diff --git "a/notebooks/v1_v2_analysis.ipynb" "b/notebooks/v1_v2_analysis.ipynb"
new file mode 100644--- /dev/null
+++ "b/notebooks/v1_v2_analysis.ipynb"
@@ -0,0 +1,1959 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "9a85640e-fa8b-4b7c-9574-008d0ab22664",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "import huggingface_hub\n",
+    "import datasets\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import matplotlib.patches as mpatches\n",
+    "\n",
+    "from datetime import datetime, timezone"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "ca02c49a-c351-4183-b5ce-e6825296ef2d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.set_option('display.max_columns', None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8c5b803d-74f3-4206-be76-e08f23e1d05d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# HF Colours\n",
+    "colors = [\"#FF9D00\", \"#FFD21E\", \"#32343D\", '#FF323D']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b564cc3c-78c4-42bc-b415-aca228e8b87e",
+   "metadata": {},
+   "source": [
+    "# Data Loading and Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80d0739a-b3a3-4331-b47b-2a99029affed",
+   "metadata": {},
+   "source": [
+    "## Load V1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "63c5d448-6473-46df-b5a7-abbb479bea9b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c53165af05a045b7aa81b155a1629986",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading readme:   0%|          | 0.00/1.21k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "975d26efb0f54c3caddcf2b407cf45fb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data:   0%|          | 0.00/1.37M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "faef61a89260472eb75df54a9d304709",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating train split:   0%|          | 0/7260 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(7260, 26)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load the v1 JSONL file\n",
+    "ds = datasets.load_dataset(\"open-llm-leaderboard/contents\", split=\"train\")\n",
+    "data_v1 = ds.to_pandas()\n",
+    "data_v1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0fdce37e-681a-4b5e-962d-c3e7156d64ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Drop contaminated models\n",
+    "# There are two of them with specific names\n",
+    "data_v1 = data_v1[~data_v1.eval_name.str.contains(\"contaminated\")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9f85eaba-5053-4a81-9cf8-1da450197ada",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7258, 26)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_v1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f5fea314-f133-4d95-8ef8-c2491a10eaaa",
+   "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>eval_name</th>\n",
+       "      <th>Precision</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>T</th>\n",
+       "      <th>Weight type</th>\n",
+       "      <th>Architecture</th>\n",
+       "      <th>Model</th>\n",
+       "      <th>fullname</th>\n",
+       "      <th>Model sha</th>\n",
+       "      <th>Average ⬆️</th>\n",
+       "      <th>Hub License</th>\n",
+       "      <th>Hub ❤️</th>\n",
+       "      <th>#Params (B)</th>\n",
+       "      <th>Available on the hub</th>\n",
+       "      <th>Merged</th>\n",
+       "      <th>MoE</th>\n",
+       "      <th>Flagged</th>\n",
+       "      <th>date</th>\n",
+       "      <th>Chat Template</th>\n",
+       "      <th>ARC</th>\n",
+       "      <th>HellaSwag</th>\n",
+       "      <th>MMLU</th>\n",
+       "      <th>TruthfulQA</th>\n",
+       "      <th>Winogrande</th>\n",
+       "      <th>GSM8K</th>\n",
+       "      <th>Maintainers Choice</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0-hero_Matter-0.1-7B_bfloat16</td>\n",
+       "      <td>bfloat16</td>\n",
+       "      <td>💬 chat models (RLHF, DPO, IFT, ...)</td>\n",
+       "      <td>💬</td>\n",
+       "      <td>Original</td>\n",
+       "      <td>MistralForCausalLM</td>\n",
+       "      <td>&lt;a target=\"_blank\" href=\"https://huggingface.c...</td>\n",
+       "      <td>0-hero/Matter-0.1-7B</td>\n",
+       "      <td>035c8193ce71be90be7d90098669afb9164ec6cb</td>\n",
+       "      <td>63.391248</td>\n",
+       "      <td>apache-2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2024-03-21T06:05:50Z</td>\n",
+       "      <td>False</td>\n",
+       "      <td>61.774744</td>\n",
+       "      <td>82.135033</td>\n",
+       "      <td>62.423731</td>\n",
+       "      <td>42.439513</td>\n",
+       "      <td>77.821626</td>\n",
+       "      <td>53.752843</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0-hero_Matter-0.1-7B-DPO-preview_bfloat16</td>\n",
+       "      <td>bfloat16</td>\n",
+       "      <td>💬 chat models (RLHF, DPO, IFT, ...)</td>\n",
+       "      <td>💬</td>\n",
+       "      <td>Original</td>\n",
+       "      <td>MistralForCausalLM</td>\n",
+       "      <td>&lt;a target=\"_blank\" href=\"https://huggingface.c...</td>\n",
+       "      <td>0-hero/Matter-0.1-7B-DPO-preview</td>\n",
+       "      <td>78040e4754051df49dd907cf1fd46a6b8a6cc30f</td>\n",
+       "      <td>64.870290</td>\n",
+       "      <td>apache-2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2024-03-23T04:13:58Z</td>\n",
+       "      <td>False</td>\n",
+       "      <td>62.713311</td>\n",
+       "      <td>82.991436</td>\n",
+       "      <td>62.700299</td>\n",
+       "      <td>45.790101</td>\n",
+       "      <td>78.847672</td>\n",
+       "      <td>56.178923</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0-hero_Matter-0.1-7B-boost_bfloat16</td>\n",
+       "      <td>bfloat16</td>\n",
+       "      <td>💬 chat models (RLHF, DPO, IFT, ...)</td>\n",
+       "      <td>💬</td>\n",
+       "      <td>Original</td>\n",
+       "      <td>MistralForCausalLM</td>\n",
+       "      <td>&lt;a target=\"_blank\" href=\"https://huggingface.c...</td>\n",
+       "      <td>0-hero/Matter-0.1-7B-boost</td>\n",
+       "      <td>ba56089eed1211f02e8d0ff47901e77b0cd48f83</td>\n",
+       "      <td>63.223517</td>\n",
+       "      <td>apache-2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2024-03-21T06:05:38Z</td>\n",
+       "      <td>False</td>\n",
+       "      <td>62.627986</td>\n",
+       "      <td>81.507668</td>\n",
+       "      <td>61.967618</td>\n",
+       "      <td>54.702404</td>\n",
+       "      <td>75.927388</td>\n",
+       "      <td>42.608036</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0-hero_Matter-0.1-7B-boost-DPO_bfloat16</td>\n",
+       "      <td>bfloat16</td>\n",
+       "      <td>💬 chat models (RLHF, DPO, IFT, ...)</td>\n",
+       "      <td>💬</td>\n",
+       "      <td>Original</td>\n",
+       "      <td>MistralForCausalLM</td>\n",
+       "      <td>&lt;a target=\"_blank\" href=\"https://huggingface.c...</td>\n",
+       "      <td>0-hero/Matter-0.1-7B-boost-DPO</td>\n",
+       "      <td>5bee9978fcf2188f1070b67f6d94be344fdd99c0</td>\n",
+       "      <td>65.985858</td>\n",
+       "      <td></td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2024-03-22T15:02:21Z</td>\n",
+       "      <td>False</td>\n",
+       "      <td>65.017065</td>\n",
+       "      <td>83.081060</td>\n",
+       "      <td>61.873805</td>\n",
+       "      <td>60.293632</td>\n",
+       "      <td>75.611681</td>\n",
+       "      <td>50.037908</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0-hero_Matter-0.1-7B-boost-DPO-preview_bfloat16</td>\n",
+       "      <td>bfloat16</td>\n",
+       "      <td>💬 chat models (RLHF, DPO, IFT, ...)</td>\n",
+       "      <td>💬</td>\n",
+       "      <td>Original</td>\n",
+       "      <td>MistralForCausalLM</td>\n",
+       "      <td>&lt;a target=\"_blank\" href=\"https://huggingface.c...</td>\n",
+       "      <td>0-hero/Matter-0.1-7B-boost-DPO-preview</td>\n",
+       "      <td>d390fb35a781129efd26d53f7ecdb513c0c3da27</td>\n",
+       "      <td>65.767435</td>\n",
+       "      <td>apache-2.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2024-03-22T07:40:42Z</td>\n",
+       "      <td>False</td>\n",
+       "      <td>64.590444</td>\n",
+       "      <td>82.871938</td>\n",
+       "      <td>62.017625</td>\n",
+       "      <td>58.859162</td>\n",
+       "      <td>75.848461</td>\n",
+       "      <td>50.416983</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                         eval_name Precision  \\\n",
+       "0                    0-hero_Matter-0.1-7B_bfloat16  bfloat16   \n",
+       "1        0-hero_Matter-0.1-7B-DPO-preview_bfloat16  bfloat16   \n",
+       "2              0-hero_Matter-0.1-7B-boost_bfloat16  bfloat16   \n",
+       "3          0-hero_Matter-0.1-7B-boost-DPO_bfloat16  bfloat16   \n",
+       "4  0-hero_Matter-0.1-7B-boost-DPO-preview_bfloat16  bfloat16   \n",
+       "\n",
+       "                                  Type  T Weight type        Architecture  \\\n",
+       "0  💬 chat models (RLHF, DPO, IFT, ...)  💬    Original  MistralForCausalLM   \n",
+       "1  💬 chat models (RLHF, DPO, IFT, ...)  💬    Original  MistralForCausalLM   \n",
+       "2  💬 chat models (RLHF, DPO, IFT, ...)  💬    Original  MistralForCausalLM   \n",
+       "3  💬 chat models (RLHF, DPO, IFT, ...)  💬    Original  MistralForCausalLM   \n",
+       "4  💬 chat models (RLHF, DPO, IFT, ...)  💬    Original  MistralForCausalLM   \n",
+       "\n",
+       "                                               Model  \\\n",
+       "0  <a target=\"_blank\" href=\"https://huggingface.c...   \n",
+       "1  <a target=\"_blank\" href=\"https://huggingface.c...   \n",
+       "2  <a target=\"_blank\" href=\"https://huggingface.c...   \n",
+       "3  <a target=\"_blank\" href=\"https://huggingface.c...   \n",
+       "4  <a target=\"_blank\" href=\"https://huggingface.c...   \n",
+       "\n",
+       "                                 fullname  \\\n",
+       "0                    0-hero/Matter-0.1-7B   \n",
+       "1        0-hero/Matter-0.1-7B-DPO-preview   \n",
+       "2              0-hero/Matter-0.1-7B-boost   \n",
+       "3          0-hero/Matter-0.1-7B-boost-DPO   \n",
+       "4  0-hero/Matter-0.1-7B-boost-DPO-preview   \n",
+       "\n",
+       "                                  Model sha  Average ⬆️ Hub License  Hub ❤️  \\\n",
+       "0  035c8193ce71be90be7d90098669afb9164ec6cb   63.391248  apache-2.0       0   \n",
+       "1  78040e4754051df49dd907cf1fd46a6b8a6cc30f   64.870290  apache-2.0       0   \n",
+       "2  ba56089eed1211f02e8d0ff47901e77b0cd48f83   63.223517  apache-2.0       0   \n",
+       "3  5bee9978fcf2188f1070b67f6d94be344fdd99c0   65.985858                   0   \n",
+       "4  d390fb35a781129efd26d53f7ecdb513c0c3da27   65.767435  apache-2.0       2   \n",
+       "\n",
+       "   #Params (B)  Available on the hub  Merged   MoE  Flagged  \\\n",
+       "0            7                  True    True  True     True   \n",
+       "1            7                  True    True  True     True   \n",
+       "2            7                  True    True  True     True   \n",
+       "3            7                 False    True  True     True   \n",
+       "4            7                  True    True  True     True   \n",
+       "\n",
+       "                   date  Chat Template        ARC  HellaSwag       MMLU  \\\n",
+       "0  2024-03-21T06:05:50Z          False  61.774744  82.135033  62.423731   \n",
+       "1  2024-03-23T04:13:58Z          False  62.713311  82.991436  62.700299   \n",
+       "2  2024-03-21T06:05:38Z          False  62.627986  81.507668  61.967618   \n",
+       "3  2024-03-22T15:02:21Z          False  65.017065  83.081060  61.873805   \n",
+       "4  2024-03-22T07:40:42Z          False  64.590444  82.871938  62.017625   \n",
+       "\n",
+       "   TruthfulQA  Winogrande      GSM8K  Maintainers Choice  \n",
+       "0   42.439513   77.821626  53.752843               False  \n",
+       "1   45.790101   78.847672  56.178923               False  \n",
+       "2   54.702404   75.927388  42.608036               False  \n",
+       "3   60.293632   75.611681  50.037908               False  \n",
+       "4   58.859162   75.848461  50.416983               False  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_v1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "90f222d7-e996-4a50-bc56-08d204032dd3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "V1 columns: Index(['eval_name', 'Precision', 'Type', 'T', 'Weight type', 'Architecture',\n",
+      "       'Model', 'fullname', 'Model sha', 'Average ⬆️', 'Hub License', 'Hub ❤️',\n",
+      "       '#Params (B)', 'Available on the hub', 'Merged', 'MoE', 'Flagged',\n",
+      "       'date', 'Chat Template', 'ARC', 'HellaSwag', 'MMLU', 'TruthfulQA',\n",
+      "       'Winogrande', 'GSM8K', 'Maintainers Choice'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Display V1 columns\n",
+    "columns_v1 = data_v1.columns\n",
+    "print(f\"V1 columns: {columns_v1}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17b08b89-da33-4c35-bd88-4408cf74d47a",
+   "metadata": {},
+   "source": [
+    "## Load V2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "374cb34f-89e9-4da2-b9a7-aef3aef33d2c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(203, 32)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load the v2 JSONL file\n",
+    "ds = datasets.load_dataset(\"open-llm-leaderboard/contents_v2\", split=\"train\")\n",
+    "data_v2 = ds.to_pandas()\n",
+    "data_v2.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "489bff2e-e70f-4d01-9918-6c3329494583",
+   "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>eval_name</th>\n",
+       "      <th>Precision</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>T</th>\n",
+       "      <th>Weight type</th>\n",
+       "      <th>Architecture</th>\n",
+       "      <th>Model</th>\n",
+       "      <th>fullname</th>\n",
+       "      <th>Model sha</th>\n",
+       "      <th>Average ⬆️</th>\n",
+       "      <th>Hub License</th>\n",
+       "      <th>Hub ❤️</th>\n",
+       "      <th>#Params (B)</th>\n",
+       "      <th>Available on the hub</th>\n",
+       "      <th>Merged</th>\n",
+       "      <th>MoE</th>\n",
+       "      <th>Flagged</th>\n",
+       "      <th>date</th>\n",
+       "      <th>Chat Template</th>\n",
+       "      <th>IFEval Raw</th>\n",
+       "      <th>IFEval</th>\n",
+       "      <th>BBH Raw</th>\n",
+       "      <th>BBH</th>\n",
+       "      <th>MATH Lvl 5 Raw</th>\n",
+       "      <th>MATH Lvl 5</th>\n",
+       "      <th>GPQA Raw</th>\n",
+       "      <th>GPQA</th>\n",
+       "      <th>MUSR Raw</th>\n",
+       "      <th>MUSR</th>\n",
+       "      <th>MMLU-PRO Raw</th>\n",
+       "      <th>MMLU-PRO</th>\n",
+       "      <th>Maintainer's Highlight</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>upstage_SOLAR-10.7B-v1.0_float16</td>\n",
+       "      <td>float16</td>\n",
+       "      <td>🟢 pretrained</td>\n",
+       "      <td>🟢</td>\n",
+       "      <td>Original</td>\n",
+       "      <td>LlamaForCausalLM</td>\n",
+       "      <td>&lt;a target=\"_blank\" href=\"https://huggingface.c...</td>\n",
+       "      <td>upstage/SOLAR-10.7B-v1.0</td>\n",
+       "      <td>a45090b8e56bdc2b8e32e46b3cd782fc0bea1fa5</td>\n",
+       "      <td>17.072003</td>\n",
+       "      <td>apache-2.0</td>\n",
+       "      <td>248</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2024-06-12T12:27:42Z</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.242126</td>\n",
+       "      <td>24.212645</td>\n",
+       "      <td>0.509387</td>\n",
+       "      <td>29.789358</td>\n",
+       "      <td>0.021148</td>\n",
+       "      <td>2.114804</td>\n",
+       "      <td>0.281040</td>\n",
+       "      <td>4.138702</td>\n",
+       "      <td>0.437156</td>\n",
+       "      <td>13.677865</td>\n",
+       "      <td>0.340010</td>\n",
+       "      <td>26.667775</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>upstage_SOLAR-10.7B-Instruct-v1.0_float16</td>\n",
+       "      <td>float16</td>\n",
+       "      <td>💬 chat models (RLHF, DPO, IFT, ...)</td>\n",
+       "      <td>💬</td>\n",
+       "      <td>Original</td>\n",
+       "      <td>LlamaForCausalLM</td>\n",
+       "      <td>&lt;a target=\"_blank\" href=\"https://huggingface.c...</td>\n",
+       "      <td>upstage/SOLAR-10.7B-Instruct-v1.0</td>\n",
+       "      <td>c08c25ed66414a878fe0401a3596d536c083606c</td>\n",
+       "      <td>19.961989</td>\n",
+       "      <td>cc-by-nc-4.0</td>\n",
+       "      <td>591</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2024-06-12T12:06:58Z</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.473661</td>\n",
+       "      <td>47.366100</td>\n",
+       "      <td>0.516249</td>\n",
+       "      <td>31.872402</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.308725</td>\n",
+       "      <td>7.829978</td>\n",
+       "      <td>0.389937</td>\n",
+       "      <td>6.942188</td>\n",
+       "      <td>0.313830</td>\n",
+       "      <td>23.758865</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>togethercomputer_RedPajama-INCITE-Instruct-3B-...</td>\n",
+       "      <td>float16</td>\n",
+       "      <td>🔶 fine-tuned on domain-specific datasets</td>\n",
+       "      <td>🔶</td>\n",
+       "      <td>Original</td>\n",
+       "      <td>GPTNeoXForCausalLM</td>\n",
+       "      <td>&lt;a target=\"_blank\" href=\"https://huggingface.c...</td>\n",
+       "      <td>togethercomputer/RedPajama-INCITE-Instruct-3B-v1</td>\n",
+       "      <td>0c66778ee09a036886741707733620b91057909a</td>\n",
+       "      <td>5.877290</td>\n",
+       "      <td>apache-2.0</td>\n",
+       "      <td>91</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2024-06-12T12:07:46Z</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.212426</td>\n",
+       "      <td>21.242636</td>\n",
+       "      <td>0.314602</td>\n",
+       "      <td>4.510786</td>\n",
+       "      <td>0.006042</td>\n",
+       "      <td>0.604230</td>\n",
+       "      <td>0.247483</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.388604</td>\n",
+       "      <td>6.408854</td>\n",
+       "      <td>0.110954</td>\n",
+       "      <td>1.217125</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>togethercomputer_RedPajama-INCITE-Chat-3B-v1_f...</td>\n",
+       "      <td>float16</td>\n",
+       "      <td>🔶 fine-tuned on domain-specific datasets</td>\n",
+       "      <td>🔶</td>\n",
+       "      <td>Original</td>\n",
+       "      <td>GPTNeoXForCausalLM</td>\n",
+       "      <td>&lt;a target=\"_blank\" href=\"https://huggingface.c...</td>\n",
+       "      <td>togethercomputer/RedPajama-INCITE-Chat-3B-v1</td>\n",
+       "      <td>f0e0995eba801096ed04cb87931d96a8316871af</td>\n",
+       "      <td>4.950649</td>\n",
+       "      <td>apache-2.0</td>\n",
+       "      <td>147</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2024-06-13T17:58:59Z</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.165215</td>\n",
+       "      <td>16.521496</td>\n",
+       "      <td>0.321669</td>\n",
+       "      <td>5.164728</td>\n",
+       "      <td>0.003021</td>\n",
+       "      <td>0.302115</td>\n",
+       "      <td>0.244128</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.368448</td>\n",
+       "      <td>5.089323</td>\n",
+       "      <td>0.112699</td>\n",
+       "      <td>1.411052</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>togethercomputer_RedPajama-INCITE-Base-3B-v1_f...</td>\n",
+       "      <td>float16</td>\n",
+       "      <td>🟢 pretrained</td>\n",
+       "      <td>🟢</td>\n",
+       "      <td>Original</td>\n",
+       "      <td>GPTNeoXForCausalLM</td>\n",
+       "      <td>&lt;a target=\"_blank\" href=\"https://huggingface.c...</td>\n",
+       "      <td>togethercomputer/RedPajama-INCITE-Base-3B-v1</td>\n",
+       "      <td>094fbdd0c911feb485ce55de1952ab2e75277e1e</td>\n",
+       "      <td>5.645099</td>\n",
+       "      <td>apache-2.0</td>\n",
+       "      <td>90</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2024-06-12T12:28:23Z</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.229363</td>\n",
