diff --git "a/competition/00a_InternLM_2.5_Results.ipynb" "b/competition/00a_InternLM_2.5_Results.ipynb"
--- "a/competition/00a_InternLM_2.5_Results.ipynb"
+++ "b/competition/00a_InternLM_2.5_Results.ipynb"
@@ -1 +1 @@
-{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{},"inputWidgets":{},"nuid":"0ea8b46b-839b-445b-8043-ccdf4e920ace","showTitle":false,"title":""},"id":"YLH80COBzi_F"},"outputs":[],"source":["%load_ext autoreload\n","%autoreload 2"]},{"cell_type":"code","execution_count":2,"metadata":{"id":"63B5exAuzq4M"},"outputs":[],"source":["from pathlib import Path\n","\n","try:\n","    from google.colab import drive\n","    drive.mount('/content/drive')\n","    workding_dir = \"/content/drive/MyDrive/logical-reasoning/\"\n","except ModuleNotFoundError:\n","    workding_dir = str(Path.cwd().parent)"]},{"cell_type":"code","execution_count":3,"metadata":{"executionInfo":{"elapsed":368,"status":"ok","timestamp":1719461634865,"user":{"displayName":"Donghao Huang","userId":"00463591218503521679"},"user_tz":-480},"id":"zFulf0bg0H-9","outputId":"debdd535-c828-40b9-efc0-8a180e5830dd"},"outputs":[{"name":"stdout","output_type":"stream","text":["workding dir: /Users/inflaton/code/engd/projects/logical-reasoning\n"]}],"source":["import os\n","import sys\n","\n","os.chdir(workding_dir)\n","sys.path.append(workding_dir)\n","print(\"workding dir:\", workding_dir)"]},{"cell_type":"code","execution_count":4,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{},"inputWidgets":{},"nuid":"9f67ec60-2f24-411c-84eb-0dd664b44775","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":589,"status":"ok","timestamp":1719462011879,"user":{"displayName":"Donghao Huang","userId":"00463591218503521679"},"user_tz":-480},"id":"DIUiweYYzi_I","outputId":"e16e9247-9077-4b0c-f8ea-17059f05a1c4"},"outputs":[{"name":"stdout","output_type":"stream","text":["loading env vars from: /Users/inflaton/code/engd/projects/logical-reasoning/.env.example\n"]},{"data":{"text/plain":["True"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["from dotenv import find_dotenv, load_dotenv\n","\n","found_dotenv = find_dotenv(\".env\")\n","\n","if len(found_dotenv) == 0:\n","    found_dotenv = find_dotenv(\".env.example\")\n","print(f\"loading env vars from: {found_dotenv}\")\n","load_dotenv(found_dotenv, override=True)"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[],"source":["P1 = \"\"\"你是一个逻辑游戏的主持人。游戏规则如下：\n","\n","1. 参与者会得到一个谜题。\n","2. 参与者可以通过提问来获取线索，尝试解开谜题。\n","3. 对于每个问题，主持人将根据实际情况回答以下五个选项之一：是、不是、不重要、回答正确、问法错误。\n","4. 回答中不能添加任何其它信息，也不能省略选项中的任何一个字。例如，不可以把“不是”省略成“不”。\n","5. 参与者需要根据回答来推理，并最终找出谜题的正确答案。\n","\n","请严格按照这些规则回答参与者提出的问题。\n","\n","谜题: {}\n","\n","实际情况: {}\n","\n","参与者提出的问题: {}\n","\"\"\""]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[],"source":["P2 = \"\"\"你是一个情景猜谜游戏的主持人。游戏规则如下：\n","\n","1. 参与者会得到一个谜面，谜面会描述一个简单又难以理解的事件。\n","2. 主持人知道谜底，谜底是谜面的答案。\n","3. 参与者可以询问任何封闭式问题来找寻事件的真相。\n","4. 对于每个问题，主持人将根据实际情况回答以下五个选项之一：是、不是、不重要、回答正确、问法错误。各回答的判断标准如下：\n","   - 若谜面和谜底能找到问题的答案，回答：是或者不是\n","   - 若谜面和谜底不能直接或者间接推断出问题的答案，回答：不重要\n","   - 若参与者提问不是一个封闭式问题或者问题难以理解，回答：问法错误\n","   - 若参与者提问基本还原了谜底真相，回答：回答正确\n","5. 回答中不能添加任何其它信息，也不能省略选项中的任何一个字。例如，不可以把“不是”省略成“不”。\n","\n","请严格按照这些规则回答参与者提出的问题。