose to...   \n",
       "\n",
       "       unsloth/Qwen2-7B-Instruct-bnb-4bit(finetuned)  \n",
       "0  Old Geng raised his rifle and squinted into th...  \n",
       "1  First thing in the morning Grannie Liu rose an...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"results/mac-results.csv\")\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['chinese',\n",
       " 'english',\n",
       " 'unsloth/Qwen2-0.5B-Instruct',\n",
       " 'unsloth/Qwen2-0.5B-Instruct(finetuned)',\n",
       " 'unsloth/Qwen2-1.5B-Instruct',\n",
       " 'unsloth/Qwen2-1.5B-Instruct(finetuned)',\n",
       " 'unsloth/Qwen2-0.5B-Instruct-bnb-4bit',\n",
       " 'unsloth/Qwen2-0.5B-Instruct-bnb-4bit(finetuned)',\n",
       " 'unsloth/Qwen2-1.5B-Instruct-bnb-4bit',\n",
       " 'unsloth/Qwen2-1.5B-Instruct-bnb-4bit(finetuned)',\n",
       " 'unsloth/Qwen2-7B-Instruct',\n",
       " 'unsloth/Qwen2-7B-Instruct(finetuned)',\n",
       " 'unsloth/Qwen2-7B-Instruct-bnb-4bit',\n",
       " 'unsloth/Qwen2-7B-Instruct-bnb-4bit(finetuned)']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[\n",
    "    [\n",
    "        \"chinese\",\n",
    "        \"english\",\n",
    "        \"unsloth/Qwen2-0.5B-Instruct\",\n",
    "        \"unsloth/Qwen2-0.5B-Instruct-bnb-4bit\",\n",
    "        \"unsloth/Qwen2-0.5B-Instruct(finetuned)\",\n",
    "        \"unsloth/Qwen2-0.5B-Instruct-bnb-4bit(finetuned)\",\n",
    "        \"unsloth/Qwen2-1.5B-Instruct\",\n",
    "        \"unsloth/Qwen2-1.5B-Instruct-bnb-4bit\",\n",
    "        \"unsloth/Qwen2-1.5B-Instruct(finetuned)\",\n",
    "        \"unsloth/Qwen2-1.5B-Instruct-bnb-4bit(finetuned)\",\n",
    "        \"unsloth/Qwen2-7B-Instruct\",\n",
    "        \"unsloth/Qwen2-7B-Instruct-bnb-4bit\",\n",
    "        \"unsloth/Qwen2-7B-Instruct(finetuned)\",\n",
    "        \"unsloth/Qwen2-7B-Instruct-bnb-4bit(finetuned)\",\n",
    "    ]\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"results/experiment-1-results.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chinese</th>\n",
       "      <th>english</th>\n",
       "      <th>unsloth/Qwen2-0.5B-Instruct</th>\n",
       "      <th>unsloth/Qwen2-0.5B-Instruct(finetuned)</th>\n",
       "      <th>unsloth/Qwen2-1.5B-Instruct</th>\n",
       "      <th>unsloth/Qwen2-1.5B-Instruct(finetuned)</th>\n",
       "      <th>unsloth/Qwen2-7B-Instruct-bnb-4bit</th>\n",
       "      <th>unsloth/Qwen2-7B-Instruct-bnb-4bit(finetuned)</th>\n",
       "      <th>gradientai/Llama-3-8B-Instruct-Gradient-1048k</th>\n",
       "      <th>gradientai/Llama-3-8B-Instruct-Gradient-1048k(finetuned)</th>\n",
       "      <th>unsloth/Qwen2-7B-Instruct</th>\n",
       "      <th>unsloth/Qwen2-72B-Instruct-bnb-4bit</th>\n",
       "      <th>unsloth/Qwen2-7B-Instruct(finetuned)</th>\n",
       "      <th>unsloth/mistral-7b-instruct-v0.3</th>\n",
       "      <th>unsloth/mistral-7b-instruct-v0.3(finetuned)</th>\n",
       "      <th>unsloth/Qwen2-72B-Instruct-bnb-4bit(finetuned)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>老耿端起枪，眯缝起一只三角眼，一搂扳机响了枪，冰雹般的金麻雀劈哩啪啦往下落，铁砂子在柳枝间飞...</td>\n",
       "      <td>Old Geng picked up his shotgun, squinted, and ...</td>\n",
       "      <td>Old Teng holds his gun up, his eyes narrowed a...</td>\n",
       "      <td>Old Geng raised his rifle and tilted his head ...</td>\n",
       "      <td>Old Jin raises his gun, squints one eye as he ...</td>\n",
       "      <td>Old Geng raised his pistol, squinted through t...</td>\n",
       "      <td>Old Geng raised his gun, squinting one of his ...</td>\n",
       "      <td>Old Geng raised his rifle and squinted into on...</td>\n",
       "      <td>The old man pulled out his gun, squinting one ...</td>\n",
       "      <td>Old Geng raised his rifle, squinting through t...</td>\n",
       "      <td>Old Geng raised his gun, squinted one of his t...</td>\n",
       "      <td>Lao Geng raised his gun, narrowed one of his t...</td>\n",
       "      <td>Old Geng raised his rifle and squinted into th...</td>\n",
       "      <td>Geng Da initiates firing, squinting to form a ...</td>\n",
       "      <td>Old Geng aimed and fired. A triangular slit op...</td>\n",
       "      <td>Old Geng raised his gun, narrowed one of his t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>次日天未明时，刘老老便起来梳洗了， 又将板儿教了几句话； 五六岁的孩子，听见带了他进城逛去，...</td>\n",
       "      <td>Next day Grannie Liu was up before dawn. As so...</td>\n",
       "      <td>The next morning, Liu Geo woke up at five o'cl...</td>\n",
       "      <td>But not before noon did Grannie Liu rise up an...</td>\n",
       "      <td>At dawn the next day, Liu Langlang got up earl...</td>\n",
       "      <td>She got up about dawn with a purpose already e...</td>\n",
       "      <td>The next morning, before dawn, Old Liu rose to...</td>\n",
       "      <td>First thing next morning Grannie Liu rose earl...</td>\n",
       "      <td>The next day, when the sun had not yet risen, ...</td>\n",
       "      <td>Grannie Liu got up before daylight was even vi...</td>\n",
       "      <td>The next morning, before the dawn had fully br...</td>\n",
       "      <td>Before dawn next morning, Granny Liu got up to...</td>\n",
       "      <td>First thing in the morning Grannie Liu rose to...</td>\n",
       "      <td>The next day, when it was still dark, Liu Lao ...</td>\n",
       "      <td>Before dawn next day Grannie Liu got up and bu...</td>\n",
       "      <td>As soon as it was light, Grannie Liu got up an...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             chinese  \\\n",
       "0  老耿端起枪，眯缝起一只三角眼，一搂扳机响了枪，冰雹般的金麻雀劈哩啪啦往下落，铁砂子在柳枝间飞...   \n",
       "1  次日天未明时，刘老老便起来梳洗了， 又将板儿教了几句话； 五六岁的孩子，听见带了他进城逛去，...   \n",
       "\n",
       "                                             english  \\\n",
       "0  Old Geng picked up his shotgun, squinted, and ...   \n",
       "1  Next day Grannie Liu was up before dawn. As so...   \n",
       "\n",
       "                         unsloth/Qwen2-0.5B-Instruct  \\\n",
       "0  Old Teng holds his gun up, his eyes narrowed a...   \n",
       "1  The next morning, Liu Geo woke up at five o'cl...   \n",
       "\n",
       "              unsloth/Qwen2-0.5B-Instruct(finetuned)  \\\n",
       "0  Old Geng raised his rifle and tilted his head ...   \n",
       "1  But not before noon did Grannie Liu rise up an...   \n",
       "\n",
       "                         unsloth/Qwen2-1.5B-Instruct  \\\n",
       "0  Old Jin raises his gun, squints one eye as he ...   \n",
       "1  At dawn the next day, Liu Langlang got up earl...   \n",
       "\n",
       "              unsloth/Qwen2-1.5B-Instruct(finetuned)  \\\n",
       "0  Old Geng raised his pistol, squinted through t...   \n",
       "1  She got up about dawn with a purpose already e...   \n",
       "\n",
       "                  unsloth/Qwen2-7B-Instruct-bnb-4bit  \\\n",
       "0  Old Geng raised his gun, squinting one of his ...   \n",
       "1  The next morning, before dawn, Old Liu rose to...   \n",
       "\n",
       "       unsloth/Qwen2-7B-Instruct-bnb-4bit(finetuned)  \\\n",
       "0  Old Geng raised his rifle and squinted into on...   \n",
       "1  First thing next morning Grannie Liu rose earl...   \n",
       "\n",
       "       gradientai/Llama-3-8B-Instruct-Gradient-1048k  \\\n",
       "0  The old man pulled out his gun, squinting one ...   \n",
       "1  The next day, when the sun had not yet risen, ...   \n",
       "\n",
       "  gradientai/Llama-3-8B-Instruct-Gradient-1048k(finetuned)  \\\n",
       "0  Old Geng raised his rifle, squinting through t...         \n",
       "1  Grannie Liu got up before daylight was even vi...         \n",
       "\n",
       "                           unsloth/Qwen2-7B-Instruct  \\\n",
       "0  Old Geng raised his gun, squinted one of his t...   \n",
       "1  The next morning, before the dawn had fully br...   \n",
       "\n",
       "                 unsloth/Qwen2-72B-Instruct-bnb-4bit  \\\n",
       "0  Lao Geng raised his gun, narrowed one of his t...   \n",
       "1  Before dawn next morning, Granny Liu got up to...   \n",
       "\n",
       "                unsloth/Qwen2-7B-Instruct(finetuned)  \\\n",
       "0  Old Geng raised his rifle and squinted into th...   \n",
       "1  First thing in the morning Grannie Liu rose to...   \n",
       "\n",
       "                    unsloth/mistral-7b-instruct-v0.3  \\\n",
       "0  Geng Da initiates firing, squinting to form a ...   \n",
       "1  The next day, when it was still dark, Liu Lao ...   \n",
       "\n",
       "         unsloth/mistral-7b-instruct-v0.3(finetuned)  \\\n",
       "0  Old Geng aimed and fired. A triangular slit op...   \n",
       "1  Before dawn next day Grannie Liu got up and bu...   \n",
       "\n",
       "      unsloth/Qwen2-72B-Instruct-bnb-4bit(finetuned)  \n",
       "0  Old Geng raised his gun, narrowed one of his t...  \n",
       "1  As soon as it was light, Grannie Liu got up an...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"results/mac-results_final.csv\")\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['chinese',\n",
       " 'english',\n",
       " 'unsloth/Qwen2-0.5B-Instruct',\n",
       " 'unsloth/Qwen2-0.5B-Instruct(finetuned)',\n",
       " 'unsloth/Qwen2-1.5B-Instruct',\n",
       " 'unsloth/Qwen2-1.5B-Instruct(finetuned)',\n",
       " 'unsloth/Qwen2-7B-Instruct-bnb-4bit',\n",
       " 'unsloth/Qwen2-7B-Instruct-bnb-4bit(finetuned)',\n",
       " 'gradientai/Llama-3-8B-Instruct-Gradient-1048k',\n",
       " 'gradientai/Llama-3-8B-Instruct-Gradient-1048k(finetuned)',\n",
       " 'unsloth/Qwen2-7B-Instruct',\n",
       " 'unsloth/Qwen2-72B-Instruct-bnb-4bit',\n",
       " 'unsloth/Qwen2-7B-Instruct(finetuned)',\n",
       " 'unsloth/mistral-7b-instruct-v0.3',\n",
       " 'unsloth/mistral-7b-instruct-v0.3(finetuned)',\n",
       " 'unsloth/Qwen2-72B-Instruct-bnb-4bit(finetuned)']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['unsloth/Qwen2-7B-Instruct-bnb-4bit',\n",
       " 'unsloth/Qwen2-7B-Instruct-bnb-4bit(finetuned)']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = [col for col in df.columns if \"7B-Instruct-bnb-4bit\" in col]\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chinese</th>\n",
       "      <th>english</th>\n",
       "      <th>unsloth/Qwen2-0.5B-Instruct</th>\n",
       "      <th>unsloth/Qwen2-0.5B-Instruct(finetuned)</th>\n",
       "      <th>unsloth/Qwen2-1.5B-Instruct</th>\n",
       "      <th>unsloth/Qwen2-1.5B-Instruct(finetuned)</th>\n",
       "      <th>gradientai/Llama-3-8B-Instruct-Gradient-1048k</th>\n",
       "      <th>gradientai/Llama-3-8B-Instruct-Gradient-1048k(finetuned)</th>\n",
       "      <th>unsloth/Qwen2-7B-Instruct</th>\n",
       "      <th>unsloth/Qwen2-72B-Instruct-bnb-4bit</th>\n",
       "      <th>unsloth/Qwen2-7B-Instruct(finetuned)</th>\n",
       "      <th>unsloth/mistral-7b-instruct-v0.3</th>\n",
       "      <th>unsloth/mistral-7b-instruct-v0.3(finetuned)</th>\n",
       "      <th>unsloth/Qwen2-72B-Instruct-bnb-4bit(finetuned)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>老耿端起枪，眯缝起一只三角眼，一搂扳机响了枪，冰雹般的金麻雀劈哩啪啦往下落，铁砂子在柳枝间飞...</td>\n",
       "      <td>Old Geng picked up his shotgun, squinted, and ...</td>\n",
       "      <td>Old Teng holds his gun up, his eyes narrowed a...</td>\n",
       "      <td>Old Geng raised his rifle and tilted his head ...</td>\n",
       "      <td>Old Jin raises his gun, squints one eye as he ...</td>\n",
       "      <td>Old Geng raised his pistol, squinted through t...</td>\n",
       "      <td>The old man pulled out his gun, squinting one ...</td>\n",
       "      <td>Old Geng raised his rifle, squinting through t...</td>\n",
       "      <td>Old Geng raised his gun, squinted one of his t...</td>\n",
       "      <td>Lao Geng raised his gun, narrowed one of his t...</td>\n",
       "      <td>Old Geng raised his rifle and squinted into th...</td>\n",
       "      <td>Geng Da initiates firing, squinting to form a ...</td>\n",
       "      <td>Old Geng aimed and fired. A triangular slit op...</td>\n",
       "      <td>Old Geng raised his gun, narrowed one of his t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>次日天未明时，刘老老便起来梳洗了， 又将板儿教了几句话； 五六岁的孩子，听见带了他进城逛去，...</td>\n",
       "      <td>Next day Grannie Liu was up before dawn. As so...</td>\n",
       "      <td>The next morning, Liu Geo woke up at five o'cl...</td>\n",
       "      <td>But not before noon did Grannie Liu rise up an...</td>\n",
       "      <td>At dawn the next day, Liu Langlang got up earl...</td>\n",
       "      <td>She got up about dawn with a purpose already e...</td>\n",
       "      <td>The next day, when the sun had not yet risen, ...</td>\n",
       "      <td>Grannie Liu got up before daylight was even vi...</td>\n",
       "      <td>The next morning, before the dawn had fully br...</td>\n",
       "      <td>Before dawn next morning, Granny Liu got up to...</td>\n",
       "      <td>First thing in the morning Grannie Liu rose to...</td>\n",
       "      <td>The next day, when it was still dark, Liu Lao ...</td>\n",
       "      <td>Before dawn next day Grannie Liu got up and bu...</td>\n",
       "      <td>As soon as it was light, Grannie Liu got up an...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>钱老板道：“是，是，多谢香主。” 在一张椅上坐了，续道：“属下将小郡主藏在猪肚里带进宫来，一...</td>\n",
       "      <td>'Thank you, Master,' said Butcher Qian, seatin...</td>\n",
       "      <td>Money Master said, 'Yes, yes, thank you for yo...</td>\n",
       "      <td>If you like,' said Butcher Qian sitting at a t...</td>\n",
       "      <td>Mr. Qian said, \"Yes, yes, thank you very much....</td>\n",
       "      <td>Of course,' said Butcher Qian gratefully. 'Tha...</td>\n",
       "      <td>The boss said, \"Yes, thank you, Master. I sat ...</td>\n",
       "      <td>'No, no, thank you,'said Butcher Qian, sitting...</td>\n",
       "      <td>Mr. Qian said, \"Yes, yes, thank you for your c...</td>\n",
       "      <td>Mr. Qian said, \"Yes, yes, many thanks, Perfume...</td>\n",
       "      <td>Yes, Goong-goong, ' said Butcher Qian, sitting...</td>\n",
       "      <td>The boss said, \"Yes, yes, thank you, Madam. Si...</td>\n",
       "      <td>Many thanks, Master,' said Butcher Qian, and h...</td>\n",
       "      <td>Yes, yes, thank you, Master,' said Butcher Qia...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>但已经晚了，物理学家静静地躺在地上，半睁的双眼看着从他的头颅上流出的血迹，疯狂的会场瞬间陷入...</td>\n",
       "      <td>But it was already too late. The physicist lay...</td>\n",
       "      <td>But it was too late; physicist lay lifelessly ...</td>\n",
       "      <td>But already too late: the physicist lay peacef...</td>\n",
       "      <td>But it was too late. Physicists lay quietly on...</td>\n",
       "      <td>But it was too late. The physicist was already...</td>\n",
       "      <td>But it was too late. The physicist lay still o...</td>\n",
       "      <td>But it was too late. The physicist lay quietly...</td>\n",
       "      <td>But it was too late. The physicist lay quietly...</td>\n",
       "      <td>But it was too late. The physicist lay quietly...</td>\n",
       "      <td>But it was too late. The physicist lay on the ...</td>\n",
       "      <td>The text is: \"But it's too late, the physicist...</td>\n",
       "      <td>But it was already late. The physicist lay sti...</td>\n",
       "      <td>But it was too late. The physicist lay on the ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>但这时，绍琳却做出了一件出人意料的事，与一位受迫害的教育部高干结了婚，当时那名高干还在干校住...</td>\n",
       "      <td>But then Shao did something that no one expect...</td>\n",
       "      <td>But this time, Rong Ling did something out of ...</td>\n",
       "      <td>However, Shen refused to make a surprising ann...</td>\n",
       "      <td>But at this time, Shen Lin made a surprising d...</td>\n",
       "      <td>But at that moment, Shao Lin did something une...</td>\n",
       "      <td>But at that time, Shao Lin did something unexp...</td>\n",
       "      <td>However, at that moment, Shao Lin took an unex...</td>\n",
       "      <td>But then, in a surprise move, she married a hi...</td>\n",
       "      <td>But, in a surprising move, she married a perse...</td>\n",
       "      <td>But then, Shao Lin surprised everyone by marry...</td>\n",
       "      <td>Shao Lin surprisingly married a high-ranking o...</td>\n",
       "      <td>But then Shao Lin did something unexpected: sh...</td>\n",
       "      <td>But at this time, Shao Lin did something unexp...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             chinese  \\\n",
       "0  老耿端起枪，眯缝起一只三角眼，一搂扳机响了枪，冰雹般的金麻雀劈哩啪啦往下落，铁砂子在柳枝间飞...   \n",
       "1  次日天未明时，刘老老便起来梳洗了， 又将板儿教了几句话； 五六岁的孩子，听见带了他进城逛去，...   \n",
       "2  钱老板道：“是，是，多谢香主。” 在一张椅上坐了，续道：“属下将小郡主藏在猪肚里带进宫来，一...   \n",
       "3  但已经晚了，物理学家静静地躺在地上，半睁的双眼看着从他的头颅上流出的血迹，疯狂的会场瞬间陷入...   \n",
       "4  但这时，绍琳却做出了一件出人意料的事，与一位受迫害的教育部高干结了婚，当时那名高干还在干校住...   \n",
       "\n",
       "                                             english  \\\n",
       "0  Old Geng picked up his shotgun, squinted, and ...   \n",
       "1  Next day Grannie Liu was up before dawn. As so...   \n",
       "2  'Thank you, Master,' said Butcher Qian, seatin...   \n",
       "3  But it was already too late. The physicist lay...   \n",
       "4  But then Shao did something that no one expect...   \n",
       "\n",
       "                         unsloth/Qwen2-0.5B-Instruct  \\\n",
       "0  Old Teng holds his gun up, his eyes narrowed a...   \n",
       "1  The next morning, Liu Geo woke up at five o'cl...   \n",
       "2  Money Master said, 'Yes, yes, thank you for yo...   \n",
       "3  But it was too late; physicist lay lifelessly ...   \n",
       "4  But this time, Rong Ling did something out of ...   \n",
       "\n",
       "              unsloth/Qwen2-0.5B-Instruct(finetuned)  \\\n",
       "0  Old Geng raised his rifle and tilted his head ...   \n",
       "1  But not before noon did Grannie Liu rise up an...   \n",
       "2  If you like,' said Butcher Qian sitting at a t...   \n",
       "3  But already too late: the physicist lay peacef...   \n",
       "4  However, Shen refused to make a surprising ann...   \n",
       "\n",
       "                         unsloth/Qwen2-1.5B-Instruct  \\\n",
       "0  Old Jin raises his gun, squints one eye as he ...   \n",
       "1  At dawn the next day, Liu Langlang got up earl...   \n",
       "2  Mr. Qian said, \"Yes, yes, thank you very much....   \n",
       "3  But it was too late. Physicists lay quietly on...   \n",
       "4  But at this time, Shen Lin made a surprising d...   \n",
       "\n",
       "              unsloth/Qwen2-1.5B-Instruct(finetuned)  \\\n",
       "0  Old Geng raised his pistol, squinted through t...   \n",
       "1  She got up about dawn with a purpose already e...   \n",
       "2  Of course,' said Butcher Qian gratefully. 'Tha...   \n",
       "3  But it was too late. The physicist was already...   \n",
       "4  But at that moment, Shao Lin did something une...   \n",
       "\n",
       "       gradientai/Llama-3-8B-Instruct-Gradient-1048k  \\\n",
       "0  The old man pulled out his gun, squinting one ...   \n",
       "1  The next day, when the sun had not yet risen, ...   \n",
       "2  The boss said, \"Yes, thank you, Master. I sat ...   \n",
       "3  But it was too late. The physicist lay still o...   \n",
       "4  But at that time, Shao Lin did something unexp...   \n",
       "\n",
       "  gradientai/Llama-3-8B-Instruct-Gradient-1048k(finetuned)  \\\n",
       "0  Old Geng raised his rifle, squinting through t...         \n",
       "1  Grannie Liu got up before daylight was even vi...         \n",
       "2  'No, no, thank you,'said Butcher Qian, sitting...         \n",
       "3  But it was too late. The physicist lay quietly...         \n",
       "4  However, at that moment, Shao Lin took an unex...         \n",
       "\n",
       "                           unsloth/Qwen2-7B-Instruct  \\\n",
       "0  Old Geng raised his gun, squinted one of his t...   \n",
       "1  The next morning, before the dawn had fully br...   \n",
       "2  Mr. Qian said, \"Yes, yes, thank you for your c...   \n",
       "3  But it was too late. The physicist lay quietly...   \n",
       "4  But then, in a surprise move, she married a hi...   \n",
       "\n",
       "                 unsloth/Qwen2-72B-Instruct-bnb-4bit  \\\n",
       "0  Lao Geng raised his gun, narrowed one of his t...   \n",
       "1  Before dawn next morning, Granny Liu got up to...   \n",
       "2  Mr. Qian said, \"Yes, yes, many thanks, Perfume...   \n",
       "3  But it was too late. The physicist lay quietly...   \n",
       "4  But, in a surprising move, she married a perse...   \n",
       "\n",
       "                unsloth/Qwen2-7B-Instruct(finetuned)  \\\n",
       "0  Old Geng raised his rifle and squinted into th...   \n",
       "1  First thing in the morning Grannie Liu rose to...   \n",
       "2  Yes, Goong-goong, ' said Butcher Qian, sitting...   \n",
       "3  But it was too late. The physicist lay on the ...   \n",
       "4  But then, Shao Lin surprised everyone by marry...   \n",
       "\n",
       "                    unsloth/mistral-7b-instruct-v0.3  \\\n",
       "0  Geng Da initiates firing, squinting to form a ...   \n",
       "1  The next day, when it was still dark, Liu Lao ...   \n",
       "2  The boss said, \"Yes, yes, thank you, Madam. Si...   \n",
       "3  The text is: \"But it's too late, the physicist...   \n",
       "4  Shao Lin surprisingly married a high-ranking o...   \n",
       "\n",
       "         unsloth/mistral-7b-instruct-v0.3(finetuned)  \\\n",
       "0  Old Geng aimed and fired. A triangular slit op...   \n",
       "1  Before dawn next day Grannie Liu got up and bu...   \n",
       "2  Many thanks, Master,' said Butcher Qian, and h...   \n",
       "3  But it was already late. The physicist lay sti...   \n",
       "4  But then Shao Lin did something unexpected: sh...   \n",
       "\n",
       "      unsloth/Qwen2-72B-Instruct-bnb-4bit(finetuned)  \n",
       "0  Old Geng raised his gun, narrowed one of his t...  \n",
       "1  As soon as it was light, Grannie Liu got up an...  \n",
       "2  Yes, yes, thank you, Master,' said Butcher Qia...  \n",
       "3  But it was too late. The physicist lay on the ...  \n",
       "4  But at this time, Shao Lin did something unexp...  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(columns=cols, axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['chinese',\n",
       " 'english',\n",
       " 'unsloth/Qwen2-0.5B-Instruct',\n",
       " 'unsloth/Qwen2-0.5B-Instruct(finetuned)',\n",
       " 'unsloth/Qwen2-1.5B-Instruct',\n",
       " 'unsloth/Qwen2-1.5B-Instruct(finetuned)',\n",
       " 'gradientai/Llama-3-8B-Instruct-Gradient-1048k',\n",
       " 'gradientai/Llama-3-8B-Instruct-Gradient-1048k(finetuned)',\n",
       " 'unsloth/Qwen2-7B-Instruct',\n",
       " 'unsloth/Qwen2-72B-Instruct-bnb-4bit',\n",
       " 'unsloth/Qwen2-7B-Instruct(finetuned)',\n",
       " 'unsloth/mistral-7b-instruct-v0.3',\n",
       " 'unsloth/mistral-7b-instruct-v0.3(finetuned)',\n",
       " 'unsloth/Qwen2-72B-Instruct-bnb-4bit(finetuned)']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[\n",
    "    [\n",
    "        \"chinese\",\n",
    "        \"english\",\n",
    "        \"unsloth/Qwen2-0.5B-Instruct\",\n",
    "        \"unsloth/Qwen2-0.5B-Instruct(finetuned)\",\n",
    "        \"unsloth/Qwen2-1.5B-Instruct\",\n",
    "        \"unsloth/Qwen2-1.5B-Instruct(finetuned)\",\n",
    "        \"unsloth/Qwen2-7B-Instruct\",\n",
    "        \"unsloth/Qwen2-7B-Instruct(finetuned)\",\n",
    "        \"unsloth/mistral-7b-instruct-v0.3\",\n",
    "        \"unsloth/mistral-7b-instruct-v0.3(finetuned)\",\n",
    "        \"gradientai/Llama-3-8B-Instruct-Gradient-1048k\",\n",
    "        \"gradientai/Llama-3-8B-Instruct-Gradient-1048k(finetuned)\",\n",
    "        \"unsloth/Qwen2-72B-Instruct-bnb-4bit\",\n",
    "        \"unsloth/Qwen2-72B-Instruct-bnb-4bit(finetuned)\",\n",
    "    ]\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"results/experiment-2-results.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv(\"results/mac-results-no-flash-attn.csv\")\n",
    "df2 = pd.read_csv(\"results/mac-results-with-flash-attn.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['chinese',\n",
       " 'english',\n",
       " 'unsloth/Qwen2-0.5B-Instruct-bnb-4bit',\n",
       " 'unsloth/Qwen2-0.5B-Instruct-bnb-4bit(finetuned)',\n",
       " 'unsloth/Qwen2-1.5B-Instruct-bnb-4bit',\n",
       " 'unsloth/Qwen2-1.5B-Instruct-bnb-4bit(finetuned)',\n",
       " 'unsloth/Qwen2-0.5B-Instruct',\n",
       " 'unsloth/Qwen2-0.5B-Instruct(finetuned)',\n",
       " 'unsloth/Qwen2-1.5B-Instruct',\n",
       " 'unsloth/Qwen2-1.5B-Instruct(finetuned)']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.columns.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'unsloth/Qwen2-0.5B-Instruct': 'Qwen2-0.5B(flash-attn:true)',\n",
       " 'unsloth/Qwen2-0.5B-Instruct(finetuned)': 'Qwen2-0.5B(finetuned)(flash-attn:true)',\n",
       " 'unsloth/Qwen2-1.5B-Instruct': 'Qwen2-1.5B(flash-attn:true)',\n",
       " 'unsloth/Qwen2-1.5B-Instruct(finetuned)': 'Qwen2-1.5B(finetuned)(flash-attn:true)'}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = df2.columns.to_list()\n",
    "new_columns = {\n",
    "    col: col.replace(\"unsloth/\", \"\").replace(\"-Instruct\", \"\") + \"(flash-attn:true)\"\n",
    "    for col in columns[2:] if not \"4bit\" in col\n",
    "}\n",
    "new_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df2[[\"chinese\", \"english\"] + list(new_columns.keys())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns=new_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .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>chinese</th>\n",
       "      <th>english</th>\n",
       "      <th>Qwen2-0.5B(flash-attn:true)</th>\n",
       "      <th>Qwen2-0.5B(finetuned)(flash-attn:true)</th>\n",
       "      <th>Qwen2-1.5B(flash-attn:true)</th>\n",
       "      <th>Qwen2-1.5B(finetuned)(flash-attn:true)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>老耿端起枪，眯缝起一只三角眼，一搂扳机响了枪，冰雹般的金麻雀劈哩啪啦往下落，铁砂子在柳枝间飞...</td>\n",
       "      <td>Old Geng picked up his shotgun, squinted, and ...</td>\n",
       "      <td>Old耿举起枪，眯着眼睛，枪声轰鸣，子弹砰砰砰地落在地上，一颗颗冰雹般的大鸟扑棱棱地落在柳树...</td>\n",
       "      <td>Old Geng raised his rifle and tilted his head,...</td>\n",
       "      <td>Old Geer lifted his gun, squinted one of his e...</td>\n",
       "      <td>Old Geng raised his gun, squinted, and emptied...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>次日天未明时，刘老老便起来梳洗了， 又将板儿教了几句话； 五六岁的孩子，听见带了他进城逛去，...</td>\n",
       "      <td>Next day Grannie Liu was up before dawn. As so...</td>\n",
       "      <td>The next day morning when the sun was still ri...</td>\n",
       "      <td>First thing that morning the old lady did rise...</td>\n",
       "      <td>The next morning, Liu Langlang got up early an...</td>\n",
       "      <td>In the predawn light she arose, dressed, and b...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             chinese  \\\n",
       "0  老耿端起枪，眯缝起一只三角眼，一搂扳机响了枪，冰雹般的金麻雀劈哩啪啦往下落，铁砂子在柳枝间飞...   \n",
       "1  次日天未明时，刘老老便起来梳洗了， 又将板儿教了几句话； 五六岁的孩子，听见带了他进城逛去，...   \n",
       "\n",
       "                                             english  \\\n",
       "0  Old Geng picked up his shotgun, squinted, and ...   \n",
       "1  Next day Grannie Liu was up before dawn. As so...   \n",
       "\n",
       "                         Qwen2-0.5B(flash-attn:true)  \\\n",
       "0  Old耿举起枪，眯着眼睛，枪声轰鸣，子弹砰砰砰地落在地上，一颗颗冰雹般的大鸟扑棱棱地落在柳树...   \n",
       "1  The next day morning when the sun was still ri...   \n",
       "\n",
       "              Qwen2-0.5B(finetuned)(flash-attn:true)  \\\n",
       "0  Old Geng raised his rifle and tilted his head,...   \n",
       "1  First thing that morning the old lady did rise...   \n",
       "\n",
       "                         Qwen2-1.5B(flash-attn:true)  \\\n",
       "0  Old Geer lifted his gun, squinted one of his e...   \n",
       "1  The next morning, Liu Langlang got up early an...   \n",
       "\n",
       "              Qwen2-1.5B(finetuned)(flash-attn:true)  \n",
       "0  Old Geng raised his gun, squinted, and emptied...  \n",
       "1  In the predawn light she arose, dressed, and b...  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'unsloth/Qwen2-0.5B-Instruct': 'Qwen2-0.5B(flash-attn:false)',\n",
       " 'unsloth/Qwen2-0.5B-Instruct(finetuned)': 'Qwen2-0.5B(finetuned)(flash-attn:false)',\n",
       " 'unsloth/Qwen2-1.5B-Instruct': 'Qwen2-1.5B(flash-attn:false)',\n",
       " 'unsloth/Qwen2-1.5B-Instruct(finetuned)': 'Qwen2-1.5B(finetuned)(flash-attn:false)'}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_columns_2 = {key: new_columns[key].replace(\"true\", \"false\") for key in new_columns.keys()}\n",
    "new_columns_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, value in new_columns_2.items():\n",
    "    df[value] = df1[key]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['chinese',\n",
       " 'english',\n",
       " 'Qwen2-0.5B(flash-attn:true)',\n",
       " 'Qwen2-0.5B(finetuned)(flash-attn:true)',\n",
       " 'Qwen2-1.5B(flash-attn:true)',\n",
       " 'Qwen2-1.5B(finetuned)(flash-attn:true)',\n",
       " 'Qwen2-0.5B(flash-attn:false)',\n",
       " 'Qwen2-0.5B(finetuned)(flash-attn:false)',\n",
       " 'Qwen2-1.5B(flash-attn:false)',\n",
       " 'Qwen2-1.5B(finetuned)(flash-attn:false)']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = df.columns.to_list()\n",
    "columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Qwen2-0.5B(flash-attn:false)',\n",
       " 'Qwen2-0.5B(flash-attn:true)',\n",
       " 'Qwen2-0.5B(finetuned)(flash-attn:false)',\n",
       " 'Qwen2-0.5B(finetuned)(flash-attn:true)',\n",
       " 'Qwen2-1.5B(flash-attn:false)',\n",
       " 'Qwen2-1.5B(flash-attn:true)',\n",
       " 'Qwen2-1.5B(finetuned)(flash-attn:false)',\n",
       " 'Qwen2-1.5B(finetuned)(flash-attn:true)']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_columns = sorted(columns[2:], key=lambda x: x.replace(\"flash\", \"a\"))\n",
    "sorted_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .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>chinese</th>\n",
       "      <th>english</th>\n",
       "      <th>Qwen2-0.5B(flash-attn:false)</th>\n",
       "      <th>Qwen2-0.5B(flash-attn:true)</th>\n",
       "      <th>Qwen2-0.5B(finetuned)(flash-attn:false)</th>\n",
       "      <th>Qwen2-0.5B(finetuned)(flash-attn:true)</th>\n",
       "      <th>Qwen2-1.5B(flash-attn:false)</th>\n",
       "      <th>Qwen2-1.5B(flash-attn:true)</th>\n",
       "      <th>Qwen2-1.5B(finetuned)(flash-attn:false)</th>\n",
       "      <th>Qwen2-1.5B(finetuned)(flash-attn:true)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>老耿端起枪，眯缝起一只三角眼，一搂扳机响了枪，冰雹般的金麻雀劈哩啪啦往下落，铁砂子在柳枝间飞...</td>\n",
       "      <td>Old Geng picked up his shotgun, squinted, and ...</td>\n",
       "      <td>Old Teng raises his gun, closing his eyes with...</td>\n",
       "      <td>Old耿举起枪，眯着眼睛，枪声轰鸣，子弹砰砰砰地落在地上，一颗颗冰雹般的大鸟扑棱棱地落在柳树...</td>\n",
       "      <td>Old Geng raised his rifle and made a twist eye...</td>\n",
       "      <td>Old Geng raised his rifle and tilted his head,...</td>\n",
       "      <td>Old耿端起枪，眯缝起一只三角眼，一搂扳机响了枪，冰雹般的金麻雀劈哩啪啦往下落，铁砂子在柳枝...</td>\n",
       "      <td>Old Geer lifted his gun, squinted one of his e...</td>\n",
       "      <td>Old Geng raised his pistol, squinted through t...</td>\n",
