{
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  {
   "cell_type": "markdown",
   "id": "929c711d-3953-4fa4-82a7-5bca07e344cd",
   "metadata": {},
   "source": [
    "# Load Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2b17a5c1-b120-4a52-b897-f9668d19b91f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datasets\n",
    "import huggingface_hub\n",
    "import matplotlib.patches as mpatches\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d90734cd-4208-4b06-9d67-b30e021e8cad",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max_columns\", None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77bb9548-7e8f-4691-bb6e-63de41318464",
   "metadata": {},
   "source": [
    "## Load and Preprocess V1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3220654b-2c87-422e-a336-047e94730cc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7260, 26)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the v1 JSONL file\n",
    "ds = datasets.load_dataset(\"open-llm-leaderboard-old/contents\", split=\"train\")\n",
    "data_v1 = ds.to_pandas()\n",
    "data_v1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6da7bed7-1c7e-4461-8cf0-372fecfe0393",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop contaminated models\n",
    "data_v1 = data_v1[~data_v1.eval_name.str.contains(\"contaminated\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "5fe0a4fb-e363-499c-a886-f2dbe2ad6a4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7258, 26)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_v1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "60a10bec-5bfb-4ab9-9280-2ca323574827",
   "metadata": {},
   "outputs": [
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       "      <td>False</td>\n",
       "      <td>64.590444</td>\n",
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       "      <td>58.859162</td>\n",
       "      <td>75.848461</td>\n",
       "      <td>50.416983</td>\n",
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      "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": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_v1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "574efc36-3434-4f7a-85a7-0cd00a22d646",
   "metadata": {},
   "source": [
    "## Load and Preprocess V2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "54532cf2-3bd7-4d4a-9efb-161fd0f679b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using the latest cached version of the dataset since open-llm-leaderboard/contents couldn't be found on the Hugging Face Hub\n",
      "Found the latest cached dataset configuration 'default' at /Users/hynky/.cache/huggingface/datasets/open-llm-leaderboard___contents/default/0.0.0/047b1ed979280dc72702d1635a8938216ccb968c (last modified on Wed Jun 26 17:19:09 2024).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(206, 32)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the v2 JSONL file\n",
    "ds = datasets.load_dataset(\"open-llm-leaderboard/contents\", split=\"train\")\n",
    "data_v2 = ds.to_pandas()\n",
    "data_v2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8b8e5ddd-0687-4102-8063-94c2f3bcca93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(206, 32)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_v2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "48a54520-eecd-4753-a93b-f4fcd209d5cc",
   "metadata": {},
   "outputs": [
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       "      <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>592</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     592   \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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_v2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e91493dd-a839-44c9-9097-88b38e1c60db",
   "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",
      "----------\n",
      "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": [
    "# Displaying the column names from both datasets to identify the correct ones to use\n",
    "columns_v1 = data_v1.columns\n",
    "print(f\"V1 columns: {columns_v1}\")\n",
    "print(\"--\" * 5)\n",
    "columns_v2 = data_v2.columns\n",
    "print(f\"V2 columns: {columns_v2}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9024d807",
   "metadata": {},
   "source": [
    "## Analysing which models are in the v2 but not the v1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "115e6f1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing 89 models\n",
      "Can only analyse 117\n"
     ]
    }
   ],
   "source": [
    "all_v2_models = sorted(list(data_v2[\"eval_name\"]))\n",
    "all_v1_models = sorted(list(data_v1[\"eval_name\"]))\n",
    "# all_v1_models[:10]\n",
    "# all_v2_models[:10]\n",
    "\n",
    "shortlist = [model for model in all_v2_models if model not in all_v1_models]\n",
    "print(\"Missing\", len(shortlist), \"models\")\n",
    "print(\"Can only analyse\", len(all_v2_models) - len(shortlist))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c650d44a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing 37 models: \n",
      "['01-ai/Yi-6B-Chat', 'CohereForAI/aya-23-35B', 'EleutherAI/pythia-2.8b', 'EleutherAI/pythia-6.9b', 'LLM360/K2-Chat', 'NousResearch/Yarn-Llama-2-7b-128k', 'NousResearch/Yarn-Llama-2-7b-64k', 'NousResearch/Yarn-Solar-10b-32k', 'NousResearch/Yarn-Solar-10b-64k', 'Qwen/Qwen2-0.5B-Instruct', 'Qwen/Qwen2-1.5B-Instruct', 'Qwen/Qwen2-72B-Instruct', 'Qwen/Qwen2-7B-Instruct', 'TencentARC/MetaMath-Mistral-Pro', 'TinyLlama/TinyLlama_v1.1', 'WizardLMTeam/WizardLM-13B-V1.0', 'WizardLMTeam/WizardLM-13B-V1.2', 'WizardLMTeam/WizardLM-70B-V1.0', 'allenai/OLMo-7B-Instruct-hf', 'databricks/dolly-v1-6b', 'deepseek-ai/deepseek-llm-7b-base', 'deepseek-ai/deepseek-moe-16b-chat', 'distilbert/distilgpt2', 'google/gemma-1.1-2b-it', 'ibm-granite/granite-7b-base', 'internlm/internlm2-1_8b', 'internlm/internlm2-chat-1_8b', 'microsoft/Phi-3-small-128k-instruct', 'microsoft/phi-1', 'mistral-community/mixtral-8x22B-v0.3', 'mistralai/Mistral-7B-Instruct-v0.3', 'mlabonne/NeuralDaredevil-8B-abliterated', 'mlabonne/phixtral-2x2_8', 'openai-community/gpt2-medium', 'openai-community/gpt2-xl', 'teknium/OpenHermes-2-Mistral-7B', 'tiiuae/falcon-40b-instruct']\n",
      "Can only analyse 169\n"
     ]
    }
   ],
   "source": [
    "# Same analysis, but we ignore the precision\n",
    "all_v2_models = sorted(list(data_v2[\"fullname\"]))\n",
    "all_v1_models = sorted(list(data_v1[\"fullname\"]))\n",
    "\n",
    "shortlist = [model for model in all_v2_models if model not in all_v1_models]\n",
    "print(\"Missing\", len(shortlist), \"models: \")\n",
    "print(shortlist)\n",
    "print(\"Can only analyse\", len(all_v2_models) - len(shortlist))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02f21ac5",
   "metadata": {},
   "source": [
    "## Adding the creation dates of models "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ab15833f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[55], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, row \u001b[38;5;129;01min\u001b[39;00m data_v1\u001b[38;5;241m.\u001b[39miterrows():\n\u001b[1;32m      2\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m----> 3\u001b[0m         data_v1\u001b[38;5;241m.\u001b[39mat[i, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdate\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mhuggingface_hub\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrow\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfullname\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mcreated_at\n\u001b[1;32m      4\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[1;32m      5\u001b[0m         \u001b[38;5;28;01mcontinue\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.6/envs/datatrove3.10/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py:114\u001b[0m, in \u001b[0;36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m check_use_auth_token:\n\u001b[1;32m    112\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m smoothly_deprecate_use_auth_token(fn_name\u001b[38;5;241m=\u001b[39mfn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, has_token\u001b[38;5;241m=\u001b[39mhas_token, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[0;32m--> 114\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.6/envs/datatrove3.10/lib/python3.10/site-packages/huggingface_hub/hf_api.py:2300\u001b[0m, in \u001b[0;36mHfApi.model_info\u001b[0;34m(self, repo_id, revision, timeout, securityStatus, files_metadata, token)\u001b[0m\n\u001b[1;32m   2298\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m files_metadata:\n\u001b[1;32m   2299\u001b[0m     params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mblobs\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m-> 2300\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43mget_session\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2301\u001b[0m hf_raise_for_status(r)\n\u001b[1;32m   2302\u001b[0m data \u001b[38;5;241m=\u001b[39m r\u001b[38;5;241m.\u001b[39mjson()\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.6/envs/datatrove3.10/lib/python3.10/site-packages/requests/sessions.py:602\u001b[0m, in \u001b[0;36mSession.get\u001b[0;34m(self, url, **kwargs)\u001b[0m\n\u001b[1;32m    594\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Sends a GET request. Returns :class:`Response` object.\u001b[39;00m\n\u001b[1;32m    595\u001b[0m \n\u001b[1;32m    596\u001b[0m \u001b[38;5;124;03m:param url: URL for the new :class:`Request` object.\u001b[39;00m\n\u001b[1;32m    597\u001b[0m \u001b[38;5;124;03m:param \\*\\*kwargs: Optional arguments that ``request`` takes.\u001b[39;00m\n\u001b[1;32m    598\u001b[0m \u001b[38;5;124;03m:rtype: requests.Response\u001b[39;00m\n\u001b[1;32m    599\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    601\u001b[0m kwargs\u001b[38;5;241m.\u001b[39msetdefault(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_redirects\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m--> 602\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mGET\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.6/envs/datatrove3.10/lib/python3.10/site-packages/requests/sessions.py:589\u001b[0m, in \u001b[0;36mSession.request\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    584\u001b[0m send_kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    585\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m\"\u001b[39m: timeout,\n\u001b[1;32m    586\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_redirects\u001b[39m\u001b[38;5;124m\"\u001b[39m: allow_redirects,\n\u001b[1;32m    587\u001b[0m }\n\u001b[1;32m    588\u001b[0m send_kwargs\u001b[38;5;241m.\u001b[39mupdate(settings)\n\u001b[0;32m--> 589\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msend_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    591\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.6/envs/datatrove3.10/lib/python3.10/site-packages/requests/sessions.py:724\u001b[0m, in \u001b[0;36mSession.send\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    721\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m allow_redirects:\n\u001b[1;32m    722\u001b[0m     \u001b[38;5;66;03m# Redirect resolving generator.\u001b[39;00m\n\u001b[1;32m    723\u001b[0m     gen \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mresolve_redirects(r, request, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 724\u001b[0m     history \u001b[38;5;241m=\u001b[39m [resp \u001b[38;5;28;01mfor\u001b[39;00m resp \u001b[38;5;129;01min\u001b[39;00m gen]\n\u001b[1;32m    725\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    726\u001b[0m     history \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.6/envs/datatrove3.10/lib/python3.10/site-packages/requests/sessions.py:724\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    721\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m allow_redirects:\n\u001b[1;32m    722\u001b[0m     \u001b[38;5;66;03m# Redirect resolving generator.\u001b[39;00m\n\u001b[1;32m    723\u001b[0m     gen \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mresolve_redirects(r, request, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 724\u001b[0m     history \u001b[38;5;241m=\u001b[39m [resp \u001b[38;5;28;01mfor\u001b[39;00m resp \u001b[38;5;129;01min\u001b[39;00m gen]\n\u001b[1;32m    725\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    726\u001b[0m     history \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.6/envs/datatrove3.10/lib/python3.10/site-packages/requests/sessions.py:265\u001b[0m, in \u001b[0;36mSessionRedirectMixin.resolve_redirects\u001b[0;34m(self, resp, req, stream, timeout, verify, cert, proxies, yield_requests, **adapter_kwargs)\u001b[0m\n\u001b[1;32m    263\u001b[0m     \u001b[38;5;28;01myield\u001b[39;00m req\n\u001b[1;32m    264\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 265\u001b[0m     resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    266\u001b[0m \u001b[43m        \u001b[49m\u001b[43mreq\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    267\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    268\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    269\u001b[0m \u001b[43m        \u001b[49m\u001b[43mverify\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverify\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    270\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcert\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcert\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    271\u001b[0m \u001b[43m        \u001b[49m\u001b[43mproxies\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    272\u001b[0m \u001b[43m        \u001b[49m\u001b[43mallow_redirects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    273\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43madapter_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    274\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    276\u001b[0m     extract_cookies_to_jar(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcookies, prepared_request, resp\u001b[38;5;241m.\u001b[39mraw)\n\u001b[1;32m    278\u001b[0m     \u001b[38;5;66;03m# extract redirect url, if any, for the next loop\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.6/envs/datatrove3.10/lib/python3.10/site-packages/requests/sessions.py:703\u001b[0m, in \u001b[0;36mSession.send\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    700\u001b[0m start \u001b[38;5;241m=\u001b[39m preferred_clock()\n\u001b[1;32m    702\u001b[0m \u001b[38;5;66;03m# Send the request\u001b[39;00m\n\u001b[0;32m--> 703\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43madapter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    705\u001b[0m \u001b[38;5;66;03m# Total elapsed time of the request (approximately)\u001b[39;00m\n\u001b[1;32m    706\u001b[0m elapsed \u001b[38;5;241m=\u001b[39m preferred_clock() \u001b[38;5;241m-\u001b[39m start\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.6/envs/datatrove3.10/lib/python3.10/site-packages/huggingface_hub/utils/_http.py:66\u001b[0m, in \u001b[0;36mUniqueRequestIdAdapter.send\u001b[0;34m(self, request, *args, **kwargs)\u001b[0m\n\u001b[1;32m     64\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Catch any RequestException to append request id to the error message for debugging.\"\"\"\u001b[39;00m\n\u001b[1;32m     65\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 66\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     67\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m requests\u001b[38;5;241m.\u001b[39mRequestException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     68\u001b[0m     request_id \u001b[38;5;241m=\u001b[39m request\u001b[38;5;241m.\u001b[39mheaders\u001b[38;5;241m.\u001b[39mget(X_AMZN_TRACE_ID)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.6/envs/datatrove3.10/lib/python3.10/site-packages/requests/adapters.py:667\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    664\u001b[0m     timeout \u001b[38;5;241m=\u001b[39m TimeoutSauce(connect\u001b[38;5;241m=\u001b[39mtimeout, read\u001b[38;5;241m=\u001b[39mtimeout)\n\u001b[1;32m    666\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 667\u001b[0m     resp \u001b[38;5;241m=\u001b[39m \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    668\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    669\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    670\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    671\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    672\u001b[0m \u001b[43m        \u001b[49m\u001b[43mredirect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    673\u001b[0m \u001b[43m        \u001b[49m\u001b[43massert_same_host\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    674\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    675\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    676\u001b[0m \u001b[43m        \u001b[49m\u001b[43mretries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    677\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    678\u001b[0m \u001b[43m        \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    679\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    681\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m    682\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m(err, request\u001b[38;5;241m=\u001b[39mrequest)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.6/envs/datatrove3.10/lib/python3.10/site-packages/urllib3/connectionpool.py:789\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m    786\u001b[0m response_conn \u001b[38;5;241m=\u001b[39m conn \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m release_conn \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    788\u001b[0m \u001b[38;5;66;03m# Make the request on the HTTPConnection object\u001b[39;00m\n\u001b[0;32m--> 789\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    790\u001b[0m \u001b[43m    \u001b[49m\u001b[43mconn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    791\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    792\u001b[0m \u001b[43m    \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    793\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    794\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    795\u001b[0m \u001b[43m    \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    796\u001b[0m \u001b[43m    \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    797\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    798\u001b[0m \u001b[43m    \u001b[49m\u001b[43mresponse_conn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresponse_conn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    799\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreload_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    800\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    801\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mresponse_kw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    802\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    804\u001b[0m \u001b[38;5;66;03m# Everything went great!\u001b[39;00m\n\u001b[1;32m    805\u001b[0m clean_exit \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.6/envs/datatrove3.10/lib/python3.10/site-packages/urllib3/connectionpool.py:536\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[1;32m    534\u001b[0m \u001b[38;5;66;03m# Receive the response from the server\u001b[39;00m\n\u001b[1;32m    535\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 536\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetresponse\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    537\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (BaseSSLError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    538\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_raise_timeout(err\u001b[38;5;241m=\u001b[39me, url\u001b[38;5;241m=\u001b[39murl, timeout_value\u001b[38;5;241m=\u001b[39mread_timeout)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.6/envs/datatrove3.10/lib/python3.10/site-packages/urllib3/connection.py:464\u001b[0m, in \u001b[0;36mHTTPConnection.getresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    461\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mresponse\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m HTTPResponse\n\u001b[1;32m    463\u001b[0m \u001b[38;5;66;03m# Get the response from http.client.HTTPConnection\u001b[39;00m\n\u001b[0;32m--> 464\u001b[0m httplib_response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetresponse\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    466\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    467\u001b[0m     assert_header_parsing(httplib_response\u001b[38;5;241m.\u001b[39mmsg)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.6/lib/python3.10/http/client.py:1374\u001b[0m, in \u001b[0;36mHTTPConnection.getresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1372\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1373\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1374\u001b[0m         \u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbegin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1375\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m:\n\u001b[1;32m   1376\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclose()\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.6/lib/python3.10/http/client.py:318\u001b[0m, in \u001b[0;36mHTTPResponse.begin\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    316\u001b[0m \u001b[38;5;66;03m# read until we get a non-100 response\u001b[39;00m\n\u001b[1;32m    317\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m--> 318\u001b[0m     version, status, reason \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_read_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    319\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m status \u001b[38;5;241m!=\u001b[39m CONTINUE:\n\u001b[1;32m    320\u001b[0m         \u001b[38;5;28;01mbreak\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.6/lib/python3.10/http/client.py:279\u001b[0m, in \u001b[0;36mHTTPResponse._read_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    278\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_read_status\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 279\u001b[0m     line \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreadline\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_MAXLINE\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miso-8859-1\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    280\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(line) \u001b[38;5;241m>\u001b[39m _MAXLINE:\n\u001b[1;32m    281\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m LineTooLong(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstatus line\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.6/lib/python3.10/socket.py:705\u001b[0m, in \u001b[0;36mSocketIO.readinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    703\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m    704\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 705\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecv_into\u001b[49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    706\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m timeout:\n\u001b[1;32m    707\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_timeout_occurred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.6/lib/python3.10/ssl.py:1274\u001b[0m, in \u001b[0;36mSSLSocket.recv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m   1270\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m flags \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m   1271\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   1272\u001b[0m           \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnon-zero flags not allowed in calls to recv_into() on \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m\n\u001b[1;32m   1273\u001b[0m           \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m)\n\u001b[0;32m-> 1274\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnbytes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbuffer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1275\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1276\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mrecv_into(buffer, nbytes, flags)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.6/lib/python3.10/ssl.py:1130\u001b[0m, in \u001b[0;36mSSLSocket.read\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m   1128\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1129\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m buffer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1130\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sslobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbuffer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1131\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1132\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sslobj\u001b[38;5;241m.\u001b[39mread(\u001b[38;5;28mlen\u001b[39m)\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "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": null,
   "id": "a8b1dcbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
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       "      <td>7</td>\n",
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       "      <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",
       "    <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",
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       "      <td>apache-2.0</td>\n",
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       "      <td>2024-03-19 11:26:56+00:00</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",
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       "      <td>2024-03-22T15:02:21Z</td>\n",
       "      <td>False</td>\n",
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       "      <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",
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       "      <td>7</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>2024-03-21 13:04:58+00:00</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  2024-03-20 05:57:38+00:00          False  61.774744  82.135033  62.423731   \n",
       "1  2024-03-19 11:27:26+00:00          False  62.713311  82.991436  62.700299   \n",
       "2  2024-03-19 11:26:56+00:00          False  62.627986  81.507668  61.967618   \n",
       "3       2024-03-22T15:02:21Z          False  65.017065  83.081060  61.873805   \n",
       "4  2024-03-21 13:04:58+00:00          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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_v1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b6e4d6c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0765d6b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "GatedRepoError",
     "evalue": "403 Client Error. (Request ID: Root=1-667d5442-15ac7a31619de50959fbd646;1b7cdc8b-f9c7-4241-a915-a754b8658606)\n\nCannot access gated repo for url https://huggingface.co/api/models/CohereForAI/aya-23-8B.\nAccess to model CohereForAI/aya-23-8B is restricted and you are not in the authorized list. Visit https://huggingface.co/CohereForAI/aya-23-8B to ask for access.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mHTTPError\u001b[0m                                 Traceback (most recent call last)",
      "File \u001b[0;32m~/.pyenv/versions/3.10.6/envs/datatrove3.10/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py:304\u001b[0m, in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m    303\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 304\u001b[0m     \u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraise_for_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    305\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m HTTPError \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.6/envs/datatrove3.10/lib/python3.10/site-packages/requests/models.py:1024\u001b[0m, in \u001b[0;36mResponse.raise_for_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1023\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m http_error_msg:\n\u001b[0;32m-> 1024\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m HTTPError(http_error_msg, response\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m)\n",
      "\u001b[0;31mHTTPError\u001b[0m: 403 Client Error: Forbidden for url: https://huggingface.co/api/models/CohereForAI/aya-23-8B",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mGatedRepoError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[16], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, row \u001b[38;5;129;01min\u001b[39;00m data_v2\u001b[38;5;241m.\u001b[39miterrows():\n\u001b[0;32m----> 2\u001b[0m     data_v2\u001b[38;5;241m.\u001b[39mat[i, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdate\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mhuggingface_hub\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrow\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfullname\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mcreated_at\n",
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      "File \u001b[0;32m~/.pyenv/versions/3.10.6/envs/datatrove3.10/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py:321\u001b[0m, in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m    317\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m error_code \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGatedRepo\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    318\u001b[0m     message \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    319\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresponse\u001b[38;5;241m.\u001b[39mstatus_code\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m Client Error.\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot access gated repo for url \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresponse\u001b[38;5;241m.\u001b[39murl\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    320\u001b[0m     )\n\u001b[0;32m--> 321\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m GatedRepoError(message, response) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m    323\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m error_message \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAccess to this resource is disabled.\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    324\u001b[0m     message \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    325\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresponse\u001b[38;5;241m.\u001b[39mstatus_code\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m Client Error.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    326\u001b[0m         \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    329\u001b[0m         \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAccess to this resource is disabled.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    330\u001b[0m     )\n",
      "\u001b[0;31mGatedRepoError\u001b[0m: 403 Client Error. (Request ID: Root=1-667d5442-15ac7a31619de50959fbd646;1b7cdc8b-f9c7-4241-a915-a754b8658606)\n\nCannot access gated repo for url https://huggingface.co/api/models/CohereForAI/aya-23-8B.\nAccess to model CohereForAI/aya-23-8B is restricted and you are not in the authorized list. Visit https://huggingface.co/CohereForAI/aya-23-8B to ask for access."
     ]
    }
   ],
   "source": [
    "for i, row in data_v2.iterrows():\n",
    "    data_v2.at[i, \"date\"] = huggingface_hub.model_info(row[\"fullname\"]).created_at"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8325e0bb-0f63-4359-8af8-9380629a9544",
   "metadata": {},
   "source": [
    "## Define Tasks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "de0e2c80-3316-411d-91ac-5893e6d8c6e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "tasks_v1 = [\"HellaSwag\", \"MMLU\", \"TruthfulQA\", \"Winogrande\", \"GSM8K\"]\n",
    "tasks_v2 = [\"IFEval\", \"BBH\", \"MATH Lvl 5\", \"GPQA\", \"MUSR\", \"MMLU-PRO\"]\n",
    "tasks_v2_raw = [f\"{task} Raw\" for task in tasks_v2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23eb53bd-9305-40c4-b73d-62d3e782924e",
   "metadata": {},
   "source": [
    "# Detect Challenging Tasks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "a1e75a38-e119-4ae2-bd4c-fb185e07db7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the mean score for each task\n",
    "mean_scores = data_v2[tasks_v2].mean().sort_values()\n",
    "# we sort the raw scores on the normalised ones\n",
    "mean_scores_raw = (\n",
    "    data_v2[tasks_v2_raw].mean().reindex(index=[f\"{s} Raw\" for s in mean_scores.index])\n",
    ")\n",
    "# mean_scores_raw = data_v2[tasks_v2_raw].mean().sort_values()\n",
    "\n",
    "# Create a DataFrame for mean scores\n",
    "mean_scores_df = pd.DataFrame({\"Normalized\": mean_scores, \"Raw\": mean_scores_raw})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "95ae2dc4-709a-4de5-9872-65c1e2c12d48",
   "metadata": {},
   "outputs": [
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         "layout": {
          "annotationdefaults": {
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          ],
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           "color": "#2a3f5f"
          },
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           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Normalized Vs Raw Scores Comparison"
        },
        "uniformtext": {
         "minsize": 8,
         "mode": "hide"
        },
        "yaxis": {
         "range": [
          0,
          60
         ],
         "title": {
          "text": "Score"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "RED = \"#FF323D\"\n",
    "ORANGE = \"#FF9D00\"\n",
    "BLACK = \"#32343D\"\n",
    "YELLOW = \"#FFD21E\"\n",
    "\n",
    "fig = go.Figure(\n",
    "    data=[\n",
    "        go.Bar(\n",
    "            x=mean_scores.index,\n",
    "            y=mean_scores,\n",
    "            name=\"Normalized Mean Scores\",\n",
    "            text=mean_scores,\n",
    "            marker_color=RED\n",
    "        ),\n",
    "        go.Bar(\n",
    "            x=[i.replace(\" Raw\", \"\") for i in mean_scores_raw.index],\n",
    "            y=[m * 100 for m in mean_scores_raw],\n",
    "            name=\"Raw Mean Scores\",\n",
    "            text=[m * 100 for m in mean_scores_raw],\n",
    "            marker_color=ORANGE\n",
    "        ),\n",
    "    ],\n",
    "    layout_yaxis_range=[0, 60],\n",
    ")\n",
    "fig = fig.update_layout(barmode=\"group\")\n",
    "fig.update_traces(texttemplate=\"%{text:.3s}\", textposition=\"outside\")\n",
    "fig.update_layout(uniformtext_minsize=8, uniformtext_mode=\"hide\")\n",
    "\n",
    "fig.update_layout(\n",
    "    title=\"Normalized Vs Raw Scores Comparison\",\n",
    "    yaxis=dict(\n",
    "        title=\"Score\",\n",
    "    ),\n",
    "    barmode=\"group\",\n",
    "    bargap=0.15,  # gap between bars of adjacent location coordinates.\n",
    "    bargroupgap=0.05,  # gap between bars of the same location coordinate.\n",
    "    legend=dict(\n",
    "        x=0,\n",
    "        y=1,\n",
    "        traceorder=\"normal\",\n",
    "    ),\n",
    ")\n",
    "with open(\"normalized_vs_raw.html\", \"w\") as f:\n",
    "    f.write(fig.to_html(full_html=False, include_plotlyjs=False))\n",
    "fig  # .to_html(full_html=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c3644018-ce17-4861-9cdc-9d3dada8773a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1400x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define the plot size\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 7), sharey=False)\n",
    "\n",
    "# Plot Normalized mean scores\n",
    "ax1.bar(mean_scores.index, mean_scores, color=\"skyblue\", edgecolor=\"grey\")\n",
    "ax1.set_xlabel(\"Tasks\", fontweight=\"bold\")\n",
    "ax1.set_ylabel(\"Mean Scores\")\n",
    "ax1.set_ylim(0, 60)  # Adjust y-axis limit\n",
    "ax1.set_title(\"Normalized Mean Scores\")\n",
    "ax1.set_xticks(np.arange(len(mean_scores.index)))\n",
    "ax1.set_xticklabels(mean_scores.index, rotation=45, ha=\"right\")\n",
    "\n",
    "# Plot Raw mean scores with adjusted y-axis\n",
    "ax2.bar(\n",
    "    mean_scores_raw.index,\n",
    "    [m * 100 for m in mean_scores_raw],\n",
    "    color=\"orange\",\n",
    "    edgecolor=\"grey\",\n",
    ")\n",
    "ax2.set_xlabel(\"Tasks\", fontweight=\"bold\")\n",
    "ax2.set_title(\"Raw Mean Scores\")\n",
    "ax2.set_ylim(0, 60)  # Adjust y-axis limit\n",
    "ax2.set_xticks(np.arange(len(mean_scores_raw.index)))\n",
    "ax2.set_xticklabels(mean_scores_raw.index, rotation=45, ha=\"right\")\n",
    "\n",
    "# Add gridlines\n",
    "for ax in [ax1, ax2]:\n",
    "    ax.grid(axis=\"y\", linestyle=\"--\", alpha=0.7)\n",
    "\n",
    "# Adding annotations to the bars\n",
    "for i, value in enumerate(mean_scores):\n",
    "    ax1.text(i, value + 0.01, f\"{value:.2f}\", ha=\"center\", va=\"bottom\")\n",
    "for i, value in enumerate(mean_scores_raw):\n",
    "    value = value * 100\n",
    "    ax2.text(i, value + 0.01, f\"{value:.2f}\", ha=\"center\", va=\"bottom\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82e499ee",
   "metadata": {},
   "source": [
    "# Analyse individual tasks trends"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac166643-9558-42d0-b5a5-901c302d01ed",
   "metadata": {},
   "source": [
    "## IFEval: Compare Chat and Pretrained Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "5b6b351d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_type_dict = {\n",
    "    \"💬 chat models (RLHF, DPO, IFT, ...)\": \"Chat Models\",\n",
    "    \"🔶 fine-tuned on domain-specific datasets\": \"Fine-tuned Models\",\n",
    "    \"🟢 pretrained\": \"Pretrained Models\",\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "2fefecdf-a62c-452f-863b-1c1ad696c3aa",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Filter the data for chat and pretrained models\n",
    "filtered_data = data_v2[\n",
    "    data_v2[\"Type\"].isin(\n",
    "        [\n",
    "            \"🔶 fine-tuned on domain-specific datasets\",\n",
    "            \"💬 chat models (RLHF, DPO, IFT, ...)\",\n",
    "            \"🟢 pretrained\",\n",
    "        ]\n",
    "    )\n",
    "]\n",
    "\n",
    "ifeval_data = filtered_data[\n",
    "    [\"Type\", \"IFEval\", \"MATH Lvl 5\", \"Average ⬆️\", \"fullname\"]\n",
    "]  # , 'IFEval Raw']]\n",
    "\n",
    "# Calculate the average scores for each type\n",
    "# average_scores = ifeval_data.groupby(\"Type\").mean().reset_index()\n",
    "\n",
    "# # Update the labels to use text instead of emojis\n",
    "# average_scores[\"Type\"] = average_scores[\"Type\"].replace(model_type_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "9ca9ff64-b652-4cd1-85b5-2d6562f5b3e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's use a nicer color palette for the plots\n",
    "colors = [\n",
    "    \"#FF9D00\",\n",
    "    \"#FFD21E\",\n",
    "    \"#32343D\",\n",
    "    \"#FF323D\",\n",
    "]  # Would be nice to edit with the HF color codes at the end\n",
    "\n",
    "if False:\n",
    "    # Plotting the average normalized scores for IFEval with updated labels\n",
    "    fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "    # Plot for IFEval\n",
    "    ax.bar(average_scores[\"Type\"], average_scores[\"IFEval\"], color=colors)\n",
    "    ax.set_title(\"Average IFEval Scores by Model Type\")\n",
    "    ax.set_ylabel(\"IFEval Score\")\n",
    "    ax.set_xlabel(\"Model Type\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    # plt.grid(True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "006ed930-4b3c-41a3-a236-88b295fdf786",
   "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>Type</th>\n",
       "      <th>IFEval</th>\n",
       "      <th>MATH Lvl 5</th>\n",
       "      <th>Average ⬆️</th>\n",
       "      <th>fullname</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>🟢 pretrained</td>\n",
       "      <td>24.212645</td>\n",
       "      <td>2.114804</td>\n",
       "      <td>17.072003</td>\n",
       "      <td>upstage/SOLAR-10.7B-v1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>💬 chat models (RLHF, DPO, IFT, ...)</td>\n",
       "      <td>47.366100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.961989</td>\n",
       "      <td>upstage/SOLAR-10.7B-Instruct-v1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>🔶 fine-tuned on domain-specific datasets</td>\n",
       "      <td>21.242636</td>\n",
       "      <td>0.604230</td>\n",
       "      <td>5.877290</td>\n",
       "      <td>togethercomputer/RedPajama-INCITE-Instruct-3B-v1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>🔶 fine-tuned on domain-specific datasets</td>\n",
       "      <td>16.521496</td>\n",
       "      <td>0.302115</td>\n",
       "      <td>4.950649</td>\n",
       "      <td>togethercomputer/RedPajama-INCITE-Chat-3B-v1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>🟢 pretrained</td>\n",
       "      <td>22.936254</td>\n",
       "      <td>0.906344</td>\n",
       "      <td>5.645099</td>\n",
       "      <td>togethercomputer/RedPajama-INCITE-Base-3B-v1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>💬 chat models (RLHF, DPO, IFT, ...)</td>\n",
       "      <td>48.023023</td>\n",
       "      <td>12.537764</td>\n",
       "      <td>22.405532</td>\n",
       "      <td>01-ai/Yi-1.5-6B-Chat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>🟢 pretrained</td>\n",
       "      <td>26.106065</td>\n",
       "      <td>5.664653</td>\n",
       "      <td>16.778059</td>\n",
       "      <td>01-ai/Yi-1.5-6B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>💬 chat models (RLHF, DPO, IFT, ...)</td>\n",
       "      <td>60.667584</td>\n",
       "      <td>23.338369</td>\n",
       "      <td>33.076818</td>\n",
       "      <td>01-ai/Yi-1.5-34B-Chat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>🟢 pretrained</td>\n",
       "      <td>31.186917</td>\n",
       "      <td>13.444109</td>\n",
       "      <td>26.787600</td>\n",
       "      <td>01-ai/Yi-1.5-34B-32K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>🟢 pretrained</td>\n",
       "      <td>28.411725</td>\n",
       "      <td>14.048338</td>\n",
       "      <td>25.812197</td>\n",
       "      <td>01-ai/Yi-1.5-34B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>204 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Type     IFEval  MATH Lvl 5  \\\n",
       "0                                🟢 pretrained  24.212645    2.114804   \n",
       "1         💬 chat models (RLHF, DPO, IFT, ...)  47.366100    0.000000   \n",
       "2    🔶 fine-tuned on domain-specific datasets  21.242636    0.604230   \n",
       "3    🔶 fine-tuned on domain-specific datasets  16.521496    0.302115   \n",
       "4                                🟢 pretrained  22.936254    0.906344   \n",
       "..                                        ...        ...         ...   \n",
       "201       💬 chat models (RLHF, DPO, IFT, ...)  48.023023   12.537764   \n",
       "202                              🟢 pretrained  26.106065    5.664653   \n",
       "203       💬 chat models (RLHF, DPO, IFT, ...)  60.667584   23.338369   \n",
       "204                              🟢 pretrained  31.186917   13.444109   \n",
       "205                              🟢 pretrained  28.411725   14.048338   \n",
       "\n",
       "     Average ⬆️                                          fullname  \n",
       "0     17.072003                          upstage/SOLAR-10.7B-v1.0  \n",
       "1     19.961989                 upstage/SOLAR-10.7B-Instruct-v1.0  \n",
       "2      5.877290  togethercomputer/RedPajama-INCITE-Instruct-3B-v1  \n",
       "3      4.950649      togethercomputer/RedPajama-INCITE-Chat-3B-v1  \n",
       "4      5.645099      togethercomputer/RedPajama-INCITE-Base-3B-v1  \n",
       "..          ...                                               ...  \n",
       "201   22.405532                              01-ai/Yi-1.5-6B-Chat  \n",
       "202   16.778059                                   01-ai/Yi-1.5-6B  \n",
       "203   33.076818                             01-ai/Yi-1.5-34B-Chat  \n",
       "204   26.787600                              01-ai/Yi-1.5-34B-32K  \n",
       "205   25.812197                                  01-ai/Yi-1.5-34B  \n",
       "\n",
       "[204 rows x 5 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
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   "cell_type": "code",
   "execution_count": 64,
   "id": "e18e3310-3992-4ec3-83d5-d1fec63bd55e",
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            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Average IFEval Scores by Model Type"
        },
        "xaxis": {
         "title": {
          "text": "Average score over all tasks"
         }
        },
        "yaxis": {
         "title": {
          "text": "IFEval Score"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "avg = \"Average ⬆️\"\n",
    "\n",
    "\n",
    "colors = [RED, ORANGE, BLACK, YELLOW]\n",
    "scatters = []\n",
    "for i, (group_name, group) in enumerate(ifeval_data.groupby(\"Type\")):\n",
    "    xval = group[avg]\n",
    "    scatters.append(\n",
    "        go.Scatter(\n",
    "            x=group[avg],\n",
    "            y=group[\"IFEval\"],\n",
    "            name=model_type_dict[group_name],\n",
    "            text=group[\"fullname\"],\n",
    "            hoverinfo=\"all\",\n",
    "            mode=\"markers\",\n",
    "        marker=dict(\n",
    "            color=colors[i],\n",
    "        ),\n",
    "        ),\n",
    "    )\n",
    "\n",
    "fig = go.Figure(data=scatters)\n",
    "\n",
    "fig.update_layout(\n",
    "    title=\"Average IFEval Scores by Model Type\",\n",
    "    yaxis=dict(\n",
    "        title=\"IFEval Score\",\n",
    "    ),\n",
    "    xaxis=dict(\n",
    "        title=\"Average score over all tasks\",\n",
    "    ),\n",
    "    legend=dict(\n",
    "        x=0,\n",
    "        y=1,\n",
    "        traceorder=\"normal\",\n",
    "    ),\n",
    ")\n",
    "with open(\"avg_ifeval_vs_all.html\", \"w\") as f:\n",
    "    f.write(fig.to_html(full_html=False, include_plotlyjs=False))\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "4135d840",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg = \"Average ⬆️\"\n",
    "ifeval = \"IFEval\"\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "for i, (group_name, group) in enumerate(ifeval_data.groupby(\"Type\")):\n",
    "    xval = group[avg]\n",
    "    yval = group[ifeval]\n",
    "    ax.scatter(xval, yval, label=model_type_dict[group_name], c=colors[i])\n",
    "\n",
    "ax.set_title(\"Average IFEval Scores by Model Type\")\n",
    "ax.set_ylabel(\"IFEval Score\")\n",
    "ax.set_xlabel(\"Average\")\n",
    "\n",
    "ax.legend()\n",
    "plt.tight_layout()\n",
    "# plt.grid(True)\n",
    "plt.show()"
   ]
  },
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   "execution_count": 66,
   "id": "faef7a9a-37e6-4dcb-af43-f01698e683a5",
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        },
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          "text": "Average score over all tasks"
         }
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       }
      }
     },
     "metadata": {},
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   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "scatters = []\n",
    "for i, (group_name, group) in enumerate(ifeval_data.groupby(\"Type\")):\n",
    "    xval = group[avg]\n",
    "    scatters.append(\n",
    "        go.Scatter(\n",
    "            x=group[avg],\n",
    "            y=group[\"MATH Lvl 5\"],\n",
    "            name=model_type_dict[group_name],\n",
    "            text=group[\"fullname\"],\n",
    "            hoverinfo=\"all\",\n",
    "            mode=\"markers\",\n",
    "        marker=dict(\n",
    "            color=colors[i],\n",
    "        ),\n",
    "        ),\n",
    "    )\n",
    "\n",
    "fig = go.Figure(data=scatters)\n",
    "\n",
    "fig.update_layout(\n",
    "    title=\"Average MATH Scores by Model Type\",\n",
    "    yaxis=dict(\n",
    "        title=\"MATH lvl 5 Score\",\n",
    "    ),\n",
    "    xaxis=dict(\n",
    "        title=\"Average score over all tasks\",\n",
    "    ),\n",
    "    legend=dict(\n",
    "        x=0,\n",
    "        y=1,\n",
    "        traceorder=\"normal\",\n",
    "    ),\n",
    ")\n",
    "with open(\"math_vs_avg_all.html\", \"w\") as f:\n",
    "    f.write(fig.to_html(full_html=False, include_plotlyjs=False))\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "3d8fbc08",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg = \"Average ⬆️\"\n",
    "ifeval = \"MATH Lvl 5\"\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "for i, (group_name, group) in enumerate(ifeval_data.groupby(\"Type\")):\n",
    "    xval = group[avg]\n",
    "    yval = group[ifeval]\n",
    "    ax.scatter(xval, yval, label=model_type_dict[group_name], c=colors[i])\n",
    "\n",
    "ax.set_title(\"Average MATH Scores by Model Type\")\n",
    "ax.set_ylabel(\"MATH Score\")\n",
    "ax.set_xlabel(\"Average\")\n",
    "\n",
    "ax.legend()\n",
    "plt.tight_layout()\n",
    "# plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a8d90b3",
   "metadata": {},
   "source": [
    "Clearly 2 clusters: one for pretrained, where performance on IFEval plateaus, whereas performance for chat models is globally correlated with average performance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fe878c5",
   "metadata": {},
   "source": [
    "## MMLU vs MMLU-Pro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "898628e2",
   "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",
       "    <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>2024-03-19 11:26:56+00:00</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>2024-03-21 13:04:58+00:00</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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7255</th>\n",
       "      <td>zorobin_mistral-class-shishya-7b-ep3_bfloat16</td>\n",
       "      <td>bfloat16</td>\n",
       "      <td>🔶 fine-tuned on domain-specific datasets</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>zorobin/mistral-class-shishya-7b-ep3</td>\n",
       "      <td>e85b73ce67deaa5b40633c5ce2545b23fa3ff3a0</td>\n",
       "      <td>44.276427</td>\n",
       "      <td>llama2</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>2024-01-28T04:36:16Z</td>\n",
       "      <td>False</td>\n",
       "      <td>46.587031</td>\n",
       "      <td>76.618204</td>\n",
       "      <td>39.065575</td>\n",
       "      <td>33.537715</td>\n",
       "      <td>69.850039</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7256</th>\n",
       "      <td>zorobin_mistral-class-shishya-all-hal-7b-ep3_b...</td>\n",
       "      <td>bfloat16</td>\n",
       "      <td>🔶 fine-tuned on domain-specific datasets</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>zorobin/mistral-class-shishya-all-hal-7b-ep3</td>\n",
       "      <td>8f15bc3f0d0235fdb67a8dfb6be36a1ac9c1b8b8</td>\n",
       "      <td>44.802767</td>\n",
       "      <td>llama2</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>2024-01-28T04:36:49Z</td>\n",
       "      <td>False</td>\n",
       "      <td>46.587031</td>\n",
       "      <td>78.868751</td>\n",
       "      <td>34.450351</td>\n",
       "      <td>35.982292</td>\n",
       "      <td>72.928177</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7257</th>\n",
       "      <td>zyh3826_20231206094523-pretrain-Llama-2-13b-hf...</td>\n",
       "      <td>bfloat16</td>\n",
       "      <td>🔶 fine-tuned on domain-specific datasets</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>zyh3826/20231206094523-pretrain-Llama-2-13b-hf...</td>\n",
       "      <td>28b3ae089b5610053f2294d24667fe248405f031</td>\n",
       "      <td>35.580577</td>\n",
       "      <td>llama2</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>2023-12-14T02:54:25Z</td>\n",
       "      <td>False</td>\n",
       "      <td>31.058020</td>\n",
       "      <td>52.031468</td>\n",
       "      <td>24.434487</td>\n",
       "      <td>44.712448</td>\n",
       "      <td>61.247040</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7258</th>\n",
       "      <td>zyh3826_GML-Mistral-merged-v1_bfloat16</td>\n",
       "      <td>bfloat16</td>\n",
       "      <td>🔶 fine-tuned on domain-specific datasets</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>zyh3826/GML-Mistral-merged-v1</td>\n",
       "      <td>17a3d5eb5dc23b8a7c29d33cfcd07140a083aa1f</td>\n",
       "      <td>73.295247</td>\n",
       "      <td>apache-2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>2023-12-23T11:32:47Z</td>\n",
       "      <td>False</td>\n",
       "      <td>71.245734</td>\n",
       "      <td>87.880900</td>\n",
       "      <td>65.417680</td>\n",
       "      <td>69.275013</td>\n",
       "      <td>80.978690</td>\n",
       "      <td>64.973465</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7259</th>\n",
       "      <td>zyh3826_llama2-13b-ft-openllm-leaderboard-v1_f...</td>\n",
       "      <td>float16</td>\n",
       "      <td></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>zyh3826/llama2-13b-ft-openllm-leaderboard-v1</td>\n",
       "      <td>70404059013c74b0641ed69d293b3d1ad708cd1e</td>\n",
       "      <td>53.858973</td>\n",
       "      <td>llama2</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>2023-12-07T03:44:45Z</td>\n",
       "      <td>False</td>\n",
       "      <td>59.641638</td>\n",
       "      <td>83.140809</td>\n",
       "      <td>60.934970</td>\n",
       "      <td>40.723683</td>\n",
       "      <td>77.348066</td>\n",
       "      <td>1.364670</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7258 rows × 26 columns</p>\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",
       "7255      zorobin_mistral-class-shishya-7b-ep3_bfloat16  bfloat16   \n",
       "7256  zorobin_mistral-class-shishya-all-hal-7b-ep3_b...  bfloat16   \n",
       "7257  zyh3826_20231206094523-pretrain-Llama-2-13b-hf...  bfloat16   \n",
       "7258             zyh3826_GML-Mistral-merged-v1_bfloat16  bfloat16   \n",
       "7259  zyh3826_llama2-13b-ft-openllm-leaderboard-v1_f...   float16   \n",
       "\n",
       "                                          Type  T Weight type  \\\n",
       "0          💬 chat models (RLHF, DPO, IFT, ...)  💬    Original   \n",
       "1          💬 chat models (RLHF, DPO, IFT, ...)  💬    Original   \n",
       "2          💬 chat models (RLHF, DPO, IFT, ...)  💬    Original   \n",
       "3          💬 chat models (RLHF, DPO, IFT, ...)  💬    Original   \n",
       "4          💬 chat models (RLHF, DPO, IFT, ...)  💬    Original   \n",
       "...                                        ... ..         ...   \n",
       "7255  🔶 fine-tuned on domain-specific datasets  🔶    Original   \n",
       "7256  🔶 fine-tuned on domain-specific datasets  🔶    Original   \n",
       "7257  🔶 fine-tuned on domain-specific datasets  🔶    Original   \n",
       "7258  🔶 fine-tuned on domain-specific datasets  🔶    Original   \n",
       "7259                                            ?    Original   \n",
       "\n",
       "            Architecture                                              Model  \\\n",
       "0     MistralForCausalLM  <a target=\"_blank\" href=\"https://huggingface.c...   \n",
       "1     MistralForCausalLM  <a target=\"_blank\" href=\"https://huggingface.c...   \n",
       "2     MistralForCausalLM  <a target=\"_blank\" href=\"https://huggingface.c...   \n",
       "3     MistralForCausalLM  <a target=\"_blank\" href=\"https://huggingface.c...   \n",
       "4     MistralForCausalLM  <a target=\"_blank\" href=\"https://huggingface.c...   \n",
       "...                  ...                                                ...   \n",
       "7255  MistralForCausalLM  <a target=\"_blank\" href=\"https://huggingface.c...   \n",
       "7256  MistralForCausalLM  <a target=\"_blank\" href=\"https://huggingface.c...   \n",
       "7257    LlamaForCausalLM  <a target=\"_blank\" href=\"https://huggingface.c...   \n",
       "7258  MistralForCausalLM  <a target=\"_blank\" href=\"https://huggingface.c...   \n",
       "7259    LlamaForCausalLM  <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",