+       "      <td>22.936254</td>\n",
+       "      <td>0.306040</td>\n",
+       "      <td>3.518608</td>\n",
+       "      <td>0.009063</td>\n",
+       "      <td>0.906344</td>\n",
+       "      <td>0.243289</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.373875</td>\n",
+       "      <td>4.001042</td>\n",
+       "      <td>0.111120</td>\n",
+       "      <td>1.235594</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                           eval_name Precision  \\\n",
+       "0                   upstage_SOLAR-10.7B-v1.0_float16   float16   \n",
+       "1          upstage_SOLAR-10.7B-Instruct-v1.0_float16   float16   \n",
+       "2  togethercomputer_RedPajama-INCITE-Instruct-3B-...   float16   \n",
+       "3  togethercomputer_RedPajama-INCITE-Chat-3B-v1_f...   float16   \n",
+       "4  togethercomputer_RedPajama-INCITE-Base-3B-v1_f...   float16   \n",
+       "\n",
+       "                                       Type  T Weight type  \\\n",
+       "0                              🟢 pretrained  🟢    Original   \n",
+       "1       💬 chat models (RLHF, DPO, IFT, ...)  💬    Original   \n",
+       "2  🔶 fine-tuned on domain-specific datasets  🔶    Original   \n",
+       "3  🔶 fine-tuned on domain-specific datasets  🔶    Original   \n",
+       "4                              🟢 pretrained  🟢    Original   \n",
+       "\n",
+       "         Architecture                                              Model  \\\n",
+       "0    LlamaForCausalLM  <a target=\"_blank\" href=\"https://huggingface.c...   \n",
+       "1    LlamaForCausalLM  <a target=\"_blank\" href=\"https://huggingface.c...   \n",
+       "2  GPTNeoXForCausalLM  <a target=\"_blank\" href=\"https://huggingface.c...   \n",
+       "3  GPTNeoXForCausalLM  <a target=\"_blank\" href=\"https://huggingface.c...   \n",
+       "4  GPTNeoXForCausalLM  <a target=\"_blank\" href=\"https://huggingface.c...   \n",
+       "\n",
+       "                                           fullname  \\\n",
+       "0                          upstage/SOLAR-10.7B-v1.0   \n",
+       "1                 upstage/SOLAR-10.7B-Instruct-v1.0   \n",
+       "2  togethercomputer/RedPajama-INCITE-Instruct-3B-v1   \n",
+       "3      togethercomputer/RedPajama-INCITE-Chat-3B-v1   \n",
+       "4      togethercomputer/RedPajama-INCITE-Base-3B-v1   \n",
+       "\n",
+       "                                  Model sha  Average ⬆️   Hub License  Hub ❤️  \\\n",
+       "0  a45090b8e56bdc2b8e32e46b3cd782fc0bea1fa5   17.072003    apache-2.0     248   \n",
+       "1  c08c25ed66414a878fe0401a3596d536c083606c   19.961989  cc-by-nc-4.0     591   \n",
+       "2  0c66778ee09a036886741707733620b91057909a    5.877290    apache-2.0      91   \n",
+       "3  f0e0995eba801096ed04cb87931d96a8316871af    4.950649    apache-2.0     147   \n",
+       "4  094fbdd0c911feb485ce55de1952ab2e75277e1e    5.645099    apache-2.0      90   \n",
+       "\n",
+       "   #Params (B)  Available on the hub  Merged   MoE  Flagged  \\\n",
+       "0           10                  True    True  True    False   \n",
+       "1           10                  True    True  True    False   \n",
+       "2            3                  True    True  True    False   \n",
+       "3            3                  True    True  True    False   \n",
+       "4            3                  True    True  True    False   \n",
+       "\n",
+       "                   date  Chat Template  IFEval Raw     IFEval   BBH Raw  \\\n",
+       "0  2024-06-12T12:27:42Z          False    0.242126  24.212645  0.509387   \n",
+       "1  2024-06-12T12:06:58Z           True    0.473661  47.366100  0.516249   \n",
+       "2  2024-06-12T12:07:46Z          False    0.212426  21.242636  0.314602   \n",
+       "3  2024-06-13T17:58:59Z          False    0.165215  16.521496  0.321669   \n",
+       "4  2024-06-12T12:28:23Z          False    0.229363  22.936254  0.306040   \n",
+       "\n",
+       "         BBH  MATH Lvl 5 Raw  MATH Lvl 5  GPQA Raw      GPQA  MUSR Raw  \\\n",
+       "0  29.789358        0.021148    2.114804  0.281040  4.138702  0.437156   \n",
+       "1  31.872402        0.000000    0.000000  0.308725  7.829978  0.389937   \n",
+       "2   4.510786        0.006042    0.604230  0.247483  0.000000  0.388604   \n",
+       "3   5.164728        0.003021    0.302115  0.244128  0.000000  0.368448   \n",
+       "4   3.518608        0.009063    0.906344  0.243289  0.000000  0.373875   \n",
+       "\n",
+       "        MUSR  MMLU-PRO Raw   MMLU-PRO  Maintainer's Highlight  \n",
+       "0  13.677865      0.340010  26.667775                    True  \n",
+       "1   6.942188      0.313830  23.758865                    True  \n",
+       "2   6.408854      0.110954   1.217125                    True  \n",
+       "3   5.089323      0.112699   1.411052                    True  \n",
+       "4   4.001042      0.111120   1.235594                    True  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_v2.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "469ebe1e-9a60-4e18-9a45-f89d68dcae79",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "V2 columns: Index(['eval_name', 'Precision', 'Type', 'T', 'Weight type', 'Architecture',\n",
+      "       'Model', 'fullname', 'Model sha', 'Average ⬆️', 'Hub License', 'Hub ❤️',\n",
+      "       '#Params (B)', 'Available on the hub', 'Merged', 'MoE', 'Flagged',\n",
+      "       'date', 'Chat Template', 'IFEval Raw', 'IFEval', 'BBH Raw', 'BBH',\n",
+      "       'MATH Lvl 5 Raw', 'MATH Lvl 5', 'GPQA Raw', 'GPQA', 'MUSR Raw', 'MUSR',\n",
+      "       'MMLU-PRO Raw', 'MMLU-PRO', 'Maintainer's Highlight'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Display V2 columns\n",
+    "columns_v2 = data_v2.columns\n",
+    "print(f\"V2 columns: {columns_v2}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "259654df-2b0a-40ce-bebf-1bf16eadb199",
+   "metadata": {},
+   "source": [
+    "## Define Tasks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3589b70a-4d04-4844-ac50-81507fd2c8d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tasks_v1 = ['ARC', 'HellaSwag', 'MMLU', 'TruthfulQA', 'Winogrande', 'GSM8K']\n",
+    "tasks_v2 = ['IFEval', 'BBH', 'MATH Lvl 5', 'GPQA', 'MUSR', 'MMLU-PRO']\n",
+    "tasks_v2_raw = ['IFEval Raw', 'BBH Raw', 'MATH Lvl 5 Raw', 'GPQA Raw', 'MUSR Raw', 'MMLU-PRO Raw']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a505711f-0fb9-45ae-b271-517f2fd4bc6e",
+   "metadata": {},
+   "source": [
+    "## Add Models Creation Date"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "22b8274d-14f6-41d9-93c7-c61323097f9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Invalid model-index. Not loading eval results into CardData.\n",
+      "Invalid model-index. Not loading eval results into CardData.\n",
+      "Invalid model-index. Not loading eval results into CardData.\n",
+      "Invalid model-index. Not loading eval results into CardData.\n",
+      "Invalid model-index. Not loading eval results into CardData.\n",
+      "Invalid model-index. Not loading eval results into CardData.\n",
+      "Invalid model-index. Not loading eval results into CardData.\n",
+      "Invalid model-index. Not loading eval results into CardData.\n",
+      "Invalid model-index. Not loading eval results into CardData.\n",
+      "Invalid model-index. Not loading eval results into CardData.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# version 1\n",
+    "for i, row in data_v1.iterrows():\n",
+    "    try:\n",
+    "        data_v1.at[i, 'date'] = huggingface_hub.model_info(row['fullname']).created_at\n",
+    "    except Exception:\n",
+    "        continue"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "dd758f84-84de-41c4-8dc5-c8082aad2210",
+   "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>eval_name</th>\n",
+       "      <th>Precision</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>T</th>\n",
+       "      <th>Weight type</th>\n",
+       "      <th>Architecture</th>\n",
+       "      <th>Model</th>\n",
+       "      <th>fullname</th>\n",
+       "      <th>Model sha</th>\n",
+       "      <th>Average ⬆️</th>\n",
+       "      <th>Hub License</th>\n",
+       "      <th>Hub ❤️</th>\n",
+       "      <th>#Params (B)</th>\n",
+       "      <th>Available on the hub</th>\n",
+       "      <th>Merged</th>\n",
+       "      <th>MoE</th>\n",
+       "      <th>Flagged</th>\n",
+       "      <th>date</th>\n",
+       "      <th>Chat Template</th>\n",
+       "      <th>ARC</th>\n",
+       "      <th>HellaSwag</th>\n",
+       "      <th>MMLU</th>\n",
+       "      <th>TruthfulQA</th>\n",
+       "      <th>Winogrande</th>\n",
+       "      <th>GSM8K</th>\n",
+       "      <th>Maintainers Choice</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0-hero_Matter-0.1-7B_bfloat16</td>\n",
+       "      <td>bfloat16</td>\n",
+       "      <td>💬 chat models (RLHF, DPO, IFT, ...)</td>\n",
+       "      <td>💬</td>\n",
+       "      <td>Original</td>\n",
+       "      <td>MistralForCausalLM</td>\n",
+       "      <td>&lt;a target=\"_blank\" href=\"https://huggingface.c...</td>\n",
+       "      <td>0-hero/Matter-0.1-7B</td>\n",
+       "      <td>035c8193ce71be90be7d90098669afb9164ec6cb</td>\n",
+       "      <td>63.391248</td>\n",
+       "      <td>apache-2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2024-03-20 05:57:38+00:00</td>\n",
+       "      <td>False</td>\n",
+       "      <td>61.774744</td>\n",
+       "      <td>82.135033</td>\n",
+       "      <td>62.423731</td>\n",
+       "      <td>42.439513</td>\n",
+       "      <td>77.821626</td>\n",
+       "      <td>53.752843</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0-hero_Matter-0.1-7B-DPO-preview_bfloat16</td>\n",
+       "      <td>bfloat16</td>\n",
+       "      <td>💬 chat models (RLHF, DPO, IFT, ...)</td>\n",
+       "      <td>💬</td>\n",
+       "      <td>Original</td>\n",
+       "      <td>MistralForCausalLM</td>\n",
+       "      <td>&lt;a target=\"_blank\" href=\"https://huggingface.c...</td>\n",
+       "      <td>0-hero/Matter-0.1-7B-DPO-preview</td>\n",
+       "      <td>78040e4754051df49dd907cf1fd46a6b8a6cc30f</td>\n",
+       "      <td>64.870290</td>\n",
+       "      <td>apache-2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2024-03-19 11:27:26+00:00</td>\n",
+       "      <td>False</td>\n",
+       "      <td>62.713311</td>\n",
+       "      <td>82.991436</td>\n",
+       "      <td>62.700299</td>\n",
+       "      <td>45.790101</td>\n",
+       "      <td>78.847672</td>\n",
+       "      <td>56.178923</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   eval_name Precision  \\\n",
+       "0              0-hero_Matter-0.1-7B_bfloat16  bfloat16   \n",
+       "1  0-hero_Matter-0.1-7B-DPO-preview_bfloat16  bfloat16   \n",
+       "\n",
+       "                                  Type  T Weight type        Architecture  \\\n",
+       "0  💬 chat models (RLHF, DPO, IFT, ...)  💬    Original  MistralForCausalLM   \n",
+       "1  💬 chat models (RLHF, DPO, IFT, ...)  💬    Original  MistralForCausalLM   \n",
+       "\n",
+       "                                               Model  \\\n",
+       "0  <a target=\"_blank\" href=\"https://huggingface.c...   \n",
+       "1  <a target=\"_blank\" href=\"https://huggingface.c...   \n",
+       "\n",
+       "                           fullname                                 Model sha  \\\n",
+       "0              0-hero/Matter-0.1-7B  035c8193ce71be90be7d90098669afb9164ec6cb   \n",
+       "1  0-hero/Matter-0.1-7B-DPO-preview  78040e4754051df49dd907cf1fd46a6b8a6cc30f   \n",
+       "\n",
+       "   Average ⬆️ Hub License  Hub ❤️  #Params (B)  Available on the hub  Merged  \\\n",
+       "0   63.391248  apache-2.0       0            7                  True    True   \n",
+       "1   64.870290  apache-2.0       0            7                  True    True   \n",
+       "\n",
+       "    MoE  Flagged                       date  Chat Template        ARC  \\\n",
+       "0  True     True  2024-03-20 05:57:38+00:00          False  61.774744   \n",
+       "1  True     True  2024-03-19 11:27:26+00:00          False  62.713311   \n",
+       "\n",
+       "   HellaSwag       MMLU  TruthfulQA  Winogrande      GSM8K  Maintainers Choice  \n",
+       "0  82.135033  62.423731   42.439513   77.821626  53.752843               False  \n",
+       "1  82.991436  62.700299   45.790101   78.847672  56.178923               False  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_v1.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "11f912a3-c2eb-4ce8-aa20-b8182d1f1ee7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# version 2\n",
+    "for i, row in data_v2.iterrows():\n",
+    "    data_v2.at[i, 'date'] = huggingface_hub.model_info(row['fullname']).created_at"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "71d5eac3-eec0-4049-9e1d-8f9298640b55",
+   "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>eval_name</th>\n",
+       "      <th>Precision</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>T</th>\n",
+       "      <th>Weight type</th>\n",
+       "      <th>Architecture</th>\n",
+       "      <th>Model</th>\n",
+       "      <th>fullname</th>\n",
+       "      <th>Model sha</th>\n",
+       "      <th>Average ⬆️</th>\n",
+       "      <th>Hub License</th>\n",
+       "      <th>Hub ❤️</th>\n",
+       "      <th>#Params (B)</th>\n",
+       "      <th>Available on the hub</th>\n",
+       "      <th>Merged</th>\n",
+       "      <th>MoE</th>\n",
+       "      <th>Flagged</th>\n",
+       "      <th>date</th>\n",
+       "      <th>Chat Template</th>\n",
+       "      <th>IFEval Raw</th>\n",
+       "      <th>IFEval</th>\n",
+       "      <th>BBH Raw</th>\n",
+       "      <th>BBH</th>\n",
+       "      <th>MATH Lvl 5 Raw</th>\n",
+       "      <th>MATH Lvl 5</th>\n",
+       "      <th>GPQA Raw</th>\n",
+       "      <th>GPQA</th>\n",
+       "      <th>MUSR Raw</th>\n",
+       "      <th>MUSR</th>\n",
+       "      <th>MMLU-PRO Raw</th>\n",
+       "      <th>MMLU-PRO</th>\n",
+       "      <th>Maintainer's Highlight</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>upstage_SOLAR-10.7B-v1.0_float16</td>\n",
+       "      <td>float16</td>\n",
+       "      <td>🟢 pretrained</td>\n",
+       "      <td>🟢</td>\n",
+       "      <td>Original</td>\n",
+       "      <td>LlamaForCausalLM</td>\n",
+       "      <td>&lt;a target=\"_blank\" href=\"https://huggingface.c...</td>\n",
+       "      <td>upstage/SOLAR-10.7B-v1.0</td>\n",
+       "      <td>a45090b8e56bdc2b8e32e46b3cd782fc0bea1fa5</td>\n",
+       "      <td>17.072003</td>\n",
+       "      <td>apache-2.0</td>\n",
+       "      <td>248</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2023-12-12 14:57:41+00:00</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.242126</td>\n",
+       "      <td>24.212645</td>\n",
+       "      <td>0.509387</td>\n",
+       "      <td>29.789358</td>\n",
+       "      <td>0.021148</td>\n",
+       "      <td>2.114804</td>\n",
+       "      <td>0.281040</td>\n",
+       "      <td>4.138702</td>\n",
+       "      <td>0.437156</td>\n",
+       "      <td>13.677865</td>\n",
+       "      <td>0.34001</td>\n",
+       "      <td>26.667775</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>upstage_SOLAR-10.7B-Instruct-v1.0_float16</td>\n",
+       "      <td>float16</td>\n",
+       "      <td>💬 chat models (RLHF, DPO, IFT, ...)</td>\n",
+       "      <td>💬</td>\n",
+       "      <td>Original</td>\n",
+       "      <td>LlamaForCausalLM</td>\n",
+       "      <td>&lt;a target=\"_blank\" href=\"https://huggingface.c...</td>\n",
+       "      <td>upstage/SOLAR-10.7B-Instruct-v1.0</td>\n",
+       "      <td>c08c25ed66414a878fe0401a3596d536c083606c</td>\n",
+       "      <td>19.961989</td>\n",
+       "      <td>cc-by-nc-4.0</td>\n",
+       "      <td>591</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2023-12-12 12:39:22+00:00</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.473661</td>\n",
+       "      <td>47.366100</td>\n",
+       "      <td>0.516249</td>\n",
+       "      <td>31.872402</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.308725</td>\n",
+       "      <td>7.829978</td>\n",
+       "      <td>0.389937</td>\n",
+       "      <td>6.942188</td>\n",
+       "      <td>0.31383</td>\n",
+       "      <td>23.758865</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   eval_name Precision  \\\n",
+       "0           upstage_SOLAR-10.7B-v1.0_float16   float16   \n",
+       "1  upstage_SOLAR-10.7B-Instruct-v1.0_float16   float16   \n",
+       "\n",
+       "                                  Type  T Weight type      Architecture  \\\n",
+       "0                         🟢 pretrained  🟢    Original  LlamaForCausalLM   \n",
+       "1  💬 chat models (RLHF, DPO, IFT, ...)  💬    Original  LlamaForCausalLM   \n",
+       "\n",
+       "                                               Model  \\\n",
+       "0  <a target=\"_blank\" href=\"https://huggingface.c...   \n",
+       "1  <a target=\"_blank\" href=\"https://huggingface.c...   \n",
+       "\n",
+       "                            fullname  \\\n",
+       "0           upstage/SOLAR-10.7B-v1.0   \n",
+       "1  upstage/SOLAR-10.7B-Instruct-v1.0   \n",
+       "\n",
+       "                                  Model sha  Average ⬆️   Hub License  Hub ❤️  \\\n",
+       "0  a45090b8e56bdc2b8e32e46b3cd782fc0bea1fa5   17.072003    apache-2.0     248   \n",
+       "1  c08c25ed66414a878fe0401a3596d536c083606c   19.961989  cc-by-nc-4.0     591   \n",
+       "\n",
+       "   #Params (B)  Available on the hub  Merged   MoE  Flagged  \\\n",
+       "0           10                  True    True  True    False   \n",
+       "1           10                  True    True  True    False   \n",
+       "\n",
+       "                        date  Chat Template  IFEval Raw     IFEval   BBH Raw  \\\n",
+       "0  2023-12-12 14:57:41+00:00          False    0.242126  24.212645  0.509387   \n",
+       "1  2023-12-12 12:39:22+00:00           True    0.473661  47.366100  0.516249   \n",
+       "\n",
+       "         BBH  MATH Lvl 5 Raw  MATH Lvl 5  GPQA Raw      GPQA  MUSR Raw  \\\n",
+       "0  29.789358        0.021148    2.114804  0.281040  4.138702  0.437156   \n",
+       "1  31.872402        0.000000    0.000000  0.308725  7.829978  0.389937   \n",
+       "\n",
+       "        MUSR  MMLU-PRO Raw   MMLU-PRO  Maintainer's Highlight  \n",
+       "0  13.677865       0.34001  26.667775                    True  \n",
+       "1   6.942188       0.31383  23.758865                    True  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_v2.head(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f19c5387-2ac7-4602-a828-d22420f9ae2e",
+   "metadata": {},
+   "source": [
+    "# MMLU vs MMLU-Pro V1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "9079d836-997d-4ec3-a7ac-879af0fd6b3f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mmlu_pro = 'MMLU-PRO' # Raw'\n",
+    "math = 'MATH Lvl 5' # Raw\"\n",
+    "gpqa = \"GPQA\"\n",
+    "\n",
+    "# Extracting the relevant information\n",
+    "v1_data = data_v1[['fullname', 'MMLU', 'GSM8K']]\n",
+    "v2_data = data_v2[['fullname', mmlu_pro, math, gpqa]]\n",
+    "\n",
+    "# Merging the two dataframes on 'eval_name'\n",
+    "merged_data = pd.merge(v1_data, v2_data, on='fullname', how='inner')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "17f4b336-a586-416e-9f80-74267bcc0f61",
+   "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>fullname</th>\n",
+       "      <th>MMLU</th>\n",
+       "      <th>GSM8K</th>\n",
+       "      <th>MMLU-PRO</th>\n",
+       "      <th>MATH Lvl 5</th>\n",
+       "      <th>GPQA</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>01-ai/Yi-1.5-34B</td>\n",
+       "      <td>77.995719</td>\n",
+       "      <td>73.237301</td>\n",
+       "      <td>40.732122</td>\n",
+       "      <td>14.048338</td>\n",
+       "      <td>15.436242</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>01-ai/Yi-1.5-34B-32K</td>\n",
+       "      <td>78.153032</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>41.212323</td>\n",
+       "      <td>13.444109</td>\n",