       "      <td>Old Geng raised his gun, squinted, and emptied...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>次日天未明时，刘老老便起来梳洗了， 又将板儿教了几句话； 五六岁的孩子，听见带了他进城逛去，...</td>\n",
       "      <td>Next day Grannie Liu was up before dawn. As so...</td>\n",
       "      <td>The next day, after dawn, Liu Geong woke up at...</td>\n",
       "      <td>The next day morning when the sun was still ri...</td>\n",
       "      <td>First thing that made him up and go out of his...</td>\n",
       "      <td>First thing that morning the old lady did rise...</td>\n",
       "      <td>At dawn on the second day, Liu Laolao got up a...</td>\n",
       "      <td>The next morning, Liu Langlang got up early an...</td>\n",
       "      <td>But she didn't begin to wake her again until t...</td>\n",
       "      <td>In the predawn light she arose, dressed, and b...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             chinese  \\\n",
       "0  老耿端起枪，眯缝起一只三角眼，一搂扳机响了枪，冰雹般的金麻雀劈哩啪啦往下落，铁砂子在柳枝间飞...   \n",
       "1  次日天未明时，刘老老便起来梳洗了， 又将板儿教了几句话； 五六岁的孩子，听见带了他进城逛去，...   \n",
       "\n",
       "                                             english  \\\n",
       "0  Old Geng picked up his shotgun, squinted, and ...   \n",
       "1  Next day Grannie Liu was up before dawn. As so...   \n",
       "\n",
       "                        Qwen2-0.5B(flash-attn:false)  \\\n",
       "0  Old Teng raises his gun, closing his eyes with...   \n",
       "1  The next day, after dawn, Liu Geong woke up at...   \n",
       "\n",
       "                         Qwen2-0.5B(flash-attn:true)  \\\n",
       "0  Old耿举起枪，眯着眼睛，枪声轰鸣，子弹砰砰砰地落在地上，一颗颗冰雹般的大鸟扑棱棱地落在柳树...   \n",
       "1  The next day morning when the sun was still ri...   \n",
       "\n",
       "             Qwen2-0.5B(finetuned)(flash-attn:false)  \\\n",
       "0  Old Geng raised his rifle and made a twist eye...   \n",
       "1  First thing that made him up and go out of his...   \n",
       "\n",
       "              Qwen2-0.5B(finetuned)(flash-attn:true)  \\\n",
       "0  Old Geng raised his rifle and tilted his head,...   \n",
       "1  First thing that morning the old lady did rise...   \n",
       "\n",
       "                        Qwen2-1.5B(flash-attn:false)  \\\n",
       "0  Old耿端起枪，眯缝起一只三角眼，一搂扳机响了枪，冰雹般的金麻雀劈哩啪啦往下落，铁砂子在柳枝...   \n",
       "1  At dawn on the second day, Liu Laolao got up a...   \n",
       "\n",
       "                         Qwen2-1.5B(flash-attn:true)  \\\n",
       "0  Old Geer lifted his gun, squinted one of his e...   \n",
       "1  The next morning, Liu Langlang got up early an...   \n",
       "\n",
       "             Qwen2-1.5B(finetuned)(flash-attn:false)  \\\n",
       "0  Old Geng raised his pistol, squinted through t...   \n",
       "1  But she didn't begin to wake her again until t...   \n",
       "\n",
       "              Qwen2-1.5B(finetuned)(flash-attn:true)  \n",
       "0  Old Geng raised his gun, squinted, and emptied...  \n",
       "1  In the predawn light she arose, dressed, and b...  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[[\"chinese\", \"english\"] + sorted_columns]\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"results/experiment-3-results.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiment 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>chinese</th>\n",
       "      <th>english</th>\n",
       "      <th>unsloth/Qwen2-0.5B-Instruct</th>\n",
       "      <th>unsloth/Qwen2-0.5B-Instruct-bnb-4bit</th>\n",
       "      <th>unsloth/Qwen2-0.5B-Instruct(finetuned)</th>\n",
       "      <th>unsloth/Qwen2-0.5B-Instruct-bnb-4bit(finetuned)</th>\n",
       "      <th>unsloth/Qwen2-1.5B-Instruct</th>\n",
       "      <th>unsloth/Qwen2-1.5B-Instruct-bnb-4bit</th>\n",
       "      <th>unsloth/Qwen2-1.5B-Instruct(finetuned)</th>\n",
       "      <th>unsloth/Qwen2-1.5B-Instruct-bnb-4bit(finetuned)</th>\n",
       "      <th>unsloth/Qwen2-7B-Instruct</th>\n",
       "      <th>unsloth/Qwen2-7B-Instruct-bnb-4bit</th>\n",
       "      <th>unsloth/Qwen2-7B-Instruct(finetuned)</th>\n",
       "      <th>unsloth/Qwen2-7B-Instruct-bnb-4bit(finetuned)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>老耿端起枪，眯缝起一只三角眼，一搂扳机响了枪，冰雹般的金麻雀劈哩啪啦往下落，铁砂子在柳枝间飞...</td>\n",
       "      <td>Old Geng picked up his shotgun, squinted, and ...</td>\n",
       "      <td>Old Tang held his gun, squinting his eyes with...</td>\n",
       "      <td>Old Teng raised his gun and looked up at a pai...</td>\n",
       "      <td>Old Geng lifted his rifle and narrowed his eye...</td>\n",
       "      <td>Old Geng raised his rifle, squinted his eyes, ...</td>\n",
       "      <td>Old Geng took up his gun, squinted one of its ...</td>\n",
       "      <td>Old耿拿起枪，眯着眼睛一搂扳机就响了枪，金麻雀噼里啪啦的往下掉，铁砂子在柳枝间飞溅，发出“...</td>\n",
       "      <td>Old Geng raised the rifle, squeezed one tiny t...</td>\n",
       "      <td>Old Geng raised his pistol, squinted, and fire...</td>\n",
       "      <td>Old Aigang raised his rifle, squinting one of ...</td>\n",
       "      <td>Old Geng raised his gun, squinting one of his ...</td>\n",
       "      <td>Old Geng raised his rifle and squinted into th...</td>\n",
       "      <td>Old Geng raised his rifle and squinted into th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>次日天未明时，刘老老便起来梳洗了， 又将板儿教了几句话； 五六岁的孩子，听见带了他进城逛去，...</td>\n",
       "      <td>Next day Grannie Liu was up before dawn. As so...</td>\n",
       "      <td>The next morning when it was still dark, Liu G...</td>\n",
       "      <td>The next day at dawn, Liu Geowon got up early ...</td>\n",
       "      <td>It was still not light when this little update...</td>\n",
       "      <td>Three or four hours before this, Grannie Liu h...</td>\n",
       "      <td>By the time the next day dawned, Liu Lao got u...</td>\n",
       "      <td>At dawn the next day, Liu Langlang got up to b...</td>\n",
       "      <td>Having been woken just before daybreak, Granni...</td>\n",
       "      <td>But by some miracle of preparation, Grannie Li...</td>\n",
       "      <td>The next morning, before dawn, Old Liu rose to...</td>\n",
       "      <td>The next morning, before dawn, Old Liu rose to...</td>\n",
       "      <td>First thing next morning Grannie Liu rose befo...</td>\n",
       "      <td>First thing in the morning Grannie Liu rose an...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             chinese  \\\n",
       "0  老耿端起枪，眯缝起一只三角眼，一搂扳机响了枪，冰雹般的金麻雀劈哩啪啦往下落，铁砂子在柳枝间飞...   \n",
       "1  次日天未明时，刘老老便起来梳洗了， 又将板儿教了几句话； 五六岁的孩子，听见带了他进城逛去，...   \n",
       "\n",
       "                                             english  \\\n",
       "0  Old Geng picked up his shotgun, squinted, and ...   \n",
       "1  Next day Grannie Liu was up before dawn. As so...   \n",
       "\n",
       "                         unsloth/Qwen2-0.5B-Instruct  \\\n",
       "0  Old Tang held his gun, squinting his eyes with...   \n",
       "1  The next morning when it was still dark, Liu G...   \n",
       "\n",
       "                unsloth/Qwen2-0.5B-Instruct-bnb-4bit  \\\n",
       "0  Old Teng raised his gun and looked up at a pai...   \n",
       "1  The next day at dawn, Liu Geowon got up early ...   \n",
       "\n",
       "              unsloth/Qwen2-0.5B-Instruct(finetuned)  \\\n",
       "0  Old Geng lifted his rifle and narrowed his eye...   \n",
       "1  It was still not light when this little update...   \n",
       "\n",
       "     unsloth/Qwen2-0.5B-Instruct-bnb-4bit(finetuned)  \\\n",
       "0  Old Geng raised his rifle, squinted his eyes, ...   \n",
       "1  Three or four hours before this, Grannie Liu h...   \n",
       "\n",
       "                         unsloth/Qwen2-1.5B-Instruct  \\\n",
       "0  Old Geng took up his gun, squinted one of its ...   \n",
       "1  By the time the next day dawned, Liu Lao got u...   \n",
       "\n",
       "                unsloth/Qwen2-1.5B-Instruct-bnb-4bit  \\\n",
       "0  Old耿拿起枪，眯着眼睛一搂扳机就响了枪，金麻雀噼里啪啦的往下掉，铁砂子在柳枝间飞溅，发出“...   \n",
       "1  At dawn the next day, Liu Langlang got up to b...   \n",
       "\n",
       "              unsloth/Qwen2-1.5B-Instruct(finetuned)  \\\n",
       "0  Old Geng raised the rifle, squeezed one tiny t...   \n",
       "1  Having been woken just before daybreak, Granni...   \n",
       "\n",
       "     unsloth/Qwen2-1.5B-Instruct-bnb-4bit(finetuned)  \\\n",
       "0  Old Geng raised his pistol, squinted, and fire...   \n",
       "1  But by some miracle of preparation, Grannie Li...   \n",
       "\n",
       "                           unsloth/Qwen2-7B-Instruct  \\\n",
       "0  Old Aigang raised his rifle, squinting one of ...   \n",
       "1  The next morning, before dawn, Old Liu rose to...   \n",
       "\n",
       "                  unsloth/Qwen2-7B-Instruct-bnb-4bit  \\\n",
       "0  Old Geng raised his gun, squinting one of his ...   \n",
       "1  The next morning, before dawn, Old Liu rose to...   \n",
       "\n",
       "                unsloth/Qwen2-7B-Instruct(finetuned)  \\\n",
       "0  Old Geng raised his rifle and squinted into th...   \n",
       "1  First thing next morning Grannie Liu rose befo...   \n",
       "\n",
       "       unsloth/Qwen2-7B-Instruct-bnb-4bit(finetuned)  \n",
       "0  Old Geng raised his rifle and squinted into th...  \n",
       "1  First thing in the morning Grannie Liu rose an...  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"results/experiment-1-results.csv\")\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['unsloth/Qwen2-0.5B-Instruct',\n",
       " 'unsloth/Qwen2-0.5B-Instruct-bnb-4bit',\n",
       " 'unsloth/Qwen2-0.5B-Instruct(finetuned)',\n",
       " 'unsloth/Qwen2-0.5B-Instruct-bnb-4bit(finetuned)',\n",
       " 'unsloth/Qwen2-1.5B-Instruct',\n",
       " 'unsloth/Qwen2-1.5B-Instruct-bnb-4bit',\n",
       " 'unsloth/Qwen2-1.5B-Instruct(finetuned)',\n",
       " 'unsloth/Qwen2-1.5B-Instruct-bnb-4bit(finetuned)',\n",
       " 'unsloth/Qwen2-7B-Instruct',\n",
       " 'unsloth/Qwen2-7B-Instruct-bnb-4bit',\n",
       " 'unsloth/Qwen2-7B-Instruct(finetuned)',\n",
       " 'unsloth/Qwen2-7B-Instruct-bnb-4bit(finetuned)']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = df.columns.to_list()\n",
    "columns = columns[2:]\n",
    "columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unsloth/Qwen2-0.5B-Instruct: {'accuracy': 0.0, 'correct_ids': [], 'meteor': 0.26682092609395136, 'bleu_scores': {'bleu': 0.050004191193532964, 'precisions': [0.32555012625848556, 0.07871253405994551, 0.025538396146217057, 0.009553670232386574], 'brevity_penalty': 1.0, 'length_ratio': 1.010036435905929, 'translation_length': 30493, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.32107735527418346, 'rouge2': 0.09481543666757632, 'rougeL': 0.2645474205481625, 'rougeLsum': 0.26425547913473013}}\n",
      "unsloth/Qwen2-0.5B-Instruct-bnb-4bit: {'accuracy': 0.0, 'correct_ids': [], 'meteor': 0.2576132895489498, 'bleu_scores': {'bleu': 0.03850233688649031, 'precisions': [0.2645428602787839, 0.060745943190507204, 0.019074366625709438, 0.007169434612941283], 'brevity_penalty': 1.0, 'length_ratio': 1.2071546869824445, 'translation_length': 36444, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.30751535945224706, 'rouge2': 0.08383610600599486, 'rougeL': 0.2517675922478758, 'rougeLsum': 0.25139603943072897}}\n",
      "unsloth/Qwen2-0.5B-Instruct(finetuned): {'accuracy': 0.00088261253309797, 'correct_ids': [147], 'meteor': 0.29032409482315213, 'bleu_scores': {'bleu': 0.06508609399238363, 'precisions': [0.3407579117113485, 0.09377291935878182, 0.03598822203642444, 0.01652015762352228], 'brevity_penalty': 0.9858565320713017, 'length_ratio': 0.9859556144418682, 'translation_length': 29766, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.323586126247744, 'rouge2': 0.11191316292616592, 'rougeL': 0.2670634549858649, 'rougeLsum': 0.2673538376679355}}\n",
      "unsloth/Qwen2-0.5B-Instruct-bnb-4bit(finetuned): {'accuracy': 0.00264783759929391, 'correct_ids': [147, 199, 533], 'meteor': 0.2882153528907209, 'bleu_scores': {'bleu': 0.06212332936010444, 'precisions': [0.33329989299090423, 0.08908275694275486, 0.03363958619691818, 0.01549364798130207], 'brevity_penalty': 0.9904816510671316, 'length_ratio': 0.99052666445843, 'translation_length': 29904, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.31981051237575686, 'rouge2': 0.10769143698198833, 'rougeL': 0.262643848734739, 'rougeLsum': 0.26277094590425254}}\n",
      "unsloth/Qwen2-1.5B-Instruct: {'accuracy': 0.00176522506619594, 'correct_ids': [658, 659], 'meteor': 0.33552118264217856, 'bleu_scores': {'bleu': 0.08285381577653864, 'precisions': [0.40636974021865224, 0.12583290620194773, 0.051405438435685916, 0.02290685609386224], 'brevity_penalty': 0.9405675222192741, 'length_ratio': 0.9422656508777741, 'translation_length': 28447, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3882007297057595, 'rouge2': 0.14102471184327187, 'rougeL': 0.32809650129939527, 'rougeLsum': 0.3281936264077426}}\n",
      "unsloth/Qwen2-1.5B-Instruct-bnb-4bit: {'accuracy': 0.0, 'correct_ids': [], 'meteor': 0.31208334401862997, 'bleu_scores': {'bleu': 0.07153184431712963, 'precisions': [0.3459574058012913, 0.10217347823188212, 0.04077178883192462, 0.018166744764445086], 'brevity_penalty': 1.0, 'length_ratio': 1.0311692613448162, 'translation_length': 31131, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3688701301259942, 'rouge2': 0.12933895802625645, 'rougeL': 0.3127979119677111, 'rougeLsum': 0.31247785624076696}}\n",
      "unsloth/Qwen2-1.5B-Instruct(finetuned): {'accuracy': 0.00264783759929391, 'correct_ids': [147, 170, 194], 'meteor': 0.35503843183028994, 'bleu_scores': {'bleu': 0.09734851870184895, 'precisions': [0.38486636126948554, 0.12903115371448134, 0.05879839025606325, 0.030757244091566802], 'brevity_penalty': 1.0, 'length_ratio': 1.0050679032792316, 'translation_length': 30343, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3808951915588314, 'rouge2': 0.15379545924290525, 'rougeL': 0.3227424936305594, 'rougeLsum': 0.32311281481354376}}\n",
      "unsloth/Qwen2-1.5B-Instruct-bnb-4bit(finetuned): {'accuracy': 0.00264783759929391, 'correct_ids': [147, 170, 533], 'meteor': 0.345364472322921, 'bleu_scores': {'bleu': 0.09480544437628514, 'precisions': [0.383379631179089, 0.12652195088012244, 0.05757323387768404, 0.03015777157092172], 'brevity_penalty': 0.9896453343069915, 'length_ratio': 0.9896985756873137, 'translation_length': 29879, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3746101417059325, 'rouge2': 0.14870278243655505, 'rougeL': 0.31763956724149944, 'rougeLsum': 0.3176042204943608}}\n",
      "unsloth/Qwen2-7B-Instruct: {'accuracy': 0.00088261253309797, 'correct_ids': [240], 'meteor': 0.3700620963466071, 'bleu_scores': {'bleu': 0.10682777284063055, 'precisions': [0.41151630221291086, 0.14519802821689615, 0.06604907715154515, 0.033000626127951085], 'brevity_penalty': 1.0, 'length_ratio': 1.011858231202385, 'translation_length': 30548, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.4175582804843597, 'rouge2': 0.16867129788757956, 'rougeL': 0.35900107583637175, 'rougeLsum': 0.35936469683784084}}\n",
      "unsloth/Qwen2-7B-Instruct-bnb-4bit: {'accuracy': 0.00264783759929391, 'correct_ids': [240, 364, 659], 'meteor': 0.3722333296692946, 'bleu_scores': {'bleu': 0.10974864974763812, 'precisions': [0.415292107511336, 0.14678679908535544, 0.0680204487361545, 0.03498779495524817], 'brevity_penalty': 1.0, 'length_ratio': 1.0080821464060947, 'translation_length': 30434, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.4173451799225595, 'rouge2': 0.16850436028376636, 'rougeL': 0.3577484528557291, 'rougeLsum': 0.3579246697205608}}\n",
      "unsloth/Qwen2-7B-Instruct(finetuned): {'accuracy': 0.00617828773168579, 'correct_ids': [147, 199, 202, 309, 350, 531, 658], 'meteor': 0.4112873282163906, 'bleu_scores': {'bleu': 0.13823813520357786, 'precisions': [0.4426776533079097, 0.18058506870661567, 0.09432987789131958, 0.05415704387990762], 'brevity_penalty': 0.9724324694495059, 'length_ratio': 0.9728055647565419, 'translation_length': 29369, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.43296519948897394, 'rouge2': 0.19919531174519026, 'rougeL': 0.3797219923806042, 'rougeLsum': 0.3796461329864944}}\n",
      "unsloth/Qwen2-7B-Instruct-bnb-4bit(finetuned): {'accuracy': 0.0088261253309797, 'correct_ids': [12, 147, 199, 309, 350, 419, 531, 534, 658, 659], 'meteor': 0.40972424327766704, 'bleu_scores': {'bleu': 0.13629523030680843, 'precisions': [0.43929173693086004, 0.1763509485570011, 0.09143024062511411, 0.05240108153395027], 'brevity_penalty': 0.9819523660115357, 'length_ratio': 0.9821132825438887, 'translation_length': 29650, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.44016795307481993, 'rouge2': 0.20207745588311687, 'rougeL': 0.3807078177580038, 'rougeLsum': 0.3806709153621652}}\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",
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       "        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>accuracy</th>\n",
       "      <th>meteor</th>\n",
       "      <th>bleu_1</th>\n",
       "      <th>rouge_l</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.002427</td>\n",
       "      <td>0.334524</td>\n",
       "      <td>0.087780</td>\n",
       "      <td>0.317040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.002665</td>\n",
       "      <td>0.052112</td>\n",
       "      <td>0.031913</td>\n",
       "      <td>0.046755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.257613</td>\n",
       "      <td>0.038502</td>\n",
       "      <td>0.251768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000662</td>\n",
       "      <td>0.289797</td>\n",
       "      <td>0.064345</td>\n",
       "      <td>0.266434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.002207</td>\n",
       "      <td>0.340443</td>\n",
       "      <td>0.088830</td>\n",
       "      <td>0.320191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.002648</td>\n",
       "      <td>0.370605</td>\n",
       "      <td>0.107558</td>\n",
       "      <td>0.358062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.008826</td>\n",
       "      <td>0.411287</td>\n",
       "      <td>0.138238</td>\n",
       "      <td>0.380708</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        accuracy     meteor     bleu_1    rouge_l\n",
       "count  12.000000  12.000000  12.000000  12.000000\n",
       "mean    0.002427   0.334524   0.087780   0.317040\n",
       "std     0.002665   0.052112   0.031913   0.046755\n",
       "min     0.000000   0.257613   0.038502   0.251768\n",
       "25%     0.000662   0.289797   0.064345   0.266434\n",
       "50%     0.002207   0.340443   0.088830   0.320191\n",
       "75%     0.002648   0.370605   0.107558   0.358062\n",
       "max     0.008826   0.411287   0.138238   0.380708"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_df = get_metrics(df)\n",
    "metrics_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>meteor</th>\n",
       "      <th>bleu_1</th>\n",
       "      <th>rouge_l</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Qwen2-0.5B-Instruct</td>\n",
       "      <td>0.266821</td>\n",
       "      <td>0.050004</td>\n",
       "      <td>0.264547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Qwen2-0.5B-Instruct-bnb-4bit</td>\n",
       "      <td>0.257613</td>\n",
       "      <td>0.038502</td>\n",
       "      <td>0.251768</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Qwen2-0.5B-Instruct(finetuned)</td>\n",
       "      <td>0.290324</td>\n",
       "      <td>0.065086</td>\n",
       "      <td>0.267063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Qwen2-0.5B-Instruct-bnb-4bit(finetuned)</td>\n",
       "      <td>0.288215</td>\n",
       "      <td>0.062123</td>\n",
       "      <td>0.262644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Qwen2-1.5B-Instruct</td>\n",
       "      <td>0.335521</td>\n",
       "      <td>0.082854</td>\n",
       "      <td>0.328097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Qwen2-1.5B-Instruct-bnb-4bit</td>\n",
       "      <td>0.312083</td>\n",
       "      <td>0.071532</td>\n",
       "      <td>0.312798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Qwen2-1.5B-Instruct(finetuned)</td>\n",
       "      <td>0.355038</td>\n",
       "      <td>0.097349</td>\n",
       "      <td>0.322742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Qwen2-1.5B-Instruct-bnb-4bit(finetuned)</td>\n",
       "      <td>0.345364</td>\n",
       "      <td>0.094805</td>\n",
       "      <td>0.317640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Qwen2-7B-Instruct</td>\n",
       "      <td>0.370062</td>\n",
       "      <td>0.106828</td>\n",
       "      <td>0.359001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Qwen2-7B-Instruct-bnb-4bit</td>\n",
       "      <td>0.372233</td>\n",
       "      <td>0.109749</td>\n",
       "      <td>0.357748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Qwen2-7B-Instruct(finetuned)</td>\n",
       "      <td>0.411287</td>\n",
       "      <td>0.138238</td>\n",
       "      <td>0.379722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Qwen2-7B-Instruct-bnb-4bit(finetuned)</td>\n",
       "      <td>0.409724</td>\n",
       "      <td>0.136295</td>\n",
       "      <td>0.380708</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      model    meteor    bleu_1   rouge_l\n",
       "0                       Qwen2-0.5B-Instruct  0.266821  0.050004  0.264547\n",
       "1              Qwen2-0.5B-Instruct-bnb-4bit  0.257613  0.038502  0.251768\n",
       "2            Qwen2-0.5B-Instruct(finetuned)  0.290324  0.065086  0.267063\n",
       "3   Qwen2-0.5B-Instruct-bnb-4bit(finetuned)  0.288215  0.062123  0.262644\n",
       "4                       Qwen2-1.5B-Instruct  0.335521  0.082854  0.328097\n",
       "5              Qwen2-1.5B-Instruct-bnb-4bit  0.312083  0.071532  0.312798\n",
       "6            Qwen2-1.5B-Instruct(finetuned)  0.355038  0.097349  0.322742\n",
       "7   Qwen2-1.5B-Instruct-bnb-4bit(finetuned)  0.345364  0.094805  0.317640\n",
       "8                         Qwen2-7B-Instruct  0.370062  0.106828  0.359001\n",
       "9                Qwen2-7B-Instruct-bnb-4bit  0.372233  0.109749  0.357748\n",
       "10             Qwen2-7B-Instruct(finetuned)  0.411287  0.138238  0.379722\n",
       "11    Qwen2-7B-Instruct-bnb-4bit(finetuned)  0.409724  0.136295  0.380708"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_df.drop(columns=[\"all_metrics\", \"accuracy\"], inplace=True, errors=\"ignore\")\n",
    "metrics_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "perf_df = metrics_df.copy()\n",
    "perf_df.drop(columns=[\"bleu_1\", \"rouge_l\"], inplace=True)\n",
    "\n",
    "perf_df[\"train-time(mins)\"] = [\n",
    "    62.99,\n",
    "    85.05,\n",
    "    0,\n",
    "    0,\n",
    "    92.74,\n",
    "    139.92,\n",
    "    0,\n",
    "    0,\n",
    "    97.77,\n",
    "    103.4,\n",
    "    0,\n",
    "    0,\n",
    "]\n",
    "perf_df[\"eval-time(mins)\"] = [\n",
    "    22.53,\n",
    "    41.88,\n",
    "    26.47,\n",
    "    36.87,\n",
    "    30.02,\n",
    "    59.6,\n",
    "    34.15,\n",
    "    50.73,\n",
    "    37.58,\n",
    "    39.87,\n",
    "    37.05,\n",
    "    36.82,\n",
    "]\n",
    "perf_df[\"GPU\"] = [\"RTX 4080\"] * 8 + [\"L40\"] * 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>meteor</th>\n",
       "      <th>train-time(mins)</th>\n",
       "      <th>eval-time(mins)</th>\n",
       "      <th>GPU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Qwen2-0.5B-Instruct</td>\n",
       "      <td>0.266821</td>\n",
       "      <td>62.99</td>\n",
       "      <td>22.53</td>\n",
       "      <td>RTX 4080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Qwen2-0.5B-Instruct-bnb-4bit</td>\n",
       "      <td>0.257613</td>\n",
       "      <td>85.05</td>\n",
       "      <td>41.88</td>\n",
       "      <td>RTX 4080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Qwen2-0.5B-Instruct(finetuned)</td>\n",
       "      <td>0.290324</td>\n",
       "      <td>0.00</td>\n",
       "      <td>26.47</td>\n",
       "      <td>RTX 4080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Qwen2-0.5B-Instruct-bnb-4bit(finetuned)</td>\n",
       "      <td>0.288215</td>\n",
       "      <td>0.00</td>\n",
       "      <td>36.87</td>\n",
       "      <td>RTX 4080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Qwen2-1.5B-Instruct</td>\n",
       "      <td>0.335521</td>\n",
       "      <td>92.74</td>\n",
       "      <td>30.02</td>\n",
       "      <td>RTX 4080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Qwen2-1.5B-Instruct-bnb-4bit</td>\n",
       "      <td>0.312083</td>\n",
       "      <td>139.92</td>\n",
       "      <td>59.60</td>\n",
       "      <td>RTX 4080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Qwen2-1.5B-Instruct(finetuned)</td>\n",
       "      <td>0.355038</td>\n",
       "      <td>0.00</td>\n",
       "      <td>34.15</td>\n",
       "      <td>RTX 4080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Qwen2-1.5B-Instruct-bnb-4bit(finetuned)</td>\n",
       "      <td>0.345364</td>\n",
       "      <td>0.00</td>\n",
       "      <td>50.73</td>\n",
       "      <td>RTX 4080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Qwen2-7B-Instruct</td>\n",
       "      <td>0.370062</td>\n",
       "      <td>97.77</td>\n",
       "      <td>37.58</td>\n",
       "      <td>L40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Qwen2-7B-Instruct-bnb-4bit</td>\n",
       "      <td>0.372233</td>\n",
       "      <td>103.40</td>\n",
       "      <td>39.87</td>\n",
       "      <td>L40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Qwen2-7B-Instruct(finetuned)</td>\n",
       "      <td>0.411287</td>\n",
       "      <td>0.00</td>\n",
       "      <td>37.05</td>\n",
       "      <td>L40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Qwen2-7B-Instruct-bnb-4bit(finetuned)</td>\n",
       "      <td>0.409724</td>\n",
       "      <td>0.00</td>\n",
       "      <td>36.82</td>\n",
       "      <td>L40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      model    meteor  train-time(mins)  \\\n",
       "0                       Qwen2-0.5B-Instruct  0.266821             62.99   \n",
       "1              Qwen2-0.5B-Instruct-bnb-4bit  0.257613             85.05   \n",
       "2            Qwen2-0.5B-Instruct(finetuned)  0.290324              0.00   \n",
       "3   Qwen2-0.5B-Instruct-bnb-4bit(finetuned)  0.288215              0.00   \n",
       "4                       Qwen2-1.5B-Instruct  0.335521             92.74   \n",
       "5              Qwen2-1.5B-Instruct-bnb-4bit  0.312083            139.92   \n",
       "6            Qwen2-1.5B-Instruct(finetuned)  0.355038              0.00   \n",
       "7   Qwen2-1.5B-Instruct-bnb-4bit(finetuned)  0.345364              0.00   \n",
       "8                         Qwen2-7B-Instruct  0.370062             97.77   \n",
       "9                Qwen2-7B-Instruct-bnb-4bit  0.372233            103.40   \n",
       "10             Qwen2-7B-Instruct(finetuned)  0.411287              0.00   \n",
       "11    Qwen2-7B-Instruct-bnb-4bit(finetuned)  0.409724              0.00   \n",
       "\n",
       "    eval-time(mins)       GPU  \n",
       "0             22.53  RTX 4080  \n",
       "1             41.88  RTX 4080  \n",
       "2             26.47  RTX 4080  \n",
       "3             36.87  RTX 4080  \n",
       "4             30.02  RTX 4080  \n",
       "5             59.60  RTX 4080  \n",
       "6             34.15  RTX 4080  \n",
       "7             50.73  RTX 4080  \n",
       "8             37.58       L40  \n",
       "9             39.87       L40  \n",
       "10            37.05       L40  \n",