       "7255               zorobin/mistral-class-shishya-7b-ep3   \n",
       "7256       zorobin/mistral-class-shishya-all-hal-7b-ep3   \n",
       "7257  zyh3826/20231206094523-pretrain-Llama-2-13b-hf...   \n",
       "7258                      zyh3826/GML-Mistral-merged-v1   \n",
       "7259       zyh3826/llama2-13b-ft-openllm-leaderboard-v1   \n",
       "\n",
       "                                     Model sha  Average ⬆️ Hub License  \\\n",
       "0     035c8193ce71be90be7d90098669afb9164ec6cb   63.391248  apache-2.0   \n",
       "1     78040e4754051df49dd907cf1fd46a6b8a6cc30f   64.870290  apache-2.0   \n",
       "2     ba56089eed1211f02e8d0ff47901e77b0cd48f83   63.223517  apache-2.0   \n",
       "3     5bee9978fcf2188f1070b67f6d94be344fdd99c0   65.985858               \n",
       "4     d390fb35a781129efd26d53f7ecdb513c0c3da27   65.767435  apache-2.0   \n",
       "...                                        ...         ...         ...   \n",
       "7255  e85b73ce67deaa5b40633c5ce2545b23fa3ff3a0   44.276427      llama2   \n",
       "7256  8f15bc3f0d0235fdb67a8dfb6be36a1ac9c1b8b8   44.802767      llama2   \n",
       "7257  28b3ae089b5610053f2294d24667fe248405f031   35.580577      llama2   \n",
       "7258  17a3d5eb5dc23b8a7c29d33cfcd07140a083aa1f   73.295247  apache-2.0   \n",
       "7259  70404059013c74b0641ed69d293b3d1ad708cd1e   53.858973      llama2   \n",
       "\n",
       "      Hub ❤️  #Params (B)  Available on the hub  Merged   MoE  Flagged  \\\n",
       "0          0            7                  True    True  True     True   \n",
       "1          0            7                  True    True  True     True   \n",
       "2          0            7                  True    True  True     True   \n",
       "3          0            7                 False    True  True     True   \n",
       "4          2            7                  True    True  True     True   \n",
       "...      ...          ...                   ...     ...   ...      ...   \n",
       "7255       0            7                  True    True  True     True   \n",
       "7256       0            7                  True    True  True     True   \n",
       "7257       0           13                  True    True  True     True   \n",
       "7258       3            8                  True   False  True     True   \n",
       "7259       1           13                  True    True  True     True   \n",
       "\n",
       "                           date  Chat Template        ARC  HellaSwag  \\\n",
       "0     2024-03-20 05:57:38+00:00          False  61.774744  82.135033   \n",
       "1     2024-03-19 11:27:26+00:00          False  62.713311  82.991436   \n",
       "2     2024-03-19 11:26:56+00:00          False  62.627986  81.507668   \n",
       "3          2024-03-22T15:02:21Z          False  65.017065  83.081060   \n",
       "4     2024-03-21 13:04:58+00:00          False  64.590444  82.871938   \n",
       "...                         ...            ...        ...        ...   \n",
       "7255       2024-01-28T04:36:16Z          False  46.587031  76.618204   \n",
       "7256       2024-01-28T04:36:49Z          False  46.587031  78.868751   \n",
       "7257       2023-12-14T02:54:25Z          False  31.058020  52.031468   \n",
       "7258       2023-12-23T11:32:47Z          False  71.245734  87.880900   \n",
       "7259       2023-12-07T03:44:45Z          False  59.641638  83.140809   \n",
       "\n",
       "           MMLU  TruthfulQA  Winogrande      GSM8K  Maintainers Choice  \n",
       "0     62.423731   42.439513   77.821626  53.752843               False  \n",
       "1     62.700299   45.790101   78.847672  56.178923               False  \n",
       "2     61.967618   54.702404   75.927388  42.608036               False  \n",
       "3     61.873805   60.293632   75.611681  50.037908               False  \n",
       "4     62.017625   58.859162   75.848461  50.416983               False  \n",
       "...         ...         ...         ...        ...                 ...  \n",
       "7255  39.065575   33.537715   69.850039   0.000000               False  \n",
       "7256  34.450351   35.982292   72.928177   0.000000               False  \n",
       "7257  24.434487   44.712448   61.247040   0.000000               False  \n",
       "7258  65.417680   69.275013   80.978690  64.973465               False  \n",
       "7259  60.934970   40.723683   77.348066   1.364670               False  \n",
       "\n",
       "[7258 rows x 26 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_v1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "3b406c77",
   "metadata": {},
   "outputs": [],
   "source": [
    "mmlu_pro = \"MMLU-PRO\"  # Raw'\n",
    "math = \"MATH Lvl 5\"  # Raw\"\n",
    "gpqa = \"GPQA\"\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",
    "# Renaming columns for clarity\n",
    "# v1_data = v1_data.rename(columns={'MMLU': 'mmlu'})\n",
    "# v1_data = v1_data.rename(columns={'GSM8K': 'gsm8k'})\n",
    "# v2_data = v2_data.rename(columns={mmlu_pro: 'mmlu_pro'})\n",
    "# v2_data = v2_data.rename(columns={math: 'math_lvl5'})\n",
    "# v2_data = v2_data.rename(columns={math: 'math_lvl5'})\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": 70,
   "id": "79650d2a",
   "metadata": {},
   "outputs": [
    {
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       "  <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": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "32eb0da4-5f9e-4d90-850e-c7bae0f0e99d",
   "metadata": {},
   "outputs": [
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            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "New scores as functions of old evaluations"
        },
        "xaxis": {
         "title": {
          "text": "v1 scores"
         }
        },
        "yaxis": {
         "title": {
          "text": "v2 scores"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "scatters = []\n",
    "\n",
    "for i, (old, new) in enumerate(\n",
    "    [\n",
    "        (\"MMLU\", mmlu_pro),\n",
    "        (\"MMLU\", gpqa),\n",
    "    ]\n",
    "):  # , (\"GSM8K\", math)\n",
    "    xval = group[avg]\n",
    "    scatters.append(\n",
    "        go.Scatter(\n",
    "            x=merged_data[old],\n",
    "            y=merged_data[new],\n",
    "            name=f\"{new} as function of ({old})\",\n",
    "            text=merged_data[\"fullname\"],\n",
    "            hoverinfo=\"all\",\n",
    "            mode=\"markers\",\n",
    "        marker=dict(\n",
    "            color=colors[i],\n",
    "        ),\n",
    "        ),\n",
    "    )\n",
    "\n",
    "fig = go.Figure(data=scatters)\n",
    "\n",
    "fig.update_layout(\n",
    "    title=\"New scores as functions of old evaluations\",\n",
    "    yaxis=dict(\n",
    "        title=\"v2 scores\",\n",
    "    ),\n",
    "    xaxis=dict(\n",
    "        title=\"v1 scores\",\n",
    "    ),\n",
    "    legend=dict(\n",
    "        x=0,\n",
    "        y=1,\n",
    "        traceorder=\"normal\",\n",
    "    ),\n",
    ")\n",
    "with open(\"new_scores_vs_old.html\", \"w\") as f:\n",
    "    f.write(fig.to_html(full_html=False, include_plotlyjs=False))\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "4b8e98d8",
   "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(\n",
    "    [(\"MMLU\", mmlu_pro), (\"MMLU\", gpqa)]\n",
    "):  # , (\"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": 73,
   "id": "ad71e9d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      15.436242\n",
       "1      15.100671\n",
       "2      15.324385\n",
       "3       8.277405\n",
       "4       8.277405\n",
       "         ...    \n",
       "208     0.000000\n",
       "209     0.000000\n",
       "210     0.000000\n",
       "211     7.829978\n",
       "212     4.138702\n",
       "Name: GPQA, Length: 213, dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_data[new]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "4a0933ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>27</th>\n",
       "      <td>EleutherAI/gpt-j-6b</td>\n",
       "      <td>26.783999</td>\n",
       "      <td>2.956785</td>\n",
       "      <td>2.676197</td>\n",
       "      <td>1.208459</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>EleutherAI/gpt-neox-20b</td>\n",
       "      <td>25.002595</td>\n",
       "      <td>5.458681</td>\n",
       "      <td>1.725030</td>\n",
       "      <td>0.604230</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>EleutherAI/pythia-12b</td>\n",
       "      <td>26.756067</td>\n",
       "      <td>1.743745</td>\n",
       "      <td>1.207890</td>\n",
       "      <td>0.906344</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>IDEA-CCNL/Ziya-LLaMA-13B-v1</td>\n",
       "      <td>27.037992</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.124778</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>PygmalionAI/pygmalion-6b</td>\n",
       "      <td>25.730836</td>\n",
       "      <td>2.047005</td>\n",
       "      <td>2.039007</td>\n",
       "      <td>0.604230</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>bigcode/starcoder2-3b</td>\n",
       "      <td>38.648602</td>\n",
       "      <td>19.636088</td>\n",
       "      <td>7.071882</td>\n",
       "      <td>1.435045</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>bigscience/bloom-3b</td>\n",
       "      <td>26.592509</td>\n",
       "      <td>1.516300</td>\n",
       "      <td>1.475694</td>\n",
       "      <td>0.075529</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>databricks/dolly-v2-12b</td>\n",
       "      <td>25.916846</td>\n",
       "      <td>1.213040</td>\n",
       "      <td>1.429521</td>\n",
       "      <td>1.435045</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>facebook/opt-1.3b</td>\n",
       "      <td>24.963046</td>\n",
       "      <td>0.151630</td>\n",
       "      <td>1.189421</td>\n",
       "      <td>0.755287</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>google/recurrentgemma-2b</td>\n",
       "      <td>34.611141</td>\n",
       "      <td>16.148597</td>\n",
       "      <td>1.992834</td>\n",
       "      <td>1.661631</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>google/recurrentgemma-2b</td>\n",
       "      <td>34.382304</td>\n",
       "      <td>15.693707</td>\n",
       "      <td>1.992834</td>\n",
       "      <td>1.661631</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>meta-llama/Llama-2-13b-chat-hf</td>\n",
       "      <td>54.636271</td>\n",
       "      <td>15.238817</td>\n",
       "      <td>10.257831</td>\n",
       "      <td>0.604230</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>mistralai/Mistral-7B-Instruct-v0.1</td>\n",
       "      <td>55.375474</td>\n",
       "      <td>14.253222</td>\n",
       "      <td>15.336879</td>\n",
       "      <td>1.510574</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>stabilityai/stablelm-2-1_6b</td>\n",
       "      <td>38.946573</td>\n",
       "      <td>17.437453</td>\n",
       "      <td>5.151079</td>\n",
       "      <td>0.151057</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>stabilityai/stablelm-2-1_6b-chat</td>\n",
       "      <td>41.472625</td>\n",
       "      <td>38.817286</td>\n",
       "      <td>6.905659</td>\n",
       "      <td>1.057402</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>stabilityai/stablelm-2-zephyr-1_6b</td>\n",
       "      <td>42.034783</td>\n",
       "      <td>35.329795</td>\n",
       "      <td>7.930703</td>\n",
       "      <td>2.114804</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>stabilityai/stablelm-3b-4e1t</td>\n",
       "      <td>45.225738</td>\n",
       "      <td>3.335861</td>\n",
       "      <td>7.432033</td>\n",
       "      <td>0.679758</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>stabilityai/stablelm-zephyr-3b</td>\n",
       "      <td>46.167947</td>\n",
       "      <td>42.153146</td>\n",
       "      <td>8.530954</td>\n",
       "      <td>4.078550</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>tiiuae/falcon-7b</td>\n",
       "      <td>27.785470</td>\n",
       "      <td>4.624716</td>\n",
       "      <td>1.392583</td>\n",
       "      <td>0.528701</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>tiiuae/falcon-7b-instruct</td>\n",
       "      <td>25.660047</td>\n",
       "      <td>4.624716</td>\n",
       "      <td>1.725030</td>\n",
       "      <td>0.604230</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>tiiuae/falcon-7b-instruct</td>\n",
       "      <td>25.836964</td>\n",
       "      <td>4.700531</td>\n",
       "      <td>1.725030</td>\n",
       "      <td>0.604230</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>togethercomputer/GPT-NeoXT-Chat-Base-20B</td>\n",
       "      <td>29.919101</td>\n",
       "      <td>6.899166</td>\n",
       "      <td>1.614214</td>\n",
       "      <td>1.132931</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>togethercomputer/LLaMA-2-7B-32K</td>\n",
       "      <td>43.325707</td>\n",
       "      <td>4.321456</td>\n",
       "      <td>8.530954</td>\n",
       "      <td>0.679758</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>togethercomputer/RedPajama-INCITE-Base-3B-v1</td>\n",
       "      <td>27.027874</td>\n",
       "      <td>1.288855</td>\n",
       "      <td>1.235594</td>\n",
       "      <td>0.906344</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>togethercomputer/RedPajama-INCITE-Chat-3B-v1</td>\n",
       "      <td>26.231263</td>\n",
       "      <td>0.530705</td>\n",
       "      <td>1.411052</td>\n",
       "      <td>0.302115</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>togethercomputer/RedPajama-INCITE-Instruct-3B-v1</td>\n",
       "      <td>25.032214</td>\n",
       "      <td>1.364670</td>\n",
       "      <td>1.217125</td>\n",
       "      <td>0.604230</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "                                             fullname       MMLU      GSM8K  \\\n",
       "27                                EleutherAI/gpt-j-6b  26.783999   2.956785   \n",
       "30                            EleutherAI/gpt-neox-20b  25.002595   5.458681   \n",
       "31                              EleutherAI/pythia-12b  26.756067   1.743745   \n",
       "40                        IDEA-CCNL/Ziya-LLaMA-13B-v1  27.037992   0.000000   \n",
       "65                           PygmalionAI/pygmalion-6b  25.730836   2.047005   \n",
       "99                              bigcode/starcoder2-3b  38.648602  19.636088   \n",
       "103                               bigscience/bloom-3b  26.592509   1.516300   \n",
       "108                           databricks/dolly-v2-12b  25.916846   1.213040   \n",
       "116                                 facebook/opt-1.3b  24.963046   0.151630   \n",
       "125                          google/recurrentgemma-2b  34.611141  16.148597   \n",
       "126                          google/recurrentgemma-2b  34.382304  15.693707   \n",
       "135                    meta-llama/Llama-2-13b-chat-hf  54.636271  15.238817   \n",
       "159                mistralai/Mistral-7B-Instruct-v0.1  55.375474  14.253222   \n",
       "185                       stabilityai/stablelm-2-1_6b  38.946573  17.437453   \n",
       "186                  stabilityai/stablelm-2-1_6b-chat  41.472625  38.817286   \n",
       "187                stabilityai/stablelm-2-zephyr-1_6b  42.034783  35.329795   \n",
       "188                      stabilityai/stablelm-3b-4e1t  45.225738   3.335861   \n",
       "189                    stabilityai/stablelm-zephyr-3b  46.167947  42.153146   \n",
       "197                                  tiiuae/falcon-7b  27.785470   4.624716   \n",
       "198                         tiiuae/falcon-7b-instruct  25.660047   4.624716   \n",
       "199                         tiiuae/falcon-7b-instruct  25.836964   4.700531   \n",
       "201          togethercomputer/GPT-NeoXT-Chat-Base-20B  29.919101   6.899166   \n",
       "202                   togethercomputer/LLaMA-2-7B-32K  43.325707   4.321456   \n",
       "208      togethercomputer/RedPajama-INCITE-Base-3B-v1  27.027874   1.288855   \n",
       "209      togethercomputer/RedPajama-INCITE-Chat-3B-v1  26.231263   0.530705   \n",
       "210  togethercomputer/RedPajama-INCITE-Instruct-3B-v1  25.032214   1.364670   \n",
       "\n",
       "      MMLU-PRO  MATH Lvl 5  GPQA  \n",
       "27    2.676197    1.208459   0.0  \n",
       "30    1.725030    0.604230   0.0  \n",
       "31    1.207890    0.906344   0.0  \n",
       "40    1.124778    0.000000   0.0  \n",
       "65    2.039007    0.604230   0.0  \n",
       "99    7.071882    1.435045   0.0  \n",
       "103   1.475694    0.075529   0.0  \n",
       "108   1.429521    1.435045   0.0  \n",
       "116   1.189421    0.755287   0.0  \n",
       "125   1.992834    1.661631   0.0  \n",
       "126   1.992834    1.661631   0.0  \n",
       "135  10.257831    0.604230   0.0  \n",
       "159  15.336879    1.510574   0.0  \n",
       "185   5.151079    0.151057   0.0  \n",
       "186   6.905659    1.057402   0.0  \n",
       "187   7.930703    2.114804   0.0  \n",
       "188   7.432033    0.679758   0.0  \n",
       "189   8.530954    4.078550   0.0  \n",
       "197   1.392583    0.528701   0.0  \n",
       "198   1.725030    0.604230   0.0  \n",
       "199   1.725030    0.604230   0.0  \n",
       "201   1.614214    1.132931   0.0  \n",
       "202   8.530954    0.679758   0.0  \n",
       "208   1.235594    0.906344   0.0  \n",
       "209   1.411052    0.302115   0.0  \n",
       "210   1.217125    0.604230   0.0  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
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           "bgcolor": "#E5ECF6",
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          },
          "scene": {
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            "gridwidth": 2,
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            "gridwidth": 2,
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            "gridwidth": 2,
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            "showbackground": true,
            "ticks": "",
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          },
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           "aaxis": {
            "gridcolor": "white",
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            "ticks": ""
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           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
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           },
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           "zerolinewidth": 2
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          "yaxis": {
           "automargin": true,
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           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
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         }
        },
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         "text": "MATH (v2) as fn of GSM8K (v1)"
        },
        "xaxis": {
         "title": {
          "text": "GSM8K"
         }
        },
        "yaxis": {
         "title": {
          "text": "MATH lvl 5"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "scatters = []\n",
    "\n",
    "for (old, new) in [(\"GSM8K\", math)]:\n",
    "    for i in range(3):\n",
    "        if i == 0:\n",
    "            tmp_data = merged_data#[merged_data[new] > 0][merged_data[old] > 0]\n",
    "            color=colors[1]\n",
    "        elif i == 1:\n",
    "            tmp_data = merged_data[merged_data[new] == 0]\n",
    "            color=colors[0]\n",
    "        else:\n",
    "            tmp_data = merged_data[merged_data[old] == 0][merged_data[new] > 0]\n",
    "            color=\"green\"\n",
    "\n",
    "        x=tmp_data[old]\n",
    "        y=tmp_data[new]\n",
    "        \n",
    "        scatters.append(\n",
    "            go.Scatter(\n",
    "                x=x,\n",
    "                y=y,\n",
    "                name=f\"{new}=f({old})\",\n",
    "                text=merged_data[\"fullname\"],\n",
    "                hoverinfo=\"all\",\n",
    "                mode=\"markers\",\n",
    "            marker=dict(\n",
    "                color=color,\n",
    "            ),\n",
    "            ),\n",
    "        )\n",
    "\n",
    "fig = go.Figure(data=scatters)\n",
    "\n",
    "fig.update_layout(\n",
    "    title=\"MATH (v2) as fn of GSM8K (v1)\",\n",
    "    yaxis=dict(\n",
    "        title=\"MATH lvl 5\",\n",
    "    ),\n",
    "    xaxis=dict(\n",
    "        title=\"GSM8K\",\n",
    "    ),\n",
    ")\n",
    "fig.update_layout(showlegend=False)\n",
    "\n",
    "with open(\"math_vs_gsm8k.html\", \"w\") as f:\n",
    "    f.write(fig.to_html(full_html=False, include_plotlyjs=False))\n",
    "\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "10194c01",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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": "e1d6d4e7",
   "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": 77,
   "id": "701c3cdd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GSM8K at 0: possible problem with the truncation\n",
      "     GSM8K  MATH Lvl 5                     fullname\n",
      "1      0.0   13.444109         01-ai/Yi-1.5-34B-32K\n",
      "7      0.0    9.592145          01-ai/Yi-1.5-9B-32K\n",
      "40     0.0    0.000000  IDEA-CCNL/Ziya-LLaMA-13B-v1\n",
      "147    0.0    0.000000    microsoft/DialoGPT-medium\n",
      "MATH at 0: likely contamination on GSM8K for high scores\n",
      "         GSM8K  MATH Lvl 5                           fullname\n",
      "24   56.633813         0.0     CohereForAI/c4ai-command-r-v01\n",
      "40    0.000000         0.0        IDEA-CCNL/Ziya-LLaMA-13B-v1\n",
      "67    7.657316         0.0             Qwen/Qwen1.5-0.5B-Chat\n",
      "71   30.098560         0.0             Qwen/Qwen1.5-110B-Chat\n",
      "73   30.856710         0.0              Qwen/Qwen1.5-14B-Chat\n",
      "74   30.629265         0.0              Qwen/Qwen1.5-14B-Chat\n",
      "81   13.570887         0.0               Qwen/Qwen1.5-7B-Chat\n",
      "82   13.191812         0.0               Qwen/Qwen1.5-7B-Chat\n",
      "84   28.203184         0.0        Qwen/Qwen1.5-MoE-A2.7B-Chat\n",
      "91   72.175891         0.0            abacusai/Smaug-34B-v0.1\n",
      "105   1.364670         0.0               bigscience/bloom-7b1\n",
      "147   0.000000         0.0          microsoft/DialoGPT-medium\n",
      "211  64.746020         0.0  upstage/SOLAR-10.7B-Instruct-v1.0\n"
     ]
    }
   ],
   "source": [
    "math_data = merged_data[[\"GSM8K\", math, \"fullname\"]]\n",
    "print(\"GSM8K at 0: possible problem with the truncation\")\n",
    "print(math_data[math_data[\"GSM8K\"] == 0])\n",
    "print(\"MATH at 0: likely contamination on GSM8K for high scores\")\n",
    "print(math_data[math_data[math] == 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "77e4aef1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        GSM8K  MATH Lvl 5                     fullname\n",
      "66  16.300227    0.453172            Qwen/Qwen1.5-0.5B\n",
      "67   7.657316    0.000000       Qwen/Qwen1.5-0.5B-Chat\n",
      "68  33.586050    2.265861            Qwen/Qwen1.5-1.8B\n",
      "69  19.029568    0.453172       Qwen/Qwen1.5-1.8B-Chat\n",
      "70  81.046247   23.036254            Qwen/Qwen1.5-110B\n",
      "71  30.098560    0.000000       Qwen/Qwen1.5-110B-Chat\n",
      "72  67.626990   16.465257             Qwen/Qwen1.5-14B\n",
      "73  30.856710    0.000000        Qwen/Qwen1.5-14B-Chat\n",
      "74  30.629265    0.000000        Qwen/Qwen1.5-14B-Chat\n",
      "75  61.106899   26.661631             Qwen/Qwen1.5-32B\n",
      "76  60.879454   26.661631             Qwen/Qwen1.5-32B\n",
      "77   7.050796    6.646526        Qwen/Qwen1.5-32B-Chat\n",
      "78  52.236543    2.416918              Qwen/Qwen1.5-4B\n",
      "79   2.426080    0.981873         Qwen/Qwen1.5-4B-Chat\n",
      "80  53.525398    4.456193              Qwen/Qwen1.5-7B\n",
      "81  13.570887    0.000000         Qwen/Qwen1.5-7B-Chat\n",
      "82  13.191812    0.000000         Qwen/Qwen1.5-7B-Chat\n",
      "83  16.982563    0.151057       Qwen/Qwen1.5-MoE-A2.7B\n",
      "84  28.203184    0.000000  Qwen/Qwen1.5-MoE-A2.7B-Chat\n",
      "85  35.708870    2.567976              Qwen/Qwen2-0.5B\n",
      "86  55.799848    6.268882              Qwen/Qwen2-1.5B\n",
      "87  72.934041   18.806647                Qwen/Qwen2-7B\n"
     ]
    }
   ],
   "source": [
    "print(math_data[math_data.fullname.str.contains(\"Qwen\")])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49d2e1a0",
   "metadata": {},
   "source": [
    "Edit: all Qwen models with bad scores are chat models!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d690147",
   "metadata": {},
   "source": [
    "## MuSR through time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8639aac7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d343dea",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "adfe1f51",
   "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",
    "yval = data_v2[\"MUSR\"]\n",
    "xval = data_v2[\"date\"]\n",
    "ax.scatter(xval, yval, c=colors[0])\n",
    "\n",
    "ax.set_title(f\"MuSR as a function of the model publication date\")\n",
    "ax.set_ylabel(f\"MuSR\")\n",
    "ax.set_xlabel(f\"Date\")\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.legend()\n",
    "# plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "518c9573-1cc8-4ba4-91d6-6e0e1f32f44b",
   "metadata": {},
   "source": [
    "# Check Zero Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "b5caa56c-4c03-4b82-8d2d-16aed7dad24c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# V2 tasks columns\n",
    "columns_to_check = [\n",
    "    \"IFEval Raw\",\n",
    "    \"IFEval\",\n",
    "    \"BBH Raw\",\n",
    "    \"BBH\",\n",
    "    \"MATH Lvl 5 Raw\",\n",
    "    \"MATH Lvl 5\",\n",
    "    \"GPQA Raw\",\n",
    "    \"GPQA\",\n",
    "    \"MUSR Raw\",\n",
    "    \"MUSR\",\n",
    "    \"MMLU-PRO Raw\",\n",
    "    \"MMLU-PRO\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "286f27a7-41c9-45dc-be0c-6a35b52f51e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter the dataframe for rows that have at least one 0 in the specified columns\n",
    "filtered_v2 = data_v2[data_v2[columns_to_check].isin([0]).any(axis=1)].copy()\n",
    "\n",
    "# Create the 'missing' column with the list of columns that have a 0 value\n",
    "filtered_v2[\"tasks\"] = filtered_v2[columns_to_check].apply(\n",
    "    lambda row: [col for col in columns_to_check if row[col] == 0], axis=1\n",
    ")\n",
    "\n",
    "# Select the relevant columns\n",
    "filtered_v2 = filtered_v2[[\"eval_name\", \"fullname\", \"Precision\", \"tasks\"]]\n",
    "\n",
    "filtered_v2 = filtered_v2.reset_index().drop(columns=[\"index\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "717d06c1-ee87-4248-a478-b54d55f2fe3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(44, 4)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_v2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "fc8c8ba1-c71a-4810-a879-703ef5741a01",
   "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>fullname</th>\n",
       "      <th>Precision</th>\n",
       "      <th>tasks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>upstage_SOLAR-10.7B-Instruct-v1.0_float16</td>\n",
       "      <td>upstage/SOLAR-10.7B-Instruct-v1.0</td>\n",
       "      <td>float16</td>\n",
       "      <td>[MATH Lvl 5 Raw, MATH Lvl 5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>togethercomputer_RedPajama-INCITE-Instruct-3B-...</td>\n",
       "      <td>togethercomputer/RedPajama-INCITE-Instruct-3B-v1</td>\n",
       "      <td>float16</td>\n",
       "      <td>[GPQA]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>togethercomputer_RedPajama-INCITE-Chat-3B-v1_f...</td>\n",
       "      <td>togethercomputer/RedPajama-INCITE-Chat-3B-v1</td>\n",
       "      <td>float16</td>\n",
       "      <td>[GPQA]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>togethercomputer_RedPajama-INCITE-Base-3B-v1_f...</td>\n",
       "      <td>togethercomputer/RedPajama-INCITE-Base-3B-v1</td>\n",
       "      <td>float16</td>\n",
       "      <td>[GPQA]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>togethercomputer_LLaMA-2-7B-32K_float16</td>\n",
       "      <td>togethercomputer/LLaMA-2-7B-32K</td>\n",
       "      <td>float16</td>\n",
       "      <td>[GPQA]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           eval_name  \\\n",
       "0          upstage_SOLAR-10.7B-Instruct-v1.0_float16   \n",
       "1  togethercomputer_RedPajama-INCITE-Instruct-3B-...   \n",
       "2  togethercomputer_RedPajama-INCITE-Chat-3B-v1_f...   \n",
       "3  togethercomputer_RedPajama-INCITE-Base-3B-v1_f...   \n",
       "4            togethercomputer_LLaMA-2-7B-32K_float16   \n",
       "\n",
       "                                           fullname Precision  \\\n",
       "0                 upstage/SOLAR-10.7B-Instruct-v1.0   float16   \n",
       "1  togethercomputer/RedPajama-INCITE-Instruct-3B-v1   float16   \n",
       "2      togethercomputer/RedPajama-INCITE-Chat-3B-v1   float16   \n",
       "3      togethercomputer/RedPajama-INCITE-Base-3B-v1   float16   \n",
       "4                   togethercomputer/LLaMA-2-7B-32K   float16   \n",
       "\n",
       "                          tasks  \n",
       "0  [MATH Lvl 5 Raw, MATH Lvl 5]  \n",
       "1                        [GPQA]  \n",
       "2                        [GPQA]  \n",
       "3                        [GPQA]  \n",
       "4                        [GPQA]  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_v2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aab6532a-3475-4354-ac4f-8354954561f7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e981349e-5e75-41e9-9eb3-549831736e8b",
   "metadata": {},
   "source": [
    "# Plot Correlations Among Tasks"
   ]
  },
  {
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    "# Selecting the relevant columns for correlation analysis\n",
    "correlation_data = data_v2[tasks_v2]\n",
    "correlation_data_raw = data_v2[tasks_v2_raw]\n",
    "\n",
    "# Calculating the correlation matrices\n",
    "# TODO: instead of this one, would be interesting to plot correlation between v2 evals vs v1 evals\n",
    "correlation_matrix = correlation_data.corr()\n",
    "correlation_matrix_raw = correlation_data_raw.corr()"
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    "                title=\"Correlation Matrix Heatmap\",\n",
    "                width=600,\n",
    "                height=500\n",
    "               )\n",
    "\n",
    "fig.update(layout_coloraxis_showscale=False)\n",
    "\n",
    "# Update layout if necessary\n",
    "fig.update_xaxes(side=\"bottom\", tickangle=-45, tickfont=dict(size=10))  # Rotate and reduce font size on x-axis\n",
    "fig.update_yaxes(autorange=\"reversed\")  # To reverse the y-axis to match traditional matrix layout\n",
    "\n",
    "# Save the plot as HTML if needed\n",
    "with open(\"correlation_heatmap.html\", \"w\") as f:\n",
    "    f.write(fig.to_html(full_html=False, include_plotlyjs=False))\n",
    "\n",
    "fig.show()"
   ]
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    "# Sort the correlation matrix by index alphabetically\n",
    "sorted_correlation_matrix = correlation_matrix.sort_index(axis=0).sort_index(axis=1)\n",
    "\n",
    "fig = go.Figure(\n",
    "    data=go.Heatmap(\n",
    "        z=sorted_correlation_matrix,\n",
    "        x=sorted_correlation_matrix.index,\n",
    "        y=sorted_correlation_matrix.index,\n",
    "        text=sorted_correlation_matrix.values,\n",
    "        texttemplate=\"%{text:.2}\",\n",
    "        zmin=0,\n",
    "        zmax=1,\n",
    "        colorscale='plasma_r'\n",
    "    ),\n",
    ")\n",
    "\n",
    "fig.update_yaxes(autorange=\"reversed\")\n",
    "\n",
    "fig.update_layout(\n",
    "    title=\"Correlation Matrix Heatmap (Normalized Tasks)\",\n",
    "    height=500,\n",
    "    width=500,\n",
    ")\n",
    "\n",
    "fig.update(layout_coloraxis_showscale=False)\n",
    "\n",
    "with open(\"correlation_heatmap.html\", \"w\") as f:\n",
    "    f.write(fig.to_html(full_html=False, include_plotlyjs=False))\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "42cc0657-755a-4458-9214-e7574573e8d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the correlation matrices as heatmaps\n",
    "fig, axes = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "sns.heatmap(\n",
    "    correlation_matrix, annot=True, fmt=\".2f\", cmap=\"coolwarm\", vmin=-1, vmax=1, ax=axes\n",
    ")\n",
    "axes.set_title(\"Correlation Matrix Heatmap (Normalized Tasks)\")\n",
    "\n",
    "# sns.heatmap(correlation_matrix_raw, annot=True, fmt=\".2f\", cmap='coolwarm', vmin=-1, vmax=1, ax=axes[1])\n",
    "# axes[1].set_title('Correlation Matrix Heatmap (Raw Tasks)')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "935ed7de-42b7-4a23-96ab-607cf4722bb7",
   "metadata": {},
   "source": [
    "# Ranking Analysis between v1 and v2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "1f73c34e-a0f5-423b-9849-92598aa9d6df",
   "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": 89,
   "id": "d2e7948b-6982-4cc0-aac3-b8188c4a27de",
   "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": 90,
   "id": "3c984e75-fc28-419e-8170-f132f897e583",
   "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": 91,
   "id": "0b397e64",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_rank_data = merged_rank_data.drop_duplicates(subset=[\"fullname\"]).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "7113d05f",
   "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>208</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>209</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>210</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>211</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>212</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>168 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",
       "208      togethercomputer/RedPajama-INCITE-Base-3B-v1  38.537852   5.645099\n",
       "209      togethercomputer/RedPajama-INCITE-Chat-3B-v1  39.527194   4.950649\n",
       "210  togethercomputer/RedPajama-INCITE-Instruct-3B-v1  39.055049   5.877290\n",
       "211                 upstage/SOLAR-10.7B-Instruct-v1.0  74.200698  19.961989\n",
       "212                          upstage/SOLAR-10.7B-v1.0  66.037836  17.072003\n",
       "\n",
       "[168 rows x 3 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_rank_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "40d53c14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# merged_rank_data[['v1_rank', 'fullname']].sort_values('v1_rank')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "b436dafe-6d6b-47a4-a552-40e4cbdb3901",
   "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": 95,
   "id": "c7693af9-918a-4f01-a9c6-4f2cb6c37ddb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating rank change and sort\n",
    "merged_rank_data[\"rank_change\"] = (\n",
    "    merged_rank_data[\"v2_rank\"] - merged_rank_data[\"v1_rank\"]\n",
    ")\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": 96,
   "id": "ca353e7e-1468-435b-b4d9-c7965262eb91",
   "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",
    "\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",
    "    )\n",
    "else:\n",
    "    top_changes = pd.concat([merged_rank_data.head(10), merged_rank_data.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": 97,
   "id": "08a6607a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Invert the order to show declines first\n",
    "top_changes_sorted = top_changes.sort_values(\"rank_change\", ascending=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "cb5e2c26-771f-4f47-b94e-4b8a0502f510",
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           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Top and Bottom Changes in Rankings from v1 to v2"
        },
        "xaxis": {
         "range": [
          -85,
          70
         ],
         "tickfont": {
          "size": 14
         }
        },
        "yaxis": {
         "autorange": "reversed",
         "linecolor": "black",
         "linewidth": 1,
         "mirror": false,
         "showline": true,
         "title": {
          "text": "model"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "colors = [BLACK if x < 0 else RED for x in top_changes_sorted[\"rank_change\"]]\n",
    "\n",
    "fig = go.Figure(\n",
    "    data=[\n",
    "        go.Bar(\n",
    "            y=top_changes_sorted[\"fullname\"],\n",
    "            x=top_changes_sorted[\"rank_change\"],\n",
    "            name=\"Top and Bottom Changes in Rankings from v1 to v2\",\n",
    "            text=top_changes_sorted[\"rank_change_info\"],\n",
    "            orientation=\"h\",\n",
    "            marker_color=colors,\n",
    "        ),\n",
    "    ]\n",
    ")\n",
    "\n",
    "fig.update_traces(textposition=\"outside\")\n",
    "fig.update_yaxes(showline=True, linewidth=1, linecolor=\"black\", mirror=False)\n",
    "fig.update_yaxes(autorange=\"reversed\")\n",
    "\n",
    "fig.update_layout(\n",
    "    title=\"Top and Bottom Changes in Rankings from v1 to v2\",\n",
    "    xaxis_tickfont_size=14,\n",
    "    yaxis=dict(\n",
    "        title=\"model\",\n",
    "    ),\n",
    "    bargap=0.10,  # gap between bars of adjacent location coordinates.\n",
    "    xaxis_range=[-85, 70],\n",
    "    autosize=True,\n",
    "    height=700,\n",
    "    paper_bgcolor=\"rgba(0,0,0,0)\",\n",
    "    plot_bgcolor=\"rgba(0,0,0,0)\",\n",
    ")\n",
    "with open(\"rankings_change.html\", \"w\", encoding=\"utf-8\") as f:\n",
    "    f.write(fig.to_html(full_html=False, include_plotlyjs=False))\n",
    "fig  # .to_html(full_html=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "fb005cca",
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "70441c19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "alignmentgroup": "True",
         "hovertemplate": "rank_change=%{marker.color}<br>fullname=%{y}<br>rank_change_info=%{text}<extra></extra>",
         "legendgroup": "",
         "marker": {
          "color": [
           -59,
           -50,
           -48,
           -36,
           -34.5,
           -33.5,
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           -30,
           30,
           31,
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           33,
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           43,
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          "coloraxis": "coloraxis",
          "pattern": {
           "shape": ""
          }
         },
         "name": "",
         "offsetgroup": "",
         "orientation": "h",
         "showlegend": false,
         "text": [
          "72 → 13",
          "97 → 47",
          "57 → 9",
          "77 → 41",
          "167 → 133",
          "167 → 134",
          "69 → 36",
          "55 → 23",
          "98 → 66",
          "80 → 50",
          "124 → 154",
          "126 → 157",
          "59 → 92",
          "53 → 86",
          "60 → 96",
          "9 → 46",
          "30 → 69",
          "46 → 88",
          "5 → 48",
          "4 → 60"
         ],
         "textposition": "outside",
         "type": "bar",
         "x": [
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         ],
         "xaxis": "x",
         "y": [
          "01-ai/Yi-1.5-34B-32K",
          "01-ai/Yi-1.5-9B-32K",
          "Qwen/Qwen1.5-32B-Chat",
          "Intel/neural-chat-7b-v3-1",
          "gpt2",
          "openai-community/gpt2",
          "openchat/openchat_3.5",
          "01-ai/Yi-34B-Chat",
          "Qwen/Qwen1.5-7B-Chat",
          "HuggingFaceH4/zephyr-7b-alpha",
          "stabilityai/stablelm-2-1_6b",
          "tiiuae/falcon-7b",
          "meta-llama/Meta-Llama-3-8B",
          "stabilityai/stablelm-2-12b",
          "meta-llama/Llama-2-70b-chat-hf",
          "upstage/SOLAR-10.7B-Instruct-v1.0",
          "stabilityai/stablelm-2-12b-chat",
          "tiiuae/falcon-11B",
          "mlabonne/Beyonder-4x7B-v3",
          "mlabonne/AlphaMonarch-7B"
         ],
         "yaxis": "y"
        }
       ],
       "layout": {
        "bargap": 0.1,
        "barmode": "relative",
        "coloraxis": {
         "colorbar": {
          "title": {
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            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Top and Bottom Changes in Rankings from v1 to v2"
        },
        "width": 700,
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "range": [
          -85,
          85
         ],
         "tickfont": {
          "size": 14
         },
         "title": {
          "text": "Rank change"
         }
        },
        "yaxis": {
         "anchor": "x",
         "autorange": "reversed",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "Model"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Using Plotly Express to create a horizontal bar chart\n",
    "fig = px.bar(\n",
    "    top_changes_sorted,\n",
    "    y=\"fullname\",\n",
    "    x=\"rank_change\",\n",
    "    text=\"rank_change_info\",\n",
    "    orientation='h',\n",
    "    title=\"Top and Bottom Changes in Rankings from v1 to v2\",\n",
    "    template='plotly',\n",
    "    color='rank_change',\n",
    "    color_continuous_scale=[ORANGE, BLACK]\n",
    ")\n",
    "\n",
    "# Additional layout settings\n",
    "fig.update_layout(\n",
    "    xaxis_tickfont_size=14,\n",
    "    yaxis=dict(title='Model'),\n",
    "    xaxis=dict(title='Rank change'),\n",
    "    bargap=0.1,  # gap between bars of adjacent location coordinates\n",
    "    xaxis_range=[-85, 85],\n",
    "    paper_bgcolor=\"rgba(0,0,0,0)\",\n",
    "    plot_bgcolor=\"rgba(0,0,0,0)\",\n",
    "    width=700,\n",
    "    height=500\n",
    ")\n",
    "\n",
    "fig.update_yaxes(autorange=\"reversed\")  # Reverse the y-axis order\n",
    "fig.update_traces(textposition='outside')  # Set text labels to be outside the bars\n",
    "fig.update_layout(showlegend=False)\n",
    "fig.update(layout_coloraxis_showscale=False)\n",
    "\n",
    "# Save the figure as HTML\n",
    "fig.write_html(\"rankings_change.html\", full_html=False, include_plotlyjs=False)\n",
    "\n",
    "# Display the figure\n",
    "fig.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "89117036-29c8-4fdb-bf8e-0bfc665566d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x3000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 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 = [\"#66c2a5\" if x < 0 else \"#fc8d62\" 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, 30))\n",
    "plt.figure(figsize=(10, 10))\n",
    "bars = plt.barh(\n",
    "    top_changes_sorted[\"fullname\"], top_changes_sorted[\"rank_change\"], color=colors\n",
    ")\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(),\n",
    "        bar.get_y() + bar.get_height() / 2,\n",
    "        rank_change_info,\n",
    "        va=\"center\",\n",
    "        ha=\"left\" if bar.get_width() > 0 else \"right\",\n",
    "        fontsize=10,\n",
    "        color=\"black\",\n",
    "    )\n",
    "\n",
    "# Adding a legend\n",
    "green_patch = mpatches.Patch(color=\"#66c2a5\", label=\"Positive Change\")\n",
    "red_patch = mpatches.Patch(color=\"#fc8d62\", 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 v\")\n",
    "plt.xlim(-70, 50)\n",
    "plt.gca().invert_yaxis()  # Invert y-axis to show declines first\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d93cd4d-c080-4376-9682-53a02f10be6c",
   "metadata": {},
   "source": [
    "# Outlier Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "8badc916-06a4-49c3-a650-ac4fa3e5acaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    16.766858\n",
       "1    19.628255\n",
       "2     5.663939\n",
       "3     4.748119\n",
       "4     5.432974\n",
       "Name: mean_score, dtype: float64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the mean score across all tasks for each model\n",
    "data_v2[\"mean_score\"] = data_v2[tasks_v2].mean(axis=1)\n",
    "data_v2[\"mean_score\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "c732a247-5470-415a-93be-3b2bb7bc3261",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'median_score'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m~/.pyenv/versions/3.10.6/envs/datatrove3.10/lib/python3.10/site-packages/pandas/core/indexes/base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3805\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[0;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'median_score'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[103], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdata_v2\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmedian_score\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.6/envs/datatrove3.10/lib/python3.10/site-packages/pandas/core/frame.py:4102\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   4101\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 4102\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   4104\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.6/envs/datatrove3.10/lib/python3.10/site-packages/pandas/core/indexes/base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3807\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[1;32m   3808\u001b[0m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[1;32m   3809\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[1;32m   3810\u001b[0m     ):\n\u001b[1;32m   3811\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[0;32m-> 3812\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3814\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3815\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3816\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3817\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: 'median_score'"
     ]
    }
   ],
   "source": [
    "data_v2[\"median_score\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52df156d-4b75-4f1b-9497-654d8334be64",
   "metadata": {},
   "outputs": [
    {
     "data": {
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PPlPv3r01ZcoU1dbyH6oAAACdAaFQ+zJNS+vWlWrVqq1at660R0xBZ2QajlRWVrIWLJiskSOTVFnpVVFRpSorvRo1KokNhACgA4R01+n58+crOTlZS5YsCRwbMmRI4N+WZemhhx7S73//e5199tmSpKefflr9+vXTq6++qosuuqjZNX0+n3w+X+D7Xbt2SZLq6+tVX1/fXo+Cw9D4OfB54EjQfxAs+hCCQf9p2dChbmVmxuvrr0vkcrmbTJ+2LEseT41++tNEDR3q7tHv3ZH0n88+264nnvhSmzbtCOzkPXRoX1155WidcMKA9iq1U7j22tH6/e/f0/ffe9SvX5QiI8NUW9ug0tIa9evXS9dcM1p+f4P8/lBX2nH4HdQ6xx+fqCVLztSGDeWqrPTJ5XJo+PB42WxGj37v6D8IFn2oZ2vt525Y+/+JsAONGDFCU6ZM0ffff6///Oc/GjBggK699lr9+te/liR9++23Sk1N1Zo1a3TssccGXjdhwgQde+yxevjhh5td884779S8efOaHV+2bJl69erVbs8CAAAAAAAAdEd79uzRxRdfrKqqKsXExBywXUiDxsjIvdNnbrrpJv3yl7/U559/rt/85jd6/PHHNWvWLOXm5uqkk07SDz/8oKSkpMDrLrjgAhmGoRdffLHZNVsa0ZicnKwdO3Yc9I1Ax6mvr9c777yj0047TeHh4aEuB10M/QfBog8hGPSfg2tp9N2wYX316193/9F3rXE4/cc0Lf3616/rq69KdNRRzUeJbtni0U9/mqgnnjiz2683Z5pWiyPTeiJ+ByEY9B8Eiz7Us+3atUt9+/Y9ZNAY0qnTpmnquOOO07333itJGjlypP773/8GgsYj4XA45HA4mh0PDw/nB6GT4TNBMOg/CBZ9CMGg/7Ts5JNTlJU1WPn5ZfJ4auV2RyojI6HHhkIH0pr+s25dqdatK1dcXJQaGvZ//wy53VH6+utybdrkUWZmv/YrtpP46U+7TlBtmla7/wzwOwjBoP8gWPShnqm1n3lIg8akpCSNGDGiybH09HT961//kiQlJiZKkkpLS5uMaCwtLW0ylRoAAABNdUTY0RKbzegRwVd7a81O3qWl7OTd2eTmbtPChXkqKCgPjOpNT4/XnDlj2IQEANAjhDRoPOmkk7Rx48YmxzZt2qTBgwdL2rsxTGJiolauXBkIFnft2qXPPvtM11xzTUeXCwAA0CUQdnR9++7kHRUV0ew8O3l3Prm525STs0IVFV4lJUXL6QyT19ugNWuKlZOzgh2PAQA9gi2UN7/xxhv16aef6t5779U333yjZcuW6YknnlB2drYkyTAM3XDDDbrnnnv02muvad26dbr00kvVv39/nXPOOaEsHQAAoFNqDDtWry6Wy+VUSopLLpczEHbk5m4LdYlohYyMBKWnx6ukpFr7L6luWZZKSqo1YkS8MjISQlQh9mWalhYuzFNFhVdpaXGKioqQ3W5TVFSEUlPj5PF4tWhRnkwzZMvjAwDQIUIaNB5//PF65ZVX9Pzzz+uYY47R3XffrYceekgzZswItPntb3+r6667TldeeaWOP/541dTU6K233gpsJAMAAIC9CDu6D5vN0Jw5Y+R2O1VYWKGamjr5/aZqaupUWFght9up7OwxrH/ZSeTnl6mgoFxJSdFNNu6R9g6eSEyM1vr15crPLwtRhQAAdIyQTp2WpDPOOENnnHHGAc8bhqG77rpLd911VwdWBQAA0PUcTtjBOoqdX1ZWshYsmByYBl9aunca/KhRScrOZhp8Z8KamgAA7BXyoBEAAABtg7Cj+8nKStbYsQPZybuTY01NAAD2ImgEAADoJgg7uqeuspN3qHY67wwa19Rcs6ZYqalxTUYUN66pOWpUEmtqAgC6PYJGAACAboKwA6HS03c6b1xTMydnhQoLK5SY+OOu0yUl1aypCQDoMUK6GQwAAADaDhuIIBTY6XyvxjU1R45MUmWlV0VFlaqs9GrUqCQtWDC5RwSuAAAwohEAAKAbYQMRdKT9dzpvHEXbuNN5YWGFFi3K09ixA3tEwM2amgCAno6gEQAAoJsh7EBHYafz5rrKmpoAALQHgkYAAIBuiLADHYGdzgEAwL5YoxEAAADAEdl3p/OWsNM5AAA9C0EjAAAAgCPSuNN5SUm1LMtqcq5xp/MRI+LZ6RwAgB6CoBEAAADAEWGncwAAsC+CRgAAAABHrHGn85Ejk1RZ6VVRUaUqK70aNSpJCxZMZqdzAAB6EDaDAQAAABAUdjoHAAASQSMAAECnZZoWwQ26DHY6BwAABI0AAACdUG7uNi1cmKeCgnL5fH45HHalp8drzpwxTEUFAABAp8QajQAAAJ1Mbu425eSs0OrVxXK5nEpJccnlcmrNmmLl5KxQbu62UJcIAACA/2OZpnzrC+XNXSvf+kJZphnqkkKGEY0AAACdiGlaWrgwTxUVXqWlxckw9k6VjoqKUGpqnAoLK7RoUZ7Gjh14xNOomZINAADQNmrz1qly8XLVbyqSVVcvIyJc4UNT5LriPEWOyQx1eR2OoBEAAKATyc8vU0FBuZKSogMhYyPDMJSYGK3168uVn192ROvhMSUbAACgbdTmrdOOOxbJrKyWvV8fGZEOWbU+1a3bpB13LFLfedk9Lmxk6jQAAEAn4vHUyufzy+ls+e/BTmeYfD6/PJ7aw742U7IBAADahmWaqly8XGZltcKGDJCtt1OG3SZbb6fCUgbIrKpW1ZPLe9w0aoJGAACATsTtjpTDYZfX29Diea+3QQ6HXW535GFdd/8p2VFREbLbbYEp2R6PV4sW5ck0rbZ4DAAAgG6tbsMW1W8q2juSsYVZKPaEONVtLFLdhi0hqjA0CBoBAAA6kYyMBKWnx6ukpFqW1TT0syxLJSXVGjEiXhkZCYd13cOZkg0AAICDMyur967JGOlo8bwR6ZBVVy+zsrqDKwstgkYAAIBOxGYzNGfOGLndThUWVqimpk5+v6mamjoVFlbI7XYqO3vMYW/e0p5TsgEAAHoamytaRkS4rFpfi+etWp+MiHDZXNEdXFloETQCAAB0MllZyVqwYLJGjkxSZaVXRUWVqqz0atSoJC1YMPmINm1prynZaMo0La1bV6pVq7Zq3bpSpqIDANBNRQwfovChKfKX7WxxFoq/rEIRw1IUMXxIiCoMDXadBgAA6ISyspI1duxA5eeXyeOpldsdqYyMhMMeySjtDb9M01KfPr1UWFihESPiZbP9+PfmxinZo0YlHfaUbPyIHb0BAOg5DJtNrivO0447FqmhaLvsCXGBXaf9ZRWyxUYr9vLzZNh61hg/gkYAAIBOymYzlJnZL6hr7Bt+eTy1Ki/fo507v1NaWpwSE6Pk9TaopKT6iKdkY6/GHb0rKrxKSoqW0xkmr7chsKP3ggWTdfzxiaEuEwAAtKHIMZnqOy9blYuXq35Tkaxyj4yIcDl+MlSxl5+nyDGZoS6xwxE0AgAAdFP7h19JSdEqKanRN99UaOPGHaqo8MrtjtSoUUnKzmbU3ZHaf0fvxs12Gnf0Liys0KJFeXryyTNDXCkAAGhrkWMy1e+4DNVt2CKzslo2V7Qihg/pcSMZGxE0AgAAdEMHCr8GDIhRUlKUCgp2KDXVrQULJiszsx8jGYPQ2h29N2woD1GFAACgPRk2mxwjUkNdRqfQM+NVAACAbu5g4ZfNZtPgwS7t2LFHNptByBik1u7oXVnZ8q6UAAAA3QVBIwAAQDfU2vDL46nt4Mq6n9bu6O1yOTq4MgAAgI5F0AgAALol07S0bl2pVq3aqnXrSmWaVqhL6lCtDb/c7sgOrqz7ychIUHp6vEpKqmVZTftZ447eI0bEa/jw+A6rqaf3fwAAEBqs0QgAALqdfXda9vn8cjjsSk+P15w5PWfDk8bwa82aYqWmxjWZPt0Yfo0alaSMjIQQVtk92GyG5swZo5ycFSosrFBi4o+7TodiR2/6PwAACBVGNAIAgG6lcafl1auL5XI5lZLiksvl1Jo1xcrJWaHc3G2hLrFDNIZfbrdThYUVqqmpk99vqqamToWFFR0efnV3WVnJWrBgskaOTFJlpVdFRZWqrPRq1KgkLVgwucMCvlD0f0ZPAgCARoxoBAAA3caBdlqOiopQamqcCgsrtGhRnsaOHdgjArbG8KtxdFtp6d7RbaNGJSk7m9FtbS0rK1ljxw5Ufn6ZPJ5aud2RyshI6LC+For+z+hJAACwL4JGAADQbRxsp2XDMJSYGK3168uVn1+mzMx+IaoyeKZptTrMCnX41dPYbEbI+lZH9//G0ZMVFV4lJf04Xbxx9GRHjuQEAACdA0EjAADoNlqz03JpadfeaflIRpCFMvxCx+nI/s/oYQAA0BLWaAQAAN1Gd99pmfUncTAd2f8PZ/QkAADoOQgaAQBAt9G403JJSbUsq+mGFI07LY8YEd8ld1refwRZVFSE7HZbYASZx+PVokV5bMTRg3Vk/2/N6Emfr2uPHgYAAIePoBEAAHQb3XmnZUaQ4VA6sv+3ZvRkRIRN5eW72Y0aAIAehDUaAQBAt9Jdd1ruCetPIngd1f8bR0+uWVOs1NS4JuG3ZVn69tsKGYahu+9epbo6dqMGAKCnIGgEAADdTnfcaXnfEWRRURHNznf19SfRdjqi/zeOnszJWaHCwgolJv646/S331aooqJWcXFOud1OdqMGAKAHIWgEAADdUnfbaflQI8hKSqo1alRSl1x/Em2vI/p/S6MnIyJsMgxDcXFOZWYmsBs1AAA9DEEjAADocKZpdavRhh3hYCPISkqqu/T6k+i69h89WV6+W3ffvUput/OQa4l2pz8EAACAvQgaAQBAh8rN3RYYAeXz9cy12440aO2u60+ia9t39OSqVVtVV8daogAA9FQEjQAAoMPk5m5TTs4KVVR4lZQU3SPXbgs2aO2O60+i+2AtUQAAejZbqAsAAAA9g2laWrgwTxUVXqWlxSkqKkJ2uy2wdpvH49WiRXkyTeuwr7tuXalWrdqqdetKD/v1HakxaF29ulgul1MpKS65XM5A0Jqbu61V12kcQTZ+/GBlZvYjZESn0biWaElJtSyr6c9i41qiI0bEs5YoAADdFCMaAQBAh8jPL1NBQbmSkqLbbO22rjQNe/+glU0yOg/WDG07rCUKAEDPRtAIAAA6hMdTK5+v7dZu62rTsNsjaEXwulJY3VWwligAAD0XQSMAAOgQbbl2W1ccHdjWQSuC19XC6q6EtUQBAOiZWKMRAAB0iLZcu+1wRgd2FvsGrS1hk4yO1V5rhuJHrCUKAEDPQ9AIAAA6ROPabW63U4WFFaqpqZPfb6qmpk6FhRWHtXZba0YH+nyda3Qgm2R0Ll0xrAYAAOjsCBoBAECHaVy7beTIJFVWelVUVKnKSq9GjUo6rGmqXXF0YFsGrQheVwyrAQAAOjvWaAQAAB2qLdZuaxwduGZNsVJT45qMSGscHThqVFKnGx3IJhmdR2vXDI2NdWjdulLWGQQAAGgFgkYAANDhGtduC+b1c+aMUU7OChUWVigx8ceNPEpKqjv16EA2yegcWhNWJyfH6k9/+kgbNuxgR2oAAIBWYOo0AADoktpqGnYosElG6B1qKntYmE3FxdVas6ZELpdTKSkuuVzOwI7UubnbQv0IAAAAnQ4jGgEAQJfF6EAE40BT2UeOTFJ5+R5t21altLQfRzs27khdWFihRYvyNHbsQPoaAADAPggaAQBAlxbsNOyewDQtwtgDaCmsNk1Ls2e/2qodqel7AAAAPyJoBAAA6MZyc7cFRuyxzmDL9g+rV63aesgdqUtL2ZEaAABgf6zRCAAA0E3l5m5TTs4KrV5dzDqDh2HfHalb0rgjtdsd2cGVAQAAdG4EjQAAAN2QaVpauDBPFRVepaXFKSoqQna7LbDOoMfj1aJFeTJNK9SldjqNO1KXlFTLspq+P407Uo8YEa+MjIQQVQgAANA5ETQCAAB0Q/n5ZSooKG/VOoNo6lA7UrvdTmVnj2GdSwAAgP0QNAIAAHRDHk/tIdcZ9PlYZ/BAGnekHjkySZWVXhUVVaqy0qtRo5K0YMFk1rcEAABoAZvBAAAAdEP7rjMYFRXR7DzrDB5aSztSs2M3AADAgRE0AgAAdGGmabUYhDWuM7hmTbFSU+OaTJ9uXGdw1Kgk1hk8hP13pAYAAMCBETQCAAB0Ubm527RwYZ4KCsrl8/nlcNiVnh6vOXPGKCsrWXPmjFFOzgoVFlYoMTFaTmeYvN4GlZRUd7l1Bg8UqAIAAKDzIGgEAADognJztyknZ4UqKrxKSvoxRFyzplg5OSsC6wguWDA5EEaWlu4NI0eNSlJ29pgus87goQJVAAAAdA4EjQAAAF2MaVpauDBPFRVepaX9OC06KipCqalxKiys0KJFeRo7dmCXX2ewtYEqAAAAQo+gEQAAoIvJzy9TQUG5kpKim6y9KEmGYSgxMVrr15crP79MmZn9uuw6g4cTqHaV4BQAAKA7s4W6AAAAABwej6dWPp9fTmfLfzN2OsPk8/nl8dR2cGVt63ACVQAAAIQeQSMAAEAX43ZHyuGwy+ttaPG819sgh8MutzuygytrWz0lUAUAAOguCBoBAAC6mIyMBKWnx6ukpFqWZTU5Z1mWSkqqNWJEvDIyEkJUYdvoKYEqAABAd0HQCAAA0MXYbIbmzBkjt9upwsIK1dTUye83VVNTp8LCCrndTmVnj+ny6xb2lEAVAACguyBoBAAA6IKyspK1YMFkjRyZpMpKr4qKKlVZ6dWoUUndZifmnhKoAgAAdBfsOg0AAELGNC3l55fJ46mV2x2pjIwEQqPDkJWVrLFjB3br97AxUF24ME8FBeUqLfXL4bBr1KgkZWeP6RaBKgAAQHdB0AgAAEIiN3dbIDzy+faGR+np8Zozh/DocNhshjIz+4W6jHbVEwJVAACA7oCgEQAAdLjc3G3KyVmhigqvkpKi5XSGyett0Jo1xcrJWdFtpv6i7fSEQBU9B6O5AQDdFUEjAADoUKZpaeHCPFVUeJWWFifD2Ps/11FREUpNjVNhYYUWLcrT2LED+R9vAN0Oo7kBAN0Zm8EAAIAOlZ9fpoKCciUlRQdCxkaGYSgxMVrr15crP78sRBWio5impXXrSrVq1VatW1cq07QO/SKgC2sczb16dbFcLqdSUlxyuZyB0dy5udtCXSIAAEFhRCMAAOhQHk+tfD6/nM6W/zPE6QxTaalfHk9tB1eGjsSoLvQ0jOYGAPQEjGgEAAAdyu2OlMNhl9fb0OJ5r7dBDoddbndkB1eGjsKoLvREjOYGAPQEBI0AAKBDZWQkKD09XiUl1bKsplNlLctSSUm1RoyIV0ZGQogqRHvaf1RXVFSE7HZbYFSXx+PVokV5TKNGt9Oa0dw+H6O5AQBdG0EjAADoUDaboTlzxsjtdqqwsEI1NXXy+03V1NSpsLBCbrdT2dljmDrYTTGqCz0Vo7kBAD0BQSMAAOhwWVnJWrBgskaOTFJlpVdFRZWqrPRq1KgkLVgwmTX6ujFGdaGnYjQ3AKAnYDMYAAAQEllZyRo7dqDy88vk8dTK7Y5URkYCIxm7uX1HdUVFRTQ7z6gudFeNo7lzclaosLBCiYnRcjrD5PU2qKSkmtHcAIBugRGNAAAgZGw2Q5mZ/TR+/GBlZvbjf7B7AEZ1oSdjNDcAoLtjRCMAAAA6DKO60NMxmhsA0J0RNAIAAKBDNY7qWrgwTwUF5Sot9cvhsGvUqCRlZ49hVBe6vcbR3AAAdDcEjQAAAOhwjOoCAADofggaAQAAEBKM6gIAAOheCBoBAOjBTNNiRBkAAACANkHQCABAD5Wbuy2wRp7Pt3eNvPT0eM2Zwxp5AAAAAA6fLdQFAACAjpebu005OSu0enWxXC6nUlJccrmcWrOmWDk5K5Sbuy3UJQIAgG7GMk351hfKm7tWvvWFskwz1CUBaGOMaAQAoIcxTUsLF+aposKrtLQ4GcbeqdJRURFKTY1TYWGFFi3K09ixA5lGjS6NpQEAoPOozVunysXLVb+pSFZdvYyIcIUPTZHrivMUOSYz1OUBaCMEjQAA9DD5+WUqKChXUlJ0IGRsZBiGEhOjtX59ufLzy9ioA10WSwMAQOdRm7dOO+5YJLOyWvZ+fWREOmTV+lS3bpN23LFIfedlEzYC3URIp07feeedMgyjydfw4cMD52tra5Wdna0+ffooKipK559/vkpLS0NYMQAAXZ/HUyufzy+ns+W/NzqdYfL5/PJ4aju4MgTLNC2tW1eqVau2at26UpmmFeqSQoKlAQCg87BMU5WLl8usrFbYkAGy9XbKsNtk6+1UWMoAmVXVqnpyOdOogW4i5CMaMzIy9O677wa+Dwv7saQbb7xR//73v/Xyyy8rNjZWc+bM0XnnnaePP/44FKUCANAtuN2Rcjjs8nobFBUV0ey819ugiAibyst3a9WqrUw57SIYwbcXSwMAQOdSt2GL6jcV7R3J2MJMCntCnOo2FqluwxY5RqSGqEoAbSXkQWNYWJgSExObHa+qqtLixYu1bNkynXrqqZKkJUuWKD09XZ9++qnGjh3b0aUCANAtZGQkKD09XmvWFCs1Na7Jf/RblqVvv62QYRi6++5VqqvruYFVV9I4gq+iwqukpGg5nWHyehsCI/gWLJjcYz47lgYAgM7FrKzeuyZjpKPF80akQ1a5R2ZldQdXBqA9hDxo3Lx5s/r376/IyEideOKJuu+++zRo0CB9+eWXqq+v16RJkwJthw8frkGDBumTTz45YNDo8/nk8/kC3+/atUuSVF9fr/r6+vZ9GLRK4+fA54EjQf9BsOhDe1177Wj9/vfv6fvvPerXL0qRkWGqrW1QUZFHXm+d3O5I9evnDBxfv75Et976ju6551SdcMKAUJcfMp2x/5impccfz9Pu3bVKT/8xOI6MjJDLFactWzz629/yNHp0vx4xgq+iYrckUzExYbLbm5+PiQlTZaWpiordHf45dsb+g66FPoRghKr/mNG9ZEY51eBvkM3hbH6+ziczyikzuhd9u5Pjd1DP1trP3bAsK2SL97z55puqqanRsGHDVFxcrHnz5mn79u3673//q9dff12XXXZZk9BQksaMGaNTTjlF8+fPb/Gad955p+bNm9fs+LJly9SrV692eQ4AAAAAAACgu9qzZ48uvvhiVVVVKSYm5oDtQho07q+yslKDBw/WAw88IKfTeURBY0sjGpOTk7Vjx46DvhHoOPX19XrnnXd02mmnKTw8PNTloIuh/xy+zz7briee+FKbNu0IrNs2dGhfXXnl6B45Mo0+1JRpWtqwoVyVlT7t2LFbf/5zrlyuSPXqFa6amjp5PF6Vle2Rz9cgv9+SaVoaN26Qbr75JPpPJ+k/ubnbdMMNb2nQoFjZ7c33+fP7TX33XZUeemhqm0yf7uy/U0zT0q9//bq+/rpEQ4a4my0NsGWLRz/9aaKeeOLMDh/h2Rn7D7oW+hCCEcr+4/tyvXbe+3eZu6plj3fLcDhk+Xzyl3tki4lWn1t/LcfoER1aEw4fv4N6tl27dqlv376HDBpDPnV6Xy6XS0OHDtU333yj0047TXV1daqsrJTL5Qq0KS0tbXFNx0YOh0MOR/O1H8LDw/lB6GT4TBAM+k/r5OZu029/uzKwbltc3N512z7/vFjffLOyR63btj/60I9++tO94dCqVVtVXd0gu71B//3vTlVWelVb2yBJsttt6tUrXPX1ptauLdNvf0v/6Sz9Jy6utySbdu1qeXOfmpoGSTbFxfUOuuau8jvl6qvHKCdnhTZsqFBi4o9rVpaUVMvtduqqq8bI4Wj+XnWUztR/0DXRh1rHMk3Vbdgis7JaNle0IoYPkWFr/geZniYU/Sd87E8V9vurVLl4ueo3Fe1dszEiXL2GH6XYy89T5JjMDq0HweF3UM/U2s+8UwWNNTU1Kiws1CWXXKLRo0crPDxcK1eu1Pnnny9J2rhxo7777judeOKJIa4UADo/dl7F4XK7I9XQYCo/v0wNDaYaGiwZhiHD2Nufdu+uk8MRpsGDXSov303/6SQOtblPSUm1Ro1KUkZGQlD36Uq/U7KykrVgweTALtylpXtHXo4alaTsbDY1AnqC2rx1zUKt8KEpcl1BqBUqkWMy1e