\n","\n","**谜面:** {}\n","\n","**谜底:** {}\n","\n","**参与者提出的问题:** {}\n","\"\"\""]},{"cell_type":"code","execution_count":7,"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>epoch</th>\n","      <th>model</th>\n","      <th>accuracy</th>\n","      <th>precision</th>\n","      <th>recall</th>\n","      <th>f1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>internlm/internlm2_5-7b-chat-1m</td>\n","      <td>0.759667</td>\n","      <td>0.741854</td>\n","      <td>0.781014</td>\n","      <td>0.758887</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-44</td>\n","      <td>0.761667</td>\n","      <td>0.810873</td>\n","      <td>0.761667</td>\n","      <td>0.780018</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-88</td>\n","      <td>0.741333</td>\n","      <td>0.816182</td>\n","      <td>0.741333</td>\n","      <td>0.769524</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-132</td>\n","      <td>0.755000</td>\n","      <td>0.809829</td>\n","      <td>0.755000</td>\n","      <td>0.775657</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-176</td>\n","      <td>0.719000</td>\n","      <td>0.803307</td>\n","      <td>0.719000</td>\n","      <td>0.750319</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   epoch                                           model  accuracy  precision  \\\n","0      0                 internlm/internlm2_5-7b-chat-1m  0.759667   0.741854   \n","1      1   internlm/internlm2_5-7b-chat-1m_checkpoint-44  0.761667   0.810873   \n","2      2   internlm/internlm2_5-7b-chat-1m_checkpoint-88  0.741333   0.816182   \n","3      3  internlm/internlm2_5-7b-chat-1m_checkpoint-132  0.755000   0.809829   \n","4      4  internlm/internlm2_5-7b-chat-1m_checkpoint-176  0.719000   0.803307   \n","\n","     recall        f1  \n","0  0.781014  0.758887  \n","1  0.761667  0.780018  \n","2  0.741333  0.769524  \n","3  0.755000  0.775657  \n","4  0.719000  0.750319  "]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["import pandas as pd\n","\n","df_p1 = pd.read_csv(\"results/mgtv-results_p1_full_metrics.csv\")\n","df_p1"]},{"cell_type":"code","execution_count":8,"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>epoch</th>\n","      <th>model</th>\n","      <th>accuracy</th>\n","      <th>precision</th>\n","      <th>recall</th>\n","      <th>f1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>internlm/internlm2_5-7b-chat-1m</td>\n","      <td>0.766000</td>\n","      <td>0.747969</td>\n","      <td>0.787526</td>\n","      <td>0.764922</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-88</td>\n","      <td>0.796333</td>\n","      <td>0.808232</td>\n","      <td>0.796333</td>\n","      <td>0.798160</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-176</td>\n","      <td>0.781333</td>\n","      <td>0.804716</td>\n","      <td>0.781333</td>\n","      <td>0.788581</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-264</td>\n","      <td>0.759000</td>\n","      <td>0.805502</td>\n","      <td>0.759000</td>\n","      <td>0.777237</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-352</td>\n","      <td>0.730333</td>\n","      <td>0.790676</td>\n","      <td>0.730333</td>\n","      <td>0.753716</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>5</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-440</td>\n","      <td>0.730333</td>\n","      <td>0.790420</td>\n","      <td>0.730333</td>\n","      <td>0.753750</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>6</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-528</td>\n","      <td>0.716000</td>\n","      <td>0.789892</td>\n","      <td>0.716000</td>\n","      <td>0.744833</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   epoch                                           