       "11            36.82       L40  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perf_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/wt3dneDgYB555BFMJlNNXoqIiIiIiIiIiFwAu4ZSS5cu5b777uPvv/9mwYIFlJSUcPnll5OXl1c6ZuLEifz88898/fXXLF26lMOHDzN8+PDS42azmX/9618UFxezcuVKPv30U2bPns1TTz1lj0sSEREREREREZEKsOvte/PmzSuzPXv2bIKDg1m/fj19+/YlKyuLWbNmMXfuXAYMGADAJ598QqtWrfj777+55JJL+OOPP9i+fTt//vknISEhdOjQgWeeeYZJkyYxdepUnJ2d7XFpIiIiIiIiIiJyDnbtlDpdVlYWAP7+/gCsX7+ekpISBg4cWDqmZcuWNG7cmFWrVgGwatUq2rZtS0hISOmYwYMHk52dzbZt28p9n6KiIrKzs0tfOTk51XVJIiIiIiIiItVKzy+rHfR9uHC1JpSyWCw8+OCD9OrVizZt2gCQkpKCs7Mzvr6+ZcaGhISQkpJSOubUQOrE8RPHyjN9+nR8fHxKX3FxcVV8NSIiIiIiIiLVy8nJCYD8/Hw7VyJw8vtw4vsi51drnr533333sXXrVpYvX17t7zV58mQeeuih0u1Dhw4pmBIRETnVXD0Gmlv1204REandHBwc8PX1JTU1FQB3d3cMBv0/vKZZrVby8/NJTU3F19cXBwcHe5dUZ9SKUOr+++/nl19+YdmyZURERJTuDw0Npbi4mMzMzDLdUkeOHCE0NLR0zJo1a8rMd+LpfCfGnM7FxQUXF5fS7ezs7Kq6FBEREREREaklzBYraxIzSM0pJNjLlW7R/jgY61doc+Jz74lgSuzH19f3rDmElM+uoZTVamXChAl8//33LFmyhOjo6DLHO3fujJOTEwsXLuS6664DYNeuXRw4cIAePXoA0KNHD5577jlSU1MJDg4GYMGCBXh7e6v7SUREREREpIGatzWZaT9vJzmrsHRfmI8rU4bFMaRNmB0rq1oGg4GwsDCCg4MpKSmxdzkNlpOTkzqkKsGuodR9993H3Llz+fHHH/Hy8ipdA8rHxwc3Nzd8fHwYO3YsDz30EP7+/nh7ezNhwgR69OjBJZdcAsDll19OXFwcI0aM4MUXXyQlJYUnnniC++67r0w3lFS92bNn8+CDD5KZmXlB5+3bt4/o6Gg2btxIhw4dqqU2ERERERFpuOZtTWb8Zxs4/UbslKxCxn+2gZm3d6pXwRTYbuVTKCJ1jV1DqZkzZwLQr1+/Mvs/+eQTRo8eDcBrr72G0Wjkuuuuo6ioiMGDB/Puu++WjnVwcOCXX35h/Pjx9OjRAw8PD0aNGsXTTz9dvcXX9FobdXRdi9GjR5OZmckPP/xQui8yMpLk5GQCAwPtV5iIiIiIiNRLZouVaT9vPyOQArACBmDaz9sZFBda727lE6lr7H773vm4urryzjvv8M4775x1TJMmTfjtt9+qsjSpRg4ODrrPVkREREREqsWaxIwyt+ydzgokZxUy/bcddG7ih6+7M34eTvi5O+Pr7oSLo7qNRGqK0d4FSPWxWCxMnz6d6Oho3NzcaN++Pd988w0Wi4WIiIjSTrUTNm7ciNFoZP/+/QC8+uqrtG3bFg8PDyIjI7n33nvJzc2t8PtPnTqVTz/9lB9//BGDwYDBYGDJkiXs27cPg8HApk2bAFiyZAkGg4H58+fTsWNH3NzcGDBgAKmpqfz++++0atUKb29vbr311jKPOj3b9YmIiIiISMOVmnP2QOpUHy1PZPz/NnDLh38z5PW/6P78Qlo8MY+4p+bRa8Yi/vXmX9z+0Wrun7uBJ374h1f+2MWs5Yl8t+Egi3emsvHAMfal55GVX4LFUjfvbBGxt1rx9D2pHtOnT+ezzz7jvffeo1mzZixbtozbb7+d+fPnc8sttzB37lzGjx9fOv5///sfvXr1okmTJgAYjUbefPNNoqOj2bt3L/feey+PPvpomdsnz+Xhhx9mx44dZGdn88knnwDg7+/P4cOHyx0/depU3n77bdzd3bnxxhu58cYbcXFxYe7cueTm5nLttdfy1ltvMWnSpHNeX1BQEJdeeunF/NGJiIiIiEgdFezlWqFxnRr7YjAYOJZfTGZ+CZn5xViskF9sJr+4gEOZBRV+T6MBfI93Wvm5O+Pr5mTrwHJ3ws/jlP3H/3nia1enutGV1RCeYij2oVCqnioqKuL555/nzz//LH1SYdOmTVm+fDnvv/8+jz76KK+88goHDhygcePGWCwWvvjiC5544onSOR588MHSr6Oionj22We55557KhxKeXp64ubmRlFRUYVu13v22Wfp1asXAGPHjmXy5MkkJCTQtGlTAK6//noWL17MpEmTznt9CqVERERERBqmbtH+hPm4nvUWPgMQ6uPK1/f0LBOsWCxWcgpNHMsvLg2qTg2sjp2yfeo/84vNWKyQkVdMRl4xkFfhWt2cHPB1PyXAci8nwPI4cdw2xtvVCWMNBkIN5SmGYh8Kpeqp+Ph48vPzGTRoUJn9xcXFdOzYkQ4dOtCqVSvmzp3LY489xtKlS0lNTeWGG24oHfvnn38yffp0du7cSXZ2NiaTicLCQvLz83F3dy8z74EDB4iLiyvdfvzxx3n88ccvqOZ27dqVfh0SEoK7u3tpIHVi35o1ayp0fSIiUvOmfgvTviu7r0UY7HzZ9nXCEXh4LizfBUUlMKQ9vDUKQnzOPe+hDJj0Bfy+GfKLIDYEPrkbuhz/X4TVClO+hQ8XQ2Ye9GoOM++AZlq+UESkQXIwGpgyLI57PttwxrETUc6UYXFndPoYjQZ83J3wcXciCo8Kv1+RyUxWfskpodVpAVaebTszv5jMgpMBl9lipaDETEGW+ZxrYJ1xDQbwcTtLgOVeNsDyOaUzy835wruyGuJTDKVmKZSqp06s/fTrr7/SqFGjMsdcXFwAuO2220pDqblz5zJkyBACAgIA2LdvH1deeSXjx4/nueeew9/fn+XLlzN27FiKi4vPCKXCw8NL14gC2216F8rJyan0a4PBUGb7xD6LxVLh6xMRkZrXOgL+nHxy+8RasXmFcPkMaN8YFh3/ncWT38Cwl+HvaWA8yyqXx/Kg1zToHwe/PwpBXrAnBfxO+azw4i/w5nz49G6IDoYnv4bBM2D7i+DqXD3XKSIitVtMkCcGOCNMCa2GDh8XRweCvR0I9q7YbYNge+hXTpGJzLySMzqzjuWXkHWWzqzcIhNWK8e7t0ousE7jWTqwTgRbJ0It235vVyem/qSnGEr1UihVT8XFxeHi4sKBAwfOeivbrbfeyhNPPMH69ev55ptveO+990qPrV+/HovFwiuvvILx+CeFr7766qzv5+joSGxs7Bn7nZ2dMZvNF3k1Z6rI9YmISM1zNEKo75n7V+yGfWmw8TnwPv57jU/vAb9xsGg7DGxT/nwv/AyRAbbOqBOig09+bbXC6/PgiWvg6i62fXPGQ8i98MN6uLlHVVyViIjUNS/M24UVuDwumDG9mta6tZAMBgPerrZb8RoHuJ//hOOKTRYyC052YJ3aeXUsv7g05Do14MrML8ZksVJkspCSXUhKdsW7ss7lxFMM1yRm0CMmoErmlIZHoVQ95eXlxcMPP8zEiROxWCz07t2brKwsVqxYgbe3N6NGjSIqKoqePXsyduxYzGYzV111Ven5sbGxlJSU8NZbbzFs2DBWrFhRJrSqqKioKObPn8+uXbsICAjAx+c892hU4fWJiEjN23MEwu8DVyfo0Qym3wSNA6HIZLvdwOWUJlhXJ9vCsMt3nT2U+mk9DG4HN7wBS3dCIz+4dyDcNcB2PDENUjJhYOuT5/i4Q/cYWLVHoZSISEO0dl8Gf+44goPRwKNDWhEb7GnvkqqMs6ORYC/XCi/mDraurLxisy3Eyj+zM+tsHVo5haYKzV/Rpx2KlEehVGXdWvsf+fnMM88QFBTE9OnT2bt3L76+vnTq1KnMWk+33XYb9957LyNHjsTNza10f/v27Xn11Vd54YUXmDx5Mn379mX69OmMHDnygmq46667WLJkCV26dCE3N5fFixcTFRVVY9cnIiI1p3sMzL7bto5UcqZtfak+T8PWF+CSWPBwsa0N9fyNtg6nx74Es8U29mz2psHMhfDQUHj8ali7Fx6YA86OMKqvLZCCM9elCvE5eUxERBoOq9XK87/tAODGLpH1KpCqLIPBgKeLI54ujkRewCory/ekcfusNecddyEBmcjpDFartfanK9Xs4MGDREZGkpSURERERJljhYWFJCYmEh0djaur/mOrL/R9FRE5j7kXf2tDZh40+Te8ejuM7Qd/bIHxn9i6m4wGuKUHbD8E3WJsC5OXx3mkbUHzlVNP7nvgU1s4tWoarNxtW3Pq8NsQ5ndyzI1v2ta6+PKBi7iAOvALKBERKWve1mTu+WwDbk4OLH2k3wWt8yRlmS1Wer+wiJSswnLXlTrxFMPlkwbUilsia7tz5Q4N2VmWFRURERG5OL4e0DwM4lNs25e3g4TXIHUmpL8H/70XDh2DpsFnnyPMF+LKPs+CVo3gwFHb1yfWrzqSVXbMkazy17YSEZH6y2S28OK8XQDc2SdagdRFOvEUQzj51MITzvUUQ5ELoVBKREREqkVuISQcsQVLpwr0sgVWi7ZBajZc1ensc/RqDruSy+7bnQxNAm1fRwfZwqeF204ez86H1Qm2Na1ERKTh+HJdEnvT8/D3cGZc36b2LqdeGNImjJm3dyLUp2zAF+rjyszbO1XpUwylYdKaUiIiIlIlHv4fDOtkC4wOH4Mp34KDEW7paTv+yVJoFQ5B3rZFyP/9X5g4BFqEn5zjsufh2i5w/+W27YlDoec0eP5HuLE7rEmADxbDB2Ntxw0GeHAIPPsDNAu1hVRPfgPhvnBN55q8ehERsaf8YhOv/7kHgAkDYvFydTrPGVJRQ9qEMSgulDWJGbXuKYZS9ymUEhERkSpxMANueRuO5kKQF/RuAX9Ps4VQYOt4mvwlZORCVBD839W20OlUCUcgPefkdtcY+P5B23lPf28LnV6/HW7rdXLMo1dCXhGMmwWZ+dC7OcybBK7O1X7JIiJSS3z0VyJpOUU09nfntu5N7F1OveNgNNAjJsDeZUg9pFCqgrQefP2i76eISNX7YsK5j8+42fY6l31vnLnvyk6219kYDPD09baXiIg0PEdzi3h/aQIADw9ugbOjVqkRqSv0X+t5ODg4AFBcXGznSqQqnfh+nvj+ioiIiIhI3fTWonjyis20beTDlW21xpFIXaJOqfNwdHTE3d2dtLQ0nJycMBqV49V1FouFtLQ03N3dcXTUfwIiIiIiInXV/qN5/G/1fgAeG9oSo9Y5EqlT9In8PAwGA2FhYSQmJrJ//357lyNVxGg00rhxYwwG/U9LRERERKSuemn+LkrMVvo2D6JXbKC9yxGRC6RQqgKcnZ1p1qyZbuGrR5ydndX1JiIiIiJSh205mMkvW5IxGOCxIS3tXY6IVIJCqQoyGo24urrauwwREREREZEGz2q1MuP3nQBc06ERceHedq5IRCpDrSIiIiIiIiJSpyzdncbKhKM4Oxh5aFBze5cjIpWkUEpERERERETqDIvlZJfUyB5NiPR3t3NFIlJZCqVERERERESkzvhh0yF2puTg5erIff1j7V2OiFwEhVIiIiIiIiJSJxSWmHnlj90AjO8Xg5+Hs50rEpGLoVBKRERERERE6oT/rtrPocwCQr1duaNXtL3LEZGLpFBKREREREREar2sghLeXhwPwEODmuPq5GDnikTkYimUEhERERERkVpv5pIEsgpKaB7iyXWdI+xdjohUAYVSIiIiIiIiUqsdzizgkxWJADw6uCUORoOdKxKRqqBQSkRERERERGq11xbspshkoVuUP5e1CrZ3OSJSRRRKiYiIiIiISK21KyWHbzccBOCxK1piMKhLSqS+UCglIiIiIiIitdaL83ZiscLQNqF0auxn73JEpAoplBIREREREZFaafXeoyzcmYqD0cDDg1vYuxwRqWIKpURERERERKTWsVqtTP99JwA3d40kJsjTzhWJSFVTKCUiIiIiIiK1zrytKWxKysTd2YF/D2xm73JEpBoolBIREREREZFapcRs4cX5uwC4s09Tgr1c7VyRiFQHhVIiIiIiIiJSq3yxNonE9DwCPJwZ17epvcsRkWqiUEpERERERERqjbwiE2/8uQeABy5rhqeLo50rEpHqolBKREREREREao2P/kokPbeIJgHu3NKtsb3LEZFqpFBKREREREREaoX03CI+WJYAwCODW+DsqI+sIvWZ/gsXERERERGRWuHNhXvIKzbTLsKHK9qE2bscEalmCqVERERERETE7hLT85i7+gAAjw1tidFosHNFIlLdtGKciIiIiIiI2N3Lf+zCZLHSr0UQPWMC7V2OSK0wZ9U+3l+6l7TcIlqFeTPtqtZ0iPQ973k/bT7MA59vZFBcCB+O7FK632q18tqC3Xy+NonsghK6RPnx7DVtiQ70qMarODt1SomIiIiIiIhdbU7K5NctyRgMMGlIS3uXI1Ir/Lz5MM/+soN/D2zGrxN6ExfmxchZq0nPLTrneUkZ+Tz/6w66Rfmfcey9pXv5ZOU+nrumDT/c1ws3J0dGfryawhJzdV3GOSmUEhEREREREbuxWq1M/30HANd2bESrMG87VyRSO3y0PJGbu0VyY5dImoV48dw1bXFzduCrdUlnPcdssfLgl5uYOKgZkf7uZY5ZrVY+XpHIhAGxXN46lFZh3rx6U3uOZBfxx/Yj1X055VIoJSIiIiIiInazZFcaf+/NwNnRyH8ub2HvckSqVU5ODtnZ2aWvoqLyu56KTRa2HsqiV+zJW1mNRgO9YgPZsD/zrPO/sXAPAR7O3NS18RnHkjIKSMspKjOnt6sTHSJ92bD/WOUv6iIolBIRERERERG7MFusvDBvJwCje0bRyNfNzhWJVK+4uDh8fHxKX9OnTy933LH8YswWK4GeLmX2B3m6kHaW2/fW7svgq7VJzLiuXbnH03ILS+eo6JzVTQudi4iIiIiIiF18v/EQO1Ny8HZ15N5+MfYuR6Tabd++nUaNGpVuu7i4nGN0xeUWmZj45SamX9cWfw/nKpmzJiiUEhERERERkRpXWGLm1T92AXBv/1h83evOB2mRyvLy8sLb+/zrpvm5O+NgNJyxqHlabtEZnU4A+4/mcfBYAXd+uq50n8VqBSDm8d9Y9J9LCfJ0LZ0j2Nu1zJxxdlrLTaGUiIiIiIiI1Lg5q/ZxOKuQMB9XRveMsnc5IrWKs6ORNo18WBmfzuDWoQBYLFZWxh9lZM8mZ4yPCfJk/oN9y+x7+Y9d5BWZmDKsNWE+bjg5GAjycmFl/FFah/sAkFNYwqakTG6/5Mw5a4JCKREREREREalRWfklvLM4AYCHBjXH1cnBzhWJ1D539o7mP19vpm2ELx0ifZi1fB/5xSZu6BwJwENfbiLEx5VJQ1ri6uRAi1CvMud7uzoBlNl/R69o3lq0h6hADyL93Xjlj92EeLtweVxIzV3YKRRKiYiIiIiISI16d0k8WQUltAjxYninCHuXI1IrDWsfTkZeMa8t2E1aThGtwr359I5uBHnZbt87lFmAwWC4oDnvubQpBcUmJn/3D9mFJXSN8uPTMd3sFgwbrNbjNxk2YAcPHiQyMpKkpCQiIvQXooiICHMv7AeceunWBv8jkohItTiUWUD/l5dQbLLw8eguDGhpnw4NkZqk3KF8RnsXICIiIiIiIg3Hawt2U2yy0D3an/4tgu1djojYkUIpERERERERqRE7U7L5dsNBACZf0eqCbz0SkfpFoZSIiIiIiIjUiBd+34nVCv9qG0aHSF97lyMidqZQSkRERERERKrdqoSjLN6VhqPRwMODW9i7HBGpBRRKiYiIiIiISLWyWq3M+H0HALd0a0x0oIedKxKR2kChlIiIiIiIiFSr3/5JYfPBLDycHXjgsmb2LkdEagmFUiIiIiIiIlJtSswWXpq/E4C7+jYlyMvFzhWJSG2hUEpERERERESqzedrDrDvaD6Bns7c1aepvcsRkVpEoZSIiIiIiIhUi9wiE28u3APAvy9rhoeLo50rEpHaRKGUiIiIiIiIVIsPl+0lPbeY6EAPbu7W2N7liEgto1BKREREREREqlxqTiEf/rUXgEcGt8DJQR8/RaQs/a0gIiIiIiIiVe7NhXvILzbTPtKXoW1C7V2OiNRCCqVERERERESkSu1Ny+XzNUkATB7aEoPBYOeKRKQ2UiglIiIiIiIiVerlP3ZhtlgZ0DKYS5oG2LscEamlFEqJiIiIiIhIldl44Bi//ZOC0QCThrS0dzkiUosplBIREREREZEqYbVamf77TgCu6xRBi1AvO1ckIrWZXUOpZcuWMWzYMMLDwzEYDPzwww9ljhsMhnJfL730UumYqKioM47PmDGjhq9EREREREREFu9KZU1iBi6ORiYOam7vckSklrNrKJWXl0f79u155513yj2enJxc5vXxxx9jMBi47rrryox7+umny4ybMGFCTZQvIiIiIiIix5ktVl74fRcAo3tFEe7rZueKRKS2c7Tnmw8dOpShQ4ee9XhoaNnHhv7444/079+fpk2bltnv5eV1xlgRERERERGpOd9uOMiuIzn4uDlx76Wx9i5HROqAOrOm1JEjR/j1118ZO3bsGcdmzJhBQEAAHTt25KWXXsJkMp1zrqKiIrKzs0tfOTk51VW2iIiIiIhIvVdYYua1BbsBuL9/LD7uTnauSETqArt2Sl2ITz/9FC8vL4YPH15m/wMPPECnTp3w9/dn5cqVTJ48meTkZF599dWzzjV9+nSmTZtW3SWLiIiIiIg0CLNX7iM5q5BGvm6M6NHE3uWISB1RZ0Kpjz/+mNtuuw1XV9cy+x966KHSr9u1a4ezszN3330306dPx8XFpdy5Jk+eXOa8Q4cOERcXVz2Fi4iIiIiI1GOZ+cW8uzgegIcGNcfVycHOFYlIXVEnQqm//vqLXbt28eWXX553bPfu3TGZTOzbt48WLVqUO8bFxaVMYJWdnV1ltYqIiIiIiDQk7yyOJ7vQRMtQL67p2Mje5YhIHVIn1pSaNWsWnTt3pn379ucdu2nTJoxGI8HBwTVQmYiIiIiISMN18Fg+n67cD8BjQ1viYDTYuSIRqUvs2imVm5tLfHx86XZiYiKbNm3C39+fxo0bA7Yupq+//ppXXnnljPNXrVrF6tWr6d+/P15eXqxatYqJEydy++234+fnV2PXISIiIiIi0hC9umA3xWYLPZoGcGnzIHuXIyJ1jF1DqXXr1tG/f//S7RPrPI0aNYrZs2cD8MUXX2C1WrnlllvOON/FxYUvvviCqVOnUlRURHR0NBMnTiyzXpSIiIiIiIhUve2Hs/l+4yEAJl/REoNBXVIicmHsGkr169cPq9V6zjHjxo1j3Lhx5R7r1KkTf//9d3WUJiIiIiIiIufwwrydWK1wZbsw2kX42rscEamD6sSaUiIiIiIiIlJ7rIxPZ+nuNByNBh4ZXP4DpkREzkehlIiIiIiIiFSYxWJlxrydANzWvTFNAjzsXJGI1FUKpURERERERKTCfv0nmS0Hs/BwdmDCZc3sXY6I1GEKpURERERERKRCik0WXpq/C4C7L40h0NPFzhWJSF2mUEpEREREREQq5PM1BziQkU+gpwt39om2dzkiUscplBIREREREZHzyiks4c2FewB4cGAz3J3t+jB3EakHFEqJiIiIiIjIeX24bC9H84ppGujBTV0j7V2OiNQDCqVERERERETknFKzC/nwr0QAHh3SAicHfZQUkYunv0lERERERETknN5YuIeCEjMdG/syuHWovcsRkXpCoZSIiIiIiIicVUJaLl+sTQJg8tBWGAwGO1ckIvWFQikRERERERE5q5fm7cJssTKwVTDdov3tXY6I1CMKpURERERERKRc6/cfY962FIwGmDSkpb3LEZF6RqGUiIiIiIiInMFqtfLC7zsBuKFzJM1CvOxckYjUNwqlRERERERE5AwLd6SyZl8GLo5GHhzUzN7liEg9pFBKREREREREyjCZLbwwz9YldUfvaMJ83OxckYjURwqlREREREREpIxvNxxkT2ouvu5O3HNpjL3LEZF6SqGUiIiIiIiIlCooNvPagj0A3N8/Fh83JztXJCL1lUIpERERERERKfXJykRSsgtp5OvGiB5N7F2OiNRjCqVEREREREQEgGN5xcxckgDAw4Ob4+LoYOeKRKQ+UyglIiIiIiIiALy9OJ6cQhNxYd5c3b6RvcsRkXpOoZSIiIiIiIiQlJHPf1ftB+CxoS0xGg12rkhE6juFUiIiIiIiIsKrC3ZTbLbQKzaAPs0C7V2OiDQACqVEREREREQauG2Hs/hh0yEAHhvSCoNBXVIiUv0USomIiIiIiDRwL8zbhdUKV7UPp22Ej73LEZEGQqGUiIiIiIhIA7YiPp1lu9NwcjDw8OUt7F2OiDQgCqVEREREREQaKIvFyvTfdwBwW/cmNA5wt3NFItKQKJQSERERERFpoH7ecpith7LxdHFkwoBYe5cjIg2MQikREREREZEGqNhk4eU/dgFwz6VNCfB0sXNFItLQKJQSERERERFpgP63ej9JGQUEe7lwR+9oe5cjIg2QQikREREREZEGJqewhLcWxQPw4MDmuDs72rkiEWmIFEqJiIiIiIg0MO8v3UtGXjExQR7c2CXC3uWISAOlUEpERERERKQBSc0u5KPlewF4dEhLHB30sVBE7EN/+4iIiIiIiDQgr/25h8ISC52b+HF5XIi9yxGRBkyhlIiIiIiISAMRn5rLV+uSAJg8tCUGg8HOFYlIQ6ZQSkREREREpIF4cd5OzBYrg+JC6BLlb+9yRKSBUyglIiIiIiLSAKzfn8Ef249gNMCkIS3sXY6IiEIpERERERGR+s5qtTL9t50A3NQ1kthgLztXJCKiUEpERERERKTeW7D9COv2H8PVyciDA5vbuxwREQAc7V2AiIiIiIiIVB+T2cIL82xdUmN7RxPi7WrnikSkouas2sf7S/eSlltEqzBvpl3Vmg6RvuWOnbc1mXcWJ7DvaB4ms5WoQA/u6hPN8E4RpWPyiky8MG8nf2w7wrH8YiL93RndM4rbL2lSQ1dUlkIpERERERGReuyb9QdJSMvDz92Juy+NsXc5IlJBP28+zLO/7ODZa9vQMdKXj1ckMnLWahY93I9AT5czxvu4OXNf/1higz1wcjCycEcqj3yzhQBPFy5tHgTAs79uZ2XCUV67qQMRfm78tSedJ3/cSoi3K4PiQmr6EnX7noiIiIiISH1VUGzmtT93AzBhQDO8XZ3sXJGIVNRHyxO5uVskN3aJpFmIF89d0xY3Zwe+WpdU7vgeMQEMaRNKbLAXTQI8uKN3NC1DvVi3L6N0zPr9x7iuUwQ9YgKI9Hfn1u6NaRXmxeakzBq6qrIUSomIiIiIiNRTH69I5Eh2ERF+btx2SWN7lyPS4OXk5JCdnV36KioqKndcscnC1kNZ9IoNLN1nNBroFRvIhv2Z530fq9XKivh09qbl0S3av3R/5yZ+/LnjCClZhVitVlYmpJOYlkefZoHnmK366PY9ERERERGReigjr5j3liQA8MjgFrg4Oti5IhGJi4srsz1lyhSmTp16xrhj+cWYLdYzbtML8nQhIS3vrPNnF5ZwyfMLKTZZMBoNPHt1G/o0Cyo9PvWq1kz+7h8umb4QR6MBo8HA9OFt6d404OIurJIUSomIiIiIiNRDby+KJ6fIRJtG3gxrF27vckQE2L59O40aNSrddnE5c22oi+Hp7MhvD/Qhr9jEyvijPPPrdiL93ekRYwudPl25j00HMvloZBca+bmxJjGDp46vKdXbDt1SCqVERERERETqmaSMfP779z4AHhvSCqPRYN+CRAQALy8vvL29zzvOz90ZB6OB9Nyyt/el5RYRVM4i5ycYjQaiAj0AaB3uQ3xqLu8uiadHTACFJWZemr+L90d0ZkBL26LmrcK82X44mw/+2muXUEprSomIiIiIiNQzL/+xixKzlT7NAu3yQVNELo6zo5E2jXxYGZ9eus9isbIy/iidmvhWeB6L1UqxyQJAidlCidmKwVA2pDYaDVit1iqp+0KpU0pERERERKQe2Xooix83HQZg0pCWdq5GRCrrzt7R/OfrzbSN8KVDpA+zlu8jv9jEDZ0jAXjoy02E+LiW/nf+zuJ42kX40MTfg2KzmcU70/h+4yGevaYNAF6uTnSP9mf6bztwdXQgws+Nv/ce5bsNB3niyriz1lGdFEqJiIiIiIjUIy/M2wnANR3CadPIx87ViEhlDWsfTkZeMa8t2E1aThGtwr359I5uBHnZbt87lFlQpuupoNjMkz9sJTmrEFcnB2KCPHjtpg4Ma39yTbm3bu3Ii/N28eCXG8nML6GRnxuPDG7B7d3t83ROg9VePVq1yMGDB4mMjCQpKYmIiAh7lyMiImJ/c7X2CLc2+B+RRKQO+mtPGiNmrcHZwcjC/1xKpL+7vUsSEZQ7nI3WlBIREREREakHLBYrM363dUndfkkTBVIiUusplBIREREREakHft5ymG2Hs/FyceT+AbH2LkdE5LwUSomIiIiIiNRxRSbbo94B7ukXg7+Hs50rEhE5P4VSIiIiIiIiddxnfx/g4LECQrxduKNXtL3LERGpEIVSIiIiIiIidVh2YQlvL9oDwMSBzXFzdrBzRSIiFaNQSkREREREpA57f2kCx/JLiA325PrOeqqXiNQdCqVERERERETqqJSsQmYtTwRg0pCWODroI56I1B36G0tERERERKSOev3P3RSWWOga5cfAVsH2LkdE5IIolBIREREREamD9hzJ4at1SQA8NrQlBoPBzhWJiFwYhVIiIiIiIiJ10Ivzd2GxwuDWIXRu4m/vckRELphCKRERERERkTpm3b4MFmw/goPRwKNDWtq7HBGRSlEoJSIiIiIiUodYrVae/20HADd1jSQmyNPOFYmIVI5CKRERERERkTpk/rYjbDiQiZuTAw9e1sze5YiIVJpCKRERERERkTrCZLbw4vydANzZJ5pgb1c7VyQiUnkKpUREREREROqIr9YdZG9aHv4ezozr29Te5YiIXBSFUiIiIiIiInVAfrGJ1/7cDcADA2LxcnWyc0UiIhdHoZSIiIiIiEgdMOuvRNJyimjs786t3ZvYuxwRkYumUEpERERERKSWO5pbxPvL9gLw8OAWODvqo5yI1H2O9i5AREREREREzmS2WFmTmEFqTiG//ZNMbpGJto18uLJtmL1LExGpEnaN15ctW8awYcMIDw/HYDDwww8/lDk+evRoDAZDmdeQIUPKjMnIyOC2227D29sbX19fxo4dS25ubg1ehYiIiIiISNWatzWZ3i8s4pYP/+bfX2xi/rYjAFzWMhij0WDn6kREqoZdQ6m8vDzat2/PO++8c9YxQ4YMITk5ufT1+eeflzl+2223sW3bNhYsWMAvv/zCsmXLGDduXHWXLiIiIiIiUi3mbU1m/GcbSM4qPOPYGwv3MG9rsh2qEhGpena9fW/o0KEMHTr0nGNcXFwIDQ0t99iOHTuYN28ea9eupUuXLgC89dZbXHHFFbz88suEh4eXe15RURFFRUWl2zk5OZW8AhERERERkapjtliZ9vN2rOcYM+3n7QyKC8VBHVMiUsfV+tXxlixZQnBwMC1atGD8+PEcPXq09NiqVavw9fUtDaQABg4ciNFoZPXq1Wedc/r06fj4+JS+4uLiqvUaREREREREylNYYiYpI591+zL4Zcthpv60rdwOqROsQHJWIWsSM2quSBGRalKrFzofMmQIw4cPJzo6moSEBB5//HGGDh3KqlWrcHBwICUlheDg4DLnODo64u/vT0pKylnnnTx5Mg899FDp9qFDhxRMiYiIiIhIlTGZLaTnFnMku9D2yiniSNbJr1OzC0nJLiQzv6RS86fmnD24EhGpK2p1KHXzzTeXft22bVvatWtHTEwMS5Ys4bLLLqv0vC4uLri4uJRuZ2dnX1SdIiIiIiLSMFitVo7ll3DkeKiUml3Ikeyik+HT8a/Tc4uwnOsevFO4OBoJ8XYlxNsFB6OBv/eevwsq2Mv1Iq9ERMT+anUodbqmTZsSGBhIfHw8l112GaGhoaSmppYZYzKZyMjIOOs6VCIiIiIiIuXJKSzhSLati+lITiEpWbaAKTXHFjalZBWSllNEsdlSofkcjAaCvVwI9nYlxMuFEG9XQn1cCT7+dYi3K6Herni7OWIw2NaHMlus9H5hESlZheWuK2UAQn1c6RbtX3UXLiJiJ3UqlDp48CBHjx4lLCwMgB49epCZmcn69evp3LkzAIsWLcJisdC9e3d7lioiIiIi0qCZLVbWJGaQmlNIsJctRLHXwtyFJWbScopIOaWb6cTtc0eyC0k93t2UV2yu8JwBHs4Ee7sS6m0LmIKPdzqFHg+bgr1dCPBwueBrdjAamDIsjvGfbcAAZYKpEzNNGRanRc5FpF6wayiVm5tLfHx86XZiYiKbNm3C398ff39/pk2bxnXXXUdoaCgJCQk8+uijxMbGMnjwYABatWrFkCFDuOuuu3jvvfcoKSnh/vvv5+abbz7rk/dERERERKR6zduazLSft5dZsDvMx5Upw+IY0iasyt7njHWbTr2V7sQaTjkXtm6Tl4sjIT62gCnEy7Xc4CnYyxVnx+p7ZtSQNmHMvL3TGX+GodXwZygiYk8Gq9VawTudq96SJUvo37//GftHjRrFzJkzueaaa9i4cSOZmZmEh4dz+eWX88wzzxASElI6NiMjg/vvv5+ff/4Zo9HIddddx5tvvomnp2eF6zh48CCRkZEkJSURERFRJdcmIiJSp83Vb+C51W4/IonUafO2JjP+sw1n3Hp24m+Vmbd3Om+ocmLdppTjodKJdZtOX8Opsus2nbh1ruzXttvqPFxqz80ktanbTEQujnKH8tn1b9x+/fpxrkxs/vz5553D39+fuXPnVmVZIiIiIiJSCWaLlWk/by93LSQrtmBq6k/biA705GjuidvpTq7blJJl267udZvqCgejgR4xAfYuQ0Sk2tSeXwOIiIiIiEidtiYxo8ztZqezAinZRQx+fVmF5quudZtERKR2UCglIiIiIiJVIjXn7IHUqVwdjUT4u9t13SYREbE/hVIiIiIiIlIlgr1cKzTukzHddFuaiIigXz2IiIiIiEiV6Bbtj7+H81mPG7A9ha9btH/NFSUiIrWWQikREREREakSiem55Bebyj12YtWnKcPitAaUiIgACqVERERERKQKpOcWMfqTtRSWWIgJ8iDU26XM8VAfV2be3okhbcLsVKGIiNQ2WlNKREREREQuSmGJmbvmrOPgsQKaBLjz1d098HV3Zk1iBqk5hQR72W7ZU4eUiIicSqGUiIiIiIhUmsVi5T9fb2bjgUx83Jz4eHRXAjxtXVJazFxERM5Ft++JiIiIiEilvfzHLn7dkoyTg4H3bu9MTJCnvUsSEZE6QqGUiIiIiIhUyldrk3h3SQIA04e3U2eUiIhcEIVSIiIiIiJywVbEp/P49/8AMGFALNd3jrBzRSIiUtcolBIRERERkQsSn5rDPZ+tx2SxclX7cB4a1NzeJYmISB2kUEpERERERCosPbeIMbPXklNooksTP168vh0Gg56qJyIiF06hlIiIiIiIVEhhiZm75qwjKaOAxv7uvD+iM65ODvYuS0RE6ihHexcgIiIiIiK1n8Vi5T9fb2bjgUx83Jz4ZExXAjxd7F1WzZirTjButdq7AhGph9QpJSIiIiIi5/XyH7v4dUsyTg4G3ru9MzFBnvYuSURE6jiFUiIiIiIick5frUvi3SUJAEwf3o4eMQF2rkhEROoDhVIiIiIiInJWK+PTefy7fwCYMCCW6ztH2LkiERGpLxRKiYiIiIhIueJTc7j7s/WYLFaGtQ/noUHN7V2SiIjUIwqlRERERETkDOm5RYyZvZacQhOdm/jx0vXtMBi04LeIiFQdhVIiIiIiIlJGYYmZu+asIymjgMb+7nwwojOuTg72LktEROoZhVIiIiIiIlLKYrHyn683s/FAJj5uTnwypisBni72LktEROohR3sXICIiIiIitccrC3bx65ZknBwMvHd7Z2KCPO1dUp0080/ba1+abbt1BDx1LQztYNtOOAIPz4Xlu6CoBIa0h7dGQYjP2ec0W2Dqt/DZCkjJhHA/GN0XnrgGTtxZabit/HNfvAUeubKKLk5EpIoolBIREREREQC+WpfEO4sTAJg+vB09YgLsXFHdFeEPM26GZqFgtcKnf8HVr8LG5yEqEC6fAe0bw6LHbeOf/AaGvQx/TwPjWe5neeFnW9D16T22kGvdXhjzAfi4wQNDbGOS3yl7zu+bYeyHcF236rtWEZHKUiglIiIiZ9jxdEt7l2B3rW61dwUiNWtlfDqPf/cPABMGxHJ95wg7V1S3DetUdvu5G22B0t/xcCjD1kG18Tnwdrcd//Qe8BsHi7bDwDblz7lyN1zdGf7V0bYdFQSfr4I1e0+OCfUte86P66F/HDQNrpLLEhGpUlpTSkRERESkgYtPzeHuz9ZjslgZ1j6chwY1t3dJ9YrZAl+sgrwi6BELRSbb7XYuTifHuDqB0WC7ne9sejaHhdtgd7Jte/N+2/ih7csffyQLft0EYy+tsksREalS6pQSEREREWnA0nOLGDN7LTmFJjo38eOl69thOLFAkVyUfw5Aj6lQWAKervD9RIiLgCBv8HCBSV/A8zfabu977EtbeJWcefb5HhsG2QXQ8hFwMNrGP3cD3Nar/PGfLgMvVxjetTquTkTk4imUEhERERFpoApLzIybs46kjAIa+7vzwYjOuDo52LuseqNFOGx6HrIK4JvVMOo9WPqELZj6+gEY/wm8Od/WIXVLD+gUZfv6bL5aDf9bAXPvg9aNYNN+ePAz24Lno/qeOf7jpbbAytW52i5RROSiKJQSEREREWmALBYr//l6MxsOZOLt6sjHo7sS4Oli77LqFWdHiA21fd05GtbuhTfmw/tj4fJ2kPAapOeAoxF8PSD03nOv/fTIXFu31M09bNttG8P+dJj+05mh1F87YVcyfDmheq5NRKQqKJQSEREREWmAXlmwi1+3JOPkYOD9EV2IDfa0d0n1nsUKRSVl9wV62f65aBukZsNVnc4874T84jOfzOdgtM17ullLbEFY+yYXVbKISLVSKCUiIiIi0sB8tS6JdxYnADB9eDt6xATYuaL6Z/IXtgXIGwdCTgHMXQlLdsD8SbbjnyyFVuG29aVW7YF//xcmDrHd8nfCZc/DtV3g/stt28M6wnM/QOMAaB0BG/fBq7/DHactZJ6dD1+vgVf0FFERqeUUSomIiIiINCAr49N5/Lt/AJgwIJbrO0fYuaL6KTUbRr5nW7jcxx3aRdoCqUFtbcd3JcPkLyEjF6KC4P+uholDy86RcMR2e98Jb42CJ7+Bez+xzR/uB3cPgKeGlz3vi79ti6ff0rNaL1FE5KIZrFZrOc2eDcvBgweJjIwkKSmJiAj9T1lERGRHy1b2LsHuWu3cYe8SRKpcfGoOw99dSXahiWHtw3nz5g560l5FzNWfEbc2+I+NIhdFuUP51CklIiIilbYuP5+PM46yrbCINLOJN8MbMdDLq/T42+lp/J6TQ0pJCU4GA3Gurvw7MIj2bm6lY7YXFvJKWipbCwsxApd7efFocAgepy+ccop0k4lX01JZkZdPjsVMFzd3Hg8JIcrZ9oipTLOZt9PTWJmXT7KpBD8HBy7z9OKBwEC8HPRkMWmY0nOLGDN7LdmFJjo38eOl69spkBIREbs6+097IiIiIueRb7HQwsWVJ0NCyj0e5ezM/wWH8ENUNP9t3IRGTk7cdTCJDJMJgFRTCXckHaCxkzNfNGnCBxGRxBcV83/JyWd9T6vVyoRDB0kqKeHtRo34NiqKMCcnxiYdIN9iASDNZCLNZOKR4CB+jIrm+dAwlufl8mRKStX/IYjUAYUlZsbNWUdSRgGN/d35YERnXJ0U0IqIiH2pU0pEREQqra+nJ309z/7Eriu9fcpsTwoK5tusLHYVFdHD0ZEluXk4GQw8GRKC8XjHxpTQEK7Zt4/9xcU0Od75dKr9JSVsLizkx6homrnYHl8/JSSEvgk5/JadzfW+vjRzceGNRidb4xs7O/PvoCAmJSdjslpxVHeINCAWi5X/fL2ZDQcy8XZ15OPRXQnwdLF3WSIiIgqlREREpGYUW618lZWJl9FIy+NhUrHVgpPBUBpIAbgYbI3cGwryyw2liq2W4+NOnmM0GHA2GNhQkM/1vr7lvn+u2YKn0ahAShqcVxbs4tctyTg5GHh/RBdig88eJIuISO0yZ9U+3l+6l7TcIlqFeTPtqtZ0iPQtd+y8rcm8sziBfUfzMJmtRAV6cFefaIZ3KruGVXxqDjN+38nqvRmYLFaahXgy8/bONPJ1K3fe6qRQSkRERKrVktxc/nP4EIVWK0GOjnwUEYmfo+1HkO7uHryYmsqsjKOM8POnwGLhtbQ0ANJM5nLni3Z2IczRkdfS05gaEoqb0cicjAxSTKaznnPMZGLm0XRu8PGtlmsUqa2+WpfEO4sTAJg+vB09YgLsXJGIiFTUz5sP8+wvO3j22jZ0jPTl4xWJjJy1mkUP9yOwnI5XHzdn7usfS2ywB04ORhbuSOWRb7YQ4OnCpc2DANh/NI/r31vFTV0ieXBgc7xcHdl9JBcXR/us7qRQSkRERKpVN3d3vouKJtNs5uusTB5KPswXjZsQ4OhIMxcXng8L44XUVF5PS8NoMHC7rx8BDg5nXfjSyWDgzUYRPJGSTI/4PTgAPdw96OPhQXnPhso1m7nn0EFiXFy4LzCwGq9UpHZZGZ/O499sABy5P/gLrt91Jeyyd1V2oKfGiUgd9dHyRG7uFsmNXSIBeO6atizamcpX65K4t1/sGeNP/8XDHb2j+XbDQdbtyygNpV6av4v+LYKZfMXJJy03CfCoxqs4N4VSIiIiUq3cjUaaODvTBGjv5saQvQl8m5XFuADbD05XevtwpbcP6SYTbkYjBuDTYxlEODuddc7Wrq58HxVNjtlMidWKv6MjN+3fRxtX1zLj8ixmxh08iIfRyFvhjXDSrXvSQMSn5nDPZ+sx4cgwn6U8FPI/e5ckIiJATk4O2dnZpdsuLi64uJzZ9VRssrD1UBb39osp3Wc0GugVG8iG/ZnnfR+r1crKhKPsTcvjsaH+gG2NwcU7U7n70hhGzFrN9sPZRPi7c2+/GAa3Dr34i6sEPX1PREREapSVk+tCnSrQ0REPo5Hfc7JxMRjo6X7+39p5OTjg7+jIvuJithUWMsDTq/RYrtnMnUlJOBngnUYRuBj1Y480DOm5RYyZvZbsQhOd3bfzUuTrGA3qFhIRqQ3i4uLw8fEpfU2fPr3cccfyizFbrGfcphfk6UJabtFZ588uLCHuqXk0+7/fGTN7LdOuak2fZrYuqfS8IvKKzcxcksClzYOYM7Ybg1uHcM9n6/l779Gqu8gLoE4pERERqbQ8i4UDxcWl24dKSthRWIiPgwO+Dg68f/QoAzw9CXR0JNNsZu6xYxwxmRjs5V16zv+OHaOjmxvuRiMr8/J4OS2ViUFBeDucfFz9vxL3MjEwiIFettBpXk42/g4OhDk6sbuoiOmpR7jM05NeHrYgK9ds5s6DSRRarLzQKJxci4Vciy0I83dwwEEdU1JPFZaYGTdnHUkZBTT2d+eD0GdxNZbYuywRETlu+/btNGrUqHS7vC6pi+Hp7MhvD/Qhr9jEyvijPPPrdiL93ekRE4D1+O8nBsWFcGefpgC0Dvdhw/5j/G/1AS5pWvPrDiqUEhERkUrbVljA6KSk0u0X0lIBuMbbmykhoSQWF/Hvw1kcM5vxNTrQxs2V/0Y2ptkpP4D9U1jA2+lp5FutNHV2ZmpIKFf5+JR5n8TiYnIsJxcxTzOZeDE1lXSTiSBHR6728eGegJPrRW0vKmRLYSEAQxL3lplrQdOmNHI686l+InWdxWLl4a83s+FAJt6ujnw8uisBf2af/0QREakxXl5eeHt7n3ecn7szDkYD6ad1RaXlFhFUziLnJxiNBqICbb+kax3uQ3xqLu8uiadHTAB+7s44Gg00O+0prDHBnqzbd6wSV3PxFEqJiIhIpXVz92B7i5ZnPf5mo4izHjthRlj4ecec/h4j/PwZ4edf6bpE6qNXFuzily3JOBoNvDeiM7GnfegQEZG6w9nRSJtGPqyMTy9d78lisbIy/igjezap8DwWq5Vik6V0znYRPuxNzyszJjEtj0a+blVX/AXQ4goiIiIiInXcV+uSeGdxAgDTh7elZ4yeNCkiUtfd2Tuaz9cm8c36g8Sn5vB/P2wlv9jEDZ1tT+N76MtNvDBvZ+n4dxbH89eeNA4czSc+NYcPl+3l+42HuLbjydsFx/WN4Zcth/l8zQH2pefx6cp9LNyZyogeFQ+6qpI6pURERERE6rCV8ek8/t0/ANzfP5Ybjj86XERE6rZh7cPJyCvmtQW7ScspolW4N5/e0Y0gL9vte4cyCzCcsk5mQbGZJ3/YSnJWIa5ODsQEefDaTR0Y1v5kV/qQNqE8d01b3l0Sz9SfttE0yJOZt3Wia9TZO9Crk8FqtTb4R3EcPHiQyMhIkpKSiIg4/20GIiIi9d2Olq3sXYLdtdq5w94liJxXfGouw99dQXahiWHtw3njpg4Yjacs5D9Xi/pzaxV83NGfY9X8OYo0YModyqdOKRERERGROuhobhFjZq8hu9BE5yZ+vHR9u7KB1EWY+afttS/Ntt06Ap66FoZ2sO2LfrD88756AG7oXv6x0e/Bp3+V3Te4HcybdHJ7dzI8MhdW7IZiE7RrDM9cD/1bX+wViYhIbaRQSkRERESkjiksMXPXnHUkZRTQ2N+dD0Z0xtXJocrmj/CHGTdDs1CwWm1h0tWvwsbnoWU4JL9TdvwHi+ClX2Fo+3PPO6QdfHL3yW0Xp7LHr3zZ9p6L/g/cnOH13+HKVyDhVQj1rZJLExGRWkShlIiIiIhIHWKxWHn4681sOJCJt6sjH4/uSsA5Hg9eGcM6ld1+7kZb59Tf8bauqdMDou/XwY3dwdP13PO6OJ09XErPgT0pMOsuW4cU2IKxd/+ErQcVSomI1Ed6+p6IiIiISB3yyoJd/LIlGUejgfdGdCY22LNa389sgS9WQV4R9Ig98/j6RNi0H8b2O/9cS3ZA8Hho8TCM/xiO5pw8FuAJLcJgzl+QVwgmM7y/CIK9oXN0lV2OiIjUIuqUEhERERGpI75al8Q7ixMAmD68LT1jAqvtvf45AD2mQmGJrQPq+4kQV87avLOWQKtw6Nn83PMNaQ/Du0J0ECSkwuNfwtAXYdU0cDCCwQB/ToZrXgOvO8FosAVS8yaBn0d1XKGIiNibQikRERERkTpgZXw6j3/3DwD394/lhi6R1fp+LcJh0/OQVQDfrIZR78HSJ8oGUwXFMHclPHnN+ee7ucfJr9s2tt2iFzMRlmyHy9rY1q66b7YtiPrrSduaUh8tgWEvw9pnIMyvii9QRETsTrfviYiIiIjUcvGpudzz2XpMFitXtgvjoUHnaUuqAs6OEBtqu3Vu+s3QvjG8Mb/smG9WQ34RjOxz4fM3DYZAL4g/YttetA1+2Qhf3A+9WkCnaHh3jC2cOv2pfSIiUj+oU0pERESkHjFbrKxJzCA1p5BgL1e6RfvjYDTYuyy5CEdzixgzew3ZhSY6N/Hj5RvaY7TD99RihaKSsvtmLYWrOkGQ94XPd/AoHM2FMF/bdn6x7Z/G035tbjTa3ltEROofhVIiIiIi9cS8rclM+3k7yVmFpfvCfFyZMiyOIW3C7FiZVFZhiZm75qwjKaOAxv7ufDCiM65ODtX+vpO/gKHtoXEg5BTYbtFbsgPmTzo5Jj4Flu2E3x4pf46WD8P0m+DarpBbCNO+g+u62p6il3AEHv0cYkNgcDvb+B7NbGtHjXoPnrrW1iH14WJITIV/dajuKxYREXtQKCUiIiJSD8zbmsz4zzZwekNJSlYh4z/bwMzbOymYqmMsFisPf72ZDQcy8XZ15OPRXQnwdKmR907NhpHvQXIm+LhDu0hbIDWo7ckxHy+FCH+4vG35c+xKhqx829cORthywHYbXmYehPvZznvmBnBxso0J9LItav5/X8GA56HEBK0j4MeHoH2Tar1cERGxE4VSIiIiInWc2WJl2s/bzwikAKyAAZj283YGxYXqVr465NUFu/llSzKORgPvjehMbLBnjb33rHHnH/P8TbbX2Vj/d/JrN2eY/9j55+zStGLjRESkftBC5yIiIiJ13JrEjDK37J3OCiRnFbImMaPmipKL8tW6JN5eHA/A9OFt6RkTaOeKREREqp5CKREREZE6LjXn7IFUZcaJfa2MT+fx7/4B4P7+sdzQJdLOFYmIiFQPhVIiIiIidVywl2uFxn2yIpFVCUexWvUos9oqPjWXez5bj8li5cp2YTw0qLm9SxIREak2CqVERERE6rhu0f6E+bhyvtWiNiVlccuHf3PNOyv4ZcthTGZLjdQnFXM0t4gxs9eQXWiicxM/Xr6hPUatASYiIvWYQikRERGROs7BaGDKsLhyFzo3HH9Nu6o1t3VvjIujkc0Hs7h/7kb6v7KET1fuI7/YVMMVy+kKS8zcNWcdSRkFNPZ354MRnXF1crB3WSIiItVKT98TERERqQcGtAzB392ZjPziMvtDfVyZMiyOIW3CAJg4qDlzVu3nv6v2kZRRwJSftvHan7sZeUkTRvaMItDTxR7lN2gWi5WHv97MhgOZeLs68vHorgTo+yAiIg2AQikRERGReuD7jQfJyC8mwMOZV29qT2Z+CcFernSL9sfhlFvAAj1deGhQc8ZfGsPX65P46K9EDmTk8+aieN5ftpfrOkdwV5+mRAd62PFqGpZXF+zmly3JOBoNvDeiM7HBnvYuSUREpEYolBI5Ya7WbOBWLXwrIlIXlZgtvL04HoDx/WK4tHnwec9xc3ZgZI8obuvehHlbU/hgWQKbD2Yxd/UBPl9zgMvjQhjXN4bOTfyqu/wG7at1SaXfu+nD29IzJtDOFYmIiNQchVIiIiIiddyPmw6TlFFAgIczt3ZvfEHnOhgN/KtdGFe0DWV1YgYfLNvLop2pzN92hPnbjtCliR/j+jZlYKsQLbpdxVYmpPP4d/8AcH//WG7oEmnnikRERGqWXRc6X7ZsGcOGDSM8PByDwcAPP/xQeqykpIRJkybRtm1bPDw8CA8PZ+TIkRw+fLjMHFFRURgMhjKvGTNm1PCViIiIiNiHyWzhneOdNnf1bYq7c+V+52gwGLikaQAfj+7Kgol9uaFzBE4OBtbtP8a4/65n4GtL+XzNAQpLzFVZfoMVn5rLPf9dj8li5cp2YTw0qLm9SxIREalxdg2l8vLyaN++Pe+8884Zx/Lz89mwYQNPPvkkGzZs4LvvvmPXrl1cddVVZ4x9+umnSU5OLn1NmDChJsoXERERsbtftiSTmJ6Hn7sTIy5pUiVzNgvx4qUb2rN80gDuuTQGL1dH9qblMfm7f+j9wmLeXrSHzNMWVJeKO5pbxJjZa8guNNG5iR8v39BeXWgiItIg2fX2vaFDhzJ06NByj/n4+LBgwYIy+95++226devGgQMHaNz4ZGu6l5cXoaGh1VqriIiISG1jtlh5a9EeAO7s0xQPl6r90S7E25XHhrbk/gGxfLHmAB8vT+RwViEv/7Gbd5ckcGOXSMb2jibS371K37c+Kywxc9ecdSRlFNDY350PRnTG1cnB3mWJiIjYhV07pS5UVlYWBoMBX1/fMvtnzJhBQEAAHTt25KWXXsJkMp1znqKiIrKzs0tfOTk51Vi1iIiISPX49Z9kEtLy8HFzYmSPqumSKo+niyN39mnK0kf789pN7WkZ6kV+sZnZK/fR7+UlTPh8I1sPZVXb+9cXFouVh7/ezIYDmXi7OvLx6K4EeLrYuywRERG7qTMLnRcWFjJp0iRuueUWvL29S/c/8MADdOrUCX9/f1auXMnkyZNJTk7m1VdfPetc06dPZ9q0aTVRtoiIiEi1sFisvLXQ1iU1tnc0Xq5O1f6eTg5Gru0YwTUdGvHXnnQ+WLaX5fHp/Lz5MD9vPkzPmADG9W3Kpc2DMBh0O9rpXl2wm1+2JONoNPDeiM7EBnvauyQRERG7qhOhVElJCTfeeCNWq5WZM2eWOfbQQw+Vft2uXTucnZ25++67mT59Oi4u5f/mafLkyWXOO3ToEHFxcdVTvIiIiEg1mLcthT2puXi5OjKqZ1SNvrfBYKBv8yD6Ng9i66EsPvxrL79sSWZlwlFWJhylZagXd/VpyrD24Tg71qnG/Grz9bok3j6+IP304W3pGRNo54pERETsr9b/lHAikNq/fz8LFiwo0yVVnu7du2Mymdi3b99Zx7i4uODt7V368vLyquKqpaGa8RMYboMH/3ty3weLoN+z4D3Wdiwz7/zzmC3w5NcQ/SC4jYaYifDM92C1nhyTWwj3z4aI+21j4h6B9/6s2usREZHayWKx8ubxLqkxvaLxcav+LqmzadPIhzdu7sjSR/pxR69o3J0d2JmSw3++3kzfFxfzwbIEcgpL7FZfbbAyIZ3J3/0DwP39Y7mhS6SdKxIREakdanWn1IlAas+ePSxevJiAgIDznrNp0yaMRiPBwcE1UKHISWsT4P1F0K5x2f35RTCkne01+cuKzfXCzzDzT/j0HmgdAev2wpgPwMcNHhhiG/PQZ7BoO3x2L0QFwR//wL2fQLgfXNW5aq9NRERqlwU7jrAzJQdPF0fG9oq2dzkARPi589SwOP59WTM+W72f2Sv3kZJdyPO/7eSthfHc2r0xY3pFE+rjau9Sa1R8ai73/Hc9JouVK9uF8dCg5vYuSUREpNawayiVm5tLfHx86XZiYiKbNm3C39+fsLAwrr/+ejZs2MAvv/yC2WwmJSUFAH9/f5ydnVm1ahWrV6+mf//+eHl5sWrVKiZOnMjtt9+On5+fvS5LGqDcQrjtXfjwTnj2h7LHHjz+gMkl2ys+38rdcHVn+FdH23ZUEHy+CtbsPWXMHhjVB/odv/N03AB4fyGsSVAoJSJSn1mtJ7ukRveMwsfdfl1S5fFxd+K+/rHc2SeaHzYe4oNle0lIy+P9ZXv5eEUiV7VvxLi+TWkRWv871Y/mFjFm9hqyC010auzLyze0x2jUWlsiIiIn2PX2vXXr1tGxY0c6drR98n7ooYfo2LEjTz31FIcOHeKnn37i4MGDdOjQgbCwsNLXypUrAdtteF988QWXXnoprVu35rnnnmPixIl88MEH9rwsaYDumw3/6gAD21TNfD2bw8JtsDvZtr15PyzfBUPbnzKmGfy0AQ5l2G7rW7wNdqfA5W2rpgYREamdFu5IZdvhbNydHRjbu3Z0SZXHxdGBm7o2ZsHES/loZBe6RflTYrby7YaDDH59GaM/WcPKhHSsp96bXo8UlpgZ99/1JGUU0NjfnQ9HdsHVycHeZYmIiNQqdu2U6tev3zl/EDnfDymdOnXi77//ruqyRC7IF6tgQyKsfabq5nxsGGQXQMtHwMFoW2PquRvgtl4nx7w1CsbNgogJ4OgARoOtU6tvq6qrQ0REaher1cqbi2xdUiN7ROHn4Wznis7PaDQwMC6EgXEhbDxwjA+W7WXethSW7Epjya402kX4cFefpgxtE4qjQ61f7rRCLBYrD3+9mfX7j+Ht6sjHo7sS4Fn+A3hEREQaslq9ppRIbZd0FP49BxZMBtcq/Fzw1Wr43wqYex+0bgSb9sODn9nWixrV1zbmrT/g73j46T/QJBCW7bR1bIX7VV3HlkidNFe3xnBr/ew8EViyO40tB7Nwc3Lgzj61t0vqbDo29mPm7Z3Zl57HR8v38vW6g2w5mMWEzzcS6e/G2F7R3Ng1Enfnuv0j6qsLdvPLlmQcjQbeG9GZ2GBPe5ckIiJSK9Xt/+OL2Nn6REjNhk7/d3Kf2WILiN7+A4o+tXU6XahH5tq6pW7uYdtu2xj2p8P0n2yhVEExPP4lfD/x5LpT7RrbwquXf1UoJSJSH1mtVt7409YldfsljQmsw503UYEePHtNWyYObM6cVfuZs2ofSRkFTP15O68v3MOIS5owqmdUnbzGr9cl8fZi25qp04e3pWdMoJ0rEhERqb0USolchMtawz8zyu4b8wG0DINJwyoXSAHkF4PxtHMdjGA53vxQYoISs+2WvTPGWCr3niIiUrstj09nU1ImLo5G7urb1N7lVIkATxcmDmrOPZfG8M36JD78K5EDGfm8tSie95ft5bpOEdzVJ5qmQXWj02hlQjqTv/sHgPv6x3BDl0g7VyQiIlK7KZQSuQhebtDmtJ83PVwgwOvk/pRM2yv+iG37nyTwcoXGgeB//Gfsy56Ha7vA/Zfbtod1hOd+gMYB0DoCNu6DV3+HOy61Hfd2h0tbwSOfg5uz7fa9pTtgzl/w6u3Ve80iIlLzTu2Suq17E4K9XO1cUdVyc3ZgRI8obu3ehPnbUnh/2V42J2Xy+ZoDfLH2AINahXD3pU3p3MTf3qWeVXxqLvf8dz0mi5Ur24Xxn0Et7F2SiIhIradQSqSavbcQpn13crvv8QXRPxkHo4+HTAlHID3n5Ji3RsGT38C9n9huDwz3g7sHwFPDT4754n6Y/CXc9i5k5NqCqeduhHsuq/5rEhGRmrVq71HW7T+Gs6ORuy+tH11S5XEwGriibRhD24SyJjGDD5btZeHOVP7YfoQ/th+hcxM/xvVtyqBWIRhPbxe2o6O5Rdwxey3ZhSY6Nfbl5Rva16r6REREaiuFUiJVbMkTZbenXmd7ncu+N8pue7nB6yNsr7MJ9YVP7q5UiSIiUsec6JK6pWskId71q0uqPAaDge5NA+jeNIA9R3L48K+9/LDxMOv3H+Pu/66naaAHd/ZpyvBOjXB1crBrrYUlZsb9dz0HMvJp7O/OhyO72L0mERGRuqJ+PHdXREREpJ76e+9RVidm4Oxg5J5+MfYup8Y1C/Hixevbs3xSf8b3i8HL1ZG96Xk8/v0/9H5hEW8t3ENmfrFdarNYrDz89WbW7z+Gt6sjH4/uSkAdXJxdRETEXhRKiYiIiNRiby2ydUnd0CWCMB83O1djP8Herkwa0pJVky/jiX+1ItzHlfTcYl5ZsJse0xcx9adtJGXk12hNry7YzS9bknE0Gnjv9s7EBteNBdlFRERqC4VSIiIiwMw/od1j4D3W9uoxBX7fdPJ4YTHc9wkE3A2ed8B1r8ORrHPPabXCU99A2H3gNhoGPg97Uk4e35cGYz+A6Adtx2MmwpRvoNhU5ZcnddS6fRmsiD+Kk4OB8Q2wS6o8ni6O3NmnKUsf7c/rN3WgVZg3BSVmZq/cx6UvLeb+uRv45+B5/uOsAl+vS+LtxfEAPD+8LT1jA6v9PUVEROobhVIiIiJAhD/MuBnWPwfrnoUBreHqV2HbQdvxiZ/Bzxvh6wdg6ZNw+BgMf+3cc774C7w5H94bA6uftj2dc/AMW8AFsPMwWKzw/h2w7UV47XbbwxEe/7J6r1XqjjcX2UKP6ztHEOHnbudqahcnByPXdGzEbw/05r9ju9GnWSAWK/yyJZlhby/nlg/+ZvGuVKxWa5W/98qEdB7//h8A7usfw41dIs9zhoiIiJRHC52LiIgAwzqV3X7uRlv31N/xtsBq1hKYe58trALbgwZaPQJ/74FLmp05n9UKr8+DJ66Bq7vY9s0ZDyH3wg/r4eYeMKS97XVC02DYlWx735dvq46rlLpk44FjLNudhoPRwL39Yu1dTq1lMBjo0yyIPs2C2HY4iw+X7eXnLcms2nuUVXuP0iLEi7v6NuWq9uE4O17872PjU3O557/rKTFbubJdGP8Z1KIKrkJERKRhUqeUiIjIacwW+GIV5BVBj1hYnwglZhjY5uSYluHQOABWxZc/R2IapGTCwNYn9/m4Q/cYWLXn7O+dlQ/+WpZGgLeOd0kN79iISH91SVVE63AfXr+5I8se7c/Y3tF4ODuw60gOD3+9mb4vLub9pQlkF5ZUev6juUXcMXst2YUmOjX25eUb2mM0GqrwCkRERBoWhVIiIiLH/XPAtl6Uyyi452P4fiLERdjCJWdH8PUoOz7Ex3asPCf2h/hU/Jz4FHjrD7h7QOWvQeqHLQczWbQzFaMB7uuvLqkL1cjXjSevjGPlY5fx6JAWBHm5kJJdyPTfd9Jr+iKe/20HyVkFFzRnYYmZcf9dz4GMfCL93fhwZBdcnRyq6QpEREQaBt2+JyIiclyLcNj0PGQVwDerYdR7sPSJmnnvQxkw5EW4oTvcpVCqwXtzoa1L6poOjYgK9DjPaDkbH3cn7u0Xy9je0fy48TAf/LWX+NRcPli2l4+XJ3JVh3DG9W1Ky1DvM841W6ysScwgNaeQIE8X5q45wPr9x/B2deST0V0J8HSxwxWJiIjULwqlREREjnN2hNhQ29edo2HtXnhjPtx0ie2JeJl5ZbuljmRBqG/5c53YfyQLwvzKntOhSdmxh49B/+egZzP4YGxVXY3UVVsPZfHnjiMYDHDfAHVJVQUXRwdu7BrJ9Z0jWLwrlfeX7WVNYgbfbTjEdxsOcWnzIO7u25QeMQEYDAbmbU1m2s/bSc4qLDOP0QDv3d6Z2GAvO12JiIhI/aJQSkRE5CwsVigqsQVUTg6wcBtc1812bNdhOHDUtuZUeaKDbMHUwm3QIcq2LzsfVifA+IEnxx3KsAVSnaNti6cbdWN9g/f28bWkhrULJyZIC4xVJaPRwGWtQrisVQibkjL5YFkC87amsHR3Gkt3p9GmkTfdowP4eHki5T2zz2LlotakEhERkbIUSomIiACTv4Ch7aFxIOQUwNyVsGQHzJ9kW6B8bD946DPw9wBvd5jwKfRoVvbJey0fhuk3wbVdwWCAB4fAsz9As1BbSPXkNxDuC9d0to0/lAH9noUmgfDyrZCWfXKus3VgSf22MyWbedtSMBhggrqkqlWHSF/eva0z+4/m8dFfiXy9Pomth7LZeij7rOcYgGk/b2dQXCgOWuBcRETkolU6lDpw4AD79+8nPz+foKAgWrdujYuL7q0XEZG6KTUbRr4HyZm2EKpdpC2QGtTWdvy122237lz3BhSZYHBbeHdM2Tl2JduennfCo1fanuA3bhZk5kPv5jBvErg6244v+Afij9heERPKzmX9X7VdqtRiJ564d0XbMJqF6BaxmtAkwINnrmnDxEHNee7X7Xy74dBZx1qB5KxC1iRm0CMmoOaKFBERqacuKJTat28fM2fO5IsvvuDgwYNYrScbm52dnenTpw/jxo3juuuuw6j7D0REpA6ZNe7cx12d4Z0xttfZnB4kGQzw9PW2V3lGX2p7iQDsOZLDb/8kA+qSsgd/D2f6Ng86Zyh1QmpO4XnHiIiIyPlVODl64IEHaN++PYmJiTz77LNs376drKwsiouLSUlJ4bfffqN379489dRTtGvXjrVr11Zn3SIiIiL1yluL4rFaYUjr0HKfBifVL9jLtUrHiYiIyLlVuFPKw8ODvXv3EhBwZqtycHAwAwYMYMCAAUyZMoV58+aRlJRE165dq7RYERERkfooPjWXn7ccBmDCZeqSspdu0f6E+biSklVY7kLnBiDUx5Vu0f41XZqIiEi9VOFQavr06RWedMiQIZUqRkRERKQhenexrUtqYKsQWof72LucBsvBaGDKsDjGf7YBA5QJpk4saz5lWJwWORcREakilVr4qaCggPz8kyu57t+/n9dff5358+dXWWEiIiIiDcG+9Dx+2GRbx+jflzU7z2ipbkPahDHz9k6E+pS9RS/Ux5WZt3diSJswO1UmIiJS/1Tq6XtXX301w4cP55577iEzM5Pu3bvj5OREeno6r776KuPHj6/qOkVERETqpXcWx2OxwoCWwbSNUJdUbTCkTRiD4kJZk5hBak4hwV62W/bUISUiIlK1KtUptWHDBvr06QPAN998Q0hICPv372fOnDm8+eabVVqgiIiISH2VlJHPdxttXVJ64l7t4mA00CMmgKs7NKJHTIACKRERkWpQqVAqPz8fLy8vAP744w+GDx+O0WjkkksuYf/+/VVaoIiIiEh99e6SeMwWK32bB9GxsZ+9yxERERGpUZUKpWJjY/nhhx9ISkpi/vz5XH755QCkpqbi7a1HGIuIiIicz8Fj+Xy97iAA/9YT90RERKQBqlQo9dRTT/Hwww8TFRVF9+7d6dGjB2DrmurYsWOVFigiIiJSH81ckoDJYqVXbACdm/jbuxwRERGRGlephc6vv/56evfuTXJyMu3bty/df9lll3HttddWWXEiIiIi9dHhzAK+WpcEwAMD9MQ9ERERaZgqFUoBhIaGEhoaWmZft27dLrogERERkfru/aUJlJitXNLUn+5NA+xdjoiIiIhdVCqUysvLY8aMGSxcuJDU1FQsFkuZ43v37q2S4kRERETqmyPZhXy+9niX1GXqkhIREZGGq1Kh1J133snSpUsZMWIEYWFhGAx6RK6IiIhIRby/dC/FJgtdo/zooS4pERERqQfmbU3m9T/3MO/Bvhd0XqVCqd9//51ff/2VXr16VeZ0ERERkQYpNaeQ/63eD9i6pPSLPREREakr/rd6P8v3pOPkYGRMryg6NvZjZXw6z/66g8T0PIZ3anTBc1YqlPLz88PfX0+JEREREbkQHy7bS5HJQsfGvvSODbR3OSIiIiIV8u6SeF5bsJuWod4kpOWyYPsR7h8Qy+yV+xjTK4rbujXBx93pguc1VqaYZ555hqeeeor8/PzKnC4iIiLS4KTnFvHZ3wcAdUmJiIhI3fLNuoNMH96Onyf0ZvaYbhSazKzff4ylj/Tj3n6xlQqkoJKdUq+88goJCQmEhIQQFRWFk1PZN9+wYUOlihERERGprz76K5GCEjPtInzo1zzI3uWIiIhIHTBn1T7eX7qXtNwiWoV5M+2q1nSI9C137LytybyzOIF9R/Mwma1EBXpwV59ohneKKHf849//w9zVB3jyyjjG9o4+Zx2HMgvoGWNbC7NbtD9ORiMTBzbH3blSsVKpSp19zTXXXNSbioiIiDQkGXnFzFm1D4B/q0tKREREKuDnzYd59pcdPHttGzpG+vLxikRGzlrNoof7EejpcsZ4Hzdn7usfS2ywB04ORhbuSOWRb7YQ4OnCpaf9Qmze1hQ2HsgkxPvMecpTbLbg4njyZjsnBwO+leyOOlWlQqkpU6Zc9BuLiIiINBQfL08kv9hM63BvBrQMtnc5IiIiUgd8tDyRm7tFcmOXSACeu6Yti3am8tW6JO7tF3vG+B4xZZ/qe0fvaL7dcJB1+zLKhFIpWYVM/Wkbc8Z2Y8wnaytczysLduPm5ABAidnKW4v24OVaNph68sq4Cs8HlQylRERERKRisvJLmL1yH6C1pERERBq6nJwcsrOzS7ddXFxwcTmzW6nYZGHroSzu7RdTus9oNNArNpAN+zPP+z5Wq5WVCUfZm5bHY0NPPqjOYrEy8ctNjOvblOYhXhWuu1uUP3vTcku3OzXx5UBG2XXGDVz4zzgVDqX8/f3ZvXs3gYGB+Pn5nfMHqoyMjAsuRERERKQ+mrUikdwiEy1DvRjUKsTe5YiIiIgdxcWV7SSaMmUKU6dOPWPcsfxizBbrGbfpBXm6kJCWd9b5swtLuOT5hRSbLBiNBp69ug19mp3skpq5NAFHBwNjekVdUN1f3t3jgsZXVIVDqddeew0vL1uK9vrrr1dLMSIiIiL1SVZBCZ+sSARsXVJGo7qkREREGrLt27fTqFGj0u3yuqQuhqezI7890Ie8YhMr44/yzK/bifR3p0dMAP8czOKTFfv49YHeF925nZFXDIC/h/NFzVPhUGrUqFHlfi0iIiIi5ft05T5yCk00D/FkSOtQe5cjIiIidubl5YW3t/d5x/m5O+NgNJCeW1Rmf1puEUHlLHJ+gtFoICrQA4DW4T7Ep+by7pJ4esQEsGZfBkfziug5Y1HpeLPFynO/bufj5YmseGzAOWvKKijh5fm7+GXLYbIKSgDwcXNiWPtw/nN5C3zcLnzh84taUyo1NZXU1FQsFkuZ/e3atbuYaUVERETqvJzCEmYtt3VJTRigLikRERGpOGdHI20a+bAyPp3Bx3+xZbFYWRl/lJE9m1R4HovVSrHJltkM79iI3rGBZY6P/Hg113aM4IYuEeecJzO/mOHvriQlu5CrOzQiNtgTgPjUHL5Zf5AV8el8N74XPhf4RL5KhVLr169n1KhR7NixA6vVWuaYwWDAbDZXZloRERGRemPOqv1kFZQQE+TBFW3D7F2OiIiI1DF39o7mP19vpm2ELx0ifZi1fB/5xSZu6Gx7Gt9DX24ixMeVSUNaAvDO4njaRfjQxN+DYrOZxTvT+H7jIZ69pg0Afh7O+J12u52j0UiQlwsxQZ7nrOWNhXtwcjCy9JH+BHmV7dSaOKg5I2et4Y2Fe3hqWA08fe+OO+6gefPmzJo1i5CQED1FRkREROQUeUUmPvprL2DrknJQl5SIiIhcoGHtw8nIK+a1BbtJyymiVbg3n97RrTQUOpRZUCaPKSg28+QPW0nOKsTVyYGYIA9eu6kDw9qHX3Qtf2w7wvPD254RSAEEe7ny2NCW/N/3W2smlNq7dy/ffvstsbGxlTldREREpF777O/9HMsvITrQgyvbqUtKREREKmdUzyhG9Ywq99jpT8R7eHALHh7c4oLmP986Uiek5RTRPOTs3VQtQr1IO239q4owXvAZwGWXXcbmzZsrc6qIiIhIvZZfbOKDZbYuqfv6x+LoUKkft0RERERqDT8PJw4eKzjr8aSMAnxraqHzjz76iFGjRrF161batGmDk1PZN77qqqsqM62IiIhInTd39QGO5hXT2N+dqztcfLu8iIiIiL31bRbES/N38dnY7jg7lv2FW5HJzCt/7OLS5kEXPG+lQqlVq1axYsUKfv/99zOOaaFzERERaagKS8y8t/REl1QMTuqSEhERkXrgocubM+ytFfR/eQkjejQhJsgTq9VKfFoun63aT7HZwms3dbjgeSsVSk2YMIHbb7+dJ598kpCQkMpMISIiIlLvfL7mAOm5RTTydWN4p3M/WllERESkrgjzceP7e3vyxA9beXHeTqzH9xuA3s2CePqq1oT7ul3wvJUKpY4ePcrEiRMVSImIiIgcZ+uSSgBsa0mpS0pERETqk0h/dz69oxtZ+SUkHs0DICrAHV9350rPWalQavjw4SxevJiYmJhKv7GIiEh9Y7YaWZPXmtQSP4KdjtHNYxsOBou9y5Ia8vW6JI5kFxHu48p1nRvZuxwRERGRauHj7kQHd98y+6xWK0fzign0dLmguSoVSjVv3pzJkyezfPly2rZte8ZC5w888EBlphUREamz5mX1YNrhcSSXnFzgMcwpjSnhHzDEZ5UdK5OaUGQy8+4SW5fU+H4xuDg62LkiERERkarT8snfWTFpAAHHQ6cxn6zhhevaEeztCkB6bjHdn/+TvdP/dUHzVvrpe56enixdupSlS5eWOWYwGBRKiYhIgzIvqwfj9z9eem/9CSklAYzf/zgzmzyvYKqe+2b9QZKzCgnxduGGLpH2LkdERESkShWZLGV+1l2TmEFhSdk7Ak7/WbgiKhVKJSYmVuY0ERGResdsNTLt8Ljj/xM2lDlmxYgBC9MOj2OQ92rdyldPFZssvLvY1iV1z6UxuDqpS0pEREQaHsP5h5xBK3CKiIhchDV5rY/fslf+/4atGEkuCWJNXuuaLUxqzPcbD3Ios4AgLxdu6dbY3uWIiIiI1BkVDqVmzJhBQUFBhcauXr2aX3/9tdJFiYiI1BWpJX5VOk7qlhKzhbcXxwNwd9+m6pISERGReslA2V/BGgwGDJVpjTpNhW/f2759O40bN+aGG25g2LBhdOnShaAg22KuJpOJ7du3s3z5cj777DMOHz7MnDlzLr46ERGRWi7Y6ViVjpO65cdNh0nKKCDQ05nbujexdzkiIiIi1cIK9H95CYbjSVResYkr3vwL4/Ftq7UyK0pdQCg1Z84cNm/ezNtvv82tt95KdnY2Dg4OuLi4kJ+fD0DHjh258847GT16NK6urpUqSEREpC7p5rGNMKc0kksCKf8WPithTul089hW06VJNTOZLbxzvEvqrj5NcXNWl5SIiIjUTy9d375a5r2ghc7bt2/Phx9+yPvvv8+WLVvYv38/BQUFBAYG0qFDBwIDA6ulSBERkdrKwWBhSvgH3LP/8XKOWgED7d12aZHzeuiXLckkpufh5+7E7ZeoS0pERETqr0g/Nzo38cPRoWqXJq/U0/eMRiMdOnSgQ4cOVVqMiIhIXdTEORlbl5SVU7ulfBxyyTJ7MS+7N7PTr2R04C/2KlGqmNli5c1FewC4s09TPFwq9SOViNQRO55uae8S7K7VrfauQETs6ZYP/2bN/w0k0NOlSufVT1AiIiIXaVb61QBc4f0XIwJ/J7XEj2CnY3Tz2Ma7qTfwypERTDs8jkDHTK70XW7naqUq/PpPMnvT8vBxc2JkD3VJiYiISP1WuRWjzk+hlIiIyEVILfHjx8x+AIwL/oEO7rvLHL8/+EvSTH7MOXolDyX9B3/HbHp6brFDpVJVLBYrby083iXVOxovVyc7VyQiIiJS/argYXtnUCglIiJyEeYc/RclVie6uG87I5ACMBhgSvgHpJt8+S2rN+P2PcEXMY/Rxm2vHaqVqjBvWwp7UnPxcnVkVK8oe5cjIiIiUiMe/nozzo7nXlPq/RFdLmjOql2hSkREpAHJt7jw2dErALgz6IezjnMwWHg18hW6e/xDrsWd0YnTOFAUUkNVSlWyWKy8ebxL6o5e0XirS0pEREQaCA8XR7xcnc75ulAX1SkVHx9PQkICffv2xc3NDavVisFQHQ1dIiIitc+3GZeRafamifNhBnmvPudYV2MJH0Y9w00JM9hR2JSRic/wTewjBDpm1VC1UhUW7DjCzpQcPF0cuaNXtL3LEREREakxU69qXeULnVeqU+ro0aMMHDiQ5s2bc8UVV5CcnAzA2LFj+c9//lOlBYqIiNRGFquBWenXAHBH4E84GCznPcfbIZ9Po6cQ4ZTCvuJwxiROJdfsVs2VSlWxWk92SY3uGYWPu7qkREREpGGorvajSoVSEydOxNHRkQMHDuDu7l66/6abbmLevHkVnmfZsmUMGzaM8PBwDAYDP/zwQ5njVquVp556irCwMNzc3Bg4cCB79uwpMyYjI4PbbrsNb29vfH19GTt2LLm5uZW5LBERkQr7M7sb+4rD8XHI4Qb/BRU+L9jpGHOaPoW/Qxb/FDRj/P7JFFu0xGNdsHBHKtsOZ+Pu7MDY3uqSEhERkYajup6+V6lQ6o8//uCFF14gIiKizP5mzZqxf//+Cs+Tl5dH+/bteeedd8o9/uKLL/Lmm2/y3nvvsXr1ajw8PBg8eDCFhYWlY2677Ta2bdvGggUL+OWXX1i2bBnjxo2rzGWJiIhU2EfHu6Ru9Z+Hu7Hogs5t6nKYj6On4WYo5K/cTjxy8EEsVt3+XptZrVbeXGT7xdjIHlH4eTjbuSIRERGRmvP5XZfg61b1XeKVCqXy8vLKdEidkJGRgYtLxe8vHDp0KM8++yzXXnvtGcesViuvv/46TzzxBFdffTXt2rVjzpw5HD58uLSjaseOHcybN4+PPvqI7t2707t3b9566y2++OILDh8+XJlLExEROa8t+bGsyWuLk6GE0YE/V2qODu67mRn1PI6Y+DGzH88n31HFVUpVWrI7jS0Hs3BzcuCuPuqSEpHyrcvP596DSVwaH0/crp38mZNT5rjVauWt9DT6xu+h4+5d3JF0gH3FxWXG3HfwIAMS4umwexd94/cwKfkwqaaSCr2/1Wpl3MGkct/7cEkJ9xxMotPuXfSO38NLqamYrNXV+yAi9c17SxPILzGXbr+7JJ6sgpN/Nx3LK2bgq0sveN5KhVJ9+vRhzpw5pdsGgwGLxcKLL75I//79KzPlGRITE0lJSWHgwIGl+3x8fOjevTurVq0CYNWqVfj6+tKly8lHDg4cOBCj0cjq1WdfcLaoqIjs7OzSV85pf2GLiIicy4kuqWE+fxHilFHpefp5beDFyDeOz3ktH6Sd+UsasT+r1cobf9q6pEb0aEJAFS/wKSL1R77FQgsXV54MKf8Jq7MyMvjs2DGmhITyReMmuBmNjDuYRJHl5LqE3dzdeS28Eb9GN+WNRo1IKi7hwUMV+4X7nGPHyl33xWy1Mv7gQUqsVv7XuAnPh4bxQ3YWb6WnV+YyRaQBWrY7jWLTyb+r3l2cQFb+yVDKZLGyN+3Cl1Kq1CIWL774Ipdddhnr1q2juLiYRx99lG3btpGRkcGKFSsqM+UZUlJSAAg57S/0kJCQ0mMpKSkEBweXOe7o6Ii/v3/pmPJMnz6dadOmVUmdIiLSsBwqDuLXzD4AjA36/qLnG+63mHSTL88nj+X55LEEOmYy3G/xRc8rVWd5fDqbkjJxcTRyV5+m9i5HRGqxvp6e9PX0LPeY1WplzrEM7g4I4DIvLwBmhIbRJyGehbm5XOHtDcAof//Scxo5OXFngD8TDh2ixGrF6RxPOt9RWMjsYxl81SSKSxPiyxxbkZdHQnERsyJjCXR0pBUwITCQV9PSuC8wEGc9QV1EzuP0vkprFXVaVqpTqk2bNuzevZvevXtz9dVXk5eXx/Dhw9m4cSMxMTFVUlh1mjx5MllZWaWv7du327skERGpIz5NvxIzDvT03Exrt8QqmXNc0PfcGWgLuB5N+jdLcjpVybxy8U7tkrqtexOCvNQlJSKVc7CkhHSzmR7uHqX7vBwcaOfqyqaCgnLPyTSb+SU7m45ubucMpAosFh5JPswTwSEEOZ7Zd7C5sIBmLi4EnnKst7sHuRYL8UUXti6iiEhVqvTjfnx8fPi///u/qqyljNDQUACOHDlCWFhY6f4jR47QoUOH0jGpqallzjOZTGRkZJSeXx4XF5cya19lZ2dXYeUiIlJf5Zjd+DxjCAB3BV58l9SpHg/7mHSTLz9k9mf8vsf5POZxOrjvrtL3kAu3au9R1u0/hrOjkbsvVZeUiFReutkEUCYYAghwdCw9dsIraanMPXaMAquV9q6uzIyIPOfcM1JT6ejmVtqBdcZ7m0wEOpz5vieOiYicj+H4q8y+KmiyrHQoVVhYyJYtW0hNTcVyyj3QAFddddVFFxYdHU1oaCgLFy4sDaGys7NZvXo148ePB6BHjx5kZmayfv16OnfuDMCiRYuwWCx07979omsQERE51VcZg8ixeBDjksSlXuurdG6jwfr/7N13dFTV3sbx70x6L6SQkEBC6B1BOoiKYEMQFMtVUcRyRb3K1Yu+4hVsWK69YUFFBTugWFBBKdKk95YQekglvU55/xgIjEmoSU4meT5rzQrnzJ59njOamclv9t6HF2JeI9MSxJL88xiT/ATftHiY5l66cIeRjo2SuuH8WCIDvQ1OIyINxZiQUEYGBXOorIy3MzN4JOUQ7zSJwVTJX4C/5+exsrCAb+N0EQYRqTl24KGvN+Dp7phwV2Kx8X+zN+Hr6QbgtN7UmTirotS8efO45ZZbyKhkYTyTyYTVaq3kURXl5+eTmHh8vnNycjLr168nNDSUpk2b8sADD/D000/TsmVL4uPjefzxx4mOjmb48OEAtG3blksvvZQ77riDqVOnUlZWxr333sv1119PdHT02ZyaiIhIpSx2Mx9mDANgbNgczKbqv2KRp9nCO82mcOPuZ9hY1Ipbdj/JrBYPE+FxpNqPJae2YncmK5Oz8HQzc/fAur88gYjUbcdGKmVYLE5T7DItFtp4ORe9Q9zdCQHiPD1p7unJRbuT2FBcTBcfnwr9riwsZH9ZGb12OY+ufeDQQbr5+DC9aTPC3N3ZWFzsdH+mpfKRWyIilRnRNcZpZNTwrk0qtjkv5oz7PatXoPvuu49rr72W//73vxUWIj8Tq1evdrpa3/jx4wEYPXo0H3/8Mf/5z38oKCjgzjvvJDs7m379+jFv3jy8vY+/aM+YMYN7772Xiy++GLPZzMiRI3n99dfPOpOIiEhl5uX04WBZJI3csrm6Bhci93cr4sP4yVyT+CJ7SqO5JflJvkqYQKBbYY0dUyr3xu+OUVKjzo8hKqjiH4IiImcixsODMDc3VhQW0Pbo3zP5Visbi4u5PjikyscdG3tQaq98FMLY0EZcExTstG/YnmQmRERwoZ9j0fXO3j68m5lJpsVSPm1vWWEh/mYzLTw9z+3ERKRBeGlU5xrp96yKUqmpqYwfP/6cClIAAwcOPOmK7SaTiSeffJInn3yyyjahoaHMnDnznHKIiIicjN0O76dfDcBNjX7C21xao8cLc8/hk/jHGZH0P7YXx3PHnseZHv9fvM1lp36wVIstoXEsTczEw83EPwe2MDqOiLiIApuNfaXH3yMOlpWxrbiYIDc3oj08uCUklHczM2nm6UmMhwevZ2QQ4e7OxUev2LehqIjNxcWc5+NDoJsb+0tLeSMjg1gPD7p4O4rjqWVljDmwnymNo+jk40O4u3uli5tHuXsQc7Tg1NfPjwRPLx5JSeHf4eFkWC28npHODcEheJrP6tpXItLAPPz1hlO2MZnghWvOrHh1VkWpa665hoULF7rElfZERETO1ZrCtmwoao2nqZSbw36slWM29Url4/gnuD7pOVYWdOTB/Q/xZtPncTOd3Xx9OTOftx4EwDXdYmgSrFFS4rq2PdnG6AiGa3tj7R1rS3ERt+7fX779fLrjokzDAwN5Niqa20NDKbLbeOLwYfJsNs7z8eG9mFi8jhaGfMwm5ufn8WZGOkV2O+Hu7vTz9ePlRtHlxSMLdpJLSymuYuRUZdxMJt6OieHJ1MPcuG8vPmYzwwKDuC8srBrPXkTqs2/WHqBJsA/towM5ydiiM3ZWRak333yTa6+9liVLltCxY0c8PDyc7r///vurJZyIiEhd8EH6cABGhPxBmHtOrR23g89u3ot7mluTJ/NzTl8mHbqTJ6OnVsuVTqRq20OasiayDW5mE/dolJSInIEevn5sbV11IdBkMnFfWDj3hYVXen8rL28+im160mM08fA86TGASu9v4uHBu6e4ip+ISFVu6tmM7zccYn9WEdd2j+Hqrk0I9j336b9nVZT6/PPP+fXXX/H29mbhwoVOV4EwmUwqSomISL2xt6Qxv+T2BuD2sDm1fvw+/ht5OfYl7tv3Hz7NvJII9yPcF/llredoSGa2vgSAEV2bEBvqa3AaEREREeM9NbwDE69sy7zNh/l69QFemLeDi9pEMOr8WAa0DKv06qCn46yKUo899hiTJ0/mkUcewaw5yCIiUo99lHEVdswMDFhNS+/9p35ADbgy+E8yLMFMOnQ3L6XeTLj7Ea5v9KshWeq7ncExrGrcFrPNyrgLNUpKRERE5BgvdzeGdWnCsC5NOHCkkG/WHODxOZux2uz8+uAA/LzOvMR0VhWl0tJSrrvuOhWkRESkXsux+PHVEceomTvCZhua5dawHxgX4Rgh9X8Hx/FrTk9D89RXx9aSuvDAOuLC/AxOIyIiIlI3mU0mTJiwY8dqO/tFps6qqjR69Gi+/FJTB0REpH6bmXUphTYf2ngn08f/1FccqWkPRX7KqJBfseHGffv+w+qCtkZHqlcSg6JZEdUBs93G9TsXGB1HREREpE4psVj5bv1BbvpgJRf+byE7UnN58qoOLHvkorMaJQVnOX3ParXywgsv8Msvv9CpU6cKC52//PLLZxVGRESkrii1ufNxxlUA3BE+u04sLm4ywbMxb5JpCWZBXg/G7HmCbxL+QyvvfUZHqxe+ODpKasCB9cTkpxucRkRERKTumDhnE3M3pBAV5M2o7rG8fkNXQv0MWuh806ZNdO3aFYDNmzc73Xe2i1uJiIjUJT/k9CfV0ogI90yGBi02Ok45d5ONN5s9zz92P83awraMTp7MtwkPE+2ZYXQ0l5Yc2Jil0Z0waZSUiIiISAUzVu4jOsiHpqG+rEzOZGVyZqXt3r25+xn1e1ZFqT/++ONsHiYiIuIS7Hb4IH04AKPDfsDTbDE20N/4mEuYFvck1yY9T2JJU25JfpJvEv5DsHu+0dFc1hetHKOk+h/cSLO8VIPTiIiIiNQtI7rG1MjMgbOb9CciIlKPLS/oxNbiBHxMxfwj9Gej41QqxD2PT+L/y4ik/5FY0pQxe55gRvOJ+JhLjI7mcvYGRLKkSScArt853+A0InVHgc3K6xkZzM/LI8tqpa2XN49GRNDRxweAdju2V/q4f4eHc3too0rvG5SUyCFLxUL/DcHBPB7ZmINlpVyye3elj305OppLAwLP8mxERORcvDSqc430e9pFqREjRvDxxx8TGBjIiBEjTtp21qxZ5xxMRETEKMdGSV0bOr9Ojz6K9szgk/j/ck3SC6wtbMu9eyfwbtzT+sbpDH3R6mLsJjN9D20kPvew0XFE6ozHDx9mV0kJz0dFE+7uztzcHG4/sJ+5cfFEeniwKKGFU/slBfk8fvgwg/0Dquzzq2ZxWE/Y3lVSwtgD+xkS4HhMY/eK/X6dnc2HWVn09/OvtnMTEZG64bQ/twYFBZWvFxUUFFRjgURERIyUWBzD73k9MGFjTNh3Rsc5pVbe+5gW9yQ37X6KBXk9+L8D9/K83a41Hk/Tfv9wFsV0AeCGHRolJXJMsc3Gb3l5vNkkhu6+vgDcGxbOwvx8vsjO5l/h4YS7O/8p8Xt+Pj18fYn1rHrh29C/PeaDrExiPTw438dxDDeTqUK/8/PzuDQwAD/zWV04XERE6rDTLkp99NFHPPnkkzz00EN89NFHNZlJRETEMNMyhgFwSeBK4rxSDE5zes7328obTV/g7r3/x1dHBhPx604eGtLa6Fgu4cujo6R6p2wmIeeQ0XFE6gwrdqyA598K3N5mM2uLCiu0z7BYWJyfz7NRUad9jFK7nbm5uYwOCamykL6luJjtJSU8Hhl5RvlFRMQ1nNHXDZMnTyY/v+5OYxARETkXmZZAZh25CIA7wmcbnObMDA5ayTNN3gbgzT8Smb5sj7GBXMAhv0b8EXseADds/83gNCJ1i5/ZjS7ePkzNzCDNUobVbuf7nBzWFxWRbrFWaP9dTg6+ZjOXnGTq3t8tyMsjz2rl6pPMwvg2J5vmnp50PTqSSkRE6pczKkrZ7faayiEiImK4zzIvp8TuRWefnXT33Wp0nDN2Q6NfGB/5GQCT5m7hh40a+XMyX7S6GJvJTI/DW2mZc9DoOCJ1znNRUdiBgUlJdNm5gxnZR7g8IBBzJYOaZuXmcGVgIF5nMMVuVk4O/f38iHD3qPT+YpuNH3NzGamlQ0RE6q0znpitNSpERKQ+KrZ58GnmFQCMDZ9dI5e8rQ33RXzBzb2aYbfD+C83sCwpw+hIddJh31AWxHYDtJaUSFWaenrySdNmrG7Zit8TWvBlszgs2InxcC4irS4sJLm0lGuCgk+774NlZSwvLGDkSR7za14eRTYbwwJVlBIRqa/O+AI9rVq1OmVhKisr66wDiYiInIttT7Y5q8f90qwHGV1DiCg8QvwbmWyzn10/dcGkre3JyC/h582HufOTNXx5Vy/aR+uPuhN92eoibGY3uqVup82RfUbHEanTfM1mfM1mcqxWlhYU8O/wCKf7Z+Vk097Lmzbe3qfd5+ycbELd3LjAv+or6n2bk81F/gEVFkcXEZH644xf4SdPnqyr74mISL1iB2YlDABgWNIS3Ow2YwOdIzeziVeu60JWwV+sTM7i1o9WMeuffYgN1ZosAKk+IfzW9HwA/qG1pESq9GdBPnYg3sOTfWVlvJieRrynp9MaUPlWK7/k5fFwRESlfdy2fx+D/AP4R0hI+T6b3c7snByGBwXhXsWX3XtLS1ldVMTUmJhqPScREalbzrgodf311xNRxZuOiIiIK1od0YZ9gY3xKStmyN6/jI5TLbw93Hjvlu5c9+5yth/O4+ZpK/nmn30I8/cyOprhvmp1IVazG13TdtL2yF6j44jUWXlWG69mpHPYYiHIbGZwQAD/CgvH44RC0k95ediBKwICK+1jf2kpR6wWp33LCwtJsVgYcZKpe7Nycoh0d6evr191nIqIiNRRZ1SU0npSIiJSH81u4RglddnelfhZig1OU32CfDyYPqYHI95exp7MQsZ8vIrP7+iFn1fDnQqT7hPEr816AHDjDo2SEjmZywIDuSyw8mLTMaOCgxkVHFzl/fMTWlTY19fPj62tTz5F+sHwcB4MDz+tnCIi4rp09T0REWnQdgdGsS6iFWablauS/jQ6TrWLDPTmk9t7EOLrwcYDOdz92RpKLa49PfFcfN3yQixmdzqlJ9IhM9noOCIiIiIN2hkVpWw2m6buiYhIvXJslFS/QxuJLDpicJqakRDuz4e3no+PhxtLdmXwn282YLM1vC+aMr0DmdesJ6BRUiIiIiJ1wRkVpUREROqTTO9AFsZ0BWBE4mKD09Ssrk1DePum83A3m5iz/hBTft5mdKRa902LgZS5edA+YzedMpKMjiMiIiLS4KkoJSIiDdbc+D5YzO60z9hN6+z9RsepcRe2juD5kZ0AeH9JMu8tbjiFmSyvAH6K7w04RklplUwRERER4zXclU5FRKRBK3bz5Mf4PgCMSFoEQIHNyusZGczPyyPLaqWtlzePRkTQ0ceHMrud1zPSWZxfwIGyUvzNbvT282V8eDgR7h5VHmdQUiKHLJYK+28IDubxyMZO++x2O3cdPMCfBQW8Ht2EQQEB1XjGDiO7xZCRX8KUn7fz7E/bCfP3YsR59f+S69+2uIBSNw/aZu6ha/ouo+OIiIiICCpKiYhIAzW/aXfyPX2Jys+gZ8pWAB4/fJhdJSU8HxVNuLs7c3NzuP3AfubGxeNrNrO1uJi7GzWijbcXuVYbz6alMu7AQb6Oi6vyOF81i8N6wvaukhLGHtjPkEoKTp8cOVIrI3juHNCctLwSpv2ZzH++2UionycDW9ffNSOzPf3LC5AaJSUiIiJSd2j6noiINDhWTMxO6A/A1UmLccNOsc3Gb3l5PBQeQXdfX5p5enJvWDhNPTz4IjubADc3psU25bLAQOI9vejs48PEiEi2lBRzqKysymOFursTfsJtUUE+sR4enO/j69RuW3ExHx/J4unGUTV67gAmk4nHLm/LsC7RWGx27pmxlg37s2v8uEaZ1WIAJe6etDqyj25pO4yOIyIiIiJHqSglIiINzsqodhzyD8e/tJBL9q0GwIodK+Bpch5H4202s7aosNJ+8mw2TECg+fTeTkvtdubm5jIiKAjTCccpstl4OOUQEyMiCXevnUHMZrOJF6/pTP+WYRSWWrnt41XsTs+vlWPXphxPX+Y27wvAjds1SkpERESkLlFRSkREGpzZCRcAcEXycrytpQD4md3o4u3D1MwM0ixlWO12vs/JYX1REekWa4U+Smw2Xk5P4/KAQPzd3E7ruAvy8sizWrk6KMhp/3NpaXT18eHiGlhD6mQ83c28c1M3OjYJIquglFs+/Iu03OJazVDT5iQMoNjdi4TsA/RIbXhXHBQRERGpy1SUEhGRBmVHcCybw5rjbrMwNHmp033PRUVhBwYmJdFl5w5mZB/h8oBAzH8bXlNmtzP+0CHswBORkad97Fk5OfT383NaGP33/DxWFhbwSMTp91Od/L3c+ei284lr5MuBI0WM/mgVucVVT0d0JXkePnzXvB+gtaRERERE6iIVpUREpEGZ3WIAABccWE+j4lyn+5p6evJJ02asbtmK3xNa8GWzOCzYifE4XkRyFKQOcshSxrTYpqc9SupgWRnLCwsYGRTstH9lYSH7y8rotWsnHXdsp+OO7QA8cOggo/ftPYczPX1h/l58MqYnYf5ebEvJ5c5PVlNqdv1rocxJ6E+RhzfNcw7SO2WL0XFERERE5G9UlBIRkQYjzSeYJdGdABiRuKjKdr5mM+Hu7uRYrSwtKOAif8e0umMFqb2lpUyLiSX4NAtSALNzsgl1c+MCf3+n/WNDGzEnLp5ZJ9wAJkRE8EwtLHp+TNNGvnx82/n4e7mzYncWL3a7AasLjy3K9/Dmu6OL2d+wY74Ln4mIiIhI/aWilIiINBjfNe+HzexGl7SdNM9NqXD/nwX5LCnI50BpKcsKCrh1/z7iPT25OiiIMrudBw4dZEtxMS9ERWMF0i0W0i0WSu328j5u27+PGUeOOPVrs9uZnZPD8KAg3P+2kHq4uzstvbycbgBR7h7EeHpW/5NwEh2aBPHezd3wcDPxZ5POvNtpOPZTP6xO+r55Pwo8fGiWe5g+hzYbHUdEREREKuH6Y/NFREROQ4G7F/PiegIwInFxpW3yrDZezUjnsMVCkNnM4IAA/hUWjofJxMGyUv7Id1ydbsTePU6P+zg2lh6+fgDsLy3liNXidP/ywkJSLBZG/G3qXl3Up0UYL4/qwv0z1zC3eV9CinO5YecCo2OdkQJ3L2YnOKZp3rDjN8wuW1oTERERqd9UlBIRkQbh12Y9KPTwITY3lW5pOyptc1lgIJcFBlZ6XxMPT7a2bnPK48xPaFFhX18/v9N67DFn0rYmDO0czZZH/8vUTlfzSbvLCCnJ49K9fxma6Uz8EN+XfE9fYvNS6Xdwo9FxRERERM7aJ8v38O6i3aTnl9A2KpDJV7WnS2xwpW3nbU7hrT+S2JNZgMVqJy7Mjzv6xzPivBgAyqw2/vfrDhZuT2dfViEB3u70axHGhMvaEBnoXYtndZyKUiIiUu9ZTWbmHF1faETSIo2cOQ3Ddi/liFcgX7a+mDe6XENwST69Dm81OtYpFbl5MqvFBQBcv2M+bvpvLSIiIi5q7oZDPP3DNp6+ugNdY4P5cGkyt0xbye8PDSTM36tC+yAfT8Zd2IIWEX54uJlZsC2Nh7/ZSCN/Ly5oFU5RmZUtB3O57+IWtI0KJKeojMlztzJ2+mrm3tfPgDPUmlIiItIALI3qSJpvKEEl+Vy0f63RcVzG6G0/c8nev7CZzEw5/2a2hMYZHemUfozvQ66XH03y07ng4Aaj44iIiIictQ/+TOb6HrGM6h5Ly8gAnhneER9PN75avb/S9r0TGnFph8a0iAigWSM/xvSLp03jAFbvyQIg0NuDz8b25MpO0SSE+3Ne0xCevKo9mw7mcDC7qDZPrZyKUiIiUq/ZoXzkzNDdS/G0WU7+AClnAv61/ht6HN5KqZsHk3qNYW9ApNGxqlTs5sk3LQcCR0dJ2W3GBhIRERH5m7y8PHJzc8tvJSUllbYrtdjYfDCHvi3CyveZzSb6tghj7d7sUx7HbrezNDGD3ekF9IgPrTpPsQWTCQK9jZlIp+l79cVMXeyaGzVFQ0Qq2hIax47QpnhYy7giebnRcVyOm93Go6s+5dG+d7E9NI6Jve/g5SVvEF6UY3S0Cn6K60WOlz+NCzK48MA6o+OIiIiIVNCuXTun7SeeeIJJkyZVaHeksBSrzV5hml64vxdJ6QVV9p9bXEavZxdQarFhNpt4elgH+rcMr7RtcZmV5+Zt46rO0QR4e5z5yVQDFaVERKRem310lNTF+9cQXJpvcBrX5G0tY/LyD3lowDj2B0Qysfcd/G/JWwSUGTPMuzIlZvcTRkkt0CgpERERqZO2bt1KkyZNyre9vCquDXUu/D3d+en+/hSUWliWmMlTP24lNtSX3gmNnNqVWW3cO3Mtdjs8PbxDtWY4EypKiRy17Uljr3ZVF7S90egEItXrkF8jlke1B+DqxMUGp3FtgWWFPL3sfcYPuJd9gY2Z1GsMzyx7D29rmdHRAJgX14sj3oFEFGZx8f41RscRERERqVRAQACBVVzt+UQhvp64mU1k5DtP70vPLyG8kkXOjzGbTcSF+QHQPjqIxLR83l6Y6FSUKrPaGDdjLQeOFPH5Hb0MGyUFWlNKRETqsTkJ/bGbzJx/eBtN89OMjuPyIoqyeXrZB/iXFrK1UTzPdb8Jq8n4jxKlZne+bnkhANftWIC7RkmJiIiIi/N0N9OhSRDLEjPK99lsdpYlZnJes+DT7sdmt1NqOf7Z6FhBak9mATPG9iTEz7M6Y58x4z9JioiI1IA8Dx9+bdoDgBGJiwxOU3/E5R3miZUf4WEtY2VUe97oPBKjV/T7tVkPMn2CCC88wqD9qw1OIyIiIlI9xvaL5/NV+/lmzQES0/J4bM5mCkstXNstFoDxX67n+Xnby9u/9UciS3alsy+zkMS0PN5fvJvZ6w5ydVfHdMEyq41/fraWTQdzePW6rljtdtLyiknLK3YqXNUmTd8TEZF66ee4XpS4e9I85yCdMxKNjlOvdMhM5pHVn/FMj9H8EteT0JJcbtn2iyFZSs1ufNnyIgBG7foDT5vVkBwiIiIi1W1o52iyCkp55bedpOeV0DY6kOljehAe4Ji+dzC7CJPp+EXPikqtPD5nMyk5xXh7uJEQ7scr13VhaOdoAA7nFDN/WyoAl7++xOlYn9/Rq8K6U7VBRSkREal3ykxufN+8H+AYJaXrk1a/PilbGLf+W97oei2ft76EkOI8hiYvq/Uc85ueT4ZvMI2Kshm8d2WtH19ERESkJo3uE8foPnGV3vflXb2dth8a0pqHhrSusq/YUF/2PHdFdcY7Z5q+JyIi9c7imM5k+gTRqCiHAQc2GB2n3rp870puOjpC6p1Ow1kS3alWj19mcuPLVhcDcK1GSYmIiIi4HBWlRESkXrEDs1pcAMBVu//Ew65CRU26ccdvXJ68DLvJzAvdbmRDWEKtHfv3pt1I8w0hpDiXS/dolJSIiIiIq9H0vQZsyncwazVsPwQ+ntCnJTx/PbR2TDclKx+e+BZ+3QT7MiA8EIZ3g6euhSDfqvu9dSpMd56eypBOMG/C8e2rXoL1eyEtF0L8YFB7eP4GiA6p/vMUkYZlQ1gLdgc1wctSymV7Vhgdp94zAfdsmE2Olz9LozvxZM9beXHJ2zTPTanR41pMZr44YZSUl81So8cTERERkeqnkVIN2KLtMG4QrJgMvz0CZVYY/BwUFDvuP3TEcfvfjbD5efj4Lpi3EW5/79R9X9oJUt46fvv8Xuf7L2wHX90HO16Eb/8FSWlwzWvVf44i0vAcGyU1eN9fBJQVGZymYXDDzn9Wz6RDRhKFHj5M7HMHh31Da/SYf8Scx2G/RgQX56n4KCIiIuKiNFKqATtx5BI4ik4R/4Q1yTCgLXSIhW8fOH5/QiQ8MwpuehssVnB3q7pvLw9oHFz1/Q9edvzfzcLhkaEw/BUos4CHi/1fabXbeSszg7m5uWRYLES4uzM8MIi7GzUqvxLC/6UcYk5urtPj+vn68V5s7En7nnnkCB9mZZJhtdLay4vHIiLp5ONTfv/ofXtZVeT8R/eooGAmNW5cTWcn4loS0/JY1bgtJruN4UlLTv0AqTaeNgtPrPyIh/vdw56gaCb2uYP/LX6T4NKCaj+W1WTmy9aOUVIjExfhbS2r9mOIiIiISM1zsT//pSblFDp+hvqfvE2gz8kLUgALtzkKXCF+cFE7ePpaaBRQedusfJix1DF90NUKUgAfZGXyRXY2UxpH0cLLk83FxTyWchh/NzM3hxwfKdDPz49nGkeVb3uaTn49sJ9zc3k+PY0nIiPp5O3Dp0eyuPPAfn6Mb04j9+NP1LVBQdwbFl6+7XOKfkXqs2l/JgPQO2UL0QWZBqdpePzLinl6+QeMH3AvB/3DeaL37Tz351R8rKXVepxFTbpw0D+cwJJ8rjDgin8iIiIiUj00fU8AsNnggU+hbyvHCKnKZOTBU7PhzotO3telneGTu2HBo441qhZtg8teAKvNud2Ez8FvDDS6C/Zlwnfjq+dcatv6oiIu8vfnAn9/mnh4MiQgkL5+vmwqKnZq52kyEe7uXn4Lcjt5Ze/jI1lcGxTEiKBgWnh58URkY7zNZmbl5Di18zabnfr1P0W/IvVVRn4J3649CMCIxEUGp2m4GhXn8syy9wksKWBnSFOe6XELZabqe12yYuLz1oMAxyip6i54iYiIiEjtUVFKABj3MWw+AF/cW/n9uYVwxYvQrglMGnHyvq7vDVd1g45NYXh3+OFhWLUbFm51bvfwlbDuGfj1EXAzwy1TwW6vltOpVV18fFhRUMCeUscfRtuLi1lbVER/fz+ndqsKC+mXuIvLd+9m8uHDZFurviJYqd3O1uJievke78NsMtHb15f1xc7T9X7IzaVP4i6uSt7Ny+lpFNlsf+9OpEH4bMVeSi02Wmfto13WHqPjNGgx+elMXjENL0spayLb8Op5o7BRPaM4lzTpzIGACAJKC7hSo6REREREXJoLTpaS6nbvx/DDOlj8OMQ0qnh/XhFc+gIEeMPsB898il3zCAgLgMRUuLjD8f1hAY5bqyhoGw2x98OKROjd8pxOp9bdEdqIApuNK5J34wZYgX+FhTM0MKi8TT8/fwYFBBDj4cG+0jJezUjnrgP7mdm0GW6VTLfLtlqwAmHuzk92Izd3dpcWlm9fERhEtIc7Ee7u7Cgp4eX0dPaUlvJ6k5gaOluRuqm4zMqny/cCjlFSmsRqvDZH9vHYX58wqddt/B7bjZDiPMZu+eGc+rSdMErq6sTF+FpKqiOqiIiIiBhERakGzG6H+6bD7NWwcCLER1Rsk1sIQ553LFz+/b/B2/PMj3MgEzLzISq46ja2oyOkSlxwrdp5eXn8kJvLi1HRtPDyZHtxCVPSUh0Lngc5ClOXBwaWt2/l5U1rLy+GJO/mr8JCevv5VdX1KY0KDnbqN9zNnTEH9rOvtJSmnmfxH0vERc1Zd5DMglKaBPvQN2WT0XHkqPPTtvPAuq95udv1fNtyIKHFuYxIWnzW/S2N7si+wMb4lRVx1e6l1ZhURERERIyg6XsN2LiP4bOlMHOcYxTU4WzHrejo8hy5hTD4OSgogWl3QG7R8TYnrg/V5iGYvcrx7/xieHgmrNgFe9JhwWYY9jK0iIQhnRxtVibCm7/C+j2wNx1+3wI3vOm4up+rjZIC+F96GmNDG3F5YCCtvLy5KiiI0aGhvJ9V9SLLsZ6ehLi5sa+s8rVQgt3ccQMyLBan/ZlWS4XRUyc6dmW+qvoVqY9sNjsfHF3g/La+cbjZNYW1Lrlk/2rGbHaMkHq/41X8HtP1rPo5cZTU8KQl+FmKT/EIEREREanrNFKqAXtnvuPnwKed9390J9x6AazdAyuTHPta/G0R8uRXIe7oBd92pBy/cp+bGTbug+lLILsAokNgcEd46lrHaCsAX0+YtQqe+NZR8IoKhks7wcThx9u4kiKbrUJ114wJ20kWyDpcVka21Up4FQUmT5OJdt7erCgsYFCA47KFNrudFYWF3BgcUmW/24sdf6SFu+lXWxqORbvSSUzLJ8DLnevOj+WA0YGkgmsSF5LlHcicFgN4+bzrCSotoFvazjPqY0VUO5KDovEtK2JY0pIaSioiIiIitUl/uTZg9hknv39gu1O3+Xs/Pp7wyyMnb9+xKfz+2Kn7dRUX+vvzblYmUR4etPDyZFtxCdOPZDHi6NS9ApuNtzMyGBwQQJi7G/tKy3gpPY2mHh70O2Eh89v272OQfwD/CHEUnW4NCeXRwyl08Paho7c3nxw5QpHNxtVH+91XWsqPubkM8Pcn2M3MjpISnk9Lo7uPD629vWv/iRAxyAdLdgNwfY9YArxdsLLdAJiAOzbPJdvLn4Wx5/F0j9E89+dUWmfvP63H24GZrS8BYNjuPwkoKzr5A0RERETEJagoJXKOHouM5PWMDJ5MPUyW1UqEuzujgoL5Z1gYAG7AzpISvsvNIffo/X39/LgvLBxP8/ExVvtLSzliPT5d77LAQLKsVt7ISCfDaqWNlxfvxsSWT9/zMJlYXljAJ0eyKLLbaezuziX+AdzdqJLV6kXqqS2HcliamImb2cStfeONjiMnYcbO+LVfkuPlz7qIVvy39+28vPhNmhRknPKxKxu3Iyk4Bp+yYoYnapSUiIiISH2hopTIOfIzu/FoRCSPRkRWer+32cz7sbGn7Gd+QosK+/4RElI+curvojw8+KRpszMLK1LPTDu6ltTlHaNoEuxjcBo5FQ+7lYl/TWdCv3+SGBzDxD538NLiNwktyavyMSeOkhqavJTAssIq24qIiIiIa9FC5yIi4pJSc4uZu+EQAGP7aZSUq/C1lPDk8g+Iys/gsF8jHu8zlgL3qqccr45ow66QWLwspVydePZX7hMRERGRukdFKRERcUnTl+2hzGqnR1wonWODjY4jZyCkJJ+nl71PcHEeu4Oa8GTPWyk1u1VoZwdmtHGMkroyeRnBpQW1nFREREREapKKUiIi4nIKSy3MWLkPgLH9NUrKFUUXZvLU8g/wKStmY3gL/tftRqyYnNqsC2/JjtBmeFlKGZm40JigIiIiIlJjVJQSERGX882aA+QUlRHXyJeL21a+npvUfS1yDvL4X9Nxt1lY0qQz73YahgUTG8MS+KNJF97rcBUAl+9ZTkhJvsFpRURERKS6aaFzERFxKVabvXyB89v7xeNmNp3iEVKXdU3fxb/XfMHz59/E3Ob9WBDbjUKPExatt9tplnvYuIAiIiIiUmM0UkpERFzK/G2p7M0sJMjHg5HdYoyOI9Vg4MH1XLJnJYBzQeqoV7uOYmlUh9qOJSIiIiI1TEUpERFxKR8s2Q3ATb2a4uupAb/1gRUT6yJbg91e8U6TYyTcux2HVVhzSkRERERcm4pSIiLiMtbvz2bVniN4uJm4pXec0XGkmmwJa06GT3B5AaoCk4l03xC2hDWv1VwiIiIiUrPqfFEqLi4Ok8lU4TZu3DgABg4cWOG+u+++2+DUIiJSE46NkrqqcxMiA70NTiPVJcsroFrbiYiIiIhrqPPzHlatWoXVai3f3rx5M5dccgnXXntt+b477riDJ598snzb19e3VjOKiEjNO3CkkJ83Oxa8vr1fvMFppDqFluRVazsRERERcQ11vigVHh7utP3cc8+RkJDABRdcUL7P19eXxo0bn3afJSUllJSUlG/n5elDrohIXffx0j1YbXb6tQijXXSg0XGkGrXP2E1YUTYZ3kGVT+Gz2wkvyqZ9xu7aDyciIiIiNabOT987UWlpKZ999hljxozBdMKH1hkzZhAWFkaHDh149NFHKSwsPGk/U6ZMISgoqPzWrl27mo4uIiLnILe4jC9W7Qfg9v4aJVXfuGHn7o1zHBt/X+z86PZdm77DjUoWQhcRERERl+VSRak5c+aQnZ3NrbfeWr7vxhtv5LPPPuOPP/7g0Ucf5dNPP+Wmm246aT+PPvooOTk55betW7fWcHIRETkXX63aT36JhZYR/gxsFX7qB4jL6ZuymYl/TSesOMdpf3hRNhP/mk7flM0GJRMRERGRmlLnp++daNq0aVx22WVER0eX77vzzjvL/92xY0eioqK4+OKLSUpKIiEhodJ+vLy88PLyKt/Ozc2tudAiInJOLFYbHy3dA8DY/vFOI2WlfumbspleKVvYEtacLK8AQkvyaJ+xWyOkREREROoplylK7d27l/nz5zNr1qyTtuvZsycAiYmJVRalRETEdfy8+TAHs4sI8/dkWJcmRseRGuaGnU4ZSUbHEBEREZFa4DLT9z766CMiIiK44oorTtpu/fr1AERFRdVCKhERqUl2u50PljgWt765VxzeHm4GJxIRERERkeriEiOlbDYbH330EaNHj8bd/XjkpKQkZs6cyeWXX06jRo3YuHEjDz74IAMGDKBTp04GJhYRkeqweu8RNhzIwcvdzE29mhodR0REREREqpFLFKXmz5/Pvn37GDNmjNN+T09P5s+fz6uvvkpBQQGxsbGMHDmSiRMnGpRURESq0/uLHaOkRpwXQyN/r1O0FhERERERV+ISRanBgwdj//slooHY2FgWLVpkQCIREalpyRkF/LYtFYDb+8UbnEZERERERKqby6wpJSIiDctHS5Ox2+GiNhG0iPA3Oo6IiIiIiFQzFaVERKTOyS4s5evVBwAYq1FSIiIiIiL1kopSIiJS58xYuY+iMivtogLpndDI6DgiIiIiIlIDVJQSEZE6pdRiY/qyPQDcMSAek8lkbCAREREREakRKkqJiEidMnfDIdLySogM9OKKjtFGxxERERERkRqiopSIiNQZdrud95fsBuDWPvF4uuttSkRERESkvtKnfRERqTOWJWWy/XAevp5u3NijqdFxRERERESkBqkoJSIidcaxUVKjuscS5OthcBoREREREalJKkqJiEidsCs1j4U70jGZ4La+cUbHERERERGRGqailIiI1AnT/kwGYEi7xjRr5GdwGhERERERqWkqSomIiOHS80qYte4gAGP7xxucRkREREREaoOKUiIiYrjPVuyl1GKjS2ww3ZqFGB1HRERERERqgYpSIiJiqOIyK5+u2As4RkmZTCaDE4mIiIiISG1QUUpERAw1e91BsgpKaRLsw6XtGxsdR0REREREaomKUiIiYhibzc4HS3YDMKZfPO5uelsSEREREWko9OlfREQMs2hnOknpBQR4uTOqe4zRcUREREREpBapKCUiIoZ5/+goqRt6NiXA28PgNCIiIiIiUptUlBIREUNsOZTDsqRM3MwmRveJMzqOiIiIiIjUMhWlRETEENOWJANwRccomgT7GJxGRERERERqm4pSIiJS6w7nFPP9hkMAjO0fb3AaERERERExgopSIiJS66Yv34PFZqdHfCidYoKNjiMiIiIiIgZQUUpERGpVQYmFGSv2AjC2n0ZJiYiIiIg0VCpKiYhIrfpmzQFyiy3ENfJlUNtIo+OIiIiIiIhBVJQSEZFaY7XZmfanY4Hz2/vFYzabDE4kIiIiIiJGUVFKRERqzW9bU9mXVUiwrwfXdIs1Oo6IiIiIiBhIRSkREak1HyzZDcBNPZvh4+lmcBoRERERETGSilIiIlIr1u07wuq9R/B0M3NL72ZGxxEREREREYOpKCUiIrXig6NrSV3VJZqIQG+D04iIiIiIiNFUlBIRkRq3P6uQnzelAI4FzkVERERERFSUEhGRGvfxsj3Y7NC/ZRhtowKNjiMiIiIiInWAu9EBpHpse7KN0REM1/ZGoxOISGVyi8v4ctV+QKOkRERERETkOBWlRESkRn35137ySyy0jPDnglbhRscREREREXEZnyzfw7uLdpOeX0LbqEAmX9WeLrHBlbadtzmFt/5IYk9mARarnbgwP+7oH8+I82LK29jtdl75bSefr9pPblEZ3eNCeHp4R+LD/GrpjJxp+p6IiNSYMquNj5Y6Fjgf2z8ek8lkcCIREREREdcwd8Mhnv5hG/8a1JIf7+tHu6gAbpm2koz8kkrbB/l4Mu7CFsy+pw/zHujPtd1iePibjSzamV7eZuqi3Xy0bA/PDO/AnHF98fFw55YPV1JcZq2t03KiopSIiNSYnzcf5lBOMWH+ngzr0sToOCIiIiIiLuODP5O5vkcso7rH0jIygGeGd8TH042vVu+vtH3vhEZc2qExLSICaNbIjzH94mnTOIDVe7IAxyipD5cmc99FLRjcvjFtowJ5+brOpOaW8OvW1No8tXIqSomISI2w2+18sGQ3ADf3isPbw83gRCIiIiIixsrLyyM3N7f8VlJS+ainUouNzQdz6NsirHyf2Wyib4sw1u7NPuVx7HY7SxMz2J1eQI/4UAD2ZxWRnlfi1GegtwddYoNZu/fIuZ3YWdKaUiIiUiNW7TnCxgM5eLmbualXU6PjiIiIiIgYrl27dk7bTzzxBJMmTarQ7khhKVabnTB/L6f94f5eJKUXVNl/bnEZvZ5dQKnFhtls4ulhHejf0rGua3p+cXkff+8zvYopgTVNRSkREakR7x8dJTWyWwyN/vbGJyIiIiLSEG3dupUmTY4va+HlVb2fk/093fnp/v4UlFpYlpjJUz9uJTbUl94Jjar1ONVFRSkREal2yRkFzN/mmJc+pm+8wWlEREREROqGgIAAAgMDT9kuxNcTN7OpwqLm6fklFUY6nchsNhF39Ep67aODSEzL5+2FifROaES4v3d5HxGB3k59tos6daaaoDWlRESk2n34ZzJ2O1zcJoIWEf5GxxERERERcSme7mY6NAliWWJG+T6bzc6yxEzOaxZ82v3Y7HZKLTYAYkN9CA/wYlliZvn9ecVlrN+fzXnNQqot+5nQSKkGLrWsjJfS01lSkE+x3U5TD0+eiWpMB28fyux2Xs9IZ3F+AQfKSvE3u9Hbz5fx4eFEuHtU2WeBzcrrGRnMz8sjy2qlrZc3j0ZE0NHHp7xNhsXCy+lpLC0oJM9mpbuPL/8XGUmcp2dtnLaI1KAjBaV8vcZxRZDb+2uUlIiIiIjI2RjbL55/f72BjjHBdIkNYtqfeygstXBtt1gAxn+5nsggbyZc2gaAt/5IpFNMEM1C/Si1Wvljezqz1x3k6eEdADCZTIzpG88bv+8iLsyP2FAfXvp1J5GBXgxuF2nIOaoo1YDlWK38Y99eevj68W5MLKFubuwtKyPQ7LhCVrHNxtbiYu5u1Ig23l7kWm08m5bKuAMH+Toursp+Hz98mF0lJTwfFU24uztzc3O4/cB+5sbFE+nhgd1u576DB3A3mXizSRP83cx8nHWE2/fvY258c3zNGsAn4spm/rWP4jIb7aMD6d28bs5dFxERERGp64Z2jiaroJRXfttJel4JbaMDmT6mB+EBjul7B7OLMJlM5e2LSq08PmczKTnFeHu4kRDuxyvXdWFo5+jyNndf0JyiUguPztpEbnEZ58eFMP22HoZdKVtFqQZsWlYmjT08eDYqqnxfzAkjlQLc3JgW63zFrIkRkVy3by+HysqI9qg4WqrYZuO3vDzebBJDd19fAO4NC2dhfj5fZGfzr/Bw9paVsaG4mO/i4ml5dFG3JyIjGZCUx0+5uVwTHFwDZysitaHEYuXjZXsAGNs/3ulNUkREREREzszoPnGM7hNX6X1f3tXbafuhIa15aEjrk/ZnMpkYP7g14wefvF1t0ZCUBuz3/Hw6eHvzwMGD9EvcxYg9yXydnX3Sx+TZbJiAwCpGM1mxYwU8//aHqLfZzNqiQgBK7Y75rF4ntDGbTHiaTOVtRMQ1zd2QQnpeCY0DvbmiY/SpHyAiIiIiIg2WilIN2IGyMr7IzqaZpyfvxcRyfXAIz6alMicnp9L2JTYbL6encXlAIP5ulQ/t8zO70cXbh6mZGaRZyrDa7Xyfk8P6oiLSLVYA4j29iHJ355WMdHKsVkrtdj7IzOSwxVLeRkRcj91u54MluwHHNzqe7nqLERERERGRqmn6XgNms9vp4O3Dg+HhALTz9mZXSQlfZh9heFCQU9syu53xhw5hxzHV7mSei4pi4uEUBiYl4Xa038sDAtlaUgyAh8nE601imHg4hd6Ju3ADevv60d/PD3sNnKeI1I6liZlsP5yHr6cbN/ZoeuoHiIiIiIhIg6aiVAMW7u5Ogpfz1e4SPD35LT/PaZ+jIHWQQ5YyPoptWuUoqWOaenrySdNmFNpsFNhshLu7M/7QQWJOWIOqvbc3s+PiybNaKbPbCXV357q9e+jg7V19Jygiter9o6OkRnWPJci36it0ioiIiIiIgKbvNWjn+fiSXFrqtG9PWSnR7sf/mDxWkNpbWsq0mFiCT1GQOpGv2Uy4uzs5VitLCwq4yD+gQpsANzdC3d3ZU1rKluLiStuISN23MzWPRTvTMZtgTN94o+OIiIiIiIgLUFGqAbslJISNRUW8m5nB3tJSfsjN4evsbG4ICQYcBakHDh1kS3ExL0RFYwXSLRbSLRZK7ccn2t22fx8zjhwp3/6zIJ8lBfkcKC1lWUEBt+7fR7ynJ1efMCVwXl4ufxUWsL+0lAV5eYzdv4+L/f3p6+dXW6cvItVo2pJkAIa0b0zTRr4GpxEREREREVeg6XsNWEcfH15vEsMr6em8k5lJjIcHj0REMjTQUTxKs5TxR34+ACP27nF67MexsfTwdRSQ9peWcsRqKb8vz2rj1Yx0DlssBJnNDA4I4F9h4XiccLW9dIuFF9LSyLBYCHd3Z1hQEHc3CqvhMxaRmpCeV8Ls9QcBGNtfo6REREREROT0qCjVwA3092egv3+l9zXx8GRr6zan7GN+Qgun7csCA7ksMPCkj7k5JJSbQ0JPP6iI1FmfrthLqcVG16bBdGum32sRERERETk9mr4nIiJnrbjMymcr9gIwtl9zg9OIiIiIiIgrUVFKRETO2qy1B8kqKCUmxIch7SONjiMiIiIiIi5ERSkRETkrNpudD/7cDcBtfeNxd9NbioiIiIiInD79BSEiImdl4c40dqcXEODlznXnxxodR0REREREXIyKUiIiclbeX5wMwA09m+LvpetmiIiIiIjImVFRSkREztjmgzks352Ju9nErX3ijI4jIiIiIiIuSEUpERE5Y9P+dIySuqJTFNHBPganERERERERV6SilIiInJHDOcXM3XAIgLH9mhucRkREREREXJWKUiIickY+XrYHi81Oz/hQOsYEGR1HRERERERclIpSIiJy2gpKLMxcuReAsf01SkpERERERM6eilIiInLavl69n9xiC/FhflzcJsLoOCIiIiIi4sJUlBIRkdNitdn5cOkeAMb0i8dsNhkbSEREREREXJqKUiIiclp+23qYfVmFBPt6cM15MUbHERERERERF6eilIiInJb3lyQDcFPPZvh4uhmcRkREREREXJ2KUiIickpr9x1hzd4jeLqZuaVPM6PjiIiIiIhIPaCilIiInNK0o6OkruoSTUSAt8FpRERERESkPlBRSkRETmp/ViE/b04BYGz/eIPTiIiIiIhIfVGni1KTJk3CZDI53dq0aVN+f3FxMePGjaNRo0b4+/szcuRIUlNTDUwsIkax2uwsT8rku/UHWZ6UidVmNzpSvfHR0j3Y7NC/ZRhtGgcaHUdEREREROoJd6MDnEr79u2ZP39++ba7+/HIDz74ID/++CNff/01QUFB3HvvvYwYMYKlS5caEVVEDDJvcwqT524lJae4fF9UkDdPDG3HpR2iDEzm+nKLy/hy1T4AxvZvbnAaERERERGpT+p8Ucrd3Z3GjRtX2J+Tk8O0adOYOXMmF110EQAfffQRbdu2ZcWKFfTq1au2o4qIAeZtTuGfn63l7+OiDucU88/P1vLOTeepMHUOvvhrHwWlVlpF+jOgZZjRcUSkoZlpMjqB8W7UyF8REam/6vT0PYBdu3YRHR1N8+bN+cc//sG+fY5v7NesWUNZWRmDBg0qb9umTRuaNm3K8uXLT9pnSUkJubm55be8vLwaPQcRqRlWm53Jc7dWKEgB5fsmz92qqXxnqcxq46OlewAY2685JpP+OBQRERERkepTp4tSPXv25OOPP2bevHm88847JCcn079/f/Ly8jh8+DCenp4EBwc7PSYyMpLDhw+ftN8pU6YQFBRUfmvXrl0NnoWI1JS/krOcpuz9nR1IySlm5e7M2gtVj/y0KYWUnGLC/L0Y1jXa6DgiIiIiIlLP1Onpe5dddln5vzt16kTPnj1p1qwZX331FT4+Pmfd76OPPsr48ePLtw8ePKjClIgLSsuruiB1on9MW0lkgDcRgV5EHP15bDvyhH2N/LxwM2s0EIDdbueDJckA3NK7GV7ubgYnEhERERGR+qZOF6X+Ljg4mFatWpGYmMgll1xCaWkp2dnZTqOlUlNTK12D6kReXl54eXmVb+fm5tZUZBGpQREB3qfVzm6Hw7nFHM4tBnKqbGc2QXiAo0gVGehF+NGfEX/72ci//hev/krOYtPBHLzczdzUq5nRcUREREREpB5yqaJUfn4+SUlJ3HzzzXTr1g0PDw8WLFjAyJEjAdixYwf79u2jd+/eBicVkdrQIz6UiAAv0vJKKr3fBDQO8ubbf/YhM7+U1Nxi0vJKyn+mnbCdkV+CzQ6puSWk5paw6WDVxzWbIMzfy2nEVcURWN408vPE3a1Oz5Ku0vtHR0mN7BZDqJ+nwWlERERERKQ+qtNFqYceeoihQ4fSrFkzDh06xBNPPIGbmxs33HADQUFB3H777YwfP57Q0FACAwO577776N27t668J9JAmE3QOLDyotSxcUxPDG1HdLAP0cE+dCSoyr6sNjuZ+SVORavU3GJSc0tIz3P8TMsrJj3PUbxKy3O03UzVIy1Nx4pXAY4iVUSAFxFHf0ae8DPMv24Vr3an57NgeyoAt/eLNziNiIiIiIjUV3W6KHXgwAFuuOEGMjMzCQ8Pp1+/fqxYsYLw8HAAXnnlFcxmMyNHjqSkpIQhQ4bw9ttvG5xaRGrLDxtT2HgwFzczhPh6kpFfWn5f4yBvnhjajks7RJ1WX25mk6NgFOhNhyanKF4VlJB2tEiVmuv4d2pecfm+tNwS0vNLsNrspOeVkJ5XwpZDJy9eNfI7Vrw6Yfrg3wpY4QFeeNRg8cpqs/NXchZv/rELux0uah1OQrh/jR1PREREREQatjpdlPriiy9Oer+3tzdvvfUWb731Vi0lEpG64khBKZO+3wLAvRe25P6LW/JXchZpecVEBHjTIz60RtZ9cjObHFP1ArzhFCOvsgocUwbT/zb66sSpg2l5juJVRn4JGfklbE2p+tgmE4T6ep4w2qqS9a8CvQn398LT/cyKV/M2pzB57lanqxmuP5DDvM0pp13YExGpKyZ9C5NnOe9rHQXb/+f4d3Ep/HsGfLECSspgSCd4+zaIrPplHdM/Kt//wg3w8JWOf1/1EqzfC2m5EOIHg9rD8zdAdMi5n5OIiEh9VKeLUiIiVXnqh61kFpTSKtKfey5MwM1sondCI6NjlXMzmwg/OrrpZGw2O1mFpc7FqhNGXqXmlZB+9D6LzU5mQSmZBaVsO0nxCiDUz7N8umBkgFf5OlcnTiEMD/DCy92NeZtT+Odna7H/rY8jBaX887O1vHPTeSpMiYjLaR8D8x89vn3iRUQf/Ax+XA9f3w9BvnDvxzDiFVg6qer+Uv72HejPG+D292Fkj+P7LmwH/3cVRAXDwSPw0Ey45jVYdpJ+RUREGjIVpUTE5SzckcasdQcxmeD5kZ3wOvEvDRdjNpsI8/cizN+L9idpZ7PZOVJYWr62ldP0wbxja185/l1mdYzSyiooZfvhvJMeP9jHnfwSa4WCFIAdx9pck+du5ZJ2jev9FQdFpH5xN0Pj4Ir7cwph2kKYOQ4uOvrC+9Fd0PZhWLELerWsvL+/9/XdGkcRqnnE8X0PXnb8383C4ZGhMPwVKLOAhz51i4iIVKC3RxFxKfklFh6bvRmA2/rE07Vpw5gTYTabaOTvRSN/L9oRWGU7m81OdlGZ83TBClMHjxevsossJz2uHUjJKeav5Kw6NRJNRORUdqVC9Djw9oDeLWHKddA0DNYkQ5kVBnU43rZNNDRtBMsTqy5KnSg1xzHSavpdVbfJyocZS6FPSxWkREREqqK3SBFxKS/O287B7CJiQnx4aEgro+PUOWaziVA/T0L9PGl7khl3drudI4VlfLlqH8/P23HKftPyik/ZRkSkruiZAB/f5VhHKiXbsb5U/ydh8/NwOBs83SHYz/kxkUGO+07H9MUQ4A0jzq9434TP4c3foLAEerWAHx46x5MRERGpx+rONchFRE5h9Z4sPlmxF4DnRnTC11N19bNlMjmKV11iT2+kmWNhdxER13BZF7i2J3Rq6ljE/KeHIbsQvlpZPf1/uAj+0Re8PSve9/CVsO4Z+PURcDPDLVPBXtkcaREREVFRSkRcQ3GZlQnfbsRuh2u7xdCvZZjRkeqFHvGhRAV5U9VqUSYgKshxNUMREVcV7AetoiDxsGNtqFILZBc4t0nNqXwNqr9bsh12pMDYgZXfHxbgONYlHeGLe+Gn9bAi8dzyi4iI1FcqSomIS3jz90SS0gsID/Bi4hXtjI5Tb7iZTTwx1PF8/r0wdWz7iaHttMi5iLi0/GJISnVcFa9bPHi4wYItx+/fcQj2ZULvFqfua9pCRx+dm526re3oCKmSsrNJLSIiUv+pKCUidd7WQ7lMXZQEwFPD2hPk62Fwovrl0g5RvHPTeTQOcp6i1zjIm3duOo9LO5xkcSoRkTrooRmwaBvsSYdlO+HqVxxT6W7oA0G+cPtAGP8Z/LHFsfD5be85FkM/cZHzNg/B7FXO/eYWwtd/VT5KamUivPkrrN8De9Ph9y1ww5uQEOnoW0RERCrSgiwiUqdZrDYmfLsRi83Ope0bq0BSQy7tEMUl7RrzV3IWaXnFRAQ4puxphJSIuKIDWY6CUGY+hAdAv9awYjKEH7146Ss3gdkEI1+DEgsM6Qhv3+bcx44UyCl03vfFCsf6UDf0qXhMX0+YtQqe+BYKShyjsi7tBBOHg5e+SxEREamUilIiUqdN+zOZTQdzCPR258lh7Y2OU6+5mU30TmhkdAwRkXP2xX0nv9/bE966zXGrin1GxX13XuS4VaZjU/j9sdPPKCIiIpq+JyJ12J6MAl7+bScAE69oR0SgrgAnIiIiIiJSX6goJSJ1ks1mZ8K3Gymx2OjbohHXdo8xOpKIiIiIiIhUIxWlRKRO+mLVflYmZ+Hj4caUqzthMmltIxERERERkfpERSkRqXMO5xQz5adtAPx7cCuaNvI1OJGIiIiIiIhUNxWlRKROsdvtTJyzibwSC51jg7mtb7zRkURERERERKQGqCglInXKDxtTmL8tDQ83Ey+M7ISbWdP2RERERERE6iMVpUSkzjhSUMqk77cAcM/AFrRuHGBwIhEREREREakpKkqJSJ3x1A9bySwopVWkP/dcmGB0HBEREREREalBKkqJSJ2wcEcas9YdxGSC50d2wsvdzehIIiIiIiIiUoNUlBIRw+WXWHhs9mYAbusTT9emIQYnEhERERERkZqmopSIGO7Feds5mF1ETIgPDw1pZXQcERERERERqQUqSomIoVbvyeKTFXsBeG5EJ3w93Q1OJCIiIiIiIrVBRSkRMUxxmZUJ327Ebodru8XQr2WY0ZFERERERESklqgoJSKGefP3RJLSCwgP8GLiFe2MjiMiIiIiIiK1SEUpETHE1kO5TF2UBMBTw9oT5OthcCIRERERERGpTSpKiUits1htTPh2IxabnUvbN+bSDlFGRxIREREREZFapqKUiNS6aX8ms+lgDoHe7jw5rL3RcURERERERMQAKkqJSK1Kzijg5d92AjDxynZEBHobnEhERERERESMoKKUiNQam83OI99upMRio1+LMK7tFmN0JBERERERETGIilIiUmu+WLWflclZ+Hi48ezVHTGZTEZHEhEREREREYO4Gx1ARBqGwznFTPlpGwAPDWlN00a+BicSERERERGp2z5Zvod3F+0mPb+EtlGBTL6qPV1igytt+/lf+5i19gA7DucB0DEmiIeHtHFqX1Bi4fl52/l1SypHCkuJDfXl1j5x3NSrWS2cTUUaKSUiNc5utzNxzibySix0iQ3m1j5xRkcSERERERGp0+ZuOMTTP2zjX4Na8uN9/WgXFcAt01aSkV9SafsVuzO5qnM0n9/Zi1n39CUqyIebp63kcE5xeZunf9zKop3pvHJdF+aPv4AxfeN54vst/LY1tbZOy4mKUiJS437YmML8bWl4uJl4fmQn3MyaticiIiIiInIyH/yZzPU9YhnVPZaWkQE8M7wjPp5ufLV6f6XtX7u+Kzf3jqN9dBAtIvx5fmQn7HZYmphR3mbN3iOMPC+G3gmNiA315caeTWkbFcCG/dm1dFbONH1PRGrUkYJSJn2/BYBxF7agdeMAgxOJiIiISK2bqS8ludFudAKpA/Ly8sjNzS3f9vLywsvLq0K7UouNzQdzuGdgQvk+s9lE3xZhrN2bfVrHKiqzUma1EezrUb6vW7MQ5m9LZVT3WCIDvVi+O5Pk9AIevyLs7E/qHKgoJSI16qkftpJZUEqrSH/uGdjC6Dh1nz6w6QObiIiISGX0ObFefE5s166d0/YTTzzBpEmTKrQ7UliK1WYnzN+5YBXu70VSesFpHeu5n7cRGehN3xbHC06TrmrPo7M20WvKAtzNJswmE1NGdKRn80ZnfjLVQEUpEakxC3ekMWvdQUwmeH5kJzzdNWNYREREREQarq1bt9KkSZPy7cpGSVWHtxcmMndDCl/c2QtvD7fy/dOX7WH9vmw+uKU7TUJ8+Cs5i/9+t5nIQG/6taz90VIqSolIjcgvsfDY7M0AjOkbT9emIQYnEhERERERMVZAQACBgYGnbBfi64mb2VRhUfP0/BLC/U9eyHpvcRLvLExixtietI06fqziMisv/rKDd2/uxkVtIgFoGxXI1kO5vLdktyFFKQ1bEJEa8eK87RzMLiI21Id/D25ldBwRERERERGX4elupkOTIJadsEi5zWZnWWIm5zULrvJxUxcl8caCRKaP6UGnGOd2ZVYbZVY7JpPzVFCz2YTdbszUSI2UEpFqt3pPFp+s2AvAlKs74euplxoREREREZEzMbZfPP/+egMdY4LpEhvEtD/3UFhq4dpusQCM/3I9kUHeTLi0DQDvLEzild928tr1XYgJ8SEtrxgAP093/LzcCfD2oGd8KFN+2oa3uxsxIT6s2J3JrLUHmHhluypz1CT9pSgi1aq4zMqEbzdit8Oo7jGGDAEVERERERFxdUM7R5NVUMorv+0kPa+EttGBTB/Tg/AAx/S9g9lFTqOePluxl1KrjX/OWOvUz78ubsmDlzhmr7xxY1demLeDB75cR3ZhGU1CfHh4SGtu6tm09k7sBCpKiUi1evP3RJLSCwgP8OKxy42ptouIiIiIiNQHo/vEMbpPXKX3fXlXb6ftpY9cdMr+IgK8+d+1nasjWrXQmlIiUm22Hspl6qIkAJ4a1p4gXw+DE4mIiIiIiEhdpZFSIlItLFYbE77diMVm57IOjbm0Q5TRkRqUd+Y7bnvSHdvtY+C/V8NlXY63Wb4LHvsKViaBmwm6NINfHgEfz8r7tNpg0rfw2VI4nA3RIXDrAJg4HI6NEjb9o/LHvnADPHxlNZ2ciKuaaTp1m/ruRmMWTRWR+uNkn3H2pEP8A5U/7qv74dqeld9nt8MT38L7f0B2AfRtBe+MgZaNj7eJ+xfszXB+3JTr4JGrzvWMROREKkqJSLWY9mcymw7mEOjtzuRh7Y2O0+DEhMJz1zs+TNntMH0JDHsZ1j3r+PC2fBdc+jw8ehW8MRrczbBhH5hP8jfz83MdHwKn3+3oY/VuuO09CPKB+y91tEl5y/kxP2+A29+HkT1q7lxFRESk4TjZZ5w20RU/i7z3O7z4I1x2ktlJL/wAr/8C0++C+Ah4/GsY8hxsfQG8T/iy7slr4I4Lj28HeFfvudUWI768LLPAxK/hp/WwO93x+XFQB8d/y+iQmj1fcS0qSonIOUvOKODl33YCMPHKdkS46ju2Cxt6nvP2M6McHz5WJDo+eDz4Kdw/xPnbvdbRJ+9z2U4Y1g2u6OrYjguHz5fDX7uPt2kc7PyY79bAhe2gecRZn4qIiIhIuVN9xvn7Z5HZq2FUT/Cv4uOo3Q6vznMUT4Z1d+z75J8QeQ/MWQPXn7BET4B3xf5dkRFfXhaWwto98PjV0LkpHCmAf30KV70Eq5+utVMXF6A1pUTknNhsdh75diMlFhv9WoRxbbcYoyM1eFYbfLEcCkqgdwtIy3F86xURCH0mQeQ/4YKn4M8dJ++nTytYsAV2pji2N+x1PKaqbx5Tc+DH9XD7BdV5NiIiIiIOf/+M83drkmH9Xrh9YNV9JKc7RvYMOmFgf5Av9ExwFGdO9NxcaHQXdP0/ePEHsFir4yxq39Dz4PIujqJUqyhHYc/f21HYA+cvL9vHOL64HNULvE6yPOyJX17GhcM1PWFwx+NfXgb5wm+POvppHQ29WsKbox3/jfZlVN2vNDwaKSUi5+SLVftZmZyFj4cbU0Z0dLokqdSuTfug9yQoLnN80Jj9ILSLgRVHP2BNmgX/u9ExHPuTJXDxs7D5eef1E070yFDILYI2D4Ob2fFB8Jlr4R99K28/fbHjG8UR59fI6YmIiEgDVdVnnL+bthDaRju+WKvK4WzHz8gg5/2RQcfvA0eR5rw4CPV3FGAe/RJSsuHlm87lTIxntcHXKyt+efmPvo4vL5NSHdMinxkF/VpX3U+fVo6pkjtTHIWuY19enuz5ySlyTO0L9q320xIXpqKUiJy1wznFTPlpGwAPDWlNbKjeYYzUOhrWP+t4w/9mJYyeCosmgu3oOsN3XQS3HR3F1DXOMQrqw4Uw5frK+/tqJcxYCjPHQfsmjm8eH/jMsQ7A6AEV23+4yPGBxruKtQdEREREzkZVn3FOLEwVlcLMZfD48Oo55vjLj/+7U1PwdIe7PnQsdn6yEUR1ldFfXhaXwoTP4YbeEKg/GeQEKkqJyFmx2+1MnLOJvBILXWKDubVPnNGRGjxPd2hx9INDt3hYtRte+8XxoQGgXRPn9m2jYV9m1f09PNPx2GNrK3Rs6rgKzZTvKxallmyHHSnw5X3Vcy4iIiIix1T1Gefd24+3+WYlFJbALf1P3texNaJScyDqhAW3U3McBZmq9GzhmL63J/3U63LWRUZ+eVlmgVFvgB1457YaOkFxWVpTSkTOyg8bU5i/LQ0PNxMvXNMJt5OthCiGsNmhpMwxzz86xFE0OtHOw9AsrOrHF5aC+W/vEm7m4x9eTjRtoeNDYueTfJgTERERqQ7HPuOcaNoiuOo8CA88+WPjwx2FqQVbju/LLXRMYevdsurHrd/rWPg7IqjqNnXZscJet3hHoalzU0dhLyrYcf+5fHnZsSnc3B8evNTx5eWJjhWk9mbAb49olJRUpKKUiJyxIwWlTPre8U4+7sIWtIoMMDiRPPoFLN7m+PZu0z7H9sJtjiHUJhM8fIXj0sffrITEw45LH28/5LwQ6MXPwpu/Ht8e2hWemQM/rnP0O3sVvPwzXN3d+di5hfD1XzB2ICIiIiLV6mSfcY5JPAyLt8PYCyvvo81Djs8x4Phc9MCl8PQc+H6No89bpkJ0MAzv5mizfBe8+rNjnaTdaY4RQQ9+Bjf1gxC/mjzb2lMbX14eK0jtOgzzH4VG+pNBKqHpeyJyxp76YSuZBaW0ivTnnoGVXPpEal1aruMDVUq242onnWLhlwlwSUfH/Q9c5lhD4MHPIKvA8e3Yb49CQuTxPpJSISPv+PYbo+Hxb+Cejxz9R4c4hnb/d4Tzsb9Y4bi88A19avw0RRqEd+Y7bnvSHdvtY+C/V8NlXZzb2e1w+Qswb6NjbZDh3St0VW7WKpg6H9bsgax8WPcMdIlzbjPwaVi0zXnfXRfB1NsRETHMqT7jgGNdy5hQx9XfKrMjBXIKj2//50rHQt93ToPsQujXCuZNOL4uppe74yp/k2Y5Cjfx4Y5RQCeuM+VKHv3CcfXkpmGQV+RYe2vhNsfzeOzLyye+dXw+7NIMpi9xfHn5zb+O93Hxs44vJu8d7Ng+9uVl00aO96l1exxfXo45OgWwzALXvAZr98APDznWnDq2kHyov2PklgioKCUiZ2jhjjRmrTuIyQTPj+yEp7sGXNYF0+48dZtHrnLcqrLnNeftAB949WbH7WTuvMhxE5HqERMKz13vWFzWbnf8cTDsZVj3rOOD/zGvznP8MXE6CoodV1Ea1Qvu+KDqdndcCE9ec3zbVxcuEBGDnc5nnGevc9yqYp/hvG0yOV7rTny9O9F58bDiydPPWNcZ8eXlwSPw/VrHv7v8n3OePx6Dge1q7HTFxagoJSKnLb/EwmOzNwMwpm88XZuGnOIRIiJypoae57z9zCjHyKkViceLUuv3wEs/wuqnIWrcqfu8+ejCv8dGX1XF1+v4IsAiIlI/GPHlZVx4xWKgSGU0xEFETtuL87ZzMLuI2FAf/j24ldFxRETqPavNMYWkoAR6H50tXVgCN74Fb91a/QWkGUsh7C7oMMEx3aOwpHr7FxERETmRRkqJyGlZvSeLT1bsBWDK1Z3w1URwEZEas2kf9J7kmE7h7+1YM6rd0VFSD34GfVrBsJOsIXU2buzjWNQ2Ohg27ocJnzvWYZn1YPUeR0REROQY/VUpIqdUXGZlwrcbsdthVPcY+rU8yaU4RETknLWOhvXPQk6R46qZo6fCoomQmAq/b3GsL1XdTlwbrmNTx2XCL37WsY7IieuKiIiIiFQXFaVE5JTe/D2RpPQCwgO8eOxyrUooIlLTPN2hRWPHv7vFw6rd8Nov4OMJSWkQfIdz+5GvQv82sHBi9WXomeD4maiilIiIiNQQFaVE5KS2Hspl6qIkAJ4a1p4gXw+DE4mINDw2u+Oy5JNHwtiBzvd1fAReuaniAunnar1jxjZRwdXbr4iIiMgxKkqJSJUsVhsTvt2IxWbnsg6NubRDlNGRRETqvUe/gMs6Q9MwyCuCmctg4TbH5bsbB1e+uHnTMIiPOL7d5iGYch1cfb5jOysf9mXAoWzH9o4Ux89j/SWlOo5zeRdo5A8b9znWrhrQBjo1rakzFRERkYZORSkRqdK0P5PZdDCHQG93Jg9rb3QcEZEGIS0XbpkKKdkQ5AudYh0FqUs6nn4fO1Igp/D49vdr4Lb3jm9f/6bj5xMjYNJIx3TB+Zvh1XmOK/3FhsLI82Hi8Oo4IxEREZHKqSglIpVKzijg5d92AjDxynZEBHgbnEhEpGGYdueZtbfPOPW+Wy9w3KoS2wgWPX5mxxURERE5V2ajA4hI3WOz2Xnk242UWGz0axHGtd1ijI4kIiIiIiIi9YyKUiJSwRer9rMyOQsfDzemjOiIyWQyOpKIiIiIiIjUMypKiYiTwznFTPlpGwAPDWlNbKivwYlERERERESkPlJRSkTK2e12Js7ZRF6JhS6xwdzaJ87oSCIiIiIiIlJPqSglIuV+2JjC/G1peLiZeOGaTriZNW1PREREREREakadvvrelClTmDVrFtu3b8fHx4c+ffrw/PPP07p16/I2AwcOZNGiRU6Pu+uuu5g6dWptxxVxabkevkz6fgsA4y5sQavIgDPvZKaKWNxoNzqBiIiIiIiIS6jTI6UWLVrEuHHjWLFiBb/99htlZWUMHjyYgoICp3Z33HEHKSkp5bcXXnjBoMQiruu9jleRWVBK68gA7hnYwug4IiIiIiIiUs/V6ZFS8+bNc9r++OOPiYiIYM2aNQwYMKB8v6+vL40bN67teCL1xqqINixo2h2TCZ4b2RFP9zpdrxYREREREZF6wKX+8szJyQEgNDTUaf+MGTMICwujQ4cOPProoxQWFp60n5KSEnJzc8tveXl5NZZZpK4rdPfijS4jARjTN56uTUMMTiQiIiIiIiINQZ0eKXUim83GAw88QN++fenQoUP5/htvvJFmzZoRHR3Nxo0bmTBhAjt27GDWrFlV9jVlyhQmT55cG7FF6ryP211Gum8IjQsy+ffgIUbHERERERERkQbCZYpS48aNY/Pmzfz5559O+++8887yf3fs2JGoqCguvvhikpKSSEhIqLSvRx99lPHjx5dvHzx4kHbt2tVMcJE6bHNoHD/E9wHg/vXf4Ot5i8GJREREREREpKFwiaLUvffeyw8//MDixYuJiYk5aduePXsCkJiYWGVRysvLCy8vr/Lt3Nzc6gsr4iJKze681nUUdpOZwXtX0jV9l9GRREREREREpAGp00Upu93Offfdx+zZs1m4cCHx8fGnfMz69esBiIqKquF0Iq7t89aDOBAQQUhxLmM3/2B0HBEREREREWlg6nRRaty4ccycOZPvvvuOgIAADh8+DEBQUBA+Pj4kJSUxc+ZMLr/8cho1asTGjRt58MEHGTBgAJ06dTI4vUjdtTswiq9bXgjAuA2zCCgrMjTPlO9g1mrYfgh8PKFPS3j+emgd7dxu+S547CtYmQRuJujSDH55xPGYU3nue3j0S/jXpfDqzY59e9Ih/oHK2391P1zb85xOS0RERERERE6iThel3nnnHQAGDhzotP+jjz7i1ltvxdPTk/nz5/Pqq69SUFBAbGwsI0eOZOLEiQakFXENVpOZV7uOwmp2o+/BjfRN2Wx0JBZth3GD4PwEsFjh/76Cwc/B1hfAz9vRZvkuuPR5ePQqeGM0uJthwz4wm07d/6okePd36NTUeX9sI0h5y3nfe7/Diz/CZZ2r59xERERERESkcnW6KGW32096f2xsLIsWLaqlNCL1w+yEAewKicW/tJB7Ns42Og4A8yY4b398F0T8E9Ykw4C2jn0Pfgr3D4FHrjre7u8jqSqTXwz/eBveHwtPz3G+z80MjYOd981eDaN6gr/3mZ6FiIiIiIiInAmz0QFEpPYc9Avj07ZDALhj81xCS/IMTlS5nELHz1B/x8+0HMeUvYhA6DMJIv8JFzwFf+44dV/jPoYrusCgDqduuyYZ1u+F2weeXW4RERERERE5fSpKiTQQNky81vVaSt086Jq2k0v2rTI6UqVsNnjgU+jbCjrEOvbtTnP8nDQL7rjQMbLqvDi4+FnYdbjqvr5YDmuTYcp1p3fsaQuhbTT0aXUuZyAiIiIiIiKnQ0UpOSdWTGwMS2Bhky5sDEvAymks8COGmNesB5vCEvCylHL/+m/q7H+pcR/D5gPwxb3H99mOzuS96yK47QLoGgev3Ayto+DDhZX3sz8T/vUJzBgH3qexEHpRKcxcplFSIiIiIiIitaVOryklddvSqA5M7TScDJ/g8n1hRdncvXFOnVg8W47L8A5kWocrARi97WcaF2YZnKhy934MP6yDxY9DTKPj+6OCHT/bNXFu3zYa9mVW3teaZEjLhfMeO77PaoPF2+HNX6FkumNNqWO+WQmFJXBL/+o4ExERERERETkVFaXkrCyN6sDTPUZX2J/hHcTTPUYz8a/pKkzVEXbgzc4jKfTwoXXWXq5K+tPoSBXY7XDfdMci4wsnQnyE8/1x4RAdAjtSnPfvPFz1VfIubg+bnnPed9t70CYKJgx1LkgBTFsEV50H4YHndi4iIiIiIiJyejR9T86YFRNTOw13bJj+Ngns6Pa7HYdpKl8dsbhJZ1ZGtcfdZuHBdV/hxsmvammEcR/DZ0th5jgI8IbD2Y5bUanjfpMJHr4CXv/FMaIp8TA8/jVsP+Q83e7iZx2joAACfBxrUp148/OCRgHH16o6JvGwYwTV2Atr/lxFRERERETEQSOl5IxtCWvuNGWvApOJdN8QtoQ1p1NGUq3lkopyPXx5p9PVAFy/YwHN8lINTlS5d+Y7fg582nn/R3fCrRc4/v3AZVBcBg9+BlkF0Lkp/PYoJEQeb5+UChlncUHBDxdBTCgM7nh2+UVEREREROTMqSglZyzLK+C02iUFRqkoZbD3Ol5Fjpc/cTkpjNr5u9FxqmSfcXrtHrnKcavKntdO/viFEyvf/+x1jpuIiIiIiIjUHhWl5IyFlpzeUJT3Ol7FpvAWDEtaQqeMJE3mq2WrItqwoGl3zHYbD6z7Cg+71ehIIiIiIiIiIuVUlJIz1j5jN2FF2WR4B1VcUwrAbsfDZqHMzYPlUR1YHtWB+JxDDEtawoUH1uFps9R+6Aam0N2LN7qMBGBY0hJaZ+83OJGIiIiIiIiIMy10LmfMDTt3b5zj2LD/bdHso9sTVs/g3fkvcMXuZXhZSkkOiubV867j5iET+aTtEDK9dYmzmvRxu8tI9w2hcUEmt2z7xeg4IiIiIiIiIhWoKCVnpW/KZib+NZ2w4hyn/eFF2Uz8azp9UzbTND+NezfO4tNfnuL2zT8QUXiEXC9/Pm99CaMHP8YL3W5gR3BsFUeQs7U5NI4f4vsAcP+6r/G2lhqcSERERERERKQiTd+Ts9Y3ZTO9UrawJaw5WV4BhJbk0T5jN244j54KKCvimsSFXJ20mGVRHfiueT+2hDXnj9hu/BHbjbaZexietIQ+KZtwt9sMOpv6odTszmtdR2E3mRm8dyVdMxKNjiQiIiIiIiJSKRWl5Jy4YT/tK+y52W30P7SR/oc2siuoCd8l9GdRTBe2NYpjW6M4wgqzGZq8lEv3rCSwrLCGk9dPn7cexIGACEKKcxm7+Qej44iIiIiIiIhUSdP3xBAtcw7y0NovmP7LM9y4/VeCi/PI8A3mo/ZXcMuQibzeeSR7AyKNjulSkoKi+brlhQCM2zCLgLIigxOJiIiIiIiIVE0jpcRQoSV53Lz9V67buYBFTboyJ6E/u4Ob8HN8b36O703XtB0MT/qT7qnbMf9tWqAcZzWZebXrKKxmN/oe3EjflM1GRxIRERERERE5KRWlpE7wtFm5ZP9qBu1fzeZGzfkuoR/LozqwLqI16yJa0yQ/nauS/mTQ/tX4WkqMjlvnzE4YQGJwDP6lhdyzcbbRcUREREREREROSUUpqVNMQMfM3XTM3E2qbwjfx/fll7ieHPQP553OVzO93aVcuvcvhu5eSuPCLKPj1gkH/cL4tO0QAO7YPJfQkjyDE4mIiIiIiIicmopSUmdFFh7hji0/cNP2X5nftDvfNe/HwYAIZrW4gDkJ/emZsoXhSUvomLkbk9FhDWLDxGtdr6XUzYOuaTu5ZN8qoyOJiIiIiIiInBYVpaTO87GWMjR5GVckL2dNRGvmJPRnbWRrlkd3ZHl0R5rnHGRY0p/El1nx9nAzOm6tmtesB5vCEvCylHL/+m8abHFOREREREREXI+KUuIyzNg5P20756dtZ29AJN8378eC2G7sDmrCK+ddxyfP/c4/ejblpl7NiAj0NjpujUv3DmJahysBGL3tZ01nFBEREREREZdiNjqAyNlolpfKfRu+5dNfnmLM5h8ILzxCZkEpr/+eSN/nf+fBL9ez8UC20TFrjB14q/MICj18aJ21l6uS/jQ6koiIiIiIiMgZ0UgpcWkBZUVcm7iQEUmL2fvlfD5amszqvUeYve4gs9cdpHuzEG7rG8+Q9pG4u9WfGuziJp1ZGdUed5uFB9d9hRt2oyOJiIiIiIiInBEVpaRecLPbuKJTFFd0imLjgWw+WrqHHzYeYvXeI6zee4ToIG9u6RPH9efHEuzraXTcc5Lr4cs7na4G4PodC2iWl2pwIhEREREREZEzV3+Gjogc1SkmmFeu68LSCRdx/0UtaOTnyaGcYp77eTu9pizgsdmbSEzLMzrmWXuv41XkePkTl5PCqJ2/Gx1HRERERERE5KyoKCX1VkSgN+MHt2bpIxfx4jWdaBsVSHGZjRkr9zHo5cXcPG0lf+xIw2ZznalvqyLasKBpd8x2Gw+s+woPu9XoSCIiIiIiIiJnRdP3pN7z9nDj2u6xXNMthpXJWXz4ZzK/bUtlya4MluzKoHmYH7f2jaODmyc+1lKj41ap0N2LN7qMBGBY0hJaZ+83OJGIiIiIiIjI2VNRShoMk8lEr+aN6NW8EfuzCpm+bA9frtrP7owC/vvdFvwufZwhe1ZyVfJSIguPGB23go/bXUa6bwiNCzK5ZdsvRscREREREREROScqSkmDFBvqy8Qr2/HAJa34ds0BPl62h+QMmNVyIHNaDKB3ymaGJf1Jh8zdmIwOC2wOjeOH+D4A3L/ua7zr8IguERERERERkdOhopQ0aP5e7ozuE8fNvZrx6YUjmZPQj3URrVka3Yml0Z1IyD7AsKQ/ueDgOjxtxqzfVGp257Wuo7CbzAzeu5KuGYmG5BAREREREZHa9cnyPby7aDfp+SW0jQpk8lXt6RIbXGnbz//ax6y1B9hx2HFhr44xQTw8pE2F9olpeTz383ZW7s7CYrPTMtKfd27qRpNgnxo+m4pUlBIBzGYTPVK30SN1G3sDIvmueT9+j+1GUnAML3e7ng/bX8EVe5ZzefJyQktq98p9n7cexIGACEKKcxm7+YdaPbaIiIiIiIgYY+6GQzz9wzaevroDXWOD+XBpMrdMW8nvDw0kzN+rQvsVuzO5qnM0510Vgpe7G1MXJXHztJX89uAFNA7yBmBvZgHXTF3Odd1jeWBQKwK83dmZmo+XuzHXwVNRSuRvmuWlcv+Gb7l168/Mi+vJ3Pi+ZPgGM6PNYL5sdREXHFjPsKQltMw5WONZkoKi+brlhQCM2zCLgLKiGj+miIiIiIiIGO+DP5O5vkcso7rHAvDM8I78vj2Nr1bv556BLSq0f+36rk7bz4/sxLzNh1mamMHIbjEAvPjLDi5sHcGjl7ctb9eskV8NnsXJqSglUoXAskJG7fqDEYmLWBbVkTkJ/dnWKI4FTbuzoGl32mfsZvjuJfRO2YKb3Vbtx7eazLzadRRWsxt9D26kb8rmaj+GiIiIiIiI1J68vDxyc3PLt728vPDyqjjqqdRiY/PBHO4ZmFC+z2w20bdFGGv3Zp/WsYrKrJRZbQT7egBgs9n5Y3sad12QwM3TVrL1UC4xob7cMzCBIe0bn9uJnSVjxmeJuBB3u40Bhzbw8pI3eXXha1y4fw1uNitbwprzTI/RjLnkUb5pMZA8j+qdfzs7YQCJwTH4lxZyz8bZ1dq3iIiIiIiI1L527doRFBRUfpsyZUql7Y4UlmK12StM0wv39yI9v+S0jvXcz9uIDPSmb4swADIKSigotfLOwiQuaBXOJ7f3YEj7SO7+bA0rdmee24mdJY2UEjkDrbP38581n3P7lh/5Mb43P8b1Js03hGkdruSzNoMZtG81w3YvITY//ZyOc9AvjE/bDgHgzs3f1/o6ViIiIiIiIlL9tm7dSpMmTcq3KxslVR3eXpjI3A0pfHFnL7w93ACw2x33XdIukrH9mwPQPjqItXuPMGPlPno1b1QjWU5GRSmRs9CoOJdbtv3C9TsW8EdMV75L6E9yUDQ/Nu/Dj8370D11O8OSlnBe2k7M2M+obxsmXut6LaVuHnRN28Ggfatr6CxERERERESkNgUEBBAYGHjKdiG+nriZTWT8bVRUen4J4ZUscn6i9xYn8c7CJGaM7UnbqOPHCvH1xN1somWEv1P7hAh/Vu85cgZnUX1UlBI5B542C0P2rWLwvlVsDEvgu4T+rGjcjtWRbVgd2YaYvDSGJS1h0P41eFtLT6vPec16sCksAS9LKfev/xZTDZ+DiIiIiIiI1C2e7mY6NAliWWJG+XpPNpudZYmZ3NKnWZWPm7ooibd+T2T67T3oFBNcoc9OMUHszihw2p+cXkCT4OpdjuZ0qSglUg1MQOeMJDpnJJHiG8r3zfvxS7MeHAiI4K0uI5ne7jKG7P2LobuXElnkXIG2YmJLWHOyvAIw22180OFKAG7d+hONC7MMOBsREREREREx2th+8fz76w10jAmmS2wQ0/7cQ2GphWu7Oa7GN/7L9UQGeTPh0jYAvLMwiVd+28lr13chJsSHtLxiAPw83fHzcpR/7hyQwH2fr6VHfCi9mzdi0c50FmxP44s7exlyjipKiVSzqMIs7tr8PTdv/4Xfmnbn++b9OOQfzrctBzK7xQB6H9rM8KTFtM/aw7KoDkztNJwMn2CnPqLz0hi6e6kxJyAiIiIiIiKGG9o5mqyCUl75bSfpeSW0jQ5k+pgehAc4pu8dzC7CZDo+t+azFXsptdr454y1Tv386+KWPHhJKwAu7dCYZ4Z35O2FiUz6fgvNw/155x/ncX5caO2d2AlUlBKpIb6WEobtXsrQ3ctYFdmG7xL6sy6iFUubdGJpk040LsjgsG8lC8nZ7RzyD2dFVHv6pmyu/eAiIiIiIiJSJ4zuE8foPnGV3vflXb2dtpc+ctFp9Tnq/FhGnR97rtGqhYpSIjXMjJ2eqdvombqNPQGN+S6hHwtiu3HYL6zyB5hMYLfzbsdh9ErZgtsZLpQuIiIiIiIi4grMRgcQaUji8g7zr/Xf8MiqT0/e0GQi3TeELWHNayeYiIiIiIiISC1TUUrEAKVunqfVLssroIaTiIiIiIiIiBhDRSkRA4SW5FVrOxERERERERFXo6KUiAHaZ+wmrCgb7FWsF2W3E154hPYZu2s1l4iIiIiIiEhtUVFKxABu2Ll74xzHxt8LU0e379r0nRY5FxERERERkXpLRSkRg/RN2czEv6YTVpzjtD+8KJuJf02nb8pmg5KJiIiIiIiI1Dx3owOINGR9UzbTK2ULW8Kak+UVQGhJHu0zdmuElIiIiIiIiNR7KkqJGMwNO50ykoyOISIiIiIiIlKrNH1PRERERERERERqnYpSIiIiIiIiIiJS61SUEhERERERERGRWqeilIiIiIiIiIiI1DoVpUREREREREREpNapKCUiIiIiIiIiIrVORSkREREREREREal1KkqJiIiIiIiIiEitU1FKRERERERERERqnYpSIiIiIiIiIiJS61SUEhERERERERGRWqeilIiIiIiIiIiI1DoVpUREREREREREpNapKCUiIiIiIiIiIrVORSkREREREREREal1KkqJiIiIiIiIiEitU1FKRERERERERERqnYpSIiIiIiIiIiJS61SUEhERERERERGRWldvilJvvfUWcXFxeHt707NnT/766y+jI4mIiIiIiIiISBXqRVHqyy+/ZPz48TzxxBOsXbuWzp07M2TIENLS0oyOJiIiIiIiIiIilXA3OkB1ePnll7njjju47bbbAJg6dSo//vgjH374IY888kiF9iUlJZSUlJRv5+TkAJCSklI7gWvA4bJSoyMYLuDAgXN6vJ7Dc38OyayeHC5Nz+G50+/yOTvn32X0PIJeE6uFnsNzp9fEc6bXxOqh18RqoOfw3FXD77NRjtUbbDabwUnqFpPdbrcbHeJclJaW4uvryzfffMPw4cPL948ePZrs7Gy+++67Co+ZNGkSkydPrsWUIiIiIiIiItLQ/fXXX5x//vlGx6gzXH6kVEZGBlarlcjISKf9kZGRbN++vdLHPProo4wfP75822KxsG3bNmJjYzGb68WMxlqVl5dHu3bt2Lp1KwEBAUbHcUl6DquHnsdzp+fw3Ok5PHd6Ds+dnsPqoefx3Ok5PHd6Ds+dnsPqoefx3NhsNlJTU+natavRUeoUly9KnQ0vLy+8vLyc9vXt29egNK4vNzcXgCZNmhAYGGhwGtek57B66Hk8d3oOz52ew3On5/Dc6TmsHnoez52ew3On5/Dc6TmsHnoez13Tpk2NjlDnuPywoLCwMNzc3EhNTXXan5qaSuPGjQ1KJSIiIiIiIiIiJ+PyRSlPT0+6devGggULyvfZbDYWLFhA7969DUwmIiIiIiIiIiJVqRfT98aPH8/o0aPp3r07PXr04NVXX6WgoKD8anxSs7y8vHjiiScqTImU06fnsHroeTx3eg7PnZ7Dc6fn8NzpOaweeh7PnZ7Dc6fn8NzpOaweeh6lJrj81feOefPNN3nxxRc5fPgwXbp04fXXX6dnz55GxxIRERERERERkUrUm6KUiIiIiIiIiIi4DpdfU0pERERERERERFyPilIiIiIiIiIiIlLrVJQSEREREREREZFap6KUiIiIiIiIiIjUOhWl5Kzs27ePytbIt9vt7Nu3z4BEIiLGWbx4MRaLpcJ+i8XC4sWLDUgkIiKuTO8rItJQqCglZyU+Pp709PQK+7OysoiPjzcgkWv65JNPKCkpqbC/tLSUTz75xIBEInI2LrzwQrKysirsz8nJ4cILLzQgket58sknKSwsrLC/qKiIJ5980oBEIlT6Hi2n5ubmRlpaWoX9mZmZuLm5GZDI9eh9RUQaCpO9suEuIqdgNptJTU0lPDzcaf/evXtp164dBQUFBiVzLW5ubqSkpBAREeG0PzMzk4iICKxWq0HJXIPNZmPRokUsWbKEvXv3UlhYSHh4OF27dmXQoEHExsYaHbHO27ZtG1988UWlz+GQIUMYOXIkXl5eRses86p6Tdy5cyfdu3cnNzfXoGSuQ6+H5y47O5vZs2dX+fvcp08foyPWeT///HP5a+L+/fux2Wz4+fnRtWtXBg8ezG233UZ0dLTRMes8s9nM4cOHK/w+Hzp0iISEBIqKigxK5jr0vlI9kpOTK31N7N27N97e3kbHq/P0viK1wd3oAOJaxo8fD4DJZOLxxx/H19e3/D6r1crKlSvp0qWLQelcj91ux2QyVdh/4MABgoKCDEjkGoqKinjppZd45513yMrKokuXLkRHR+Pj40NiYiJz5szhjjvuYPDgwfz3v/+lV69eRkeuc9auXct//vMf/vzzT/r27UvPnj25+uqr8fHxISsri82bN/PYY49x33338Z///IcHHnhAxalKjBgxAnC8Jt56661Oz5HVamXjxo36wHaaqno93LBhA6GhoQYkch2HDh3iv//9LzNmzCA6OpoePXrQpUuX8t/nP/74g//97380a9aMJ554guuuu87oyHXO7NmzmTBhAnl5eVx++eVMmDCh/H3l2Gvi/Pnzeeqpp7j11lt56qmnKhQLBF5//XXA8Zr4wQcf4O/vX36f1Wpl8eLFtGnTxqh4LkHvK9VjxowZvPbaa6xevZrIyEin3+ekpCS8vb35xz/+wYQJE2jWrJnRcescva9IbVJRSs7IunXrAMcfD5s2bcLT07P8Pk9PTzp37sxDDz1kVDyX0bVrV0wmEyaTiYsvvhh39+O/ilarleTkZC699FIDE9ZtrVq1onfv3rz//vtccskleHh4VGizd+9eZs6cyfXXX89jjz3GHXfcYUDSumvkyJE8/PDDfPPNNwQHB1fZbvny5bz22mu89NJL/N///V/tBXQRx4rHdrudgIAAfHx8yu/z9PSkV69e+n/vFEJCQspfD1u1auVUmLJareTn53P33XcbmLDu69q1K6NHj2bNmjW0a9eu0jZFRUXMmTOHV199lf379+u9+m9eeOEFXnnlFS677DLM5oqrW4waNQqAgwcP8sYbb/DZZ5/x4IMP1nbMOu+VV14BHK+JU6dOdZqq5+npSVxcHFOnTjUqnkvQ+8q569q1K56entx66618++23FUbOl5SUsHz5cr744gu6d+/O22+/zbXXXmtQ2rpJ7ytSmzR9T87KbbfdxmuvvUZgYKDRUVzS5MmTy3/++9//dvom8diHtpEjRzoV/eS4bdu20bZt29NqW1ZWxr59+0hISKjhVK6lrKys0mJedbVvaCZPnszDDz/sNHpUTs/06dOx2+2MGTOGV1991WmU6LHXw969exuYsO7LzMykUaNGNdZe5ExdeOGFzJo1i5CQEKOjuKzJkyfz0EMP4efnZ3QUl/PLL78wZMiQ02qbmZnJnj176NatWw2nci16X5HapKKUnJWcnBysVmuFKRVZWVm4u7urWHWapk+fznXXXac57SIuLjk5GYvFQsuWLZ3279q1Cw8PD+Li4owJ5kIWLVpEnz59VPwUERERaUBUlJKzctlllzF06FDuuecep/1Tp07l+++/56effjIomTQEGzduPO22nTp1qsEkruvYuh+n4/7776/BJPXDBRdcwJgxYxg9erTT/s8++4wPPviAhQsXGhOsjsvNzS3/EuNUi/bqy46qff/996fd9qqrrqrBJK7r2JqZp+Pll1+uwST1w8iRI+nRowcTJkxw2v/CCy+watUqvv76a4OS1W3nnXceCxYsICQkpHyph6qsXbu2FpO5ljNZBF7vLZXT+4rUJhWl5KyEhoaydOnSClOotm/fTt++fcnMzDQoWd0XGhrKzp07CQsLK19LpSqVXQpYHFekMZlMVS6MfCJdsaty8fHxTtvp6ekUFhaWry+VnZ2Nr68vERER7N6924CEriUwMJC1a9fSokULp/2JiYl0796d7OxsY4LVcSdece/Y7/XfHfs91+9y1f6+BtKx18cTt4/R81i5Cy+80Gl77dq1WCwWWrduDTiueObm5ka3bt34/fffjYjoUsLDw/n999/p2LGj0/5NmzYxaNAgUlNTDUpWt504FfzYUg9VeeKJJ2opleup6v2kMnpNrJzeV6Q2aaFzOSslJSVYLJYK+8vKynSZ31N45ZVXCAgIAODVV181NoyLSk5OLv/3unXreOihh3j44YfL151Zvnw5L730Ei+88IJREeu8E5/DmTNn8vbbbzNt2rTyP8B27NjBHXfcwV133WVURJdiMpnIy8ursP/YVGep3O+//14+DfyPP/4wOI3rstls5f+eP38+EyZM4Nlnn3V6TZw4cSLPPvusURHrvBP//3v55ZcJCAhg+vTp5WsiHTlyhNtuu43+/fsbFdGl5OfnV7oupoeHxxmNYmloTiw0qeh09k78fd6zZw+PPPIIt956q9Nr4vTp05kyZYpREes8va9IbdJIKTkrF154IR06dOCNN95w2j9u3Dg2btzIkiVLDEomDU2PHj2YNGkSl19+udP+n376iccff5w1a9YYlMx1JCQk8M0339C1a1en/WvWrOGaa65xKmBJ5YYOHYqPjw+ff/55+dWmrFYr1113HQUFBfz8888GJ5SGokOHDkydOpV+/fo57V+yZAl33nkn27ZtMyiZ62jSpAm//vor7du3d9q/efNmBg8ezKFDhwxK5jp69OjBlVdeyX//+1+n/ZMmTWLu3Ll6bz4Dq1evLv+9bdeunRbkPkMXX3wxY8eO5YYbbnDaP3PmTN577z1Nrz8Nel+RmqaRUnJWnn76aQYNGsSGDRu4+OKLAViwYAGrVq3i119/NTida7FarcyePdvpA8ewYcNwd9ev5+nYtGlThalo4JietnXrVgMSuZ6UlJRKRz5arVZNsThNzz//PAMGDKB169blIymWLFlCbm6upvqcgSNHjjBt2jSn18PbbrutwkU1pGpJSUnl03BPFBQUxJ49e2o9jyvKzc0lPT29wv709PRKR0RKRY8//jgjRowgKSmJiy66CHB8Tvz888+1ntRpOnDgADfccANLly51mlrfp08fvvjiC2JiYowN6CKWL1/O1KlTK+zv3r07Y8eONSCR69H7itQ086mbiFTUt29fli9fTmxsLF999RVz586lRYsWbNy4UUPbz8CWLVto1aoVo0ePZvbs2cyePZvRo0fTsmVLNm/ebHQ8l9C2bVumTJlCaWlp+b7S0lKmTJlSYc0zqdzFF1/MXXfd5bRo6po1a/jnP//JoEGDDEzmOtq1a8fGjRsZNWoUaWlp5OXlccstt7B9+3Y6dOhgdDyXsHjxYuLi4nj99dc5cuQIR44c4fXXXyc+Pp7FixcbHc9lnH/++YwfP96poJyamsrDDz9Mjx49DEzmOq6++mpuu+02Zs2axYEDBzhw4ADffvstt99+OyNGjDA6nksYOnQoc+bMITExkXvuuYd///vfHDhwgPnz5zN8+HCj47mEsWPHUlZWxrZt28jKyiIrK4tt27Zhs9lUTDkDsbGxvP/++xX2f/DBB8TGxhqQyPXofUVqmqbviRiod+/ehIeHV1i34tZbbyU9PZ1ly5YZnLDu++uvvxg6dCh2u738SnsbN27EZDIxd+5cvVmehvT0dEaPHs28efPw8PAAwGKxMGTIED7++GMiIiIMTigNQceOHenduzfvvPOO0xTIe+65h2XLlrFp0yaDE7qGxMRErr76anbu3Fn+B9f+/ftp2bIlc+bMqbAYv1RUWFjIQw89xIcffkhZWRkA7u7u3H777bz44ov4+fkZnFAaAh8fH5YtW1bp1Pr+/ftTWFhoUDLX8tNPPzFy5EhatGhBz549Acdnx127dvHtt99WWP5BKtL7itQ0FaXkrOzbt++k9zdt2rSWkrg2Hx8fVq9eXem6Feeff74WjT9NBQUFzJgxg+3btwOO0VM33nij/nA4Qzt37ix/Dtu0aUOrVq0MTuQ6TjWSZ8CAAbWUxHX5+Piwfv368sX2j9mxYwddunTR6+EZsNvt/Pbbb06viYMGDTrtq1GJQ0FBAUlJSYBj7T29p0htatWqFZ999lmFL9f++usvbrzxRhITEw1K5nr279/PO++84/SaePfdd2uk1BnQ+4rUJC1aI2clLi7upC9CutrU6WnVqhWpqakVilJpaWn61uEM+Pn5ceeddxodw+XFxcVht9tJSEjQmmZnaODAgRX26XLJZ+a8885j27ZtFYpS27Zto3Pnzgalck0mk4nBgwczYMAAvLy89EfDWUpJSSElJYUBAwbg4+OD3W7Xc3mazGazPieeoxdffJH77ruPt956i+7duwOORc//9a9/8b///c/gdK4lNjZWV4k7R3pfkZqkvzrkrKxbt85pu6ysjHXr1vHyyy/zzDPPGJTKNZx4KeQpU6Zw//33M2nSJHr16gXAihUrePLJJ3n++eeNiuhyPv30U9599112797N8uXLadasGa+88grNmzdn2LBhRser8woLC7nvvvuYPn064Bgx1bx5c+677z6aNGnCI488YnDCuu/IkSNO28deEx9//HG9Jp7Exo0by/99//33869//YvExESn18O33nqL5557zqiILsdms/HMM88wdepUUlNTy3+fH3/8ceLi4rj99tuNjljnZWZmMmrUKP744w9MJhO7du2iefPm3H777YSEhPDSSy8ZHbHOmz17ttP2sdfE6dOnM3nyZINS1X0hISFOf+wXFBTQs2fP8i+KLBYL7u7ujBkzRmtznYElS5aUf078+uuvadKkCZ9++un/s3fnYTXm///An+eUUimVhEhpLE1aSVSohJRBmBkzQmoYjX0djCl1jGwzpMFgbDXGx5p9mWhXjLVCSKRCMUoSaT2/P/w6X8dpOZ2Wd3e9Htflupz7Pn88r3N1Xvd93vf7/XqjS5cuEjvKEUl0XSH1jZbvkTp1+vRprFu3jrZXrcKnTw/Lv4Llxz5+TU8Sq/fHH3/Ax8cHc+fOxS+//II7d+7AwMAAe/bsQVBQECIiIlhHbPTmzJmD2NhYBAQEYNiwYUhMTISBgQGOHz8OX19fiUFoIr2oqCjMnz+ftj+vRHk9rO5WhOqh9AQCAYKCgiAQCDB16lTcvn0bBgYGOHDgAAICAnDp0iXWERu9SZMm4cWLF9ixYwc+//xzJCQkwMDAAP/88w/mz5+PO3fusI7IWfv27cOBAwdw/Phx1lEapfKHQ9Jwd3evxyRNx5EjRzBx4kS4ubnhr7/+QlJSEgwMDLBp0yacOXMGZ86cYR2x0aPrCqlvNChF6lRKSgrMzMzw9u1b1lEaraioKKnfa2dnV49JmgYjIyP4+/vD1dUVqqqqoh8Pt2/fhr29PV6+fMk6YqOnp6eHAwcOoF+/fmKfYUpKCnr16iU2u4/UzL1792BpaYn8/HzWURqltLQ0qd+rp6dXj0majq5du2Lbtm1wdHQU+z7fu3cP1tbWErP6iKT27dvjn3/+gZmZmdhn+OjRI5iamtL3uRboMyQNzcLCAvPmzcOkSZPEvs83b96Es7MzsrKyWEds9Oi6QuobLd8jMvn0R6pQKERmZiZ8fX3RrVs3Rqm4gQaa6lZqaqrEzjQAoKioSIOjUvrvv/8q3GHv7du31DNASh8vQwP+ryauXr0a5ubmbEJxAA001b2nT59W2JOwrKxMtJMcqdrbt2+hrKwscTwnJweKiooMEjUNBQUFCAwMRMeOHVlH4SQPDw+sXLkSOjo6rKNwyv379yvcbKR169bIzc1t+EAcRNcVUt9oUIrIRF1dXeLHqlAohK6uLvbv388oFfcNGjQIu3fvph9qNdClSxfEx8dLfGbnzp3D559/zigVt1haWuL06dOYNWsWgP9bSrpjxw5YW1uzjMYZ5ubmFS5D69evH3bt2sUoFbeVL5eiBx01Y2RkhJiYGImaePjw4QoH8ImkAQMGIDg4GCtWrADwoSaWlZVh7dq1cHBwYJyOGz7tjSQUCvHmzRsoKytj7969DJM1fp8+5Cj3999/Y9SoUTAwMAAAmJqaNmQszmrfvj1SUlKgr68vdvzixYuiz5JUja4rpL7RoBSRyad9evh8Ptq2bYuuXbvSrl1SOHHiRIXHo6OjcerUKdEWtSNHjmzIWJw0f/58zJgxA+/fv4dQKMSVK1fwv//9D6tWrcKOHTtYx+MEf39/ODs7IykpCSUlJdi4cSOSkpIQFxdXo+WmzVlqaqrY6/Ka2LJlS0aJuCMwMLDC4+np6di9ezfat28P4EMjdFI9Hx8fuLu74+nTpygrK0NISAju37+P4OBgnDp1inU8Tli7di0cHR1x7do1FBUV4ccff8SdO3eQk5OD2NhY1vE4ISAgQOx1eU3s27cvNDQ02ITiiMoecgDA2LFjRbtAUp896UydOhVz5szBrl27wOPx8OzZM1y6dAkLFy6Et7c363icQNcVUt+opxSpseLiYkybNg3e3t7o0qUL6zicJE1zX7rhkN7ff/8NX19fPHz4EACgo6MDPz8/2g2kBh4+fIjVq1cjISEB+fn56NWrFxYvXgwTExPW0Rq94uJiDBs2DFu3bqVZPTLg8/no2LGjxAONtLQ06OjooEWLFuDxeHj06BGjhNwTExMDgUAg9n328fHB0KFDWUfjjNevX2PTpk1in+GMGTPQoUMH1tEavZKSEvj7+8PT0xOdOnViHYdzzM3N0alTJ/z6669QUlIC8GGmWbdu3XD27FnRdYZm1UtHKBTC398fq1atwrt37wB8aPGwcOFC0WxIUj26rpD6RINSRCatW7dGfHw8DUrJyNnZGXJycti1a5dYL58WLVogISEBRkZGDNNx17t375Cfn19hfyRC6lPbtm0RFxdHg1Iy8PLywr///ot9+/aJLbmlekgId6mqquLWrVsSS6ZI9cpn550/fx579+4VLY+imlg7RUVFSElJQX5+PoyMjNCqVSvWkQgh/x+tsyIycXV1xbFjxzBv3jzWUTjp7Nmz2LBhAywtLbFlyxZ88cUXrCM1CcrKyhU2pyXVKysrQ0pKCl68eIGysjKxcxU1CCXiJkyYgJ07d2L16tWso3DO1q1bcfToUTg5OeHHH3/EzJkzWUdqEoqKiir8Pnfu3JlRIm7Jzc3FlStXKvwMJ02axCgVdwwaNAhR20urCwAAl6xJREFUUVE0KCUDBQUFBAQE4OzZsxg5ciSmT5+OxYsXs47FeQoKCjSgV0t0XSH1hQaliEy6desGgUCA2NhY9O7dGyoqKmLnqfdH9ebNmwcHBwe4ubnh5MmT2LBhA+tInPT8+XMsXLgQYWFhePHihcSSSFoCWb3Lly9j/PjxSEtLk/j8aBmpdEpKSrBr1y5cuHChwpq4fv16Rsm4YfTo0bCyssKkSZNw+vRp7N69m3Ukznrw4AE8PT0RFxcndpz60Ejv5MmTcHNzQ35+PtTU1MQadvN4PBqUkoKzszOWLFmCW7duVVgTqWdm9ZydnXHt2jV4eHjg7NmzrONw1tu3b7F69WrRfeKnAyq0NLx6dF0h9Y2W7xGZVLVsj3p/1ExBQQHmzZuH8PBwPHr0CImJifQkpwacnZ2Rnp6OmTNnokOHDhK7Qo4aNYpRMu4wNzdH9+7d4efnV+Fn2Lp1a0bJuKO6Hbk+3RyCVEwoFGL16tUIDAzEf//9R/VQBra2tpCXl8eSJUsq/D6bmZkxSsYd3bt3h4uLC/z9/Wn2rYz4fH6l5+hHbM0FBgYiIiICv//+O/XpqqFvv/0WUVFRmDhxYoU1cc6cOYyScQddV0h9o0EpQhqJEydOICIiAkuXLqWeSDWgqqqKmJgYmJubs47CWSoqKkhISEDXrl1ZRyEEAHDjxg3ExMRg0qRJtFNXDamoqOD69eswNDRkHYWzVFRUcOvWLdounpAmQF1dHadPn4atrS3rKJxF1xVS3yp/jEFIFQQCgWgHi48VFBRAIBAwSMR9vXr1wq+//koDUjWkq6tb5S6GpHp9+/ZFSkoK6xic5unpiTdv3kgcf/v2LTw9PRkk4raCggJ4eXnRgJQMjIyM8PLlS9YxOM3JyQnXrl1jHYPTgoODUVhYKHG8qKgIwcHBDBJxm5qaGq1CkJGGhgY0NTVZx+A0uq6Q+kYzpYhM5OTkkJmZKTGAkp2dDW1tbZqWLQM1NTXEx8fTk9kaCg0NxW+//YZt27ZRQ1UZHT16FD///DMWLVoEExMTtGjRQuy8qakpo2TcUVlNfPnyJdq3b4+SkhJGybiJ6qHswsPD8fPPP8Pf37/C77OamhqjZNyxc+dOCAQCeHh4VPgZUj+k6tF9Yt1SVVVFQkIC1UQZ7N27F8ePH0dQUBAtx5URXVdIfaNBKSITPp+P58+fo23btmLHw8PDMW7cOPz333+MknEX3XDIRkNDA+/evUNJSQmUlZUlLpQ5OTmMknFHRb0/eDweNbCUQl5eHoRCITQ0NPDgwQOxmlhaWoqTJ09iyZIlePbsGcOU3EP1UHbl3+dPe37Q91l61A+p9iq7T0xISICDgwNdm2uIaqLsLCws8PDhQwiFQujr60vcJ964cYNRMu6g6wqpb7T7HqkRDQ0N8Hg88Hg8dO/eXaw4lZaWIj8/H15eXgwTkuYmICCAdQTOS01NZR2Bs9TV1cVq4qd4PB78/PwYJCPNFTXVr71Pd+ci0rOwsBDVREdHR8jL/99PjdLSUqSmpmLYsGEME3LThAkTaDaKjFxdXVlH4Dy6rpD6RjOlSI0EBQVBKBTC09MTAQEBYrtyKSgoQF9fH9bW1gwTcteqVavwww8/QF1dnXUUQoiUoqKiIBQKMWjQIBw5ckSsb4WCggL09PSgo6PDMCE37du3D6NGjZLYRp4Q0riVD8L7+flhwYIFaNWqlehc+X3i2LFjoaCgwCoiIYSQRoYGpYhMoqKiRNuDkrpR/lX8dGosqVp6enqV5zt37txASbiruqazkyZNaqAk3JWWlobOnTvT97eOZGRkAPiwkQGpmejo6CrPDxw4sIGScFd1G7b4+Pg0UBLuCgoKwjfffANFRUXWUTgtLCwMGzZswN27dwEAn3/+OebOnYvBgwczTkaaE7qukPpGg1JEJjdu3ECLFi1gYmICADh+/Dh2794NIyMj+Pr60hOwGti5cyc2bNiABw8eAAC6deuGuXPnYsqUKYyTcQOfz69yIIDWuVfv0x3OiouL8e7dOygoKEBZWZl6f0jh3LlzaNWqFfr37w8A2Lx5M/78808YGRlh8+bNtIucFEpKSuDn54fAwEDk5+cDAFq1aoVZs2Zh+fLlEn1ASMUq6xFXjmpi9SwsLMReFxcXIzU1FfLy8vjss8+oB40UMjIywOPx0KlTJwDAlStXsG/fPhgZGeH7779nnI4btmzZgjlz5uDLL78UrUK4fPkyDh8+jA0bNmDGjBmME3ID3SfWHl1XSH2jaS5EJtOmTcOSJUtgYmKCR48eYdy4cRgzZgwOHTqEd+/eUZ8fKfn4+GD9+vWYNWuW6Ibj0qVLmDdvHtLT06t9WkuAmzdvir0uLi7GzZs3sX79eqxcuZJRKm559eqVxLEHDx7ghx9+wKJFixgk4p5FixZhzZo1AIBbt25h/vz5WLBgASIiIjB//nzs3r2bccLGb9asWQgJCcHatWvF6qGvry+ys7Pxxx9/ME7IDZ9+n8trore3N9VEKX16XQE+bGowefJkjB49mkEi7hk/fjy+//57TJw4EVlZWRg8eDCMjY3x999/Iysri2abScHf3x8bNmzAzJkzRcdmz54NW1tb+Pv706CUlI4ePSr2urwmBgUFUc9HKdF1hdQ7ISEyUFNTE6akpAiFQqFw9erVwqFDhwqFQqHw4sWLwk6dOrGMxilaWlrCffv2SRzft2+fsE2bNgwSNR2nTp0S2tnZsY7BaVevXhX26NGDdQxOUFFREaampgqFQqFw+fLlwrFjxwqFQqHw+vXrwnbt2jFMxh1qamrCM2fOSBw/ffq0UE1NjUGipiUyMlLYq1cv1jE4LTExUainp8c6Bieoq6sL7927JxQKhcKNGzcKbWxshEKhUPjPP/8Iu3TpwjIaZ6ioqAgfPHggcTw5OVmooqLCIFHT8vfffwtHjhzJOgan0XWF1JXK97wlpApCoVC0O82FCxfg4uIC4EP/j5cvX7KMxinFxcWwtLSUON67d2+UlJQwSNR09OjRA1evXmUdg9Pk5eXx7Nkz1jE4QUFBAe/evQPwoSYOHToUAKCpqYm8vDyW0ThDUVER+vr6Ese7dOlCS8LrQLt27XD//n3WMTjt9evXeP36NesYnFBcXCzqJ3XhwgWMHDkSAGBoaIjMzEyW0Thj5MiRErN8gA8tM7744gsGiZqWfv36ISwsjHUMTqPrCqkrtHyPyMTS0hK//PILBg8ejKioKNGyitTUVLRr145xOu6YOHEi/vjjD6xfv17s+Pbt2+Hm5sYoFbd8+oNfKBQiMzMTvr6+6NatG6NU3HLixAmx1+Wf4aZNm2Bra8soFbf0798f8+fPh62tLa5cuYIDBw4AAJKTk0U9VUjVZs6ciRUrVmD37t2iH7OFhYVYuXKl2PIVUrXExESx1+Xf59WrV8Pc3JxNKI4JDAwUe13+Gf71119wdnZmlIpbevbsia1bt2L48OE4f/48VqxYAQB49uwZ2rRpwzhd4/Xx356RkRFWrlyJyMhIsZ5SsbGxWLBgAauITUJBQQECAwPRsWNH1lE4ga4rpL5Ro3Mik8TERLi5uSE9PR3z58/H8uXLAXzoCZKdnY19+/YxTth4zZ8/X/T/kpIS7NmzB507d0a/fv0AAP/++y/S09MxadIk/P7776xickZFDSyFQiF0dXWxf/9+0Y0cqdynDSx5PB7atm2LQYMG4bfffkOHDh0YJeOO9PR0TJ8+HRkZGZg9eza+++47AMC8efNQWloq8SOXfDBmzBix1xcuXICioiLMzMwAAAkJCSgqKoKjoyNCQkJYROSc8pr46e1dv379sGvXLhgaGjJKxh1dunQRe83n80U1cenSpVBVVWWUjDsiIyMxevRo5OXlwd3dHbt27QIA/PTTT7h37x59nyvx6d9eZXg8Hh49elTPaZoGDQ0NsftEoVCIN2/eQFlZGXv37hXN4iOVo+sKqW80KEXq1Pv37yEnJ0e7JFXBwcFBqvfxeDyEh4fXcxrui4qKEntd/uOha9eukJenyaCENGYeHh5Sv5eaxUsnLS1N7HV5TWzZsiWjRKS5Ki0tRV5entjuo48fP4aysjK0tbUZJiPNyZ49e8QGpcprYt++fWlnXCnRdYXUNxqUIrVSVFSEFy9eiPpLlevcuTOjRKS5iY6Oho2NjcQAVElJCeLi4jBw4EBGybhDIBBg4cKFUFZWFjteUFCAdevW0S5JUiorK0NKSkqFNZH+DklDCQ4Oxrhx40RLIMsVFRVh//79mDRpEqNk3OHp6YmNGzdKzIh6+/YtZs2aJZr1Qwhp/NLT06Grqysxq778HP1mqR5dV0h9o0EpIpPk5GR89913iIuLEzsuFArB4/FQWlrKKBlpbuTk5JCZmSnx1DU7Oxva2tr0tygF+gxr7/Llyxg/fjzS0tIkprdTTSQNib7PtVfZZ/jy5Uu0b9+eNiKRwvPnz7Fw4UKEhYXhxYsXEnWR/g6rV1paij179og+w08fdtBseulQTaw9+gxJfaO1LUQmHh4ekJeXx6lTp9ChQ4cKnz6Q6r19+xarV6+u9IaD+gVUr3wg9FPZ2dlQUVFhkIh7KvsMExISoKmpySAR93h5ecHS0hKnT5+mmigj+hFbNyr7Pj958gStW7dmkIg78vLyIBQKRT1nPl6aUlpaijNnztCyMylNnjwZ6enp8Pb2ppooozlz5mDPnj0YPnw4jI2N6TOUUWXzL/Lz82n5mZToukLqGw1KEZnEx8fj+vXr1NiulqZMmYKoqChMnDiRbtpqqLxBMo/Hw+TJk8WmFJeWliIxMRE2Njas4nFCefNPHo+H7t27i/39lZaWIj8/H15eXgwTcseDBw9w+PBhdO3alXUUzqIfsbVjYWEh+j47OjqKLWkuLS1Famoqhg0bxjBh46euri5WEz/F4/Hg5+fHIBn3XLx4ETExMbQzVy3s378fBw8ehIuLC+sonFS+sRCPx4OPj49Yi4LS0lL8+++/9PdZDbqukIZCg1JEJkZGRnj58iXrGJx39uxZnD59Gra2tqyjcE75kxmhUAhVVVUoKSmJzikoKKBfv36YOnUqq3icEBAQAKFQCE9PT/j5+Yk97VJQUIC+vj7tXiilvn37IiUlhQalaoF+xNaOq6srgA8PjZycnNCqVSvRufLv89ixYxml44aIiAgIhUIMGjQIR44cEZspqqCgAD09Pejo6DBMyB26urqVzlAh0lFQUKBrSi3cvHkTwIf7xFu3bkFBQUF0TkFBAWZmZli4cCGreJxA1xXSUKinFJFJeHg4fv75Z/j7+8PExERitz01NTVGybilS5cuOHPmDD7//HPWUTjLz88PCxcupKV6tRAVFQVbW1varbAWjh49ip9//hmLFi2qsCaampoySsYdRkZG+Pvvv2FhYcE6CqcFBQVh3LhxtCylFtLS0tC5c2earVcLoaGh+O2337Bt2zbo6+uzjsNJv/32Gx49eoRNmzbR32IteHh4YOPGjfTbpBboukLqGw1KEZnw+XwAkLhIUqPzmtm7dy+OHz+OoKAgiZ3PiPRKSkoQGRmJhw8fYvz48VBVVcWzZ8+gpqYm9lSHVO7hw4fYvXs3Hj58iI0bN0JbWxtnz55F586d0bNnT9bxGr3ymvgxHo9HNbEG6Eds3cnNzcXhw4fx8OFDLFq0CJqamrhx4wbatWuHjh07so7HCTExMdi2bRsePXqEQ4cOoWPHjvjrr7/QpUsX9O/fn3W8Rk9DQwPv3r1DSUkJlJWVJQbqc3JyGCXjjtGjRyMiIgKampro2bOnxGcYEhLCKBk3paSk4OHDhxg4cCCUlJQq7ZNEKkbXFVKf6LE4kUlERATrCE3Cb7/9hocPH6Jdu3bQ19eXuOG4ceMGo2TckZaWhmHDhiE9PR2FhYUYMmQIVFVVsWbNGhQWFmLr1q2sIzZ6UVFRcHZ2hq2tLaKjo7Fy5Upoa2sjISEBO3fuxOHDh1lHbPRSU1NZR+C8cePG4d27d/jss8/oR2wtJCYmYvDgwWjdujUeP36MqVOnQlNTEyEhIUhPT0dwcDDriI3ekSNHMHHiRLi5ueHGjRsoLCwEALx+/Rr+/v44c+YM44SNX0BAAOsInKeuro7Ro0ezjsF5OTk5+OqrrxAREQEej4cHDx7AwMAA3333HTQ0NPDbb7+xjtjo0XWF1DeaKUUIQ9U1TF2+fHkDJeEuV1dXqKqqYufOnWjTpg0SEhJgYGCAyMhITJ06FQ8ePGAdsdGztrbGV199hfnz50NVVVX0GV65cgVjxozBkydPWEckzUBQUFCV593d3RsoCbc5Ojqid+/eWLt2rdj3OS4uDuPHj8fjx49ZR2z0LCwsMG/ePEyaNEnsM7x58yacnZ2RlZXFOiIhREqTJk3CixcvsGPHDnz++eei7/M///yD+fPn486dO6wjNnp0XSH1jWZKkRpJTEyU6n3UP0U6NOhUezExMYiLixNrYAkA+vr6ePr0KaNU3HLr1i3s27dP4ri2tjZtaFCNEydOSPW+kSNH1nMS7qNBp7px7do1bN++XeJ4x44daTBFSvfv38fAgQMljrdu3Rq5ubkNH4hD8vLypHof9feR3osXL3D//n0AQI8ePaCtrc04EbeEhobin3/+QadOncSOd+vWDWlpaYxScQtdV0h9o0EpUiPm5uaiPimVof4pNXft2jXcvXsXwIdmv71792aciDvKysoq/Ht78uQJVFVVGSTiHnV1dWRmZqJLly5ix2/evEl9AqpRvjNNVagmSq+0tBRHjx4Vq4ejRo2iJvw1oKioWOHAQHJyMtq2bcsgEfe0b98eKSkpEr3NLl68CAMDAzahOEJdXb3KPj3UZ096eXl5mDFjBvbv3y/6vOTk5DBu3Dhs3rxZbMdcUrm3b99W2Lc1JycHioqKDBJxD11XSH2juzxSI9Q3pW49efIE3377LWJjY6Gurg7gQyNBGxsb7N+/X+KpDpE0dOhQBAQEiJ7g8Hg85OfnY/ny5XBxcWGcjhu++eYbLF68GIcOHQKPx0NZWRliY2OxcOFCTJo0iXW8Rq2srIx1hCbjzp07GDlyJLKystCjRw8AwJo1a9C2bVucPHkSxsbGjBNyw8iRIyEQCHDw4EEAH2pieno6Fi9eTFt3S2nq1KmYM2cOdu3aBR6Ph2fPnuHSpUtYuHAhvL29Wcdr1KjnaN2ZOnUqbt68iVOnTsHa2hoAcOnSJcyZMwfTpk3D/v37GSfkhgEDBiA4OBgrVqwAANF9ztq1a+Hg4MA4HTfQdYXUN+opRQhDw4YNQ25uLoKCgkQ/wu7fvw8PDw+oqanh3LlzjBM2fk+ePIGTkxOEQiEePHgAS0tLPHjwAFpaWoiOjqZp7lIoKirCjBkzsGfPHpSWlkJeXh6lpaUYP3489uzZAzk5OdYRSTNgbW2Ntm3bIigoCBoaGgCAV69eYfLkyfjvv/8QFxfHOCE3vH79Gl9++SWuXbuGN2/eQEdHB1lZWbC2tsaZM2egoqLCOmKjJxQK4e/vj1WrVuHdu3cAPswUWLhwoeiHLSH1TUVFBf/884/Ebo8xMTEYNmwY3r59yygZt9y+fRuOjo7o1asXwsPDMXLkSNy5cwc5OTmIjY3FZ599xjpio0fXFVLfaFCK1JqJiQnOnDkDXV1d1lE4R0lJCXFxcbCwsBA7fv36dQwYMEB0M0yqVlJSgv379yMxMRH5+fno1asX3NzcoKSkxDoap6Snp+P27dvIz8+HhYUFunXrxjoSJ6mpqSE+Pp6W+dSQkpISrl27hp49e4odv337Nvr06YOCggJGybjp4sWLYjVx8ODBrCNxTlFREVJSUpCfnw8jIyO0atWKdSROGj58OHbs2IEOHTqwjsIpnTt3xunTp2FiYiJ2PDExES4uLrQJSQ28fv0amzZtQkJCgqgmzpgxg/4ma4iuK6S+0PI9UmuPHz9GcXEx6xicpKurW+FnV1paCh0dHQaJuEleXh4TJkxgHYPzOnfujM6dO7OOwXn0rEc23bt3x/PnzyUGpV68eIGuXbsySsVd/fv3l5hhQWpGQUEBRkZGrGNwXnR0NA0qy+Dnn3/G/Pnz8ddff6F9+/YAgKysLCxatIiWkdZQ69atsWzZMtYxOI+uK6S+0KAUIQytW7cOs2bNwubNm2FpaQngQ9PzOXPm4Ndff2WcjjsePHiAiIgIvHjxQqLHj4+PD6NU3FFaWoo9e/YgLCysws8wPDycUTLS1H3cOHXVqlWYPXs2fH190a9fPwDA5cuXIRAIsGbNGlYROSksLKzS7/OuXbsYpeKOt2/fYvXq1ZV+ho8ePWKUjDR1FhYWYo3iHzx4IPbAKD09HYqKivjvv/8wbdo0VjE5Jzc3F1euXKnw+0y9M6VD1xVSn2hQitTagAEDaJlUDWhoaIjdcLx9+xZ9+/YV7S5VUlICeXl5eHp6SrWzV3P3559/4ocffoCWlhbat28v9tnyeDwalJLCnDlzsGfPHgwfPhzGxsZV7pxEqjdhwgTa7lxKn+7UJRQK8fXXX4uOlc86GzFiBO3WJSU/Pz8IBAJYWlqiQ4cO9H2WwZQpUxAVFYWJEyfSZ1gH9PT00KJFC9YxOIHu++reyZMn4ebmhvz8fKipqUncJ9KgVPXoukLqG/WUIqSBBQUFSf1ed3f3ekzSNOjp6WH69OlYvHgx6yicpaWlheDgYNqtkDS4qKgoqd9rZ2dXj0majg4dOmDt2rWYOHEi6yicpa6ujtOnT8PW1pZ1FEJILXXv3h0uLi7w9/eHsrIy6zicRNcVUt9ophSRSXZ2Ntq0aQMAyMjIwJ9//omCggKMHDkSAwYMYJyucaOBprr16tUrfPXVV6xjcJqCggL17KmFN2/eIDk5GT169ECrVq1w48YNBAQEoKCgAK6urnBzc2MdsdGigaa6V1RUBBsbG9YxOE1DQwOampqsYzQZb9++xYEDB1BQUIChQ4fSJhoymD59OgQCAbS0tFhH4ZynT59i9uzZNCBVC3RdIfWNzzoA4ZZbt25BX18f2traMDQ0RHx8PPr06YMNGzZg+/btcHBwwLFjx1jH5KThw4cjMzOTdQzO+eqrrxAaGso6BqctWLAAGzdupAbdMoiOjkbHjh3Rp08f6OnpITQ0FPb29rh69Sru3r2LSZMm4c8//2Qdk3NMTEyQkZHBOgYnTZkyBfv27WMdg9NWrFgBHx8f2gFXBunp6bCzs4OqqiqGDBmC9PR09OrVC1OmTMGsWbNgbm6O6Oho1jE5Z+/evWI9+Ij0nJyccO3aNdYxOI2uK6S+0fI9UiPOzs6Ql5fHkiVL8Ndff+HUqVNwcnIS/eiaNWsWrl+/jsuXLzNOyj2qqqpISEigbeRraNWqVVi/fj2GDx8OExMTib4Vs2fPZpSMO0aPHo2IiAhoamqiZ8+eEp9hSEgIo2SN38CBA9GtWzcIBALs2rUL69evxw8//AB/f38AwC+//ILDhw8jPj6ebVCOoXoouzlz5iA4OBimpqYwNTWV+D6vX7+eUTLusLCwwMOHDyEUCqGvry/xGd64cYNRssbv66+/RkZGBmbOnImDBw8iOTkZn332GXbu3Ak+n48ffvgBOTk5tIFGDVFNlN3OnTshEAjg4eFR4X3iyJEjGSXjDrqukPpGg1KkRrS0tBAeHg5TU1NRw8CrV6+id+/eAIB79+6hX79+yM3NZRuUg+iGQzZdunSp9ByPx6NdkqTg4eFR5fndu3c3UBLuUVdXx+XLl2FoaIiioiIoKSnhxo0bMDMzAwCkpKTAwsICb968YZyUW6geys7BwaHSczwejwYDpODn51fl+eXLlzdQEu5p3749Tpw4ASsrK+Tk5EBLSwuxsbGwtrYGACQkJMDR0REvX75knJRbqCbKjs+vfGEQj8ejTTSkQNcVUt+opxSpkZycHLRv3x4A0KpVK6ioqEBDQ0N0XkNDg358yYh2p5FNamoq6wicR4NOssvLyxP1nlFQUICysjJUVVVF51VVVWkJkAxoV1fZRUREsI7AeTToJLsXL15AT08PAKCpqQllZWW0a9dOdL59+/Z49eoVq3icRffWsisrK2MdgfPoukLqG/WUIjX26TagtC2o7NLT00V9fG7fvg1dXV0AH7ZBT09PZxmNECIFHo8nsb001UTZREdHo6SkBABw5swZdOjQAQBQUlJCPWgI4ZBPayKRjZycHF68eCFxPDs7G3JycgwSEUJI/aCZUqTGJk+eDEVFRQDA+/fv4eXlBRUVFQBAYWEhy2ic06VLF2RmZkJbW1vseE5ODrp06UJTiisxf/58rFixAioqKpg/f36V76V17hXr1asXwsLCoKGhAQsLiyp/OFD/lMoJhUI4OjpCXv7D5fTdu3cYMWIEFBQUAEA0yEKq5+DgUGE9fP36NRwcHKgeVmHMmDHYs2cP1NTUMGbMmCrfSz3iKqapqYnk5GRoaWlBQ0OjypqYk5PTgMm4x8fHR7TTWVFREVauXInWrVsDAM0crYHKOqwUFhaKrjGkYoGBgfj+++/RsmVLBAYGVvle6j1aMbqukIZEg1KkRtzd3cVeT5gwQeI9kyZNaqg4nCcUCiu88c3Pz0fLli0ZJOKGmzdvori4GMCHAZPKfjzQE9rKjRo1SjS47OrqyjYMh326zGfUqFES7xk7dmxDxeG0yuphdna26MEHqVjr1q1Fn52amhrVPhls2LBBtPQ2ICCAbRgOGzhwIO7fvy96bWNjI9HbceDAgQ0di1PKB1F4PB527NiBVq1aic6VlpYiOjoahoaGrOJxwoYNG+Dm5oaWLVtiw4YNlb6Px+PRoFQl6LpCGhI1OieEgfLZPRs3bsTUqVNFTxSBDzcc//77L+Tk5BAbG8sqYqOWmJgIY2PjKptXkqp9/BQxPT0dnTp1os+TMFH+BPb48eMYNmyYaLAU+FAPExMT0aNHD5w7d45VxEbvxIkTcHZ2pr6EtfDxDNzo6GjY2NiIZkAS0pDKN3BJS0tDp06dxJbqKSgoQF9fHwKBAH379mUVsdF7/fq1aHYekQ1dV0hDokEpQhgo38UiKioK1tbWYtOwy284Fi5ciG7durGK2KjJycmJlvkYGBjg6tWraNOmDetYnCIvL49nz55BW1tb7PMkpKGV7/4YFBSEr7/+WqzBeXk9nDp1KrS0tFhFbPTk5OSQlZWFtm3b0vdZRi1atMCTJ0/Qrl07+gxJo+Dg4ICQkBCxDYWIdD7+Dg8aNAghISFQV1dnHYtT6LpCGhI9AiI18uLFC7GCFB8fjw0bNiAlJQUdOnTAzJkzYW9vzy4gR5TvYuHh4YGNGzdCTU2NcSJuUVdXR2pqKrS1tfH48WPaWUUGOjo6OHLkCFxcXCAUCvHkyRO8f/++wvd27ty5gdNxx5UrV9C7d2/Rk+xTp05h3bp1opo4e/ZsWtJcjfLdH/X19bFo0SKxmaNEOm3btsXly5cxYsSISpdBkqrp6+sjMDAQQ4cOhVAoxKVLlyodDKDlZ5UbMWIEvv76a3z55Ze0g2Yt0Y5nsmvVqhWys7Ohra2NyMhIUcsHIj26rpCGRDOlSI18PFIeFxcHe3t72NjYwMrKCvHx8YiIiEBYWBjdsEnp9evXKC0tFW0pXy4nJwfy8vI0WFWJ77//HsHBwejQoYNo6VllO9F82suCfLB9+3bMmjWrykbc5Tch1GC6ch/XxJMnT8LV1RUTJkxA3759cfPmTezZswcHDx7E6NGjWUdt9FJTU1FSUiIxQ/TBgwdo0aIF9PX12QTjAF9fXwgEAql+NND3uWLHjh2Dl5cXXrx4AR6PV2mTaaqJVePz+ZCTk4OKigq+/fZbTJkyBb1792Ydi5PGjh0LKysrLF68WOz42rVrcfXqVRw6dIhRssZv7NixiI2Nxeeff46oqCjY2NhU2hw+PDy8gdNxA11XSEOiQSlSI3w+H1lZWdDW1sbQoUOhq6uLnTt3is7PnTsXt27dQlhYGMOU3OHs7IwRI0Zg+vTpYse3bt2KEydO4MyZM4ySNX7nzp1DSkoKZs+eDYFAIGpQ+6k5c+Y0cDLuePPmDdLS0mBqaooLFy5UugTSzMysgZNxx8c1ccCAAejfvz9WrVolOu/v74+TJ0/i0qVLDFNyg52dHTw9PSU21Ni7dy927NiByMhINsE44t69e0hJScHIkSOxe/fuSpeqVNSMn/yf/Px8qKmp4f79+5UuVaFeNZXj8/m4ffs2QkNDsWvXLty5cwcmJiaYMmUK3NzcaClaDbRt2xbh4eEwMTERO37r1i0MHjwYz58/Z5Ss8SsoKEBQUBAePnyI3377TaJ/68eqaoTe3NF1hTQUGpQiNfLxDzAdHR2EhISgX79+ovN37tyBvb09/vvvP4YpuUNTU1P0JOdj9+7dg62tLbKzsxkl4w4PDw8EBgZWOihFqhcUFIRvvvlGrME0kc7HNbFdu3Y4c+aM2KyA+/fvo1+/fnj16hXDlNygpqaGGzduoGvXrmLHU1JSYGlpidzcXDbBOMbPz4+WQdZSVFQUbG1tqdG5DD6uicCHJc47d+7EgQMHUFRUBFdXV0yZMgWDBg1inLTxU1JSQnx8PHr06CF2/N69e7CwsEBBQQGjZNzi4OCAo0ePUk+pWqDrCqlvtNUSqbE3b94gLy8PLVu2lPgR27JlS7x7945RMu4pLCyscPlUcXEx3WxIaffu3TQgJYOPn0e4u7vTgFQtJCUlITExEUpKShX2N6tqiST5PzweD2/evJE4Xr7MmUhn+fLl9MNBBm/fvhX9387OrtoBqY/fTypnZWWFbdu24dmzZ9iyZQsyMjIwZMgQ1rE4wcTEBAcOHJA4vn//fhgZGTFIxE0RERE0IFVLdF0h9Y0GpUiNde/eHRoaGnj8+DGuXbsmdu7OnTvQ0dFhlIx7rKyssH37donjW7dupR4MVfDy8sKTJ0+keu+BAwfw999/13Mi7unZsyf279+PoqKiKt/34MED/PDDD1i9enUDJeMeR0dHmJubIz09HbGxsWLnbt68SY3ipTRw4ECsWrVKbACqtLQUq1atQv/+/Rkma/yGDRuGy5cvV/u+N2/eYM2aNdi8eXMDpOKWrl27YvXq1cjMzKz0PUKhEOfPn4ezszMCAwMbMB33KSsrY/LkyYiJicHdu3dZx+EEb29vrFixAu7u7ggKCkJQUBAmTZqElStXwtvbm3W8Rm316tVSPyT/999/cfr06XpOxD10XSENieYlkxr5dCeQDh06iL1OTU3F999/35CROO2XX37B4MGDkZCQAEdHRwBAWFgYrl69itDQUMbpGq+2bduiZ8+esLW1xYgRI2BpaQkdHR20bNkSr169QlJSEi5evIj9+/dDR0enwoG/5u7333/H4sWLMX36dAwZMqTSz/DOnTuYOXMmfvjhB9aRG6XU1FSx161atRJ7XVRUJNGkllRszZo1GDhwIHr06IEBAwYAAGJiYpCXl0eNaKvx1VdfYezYsWjdunWVNfHMmTMYPnw41q1bxzpyoxMZGYmffvoJvr6+MDMzq/AzvHTpEuTl5bF06VJMmzaNdeRGyc7OrtKG0uW6d+/eQGm4bcSIETh27Bj8/f1x+PBhKCkpiXpA2tnZsY7XqCUlJUFPTw9fffWVqCa2bdsWwIfZy+U1ce/evXj27BmCg4MZJ2586LpCGhL1lCKEsfj4eKxbtw7x8fGiG46lS5dK7EBFxD1//hw7duzA/v37kZSUJHZOVVUVgwcPxpQpUzBs2DBGCbnh4sWLOHDgAGJiYpCWloaCggJoaWnBwsICTk5O1JiWNKhnz55h06ZNSEhIENXDmTNnSuxQSiQVFhbi0KFDOHDgAC5evIjXr18D+LAs0sjICE5OTvjuu+8kehgScenp6Th48CAuXrxYYU10dnaudLdXQkjjkZCQgE2bNuHw4cPIy8uDnJwcFBUVRTOoLCwsMGXKFEyePBktW7ZknLZxousKaSg0KEUI4bxXr14hPT1d9OPhs88+k2oLW0IIaapev36NgoICtGnTBi1atGAdhzRjq1evhpeXF/X1IUyUlZUhMTFRbJDZ3NwcWlparKNxDl1XSH2hQSlSp9zd3ZGRkUFLLaSUnp5e5XnqRVO99PR06OrqVjgIlZ6eTp+hFIKDgzFu3DiJZudFRUXYv38/Jk2axCgZ9w0ePBiPHj3Co0ePWEdp9KKjo6s8P3DgwAZKwm3R0dGwsbGRaNRdUlKCuLg4+hylIBAIsHDhQonGvgUFBVi3bh18fHwYJeMmNTU1xMfHw8DAgHUUTuHz+VU+YKMNIEhDoesKqW80KEXq1E8//YTMzEzs3r2bdRROoBuO2pOTk0NmZqZo++ly2dnZ0NbWps9QCvQZ1p/Nmzfj5cuXWL58OesojR6fL7n3ysf1kf4OpUPf59qjz7BuqaqqIiEhgQalauj48eNir4uLi3Hz5k0EBQXBz88P3333HaNk3ELf59qjz5DUN2p0TuqUv78/6wiccvPmTbHX5Tcc69evx8qVKxml4hahUFjhwF5+fj71CJBSZZ/hkydP0Lp1awaJmo4ZM2awjsAZr169EntdXg+9vb2pHtZAZd/n7OxsqKioMEjEPZV9hgkJCdTfjDSYUaNGSRz78ssv0bNnTxw4cIAGpaRU2fyLwsLCapvykw/oukLqGw1KEcKQmZmZxLHy3S3WrVuHMWPGMEjFDfPnzwfwYSaFt7e32DKL0tJS/PvvvzA3N2eUjhssLCzA4/HA4/Hg6OgoNi27tLQUqamp1CieNJiKBkCHDBkCBQUFzJ8/H9evX2eQijvKrxc8Hg+TJ08WW45bWlqKxMRE2NjYsIrHCRoaGqKa2L17d4mZevn5+fDy8mKYkJuSkpKgo6PDOkaT0a9fP9rpWgqBgYEAPtTEHTt2iO2OW1paiujoaBgaGrKKxwl0XSENhQalSI0lJSVh06ZNuHTpErKysgAA7du3h7W1NWbOnAkjIyPGCbmvR48euHr1KusYjVr5LDOhUIhbt26JPe1SUFCAmZkZFi5cyCoeJ7i6ugL4sAOkk5OT2A2bgoIC9PX1MXbsWEbpmoaHDx9i6tSp1GevFtq1a4f79++zjtHolQ/qCYVCqKqqQklJSXROQUEB/fr1w9SpU1nF44SAgAAIhUJ4enrCz89PbKC0vCZaW1szTMhNurq6rCM0GQUFBQgMDETHjh1ZR2n0NmzYAOBDTdy6davYrpnl3+etW7eyiscJdF0hDYV6SpEaOXv2LFxdXdGrVy84OTmhXbt2AIDnz5/j/PnzuH79Oo4fPw4nJyfGSbkhLy9P7LVQKERmZiZ8fX1x7949xMfHswnGIR4eHti4cSPU1NRYR+GsoKAgjBs3jpY71oOEhAT06tWL+i1IITExUex1eT1cvXo1SkpKcPHiRUbJuMXPzw+LFi2SaNJNpBcVFQUbGxvaXUpGW7ZsQUhICDQ1NTFt2jQ4OjqKzr18+RJWVla0+YMUymfulRMKhXjz5g2UlZWxd+9ejBw5kmE67nBwcEBISAg0NDRYR+EsPz8/LFy4kJbqkXpDg1KkRszMzDBq1CgIBIIKz/v6+iIkJETixwWpWEWNzoVCIXR1dbF//356IiuF169fo7S0VKLPR05ODuTl5WmwitSr8uUBlXn69Cl+/fVXGpSSQnk9/PS2pF+/fti1axcts5BSamoqSkpK0K1bN7HjDx48QIsWLaCvr88mWCOXl5cnul58+sDoU3RdqVxgYCCWLl0KDw8PvH79GgcPHoSvry+WLl0K4MNDTB0dHaqJUggKChJ7zefz0bZtW/Tt25cGWAghTQoNSpEaUVJSQnx8PHr06FHh+fv378Pc3BwFBQUNnIyboqKixF6X33B07dpVYttVUjFnZ2eMGDEC06dPFzu+detWnDhxAmfOnGGUrHHT1NREcnIytLS0JJ7GfionJ6cBk3ELn89Hhw4dKm2WWlRUhKysLPoBJoW0tDSx1+X1kGbw1YydnR08PT3h7u4udnzv3r3YsWMHIiMj2QRr5D7eXaqynXHLm/3S97lyPXv2xLJlyzB+/HgAQFxcHFxdXeHl5QWBQECDUlIqKSmBv78/PD090alTJ9ZxOG3s2LGwsrLC4sWLxY6vXbsWV69exaFDhxgla9x69eqFsLAwaGhoiHqQVubGjRsNmIw0RfSrl9SIvr4+Tp8+Xemg1OnTp6Gnp9fAqbipuLgYQUFB8Pb2RpcuXVjH4ax///0X69evlzhub2+PZcuWMUjEDRs2bICqqiqAD31UiGz09PSwZs0afP311xWej4+PR+/evRs4FfcUFxfD09MTW7dulZjhQ2rm5s2bsLW1lTjer18/zJw5k0EibggPDxfNuI2IiGCchrtSU1PFGh/b2NggPDwcgwcPRnFxMebOncsuHIfIy8tj3bp1mDRpEusonBcdHQ1fX1+J487Ozvjtt98aPhBHjBo1StTYvLwHKSH1hQalSI0IBAKMHz8ekZGRGDx4sFhPqbCwMJw7dw779u1jnJIbWrRogSNHjsDb25t1FE4rLCxESUmJxPHi4mKasVeFj2dRfDqjgkivd+/euH79eqWDUhUtRyOSWrRoQcu+6wiPx8ObN28kjpcvdSYVs7Ozq/D/pGa0tLSQkZEhtkzU2NgY4eHhGDRoEJ49e8YuHMcMGjQIUVFRtOS2lvLz8yuczdyiRYtql+o2Z8uXL6/w/4TUB1q+R2osLi4OgYGBFe6+N2fOHOqDVAPu7u4wNzfHvHnzWEfhLAcHBxgbG+P3338XOz5jxgwkJiYiJiaGUTJuKS0txdGjR3H37l0AgJGREUaNGkXLSKuRlJSEd+/ewdLSssLzxcXFePbsGc0glcK8efOgqKiI1atXs47CaSNGjICSkhL+97//iXabKi0txbhx4/D27VucPXuWcUJuePXqFXbu3ClWEz08PCT6FxJx48ePR7t27UQ7n33szp07cHBwQHZ2Ng2QSmHr1q3w8/ODm5sbevfuLdFkmhqdS8fKygpffPEFfHx8xI77+vri5MmTuH79OqNk3HPt2jWxmkgzwUldoUEpQhj65Zdf8Ntvv8HR0bHCG47Zs2czSsYdsbGxGDx4MPr06SPa4ScsLAxXr15FaGgoBgwYwDhh43fnzh2MHDkSWVlZoqW5ycnJaNu2LU6ePAljY2PGCUlzMGvWLAQHB6Nbt24V1sOKlukSSUlJSRg4cCDU1dVF9S8mJgZ5eXkIDw+n77MUoqOjMWLECLRu3Vo04Hz9+nXk5ubi5MmTGDhwIOOEjVdiYiKuX78ODw+PCs/fvn0bR44coZkXUuDz+ZWeo95m0jt58iTGjBmD8ePHY9CgQQA+3Cf+73//w6FDh2hpmhSePHmCb7/9FrGxsVBXVwcA5ObmwsbGBvv376e+Z6TWaFCK1Nrq1avh5eUlKlJEelX1kuLxeLRlspTi4+Oxbt06xMfHQ0lJCaampli6dCn1ppGStbU12rZti6CgINGOPq9evcLkyZPx33//IS4ujnFCbqGaKBsHB4cqz1OfH+k9e/YMmzZtQkJCgqgmzpw5k2b5SMnExATW1tb4448/xGabTZ8+HXFxcbh16xbjhISQmjh9+jT8/f3F7hOXL19OS3WlNGzYMOTm5iIoKEj08PL+/fvw8PCAmpoazp07xzgh4ToalCK1pqamhvj4eBgYGLCOQgiRgZKSEq5du4aePXuKHb99+zb69OlDvblqiGoiIdxW2U7DtMNwzeTm5uLKlSt48eIFysrKRMd5PB4mTpzIMBk3BAcHY9y4caJm0+WKioqwf/9+aoJOGoySkhLi4uJgYWEhdvz69esYMGAA3r17xygZaSqoWQipNRrXlJ1AIMDChQuhrKwsdrygoADr1q2TWP9OJKWnp1d5vnPnzg2UhLu6d++O58+fSwxKvXjxAl27dmWUiruoJsrG09MTGzduFO0KWe7t27eYNWsWdu3axSgZt0RHR1d5npaeVa9Xr164e/euxKDU3bt3YWZmxigVt5w8eRJubm7Iz8+Hmpqa2HbyNCglHQ8PDwwbNgza2tpix9+8eQMPDw8alCINRldXF8XFxRLHS0tLoaOjwyARaWpophSpNVVVVSQkJNCsABnIyckhMzNT4oYjOzsb2tra1C9ACnw+X+xm91P0GVbs4x1nLl68iB9//BG+vr7o168fAODy5csQCARYvXo1XFxcWMXkJKqJsqmsHr58+RLt27evcJdNIqmiPjQf10iqiRX7ePfHu3fv4scff8SsWbPEauLmzZuxevVqjBs3jlVMzujevTtcXFzg7+8v8eCNSIfP5+P58+do27at2PGEhAQ4ODggJyeHUTJuofvE2jt+/Dj8/f2xefNmUZ+9a9euYdasWVi8eDH15SK1RoNSpNYyMjKgo6Mj6rtApFfZDUd4eDjGjRuH//77j1Ey7khISBB7XVxcjJs3b2L9+vVYuXIlxowZwyhZ4/bpTVr5paD82Mev6YatZqgm1kxeXh6EQiE0NDTw4MEDsXpYWlqKkydPYsmSJbSVvJRev34t9rq8Jnp7e2PlypWiDSGIuPKaWN1tMdVE6aioqODWrVs0OC8DCwsL8Hg8JCQkoGfPnmK74JaWliI1NRXDhg3DwYMHGabkjuPHj4u9Lq+JQUFB8PPzw3fffccoWeOmoaEhdp/49u1blJSUiP4ey/+voqJCA6Sk1mj5Hqk1XV1d1hE4p7zQ83g8dO/eXeIpdn5+Pry8vBgm5I6KllJYWlpCR0cH69ato0GpSlDT6PpDNbFm1NXVxerhp3g8Hvz8/Bgk46bWrVtLHBsyZAgUFBQwf/582v68EqmpqawjNClOTk64du0aDUrJoHzWSXx8PJycnNCqVSvROQUFBejr62Ps2LGM0nHPqFGjJI59+eWX6NmzJw4cOECDUpUICAhgHYE0IzRTitTYli1bEBISAk1NTUybNk3sqevLly9hZWVFu8ZVIygoCEKhEJ6enggICBD7EVF+w2Ftbc0wIfelpKTAzMwMb9++ZR2FNHFUE2snKioKQqEQgwYNwpEjR8R2iFNQUICenh71rKgD9+7dg6WlJfLz81lHIc3Azp07IRAI4OHhARMTE7Ro0ULs/MiRIxkl446goCB88803Eo3OSd149OgRTE1NqSYS0gjQoBSpkcDAQCxduhQeHh54/fo1Dh48CF9fXyxduhQA8Pz5c+jo6NDUdilFRUXB1tZWbGo2qZmPeyMBH5adZWZmwtfXF/fu3UN8fDybYBw2aNAg7N69G3p6eqyjNHpUE+tOWloaOnfuXGXvD1K9j3sjAf9XE1evXo2SkhJcvHiRUTLuMjAwwD///INu3bqxjsIZFfU2K0dLIKWTkZEBHo+HTp06AQCuXLmCffv2wcjICN9//z3jdNxWUFCApUuX4uzZs7h//z7rOJzj4eGBlStX0gMjUmfolzCpkW3btuHPP//E+PHjAQA//PADXF1dUVBQAIFAwDgd96iqquLu3bswMTEB8GHd++7du2FkZARfX18oKCgwTtj4lS/9+ZhQKISuri7279/PKBU3nDhxosLj0dHROHXqlGgZGj3RrhzVxLpz9+5dZGRkoH///gCAzZs3488//4SRkRE2b94MDQ0Nxgm5wdzcvMLeSP369aMdDKsRGBhY4fH09HTs3r0b7du3BwDMnj27IWNxUllZGesInDd+/Hh8//33mDhxIrKysjB48GAYGxvj77//RlZWFu3QLKVPeyMJhUK8efMGysrK2Lt3L8Nkjd+nDznK/f333xg1apRoea6pqWlDxiJNEM2UIjWirKyMpKQk6Ovri47dvn0bgwcPhoeHB+bOnUuzAmqgT58+WLJkCcaOHYtHjx7ByMgIY8aMwdWrVzF8+HBazy2FqKgosdd8Ph9t27ZF165daQZaNaRp7EtPtKtGNbHumJiYYM2aNXBxccGtW7dgaWmJBQsWICIiAoaGhti9ezfriJyQlpYm9rq8JrZs2ZJRIu7g8/no2LGjxLUjLS0NOjo6aNGiBXg8Hi3HraH379/T358MNDQ0cPnyZfTo0QOBgYE4cOAAYmNjERoaCi8vL/o7lFJQUJDY6/Ka2LdvX3rYUY2q7hPLj9N9IqkL9IuN1IiWlhYyMjLEfoAZGxsjPDwcgwYNot2Raig5ORnm5uYAgEOHDsHOzg779u1DbGwsvvnmGxqUqkZxcTGCgoLg7e2NLl26sI7DOU5OTpCTk8OuXbugra0tOt6iRQskJCTAyMiIYTpuoJpYd1JTU0V/c0eOHMGIESPg7++PGzduwMXFhXE6biguLoanpye2bt1KS81k8P333+Pff//Fvn378Pnnn4uOt2jRAqGhoVQTa6C0tBT+/v7YunUrnj9/juTkZBgYGMDb2xv6+vrUXFoKxcXFon5SFy5cEM1aNjQ0RGZmJstonFFSUoK0tDR4enqKlkES6ZmamqJTp0749ddfoaSkBODDTLNu3brh7NmzdJ0hdabyBd+EVKB///4ICQmROG5kZISwsDCcPXuWQSruEgqFoinuFy5cEP3w0tXVxcuXL1lG44QWLVrgyJEjrGNw1tmzZ+Ho6AhLS0ucOnWKdRxOoppYdxQUFPDu3TsAH+rh0KFDAQCampoSveNIxVq0aFHpcgtSva1bt8LHxwdOTk7YtGkT6zictnLlSuzZswdr164Va0VgbGyMHTt2MEzGHT179sTWrVsRExOD8+fPY9iwYQCAZ8+eoU2bNozTcYO8vDzWrVuHkpIS1lE46cqVK+jatSvGjh2LnJwc6OnpiR7C6ejoQE9Pj/qPkjpBg1KkRpYsWVLpuuGePXsiPDyc1rjXgKWlJX755Rf89ddfiIqKwvDhwwF8mDHQrl07xum4wdXVFceOHWMdg7PmzZuHEydOYPHixZg2bZpoUIBIh2pi3enfvz/mz5+PFStW4MqVK6J6mJycTE+4a2DChAnYuXMn6xicNXr0aFy6dAlHjx6Fs7MzsrKyWEfipODgYGzfvh1ubm6Qk5MTHTczM8O9e/cYJuOONWvWYNu2bbC3t8e3334LMzMzAB/6QVpZWTFOxx2DBg2SaPVApKOgoICAgAD8+uuvGDlyJFatWkX94ki9oOV7pEZMTU2rbGZnbGwMY2PjBkzEbQEBAXBzc8OxY8ewbNkydO3aFQBw+PBh2NjYME7HDd26dYNAIEBsbCx69+4NFRUVsfPUkLZ65ubmuHbtGubNmwdzc/Mqe0wRcVQT686mTZswffp0HD58GH/88Qc6duwI4MOMvvIZAqR6JSUl2LVrFy5cuFBhTVy/fj2jZNzRsWNHXLhwAatXr4aFhQXVRBk8ffpUdE/zsbKyMhQXFzNIxD329vZ4+fIl8vLyxHofff/991BWVmaYjFucnZ2xZMkS3Lp1q8KaSJu5VM/Z2RnXrl2Dh4cHzQAn9YIanROZ5ebm4sqVK3jx4oXYqDmPx8PEiRMZJuO+9+/fQ05ODi1atGAdpdGrqpcUNaStuRMnTiAiIgJLly4V6zNFqkc1kTQGDg4OVZ6PiIhooCRNw40bNxATE4NJkyZRU+Qa6N27N+bNm4cJEyZAVVUVCQkJMDAwgEAgwPnz5xETE8M6Imkm+PzKFwZRk+6aCwwMREREBH7//XeaxUzqDA1KEZmcPHkSbm5uyM/Ph5qamthWqzweDzk5OQzTcU9RUZHED1kA6Ny5M6NEpLl68uQJOnToILbcglSPamLdKCsrQ0pKSoX1cODAgYxSkeYsNjYWlpaWoobTRDrHjx+Hu7s7li5dCoFAAD8/P9y/fx/BwcE4deoUhgwZwjpio/f8+XMsXLgQYWFhePHihcSMPRpMIYQ0FTQoRWTSvXt3uLi4wN/fn6YQ10JycjK+++47xMXFiR2nLValJxAIsHDhQom/w4KCAqxbt476+dSQmpoa4uPjYWBgwDoKp1BNrL3Lly9j/PjxSEtLk/jxRfVQep6enti4cSNUVVXFjr99+xazZs3Crl27GCXjJqqJsouJiYFAIEBCQgLy8/PRq1cv+Pj4iDYxIFVzdnZGeno6Zs6ciQ4dOog97ACAUaNGMUrGLcHBwRg3bpzEwHJRURH279+PSZMmMUrGTVQTSX2gQSkiExUVFdy6dYsKUi3Z2tpCXl4eS5YsqfCGo7ypJamcnJwcMjMzJZaaZWdnQ1tbm37I1tDHyyyI9Kgm1p65uTm6d+8OPz+/Cuth69atGSXjlspq4suXL9G+fXvahaqGqCYSVlRVVRETEwNzc3PWUTiN7hPrFtVEUh+o0TmRiZOTE65du0YFqZbi4+Nx/fp1GBoaso7CWeWzyj6VkJAATU1NBolIc0Q1sfYePHiAw4cPV9gcmVQvLy8PQqEQQqEQb968QcuWLUXnSktLcebMGeoTRxqMj48PHBwcYG1tLfa3SKSnq6tLTfbrQGX3iU+ePKGHHYQ0EjQoRWQyfPhwLFq0CElJSTAxMZFoyE07WUjHyMgIL1++ZB2DkzQ0NMDj8cDj8dC9e3exG47S0lLk5+fDy8uLYUJu+umnn2gwTwZUE2uvb9++SElJoUEpGamrq4vVxE/xeDz4+fkxSMZt27ZtQ7t27VjH4JxLly5h/fr1KCkpQZ8+fWBnZwd7e3vY2tpCSUmJdTxOCAgIwJIlS7Bt2zbo6+uzjsM5FhYWopro6OgIefn/+9lbWlqK1NRU2tlVBhMmTICamhrrGKSJoeV7RCa0k0XdCA8Px88//wx/f/8Kf8hS0a9cUFAQhEIhPD09ERAQIPa0S0FBAfr6+rC2tmaYkJvKLwkVPVUklaOaWHtHjx7Fzz//jEWLFlVYD01NTRkl44aoqCgIhUIMGjQIR44cERtcVlBQgJ6eHnR0dBgm5KaMjAwAH2atkJopKSnBv//+i+joaERFRSEuLg6FhYXo06cPLl68yDpeo6ehoYF3796hpKQEysrKEjWRNtCoWvkgvJ+fHxYsWIBWrVqJzpXfJ44dOxYKCgqsIhJC/j8alCKEofIfsp8OAFCjc+lFRUWJenMR2e3cuRMbNmzAgwcPAADdunXD3LlzMWXKFMbJSHNR0cAej8ejelhDaWlp6Ny5Mw0s10JJSQn8/PwQGBiI/Px8AECrVq0wa9YsLF++XGJwgFQtOTkZERERuHDhAo4dO4bWrVvTLHEpBAUFVXne3d29gZJwW1BQEL755hvaQbOWwsLCsGHDBty9excA8Pnnn2Pu3LkYPHgw42SkKaBBKVJr79+/p34BMoqKiqryvJ2dXQMl4a4bN26gRYsWMDExAfBhG+rdu3fDyMgIvr6+9ARMCj4+Pli/fj1mzZolml126dIlbNq0CfPmzYNAIGCckFuoJsomLS2tyvN6enoNlITbzp07h1atWqF///4AgM2bN+PPP/+EkZERNm/eDA0NDcYJG78ffvgBISEhEAgEYjXR19cXrq6u+OOPPxgnbPy2b9+OyMhIREVFobCwEAMGDIC9vT3s7e1hampKg6akwWRkZIDH46FTp04AgCtXrmDfvn0wMjLC999/zzgdN2zZsgVz5szBl19+KaqJly9fxuHDh7FhwwbMmDGDcULCeUJCZFBSUiIUCARCHR0doZycnPDhw4dCoVAo/Pnnn4U7duxgnI40J5aWlsLDhw8LhUKh8OHDh0JFRUXht99+K+zatatwzpw5bMNxhJaWlnDfvn0Sx/ft2yds06YNg0TcQzWRNBbGxsbC06dPC4VCoTAxMVGooKAgXLp0qbBfv37CyZMnM07HDWpqasIzZ85IHD99+rRQTU2NQSLu4fF4Qm1tbeGaNWuEb968YR2HU16/fi3VPyKd/v37C4ODg4VCoVCYmZkpVFVVFVpbWwu1tLSEfn5+jNNxQ8eOHYW///67xPFNmzYJdXR0GCQiTQ2tdyEyWblyJYKCgrB27VpMnTpVdNzY2BgBAQH47rvvGKZr/BITE6V6H/VQqV5ycrJou+RDhw7Bzs4O+/btQ2xsLL755hsEBAQwzccFxcXFsLS0lDjeu3dv2j5eSlQTZXfixAmp3kfN4qWTmpoKIyMjAMCRI0cwYsQI+Pv748aNG3BxcWGcjhsUFRUrbCzdpUsXmn0rpZCQEERHR2P//v1Yvnw5LCwsRDOl+vfvD2VlZdYRG63yTQsqI6QlzTVy+/ZtWFlZAQAOHjwIExMTxMbGIjQ0FF5eXvDx8WGcsPHLzc2tsCn80KFDsXjxYgaJSFNDg1JEJsHBwdi+fTscHR3FdjgzMzPDvXv3GCbjBnNzc1GvlMrQDYd0hEIhysrKAAAXLlzAF198AeBDU1rqWSGdiRMn4o8//sD69evFjm/fvh1ubm6MUnEL1UTZubq6VvseqofSU1BQwLt37wB8qImTJk0CAGhqaiIvL49lNM6YOXMmVqxYgd27d4v60BQWFmLlypWYOXMm43Tc4OrqKvpuv379GjExMTh06BC++OIL8Pl8vH//nm3ARiwiIoJ1hCaluLhY9D2+cOGC6AGHoaEhMjMzWUbjjJEjR+Lo0aNYtGiR2PHjx4+L7rsJqQ0alCIyefr0aYXbdpeVlaG4uJhBIm5JTU1lHaHJsLS0xC+//ILBgwcjKipK1OsjNTWVtvGuwvz580X/5/F42LFjB0JDQ9GvXz8AwL///ov09HTRD1pSNaqJsisfVCZ1o3///pg/fz5sbW1x5coVHDhwAMCHWaXlPVWIpDFjxoi9vnDhAjp16gQzMzMAQEJCAoqKiuDo6MgiHidlZ2cjKioKkZGRiIyMxJ07d6ChoYEBAwawjtaoUT/RutWzZ09s3boVw4cPx/nz57FixQoAwLNnz9CmTRvG6RqvwMBA0f+NjIywcuVKREZGivWUio2NxYIFC1hFJE0IDUoRmRgZGSEmJkai8ezhw4dhYWHBKBV3UMPeuhMQEAA3NzccO3YMy5YtEw0MHD58GDY2NozTNV43b94Ue927d28AwMOHDwEAWlpa0NLSwp07dxo8GxdRTSSNxaZNmzB9+nQcPnwYf/zxBzp27AgAOHv2bIXLL8gHrVu3Fns9duxYsde6uroNGYfzTExMkJSUBE1NTQwcOBBTp06FnZ0dtSWQ0fDhw7Fjxw506NCBdRTOWbNmDUaPHo1169bB3d1dNNB84sQJ0bI+ImnDhg1irzU0NJCUlISkpCTRMXV1dezatQs///xzQ8cjTQztvkdkcvz4cbi7u2Pp0qUQCATw8/PD/fv3ERwcjFOnTmHIkCGsI3KOiYkJzpw5Qze+deT9+/eQk5OjrbtJg6CaWLfU1NQQHx8PAwMD1lEIITLYvHkz7O3toa2tDUVFRaipqbGOxGmqqqpISEigmiij0tJS5OXlie0++vjxYygrK0NbW5thMkIIQINSpBZiYmIgEAiQkJCA/Px89OrVCz4+Phg6dCjraJxENxy1U1RUhBcvXkgsBercuTOjRKS5oZpYd6ge1k5ZWRlSUlIqrIkDBw5klIo0F7m5uVi2bBkOHDiAV69eAQDatm0LDw8PeHt7U5NzGVBNJIQ0ZTQoRUgjQTccsklOTsZ3332HuLg4seO0O4303r59i9WrVyMsLKzCH7GPHj1ilIw0V1QPZXf58mWMHz8eaWlpEptpUE2UzvPnz7Fw4UJRTfz0c6TPsHI5OTmwtrbG06dP4ebmhs8//xwAkJSUhH379sHQ0BAXL15EYmIiLl++jNmzZzNOzA3GxsY4e/YszaaXAX2fa6+0tBR79uyp9D4xPDycUTLSVFBPKSITHx8fODg4wNraGi1btmQdp0kYMGAAlJSUWMfgHA8PD8jLy+PUqVPo0KFDldsok4pNmTIFUVFRmDhxIn2GMqKaWLcmTJhAy31k5OXlBUtLS5w+fZq+zzKaPHky0tPT4e3tTZ9hDQkEAigoKODhw4cSm40IBAIMHToUEydORGhoqFgjZVK127dvs47AWfR9rr05c+Zgz549GD58OIyNjekzJHWOZkoRmQwZMgSXLl1CSUkJ+vTpAzs7O9jb28PW1pYGVkiDUlFRwfXr12FoaMg6Cmepq6vj9OnTsLW1ZR2Fs6gmksZCRUUFCQkJFe4GSaSjqqqKmJgYmJubs47COfr6+ti2bRucnJwqPH/u3Dm4uLhg+fLlWL58eQOn4663b9/iwIEDKCgowNChQ9GtWzfWkTiDvs+1p6WlheDgYLi4uLCOQpooPusAhJvOnz+P3NxchIWFwcXFBdeuXcOYMWOgrq6O/v37s47HGdnZ2aL/Z2RkwMfHB4sWLUJMTAzDVNxiZGSEly9fso7BaRoaGtDU1GQdg9OoJtbOmzdvcP36deTn5wMAbty4gUmTJuGrr77C33//zTgdt/Tt2xcpKSmsY3Carq6uxBIfIp3MzEz07Nmz0vPGxsbg8/k0IFWF9PR02NnZQVVVFUOGDEF6ejp69eqFKVOmYNasWTA3N0d0dDTrmJxB3+faU1BQoAcdpF7RoBSRmby8PGxtbTF27FiMHj0aTk5OKCsrw71791hHa/Ru3boFfX19aGtrw9DQEPHx8ejTpw82bNiA7du3w8HBAceOHWMdkxPWrFmDH3/8EZGRkcjOzkZeXp7YP1K9FStWwMfHB+/evWMdhdOoJsomOjoaHTt2RJ8+faCnp4fQ0FDY29vj6tWruHv3LiZNmoQ///yTdUzOmDVrFhYsWIA9e/bg+vXrSExMFPtHqhcQEIAlS5bg8ePHrKNwjpaWVpWfW2pqKu12Vo2FCxeiqKgIW7duhbKyMpycnNCtWzdkZmbi+fPncHZ2hq+vL+uYnEHf59pbsGABNm7cSIN7pN7Q8j0ik+3btyMyMhJRUVEoLCzEgAEDYG9vD3t7e5iamtJa42o4OztDXl4eS5YswV9//YVTp07ByclJ9MNr1qxZuH79Oi5fvsw4aePH538YW//0b44anUvPwsICDx8+hFAohL6+Plq0aCF2/saNG4yScQfVRNkNHDgQ3bp1g0AgwK5du7B+/Xr88MMP8Pf3BwD88ssvOHz4MOLj49kG5YjymvgxHo9HNbEGNDQ08O7dO5SUlEBZWVmiJubk5DBK1vh5enri4cOHOH/+PBQUFMTOFRYWwsnJCQYGBti1axejhI1f+/btceLECVhZWSEnJwdaWlqIjY2FtbU1ACAhIQGOjo40S1xK9H2uvdGjRyMiIgKampro2bOnxGcYEhLCKBlpKmhQisiEz+ejbdu2WLBgAaZPn45WrVqxjsQpWlpaCA8Ph6mpKfLz86GmpoarV6+id+/eAIB79+6hX79+yM3NZRuUA6Kioqo8b2dn10BJuMvPz6/K87TMonpUE2Wnrq6Oy5cvw9DQEEVFRVBSUsKNGzdgZmYGAEhJSYGFhQXevHnDOCk3pKWlVXleT0+vgZJwV1BQUJXn3d3dGygJ9zx58gSWlpZQVFTEjBkzYGhoCKFQiLt372LLli0oLCzE1atX0blzZ9ZRGy0+n4/MzExRo/hWrVohMTFRtBvp8+fPoaOjQwPMUqLvc+15eHhUeX737t0NlIQ0VTQoRWRy7NgxREdHIzIyEnfv3oWFhYVoVkD//v2hrKzMOmKjxufzkZWVJZrC/un253TDQQi3UE2UHdVDQpqW1NRUTJ8+HaGhoaLlPjweD0OGDMGmTZuoN001qCYSQpobedYBCDe5urrC1dUVAPD69WvExMTg0KFD+OKLL8Dn8/H+/Xu2ATng0+U8tLynZqTtjWJqalrPSZqOa9eu4e7duwA+NJAvn7lHqkc1UXY8Hk+s/n36mkjnxIkTUr1v5MiR9ZykaSgtLcXRo0fFauKoUaMgL0+3ztXp0qULzp49i1evXuHBgwcAgK5du9KGGjXg4+MjephRVFSElStXonXr1gBA/R+lJG1fUTU1tXpO0nS8ePEC9+/fBwD06NGD+sOROkMzpYjMsrOzERUVhcjISERGRuLOnTvQ0NDAgAEDcPToUdbxGjU+nw9nZ2coKioCAE6ePIlBgwZBRUUFwIe+C+fOnaOnYFXg8/miPimVof4p0nny5Am+/fZbxMbGQl1dHQCQm5sLGxsb7N+/H506dWIbkCOoJsqGz+fD2NhY9GM/MTERhoaGon40JSUluHPnDn2Xq1FRL6lPUU2Uzp07dzBy5EhkZWWhR48eAIDk5GS0bdsWJ0+ehLGxMeOEpCmzt7eXamA+IiKiAdJwV/l9YmWoz5708vLyMGPGDOzfv1/0ecnJyWHcuHHYvHmzaMCUEFnRoBSRiYmJCZKSkqCpqYmBAwfC3t4ednZ2NCtFStWtzS5Ha7QrV13flHLUP6V6w4YNQ25uLoKCgkQ/wO7fvw8PDw+oqanh3LlzjBM2flQTZVddT7Ny1NuMNBRra2u0bdsWQUFB0NDQAAC8evUKkydPxn///Ye4uDjGCQkh1amu52g56j1avXHjxuHmzZv4/fffRQ33L126hDlz5sDc3Bz79+9nnJBwHQ1KEZls3rwZ9vb20NbWhqKiIk19JYTDlJSUEBcXBwsLC7Hj169fx4ABA2ipgBSoJhLSdCgpKeHatWvo2bOn2PHbt2+jT58+KCgoYJSMEEIanoqKCv755x/0799f7HhMTAyGDRuGt2/fMkpGmorq53oT8onc3FwkJSXBzs4O7du3h4aGBtq3b4+lS5fSj1fClImJCTIyMljH4BxdXV0UFxdLHC8tLYWOjg6DRNxCNZE0Vmpqanj06BHrGJzTvXt3PH/+XOL4ixcvqEk3qXcjRozAX3/9RYOf9WD48OHIzMxkHYNz2rRpU+ESvdatW4tmkxJSGzQoRWokJycHffv2RVBQEMaOHYvffvsNv/32G0aOHInff/8dAwcOxPv373HlyhUEBgayjttovXjxQux1fHw83N3dYWtriy+//BKRkZFsgnHc48ePKxxcIVVbt24dZs2ahWvXromOXbt2DXPmzMGvv/7KMFnjRzWx9q5cuSLW0+PUqVOws7NDx44dYWlpieDgYIbpuI0mw0svLy9P9G/VqlWYPXs2Dh8+jCdPnuDJkyc4fPgw5s6dizVr1rCOSpq406dPw9PTEx06dMAPP/yA69evs47UZERHR9Ngnwx+/vlnzJ8/H1lZWaJjWVlZWLRoEby9vRkmI00FLd8jNTJ37lyEhYXhwoULaNeundi5rKwsDB06FD169EBoaCgCAwPh7u7OKGnjJicnh8zMTGhrayMuLg729vawsbGBlZUV4uPjERERgbCwMAwcOJB1VE75dNtkUjkNDQ2xBqBv375FSUmJqNl0+f9VVFSQk5PDKmajRzWx9j6uhydPnoSrqysmTJiAvn374ubNm9izZw8OHjyI0aNHs47KOVQTpfdpU+Ty2+PyYx+/psbIpD7x+Xzcvn0boaGh2LVrF+7cuQMTExNMmTIFbm5uNDOlFqgmSs/CwkKsJj548ACFhYXo3LkzACA9PR2Kioro1q0bbty4wSomaSJoX1tSI8eOHcO2bdskfnwBQPv27bF27Vq4uLhg+fLl9OOrCh+PBfv6+mLixInYuXOn6NjcuXPh5+eHsLAwFvE4a8CAAVBSUmIdgxMCAgJYR2gSqCbW3sf1cO3atfjxxx+xatUq0bEuXbpg7dq1NCglgwkTJlB/MynRTmakMdHS0sLcuXMxd+5cXLlyBTt37sTPP/+MH3/8Ea6urpgyZQoGDRrEOibn6OnpoUWLFqxjcIKrqyvrCKQZoZlSpEYUFRXx8OHDSreIf/LkCfT19VFSUtLAybiFz+cjKysL2tra0NHRQUhICPr16yc6f+fOHdjb2+O///5jmJIQUh2qibX3cT1s164dzpw5g969e4vO379/H/369cOrV68YpiSEkIbxcU382Lt373Dw4EHs3LkTcXFxNGOPENJkUE8pUiNaWlp4/PhxpedTU1MlLqKkYm/evEFeXh5atmwJRUVFsXMtW7akBslSys7OFv0/IyMDPj4+WLRoEWJiYhim4i5qAlozVBPrRlJSEhITE6GkpISysjKJ8zSoJ503b97g+vXryM/PBwDcuHEDkyZNwldffYW///6bcTpuog00SGOhrKyMyZMnIyYmBnfv3mUdh5Pevn2LXbt2YfPmzXjw4AHrOJw0ffp0vHz5knUM0sTQoBSpEScnJyxbtgxFRUUS5woLC+Ht7Y1hw4YxSMY93bt3h4aGBh4/fizWYBr4MFOKdj2r2q1bt6Cvrw9tbW0YGhoiPj4effr0wYYNG7B9+3Y4ODjg2LFjrGNyDjUBrRmqiXXD0dER5ubmSE9PR2xsrNi5mzdvinpYkMpFR0ejY8eO6NOnD/T09BAaGgp7e3tcvXoVd+/exaRJk/Dnn3+yjsk5tIEGaWh2dnZQUFCo8j3du3dvoDTclZ6eDjs7O6iqqmLIkCFIT09Hr169MGXKFMyaNQvm5uaIjo5mHZNz9u7di7y8PNYxSBNDy/dIjTx58gSWlpZQVFTEjBkzYGhoCKFQiLt372LLli0oLCzE1atX6QdENaKiosRed+jQQewGY+PGjSgqKsKiRYsaOhpnODs7Q15eHkuWLMFff/2FU6dOwcnJSfSja9asWbh+/TouX77MOCm3UBPQmqGaWHtpaWlir1u1aoU2bdqIXpfvvjdp0qQGzcU1AwcORLdu3SAQCLBr1y6sX78eP/zwA/z9/QEAv/zyCw4fPoz4+Hi2QTmGaiIh3PT1118jIyMDM2fOxMGDB5GcnIzPPvsMO3fuBJ/Pxw8//ICcnByEh4ezjsopVBNJfaBBKVJjqampmD59OkJDQ8V2oxkyZAg2bdqErl27Mk5ImgMtLS2Eh4fD1NQU+fn5UFNTw9WrV0W9aO7du4d+/fohNzeXbVCOMTY2xtmzZ6Grq8s6CmdQTSSNgbq6Oi5fvgxDQ0MUFRVBSUkJN27cgJmZGQAgJSUFFhYWePPmDeOk3OLi4oKdO3eiQ4cOrKOQZmz16tXw8vKCuro66yic0b59e5w4cQJWVlbIycmBlpYWYmNjYW1tDQBISEiAo6MjLUWrIRqUIvWBBqWIzF69eiVaj921a1doamoyTkSak08bgX56kXz+/Dl0dHSoEagU0tPToaurK7b1L/BhV7SMjAya5SMlqomEJaqJdSc6Oho2NjaQlxffpLqkpARxcXEYOHAgo2SkuVJTU0N8fDwNBNQAn89HZmamaHfcVq1aITExkWoiIY0Q9ZQiMtPQ0ICVlRWsrKzox1cdc3d3p61+pfDpIMqnr4l0unTpUuFOjzk5OejSpQuDRNxENbF+DB48mH6ISYHH44nVwE9fE+k5ODggJydH4vjr16/h4ODAIBFp7mgOgWw+rYlENnJycnjx4oXE8ezsbMjJyTFIRJoa+erfQghpaB07dgSfT2PG1Zk8ebJo58L379/Dy8sLKioqAD40mSbSEQqFFd6s5efno2XLlgwSEfJ/Ro8eTcsrpCAUCuHo6Cia3fPu3TuMGDFC1DCZdjCUXmU1MTs7W3SNIYQ0fj4+PlBWVgYAFBUVYeXKlWjdujUA0C7XNVDZoGhhYWG1TfkJkQYt3yOEcJKHh4dU79u9e3c9J+Gu+fPnA/jQWH/q1KmiGzcAKC0txb///gs5OTmJ3dAIIY2Pn5+fVO9bvnx5PSfhrjFjxgAAjh8/jmHDhokeegAfamJiYiJ69OiBc+fOsYpImqmMjAzo6OjQrJQasLe3l2p2VERERAOk4abAwEAAwLx587BixQq0atVKdK60tBTR0dF4/Pgxbt68ySoiaSJoUIoQQpqp8mUoUVFRsLa2FnvapaCgAH19fSxcuBDdunVjFZEQQhpM+cOOoKAgfP3111BSUhKdK6+JU6dOhZaWFquIhBDSYMpbOKSlpaFTp05ig6LlNVEgEKBv376sIpImggalCGEkKSkJmzZtwqVLl5CVlQXgw04h1tbWmDlzJoyMjBgnJM2Fh4cHNm7cCDU1NdZRCJHw8OFDTJ06lbbtJg3Gz88PixYtEps9SkhD2rJlC0JCQqCpqYlp06bB0dFRdO7ly5ewsrLCo0ePGCYkzYmDgwNCQkKgoaHBOgppoqhpDSEMnD17FhYWFrh58yZGjRoFHx8f+Pj4YNSoUUhISECvXr3wzz//sI7ZqH3acDE+Ph7u7u6wtbXFl19+icjISDbBOCggIKDCfjM5OTnIy8tjkIiQ/5Ofn4+oqCjWMRq9K1euiO0iderUKdjZ2aFjx46wtLREcHAww3TcMmnSJDx9+lTi+IMHD/D48eOGD0SalcDAQCxatAiGhoZQVFSEi4sLVq1aJTpfWlqKtLQ0hgm5YcSIEfjrr79QUFDAOgrnRURE0IAUqVc0U4oQBszMzDBq1CgIBIIKz/v6+iIkJASJiYkNnIw75OTkkJmZCW1tbcTFxcHe3h42NjawsrJCfHw8IiIiEBYWRlt3S8HZ2RkjRozA9OnTxY5v3boVJ06cwJkzZxglI81Bec+Kyjx9+hS//vorbdtdjY9r4smTJ+Hq6ooJEyagb9++uHnzJvbs2YODBw9i9OjRrKM2enZ2dvD09IS7u7vY8b1792LHjh300IPUq549e2LZsmUYP348ACAuLg6urq7w8vKCQCDA8+fPoaOjQzWxGnw+H3JyclBRUcG3336LKVOmoHfv3qxjcdLYsWNhZWWFxYsXix1fu3Ytrl69ikOHDjFKRpoKGpQihAElJSXEx8ejR48eFZ6/f/8+zM3N6elOFfh8PrKysqCtrY2hQ4dCV1cXO3fuFJ2fO3cubt26hbCwMIYpuUFTUxOxsbH4/PPPxY7fu3cPtra2yM7OZpSMNAd8Ph8dOnSodAefoqIiZGVl0Q+wanxcEwcMGID+/fuLza7w9/fHyZMncenSJYYpuUFNTQ03btxA165dxY6npKTA0tISubm5bIKRZkFZWRlJSUnQ19cXHbt9+zYGDx4MDw8PzJ07lwalpMDn83H79m2EhoZi165duHPnDkxMTDBlyhS4ubnRzJ8aaNu2LcLDw2FiYiJ2/NatWxg8eDCeP3/OKBlpKmj5HiEM6Ovr4/Tp05WeP336NPT09BowEbfdvn0bU6dOFTs2depUmmkmpcLCwgqX7xUXF9PAKKl3enp62LBhA1JTUyv8V1WtJBVLTk7Gl19+KXZs7NixuHfvHqNE3MLj8fDmzRuJ469fv6aBAFLvtLS0kJGRIXbM2NgY4eHh2L17N3788UdGybhHS0sLc+fORWJiIi5duoS+ffvi559/RseOHTF+/HjqVSil/Pz8Ch8ctWjRgto8kDpBg1KEMCAQCLB48WKMHDkSgYGBOHDgAA4cOIDAwECMGjUKS5cuxcqVK1nHbPTevHmDvLw8tGzZUmzrbgBo2bIl3r17xygZt1hZWWH79u0Sx7du3UpT3Um96927N65fv17peR6PB5rULZ2kpCQkJiZCSUkJZWVlEucrGnwmkgYOHIhVq1aJDUCVlpZi1apV6N+/P8NkpDno378/QkJCJI4bGRkhLCwMZ8+eZZCK+6ysrLBt2zY8e/YMW7ZsQUZGBoYMGcI6FieYmJjgwIEDEsf3799PGzOROiHPOgAhzdFXX32Fjh07IjAwEL/99pvE7nuRkZGwtrZmnLLx6969OwBAKBTi2rVrsLCwEJ27c+cOdHR0WEXjlF9++QWDBw9GQkKCaIefsLAwXL16FaGhoYzTkaZOIBBUOYBsZGSE1NTUBkzEXY6OjqIBvNjYWPTp00d07ubNm+jcuTOraJyyZs0aDBw4ED169MCAAQMAADExMcjLy6OZFaTeLVmypNKB+p49eyI8PBxHjhxp4FRNh7KyMiZPnozJkycjOTmZdRxO8Pb2xpgxY/Dw4UMMGjQIwIf7xP/973/UT4rUCeopRQjhpE934+rQoYNokAoANm7ciKKiIixatKiho3FSfHw81q1bh/j4eCgpKcHU1BRLly5Ft27dWEcjhEjh0924WrVqhTZt2ohel+++N2nSpAbNxVXPnj3Dpk2bkJCQIKqJM2fOhKamJutohBApODg44OjRo1BXV2cdpUk4ffo0/P39xe4Tly9fDjs7O9bRSBNAg1KENBKrV6+Gl5cXXTwJIc0e1UNCCAFyc3Nx5coVvHjxQmxJLo/Hw8SJExkmI4SQukODUoQ0EmpqaoiPj4eBgQHrKKSZSU9Pr/I8LfkhDY3qIWEpOjq6yvMDBw5soCSkOTt58iTc3NyQn58PNTU18Hg80Tkej4ecnByG6biJHngQ0jjRoBQhjYSqqioSEhLoR1gdcXd3R0ZGBvX/kAKfzxe72f0U7TZFGhrVw7o3ePBgPHr0CI8ePWIdpdHj8yX3Afq4RlJNJA2he/fucHFxgb+/P5SVlVnHaRLogYds6D6R1DdqdE4IaZI6duxY4Q8LIunmzZtir4uLi3Hz5k2sX7+edoEkpIkYPXo0Xr58yToGJ7x69UrsdXlN9Pb2pppIGszTp08xe/ZsGpCqQzQXQzZHjx4Ve11eE4OCguDn58coFWlKaKYUIY1ERkYGdHR0ICcnxzoKIQA+NLVct24dIiMjWUchzQzVQ9IYRUVFYf78+ZXujEZIXRozZgy++eYbfP3116yjNBk0C7du7du3DwcOHMDx48dZRyEcR4NShBBCKpSSkgIzMzO8ffuWdRRCCGHu3r17sLS0RH5+PusopBnYuXMnBAIBPDw8YGJighYtWoidHzlyJKNk3EUPPOrWo0ePYGpqSjWR1BoNShHCyJYtWxASEgJNTU1MmzYNjo6OonMvX76ElZUV9f6oRlJSEjZt2oRLly4hKysLANC+fXtYW1tj5syZMDIyYpyQG/Ly8sReC4VCZGZmwtfXF/fu3UN8fDybYKTZoHpY/x4+fIipU6dSnz0pJCYmir0ur4mrV69GSUkJLl68yCgZaU6qakHA4/Gojw9hqqCgAEuXLsXZs2dx//591nEIx1FPKUIYCAwMxNKlS+Hh4YHXr1/DxcUFvr6+WLp0KYAPDQPT0tIYp2zczp49C1dXV/Tq1QujRo1Cu3btAADPnz/H+fPn0atXLxw/fhxOTk6MkzZ+6urqEg0shUIhdHV1sX//fkapSHNB9bBh5OfnIyoqinUMTjA3NwePx5PoP9OvXz/s2rWLUSrS3JSVlbGOwHn0wKNuaGhoiN0nCoVCvHnzBsrKyti7dy/DZKSpoJlShDDQs2dPLFu2DOPHjwcAxMXFwdXVFV5eXhAIBHj+/Dl0dHToKVgVzMzMMGrUKAgEggrP+/r6IiQkROKJN5H06Q9VPp+Ptm3bomvXrpCXp2cXpH5RPawbgYGBVZ5/+vQpfv31V/ocpfDpIGh5TWzZsiWjRKS5e//+Pf391dCnDzwOHjwo9sCDri3SCwoKEntdXhP79u0LDQ0NRqlIU0KDUoQwoKysjKSkJOjr64uO3b59G4MHD4aHhwfmzp1LF8pqKCkpIT4+Hj169Kjw/P3792Fubo6CgoIGTsYtxcXFmDZtGry9vdGlSxfWcUgzRPWwbvD5fHTo0AEKCgoVni8qKkJWVhZ9jtUoLi7GsGHDsHXrVnTr1o11HNKMlZaWwt/fH1u3bsXz58+RnJwMAwMDeHt7Q19fH9999x3riI0aPfCoGyUlJfD394enpyc6derEOg5pomi/dEIY0NLSQkZGhtgxY2NjhIeHY/fu3fjxxx8ZJeMOfX19nD59utLzp0+fhp6eXgMm4qYWLVrgyJEjrGOQZozqYd3Q09PDhg0bkJqaWuG/quol+T8tWrSgGbakUVi5ciX27NmDtWvXig02GxsbY8eOHQyTcUNqaipsbGxEr21sbBAeHo7t27eLZkuR6snLy2PdunUoKSlhHYU0YTQoRQgD/fv3R0hIiMRxIyMjhIWF4ezZswxScYtAIMDixYsxcuRIBAYG4sCBAzhw4AACAwMxatQoLF26FCtXrmQdkxNcXV1x7Ngx1jFIM0X1sG707t0b169fr/R8RT2SSMUmTJiAnTt3so5Bmrng4GBs374dbm5uYrvFmZmZ4d69ewyTcQM98Kg7gwYNop6EpF5RsxBCGFiyZEmlPx569uyJ8PBwmr1Sja+++godO3ZEYGAgfvvtN4nd9yIjI2Ftbc04JTd069YNAoEAsbGx6N27N1RUVMTOz549m1Ey0hxQPawbAoEA7969q/S8kZERUlNTGzARd5WUlGDXrl24cOFChTVx/fr1jJKR5uTp06fo2rWrxPGysjIUFxczSMQt5Q88BgwYIHa8/IGHg4MDo2Tc4+zsjCVLluDWrVsV1sSRI0cySkaaCuopRQghzVxVvaR4PB7tTEMIaVaq+7EaERHRQElIc9a7d2/MmzcPEyZMgKqqKhISEmBgYACBQIDz588jJiaGdcRGLTExEdevX4eHh0eF52/fvo0jR45g+fLlDZyMe/j8yhdX8Xg86stFao1mShHCWG5uLq5cuYIXL16Ibf/L4/EwceJEhsm4Z/Xq1fDy8oK6ujrrKJxCsydIY0H1sG5RTZQNDTqRxsDHxwfu7u54+vQpysrKEBISgvv37yM4OBinTp1iHa/RMzU1hampaaXnjY2NYWxs3ICJuOvj6zEh9YFmShHC0MmTJ+Hm5ob8/HyoqamBx+OJzvF4POTk5DBMxz1qamqIj4+HgYEB6yicIhAIsHDhQigrK4sdLygowLp16+Dj48MoGWlOqB7WPaqJsvH09MTGjRuhqqoqdvzt27eYNWsWdu3axSgZaW5iYmIgEAiQkJCA/Px89OrVCz4+Phg6dCjraJxCDzxqJzg4GOPGjYOioqLY8aKiIuzfvx+TJk1ilIw0FTQoRQhD3bt3h4uLC/z9/SUGBEjNfTy9nUhPTk4OmZmZ0NbWFjuenZ0NbW1tmpZNGgTVw7pHNVE2ldXEly9fon379rQLFSEcQg88ao/uE0l9o933CGHo6dOnmD17Nv0AI0wJhUKxm7RyCQkJ0NTUZJCINEdUDwlreXl5eP36NYRCId68eYO8vDzRv1evXuHMmTMSP8oIqS8+Pj6IiIjA+/fvWUfhtAULFsDT0xP5+fnIzc3Fq1evRP9oQEo6ld0nPnnyBK1bt2aQiDQ11FOKEIacnJxw7do1eopdR5KSkqCjo8M6BmdoaGiAx+OBx+Ohe/fuYjccpaWlyM/Ph5eXF8OEpDmhelj3qCbWjLq6ulhN/BSPx4Ofnx+DZKQ5unTpEtavX4+SkhL06dMHdnZ2sLe3h62tLZSUlFjH4wx64CE7CwsLUU10dHSEvPz/DR2UlpYiNTUVw4YNY5iQNBU0KEUIQ8OHD8eiRYuQlJQEExMTtGjRQuw8bbFaM7q6uqwjcEpAQACEQiE8PT3h5+cn9rRLQUEB+vr6sLa2ZpiQNCdUD+se1cSaiYiIgFAoxKBBg3DkyBGxmaIKCgrQ09OjQT7SYM6fP4+SkhL8+++/iI6ORlRUFAIDA1FYWIg+ffrg4sWLrCNyAj3wkJ2rqysAID4+Hk5OTmjVqpXoXPl94tixYxmlI00J9ZQihCHaYrV2tmzZgpCQEGhqamLatGlwdHQUnXv58iWsrKzw6NEjhgm5ISoqCra2tmJPwAhpaFQPa49qYt1IS0tD586dK1yuQggLycnJiIiIwIULF3Ds2DG0bt0aL1++ZB2LE3bu3AmBQAAPDw964CGjoKAgfPPNNxKNzgmpK9RTihCGysrKKv1HP8CqFhgYiEWLFsHQ0BCKiopwcXHBqlWrROdLS0uRlpbGMCF3qKqq4u7du6LXx48fh6urK3766ScUFRUxTEaaE6qHtUM1se7cvXsXsbGxotebN2+Gubk5xo8fj1evXjFMRpqT7du3Y/z48ejYsSNsbGxw7tw59O/fH9euXcN///3HOh5nTJ06FRkZGRAIBPjqq6/g6uoq+jd69GjW8Thh0KBBYn9zV65cwdy5c7F9+3aGqUhTQoNShDQS1MiyZrZt24Y///wTmzZtwl9//YWIiAhs2LABPj4+rKNxzrRp05CcnAwAePToEcaNGwdlZWUcOnQIP/74I+N0pDmielhzVBPrzqJFi5CXlwcAuHXrFubPnw8XFxekpqZi/vz5jNOR5sLLywthYWGYM2cOHj9+jKNHj2LOnDkwMzOjWXw1QA88am/8+PGIiIgAAGRlZWHw4MG4cuUKli1bBoFAwDgdaQpoUIoQhkpLS7FixQp07NgRrVq1Ei2r8Pb2xs6dOxmna9xSU1NhY2Mjem1jY4Pw8HBs374dS5cuZZiMe5KTk2Fubg4AOHToEOzs7LBv3z7s2bMHR44cYRuONBtUD2uHamLdSU1NhZGREQDgyJEjGDFiBPz9/bF582acPXuWcTrSXISEhMDNzQ379+9H27ZtYWNjg59++gmhoaF49+4d63icRA88ZHP79m1YWVkBAA4ePAgTExPExcXh77//xp49e9iGI00CDUoRwtDKlSuxZ88erF27FgoKCqLjxsbG2LFjB8NkjZ+WlhYyMjLEjhkbGyM8PBy7d++mGT41IBQKUVZWBgC4cOECXFxcAHxokkw9K0hDoXpYO1QT646CgoLoR/+FCxcwdOhQAICmpqZoBhUh9c3V1RXr16/HjRs3kJWVhZ9++glPnz7FF198IdaEn1SNHnjUXnFxsaif1IULF0R9uAwNDZGZmckyGmkiaFCKEIaCg4Oxfft2uLm5QU5OTnTczMwM9+7dY5is8evfvz9CQkIkjhsZGSEsLIyeZteApaUlfvnlF/z111+IiorC8OHDAXyYLdCuXTvG6UhzQfWwdqgm1p3+/ftj/vz5WLFiBa5cuSKqicnJyejUqRPjdKQ5yc7ORkhICLy9vfHTTz9h7969UFNTg7OzM+tonEEPPGqvZ8+e2Lp1K2JiYnD+/HkMGzYMAPDs2TO0adOGcTrSFNCgFCEMPX36FF27dpU4XlZWhuLiYgaJuGPJkiUwNTWt8FzPnj0RHh5OvVSkFBAQgBs3bmDmzJlYtmyZ6G/y8OHDYsuBCKlPVA9rh2pi3dm0aRPk5eVx+PBh/PHHH+jYsSMA4OzZs6IfY4TUNxMTE2hra2PatGl4+vQppk6dips3b+Lly5c4evQo63icQQ88am/NmjXYtm0b7O3t8e2338LMzAwAcOLECdGyPkJqgycUCoWsQxDSXPXu3Rvz5s3DhAkToKqqioSEBBgYGEAgEOD8+fOIiYlhHZE0Y+/fv4ecnJzE9smE1Aeqh4QQ8n82b94Me3t7aGtrQ1FREWpqaqwjcZKSkhLu3bsHPT09sWtLUlISrKyskJ+fzzoiJ5SWliIvLw8aGhqiY48fP4aysjK0tbUZJiNNgTzrAIQ0Zz4+PnB3d8fTp09RVlaGkJAQ3L9/H8HBwTh16hTreJyRm5uLK1eu4MWLF6LeSADA4/EwceJEhsm4paioSOIzBIDOnTszSkSaE6qHdYdqYu2VlZUhJSWlwpo4cOBARqlIc5Gbm4ukpCQsX74cr169AgC0bdsWHh4e8Pb2hrKyMuOE3GFkZISYmBjo6emJHT98+DAsLCwYpeIeOTk5sQEpANDX12cThjQ5NFOKEMZiYmIgEAiQkJCA/Px89OrVCz4+PqLGqqRqJ0+ehJubG/Lz86Gmpia2TTKPx0NOTg7DdNyQnJyM7777DnFxcWLHhUIheDwebZlMGgzVw9qjmlh7ly9fxvjx45GWloZPb5OpJpL6lpOTA2trazx9+hRubm74/PPPAQBJSUnYt28fDA0NcfHiRSQmJuLy5cuYPXs248SN2/Hjx+Hu7o6lS5dCIBDAz89P7IHHkCFDWEds9J4/f46FCxciLCwML168kKiLVBNJbdGgFCGE07p37w4XFxf4+/vTk0MZ2draQl5eHkuWLEGHDh3EfsQCEPUOIIQ0flQTa8/c3Bzdu3eHn59fhTWxdevWjJKR5mDu3LkICwvDhQsXJDYbycrKwtChQ9GjRw+EhoYiMDAQ7u7ujJJyBz3wqB1nZ2ekp6dj5syZFdbEUaNGMUpGmgoalCKEIR8fHzg4OMDa2hotW7ZkHYeTVFRUcOvWLRgYGLCOwlkqKiq4fv06DA0NWUchzRjVw7pBNbH2VFRUkJCQUGHjfULqm76+PrZt2wYnJ6cKz587dw4uLi5Yvnw5li9f3sDpSHOkqqqKmJgYmJubs45CmijafY8Qhi5duoQRI0ZAXV0dAwYMwM8//4wLFy6goKCAdTTOcHJywrVr11jH4DQjIyO8fPmSdQzSzFE9rBtUE2uvb9++SElJYR2DNFOZmZno2bNnpeeNjY3B5/NpQEpKPj4+iIiIwPv371lH4SxdXV2JJXuE1CVqdE4IQ+fPn0dJSQn+/fdfREdHIyoqCoGBgSgsLESfPn1w8eJF1hEbveHDh2PRokVISkqCiYmJxE5xI0eOZJSMO9asWYMff/wR/v7+FX6GtOMPaQhUD+sG1cTamzVrFhYsWICsrKwKP0NTU1NGyUhzoKWlhcePH6NTp04Vnk9NTaXdzmrg0qVLWL9+PUpKStCnTx/Y2dnB3t4etra2UFJSYh2PEwICArBkyRJs27aNmpuTekHL9whpJJKTkxEREYELFy7g2LFjaN26Nc1ekQKfX/mET2pIK53yz/DTHgHU6JywQvVQdlQTa6+iz5DH41FNJA3C09MTDx8+xPnz56GgoCB2rrCwEE5OTjAwMMCuXbsYJeSeTx94xMXF0QOPGtDQ0MC7d+9QUlICZWVliYF62kCD1BYNShHC0Pbt2xEZGYmoqCgUFhZiwIABsLe3h729PUxNTSUGCQipD1FRUVWet7Oza6AkpDmjekgai7S0tCrPf7q1PCF16cmTJ7C0tISioiJmzJgBQ0NDCIVC3L17F1u2bEFhYSGuXr2Kzp07s47KOfTAQzZBQUFVnqdm+6S2aFCKEIb4fD7atm2LBQsWYPr06WjVqhXrSJz2/v17apBMCEdRPax7VBMJ4abU1FRMnz4doaGhol4+PB4PQ4YMwaZNm6gJfw3QAw9CGj8alCKEoWPHjiE6OhqRkZG4e/cuLCwsRBfK/v3703beUigtLYW/vz+2bt2K58+fIzk5GQYGBvD29oa+vj6+++471hEbrcTERKneR/1TSEOgelg3qCbK7sSJE1K9j/pykYby6tUrPHjwAADQtWtXaGpqMk7EPfTAQ3Z5eXlSvY96j5LaokEpQhqJ169fIyYmBocOHcL//vc/8Pl82ilECgKBAEFBQRAIBJg6dSpu374NAwMDHDhwAAEBAbh06RLriI0Wn88X9UmpDPVPISxQPZQd1UTZVdWPqxzVREK4hR54yK78PrEy1GeP1BXafY8QxrKzsxEVFYXIyEhERkbizp070NDQwIABA1hH44Tg4GBs374djo6O8PLyEh03MzPDvXv3GCZr/FJTU1lHIEQM1cPao5oou7KyMtYRCCF1zNXVFa6urgDEH3h88cUX9MCjGhEREawjkGaCBqUIYcjExARJSUnQ1NTEwIEDMXXqVNjZ2dFyqRp4+vRphb0VysrKUFxczCARd1CzXtKYUD2sG1QTCSFEHD3wkA1tdEMaCg1KEcKQl5cX7O3toa2tDUVFRVqTLQMjIyPExMRIDLAcPnwYFhYWjFJxl4mJCc6cOQNdXV3WUUgzQ/WwblBNrFtqamqIj4+HgYEB6yiEEBnQA4+6NXz4cOzYsQMdOnRgHYU0ITQoRQgjubm5SEpKwvLly/Hq1SsAQNu2beHh4QFvb29a4y4lHx8fuLu74+nTpygrK0NISAju37+P4OBgnDp1inU8znn8+DHNpiANjuph3aGaWLeo9Soh3EYPPOpWdHQ0CgoKWMcgTQw1OieEgZycHFhbW+Pp06dwc3PD559/DgBISkrCvn37YGhoiIsXLyIxMRGXL1/G7NmzGSdu3GJiYiAQCJCQkID8/Hz06tULPj4+GDp0KOtonKOqqoqEhASaFUAaDNXDukc1se5QTSSEu3Jzc7Fs2TIcOHCAHnjUEaqJpD7QTClCGBAIBFBQUMDDhw/Rrl07iXNDhw7FxIkTERoaisDAQEYpuWPAgAE4f/486xhNwoABA6CkpMQ6BmlGqB7WPaqJdWfChAk0s4IQDqrqgcfvv/+O8+fP0wMPGejp6aFFixasY5AmhmZKEcKAvr4+tm3bBicnpwrPnzt3Di4uLli+fDmWL1/ewOm4xcfHBw4ODrC2tkbLli1ZxyGE1BDVw7pFNZEQQoC5c+ciLCwMFy5ckHjgkZWVhaFDh6JHjx6iBx7u7u6MkhJCaFCKEAYUFRXx8OFDdOrUqcLzT548gb6+PkpKSho4GfcMGTIEly5dQklJCfr06QM7OzvY29vD1taWZvxIKTs7G23atAEAZGRk4M8//0RBQQFGjhxJO9OQekf1sG5RTaydN2/eIDk5GT169ECrVq1w48YNBAQEoKCgAK6urnBzc2MdkRAiBXrgUT/evn2LAwcOoKCgAEOHDkW3bt1YRyJNAJ91AEKaIy0tLTx+/LjS86mpqdDW1m64QBx2/vx55ObmIiwsDC4uLrh27RrGjBkDdXV19O/fn3W8Ru3WrVvQ19eHtrY2DA0NER8fjz59+mDDhg3Yvn07HBwccOzYMdYxSRNH9bBuUU2UXXR0NDp27Ig+ffpAT08PoaGhsLe3x9WrV3H37l1MmjQJf/75J+uYhBApZGZmomfPnpWeNzY2Bp/PpwGpKqSnp8POzg6qqqoYMmQI0tPT0atXL0yZMgWzZs2Cubk5oqOjWcckTQANShHCgJOTE5YtW4aioiKJc4WFhfD29sawYcMYJOMmeXl52NraYuzYsRg9ejScnJxQVlaGe/fusY7WqP34448wMTFBdHQ07O3t8cUXX2D48OF4/fo1Xr16hWnTpmH16tWsY5Imjuph3aOaKJuff/4ZX331FTIyMjB37lyMGzcOM2fOxN27d3H79m34+flh8+bNrGMSQqRADzxqb+HChSgqKsLWrVuhrKwMJycndOvWDZmZmXj+/DmcnZ3h6+vLOiZpAmj5HiEMPHnyBJaWllBUVMSMGTNgaGgIoVCIu3fvYsuWLSgsLMTVq1fRuXNn1lEbve3btyMyMhJRUVEoLCzEgAEDYG9vD3t7e5iamoLH47GO2GhpaWkhPDwcpqamyM/Ph5qaGq5evYrevXsDAO7du4d+/fohNzeXbVDSpFE9rFtUE2Wnrq6Oy5cvw9DQEEVFRVBSUsKNGzdgZmYGAEhJSYGFhQXevHnDOCkhpDqenp54+PAhzp8/DwUFBbFzhYWFcHJygoGBAXbt2sUoYePXvn17nDhxAlZWVsjJyYGWlhZiY2NhbW0NAEhISICjoyNevnzJOCnhOhqUIoSR1NRUTJ8+HaGhoSj/GvJ4PAwZMgSbNm1C165dGSfkBj6fj7Zt22LBggWYPn06WrVqxToSZ/D5fGRlZYmeFH66ze/z58+ho6OD0tJSljFJM0D1sO5QTZQd1URCmg564FF7fD4fmZmZokbxrVq1QmJiItVEUufkWQcgpLnq0qULzp49i1evXuHBgwcAgK5du0JTU5NxMm4JCQlBdHQ09u/fj+XLl8PCwkI0K6B///5QVlZmHbFR+3TWBM2iICxQPaw7VBNlx+PxxGrgp68JIdzRqVMnXLp0CdOnT8fSpUsrfOBBA1LV+7QmElIfaKYUIaTJeP36NWJiYnDo0CH873//A5/Px/v371nHarT4fD6cnZ2hqKgIADh58iQGDRoEFRUVAB+mt587d46egBHCUVQTa4bP58PY2Bjy8h+e2SYmJsLQ0FC09KekpAR37tyhmkgIx9ADD9nw+Xx8//33oocZmzdvxoQJE9C6dWsAwLt37/Dnn39STSS1RoNShBDOy87ORlRUFCIjIxEZGYk7d+5AQ0MDAwYMwNGjR1nHa7Q8PDyket/u3bvrOQkhpC5RTZSNn5+fVO+j3boIIc2Bvb29VLOjIiIiGiANacpoUIoQwmkmJiZISkqCpqYmBg4cCHt7e9jZ2cHU1JR1NEIIaXBUEwkhhBDCJdRTihDCaV5eXrC3t4e2tjYUFRWhpqbGOhIhhDBDNZEQQgghXMJnHYAQQmSVm5uLpKQk2NnZoX379tDQ0ED79u2xdOlSvHv3jnW8Ru/Fixdir+Pj4+Hu7g5bW1t8+eWXiIyMZBOMECITqom1c+XKFbHeKKdOnYKdnR06duwIS0tLBAcHM0xHCCENa8SIEfjrr79QUFDAOgpp4mj5HiGEk3JycmBtbY2nT5/Czc0Nn3/+OQAgKSkJ+/btg6GhIS5evIjExERcvnwZs2fPZpy48ZGTk0NmZia0tbURFxcHe3t72NjYwMrKCvHx8YiIiEBYWBgGDhzIOiohpBpUE2vv45p48uRJuLq6YsKECejbty9u3ryJPXv24ODBgxg9ejTrqIQQUu/4fD7k5OSgoqKCb7/9FlOmTEHv3r1ZxyJNEA1KEUI4ae7cuQgLC8OFCxfQrl07sXNZWVkYOnQoevTogdDQUAQGBsLd3Z1R0saLz+cjKysL2traGDp0KHR1dbFz507R+blz5+LWrVsICwtjmJIQIg2qibX3cU0cMGAA+vfvj1WrVonO+/v74+TJk7h06RLDlIQQ0jD4fD5u376N0NBQ7Nq1C3fu3IGJiQmmTJkCNzc3aGhosI5ImggalCKEcJK+vj62bdsGJyenCs+fO3cOLi4uWL58Oe2UVImPf4Dp6OggJCQE/fr1E52/c+cO7O3t8d9//zFMSQiRBtXE2vu4JrZr1w5nzpwRmxVw//599OvXD69evWKYkhBCGsbHNRH4sMR5586dOHDgAIqKiuDq6oopU6Zg0KBBjJMSrqOeUoQQTsrMzETPnj0rPW9sbAw+n08/vqrx5s0b5OXloWXLllBUVBQ717JlS+pDQwhHUE2sG0lJSUhMTISSkhLKysokzpeUlDBIRQgh7FlZWWHbtm149uwZtmzZgoyMDAwZMoR1LNIE0KAUIYSTtLS08Pjx40rPp6amip7skMp1794dGhoaePz4Ma5duyZ27s6dO9DR0WGUjBBSE1QT64ajoyPMzc2Rnp6O2NhYsXM3b95E586dGSUjhJDGQVlZGZMnT0ZMTAzu3r3LOg5pAuRZByCEEFk4OTlh2bJlOH/+PBQUFMTOFRYWwtvbG8OGDWOUjhsiIiLEXnfo0EHsdWpqKr7//vuGjEQIkRHVxNpLTU0Ve92qVSux10VFRVi8eHFDRiKEEGbs7Owkrief6t69ewOlIU0Z9ZQihHDSkydPYGlpCUVFRcyYMQOGhoYQCoW4e/cutmzZgsLCQly9epWeahNCmgWqiYQQQgjhIhqUIoRwVmpqKqZPn47Q0FCUlzIej4chQ4Zg06ZN6Nq1K+OEhBDScKgmEkIIqU+rV6+Gl5cX1NXVWUchTQgNShFCOO/Vq1d48OABAKBr167Q1NRknKhpcHd3R0ZGBsLDw1lHIYTUANXE+jF48GA8evQIjx49Yh2FEEKYUFNTQ3x8PAwMDFhHIU0I9ZQihHCehoYGrKysWMdocjp27Ag+n/bDIIRrqCbWj9GjR+Ply5esYxBCCDM0n4XUB5opRQghhBBCCCGEkCqpqqoiISGBZkqROkWDUoQQQgghhBBCCKlSRkYGdHR0ICcnxzoKaUJoXQYhhDRjSUlJmD59OiwsLNChQwd06NABFhYWmD59OpKSkljHI4SQRuPhw4cYNGgQ6xiEEMKMrq4uDUiROkc9pQghpJk6e/YsXF1d0atXL4waNQrt2rUDADx//hznz59Hr169cPz4cTg5OTFOSggh7OXn5yMqKop1DEIIaTBbtmxBSEgINDU1MW3aNDg6OorOvXz5ElZWVrT5A6k1Wr5HCCHNlJmZGUaNGgWBQFDheV9fX4SEhCAxMbGBkxFCSMMLDAys8vzTp0/x66+/orS0tIESEUIIO4GBgVi6dCk8PDzw+vVrHDx4EL6+vli6dCmADw8xdXR0qCaSWqNBKUIIaaaUlJQQHx+PHj16VHj+/v37MDc3R0FBQQMnI4SQhsfn89GhQwcoKChUeL6oqAhZWVn0A4wQ0iz07NkTy5Ytw/jx4wEAcXFxcHV1hZeXFwQCAQ1KkTpDy/cIIaSZ0tfXx+nTpysdlDp9+jT09PQaOBUhhLChp6eHNWvW4Ouvv67wfHx8PHr37t3AqQghhI3U1FTY2NiIXtvY2CA8PByDBw9GcXEx5s6dyy4caVJoUIoQQpopgUCA8ePHIzIyEoMHDxbrKRUWFoZz585h3759jFMSQkjD6N27N65fv17poBSPxwMtMCCENBdaWlrIyMiAvr6+6JixsTHCw8MxaNAgPHv2jF040qTQ8j1CCGnG4uLiEBgYiEuXLiErKwsA0L59e1hbW2POnDmwtrZmnJAQQhpGUlIS3r17B0tLywrPFxcX49mzZzSDlBDSLIwfPx7t2rXDhg0bJM7duXMHDg4OyM7OpuV7pNZoUIoQQgghhBBCCCEiiYmJuH79Ojw8PCo8f/v2bRw5cgTLly9v4GSkqaFBKUIIISKrV6+Gl5cX1NXVWUchhBDmqCYSQggh9YsGpQghhIioqakhPj4eBgYGrKMQQghzVBMJIQTIzc3FlStX8OLFC5SVlYmO83g8TJw4kWEy0hRQo3NCCCEi9JyCEEL+D9VEQkhzd/LkSbi5uSE/Px9qamrg8XiiczQoReoCn3UAQgghhBBCCCGEND4LFiyAp6cn8vP/X3v3GhPl1a5x/BpUEFFSpoIEBQlSq1QwEMUCIvhWHKvRNjX9IhrPEQ9Bk4ZW2zgaG22jsW2IVfFISaO2KpJoWouHguAR2xSNA2oRrdLEtgImqCCn/aH7HTeB7uzsV59H5/n/kvkw65aZa/hwG+6staZB9fX1qqurcz9qa2vNjgcPwPE9AIDb7du3FRISom7dupkdBQBMR08EYHV+fn66fPkyx5jxzLBTCgDgFhoayh9fAPDf6IkArM7hcOjixYtmx4AH404pALCwzZs3Kz8/X3a7XQsWLNAbb7zhrv3111+Kj4/XjRs3TEwIAMahJwJAR5MmTVJWVpZcLpeio6PVo0ePDvUpU6aYlAyeguN7AGBR2dnZWrFihWbPnq379+/r22+/1erVq7VixQpJ0t27dxUSEqLW1laTkwLAs0dPBIDOvLz++XCVzWajJ+I/xk4pALConJwcbd++XdOmTZMkLVy4UG+//bYePXqkNWvWmJwOAIxFTwSAztra2syOAA/HUAoALKq6ulqJiYnu54mJiTp58qTGjRun5uZmLVu2zLxwAGAweiIA/O8aGxvVs2dPs2PAwzCUAgCL6tu3r27fvq3w8HD32rBhw3Ty5En961//0u+//25eOAAwGD0RADprbW3VunXrtHXrVt29e1fXrl1TRESEVq5cqfDwcM2dO9fsiHjB8e17AGBRo0ePVn5+fqf1qKgonThxQt9//70JqQDAHPREAOhs7dq1ys3N1fr16+Xt7e1eHzZsmHbs2GFiMngKhlIAYFHLly9XTExMl7XXXntNJ0+elNPpNDgVAJiDnggAneXl5Wnbtm1KT09Xt27d3OvDhw9XZWWlicngKfj2PQAAAAAA0Imvr68qKys1cOBA9enTR+Xl5YqIiJDL5VJ8fLwaGhrMjogXHHdKAQBUX1+vCxcu6I8//ujwLSs2m00zZswwMRkAGI+eCAB/i4qKUklJiQYOHNhh/cCBA4qNjTUpFTwJQykAsLjDhw8rPT1dDQ0N8vf3l81mc9f4AwyA1dATAeAJp9OpmTNnqqamRm1tbcrPz9fVq1eVl5enI0eOmB0PHoDjewBgcYMHD9bEiRO1bt069erVy+w4AGAqeiIAdFRSUqI1a9aovLxcDQ0NiouLk9Pp1Pjx482OBg/AUAoALM7Pz0+XL19WRESE2VEAwHT0RAAAjMO37wGAxTkcDl28eNHsGADwXKAnAsATTqdTP/74oxobG82OAg/FnVIAYHGTJk1SVlaWXC6XoqOj1aNHjw71KVOmmJQMAIxHTwSAJ86ePavPPvtMLS0tGjlypFJSUpSamqqkpCT5+vqaHQ8egON7AGBxXl7/vGnWZrOptbXVwDQAYC56IgB01NLSovPnz+vUqVMqLi7WmTNn1NTUpJEjR6q0tNTseHjBsVMKACzuf37dOQBYHT0RADrq3r27kpKSFBgYKLvdrj59+qigoECVlZVmR4MH4E4pAIAb9wUAwBP0RABWt23bNk2bNk39+/dXYmKijh49qtGjR+vixYv6888/zY4HD8BQCgAsrrW1VR9//LH69++v3r1768aNG5KklStXaufOnSanAwBj0RMB4ImMjAydOHFCS5cu1c2bN3Xo0CEtXbpUw4cPl81mMzsePABDKQCwuLVr1yo3N1fr16+Xt7e3e33YsGHasWOHickAwHj0RAB4Ij8/X+np6dq3b58CAwOVmJioDz/8UIWFhXr48KHZ8eABuOgcACwuMjJSOTk5euONN9SnTx+Vl5crIiJClZWVSkhIUF1dndkRAcAw9EQA6Nr9+/dVUlKi/fv3a+/evfLy8uKYM/5jXHQOABZXU1OjyMjITuttbW1qbm42IREAmIeeCAAd3bt3T8XFxSoqKlJRUZGuXLmigIAAJScnmx0NHoChFABYXFRUlEpKSjRw4MAO6wcOHFBsbKxJqQDAHPREAHgiOjpaLpdLdrtdY8aM0fz585WSkqKYmBizo8FDMJQCAItzOp2aOXOmampq1NbWpvz8fF29elV5eXk6cuSI2fEAwFD0RAB4IiMjQ6mpqQoKCpKPj4/8/f3NjgQPw0XnAGBxb731lg4fPqzjx4/Lz89PTqdTFRUVOnz4sNLS0syOBwCGoicCwN/q6+vlcrmUkpKi4OBgBQQEKDg4WCtWrOCSczw1XHQOAAAAAADcamtrlZCQoJqaGqWnp2vo0KGSJJfLpT179mjIkCEqLS3VpUuXdO7cOWVmZpqcGC8qju8BgMU5nU6NHTtWCQkJ6tmzp9lxAMBU9EQAkNasWSNvb29VVVWpX79+nWrjx4/XjBkzVFhYqOzsbJNSwhOwUwoALC4tLU1nz55VS0uLRo4cqZSUFKWmpiopKUm+vr5mxwMAQ9ETAUAKDw9XTk6OHA5Hl/WjR49q4sSJWrVqlVatWmVwOngShlIAALW0tOj8+fM6deqUiouLdebMGTU1NWnkyJEqLS01Ox4AGIqeCMDqfHx8VFVVpQEDBnRZv3PnjsLDw9XS0mJwMngaju8BANS9e3clJSUpMDBQdrtdffr0UUFBgSorK82OBgCGoycCsLq+ffvq5s2b/ziUqq6uVlBQkMGp4InYKQUAFrdt2zYVFRWpuLhYTU1NSk5OVmpqqlJTUxUTEyObzWZ2RAAwDD0RAKQ5c+aoqqpKx44dk7e3d4daU1OTHA6HIiIitGvXLpMSwlMwlAIAi/Py8lJgYKDee+89LVq0SL179zY7EgCYhp4IAH8fzxsxYoR8fHy0ePFiDRkyRO3t7aqoqNDmzZvV1NSksrIyhYWFmR0VLziGUgBgcQUFBTp16pSKiopUUVGh2NhY966A0aNHq1evXmZHBADD0BMB4G/V1dVatGiRCgsL9e+xgc1mU1pamjZt2qTIyEiTE8ITMJQCALjdv39fJSUl2r9/v/bu3SsvLy81NjaaHQsATEFPBACprq5O169flyRFRkbKbrebnAiehIvOAQC6d++eiouLVVRUpKKiIl25ckUBAQFKTk42OxoAGI6eCABPBAQEKD4+3uwY8FDslAIAi4uOjpbL5ZLdbteYMWOUmpqqlJQUxcTEmB0NAAxHTwQAwDjslAIAi8vIyFBqaqqCgoLk4+Mjf39/syMBgGnoiQAAGMfL7AAAAPPU19fL5XIpJSVFwcHBCggIUHBwsFasWKGHDx+aHQ8ADEVPBADAWBzfAwCLqq2tVUJCgmpqapSenq6hQ4dKklwul/bs2aMhQ4aotLRUly5d0rlz55SZmWlyYgB4duiJAAAYj+N7AGBRa9askbe3t6qqqtSvX79OtfHjx2vGjBkqLCxUdna2SSkBwBj0RAAAjMdOKQCwqPDwcOXk5MjhcHRZP3r0qCZOnKhVq1Zp1apVBqcDAGPREwEAMB5DKQCwKB8fH1VVVWnAgAFd1u/cuaPw8HC1tLQYnAwAjEdPBADAeFx0DgAW1bdvX928efMf69XV1QoKCjIuEACYiJ4IAIDxGEoBgEU5HA599NFHevz4cadaU1OTVq5cqQkTJpiQDACMR08EAMB4HN8DAIu6c+eORowYIR8fHy1evFhDhgxRe3u7KioqtHnzZjU1NamsrExhYWFmRwWAZ46eCACA8RhKAYCFVVdXa9GiRSosLNS//zuw2WxKS0vTpk2bFBkZaXJCADAOPREAAGMxlAIAqK6uTtevX5ckRUZGym63m5wIAMxDTwQAwBgMpQAAAAAAAGA4LjoHAAAAAACA4RhKAQAAAAAAwHAMpQAAAAAAAGA4hlIAAAAAAAAwHEMpAACA/6eioiLZbDbV19f/n38mPDxcX3zxxTPLBAAA8KJgKAUAADzWrFmzZLPZlJGR0am2ePFi2Ww2zZo1y/hgAAAAYCgFAAA8W2hoqPbt26dHjx651xobG7Vnzx6FhYWZmAwAAMDaGEoBAACPFhcXp9DQUOXn57vX8vPzFRYWptjYWPdaU1OTMjMzFRQUpJ49e2r06NEqKyvr8FrfffedBg8eLF9fX40dO1Y3b97s9H6lpaVKTk6Wr6+vQkNDlZmZqQcPHnSZrb29XatXr1ZYWJh8fHwUEhKizMzMp/PBAQAAnnMMpQAAgMebM2eOdu/e7X6+a9cuzZ49u8O/ef/993Xw4EF99dVX+vnnnxUZGSmHw6Ha2lpJ0u3bt/XOO+9o8uTJ+uWXXzRv3jwtX768w2tUVVVpwoQJmjp1qi5duqRvvvlGpaWlWrJkSZe5Dh48qM8//1w5OTm6fv26CgoKFB0d/ZQ/PQAAwPOJoRQAAPB406dPV2lpqW7duqVbt27p9OnTmj59urv+4MEDbdmyRRs2bNCbb76pqKgobd++Xb6+vtq5c6ckacuWLRo0aJA2btyoV199Venp6Z3uo/rkk0+Unp6uZcuW6ZVXXlFiYqKys7OVl5enxsbGTrl+++03BQcHa9y4cQoLC1N8fLzmz5//TH8XAAAAzwuGUgAAwOMFBgZq0qRJys3N1e7duzVp0iT17dvXXa+qqlJzc7OSkpLcaz169FB8fLwqKiokSRUVFRo1alSH101ISOjwvLy8XLm5uerdu7f74XA41NbWpurq6k653n33XT169EgRERGaP3++Dh06pJaWlqf50QEAAJ5b3c0OAAAAYIQ5c+a4j9F9+eWXz+Q9GhoatGDBgi7vherqUvXQ0FBdvXpVx48f17Fjx7Ro0SJt2LBBxcXF6tGjxzPJCAAA8LxgpxQAALCECRMm6PHjx2pubpbD4ehQGzRokLy9vXX69Gn3WnNzs8rKyhQVFSVJGjp0qC5cuNDh586dO9fheVxcnFwulyIjIzs9vL29u8zl6+uryZMnKzs7W0VFRTp79qwuX778ND4yAADAc42dUgAAwBK6devmPorXrVu3DjU/Pz8tXLhQWVlZstvtCgsL0/r16/Xw4UPNnTtXkpSRkaGNGzcqKytL8+bN008//aTc3NwOr/PBBx/o9ddf15IlSzRv3jz5+fnJ5XLp2LFj2rRpU6dMubm5am1t1ahRo9SrVy99/fXX8vX11cCBA5/NLwEAAOA5wk4pAABgGf7+/vL39++y9umnn2rq1KmaMWOG4uLi9Ouvv+qHH35QQECApL+P3x08eFAFBQUaPny4tm7dqnXr1nV4jZiYGBUXF+vatWtKTk5WbGysnE6nQkJCunzPl156Sdu3b1dSUpJiYmJ0/PhxHT58WC+//PLT/eAAAADPIVt7e3u72SEAAAAAAABgLeyUAgAAAAAAgOEYSgEAAAAAAMBwDKUAAAAAAABgOIZSAAAAAAAAMBxDKQAAAAAAABiOoRQAAAAAAAAMx1AKAAAAAAAAhmMoBQAAAAAAAMMxlAIAAAAAAIDhGEoBAAAAAADAcAylAAAAAAAAYLj/AuDcijw2Rj/gAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from llm_toolkit.translation_utils import plot_times\n",