+4DMJfoJsLadCYk5OjM888U4MHD9YPP/ygO+64Q3a7XdOnT1dsbKyuuOIK3XTTTYqLi1NMTIyuu+46nXjiiew4DQCtwM6rOFzp6fHy+fyqrW1Q794R8vl8stmM/+s/lurrTZmmpZgYh+x2G/2nk7DZDM2Zs3cEX2FhyyP4srPHBB3+dbXfKVlZyRo7dqDy88vk8dTK7Y5URkZCyENQAO2vNm+ddtyxSGZltez9+uzd1bjWp7p1m7TjjkXqOy+bsDFEDJtNjhGpoS4DQDsKadD4/fffa/r06dq5c6fi4+N18skn69NPP1V8fLwk6cEHH5TNZtP5558vn8+nKVOm6LHHHgtlyQDQZXg8tfL5/HI6W/5V73SGqbTUL4+ntoMrQ2dVUFAuh8OmyMgweb312ruKsyXL2juazW43ZLMZqq6uU+/e4fSfTqQjRvB1xd8pNpvRKUJPAB3HMk1VLl4us7JaYUMGBP4wYvR2ykgZoIai7ap6crkcx2Uwkg4A2kFIg8YXXnjhoOcjIyO1aNEiLVq0qIMqAoDuw+2OlMNhl9fb8rptXm+DHA673O7IEFSHzsjjqVVYmF0jRsSrsNCjnTu98vst2WyGwsJscjrD1dDgV329X16vQf/pZNp7BB+/UwB0BXUbtqh+U9HekYwtjL62J8SpbmOR6jZsYWQdgDZjWpa2eyq1p86nXhEODXC7ZDN65iyKTrVGIwCg7XTUum3oPhqDpMjIcB1/fH99/vkPqq6uk9MZpvBwu/x+U6a5N3Sk/3RO7TmCj98pALoCs7J675qMkc03CJW0dxp1uUdmZXUHVwagu9pcWqZ3CzaouKpS9X5T4XabkmJdmpQ+XEf363n/XcRYcQDophrXbXO7nSosrFBNTZ38flM1NXUqLKxos3Xb0PWZpqV160q1c+fenYSLi3fJMAwdfXQfOZ1hqqvzq6HBlNdbL4cjTGVlu+k/PRC/UwB0BTZXtIyIcFm1vhbPW7U+GRHhsrmiO7gyAN3R5tIyvfD5F/pu5045IyLUNzpKzogIfVexUy98/oU2l5aFusQOx4hGAOjG2HkVh5Kbuy3QP3y+vYFiRYVXXm+pjjoqTsOG9dG331aqqqpWhmHI5YrU6NH0n56K3yndl2Wa7ASLbiFi+BCFD01R3bpNMlIGNBt97S+rkOMnQxUxfEgIqwTQHZiWpXcLNmiPz6f4mB83y4sMD5cjLEzl1dVauWGDUhPie9Q0aoJGAOjm2HkVB5Kbu005OStUUbF3JGPjTsV1dX7V1NRp+/YqhYXZlZTUW8cdl6Rp04Zq3LhB9J8ejt8p3U9t3jpVLl6u+k1Fe6ecRoQrfGiKXFecx8686HIMm02uK87TjjsWqaFou+wJcYFdp/1lFbLFRiv28vMI0gEEbbunUsVVlYrp5WxxTdgYp1M/VFZqu6dSyXHuEFXZ8QgaAaAHYOdV7M80LS1cmKeKCq/S0n5cby8qKkLHHJOgb76pUGqqWzfffJL69HESJKEJfqd0H7V567TjjkUyK6v3bp7xf4FM3bpN2nHHIvWdl03Y2A7YNKB9RY7JVN952T8G6OUeGRHhcvxkqGIvJ0AH0Db21PlU7zcVG9ZytBZhD9Muf6321LW8lEN3RdAIAEAPlJ9fpoKCciUlRbf4F9ikpGj98EO1+vRxEigB3ZRlmqpcvFxmZbXChvw4xdTo7ZSRMkANRdtV9eRyOY7LYPRXG2LTgI4ROSZT/Y7LYEkAAO2mV4RD4Xab6hoaFBke3ux8nb9B4XabekW0vDlVd8VvWQAAeiCPp1Y+n19OZ8t/c3Q6w+Tz+eXx1HZwZQA6St2GLarfVLR3JGMLf3CwJ8SpbmOR6jZsCVGF3Q+bBnQsw2aTY0SqnFnHyjEilZARQJsa4HYpKdalXV6vLMtqcs6yLO3yetXf5dIAtys0BYYIv2kBAOiB3O5IORx2eb0NLZ73ehvkcNjldkd2cGUAOopZWb13TcbIlkdaGJEOWXX1MiurO7iy7mn/TQMiw8NlMwxFhocrPjpae+p8Wrlhg8z9/mcVANA52QxDk9KHq1eEQ+XV1aqtr5dpWqqtr1d5dbV6RTj0s+HDe9zSGASNAAD8f/buPb6tu74f/+scSedItmRJviuOa6eOXTuuA0lTN/WgXBrSwZcBS38bbJQO2vHl4sC+MLPRPeBXCt2Xy7IvMJJyW8sXyq8LYwS+W+ELoeXSgUudtKH1nDhJ3ThxXd91sWRdjnTO+f3hSoljOZFs3fV6Ph48tlqO/ZEs+fI+7/f7VYa6u+vR1VWH6Wl/0iuw09N+bNtWh+5ujvERlSrRYYMgmaCHk++O0sMRCJIJosOW45OVpnRCA4iIqDi0N9TjHTfuwjXVNQgpCuYDAYQUBS01NXjHjbvKciUGdzQSERGVIVEUsH9/LwYGjmJszI3Gxoup09PTfjidFvT39zIApkxpms5U6TIgdW6BqaMVyvAZCK1NK4pfuq5DnXVD3t4BqXNLHk9ZOhgaQERUmtob6tFWX8eQr5ex0EhERFSm+vqaceDAXhw8OIRTp+YwM6NClg3YudOF/v5e9PU15/uIlAeDgxOJ50Qksvyc6Oqqw/79fE6UGkEU4bh7H+bvPYTY+CQM9dWJ1Gl11g3RboP9rn3ca5chDA0gIipdoiCgudqZ72MUBBYaiYiIylhfXzN2797M7jUCsFxkHBg4Crc7BJfrYpfriRNTGBg4igMH9rLYWGLMvT2ova8f3gePIHpmHPqcB4Jkgry9A/a79sHc25PvI5aMeGjABfcCZKNxVQfpYiiElpqasgsNICKi0sJCIxERUZkTRQE9PQ35PgatIVdjzJqm4+DBIbjdIWzdWp0oglitEtraqjE25sahQ0PYvXszC9Elxtzbg4Zd3VBGz0Hz+iE6bJA6t7CTMcPioQGHjx3HnN+PKosFksEIRY1hMRQq29AAIiIqLSw0EhEREaVA03ScPDkLADh5chY9PZuyXnDL5RjzyMgsTp2ag8tlSxpU0dhow8mTcxgZmWVhugQJogh5W1u+j1Hy4qEBj50axZTPi0U1DJNBREtNDW7t7CzL0AAiIiotLDQSERERXUW84PfCC/O4555GvO99j+Laa2uzurcw12PMHk8YkYgKiyX5r4cWixEzMyo8nnDGPidROWJoABERlTLOQxAREVHZ0jQdw8MzeOKJ8xgenoGm6aveJ17we+aZKdjtZgCA3W5OFPwGByeycq5Lx5itVgkGg5gYY/Z4Qjh0aCjpedfL6TRDlg0IhWJJbw+FYpBlA5xOc8Y+J1G5iocGXNfYiOZqJ4uMRERUMtjRSERERGUplbHkywt+krRcDKiszO7ewnyMMXd316Orqw4nTkyhra16VVDF9LQfO3e60N3N0U4iIiIiSo4djURERFR2Lu1SdDgsaG11wOGwrOpSTKfgl0mpjDFHIpkdYxZFAfv398LptGBszI1AQIGqaggEFIyNueF0WtDf38sgGCIiIiJaEwuNREREVFbSGUvOR8EPyN8Yc19fMw4c2IsdO1zwekMYH/fC6w1h505XxndCEhEREVHp4eg0ERFRlmiajpGRWXg8YTidZnR317MbrACk06V4acHPapVWfaxsFfzyOcbc19eM3bs3l+Rzl69JIiIiouxioZGIiCgLUtn/R/mRTrryq151zYqCH5Cbgl98jHlg4CjGxtxobLyYOj097c/6GLMoChnb/Vgo+JokIiIiyj6OThMREWVYqvv/KD/SGUu+fG/h0pICAFhayv7ewr6+ZnzhC2/Ali1OvPiiD6Oj8/B4ghxjXge+JomIiIhyg4VGIiKiDEpn/x/lR3wseXraD11f+XWIdylu21aX6FK8dG+hz7e8i9HnC2e94Dc4OIEHHjiGl17yQ1V1GAwiNm2qwgc+cCOLjGnga5KIqHBouo4Jtwenp6cx4fZA0/m9l6jUcHSaiIgog9LZ/1dqo6nFYj1jyfG9hcPDL2F8/Di+/vU3o6dnU9ZGl+MdeG53CC6XDZs22RAKxXDunAd/8zc/Z0djGviaJCIqDGdnZvHYqVFM+byIqhpMBhEuuwN7ujrR3pD5ncNElB/saCQiIsqgfKUUU3rWk64sigK2bVv+Q2jbtuyFiLADL7P4miQiyr+zM7M4fOw4LiwswCJJqLVZYZEkXHAv4PCx4zg7M5vvIxJRhrCjkYiIKIPylVJM6SvUdGV24GUWX5NERPml6ToeOzWKYCSCuqqLP9vMJhNkoxFzfj8eHx1FW30dRCG/P4OJaOPY0UhERJRB6e7/o/yKpyvfcksLenoa8l5kBNiBl2l8TRIR5dekx4spnxdVFZakF9CqLBa85PVi0uPNzwGJKKNYaCQiIsqgy1OKAwEFqqohEMh+SjGVhnRSsenq+JokIsqvoBJBVNUgGZNfQJMMRkRVDUElkuOTEVE2sNBIRESUYevZ/0cUV4odeJqmY3h4Bk88cR7DwzM53y/J1yQRUf5USDJMBhFKLPkFNEWNwWQQUSHJOT4ZEWUDdzQSERFlQaHu/6PCt55U7EI2ODiBgweHcOrUHCIRFbJsQFdXHfbv781pgY+vSSKi/GhyOuCyO3DBvQDZaFwxPq3rOhZDIbTU1KDJ6cjfIYkoY1hoJCIiypL4/j+idMU78OIFupmZ5QLdzp0u9PfntkC3EYODExgYOAq3OwSX62LB9MSJKQwMHM15NyFfk0REuScKAvZ0deLwseOY8/tRZbFAMhihqDEshkKokGTc2tnJIBiiEsFCIxEREVEBKvYOPE3TcfDgENzuELZurU50sFitEtraqjE25sahQ0PYvXtz0dwnIiJan/aGerzjxl147NQopnxeLKphmAwiWmpqcGtnJ9obimcdCBFdGQuNRERERAWqmDvwRkZmcerUHFwuW9KU0cZGG06enMPIyGzR3sdc0jUNyug5aF4/RIcNUucWCCLXrRNR8WhvqEdbfR0mPV4ElQgqJBlNTgc7GYlKDAuNRERERJRxHk8YkYgKiyX5r5sWixEzMyo8nnCOT1Z8wkPD8D54BNEz49CVKATJBFNHKxx374O5tyffxyMiSpkoCGiudub7GESURbwMSkREREQrZCIl2uk0Q5YNCIWSp4yGQjHIsgFOp3mjxy1p4aFhzN97CMpzZyDabTA2uyDabVCGz2D+3kMIDw3n+4glS9c0RE6OITT4e0ROjkHXtHwfiYiIqOCxo5GIiIiIEjKVEt3dXY+urjqcODGFtrbqVSmj09N+7NzpQnc393KtRdc0eB88As3rh3FLU+IxFCotEFqbEBufhO+hI5B3dXOMOsPYRUpERLQ+/I2EiIiIiABcTIl+5pkpOBwWtLY64HBYEinRg4MTKX8sURSwf38vnE4LxsbcCAQUqKqGQEDB2JgbTqcF/f29DIK5AmX0HKJnxmFoqEm659JQXw3l9DiU0XN5OmFpYhcpERHR+rHQSERERESrUqKtVgkGg5hIifZ4Qjh0aCitMeq+vmYcOLAXO3a44PWGMD7uhdcbws6dLhw4sDetDslypHn9y910Zjnp7YJZhq5EoXn9OT5Z6bq8i1SstEAwiBArLTC2NkHz+eF76AjHqImIiNbA0WkiIiIiylpKdF9fM3bv3oyRkVl4PGE4nWZ0d9ezkzEFosMGQTJBD0cgVFpW3a6HIxAkE0SHLQ+nK03pdJHK29rydEoiIqLCxUIjEREREWU1JVoUhbSKk7RM6twCU0crlOEzEFqbVu25VGfdkLd3QOrcksdTlpaUukjnPOwiJSIiWgNHp4mIaMMykVBLRPnFlOjCI4giHHfvg2i3ITY+CW0pCF1VoS0FERufhGi3wX7XPgbBZNClXaTJpNJFyrRqIiIqZ+xoJCKiDclUQm0+aJrOcU6ilzElujCZe3tQe1//xQTkOQ8EyQR5ewfsdzEBOdM22kXKtGoiIip3LDQSEdG6xRNq3e4QXC4bLBYjQqFYIqG2kMMennpqEg888HRRFkiJsiGeEj0wcBRjY240Nl58TU9P+5kSnUfm3h407OqGMnoOmtcP0WGD1LmFnYxZEO8inb/3EGLjkzDUVy+PS4cjUGfdV+wijadVa17/8o7Hl/9dPK269r5+FhuJiKjk8bcTIiJal2wk1ObSJz7xCzzzzBQcDgtaWx1wOCyJAung4ES+j0eUF0yJLlyCKELe1gZL3yshb2tjkTGL4l2kUk8HNF8AsYlpaL4A5O0daxYLmVZNRES0jB2NRES0LtlKqM22eOHT41kukMbPHi+Qjo25cejQEHbv3szOLSpLTIkmSr+LlGnVREREy1hoJCKidclmQm02jY7OAQAaG61FVSAtJtx9WfyYEk2ZoGtaUY97x7tIU8G0aiIiomVpFxr/67/+C9dff33S2370ox/hbW9720bPREREReDShFqrVVp1e6Em1Hq9y0miZrMRySbYCrVAWiyKORyIiDKn3EJRLk2rFiotq25PJa2aiIioFKR9SfG2227DuXPnVr39Bz/4Ad75zndm5FBERFT44gm109N+6PrKPYzxhNpt2+oKLqHW4VjuNgmHY0lvL9QCaTGIhwNx9yVReYuHoijPnYFot8HY7IJotyVCUcJDw/k+YsbF06rV2YWkPxPVWTek61oTadW6piFycgyhwd8jcnKMuxuJiKhkpF1o/Mu//Evs2bMH09PTibd973vfw5133on//b//dybPRkREBSyeUOt0WjA25kYgoEBVNQQCCsbG3AWbUNvZWQcAmJkJFFWBtNAVezgQEWVGuYaixNOqRbsNsfFJaEtB6KoKbSmI2PjkirTq8NAwZj7wGcz234+5jx3AbP/9mPnAZ0qyAJtPLOYSEeVH2qPT9913H9xuN/bs2YMnnngCP/3pT/GXf/mXePjhh3H77bdn44xERFSg4gm18VHZmZnlUdmdO13o7y/MUdl44dPhWC6QNjbaYLEYEQrFMD3tL9gCaaEr1nAgIsqscg5FiadVJ0bG5zwQJBPk7R2w37U8Mh7v9tS8/uXHyCxDD0cS3Z5rpVpTesptdJ+IqJCsKwzmK1/5Ct75zndi9+7dmJycxL/8y7/grW99a6bPRkRERaBYE2rvv//1eOCBp4umQFroijUciIgyq9xDUa6UVn15t2e8ECtUWiC0NiE2PgnfQ0cg7+ouqtCcQsNiLhFRfqVUaPz3f//3VW/bt28f/vM//xN/9md/BkEQEu/zlre8JbMnJCKigleMCbU33dSEvr6WoiuQFqpiDQciosxiKMraadXl3O2ZKyzmEhHlX0qFxislST/00EN46KGHACz/gFRVNSMHIyIiyrZiLJAWqng40IkTU2hrq17xR3R89+XOnS7uviQqcfFQFGX4DITWplXfC9RZN+TtHYlQlHJS7t2eucBiLhFR/qV0GUfTtJT+xyIjERFRZmmajuHhGTzxxHkMD88UbJhKsYYDEVFmpROKUm4u7fZMphy6PbMtpWKuEmUxl4goi9a1o/FyXq8XDocjEx+KiIiIXjY4OJEI2olElvdIdnXVYf/+wtwjWYzhQESUeamEopQjdntmH0f3iYjyL+1C4+c//3m0trbi7W9/OwDgT/7kT/CDH/wALpcLP/nJT/CKV7wi44ckIiIqN4ODExgYOAq3OwSX62Iy9okTUxgYOIoDB/YWZOGuWMOBiCizrhSKUq7i3Z7z9x5CbHwShvrqRFCJOusu627PTGExl4go/9L+Kfa1r30Nzc3Lf9j8/Oc/x2OPPYaf/vSneOMb34iPfexjGT8gERFRudE0HQcPDsHtDmHr1mpYrRIMBhFWq4S2tmp4PCEcOjRU0GPUPT0NuOWWFvT0NLDISFSm4qEolr5XQt7WxgIaLnZ7Sj0d0HwBxCamofkCkLd3MA05Azi6T0SUf2l3NE5PTycKjY8++ij+9E//FHv37kVraytuuummjB+QiIio3IyMzOLUqTm4XLaky+wbG204eXIOIyOzDLMhoozRNY0diDnAbs/s4ug+EVF+pV1odDqdmJiYQHNzM37605/i/vvvB/ByKzrDYIiIiDbM4wkjElFhsST/MW2xGDEzo8LjCef4ZERUqsJDwxcLM0oUgmSCqaMVjrtZmMmGeLcnZQeLuURE+ZN2oXHfvn348z//c7S3t2NhYQFvfOMbAQAnTpzA1q1bM35AIiKicuN0miHLBoRCMVit0qrbQ6EYZNkAp9Och9MRUakJDw1j/t5D0Lx+GBpqEnsDleEzmL/3EEd6qSixmEtElB9pX9L54he/iP3792Pbtm34+c9/DqvVCgCYmprCBz/4wYwfkIiIqNx0d9ejq6sO09N+6PrKPYy6rmN62o9t2+rQ3V2fpxMSUanQNQ3eB49A8/ph3NIEsdICwSBCrLTA2NoEzeeH76Ej0DUt30clIiKiIpB2R6PJZMLAwMCqt3/kIx/JyIGIiIjKnSgK2L+/FwMDRzE25kZj48XU6elpP5xOC/r7exmyQkQbpoyeQ/TM+HInY5KdsIb6aiinx6GMnmN3GBEREV1V2oXGuJMnT+LChQtQFGXF29/ylrds+FBERETlrq+vGQcO7MXBg0M4dWoOMzMqZNmAnTtd6O/vRV9fc76PSEQlQPP6l3cymuWktwtmGfqcB5rXn+OTERERUTFKu9D4wgsv4I//+I8xPDwMQRASI13xK6AMhCEiIsqMvr5m7N69GSMjs/B4wnA6zejurmcnI1GZyEUKtOiwQZBM0MMRCJWW1WcIRyBIJogOW0Y/LxEREZWmtAuNf/VXf4UtW7bg8ccfx5YtWzA0NISFhQX89V//NQ4cOJCNMxIREZUtURTQ09OQ72MQUY7lKgVa6twCU0crlOEzEFqbVoxP67oOddYNeXsHpM4tGfucREREVLrSviT65JNP4tOf/jRqa2shiiJEUcSrXvUqfPazn8WHP/zhbJyRiIiIiKhsxFOglefOQLTbYGx2QbTbEinQ4aHhjH0uQRThuHsfRLsNsfFJaEtB6KoKbSmI2PgkRLsN9rv2ZbyTkoiIiEpT2r8xqKoKm215dKK2thYvvfQSAKClpQWnT5/O7OmIiIiIiMpIPlKgzb09qL2vH1JPBzRfALGJaWi+AOTtHai9rz+jHZTlQNN1TLg9OD09jQm3B9rLq6aIiIjKQcqj00888QRuvvlmXH/99Xj22WexZcsW3HTTTfjCF74ASZLwjW98A9dee202z0pEREREVNLylQJt7u1Bw67urO+ELHVnZ2bx2KlRTPm8iKoaTAYRLrsDe7o60d5Qn3i/XOzfJCIiyoeUC42ve93rMDU1hU984hNYWloCAHz605/Gm9/8Zrz61a9GTU0Nvve972XtoEREREREpS6fKdCCKGa0eFluzs7M4vCx4whGIqiqsMBuNEKJxXDBvYDDx47jHTfuQntDfc72bxIREeVDyoXGeLr0bbfdlnjb1q1bMTo6CrfbDafTueqqKxERERERpY4p0MVJ03U8dmoUwUgEdVW2xN9FZpMJstGIOb8fj4+Ooml8Gu5PPQDN61/uWjXL0MORxP5NjqoTEVGxS6s/f61CYnV1NYuMRERERJQyTdMxPDyDJ544j+HhGWga99gBF1Og1dmFxIX+uHgKtHRdK1OgC8ykx4spnxdVFZakI+9VFgte8nhx5l8ezen+TSIiolxLuaMRAN797ndDlpOPccQdOXJkQwciIiIiotI2ODiBgweHcOrUHCIRFbJsQFdXHfbv70VfX3O+j5dX8RTo+XsPITY+CUN9daLrTZ11MwW6QAWVCKKqBrsx+Z9XksEIX2gR/qlZ1OV4/yYREVEupVVotNlssFhWj3AQEREREaVicHACAwNH4XaH4HLZYLEYEQrFcOLEFAYGjuLAgb1lX2yMp0An9vjNeSBIJsjbO2C/i3v8ClGFJMNkEKHEYjCbTKtuV9QYjLoO81IYgjX52Hs2928SERHlSlqFxn/6p39CfX391d+RiIiIiOgymqbj4MEhuN0hbN16cfWO1Sqhra0aY2NuHDo0hN27N0MUy3stD1Ogi0uT0wGX3YEL7gXIRuOKjkVd17EYCqHZakP9UgS6UeL+TSIiKlkp/6bCHYxERFQMuPeNqPDomobIyTGceuRXWHruDDY1WpOOjjY22nDy5BxGRmbzdNLCEk+BtvS9EvK2NhYZC5goCNjT1YkKScac349wNApN0xGORjHn96NCkrGndydk7t8kIqISl3bqNBERUaHi3jeiwhMeGk6MABs8QfTPB+GJ1uI36MG4zbXifS0WI2ZmVHg84Tydlmj92hvq8Y4bd+GxU6OY8nmxqIZhMohoqanBrZ2daG+oR5j7N4mIqMSlXGj85S9/ierq6myehYiIaN24942o8ISHhjF/7yFoXj8MDTUQK2wIzk1iU2AObz0/iP/T0rei2BgKxSDLBjid5jyemmj92hvq0VZfh0mPF0ElggpJRpPTAfHlDl7u3yQiolKXcqHxNa95TTbPQUREtG7p7H0jotzQNQ3eB49A8/ph3NIEQRBg1QFTVSVe9BpwTSyEV03/F85bG6ELAnRdx/S0Hzt3utDdzZ3gVLxEQUBztXPN27l/k4iISllaYTBERESFaGRkFqdOzcHlsl1171tnJ7vziXJBGT23PC7dUJN4XQoCsGWLAydPzmE2YkL90gLqAvN4QbBjetoPp9OC/v7esg+CodIX379JRERUalhoJCKioufxhBGJqLBYkv9Y4943otzTvH7oShSCWV7xdqfTgm3b6jD+ghuCz4PFFxfgtUnYudOF/n7uU80XTdcx4fbggnsB0AW01FZjs9OZGPklIiIiSgULjUREVPScTjNk2YBQKAarVVp1O/e+EeWe6LBBkEzQwxEIlZYVtzmdFlRd50RwRsBH+t8A2yvb0d1dz07GPDk7M4sfnvg9xhfmocRUAIBkMKC1thZ/vOOVaG/gKDsRERGlJu1FIK95zWvwne98B6FQKBvnISIiSlt3dz26uuowPe2Hrusrbovvfdu2rY5734hySOrcAlNHK9TZhaSvS23ODfsrt+Lmd/ahp6eBRcY8OTszi/89+CTOzs4ipmqQjUZIRgOimoazsy/fNjOb72MSERFRkUi70Lhjxw4MDAygsbER733ve/G73/0uG+ciIiJKmSgK2L+/F06nBWNjbgQCClRVQyCgYGzMzb1vRHkgiCIcd++DaLchNj4JbSkIXVWhLQURG5+EaLfBftc+BmDkkabr+PnJU1gIBCAKAmSjEQZRhFE0wGwyQQCwsLSEx06NQrusWExERESUTNq/2X3pS1/CSy+9hG9961uYnZ3FLbfcgm3btuHAgQOYmZnJxhmJiIiuqq+vGQcO7MWOHS54vSGMj3vh9Yawc6cLBw7sLbi9b5qmY3h4Bk88cR7DwzPQNP4RT6XH3NuD2vv6IfV0QPMFEJuYhuYLQN7egdr7+mHu7cn3EcvapMeLC243dABGUVwRpiUAMBkN0HQd5xcWMOnx5uuYROsW3z16enoaE24PC+ZERDmwrh2NRqMR+/btw759+zA7O4tvfOMb+OQnP4m/+7u/w5ve9CZ8+MMfxutf//pMn5WIiOiK+vqasXv3ZoyMzMLjCcPpNBfk3rfBwQkcPDiEU6fmEImokGUDurrqsH8/gzCo9Jh7e9CwqxvK6DloXj9Ehw1S5xZ2MqZA03VMerwIKhFUSDKanI6MhrMElQgUdXknY7KPKwoioKuIqiqCSiRjn5coF87OzOKxU6OY8nkRVTWYDCJcdgf2dHVy7ygRURZtKAxmaGgI3/rWt3D48GHU19fj3e9+NyYnJ/HmN78ZH/zgB3HgwIFMnZOIiCgloiigp6ch38dY0+DgBAYGjsLtDsHlssFiMSIUiuHEiSkMDBwtyO5Loo0SRBHytrZ8H6Oo5KJIUiHJkAwGLGG5qGm4rNio6RogCDAZDKiQ5OQfhKgAnZ2ZxeFjxxGMRFBVYYHdaIQSi+GCewGHjx3HO27cxWIjEVGWpH0peXZ2Fv/4j/+I66+/Hq9+9asxNzeHf/mXf8H4+Djuu+8+/PM//zOOHj2Kr33ta9k4LxERUdHSNB0HDw7B7Q5h69ZqWK0SDAYRVquEtrZqeDwhHDo0xDFqojIXL5JcWFiARZJQa7PCIkmJIkmmwlmanA5cU10NQQBimrYitEcHEI2pEAUBLTU1aHI6MvI5ibJN03U8dmoUwUgEdVU2mE0miIIAs8mEOpsNQSWCx0e5d5SIKFvS7mjcvHkz2tracNddd+Hd73436urqVr3P9u3bceONN2bkgERERKViZGQWp07NweWyrdiFBgCCIKCx0YaTJ+cwMjJb0F2ZRJQ9lxdJ4t8rzEYTdFnH/NIS/uPZ5/DhPa+HcYPj56Ig4A3bujDp9WLW70ckFoPRYACgI6pqAICaykrs6erM6Mg2ZX8svpxNeryY8nlRVWFJ+rO2ymLBS14vJj1eNFc783RKIqLSlXah8fHHH8erX/3qK75PVVUVfvnLX677UERERKXI4wkjElFhsST/8WuxGDEzo8LjCef4ZERUKJIVSZYiEcz7AwjHYlA1Daemp/Dlx36Bt7xi+4bHP9sb6vHuvpvxwxO/x/jCPJRYDAAgGQ3YUluLt73ylRwxzTDuDsyuoBJBVNVgNyb/WSsZjFhUw9w7SkSUJWkXGuNFxtnZWZw+fRoAcN1116G+nj8UiYiIrsTpNEOWDQiFYrBapVW3h0IxyLIBTqc5D6cjokJweZFkKRLBS14fVE2D0SDCKC7vmpv0ejK2a669oR4Dt70BE24PLrgXAF1AS201Njud7LLLMO4OzL4KSYbJIEKJxWA2mVbdrqgxmAwi944SEWVJ2vMWfr8f73rXu9DU1ITXvOY1eM1rXoOmpibccccd8Pl82TgjERFRSejurkdXVx2mp/0rdqEBgK7rmJ72Y9u2OnR3849MonJ1aZEEOjDvD0DVNEhGIwyCCF3XIYoiaqzWjO6aW97FWI1Xt7fj1R1bcU11NYuMGcbdgbnR5HTAZXdgMRRK+rN2MRTCJoeDe0eJiLIk7ULjX/7lX+Kpp57Co48+Cq/XC6/Xi0cffRTHjx/H+973vmyckYiIqCSIooD9+3vhdFowNuZGIKBAVTUEAgrGxtxwOi3o7++FKPKPe6JydWmRJKQoCMdiMBpECFguksQ0DWaTERaTacWuOSp86ewOpPUTBQF7ujpRIcmY8/sRjkahaTrC0Sjm/H5USDJu7eTeUSKibEm70Pjoo4/ioYcewm233YaqqipUVVXhtttuwze/+U38x3/8RzbOSEREVDL6+ppx4MBe7Njhgtcbwvi4F15vCDt3unDgwF709TXn+4hElEeXFknml5agahoECFA1DYqqwiCKqLVaIQgCJIMRUVXjrrkiER+Ll66wO5Bfz8xob6jHO27chWuqaxBSFMwHAggpClpqajieTkSUZWnvaKypqYHdbl/1drvdDqdz/aldn/vc53DPPffgr/7qr/ClL30JABAOh/HXf/3XOHz4MCKRCG677TY88MADaGhgEicR5Yam6RgZmYXHE4bTaUZ3dz27zWjD+vqasXv3Zj63iCipeJHk3599DqPTU1BiMYiiCItkQq3Vikp5ebccd80Vl2LYHahrGpTRc9C8fogOG6TOLRA2mG6eL+0N9Wirr2O6NxFRjqVdaPzEJz6Bj370o3j44YfR2NgIAJiensbHPvYxfPKTn1zXIY4dO4avf/3r2L59+4q3f+QjH8GPf/xjfP/734fdbsf+/fuxb98+/Pa3v13X5yEiSsfg4AQOHhzCqVNziERUyLIBXV112L+/l11ntGGiKKCnhxfOiCi59oZ6/NWe1+PLj/0Ck14PaqxWWEymxMhtfNdcS00Nd80VifhY/AX3AmSjccX4dCF8PcNDw/A+eATRM+PQlSgEyQRTRyscd++DubcnL2faKFEQ0Fy9/mYYIiJKX0qFxh07dqz4QXj27Flcc801uOaaawAAFy5cgCzLmJubS3tPYyAQwDvf+U5885vfxP333594u8/nw4MPPohHHnkEr3/96wEA3/rWt9DV1YXf/e532L17d1qfh4jyq9g6AwcHJzAwcBRudwgulw0WixGhUAwnTkxhYOAoR1yJiCjrjKKIt7xiOw4fO45AOAzx5XFpRY1hMRTirrkiEx+LP3zsOOb8flRZLAXz9QwPDWP+3kPQvH4YGmogmGXo4QiU4TOYv/cQau/rL9piIxER5VZKhca3ve1tWTtAf38//tt/+2/Ys2fPikLj008/jWg0ij179iTe1tnZiWuuuQZPPvnkmoXGSCSCSOTiXpPFxUUAQDQaRTQazdK9oHTEvw78epSPp56axDe+8TTOnJlPdAZ2dNTiv//3G3DTTU1pfaxcPH80TcfXvjaEpaUwurqqExdazGYJDkc1zp3z4OtfH8INNzQUdLGUkuP3INoIPn9oI9bz/GmtduJPdrwSvzpzFtOLXiypYRgNAlqrq/Ga9na0Vjv5fCwiG/16ZuN7kK5pmP/2jxANhmBsvwa6IEAHANkEoaoS0QsvYeE7P0LdKzqKdoyalvFnGG0Un0PlLdWvu6Drup7ls6zp8OHD+Pu//3scO3YMZrMZr33ta/HKV74SX/rSl/DII4/gPe95z4qiIQD09vbida97HT7/+c8n/Zif+tSncN999616+yOPPIKKioqs3A8iIiIiIiIiIqJSFQwG8ed//ufw+Xyoqqpa8/3S3tGYKRMTE/irv/or/PznP4fZbM7Yx73nnnvw0Y9+NPHfi4uLaG5uxt69e6/4QFDuRKNR/PznP8cb3vAGmJIswqbSoWk63vve/8Czz07j2mudq3YRnTvnwSte0YhvfOOPUu4MzMXzZ3BwAv/jf/wU11xjh8Gw+sq9qmq4cMGHL33pDzk+XYT4PYg2gs+f4qLpOqa8vkQQhMthz+uYMZ8/tFHZeA6FnxrG/Cf/CcamRghJfu/RVRWxyRnUfubDMN/E8elixu9BtFF8DpW3+MTw1eSt0Pj0009jdnYWO3fuTLxNVVU88cQTOHjwIH72s59BURR4vV44HI7E+8zMzCRCaJKRZRmyvDqpzWQy8YVQYPg1KX3DwzMYHp5DdbUVsdjlf9gJcDqteO65OZw540k7FCObz5/q6koAIhYXY7BapVW3BwIxACKqqyv5HC5i/B5Ea9F0/aoppXz+FL6zM7N47NQopnxeRFUNJoMIl92BPV2daG+oz+vZcvH8KaX0YFotk88hrdoOIwSIgSDESsvq25eCMEKAVG3n970SwZ9htFF8DpWnVL/meSs03nrrrRgeHl7xtve85z3o7OzE3/7t36K5uRkmkwmPP/44br/9dgDA6dOnceHCBdx88835ODIRpcnjCSMSUWGxJP9WY7EYMTOjwuMJ5/hkV9bdXY+urjqcODGFtrbqVZ2Y09N+7NzpQnd3fv9QJaLMK+TiFKXu7MwsDh87jmAkgqoKC+xGI5RYDBfcCzh87DjeceOukv56lmJ6MGWP1LkFpo5WKMNnILQ2rfq9R511Q97eAalzSx5PSURExSJvlzVtNhuuv/76Ff+rrKxETU0Nrr/+etjtdtx999346Ec/il/+8pd4+umn8Z73vAc333wzE6eJioTTaYYsGxAKxZLeHgrFIMsGOJ2ZW5+QCaIoYP/+XjidFoyNuREIKFBVDYGAgrExN5xOC/r7exkEQ1Ri4sWpCwsLsEgSam1WWCQpUZw6OzOb7yNSCjRdx2OnRhGMRFBXZYPZZIIoCDCbTKiz2RBUInh8dBRa/taUZ1U8PVh57gxEuw3GZhdEuy2RHhweGr76B6GyIogiHHfvg2i3ITY+CW0pCF1VoS0FERufhGi3wX7XvqLriNV0HRNuD05PT2PC7SnZ1zwRUaFZd0ejoig4d+4c2traYDRmpzHyi1/8IkRRxO23345IJILbbrsNDzzwQFY+FxFlXjF3Bvb1NePAgb04eHAIp07NYWZmOS17504X+vt7uZuRqMRcXpxKpM2bTJCNRsz5/Xh8dBTvuZkXOwvdpMeLKZ8XVRWWFT93AEAQBFRZLHjJ68Wkx4vmameeTpkduqbB++ARaF4/jFsudqYJlRYIrU2IjU/C99ARyLu611U04jh26TL39qD2vv6LnbBzHgiSCfL2DtjvKr5OWHanExHlT9oVwmAwiA996EP49re/DQA4c+YMrr32WnzoQx9CU1MTPv7xj6/7ML/61a9W/LfZbMahQ4dw6NChdX9MIsqfeGfgwMBRjI250dhog8ViRCgUw/S0v+A7A/v6mrF792aMjMzC4wnD6TSju7u+YM9LROuXanFqyuvL0wkpVUElgqiqwb7GhXDJYMSiGkZQieT4ZNmnjJ5D9Mw4DA01SZ/HhvpqKKfHoYyeg7ytLa2PHR4ahvvBI5icnUNIBCwa0FRfh+oSHsdOZV9rKTH39qBhV3fRF5PLfXUCEVG+pV1ovOeee/Dss8/iV7/6Ff7wD/8w8fY9e/bgU5/61IYKjURUeoq9M1AUhbSDaoio+JRzcarUVEgyTAYRSiwGc5Kl5Yoag8kgokJaHR5Y7DSvf3knozn5fRPMMvQ5DzSvP62PGx4axjP/9G082VKL+c5uxIwijDENtfM+3PxP38bOD/9FyRUby7UjThDFtIvQhSTV7vS2+rqSLhoTEeVT2oXGH/3oR/je976H3bt3r7hS2t3djbGxsYwejohKAzsDiajQlXNxqtQ0OR1w2R244F6AbDSuWtuxGAqhpaYGTU5H/g6ZJaLDBkEyQQ9HICRJD9bDEQiSCaLDlvLH1DUNz37vx/hJdzPCVjNsSgymkIqoQcBUoxM/sVlg+Ncfo3ed49iFiB1xxaucVycQERWKtH8bmJubQ3396h+sS0tLq76ZExHFxTsDb7mlBT09DSwy5pGm6RgensETT5zH8PAMNI3L0YnixanFUAj6ZYEB8eLUJocDLoc9TyekVImCgD1dnaiQZMz5/QhHo9A0HeFoFHN+PyokGbd2dpZkN1M8PVidXUj6PFZn3ZCua00rPTh86gX8pkJEuFJGTSgKWdUhApBVHTWhKMIVMn5rERE+9UKG701+lHuYULGLd6dLV+hOj6oau9OJiLIo7ULjrl278OMf/zjx3/Hi4j//8z/j5ptvztzJiIgo4wYHJ3DHHUdw550/xPvf/yjuvPOHuOOOIxgcnMj30ajEFXqBu5yLU6WovaEe77hxF66prkFIUTAfCCCkKGipqSnpbrRspAdPzs5hrsoCm6Li8me/AMCmqJitsmBydi6j9yVf0umIo8JzaXd6MuxOJyLKvrRHp//n//yfeOMb34iTJ08iFovhy1/+Mk6ePInBwUH8+te/zsYZiYgoAwYHJzAwcBRudwgu18VgnhMnpjAwcBQHDuwt+J2ZVJwGBycSe1ojkeU9rV1dddi/v7D2tMaLU/G9bItqGCaDiJaaGtzaubyXLRqN5vuYlKL2hnq01deVVZgHkPn04JBFhmo0wBiOAgbDqtuN0RhUswkhy+rCTTGGqXBfa3Er59UJRESFIu1C46te9So8++yz+OxnP4uenh4cPXoUO3fuxJNPPomentJaAk1EVCo0TcfBg0Nwu0PYurU68Yu31Sqhra0aY2NuHDo0hN27N3OsnTKq2Arc5VqcKlWiIJTlHrZMpgfb21tgPH4c0aUIDEkKjVFVg8lkhL29ZcXbizVMhftai1u8O/3wseOY8/tRZbFAMhihqDEshkLsTiciyoG0Co3RaBTve9/78MlPfhLf/OY3s3UmIiLKsJGRWZw6NQeXy5Z0FKyx0YaTJ+cwMjLLlG3KmGItcJdrcYpKS6bSgzdXV2PzpkacUydh8ixBlE0QRBG6pkGLRBFwVmLLpkZsrq5O/JtiDlNhR1zxS6U7nYiIsietQqPJZMIPfvADfPKTn8zWeYiIKAs8njAiERUWS/Jv+xaLETMzKjyecI5PRqWMBW6i4icKAvb+wW78yy9/Da/RiErfEoyRCGJGI5bq7KiqcWLvH+xOdIhdHqYSf+2bTSbIRiPm/H48PjqKtvq6guwqY0dcaWB3OhFR/qQ9P/G2t70NP/rRj7JwFCIiyhan0wxZNiAUSr4cPRSKQZYNcDrNOT4ZlbJUCtyRCAvcRIWuvaEef/a61+Da7g6obZsRaNsMtW0z2ro78Gevu2VFh1gphKmUa5hQqYl3p1/X2IjmaieLjEREOZL2jsb29nZ8+tOfxm9/+1vccMMNqKysXHH7hz/84YwdjoiIMqO7ux5dXXU4cWIKbW3Vq0bBpqf92LnThe7u0vnjSdN0jIzMwuMJw+k0o7u7vqDGc8vBpQVuq1VadTsL3ETFI9UOsVIJU2FHHBER0fqkXWh88MEH4XA48PTTT+Ppp59ecZsgCCw0EhEVIFEUsH9/LwYGjmJszI3GxouhHNPTfjidFvT395ZMIe5KKcc33tiY7+OVjXIscJe6YkwRpsxJZX9pKYWpcF8rERFR+tIuNJ47dy4b5yAioizr62vGgQN7EwW4mZnlAtzOnS709/cWVPLvRlwt5fgLX7g130csG+VW4C51xZoinGu6pmUk7blYMUyFiIiovKVdaLyUrusAsGr/ChERFaa+vmbs3r25ZEeKU0k5/uY3n8a+fRv68UdpKJcCd6kr5hThXAoPDcP74BFEz4xDV6IQJBNMHa1w3L0P5t6efB8vJximQkREVN7W9ZfWd77zHfzDP/wDzp49CwDo6OjAxz72MbzrXe/K6OGIiCjzRFEo2YTfVFKOT5+eB8Dx6Vwq9QJ3qUuaIqzrkMIKaqI65iMBPH7qVMGmCF8qm92G4aFhzN97CJrXD0NDDQSzDD0cgTJ8BvP3HkLtff0w7OjMyOcqdPEwlXgH7KIahskgoqWmBrd2sgOWiIiolKVdaPxf/+t/4ZOf/CT279+PP/iDPwAA/OY3v8H73/9+zM/P4yMf+UjGD0lERJSKVFKOvV41x6cioLQL3KXu8hRhzbOI6MQ09EAQuq7DbDLi/IIX50QL2m7pzfdx15TNbkNd0+B98Ag0rx/GLU2JCx1CpQVCaxNi45PwPXQEzi9/PBN3pSgwTIWIiKg8pV1o/MpXvoKvfvWruPPOOxNve8tb3oLu7m586lOfYqGRiIjyJtWUYyJK3aUpwppnEcqZcehRFYJsgiiKMGkaAkoUL33z+2gyWwpyRDiVbsONnFsZPYfomfHlj52km9pQXw3l9DiiZ85v9K4UFYapEBERlZ+0Z0WmpqbQ19e36u19fX2YmprKyKGIiIjWI55yPD3tT+wRjounHF93XW2eTkdUnBIpwtHocidjVIVYYYZgMACCgJhkgslggNm9CN9DR6BrWr6PvMLl3YZipQWCQYRYaYGxtQmaz7/hc2te/3KXpDl5krJglqErUWi+wLo/BxEREVExSLvQuHXrVvzrv/7rqrd/73vfQ3t7e0YORUREtB7xlGOn04KxMTcCAQWqqiEQUDA25obTacF733tDvo9JVFTiKcI+nx9aIAhBNiVu0wEEJAPqlyJoNElQTo9DGT2Xv8MmkWq34UbOLTpsECQT9HAk6e16OAJBMkG0W9f9OYqBrmmInBxDaPD3iJwcK7iiMxEREWVf2qPT9913H97+9rfjiSeeSOxo/O1vf4vHH388aQGSiIgol66WcnzjjY34yU+ezfcxiYpGPEX4kakZuK1m2DTApOmIGkQEJAMsMQ03v+iBwSwjNueB5vXn+8grpNRtuMFzS51bYOpohTJ8BkJr04qCpq7rUGfdkLd3wNTRArxwKu2Pn80Qm0xh4jYREREB6yg03n777XjqqafwxS9+ET/60Y8AAF1dXRgaGsKOHTsyfT4iIqK0XSnlOBqN5vt4REWnvaEef3LtVvzfk+cwX21DQDbAqGnY5A/j5hc9aPGFoMW79hy2fB93hUu7DYVKy6rb9QycWxBFOO7eh/l7DyE2PglDfXViD6Q664Zot8F+1751FQeLoYCX7R2YxVBoJSIiomVpFxoB4IYbbsB3v/vdTJ+FiIgoY5hyTJRZXTe+Ao6H/h2Tz4xBaXGhIqahfikCESu79qTOLfk+6gqpdhtu9Nzm3h7U3td/sSg454EgmSBv74D9ruWiYLoXOrJdwMuEVBO35V3dJVtoJSIioovSLjT+5Cc/gcFgwG233bbi7T/72c+gaRre+MY3ZuxwRES0Nk3Tk3bsERFlgyCKqL57H7R7D0H7/dlE156Wga69bMpmt+HlzL09aNjVnZHuu2wX8DIlnR2Y8ra2tD52MRRaiYiIaKW0fyv5+Mc/DlVVV71d13V8/OMfz8ihiIjoygYHJ3DHHUdw550/xPvf/yjuvPOHuOOOIxgcnMj30YiohMW79qSeDmi+AGIT09B8AcjbOwq66JPLcwuiCHlbGyx9r4S8rW3dRcBchNhkQsqJ22nuwMxFWjgRERFlXtodjWfPnsW2bdtWvb2zsxPPP/98Rg5FRJSKcu3oGxycwMDAUbjdIbhcNlgsRoRCMZw4MYWBgaM4cGAv+vqa831MIipRmezay6ViO3cuQmwyIVs7MLPZKUlERETZk3ah0W6344UXXkBra+uKtz///POorKzM1LmIiK7oqacm8cADT+PUqTlEIsupwl1dddi/v7eki2yapuPgwSG43SFs3Vqd+OPLapXQ1laNsTE3Dh0awu7dm8ui6Ho1yYrRRLRx8a69YlNM585FiE0mZGsHZrEUWomIiGiltC/hvvWtb8X/+B//A2NjY4m3Pf/88/jrv/5rvOUtb8no4YiI1vKJT/wCzzwzBYfDgtZWBxwOS6Kjr5THh0dGZnHq1BxcLlvSDo/GRhtOnpzDyMhsnk5YONYaL3/qqcl8H42I6KriBTx1dgG6rq+4LV7Ak65rzXv4TnwHpmi3ITY+CW0pCF1VoS0FERufXPcOzEsLrckUSqGViIiIVkq70PiFL3wBlZWV6OzsxJYtW7BlyxZ0dXWhpqYGBw4cyMYZiYgSNG35jy2PZ7mjz2qVYDCIiY4+jyeEQ4eGEu9XajyeMCIRFRZL8oZ0i8WISESFxxPO8ckKS3y8PFkx+hOf+EW+j0dEdFXZKuBlQzZ2YBZLoZWIiIhWWtfo9ODgIH7+85/j2WefhcViwfbt23HLLbdk43xERCuMjs4BABobrVft6OvpacjHEbPK6TRDlg0IhWKwWqVVt4dCMciyAU6nOQ+nKwxXGy9/8UVP4v2IiApZvIDnffAIomfGoc95IEgmyNs7YL9rX0GF72R6B2Yu08KJiIgoc9IuNALLf8zv3bsXe/fuzfR5iIiuyOtdHqEym41IFjRpsRgxM1O6HX3d3fXo6qrDiRNTaGurXrULa3raj507XWW9i/Bq4+UNDVYAy0XrV7yiKR9HJCJKWTGF2GR6B2YxFVqJiIhoWcqFxieffBILCwt485vfnHjbd77zHdx7771YWlrC2972NnzlK1+BLCdf2ExElAkOhwyvFwiHY5Ck8uvoE0UB+/f3YmDgKMbG3GhsvJg6PT3th9NpQX9/b1kHwVxtvNxsXn57vGhNdDld04qiqEPlo5hCbDKtmAqtRERElEah8dOf/jRe+9rXJgqNw8PDuPvuu/Hud78bXV1d+Id/+Ads2rQJn/rUp7J1ViIidHbWYXwcmJkJYPNmZ1l29PX1NePAgb04eHAIp07NYWZmOXV7504X+vtLO3U7FVcbLw+HYwCWi9aUfcVWtAsPDV/snlKiECQTTB2tcNzN7imifCnnQisREVGxSbnQ+Pvf/x6f+cxnEv99+PBh3HTTTfjmN78JAGhubsa9997LQiMRZVW8U8/hsJR1R19fXzN2796MkZFZeDxhOJ1mdHfXl/z9TsXVxstnZgIAHOjsrMvfIctEsRXtwkPDmL/3EDSvH4aGmsQ+OGX4DObvPYTa+/ph2NGZ72MSERERERWslFsKPB4PGhouBiv8+te/xhvf+MbEf994442YmJjI7OmIiNZw//2vx44dLni9IYyPe+H1hrBzpwsHDuwtm44+URTQ09OAW25pQU9PA4uML4uPlzudy8XoQECBqmoIBBSMjbnhcFgS70fZEy/aKc+dgWi3wdjsgmi3JYp24aHhfB9xBV3T4H3wCDSvH8YtTRArLRAMIsRKC4ytTdB8fvgeOgI92XJYIiIiIiICkEZHY0NDA86dO4fm5mYoioJnnnkG9913X+J2v98Pk8mUlUMSEV3uppua0NfXwo4+SupK4+Uf+MANWFh4Nt9HLGmXF+3iXaVCpQVCaxNi45PwPXQE8q7ughmjVkbPIXpmfLmTMUmIkKG+GsrpcUTPnM/TCYmIiIiICl/KhcY3velN+PjHP47Pf/7z+NGPfoSKigq8+tWvTtz+3HPPoa2Nu1OIKHfiHX1Eyaw1Xq6qMfzkJyw0ZlOqRTtl9FzB7F3TvP7l8W5z8t2dglmGPueB5gvk+GSFpdh2bhIRERFRbqVcaPzMZz6Dffv24TWveQ2sViu+/e1vr0h8feihh7B3796sHJKIiGg9khWjVTVPhykjKRftvP4cn2xtosMGQTJBD0cgVFpW3a6HIxAkE0S7FVjIwwELQLHt3CQiIiKi3Eu50FhbW4snnngCPp8PVqsVBoNhxe3f//73YbVaM35AIiIiKi4pF+0ctjycLjmpcwtMHa1Qhs9AaG1aFSKkzrohb++AqaMFeOFUHk+aH6kE5RRKsVHTdUx6vAgqEVRIMpqcDogC12oQERER5ULKhcY4u92e9O3V1dUbPgwREREVv1SLdlLnljyeciVBFOG4ex/m7z2E2PgkDPXViWKaOuuGaLfBfte+shwTLqadm2dnZvHYqVFM+byIqhpMBhEuuwN7ujrR3lCf17MRERERlYPy+22ZiIiIsipetBPtNsTGJ6EtBaGrKrSlIGLjkwVbtDP39qD2vn5IPR3QfAHEJqah+QKQt3cUVMderqWzczOfzs7M4vCx47iwsACLJKHWZoVFknDBvYDDx47j7MxsXs9HREREpUvTNZyeO42hiSGcnjsNTdfyfaS8SbujkYiIiOhq4kW7xE6/OQ8EyQR5ewfsdxXuTj9zbw8adnUz8OQSxbBzU9N1PHZqFMFIBHVVtkRB1GwyQTYaMef34/HRUbTV13GMmoiIiDLq6cmn8fCJh/H8wvNQVAWSQcLWmq1414534YamG/J9vJxjoZGIiIiyoliLdoIoFkwadiEolJ2bV0q8nvR4MeXzoqrCkrTrsspiwUteLyY9XjRXO7N6TiIiIiofT08+jc/+6rPwhX2oq6yD2WhGOBbGyOwIPvurz+Ke195TdsVGFhqJiIgoa1i0K36FsHPzaonXQSWCqKrBbkz+q61kMGJRDSOoRDJ6risVP4mIiKi0abqGh088DF/YhxZHS+J3pEqpEhWmCpz3ncd3T3wXOzbtgCiUz+8HLDQSERER0ZryHZSTSuJ1xdbNMBlEKLEYzCbTqo+hqDGYDCIqpOTj3+s915WKn0RERFTazs6fxfMLz6Ousi7pREVdRR3OLpzF2fmzuK7uujydMvfKp6RKRJSEpukYHp7BE0+cx/DwDDRNz/eRiIgKTr6Cci5PvBYrLRAMIsRKC4ytTdB8fvgeOoJN9iq47A4shkLQNQ2afwmaexGafwm6pmExFMImhwNNTkdGzhUvfirPnYFot8HY7IJotyWKn+Gh4Yx8HiIiIipcvrAPiqrAbDQnvd1sNENRFfjCvhyfLL/Y0UhEZWtwcAIHDw7h1Kk5RCIqZNmArq467N/fi76+5nwfj4iooORj52aqidex0+PY09WJf/nlrzF1dhyVviUYYzHEjEYs2Sthq3Hi1s7OjATBXF78jJ9LqLRAaG1CbHwSvoeOQN7Vve7HhiPZREREhc9utkMySAjHwqiUKlfdHo6FIRkk2M32PJwuf1hoJKKyNDg4gYGBo3C7Q3C5bLBYjAiFYjhxYgoDA0dx4MBeFhuJiC6T652b6SReNweC2PPoU3iypRbztXYsGS0wxjQ0vrSAm588jeZrrwMa6jd8plSLn8rouXU9VhzJJiIiKg7tte3YWrMVI7MjqDBVrNpjPRecw/X116O9tj2Pp8w9XholorKjaToOHhyC2x3C1q3VsFolGAwirFYJbW3V8HhCOHRoiGPURER5dmnidTKJxOuqSngfPILNY1N4+zk3/vTUFP54dBp/emr5vze/MAXfQ0egqiom3B6cnp7GhNsDTU//+3xKxU8lCs3rT/tjcySbiIioeIiCiHfteBfssh3nfeexpCxB1VQsKUs47zsPu9mOO3bcUVZBMAA7GomoDI2MzOLUqTm4XLak3SiNjTacPDmHkZFZ9PQ05OmUlG+apmNkZBYeTxhOpxnd3fUQxY2PXRJR6lJNvNaBRJehKAhoXIrE3wlaIAjIMs5Oz+DfHv0pZqAiqmowGUS47A7s6epEexqdjpcWP4VKy6rbE8VPhy2t+5qLkWwiIiLKrBuabsA9r70HD594GM8vPI/54Dwkg4Tr66/HHTvuwA1NN+T7iDnHQiMRlR2PJ4xIRIXFkvxboMVixMyMCo8nnOOTUaHg/k6iwpBq4rW+uLSqy1DzLiJ6YRraUhAXNlXj5zu6EDk/geqGWtirHVBiMVxwL+DwseN4x427Ui42plr8lDq3pHVfsz2STYWFF7OIiErHDU03YMemHTg7fxa+sA92sx3tte1l18kYx0IjEZUdp9MMWTYgFIrBapVW3R4KxSDLBjidydPDqLRxfydRYYknXif2Fs55IEgmyNs7YL9reW9h5OTYii5DzbsI5fQ49GgMkCUc292FsEVG9fwiBH8I6DDA7KyCbDRizu/H46OjaKuvSyksJtXiZ7pdh+nso6TixotZRESlRxREXFd3Xb6PURBYaCSistPdXY+urjqcODGFtrbqVd0o09N+7NzpQnf3xkMDqLhcvr8z/tyI7+8cG3Pj0KEh7N69mZ0nRDl0tcTrFV2GLZsQvTANPRqDUGHBdI0N89U22EIKRKsFejCC2MQ0JMfy+owqiwUveb2Y9HjRXO1c9bmTJUCnUvxMV7ZGsqmw8GIWERGVOhYaiajsiKKA/ft7MTBwFGNjbjQ2XvxFf3raD6fTgv7+XhaSsqwQxsYuP4Om6dzfSVSgrpR4fWmXYfTMeaiLgeWiXUxFWBSgGg0w6QIECIBsghYIQgsEIdoqIRmMWFTDCCqrA2eulgB9peJnurI1kk2FgxeziIioHLDQSERlqa+vGQcO7E2MLs3MLI8u7dzpQn8/R5eyrRDGxp56ahIPPPD0ijPU1FTA4wnD5UreMcT9nUSZlaxbcL2FuniX4cLnH0RsZh5QABhEVBgMMBqNiBl0GFQdgigup01HVQCAosZgMoiokFaOLMcToDWvf3lv4svj0fEE6Nr7+mHu7cnYvsRsjWRT4WAYHRERlQMWGomobPX1NWP37s1576orJ5qm4//7/57D5z//WywtRdHSYkdFhSkvY2Of+MQvMD0dXDG69sILHszNLWF62oKmpqpV/4b7O4ky52rdguth7u1BzX39mHnvvRDNZoi2CjRZK9AQieIlqxlSKApo2nKRx2SArutYDIXQUlODJqcj8XHylQCdjZFsKhwMoyMionLAQiMRlTVRFNg1kCODgxP4yleewk9/+jyCwRhkWYSiqNiyxQGn05KzsTFN0wEAHs/q0bWurlrMzwcxNuaGy2WFeEkBIZX9nYUwDk5UDFLtFlwPeVsb5O3XLY8gu2ohCgJunvDgJ+0NWLCYYPVFIVsroMgmLPr9qJBk3NrZuSIIJp8J0JkeyabCwTA6IiIqByw0EhFR1sWX309N+RGNaqisNEEUBfh8YZw8OYdt2+rgdFpyMjY2OjoHAGhstK4qIIiiiK1bq3H69AJOnpxDa6sz5f2dhTAOTlQMst0tmGwE+RpVxR8+F8aTmxyYq7Eh3FANKRpFS00Nbu3sRHvDyosH+U6AvtI+SipeDKMjIqJywEIjERFl1aXL7xsbbZifD8JoFCEIAiorJSwtKTh3zguHw5yTsTGvdznwwWw2QtNW397YaIXbHUJbWzUWFoIp7e9kiihR6nLRLZhsBLlZMmGLbEHwttcj1nENKiQZTU7Hik7GOCZAUzYwjI6IiMoBC41ERJRVly6/1zQdoihCVXUYjct/SJnNRgQCEfj9CkRRyPrYmMMhw+sFwuEYJCn56JrTacY//uNeiKJw1TFopogSpSdX3YIbGUFmAjRlC8PoiIio1LHQSEREWXXp8ntRFGC1SvD5wjAYTBAEAaIoQNN0KEoMXm8462NjnZ11GB8HZmYC2LzZueboWk9PQ0qFQaaIEqUn3i2ohiOYr3cgZDLAElVRvxSBiMx2C653BJkJ0OnJZHp4OSjkMDpN1zHp8SKoRK7Y9UtERLQWFhqJiCirLl9+39rqwKlTcwgGo5BlIwAdug7MzCyhsdGa9bGx+Md2OCwZGV1jiihReqTOLXhp53X4jRmYr3cgJoowahrqgxHsvuBG07kXC6JbkAnQqclGeng5KMQwurMzs3js1CimfF5EVQ0mgwiX3YE9Xav3mBIREa2FhUYiIsqqy5ffV1db0NVVh/FxLwKBCMLhGCorJezevTmnwSn33/96PPDA0xseXWOKKFF6np+bx89ubMPizDys/hCsBhExkxGTlTJ+3FKNN/ld2Fkg3YJMgL6ybKaHX4pddtl3dmYWh48dRzASQVWFBXajEUoshgvuBRw+dhzvuHEXi41ERJQSFhqJiCirki2/t9tltLdX4/x5LyorJfzt3/4B3vnO7TkdG7vppib09bVseHSNKaJEqdN0HY+dGkXIZESjqwExdQZ6IAiTEkV1UICnpgon9t2C3Tden++jJjAB+iJN0y9+z7RLqPvnH2QtPTyOXXbZF39dBiMR1FVdXANiNpkgG42Y8/vx+Ogo2urrWOAlIqKrYqGRiIiybq3l9319zXldfp+J0TWmiBKlbtLjxZTPi6oKCwwmEwzOKmiBIBBVAZMB1bIJM9EoJj1eNFc7c3Yu7hi8usHBicT38EhERbu4iIGl51DbWgtHltLD2WWXG5e+LpPtGq6yWPCS15vz1yURERUnFhqJKCtWdD0U0JJzyp9CXn6/UUwRpWwqpSJYUIkgqmqwG1/+FVQQINoqE7dLmo7FcARBJZKzM3HH4NUNDk5gYOAo3O4QXK7liykN837E5iI4+cIiOi1mKJtXBvtsND2cXXa5s+p1eRnJYMSiGs7p65KIiIoXC41ElHGXdz3IsgFdXXU53b9HhSFZwbnQlt9nSikXUil/Sq0IViHJMBlEKLEYzCbTqtsVNQaTQUSFJOfkPLnaMVjMNE3HwYNDcLtD2Lr1kvUQNitEswneRiu+192E6Gb7imCfm85MoXkD6eHsssudQntdEhFRcWOhkYgyKlnXQygUw4kTUxgYOIoDB/ay2FgmyrHgXIgpolS8SrEI1uR0wGV34IJ7AbLRuGqn6WIohJaaGjQ5HVk/i65p8D54JOs7BovdyMgsTp2ag8tlW/H1mrZUY3RrK8ZuacaSxYyacAw2AYgaBExazfjJdS78UY0Dm9eZHs4uu9wppNclEREVv/L9rYmIMu7yrgerVYLBIMJqldDWVg2PJ4RDh4agaXq+j0pZFi84P/PMFBwOC1pbHXA4LImC8+DgRL6PSFTQLi+CiZUWCAYRYqUFxtYmaD4/fA8dga5p+T5qWkRBwJ6uTlRIMub8foSjUWiajnA0ijm/HxWSjFs7O3MyCquMnkP0zPhyEfcqOwbLmccTRiSiwmJZWfDTBeBY3zaELTIc8z6YIlGIug5JicG54EfYIuF43zbo6/xaXtpllwy77DKnkF6XRERU/FhoJKKMWavrAVj+o62x0YaTJ+cwMjKbpxNSLrDgTLRx+SiCabqOCbcHp6enMeH2QNOz8xptb6jHO27chWuqaxBSFMwHAggpClpqanIa7qF5/cvj6ObkhSrBLENXouveMVgqnE4zZNmAUGhlwc9QpSNcLWMxIiMsShA0FVo4AkRjMFZVwtlYhxnD8gj0esS77BZDIeiXPRfjXXabHA522WVIobwuiYio+HF0mogyZq2uhziLxYiZGRUeTzjHJ6NcSqfgzDFjouRSKoJtIGjjcmdnZvHYqVFM+byIqhpMBhEuuwN7ujqzUmBob6hHW30dJj1eBJUIKiQZTU5HTjumRIcNgmSCHo5AqLSsul0PRyBsYMdgqejurkdXVx1OnJhCW9vFHY2CCYAIeCIitOpGbNpaBcSW08NFawWMOhAIBNY92hzvsjt87Djm/H5UWSyQDEYoagyLoRC77LKgEF6XRERU/NjRSEQZs1bXQ1woFIMsG+B0mnN8MsqlVArOkQgLzkRXcmkRLJlMFsHOzszi8LHjuLCwAIskodZmhUWScMG9gMPHjuPsTHa60EVBQHO1E9c1NqK52pnzYobUuQWmjlaoswtJO+bUWTek61ohrXPHYKkQRQH79/fC6bRgbMyNQECBqmpY8sUQCamQzCJaWx0QqyohVlctp4gLQkZGm9lll3v5fl0SEVHxY0cjEWXMWl0PwPIfbdPTfuzc6UJ3N/8wKGWXFpytVmnV7Sw4E11dvAimDJ+B0Nq06vupOuuGvL1jw0UwTdfx2KlRBCMR1FVd7EI2m0yQjUbM+f14fHQUbfV1JVdwEEQRjrv3Yf7eQ4iNT8JQX50I3FFn3RDtNtjv2nfVIBhd06CMnoPm9UN02CB1bim58Ji+vmYcOLA3EfA1M7Mc8NXYa4VziwzHZd/PMxkgwi47IiKi4sJCIxFlTLzrYWDgKMbG3GhsvJg6PT3th9NpQX9/L0SRfxyUMhaciTYuU0Wwq5n0eDHl86KqwpJ01UGVxYKXvF5MerxornZu6HMVInNvD2rv64f3wSOInhmHPueBIJkgb++A/a59V031Dg8NX/y3ShSCZIKpoxWOu6/+b4tNX18zdu/ejJGRWXg84eWLSnXA944/nfXR5niXHRERERU+FhqJKKPW6nrYudOF/v5e9PU15/uIlGUsOFMxKeRutI0WwVIRVCKIqhrsxuS/EkoGIxbV8Lr37BUDc28PGnZ1p/08CA8NY/7eQ9C8/uXQnpcLwcrwGczfewi19/Xntdio6XrGuwBFUVi1W/cdN+5K7PdcVMMwGUS01NTg1s7s7PckIiKiwsZCIxFlXLKuh+7uehaWyggLzlQMiqEbbb1FsFRVSDJMBhFKLAazybTq9kzs2SsGgihC3taW8vvrmgbvg0egef0wbrk42i5UWiC0NiE2PgnfQ0cg7+rOS+E6l+E+HG0mIiKiS7HQSERZkazrgcoLC85UyAq9G+1S6RbB0tHkdMBld+CCewGy0bhq1UGm9uyVGmX0HKJnxpefO0lGzg311VBOj0MZPZe1r91a4uE+wUgEVRUW2I1GKLFYItwnGyEqHG0mIiKiuMKYDSIiorRomo7h4Rk88cR5DA/PQNP0q/+jPIgXnG+5pQU9PQ0sMlJBuLwbTay0QDCIECstMLY2QfP54XvoCHRNy/dRs04UBOzp6kSFJGPO70c4GoWm6QgrUbzk9UEUBPQ0NeX7mAVH8/qXu2DNqzs9NQAztVU4V12JiZlZaHruvj9fHu5jNpkgCgLMJhPqbDYElQgeHx3N6ZmIiIiovLCjkYioyAwOTiRGkiOR5ZHkrq467N/PkWQi4Oq76Qq5Gy0f2hvqV+zZm48EEFIUQAB0XcLPRkbw3IuTGR+7zdZ+TE3Ts95JLTpsECQT9HAEQqUl8fbzdguebHZi1mxCDE0wz05i06//Mysjy8mUe7gPERER5R8LjURERWRwcAIDA0fhdofgcl0MWTlxYgoDA0dx4MBeFhuprKWym+5K3WgAlseo5zzQvP5cHj2v4nv2nnz+Bfx4eBgCgGprJWSTKStjt9naj5mrCzFS5xaYOlqhDJ+B0Lq8o/G83YKftDcgaBRh9QVRVWEGbJUrHrvWLBf3GO5DRERE+cbRaSKiIqFpOg4eHILbHcLWrdWwWiUYDCKsVgltbdXweEI4dGioYMeoqXzka7Q/vpvuwsICLJKEWpsVFklKFHrOzswCWNmNlowejkCQTBAdtpycu5A8NzkJXdexyemARZKyMnYb34+pPHcGot0GY7MLot2W2I8ZHhpe18eNX4h55pkpOBwWtLY64HBYEhdiBgcnNnTuSwmiCMfd+yDabYiNTyK2FMRgkxNBgwDngh+yAEjNjTBLUk5Hli8N90mmXMJ9iIiIKH9YaCQiKhIjI7M4dWoOLpct6UhcY6MNJ0/OYWRkNk8nJFou9txxxxHceecP8f73P4o77/wh7rjjSEaLPMmks5su3o2mzi5Av6zwo+s61Fk3pOtaIXVuyeqZC006Y7frla39mPm4EGPu7UHtff2QejowLeiYlUTYAmEYqyohdbRCdFYBWPnYTXl9Gfv8ycTDfRZDoaTP7cVQCJscjjXDfYpl/y8REREVLo5OExEVCY8njEhEhcWS/Fu3xWLEzIwKjyec45MRLcvnaH+6u+kcd+/D/L2HEBufhKG+OpE6rc66IdptsN+1LyP7AotJLsZus7UfM50LMT09Des+/+XMvT1o2NWNuaHfA2NnUGGxwGirBC47Q65GluPhPoePHcec348qiwWSwQhFjWExFEKFJOPWzs4VO0vjuP+XiIiIMqG8foMmIipiTqcZsmxAKJR8JC4UikGWDXA6zTk+GVH+R/vjRTLpCkWyqKolCj2XdqNpvgBiE9PQfAHI2ztQe1//hvYEFqtcjN2mtB9Tiaa9HzOVCzGRSHYuxAiiCEfHFkjWCsQs8qoiI5DbkeV4uM811TUIKQrmA8vhPi01NWvu2Mzl2DkRERGVNnY0EhEVie7uenR11eHEiSm0tVWv6NrRdR3T037s3OlCd3f2k02JLpevjrK4S4tkZpNp1e3JCj3xbrRsJB8Xo/jY7QX3AmSjcdX3mMVQCC01NWuO3aZirbTmxOdZ537MSy/EWK3SqtuzfSEm1cfO5bDj2aycYKV4uM+V0tfjLr9IED97/CLB2Jgbhw4NYffuzRlP7yYiIqLSU56/SRMRFSFRFLB/fy+cTgvGxtwIBBSoqoZAQMHYmBtOpwX9/b38Q5DyIp8dZcD6d9MJogh5Wxssfa+EvK2tbIuMwMWx2wpJxpzfj3A0Ck3TEY5GMef3X3HsNlXZ2o8ZvxAzPe1P+nGnp/3Ytq0uaxdicvHYredMzdVOXNfYiOZq55qfm/t/iYiIKJPK97dpIqIi1NfXjAMH9mLHDhe83hDGx73wekPYudOV1f13RFeT79H+9RZ6NF3HhNuD09PTmHB7sp4KXOjWM3abjsvTmrWlIHRVhbYURGx8ct37MQvhQky2H7tsyfdFAiIiIiotHJ0mKiKapmNkZBYeTxhOpxnd3fUZ/6MpF5+DNqavrxm7d2/m14kKSiGM9scLPY+dGsWUz4tFNQyTQURLTQ1u7excVeg5OzObeN+oqsFkEOGyO7Cna/X7Fjtd01aMiJs6WhA9cz7pyHg6Y7frEd+P6X3wCKJnxqHPeSBIJsjbO2C/a9+692PGL8TEA01mZpYDTXbudKG/PzeBJtl+7LIh32PnREREVFpYaCQqErlIg2TiZPEQRSEre+6I1iveUTYwcBRjY240Nl5MnZ6e9udstD/VQs/ZmVkcPnYcwUgEVRUW2I1GKLEYLrgXcPjY8YLuQEtXeGj4YlFPiQIxFZoShSAZIRiNECQTTB2tsL/nbRCrrIni4+Ys7qvM1n7MQrgQEx9ZLhaFcJGAiIiISgcLjURFIJ4G6XaH4HJd/OM9ngaZiZHZXHwOIipthdBRBly90KPpOh47NYpgJIK6qot76cwmE2SjEXN+Px4fHUVbfV1Bd6KlIjw0jPl7D0Hz+mFoqIEeiUAZHYceUSCYJZg6tkA0SwgPDSP0q2MwOGyA0ZAoPjruXtlhmMmu9/h+zEzjhZj0FMpFAiIiIioNLDQSFbhcpEEycZKIMqUQOsquZtLjxZTPi6oKS9LwiyqLBS95vZj0eIuqM+1yuqbB++ARaF4/jFuaIACIjE0AmgahygqEIlAnZ4DmRmj+JejBMCAKkLdfBz0cQeT4CGZHnofjw++E7f/Ziyd/N8mu9zzRdD2r49iFcpGAiIiIih8LjUQFLp00yPV2cOTicxBRabp895/UuQWiKBb094qgEkFU1WA3Jv81SDIYsaiGEVQiOT7Zxl369VAXvFBOn4OhoQaCIEDzL0FbCkKQJUAQoEsmqIEl6C+8CERjECot0CMK1NkFqPNeqIElIBLF/N99GRcO/wJfP+fEMyEbu95zLFe7RIvhIgEREREVPhYaiQpcKmmQMzMbS4PMxecgotJz+e6/ZOO22e7EWo8KSYbJIEKJxWA2mVbdrqgxmAwiKiQ5D6dbv8u/Hno0BnXeA8FshlhpgR5TAU0HDC/vQTSIQERb7mSUJQhGEXpYQfTcJKDrEGUJutEIKDGEjo3gT2Im2DpuwfmXA0PY9Z59ud4lyrFzIiIi2igWGokyKBuJzblIg2TiJBGl6/Ldf4JZhh6OQBk+g/l7D6H2vn5MtDQUZKpzk9MBl92BC+4FyEbjqvCLxVAILTU1aHI6Uv6YyTo7sxWkkkyyr4e64EFschbRM+cgdF0LwWgARAFQNcBgWP6/AKC/XHxUNeixGAQYIVgrErdpWhSzqgl2MYZXz/wXLtgaob/8mLHrPXvKaZcoERERlQ4WGokyJFuJzblIg2TiJFFxyXdRa9Xuv3jRqdICobUJsfFJPPuvP8bR1/YgGFEKLtVZFATs6erE4WPHMef3o8pigWQwQlFjWAyFUCHJuLWzM+XiTSqdndm01tfDWFeNmHMemtuH2IUpSNe3Q6ysgOYPAGYzoEQhViyPS0PVoIeXR8UFyyWdnKoGXRAQ1UUE5Eo0hjxoDLkxVVGTeBd2vWdHuewSJSIiotKSu79KkvjqV7+K7du3o6qqClVVVbj55pvxf//v/03cHg6H0d/fj5qaGlitVtx+++2YmZnJ44mJkosnNj/zzBQcDgtaWx1wOCyJ3VWDgxPr/tjxNEin04KxMTcCAQWqqiEQUDA25s5IGmQuPgcRZUZ4aBgzH/gMZvvvx9zHDmC2/37MfOAzCA8N5+wMyug5RM+MJ3b/XUoQBAj11fiNRcSSfwl1VTaYTSaIggCzyYQ6mw1BJYLHR0eh6XrOzny59oZ6vOPGXbimugYhRcF8IICQoqClpiatImi8k1B57gxEuw3GZhdEuy3R2ZmLr8uaXw9BgNTigiBLUN2LUOc9MG6uBwQR+mIAMAgwtLggmCXoS0HoogAYDYnRal3XASUKVJgRMZgQhgCjpsISW7m7kl3v2RHfJSpdYZdoVNWKcpcoERERla68djRu3rwZn/vc59De3g5d1/Htb38bb33rW3HixAl0d3fjIx/5CH784x/j+9//Pux2O/bv3499+/bht7/9bT6PTbRCLhKbc5EGycRJypZsrBQoVyvGY+uroasqtKUwIsdHMH9+CrWf7s9JB53m9S937pmT7zCcq63CXJUFVYKYl06sVDs+2xvq0VZft+4dkql0dvoeOgJ5V/e6O05TuS9X+nqIjipIna1QTo9D8wUgmIww1ldDU6IQJCMQCEKsqgQ0HYLJCNW/tLzLEQKgRCGYjDBf2wTruSUo3gBikoiQ8eLnYdd79pTqLlEiIiIqbXktNP7RH/3Riv/++7//e3z1q1/F7373O2zevBkPPvggHnnkEbz+9a8HAHzrW99CV1cXfve732H37t35ODLRKrlKbM5FGiQTJynTsrVSoBxdWtQSnTZEX3gR2lIQ0HToAqC6fXB/4SG4/vUfsz5GLTpsECQT9HAEQqVl1e1BXYNqNMAkrS6OANlNdU53jFkUhHUXO6/W2Wmor4ZyehzK6DnI29qydl+u9vUQZAmmlk1wfvQvYKhxQHTYYOpoQfTM+UQBU/MvwffgEQR/8RT0pRAEWYJYVQnjNY0wOKqwRTdi/tkFnFUdeF61wqxqCIVimJ72l0TXeyGGFmVjl2i2FeLjSERERLlVMDsaVVXF97//fSwtLeHmm2/G008/jWg0ij179iTep7OzE9dccw2efPLJNQuNkUgEkcjFP1wWFxcBANFoFNFoNLt3glIS/zqUytfD7V4CoKGqygiDYfXtVVVGeL0a3O6ljNznzs7qxP+vqjGo6oY/ZF4+x3qV2vOnlD311CQ+8YlfwOMJobHRCrPZiHA4hpMnp/F3f/dz3H//63HTTU05P1exPoeU0XMIj78I2CuhnH9pObSj0pII8VAjESw9dxref/sZrH+85+ofcAOEts0Qtl0LZeR5GKsqVxVATAuLMEsmaLIJQpLx6FgsCtkgQBYNGf06RJ4+iYX/+U1ovuWOT0GWoUciCJ1+AZH7v46av3sv5Bu2behzXPr8Ud0+xKDDaK2Ablhd3NWtFYj5/FDcPohp3s907svVvh4x7yKk67dC2ntzogit6jrE9msSO3REANWvvAfy//kFvF/7V+ihCISmOuhmM6KRCCyhRVR3NmHMth3BmQg8viBk2YDeXhfe+94bcOONjUXxmtJ0HVNeX6IQ5nLYcW5uHr86cxbTi17EVB1Gg4DGKgde29GOtvq6jH7+dL//vK59K37wjB9uvx82sxkmgxFRNQZ/OAyrScJrt26FGouhEH5Mj83O5exxLGfF+jOMCgOfP7RRfA6Vt1S/7oKu53FBEoDh4WHcfPPNCIfDsFqteOSRR/CmN70JjzzyCN7znvesKBoCQG9vL173utfh85//fNKP96lPfQr33Xffqrc/8sgjqKioyMp9ICIiIiIiIiIiKlXBYBB//ud/Dp/Ph6qqqjXfL+8djddddx1+//vfw+fz4d/+7d/wF3/xF/j1r3+97o93zz334KMf/WjivxcXF9Hc3Iy9e/de8YGg3IlGo/j5z3+ON7zhDTAl2TlUbDRNx3vf+x947rlpbNniXNVJcu6cB694RSO+8Y0/yupY2VNPTeIb33gaZ87MJ0ZUOzpq8d//+w156RrLllJ7/pSqkydn8b73PQq73YzKSmnV7UtLCny+ML7+9Tdj27bc7nUr1ueQMnoOs/v/HtHpOYiShMtbqPWYCkQVGBpr0XDwE5A6t2T9TJGnT8L78L8j9vyFi6O97dfAfsdb8GJzHX7wzAmEosqqTiyLScLtO3dktMtJGT2HuYF/gFhlhVixenxYCwahLS6h7sDHNvTYXPr8MRoMmP3oFxAdeR7iNZswb5URMhpgiamoDUSgXXgJ0vVbUfePf5PWOPt678uVvh7Sjs7lUWlfAKLdClNHy+pdj5qO0dE5eL0ROKoktBmWoC8urfn+xWRsdm75+agosFnMMBmNUKJRvOjxQNV0bHY6UGm+GGaj6zoWAgE0V1fjzpt3Y8a3uKILcr3jwOv9/pOsE7NQRpI1Xce3fvskXnS7UWOzrvo9KP44vrvv5oI5czEr1p9hVBj4/KGN4nOovMUnhq8m74VGSZKwdetWAMANN9yAY8eO4ctf/jLe/va3Q1EUeL1eOByOxPvPzMygsbFxzY8nyzJkefVSbJPJxBdCgSmlr8n739+LgYGjGB11o7HRBovFuGJ31fve1wtZXl1syZTBwQn8zd88Drc7BJfLhurq5c9/7NgUnn/+cRw4sLfk9uGV0vOnFPl8MSwuxlBdbUSyDnuDwYjFxRh8vljevo7F9hwydm+FVOuEdvYChCoRQkxL3KbrOhCKQLRVQIiqEP3BnNw30+5XoLK3J2lYSSeAPzUa8dipUUz5vIiGIzAZRGyuqcGtnZ0ppzqnKuYPQgyEYHQ6IMRWD5KKBiNigVDGHpv486f2L96GZ/7p23iy2Y752irEjAYYYypq5xdxsxbFzjvfBinJ7yWZvC/xwBiDpqP2A++AAEBbXEp8PSLHR+D+8OeuuOvxivtUe4r754em6/jl2ecRUBTUVV3cp6wJImIQoELHXDCICrMFiNfBBAGVFgteWFjAA7/+T/hCQURVDSaDCJfdgT1dG3sOr+f7T2uBjh9PuD14adGHygoLIIpYMSr18uM46fNhNrCUlfCnclVsP8OosPD5QxvF51B5SvVrnvdC4+U0TUMkEsENN9wAk8mExx9/HLfffjsA4PTp07hw4QJuvvnmPJ+SaKV8JjbnIvWaKF1OpxmybEAoFIPVurrIHgrFIMsGOJ3mJP+akhFEEba3/yEix/4LCIahy3JiP2M8HdhQXw3oOkSHLafnWivoZKOpzum4WiCKHo5AkEwZf2wmWhrw2JtvwuKCFzbfEgzRGFSTEdObavBYTxvqWhrQnubHTOe+XCkwRt7WtjKpvKEGglmGHo5AGT6D+XsPofa+fjwTc2Bg4GjiYlX8YtmJE1MYGDha9BerJj1eTPm8qKqwrOi2UzVteaeoQUQ4GkM4GoX5kgCjqKrCvRRENKairsoGu9EIJRbDBfcCDh87jnfcuCvjBfNiFFQiiKoa7Mbkf1ZkM/yJiIiICk9eC4333HMP3vjGN+Kaa66B3+/HI488gl/96lf42c9+Brvdjrvvvhsf/ehHUV1djaqqKnzoQx/CzTffzMRpKkj5SmzOVeo1UTq6u+vR1VWHEyem0NZWvWqUbnraj507Xeju5h/p6bD9P3vh/9efIfL0SSAWA6I6IAoQqyphaG6E7l2EvL1jw2PTmUyO3Uiqczqkzi0wdbRCGT4DobVp1XNOnXVn5LG5lKbreOzUKEImIzZtbUVgbhHRcBQGswmuuirMLQXw+Ogo2urr0nr8Lr8vuiBgtlJGyGSAORpD9bkXYdneAW0xgIX7vrpmEbHm3g/A960fQfP6Ydxy8TERKi0QWpsQG5+E98EjOLTYXdIXq9YqhBlEEYIgQIAAXdegahe7hKEDc4t+6LqOGpsV5pev4JtNJshGI+b8/nV9bUtRhSTDZBChxGKJx+lSihqDySCiQkqvs5eIiIiKU14LjbOzs7jzzjsxNTUFu92O7du342c/+xne8IY3AAC++MUvQhRF3H777YhEIrjtttvwwAMP5PPIRFckikLOi3keTxiRiAqLJfnL2WIxYmZGhccTzum5qLyJooD9+5dXCoyNJV8p0N/fW7SFi/io6uXjwtkmiCJq/uYuzP2/B6HNuSFWWRPJ09qcB6LdBvtd+zZ0lrMzsxfHnTM4KpptgijCcfc+zN97CLHxyeWk5pcLb+qsOyOPzeXinXKIivj9szPwBxRoqg7RIMD2UghN19jwkteLSY83rWLrpfflhWAAQzdsxVxVBWICYIhEUb/Jjr2v6YN+lSKi58vfhTo9v1yETHIhylBfjcVnn0cgIMHl2lSyF6vWKoSZTUaYTUYEI8ry43HJcyOkKAhFo7BIJlguK54JgoAqi2VdX9tS1OR0wGV34IJ7AbLRuKrIvxgKoaWmBk1OR/4OSURERDmT10Ljgw8+eMXbzWYzDh06hEOHDuXoRETFhyOqVCg0TV/R0bt79+a8rRTIpiuNqsb33WWTubcHdZ/enziD5lmEIJkgb++A/a6NneHszCwOHzuOYCSCqgpL0Y2Kmnt7UHtf/8Wvz5znqo/NRorGQSWCxUAEL54NIBrVIMtGGGQBqqrD54sgeDqKzVut6xoZNff2wPe3f4Gjzz2HoKbCGgjBqmpQqyox216HI/PTuNXjRusViojRsQlA02BoqE36OQSzDC2sQAqHS/pi1VqFMEEQUFNZiaWIkuhK1DQdihrD/NISBEFAnW31tADAceBLiYKAPV2dOHzsOOb8flRZLJAMRihqDIuhECokGbd2dpZ95ycREVG5KLgdjUSUHo6oUiG4UpDEd7+7L+crBbIllX13uSo2NuzqzmhXZXwMOBiJrAjMKLZR0XQem40WjS0mCe75MFRoqKy82PVmNAowGk0IKVG455dTttOl6TqeQBRKUz0ahZfDf0wGiNYKWAHMzs7jd+0utIwtJP33glkGNG05nOMKux5FswQlZi7pi1VXKoQFFQX1NhvsFgsCkTAWw+Hl0CKHAzOLizBdlu4ex3Hgldob6vGOG3cluqEX1eXHsSVL4U9ERERUuFhoJCpypT6iSoVvcHCipIMk4nRNg/fBI1ccVfU9dATyru6cjVGvFcKyHmsFZgDFNyqaymOTiaKxd1KBb0pBZYMRCOm4GFkMADosdiN8LynwTiq4pia9+xD/etgrLDBcProLwCbLmKu2YuaCG5u01f9eD0cgWCtgbKxF7MLUmnsrq17RAetiO878frqkL1ZdrRB2eWiRy2HHN5/4DceB05DL8CciIiIqXCw0EpWAfKZeU/G5fMR5Ix2G5ZR6royeQ/TM+BX33Smnx6GMnstoATBXLg/M0HUd4WgMqqbBIIqQDAYsqlpJjIpmqmjs9UYwdVzB1tuMECsBLawDKgADIJoBTQGmnlbg/W/pP2ZXS/I1V1nhNctYWgpCN1esGX5T9Z4/xsKnHlhzb6Xj7n3ofzl1utQvVl2tEHZ5AZ3jwOnLVfgTERERFS4WGolKRL5Sr6m4XGnEeT0F6VJOPb98d5/qWVwerzUnH5UUzDL0OQ80rz/HJ82MSwMzVE3DfCCAcDQGXdchCMLymKhJKolR0UwVjZ1OM6ILAtzHAOf1Agw2HRABaEDMK8A7oiO6IKxr7PjqSb4q5Go7KiTpiuE3qeyt7APK5mJVOoUwjgMTERERpY+FRqISko/Uayoe2RhxLsbUc11bnjMNPzUMrdqedH9fst19hoZaIKZecd+dIJkgOmw5uR+ZFg/MeH52BqFoFKqmwyiKEEURmq4jqESh60AoquT7qBumef0ZKRpfuiPXGHTCaAcEE6BHgZgPOD/mWffYcUpJvpsase0j78biQz+8YvhNKnsrebEquXyPA2eyA52IiIgoF1hoJCIqA9kacS621PPw0DDmv/0jYO92zH/yn2CEsCr8Y63dfdHzk4kClanr2jVHVaXOLXm6dxsjCgJu7bwO//XSS4jEVMhGw8tFRg0xTYNkMEA2GvGL0dPYWl9f1COjosMGQTJtuGi8ckeuJ6Njx6km+VY01MNy4/VXDb9JZW8lL1Yll69x4Ex3oBMRERHlQva31RMRUd6lM+KcjnhH1/S0H7qur7gtHiSxbVtdQQRJxAuI0f96HgBgbGqEaLclwj/CQ8OrdveJlRYIBhFipQWmLZshVJihBcOInZuEthSErqrQloKIjU8mRlVzEQSTLRZJglWWUGEyQdPx8hi1DotkQpPTgVqbNREIU8ykzi0wdbRCnV1I+rxVZ92QrmtNqWgc35G7Y4cLXm8I4+NeeL0h7Nzp2nAQUnx095rqGoQUBfOBAEKKgpaaGrzjxl2J0d14EdHS90rI29qK+jmYbbqmIXJyDKHB3yNycizR4Vxo4h3ozzwzBYfDgtZWBxwOS6IDfXBwIt9HJCIiIkqKHY1ERGUgWyPOxZJ6rsZUnP/HR6BPuaFv2QwAiQLipeEf9grzFXf3mVpciE3NwdjigjqzsOaoarEKKhGIgojW2trErkaDKC7vCBSWO2MX1XDRB8IIogjH3fswf++hK+43TLVgl82x43yP7paSZCsRLu9oLgTlFLJFREREpYeFRiKiMpDNEedCTz0fHJzA9z/7E/y3p0fghwnK0izMADzeEGqt0srwj+fOrtrdp+s69EAQejQGGETAYIBj/5/DUG2/4qhqMUoEkKgxmKVkASSx5VCYEgiESSUkJR3ZHDtmku/GrbUSId7RXHtff8EUG0s5ZIuIiIhKHwuNRERl4NLQira26lX7Baen/esOrQAy29GVyfCD+Piha2YSsqBjyWyGybhcEDw9Og9DmxNOpyUR/gEBK3b3qd5FxC5MQ1sKApoOQAeMRsRemkXFq3au60yFLKUAkpoaNDkd+TtkBqUSkkLF7/KVCPHntXBZR7O8q7sgvvbFGLJFREREFMdCIxFRGcjFiHMmOroyGX5w6fhhd0sdtDEjJD0GxbDcqReNahg/54XDYUmEf8g97TB1tEIZPgPNUYXYmfHlTkZJAiQRejAIQdWw+NCPILU2Zb0DKteJs6kGkKxnbFfXtIIs6KUSkpJPhfq4FRNl9NwVVyIkOppHzxXEc6HYQraIiIiILsVCIxFRmSiGEeeBgaNwu0NwuS4WQuPhB+kGa1w6fjhTYcKMxYnNS3OY05f/OJdlA/wBBYHFMMzulxOjt7XBcfc+zP2/BxE99QKgqkCFGYKmQw+HIUoSTB0t0Lz+rHdA5StxNh5A8tipUUz5vFhUwzAZRLTU1ODWzs5EAElcKsXQYtmNdzXxoh+wXLwydm/NatHvSo+bzE7MlMXT4i9diXCpeEez5vXn+GTJZbsDnYiIiCibWGgkIioj2Qyt2IhshB9cOn6oCwL+s/F6vPX8IGoUPxYAGARAUBVoL05DdDkT4R/m3h447vpjzP3dl6HrGoSwAl0UINqsMF3TCNFRBUEyZbUDKtNF13S1N9Tj2toanD8xgoB3EVZHFVp2dMNgMKw659WKocW0G+9K4kW/8PiLwAffjLmBf4C5dXPWiqVXetxm//ofYGiogbbgLerCba6IDtuKlQiXi3c0iw5bHk63WrGEbBERERElw0vfRERlJj7ifMstLejpaSiIP1bTCT9I1aXjhwAwbnPh/7T04aWK2uXbIwHYEIPQee2qYpdxcyOM9TWQr2+HtK0N8vXtkHvaITqqls9klqEr0ax0QF1edLVaJRgMYqLo6vGEcOjQEDRNz/jnjgsPDWPug/fD9LEvwnHv12D62Bcx98H7ER4aTrxPvBj6zDNTcDgsaG11wOGwJIqhg4MTq3bjiZWWRNq3sbUJmm+5M1TXtKzdl0yIF/2U585ArLICAMQqa6JYeunjkglXetxEhw2x8UlEnj4JocoKY7MLot2WtbOUAqlzC0wdrVBnF6DrK183uq5DnXVDuq4VUueWPJ1wtXgH+o4dLni9IYyPe+H1hrBzpyvrFxqIiIiINoIdjURElHfZCD9INn44bnPhJWcj7gTwVfN2NHa7cOC7fwmDcWWnnuiwQZBNEIxGiDnugMp34mwqHYjSrutT6kDdWfWKotqNl8zlRT/NtPwcFSssELMUJLLmTkFdR2xiBjoAAYCg6xAMYsGGmhQKQRThuHsf5u89hNj4JAz11YnntTrrhmi3JTqaC0mhdqATERERXUlh/UZFRERr0jQdw8MzeOKJ8xgenslqR1uuXd59eLn1hB/Exw+dTgvGxtwIBBSoqoZAMAoAmKq7Bn/y8TetKjICKzugNE2HfzGChYUQ/IsRaFp2O6BSKbpGItlJnE21A3FkePqKxVBXgxWBZ8/ghR/8JzT/EgR5daAFkN3O0ExJJ0gkU9baKagFgtCWgstv1/XloKIsn6WQaLqOCbcHp6enMeH2QNNT/x5o7u1ZLpL3dEDzBRCbmIbmC0De3lHQ4/uF2IFOREREdCXsaCQiKgL5CgbJlWyFHyQLwKmqWv7Rd//9r1/zsYt3QJ376BfhGxzFTMyEkG6ARVDRYIzC3lyL+ix1QOUzcTbVopr/ubE1i6Gt/im8amoYTt88xH81Ibbog7YUgnTt5sToeVyh7cZLJh9BImvtFNRjKqDpgAGAKEAwrXz8Cy3UJJPOzswmAoqiqgaTQYTL7sCertUBRWsx9/aggSE6RERERFnF36yIiApcKrvwit2a3YcBBWNj7g2FH/T1NeO7392H73znj/G1r70ZX//6mwEAN93UdMV/90zMgc8ttWMkZkOVGEOzIYQqMYYR1YbPLbXjmZhjPXf1quJF1+lpf9J9ctPTfmzbVpeVxNmUimpKFHYhmrQDtdU/hbeeH8SmwByCBgliSxNEawU0XwCR0XPQvIsr7ksh7sa73KVFv2SyUSxda6egYDRAFwBEFIiVFRCsFVk/y1o20l2YrrMzszh87DguLCzAIkmotVlhkSRccC/g8LHjODuT+u5WQRQhb2uDpe+VkLe1schIRERElGHsaCQiKmDZSGMuVMm6D2XZgJ07Xejv31jnZnz8EACi0SjGx6/8/vHH/ZmQDe5XvgmusAeWWAQho4wpsxPPv+BBLEuPez4TZ1cU1SosCPgjUKIaJJMIq01OFLLaXtmMri73ig5UQdfx6un/QkUsgvNaBewOM6yOCmjXboZyehx6MAzlhRchb++AHlEKejfepeJFP2X4DITWlcXpeLFU3t6R0WLpWjsFIQgQIEDXdRiaG1d1/mbjLMlkorswVZqu47FTowhGIqirujiqbzaZIBuNmPP78fjoKNrq6yAKxf09kIiIiKgUsNBIRFTA8h0MkmuFEn5w6eMOUcRURc2K27P9uGez6Hol8aKad+gkxkISAktRaJoOURRgrTShzaLAedM2yNuuxf798opi6LW6D3VLC5iJSTDJBrRucUAQAIOjCtJ1rYi+8CK0QBDRsQmItkrI2ztgv2tfwe7Gi7u86IemOgCAFgxCm5zLWrE0vlPQ++ARRM+MQ5/zQJBMMN+wDbGZBejeRWiSMeehJvHuwmAkgqoKC+xGI5RYLNFd+I4bd2W02Djp8WLK50VVhSXp98AqiwUveb2Y9HjRXO3M2OclIiIiovVhoZEohzRNz3sBhYpLNtKYC92l3Ye5cvlrc2EhlPfHPR9FV0EUMdH7Kiz85FlUKH7AXImYZIIxFkWFdxHjS2b4b3wVGkRxVTF00b8AIRaD2W5Dy7XVcDov7hY0OKogbO9AbGwC9vf9KSx/sKOoduNdWvQLj78IANAWl2DJcrF0rZ2CkeMjqwqQuSjc5qO7MKhEEFU12I3JX4uSwYhFNYygkny0nYiIiIhyi4VGohwp9TAPyo58BoOUi2SvTZfLhlhMzfvjnuuiq6bp+OLjXoSk7fizyvNoCHlhVCKIiQZMOxpxOHYNKn7hxcPvWe5yvLQY6v/9WdQeegEVDU4YrJbVHzyiQLRVwvIHOyBva8vZfcqUeNEvOPI88MIp1B34GCq6t2a9WBrfKZjsLLkONclHd2GFJMNkEKHEYjCbTKtuV9QYTAYRFVLyvaJERERElFssNBLlQDzMw+0OweW6uG8tHuZx4MBeFhspqa6uOrhcNoyMzKKlxYGqKjnxB/5G0php2VqvzRde8MDtXu4m7elpyFgKdiZpuo5JjxdBJYIKSUaT07HhLrL4yLjjmi34bmUHGkPuxG7KaUs1/EtRjF82Mh4vhurddZj5zeNQhs9ArGzK2/7AbBJEcfn8L5zKe0dmsgJktuWju7DJ6YDL7sAF9wJko3HV82oxFEJLTQ2anI6MfU5am6ZrODt/Fr6wD3azHe217RCF4uhMJiIiotxgoZEoy8opzIMyK95p9/zzC5ibC2JmZglVVTLa2pwwm02JYJAPfOBGjuSvQ7LXpq7r0DQdTqcZi4sRLC1F8fzzC3C5qnIWyJKKbIVxXDqqrwvCqt2UVxoZXyvAJJf7Aym78tFdKAoC9nR14vCx45jz+1FlsUAyGKGoMSyGQqiQZNza2ckgmBx4evJpPHziYTy/8DwUVYFkkLC1ZiveteNduKHphnwfj4iIiAoEC41EWVZuYR6UGZd22jU12VFTU4EXXvDC5wvj97+fQXNzFW68cRNuuaUFDzxwjCP56zA6OrfitenxhHDunBeBQASapkPXl1+jTmcFvN5QzgJZriabYRzxUf1wMIqthsCKbkZdEK46Mr5WgEmxBL/QleWru7C9oR7vuHFXori+qIZhMohoqanBrZ2ZT7qm1Z6efBqf/dVn4Qv7UFdZB7PRjHAsjJHZEXz2V5/FPa+9h8VGIiIiAsBCI1HWlWOYB21Msk47q1VCTU0FFhcjOH/ei/b2arzvfbvw8Y8/xpH8dfJ6I4nXpscTwsmTc1AUFWazEaIoQFU1BAIKFhfD+PjHX4WWFkfeO0azHcbR3V2Pva4oOp79T2w1LsGoaYiJBsxYnHii4Xo8PitddWQ8X/sDKfuy0V2Y6gqA9oZ6tNXXZXxdAF2dpmt4+MTD8IV9aHG0JL7vVEqVqDBV4LzvPL574rvYsWkHx6iJiIiIhUaibGOYB6VrrS5YQRBgt5tx7bXVmJxcxOc+9xuO5G+AwyFDlg0IBqM4d84LRVFRWXnpa1SA2WxEMBjFT3/6PB5+eF/eH8tsh3Eox/8L7148gTndiwXFDJgtkKHB5Z/FH/qegL/xJryv/61XfRzysT+QciOT3YXprgAQBSFjITOZlI19qdn4mOt1dv4snl94HnWVdUm/79RV1OHswlmcnT+L6+quy8sZiYiIqHCw0EiUZd3d9ejqqsOJE1Noa6suyFAJKiypdMGeP69gacmNzZurOJK/Tp2ddejqqsPvfvci/P4wzOaLj7eu64hEYrDbzWhpcRTMY5nNMA5d0+B98Agq1Qj0V1wL/7gPgYCCsAb4hUq0SCF87Fovunc3bfRuUJHLRHdhNlcA5NKZ6Rn8x3PDmFn0QdMBi8mITQ7nhvalZmsH63r5wj4oqgKzcY2VCUYz5oPz8IV9OT4ZERERFSIWGomyTBQF7N/fi4GBoxgbc6Ox0VZQoRJUeFLpgjUYlkd7OZK/fvHX5nPPzSAS0WA06hBFHaq6XGSUJANaWx2oqDBhdnYpp4+lpulJA36yGcahjJ5D9Mw4DA01cFZa4HBWIOCPQIlqkEwiKkQVunsWyug5divShroLs70CIFd+OXoa//bMCUSiUYiCAFEUEYlFEVQUzAcC6yqWFmIB1m62QzJICMfCqJQqV90ejoUhGSTYzfacnouIiIgKExepEOVAX18zDhzYix07XPB6Qxgf98LrDWHnThf36NEq8S7Y6Wk/dF1fcVu8C7ajowZVVTJCoVjSj8GR/NT09TXjb//2D1BRYYSiqAiFoojFNNjtZnR11aG62pLzx3JwcAJ33HEEd975Q7z//Y/izjt/iDvuOILBwYlEGMdiKJT0ubEYCmGTw7GuMA7N64euRCGYl4uUggDYqmTU1Fhgq5IhWmToShSa15+Ju0llLJ0VAIXqzMwMfvDMCYSjUchGI2SjEQZBgBJTEYpG4Q0u4fHRUWiXvU6v5PICrNlkgigIMJtMqLPZEFQiaX/MTGivbcfWmq2YC84l/b4zF5xDe0072mvbc3ouIiIiKkzsaKSStlZX0Ebfdz36+pqxe/fmrH4OKg2pdMHec8+r8cADxziSnwHvfOd2/OQnZ/G7372IxkYbJMkAm02CIAg5fywvTRtfK+An02EccaLDBkEyQQ9HIFRaVt2uhyMQJBNEhy0Td5XKWDZXAOSCpuv4j2eHXy4yGmB4OejIIAgQBQGKqkJRVUx6PGntS832Dtb1EgUR79rxLnz2V5/Fed951FVcTJ2eC87Bbrbjjh13MAiGiIiIALDQSCVscHACBw8O4dSpOUQiKmTZgK6uOuzf37uqgzCd990IURTyvuONikO8Czb+vJyZWX5e7tzpQn//8vNSFAWO5GeAKAr40IduwvnzPng8ITQ22qBpOkKhaE4fy2Rp48DqgJ+HH96XsTCOS0mdW2DqaIUyfAZCaxMEAFogCD2mAgYR2rwX8is6IHVuuerH0jUdC9MKIiENskVETaMEgc9Felk2VwDkwqTHi5nFRQiCAINoWHGbIAgwiiKiqoZQNJZWsTSVAuxcMIAnj12Ad5OS04uVNzTdgHteew8ePvEwnl94HvPBeUgGCdfXX487dtyBG5puyMk5iIiIqPCx0EglKZWuoHgBMZ33Jcqlq3XBplKMpNQUwmO5Vto4kDzgZ6NhHJcTRBGOu/dh/t5DiJ56AVo4Ai0cAVQNUDWI1gqYd78CgnjlrqWpcyE89xsfPNMKVFWHwSDA2Shh+6vscG1Z3SlJ5Se+AuCCewGy0biqI3sxFEJLTc26VgDkQlCJQNd1iIIATddguKyTTxQERFUVooC0iqVXKsB63CGMT3gQUWP4/FcHofmFrFwQvZIbmm7Ajk07cHb+LHxhH+xmO9pr29nJSERERCuw0EglJ9WuoN27NwNAyu/LzjDKh6t1wXIkP3Py/VimkjZ+acDPRsI41mLu7YHt7X8I92f/GVogCMEgAgYDhMoKCGYJ/u/9FPK2Nph7e5L++6lzIfz2/8wjHNJQaTPAYBKgRnXMvRjBb//PPP7grbUsNhJEQcjaCoBcqJBkmCUTwtEoFFWFaBRx6UlVXYem62i029Mqlq5VgPW4Qzh5cg5ipQ7Nb0CD1YaQQc3LBVFREHFd3XU5+VxERERUnFhopJKTTlcQgLQ6iIgKEUfyMyefj2UqaePZDqXRNQ2hJ5+F6KyCqaMFiKkQTEYI1goAQGx8Er6HjkDe1b2qs1HXdDz3Gx/CIQ32motFElEWYJcE+BZiGP6ND40t5oyPUeuaBmX0HDSvH6LDBqlzy1U7Lym/2hvqs7ICIBeanA5ssjsQjCiIaRqUWAxGgwhREKFqKiIxFRaTCW/u6UmrWLpWAXZ8wgOxUodJMCB4wQiDQYTVauAFUSIiIipILDRSyUm3Kyid9yUiypZ42ng+A36U0XOInhmHsbEWYpJAGEN9NZTT41BGz0He1rbitoVpBZ5pBZU2Q9ILNxU2A9zTChamFdRuytzuvfDQMLwPHkH0zPhyarZkgqmjFY67963ZeUmFob2hPuMrAHIhXhCcDwQAAEosBkVVoekqdF2HxWTC7Tt3oKMx/YsWlxdg54IBRNQYNP9ykTHmvlhA5wVRIiIiKkS83E8l59KuoGQu7QpK532JiLIpnjbudFowNuZGIKBAVTUEAgrGxtw5CaXRvP7lYp05eSFQMMvQlSg0r3/VbZGQtryT0ZT8fEaTAFXVEQlpGTtveGgY8/cegvLcGYh2G4zNLoh2G5ThM5i/9xDCQ8MZ+1yUHfEVANc1NqK52lnwRca4eEFwa309HJUVqDKb4ayowDaXCx++9XV4Xef6x4vbG+rxvte8Gu999auxu7odzz8aRvBpw4oiY5zFYkQkwguiREREVDjY0UglJ92uoCu979TUItraqrGwEMLw8ExZ7b7TNJ17/4jWoOn6hruwko375juURnTYIEgm6OEIhCQdjXo4AkEyQXTYVt0mW0QYDMs7GUV59WMRiy4Hw8iWzFzj1DUN3gePQPP6YdzSlPj+LVRaILQ2XXHMmygTstmRGS/Aejcp0PwCQgYVVqth1fvxgigREREVGhYaqeTEu4IGBo5ibMyNxsaLSdLT0/5VXUFrve8LL7ixtBSFprnxwQ/+GLJsyHnCY74MDk4kCh2RiFpW953oas7OzCbGGqOqBpNBhMvuwJ6u1PfKXWnct6+vJ2+hNFLnFpg6WqEMn4HQ2rTq4os664a8vQNS55ZV/7amUYKzUcLcixHYJWHVvw36VdRvllHTuHr/5HrEx7wNDTVJR7WvNOZNlCnZCGW6VCGsVCAiIiJKBy/xU0mKdwXt2OGC1xvC+LgXXm8IO3e6VqUzJnvfyUkfgsEoKislNDVVobXVAYfDkkh4HBycyOO9y67BwQkMDBzFM89MweGwlNV9J7qaszOzOHzsOC4sLMAiSai1WWGRJFxwL+DwseM4OzN71Y+RyrhvPJTmllta0NPTkLNuYkEU4bh7H0S7DbHxSWhLQeiqCm0piNj4JES7Dfa79iXtEBREAdtfZYfZIsK3EIMS0aBpOpSIhz92IAABAABJREFUBt9CDOYKET2vsmcsCGYjY95ExaIQVioQERERpYMdjVSy+vqaU+4KuvR9FxZC+MIXfosXXvBg69aL3QNWq1TyCY+apuPgwSG43aGyu+8bxVHz0qfpOh47NYpgJIK6qotJ9WaTCbLRiDm/H4+PjqKtvm7N0clMjftm8/lm7u1B7X39Fzsu5zwQJBPk7R2w33XlgBXXFgv+4K21eO43PnimFYQCy+PS9Ztl9LzKDteW1ePY67WRMW+iYpLvlQpERERE6WChkUpavCsonfcdHp7B1JQfLpct6TheKSc8jozM4tSpubK872tJpaDDUfPyMOnxYsrnRVWFJenro8piwUteLyY93jVHKTMx7puL55u5twcNu7pX7ZBMZdeha4sFjS1mLEwriIQ0yBYRNY1SxjoZ4zYy5k1UbNK5eEpERESUTyw0Uslab8ePxxNGJKLCYkn+8rBYjJiZKc2Ex3K+78mkUtCJj5q73SG4XBd3fMZHzS8f1afiFVQiiKoa7Mbkrw/JYMSiGkZQiaz5MVIa953zJMZ9Lw+MedptxMDfPJaT55sgiuvebSiIAmo3Jb+PmRIf856/9xBi45Mw1FcvP37hCNRZ9xXHvImKUToXT4mIiIjyhYXG/5+9Pw+T677rfPHX2Wuv6n2T1C1rsRZLjiRLXuKYQBJzWQPOnRmGOA6QXwaCPfPcAV9+v8DMz8nvkvGFyR3uMxclA0zCJA4hAxdnfIEAhhAnseVEtiLstqzdai2t3rv25ey/P0rVa3V39V7d/X09T548VlWf+tY5p05Vvev9/rwFm5LlOH4aGgIYhkKx6BCJzC4t2MwNj1v5uc+kFgHxgQe2iaj5FiKkG2iKjOU4BDRt1u2W66ApMiF9boFtMXHf0ulexr/wPP3DIxRlCHowcLlIW3E7jfv3ivON5cW8BQKBQCAQCAQCwcojhEbBpqNWh9lcjset3PC42Z77Ul2ttc6qDIe1LRs193yf/mSKgmUS0g26GhJzziXcLHQ1JOiIJ7gxPoahqrNeH5like6mJroaEnNuo9a4r5fNc/b/+jKvdjczuu8gjiojWw6hHWN86PUbfD8Xoy/aMfG3m/18m4/5Yt4zHaG1xr8FAoFAIBAIBALB0hBCo2BTUatA5Hk+n/vca3M6Hp966gRPP/0iV6+O094+KVYODmZXtOGx3gpEKu2Wa/HcV5vluFprnVX5gx8MYpougYBCJmNi2y6aphCN6kiStGmj5peHhvmH8+e5PTSC7ThoqkpnWwvv37+fPW0bQ4ReCrIk8f79+/jaa68zks0SCwbRFRXLdcgUi4R0g/ft2zev4FpL3Df2Cz/Dm3/2Db5xcDulSICo5aAVXQqez3BnI/lHIxz8zg2u++34Ux5rs55vtVAt5l063TvpdLRsJF1D29tD4mPC6SgQCAQCgUAgEKwWQmgUbCpqEYhee+02b731DUzTmdfxuNoNj/VaILIZ2i2XOzex1lmV4OM4Lj/4wSClkoPneciyTCSi09OTQNeVTRc1vzw0zJ9+69tkxlJE03kM28HVVN4ZTfKng8P8yx/+oU0tNu5pa+Xnjt/HP5y/wEA6RcYtoSky3U1NvG/fvpqe+0JxXz8c5OWQTCls0FS0qVzJAp5PMFOi2Bhk+HAzbW+OMxhqmtjuVhptsBCl072MPnMSL5UtF+/cEXOt3kuMPnOS5k8/KcTGBdiKrmWBQCAQCAQCwfIRQqNgU7GQQBQIKAwN5YjHAxw+3DbvjLPVbHis9wKRjdxuWaurdb45drXOqpRlifHxEtmsSSRioCgKruuTTpd4++0RYjGdhx/esWGi5gvh+T4vvvI9MkOjNCTzyIaGFDBQPI/ESJqk4/DiK99j18/+1KYWJPa0tbKrtWVZIsx8cd8r33qVkViQqOUydYuqKqMpEm7GJN0SwYg44JVv24ijDVYL3/NIfeF5vFQWdedkPF0KB5F6unD6+kl/8XmM+w6KGPUclF3LZTHddj00RaYjnuD9+2sT0wUCgUAgEAgEWxchNAo2FQsJRKOjRSzLo60tUtNMvdVoeFyqELbWMeuN2m5Za+x5vjl2tcyqPHKknb/92yuEwzqu62GaDoGAiqJIGIZCLmehKBKf+MTxDSHQ1sKt8XFu3R4kki2hhCZdc5KioIQUItkS/bcHuTU+zo6mpnm2tPGRJYntjQ3L2sZcrc7FoIGrKqglGxRl2m2hkIaXK1GUdVKyT3KsSD5vkU6btLVFNsxog9XEunAN+1Jf2clY5RqgtDZiXezDunBtya3aK0G9OgYvDw3ztddep2CaxEJB4qqK5TjcGB/ja6+9zs8dv0+IjQKBQCAQCASCORFCo2BTsZBANDycQ9cVmptnt73C2sw4W4oQVq8x63qk1tjzfMe4llmVP/Zje/hP/+lV7rqrAdt2uXYtRS5n4nk+siwRjwdIJALE43M3EG800pev49gOEaW6C0xTZPK2Q/rydViC0Oh7PmMDJrkLt9CsAk07Qhj779pQrrOVKB+J7+lGff117LyJMkNo1DQFPaRS8CW+e9Fk6J3bQPm83rWrccWex0qy1j+SeKlseSZjoPprTwoY+CNJvFR21dawEPXqGPR8n384f4GCadISm3yPCmgahqoyks3yzQsX2NXaUheiqEAgEAgEAoGg/hBCo2BTUYtAZBgKpZJLJKLM+vu1mHG2WCGs3mPW9UatseeFjvFCsyodx5s4jpGITiIRIJu1JgphQiGV69fTm6qYI1g0URwXR1NRPH/W7Y6mojguwaK56G0PXCty9n/0MXY1jWt7yJ5D1HqHPaG/Y/cvPrIh5umtVPnItsZGtnW2c83tR6tE1O80KHumTT4eIjMoIRdV9u9vIRLRUBSZW7fSK3pNWAnRdD1+JJETUSRdwy+ZSOHZPyr5JRNJ15AT0VV5/IWoZ8dgfzLFQDpFLBSs+kNYLBjkdipFfzK1bEevYHPhez5jgxZm0cMIyjS160hb3F0tEAgEAsFWRQiNgk3HfALRJz5xnM997rV5I7GrPeNsMULYSswb3GrM5Wr1fZ9MxuT69RT33FO+z0LMN6uyt3do2nGUJIlYbNJBlctZm66Yo6u1hZZMkYGWGHrJmTY/0AeyukLnSJ6u1oX37VQGrhX59n+9RHE4h55Pons2rqqTCrbwhhnF/d+/zt3/HybEuuU65Dy/LJJeHhoiGgqvSGR1JctHZEni0Xc/wJ9+69ukVJVwOo9qmjiqSr4lTrYgc+51k4MHW6ddwyIRfcnXhJmiopfJkf7j/7Es0bTWH0lWQtCcir5vJ9reHqzeS0g9XbOu8+7wOMbhvej7di75MZbKWjsGFxvPLlgmtusRV6t/PNQVlYxbomAt/scEweZl4FqRN19Okxy0cF0fRZFoaNc5/HCcjp3VEyQCgUAgEAg2L0JoFNQNKxmvm08gkmVpXsfjas84q2X+X0XsXIl5g1uNaq7WUsnmnXdSpNMlJEni8uVxnnji6zW5quaaVbmY47hZCOy/i4cLHn9ZMBkLB4haLprrYSsyWV0hmC/x7qJHYP9dNW/T93zO/o/rFIdzBNODyKoCiozq2SjZYQqRFq4G9tP2xedpu+8gr36vf1kOuctDw3zz7fN0AF89/TryCkRWV6N8ZE9bK//yh3+Ifzh/nttDI5QcB01VaTKinPqD60S98IpdE2Y6MXFc3FQWKRhA6+lckmha648k71KSZL749WW7QKftB1km8bHHGH3mJE5fP0pr48RzcIfHkeNR4r/02MSxWMtZiWvpGFxKPDukG2iKjOU4BDRt1u2W66ApMiF9/pEQ9Tp/UrDyDFwr8soLo5SKHuGogqJJuLbPyC2TV14Y5d0fbBZio0AgEAgEWwwhNArqgtWI180lEC0UiV3tGHIt8e6K2LkS8wa3IlOP8euv93PrVhbP84nHA+za1YBhqMuOni/mOG4WJFnm3n/xE7j/+Uu82t3MaHOMnK6hOi4dg0kevD7Kvf/mo4tyo40NmIxdTaLnk2WRsSJGSBKSImEUM+SCLYz25bjxtVd5+vcvLHmMQCWyapomHapMYySM6brLjqyuVvlItXbrd3rTfPn6FXp6wlX/ZrHXhFlOTEPHfPMiXjaP5Lr4to0cDi5aNK3lRxL37Nvc+o1vELBKy3aBziRw4hDNn35yUkAdSSLpGsbhvcR/aVLEXOtZiWvlGFxqPLurIUFHPMGN8TEMVZ31A0qmWKS7qYmuhsS8j12P8ycFK4/v+bz5cppS0SPeNHm+yIZEXJdIjzn0vpymvTsgYtQCgUAgEGwhhNAoWHfWYwbhXI5HgN7eoVUvLahV7FypeYNbkYce2s6JE1389E//Kabp0t2dIBYzJr4IrUT0fL1F6/UgcOIQR//NR+n5wvP0v/02RRmCHmxra6Hh33x00cJQ7sItXNtH82yoUjKj+DYmCpYl8fXnXmd8XFnSGIGpkdXWaASKBWRJWpHI6mqWj8xst041Wit2TajmxPSyeXzTKs82tBycG4PI8bJYuBjRdKEfSUIBhUczF/B0G/VAz4q4QGcSOHGItvsOzhnLXo9ZiSvlGJyP5cSzZUni/fv38bXXXmckkyXig+76WIpEToKQYfC+ffvmfI3U8/xJwcozNmiRHLQIR5WqPyiEogrjgxZjgxbNnZunGE0gEAgEAsH8CKFRsK6s5wzCmY7HtS4tmC/eXWErxnNXkvPnRxgYyHLXXY2zRJmZMdNKVH2xInMtx3GzEThxiI77DtK0AnP1NKuA7Dm4qo7s2ZOORt8HH1xVR/Fs/FKBt4cLdHR0LikyvJqR1bUsH1nJa0I1J6bvuOD5SIaCj4SXL+DnCkjRsoOyVtF0oR9JGlIj7PCyyM3Vj+dSXaAVpo/iiHDwgbumvSbXq115JRyDC7Hcc31PWyuPRRp48fwPGJJ9HElC9X3aPIlHjx+dUygUjdVbD7PolWcyatWPp6pJFHM+ZtFb45UJBAKBQCBYT4TQKFhX6mUG4Xo1O88V7556+1aL564ktUbPv/vdGzz77MtLFpkXOo6bEUmWlyQAzaRpR4io/Q6pYAtqdghJkvA9D3wfHzCDjcSS1wlIea64UbqXOEZgNSOra1k+spLXhGpOTElVQJbA9coOU9vHt53J51OjaLqQIFoaSRLWIdxUfTvLcYHW8qPRerUrT3MMZrPEgkF0RcVyHTLFIiF9fsdgLSz3XC+d7iX+O1/iZ1NZxnZ3UYoECeSKNF3tR/3HXkqhSFXnsmis3noYQRlFKc9klI3Z56xjl4thjODSy50EAoFAIBBsPMQ7v2BdqUUIMs3VnUE401UZiegoijzhqkwmi5w8eRrP81dtDfNRieceOdJBKlWkry9FKlXk6NGOVRNANwtTXVXVKBYdHMfjD/7gDD/4wQCJRJCengSJRHBCZD516uYar3prYey/iz3BfnS3RCHcgi2p+D44aoBipBXNKtB96R/RzBKHpOS8x3K+yPDUyGo1lhNZrZSPyPEoTl9/2QXounj5Ak5f/6zykVrwfJ+b40kuDg5yczw50ZQNK3dNmOrEnPi3SAg5HMI3LTzLxvWgYPllg+kd0VS/u2dB0bQiiDY0BLl6dZxczsJ1PXI5i6tXx5HjMRpaY2BWF7uW6gKt/Gi00Ou5Isbp84hxtuutSrvynrZWfu74fexobKJoWYzmchQti+6mphWJFi/nXJ8ap9d3dtHpSdyVKdHpSeg9XXjpLOkvPl/+MWAGtezTomVzYXBg1jkt2Jg0tes0tOvksy7+jOPp+z6FrEtju05T+2xXs0CwFni+x8WRi5y+eZqLIxfxfOGurQfEcREINj/C0ShYV+phBmG9uCrnYyvGc1eChVxVAwNZLMtFkmDPnqY1je4LykiyzO5ffAT32a9zRd5NLtKGFYgjuzax5HW6r3yblkYPyZH558p1/t1AgvDupkVHhqdGVgORyLTbViKyWmv5SC3UUqaxEteEqk5MSaKYaMQdyaC6FjlJ450LSRpupNke8Ym0N9Qsms47w/QT95F4Lr+iLtDFjOJYi1mJ81Gt6GelmpmXE89eTrHRtH2qqni5AtguaApFTWUok6Hk2PzdufN89/IVURCzCZBkicMPx3nlhVHSYw6hqIKqSTh2WWQMhGQOPRwXRTCCdeFM/xmeO/scV8auYLkWuqKzu2k3HznyEY51HVvv5W1ZxHERCLYGQmgUrCv1MINwozQ7b8V47nJZKGYaCKh4nkdnZ6xuReatQODEIXb+y34iv/mfyQZbcNQgql0k5iTR7upCbWjCyxc44Ca5RzfpXUJkeGpkdSyX4y5FxvN8TK8SWdV5JBDFfPWNJc+cXKh8pBYWU6ax3GtCxYk5+sxJnL5+lNZG0iWfq9fTNPkqIUXCUgw6KFLMwmmngf1P/TO2LUI0nU8QLenTH7vSOu0Ojy/JBbqYH40O3tO66rMSF2Jm0c9Kbnep8ezlFBtVBM7r/bdhKAm5QjkmH9AZaozgqCqhgE57PIa9Am3vgvqgY2eQd3+wmTdfTpMctCjmynHp1m0Ghx6O07Fz9txagWC1OdN/hmdfepZ0KU1LuIWAGqDklDg3fI5nX3qWT773k0LUWgfEcREItg5CaBSsK/Uwg7AeXJWC1WM+V9W7372D3//903UvMm8WPN+f08GlbWtHbW2guTECroukRZEibZNtxAGDkAb/68cP83vfyi6p5bsSWf3m2+chk2I8n0dWZLahcN+3eon+4P9mxLKRdA1tbw+Jjy3OiQjLm125mDINYEXccFOdmNalPtLXxjAsn5sNXbzSdg8lVSfomBQUnVduw5F/TPHcL/rTrsm+5zM2aGEWPYygTFO7Ps3BNJcgupIuUM/3uTo4it7iE2lUIO8D0/fH1NfzzHZlxVPwXQlJ8XFld8F25Xqncq5XnLEZt4SmyHQ3NfG+fXO7CJdTbCRLEo+g8bXbI4wpMpGAjubDaCyAA2imRUswiCLLKLIsCmI2ER07g7R3B+a9Dghm4/kel0cvky6liQfi7GnegyyJqVbLxfM9njv7HOlSmu5E98R7aVgPE9JCXE9f5ytnv8KRziNif68h4rgIBFsLITQK1p35hKBPfOI40ajOd75zfdXiwvXgqhSsLnO5qs6dG+aP/uiMEJnXgGpx4PZYnHu3baM5GkbTZRRDB0VGkqVyAUmuAJFQuSDmjsBx+N27+MrH7lpyZHhPWys7GhL87d/8DT9/4j70q/1oz3weUlloaUBtbcI3LazeS4w+c5LmTz+5aLFxqdRapvHqlXd4s79/3mj1Yqg4Mc/95ev8X5/8G+SOGJmmNvwZa2jrsGY5fAeuFSecTK5bdjI1tOscrsHJ5HseUiRE7CM/hZfMIDfGURpiS3KB/sP5C7wzNsrOHw0g4+LnPUpXFZzxye3MfD3vaWvlYHAHX3/zn7BUGyQffAnd0fjZ+/dseJfdQvHsasL/coqNfM+j6U9f5EeHhjh94m5GwgZpVcHSVAKOS2Myh1GwoakBJEkUxGwyJFmiuXN1Rg1sRkR8dPW4PHqZK2NXaAm3VH0vbQm1cHnsMpdHL3N3y93rtMqthzguAsHWQgiNgrqgmhCUTpt87nOvLaoJ2PP8RQsQ9eCqFKw+1VxVQmReG6rFgdOFAm/23+Kfbt4kGggQ1nUS77uXY3/3GttvjYLvgywhh0Mo29vxU5kJgUNaZmS4IrRsvzHC2P/yH3GGx0FVkDI53MExtB3tqD1dOH39pL/4PMZ9Bxcdo14KtbQFj5o5/rq3F9/3F4xWLwZJlhlvaOOfaKanIYFSxV020+E7cK3IKy+MUip6hKMKilZunx25ZfLKC6O8+4PNc4qNpdO9k07GGS7SpUbNG2Mhhm7kyRYswg0qoUMuhV5wxuWqr+dTp27y2X//fcbHi+zYHyUYVShmXW5eSHH1r75PezBe12Vb8zmEK0yNZ0+9/2g2zxu3bjGYSc8Sq7d/bGmR9sp8x53xKDvP3WY4bPBOQ4iXdzTRnDeRkfByBbxcATkaBpbX9i4QbFREfHR1SZfSWK5FQK3+I3FADTBaGCVdSq/xyrY24rgIBFsLITQK6oapQtCpUzf5jd/4e8bHi3R0TAp/lebQas2qp07dnHBF1ipMVpi3tKCGSOZmZini7UZBiMyrz7Q4cDSCny+SL2UZtU18ScIHHM/FMC1uh3XG3v8uPvDiGXYMZ8p/n87ipjJoPV2Lntm3EKPPnMQbHkcK6Ei6Bq6Hl81hXexDv7tn3uKL1WDBghLHoWhZSEBnQ2LeaPVSYqiLGSPhez5vvpymVPSIN03OOJQNibgukR5z6H05TXt3YFZ8snS6t7zvU9ly6cgdIWuxLtJqUfOengbefnuE7LBDuFlF3+mSujH79TyrOMaR8JJgoGyIIqhaCoPmun/etMiWyvHx1liU5mhklli9fQmR9qnzHSWgPV8WD1/vTOAoEoYvl5umbXfib1a7dEcgqDdEfHT1iQfi6IpOySkR1sOzbi85JXRFJx6Ir8Pqti7iuAgEWwshNArqjsU0h1a+AJ46dZOnn35xUcLkTESz82yWI95uFITIvLpU4sBRx8V66wpersBIYwRHV1F9CTWgYzouzmiShnSB8XiY799/gPY//y4qIKkyEhJqWxPGfQdXZE2+5wHgZXJImgKaBkigKEjBIH6xiH1jEP3AXfiWXbX4YjVYqC14LJ8HCRoj4Xmj1UuNoS7G4Ts2aJEctAhHlaprCUUVxgctxgataXFK3/NIfeF5vFQWdedkNFcKB5EW6SKtFjVvaAxy4EALfX0p8jkLOQAlxZ71el5McUy9FUEtpjBo1v2DQdKFIj7gA6O5PLqqEjaMaWL1v3rkPYsuNqo237E1b9JaMLkdCaBZpfK+1hRg7Up3BIJ6QsRHV589zXvY3bSbc8PnCGmhWe9lI4UR7mm9hz3Ne9ZxlVsPcVwEgq2F+KlMUHcs5gsgzBYmIxEdRZEnhMlkssjJk6fxPH/Bx664Kh95pJtDh9q2vMj49NMv8oMfDJBIBOnpSZBIBCfE21Onbq73EleMhx7azle+8hhf/vLP8l/+y0/y5S//LM8995gQGVeAgmVi5Yr4V27iZfJYQR1L11A8H8lxoFDEs23Mokmu5OGP5ulvjHE62sKbhQCX/ARmdzfuWArrwrUVWZN96ToASlMDyDK43rTbJV3Hyxdwx9NzFl+sBpWCkpBuMJLNUrJtPM+nZNuMZLMYqkJQ0zGquB2hHEO1XW/JMdSKw7ehIcjVq+Pkchau65HLWVy9Oj7NEWgWvfJMRq36NVLVJFzXxyxO37eVeK3S1lT1+j7VRboQlai5PiNq3tAY5MiRDg4daKNzW5T/7//vkVmv52SyhGm68xZBmWb9FUHNdHEGNA1ZkghoGi3RKAXL5JsXLpSdg1XuD2C6LrqqoCsKrucxmsvh+/4ssbpSbBR86F0YB3YtKPxW5ju6w2P4dx5fBh68mSTgeIwbKnYsDOHgxDk9XwO2QLAZqSU+armWiI8uA1mS+ciRjxA34lxPXydv5XE9l7yV53r6OvFAnMePPC4co2uMOC4CwdZCvJIFdcfML4C+75PJmIyNFchkTAIBZdoXwMUKk/PheT69vUN85zvX6e0dqkmc3IyspHi7URAi8+oQ1DSkZBrbBzkUwFNVfAmkOw5CzwcsB79gorkWUdtEVSWawh7tFMllirx5OUUhVVgxZ6GXzgEgN8SQwyGw7AlhBABFxnc9vNEk+t09VYsvVotKW/COxiaKlsVoLkfRsuhuauLHDx0ibOhYjlP1b1cihlpx+B450kEqVaSvL0UqVeTo0Y5pznAjKKMo5ZmM1XDscjGMEZz+MWNqvLYaUsCo2UU6NWo+e0NghBTiUYN79rXPej1PjYlXo56KoDzf5+Z4kouDg5y5foPbqeSChUH9yRQw2/Xpeh6+7yNLMpIkocoyJduhZJf3w3LEakmWSXzsMeR4FKevHy9fwHddtt8e40e/3UtnpojV1shoLj9xTi91pqhAsFGZGh+thoiPrgzHuo7xyfd+koOtB8mYGW5lbpExM9zTeg+f/CExA3O9EMdFINg6iOi0oO6Y+gXQslz6+lLkchae5yHLMoGASjyuT3wBrMWZMrXAYC62Qky4VjZyrHAz4HveoiKL9UzLYJLmoRQDLTGMkoPs+ZVyXyQfHE1Bz5sES2Vho6RpyI6HWrKIyg67/By3Sx7DKZ3uWASorQRjPuR4BMYAy0Ld0Y59sQ+/aOLrGigy2DY47rzFF6vJzLbgoKbRMpjEG8rwT5LCrUIRIzY7Wr1SMdRaxkg0tes0tOuM3DKJ69KstRSyLq3bDJrap896rBavnUqlXbwWF+lCUfP59sdGKYKaOYvR9TwypRKdqlp1jufMcpWZBUOKXBYYPd9DkWRkScLxytuF5YvVgROHaK4y33HP3T0c/bFHGduzfcmvW4FgMyDio2vHsa5jHOk8wuXRy6RLaeKBOHua9wjH3DojjotAsDUQQqOg7qh8AXzllRtkMha27WIYKoqi4Lo+qVQRz/NIp8tfpBZTYDAXKzHjcTOVpqyUeCtYPPO18dZSkFF3pHPc/9Z1/u49BxkPaoQtF931KKnlD5SK5xMfL7vXfKAQDdLSP07DUJoSCgEctpHnjJUg4YYJLLIEoxra3m545zzuyDhqZxva3T04Nwbx8gWwPXzbRWlrpPl3fm3d9nmlLbhyPozeOR/e1dXE0MMHGC6ZxBsT6IqK5TpkisUVjaFWa2mfiiRLHH44zisvjJIecwhFFVRNwrHLImMgJHPo4fisIphKvNbqvYTU0zXrS7Y7PD7RLr7gGu9Ezb/22uuMZLPEgsGa98dGKIKqzFbMmyYhJYAhyxR9E8d1uZ1Ksa2hgbAxXRCcKRTOLBgKaBoBVaVo28hquZxFkiQUWV4xsTpw4tCc8x1Dy9khAsEmoBIfffalZ7mevk5LaLJ1eqQwIuKjK4wsyWLWZR0ijotAsPkR72KCukOWJX71V4+Tz9sUChaGoaAo5ZZQ03QIhTTCYZ3Pf/41PM+fECYHB7PT449MOlMOHGiZ05myEjHhU6du8vjjz/PEE1/nV37lr3jiia/z+OPPb9g5hhspVriZqLTxWm9eQo5HUbd3IMejE228pdO9673EeZka8bw5nsTzfeRElO5UgR/rvUlnroSpyih3XkuyD7F0AaNkkdUMUk0xgkWLe1+9gOT7yPhIEiiSz6u0cmFohK+99jo3xsYI6jrN0QhBXZ8owbg8tPB4BGDCoSjHyhFPSVPRDtyFtms7ckMMbfcO2j737wnef3jV9lUtVDsfeiyfR//xDVov3yKfykyLVq91DLVjZ5B3f7CZlm0GVtEjm3Swih6t2wze/dPNdOyc7VicK17r5Qs4ff2LdpHOFzVfaH/UGhNfDyqzFdO5ElJGJzvkkhy0MUdlFE/FdjxGsrlp73kVobAzkZgQCiuuz0yxWL6vBM3RCIosY9o2tutiqCrgr+jMxMXOdxQIthIiPioQCASCzY5wNArqknjcoLExiKLIlEo2luUiyxLxeICdOxNomjItuvvUUyf49V//O95+e4R43CAc1lFVmaGh3ILOlOXGhBfrhlxL5+NSH2ujxAo3EyvZxrsezIx4VpyG79u3l9jeHrb1XmKH6TISCVDUFJKGysXmCEMypBujmA4k+jPc9+rbbL8+jIyPh0TW17B8iWQgytXiIAXHpCUawc8XwS6iawotkQgjuRzfvHCBXa0tNYskTb/5cXJ//D+muUeD9x8m/kvr7x6d73y4y/fZ9uJZUmmLwP/6i4QDgXWLoXbsDNLeHWBs0MIsehhBmaZ2fZaTcSpzxWuNw3uXtO9nRs0XE8utJSa+HvQnU9wYTuJlFGzXR1ElJBl8DwJWgLyaJ1cyyRSLRAPBOV2c1VyfQU2nORJmOJvD87w7cxptupuaeN++2p3BAoFg6Yj4qEAgEAg2M0JoFKwbleKVal/ukskSqipz9Gg7hYKDbbtomkI0qpcH2rverOhuJGJw4cIot2+XY5jBoMbx45186lPvndeZspyY8Ew3ZEUMqLghr14d5+TJ0zzwwDZkWVrTOZCLfayZouSv/upxfuM3/r5uY4WbjcW08RoHdq3TKqtTiXgWTJNYKEhcVbEchxvjY/z318/wU+89QuO5K7gX+2jd1oYUNOgumRz83jgjLQlujRcYGXOQB/OkZYkLfgxN8nHvzI+LSR5dD3SR90tEHRfrrSv4ucJEU64UCRHpapkowdje2DCxtmrzLisYxw4QPnGoLudhmm9fxXzzInIggJ8rQGRylpckSWitjTT902VaUwWMAx3rulZJlmjuXNxMv/nitUuhEjVf0t8uEBNfLeabxZovmeTzNpproOoSlSuCJENQ1vEdn4JcoGBamI6LpshzCoUV12flh4CMW0JTZO7dto3D27pojoTFzESBYB0Q8VGBQCAQbFaE0ChYNz7+8b+kt7e6CFaJ7pZKLrHY7C+wU6O7Ux2F997bjut65HI2qVSRbHbh5sxqMx593yebLc+HtG0PXa8eE16MGzKbtZY9B7JWFuuynEuU/OhH7+Xb377O+fMjDA2V//3o0Q6efHLrFeSsNjW18Y4kV6x5eaWoRDwLpklLbPJ1ENA0tGyBoYHb/ONgkp8pFCGTx01nUWIR5HiE4OG93PMLP0PwP36VxKVerrg6tithT0z18OlWbG6FmvmhX3iA14YvEOi7jWe7SIaGLMv4noeXyUOxhNXTOa0td655l5Ff/JmJ+1QinvVE6XQvY7/zBZybg0iKAoqMHA6h7mhHScSA+j0fFkM97vu1YqFZrHZWwnckUD2kGVNuJECTFYJOgP9p1710dUYWFAqX4/oUCAQCgUAgEAgWgxAaBWvO97/fD8AbbwzS2BipKoI98MC2iejuXXc1kMvZE67GSESbiO7u39/CE098fZajMJEI0tUVneUorMbMmHAqVeLatRS5nInrejiOT3t7ZKJ8Ziq1uiHHxor84R+eqdn5uBwcx+O3f/s79Pdn6O5OEA5rSJI0r8tyLlGyry/F7/7uB4jHjbqKFW5GVrKNdy3pT6YYSKeIhYLTxHYvmcG+fJ2wDyONUVJH99E6ksa9NYgUDJD4xL8g8j8/iiTLFH98lMKrF+lRSgy72p0SGJcW2SIn6YSe+CDHj27j9T/9PrYPgdCk6C8pClJIoWTZyMk0wTtNvJX5hl4qW3aJBgz8konVe4mx//BH8LEPrPm+qoXKut3hcVAV0DRAwsvksS/2wd09KIlY3Z4PgoWZ79wcfeYkzZ9+kkTjLkJ2iLyRQ0Nh0tMIPj6mbBExI+yJdbK9vbaKleW4PgUCgUAgEAgEglpZ/3yYYEvheT5/+IdnALjrroY5i1cAnnrqBKoq893v3uDMmdu8+eYQZ87c5rvfvYGqyjz55AnOnx+p2VE4F5X20YaGIG+9NUxv7xCpVPGOmCZhGAq+7/Mbv/H3s8pdai1NGRsrLHudtXDq1E1++qf/lG99q4/R0QJvvDHI2bODJJPFqo9VSxHO5z//GgcPtvLII90cOtQmRMZVotLG6w6PVS01cofH0e/uqamNdy0pWCa266GrU8R238e+OYhvu+i6hqMqlAwNJRJCu3sneB6Ff/w+UL4m/N43U/zX4LsYa+nA2J3Ae3cX2Yd7uHr3Lp5LHOMv+2Sab4/TPJQiFwsys5bJB3KxIM1DKVoGk7PmG8rhIJIiI4eDqD1deJk7LdeeN+v5VCu0WSumrlvb240SjYBtg6owtL2Fvs4m+gsFHM+r2/NBMD8LnpvpLOkvPk8wKNNjbkPzVfIUcXDw8XFwyFNE81W6zW0EQ0rNj72e57ZAIBAIBAKBYOsgHI2CNeXcuWEuXRoF2pcguPmAxNQ/W858xak89NB2fvd3P8CHP/w8pumiqjKeB/F4gJ6eBA0Ngaquw1pLU5qaQiuyzvmoOBNv3cogyxKhkIrvQzpd4u23RzhwoIWGhuC0x1puEc5U1rLkZjNSaeMdfeYkTl8/SmvjhNPJHR5fdBvvWhHSDTRFxnIcAnfchF6ugJ8rIBkaliKjeh5B2wVmz5u85EY4f36EtmM7+X+O7ERv8ECRQALPldBGoO+VMa69cYv737rO373nIONBjYjlorketiKT0xVCtsuJt65DOrfwvMuWsqvLvnQd/dDeidvmKrR5//61KciYtm5ZRtvRztVclu8f281oSwJXkVEcl9ZMgQcVn6N1eD5U8HxfxHSrUOss1pbULe5qbsEd8hlsuE1WyuNhIyOR8KO0pTrZ1dZCU7s+7+NV5kBeHhjk2/kUQ56L7a39uS0QCAQCgUAg2DoIoVGwplSEwbmYGTW2bY/3vGfHrOj0O+8kOXnyNL/xG++eNV9xKoWCjed5XL06vqD4FY8bNDQYtLaG0TR5WvkMUFV0q7ghn376xXlLU6JRfd51Tp05uRSmOhN7ehKk0yaeB6oqEw7r5PMW166lSCQC0x5rpYTatSy52cysdBvvWtDVkKAjnuDG+BiGqpZfL7ZbLmqRyyJgZ7ZEa35y9MDU+YJJR0Vr8ml6CJQweL4MlUuEAsE26PxhlYuey/FUgR/rvcn397YzHDLI6Sqq59GZLXH/5QG2pwrIiejC8y6N8r976dzEv81XaPO1117n547ft+qCzMx13+xu4+/vOkgBiGQKqLaNoyoMdDbxD+/aS0t3G3tWdUWTzFdcMpP1FmzrmVpnsfrpHIcf7iL7gk18OIYbK+FpDrKtomQCBEMKhx6OT7R7VxN2rdfeIvWF57maHOdvjt5FKaARRSba2YoTDKzpuS0QCAQCgUAg2DoIoVGwplSixnNRLWosy/KsQpiK6AfM6SgcGyvw1lvDqKrMf/yPpxYUv5LJEpbl0dkZQlFmf4GeS3R76KHtfPazj04IbdVKUzzPr8n5ePDg0r7sTXUmhsMakYhOOl1CUcrzGQMBlVzOJJMxGRnJTzzWuXPDyxZAF1s8I5iflW7jXW1kSeL9+/fxtddeZySbJRYMoqoylqaSC2qEHI8HbyWnzemYOl8w4Rh03KcjBymblqf+DuGAJ4MRk7kYdXhgbw/bei+xw3QZiQQoagpB26UlV8Lr60c/vBd9306sC9eQdA23ZDLampi4X2veRAZ8syx6yvEIMH+hjaGqjGSzfPPCBXa1tqyqK2/qnE4/HOTV7Q0UAzrNBQsCBr6qonge0a4OxiRvTdYECxeXTKUeBNt6ZjGzWDt2Bnn3B5t58+U0yUEZ1/VRFInGbTqHHo7TsbP899WE3TYXjjz/HTqvDvD9Hz9GKRygMVcC08bJF9H39tCSiK7ZuS0QCAQCgUAg2DoIoVGwphw82Mrevc0Ad+bQLS9q/IMfDPDwwzs4d26EK1fGJ4SuwcEc58+PArB7dyPt7ZEFxa9q7dNTmU90e+ih7TzwwLY5o8O1Oh+XGjWe6kyUJImengRvvz1CLmehaTKyLOE4Htevp+jqik08Vq3R77kE0JkzHlez5GYrsRHaeGc6qP7F8WN88/zFstiBB7EQHQPjPDRepDszKc5X5k0ad0TBRDJFvEvDw0PyZp8jruOh6TIpr0jxwz+G8ewAXl8/rfNEy/V9O7l99G5eDsBoawJHLse3W9IF7r90m45b5dEM2t5uoFxoczuVxNBU8qaFIssEtPJrSZIkYsEgt1Mp+pOpVS3TqMzptHovMXrPXQyHDKKmXb5Kqgq+ZaPEwiixMDHHWZM11VJcUhEb60WwrWemHmOpp2vWNXfqawOgY2eQ9u4AY4MWZtHDCMo0tesTTsaqwq5t03frFgMHt/NAa4KRWIio5SArCoQUvEIJ5+YgeiK6Zue2QCAQCAQCgWDrIIRGwZoiyxL/6l8dY2zsDa5dS9LQEFlS1HhwMMfAQI7f+Z2XkWUZx3ExTY/+/gyKIjE4mEPTZA4daqWxsdzIuZD4tVzRTZaleecY1uJ8XCozRVJJKsemczkX03Tw/fL8r66u2DSRdbkC6ErOeBRMUu/zLueKxr5v390EdZ2CZaJeuoH211+EdBZvHlGwaFvEEjrpQgnH8VBkCR/wffA9H1mW0DQZ23Vx9u6gq4Zo+ZWRUf7u+C4yQ6NEskUivo/tufQHDb6xu40P9A8BYJ29gP7AvVwaGmQklwfKr3VJkghoKs2RCGHDQFdUMm6JgjW7eX4lmTqnMzeaxJG60BwP3/PwTRtJU1C3t4MkrcmaZhaXVF7jUjiI1NOF09dP+ovPY9x3EEmW52wgB9ZUsK1nljKLVZIlmjtnR63nEnZ106ZhLMN4IsxZTcGRZTR3srBMMjS8XAEvV0APh9bk3BYIBAKBQCAQbB2E0ChYc+6/v4tvfOMNDh9up7d38VHj8fEC58+Pomky7e1RQiGNYtFhYCCDYWj85E/u4S/+4jzt7RGi0elfzuYTv1bbdQgLOx+XylSRtLExyPnzo1iWSyxm4Hk+hYKDpkl4VVp2lyOArtSMx5Wk3kW6haj3eZfzRWP/++tn+Lnj93F3ezu0t1MKBBcUBUO6QSRkUHJtTN/DtlwqZbiSBModwUVXFEK6sWC0vCK+FDWV9o427OKtcouz79NQtEg2xzj7yL3cBYz9hz/i+q99mJdSIziui6YoaIqC57gUiya3TYvOxgYURUFTZEJ69bl6K0llTufoV/8SxXawHAfD9VBiYdTt7cgNMQAs11n1NdVaXGJduIZxYNdEA3lcrX49WCvBtt5ZqVmscwq7tgu+T9RySRvlgiZbkTDc8gtLkuVy47Ttrsl5JBAIBAKBQCDYWgihUbBu/NEf/RSXLiUXFTUuFGx6e8uxx3vuaZ0QEiMRnd27m7h6dZzTp/vvtC5rVR93PvFrNV2HFRZyPi51m089dYJf//W/o7d3GMfxiER0XNfHtl3CYY19+5pJJotV3ZxLFUCXEzdfDepdpFuIepp3Wa1cAlhUNLaWeZNdDQm6mxoZy+VB8pElCUkpt8tLgC95mCWfUDgwsYb5ouVTxRdFVXE0DUnXkAwNJJmYpDDWFOUuwMlkefG1H2Df1UlY1ymZFp5ZAtdFwcdRFIZuDRKKhNi5vWvi8VebwIlDHDp2gFf++m+5mc0SCQZRouGy8krZdZkpFulualrVNdVaXOKlssCMBnJVxcsVyqKXpiBHQkLUmsJKzGKdU9jVFCRJQrUdMDTipk3GUNGL5Ri+73lIkoSvymtyHgkEAoFAIBAIthZCaBSsGwsJbg88sI1PfOI+vvSlN7h1K4MsS3heeRj+7t3NNDWFpt2/4la8eTMNSEsWv1bLdbjaPPTQdn71V4/zb/7N3+B5EsWijSzLxOMBenoSNDYGMQx1zijzUgTQ5cbNV5J6EumWQj3Nu5wrGn14W9eio7ELzZuUJYn37dvHq29eh4CPashQMd4qICNRSDpcejMLH2LqWNeqTBVfvFwBP19AChpISrmESnM9indcXuM7OxmSfWI+hCWFftPEQkKVJWQkJHwKikxoeJz3bOtZ07mCiqLw6P3H+dprrzNmmcQcB11RsVyHTLFISDd43759a1ZOs1BxCUw2kF/vvw1DScgVJqLoREKk2xro2dYpRK07LHcW6zRhV5v8YU2OhJAiIexCCc3zOHo7xfe3NzIe1IhYLkqxhBePkPE9Qsbqn0cCgUAgEAgEgq1FfdaYCrY0nufz3HNv8Oijz/GZz3yHW7fSmKZDMKjynvd009YWpr09UvVvK2Uo27bFGBzM3imcmaQifh040DKv+FUR3R55pJtDh9rqXmSs0N2doL09wpEj7Rw61Ma73tXOkSPtNDaWRYJgUMU0Vy7KXHFSNjQEuXp1nFzOwnU9cjmLq1fHVyRuXgszRbpIREdR5AmRruLk9Dx/4Y2tE4uZd7maVKLRN8bGCOo6zdEIQV3nxvgY3+jtJW9a6KoKPmQzJuNjRbIZE/xyNNZ2vUVHY80RuPo3JawRGXyQ1PL/8MAahezrKudeHa/puU8VX7DdstA1xSVmK+ViGIBS2MCRJFTXQ7s9Qst4joDn48kytiLjSzKqD/edu0Hz117ErzJ6YDXZ09bKzx2/jx2NTRQti9FcjqJl0d3UtCbtzZXiEnd4rOq11B0eR7+7Z6K4RJYkHkFDuz3CGB5mQIeAgRnQGaO8j9/ja0LUWiEqwm6mWJx+fCQJdVsbuWiA5uEUh98Z5McuDtCRylP0XNLxMFZbI93Na3MeCQQCgUAgEAi2FsLRKKgrTp26yac+9RKvvHITx3FRFHniC9Tt21neemsERSkLLzMddFB2KwYCKh/96L18/vOvr9qsxXqloSFAIKCiqgqJxNpEmdcibr4QtYh0588N8/ZfvsauFn1JMcXVph7mXS7UGnw7laZoW4yM5rh9M0c2Z+G5PrIiEY3odO2IogUXH41NJkskr9vEpRBmg4SSKL/m3RS4GRnZ9WsWyCviy43xMZpUuRwR9TwkRcEHcrpCV74shAbyJqrvY1s2cq5ASFMJZUtYiowrS7iAJ0vcZflY70zOIlxL9rS1squ1ZVaMfS3EusUWl/ieR9OfvsiPDg1x+sTdjIQN8ncav7vyJsdPX6T56jj+I/fX1WtvoyJLEu/fv4+vvfY6I9kssWBw0vWqSsTaWnj4RhrSObpGknzookbyXXtQfvq9JO7dv2bnkUAgEAgEAoFgayGERkHdcOrUTX791/+Oc+dG8H2fUEi745ArN89KEriuh2XBhQtjjIwUuPvu5gm33tSo7oc/fJhduxrXRPyqp/KR9Yoyr3fcfCGR7oAzyrHRH6D89iuM6CDpGtreHhIfq714YbWph3mX08olAC+bnzZjrykcJlMocSOZJJ/2MAwVxZBwXZ902sS+bXNgR8eio7GTz91FUXTczPTbi0W75uc+VXwZM00CsTBKOocb0cjpCkHH40R/ipH93TReu03bgW0MShDz/YniGcP18F0YD2p0Zku02R6eZU/MIlxrZElat5bmxRSXVMpjdsaj7Dx3m+GwQVFTCNourXkTUKaVx2xEfM9nbNACYGzQoq1LRVrHH60qrtfKqIOMW0JTZLqbmnjfvn3s/uePTZsDuaPOfmARCAQCgUAgEGw+hNAoqAsq0dfBwTzgEwxq5PMWvg+KIuE4ZYeTLJf7EDwPkskivb1DHDzYQiCgzXIrLkb8WqpYWG/lI2vRnD3fY690yU2tzCfS9WQH+Mnrpwi4JZT4dtTmKH7JxOq9xOgzJ2n+9JN1ITbuP9DCgeONXL42Riyq4mUlKgMJ12reZWW+YTRbwLw1hD9lxp4UCaFsa8PMuziWR7RVxSsBro9qgB5XKWYdLv5jBh5jwVmKU1lpgXyq+NJv2eTwUWyHzmSJB26n6BrLMAKosSiPHj/KX4wMkIyFiNguOuV4dUWUfPBWEmnGLML5qFais9FdY7UWl0wtj5GA9vz0CL0/ozxmozFwrcibL6dJDhcJ7oaX/myYhtYghx+O07Fz9gzLtWIh1+tGFXUFAoFAIBAIBBsTITQK1oyKmDc+np/47wqV6GsiYTA6mi/P/3I9ZFnCcbwp2yj/v6JI+L6PaTq8/fYoO3fGq7oVaxG/5hML5xMql1o+stoOyHqIMq81cwlVku/znsFeDKtIPtFEpDVebjMOB5F6unD6+kl/8XmM+w6uq8unUr7S/l4Fb18QxzLx8wpWn0L2lrtmkf+QbqCYFoXrA2glG8nQkGUZ3/PwMnkKV27gy2EKl3SUHgkl6pcn/XrgpCTy51TeuTxetWxoPqoJ5JE2Bdt3Gb6dX9Jznyq+pN44j/vCSzS8cRnJsvEiZVGo6Tc/TvcD96INDvLXz/0FQxLkAxqq59OZLfHgrSQ7UgWc4XGMw3snZhHOxVwlOu/fv2/Dz8GrpbhkseUxG4mBa0VeeWGUUtEjHLvjfA3IjNwyeeWFUd79weZ1FRvX0/UqEAg2FxXntln0MIIyTe36ujq3BQKBQLDxEEKjYE2YKuaBx6c+1cXHP/6X/MqvlIWvSvS1qSmELEu4rofvl91MfpX+jvLcRon29gi6rvLv//0P8bM/u3/RIsx8YuEv//Jf0tERZWQkX1WAXKgh+Pd///uEwxrptDkhKH7ve7fWxAG53lHmtWYuJ2djcphEapSsHmLXXQ1MNZZJkoTS2rjuUc5K+UrBNGluCBNSdK7fSmElHPy7XaxRZ81E4s54jKZbI/TrKk2yMmFKlBQFQgo5BdpujjE20EVuREGJ+Uga+Da4GWnOWYq+5y3ohqsI5Cefe5VcPIuecAlrEq1qlD1dLbTsWtzcR5gUX7b/8EP4P/TAxBq8aAjeOY9x7AAAe9vb2fbIe3j79/4bBcsiHA7RZntIJROnyizCakw9jrFQkLiqYjkON8bH+Nprr2+J0o1KeYzVewmpp2uWM9WtUbCtN3zP582X05SKHvEmFWQPD1ANmXiTTHrMofflNO3dAfFlXCAQbGgmnNuD5dFFiiLR0K6vu3NbIBAIBBsLITQKVp2ZYl4sVj7t3nxzcML5V4m+KopEJGKQTBYBqrYES1JZWPI8n2LRJhzWaWkJL1pEm9lUPFUsbGgIcvbsINevpzl6tINQSJvmVvzEJ+6bt3wkGNT4m7+5wg9+MIgsSxiGQnNzmMHBLLbtLcoBuVTWIspcT/Mpqzk5E2SIBiS27WunoWH2B2RpnaOc1cpXAk0ajY0hMpkS48U8xw8lePon34+qyDUJdsvBudjHidcu8bf372U8qBGxXDTXm4wSFy3u+d4FBiSDTFMbrYNJgo5JUTUYDDZWnSNZOt07Od/Psuedj9myy2DfT4QZz8oYaAR0FSOokCkVly3WTXXk2bYN75yfdnvo/sPc82u/OLFW785aq80inMm8JTqKwvBYkhe/+yrb9h8ksP+uTTsjb7HlMRuFsUGL5KBFOKqUy4Wm3CZJEqGowvigxdigRXPn4gVxgUAgqAemObejCoom4dp+3Ti3BQKBQLBxEEKjYFWpJuYpSvm2nTsbuHBhnJMnT/OlL/3sRPS1pydOoWBjWW5VN2NFZJRliXzeprHRX1JBxlxNxb7vc/16+o77zb8zJ1Ke5lb88pffoFRyqpaPjI8XuXYtSaFQvr2jI0qhYPP979/Ctj2OHm2fmCM4dZsnT57mgQe2bRjXYb3Np4TZTs7G5BCx/7MPJVBd2FjvKOd85SuxWAg9pFCwSgyk07RcuVWzYLdUvFSW7f1j/PjlIb7X3cRwyCCnq6ieR2e2xP3Xx5AGx2mjj59OvklbMYXquTiywlAwwZ863XTef2hilmLpdC+jz5zES2VR2pomRKdq8zEnxTqL7c2Jaa9JQ1MZyWb55oUL7GptWbWZh7XOIpzJtOM4ZW1eMoN9c5BAyeSmpvDWn/w921tb6qqEaKVZTHnMRsEsemVnj1b9vFM1iWLOxyx6VW8XCASCememc7vyXiYbEnFdEs5tgUAgECwKITQKVoxq7ra5xDwoO0Ha26O8/fYI58+PTERfx8eL7NyZ4OrVcTIZa9bjuO6k+lgsOkSj+pIKMuZqKs5mLXI5i0BAxbZdbNudteabNzOAP6t8xPd9+vpSd4Q3mVgsgKLI+H7ZiSlJ0NeXpqFhUpCYuh8WO9tuvVjqfMq1YKqT0/e2M/SN+o1yLlS+om5rw1ZlUm+cR/rdL9ck2C2Hyoy9HYMpdmTN2a3B+QK5mMFPjF1DLjnkAmEcPYTqWrSkhvhlLU3Tj7wHWZbwPY/UF57HS2VRd07u+7nmY84l1kH5NRILBrmdStGfTNU8i24pDtBaZhHOpHIc4+rktcRLZrAu9eHbLpqhkQ/omI2xuishWg2WKtjWK0ZQRlHKzh7ZmP0F27HL8UIjWB/PT8xXEwgEi2Wmc3sqwrktEAgEgsWyrp+Kn332WY4fP040GqW1tZWf+Zmf4eLFi9PuUyqVePLJJ2lqaiISifChD32IoaGhdVqxYC5OnbrJ448/zxNPfJ1f+ZW/4oknvs7jjz/Pd797o6qYVyEYVCdmulWir0eOdOD7Pk1NITSt/AVvJopSmdMIqVSJ733v1qLXPLWpeCq27eJ53kREW9OUWWuWZYlt2+IMDmYn1gEVkbLctBqNBohG9Snb9AkGVXI5k2zWmrXNarPt6pGZLtVIRJ/m+Ewmi5w8ebpq7H2tqUQ55XgUp68fL1/Ad128fAGnr3/do5wT5Svv3MTL5EFTkQMGaCpeJk/+2k2UkoX7wksTgp0cDiIpMnI4iNrThZfOkv7i8/je8t1UlRl77vAYku/TnjfZmSrQnjeRfB93aBxD8elsDpBPNJFzFQolh5yrUEw00dOsseO1lycEPvtSX1kYrfKlZep8TJgU63S1+rVCV1Rs16NgmVVvn0npdC9Dn/jfGH7ytxn5Xz/L8JO/zdAn/jdKp3snzs1Tp27S2zu07HM1pBtoiozl3LmW+D72zUF820UOBXD0csFMWFFX/JjVKxXBNvjQuzAO7Jr3Neb5PjfHk1wcHOTmeBKvmpV9HWlq12lo18ln3WnXeyi/DxWyLo3tOk3t+hxbWDsGrhV58U+G+Ic/GeKlPx/mH/5kiBf/ZIiBa8X1XppAIKhjanFuu65wbgsEAoGgNtbV0fjtb3+bJ598kuPHj+M4Dr/5m7/Jo48+yttvv004HAbg3/7bf8tf//Vf8+d//ufE43GeeuopHnvsMV555ZX1XLpgCvO5286dG8Fx3FnOvwozZ7pNjb6OjRX53d99hStXxhgZKdxxN95xe0llsTEeLzsGlxI7nqupWNMUZFmiWHRoaAhOiIUz1/zRj97L5z//+rTykUzGpFRyCIU0du5MzNhm2dnoef40l2S1/VDP1OpSrRd3Zj1HORcsX1EluvoGSPzTpZoEu+UW2iw0Y08yNCTPJdrRypFQkFzWxLI9dE0mEjXwC4WJtXipbDniHajufJg5H3OqWBfQtFn3t1wHTZEJ6Qs7KeaLbF/7td/juaaj3P9L2/lf/pe/BeRlR/67GhJ0xBPcGB/DUNWyMzVXQDI0fCCnK3RmS7TmzbopIVosnu/Tn0xRsExCukFXQ2JFIuwboalbkiUOPxznlRdGSY85hGKgALbpUch4BEIyhx6Or7trUMxXEwgES2WjObcFAoFAUN+sq9D4t3/7t9P++7/9t/9Ga2srZ86c4ZFHHiGdTvOFL3yBr371q/zIj/wIAH/8x3/M/v37+d73vscDDzywHssWTGG+QpVduxq5cmUcy/IYGMiwe3fTrOjq4GCWo0c7pkWfp0ZfdV3hE5/4a4rFDNGofqeRuizUGYbKvn3NaJqyJGFrrqZiSeJO4zX09MTnXPOHP3yYXbsap5WPeJ5HOKzT05OYVj4SjepEIjrJZBFVne6SnGs/1CtzRc4rBIMqQ0P15c6s1yjnguUrlsN9r55HyhWQ2luqbkMKGLgjSW4ODeM2hpctAs0nzAaOHyL9xeeRAgaSBNHYDNFvinhYiWH7JRMpPFvcmDkfc6ZYN/N1lykW6W5qoqshMe/654tsp+ONjL7xDrsGXgO2s2NHnExm+ZF/WZJ4//59fO211xnJZolYLj4+rqaSM1SCjseDt5ITEYL1LiFaLKslBm6kpu6OnUHe/cHmchvrcJEgYJU8WrcFOVQHbaxivppAIFgOFef2yC2TuC7Neg8uZF1atxl14dwWCAQCQf1TVzMa0+k0AI2NjQCcOXMG27Z5//vfP3Gfffv2sWPHDl599dWqQqNpmpjmZLQuk8kA5ZZR27ZXc/lbkrffHuadd0bp7o6i6zO/vEh0d0e5fTtLMKhx61aS1tYwtu0DIa5dG6O1NcwnPnEM13Vw3dnbP368nV/5lSN86lMv3Yms+WiaRHNziO7uBIlEANf1SKU8xsfziz7Gx4+387u/+z7+8A/PcOnSKKlUudTkPe/ZxtBQjny+RCSiEgiolEoOQ0M52tpCE2s+frydP/7jn+LChRFSKZNYTOc//afv0ds7hKr6Uz6oSezZk+DNN000TcYwQJa9qtusth/qiXhcJRZTcV2HQGD2B07LcojFVOJxdVVec5VtLmXb8p4dE2KP47qs9862xtN0jqT5n66N8Nr2RkaDBkVDQ/U8uvImx2+N0zGWwQ3oSK6DbMwWM/pCKqff/y7Sw/24ySFURaI9luC9e/ewq7W6OLkQypF9NP7n/w/2pet46RxyPIK2t7v833/2NzhzrMWzTLxIEC8aQtm1DenAXVjnrqDGwrO+tDipDPo9u5F2bZs4lj+8Zzf/95kMQ6kUAV0joGhIEuRMk4im897du3Edh/mOmnXhGqW+W8jbWvG0ybc434d3BvJYRoS7wjYZIBCQURSdRKKRa9eS/MEfnObYsbYlFTL1NDbwz468i5cuXWZgeAQzFkZFoitvcqI/xba8hasqs/ZTvb8vXR0e4S9+cJaiZRENBtCCKrbj0D8+xp+dfo0PHT1S83nm+T4DqTQFyySg6Xzz7bcxTZPWaGTi/AiqKoFIhLFcjn88f54dK+ScXAmat6n88D9vZHigyJk34OEPNdLaEUSSpXU/jmODFsnhIuGYDLI3rRkbCUIxGB8uMtRfEELBOrOc97DVxvc97MwVPCuDrMfQYruRJOFgqzdW6xw6+GCY7/11iXTSJBSZdEUXci6BsMyBB8M4rsO8b8KCuqeer0GCjYE4h7Y2tR53yZ85cGid8DyPn/7pnyaVSvHyyy8D8NWvfpVf/MVfnCYcApw4cYIf/uEf5nd+53dmbedTn/oUn/70p2f9+1e/+lVCodDqLF4gEAgEAoFAIBAIBAKBQCDYpBQKBX7+53+edDpNLBab835142h88skneeuttyZExqXyyU9+kl/7tV+b+O9MJsP27dt59NFH590RgqXx9tvD/PIv/xXxeIBweLZLIp+3SKdLPPnkCf7rf/0BQ0M5WltD/Pqvt/KHf5ikvz9PIhHkt3/7R7j//q6qj+F5Ph//+F/y5puD7NzZMMsZde1aknvvbecP//CnluREmg/P8yfciomEwb59LTU9xve/3z/hkiw3UCvcfXczH//4MY4f71zSNuuJ73+/n3/37/6RVKpIW1tkmuNzoeO5XGzb5u///u/5wAc+gFZllt9Gwvc8hn/td7HPXUHd0Tnb9XfjNvo9u4n9/E8y/r9/AS+TRWlpQDIMXNPk+X2dDLXEaGtvRW6ITfvbsVyO7Y2N/MJDD66oI8w88zZj/+GPpq3FN03ckSRSNEL8Iz+J2tk24YK0zl4g9dz/g3PlRnlmo66h7dlB/PGfxjh2YGK705xzgQCe72M6NkXbIRYw+NDRozU556wL1xh5+j8ixyLIoUnX5fh4kXNvDxPXIKj7ZP63f8FXv1rAsXzaSkl0q8Q7wxa//p//GQ893L3g4yx0bZhvP8mxKE2/+fFpz78e6U+m+G+vniKo6RhVXmumbVO0LX7hwYdmRdqnuhfH8gW+c+kSRcsuuyJVlXSxwGAqg6YodCbihIzpMXzP8xnP5/n5E/exp239Z71OpR6vQWODFi/92TBGQEY1ZjvQbNPDKnm895+3CkfjOlOP509p9J8Ye/OzuGYGNdiEpATw3RJOaQxFj9F0+GkCze9a72UK7rDa55Dv+YwP21hFDz0o09iqiZELm4h6vAYJNhbiHNraVBLDC1EXQuNTTz3FX/3VX/Gd73yHbdu2Tfx7e3s7lmWRSqVIJBIT/z40NER7e3vVbRmGgWHMLgvQNE28EFaBQ4c6ueuu5lmFKlAWO27cyHLkSAff+MZVhoaK7N7dMhGxNgyDbdt0rl4d5/OfP8NDD3XPKbj9yq+UZyleuDA5S7FYdBgczNLQEOSXf/kEhrE6X57uvXfxgtnDD/fw0EPdnDs3TDJZoqEhwMGDrRPPbynbrCcefriH//AfPjAxn7Iiph482M6TTy69VGMxbJbXdPNHf6ZcXHLlBvKU8hVveBwtHqXpiZ8hcOIQ2r/75cm5iZbNSGuC0bYE8ZZGpMb4jKikRDgYpD+dZjiXZ3tjw6LX5Xl+1fNXe+Be1BlrkXQNo6URfMh9/s8mBcW9PSQ+9hhd/9dvzTsf0/N9vnX5CjnLoiU2WTIUwCDm+4xks7x05Qp7OzsWFE3Vg7sJ9GzD6r2E3NM1ZVs+sukQLeW53dxJAOhIDvLQjV7aiklkx8H0Jdr/SwY3/PPzlgSdOnVz1rk/s1Bmrv0U2nfXupcQ1YrpuZiuTzSk4VfZ76qqYZZMTM+d9lqcOtPRclwypSKeD12JOLpevk4bmoGkKJiex3A+T7cRgCkPYXoOsiITDYXr9nVeT9egti6VhtZgeb5akzx7vlqmPE+yrSskBIM6oV7OH9/3yL3zHFijBKLdd84dH2QDVe3Azt8g985XiLQfEzHqOmM1z6H27eIHic1OvVyDBBsXcQ5tTWo95usqNPq+z7/+1/+ar3/967z00kvs3Llz2u3Hjh1D0zS++c1v8qEPfQiAixcvcuPGDR588MH1WLJgBnMVqkwVAX/sx3bzn/7Tq8tqKX7ooe189rOPTiteMQyFo0c71kzYWixTS202I1MbwquJqYLaqLUVe2ahTVr28G9fx4hGq25XV1QybomCZVa9fT4WEtJmrsW+NUjmi/8DLz275Xn0mZM0f/rJeYW1/mSKgXSKWChY9RoRCwa5nUrRn0wtKJpKskz8lx7jnWf/O9aYhxHTiSs5QrJLt1Jk1FI51bqfHwF+4sZpwvkcaS1E2tJoisgEb9yYd82nTt3k6adfZHy8SEfH5PWuWqHMQiVEq9XkvFIspQl8ZsGLofkkC4WywzGdoVOSCBsGAU0loKkUTIuiZVOybQJ6+TEWU/4jKDOrGTuqoGoSjl0ucaiXZmxB/WGlL2FlrqIGW6pef1WjGStzBSt9CSOxb51WKRAIBAKBYCOxrkLjk08+yVe/+lVeeOEFotEog4ODAMTjcYLBIPF4nI997GP82q/9Go2NjcRiMf71v/7XPPjgg6Jxuo5YSAR0HG9FWoqFsFV/bHYxda2otRVbkmWMA7sASIwn0YZuLUoEquB73pyPVauQVlmL73mkP/ECXnp2y7PU04XT10/6i89j3HdwzpbvgmViux5xtfo1YjGi6cC1Im9eamHsyBPY41mkUpFoYZi7sj8gct9+Pv9OgjeHNX4ECLgmg2oEs+ShGQpde1pQE4E51+x5Pr//+6cZHy+ye/ekgzsS0dm1q5GrV8c5efI0DzywbeK6NPWYTWW1mpxXksU2gXu+zz+cv0DBNCecqblS+ZgZqortuozmcoR1A0mSaI5EuG2nMF2XomWhqyqW65ApFgnpBu/bt6+uhNd6Z1oz9qBFMeejKBKt24y6aMYW1CeelcZ3TaRA9dEUkhrAN0fxrPQar0wgEAgEAsFGZV2Fxs9//vMAvPe9753273/8x3/ML/zCLwDwe7/3e8iyzIc+9CFM0+RHf/RH+dznPrfGKxUsxHwiYG/vEIahUCw6RCKzoxjFooNhKDQ0BBZ8HCFsCTYrcwlSc7FYEahC6XTvrDhvJeKs33fPooU068I17Et9ZSdjFTeM0tqIdbEP68K1OZ/fUpxz1Ri4VuSVF0YpFT3CTSEi7WHsdJFcroWLBw7z8D/v5JeHxnj+s38DwKirYzs+8bhBz84EDQ1lIWauNZ87N8z58yPLcmfDbNdfXFWxHIcb42N87bXX+bnj99WF2ChLEu/fv4+vvfY6I9kssWAQXZlbDKzmTFXkcozX931URaZkOxPuxbBuEAsGSRYKFGwLO+eiKQrdTU28b1/9CK4biY6dQdq7A4wNWphFDyMo09SuCyejYE5kPY6kGPhuCUkNz7rdd0pIioGsx9dhdQKBQCAQCDYi6x6dXohAIMDJkyc5efLkGqxIsBzmEgEPHmxl//6WiTmOUwdx+b7P4GCWo0c7OHhQfKkUCGplsSIQlEXG0WdO4qWqR5zTT/zLRQtpXipbFiwD1UVAKWDgjyTxUtk5n8tSRdOp+J7Pmy+nKRU94k2T29AbghQTOYYyeV5+pZ8PfXgXB//tMV4av8Wu/W0EZIlI1GDq051rzclkadnu7GquP4CApmGoKiPZLN+8cIFdrS114ebb09bKzx2/b8J9mXFLaIpcVQys5kytRKSLlo2mKPi+h+t55E2TkUyWvGWhyDKKLJMIhnh4924e3H1XXTz3jYokSzR3zi/KCwQV9Phe9NguzOTbaOHQ7FIycxSj4SB6fO86rlIgEAgEAsFGoi7KYASbm5lzHHfsiAIh8nmLGzfKcxyffPKEiEALBItkUgQ6z+2hEdKOg6aqdLe18L79+6eJQL7nkfrC83ipuSPO3p9/A6u0fVFCmpyIIukafslECs+OZvolE0nXkBPVZ0nC0kTTmYwNWiQHLcJRZeK5jZLiqnyDjJTHbfC4ZMrcfvEW70uUG7oTIRlNn+2ynmvNDQ2BZbuzV3Ie5Vqxp62VXa0tc86TrMyaHM7k8H0f07YJ3tmvExHpVBrLcUCSKNk2o/k8tutOtE5rikKqWOAfL16kNRYVbkaBYI2QJJnE3o8yevYz2PkbqEZzOS7tlHDMURQ9TmLvE6IIRiAQCAQCQc0IoVGwJkyd4/jOO6MApNOlui5zEQg2AtuvD/HBP32J/uERijIEPehqbaHxY40wRaypJeJs3L7NbiVBphitWUjT9+1E29uD1XsJaUrLM5TdMO7wOMbhvej7ds7a3lQW45yrhln0cF0fRZsUGd9ULmH5NkFfR0Km6NtcGx/hz80MxyRwR8ZRO9tqXvNMd/bMv6vFnb2S8yjXElmSqgqf02dNumSKRVLFAl2JBJFA+TwJGwYd8Rj9qRQyEqP5PI7rEjEMWqIRwkbZfVePjk7BxsTzPS6PXiZdShMPxNnTvAdZCGVzEmw5RvOR3yJ16UtYmav45iiSYmA0HCSx9wmCLcfWe4kCgUAgEAg2EEJoFKwZlTmOvb236et7nT/4g5/k0KHODedk9Dy/pkKaWu8nECyVqVHozrYmJK0chXaqtD3XEnEOyD4Htof4H33ZmoU0SZZJfOwxRp85idPXj9LaOBHJdofHkeNR4r/02JxFMFNZyDk3H0ZQRlEkXNtHMuCqfAPLt4kQxMYhRw5HcVEcMNMmJGLc7Gmn59Ktmtc8053d3j5ZljM4WJs7e6XmUdYD1WZNaorCQDrDjfEkHfEY8WAIy3UoWBad8QRHdmznu5cvE9J1YsHprs56dXSuN/XeTl5vnOk/w3Nnn+PK2BUs10JXdHY37eYjRz7CsS4hmM1FsOUYgeYjWOlLeFYaWY+jx/cKJ6NAIBAIBIJFI4RGwZoiyxIHDrTS1wcHDmw84e3UqZsT7dqmWW7X3r+/haeemu7KrPV+AsFSqSUKPbU5uaaIs6Hxsx+5j2///oVFCWmBE4do/vSTkyUzI0kkXcM4vJf4Lz02IXbWwlzOuYVoatdpaNcZuWWCXiIj5Qn6OjYOGSmP53vIsoKhyfieB8DfP3qMn2xqoPMHF2te81R39vnzIwwNlV/ftbqzV2IeZT0w16zJhnAYTVHoT6UZzeWwHBddnSx48XyPU1ffIRqcHR2H+nV0rhcboZ28njjTf4ZnX3qWdClNS7iFgBqg5JQ4N3yOZ196lk++95NCbJwHSZIxEvvWexkCgUAgEAg2OEJoFCyZrebYO3XqJk8//SLj40U6OiYFmLNnB3j66Rf57Gcf5aGHttd8P4FgOSy27bnWiPPxn3uQz/bsWLSQFjhxiLb7DmJduIaXyiInouj7dtbkZFwJJFni8MNxXnlhlIFMCbfBQ0ImR64sMiKja+UGZEVRACipMmd/6kEO/9L/DOlczWuuuLOXcv1biXmU9cB8syYjgQDdjQrpYpGfOHwPPU3NEy68m+PJTePoXG2W2k6+VR2Qnu/x3NnnSJfSdCe6J87LsB4mpIW4nr7OV85+hSOdR0SMWiAQCAQCgWAVEUKjYElsNcee5/n8/u+fZny8yO7dk5HSSERn165Grl4d5+TJ05w40VXT/R54YNumFmUFq89i254XE3FeqpAmyTLGgV0r/lxrpWNnkHd/sJnvfLfEJbM8k9FRXGRZQdfK0WooCxIAUcPgdjrN6OHDbD+4e1GPJcvSRPP2YlnuPMq1Yj7BasFZk3fcmq3R6DSH6mZxdK42S20n38oOyMujl7kydoWWcEvVH19aQi1cHrvM5dHL3N1y9zqtUiAQCAQCgWDzI4RGwaJZimNvqvsxHt94p925c8OcPz9CR0e06heY9vYob789wgsvXKjpfufODS9ZpBAIYGltz4uJOC9HSFtPOnYG+Z937GLgxVtcGx9BccC442QE8AHPLQuNkUCAkXx+XWK6y5lHuRYsJFgtddbkZnF0rjZLaSdfqgNys5AupbFci4BavfU9oAYYLYySLqXXeGUCgUAgEAgEW4uNp/gI1pVanX1THXsz3Y+xmMonP9nOq6/eoqEhtKTo9VrHtpPJEqbpEgxWf8kEgypDQy63b2drul8yWVq1tQo2F77nVY0jL7Xteb0jzmuBosj8xNGDfOnV75FPmzieh6ooeL6H43rod56rvc4x3aXOo1xtahGsdrW2LNmZuFEcnevJYtvJl+qA3EzEA3F0RafklAjr4Vm3l5wSuqITD8TXYXUCgUAgEAgEWwchNAoWRa3Ovopjr5r70XUdAH7+5/+CUMhAVeWao9ee5/Mnf/ImX/rSG9y6lUGWpVWNbVcEzatXx/E8j0LBJhqdLUoUiw6GodDZGcUwFIpFh0hEn/N+DQ3VHRdbga0223M5lE73TroPLRtJ19D29pD4WNl9uNS25/WOOK8Fe9paeeKB+/mD73yXdLGI63nIskxQ12gNh8F1yJZKbKshpruVZt4tRrBajjOx3h2dc1E5F6DsOtzR0rwqa16sY3QpDsjNxp7mPexu2s254XOEtNAs8XukMMI9rfewp3nPOq5SIBAIBAKBYPMjhEbBoqjV2ZdMluZ0PxaL5dhiLmdi23D0aDulkrtgWcqpUzd55pmXOHXqBo7jo2kysZhBe3tkVYpWZjoxh4YK3L6d4557WmlqCk3cz/d9BgezHD3awQc/uI+/+IvznD07wK5djbO+6FTud/Dg1nTsbLXZnsuhdLqX0WdO4qWy5cKXOyKi1XuJ0WdO0vzpJ1e07Xkzsre9jV/+offw3KvfJ29ZxAIBooEAjmODC0FNXzCmu9Vm3i1GsFquM7FeHZ1zUTkXhtJJjgD/7dVTtMUbVuVcWOwsy8U6IDcjsiTzkSMf4dmXnuV6+jotocnW6ZHCCPFAnMePPC6KYAQCgUAgEAhWGSE0ChZFQ0OgZsdeNfej7/vcuJECGgmHdXI5m0LBIRYz5i1LOXXqJr/+63/HuXMj+D7EYjq+D5mMSanksH9/M+PjxRUrWqk83uBgnkTCoKkpRCCgcv78KGfPDrJ/fzPt7RGKRYfBwSwNDUGefPIEqirz1FMnePrpF7l6dZz29skZllPvtx4OvvV2Eoo27trxPY/UF57HS2VRd07GoqVwEKmnC6evn/QXn8e47+CWiEIvh71tbTzx4AMTYthoLodxpxTmQ0ePzCsQbaWZdxWn3oXBAQqWTSw4e+4nzBasNqozcbFMPRcSwQDYFkFNX7VzYbGzLJc6M3OzcazrGJ987yd57uxzXBm7wmhhFF3Ruaf1Hh4/8jjHuo6t9xIFAoFAIBAINj1CaBQsioMHW9m/v6Umx97LL9+Y5X7MZi1yORsoz1HzPB/bdoG5y1IqzsjBwRySBMGghnxHRAmHdfJ5i76+NHv2NK5I0Yrn+XzqUy9x7twI4DM6mkeWJSIRg927G7h+Pc21a0mKRZtAQOXo0Q6efHLSkffQQ9v57GcfnXDuDQ2VnXsz77eWrLeTcCmzPbcy1oVr2Jf6yk7GKq4ypbUR62If1oVrGAd2bYko9HKYKYYZssIbr55iV2vLnH+zlWbeTXVtFi2bdLGA6di0xWKEjenCVDXBaqM5ExfLzHNBBrAtDE2jRdNW7VxYjGNUtHlPcqzrGEc6j3B59DLpUpp4IM6e5j3CySgQCAQCgUCwRgihUbAoZFmq2bFXzf1o2y6+X45Oe56PLEtomjKx/WplKRVnZCIRZHS0gKJM/yIXCKjkciaO42Gayy9a+ZM/eZNXXrmJ7/uEQhqyLOF5Pul0iWLRZteuRopFm9/4jYe5776Oqs7Ahx7azgMPbKuLWYT14CRc7GzPrY6XypZnMgaqu4+kgIE/ksRLZdd4ZRuXqWKYbdu8scD9t8rMu1muzWCIkm1TsGxup1J0JhITYuNWE6wqzDoXfH/ittU+F2p1jIo27+nIkszdLXev9zIEgnnxfE8I4gKBQCDYlAihUbBoanXsVXM/apqCdOdDlGk6RCIG0ehkBLtaWUplLmRTUxBZlnFdH1Wd/LJUEQLzeWvZRSue5/OlL72B47jE44EJgUFRpAn35NBQjkQiwK5dDfOKYrIsrbtoVi9OwsXM9hSAnIgi6Rp+yUQKz46w+iUTSdeQE9F1WN3WYCvMvKs49fKmSSwQwHU9LN+hLR6jP5nCdFyGMhm6G5uwPXdLClaw/udCrY5R0eYtEGwczvSfmYj4W66FrujsbtrNR458RET8BQKBQLDhEUKjYEnU4tir5n4MhVQCgfJpp6oyO3cmps1vrFaWUnFGKopMJKKTTpdQFG3i7zzPR5IgnTZ58MFtyypaOXdumFu30miaMkvQhLJ7MpMxicWMDdEcXS9OwsXM9hSAvm8n2t4erN5LSD1ds2KQ7vA4xuG96Pt2ruMqNzdbYeZdfzJF39goBdMiVSzi+z6SJBFQVZoiYdKFIiXbZjCTIaRrW1aw2kjnwlaZmSkQbGTO9J/h2ZeeJV1K0xKeLC06N3yOZ196lk++95NCbBQIBALBhkb48wVLpuLYe+SRbg4daqvqiKu4H48c6SCVKnL9eppYrPxFLRYz7gh6HrmcxdWr41XLUirOyKGhHN3dcXRdoVCwcRwP3/cpFGwkSaKtLbLsopVkslSOwsUMTNPBnxKRA5AksG2P7dtjG6I5uhYn4UrEzReicgwHB7Oz9mlFYD5woGVD7NO1QJJlEh97DDkexenrx8sX8F0XL1/A6etHjkeJ/9JjovBlFanMvMvcEeCmUokQdyYSGzpCfGloiPF8AdNxUGQJXVVRZImibTOeL9ASjZIIhvjRg/v5+Hvew7965D1bTmSEjXcuVByQd7e3s72xQYiMAkEd4fkez519jnQpTXeim7AeRpEVwnqY7ng3aTPNV85+Be/OmCGBQCAQCDYi4luqYNV56KHtfOUrj/HlL/8s/+W//CRf+cpjANx//zZSqSJ9fSlSqSJHj3ZUnRdYcUY2NARJJov09CQmhMB0uiwM3n//Nv6P/2P5swYbGgIEAirt7ZFZgqbjeOTzNqoq88QT926I0pKpTsJqrKaT0PN8enuH+M53rnPu3DC/+qvHaWgIcvXqOLmctaDAvNUJnDhE86efRD+0Fy+dw7k5iJfOYRzeS/OnnyRw4tB6L3FTU5l5F9INRrJZSraN5/mUbJuRbHbDR4g93+efbt7C9300RUGRZCRAkWR0VcX1PEZyWQK6xr72ji0tWM08F0y7XGhmbpJzQSAQrB2XRy9zZewKLeGWqkmTllALl8cuc3n08jqtUCAQCASC5SOi04I1Yeq8Qtu26euDP/qjn+LSpWRNZSkz50LG4waxmM727XGeeOJePvzhwysiUk2dK7lvXzPXr6fJ5Swsy7tTXCNz//3b+PCHDy/7sdaCxbSEryRztVx/9KP38u1vX1/VNm7P9ydig0FNo2UwCekcciKKvm/nol2AvudhXbiGl8oueRtLJXDiEG33HVy3x9/q7Gpt4Uf23c0rV64wni8gSaApyqaIEPcnU6QKeYK6huW4yJI0cX2QoOxstGx2NoXqxqm3nkydfziUTgJQtK1NcS5Mxfc9rPQlPCuNrMfR43sn5ioLBILlky6lsVyLgFr9B96AGmC0MEq6lF7jlQkEAoFAsHIIoVGwbiy2LGUtmpynzpVMJovs2dN4x3lnk0oVaW+P8KlPvXfDOO8W0xK+UszXct3Xl+J3f/cDxOPGqhzDy0PDE0UIVq6IlEzTPJTi/reu050qoO3tIfGxx2p2A5ZO95L6wvPYl/rKLdC6tuhtLBdJljEO7FqTxxJMMu1cclwAEsEQD+/ezYO779rw7rWCZeJ4Pi3RKIPpDJbrosoysiTh+T6uV57XeO/2rg3/XFeKyvzDGyOjvPHqKX7hwYfY0dK8afZPceQMqUtfwspcxXdNJMVAj+0isfejBFvEvDiBYCWIB+Loik7JKRHWw7NuLzkldEUnHoivw+oEAoFAsBx8z2ds0MIsehhBmaZ2HWmD6AYrjRAaBeuK5/mLEg7Xosl5pnuy4sh76KHtS3LeLfY5rjS1toSvBLW0XH/+86/x3HOPrfg+uDw0zNdee52CaRJ1XIy+29g+DLTE+Lv3HOTHem+yrfcSo8+crCl6XDrdy+gzJ/FSWZS2JqSAgV8ysRaxDcH8rKdbdD6mnkuxUJB4SMVyHFLFAv948SKtseiGd7BVCk40RaEzEWc0l6NkOziehyRJ6KpCSNPZ29a+3kutK2RJoqshwRuwqUpWiiNnGD37GVwzjRpsQQq04LslzOTbjJ79DM1HfkuIjXWAcJxufPY072F3027ODZ8jpIVmJU1GCiPc03oPe5r3rOMqNxee73F59DLpUpp4IM6e5j3I4nUjEAhWmIFrRd58OU1y0MJ1fRRFoqFd5/DDcTp2Btd7eWuOEBq3GOstek3l+9/v53OfOzMrXvvUUysrfi2FlXJPzhUhXuvnuBZuUFi/lmvP9/mH8xcomCYt0QjWW1fwbJdAKIBRchgPanx/Tzs7TBevr5/0F5/HuO/gnKKW73mkvvA8XiqLunOy9VkKB5F6unBq2IZgfurBLVqNaedSbPI8Dmgahqoyks3yzQsX2NXasqFFpkrByY3xMVqiUXY0NlKyHVyvPCYiWyzS09wsYtNbAN/3SF36Eq6ZRot0T17v1DBaOISdv0Hq0pcJNB8RotY6IhynmwNZkvnIkY/w7EvPcj19nZbQZOv0SGGEeCDO40ceF0LYCnGm/wzPnX2OK2NXsFwLXdHZ3bSbjxz5iGj2FggEK8bAtSKvvDBKqegRjioomoRr+4zcMnnlhVHe/cHmLSc2inexLcSpUzd5/PHneeKJr/Mrv/JXPPHE13n88ec5dermuqzn3/27f+QHPxggkQjS05MgkQhy9uwATz/94rqtaSq1tGrPRyVCXC/PcbnPpxbWq+W6P5liIJ0iFgri54v4uQKSUW43l4CI5TIcNhiJBFBaG7Eu9mFduDbn9qwL17Av9ZWdjFUE01q2sVh8z2f0tkn/1SKjt018z1/4jzYoFbeo9eYl5HgUdXsHcjw64RYtne5ds7V4vs/N8SQXBwe5OZ7k5nhy4lyqduxjwSC3Uyn6k6k1W+NqMKvgxHEwVBVVkcmVSoSNwKYoOJl5fD1/876uloqVvoSVuVp2MlY551WjGStzBSt9aVUefytd+5ZKxXFqjp/D9FXyShTTVzHHzzF69jMUR86s9xIFi+BY1zE++d5PcrD1IBkzw63MLTJmhnta7+GTP/RJIYCtEGf6z/DsS89ybugcMSPGttg2YkaMc8PnePalZznTL143AoFg+fiez5svpykVPeJNKpohl7sdDJl4k0qp6NH7cnrLfb4RjsYtwnxz855++sWqbc+rhXfnRZZMzh2vPXnyNA88sG3DzEKcSS0R4o3+HKsxteU6EtFn3b5aLdcFy8R2PeKqCnYR3/eRpzgNNdcjp6sUNaUcgR5J4qWyc27PS2XLLruAUfX2atuYWkIT0o1FxSq3ktW+ntyiV4dH+NblKwykU9iuh6bIRAyDvGkRD4Wq/o2uqGTcEgXLXNW1rQVTC04G0ikybglNkTdNwcnUOZuV49sRT/D+/Rv/ua0knpUuO+QCLVVvl9QAvjmKZ618OcVWuvYtlYrjNJ8f4oblULCH8XwfWZIIaSF2OCaqcJxuOI51HeNI5xER6V0lPN/jubPPkS6l6U5MOrXDepiQFuJ6+jpfOfsVjnQeEftcIBAsi7FBi+SgRTiqVP3BNhRVGB+0GBu0aO6s/t1yMyKExi1AvYleFy6MANDeHlnTeO1asl4R4vVmvVquK/PmLMdB18oXed/zkBQFAFuRUT2PoO3il0wkXUNOROfcnpyIIula+b7h2V92Z25jOYLGVrPal86/w83hEcwD3YQVlda8OWGtn+kWXe0SnL/4wVlyllWew6iW5zAOZ3NkSyXShQIN4dmD+i3XQVNkQvrm+KBQKThZqkher8yas3nn+N4YH+Nrr73Ozx2/T4iNd5D1OJJi4LslJHX2Oe87JSTFQNZXtpxiq137loqVvkR6tJe+QoaC56ErBrIk4/keOStHnyPTM/omjelLGIl9671cwSKQJZm7W+5e72VsSi6PXubK2BVawtWd2i2hFi6PXeby6GVxDAQCwbIwi175x1Kt+mdnVZMo5nzMorfGK1tfxE84W4DFiF4rgef59PYO8Z3vXKe3d2jCwVghlSo7gQKBtY3XriXrFSFebyot1w0NQa5eHSeXs+60dltcvTq+Ki3XMDlvLlMslp1xkRC+aQPgAzldoTVv0pIr4Q6Po9/dg75v55zb0/ftRNvbgzs8hj8jaun7/rRtVASN62NjBGyXRtsjYLvcGCsLGpeH5n5dbTWr/eWhYb5w/hx/8dDdvPCubv7sYCd/dk8n1+OTYoIUMPAte17H6XKpxGeLlkVLLEpA05AliYCm0RmPI8syw9ksnjf9A4Hv+2SKRToTiU01u1CWJLY3NnB3ezvbGxs2vMg4c87m1OPbEo1SsEy+eeGCiFHfQY/vRY/twimNVr3eOeYoemw3enzvij3mVrv2LQfHTJEujlL0XIJaCEUu/5imyApBLUTRc0kXR3HM1LIfy/M9Lo5c5PTN01wcuYjnb60vRYLNQ7qUxnItAmr1BE1ADWC5FunSyju1BQLB1sIIyihK+cfSajh2ObFhBLeW9CYcjVuAWkSvoaGVEb1qKT9JJAxSKSiVHHR97eK1a8l6RYjrgbVsua5QmTf3tddeZySXI9LVAsUSJcsmFwsSsl3uvzyA19ePHI8S/6XH5o3lSrJM4mOPMfrMSZy+fpTWxonWaXd4fGIbviTxD+cvkEtnSAwlIVfA9v3yLL9IiHRbw7zFIVvJal8RZPO2SdB2UZ0Sjq5yOxLgG3va+PHLQ3SnizU5TpfLQKr8xSIaDMze77JEazTCYDrDQDpNUySCrqhYrkOmWCSkG5tiduFmZurM1oXmbG5vbFixx92ozaaSJJPY+1FGz34GO38D1Wgux6WdEo45iqLHSex9YkVjuVvp2rdcbuVTFByHkKJTTfYLKxoFx+JWPsVyfFmiNEOwmYgH4uiKTskpEdZnO7VLTgld0YkHVtapLRAIth5N7ToN7Tojt0ziujQr0VfIurRuM2hqn60JbGaE0LgFWCvRq9Y5kPv2tdDXB0NDObZta1izeO1asl4R4nphrVqupzJz3pzV04mUTNM5lOLEW9fZniqgH95L/JdqazUOnDhE86efnGxGHkki6RrGlG3cHE/SPzBI6OYQfslGMjRkWcb3PPxMnoBtcUuW5xQ05rLa+/hkyGHqNlZJolhoXrH9tB5MdZi1NjVgDYzhZfIYioJetBkPary6rYFtqQLe8DjG4b3zOk6XS2W+oqZWfwuMB0PkTYuWSJS8ZW662YW1spy5o+vJtJmtVViNOZsbXaQJthyj+chvTbYam6NIioHRcJDE3idWvNVYxIxqJ60mGPBUdsoOKV+Dqa9B3yeKwzVPJawmlvwYldKMdClNS3iyBblSmvHJ94qCEsHGYk/zHnY37ebc8DlCWmjW5/CRwgj3tN7DnuY967hKgUCwGZBkicMPx3nlhVHSYw7BkIksO3ieSrFgEAjJHHo4jrSJehlqQQiNW4C1EL2WMgcykSjHa9vbJ0XJwcHsqsVr15JKhPjpp1/ctM9xISot12vJzHlzQU2jZTAJ6RxyIoq+b+eiCkYCJw7Rdt9BrAvX8FLZWdvIl0qY42mMkoUVMnAlCcUHQ1GQQwpqsUQhmSZfqu4Wnmq1l43yuTBKiqvyDTJSHtf3kOIypcvD/ET04DSBy/e8OddVb0xzmMky2vZ2rEt9eIUSkqERMSWGgxoD6TQdNThOl0tlvqLtVHdVW65D2ND5l/cfR0LacELbSrCRi1SmzmwNaNqs21d6zuZmEWmCLccINB/BSl/Cs9LIehw9vndVCkaqXfumslVjRtWIBxs4RQttjNBAkZyv46Cg4hLBIo/MKVo4FlyaO3elSzN8z2ds0MIsehhBmaZ2fct9uQKxH9YbWZL5yJGP8OxLz3I9fZ2W0OS1eaQwQjwQ5/Ejj28I17lAIKh/OnYGue+Hxvinb/WTGQvjuQqy4hJP5Lj3kW107Fyb0t16QgiNW4C1EL0WMwdy375GAH77t3+Ez33uzJrFa9ea9YgQCybnzU3Q1LSs7UmyPGcpiX57BNe0GGyKYqsKviQh+T6G7dJQspGDBnLRRL89Ap0ds/5+ptV+TErzpnIJy7cJ+DqqJaMEfIYKqWkFFqXTvZNOS8tG0jW0vT0kPlabW3Otmekwkxti6Ht7sG8O4ucKKLaNEw3hHNhJ8z/7iVV/Dh2JOG8A2VKJRk2b9eNLpliku6mJbQ0bf17hUtjoRSqVma03xscwVHXO47sSczY3W7OpJMlrUigiYkYLUxGqwoVthPQf5s8KL/JjIZtmP4+ChYvMoBTlb0oqoZZ7l+zMWsnSDNEiXkbsh/rgWNcxPvneT064zUcLo+iKzj2t9/D4kcc3xA9AAoFgY1AcOYMy8hnu3Z2h4BzE8eKocpqQ9jbKSJTiyG+teDqk3hFC4xZhtUWvpcyBvP/+Lh56qHtN47VrzXpEiAVrR+qNCxR1FdPQ0E0HGQ8fiZKuMqzKGK7PjsEkbaZb9e+nWu1TYzaXm25g+TYhL4jngKJAY5OBHpQZyWb55oULdPUNMv6pz+GlsihtTROzI63eS4w+c5LmTz9Zd2JjNYeZ3BDDSETxcgVKpkVAgq4n/gWBZQrDtVARD4Oazkg2SywYFHMY7zCzSKUiPAQ0DUNVJ87DueaO1gPTZrau8vGtl2bTjeaemhkzCkUVVE3Cscsi41aNGVWYKVTt8/4519z9/GniRTqah4grCmnX5e1Cnlggwb9ahjNrammGj082a2HbLpqmEI3qBNQAo4XRBUszRIt4GbEf6otjXcc40nlkQ87PFQgEGwPf90hd+hKumUaPdmNIOSB357bt2PkbpC59mUDzkVVJidQrQmjcQqym6LXUOZDrEa9da7bCc9yKuK7LS4MDaEEVT5ZxNRnJ8ZB8H8X1sHQVybc5cek26odjc26nY2eQd3+wme989xYZM4/q6PiAHpSJNaoYIQWgXGCRTHHppZdpTGVRd3ZNCBtSOIjU04XT10/6i89j3HewrmLUczrMJAkpEiLnu2UHYWPjmq7rQ0eP8K3LVxhIp7bsHEaYHsMf0GUGUmtfpLLSzJzZulrHt5Zm01pEmuWwUd1TlWtfZe3FXHntrdsMDtX52leTakJVyA7jJQ8RG23jDeW/83bgErqic7D10LKdWZXSjKHxNIM3LIxcE5oXxpaLmJEh2nfo6Mb8pRkzW8Qr1w7ZkIjrEukxh96X07R3Bza1eCz2Q30iS/Kq/tAjEAi2Nlb6ElbmKmqw+o/OqtGMlbmClb60JqmRekEIjVuM1RK9FjMH0nWdFX98gWCtuX72HMMBhcZMHjdbINUQxdRVfAkkHwIli0DJIpyILVhs0rEzyL1GlDMvy8T1AKoqoxsyTHmv0hWVdDFDdmCYlramqm9kSmsj1sU+rAvXZsW9pxZ7BHUd34eSba3J7MG1dJgthl2tLezt7NiQhScrxcwYfv+2ZoqPHCTa0gSNs4WF1ShSWS1mzmxdjeO73s2mG9091bEzSHt3YEO5MVeT+YSqtrYIgbEAO7V/w/b3p0gEV8aZtad5Dwm/kxvX8zyYe4wmuRNV0XCwGcve5s3r32DHnqZ5o9miRbyM2A8CgUCw9fCsNL5rIgVaqt4uqQF8cxTPWr0fnesRITQKVoTFzIF0q6dIBYINRS6VwZEkNE1DzxcJ3B7DCuq4iozieqgli3Qigv/g4ZrcheGAQcBQkHUfXZt9f8t1UH2fQL6EFIlW3YYUMPBHknip7LR/n1rskTctipYFUjk6HDb0VS/58HyfgKbx0K6d/NPNflLFQt04CGfN9NxClE73MvrMyWkx/JAmoRRKFN65SUSSkBumu3FXukhltVnt47uezaabxT0lyZIQXe5Qi1BlpQLsUu+luWWF9pkvIb36AA+HOgiqIQp+jgI2Khqt6jYeLjzOze8NwBPStB+/piJaxMuI/SAQCARbD1mPIykGvltCUmf/6Ow7JSTFQNZX50fneqV+snWCDU9lDuSRIx2kUkX6+lKkUkWOHu3gs599VJSfCDYVkUQM1fdxNBUpHEJSVfSSTTBXQi/ZuIaOKsk0HK7NIl+JF2eKRXzfn3ZbpcCiMxKlNW/il6q7yfySiaRryIlJIbJS7HFjbAyAgmVhex6O61G0LYCJko/LQ8NL2RXzcnlomD/49nf5o+9+tyx2jqXxixL74538vx5+mH/1yHu2VEy5XvA9j9QXnse7E8OXw0EkRabdk2i1PbK6in1zEKacixPnYSKxIkUqm4FKs2nciHM9fZ28lcf1XPJWnuvp66vabLoY95RgY1CLUOW6KytUvfXWEE3ZvYStVrJeGlsp4CkWtlIg56UJW600Zfbw1ltDc25jaot4NbZKi7jYDwKBQLD10ON70WO7cEqjVb/DOeYoemw3enzvOq1wfRCOxi2K5/mrMqtRlJ8ItgrdRw7S9q3v0q9KNCEhayq+44Lv40uQC2hsc8v3q4Va4sXvv+8oxt7XsHovIfV0zXJPucPjGIf3TkS1pxV7RKPcGB+fcBfi+1iuS6ZUYntDA6O53IqXfExtL8aW6b+RI2/aqEG4+M4If/d/9/HkRx4UP0KsA9aFa9iX+spOxinHWwYeupXkr3e1MCr7NKSzBGLRdY+51zPr1Wy6Xu4p3/ew0pfwrDSyHkeP791Sw81Xk6lClWzMPq6rIVQNXC8RD4awbZ+A3YGnmPiSh+TLyK6BDcSDYQaulzh8uPo2RIt4GbEfBAKBYOshSTKJvR9l9OxnsPM3UI3mclzaKeGYoyh6nMTeJ7bcZyUhNG5BTp26OdE+bZrl9un9+1t46qnlt0+DKD8RbA0UReHR40f52muvM6bKRBwPXZGxJMipMkHX4wPH70NRlJq3WUuBReljjzH6zEmsvn7GdnVRigQI5Eo0Xe1HjUeJ/9JjE1Ht/mSKgXS52MN0HEqOg6rI5fSbJKHKMiXbwXTcFS/5mCpyqo7K+Quj2LaHYajIJighSIczPP30i8LxvA54qSy+ZSMFZscvu9NFfvzyEKeaQiTbmsjlcnURc69n1qPZdD1EqeLIGVKXvoSVuVqeR6QY6LFdJPZ+lGDL6giqW4n1EKpCAQ1VkSk5FooqI7vTi41sxyWg6oQC2pzbEC3iZcR+EAgEgq1JsOUYzUd+a/IzkjmKpBgYDQdJ7H1iS35GEkLjFuPUqZs8/fSLjI8X6eiYnKN49uyA+MIvECySg+99kJ8DXnztBwwpEjlJQvV9trnwgeP3cfC9Dy56mzMLLKYWt9wcT9J1/B7S/++Plh9T9nHuPGbb4W4ePX6UbScOTWyrYJnYrkdcVSmaFr7vI0uTwqcsSTieh+t5hHR9RUs+KiJnNBjgwvlRfMUnHNKg0gVl+SS6NG4reU6ePM0DD2wTzuc1RE5EkXStHLcPzy4L2TGYpPPiTdyf/XHsba1brihnKa7/tW42XWtRqjhyhtGzn8E10+VmxUALvlvCTL7N6NnP0Hzkt7bkB+mVZC6hyi7lKWQ8jJDCPe9uWVGhat+BJl7Rx8mXfBTFh6mvcd/HdXy0kMK+A03zbke0iJdZzH7wPV8UIQkEAsEmIdhyjEDzEZH6uIMQGrcQnufz+79/mvHxIrt3TzZDRyI6u3Y1cvXquPjCPwXf8xkdMBm5ZQF++Qtbh7FhPwT6nod14RpeKouciKLv21lTSclWZ2pbczWx5eB7H2Tfe05w/ew5cqkMkUSM7iMHF+VknEmlwOLy0DB//eZbDKRT2K6HpshEjADpYhH3rk5iPmiuj61IDEvwfC6JPjQ84TgL6QaaImM5DoosI0kSnu+h3HnD83y/3FYty8su+Zi5n/JmibxpMZrJ44ddwlEF8PEd8PISvg3I0NoZ5u23Rzh3blg4odcQfd9OtL0988bwA4f30nr0ni13nVht1/9KsZbuKd/3SF36Eq6ZRot0T5wvkhpGC4ew8zdIXfoygeYjW/YD9UoxVaga609jF9JIFIiE+tnZeQppQKEYXTkHaXOHwfa7wrgXPcZzeQKGiqxIeK5PyXRojITZvitMc8fC7w2iRbxMLfth4FpxQox03bIY2dCuc3gLibJrgRj1IBAI1hJJkjEStc3n3+wIoXELce7cMOfPj9DREa06OL69PSq+8N9h4FqR7//tOMM3SjhOeairqsm0bje4/39q3HAfAkune0l94XnsS33luKSuoe3tIfGxxwhMccAJpjO1rbki9FVraFYUhbvum2N41TIeuzLfMBYKEldVTNvmndERXM9nR2MDwUA54qYCAd9nJJudNmexUjBzY3yMlkiUgKpStG1kVQbfx/E8grqGoSqM5nJ0NzUtqeSj2n7SZIV0sYjv+Xiuj+xLSBJIKigxHzcPeKBJCqbpkkyWVnT/CeZHkmUSd2L4Tl8/SmtjubW8ZOIOjyPPiOFvFWpx/R8/3r7ey5xgrVxkVvoSVuZq2clY5fODajRjZa5gpS+JD9grQMfOIPHw21z77hco5T0CEYN4Ig1eCTM5uqIOUkmW+JGf7eBvvuKjDSmkcgVKpo2mqbQ1xmhpC/IjP9NRs1goWsTLzLcfBq4VeeWFUUpFj3BUQdHKIxBGbpm88sIo7/5g84b7nFmPiFEPAoFAsH5srW8QW5xksoRpugSD1fXlYFAVX/gpfwD81n8fZuCdIp5bHqivaRKu4zHwTolv/dkwA9eK673Mmimd7i3P9HvzEnI8irq9Azkexeq9xOgzJymd7l3vJdYFvudhvn2V4ql/wnz7KpcGByfamoO6TnM0QlDXV7WhucK0EpdYlICm3XFRVv4HY/k8TCk2kyRp2pxFmCyYCekGI7kssWAAWZIo2TbmHZdjLBBgNJdbcsnH1Fbrif2k6fSnUriedyeGJ5XX6gNu+T+VMDgZidxQ2THW0BCY/4EEK07gxCGaP/0k+qG9eOkczs1BvHQO4/Bemj/95Jb7EWKm6z8S0VEUecL1n0wWOXnyNJ5XvVF2vejYGeTRD7fx/g+38d5/1sr7P9zGBz7ctqJChWel73xRr/46ldQAvmviWekVe8ytjO97pC9/ibB+kfbtFg2NOWRZQVbDaOEduFaG1KUv4/srU/TTsTPIjz3eyb0nmtizq4m772pgz84wh96l86M/3yFErxXE93zefDlNqegRb1LRDBlZltAMmXiTSqno0ftyGr/OrjMbjcqoB3P8HIoWRQtvQ9GiE6MeiiNn1nuJAoFAsKkRjsYtRENDAMNQKBYdIpHZM5uKRWfLf+H3PZ83vpsim3KQZAlVY8K9oSsSlumRTTq8+d007d2Buo8D+Z5H6gvP46WyqDsn45FSOIjU04XT10/6i89j3HdwyzmXpjLT8enrGn/94/eR62yirbV5Yr8FNA1DVWc5B1eaqSUuU91Djufieh6yJFG0bIq2TVCfHNCvK+qsOYszC2ZCuk7RskCCoFb+26WWfMwURKfFbwFFlvF9H1WXsC0PVZIntFJJAmtQZnAwxdGjHRw8KApG1oPAiUO03XdQjFWgdtf/hQsj67TCuVltF5msx5EUA98tIanhWbf7TglJMZD1+KqtYSuxHg7Siovyxut/RSE5hqqkiMXGkQbuWtGo9lZnbNAiOWgRjipVj20oqjA+aDE2aAln6BIRox4EAoFg/RFC4xbi4MFW9u9v4ezZAXbtapw1k2twMLvlv/CPDVqM3jLBB0Vl1odAVZNxbY+RfnNDfAi0LlzDvtSH0tZU9QOt0tqIdbEP68I1jAO71mmV60vF8emlsuX9FDAY0CSG8AjdHMLXDaSG2MT9ZzoHV6KheSZTS1wq5E2TkWwWx3Un/u12KkV7PEbYKJ+Hc81ZnK9gZjklH3MJoq5Xdtnoqorjumi6hOfZ+PiABBa4Lty6kqWhIciTT54Qc2HXEUmWt+zrfyq1uP6HhlxSqZUpTNpI6PG96LFdmMm30cKhWZ8fHHMUo+EgenzvOq5y8zDhIA20VL1dUgP45uiKOkiLI2cY+6fPEHDTRNpakJQAvhsRZT8rjFn0yjMZterveaomUcz5mMWVcatuRcSoB4FAIFh/xM84WwhZlnjqqRM0NAS5enWcXM7CdT1yOYurV8fX/Qu/5/n09g7xne9cp7d3aF3iaWbRw3HuyCFVdkPl34p5h9Pfu71u66wVL5Utz2QMVBdEpYCBb9l4qewar6w+mOn4lMNBJEXGjIVxAzpKyca5OQj+9GOsKyq2661YQ/NMppa4QFlkvJ1KY9rOhCDoA6bjcDuVJm+a+L5PplikM5GoOmexUjBzd3s7Oxob6W5q5O72drY3NizZlVkRRHV1ujBTKZ6BctlMZ1OCrngCqaBQGHbJjTg4JY89O5tE072gbpjq+q9GxfWfSNT3D0yrgSTJJPZ+FEWPYedv4Nl5fN/Fs/PY+RsoepzE3ieEO2iFmOogrcZKO0hnOsBkNYwkrV5UeytjBGUUpTyTsRqOXZ6zagTFa2mpiFEPAoFAsP4IR+MW46GHtvPZzz460ag5dGc+2tGjHTz55Po1atZLy6cRlFFVCYmytjRTf7FtD9N0yaWKfOl/f4O8XazLNtIKciKKpGv4JRMpPHvGkl8ykXQNORFdh9WtP3M5PoO2i+r5uCEDJVfAyxWQo5NxweU2NC/E1BIXXSkXtbieh6GquL5P0bKQJQlNkXFsm8HRcSK6TjgaXtKcxaUyVRANaJMR7oCmEVBV8paFIsuoskJbc4C2piiZTInxYp6OaIKnf/39qIr4MiWoD2p1/e/b10Jf3/qtc63wPX9Ga+5Rmo/81mS5gjmKpBgYDQdJ7H1CuN1WkLV2kAoH2NzMfh0sr0W7qV2noV1n5JZJXJdmHdtC1qV1m0FT++wRR4LaEKMeBAKBYP0RQuMW5KGHtvPAA9s4d26YZLJEQ0OAgwdb183JWEvL51qJeE3tOs3bDPKZAq4DkuZPfAi0bI9S0cHzfDJmgVizilYMrss6a0XftxNtbw9W7yWknq5ZH2jd4XGMw3vR9+1cx1WuH3M5PlvzJq0Fk/5IgIaiCfZkXLniHFxqQ3MtVEpcvvba6wyk0xQtG1WW8fBxvbKDUPV9nEKpHIGWoOPGEO9P2Wxv3QGLnLW4VKYKooaqTp5fEjRFwuTHLSotMJ7nY7kOJcmiORHhQ8ffNUtk9Hx/It69nEi3QLAUKq7/p59+katXx2lvn3w/GhzcWjH/gWvFiSZr1y07rBradQ4/fID2h/5PrPQlPCuNrMfR43trcjKutGCzmak4SEfPfgY7fwPVaC67sJwSjjm64g7S9YhqbwTmfh0svdFdkiUO///Z+/Mouc77vhP+PHevqq6ll+oFjVVYCAoEJBAkRJGSTVmLLXssOUpiK7EoJfZJ/GakmTgZzpyXUd7I9hmZGUdnxpMRJ8l47FgmZctJLNuKHSuyFNGmRIogIVCEQIBogFgbQHdXd9ded3/ePwrd6KW60UtVV3Xjfs7ROSKqu+qpW/dW1/Ot7+/7fU+a7/5pjsKkTzypoukC36uLjFZc4fB70tG1sQ6iqIeIiIiI9hMJjfcoiiI4fHig3ctY1PI582FgpuXz4sUpnnnmBI88sn1DNndCEbzjvRmmbrgUJj18V6Bo9fEWtxaABDf0uFmdnNdGutHrXClCUcj84sfIfe4Z/MujqP099XFp2yEYn0JJJ0n/wsfuyeIHWNrxqQDvvjbNn+/NMp2K062AdVsoK9Zqa25oXg0zJS5f/f4p8tUaUkoURSFm6PRKBfXiNRwgMHUqMYMfuTzJ0Nkr5N64vCGNwWEoOXNmnD43w1V/moliiVQ8hqFquIFP1XXpTyZJx2KUHZuS7aArCoOxbh4e2Et3kEaGcnYzNTI2PltY4wUhuqowlM7wgftXX1ITEbEapAxnhbNjb0/zhX/1Ab74zKtLuv49z2v3klvKzUs1vvunOexaSCKpour1Mc+J6w7f/dMcj320j6E9q3O1tUKw2erEssc2zEEaOcAWs7LrYG3n7tCeGI99tG/2mqiV69dE/3aTw9E1sW42WqiPiIiIiFhMJDRGtJWVtnyeOTO+YcLo0J4Y7/u5fl7++hTjV218TyJD8IOQ6VqZK8Vb5O1y29e5Uqzjh+n71U/faVWemEYYOuaRA6R/4WMtF6Q6meUcnzvzVX78hTO88tghpjWVcrmMriprbmheC/sH+vk773qY//vbf4WhqsQMA0vXcE6PEHoBsbiFIxQsP6RL1dA2qEl8YdRB9y6dt703Tv/uEMNS5x2nmRKaa1dKXDvtEI4ZXPpByFV1bFZoKMdLfOWVV6k6Dql4jLSm4fo+V6cm+corr/Lxhx+KxMaIllCbOHlHyAkchGqyJ7WX//c3P8nFsfd0hOt/I5Gh5PXvFLBrIeneOy5lxRSkDUFh0uf0dwoM7rJW7LhqpWCz1Yllj2H1HV2Tg3Q1RA6w+bTiOljI0J4Yg7usyOXbIjZSqI+IiIiIWEwkNEa0lZW2fE5PNw5EbxVDe2J89Je2kbvpMHHd5fybk3zhi98l1aehNsiUa9c6V4p1/DADDx3CPXeJMF9CySQxDu65Z52MM9zN8bk7neTBH3kvk/t3tG2cd3t3N7t6e7k6NUlG1wjLVWS5ijB1JFA2VLaVbPorzoY0iS8VdfDqs3kG3ubwP/yThzn+4I55x0krWNz4qzJezVgkNHznTye4vv8iVcchm7rzhYOl65iaxkSpxLfOnWNvfzYao45oKrWJk+ROfZ7AKdSz6awsMrBxpt9g8rVfZ9/RzxI7fG9tRidvuUzfckkk1YZf/sWTKlO3XCZvufRtu3tG7UYINks97lYRcIRQWp6LGDnA5tPs62AphCLW9fsRy7NRQn1ERERExGIioTGircxt+ezqWhx8PdPy2d3duDmulQhFkB22yA5bBPEaFa+GXot13DpXilCUlghPm52VOD7jbVzf3LzGiVKJLjdAIgl0jbKpEfND3n19mpmPzcIykRPTLWkSX0nUwVf//Qh/48cO32nHvovQMFrIc3V8mv6BeMMNXSoW40Y+z+h0nh093U1/Tq0myp3sTBa27M6ce0JLoCfieOUrTJ35It33/0NUs/ue2Zw6tbA+2qw3Pkc1XVArS5za8u3DM0Lf+DWbiWs28RYLNnOJxrTXRuQAu0OzroOI9rMRQn1ERERExGIioTGiray05fPQofaOTW6WdUasjU53fM7kNX7z7DlujI1jJ+NoCLaVbN59fZpdhdrsz7aySXwtUQd3c4aoiRDXDyBofKwNVaMY2FRdp+nPp9VEuZOdy3Itu6FXJHALeLdewi1cQDFSGKm9ZA58alOLLStx+ZkxBVWtO44Vc7HI4nt14c6MLf3eOFfos2sh1ZKPa0tSvWDF1Xk/22zBJhrTXh+RA6xOM66DiIiIiIiIe5lIaIxoK5ul5XOzrDNiPqtxk3W643P/QD97+7Ncn5ri+tO/hX72EkPpNOoGNomvJergbs4QSzMQUuB6PlZMXXS7G/joqkLcaM14WatGLEfGxqPcyQ5mqZbdwC3gFkYIA68uhJu9KJqFM/0GuVOfp+/oZ9EyR9q06rWzUpdf76BB96DBxHWHtCEWfalWLQX0bzfpHTQaXju3rtjzhD7dFNhlH9cOmRrz6BmYLzY2U7Bp15h2qwllyEhuhIJdIG2l2d+3H6WVwp8UlKp77ryuKQGb53A1hdVcBxERERERERGLiYTGiLbz6KM7+MIXPjRbLtGo5bMT2CzrjKizFd1kihDs7O2l/2d/itznniG8PIrYwCbxtUQd3M0ZEnfjJNUEZb9GUhqLNnTFWo1dvb0Md2ea/nzGrticeanS9BHLUEq+efZclDvZwTRq2ZWAV7mODH2EaoL0UVQTZWacunKV/Pnfo/fh39jw9a5HEF+Ny08ogiPvSfPdP81RmPSJJ1U0XeB7dXHFiiscfk+aW1fsxcLlgE6tEi4S+syYil0LCAMoTfmYMQUhRNMFm43K1dtITo6e5NlTz3Jh8gJu4GKoBvt69/HE0Sc4Ntx8d200dl5npdfBZhKsIyIiIiIiNpJIaIzoCB59dAePPLKdM2fGO7rlc7Os815nq7vJ2tUkvpYIgbs5Q2qlkAeGd/NW/C0mSiVSsRiGquEGPsVajbhh8v6DB1siyH3vzyexq+Ku4stqRZ7R6Tw3C3lS8VjLcie3UtlFO2jUshv6ZUK/AooO0kXRkrMipBACzezDLV7AK17Y0LWuR/xZi8tvaE+Mxz7aN/uYtXL9Mfu3mxx+TxqgoXB564pDrRSQ6dfnnffJHh1/TOL7IY4d4tRCFFWsSbBZ7rzfarl6J0dP8vTzT1OwC2QTWSzNwvZtzoyf4ennn+apx59qqtgYjZ3P527Xwb10LCIiIiIiIlZLJDTeo4Sh7DixTFHEbK5bJ7NZ1tluZBi2JfPwXnGTtSNXcrkIgbGbRQ4nHH758STeubdm17ISZ8hj793N8XjvrAO1GNjoqsKu3l7ef7D5DlQZSgBsOyTday4rvjR0bt1F5Km6Dl4QktYa/4ldb+7kZnUddZI42qhlV4Zu3c1IgFB09K7t8wQzoVlIJ0foFjdsnesVf9bq8hvaE2Nwl7Xo9QL4xpfHGgqX8aRKpeBTLQUkUncez4ordA8YFCfrmY3lQj0mYbWCzd3O+62UqxfKkGdPPUvBLrArc6esKGEkiOtxrhSu8Nyp5zi67WhTxqi36tj5elnqOriXjkFERERERMRaiITGe5AXX7w2O/7rOPXx3/vvz/KZz0Tjv/cyzRQG7ROn77jtXA9h6OgHdpP5xda57WbYCDdZp9COXMlGEQJHmOJ/1C9zv+oQ/93XGP/9+a/3QmdIddJFIaC3V+UdH952W2iIsbc/uyENzVPjHgCJruXFlzdPlvjhdwurFnnihomuKri+j6Xri25fT+7kQuEpDCWeK7l12aY46fGeDnUddaI4urBlN3RLICWKnkBP7kY10vN+Xvo2QjVRjBQw3vL1NUP8WY/LTyhi0Yhx7oazpHCpagJFAc8O8RyJYd253YorKKpBtRTw4I9107/DbPr49+Aua8vk6o3kRrgweYFsYnFZkRCCbDzLyOQII7kR7svet+7H24pj582i0XXQLMIgYOzCCLVShVgywcC+/Sjq4qziiIiIiIiIzUYkNN5jvPjiNZ588htMTdUYGrrjRjp16iZPPvkNvvCFD0Vi4z1IM4VB+8Tpen5gvoQ60DubH+iePk/uc8/Q96ufbqnY2Go3WcT8CIHKS6/T8+WvYLn2sq/30J4YmfELXP3uf6N6fRLdLpMKplFO78a+fZ4pQmyI+OveFlWU5cSXkuTcK6U1iTzD3RmG0hmuTk1iatoiwWOtuZNzhSczppCf8HCdECRIJHYl4MTXp/jIL23rKMdNJ49kzm3ZDZw8U2/8W7zyNRQ9Ne/npJT4Tg6z+xB6ah/Q+vHpZog/zXb5LSdcGqaCYSnY1ZAgCIE79zlX6Lv/4eSqzs/VCK5bJVevYBdwAxdLsxrebmkWuWqOgl1oyuNttbHzzcDlU6/x2rdHKeYThIGKopZIZS7yzvcNs/voO9u9vIiIiIiIiHXR+fMjEU0jDCVf/OIJpqZq7NvXQ1eXgaoqdHUZ7N3bw/R0jWeeOUF4e6ww4t5gRhh0Xz+Pkk6i7RhCSSdnhSL7xOkV35cMQ/K//VXCfAltzzBKIoZQFZREDG33MGGhROF3vooMW7dZmesma0SrW4zvFRRF8MChLLtPvojl2nd9ve0Tp5n8lWeI/eAE/Vqenj4VdY3n2Xoxbosqodf4vc73JBIo570ViTwLUYTgA/cfJG6YTJRK2J5HGEpsz2OiVFpR7qQMJbkbDqMXa+RuOLNjx9O3XDRdkB/3cOwQVRWoukDTFMJQcvOSzZsnS2s/OE1iZv3XL1R55RvT2NW6SKSbCooi0E2FdK+GXQs5/Z3C7Dh7OxBCwcwcJD7wCL0P/A9oZhqvcpXQqyBlQOhV8CpXUY00mQOfRLSy8XcOKxF/gmB58WcmI7VSCpBy/jGeEf96Bo0Vu/zmCpeNiCc1FEVQLYW4TkgYSlwnpDDpr1noW43gOuOezm43cWshpWkftxbSv93ksY+0RtBudK2ul7SVxlANbN9ueLvt2xiqQdpKN7x9tcx9XSVQdivk7Txlt4Jkc42dbwYun3qN735tkunJFLrukugqo+su05Mpvvu1SS6feq3dS4yIiIiIiFgXkaPxHuLMmXHOnp1gaCjZ8MP64GCSN96Y4MyZ8SiD8B5hoTA4c16IRAyxexj/8iiF3/kq5kOHVjRG7Z67hHf+ct3Z1uAcU/t7cN+8jHvuUstGflvlJotYzEpfb+eNi009z9ZLT399nLlSDkh3Lz5HqqWAroxGOe+t2eGzf6Cfjz/80JpyJ5caMx7aYxH4EqcWEAQS3ZizNgG6IXDtuhPzvmOrc401k7nrd+2QStHHMBWcmooVn5N52IEjmQvHqaWTQ6gmZvchMgc+SSx7DM/zNmQtzXAjNrs9927lTr4vGdpjYSYU8mNeUwo0Vuu228hcvVZFAuzv28++3n2cGT9DXI8vOs4T1Qke6H+A/X37m/E0Zl/Xa5cLTIqrVLwKoZQoQpDQE/TKnezcnd4UY+edThgEvPbtUVw3RVdXmZmXVtcDNK1MudzFD749ys4jh6Mx6oiIiIiITUskNN5DTE/bOE5ALNb4ZY/FNMbGAqanG3+DHrH1aLYwGOZL9dFrq7FgICwTOTFNmG+d42rGTfaVV17d8Bbje42Vvt7u6yNtF6DnPeZtwcGylCXFl4MPJXnt+fy6RJ79A/2rzp1cbsx46paL54W4doimL35cKUFRBeW81zbhbuH6FQ2qJYHrhkyPuXQPGFjxO2vvxJHMuePUoVtAMdIY6QMb5mSc4W6i3kozB5vZnrsS4fL4T/Q0Vehbi+Dayly9GVoZCaAIhSeOPsHTzz/NlcIVsvE7rdMT1QnSVppPHP1EU4pgoH68zPtuMfbmJIpnYZgg1AAZqIRlizHtOvvuqyGUwaY83r3M2IURivkEllVj4Z8CIcCyahTyCcYujDB038H2LDIiIiIiImKdRELjFmZhs3Q6bWKaKrWaT1fX4o1JreZjmird3Y0zgSK2Hs0WBpVMEmHoSNtBJBZvsKTtIAwdJZNc17rvxmrcZO1qx94KrPT1RtB2AboRj/xUL2deqjQUXwZ3WVw5V123yLOa3Mm7ZtHlfAIPggBUXSKYv6bAB8NSQNAW4a7R+qUEoVAXinxJacrDit85Dzp1JHNmnLqta2iiG7GZLr+VCpfNEvqaJbg2k41oaT42fIynHn+KZ089y4XJC+SqOQzV4IH+B/jE0U9wbPhY055PKEP+ZPx3uTFQ43DxJ7FqfQhfRSoBtVSOHyb/C7nxOD8ijzRN3LxXqZUqhIGKFgsa3q5pAY6tUitVNnhlERERERERzSMSGrcojZqlDx7sI5tNcO1agb17exZ9WL91q8SDDw5x6NDSI30RW4tmC4PGwT3oB3bjnj6P2D286BwLxqcwjxzAOLinac9hKVbiJmtnO/ZWYMWv9wP7KaW24dODqUIqmGbu1nsjBOiZjEOnFqIadRFuYJfF8Nu6lhRfNrpY4q5ZdCkV3w9RFAg8QJcIUXcyBn7dzRhPqiBpi3DXaP26Kepj03aIooLr1LP7DFPZdE3A7aDZbsRmiX8bOZ7c7PHvZrBRLc3Hho9xdNtRRnIjFOwCaSvN/r79TRf7ZlquU9kUV7f9GVa1D9W3CDQbO55D8yqMTI42reX6XiaWTKCoJXxfRdcXi42+r6KoAbFkog2r23rIUDJ502H8ugMIstsN+obMTVEKFREREbGZiYTGLchSzdKvvXYLTVPQNIWLF6cYHLxz261bJbq7Y3z608dRoj++9wzNFgaFopD5xY+R+9wz+JdHUft7ZluIg/EplHSS9C98bMMcg8u5ydrdjr0VWMnrbf/03+K1U12M7/9bBG6AqgmSfp59tTP0+uMbIkAvylHTQ2L7YOyKzfZ9ySWFgGaKPCthJVl0uq4QG1TJT3iEASABAaal0NWt4tRk24S7RusXQpDs0fDGPMJAgpQEnsQl3HRNwO1iI0W91bAR48kzbPS1eDc2sqVZEUrLxb15LdcC7ERu3u3Nbrm+lxnYt59U5iLTkyk0rTxvfFpKsO0YPb1FBvY1J3/zXubmpRovf32K8WsOvle/FjVN0L/T4l0/0bPh7xsRERER9xKR0LjFWNgsPSMczTRLX7w4xY4daXp7Y5w7l2NsrO52fPDBIT796eM8+uiONj+DiI2kFcKgdfwwfb/66TtOwYlphKFjHjlA+hc6wynY7BKce5nlXm/7p/8Wr4yksGsusaE04VtXCFzIx3p4vetdPJD7azLXzrRUgG6Yo3a7kfx7fz7JYx/Rlt1sbKTIs6IsOk3wjh/JcPo7earlEMNS0A2BUKBWDtsq3C21fiuu0jMA+QkPz5HUygGGpbRNJNqMbKSo16l0kuDajKKeTmJuy3XCWOyka3bL9b2Moqq8833DfPdrk5TLXVhWDU0L8H0V245hGC7veN9wVASzTm5eqvHt/zBOIeeDkOi6QAKhDzffqvHtPxznfT/XH/39iYiIiGgRkdC4xVhJs/TERIV/9a8+iKKI2fzGQ4f6IyfjPUorhEHr+GEGHjrUsdmHndCOvZVo9HrrB3bzl38wgV1zbmeYJQm0XfjXbqKWJ6kaGS5ab+fdRzwyLRKgl8pRE6ZCCNh2uKIctY0SeVaaRXffsSTpPn3W3eXUwra6u1ayfjOmYMYVBnaaPPi+bsyE2hGuvIjNRacIrp2YG7keNrrlei3Mjb/oFFfvWtl99J3Aa7z27VGK+QSOXR+X7ukt8o73Dd++PWKtyFDy+gt5StN1kdEw73z2VBSJ7wlKeZ/XX8ivK0c1IiIiImJpIqFxi7HSZulCweFHfmTXsve1sEwmEiO3Lq0QBoWiNBTpOmGz0KgEJwTGEyY1XcWKafS0oZxkM7Pw9c7dcBZlmKmZJGomSViuotR87DCL9vd/Amu4NQVUy+WoAcS7mpOj1ixWk0XXSe6ula4/llB56IPRuFrE5qcTcyPXw0a3XK+WRfEXqqB70ODIJnZE7z76TnYeOczYhRFqpQqxZIKBffsjJ2MTmLzlMjHqIkOJrs8/Z4UQqFq9PG3iutMxf/8jIiIithqR0LjF6O62mtIs3ahM5v77s3zmM9F49VZlKWGwmXTKZmFhCc6VdIyXdnQzHjfxFQXN9+kbSvNhVfL2DVvV1mK5DDOlK44RlzjTPo4tm/7YoZSMTue5crNEXpQY0huP+6m6IAiak6PWLFaTRdcp7q65dFqWXkRnE8qw5UUnrWKrnesb2XK9GhrGX3iSiesO3/3THI99tG/THesZFFVl6L72tttvRZxaiO/d/mzRQOuf+d7R9zvr739ERETEViISGrcYhw71c//9WU6durnmZumlymROnbrJk09+gy984UOR2BixajppszC3BOfqO/bxF/sHqOoqScdDD3xc1+PWYDd/NHEDfWyY/QNbq4l9I1yl7cowGxkb55tnz3GzkMd2AmrpkGsywX520kdm3s8GHZqj1oluxdXQCevvBOf0ZqfVIuDJ0ZOzopYbuBiqwb7efTxx9Im2iVqrpRPO9WayUS3XK2Wp+AvFFKQNQWHSX1H8RcS9hRlT0Ga+5LxdmDYXeVuD1LTO+/sfERERsVWIhMYthqIIPvOZ4zz55DfW1Cy9kjKZZ545wSOPbI/GqCNWTKdtFmZKcMY/9wwv9sSoqoLeigNhiHQ8TF2lK9vLpOvyrXPn2NufRWkwersZ2ShXaTsyzEbGxvnKK69SdRxS8RjpmMZYpcq0V+R15TxHwgPzxMZqOaB/W7wjc9Q60a24Gtq5/k5xTm9mWi0Cnhw9ydPPP03BLpBN3BnTPTN+hqeff5qnHn9q04iNm/1aXchGtFyvlOXiL4QQxJOdFX8R0Rn0Dhpkhw0qBR/PC+dlNEpZH5sWArKbKEc1IiIiYrMRfY2zBXn00R184Qsf4ujRIfL5Gpcv58nnazz44NBd3YgrKZN5440JzpwZb/q6ZSjJ3XAYvVgjd8NBhs0fqYxoD6vZLGwU1vHDeE/9ArmhHpJlG2k74PmoqQTGgd2oPWlSsRg38nlGp/Mbtq5WMuMqnbjuYMYUkt0aZkyZdZXevFRr2mPNZJhZMYXCpI/rhIShxHVCCpN+0zPMQin55tlzVB2HbCqJpesoiqCn1yJBDDtwGZFXCcIQz6mPSlmxzZWjFnF3NvIc36rMiIBnxs6QMlNsT20nZaZmRcCToyfXdf+hDHn21LMU7AK7MrtIGAlURSVhJNiV3kXBKfDcqecIZTTSeK+zXAQHgNaB8RcR7UcogiPvzZDs1kAKXKd+HgWBxHfr+41kt8aR92aiv/8RERERLSJyNG5RHn10B488sn3VZS4rLZOZnrabut7IgbI5Wel44ko2C7Xyxm8W/AM7ITdKfJuC4oegqyhd8dkAH0PVKAY2VdfZ0HW1gna4Sjcyw2x0Os/NQp5UPDa/7Tiu0jtowqSk6Fe4WSyQEXFiwCM/2Ru9v2whOs05vRlZKALOHMOEkSCux7lSuMJzp57j6Lajax6nHcmNcGHyAtlEtuEXT9l4lpHJEUZyIx3jrItoD+2K4GgGUoa4hfOEbgHFSGOkDyA2Sf7oVmBoT4z3/Ww/L399ivFrDr5X/3ypa4L+nTGO/0RUTBYRERHRSiKhcQujKILDhwdW9TvNKpNZDZ2U3RexclYjDi/cLITlKtLzEbqG0hVv22YhbpjoqopvGFi6vuh2N/DRVYW4sflHsto1grZRGWZV18ELQtLa4j9rZlxl0Iozli/xwINJ9vX38vL3YWBXa9qutwKbMeMwGrNcPxshAhbsAm7gYmmNrz9Ls8hVcxTswpruvxVsxuthK9COCI5mUJs4Sf78l3CLF5GBg1BNjNReMgc+RSy7OSIBtgJDe2J89Je2MXnTYfy6Awiy2w36hszo+o2IiIhoMZHQGDGPZpTJrIbIgbI5Wa04PLNZGB8pEsvfQJZrEIagKIiuGLXMNgb2pzZ8szDcnWEoneHq1CSmpi0634u1Grt6exnuzmzoulpBO12lczPMWrVhr4vGCq7vLykaW6bKrj1JepOdtSntNDarw7xTndOtohXXUr5WwKz00efvQnoudjw3r0ihGSJg2kpjqAa2b5MwEotut30bQzVIW43b4jeazXo9tIqNFF1nIji++6c5CpM+8aSKpgt8ry4yNjuCoxnUJk6SO/V5AqeAFssirCwysHGm3yB36vP0Hf1sJDZuIEIR9A1b9A1HXyxGREREbCSR0Bgxj/WWyayWyIGy+ViLOCwUwcGeSXJjOcqYWAaoIiSQCrZjoo/d5L53eQhlcEOfiyIEH7j/IF955VUmSiVSsRiGquEGPsVajbhh8v6DB7dEEUwnuEpbuWFfjWgc+P56n8qWZTM7zDfzmOVqacW1dPNSjavfzHDs2t9Dw0CoEjueI7ftFNXUKNAcEXB/33729e7jzPgZ4np80bU6UZ3ggf4H2N+3f82P0Sw28/XQCtohum5kBMd6kTIkf/5LBE4BvetO9IDQEuiJOF7lKvnzv4fVdzQao46IiIiI2NJEf+UiFrGeMpnVEgV9bz7WUuwiwxDza/+JQ2/9BRlRwNMsqloST7PIiDyHLv0F1n/+T8hw41/n/QP9fPzhh9jZ00vNdcmVy9Rcl129vXz84YfYP9Ac9267mXGVlser2KfP45wewX3jIs7pEezT56mMV+kZNFrmKl1pScdaS6FmROO4YTJRKmF7HmEosT2PiVJpS4nGrWLhlwi6qaAoAt1USPdq2LWQ098pdGxR18w5XikFSDl/jTNjlq08xzeKVhTezNynM2GiWVDUxvBVm1h5gG1vvY94cXhWBNzfu39dIqAiFJ44+gRpM82VwhUqboUgDKi4Fa4UrpC20nzi6CfWnAHZLDb79dBs2lm0NLQnxod+foAP/PwAj//tfj7w8wN88OcHOkpkBHAL53GLF+tOxgafjzSzD7d4Abdwvk0rjIiIiIiI2BgiR2NEQ9ZaJrNa7iUHylZhLeOJ7rlLeOcvk00n6S99m6LajauYGKFDKphGxqu4b47hnruE+fa961qfDEPcc5cI8yWUTBLj4B6Esvz5s3+gn739WUan81Rdh7hhMtyd2VKiVDtdpSt1wUopOf3d4prdMjOi8TfPnuNmIU8xsNFVhV29vbz/4MEtIxq3is3uMN+MY5arpRVxI/Pus09DOANUJ0tUwgKGYRNze8lcP8wbQy+SjjVHBDw2fIynHn+KZ089y4XJC+SqOQzV4IH+B/jE0U9wbLh1o6UrLenY7NdDM+mEmJu5ERydSugW6pmMVrbh7UKzkE6O0O2c/NGIiIiIiIhWEAmNEUuyljKZ1bJZg77vZVYqDhuWIHfDqQuOVyqEro9qmQggHUxDMOeXLBM5MU2YL61rbfaJ0+R/+6t45y8jXQ9h6OgHdpP5xY9hHT+87O8qQrCjp3tdj9/J3HGV1ri893FKWgZHqCgyIONPs/utv8L6zzHkh++/qzC7WlayYR+76jB5c4IgYF0jiveCaNwqtkLG4WYas1wLrRC/Ft5n2kqzv3cf14vXqbgVymIKs9LLg4n38rPv/cmmiYDHho9xdNtRRnIjFOxC/XH79rfUybiako6tcD2EMmzK8e0E0bVZz6WVKEYaoZrIwEZoi/NHpW8jVBPF6Iz80YiIiIiIiFYRCY0RbWXGgfKdP80xfsNB6CGmqRCzjC3jQNlqrEQcTmY0vv+taabHPIJAovhxrP1/k721N8iy+Jt8aTsIQ0fJJNe8LvvEaXKfe4YwX0Id6EVYJtJ2cE+fJ/e5Z+j71U/fVWzcymykq3QhK9mwV8s+pqXQO2Ss2y2z1UXjZjNT7lDIeUjJpneYb1TTeTtohfjV6D7TVpqUlabiVvB8D7+k88mH/inbhxeLJ+tBEcqa26tXy91KOnrf+Vmq/gOz54xhiU09cXFy9OSsY9QNXAzVYF/vPp44+sSqxeJ2i67NfC6txEgfwEjtxZl+Az2xOH/Ud3KY3Ycw0gfauMp7j5W6mCMiIiIimkckNEa0nUs3c3zz1A+JeUnSsTiaqqAbKjv2JnjsI/2b3oGy1bjbeKKiQKXoU8rPdaapFFM7OK2lOOKdos8fn70/KSXB+BTmkQMYB/esaU0yDMn/9lcJ8yW0PcN3AtgTMcTuYfzLoxR+56uYDx1a0q23kU2a7SDMl+ouzxa7ShtxNxdsrRIQ+pJ4VzSiuJBWn5dzyx18P6RaDKgUfHoGDWIJ9c46NpnDfDOMWa6FVsSNLHWfAugyErgyxDVDrPjm/ch4t5KOsVGNk8/dwg6yd2IbBnSsLpVy3t90ExcnR0/y9PNPU7ALZBNZLM3C9m3OjJ/h6eef5qnHn1qVQNfOmJtmP5dWIoRC5sCnyJ36PF7lKprZVx+X9m18J4dqpMkc+GQkcm0gq3ExR0REREQ0j837qTFiS/Dii9d48slvMDVVY2goSX86SegLbowVkN8P2POuDzG0p3nlM/caa8krXAlLjSdmhw1qlbC+MVuY47S9i/xlnwvOHrorl1FuOw6D8SmUdJL0L3xszWubceupA70NhSq1vwf3zctLuvXa0aS50SiZJMLQ6+7RxOLn1AxX6VLczQVbK4UoqsBMNH79N8OIYito9Xm5sFE3rqtousL0mEtu1KW7XyeeUrdUxuFmpxVxI/dChMlyJR1T09s5e/FxXNck1W8TtxL12IZRF0UBRbCpMj9DGfLsqWcp2AV2Ze6IqgkjQVyPc6VwhedOPcfRbUdXPHrcrnOkFc+l1cSyx+g7+tk74paTQ6gmZvchMgc+GYlbG8jdXMx9Rz8bvR4RERERLSISGiPaRhhKvvjFE0xN1di3rwchBNXAAQHdAwYXL07xzDMneOSR7U0vobkXWE9e4UpoNJ6IlHzz98cb5jhp3Um6fCjfEhRyMZITNxCGjnnkAOlfWN+a5rr1GiGWcestFFvWmg3Y6RgH96Af2I17+jxi9/CijeJ6XaXLcTcXrBlXUDUIfVDVxb+/WrdMqwT2jaTV5+VS5Q5daQ1Vg6lbHqVpn8CXqNrWyTjc7Ky28GauI9awBAJwbDnPHXsvlOgsVdIhJVx86xieHyceu4GmdaMoYl5sQzKjYSYU8mNeWzI/V+tqHsmNcGHyAtlE4+bjbDzLyOQII7mRFY+tt+scmfdcgL6wjIWHjU5OJNb0XDaCWPYYVt/RaFy3jdzNxexVrpI//3tYfUej1yUiIiKiBURC4z1GJ42Hnjs3wdmzEwwNJRt+GB4cTPLGGxOcOTPe8lKarcZG5RUuHE8cvVhbNsfJ6O3CERaJj/5DsrFy00Sgtbr1OqFJc6MQikLmFz9G7nPP4F8eRe3vmT0vmuEqvRvLlXQ88Gia0y8WmuKWWa3A3knviXPX1Orzcrlyh1hCo29YUCsFHP2xDP07rI44LhF1Vlp4M9cR69SCuiNYCExLwYwpdA/o7Lo/QbJHx4wpPPrTfZx+cWuW6CxV0lEsZSmVejGNIopQEMqdj8UzsQ21csBjH+kFITb8fWItruaCXcANXCzNani7pVnkqjkK9uqaj9tRtDTzXPYqDg95b9ErK6iEBChMigSvKsO8HLirfi4bgRAKZuZgu5dxz7Kci1kIgWb24RYv4BbOR69TRERERAuIhMZ7iE4bD83nHRwnIBZrfBrGYhpjYwHT0/YGr2xz04y8wrWyohwnTdB1/w5iTcxPW6tbrxOaNDcS6/hh+n7103eEuInpprlKV8JyJR1CYd1umdUK7GNXbM68VOmY98QZNuK8vFu5g24o2EpIus/YEuf+VuNuhTdzHbGaXhfIwgAkIU5Nompw+Y0ql35YJdalYMZUugcNDj+WwrDUjhLem8FSJR2eaxGEKoaoIPQuFK1r3u/NxjbYkuG9jYW7VrFWV3PaSmOoBrZvkzAWl/fYvo2hGqSt1Tcfb3TRUtpKc0Dzedw/T0KElDHwUdEIGJAlHvfPM61l1/RcIrY2S7mYZxCahXRyhG7nidQRERERW4FIaLxH6MTx0EzGxDRVajWfrq7FTqVazcc0Vbq7N/bD/WZnvXmF66FdOU5rdeu1u0mzHVjHDzPw0KG2jRYvVdKxXrfMagT2Gb7355PYVTH/PfGaw/P/aYJD706x7W2xtggtG3FetrPcIaI5LHUtzXXEpnpUJm94hCFoRt1l5TohxUkfoYJAEARgWIKJ6w4vfm2Sxz7ax/Deze1gXMhSJR2qmEahRhjGsBLbF/1eu66D9bia9/ftZ1/vPs6MnyGuL24+nqhO8ED/A+zv27+mtW1k0dK+3r182HLRXYdpNQUzAjEa01IlERb5sOWxr7e5n2UiNj9LuZhnkL6NUE0UIxKp20EnTpNEREQ0l0hovAfo1PHQgwez3H9/llOnbrJ3b8+iD8O3bpV48MEhDh3q37A1bQXWk1e4XtqZ9bUWt969KrYIRWm6yNwM1uOWWY3ALvbWC6ZsOyTda87+vBtIXDvAmQ556c8mSfVo9AyZG+5w3Ijz8l4oAFkpW23DM9cR67vgOiGqxuxrLEMIJZi6Akg8JwQE6V5tS8VFLKRRSUciliPVXaNU3Yeiz3cztvM6WI+rWREKTxx9gqeff5orhStk43eamieqE6StNJ84+omOKU9ZDr94gb2mzmXPpObXMFQTRSiEMsS93SC819TwixdQo/HXiDks5WKG+rXtOznM7kMY6QNtXOW9SadN2EVERLSGSGi8B+jU8VBFEXzmM8d58slvcPHiFIODSWIxjVrN59atEt3dMT796eNREcwqaWe7MLQnx2mG1br1IrGl81irW2Y1Anth3AMg0XXnPdGuhkyP3f7QqwmkBFUVbXF9b8R5eS8UgKyErbjhmeuIdWshyFkjGGFYL0ARUP/3utZIGEiEULZcXMRCGpZ0HNzJi1+b7KjrYL2u5mPDx3jq8ad49tSzXJi8QK6aw1ANHuh/gE8c/QTHhjdH027oFrBUlZ09+7leukHVrRBKiSIESTPJcHIbVlCMxl8jFrGUi1n6Nr6TQzXSZA58MiqC2WA6ccIuIiKiNURC4z1AJ4+HPvroDr7whQ/xxS+e4OzZCcbGAkxT5cEHh/j0p4/z6KM7Gv7eVnOgNJN2tgvPsFE5TqGUjE7nqboOccNkuDuDsgq3XiS2bE4aXf+rEdjd2+91ypz3xNKURxBIdEPUrxMPFFWQ7lU33OW1UedlO78U6AS26oZnniNWFSBui4sCkLf/B7P/jqif67A14yIWsrCkY1sGHvuo6KjroBmu5mPDxzi67SgjuREKdoG0lWZ/3/5N4WScYWb8NaUbHOo/RMWt4IUeuqKTMBJIr0JANP4a0ZhGLmahmpjdh8gc+CSx7OYQ3LcKnTphFxER0RoioXELEoaSM2fGmZ626e62GOxJd/R46KOP7uCRR7bPW/OhQ/1LOhm3ogOlmbS7XfjOOlqb43RxfIJvj1zgZiGPF4ToqsJQOsMH7j/I/oGVj9vf62LLZmOp6//wo0MrFtiN0RoAoSdRjfpoqeuEaNrt0dI54stSru9Wf9mxUeflRpc7dApbecMz1xGb6lExTAXHDhF6/cSWEhQVhJD4HpiWgn77s0G7Pw+0AinD+Q7G9IFFLqZOuw6a5WpWhMJ92fuauraVHM9msXD8tWtOuU00/hqxEhq6mFt4zkYsTadO2EVERLSGSGjcYrz44rVZd6Dj1N2B99+f5aceOkqlpM5+YJVAqeTgugGBrbD9bfG2jocqiuDw4YG7/txWdaA0m3a3C8MSbkPRvE3bH33/FGXXJRWPkdY0XN/n6tQkX3nlVT7+8EOrFhs7aZMZ0Zjlrv8X//MkD3/kb6Fe/b/uKrD39OsAVMoBqW6VvCxTtKoYik7cjRH4Yp74stDltVFfdmzUebmR5Q6dQis2PJ3itJ/riC1OBcSSKp4b4rv11mmhAAL8267dZE9daN2KcRG1iZN33Ey3M/2M1F4yBz61yM3USddBp7rtV3M8m0E0/hrRDBa6mCPaQydP2EVERDSfSGjcQrz44jWefPIbTE3VGBq6k3d46tRNyrmQn3nvgxQmwZM+167nsasBlm7gBT4nLr1Jct8DS44qdwJb2YHSCtbbLizDcM2/OzI2zjfPnmvoNtzbn12XABnK+txfzXXJppKz54Gl65iaxkSpxLfOnWNvf3ZV99tJm8yIxazk+n9zqpf3/sqnKfzO8gL7zPtDLVHmtD9K1ariDQYoUiHmxBgqDtOXvlMqM9fltdFfdkTnZWto9oan05z2Cx2xZkzBqYUIoaBa4NoSISDVo2FY9SbqrRYXUZs4Se7U5wmcAlosi7CyyMDGmX6D3KnP03f0sx09Otlpbvt2Hc9o/DUiYmtwrxYwRkTcq0RC4xYhDCVf/OIJpqZq7Nt3p8G5q8tg794eRi6O8fKF87z3yAGuvVkEqRI3VSqezfmxG7zx1g1Oj9zgC1/4UMeKjZHlfvWstV3YPnH6jhvS9RCGjn5gN5lfvLsbcmRsnK+88ipVx1nkNvzdF18iHYtRduw1jzvfzNdD35Mxq+F5kIrFuJHPMzqdZ0dP96qf+0Ja7cyMWBkrvf4r7z/AwL/5/61IJB8dukK1aKO5BqqnERBSjVUYTVyiDwOLzDyXV0+/zl/+wXj0ZccWoJkbnk512i90xBqWQACOLSlNeVw+WyE/5lGa9rdcXISUIfnzXyJwCuhdu2avVaEl0BNxvMpV8ud/D6vvaEe74TrFbb+a4ymh6bmQ0fhrRMTmJypgjIi4t4iExi3CmTPjnD07wdBQsuEmfHAwycs/uMK1iSkK4x5v292LLwPKbg0U2Lu3h4sXp3jmmRM88sj2jmx6jiz3G4N94jS5zz1DmC+hDvTOjp+6p8+T+9wz9P3qp5cUG0Mp+ebZc1QdZ5Hb0A8Crk5NM1kus7O3h3RcX9O4c9V1ANC1xm9fhqpRDOzZn1sPyzkzVzOaHbF+VnP9301gn3HF+vjsGe7GcyVONaA05ROGKo7qcIGrJO0ktVI46/KaGveiLzu2CM3a8HS6035JR+zeGPcdSy4SsKSQvDnx5qYtD5nBLZzHLV6sO+8aXKua2YdbvIBbON/xI5Wd4Gpe6fF87fyf8KWRv+bC5AXcwMVQDfb17ONjOz7F3uTBdQml0fhrRMTmplMjISIiIlpDJDRuEaanbRwnIBZr/JLGYhpXrjhUKi7bt6fJO+V5t8+IkW+8McGZM+MrykvcaCLLfeuRYUj+t79KmC+h7blTqCESMcTuYfzLoxR+56uYDx1q7BCbznOzkCcVj83fjEiYLFfq/18IQKAIsaZx57hR33B5vo9hLBYB3MBHV5XZn1sryzkz15IDGbE+mupAm+uKVQSGJTAsBcNSKE75hI4kL8pMuSV2be+ZdXmNXqxFX3ZsEZq14dnMTvuFAtbJ0ZM8e+rZ+SJR7z6eOPoEx4Y313hq6BbqGYJWtuHtQrOQTo7QLWzwyjYnKzmetcp1vvzqv+OMHZJNZLE0C306i3XyHXz3pSLnY1fpsmJReV9ExD1Mp0VCREREtI5IaNwidHdbmKZKrebT1bVYfKnVfFRVIQjCZcXIsbGA6Wm71ctdE5HlvvW45y7hnb9cdzI22DSr/T24b17GPXepoWOs6jp4QUh6gdvQ9jxsvy4ABqEkCO8IMasddx7KpPkBULJtenR90XlQrNXY1dvLcHdm9QfgNss5M9eTAxmxdpp5/S/lijXjKtmYim1rTFUCjjya5PjbB2bFpujLjq1FMzY8W8Vpf3L0JE8//zQFuzArEtm+zZnxMzz9/NM89fhTLREbW1WgoxhphGoiAxuhJRbdLn0boZooRnrdj3UvcLfjGfo2U3aJCUdhV+ZAXWQvDrPt2vtQfYuSkmNSXKMndrDtkQIRERHtpVMiISIiIlpLW3dDf/3Xf81P//RPs23bNoQQ/Mmf/Mm826WU/It/8S8YGhoiFovxgQ98gJGRkfYstsM5dKif++/PcutWCXl7LHAGKSW3bpU4cKCHVMqgVvMb3ket5mOaKt3d1kYsedXMOFCsmEJh0sd1QsJQ4johhUk/stw3gTBfqmcyWo2dN8Iyka5HmC81vD1umOiqguvPP8eCMLx9XtYFInWBG9JQNbwgXNG484ywF9MNJkolbM8jDCW25zFRKhE3TN5/8OC6BMAlnZksFkY3IzKU5G44jF6skbvhIEN5919qwX2shmZe/3NdsYsfCNDqX8hs29Y17/5mxM5KKWj4PlstBfQMGtGXHZuIoT0xPvTzA3zg5wd4/G/384GfH+CDPz+wYgFkrvjciM0gPocy5NlTz1KwC+zK7CJhJFAVlYSRYFd6FwWnwHOnniOUzRVLb16q8Y0vj/HNL4/x/H8c55tfHuMbXx7j5qXauu/bSB/ASO3Ft3MNr1XfyWGk9mGkD6z7se4F7nY8a5UbXPEFfuz2JISEvhtHUX0Lx5pG1aHilXFFjXSvhl0LOf2dQsv/bkRERHQmM4764b0x+raZ0d4tImIL0tZPvpVKhXe84x0888wzDW//jd/4Df71v/7X/Nt/+295+eWXSSQS/PiP/zi23ZmOu3aiKILPfOY43d0xLl6colx2CYKQctnl4sUpurtjPPXUe3n72/uXFSPf/vYshw517jjojAMlu93ErYWUpn3cWkj/dpPHPhJ9Ow718WfnjYvUXnwN542LyHDlm0Mlk0QYOtJuLPhJ20EYOkomuei2IAhwL14lZrvkpvPzHndGWPSDAEvXsPT5TrK1jDv/zQePsrOnl5rrkiuXqbkuu3p7mzLSPOPMNJbJgVypMNpK1iL2NWNz30qBYDmadf0PZeouppJtN3wvLNZqbMtkFrlioy871sZGi9KrZT0bnq0gPo/kRrgweYFsonH+XjaeZWRyhJFc877onSnQmbjuYMYUkt0aZkyZdbut971ECIXMgU+hGim8ylVCr4KUAaFXwatcRTXSZA58MioTWSF3O56BavG8l8TU6+/BVrUPq9qHZ5RBgCoUQinxQ29RpECnI2WIkz9HbfxlnPw5ZJMF94iIiIiIiK1IW0enP/zhD/PhD3+44W1SSn7zN3+Tf/7P/zkf/ehHAfi93/s9BgYG+JM/+RM+/vGPb+RSNwWPPrqDL3zhQ3zxiyc4e3aCsbEA01R58MEhPv3p4zz66A4URfDkk9/g4sUpBgeTxGIatZrPrVslurtjfPrTxzuyCGYukeV+aZZrizYefuCu7cnGwT3oB3bjnj6P2D28aDw1GJ/CPHIA4+Ceeb935vmX+MYr32dMkdQ0lWrMoFyp0p/oItPXA9Q3rKGU9CYSTRl33tuf5cC2oZY0Qs91Zlq6vuj2ZuVAroebl2qzI59BUHdN3S37qhntuO1u2G3G9b/QFZuKxTBUDTfwKdZqy7pio3yh1bGW83QzsRXC7Qt2ATdwsbTG0wyWZpGr5ijYzckz3KgCnVj2GH1HP0v+/JdwixeRTg6hmpjdh8gc+CSx7ObKnWw3yx1Pp/9xRl/8bVK+XXfE+hYiVAmVums8kCGKEGhK/e/pZokUqE2cvPN8AwehmhipvWQOfCo6fyIiIiIiIpahYzMaL126xK1bt/jABz4w+2/pdJp3vetdvPTSS0sKjY7j4Dh3XEbFYhEAz/PwPK+1i+4AHn54kH//73+ac+cmyOcdMhmTgwezKIrA8zwefniQ3/iN9/P//D8nOX8+Rz5fFyOPHx/iH/yDYzz88GDLj9PM/a/3cdJZhRlTrh/4EKx3ZZsb5+QbTP76bxEWSqj9PQjTRDoOtTff4sL//Ryvf+RRxlXwA4mmCgZTGR4/sJ+9/fPD3bv+/s8w+eu/hXNjDDXbPXs/wcQ0Sl83ib/3M/hBAEH9gJ/7zgn+0/dPYWsKXYEk4waUQ5epuEGuWsEe90kkE+zv6yVfs3FcF0NR0FUNL/Ap2TZdusHj+/YR+P5dX8aF589gsgvoAljR76+E/q4E21Jprk9PYanqImG0Uquxo6eH/q5EW95Xxq7YfO/PJ7HtkESXiqILQk8ycaPKd79m88hP9TKwa75oIEPJD74zie14pPo0hJCARFMhZQqKUx6vf2eS3m39S27um3EfzWI91//Ma/Yz7zjCCxff4lYxTyWw0VTB7p4efnT/fnb3dC/52vZt13jfz/YwNe7h1kKMmEJPv464/T4bUWct52k7CGXIxcmLFJ0iKTPF3t69yzYtL3wP6tuu8ch/l+GHLxXIj3vUqnVBNbtd59C70/Rt1zr6vOjSukioCXzfx2zw5YnjOyTUBF1aF57nIUPZ8NxfKZO3XKbHayRSCigh83ygAuIpmBqvMTZaXbcTVMscoffhf4VXvEDoFlGMFHpqH0IobXtNmvUZqB0sdTx7gPvOfYezubOktCRppYiu2SgSHCXEkQ5Jq4u4aSEJ8P0QVQ9RjbBjj4Ode43J179A4BTRYr0opoUMbKpTIzjf/w16jzyJ1ffOdT2GlGHDc/NubOZzKKL9ROdPxHqJzqF7m5W+7kIunPVpE0II/viP/5if+ZmfAeDFF1/kscce48aNGwwNDc3+3M/+7M8ihOAP//APG97Pr/zKr/Crv/qri/7993//94nH4y1Ze0RERERERERERERERERERERExFalWq3yd//u36VQKJBKpZb8uY51NK6Vp556in/6T//p7H8Xi0V27NjBhz70oWUPRMTG4Xkef/mXf8kHP/hB9AZjqe1m7Io960yZGfXL9Os88O50R7hvGuGeu8TEk/8KJdWFEr8zjhgCX337IDfjJt2FCsahvShd9cZIKSWT5TI7enr4e4++e96Y6MXxCZ4/f56b4zl830fTNIb6+3j8wIF5DsjLp87wu9/5LlYoMVnsaLGROIrg773nMXYfPVRfk5TczBdmx52HMulVjTtv5PlTPw4j3CrmZ52gQ+kMP7p/sRN0o5i85fL8fxjHtBQ0c7HzwXNCXDvk8Z/tn+cGunnJ5oU/maArozWMRwhDSTnv896fyTK0p/F53oz76AQ6/T1oK7DW83Qjee3ma/zvL/zvFJwCffG+2ablXC1HWs/w/3n7/8SergOLXHvNOH867e/M7LFwC/TFFhwLM80/fc8/Zcg92NChWikHWJayYofqZjg3WslWff+xc69x5ZVfpVy9xaQfUiofoHzj5xFhAtOskejehRRJquUAK6bwyE92hqO5EW7hPGMnPouqd6E0atn2KgR+mYHjn19TodBCt6RQ625J355ENVJ3dUtu1XMoYmOIzp+I