model  accuracy  precision  \\\n","0      0                 internlm/internlm2_5-7b-chat-1m  0.766000   0.747969   \n","1      1   internlm/internlm2_5-7b-chat-1m_checkpoint-88  0.796333   0.808232   \n","2      2  internlm/internlm2_5-7b-chat-1m_checkpoint-176  0.781333   0.804716   \n","3      3  internlm/internlm2_5-7b-chat-1m_checkpoint-264  0.759000   0.805502   \n","4      4  internlm/internlm2_5-7b-chat-1m_checkpoint-352  0.730333   0.790676   \n","5      5  internlm/internlm2_5-7b-chat-1m_checkpoint-440  0.730333   0.790420   \n","6      6  internlm/internlm2_5-7b-chat-1m_checkpoint-528  0.716000   0.789892   \n","\n","     recall        f1  \n","0  0.787526  0.764922  \n","1  0.796333  0.798160  \n","2  0.781333  0.788581  \n","3  0.759000  0.777237  \n","4  0.730333  0.753716  \n","5  0.730333  0.753750  \n","6  0.716000  0.744833  "]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["df_p2 = pd.read_csv(\"results/mgtv-results_p2_full_metrics.csv\")\n","df_p2"]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>epoch</th>\n","      <th>model</th>\n","      <th>accuracy</th>\n","      <th>precision</th>\n","      <th>recall</th>\n","      <th>f1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat</td>\n","      <td>0.783667</td>\n","      <td>0.766712</td>\n","      <td>0.792917</td>\n","      <td>0.767940</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>0.568667</td>\n","      <td>0.807123</td>\n","      <td>0.568667</td>\n","      <td>0.625399</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>0.704333</td>\n","      <td>0.810817</td>\n","      <td>0.704333</td>\n","      <td>0.742186</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   epoch                                              model  accuracy  \\\n","0      0                shenzhi-wang/Llama3-8B-Chinese-Chat  0.783667   \n","1      1  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.568667   \n","2      2  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.704333   \n","\n","   precision    recall        f1  \n","0   0.766712  0.792917  0.767940  \n","1   0.807123  0.568667  0.625399  \n","2   0.810817  0.704333  0.742186  "]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["df_p1_llama3 = pd.read_csv(\"results/mgtv-llama3_p1_full_metrics.csv\")\n","df_p1_llama3"]},{"cell_type":"code","execution_count":10,"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>epoch</th>\n","      <th>model</th>\n","      <th>accuracy</th>\n","      <th>precision</th>\n","      <th>recall</th>\n","      <th>f1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat</td>\n","      <td>0.730000</td>\n","      <td>0.770974</td>\n","      <td>0.730000</td>\n","      <td>0.746291</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>0.773000</td>\n","      <td>0.773916</td>\n","      <td>0.773000</td>\n","      <td>0.764280</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>0.704667</td>\n","      <td>0.814516</td>\n","      <td>0.704667</td>\n","      <td>0.745365</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>0.679333</td>\n","      <td>0.803070</td>\n","      <td>0.679333</td>\n","      <td>0.724637</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   epoch                                              model  accuracy  \\\n","0      0                shenzhi-wang/Llama3-8B-Chinese-Chat  0.730000   \n","1      1  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.773000   \n","2      2  