    "\n",
    "plot_times(perf_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiment 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chinese</th>\n",
       "      <th>english</th>\n",
       "      <th>unsloth/Qwen2-0.5B-Instruct</th>\n",
       "      <th>unsloth/Qwen2-0.5B-Instruct(finetuned)</th>\n",
       "      <th>unsloth/Qwen2-1.5B-Instruct</th>\n",
       "      <th>unsloth/Qwen2-1.5B-Instruct(finetuned)</th>\n",
       "      <th>unsloth/Qwen2-7B-Instruct</th>\n",
       "      <th>unsloth/Qwen2-7B-Instruct(finetuned)</th>\n",
       "      <th>unsloth/mistral-7b-instruct-v0.3</th>\n",
       "      <th>unsloth/mistral-7b-instruct-v0.3(finetuned)</th>\n",
       "      <th>gradientai/Llama-3-8B-Instruct-Gradient-1048k</th>\n",
       "      <th>gradientai/Llama-3-8B-Instruct-Gradient-1048k(finetuned)</th>\n",
       "      <th>unsloth/Qwen2-72B-Instruct-bnb-4bit</th>\n",
       "      <th>unsloth/Qwen2-72B-Instruct-bnb-4bit(finetuned)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>老耿端起枪，眯缝起一只三角眼，一搂扳机响了枪，冰雹般的金麻雀劈哩啪啦往下落，铁砂子在柳枝间飞...</td>\n",
       "      <td>Old Geng picked up his shotgun, squinted, and ...</td>\n",
       "      <td>Old Teng holds his gun up, his eyes narrowed a...</td>\n",
       "      <td>Old Geng raised his rifle and tilted his head ...</td>\n",
       "      <td>Old Jin raises his gun, squints one eye as he ...</td>\n",
       "      <td>Old Geng raised his pistol, squinted through t...</td>\n",
       "      <td>Old Geng raised his gun, squinted one of his t...</td>\n",
       "      <td>Old Geng raised his rifle and squinted into th...</td>\n",
       "      <td>Geng Da initiates firing, squinting to form a ...</td>\n",
       "      <td>Old Geng aimed and fired. A triangular slit op...</td>\n",
       "      <td>The old man pulled out his gun, squinting one ...</td>\n",
       "      <td>Old Geng raised his rifle, squinting through t...</td>\n",
       "      <td>Lao Geng raised his gun, narrowed one of his t...</td>\n",
       "      <td>Old Geng raised his gun, narrowed one of his t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>次日天未明时，刘老老便起来梳洗了， 又将板儿教了几句话； 五六岁的孩子，听见带了他进城逛去，...</td>\n",
       "      <td>Next day Grannie Liu was up before dawn. As so...</td>\n",
       "      <td>The next morning, Liu Geo woke up at five o'cl...</td>\n",
       "      <td>But not before noon did Grannie Liu rise up an...</td>\n",
       "      <td>At dawn the next day, Liu Langlang got up earl...</td>\n",
       "      <td>She got up about dawn with a purpose already e...</td>\n",
       "      <td>The next morning, before the dawn had fully br...</td>\n",
       "      <td>First thing in the morning Grannie Liu rose to...</td>\n",
       "      <td>The next day, when it was still dark, Liu Lao ...</td>\n",
       "      <td>Before dawn next day Grannie Liu got up and bu...</td>\n",
       "      <td>The next day, when the sun had not yet risen, ...</td>\n",
       "      <td>Grannie Liu got up before daylight was even vi...</td>\n",
       "      <td>Before dawn next morning, Granny Liu got up to...</td>\n",
       "      <td>As soon as it was light, Grannie Liu got up an...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             chinese  \\\n",
       "0  老耿端起枪，眯缝起一只三角眼，一搂扳机响了枪，冰雹般的金麻雀劈哩啪啦往下落，铁砂子在柳枝间飞...   \n",
       "1  次日天未明时，刘老老便起来梳洗了， 又将板儿教了几句话； 五六岁的孩子，听见带了他进城逛去，...   \n",
       "\n",
       "                                             english  \\\n",
       "0  Old Geng picked up his shotgun, squinted, and ...   \n",
       "1  Next day Grannie Liu was up before dawn. As so...   \n",
       "\n",
       "                         unsloth/Qwen2-0.5B-Instruct  \\\n",
       "0  Old Teng holds his gun up, his eyes narrowed a...   \n",
       "1  The next morning, Liu Geo woke up at five o'cl...   \n",
       "\n",
       "              unsloth/Qwen2-0.5B-Instruct(finetuned)  \\\n",
       "0  Old Geng raised his rifle and tilted his head ...   \n",
       "1  But not before noon did Grannie Liu rise up an...   \n",
       "\n",
       "                         unsloth/Qwen2-1.5B-Instruct  \\\n",
       "0  Old Jin raises his gun, squints one eye as he ...   \n",
       "1  At dawn the next day, Liu Langlang got up earl...   \n",
       "\n",
       "              unsloth/Qwen2-1.5B-Instruct(finetuned)  \\\n",
       "0  Old Geng raised his pistol, squinted through t...   \n",
       "1  She got up about dawn with a purpose already e...   \n",
       "\n",
       "                           unsloth/Qwen2-7B-Instruct  \\\n",
       "0  Old Geng raised his gun, squinted one of his t...   \n",
       "1  The next morning, before the dawn had fully br...   \n",
       "\n",
       "                unsloth/Qwen2-7B-Instruct(finetuned)  \\\n",
       "0  Old Geng raised his rifle and squinted into th...   \n",
       "1  First thing in the morning Grannie Liu rose to...   \n",
       "\n",
       "                    unsloth/mistral-7b-instruct-v0.3  \\\n",
       "0  Geng Da initiates firing, squinting to form a ...   \n",
       "1  The next day, when it was still dark, Liu Lao ...   \n",
       "\n",
       "         unsloth/mistral-7b-instruct-v0.3(finetuned)  \\\n",
       "0  Old Geng aimed and fired. A triangular slit op...   \n",
       "1  Before dawn next day Grannie Liu got up and bu...   \n",
       "\n",
       "       gradientai/Llama-3-8B-Instruct-Gradient-1048k  \\\n",
       "0  The old man pulled out his gun, squinting one ...   \n",
       "1  The next day, when the sun had not yet risen, ...   \n",
       "\n",
       "  gradientai/Llama-3-8B-Instruct-Gradient-1048k(finetuned)  \\\n",
       "0  Old Geng raised his rifle, squinting through t...         \n",
       "1  Grannie Liu got up before daylight was even vi...         \n",
       "\n",
       "                 unsloth/Qwen2-72B-Instruct-bnb-4bit  \\\n",
       "0  Lao Geng raised his gun, narrowed one of his t...   \n",
       "1  Before dawn next morning, Granny Liu got up to...   \n",
       "\n",
       "      unsloth/Qwen2-72B-Instruct-bnb-4bit(finetuned)  \n",
       "0  Old Geng raised his gun, narrowed one of his t...  \n",
       "1  As soon as it was light, Grannie Liu got up an...  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"results/experiment-2-results.csv\")\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unsloth/Qwen2-0.5B-Instruct: {'accuracy': 0.0, 'correct_ids': [], 'meteor': 0.26453254295068257, 'bleu_scores': {'bleu': 0.04698039499333136, 'precisions': [0.309245348800355, 0.07347623117890723, 0.023966406063295892, 0.008945686900958467], 'brevity_penalty': 1.0, 'length_ratio': 1.0450811526995694, 'translation_length': 31551, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.311193579937054, 'rouge2': 0.08909098431958892, 'rougeL': 0.25609252628986867, 'rougeLsum': 0.25599725080528407}}\n",
      "unsloth/Qwen2-0.5B-Instruct(finetuned): {'accuracy': 0.00176522506619594, 'correct_ids': [147, 533], 'meteor': 0.28664792904792147, 'bleu_scores': {'bleu': 0.0633353272697663, 'precisions': [0.33089419978517726, 0.08866324714749294, 0.0345015434901035, 0.016769504485747815], 'brevity_penalty': 0.9867295481943301, 'length_ratio': 0.9868168267638291, 'translation_length': 29792, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.31737609540000844, 'rouge2': 0.10644051608097954, 'rougeL': 0.26125759928324677, 'rougeLsum': 0.26091873404690213}}\n",
      "unsloth/Qwen2-1.5B-Instruct: {'accuracy': 0.0, 'correct_ids': [], 'meteor': 0.3108076173265163, 'bleu_scores': {'bleu': 0.07171893503391259, 'precisions': [0.39917895129688374, 0.11838516093835243, 0.0465865090686774, 0.01994873985476292], 'brevity_penalty': 0.8809955157186649, 'length_ratio': 0.8875455448824114, 'translation_length': 26795, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.36703263137712216, 'rouge2': 0.12681326966088435, 'rougeL': 0.3103553529465547, 'rougeLsum': 0.3108439314288456}}\n",
      "unsloth/Qwen2-1.5B-Instruct(finetuned): {'accuracy': 0.00441306266548985, 'correct_ids': [147, 170, 309, 526, 533], 'meteor': 0.3412870633079792, 'bleu_scores': {'bleu': 0.09439554395746873, 'precisions': [0.3842746400885936, 0.1271192353310542, 0.05777680938373824, 0.029647860658841348], 'brevity_penalty': 0.9869644625887559, 'length_ratio': 0.9870486916197416, 'translation_length': 29799, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.37294305507732706, 'rouge2': 0.14604688226310758, 'rougeL': 0.31770893394420635, 'rougeLsum': 0.3179662550446343}}\n",
      "unsloth/Qwen2-7B-Instruct: {'accuracy': 0.00088261253309797, 'correct_ids': [77], 'meteor': 0.3706126714366309, 'bleu_scores': {'bleu': 0.10881761334734624, 'precisions': [0.41661179878851723, 0.14690695209109872, 0.06769832799715404, 0.033841135698135585], 'brevity_penalty': 1.0, 'length_ratio': 1.006160980457105, 'translation_length': 30376, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.4169212377209478, 'rouge2': 0.16738907420004812, 'rougeL': 0.35933914476241274, 'rougeLsum': 0.3594104389179988}}\n",
      "unsloth/Qwen2-7B-Instruct(finetuned): {'accuracy': 0.00617828773168579, 'correct_ids': [147, 199, 309, 526, 531, 658, 935], 'meteor': 0.4016299621577931, 'bleu_scores': {'bleu': 0.1340342690832733, 'precisions': [0.44661102067675684, 0.1784426820475847, 0.0918075911311537, 0.05294140732310349], 'brevity_penalty': 0.9554111104454712, 'length_ratio': 0.9563762835375952, 'translation_length': 28873, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.4332992907580583, 'rouge2': 0.19870320112127682, 'rougeL': 0.3799161621619124, 'rougeLsum': 0.37983202851331527}}\n",
      "unsloth/mistral-7b-instruct-v0.3: {'accuracy': 0.00088261253309797, 'correct_ids': [77], 'meteor': 0.3221588341261717, 'bleu_scores': {'bleu': 0.08500847701637192, 'precisions': [0.3839848248763634, 0.12152594314699355, 0.05129145876137364, 0.023885167464114832], 'brevity_penalty': 0.9776268817156146, 'length_ratio': 0.9778734680357735, 'translation_length': 29522, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.36749848396489115, 'rouge2': 0.13267900787227876, 'rougeL': 0.3179196454915659, 'rougeLsum': 0.31792155927408666}}\n",
      "unsloth/mistral-7b-instruct-v0.3(finetuned): {'accuracy': 0.00529567519858782, 'correct_ids': [77, 147, 199, 240, 309, 364], 'meteor': 0.38725013933546865, 'bleu_scores': {'bleu': 0.12550528350380133, 'precisions': [0.4230690780527604, 0.16504614079128804, 0.083050972304066, 0.04753304641869044], 'brevity_penalty': 0.9740309389176179, 'length_ratio': 0.9743623716462405, 'translation_length': 29416, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.4145919330274994, 'rouge2': 0.1861726006282483, 'rougeL': 0.3585578629853423, 'rougeLsum': 0.3586207093342768}}\n",
      "gradientai/Llama-3-8B-Instruct-Gradient-1048k: {'accuracy': 0.0, 'correct_ids': [], 'meteor': 0.31732985329639374, 'bleu_scores': {'bleu': 0.05328901091080452, 'precisions': [0.23537940003837707, 0.07314835044789163, 0.03125210027555615, 0.014986438652139935], 'brevity_penalty': 1.0, 'length_ratio': 1.5535939052666445, 'translation_length': 46903, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3642139919684396, 'rouge2': 0.13013623772550284, 'rougeL': 0.3113557371417485, 'rougeLsum': 0.3113699185025507}}\n",
      "gradientai/Llama-3-8B-Instruct-Gradient-1048k(finetuned): {'accuracy': 0.01235657546337158, 'correct_ids': [41, 77, 133, 147, 199, 301, 348, 364, 413, 526, 533, 778, 893, 1011], 'meteor': 0.3921204969248105, 'bleu_scores': {'bleu': 0.12623244689421761, 'precisions': [0.43614474551573396, 0.16861682918020945, 0.08602838748541095, 0.048517732169536844], 'brevity_penalty': 0.9536797061184072, 'length_ratio': 0.9547201059953627, 'translation_length': 28823, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.4266903270472449, 'rouge2': 0.19287622866916668, 'rougeL': 0.3680571409424248, 'rougeLsum': 0.3681316523625903}}\n",
      "unsloth/Qwen2-72B-Instruct-bnb-4bit: {'accuracy': 0.00088261253309797, 'correct_ids': [533], 'meteor': 0.39327797374995854, 'bleu_scores': {'bleu': 0.12559679608930588, 'precisions': [0.43200657894736844, 0.1652031298048997, 0.08068242402701262, 0.04321421958896501], 'brevity_penalty': 1.0, 'length_ratio': 1.0069559456773767, 'translation_length': 30400, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.44067456878602607, 'rouge2': 0.19005982474853128, 'rougeL': 0.38306694667909413, 'rougeLsum': 0.3831992922806715}}\n",
      "unsloth/Qwen2-72B-Instruct-bnb-4bit(finetuned): {'accuracy': 0.00794351279788173, 'correct_ids': [147, 199, 240, 272, 364, 419, 658, 659, 820], 'meteor': 0.45716060031914707, 'bleu_scores': {'bleu': 0.17001816364266625, 'precisions': [0.48491918577128557, 0.2166942090643789, 0.12017135952546593, 0.07191963944694829], 'brevity_penalty': 0.9793863208805533, 'length_ratio': 0.9795958926796953, 'translation_length': 29574, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.48438121516267174, 'rouge2': 0.24299836033305106, 'rougeL': 0.42482240676008176, 'rougeLsum': 0.42475113145470866}}\n"
     ]
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    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accuracy</th>\n",
       "      <th>meteor</th>\n",
       "      <th>bleu_1</th>\n",
       "      <th>rouge_l</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.003383</td>\n",
       "      <td>0.353735</td>\n",
       "      <td>0.100411</td>\n",
       "      <td>0.337371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.003926</td>\n",
       "      <td>0.055771</td>\n",
       "      <td>0.037567</td>\n",
       "      <td>0.050444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.264533</td>\n",
       "      <td>0.046980</td>\n",
       "      <td>0.256093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000662</td>\n",
       "      <td>0.315699</td>\n",
       "      <td>0.069623</td>\n",
       "      <td>0.311106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.001324</td>\n",
       "      <td>0.355950</td>\n",
       "      <td>0.101607</td>\n",
       "      <td>0.338239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.005516</td>\n",
       "      <td>0.392410</td>\n",
       "      <td>0.125756</td>\n",
       "      <td>0.371022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.012357</td>\n",
       "      <td>0.457161</td>\n",
       "      <td>0.170018</td>\n",
       "      <td>0.424822</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        accuracy     meteor     bleu_1    rouge_l\n",
       "count  12.000000  12.000000  12.000000  12.000000\n",
       "mean    0.003383   0.353735   0.100411   0.337371\n",
       "std     0.003926   0.055771   0.037567   0.050444\n",
       "min     0.000000   0.264533   0.046980   0.256093\n",
       "25%     0.000662   0.315699   0.069623   0.311106\n",
       "50%     0.001324   0.355950   0.101607   0.338239\n",
       "75%     0.005516   0.392410   0.125756   0.371022\n",
       "max     0.012357   0.457161   0.170018   0.424822"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_df = get_metrics(df)\n",
    "metrics_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>accuracy</th>\n",
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       "      <th>bleu_1</th>\n",
       "      <th>rouge_l</th>\n",
       "      <th>all_metrics</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Qwen2-0.5B-Instruct</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Qwen2-0.5B-Instruct(finetuned)</td>\n",
       "      <td>0.001765</td>\n",
       "      <td>0.286648</td>\n",
       "      <td>0.063335</td>\n",
       "      <td>0.261258</td>\n",
       "      <td>{'accuracy': 0.00176522506619594, 'correct_ids...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Qwen2-1.5B-Instruct</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.310808</td>\n",
       "      <td>0.071719</td>\n",
       "      <td>0.310355</td>\n",
       "      <td>{'accuracy': 0.0, 'correct_ids': [], 'meteor':...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Qwen2-1.5B-Instruct(finetuned)</td>\n",
       "      <td>0.004413</td>\n",
       "      <td>0.341287</td>\n",
       "      <td>0.094396</td>\n",
       "      <td>0.317709</td>\n",
       "      <td>{'accuracy': 0.00441306266548985, 'correct_ids...</td>\n",
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       "      <td>Qwen2-7B-Instruct</td>\n",
       "      <td>0.000883</td>\n",
       "      <td>0.370613</td>\n",
       "      <td>0.108818</td>\n",
       "      <td>0.359339</td>\n",
       "      <td>{'accuracy': 0.00088261253309797, 'correct_ids...</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Qwen2-7B-Instruct(finetuned)</td>\n",
       "      <td>0.006178</td>\n",
       "      <td>0.401630</td>\n",
       "      <td>0.134034</td>\n",
       "      <td>0.379916</td>\n",
       "      <td>{'accuracy': 0.00617828773168579, 'correct_ids...</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>mistral-7b-instruct-v0.3</td>\n",
       "      <td>0.000883</td>\n",
       "      <td>0.322159</td>\n",
       "      <td>0.085008</td>\n",
       "      <td>0.317920</td>\n",
       "      <td>{'accuracy': 0.00088261253309797, 'correct_ids...</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mistral-7b-instruct-v0.3(finetuned)</td>\n",
       "      <td>0.005296</td>\n",
       "      <td>0.387250</td>\n",
       "      <td>0.125505</td>\n",
       "      <td>0.358558</td>\n",
       "      <td>{'accuracy': 0.00529567519858782, 'correct_ids...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Llama-3-8B-Instruct-Gradient-1048k</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.317330</td>\n",
       "      <td>0.053289</td>\n",
       "      <td>0.311356</td>\n",
       "      <td>{'accuracy': 0.0, 'correct_ids': [], 'meteor':...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Llama-3-8B-Instruct-Gradient-1048k(finetuned)</td>\n",
       "      <td>0.012357</td>\n",
       "      <td>0.392120</td>\n",
       "      <td>0.126232</td>\n",
       "      <td>0.368057</td>\n",
       "      <td>{'accuracy': 0.01235657546337158, 'correct_ids...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Qwen2-72B-Instruct-bnb-4bit</td>\n",
       "      <td>0.000883</td>\n",
       "      <td>0.393278</td>\n",
       "      <td>0.125597</td>\n",
       "      <td>0.383067</td>\n",
       "      <td>{'accuracy': 0.00088261253309797, 'correct_ids...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Qwen2-72B-Instruct-bnb-4bit(finetuned)</td>\n",
       "      <td>0.007944</td>\n",
       "      <td>0.457161</td>\n",
       "      <td>0.170018</td>\n",
       "      <td>0.424822</td>\n",
       "      <td>{'accuracy': 0.00794351279788173, 'correct_ids...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            model  accuracy    meteor  \\\n",
       "0                             Qwen2-0.5B-Instruct  0.000000  0.264533   \n",
       "1                  Qwen2-0.5B-Instruct(finetuned)  0.001765  0.286648   \n",
       "2                             Qwen2-1.5B-Instruct  0.000000  0.310808   \n",
       "3                  Qwen2-1.5B-Instruct(finetuned)  0.004413  0.341287   \n",
       "4                               Qwen2-7B-Instruct  0.000883  0.370613   \n",
       "5                    Qwen2-7B-Instruct(finetuned)  0.006178  0.401630   \n",
       "6                        mistral-7b-instruct-v0.3  0.000883  0.322159   \n",
       "7             mistral-7b-instruct-v0.3(finetuned)  0.005296  0.387250   \n",
       "8              Llama-3-8B-Instruct-Gradient-1048k  0.000000  0.317330   \n",
       "9   Llama-3-8B-Instruct-Gradient-1048k(finetuned)  0.012357  0.392120   \n",
       "10                    Qwen2-72B-Instruct-bnb-4bit  0.000883  0.393278   \n",
       "11         Qwen2-72B-Instruct-bnb-4bit(finetuned)  0.007944  0.457161   \n",
       "\n",
       "      bleu_1   rouge_l                                        all_metrics  \n",
       "0   0.046980  0.256093  {'accuracy': 0.0, 'correct_ids': [], 'meteor':...  \n",
       "1   0.063335  0.261258  {'accuracy': 0.00176522506619594, 'correct_ids...  \n",
       "2   0.071719  0.310355  {'accuracy': 0.0, 'correct_ids': [], 'meteor':...  \n",
       "3   0.094396  0.317709  {'accuracy': 0.00441306266548985, 'correct_ids...  \n",
       "4   0.108818  0.359339  {'accuracy': 0.00088261253309797, 'correct_ids...  \n",
       "5   0.134034  0.379916  {'accuracy': 0.00617828773168579, 'correct_ids...  \n",
       "6   0.085008  0.317920  {'accuracy': 0.00088261253309797, 'correct_ids...  \n",
       "7   0.125505  0.358558  {'accuracy': 0.00529567519858782, 'correct_ids...  \n",
       "8   0.053289  0.311356  {'accuracy': 0.0, 'correct_ids': [], 'meteor':...  \n",
       "9   0.126232  0.368057  {'accuracy': 0.01235657546337158, 'correct_ids...  \n",
       "10  0.125597  0.383067  {'accuracy': 0.00088261253309797, 'correct_ids...  \n",
       "11  0.170018  0.424822  {'accuracy': 0.00794351279788173, 'correct_ids...  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"results/experiment-2-results.csv\")\n",
    "metrics_df = get_metrics(df)\n",
    "metrics_df.describe()\n",
    "plot_metrics(metrics_df, figsize=(18, 5), ylim=(0, 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/inflaton/code/engd/projects/llm-finetuning/llm_toolkit/translation_utils.py:144: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  barplot.set_xticklabels([\"METEOR\", \"BLEU-1\", \"ROUGE-L\"])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(metrics_df, figsize=(18, 5), ylim=(0, 0.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiment 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Qwen2-0.5B(flash-attn:false): {'accuracy': 0.0, 'correct_ids': [], 'meteor': 0.2596076617917167, 'bleu_scores': {'bleu': 0.04409425333473452, 'precisions': [0.33363639542609363, 0.07631808913486746, 0.023275240156433914, 0.007970813340207003], 'brevity_penalty': 0.9458179749826672, 'length_ratio': 0.9472341835044716, 'translation_length': 28597, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3097557467235248, 'rouge2': 0.08642674668219225, 'rougeL': 0.25214875199618664, 'rougeLsum': 0.2522851845174793}}\n",
      "Qwen2-0.5B(flash-attn:true): {'accuracy': 0.0, 'correct_ids': [], 'meteor': 0.2604145257072387, 'bleu_scores': {'bleu': 0.046012982792608105, 'precisions': [0.3368365553602812, 0.07779038693853645, 0.02424219286859586, 0.009012601690859786], 'brevity_penalty': 0.9406727862276781, 'length_ratio': 0.9423650215303081, 'translation_length': 28450, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3101517051554241, 'rouge2': 0.08890555581222168, 'rougeL': 0.2547030960897408, 'rougeLsum': 0.2547066305272284}}\n",
      "Qwen2-0.5B(finetuned)(flash-attn:false): {'accuracy': 0.00264783759929391, 'correct_ids': [147, 199, 533], 'meteor': 0.29068031845703574, 'bleu_scores': {'bleu': 0.060746076799314715, 'precisions': [0.33972489966727265, 0.09057815845824412, 0.03312637097074023, 0.015400729304057724], 'brevity_penalty': 0.9650542580679092, 'length_ratio': 0.9656508777740974, 'translation_length': 29153, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.320332395077233, 'rouge2': 0.10635595885389075, 'rougeL': 0.26536204016272524, 'rougeLsum': 0.26517589740496317}}\n",
      "Qwen2-0.5B(finetuned)(flash-attn:true): {'accuracy': 0.00264783759929391, 'correct_ids': [147, 199, 533], 'meteor': 0.2875255734599462, 'bleu_scores': {'bleu': 0.0641508398737298, 'precisions': [0.33414174077068176, 0.09091227455787078, 0.03518174195194867, 0.01695559610705596], 'brevity_penalty': 0.983232969050409, 'length_ratio': 0.9833719774759854, 'translation_length': 29688, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3204812146158952, 'rouge2': 0.1064017588261093, 'rougeL': 0.26577679033295876, 'rougeLsum': 0.2656659730861177}}\n",
      "Qwen2-1.5B(flash-attn:false): {'accuracy': 0.00088261253309797, 'correct_ids': [77], 'meteor': 0.3140903672178899, 'bleu_scores': {'bleu': 0.07105708852581612, 'precisions': [0.3965299918439979, 0.11779729886614296, 0.045886780237122164, 0.01916228590808886], 'brevity_penalty': 0.8876072487625101, 'length_ratio': 0.8934746604836038, 'translation_length': 26974, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.37157986085405303, 'rouge2': 0.13207331381981324, 'rougeL': 0.3157553794523721, 'rougeLsum': 0.315660601864261}}\n",
      "Qwen2-1.5B(flash-attn:true): {'accuracy': 0.0, 'correct_ids': [], 'meteor': 0.31196189387338386, 'bleu_scores': {'bleu': 0.07269636467609625, 'precisions': [0.3994544911074578, 0.11993289376146073, 0.046890303623898136, 0.020746000513303105], 'brevity_penalty': 0.8798465412731852, 'length_ratio': 0.8865187148062272, 'translation_length': 26764, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.36732209695480655, 'rouge2': 0.12854797416384176, 'rougeL': 0.3105303328656307, 'rougeLsum': 0.3102172412975642}}\n",
      "Qwen2-1.5B(finetuned)(flash-attn:false): {'accuracy': 0.00264783759929391, 'correct_ids': [77, 170, 533], 'meteor': 0.3490204861480539, 'bleu_scores': {'bleu': 0.08451008426675863, 'precisions': [0.34023887851575235, 0.11221172718351324, 0.050447931888938306, 0.026483085740637045], 'brevity_penalty': 1.0, 'length_ratio': 1.1176217290493542, 'translation_length': 33741, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.377001533571725, 'rouge2': 0.1501932422968003, 'rougeL': 0.3208875552321767, 'rougeLsum': 0.3206919463211514}}\n",
      "Qwen2-1.5B(finetuned)(flash-attn:true): {'accuracy': 0.00264783759929391, 'correct_ids': [147, 170, 533], 'meteor': 0.3494130868780632, 'bleu_scores': {'bleu': 0.09464090787225404, 'precisions': [0.3858167437495811, 0.12861870754224003, 0.05755213055303717, 0.02944845575171058], 'brevity_penalty': 0.988272274882654, 'length_ratio': 0.988340510102683, 'translation_length': 29838, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3762054404870173, 'rouge2': 0.15064305035657688, 'rougeL': 0.3185654749170195, 'rougeLsum': 0.3189669800519496}}\n"
     ]
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>meteor</th>\n",
       "      <th>bleu_1</th>\n",
       "      <th>rouge_l</th>\n",
       "      <th>all_metrics</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Qwen2-0.5B(flash-attn:false)</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.259608</td>\n",
       "      <td>0.044094</td>\n",
       "      <td>0.252149</td>\n",
       "      <td>{'accuracy': 0.0, 'correct_ids': [], 'meteor':...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Qwen2-0.5B(flash-attn:true)</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.260415</td>\n",
       "      <td>0.046013</td>\n",
       "      <td>0.254703</td>\n",
       "      <td>{'accuracy': 0.0, 'correct_ids': [], 'meteor':...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Qwen2-0.5B(finetuned)(flash-attn:false)</td>\n",
       "      <td>0.002648</td>\n",
       "      <td>0.290680</td>\n",
       "      <td>0.060746</td>\n",
       "      <td>0.265362</td>\n",
       "      <td>{'accuracy': 0.00264783759929391, 'correct_ids...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Qwen2-0.5B(finetuned)(flash-attn:true)</td>\n",
       "      <td>0.002648</td>\n",
       "      <td>0.287526</td>\n",
       "      <td>0.064151</td>\n",
       "      <td>0.265777</td>\n",
       "      <td>{'accuracy': 0.00264783759929391, 'correct_ids...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Qwen2-1.5B(flash-attn:false)</td>\n",
       "      <td>0.000883</td>\n",
       "      <td>0.314090</td>\n",
       "      <td>0.071057</td>\n",
       "      <td>0.315755</td>\n",
       "      <td>{'accuracy': 0.00088261253309797, 'correct_ids...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Qwen2-1.5B(flash-attn:true)</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.311962</td>\n",
       "      <td>0.072696</td>\n",
       "      <td>0.310530</td>\n",
       "      <td>{'accuracy': 0.0, 'correct_ids': [], 'meteor':...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Qwen2-1.5B(finetuned)(flash-attn:false)</td>\n",
       "      <td>0.002648</td>\n",
       "      <td>0.349020</td>\n",
       "      <td>0.084510</td>\n",
       "      <td>0.320888</td>\n",
       "      <td>{'accuracy': 0.00264783759929391, 'correct_ids...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Qwen2-1.5B(finetuned)(flash-attn:true)</td>\n",