+       "      <td>15.100671</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>01-ai/Yi-1.5-34B-Chat</td>\n",
+       "      <td>77.082840</td>\n",
+       "      <td>71.645186</td>\n",
+       "      <td>39.116061</td>\n",
+       "      <td>23.338369</td>\n",
+       "      <td>15.324385</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>01-ai/Yi-1.5-6B</td>\n",
+       "      <td>64.726895</td>\n",
+       "      <td>50.341168</td>\n",
+       "      <td>23.343307</td>\n",
+       "      <td>5.664653</td>\n",
+       "      <td>8.277405</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>01-ai/Yi-1.5-6B</td>\n",
+       "      <td>65.002720</td>\n",
+       "      <td>49.810462</td>\n",
+       "      <td>23.343307</td>\n",
+       "      <td>5.664653</td>\n",
+       "      <td>8.277405</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                fullname       MMLU      GSM8K   MMLU-PRO  MATH Lvl 5  \\\n",
+       "0       01-ai/Yi-1.5-34B  77.995719  73.237301  40.732122   14.048338   \n",
+       "1   01-ai/Yi-1.5-34B-32K  78.153032   0.000000  41.212323   13.444109   \n",
+       "2  01-ai/Yi-1.5-34B-Chat  77.082840  71.645186  39.116061   23.338369   \n",
+       "3        01-ai/Yi-1.5-6B  64.726895  50.341168  23.343307    5.664653   \n",
+       "4        01-ai/Yi-1.5-6B  65.002720  49.810462  23.343307    5.664653   \n",
+       "\n",
+       "        GPQA  \n",
+       "0  15.436242  \n",
+       "1  15.100671  \n",
+       "2  15.324385  \n",
+       "3   8.277405  \n",
+       "4   8.277405  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2f7240f-8a99-4463-bd63-ea32fcc9e4fe",
+   "metadata": {},
+   "source": [
+    "- MMLU and MMLU_Pro are well correlated - overall, a model with High MMLU has a high MMLU Pro score\n",
+    "- For MATH vs GSM8K, we identify 3 groups:\n",
+    "    - \"High\" MATH score, very low v1 score (2 outliers): possible overfitting on MATH, or , more likely, one of these models with issues with eos tokens on GSM8K\n",
+    "    - Correlation between v2 and v1 score (most models)\n",
+    "    - Low MATH score, high GSM8K score: likely overfitting on GSM8K"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "32e93390-9183-46ef-9fe1-5acf47f8c195",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(8, 6))\n",
+    "\n",
+    "for i, (old, new) in enumerate([(\"MMLU\", mmlu_pro), (\"MMLU\", gpqa)]): #, (\"GSM8K\", math)\n",
+    "    yval =  merged_data[new]\n",
+    "    xval = merged_data[old]\n",
+    "    ax.scatter(xval, yval, c=colors[i], label=f\"`{new}=f({old})`\")\n",
+    "\n",
+    "ax.set_title(f\"New scores as functions of old evaluations\")\n",
+    "ax.set_ylabel(f'v2 scores')\n",
+    "ax.set_xlabel(f'v1 scores')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.legend()\n",
+    "#plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "42776db9-3b2c-4658-8bfd-cad0d44257a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(8, 6))\n",
+    "\n",
+    "for (old, new) in [(\"GSM8K\", math)]:\n",
+    "    yval =  merged_data[new]\n",
+    "    xval = merged_data[old]\n",
+    "    ax.scatter(xval, yval, c=colors[0], label=f\"`{new}=f({old})`\")\n",
+    "\n",
+    "ax.set_title(f\"MATH (v2) as fn of GSM8K (v1)\")\n",
+    "ax.set_ylabel(f'MATH')\n",
+    "ax.set_xlabel(f'GSM8K')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "#plt.legend()\n",
+    "#plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f22371aa-3f2c-4bf8-84e0-d887eefbb4ac",
+   "metadata": {},
+   "source": [
+    "- MMLU and MMLU_Pro are well correlated - overall, a model with High MMLU has a high MMLU Pro score\n",
+    "- For MATH vs GSM8K, we identify 3 groups:\n",
+    "    - \"High\" MATH score, very low v1 score (2 outliers): possible overfitting on MATH, or , more likely, one of these models with issues with eos tokens on GSM8K\n",
+    "    - Correlation between v2 and v1 score (most models)\n",
+    "    - Low MATH score, high GSM8K score: likely overfitting on GSM8K "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbac9655-0697-4b5c-88f3-2286038d4c67",
+   "metadata": {},
+   "source": [
+    "# Ranking Analysis between V1 and V2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "dd7a03bc-183e-4e57-9791-025ff634c76f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Extracting the relevant information for ranking comparison between v1 and v2\n",
+    "# We actually want to merge with correct precision if possible, else merge on what's available\n",
+    "v2_rank_data = data_v2[['fullname', 'Average ⬆️']]\n",
+    "v1_rank_data = data_v1[['fullname', 'Average ⬆️']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "597ed839-45af-49cf-a160-f9b47eccfc63",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Renaming columns for clarity\n",
+    "v2_rank_data = v2_rank_data.rename(columns={'Average ⬆️': 'v2_score'})\n",
+    "v1_rank_data = v1_rank_data.rename(columns={'Average ⬆️': 'v1_score'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "865a4e21-0a4b-4a7e-b3e6-ebd23b4d9ec9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Merging the two dataframes on 'eval_name'\n",
+    "merged_rank_data = pd.merge(v1_rank_data, v2_rank_data, on='fullname', how='inner')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "edbe899e-4603-4e52-a64d-39a5db8097b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged_rank_data = merged_rank_data.drop_duplicates(subset=['fullname']).dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "63cda705-29b5-41ff-b535-a4a8d1ee4e32",
+   "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>fullname</th>\n",
+       "      <th>v1_score</th>\n",
+       "      <th>v2_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>01-ai/Yi-1.5-34B</td>\n",
+       "      <td>73.504618</td>\n",
+       "      <td>25.812197</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>01-ai/Yi-1.5-34B-32K</td>\n",
+       "      <td>60.700977</td>\n",
+       "      <td>26.787600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>01-ai/Yi-1.5-34B-Chat</td>\n",
+       "      <td>74.823763</td>\n",
+       "      <td>33.076818</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>01-ai/Yi-1.5-6B</td>\n",
+       "      <td>61.566520</td>\n",
+       "      <td>16.778059</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>01-ai/Yi-1.5-6B-Chat</td>\n",
+       "      <td>66.167303</td>\n",
+       "      <td>22.405532</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>204</th>\n",
+       "      <td>togethercomputer/RedPajama-INCITE-Base-3B-v1</td>\n",
+       "      <td>38.537852</td>\n",
+       "      <td>5.645099</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>205</th>\n",
+       "      <td>togethercomputer/RedPajama-INCITE-Chat-3B-v1</td>\n",
+       "      <td>39.527194</td>\n",
+       "      <td>4.950649</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>206</th>\n",
+       "      <td>togethercomputer/RedPajama-INCITE-Instruct-3B-v1</td>\n",
+       "      <td>39.055049</td>\n",
+       "      <td>5.877290</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>207</th>\n",
+       "      <td>upstage/SOLAR-10.7B-Instruct-v1.0</td>\n",
+       "      <td>74.200698</td>\n",
+       "      <td>19.961989</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>208</th>\n",
+       "      <td>upstage/SOLAR-10.7B-v1.0</td>\n",
+       "      <td>66.037836</td>\n",
+       "      <td>17.072003</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>166 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                             fullname   v1_score   v2_score\n",
+       "0                                    01-ai/Yi-1.5-34B  73.504618  25.812197\n",
+       "1                                01-ai/Yi-1.5-34B-32K  60.700977  26.787600\n",
+       "2                               01-ai/Yi-1.5-34B-Chat  74.823763  33.076818\n",
+       "3                                     01-ai/Yi-1.5-6B  61.566520  16.778059\n",
+       "5                                01-ai/Yi-1.5-6B-Chat  66.167303  22.405532\n",
+       "..                                                ...        ...        ...\n",
+       "204      togethercomputer/RedPajama-INCITE-Base-3B-v1  38.537852   5.645099\n",
+       "205      togethercomputer/RedPajama-INCITE-Chat-3B-v1  39.527194   4.950649\n",
+       "206  togethercomputer/RedPajama-INCITE-Instruct-3B-v1  39.055049   5.877290\n",
+       "207                 upstage/SOLAR-10.7B-Instruct-v1.0  74.200698  19.961989\n",
+       "208                          upstage/SOLAR-10.7B-v1.0  66.037836  17.072003\n",
+       "\n",
+       "[166 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_rank_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "9e98fda2-590e-4333-9d32-44bcfc961464",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calculating rank for v1 and v2 based on scores\n",
+    "merged_rank_data['v1_rank'] = merged_rank_data['v1_score'].rank(ascending=False)\n",
+    "merged_rank_data['v2_rank'] = merged_rank_data['v2_score'].rank(ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "3d9a8416-8c50-4290-83a3-34651e0af8a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calculating rank change and sort\n",
+    "merged_rank_data['rank_change'] = merged_rank_data['v2_rank'] - merged_rank_data['v1_rank']\n",
+    "\n",
+    "merged_rank_data = merged_rank_data.sort_values('rank_change')\n",
+    "merged_rank_data = merged_rank_data.dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "ebf1266a-0598-4279-9636-9fe23ab3a720",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Filter to show only top 10 improvements and top 10 declines\n",
+    "show_all = False\n",
+    "\n",
+    "if show_all:\n",
+    "    # Show all changes\n",
+    "    top_changes = merged_rank_data\n",
+    "else:\n",
+    "    # Sort by rank change\n",
+    "    merged_rank_data_sorted = merged_rank_data.sort_values('rank_change')\n",
+    "    # Concatenate top 10 declines and top 10 improvements\n",
+    "    top_changes = pd.concat([merged_rank_data_sorted.head(10), merged_rank_data_sorted.tail(10)])\n",
+    "\n",
+    "# Create a column for detailed rank change information\n",
+    "top_changes['rank_change_info'] = top_changes.apply(\n",
+    "    lambda x: f\"{int(x['v1_rank'])} → {int(x['v2_rank'])}\", axis=1\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "d1b57df5-1b71-4fee-b0e4-d2706a59feb7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Invert the order to show declines first\n",
+    "top_changes_sorted = top_changes.sort_values('rank_change', ascending=True)\n",
+    "\n",
+    "# Positive changes should be green, and negative changes should be red\n",
+    "colors = ['#FF9D00' if x > 0 else '#32343D' for x in top_changes_sorted['rank_change']]\n",
+    "\n",
+    "# Plotting the top and bottom rank changes with corrected colors and adding a legend\n",
+    "plt.figure(figsize=(10, 8))\n",
+    "bars = plt.barh(top_changes_sorted['fullname'], top_changes_sorted['rank_change'], color=colors)\n",
+    "\n",
+    "# Annotating the bars with rank change details\n",
+    "for bar, rank_change_info in zip(bars, top_changes_sorted['rank_change_info']):\n",
+    "    plt.text(\n",
+    "        bar.get_width() + (1 if bar.get_width() > 0 else -1), \n",
+    "        bar.get_y() + bar.get_height() / 2, \n",
+    "        rank_change_info,\n",
+    "        va='center', ha='left' if bar.get_width() > 0 else 'right', \n",
+    "        fontsize=10, color='black'\n",
+    "    )\n",
+    "\n",
+    "# Adding a legend\n",
+    "green_patch = mpatches.Patch(color='#FF9D00', label='Positive Change')\n",
+    "red_patch = mpatches.Patch(color='#32343D', label='Negative Change')\n",
+    "plt.legend(handles=[green_patch, red_patch])\n",
+    "\n",
+    "plt.xlabel('Rank Change')\n",
+    "plt.ylabel('Model')\n",
+    "plt.title('Top and Bottom Changes in Rankings from v1 to v2')\n",
+    "plt.xlim(-80, 80)\n",
+    "plt.gca().invert_yaxis()  # Invert y-axis to show declines first\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1adaffc-c98d-42c2-966e-69c0a228a6b5",
+   "metadata": {},
+   "source": [
+    "# Params and Performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "7de00d20-06e9-4721-b2d3-5bbffdeec218",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full=False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "d78410a9-a826-4b07-b4d6-896b4bc5c39d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "threshold_1 = datetime(2023, 10, 1, tzinfo=timezone.utc)\n",
+    "threshold_2 = datetime(2023, 12, 1, tzinfo=timezone.utc)\n",
+    "threshold_3 = datetime(2024, 2, 1, tzinfo=timezone.utc)\n",
+    "threshold_4 = datetime(2024, 5, 1, tzinfo=timezone.utc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "5ee768cd-f0ee-4272-83a4-667b9147efdc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calculate the mean performance score across the tasks for both versions\n",
+    "data_v1['median_score_v1'] = data_v1[tasks_v1].median(axis=1)\n",
+    "data_v2['median_score_v2'] = data_v2[tasks_v2].median(axis=1)\n",
+    "\n",
+    "# Extract the necessary columns for plotting\n",
+    "if full:\n",
+    "    v1_data = data_v1[['fullname', '#Params (B)', 'median_score_v1', 'date']]\n",
+    "    v2_data = data_v2[['fullname', '#Params (B)', 'median_score_v2', 'date']]\n",
+    "\n",
+    "    # Merge the data on fullname\n",
+    "    merged_data = pd.merge(v1_data, v2_data, on=['fullname', 'date', \"#Params (B)\"], how='outer')\n",
+    "else:\n",
+    "    v1_data = data_v1[['fullname', '#Params (B)', 'median_score_v1']]\n",
+    "    v2_data = data_v2[['fullname', '#Params (B)', 'median_score_v2', 'date']]\n",
+    "\n",
+    "    # Merge the data on fullname\n",
+    "    merged_data = pd.merge(v1_data, v2_data, on=['fullname', \"#Params (B)\"], how='outer')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "03e4d74c-96c2-493d-85e0-b9bacca5475b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged_data.dropna(subset=\"date\", inplace=True)\n",
+    "\n",
+    "merged_data[\"date\"] = pd.to_datetime(merged_data[\"date\"])\n",
+    "for row in merged_data[\"date\"]:\n",
+    "    if not isinstance(row, pd._libs.tslibs.timestamps.Timestamp) or not isinstance(row, datetime):\n",
+    "        print(row, type(row))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "c05cc61a-23f9-4042-98cd-a4bf931ede25",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+QAAAK9CAYAAACtq6aaAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdeXxU1f3/8de9k1myTAKEsCNiWIWIFapV2/p1pRWXqhVxqbh81VbrbmupuxWXflXQKlb7bbW22qqtXa2ty9faX90qVZGiskYQEIQAyWSSWe/9/XEySSYbmZBkksn7+XjEmzn3ZOZk7oB87jnn87Fc13URERERERERkV5lZ3sAIiIiIiIiIgORAnIRERERERGRLFBALiIiIiIiIpIFCshFREREREREskABuYiIiIiIiEgWKCAXERERERERyQIF5CIiIiIiIiJZoIBcREREREREJAsUkIuIiIiIiIhkgQJyERHpFZZlcfPNN2f8cx9//DGWZfHYY491+5j6mr333ptzzjmnx57/sccew7IsPv744x57jVz0i1/8gilTpuD1ehk0aFC2hyMiIjlEAbmIyACSCsgsy+Kf//xnq/Ou6zJ27Fgsy+K4447Lwgj33NatW7nmmmuYMmUKBQUFFBYWMnPmTG677TZ27dqV7eH1OUuWLOl3Nzv+/ve/N36OLcvC6/Wyzz77cPbZZ7Nu3bpufa2PPvqIc845h/Lycn7yk5/wyCOPdOvzi4jIwJaX7QGIiEjvCwQCPPnkk3zxi19Ma3/11VfZuHEjfr8/SyPbM2+//TbHHnsstbW1nHXWWcycOROApUuXcuedd/KPf/yDF154IcujzJ5vfOMbzJs3L+36LlmyhKFDh/bozHxPueyyy/j85z9PPB7nnXfe4ZFHHuG5555j+fLljBo1qlte4+9//zuO43DfffcxYcKEbnlOERGRFAXkIiID0LHHHsszzzzD/fffT15e0/8KnnzySWbOnMn27duzOLqu2bVrFyeddBIej4d3332XKVOmpJ1fuHAhP/nJT7I0ur7B4/Hg8XiyPYxu86UvfYmvf/3rAJx77rlMmjSJyy67jJ///OcsWLBgj547HA5TWFjIZ599BtCtS9Xr6uooKCjotucTEZH+S0vWRUQGoNNPP52qqipefPHFxrZYLMZvfvMbzjjjjDZ/JhwOc/XVVzN27Fj8fj+TJ0/m7rvvxnXdtH7RaJQrr7ySsrIygsEgJ5xwAhs3bmzzOTdt2sR5553H8OHD8fv9TJs2jZ/97Gdd+p0efvhhNm3axL333tsqGAcYPnw4119/fVrbkiVLmDZtGn6/n1GjRnHJJZe0Wtb+X//1X0yfPp3333+fww47jIKCAiZMmMBvfvMbwKwqOOigg8jPz2fy5Mm89NJLaT9/8803Y1kWH330EXPnzqW4uJjS0lIuv/xyIpHIbn+vXbt2ccUVVzS+7xMmTOCuu+7CcRzAbDM4/PDDKSsrawwewVzPiooKysvLCYfDQOs95HvvvTcrVqzg1VdfbVz+/V//9V+sW7cOy7JYtGhRq/G8/vrrWJbFr371qzbHu3XrVvLy8rjllltanVu5ciWWZfHAAw8AEI/HueWWW5g4cSKBQIDS0lK++MUvpn0uM3HEEUcAUFlZ2dj2/PPP86UvfYnCwkKCwSBz5sxhxYoVaT93zjnnUFRUxNq1azn22GMJBoOceeaZ7L333tx0000AlJWVtcqDkMnn59///jdf/vKXKSgo4Pvf/35jboS7776bBx98kH322YeCggKOOeYYPvnkE1zX5Qc/+AFjxowhPz+fE088kR07dqQ99x/+8AfmzJnDqFGj8Pv9lJeX84Mf/IBkMtnmGD744AMOP/xwCgoKGD16ND/84Q9bvYeRSISbb76ZSZMmEQgEGDlyJCeffDJr165t7OM4DosXL2batGkEAgGGDx/ORRddxM6dOzt/sUREBFBALiIyIO29994cfPDBaUHV888/T3V1NfPmzWvV33VdTjjhBBYtWsRXvvIV7r33XiZPnsx3vvMdrrrqqrS+//3f/83ixYs55phjuPPOO/F6vcyZM6fVc27dupUvfOELvPTSS3z7299uXBJ8/vnns3jx4ox/pz/+8Y/k5+c3zpjuzs0338wll1zCqFGjuOeeezjllFN4+OGHOeaYY4jH42l9d+7cyXHHHcdBBx3ED3/4Q/x+P/PmzeOpp55i3rx5HHvssdx5552Ew2G+/vWvEwqFWr3e3LlziUQi3HHHHRx77LHcf//9XHjhhR2Osa6ujsMOO4xf/vKXnH322dx///0ceuihLFiwoPF9tyyLn/3sZ0QiEb75zW82/uxNN93EihUrePTRRyksLGzz+RcvXsyYMWOYMmUKv/jFL/jFL37Bddddxz777MOhhx7KE0880epnnnjiCYLBICeeeGKbzzl8+HAOO+wwnn766VbnnnrqKTweD6eeeipgrsEtt9zC4YcfzgMPPMB1113HXnvtxTvvvNPh+9KeVNBYWloKmGRsc+bMoaioiLvuuosbbriBDz74gC9+8YutEtslEglmz57NsGHDuPvuuznllFNYvHgxJ510EgAPPfQQv/jFLzj55JMbx97Zz09VVRVf/epX2X///Vm8eDGHH35447knnniCJUuWcOmll3L11Vfz6quvMnfuXK6//nr++te/cu2113LhhRfypz/9iWuuuSbteR977DGKioq46qqruO+++5g5cyY33ngj3/ve91q9Nzt37uQrX/kKM2bM4J577mHKlClce+21PP/88419kskkxx13HLfccgszZ87knnvu4fLLL6e6upr//Oc/jf0uuugivvOd73DooYdy3333ce655/LEE08we/bsVr+7iIjshisiIgPGo48+6gLu22+/7T7wwANuMBh06+rqXNd13VNPPdU9/PDDXdd13XHjxrlz5sxp/Lnf//73LuDedtttac/39a9/3bUsy12zZo3ruq773nvvuYB78cUXp/U744wzXMC96aabGtvOP/98d+TIke727dvT+s6bN88tKSlpHFdlZaULuI8++miHv9vgwYPdGTNmdOp9+Oyzz1yfz+cec8wxbjKZbGx/4IEHXMD92c9+1th22GGHuYD75JNPNrZ99NFHLuDatu2++eabje1/+9vfWo31pptucgH3hBNOSBvDxRdf7ALusmXLGtvGjRvnzp8/v/HxD37wA7ewsNBdtWpV2s9+73vfcz0ej7thw4bGtocfftgF3F/+8pfum2++6Xo8HveKK65I+7nU9a+srGxsmzZtmnvYYYe1eo9Sz/fhhx82tsViMXfo0KFpY2xL6meXL1+e1r7vvvu6RxxxROPjGTNmpH3OOuuVV15pvE7btm1zN2/e7D733HPu3nvv7VqW5b799ttuKBRyBw0a5F5wwQVpP7tlyxa3pKQkrX3+/Pku4H7ve99r9Vqp67dt27bGtq58fn784x+nPW/qc11WVubu2rWrsX3BggUu4M6YMcONx+ON7aeffrrr8/ncSCTS2Jb6M9LcRRdd5BYUFKT1S43h8ccfb2yLRqPuiBEj3FNOOaWx7Wc/+5kLuPfee2+r53Ucx3Vd1/1//+//uYD7xBNPpJ3/61//2ma7iIh0TDPkIiID1Ny5c6mvr+fPf/4zoVCIP//5z+0uV//LX/6Cx+PhsssuS2u/+uqrcV23cZbtL3/5C0CrfldccUXaY9d1+e1vf8vxxx+P67ps37698Wv27NlUV1dnPEtaU1NDMBjsVN+XXnqJWCzGFVdcgW03/a/wggsuoLi4mOeeey6tf1FRUdrKgcmTJzNo0CCmTp3KQQcd1Nie+r6tTN+XXHJJ2uNLL70UaHrP2vLMM8/wpS99icGDB6e9R0cddRTJZJJ//OMfjX0vvPBCZs+ezaWXXso3vvENysvLuf322zvzdrRp7ty5BAKBtFnyv/3tb2zfvp2zzjqrw589+eSTycvL46mnnmps+89//sMHH3zAaaed1tg2aNAgVqxYwerVq7s0xvPOO4+ysjJGjRrFnDlzCIfD/PznP2fWrFm8+OKL7Nq1i9NPPz3tvfN4PBx00EG88sorrZ7vW9/6VqdeN9PPj9/v59xzz23zuU499VRKSkoaH6c+Q2eddVZafoeDDjqIWCzGpk2bGtvy8/Mbvw+FQmzfvp0vfelL1NXV8dFHH6W9TlFRUdp18/l8HHjggWmf1d/+9rcMHTq08bPZnGVZgPlMlpSUcPTRR6e9rzNnzqSoqKjN91VERNqnpG4iIgNUWVkZRx11FE8++SR1dXUkk8l2l3uvX7+eUaNGtQp4p06d2ng+dbRtm/Ly8rR+kydPTnu8bds2du3axSOPPNJuGanm+6E7o7i4uM2l4m1JjbfluHw+H/vss0/j+ZQxY8Y0BiQpJSUljB07tlUb0OZe2okTJ6Y9Li8vx7btDmuCr169mvfff5+ysrI2z7d8j376059SXl7O6tWref3119MCtkwNGjSI448/nieffJIf/OAHgFlePXr06Ma92u0ZOnQoRx55JE8//XTjzz711FPk5eU1LvkGuPXWWznxxBOZNGkS06dP5ytf+Qrf+MY32G+//To1xhtvvJEvfelLeDwehg4dytSpUxuD2FSQ395Yi4uL0x7n5eUxZsyYTr1upp+f0aNH4/P52nyuvfbaK+1x6jPUmc/WihUruP766/m///s/ampq0vpXV1enPW7rMzx48GDef//9xsdr165l8uTJaTcCWlq9ejXV1dUMGzaszfOZ/rkVERnoFJCLiAxgZ5xxBhdccAFbtmzhq1/9ardmku5IKiHZWWedxfz589vs09mgLGXKlCm89957xGKxdoOfrmovM3l77W6LRHdtaRkctcVxHI4++mi++93vtnl+0qRJaY///ve/E41GAVi+fDkHH3zwbl+jI2effTbPPPMMr7/+OhUVFfzxj3/k4osvTpsVbs+8efM499xzee+999h///15+umnOfLIIxk6dGhjny9/+cusXbuWP/zhD7zwwgv87//+L4sWLeLHP/4x//3f/73b16ioqOCoo45q81zqM/aLX/yCESNGtDrfMuj0+/2d+r26oqMbI139bO3atYvDDjuM4uJibr31VsrLywkEArzzzjtce+21jb9/Z5+vsxzHYdiwYW3mFwDavXkkIiJtU0AuIjKAnXTSSVx00UW8+eabacuLWxo3bhwvvfQSoVAobZY8tSx23LhxjUfHcRpn2lJWrlyZ9nypDOzJZLLdgCpTxx9/PG+88Qa//e1vOf300zvsmxrvypUr2WeffRrbY7EYlZWV3Tam5lavXs348eMbH69ZswbHcdh7773b/Zny8nJqa2s7NZ5PP/2USy+9lGOOOQafz8c111zD7NmzG3/X9nR0Y+ArX/kKZWVlPPHEExx00EHU1dXxjW98Y7djAfja177GRRdd1Pi5WrVqVZulyIYMGcK5557LueeeS21tLV/+8pe5+eabOxWQdyS1SmPYsGHdfj2z8flp6e9//ztVVVU8++yzfPnLX25sb55hPlPl5eW89dZbxONxvF5vu31eeuklDj300D1agSEiIob2kIuIDGBFRUU89NBD3HzzzRx//PHt9jv22GNJJpON5apSFi1ahGVZfPWrXwVoPN5///1p/VpmTfd4PJxyyin89re/TcvenLJt27aMf5dvfvObjBw5kquvvppVq1a1Ov/ZZ59x2223AXDUUUfh8/m4//7702YIf/rTn1JdXd1mVvg99eCDD6Y9/tGPfgQ0vWdtmTt3Lm+88QZ/+9vfWp3btWsXiUSi8fEFF1yA4zj89Kc/5ZFHHiEvL4/zzz9/tzOghYWFrUp1peTl5XH66afz9NNP89hjj1FRUdHplQuDBg1i9uzZPP300/z617/G5/Pxta99La1PVVVV2uOioiImTJjQOMu/J2bPnk1xcTG33357m5