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.704667   \n","3      3  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.679333   \n","\n","   precision    recall        f1  \n","0   0.770974  0.730000  0.746291  \n","1   0.773916  0.773000  0.764280  \n","2   0.814516  0.704667  0.745365  \n","3   0.803070  0.679333  0.724637  "]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["df_p2_llama3 = pd.read_csv(\"results/mgtv-llama3_p2_full_metrics.csv\")\n","df_p2_llama3"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[{"data":{"text/plain":["3"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["new_len = min(len(df_p1), len(df_p2), len(df_p1_llama3), len(df_p2_llama3))\n","new_len"]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[],"source":["# df_p1 = df_p1[:new_len]\n","# df_p2 = df_p2[:new_len]\n","# df_p1_llama3 = df_p1_llama3[:new_len]\n","# df_p2_llama3 = df_p2_llama3[:new_len]"]},{"cell_type":"code","execution_count":25,"metadata":{},"outputs":[],"source":["def plot_results(df_p1, df_p2, best_p1, best_p2, color_p1=\"red\", color_p2=\"blue\", model_name=\"InternLM2.5_7b\"):\n","    sns.lineplot(\n","        x=\"epoch\",\n","        y=\"accuracy\",\n","        data=df_p1,\n","        ax=ax[0],\n","        color=color_p1,\n","        label=f\"{model_name}: P1\",\n","    )\n","    sns.lineplot(\n","        x=\"epoch\",\n","        y=\"accuracy\",\n","        data=df_p2,\n","        ax=ax[0],\n","        color=color_p2,\n","        label=f\"{model_name}: P2\",\n","    )\n","    sns.scatterplot(\n","        x=\"epoch\", y=\"accuracy\", data=best_p1, ax=ax[0], color=color_p1, s=50\n","    )\n","    sns.scatterplot(\n","        x=\"epoch\", y=\"accuracy\", data=best_p2, ax=ax[0], color=color_p2, s=50\n","    )\n","\n","    sns.lineplot(\n","        x=\"epoch\",\n","        y=\"f1\",\n","        data=df_p1,\n","        ax=ax[1],\n","        color=color_p1,\n","        label=f\"{model_name}: P1\",\n","    )\n","    sns.lineplot(\n","        x=\"epoch\",\n","        y=\"f1\",\n","        data=df_p2,\n","        ax=ax[1],\n","        color=color_p2,\n","        label=f\"{model_name}: P2\",\n","    )\n","    sns.scatterplot(x=\"epoch\", y=\"f1\", data=best_p1, ax=ax[1], color=color_p1, s=50)\n","    sns.scatterplot(x=\"epoch\", y=\"f1\", data=best_p2, ax=ax[1], color=color_p2, s=50)"]},{"cell_type":"code","execution_count":26,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Best P1 accuracy:\n","0.7616666666666667\n","Best P2 accuracy:\n","0.7963333333333333\n","Best P1 accuracy llama3:\n","0.7836666666666666\n","Best P2 accuracy llama3:\n","0.773\n"]},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABN4AAAHZCAYAAABZzPY5AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/TGe4hAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOydZXRUVxeGn5m4QIIEKQQLECRIcHenoVihxd0pWkhLimtDi5bSAsGhxT6guLtboUhwh+AE4nK/H5uZZGJIEoKcZ627krn3XDuZzJz7nr3frdM0TUOhUCgUCoVCoVAoFAqFQqFQJCn6lL4AhUKhUCgUCoVCoVAoFAqF4lNECW8KhUKhUCgUCoVCoVAoFApFMqCEN4VCoVAoFAqFQqFQKBQKhSIZUMKbQqFQKBQKhUKhUCgUCoVCkQwo4U2hUCgUCoVCoVAoFAqFQqFIBpTwplAoFAqFQqFQKBQKhUKhUCQDSnhTKBQKhUKhUCgUCoVCoVAokgElvCkUCoVCoVAoFAqFQqFQKBTJgBLeFAqFQqFQKBQKhUKhUCgUimRACW8KheKTYsCAAbi6uuLj45PSl6JQKBQKhUKh+MDx9PTE1dU13mXTpk0m7V++fEm1atVYtWpVCl2xQqH42DBP6QtQKBSKpOLFixds27aNvHnz8vfff9O+fXt0Ol1KX5ZCoVAoFAqF4gPGycmJ6dOnx7ktR44cxt+fP39Ojx49uHPnznu6MoVC8SmghDeFQvHJsG7dOgCGDBlC27ZtOXToEGXLlk3hq1IoFAqFQqFQfMhYWlpStGjRBNts376dMWPGEBAQ8H4uSqFQfDKoVFOFQvHJsHLlSsqWLUuZMmXInj07