       "      <td>0.002648</td>\n",
       "      <td>0.349413</td>\n",
       "      <td>0.094641</td>\n",
       "      <td>0.318565</td>\n",
       "      <td>{'accuracy': 0.00264783759929391, 'correct_ids...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     model  accuracy    meteor    bleu_1  \\\n",
       "0             Qwen2-0.5B(flash-attn:false)  0.000000  0.259608  0.044094   \n",
       "1              Qwen2-0.5B(flash-attn:true)  0.000000  0.260415  0.046013   \n",
       "2  Qwen2-0.5B(finetuned)(flash-attn:false)  0.002648  0.290680  0.060746   \n",
       "3   Qwen2-0.5B(finetuned)(flash-attn:true)  0.002648  0.287526  0.064151   \n",
       "4             Qwen2-1.5B(flash-attn:false)  0.000883  0.314090  0.071057   \n",
       "5              Qwen2-1.5B(flash-attn:true)  0.000000  0.311962  0.072696   \n",
       "6  Qwen2-1.5B(finetuned)(flash-attn:false)  0.002648  0.349020  0.084510   \n",
       "7   Qwen2-1.5B(finetuned)(flash-attn:true)  0.002648  0.349413  0.094641   \n",
       "\n",
       "    rouge_l                                        all_metrics  \n",
       "0  0.252149  {'accuracy': 0.0, 'correct_ids': [], 'meteor':...  \n",
       "1  0.254703  {'accuracy': 0.0, 'correct_ids': [], 'meteor':...  \n",
       "2  0.265362  {'accuracy': 0.00264783759929391, 'correct_ids...  \n",
       "3  0.265777  {'accuracy': 0.00264783759929391, 'correct_ids...  \n",
       "4  0.315755  {'accuracy': 0.00088261253309797, 'correct_ids...  \n",
       "5  0.310530  {'accuracy': 0.0, 'correct_ids': [], 'meteor':...  \n",
       "6  0.320888  {'accuracy': 0.00264783759929391, 'correct_ids...  \n",
       "7  0.318565  {'accuracy': 0.00264783759929391, 'correct_ids...  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"results/experiment-3-results.csv\")\n",
    "metrics_df = get_metrics(df)\n",
    "metrics_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/inflaton/code/engd/projects/llm-finetuning/llm_toolkit/translation_utils.py:144: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  barplot.set_xticklabels([\"METEOR\", \"BLEU-1\", \"ROUGE-L\"])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(metrics_df, ylim=(0, 0.4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>meteor</th>\n",
       "      <th>bleu_1</th>\n",
       "      <th>rouge_l</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Qwen2-0.5B(flash-attn:false)</td>\n",
       "      <td>0.259608</td>\n",
       "      <td>0.044094</td>\n",
       "      <td>0.252149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Qwen2-0.5B(flash-attn:true)</td>\n",
       "      <td>0.260415</td>\n",
       "      <td>0.046013</td>\n",
       "      <td>0.254703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Qwen2-0.5B(finetuned)(flash-attn:false)</td>\n",
       "      <td>0.290680</td>\n",
       "      <td>0.060746</td>\n",
       "      <td>0.265362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Qwen2-0.5B(finetuned)(flash-attn:true)</td>\n",
       "      <td>0.287526</td>\n",
       "      <td>0.064151</td>\n",
       "      <td>0.265777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Qwen2-1.5B(flash-attn:false)</td>\n",
       "      <td>0.314090</td>\n",
       "      <td>0.071057</td>\n",
       "      <td>0.315755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Qwen2-1.5B(flash-attn:true)</td>\n",
       "      <td>0.311962</td>\n",
       "      <td>0.072696</td>\n",
       "      <td>0.310530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Qwen2-1.5B(finetuned)(flash-attn:false)</td>\n",
       "      <td>0.349020</td>\n",
       "      <td>0.084510</td>\n",
       "      <td>0.320888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Qwen2-1.5B(finetuned)(flash-attn:true)</td>\n",
       "      <td>0.349413</td>\n",
       "      <td>0.094641</td>\n",
       "      <td>0.318565</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     model    meteor    bleu_1   rouge_l\n",
       "0             Qwen2-0.5B(flash-attn:false)  0.259608  0.044094  0.252149\n",
       "1              Qwen2-0.5B(flash-attn:true)  0.260415  0.046013  0.254703\n",
       "2  Qwen2-0.5B(finetuned)(flash-attn:false)  0.290680  0.060746  0.265362\n",
       "3   Qwen2-0.5B(finetuned)(flash-attn:true)  0.287526  0.064151  0.265777\n",
       "4             Qwen2-1.5B(flash-attn:false)  0.314090  0.071057  0.315755\n",
       "5              Qwen2-1.5B(flash-attn:true)  0.311962  0.072696  0.310530\n",
       "6  Qwen2-1.5B(finetuned)(flash-attn:false)  0.349020  0.084510  0.320888\n",
       "7   Qwen2-1.5B(finetuned)(flash-attn:true)  0.349413  0.094641  0.318565"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_df.drop(columns=[\"all_metrics\", \"accuracy\"], inplace=True, errors=\"ignore\")\n",
    "metrics_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_minutes(time_str):\n",
    "    parts = time_str.split(\":\")\n",
    "    if len(parts) == 3:\n",
    "        h, m, s = parts\n",
    "    else:\n",
    "        h, m  = parts\n",
    "        s = 0\n",
    "    return int(h) * 60 + int(m) + int(s) / 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_times(metrics_df, df_time):\n",
    "    train_time = []\n",
    "    eval_time = []\n",
    "    for idx, row in metrics_df.iterrows():\n",
    "        model_name = row[\"model\"]\n",
    "        with_flash_attn = \"true\" in model_name\n",
    "        finetuned = \"finetuned\" in model_name\n",
    "        model_name = model_name.split(\"(\")[0]\n",
    "        # print(model_name, with_flash_attn)\n",
    "        model_time = df_time[df_time[\"model\"] == model_name][\n",
    "            df_time[\"with_flash_attn\"] == with_flash_attn\n",
    "        ].iloc[0]\n",
    "\n",
    "        if finetuned:\n",
    "            train_time.append(model_time[\"train_time\"])\n",
    "            eval_time.append(get_minutes(model_time[\"fine_tuned_model_eval_time\"]))\n",
    "        else:\n",
    "            train_time.append(model_time[\"train_time\"])\n",
    "            eval_time.append(get_minutes(model_time[\"base_model_eval_time\"]))\n",
    "\n",
    "    return train_time, eval_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_perf_df(metrics_df, bnb_4bit=False):\n",
    "    df_time = pd.read_csv(\"results/model_training_evaluation_times.csv\")\n",
    "    df_time = df_time[df_time[\"4bit\"] == bnb_4bit]\n",
    "    df_time.drop(columns=[\"4bit\"], inplace=True)\n",
    "    # print(df_time.to_markdown())\n",
    "    perf_df = metrics_df.copy()\n",
    "    perf_df.drop(columns=[\"bleu_1\", \"rouge_l\"], inplace=True)\n",
    "\n",
    "    perf_df[\"train-time(mins)\"], perf_df[\"eval-time(mins)\"] = get_times(metrics_df, df_time)\n",
    "    if bnb_4bit:\n",
    "        perf_df.drop(columns=[\"meteor\"], inplace=True)\n",
    "    \n",
    "    return perf_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/7x/56svhln929zdh2xhr3mwqg4r0000gn/T/ipykernel_19830/2495696642.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  model_time = df_time[df_time[\"model\"] == model_name][\n",
      "/var/folders/7x/56svhln929zdh2xhr3mwqg4r0000gn/T/ipykernel_19830/2495696642.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  model_time = df_time[df_time[\"model\"] == model_name][\n",
      "/var/folders/7x/56svhln929zdh2xhr3mwqg4r0000gn/T/ipykernel_19830/2495696642.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  model_time = df_time[df_time[\"model\"] == model_name][\n",
      "/var/folders/7x/56svhln929zdh2xhr3mwqg4r0000gn/T/ipykernel_19830/2495696642.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  model_time = df_time[df_time[\"model\"] == model_name][\n",
      "/var/folders/7x/56svhln929zdh2xhr3mwqg4r0000gn/T/ipykernel_19830/2495696642.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  model_time = df_time[df_time[\"model\"] == model_name][\n",
      "/var/folders/7x/56svhln929zdh2xhr3mwqg4r0000gn/T/ipykernel_19830/2495696642.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  model_time = df_time[df_time[\"model\"] == model_name][\n",
      "/var/folders/7x/56svhln929zdh2xhr3mwqg4r0000gn/T/ipykernel_19830/2495696642.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  model_time = df_time[df_time[\"model\"] == model_name][\n",
      "/var/folders/7x/56svhln929zdh2xhr3mwqg4r0000gn/T/ipykernel_19830/2495696642.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  model_time = df_time[df_time[\"model\"] == model_name][\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from llm_toolkit.translation_utils import plot_times\n",
    "perf_df = get_perf_df(metrics_df)\n",
    "plot_times(perf_df, ylim=0.358)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(252.61249999999998, 94.34583333333333, 441.3041666666667)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perf_df[\"train-time(mins)\"].mean(), perf_df[\"eval-time(mins)\"].mean(), perf_df[\n",
    "    \"train-time(mins)\"\n",
    "].mean() + 2 * perf_df[\"eval-time(mins)\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/7x/56svhln929zdh2xhr3mwqg4r0000gn/T/ipykernel_19830/2495696642.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  model_time = df_time[df_time[\"model\"] == model_name][\n",
      "/var/folders/7x/56svhln929zdh2xhr3mwqg4r0000gn/T/ipykernel_19830/2495696642.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  model_time = df_time[df_time[\"model\"] == model_name][\n",
      "/var/folders/7x/56svhln929zdh2xhr3mwqg4r0000gn/T/ipykernel_19830/2495696642.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  model_time = df_time[df_time[\"model\"] == model_name][\n",
      "/var/folders/7x/56svhln929zdh2xhr3mwqg4r0000gn/T/ipykernel_19830/2495696642.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  model_time = df_time[df_time[\"model\"] == model_name][\n",
      "/var/folders/7x/56svhln929zdh2xhr3mwqg4r0000gn/T/ipykernel_19830/2495696642.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  model_time = df_time[df_time[\"model\"] == model_name][\n",
      "/var/folders/7x/56svhln929zdh2xhr3mwqg4r0000gn/T/ipykernel_19830/2495696642.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  model_time = df_time[df_time[\"model\"] == model_name][\n",
      "/var/folders/7x/56svhln929zdh2xhr3mwqg4r0000gn/T/ipykernel_19830/2495696642.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  model_time = df_time[df_time[\"model\"] == model_name][\n",
      "/var/folders/7x/56svhln929zdh2xhr3mwqg4r0000gn/T/ipykernel_19830/2495696642.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  model_time = df_time[df_time[\"model\"] == model_name][\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from llm_toolkit.translation_utils import plot_times\n",
    "\n",
    "perf_df = get_perf_df(metrics_df, bnb_4bit=True)\n",
    "plot_times(perf_df, ylim=0.358)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(248.74249999999998, 102.02708333333334, 452.7966666666666)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perf_df[\"train-time(mins)\"].mean(), perf_df[\"eval-time(mins)\"].mean(), perf_df[\n",
    "    \"train-time(mins)\"\n",
    "].mean() + 2 * perf_df[\"eval-time(mins)\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiment 4 - Performance vs Epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chinese</th>\n",
       "      <th>english</th>\n",
       "      <th>Qwen/Qwen2-0.5B-Instruct_checkpoint-560</th>\n",
       "      <th>Qwen/Qwen2-0.5B-Instruct_checkpoint-1120</th>\n",
       "      <th>Qwen/Qwen2-0.5B-Instruct_checkpoint-1680</th>\n",
       "      <th>Qwen/Qwen2-0.5B-Instruct_checkpoint-2240</th>\n",
       "      <th>Qwen/Qwen2-0.5B-Instruct_checkpoint-2800</th>\n",
       "      <th>Qwen/Qwen2-0.5B-Instruct_checkpoint-3360</th>\n",
       "      <th>Qwen/Qwen2-0.5B-Instruct_checkpoint-3920</th>\n",
       "      <th>Qwen/Qwen2-0.5B-Instruct_checkpoint-4480</th>\n",
       "      <th>...</th>\n",
       "      <th>Qwen/Qwen2-1.5B-Instruct_checkpoint-560</th>\n",
       "      <th>Qwen/Qwen2-1.5B-Instruct_checkpoint-1120</th>\n",
       "      <th>Qwen/Qwen2-1.5B-Instruct_checkpoint-1680</th>\n",
       "      <th>Qwen/Qwen2-1.5B-Instruct_checkpoint-2240</th>\n",
       "      <th>Qwen/Qwen2-1.5B-Instruct_checkpoint-2800</th>\n",
       "      <th>Qwen/Qwen2-1.5B-Instruct_checkpoint-3360</th>\n",
       "      <th>Qwen/Qwen2-1.5B-Instruct_checkpoint-3920</th>\n",
       "      <th>Qwen/Qwen2-1.5B-Instruct_checkpoint-4480</th>\n",
       "      <th>Qwen/Qwen2-1.5B-Instruct_checkpoint-5040</th>\n",
       "      <th>Qwen/Qwen2-1.5B-Instruct_checkpoint-5600</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>老耿端起枪，眯缝起一只三角眼，一搂扳机响了枪，冰雹般的金麻雀劈哩啪啦往下落，铁砂子在柳枝间飞...</td>\n",
       "      <td>Old Geng picked up his shotgun, squinted, and ...</td>\n",
       "      <td>Old Trinket raised his gun and squinted his tr...</td>\n",
       "      <td>Old Geng raised his gun, his eyes narrowed. Th...</td>\n",
       "      <td>Old Geng held his gun up, half-closed, and coc...</td>\n",
       "      <td>Old Geng raised his gun, his triangular eye ha...</td>\n",
       "      <td>Old Geng took out his pistol, squinted over a ...</td>\n",
       "      <td>Old Geng held his rifle up and cocked it over ...</td>\n",
       "      <td>Old Geng held his gun to his chest, eyes on a ...</td>\n",
       "      <td>Old Geng took up his gun and raised a triangul...</td>\n",
       "      <td>...</td>\n",
       "      <td>Grannie Geng held up his gun with one eye, nar...</td>\n",
       "      <td>Old Geng raised his rifle and squinted at it t...</td>\n",
       "      <td>Old Geng took his gun off the table and raised...</td>\n",
       "      <td>Old Geng raised his rifle and squeezed the tri...</td>\n",
       "      <td>Old Geng took aim and squeezed the trigger; do...</td>\n",
       "      <td>Old Geng took a step forward, raised his pisto...</td>\n",
       "      <td>Old Geng raised his pistol, opened it up, and ...</td>\n",
       "      <td>Old Geng took a shot with his rifle. A spray o...</td>\n",
       "      <td>Old Geng took a step forward, raised his rifle...</td>\n",
       "      <td>Old Geng reached for his rifle, wedged it to h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>次日天未明时，刘老老便起来梳洗了， 又将板儿教了几句话； 五六岁的孩子，听见带了他进城逛去，...</td>\n",
       "      <td>Next day Grannie Liu was up before dawn. As so...</td>\n",
       "      <td>In the morning she was up early for breakfast ...</td>\n",
       "      <td>In the morning, however, when the sun was just...</td>\n",
       "      <td>In the morning when the sun was just rising, G...</td>\n",
       "      <td>It was still dark before she got up for breakf...</td>\n",
       "      <td>In the early hours of the next day, when it wa...</td>\n",
       "      <td>By day's dawn her old lady had risen from bed ...</td>\n",
       "      <td>By the time the next morning was over, Grannie...</td>\n",
       "      <td>It was just now six o'clock that the old woman...</td>\n",
       "      <td>...</td>\n",
       "      <td>By morning of the next day, Old Liu got up ver...</td>\n",
       "      <td>At dawn the next day, Grannie Liu got up and w...</td>\n",
       "      <td>By midnight, Grannie Liu had risen from her be...</td>\n",
       "      <td>When she arose from her bed at daybreak the ne...</td>\n",
       "      <td>As soon as it was light outside, Grannie Liu r...</td>\n",
       "      <td>By daybreak she was up and dressed, having ins...</td>\n",
       "      <td>At daybreak the old woman got up and dressed h...</td>\n",
       "      <td>When she woke from her nap, Aunt Liu dressed h...</td>\n",
       "      <td>Then at daybreak the old woman was up and abou...</td>\n",
       "      <td>Grannie Liu got up very early the morning of t...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             chinese  \\\n",
       "0  老耿端起枪，眯缝起一只三角眼，一搂扳机响了枪，冰雹般的金麻雀劈哩啪啦往下落，铁砂子在柳枝间飞...   \n",
       "1  次日天未明时，刘老老便起来梳洗了， 又将板儿教了几句话； 五六岁的孩子，听见带了他进城逛去，...   \n",
       "\n",
       "                                             english  \\\n",
       "0  Old Geng picked up his shotgun, squinted, and ...   \n",
       "1  Next day Grannie Liu was up before dawn. As so...   \n",
       "\n",
       "             Qwen/Qwen2-0.5B-Instruct_checkpoint-560  \\\n",
       "0  Old Trinket raised his gun and squinted his tr...   \n",
       "1  In the morning she was up early for breakfast ...   \n",
       "\n",
       "            Qwen/Qwen2-0.5B-Instruct_checkpoint-1120  \\\n",
       "0  Old Geng raised his gun, his eyes narrowed. Th...   \n",
       "1  In the morning, however, when the sun was just...   \n",
       "\n",
       "            Qwen/Qwen2-0.5B-Instruct_checkpoint-1680  \\\n",
       "0  Old Geng held his gun up, half-closed, and coc...   \n",
       "1  In the morning when the sun was just rising, G...   \n",
       "\n",
       "            Qwen/Qwen2-0.5B-Instruct_checkpoint-2240  \\\n",
       "0  Old Geng raised his gun, his triangular eye ha...   \n",
       "1  It was still dark before she got up for breakf...   \n",
       "\n",
       "            Qwen/Qwen2-0.5B-Instruct_checkpoint-2800  \\\n",
       "0  Old Geng took out his pistol, squinted over a ...   \n",
       "1  In the early hours of the next day, when it wa...   \n",
       "\n",
       "            Qwen/Qwen2-0.5B-Instruct_checkpoint-3360  \\\n",
       "0  Old Geng held his rifle up and cocked it over ...   \n",
       "1  By day's dawn her old lady had risen from bed ...   \n",
       "\n",
       "            Qwen/Qwen2-0.5B-Instruct_checkpoint-3920  \\\n",
       "0  Old Geng held his gun to his chest, eyes on a ...   \n",
       "1  By the time the next morning was over, Grannie...   \n",
       "\n",
       "            Qwen/Qwen2-0.5B-Instruct_checkpoint-4480  ...  \\\n",
       "0  Old Geng took up his gun and raised a triangul...  ...   \n",
       "1  It was just now six o'clock that the old woman...  ...   \n",
       "\n",
       "             Qwen/Qwen2-1.5B-Instruct_checkpoint-560  \\\n",
       "0  Grannie Geng held up his gun with one eye, nar...   \n",
       "1  By morning of the next day, Old Liu got up ver...   \n",
       "\n",
       "            Qwen/Qwen2-1.5B-Instruct_checkpoint-1120  \\\n",
       "0  Old Geng raised his rifle and squinted at it t...   \n",
       "1  At dawn the next day, Grannie Liu got up and w...   \n",
       "\n",
       "            Qwen/Qwen2-1.5B-Instruct_checkpoint-1680  \\\n",
       "0  Old Geng took his gun off the table and raised...   \n",
       "1  By midnight, Grannie Liu had risen from her be...   \n",
       "\n",
       "            Qwen/Qwen2-1.5B-Instruct_checkpoint-2240  \\\n",
       "0  Old Geng raised his rifle and squeezed the tri...   \n",
       "1  When she arose from her bed at daybreak the ne...   \n",
       "\n",
       "            Qwen/Qwen2-1.5B-Instruct_checkpoint-2800  \\\n",
       "0  Old Geng took aim and squeezed the trigger; do...   \n",
       "1  As soon as it was light outside, Grannie Liu r...   \n",
       "\n",
       "            Qwen/Qwen2-1.5B-Instruct_checkpoint-3360  \\\n",
       "0  Old Geng took a step forward, raised his pisto...   \n",
       "1  By daybreak she was up and dressed, having ins...   \n",
       "\n",
       "            Qwen/Qwen2-1.5B-Instruct_checkpoint-3920  \\\n",
       "0  Old Geng raised his pistol, opened it up, and ...   \n",
       "1  At daybreak the old woman got up and dressed h...   \n",
       "\n",
       "            Qwen/Qwen2-1.5B-Instruct_checkpoint-4480  \\\n",
       "0  Old Geng took a shot with his rifle. A spray o...   \n",
       "1  When she woke from her nap, Aunt Liu dressed h...   \n",
       "\n",
       "            Qwen/Qwen2-1.5B-Instruct_checkpoint-5040  \\\n",
       "0  Old Geng took a step forward, raised his rifle...   \n",
       "1  Then at daybreak the old woman was up and abou...   \n",
       "\n",
       "            Qwen/Qwen2-1.5B-Instruct_checkpoint-5600  \n",
       "0  Old Geng reached for his rifle, wedged it to h...  \n",
       "1  Grannie Liu got up very early the morning of t...  \n",
       "\n",
       "[2 rows x 22 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"results/mac-results_lf.csv\")\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['chinese',\n",
       " 'english',\n",
       " 'Qwen/Qwen2-0.5B-Instruct_checkpoint-560',\n",
       " 'Qwen/Qwen2-0.5B-Instruct_checkpoint-1120',\n",
       " 'Qwen/Qwen2-0.5B-Instruct_checkpoint-1680',\n",
       " 'Qwen/Qwen2-0.5B-Instruct_checkpoint-2240',\n",
       " 'Qwen/Qwen2-0.5B-Instruct_checkpoint-2800',\n",
       " 'Qwen/Qwen2-0.5B-Instruct_checkpoint-3360',\n",
       " 'Qwen/Qwen2-0.5B-Instruct_checkpoint-3920',\n",
       " 'Qwen/Qwen2-0.5B-Instruct_checkpoint-4480',\n",
       " 'Qwen/Qwen2-0.5B-Instruct_checkpoint-5040',\n",
       " 'Qwen/Qwen2-0.5B-Instruct_checkpoint-5600',\n",
       " 'Qwen/Qwen2-1.5B-Instruct_checkpoint-560',\n",
       " 'Qwen/Qwen2-1.5B-Instruct_checkpoint-1120',\n",
       " 'Qwen/Qwen2-1.5B-Instruct_checkpoint-1680',\n",
       " 'Qwen/Qwen2-1.5B-Instruct_checkpoint-2240',\n",
       " 'Qwen/Qwen2-1.5B-Instruct_checkpoint-2800',\n",
       " 'Qwen/Qwen2-1.5B-Instruct_checkpoint-3360',\n",
       " 'Qwen/Qwen2-1.5B-Instruct_checkpoint-3920',\n",
       " 'Qwen/Qwen2-1.5B-Instruct_checkpoint-4480',\n",
       " 'Qwen/Qwen2-1.5B-Instruct_checkpoint-5040',\n",
       " 'Qwen/Qwen2-1.5B-Instruct_checkpoint-5600']"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.read_csv(\"results/experiment-2-results.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chinese</th>\n",
       "      <th>english</th>\n",
       "      <th>unsloth/Qwen2-0.5B-Instruct</th>\n",
       "      <th>unsloth/Qwen2-0.5B-Instruct(finetuned)</th>\n",
       "      <th>unsloth/Qwen2-1.5B-Instruct</th>\n",
       "      <th>unsloth/Qwen2-1.5B-Instruct(finetuned)</th>\n",
       "      <th>unsloth/Qwen2-7B-Instruct</th>\n",
       "      <th>unsloth/Qwen2-7B-Instruct(finetuned)</th>\n",
       "      <th>unsloth/mistral-7b-instruct-v0.3</th>\n",
       "      <th>unsloth/mistral-7b-instruct-v0.3(finetuned)</th>\n",
       "      <th>gradientai/Llama-3-8B-Instruct-Gradient-1048k</th>\n",
       "      <th>gradientai/Llama-3-8B-Instruct-Gradient-1048k(finetuned)</th>\n",
       "      <th>unsloth/Qwen2-72B-Instruct-bnb-4bit</th>\n",
       "      <th>unsloth/Qwen2-72B-Instruct-bnb-4bit(finetuned)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>老耿端起枪，眯缝起一只三角眼，一搂扳机响了枪，冰雹般的金麻雀劈哩啪啦往下落，铁砂子在柳枝间飞...</td>\n",
       "      <td>Old Geng picked up his shotgun, squinted, and ...</td>\n",
       "      <td>Old Teng holds his gun up, his eyes narrowed a...</td>\n",
       "      <td>Old Geng raised his rifle and tilted his head ...</td>\n",
       "      <td>Old Jin raises his gun, squints one eye as he ...</td>\n",
       "      <td>Old Geng raised his pistol, squinted through t...</td>\n",
       "      <td>Old Geng raised his gun, squinted one of his t...</td>\n",
       "      <td>Old Geng raised his rifle and squinted into th...</td>\n",
       "      <td>Geng Da initiates firing, squinting to form a ...</td>\n",
       "      <td>Old Geng aimed and fired. A triangular slit op...</td>\n",
       "      <td>The old man pulled out his gun, squinting one ...</td>\n",
       "      <td>Old Geng raised his rifle, squinting through t...</td>\n",
       "      <td>Lao Geng raised his gun, narrowed one of his t...</td>\n",
       "      <td>Old Geng raised his gun, narrowed one of his t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>次日天未明时，刘老老便起来梳洗了， 又将板儿教了几句话； 五六岁的孩子，听见带了他进城逛去，...</td>\n",
       "      <td>Next day Grannie Liu was up before dawn. As so...</td>\n",
       "      <td>The next morning, Liu Geo woke up at five o'cl...</td>\n",
       "      <td>But not before noon did Grannie Liu rise up an...</td>\n",
       "      <td>At dawn the next day, Liu Langlang got up earl...</td>\n",
       "      <td>She got up about dawn with a purpose already e...</td>\n",
       "      <td>The next morning, before the dawn had fully br...</td>\n",
       "      <td>First thing in the morning Grannie Liu rose to...</td>\n",
       "      <td>The next day, when it was still dark, Liu Lao ...</td>\n",
       "      <td>Before dawn next day Grannie Liu got up and bu...</td>\n",
       "      <td>The next day, when the sun had not yet risen, ...</td>\n",
       "      <td>Grannie Liu got up before daylight was even vi...</td>\n",
       "      <td>Before dawn next morning, Granny Liu got up to...</td>\n",
       "      <td>As soon as it was light, Grannie Liu got up an...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             chinese  \\\n",
       "0  老耿端起枪，眯缝起一只三角眼，一搂扳机响了枪，冰雹般的金麻雀劈哩啪啦往下落，铁砂子在柳枝间飞...   \n",
       "1  次日天未明时，刘老老便起来梳洗了， 又将板儿教了几句话； 五六岁的孩子，听见带了他进城逛去，...   \n",
       "\n",
       "                                             english  \\\n",
       "0  Old Geng picked up his shotgun, squinted, and ...   \n",
       "1  Next day Grannie Liu was up before dawn. As so...   \n",
       "\n",
       "                         unsloth/Qwen2-0.5B-Instruct  \\\n",
       "0  Old Teng holds his gun up, his eyes narrowed a...   \n",
       "1  The next morning, Liu Geo woke up at five o'cl...   \n",
       "\n",
       "              unsloth/Qwen2-0.5B-Instruct(finetuned)  \\\n",
       "0  Old Geng raised his rifle and tilted his head ...   \n",
       "1  But not before noon did Grannie Liu rise up an...   \n",
       "\n",
       "                         unsloth/Qwen2-1.5B-Instruct  \\\n",
       "0  Old Jin raises his gun, squints one eye as he ...   \n",
       "1  At dawn the next day, Liu Langlang got up earl...   \n",
       "\n",
       "              unsloth/Qwen2-1.5B-Instruct(finetuned)  \\\n",
       "0  Old Geng raised his pistol, squinted through t...   \n",
       "1  She got up about dawn with a purpose already e...   \n",
       "\n",
       "                           unsloth/Qwen2-7B-Instruct  \\\n",
       "0  Old Geng raised his gun, squinted one of his t...   \n",
       "1  The next morning, before the dawn had fully br...   \n",
       "\n",
       "                unsloth/Qwen2-7B-Instruct(finetuned)  \\\n",
       "0  Old Geng raised his rifle and squinted into th...   \n",
       "1  First thing in the morning Grannie Liu rose to...   \n",
       "\n",
       "                    unsloth/mistral-7b-instruct-v0.3  \\\n",
       "0  Geng Da initiates firing, squinting to form a ...   \n",
       "1  The next day, when it was still dark, Liu Lao ...   \n",
       "\n",
       "         unsloth/mistral-7b-instruct-v0.3(finetuned)  \\\n",
       "0  Old Geng aimed and fired. A triangular slit op...   \n",
       "1  Before dawn next day Grannie Liu got up and bu...   \n",
       "\n",
       "       gradientai/Llama-3-8B-Instruct-Gradient-1048k  \\\n",
       "0  The old man pulled out his gun, squinting one ...   \n",
       "1  The next day, when the sun had not yet risen, ...   \n",
       "\n",
       "  gradientai/Llama-3-8B-Instruct-Gradient-1048k(finetuned)  \\\n",
       "0  Old Geng raised his rifle, squinting through t...         \n",
       "1  Grannie Liu got up before daylight was even vi...         \n",
       "\n",
       "                 unsloth/Qwen2-72B-Instruct-bnb-4bit  \\\n",
       "0  Lao Geng raised his gun, narrowed one of his t...   \n",
       "1  Before dawn next morning, Granny Liu got up to...   \n",
       "\n",
       "      unsloth/Qwen2-72B-Instruct-bnb-4bit(finetuned)  \n",
       "0  Old Geng raised his gun, narrowed one of his t...  \n",
       "1  As soon as it was light, Grannie Liu got up an...  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Qwen2-0.5B-Instruct checkpoint-560 1\n",
      "Qwen2-0.5B-Instruct checkpoint-1120 2\n",
      "Qwen2-0.5B-Instruct checkpoint-1680 3\n",
      "Qwen2-0.5B-Instruct checkpoint-2240 4\n",
      "Qwen2-0.5B-Instruct checkpoint-2800 5\n",
      "Qwen2-0.5B-Instruct checkpoint-3360 6\n",
      "Qwen2-0.5B-Instruct checkpoint-3920 7\n",
      "Qwen2-0.5B-Instruct checkpoint-4480 8\n",
      "Qwen2-0.5B-Instruct checkpoint-5040 9\n",
      "Qwen2-0.5B-Instruct checkpoint-5600 10\n",
      "Qwen2-1.5B-Instruct checkpoint-560 1\n",
      "Qwen2-1.5B-Instruct checkpoint-1120 2\n",
      "Qwen2-1.5B-Instruct checkpoint-1680 3\n",
      "Qwen2-1.5B-Instruct checkpoint-2240 4\n",
      "Qwen2-1.5B-Instruct checkpoint-2800 5\n",
      "Qwen2-1.5B-Instruct checkpoint-3360 6\n",
      "Qwen2-1.5B-Instruct checkpoint-3920 7\n",
      "Qwen2-1.5B-Instruct checkpoint-4480 8\n",
      "Qwen2-1.5B-Instruct checkpoint-5040 9\n",
      "Qwen2-1.5B-Instruct checkpoint-5600 10\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'epoch': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
       " 'Qwen2-0.5B-Instruct': [0.26453254295068257,\n",
       "  0.28906766286950575,\n",
       "  0.3075388134142166,\n",
       "  0.3232125016634757,\n",
       "  0.3141676906431015,\n",
       "  0.31468732087511564,\n",
       "  0.3060953047058868,\n",
       "  0.29569751947150547,\n",
       "  0.29297589531864165,\n",
       "  0.2833319356953958,\n",
       "  0.28432663251720675],\n",
       " 'Qwen2-1.5B-Instruct': [0.3108076173265163,\n",
       "  0.3555548051770412,\n",
       "  0.364551066769633,\n",
       "  0.3723931629938662,\n",
       "  0.35847259317675817,\n",
       "  0.35988930837184085,\n",
       "  0.3460642024871934,\n",
       "  0.3479480952549209,\n",
       "  0.33844145976530193,\n",
       "  0.3380289789419591,\n",
       "  0.3339867178782917]}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re  # Import the re module for regex operations\n",
    "\n",
    "dict = {\n",
    "    \"epoch\": [],\n",
    "}\n",
    "\n",
    "for col in df.columns[2:]:\n",
    "    # Split using regex\n",
    "    parts = re.split(r\"[/|_]\", col)  # Use re.split() to split by regex\n",
    "    # print(parts)\n",
    "    model_name = parts[1]\n",
    "    checkpoint = parts[2]\n",
    "    epoch = int(checkpoint.split(\"-\")[1]) // 560\n",
    "    print(model_name, checkpoint, epoch)\n",
    "\n",
    "    if model_name not in dict:\n",
    "        dict[model_name] = []\n",
    "        metrics = calc_metrics(df[\"english\"], df2[f\"unsloth/{model_name}\"])\n",
    "        dict[model_name].append(metrics[\"meteor\"])\n",
    "        dict[\"epoch\"].append(0)\n",
    "\n",
    "    metrics = calc_metrics(df[\"english\"], df[col])\n",
    "    dict[model_name].append(metrics[\"meteor\"])\n",
    "    dict[\"epoch\"].append(epoch)\n",
    "\n",
    "dict[\"epoch\"] = dict[\"epoch\"][: len(dict[model_name])]\n",
    "dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create df from dict\n",
    "perf_df = pd.DataFrame(dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from numpy import argmax\n",
    "\n",
    "# Assuming perf_df.set_index(\"epoch\").plot() generates a line plot\n",
    "ax = perf_df.set_index(\"epoch\").plot(\n",
    "    figsize=(10, 5), title=\"METEOR score vs epoch\", grid=True\n",
    ")\n",
    "\n",
    "# Loop through each line to annotate the last point\n",
    "for line in ax.lines:\n",
    "    # Get the data\n",
    "    xdata, ydata = line.get_data()\n",
    "    for index in [0, 1, argmax(ydata), -1]:\n",
    "        ax.annotate(\n",
    "            f\"{ydata[index]:.3f}\",\n",
    "            xy=(xdata[index], ydata[index]),\n",
    "            textcoords=\"offset points\",\n",
    "            xytext=(0, 1),\n",
    "            ha=\"center\",\n",
    "        )\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
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