m/u/IZS8nG56el1Ix389ePxWIsWbKky895yimnsH379lZ/zpu/zty5c0kmk41bEZpLJBLtfpZERKRtmiEXERng2lsy3tzxxx/P4YcfznXXXcfHH3/MjBkzeOGFF/jDH/7AFVdc0Tgbuf/++3P66aezZMkSqqurOeSQQ3j55ZdZs2ZNq+e88847eeWVVzjooIO44IIL2HfffdmxYwfvvPMOL730Uquay7szePBgfve733Hsscey//77c9ZZZzFz5kwA3nnnHX71q181LuEuKytjwYIF3HLLLXzlK1/hhBNOYOXKlSxZsoTPf/7zu01a1hWVlZWccMIJfOUrX+GNN97gl7/8JWeccQYzZsxo92e+853v8Mc//pHjjjuOc845h5kzZxIOh1m+fDm/+c1v+Pjjjxk6dCiPPvoozz33HI899ljjPugf/ehHnHXWWTz00ENcfPHF7b7GzJkzeeihh7jtttuYMGECw4YNS9t3nSq39sorr3DXXXdl9DufdtppnHXWWSxZsoTZs2e32hKx77778l//9V/MnDmTIUOGsHTpUn7zm9/w7W9/O6PXaUtxcTEPPfQQ3/jGNzjggAOYN28eZWVlbNiwgeeee45DDz20zcCzM7Lx+WnpkEMOYfDgwcyfP5/LLrsMy7L4xS9+kfES9ObOPvtsHn/8ca666ir+9a9/8aUvfYlwOMxLL73ExRdfzIknnshhhx3GRRddxB133MF7773HMcccg9frZfXq1TzzzDPcd999nS49KCIiqOyZiMhA0rzsWUdalj1zXdcNhULulVde6Y4aNcr1er3uxIkT3f/5n/9pLIeUUl9f71522WVuaWmpW1hY6B5//PHuJ5980qrsmeu67tatW91LLrnEHTt2rOv1et0RI0a4Rx55pPvII4809uls2bOUzZs3u1deeaU7adIkNxAIuAUFBe7MmTPdhQsXutXV1Wl9H3jgAXfKlCmu1+t1hw8f7n7rW99yd+7cmdbnsMMOc6dNm9ap98h1XRdwL7nkksbHqbJZH3zwgfv1r3/dDQaD7uDBg91vf/vbbn19favnbFlSLBQKuQsWLHAnTJjg+nw+d+jQoe4hhxzi3n333W4sFnM/+eQTt6SkxD3++ONbjeWkk05yCwsL3XXr1rmu23bZsy1btrhz5sxxg8GgC7RZAm3atGmubdvuxo0bW53rSE1NjZufn99Yjq2l2267zT3wwAPdQYMGufn5+e6UKVPchQsXurFYrMPnTZU9e+aZZ3Y7hldeecWdPXu2W1JS4gYCAbe8vNw955xz3KVLlzb2mT9/vltYWNjmz7dV9ixlTz4/qc/1//zP/3Tqd2vrz+5rr73mfuELX3Dz8/PdUaNGud/97ncbS++98sorux3D/Pnz3XHjxqW11dXVudddd507fvz4xj+TX//61921a9em9XvkkUfcmTNnuvn5+W4wGHQrKirc7373u+7mzZtbvY6IiLTPct09uJUqIiIiHbr55pu55ZZb2LZtW1pCs/7kc5/7HEOGDOHll1/O9lBERERyivaQi4iISLuWLl3Ke++9x9lnn53toYiIiOQc7SEXERGRVv7zn//w73//m3vuuYeRI0dy2mmnZXtIIiIiOUcz5CIiItLKb37zG84991zi8Ti/+tWvCAQC2R6SiIhIztEechEREREREZEs0Ay5iIiIiIiISBYoIBcRERERERHJgpxP6uY4Dps3byYYDGJZVraHIyIiIiIiIjnOdV1CoRCjRo3CttufB8/5gHzz5s2MHTs228MQERERERGRAeaTTz5hzJgx7Z7P+YA8GAwC5o0oLi7O8mjaF4/HeeGFFzjmmGPwer3ZHo70AF3j3KdrnPt0jXObrm/u0zXOfbrGua+/XOOamhrGjh3bGI+2J+cD8tQy9eLi4j4fkBcUFFBcXNynP1jSdbrGuU/XOPfpGuc2Xd/cp2uc+3SNc19/u8a72zatpG4iIiIiIiIiWaCAXERERERERCQLFJCLiIiIiIiIZIECchEREREREZEsUEAuIiIiIiIikgUKyEVERERERESyQAG5iIiIiIiISBYoIBcRERERERHJAgXkIiIiIiIiIlmggFxEREREREQkCxSQi4iIiIiIiGSBAnIRERERERGRLFBALiIiIiIiIpIFCshFREREREREskABuYiIiIiIiEgWKCAXERERERERyQIF5CIiIiIiIiJZoIBcREREREREJAsUkIuIiIiIiIhkgQJyERERERERkSzIy/YAREREpA9yHYiugmQ1eErAPwks3ccXERHpTgrIRUREJF14KVT9HKJrwI2B5QP/BCidD4Wzsj06ERGRnKFb3SIiItIkvBS2LIT6FeApBu8Yc4ysMO3hpdkeoYiISM5QQC4iIiKG65iZ8UQ1+MaBXQiWxxy948zy9arHTT8RERHZYwrIRURExIiuMsvUvWVgWennLAs8ZRBdbfqJiIjIHlNALiIiIkayumHPeKDt83bAnE9W9+64REREcpQCchERETE8JSaBmxtp+7wTMec9Jb07LhERkRylgFxEREQM/ySTTT2xDVw3/ZzrQnIb+CeafiIiIrLHFJCLiIiIYdmmtJmnBOLrIRkGN2mO8fWmvfRs1SMXERHpJvo/qoiIiDQpnAUjroPANHBqIL7RHAPTTbvqkIuIiHSbvGwPQERERPqYwllQcIDJpp6sNjPj/kmaGRcREelmWf0/azKZ5IYbbmD8+PHk5+dTXl7OD37wA9xm+9Zc1+XGG29k5MiR5Ofnc9RRR7F69eosjlpERGQAsGwITIHCg8xRwbiIiEi3y+r/Xe+66y4eeughHnjgAT788EPuuusufvjDH/KjH/2osc8Pf/hD7r//fn784x/z1ltvUVhYyOzZs4lE2skAKyIiIiIiItIPZHXJ+uuvv86JJ57InDlzANh777351a9+xb/+9S/AzI4vXryY66+/nhNPPBGAxx9/nOHDh/P73/+eefPmZW3sIiIiIiIiInsiqwH5IYccwiOPPMKqVauYNGkSy5Yt45///Cf33nsvAJWVlWzZsoWjjjqq8WdKSko46KCDeOONN9oMyKPRKNFotPFxTU0NAPF4nHg83sO/UdelxtaXxyh7Rtc49+ka5z5d49ym65v7dI1zn65x7usv17iz47Nct2Wh0d7jOA7f//73+eEPf4jH4yGZTLJw4UIWLFgAmBn0Qw89lM2bNzNy5MjGn5s7dy6WZfHUU0+1es6bb76ZW265pVX7k08+SUFBQc/9MiIiIiIiIiJAXV0dZ5xxBtXV1RQXF7fbL6sz5E8//TRPPPEETz75JNOmTeO9997jiiuuYNSoUcyfP79Lz7lgwQKuuuqqxsc1NTWMHTuWY445psM3Itvi8TgvvvgiRx99NF6vN9vDkR6ga5z7dI1zn65xbtP1zX26xrlP1zj39ZdrnFqpvTtZDci/853v8L3vfa9x6XlFRQXr16/njjvuYP78+YwYMQKArVu3ps2Qb926lf3337/N5/T7/fj9/lbtXq+3T1+wlP4yTuk6XePcp2uc+3SNc5uub+7TNc59usa5r69f486OLatZ1uvq6rDt9CF4PB4cxwFg/PjxjBgxgpdffrnxfE1NDW+99RYHH3xwr45VREREREREpDtldYb8+OOPZ+HChey1115MmzaNd999l3vvvZfzzjsPAMuyuOKKK7jtttuYOHEi48eP54YbbmDUqFF87Wtfy+bQRURERERERPZIVgPyH/3oR9xwww1cfPHFfPbZZ4waNYqLLrqIG2+8sbHPd7/7XcLhMBdeeCG7du3ii1/8In/9618JBAJZHLmIiIiIiIjInslqQB4MBlm8eDGLFy9ut49lWdx6663ceuutvTcwERERERERkR6W1T3kIiIiIiIiIgOVAnIRERERERGRLMjqknURERHpo1wHoqsgWQ2eEvBPAquD+/iZ9hcREREF5CIiItJCeClU/Ryia8CNgeUD/wQonQ+Fs/a8v4iIiABasi4iIiLNhZfCloVQvwI8xeAdY46RFaY9vHTP+ouIiEgjBeQiIiJiuI6Z6U5Ug28c2IVgeczRO84sR6963PTrSn8RERFJo4BcREREjOgqs+zcWwaWlX7OssBTBtHVpl9X+ouIiEgaBeQiIiJiJKsb9oAH2j5vB8z5ZHXX+ouIiEgaBeQiIiJieEpMQjY30vZ5J2LOe0q61l9ERETSKCAXERERwz/JZEdPbAPXTT/nupDcBv6Jpl9X+ouIiEgaBeQiIiJiWLYpVeYpgfh6SIbBTZpjfL1pLz27qb54pv1FREQkjf4PKSIiIk0KZ8GI6yAwDZwaiG80x8B0096yrnim/UVERKRRXrYHICIiIn1M4SwoOMBkR09Wm5lu/6T2Z7oz7S8iIiKAAnIRERFpi2VDYErP9RcREREtWRcRERERERHJBgXkIiIiIiIiIlmggFxEREREREQkCxSQi4iIiIiIiGSBAnIRERERERHp+1zHVPQAc3Sd7I6nGyjLuoj0T6m/kFViSURERCT3hZdC1c+hbj1wPmxcAAXjoHS+Kb/ZTykgl85TACR9RXgpbH8MIivAjYLlh8A0GHpOv/4LWURERETaEF4KWxZCoho8I02bJ2j+LbhlIYy4rt/+G1ABuXRO6o5UdA24MbB84J+Q/TtSukkw8ISXwqYFENsEuE3tia0QXQmj7+i3fyGLiIiISAuuY+KQRDX4xoHjNe12IdjjIL4eqh6HggP6ZRyggFx2r/kdKW8ZWAFwI9m/I9VXbxJIz3Ed2LoIYusBj7nmeICk+QzE1sPWxTD+8X75F7KIiIiItBBdZf697y0Dy0o/Z1ngKYPoatMvMCU7Y9wD+herdKzlHSm7ECyPOXrHmZnpqsd7P6FC6iZB/QrwFIN3jDmmbhKEl/bueKR3RD6C+vcBC6x8sPLMX8RWnnmMBfXLTD8RERER6f+S1Q2Tb4G2z9sBcz5Z3bvj6iYKyKVjmdyR6i199SaB9LzICrM6wwq0/XlsvnpDRERERPo/T4lZFelG2j7vRMx5T0nvjqubKCCXjvXFO1J98SaB9DK3neZ22kVERESkf/JPMttSE9ta/1vPdSG5DfwTTb9+SAG5dKwv3pHqizcJpHcEpjXMgkdpHZS7QNScD0zLwuBEREREpNtZtskR5SkxCdySYdOeDJvHnhIoPbvf5g/qn6OW3tMX70j1xZsE0jsCUyB/P8CFZJ0JzN2YOSbrTHv+jH6Z0ENERERE2lE4yySSDkwDJ2TanBAEpvfrkmeggFx2p607Um4yu3ek+uJNAukdlg3Dr4S8YUAcnFpwasyRuGkffkW/vUMqIiIiIu0onAVj74Mxd5jHY+6AsYv7dTAOCsilM9LuSNVAfKM5ZuuOVF+8SSC9y84Hy9/iK2DaRURERCQ3WXbTpJt/Uk78e191yKVzCmdBwQEmUVqy2gS92fxDkLpJkKpDntxulqkHpptgvJ/fKZN2pDLsu0koOBDcOnDjYHnBKoDEBpNhv+CAnPgLWkRERERymwJy6TzL7lt7c/vaTQLpec0z7Ns2UJR+vnmG/b70WRURERERaYMCcunf+tpNAulZncmwn9yuDPsiIiIi0i9oKlFE+g9l2BcRERGRHKKAXET6D2XYFxGRbHMdiHwE4bfM0XWyPSIR6ce0ZF1E+o9Uhv0tCxsy6peZZepOxATjyrAvIiI9Kby0KaGsGzOrsvwTzP+blFBWRLpA/2oVyQUD6W59XyvDJyIiA0N4qbkhXL8CPMXgHWOOkRWmPbw02yMUkX5IM+Qi/d1AvFuvDPsiItKbUmU3E9XgGweWZdqtQvAWmFVbKrspIl2gvzFE+rOBfLc+lWG/8CBz1D+ARESkpzQvu5kKxlMsK73spohIBvQvWJH+quXdersQLI85eseZmeOqx3N7+bqIiEhv6EzZTTemspsikjEF5CL9le7Wi4iI9A6V3RSRHqKAXKS/0t16ERGR3qGymyLSQxSQi/RXulsvIiLSO1JlNz0lJoFbMgxu0hzj61V2U0S6TH9riPRXulsvIiLSe1R2U0R6gMqeifRXqbv1WxY23J0vM8vUnYgJxnW3fmBzHZWFExHpbiq7KSLdTAG5SH+WulufqkOe3G6WqQemm2Bcd+sHpvBSqHoM6j8wWxqsAOTvC6Xn6DMhIrKnUmU3RUS6gQJy6TzNuPVNulsvzYWXwqYFEN/cUPLOBSxIbIXIShh9h4JyERERkT5CAbl0Tnhp0yysGzOzsP4JZsm0/nGffbpbL2AC8K2LILYeXBs8fsADJMGJmvati2H847phIyIiItIH6F9ksnvhpWafcv0K8BSDd4w5RlaY9vDbEPkIwm+Zo+tke8QiA1PkI6h/H1wLPAWYe66WOdoF5vv6ZaafiIiIiGSdZsilY65jZsYT1eAbB5Zl2q1C8BZAdCV8cjnYxUC88zPnWv4ue0qfodYiK8yecTsfE4g3ZwF+cOtNv/x9szBAEREREWlOAbl0LLrKLFP3ljUF4ylONSR2gRuH/FLwDDfBQGrmvL0SIFr+LntKn6HdaBmMp5ots6VcRERERPqEAT6dJLuVrG4IeAItTrgQ22hmKa08IA8sD9iF4B1nfq7q8dbL13e7/H1pb/1m0l/pM9S+wDTzZ9WNQLJh37hTb47JZFPG9cC0bI9URERERFBALrvjKTGzj24kvd0Jmy87zywTtrxN5yzL1MSOrjYz7Cktl7/bhZ0L4kVS9BnqWGAK5O9nVq2wE9wQuGFzZGfDapYZSgAoIiIi0kcoIM8lrtP9ydX8k8xS4MQ2cJutdXXj4CbBTZhgyC5M/zk7YGbWk9VNbR0tf28viBdpLu0zBMRrIFpljhb6DFl2w97wZDsdkpA/VXvtRURERPoI7SHPFT21p9ayzXNsWQjx9SbgsQMmECdpXsc3po395RFzzlPS1Nbu8vcGdgCS29ODeJHmUp+hWATCKyFe27BtwgZvERSOAzs2cD9DTgJ2Pttxn53PwvBrzeoWEREREckqTZPkgp7eU1s4yyRoC0wDpwbiG01AnldmAm67JL2/60JyG/gnmhn2lPaWv6e0FcSLNOcpgUQcaj6AWDVgNQSWlnlc84E5P1A/QzV/g2RVx32SVaafiIiIiGSdpkj6u92VJYuvN3tqCw7Ys2WqhbPMczQvM5Woga13pM+cOxETjHtKoPTs9NdMLX+PrDBjaz6rngriA9PTg3iR5nwToC4MVgwcB9woJm24ZfaS5zkQrjP9BqK6ZTSlUW8r07prvuqWwaA5vTcuEREREWmTZsj7u97cl23ZJhlU4UHmWHRg65lzp8YE1W2VPEstf/eUmCA+GTb70JPhhqC+jSBepLldH8KWakgmwOs0/A1mmaPXMe1bd5l+A5Fb3739RERERKRHaYa8v8v2vuy2Zs79k9oPqlPL31P73ZPbzTL1wHQTjKuGtHQkWgU7t0G9A2X5EPBAngWOC3UJ2FYPkVrTbyDqbDkzlT0TERER6ROyGpDvvfferF+/vlX7xRdfzIMPPkgkEuHqq6/m17/+NdFolNmzZ7NkyRKGDx+ehdH2Uc33ZVuFrc/3xr7s1Mx5Z2UaxIukRKogGYFQAkJhyM8Djw1JB+oTNO4pjwzQgLxgv6Y65GlL15tVSLACpp+IiIiIZF1WI6C3336bTz/9tPHrxRdfBODUU08F4Morr+RPf/oTzzzzDK+++iqbN2/m5JNPzuaQ+572ypJB+8nV+oKWy98VjEtn+AeDkwQcwDVBeG2sIRh3TbuTNP0GosAUKJiFudea2sLSPDDPg4LPqw65iIiISB+R1SiorKyMESNGNH79+c9/pry8nMMOO4zq6mp++tOfcu+993LEEUcwc+ZMHn30UV5//XXefPPNbA67b9G+bBlIojtIm+1tk9vQbwCybBh+Jfj3AasA8AFec7QKTPvwK/T3gYiIiEgf0Wf2kMdiMX75y19y1VVXYVkW//73v4nH4xx11FGNfaZMmcJee+3FG2+8wRe+8IU2nycajRKNRhsf19TUABCPx4nH4z37S+yB1Ni6NEbfDBi6AHY8CdF14NaA5QX/DBhyujnfh3/3gWKPrrEY9SGw8jEz5DaNWcOxGr4a2utDWfnM94lr7JsBwxdC1RMQ+chkorf8EJgKpWfo74M91CeusfQYXd/cp2uc+3SNc19/ucadHZ/lui3XOWfH008/zRlnnMGGDRsYNWoUTz75JOeee25acA1w4IEHcvjhh3PXXXe1+Tw333wzt9xyS6v2J598koKCgh4Zu4iIiIiIiEhKXV0dZ5xxBtXV1RQXF7fbr8/MkP/0pz/lq1/9KqNGjdqj51mwYAFXXXVV4+OamhrGjh3LMccc0+EbkW3xeJwXX3yRo48+Gq/X27UncR2IrYFkDXiKTS1mLU3tM7rlGg90O/4DLx4LiUhDzoSGveRYgG1K/eUF4Oi/wJDpvT68PnWN9fdBj+hT11i6na5v7tM1zn26xrmvv1zj1Ert3ekTAfn69et56aWXePbZZxvbRowYQSwWY9euXQwaNKixfevWrYwYMaLd5/L7/fj9/lbtXq+3T1+wlC6PM7y0qZSYGzOZ1f0TzP5ylRLrU/rLZ7FPGjYDhk6Hrf8E2wNWKnmZC27C5E8Y+nnTL4vBZ9avcXgpVD0G9R80VGAIQP6+UHqO/j7oJlm/xtKjdH1zn65x7tM1zn19/Rp3dmx9Yrrk0UcfZdiwYcyZM6exbebMmXi9Xl5++eXGtpUrV7JhwwYOPvjgbAyz7wovhS0LoX6FmQnzjjHHyArTHl6a7RGKdA/Lhs/dAsGG2V43AW7cHC2Paf/czQN7Jji8FDYtgNArEP8UEjvMMfSKadffByIiIiJ9RtZnyB3H4dFHH2X+/Pnk5TUNp6SkhPPPP5+rrrqKIUOGUFxczKWXXsrBBx/cbkK3Acl1zMx4ohp848ySXTA1yb0FJtN61eOm7ndPBSmuo5ri0nuGHwKHPgwf/Aiq3jV1yT0BKD0A9v22OT9QuQ5sXQSx9eDa4PEDHiAJTtS0b10M4x/Xn1ERERGRPiDrAflLL73Ehg0bOO+881qdW7RoEbZtc8oppxCNRpk9ezZLlizJwij7sOgqs0zdW9YUjKdYFnjKILra9OuJ2sPhpbD9MTMb35jNeRoMPSe7S2N1kyC3DT8Ehn0Bdq6A2E7wDYbB03SNIx9B/fvgWuApoKkWeZ5Z4u+EoX6Z6Ze/bzZHKtngOLC2EkIhCAahfDzYA/zPjIiISJZlPSA/5phjaC/ReyAQ4MEHH+TBBx/s5VH1I8nqhj3jgbbP2wFIbjf9ultqaWxsE2m1oRNbIboSRt+RnaBc++kHBsuGIRXZHkXfEllh9ozb+TQF4ykW4Ae33vRTQD6wLFsOTz0LletN2TuvF8aPg9NOhhn6cyQiIpItujXe33lKTMDpRto+70TMeU9J975u86WxuOY1rPyGsbhNS2Ndp3tfd3e0n16E1sF4qrmddslty5bDoiWwcrWZGR810hxXrjbty5Zne4QiIiIDlgLy/s4/ycz+JrY1lIFqxnUhuQ38E02/7pRaGovVEIjnmX/sW3nmMVbT0ti2uI45F37LHLsjcG+5n94uNIm+7ELwjjOrBKoe7/2bBCK9JTDNrJZxI23/fZDKuB6Ylp3xSe9zHDMzHgrBmNFQkG+WqRfkm8ehEDz9O9NPREREel3Wl6zLHrJssxR7y0KTwM1TZpapOxETjHtKoPTs7t9bm1oaaxW0vXedALh1bS+N7akl5dneTy+SbYEpkL8f1L1tlqbjozGpmxsDXMifoc//QLK20ixTLy1t++/F0iGw7mPTb2J5VoYoIiIykGmGPBcUzoIR15lZL6cG4hvNMTDdtPfovmm3YeatWfkp1209O5fSk0vKO7Of3o31zH56kb7AsmH4lU0VF9yYCczdmDnnGwfDr1Dyu4EkFDJ7xgP+ts/7/eZ8KNS74xIRERFAM+S5o3CWKW3WW5nFU0tjnfqGhkSzkw0fK7vF0tieLtHWfD+9Vdj6fE/tpxfpSwpnmYSKraofTIehSmw44ASDJoFbJGqWqbcUjZrzwWDvj01EREQUkOcUy+69paiBKeDbGyLvt3EyZg6+qenj6ekl5an99JEVJsBv/hqp/fSB6d2/n16kr+ntG3TSd5WPN9nUV66G/NGt/16s2gFTJpl+IiIi0uv0rzPZQ+1lbW6jvaeXlKf203tKzGx7Mgxu0hzj63tuP71IX5S6QVd4kDnqcz8w2bYpbRYMwsZNUFcHyaQ5btxk2ueepHrkIiIiWaL/A0vXRD6C2MeAF5M4ym725TPtscr0LOu9UaItq/vpRUT6oBkVcOXFMHkihGrh0y3mOGWSaVcdchERkazRknXpPNdpWgIbfsvsH7cLTKkzNwk4gG1KjTkJk0yqeZb13lpSruW6IiLpZlRAxTSTTT0UMjPj5eM1My4iIpJlCsilc1qWKnPqgIQJxC2vCcqbsyxomWi9N0u09eZ+ehGR/sC2VdpMRESkj1FALruXKlWWqG5IyBaARBXEt4AbBscG29fU33UbMp23yLIOTUvKU8F9crtZph6YboJxLSmXzmq+YkOrIERERESkH1JALh1rr1SZtwxixeBUNwTlDmCZXG5uEnAhf0bbs9RaUi57quWKDctntkOUqqyXiIiIiPQfioCkY+2WKrMgUI5J6pYEt9Z8ObWm7nHeMBh+RftBtjJAS1elVmzUrwBPMXjHmGNkhWkPL832CEVEREREOkVRkHRsd6XKrDzMtHhew5cX8IOd32tDlAGk5YoNu9AkEbQLwTvOfF6rHjf9RERERET6OAXk0rF2S5W5ENsIbgLzMcozgZGVB54icMIKjKT7tbtiA/PYUwbR1aafiIiIiEgfp4BcOpYqVZbYZpK1pThhM0tJ3Dy2/GAVNgTvtWamsn6ZAiPpXrtbsWEHzPlkde+OS0RERESkCxSQS8dSpco8JaZUWTJskrYlQkC0oU+qpnhDcE6goc92SOzM0sAlJ7W7YqOBEzHnPSW9Oy4RERERkS5QQC67lypVFpgGTg3EN5ojFuADouCETEI3J2RmyLHAjUNyV1aHLjmmvRUbYB4nt4F/ouknIiIiItLHqeyZtNZWfefCWZC/P4ReNPXHk9Xw2RIgBq6FCc5twG3YVx43y4o9g7L4i0jOSa3Y2LLQrNjwlJll6k7EBOOeElPPXln7RURERKQfUEAu6dqr71x4EITfhLoVJvjBAhKA2/DVFltLh6X7pVZspD6nye3mcxqYboJx1SEXERERkX5CAbk0SdV3TlQ3ZLEOmL26df+G6hcgmYSYY2bQPYDPNXF5m9o9IbLnCmdBwQGtV3JoZlxERERE+hEF5GK0rO/cWFKqABLhhmRZgL9FoO3SEHvbNAXhqVlzR9mupedYNgSmZHsUIiIiIiJdpoBcjPbqOzsNJcwaY22rIQhvtky9MShPfePFTKEnldRNRERERESkHQrIxWivvnO8Oj34hobg2yJ977gPbD+Nyd3cerC8SuomIiIiIiLSDm24FKO9+s5ONP2x5TR9pXHBSt3fiQA25A2FvME9NGAREREREZH+TQG5GO3Vd7b9nXwC18yKkwCrCPIGQf4M1YMWERERERFphwJyMVL1nT0lpr5zMgxuEmxvs05uG18NvHs1BPX7gCcI3uHZrQftOhD5CMJvmaPbckZfREREREQku7SHXJq0Vd/ZSZokb+0GtG5D0J00CeAsH+RnuR50eClUPQb1H5gl+FYA8veF0nNUo1pERERERPoMBeSSrmV958g62Lig47LiDlByIngHgXcEBI8GO0sfrfBS2LQA4psbbiI0ZH5PbIXIShh9h4JyERmYHAfWVkIoBMEglI8HWwvlREREskkBubTWvL7zzndMArfG0mYtuJjzO54Eb7GZIa/+i1n+3tuBr+vA1kUQWw+uDR4/jeXXnKhp37oYxj+evaX0IiLZsGw5PPUsVK6HeBy8Xhg/Dk47GWZUZHt0IiIiA5aiEulYdKs5tjdD3rw+uXcMeIohsgK2LDSz1b0p8hHUv2/G4inA3G+yzNEuMN/XLzP9REQGimXLYdESWLnazIyPGmmOK1eb9mXLsz1CERGRAUsBuXQsVNm5fk4MLA/YheAdZ5a7Vz3eu8nUIivMnvHGeujNWYDfnI+s6L0xiYhkk+OYmfFQCMaMhoJ8s0y9IN88DoXg6d+ZfiIiItLrFJBLx3Z82Ll+sdqm7y0LPGUQXW32orelR7OgtzOdb3W0EV5EJAetrTTL1EtLW/8daFlQOgTWfWz6iYiISK/THnLpWCIOjgt2wz/kUpXOWk1AJyG6AewAeIaaY3K7mSlvKby0KZO7GzP7zv0T9nzfeWCayajuRoDC9H98um5TxvXAtK6/hohIfxIKmT3jAX/b5/1+2LHT9BMREZFepxly6Vj+eIglTVDeXjAO4HUgtsYsB697HaLrTaDtKUnvF15q9pfXrzD7zTu777wzM+qBKZC/H+CCWw9uoiEQT5jHuJA/oylhnYhIrgsGTQK3SLTt89GoOR8M9u64REREBNAMuezO3hfC2n+A7WtYim4Dlgl0oWnm3LIwGc1dM+sd/xg8+4F/UtNzuY6ZGU9Ug29c0wy2VQjeAoivN/vOCw5Iz4Le2Rl1y4bhVzaUPdtk+jY/5x0Dw69QhnURGTjKx5ts6itXQ/7o1iuHqnbAlEmmn4iIiPQ6RSbSscLRUAUkmy1bp0Uw3ihV97vhfGMt8AbRVSao9pa1vZexrX3nmc6oF84ytcaLjjA10fMGm2PREapBLiIDj22b0mbBIGzcBHV1kEya48ZNpn3uSapHLiIikiWaIZeODZ4Gw78A9rsmAG+c1W7eySItEAcgD5waCL0IJV81TcnqhhnuQNuv1XLfeVdn1AtnmbboKvNcnhIzU6+ZcREZiGZUwJUXN9Uh37HTLFOfMskE46pDLiIikjUKyGX3CurA9jTsI28IvC0LPB1lLY+Da0N8S1OTp8QsN3cjJqhuyYmk7zvPZEa95b5wy9ZecRGRlBkVUDHNZFMPhczMePl4zYyLiIhkmQJy6VjdB+B8AtgmILeaZ3bbXRkxx2RcT/FPMnu/IyvMDHfLvYzJbRCY3rTvPNMZdRERaZ9tw8TybI9CREREmtGtcenYhr+DHYdEHjh+cLzmy+1sTe9m/SzbJGLzlJjl5skwuElzjK837aVnNy0tbz6j3paWM+oiIiIiIiL9iAJy6VikIRi2HLCjJji342C76f3c1j8KQPzT9MeFs2DEdaYWuFMD8Y3mGJhu2psnXUvNqCe2NWV1b3y9hhl1/8T0TO4iIiIiIiL9hJasS8fyKyBkQV6iYcl6Q2I3F9IKk7c3YV7/Yeu2ziZdS82ob1nYMINeZpapOxETjLecUW/OdWDnCojtBN9gk5xOSd1ERERERKQPUUAuHZtwOPzTgkEueDu7TL2Z5rXAm+ts0rXUjHqqDnlyu1mmHphugvG2yphtfR0+fAB2fQjJKHj8MGgqTP02DD8k899BRERERESkByggl47VrIREso2a452UN2jPx5BJGbOtr8O/roHYDsgfCXn5kKiHqndN+4F3KygXEREREZE+QQG5dKx+KwxKNiRxa29puku7a9YLD+yecXRmRt11zMx4bAcEJzRlcfcWQV45hNbChw/CsC9o+bqIiIiIiGSdohLpWPgfkNesxJnb7Gu38kwN8d6yc4VZpp4/su265fkjYNcHpp+IiIiIiEiWKSCXjoWqOrdcva0A3fL3bkmy2E6zZzwvv+3znnxzPraz98YkIiIiIiLSDgXk0rFkJnvHUzPpXpN4zfL00KDa4RtsErgl6ts+n6w3532De3dcIiIiIiIibVBALh0r+nKHW8QbWX6wihq+vGAFwBMEJ9QbozQGTzPZ1Ou3tF23vH4LDNrX9BMREREREckyBeTSmutA5CMIvwXD/RBPdhyUu5gkaVYSLNcE4t6xZrl6by5Zt2xT2sw32CRwi9eCkzTH0FrTPvUSJXQTEREREZE+QVnWJV14KVQ9BvUfgBuBWC3gQtIxe8mb7yd3G/6TcMAaBAUjG2bHCyCxAfzTTXmy3jT8EFParLEO+VazTL30ABOMq+SZiIiIiIj0EQrIpUl4KWxaAPHNZpYc1wTlHttkKY8nIemamXIX8FiQ5zHLwWPVEJwMTsQE454SKD07O7PRww8xpc12rjAJ3HyDzTJ1zYyLiIiIiEgfooBcDNeBrYsgth5c28wq44FEtKmEmG2ZQNy2wHGhNga1EQgGIJiA+EaTzC0w3QTjhbN6Z9xtBd6WDUMqev71RUREREREukgBuRiRj6D+fXAt8BTQuGHc9kEiboLwPNvsycZt2lNeH4dtYdj/OBhyGHhHQPBosHvho7X1dfjgR1D1LiQj4AlA6edg30u1NF1ERERERPo8BeRiRFaY5el2PmnZ2zx+SIbNLLmFmRmPJU2AXuCFvYZAIgn5H8OOT8wMefVfoHR+z86Qb30dXrsIwuvNLHnjDYLNsPM/cOjDCspFRERERKRP06ZaaaFlKnUP2M3qiaeqiTkuJFwI5EFRPuQVgV0EVh7U/we2LDR70rtL88zv9R/AuzdBaI2ZsfcEwFtgjk7StL97c8M+eBERERERkb5JM+RiBKaZ2uFuBChs2jeOA7bdMAvdkG09xe8BbJP0Lbq2IZa3wSoENwpVj0PBAXueTC28FKp+DtE14MYgmYSClVDohVh+Uz/LA94iiIdg+1LYsRxKZ+zZa4uIiIiIiPQQzZCLEZgC+fth9ofXg5swAbibMG0W4HpN0G77wJsPeV4TjDeuF88H8sANQbIa6pdBdNWejSu81My2168ATzF4x5hM7wELxhRDQRvF0T35kKiDqnf27LVFRERERER6UNYD8k2bNnHWWWdRWlpKfn4+FRUVLF3atNTZdV1uvPFGRo4cSX5+PkcddRSrV6/O4ohzlGXD8CvBNw6wTPkyp9bMSGOBlQ/BGVD2eRg6EwZNNjPn5ofB9jbsM88D8sFNQmI7JHZ2fUyuY2bGE9VmXHahmQW3fBB1wAOUetr6wa6/poiIiIiISC/JakC+c+dODj30ULxeL88//zwffPAB99xzD4MHD27s88Mf/pD777+fH//4x7z11lsUFhYye/ZsIpFIFkeeZc33U0c+6r690oWzoOBMqA9ANAKxKERjkMgzy9DtEvAWg78UPD5M4Os2BOHNAmPLwsyUxyG5q+vjia4yy9S9Zc2W0APeEnMDIZ4Evw3+Zudc12Rcz8uH0gO6/toiIiIiIiI9LKt7yO+66y7Gjh3Lo48+2tg2fvz4xu9d12Xx4sVcf/31nHjiiQA8/vjjDB8+nN///vfMmzev18ecdeGlsP2xhqzoUbD8Zv/30HP2PKv51tfhX/eYzOV+CzyWWR7usWFsEqyPwDcUsMEJ0zgTbQWApLkxYNmY4DwBlhc8g7o+nmS1maG3AuntviD4BkFsB2aPe9KUa3OT4ETNuIZ+XnXIRURERESkT8tqQP7HP/6R2bNnc+qpp/Lqq68yevRoLr74Yi644AIAKisr2bJlC0cddVTjz5SUlHDQQQfxxhtvtBmQR6NRotFo4+OamhoA4vE48Xi8h3+jrkuNrcMx1r0Lm2+G+Kc0Lcuug9hrULcORt0MBZ/r2gBcB95ZCKFNgA/iAUg0JHOrj8DWevBUQ6K22Yx8PiY4T5C+TNwyNwryhgHF0NX33S0CpxASCbD96eeKpsOu5ZCMQTzRkGyuIaFc0ViouNGUYyPZtdfuAZ26xtJ/OQ7xNesAiK9aAxP2abatQ3KF/hznNl3f3KdrnPt0jXNff7nGnR2f5bpu1jbcBgJm5vOqq67i1FNP5e233+byyy/nxz/+MfPnz+f111/n0EMPZfPmzYwcObLx5+bOnYtlWTz11FOtnvPmm2/mlltuadX+5JNPUlBQ0HO/jIiIiIiIiAhQV1fHGWecQXV1NcXFxe32y2pA7vP5mDVrFq+//npj22WXXcbbb7/NG2+80aWAvK0Z8rFjx7J9+/YO34hsi8fjvPjiixx99NF4vd7WHSIfwcfnNizhLiC9XrgLbp1Jdrb3oyZjeqbW/BLeusokZ0vGgETTub2HQL4XYkkYvC8UjDF7tevfB2cXZma6+c2Ohpnp/Jkw7uHMyp65DsTWQLLGZFVPhOCzexsel4IdMAnnklXm/PCrIRqA2C6zjH3QlD0vs9ZDdnuNpX/6zwfw4E+gtpZ4WRkvHvVFjn7pn3i3bYOiIrjkApi+b7ZHKd1Ef45zm65v7tM1zn26xrmvv1zjmpoahg4dutuAPKtL1keOHMm++6b/Q3Xq1Kn89re/BWDEiBEAbN26NS0g37p1K/vvv3+bz+n3+/H7/a3avV5vn75gKe2Os/YjsKrBLgCrjeUPrgVuNSQ+Am8X9k57ADfcsMy7WZK4fC/kxyEZNfcA7Dh4EpCsBbsWkxew4VyqDrldCJ6hwFZwKjt/g6BlvXHLB/4JUHqCSWAXXQNOQ3vhJCg9e8/3zWdBf/ksSic4Djzze9i5C8aMhjzzV6rX58M7fDhs3AS/+QPMqNDy9RyjP8e5Tdc39+ka5z5d49zX169xZ8eW1YD80EMPZeXKlWltq1atYty4cYBJ8DZixAhefvnlxgC8pqaGt956i29961u9Pdw+op0FDXu60GHI/g17w1tkbPfYJuaONzy/nYDoBlOf3EmYGwSuC/5xpjSa5TUBOQ7EN5rEbJ2RqjeeqG7Iqh4AN2KS18U3wojvgydons9TAv5JfXYmXAaQtZVQuR5KS9MrAYB5XDoE1n1s+k0sz8oQRURERKTvympAfuWVV3LIIYdw++23M3fuXP71r3/xyCOP8MgjjwBgWRZXXHEFt912GxMnTmT8+PHccMMNjBo1iq997WvZHHrvC0xrCFKjDWXGWixZJ2rOB6Z1/TUsmsX7Dc+fdE2M7veALw8Slemv67hmmbunGOyiplPJiJnJ9pTs/nVb1htPBTZWIXgLIL4eqn4BYxd3Pgh3HVM2TQG89KRQyCQtDLRelQOA3w87dpp+IiIiIiItZDUg//znP8/vfvc7FixYwK233sr48eNZvHgxZ555ZmOf7373u4TDYS688EJ27drFF7/4Rf761782JoQbMAJTIH8/qHsbnDqTxdzymFJfbkOpr/wZXds/DrDjPcxUuIuJwBsi8/qYmQEP+Jp19jScTwJRcH0N9b93mhlyqwCS2yAw3QTCu9NevXEwjz1lEF1t+nXm92t36fv8frnEXfqwYBC8XohEoSC/9flo1JwPBnt/bCIiIiLS52U1IAc47rjjOO6449o9b1kWt956K7feemsvjqoPsmwYfiVsWgDxzSbQdF1MiTEPeMfC8Cv2bBbYssETMPu03YR5fssyM+NNnWianW+YUndDUPd+QzBtmefxjjJ7vDsznvbqjafYAUhu79zy946Wvm9ZCCOuU1Au3ad8PIwfBytXQ/7o9HOuC1U7YMok009EREREpAWt4e1PCmfB6DsgeDh4R0LeEHMMHmHa9yTQLD0A8vJNIO4rAW+JOZYOgTzbLF1vXM6exDxoHqgnzDJx18l8P7unxMxiu5G2zzudXP7ecum7XWhuVtiF4B1nAvqqx5vVURfZQ7YNp51sZsA3boL6OtNeX2ceB4Mw9yQldBMRERGRNmV9hlwyVDgLCg7o/v3RQypg6Odh6/+DeK0Jzi0P+Bqe13XB9ZokbjiAbZbKN99zblk0Zll3wib4LThg92PzTzJLyiMrzJ7x5svWXbfj5e+OYxJmhUJQuA3sblz6LtIZMyrgyovhqWfhk02mLRQ2M+NzTzLnRURERETaoIC8P7Ls7g8oLRs+dzO8dhGE15ukbC4Q9Zo94rbHZFx36miKwpvNNFu+hiXnSXBrIWlD/bLOBb+WbfZ3b1loErh5yprVG99mbjq0tfx92XITBFWuN4m19qmFEzZBcT60Veovk6XvIpmYUQEV02DVGlj5IVx3DUyaoJlxEREREemQ/rUoTYYfAoc+DGPmQMEYCAyFeBngM3ncSGD2jXta/6wdMLPQVh6Qb5LNJbZDYmfnXrtwltnfHZgGTo0pdebUmJnxtvZ9L1sOi5aYvbvBIIwaaQL3ugR8Ugk1Na1fo7NL30W6wrab9oqXj1cwLiIiIiK7pRlySTf8EBj2Bdi5AmI7zV7yHddB5D8NHdwWR2gVoFsWuHkmUVtyV+dfu7PL8R3HzIyHQjBmdNPy9PrhEC6Fkm2w+VMTqKfO7W7pu4iIiIiISC9TQC6tWbbZUw4Q+Qh2JMG3d0N29zhpS9VT3GTD7DiYYD1hSqB5BmX+2rtb4r620ixTLy01j8NhSCQgLw8+mASfD4F/B4SroHDw7pe+i4iIiIiIZIECculYqiSZbxx49zZ7sJ2GbOjxjQ010B3McvaGuujEABvyhkLe4O4fUyhk9ozHorDhE6irA8cF24JPCiA8GaavgSHbIbrd1GwPTIehqkMuIiIiIiJ9hwJy6VjzkmR2IdjDTLvrmj3eyZ0mCHcdoB6wwSoCjxfyZ/TM8vBgEBJJWPexWb7u85n9uo4DtbWwKQLTaNjD62AywGdYik1ERERERKSHae2udCxVkiyxLb2+uGWBdwxggZ0P/vKGvvuAJwje4T23PHz8ODNDHo2BPwAeT0NZMw9MTsLXtsCIWigYBf6Jpgxa5AOTxT28tPvHIyIiIiIi0gUKyKVjqZJknhJTkiwZNjPiyTA4u8xS9vz9gSQ4teAmIL+dzOjdpXI9ePPA74NoFJLJhhn7BByyFYpc2BGAiGVqqduF4B1nlt9XPd4wmy8iIiIiIpJdWrIuu5cqSVb1c4iuMfvILZ/Zl116ducyo3enUMgkcNtnPHy6xSR1cxwYE4fRCYgVmhnzRLLpZyzL1DePru5cbXQREREREZEepoBcOmd3Jcl6M8ANBsHrhVisqc0FCpKQ50K9bRK85bUox2YHzM2EZHXvjVVERERERKQdCsj7Mtfp3Znn3elMSbLeUD4eBg+C9943M99+v0ngFgeiLiTrwF8CBQXpP+dEzMy+pyQboxYREREREUmjgLyvCi9tWiLuxkwg6Z9g9nOrdJfZM+5iZsJTtvhhiw/GRkxg3rJ/cptZZt8Tmd9FREREREQypKRufVF4qckIXr8CPMUmm7mnGCIrlCkcYG0l7Ko22daLiiCRgEgU4kn41yhIFkBRCMJVTQno4uvNzHhPZX4XERERERHJkGbI+xrXMTPjiWqTwdxqmAG2CsFbYALLqsfNfu6BGliGQqbs2aiRUDYU6upMUJ6XB4EC+PdnsM9yGFID8XB6AjqtLhARERERkT5CAXlfE1tjlql7y5qC8RRlCjdSSd0iUSjIh8LC9PMbgrCiAmaeAaOL+8b+exERERERkRYUkPc1yZqGPeOBts9nM1O445jl4qGQCYrLx5tkar2tfLxZrr5yNeSPTr9x4bpQtQOmTIKJR2ZnfCIiIiIiIp2ggLyv8RSbJdZuxCxTbylbmcKXLYennoXK9Wa5uNdrguLTToYZFb07Fts2r7toCWzcBKVDTKb1aNQE48EgzD1JwbiIiIiIiPRpilj6Gt8Ek009sc3M9jaXyhTun9i7mcKXLTfB78rVJtgdNdIcV6427cuW995YUmZUwJUXw+SJEKqFT7eY45RJpr23bxKIiIiIiIhkSDPkfY1lm9JmWxY2ZAYvM8vUnYgJxns7U7jjmJnxUAjGNFseXpBvlotv3ARP/w4qpvX+jPSMCvO6fWEZvYiIiIiISIYUkPdFhbNgxHVNdciT27OXKXxtpVmmXloKFhDYBp4IJAMQGWqWi6/72PSbWN5740qx7ey8roiIiIiIyB5SQN5XFc4ypc2iq0wCt2xlCk+VGBuyHcrehUAVWElwPRAphW37ww6P6SciIiIiIiKdpoC8L7Ps7Jc2CwZh6A4Y+Q54Y5AoBCcP7ATkb4WRr8CuA0w/ERERERER6TQF5NKxfcbB1A1AHUSHNu0hd3wQ9YK9HaZ+YvqJiIiIiIhIpyn7lXSs+kMYFodEEUSikEw2ZHtPmseJIhgWM/1ERERERESk0xSQS8diO8Fnw7h9oKgQEqlAPGkejxtvzsd2ZnukIiIiIiIi/YqWrOcS1+n+JHC+weDxgy/P1PyuqzPBeJ4HCgogEYaY3/QTERERERGRTlNAnivCS5vKpLkxUybNP8HUNN+TMmmDp8GgqVD1LgTLobCw6ZzrQv0WKD3A9BMREREREZFO05L1XBBeClsWQv0K8BSDd4w5RlaY9vDSrj+3ZcPUb5sZ8NBaiNeCkzTH0FrTPvWS3i/HJiIiIiIi0s8piurvXMfMjCeqwTcO7EKwPOboHWeWr1c9bvp11fBD4MC7ofRzENsFtR+bY+kBpn34Id30y4iIiIiIiAwcWrLe30VXmWXq3rKmkmQplgWeMoiuNv32pKb58ENg6Cz44AEIr4fCcbDvt8Hj27Pxi4iIiIiIDFBdCsjXrl3Lo48+ytq1a7nvvvsYNmwYzz//PHvttRfTpmkvca9KVjfsGQ+0fd4OQHK76bcnPnwY3r8dIp+Z2XbLhg/ug/2+D1Mv2rPnFhERERERGYAyXrL+6quvUlFRwVtvvcWzzz5LbW0tAMuWLeOmm27q9gHKbnhKTAI3N9L2eSdizntKuv4aHz4Mb19jErjZAfAWm2P9FtP+4cNdf24REREREZEBKuOA/Hvf+x633XYbL774Ij5f03LlI444gjfffLNbByed4J9ksqkntpms5825LiS3gX+i6dcVTsLMjDsx8JaYJeqWbY7eEtP+/h2mn4iIiIiIiHRaxgH58uXLOemkk1q1Dxs2jO3bt3fLoCQDlm1Km3lKIL4ekmFwk+YYX2/aS8/uehb09X8wy9Q9Be3sUS+AyFbTT0RERERERDot4yht0KBBfPrpp63a3333XUaPHt0tg5IMFc6CEddBYBo4NRDfaI6B6aZ9T+qQ1282e8btdtIN2HnmfP3mrr+GiIiIiIjIAJRxUrd58+Zx7bXX8swzz2BZFo7j8Nprr3HNNddw9tln98QYpTMKZ0HBASaberLazIz7J+15ffD8UeY5nETbGdWdhDmfP2rPXkdERERERGSAyThau/3225kyZQpjx46ltraWfffdly9/+csccsghXH/99T0xRuksyzalzQoPMsc9DcYBxp0IgWGQrGtnj3qdOT98CoTfgshHe1bzXEREREREZIDIaIbcdV22bNnC/fffz4033sjy5cupra3lc5/7HBMnTuypMUo22XmmtNnb10C82uwZt/PMzHiyDoIFMHlf2HRNQ/k1n0kyVzp/z5bKi4iIiIiI5LiMA/IJEyawYsUKJk6cyNixY3tqXNKXpOqMp+qQJxvqkA8ZAeNHQ14ErCITjONA/X9gy8I9378uIiIiIiKSwzIKyG3bZuLEiVRVVWlGfKCZehFMPBfe/l+oWQ/Fe8GoD6DuXXDCEP0McAAbrEJwo1D1uNnX3h1L50VERERERHJMxknd7rzzTr7zne/w0EMPMX369J4Yk/RFy5bDU89C5XqIx2FUJZz2AQQT4LEAH+ABkuCGIGlD/TKTZC4wJcuDFxERERER6XsyDsjPPvts6urqmDFjBj6fj/z8/LTzO3bs6LbBSZY4DqythFAIgkGoDcN9D0FNDXg8gAveevDVQRygEPJSH6U8cD3g1kFiOyR2Zu/3EBERERER6cMyDsgXL17cA8OQPqPlTHheHoRqoa4OIhFIJMAFSsNgJyFuQzIGRV7AMs9hWeDmmSRvyV1Z/GVERERERET6rowD8vnz5/fEOKQvWLYcFi0xM+OlpRDww85dsG2bSeQGZobctiDsgYQFHgfiCUgmwZP6OLlAAiwveAZl53dpj+t0f612ERERERGRLsg4IAdIJpP8/ve/58MPPwRg2rRpnHDCCXg8nm4dnPQixzEz46EQjBltZrkBbLspGLcs8xig3gs1eRCMgzcJbgLwgJsEYoANeUMhb3AWfpl2hJdC1c8hukYl2kREREREJOsyDsjXrFnDsccey6ZNm5g8eTIAd9xxB2PHjuW5556jvLy82wcpvWBtpVmmXlraFIwDhMNN37uu+bIs2OyD9X4od8wsuTduZp+xTQk0jxfyZ5gZ6L4gvNSUYktUg7cMrAC4EYisUIk2ERERERHJiozX6l522WWUl5fzySef8M477/DOO++wYcMGxo8fz2WXXdYTY5TeEAqZPeMBf8f9XLfhaMH/lcCuPKj3QKTMBN/+fcATBO9wKD27bywHdx0zM56oBt84sAvB8pijd5xZvl71eMMNBRERERERkd6R8Qz5q6++yptvvsmQIUMa20pLS7nzzjs59NBDu3Vw0ouCQfB6IRKFgmaZ89sK0FNB+WofPDUEjqqBMhucWrMMPH+6Ccb7yoxzdJVZpu4tS5/9B/PYUwbR1SrRJiIiIiIivSrjgNzv9xMKhVq119bW4vP5umVQkgXl42H8OFi5GvKb7SEfMgR8PojFGrKnp5atYxK8rcuHP4yBebeAVds3E6Ulqxv2jAfaPm8HILnd9BMREREREeklGUdNxx13HBdeeCFvvfUWruviui5vvvkm3/zmNznhhBN6YozSG2wbTjvZzJRv3GTKnCWTptRZUaHJrG5Z4PebGXSf3ySC8/vgnG9A0TQoPMjMMPelYBzMTQLLZ/aMt8WJmPOekt4dl4iIiIiIDGgZR073338/5eXlHHzwwQQCAQKBAIceeigTJkzgvvvu64kxSm+ZUQFXXgyTJ5ra459uMccDZ8JZp8GwMnCSEI2a47ChcMXFcMqJ2R55x/yTTDb1xLam5fYprgvJbeCf2HcS0ImIiIiIyICQ8ZL1QYMG8Yc//IE1a9Y0lj2bOnUqEyZM6PbBSRbMqIBpU+HVf8K27VA2FA77IuTlwSUXtt3e12t7W7YpbbZlIcTXmz3jdsDMjCe3mTH3lQR0IiIiIiIyYHSpDjnAhAkTFITnomXLTT3yyvUm67rXCy+/apazz6iAI/8rvX9/qe1dOMuUNkuNNbndjDXQxxLQiYiIiIjIgJFxQH7KKadw4IEHcu2116a1//CHP+Ttt9/mmWee6bbBSS9bthwWLTEl0EpLTYb1SNQkelu0xCxnn1HR1L+/1fYunAUFB/Tt2XwRERERERkwMo5E/vGPf3Dssce2av/qV7/KP/7xj24ZlHSR60DkIwi/ZY6Z1NV2HDMzHgrBmNEmcZttm+OY0ab96d+ZfqnX6o+1vS3bJJ7rqwnoRERERERkwMh4hry98mZer5eamppuGZR0wZ4uHV9baZapl5a2Xau7dAis+9j0m1iu2t4iIiIiIiJ7KOPpwYqKCp566qlW7b/+9a/Zd999u2VQkqHU0vH6FeApBu8Yc0wtHQ8v3f1zhEJmz3jAbzKPh8NQXW2OrmvKncXjph90rra3G1NtbxERERERkXZkPEN+ww03cPLJJ7N27VqOOOIIAF5++WV+9atfZbx//Oabb+aWW25Ja5s8eTIfffQRAJFIhKuvvppf//rXRKNRZs+ezZIlSxg+fHimw85dLZeOp2arrULwFpis4lWPm73THS3PDgZNArftVbBjp6lD7rim/nhBAQwZbM4Hg6Z/89reVmHr5+tvtb37eqZ4ERERERHJORkH5Mcffzy///3vuf322/nNb35Dfn4+++23Hy+99BKHHXZYxgOYNm0aL730UtOA8pqGdOWVV/Lcc8/xzDPPUFJSwre//W1OPvlkXnvttYxfJ2d119Lx8vEwqATeWw4eG3w+s4fccaC2Fqpr4HP7mX7QVNs7ssIE/s1fO1XbOzC9f9T27i+Z4kVEREREJKd0qezZnDlzmDNnTvcMIC+PESNGtGqvrq7mpz/9KU8++WTjTPyjjz7K1KlTefPNN/nCF77QLa/f73Vm6Xhye+eWjqeCatdNb3ddsFr2zZHa3v0tU7yIiIiIiOSMLtchB7Ok/KmnniIcDnP00UczceLEjJ9j9erVjBo1ikAgwMEHH8wdd9zBXnvtxb///W/i8ThHHXVUY98pU6aw11578cYbb7QbkEejUaLRaOPjVKK5eDxOPB7PeHy9JTW2jMfoFoFTCIkE2P7W55NRc94tMnvA27O2EkK1JmHbzl1QX2dmx20LhgyBwYPM+VVrmmbJfTNg6ALY8SRE14FbA5YX/DNgyOnmfB9+z3Ed+OyXEK0HXzm4FrgAfrCLIfYJfPYEjK7olhsLXb7G0m/oGuc+XePcpuub+3SNc5+uce7rL9e4s+OzXLfldGjbrrrqKuLxOD/60Y8AiMViHHjggXzwwQcUFBSQSCR48cUXOfjggzs9yOeff57a2lomT57Mp59+yi233MKmTZv4z3/+w5/+9CfOPffctOAa4MADD+Twww/nrrvuavM529qXDvDkk09SUFDQ6bGJiIiIiIiIdEVdXR1nnHEG1dXVFBcXt9uv0zPkL7zwArfffnvj4yeeeIINGzawevVq9tprL8477zxuu+02nnvuuU4P8qtf/Wrj9/vttx8HHXQQ48aN4+mnnyY/P7/Tz9PcggULuOqqqxof19TUMHbsWI455pgO34hsi8fjvPjiixx99NF4vd7MfrjuXdh6NyRrwFPabOl4lcm2PvwaKPhcx8+xthIW3g3BImjrva+vg1AYrrumaYa8v6tbCptvBe8oU0O9JTcJ8c0w6kYo2PNl63t0jaVf0DXOfbrGuU3XN/fpGuc+XePc11+ucWdLgnc6IN+wYUNaWbMXXniBr3/964wbNw6Ayy+/nGOPPTbDYaYbNGgQkyZNYs2aNRx99NHEYjF27drFoEGDGvts3bq1zT3nKX6/H7+/9dJtr9fbpy9YSpfGWXIg5H23KTGZ05CYrHCS2cfdmT3QkybA2NGwcjWMGd06SdvWbTBlkuln9/F94Z3lH2z+BNi1YLeRKT4ZNuf9DRnmu0l/+SxK1+ka5z5d49ym65v7dI1zn65x7uvr17izY+t0ZGXbNs1Xt7dMrDZo0CB27tyZwRBbq62tZe3atYwcOZKZM2fi9Xp5+eWXG8+vXLmSDRs2ZLQsfsAonAVj74Oxi2D07eY4dnHnE5LZNpx2silrtnGTKXuWTJrjxk2mfe5JuROMQ1Om+MS2thPZJbeBf2L/yBQvIiIiIiL9Tqejq6lTp/KnP/0JgBUrVrBhwwYOP/zwxvPr16/PuD74Nddcw6uvvsrHH3/M66+/zkknnYTH4+H000+npKSE888/n6uuuopXXnmFf//735x77rkcfPDByrDeHss2pc0KDzLHTBORzaiAKy+GyRNNArdPt5jjlEmmvWIarF4L77xnjo7TI79Gr0lliveUmEzxybBZpp4MN2SO7yeZ4kVEREREpF/q9JL17373u8ybN4/nnnuOFStWcOyxxzJ+fNNe4r/85S8ceOCBGb34xo0bOf3006mqqqKsrIwvfvGLvPnmm5SVlQGwaNEibNvmlFNOIRqNMnv2bJYsWZLRa0iGZlSYwHttJYRCZma8fDwsXwHX3WqWtEdj4PeZwH3eKeZn+qvCWaa0WWq5f3K7We4fmN755f4iIiIiIiJd0OmA/KSTTuIvf/kLf/7znznmmGO49NJL084XFBRw8cUXZ/Tiv/71rzs8HwgEePDBB3nwwQczet6c4TitA+PeWDJu26b8Wcqy5XDb3bB1KzgupjaYBdurYE0lXH9N/w/KCw6A6CpTr91TYpapa2ZcRERERER6UEZ1yI888kiOPPLINs/ddNNN3TIgabBsOTz1LFSuN7W8vV4YP87s8+7N4Ndx4Mc/M/vIPTb4fCZgdxyIxUz7w4/CA3f37/3lqeX+IiIiIiIivaQfR1A5bNlyWLTELA8PBmHUSHNcudq0L1vee2NZvRY+XGmyrgcC4PGY7z0e89iy4IOPTD8RERERERHpNAXkfY3jmJnxUMiUHyvINzPPBfnmcSgET/+u9xKqrVwNkajZM968FBqYx36fOb9yde+MR0REREREJEcoIO9rKtebr9LStgPg0iGw7mOzt1xERERERET6LQXkfU1trdkzHvC3fd7vN+dDod4Zz5RJZizRqMnl1pyLaQ/4TT8RERERERHptC4F5IlEgpdeeomHH36YUENguHnzZmpra7t1cANSUZFJ4BaJtn0+GjXng8HeGc+EfWDfhmRn9XWQTILrmmN9nWnfd4rpJyIiIiIiIp2WcUC+fv16KioqOPHEE7nkkkvYtm0bAHfddRfXXHNNtw9wwBk/znxVVZnAtznXhaodsM/epgRab7BtuOhcs3/d4zGZ1esj5ujxmPaLzu3fGdZFRERERESyIOMo6vLLL2fWrFns3LmT/Pz8xvaTTjqJl19+uVsHNyDZtiltFgyakmJ1DbPSdXXmcTAIc0/q3QB4RgVcdw0cejAMGwaDS8zxiweb9v5cg1xERERERCRLMqpDDvD//t//4/XXX8fn86W177333mzatKnbBjagzaiAKy9uqkO+Y6dZpj5lkgnGsxEAz6iAimkmmVwoZG4MlI/XzLiIiIiIiEgXZRyQO45DMpls1b5x40aCvbWveSDoiwGwbcPE8uy9voiIiIiISA7JOLo75phjWLx4ceNjy7Kora3lpptu4thjj+3OsUkqAD5gf3PUbLSIiIiIiEjOyHiG/J577mH27Nnsu+++RCIRzjjjDFavXs3QoUP51a9+1RNjFBEREREREck5GQfkY8aMYdmyZTz11FMsW7aM2tpazj//fM4888y0JG8iIiIiIiIi0r6MA3KAvLw8zjzzTM4888zuHo90huvAzhUQ2wm+wTB4Glhazi4iIiIiItKfZByQ33HHHQwfPpzzzjsvrf1nP/sZ27Zt49prr+22wUkbtr4OHz4Auz6EZBQ8fhg0FaZ+G4Yfku3RiYiIiIiISCdlPK368MMPM2XKlFbt06ZN48c//nG3DErasfV1+Nc1UPUO+AZBcG9zrHrXtG99PcsDFBERERERkc7KOCDfsmULI0eObNVeVlbGp59+2i2Dkja4jpkZj+2AonKIWVATNseifczy9Q8fNP1ERERERESkz8t4yfrYsWN57bXXGD9+fFr7a6+9xqhRo7ptYNLCzhUNy9SDsGoN1NWD45hSaAX5MLwYdn1g+g2pyPZoRUREREREZDcyDsgvuOACrrjiCuLxOEcccQQAL7/8Mt/97ne5+uqru32A0iC2E+qqYVMSEknwecH2gZOE2jBE6mF0nuknIiIiIiIifV7GAfl3vvMdqqqquPjii4nFYgAEAgGuvfZaFixY0O0DlAbeEqiuN0vSA0GwGto9Hgh4IB6C6rjp11McB9ZWQigEwSCUjzcz9CIiIiIiIpKxjANyy7K46667uOGGG/jwww/Jz89n4sSJ+P3+nhifpFQVwI58KNkFCZemiBywXCiIwY4hpl9pD7z+suXw1LNQuR7icfB6Yfw4OO1kmKEl8iIiIiIiIpnq8vRmUVERn//855k+fbqC8d5QG4bKSeDmg28X2DHAMUffLnAKoHKi6dfdli2HRUtg5WozMz5qpDmuXG3aly3v/tcUERERERHJcRnPkIfDYe68805efvllPvvsMxwnPav3unXrum1wA57rQHQVJKuhsAbCI6GyGEa/D6OrwR+DqAWbhsGm6RAOmkC5OzmOmRkPhWD0KKivN9/n5ZnHmzbD07+DimnZWb7e/D3ylIB/ElhaRi8iIiIiIn1fxgH5f//3f/Pqq6/yjW98g5EjR2JZ1u5/SDIXXgpVP4foGnBjJoHbGRHYGIUZQL7fLFV3LdjLhb/vgPwKs6+7O62tNMvU/X5YuQbCtU3Z3QuLoHQwrPvY9JtY3r2vvTst3yPLB/4JUDofCmf17lhEREREREQylHFA/vzzz/Pcc89x6KGH9sR4BEyguWUhJKrBWwZWANwIjNsMe+1K72u5UFgPs9dB3uzun6UOhczXrmqIxtLPxXaYAH1QienTm9p7jyIrTPuI6xSUi4iIiIhIn5Zx9DZ48GCGDBnSE2MRMEuwq35uAk3fOLALwfKAVQBWrcnlZgFOsy8s8DrgfQqcRPeOp6gIqmugPmIe2zZ4bHN0gUjEnC8q6t7X7Uh775FdCN5xZvl61eOmn4iIiIiISB+VcUD+gx/8gBtvvJG6urqeGI/E1pgl2N4yaL4dIPEZkGgKyPM84Mkze7ltj9k3ndwBNX/r3vE4DsRi5jVtu2FMljmmZuNjMdOvt0RXtf0egXnsKYPoatNPRERERESkj8p4yfo999zD2rVrGT58OHvvvTderzft/DvvvNNtgxuQkjUN+6ED4LrghMGNQ6Kqdd+0YNQGElC/HAbN6b7xrFpjZsJtu2nveErqsdvQb8qk7nvdjiSrm96jttgBSG43/URERERERPqojAPyr33taz0wDGnkKTbJyRLbILHdBOQ4bSxFb5lMr2GG2i7o/jHZtqk7nohDMmkC8NQsfZ7X1CXvTZ4S8x65EbAKW593Iua8p6R3xyUiIiIiIpKBjAPym266qSfGISm+CeAZAnVvAx4TWOIBK2ZmhdvlAF4IHtm945k8EQJ+iMWhoAAc1+zNtmywLbO3POA3/XqLf5LJph5ZAd6C9JUCrgvJbRCYbvqJiIiIiIj0USrY3Ke55mBhkpalzYo7zY5xcy6wL+RP7d4hTCyHqZNNoBuNNsyM55ljNGra953SuyXPLNuUNvOUQHw9JMPgJs0xvt60l56teuQiIiIiItKnZRyxJJNJ7r77bg488EBGjBjBkCFD0r5kD8XWmORs/glm+ToJcOvN0TMYSO3ZTwXiDpAH3jEw6sbuD0JtG755HowZbb6PxUxm9VjMPB4zGi46t/vLre1O4SxT2iwwDZwaiG80x8B0lTwTEREREZF+IeMl67fccgv/+7//y9VXX83111/Pddddx8cff8zvf/97brzxxp4Y48CSSurmHQPe4U1J3SyvKeuV2GkyiFv5gGsSmBUcYGaMeyoInVEB118Dv/4trFxt6pH7fSaJ22knm/PZUDjL/O7RVSaBm6fELFPXzLiIiIiIiPQDGQfkTzzxBD/5yU+YM2cON998M6effjrl5eXst99+vPnmm1x22WU9Mc6BI5XULZWwzG5R39vyg28vGHYZ5A3pvSB0RgVUTIO1lRAKQTAI5eN7f2a8JcuGwJTsjkFERERERKQLMg7It2zZQkWFmREtKiqiutqUljruuOO44YYbund0A5FvQucSlhXP7v2ZYNvu3b3iIiIiIiIiOSzjiG7MmDF8+umnAJSXl/PCCy8A8Pbbb+P3+7t3dANR84RlsfUN5c+qzDGmhGUiIiIiIiK5IuOo7qSTTuLll18G4NJLL+WGG25g4sSJnH322Zx33nndPsABqXAWDDrFLFuv/wjq/2OObsS0K2GZiIiIiIhIv5fxkvU777yz8fvTTjuNvfbaizfeeIOJEydy/PHHd+vgBqzwUqj6OSR20XTPxDaPq35u9kwrKBcREREREenXMg7IWzr44IM5+OCDu2MsAuA6sHWRWZ7u2uAJAB4gCU7UtG9dDOMfH1jL1h2n7yWUExERERER2QNdCsg3b97MP//5Tz777DMcx0k7pyzreyi6CurfB9cCTwGQSuqWB7bHlEGrXwaRjyB/32yOtPcsWw5PPQuV6yEeB68Xxo/Lbsk1ERERERGRPZRxQP7YY49x0UUX4fP5KC0txWqWBdyyLAXkXeE6JhAHCP0dnHqwmwfjKRbgB7feZGEfCAH5suWwaImZGS8thYAfIlFTD33RErjyYgXlIiIiIiLSL2UckN9www3ceOONLFiwAFtLhvdcar943XrgfNj5GyABbhIsb+v+lgVubw8ySxzHzIyHQjBmdFMJuIJ8yB8NGzfB078z9dH1WRQRERERkX4m4yimrq6OefPmKRjvDuGlsGUh1K8AT9C0eYYAFrhhcGLp/V3XZFq3AhCY1uvD7XVrK80y9dLS9HrsYB6XDoF1H5t+IiIiIiIi/UzGUfX555/PM8880xNjGVhcpyGTejX4xoFdaNq9Q8EubugTBifeEIgnzFJ1XMifYTKt57pQyOwZD7RT397vN+dDod4dl4iItOI4DqvXrOWdd95j9Zq1rXLMiIiISGsZL1m/4447OO644/jrX/9KRUUFXm/6sup777232waX06KrILoGvGUtZn8tCJRD/QdmNtyNAvGGUzZ4x8DwKwZGhvVg0CRwi0TNMvWWolFzPhjs/bGJiEijZcuW89TTz1L58XrisThen5fxe4/jtLknM0N5PkRERNrVpYD8b3/7G5MnTwZoldRNOilZDW7MLD9vyTPIzIBHV4MdNMG35YfAdBg6HwoOMFnWk9XgKQH/pNwM0MvHm2zqK1ebPePNP1+uC1U7YMok009ERLJi2bLlLLpvCaGaEKWlpQQCfiKRKCtXrWbRfUu48vKLFZSLiIi0I+OA/J577uFnP/sZ55xzTg8MZwDxlIDla9gTXtj6vOUH314w7DLIG9IUeNe9A59cbmbX3Zh5Dv8EKJ0PhbN6dsypbPC9dSPAtk1ps0VLTAK30iFmmXo0aoLxYBDmnqSEbiIiWeI4Dk89/SyhmhBjxoxuvDFfUJBPfv5oNm7axNPP/I6KimnKPSMiItKGjANyv9/PoYce2hNjGVj8k0wgHVkB3oL0c64LyW1mRrx4dlPQm0oCl6huWOoeMAF9ZIVpH3FdzwXlqWzwvX0jYEaFKW2WqkO+Y6dZpj5lkgnGNesiIpI1a9dVUvnx+lZlUMGsmisdMoR1lR+zdl0lEyeUZ2mUIiIifVfGAfnll1/Oj370I+6///6eGM/AYdkmmN2yEOLrgZGmPRmG5KdmBrr07KZgvGUSuNQ/fKxCE9DH10PV42Y5e3fPWmfzRgCYoLtimsmmHgqZmfHy8ZoZFxHJslBNiHgsTqCd5Jt+v58dO3YSqlHyTRERkbZkHJD/61//4v/+7//485//zLRp01oldXv22We7bXA5r3CWCWYb65ADTggKpptgvPle8cQOiKxuIwkc5rGnzOw5j67q3gzs2bwR0Jxtw0TNroiI9CXB4iBen5dIJEpBG8k3o9EoXp+XYLGSb4qIiLQl44B80KBBnHzyyT0xloGpcJYJZsMfwso1MOYOKJzaeq+4m4DEdrADTSXSmrMDkNxugvfu1DwbPEA4DIkk5HmgoKDnbgSIiEifV77PeMbvPY6Vq1aTnz86bdm667pU7djBlMmTKN9HyTdFRETaklFAnkgkOPzwwznmmGMYMWJET41p4LFss6ecNU2J21ouEU/ugPhmE/xak8HKAzcOltcE6E7E7Ov2lHTv2FLZ4GvjsHk11NaC44JtQVERjBoGgVj33wgQEZE+z7ZtTpt7MovuW8LGTZsoHTIEv99PNBqlascOgsEgc089SQndRERE2pFRQJ6Xl8c3v/lNPvzww54aj7S3RDxvKNiDTGBe9x8TkFsuYINVALYXCmY1BPbdyFMCEQc+Xgu1CZNwLiW2E2Ih2HtI998IEBGRfmHGjAquvPzixjrkO3bsxOvzMmXyJOaeepJKnomIiHQg4yXrBx54IO+++y7jxo3rifFIbE3TEvG0veIW5A022ddJAB4gAMTB2QmuDwoP7P593N4JsDoGwTCEvOnP7zoQCMPqIEyb0L2vKyIi/caMGRVUVExj7bpKQjUhgsVByvcZr5lxERGR3cg4IL/44ou5+uqr2bhxIzNnzqSwMH0/83777ddtgxuQkjUNZcUCLU64kNwF+DABuQtEABvsIWDnQfhfMOSM7g3K11TCn71wog1D41BtQ8wCnwslDtTZ8Oc8mF4Jkyd23+uKiEi/Ytu2SpuJiIhkKOOAfN68eQBcdtlljW2WZeG6LpZlkUwmu290A5Gn2OwFdyMmk3mKEzZftg/wgm+82T/euIe8rmeSq61cDR/YUD8EjqyBUXETiCcsWO+D/yuGStv0U0AuIiIiIiLSaRkH5JWVlT0xDknxTQD/BFPj21vQtGzdjYPbcLPDUwx5LZa091SWddcFJwlrfLB+BIyKQUHDzPhmH8QTYCXT95aLiIiIiIjIbmUckGvveA+zbCidb7Ksx9ebsmJ2wJQ9I2lmz31jWtci76ks6wUFQMNrOcBGnwm+G1/fMl8FBd37urnGcWBtw82stZUwaYKprS4iIiIiIgNWlyKCtWvXcumll3LUUUdx1FFHcdlll7F27do9Gsidd96JZVlcccUVjW2RSIRLLrmE0tJSioqKOOWUU9i6desevU6/UDgLRlwHgWng1EB8ownI88pMwG23CLpd1yR780/s/izrg0vA5zXfJxLmK5ls+h7M+cHKst6uZcvh+h/AwrvN44V3m8fLlmd3XCIiIiIiklUZB+R/+9vf2HffffnXv/7Ffvvtx3777cdbb73FtGnTePHFF7s0iLfffpuHH364VUK4K6+8kj/96U8888wzvPrqq2zevJmTTz65S6/R7xTOgrH3wdhFMPp2cxyzGLzDzcx5MmyWsCfDDTPpJVB6dvdnWS8pgeLijvsUF5t+0tqy5bBoidljHywybcEi83jREgXlIiIiIiIDWMZL1r/3ve9x5ZVXcuedd7Zqv/baazn66KMzer7a2lrOPPNMfvKTn3Dbbbc1tldXV/PTn/6UJ598kiOOOAKARx99lKlTp/Lmm2/yhS98IdOh9z+W3TpBm3WdqVMeXWP2jFs+CEw3wXjBAbBjuakP7hsMg6fteYA+fpxZWm1Zbe8Ttyzw2KafpHMceOpZCIVgzGjIa/jjlp9vHm/cBE//Diqmafm6iIiIiMgAlHFA/uGHH/L000+3aj/vvPNYvHhxxgO45JJLmDNnDkcddVRaQP7vf/+beDzOUUcd1dg2ZcoU9tprL9544412A/JoNEo0Gm18XFNTA0A8Hicej2c8vt6SGttux+ibASPuNvXKkzUmwZtvAmx7G/51DlSvgmQUPH4omQSTLoRhB3V9YGsrTcDt9bYfkNs2rFkH5eO7/jq5aG0lfLIJhg+DvDziHhN0m2MeDC+DDRth1Rq9dzmi03+Opd/SNc5tur65T9c49+ka577+co07O76MA/KysjLee+89Jk5ML3H13nvvMWzYsIye69e//jXvvPMOb7/9dqtzW7ZswefzMWjQoLT24cOHs2XLlnaf84477uCWW25p1f7CCy9Q0A8Sj2W+7P8zYE3D96eYHGupq1oDLK0C/rJngzr/jN33Wfmh+ZJ0X5/TqunFQ2elN+i9yzld3b4j/YeucW7T9c19usa5T9c49/X1a1xXV9epfhkH5BdccAEXXngh69at45BDDgHgtdde46677uKqq67q9PN88sknXH755bz44osEAoFMh9GuBQsWpI2jpqaGsWPHcswxx1C8u73QWRSPx3nxxRc5+uij8Xq9nf9B14HXLoAdy6Bon/Ts664LtZUwZAYc+kjXlq+/8g+45U5wMTPlWM2yrLuQdMxNgJu+B4d/OfPnz2VrK00Ct2AR5OcT99i8eOgsjn5tKd6kA/V1EArDddfk3gy540DleqithaKipq0POa7Lf46l39A1zm26vrlP1zj36Rrnvv5yjVMrtXcn44D8hhtuIBgMcs8997BgwQIARo0axc0338xll13W6ef597//zWeffcYBBxzQ2JZMJvnHP/7BAw88wN/+9jdisRi7du1KmyXfunUrI0aMaPd5/X4/fr+/VbvX6+3TFywl43HuWA7VyyF/CFhxqKuDRBLyPKYUWf5gqH4falfBkIrMB1QfgWjMfO/xmGPzsmfJZFO/fvD+9qpJE2DsaJPAbcxoUn/cvEkHbyIBW7fBlEm5VwJt2XKzd75yPcTj5nMxfhycdjLM6MJnsB/qL3/fSNfpGuc2Xd/cp2uc+3SNc19fv8adHVunooA//vGPjWvgLcviyiuvZOPGjVRXV1NdXc3GjRu5/PLLsVrWxu7AkUceyfLly3nvvfcav2bNmsWZZ57Z+L3X6+Xll19u/JmVK1eyYcMGDj744E6/Ts6L7TR7xusSJvBbuQZWrzXHlauhLm7Ox3Z27fktqyEQd9sue+a65nwG137AsG0ThAaDJoFbfcOylfo68zgYhLkn5V4w3phVPgijRpqjssqLiIiIiLTSqRnyk046iS1btlBWVobH4+HTTz9l2LBhBIPBLr9wMBhk+vTpaW2FhYWUlpY2tp9//vlcddVVDBkyhOLiYi699FIOPvjg3Muw7joQXWW+j66CvKmdX17uGwwxBzavg5hlaoLbPnCSUBuG9bUwKmj6dcWUSeY56xIdjMFr+klrMyrgyovNjPEnm0xbKGzer7kn5daMccus8qmbNAX5kK+s8iIiIiIiLXUqIC8rK+PNN9/k+OOPx3XdjGbC98SiRYuwbZtTTjmFaDTK7NmzWbJkSa+8dq8JLzVlzOrWA+fDxgVQMA5K55ta5LtTMhU+80JeLdhDzZ7uRMIEQwE/2Nvhs1LTr7Mcx+x/DoVMia5AAOrrASs9kHIcwDV99tk7s997IJlRAdOmwt//H4RrYP7p8F9faiqDlivWVppl6qWlrVdMWBaUDoF1H5t+E8uzMkQRERERkb6kUxHBN7/5TU488UQsy8KyrA73cCdTe4q74O9//3va40AgwIMPPsiDDz7Y5efs08JLYctCSFSDZ6Rp8wQhssK0j7hu90H5uvXw4V4waSvYn0HUCwkb8hwojEMyH1aNNf06EwS13P/rOBBp2B+eTKaXPrMt8PrMXvXKTj7/QJR6Tz/ZZLKu//xX8H//yL091aGQ+cwEWudwAMDvhx07TT8REREREelcQH7zzTczb9481qxZwwknnMCjjz7aqhyZZMh1zMx4ohq8e0GiIXGa60LeXpDYAFWPQ8EBHS9fD4VgYwFs3wcmfwyD66EgDkkbPiuCVeMgUtC5ICi1/zcUMrOcAT9sqzJBVl6eCbyjUROk27bJnj1imFkaryCrbc3f0+ENZQGDRU17qq+8OHeC8mDQ3LiJRM0y9ZaiUXN+D7a6iIiIiIjkkk6vmZ0yZQqTJ09m/vz5nHLKKRQVFfXkuHJfdBVE14AdgOgHZpk5QPQjSOaBZyhEV5t+gSntP09RkQmIdxTBrs9DsBq8MYj7IFQCdfXgC5t+HWlv/29RAfh8Znxer1mankg0Bej19eb1FGS11vI9TS1Rz883j3NtT3X5eJNNfeVqs2e8ZQm+qh1m73yulXgTEREREemijKIA13V54okn+PTTT3tqPANHstp8xT4BJ0TTvZE88zj+SVOfjqSWkLuuCcw32VAZMMfacPr5jrS3/7egwHxhQThs2kpKoLDQfF+1wwTpPR1kOY7JHv/Oe+boOD37et0hkz3VuaBlVvm6OrPNoS6Hs8qLiIiIiOyBjLJK2bbNxIkTqaqqYuLEiT01poHBDkIyBG4MrEKgocY3HiAf3LA5b+9m5jkcNlnOw3Um6LZtc5vFxdQjtywIepuC6fa0t//XsmDUiIZ65BET5AcCZvlx1Y7eCbL6a13rgbinunlW+cr15vfzenMzq7yIiIiIyB7KOM3znXfeyXe+8x0eeuihVmXLpItaJq23MAF1ZxQVQSwOHtv8oJMEp+E58vLME8Xiu1+y3tH+3+JiGD0SNn8K0Rh8uqX3gqy29rVHov1jD/ZA3VM9o8Isw09l6g8GzQoKzYyLiIiIiKTJOCA/++yzqaurY8aMGfh8PvLz0wONHTt2dNvgcpoTMhnVEzXg1tN0KZINj73mvLOb2dPUUnTbA/kBcFyTMM6yTRb0+kh6v/bsbv9vNAqHHATnnAm1tb0TZPX3utYt39Pmcn1PtW0r676IiIiIyG5kHJAvXry4B4YxAHlKGr4GQWI7OA1J3UiYZep5QwHX9OlIOAxFhWYpeTRmlq978kwwm3pcVLj7Jeup/b+LlphAt3SIWVLdfGn6aSfDpAnd8Mt3Un+va93yPR1eZtrr62DrNu2pFhEREREZ4DIOyOfPn98T4xh4/JPAP8HUHA/s21T2LDAF8nwQ3wCB6aZfR4JB81VcbPbr1tWZWXLbMoH4kMFmNrYzy6L72v7fXNiD3fw9/WSTaQuFtadaREREREQyD8gB1q5dy6OPPsratWu57777GDZsGM8//zx77bUX06ZN6+4x5ibLhtL5sGWhCb4Zadpdyzz2lEDp2R3XIIf0ZdGTJpgyZKmyZPn5sGlzZsui+9L+31zZg516T1etgZUfwnXXmGulmXERERERkQEt44jg1VdfpaKigrfeeotnn32W2tpaAJYtW8ZNN93U7QPMaYWzYMR1EJjWtFfcCZmZ8RHXmfO707zU1KbNZil3UZE5btrctWXRqf2/B+xvjtkKHFM3G6qqWu+BT+3B7o2Sa93BtpvGqQRnIiIiIiJCFwLy733ve9x22228+OKL+Hy+xvYjjjiCN998s1sHNyAUzoKx98GYO8zjMXfA2MWdC8ZTUsuiJ0+EUK3Jgh6qNTPjfTkL+e6orrWIiIiIiOSwjJesL1++nCeffLJV+7Bhw9i+fXu3DGrAseyGveJrzHF3y9TbMqMCpk2FV/8J27ZD2VA47IsNpc/6sb62r11ERERERKSbZBytDRo0iE8//ZTx49OXCb/77ruMHj26nZ+SbuU4rfd4L1/RFLTG4yZofflVM8Pc34PWvrSvXUREREREpJtkHJDPmzePa6+9lmeeeQbLsnAch9dee41rrrmGs88+uyfGKM0tW9468B5UYvZTJ5OmRFjAbxKhrVxtSm5lumy9rYA/28Gv6lqLiIiIiEiOyTggv/3227nkkksYO3YsyWSSfffdl2QyyRlnnMH111/fE2OUlGXLTYAdCjUF3vURWPEhxBMwZWJTNvKCfMgfbfZaP/07M8PcmaC6rYB//LjcmGkXERERERHpQzKe9vT5fPzkJz9h3bp1/PnPf+aXv/wlH330Eb/4xS/weDw9MUYBM2v91LMmGB8z2gTctg00ZB+3gE+3pmcjtywoHQLrPjYz3ruTCvhXrjYz46NGmmNqpn3Z8h74xURERERERAamTs+QO47D//zP//DHP/6RWCzGkUceyU033UR+fhv1oaX7ra00s9alpSbQTkkkwHHB7zfZx+vqoLCw6bzfbxKhhUIdP3/LgD/1Gl2daRcREREREZEOdTqyWrhwId///vcpKipi9OjR3HfffVxyySU9OTZxHFi9Ft55Dz5cCbGYWabeXF4e2A3Bs+OaAL25aNQsOw8GO36t9gJ+yHymXURERERERHar0zPkjz/+OEuWLOGiiy4C4KWXXmLOnDn87//+L7ZmTLtfy73cjgs7dpgZ72FlTf0KCsxXTQg8dnqZM9c1yd6mTDKJ2ToSCpnXaRnwp3R2pr2n9MVEcyIiIiIiInug0wH5hg0bOPbYYxsfH3XUUViWxebNmxkzZkyPDG7Aai952/btJkD3+6Gk2PS1LBg5AqprGma2LZNtPRo1wXgwaOp17y54DQbNTHok2pQYrrnOzrT3BCWaExERERGRHNTpKcZEIkEgEEhr83q9xOPxbh/UgNZ8L/foUeA6DbPSLuwz3iRvW7sOwmETeNfVQU2N2fc9fV+orYVPt0Co1syMd7bkWfl4E+RWVaUnhoOmmfZ99t79THt3U6I5ERERERHJUZ2eIXddl3POOQe/v2lJcyQS4Zvf/CaFzZKIPfvss907woGmcn3TLPiqNSbgdlyzT7ygAIYPg527TIBs22a2eMokMwteMa3ry7pt28w4L1piEriVDjFjyHSmvTsp0ZyIiIiIiOSwTgfk8+fPb9V21llndetgBDPDHQqZGe5kEnw+E2w6jjlXXw/BIjjnTBgzqnXgPbG86689o8LMqKeWh+/YmR7w9/by8EwSze3J7y0iIiIiIpIFnQ7IH3300Z4ch6QUFkJt2OyVLiiApGMyp1s2BAJmxrw2bILkSRO6//VnVOzZTHt36uuJ5kREcpjjOKxdV0moJkSwOEj5PuOVxFVERKSbdTogl16S2r/tumZG3HHAxewdt20zM2y5kFwL4SrwlIB/kgnYu4tt940Z576caE5EJIctW7acp55+lsqP1xOPxfH6vIzfexynzT2ZGUqmKSIi0m10q7uvqasDnxcSSfOF1VBn3DKPx4XhnM0QuwM2fR8+uRI+uRzCS7M98u7XVxPNiYjksGXLlrPoviWsXLmaYFGQUaNGEiwKsnLVahbdt4RlSqYpIiLSbRSQ9zUFBRCLg8fTUFPcNUndcGFyHObtgFF1YBeDdwx4iiGyArYszL2gPJVoLhg0Cdzq6poyy2/clJ1EcyIiOcxxHJ56+llCNSHGjBlNQUE+tm1TUJDPmNGjCYVCPP3M73AcJ9tDFRERyQmKZPqaVPIy2zL7yQuLoLAAigrh6DAUOLDNC3Y+WB6wC8E7DpLVUPW4KZOWS1KJ5iZPNInuulLSTUREOmXtukoqP15PaWkpVotkmpZlUTpkCOsqP2btusosjVBERCS3aA95XxMOm+A75EI0YrKse/JgeB2U1UONF/IalrSnWBZ4yiC6GqKrIDAle+PvCX0p0ZyISA4L1YSIx+IE2kmm6ff72bFjJ6EaJdMUERHpDl0KyFevXs0rr7zCZ5991mrZ2o033tgtAxuwiopMwFlcbPZIh8Mm07o3An4LYvlm9jzPk/5zdgCS281MeS7qK4nmRERyWLA4iNfnJRKJUtBGMs1oNIrX5yVYrGSaIiIi3SHjgPwnP/kJ3/rWtxg6dCgjRoxIW9JmWZYC8q5wHDP7m/p+73Hw/n+azltAnQcSFlgxyB9k9pqnPUcELJ/Jui4iItIF5fuMZ/ze41i5ajX5+aPT/h/vui5VO3YwZfIkyvdRMk0REZHukHFAftttt7Fw4UKuvfbanhjPwLNsOTz1LHyyCb4+B+6412QQ37kLnASMB4qAWgs22rBX1MygN9/b57qQ3AaB6aYEmoiISBfYts1pc09m0X1L2LhpE6VDhuD3+4lGo1Tt2EEwGGTuqSepHrmIiEg3yTgg37lzJ6eeempPjGXgWbYcFi0x+6KHDzNtRYWw4kMYH4ajQzC6HrwuxC2o9ptEbslNkAyaxG5OxATjnhIoPbt765GLiMiAM2NGBVdefnFjHfIdO3bi9XmZMnkSc089SXXIRUREulHGAfmpp57KCy+8wDe/+c2eGM/A4ThmZjwUgjGjG0qcAbgwvg7mbYPSBHispvbBEaj3QZUNg7aDzzLL1APTTTBeOCtbv42IiOSQGTMqqKiYxtp1lYRqQgSLg5TvM14z4yIiIt0s44B8woQJ3HDDDbz55ptUVFTg9XrTzl922WXdNrictrYSKtdDaWn68vNEAo7dBsPi4ABJD2CB5YLtQFEEtnshdimMH2Vmxv2TNDMuIiLdyrZtJk5QMk0REZGelHFA/sgjj1BUVMSrr77Kq6++mnbOsiwF5J0VCkE8Di1Lywyrg3ERcIGYZWbIbQtcC5IWWAkYXWuSuhUelJWhi4iIiIiIyJ7LOCCvrKzsiXEMPMEgeL0QiULz0jKjIuBzIQpmZrz5D1kQsyHgQNmuXh2uiIiIiIiIdC+tc86W8vEwfhxUVZks6c1ZVvpjl4ZM6o65YrbH1OUWERERERGRfivjGXKAjRs38sc//pENGzYQi8XSzt17773dMrCcZ9tw2skmy/rGTTC8zLR/mm8yqvtcSHj+P3t3Hh9FfT9+/DWz9yabhIQ7HDnIgRgD6K+KWgG1Sj2qiIJWRYVqWy+KYlvvW2uLIljRagVslSoUrK3fHlIVFO+ChCgYkhCuQALJhmQ3mz1nfn8su2ZJAgkkZBPez8cDYWcmM59kkrjveX8+7zdoOuGIHDAYwtlxgxWso7pt6EIIIYQQQgghjl6HA/L33nuPH/3oR2RlZfHtt99y4oknsm3bNnRdZ+zYsV0xxt6rsABm3/xdH3KA7SrsHwADa8BsAEzh9eOKDgTC2XNbIVjzu3PkQgghhBBCCCGOUofnPd99993MmTOH4uJirFYrK1asYOfOnYwfP176kx+JwgJ47H64d0749b13wekLwJoRnpquBEH1H/jbAObhMOAXUlVdCCGEEEIIIXq4Dkd1mzdvZvr06QAYjUaamppITEzkkUce4amnnur0AR6X7CdD+pPgmAimQWBMDf/tODu8XfqNCyGEEEIIIUSP1+Ep6wkJCdF144MGDaK8vJxRo8LrmWtqajp3dMeDouLvpqxffiE8PheGpofXl5+0AHxbIFQv/caFEEIIIYQQopfpcEB+2mmnsXbtWkaOHMkFF1zAnXfeSXFxMStXruS0007rijH2XkXF4aJuLhcM6B/e5kiEktLw9tk3h6e0CyGEEEIIIYTodTockD/zzDO43W4AHn74YdxuN2+++SY5OTlSYb0jNC2cGXe5YEg6GA/cCpst/HpXJSx7CwpGSYszIYQQQgghhOiFOhyQZ2VlRf+dkJDAiy++2KkDOm6UV0DFdkhLa9l3XFEgLRW2bgsfl5PdLUMUQgghhBBCCNF1JPXaXVwuCATAaml9v8US3u9yHdtxCSGEEEIIIYQ4JtqVIU9NTWXLli307duXPn36oByc0W3G6XR22uB6NYcDTCbw+sBua7nf5wvvdziO/diEEEIIIYQQQnS5dgXk8+bNw3EgMHz22We7cjzHj+xMyBweLuBmS4/dp+tQ64T83PBxQgghhBBCCCF6nXYF5Nddd12r/xZHQVXDrc3mLQwXcBvQL7y9yQPV+8KZ8amTpaCbEEIIIYQQQvRS7QrIGxoa2n3CpKSkIx7McaewINzaLNKHHMDVGM6MT50sLc+EOARN0yjfWoGrwYUjyUF2ViaqPMASQgghhBA9SLsC8pSUlEOuG28uFAod1YCOO4UF4dZmW8qgZDPcOwdyR0hmXIhDKCoq5s1lK6nYtp2AP4DJbCIzYzjTpl5GoTzIEkIIIYQQPUS7AvIPPvgg+u9t27bx61//muuvv55x48YB8Omnn/Lqq6/y5JNPds0ohRDigKKiYubNX4irwUVaWhpWqwWv10fJllLmzV/I7Fk3S1AuhBBCCCF6hHYF5OPHj4/++5FHHuGZZ57hqquuim770Y9+REFBAS+99JKsMe+oouLvpqxffiE8PheGpofXl0tQIUQMTdN4c9lKXA0uhgxJj87csdtt2Gzp7KqsZNnytygoGCXT14UQQgghRNzr8DvWTz/9lFNOOaXF9lNOOYUvvviiUwZ13CgqDhd1KykFR2J4myMx/HrewvB+IURU+dYKKrZtJy0trcUyGkVRSEtNZWvFNsq3VnTTCIUQQgghhGi/DgfkQ4cO5eWXX26x/Y9//CNDhw7tlEEdFzQtnBl3uWBIOtgO9CK32cKvXS5Y9lb4OCEEAK4GFwF/AKvV0up+i8VCwB/A1eA6xiMTQgghhBCi49o1Zb25efPmMWXKFP71r39x6qmnAvDFF19QWlrKihUrOn2AvVZ5BVRsh7Q0OLhgnqJAWips3RY+Lie7W4YoRLxxJDkwmU14vT7sdluL/T6fD5PZhCPJ0Q2jE0IIIYQQomM6nCG/4IIL2LJlCxdffDFOpxOn08nFF1/Mli1buOCCC7pijL2TywWBALSR6cNiCe93SaZPiIjsrEwyM4ZT66xF1/WYfbquU+t0kpWZQXZWZjeNUAghhBBCiPbrcIYcwtPWn3jiic4ey/HF4QCTCbw+aCXTh88X3u+QTJ8QEaqqMm3qZcybv5BdlZWkpaZisVjw+XzUOp04HA6mXjFZCroJIYQQQoge4YjetX700Udcc801nH766VRWVgLw5z//mbVr13boPC+88AInnXQSSUlJJCUlMW7cOP71r39F93u9Xm655RbS0tJITExkypQpVFdXH8mQ4092JmQOh9paOCjTh65DrROyMsLHCSGiCgsLmD3rZvJyc3C53ezZU4XL7SY/L1dangkhhBBCiB6lwxnyFStWcO2113L11Vezfv16fD4fAPX19TzxxBP885//bPe5hgwZwm9+8xtycnLQdZ1XX32VSy65hK+++opRo0Yxe/Zs/u///o/ly5eTnJzMrbfeymWXXcbHH3/c0WHHH1UNtzabtxB2VcKAfuHtTR6o3hfOjE+dHD5OCBGjsLCAgoJRlG+twNXgwpHkIDsrUzLjQgghhBCiR+nwu9fHHnuMF198kZdffhmTyRTdfsYZZ7B+/foOneviiy/mggsuICcnh9zcXB5//HESExP57LPPqK+v55VXXuGZZ57h7LPP5uSTT2bx4sV88sknfPbZZx0ddnwqLIDZN0NeDrgaw9tcjZCfG94umT4h2qSqKjkjshk7djQ5I7IlGBdCCCGEED1OhzPkJSUlnHXWWS22Jycns3///iMeSCgUYvny5TQ2NjJu3DjWrVtHIBDg3HPPjR6Tn5/PsGHD+PTTTznttNNaPY/P54tm7QEaGhoACAQCBAKBIx5flzkhHx78NYGyrVC2hcCvZ8OIrHBmPB7HK45Y5PsvLr8PRaeQe9z7yT3u3eT+9n5yj8M0TaNi23bcLjeJjkQyM4b3mgfbco97v55yj9s7vg4H5AMHDqSsrIyMjIyY7WvXriUrK6ujp6O4uJhx48bh9XpJTEzkrbfe4oQTTmDDhg2YzWZSUlJijh8wYABVVVVtnu/JJ5/k4YcfbrH93XffxW63d3h8x9qqsi1QtqW7hyG60KpVq7p7CKKLyT3u/eQe925yf3s/ucexSr7d3N1D6HRyj3u/eL/HHo+nXcd1OCC/8cYbmTVrFosWLUJRFHbv3s2nn37KnDlzuP/++zs80Ly8PDZs2EB9fT1//etfue6661izZk2HzxNx9913c8cdd0RfNzQ0MHToUM477zySkpKO+LxdLRAIsGrVKn7wgx/ELAUQvYfc495P7nHvJ/e4bb0h4yb3t/c73u/x119v4vkXXsbtcpParFOJs85JYmIit/z8Rk488YTuHuZROd7v8fGgp9zjyEztw+lwQP7rX/8aTdM455xz8Hg8nHXWWVgsFubMmcNtt93W4YGazWZGjBgBwMknn8yXX37J/PnzmTZtGn6/n/3798dkyaurqxk4cGCb57NYLFgsLXt7m0ymuL5hET1lnOLIyT3u/eQe935yj2MVFRXz5rKVVGzbTsAfwGQ2kZkxnGlTL+uRnQ/k/vZ+x+M91jSN5X/9G3XO/QwZko6iKOi6jtlsZkD/AeyqrOSvK96msLCgxz1Ma83xeI+PN/F+j9s7tg7/tCmKwr333ovT6eTrr7/ms88+Y9++fTz66KMdHmRrNE3D5/Nx8sknYzKZeO+996L7SkpK2LFjB+PGjeuUa4meT9M0SsvKWb9+A6Vl5Wia1t1DEkKI40pRUTHz5i+kpKQUR6KDwYMH4Uh0ULKllHnzF1JUVNzdQxRCAOVbK6jYtp20tDQURYnZpygKaampbK3YRvnWim4aoRDHpw5nyCPMZjMnnHB0U1ruvvtufvjDHzJs2DBcLhdLly5l9erV/Oc//yE5OZmZM2dyxx13kJqaSlJSErfddhvjxo1rs6BbjxUMwpoDPdzXrIUJ3wfjEd+a40Zvy8gIIURPo2kaby5biavBFc24AdjtNmy2dHZVVrJs+VsUFIzqFRk3IXoyV4OLgD+A1dpyJimEZ5k6nXW4GlzHeGRCHN/aHfXNmDGjXcctWrSo3Rffu3cv06dPZ8+ePSQnJ3PSSSfxn//8hx/84AcAzJs3D1VVmTJlCj6fj/PPP5+FCxe2+/w9woq3YdFr0OiG2T+Dx34b7k0+4xqYckl3jy5uRTIyrgYXaWlpWK0WvF5fNCMze9bNEpQLIUQX60jGLWdEdjeNUggB4EhyYDKb8Hp92OxWPB4PwUAQo8mI3W7H5/NhMptwJDm6e6hCHFfaHZAvWbKE4cOHM2bMGHRd75SLv/LKK4fcb7Vaef7553n++ec75XpxZ8Xb8OxC8Acg8UAFeIMBamrD20GC8lZIRkYIIeKDZNyE6DmyszLJzBjOxo1fEwgEafI2oWk6qqpgs9owmYwUFhaQnZXZ3UMV4rjS7oD85z//OX/5y1+oqKjghhtu4JprriE1NbUrx9a7BYPhzLjPD4oS/hti/178GlxyoUxfP4hkZIQQIj40z7jZ7bYW+yXjJkT8UFWVk8eO5qO1n+D3B7BaLFgsJoKBIPvr6zGbTYwdUyjJDCGOsXb/xD3//PPs2bOHX/7yl/zjH/9g6NChTJ06lf/85z+dljE/rqxZC7VO0DXQNFAO3ApFDb/WNahxfre2XES1JyMT8AckIyOEEF0sknGrdda2eC+g6zq1TidZmRmScRMiDmiaxrr1G0hyJJGSkoyOjs/nR0enT0oySUlJrP+qSArkCnGMdegRmMVi4aqrrmLVqlVs2rSJUaNGcfPNN5ORkYHb7e6qMfZO1fvCWXIdMKjhLDmE/zao4e3BYPg4EaN5RqY1kpERQohjQ1VVpk29DIfDwa7KSjweD6FQCI/Hw67KShwOB1OvmCwZNyHiQGSGYXr6YPLzcsnLzSFnRBZ5uTnk5eWSPniQVFkXohsc8f8hVVWN9i8MhUKdOabjg6YB+oFAXDlop3Jgu37gONGcZGSEECJ+FBYWMHvWzeTl5uByu9mzpwqX201+Xq4U2BQijjSfYagoCgkJCSQnJ5OQkICiKDLDUIhu0qHFyT6fj5UrV7Jo0SLWrl3LRRddxO9//3smTZokT787KncEqIYD09MPmvKv6+E/qiF8nIgRycjMm7+QXZWVpKWmYrFY8Pl81DqdkpERQohjrLCwgIKCUZRvrcDV4MKR5CA7K1N+DwsRR6TmgxDxqd0B+c0338wbb7zB0KFDmTFjBn/5y1/o27dvV46td+uTAmmp4YrqoVD4D3z3b0UJ7++T0p2jjFuRjEykD7nTWYfJbCI/L5epV0yWjIwQQhxjqqpKIU0h4lhkhmHJllJstvSYwriRGYb5ebkyw1CIY6zdAfmLL77IsGHDyMrKYs2aNaxZs6bV41auXNlpg+vVsjNhzEnw6Rfgcn83NT3yd2ICjC0MHydaVVhYQMGokZSvWYtrXw2Ofn3JHn8mqlSlF0IIIYSIITMMhYhP7Y5cpk+f3qLFlDgKqgonj4aPPgn/22oOb7eawxnyYDAckMsvxbYVFaO+uZKciu0QCIDJBO+tgWmXgWTIhRBCCCFiyAxDIeJPuwPyJUuWdOEwjkOaBus2QFJSOPgOBcPbDQZITQGDEdYXweSLJShvTVExzFsILhekpYHVAl4flJSGt8++WYJyIYQQQoiDSM0HIeKLzO3tLuUVULEd0geDzQr+Ay28crLBbIGmJti6LXxcjqzJi6Fp8ObKcDA+JP27lnF2G9jSYVclLHsLCkbJwwwhhBBCiINIzQch4ocE5N3F5QpPs7ZawgGlPSG83Z4QnrJusYCzLnxcnNA0LT6epkYeZqSlfReMR0SK4cnDDCGEEEJ0gbh5PySE6BUkIO8uDkd4zbPXF87sHsznC+93xEfriaKi4uh6o4A/gMlsIjNjONOmXnbs1xs1f5jRmjh8mCGEEEKIni+u3g8JIXoFeZzXXbIzIXM41Na23oe81glZGXFRZb2oqJh58xdSUlKKI9HB4MGDcCQ6KNlSyrz5CykqKj62A2r+MKM1cfYwQwghhBA9X9y9HxJC9AoSkHcXVQ1XA3c4wmuemzzh7U2e8GuHA6ZO7vY10Jqm8eaylbgaXAwZko7dbkNVVex2G0PS03G5XCxb/hZapF3bsdCDHmYIIYQQoueLy/dDQoheQQLy7lRYEK4GnpcDrsbwNlcj5OfGTZXw8q0VVGzbTlpaWou2d4qikJaaytaKbZRvrTh2gzr4YYbHE15374mvhxlCCCGE6B3i8v2QEKJXkDXk3a2wIFwNfEsZlGyGe+dA7oi4CSZdDS4C/gDWNtZrWywWnM46XA3HeL125GHGmyvDBd6cdeFp6vm54WA8Dh5mCCGEEKJ3iNv3Q0KIHk8CcnFIjiQHJrMJr9eHvZXicz6fD5PZhCOpG9ZrRx5mlFeEC7g5HOFp6nHyMEMIIYQQvUNcvx8SQvRoEpB3t6LicJZ3ZyVcfiE8PheGpoenZMdBljc7K5PMjOGUbCnFZkuPmaal6zq1Tif5eblkZ3XTem1VldZmQgghhOhScf9+SAjRY0kqsTsVFcO8hVBSCo7E8DZHYvj1vIXh/d1MVVWmTb0Mh8PBrspKPB4PoVAIj8fDrspKHA4HU6+YLP03hRBCCNFryfshIURXkd8a3UXTwplxlwuGpIPtwPQnmy382uWCZW+Fj+tmhYUFzJ51M3m5ObjcbvbsqcLldpOfl8vsWTdL300hhBBC9HryfkgI0RVkynp3Ka8IFyNLS4ODqnWiKJCWClu3hY+LgynZhYUFFBSMonxrBa4GF44kB9lZmfIkWAghhBDHDXk/JITobBKQdxeXCwIBaKNaJxZLuHK4qwPVOjWtSwucqapKzojufzgghBBCCNFd5P2QEKIzSUDeXRyOcJsurw9aqdaJzxfe72hntc5IcbiK7eFA32SCzOGdWhxO0zR5IiyEEEIIIYQQnUQC8u6SnRkOmEtKwZYeu0/XodYZ7qmd3Y5qnZHicC5XeAq81RIO9CPF4WbffNRBeVFRMW8uW0nFtu0E/AFMZhOZGcOZNvUyWTMlhBBCCCGEEEdA0pvdRVXD2WuHA3ZVQpMnvL3JE37tcMDUyYefcn5wcTi7Lfwx9s4rDldUVMy8+QspKSnFkehg8OBBOBIdlGwpZd78hRTFQTV4IYQQQgghhOhpJCDvToUF4ex1Xg64GsPbXI3hzHh7s9odKQ53BDRN481lK3E1uBgyJB273YaqqtjtNoakp+NyuVi2/C20OKgGL4QQQgghhBA9iUxZ726FBVAwCraUQclmuHcO5I5ofzG2rigO10z51goqtm0nLS0N5aCAX1EU0lJT2VqxjfKtFVLgRAghhBBCCCE6QDLk8UBVv1sr3tHK6M2Lw7Wmo8XhDuJqcBHwB7C2EfBbLBYC/gCuhiML+IUQQgghhBDieCUBeU8XKQ5XWxsuBtdcpDhcVkb7isO1wpHkwGQ24W0j4Pf5fJjMJhxJRxbwCyGEEEIIIcTxSgLynu7g4nAeD4RC4b87UhyuDdlZmWRmDKfWWYt+UMCv6zq1TidZmRlkZx1ZwC+EEEIIIYQQxysJyHuDmOJwbthTFf67I8Xh2qCqKtOmXobD4WBXZSUej4dQKITH42FXZSUOh4OpV0yWfuRCCCGEEEII0UFS1K23iBSHK68IF3BzODq+Hr2tUxcWMHvWzdE+5E5nHSazify8XKZeMVn6kAshhBBCCCHEEZCAvDdRVcjpmkrnhYUFFBSMonxrBa4GF44kB9lZmZIZF0IIIYQQQogjJAG5aDdVVaW1mRBCCCGEEEJ0EklvCiGEEEIIIYQQ3UAy5OKoaZomU9mFEEIIIYQQooMkIBdHpaioOFrsLeAPYDKbyMwYzrSpl0mxN9Gl5EGQEEIIIYTo6SQgF0esqKiYefMX0lDfQEJCAuYEM5qm8W3JFubNX8jsWTdLUC66RFFRMUv/spwNRRvx+fxYLGZGF57Ej6+6Qr7nDggGg6z5cC379tbQr39fxp91Jkaj/MoXQgghhIgn8u5MHBFN03hz2Ur27a0hGApSU1uLpumoqoLNZsPn87Ns+VsUFIySrKXoVEVFxfzy1w9QU1Mb3eZywX/fW82GomJ++5tHjvugfMWKt1m05DXq6urQNA1VVenTpw8zrr+GKVMu6e7hCSGEEEKIAyRSEkekfGsFmzZ/i8vlorGxEaPBiNViwWgw0tjowdXg4ptNmynfWtEt49M0jdKyctav30BpWTmapnXLOETn0jSNhx99KiYYb66mppZHHn3quL7fK1a8zbMLFlJTU4vJaCIhIQGT0URNTS3PLljIihVvd/cQhRBCCCHEAZIhF0ekfn89Tmc4+2a1WlEUBQCDwYBVVfF6vTidddTvr+/ScbQ2LfebbzbLuvZeavO3JezYsSP6OvJ9B6DrOgDbd+xg87cljDph5DEfX3cLBoMsWvIafn+AxMRE1ANfH7PZjNFkwu12s3jJa1xyyYUyfV0IIYQQIg7IOzJxROobGggGg5iMppigCMJBksFgIBAMUN/Q0GVjaG1abmJCIharBbPJRFpaGlarBa/XR8mWUlnX3gv84x//4kDcDXwXhDen6+HjjseAfM2Ha6mrq8NqsUSD8QhVUbBaLDjr6ljz4VrOOXtC9wxSCCGEEEJEyZR1cUSSk5MxGo0EQ0EODol0IBgKYjQaSU5O7pLrtzYt12g04ayrY8+eKgwGI3a7DVVVsdttDElPx+VysWz5W8f1dOaezuVyd+pxvc2+vTVomobR1PqzVqPJiKZp7Ntbc4xHJoQQQgghWiMBuTgiyclJpKamYjAY8HqbCIVC6OiEQiG83iZU1UBqairJyUmdfu2Dp+WazWZURcVgMESP2VVZGRN4K4pCWmoqWyu2ddu6dnH08vPzOvW43qZf/76oqkowEGx1fzAQRFVV+vXve4xHJoQQQgghWiMBuTgi2VmZnDAyjySHA7vNjj/gp7HRgz/gx263k5zkYNQJ+WRnZXb6tdualqvrGoqioKoKfr8fp9MZ83EWi4WAP4CrwdXpYxLHxhWXX9JiicTBFEXhisuPz0ri4886kz59+uD1+dAOms6v6Tpen4/UPn0Yf9aZ3TRCIYQQQgjRnATk4oioqsq0qZdhs9vxer1oWvjNv6bpeJu82Ox2pl4xuUtanrU1LVdRVBQFInPofX5/zH6fz4fJbMKR5Oj0MYljY+euSlJSDr0MIiUlmZ27Ko/RiOKL0WhkxvXXYDaHC7j5/X40XcPv9+N2uzGbTdxw/TVS0E0IIYQQIk7Iu7JeRNM0yrdW4Gpw4UhykJ2V2eU9wCPxr6oq6LoSDoiV8Pau0nxartlsjm43GAwY1HAxOVCwNNun6zq1Tif5ebldkrUXx4arwUVKcjIGg6HV1md9+6bhSEw8rmdBRPqMRwoe+vy+8DT1vmncIH3IhRBCCCHiigTkvURRUfExbfWlaRpvLltJKBRizJiTaGpqIhgIYjQZsdlsVFbuZtnytygoGNXpDwUi03JramoxmkzRaesKYLaYCQQDqKqK1WolFArh8/modTpxOBxdlrUXx4YjyUEwFKKpqQmr1Qro6Lp+YBq7gsfThM1mO+5nQUyZcgmXXHJhi5aAkhnvmbrjYasQQgghjg15d9YLFBUVM2/+QlwNrmPW6qt8awUV27aTlpaGqqgk2BNi9jcvoJYzIrtTrx2ZlvvsgoW43W6sFgtGk5FgIIjX58NstpCZOZyq6mp8Pj8Wi5mCE0dx5bQp0vKsh8vMGE4gEMDn88f02YbwGmm3200wECAzY3g3jjI+GI1GaW3WCxzrh61CCCFEPIs8pIZwPJKbM6LHP6Tu2aMX0Uy1q8HFkCHpx6zVl6vBRcAfwGq1tLq/qwuoTZlyCb+4/Wb69k0jEAzQ2NhIIBigX980Jk74Pvv311NT46S+voGaGicbi7+hrGxrl4xFHDsV27ZjMhmxmM34fF58fj/+gB+f34/P58ViMWM0GanYtr27hyrEUYs8bC0pKcWR6GDw4EE4Eh3Rh61FRcXdPUQhhBDimCkqKube+x7hwYceB+DBhx7n3vse6fH/P5QMeQ/XPFN9cPXpg1t9dWam2pHkwGQ24fX6sNttLfYfiwJqrU3LddbWseD3L+L3B2Iy5zU1tTy7YGH040TP5GpwYTQYGTBwAJW7KvF6fdF9ZrOZQYP6o2v6cb2GXPQOBz9sjfx+t9tt2Gzp7Kqs7LJlQUIIIUS8KSoq5rEn5lJdVR0t7LxvXy179lRTVl7BfffM6bEzx+T/4j3cscxUa5pGaVk569dvQNd0MoYPo9ZZi35Qe6VIAbWszIwuL6AWmZZ75ZWXM/6sM1nyp6Ut+pObzWYSExPx+wMsXvIawWDrPZpF/AuvIQ9SXVWNoiok2O0kJCSQYLejKArVVXsJhoLH/Rpy0fN15GGrEEII0ZtpmsaLLy1i165KNF3DbDIBYDaZ0DSNXbsq+cNLizt9RvCxIhnyHu5YZapbW8fYJyUFVTWwq7KStNRULBZLtxZQa6s/OYCqKFgtFpx1daz5cK2sre2hwmvIg/j8LdeQG6NryIOyhlz0eO152Op01slsECGEEL1eaVk5mzeXoCgKVosVVTUAoKoGrBYrnqYmNm3+ltKycvJyc7p5tB0nGfIeLjsrk8yM4V2aqW5rHWP13r0oQP/+/XC53ezZU4XL7SY/L7dLCskdTlv9ySOMJiOaprFvb80xHZfoPOE15CYslvAa8lAohI5+oJq+F7PZjNFkkjXkQDAY5L33V/PGG3/lvfdXy8yQHqb5w9bWHItlQUIIIUQ8KCkpxev1YbGYW501ZrGYwwWtS0q7aYRHRzLkPZyqqkybehnz5i/skkx1e9Yx9klJ4RezbqbR3djuljxd0canrf7kEcFAMNyPuX/fo7qO6D7hNeQGsjIzqKrai6fJgxbQUVWFxMREBg7oj9vdeNxnDVeseJtFS16jttaJpmmoqkpaWiozpA95jxF52FqypRSbLT3mDUjkYWt+Xm6XLguSdmtCCCHiit7B7T2EBOS9QGFhAbNn3RydUu501mEym8jPy2XqFZOPKlPdfB1jKBRix46dNHl92KwWhg0bSlpqKhXbtqOqKmPHjm7XObuqjU9b/ckh3BLL6/PRr28a488684ivIbpXJGtoNlvIy8/B4/EQDAQxmozY7XaaPE2YzP7jOmu4YsXbPD3v9/j9/ug2TdOort7L0/N+D0hhw56gqx+2Ho60WxNCCBEv8vNysVot+Pw+DMbY8FUHfH4fVquF/Lzc7hngUZKAvJcoLCygoGBUp2czIusYt+/YQU1Nbcy+yt176Ns3FUeio90Zya7smX74/uQmbrj+GoxG+bbvqZpnDYfY0kmwJ0T3HausYTwLBoP84aXF+Hw+FEXBYDCgKKDrEAqF8Pt9/OHlxVxyyYXyc9ADdOXD1kPpyt/TQgghREeNGJHFCSPz2VC0kaYmD0ZDIgCaFqKpyQPACSPzGTEiqzuHecTkHVkvoqpqp7Y2g3BGsqa2BpersdX9NTVOfD5fuzKSx6KNTyTzt2jJa9TV1eHz+8LT1PumcYNM1+3xujtrGO9Wr15L3f79KAoxAXfkdTAYoK5uP6tXr+Xccyd030BFu3XVw9a2SLs1IYQQ8UZVVX560w08/sRcqqqq8QfCswD9AT8Gg4GBAwbw05tu6LH/X5KAXBxS+uBBbQbjES5XI+mDBx32XMeqZ3pr/cnHn3WmZAR7ie7KGvYEm7/9Fk3TMLTxPyRVUQlpGpu//VYC8h6kKx62tuVY/Z4WQgghOqKwsIB775nDG8tWsvVAy89+/fqRnZXZ45dTdWuE8uSTT7Jy5Uq+/fZbbDYbp59+Ok899RR5eXnRY7xeL3feeSdvvPEGPp+P888/n4ULFzJgwIBuHPnx46WXl7T7uF/MuvmQxxzLNj6R/uSidyosLGDUqJHy0OUgVqs1/A+ljQOUg44T4iDSbk0IIUS8iswa21JaRsm3m3n4oXvIzRnRYzPjEd06+jVr1nDLLbfw2WefsWrVKgKBAOeddx6Njd9lZGfPns0//vEPli9fzpo1a9i9ezeXXXZZN446fmmaRmlZOevXb6C0rBxN0476nLt2VbbruG+/3XLY60obH9FZioqKeeDBx/njK3/iryve5o+v/IkHHnycoqLi7h5atzrzzHEYjQY0TUc/6OdQ1zQ0TcdoNHDmmeO6aYQi3snvaSGEEPFMVdVoraDe0v2jW9NJ//73v2NeL1myhP79+7Nu3TrOOuss6uvreeWVV1i6dClnn302AIsXL2bkyJF89tlnnHbaad0x7LjUVRVxExISDn8QULKllCeefPqQ142HNj6i55OCU23Ly80hJ2cE3367hZCmoRJOiusQfVCWm5NDXm5Odw5TxDH5PS2EEEIcW3E1v7O+vh6A1NRUANatW0cgEODcc8+NHpOfn8+wYcP49NNPWw3IfT4fPt93T/YbGhoACAQCBAKBrhz+UYmM7UjG+PXXm3j+hZdxu9ykNitytbWiguee/wO3/PxGTjzxhCMa1/nnnc27q94/7HHp6YPo17ffYa97xeWX8vwLL1O9t5rUPn0wmy34/T6cdXX06ZPC5VMuIRQKEQqFjmi88exo7rEI0zSNv/71b3ibmhg+fFg0WDCZTCQmJrB7z27+uuJt8vNzu+WJaTzc4ztn38qjj/+WvXtrYmarqKpK//59uWP2Lb32Z+xYiId73NXk93Tvvr/HO7nHvZ/c496vp9zj9o5P0XU9Llqpa5rGj370I/bv38/atWsBWLp0KTfccENMgA3wve99j4kTJ/LUU0+1OM9DDz3Eww8/3GL70qVLsdvtXTN4IYQQQgghhBDiAI/Hw49//GPq6+tJSkpq87i4yZDfcsstfP3119Fg/Ejdfffd3HHHHdHXDQ0NDB06lPPOO++QX4juFggEWLVqFT/4wQ8wmUzt/rjyrRU8/sRcHImJ2Gy2Fvubmjy43I3ce8+cdk8x1DSNim3bcbvc2BPsLF26nI/WfoLf3/pTnr5908jJyUZpVknqcNdtfo1ERyKZGcN7xRqQQznSeyy+U1RUzNPPPMfAgQNb/X4JhUJUV1dz5x23dcu09Xi6x8fjz9ixEE/3uKsdj99Dx9P9PV7JPe795B73fj3lHkdmah9OXATkt956K++88w4ffvghQ4YMiW4fOHAgfr+f/fv3k5KSEt1eXV3NwIEDWz2XxWLBYmlZHdZkMsX1DYvo6Dg9jR6aPE30SUlpdfqgwWCkydOEp9HTrvO2thbd5/PT2OihzckUus7e6r34/H4sZjOpqamHva6maRgMBlRVxWAwYDKZev0bvYie8r0Yj5JTkkFRaGz0YLe3fADl8XhAUUhOSe7Wr3G83OP8vNzuHkKvFS/3uKsdr99Dx8v9PZ7JPe795B73fvF+j9s7tm4NyHVd57bbbuOtt95i9erVZGbGZlJPPvlkTCYT7733HlOmTAGgpKSEHTt2MG6cVAmG2Iq4rQUoHamI21qxLI+nifLyCnRdx2AwgK6jA+g62oEAffeeKvZUVUfPYzaZGDCgP1abtdXrdlUBOtH7ScEpIYQQQgjRm3RrSvKWW27htddeY+nSpTgcDqqqqqiqqqKpqQmA5ORkZs6cyR133MEHH3zAunXruOGGGxg3bpxUWD8gEqDUOmtbZLAjAUpWZsZhAxRN03hz2UpcDS6GDEnHbrehqipebxOaphGOe3TMFgtGoxGzxRITDAEoioKiKPgDAXbuqsRoMLa4biToLykpxZHoYPDgQTgSHdEK2cd72ypxaKqqMm3qZTgcDnZVVuLxeAiFQng8HnZVVuJwOJh6xeTjZraFEEIIIYTo2br1XesLL7xAfX09EyZMYNCgQdE/b775ZvSYefPmcdFFFzFlyhTOOussBg4cyMqVK7tx1PGlswKU8q0VVGzbTlpaWkyg7fP7w//QIRTSaGpqwu/34/V6Yx4A6LqOpmlomoau6+i6TvXevTFVntsK+u12G0PS03G5XCxb/lan9E8XvVdhYQGzZ91MXm4OLrebPXuqcLnd5OflHtctz4QQQgghRM/T7VPWD8dqtfL888/z/PPPH4MR9UyRACUyDdzprMNkNpGfl8vUKya3K0BxNbgI+ANYrbHr7y1mc7vu08EURaGhwcWaD9dyztkTgLaD/sjxaampbK3YRvnWCnJGZHf4muL4UVhYQEHBKMq3VuBqcOFIcpCdlSmZcSGEEEII0aPERVE3cfSONkBpay1682J67aUoCrquEwwGqd67L7q9raA/wmKx4HTW4Wpwdfia4vijqqo8uBFCCCGEED2aBOS9yNEEKG0Vy9q/f3+7zxH5mEhGXdd1tNB30887swCdEEKIrqVpmsxCEUIIIbqYBOQC+G4t+rz5C9lVWUlaaioWiwWX293uc+i63mIq+ojs74q6HcsK2fJGUhzvgsEgaz5cy769NfTr35fxZ52J0Si/8kX7SDcMIYQQ4tiQd2ciqrW16E1N3g6do/l6c4PBQKPHE33dVtDv8/modTo7rUK2vJEUx7sVK95m0ZLXqKurQ9M0VFWlT58+zLj+GqZMuaS7hyfiXGstML1eX7QbhhRPFEIIITqPpAxFjMLCAh568G4uvOB8Tj31FMaMPumIz2UymUhOTm5x/q6skC1t1cTxbsWKt3l2wUJqamoxGU0kJCRgMpqoqanl2QULWbHi7e4eoohj0g1DCCGEOLYkQy5iHJxZC4WO/E2XqiokOVquB++qCtkHv5GMTIm3223YbOnsqqxk2fK3KCgYJdPXRa8UDAZZtOQ1/P4AiYmJqAd+BsxmM0aTCbfbzeIlr3HJJRfK9HXRKumGIYQQQhxbEpWIqNYya4YjDFyjBd5ovWVapADd2LGjyRmR3SkBckfeSArRG635cC11dXVYLZZoMB6hKgpWiwVnXR1rPlzbTSMU8a493TAC/oB0wxBCCCE6iQTkAmiZWTObzaiKimowHNH5dF1H07Rj+qZN3kiK492+vTVomobR1Hr222gyomka+/bWHOORiZ6ieTeM1kg3DCGEEKJzSUAugENn1o5UIBCkvqGhU87VHvJGUhzv+vXvi6qqBAPBVvcHA0FUVaVf/77HeGSip4h0w6h11sYU6YTvumFkZWZ0SjcMIYQQQkhALg5oK7N28NTvjgiFQiQkJBzt0NpN3kiK4934s86kT58+eH0+NF0jGAoRCAYIhkJouobX5yO1Tx/Gn3Vmdw9VxKlINwyHw8Guyko8Hg+hUAiPx8OuyspO64YhhBBCiDD5P6oA2s6sHW22fMuWsqP6+I6QN5LieGc0Gplx/TUYDAbq6xtwuVy43Y24XC7q6xswGAzccP01UtDtOKVpGqVl5axfv4HSsvI2K6V3dTcMIYQQQnxH3pUJ4LvMWk1NLUaTKRqIt16Srf22Vmw76rF1RGu91E1mE/l5uUy9YrK8kRS93ogRWaSlpVJdvTcm4FJVlbS0VEaMyOrG0YnuUlRUHP29GPAHMJlNZGYMZ9rUy1r9vdhV3TCEEEIIEUsCcgF8l1l7dsFC3G43VoslWgDq6HTOevSOkDeS4ngVaf1nNpk49XunUFdXh8/vx2I206dPH3bv2SOt/45DRUXFzJu/EFeDi7S0NKxWC16vj5Itpcybv7DNrHekG4YQQgghuo4E5CJqypRLAKJ9yH3+cHG0yBv3pCQHPp8fTdPQNI1AIHDYc2ZlDuu6AR+CvJEUx6Pmrf8MBgN9+8YWb5Me0sefyEMaV4OLIUPSo3VB7HYbNls6uyor5SGNEEII0Y0kIBcxpky5hEsuuZA1H65l394a+vXvS22tk2fnv0B9fQOgo3dgHvuIbJkeK8Sx0p7Wf05nnbT+O440f0hzcJFORVHkIY0QQgjRzSQgFy0YjUbOOXtC9HVRUTEORyI1NbUdPtfWiu2cfvppnTg6IURbmrf+s9ttLfZL67/jjzykEUIIIeKbBOQ9UDAYjMlgjz/rzC6rmqxpGn94aTENDS7MZhMGQ/g6gUCAYLD1XscRiqKgGmQKpBDHSqT1X8mWUmy29JiMaKT1X35errT+O47IQxohhBAivklA3sOsWPF2dI23pmmoqkqfPn2Ycf010TXgnamsbCubNn+LooDdnkAopKHr4UJvhwvIjUYjA/r3a3WfpmlSdE2IThZp/Tdv/kJ2VVaSlpqKxWLB5/NR63RK67/jkDykEUIIIeKbBOQ9yIoVb/PsgoX4/YFoFfRgIEhNTS3PLlgI0OlB+bclW/B6fZhMJtzuRkKhYLvXkNusVsafdWaL7R1tvyOEaD9p/Seak4c0QgghRHyTgLyHCAaDLFryGn5/gMTExGifcLPZjNFkwu12s3jJa1xyyYWdPn1d13U8Hk/HP1Bp2fLsSNvvCCHaT1r/iebkIY0QQggRvyQg7yHWfLiWuro6rBZLNBiPUBUFq8WCs66ONR+ujSnIdrRyc0YccS/ypqammPF0RfsdmfouROuk9Z9oTh7SCCGEEPFJAvIeYt/eGjRNw2hq/ZYZTUZ8fh/79tZ06nVDWqhdAbmiKNE/qqoQDIYIBoNU790XPaaz2+/I1HchhGg/eUgjhBBCxB8JyHuIfv37oqoqwUAQs9ncYn8wEERVVfr179up1/3kk8/bdZyiKBgMhpjXuq6hhb4L5o+m/c7BmXC3y838516Uqe9CCCGEEEKIHksC8h5i/Fln0qdPH2pqajGaTDHT1jVdx+vz0a9vWqtF1I5G4xGsHdc1DV3XUVUDuTnfZWOat9+x2a14PB6CgSBGkxG73d5m+53WMuENDS7QYcSIrE6Z+i6EEEIIIYQQx5oE5D2E0WhkxvXX8OyChbjd7pgq616fD7PZxA3XX9PpBd20YPvWj2uahqoo6Af+rSgKaWl96JPaJ3pMpP3Oxo1fEwgEafI2oWk6qqpgs9owmYwUFhbEtN9prQhc3f46amudmE0mXC4XSUlJ0eOPZOq76JmkfoAQQgghhOjpJCDvQSItzSJ9yH1+X3iaet80buiiPuQGQ/sDnNCBteZms4mEhATGjhkdE1yrqsrJY0fz0dpPoq3bLBYTwUCQ/fX1mM0mxo4pjAZVbRWBMxqMGAwGNF1j954qHA5HzJr0Q019F72D1A8QQgghhBC9gQTkPcyUKZdwySUXsubDtezbW0O//n0Zf9aZnZ4ZjzBZTO06zuFIIC2tL6qiEAwFSU5ObtHbVtM01q3fQJIjiWAoSFNTEz6fH1VV6JOSjMFoZP1XRUyefDGqqrZZBM5oMmIwqCgoeDwePB4PCQkJ0f1tTX0XvYO0zhNCCCGEEL2FBOQ9kNFo7NTWZoeyZ091O49UUAgHy7m5I1rtbRsJsNPTB2O1WnA6nfj8fixmM6mpqXi93pip5m0VgbPb7dhtdlxuF4qi4HK7CQaDGI1GbDYbtU4n+Xm5Mdl50Tt0Res8IYQQQgghuosE5OKQmpq87TouPy+Xm268/pBreSMBtt/vZ8eOnXiaPNE15DU1TgYM7E/AH4hONW9eBM5ut0XPo6AwePBASra48Hp97NixE1VRQQlPix8wYECL7LzoHTq7dZ4QQgghhBDdSSIWcUhD0ge367j8/FzGjh1NzojsNgNhR5KDYCjI1q0VuBvdGA3GcHE6gxF3YyMVW7cRDAWjU80jReBqnbXouh5zLl3XCYU0VFUNt1hrti82TBO9SXta5zV/qCOEEEIIIUQ8k4BcHNJPZk7vtOMyM4YTCATD09QtVgwGQ7R/ucViwef3EwwEycwYDoSz3dOmXobD4WBXZSUej4dQKESjx0N5eQWKonDCCfmccEI+uTnZjByZx+jRJxHSQixb/haa1r4K8aLnaD5rojVSP0AIIYQQQvQkEpCLQ/r8i/8dNuOsHDjucCq2bcdkMmGxmPH5vIRCIXR0QqEQPp8Xs9mM0WSiYtv26McUFhYwe9bN5OXm4HK72bOnitpaJ6pBJTNjOCnJySTYE0g+8LeqqDHTlkXvcrhZE7VOJ1mZGVI/QAghhBBC9AiyhlwcUvXefaAocFDw05wOfP3NZvqkpBx2DbnRYCArM4Oqqr3hNeSB8BryxMREBg7oj9vd2GK6cWFhAQUFo6I9p3dV7ubVV5fSt29aq+ORtme9V2TWxLz5C9lVWUlaamp4doXPR63TicPhkPoBQgghhBCix5CAXBySFtKAtoPxiL/97R3eeeffWK0WThx1AldOm9KiynpkurHZbCEvPwePx0MwEMRoMmK322nyNGEy+w873Tghwd5qsbcImbbcu0VmTUT6kDuddZjMJvLzclut7i+EEEIIIUS8koBcHFJuTvahkuNRDQey0YoC1dX7+OabzTz6yH0xwVFkunHJllKG2NJJsH/XOzwy3bi1dmVFRcXR4CvgD2Aym3C53LhcbkZkZ8VU246cJy83B03TWL9+wyGz9qJnOnjWhNxjIYQQQgjRE0lALloIBoOs+XAt+/bW0OhpatfHRIqzhaufh9i9p4q5zzzHq4tfjAZJRzLduKiomHnzF+JqcJGWlobVasHr9eFqcFHf0EBZ+VbSBw+KOY9BNVC3fz8PPfxkNIDPzBjOtKmXSfa0F1FVVVqbCSGEEEKIHk0CchFjxYq3WbTkNerq6tA0jUAg2M6P1FEUJfonGAyyZUsZW7aUkZ+fGz2qI9ONNU3jzWUrcTW4GDIkPZoJt9ttZGdnUV6+FR1ocLkIHjjPgP792VdTS3XVXhISEjAnmNE0jW9LtjBv/kJmz7pZgnIhhBBCCCFEXJCAXEStWPE2zy5YiN8fCPcHNxlpaHC1q31Y82ntiqKgqirBYJCP1n4SE5BD+6cbl2+toGLbdtLS0mKmpUeuMXjwIFxuNz+ZOZ0+KSkkJiayeMlrbN++k2AoSE1tLZoWLhpns9nw+fwsW/4WBQWjZGqzEEIIIYQQottJQB7HNE07Zmtkg8Egi5a8ht8fIDExEfVAAKyq6hH1844E0N4mb6v72zPd2NXgIuAPYLVaWt1vsVioddbR2OihT0oKO3ftYtPmb3G5XGi6htlkRjWFx9/Y6EFVFL7ZtJnyrRW9bqrzsfxeEUIIIYQQQnQOCcjjVGuFzLpyHfSaD9dSV1eH1WIBXccXCKBpWotez21R1djCapqmoaoK+SNzD/FRhxapyt5WNfXaWie1tbUsefV1VCWckd+zpxqDwUCC3R59KGAwGLCqKl6vF6ezjvr99Uc8pnh0rL9XhBBCCCGEEJ1DUmhxKFLIrKSkFEeig8GDB+FIdFCypZR58xdSVFTc6dfct7cGTdMIaSHqGxpobGykqamJUCjUzjMo6LoeDcZ1XadPSgoTJ5x1xGOKVGWvdda2eDBQX1/Ptu3b0UIaaalpDB48CJPZhKaFCAaDBA8ad6ToXDAYpL6h4YjHFG+643tFCCGEEEII0TkkII8zBxcys9ttqKqK3W5jSHo6LpeLZcvf6tA0ck3TKC0rZ/36DZSWlbf6sf3690XTdJqavO3Oih98DU3T0EIhdF3HYjHz05tmYDQe+SSMSFV2h8PBrspKPB4PoVCIRo+H8vIKdB2ys7NISLCjqioJCQkoSvjBgNcb+3noQDAUxGg0kpycfMRjiidd8b0ihBBCCCGEOHZkynqcqdi2/ZCFzNJSU9lasa3d66DbO535jNNPiwlgI9c+ODgPb9cBBaPBQCAYrsKuquHq6qpBJS01lRk3XMuUKZcc4VfhO61VZdd0HdWgkjlkOMnJSdFjTSYjZrMFv99HMBgkEAh/vlpIwx/wo6oGUlNTYz6mJztc0buOfq8IIYQQQgghji0JyOOM2+U+bCEzp7MOV4PrsOdqq4d3ZDpz8xZgH3/yGaqiEMml6rreIsiLCG9XCIZCKIqCxWLmuuk/JsnhoF//vow/68yjyowf7OCq7Lsqd/Pqq0vp2zct5ji73Y4jMZH6hhDBYIhgKEjIG17LnpCQgMloZNQJ+WRnZXba2LpTe4retfd7RQghhBBCCHHsSUAeZxIdiYcsZObz+TCZTTiSHIc8z6F6eNts6eyqrIxpAbZvbw2KqmAzWfH5fWia3urU9fA6cQhnycMF0/r168vEiWd1aRa2eVV2R5IDs8WM1+vDZrPi8XgIBsPT0QcNGkCjx4Ouw+DBg7DbbGi6hqexCUeSg6lXTO411ccPV/Suvd8rQgghhBBCiO4hAXmcycwYTmbGcEq2lGKzpcdkqXVdp9bpJD8v97BZ3o5OZ+7Xvy+qqmIwGEhOTiZwoMq6oig0NTWhaa2vKw+FQhgNxmOadY4Ueyva+DXBYDA6vki/cZvVQlKSA4PBQGOjB5PZRH5+LlOvmNyrqo5Hvg5H+70ihBBCCCGE6B4SkMeZSCGzefMXsquykrTUVCwWCz6fj1qnE4ejfVnejk5nHn/WmfTp04eamlqMJhNmkxkIB9xtBeMR23fsJBgMYjabj+Az7jhVVTl57Gg+WvsJfn8Aq8WCxWIiGAiyf389ZrOJG2+8gdGjC3p1X+7O+l4RQgghhBBCdA95px6HIoXM8nJzcLnd7NlThcvtJj8vN2bd96E0n87cmoOnMxuNRmZcfw1mswm3243f70fTNRobGw97LU3T+MtflnfskzwKmqaxbv0GkpIcpCQno6Pj8/nR0UlJSSY5KYkNGzaSnZXJ2LGjyRmR3WuD0s74XhFCCCGEEEJ0D8mQx6mDC5m1J8uraVr0+ITEBDIyhrOlA9OZI1XRX1n8Z5xOJ01eb7v7kP/n3fcpKBh11Nno5p9DW+eKTMdPH5yOzX5gDXkgiNFkxG630+RpOq6qix/J94oQQgghhBCi+0lAHseaFzI7nNbam6WkJGNQDR2azjxiRBaFJ53I199swuv10dDgInigtdmhbNu+nQcefByzxUJeXg5XHtRW7Ug/h9ZatDWfjq+gkGBPiDnP8VhdvCPfK0IIIYQQQoj4IAF5L9BWe7O9e/ehqgYG9O9P3f79OJ114QJnea0XOIucp6G+gZTkFNQ+KqqiUFPrPOwYQqEQdfvrURSo2VdDedlW7r1nTruD8o60aJPq4kIIIYQQQojeQALyHq497c1SUpL5xaybcbvdbU5njpxn394agqEgNbW1B4q5HbqgW4TJZAIUFCAYCrGrspI/vLSY3z8397BTpzvaok2qiwshhBBCCCF6A1lk2sO1p71ZxbbtKKpyyAJn5Vsr2LT5W1wuF42NjRgNRqwWCyajqV3jaGry0tTUhKepCb/fTyiksWnzt5SVbe2UzyGyJhy+qy7ucDjYVVmJx+MhFArh8XjYVVkp1cVFt4jUP4Dw97Smad08IiGEEEIIEe8kYunh2tPeLOAPHHY9df3+epzOOjRNw2qxYjAYwsGxonQ4sNV1nWAwiMfj4duSLV3yOUh1cRFPioqKue/+R3n8ibkAPP7EXO67/1GKioq7eWRCCCGEECKeyZT1Hq75eurWKo63dz11fUMDwWAQk9EUOwX8QJZPVVV0Xadfv74YjQb8/gA1NbWHPGcopBFqR0G4I10TLtXFRTxoXv9gwID+ADgSE1utfyCEEEIIIURzEpD3cJH11Bs3fk0gEKTJ24Sm6aiqgs1qw2QyUlhYcNj11MnJyRiNRoKhICbMREJy5UBwq+vhcw4Y0J+U5GR27ao8bEAO4HIfvo/50awJl+riojsdXP/AaAz/SrXZbAxJb1n/QAghhBBCiOYkIO/hVFXl5LGj+WjtJ/j9AawWCxaLiWAgyP76esxmE2PHFB42GEhOTiI1NRWn04nX24TZZEY1qKDrKAroOhiNJkym8LeMPxBo1/icdfvb9TlMm3oZ8+Yv7FCLNiG6W0fqH8iDIyGEEEcqFAoRaOd7r+NdIBDAaDTi9XoJhULdPRzRBeLlHptMJgwGw1GfRwLyHk7TNNat30CSI4lgKEhTUxM+nx9VVeiTkozBaGT9V0VMnnzxIQPa7KxMThiZF5tpD4Sz4omJiXg8HoxGA6AQCoXw+33tGp+3qaldx0XWhEf6kB+uRZsQ8aA99Q+czrrD1nAQQgghWqPrOlVVVezfv7+7h9Jj6LrOwIED2blzZ4uH5aJ3iKd7nJKSwsCBA49qHBKQ93CRDF16+mBstgNryINBjMbwGvKmpqZ2ZegiWerde6pwNbjoP6AvqqKi6RqexibS0tLo1zeNuv37qXPWoartexqUn5/b7s/lWKwJj1TCljXnojMcaf0DIYQQoj0iwXj//v2x2+3dHnz0BJqm4Xa7SUxMlPd4vVQ83GNd1/F4POzduxeAQYMGHfG5ujUg//DDD/nd737HunXr2LNnD2+99RaXXnppdL+u6zz44IO8/PLL7N+/nzPOOIMXXniBnJyc7ht0nGmeoVMUhYSEhJj9HcnQHZylDvi94Sx1fjhLPWrUSNZ8uJZ9e2sIBEM8v/APBAJtF22zWMycMDK/Q59PV64JLyoqbva5BTCZTWRmDGfa1MskAy+OyMH1D5o7XP0DIYQQ4lBCoVA0GE9LS+vu4fQYmqbh9/uxWq0SkPdS8XKPbbZwMmbv3r3079//iKevd2tA3tjYSGFhITNmzOCyyy5rsf+3v/0tCxYs4NVXXyUzM5P777+f888/n02bNmG1WrthxPGnszN0bWWpi4u/4YEHH48JZpMcSTjr6tB1vcV5VFVldOFJjBiRddSf46G0N+PdvBJ2WloaVqsFr9cnlbDFUTm4/sGA/v0AaGryUL13n9Q/EEIIccQia8btdns3j0QI0ZbIz2cgEOiZAfkPf/hDfvjDH7a6T9d1nn32We677z4uueQSAP70pz8xYMAA/va3v3HllVcey6HGraOpUN6Wg7PUbQWztYeosm4ymTjn7PFdGoi0N+N9cCXsyNfIbrdhs0klbHF0IjNL3nhzBVsrKgCodTrJy82R2RdCCCGOmkxTFyJ+dcbPZ9yuIa+oqKCqqopzzz03ui05OZlTTz2VTz/9tM2A3Ofz4fN9V3CsoaEBCD+1iOfqlJGxHckYr7j8Up5/4WWq91aT2qcPZrMFv9+Hs66OPn1SuHzKJYRCoSOqQqhpGn/969/wNjUxfPiw6Ded0WTEbLZgtVoxGFRUVTnQGk3FbkvAZDKyoaiYiy6a1CVB7tdfb+L5F17G7XKT2qwq+9aKCp57/g/c8vMbOfHEE4DwOvuduyoZMKB/tC1VcwP692PHzl1sKS3r0qnFR3OPRXzTNA2DwYDRaALCHQlUVUXTNLnfvYz8HPducn97v550jwOBALquo2kamqZ193B6jMjMzcjXTvQ+8XSPNU1D1/VWM+Tt/T2j6K3NN+4GiqLErCH/5JNPOOOMM9i9e3fMIvmpU6eiKApvvvlmq+d56KGHePjhh1tsX7p0qUz5EUIIIYQQPYLRaGTgwIEMHToUs9nc3cPpUS666CIKCgp48sknj+o8//d//8f999/P9u3buemmm476fKL38fv97Ny5k6qqKoLB2NpaHo+HH//4x9TX15OUlNTmOeI2Q36k7r77bu64447o64aGBoYOHcp55513yC9EdwsEAqxatYof/OAHmEymQx6raRoV27bjdrlJdCSSmTE8mo1rbfvRKCoq5ulnnmPgwIEx52poqKd86zYsZjM+v5/srAySkpKj+0OhENXV1dx5x22dPmW3fGsFjz8xF0diYrSYQnNNTR5c7kbuvWcO2VmZHT6+q3TkHoueQdM0Hnvst5SWlTN48GCMRgNnnH4KH3/yP4LBELv37CZnxAjuu/cuWQ7RS8jPce8m97f360n32Ov1snPnThITE3tU7aQf/ehHBAIB/vWvf7XY99FHHzFhwgS++uorTjrpJGbNmsUnn3zC119/zciRI1m/fv1RXz+SazSbzUf93v+OO+7g+uuv57bbbsPhcOBwHNuuKU1NTTz11FO88cYbbN++HYfDwYQJE3jwwQcZNWpUu89jMBhYsWJFTPHsg23bto3HHnuMDz74gKqqKgYPHszVV1/NPffcE/NAaOPGjdx22218+eWX9OvXj1tvvZW77roruv/ll1/mtdde4+uvvwbg5JNP5rHHHuN73/te9JiHH36YN998k507d2I2mzn55JN59NFHOfXUU9v1+ei6jsvlwuFwdPuSDq/Xi81m46yzzmrxcxqZqX04cRuQDxw4EIDq6uqYDHl1dTWjR49u8+MsFgsWS8uewCaTKe5/8ULsOFsrWFZc/M0h103n57W/zVh7JKckg6LQ2OiJLRqnKASDQULBEDo6kf7kER6PBxSF5JTkTv+6exo9NHma6JOS0uo0fIPBSJOnCU+jB5PJRG7OCIYOSadkSylD0luus6/eu4/8vFxyc0Yck6Cpp3wvisMrLSunbGsFSUnJaJpGKBT+3gqFwtMLkxxJlJVvZcfOXV3WPUB0D/k57t3k/vZ+PeEeh0IhFEVBVdWjf3+iaVBeAS4XOByQnQld9J7nJz/5CVOmTGH37t0MGTIkZt+rr77KKaecEn0vrygKM2bM4PPPP2fjxo2d8j6s+RTmozmf2+1m7969TJo0qcXn0RF+v/+IZjj4fD7OO+88duzYwdNPP82pp55KdXU1Tz75JOPGjeO///0vp512WrvPd7jvoy1btqDrOn/4wx8YMWIEX3/9NTfeeCMej4e5c+cC4QBz0qRJnHvuubz44osUFxczY8YM+vTpw0033QSEu2hdddVVnH766VitVp566ikmTZrEN998Q3p6uCNNXl4ev//978nKyqKpqYl58+YxadIkysrK6Nev32E/l8g9jvx8dCdVVVEUpdXfKe39HRO3KZvMzEwGDhzIe++9F93W0NDA559/zrhx47pxZMdGUVEx993/KA88+DhPPPk0Dzz4OLfcdiePPzGXkpJSHIkOBg8ehCPREa0UXlRU3OnjiBSNq3XWxlRTt9vt2Kw2vD4fNpsNHZ36+noaPY1oukat00lWZkaXZJybV5ZvzcGV5SOVsB0OB7sqK/F4PIRCITweD7sqK6UStjhizdsOtsZisRDwB9rVdlAIIYToEkXFcN+j8MDj8MTT4b/vezS8vQtcdNFF9OvXjyVLlsRsd7vdLF++nJkzZ0a3LViwgFtuuYWsrM7vyhMMBrn11ltJTk6mb9++3H///THvZX0+H3PmzCE9PZ2EhAROPfVUVq9eDcDq1auj2fCzzz4bRVGi+1asWMGoUaOwWCxkZGTw9NNPx1w3IyODRx99lOnTp5OUlBQNVNeuXcv3v/99bDYbQ4cO5fbbb6exsbHN8T/77LN8+umnvPPOO0ydOpXhw4fzve99jxUrVjBy5EhmzpwZ8/ksWrQoOq5BgwZx6623RscDMHnyZBRFib4+2KRJk1i8eDHnnXceWVlZ/OhHP2LOnDmsXLkyeszrr7+O3++PXuvKK6/k9ttv55lnnok55uabb2b06NHk5+fzxz/+EU3TYmK6H//4x5x77rlkZWUxatQonnnmGRoaGti4cWObX4/erFsjELfbzYYNG9iwYQMQLuS2YcMGduzYgaIo/OIXv+Cxxx7j73//O8XFxUyfPp3BgwcfcrpFbxCpat488E5MSOSbbzazc1clSUkO7HZbuICa3caQ9HRcLhfLlr/V6YUN2gpmmzxNmExGDAYDbpebb7/dwpbScjZvLmHDVxsxqIYuC3LbekgA31WWP/hhQKQSdl5uDi63mz17qnC53eTn5UrLM3HEOvpwSAghhDimioph3kIoKQ1nxgcPCv9dUhre3gVBudFoZPr06SxZsiTmfdry5csJhUJcddVVnX7N1vzpT3/CaDTyxRdfMH/+fJ555hn++Mc/RvffeuutfPrpp7zxxhts3LiRK664gkmTJlFaWsrpp59OSUkJEA7A9+zZw+mnn866deuYOnUqV155JcXFxTz00EPcf//9LR4+zJ07l8LCQr766ivuv/9+ysvLmTRpElOmTGHjxo28+eabrF27Nho0t2bp0qX84Ac/oLCwMGa7qqrMnj2bTZs2UVRUBMALL7zALbfcwk033URxcTF///vfGTFiBABffvklAIsXL2bPnj3R1+1RX19Pampq9PWnn37KWWedFZPxP//88ykpKaGurq7Vc3g8HgKBQMx5mvP7/bz00kskJye3+FyPF906Zf1///sfEydOjL6OrP2+7rrrWLJkCb/85S9pbGzkpptuYv/+/Zx55pn8+9//7lHraDqqeYuu9CGDaWpqwuVyEQiGq/QpCuypqiYpOQkF5cA2hbTUVLZWbKN8a0WnT42NBLN/eeOvfP3NJnw+PxaLOdxCTFWp31+PpusoSnjqOgp0ZaXAg3s/pzWrsl7rdLaZ8W6rx7pkxsWROrjtYHNH2nZQCCGE6BSaBm+uDE9TH5IefhMJYLeBLR12VcKyt6BgVKdPX58xYwa/+93vWLNmDRMmTADCAeGUKVNITk4+9Ad3kqFDhzJv3jwURSEvL4/i4mLmzZvHjTfeyI4dO1i8eDE7duxg8ODBAMyZM4d///vfLF68mCeeeIL+/fsDkJqaGl1K+8wzz3DOOedw//33A5Cbm8umTZv43e9+x/XXXx+99tlnn82dd94Zff2Tn/yEq6++ml/84hcA5OTksGDBAsaPH88LL7zQamyzZcuWmDipuZEjR0aPGT16NI899hh33nkns2bNih7z//7f/wOITgFPSUmJfh7tUVZWxnPPPRedrg5QVVVFZmbse5oBAwZE9/Xp06fFeX71q18xePDgmM5ZAO+88w5XXnklHo+HQYMGsWrVKvr27dvu8fUm3RqQT5gwoUWGszlFUXjkkUd45JFHjuGoulfFtu1UbNuOxWJhS0kZniYPmqaj6xoBfwCL1YrH48Hj8ZBgT4h+nMViwems67KpsWVlWyn+ehO1tU40TUNVVeqc+7Hb7YwefRJNTU0Eg0GMRiM2m43K3bu7tLd35CFBZD2901mHyWwiPy+XqVdMbjPjfXCPdSGOxsEPhwb0D/9Pr6nJQ/XefbIcQgghRPcpr4CK7ZCW9l0wHqEokJYKW7eFj8vp3PdG+fn5nH766SxatIgJEyZQVlbGRx99dNTv6X/2s5/x2muvRV+73e42jz311FNj6gaNGzeOp59+mlAoRHFxMaFQiNzc2NpLPp+PtLS0Ns+5efNmLrnkkphtZ5xxBs8++yyhUCja9uqUU06JOaaoqIiNGzfy+uuvR7dFWnZVVFREA+yDtacZ1t69e9m9ezfnnHPOYY9tr8rKSiZNmsQVV1zBjTfeeMTn+c1vfsMbb7zB6tWrWzx0mDhxIhs2bKCmpoaXX36ZqVOn8vnnn0cfhBxP4rao2/HK7XLjcrlwudyEQiHMJjOqWSXgD+DT/Xi9XkwmI8FAbFn9rpwau2LF2zy7YCF+fwCrxYLRZMTv9+PxNOEPBKiu3sugQbFP3LoyYx8hGW8RD5o/HNq5qxIAl7vxsA+HhBBCiC7lckEgAG3UOcFiAWdd+LguMHPmTG677Taef/55Fi9eTHZ2NuPHjz+qcz7yyCPMmTPnqMfmdrsxGAysW7euRe/oxMTEoz5/QkJCzGu3281Pf/pTbr/99hbHDhs2rNVz5Obmsnnz5lb3Rbbn5ua22kHoaOzevZuJEydy+umn89JLL8XsGzhwINXV1THbIq8Pzr7PnTuX3/zmN/z3v//lpJNOanGdhIQERowYwYgRIzjttNPIycnhlVde4e677+7Uz6cnkIA8ziQkJOB2NxIIBLDbE4g81zOZTBiNRoLBAIFAOBMd0ZVTY4PBIIuWvIbfHyAxMRH1wJNGg8GAoijous7OXZUMGNA/JhDu6ox9hGS8RTyIPBzaUlpGybebufeeOcesar8QQgjRKocDTCbw+sLT1A/m84X3d1Err6lTpzJr1iyWLl3Kn/70J37+858fdYuq/v37tzuD+sUXX8S8/uyzz8jJycFgMDBmzBhCoRB79+7l+9//fruvP3LkSD7++OOYbR9//DG5ubktAvvmxo4dy6ZNm6Lrutvjyiuv5N5776WoqChmbbWmacybN48TTjiBwsLCaKG29957r80p7iaTqdXORAerrKxk4sSJnHzyySxevLjF+5hx48Zx7733EggEohXEV61aRV5eXsx09d/+9rc8/vjj/Oc//2kxW6Atmqbh87Vek6e3k3eLcUaPrr5WoNk0FUVRsFos4e3oNHm9x6RS+JoP11JXV4fVYokG4+HxqKiqgqIo+P1+nE5nzMdJMStxvFFVNfpATGZqCCGE6HbZmZA5HGprY95TAuHXtU7Iyggf1wUSExOZNm0ad999N3v27IlZYx1RVlbGhg0bqKqqoqmpKVrs2e/3H/X1d+zYwR133EFJSQl/+ctfeO6556JrrHNzc7n66quZPn06K1eupKKigi+++IInn3yS//u//2vznHfeeSfvvfcejz76KFu2bOHVV1/l97///WGz9r/61a/45JNPuPXWW9mwYQOlpaW8/fbbhyzqNnv2bL73ve9x8cUXs3z5cnbs2MGXX37JlClT2Lx5M6+88kr0AcdDDz3E008/zYIFCygtLWX9+vU899xz0XNFAvaqqqo2i69VVlYyYcIEhg0bxty5c9m3bx9VVVVUVVVFj/nxj3+M2Wxm5syZfPPNN7z55pvMnz8/WgcM4KmnnuL+++9n0aJFZGRkRM8RWV7Q2NjIPffcw2effcb27dtZt24dM2bMoLKykiuuuOKQX8feSjLkccbT6CExIQG33ojX58NsMqGqKpqmEQyFsFmtaLqG2+3G2+Rt17rpo7Fvbw2apmE0xX6rGAwGDKqBQDA8dd7X7BenFLMSQgghhOhmqgrTLgtXU99VGV4zbrGEM+O1znBmfOrkLutHDuFp66+88goXXHBBtHhacz/5yU9Ys2ZN9PWYMWOAcOelttpztde1115LU1MT3/ve9zAYDMyaNSvaggzCReYixdAqKyvp27cvp512GhdddFGb5xw7dizLli3jgQce4NFHH2XQoEE88sgjrT5saO6kk05izZo13HvvvXz/+99H13Wys7OZNm1amx9jtVp5//33eeKJJ7jnnnvYvn07DoeDiRMn8tlnn3HiiSdGj73uuuvwer3MmzePOXPm0LdvXy6//PLo/qeffpo77riDl19+mfT0dLZt29bieqtWraKsrIyysrIWfdcja9mTk5N59913ueWWWzj55JPp27cvDzzwQMzX9YUXXsDv98dcH+DBBx/koYcewmAw8O233/Lqq69SU1NDWloa/+///T8++ugjRo0adcivY2+l6O2pFtCDNTQ0kJycTH19PUlJSd09nDYFAgH++c9/kpc/kocf+Q0KCrVOJ42NjdEiaokJCaSmpqLpGjf+5Dr6pKR0+brp995fzf0PPIbJaIppcQDh6ewutxtd18nKzGDgwAExlc6PRTsxTdN6zBryyD2+4IILotN8RO8i97j3k3vcu8n97f160j32er1UVFSQmZl5dB2GiorD1dYrtofXlJtM4cz41MnQC+ucaJpGQ0MDSUlJcfueUBydeLrHh/o5bW8cKhnyOJOZMZzMjOEUbfw6+jQq8shE03XqGxoYXVjAxAlnHZNvwPFnnUmfPn2oqanFaDLFTFtXDQZUVcVkMqIaVPbsqeryjH1zRUXF0SrrAX8Ak9lEZsZwpk29TAppCSGEEEJAOOguGBWupu5yhTPj2ZldmhkXQrSfBORxRlVVTh47mo/WfhKtam6xWAgGgtTXN2A2mxg7pvCYPQ0yGo3MuP4anl2wELfbHa2yHgwE8fp8WCxmbrv1p6SlpbJvbw39+vdl/FlnxhSd6wpFRcXMm78QV4OLtLQ0rFYLXq+Pki2lzJu/8Jhk54UQQgghegRV7fTWZkKIziEBeZzRNI116zeQlOQgGAjR5G3C5/OjqgopKcmYjEbWf1XE5MkXH7OgfMqUcL/FRUteo7bWSZPXi6qq9O2bxnnnTmTDhuKYLPV7763p0iy1pmm8uWwlrgYXQ4akRwta2O02bLZ0dlVWdmkPdCGEEEIIIYToDBKQx5mKbdup2Lad9MHp2OxWPB4PwUAQo8mI3W6nydPU5f29WzNiRBYFJ57A199swufzY7GYSU8fzMeffE4oFDqmWeryrRVUbNtOWlpai/YZiqIckx7oQgghhBBCCHG0JH0YZ9wuNwF/AKvVAjpEu6Ad+LfFYiHgD3R5f+/mItPDt2wpo09KHwYPGkRKcgqbNpWwc1clSUkO7HYbqqpit9sYkp6Oy+Vi2fK30DSt08fjanB99zVqRXd8jYQQQgghhBCioyRDHmcSHYmYzCZqampxOuvwNHnQNB1VVbDb7KSm9jmm/b0j08P37a0hGApSU1uLpoWfDvj9AVRVZU9VNUnJSSiEs9VdnaV2JDkwmU14vT7sdluL/dIDXQghhBBCCNETSEAeZzIzhpOSksyGDcUYDCpmkxnVFO5D7m50U9/QwJjRJ3Vpf+/mrcTq9u/nm02bcblcaLoWHY/f70fTNHRNw+Vy4/F4SLAnRM9hsVhwOuu6JEudnZVJZsZwSraUYrUNpqmpKTqt32azSQ90IYQQQgghRI8gAXkcimSaoy3iDyyT1nWdg5ZMd7qDW4kFg0H27KnGYDBgt9nQdJ1gMAiKgqIo6LpOIOAnEAjGnKcrs9SqqjJt6mU8/sRcNny1EU3X0HVQFFAVlYEDBzD1islS0E0IIYQQQggR1yQgjzMV27ZTt38/mRnDqa110uhpJOTVMBhUEhMTSUtNpW7//i6ZCt5aK7E9VVVoWghN03AFg+Gs+IHjFQBFQdM0gsFA9Dy6rh+TLLUe/ZeCoujhESnNtwshhBBCCCFE/JKAPM5EirolJiaCApqmo+v6gXXbYDKbaHQ3dvpU8OatxNKHhKeBu1wuDAYDEA6yQ3psqKuHd6AoCsGgRigUwufzUet04nA4uixLHRmrFgoxevRJ4SnrwSBGY3jKeuXu3dL2TAghhBBCCBH3JFqJA5E12wD76+sJhoKUlpVTV7efQMBPMBgkEPBTV7efsrKtBEPBTp8KHmklZrFY2FJSRklJKaVlW9m1qxK9HSlnv9/Hnj1VuNxu8vNyu6zlWfOxpqWloaoqCQkJJCcnk5CQgKqqMQXlhBBCCCHEsXPxxRczadKkVvd99NFHKIrCxo0bAbj99ts5+eSTsVgsjB49ulPHsXPnTmbMmMHgwYMxm80MHz6cWbNmUVtb2+5zrF69GkVR2L9//2GPu+SSSxg0aBAJCQmMHj2a119/vcVxy5cvJz8/H6vVSkFBAf/85z+j+wKBAL/61a8oKCggISGBwYMHM336dHbv3h1zjh/96EcMGzYMq9XKoEGDuPbaa1scczBFUfjb3/7W7s+7LQ899BCKorR6f3/3u9+hKAoTJkw46usc7OCvzZAhQ/jZz37W4vN2Op1cffXVJCUlkZKSwsyZM3G73dH97b1PEW+88QaKonDppZd2+ufUnATk3ayoqJj77n+Ux5+YC8DiJa9TXb0Xr9eLpmmoqgGjwYCqGtA0Da/XS2Ojh1AwxPr1GygtK++U1mKuBhcul4vK3btxN7oxGoxYrRZU1XDYj9V1nZ/eeAP33H0njzx8L48+cl+XBeORsXZ22zNN0ygtK+/Ur6kQQgghRFzQNXAWQ9WH4b/1rnufM3PmTFatWsWuXbta7Fu8eDGnnHIKJ510UnTbjBkzmDZtWqeOYdu2bXzve9+jtLSUv/zlL5SVlfHiiy/y3nvvMW7cOJxOZ6de75NPPuGkk05ixYoVbNy4kRtuuIHp06fzzjvvxBxz1VVXMXPmTL766isuvfRSLr30Ur7++msAPB4P69ev5/7772f9+vWsXLmSkpISfvSjH8Vca+LEiSxbtoySkhJWrFhBeXk5l19+ead+PocyaNAgPvjggxb3d9GiRQwbNuyozx8IBFpsO/hr89e//pWysrIWgfLVV1/NN998w6pVq3jnnXf48MMPuemmm6L723OfIrZt28acOXP4/ve/f9Sf0+HIlPVu1HzN9oAB/QEwGU0tCqShADrRImr19Q38YvavCGkaVquFE0edwJXTphxVEJyYmIjb3UggEMBuT4jUkSMUDB7y4yK279jJ+eefe8TX74jObnt2cCE7k9lEZsZwpk29rEsfLAghhBBCdLnqT2Dz72H/Zgj5wGCBlJEw8lYYcHqnX+6iiy6iX79+LFmyhPvuuy+63e12s3z5cn73u99Fty1YsACAffv2RbPmnWHOnDmYzWbeffddbLbwe8Vhw4YxZswYsrOzuffee3nhhReA8PvGBx54gKVLl7J3716GDh3K3XffzTnnnMPEiRMB6NOnDwDXXXcdS5YsaXG9e+65J+b1rFmzePfdd1m5ciUXXXQRAPPnz2fSpEncddddADz66KOsWrWK3//+97z44oskJyezatWqmPP8/ve/53vf+x47duyIBruzZ8+O7h8+fDi//vWvufTSSwkEAphMphZjy8jIAGDy5MnRj9m2bRsAL7zwAnPnzmXnzp1kZmZy3333ce211x7ya9u/f39OPvlkXn31Ve69914gHOjW1NRwxRVXsGnTpuixX375Jffccw9fffUVgUCA0aNHM2/ePMaOHRs9RlEUFi5cyL/+9S/ee+897rrrLh566KGYax78tdE0jd/+9recc8450a/N5s2b+fe//82XX37JKaecAsBzzz3HBRdcwNy5cxk8eHC77hNAKBTi6quv5uGHH+ajjz467AyJoyUZ8m7SfM32kCHp0V8WwdB3AbCigK5rhEIauq6hquEwORQKse9An/I9e6p47/013P/AYxQVFR/xePTmpdqazVHX2vkEtaqq+oiv3VGRtme1ztrvKtEfECkol5WZ0a6CcpGHIiUlpTgSHQwePAhHooOSLaXMm7/wqL6mQgghhBDdqvoT+GIO1K4Hcwo4MsJ/134V3l79Sadf0mg0Mn36dJYsWRLzPm358uWEQiGuuuqqTr9mc06nk/fff5+f//zn0ffXEQMHDuTqq6/mzTffjI5t+vTp/OUvf2HBggVs3ryZP/zhDyQmJjJ06FBWrFgBQElJCXv27GH+/PntHkd9fT2pqanR159++innnhubvDr//PP59NNPD3kORVFISUlp83N9/fXXOf3001sNxiEcFEN4dsKePXuir9966y1mzZrFnXfeyddff81Pf/pTbrjhBj744IPDfm4zZsyIeTCxaNEirr76asxmc8xxLpeL6667jrVr1/LZZ5+Rk5PDBRdcgMsVO4v1oYceYvLkyRQXFzNjxozDXh+goaEh5mvz6aefkpKSEg3GAc4991xUVeXzzz9v8zwH3yeARx55hP79+zNz5sx2jeVoSUDeTZqvg1Za6WVmUFUURcFms5GQYMduT4hZy60qCkajEVU1EAqF2L2nirnPPHfEU60b3Y0kJiRgNpnw+nyEQqFwmzVa77OmHGh7FjFw4MAjuu6RiLQ9czgc7KqsxOPxEAqF8Hg87KqsbHdBuYMfitjtNlRVxW63MSQ9HZfLxbLlb8n0dSGEEEL0PLoWzoz7neAYAaZEUAzhvx3Z4K+Dzc93yfT1GTNmUF5ezpo1a6LbFi9ezJQpU0hOTu706zVXWlqKruvk5+e3un/kyJHU1dWxb98+tmzZwrJly1i0aBGTJ08mKyuLc845h2nTpmEwGKKBWv/+/Rk4cGC7x75s2TK+/PJLbrjhhui2qqoqBgwYEHPcgAEDqKqqavUcXq+XX/3qV1x11VUkJSXF7PvVr35FQkICaWlp7Nixg7fffrvNsfTr1w+AlJQUBg4cGH09d+5crr/+em6++WZyc3O54447uOyyy5g7d+5hP7+LLrqIhoYGPvzwQxobG1m2bFmrgfTZZ5/NNddcQ35+PiNHjuSll17C4/HEfF8A/PjHP+aGG24gKyurXdPevV4vDz30EFdeeWX0a1NVVUX//v1jjjMajaSmprb5NW7tPq1du5ZXXnmFl19++bDj6CwSkHeTttZBJybYUVUVnXCF9VAwRCgUIhgMEgqFoscpBwJ2VVUxGo3ous6WLWVs2VJ2RONxJDlwJIUzxIkJCQRDQbw+H6qh5bdIJBCPPFk0GAyc9f3On/J0KIWFBcyedTN5uTm43O4jKih3qIciiqJIcTghhBBC9Fx134SnqdsGhaddNqcoYBsI+zeFj+tk+fn5nH766SxatAiAsrIyPvroo6POOP7sZz8jMTEx+udQDp5F2ZoNGzZgMBgYP378UY2ruQ8++IAbbriBl19+mVGjRh3ROQKBAFOnTkXX9ejU+ubuuusuvvrqK959910MBgPTp09v1+fb3ObNmznjjDNitp1xxhls3rz5sB9rMpm45pprWLx4McuXLyc3NzemLkBEdXU1N954Izk5OSQnJ5OUlITb7WbHjh0xxzXPah9OIBBg2rRp6LrOwoUL2/1xB2vtPrlcLq699lpefvll+vbte8Tn7ihZQ95N2loHbbcn4EhMZH99PQBNXm+Lj1UIZ8ijrw8E5sFgkI/WfkJ+fm6HxxOZBl6ypZTc3BHRVmIGg4EtpeU0NjZGj23+A68oCnl5OeTmjujwNY9WYWEBBQWjKN9agavBhSPJQXZWZrtbnbWnOJzTWdfpLeaEEEIIIbqcvy68ZtzYst4OAAYbhKrDx3WBmTNnctttt/H888+zePFisrOzjzrwfeSRR5gzZ84hjxkxYgSKovDtt9+2un/z5s306dOHfv36tZjSfrTWrFnDxRdfzLx585g+fXrMvoEDB1JdHbvEs7q6usUs00gwvn37dt5///0W2XGAvn370rdvX3Jzcxk5ciRDhw7ls88+Y9y4cZ36+RzKjBkzOPXUU/n666/bnGZ+3XXXUVtby/z58xk+fDgWi4Vx48bh9/tjjktISGjXNSNfmx07dvDWW2/FfG0GDhzI3r17Y44PBoM4nc4WX+O27lN5eTnbtm3j4osvjm6LzJQ1Go2UlJSQnZ3drrF2hGTIu0lb66AVRTnsN6Wiqi2edEYyvN6mlgF8ezSfBl65ezeKopCYmIiqqjgciZhMphaBrsFgYPCggcy547Zu6/etqio5I7IZO3Y0OSOyOzSO5g9FWtPR4nBCCCGEEHHD3CdcwC3Y1Pr+UFN4v7lPl1x+6tSpqKrK0qVL+dOf/sSMGTNaXabZEf3792fEiBHRP61JS0tj4sSJvPDCCzQ1xX7uVVVVvP7660ybNg1FUSgoKEDTtBZTqCMia6Kbz1Jty+rVq7nwwgt56qmnYip7R4wbN4733nsvZtuqVatiguhIwFlaWsp///tf0tLSDnvdSMDo87X+fhbCGe2DP4eRI0fy8ccfx2z7+OOPOeGEEw57TYBRo0YxatQovv76a3784x+3eszHH3/M7bffzgUXXMCoUaOwWCzU1NS06/wHa/61effdd1us+x43bhz79+9n3bp10W3vv/8+mqZx6qmnRrcd6j7l5+dTXFzMhg0bon9+9KMfMXHiRDZs2MDQoUOPaOyHIxnybhIJgOfNX8iuykoG9A+v52hsdFN14OmZqqqgh8utNQ/aw+3Q1Jip4+FtCvkjO54dj4hMA49UHHc66zCZTYwuLGDsmEL+t+4rvv5mE16vL1zd/cRRXNmDK5E3nxVgs6XH/E8iUhwuPy+3XcXhhBBCCCHiSp9R4WrqtV+BMTs2maPr0FQFaWPDx3WBxMREpk2bxt13301DQwPXX399i2PKyspwu91UVVXR1NTEhg0bADjhhBNaFAjriN/+9rdMmjSJ888/n8cee4zMzEy++eYb7rrrLtLT03n88ceBcAXy6667jhkzZrBgwQIKCwvZvn07e/fuZerUqQwfPhxFUXjnnXe44IILsNlsrU6V/+CDD7jooouYNWsWU6ZMia5ZNpvN0cBx1qxZjB8/nqeffpoLL7yQN954g//973+89NJLQDjgvPzyy1m/fj3vvPMOoVAoep7U1FTMZjOff/45X375JWeeeSZ9+vShvLyc+++/n+zs7ENmxzMyMnjvvfc444wzsFgs9OnTh7vuuoupU6cyZswYzj33XP7xj3+wcuVK/vvf/7b76/z+++8TCATaLDqXk5PDn//8Z0455RQaGhq46667jmhWQmtfm+rqajweD3379sVsNjNy5EgmTZrEjTfeyIsvvkggEODWW2/lyiuvZPDgwcDh75PVauXEE0+MuXbkczt4e2eSgLwbNQ+Ad+6qBKB67z50Xcdms2G1WAhpGrqmoagKDc2mTkeC8si/dV0ntU8fJk4466jH1NY08MmTLz7i6eHx6OCHImmpqVgsFnw+H7VOZ7uLwwkhhBBCxB1FDbc2+2IOuMrDa8YNtnBmvKkqnBkfeUv4uC4yc+ZMXnnlFS644IJoUNTcT37yk5js9JgxYwCoqKiItus6EtnZ2XzxxRc8/PDDTJ06NTpt+dJLL+XBBx+Mya6+8MIL3HPPPdx8883U1tYybNiwaHus9PR0Hn74YX79619He1a31vbs1VdfxePx8OSTT/Lkk09Gt48fP57Vq1cDcPrpp7N06VLuu+8+7rnnHnJycvjb3/4WDfQqKyv5+9//DsDo0aNjzv/BBx8wYcIE7HY7K1eu5MEHH6SxsZFBgwYxadIk7rvvPiyW1pdgAjz99NPccccdvPzyy6Snp7Nt2zYuvfRS5s+fz9y5c5k1axaZmZksXryYCRMmtPvrfLhZva+88go33XQTY8eOZejQoTzxxBOHXXLQmvZ8bQBef/11br31Vs455xxUVWXKlCnR1nrQvvvUHRS9oxUAepiGhgaSk5Opr69vdQ1GPNA0jS2lZZR8u5mdu6p47fU3SUhIQD3oF6TX58Pj8URfN8+gm80m7px9G1OmXHKMR9/ztdaHPCszg6lXTO7U7H8gEOCf//wnF1xwQZutKUTPJve495N73LvJ/e39etI99nq9VFRUkJmZidVqPfITtdqH/IRwMN4Ffci7m6ZpNDQ0kJSUJEmVXiqe7vGhfk7bG4dKhjwOqKpKdlYmJd9uJj8/N1ygLRBsMVXHarEQDAbx+/0YDCqgoBpU0lJTmXHDtRKMH6HCwgJGjRrJmg/Xsm9vDf3692X8WWdiNMqPhxBCCCF6uAGnQ//TwtXU/XXhzHifUV2aGRdCtJ9EHHHmjNNPo0+fPtTU1GI0mWKqqWsH1ooPHDiA22/7KXXO/RI8doLWMuTvvbeGaT14fbwQQgghRJSiQqq8pxEiHkkUF2eMRiMzrr+GZxcsxO12Y7VYMJqMBAPhvuBms4kZ11/D+eed2+JjNU3rVWu8j4WiomLmzV+Iq8FFWloaVqsFr9dHyZZS5s1f2O6e5kIIIYQQQgjRURKQx4FIIA1QvrWCyZPDve8WLXmNuro6fH4fqqrSr28aN1x/TatT01vL8mZmDJcs7yFomsaby1bianAxZMh3Vdbtdhs2Wzq7KitZtvwtCgpGyYMNIUSPJw9thRBCiPgjAXk3iwTSO3dVcvllF/L4E3MZOiSdaVMv4+23/tLquuZgMBizPSU5meeef6nTsrxtvWnrbW/myrdWULFtO2lpaS36YiqKQlpqKlsrtlG+tYKcEdndNEohhDh68tBWCCGEiE8SkHejyHTphgYXyckOAIxGAyUl3wXS55w9IeZjVqx4m0WLX6PW6Yy2PlNVlQS7nRNOyD/qLG9RUTFvvLkitt/4qBM45eQxrFu/oVe9mXM1uAj4A1itrbeJsFgsOJ11uJq1mxNCiJ5GluYIIYQQ8UsC8m4SmS69b98+goEQDQ31AOzcuQuDasTn87UIpFeseJun5/0ev98fcx5d1/H7/VRVVTNo0MDovo5meYuKirn/gceoqt6LpoXQdVAUqKraywerPyItrQ/pg9N7zZs5R5IDk9mE1+vDbre12O/z+TCZTTiSHN0wOiGEOHqyNEcIIYSIb/J/325SvrWCTZtLcLncNHoaMRrCz0aMBiONnkbqG1x8s+nb6NryYDDIiy8twufzAWAwGDAaDKhq+M2Vruts37ETTdNirmOxWAj4A4fN8mqaxtxnnmP3nipCoRCqasBoNKIo4anqgUAAl8uNzW5FVVXsdhtD0tNxuVwsW/5Wi+v2BNlZmWRmDKfWWYuu6zH7dF2n1ukkKzOD7KzMbhqhEEIcnY4szRFCCCHEsScBeTepr2/A6XQSCoWwWq1EwkEdsFqtaFoIp9NJfX0DAB+s/oj9++tRFAWjwRB+Y6UoKM16SAYCAWpra2Ou094s75YtZWzZUoau6xiNRlRVRVGUmIyJx9NEo7sx+vpYvpnTNI3SsnLWr99AaVl5pzwAUFWVaVMvw+FwsKuyEo/HQygUwuPxsKuyEofDwdQrJkvWSAjRY7VnaU57HtoKIYQQomvIlPVuUl9fTzAYRFUNNLobMRgNADQ2NhIKhjAcKN5WXx+eyv7ttyVomobBoIbnkR+gKAqKokQzvHX792M2mzEajdhsNmqdTvLzcg+b5f1o7ScHxqPGZFGaZ451XcfprCMxMTG67Viss+7KYkSFhQXMnnVz9PxOZx0ms4n8vFymXjG5R07FF0KICFmaI4QQoqutXr2aiRMnUldXR0pKyhGf529/+xtz5syhoqKC2267jWeffbbTxhjPJPXXTZKTklAUBZ/XSyAYjGa6FUUlEAzi9XpRFIXkpCQgnDUHQG95LkOzDG5tbR2lpeVs/raEDUUbUVVDu7K83ibvgeu3nNLYXEgLxbzu6jdzkWJEJSWlOBIdDB48CEeiI7p+vaio+KivUVhYwGOP3s8jD9/LPXffySMP38ujj9wnwbgQoseTpTlCiGPt4osvZtKkSa3u++ijj1AUhY0bNwJw++23c/LJJ2OxWBg9enSnjeHmm2/GYDDws5/9rMW+W265BUVRuP766zvtehFOp5PbbruNvLw8bDYbw4YN4/bbb48m2CJ27NjBhRdeiN1up3///tx1110Eg8Ho/pUrV/KDH/yAfv36kZSUxLhx4/jPf/7T5nV/85vfoCgKv/jFLzr9czqWfvrTn3L55Zezc+dOHn30Ua6//nouvfTS7h5Wl5OAvJs4khyoqopO20GwqqrRQPf7Z56O0WhA07WWb6qa/Ttyzui52jme/JG5qKoSLRLX2ngATCYT9fX1NHoa0XStS9/MHVyMyG63ddn6dVVVyRmRzdixo8kZkS3T1I9SVywxEEJ0nCzNEUIAoGvg/RYaPw//rXfd/5dnzpzJqlWr2LVrV4t9ixcv5pRTTuGkk06KbpsxYwbTpk3r9HEMHTqUN954g6ampug2r9fL0qVLGTZs2FGfPxAItNi2e/dudu/ezdy5c/n6669ZsmQJ//73v5k5c2b0mFAoxIUXXojf7+eTTz7h1VdfZcmSJTzwwAPRYz788EN+8IMf8M9//pN169YxceJELr74Yr766qsW1/zyyy/5wx/+EPM17Yncbjd79+7l/PPPZ/DgwTgcnZfsa14QOx7J/4G7iYISLsxmNGAwGNAPBCy6pkW3K4rCF1+s5403/srOXZWMGJENKIRCoeifYDBIKBTOWtvtNrKyMhg8eCAZGcMoLCwgpIXaFbROnHAWKSkp6LqOpmlomkYoFGoRnFdVVbOltJzNm0vY8NVGDO3MwB8JKUbUMxUVFXPf/Y/ywIOP88STT/PAg49z3/2PdspsBiFEx0WW5uTl5uByu9mzpwqX201+Xm6P7ZIhhOiAxv/BzlmwczZU3hP+e+es8PYucNFFF9GvXz+WLFkSs93tdrN8+fKY4HTBggXccsstZGVldfo4xowZw9ChQ1m5cmV028qVKxk2bBhjxoyJOfbf//43Z555JikpKaSlpXHRRRdRXl4e3b9t2zYUReHNN99k/PjxWK1WXn/99RbXPPHEE1mxYgUXX3wx2dnZnH322Tz++OP84x//iGbA3333XTZt2sRrr73G6NGj+eEPf8ijjz7K888/Hw0cn332WX75y1/y//7f/yMnJ4cnnniCnJwc/vGPf8Rcz+12c/XVV/Pyyy/Tp0+fw35NvvzyS37wgx/Qt29fkpOTGT9+POvXr485RlEU/vjHPzJ58mTsdjs5OTn8/e9/jznmn//8J7m5udhsNiZOnMi2bdsOe+1nnnmGgoICEhISGDp0KDfffDNutxsIT3mPBOBnn302iqIwYcIEXn31Vd5+++3oEt3Vq1cDsHPnTm644QZSU1NJTU3lkksuiRlDJLP++OOPM3jwYPLy8g47vu4kAXk3cbvdJCYmYLFYMBhUbLbwlHSbzYrBoKIoKk1NXp5f+BLPLljIgw89zt69+7DbbTFBcyRgNhgMmE1mdu7cxZ491ezYsYvSLeVYzJZ2Ba1Go5Gf3TQDk8kUDcYPDsrNZhOaFrl2eFsrM+g7jRQj6nmOxRIDIUTHydIcIY5Tjf+Dqseh6RswJIFpSPhv7zfh7V0QlBuNRqZPn86SJUtiEjvLly8nFApx1VVXdfo12zJjxgwWL14cfb1o0SJuuOGGFsc1NjZyxx138L///Y/33nsPVVWZPHlyi4TWr3/9a2bNmsXmzZs5//zz2zWG+vp6kpKSMBrDpbs+/fRTCgoKGDBgQPSY888/n4aGBr755ptWz6FpGi6Xi9TU1Jjtt9xyCxdeeCHnnntuu8bicrm47rrrWLt2LZ999hk5OTlccMEFuFyx76Uffvhhpk6dysaNG7ngggu4+uqrcTqdQDgYvuyyy7j44ovZsGEDP/nJT/j1r3992GurqsqCBQv45ptvePXVV3n//ff55S9/CcDpp59OSUkJACtWrGDPnj38/e9/Z+rUqUyaNIk9e/awZ88eTj/9dAKBAD/84Q9JTExkzZo1fPzxxyQmJjJp0qSYTPh7771HSUkJq1at4p133mnX16e7SFG3buJIcuBwOEhyJOF01hEMhae96ISrnDc2emIqnkO4MrumadEf6MgvuUjw3ORtwmqxoprCrcrcjY00eb04EhPaFbSOGJFFcnISNTW1LfapqorJaMLr86JpOqqqYLVaaPJ4uqyHbfNiRDa7FY/HQzAQxGgyYrfbpRhRnJF+x0LEt8jSHCHEcULXoPZVCNaDefh3RYGVBDDZIbAdav8E9rGgdO7/l2fMmMHvfvc71qxZw4QJE4DwdPUpU6aQnJzcqdc6lGuuuYa7776b7du3A/Dxxx/zxhtvRDOtEVOmTIl5vWjRIvr168emTZs48cQTo9t/8YtfcNlll7X7+jU1NTz66KPcdNNN0W1VVVUxwTgQfV1VVdXqeebOnYvb7Wbq1KnRbW+88Qbr16/nyy+/bPd4zj777JjXL730EikpKaxZs4aLLroouv3666+PPjh54oknWLBgAV988QWTJk3ihRdeIDs7m6effhqAvLw8iouLeeqppw557ebr2zMyMnjsscf42c9+xsKFCzGbzfTv3x+A1NRUBg4cCIDNZsPn80VfA7z22mtomsaCBQtITk5GVVUWL15MSkoKq1ev5rzzzgMgISGBP/7xj5jN5nZ/fbqLvCvuJpFCOz6/j6zsDOx2OxAu3ub1eqPBtt/vx+v14fX6otnqUCjY7InjgUy1rhMKafgDATxNTfgDASwWM4FAAHdjIwmJCYccj6Zp/OGlxTQ0uLBYzNhsNmw2GxaLJbq/0eMhFNKi16qvb2Dvvn0x/dK74mu0e/duvt28hZKSUkrLtlJSUsq3m7ewe/ceKUYUR2SJgRBCCBFHfFvAVwamfjEdeoDwa0M/8JWGj+tk+fn5nH766SxatAiAsrIyPvroo5jp6kfiZz/7GYmJidE/h9OvXz8uvPBClixZwuLFi7nwwgvp27dvi+NKS0u56qqryMrKIikpiYyMDCBcfK25U045pd1jbWho4MILL+SEE07goYceavfHHWzp0qU8/PDDLFu2LBq07ty5k1mzZvH6669/V/i5Haqrq7nxxhvJyckhOTmZpKQk3G53i8+z+Xr0hIQEkpKS2Lt3LwCbN2/m