9RKdQ/c2MxPDd6NjhcbBwUEAxsbG5jkax8bGeOc737nk75mmiWkuHv3RdT26EDqMTnxNbl6q8b0/y9/On9Nn8+dy1wO+92f5lufPrRW/VEUp19C6Mwj/zgzpRMJkzDKIOz5hECK9EDkj6glBIhZjtFBgvFxhR083ACNj4/zHU69RdRxSqS4MVcUplrl84xb/ITfFx9/7KAduX5+1YhkHSASSsIFWqElJURHUiuV5r/XuJoh0qz1/QilXned4cHhby3Ig10rg+gSegtqlIeTidWiaQs3zCVxl3vGJd4WoQiN0FdQGm/vArd8e7zKWPK7NuI9OohPfgzoJGco1Z2Gu9TxtFlKGuIXzhG4BxUhjpA/MG0kMZciXX/8yOTvHrswuhBBIJKZhsq/2MPEL9/Hqmy7X41OoWuNcybWeP534d+bhnQ/z5ONP8uypZ7kweQG36mKoBvdl7+MTRz/Bg0MP8o0vj2FXBeleczZOQjUg3a1RmPR546UKw2/ruus5MjCs0d0fY+K6Q7pXWRRNUS2G9G+PMTAc7+hsy/WyEe8/d7sOmvk45beepUvUyAwcocer4vV5VLr/mtFr76FUSFIcr2ImU/Rvi3d8nq0fllHCMpqWQQh/0e2KriHd+s+s9jWcOVa4OazkrtvnvwTFRNOG8CpXKb/1HF2Dx+76WkV/wyLWQ3T+RKyX6By6N1npa96xQuOePXsYHBzkW9/61qywWCwWefnll/lH/+gftXdxEVuSjQqoXyvLCWVz26JFov7hXQITZgZHNbBqDggBujrvPg1VoxjYs+3JoZR88+w5qo5DNpVE5kt4124hylWSUjKdivPnz/4R23/kvcTfdYSuTApNSjwBjWpRXFEXG7sy7XUTj4yN882z57hZyOMFIbqqMJTO8IH7D961oVoRYlaE7QTMmIKq1oUJxVx8Hvpe3R1lxuZvUGbacSeuO6QNsXhzXwro324u6yBqxn1EbA7WW+Ky1vO0GaykwGGppuV4cZhtl96H8AzK6jRKVx+miM0rO+rbvvaPTp38d+bY8DGObjvKSG6Egl0gbaXZ37cfRSjkbjhM33JJJNWGzdRzW4T7ti1fkrUVCnQ2AxtZZOIWzuMWL6LFsihC0GXUXYDd1hTD/V8jP9WFY0uGHvrvGdy/o+NfW8VII1QTGdiIBo5G6dsI1UQx0qu+77nHqtG1pJl9uMULuIXzmJmDa34OERERERER7aT5n/BXQblc5rXXXuO1114D6gUwr732GlevXkUIwS//8i/zv/6v/ytf+9rXOH36NJ/85CfZtm3bbI5jREQzmbzlrngjtdGMjI3z7/7qBX7rhRf43Re/x2+98AL/7q9eYGRsHLjTFh2MT9ZHorV+TiTfx0XzGK6IM232UEwN4ynzx5QWtiePTue5WciTiseQ+RLu+cuExQroGqpl0uUFjBHyxv/xu9gnTrPr6CEGQkFJU1gY9iqBsqYwGAp23R6bbgcjY+N85ZVXuTo5Scww6Et2ETMMrk5N8pVXXp09hpuFGbGvUgpYGLE7I/b1DBqLxL6Zzb0VUyhM+rhOSBhKXCekMOmvaHPfjPvoVGQoyd1wGL1YI3fDQYYdEV/cFmaaxSeuO5gxhWS3hhlTZsW2m5dqd72PtZ6n66U2cZLcqc/jTJ1B1ZPoie2oehJn+g1ypz5PbeIksETTsoS+G0dRfQvPyuMpDgEeuqmQ7tWwayGnv1NY17nRyX9nABShcF/2Po7vOM592ftmS3GcWlgXnPXG17amC4Jg5S3CM+3t2e0mbi2kNO3j1kL6t5s89pHOnBzYTKz0OmgWoVu4LWYuHoUWAjI9BXrSb5DpLmyKvw9G+gBGai++nWv4/uU7OYzUvjWNTS93rACEZiEDh9BtTst7REREREREO2iro/HVV1/lfe973+x/z2QrfupTn+J3f/d3+V/+l/+FSqXCP/yH/5B8Ps973vMevv71r2NZnZnpErG5WclGqlZe+UZqKWQY4p67RJgvoWSSGAf3IJSlNf8ZoaziOKQkJAOJF0quTtaFso8//BD7B/rJ/OLHyH3uGcZzCmf2HsdTLTLlEskSFJM+Kl1Mjbn0DBqYcRUpJcVajV29vQx3ZwCoug5eEJJWVbxrt5BegIhbOKpCqAhEKPENlarrUvidr9L/0CE+9PCDfOWVV5nUFLr8EEPWnYxlTSEWhHzw4YdQVXXJ59dKFjo0Zzb2lq5jahoTpRLfOneOvf3Zto5Dr4b1uIFmNvczTrVaue4q699urniUrRn30W5mhKKbl2ziXSGuHXD6u8U1u/e2EgsddzKoIH0fVdVI9yZW7Lhrh2tNypD8+S8ROAX0rl2z17vQEuiJOF7lKvnzv4fVd5S0lcZQDWzfJnHbfWVV+7CqfXhGmYAQRQg0pT4eMlcEnBpfe8viRv2daQZhEDB2YYRaqYLrJlDVWFMdqkN7YgzustY8nr+RhDJs6PTcSFY6Br2a66BZY9StdAC2AyEUMgc+Re7U5/EqV9HMvroA6Nv4Tg7VSJM58Mk1Hb+tdqwiIiIiIiIa0Vah8fHHH1/0TeFchBD82q/9Gr/2a7+2gauKuFdZatQvLFeRno+Phqoa6xr1s0+cJv/bX8U7fxnpeghDRz+wm8wvfgzr+OFFPz8jlJULRTJj01Cu4kmJEIJUV5zCQPesUGYdP0zvr3yaV5+7ybQeogWjBKjsKWQ421PBUV00z6Aw6ZHSQop2jbhh8v6DB2dFtrhhoqsKTrGMKFexuyymExauqiBF3aWohpLiUC/uG5exz75F6shBHqtV+f7Z8xRVSVkINCnZHsAHH36IQ4+/e83Ha73MdWg2cg+lYjFu5POMTuc7ajz6bqxH7Fvp5n65fL7NJBAs5OalGj/4ziSk4IU/mSD0VJxqgGYK0j138vLmjsreS2LjjOPOsqrY09fxbJ9QKigiRLc0LGs7U7fEikZkN1qUbjSSKCUUS1k810IVSShcxC2cZ3/fAfb17uPM+BniehwhBKpvIUKVUPi4gUPSTM6KkHBHBHTXIQK2c6R8NVw+9RqvfXuUYj5BGKgIpYQfpqiVE/QNdzUtNkEo4q7nUbs5OXryTnZlUM+u3Ne7jyeOPsGx4eaOIC9FbeIk029+ifLUGcLAQVFNunoO0X3f4jHodozmzjgAnek30BPxReeH7+Qwuw+tyQHYLmLZY/Qd/eyd8XMnh1BNzO5DZA58cs3j51vxWEVERERERCykYzMaIyI2moX5c2GhjH/tJmG5hgxDqrFeurUSiWsF2LZYFFyKmWzF/A/O4vy7/0TflTH0gV6EZSJtB/f0eXKfe4a+X/30IrFxdDrP6M1bxK+NIW0PYeooioIMQ2SxguW5XFeUWaHsTFcPL7/9OhV1glAEqKikFJ3d4XZyTFPQKhR9F1E12JXt5f0H52cUDndnGEpnuDJ6A01XySVjhIpADSUiBE8VSCF4ee8ATs3m5tkzjF8QeEGItmcbvRIeMBPcN9DPrqOH2uZknGHWoak1fqtbmFG5mViP2He3zf1K8vk2g0CwkJmRYNvxSKagK6ORu3p7BFzWRz91U+mIvLx24dRCPKdGUBmlWssQBDEkAoFErdaIx0YJtR2rGpHdKFF6diTRyiKB0bEeLl8+Tq0ygJQGquKTiF3lwcEKux9UeOLoEzz9/NNcKVwhG8+iK1V84eF6Ppqmsz21nbmrnBEBjXWIgJsh5/Tyqdf47tcmcd0UllVDiwX4vopXcbEdk9xomXQ2cU/kKp4cPcnTzz9NwS6QTWRn27jPjJ/h6eef5qnHn2q52FibOMnl7/0zSpUb5PwQR4IpoLc6TmH6TXY/8uvzRK+510EjhGYhnVxTR3OFUEjvf4KJV/8FbuFNFKsX1eyBJjgA20ksewyr72hTC3Va6ZaMiIiIiIjoFCKhMSLiNnNH/fLXK2i3RlFdm9CKY2sJjMBm15vfYuJX8nhP/QL+gZ13bSGeLSHJ56mNjqEe20P/kV08en2aXYUaIhFD7B7GvzxK4Xe+ivnQoXlj1BXbxpkqYNkeSvxOZIBQVURcRavZVKcLVGybkbFx/vTsKQpqlbhqomEREJCnREW1ORzsR5MahZLNu9/Vz9HDg4vWrQjBB+4/yO+PTzDa00WoCIxAIgX4qkCV0F9xKBga33p4H0nPIZNIkdY0XN9nqlbjVemyb8dg20VGuOPQdH0fq0FD1sKMys1GK8S+WTGuFpJIqlvG4Td3JDjVpyGBwJP4vkQ3BEEApSkfM1ZvwV1twcVWwbQEvl3EtgcABUXxbottAj+IUyxbWFYR01q5oLRRovTMSGKhmmPkVjdjl36U0E+g6JNomo9GgkJpJye+ncLsrnFszzGeevypWbdazn+NjPEgfe4e+nuSpK07o4tzRcCe/rU3LHZ6EUoYBLz27VFcN0VXV5mZPxG6HpBOT1MogPQN3JpFrcymi01YDaEMefbUsxTswmwzOUDCSBDX41wpXOG5U89xdNvRlo1RSxny1mu/Sb54hbFQwVAtTKEQypBRz8YvXiH8wW/y9vd/aVaYasdobm3iJIWRZwkDm8At4NuTCEVHs/owe4+sywHYboRQml7K0iq3ZERERERERKcQCY0RHcFqcwtbxdCeGI/+dC+v/OZ3KWDhJpMoMqDbn2Rv7QylvSb/sWc3k6+/hsyNoqvqku3FM9mKVcchKcGcLOJbBjeTFv9l/wA/OTJWFxuFQO3vwX3zMu65S5hv3zt7H8aNCVTbIYibDS9WP2ai1Bz00XG+USlgBy5xGUMNFVDqo84aGrZ0eEu5xjtrh9DDGLv6epYUR/cP9PP+o0d47vkXkGGIryoICZYf0m17WF7ApKHi6hqpVNesgNeJuYczDs2rU5OYmrbIPVSs1djZ04NE8uatW3cVjrc6ndyIu17ml3BIJBAGgKyLP6qol9p4jsS4LaJ1Ul7eRpGIXSbwA8JQQ1UqCCQgAIEiQvwgRhhUScQuA53ViGqkD1DWepgee5WJ0ScJgy50awyAIAwIRR4zIXHdHbPn8cKmZZlLc/Wv4jiVEFcJWyICdnLO6diFEYr5BJZVY+HboBCQSBRxXYN3PhYnObBrU8UmzLBU3uLCfw9l2LCZHOojyNl4lpHJEUZyI+zv2z/vd/f27uXi5MV1Zzra+TcpTL7OdCiI6XdEQ1WoxJQEea+MyL2OnX+TWPf9wMaP5s4UzwROAT02gJ7YSehO18VGzSK9/xORcNaAVrglIyIiIiIiOoVIaIxoO6vNLWw1vc5Njv7w9yn37cZPpDBCh1QwzdV0jL/YP0hVFZhVB8MPEZq+qJQFFpeQyOkSjpSYEoyax1RM56Xt3ewo1FCgPkY9MU2YL81by4ATkJ0qc3OwG6PmzRvjk0A5ZjB0a5qgVOFmKU9PMk6pFlJxHBzVxpcB8nYf9C0xyUXvBu8c3HXXsbxsMkkmHse4Pl4vztBNNKmiBGAHHp4WQ6gqCyNWOy33cMah+ZVXXmWiVCIVi2GoGm7gU6zVUIRCyXb4f1/4Dl4QoqvKksJxu1guK3FmLL/qOk0RSVfTiLvZHH7zSzjqJ66iUtfQZF1EQUIY3DmpOyUvbyO59eYZFPpQhIOUJlJ6QAiI2+KjhxA+UzfKDPe0e7XzkcB/rgjeXt2JcHag69MIAQKJIcCTMBUIdnbPP49nmpYB2AE7M7VlRUDPW3sZzAydmnNaK1UIAxUtFjS8XdMCHFvFMCoM7918Dsal8haPbz/Oiesn5v17d6ybol2kP9H4b4GlWeSqOV65/gr/5uV/M/u7QRjgBR6aqqEp2royHa9OnCEIbIQab/wDqkUQVLk6cYb7bguNGzmau1TxjGJlUc0+vMpVCiPPEcseiwS0BrTCLRkREREREdEJREJjRFuxT5wm97lnCPMl1BXmFraaMF8C1yOjlhFetf5vwEs7uimaGr4QlEwNqlWE62CqKm4QzHPxLSwhkXpduJFhiFBVutyA8YTJeMJksOIgbQdh6CiZ5Ly1aN0pHhm5ydd7kkzFdLrcAD0I8VSFsqESc30eGbmJ89MWXj4kHdfQkzYVv0IYhqghKBJCIfHVkKvxK/zI4W133czGDROjK44yPIyd8/DC24qMKpG6hzR8FE1FbeA67bTcw/0D/Xz84YfqI+yFPMXARlcVehIJ8tUa05UKqXhsdvz76tRi4XgjCEPJmTPjTE/bdHdbHDrUz9gVe8msxHK8NPucmiWSbqZG3NUyt4RDuz3Vr5sKhqng2CGqBghQ1JkSkebk5S0nFHcaUoYURn+AEI/TFR/FdrIEoYWUAoREUytY5hSBTOEFXe1e7iJGciN8pzBFVR5kW2gS06dQCJEIHDSmhUnJdxmQNcLAWPI83igRsBNzTmPJBIpawvdVdH2x2Oj7KooaEEsuHsntdJbKWzw5epJvXfwWGSvDjvSO2X+/mr/KVG2KicoEg8nBRfdn+zZ+6PMHr/8Bru+STWRxAoeR3Ahu4GJqJvt69mFq5rxMxyP9R1a85lJYF8gNAX6D2w3qt5cWnMobNZrbjuKZiIiIiIiIiM4nEhoj2oYMQ/K//VXCfAltz/Dsh9S75Ra2GiWTRBh6XfxL1B0b4wmT68kYVU1FCtD8AFVVkIrA9n1EEHApl5t18S0sIVG64oiuOGGxgoir6EFI2dCoagpBsYx//RbGwbehH9g1by3GwT3s7e7hx184w4nj9zGRMCkbGloYsq1k8/CJN9k3OICzfxf62HVczydfLSKkhxYqSFFfL0iUMETxipx86xTH37F9WefbcHeGbi3JBTuHZSbQhITbI5Q2Gr4sERfKpsk93D/Qz97+7Kz7z9IN/svp00yVK2RTydlzr13j3y++eI0vfvEEZ89O4DgBpqly/Mgu3rXvAIamLcpK/M9/dpHr2y7j4TVVJG13I64MJZM3HcavO4Agu92gb8hsisAzt4QjNee5JXt0vFtOfWQ6pqDq4DphU0ZlV1Kq00m4hfMowSiqGqAoDqmuS/hBDClVhAjQ1Cq+r4OSoqtvd7uXu4iCXcANXHKWRVJVKcseFMUhQOCiAZJQ2riuh6may57HnSQCbqRYPbBvP6nMRaYnU2haed74tJRg2zF6eosM7NvfksdvFfPyFtO7iNWyqDWLuGpzKbiMF3j4gU/ciCMQJIwE+3r3MW1Pc6Vwhf5EP8qczyFSSsYr4/iBjytcdqV3YdWyXBu/RXe4g6o1gR3WuFm6ydv7386u9K7ZTMd/+aF/ueJ1J3sOcYEYO7EpyAQLX5AENleIMdxzaNHvbsRobjuKZyIiIiIiIiI6n0hojGgb3vkreOcv152MDb4JXyq3sNUYB/egH9iNe/o8YnddAK3oKmVDIxRguj5S05FCRUgwNA3H8yjUbMpO3cW3qIRECPQdg7jnLxNWa7iWjmpLlNfexLkyBorAf+s645/+/LyRcaEoZH7xY+z+3DNs/7MTTO4dxu6ysMo2vRdH0dJJ0r/wMYyeHobSGS5OjFOzbTQvqLdTEyAR+ArE/IDufIlRQq5PTbGzt3fe8567mTUsQTY3yOUgj2PYKBioqAQE+HhooYr0FKSUDXMPd/X2Mtyd2bDXbCUoQsyOcl+bmuZmoTDrOJ3LRo9/v/jiNZ588htMTdUYGkoSi2nUaj6mneDmaJnte7rQzbqgq5iClAGn/WtUizX2DHfPCg7NEEnb2Yh781KNl78+xfg1B9+r23M0TdC/0+JdP9GzbmFubglHccoj2V93kSqqwIipKGqIaSmU80FT8vLWW6ojZbjh2V2hWyAZv0oylSefz6IqN9DUKjPz5TIMcb1usjtUeoc6Q4SbS9pKY6gGeeM6dnySWHmAmppnJnMiCEMUBEFNp2eX0dZm55Wy0WK1oqq8833DfPdrk5TLXfXWaa3eOm3bMQzD5R3vG0bpgLKv1TCSG+HC5AXeFj7I9vOPYlX7EKGKL1w0cYA3Ul9nzD9Hxa3QZdTduopQ2JXexaXpS1yYusBwanjW7ThRncDUTKSUs/epV7oZ8AJC4VMwbnA2/XVuiDeoePX7nMl0vDh5ccXr3p+9jz/uOkJ3+QQZtUZFGvioaAQkcCmEkqtdR/i5mdH/BbR6NLcdxTMRERERERERnU8UmLKJkaEkd8Nh9GKN3A2nnqW3iQgL5Xomo9V4wyosE+l6i3ILW82MuKekk/iXRwkrVaoqBAKUUOJpFp5q4trh7P8ECkEYzAqNMyUkxVoNeTvIUOlOoW3rR4YhJSHouzpO9uJ1ALRd21CHsrMj4/aJ07PrsY4fro+QHz5A//UJdrz+Fv3XJ4gdOTA7Wj6TRagHkkAASr3CQSIJFIkqQ7prLoaq4Hk+hZEr857zzUs1vvHlMb755TGe/4/j/Ncv3aL6psnB2j66SeIKn4qwcYVPD2nu9/dieAY380VszyMMJbbnMVEqETdM3n/wYEcXqsw4Tg2t8XcthqrhBSFV12npdRaGki9+8QRTUzX27euhq8tAVRUGe9IM9qQpVW2uXMkz9xFLokLNqKK5Bp47fy0LRdLVMiPGWTGFwqSP64SEYb0kpTDpt6wR9+alGt/+D+PcfMsm8EN0XaDpgjCAm2/V+PYfjnPzUm3djzNTwtG3rS4wlfM+bi1k2x6Ln/z7Q3z47w/x+N/u5wM/P8AHf35gzULOwlId3VRQFIFuKqR7NexayOnvFJY8l2oTJ7n14i9z66X/ibETn+XWS/8Tt178ZWoTJ9f83FeCYqRRNIO37T6BYQbUnGH8wETKED8wqbnb0A2PI+/t7sjx7/19+9nXu4+J2jgTQ98n0BxMO4MS6BAKAhcywTaSXVZbm51XyoxYPXHdwYwpJLs1zJgyK1Y345poxO6j7+Sxj/TS3VvE8wwq5S48z6Cnt8hjH+ll99F3tuRxW4UMJTevlRkafxd7L3+EWHkQX7NxrAKeUqPX2c0jU58iW9uPH84fUM4msvTEe9iZ2UnRKXK9eJ2iU+SB/gf4+JGP01fbz55rHyZWHsRTahTVMVylQo+zm4dzT5Ct7ccL6pmelmbhBi5Fp7jitStC4YMP/2P+q9jNJU/BkC5pWcOQLm95Ct9QdvOBh/9xy1qv78ZM8Yxv52Y/68wwUzxjpPY1rXgmIiIiIiIiYnMQORo3KZttJK8RSrpr0YjyXJbKLdwIZsS9mZIac1SiHNqBr+ooKLcVeglSEAQhMgzRdIWgJBi9WMOMKbz/4H384asnZ0tItFKVyniOcqaLWMXmoVdHEIk4IggJb+Ug3YW2xMi4dfwwAw8dWraZe/9APx9K9fCH0/l6UzQCQb0tOlNzifshjq6h+gGx2p38xEbOq2oxwHd9tLE4R8Qh3HgVFx8DjRRdSAFmKY6zbZK8W5rNPdzV28v7D3ZOkcpSLHKcLmBm/Ls6IfnGX4617Do7c2acs2cnGBpKznMP6oqKoiiomqBccimVHFLJuiDv4iNFiCKVecUlM6w3I3OjG3FlKHn9hTylaR+ExDDvnNOKIvE9QSnv8/oL+aa0XQ/tidG7rZ+/+Dq892eyxLuMpo+irqdUZ26DqxbLIqwsMrBxpt8gd+rz9B39bMsaXGdEg3RwisMPWLx16SGKpV48T0VRAtKpaxy4/yo7HvhgSx5/vShC4YmjT/D0809z1nkRZ4fN8PijGNUelMDAUkKGdnXxng8t7ybtBNrdAL/76DvZeeQwYxdGqJUqxJIJBvbt33ROxpnPSjeup3h77mdQpEag2QhdBeEhNZ+SNk0qGOSB0k8yJr457/dt3yZlpvhnj/8zFKHMa5E+PzHC2SkDxTNxYtNI6UMAnnDwlTGSXn/9PpVvzt6XoRqkzBRjjK34ORwbPgaPf55nv/97fHvqDHro4CkmXT0P8IkHV18w00w2sngmIiIiIiIiYvMQCY2bkPWO5HUK+oFdi0aUZ5BSEoxPYR45gHFwT1vWN1fcs2+NY7x1lQAXSYgM7oTkS1WAVBC2xuvfKHHxdo5d94DO+7KDvBZeY6xQwp7MoyoK20o2x771fXbkSgizLjLIWg3v6i3Mw8klR8aFoiw7Qm6fOM3+3/sL3jaUYHR7lq6KjYbAVFUUXUMCJUNl20SF4f56ntJSm1nDqotcQSApT/lk4/PFXs8L6Q0z/NjRAzixWsPW42Y3IjeTGcfp1alJTE1rOP7db2a48C0PpyZbdp1NT9s4TkAsNv+t2AsDQhmiayqO4+N5d5L+DTSEVAhFOFtcgqznCoaBxJM+urK+jMyNbMSdvOUyMeoiQ4muz9+MCiFQNUngw8R1p2lt1zPPY2iPhd5AaF4vay3VWarBVWgJ9EQcr3KV/Pnfw+o72pKN+1zRIMVLHDtygVJtO66tosgbZLqrZB98qqNFg2PDx3jq8aduNwuf4dLga/R4O9mT2M+HD3+Q9x65v+OdjNAhDfCKoNgjKMQD0pZgYBMct7nM/awUN0yKwiXARQ1MrEovdmISVBtVVamEU/R4O/H9XThmDqj/LZioTvBA/wPcl71vkWuw19tFrz9FUZ2qh4soGpqi4oc+QihUlGl6/J343i5sY2L2vvb27mWEkVU9l2PDxzi67SgjuZF5Yme7nIxz2ajimYiIiIiIiIjNQyQ0bjI20uXQ6gD6mRHl3Oeewb88itrfM9s6HYxPodzOH9yIIhgZhg3dgjPiXjo9TNcbk/iqhxQSXxUzsWWovgAUErUu0nQR69Zwp8rcenUCzS7x2JXv4atTlGs1urrTDNQ8/KsTMGdkXBgGYaVKWK4i4hZyYnpVI+Nz27sfmdD5r91J7JhJV6lK6Di4qS7KcZNYxeaxWoh1/9uApTezuinqbby1EMcOcJ1w1mk2N6svO2QiFGvRekbGxpveiNxMZkbNv/LKq7OOU0PVcAOfYq1G3DDITg7i1GRLr7PubgvTVKnVfLq67uTFld0aZdemS4+hKO48AS4pE8S8OLVYBd0QONWA4pSP54SEUmKrNn16CiVvQs/aj9FGlWE4tRDfu+3MbHAoZ05L3988bddrLdXphAbXhaJBXD1FImVipPaROfCPN4Vo0MmizEppdwP8ydGTt8XaC7iBi6Ea7OvdxxNH2+ugWykLPys51RBdMQhCFw8bXZoYtRTlRAWBIFBc1FDHr0EQC2ZzGNNWmk8c/UTDc8e1JWmjh5I/Sc2vYqgmpmbiuz5+GKAKF5MYvg1X/CvL3tdKUITCfUtkMbabjSieiYiIiIiIiNg8RELjJmOjXA4bNZq9cERZTkwjDB3zyAHSv3CnFKWV2CdO33l810MYOvqB3fNKWbxqwLbRNPZAAddQMD2BkAIpJK6qoAU6AxNZ1H4FWSwj37qM5XlM9KU4eeQRjlz4Jjt+cB7FmkDsHARFQBCCdnsMTVXAlUg/gFWOjC9s795bKPHj33qNl4/tI5fNUFEV1DBk6OYU7746yTv+x0/NirdLbWaFECR7NLxbLr4Hbi1E0wW+J+/axjsyNs5XXnmVquM0tRG52ewf6OfjDz80K4jOHf9+qG8vF84GmEmlpdfZoUP93H9/llOnbrJ3b8+8x7qaH2NX1xB9mSSWoROGcvb4703u4HrqMjenigRFFREooIU4ioshNbK5IV762iSPfVR0vLvZjClo+h1n5kKxcSb2S9Na13bdbNZaqtMpDa5bQTRYjSjTjuKdu9HOBviToyd5+vmnKdgFsonsbAHKmfEzPP380zz1+FNNExtbdewXflZSVIGqKMTVBHZgE4QeItDBU8nE0qTVHioVl3yYY6p4HUM1eKD/AT5x9BNLPlczptBlxXibspeb9nUqboVQSgzVRBKihRZ+WL/Puffled66n18n0urimYiIiIiIiIjNQyQ0bjI2wuWw0aPZK8kfbBVznYDqQO+so3KmlGW2bGXsJpkpwX1eL9cGi5Qtn0CViFCQqHUxmMuSrMSRKRv/xk2mYjWu3OdSiBcIFHhj5xGG9vRy/OU32ZWbRsRjyFIVqd4WsoKwLj6qyqpHxt1zl+a1d4tMir3A9r/8AWMJnZquYVUddh54G33/5FPzxNvlNrNWXCXVq1Oc9PB9SWnav2tWXygl3zx7jqrjkE3dyR1sRiNyK9g/0M/e/uyiEe+bb9m8GYy33E2kKILPfOY4Tz75DS5enGJw8E7r9IULkxR3OPzcTxzDrYULshL7KcWyPPf115iihDAlCgrdMsVeuYPeVHpZ12UnjbX3Dhpkhw0qhfqI+NyMRinrY9NCQLZFbdfLsVZX99yG68KkTzypNhTqAXI3nNn774p1ToPrQtFAhpLcTaflo/QbTW3i5J2Rz8BBqCZGai+ZA59qq3uzXQ3woQx59tSzFOwCuzJ3xvcTRoK4HudK4QrPnXqOo9uOrtsh2spjv/Cz0qxL34YuI0EgQwIP+jJ7SafiFCd99hww+Kkff5KiszIX7J3XSHJ/9u1UvSp+6KEpOnE9Rm68Rizr8jf/xpMcyK7fUbuW96NWT6ZERERERERERDQiEho3Ga12ObQrgP5u+YOtYKETcDYPLRFDLChlSYsyyeo4ob6Dd76lUY75eGoIoYnvDaIgUP0aWs1nXBQ5vb+CbQZIJKGAQNUoHxhmvD/DT/35q/R1bcOerKLVyiTtHLgOSjxGmMujZFY3Mh7mS4vau5VMilg6yc5yldD1CGs+PZ/5u4sconfbzPqeZNf9cY69vxvHlnfdqIxO57lZyJOKxxo6Aec2Iu/o6V7R82s1ihCL1rKRbqJHH93BF77wIb74xROcPTvB2FiAaao8+OAQn/70cd79yPaGG0X9hsL9Ewdx4zVCEcwW9QjqY/1LuS47baxdKIIj780wedOlkKs3Xava7ZxPv34epvp0jrw3s6Eb5PW6uu9WqgPwjS8vKBoaSDOcfoyu4L+hJ+KLr0cnh9l9aMMbXFvlcG+3CNLO4p27sVKxutnHayQ3woXJC2QTWQTQF5ax6sPG5ESCbDzLyOQII7mRdY3xtvrYL3wPn3Xpj3n4nkQoCoqQGIpBcdLHiisceU+Gof6hhvfX0HmpKLOvUXHSJ56METfi+J6kOBmQSlo89qHtDPWvsb1+zmNOjKU5eyrN9Ji34mtwK5QGRrSHUIabOnoiIiIiIqL9RELjJqPVLoeOCKDfIBY6AecihJhXyqJ2J3nb1Pf5YVeWqpYmbldRZYAjLIqaipABca8ISobzO8tULR8ANRQooUAlwFdUpnpS/Nl/90EOv/U25B4dxbFJTI+y663n6TfLmO9Y/ci4kkk2bu8WAiWZgEoVkgnU7tSi311qM+sValTLAWZM5fBjWfqGF+cwNqLqOnhBSFpb/NYipSQMJWXH5fJkrqPKYRay0W6iRx/dwSOPbOfMmXGmp226uy0OHepHuS0iNLrWnFpIGEC3lkRpEG7YyHXZjrH2lYhJQ3tivO9n+3n561OMX3Pwb5ff6Jqgf2eM4z/Rs6Eb42a5upcq1bl1xW58/6MuhfGPcd+OCdKc7ogG11Y53OeKIL4fgoSujM7Bh5PcdyzZcsGx3cU7K2GjG+ABCnYBN3DZqzg85L1Fr6ygEhKgMCkSvKoM83LgUrDXPr6/8NgjBKFfhtBHMXsI7Ml1H/tG7+FWXKVnAIpTPnYlQNUFMmD2eA7uNnHy5xaNcS/nvBzac6wlr9Hcx5ycHObshb+NFxTp6kmim114ruTWZZvipMd7GlyDnVQa2InRBBFLs9nzWSMiIiIiOoNIaNxktNrl0O4A+o2kkRNwLsIyZ0tZrEeOMLBdR178cy6/7XFKWgZHUVFkgBnWwPcxY4JSGgrx2yKjL0AIhAxRPRehatiGznS3YDI5SdwDRTMIYvu5cP8D9H8gQf+Pvm3VI+PGwT3rau+eu5mdfKuIN1VC2DWS1XHeNvV9lMs69i/eXfyUMkR3bqBKG8cOiVmp2SaPiuOQK5epuR5BGPLnr/+QH47e7JhymIW0w02kKILDhwdW/POGJZChpFoMMCwF3ZwviC50XTZ7rH0lm8fVOGqG9sT46C9tY/Kmw/h1BxBktxv0DZkb6nJrtqt7YanO3e8/xo3iPyA7/G/xSu1tcG2Vw32uCKLpArcm8ZyQ4pTPrcs2514t8a4Wi8udULyzEjayAR4gbaU5oPk87p8nIULKGPioaAQMyBKP++eZ1rKkrbWP78899qFXxKtcJ/QrIEMQCkIxcSZfX9exX+o9XFEFuimIdekceneabW+L0TtoYE9+n1svLhYTY/3vonTpj5Z1Xg7tObboNerp15ka9xi9WFv1azbX7ala/Vy99ZN4fheaOkV+TCWQGqAgkdiVgBNfn+Ijv7Rt9v7bNZmy1HPpxGiCiMbM5LMW7TxvjydIq10UgoA3xn/Y9HzWiIiIiIitTSQ0bkJa6XJoZwD9RrOkE/A2ck4py0xDtv+5Z+h+/Tkqw/vwY0m0Wgk7X+Ps7g9TTfeRI0+oSoTPrMimBh4gCBQdISHQJCP31VBRUPFJKlMMTm/j3PUudjSq3b0LzWjvHtoTIzN+gbf+6A9xqyFm2iStlkF3cE9PzsurbMTMZkIULpJxHuZWpZde8xpG1w5qWNzIFwjCeitywjRIx2IdVw6zkHa4iVbKzUs1Xn8hT7Uc4rs+qlbPH0v2aFhxtaHrsplj7SvZPK7FUSMUQd+wtWIHbStotat7JfdfyHdh7PkNehOX2+oCasWxmCuCmDHB9LhPGEhUDVRd4LmSsSs23/nTXEOnVrPolOKdlbBRDfAA+3r38mHLRXcdptU7XxZ5aExLlURY5MOWx77etUedzBz7UDHxiheRoY9QDVAUkCGhX0V6ZWrjr6xZaJQypKf7Cg/9SIWzp5IUpqzZ9/CBHda89/Alx7inzlC9+R2EFsNM37e861VRZl+jm5dq/OUfjK9pZHmh27NY6qdU6kPVfCrVYcJAoGg2qp4AKfC8kJuXbN48WeLgw/WphU6ZTOnkaIKIxczks/a6t/hEwqdPjqEGdTdzLp7gL2p20/JZIyIiIiK2PpHQuElplcuhXQH07WC1TsC5DdnK7YZq6fvEXZ9D8r9wyX6EG6qF7AKF207GwEWRIaGiECqCUKlX6KqKShdxAgIKlKhkLqLmBA/e6l7TB//1tnfLMKTwO18lOXbpdl5lACjQIK9yoWA5dzOhx7I82j3FX0xlyDkKXd5b5JVB/KD+s7qqkk3WW5RNvfPKYRay0W6ilTBXwEv1aBQn65ldTi3Eu+WS6tXxPbnIdbncWDuAoWoUA5uq6yz7+CvZPFq9D3aMo2a1tNrVveL7tyV9w+1tcG3FsZgRQeJdCoVcXWTUdGbPEU0XBH5ItRS09BxRjPYX73RiDppfvMBeU+eyZ1LzaxiqiSIUQhni3v5SYa+p4RcvoK5RBFSMNEIx8MpX6yKjFrvzFZtQEYqJDKqUR/8b6f0/v2qBfe4XIQQO9w9Z2EMPYwx8lNTQ/fPew5cboVesEK9yHbXB4y90vRrpA7iF89x8q8KJb6dwHYNEqvEXLH3bl/7ovdBp67kWQajheSZhqKJqbt35SYBQVHRD4NqSc6+UZiMHOmEyZTNEE0TMZyQ3gj/1Oj9rFkjI+W7mQVniZ02FP576wbrzWSMiIiIi7g0ioXET0wqXQ7sC6NvBSpyAyb//N7ieL9xp5334gdmGbPulH1B89mtIxSPbZZP1XiJzKcFbw9sJVYHm+wgpbz8Y+IoEAUgwAg2hCjQ0EqiURY0r5nVq1bW7RBq1d2v37eZGoUj11q1l24VXk1c5t7Sn0WZit1bhJ3uv8FJxiNGaRdnzUDWDuKHT19VFwjRn77cTy2EWI0nmr5O4fUwZ3ANrcJ42ZSUNRuI0XaE05eHYAb4HpUmPnffHOfLezDz3TNww0VUF1/exdH3RfbuBj64qxI2l31NWunnUdx/qCEfNWmi1q3szucZbsdYZESQMxe3iH+adI0KAQGBaSkvPESN9ACO1F2f6jbYU73RqDlroFrBUlZ09+7leukHVrRBKiSIESTPJcHIbVlBcl9PTSB9Aiw3gFi8i1Ni8d1MpJUgXVU/i18ZWPT691Bchlv0C6uQPSOz8LEK5c3yXHaGXAQiFMLCRfgWhd82//bbrtTZ+gqkz/zdO4S2+//rfpVLaRVeqiiK2oyjpRV+wPP6zPUuuf6HTVjdsAHzfQFH8mY8QIOufJ6QERRWU897stdIJ7zGbJZog4g6F2jSPMkGckGlii9zMGWo8ygSF2nSbVxoRERERsRmIhMaIRXTyyGizWc4JmPv4h/jPtTw3X7i8qJ1338E9TP+fzyEdb15j9d6pWwzfMLm2sx9PV9GkRIQQqMqsNqV7oKOAWv9vgcAMDSp6lXxQYgfxNT+fue3dI2PjfPOF766oXXg1eZVzWWozscsqssMscrKQ5r8V9tGXStEVzyzS51bqomsX9onTd84N10MYOvqB3WRWkFnZChqNxFlxBStu4johbi3E9yXH3t+9aPx4uDvDUDrD1alJTE1bJKwUazV29fYy3J1Z8vHvtnlUjSy50Qpu+TKubRFLNt7IdnLW60pd3T0DWsPiiGbd/4xrXMoQJ/8mzvQPEYDRfQgzc3BDXEDNcrjPy/P006hqHN+VIGf3snN+FhCgmwK7GrbsHBFCIXPgU+ROfR6vcnVDi3dmctAKdoFsIoulWdi+zZnxM23PQZtxeqZ0g0P9h6i4FbzQQ1d0EkYC6VUIWJ/TUwiFxPD7qI6/BKGLRICoj01L6SKEhta1g9ArrUrQXIuLbtkRekUDoYIMkKG3+PF8GykDChf+ABm4VLwHqNR2YlkVpF/GLYxgpPejGul5X7BMjS++r9mHXOC0TSUniFklqpUMKD7yzkGsT174YFj1zxcz10onTKZspmiCiDppP8+Q4lPEWPzGLAQlqTGkuKT9fFvWFxERERGxuYiExoiGdOLIaKto5AS80pPgq69+f8l23r+ZHSJ52wEohWAsYVLTVUwR8qN//UP+7KcepphK4KsKCEk4MzoaCBJVBazZ7UJ94+ArCEuiJ2XjRa6S1bYLz82rlIkY47efT8wL6K84MCevci7LbSYUAdtjDrGig0rQ0AS4Ehddu7BPnCb3uWcI86W60/O229U9ff6umZWtYrmROMNU0HRBadrHsRefR4oQfOD+g3zllVeZKJVIxWIYqoYb+BRrNeKGyfsPHlx2hH2513tyapiLbz1IsZAiVAW1io/nhKSzOlZcnfez7XbtSVnfkNsTrxLGu+eJhCtxdR944BZj3/v1NRUcrMY1Xps4yeTrv4kz9TphUHc2CdXC6jlM75F/0vJ8s2Y43BfmeaJYWOo/Il/cB0KrG7Pm/HrgS0xLQSii5edILHuMvqOfZfrNLzF5o4rraBimRu+2Abrva03xzkwOWsEusCtzRwxLGAniepwrhSsbloPWqNBpodOzy0jM+fmVOz3vNhYe6z+OFh9CuiXCwLk9DqygaEn0ru0IoSFVd1WC5lpcdMuN0CtaF4pqEXolEPM/Lksp8ewJZOAhUdC7dhFMdhOGGpoWIkSMMKjVR69vP4eZL1jcZcTzRk7bnTtPMzU9TBjoKIqLUDSkVAj8upsxnlRBMnutdMJkSidEE0Ssju2JDHlNI+95WMpiEboSeGzTNbYnMhu/uIiIiIiITUckNN6jyFDeVURs5mi2DMNZIS9Mrt2x1yrmOgFDKfnWX72wbDvvt69d5adcj+uDGb63q5fxuImvKGhhSO+2NMdffpPLO/u5sWMQx4ojhYokxHItEDq+7aMpCqgagS+RakgioZNYwlG4GtbSLjyTV3lhbIwTh7YxkbjzfLIVh+Mn3mT/fbsXNVffbTORFZNk9QEmXIkVl2ty0bUDGYbkf/urhPnSPMeqWEFmZStZ70jc/oF+Pv7wQ3zz7DluFvIUAxtdVdjV28v7D969AXyp13tyapjTP/wxXNfE1KeJ9QoCX8GxQ6bGXHoGjFmxsd1Zr7WJk0yeew54nPGTv4amKYtEwuVc3QceuIU68Ws46yg4WIlrvDZxkvET/wyvfAUpBUK97SQPHWq5Vxk/8RT9x59uudi4Hof7UmOsO/v/lHLx53DkDgJPBb1uYwz8+n13dWsbdo7ky2/n9FtPMXWjjO8HaJpKj93FkeEMscZmrHUxkhvhwuQFsonGYlg2nmVkcqTlOWjLFTqt1+l5cvQkz37/96hMnUEPHTzFJNFziCce/OSsU9NIH8DqOYw9dQbT7AXpIxR99n3Fq1xd9ej6Wlx0y43QIyWKagEhvjOJEMq8Y6GoJlKGs8Kmbtgoik8QaGiah1AMpFch9MsoWtfs+7OxjHjeyGm7Y/h1rly5n+n8dkKpIqSFCMC0FLq6VZyaXHSttHsypd3RBBGrRzMzpGN9TAc5ql51UT5rXFVJx/rQzEy7lxoRERERsQmIhMZ7kJuXarMfPlfbiLgWFo6fhl0x+O//O5yTb6A/8o6mP956WUk7761imdP3DXNi/wA1UyfpeOiBj6cKbg33Mt1l8a6/vkbKfphawiJWqXB5R4li2kMKnUDoSDtEjUl0S+DHAnYN9DVFcFtLu7BQFCb/zof4r6+8Sk1V6s9HgifghqXxXx+7n+6HH2Jggai23GYilJKgdIN361m+GR5iolgiFV+9i64drDWzstU0YyRu/0A/e/uzjE7n72SPLpHduZBGr7eUcPGtY7ieRcy6gWok0cwu0tmQqVsOvivJ5zyyw4LAp61ZrzPCl2vXgMfRE8MoYbmhSNjI1d0zoDH2vV/HaULBwXKucSlD8m9+Ca8yipQKih6fNQRLoUJg41VuMv3mlzakTGEtDvflxlgHhn3gDxm59nNMT+/GcySKIjEshXhSw6mFG3KOzG9Gj90p7hh1l2xGXy8Fu4AbuFha42Z1S7PIVXMU7NaNlK6k0Knv6GfvCJFODqGamN2HyBxY3ul5cvQkX37+sxwPRtmuh+iAB1yfHuPLz78Jj3+eY8PH5glqgTs1R9Csrnl0fS0uuruN0GvxAZJ7PkZt/OVFxyKWfYj8+S8h1PprmUpOkExOki8MkFCnEUIhJESG/vzYhf7FGblzmXHazj3+9+39M94Y+The2I+ZMNENgVCgVl76WmnnZEo7owki1oaRPkC67zC7w+9z1fWoetU7+axGFzsMnXTfkUgcjoiIiIhYEZHQeI8xf2PVuBGxmRurRuOnfuADMPnrv4X2z3+pLVl3y7Gidl5N5bXDu6nJkN6qOysCmIHECAJycZOXf/QYe6+k6B6drBccqHHOddm4RhXNM1B8n64U2AakTKtpgtta2oVDKflrPNxtWXrHpsG2kVJiCEFfV5z8tl5eEB733/7QOcNSmwl/ehJ/8iqyDNu+c4X3q3/NiYcPMLk9S9E0VuWiawdrzaxsNc0aiVOEWFP5TqPXu1jdQbHUjaFNoigaemI7UM+O7Bk0KUy4uHZIYcLHsJS2Zb3OF772QgmEUFGWEQkXurqd/Lm7j2YWRjh/6RsU9J67Ngkv5Rp3C+dx8meQSBTNnJc6IIQAxUCGLk7+zIaVKazW4X63Mdb+gSI9Pf8H5cQXuHAmSbngzz7PjThHGhUrQeub0dNWGkM1sH2bhLFYDLN9G0M1SFutGSldaY7h4KP/B4N9RxeNVi8nDIUy5C9f+T/58fAyaV2ljEH1dmvtHt2lN7jMyy//Ogcf+5/RzAxW39E1C5qNWKuLrpGwt3Ad6X1/Z9GxcAvnKVz8yqywKQTsfdtJTv/wx6hUuzGNIqpQ8X2NWtFflXgeyx7DmnP8h4w0A+/ayenvFpm+LRyuxKHYitLApVg8Ln90NpqgPHWGMHBQVJNk7wNren0jWsvM33e/cp0DWgFXHcQTCroMMYIyqpmJxOGIiIiIiBUTCY33EBu9sVpq/FQx6x+Iw2Kp5eOnc0e2lUwS4+Ceuz7WStp5JVDu7yZ5bQxpe2DqCEVBhiHS8YhZMfIZDX+yiiLqG55eqXL/qMXl/jIly6Oqge577No22FTBbS3twqPTeW7kp4klE/jpJIrtYgYSYWgoXXHSvr9kO/TCTVqYHyUYKyAmTIzr96HpQ+y2HYb/+EVyOwcwf+lvkXnH/St20S3HWl7flTA3s1IkFm/g5BKZlRvBRo3ENcpvE0JZ9HrXiiXCQEGPmxhdw7N5ZFAXG40dJvlxj3f8aIbhfbG2Zb3OFb5kI5eq2Ycz/UNKl/4Is+dwQ1HlbqOZRd+lVLzGl7/zv3HO19fcJBy6BaTv3G6WbXA+3/436dsdW6awkjFWnBx77y9w6L33bbjrqlGx0uzaWtiMvr9vP/t693Fm/AxxfbEYNlGd4IH+B9jft78pjxeEPhcuf5NK9RaJ+CA7MztWlWO4GhF7ZOJNdpdfJ6mKRa21dhiyTbEZqL3G6Ev/M6aZmh3VHnz0N1claC7Felx0C4W9hesQQll0LBoJm709oxx+4L9x8a1jFAtJXDGE6Vr0bzdm3589b+kymIXPZ+5jbsvA0O5YR2ZnL9Wifnz7cU5MCyrTAj0UeIogIeAJFyKZsfOY+/ddFC9izMQq9ETicERERETE6oiExnuIjd5YLTd+CqBmu9c1fnq3nMm1NgavpJ23JxFnCoi/bQfB9TFkuT5iIoRATSUwMj2EdkCoBQj9zohcT9mku2xQNFxKusm7f6yPh3/kSFNHh9fSLnx+7BYT5crszwghsHSNPsMiIcRd26FnNmnO9Jvk/uW/RrxxEz1128ml1rMNjd3D9F8exfzDv6T/Rx9peE6sBufkG0z++z9pSSP0TGale/o8YvfwomMYjE9hHjmwKLNyo2j1SNxy+W2x7LF5m3Ltepnzo2m0RAzVWLyB9736SOzwvtiGOWsaMVf4WliVE7gF3PI1QjdP7vXfxFcs3NgQ+u6/yf59H511JC43mpm3C1ydGsGQLoqeYnu8d81NwoqRRmgmuKJekiHml+lwu8xGaFbHlimsZox1I11XMyxXrASta0ZXhMITR5/g6eef5krhCtn4ndbpieoEaSvNJ45+oilFMK+98WVGf/hviDFUAaQAANPtSURBVLuTqAQUUBnVuxjWBF09zW8DLk2doZ8aFeLzGn4s6dFLFeX2ledrCeJ6clW5pitlJe7EpWgkJi7HUsJmd/I877jvJaruAWK7/wGpoYGmvT+341q5G0u1qJ8cPcm3Ln6LjJVhR3rH7L9fnnij7e3qEUtzN9E9IiIiIiJiJURC4z3ERm+s7jp+apr1zMY1jJ/eLWdyrY3BMgzxzl3ivYHKVwO5ZK7gY/v28V/PnMGPG1iH9xOWq+AFoKsoXXGcsotyo4hiN3jeCOJFHTOZZO+R+5qeT7jaduGRsXGef3MEPwjQVRVdVQmlpOZ63MgX2JZJoypKw3boha43bmrIV2z09M5FG+VmZxtO/vpvISamW9IILRSFzC9+jNznnsG/PIra3zP7GMH4FEo6SfoXPrahRTCL19iaDedK8tti2WOzm/JtKUnPqbF15Ua2irlfRih+GhQLGdig3jlugVvALYwQBi4hCqO2TdXP01W8hj32ff74zB/zwYf/MceGjy05mimB64VrxKXDlNZDRc+iCrHmJmEjfQAzcwi/OlYfN9TmZDRKiQxdEApmpnPLFFYzxrqUe7aVrLdYaT0cGz7GU48/NesAy1VzGKrBA/0P8Imjn2iK+PLaG18md+ppktKlpsQI0VDwSXp5fD+gWLxKOr34i5L1tAEnFagJqAFzj1pa1lAJcVHQCNGEsmxkwXrZSKFkKWHT6nk7vdt+FD3hoBiXQBwA2u88bDZLtajH9Th+6OMFHn7gEzfiCNb+nhixsaxWdI+IiIiIiFhIJDRuYsJQcubMONPTNt3dFocO9aMs8435Rm+s7jp+6qxt/PRuOZOP/nQvyhoag+c6IFOux/uHe5fMFdzbn+X166N112AyiZK849iRUlILHXq0LvSwl7A6iWLqoCgQhoSOhxPvY2BPit6h1jgTVtouPNNQ7QUBCcPA9n0QAlUIFCFwg4CJUpmYrrG7b35ZTSPXm+r2EvTkUfW+hutqRrahDOtCeFgoYTZ4fd3L1/n+c7+NOvhxMrHuZTPylsM6fpi+X/30HVfsxDTC0DGPHCD9C+t3TXYiK81vs/qOghSzIt6ug3GKk966ciObzeIvI+JY6j9iZ/+f0ret7qySgF+5Thh4hEhKQchUWMNQLcpYZKiyo3iKf/n8r/P/ffyfcWz4WEMHU6U2TdzL4ygmp7Qd89xca2kSFkIhc9+ncAtv4pWvEPoVhHL7vSJ0kEj0+DBdw49jT7zSkY6TlY6x2rlTy7pnW0UzipXWw7HhY7xz6B3zxpr37f4AqrL+j2VB6DP6w39DUrpU1NTs+RhiUECjS04jK9cIU7vmvTeutw14Z/YQk6qF9GxQugAwpI9BgI8CMkAIlZhR/5vfaFS7WWykULJQ2PTKo5RvfJvpc7+9oed0O1iqRb3iVah6VSzNoupXqbgVuoz6ObGR7eoRERERERER7SESGjcpL754jS9+8QRnz07gOAGmqXL//Vk+85njPProjoa/0zto0D2gc/2tGqoVYhgqyWS9aKAVG6vlxk8Bgolp4gfftqrx0+VyJlM6TI/7fO+rNzhwtUJmFY3BjRyQS+UKCilxz761vOvRNPnguw5zdaJKbVzHqE6jOi6BZuCmB4lnu3jnR3e2VHxZSbvwTEN1Oh6jyzK5kS/g+j6aqqAIBUUIKo5D0ppfVrOU682rXiZ4TwHlhyl0e3jRmpqRbeidvwJQdxkueH1/2DXJf3rPVa6YPyT889cw411rysibwTp+mIGHDrUkB7ITuVuBx4wocO2H5zn7Wmaeo9jqUtFNBbsctCw3cqUs9WVEKb+Psxd/jvv4I0hC4JUIvBJCSNwgJBcqxPQ7XxpUpcUu3SduT8y6bxo5mIIg4GpocNY4wC0ls2g9a2kSjmWP0X/815l8/Tdxpl4nDGoACNXCSO5G1ZMdL2TcbYwVWJF7thU0q1hprcx8UWMVL2Lefg3Hb/6XpryGFy5/k7g7SU2JzRO9AVAUpsMYvbJGdfoc8eSuprUBW5n7SPceQY6/wphXwVAtFBECEl+GmEKgG0mUOaP06xnV7iRmhM3axEkKF55b9pzWMkfavdymsVSLuhd4hDLEVE2cwMEP/Xm3b0S7ekRERERERET7iITGTciLL17jySe/wdRUjaGhJLGYRq3mc+rUTZ588ht84Qsfaig2vvS963z1Wz+kX82iqxq252LFVXZsz6ALrekbq6XGT8PbOX9KavXjp7mbDhPXbDRdwXMkulkXQOxqQGnKx7FDKnlJYdffoJs8+2pv0OuPz1/XAlfdUqU1jXIFnVd+uGLX4/6Bfnala7z+Qp6pq2kcN0AzVAZ3Jjjy3syGiC93axee21CtCMG2TJpcqYzt+/iyvjHQVJX33Xdg1gW5rOutZx/h1Em87W+ijgwtcss0I9swLJTrj2ea9bKM25xO5PjittcpqS49JZWk3odrGmvKyJuLUJR1j3lvFlZS4DE5luLCGYnrOfNEvHLex7QE73w8Q7JHb1tRwbJfRvQIChNZro9/EJGEoHoLQp9Q6+KGV8NXLeamIfqoqLhss1KcnuO+Wehgulqe5g9e+L9IhiaL0wjX3iQcyx5j+Me+hJN/E2f6h7e/FBIULnwZv3pzw8W5tbDUGCvArRd/eUXu2VY5NTeqWGkhK40nWCuV6i1Ugv8/e/8dJll2l+mi71pru/CRPquyvO+u7paqHVJLQgKEMLo4YQaOpAHEHOZwNEbPXLiDOXc03HM0gnHAGTTMmRkhkBAIhhFeIC8h393Vrrq6y9vMSp8ZPrZd6/4RmVnpK7Mqq7u6tN/n6W6pIjL23mvviIr95ff9PvQaX/FaeDgmoOT2ksT1W257nkcIyb5Xv4dLX/9lrOY1puMA3yQYacgIgVIZssV9S92jtxDVvtPYqCO855F/+zLv6daxVou6rWykkMQ6RgqJtcype7vb1VNSUlJSUlJeXlKh8RWG1obf/u3HmZlpc+DAdUdXPu+wf38358/P8IEPPM5rXrNjSYx6sTh5dH+bQ4PbybkeOoKrl2rsv6fE676/f8tvrFaLn+p8Zxvdv/gziHyW9lef2ZBTbPRim2/87Qy12RgpBUKA40rcrKRRTdCJQSowiUDpmIrdw3P51/BA4+tLxMblrrr1SmsWOyDrf/opah/68w25Huedf7e7tONWWd5QnXNdco6LH0UkWhNrTWI0hwYGFn5msesNIUgaLYhisDsN1VbPLqLgMuFT52h7ryLOFLDadXIj51BbMNtQlvIw3Yne43TctxrD/+w9T12FDLUzYBKU46TzoDbJjQo8dBRweeR7CCJFuX/15vorp1p859sHNnSN347ZfKuVXiVhlag5jImaWNjUqmVK1MjvfivtkU/QwqXZvoK3bNsWCQkSrbKESWWJ+2ZxNPNgn2b/C3/XaRK2MvTRwiPCx2aS7C01CQsh8bruweu6B2M0Y199Dzqsbbk4d6NyrVthtRhrUDm1qfbj28VWfkZv5HrezHiCm30v5LKDVFFIYjQrEwqKGB+b7iM/x2DPvi19/2X6HmLPa/4Ns6d/n66Zk+jER0ZVJOCUDi9ppb/VqPadwvx592dO4M+exPJ6172mo9q5l2lPt561WtRzdufv3tn2LF1e1xIR8na0q6ekpKSkpKTcWaRC4yuMkycnePHFSbZtK6z6RXZwsMALL0xy8uQE99/fEYeWi5OhCHh+4iJ5J4MlFBcuT7OnWeAn/vntadBdHj/VhSxceJHqR/8a88KFDTUGz0chm/UEKUApQEDga9rNBCHAdgXGCKTU2Eqj6uO0ioOczxyluz6xEBFf7qpbXFqTGMO4NLQEZA0MaIH0XPTEDPU//rsNuR5XnJctLO0wWm8qxnsj8WDVhmoBnmN3bgbq9RUN1fOuN+1niIfPYBpt0BqkROQzWDsGqGYe4srBt1FvdqGFQmYSSoOv49XfvR3v0aO3tAb2od1w4UWSyRms7QMIIbjo1bjs1eiJPQgiZDGPyGeBdB7UZrhRgcfsjE2jvYt8b3bVz5+NNNdrozk7dZbGxOMUxj6HG4xjdLhl8d/lpVfzZS9GxwjlYDuSsG0D0B5/HDuzDbt+BSk6+6bmG56NIU/ImCgwnKh13TfzTcIf/cKv8HDrcXZYGhuIgOFY8qQ3xNu3oEl4o9H2zYpz1y60ePbzw8xOaLRWWI5L14BN6b4aPg08k2f/4G56t7lbJj5uxD37UkVqt+Iz+kZN7fPcrnO4mAN73sy5J3sohBM0hb00Pm0Mnm5TdwY4sPctWzITcjmrzSysnP0IOqqipX1LUe2XozhoPRafdx1Uif1JTFiH/K4loiosvqZrL9Pebj3rtahb0sJWNpayaIWt29aunpKSkpKSknLnkQqNrzBmZ32CICGTWf3UZTIW4+MJs7PX647XEicbYWfml5MXK8TJrWZx/LTx9WcBiJ4/h9NV6sSp/YCpcxXGfu0v6P/fBNvefHThhnZxFLJ7wGYqNgS+xnYERhniqNOzYiJNHMTYURvVrEEY4VQmqeaLVEWJYmN01cbg+dKaiybiazt6mezKkSiFShL6Zpu89vIUO4whuTZ5Q9fjVrQpr8XispqNirPrNXPD2g3VzVZIzffJOQ7ffnhpM7Z0SuBrwisXEG3AdRBSYrTG1JqMn7I40/5xdPdecoMSZWISYVGPbZ44K3Eutm/JObtw3oqFhUh+LeMTEuM0AMvG2jmIWNTwmc6D2hg3KvBIeDVY3Ws21xtt8Nuaiav+qq6w4yPH+cjTHyGeeY7vk6O00UR2gYHiLoq2sxAd7Xn1r9CK77sph9ny0quoOYzRMdLqXHNxbKPUXKFQVAOnF9frYZs/y1TURogcFgl5QnxsnlY7mKhP3dB9c68D/2vRUG/CVAyBAVfAfgeO5Qx7tmD07e0Q5y49/Qxf/es6gS9xnQqOign9DKde6CZ+ziJSPog2X7Fn2L6jxJvesndLnO83cs++kiK1m4lCvxQCq5IWQ/f9HFNPv59cUsOXGRIsFBFu0qYtLNSeH7qtAt1iF2umH+zC7jVndW70FwsbFXNfKlacdytLHM6iozph9SxO6eBSB+fCNV0EJtZ+4VcYa7WoPzz0MI/seITHhx+/be3qKSkpKSkpKXcmqdD4CqOry8N1Fe12TD6/8s613Y5xXUVX1/XB3DcjTt4ujNZUPvKX8JYHsHZvRyaaaaufc+Wj1HeUSWKD9Tct+sbGFuYYzkchHVcQtA2ZvCIKNVFoOkYNAzoxRGGMMjFZ3UDmshgrRLUDgsjQGpklT2PVxmDnyF6uHtjOJ3aU8TMu+aaPlfjESjHaW+QTOZfvlYKdlycR3uqul61oU16P1cpqjB8QnjjD1Hs/QO+vvnvJMd2omft1P9C7IBYsbqi+MjFLs9nAxIJclGUw2MHFpqHw+uvioF04gLkKOD6I0oKgJ5REZzyuzHwnYZClZyiLnBMFLcAxhup0zIkvVxnc7d2yM6rnl/9XGh/6c6Izl8gGVextEJY8ijt2osrFpeuXzoPaMOsVePTvfTv21cyK5nq/panPRATtBG3gqc/OcvVMe4mofXzkOO//wvup+RX+caZGEck0HmHYpj5zgQM9ByjldjE+ovjGh4ephTmEVmRcd4VAvh6L24QLsoWJmgjV+aw0BvwgT7lrDBBYbg9JMEPX4Z+hcf7P8CeOI5IaSJtRkefr9PNUvXpD9818HDZDTGHgEcpRi0hH2NIma2eJbyIOO+/8rPqd7R/sPbjl4lxr4jhPf/oCgT9ILjeLlJIoMUS+jxUqBC5SQD17jXZiiC8ZPvk/LvNdP7r7lsXGG7lnXymR2s1GoW/mHN6Mi+/V976dZ4CR53+HbDiNa1oEJmE0UbxgPEZf/AR/N36Zdz74D18SwWetWZ0bfT/c7rmWm2W18y4AZRdIwhromKgxjOwqIoRYck3bxQPA3ROfho7YeGz7sZWfWULy46/68VX/PCUlJSUlJeXuJRUaX2EcPdrPPff08fTTo+zf373i5mxsrM6DD27j6NH+hT+/GXHydhGeukh87gq85QGEEExb/TyXfw2hdPB0C8+ExE3BxIUmX5mJed0P9DI5ElCdiTDadEQtAcoSSAVxZOYPHkuH5E0dR0QACM/BeFlUZMgMFOn7P34W9979K6LG2hi+vrsP35F0zdQ74pgAFSXYM3Vmu/N849AQO8YqnfmOuZU32DfbpqyNWbcVGtYvqxF7hogvjVD93Y/jPnx0zlm4dhnG/By95WLfwYF+ss08n3zuMs0wpJhxKVt5tM0KcTI6fRn59TL6dRVMtgmBB4kClVA3/TTCXXjNJrR8mIsvw8ajtRvFfeheco/eT3jqIt2zVQ5d+s+c8i9TLi09B+k8qM2zZoGHEXQ9Nc7kcEDJEXNFTJrZ8ZA41ggEXlaSLagl183AHpePPP0Rqn6Vh4q9DMTjNHFQwiIjLdpRi5HaMNXkMV489R3oJENTnaIhDXbSRePSNurT0cI1uJ7wsrhNuDatUdrGdiRxbOMHeRzbZ++eZ7jEMYTlocMAOz/EvW/+fZ458+d86sWPc646xpVEYCuzIffN4jisFIK8s1RA2mwcdt75eW76HGES4iin057+6rezfYvEOWM0V578a+r1N5DJBEjZiYz7cUQ73IUQFkr4GO1g46GckHo0hl21eO7LlSWfHzcjhN3IPXsr7ccvJZuNQm9WYL0VF9+r73079x/5B3zyqf+Hp05+mHtEje2uZDchEaMMz47z0S+chje97yURG1eb1bkRXoq5lptltfMuADu3AxOfResQHdXRUQ0hrFfUNX2zSCFXHU2y1p+npKSkpKSk3L2kQuMrDCkF/+SfPMrP//ynOH9+hsHB663TY2N1uroyvPvdjy4pgrkZcfJ2MT8PEcAA5zJHCaVDLql1fHFSYAUBmUxMo23z+N/N0KzHJJHBsgVSCUysiX2NVIJyr011MiIONPl4Fkcm148N8FWWcjhOfvwCQspV5xlefvokExmLYhgjLYtIWBihECbBljGFxDBR8Jjev53eFy4h9gytWMObaVM+Oz7BZ148xWi1QpRobCXZVirz5nuOLDQ8w8bLauZj26uVYSx+/mpin9GG579Sw2lm6espLvycclkhTupKHTGcxXnxVUT7LmDyNYzrI7Qimd6GCfKo1iwmilccs2UL2g1D0NYbXqf1mI/ku8BP7fm5VedEpfOgbo5VRQHBgohXnY7JFhS16TmRUXTen8VuC8dT2K5cuG6quSrnps/Rl+sjI0IUmnhRv7OjXCK/yuXL90CSIeeMkBfQh6Cl20zrWajfy4kvVynlXqB6dn3hZb5N+JnPtZm8nCNsOiilKZfG2L/vKcpd41yqHVviHhNCcuzw23jVoR/ctPtmK+Ow887Pql+lL3f9Oj45cZL3f/HX+T+O/RA9zeFbFufC6hlas9No46FUZ2ZcrBOi2MYkGYSIkGgEIEznly+O5VKPphgd6Vr4/LgVIWw99+yttB+/lGz23G9GYN0KF58QkueGv8EjskrJUjRwaKGwSNhrh/Qkl/jME7/Fse2/d8d+Pr4Ucy03y1rnXTklnNJBwsZVdFghao2i3PKSazqKopdkH1NSUlJSUlJSXi5SofEVyGOP7eTf//u38Nu//TgvvjjJ+HiC6yoefHAb7373ozz22M4lz78ZcfJ2MT8PEaCmytStMp5uXZ+mN1cqIh2LjCUZv+LjZCReThG2Ymj7ECdIA4lyqA+3sF0FSUxg5ZCmhTIJiVD4MoujQ/YHL0IYrRlrblRqxEJgrCzV7gESYWPouBOUicgkdWIZYV77KuTI9MJcwPn48mpzH2/E2fEJPvbEk7SCgGI2Q8myCOOYKzPTfOyJJ/nxRx5eEBsXl9VoYCLn0rYVmSihvxmsiG0vL8NYzmpi32bEycLcOZSjBbzat6ALVbAjiGxyrT2ovCGxHIS98uMljjqzIt3M1t/QrjUnKp0HtbXMi3jPfanCxJUGfqszI9X1FIVuCy/bERAXXzfOcIMwCTuiGYYEiUVCNPdXUFYkeMEgE+2dONY0QoCeC+XnhMamxWxyhanhnVz80gfJOadvKLxs25th4Kf2c+aTf0RzepRMsUSpOIUQkJjOduNgmmz3oSUOwJtx32xVpFkbveD83F2+7txa3J7++xee5H2P/hLVsx+5JXFOh1UsVUEpTZJYWFaEQWPMXNOWMGAEoDFCg4GM0MQiJA58glZMe/L5WxbCbjVS+3JzM+d+IwLrYhefldsFSQsd1hDSxsruJG5dXdPFt9hheqUxzZ7GsxSUYJbMQjFMhEXFKEqyya7Gc5ydPM3h/ntu72LdJHdScdA865135ZRwChZJMEn3vf8bbvf9r6hrOiUlJSUlJSXlVkmFxlcojz22k9e8ZgcnT04wO+vT1eVx9Gj/mmLhZsXJ24VzZC/WgV0ARMJGC4XS151vJoiQxRwyn8W0EuLYUMwqZNBmZjYiMZJOstkg0ETGxq1Oc2D0G8wMHKHu9hBIhTQJXdE0+9sn6apfQa8Ta86XiwgsKnYJhYU0CRKDQRBJh0CVkaaB3HOYnn99D9XfnStkmZxFOPaqcx/XQxvDZ148RSsI6CteL+jxbBvXspis1/nsqVPs7+/EMOfF2UsZi28cGmQi6xJLiaU1/a2Abzkzys5Fx7e8DGM5q4l9mxEne47sxT60h/DEGaw9Q6h6eeF5RTNLvj5GrXs3+WURc2MMrXpC/w6XnsEtaMZgZav2g9seXHNOVMrWUc6/wP37fp/zQQ9nGt9NxpvFzihsawdwXVCZv248k8dRDn7sM2XnmBY5BkydWaNACIq6RZjsRhsbJQPM3DvcABKDjcEz0wStPH5TU+7eWHxSKsXuR/5fHTEsPIeJe8Hy0FEAsGVRxq2aN3h26uyC83M1wX++Pf2qKHLosd+8JXFOOiWKxRny+XGqtSFyahZhDJZISDAYLQAbrUIc2aRk2tgmRuMi40nqpz+O8c5uSZz1ZiO1LwfzIh50nHaZ7sM3de5vJLDOu/iE5RFWXkTHTUADEmnlUF7Pqi6+5Q5TE7U5IurUyC1tnwYQgqbxGKBFfeYk3KFC451YHHSj93wSTOF23Udh7w+nAmNKSkpKSkrKNx2p0PgKRkqxqZbozYqTN4vRmvDURXSljiwXcI7sXXD6CSkpv/P7YfwS8toIcm9EgkIlATXLJ+qRZIdKOBiisDN/0bYFXBml0Exo53qIhYOm4ziUaHaNPcGuq19j9+TTtA4eJZIejg4oJrNgDPENYs27XnUv7hdfpGEn5EOzUG7SkTIFvm3I+X2ceqrExKDH/f+fX6A/GF31+DayNpeeep6Ra6MUHIflKy+EoJjJcK1SYWS2ws7uLpwje7n24GE+0ZfBz3kUwhg7iYmUYCTv8YnD2/i+njI75o5vcRnG/By9he2vIfZtRpwUUlL+mbcx9d4PrOru3Df4PKcPH12I1lq2II462/WykvtfX7rlIhiA8cs+J7/WXLVV+/Dew3PrbZgeDW+qvXizLBc9b+e2Xk4Wxzm7e+7DGY4QSmLilU2r89fN/sHdHBg9wMmJk2RLu3nK2sm3RWfpok2gFS4xqDpSRCTaRapgYXudd6TB0xaYFl7eRYil0cP14pOruce0zAPQ88D/e0viuVs1b7DqVxecn6uxuD39VsU5p3QIt7SP3ds+yYutf0CjnsWxfTKyTSwD4iQHMkR7s/TSRKIJNOi4l77uSZzoc/iNSezi3jsmznq7mRfx2tUrwE8z/vivkCntItP/LcQ3EWdf7xzqsEoSVDFRDWMShHBASDAaHdcxzRbCLi5x8a0WtQ7rV3EwlGmTGIUv7CXbCYGigMIdrIUtFvWsXJbW8rKll6E46G6ZMZqSkpKSkpKScjtIhcZvMjYrTm4W//ETzH7w48xcaRIaG0dEdO/K0fUz1x1/7kP3wicu0b2vRL4xweWeAuP9MzTzBu0qlLpAwYzTn2zDkzmSyWmYqWErid0cJXFzGCHRQqBR9DtVhGMhXYvcuec3HWuuTCZsDw9Qt87QsmPcWCKMRaQkoRVhJYrB2e2oLsnkcMBX/2qa1/3Adrbdu7nWVf/xE1Q++HFGKrP433KQTMMnyGexdw4iu663JDvKopb4tMKO2GKE4MnX3Yt/dYSu6TrStRFS4oQJXfU2s105nnxsL4/MtV4uLsPYqNi3WXHSe/R+en/13VQ+uNLdefhdP0R/3y6e+3KV2bGQdqMjNvXvcLl/g83BG+HrfzON3xJrtmoDC/uwXIjcqn2YZ/Ri+yXb1svJ8lKGMg0KhWkq1QFy2QSj20TNYZRTWnLd9G5zeSfvXJih2cr2odV+Xh1fZlDXsAXYmcvkMleotQ7gytEF85WZ+1ccd1MuXKVUrsKi+Y7zrBefXO4e0zLPia+cw+t99ZatzVbMGyx5pQXnZ85Z6dzayvb0eaEkrPwSB4f+E1fHvo9Gexdal7FUE2MEQgWUTAO0oZV4mKSbrBtzYP9zWJkeAn+UpD2F5Q2sFBtXOR+rNmm/QoSYxSKe8rZBBMrOE8y+QNwcprD3h2lPfGPLZk0Ku4CO6qAjhFrkmBMKjIdJWpiojrA7Tva1ClMymV6qjctIoynh42MtdTYmPsr22NV39JbX6HYxf61e+vovUxt/gulYExhwBfRYkmJuOwMvg6h3N8wYTUlJSUlJSUm5HaRCY8qW4T9+gtO/9mdcyN1PY+82tLSQOiZfH2Xfr/0Zh3+RJfHi0ju+j8wffJ6LuwcJLYMXCtwYEk8wI6rUc1WODHs0W11kgnDB/SdlG5H1aOX76IqmKakGiWVRfOf30378xILwhW1h7d5O7jtfi8hnMVqvKjYGbU2JXu4NDBfVFZqOT6w0wkDez7C9NkQuLCOVpNSzemvzPGu5Of3HTzD13g+gK3WyB4awlSL2HEStSXjmEs6hPQtiY5jE2EqSdTpFLSOzFcYVdA/2IRMwjRbamI5rqJija7CPccWCAxIWzdHboNh3M+Kk9+j9DDx8dNXj3QYM7vZui8PP6I7T1fc1pR531Vbtx/9uhtBPCHyzphC5VQLg6MU2X/mLKfy23rJt3anuyNVKGfbvO86J57+dZqsL11EQtghaDdotd8l1s3yG5jeSkGfUAN9Z3MdbkgvocJb+/r+kdfVnCcJt2NYsUgYY7RLGXViqyb5dXwbtg9x8fHKxe6xTxnBuy9fnVucNHuw9yIGeOeenvTKOudXt6V7vMZQ3QHfxacqF36TZ2kmUFJC2z4SfpTL+7QT+Tvy4iJSacnmSIwdP0NM9QhLbIG101MDETYSdX/Lay8/Hmk3ax955x89PXS7iaTquQGnlkNYuouYV2hOPM/Da/0hUO7dFsyY7vzjSsELERcyPFeg8D9YuTJF2HtspEgcVLBOiiIix0UYTJj4DylDqfQCvfGe3Ar8QwkdrgocT2GFBFxAB50I4ngjeHsLLcRW90meMpqSkpKSkpKTcDlKhMWVLMFpz7kN/z/PdryfyCnimhdItEmFRLe3kebeM9Xt/z70PX3dNTL3/v3Py/h1I2aQQZNHCRmsQzYScSQisFpOFMfZUHNqFfpx2FRWHJNImFHkcv8H+8CT4AcKx8V77Koo//YOEpy7if+1Zmp/+KvHoJJX//DH4L3+CvW8nXe95B5nXvGrJvtdnIpr1BBOX2SPKtOwmIRGOsCmSx0QGrTX4PiKbXbW1Ga47FqMzlzrlLY6NfWgPpZ/+Qaof+nN0pY61d4hBLehvBVzLe3QjMC2f+OoYTrmAAWrtNrt7ehjqKgPQCgOiRFPqLiO7y+hGC6IEbIXMZ7EMNBqNBQfkPNv2ZjYl9m1WnITrrc+rIaRYsj5bxcxEJzaby69dXDN+NcDNSLoH7FWFyLWE4s1itOG5L1fx25pSj7Ul27qT3ZGrlTL0dI9w/32f4/yFh6jXewjiDI7Sq143Dw09tGKG5oGe/Ux89V9QG/kMPfkXMDv/G2MT30+7vZMoLiNFRD57jp2HLrBtKENQuXhLcxBvxOIijZsRDW4l0iyF5J3H3vmStaeH1TMk4Qxu932AIdMVgwRp9bINaAz+MY0pSeQeoVjMUipMLpjhpJVHWnl0OItOIuSiRO7y8zHfpF3zK9ybzVFSeapJwgsTz/P+L7yfX3rTL93RYuMKEc9cf2xxTDyqnduymLiJakingAlqmKQN8np02ugQIezO41GnMXytwhQBZIv7aM28iNJtLB3QNDGugCFbUcztZM+r3nNHC2PzJUkn/YRa6VH6aOER4WMz6WS5XLvCHzz9Bxzbfuxlcci+kmaMpqSkpNwJ3Op3rZSUlDufVGi8C1lvRuLtInjxAmfbQ0S5PDldW3AfWiYmZ2o03QJnGkPsO3mexCQAjCcRU/1lymGAm/hEwiYULr620dJC6AIT27IMTrYpNAJCt0CYKSKTmOLMZXZf+hJdR4oki2YwCikxjRb1P/0U8egUJggwfghJQjwyQfD0i3T/0j+i9FM/CHREnRNfrsy55ATKgnycJwwMYEiMj9YCO/Exp8cJ8hnkjm0k2lvS2uw/foLJf/Xb6LmotuwqgZKEJ84w+S9/A/wAta1zkyqA116d5RMHB5jJ2OQw2M0WcbVOQ0LWcfmOI0eQc3f0WcfFVpIwjvFsG1lY6uYK42iJA3IxmxX7NitOvhyEc+su1yiuMdoQR5pil7WmELmaUHwzzLd1Z/OSKDDoRCOVwHbFTW3rRu7I135/D43C1ZcterpWKUNP9wjdXSNUZvIEvmHbw/87gwcHVr1uVmt1Lh/+ScLqaahfpL9wkr7cSdrt3cRJAduqk8tPMvjgL+GWj9zWmWjLizSEcnGK+ykf+smXLAb5Uranz4tT0utDiJVx9Ey2C5l7DqdoYWe3LXlMAJbXRxTVSYJJpHJWPR8G+MjTH6EnHOMduZheM45KNAmSqWyOv237t1Uk2oq49svReiydUucfu0wSTHXKYLQGIZF2AeX2gjALjtEbtSB7xb1EzasMyAw9JkEpj+7+Y3S9hNf2zbKkJElKprjunhWwUJJ0dursphvjU1JSUlJeWu6E71opKSm3n1RovMtYy1VX/pmNtyLfDNNXWtSdHjzTXllwAnimTd3u4uL7PoY3ehb+xQ/TDHyiKCIfJGBbGCRtmUULgUwi7DghtqGZz5MNDHtPfZJMYxo7bJGvXUMYQ5T0YO0YWJjBaLSm8sGPE49OoRtNiGJwHHAdRKLR9SYz7//vOPfsw3vkfp77cpXAN3QP2sxOxCSxQchO360xhthYWERkaSFsC11rEp4bQQztWGhtNloz/W9/l+j0JQCSmSpIgcxlUTsHSUYn0NUG1p6hhTXZXW3zvWfH+drOLiayLg1L4oUhu4e28R1HjnBwoH/huUNdZbaVylyZmca1rBVOruUOyFvldjkRtwpnbt11ZFCrlFdHQcduZDk3btC+VYK2JmgntOoQhbrjdBLguJJCt4XjyQ1v60buyPHxBh/+k+f4yuB/IdTByxI9Xa9pFQw59yTdg0cZPHRoZdxzHTJ9D9H/6L9h8vj/j2DmeYzU2LlLgELaOaRdpn7xf+Ie+5XbNhNttSINk/gEsy8w9fT76D32Ky+p2PhStKffqM1XoJDKQ0d1jBlc8dljtI/X9xDSKRPVL6x6Pk5PniaeeY4fc6vkjKaBQ4zCImHQ1PkxV/JnM8/eFpHo+MhxPvLUh2nOnMTWAZF0yXUf5Z0P/sNNvWdejtbjxe81p3wvJmmCjkFaCJUjbl5Z4uC9UQtyO6hwzhT4i4aLrcPOWliCd75MkePNsJmSpJSUlJSUO5c76btWSkrK7SUVGu8iFs8BVAM9C4Uo4YkzTL33A/T+6rtvm9gYOVm0bKKSJqhVXEx+m0Tk8CfqZL3OzYIXa1QYE4UREmhmejBCoiIfgFiBTCBfqxDZ25jc/gCv+sp/QxjdUS8NqP7uJccVnrpIePoiJgg6ImPGu37DZSnIZdCNFrO/+RGcf/9/MjsWkisobFciBgT1mZjATzCmI1YZAdmkgWMCUBKyHkGSoXvqMt39nchw/U8/RfD4ic42XLfzvEST1FpURwOS/sPI8CrWTAW7v2dhTXZX2+ysthmThlYYMvQLb2DPQ/cvOBkX1k4I3nzPET72xJNM1usUMxkcZREmMbV2e4UD8m6nu7+T0Ww2EkpdK4XXwNfYlmC5idcYQxR0Hjfa4Hi3vl71mYh2oyMiKrvTsWAMBL4mGo8odlsLbd03Yt4dmSsoEIJG2CTWEZa0iXXEaHgV0cow2L8fXazhxz4nJ05uOHq62OksSjnYFmPi2qYiK7ezadXrPYaV30XcnkY5nYILaReQc7P/ouYVKmc+zOBjv8HgFs9EW6tIQ1g57Fx2Ydte77FNb+dm40GLnZ+3qz39RuJUEkzh9jyAjhprnu+e+9+z7oy6anuWx5gki2aWzEIRSYTFrFGUafMYk1Tbs7d8PIs5PnKcj37hV3g0GWGH3ZmqGAHDs+N89Aun4U3v27DYuHydFrOVsf3FLH6vxfNrbxc6a9+8suK9tt57s9EYZrhV4a/CMs3MwEIc/9LkC6+I6PpLWZKUkpKSknJ7uJ3ftVJSUu48UqHxLmHeyTc/B3DhwzuXQewZIr40QvV3P4778NEF599WxqvzR3agnBnihsZWyyJ4BuJQo+yYzFAXstWZJdg71aBcTxjvLVBoGRJhI00yNwDfELqGYk1SaEgSq0qjtJ1G904Ks1fBsUAb8m97MyZOCF44j3NkL7pSxzRanbi046zShKowShKdv0p4apgksVFzEVwvq3AzkmC2TfPCJC23jBYKjMEgSITCl1kc02b3hS8SndmHc2Qv9T/+O0ycIIq5hb8YZ3r3c2nX62hm+0ksB7k9pBhMccC6Qm88cX1/jKHv/AjuA4fof/C+NV1gBwf6+fFHHuYzL55itFqhlvjYSrK7p2eFA/JOZauuuXmBxfPkqsU1uYKi1GvTqMQdAVkI/FZCfSYmDDRJbLAdyVOfneWBN5Rveu6h0YbLLzYRErQ2SDl/ww/CNsSRoToVsefe7EJb93oEbU2SGJpJnZGJYZphE20MUgjiJMYYQbcYIi+6aMomOSdH1s5yuXr5htHTxU7npHua5FXjMBAju7PIfHFTkZXb1bQaVs8Q1S/gFHYgV3GNzc/BC6tncMtHtnQm2lpFGrB0Bt/8tjfKVsSDbufMzo0Ixz33vwfghud7rXUpxRW2yZgaztK2484OUDcW22RIKa7c0rEsRhvNp5/4Lb5LX6JkKxo4tOZclHvtkJ7kEp954rc4tv33NuQSXb5OwhnsbCdqkoRjWxLbX40bvde83mMElVPosIqwCwgrS2HPD9IY+Ryt+lV0EiCVy7kI/ioskxSPkJs7B5v5/Hi5ealLklJSUlJStp7b9V0rJSXlziQVGu8SwlMXic5c6jgZV/nwVv3dhKcvEZ66iGm0mP3v/5PwxBmMHyI8B+f+Q3T9ox++acdjzzaXnr1FJs4aVGsK6dogJWiNbgcEbplyMk1JV0jmHCHP3/9j5FtdXLMqTHVrrETgJAJjQeCAHQl2X7YRCFQcEnpFIisDloS4E7eu/cnfUf/Dv1mIiOe+41s6200ScFcRdxLdcRxqjR22UKpMEhmkKxbWyhYx2aCCtCQtq0AiLVoUkCahK5pmX/MEpcoldKVOeOoiybVJcCyENqBgpryHFw9/P5GdwfWruO0KiZehWtzJc3EP9/lfx7EhjAVqcoJSqbAQ/V6PgwP97O/vY2S2QisMyDouQ13lJU5Gbcy6j79c3I5I/2ve2sPJrzVXLa4BFhq0LVtQm47QCRgMli0odFtMjoS31EA9PRYyOx5R6rWpz8REoUFZYs7VKDBGgxHsvie3IfeZm5EE2mdk6gqBaOIoFyUkYRIRJCGOyZLIkMTyF35GCHHD+WSLnc7cK0iOjWCsAKqKpOojD5QJks1FVm5H0+rLMQdv+bZbMkcUV7ClTc7JLYyBuJltb0U86HY0mi9nPTGrdPAdXCHfiW8f/Dl22UC0MRfs/GzEenMUT0oqSYQnV34mN5OI7bbFjlz5lo5jMWcnT7On8RwFJVa4KCtGUZJNdjWe4+zkaQ7337Pu/l+Prh9bWKd29QoASdwgswWx/fVY673mTz3N6FfeQ2PmJDqsIZM2UioS6TId+kwkhhNxhnNacqLZYKi4k8FVvhvczHzDrZh7uRle6pKklJSUlJSt5+X8npeSkvLSkwqNdwm6Uu8ION7qs/WE52ImZ/G//izVD/058fB4J+NpDAhBe2ya6MUL9P+HX7gp4UdIwbEf3M2XPnqF9oSF05pFBSGJ5RAUBnDadQ4k5xDArN35C6ZWGqJ7qs6RJMulnSGNvCayDFYiKTUlu6/YlKudfUxspzO3MWx0REZjIJ9FlYvIjLcQEY8uX0N1l4hHJhCJ7sSlF2HCEJnxkIUcPbuydE05TA4HlBxx3QVqWxgpSVAMBMMcbj1DJF0cHVBMZtHNFtXidnQ7j11tYoRE5nOYRhMyWS7veh2RnSHbmuokvLXG8RTZfV1Mj3g86bwFK25jpIU1oOjeU+RY3262cWOkEOzs7lr1sbPjEwuOxyjR2EqyrVTmzfe8vI7H2xXpH9jtMbQvv2Zxzet+oJdnv1Thyost4sggFXieotBt42Ulxhiq0zHPfamC7QgC32wqljrvQCx0WVi2pD4TEQbX5zS6nkRZkkK3fcPXAugasBiX57DCIjJrYO6eWQqBQJBNSkw5l2hnJhGLJqGuN59ssdNZ7d1OcORxjB0hWgWE3Wk81yMV7PsOEm8ysrLVTasvxxy8eU7NjtBuTFKJR/GNQApB1smxo7iDslfa9La3Ih50OxrN12I1Mev5Rp1ff/yjnJs+R5iES2eC9q9/3o+PHF8os+nVdd5pRZAYGqZBxsoghUQbTZgEZJWilOnFcsu3dAxGm4XPgvGx8/QZn+YikXEBIWgajwFa1GdOwipC4+L9X37sDz72m7SmX+TEV84x8Oj7yPbcc9sjXsvfa+3J41z6+i9Tb16jEUd0iwgLgxAQmTptbdOvJK93AyaiAq3I52r1Khk7syJevNn5huutze2MX29lSdJLLZSmpKSkpLy83/NSUlJeelKh8S5BlgsIx8b4ASK30uFi/ADh2NT+5JNEF0cQSnZKUubmCRKGRJdGmPl3v8u2P/4PNxVp3bY3wxvevovnvlRh5kqJIEywHMVA2bD9b/+SbruGyWW4kLkHQYijElrFASQue65CMxeRWJqMVOwpZkisMRK3hg4iQq9EceYy+dkROkoO0GgRnbqIfWAnqlxciIjL7X3IXLZTBpPLICwFicaEISiF9Fycw3twDu9ld6vJ9EjAzHhEvqxQYUDsR7Syvdh+nf3RScrJLHSKspmy+jlX2Etz9w7E4zYyyeId+mH2TR2neOpJqnaZRm4AN6ghjMFojZACa3s/sZsjzlgkliFbypPNW2jHY6ae3LI76ez4BB974klaQUAxm6FkWYRxzJWZaT72xJP8+CMPvyxi42Yj/ZtlveKabXsz2I5gaiTEsgWOJ3Hc69sQQmDZgssvtpgcCRGCTcVS3YxEqY7DzMtKvKxLGGh0AnJO3w7nBNCNcG76DGPFv2BX40eRQQnfbqFVjNIupaRAqJo8m/9remNF3rneurrefLLFTmdTrGHyNUTgXRcqXRvdaGOa7Zc9snKjeYG3Yw4edISTX3vyo/xQotljaSpk0RgaQZ1z0+c40L2fbFLd8LaNNoydPcPoJXAz91GmseTxjcaDFs/svN3t6Z3XvC5mHR85zvu/+OtU/Sp9uevusY3MBD0+cpz3f+H9Cz8rVR+VqEkvVUaTmCAJEHTE3IKTZ6djU+p94JbO6/J4eRTvwjI/T6b/02RLF1Y8PwSKAgqrvDWX7/9qx/5A/wPAuVt28W6UJcKYWyB+9jeo1C4zrgW7lcYCIiSxTnCBkjRMihxdtHmjVeXvlU2kI4arwxTd4pLraTPzDTeyNrdbbLzVkqSXSyhNSUlJ+Wbn5fqel5KS8vKQCo13Cc6RvdiH9hCeOIPYM7TiwzuZmMHatY32E8933C/LSlKM8hCtNv7TpwheOI93383NOtq2N8Pgbm+Jy6y732LihE14YprmgfuoqxJFJmm6vSSJQsYhSkmKukDc6uxTI2NTPHoQqm38aR97Yozd5z4/JxrGc/Foja7UCE+exzm6H1Uuovq70dNViu/6QWof+nN0o4VREpTsOBk9FzXYi/99P8Kn/2iS2bGQKDQEjQh/qokVtbBjn1JrnF1nv0AxHEPv3obwXKZMNycyx4icHPnBEk63RRIpasWdPG8Vuc+xSSYrJFLhhgFGGIQQyFIeNdjL7EjYmeVnCexiDpVTKLhld5I2hs+8eIpWENBXLCycV8+2cS2LyXqdz546xf7+vpc8Rr2ZSL977/4t337gd1w+2YJCLltXv5VQm46Io06cOltQq8ZS1yrz6Bl06Bpc6oidFzLn3ZL9O9wNzWdsTx4nevY3+IHiEyRqmvGJt9L0d9GO8oTSMOFd4OnsXzDhnqGUXI83aqMZqY2wq7wLbTTa6CU33YudzsZuYGSCSK43t3bmtUYQxYhc7mWNrNzOopm10Ebzkac/QsWvcbp4mO3xObrxaeBgWRmSuEm9eoZ818ENbXte8JoehrDxoyhLUChMs3/fcXq6R64f6wbiQfOO2fkZssuxbEG7bmhMXiRnbU18Ha6vSdWvsrt83Y25kZl+a/3sc2Iv3xadYbtoE0qHbV17sI3GSRoot3xL53W1eHnY9pgdP0DrygByzx/hFc4u/aHER9keu/qO3tSx/9pbfu2m9vVmWC6M7VaaH5dXaBpByc7gmjrJvP3ZCCJhsIlxSGjgMCgCDnseJ9stmmGTZtRc+EXFZuYb3sp1sZUsLknaLC+3UJqSkpLyzczL8T0vJSXl5SMVGu8ShJSUf+ZtTL33A8SXRlD93QjXIZmpoqdmkaUCzn0HaH/5Kch0hAYTJ2A0iI4Qh+tg2j7hc2dvSmhcHF1zM5Lte6+LZuWfeRuT7/0A0xWLsKsTJdUGZBwipEBmMyhbISJNFECzFnccZpZNpjVM76WvY4kEshloNjtCowTiBOOHxFdGkaXCQkQ8+60Pk/22R5n9jQ8TXRgGrZGFHM7hPfjf9yM8cbaI3w7IFRSeadMeHqGl8ljScDB8kZ2N54nDMXTLJxmdwlgW5w++njhXojyUx+qaa8V1BaUdeSqXYi7k7uOQfA5lK3Q+jxW2wXWw9+8iCg1hoFFKoI2hYTepk+BgURT5W3InjcxWGK1WKGYzq4p5xUyGa5UKI7OVNWPXt4uNRvp1pX5btr/YdTg/hxM6N9j1mXjBfeh4EinFilhqKfcC1bNrl3k88PrSwizI5aU0XlZy/+tLNxSO52f5Oa1JfCxM4QIDhd9GtQdpxyWOez1ctiaYnDpLrGNiHZPohMnmJJerl9FaY4zhF/72F1Y4cxY7nYlshFagEkg6H/1G6857ybbuiMhKpu8hel79S8yc/ABR/RKYBGEXbqloZvnn0mLh9/z0ec5Nn6Mv18c1lePz4iAPxlfpMU0UIZGSXIgV5X3v3NQ8xUxWYiUzJCZDpTrAiee/nfvv+9yC2LiRtV7r2p0nbNXQQYX66Q/C1Us3VTSzGmenzi6syWqfJ+vN9Fvys0CvbuAR4WPzeesgD5hLdOsmdjiN6xRwuu+7pfmGa8XLvVyeXKFJs55jevTbGcieRkkxF9f2GVCGUu8DeOV19n+dYz8/ff6m9nezrCaM5dtXwMS0tCRrIgSdsjJjgLn/LYwBExMLF0XI7kIPZ4IIP/apB3UyVmbT8w1v5bq4E7hThNKUlJSUb2ZuV6FgSkrKnUcqNN5FeI/eT++vvpvKBz9O+Mwp4skZTBQjbAscm/aXnsJoqGcHiHCw/Tr5yjWEMKAUOPbCfLnNcqNm1Nm+A5x4y3uYuNQkUB3RSQuBsm2k5yDsTtZUKomyNF5Ose+BHOOnKlQDxaV9b+Tq3teTb4yz64VP0jVzsbOjSoFO0LUmptECKRCOjSwXcO/dj/eH/3ZJ07F9aA+f/qNJ/HawcGManBnFCtsUs4amLDLh7WJ37jzOPfuILg5j7RlCv/Md+E/lKJY9LHfpTYjVVSAfQ2NMYKa+TiEzRjW/HTtrYe/chioXiFoaow3VbI3RvmHCjA8YLCyKJsc+ZydOI0/Q1pte+1YYECWakrX629lRFrXEpxUGm37tzbCaoLPRSL8sF27LPq3mOgSIgo7wazB4nloRqc4WFFMjVS5+6YPknNNrlnls2/sQr/uB3oVrf3kpzY3i14tn+WVLB7HCF2gEdaSdJc5O0MNlHhAFrlhHKXklhBBEScS5mXPMtGaQQrK3ay99ub5VnTmLnc4qux3RKKKLs9DKdeLTQYQs5hC5DHHzysseWWlPHqd69iMk7QkwGiEUdmaA0sF33NSXz9GL7c4oh2sN4jjBshTd2/Mcfazj6KoFNcIkxLM6v3y5Jstcs0v0miYeES0teaYxy35vaN3trBS8bPwgC1GdXDah2eri/IUH6e7qjH7YSDxorWsXIA6qNKZnKOYvUe5qIq0dmy6aWYuqX12yJstZb6bf/M/ulwEPRxfmBFtNgmRa5HhS7eJqMM0/2//T7N7xmlt2YC6Pl+u4gdExQlrkSnvR0SWCYBdJa4DYu4wrYMhWFHM72fOq96zY9kaPvRbUbnqfN8pawlikXOIIHAytKMTYnUEIYu5fAtBADFgkJEgsp4udJZvh2jB+7DNcG970fMNbuS4WH9PLNRvxlS6UpqSkpNwt3I5CwZSUlDuPVGi8y/AevZ+S1kz+y99AFvOovm5kdwmCkImKzYVveReN4ja0spE6Jl8dZffZz9M1dRaaMSLr4d6/OTfjjZpRjzxa4NTjdXw/Q/FAjmi0BYCRNrFQSCkXtM0k7hRyYODiiSZJK+m0Q/stEsejWtrBqWP/gCNP/zFdM5cWRFGjNTqK0dUGwaseYcoexBnxEUDgbsfd0xG+VtyYNlroRhsx11Dt6RY1q0xNdVFKZrEGekjGpkjcHNqyV40xGmMglyXpcdDf9rO8WrZ44oUsvrGRGYXQhiTRjJVGGe6/gpadgY8CUCSExDRkm0PuAdzM4KbPedZxsZUkjGM8e2XxSJjE2EqSdW59jttarCU03//YthtG+t0HDuEc2Xtb9ktIsarrMPA1STzfQL1yzSxbELWq+E1NuXv9Mo/VxgVstFAmrJ4hrJ3HynRi7TuKOzg3fY521MJRLnUcuk2dqH6WgcIAv/itv0jOyfFvvvBvEAgOdB9Azs22XNWZs8jpnFy6hpXZQXSsgcnUMTUFtoMcKhM3r7wkkZW1YuiwsqXZyvRjEp+oeZXpZ96P2KR4NnqxzZf+52Va1RqWNYklfJLAY/RsH9WpHO5+KLpFHOXgxz45Z24wuRBMiY4Q2Yyb2Mq94fy61eYp2rkdhNWzGN3GdRT1ei+VmTw59+SG1nqtazeODLWJOrZqcODgKZSd7Tx/E0Uz61HySgtrknWyNMMmsY6xpEXOya0706/klThkxbwpPkNOaBo4xCgsEgZMnTfps/yZ6iM38Frc8q2LOfPxcmHq+LPDmKiJQSOQCDtHttBHUjEMurso52tI5VLoWdtFufjYF66HRcwfe9EtMs74Le//eqwljM2IAiOJYq+lGdMJAQoX3XEyAjaGloEIRYmQMVFgkix+MsUb976Rf/zoP6Ye1Dct9G10bdZ6r7zcsxG3QihNSUlJSdkatrpQMCUl5c4jFRrvMozWVD/05xBGOPfuX7hBmfZ288LQtxC2NU67iopDEtul1rWTU8d+lCPHP0bX1DlkLoO9CdHnhs2oUzFPfmYWZQnKvTZCCPI9i2bWGYgijS0kSdwRqLIFi9psjEGQyypCyyaxXGQc4FCnne3i3L1v5aG//22kmCuGQTDl57h05K20Bw8T/dFExx0oBK4ncTOSrkGHbXu9JXPPTBTDfHwUUCYhkIpQupBcj/baYQulyitijH4roT4TE/garQ3PnbLp29HPvW/0GDnvL7jcqm6V4f7LJDJBGZCmYx5NRIwWSadAousaXQNLZ4ZthKGuMttKZa7MTONaFgIxV0piEBJqYZvdvT1sL5WYuhZsWAxbzaG42vPXE5q/+lfTPPL9P4K68p+uR/rnWqeTiRlkqUDpXW+7qSKYjbJtb2aF69Bog+1ICt0WXnbltiO/iaCFl3cRIlry2GplHuuV0qyHDqudSLbXaWIveyUO9BxguDZMK2wSGY0nY4527eY7HnkPDw09xOnJ08y2ZxkqDi2IjIv3bbkzZ7HTOTpzCVUZInnVOAzGyC4PMhq3ePsjK+3J49ejMsti6F7vsVtuaV6M0YanP3ORxuwMtn2V0JjO21wEWKpGc3YIF9jXtY8DPQc4OXGSrL1yMPlG59etNk9ROSWc0kGi5jCELYI4Q+Abugc3vtarXbuSkFLuAvv2Hqe3Z2bJ8zdaNLMeB3sPcqDnAMdHjhMnMa24tTD7M2tlsZTFw0MPr7omB3r28z1eiB0GzMoCjtC4xGgEs8Yjp+t8jxdxoGdr5rG6GYnApz17GSVbCOUghcQYjY7qRH6CndnD7m95D+WuG7sm5o/9RtfD/p79nOXsqq+xVawljGXdPF+ol+iTswxIQ81Y9IgQpWMUEBmYSSBvGjSUx9dkH5drVyh5Jd557J3cs0rL9kbY6Nqsdl3cCbMRb1UoTUlJSUlJSUlJ2Tip0HiXsVr5hgHOZY4S4ZBpDiO0BgFW7KMaPu18P5cPfwflxjAynyU6c3nDxRw3aka1PUF9NOmIVPMzkYqqk9CWoBMwyZyT0ZPkvIRWpY2OFLGUzLQESX4bJmMWvbIhHLyH42/8Zxx47i/pmjlPZfAQp+//EZJyD3bOI5iOOq+NJmgb3KxkcjhgZixEJ2ZBMBS21REZtQYlSYRCmgRHd2LG89Henl1ZuqaWxhj9VsLMeIRODMYYvIwkV1BMDgfUpyMe+/4eHE/RbiV8+Nkn0bUIlUiEUSAMAoM0kGBIREKY9blWrW56jqIUgjffc4SPPfEk16Zr0FSYUJKQEFkRnmWzt7yDz/zhxJrR9uXcKAq/cCZuJDRPx5ye6eEN//rdVH+3I3SZyVmEY+M+cIjSu96G9+j9mzreG7GaQLrcdeh4gqc+O8vkSIgxZsVNc6umyWdHKJWrgFqxjY2UeWwE6ZQQysUkPsLq3PyWvRIlr0QzbBJFdezE55++4ZfJdHUEgptx5niP3s/Aw0cXxgiIUg62xZi49pJEVpa7FZfH0EsH3rHg7Fztc2Sz4tn0aMDE8DRCTuIbgxQKKcEYCEyCJacARWUi5p3H3sn7v/B+Llcv05e9LoJsZn7dWvMUlVNCOSWCVgNHabY9/L8zeGhza7382qV9kvDiB3HyQ9yOa1MKyaM7HuWz5z9LlER4loerXGIdM+vPYiubR3Y8suqaxLVz7HdtRiOLXl3FkwZB5+8gXwsa0mG/axHXzqG2wMnQPWCRdc4xWy+Sz3eKnwCEUCAz+K0c3fnzDBz4bqRauVarHftWXA9bwVrCmEDQzu/lw7Ntvsvz2ScT6okm2wkC0EJgScHZyPC3tYiq1+KBwQc2HJFei5tdmztlNuKtCKUpKSkpKSkpKSmbIxUa7zJWK9+oqS7qVhk3bCAFGCkQSoHWCANuUKfZvZPgwUdxJi9vuJjDaMPEVR+/rbFXKSuAjghmDLDo/kEIgQFsVxIjMMZQyMSomUlakYVGkjhFTGQQloURCqMWvb6OwRgape2cevjHuXf404x+x9vRsptSj2L6WoTWYDkda34UGtr1hN4hh+pUTJIYGrWYcq/dEZkcC9PyMbksTZUnn9QxdNqc56O97j37eCAbLMQYM3lJbTpGxwaDwbIkxR4H25ULAtvzX6nxnW8f4Gplhun2FAqFpTSJFhgjwEgQBoUGFRKJ+KbnKB4c6OfRzBE+feEULauFlhppJLl2ju317Vw6ZXCzPqUea0W0fb5deZ4bReEXP/9GQvN8yU3zOw4x8Dv/3yXzMp0je7fcyXgjgXSx6/CBN5TXLHJxs4q9278K2ge50v2yvMxjvUjwejilQzjF/QSzL2Dnrt/8CiBnZ4nCKdye+5aUVtysM0dIeVuavW/E4jmUy92KVjbLzETM9JPHkbqH7v7V12yz4llt8hJxGIMdoOR1gUkIUEKhTQBkqU9d5qEHH+KX3vRLC7HOqdbUpufXrTdP0RhDu+XSv8Nl8ODAivfJRljsmA0qecauOkvE6cXcaqmPNprHhx+n7JWJdUwrahEkAVJIujJdWNLiieEn+PFX/fhKUSms4piI7ZYgTiA0nXmBEshKKCqBbaItazaP6mfZve0TNOs/TLPVhec2UComSSz8II/jtNi17RNE9f0bdnc+NHTj6yGKohu/0C2ynjBWckuctrr5cGwo+1VsbQiFRdbOsqvQi7LLXLYME/EIh8t7+Hff+++w5K1/3dvI2iznTpmNeCeJyCkpKSkpKSkpdzup0HiXsVr5RihdtOjMyTLQGRifzxJLB43otFbaWeJmfkkxh9F6TWFoXtCZvOrTqsf4jRg3oyh020uiqNrMuUxW6Tjp6rOZnUio6QZ+u4onYwajWdpOFz5AHJMYgVn44j/XVCMVxhjsqEVc7OLK9/wjfJEll5HEIZ12Z4uFmxpldaLEUWjIFhXNKqjQZ+b5aZzWLDIM8EWWlurHCIVQgifz30q+Psa+wec5/K4fQki5JMY4edXHbydIAd6y414ssE2PhVRb59FJ1HHZoLGtAGMkGNFxNoqEQEuEidado7helPnahRZXPwd7g4NE+TaJlWAlFnbTwySCGIOdaOy50pPl7cqDuzsN4RtxKC5+/mqR0cVYtqDdMHMlN4J6eQeBO7f/N9M6tA6bEUhh9VjqfJHLfa/rQ4wqgtmpJQIgdISjxWUe60WCbxSPFUJSPvSTTD39PqLmFSy3tyOqxT5xMLXqLL+bdeZsNAq/1SyeQ4kQNMImkY5oVvdx7errqdW7SGKBkhGFq7Mc2P/sQjvzwr5vUjybaZ8DYWOMB4QrHjemcx3Mti8AR3lo6CGObT9200UV681T3EwD+UZYS5zuHNfGimbWY14Y2lnaSdbO0oyaREmErWxydo5W1FpTGBJ2AR3VUWiUU8I2emFmohQSkhY6qiPsub9jblKgn0eHVboKp7n/vs9x4eIj1Os9BIGFlDHl0hj79j5J0T29aWHzVq+HreBGwthAYYD/5YH/hd/5xu9gbI+8kyfn5KjMfa7mFeySivHmOOenz2+ZiLd4bWb9WSrtCmWvTN7JL0TsF3MnzUa8GaE0JSUlJSUlJSVl86RC413G4pbZ+fINRwdIk5BYDlIIYjtL2+snFg7zgWSJodGUDB7eg3NkL/7jJxZmupkwQjg29qE9lH/mbcz2HVgQdLIFRegbQl/jtxPicUPXgIOXlRhjiHxDpqAIAk1mWUS1ka1zbttV2lRQUQNbCurNhGwlj9P0iZRHx9u1+Oa8IzYKCcn2HZQGPerVBGM02aIibGswsNg4IeayezoBxxOIMGDHmc8xbQ3QKGwjynQTCA+hE3K1UTzdJnE8at27OX34KP19u9g291rzMcYXn6jx+CdnKZQVbmZlJM+yBa26ZuJqQCOKsDTYUhMZhTAaIfTCYcVaYRB0WTbbS6sLKes59QZ3ezz5mVnCQOO4Ei/JQ6dvBm0ZgqgzqzEKzcJzOuuyVBDt3e5u2KE4//y1IqPzxFFnX+szEZ/66o2j2DfLZgXSedYrcmkXOgJg2LhKK7yX2JSwRJWs8wKW2xEA/amn140Eb6T9N9P3EL3HfuW6WBlMIZSL27X6LL+bceZsNAp/O5ifQ1nDZXjmEq2wiV8/RG3kjZikQM5r4XptNDkqlX6ee/7beeC+zy2IjTcjnpmyj+ON4bcPYNT4ks8DY8AkJaCGKV13EEshb0mMWU+43kgD+Vqs1tS7WXF6MywWhoQQ5J38ksfXF4autx5LIbCEYj7ebbjubgRxSwL9PPOjB7pL5+h5aJRavY8o9LAdn2JhEhM3SeKbc3fe6vWwFdxIGEt0suAKXOzcned2iXhSSBphg489+7EblrvcabMR7wQROSUlJSUlJSXlbicVGu8yxKKW2fnyjYKXUAimqchurFyJZqYPjUSaBIFBCwutDRe3v4Yd39dL8ORJpt77AXSl3pn1OFfeEZ44w+R7P8CJt7wH388sCDrFHuZmFUIcaqqjLeiS+ImNl5MceaTEqcfrC04fYxIs4NnkLKEdU56pkDURsWMxVvBI8pMcOB8izZ5FiuGcm3H+OHVMlLgY3REOhJwTu5QAMfdn843U142QxJHBzFboHj/D/sGL1BrdPJd/DSjIJ7MI3UJkPbxDu8nns6sKVEIK+nd6eBmJXMOh1KontGoJT31uFqP7UMUBtNdCWAmxkCijwUhibREDSkvKF/fwmT+cWCH+3Mipd9/rSlQnIqQULE9ldhysdNZJd8TWxSx1HK5earHe828UGW3VEwplixNfrhD4ZkNOw5thswLpksfXKHLJ9D1E0vevePbzI9QqOXSikCqhWH4Lr/62IbzeBxj76nu2pMAk0/cQXu+xDbu7NuPM2azTc6OsJoCtdrMunRJ+knClepaW1tjSoz31fegkj7RHCI3AEg6ZQi9KDtNsdXH+/AN0la9AcnPiWbHnKKL/X2MNDxAHA1h2BSEDEu2SRGUcuwZAofvILbvqFnMrDeSrsV5T772bEKc3w60IQyaqIZ0CJqhhkjZIpzOM12iMDhHCRjoF/MknqV/6s1sS6GGlu7NUnLy+L1vg7rwTWE8YOz15+mUR8TZT7nInzka8E0TklJSUlJSUlJS7mVRovAtZ3jJrJmfZW/4aJ/e/lbq7A60FKvExsUYLC2kSCqJOMrCNU9MFnL/8r+hKHWvv0HXxJJdB7BliZjxi5lKN3MH8wmNeVlHOtahN+ETGIogkjeoMXVadV3/3dnY/tpOebS7f+LsZJq4GxDpkaBdExOQSByc2KFuiEkNXO2YsFzM8WGP3SIwRqqMQLo/ZxjEmkEShje0KciWb2kxEsVvhuJLA1wi746CcL5qxHUHlWpt89RrlYoIUDgIIpUdW15ECyLiYMO4IdM02nomZvhwxPVqmd+h69Gs9ga3djJkdD5FKzAk7OfbUCrygEkITo+yAGEWiO6U4lhEcbhXY5g6sEH824tQ79UR9Tmy9LrAakyworJ1wfIflppd5x6Gb6QgrG3Uozj//hpHRjMQAgW825TTcLJsVSDfC6MU2T36xB9/vItcToGRMoi0aLZcnvyiR1hnMogITA+i40ZkhKi3UJgtMhJCbagneiDPnZp2eN2I9AWx5/NAqHuB8EFEwAcYqErR3EflDWHYFqRTKxDS1IZcdwLUyaDFOrd7LzISmVKrflHh2sO8wX+wLOCz+KxMT30ezvQttiigRkcuew+v/JPAOdtBk7KvvuSVX3XJutoF8ORsRcx587De3RCRdLBoX3AL7u/fzwuQLmxaGpFPq/GOXSYIpdNzsFG0JibQLKLcX0DRGPrslAv2S0QONy4QqTyQkttE4SQPllm/J3Xmns1kRbytE9c2Wu6SzEVNSUlJSUlJSvvlIhca7lOUtswPlAm6jl7//symkBmNlwTG4SlMoSTI9PYSBZuZKk5krTcqLWqvnEUKQ9PUTBwky9MHNApBU6ohLlyhGMbGXp20XOVR/iqELX0Wdy+P3vRv6DhD5CW5GIvOdQfpZ5ZJEglp2gFxcJUsbx0RkfEMzl9B2W3hhYZWjM2jlQJwQVNpsP1LivsdKfPWvpqjNJGQKiijUxGGndVopQSavqE7HuFbC3rFvILs6QsD8/EqpI2qZmEglqHpI6cxFCBOMNgReF6P/16fJ//TrFxqS1xLYolAzMxYBgu5BZ2Em4vbsbmSjxTnXwjcGBCityCeaA60M27KHUY5cIf5sxKnXqEQIBZYliMIYI3wwcWelDEAWUFiOWIhNdx7riIH9O1x6Bh1gYw7Fxc+H9SOju45keeYLlZtyGm6GzQqkN2KJQNdrIYQNdD4wnYyhOh1z8usxRwZDLM8jCatEzeGOsGI6wopQWaSyb7n4Yj1x4EbOnFtxeq7FvABWaVfIu3kc5aCN5vnx51e4mQDOTZ/nb32HH7JcumgzEXkYbSPtAJuEBMlkIsiFLfJOiWxPkXiqRf7AP2fwQO6mxJBg6mne4LSp5y+xN/tviYJd6KSAreoo7wqfk3s5BMyc+I8QTt2Sq+52sBkxZzPi9GqsJhp3Z7pRQm1aGFrsMHTK92KS5oLwLlSOuHkFO7+TqDW+ZQ3jmb6HmN7xw4w8/ztkw2soEhIUTaeHHfvfxs6X6RxuFTcS9Tcq4m1FVB1urtwlnY2YkpKSkpKSkvLNRSo03oUsKX0o76DnSCe6VzjfJltUeFkFBqQS2O51McmyBc0wITQ2wjOrvrZjGaSOifwYNacBxldHIYqRWQ8pwDEB3bKGvWc78aURKr/7cZ577GcJfEP3gM3U3P2pDkEbMNKm7nQTaJ9sWCVXn6Ld3UNkG7xIsjw2DYCQaMCqTHLfYzvZvn+p2OVmJEFbI4TE8Tob7N/hcmRHhPjsGMYvIHIZHB1QyzY41Veh6YUkaFRiyLd89kwVKbbzKECcPsPUe5+l91ffvSA2LhfYWg1NXTRoZELynoNUTqfVWgiUU6KrfS8HRn1qIiFRMbaWFI1FqTeDcoqdw1om/mzEqYeAQslmNmiACUgSiVSq42Oca/0WaJTShIFet6TiZkst1oqMXrvob7nTcDVuRiBdj40IdJVpl0ZpN0U1Rty8itExQjkgO1FRHdUwkSBqjJDpv7njWi4OID18HsEZ+AGK2+65YSx3q52e8wLYeGOcWMdMtiYXCiCydpYgDpa4maAz8+9MbPEF7xAP6xGyVhVLhgjtEKiIqvRoxAmx7vwCIo4MluMyk8kzVa9Sis5uaobafMt1hhjTfR/DtRESMY40o2gh6LYyfHf3Di60IQlqeIVbc9XdDl6qpt61XJOjjVGUUAzmBhltjBLEAa7lcl//fbzzwZWu1ev7dt1hGM/Pj7QLnfmRzSsop0Ru+7dROfNhhFq9HGSzDePHR47z/qf/jJrvcW/2ECWlqCYJLzSaFJ/+M36pdOQVK2RtNKJ8IxGvPXn8lmfJznOz5S7pbMSUlJSUlJSUlG8eUqHxFcxqrdBjl4M1Sx/cjMSyJCIIsIgRtoXwsguv17nBVzgiWtJavZhCY5SCNU0j3IZrDKbZRjfaCLdTLOPLLF3RNMVktiOw9XczfbnJzI4muXKnXAC/c+McG40Qaq7tWBBhU1dlHDmO125gR2Jpq8sCHXFN6oQ9l75ET7AN2L9C7HI8ARomrwWAoG+HQ89AHxOLynJm8z4Xhq7hW5JMKHBDSJSgWtK8kKux91qePZUqXQM2yaUJqr/7cdyHjy60b89v8/iL1/j0C2cYrVQJ4wSJJBNm2Dk1xI5cDwCVGQdjbHKxRsSgLEi0pDotkJZeaK1eLP5sxKlnWZIjD+d56pMXSJSNlh46sUmMACSOHbBj25cIzV789v03LKm42VKL1SKjW+00XIutbv3dkEBnHIx9gKjxdYzWSDu7IIcbJAIJQtK49gUKe39w04LVcnFgpnWM8+dfTb3WhTZ17OwZenf28sAbymuek61e/7NTZ3lu7DlqQQ1tNI5ykEKijaYRNpBC8uzYs0sEsPmZf+e1y5h9H73FFr3ZGrI5REtVSUyCFBpL2hhjmJltMmmf4w+e+C+EOlg3lr0ai1uuy1aOUqZMc67p2pY2GQGhPwaAlVndub3cVbeVcxw3wkvR1Hsj1+TpqdO0wyZ7bIGlAmIhMNxYkL5RuZG0c1TP/zEm8RHWyrmCm2kYX3oMe2gKQRPAgl2OWRHjfSWxGVfreiLevPC+FVF1uLUZnulsxJSUlJSUlJSUbw5SofEVymqt0NUjD/PC0JsJlbdq6cPDB2tkrlxjNi6QbU8jpETmM1g7tyFLeVr1hL5dObqv5ohOjCy0Vs9jjEFPzHBo+wgvlB+iOh3jmRijDYly8FUWR4fsb59cEFyE5xL6NnGYoGyBMQamXdgJsR1hh4rOcESJjBMSKamWymwfmSTTdjAy6oiNi2+AjFn4r9euoiv1hYcWi12jF9s8+6UKU8MBcWywLEHvDpd7vv9HUFf+E+GlEb72PQ+B9Mn6BYyRGAyWEchA0nQ1o71TfPvoJaQQ0N9NePoS4amLuPfuX9jmuclJ/vLU00xX29ixjRtZGKlpuU3OWReIpwzlqIROTGdGoukIqEKCbQmi0FCfifCynf1eLP5s1Km35+Ao7fMf49Lwt1OvDxDHLkJo8vkZDh/6Gt3FM8TR3+Hu//doa/cNSyq2qtRiq52G63GzAulqItKGBDpLUNrxANEFjRCdWnMzX3xhQoS0sHI7iOrnNxwDXbxPi8WBmdkdnHj+jYSBhWPP4Ig2STjF6Nk21Ykqb/jh3ase31av/2x7lqnWFFprMnZm4fWUUHjCox21mW5NM9ueXfiZAz37ebTcz+jMGUpqB1MyT2voebZf6MMNytTFNLlMHsdkGB9vMB4M82z5Lyl6BTyrb82SibWYb7kWXh/Q8ULnFwkixiSYZKLz+wrlASvd24tddVsVOd0ML0VT73LXpDGGZtQkSiKCOGAwnuG7PJ8DroMrJBFNhmc/z0e/cAbe9L51z8N65UbG6CUFLsuvyc0UuLxUzs+Xg80e21oi3mLhfSui6ndiuUtKSkpKSkpKSsqdRSo0vgLxHz+xohVa+wFnG9toXatQ3lPGcju55vnSh8pwk2f+53l2X/0qjQNvpZXvx4ubmFqL8OwI8eAOMj0ZHnhDma4jS1ur51unk4kZZKnA/p/6Vrr7OoLOzOWIwOtCAV3RNPvbJ+mJJxb21fgBjpBYjiKJDMZAFHZu7C1t4Vs+dmwjkSRKEzoalbgcbHQxpWxUHCLmBBwjFVrZHTEH0JbDmX3fSX+QZxdLHZ6TQZ4vfcOmUYnBXA9fN2stZsp5XvdP/imVT/8FE1mHUrWGwKfldREri0R2Yse5MCG2ZhjRY1TiiEzGoXciWiJsamP4zIsvMltr40UetiMJtUZrgRcrfNtntHgNbziP40p0Ao4niU1CFBikJVCWIgw6sWbbEUvEn4069Ux0ma7CafoealJvDBKFHrbjUyxMzpXDeBBMUe6qkunfWMPwVpRabLXT8EZsViBdS0QqHfxJugaHbijQ9e3qYWykG3SIjtud+YxIpFXAzu9A2vnO7MZNzmlcLA6A4Ny5Bwh9RcYbmXMdSSzaWNYVWpVtPPMZzeDP3LPiOLd6/St+hTiJcZSzumghLcIkpOJXlqzvj5kLVJxJkmCciszx9cwMF3a26Rp5FaVwiB4zSNjWTNjneDL/p4SFCfBBu5q8k2d3afeG3WnSKSGUe0PHHBpM4oNceY3PPydqjFA99wdbEjndDC+FmLPYNVn1qwxXh2lGTbTR7BM+7yzG5IVAC0mAhcCw10ro0Zf4zBO/xbHtv7fueVir3GgrC1xeCufny8WKYzOGXtPEI8LHJlEeU0l4w2NbLrwvZ7NR9bTcZSWLy5TSSHhKSkpKSkpKSio0vuIwWlP54MdXtEI3ittpFLfjtqokw22sruslKkIInMoENasLb1uJV7Ue51zmKHWrjC7kEIFPefYyD//k6zquqL0rW6uFY+M+cIjSu96G9+j9bIOOoDNaZvT/+jTi9Bm6BuyO829+X40hmZih54FDdO/KMTkcYhNh4k78btfwANfKU7QyAcZKsExMqWEzMD3AQPUc090GIy2ETkAItHIwQrAgGxpDK9fDN551Ma2TuH/5p0RnLqHDmK8deQfVwm6kpVCunBPbIImhOh3xzJUyB3/+JzF//wUyIkbZLnmZofXiVbRlI3WCH7eY7CnxyQd2ohKNSjS92wq85fIVXvXYqwEYma1wdaqCFTqdWDpg2ZIo0BhjsCKbttui5TSRUR6hNNNyjHbUJp8MIhMF0mDjEbY1rbpZIf6smAVZDZAyoadf8qpv28G2vRmCSmlOPPEpFSevnwOgETaJojq2NmAXb+PVuTornIZ1gxQhPb0BR19jMbhnaEu3t1GBdL25ZdPPvI/D9/0r6tM96wp0yu38I608AjA6QkgbYeUQQqCj5oZjoItJgll0WEMIm3q1j1qtjOPMdEYNwKIWcQfHrjA1IpkeDZY0o89zs07P1ShnytjKJtYxjnGWjk41kOiI/Y6kNxylduHjVM5+hLg9gZMEdClFomO6TJXt8bN8wh1j6qGQ1+28h/2FXXzl2uf5o+O/RJD46KnOZ4QUkqJbZF/3vg270xYXkqzpmCs/ADMQ+9NY1rY1XXX1kc/Tbk0SuX3YGnKWQr4EcxxfCjFn3jU52ZxkuDbcOafKAaN5ayahW4IShpzxUQIMghBJlxTsajzH2cnTHO6/56a2vVUFLi+F8/PlYvGxHbQiHoyv0mOaKDQJknHj8nmreMNj26jwvpnPqLTc5To3KutJSUlJSUlJSflmJBUaX2GEpy4SnbnUcTIuujmeb0+2bIFutNGNFjLfmb+oGy1Eo4HOdBOFHn3RGN31CWqqi1C6WM0q+anL9AT7gE4keHlr9fwMyPnZhDAn6Ax55H/69Uy991mSSxOwigOy/K63cWRyhuknazRiC+N0xJB8NcO+2gH8TAsnmsSrNPDaHolrsC6cxc3eR+gW0JaLFrIjMkoNc1KLFBHZwhTtacXTz13k2ItnsQa6qee3Uc0NgdaIIEBYWYStOgls2xCHgvHLTbqO/ykiydIWAY7RSCuLVTCYqSYtKZkcKJEoidcOyTbbxJbF2EAX/+PyRawvfI2jb3otrTAgihOksRfS3UpB4miiUENiSJQmVjFB1KKuRgl1E8dxCdUMtl9Cxg4xMY22Ztfe0ppzE0u5F7jy5F/Tmp3GUhWKxRnE6D7ahZ/E6z22Qlyp+FWGa8O0wgbdhIyQ5T9/+b/wzgf/4Ut+AzTvNLz24jNMn/0kIjxHPnMZM+wwVru9MdTV2MjcMrf1IR77/vdz4iu1NQU6YxaLWrtWCO2biYHO0548zuwL/5W4PUniT9Gs2ehEoqwQsWgKZCf7K7FsCFsJjalL9A6tHn28mSj8ai6dLq+L3mwvk81J2nF7yYzGvaLN9xYjdtuS4rkPMRlV0Tqcm1UJlnJRVgatI1TS5kedJkPf8hPk+o9xfOQ4H3jx12nHLaAjtAkEGk3Vr/Li5Isc6jlEOOfgWs9BtMQxN19IYnmdQpJgCuWUKB34CXj8GsoprvmcCW8XzbMfphLH+PVZpBBknRw7ijsoe6VNR043y+0Wcw72HmR/934+f/HzGGPwrM4M3UEiDjmGnAAlIDYGLTqORpcEG8FOWtRnTsIGhcbl56sW1Pj1LShwuZtjvPPH1p58km/z6njENHCIUVgk9OkaP+Zqdprauq+zIeF9k59RkJa7wMbLelJSUlJSUlJSvtlIhcZXGLpS78xk9JY6thwdIE1ComxUEGKieOExE8UkKJTQODoAOlJdKZmFBIxKiMOlkWAAIeWSWYRr4T26vgMSQP32f+LeuMyFPW9g0uo42BKpsIM2pXYduxGgE0k7X6Y4e4XBqRcZm3k1s9370bZLYmU6JkbA9+okKsETAVJOoOIGNa9M6+BRykmFqtNHIi2UjhDaYHwfYXecHEIIpIqJgwQz1qK/LLgWluimiY4aUBaEdYeZQpHYssg0ffJNHww4UUzXTJ3ZniKfeuIpjrzhUbKOi20p2kJjtERIiHRMK25ihEbaHZej1Jq2qFNLpinYBZRUJDIgURN47R5mrTGuHXyGn/6xX0YptWKN25PHmX7mfXhJlfxAH0J5mCS/JMK5WFxpCY/zlatIHdKvIBAZnmEnJydfeNlugPzpp9Aj76OoqljdfQg19JLEUOdZInZEM3jVczecWzZ49ApvefvhNQW6eVFr8qn30ayeI7IL2FaOjJQkc4LVRmOgsNRlKe08Om5hWy2kCEm0hZAxnXevRggLIRRJYiFFiK0a6772ZqLwa7l03vHqd3D/4P0cHzlOnMS04hbaaO6xNT+Vj8kJ0E4BLztAMDsNOsQYMCqDERphQCmnMwohaVN54Xfweh/kw09/mOn2dGdGqrQWBFVpJFpogjjgSuUK/fl+Rmoj/OHf/OG6DqJVC0mkQ5jdRX3g26jT+TzoeeDnaVz4yIrSkvGuR/mDpz/M95s2RubwhCLSEVW/SjNscrj3EEW3QNC6xsnhr5OLxG0RWdYTc241rimF5E373sRnz38Wg+m0hyPJi4SyBAkE83r2nLAdIrFNTFFobJINbWe1a6nm1zAYDvcevqUCl7s5xiuF5J2vfjsXvvj3yKTNlMwhhUIbTSsJsVSGQ06W6tk/INP30JqfMRsR3jfzGbV8H19psy+3is2U9bwSr7+UlJSUlJSUlFshFRpfYchyAeHYK1qhi8kshbhCRXWTlW2EvejUWhaBW6DsT1FMZle8pvEDhGMjy4UVj22UtRyQAOM/93+iK3X69xbpHf9bzje7qXzHQ7hxC7c+i4oDYsshyJWw/Qa7z3wWkUTsGf4a7WwPbVFGODbN3CzjPdO03QAtNJaIGY81u6oNvLBM1HJZfO9rBAgFDauBtkNsHLJRFp0EgMLyenht6QqfmMkzo3N4Ok/YzNIuW7TdGKnBiXNEto8dtUBJJIJ8o814Icvlp0+y56H72dlb5oX6JNKX2I7Ej9tINLbUhCoh72fpty7SJoOKB2mEDSxXII2FHeaInSbjO7/MSHiSczPnVty4bbQ1dPCx36D32K8we/r3uTb8RUqmjbRcJkSOp62dzMgyu29jE+t6wsdWN59uluVixxEr4u3OFIXSQcqrxAkXzy27kUD3QgifbufZ07hAP+PYApTyKPU+wL5XvWfD4unyNVJRjbB6lnzmIrnMFeqtAyg52lk7IREqgzHg+zlKhSv0DG2/6fVZzHounV/74q/xtqNvY7g6TNWvMlAYwJKSHzEXyRNRkVkOlPYg0B0/ojEYDEncpE3HpaikwrM8pJBEtYucu/QZXhh/AYHAljaJSVCiI7YLITpiI5paWGO3tZs/eOYPqPm1GzqIFheSvDjyBH957vM8MTlOcOkj5NTH+anyT/EX107zyMGfY5cNRDWkU8IqHuDXP/HzzAZtZMbFMjHVsE1iEowxhEnIifHn6bIz2Drg95/4EFPyT29bXHI1MWer4ppDxSG6s91EcbQgGuccjQVEsFASo43BmM44CABbGj714p/SKhxYd3urXUsz7RkmW5MdwTGoLYn+3kyBy90c470vXyCXLzHqS+IoQJsIKQQFt8BQcQd5ZW3IVXujJvCX0k1+t3A3FxGlpKSkpKSkpNwqqdD4CsM5shf70B7CE2eWtEILYH/7JM/Zx2gX+rFsD0ubzkw538axDXvOfxl6TafFeY75OYruA4cWhMGNsrh8ZV5YXO6ADF44vzTqHcVsHz5OhYcoV67QcPsJ3TyKhFJ9hF3nv0jX9EVwbLrGTnNPGHLmgbdwdnsv1/rH0ELjxQpbJGipqdmGU92G/XEd1ZiGCMrxFMokVLItJnvHaLk+xgKJIhNm6JvopWwydJXHKXs1vrf7Al+c2s9Iu5tEgFEx0kCuqRDCpVFyyTfHcaI2YLCimKYUNCo1pBC8+Z57GJ2uM520iUMLkyRYUhMogZXYbKs7dA19hP7EZ3LyBzCt3Ug/j1CGdn6cqe1PE+UnCWurD/bfTGtopu8hrpgsHzr/Ar2OB3aBKZFbOOebuQFarY15LRHwRsLHVjefbobVxA4ZTdOMrzE7cxZ6DlNeNudso3PLFr92f/YedqgEmbS41q7RGm/xiyFs9BZ++Ropp4RTOkjUuMqugb/m9JV/RDvYhuPUsB2LRHv4fhZLVtl34Hmc0o/c5ApdZyMunSeGn+BfvvFf8tFnPsq56XOU4zp9tk9iFzlQ2kPZK5HEjTlxypDQKZa3kGgEsY5ph02yysYYTbM1hp/4GGPI2BlaUYvEJAvxaUPndRAQmYhW0Nqwg0gIyfPNJu9/5i8Wzr+TBIxXx6EM/+FL/4GufBcPDD7QuVb7j3B68jTnps9Rygwxapr0xhViDVIopBAkRhMlIRYR46qIzO+jmAQ3FZfczHtsnq2Ma5a8EkW3SLFYxGCIdUwPTTSnUVoTG4MBtL7+GxwlQCM4VbnGl+a2t5rrElj1WrKVjRKKRCcMV4cpusUlnwk3U+Byt8Z4dVjFU4qD/ffRinwiHWFLm5yT6wwQMcmGi1zWawJP2Tx3cxFRSkpKSkpKSsqtkgqNrzCElJR/ZvVW6NLEU9w32GTk6I9Ra+vrM+V2uhx+VRF1tkJ4qc70/iH8vIfX8Ok5P4JVKlB619uWzF+8Ef7jJ65HpcMI4djYh/ZQ/plOWcw8y6PewrY6qgNw/8n/QZMiUSyxbU3Bn6Ge6WVmx71kB4oUdYWgr8jVe05wpWcviQBhIFAgY7ATgUqgaUnGeibITJ0A7ulEwt2rXBlqkKgEJ1LIyEZLQ8Nu0B5sU2q7C6Upu9wah8d2kG91odwpQp1wrgBKg0pCEuXQznRjRyMIA7FtYWlDvtwpVjk40M87X/8of/3kSS6MTZJEBoxFLlDs9NvsGvgr7Nx56mGd3XtOETf38A35ajI5Gz87BQL8cO3Sgs22hlaDOpcTSWL3o+TKGPZGboDWamNebZbimsLH+En+1Wf+FT/xwE/wSM7D3cLm042yRDgr7aKPFh41fCtDjS66kxlGqlcpeaXr0w83OLdsNVFuBsAqYTuDVDbpHF3tPCunhOwuMZAZA/n7XBn9blrhQaK2jaRF3jvJ7oFPULIrjH/tX9zyrMuNunSKbpH/+Nb/yNmpszTHv0b+/O+SLe5DzjkRpZWjrQ0enbe7oBO/FQiUUCgT09KaLjtPLjuIpzrzAaWQZO0sfuyT6ASNBtNx9XV5XbTC1oYcRId6DxJWzxAHFf7myf9Kza+wu7yHalDlwswFlJnbTyFpBA2eH39+QaRLdNIRyy2Pv5oV/KgL25WgYiA0BldoyhIaBj7RsnCyipzafFxyM++xeZZfcwhohs2OQJjpYbo1valrbvGMw92lzjUstEU7dMmKABdNaEADCrCFQEhBC5dSfifn6tP831/9vyl7Zc7PnF/yS4Y37n3jqteSJS2UVAgEzahJM2qSd/ILj99sgcvdGOOdL3Ih8cmvUnaz2SKXtZrAb5abEcrvFu7mIqKUlJSUlJSUlFslFRpfgaw3E/Hwu36IBx5ebabcICdzP8mnnniKcWmIhcAyhoEHdvOWRx5kxyJx8Eb4j59g6r0fQFfqHafinNAZnjjD1Hs/QO+vvntBbFwe9Rb5LDLXKanBaIqNa5hGi8rOozzzwE/QzPajLQflKgJvnFM7Jmh7MUZGyMRGIIiEIbEhZwQqcXHjmDBTZ7SR4OqIoLvEWP8VtCzgBl5n3psUyETgaY/QaTJbHJmv06BW76Ne66JbVlB+gNEwloVaNkG1JFLHxJZLbLmooEUjn2MHkt3Hji6sycGBfv759/bx5ef+mhOPf47Y5Ci5U/T1voiUBrBQwqJiQnbnLvK8k6Pl9neW4QalBYtbQ1E5avU+otDDdnyKhckVN5u3egO0Xhvz8lmKazngIh3RCBpUahX+3Zf+HY+UenmXO0VZeJRzAyu2eTPNpxthXjh7MOvxWHxySWtrCxspLTJRhWZrilyma1Nzy7Y6OrdWO6wA7MwgA9vH6S5+gEZ4mKAdY6s6xeIsTmEnUg1syazLzbh05oWdwDaMXf0fkPgwt9/NsMWYVuyam/VnYE5mNFhoEiEItCHwBjmw583cO/C3jDfHCeKArJ3Fdm0SnZDohEhHKKE42HOQ8eb4DfetMfE4Y2c+QFg7TxDWeUN9kqOZHM+ZCi9UrxHrmNzczFbHcmglLXqzvUy3OyLdzz76szjKYdaf5ZIf0oXHm9yILplggEAbzkeCz4RZTscR94YdoWwz53zxeyxUeSLlYBuNmTm57jlcfM1Vgyoj1RGaUbMzX1FIXOXy3NhzG7/mVplxmCiPYbJsNyGWUDhCIzuDGomw0AhGZZFpmcdTTY6PHKcn28NQcWiJu/LU5ClaYYv+XP+SbeacHDk7Ry2oIYQgSqKFx17pBS5bze0qctkKbkYov5u4m4uIUlJSUlJSUlJulVRofIVyo1bo3u0uOkkYP3eWi9eaTBjNp+szNPdtp2jASgxtIxjB8Ccz07x9bJxDgysFoOUYral88OPoSh1r76Lodi6D2DNEfGmE6u9+HPfhowgpV416Wzvmbjx9HxNGzA4e4tS9P0hkZ3GDOpYVMZXVPLuvQWg5SGGRCJAqRmiFMpJEQNuCMk1cZ5yayPCpPa/Fd/OEhSx1x8KOmigSpF3ECIEQAscT5K06VaMZD7Nsc1v4rYQk1jhOp/FWSIddszEvDMY08hrHj1CxIrAkQS5PVhve8poHV5S2nJsY55mrVa7Y+wiMQoltFOMhDqrn2C5H6LIsGlGAJSBjYuo62VBpwfzN5uhFnytjb6Ve70FrCyljCoVpdg3+Ddv2DizcbN7KDdBmZymuJrZV/Arnps8R6xhXuWijmZZ5LoUT7K6cA+lSzpSX7NNW3jAvdtnUZ64ypCt8N3UyZmlra5GABMG0UfRENSLd3NTcsq2Ozt1IVDDaJ7v9W8nMzW5UuSGUvXPBiXmjWZeL10XYRcBgovoSJ9LNiNSr7XekI5pGMkOWHloIwCLGIIlQREgqCKyBb0NJi3947B9yevI0VypXaEZNXNVxQEc6whjD9vJ23nbf2/jgkx9cd98OWTGFS39EoAOsTB8RDk0zxXbalMPTvGg055XH/KLNl6rEOl4QCQEO9BygMvoV/mm+xS4LbGHAwEwi+GRT8KXQJu9m0MYn1teLtzZyzuffY83mOFfCmFY0gTam02ptZ9kVB1hrnMP5ay5IAi7MXCDW8ZLm71bUohE2eHz48Q27+1bMOExCPm8V+TFXk5WSCb+BpRwk4BLjC4enrZ0YAVPNqYW1mz8n81H2s9NnqYd12lGbvHvdsSgQDJWGaEw2iHRErGOSDX4WfrNxO4tcboXN/DLqbuVuLiJKSUlJSUlJSblVUqHxFYwRgonBblrdObKOy5AQC6LDpaef4ZnPj1Cr5EgSyanBcZpewLauLNLJU5mJiYLOcP8p1eajf/cMP/utb2BoX3bdbYanLi7MXDRCMJ5zaduKTJTQ3wxQ/d2Epy8RnrqIe+/+haj35Hs/wMx4RNLXjyp3dV5MSBCKK/e8hcjOkm1NITMeM+WQ53dXCK0EoQElgQQjBInSWCbCAowE175GiENL56FoU56pY2lDo7dE7CjaOcNAUeFKG6kEjiuJg14mZkepNabRcZbxsELLbWIlNkEmZNYTTDgQA5GCIG8Qpk1sueytt/mu17+Go2967ZJ1ef70l/iTp56jGSXkaJMnIjIWVd3LSf1a+qxPMSSugi0wQuLGVYb9zrytXcVdvGn/m8g7+QVn0mKEkATZd/Hi+WnC0MHzWlhWQhwrZme7aTb/Ad1HexZuNte6AWpHba7Vr5GxM3zr3m9d/fxucpbicrHNYBipjhDrmKzlMSRjbBPTK2NO24foCU9iVc9QsI4ib8MN83KXTVEbftqZQBrBtCgszKqMsJg1ihJNmsJB3v8LDOR7NhX/2+ro3EZEhfz2NzF7+oPY+Z3IZSU26826XLwuOqyiwzoGkHYB5ZYWnEgHe49tWqRebb9tofCEQaCZIUeAIkuEwBAKi3Hj8nld5J/2Pwp0xK73veV9/NZXfosT4ydox22gI9w9MPAA/+x1/4xj24/xxYtfXHPfploTvD0f4mixIJLbGmKhmMaiC5/v8iL+n/b1Eq3595ut7AWRsB7U+al9jzBR+RRWoqloSYzEBrpUwlvzhtmmxYW5n7Xk9b9GN3LOw+oZqlMnuNSq0dIaR7kLQmEjbHApluyZeo7uVeaVzl9zVytXiXVMxsosiKZKKFzl0opbfOH8F3j7q9++YZFjtRmHO02N4RP/GWv067hEaGExJoo8be3kmizTDBs0oga2srEte8nrCSHYXtxObbLGtfo1DjoHl5yvklvqjCuYczQO14bvmgKXreZOK3J5uYu97iTu5iKilJSUlJSUlJRbIRUaX6GcHZ/gMy+eYrRaIUo0tpJsK5V58z1HsK9d4yt/OU0YFvG8NlEhoOWEqNBmZixBqDagUJZASQHaYTqs88m/vsz3fN8etu3NrLnd+ZmLVwbLfH13DxNZl1hKLK3pbwW85vI0Q5Oz6Ep94Wdm+w5w4i3vYfpijTiM8XMtttPk6ve9lX33HsK/soO8DhAzCdOyytN7qwSuwYiOmCg6PRAYOjctkej8PwG0yVCjhNTQHyYI1yAH8yhloSyLKNFM+3X6C0WUlBgEscqB08PX4m+hOq2Jtaa9bYrEWAipCWWE6Wyi8186/60XM1T3bMe5Z2nhTXPiST759N/TjAr0WAE67pRXKEJcPUWFbp6KH2HQHiUrBVI5vL1U4Fj5Dfzl2DnGG+N85OmP8MfP/fGqzbFGG04/P4iWGfLFKxC3MFpjSUm+KPCjXZx5vsTuVxmMMJydOkuiE97+6rfzhYtf4Pz0eYarw9TDzjkxGD745Af54sUvrtjWanMCDZ05cJGOsIXCSYKFWYrLxbZm2Jm5dp8r+C6vznYZY2GwxCVmdYHn5RBD8TRd7UkcKW75hnlJ07U/QubCR9DhdZeN8GcoiYTIGDwR47NIEBGC2Rh227Cray+Zrns2te3bEZ27kaiAiTc1rxOWuo+EypAENYyOEAJMVAO3tMSJdDMuneX77SQBZcviYqg5kznMNVmm1zTxiGgbi6fqUxztv3/J2jw09BC/96O/x+nJ05wcPwnA0YGjHO47vLC99fbtkOey302wMn0gBI2wSZREOJaLH7VpKJudVsh2GTFFRxiPkoisnaUZNhlvjJPohJydoXv2G3i5Lk7Upwl1hAASIYiMTZ+M+e5MwG/WoOCWFkTmjZ7zOKhQbU/R1gkZ+7pYrIQiI7O0oybV9hRxUGF51/nB3oP05/s5P3N+icg4v/1QhxScAmONsU233a4243D/t/03PvZXP8vozBlK+R1My/x1sT6JiJKIrkzXQhR9MRkrQ8EtkLEyq56vgcIAv/itv0jBLdxVBS63g9tV5LLk83OD6/9yFnvdidytRUQpKSkpKSkpKbdCKjS+Ajk7PsHHnniSZhBQdAwFlRAZxZXpaT72+BNsvxAhwx7y+QZCQCw1RhocmRBFDmiDl7vufrSlIhQhzTDkxJerDO72EFKsum1ZLnB1qIe/OzhA27UpBBF2EhMpwbW8xycODvDdM1UGygUARi+2+cpfTNH2PWbvqXNVTRCIgO14/NWRfvrzhmI+YGe+h5kdFieZJJQgkSRoYF7sm/vSbpi70TUYBDOmB4GhL2xCsYq2IhI1jTBd+KENKBpJQhDNIKXEtSyM0WgjUO4uCnaddmOUmq2JdWcG2wKLlkCIzqavzMzyoa98lZ9+3WMcHOjHGM2Z5/+EyXAbWRnSiFugE9w5WVRKyJkGVfppyW0UrAp28QCt1jjRpT9l2O+mN9e/bnPs9FjI7FhIviuH7R5Fxw2MjhHSQlp5VKCZGQv50nPP8PHh31/S/Ly/ez/fsf87+OTZTyKEWDFHbfm2ls8JrPhVhmvDtMIm2hg8YShbFrOzIxzrXym2xTrmoIp4RzYih2FGGxJh0SUcBkydomjzx1GBA4d+jqPdO2/phvmZ0Wf46HOd5uMoCfhpZ5J9VkK+dIjynNtPKhslbBITkU8aNGUBKRTaaMIkwFYO3V4Botqmt3+7onPriQpB5dSqcxznWT7rcrH7yMrvJqy8ACZB2jnCOIDYJ2iMkO95NUlrmMqZD/PgY79xUy6d5fs9OzvCnz/5USr16v+/vTuPk6uq8///Oneptaur9+50OgnZN0iAGCIgwgiyDoPrMAoaBgYHRUf066jIoDJ+GRgZd/y5jCiyiTCijopfQYQoO0TCkoRshHT29Fpd+93O74/qrqSXJJ2uLB34PH30Q1LrqXtPbrrf/TnnQ2MshG9FKXiKztwuZkbCLJ16Im5q7aDzbyiDuU1zmds0cui7rwqipVNPJLLxp/S5Dlu6Xy/PWU2A63sE2qdBKUKBg69LXZQDHdCb76Uj24FGY2Dwlf+9ks/XGzQkJzM/OoE1nWvKS5Rtwybv52kz87SZPlasniAIDuicb8n2kvM8Ymao/wo3WNy0yXkOW7K9DI0JDWXwjmnv4On2p3F8p9xApzSfHSzDYlJyEmknfVC63ZqGxQVvuZqbHruJ9ekuGmPG7jme7cAyLBpiDcMCJyhVd1aHq7niLVewbOMyqfiq0MFu5LJ86/Ly36M9m/gM/eXTUAfanGw0xhJ4VkIHeoR9rEf+nmc03oiNiIQQQgghKiFB41Em0Jo/rn6VTC5Fjd6FLuZwdQDKoNqM0VWoJ6PCLIzkBgpPsLWBocHr7/2KhsD3y3sM+vgYGCSiYbp3OHTtcEp7PGrN1p5eck6xtDS7tgZr9jE8u3gWeQX1OaecxYV9TSjv0mUpnls8i7fOPgYdaF56PMUOr4dNTa/TrVJoNLZSQITA12zt62FnIk3YU2yytuISgFKYmJQiAj38IGgYCB41BgqPWLibvA7Ra9Tj6gieVvjlNhQKs/9gZIpFtNbEw2GaqhMErmJnOgsEmMrHw2ZQwtjfxqL0TiYB0JnN8sfVrzK9qRE3tZa+9E4c2tBuCpSPoQw8IISP1mBrl5wyKVp1hJKNGKEk23s3Ua+znFg9iy5z8N5mQzvXFvMBvq8x7dK4DKuKPVm2orsnz0+fvod2e+Xgzs+7VvL4pseJ2TFmN8wuBwJ7e68999vLmS7ruzfgBW5peSeKGnJsdAJ+9fzdfL5qCosmLhoUtlXZMS6MusQI2OaXfgCL21Fc1b9UWWc53c6QqJtPdC9h0mh9/S9fp7PQSWO8kUmGR6u3jV1uwI7uDcyon0FNJAnKAsPEACLawwyK5LWBoRSJcIKJ8Qaihh5zE5pDtXRub6HCgTaH2LP6SPtZAi+LozW5fE9p2TBg+Sle3PIUrdWtNPRXIo2lSifQwbBqxM+e/nnuXnF3+djMsjwuqXKYHvaJbPwpO9rvPeAGEnsbm5tay8b1P6Y9tW7YkuRA5zEDD89UpANN3i8tzS74BZzAwcAoN1Nxi910Zz1cq4qJyUnMbphdDtsLXgFTKWrsCIuaZrKi4B/wst+UVcP2wGKq4dGr7XKFYP9JJIHHxsAibtWM+PzFbYtpSbSQKWYo+sXy8u9EKEFbsg3LsCj6xYPW7XZvc/yE1hPoznWzPbMdrfVeK3ovmncRF827aJ9z6XAHTW92y7cu56bHbiJVSA3792LoL5+G2lvTqgEH2thrrIHnWG3fmOelx1P07HBK/66aitqWEAveltznag4hhBBCCDF6EjQeZbb29LK1azsxdytal76hxzBAB2gvQ8hRpEINZEJ5Er6BwiDhmVR5Fr2mj637e796XimvMw0KONToamqtqlL1Xz7Y69LsBRMn0tXWSGLTdnTBhbCNMgx0EKCLLlVRm85JE/jr5i0EGYNXurexuWYraZUrVwwN/Diat3OYbhjHzPOCXoOJIoyNiwv9jw3wBx+A/iXNJRoI8JVFB7W4RpTAMDF0AJgMhIygcYMAyzSJ2jZ51y2/iEMYR4cwcAhUqWXFoGxT9a+f1hqtAhQGgdZs6upia08vDV6KiM5C4KAxsFWp0rJUzxigFbjaBgKqk22YoRwZJ0ufW6DRMIgqjz2N1Lk2HDUwTYXvaozw8KoLz9WknG56/F1MaZwyKExs0A1s7duKqczB+ele3mtgv72Ov95IumctZuARsuJY+FRRpECYtdEZ9KZT5YByzyAi2/UKbVZAd6CxjBBRO4Jl2ANv2L9UWTPZZswCXaoDSxV3d7qOBT3YCiwrTs7Ls7VvC6DZktpKrecQwQMUYcOmvqqFmkhtqQIz206ourImNIdz6dyBNofYs/pIu2k8zyHrOf2Fwar/7yRo32VtTzt2op6m/kqkA6nSWb51eXl/xYJXAEr7Kx7XfBz/csq/kAgnyOx6lsTrPyMUqNKySzMy5gYSI43Nqp7BhqJLQhfRVnU5wDOVSZUdJ+730W3W8YFTruIvG5/Y/TxlMDVkUGeFKCobtImj+9ie2sSEZBs1kdJ+guXtAwKPMC7XnXwz7Z6x33M+NERLRJI8SSPNdFBLnoze3aCoCocsBk/SyKJo7YiffWbDTBa0LOCVna/QEGvACzxs0y4vX96U2nTQu93ubY6/sO2FUVf07m0uHe6g6c0u0AF3vnAnqcLu6yfs/ZdPQx3MTtiVBJ5jMbDCopAPiCdMTLv072rHliJP/LqTUy9qkLBRCCGEEOIgkKDxKJMtFijme4joAsqO7c6OlAlmFIs8moC056AcH6XANEzasiGyiQLFUBHbszALDo6pccIa2wwznTZ8F0xTsb3Qze9Xv0iuWKQ6FiVpWTieR3t3F5u7uymaBslpk/C37ERncgT91SxmdRw1oYFO3+X+558ncKGnKg9KU/pfqXpyQIDGsQqgwLFdAGJBpL8i0CcYWFiohlQ1lsNGVfo/DWmjGlsF5aAv0Kq/nrH0XEu5NEZNQuEE7d09uH5AwfXwA02gLJQuUqqSVLsDOU3/um2F1mY5twx8H8ct0LNzBTF/FTWF1TRxDLtUC3Hdg0bhq9LYtIaMqiLBLqp0BxDHDVwsHeBjDd4zsN/QzrX1LSFqW0J0bCmSDKlhP9j19ubpsjYRqfNQKjTotbzAw1AGBa9Arphhiq2I4FLAplPFR+ySG21cRH7ah9j4zA20mgG2LnUN7lMRVpoT2GbU0BgLDQooy0HEugcovPSf5Ap50BqFgdb6oCxVHrChawPAoCWbBWx8DCx8QmaYvmKaTOdatA4wzSgRcpgE+H6BnekdVJk2nnPwmtAYymBmw8xyELOuc90hCxsPpDnEntVHKBMncEt/L/qP20DNrzZMQoFPR76XGUMqZvdn+dblXPfQdbT3tqOUKu0dCBT9Is9vfZ5/e/jfuPGdX6G19zmKQfGQNZBY37WB3xdCvNsKjxzgGWH+6Mb4xOSTqY824q3zWBAOcU7Mo830sVUeV+fZFpikA4N6w6Uz00FTVRMKqArF0VrjZtsJ1c4nUjOb2fsZ60gh2vT66fRF2rgv63Be1KNBZzFx8DHYoRL8vmARa1y416BwzyX7XfmucsCXc3OHtNvtSOFupRW9hztoErCucx3ru9bTGB95j8Whv3wa6mB1wq408DxQAyssCvmAZL1Vfj8jrEiGFKkub79bxwghhBBCiNGRoPEoE3K2Y+oCvhEbdvK8wCWPg6FcXJ0kHc5gaYg4LqGMz8wsbExGyURzZKNuKTfToL08a3mN1sIUZjY38lzHWnLFIo3VifI34xHbJmxZbOvtJe86eLV1RI6biZ/qRHsOygpRiCTY3pvC831ioRCYBt253O7AkGFFdQy9I2cUhtw+wtLpQS9klm/wAUuVlisDBJSiSBMfzwenbwN5wwLdSgD4QYBpGASAG5gYyh4cMo4wWqUhCCDI95JbdQ893npU4HCi8Sx/8s+mh1qqyGDqUphXUFWEKTLZeAFPVwNgK4uk8tlJjE61/27FylAseFuSJ37dSarLI5YwsWyF52pyaR8j5LEh+hhxu9Q6QmtN1i01wnADF1OZzDAd3q3XMcF1SoEbBl0qzpO6kV0jdMlNRSbyE6eR0yMBJ9BBklKl2GK/nWlBF88bE3nGdwYFlIYyOKbpWHbEm5gVVWzJdpb3yTtYS5UB+oqlkDJiRcpBcqeK06XiNOs0DpHSfnXKoipchaM1Re2i0URNi3CQpze9mdpJZxy0rq0HoyrrQJaPDt0PUdnVgEa7aYq9r5b3Pdyz+qiXCIVAE1PgAKCxgDyKAtBkKtY7AW7HJs6tmz/qMd/x1zvY1rcN0zCJWJHyNcMyLHJuju3p7fxu+fdYau84pA0kUoUUaz2LxyKzeEuwlfohAd5ycyJrC6W9CzuzndRQw6VVHlVGqbO0oyEETDF9fA2+1qjCdoJwfExByt5CtFW7VpUqjKni/8t5zIs2kzRNUr7PqlyW6kgNH9lPUDieut2OtaL3cAdNoiRVSOH4DhErMuL9I/3yaaiD0Qm70sDzQA3sdRxPmCilhu11HEuYg7aOEUIIIYQQYydB41GmOeLQYPayw28iZBTK23tpIOcWyFCDYeXZ3JAiQIEKCPmappRBU6qZNnKsinmlEE6VCvYcW7NT99CXzDN1ps32XSmqY9ERv/mvi8fJOQ6dfd00qA60nwMdgG+wM9uAq0NURaJUR6Nkiw6qtIK4nNmNHBvuceug99xLyLgXAQZOUAoXdz9T4WOiUfTQQE3QhUEWN4hjGIqil8UJcigV7i9Y3KPV9KAP3z8iTWmJdy5KfVUHWkOgDCaZm/kbHmJ5sJhu3UhAFSYeTWxnjvFXbLUDSx1L4GYJOZ0EdpyHchF0eMiOkHvpXDthapRTL2oo7y2Vz5T2lmpqC5M8tsD/vLQF06vGCzy2pLaQdbMEOkChmG4WuazKpQFFjki5yqtJp3mH30td8iRmNswcFHQ9v/V5aoo7OM7MEVGaHYGBr0wSdohm0pzhraXHahwWUA4EW7pnFfMa55Fzc6XlpoZ90JYqV4er2clOCl6BcKj/B0Kl+Ks1ib9x11Gts+Txsa0otvaowiGjwjxqzcBRNrhpOosFPjPzKiY07t4nUutgTF1dD0ZV1liCyoF9HPMdy+lZ9b3SD/x+EWWGB+17OFB9pPra6fEhbEFYadDgAb2BQbOhyQYGv8sp3p3pGPW5WNe5jpW7VqLRhMzQ4GuGgrAVxvEdtnevJZP0QYWxg1KYtOe8H0sDiaEGOqBvCMLssI8td7keqN7NujlCZmnvwqZ4Aw4eMRWwM+ivZAaKwM7AoEn5dAWK+vgx+F76gIOU0YRorYlWaiI1rO/egOPnCZkh5jcdN+qgcDx1ux1LM4zDHTSJkoG/JwWvUO6Yvqehv+jam0o7YR+MwPNADOx1rHSaQs8WtJtFU9oORdlxzGgbvh+jmB+pRZMQQgghhDgQEjQeZaxwDUvir/FQpoYuP0LCcLBVQCGA3qAWlwhhowChXjy/Cl8pChakmxx21L5O0fZx+5fXahRK9xcNGgF5I8Mz217CVSGS1shTI2zZREyN6XbTqSFhhrENTdY3yQUGlnKpj5T2bTINA8NQ+PvKC4dVLA7sqzh6qr8DNf0/NiSNIqkg3H/b7pTTx6abRixcLJ2nL5ejs7AdN3AIq4EfdjQKHz3CkmZ0aalp2IPGrnr6YnXUJIrknCymhlZjM81qC100UtRRLJXniWwHuyzNMSGD3p5X0dEGkg0LqJ5+El0v/JLUAXQrnjA1SsuUyLBumVo1MWPrDJZvXU7WSTNBOUwNWeS1xUbX4/yoS5XSbPU0UUthKChoRZ+vaTbhb6s0L2xdzp39TTv6in10ZnZxTdIhDuwMTEARBD69TgHHjlGj85wXcZlRP33wudhjWZ2XbScWbkCFE6VqsGw7ZihJcualrK0gHJleP511rKMz30mr3VoOKbYZNTxqzWBGbhXNhqJK+QRodqgEL1iT2GbUlOaBHWVLfgupYrr8mvmO5burc4aEdeGGvYc5B6Mqq5KgMt+xnM4Xbix1lY42lvZiHGHfw4YTrqNj+c0Y2W5yGmL9EzqvDQzgdd/iwZzFWi+gOdE86nORKqQoekWAET+foQz8wGdTppsdlkc+m8JTJrFQnLbq0v6HcOANJEYyqAN6cgqdxu4l4EMD/Ana4dF1r9HjawZ6ZO1+MHQHmnrLYvZJX8Iy7QMOUkYTonXluvj86Z/HUPvf53FvjuZut4c7aBIlg/6e2MP3WBzpF117U0kn7IMVeI5WOGqgKJDv2YRp5FBmCEMZaB0QuGncwiZUaCrhqFTPCiGEEEJUSoLGo0woOYsZDTXo4Bmed0+gw4mTQUHgAZoIBeL00aMa0JaLpfv3RtQm6bDBQJMURQBaoZUiMMFWBQIsdqTT1FbV4XgeEXt42OZ4LuGgj7dGXmSDnkWHEyfrKwJtYClNg9FDyMmBriViW5gGuOWkcWiAuLcE8kDCRr3H/5cCx3QQwsAnoBSQldrfBJR2frQJCDhBLWOneTZF18MywmgdoHWADRgoAr3HFo39TA01DkzrNbBzMVw3iq81vg7QykDpgG0B+HThBj5JA150DO7JKBbXNBFxPIyiwT+f+EEWT1rCtck5B7z0URlq2LIuheKS4y9h55Y/cWYsy2QbbIoUtabL07Ra0BWAi4ehCigGui5XUxtvwMhu4cfLvszagkPEjtCZ6WSC4TDJ1HQF4GkfUxmYysDXfqkyzI4wPWzh9a3HHPKD5r6W1e2sPYmvPXt3RUuMB4KYZCg5rAnFk7kUy81jqHH7mGTXgJ0oLU/f44fpoT/A7iuse/3pL/Brv5nHU90jjrfSqqxKgko/8Nj80q342Z2YsTZsq1QlONK+h9HGRcw/604uue1UfCdFYEaxlUXM0GS1wWbPoM/J0FTVxFkzzhrVeYBSWBC2wuXPUloSvJvjOzi+w0Zt0qXiTDIcurDIFNOs71rPjPoZJMPVB9RAYm/23Ltwf81JlJcpja8/QDcMo9QkC00QBARK0RitwfBzhOuXHPBYRhuipYtpTpp00pg/83i2v60ADnfQJEoO5O/JoXQwA8/RqGu2iIXW05OupqpKl/9JUMoEI0ohF6euaj11zTMOyvsJIYQQQryZSdB4lBmoGJuavZG24kN0Vx1DQcXpzhVZ1jeVKHl6aCDAxMRDqVIA5ytjUH6nMcrhiwYcIlg4+Noiavr05fOELWvYN/+pbB+NqoMTajIsstawy42R922ygcWfuqcQMkwCL0vgZTGsOE6QhoFqQT1S4LivUHF0gaNCYxPgYvQ3nDEI4WIQ4PZXJqr+akebIjYOE8x2muvbebZnJUm7Dbc4CU9niKBpNaI4hVYs7ROYmqIBgQGzu6Elr/D9MI7hYllZtPb7A0kDS4EOTPo8j7ACF8hqAxS8lEmjlMILMqx98F/5+gVf5y1tbzloSx+r81tYGneIaEV3oMkHAWEFx9iQNCHrGRT90vFsS06kJlJLPBRHa5+tOzcROEmSkRbWdq7FCRyqbYOQ8nGCUnzr66B/QbpCAY3VbUSUu9elriMtq3slk+amZf950Bo/fPq0T3P3S3cPC2ovOf4S7lpxF8/vWsmUUMs+f4DVOqB37U/xi6lhTUpypktP90om+dtIho4lbEeHjdcP/IqqskYbVD607iHqonWDuv3+7vnvclr6abJa4WX7BlUJDt338JVsljtfuJPtOsLOYgpNDktZRO0oEFDwctimzVUnXYVljP6fhZkNM5nfNJ9d2V2l46B279GIhpyTA6AmWsfK8ESavPU04JCxQqS9IrtSrxOLJTHDNdTM+jAaWNuxZsx/H0a7d6ERqgZ2MaN2Mq/3bcLxnfLlJmSFmFbdQjIcG3OF5Zs9RBvNVgCHO2gSu42HPT4Pd+DpptcxZcKDZNPvJZurJRLOYJoevm9RKFYRCuWYPOFB3PT0MVdpCiGEEEKIEgkaj0J7Vow19i/19IxWAmXhaYMipYo+KNUulr6MPTK7QR1Pyrd52BhoZiQcNhbidKTTVEejhEwLx/foy+eJWrDYWo1pRVAKWkKlICHQ8Eq2kW3FOLUqD4FL1skSBN2EqaVIFJTuH5c1wvvvSfePKEBj7uUxpceZ+Bh4WIaCQONioku3YOATIkuYHAZu/26NLg615HWc5uo2DPM5MLfj6hi2qgNSRIwelKrHC+KYQQFlQE2hFDKiFY5XRzy6nkh0S38lKSitCRSYpo3yfepNxWteqUZqvq0JTMUOwji+SUeugy898iX+/ax/Z9HERRUvfdQ6wH39F8RVQNpM4jppUOApk06tqcGj2dCkA4NAB6QKKSZWt6GAbL6HnOcRjtSzrXcbXuChlCKnFS4BEaXJ61Kna0OZxEMxXN8lYhgoY99LXfdcVhfogDsf//RBbfxw/ITjWTRp0YhBrVJqVD/AFntfxenbgBVtBKXIONlSV3DDYkvfVtxAMcWGyTZ0Guaw8X7kpI9UFCjtr/Kt6BfZnNrMV5d9Fcu0CJkh6qJ17MruYioZwmEoEAX0oCrBmkiyvO/h6q3PcdOKX5ePfSKcYFNvKVxLO2lsw6apqomrTrqKDx7/wVEd+wGGMvjwiR9mTeca2nvbybk5wmapwjHv5dFoIlaESclJbDOTPKpmcqK3mXqdJWZqim4WJ76AScddzSoH7vztp8h2r8QOirhGmHjdfD504ocPKPgYzd6FdvUMYD11pqJl0lvpynVR9IqErTD1sXr87GZC1TPGXGH5Zg7RRrsVwHiprHuzGg97fB7OwDNwUtQm1nDcsX/itY2LSafrKRYtDMOjJrmDaVOfpzq8pqJ9YoUQQgghRIkEjUepoRVjxc5d2M+vo+BH+pcK715SrBlodrC/CsHSY45vNFjS8hb+uPpVtqd66fML2KbBlPp63t4WJb62D+0nUNbuYMVQcHL1Nh7smkKPX01tYFDULgoDGw8PDxuHCDkMTKCuPL6hY7Jw8ctTs7THoolH0N9DemCcIYqY+ASAFxhoZWLrIkUdIkQP1SpHoIuDfsh3sbFwCUJhTj32Eu5a/wRru9ZSH7Ix/TimqsYjjR3aiVOcTN60sIOAiX39lYxeHbaZoa3pf9nlFahTAXb/bpDZAHJ+nmZD42OQUAGfSmpCCgKVZ2vg8lAhwssFg2hhBw88fgOxxVcx65izMUdRRba3pYhOai2h/HbS2LjaK8WzqtRZswDktCKmNFGlUWaIrJMl62SJ2zH8YifbA4sdRpismyVkhvACj00ebPEUUy1N3tcYyiwtK+3vIG27aUKNJ+wziNlzvN35btZ2rCUeitNb6MU2beJ2HKVURY0f9rZH3Wh/gA2cFNovstML2JFZS8Erlmej4ztErQimDtBuHz2BWx73wHiBigKlfVW+9RZ6Wde5Dtd3qY5UUxetI+/mWbFjBZ7vMb1pKoFKYROAsogaMfJujq19W0hGkqV9D40Q/7v+0UEBbzwUZ2L1RDqyHexI72Bq3VTu/Ps7CZmhUR/3ocf6xrNv5FtPfIuXd75M3ssDpa7TpjKZ3TC7HLRuM2rYZidp0FlCQZHXM51c2HAuz2xZxdMv/ZhTVAeT7NIOqS6wpWcndz+2Bs648YBCh/3tXTiwz6IZqsbPbqYh3ICK1fXvJbp51J2l9/X+b8YQ7UC3AhgPlXUHauC6BqWK5Dktc47a8zge9vg8XIGnEUqizDB1yfXUL9pOX7oR14lghwpUJzrQXhbfq2yfWCGEEEIIUSJB41Fsz4qxNv00TdZyNusmVFBaJqzY3T1xoKPy4BrC4eGjiU+kegYzmpuY3tTI1p5eck6RWCjMxNoaFJodO6ZT7FmFHR8crLSFUiwKr+RlbwFpR+H4HpoITcYWGoztbAva6AtqMPurFEvhoUFQHoPGwsXGLXWK7l92HCFHAx1oTBwsOmjpH31AAPgotDZReP3Lvx2y2iRpFvuXMGsCXVopHlBFNdspNszhc//v82zq3UR3rpuuXBe1dgeTQsdRMOpQpoWKdJDMV9Pc3UA0H8dRLonYeiY1/S/x+CrWu1BQ0GqWglZXQ1wpdvmaBjOg3oCeAPqwCKM4xvS4PJamKxxQY+Sxij1se/IatjxXS/PUC5k96317bTaxr6WI82yPkKGw7SqyxT50fxg4cI53+QZTLJ9mEzLKIBN4uG4a1+nEDCV5kjBZL0+gAyJWBNM38QKP3+ZMLk9oJpiaPq0paI3yC7SEQkSiDfsMYoaON+tk6czuYnrIImwqMtqg3UgwMTmJZCR5SBo/jOYH2Fd7tpLp20GHkycflJYrW4aJaVj4QYDv5UgbipXpTbzuKQxlELfjTEhMKFUEFtMVBUp7q3zTaLb0bsHxHWqjtTTEGkr3KVD9FaYr+rp5Z22cFp2mR5ugFCEzXAqSixlCbhdObDLPdewctjTbUAbNVc1UharoK/axsXtjRYHDoomLuP39t7OmYw0rd64ESuHSrU/dWt7DsUwpOlUVO7IZtmTTbHvuv6ktbuPSWJ6kYZAnSk6FsfCZajvU+6/zx+e+xQmttx/08KF+wWfIvHbnsL1ER9NZen+OxhCtUmPZs3Q8VNaN1sB1bVPXJi6ruYzrHrqOKfVTDmiPWTHc4Qg8Q8lZhKp3f++SrO4o36e1Pij7xAohhBBCiBIJGt8gBrpR92ZqKLhhfG1i4aGVga8NjP6qO39YBeHuZdQ2LnHTx4u0AWAoxaS62iHvpMpdhd1sO1a4AWVF2JSN8GSqmQ6/FseuRvkOzYkaCupVpud+zzGGR633FH00EVLVwEf5Z+PrtKtj2KKnsCaYS4EIoHCxMSg1ZFBoqukjhIsL+ERJ0kueKC4hBhrbGDjYeJg6w5bCShpDC9jq11NnpIkrF5RNnirQBVbl/8pfNgSErBDNVc3URGt4res1dhXWsauwhrfWNDIzFCVq5YlbHRTDx+B5CUyzj/roJkylKVLa+9DR8GwRni8ourSFg8XfxQo0odnml6q6DGVQBNKB5hgzoMGA1zywtGaiGRD1dqHX/ZiNW35HsnkxNbOWDgo59rcU8bq3fJA6M8zEqggZN4fjOwRojP6GNkU0Xb4ibSWoxiFieNh+gXD9sTTNvBTr2bvp2/ZCKZrWAVErStbN8krR50fa4O/imsmWptrQGIaiquF4GhdeQ7Rx0YhVli9se2HQeIt+kUR+M0uTHpNtj7AqLWTf7BV4KJUhxVwswzoke9bt6wfY5VuXc9Pzd3F+scgx1sCye4UX+PjaBzRJpdngKrYFNhHLItABaSdNtjtLfayeZCTJ7MbZYw6U9lb51p3vprfYS9gKMyk5qRzauL5LQEDYDJNxczypJ3E+BWrJk9EhwEBpHz+3BTPeQrr5byi+fieNh6Gzr6EM5jbNZW7TXKBU+fWHdX8YsdqzN9/Lhu4NpQrRUJRzlUPCUGz1NIZfIB4y0YZNrzZJGlkmZ15iXccaZve/9sESaTieqpZFg/YSHW1n6dE4mkK0g2GsnaTHQ2Xd/ux5HZ4QnwBAIpwY8x6z4vAa2N966Pcu2ivgFTsrrmIWQgghhBC7SdD4BrFnN+plhZNoLyYp6hAmAWHDI0SRVFAFlJYmB/3x3EDQGCVP0ixixVuJh0f+IXHA0K7CmzKK/5eeT19gk9MFct4WDGXTW6gmEZ7K02oBSe9pmk1NJOigSOmHzLClmcurzNKvMsdYxV/9k9ihW/CwMXGpJlVakkycXmqx8GhS23mL+Rxaw+P+aezS9aAUYVUkQSevFl5gW34zGW8XzeETKZoNhFQVBj6e7mCL8zI7ClsIm2GWtC3BMAy8ggcKbNPG8R0ey3WxIKRpVgHaBDuysdQkov/zbwsUvylEaS8WyWrFZs/ADXwMBWc0tTFDtdPtOYDub4BTqiRtMHdXmEbRNFtgaZ+iMrHwcQvdRLpX0vnCjTSccF05yNvfUsQ71v6Zf62dTqx3FbPrZ7GyYxWO75SW0CuoMxW7zBr+YC/Ezaxnfu0UPnHaF4jUzEYpgw+dYLA5tZmeQg8Fr0DMihExI+R1ntWuZkOfzSQroC2a5GOnfY55s96NUsaIVZbT66bTk+8pjxcFVuda3l/lEKfUqKYbTUwZTLc0l5o57stsYLtZy3HNxx22PesGjmtHtovfuyEuqyrSbGp6A4WDwiIgaWoyAfwua6CNUhWhqUwiREg7abzAY3r9dKCyQGmkyjcv8EpVq3UzBoWvtmmXXzPQARt1lEdDe+x7iEdRgZmcTcNxV5OjipD58yPSlGRvIWrezbOmaw0omFk/kxaVZ2IQkAoMTKXwdUDeLZAI26AUWR2hmRzp7pVwkINGGFwZfigcDSHawfJGbYIz9Dpsq1KTsXgozhR7ypj2mBWH39DvXQ52FbMQQgghhCiRoPENYlA3aut3rIou4K/5NlJeCAIf21TMrE2wLRuQLeYxtYuJh4VHgjQJo0g6NIW2hglMrK3Z7/sN7BFZ6F3D3X9+lu1+H3ndR8gMEVE2buCSdjtw/Brqq97OnZ0vcXYoT5vlk8AnCxQDaPehwYQWtZkLrS3s1I04RAmRp0l1oICdupFeHaVa5XG9Dhxf02DC+6yfsSFoJBVE6fLzPO8VWecGTLECYmoj+WI7G3QzphHFCbIYZh7Hd1CUQqOclyNux9mS2oIXeITNMF7gsdpV/CgX4X2RPNMsj0j/z43FANa48KtinLWeRcZxMFVpGbihSo1WwtohYpnYqgpVzBDo0sLwqNJEFRQ1hBQ0GKW/fEUNEIAyMLWHY8QIOX30rr2DSMMJo1uK2L2e9JwriOW2EHNSzK+fxtqeTRiBQ5OpUcoiRQQvs45cuIkzF19DtHZ3YLNo4iK+cMYX+NYT3+Kv2/5aahBi2tRF60iEE+S9PGYkyT+f+WVObFsM7L3KcsX2FXTluphaNxWlFNlimrOsFAkFOwIDTweAJq+gGCiaDM3bzV5+bTUf1j3rBo5rdaSaV/Pd3J2v4uxIgYmGR41RqlRd78BvswarXYiHfExlEuigHKrahs2Grg3lEKmSQGloUNmd7+Y7T35n2LLjuB0nbsdJFVOYysQyLLYZVWyzk9QHGVKZLbTWzeILZ/wQ07CYqYMj2pRkpBA10AGmMplSN4XaaC1VxTy2gl4NKIWBga99/MDDNCwcoFpBQvKbce+N2gRnLEvCxfg0dH/rg13FLIQQQgghJGh8Q9nzt/XH9a1hnvkSnboJLzqVpmnnM23aKaxc8xfuem4Fed8gqvKEKKCVRa9OEvO7eVvLtD3299s3pQw6dBPrUznyOkfUjuIGHjkni6+90r5Hfg9uX4xteiI/KHRSH2SYZsLZNaXlwwqwVSnYiRqaRrULBQx8yx8AzWoX1UFpH8T1QD6A7gAmm5pl2V285CjaPcW8kOLqqoA2s9SAxdEeW/xt/DZnssqHECFMZaJVaadK13fJki03QQH62+hoVhZhRc5gogHTS8UrbHDhdU8RDxlYhlF+rOr/H0BBW/gYhJUiYkVQqrQc2dLOHvWjEDJKzS6UUmitS724FfiBgxVpwOlbj5NaS6rQN2gpotaarJvF9UuNSSJmBMd3SEUmMrn/3Ku+DcyNxnCLBQzto7THKWxnSdjCrKlj6gg9Pwb22Pv1ql/zwMoH2J7ejkIRtsIc2zykgco+qiwbY43szOykM9NJc1UzdTrNRDOgNzBQysA0FH7glz9zbwBTbLhs5jsO67LDgSWetdFaDGWwxjPZkEvQZvjEVUCv57M6nyNkRjCVhx/4FHQBQxkkQgkmVE8gXUwf1D0l9wwqAx3w/9b+v2GBjVKKidUT6d3VizJKt/mBT97Ls7ZvO1G7irNnXFj+oXk8NCUZGqK297bzw+d+SGOssfQAO0HgGdgEuNpAAYEuNR4yAfwCph1hcuP8QzZGcXCMh/l2KIx1SbgYnw51FbMQQgghxJudBI1HsZH2xxv62/qJe/y2XuuAhq7/4W9CG/lzbi59QR0Zoig8qtUWTrVepb7zdfSsU1H9FXr7Wwq6ZtdrOL5LyDBwA4+sk+1vRmJgGKUxag0Zp0hBZykYIeK4ABSAhCqFig7gazAVe7SwKRkIIzMB5Pu72YQVZDW85Che9xRzbc3liYCq/gYs3UGpcnCapbm8yuPHGYsOK0ldrI5NvZuA0jJU13f7K6wUk4wAQpDyA9rdHD6ajYHBRm/weLJOlkQ4gWmUmqYYpR0lMZRBxq6nU2Wo87sxwrVMTE7i9d7XKRSKBJTGNFDVWG7Mo3ZHlqYZLu0bVewkcFIkIzXlpYhe4LEltYWsmyXQpfcLm2GqwlUkI0mijbOJNJxAeuOv6Hr5Gxiuiaa0153SClt7qPQadj37BZpO+o9hy8QMZfDu+e/monkX7fO876u6x7ZsbNMm42bIOlkaFLur1SgFuaZhErOjpTBMB0QpMqX+mFHO+oNjYImnqUzidpy0k8ZUUTYHpUviwNLzgIC6WB1Taqbgax/LsIiH4uScHEWzeMiWgO4rsOkt9jK5djJNsSa6891s7dtKupgGSkH0bc/fxrKNy8oNKsZDU5I9Q9Shy2u7VBU9ZjUNupftvodSRn9rKE3ezdJsapINC4jUSKXY0WA8zLeD7Y26JFwIIYQQQohDQYLGo9S+uhAvmriIcM2c3UHhludJRpJMtgJ2bH8WP7+TxaykRzWS0aVlynHdQS4PO3d0UZdayyvZLHf+9Q6y3SuxgyKuESZeN58PnfjhQZVtm1Mb8QMXZRoU3AwRAkKmgUmAD3iEKGofX+dpUy5NIZOJ/RFbUu2uXLQVeLpU1Wf23+ZT+u+BCseChpiCnIY6A9a7pU7aC0OK98Y1VQZs8ykvjS7qgJ4A2ky4utbmT6E2Oowq2lPtAMSsGDlyzLUDzoumaTN9THwsDekAHi/AA1k4KQwNpqLT1zxdLLWfadJZ6sIhOl2P110PjSIZTqKU4rmC5tyQySRLEzctplRP4jUnjcJBKegODFpUgEUpVA0AG3CVTX2sAe3mUGYYI5RkZrK0FHH51uVknTQTlMPUkEVeW7R7ir5iX6lBSX/QBJDZ9ih+sQelLAwzskcn4zCBl8PNbisvzd5zuZjWAU5qLX6xl9ZiD5PCNZi2ZnCUuO/qnrgdJx6K05vvxfMdam0T21NU4ZPR/dWdhkXILC0JDtxMRdVqA2Pe2xK4vd2/5xLP1upWNnRvIO/lCZkhFAo3cEvhqoZJ1ROZaisiaApAR3B4loDuL7A5ofUEfr3yl/zvc7diGiHisRbSdh0FvzisQcXhaEqyv3MxYKTlta+EpnEaq2lVBbp8H1cZWNqjxTapjk/imIXXjGlp42h+WXI0ONo+x9HWBGd/x3fonN3zoni4loQfbXNgPDlaj93ROu43mrGeBzl/Qggh3syOiqDxu9/9Lrfccgs7duxg4cKFfOc73+Gkk0460sM6YvbXhfjaM64FGBZEnqZ6OMfYyUSL/gq6XeQD2OlDRpeWJPfmdrFyy9Pc/eK9nORvpc0OSiEYsKVnJ3c/tgbOuLH8+q/sfIWIdyK1upk2o0hCgaX8/uWP0EM1Mb2d99d00GQG1BpZDMJsBKbZlJcTG8DQRGvPb8dsBRMtmEBpmXVPAEkD/k8NxFVAnVkKIBMBZFFUKU2rCXGzFFbWqjyXeMvZ6IcJVzfymo7S3tfOCZEwZ1Q5RPBRaBqN0ns1AzNCsLS6FARqNFpDNoBdvsZSLmHDw9XQ7sGDeYuaiMk5/qtMjmjq7CqsoIjTuxrbjFBlGLQTp8bP0WQE2P0hq+5vFONoMGOtoMErdhKunV8Oai45/hJ2bvkTZ8ayTLYhpIo4WrHZgz8UImw3o9y94m5OnHgibmotxd5VoDXKCA2qOFSAYYYJAodizys4qbXl5WP5juX0rv0pxa6X8Qqd6MBFGRZmtJFI3XGDOmHvq7pnYL+yiX6Ki7xXmaw0SUNTpzRp7dIRGGgrjB/4OH6homq1QucKMq/dWdrU3y+izDCh6unlsQ58pr3dP1AxmCqmaKtuozPbScbN4PoulmExv2k+LX43p/lraHP2+HvgGTwfmcglh2EJ6L4Cm9yu5wit+gYfsjuIhi189Rpd/k7+ak0ilhzeoOJQNiXZ37He00jVmputBA+rqSzUrzPBcqmPVBMJJUjUHzvmJg37+mXMgqYFB+ujH3L7+6XSeHW0NMEZzfEdOmcnxCZAtFTdvj23/ZAvCT9a58B4cLQeu6N13G80Yz0Pcv6EEEK82Smttd7/w46cn//853z4wx/m+9//PkuWLOGb3/wm999/P2vWrKGpqWm/z+/r6yOZTJJKpaiurj4MIz60Ah3w6d99mpU7Vw7aHw9KlRWbUptoTbSSKWboK/aVg0gzvYb3qm20WaVQy6MUdNmqVDnY7pZuqzJguT2Vt/jbSFomGUJ4lLoix3Ho833+bM/hVc+kr9BHQ6wBK9NHo3UyPhGqyGDj4mKTo4oIBf7GfIiJxmb8/nF6Oszm6v9kVuZzGBSBYRnjIAMTdOAxAaXqx54AdvilbtDT7NLjfCBFiHrlEaLUiCXo/6w+pb2ZjNhECrP+mds3PMtbU8uYoLM4gcckq/Qefv97mXuMoaBLrx/pH8RWDzq1Tcw0abQsoqE4tmljAtGqiRhmhMDL4+W2ocwoDzoJXkzt4NJQN9W6gEmpelP1j89XikTNXHRQxAwlaTjhC+VwZc26X7Lx6WuJaI/uAIpaE1aKOhM8M8LDaiorHc3Xzv8ak3Uv25/8NEGxF7VHNePuY6kJvDxWuI6WU75GtGkJ+Y7ldL5wI15uJ76TQgcByrQg8ECZGKEkdqxpUCfsT//u06zctZIpyeFz0Ox7lX8I9xEzNF1egKF9Wg0HC42LYkcQwlcm9ZZBdbyVY946fBn3vriuy4MPPsjCyD3gdGJFG1FmBO0X8AqdmKFqElPfS3rjL/CLqRHvH/gsQ38Y0FrTkmjhPfPfwzmNbWx65t9IZ7fR6QUUdWnJ/ljHfTDlO5az+dnr2dWzgawRQSsbC58qHArYPGrPZJ1n01fs42vnf+2QBj4D82d/x3qokX4Qm1k3gw/PejtzaidW1KRhb7+M6ch1kAwn+dxpn2PbC9s4//zzsW37YByGQ2J/n2OgYlWMzYEe34E5u6lrE5fVXMbtvbdzTP0xh3RJuMyBsRuvx27g37C9XX/G67jfbMZ6Hg7H+dvfHBJiX2T+iErJHHpzG22+Nu4rGr/+9a9z5ZVX8o//+I8AfP/73+d3v/sdP/7xj/n85z9/hEd3+O2v+2VDrIGXdrxEdbiamfUzS81IAp8lwU5sC9Ia4v3LlAMohyfN/RWB612YGGwlETboIQr97+Fi0atNkkaW6dlVPOG1MKN+Fgo4O76WwH2ItXoxfbqRHFWYeDSo7ZxkPkebsRkohX15FAOXo4EAcDStZwb2bVT9X5aCkKHwAgND+/iUAkIbaDQcTErBoAaULlWkGFYcFXgop4fmnmf5j1M/wpbHX8FVEzAz61Haw+8PFM0h7x/qD2R1//s3WVBdNY3qaA1RK0qx8znwFZHGxeVmOqZdhVE9EzfbzumJGI35HFr7bDaqCaEJB0WqcLGVIqIMvOwWYq1nDKrg0jrAff0XxFVA2kwSIsBCY6DIK5Na8ryVXazwq0kVUhiJJMqKQBHQAaghn0QHpT0hrdLSbK0Detf+FL+YIgg80BrDjpX2yFM2OihA4OIVU+Xl1vvaP7Azt4srIzkmRBNU1cym1s3hBi62X8AsdmIVezjGMlDRljFXq2ldmg1+sY9IYnfQqaw4djyGm9lE98rvoYwQocQxw+/Ptpc/y94qBhWw48lriOKRaF5MzcDnMGxidgxvj9c43N1Ky+fMSbFLW0SUjVIKF4seXZoTJ3ib2WzOpdN3DmmDij3nj101wrnYx3E6VMtr99WsKGbH2JTaxM9f/DmncVpF73OojeZz7FmxKg7MWI7vwJx9dcerrHt2HTeefSNzWuYcsuMvc2DsjtZjd7SO+41mrOdBzp8QQghRMq6DRsdxWL58Oddee235NsMwOOuss3jqqadGfE6xWKRYLJb/3NfXB5SSd9d1D+2AD4OebA860FRZVZjD4jAwtIH2NXXhOmxVivSM/DYmmhY9gUU6gDaL/q7MpQDP1aVKxl4PVhThXQmTDAn00JBKQZ+GCWaWeVYVWtnUBxmalcK1M8zwH2SXbqBIlDB5GlQnMUMTEC6/hKlMBqadR7i8PHqgGnFoJeEAH0qhZ38n2gAIAzFlkdE+2UATN0pVmXvufagHGswYIQyzCm0E+IFDrnst4a5VmCqEZYRxtI0mhDIUWnsEe6nzHZhBpoI6O4ZtJwncDL4OoRV4XhHTqhp82EItRAqdzIvVsNOJ4rkOjtbkVIiiHWVCrB5LaQLtkJj1T1g1c8pz1Umtxcx10UcVATamYQ46ZmltUacdppkWVVYVKjYVq/o4nFwvBBrVH0INPD7w8ygVxUouRMWmkutaTT7VDlYNXiGFMhOogXdQpbDR9zxCkRryqU3kulYTSs5iQdMCPnfa57j3xXt5rfs1UvkUISPE6fXTmIdJNNaIxiZqJ4kOjDc6EaPQhe+mqT/uGuITz0Qp44D/Xua715aGF2kmwN6jq05JYNXgZNcTTs4Y8X4Vahn0WQCm1Uwr3+97Pk5qLflUO2ZkwvDPsZfXOFwGxkZoAoYq4mur/+8VoAbmhEetl6bHjFNlVR2ya9+ex2m0x3qoocfeL9c+j826znVs6trEhPiE8jVw94BgQmwC7d3tUMO4/jdhNJ/j9a7XeXXHq4d0b8A3qkqO7zHJY1jHOo5JHnNQ5uyhGOOb3Xg+dgPXnZGuP+N53G8mYz0Ph+v87WsOCbE/Mn9EpWQOvbmN9ryP66XT27ZtY+LEiTz55JOcfPLJ5ds/+9nPsmzZMp555plhz/nyl7/MDTfcMOz2e+65h1gsdkjHK4QQQgghhBBCCCHEG00ul+ODH/zg0b90+kBde+21fPrTny7/ua+vj0mTJnH22We/YfZo/MIfvsDqztVMrp48bH+8Dd0bSBfTzKyfSVW4VFlnZNv5W38DWQ35/scqIEppCbJBqYrwOynF9Lrp/IO9ix7XwbTjDOU5acJ4PGTPIxdupD7IcLa7mkAr6sjhavB0gKFKdXERgoHV1/1NTxQWIV6r+nemZr6I3b9H40BFI4w8KQcqGhUag1KjGVfDRs8grxUKqA+FqadAtH+htVZgKBvTiqEMs/8YBQSBgx1tpvmtt9D76m3kO5fj5ztLR8UwIAjQA6PRu5dLB/1jMCi9X7hmDla0kcDNUOhZjVYQqZ07rKIxcLN4xS40YEfqMazhxzVws/hehuaTbhxU+eWk1rLz2evIBorXUtvwAxfbDGEog0AHKL9AlalIHH89C2ZeWH5eoXMF3Su/S6FnFdovlOaBGSFcN5+6eR8j0nD8oNdHmbiZTShlDe7aHARo7RGqmoLGHza+oQZez7SrDuhzHohc16s89sxrHBf9JbYdHna/W+jESZUqGq1Iw5jGcDg+x1jtObY+z+e17tcGzQtLe1hBgV8b0/jQ267j+AnHH5axjJfjtK5zHdc9dB2JcGJYsyIoNfAoFAtcUnMJ73znO8ft3jKj+RzpYpobz75RKpvGoJLj67ouDz/88CGfPzIHxm48H7t9zZ/xPO43k7Geh8N1/g7XNUi8Mcn8EZWSOfTmNrBieH/GddDY0NCAaZrs3Llz0O07d+6kpaVlxOeEw2HC4eHhg23bb5i/CB888YPc9NhNbEhtGLQ/Xkeug2QsyYTkBLZntjMlVNofJog2szW1hqmWT8HfvXdhjtJ/tJqlvRlzkTb+8W++RGT1t1G7nmOn6xAyI+VQq9QlOGCnUc3L+V4mh2rYqcL0KItm0njaJYSHZdhE7Rha+ygv2x/LaVAK24zjB6UQL6SKpWYweveKy4GIa2AvRigFhgpFoDVGfwMVQ0FeWZh2mJDvUB1OMrdpLvmedQReBiMolPZKNEMYqrSDY2npcA5LmcTqZhFvmIc551J2Ld+IU9hW2r8wGNjd0S+/98CITGWhtNc/0BB2JImhPJQVwlQOoLCsMIbyyudKa43v7CBWNx+tA5ze1RjW8IDYd3YQrZ1PrH7uoKDPqp9LNDkZo2cVM+va2JLeSs7J4vYfiwm2oqrheObNfdeg59kTFlPV8mOKva/i9KxEA+HaYwnXzB7x9YvdK7FME+2lUWa0dMa0RusChlUFfi/RumOHjW+o8uv1rDqgz3kgonWzgNfQxZ0Y9oRh72F4vYQiCfB6MUiOaQyH43OM1Z5jq4tPxqyfxJa+Lf3zIiCpPFKhJpaech2L2hYftrGMl+M0p2UOU+qnlJoV2cObFW3PbWdh40IIxve/C6P5HMc2HXtI9wh8IzsYx/dQzx+ZA2N3NBy7kebP0TDuN4OxnofDff7G879hYvyT+SMqJXPozWm053xcf5cSCoVYtGgRjzzySPm2IAh45JFHBi2lfrNZNHER155xLfOb5tNX7GNL3xb6in0c23Qs155xLZ889ZMkw0k2pTaRdbJo4BnVTCaACSbEDIVJqYNyqwmZAFaGp/P1v/0mi9oWM+34a6irnsJE28QIihS9PEZQZKJtUlc9hWkLP0l1uKb0+m6O542JZLWBE2i0UkSVwsDH1B6GGS5VCRomyghh4KKMUP8nUSiscsOZgb0ZvT1Txv6Q0aA0ZtW/r6SHQYCBGTiErRBtVQ342c1Eq5ppOeFfCScmo9Hg5wgCr/TlZVEE2PFWamZ9GKUMoo2LaFp0PaGaeQPvTukdzIG3L32ZUZSx+y+VFZuAUrpUrZjbjB2fiB1vxcu2E7hZtPYJ3Cxuth0zlKRm1oepnX0ZZqgadx+PGRrGKGVQM2spZqiamJ9ibu0xzGucy+y6KcyrqmNC7UymLbxmxBBHKYNI7Tyqp72f5LT3E6kdHvaUXz+cxDBsQBF4OYLAQft5wADDxgrXjDi+kd5zYLwH8jkPxMBz9/oe4Rrq5n8UK5wc8xgOx+cYq6FjqzYt5jXOYV7dNOZW1dBUO52/OePrhzxkHGks4+E4DTQr2vMa6Ac+WSfLptQmkpEkFy+8+LCNZ6xG8zkuPeFSCRvG6Gg4vkfDGMero/XYHa3jfqMZ63mQ8yeEEEKUjOs9GgF+/vOfs3TpUn7wgx9w0kkn8c1vfpP77ruPV199lebm5v0+f7Ttt49GgQ722rF1+dbl3PnCnazvWo/jO4TMEDMMhxP8dlqUg4XGRbFDh2DS3/Hhd9w86BuffMdyetb8lEz3SgK/iGGGB3UJHvr6syyP8yIO0y0fy8ugAxdl2JjRBuxYGwBubgt+vgMvMFkT/XfmFP4dy/BQZgjXyRAERdAaDXgoUBZhpTC0C2g0CheD14IIf/VizLYcJluaukiCaDhJqHpGeXz5juV0vfQNCt0vD146XL+A+uOuGdbpWOuArpe/Qd+6e/HdPkpllkH/umnVH4YamKFqzGgL4KP9IsoMl98XoHftT3H6Ngy7b+D98h3L9/uYkYz1eaM18PrFrpfxCp2Dzl+kbsEBv8+hHK/rujz44IO846RWMq/dudf3OBhjONTHvRLjaWzjaSwDRroGzqyfyaUnXMqCpgU8+OCDnH/++eP+N7H7+hyLJh7ZOfhGMJbjO3ANOlzzR+bA2I3HYzea+TMex/1mNNbzcKjP3+G+Bok3Fpk/olIyh97cRpuvjfugEeDWW2/llltuYceOHRx//PF8+9vfZsmSJaN67hs5aNyfkYJIP/D4y8t3kkpvIZlo47TjPoRthkZ8vtYBTmotgZPCCCUJJWcNqkwa+voz6qfj9a3HL/biF3swwzWY4dry3mxOai1+sYdiroc/v+Ry5inTsCwL7fah7ARB4LN582Pk3Dyx+mOZPuUsvL4NFLpfws/vxIy2EK6dT7tnkCqmSYYTTLYBt2/E8Wkd7Hfp8LBjFnhktzzc/37NRFv/hvy2R8t/jre9E6WMvR6X/R2z0T5mLOejUgOvP9L5G8v7HKrx7vmPm2WZ+3yPgzGGQ33cKzGexjaexjJgb7+MOdq+QdrXL5VE5Q70+B6J+SNzYOzG27Eb7fwZb+N+sxrreTiU5+9o+zdMjC8yf0SlZA69uY02XxvXezQO+PjHP87HP/7xIz2Mo46hDGY3zh58mxniHcdfMarnK2UQrplzQK9v7uPxA69luy689CDhmjnDLk6z648b9Gerdi6R2rmDHzOq0e9eOhypnbf/B/czDIvE5PMG3Tb0z8Bej8v+jtloH3Mwn3ekXv9Qj3c073EwxnA4PsdYjaexjaexDBjpGnU0eqN8jvHqaDi+R8MYx6uj9dgdreN+oxnreZDzJ4QQ4s1MfjUqhBBCCCGEEEIIIYSomASNQgghhBBCCCGEEEKIiknQKIQQQgghhBBCCCGEqJgEjUIIIYQQQgghhBBCiIpJ0CiEEEIIIYQQQgghhKiYBI1CCCGEEEIIIYQQQoiKSdAohBBCCCGEEEIIIYSomASNQgghhBBCCCGEEEKIiknQKIQQQgghhBBCCCGEqJgEjUIIIYQQQgghhBBCiIpJ0CiEEEIIIYQQQgghhKiYBI1CCCGEEEIIIYQQQoiKSdAohBBCCCGEEEIIIYSomASNQgghhBBCCCGEEEKIiknQKIQQQgghhBBCCCGEqJgEjUIIIYQQQgghhBBCiIpJ0CiEEEIIIYQQQgghhKiYBI1CCCGEEEIIIYQQQoiKSdAohBBCCCGEEEIIIYSomASNQgghhBBCCCGEEEKIiknQKIQQQgghhBBCCCGEqJgEjUIIIYQQQgghhBBCiIpJ0CiEEEIIIYQQQgghhKiYBI1CCCGEEEIIIYQQQoiKSdAohBBCCCGEEEIIIYSomASNQgghhBBCCCGEEEKIiknQKIQQQgghhBBCCCGEqJgEjUIIIYQQQgghhBBCiIpJ0CiEEEIIIYQQQgghhKiYBI1CCCGEEEIIIYQQQoiKSdAohBBCCCGEEEIIIYSomASNQgghhBBCCCGEEEKIiknQKIQQQgghhBBCCCGEqJgEjUIIIYQQQgghhBBCiIpZR3oAh5rWGoC+vr4jPBIxwHVdcrkcfX192LZ9pIcjjjIyf0SlZA6JSsj8EZWQ+SMqJXNIVELmj6iUzKE3t4FcbSBn25s3fNCYTqcBmDRp0hEeiRBCCCGEEEIIIYQQR690Ok0ymdzr/UrvL4o8ygVBwLZt20gkEiiljvRwBKUUfNKkSWzevJnq6uojPRxxlJH5Iyolc0hUQuaPqITMH1EpmUOiEjJ/RKVkDr25aa1Jp9O0trZiGHvfifENX9FoGAZtbW1HehhiBNXV1XJxEmMm80dUSuaQqITMH1EJmT+iUjKHRCVk/ohKyRx689pXJeMAaQYjhBBCCCGEEEIIIYSomASNQgghhBBCCCGEEEKIiknQKA67cDjMl770JcLh8JEeijgKyfwRlZI5JCoh80dUQuaPqJTMIVEJmT+iUjKHxGi84ZvBCCGEEEIIIYQQQgghDj2paBRCCCGEEEIIIYQQQlRMgkYhhBBCCCGEEEIIIUTFJGgUQgghhBBCCCGEEEJUTIJGIYQQQgghhBBCCCFExSRoFIfNl7/8ZZRSg77mzJlzpIclxqk///nPXHjhhbS2tqKU4le/+tWg+7XWfPGLX2TChAlEo1HOOuss1q1bd2QGK8ad/c2fyy67bNj16Nxzzz0ygxXjzk033cTixYtJJBI0NTXxrne9izVr1gx6TKFQ4Oqrr6a+vp6qqire+973snPnziM0YjHejGYOnXHGGcOuQ1ddddURGrEYT773ve+xYMECqqurqa6u5uSTT+b3v/99+X65/oj92d8ckuuPOBA333wzSimuueaa8m1yHRL7IkGjOKzmz5/P9u3by1+PP/74kR6SGKey2SwLFy7ku9/97oj3f/WrX+Xb3/423//+93nmmWeIx+Occ845FAqFwzxSMR7tb/4AnHvuuYOuRz/72c8O4wjFeLZs2TKuvvpqnn76aR5++GFc1+Xss88mm82WH/OpT32K3/zmN9x///0sW7aMbdu28Z73vOcIjlqMJ6OZQwBXXnnloOvQV7/61SM0YjGetLW1cfPNN7N8+XKef/553vGOd3DRRRexcuVKQK4/Yv/2N4dArj9idJ577jl+8IMfsGDBgkG3y3VI7JMW4jD50pe+pBcuXHikhyGOQoD+5S9/Wf5zEAS6paVF33LLLeXbent7dTgc1j/72c+OwAjFeDZ0/mit9dKlS/VFF110RMYjjj67du3SgF62bJnWunS9sW1b33///eXHrF69WgP6qaeeOlLDFOPY0Dmktdann366/uQnP3nkBiWOKrW1tfpHP/qRXH/EmA3MIa3l+iNGJ51O65kzZ+qHH3540JyR65DYH6loFIfVunXraG1tZdq0aVxyySW0t7cf6SGJo9DGjRvZsWMHZ511Vvm2ZDLJkiVLeOqpp47gyMTR5LHHHqOpqYnZs2fz0Y9+lK6uriM9JDFOpVIpAOrq6gBYvnw5rusOugbNmTOHyZMnyzVIjGjoHBpw991309DQwLHHHsu1115LLpc7EsMT45jv+9x7771ks1lOPvlkuf6IAzZ0Dg2Q64/Yn6uvvpoLLrhg0PUG5PsgsX/WkR6AePNYsmQJt99+O7Nnz2b79u3ccMMNnHbaabzyyiskEokjPTxxFNmxYwcAzc3Ng25vbm4u3yfEvpx77rm85z3vYerUqWzYsIEvfOELnHfeeTz11FOYpnmkhyfGkSAIuOaaazj11FM59thjgdI1KBQKUVNTM+ixcg0SIxlpDgF88IMfZMqUKbS2tvLSSy/xuc99jjVr1vDAAw8cwdGK8eLll1/m5JNPplAoUFVVxS9/+UvmzZvHihUr5PojRmVvcwjk+iP279577+Wvf/0rzz333LD75PsgsT8SNIrD5rzzziv/94IFC1iyZAlTpkzhvvvu44orrjiCIxNCvNn8wz/8Q/m/jzvuOBYsWMD06dN57LHHOPPMM4/gyMR4c/XVV/PKK6/InsJizPY2hz7ykY+U//u4445jwoQJnHnmmWzYsIHp06cf7mGKcWb27NmsWLGCVCrF//zP/7B06VKWLVt2pIcljiJ7m0Pz5s2T64/Yp82bN/PJT36Shx9+mEgkcqSHI45CsnRaHDE1NTXMmjWL9evXH+mhiKNMS0sLwLDOZjt37izfJ8SBmDZtGg0NDXI9EoN8/OMf57e//S2PPvoobW1t5dtbWlpwHIfe3t5Bj5drkBhqb3NoJEuWLAGQ65AAIBQKMWPGDBYtWsRNN93EwoUL+da3viXXHzFqe5tDI5Hrj9jT8uXL2bVrFyeeeCKWZWFZFsuWLePb3/42lmXR3Nws1yGxTxI0iiMmk8mwYcMGJkyYcKSHIo4yU6dOpaWlhUceeaR8W19fH88888ygvWeEGK0tW7bQ1dUl1yMBgNaaj3/84/zyl7/kT3/6E1OnTh10/6JFi7Bte9A1aM2aNbS3t8s1SAD7n0MjWbFiBYBch8SIgiCgWCzK9UeM2cAcGolcf8SezjzzTF5++WVWrFhR/nrLW97CJZdcUv5vuQ6JfZGl0+Kw+cxnPsOFF17IlClT2LZtG1/60pcwTZMPfOADR3poYhzKZDKDfqu6ceNGVqxYQV1dHZMnT+aaa67h//7f/8vMmTOZOnUq119/Pa2trbzrXe86coMW48a+5k9dXR033HAD733ve2lpaWHDhg189rOfZcaMGZxzzjlHcNRivLj66qu55557+PWvf00ikSjvN5RMJolGoySTSa644go+/elPU1dXR3V1NZ/4xCc4+eSTeetb33qERy/Gg/3NoQ0bNnDPPfdw/vnnU19fz0svvcSnPvUp3v72t7NgwYIjPHpxpF177bWcd955TJ48mXQ6zT333MNjjz3GH/7wB7n+iFHZ1xyS64/Yn0QiMWhPYYB4PE59fX35drkOiX060m2vxZvHxRdfrCdMmKBDoZCeOHGivvjii/X69euP9LDEOPXoo49qYNjX0qVLtdZaB0Ggr7/+et3c3KzD4bA+88wz9Zo1a47soMW4sa/5k8vl9Nlnn60bGxu1bdt6ypQp+sorr9Q7duw40sMW48RIcwfQP/nJT8qPyefz+mMf+5iura3VsVhMv/vd79bbt28/coMW48r+5lB7e7t++9vfruvq6nQ4HNYzZszQ//qv/6pTqdSRHbgYFy6//HI9ZcoUHQqFdGNjoz7zzDP1Qw89VL5frj9if/Y1h+T6I8bi9NNP15/85CfLf5brkNgXpbXWhzPYFEIIIYQQQgghhBBCvPHIHo1CCCGEEEIIIYQQQoiKSdAohBBCCCGEEEIIIYSomASNQgghhBBCCCGEEEKIiknQKIQQQgghhBBCCCGEqJgEjUIIIYQQQgghhBBCiIpJ0CiEEEIIIYQQQgghhKiYBI1CCCGEEEIIIYQQQoiKSdAohBBCCCGEEEIIIYSomASNQgghhBDiqHH99dfzkY985ICe89hjj6GUore3F4Dbb7+dmpqagz+4/Xj99ddRSrFixYoxPf+tb30rv/jFLw7uoIQQQgghDiIJGoUQQgghDrLLLrsMpRRXXXXVsPuuvvpqlFJcdtllh39gQ/i+z80338ycOXOIRqPU1dWxZMkSfvSjHx3poY1ox44dfOtb3+K6664bdt9TTz2FaZpccMEFFb3H7bffjlJqn1+vv/56Re8xVv/2b//G5z//eYIgOCLvL4QQQgixPxI0CiGEEEIcApMmTeLee+8ln8+XbysUCtxzzz1Mnjz5CI5stxtuuIFvfOMbfOUrX2HVqlU8+uijfOQjHylX/h0KjuOM+bk/+tGPOOWUU5gyZcqw+2677TY+8YlP8Oc//5lt27aN+T0uvvhitm/fXv46+eSTufLKKwfdNmnSpDG/fiXOO+880uk0v//974/I+wshhBBC7I8EjUIIIYQQh8CJJ57IpEmTeOCBB8q3PfDAA0yePJkTTjhh0GODIOCmm25i6tSpRKNRFi5cyP/8z/+U7/d9nyuuuKJ8/+zZs/nWt7416DUuu+wy3vWud/Ff//VfTJgwgfr6eq6++mpc193rGP/3f/+Xj33sY7z//e9n6tSpLFy4kCuuuILPfOYzg8b21a9+lRkzZhAOh5k8eTI33nhj+f6XX36Zd7zjHUSjUerr6/nIRz5CJpMZNq4bb7yR1tZWZs+eDcDmzZv5+7//e2pqaqirq+Oiiy7ab6Xgvffey4UXXjjs9kwmw89//nM++tGPcsEFF3D77bfv83X2JRqN0tLSUv4KhULEYrHynx9++GGWLFlCIpGgpaWFD37wg+zatav8/J6eHi655BIaGxuJRqPMnDmTn/zkJyO+l+/7XH755cyZM4f29na01nz5y19m8uTJhMNhWltb+Zd/+Zfy403T5Pzzz+fee+8d8+cTQgghhDiUJGgUQgghhDhELr/88kEh049//GP+8R//cdjjbrrpJu644w6+//3vs3LlSj71qU9x6aWXsmzZMqAU9rW1tXH//fezatUqvvjFL/KFL3yB++67b9DrPProo2zYsIFHH32Un/70p9x+++37DN1aWlr405/+REdHx14fc+2113LzzTdz/fXXs2rVKu655x6am5sByGaznHPOOdTW1vLcc89x//3388c//pGPf/zjg17jkUceYc2aNTz88MP89re/xXVdzjnnHBKJBH/5y1944oknqKqq4txzz91rxWN3dzerVq3iLW95y7D77rvvPubMmcPs2bO59NJL+fGPf4zWeq+fqRKu6/KVr3yFF198kV/96le8/vrrg5bBDxyn3//+96xevZrvfe97NDQ0DHudYrHI+9//flasWMFf/vIXJk+ezC9+8Qu+8Y1v8IMf/IB169bxq1/9iuOOO27Q80466ST+8pe/HJLPJoQQQghRKetID0AIIYQQ4o3q0ksv5dprr2XTpk0APPHEE9x777089thj5ccUi0X+4z/+gz/+8Y+cfPLJAEybNo3HH3+cH/zgB5x++unYts0NN9xQfs7UqVN56qmnuO+++/j7v//78u21tbXceuutmKbJnDlzuOCCC3jkkUe48sorRxzf17/+dd73vvfR0tLC/PnzOeWUU7jooos477zzAEin03zrW9/i1ltvZenSpQBMnz6dt73tbQDcc889FAoF7rjjDuLxOAC33norF154If/5n/9ZDiTj8Tg/+tGPCIVCANx1110EQcCPfvQjlFIA/OQnP6GmpobHHnuMs88+e9hYByr+Wltbh9132223cemllwJw7rnnkkqlWLZsGWeccca+Ts+YXH755eX/njZtGt/+9rdZvHgxmUyGqqoq2tvbOeGEE8qB6DHHHDPsNTKZDBdccAHFYpFHH32UZDJZ/owtLS2cddZZ2LbN5MmTOemkkwY9t7W1lc2bNxMEAYYhNQNCCCGEGF/kuxMhhBBCiEOksbGxvJT3Jz/5CRdccMGw6rb169eTy+V45zvfSVVVVfnrjjvuYMOGDeXHffe732XRokU0NjZSVVXFD3/4Q9rb2we91vz58zFNs/znCRMmDFrWO9S8efN45ZVXePrpp7n88svZtWsXF154If/0T/8EwOrVqykWi5x55pkjPn/16tUsXLiwHDICnHrqqQRBwJo1a8q3HXfcceWQEeDFF19k/fr1JBKJ8uetq6ujUCgM+sx7GtjrMhKJDLp9zZo1PPvss3zgAx8AwLIsLr74Ym677ba9fu5KLF++nAsvvJDJkyeTSCQ4/fTTAcrn4qMf/Sj33nsvxx9/PJ/97Gd58sknh73GBz7wAbLZLA899FA5ZAR4//vfTz6fZ9q0aVx55ZX88pe/xPO8Qc+NRqMEQUCxWDwkn08IIYQQohJS0SiEEEIIcQhdfvnl5aXE3/3ud4fdP7Cf4e9+9zsmTpw46L5wOAyU9ib8zGc+w9e+9jVOPvlkEokEt9xyC88888ygx9u2PejPSqn9dig2DIPFixezePFirrnmGu666y4+9KEPcd111xGNRg/sw+7FnkEklD7zokWLuPvuu4c9trGxccTXGAhoe3p6Bj3mtttuw/O8QZWOWmvC4TC33nrroCCvUgNLxc855xzuvvtuGhsbaW9v55xzzikv+T7vvPPYtGkTDz74IA8//DBnnnkmV199Nf/1X/9Vfp3zzz+fu+66i6eeeop3vOMd5dsnTZrEmjVr+OMf/8jDDz/Mxz72MW655RaWLVtWPrfd3d3E4/GDdm6EEEIIIQ4mqWgUQgghhDiEBvYdHNiXcKh58+YRDodpb29nxowZg74Guhs/8cQTnHLKKXzsYx/jhBNOYMaMGXut/KvUvHnzgFKoNnPmTKLRKI888siIj507dy4vvvgi2Wy2fNsTTzyBYRjlpi8jOfHEE1m3bh1NTU3DPvPegsHp06dTXV3NqlWryrd5nscdd9zB1772NVasWFH+evHFF2ltbeVnP/vZWA7BXr366qt0dXVx8803c9pppzFnzpwRK0YbGxtZunQpd911F9/85jf54Q9/OOj+j370o9x888383d/9XXkfzgHRaJQLL7yQb3/72zz22GM89dRTvPzyy+X7X3nllWHNhIQQQgghxgupaBRCCCGEOIRM02T16tXl/x4qkUjwmc98hk996lMEQcDb3vY2UqkUTzzxBNXV1SxdupSZM2dyxx138Ic//IGpU6dy55138txzzzF16tSKxva+972PU089lVNOOYWWlhY2btzItddey6xZs5gzZw6WZfG5z32Oz372s4RCIU499VQ6OjpYuXIlV1xxBZdccglf+tKXWLp0KV/+8pfp6OjgE5/4BB/60IfK+zOO5JJLLuGWW27hoosu4t///d9pa2tj06ZNPPDAA3z2s5+lra1t2HMMw+Css87i8ccf513vehcAv/3tb+np6eGKK64YFlC+973v5bbbbuOqq66q6BjtafLkyYRCIb7zne9w1VVX8corr/CVr3xl0GO++MUvsmjRIubPn0+xWOS3v/0tc+fOHfZan/jEJ/B9n7/927/l97//PW9729u4/fbb8X2fJUuWEIvFuOuuu4hGo0yZMqX8vL/85S8j7mEphBBCCDEeSEWjEEIIIcQhVl1dTXV19V7v/8pXvsL111/PTTfdxNy5czn33HP53e9+Vw4S//mf/5n3vOc9XHzxxSxZsoSuri4+9rGPVTyuc845h9/85jdceOGFzJo1i6VLlzJnzhweeughLKv0++jrr7+e//N//g9f/OIXmTt3LhdffHG5ii8Wi/GHP/yB7u5uFi9ezPve9z7OPPNMbr311n2+bywW489//jOTJ0/mPe95D3PnzuWKK66gUCjs8zj90z/9E/fee295Ofhtt93GWWedNWIV5Hvf+16ef/55XnrppbEenmEaGxu5/fbbuf/++5k3bx4333zzoCXRAKFQiGuvvZYFCxbw9re/HdM0uffee0d8vWuuuYYbbriB888/nyeffJKamhr++7//m1NPPZUFCxbwxz/+kd/85jfU19cDsHXrVp588skRO5cLIYQQQowHSmutj/QghBBCCCGE2B+tNUuWLOFTn/pUufnLm8nnPvc5enp6hi3FFkIIIYQYL6SiUQghhBBCHBWUUvzwhz8c1on5zaKpqWnYUm0hhBBCiPFEKhqFEEIIIYQQQgghhBAVk4pGIYQQQgghhBBCCCFExSRoFEIIIYQQQgghhBBCVEyCRiGEEEIIIYQQQgghRMUkaBRCCCGEEEIIIYQQQlRMgkYhhBBCCCGEEEIIIUTFJGgUQgghhBBCCCGEEEJUTIJGIYQQQgghhBBCCCFExSRoFEIIIYQQQgghhBBCVEyCRiGEEEIIIYQQQgghRMX+f0CEwjO6lAhRAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1600x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a large figure to accommodate all plots\n",
    "plt.figure(figsize=(16, 12))\n",
    "\n",
    "# Scatter plot for each dataset with a unique color and label\n",
    "colors = [\"#000080\", \"#DC143C\", \"#228B22\", \"#DAA520\", \"#9370DB\", \"#5F9EA0\", \"#FF6347\"]\n",
    "labels = tasks_v2\n",
    "for task, color, label in zip(tasks_v2, colors, labels):\n",
    "    plt.scatter(\n",
    "        data_v2[\"mean_score\"], data_v2[task], alpha=0.7, color=color, label=label\n",
    "    )\n",
    "\n",
    "# Label the axes\n",
    "plt.xlabel(\"Mean Score (All Tasks)\")\n",
    "plt.ylabel(\"Score by Task\")\n",
    "\n",
    "# Title of the plot\n",
    "plt.title(\"Outlier Detection: Mean Score vs. Task Score\")\n",
    "\n",
    "# Add a grid for better readability\n",
    "plt.grid(True)\n",
    "\n",
    "# Add a legend to identify the datasets\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7767074b-1032-44cb-bb5e-3e553856cd9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot average curves for each evaluation as a function of the median\n",
    "plt.figure(figsize=(16, 12))\n",
    "for task, color, label in zip(tasks_v2, colors, labels):\n",
    "    sorted_data = data_v2.sort_values(\"mean_score\")\n",
    "    plt.plot(\n",
    "        sorted_data[\"mean_score\"],\n",
    "        sorted_data[task].rolling(window=20, min_periods=1).mean(),\n",
    "        color=color,\n",
    "        label=label,\n",
    "    )\n",
    "\n",
    "plt.xlabel(\"Mean Score (All Tasks)\")\n",
    "plt.ylabel(\"Average Score by Task\")\n",
    "plt.title(\"Average Curves for Each Evaluation as Function of the Mean\")\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a0b6cfc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: jinja2 in /Users/hynky/.pyenv/versions/3.10.6/envs/datatrove3.10/lib/python3.10/site-packages (3.1.4)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /Users/hynky/.pyenv/versions/3.10.6/envs/datatrove3.10/lib/python3.10/site-packages (from jinja2) (2.1.5)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install jinja2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9f7089c-34d7-408a-a6b0-038b41d61ce4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
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       "  background-color: lightgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_592f0\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_592f0_level0_col0\" class=\"col_heading level0 col0\" >eval_name</th>\n",
       "      <th id=\"T_592f0_level0_col1\" class=\"col_heading level0 col1\" >mean_score</th>\n",
       "      <th id=\"T_592f0_level0_col2\" class=\"col_heading level0 col2\" >IFEval</th>\n",
       "      <th id=\"T_592f0_level0_col3\" class=\"col_heading level0 col3\" >BBH</th>\n",
       "      <th id=\"T_592f0_level0_col4\" class=\"col_heading level0 col4\" >MATH Lvl 5</th>\n",
       "      <th id=\"T_592f0_level0_col5\" class=\"col_heading level0 col5\" >GPQA</th>\n",
       "      <th id=\"T_592f0_level0_col6\" class=\"col_heading level0 col6\" >MUSR</th>\n",
       "      <th id=\"T_592f0_level0_col7\" class=\"col_heading level0 col7\" >MMLU-PRO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_592f0_level0_row0\" class=\"row_heading level0 row0\" >65</th>\n",
       "      <td id=\"T_592f0_row0_col0\" class=\"data row0 col0\" >meta-llama_Meta-Llama-3-70B-Instruct_bfloat16</td>\n",
       "      <td id=\"T_592f0_row0_col1\" class=\"data row0 col1\" >36.183402</td>\n",
       "      <td id=\"T_592f0_row0_col2\" class=\"data row0 col2\" >80.990771</td>\n",
       "      <td id=\"T_592f0_row0_col3\" class=\"data row0 col3\" >50.185133</td>\n",
       "      <td id=\"T_592f0_row0_col4\" class=\"data row0 col4\" >23.338369</td>\n",
       "      <td id=\"T_592f0_row0_col5\" class=\"data row0 col5\" >4.921700</td>\n",
       "      <td id=\"T_592f0_row0_col6\" class=\"data row0 col6\" >10.920573</td>\n",
       "      <td id=\"T_592f0_row0_col7\" class=\"data row0 col7\" >46.743868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_592f0_level0_row1\" class=\"row_heading level0 row1\" >125</th>\n",
       "      <td id=\"T_592f0_row1_col0\" class=\"data row1 col0\" >Qwen_Qwen2-72B-Instruct_bfloat16</td>\n",
       "      <td id=\"T_592f0_row1_col1\" class=\"data row1 col1\" >42.486308</td>\n",
       "      <td id=\"T_592f0_row1_col2\" class=\"data row1 col2\" >79.891687</td>\n",
       "      <td id=\"T_592f0_row1_col3\" class=\"data row1 col3\" >57.483009</td>\n",
       "      <td id=\"T_592f0_row1_col4\" class=\"data row1 col4\" >35.120846</td>\n",
       "      <td id=\"T_592f0_row1_col5\" class=\"data row1 col5\" >16.331096</td>\n",
       "      <td id=\"T_592f0_row1_col6\" class=\"data row1 col6\" >17.167969</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_592f0_level0_row2\" class=\"row_heading level0 row2\" >66</th>\n",
       "      <td id=\"T_592f0_row2_col0\" class=\"data row2 col0\" >meta-llama_Meta-Llama-3-70B_bfloat16</td>\n",
       "      <td id=\"T_592f0_row2_col1\" class=\"data row2 col1\" >26.365471</td>\n",
       "      <td id=\"T_592f0_row2_col2\" class=\"data row2 col2\" >16.031906</td>\n",
       "      <td id=\"T_592f0_row2_col3\" class=\"data row2 col3\" >48.709813</td>\n",
       "      <td id=\"T_592f0_row2_col4\" class=\"data row2 col4\" >16.540785</td>\n",
       "      <td id=\"T_592f0_row2_col5\" class=\"data row2 col5\" >19.686801</td>\n",
       "      <td id=\"T_592f0_row2_col6\" class=\"data row2 col6\" >16.011198</td>\n",
       "      <td id=\"T_592f0_row2_col7\" class=\"data row2 col7\" >41.212323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_592f0_level0_row3\" class=\"row_heading level0 row3\" >61</th>\n",
       "      <td id=\"T_592f0_row3_col0\" class=\"data row3 col0\" >microsoft_Orca-2-13b_bfloat16</td>\n",
       "      <td id=\"T_592f0_row3_col1\" class=\"data row3 col1\" >18.136816</td>\n",
       "      <td id=\"T_592f0_row3_col2\" class=\"data row3 col2\" >31.279339</td>\n",
       "      <td id=\"T_592f0_row3_col3\" class=\"data row3 col3\" >27.308019</td>\n",
       "      <td id=\"T_592f0_row3_col4\" class=\"data row3 col4\" >0.981873</td>\n",
       "      <td id=\"T_592f0_row3_col5\" class=\"data row3 col5\" >4.026846</td>\n",
       "      <td id=\"T_592f0_row3_col6\" class=\"data row3 col6\" >25.787760</td>\n",
       "      <td id=\"T_592f0_row3_col7\" class=\"data row3 col7\" >19.437057</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1784f12d0>"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define a function to highlight the maximum value in each column\n",
    "def highlight_max(data, color=\"lightgreen\"):\n",
    "    attr = f\"background-color: {color}\"\n",
    "    if data.ndim == 1:\n",
    "        is_max = data == data.max()\n",
    "        return [attr if v else \"\" for v in is_max]\n",
    "    else:\n",
    "        is_max = data == data.max().max()\n",
    "        return pd.DataFrame(\n",
    "            np.where(is_max, attr, \"\"), index=data.index, columns=data.columns\n",
    "        )\n",
    "\n",
    "\n",
    "# Find the best model for each task based on the maximum score for that task\n",
    "best_models = data_v2.loc[data_v2[tasks_v2].idxmax()]\n",
    "best_models_unique = best_models.drop_duplicates(subset=[\"eval_name\"])\n",
    "\n",
    "styled_best_models = best_models_unique[\n",
    "    [\"eval_name\", \"mean_score\"] + tasks_v2\n",
    "].style.apply(highlight_max, subset=tasks_v2)\n",
    "\n",
    "styled_best_models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57aad5f2-6b69-4546-a409-697bb523a176",
   "metadata": {},
   "source": [
    "# Params and Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d559d71",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime, timezone\n",
    "\n",
    "threshold_1 = datetime(2023, 10, 1, tzinfo=timezone.utc)\n",
    "threshold_2 = datetime(2024, 4, 1, tzinfo=timezone.utc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77596d81",
   "metadata": {},
   "outputs": [],
   "source": [
    "full = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dfd3f086-9e52-4fcb-a7b1-77a6a6176b52",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the mean performance score across the tasks for both versions\n",
    "data_v1[\"Mean_Score_v1\"] = data_v1[tasks_v1].mean(axis=1)\n",
    "data_v2[\"Mean_Score_v2\"] = data_v2[tasks_v2].mean(axis=1)\n",
    "\n",
    "# Extract the necessary columns for plotting\n",
    "if full:\n",
    "    v1_data = data_v1[[\"fullname\", \"#Params (B)\", \"Mean_Score_v1\", \"date\"]]\n",
    "    v2_data = data_v2[[\"fullname\", \"#Params (B)\", \"Mean_Score_v2\", \"date\"]]\n",
    "\n",
    "    # Merge the data on fullname\n",
    "    merged_data = pd.merge(\n",
    "        v1_data, v2_data, on=[\"fullname\", \"date\", \"#Params (B)\"], how=\"outer\"\n",
    "    )\n",
    "else:\n",
    "    v1_data = data_v1[[\"fullname\", \"#Params (B)\", \"Mean_Score_v1\"]]\n",
    "    v2_data = data_v2[[\"fullname\", \"#Params (B)\", \"Mean_Score_v2\", \"date\"]]\n",
    "\n",
    "    # Merge the data on fullname\n",
    "    merged_data = pd.merge(\n",
    "        v1_data, v2_data, on=[\"fullname\", \"#Params (B)\"], how=\"outer\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e377ade",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NaT <class 'str'>\n",
      "NaT <class 'str'>\n",
      "NaT <class 'str'>\n",
      "NaT <class 'str'>\n",
      "NaT <class 'str'>\n",
      "NaT <class 'str'>\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/cj/cyw80lln0mz_9hlly0x3qvf80000gn/T/ipykernel_54935/29333465.py:3: FutureWarning:\n",
      "\n",
      "In a future version of pandas, parsing datetimes with mixed time zones will raise an error unless `utc=True`. Please specify `utc=True` to opt in to the new behaviour and silence this warning. To create a `Series` with mixed offsets and `object` dtype, please use `apply` and `datetime.datetime.strptime`\n",
      "\n"
     ]
    }
   ],
   "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 isinstance(row, str):\n",
    "        print(row, type(row))\n",
    "\n",
    "# dropna\n",
    "merged_data.dropna(subset=[\"Mean_Score_v1\", \"Mean_Score_v2\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae5c5cb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "166\n",
      "166\n",
      "37\n",
      "90\n",
      "39\n",
      "166\n"
     ]
    }
   ],
   "source": [
    "print(len([x for x in merged_data[\"Mean_Score_v1\"] if x > 0]))\n",
    "print(len([x for x in merged_data[\"date\"] if x != float(\"nan\")]))\n",
    "print(len(merged_data[\"#Params (B)\"][merged_data[\"date\"] < threshold_1]))\n",
    "print(\n",
    "    len(\n",
    "        merged_data[\"#Params (B)\"][threshold_1 < merged_data[\"date\"]][\n",
    "            merged_data[\"date\"] < threshold_2\n",
    "        ]\n",
    "    )\n",
    ")\n",
    "print(len(merged_data[\"#Params (B)\"][merged_data[\"date\"] > threshold_2]))\n",
    "print(len([x for x in merged_data[\"Mean_Score_v2\"] if x > 0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4047afd0",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
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    "merged_data[\"#Params (B)\"][merged_data[\"date\"] < threshold_1]"
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            "ticks": ""
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           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
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          "xaxis": {
           "automargin": true,
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           "ticks": "",
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          "yaxis": {
           "automargin": true,
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           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
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           "zerolinewidth": 2
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        },
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         "text": "Size of models vs Performance"
        },
        "xaxis": {
         "title": {
          "text": "Number of parameters (in B)"
         }
        },
        "yaxis": {
         "title": {
          "text": "Mean Performance Score"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "fig = go.Figure(\n",
    "    data=[\n",
    "        go.Scatter(\n",
    "            x=merged_data[\"#Params (B)\"][merged_data[\"date\"] < threshold_1],\n",
    "            y=merged_data[\"Mean_Score_v1\"][merged_data[\"date\"] < threshold_1],\n",
    "            name=f\"V1 - before Oct 2023\",\n",
    "            text=merged_data[\"fullname\"][merged_data[\"date\"] < threshold_1],\n",
    "            mode=\"markers\",\n",
    "            marker=dict(\n",
    "                color=RED,\n",
    "            ),\n",
    "        ),\n",
    "        go.Scatter(\n",
    "            x=merged_data[\"#Params (B)\"][threshold_1 < merged_data[\"date\"]][\n",
    "                merged_data[\"date\"] < threshold_2\n",
    "            ],\n",
    "            y=merged_data[\"Mean_Score_v1\"][threshold_1 < merged_data[\"date\"]][\n",
    "                merged_data[\"date\"] < threshold_2\n",
    "            ],\n",
    "            name=\"V1 - Oct 2023 to Mar 2024\",\n",
    "            text=merged_data[\"fullname\"][threshold_1 < merged_data[\"date\"]][\n",
    "                merged_data[\"date\"] < threshold_2\n",
    "            ],\n",
    "            mode=\"markers\",\n",
    "            marker=dict(\n",
    "                color=ORANGE,\n",
    "            ),\n",
    "        ),\n",
    "        go.Scatter(\n",
    "            x=merged_data[\"#Params (B)\"][merged_data[\"date\"] > threshold_2],\n",
    "            y=merged_data[\"Mean_Score_v1\"][merged_data[\"date\"] > threshold_2],\n",
    "            name=\"V1 - Mar 2024 and after\",\n",
    "            text=merged_data[\"fullname\"][merged_data[\"date\"] > threshold_2],\n",
    "            mode=\"markers\",\n",
    "            marker=dict(\n",
    "                color=YELLOW,\n",
    "            ),\n",
    "        ),\n",
    "        go.Scatter(\n",
    "            x=merged_data[\"#Params (B)\"],\n",
    "            y=merged_data[\"Mean_Score_v2\"],\n",
    "            name=\"V2\",\n",
    "            text=merged_data[\"fullname\"],\n",
    "            mode=\"markers\",\n",
    "            marker=dict(\n",
    "                color=BLACK,\n",
    "            ),\n",
    "        ),\n",
    "    ]\n",
    ")\n",
    "\n",
    "fig = fig.update_layout(\n",
    "    title=\"Size of models vs Performance\",\n",
    "    yaxis=dict(title=\"Mean Performance Score\"),\n",
    "    xaxis=dict(title=\"Number of parameters (in B)\"),\n",
    "    legend=dict(\n",
    "        x=0.75,\n",
    "        y=1,\n",
    "        traceorder=\"normal\",\n",
    "    ),\n",
    ")\n",
    "fig\n",
    "with open(\"model_size_vs_perf.html\", \"w\") as f:\n",
    "    f.write(fig.to_html(full_html=False, include_plotlyjs=False))\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36969435-f409-4516-b404-e47254f46ee1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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 = [\n",
    "    \"#FF323D\",\n",
    "    \"#FF9D00\",\n",
    "    \"#FFD21E\",\n",
    "    \"#32343D\",\n",
    "]\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.scatter(\n",
    "    merged_data[\"#Params (B)\"][merged_data[\"date\"] < threshold_1],\n",
    "    merged_data[\"Mean_Score_v1\"][merged_data[\"date\"] < threshold_1],\n",
    "    label=\"V1 - before Oct 2023\",\n",
    "    alpha=0.7,\n",
    "    color=colors[0],\n",
    ")\n",
    "plt.scatter(\n",
    "    merged_data[\"#Params (B)\"][threshold_1 < merged_data[\"date\"]][\n",
    "        merged_data[\"date\"] < threshold_2\n",
    "    ],\n",
    "    merged_data[\"Mean_Score_v1\"][threshold_1 < merged_data[\"date\"]][\n",
    "        merged_data[\"date\"] < threshold_2\n",
    "    ],\n",
    "    label=\"V1 - Oct 2023 to Mar 2024\",\n",
    "    alpha=0.7,\n",
    "    color=colors[1],\n",
    ")\n",
    "plt.scatter(\n",
    "    merged_data[\"#Params (B)\"][merged_data[\"date\"] > threshold_2],\n",
    "    merged_data[\"Mean_Score_v1\"][merged_data[\"date\"] > threshold_2],\n",
    "    label=\"V1 - Mar 2024 and after\",\n",
    "    alpha=0.7,\n",
    "    color=colors[2],\n",
    ")\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[\"Mean_Score_v2\"],\n",
    "    label=\"V2\",\n",
    "    alpha=0.7,\n",
    "    color=colors[3],\n",
    ")\n",
    "plt.xlabel(\"Number of parameters (in B)\")\n",
    "plt.ylabel(\"Mean Performance Score\")\n",
    "plt.title(\"Model size vs Performance\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b946604f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5PV5u2Ww2ypQp4zK/AJDtzSMhhBCuSUAuhBDF2P3338/jjz/O77//7jS8OLOKFSvy448/EhcX59RLbh8WW7FiRcfWZrM5etrsDhw44HQ8ewZ2q9WabUB1vbp168aWLVv47LPP6NOnT4517e09cOAAVapUcZSnpqZy9OhRt7Upo0OHDlG5cmXH48OHD2Oz2ahUqVK2z6latSrx8fG5as9///3Hk08+SceOHfH09GTcuHFEREQ4Xmt2crox0KlTJ4KDg1m2bBnNmjUjMTGRRx999JptAbjvvvt4/PHHHZ+rgwcPulyKrFSpUgwcOJCBAwcSHx/PPffcw+TJk3MVkOfEPkqjTJkybr+e+fH5yWzDhg1cvHiR1atXc8899zjKM2aYv15Vq1Zl69atWCwWTCZTtnV+/PFH7rrrrpsagSGEEEKROeRCCFGM+fn5sWDBAiZPnky3bt2yrde5c2esVqtjuSq7WbNmoWka//vf/wAc27lz5zrVy5w13Wg00qNHDz777DOn7M1258+fv+7XMnToUEJDQ3nqqac4ePBglv3nzp3j5ZdfBqB9+/Z4enoyd+5cpx7ChQsXEhMT4zIr/M2aN2+e0+O33noLuPqeudKzZ0+2bNnC999/n2XflStXSEtLczx+7LHHsNlsLFy4kPfeew8PDw8iIyOv2QPq6+ubZakuOw8PD/r06cMnn3zC4sWLqVevXq5HLpQoUYKIiAg++eQTVqxYgaenJ/fdd59TnYsXLzo99vPzo1q1ao5e/psRERFBQEAAr7zyisvM3zfyGbPLj89PZvYe74znT01NZf78+Td8zB49enDhwoUsP+cZz9OzZ0+sVqtjKkJGaWlp2X6WhBBCuCY95EIIUcxlN2Q8o27dutGmTRsmTJjAsWPHaNCgAT/88ANffvklo0ePdvRGNmzYkD59+jB//nxiYmJo0aIF69ev5/Dhw1mO+dprr/Hzzz/TrFkzHnvsMW677TYuXbrEX3/9xY8//phlzeVrKVmyJJ9//jmdO3emYcOG9O3blyZNmgDw119/8fHHHzuGcAcHBzN+/HimTJlCp06duPfeezlw4ADz58/n9ttvv2bSshtx9OhR7r33Xjp16sSWLVtYunQpDz/8MA0aNMj2OU8//TRr1qyha9euDBgwgCZNmpCQkMDu3bv59NNPOXbsGKVLlyYqKopvvvmGxYsXO+ZBv/XWW/Tt25cFCxYwbNiwbM/RpEkTFixYwMsvv0y1atUoU6aM07xr+3JrP//8M6+//vp1veZevXrRt29f5s+fT0RERJYpEbfddhutW7emSZMmlCpVim3btvHpp58yYsSI6zqPKwEBASxYsIBHH32Uxo0b07t3b4KDgzlx4gTffPMNd911l8vAMzfy4/OTWYsWLShZsiT9+/dn5MiRaJrGRx99dN1D0DPq168fS5YsYezYsfzxxx+0bNmShIQEfvzxR4YNG0b37t1p1aoVjz/+OK+++io7duygY8eOmEwmDh06xKpVq5gzZ06ulx4UQgiBLHsmhBDFScZlz3KSedkzXdf1uLg4fcyYMXpYWJhuMpn06tWr62+88YZjOSS7pKQkfeTIkXpQUJDu6+urd+vWTT958mSWZc90XdfPnj2rDx8+XA8PD9dNJpMeEhKit2vXTn/vvfccdXK77JndmTNn9DFjxug1atTQzWaz7uPjozdp0kSfNm2aHhMT41T37bff1mvVqqWbTCa9bNmy+hNPPKFfvnzZqU6rVq30OnXq5Oo90nVdB/Thw4c7HtuXzdq3b5/+4IMP6v7+/nrJkiX1ESNG6ElJSVmOmXlJsbi4OH38+PF6tWrVdE9PT7106dJ6ixYt9BkzZuipqan6yZMn9cDAQL1bt25Z2nL//ffrvr6++r///qvruutlz6Kjo/UuXbro/v7+OuByCbQ6deroBoNBP3XqVJZ9OYmNjdW9vb0dy7Fl9vLLL+t33HGHXqJECd3b21uvVauWPm3aND01NTXH49qXPVu1atU12/Dzzz/rERERemBgoG42m/WqVavqAwYM0Ldt2+ao079/f93X19fl810te2Z3M58f++f6jTfeyNVrc/Wz+9tvv+l33nmn7u3trYeFhenPPPOMY+m9n3/++Zpt6N+/v16xYkWnssTERH3ChAl65cqVHT+TDz74oH7kyBGneu+9957epEkT3dvbW/f399fr1aunP/PMM/qZM2eynEcIIUT2NF2/iVupQgghhMjR5MmTmTJlCufPn3dKaFaYNGrUiFKlSrF+/fr8booQQghRpMgcciGEEEJka9u2bezYsYN+/frld1OEEEKIIkfmkAshhBAiiz179rB9+3ZmzpxJaGgovXr1yu8mCSGEEEWO9JALIYQQIotPP/2UgQMHYrFY+PjjjzGbzfndJCGEEKLIkTnkQgghhBBCCCFEPpAeciGEEEIIIYQQIh9IQC6EEEIIIYQQQuSDIp/UzWazcebMGfz9/dE0Lb+bI4QQQgghhBCiiNN1nbi4OMLCwjAYsu8HL/IB+ZkzZwgPD8/vZgghhBBCCCGEKGZOnjxJ+fLls91f5ANyf39/QL0RAQEB+dya7FksFn744Qc6duyIyWTK7+aIPCDXuOiTa1z0yTUu2uT6Fn1yjYs+ucZFX2G5xrGxsYSHhzvi0ewU+YDcPkw9ICCgwAfkPj4+BAQEFOgPlrhxco2LPrnGRZ9c46JNrm/RJ9e46JNrXPQVtmt8rWnTktRNCCGEEEIIIYTIBxKQCyGEEEIIIYQQ+UACciGEEEIIIYQQIh8U+TnkQgghhBBCFFdWqxWLxZLfzbhlLBYLHh4eJCcnY7Va87s5Ig8UlGtsNBrx8PC46aW1JSAXQgghhBCiCIqPj+fUqVPoup7fTblldF0nJCSEkydP3nSgJAqmgnSNfXx8CA0NxdPT84aPIQG5EEIIIYQQRYzVauXUqVP4+PgQHByc74HLrWKz2YiPj8fPzw+DQWbnFkUF4Rrruk5qairnz5/n6NGjVK9e/YbbIgG5EEIIIYQQRYzFYkHXdYKDg/H29s7v5twyNpuN1NRUzGazBORFVEG5xt7e3phMJo4fP+5oz42QT6kQQgghhBBFVHHpGRciP7jjhoAE5EIIIYQQQgghRD6QgFwIIYQQQgghhMgHEpALIYQQQgghBNC6dWtGjx5908f54osvqFatGkaj0S3HE0WXBORCCCGEEEKIfNetWzc6derkct+mTZvQNI1du3YBMHLkSJo0aYKXlxcNGza8ha3Mnccff5wHH3yQkydP8tJLL93y8yclJTFp0iRq1KiBl5cXpUuX5qGHHmLv3r3XdRxN0/jiiy9yrHPs2DEiIyOpXLky3t7eVK1alUmTJpGamupUb9euXbRs2RKz2Ux4eDjTp0932v/+++/TsmVLSpYsScmSJWnfvj1//PGHU53Jkydz2223Ua5cOYKCgmjfvj1bt269rtdU0EhALoQQQgghhHDNZoNDR+CvHWprs+XZqSIjI1m3bh2nTp3Ksi8qKoqmTZtSv359R9mgQYPo1atXnrXnRsXHx3Pu3DkiIiIICwvD39//ho6TOaDNrZSUFNq3b8+iRYt4+eWXOXjwIN9++y1paWk0a9aM33///YaOm51//vkHm83Gu+++y969e5k1axbvvPMOzz//vKNObGwsHTt2pGLFimzfvp033niDyZMn89577znqbNiwgT59+vDzzz+zZcsWwsPD6dixI6dPn3bUqVGjBnPnzuW3337jl19+oVKlSnTs2JHz58+79TXdShKQCyGEEEIIIbLauRsmvgQvToNXZqrtxJdUeR7o2rUrwcHBLF682Kk8Pj6eVatWERkZ6SibO3cuw4cPp0qVKm5vR1paGiNGjCAwMJDSpUvzwgsvoOu6Y39KSgrjxo2jXLly+Pr60qxZMzZs2ACooNIegLdt2xZN0xz7PvvsM+rUqYOXlxeVKlVi5syZTuetVKkSL730Ev369SMgIIAhQ4YA8Ouvv9KyZUu8vb0JDw9n5MiRJCQkZNv+2bNns2XLFr7++mt69uxJxYoVueOOO/jss8+oXbs2kZGRTq9n0aJFjnaFhoYyYsQIR3sA7r//fjRNczzOrFOnTkRFRdGxY0eqVKnCvffey7hx41i9erWjzrJly0hNTXWcq3fv3owcOZI333zTqc6wYcNo2LAhtWrV4oMPPsBms7F+/XpHnYcffpj27dtTqVIl6tSpw5tvvklsbKxj5ERhJAG5EEIIIYQQwtnO3TBrPhw4BP7+EBaqtgcOqfI8CMo9PDzo168fixcvdgoYV61ahdVqpU+fPm4/pysffvghHh4e/PHHH8yZM4c333yTDz74wLF/xIgRbNmyhRUrVrBr1y4eeughOnXqxKFDh2jRogUHDhwAVAD+33//0aJFC7Zv307Pnj3p3bs3u3fvZvLkybzwwgtZbj7MmDGDBg0a8Pfff/PCCy9w5MgROnXqRI8ePdi1axcrV67k119/dQTNrixfvpwOHTrQoEEDp3KDwcCYMWPYt28fO3fuBGDBggUMHz6cIUOGsHv3btasWUO1atUA+PPPPwE1OuG///5zPM6NmJgYSpUq5Xi8ZcsW7rnnHjw9PR1lERERHDhwgMuXL7s8RmJiIhaLxek4GaWmpvLee+8RGBiY5bUWJh753QAhhBBCCCFEAWKzwcrVEBcH5cuBfS1zH2/wLgenTsMnn0O9OuCGdZgzGjRoEG+88QYbN26kdevWgAoIe/ToQWBgoFvPlZ3w8HBmzZqFpmnUrFmT3bt3M2vWLB577DFOnDhBVFQUJ06cICwsDIBx48axdu1aoqKieOWVVyhTpgwApUqVIiQkBIA333yTdu3a8cILLwBq6PW+fft44403GDBggOPcbdu25amnnnI8Hjx4MI888ogjMVz16tWZO3curVq1YsGCBZjN5iztP3jwIG3atHH52mrXru2o07BhQ15++WWeeuopRo0a5ahz++23AxAcHAxAiRIlHK8jNw4fPsxbb73FjBkzHGXR0dFUrlzZqV7ZsmUd+0qWLJnlOM8++yxhYWG0b9/eqfzrr7/m4YcfJjExkdDQUNatW0fp0qVz3b6CRnrIhRBCCCGEEFcdOQpHj0NQ0NVg3E7TIKgU/HtM1XOzWrVq0aJFCxYtWgSo4G7Tpk1Ow9VvxNChQ/Hz83N85eTOO+9Ey/C6mzdvzqFDh7BarezevRur1UqNGjWcjrdx40aOHDmS7TH379/PXXfd5VR21113OY5r17RpU6c6O3fuZPHixU7nioiIwGazcfRo9u9/xhEG2Tl37hxnzpyhXbt216ybW6dPn6ZTp0489NBDPPbYYzd8nNdee40VK1bw+eefZ7np0KZNG3755Rd+/fVXOnXqRM+ePTl37tzNNj3fSA+5EEIIIYQQ4qq4OLBYwOzler+XF1y6rOrlgcjISJ588knmzZtHVFQUVatWpVWrVjd1zKlTpzJu3Libblt8fDxGo5Ht27djNBqd9l0r0M8NX1/fLOd7/PHHGTlyZJa6FSpUcHmMGjVqsH//fpf77OU1atTA29v7Jlvr7MyZM7Rp04YWLVo4JWsDCAkJ4ezZs05l9seZe99nzJjBa6+9xo8//uiUxM/O19eXKlWqEBAQQIsWLahevToLFy5k/Pjxbn09t4oE5EIIIYQQQoir/P3BZILkFDVMPbOUFLX/BrOHX0vPnj0ZNWoUy5cvZ8mSJTzxxBNOPdY3okyZMo6h5NeSeRmt33//nerVq2M0GmnUqBFWq5Vz587RsmXLXJ+/du3a/Pbbb05lv/32GzVq1MgS2GfUuHFj9u3b55jXnRu9e/dmwoQJ7Ny502lutc1mY9asWdx22200aNDAkaht/fr12Q5xN5lMTj342Tl9+jRt2rShSZMmREVFYcg0laF58+ZMmDABi8WCyWQCYN26ddSsWdNpuPr06dOZNm0a33//fZbRAtmx2WykpKTkqm5BJEPWhRBCCCGEEFdVrQyVK8LFi5B56LOuw8VLUKWSqpcH/Pz86NWrF+PHj+e///5zmmNtd/jwYXbs2EF0dDRJSUns2LGDHTt23PBSYRmdOHGCsWPHcuDAAT7++GPeeustxxzrGjVq8Mgjj9CvXz9Wr17N0aNH+eOPP3j11Vf55ptvsj3mU089xfr163nppZc4ePAgH374IW+//fY1e+2fffZZNm/ezIgRI9ixYweHDh3iyy+/zDGp25gxY7jjjjvo1q0bq1at4sSJE/z555/06NGD/fv3s3DhQscNjsmTJzNz5kzmzp3LoUOH+Ouvv3jrrbccx7IH7NHR0dkmXzt9+jStW7emQoUKzJgxg/PnzxMdHU10dLSjzsMPP4ynpyeRkZHs3buXlStXMmfOHMaOHeuo8/rrr/PCCy+waNEiKlWq5DhGfHw8AAkJCTz//PP8/vvvnDhxgu3btzNo0CBOnz7NQw89lOP7WJBJD7kQQgghbp5ug5SDYI0BYyB41QBN7vsLUSgZDNDrAZVN/dRpNWfcy0v1jF+8pHrGe97v9oRuGUVGRrJw4UI6d+7sSJ6W0eDBg9m4caPjcaNGjQA4cuRItlm5c6tfv34kJSVxxx13YDQaGTVqlGMJMlBJ5uzJ0E6fPk3p0qW588476dq1a7bHbNy4MZ988gkvvvgiL730EqGhoUydOtXlzYaM6tevz8aNG5kwYQItW7ZE13WqVq2a4/rrZrOZn376iVdeeYXnn3+e48eP4+/vT5s2bfj999+pW7euo27//v1JTk5m1qxZjBs3jtKlS/Pggw869s+cOZOxY8fy/vvvU65cOY4dO5blfOvWrePw4cMcPnyY8uXLO+2zz2UPDAzkhx9+YPjw4TRp0oTSpUvz4osvOr2vCxYsIDU11en8AJMmTWLy5MkYjUb++ecfPvzwQy5cuEBQUBC33347mzZtok6dOjm+jwWZpudmxn8hFhsbS2BgIDExMQQEBOR3c7JlsVj49ttv6dy5s2MYhyha5BoXfXKNiz65xtlI2AYXP4SUw6CnguYJXtUgqD/45m7IYUEg17foK07XODk5maNHj1K5cmWXmbhzZedulW396HE1p9xkUj3jPe+HBvXc2l53sdlsxMbGEhAQkGXYtCgaCtI1zunnLLdxqPSQCyGEEOLGJWyD6GmQFgOmYNDMoCdD8l5VHjKhUAXlQogMGtRTS5sdOaoSuPn7q2HqEugK4TYSkAshhBDixug21TOeFgOeFa8uj6T5gskHLMfh4hLwaSzD14UorAwGqF41v1shRJEl/zsKIYQQ4sakHFTD1E3BrtcqNgZDyiFVTwghhBBZSEAuhBBCiBtjjUmfM57N/FSDWe23xtzadgkhhBCFhATkQgghhLgxxkCVwE1Pdr3flqz2GwNvbbuEEEKIQkICciGEEELcGK8aKpt62nnXaxVbz4NXdVVPCCGEEFlIQC6EEEKIG6MZ1NJmxkCVwM2aALpVbS3HVXlQP0noJoQQQmRD/ocUQgghxI3zbaqWNjPXAVssWE6prbmuLHkmhBBCXIMseyaEEEKIm+PbVC1tlnJQJXAzBqph6tIzLoQQQuQoX/+ntFqtvPDCC1SuXBlvb2+qVq3KSy+9hJ5hHpqu67z44ouEhobi7e1N+/btOXToUD62WgghhBBZaAYw1wLfZmorwbgQQghxTfn6v+Xrr7/OggULePvtt9m/fz+vv/4606dP56233nLUmT59OnPnzuWdd95h69at+Pr6EhERQXJyNhldhRBCCCGEEIVOt27d6NSpk8t9mzZtQtM0du3aBcDIkSNp0qQJXl5eNGzY0K3tOHnyJIMGDSIsLAxPT08qVqzIqFGjuHjxYq6PsWHDBjRN48qVKy73Dxw4kIkTJwKgaRpms5njx4871bnvvvsYMGDAjb6MXHn//fdp2bIlJUuWpGTJkrRv354//vjDqc61OkiPHTtGZGSkUyfrpEmTSE1NddQ5cOAAbdq0oWzZspjNZqpUqcLEiROxWCx5+voKg3wNyDdv3kz37t3p0qULlSpV4sEHH6Rjx46OD4Gu68yePZuJEyfSvXt36tevz5IlSzhz5gxffPFFfjZdCCGEEEKIos9mg9P/wKGtamuz5dmpIiMjWbduHadOncqyLyoqiqZNm1K/fn1H2aBBg+jVq5db2/Dvv//StGlTDh06xMcff8zhw4d55513WL9+Pc2bN+fSpUs3fQ6r1crXX3/Nvffe6yjTNI0XX3zxpo99vTZs2ECfPn34+eef2bJlC+Hh4XTs2JHTp0876lyrg/Sff/7BZrPx7rvvsnfvXmbNmsU777zD888/7ziGyWSiX79+/PDDDxw4cIDZs2fz/vvvM2nSpFv+mguafJ1D3qJFC9577z0OHjxIjRo12LlzJ7/++itvvvkmAEePHiU6Opr27ds7nhMYGEizZs3YsmULvXv3znLMlJQUUlJSHI9jY2MBsFgsBfoOjL1tBbmN4ubINS765BoXfXKNiza5vkVfcbrGFosFXdex2WzYbjSI/nc72sYlEH0ELClg8oKQquit+kGVJu5tMNC5c2eCg4OJiopiwoQJjvL4+HhWrVrF66+/7ngts2fPBuDcuXPs2rXLUW6f+mp/7ddr2LBheHp6snbtWry9vQEoX748DRo0oHr16jz//PPMnz8fUHHHpEmT+Pjjjzl37hzh4eE8++yztGvXjjZt2gBQsmRJAPr160dUVBQAv/76KyaTiSZNmjjaOHz4cGbNmsVTTz1F3bp1Ha8h4+tISUnhmWeeYeXKlcTGxtK0aVNmzpzJ7bffjs1mo1KlSowfP54nnnjC8Xr+/vtvbr/9do4cOULFihWzvN6PPvrI6fF7773HZ599xrp16+jXr5+jg3TChAl069YNgMWLFxMaGsrq1avp3bs3HTt2pGPHjo5jVKpUiaeeeop33nmH6dOnO8oqVarkqBMeHs7DDz/Mpk2brvs63ew1diebzYau61gsFoxGo9O+3P6eydeA/LnnniM2NpZatWphNBqxWq1MmzaNRx55BIDo6GgAypYt6/S8smXLOvZl9uqrrzJlypQs5T/88AM+Pj5ufgXut27duvxugshjco2LPrnGRZ9c46JNrm/RVxyusYeHByEhIcTHxzsNHc4t4/EdeH/7JlpiLDb/0uBTCizJGI7vRl/1Ekmdx2Kt2NDt7e7ZsydRUVGMGDECTdMAWLZsGVarlS5dujg62+xSUlKwWq1ZyuPi4q773JcvX+aHH35wDKXOGFD5+Pjw0EMPsXLlSl599VU0TWPQoEH88ccfvPbaa9StW5fjx49z8eJFAgMDWbJkCf369ePPP//E398fs9nsaOOnn35Kx44dndrYqFEjIiIiePrpp1m5ciUAaWlpWCwWx/Oee+451qxZw7x58wgPD2fu3Ll06tSJv/76i5IlS3L//fezdOlSRywFKnhu1qwZJUuWzPIeuRIXF4fFYnG099ixY0RHR3PnnXc6nq9pGk2aNOGXX36hc+fOLo9z7tw5AgMDsz3nv//+y3fffUfXrl1z1a7s2prfUlNTSUpK4pdffiEtLc1pX2JiYq6Oka8B+SeffMKyZctYvnw5derUYceOHYwePZqwsDD69+9/Q8ccP348Y8eOdTyOjY11DL0ICAhwV9PdzmKxsG7dOjp06IDJZMrv5og8INe46JNrXPQVq2us2yD1MFhjwRgAntWKfKK2YnV9i6nidI2Tk5M5efIkfn5+mM3m63uyzYa27TNIjoeylTGkB8Z4eoKPP1w4gd+21eh17gaDe38vDB06lLfeeou///6b1q1bA7By5UoeeOABwsPDs9T38vLCaDQ6/s7XdZ24uDj8/f0dAX1u7d+/H13Xadiwocu4oX79+nz44YekpKRw5coVPv/8c77//nvHaN6Mw+nLlSsHQJUqVShRooTTcb7//ntmzpzpdA5vb2+mT59Ow4YN2blzJy1btsTDwwOTyURAQAAJCQksWrSIRYsW0aNHD0AN469SpQqrVq1i3LhxDBw4kHnz5nHlyhUqVKiAzWbj888/5/nnn891HDR+/HjCwsK49957MZvNxMfHO15HxmOEhYVx6dIll8c9fPgw77//PtOnT8+y/+677+avv/4iJSWFxx57jNdeew3DdX6GbuYau1tycjLe3t7cc889WX7OcnujIV8D8qeffprnnnvOMfS8Xr16HD9+nFdffZX+/fsTEhICwNmzZwkNDXU87+zZs9kmb/Dy8sLLyytLuclkKhS/eAtLO8WNk2tc9Mk1LvqK/DVO2AYXP4SUw6CnguYJXtUgqH+xWFe8yF9fUSyusdVqRdM0DAbDdQc8/HdQDVMvUSZrwK1pEBAM0YfRzh6GcrXc12jgtttuo0WLFixevJi2bdty+PBhNm3axM8//+zyddgDMvs++xBm+2u3Gzp0KEuXLnU8tgeaGdnrZ36uq3Pt2rULo9FImzZtXNa1l2V+//fv38+ZM2fo0KGDU7nBYKBu3br069eP559/nt9++w1N0xxtOXr0KBaLhZYtWzqe5+XlxR133ME///yDwWCgcePG1K5dmxUrVvDcc8+xceNGzp07R69evXL1GXjttddYuXIlGzZscIwszu51ZGxbRqdPn6Zz58489NBDPP7441nOsXLlSuLi4ti5cydPP/00b775Js8888w125ZRdtc4PxgMBjRNc/k7Jbe/Y/L1FSQmJmZ5E41Go+NNrly5MiEhIaxfv96xPzY2lq1bt9K8efNb2lYhhBCiWEjYBtHTIGmv6hk3lVfb5L2qPGFbfrdQCJHXEmMgLRVM2fSse5rV/sSYPDl9ZGQkn332GXFxcURFRVG1alVatWp1U8ecOnUqO3bscHy5Uq1aNTRNY//+/S7379+/n5IlSxIcHOyYX3691qxZQ4cOHbIdtTBlyhT++uuvG05g/cgjj7B8+XIAli9fTqdOnQgKCrrm82bMmMFrr73GDz/84NTTn7GDNKOzZ8869tmdOXOGNm3aOPKEuRIeHs5tt91Gnz59eO2115g8eTJWq/W6XmNRk68Bebdu3Zg2bRrffPMNx44d4/PPP+fNN9/k/vvvB9Rdj9GjR/Pyyy+zZs0adu/eTb9+/QgLC+O+++7Lz6YLIYQQRY9uUz3jaTHgWREMvqAZ1dZUEawxcHGJqieEKLp8AsHDEyzZLDOcmqz2+wTmyel79uyJwWBg+fLlLFmyhEGDBt300OQyZcpQrVo1x5crQUFBdOjQgfnz55OUlOS0Lzo6mmXLltGrVy80TaNevXrYbDY2btzo8lienp4AWYLNL7/8ku7du2fbzvDwcEaMGMHzzz/v9NyqVavi6enJb7/95iizWCz8+eef3HbbbY6yhx9+mD179rB9+3Y+/fRTp/nk2Zk+fTovvfQSa9eupWlT51FQue0gPX36NK1bt6ZJkyZERUXlqufaZrNhsVjyPTFbfsvXIetvvfUWL7zwAsOGDePcuXOEhYXx+OOPO6X8f+aZZ0hISGDIkCFcuXKFu+++m7Vr117/XBghhBBC5CzloBqmbgpWw1Iz0jQwBkPKIVXP7N5hqkKIAiS0BoRUg1N7obSP8+8DXYe48xBeV9XLA35+fvTq1Yvx48cTGxvrci3uw4cPEx8fT3R0NElJSY5e71q1bu5309tvv02LFi2IiIjg5ZdfpnLlyuzdu5enn36acuXKMW3aNEBlDe/fvz+DBg1i7ty5NGjQgOPHj3Pu3Dl69uxJxYoV0TSNr7/+ms6dO+Pt7U1iYiLbtm1jzZo1ObZh/PjxvP/++xw9etSxrJuvry9PPPEETz/9NKVKlaJChQpMnz6dxMREIiMjHc+tVKkSLVq0IDIyEqvV6rS0miuvv/46L774IsuXL6dSpUqOxNl+fn74+fk5dZBWr16dypUr88ILLzh1kNqD8YoVKzJjxgzOnz/vOL69F33ZsmWYTCbq1auHl5cX27ZtY/z48fTq1avITx+5lnwNyP39/Zk9e7Zj2QJXNE1j6tSpTJ069dY1TAghhCiOrDHpc8azueltMIP1gqonhCi6DAZo3R8+mwYXjoN/sBqmnpqsgnHvQGjVz+0J3TKKjIxk4cKFdO7cmbCwsCz7Bw8e7NQ73ahRIwCOHDlCqVKlbvi81atXZ9u2bUyaNImePXty6dIlQkJCuO+++5g0aZLTsRcsWMDzzz/PsGHDuHjxIhUqVHCsvV2uXDmmTJnCc889x8CBA+nXrx8tW7bkjjvuoHTp0jm2oVSpUjz77LNO63iDmuNts9l49NFHiYuLo2nTpnz//feOpdXsHnnkEYYNG0a/fv2uObR+wYIFpKam8uCDDzqVT5o0icmTJwPX7iBdt24dhw8f5vDhw5QvX97pOPYlyjw8PHj99dc5ePAguq5TsWJFRowYwZgxY3JsX3Gg6fZ3qYiKjY0lMDCQmJiYAp9l/dtvv6Vz587F/i5RUSXXuOiTa1z0FflrnPwPnByj5owbfLPutyaALRbCZxXJHvIif31FsbrGycnJHD16lMqVK9/4yNIj22HDhxB9WM0Z9/CE0OoqGK/q/nXI3cFmsxEbG0tAQEC+J/zK7N577+Xuu+++7iRmwllBusY5/ZzlNg7N1x5yIYQQQhQgXjVUNvXkvWByMUzVeh7MdVU9IUTRV7UJVG6ksq4nxqg546E18rRnvCi7++676dOnT343QxQwEpALIYQQQtEMammz6GlgOa7mjBvMYEtWwbgxEIL6Ffn1yIUQGRgMbl/arLiSnnHhivyPKoQonHSbGl6bsFVtJeuzEO7h2xRCJoC5jhqebjmltua6qrwYrEMuhBBC3CrSQy6EKHwStqmlmVIOpyeg8lTDbIP6S7AghDv4NgWfxiqbujVG9Yx71ZCecSGEEMLNJCAXQhQuCdvUcNq0mPSlmcygJ6s5r9HTpAdPCHfRDEUycZsQQghRkMitbiFE4aHbVM94Wgx4VlRZoDWj2poqqp68i0tk+LoQQgghhCgUJCAXQhQeKQfVMHVTsHP2Z1CPjcGQckjVE0IIIYQQooCTgFwIUXhYY9LnjGeznqrBrPZbY25tu4QQQgghhLgBEpALIQoPY6BK4KYnu95vS1b7jYG3tl1CCCGEEELcAAnIhRCFh1cNlU097TzouvM+XVfrJHtVV/WEEEIIIYTbTZ48mYYNG+Z3M4oMCciFEIWHZlBLmxkDwXIcrAmgW9XWclyVB/WTpZmEEEIId9FtcGk3RP+itnmYOLVbt2506tTJ5b5NmzahaRq7du0CYOTIkTRp0gQvLy+3BocDBgxA0zQ0TcNkMlG2bFk6dOjAokWLsNny5rW3adOGDz74gGPHjqFpGjt27Lgl58n81bdv3zw5L8DOnTvp06cP4eHheHt7U7t2bebMmZOl3oYNG2jcuDFeXl5Uq1aNxYsXO+1/9dVXadasGeHh4YSEhHDfffdx4MABl+fUdZ3//e9/aJrGF198kQevyj1k2TORe7pN1qQV+c+3qVrazL4OufWCGqZurquCcVnyTAghhHCPs5th/9twZT9YU8DoBSVqQ+0RULaF208XGRlJjx49OHXqFOXLl3faFxUVRdOmTalfv76jbNCgQWzdutURpLtLp06diIqKwmq1cvbsWdauXcuoUaP49NNPWbNmDR4e7guhLl26xG+//caKFStISkpy23Fzc54ff/yROnXqOOp4e3vn2fm3b99OmTJlWLp0KeHh4WzevJkhQ4ZgNBoZMWIEAEePHqVLly4MHTqUZcuWsX79egYPHkxoaCgREREAbNy4kSeeeILatWtjNpuZOHEiHTt2ZN++ffj6+jqdc/bs2WiZkwAXQBJNidxJ2AYnR8HJMXD6ebU9OUqVi/yn2yD5H0jYqrZFfdkv36YQPgfCZ0G5V9Q2fLYE40IIIYS7nN0Mf4yDi3+BZwnwr6S2F/9W5Wc3u/2UXbt2JTg4OEuvaHx8PKtWrSIyMtJRNnfuXIYPH06VKlXc3g4vLy9CQkIoV64cjRs35vnnn+fLL7/ku+++c2rblStXGDx4MMHBwQQEBNC2bVt27tzpdKyvvvqK22+/HbPZTOnSpbn//vud9n/zzTc0btyYsmXLZmmH1WolMjKSypUr4+3tTc2aNbP0Kg8YMID77ruPGTNmEBoaSlBQEMOHD8disVzzPEFBQYSEhDi+AgMDc/26AN59913Cw8Px8fGhZ8+exMRkn1R30KBBzJkzh1atWlGlShX69u3LwIEDWb16taPOO++8Q+XKlZk5cya1a9dmxIgRPPjgg8yaNctRZ+3atQwYMIDatWvToEEDFi9ezIkTJ9i+fbvT+Xbs2MHMmTNZtGhRtm0qKCQgF9eWsA2ip0HSXjAGgKm82ibvVeUSlOev4nqzRDOAuRb4NlNbGa0hhBBCuIduUz3jqZfAvxqY/EAzqq1/VUi9DPvnub0DwMPDg379+rF48WL0DLliVq1ahdVqpU+fPm493/Vo27YtDRo0cAogH3roIc6dO8d3333H9u3bady4Me3atePSpUuACoLvv/9+OnfuzN9//8369eu54447nI67Zs0aunfv7vKcNpuN8uXLs2rVKvbt28eLL77I888/zyeffOJU7+eff+bIkSP8/PPPfPjhhyxevDjLTY2czpPZtV4XwOHDh/nkk0/46quvWLt2LX///TfDhg3L1fHtYmJiKFWqlOPxli1baN++vVOdiIgItmzZkuMxAKfjJCYm8vDDDzNv3jxCQkKuq035Qf6CFTnTbWpocFoMeFYEg6/6hWzwBVNFNXz94pKi3yNbUMnNEiGEEEK42+W9api6dyhkHvKraeAdAlf2qXpuNmjQII4cOcLGjRsdZVFRUfTo0cPRg5tfatWqxbFjxwD49ddf+eOPP1i1ahVNmzalevXqzJgxgxIlSvDpp58CMG3aNHr37s2UKVMcPbrjx493HC8lJYW1a9dy7733ujyfyWRiypQpNG3alMqVK/PII48wcODALAF5yZIlefvtt6lVqxZdu3alS5curF+//prnadGiBX5+fo6vv//+O1evCyA5OZklS5bQsGFD7rnnHt566y1WrFhBdHR0rt7LzZs3s3LlSoYMGeIoi46OzjJSoGzZssTGxroczm+z2Rg9ejR33XUXdevWdZSPGTOGFi1a5PoGRH6TOeQiZykH1TxdU7DrX8jGYEg5pOqZa+VPG4urzDdL7NdH8wWTj0pydnEJ+DSW3mMhhBBC5F7qZTVn3CObOcVGb7CeVfXcrFatWrRo0YJFixbRunVrDh8+zKZNm5g6depNHXfo0KEsXbrU8Tg+Pv66j6HrumNO8s6dO4mPjycoKMipTlJSEkeOHAHUsOnHHnss2+P99NNPlClTxmked2bz5s1j0aJFnDhxgqSkJFJTU7MksatTpw5Go9HxODQ0lN27d1/zPCtXrqR27dqOx+Hh4XzwwQfXfF0AFSpUoFy5co7HzZs3x2azceDAgWv2Su/Zs4fu3bszadIkOnbsmGPdnIwYMYI9e/bw66+/OsrWrFnDTz/9xN9//33Dx73VJCAXObPGgJ4Kmtn1foNZJdWyZj9nROQRuVkihBBCiLzgWVIlcEtLUsPUM7Mmqf2eJfPk9JGRkTz55JPMmzePqKgoqlatSqtWrW7qmFOnTmXcuHE3dYz9+/dTuXJlQAX0oaGhbNiwIUu9EiVKANdOkrZmzZpse8cBVqxYwbhx45g5cybNmzfH39+fN954g61btzrVM5lMTo81TXPKCJ/decLDw6lWrZpTWW5e183Yt28f7dq1Y8iQIUycONFpX0hICGfPnnUqO3v2LAEBAVney6effpq1a9fyyy+/OCUA/Omnnzhy5EiWtvbo0YOWLVu6fF35TQJykTNjoMpgrSerntfMbMlqvzF/hxAVS3KzRAghhBB5oWQdlU394t/gUdX5xr+uQ1I0BDVW9fJAz549GTVqFMuXL2fJkiU88cQTN50tu0yZMpQpU+aGn//TTz+xe/duxowZA0Djxo2Jjo7Gw8ODSpUquXxO/fr1Wb9+PQMHDsyyT9d1vvrqK6de+8x+++03WrRo4TQ3O2MvdW7k5jwZ5eZ1AZw4cYIzZ84QFhYGwO+//47BYKBmzZrZPmfv3r20bduW/v37M23atCz7mzdvzrfffutUtm7dOpo3b+70ep588km++eYbfv75Z8cNErvnnnuOwYMHO5XVq1ePWbNm0a1bt2zblp8kIBc586oBXtXUnGSTT9ZfyNbzarkprxr518biqrjfLJFl+IQQQoi8oRnU0mZ/jIO4I2rOuNFb9YwnRaue8drD8+z/XT8/P3r16sX48eOJjY1lwIABWeocPnyY+Ph4oqOjSUpKcqzdXavWzY8KTElJITo62mnZs1dffZWuXbvSr18/ANq3b0/z5s257777mD59OjVq1ODMmTOORG5NmzZl0qRJtGvXjqpVq9K7d2/S0tL49ttvefbZZ9m+fTuJiYncfffd2bajevXqLFmyhO+//57KlSvz0Ucf8eeff2YJQnOSm/NklJvXBWA2m+nfvz8zZswgNjaWkSNH0rNnz2yHq+/Zs4e2bdsSERHB2LFjHXPNjUYjwcHBgJpW8Pbbb/PMM88waNAgfvrpJz755BO++eYbx3GGDx/O8uXLWbZsGf7+/o7jBAYG4u3t7cgYn1mFChWu6327lSQgFznTDBDUXyUIsxxXw6ANZhXsWc+rQCionwRC+aE43yxJ2HZ1HXI9Vd148KqmPquy9JkQQghx88q2gDtmZFiH/Kwaph7UWAXjebAOeUaRkZEsXLiQzp07O3phMxo8eLBT4rdGjRoBqgc5Y8btG7F27VpCQ0Px8PCgZMmSNGjQgLlz59K/f38MBvU3r6ZpfPvtt0yYMIGBAwdy/vx5QkJCuOeeexyJyVq3bs2qVat46aWXeO211wgICOCee+4B4Msvv6Rz585Oa5rbh5nbyx5//HH+/vtvevXqhaZp9OnTh2HDhvHdd9/l+rW4Ok9OcvO6AKpVq8YDDzxA586duXTpEl27dmX+/PnZHvfTTz/l/PnzLF261Km3vmLFio5EeZUrV+abb75hzJgxzJkzh/Lly/PBBx841iAHWLBgAaCWyMsoKirK5Y2bwkDTM64pUATFxsYSGBhITEwMAQEB+d2cbFksFr799ls6d+6cZR5IgeAyAKqugnEJgHIlT66xPcu6Ncb1zZKQCUXv+thfc1pM+vx5sxolkJb/r7nA/xyLmybXuGiT61v0ueUaF5IRWsnJyRw9epTKlStjNmczvS03dJvKpp56WfWMl6xTIF+vnc1mIzY2loCAAEfwXBDVr1+fiRMn0rNnT0fZ77//TvPmzTl//jylS5fOs/MUdgXpGuf0c5bbOFR6yEXu+DZV2boLwX9AxYpvUxWA2m+WWC+omyXmukXzZolklhdCCJGfiuMILc0AperldyuKlNTUVHr06MH//vc/ANLS0jh27BhvvPEGDRo0cFswnvk8omCSgFzknmaQbN0FUXG6WeKUWR6wxYNuAc0EBl/JLC+EECLvZDdCK3mvKi+Ko9JEnvD09GTSpEmOx3v27KFFixY0bNiQJUuW5Nl5RMEkAbkQRUFxuVlizyxvS4GUo2BLAGyAQQXkpjC1XzLLC3HzCsmwXCFuCRmhJfJQw4YNSUxMzO9miHwiAbnIH/KHnrgRxkDQ0yD5IKCDwRMwAlawxalyU3DRzSwvxK1SHIflCpETpxFamZbf0jQZoSWEuGESkItbz51/6ElgX7x4VlND1PVUMPgD9mvtkf59nNrvWS3/2ihEYSfDcoXIyj5CS8smOZrBrPK4yAgtIcR1koBc3Fru/ENPenCKn9TD6fPFvYBk0DP0kJMKmpfan3pYeiiEuBEyLFcI14yB6u8MPVn9PGRmS1b7ZYSWEOI6yf+m4tbJ/IeewRc0Y/rc34rqrvLFJaretdgD+6S9YAwAU3m1tQf2Cdvy/vWIW88aA5qHWnLP4A+kAUlqa/BX5ZqH9FAIcaOuZ1iuEMWJVw110z/tPGReMVjX1XKjXtVVPSGEuA4SkItbx11/6LkzsBeFi72HwuAF5jrqy6vW1e81L+mhEOJm5GZYriROFMWRZlAj8IyBaqSINQF0q9pajqvyoH4yckQIcd3kt4a4ddz1h5704BRfGXsoAIx+4FFSbUF6KIS4WRmH5boiw3JFcebbVE2tM9cBWyxYTqmtua7kVhBC3DCZQy5uHXfNv5LEKsWXvYcielp6j0Swut62ZBWMSw+FEDfHftMrea+aM57xpqd9WK65rtz0EsWXb1OVQ0ESygoh3ER+e4hbx13zr6QHp3iTHorc0W2Q/A8kbFVbmcIhckOG5QpxbZpBJQ71baa2Rf3nQbdB3F649Jva5uH/J926daNTp04u923atAlN09i1axcAI0eOpEmTJnh5edGwYUO3tWHAgAFomsbQoUOz7Bs+fDiapjFgwAC3nW/jxo2Eh4c7lW3ZsgWj0UiXLl3cdp6MpkyZQt++fQGoVKkSmqaxYsWKLPXq1KmDpmksXrw4T9ph9+qrr3L77bfj7+9PmTJluO+++zhw4IBTneTkZIYPH05QUBABAQH069ePs2fPOvbv3LmTPn36EB4ejre3N7Vr12bOnDnZnvO3337Dw8PDrZ+dG1XEf4OIAsVdf+hJYhXh2xTC50D4LCj3itqGz5Zg3C5hG5wcBSfHwOnn1fbkKEl2KHJHbnoJIewub4VdQ2D3UNg3Rm13DVHleSAyMpJ169Zx6tSpLPuioqJo2rQp9evXd5QNGjSIXr16ub0d4eHhrFixgqSkJEdZcnIyy5cvp0KFCjd9fIvF4vj+yy+/pFu3bk77Fy5cyJNPPskvv/zCmTNnbvp8VqsVm+3qjZQvv/ySe++91/E4PDycqKgop+f8/vvvREdH4+vrYlSrm23cuJHhw4fz+++/s27dOiwWCx07diQhIcFRZ8yYMXz11VesWrWKn3/+mejoaB588EHH/u3bt1OmTBmWLl3K3r17mTBhAuPHj+ftt9/Ocr4rV67Qr18/2rVrl+evLTckIBe3ljv+0JMeHAHFr4cit2QFAuEOctNLCHF5KxyYCLE7wSMQvCuobdxOVZ4HQXnXrl0JDg7O0iMbHx/PqlWriIyMdJTNnTuX4cOHU6VKFbe3o3HjxoSHh7N69WpH2erVq6lQoQKNGjVyqrt27VruvvtuSpQoQVBQEF27duXIkSOO/ceOHUPTNFauXEmrVq0wm80sW7bMsX/NmjVOwXF8fDwrV67kiSeeoEuXLlneiw0bNqBpGt988w3169fHbDZz5513smfPHkedxYsXU6JECdasWcNtt92Gl5cXJ06cAODkyZPs3bvXaSTCI488wsaNGzl58qSjbNGiRTzyyCN4eDjPcH7zzTepV68evr6+hIeHM2zYMOLj4wFISEggICCATz/91Ok5X3zxBb6+vsTFxbl8v9euXcuAAQOoU6cODRo0YPHixZw4cYLt27cDEBMTw8KFC3nzzTdp27YtTZo04e2332bz5s38/vvvgLo5M2fOHFq1akWVKlXo27cvAwcOdLqGdkOHDuXhhx+mefPmLttzq8lfsOLWc8cfetKDI0RWGVcgMIWDLQEsp9XWI1xWIBDXR256CVF86TY48T5YroBPZfBIX9HGwxe8K0PaFbXfzf+feHh40K9fPxYvXoyeYRTkqlWrsFqt9OnTx63ny8mgQYOceo0XLVrEwIEDs9RLSEhg7NixbNu2jfXr12MwGLj//vudeqQBnnvuOUaNGsX+/fuJiIgAYO/evZw7d462bds66n3yySfUqlWLmjVr0rdvXxYtWuT0Xtg9/fTTzJw5kz///JPg4GC6devm1POemJjI66+/zgcffMDevXspU6YMoG4AtG7dmoCAAEfdsmXLEhERwYcffuh47sqVKxk0aFCW8xoMBubOncvevXv58MMP+emnn3jmmWcA8PX1pXfv3ll626OionjwwQfx9/fP5t12FhOj8kCVKlUKUL3fFouF9u3bO+rUqFGDChUqsGXLlhyPYz9Gxrb8+++/TJo0KVdtuRUkqZvIH/Y/9G6GJFYRwpl9BQJskLAZsAA6oAEm8Cx3dQWCm/35E0IIUXTF74eEA+BV1vWKNp5l1P74/eBfx62nHjRoEG+88QYbN26kdevWgAqievToQWDgrcsP1LdvX8aPH8/x48cBNed4xYoVbNiwwalejx49nB4vWrSI4OBg9u3bR926dR3lo0eP5oEHHnCq++WXXxIREYGnp6ejbOHChY753Z06dSImJsbpvbCbNGkSHTp0AODDDz+kfPnyfP755/Ts2RNQw+Lnz59PgwYNspyze/fuWV7voEGDeOqpp5gwYQKffvopVatWdTm/evTo0Y7vK1WqxMsvv8zQoUOZP38+AIMHD6ZFixb8999/hIaGcu7cOb799lt+/PHHLMdyxWazMXr0aO666y7H+xcdHY2npyclSpRwqlu2bFmio6NdHmfz5s2sXLmSb775xlF26NAhnnvuOTZt2pSl5z8/SeQiCjfpwRHiKmsMWM5B6jEgFRWMk75NVeWWc7ICgRBCiJxZroAtBYzZrGhj9Fb7LVfcfupatWrRokULFi1aBMDhw4fZtGmT03D1GzF06FD8/PwcX9cSHBzsGDIeFRVFly5dKF26dJZ6hw4dok+fPlSpUoWAgAAqVaoE4Bgibte0adbRm5nnch84cIA//vjDMRLAw8ODXr16sXDhwizPzTjculSpUtSsWZP9+/c7yjw9PZ3m2wPExsayceNGp3PadenShfj4eH755RcWLVrksncc4Mcff6Rdu3aUK1cOf39/Hn30US5evEhiYiIAd9xxB3Xq1HH0ti9dupSKFStyzz33uDxeZsOHD2fPnj0uk8zl1p49e+jevTuTJk2iY8eOgJpH//DDDzNlyhRq1ChYeaYkehFCiKJC8wXrZa4G4pnpar+rZQeFEEIIO1MJMHiBNZsVbaxJar+pRJ6cPjIyks8++4y4uDiioqKoWrUqrVq1uqljTp06lR07dji+cmPQoEEsXryYDz/8MNsAtVu3bly6dIn333+frVu3snWrmlufmprqVC9zcrT//vuPv//+2ymT+sKFC0lLSyMsLAwPDw88PDxYsGABn332mWMYd255e3ujZRrd8N1333HbbbdlyeoOKvh/9NFHmTRpElu3buWRRx7JUufYsWN07dqV+vXr89lnn7F9+3bmzZuX5fUOHjzYMfc9KiqKgQMHZmmLKyNGjODrr7/m559/pnz58o7ykJAQUlNTuXLlilP9s2fPEhIS4lS2b98+2rVrx5AhQ5g4caKjPC4ujm3btjFixAjHezt16lR27tyJh4cHP/300zXbl1ckIBdCiKIi5ShgvUYla3o9IYQQIht+tcG3JqSedb2iTeo5td+vdp6cvmfPnhgMBpYvX86SJUsYNGhQrgK6nJQpU4Zq1ao5vnKjU6dOpKamYrFYHPO+M7p48SIHDhxg4sSJtGvXjtq1a3P58uVcHfurr76iRYsWjjnOaWlpLFmyhJkzZzrdONi5cydhYWF8/PHHTs+3JzMDuHz5MgcPHqR27ZyvR3bD1e0GDRrExo0b6d69OyVLlsyyf/v27dhsNmbOnMmdd95JjRo1XGaB79u3L8ePH2fu3Lns27eP/v3759guXdcZMWIEn3/+OT/99BOVK1d22t+kSRNMJhPr1693lB06dIgTJ044jRTYu3cvbdq0oX///kybNs3pGAEBAezevdvpvR06dCg1a9Zkx44dNGvWLMc25qWCM3heCCHEzUnanft6Jbtdu54QQojiSTNAhcdUNvWko2rOuNFb9YynngOPEmp/Hk0V9PPzo1evXowfP57Y2FiX634fPnyY+Ph4oqOjSUpKcvR616rlvhwpRqPRMQzcaDRm2V+yZEmCgoJ47733CA0N5cSJEzz33HO5Onbm7Opff/01ly9fJjIyMstc+R49erBw4UKntdGnTp1KUFAQZcuWZcKECZQuXZr77rsv2/OlpaXx3XffMW7cuGzr1K5dmwsXLuDj4+Nyf7Vq1bBYLLz11lt069aN3377jXfeeSdLvZIlS/LAAw/w9NNP07FjR6febleGDx/O8uXL+fLLL/H393fMCw8MDMTb25vAwEAiIyMZO3YspUqVws/Pj+HDh9O8eXPuvPNOQA1Tb9u2LREREYwdO9ZxDKPRSHBwMAaDwWlOP6ibNGazOUv5rSY95EIIUVToSdeucz31hBBCFF8lm0HNl8G/AaTFQtIJtfVvoMpL5m2PYmRkJJcvXyYiIoKwsLAs+wcPHkyjRo149913OXjwII0aNaJRo0ZuWbc7o4CAAKeM5BkZDAZWrFjB9u3bqVu3LmPGjOGNN9645jETEhJYv369U0C+cOFC2rdv7zJxXY8ePdi2bRu7du1ylL322muMGjWKJk2aEB0dzVdffeWUHC6zjRs34ufnR+PGjXNsW1BQEN7e3i73NWjQgDfffJPXX3+dunXrsmzZMl599VWXdSMjI0lNTc12qH9GCxYsICYmhtatWxMaGur4WrlypaPOrFmz6Nq1Kz169KB169aULVvWaXm1Tz/9lPPnz7N06VKnY9x+++3XPH9+03RXefSLkNjYWAIDA4mJicn2h6kgsFgsfPvtt3Tu3BmTyZTfzRF5QK5x0Zfv1/jiCjjz7LXrhb0OQb3zvj1FUL5fY5Gn5PoWfcXpGicnJ3P06FEqV66M2ZxNcrbc0G0qm7rlipoz7le7QCfRtdlsxMbGEhAQgMFQMNu5evVqJk6cyL59+677uRs2bKBNmzZcvnw5S9bxnIwcOZK0tDRHNvS89tFHHzFmzBjOnDmT442CG1GQrnFOP2e5jUNlyLoQQhQVPvUBT1SG9ex4ptcTQgghckEzuH1ps+LOz8+P119//Zaes27duk7zrfNKYmIi//33H6+99hqPP/6424Pxoqhg3jYSQghx/bxqgPEaI4GMAaqeEEIIIfJFx44d6dbt1uZyGTJkCPXq1cvz80yfPp1atWoREhLC+PHj8/x8RYEE5EIIUVSkHgajH2jegBHQMnwZVbnRT9UTQgghRKHTunVrdF2/ruHqt9LkyZOxWCysX78+V+u9CxmyLvKLboOUg2CNAWOg6rErwPORhCgUrDGgeYC5FqSeBlscYAMMYPAHz3KqzHp9a5kKIYQQQoi8IQG5uPUStsHFDyHlMOipoHmCVzUI6g++TfO7dUIUXsZA9fNk8ALvumBLAN0CmgkMvmBLBC1F1RNCCCGEEPlOuiTFrZWwDaKnQdJeNZfVVF5tk/eq8oRt+d1CIQovrxrq5lbaefXY6AceJdUWwHoevKrLHHIhhBBCiAJCAnJx6+g21TOeFgOeFVWPnWZUW1NFNYz24hJVTwhx/TSDGmliDATLcbAmgG5VW8txVR7UT6aHCCGEEEIUEPJXWVGi2yD5H0jYqrYFLbBNOaiGqZuCQdOc92kaGIMh5ZCqJ4S4Mb5NIWQCmOuALRYsp9TWXFeVy7QQIYQQQogCQ+aQFxWFYV62NSa9bWbX+w1msF6QhFNC3CzfpuDTWBInCiGEEEIUcPLXWVFQWOZl2xNO6cmu99uS1X5JOCXEzdMMKtu6bzO1lWBcCCGEEAVY69atGT169E0dIzExkR49ehAQEICmaVy5csUtbctL8hdaYVeY5mVnTDil6877dF0STgkhhBBCFDS3cEpkt27d6NSpk8t9mzZtQtM0du3aBcDIkSNp0qQJXl5eNGzY0G1tGDBgAJqmMXTo0Cz7hg8fjqZpDBgwwG3nc2X16tV06NCB4OBgAgICaN68Od9//32WevPmzaNSpUqYzWaaNWvGH3/84dh36dIlnnzySWrWrIm3tzcVKlRg5MiRxMS4Hol68eJFypcvX2iC2Ox8+OGHbNq0ic2bN/Pff/9x+fJlNE1jx44d+d20bElAXtgVhHnZuf1FLQmnhBBCCCEKj4RtcHIUnBwDp59X25Oj8mz0ZWRkJOvWrePUqVNZ9kVFRdG0aVPq16/vKBs0aBC9evVyezvCw8NZsWIFSUlJjrLk5GSWL19OhQoV3H6+zH755Rc6dOjAt99+y/bt22nTpg3dunXj77//dtRZuXIlY8eOZdKkSfz11180aNCAiIgIzp07B8CZM2c4c+YMM2bMYM+ePSxevJi1a9cSGRnp8pyRkZFO721hdeTIEWrXrk3dunUJCQlByxwf3QSLxeK2Y2UkkU9hl5t52Xpq3s3Lvt5f1JJwSgghhBCi4MuHKZFdu3YlODiYxYsXO5XHx8ezatUqp2By7ty5DB8+nCpVqri9HY0bNyY8PJzVq1c7ylavXk2FChVo1KiRU921a9dy9913U6JECYKCgujatStHjhxx7G/bti0jRoxwes758+fx9PRk/fr1Ls8/e/ZsnnnmGW6//XaqV6/OK6+8QvXq1fnqq68cdd58800ee+wxBg4cyG233cY777yDj48PixYtAqBu3bp89tlndOvWjapVq9K2bVumTZvGV199RVpamtP5FixYwJUrVxg3blyu3p9nn32WGjVq4OPjQ5UqVXjhhRecgtXJkyfTsGFDPvroIypVqkRgYCC9e/cmLi7OUSchIYF+/frh5+dHaGgoM2fOvOZ5jxw5Qvfu3QkNDaV8+fI0a9aMH3/80bG/devWzJw5k19++QVN02jdujWVK1cGoFGjRo4yuw8++IDatWtjNpupVasW8+fPd+w7duwYmqaxcuVKWrVqhdlsZtmyZbl6f66XBOSF3a2cl525Jzz+D/ULOXEPaB5g8FPbpD05/6L2bQrhcyB8FpR7RW3DZ0swLoQQQghREOTTlEgPDw/69evH4sWL0TNMb1y1ahVWq5U+ffq49Xw5GTRoEFFRUY7HixYtYuDAgVnqJSQkMHbsWLZt28b69esxGAzcf//92GzqvRk8eDDLly8nJSXF8ZylS5dSrlw52rZtm6u22Gw24uLiKFWqFACpqals376d9u3bO+oYDAbat2/Pli1bsj1OTEwMAQEBeHhczeu9b98+pk6dypIlSzAYchca+vv7s3jxYvbt28ecOXN4//33mTVrllOdI0eO8MUXX/D111/z9ddfs3HjRl577TXH/qeffpqNGzfy5Zdf8sMPP7Bhwwb++uuvHM8bHx9P586dWbduHRs3biQiIoJu3bpx4sQJQN00eeyxx2jevDn//fcfq1evdgzj//HHHx1lAMuWLePFF19k2rRp7N+/n1deeYUXXniBDz/80Omczz33HKNGjWL//v1ERETk6v25XpJlvbCzz8tO3gsmH+dh6/Z52ea6Nz8v21UWd2sM2FJAN0Daf4ANMIDmD3qK+kXt09j1EHR7wikhbpRukyziQgghRF64nimRbv57btCgQbzxxhts3LjR0ZsZFRVFjx49CAy8dYl/+/bty/jx4zl+/DgAv/32GytWrGDDhg1O9Xr06OH0eNGiRQQHB7Nv3z7q1q3LAw88wIgRI/jyyy/p2bMnAIsXL3bMVc+NGTNmEB8f73j+hQsXsFqtlC1b1qle2bJl+eeff1we48KFC7z00ksMGTLEUZaSkkKfPn144403qFChAv/++2+u2jNx4kTH95UqVWLcuHGsWLGCZ555xlFus9lYvHgx/v7+ADz66KOsX7+eadOmER8fz8KFC1m6dCnt2rUD1Nzv8uXL53jeBg0a0KBBA2w2G7GxsUydOpUvvviCNWvWMGLECEqVKoWPjw+enp6EhIQAEBsbC0BQUJCjDGDSpEnMnDmTBx54AIDKlSuzb98+3n33Xfr37++oN3r0aEedvCIBeWFnn5cdPS19HnawGqZuS1bBuDvmZduHLFmugIefCsZtCWCJRgXhmX6Z6Kmge0LSzjz5RS1EoVjmTwghhCis8nGp2lq1atGiRQsWLVpE69atOXz4MJs2bWLq1Kk3ddyhQ4eydOlSx+P4+Pgc6wcHB9OlSxdHb32XLl0oXbp0lnqHDh3ixRdfZOvWrVy4cMHRM37ixAnq1q2L2Wzm0UcfZdGiRfTs2ZO//vqLPXv2sGbNmly1e/ny5UyZMoUvv/ySMmXKXMcrvio2NpYuXbpw2223MXnyZEf5+PHjqV27Nn379r2u461cuZK5c+dy5MgR4uPjSUtLIyAgwKlOpUqVHME4QGhoqGN++5EjR0hNTaVZs2aO/aVKlaJmzZo5njc+Pp7JkyfzzTffcObMGaxWK0lJSY4e8txKSEjgyJEjREZG8thjjznK09LSstz0ado07/+ulIC8KLDPy7YHKNYLKkAx11XB+M0EKPYhS6lnQU+DlHOALT1LutVeCeePkk0NobechbTLN35uIVyx3yBKi0m/c29Wnzf7nDbJRSCEEELcnIxTIjXfrPvzeKnayMhInnzySebNm0dUVBRVq1alVatWN3XMqVOn5nqOtN2gQYMc87/nzZvnsk63bt2oWLEi77//PmFhYdhsNurWrUtqaqqjzuDBg2nYsCGnTp0iKiqKtm3bUrFixWuef8WKFQwePJhVq1Y5DU8vXbo0RqORs2fPOtU/e/asUy8wQFxcHJ06dcLf35/PP/8ck8nk2PfTTz+xe/duPv30UwDHNIHSpUszYcIEpkyZkqVNW7Zs4ZFHHmHKlClEREQQGBjIihUrsswBz3geAE3THDcrbtS4ceNYt24d06dPJyQkhODgYHr27On0XueG/WbM+++/73RTAMBoNDo99vV18fl3MwnIiwrfpmp4uLuH8KYchKRdYItRGdHxUF+6qyyD9p5yAyooT4G0Szd3fiEyyjynzT7US/NVUzYsx3OeKiGEEEKIa7tVUyKz0bNnT0aNGsXy5ctZsmQJTzzxxE1nyy5Tpsx19zB36tSJ1NRUNE1zOX/44sWLHDhwgPfff5+WLVsC8Ouvv2apV69ePZo2bcr777/P8uXLefvtt6957o8//phBgwaxYsUKunTp4rTP09OTJk2asH79eu677z5ADRFfv369UwK52NhYIiIi8PLyYs2aNZjNziMePvvsM6dM8n/++SeDBg1i06ZNVK1a1WW7Nm/eTMWKFZkwYYKjzD6sP7eqVq2KyWRi69atjqz1ly9f5uDBgzneePntt98YMGAA999/P7GxsRgMBo4dO5bjuTw9PQGwWq2OsrJlyxIWFsa///7LI488cl1tzwv5GpBXqlTJ5QUcNmwY8+bNIzk5maeeeooVK1aQkpJCREQE8+fPzzJfQqTLi3nZaZch7QLY7HeeUlE94pnWEbfPH1cNSd+vgS3n4UBCXJd8nNMmhBBCFBu3YkpkDvz8/OjVqxfjx48nNjbW5brfhw8fJj4+nujoaJKSkhzrTNeq5b7//41GI/v373d8n1nJkiUJCgrivffeIzQ0lBMnTvDcc8+5PNbgwYMZMWIEvr6+3H///Tmed/ny5fTv3585c+bQrFkzoqOjAfD29nYMqR47diz9+/enadOm3HHHHcyePZuEhARH4rnY2Fg6duxIYmIiS5cuJTY21jGfOjg4GKPRmCXovnDhAgC1a9emRIkSLttWvXp1Tpw4wYoVK7j99tv55ptv+Pzzz3N8PZn5+fkRGRnJ008/TVBQEGXKlGHChAnXTCpXvXp1Vq9eTZcuXUhISGD69OnX7HUvU6YM3t7erF27lvLly2M2mwkMDGTKlCmMHDmSwMBAOnXqREpKCtu2bePy5cuMHTv2ul7PzcrXLqQ///yT//77z/G1bt06AB566CEAxowZw1dffcWqVavYuHEjZ86cyfNJ9SIT6xWVuI209C/IMmccUAG5vY6999wovZTCvfJ7mT8hhBCiuMjnpWojIyO5fPkyERERhIWFZdk/ePBgGjVqxLvvvsvBgwdp1KgRjRo14syZM25tR0BAQJb50XYGg4EVK1awfft26taty5gxY3jjjTdc1u3Tpw8eHh706dMnS091Zu+99x5paWkMHz6c0NBQx9eoUaMcdXr16sWMGTN48cUXadiwITt27GDt2rWOjsu//vqLrVu3snv3bqpVq+Z0nJMnT97guwH33nsvY8aMYcSIETRs2JDNmzfzwgsvXPdx3njjDVq2bEm3bt1o3749d999N02aNMnxOW+++SYlS5bk7rvvpk+fPkRERNC4ceMcn+Ph4cHcuXN59913CQsLo3v37oD6/HzwwQdERUVRr149WrVqxeLFix3LpN1Kmp5xTYF8Nnr0aL7++msOHTpEbGwswcHBLF++nAcffBCAf/75h9q1a7NlyxbuvPNOl8dISUlxWlYgNjaW8PBwLly4kO0PU0FgsVhYt24dHTp0yDLnIl/F/w4nhqAC7uuk+UDlJdJTma7AXuPCJOUgnBoPRn+19Epm1gSwxUH5V/NsGF1O5BoXfXKNiza5vkVfcbrGycnJnDx5kkqVKl0zAMxRIVvVRNd14uLi8Pf3v+lh7u507NgxqlevztatW68ZRIqcFaRrnJyczLFjxwgPD8/ycxYbG0vp0qUdy81lp8AE5KmpqYSFhTF27Fief/55fvrpJ9q1a8fly5edhkxUrFiR0aNHM2bMGJfHmTx5sssEBMuXL8fHxyevmi+EEEIIIUSB4eHhQUhICOHh4Y55tOLWs1gsXLp0iRdeeIHjx4/z/fff53eThBulpqZy8uRJoqOjSUtLc9qXmJjIww8/fM2AvMAkdfviiy+4cuWKY45IdHQ0np6eWeYvlC1b1jGPwpXx48c7jfu395B37NhReshvxHX3kGvqS/MEjzJQYXa+9FQWRAX2Ghc2iX/D2RlgjQVjUIY5bRfBGABlx4FPo3xpmlzjok+ucdEm17foK07X2N5D7ufnd3M95IVMQeo9BdiwYQPt2rWjRo0afPLJJwU6HiksCtI1Tk5Oxtvbm3vuucdlD3luFJiAfOHChfzvf/9zOUfkenh5eeHl5ZWl3GQyFYpfvAWunYZ4MFi4usRZesCNDecg3TN9+JIGBn/wDFNDh7V4cMfrKWTDpXJS4K5xYRN4B3g8AxcWq+yvaSmgeYFPXShdMNYhl2tc9Mk1Ltrk+hZ9xeEaW61WNE3DYDBcM1lWUWJP8mV/7fmtbdu2FJAByUVGQbrGBoMBTdNc/k7J7e+YAhGQHz9+nB9//JHVq1c7ykJCQkhNTeXKlStOveSu1tcTechYAgxeYLOgMqdbydpbbgTPKmD0Ac2k5vbaEkFLcc/6lAnbrq6xrqeq3nevair7ZwEIvkQ+0Rz/qK0m/9k5saVB3DqwRIMpBPw7gKFA/MoXQgghhBDp8v+2ERAVFUWZMmWc1thr0qQJJpOJ9evXO8oOHDjAiRMnaN68eX40s3jyKAkepUHzUF8GHzD4pSfUMnJ1eHqgqmv0U8+zngev6jc/XD1hm1pyI2mvGo5sKq+2yXtVecK2m32ForBxfCb2qeXPvKqrbfI++UzYXVwKB++Bk6Pgv2lqe/AeVS6EEEIIIQqMfO8usdlsREVF0b9/fzw8rjYnMDCQyMhIxo4dS6lSpQgICODJJ5+kefPm2WZYF3nAqwZ41wd9u+ol1xNRveQGMASAHg+aMX1pcqt716fUbapnPC0GPCteXXda8wWTj1oX8+IS8Gmc9TxFaIi7yOBmPhPFxcWl6saEzaLm12smwAJp51Q5QFDffG2iEEIIIYRQ8j0g//HHHzlx4gSDBg3Ksm/WrFkYDAZ69OhBSkoKERERzJ8/Px9aWYxpBjU03HIK0q6A7g16WnqPuRG0YJW8zXoJLBdVb7m5rgrGb3Y4ecpBNUzdFHw18HK0SwNjMKQcUvUyLq0mQ9yLrhv9TBQXtjQ4Pz89GPfPcFPCCzCBHqf2l+wtw9eFEEIIIQqAfP+LrGPHjtkmOjCbzcybN4958+bd4lYJJ75NwacpXPwI9KSr5Zo3BD0KIePzpjfaGpMeUGeTGdRgBusFVc/OPpw5LSY9aDODnnx1iHvIBAnKC7Mb+UwUJ3HrIO1Ces94pp9BzQC6We2PWweB/8ufNgohhBBCCId8D8hFIXBxqRoGrCc7l+tJqtyzYt4MgTUGqt5tPVkNSc7Mlqz22xPHyXDmou96PxPFjSVa/Rxo2WT11EzqPbJkv3SkEEIIIYS4dSQqETmzpcHZWaCnoDJaG1H3cdITuukpED0LEvdCwlZI/kcFBO7gVUMNNU87D5lHUeh61sRx1zOcWRRO1/uZKG5MIek3myyu9+sWtd8kK1UIIYQQQhQEEpCLnMV+r+aHYw9+rEAaV9cl18F2AU4MgdPPw8kxKqOzOzJd2+evGwNV77Y1QSWOsyaox5kTx+VmOLOeWnyHMxcF1/uZKG78O6hVEfTkrDfGdBuQrPb7d8iX5ol8ptvUTVN33zwVQhRpNpuNQ4eP8NdfOzh0+IhjDei80K1bNzp16uRy36ZNm9A0jZ07d9KnTx/Cw8Px9vamdu3azJkzJ8/aJERekyHrImfJu7m67njmuf4ZH9vUkmTunq/t21Qdx56kzXoh+8RxMpy5eLiez0RxY/CA4GHq50+PU3PGNZPqGSf98x88TBK6FUeS7FIIcQN27tzNyk9Wc/TYcSypFkyeJipXqkivng/QoEE9t58vMjKSHj16cOrUKcqXL++0LyoqiqZNm7J9+3bKlCnD0qVLCQ8PZ/PmzQwZMgSj0ciIESPc3iYh8pr8VSZyll1vc9aKYItVf/ybKoDlhPvma/s2Vce5VuI4+3Dm5L1qznjGYev24czmusV3OHNRktvPRHFkz+dwfr5K4GZLVu+LRxkVjMuSZ8WPJLsUQtyAnTt3M2vOfOJi4wgKCsJs9iI5OYUDBw8xa858xowa5vagvGvXrgQHB7N48WImTpzoKI+Pj2fVqlW88cYbWVZmqlKlClu2bGH16tUSkItCSQJykTNT5dzVs5yDtIuAAYz+alisO5ef0gzXPo59OHP0tPThy8FqmLo710YXBUduPhPFVVBftbRZ3DqVwM0UooapS8948SPJLoUQN8Bms7Hyk9XExcZRvnw5tPTfHT4+3nh7l+PU6dN8supz6tWrg8Hgvt8dHh4e9OvXj8WLFzNhwgTHeVetWoXVaqVPnz4unxcTE0OpUqXc1g4hbiX531fkTE/MZcU0NQxST1a9cilHVc/lrZ6vbR/ObK6jeuwtp9TWXFd6gUTxYvBQS5uVHqi2EowXT5LsUghxA478e5Sjx44TFBTkCIrtNE0jqFQp/j16jCP/HnX7uQcNGsSRI0fYuHGjoywqKooePXoQGJh12uHmzZtZuXIlQ4YMcXtbhLgV5C80kZVuuzoUOHlvbp+EysKe/r2eBGkaGPzzqJE5kOHMQgih5CbZpfWCJLsUQjiJi43DkmrBbPZyud/Ly4tLly4TFxvn9nPXqlWLFi1asGjRIlq3bs3hw4fZtGkTU6dOzVJ3z549dO/enUmTJtGxY0e3t0WIW0ECcuEsc+KftCvX8eTMWZ1T8i+LrwxnFkIISXYphLgh/gH+mDxNJCen4OPjnWV/SkoKJk8T/gF50/ESGRnJk08+ybx584iKiqJq1aq0atXKqc6+ffto164dQ4YMcZpvLkRhI12G4ip74p+kvWAMUFnTb6qH2wYp+93WPCGEENfJnuwy7bxKbpmRPdmlV3VJdimEcFK1SmUqV6rIxUsX0TP97tB1nYuXLlGlciWqVsllrqHr1LNnTwwGA8uXL2fJkiUMGjTIaej83r17adOmDf3792fatGl50gYhbhUJyIWSOfGPwRc0I3gEA8YbPKgNbGnZn0/WwxVCiLxlT3ZpDFQJ3KwJoFvV1nJckl0KIVwyGAz06vkA/v7+nDp9msTERKxWK4mJiZw6fRp/f396PnS/WxO6ZeTn50evXr0YP348//33HwMGDHDs27NnD23atKFjx46MHTuW6OhooqOjOX/+fJ60RYi8Jv8DCyW7xD9GP9Vb7pgfrmX4PhesLuYWJWyDk6Pg5Bg4/bzanhylyoUQQriXJLsUQtyABg3qMWbUMGrWqE5cfDz//RdNXHw8tWrWyJMlzzKLjIzk8uXLREREEBYW5ij/9NNPOX/+PEuXLiU0NNTxdfvtt+dpe4TIKzKHXCjZJf7RNPCqAkn7VaI2TKpMtwIZe78zBun61TIt00dM1sMVQohbT5JdCiFuQIMG9ahXrw5H/j1KXGwc/gH+VK1SOc96xjNq3rx5luHyAJMnT2by5Ml5fn4hbhUJyIWSU+IfYwnwrAypJ9Lr6WoouvUcV4PvzL8wNcADPEOvFsl6uEIIkX8k2aUQ4gYYDAaqV6ua380QosiSgFwo9sQ/yXtVcJxx2LquA8ng3wbKPAG2OBVEnxgClrOo4Nua4WBGQAdTGfDvcLX4etbDlT8ahRBCCCGEEEWcdEMKJVeJfx692nNt8IDSQ8Hgmf58swrS7UPeDV4QPEzVs8vNerh6qqyHK4QQQgghhCgWpIdcXGVP/GNfh9x6QQ1jN9cF3zvg0kdX1yfXPFWPeqm+EPMNpF0A3aICdlMZFYwH9XU+vqyHK4QQQgghhBAOEpALZ64S/1jjIPoV14nYLKeg/CywXgJLNJhC1DB1g4uP1rWGxVvPq+Bf1sMVQgghhHALV4nRhBDu4Y6fLwnIRVYZE//oNrUkWU6J2C4tg/DZ107EZh8WHz0tfRh8sBqmbktWwbishyuEEEII4RZGoxGA1NRUvL2987k1QhRNiYmJAJhMphs+hgTkImfuTsSW07D4oH6y5JkQQgghhBt4eHjg4+PD+fPnMZlMt2SpsoLAZrORmppKcnJysXnNxU1BuMa6rpOYmMi5c+coUaKE4wbYjZCAXOQsN4nYLOcg9geI35LzkHU7WQ9XCCGEECJPaZpGaGgoR48e5fjx4/ndnFtG13WSkpLw9vZGy9yZJIqEgnSNS5QoQUhIyE0dQwJykbNrJWJLOQ6Wk3D2LUBXQbVHaddJ3TKS9XDFzdJtclNHCCGEyIGnpyfVq1cnNTU1v5tyy1gsFn755RfuueeemxpGLAqugnKNTSbTTfWM20lALnKWUyK2lFNgOaa+N3gDJsACaefUPHHIOSh3xV1BlgRrRVvCtqvTHjJm/Q/qL9MehBBCiAwMBgNmczYjHYsgo9FIWloaZrNZAvIiqqhdYwnIRc6yS8SWlgiph9Pr+IHmlf4EL8AEehycnw8le+c8fD0jdwVZEqwVbQnb1OfRVdb/6GkqR4FcZyGEEEIIUQhIl6G4NnsiNnMdsMWqpc7SzqCGqHuDwRP0FNCT1FbTALNamzxuXe7OYQ+ykvaCMQBM5dXWHmQlbLu1xxEFk25TN1vsWf8NvqAZ1dZUUY2IuLhE1RNCCCGEEKKAkx5ykTuZE7HF/QznPwAdsF4GMgZABsCsyizR1z525iDL1dJqF5eo8+c07NxdxxEFl7uz/gshhBBCCJGPJCoRuWdPxObbDLwboKLxRK4G4/YAyZZerqus69dyPUHWrTiOKLhyk/VfT1X1hBBCCCGEKOAkIBc3xrcNKiC30zJtUft921z7WLkJsmwpkLQbErZC8j+uhyRLsFb0Zcz674otWe03Bt7adgkhhBBCCHEDZMi6uDEJP2cq0F1WI+FnCPxfzse61tJqaefV17n5qpc+uyRt1zqOBGuFX05Z/3UdrOfBXFfVE0IIIYQQooCTHnJxYyzRqI+PD8694qQ/9lH7czOH3B5kpZ1XQVVGaZfTs6XbwCM45yRtOR3HHqx5VZdgrTCzZ/03BqqcANYE0K1qazmuyoP6SY4AIYQQQghRKMhfrSL3dJsaLp6wVQVBmiE9FjfiPGTdqDaaIXdzyLMNsuKvzvc21wSjX84ZtSVYKx5cZf23xaqecVnyTAghhBBCFCIyZF3kTua1vTGl90In4gjC0UhPu66WQDOWBv8OuTu+Pciyn8N6If34BtXr7VHCuX52GbVdHUfzVMFaUD8J1oqKzFn/jYFq5IPcbBFCCCGEEIWIBOTi2uxre6fFpGcwN6uAOyVjJT3TFjCFXV+AlDnISj0B5xaooequGMwq4M6cpE2CteLBnvVfCCGEEEKIQkoCcpEzp7W9K4CeqIYH2yygmdKHi9u/dFQvuacKxrFc/3rQGYMsYyAYvG4sSZsEa0IIIYQQQogCTgJykTP72t4GMyTvA1sCYFPDyfVUwBPVVW5EBeRGMASoIea2uJtbYkwyaouc6DYZBSGEEEIIIQo1CchFzqwx6istFrCCwRMVfKemrwWdlF4xw5B12yVIjgdT2ZtbYsyepC16WnpStuD0NcmTVTAuSdqKr8w5DbJbCk8IIYQQQogCTCIZkTODP1jjQLeAwVvF3bqFrB+djIndbCpYtyaAZ7WbO79k1BaZ2XMaJO1VS+DltBSeEEIIIYQQBZj0kIvc0Wzpw8/tc8XBKYGbYx55xqJENaTY+7abO/eNJGmT4cxFk1NOg4pXpzFovmpag+W4WgrPp7FcbyGEEEIIUeBJQC5yZotTw4Ft8WQJuK9FT1W9ljcbkMP1JWlL2AYXFqtz6ymgeake9tIDpEe9sLPnNDAFO+cUgOyXwhNCCCGEEKKAkoBc5Mzgr3q6nXrDcystPQv7LZSwDU6Ph9TTOLU57SykHIByr0pQXphZY9LnjJtVYj9bgppCoZnA4Jv9UnhCCCGEEEIUQBKQi5zptvRs6uD8cXExRD3rk0HzyZt2uTydDc7OgtTjgFH17GMErOo1pB6Hs7Oh8hIZzlxYGQPVdU07D2kXrmb9x6ACcmPp7JfCE0IIIYQQooCRqETkLGU/KlFberI2SP8+l2zx7m9TdpL/gaRdgAaaN2geahiz5qEeo0HSTlVPFE5eNcBYSg1bt8ahbhJ5q601DlIPgzFIlsITQgghhBCFgvSQi6xsaRC3DizRkPJv+lxdb8ACuhXnxG7XYD2fd+3MLHmvyu6u+bieX4wZ9ET3zWsX+Sz9M6jZv72RaRVCCCGEEELkHwnIhbOLS+H8fDUcWLcH3ulBuDFQfa/b1LxdPSnnYwEYy+Rte13KJjDTJWAr9FIOgvWSWnPcPmRdTwUMaukzj9JgvShJ3YQQQgghRKEgAbm46uJStY6zzaKSY2kmFezo8UCaSpRl8Esvt+bigBoY/fO2zRmXN9N8AK/0zOoeOA+t14EUlQzMXCdv2yTyjj2pm6k8mMpmTeqm29Ra9ZLUTQghhBBCFAISkAvFlqZ6xm0WlVndnvRMM4NNAz0OsKZnXM9N5nRNPdejVN61OWGbWpM65XB65m1PMHiBNVm1U/MCzahuHugpgA7eDaTntDCzJ3XTk9Xa4wY/5/22ZEnqJkR2Mt7ANAaqXAuS4FIIIYTIVxKQCyVunRoCbDBn/QPNHuRiAVJdPds1YyB4lHRnK69K2KZ689Ni0tekNqsgzRoDNqOqo6emD1PXVGBuCoeyo+UP0MLMq4Yarp68F0yZcgXouspZYK4rSd2EyMzVDUyvahDUX5aCFEIIIfKRRCZCsUSnzxk3Zd1nS0IF46j9mj8qyZudEfVRsn+Z1FYzgmc197dVt6k/LNNiwLOiGqqsGdXWqyZ4BKmeeY8QtTWFgn9bWYO8KNAMKoAwBoLlOFgT1AgIa4J6bAyEoH5y00WIjOw3MJP2qlwLpvJqm7xXlSdsy+8WCiGEEMWW9JALxRSSHsRYAK+r5TabykxuZzCroeC6EWwpqOHrVhWka1p6j3QaKiA3qWWo3D1EPOWg6uUxBbvOpm4KB1sslBmpAnIZmlm0+DaFkAlXe/usF1Rvn7muCsblposQV2W+gWn/nan5qlEmluNwcQn4NJbfkUIIIUQ+kIC8MMqLeYD+HVSG6rRzqF5w+/EsXM1argGG9Azr1gxleno9g3qeIQBMYWCLy5vkWvbEXprZ9X6DWQVpHqXAt5n7zy/yn29TFUDIfFghcnatG5jGYEg5JCsTCCGEEPlEAvLCJq/mARo8IHiYGr6ox4Fuz7KekrES2OK5GqDbcMx6MJVVQ8Xt2a6tiaCl5E1yrcyJvTKTxF7Fg2aQAEKIa8ntDUxZmUAIIYTIF9KdVJjY5wEm7lHLehn81DZpj3vmAQb1VUOBPcoAqaqH25FR3d4Tbv/eLn2tckMAGEuqNumo5Fpe1fMmuZY9sVfa+axri9sTe+XVuYUQojDJeAPTFbmBKYQQQuQr6SEvLOzzAFPPgp4GKedw9FBrvqon2x3zAIP6QsneKuu6JRoMpeDMs6AnpVdwteSZBgZvNYzdlqwC4rxMrmVP7BU9LT2RV7Dq5bkV5xZCiMJEViYQQgghCjQJyAuLlIOQtAtsMaig2BOV3dyqhphbDZC00z3zAA0eEPg/9X3yP2DwAWsSV3vIMzNCynHVFs0M3o0geEDeJteSxF5CCHFtcgNTCCGEKNAkIC8s0i6rdcJ1K2gZezk8VMZzPVHtT7vs/vPqKTiC/ywMQJoKiO1J35L/guQ78z4olsReQghxbXIDUwghhCiwJCAvLKxXVHZzzdN1plzdQyXusV5x/3kdy5u5Yi/3VMPWsahM7dHTVHFQX/e2JzNJ7CWEENcmNzCFEEKIAkkC8sLCWCI963maCsqdEqulr/2tmVQ9dzIEqGNnO1w9neaR/oedF2BSw+jPz1fz0Q3yMRNCiHwnNzCFEEKIAkdujRcWHiXVOuEYVII1PT1I1tPSE64Z1H6Pku49b9plrhmMg0o6Z6cZALMaQh+3zr3tEUIIIYQQQogiQgLywsKrBnjXB48SoPkB9kA8TT32KAHeDdyfKTflUC4rZhrSrplUkG6Jdm97hBBCCCGEEKKIkLHEhYU9U67lFKTFgDEEdT/FBtYE8MijTLnJ+3NZ0aLmsGMAzZg+390AphD3tkcIIYQQQgghioh87yE/ffo0ffv2JSgoCG9vb+rVq8e2bdsc+3Vd58UXXyQ0NBRvb2/at2/PoUO57bUtYuyZcr3rqKHqtni19a6ryvMkU65fLuult8cWB9ZY1XvvURr8O+RBm4RAjcBI/gcStqqtnl3iQSGEEEIIIQqmfO0hv3z5MnfddRdt2rThu+++Izg4mEOHDlGy5NV50NOnT2fu3Ll8+OGHVK5cmRdeeIGIiAj27duH2WzOx9bnk1udKfe6ksSl99hjUd8HdpGEbiJvJGy7uoSTnqoSHXpVU6NIZAknIYQQQghRSORrtPT6668THh5OVFSUo6xy5cqO73VdZ/bs2UycOJHu3bsDsGTJEsqWLcsXX3xB7969b3mbC4RbmSnXYLyOylZU9ncvMPqD5azqtZRldYQ7JWxTy+qlxYApGDQz6MmQvFeV59loESGEEEIIIdwrXwPyNWvWEBERwUMPPcTGjRspV64cw4YN47HHHgPg6NGjREdH0759e8dzAgMDadasGVu2bHEZkKekpJCSkuJ4HBsbC4DFYsFiseTxK7px9rYVuDamnANbLkYiGPzBIxgMZjAGgS0JEo9Bwn73J5orpArsNS5MdBucWwopSeBZFXQtfREAL7VEX+pJOLcMytXLlxtBco2LPrnGRZtc36JPrnHRJ9e46Css1zi37dN0Xc/FmlZ5wz7kfOzYsTz00EP8+eefjBo1infeeYf+/fuzefNm7rrrLs6cOUNoaKjjeT179kTTNFauXJnlmJMnT2bKlClZypcvX46Pj0/evRghhBBCCCGEEAJITEzk4YcfJiYmhoCAgGzr5WtA7unpSdOmTdm8ebOjbOTIkfz5559s2bLlhgJyVz3k4eHhXLhwIcc3Ir9ZLBbWrVtHhw4dMJlM+d2cq658B2eevnY9z1rgWfbqY2uCSvBW/lXpIU9XYK9xYZK4Dc5MBVOYyuafmW4FyxkIexF8bv2wdbnGRZ9c46JNrm/RJ9e46JNrXPQVlmscGxtL6dKlrxmQ5+uQ9dDQUG677Tanstq1a/PZZ58BEBKilsw6e/asU0B+9uxZGjZs6PKYXl5eeHl5ZSk3mUwF+oLZFbh2+lYBQwrp44Kz55EGnFfrj2s+YP0PfOqCb22ZQ55JgbvGhYlXSfVbyxAPBt+s+60Jar9XScjH91iucdEn17hok+tb9Mk1LvrkGhd9Bf0a57Zt+Rop3XXXXRw4cMCp7ODBg1SsWBFQCd5CQkJYv369Y39sbCxbt26lefPmt7StxZY1Jnf1kg9B0n5I3A2JfwLGvFkXXRRvXjVUNvW085B5cI+ug/U8eFWXURlCCCGEEKJQyNdoacyYMfz++++88sorHD58mOXLl/Pee+8xfPhwADRNY/To0bz88susWbOG3bt3069fP8LCwrjvvvvys+kFU16sy5y0l2v2jgNquTPt5s8nRE40g1razBgIluOqR1y3qq3luCqXG0FCCCGEEKKQyNch67fffjuff/4548ePZ+rUqVSuXJnZs2fzyCOPOOo888wzJCQkMGTIEK5cucLdd9/N2rVri+ca5DnJq3WZ087nrp5HUPq83vQh62kn4OIStWa6BEfCnXybqqXN7J936wX1eTfXVcG4LHkmhBBCCCEKiXwNyAG6du1K165ds92vaRpTp05l6tSpt7BVhUxersusJ+eunuYBHiWvPjYGQ8ohSDl469ZMF8WHb1N1syfloJpWYQxUw9Tl5o8QQgghhChE8j0gFzdJt6mewrQY8KwIWvqwcc0XTD5qGO/N9FSbb7t2HQCDX6bHZtVzmds56EJcL80gN3uEEEIIIUShJt1JhV3KQTVs1xR8NRi30zTnnuob4dMA8LxGJQ2MmVL525LVMGJj4I2dN7fyYt68EEIIIYQQQtwC0kNekOm2aw/JtcakzxnPZk79zfZUe9VQwbb1Qg6VPJx7yO3Zrs118zbbdV7NmxdCCCGEEEKIW0AC8oIqt8GmMVDt05PVMPXMbranOvUwGP3AlpA+nzxzxnVNlVkvgLGUOp/1fN5nu77mvPnnwegv84uFEEIIIYQQBZYE5AXR9SRps6/LnLxXzRnPOGzdHT3V1hiVsM1cCyxnwBqHWuLMoAJeQwCk/QfWWLAl3pps19eaN59yAE6OUm3DIj3nQgghhBBCiAJJAvKC5nqTtNnXZY6elr4Oc7Aapu6unmp7D7zBC8x10nvKLWp5M4NvehDuAWVHgUepW9MbndO8eVsMpF1RbfQOAmNZ92WcF0IIIYQQQgg3kjG8BU3q4etP0mZfl9lcB2yxYDmltua6Nx+A2nvg7euRG/3U8mbG9Dnj1vNgrgEBEeDbTPWk5/XQ8GznzeuQekrd1NA8AA/QjOrGgamiet7FJZL4TQghhBBCCFEgSA95QWONvbEkbe5clzlzMrmgfhD9St70wN+I7ObN2xLUl8ED0FUvvl3mmxmyXJYQQgghhBAin0lAXtAYA248SZs71mVO2AYXFqsh3noKaOlD1Uv0UEuLpRxWNwRuxVzx7GQ3b163gG5V3xsDVM94RrI2ushL9htZoLYetSWRoBBCCCGEyJEE5AWNZ7W8TdKWk4RtcHo8pJ7GKZt62lmVKC1sGngE5H/m8uzmzetpgFXdLPAs72J++S1aG10UP/ZVERKPA5Fwajz4VJREgkIIIYQQIkfSfVPQ2INNY6AKNq0JqtfXmpAefObREHHdBmdnQepx1HBvT9C803vrdVV+bq4Kwm/VXPGcuJo3r6eBR7B6jwyZgm77zQyv6nm7NroofuyrIiTtVSsPgNraEwkmbMvf9gkhhBBCiAJLesgLInuwaV+H/FYMEU/+B5J2AVp6IG7vXfYAjKAnQNJOVc/7Nvef/0a4mjefFgtnXy04891F0ZZ5VQRbet4Cgy8YKmZdFUEIIYQQQogMJCAvqNyZpC03kvemz1v3yTrUW9MAs0qYFvcj2OLyd8i6U9tczJvXbvHNDFF85bQEnyQSFEIIIYQQ1yABeUHmjiRt1013XWyzAGlw6WO48qUKcr2qFcw5srf6ZoYovrJdgi+dJBIUQgghhBA5kAhFKOY6KqjQU8gSlOupQCKggTEITOVVFvOCPEfWfjOjIMx3F0VXxiX4XJFEgkIIIYQQIgcSpQjFXAu86wM62BLTM5brqmfclqDqGALTh+Ya1RxZU0XV83dxiZpLW5DoNjXfPWGr2ha09omiwb4EX9p5lTgwI0kkKIQQQgghrkGGrAtFM0DZMWrZM8sZ1Suu6+lBhq56z81VgAzzZAvqHFn7ElQph9OHExfg4fWicMu8BB+hqtyaANb/JJGgEEIIIYTIkfyVKK7ybQrlXgX/NmAKBY9S6svgo4JtY4mszzGYVdBbUObIOi1BFVA4hteLws1pCb44VWaLU4kEQybITSAhhBBCCJEt6SEXzjInREu7pNYfN3i5rl+Q5shmXoLKnvVa8wWTjyxBJfKO/ecmYT8cOAzlXwXf2vI5E0IIIYQQOZK/FkVWGROiBUQUnjmy17MElRDuphmu/hxIVn8hhBBCCJEL8hejyJl9jqwxUPUwWxNAt6qt5XjBmiObmyWoCtLweiGEEEIIIUSxVgCiKFHgOc2RjQXLKbUtaHNkZQkqIYQQQgghRCEic8hF7mSeW24MLHjDcu1LUCXvVXPGMw5btw+vN9ctGMPrhRBCCCGEEMVeAYqmRIGXcW65uVbBCsahcA2vF0IIIYQQQhR70kMucs+aCpeWQOpJ8AyHUv3A6JnfrXJmH15vX4fcekENUzfXVcF4QRleL4QQQgghhCj2JCAvjHTbrR86/t80uPhR+vxsHdDg7AwIehRCJ+TtuXPi6r0oDMPrhRBCCCGEEMWeBOSFTcK2q72/eqrq/fWqpoZq51Xv73/T4MIHgA01y8GovteT0svJn6D8Wu+Fudatb5MQQgghhBBC5JJ0GRYmCdsgehok7QVjAJjKq23yXlWesM3957Smqp5xbKj7N/aPjCH9sU3tt6a6/9w5yY/3QgghhBBCCCHcSALywkK3qd7gtBjwrAgGX9CMamuqqIZmX1yi6rnTpSXpw9Q1wAqkZfiyqnI9WdW7VfLrvRBCCCGEEEIIN5KAvLBIOaiGZpuCnZfzAvXYGAwph1Q9d0o9iZozbv/SMnxlKE896d7z5iTjewFgjYe0y2oLefdeCCGEEEIIIYQbyRzywsIakz5P2ux6v8GsMopbY9x7XlO5DA8y3QhwBOWZ6+Ux+3thSwHLUbAl4JjfbvAFU5ja7+73QgghhBBCCCHcSHrICwtjoEpapie73m9LVvuNge49r29zrgbieqad9scaeIRAwlZI/ifvh4obA0FPU73gtjh1ft2otrY4Va6nuf+9EEIIIYQQQgg3kh7ywsKrhsognrwXTD7Ow9Z1Hazn1VrbXjXcfOJEFdhar9hP5qKOB5x9RS0rpnmBuQ6UHpB3Wd89q4FuUTch0IAU1S5dQ91j0tV+z2p5c34hhBBCCCGEcAMJyAsLzaCW84qeBpbjap60wayCUut5FTQH9XPPWtu2NIhbB5Zo0K1gCgW8wXoWNTTc0ShUAJym5nDbz512FlIOQLlX8yYoTz2c3gtvTS/IsBQbaapduk3Vk6XPhBBCCCGEEAWUBOSFiW9TCJlwde1t6wU1TN1cVwXj7gh+Ly6F8/Mh7UJ60KupDOZGX/C5B9JOq/XHNTOkngdigfSecc0DsKr526nH4exsqLzEPTcJMkq7nD5U3RPVY2/FkfEdE46h62mX3XteIYQQQgghhHCjGwrIP/roI9555x2OHj3Kli1bqFixIrNnz6Zy5cp0797d3W0UGfk2BZ/GKoO4NUb1jHvVcE/Qe3Gp6oG3WVTvu2YCLGBLhLQUYI8aBm4wQ9pFIE49T/MFgyn9IB6AEfQESNqp5pR733bzbcvIekUNSTd4ps+rt+JI6qYZwZaantTtinvPK4QQQgghhBBudN1R3IIFCxg7diydO3fmypUrWK1q2HCJEiWYPXu2u9snXNEMaii2bzO1ddcw9fPz04Nx//Qe7/Seb0MgoIE1AWwxYDkF1ouAnh6Me2Zqn6Z60PVkNefd3Ywl1M0CPS19JTYPFZhrHulT3NPUfmMJ959bCCGEEEIIIdzkuiO5t956i/fff58JEyZgNBod5U2bNmX37t1ubZy4heLWqWHqBnPWAF8zgOajeqJLPAjlXoFSfQAP1SPtiu4q+ZubeJQEj9Lp505KD8x1tSVJlXuUVvWEEEIIIYQQooC67oD86NGjNGrUKEu5l5cXCQkJbmmUyAeW6PQ54ybX+zUTqkfcqHrm/duDwRv09AznTnQgRfWSm+u4v61eNcC7fvpScP6oRG5Jaqv5q3LvBnmQcV4IIYQQQggh3Oe6A/LKlSuzY8eOLOVr166ldu3a7miTyA+mkPSecYvr/bpF7TeFqMfmWiooRldzzPU09b2eph6jq6A4L7Kc2zPOm8qqZHOeVVTw7VlFPTaVdV/GeSGEEEIIIYTII9ed1G3s2LEMHz6c5ORkdF3njz/+4OOPP+bVV1/lgw8+yIs2ilvBv4Ma5p12DjA5B7O6DUgGjzKqHqj9ZcfA6fFgOaOSqOk6jqzspnAoOzrvguLMGedtqWoeuXc992WcF0IIIYQQQog8dN0B+eDBg/H29mbixIkkJiby8MMPExYWxpw5c+jdu3detFHcCgYPCB6msqzrcaCnZ1nXLUCyCnaDh6l6dr5N1VrjFxdD0j6VxE0zg3cd1YOd10FxXmacF0IIIYQQQog8dl0BeVpaGsuXLyciIoJHHnmExMRE4uPjKVOmTF61T9xKQX3V1r4OuS1ZBbceZVQwbt+fUX4HxfaM80IIIYQQQghRyFxXQO7h4cHQoUPZv38/AD4+Pvj4+ORJw0Q+CeoLJXurrOuWaDVn3L+Dc894ZhIUCyGEEEIIIcR1u+4h63fccQd///03FStWzIv2iILA4AGB/8vvVgghhBBCCCFEkXbdAfmwYcN46qmnOHXqFE2aNMHX19dpf/369d3WuGJPt8n8aCGEEEIIIYQooq47ILcnbhs5cqSjTNM0dF1H0zSsVqv7WlecJWy7mkFc/z97/x4fZ13n//+P65q55pRM0iRt6blND2mhLeW0KMpyBhdZBVopyrIIuCdBxLKuLh52QUFkF7cUpZ4+SldZhHLSdf2qPxYFrAIrxYZyaJq2aUvSYw5NJpnM8bp+f1yZadIkbaadyWHyvN9uYTrXXJl5J1dD85rX+/V69XQQ988fnmZpIiIiIiIiUnA5B+QNDQ2FWIf01vWa2+081Q7WJLdzuROD2Fvu8SlfHJmgXBl7ERERERGRvMk5IFfteIE5tpsZT7WDbzYYhnvcKAErBMld0PIjt7P5cAbDXa9B8zr3TQEnDoYfAoth4o3K2IuIiIiIiByHnANygO3bt/Pggw9mu62fcsop3H777cybNy+vixuXEtvcberWpMPBeIZhgGcSxOvdTPVwdTbveg2a7oREE+AcPp7aD/E6dxa5gnIREREREZGc5Jxi/fWvf80pp5zC//3f/3Hqqady6qmn8uqrr7J48WKee+65QqxxfEl39NSMBwZ+3Ay4j6fbh2c9jg37V0NiF+C4texG0L11HPf4/gfd80RERERERGTIcs6Q//M//zOrVq3i61//er/jn//857n00kvztrhxyVPWE+zGgBDYXeAkwbDALAE75j7uKS/cGnrXiiebofsNwOgJxDNZey/gAacLumshtgWCpxRuTSIiIiIiIkUm54D8nXfeYf369f2O33zzzTz44IP5WNP45pvvdlOPbgQ75Qa82IDp1pGbXgid5TZUK4Qju7unu8DuPCIY72EYQACcqFtbroBcRERERERkyHLesj5p0iQ2bdrU7/imTZuYPHlyPtY0vhkmlLwH0m1gt4KTCXoN9366DUrOLkxDt0x39+633Ey9NQPMIGCD0+1m6o/kOP2PiYiIiIiIyDHlnCH/27/9W/7u7/6OHTt28L73vQ+A3//+99x///3ccccdeV/guOPY0PUqeCrATrrZZ2JuAG5UgGlB1/9B5XX5DcoH6+7umQjsBlJgd4PHC2Qy5Q4Qd+vdA4vztxYREREREZFxIOeA/Mtf/jLhcJhvfOMb3HnnnQBMmzaNu+66i09/+tN5X+C4k+my7psJ5gA15OloYbqsx7cO3N3dUwqeMKQPAYmehnM+cNLu+DMcCC4bvo7vIiIiIiIiRSLngNwwDFatWsWqVauIRCIAhMPhvC9s3OrTZd0As7Tv42YA0s3577Kebj/8uo7T940AX7XbtM3pBjsBRspdm+EBayac9JnhnYleCL0b2XnK3Rr9sf41iYiIiIjIqJZzQN7Q0EAqlWLBggV9AvH6+nosy2LOnDn5XN/407vLulHS//FCdVn3lLvPmzoIqWY3IM80kzNLwHsSpFvc9eG4gXtwMVR9fOzPID+ykZ3hcxvrFcPXJiIiIiIio1bOKcAbb7yRP/zhD/2Ov/rqq9x44435WNP4lumynjrYv2Ga40D6IPgX5L/Lur8GPJVuUJqOuE3kHI97m45Aag/4l8KUL8DEv4Up/wQzVo/9gHWgRnaeMrdr/L573cdFREREREQKIOeA/E9/+hPvf//7+x1/73vfO2D3dcmRYbqZWU85JHe5Y8ectHub3OUer7qhgNupbTc773T1+oiBk4LEVjj4LWh9FA48BI2rxnbAemQjO7PE3YZvloA1292+3vIj9zwREREREZE8yzmqMwwjWzveW3t7O+l0Oi+LGvdKzoIpX3Q7l9sdkGx0bwNL3OOFyErHt7qvQ+8u6r3Z7sg1w1s8WeTBGtmBe98z6XADPRERERERkTzLOSA/77zzuO+++/oE3+l0mvvuu49zzz03p+e66667MAyjz8eiRYe7dcdiMW699VaqqqooLS1lxYoV7N+/P9clj00lZ8HMNTBzNUz/mns788HCbRFPtbm14w7gmeAG3GYpmGEOtxqw3T8XSxa5dyO7gZgB9/F8N9ATERERERHhOJq63X///Zx33nksXLiQP//zPwfgd7/7HR0dHfzmN7/JeQGLFy/mf//3fw8vyHt4SatWreIXv/gFTz75JOXl5XzqU59i+fLl/P73v8/5dcYkwxy+cWLpQ25XddOHmyH3ujdOCkj3HHPcenI4PIatdxZ5rI0+yzSyG+4GeiIiIiIiIhxHQH7KKafwxhtv8K1vfYva2lqCwSA33HADn/rUp6isrMx9AV4vU6ZM6Xe8vb2dH/zgBzz22GNcdNFFADzyyCOcfPLJvPLKK7z3ve/N+bXkKDwT3CDbSQG+Xlu4nZ6Pngx4shFSBtnu69a0sZtF9te4DfRib4EV6rttPdNAL7Ak/w30REREREREOI6AHGDatGl87Wtfy8sC6uvrmTZtGoFAgHPOOYf77ruPWbNmsXHjRpLJJJdcckn23EWLFjFr1ixefvnlQQPyeDxOPB7P3u/o6AAgmUySTCbzsuZCyKxt5NZYBsY0sFt6tp/7ALMnHvcdPs0sAccCbHcmeWo3eKrAKYVR/P0dVPlfQ+wBiO11vw4z4GbG0y3gmQjl10MqjbtL4MSM/DWWQtM1Ln66xsVN17f46RoXP13j4jdWrvFQ12c4zpGztQbW3NxMV1cXs2fPzh576623eOCBB+jq6uKqq67iuuuuy2mRv/zlL+ns7GThwoXs3buXu+++m6amJt58801+/vOfc9NNN/UJrgHOPvtsLrzwQu6///4Bn/Ouu+7i7rvv7nf8scceIxQK5bQ+ERERERERkVxFo1Guu+462tvbKSsrG/S8IWfIb7vtNqZNm8Y3vvENAA4cOMCf//mfM23aNObNm8eNN95IOp3mr//6r4e8yMsvvzz751NPPZX3vOc9zJ49m/Xr1xMMBof8PL3deeed3HHHHdn7HR0dzJw5k8suu+yo34iRlkwmee6557j00kuxLGtkFhH9E+x/wB0DZpa4NeyZcWvZOvLeDMAC6ySY9eDY3trt2JDYBukOt6Gdb37eR8uNimssBaVrXPx0jYubrm/x0zUufrrGxW+sXOPMTu1jGXJA/sorr7Bu3brs/R/96EdUVlayadMmvF4vDzzwAA8//HBOAfmRJkyYQE1NDdu2bePSSy8lkUhw6NAhJkyYkD1n//79A9acZ/j9fvx+f7/jlmWN6guWMaLrLD8bvJ9zZ3PHt7m14Z4k2D31406CwzXlhvthAGYXGJ0wBr6/R+VbPCwvM1b+Lsrx0zUufrrGxU3Xt/jpGhc/XePiN9qv8VDXNuQU4L59+5gzZ072/m9+8xuWL1+e7Yr+4Q9/mPr6+txWeYTOzk62b9/O1KlTOfPMM7Esi+effz77eF1dHbt37+acc845odeRozhy3NpJn3ebvQGY5T3j0MI9t+W488kjPePRREREREREZKiGnCEvKyvj0KFD2Rry//u//+MTn/hE9nHDMPrVex/LZz/7WT70oQ8xe/Zs9uzZw7/+67/i8Xj42Mc+Rnl5OZ/4xCe44447qKyspKysjNtuu41zzjlHHdYLrfe4te63ex3vNQ4NwHDcZPlIcmx35Fq63R1P5q/J+1ZzERERERGRQhhyQP7e976Xhx56iO9///s888wzRCKR7DgygK1btzJz5sycXryxsZGPfexjtLS0MGnSJM4991xeeeUVJk2aBMDq1asxTZMVK1YQj8f5wAc+wNq1a3N6DTlBdgQ8YUh1gNONOxLNA04aSACW+7gdGf61db3Wd3u94XPHmFV93M30i4iIiIiIjGJDDsi/+tWvcvHFF/Poo4+SSqX4whe+QEVFRfbxxx9/nPPPPz+nF3/88ceP+nggEODhhx/m4Ycfzul5JY885T0fEyDVDHZXTy256W5T904EHPec4dT1Guy7121AZ00CIwBOzJ0pvu9emPJFBeUiIiIiIjKqDTkgP/XUU3nnnXf4/e9/z5QpU3jPe97T5/GPfvSjnHLKKXlfoIwwf42bdY69BYFTwImCk3Tryo0QJHdDYMnwdlh3bDcznmoH3+yerfSAUQJWyO0K3/IjCJ2h7esiIiIiIjJqDTkgB5g4cSJXXnnlgI9dccUVeVmQ9DIa6qMN090Cvu9eN/j2TAKzDOwYpHa766q6YXjXFd/qblO3Jh0OxrPrNdw1xuvd8zK18CIiIiIiIqNMTgG5DKPRVB9dcpa7BTyznnSzu57AEjcYH+71pNt7vieBgR83A+4a0+3Duy4REREREZEcKCAfjUZjfXTJWe4W8JHO2IP72obP/Z4YJf0ft2Pu48Nd1y4iIiIiIpIDBeSjzWiuj+49Dm0k9a5rt0J9t607DqQPDn9du4iIiIiISI7U8Wq0SWwben30eJWpa/eUu29QpLvcMWzpLvf+SNS1i4iIiIiI5Oi4Ipbt27fzpS99iY997GMcOHAAgF/+8pe89dZbeV3cuJTuOHZ9tJMYuD7asSG2BbpedW8du7BrHUmZuvbAYrA7INno3gaWaOSZiIiIiIiMCTlvWX/xxRe5/PLLef/7389LL73Evffey+TJk6mtreUHP/gBTz31VCHWOX54yo6vPno0NYEbLqOprl1ERERERCRHOUcu//zP/8w999zDc889h8/nyx6/6KKLeOWVV/K6uHHJN98NpFMH3Xro3jL10f4FfeujM03gut9yA3prhnubaQLX9drwfg3DKVPXXvIe91bBuIiIiIiIjBE5Ry+bN2/m6quv7nd88uTJNDc352VR41qu9dFHNoEzS8DwuLfWbDdz3PKj4t6+LiIiIiIiMgblHJBPmDCBvXv39jv+pz/9ienTp+dlUeNeLvXR8a3D1wRuPNWoi4iIiIiIFFjONeQf/ehH+fznP8+TTz6JYRjYts3vf/97PvvZz3LDDTcUYo3j01Dro9Ptx24Cl24euAlcLsZjjbqIiIiIiEgB5Zwh/9rXvsaiRYuYOXMmnZ2dnHLKKZx33nm8733v40tf+lIh1ihH4yk/3ARuIIM1gcvFeK5RFxERERERKZCcM+Q+n4/vf//7/Mu//AubN2+ms7OT008/nQULFhRifePX0TLSvTPnZhh88yD+NlihvtvWM03gAkv6NoHLxZE16pnnN0rc10vucmvUQ2eooZqIiIiIiEgOcg7IM2bOnMnMmTPzuRbJyGSkU+09teEBNwMeewua7gTvZEi3Hg7UPZWAp6fp2yR3m7odc4PxI5vA5SqXGvXAohP+0kVERERERMaLnKO0FStWcP/99/c7/m//9m9cc801eVnUuHa0runmBEjsgu5N4Akf3jqe6mmy55167CZwuRpKjbqTOPEadRERERERkXEm5wz5Sy+9xF133dXv+OWXX843vvGNfKxpfEtsGzgj7ThuoI3RsxU9CmYSDAusWZDcDd5KmPpFsCODN4HLVe8adaOk/+P5qFEXEREREREZh3IOyDs7O/H5fP2OW5ZFR0dHXhY1rqU7Bs5I213uBx43OI5vBQzAdLPl3oluIG+YUPKe/K3HX+PWrsfeKkyNuoiIiIiIyDiVc/p06dKlPPHEE/2OP/7445xyyil5WdS45ikbuGu6kwQnBU4USPfcT7jnpZoh3uBuG8/31nHDdBvJecrdGvV0Fzhp9za568Rr1EVERERERMapnDPkX/7yl1m+fDnbt2/noosuAuD555/nJz/5CU8++WTeFzju+OYPnJE2vG4AjtNzoombIXcAG5xuSBlu1/V8KznLrUXPdH1PN7tvGgSWuMG45pCLiIiIiIjkLOeA/EMf+hA//elP+drXvsZTTz1FMBjk1FNP5X//9385//zzC7HG8SWTkd53LyR2glnqHkt1AnbPSR7cYByy29ZJgxN3m8IVQslZfcet5atGXUREREREZJw6rrFnV1xxBVdccUW+1yIZJWfBhBVwcC3Em3qC7ExmPBOIO70+wSFbfRB/B0JLCrMuw9RoMxERERERkTw57jnkiUSCAwcOYNt9M7KzZs064UWNe12vwaGnwfBDYCFgunXimS7rmLjZcqfnvtf9MJIjt2YRERERERHJSc4BeX19PTfffDN/+MMf+hx3HAfDMEin03lb3LjUZw75nF415D5I7uXwtvUgGJnMuBfodjuzBxaPxKpFREREREQkRzkH5DfeeCNer5f/+Z//YerUqRi9x2DJ8XHsnjFmQOT5XnPIAbvT7aiO1+3Anm4DkkAKnCPqyEPLtKVcRERERERkjMg5IN+0aRMbN25k0SIFfnnR9ZqbEY/uAj4B+/8DnL3gnQp2Q8/scZvDXdUzjqghNywo/ws1WRMRERERERkjco7eTjnlFJqbmwuxlvGn6zW3m3r3W+DpGVfmKQM7DoltbjdzwwtGEPCCHe35RBMIuDXmRhA8k8BbBV3/V7gu6yIiIiIiIpJXOQfk999/P5/73Od44YUXaGlpoaOjo8+HDFGfWvHZYJa4xz2VuJfFASdN3xFnPU3cDAuCSyF4CoSWul3VrZkQrz+89V1ERERERERGtZy3rF9yySUAXHzxxX2Oq6lbjuJbe9WK99qK7kTdbeeOCaTATrgN3Zwkh8ebmWCaYJYd/jwzAOlmN6suIiIiIiIio17OAflvf/vbQqxj/Em3g5NwO6P35qR6AvQSNzgnBWTe5DDdLeqG3ROg92LH3MDdU174tYuIiIiIiMgJyzkgP//88wuxjvHHU96T+Y6BUXL4uOHFDbwBAuCf625RxwuJnWB3gOPpOdbDcSB9EAJLwF8zrF+GiIiIiIiIHJ+cA/KMaDTK7t27SSQSfY6feuqpJ7yoccFfA/75EHsLrNDh42YJGCGw28CsBG/vLe0zIfame9/BrTG3Y24w7imHqhvUZV1ERERERGSMyDkgP3jwIDfddBO//OUvB3xcNeRDZJhQ9XG3y3pyFzDVPZ6OgmmB4wOzp7O6GXADb/uQ2wDOOxnSrZBucbPsgSVuMF5yVmHXnJmXnm533wDw1+gNABERERERkeOUc0D+mc98hkOHDvHqq69ywQUX8Oyzz7J//37uuecevvGNbxRijcWr5CyY8sVec8gBOwKhs6DkbOh61W38lm7uG3iHzhj+wDgzLz2+raf23edm+Ks+Xvg3AkRERERERIpQzgH5b37zG372s59x1llnYZoms2fP5tJLL6WsrIz77ruPK664ohDrLF4lZ7kBdtc7ULcNZtwHJSe7AXbldYMH3oFFw7fGzLz05CHwlrrBODZ0v+ken/JFBeUiIiIiIiI5yjkg7+rqYvLkyQBUVFRw8OBBampqWLp0Ka+//nreFzguGGZPM7ZtfYNuwxzewHsgmXnpif1gJyG1F7BxG8+FwYlDy4/cNxW0fV1ERERERGTIco6gFi5cSF1dHQDLli3ju9/9Lk1NTXznO99h6tSpeV+gjLD4Vuh+w61Zt1vcANxJurd2C6RaobvWPU9ERERERESGLOcM+e23387evXsB+Nd//Vf+4i/+gv/6r//C5/Oxbt26fK9PRlqqDZL73fFsGPR9D8d2jyf3u+eJiIiIiIjIkOUckF9//fXZP5955pns2rWLLVu2MGvWLCZOnJjXxckokGp1s+HZYNzo9aCJG5TH3fNERERERERkyI57DnlGKBTijDPOyMdaZDSyO3v+4NA3GKfnfs/x7HkiIiIiIiIyFDkH5I7j8NRTT/Hb3/6WAwcOYNt2n8efeeaZvC1OBjGc88ANE/AA6Z6PTJbcwW3uZriPq6GbiIiIiIhITo5rDvl3v/tdLrzwQk466SQM48isqRTUcM8DDywGMwh2DDcAT3M4W+4BTDAD7nkiIiIiIiIyZDkH5D/+8Y955pln+OAHP1iI9cjRZOaBpw6Bp/c88LcKNw88sAiCp0LXy7jBeIYDpAAPBJeN/Hi20S6zqwHcW+/J2lUgIiIiIjLO5RyQl5eXM3fu3EKsRY4mMw88mZkHfoDD88BDbrfzQswDN0wIngJdvx/kBBuCCi6PKrOrIboL+AQ03gmh2YXb1SAiIiIiImNCzlHUXXfdxd133013d3ch1iODycwDTx3qaaBm4L6f0tNQLXWoMPPA7RS0/wJ3e7qF+1cm82G5x9t/4Z4n/WV2NXS/BZ6we8wThljProau10Z2fSIiIiIiMmJyzpCvXLmSn/zkJ0yePJk5c+ZgWVafx19//fW8LU56SbVBqhmcFOD0jCLrXcttuI/nex545Dn3ec1gzxb5tJutzzR7sxPu45HnoPzy/L72WJfZ1ZBqB99ssHt+VswSMGdDcldhdjWIiIiIiMiYkHNA/vGPf5yNGzdy/fXXq6nbcEofAjvO4TruTJY6U8sN2LZ7Xj4l9/UE4Jb7UgAYh/9sWG7Dt+S+/L5uMYhvdZvvWZPgyJ8TwwDPJIjXu+epBl9EREREZNzJOSD/xS9+wa9//WvOPffcQqxHBmOW4daMO2Qz4tBza+IG6nbPeXlkTXGzt06sJzt/ZJd1r/u4NSW/r1sM0u09nfADAz9uBiDd7J4nIiIiIiLjTs4B+cyZMykry3PQJ8dmd3A4I+4c8WAmQDZ7zsuj8KVght3AMZuVz7wZkAISYE50z5O+POXuNn8nBkZJ/8ftmPu4p3z411Zoma7y6Xb36/PXaFu+iIiIiMgRcg7Iv/GNb/C5z32O73znO8yZM6cAS5IBeSaA6XdrtoH+mWrA9Lnn5ZNhgjUN0i24GfpMRt4+fN+armBrIP4ad0Z87C2wQn0fcxxIH4TAEve8YpLpKh/f1rNDwOd+H9RVXkRERESkj5wD8uuvv55oNMq8efMIhUL9mrq1trbmbXHSi7cCvBMheRA3EPa7dchOpobcdB/3VuT3deNb3ee35kBqrxtgkQIMdyu2dwqQVB30QAzTDUL33es2cGOqezzdBem9bua46obiejMj01U+1d5TOx9wdwhkuspP+aKCchERERGRHjkH5A8++GABliHH5K+B4KngbHTnkDtRIO0Gc0YYTAuCy/Kfbc3UQftng78aUgfdDu+GH7yTABuSjaqDHkzJWW4Qmp1DDtgRCC1xg/FiCk6P7CqfaWRnlLg7BNRVXkRERESkj5wC8mQyyYsvvsiXv/xlqqurC7UmGUgm25psdAMezxSyW8fTXeAtULa1dx20WQLW5L6Pp4u4DjpfSs5yg9Cud6BuG8y4D0pOLr6gVF3lRURERERyklNEYFkWTz/9dKHWIseSybYGF7sdz+1O9za4pHBbgTN10KmDPdvje8nUQfsXFF8ddL4Z5uHvUbE2OBtKV3knod0UIiIiIiI9ct6yftVVV/HTn/6UVatWFWI9ciyZbOtwdbA+sg7aM8kNrOyYG4wXYx10IWS6joN76y3CDPl47iovIiIiInIccg7IFyxYwFe+8hV+//vfc+aZZ1JS0vcX709/+tN5W5wMwjCHd8tv7zro+DZ3BJrhczuEF1sddCFkuo5HdwGfgMY7ITS7+LqOH9lVvve29WLuKi8iIiIicpxyDsh/8IMfMGHCBDZu3MjGjRv7PGYYhgLyYlVyFgRPg8hzkNwH1pSeGeU5/xUaX3p3Hff0dFn3hIuz67h2U4iIiIiI5CTnaKqhoaEQ65DRbqDZ0u3/X/FlefPpyK7jds+IQLMEzNnF2XVcuylERERERIbshNKbTk+TL+PIjspSXDRb+viM167jw93nQERERERkjDqu35B/9KMfsXTpUoLBIMFgkFNPPZUf//jH+V6b5MqxIbYFul51bx07P8/ZO8trloDh6RmBNtsNuFp+lJ/XKjbjuet4ps9ByXvcWwXjIiIiIiL95Jwh/4//+A++/OUv86lPfYr3v//9AGzYsIF/+Id/oLm5Wd3Xj0c+OnAPtKXcP//Et5SP1yxvPqjruIiIiIiIHEXOaatvfvObfPvb3+b+++/nwx/+MB/+8If5t3/7N9auXctDDz103Av5+te/jmEYfOYzn8kei8Vi3HrrrVRVVVFaWsqKFSvYv3//cb/GqNT1Grx7u9t5G9zbd293j+fyHPvuhe63wFMG1gz3NrOlPJfngr6Z9u7NYMfHZ5b3RGmGu4iIiIiIHEXOAfnevXt53/ve1+/4+973Pvbu3Xtci/jjH//Id7/7XU499dQ+x1etWsXPf/5znnzySV588UX27NnD8uXLj+s1RqU+gXTYPda7A/dQAul8bynPvEHw7ipo+gIcWOsGlKmDA5+vLO/gMl3HPeVuA7d0l3s83dXThVxdx0VERERExrOcI4H58+ezfv36fsefeOIJFixYkPMCOjs7+au/+iu+//3vU1FRkT3e3t7OD37wA/7jP/6Diy66iDPPPJNHHnmEP/zhD7zyyis5v86oM1AgDbkH0rlsKT+WgTLt3klA2n2NVNsRX8MwZnkLUR8/HDJdxwOLwY64x+yI23VczfBERERERMa1nGvI7777bq699lpeeumlbA3573//e55//vkBA/VjufXWW7niiiu45JJLuOeee7LHN27cSDKZ5JJLLskeW7RoEbNmzeLll1/mve9974DPF4/Hicfj2fsdHR0AJJNJkslkzusrmPhWiO5yZ1PbFsm0eykytzAVojuh652jB7vxNkgBRik4nv6PO6WQ6nDP8xzl63dsOPAoxLvBNw8cAxwAP3iXQvxt6N4F/iCYwZ7Z0i3gmQjl10MqDaSP61txTNE/QetjEN8BThIMC/xzofI6CJ1emNfMJ98ymPIAyehWqNtBcsq9EOrpOj6a/k7KCcv8P2ZU/b9G8krXuLjp+hY/XePip2tc/MbKNR7q+gzHObK49dg2btzI6tWreeeddwA4+eST+cd//EdOPz234Ojxxx/n3nvv5Y9//COBQIALLriA0047jQcffJDHHnuMm266qU9wDXD22Wdz4YUXcv/99w/4nHfddRd33313v+OPPfYYoVAop/WJiIiIiIiI5CoajXLdddfR3t5OWVnZoOcNKUN+xx138NWvfpWSkhJeeukl3ve+9/Hoo4+e0ALfffddbr/9dp577jkCgUEahh2HO++8kzvuuCN7v6Ojg5kzZ3LZZZcd9Rsx7OJb3QZunjCYJSTTXp575wNcevKvsTwpt87YjsCM+46eIXdsaPq8u43bmtl327rjQPJdCJwM079+9Frl6Guw5ytgTXNr0Pu9ThoSTTDpE+DraRrnm1/Y+ufM19a9BXwn8LWNEslkkueee45LL70Uy7JGejlSALrGxU/XuLjp+hY/XePip2tc/MbKNc7s1D6WIQXk3/zmN/n85z9PSUkJF154IXv37mXy5MkntMCNGzdy4MABzjjjjOyxdDrNSy+9xLe+9S1+/etfk0gkOHToEBMmTMies3//fqZMmTLo8/r9fvx+f7/jlmWNrgvmPRlCs90Gbubs7GHLk8Iyk5DeC6ElUDKEEWiTr3drv9Pb3ZpxM+BuKbcPgq8cJv8V+Pp/T/rwV7h/G8zOw/XsvaW7wDIgvHT4xpvFtkCqDgLlYA6wHd4oh9QWsBvG1Mi1Ufd3UfJO17j46RoXN13f4qdrXPx0jYvfaL/GQ13bkALyOXPm8NBDD3HZZZfhOA4vv/xynwZsvZ133nlDeuGLL76YzZs39zl20003sWjRIj7/+c8zc+ZMLMvi+eefZ8WKFQDU1dWxe/duzjnnnCG9xqiW6cC971634zZT3ePpLjcYz6UDd6ZxWPM6N8B34mD43cZhE4c4hzwzoiv2Flih/tno9EHwL3az1l2vuuvz1xQ2M51u75mpHnDXYHcdriE3S9w3HtLNGrkmIiIiIiJj0pAC8n//93/nH/7hH7jvvvswDIOrr756wPMMwyCdHlpjr3A4zJIlS/ocKykpoaqqKnv8E5/4BHfccQeVlZWUlZVx2223cc455wza0G3MyQTSLf/pNngDd5t6aIkbjOfagdvI/se9NXJoD3DkGwS9M+3pg4DHbeK2+1PgxNwgOXgKVN1YuE7hnnJ3pFrqIKSa3YAcGzDdgNwzUSPXRERERERkzBpSQH7VVVdx1VVX0dnZSVlZGVu3bmXSpEmFXhurV6/GNE1WrFhBPB7nAx/4AGvXri346w6rkrMgdIbbTb1um1szPpRt6r1lxpUlD4G3FAgDtju+bN+9Qx+v1fsNgvg2N/ts+MA7DRI7obu2Z9yYAxiQ2g+xOph+X2GCcn8NeCoh+kfA49a2Ox73PYd0BNKHIHR24UeuiYiIiIiIFEBOY88CgQCPPPIIfr+f8vL8ZyVfeOGFfq/38MMP8/DDD+f9tUYVw+wJKrflvg08M888sR+cFMQPkM0iGyXu9vWWH7lB/1C3v4fOcJvOpdvBDMPeeyF1ABwTPH7AA6TBjkNiF+x/EKp/VMDt67a7VT2T8M/cDtR8TkREREREZIzIKYLyer188pOfxLbtQq1HchXfCt1vgN0OTgT3PZage+tE3KC6u9Y9b6gM022SVvIe9373G+5cck+o5/kN99YMuX/urnUbsOVbfCskG3u9Zp9FuseT7+b2tYmIiIiIiIwSOac0zz77bDZt2lSApchxSbW59dVOGgj0xK3JntuAezzV7J53PGJvuTXjZk+XdiflNlpzUj0n+N3HY2+d6FfSX/ZrAzwTwCwDs9S99UzoOecEvjYREREREZERlNOWdYBbbrmFO+64g3fffZczzzyTkpK+I7JOPfXUvC1OhiB9yN3OjQecTrDTZGu8DQ9upjzpnncinBQ43T2Bf+/nL+CogczXZvp6Xu/Iv65e982BE/3aRERERERERkDOAflHP/pRAD796U9njxmGgeM4OXVZlzzxTABMN1jGcP+MCTg9Weyk2xE9k1HOVWAxYIETzbxgr+dP9jx/qOe8PPNMcEecOSnA138UGyn38eP92kREREREREZQzgF5Q0NDIdYhx8tTzrErD8zjHw3mrwEz6M5Hz75OtqsaYLuPF6LTubcCvBPdbel0g+Mj21COhJuh9050zxMRERERERljcg7IZ8+eXYh1yInIbB13HNwO6zZusOxxs8on0o08sQ08pZDuBJI9z519YSDgPp7Y5jaCyyd/DQRPhehGsFPgdAEJ3A7yYTC9EFymsWciIiIiIjImHdecqh//+Me8//3vZ9q0aezatQuABx98kJ/97Gd5XZwMgR0BT9jdlm5Y7vZxo6Tn1urZrh52zzse6Xa3djt4MniqwPD3PK/fvR882X083Z7frwvcbu9VHwfrJPCUgG+uG3z75rr3rZOg6oYCjlsTEREREREpnJwjmW9/+9vccccdfPCDH+TQoUPZmvEJEybw4IMP5nt9ciyecvfDN7MnMHfASLu3njBYMw+fc7zPb/jcLuuhJRA6FYKL3dvQkp4A3Xf8z38sJWfBlC9CcAmQArvTvQ0udY+XnFWY1xURERERESmwnLesf/Ob3+T73/8+V111FV//+tezx8866yw++9nP5nVx456dgshzkNwH1hQIX+pu0+7NXwP++e7YMf8pbvM1J3k4W57aDf4lx7+tu/fzW7PdsWMZjgPpgxA4gecfipKzIHSGO2883e4G//4aZcZFRERERGRMO66mbqeffnq/436/n66urrwsSoCWR+Hg2p453LYbfHonwqRboOr6w+dltnXvu9cNvj2TwFMGdqznfvmJbevu/fzJXe7zmwH3+dMHT/z5c1lHvmvURURERERERlDOUVR1dTWbNm3qd/xXv/oVJ598cj7WJC2P9gTAB3q2i4fd29QB93jLo33Pz2zrDiwGuwOSje5tYEl+tnUX+vlFRERERETGoZwz5HfccQe33norsVgMx3H4v//7P37yk59w33338f/+3/8rxBrHFzvlZsbtZE8gnnnPxI/bST3iPh48ze06ntm+Xeht3do2LiIiIiIiklc5B+R/8zd/QzAY5Etf+hLRaJTrrruOadOmsWbNGj760Y8WYo3Fz7HdQBeg9TF3m7oZ6B/sGiY4FiT3wq6/B0/AzZz757vbykvOKuy2bm0bFxERERERyZucA/J4PM5VV13FX/3VXxGNRuns7GTy5MmFWNv40PUatPwnRHcBn4CWR8BJAD53zHdvThKcGGCD6QFrhns/9pa7lV3bx0VERERERMaMIe83PnjwIJdffjmlpaWUlZXx3ve+l7179yoYPxFdr7mBdPdb7ogy6Ll13O3oTrLXyQ443e4tJphlYHjALHG7n6fboeVHbrZdRERERERERr0hB+Sf//zn2bRpE1/5yld44IEHOHToEH/zN39TyLUVN8d2M+OpdvDNdgNrAO903HpxG+wobgAOkAYn5f7RsMAz8fBzGYbb/Txef3jru4iIiIiIiIxqQ96y/txzz7Fu3To+8IEPAPCXf/mXnHzyycTjcfx+f8EWWLTiWyG+DaxJbkCdYZrgn+MG1yTB7gYj0LON3QY84JvjntebGYDUQejerKZrIiJywmzbZvuOBiIdEcJlYebNrcY88t8eEREROSFDDsj37NnDsmXLsvcXLFiA3+9n7969zJkzpxBrK27pdjfINgL9H/PNcDPoiR09deNJ3IJyL/hmuo8fKXnQDcgPfNsN8I9s9iYiIjJEtbWbeWL9MzTs3EUykcTyWVTPmc21K5ezbNnSkV6eiIhI0cjprW6Px9PvvuM4g5wtR+Upd4NmJzbw494q8C2AKf8MU78IMx+E8F/0dFo/4nueOgSJbYAN1kS32Zun7HCzt67XCvzFiIhIsait3czqNWupq6snXBpm2rSphEvD1G2tZ/WatdTWbh7pJYqIiBSNIWfIHcehpqYGo9f26s7OTk4//fQ+W9haW1vzu8Ji5a9xM9ixt8AK9X3McSB9EIJLYOLNh7edWye5AXZyl1szbgYg3Q3xLYef0yx1/2yUuM+b3OU2ewudoe3rIiJyVLZt88T6Z4h0RJgxY3r23/xQKEgwOJ3GpibWP/ksS5cu1vZ1ERGRPBhyQP7II48Uch3jj2G628kzATZT3ePpLkjvdTPolde7teaZmvDQGe5os5b/dOvP0809XdU9bt25t+KI1zii2ZtmiIuIyFFs39FAw85dVFVV9XkDHsAwDKoqK9nRsJPtOxpYMH/eCK1SRESkeAw5IP/4xz9eyHWMTyVnHQ6wo7vcY3YEQkug5Gxo/bEbeDuJvjXhM9ccDtQTu926ce+kgV/DDLiBe7p9+L4uEREZkyIdEZKJJIHAwM1a/X4/ra1tRDoiw7wyERGR4jTkgFwKpOQsN/Pd9Q7UbYMZ94ERhX1fc0eiWZN6uqzHDteET/ni4UZtnnIw/e7jRkn/57djbjDvKR/er0tERMaccFkYy2cRi8UJhYL9Ho/H41g+i3BZeARWJyIiUnxUADYaGKZb/w3gm+/WfPeeT2543FtrtpvpbvlRz1Z1Dteipw72b/aWqUX3Lzj8/CIiIoOYN7ea6jmzaWlt6de01XEcWlpbmVs9h3lzq0dohSIiIsVFAflok9g28Hxy6F8TDodr0T3lbi16uguctHub3OUer7pBDd1EROSYTNPk2pXLCYfDNDY1EY1GSafTRKNRGpuaCIfDrLzmajV0ExERyRP9izrapDsGn08Obk24k+hbE56pRQ8sBrsDko3ubWBJ3+3tIiIix7Bs2VJW3X4LC2sWEOnsZO/efUQ6O1m0sIZVt9+iOeQiIiJ5pBry0cZTdng+eS414Zla9N5d2f01yoyLiEjOli1bytKli9m+o4FIR4RwWZh5c6uVGRcREcmznAPydDrNunXreP755zlw4AC2bfd5/De/+U3eFjcu+eb3mk8eBCcKThIMC4yQWxMeWDJwTbhharSZiIjkhWmaGm0mIiJSYDkH5Lfffjvr1q3jiiuuYMmSJf3mlMoJytSEN90J0T/2NG9zAMN9zJqmmnAREREREZEikHNA/vjjj7N+/Xo++MEPFmI90lu2w63e9BARERERESk2OQfkPp+P+fPnF2ItAm5GvOU/3U7pobP7b1lP7XbHnoXOUJZcRERERERkDMs5ovvHf/xH1qxZ028+qeRJ77FnpuEmxzMf5gBjz0RERERERGRMyjlDvmHDBn7729/yy1/+ksWLF2NZVp/Hn3nmmbwtblzKjD2z4xBvALsLsAETzBK3hvzIsWciIiIiIiIy5uQckE+YMIGrr766EGsRcMeeOSmIbQUcMH2AB0iDHXGPW5P6jz0TERERERGRMSXngPyRRx4pxDokw5rr1ow7CTDDHK4q8Pb8OeI+7lMdv4iIiIiIyFimrmCjTXKH28DN9AMxN1vuOO4tMTD87uOJbSO9UhERERERETkBOWfIAZ566inWr1/P7t27SSQSfR57/fXX87KwccWxDzdp634b8IB/AST2uNvUszXkYfBNc4+phlxERERERGRMyzlD/tBDD3HTTTdx0kkn8ac//Ymzzz6bqqoqduzYweWXX16INRa3rtfg3duh8U73/sEfQOogpA8BTv8POwGGTzXkIiIiIiIiY1zOAfnatWv53ve+xze/+U18Ph+f+9zneO655/j0pz9Ne7uytjnpeg323Qvdb4En7B6zKt0a8cRONwtu+MEodW/tTneruqcK/DUjunQRERERERE5MTkH5Lt37+Z973sfAMFgkEgkAsBf//Vf85Of/CS/qytmjg0t/wmpdvDNdkeagXtr+HtO6qkfN7KfNAILFRERERERkULIOSCfMmUKra2tAMyaNYtXXnkFgIaGBhxHAeOQxbdCfJs7wswwDh+3u4AUGEHcSDwBTrd7zFMG/vmQbjlccy4iIiIiIiJjUs5N3S666CL++7//m9NPP52bbrqJVatW8dRTT/Haa6+xfPnyQqyxOKXb3dFmRgC3Nryz53jEzZ4bATce980GM9jTeb3EfSzZqKZuPWzbZvuOBiIdEcJlYebNrcY0NTxgPNC1FxEREZGxLueA/Hvf+x62bQNw6623UlVVxR/+8Ac+/OEP8/d///d5X2DR8pS7zdlSByHVDKmUezzxLhg9nesNE8wy8JQe/jw7pqZuPWprN/PE+mdo2LmLZCKJ5bOonjOba1cuZ9mypSO9PCkgXXsRERERKQY5p5NM08TrPRzHf/SjH+Whhx7itttuw+fz5XVxRc1fA55Kd9t6qoPseyOGD7DdbeqZrHiG40D6oDsSbZw3daut3czqNWupq6snXBpm2rSphEvD1G2tZ/WatdTWbh7pJUqB6NqLiIiISLE4rv2dv/vd77j++us555xzaGpqAuDHP/4xGzZsyOvixo3eNeTgBuLgZsPtLnDSkO6C5C43M151g5s9H6ds2+aJ9c8Q6YgwY8Z0QqEgpmkSCgWZMX06kUiE9U8+m93JIcVD115EREREiknOUd3TTz/NBz7wAYLBIH/605+Ix+MAtLe387WvfS3vCyxa8a2QbnWbtHnCQM+WdVLgmQDWHDcwTzW7NeN2BwSWwJQvQslZI7fuUWD7jgYadu6iqqoK44g3MwzDoKqykh0NO9m+o2GEViiFomsvIiIiIsUk5xrye+65h+985zvccMMNPP7449nj73//+7nnnnvyuriilmnqZs0A70mQdN/YwL8ILD9gQ8KEyZ8E3yw3M+6vGdeZ8YxIR4RkIkkg4B/wcb/fT2trG5GOyDCvTApN115EREREiknO0V1dXR3nnXdev+Pl5eUcOnQoH2saHzJN3ZyYu2U907jNU+ret2Ng+iG4FEreA4FFCsZ7hMvCWD6LWCw+4OPxeBzLZxEuCw/zyqTQdO1FREREpJgc1xzybdu29Tu+YcMG5s6dm5dFjQv+Gne7euqg26ytNzVvO6p5c6upnjObltYWnCO+d47j0NLaytzqOcybWz1CK5RC0bUXERERkWKSc0D+t3/7t9x+++28+uqrGIbBnj17+K//+i8++9nP8slPfrIQayxOhglVH3cz5cldbtM2UPO2ITBNk2tXLiccDtPY1EQ0GiWdThONRmlsaiIcDrPymqs1k7oI6dqLiIiISDHJuYb8n//5n7Ftm4svvphoNMp5552H3+/ns5/9LLfddlsh1li8Ss5ym7S1/CdEd7nH7AiElrjB+Dhv3nY0y5YtZdXtt2RnUbe2tmH5LBYtrGHlNVdrFnUR07UXERERkWKRc0BuGAZf/OIX+ad/+ie2bdtGZ2cnp5xyCqWlpYVYX/ErOQuCp0Hb/w/qkjDpH6DiMjBzvjTjzrJlS1m6dDHbdzQQ6YgQLgszb261sqPjgK69iIiIiBSD4476fD4fp5xySj7XMj51vdYrQ/4JOPgd6Pqlu51dGfJjMk2TBfPnjfQyZATo2ouIiIjIWDfkgPzmm28e0nk//OEPj3sx407Xa7DvXki1g2eqe8wThthb7nHNHBcRERERESlaQw7I161bx+zZszn99NP7dTeW4+DYbmY81Q6+2WBb7nGzBMzZbmO3lh9B6Aw1dhMREREZJWzbVsmUiOTNkAPyT37yk/zkJz+hoaGBm266ieuvv57KyspCrq24xbdCfBtYk9y5470ZBngmQbzePS+waGTWKCIiIiJZtbWbs01Fk4kkls+ies5srl25XE1FReS4DPntvIcffpi9e/fyuc99jp///OfMnDmTlStX8utf/1oZ8+ORbgcnAUZg4MfNgPt4un141yUiIiIi/dTWbmb1mrXU1dUTLg0zbdpUwqVh6rbWs3rNWmprN4/0EkVkDMppf43f7+djH/sYzz33HG+//TaLFy/mlltuYc6cOXR2dhZqjcXJUw6GD5zYwI/bMfdxT/nwrktERERE+rBtmyfWP0OkI8KMGdMJhYKYpkkoFGTG9OlEIhHWP/kstm2P9FJFZIw57oIX0zQxDAPHcUin0/lc0/jgrwH/fEgddOvJ7Z43NOxO9376IPgXuOfJ4BwbYlug61X31tE/hCIiIpJf23c00LBzF1VVVRhHlBoahkFVZSU7GnayfUfDCK1QRMaqnALyeDzOT37yEy699FJqamrYvHkz3/rWt9i9e7fmkOfKMN3RZngg+kfofts93v22ex8PVN2ghm5H0/UavHs7vLsKmr7g3r57u3tcREREJE8iHRGSiSSBgH/Ax/1+P8lEkkhHZJhXJiJj3ZCbut1yyy08/vjjzJw5k5tvvpmf/OQnTJw4sZBrGz9Ug5+73iPjrEluLb4T08g4ERERybtwWRjLZxGLxQmFgv0ej8fjWD6LcFl4BFYnImPZkNOv3/nOdygrK2Pu3Lm8+OKL/N3f/R3Lly/v95GLb3/725x66qmUlZVRVlbGOeecwy9/+cvs47FYjFtvvZWqqipKS0tZsWIF+/fvz+k1Rq3M2DMnDYGzwNszh9w7FfxnAml37Jm2YPd35Mg4swQMj3trzXYb4el7JyIiInkyb2411XNm09La0q+ZseM4tLS2Mrd6DvPmVo/QCkVkrBpyQH7DDTdw4YUXMmHCBMrLywf9yMWMGTP4+te/zsaNG3nttde46KKLuPLKK3nrrbcAWLVqFT//+c958sknefHFF9mzZ0/OQf+olRl7hg3dr0Byh3s8ucO9b9uHx55JX7mMjBMRERE5QaZpcu3K5YTDYRqbmohGo6TTaaLRKI1NTYTDYVZec7XmkYtIzoa8ZX3dunV5f/EPfehDfe7fe++9fPvb3+aVV15hxowZ/OAHP+Cxxx7joosuAuCRRx7h5JNP5pVXXuG9731v3tczrNLtkDwA6VbABnqPP4tDchfYlRp7NpChjIxLN+t7JyIyjGzbZvuOBiIdEcJlYebNrVZwIkVl2bKlrLr9luwc8tbWNiyfxaKFNay85mrNIReR4zLkgLzQ0uk0Tz75JF1dXZxzzjls3LiRZDLJJZdckj1n0aJFzJo1i5dffnnQgDwejxOPx7P3Ozo6AEgmkySTycJ+EblIByEZBXwAJG1/n1sA7GjPeaNo3aOBUwp2CaRSYA7QXCUddx93SkfV9y7z929U/T2UvNI1Ln66xgN78823eeaZ/2bn7ndJJZN4LYs5s2ayfPmHWbLklJFe3pDp+ha/E73Gp5yyiH/9l3+mYecuOiOdlIZLqZ4zG9M09fdmlNDPcfEbK9d4qOsznCMLYYbZ5s2bOeecc4jFYpSWlvLYY4/xwQ9+kMcee4ybbrqpT3ANcPbZZ3PhhRdy//33D/h8d911F3fffXe/44899hihUKggX4OIiIiIiIhIRjQa5brrrqO9vZ2ysrJBzxvxDPnChQvZtGkT7e3tPPXUU3z84x/nxRdfPO7nu/POO7njjjuy9zs6Opg5cyaXXXbZUb8Rw27/f0DLD7N3k7af53Z/lUtnfRnL7PUmRNXNcNIdAzzBOBf9E+x/ANId4Klyt6nbMUi3gKcMTvoshE4f6VX2kUwmee6557j00kuxLGuklyMFoGtc/HSN+7Jtm3vu+Tfqt21n2rRpfeYzO47Dnr17WDB/Pl/64j+Nie3rur7FT9fYZdv2gFn+YqBrXPzGyjXO7NQ+lhEPyH0+H/PnzwfgzDPP5I9//CNr1qzh2muvJZFIcOjQISZMmJA9f//+/UyZMmXQ5/P7/fj9/bcxW5Y1ui6Y0QlmrN9hy4xj9T5udMJoWvdoUX42eD/ndluPbwM7AYYPSmrc+e2jeOTZqPu7KHmna1z8dI1d9du2s21HA2Vl5dh2/8kWZeEytm3fwe53G1kwf94IrPD46PoWv/F8jWtrN2fr4JOJJJbPonrObK5dubyo6uDH8zUeL0b7NR7q2kY8ID+SbdvE43HOPPNMLMvi+eefZ8WKFQDU1dWxe/duzjnnnBFeZR4EhlhTN9TzxqOSsyB0httNPd0OnnLw14BRHO/wioiMdpGOCMlEkkBggH4euG+St7a2EemIDPPKRGQgtbWbWb1mLZGOCFVVVQQCfmKxOHVb61m9Zi2rbr+lqIJykbFgRAPyO++8k8svv5xZs2YRiUR47LHHeOGFF/j1r39NeXk5n/jEJ7jjjjuorKykrKyM2267jXPOOWfsd1gHCC3D7azeP0t+WKDnvNFhVHbQNUwILBrZNYiIjFPhsjCWzyIWixMKBfs9Ho/HsXwW4bLwCKxORHqzbZsn1j9DpCPCjBnTsyUmoVCQYHA6jU1NrH/yWZYuXTzyv9+JjCMjGpAfOHCAG264gb1791JeXs6pp57Kr3/9ay699FIAVq9ejWmarFixgng8zgc+8AHWrl07kkvOn8AiN8Pb9TKQHuAEL5T82agJNsfL9iYRERm6eXOrqZ4zm7qt9QSD0/vVkLe0trJoYQ3z5laP4CpFBGD7jgYadu6iqqqqz88qgGEYVFVWsqNhJ9t3NIypEhORsW5EA/If/OAHR308EAjw8MMP8/DDDw/TioaRYcJJq6DpAMQbAU/PA5b7Z/90OOkzo2L7tbY3iYjIQEzT5NqVy1m9Zi2NTU1UVVbi9/uJx+O0tLYSDodZec3VyraJjAIqMREZnfQv5EgqOQum3wdlF4M13T1mTXfvT79vVDQmO3J7UygUxDRNQqEgM6ZPJxKJsP7JZwds5iMiIsVv2bKlrLr9FhbWLCDS2cnevfuIdHayaGGN3rAVGUV6l5gMRCUmIiNj1DV1G3cyjcm63oH6bTDrP6Dk5FGRGQdtbxIRkWNbtmwpS5cuHn19RkQkSyUmIqOT/qUcDQzT7Q4Oo65L+FC2NyUTSW1vEhEZ50zTZMH8eZxxxmksmD9PwbjIKJMpMQmHwzQ2NRGNRkmn00SjURqbmlRiIjJC9BNXTBwbYlug61X31jnxbeTa3iQiIiJSHFRiIjL6aMt6seh6DVr+E+LbwEmA4QP/fKj6+AnVog9le9PCmgXYts3rr2/SNkURERGRUUwlJiKjiwLyYtD1Guy7F1LtYE0CIwBODGJvucenfPG4g/JjddD1mB7aDh3irrvv0zg0ERERkTEgU2IiIiNPb4WNdY7tZsZT7eCbDWYJGB731poN6XZo+dEJbV8fbHvTSZMn4wAH9h8kXBpm2rSphEvD2XFotbWb8/d1ioiIiIiIFBllyMe6+FZ3m7o1CY7ogo5hgGcSxOvd8wKLjvtljtzeVFpayiPrHmX//gPMmHF4K3soFCQYnE5jUxPrn3yWpUsXawuUiIiIiIjIABQpjXXp9p6a8cDAj5sB9/F0+wm/VO8OuoZpsHPX7iGNQxMREREREZH+FJCPdZ5yt4GbExv4cTvmPu4pz+vLahyaiIiIiIjIidGW9bHOX+N2U4+9BVao77Z1x4H0QQgsOTznPE96j0MLhYL9Htc4NJGRlUqlePGlDRw80MykyRM5/7xz8Xr1v3wRERGR0US/nY11humONtt3LyR3uTXjZsDNjKcPupnxqhvc8/JoKOPQFi2sYd7c6ry+rogc29NP/4wfrnuUtrY2bNvGNE0qKiq4+cbrWbHiypFenoiIiIj00Jb1YlByljvaLLAY7A5INrq3gSUnNPLsaDLj0MLhMI1NTUSjUdLpNNFolMamJsLhMCuvuVoN3USG2dNP/4wHH1pLc3MLlteipKQEy2vR3NzCgw+t5emnfzbSSxQRERGRHsqQF4uSsyB0httNPd3uZsb9NXnPjPeWGYf2xPpnaNi5i9bWNiyfxaKFNay85mrNIRcZZqlUih+ue5REIklpaSlmz84Vn8+H17Lo7OzkkXWPcuWVV2j7uoiIiMgooN/IiolhntBos+Nx5Di0cFmYeXOrlRmXgrNtW3/vjvDiSxtoa2sj4Pdng/EM0zAI+P20trXx4ksbuPiiC0ZmkTJm6GdMRESk8BSQywnLjEMTGS61tZuzOzOSiSSWz6J6zmyuXbl8XO/MOHigGdu28VoD/6/da3mJJ+IcPNA8zCuTsUY/YyIiIsNDb3WLyJhSW7uZ1WvWUldXT7g0zLRpUwmXhqnbWs/qNWuprd080kscMZMmT8Q0TVLJ1ICPp5IpTNNk0uSJw7wyGUv0MyYiIjJ8FJAXEdu2qd+2nddf30T9tu3Ytj3SSxLJK9u2eWL9M0Q6IsyYMZ1QKIhpmoRCQWZMn04kEmH9k8+O27/75593LhUVFcTicWzH6fOY7TjE4nEqKyo4/7xzR2iFMtrpZ0xERGR4act6kdD2QhkPtu9ooGHnLqqqqvqM2gMwDIOqykp2NOxk+46GcVlG4fV6ufnG63nwobV0dnYS8PvxWl5SyRSxeByfz+KmG69XQzcZlH7GREREhpcy5EVA2wtlvIh0REgmkgQC/gEf9/v9JBNJIh2RYV7Z6LFixZV85tO3MHFiFclUkq6uLpKpJJMmVvGZT9+iOeRyVPoZExERGV5Kk4xxR24vzGQ0QqEgweB0GpuaWP/ksyxduljdcWXMC5eFsXwWsVicUCjY7/F4PI7lswiXhUdgdaPHihVXcuWVV/DiSxs4eKCZSZMncv555yozLseknzERERnNMhNAwN3VVbNg/piPcfTb2Rin7YUynsybW031nNnUba3HH5hKW1sb8XgCv99HRUUFLa2tLFpYw7y51SO91BHn9Xo12kxy1vtnLBic3uffFcdx9DMmIiIjJlOi+25jEx9ZfgX3fu0BZs6YPuZLdMf22wmi7YUyrpimybUrl5NMJHn11dfYsqWehoZdbNlSz6uvvkYykWTlNVeP+XdKRUZK5mcsHA7T2NRENBolnU4TjUZpbGoiHA7rZ0xERIZd3xLdUgDCpaVFUaKrf1HHuN7bCwei7YVSbLZt20FzS2u/Ls+2bdPc0sq2bTtGaGUixWHZsqWsuv0WFtYsINLZyd69+4h0drJoYQ2rbr9lTGchRERk7DmyRDcYdEuqgsHimACiLetjnLYXyniSSqX44bpHSafTlJeXYdsOjmNjGCamadDZ2cUj6x7lyiuvUL20yAlYtmwpS5cuZvuOBiIdEcJlYebNrVZmXEREhl2xl+jqX9YxTtsLZTx58aUNtLW1EfD7MQ0Tr8eD5bXwejyYhknA76e1rY0XX9ow0ksVGfNM02TB/HmcccZpLJg/T/+OiIjIiCj2El3961oEhmt7oW3b1G/bzuuvb6J+2/Yxuy1Exq6DB5qxbRuvNXD222t5sW2bgweah3llIiIiIlIIxV6iqz2dRaLQ2wszXQ0bdu4imUhi+Syq58zm2pXLWbz4ZI1XkmExafJETNMklUxh+Xyk0+nslnWPx0MqmcI0TSZNnjjSSxURERGRPDiyRLe3YijRVdRURDLbC/Mt09Uw0hGhqqqKQMBPLBanbms9X/6Xe+iOxejq6sK2bUzTpKKigptvvJ4VK67M+1pkfDv/vHOpqKjg4MFmYvE4jmPjOGAYYBgmjuMweZL7ppCIiIiIjH2ZEt3Va9bS2NTESZMnAdDdHWX/gYNjvkR3bK5ahs2RXQ1DoSCmaRIKBfGYHvbs3Udb2yG8Hi8lJSVYXovm5hYefGgtTz/9s5FevhQZr9fLZZdciOPYpFKpnmDcwHHouW9z6SUXaocG7vfj+d+8wOOPP8Xzv3mBVCo10ksSEREROS59S3S7AIh0dhXFBBD91ipHNVhXQ8exaWxqwnEcTNPE4/ViGiY+nw+vZdHZ2alu15J3tm2z/8BBJkyYQGdnF8lkEsdxAPD7fZSWlHDgYHN2t8Z49fTTP+OH6x6lpWc8nGmaVFVVaueKiIiIjFmZEt2t9duo2/IOX/zCZ6lZMH/M/86nSEmOarCuhi2trSSTyewPgGPb4PEAYBpGn27XF190wXAvW4pU5g2i6jlzCAT9tLa2Eo8n8Pt9VFZWEuuOjemxF/nw9NM/4xurv0Uikcges22b/fsP8I3V3wJQUC4iIiJjkmmazJtbTd2Wd4pmHOfY/wqkoAbrapiIJ7LbhXEcbMchmUySSqdxHEfdrqUger9BlNmv0ft2rI+9OFGpVIrvfu8R4nH359Xj8eD1evD0vFmWSMT57vcf0fZ1ERERkVFCGXI5qiO7Gma2rfv8PsAhnU5jGAbd3d2AGxR5PG4AoG7Xkm+ZN4jefbeRffv392xZd5u6WZbFlJNOIhAMjNmxFyfqhRc20HboEIZBn1KRzP1UKklb2yFeeGEDl1xywcgtVEREREQAZcjlGDJdDcPhMI2NTbS0ttDW1gaO29W693mmaWKYJql0mu5YjNKSEnW7lryaN7car9fL7ncbSSTcYBzAcSCRSLL73Ua8Xu+YHXtxot7ZssWtGTcG/l+7aZjYts07W7YM88pEREREZCAKyOWYli1byoeu+Ati8TjbtzdQt7We7TsaDtePO062sZbjONi2jWEYnDTlpKKo65DRw7Zt3n236ajnNL7bhG3bw7Si0SUQCLh/MAY5wTjiPBEREREZUYqW5Jhqazfz81/8Cr/Px7y5c1lYU8OM6e72dZ9lYVkWjmOTTqdxHBu/38fMGdNJpVJs39Ew4HPatk39tu28/vom6rdtH7cBlOTmty+8RFdXZ5+O/70ZhkFnVye/feGlYV7Z6HDuuefg9XqwbcdttNiLY9vYtoPX6+Hcc88ZoRWKiIiISG+qIZej6j2HfObMGdlAyDQNvF4PjuNQWhpm4sRKkokEPr+PqspKbNth7959AzbXqq3dzBPrn6Fh5y6SiSSWz6J6zmyuXbl8TM8QlMLb8s5WbNtxexQYBg7urgzDMDAA23H7Gmx5ZyuXXnLRSC932C2sWcCCBfPZsmUradvGxE2KO5B906tmwQIW1iwYyWWKiIiISA9lyOWoBptD7vV6MU0Tr8dLd3eUUCjItGnTmFg1EcMwicfjWD6rX3Ot2trNrF6zlrq6esKlYaZNm0q4NEzd1npWr1lLbe3m4f4SZQwJBN2t1k5PJzfDMNzeBYYBhpEtncicN96Ypsk//eOnmTp1Cqbp1ounbTs7i3zq1Cl89h9vUymJiIiIyCih38rkqAabQx4KhQgFQ6TTadJpm85IJ+3t7XRFu7Adm5bWVuZWz+nTXKt3tn3GjOmEQkFM0yQUCjJj+nQikQjrn3xW29dlUH9+7vvwet2RepngOyPTv8Dr9fLn575vhFY48pYtW8o9X/kSF114HlOmTKaiYgJTpkzm4ovO556vfEm7UMYglfiIiIgUL21Zl6PqPYc8FApmjxuGwbRpU6jbuo1YLMaOhp2A4Y5X8niZNm0qK6+5uk8mbrBse+b5qior2dGwk+07Glgwf94wfYUyltTUzKemZj7vvFNHKpXC4/Fg9GTGMyP4MueMZ8uWLWXp0sVs39FApCNCuCzMvLnVyoyPQSrxERERKW767UyOKjOHvKW1hXQ6TXNzM0179tDc3Ew6nSaRSACQSqVJpVIkkym6YzHaOzr6Pddg2fYMv99PMpEcsO5cBNwt2Z+94zamTZ2Cx+PBtt2/d7adxuPxMG3qFD57h7Zkg/u9WjB/HmeccRoL5s/T92QMUomPiIhI8dNvaHJUmTnkiUSSV//vNbbU1dPQsIstdfW8+dY72a2TmTnkmXrelpZWHvjGN/tsreydbR/IYHXnIr0tW7aUr37lS1x80flMnTqFysoKpk6dwsUXX8BXtSVbioRKfERERMYHbVmXY9q2bQctLa19fvHrXb9rGAYej+fwYz2NpLbW11O3tZ6TFy0EDmfb67bWEwxO77Nt3XEcWlpbWbSwpk/duchAtCVbit1oKPGxbVs/YyIiIgWmgFyOKpVK8cN1j5JOpykvL3PnGzs2iUSCeNzdru4G5w7ugCUwTBMTh1QqzYYNL2cD8ky2ffWatTQ2NVFVWYnf7ycej9PS2ko4HO5Xdy4iMh4NpcSntbWtYCU+ql0XEREZHgrIpZ/eWZG336mjra2NgN+PaZiYHgAPiUSyz+ek07Y7C7rng54EeiwW63PesmVLWXX7Ldlf9Fpb27B8FosW1rDymqv1i54MiYIFKXaDNdTMKGSJT6Z2PdIRoaqqikDATywWz9aur7r9Fv2ciYiI5IkCcunjyECns6uLRCKJZVl9zvN6vcTjh2vBe29nz3S9Nk2Tkxct6vca2m4sJ0LBgowHI1Xic2TteuZ1Q6EgweB0GpuaWP/ksyxdulj/zxYREckD/WsqWQN39C3FcRyi0SipVCp7ruUd/L2cTH15aWkpF1xw7oDnqAO0HA81uho6za4e2zIlPuFwmMamJqLRKOl0mmg0SmNTU8FKfHKpXRcREZETpwy5AINnRaZOnUJj0x4SiQTR7m4Cfj+2Y2MYJoYBvXq79VNZMUGBtuTVaGh0NRZoS39xGIkSn5GuXRcRERlvFJALMHigY5oms2bOYPuOBlKpFJ29suRHY5omLa2tbNu2g5qa+YVatowzChaOTVv6i8twl/iMZO26iIjIeKT0pQDHDnSco6XC6TuHvOcT6O6OsaVua76XKuOYZtkfnbb0F6fhLPHJ1K63tLb0+/9+pnZ9bvUcjacUERHJEwXkAgwe6Ni2za5du4/5+aZp4vF48Hg8WF4vDpBOp3H0i7/kkYKFo1P9r5yokapdFxERGa/0L6oAgwc6LS0tJIewTb13xi3z2YYBoZJQvpc6JKlUiud/8wKPP/4Uz//mhT4N6WTsUrBwdEPZ0p9MJMf1ln45tkzt+sKaBUQ6O9m7dx+Rzk4WLaxRyYOIiEieqYZcgMOBzuo1a2lsaqKqstKtx21rG9Ln27adDYIywbnP56OioqJgax7M00//jB+ue5S2trbsuioqKrj5xutZseLKYV+P5Jdm2Q9O9b+SLxpPKSIiMjwUkEvWQIFOZ2fnkD8/E4gbBliWj0mTJlFeXlao5Q7o6ad/xoMPrSWRSBLw+/FaXlLJFM3NLTz40FoABeVFQMHCwEZqdrUUp0ztuoiIiBSOAnLp48hA5zvfe4SNG/80pM/1er14PCahUAifZbH4lEXD+ot/KpXih+seJZFIUlpaitkTjPh8PryWRWdnJ4+se5Qrr7wC71HmqMvYoGChv8F2usTjcVpaW8f9ln4RERGR0UZRifTTO9CZNLFqSJ9TWlrKnNkzsR2baFc34bLh/8X/xZc20NbWRsDvzwbjGaZhEOjZgv/iSxu4+KILhm1dIsNJW/pFRERExg4F5HJUO3fuGtJ5iUScrq6o+4v/opH5xf/ggWZs28ZrDfzX2mt5iSfiHDzQPKzrEhlu2tIvIiIiMjYoIJejiicGnvd8pIkTq/jCnf84or/4T5o8EdM0SSVTWD6fO3bNsTEMdyRbKpnCNE0mTZ447GsTGW7a0i8iIiIy+ikgl6MKBvp3ah7IhPJyzjjjtMIu5hjOP+9cKioqOHiwmVg8juPYOI7bZM4wTBzHYfKkiZx/3rkjuk4RERERERHQHHI5hksuuSCv5xWS1+vlsksuxHFsUqlUTzBu4Dj03Le59JIL1dBNRERERERGhRENyO+77z7+7M/+jHA4zOTJk7nqqquoq6vrc04sFuPWW2+lqqqK0tJSVqxYwf79+0doxePPjBl9RycNxDAMZsyYPkwrGpxt2+w/cJAJ5RPw+Xw4joNt2ziOg9/vY8KECRw42JwdzyZjm23b1G/bzuuvb6J+23ZdVxEREREZc0Y0Vfjiiy9y66238md/9mekUim+8IUvcNlll/H2229TUlICwKpVq/jFL37Bk08+SXl5OZ/61KdYvnw5v//970dy6ePG+eedS2VFBS2trYOeU1lZkfM2cNu2895wavuOBhp27qK6eg6BgJ/W1lbiiQR+n4/KykpisRg7GnayfUeDamvHuNrazdku4slEEstnUT1nNteuXK4u4iIiIiIyZoxoQP6rX/2qz/1169YxefJkNm7cyHnnnUd7ezs/+MEPeOyxx7jooosAeOSRRzj55JN55ZVXeO973zsSyx5XTNNk8kmTaG1rA8BxnOxjmcz55MmT2L6jga7OriEF14UKpiIdEZKJJIGAH9M0mTixb/M2v99Pa2sbkY7Icb+GjLza2s2sXrOWSEeEqqoqAgE/sVicuq31rF6zllW336KgXERERETGhFFVTNve3g5AZWUlABs3biSZTHLJJZdkz1m0aBGzZs3i5ZdfHjAgj8fjxOOHO4N3dHQAkEwmSSaThVz+CcmsbbStcfuOBgzDYG71bA4cOEgimco+5vNZlJeVceDAAf7lX+/BNEy8lsWcWTNZvvzDLFlySr/ne/PNt3n429+nM9JJZWUlfr+feDzOjoYGvvnwd7n1k3874OcNRagkRDAUJJVKEQz2b0aXSMQJhoKESkIj8n0erdd4LLFtm6ee+imx7m5mz56VfVPIsixKS0vYs3cPTz39MxYtqhmRTv+6xsVP17i46foWP13j4qdrXPzGyjUe6voMp3fKcwTZts2HP/xhDh06xIYNGwB47LHHuOmmm/oE2ABnn302F154Iffff3+/57nrrru4++67+x1/7LHHCIVChVm8iIiIiIiISI9oNMp1111He3s7ZWVlg543ajLkt956K2+++WY2GD9ed955J3fccUf2fkdHBzNnzuSyyy476jdipCWTSZ577jkuvfRSLMsa6eVkbd/RwL1fe4BwaSmBQIDu7iipVAqP10Pju3vp7Irg9XpZMH8eoZBb9+84Dnv27mHB/Pl86Yv/lM1U9n6ugTLY3d1RIp1dfPELn2Xe3OrjWm82A9/ZSWVFBT6fn0QiTmtbG6WlpSeUgT9RhbrGb775Ns8889/s3P0uqWTymLsUxrLa2s184z++yZQpUwbMgKfTafbv388/3nHbiGxbHy0/x//93/8fP/6vJzh0qA3bdjBNgwkTKvjrv7qWD3/4gyO2rmIwWq6xFIaub/HTNS5+usbFb6xc48xO7WMZFQH5pz71Kf7nf/6Hl156iRkzZmSPT5kyhUQiwaFDh5gwYUL2+P79+5kyZcqAz+X3+/H7/f2OW5Y1qi9YxmhbZ82C+cycMZ3aN94klUrR3d2NbTuAQyKRxDRNJkwI4vP5SafT2c8rC5exbfsOdr/bmG2gFu2K0h3tpmLChD7nZng8Xrqj3US7osf9PTj99GXcduvf96tRnzd3LiuvuXpU1Bbn8xrX1m5mzTe/c7ieesIEYrE4b2+p491vfqfo6qnLJ5SDYdDVFSUU6v+mTjQaBcOgfEL5iP4cjeTP8dNP/4wHH1pLIpEk4PdjWRapZIq9e/fx4ENrAVix4soRWVsxGW3/r5b80vUtfrrGxU/XuPiN9ms81LWNaEDuOA633XYbzz77LC+88ALV1X2zomeeeSaWZfH888+zYsUKAOrq6ti9ezfnnHPOSCx53DFNkzPPOI3fbfhD9hd8v98iHo+TTqdxHIfysrJ+o9EGaqAWLgtj+SxisfiAwVQ8HsfyWYTLwie05mXLlrJ06eK8d3EfbWzb5on1zxDpiPQZTxcKBQkGp9PY1MT6J59l6dLFRfO1z5tbTfWc2dRtrScY7DuSz3EcWlpbWbSw5rh3WIx1qVSKH657lEQiSWlpKWbP98fn8+G1LDo7O3lk3aNceeUVeL2j4v1YERERkXFtRH9Lv/XWW3n00Ud57LHHCIfD7Nu3j3379tHd3Q1AeXk5n/jEJ7jjjjv47W9/y8aNG7nppps455xz1GF9mNi2zcbXN1FWFmZCeTkODvF4AnCDda/HQ3tHB0e2IhgouM4EUy2tLf3OzwRTc6vn5CWYMk2TBfPnccYZp7Fg/ryiCUh7y4x5q6qq6veGiGEYVFVWZse8FQvTNLl25XLC4TCNTU1Eo1HS6TTRaJTGpibC4TArr7m6KK/3ULz40gba2toI+P3ZYDzDNAwCfj+tbW28+NKJlQaJiIiISH6M6G+t3/72t2lvb+eCCy5g6tSp2Y8nnngie87q1av5y7/8S1asWMF5553HlClTeOaZZ0Zw1eNLJuibPm06ixYtYNasGUybehJzZs+isrICwzSIRqPuVuEegwXXCqbyq/eYt4H4/X6SiWTRjXlbtmwpq26/hYU1C4h0drJ37z4inZ0sWlhTdFv0c3XwQDO2beO1Bs5+ey0vtm1z8EDzMK9MRERERAYy4lvWjyUQCPDwww/z8MMPD8OK5EiZoC+RiLNr9266urqwbRvTNPH73HnfiUSCzs4uAoEA8XicltbWQYPrTDCVqfFubW3D8lksWlgzamq8x4rhKgEYjcZLWUKuJk2eiGmapJIpfD5fv8dTyRSmaTJp8sQRWJ2IiIiIHElFhHJU4bIwqXSa+m3bSSaS9H4LJZFI4DE9eL0WrW2tNLe04Pf7WLpkMR+9dsWgwbWCqfwY7/XUmbIEOez8886loqKC5uYWvJbVZ9u67TjE4nEmTazi/PPOHcFVykixbVv/3xURERllFJDLUVXPmU1XVxexWBzDMPB4PBiAgztiKp1OYqSSxONxHMfBNE021W7mrDNPP2q2O1/BVCqV4sWXNnDwQDOTJk/k/PPOHTfNqjIlAKvXrKWxqYmqykr8fv8xdylI8fJ6vdx84/U8+NBaOjs7Cfj9eC0vqWSKWDyOz2dx043Xj5ufETmstnZzv+kT1XNmc+3K5dqZJCIiMoL0W5kc1fYdDcRiMQzDyGZgjyw0cBxwHBtwg/QDBw7y799YAxR2vNLTT/+MH657lLa2tuw2+oqKCm6+8fpxM9ZJJQBypMzf/czPRjwRd7epT6zipnH0syGH1dZuZvWatYfHIwb8xGJx6rbWs3rN2nHfe0FERGQkKSCXo6qrqyeZTBEMBkkmkz3zwx3AOGoPgGQyxbce/l7BxisdOWs5kwVsbm4Zd7OWVQIgR1qx4kquvPKKcbt7RA4bj+MRRURExhL9diZD5jgOtm1n7vV7PJtB7wnUOyIRnv/NC3zgskvyug7NWu5P9dRyJK/Xy8UXXTDSy5ARlst4RP0/REREZPjp7XA5qkULa7AsL9FolFQqddRzHcfBcZw+v/S9+spreV+TZi2LiAzNeB2PKCIiMlYoIJejmjt3Tq+s+ND03sp+7MF2udOsZRGRoek9HnEgxTweUUREZCxQQC5HtbV+26C/yA3FOeecncfVuHrPWh6IZi2LiLgy4xFbWlv69f3IjEecWz2naMcjioiIjHYKyOWofvrTX5zQ55/35+/L00oOy8xajsXj2Ef8gpmZtVxZUaFZyyIy7mXGI4bDYRqbmohGo6TTaaLRKI1NTRqPKCIiMsL0L7Ac1bbtO07o83+34Q95WslhmVnLPp/bwC2RSGA7NolEgs7OTs1aFhHpJTMecWHNAiKdnezdu49IZyeLFtZo5JmIiMgIU8QiR2UdZ1BrGO5YtC3vbOXSSy7K86o0a1lEJBcajygiIjI6KSCXozrttKW8tvFPQzrXNM0+o88cxyEQDAx4rm3bJ/yLoWYti4gMncYjioiIjD6KXOSoLrjgz/nBD3/crxnQkUzTxOPxAG4wnk6n8Xq9/Pm5/WvIa2s388T6Z2jYuYtkIonls6ieM5trVy7PeeukZi2LiIiIiMhYpYBc+kmlUtmsc9XESmbNnMmu3buP+jkGh7Pi6XQawzCoqZlPTc38PufV1m5m9Zq1RDoiVFVVEQj4icXi1G2tZ/WatXmrZ8xHBl5ERERERKSQFJBLH08//bNsXbZt2xiGQTp97DnkadsG28YwwOPxMOWkyXz2jtv6BMG2bfPE+meIdESYMWN6dnt7KBQkGJxOY1MT6598lqVLF59Q8JzPDLyIiIiIiEihKCCXrKef/hkPPrSWRCJJwO/Ha3mJdcdIpJNH/TzDMKiqqsC2HQIBP0uWLOajAwS/23c00LBzF1VVVdlgvM9zVFayo2En23c09KtzHGrGe7gy8CIiIiIiIidKAbkA7jb1H657lEQiSWlpKWYmYD4icPZ6PTiOG0C72fM0tm1z4QXnc9mlFx41WI50REgmkgQC/gHX4Pf7aW1tI9IR6XN8qBnv4crAi4iIiIiI5IOiEgHgxZc20NbWRsDvPxyMQ79mbo7Tt5t65jaRiB/zNcJlYSyfRSw28LnxeBzLZxEuC2ePZTLedXX1hEvDTJs2lXBpOJvxrq3dnD03lwy8iIiIiIjISFOGXAA4eKAZ27bxWn3/SmQ6p2f0DtAdx8G23fryF178Hb/b8DJ+v48li0/hYx/9SL+t4fPmVlM9ZzZ1W+sJBqf3CZodx6GltZVFC2uYN7cayD3jfbwZeBERERERkZGgDLkAMGnyREzTJJVM9Tnus6x+5/bupp4J0NvbI7S1HWLfvgP85rcv8aV/uadP9hrczPq1K5cTDodpbGyipbWFtrY2WlpbaGxsIhwOs/Kaq7PbyXPNeB9PBl5ERERERGSkKCAXAM4/71wqKiqIxePYvbLgpmkSDAaz9zNZ8d7BOLiZdI9pYpomtm2zd+8+HvjGN7MZ9Ixly5byoSv+glg8zvbtDdRtrWf79gZi8TgfuuIv+mTVh5LxTiaS2Yx3JgPf0toywFZ7NwM/t3pONgMvIiIiIiIykhSQj0GpVIrnf/MCjz/+FM//5gVSqdRRz7dtm/pt23n99U3Ub9veL0gG8Hq93Hzj9fh8Fp2dnSQSCWzHJpFIkEwmsSwv4XApXq8HwzD6bGX3ejxuXXlPQO7pyXBvra+nbmt9n9eprd3Mz3/xK/w+H/PmzmVhTQ3z5s7F7/fx81/8qk9WPdeMd58MfFMT0WiUdDpNNBqlsal/Bl5ERERERGQkqYZ8jDlyTrhpmlRUVHDzjdezYsWV/c7PZSZ35vMzzx9PxDFNk0kTq7jpxuv50IcuZ/2Tz7J37z527drNy6/8EY/HxAEc28ZxnOzWctM0SKXSbNjwMicvWgj0rQmfOXNGn23olZUV/WrCc605BzcDv+r2W7Jfc2trG5bPYtHCGlZec7VGnomIiIiIyKihgHwMGWhOeCqZorm5hQcfWgvQJyg/npncK1ZcyZVXXsGLL23g4IFmJk2eyPnnnctbb73DXXfflw3s29s7AEin+2fbATKhcywWyx7rXRMO0NXVRSqVwuv1EgqF+s0hz2S8V69ZS2NTE1WVlfj9fuLxOC2trYTDYT7ykav6zSdftmwpS5cuHtLcchERERERkZGigHyMGGxOuM/nw2u528wfWfcoV155BV6v94Rmcnu9Xi6+6ILs/Uxg39ERoaQkiM+ySCRT0NY26HozFdwnL1qUPZapCU8kEuze/S7R7ii27WCaBqFgiJOmTO5TEw5Hz3ifcfoynnzy2UGz/wvmzzvB77qIiIiIiEjhKCAfIwabEw5gGgYBv5/WtjZefGkDF190QU4dyo8WuGYC+4MHD5KIJ9m3b9+gWfEjGYbBuee+N3s/XBYmlU6xY0cDtmPjs3yYltsErrOri+iOnVRWVfTrgj5Qxrsz0smab35n0Oz/7Z/+JKWlJcqQi4iIiIjIqKWAfIwYbE54htfyEk/EOXigGRhah/KhzOTevqOBt9+po7W1jUQimfO6N2x4hUsuuQCA6jmzSSZTxBOJPll+j8eD3zTp7OwklUxRPWd2v+cxTTP7xoFt23zpy18dNPu/ffsOvvTlrxIOl5JKpo5aNy8iIiIiIjJSlDIcIwabE56RSqbcBmyTJwL5m8nd3t7B/v37jysYdxyHd7Zsyd5v2LkLy7Lw+33E4zF3dBruPPN4PJbdft+wc9dRn/do2f9IJEJ7R4SWllYsr8W0aVMJl4azmfMjZ6OLiIiIiIiMFAXkY8Rgc8IBbMchFo9TWVHB+eedC+RvJndLS8txBeMZfv/hDH2kI4LX42Fu9RxKS0pJpVPEYnFS6RSlpaXMmzsHr8dzzKz9YNl/B4c9e/Zh22m8Xg9erzuOLRQKMmP6dCKRCOuffHbAsW8iIiIiIiLDTQH5GHG0OeGdnZ34fBY33Xg9Xq+7pT1fM7l37Xp3yGs0DMP96HVszpxZ2T9nsvY+n5+FixawcOECFsyfy8KF7p99Pt+QsvaDZf+j0SjR7ihejxfTNLPfi8zaetfNi4iIiIiIjDQF5GPIihVX8plP38LEiVUkU0m6urpIppJMmljFZz59S7855JkO5QtrFhDp7GTv3n1EOjtZtLBmwJFnA+k9tuxYHMdxP3ru+3w+Kisrs4/3ztrjQEmohPLyckpCJeAw5Kz9YNn/VDJFOm2TttOEQiFCoVCfz/P7/f26uIuIiIiIiIwUNXUbYwabE947G9zb8czktm07e/5QO6ofyev1ctJJkykvL8seG8pc8aFk7Qd7nlQ6RTqdxuezmDZ1Sr/68qHWzYuIiIiIiAwHBeSjWO/AuHcgfeSc8GPp3aH8WGprN2dnficTSeKJgZvCDaR3AGwYBtOmTumX7T7aXPGV11w9aNb+yO/F0qWLWXX7LTz+xNPUba0nHk/g9/uorKzAMAzC4b5Bd6ZuftHCmmNm4EVERERERIaDAvJR6sjAeDhGd9XWbmb1mrV9Zns3NjYN6XP9fh+WZeE4YNvpo2bWj5a1H+hNiM2b3xrwe3HmGaeBYQCZunWD2bNn0tzcekIZeBERERERkeGggHwUGigwjsXi2dFdQ63/zoVt2zyx/hkiHRGmT59Ka1sbhw610dLaOqTPT6XSmKYH0zQoLS2lsqKCtkOH2L6jYcDs/EBZ+4HehKiYMIGDzS3Y6XSf78Ubb7zJ7zb8gbJwGdOnT8seP3DgIB7Tw+TJkzh0qH3IGXgREREREZHhpoB8lOkdGM+YMT27DTwUChIMTqexqYn1Tz7L0qWL85rpzcz2tm2b1zb+iWQyiePQb2TaYLxeLxMmlBPw+5k6dQqA20RuiA3UBnoTojsW48033yaZSrFoYQ2hUBCAYChAMpkikUiSTqcIBgMYhtHne1QxYQKfuf0Wujq7hlQ3LyIiIiIiMtwUkI8yDTt30bBzF1VVVf2akh05umuodeFDEemI0NLSSmtrG+BmsE3TIJVKDenz4/E4+/btB2DPnr2cdNJkAsHAkBqoDfYmhDtZ3MEwDPbu20dZWRjDMIhGo3THugn4/US7u4lGo5SUlACHv0cNO3dhmiZnnHFazt8LERERERGR4aCU4SjTGekkmUgSCPgHfLxQo7tCoRDt7R04joPH48E0zX5vCByLxzQxTZNEMsm7jU14Pd4hNVDLZOePfBMilUzhOG59ejQaJRqNZo/btoPX8mLbTr83DTTeTERERERExgIF5KNMabgUy2cRiw3c3bxQo7vebWzEttP9gnCPxzOkz/d4PBim0Se7vf/AAWz72GPTIh2RAd+E8FpeTNMAhz6Bd+Z4KpnCNI1+I9803kxERERERMYCBeSjTPWc2VTPmU1La0u/+u3M6K651XPyPrqrpbkVwzCznc4dx8l+DIVt26TTaWzbxvJ6CQSCdHZ28uJLG475ueGy8IBvQoRCIULBEPFEvE/gHQqFCAaCxOJxgsEgjuPQ3t5OV1cXtm0X7HskIiIiIiKSTwrIRxnTNLl25XLC4TCNTU1Eo1HS6TTRaJTGpqaCje6aNHkiXq8Hn2Xh9XhwbBu752MoegfuDuDxuIH9wQPNx/zceXOrB3wTwsBg6tST+pybTqfpjnZjWV48Hg+dnZ1sqdtKff123tlSx6baNzBNj8abiYgcwbZt6rdt5/XXN1G/bfuQ//8uIiIihaOmbqPQsmVLWXX7LdkRYMMxuuv8886loqKC5uYWSkpK3Oy4bZNMpYjFYtnzLMsLGNmMeIZhGHg8HhzHDZq7uqJ4vV4mTZ54zNfOvAmxes3afvPDOyIRZkyfTtXESg4daqet7RCWz2LWrJmYpknboXYcx8YBMpvlc6t8FxEpfgONlayeM5trVy7XOEgREZERpIB8lFq2bClLly5m+44GIh2Rgo/u8nq93Hzj9Tz40Fq6uroI+P14LS/JZDJ7jmEYuAns/tvY3XUZGIZ7nm3beDwmf37u+4b0+sd6E6L396K0tJRH1j3K/v0HOP30U+nu7iaVTOG1vASDQZqa9vDE+mcIBAMaeyYi495AYyVjsTh1W+tZvWYtq26/RUG5iIjICFFAPoqZpjngaDPbtgcM1Ac7PlQrVlwJwA/XPUpbWxvxhFvT7fF4eoJx5/AWxwHq2x3bzVTbto1pmpSVhdm1+90hj2c71psQmeep37adnbt2U1VVhWmYlIRK+jyP3+fnDy+/ypa6rZiGqUyQiIxbg42VDIWCBIPTaWxqYv2Tz7J06WK9aSkiIjICFJCPMYNtOzzzjNPY+PqmE96OuGLFlVx55RW8+NIGDh5opmpiJb/5zUu8sfktkslkT027DTikkikM08g2f0v3BOk+n4/p06fi2E7Oo8cGexOit95d2R3HIRqNkkql8Hq9pFIpmvbsIR6L4/cFmDSpSpkgERm3BhsrCe5upqrKSnY07GT7joYhv3kqIiIi+aOAfAwZbNvhG2+8ye82/IGycBnTp0874e2IXq+Xiy+6IHt/8qRJ7N23n46ODk6aPAnDNOmOdvNuYxM+n0V19Ry6urqIxeIEAn6mTp1CPBYn0tlZkNFjma7szc0ttLa2Ee2OYttOdhSa7dj4/D5KS0OYpqlMkIiMW4ONlczw+/20trbl/OapiIiI5IcC8jFisG2HwVCAZDJFIpEknU4RDAYwDOOEgtBUKpXNkE+aPJHzzzu3T313sjuG1/IysaqSRCLBvr376Y51Z4PiSEcnluVl2bKlBRk9Nm9uNRMmlLNp02Y8HhOf5cO0TBLJJImemveSEotQKJT9HGWCRGQ86j1WMhQK9ns8Ho9j+ayCvHkqIiIix6aAfIwYbNthNBqlO9ZNwO8n2t1NNBqlpMStqT6eIPTpp3+WrSHP1IJXVFRw843Xc89Xv9ynvnvTps089M1vk0gkCfj9+P0WqWSKQ+3t+HwWZ5y+rGCZaKOnl3p2TJoB4G6dN01joL5zygSJyLiTGStZt7WeYHB6n38/HMehpbWVRQtrCvLmqYiIiByb9u2OEYNtO0wlU9i2g9fyYtsOqVSqz+N+v59kIjmkIPTpp3/Ggw+tpbm5BctrUVJSguV1t4Y/+NBann325yyYP48zzjiNeXOr+dOfaikLlzFhQjkODvF4AgeHignllJWV8fqfagsy53b7jgbaDh2ies5swqVhUukUsVi8p7O7B78/QDLl1rv3pkyQiIw3mbGS4XCYxqamnj4gaaLRKI1NTYTDYVZec7XKeEREREaIMuRjxGDbDr2WN1s7bZoGXm/fSzrUIDSVSvHDdY+SSCQpLS3F7Mmi+Hw+vJZFZ2cnj6x7lCuvvAKv15vN2E+fPo1gMNCnsVooFKK7u7tg28Mzb05MmzaVSZMnuq+ddF9797uNdHZ2YttGnzcnlAkSkfHqWGMl1ehSRERk5CggHyMG23YYCoUIBoIcam/PZqrb29uzM7mHGoS++NIG2traCPj92WA8wzQMAn4/rW1tvPjSBi6+6II+GXvDMLLb5DMKuT38yDcneo89mz5tKvXbdpBMJkmlUqTTaeLxOC2trcoESUFlxg6Cu4ujZsF8/V2TUeNYYyVFRERkZCggHyMy2w5Xr1lLY1MTVZWV+P1+NwNuefF4PHRGOtmyZSuOA4YBpmEyZcpJQwpCDx5oxrZtvNbAfyW8lpd4Is7BA83AyDYKOlpNZDgcprwsjAMkUyn27t2nTJAUXGYc4buNTXxk+RXc+7UHmDljes5jB0UKaShjJUVERGR4KSAfQwbbdjhr1kwM06T9UDu242AYDmDAwL3NBjRp8kRM0ySVTOHz+fo97m6JN5k0eSIwso2C+rw50dhEqCSIaZjYjk20q5uJkyZy+6c/SWlpiTJBUnC9xxGedNJkAMKlpcc9dlBERERExg8F5GPMkdsOS0tLeWTdo+zff4DTTjuV7u7ubC13MBikac+eIY09O/+8c6moqKC5uQWvZfXZtm47DrF4nEkTqzj/vHOBo2fsh2N7+LJlS/nQFX/BDx95lKY9e7Md4auqKrnuims4/bRTC/K6Ir0dOY4w08MhGAwyY/rxjR0UERERkfFDvyGOQZlth2eccRqGabBz126qqqowTZOSkhLKy8spKSlxA9ReY8+Oxuv1cvON1+PzuQ3cEokEtmOTSCTo7OzE57O44a8/xuNPPM2/P7CGR//rCU4+eSGrbr+FhTULiHR2snfvPiKdnSxaWFPwrGBt7WYeX/8MHR0dmKaJx+PBNE06Ojp4fP0z1NZuLthri2QMNo4Q+o8dFBERERE5kjLkY9xg49AycmmutmLFlQDZOeTxRNzdpj6xilmzZrLmm98mHo9na9S//Z3/x8qPXN1vPnmht4fbts13vvdDGhub8HhMtxGdaWLbNolkgsbGJr77vUf41jcfUFZSCiqfP38iIiIiMv4oIB/FMl2bjxbo5ru52ooVV3LllVfw4ksbOHigmUmTJ1L7xpv85CdPYtsOpmlimgaO4xCLxXn0sScAuP32W078Cx6i+m3beeedOgzDIOAPZDOTHo+HgBkg2t3N2+9soX7bdhbWLBi2dcn4M5LNDUVERERk7FNAPkpltmTX1dWTiMfx+f0sXLiAjx7RtbkQzdW8Xi8XX3QBAIlEgn/513uxbSe7LTzDMAzS6TTrn3qWT37ybwZsBlcIdXX1xGJxgsHAgNuE/X4f3d0x6urqFZBLQfX++QsEphGPxwCIRruwLF9BmxuKiIiIyNin/byjUG3tZu792gNs2PAyBw4coO1QOwcOHOD3G17m3q890Kc+OtNcLRwO09jURDQaJZ1OE41GaWxqOuHmauuffJZ4PN6TGe/7HJlj8Xic9U8+e0Jf83EZrIX8UFvLi5ygzM+faXrYVPsGdVvrAajbWs+m2jcwTU9BmxuKiIiIyNim3xJHGdu2+e73HuHdxiZsO43P8hEMBvBZPtJ2msYmtz7atu3s52TGoRWiudrevft6asaNAR83DAPHcc8bLosW1hAI+Ikn4v1ibweIJ+IEAn4WLawZ8nPatk39tu28/vom6rdt7/P9FTkWA3AGeCNo4J8aERERERGXtqyPMjt27OTtd7ZgGBAMhrK/0Hs8HoLBENFoF2+/s4Vt23ZQUzM/+3lHjkPLV3O1qVOnYBju9veBOI6DYbjnDZf58+dyysmL2FT7Bt3dUfw+P6bHxE7bxBNxAE45eRHz588d0vPV1m7OznZPJpJYPovqObO59ojyAJEjZcaepdNpTj/9VJLJBAALFy7Asnw0NQ1t7KCIiIiIjE8j+hviSy+9xIc+9CGmTZuGYRj89Kc/7fO44zj8y7/8C1OnTiUYDHLJJZdQX18/MosdJlvrtxGLxfH7/P2yawbg9/mJxeJsqdva73N7j0NbMH9eXgKAlddcjd/vx7btflnjzDG/38/Ka64+4dcaKtM0fTZIKwAAOPNJREFU+fu/u4kZ06fjMT0kkgm6u2Mkkgk8Hg8zpk/n7//upiF9/bW1m1m9Zi11dfWES8NMmzaVcGmYuq31rF6zVuPT5Kh6jz0zDZNQsASAULAE0xj62EERERERGZ9GNCDv6upi2bJlPPzwwwM+/m//9m889NBDfOc73+HVV1+lpKSED3zgA8RisWFe6QgYbK/rMO+B9fl8rPzI1Zim28AtnU5j23b2z6ZpsPIjVw9bQ7eMZcuW8sUvfJb3n3sOkydPpmJCOZMnT+bc95/DF7/w2SFltjPZzUhHhBkzphMKBTFNk1AoyIzp04lEIqx/8lltX5dBDWXsWTKR1NgzERERERnQiG5Zv/zyy7n88ssHfMxxHB588EG+9KUvceWV7nzsH/3oR5x00kn89Kc/5aMf/ehwLnXYLFgwz62PjifwBD39uqbH4wkCAbfj+nDJjDR74slniMcT2eN+v49rr1k+rCPPejvRbfq9s5sDdWvvnd1cMH9eIb4EGeM09kxERERETsSorSFvaGhg3759XHLJJdlj5eXlvOc97+Hll18eNCCPx+PE4/Hs/Y6ODgCSySTJZLKwiz4BmbXNnjWTJUtOYfPmt7CdND6vD8MwcRybRDKBZXlZumQxc2bPKtjXY9s2DTt30RnppDRcSvWc2Zx33vs50HyQN954m0Qijs/n59RTT+G8894/4t/XObNnZf+cydwPRfuhdnAcSkpCAwbxoVCISCRC+6H2vHyNmecY6e+X5M+smTOYP7ea+u3bKS0tweNx/x55PCaO49AR6WDB/PnMmjlD171I6Oe4uOn6Fj9d4+Kna1z8xso1Hur6DGewbl3DzDAMnn32Wa666ioA/vCHP/D+97+fPXv2MHXq1Ox5K1euxDAMnnjiiQGf56677uLuu+/ud/yxxx4jFAoVZO0iIiIiIiIiGdFolOuuu4729nbKysoGPW/UZsiP15133skdd9yRvd/R0cHMmTO57LLLjvqNGGnJZJLnnnuOSy+9FMuy+O///v/40aNP0Nraim3bmKbbIOqvr7+WD3/4gwVZw5tvvs2/P/AQ+w8cwHHsnjFODslkGtM0WVgzj7Ky8uz5juOwZ+8eFsyfz5e++E9jrou0bdvcc8+/Ub99O9OmTutXHpDvr+3IayzF48033+aZZ/6bxj17uerDH+Cn//1rZkyfxvKrP8SSJaeM9PIkj/RzXNx0fYufrnHx0zUufmPlGmd2ah/LqA3Ip0xxx2jt37+/T4Z8//79nHbaaYN+nt/vx+/v32DJsqxRfcEyLMvi7be38MT6Z2htbSWZTPaMFjNoaW3lifXPMH/+3LyP47Jtm+//v/+kYecuTNPA7/Pj8ZgkE8meJnoOOxp2sWTJKRi9OsuVhcvYtn0Hu99tHJN11h/5yFWsXrOWXbt3U1VZid/vJx6P09LaSjgc5iMrrhzw79OJGCt/F2XoTj99GcuWLWVr/TbqtrzD5z/3GWoWzB9zb1LJ0OnnuLjp+hY/XePip2tc/Eb7NR7q2kbtb4vV1dVMmTKF559/Pnuso6ODV199lXPOOWcEV1ZYtm3z3e89wu53G4l1d/fpah6LdbP73Ua++71H8t75e9u2HX3mn3s8HgwMDMPIZo4jkU6iXdE+nzfWu0gvW7aUVbffwsKaBUQ6O9m7dx+Rzk4WLaxh1e23aA65DJlpmsybWw2QU3NBERERERm/RjRD3tnZybZt27L3Gxoa2LRpE5WVlcyaNYvPfOYz3HPPPSxYsIDq6mq+/OUvM23atGydeTHasWMnm998i2QyiWmamKaJATi4wXoymWTzm2+xbdsOamrm5+11t9RtJRaLEwwEwHFI2TaObWP3tBgwDRPbtuns7KSkpCT7ecXQRfpEu7WLiIiIiIgcjxENyF977TUuvPDC7P1M7ffHP/5x1q1bx+c+9zm6urr4u7/7Ow4dOsS5557Lr371KwKBwEgtueDqtm4lFothGEY2ILR7tqybptu5ORaL8fY7W/IakGek0mlisRjpdBq3hNzBwX1D4Mj+f47j0NLaysKaBdi2zeuvbxqzwaxpmmNyy72IiIiIiIxdIxqQX3DBBf2CvN4Mw+ArX/kKX/nKV4ZxVSOrubkF23YD8HQ63ef7k9k6btsOBw825/V1Fy5cgGV56e7uzgb/mUrxzDoMw8Dj9ZJOp7N11h7TQ9uhQ9x1930kE0ksn0X1nNlcu3K5tnuLiIiIiIgcxdhKY44DkyZOBNzscyYIznxkjgFMnjwpr687b241gUCgz2sYR5xjWRbpVCpbZ33S5Mk4wIH9BwmXhpk2bSrh0jB1W+tZvWYttbWb87rGgdi2Tf227bz++ibqt23Pe229iIiIiIhIoYzaLuvj1fz58/B4TNJpN7AcaAeBx2OysGZBXl+3YecuSkpKiEajJJPJPoGtaRpYlo/KigqmTJlCpDPCtKlTiMXi7N9/gBkzpmez96FQkGBwOo1NTax/8lmWLl1csO3rtbWbeWL9MzTs3KXsvIiIiIiIjDkKyEcZ0zTx+/1Eo92DnuP3+Wls3EM0Gs1bzXakI4LX42HB/Hns3befrq6u7Pzz0pJSEskEe/buY8/efQD86U9vAFBRMYGZxow+z2UYBlWVlexo2Mn2HQ0Fqc2urd3M6jVriXREqKqqIhDwE4vFs9l5dUgXEREREZHRTgH5KNPV1UV5eRmO4xCPxemdHzcMsLwWtuPwzYe/i+X15i0rHC4LY/ksfD4/ixbVEI1GSSVTeC0vu3c30d4+8GD7trZDvLNlKycvqulz3O/309raVpBxaLZt88T6Z4h0REYsOy8iIiIiInKiFK2MMqXhUsLhMLNmzqSyJ/Pr81kEAn7KysowTLfZm89nUVISwuv1UFd34jXb8+ZWUz1nNi2tLX22yadTaZqbDzeQ613TntHc3Ew6nerzfIUch7Z9RwMNO3dRVVXVZx2Z9fXOzouIiIiIiIxWypCPMtVzZlM9ZzZ1W+tZuHA+3d3dpJIpPF4vu3c3kkym8Ho87Nu3H8dx67uDgSDxePyEssKmaXLtyuXc+7UH2PSnN7AdG8eBZDI5pM/ftftd5lZXA4fHoS1aWMO8udU5r+VYIh0RkokkgYB/wMcLmZ0XERERERHJF2XIR5lMYBwOh2lq2oOBQWlpKfF4jPb2drcLOu7W9UDAj9fjpSvaRXtHhLfe3nLCWeFeQ9YwjIGbyg2ks7OLdDpNNBqlsamJcDjMymuuLsiW8cz2+lgsPuDjhczOi4iIiIiI5Isy5KPQsmVLWXX7LdkO4q2tbaRSKRzHwev1EAoFiccT2PE0punB7/cTi8VobW0dtNb7WDJ12XY6zWmnnepm5lMp9u3fz4EDA888z4xiAzdTv3fvPiyfxaKFNay85uqCNVXLbK+v21pPMDi9z7b1483O27bN9h0NRDoieWuUJyIiIiIicjQKyEepZcuWsnTp4myQ+PY7dTy89nvYtsOhQ+19zu3u7sbr9eI4Du3t7YM849H1rss2TZOSkhIASktL+gTk2RnlfYJxkwf/434SicSwBLOZXQSr16ylsamJqspK/H4/8XicltbWnLPzGp8mIiIiIiIjQSnAUSCTnQU3MO49AzwjXFpCOm2TSqX6PQaQSqVIp9OUl5Ud1xoGq8v2eLxMnDix3/m9t7JfdulFLFlyCmeccRoL5s8blsxyZhfBwpoFRDo72bt3H5HOThYtrMlp5FlmfFpdXT3h0jDTpk0lXBrOjk87kUZ5IiIiIiIiR6MM+QjLZGffbWziI8uv4N6vPcDMGdM584zT2Pj6pmzWNp1Ok06nj/pcqVSaYCh4XOvoXZcdDAX6jD2bNm0Kra2tA75RUF5exsprrj6u1zxRR+4iyDU7r/FpIiIiIiIykhSQj6BMdjbSEeGkkyYDEC4tpfaNN/ndhj9QVhZm+rTpBAJ+du7aPaTn3PjaJhafcnLOa8nUZb/xxpskkym6Y93YtoNpGiSTSQzDYMKECRiGQyyWIBj0M3/+PA4cODiiQatpmiyYP++4PjeX8WnH+xoiIiIiIiKDUdpvhByZnQ0G3cx2IBAglUqRSCR7Mt4BTNMkkRi4o/iR6rbWH9d6TNPkzDNOo72jg0Pt7RgY+P0+HMch0ZOhj0QiHDrUQSwWo62tndpNm7HT9pid+T2U8WnJRFLj00REREREpCCUIR8hvbOztm2zZ88eAHbt2kW0K0rA7yca7aapaS+mAcnkwLXjR0ql0rz++qbj2r698fVNlIXLSKVTdHV1kexO9p6DRjqdxmOaGKaB40AimaSxaQ8VFeVjMmjtvU0/NMBWf41PExERERGRQlJAPkIy2dm9+/a548IsC4A9e/eTSCTweDyk02l27twFDH0e+MbXN7F9+46cO4Vn3iAoLy+jpaW15zUHeF3DDcYNw8A0TWw7TXt7B6FQKIevPneFGEtWiPFpIiIiIiIiQ6WAfISEy8Icaj9EW9vAY8oyDdwygW86nR5SUF5SEmLatKnEYvFsp/ChdB2PdESIRCJEIp2k02l8lg8zYBKLxUkmk/3WlVmb44BtO7zb2MSiRTVD+dJzVqixZPkenyaHaa67iIiIiMixKSAfITNnTOfQoY5jnmcYRp+g/FjCpSWYpplzp/DS0lI6O7tIJpOEQiUYg555WO83CFqaW4bwGbnr3fiuqqqKQMCf85sNR5MZn5YJ+Ftb27B8FosW1rDymqs1h/w4aK67iIiIiMjQKCAfIU8+9bMhZbxt2+7XAXwwpmlSUVmRvZ9Lp3AnWyxu4DgOadvGcexjrtFdm0PVxMohrTEXvRvfTZ8xje7ubiKRCF7Ly/Tp02hq2pOXDu8nOj5tqF9L71nzNQvmF2XGuNBvoIiIiIiIFBMF5CNky5a6IZ3nOM6A87/BDcAdx8kGzV6vh1gsRkmoJHuO3++ntbXtmE3Xujq7KC0poT3VQSQS6QnGj1677m5Zd/B4vMycMWNIX08uMnXtfr+frXXbiHZHs6PYQsEQlZUVeRtLdiLj045lsFnzxZYx1lx3EREREZHc6LfiERIOlw7pPK/XS0koRCDg79m+7h7PBMMZfr8f0/QQiXTS3t5OV1cXjuMMuVN4uCyM1/L2BP8OYGS3yw8m8/qhUIhoNDqkrycXmbr2pj176OzqxOvxEgj48Xq8dHZ10rRnj1v3Poo7vGcyxnV19YRL3WseLi3NZoxrazeP8ArzJ5e57iIiIiIiogz5iPnQhy7nyad+eszzvF73EhmGmw03DAPL8jB92jTiiQR+v4+ODrcZm22n2b27EXAwTZOSkhK8Xi+nLVt6zE7h1XNmk0ymSKXTlJaG3cy7bZO27UGDbcMw8Hg8OI5NaenQ3mDIxWB17R6Ph4AnSDTaRWdnV0FeOx+OzBhnrmUwGGTG9OLLGA9lrvtQdmuIiIiIiIwXCshHyMKaBQT8fmLx+FHPi8VixI8Yx+Xz+TjY3Jzdvg1kO6HbdrxnLBnEYnF8Ph9nnL7smAFfw85dWJaF3+8jkYjjs3x4LC92PDHo5/TO0DsMbSxbLnrXtWe/qMMv7h4v0GvnQy4Z40Jtlx9OmusuIiIiIpKbsZ+WG6N27NhJepDa8CP1rhMHN9A2MPD7feBAV1e017mHbx3HIZ1O8fxvXhy0Dj0j0hHB6/Ewt3oOpSWlpNIpYrE4afvond1TqRSGYdDV2TWkryUXmbp2n2URi8ezo9/S6TSxeByfZVFaUlKQ186HoWSMk4lk0WSMM3PdW1pb+vUeyMx1n1s9R3PdRURERER6KCAfIW+9/U6f+d65ME0D27GJxxP9AmaPx5P9ME2TVCrN5jffYtu2HUd9zkx20+fzs3DRAhYu/P+3d+fRUVRp/8C/vaTXLJ2FbEAWyAaKGEA4ARFUFBQFAYHRiAEZBIUXkGERVOB1BxSEGYR55zeAC/uIjnpAD4MQEoWwhEUIJAGDkI0IgaQ7naWX+/sjdpvOAiEkVNLz/ZyTc1JVt6qe7ieBPH3r3huN6KhOCA/veNN4zGYzdDpdk17LzWLy8vZCaGgIPPX66g8JKithtVnh6alHaGiIs01rVLPHuD7u1mPsWNfdy8sLuXl5MJvNsNlsMJvNyM3L47ruRERERES18C9jiWRmZjf5XKvVhvCwjoiO6oTAwHYuxxxrlsvlcijkcshkMlRUVCDjzNkbXrNm7yYEoNfp4ePjA1kjViS32wUu5eY1+fXcLKbKqkrExEQhNqb6Q4LYmGjEREehsqqyVfe41nxP7XY7zObqnnyzuQx2u90te4wd67rHxkTDaDKhoKAQRpMJcbExXPKMiIiIiKgWjiGXiFZbd4xtYwkhYLFaEBAQgOslJS7HHOuWy2TVpbRcLoPNZsdvv1254TUdvZsrVn6M3Lw8+Pv5Qa1Ww1TWuNnTr1652tSX06iY8vLz4e/nB09PT1RWViIvP7/V97g64n/73Q9w/MRJKJUKDBn8IDKzsmG12hAUFNSq42+qO7GuOxERERGRO2BBLhGDwfu2zjeZqgtllUrlsr/2WHFHcV67J70+jt7Nrdt2IOfCryguvgar1XrT84Swwz/A/xaib7z6YvJQeSAuNgZjRo9oEz2uMvwxtr/2fnfVkuu6ExERERG5CxbkEimvqLit8x2zq3t5etZZk7wmIQTUahW6xMU26rq1ezcLCn/DwkVv3fAcIYCQ4KBbewG3oK32uDqWPbPZbIiPvwcWS/WM9bGx0fDwUCEvL9+tlj0jIiIiIqJbwypAIpbKpk3o5iCTyWGz2X7//sZ9rZ6enujUKaLR13b0bvbocS/y8xs3NvzAwUONvn5T1IwpOqpzmyhgay57JpfJodPqAQA6rR5ymdxl2TMiIiIiIvrv0/qrGjcV1yXG2cvdFELYUVBQiOJr16DRqKHy8Pj98fTq4zIZIJfJoNGoodPpkHPh1ybdp6KRPfmNbfff5L9t2TMiIiIiIro1fGRdIg8OfAAGgwHFxdecM6M3lkKhwNgxo9C1Syxy8/LxyaebIZfJkJeXjyqLBYAAIIOHygOhISEQQjS56OsSFwe5XA4h7FAoFM71zR3Fv81mg0wmR5e4uCZd353VXPZMp6s7iZ+7LXtGRERERES3hj3kElEqlZjy4gtQq3+flK2BMeA1yWQyKJVKaDRqxMVGo0ePe9GlSyxsNhsuFxVBLpdDr9fBy9MTer0OcrkchZeLYLXZmlz0DRx4P3wNBgDVy6054qi57etrwMCB9zfp+rfLbrcj+9x5pKcfR/a583UmtZNSzWXPao/xF0K45bJnRERERETUeOwhl9CoUcMBAP9v3acwmUwAAIVC7uwxd4wRdxBCQC6Xw8/PFz4GHwBAZEQ4LBYLKiur4OnpCXmN8eR2pYDJZILVYkFkRHiTYlQqlZj84gQs+3AVLBZLnZiUSg9MnjQBSuWd/1E6ceJn5+zrlioLPFQeiIwIx9gxI1vF7Ou1l5IL+n2m+/JyMy4X/dbql20jIiIiIqKWxUpAYlFRnXBv925o16562bB27QLg5elZp/B1qKqqgpenl7NXNefCr/DwUEKtUqGyshI2mw1CCNhsNlRWVkKtVkHpoWzyGHJHjIHtAqBQKFz2KxQKBLYLQFRUpyZfu7Fq94QfO3YCK1Z+jMzMbHh5eiE0NARenl7IzMrGipUf48SJn1s8psZwLNsWGxMNo6kMAGA0lSEuNgavzHi5VXxwQERERERE0mAPuYROnPgZK1Z+DGOpEcFBwQCA4KBA5ObmO9vULIKrx2sDl4uKYLfbIZfLYSw1QqlQolOnSBQWXoa53Ay7RUAul8HTU4+goECUmcqaPIbcuXSX3QaDwQcmkwl2u+P6nrDb7S2+dFd9PeGlpUZAVH9Y4HiEXqfTQqttj9y8vFa1nJhj2bas7HPIPHsGry2YjZjoqFYRGxERERERSYcVgUQcha6x1Ij2HUIhUD32+fr1Utjt9t8nTZNBCPvv448FVCoPaDRamEwmJO9PBfDHxGEqlQqxsdGIjYlGdFQnxMZUf69WqW5r4rDzv+Qg40wmjEYTzGYzNGoNPD310Kg1MJvNKCk14nTG2RZbusvxoUXNnnClUoGrV4thNBphNLp+0CCTyVrlcmJyudz5VENbWEOdiIiIiIhaHqsCiTjWqFar1cjKPIfs7PMAgPyCAgBwFuRarQ56vQ56vSf0ek+o1SrY7Xb8VnQFgOvEYQCg1+vh4+MDvb56zevbnTispKQUxcXFsNls0Gi0UCgUkEEGhUIBjUYLu92G4uJilJSU3u5bUkfNDy06dGgPnU4LuVwOpUIJhUIBu7Ajv6CwzoRpXE6MiIiIiIjaAhbkEjGWVvfu5uXnw1RmglJRPXpAqax+RN0xW7hcJoOH0gNKhQIyAFaLFXK5HO0CA6qP/z5xmJeXF3Lz8mA2m2Gz2WA2m5Gbl3fbE4eVlJTAarVCqVCi9qrpMgBKhRJWqxUlJSVNuv6NOD608Pf3dz6WDgBKDyUUCjkUcgXMZjPMZrPLeVxOjIiIiIiI2gIW5BLx9PSEyVQGi8UCjUYLuby6EFep1M7i0/HouoNdCFRUVsLP1xcDHvhjmTHXicNMKCgohNFkapaJw3y8vaFUKmGz2+pdustms0GpVMLH27vJ92iIsdQIS5UFGo3aZb9Op4NOq4PVZoXdbofVanWJicuJERERERFRW8BJ3SQi4ChuZS5rkMsgg0ajQXl5OQCgymKBXCGH1WJFRWUlVCoPTBj/XJ1lxhwTh53/JQfGUiO8vL2aZayyj8EHfn6+KL56rfr+Hh6Qy+Ww2+3O2Gouw9acHOPjKyoqodNpnftlkCE0NBjZ58ywWCywWq3OWeWvFhdzOTEiIiIiImoTWJBLpMxUBk+9HiZRhorKyhqPqlcvd6ZWq2CxWGGzWVFW9vtj6gH+mDD+Oef65bXJ5XJER3Vu1jg7d4pE1y5xOHHyFKxWK8rLy/+YxV2vg0KpxF1du7RIb7RjfHxmVja02vYuTwt4eXnBx9sLAoDFakVBQSE8VB6Ii43BmNEjuJwYERERERG1eizIJeLl7QUvby94e3ujuPgarDYLAMBqs8LTUw8/X18IYcfQoUOgVCjQLjAAAx64v07PeEtzjFHPLyhEaWkpAtsFQCaXQ9jtKDOb4e3t3WK90Y57r1j5MXLz8uDv5we1Wu3sCQ9oF4AZ01+Cp6e+WZ8KICIiIiIiuhNYkEukZu9vTEwULJYqAEB0VGd4eKiQl5+PuNgYPPvMaMkLTMcYdeda4OUV8FB5oEtcbIv3Rte+d3HxNfaEExERERGRW2BBLpGavb95+fkICmwHoHq5s7z8/FY3Drqlxqi39nsTERERERG1FBbkEqrZ+3spNw8AYDSVtdre35YYo94W7k1ERERERNQSWJBLzNH7m5V9Dplnz+C1BbMREx3F3l8iIiIiIiI3x6qvFZDL5c5ZyvkoNhERERER0X8HVn5EREREREREEmBBTkRERERERCQBFuREREREREREEmBBTkRERERERCQBFuREREREREREEmBBTkRERERERCQBFuREREREREREEmBBTkRERERERCQBFuREREREREREEmBBTkRERERERCQBFuREREREREREEmBBTkRERERERCQBFuREREREREREEmBBTkRERERERCSBNlGQr169GhEREdBoNOjTpw8OHTokdUhEREREREREt6XVF+Rbt27FrFmzsGjRIqSnp6N79+4YPHgwioqKpA6NiIiIiIiIqMlafUG+fPlyTJo0CRMmTEDXrl2xdu1a6HQ6rFu3TurQiIiIiIiIiJpMKXUAN1JVVYWjR49i/vz5zn1yuRyDBg3CgQMH6j2nsrISlZWVzu3S0lIAgMVigcViadmAb4MjttYcI90e5tj9Mcfujzl2b8yv+2OO3R9z7P7aSo4bG59MCCFaOJYmy8/PR/v27fHTTz8hISHBuX/u3LlITk5GWlpanXMWL16M//3f/62zf9OmTdDpdC0aLxEREREREZHZbMazzz6LkpISeHt7N9iuVfeQN8X8+fMxa9Ys53ZpaSk6duyIRx999IZvhNQsFgt2796NRx55BB4eHlKHQy2AOXZ/zLH7Y47dG/Pr/phj98ccu7+2kmPHk9o306oL8oCAACgUCly+fNll/+XLlxEcHFzvOWq1Gmq12rnteACgvLy8VSfMYrHAbDajvLwcVqtV6nCoBTDH7o85dn/MsXtjft0fc+z+mGP311ZyXF5eDuCPerQhrbogV6lU6NmzJ/bs2YOnnnoKAGC327Fnzx5MmzatUdcwGo0AgI4dO7ZUmERERERERER1GI1G+Pj4NHi8VRfkADBr1iwkJSWhV69e6N27Nz766COUlZVhwoQJjTo/NDQUly5dgpeXF2QyWQtH23SOR+svXbrUqh+tp6Zjjt0fc+z+mGP3xvy6P+bY/THH7q+t5FgIAaPRiNDQ0Bu2a/UF+dixY/Hbb79h4cKFKCwsxL333ovvvvsOQUFBjTpfLpejQ4cOLRxl8/H29m7VP1h0+5hj98ccuz/m2L0xv+6POXZ/zLH7aws5vlHPuEOrL8gBYNq0aY1+RJ2IiIiIiIioLZBLHQARERERERHRfyMW5K2EWq3GokWLXGaIJ/fCHLs/5tj9Mcfujfl1f8yx+2OO3Z+75VgmbjYPOxERERERERE1O/aQExEREREREUmABTkRERERERGRBFiQExEREREREUmABTkRERERERGRBFiQtxKrV69GREQENBoN+vTpg0OHDkkdEjXBe++9h/vuuw9eXl4IDAzEU089hczMTJc2FRUVmDp1Kvz9/eHp6YlRo0bh8uXLEkVMt+v999+HTCbDzJkznfuY47YvLy8Pzz33HPz9/aHVatGtWzccOXLEeVwIgYULFyIkJARarRaDBg1Cdna2hBFTY9lsNrzxxhuIjIyEVqtF586d8dZbb6HmHLfMb9uyf/9+PPnkkwgNDYVMJsNXX33lcrwx+SwuLkZiYiK8vb1hMBgwceJEmEymO/gq6EZulGOLxYJ58+ahW7du0Ov1CA0NxfPPP4/8/HyXazDHrdvNfo9rmjJlCmQyGT766COX/W01xyzIW4GtW7di1qxZWLRoEdLT09G9e3cMHjwYRUVFUodGtyg5ORlTp07FwYMHsXv3blgsFjz66KMoKytztnnllVfwzTffYPv27UhOTkZ+fj5GjhwpYdTUVIcPH8bf//533HPPPS77meO27dq1a+jXrx88PDywa9cuZGRk4MMPP4Svr6+zzdKlS7Fq1SqsXbsWaWlp0Ov1GDx4MCoqKiSMnBpjyZIlWLNmDf72t7/hzJkzWLJkCZYuXYq//vWvzjbMb9tSVlaG7t27Y/Xq1fUeb0w+ExMTcfr0aezevRvffvst9u/fjxdffPFOvQS6iRvl2Gw2Iz09HW+88QbS09OxY8cOZGZmYtiwYS7tmOPW7Wa/xw5ffvklDh48iNDQ0DrH2myOBUmud+/eYurUqc5tm80mQkNDxXvvvSdhVNQcioqKBACRnJwshBDi+vXrwsPDQ2zfvt3Z5syZMwKAOHDggFRhUhMYjUYRHR0tdu/eLQYMGCBmzJghhGCO3cG8efPE/fff3+Bxu90ugoODxbJly5z7rl+/LtRqtdi8efOdCJFuw9ChQ8ULL7zgsm/kyJEiMTFRCMH8tnUAxJdffuncbkw+MzIyBABx+PBhZ5tdu3YJmUwm8vLy7ljs1Di1c1yfQ4cOCQDi119/FUIwx21NQznOzc0V7du3F6dOnRLh4eFixYoVzmNtOcfsIZdYVVUVjh49ikGDBjn3yeVyDBo0CAcOHJAwMmoOJSUlAAA/Pz8AwNGjR2GxWFzyHRcXh7CwMOa7jZk6dSqGDh3qkkuAOXYHX3/9NXr16oXRo0cjMDAQ8fHx+Mc//uE8npOTg8LCQpcc+/j4oE+fPsxxG9C3b1/s2bMHWVlZAIATJ04gNTUVjz32GADm1900Jp8HDhyAwWBAr169nG0GDRoEuVyOtLS0Ox4z3b6SkhLIZDIYDAYAzLE7sNvtGDduHObMmYO77rqrzvG2nGOl1AH8t7ty5QpsNhuCgoJc9gcFBeHs2bMSRUXNwW63Y+bMmejXrx/uvvtuAEBhYSFUKpXzPwiHoKAgFBYWShAlNcWWLVuQnp6Ow4cP1znGHLd9v/zyC9asWYNZs2ZhwYIFOHz4MKZPnw6VSoWkpCRnHuv7d5s5bv1effVVlJaWIi4uDgqFAjabDe+88w4SExMBgPl1M43JZ2FhIQIDA12OK5VK+Pn5MedtUEVFBebNm4dnnnkG3t7eAJhjd7BkyRIolUpMnz693uNtOccsyIlayNSpU3Hq1CmkpqZKHQo1o0uXLmHGjBnYvXs3NBqN1OFQC7Db7ejVqxfeffddAEB8fDxOnTqFtWvXIikpSeLo6HZt27YNGzduxKZNm3DXXXfh+PHjmDlzJkJDQ5lfojbOYrFgzJgxEEJgzZo1UodDzeTo0aNYuXIl0tPTIZPJpA6n2fGRdYkFBARAoVDUmYH58uXLCA4Oligqul3Tpk3Dt99+i71796JDhw7O/cHBwaiqqsL169dd2jPfbcfRo0dRVFSEHj16QKlUQqlUIjk5GatWrYJSqURQUBBz3MaFhISga9euLvu6dOmCixcvAoAzj/x3u22aM2cOXn31VfzpT39Ct27dMG7cOLzyyit47733ADC/7qYx+QwODq4zka7VakVxcTFz3oY4ivFff/0Vu3fvdvaOA8xxW5eSkoKioiKEhYU5//b69ddf8Ze//AUREREA2naOWZBLTKVSoWfPntizZ49zn91ux549e5CQkCBhZNQUQghMmzYNX375JX744QdERka6HO/Zsyc8PDxc8p2ZmYmLFy8y323Eww8/jJ9//hnHjx93fvXq1QuJiYnO75njtq1fv351livMyspCeHg4ACAyMhLBwcEuOS4tLUVaWhpz3AaYzWbI5a5//igUCtjtdgDMr7tpTD4TEhJw/fp1HD161Nnmhx9+gN1uR58+fe54zHTrHMV4dnY2/vOf/8Df39/lOHPcto0bNw4nT550+dsrNDQUc+bMwffffw+gjedY6lnlSIgtW7YItVotNmzYIDIyMsSLL74oDAaDKCwslDo0ukUvvfSS8PHxEfv27RMFBQXOL7PZ7GwzZcoUERYWJn744Qdx5MgRkZCQIBISEiSMmm5XzVnWhWCO27pDhw4JpVIp3nnnHZGdnS02btwodDqd+Pzzz51t3n//fWEwGMS///1vcfLkSTF8+HARGRkpysvLJYycGiMpKUm0b99efPvttyInJ0fs2LFDBAQEiLlz5zrbML9ti9FoFMeOHRPHjh0TAMTy5cvFsWPHnDNsNyafQ4YMEfHx8SItLU2kpqaK6Oho8cwzz0j1kqiWG+W4qqpKDBs2THTo0EEcP37c5e+vyspK5zWY49btZr/HtdWeZV2ItptjFuStxF//+lcRFhYmVCqV6N27tzh48KDUIVETAKj3a/369c425eXl4uWXXxa+vr5Cp9OJESNGiIKCAumCpttWuyBnjtu+b775Rtx9991CrVaLuLg48X//938ux+12u3jjjTdEUFCQUKvV4uGHHxaZmZkSRUu3orS0VMyYMUOEhYUJjUYjOnXqJF577TWXP9yZ37Zl79699f7fm5SUJIRoXD6vXr0qnnnmGeHp6Sm8vb3FhAkThNFolODVUH1ulOOcnJwG//7au3ev8xrMcet2s9/j2uoryNtqjmVCCHEneuKJiIiIiIiI6A8cQ05EREREREQkARbkRERERERERBJgQU5EREREREQkARbkRERERERERBJgQU5EREREREQkARbkRERERERERBJgQU5EREREREQkARbkRERERERERBJgQU5ERG3Ovn37IJPJcP369UafExERgY8++qjFYroTZDIZvvrqq2a73vjx4/HUU0812/VqqqqqQlRUFH766acWuX5b15LvfU1VVVWIiIjAkSNHWvxeRER061iQExFRsxo/fjxkMhmmTJlS59jUqVMhk8kwfvz4Ox9YI5SWluK1115DXFwcNBoNgoODMWjQIOzYsQNCCKnDa3YrV67Ehg0bnNsDBw7EzJkzm+Xaa9euRWRkJPr27Vvv8WXLluHZZ58FAGzatAkPPfSQy3HHhy6Or6CgIIwaNQq//PJLs8QntdrvfVMsXrzY5T3y8fFB//79kZyc7GyjUqkwe/ZszJs37zYjJiKilsCCnIiIml3Hjh2xZcsWlJeXO/dVVFRg06ZNCAsLkzCyhl2/fh19+/bFp59+ivnz5yM9PR379+/H2LFjMXfuXJSUlEgdYrPz8fGBwWBo9usKIfC3v/0NEydObLDNgQMH0K9fPwBASkqK8/vaMjMzkZ+fj+3bt+P06dN48sknYbPZmhSXxWJp0nktobne+7vuugsFBQUoKCjAgQMHEB0djSeeeMLl5zUxMRGpqak4ffr0bd+PiIiaFwtyIiJqdj169EDHjh2xY8cO574dO3YgLCwM8fHxLm0rKysxffp0BAYGQqPR4P7778fhw4dd2uzcuRMxMTHQarV48MEHceHChTr3TE1NRf/+/aHVatGxY0dMnz4dZWVljY55wYIFuHDhAtLS0pCUlISuXbsiJiYGkyZNwvHjx+Hp6QkAuHbtGp5//nn4+vpCp9PhscceQ3Z2tvM6GzZsgMFgwLfffovY2FjodDo8/fTTMJvN+OSTTxAREQFfX19Mnz7dpbCMiIjAW2+9hWeeeQZ6vR7t27fH6tWrbxjzpUuXMGbMGBgMBvj5+WH48OHO9+bs2bPQ6XTYtGmTs/22bdug1WqRkZEBwPWx6fHjxyM5ORkrV6509rjm5OQgKioKH3zwgct9jx8/DplMhnPnztUb19GjR3H+/HkMHTq0wdhrFuSpqakNFuSBgYEICQnBAw88gIULFyIjIwPnzp3D4cOH8cgjjyAgIAA+Pj4YMGAA0tPTXc6VyWRYs2YNhg0bBr1ej3feeQc2mw0TJ05EZGQktFotYmNjsXLlSpfzHO/Lu+++i6CgIBgMBrz55puwWq2YM2cO/Pz80KFDB6xfv955TlVVFaZNm4aQkBBoNBqEh4fjvffea/D1135kfeDAgZg+fTrmzp0LPz8/BAcHY/HixQ2e76BUKhEcHIzg4GB07doVb775JkwmE7KyspxtfH190a9fP2zZsuWm1yMiojuLBTkREbWIF154waVgWbduHSZMmFCn3dy5c/HFF1/gk08+QXp6OqKiojB48GAUFxcDqC46R44ciSeffBLHjx/Hn//8Z7z66qsu1zh//jyGDBmCUaNG4eTJk9i6dStSU1Mxbdq0RsVqt9uxZcsWJCYmIjQ0tM5xT09PKJVKANWF1JEjR/D111/jwIEDEELg8ccfd+l9NZvNWLVqFbZs2YLvvvsO+/btw4gRI7Bz507s3LkTn332Gf7+97/jX//6l8t9li1bhu7du+PYsWN49dVXMWPGDOzevbvemC0WCwYPHgwvLy+kpKTgxx9/hKenJ4YMGYKqqirExcXhgw8+wMsvv4yLFy8iNzcXU6ZMwZIlS9C1a9c611u5ciUSEhIwadIkZ49rWFhYnTwCwPr16/HAAw8gKiqq3thSUlIQExMDLy8vl/3vv/8+DAYDDAYDCgsLMWDAABgMBpw6dcr5wUJqamq91wQArVYLoLr4NRqNSEpKQmpqKg4ePIjo6Gg8/vjjMBqNLucsXrwYI0aMwM8//4wXXngBdrsdHTp0wPbt25GRkYGFCxdiwYIF2LZtm8t5P/zwA/Lz87F//34sX74cixYtwhNPPAFfX1+kpaVhypQpmDx5MnJzcwEAq1atwtdff41t27YhMzMTGzduRERERIOvpT6ffPIJ9Ho90tLSsHTpUrz55psN5r8+lZWVWL9+PQwGA2JjY12O9e7dGykpKbcUDxER3QGCiIioGSUlJYnhw4eLoqIioVarxYULF8SFCxeERqMRv/32mxg+fLhISkoSQghhMpmEh4eH2Lhxo/P8qqoqERoaKpYuXSqEEGL+/Pmia9euLveYN2+eACCuXbsmhBBi4sSJ4sUXX3Rpk5KSIuRyuSgvLxdCCBEeHi5WrFhRb8yXL18WAMTy5ctv+NqysrIEAPHjjz869125ckVotVqxbds2IYQQ69evFwDEuXPnnG0mT54sdDqdMBqNzn2DBw8WkydPdm6Hh4eLIUOGuNxv7Nix4rHHHnNuAxBffvmlEEKIzz77TMTGxgq73e48XllZKbRarfj++++d+4YOHSr69+8vHn74YfHoo4+6tHfkymHAgAFixowZLjHk5eUJhUIh0tLShBDV+QkICBAbNmxo8H2aMWOGeOihh+rsv3btmsjJyRGLFi0SgwcPFjk5OWL16tXivvvuEzk5OSInJ8eZr71797rkOD8/X/Tt21e0b99eVFZW1rm2zWYTXl5e4ptvvnF5v2bOnNlgnA5Tp04Vo0aNcnlfwsPDhc1mc+6LjY0V/fv3d25brVah1+vF5s2bhRBC/M///I946KGHXN7fG6nvvb///vtd2tx3331i3rx5DV5j0aJFQi6XC71eL/R6vZDJZMLb21vs2rWrTtuVK1eKiIiIRsVGRER3jlLCzwKIiMiNtWvXDkOHDsWGDRsghMDQoUMREBDg0ub8+fOwWCwujyt7eHigd+/eOHPmDADgzJkz6NOnj8t5CQkJLtsnTpzAyZMnsXHjRuc+IQTsdjtycnLQpUuXG8YqGjlh25kzZ6BUKl3i8ff3R2xsrDNeANDpdOjcubNzOygoCBEREc7H3h37ioqKbvi6EhISGpwZ/sSJEzh37lydXuiKigqcP3/eub1u3TrExMRALpfj9OnTkMlkjXqtDqGhoRg6dCjWrVuH3r1745tvvkFlZSVGjx7d4Dnl5eXQaDR19jt6xw8dOoRRo0YhIiICx44dw7BhwxrsTe7QoQOEEDCbzejevTu++OILqFQqXL58Ga+//jr27duHoqIi2Gw2mM1mXLx40eX8Xr161bnm6tWrsW7dOly8eBHl5eWoqqrCvffe69Lmrrvuglz+x4OEQUFBuPvuu53bCoUC/v7+zhyOHz8ejzzyCGJjYzFkyBA88cQTePTRRxt8j+pzzz33uGyHhITU+RmpLTY2Fl9//TUAwGg0YuvWrRg9ejT27t3r8tq1Wi3MZvMtxUNERC2PBTkREbWYF154wfnY+M3GQ98Ok8mEyZMnY/r06XWONWYSuXbt2sFgMODs2bPNEo+Hh4fLtkwmq3ef3W5v8j1MJhN69uzp8iGEQ7t27ZzfnzhxAmVlZZDL5SgoKEBISMgt3+vPf/4zxo0bhxUrVmD9+vUYO3YsdDpdg+0DAgLw888/u+xLSUnBY489BqD6kf59+/bhlVdeQXl5OTw8PPD+++9jwYIFWLBgQZ3zvL29ERgY6PLhQ1JSEq5evYqVK1ciPDwcarUaCQkJqKqqcjlfr9e7bG/ZsgWzZ8/Ghx9+iISEBHh5eWHZsmVIS0tzaXerOezRowdycnKwa9cu/Oc//8GYMWMwaNCgOsMSbqQpPyMqlcpl6EB8fDy++uorfPTRR/j888+d+4uLi11+LoiIqHVgQU5ERC3GMZ5ZJpNh8ODBdY537twZKpUKP/74I8LDwwFUj40+fPiwc/mtLl26OHsAHQ4ePOiy3aNHD2RkZDQ4pvlm5HI5/vSnP+Gzzz7DokWL6owjN5lM0Gg06NKlC6xWK9LS0pzLeV29ehWZmZn1jsu+VbVf18GDBxvs3e/Rowe2bt2KwMBAeHt719umuLgY48ePx2uvvYaCggIkJiYiPT3dORa7NpVKVe8M5o8//jj0ej3WrFmD7777Dvv377/h64iPj8eaNWsghHD2yPfq1QvHjx/H0aNHMXfuXOzZswcXL17EsGHDkJ6eDrlcDj8/vzrXioyMrHc28h9//BEff/wxHn/8cQDVcw1cuXLlhnE5zuvbty9efvll576aTxTcDm9vb4wdOxZjx47F008/jSFDhqC4uLje19WSFAqFywoHAHDq1Kk6EyoSEZH0OKkbERG1GIVCgTNnziAjIwMKhaLOcb1ej5deeglz5szBd999h4yMDEyaNAlms9m5ZNaUKVOQnZ2NOXPmIDMzE5s2baqzfvO8efPw008/Ydq0aTh+/Diys7Px73//u9GTugHAO++8g44dO6JPnz749NNPkZGRgezsbKxbtw7x8fEwmUyIjo7G8OHDMWnSJKSmpuLEiRN47rnn0L59ewwfPvy23iugulhcunQpsrKysHr1amzfvh0zZsyot21iYiICAgIwfPhwpKSkICcnB/v27cP06dOdE41NmTIFHTt2xOuvv47ly5fDZrNh9uzZDd4/IiICaWlpuHDhAq5cueLsnVUoFBg/fjzmz5+P6OjoOo/W1/bggw/CZDK5LLOl1WoRFRWFnJwcDBw4EFFRUcjNzUW/fv0QExODqKioWypco6Oj8dlnn+HMmTNIS0tDYmJigx801D7vyJEj+P7775GVlYU33nijzqz+TbF8+XJs3rwZZ8+eRVZWFrZv347g4OAWWVauJqvVisLCQhQWFiI7Oxtvv/02MjIy6vw8pqSk3PIj9ERE1PJYkBMRUYvy9vZusAcXqJ55e9SoURg3bhx69OiBc+fO4fvvv4evry+A6kfOv/jiC3z11Vfo3r071q5di3fffdflGvfccw+Sk5ORlZWF/v37Iz4+HgsXLqx3xvSG+Pn54eDBg3juuefw9ttvIz4+Hv3798fmzZuxbNky+Pj4AKieYbxnz5544oknkJCQACEEdu7cWedx46b4y1/+giNHjiA+Ph5vv/02li9fXu+TBUD1OPX9+/cjLCwMI0eORJcuXTBx4kRUVFTA29sbn376qXNGd6VSCb1ej88//xz/+Mc/sGvXrnqvOXv2bCgUCnTt2hXt2rVzGY89ceJEVFVV1TtTfm3+/v4YMWJEvY/T79u3Dw888AAAIDk52fn9rfrnP/+Ja9euoUePHhg3bpxz6bybmTx5MkaOHImxY8eiT58+uHr1qktveVN5eXlh6dKl6NWrF+677z5cuHABO3fudBmH3hJOnz6NkJAQhISE4N5778W2bduwZs0aPP/88842Bw4cQElJCZ5++ukWjYWIiG6dTDR2JhsiIiJqMREREZg5c6bzUf3WJiUlBQ8//DAuXbqEoKCgm7Y/efIkHnnkEZw/f95lMju688aOHYvu3bvXGZ9PRETSYw85ERERNaiyshK5ublYvHgxRo8e3ahiHKh+amHJkiXIyclp4QjpRqqqqtCtWze88sorUodCRET1YEFOREREDdq8eTPCw8Nx/fp1LF269JbOHT9+PLp169ZCkVFjqFQqvP76640aX09ERHceH1knIiIiIiIikgB7yImIiIiIiIgkwIKciIiIiIiISAIsyImIiIiIiIgkwIKciIiIiIiISAIsyImIiIiIiIgkwIKciIiIiIiISAIsyImIiIiIiIgkwIKciIiIiIiISAL/HyoiXJTamVzbAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datetime import datetime, timezone\n",
    "\n",
    "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)\n",
    "\n",
    "# Plot the relationship between model complexity and performance for both versions\n",
    "colors = [\n",
    "    \"#FF323D\",\n",
    "    \"#FF681F\",\n",
    "    \"#FF9D00\",\n",
    "    \"#FFB80F\",\n",
    "    \"#FFD21E\",\n",
    "    \"#32343D\",\n",
    "]\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.scatter(\n",
    "    merged_data[\"#Params (B)\"][merged_data[\"date\"] < threshold_1],\n",
    "    merged_data[\"Mean_Score_v1\"][merged_data[\"date\"] < threshold_1],\n",
    "    label=\"V1 - before Oct 2023\",\n",
    "    alpha=0.7,\n",
    "    color=colors[0],\n",
    ")\n",
    "plt.scatter(\n",
    "    merged_data[\"#Params (B)\"][threshold_1 < merged_data[\"date\"]][\n",
    "        merged_data[\"date\"] < threshold_2\n",
    "    ],\n",
    "    merged_data[\"Mean_Score_v1\"][threshold_1 < merged_data[\"date\"]][\n",
    "        merged_data[\"date\"] < threshold_2\n",
    "    ],\n",
    "    label=\"V1 - Oct/Nov 2023\",\n",
    "    alpha=0.7,\n",
    "    color=colors[1],\n",
    ")\n",
    "plt.scatter(\n",
    "    merged_data[\"#Params (B)\"][threshold_2 < merged_data[\"date\"]][\n",
    "        merged_data[\"date\"] < threshold_3\n",
    "    ],\n",
    "    merged_data[\"Mean_Score_v1\"][threshold_2 < merged_data[\"date\"]][\n",
    "        merged_data[\"date\"] < threshold_3\n",
    "    ],\n",
    "    label=\"V1 - Dec/Jan/Feb 2024\",\n",
    "    alpha=0.7,\n",
    "    color=colors[2],\n",
    ")\n",
    "plt.scatter(\n",
    "    merged_data[\"#Params (B)\"][threshold_3 < merged_data[\"date\"]][\n",
    "        merged_data[\"date\"] < threshold_4\n",
    "    ],\n",
    "    merged_data[\"Mean_Score_v1\"][threshold_3 < merged_data[\"date\"]][\n",
    "        merged_data[\"date\"] < threshold_4\n",
    "    ],\n",
    "    label=\"V1 - Mar/Apr/May 2024\",\n",
    "    alpha=0.7,\n",
    "    color=colors[3],\n",
    ")\n",
    "plt.scatter(\n",
    "    merged_data[\"#Params (B)\"][merged_data[\"date\"] > threshold_4],\n",
    "    merged_data[\"Mean_Score_v1\"][merged_data[\"date\"] > threshold_4],\n",
    "    label=\"V1 - May 2024 and after\",\n",
    "    alpha=0.7,\n",
    "    color=colors[4],\n",
    ")\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[\"Mean_Score_v2\"],\n",
    "    label=\"V2\",\n",
    "    alpha=0.7,\n",
    "    color=colors[-1],\n",
    ")\n",
    "plt.xlabel(\"Model Complexity (#Params in B)\")\n",
    "plt.ylabel(\"Mean Performance Score\")\n",
    "plt.title(\"Model Complexity vs Performance\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fa0cd00-64fd-42c9-8956-e8af0cab105f",
   "metadata": {},
   "source": [
    "## Outliers analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6a86455-3383-4ec9-a87b-8ef6a23ebeb2",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Median_Score_v1'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m~/miniconda3/envs/open_llm_leaderboard/lib/python3.10/site-packages/pandas/core/indexes/base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3805\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[0;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Median_Score_v1'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[56], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Identify models with 0 parameters but high median scores\u001b[39;00m\n\u001b[1;32m      2\u001b[0m zero_params_high_score_v1 \u001b[38;5;241m=\u001b[39m v1_data[\n\u001b[1;32m      3\u001b[0m     (v1_data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m#Params (B)\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m----> 4\u001b[0m     \u001b[38;5;241m&\u001b[39m (\u001b[43mv1_data\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mMedian_Score_v1\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m>\u001b[39m v1_data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMedian_Score_v1\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mmedian())\n\u001b[1;32m      5\u001b[0m ]\n\u001b[1;32m      6\u001b[0m zero_params_high_score_v2 \u001b[38;5;241m=\u001b[39m v2_data[\n\u001b[1;32m      7\u001b[0m     (v2_data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m#Params (B)\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m      8\u001b[0m     \u001b[38;5;241m&\u001b[39m (v2_data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMedian_Score_v2\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m>\u001b[39m v2_data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMedian_Score_v2\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mmedian())\n\u001b[1;32m      9\u001b[0m ]\n\u001b[1;32m     11\u001b[0m zero_params_high_score_v1\n",
      "File \u001b[0;32m~/miniconda3/envs/open_llm_leaderboard/lib/python3.10/site-packages/pandas/core/frame.py:4102\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   4101\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 4102\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   4104\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[0;32m~/miniconda3/envs/open_llm_leaderboard/lib/python3.10/site-packages/pandas/core/indexes/base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3807\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[1;32m   3808\u001b[0m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[1;32m   3809\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[1;32m   3810\u001b[0m     ):\n\u001b[1;32m   3811\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[0;32m-> 3812\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3814\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3815\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3816\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3817\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Median_Score_v1'"
     ]
    }
   ],
   "source": [
    "# Identify models with 0 parameters but high median scores\n",
    "zero_params_high_score_v1 = v1_data[\n",
    "    (v1_data[\"#Params (B)\"] == 0)\n",
    "    & (v1_data[\"Median_Score_v1\"] > v1_data[\"Median_Score_v1\"].median())\n",
    "]\n",
    "zero_params_high_score_v2 = v2_data[\n",
    "    (v2_data[\"#Params (B)\"] == 0)\n",
    "    & (v2_data[\"Median_Score_v2\"] > v2_data[\"Median_Score_v2\"].median())\n",
    "]\n",
    "\n",
    "zero_params_high_score_v1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad0e0c64-a30d-4833-9575-dd4c56c1ef2d",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Median_Score_v1'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m~/miniconda3/envs/open_llm_leaderboard/lib/python3.10/site-packages/pandas/core/indexes/base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3805\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[0;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Median_Score_v1'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[143], line 8\u001b[0m\n\u001b[1;32m      3\u001b[0m data_v2 \u001b[38;5;241m=\u001b[39m data_v2\u001b[38;5;241m.\u001b[39mdrop_duplicates(subset\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124meval_name\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      5\u001b[0m \u001b[38;5;66;03m# Identify models with 0 parameters but high median scores and include task scores\u001b[39;00m\n\u001b[1;32m      6\u001b[0m zero_params_high_score_v1 \u001b[38;5;241m=\u001b[39m data_v1[\n\u001b[1;32m      7\u001b[0m     (data_v1[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m#Params (B)\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m----> 8\u001b[0m     \u001b[38;5;241m&\u001b[39m (\u001b[43mdata_v1\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mMedian_Score_v1\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m>\u001b[39m data_v1[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMedian_Score_v1\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mmedian())\n\u001b[1;32m      9\u001b[0m ]\n\u001b[1;32m     10\u001b[0m zero_params_high_score_v2 \u001b[38;5;241m=\u001b[39m data_v2[\n\u001b[1;32m     11\u001b[0m     (data_v2[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m#Params (B)\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m     12\u001b[0m     \u001b[38;5;241m&\u001b[39m (data_v2[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMedian_Score_v2\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m>\u001b[39m data_v2[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMedian_Score_v2\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mmedian())\n\u001b[1;32m     13\u001b[0m ]\n",
      "File \u001b[0;32m~/miniconda3/envs/open_llm_leaderboard/lib/python3.10/site-packages/pandas/core/frame.py:4102\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   4101\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 4102\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   4104\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[0;32m~/miniconda3/envs/open_llm_leaderboard/lib/python3.10/site-packages/pandas/core/indexes/base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3807\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[1;32m   3808\u001b[0m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[1;32m   3809\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[1;32m   3810\u001b[0m     ):\n\u001b[1;32m   3811\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[0;32m-> 3812\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3814\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3815\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3816\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3817\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Median_Score_v1'"
     ]
    }
   ],
   "source": [
    "# Dropping duplicated rows based on eval_name\n",
    "data_v1 = data_v1.drop_duplicates(subset=\"eval_name\")\n",
    "data_v2 = data_v2.drop_duplicates(subset=\"eval_name\")\n",
    "\n",
    "# Identify models with 0 parameters but high median scores and include task scores\n",
    "zero_params_high_score_v1 = data_v1[\n",
    "    (data_v1[\"#Params (B)\"] == 0)\n",
    "    & (data_v1[\"Median_Score_v1\"] > data_v1[\"Median_Score_v1\"].median())\n",
    "]\n",
    "zero_params_high_score_v2 = data_v2[\n",
    "    (data_v2[\"#Params (B)\"] == 0)\n",
    "    & (data_v2[\"Median_Score_v2\"] > data_v2[\"Median_Score_v2\"].median())\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a42f2ec-c6b9-4be3-a8fa-c466a77dbd29",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'zero_params_high_score_v1' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[144], line 15\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m pd\u001b[38;5;241m.\u001b[39mDataFrame(\n\u001b[1;32m      9\u001b[0m             np\u001b[38;5;241m.\u001b[39mwhere(is_max, attr, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m), index\u001b[38;5;241m=\u001b[39mdata\u001b[38;5;241m.\u001b[39mindex, columns\u001b[38;5;241m=\u001b[39mdata\u001b[38;5;241m.\u001b[39mcolumns\n\u001b[1;32m     10\u001b[0m         )\n\u001b[1;32m     13\u001b[0m \u001b[38;5;66;03m# Create a mask to filter rows with at least one max value in tasks\u001b[39;00m\n\u001b[1;32m     14\u001b[0m mask_v1 \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m---> 15\u001b[0m     \u001b[43mzero_params_high_score_v1\u001b[49m[tasks_v1]\n\u001b[1;32m     16\u001b[0m     \u001b[38;5;241m==\u001b[39m zero_params_high_score_v1[tasks_v1]\u001b[38;5;241m.\u001b[39mmax(axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m     17\u001b[0m )\u001b[38;5;241m.\u001b[39many(axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m     18\u001b[0m mask_v2 \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m     19\u001b[0m     zero_params_high_score_v2[tasks_v2]\n\u001b[1;32m     20\u001b[0m     \u001b[38;5;241m==\u001b[39m zero_params_high_score_v2[tasks_v2]\u001b[38;5;241m.\u001b[39mmax(axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m     21\u001b[0m )\u001b[38;5;241m.\u001b[39many(axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m     23\u001b[0m \u001b[38;5;66;03m# Filter dataframes based on the mask\u001b[39;00m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'zero_params_high_score_v1' is not defined"
     ]
    }
   ],
   "source": [
    "def highlight_max(data, color=\"lightgreen\"):\n",
    "    attr = f\"background-color: {color}\"\n",
    "    if data.ndim == 1:\n",
    "        is_max = data == data.max()\n",
    "        return [attr if v else \"\" for v in is_max]\n",
    "    else:\n",
    "        is_max = data == data.max().max()\n",
    "        return pd.DataFrame(\n",
    "            np.where(is_max, attr, \"\"), index=data.index, columns=data.columns\n",
    "        )\n",
    "\n",
    "\n",
    "# Create a mask to filter rows with at least one max value in tasks\n",
    "mask_v1 = (\n",
    "    zero_params_high_score_v1[tasks_v1]\n",
    "    == zero_params_high_score_v1[tasks_v1].max(axis=0)\n",
    ").any(axis=1)\n",
    "mask_v2 = (\n",
    "    zero_params_high_score_v2[tasks_v2]\n",
    "    == zero_params_high_score_v2[tasks_v2].max(axis=0)\n",
    ").any(axis=1)\n",
    "\n",
    "# Filter dataframes based on the mask\n",
    "filtered_v1 = zero_params_high_score_v1[mask_v1]\n",
    "filtered_v2 = zero_params_high_score_v2[mask_v2]\n",
    "\n",
    "filtered_v1 = filtered_v1[\n",
    "    [\n",
    "        \"eval_name\",\n",
    "        \"fullname\",\n",
    "        \"Precision\",\n",
    "        \"#Params (B)\",\n",
    "        \"ARC\",\n",
    "        \"HellaSwag\",\n",
    "        \"MMLU\",\n",
    "        \"TruthfulQA\",\n",
    "        \"Winogrande\",\n",
    "        \"GSM8K\",\n",
    "        \"Median_Score_v1\",\n",
    "    ]\n",
    "]\n",
    "filtered_v2 = filtered_v2[\n",
    "    [\n",
    "        \"eval_name\",\n",
    "        \"fullname\",\n",
    "        \"Precision\",\n",
    "        \"#Params (B)\",\n",
    "        \"IFEval\",\n",
    "        \"ARC Challenge\",\n",
    "        \"BBH\",\n",
    "        \"MATH Lvl 5\",\n",
    "        \"GPQA\",\n",
    "        \"MUSR\",\n",
    "        \"MMLU-PRO\",\n",
    "        \"Median_Score_v2\",\n",
    "    ]\n",
    "]\n",
    "\n",
    "# Highlighting the highest scores per task in each row\n",
    "highlighted_v1 = filtered_v1.style.apply(highlight_max, subset=tasks_v1)\n",
    "highlighted_v2 = filtered_v2.style.apply(highlight_max, subset=tasks_v2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d84a47e-6d51-4dc0-bb6c-bc9b416cf3d1",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'highlighted_v1' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[145], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mhighlighted_v1\u001b[49m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'highlighted_v1' is not defined"
     ]
    }
   ],
   "source": [
    "highlighted_v1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a68a15c7-29ec-43a5-aee1-db795a544a5d",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'highlighted_v2' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[146], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mhighlighted_v2\u001b[49m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'highlighted_v2' is not defined"
     ]
    }
   ],
   "source": [
    "highlighted_v2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f1ac894-d5d4-4411-bc57-05cb60d65a63",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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