1FNPjTl+3Lhxh732f//7X8455xzS09NxOBxce+211NbW4vF42j1+gKKiIsrKyhg6dChJSUkkJiaSmpqK1+uNWWZQUFDQI4JxkIC820QK7dTV7efzz//Hvn3hrHRtbR3BYOiQH6vrNFtH/t1UmmAwSFNTEz6fj6amJvbvrycYDGfelcOUdysr28qmzd+iKGC3J2CzWrFZrZhMpkN+XCiksXv3nmi/9M6kqionjx1NfUMD++vr0XUdg6qi6zr76+upb2hg7JjCHpFt1TQtGoiWb63olUXOZImBEEIIEUdC9aD7QWkjYFOt4f2h+tb3H6WZM2eyYsUKXC4XixcvJjs7m/Hjxx/VOR955BE2bNgQ/dMeM2bMYMmSJbz66qvMmDGj1WMuvvhinE4nL7/8Mp9//jmff/450LIYWELCoRNcES6Xi0mTPQ7d3wAALn9JREFUJuFwOHjrrbdi3k8PHDiQ6urqmOMjr5tngiGcBf/JT37CsmXLYqalr1u3jr179zJ27FiMRiNGo5E1a9awYMECjEZjtL7Uwa677jo2bNjA/Pnz+eSTT9iwYQNpaWktPs+D3/8rinJU7123bdvGRRddxEknncSKFStYt24dzz//PNDxgmtut5uTTz6ZDz/8kPXr10e/F7Zs2cKPf/zj6HHtvVfxQKasd6Nly9/qkkC2uVAovNY8UjShLd+WbAlPDbdaY0L31ipItrxGiOrqmqMcaUuaprFu/QasFivBYIimA63ZILye3Wq1sv6rIiZPvjiug/JIH/Wduyq5/LILefyJuQwdkt4pfdTjifQ7FkIIIeKIIRkUM+je8DT1g2ne8H5D10whnzp1KrNmzWLp0qX86U9/4uc//3mLGXQd1b9//2iWuL0ia4sVRWl13XdtbS0lJSW8/PLLfP/73wdg7dq1RzzGhoYGzj//fCwWC3//+99bZLDHjRvH448/zt69e6Ofy6pVq0hKSuKEE06IHveXv/yFGTNm8MYbb3DhhRfGnOOcc86huDi2Ls8NN9xAfn4+v/rVrzAYDK2O7eOPP2bhwoVccMEFQDjTXlPTsffwI0eObFHk7bPPPjvkx6xbtw5N03j66aej79mXLVt22GuZzeYWDxfGjh3Lm2++Sd++fRkyZEhcxwDt1fM/gx7K6/Xy7//896jO0d5fah5PE1ZbO6ezHHTKULOeiIfy0VH84mpL+dYKNm3+Fp/Ph6oqJNjtJCQkkGC3oygqPq+PbzZtjusp0LFFzsJTqxyJib2yyJn0OxZCCCHiiCUXLCMguC88vbI5XYfQPrDkhI/rAomJiUybNo27776bPXv2tNr3u6ysjA0bNlBVVUVTU1M029mZbaoMBgObN29m06ZNrQaqffr0IS0tjZdeeomysjLef/997rjjjiO6VkNDA+eddx6NjY288sorNDQ0UFVVRVVVVTSwPO+88zjhhBO49tprKSoq4j//+Q/33Xcft9xyS3Sp6NKlS5k+fTpPP/00p556avQckX7mDoeDE088MeZPQkICaWlpMWveD5aTk8Of//xnNm/ezOeff87VV1+NzdYyiXIoP/vZzygtLeWuu+6ipKSEpUuXtqiof7ARI0YQCAR47rnn2Lp1K3/+85958cUXD3utjIwMNm7cSElJCTU1NQQCAa6++mr69u3L1VdfzUcffURFRQWrV6/m9ttvb7XVXk8gAXk3ee75l7r0/M2DdV3XWbduwyGPz8vLwWq14PP5Y4Kp9lZRr9/f+Zn++v31OJ11aJqGzWrDbDZjNpkwm83YrFY0TcPprKN+f9dMtTpaBxc5i/zCs9k6v496PJB+x0IIIUQcUVRIuy6cAQ9sh1Aj6KHw34Ht4e1p0zu9oFtzM2fOpK6uLtpb+mA/+clPGDNmDH/4wx/YsmULY8aMYcyYMezevbtTx5GUlERSUlKr+1RV5Y033mDdunWceOKJzJ49m9/97ndHdJ3169fz+eefU1xczIgRIxg0aFD0z86dO4HwA4J33nkHg8HAuHHjuOaaa5g+fTqPPPJI9DwvvfQSwWCQW265JeYcs2bNOqJxRbzyyivU1dUxduxYrr32Wm6//fYOzzgYNmwYK1as4G9/+xuFhYW8+OKLPPHEE4f8mMLCQp555hmeeuopTjzxRF5//XWefPLJw17rxhtvJC8vj1NOOYV+/frx8ccfY7fbWb16NUOGDOHyyy9n5MiRzJw5E6/X2+Y9jncyZb2bfPnl+sMfdBRa6x9+KDkjshk5Mo8NG4rx+ryYTWZUVUVVFA69oj0sNzfnKEbbuvqGBoLBICajqdUiYQaDgUAwQH1D1077P1LNi5wBeDyN0b/NZktMkbPeUvk40u/4zWUrqdi2HaezDpPZRH5eLlOvmNyrpugLIYQQcS/hFBh4b7jauq8MQjXhaerWE8PBeEL7i5QdiXHjxrV4T9rcwdXOO8vChQsPGZz97W9/i3l97rnnsmnTpphtzcedkZFxyM8jYsKECe06bvjw4fzzn/9sc/+RfF3a8zFjxoxpUZX98ssvj3nd2vj3798f8/qiiy6KqcoOtNpOrrnZs2cze/bsmG3XXntt9N8pKSktrt2vXz/efffdFucaOHAgL7zwAklJSa0meg6XsY83EpB3k2CgfVPBD+VwP/DfBbE6gwYNPOSxqqrys5tm8NgTc6mursYf8IMebsPWHj+96fp2HdcRycnJGI1GgqEgJswxs+l1IBgKYjQaj2n7jI6IFDnz+/3s2LGTYCjApPMnUlpWjtFgYsDA/r2yyFlhYQEFBaMo31qBq8GFI8lBdlamZMaFEEKI7pBwSri1mW9LuICbITk8Tb0LM+NCiPaTgLybZGVlsP2gFgMdoSgK6oGK482nPEfWxkSqIWqahtVqYeoVkw97zsLCAu67Zw5vvLmCki2l+Hx+FAX27q1ps1ojgMORQOXuPZ2e5U1OTiI1NRWn04nX2xTO2htUtJCGP+BHVQ2kpqaSnByf01McSQ6CoSBbt1ag6RqJB6o9Gg1G3I2NeLZuIzWtT68scib9joUQQog4oqhgze/uUQghWiEBeTfJzs7gg9UfHvHHHxyIN9/ePBhXVYWpl09udx++g7Obuyp384eXFlNTU0uwlQJvRqOBxITELsnyZmdlcsLIPDZu/JpAIEiTtwktoIcLvCUkYDIaOWFkHpqmsX79hrjLxGZmDCcQCOLz+0lMTERVww9LVNWAxWLB7XYTDATJzBjezSMVQgghhBBCdAcJyLvJ4MNMIT8cVVWjWfK0tFSGDxtK0cZifD4fmgaKQjgzfvlkZs26ucPnjmQ3ExITaGxsxGAwkJiQgNfrJaRpGFQVq9VKk9eLu7GRhMTO7/UXKRK2e08VrgYX/Qf0RVVUNF3D09iEwWCgbv9+Hnr4SQL+ACazicyM4XHTTqxi23ZMJhMWixmfz4vJGA7INS2Ez+fFbDZjNJmo2LZdsslCCCGEEEIchyQg7yb5I/Ow2214PE0d+jhFUdB1ncsmX0xmxnD69e/L+LPOxGg04vf7Wbb8LfbsqWLQoIFMvaL9mfE2r8d369AVVcVut0f36c3+qxzcL62THFwkLOD3YjKbGDCgP/tqatlbvY+0tDSsVgtery/aTmz2rJu7PSh3NbgwGgxkZWZQVbWXYCjc0z0YCpKYmMjAAf1xuxt73RpyIYQQQgghRPtIQN5NckZkc+KJJ/DVV0UEg22vzzYYVHT9u0Bc13XsdhtTLruE3NwRMceazWauuXpap47T7XaTmJiAy6W3uo7bZDKRmJiA2+3u1Os2d/A0+sTERBYveY3q6r0MGZIeLV5nt9uw2dLZVVnJsuVvUVAwqlunrzuSHJjMJsxmC3n5Ofj9PgBycrIxmy00eZowmf29cg25EEIIIYQQ4vDiY7HtcShS1Xzo0KHYbDYslnAm22IxR4PIcBAePj5SUd1oNFBw4ihGjMg6JuN0JDlwOBykDx5MYkIiwVAQr9cXzfKmDx6Mw+Ho8qAyMo1+7NjRKKrCtu07SEtLa7UdWvN2Yt0pOyuTzIzh1DprQQe7LTyt325LAB1qnU6yMjPIzsrs1nEKIYQQQgghuocE5N0oUtX8zDNOY8CAfgAMGNCPU04eQ/rgQdisVkwmI0ajEZPJiNVqYdjQofz0phuOWeY3ElT6/D5y80aQl5dDzogs8vJyyM0dgc/vO+ZBZaSdmNVqaXW/xWKJi3ZikTXwDoeDXZWVNDV5AP5/e3ceHtPZ/w/8PTPZI8mIyCZrZbFU0xBcoXSxxJ6ipJ6UhBRpqaCInaetvaW0Sp9etdQuLYrH8lVLJESQiC2RhEatkRIkkWVG5v794Zd5jCQEiTOZvl/Xlesy91nmM/NOIp8559wHRUWFuHb9OqysrNC/X2+9mYSOiIiIiIheLZ6yLrGy07EzMi8i/UIa/j1zCny8vXD27Hls3LwF6emZUJWUwMTUFI18vV/5hGVlTeWixT/g+vUbqGdrizp16qCkpATXr9+QpKksOxW8uLgEFhbm5ZaXlJTA2MRYL04Ff/wa+KvXrgMA8gseoJGvD/r36y35de5ERERERCQdNuR6QC6Xo+Frnki/kKa9bdeT101LeUuvJydWy829C2MTY8mayrKj9ukZmTA3b6Bz2roQAndyc9HI10dvTgV/8kOXKZPHwcfbi0fGiYiIiIj+4diQ67HHbz8mNX36gODxo/bXrl9HPVtbmJqaoqSkBHdyc/XyVPCKPnQhIiIiMjQ9e/aEWq3Gnj17yi2Li4tD+/btkZKSgrlz5yI+Ph63b9+Gh4cHIiMjERUVJUHFRNJiQ05Vpm8fEOjTUXsiIiIifaXRaF7ZQZWIiAj07dsX165dg4uLi86ylStXIiAgAElJSbC3t8fatWvh6uqKo0ePYtiwYVAoFBg5cmSN1EWkr2pFQ7506VIsWLAA2dnZ8PPzw3fffYdWrVpJXRZJTJ+O2hMRERHpo9Onz2oPYKhVahibGMPTw73G5iXq0aMH6tevj1WrVmHq1Kna8YKCAsTExGDBggUYMmSIzjavvfYaEhISsGXLFjbk9I+j953Lpk2bMHbsWMyYMQPJycnw8/NDUFAQcnJypC6N9MDjt0Pz9mrIZpyIiIjo/zt9+iwWLf4B6emZsKpjBWdnJ1jVsUJ6RiYWLf4Bp0+frfbnNDIywqBBg7Bq1SrtbXsBICYmBqWlpRgwYECF292/fx+2trbVXg+RvtP77mXhwoUYOnQoBg8ejCZNmmD58uWwsLDAihUrpC6NiIiIiEgvaTQabNq8Bfl5+XBxaQALC3PI5XJYWJjDpUED5OfnY3PMVmg0mmp/7iFDhuDSpUuIjY3Vjq1cuRJ9+/aFjY1NufWPHj2KTZs2YdiwYdVeC5G+0+tT1lUqFZKSkjBp0iTtmFwuR8eOHZGQkFDhNiUlJSgpKdE+zsvLAwCo1Wqo1eqaLfgllNWmzzXSy2HGho8ZGz5mbNiYr+GrTRmr1WoIIaDRaF6oab548U9kXf4LtvVsARkg8L+j1ZABtra2+DPrMi5e/BNeXq9VY+WAj48P2rRpg59//hnt27fHxYsXERcXh5kzZ5Z7LefOnUNwcDCmT5+Ojh07vvQHBGVH5cveOzI8+pSxRqOBEAJqtRoKhUJnWVV/z+h1Q3779m2UlpbCwcFBZ9zBwQEXLlyocJs5c+bg3//+d7nx//u//4OFhUWN1Fmd9u3bJ3UJVMOYseFjxoaPGRs25mv4akPGRkZGcHR0REFBAVQq1XNvn52djeLiYiiVNnj48GEF+1eguLgY2dnZsLe3q46SdQwYMADR0dGYPXs2fvzxR3h6esLf3197sAwALly4gF69emHQoEH47LPPdJa9rPz8/GrbF+knfchYpVKhqKgIhw8fLvdzVlhYWKV96HVD/iImTZqEsWPHah/n5eXB1dUVnTt3hrW1tYSVPZ1arca+ffvQqVMnGBsbS10O1QBmbPiYseFjxoaN+Rq+2pRxcXExrl69ijp16sDMzOy5t3d0dISZmRkePiyFhYVJueWFhUUwMzODo6NjjfyNPGjQIEyaNAk7d+7E5s2bERkZqXO6+vnz5xEcHIywsDDMmzev2p5XCIH8/HxYWVlBJpNV235Jf+hTxsXFxTA3N0f79u3L/ZxW9QMmvW7I7ezsoFAocOvWLZ3xW7duwdHRscJtTE1NYWpqWm7c2NhY73/xArWnTnpxzNjwMWPDx4wNG/M1fLUh49LSUshkMsjl8heatNbL6zV4ergjPSMTFuYNdBoXIQRyc3PRyNcHXl6v1cikuNbW1ggJCcGUKVOQl5eHwYMHa5/n3Llz6NChA4KCgvD5559rJ2tWKBSoX7/+Sz1v2SnMZe8dGR59ylgul0Mmk1X4O6Wqv2P0+rvUxMQELVq0wP79+7VjGo0G+/fvR2BgoISVERERERHpL7lcjpD+fWBlZYVr16+jsLAQpaWlKCwsxLXr12FlZYX+/XrXaEMTERGBu3fvIigoCM7OztrxX3/9FX///TfWrl0LJycn7VfLli1rrBYifaXXDTkAjB07Fj/99BNWr16NtLQ0fPLJJ3jw4AEGDx4sdWlERERERHrLz68ZxkR9Cl8fb+QXFODmzWzkFxSgka8PxkR9WiP3IX9cYGAghBD473//qzM+c+ZMCCHKfV2+fLlG6yHSR3p9yjoAhISE4O+//8b06dORnZ2NN998E3v27Ck30RsREREREeny82uGZs2a4tKfWcjPy4eVtRUavuYp+am+RPSI3jfkADBy5EiMHDlS6jKIiIiIiGoduVwOb6+GUpdBRBXgR2NEREREREREEmBDTkRERERERCQBNuRERERERHpKCCF1CURUier4+WRDTkRERESkZ8ruYVxYWChxJURUmbKfz6rec7witWJSNyIiIiKifxKFQgGlUomcnBwAgIWFBWQymcRV6T+NRgOVSoXi4mLOJG+g9CFjIQQKCwuRk5MDpVIJhULxwvtiQ05EREREpIccHR0BQNuU07MJIVBUVARzc3N+gGGg9CljpVKp/Tl9UWzIiYiIiIj0kEwmg5OTE+zt7aFWq6Uup1ZQq9U4fPgw2rdv/1KnEZP+0peMjY2NX+rIeBk25EREREREekyhUFTLH/7/BAqFAg8fPoSZmRkbcgNlaBnzwgoiIiIiIiIiCbAhJyIiIiIiIpIAG3IiIiIiIiIiCRj8NeRlN2vPy8uTuJKnU6vVKCwsRF5enkFcC0HlMWPDx4wNHzM2bMzX8DFjw8eMDV9tybis/yzrRytj8A15fn4+AMDV1VXiSoiIiIiIiOifJD8/HzY2NpUul4lntey1nEajwY0bN2BlZSX5feqeJi8vD66urrh69Sqsra2lLodqADM2fMzY8DFjw8Z8DR8zNnzM2PDVloyFEMjPz4ezszPk8sqvFDf4I+RyuRwuLi5Sl1Fl1tbWev2NRS+PGRs+Zmz4mLFhY76GjxkbPmZs+GpDxk87Ml6Gk7oRERERERERSYANOREREREREZEE2JDrCVNTU8yYMQOmpqZSl0I1hBkbPmZs+JixYWO+ho8ZGz5mbPgMLWODn9SNiIiIiIiISB/xCDkRERERERGRBNiQExEREREREUmADTkRERERERGRBNiQExEREREREUmADbmeWLp0KTw8PGBmZobWrVvj+PHjUpdEL2DOnDlo2bIlrKysYG9vj/fffx/p6ek66xQXF2PEiBGoV68e6tSpg759++LWrVsSVUwva+7cuZDJZBg9erR2jBnXftevX8dHH32EevXqwdzcHM2aNcPJkye1y4UQmD59OpycnGBubo6OHTsiMzNTwoqpqkpLSzFt2jR4enrC3NwcDRs2xJdffonH57hlvrXL4cOH0bNnTzg7O0Mmk2Hbtm06y6uSZ25uLkJDQ2FtbQ2lUomIiAgUFBS8wldBT/O0jNVqNaKjo9GsWTNYWlrC2dkZgwYNwo0bN3T2wYz127N+jh8XGRkJmUyGb7/9Vme8tmbMhlwPbNq0CWPHjsWMGTOQnJwMPz8/BAUFIScnR+rS6DnFxsZixIgROHbsGPbt2we1Wo3OnTvjwYMH2nXGjBmDHTt2ICYmBrGxsbhx4wb69OkjYdX0ok6cOIEff/wRb7zxhs44M67d7t69i7Zt28LY2Bi7d+9GamoqvvnmG9StW1e7zvz587FkyRIsX74ciYmJsLS0RFBQEIqLiyWsnKpi3rx5WLZsGb7//nukpaVh3rx5mD9/Pr777jvtOsy3dnnw4AH8/PywdOnSCpdXJc/Q0FCcP38e+/btw86dO3H48GEMGzbsVb0EeoanZVxYWIjk5GRMmzYNycnJ2LJlC9LT09GrVy+d9ZixfnvWz3GZrVu34tixY3B2di63rNZmLEhyrVq1EiNGjNA+Li0tFc7OzmLOnDkSVkXVIScnRwAQsbGxQggh7t27J4yNjUVMTIx2nbS0NAFAJCQkSFUmvYD8/Hzh7e0t9u3bJ95++20RFRUlhGDGhiA6Olq89dZblS7XaDTC0dFRLFiwQDt27949YWpqKjZs2PAqSqSX0L17dzFkyBCdsT59+ojQ0FAhBPOt7QCIrVu3ah9XJc/U1FQBQJw4cUK7zu7du4VMJhPXr19/ZbVT1TyZcUWOHz8uAIi//vpLCMGMa5vKMr527Zpo0KCBOHfunHB3dxeLFi3SLqvNGfMIucRUKhWSkpLQsWNH7ZhcLkfHjh2RkJAgYWVUHe7fvw8AsLW1BQAkJSVBrVbr5N2oUSO4ubkx71pmxIgR6N69u06WADM2BNu3b0dAQAD69esHe3t7+Pv746efftIuz8rKQnZ2tk7GNjY2aN26NTOuBdq0aYP9+/cjIyMDAHD69GnEx8eja9euAJivoalKngkJCVAqlQgICNCu07FjR8jlciQmJr7ymunl3b9/HzKZDEqlEgAzNgQajQYDBw7E+PHj0bRp03LLa3PGRlIX8E93+/ZtlJaWwsHBQWfcwcEBFy5ckKgqqg4ajQajR49G27Zt8frrrwMAsrOzYWJiov0PooyDgwOys7MlqJJexMaNG5GcnIwTJ06UW8aMa78///wTy5Ytw9ixYzF58mScOHECo0aNgomJCcLCwrQ5VvR7mxnrv4kTJyIvLw+NGjWCQqFAaWkpZs2ahdDQUABgvgamKnlmZ2fD3t5eZ7mRkRFsbW2ZeS1UXFyM6OhoDBgwANbW1gCYsSGYN28ejIyMMGrUqAqX1+aM2ZAT1ZARI0bg3LlziI+Pl7oUqkZXr15FVFQU9u3bBzMzM6nLoRqg0WgQEBCA2bNnAwD8/f1x7tw5LF++HGFhYRJXRy9r8+bNWLduHdavX4+mTZsiJSUFo0ePhrOzM/MlquXUajX69+8PIQSWLVsmdTlUTZKSkrB48WIkJydDJpNJXU614ynrErOzs4NCoSg3A/OtW7fg6OgoUVX0skaOHImdO3fi4MGDcHFx0Y47OjpCpVLh3r17Ousz79ojKSkJOTk5aN68OYyMjGBkZITY2FgsWbIERkZGcHBwYMa1nJOTE5o0aaIz1rhxY1y5cgUAtDny93btNH78eEycOBEffvghmjVrhoEDB2LMmDGYM2cOAOZraKqSp6OjY7mJdB8+fIjc3FxmXouUNeN//fUX9u3bpz06DjDj2i4uLg45OTlwc3PT/u31119/4fPPP4eHhweA2p0xG3KJmZiYoEWLFti/f792TKPRYP/+/QgMDJSwMnoRQgiMHDkSW7duxYEDB+Dp6amzvEWLFjA2NtbJOz09HVeuXGHetUSHDh1w9uxZpKSkaL8CAgIQGhqq/Tczrt3atm1b7naFGRkZcHd3BwB4enrC0dFRJ+O8vDwkJiYy41qgsLAQcrnunz8KhQIajQYA8zU0VckzMDAQ9+7dQ1JSknadAwcOQKPRoHXr1q+8Znp+Zc14ZmYm/vjjD9SrV09nOTOu3QYOHIgzZ87o/O3l7OyM8ePHY+/evQBqecZSzypHQmzcuFGYmpqKVatWidTUVDFs2DChVCpFdna21KXRc/rkk0+EjY2NOHTokLh586b2q7CwULtOZGSkcHNzEwcOHBAnT54UgYGBIjAwUMKq6WU9Psu6EMy4tjt+/LgwMjISs2bNEpmZmWLdunXCwsJCrF27VrvO3LlzhVKpFL///rs4c+aMCA4OFp6enqKoqEjCyqkqwsLCRIMGDcTOnTtFVlaW2LJli7CzsxMTJkzQrsN8a5f8/Hxx6tQpcerUKQFALFy4UJw6dUo7w3ZV8uzSpYvw9/cXiYmJIj4+Xnh7e4sBAwZI9ZLoCU/LWKVSiV69egkXFxeRkpKi8/dXSUmJdh/MWL896+f4SU/Osi5E7c2YDbme+O6774Sbm5swMTERrVq1EseOHZO6JHoBACr8WrlypXadoqIi8emnn4q6desKCwsL0bt3b3Hz5k3piqaX9mRDzoxrvx07dojXX39dmJqaikaNGon//Oc/Oss1Go2YNm2acHBwEKampqJDhw4iPT1domrpeeTl5YmoqCjh5uYmzMzMxGuvvSamTJmi84c7861dDh48WOH/vWFhYUKIquV5584dMWDAAFGnTh1hbW0tBg8eLPLz8yV4NVSRp2WclZVV6d9fBw8e1O6DGeu3Z/0cP6mihry2ZiwTQohXcSSeiIiIiIiIiP6H15ATERERERERSYANOREREREREZEE2JATERERERERSYANOREREREREZEE2JATERERERERSYANOREREREREZEE2JATERERERERSYANOREREREREZEE2JATEVGtc+jQIchkMty7d6/K23h4eODbb7+tsZpeBZlMhm3btlXb/sLDw/H+++9X2/4ep1Kp4OXlhaNHj9bI/mu7mnzvH6dSqeDh4YGTJ0/W+HMREdHzY0NORETVKjw8HDKZDJGRkeWWjRgxAjKZDOHh4a++sCrIy8vDlClT0KhRI5iZmcHR0REdO3bEli1bIISQurxqt3jxYqxatUr7+J133sHo0aOrZd/Lly+Hp6cn2rRpU+HyBQsW4F//+hcAYP369Xjvvfd0lpd96FL25eDggL59++LPP/+slvqk9uR7/yJmzpyp8x7Z2NigXbt2iI2N1a5jYmKCcePGITo6+iUrJiKimsCGnIiIqp2rqys2btyIoqIi7VhxcTHWr18PNzc3CSur3L1799CmTRv88ssvmDRpEpKTk3H48GGEhIRgwoQJuH//vtQlVjsbGxsolcpq368QAt9//z0iIiIqXSchIQFt27YFAMTFxWn//aT09HTcuHEDMTExOH/+PHr27InS0tIXqkutVr/QdjWhut77pk2b4ubNm7h58yYSEhLg7e2NHj166Hy/hoaGIj4+HufPn3/p5yMiourFhpyIiKpd8+bN4erqii1btmjHtmzZAjc3N/j7++usW1JSglGjRsHe3h5mZmZ46623cOLECZ11du3aBR8fH5ibm+Pdd9/F5cuXyz1nfHw82rVrB3Nzc7i6umLUqFF48OBBlWuePHkyLl++jMTERISFhaFJkybw8fHB0KFDkZKSgjp16gAA7t69i0GDBqFu3bqwsLBA165dkZmZqd3PqlWroFQqsXPnTvj6+sLCwgIffPABCgsLsXr1anh4eKBu3boYNWqUTmPp4eGBL7/8EgMGDIClpSUaNGiApUuXPrXmq1evon///lAqlbC1tUVwcLD2vblw4QIsLCywfv167fqbN2+Gubk5UlNTAeieNh0eHo7Y2FgsXrxYe8Q1KysLXl5e+Prrr3WeNyUlBTKZDBcvXqywrqSkJFy6dAndu3evtPbHG/L4+PhKG3J7e3s4OTmhffv2mD59OlJTU3Hx4kWcOHECnTp1gp2dHWxsbPD2228jOTlZZ1uZTIZly5ahV69esLS0xKxZs1BaWoqIiAh4enrC3Nwcvr6+WLx4sc52Ze/L7Nmz4eDgAKVSiS+++AIPHz7E+PHjYWtrCxcXF6xcuVK7jUqlwsiRI+Hk5AQzMzO4u7tjzpw5lb7+J09Zf+eddzBq1ChMmDABtra2cHR0xMyZMyvdvoyRkREcHR3h6OiIJk2a4IsvvkBBQQEyMjK069StWxdt27bFxo0bn7k/IiJ6tdiQExFRjRgyZIhOw7JixQoMHjy43HoTJkzAb7/9htWrVyM5ORleXl4ICgpCbm4ugEdNZ58+fdCzZ0+kpKTg448/xsSJE3X2cenSJXTp0gV9+/bFmTNnsGnTJsTHx2PkyJFVqlWj0WDjxo0IDQ2Fs7NzueV16tSBkZERgEeN1MmTJ7F9+3YkJCRACIFu3brpHH0tLCzEkiVLsHHjRuzZsweHDh1C7969sWvXLuzatQtr1qzBjz/+iF9//VXneRYsWAA/Pz+cOnUKEydORFRUFPbt21dhzWq1GkFBQbCyskJcXByOHDmCOnXqoEuXLlCpVGjUqBG+/vprfPrpp7hy5QquXbuGyMhIzJs3D02aNCm3v8WLFyMwMBBDhw7VHnF1c3MrlyMArFy5Eu3bt4eXl1eFtcXFxcHHxwdWVlY643PnzoVSqYRSqUR2djbefvttKJVKnDt3TvvBQnx8fIX7BABzc3MAj5rf/Px8hIWFIT4+HseOHYO3tze6deuG/Px8nW1mzpyJ3r174+zZsxgyZAg0Gg1cXFwQExOD1NRUTJ8+HZMnT8bmzZt1tjtw4ABu3LiBw4cPY+HChZgxYwZ69OiBunXrIjExEZGRkRg+fDiuXbsGAFiyZAm2b9+OzZs3Iz09HevWrYOHh0elr6Uiq1evhqWlJRITEzF//nx88cUXleZfkZKSEqxcuRJKpRK+vr46y1q1aoW4uLjnqoeIiF4BQUREVI3CwsJEcHCwyMnJEaampuLy5cvi8uXLwszMTPz9998iODhYhIWFCSGEKCgoEMbGxmLdunXa7VUqlXB2dhbz588XQggxadIk0aRJE53niI6OFgDE3bt3hRBCREREiGHDhumsExcXJ+RyuSgqKhJCCOHu7i4WLVpUYc23bt0SAMTChQuf+toyMjIEAHHkyBHt2O3bt4W5ubnYvHmzEEKIlStXCgDi4sWL2nWGDx8uLCwsRH5+vnYsKChIDB8+XPvY3d1ddOnSRef5QkJCRNeuXbWPAYitW7cKIYRYs2aN8PX1FRqNRru8pKREmJubi71792rHunfvLtq1ayc6dOggOnfurLN+WVZl3n77bREVFaVTw/Xr14VCoRCJiYlCiEf52NnZiVWrVlX6PkVFRYn33nuv3Pjdu3dFVlaWmDFjhggKChJZWVli6dKlomXLliIrK0tkZWVp8zp48KBOxjdu3BBt2rQRDRo0ECUlJeX2XVpaKqysrMSOHTt03q/Ro0dXWmeZESNGiL59++q8L+7u7qK0tFQ75uvrK9q1a6d9/PDhQ2FpaSk2bNgghBDis88+E++9957O+/s0Fb33b731ls46LVu2FNHR0ZXuY8aMGUIulwtLS0thaWkpZDKZsLa2Frt37y637uLFi4WHh0eVaiMiolfHSMLPAoiIyIDVr18f3bt3x6pVqyCEQPfu3WFnZ6ezzqVLl6BWq3VOVzY2NkarVq2QlpYGAEhLS0Pr1q11tgsMDNR5fPr0aZw5cwbr1q3TjgkhoNFokJWVhcaNGz+1VlHFCdvS0tJgZGSkU0+9evXg6+urrRcALCws0LBhQ+1jBwcHeHh4aE97LxvLycl56usKDAysdGb406dP4+LFi+WOQhcXF+PSpUvaxytWrICPjw/kcjnOnz8PmUxWpddaxtnZGd27d8eKFSvQqlUr7NixAyUlJejXr1+l2xQVFcHMzKzceNnR8ePHj6Nv377w8PDAqVOn0KtXr0qPJru4uEAIgcLCQvj5+eG3336DiYkJbt26halTp+LQoUPIyclBaWkpCgsLceXKFZ3tAwICyu1z6dKlWLFiBa5cuYKioiKoVCq8+eabOus0bdoUcvn/TiR0cHDA66+/rn2sUChQr149bYbh4eHo1KkTfH190aVLF/To0QOdO3eu9D2qyBtvvKHz2MnJqdz3yJN8fX2xfft2AEB+fj42bdqEfv364eDBgzqv3dzcHIWFhc9VDxER1Tw25EREVGOGDBmiPW38WddDv4yCggIMHz4co0aNKresKpPI1a9fH0qlEhcuXKiWeoyNjXUey2SyCsc0Gs0LP0dBQQFatGih8yFEmfr162v/ffr0aTx48AByuRw3b96Ek5PTcz/Xxx9/jIEDB2LRokVYuXIlQkJCYGFhUen6dnZ2OHv2rM5YXFwcunbtCuDRKf2HDh3CmDFjUFRUBGNjY8ydOxeTJ0/G5MmTy21nbW0Ne3t7nQ8fwsLCcOfOHSxevBju7u4wNTVFYGAgVCqVzvaWlpY6jzdu3Ihx48bhm2++QWBgIKysrLBgwQIkJibqrPe8GTZv3hxZWVnYvXs3/vjjD/Tv3x8dO3Ysd1nC07zI94iJiYnOpQP+/v7Ytm0bvv32W6xdu1Y7npubq/N9QURE+oENORER1Ziy65llMhmCgoLKLW/YsCFMTExw5MgRuLu7A3h0bfSJEye0t99q3Lix9ghgmWPHjuk8bt68OVJTUyu9pvlZ5HI5PvzwQ6xZswYzZswodx15QUEBzMzM0LhxYzx8+BCJiYna23nduXMH6enpFV6X/byefF3Hjh2r9Oh+8+bNsWnTJtjb28Pa2rrCdXJzcxEeHo4pU6bg5s2bCA0NRXJysvZa7CeZmJhUOIN5t27dYGlpiWXLlmHPnj04fPjwU1+Hv78/li1bBiGE9oh8QEAAUlJSkJSUhAkTJmD//v24cuUKevXqheTkZMjlctja2pbbl6enZ4WzkR85cgQ//PADunXrBuDRXAO3b99+al1l27Vp0waffvqpduzxMwpehrW1NUJCQhASEoIPPvgAXbp0QW5uboWvqyYpFAqdOxwAwLlz58pNqEhERNLjpG5ERFRjFAoF0tLSkJqaCoVCUW65paUlPvnkE4wfPx579uxBamoqhg4disLCQu0tsyIjI5GZmYnx48cjPT0d69evL3f/5ujoaBw9ehQjR45ESkoKMjMz8fvvv1d5UjcAmDVrFlxdXdG6dWv88ssvSE1NRWZmJlasWAF/f38UFBTA29sbwcHBGDp0KOLj43H69Gl89NFHaNCgAYKDg1/qvQIeNYvz589HRkYGli5dipiYGERFRVW4bmhoKOzs7BAcHIy4uDhkZWXh0KFDGDVqlHaiscjISLi6umLq1KlYuHAhSktLMW7cuEqf38PDA4mJibh8+TJu376tPTqrUCgQHh6OSZMmwdvbu9yp9U969913UVBQoHObLXNzc3h5eSErKwvvvPMOvLy8cO3aNbRt2xY+Pj7w8vJ6rsbV29sba9asQVpaGhITExEaGlrpBw1Pbnfy5Ens3bsXGRkZmDZtWrlZ/V/EwoULsWHDBly4cAEZGRmIiYmBo6NjjdxW7nEPHz5EdnY2srOzkZmZia+++gqpqanlvh/j4uKe+xR6IiKqeWzIiYioRllbW1d6BBd4NPN23759MXDgQDRv3hwXL17E3r17UbduXQCPTjn/7bffsG3bNvj5+WH58uWYPXu2zj7eeOMNxMbGIiMjA+3atYO/vz+mT59e4YzplbG1tcWxY8fw0Ucf4auvvoK/vz/atWuHDRs2YMGCBbCxsQHwaIbxFi1aoEePHggMDIQQArt27Sp3uvGL+Pzzz3Hy5En4+/vjq6++wsKFCys8swB4dJ364cOH4ebmhj59+qBx48aIiIhAcXExrK2t8csvv2hndDcyMoKlpSXWrl2Ln376Cbt3765wn+PGjYNCoUCTJk1Qv359neuxIyIioFKpKpwp/0n16tVD7969Kzyd/tChQ2jfvj0AIDY2Vvvv5/Xzzz/j7t27aN68OQYOHKi9dd6zDB8+HH369EFISAhat26NO3fu6Bwtf1FWVlaYP38+AgIC0LJlS1y+fBm7du3SuQ69Jpw/fx5OTk5wcnLCm2++ic2bN2PZsmUYNGiQdp2EhATcv38fH3zwQY3WQkREz08mqjqTDREREdUYDw8PjB49Wnuqvr6Ji4tDhw4dcPXqVTg4ODxz/TNnzqBTp064dOmSzmR29OqFhITAz8+v3PX5REQkPR4hJyIiokqVlJTg2rVrmDlzJvr161elZhx4dNbCvHnzkJWVVcMV0tOoVCo0a9YMY8aMkboUIiKqABtyIiIiqtSGDRvg7u6Oe/fuYf78+c+1bXh4OJo1a1ZDlVFVmJiYYOrUqVW6vp6IiF49nrJOREREREREJAEeISciIiIiIiKSABtyIiIiIiIiIgmwISciIiIiIiKSABtyIiIiIiIiIgmwISciIiIiIiKSABtyIiIiIiIiIgmwISciIiIiIiKSABtyIiIiIiIiIgn8PxxebR5+eqeEAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the relationship between model complexity and performance for both versions\n",
+    "colors = ['#FF323D', \"#FF9D00\", \"#FFD21E\", \"#32343D\", ] \n",
+    "plt.figure(figsize=(12, 8))\n",
+    "plt.scatter(\n",
+    "    merged_data['#Params (B)'][merged_data['date'] < threshold_1], \n",
+    "    merged_data['median_score_v1'][merged_data['date'] < threshold_1], \n",
+    "    label='V1 - before Oct 2023', alpha=0.7, color=colors[0])\n",
+    "plt.scatter(\n",
+    "    merged_data['#Params (B)'][threshold_1 < merged_data['date']][merged_data['date'] < threshold_2], \n",
+    "    merged_data['median_score_v1'][threshold_1 < merged_data['date']][merged_data['date'] < threshold_2], \n",
+    "    label='V1 - Oct 2023 to Mar 2024', alpha=0.7, color=colors[1])\n",
+    "plt.scatter(\n",
+    "    merged_data['#Params (B)'][merged_data['date'] > threshold_2], \n",
+    "    merged_data['median_score_v1'][merged_data['date'] > threshold_2], \n",
+    "    label='V1 - Mar 2024 and after', \n",
+    "    alpha=0.7, color=colors[2])\n",
+    "plt.scatter(\n",
+    "    merged_data['#Params (B)'],\n",
+    "    merged_data['median_score_v2'], label='V2', alpha=0.7, color=colors[3])\n",
+    "plt.xlabel('Model Complexity (#Params in B)')\n",
+    "plt.ylabel('Median Performance Score')\n",
+    "plt.title('Model Complexity vs Performance')\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "4276cef4-b614-494b-b962-d68c563264ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the relationship between model complexity and performance for both versions\n",
+    "colors = ['#FF323D', \"#FF681F\", \"#FF9D00\", \"#FFB80F\", \"#FFD21E\", \"#32343D\", ] \n",
+    "plt.figure(figsize=(12, 8))\n",
+    "plt.scatter(\n",
+    "    merged_data['#Params (B)'][merged_data['date'] < threshold_1], \n",
+    "    merged_data['median_score_v1'][merged_data['date'] < threshold_1], \n",
+    "    label='V1 - before Oct 2023', alpha=0.7, color=colors[0])\n",
+    "plt.scatter(\n",
+    "    merged_data['#Params (B)'][threshold_1 < merged_data['date']][merged_data['date'] < threshold_2], \n",
+    "    merged_data['median_score_v1'][threshold_1 < merged_data['date']][merged_data['date'] < threshold_2], \n",
+    "    label='V1 - Oct/Nov 2023', alpha=0.7, color=colors[1])\n",
+    "plt.scatter(\n",
+    "    merged_data['#Params (B)'][threshold_2 < merged_data['date']][merged_data['date'] < threshold_3], \n",
+    "    merged_data['median_score_v1'][threshold_2 < merged_data['date']][merged_data['date'] < threshold_3], \n",
+    "    label='V1 - Dec/Jan/Feb 2024', alpha=0.7, color=colors[2])\n",
+    "plt.scatter(\n",
+    "    merged_data['#Params (B)'][threshold_3 < merged_data['date']][merged_data['date'] < threshold_4], \n",
+    "    merged_data['median_score_v1'][threshold_3 < merged_data['date']][merged_data['date'] < threshold_4], \n",
+    "    label='V1 - Mar/Apr/May 2024', alpha=0.7, color=colors[3])\n",
+    "plt.scatter(\n",
+    "    merged_data['#Params (B)'][merged_data['date'] > threshold_4], \n",
+    "    merged_data['median_score_v1'][merged_data['date'] > threshold_4], \n",
+    "    label='V1 - May 2024 and after', \n",
+    "    alpha=0.7, color=colors[4])\n",
+    "#plt.scatter(merged_data['#Params (B)_x'], merged_data['median_score_v1'], label='V1', alpha=0.7, color=colors[0])\n",
+    "plt.scatter(\n",
+    "    merged_data['#Params (B)'],\n",
+    "    merged_data['median_score_v2'], label='V2', alpha=0.7, color=colors[-1])\n",
+    "plt.xlabel('Model Complexity (#Params in B)')\n",
+    "plt.ylabel('Median Performance Score')\n",
+    "plt.title('Model Complexity vs Performance')\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a7ea8628-e7b1-4806-a525-cc36fcc6eefe",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Leaderboard EDA",
+   "language": "python",
+   "name": "leaderboard_eda"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}