f/31V6w2q1evplGjRhQpUoQqVarwyy+/EBoaatx+6tQpOnToQLFixShTpgz9+/fHz88PgFWrVuHq6srt27dNjlmtWjU8PT2Nr11dXZk+fTqNGzemcOHCxhnUo0eP0rFjR0qWLImbmxvVqlVj2rRpREZGGvd9+fIlo0aNomLFihQtWpQmTZqwa9cuACZMmEDhwoV58eKFyflnzJhB8eLFCQoKSlwHKhQKhUKhUChi4e/vT69evShZsiSzZ89O6ctRKBQfGUp4UygUnwSXLl3izJkzNGzYEICGDRuyfft2Hj16ZGyzePFiBg8eTMGCBZk+fTpdunRh4cKFjB49GoBz587RqlUrQkJC+PnnnxkxYgT//fcfHTt2JDw8/K2uZ+bMmXh4eDB16lRq167NhQsXaNeuHY6OjkyaNInff/+dEiVKMH36dDZu3AhAREQEHTp04J9//qFr167MmDGDXLly0bNnT44dO0bTpk0JCQmJ5TWyZs0a6tWrh42NTSJ6UKFQKBQKheLzJTw8PNaiaRoA1tbWrF+/ngkTJpAmTZoUvlKFQvGxoVJNFQrFJ8HKlStxdHSkWrVqADRq1Ihp06axYsUKunXrRmRkJL/99hs1atQwCm0AQUFBrF+/nrCwMGbOnImjoyM+Pj5YWVkBkCFDBgYMGMClS5fe6npKlChB+/btja9Xr15NuXLl8Pb2Rq+XOY/y5cuzY8cODh8+TP369dmzZw///vuv8ToBypQpw61btzh06BC9evXC3d2dNWvW8PXXXwNw4sQJrl+/zvjx49+98xQKhUKhUCg+Y+7cuUPBggVjrR8wYABdunTB0tKSXLlypcCVKRSKTwElvCkUio+esLAw1q5dS40aNQgODiY4OBg7OzuKFy/OsmXL6NKlC9euXePx48fUrFnTZN+OHTvSsWNHAI4fP07lypWNohuAu7s7O3bsAOD8+fNvfE358+c3ed2wYUMaNmxISEgI165d48aNG5w/f56IiAjCwsKM57ewsDCKhwB6vd4kZbZJkyb89NNP3LlzhyxZsvC///2PnDlz4u7u/sbXplAoFAqFQqGIwsnJid9//z3W+kyZMqXA1SgUik8NJbwpFIqPnl27dvH48WNWrFjBihUrYm3fu3cv9vb2AKRLly7e4zx79izB7W+Dra2tyevg4GBGjRrFmjVrCA8PJ2vWrLi7u2Nubm5MY3j27BmOjo7GiLi4qFevHmPHjmXNmjV07NiRjRs30qVLlyS5ZoVCoVAoFIrPEUtLSwoVKpTSl6FQKD5RlPCmUCg+elauXImzszNjxowxWa9pGr169eKvv/6if//+ADx58sSkzdOnTzl37hzu7u6kSpUq1naA3bt3kz9/fnQ6HYBJMQTgjapbjRkzhs2bNzN58mTKlStnFOaiV11NlSoVz549Q9M047lAvOc0TaNgwYLY2dlRp04dNm7cSN68eQkMDOSrr7567fkVCoVCoVAoFAqFQvH+UcUVFArFR83Dhw/Zu3cv9evXp3Tp0iZLmTJlqFOnDrt37yZ16tSkSZOGnTt3muy/Zs0aunTpQlhYGCVKlGD//v0mVU7PnTtHly5dOHv2rDFq7v79+8btV65c4dmzZ6+9zuPHj1O6dGlq1KhhFN3+++8/njx5YhTySpQoQVhYGHv27DHup2kaP/zwA3/88YdxXdOmTbl48SLz58+nXLlyZMyY8e07TqFQKBQKhUKhUCgUyY6KeFMoFB81q1evJjw8nPr168e5vWHDhixfvpxly5bRu3dvRo4cSbp06ahWrRrXrl1j6tSptGzZEgcHB3r06EHz5s3p2rUrbdq0ITg4mMmTJ1O4cGHKly9PcHAw1tbWjB8/nj59+hAQEMDUqVNxdHR87XUWLlyYjRs3snTpUlxcXLhw4QK///47Op2OoKAgAKpUqYK7uzuenp707dsXZ2dn1qxZw5UrVxg1apTxWMWLFydnzpwcOXKESZMmJUk/KhQKhUKhUCgUCoUi6VHCm0Kh+KhZtWoVefLkIW/evHFuL168OFmzZmX58uXs3LkTW1tb5syZw99//02mTJno3LkznTt3BqBAgQIsXLiQX375hb59+2Jvb0/lypUZOHAglpaWWFpaMm3aNH755Rd69uxJlixZ6NWrF6tXr37tdXp6ehIWFsbkyZMJDQ0la9asdO/encuXL7Njxw4iIiIwMzNj1qxZTJw4kSlTphAUFISrqys+Pj4ULlzY5HhVqlThyZMnxuqnCoVCoVAoFAqFQqH48NBpBldvhUKhUHwUaJpG/fr1qVChAj/++GNKX45CoVAoFAqFQqFQKOJBRbwpFArFR8LLly+ZN28eZ86c4datW7Ru3TqlL0mhUCgUCoVCoVAoFAmghDeFQqH4SLC2tuavv/4iMjKSsWPH4uzsnNKXpFAoFAqFQqFQKBSKBFCppgqFQqFQKBQKhUKhUCgUCkUyoE/pC1AoFAqFQqFQKBQKhUKhUCg+RZTwplAoFAqFQqFQKBQKhUKhUCQDSnhTKBQKhUKhUCgUCoVCoVAokgFVXOENOHnyJJqmYWFhkdKXolAoFAqF4iMhLCwMnU6Hu7t7Sl+KIgHUOE+hUCgUCsXb8jbjPBXx9gZomkZy1qDQNI3Q0NBkPcenjOq/xKH6L3Go/kscqv8Sh+q/xJHc/Zfc4wdF0qDGeR82qv8Sh+q/xKH6L3Go/kscqv8Sx4c0zlMRb2+AYQa0UKFCyXL8wMBAzp8/T+7cubG1tU2Wc3zKqP5LHKr/Eofqv8Sh+i9xqP5LHMndf2fOnEnyYyqSHjXO+7BR/Zc4VP8lDtV/iUP1X+JQ/Zc4PqRxnop4UygUCoVCoVAoFAqFQqFQKJIBJbwpFAqFQqFQKBQKhUKhUCgUyYAS3hQpypYtULIk2NpCzpwwcSLElyY9bx7odLEXOztbSpQozqJFZiZt3dzA2hpy5YKRIyE83PR4ISHw44+QLRvY2ECRIrBsWbLdqkKhUCgUCsVnRUqO827cgGbNIEMGSJ8eGjaEK1eS604VCoVCoYgf5fGmSDEOHYIvv4TmzWHUKNi3DwYNkoGTp2fs9vXrw8GDsdd36BDJkyfh1K4dAcCUKdC3LzRtCt7e8PAhDB0K//4LK1dG7deqlQwIx4+HPHlg4UL45htInRrq1Emee1YoFAqFQqH4HEjJcV5QENSsKeeaNk0mWIcOhSpV4MwZcHRMrrtWKBQKhSI2SnhTpBjDhoG7uwheIGJXWBiMHQt9+sggKTpOTrJEZ+pU8PXV4eNzBSen7EREyKxnzZqwfHlUu2LFoFAh2LpVtu3dCytWwIYNULeutKleHS5fho0blfCmUCgUCoVCkRhSepx36RJs2ybjOwBXV8iXD9asgbZtk+++FQqFQqGISYoLb5GRkUyfPp3ly5fz4sULSpYsydChQ3F2do6z/ePHjxk7diz79+9H0zTKlSuHp6cnGTNmNLbZuHEj06ZN4/bt2+TKlYvBgwdTtmzZ93VLijcgJAR27YIRI0zXN20KP/8ss6I1ayZ8DD8/8PKCzp3DcXMLNK578kRmWKPj5iZpBuvXy3FXrAAXlyjRDSSdYf/+xN+bQqEQIiIiCAsLS7BNSEiI8ader9wP3hbVf4kjMf1nYWGBmZnZ6xsqFJ8hKT3OCw6W9alTR7VJl05+Pn787velUCgUCsW7kOLC24wZM1iyZAnjx48nU6ZMeHt706lTJ/755x8sLS1jte/bty/h4eHMnTsXTdMYMWIEPXv2ZMWKFQAcOnSI77//nkGDBlG+fHlWrFhBly5dWL16NS4uLu/79hTxcPUqhIZC3rym63Pnlp++vq8fkA0bBno9DB0axr17ss7REczNxdcjOk+fynL1qrw+dUoGaUuWSPrDpUuSbjpunHiAKBSKd0fTNO7fv8+zZ89e2zYyMhJzc3Pu3r2rhKN3QPVf4khs/zk6OpIpUyZ0Ol0yXJ1C8fGS0uO8WrUgf35JbZ0zRzzm+vYFe3s1zlMoFArF+ydFhbfQ0FB8fHwYOHAgVapUAWDSpElUrFiRLVu28GWM6Sx/f3+OHDnC77//Tv78+QHo0qULPXr04NmzZzg6OjJr1ixq1KhBmzZtABg8eDAnT55k/vz5jBw58r3enyJ+nj+Xn9FnIgFSpZKf/v4J7//gAcyfDwMGyCDMMCCztRUvkenToWBBaNRI2vbpIwO1gABp9/ChiG3Hj8OYMZA5M8yYAY0bS/qpSjVVKN4dg+iWIUMGbG1tExQlIiIiCAkJwcrKSkUPvQOq/xLHu/afpmkEBgby4MEDADJnzpxcl/jBkxKZC0+fPmX06NHs2bMHnU5H/fr1GTRoEDYxcxcVKUZKj/OsrUVw8/CQDAcAKyv45x8pxqBQKBQKxfskRYW3CxcuEBAQYDKYSp06NQUKFODo0aOxhDdra2vs7OxYvXo1pUqVAmDNmjXkzJmT1KlTExkZyYkTJ/CM4dhaunRptmzZkvw3pHhjIiMT3v66wIPZsyEiQgZaMZk5UwZXnTpBx47iIeLpCS9eyIANZBb23j0R3ooVk3XVqkll05EjlfCmULwrERERRtEtnSGv5zXtQT7flXD09qj+SxyJ6T+DyPPgwQMyZMjw2fZ/SmQufPfddwQFBTFv3jz8/f0ZMmQIgYGBTJgw4b3euyJ+Unqct3s31K4N5ctD//5gZgZ//CFC3caNULHiu92XQqFQKBTvQooKb/fv3wdizxRnyJDBuC06lpaWjB8/nqFDh1KiRAl0Oh0ZMmRg0aJF6PV6nj17RmBgIJkyZXqj470Nhtnt5CAoKMjk5+eApaUOsOHRoxACAyOM6588AbDFxiaUwMDw+HZn2TJrqlfXsLMLidV/er1UsBo3Dm7e1JEtm4a9PcyaZU2OHJEEBoZiZ2dNpkyQL18w0f+slStb4ONjTmDg5/O3+Bzff0mJ6j9TQkJCiIyMxMrKyihqJISmacafb9JeYYrqv8SR2P6zsrIiMjISf39/rKys4jz+p5yGmhKZCydPnuTIkSNs2LDBKMSNHDmSTp060b9/f5PIOUXK4eAgP1+8MF1viHQzbI+PFSskXdTJCWIOv+3tJZptyhRJOc2eXdbNnh2VyjpmDGTJIlkMhn/NWrWgXDno1w+OHUvc/SkUCoVC8TakqPBmeFCNOSNqZWXFc0OMejQ0TeP8+fO4u7vTqVMnIiIimDRpEj169GDp0qUEv3JSjet4BgPldyUsLIzz588n6hiv4/r168l6/A+J0FAdZmbuHDz4kDx5/Izr//vPFsiPtfU1zp9/Gee+Dx5Y8O+/hWnU6Brnzz8xrjf03969DqRKFU7RogHodHDrFjx5Ys6dO4XJnPke588/IEOGnNy4kZpz584T/Zno4UNnLC3TJvvf+kPkc3r/JQeq/6IwNzcnNDT0rQSHxH5Gf+6o/ksc79p/oaGhhIeHc9VgLBUHcUV9fSqkRObCsWPHcHJyMvHtLVWqFDqdjuPHj1OvXr3kul3FW+DiIlFmly+brje8fqW7xsmdO3DypHiyxcW6dZAmjUSzFSwo6x48gNu3o7IYbtyAEiWiRDeQidkKFeC3397plhQKhUKheGdSVHiztrYGZOBq+B1kAByXT8fGjRtZtGgRO3fuxN7eHoCZM2dStWpVVqxYwVdffWU8XnTiO97bYGFhQW7DNFoSExQUxPXr18mRI8dn5U9Svnwkhw5lZuzYtEbxa/FiCxwcNJo0cTamC8Tk4kVJ52nSJBO5cn3B06d6IiMj0ev1pEkTiZeXOU+ewM6dUQ9Sw4ZZYGYG7dqlJ1eudDRtasa2bebcvVuQGjUkHyI0FI4ds6ZixUjjTPznwOf6/ksqVP+ZEhISwt27d7GysjL5XI8PTdOMHlufcmRQcqH6L3EkRf+Zm5uTLVu2OCPeLsdUHT4xUiJzwc/PL9b5LC0tcXR05J7BCOwdUJkNSU/58lasWAE9eoQYx3l//WWBg4M5bm5BsSLZDOzZYwZYUaxYEIGBep4+tSBDBjeePtUDIfz2W+xxnre3BWZm5lSvHkxgoEaePJYcPqzn6dNgo/imabB/vxU5c+oIDAxO1nv/kPhc339Jheq/xKH6L3Go/kscyd1/b5PZkKLCm2Hg9ODBA7Jly2Zc/+DBA1xdXWO1P3bsGDlz5jSKbgAODg7kzJmTGzdu4OjoiK2trdHsOPrxEpt6oNPpsI1PCUoibGxskv0cHxLDhkGNGtCunS0dOsCBAzB5MowfD+nT2+LvD+fOyaypk1PUfhcvygxmxow2zJwp+9y+DVmzyuxo+/ZivDtkiC0NGsD27TBxIgweDG5uIox06AB//gkdO1ozbpzsO2WKzLKuWKHH1jbFC/6+dz63919So/pP0Ov16PV6zMzM3sjzypDep9PpPjiPLE9PT+7cucPChQvfeJ+dO3fi7OycbBM1BlxdXRk3bpxxwilm/3l6evK///2PqlWrMnPmzFj7r1+/nv79+1OqVCnj/d27dw9vb28OHz5MaGgohQsXxtPTkzx58sR7HV5eXixfvtxkXZYsWdixY8cb3Ufr1q05cuRInNsmTJhAw4YNqVatGo0aNaJ3795vdMw3OY9Op8PGxgYXFxfatm1LvXr1Yr3/jh8/TqtWrRKMgDYzM0Ov12NjYxOn0Pypi6EpkbkQFBQUZxRhYrMbVGZD0vPtt6no0SMPX30VQoMGjzh92h4fn0z06nWHGzf8ePlSz7VrNmTNGkKaNFH2Irt2ZcbSMhNp0uj57Tdzpk0zM47zeveOoHXrCFq2tKRTpxdUrvyMI0dS4+OTmbZt7xMScofz56F5c1u2bnWlVq1wvv32AWZmGmvXpufwYRsmTLjK+fPPUq5jUojP7f2X1Kj+Sxyq/xKH6r/EkZz996aZDSmqLuTLlw97e3sOHz5sFN78/f05d+4crVq1itU+U6ZMrF+/3jg7DRAYGMjt27dp0KABOp2OYsWKceTIEb7++mvjfocPH6ZEiRLv56YUb0y1arBypQhwDRuKF4e3t1SwAjhxAqpWhblzoV27qP38/KRKVp060sbArVuyb/HiYrw7daoY6WbPLr9Hf2azsIBt2+DHH2HIEPEgKVZM1hnSFBQKheJtuHPnDt26dWPBggXJLry9CRYWFuzfv5+XL1+aTFgBbNiwwUQUCg0NpUuXLjg6OjJz5kysra2ZNm0abdu2Zd26daRNmzbOc/j6+tKtWzeT7+y3EVCnTZtGWFiY8bWmafTr14/nz59Ts2bNNz7Om1C3bl2GDBliPE9AQACzZs1i0KBBZM2aFXd3d2Pb48eP06NHDyJf5xD/mZMSmQvW1taxthvaJGbyQ2U2JD3584OjYyijRzvw/feOfPGFxpgxYfTpkxZIy549etq3t2bmzBBat47yWNQ0CxwcdDRoYBVrnDdokBnFi5vx+++RTJ3qxOrVGciWTWPixFC6d08NpDaeO1u2UEaOtGPoUBcsLaFQoUg2bgyhYsXMwOdTifhzff8lFar/Eofqv8Sh+i9xJHf/vU1mQ4oKb5aWlrRq1YqJEyeSNm1asmTJgre3N5kyZaJWrVpERETw5MkTUqVKhbW1NQ0bNmTOnDn07duXPq/KHE2ePBkrKysaN24MQPv27enSpQsFChSgUqVKrFy5kvPnzzNmzJiUvFVFPDRqJEtcVKkiaQExmTpVzHMNAl1Mjh+Hli3h9GnxF4mPNGng999lUSgUisSixfWBlYK4ublx5coVduzYQYMGDYzrX758yd69eylevLhx3bFjx7h48SJ79uwxRoh7e3tTunRpduzYQdOmTWMdX9M0Ll++TJcuXXCKHpb8Fjg6Opq8XrRoEadPn2bNmjXY2dm90zHjw9raOtZ1/vTTT2zcuJENGzbg7u5OeHg43t7eLF68mLx58/Ls2bMkvYZPjZTIXMiUKRPbtm0z2R4aGmqspvyuqMyG5OGbb2QRdIDlq0UmUOVj0zRNe8YMyJfvdeM8PWfOGMZ5psc1ULmyZD1EYfZq+Tz5HN9/SYnqv8Sh+i9xqP5LHMnVf2+T2fCaYt7Jz3fffUfTpk3x8vLi22+/xczMjDlz5mBhYcG9e/eoUKECGzZsAMTjY8mSJWiaRtu2bWnfvj0WFhYsWbKEVKlSAVChQgXGjh3L0qVLadSoEYcOHWLmzJkmJryKj5vHjyW9NCEmTYJHj97L5SgUis+EatWqMWfOHHr37o27uzulS5dm9OjRhIeHc/v2bapXrw5AmzZtmDZtGgBXrlyhc+fOuLu7U6FCBQYMGMDDhw+Nx2zdujU//fQTX3/9NSVKlGDt2rV4enri6enJhAkTKFu2LEWKFKFr1674+fnFeV3xYWFhQfXq1dm0aZPJ+m3btuHq6oqzs7NxXZ48efjzzz9NbBn0ehki+BvKEMbg5s2bBAYGkitXrre6rvh48uQJkydPpnv37rGO+fDhQzp16kShQoWoVq0aixcvNtnu6upq7PO3wdxc5h8NaQKBgYEcPXqU2bNnxxl5rzAleuaCAUPmQsmSJWO1z5QpEzdu3DBJCTVkLuTIkcMkcyE60TMXSpYsyf3797lx44Zxu6F9dDFZ8fHyJuO8n3+WggoKhUKhUHwMpLiRlZmZGd9//z3ff/99rG1Zs2bF19fXZJ2Li0ucfjXRadiwIQ0bNkzKy1R8QDx5IukGCXHrFnTpIimsdepA5s8no0Ch+DDRNOJ00o6IgOBg+ZlcHm+2tpBEXltTpkxh4MCBDBo0iCNHjjBkyBDc3Nzw8PBg+fLlfP3110ybNo3y5cvj5+dHixYt8PDwwNPTk6CgIKZNm0bz5s1Zt26dceZt+fLleHt74+rqipOTEwcOHGDdunV4eHiwaNEiHj9+TP/+/Zk8eTLjxo17q+utW7cuvXv3Nkk33bBhA/Xr1+fChQvGdk5OTlSuXNlk34ULFxIcHEz58uXjPPbFixeN7fbs2YNer6dSpUr069fPOBn2NsyaNQtra2s6duwYa9uyZcvo27cvQ4YMYd++fYwZM4YMGTIY01H37dv31jOZT58+Zdq0aQQHBxuPkzp1alatWgVg/KmIn5TIXChSpAjFihWjX79+DB8+nMDAQIYOHUrDhg0T7eer+DAwVKRPiPv3JZ20YkWoVEmWYsXESkShUCgUig+NFBfeFIo3JTxc0kJ/+unN2q9dKwvIYKxePahbF0qXTr7ne4VCEQeaBhUqSAWVGJgBSZtQGAfly8PevUkivlWoUIE2bdoA4OzszMKFCzlx4gQNGzY0+qA5ODhgZ2fHrFmzyJQpE15eXsb9J0+eTJkyZdi0aZNRaMifPz8eHh4m50mVKhUjR47EwsICFxcX6tWrx+7du9/6esuVK4eNjQ07d+7Ew8OD58+fc/DgQUaNGmUivMVk69at/PLLL7Rr1y7OlEEQ4U2v15MhQwZmzpzJzZs3+fnnn7l06RLz5883Rsy9CS9fvmTZsmX06tUrzuqgNWrUoFu3bgDkzJmTU6dO4ePjYxTM3iTV9Z9//mHz5s2AFPUICQnhiy++YNiwYRQqVOiNr1VhynfffUd4eDheXl4EBwdTsmRJY+aCIRJ03LhxNG7c2Ji54O3tTdu2bdHr9ZQoUSLOzIUZM2YwadIkcufObZK5oNPpmD59OiNGjKBt27ZYWVlRp04dfvjhh5TsBkUScveuCGjR7B9jodPB8+ewbp0sIHMs5cpFCXGlS8MbFNdWKBQKhSLZUcKb4qNg+3bo0wfOnpXXX3whA7P46NMHUqWCTZvg2DEpwnDiBIweDWnTQu3aIsLVqWNaMVWhUCQTn0h1x5i2BalSpTIpDhCdc+fOcenSJRPTfhAT+CtXrhhfZ8+ePda+2bJlwyJa6EZC50kIQ7rp5s2b8fDwYMuWLRQtWjTByKClS5cyatQoGjRowKBBg+Jt1717d1q0aEGaNGkAyJs3L05OTjRr1owzZ85QpEiRN77Obdu2ERoaSpMmTeLcHjOFsEiRIm8tRFarVo2BAwcCkkZrb29P6tSpjZU0Fe9GSmQupEuXjqlTp77T9So+bI4dgwYNEhbdQFJNK1aE/fthzx6ZW3nyRIpkGSwALS1FfDMIcWXLythQoVAoFIr3jRLeFB80166Jue7//iev06WDMWPgq6/gyy/FYDcmxYuDlxekTw+jRkkV1M2bYcMG+fnkCSxdKotOByVLRkXDlSgBbxGkoVAo3gSdTp6K4kg1jYiIIDg4GGtr67eqhvlWJGGqaVwlw+MrqhAZGUmZMmUYNmxYrG3RUzGt4wjJeNPS5G9CvXr16NGjBwEBAWzcuJF69erF29bb25vZs2fTvn17Bg8enKBprF6vN4puBvLkyQPA/fv331p4q1y5MqlTp473XNGJjIx86z6ys7OLJXJGRETE01qhULxvVqyANm0gKEiKK5ibw3//xW5XvLhUu0+fXoS1/v0hMhLOnRMRbs8e2L1b0lH37pVlzBjJdihWLEqIq1BBJmMVCoVCoUhulMSg+CAJCBDxLH9+Ed3MzOC77+DSJejaFTJlgo0b4ddfIVs2eabOlk1eb9okgzEDGTPKQO6vv+DhQ9i3D378EYoWlQy4I0dg+HAZvGXKFNX2yZOUunuF4hNEpwM7u5RZ3lO0XUyRKk+ePFy5coXMmTOTPXt2smfPjoODA2PHjjX6o70PypQpg62tLatXr+bYsWPUrl07znYG0W3w4MF4enq+tlLToEGDaNeuncm6M2fOAJA7d+63usZjx45RtmzZeLefNYQ7v+L48eNGkU+hUHzcaJoIY19/LaJb3bpw+DDs2PFm4zyQSVM3N+jRQ8Zwd+/KmHHOHBnX5cghVqJHj8Ivv8gEbrp0ULgw9OoFy5bBvXspcvsKhUKh+AxQEW+KDwpNkwHT99/DnTuyrnp1mDIFChY0bevkJGLcN99EEB4ejrm5ORkymCXo32ZuLnZP5cvLIO/uXRnAbdgAW7aIMLdwoSx6PZQpExUNV7SoioZTKBTxYzD3v3jxIgUKFKBFixb8/fffDBw4kB49egAwYcIEfH19yZs3b6LPd/HiRfbu3UtoaCiWlpbo9XocHR0pXLiwSTtzc3Nq1qzJr7/+SsmSJY1edNE5fPgws2fPpnXr1nh4eJhUXrW1tcXOzo7g4GBevHhB2rRpMTMzo3bt2vTo0YPp06fToEEDrl27xsiRI/nyyy/fqpL4vXv3ePr0Kfny5Yu3zfr168mXLx9VqlRh27ZtbN26lfnz5xu3P3z40HidCoXi4yEkBDp1gkWL5HWfPjBxoozX4O3HeQZ0OsidW5YOHWTdzZsS/WaIirtwAc6ckeW336RNnjxREXGVKolgp1AoFApFYlEyQiLZcmULJWeVxHaMLTmn5GTigYnxph3NOzUP3QhdrMXO244S60qw6L9FxrarL6ym+J/FsR9rT+6puRmxawShEaHxXseaC2vQjdCx6/qupL7F98aJE+LX0aKFiG45c8KqVbB1a2zRzYCZGTg4hPDgwX84OIS8ddGEL76QAdmKFVK+ftcuGDRIZk0jI8UL3stL0hqyZIlq+/x5om9XoVB8YqRJk4YmTZrw888/M2XKFJydnVm0aBEBAQF8++23tGrVCgsLCxYsWBCn+PW2zJ07l65du9K7d2+6du1K586d8fb2jrNtvXr1ePnyJfXr149z+7pX7uQLFy6kQoUKJouPjw8g1VArVKjAvVdhIdWrV2fy5Mls374dDw8PhgwZQq1atRg7duxb3YdB5HN0dIy3TceOHdm5cycNGjRg5cqV/PLLL5QuXdq4Pfp1KhSKj4MHD6BaNRHdzMykgNbkyVGiGyR+nBedbNmgZUv44w84f16sSFasEHGvaFER6wxRcm3byjg0e3Zo3RpmzQJfX5kgfp9s2SKWKLa2cj0TJ8Z/DfPmyT3EXOzsbClRojiLFkV13urVMra1txdxcsQICI3xmHHwIFStKufOkEHGwH5+yXarCoVC8Umj0+JTiRRGDKkzMaueHbp9iEpzK9HcrTktC7Vk3819jN07lrHVx+JZwTPWcR4GPOTK0yux1ndY04EnL59wuONhsqfPztYrW6m9qDbN3ZrToWgHzj48yw/bf6CTeyem1ZsWa//HgY8pOKMgfgF+7Gy7kyo5qiTNjb8nHjyAIUNkoKNp8gX/44/i7fYm1agCAwM5f/48+fPnN0acJAW3bkk664YNYtQbEBC1zcxMouYM0XCFCn283vHJ1X+fC6r/TAkODubatWvkzJkzTu+ymLwXj7dPGNV/iSOx/fe693t84wfFh0Vy/53U90Rs/vsPPDzg+nVwcBABrEaNuNu+r/579iyqWMOePVLoITzctE2GDKYRcW5uJEoMTIhDh+QczZuLYLhvH4wdK4tn7McMHj6EK7EfM+jQIZInT8I5fDic7Nlt2bpViow1by5i2tmz8MMPEnk47dVjxpEjMhmeP7+M0W1sRPS7eRNOnpS/2eeC+v9NHKr/Eofqv8SR3P33NuMHlWqaCIbtGoZ7ZncWNloIQJ3cdQiLCGPs3rH0Kd0HGwsbk/ZOdk442ZmW0Jx6eCq+j33xKeeDk61sm3tqLtkcsrGo0SLM9GbUdKmJ30s/fj30K7/W/hULMwuTY/TY0CPWuo+BsDAJ7R8+PCqCrEULmDABsmZN0UsDwNkZunSRJSREBjwbNshy4ULUwMzTU6Lh6tWTpXp1VTVLoVAoFAqFIi42bIBvvoEXL8DFBdatk2IKKY2jI9SvLwvIhOuhQ1HFGg4dksniFStkMexToUKUEFesGFgk0ZB82DBwdxf7E4A6dWTsPHaspOTamD5m4OQkS3SmTgVfXx0+PldwcpLiMnPnSvSfIdKwZk2JZPv1V1ksLMSOxcEBdu4EQw2datXk7/Tzz7JdoVAoFG+OSjV9R0LCQ9h1fReN8jUyWd+0QFNehL5g3819rz2G30s/vHZ40bloZ9zSuBnXB4cHY2dph5k+agotnW06QiNCeRH6wuQYf//3N1uvbOXnGj8n8o7eL1u2QJEi0K+fiG7FiomwtXjxhyG6xcTKSgS1X36R9ISrV0U0rF9fBj537kgaQqNGYtYbva2KKVUoFJ8bJ0+exN3dPcHFM66QDYVC8cmiaeLZ6+EholvlylJE4UMQ3eLCzk7GcyNGiBXJ8+dRFVJr15Y0zWfPRDgcNEh8gR0dRcgaNUrEuqCgdzt3SIics5HpYwZNm0rf7Xv9YwZ+fmKX0rlzOG5uUVXFg4Pl3qJH6qVLJ6mmL149Zpw/L4Ji9MLVtrZSiGz9+ne7J4VCoficURFv78jVp1cJjQglbzpTg+zcaaWSm+9jX2q61EzwGMN2DUOv0zO0wlDuXYsqpdSzZE/qLq7LxAMT6VSsExceXWDyocnUy1OPtDZRvkB+L/3ouaEnU+pMIXOqzEl4d8nHlStS9n3tWnmdPr3M3HXokHyh+slBzpxSOatHDxlU7d4taanr18s97tghy8CB4g9iiIarWlUGO0nFli2SAnD2rFRv7dlTUnTjSnudNw/at4/rKLZAcf74I4QuXWTN6tUyaPT1lUqvrVtLGoKlZdzX0bSpDEDnzUuKu1IoFB87BQoUYPXq1Qm2UYUQFIrPh7Aw8VKbOVNed+wIM2bEP674ELGyEjGqQgWxRAkPh1OnojIg9u6FJ0/EnmTbNtnH0hJKlYqKiCtX7s2yIq5eFSEsZh0eQ8FoX18R+BJi2DApCjZ0aJhJxdaePcUmZeJESS+9cEG89erVA4P9aPr0cONG7GNeuSLXplAoFIq3Qwlv78jzEMmNTG2V2mR9Kiv5NvUP8U9w/wcBD5j/73wGlB2Ao7Uj94j6RqyWsxqDyg/i+63f8/3W7wFwz+TOksZLTI7RZV0XyjqXpXWR1h98UYWXL2WG8NdfZSBhbi7l24cNk9nBjxkbGwn/r1NHZnIvXZI0io0bZbbyxg0xDP79dxm0Va4cJcTlyfPu5z10CL78Ujw6Ro2S2c9Bg2QgGFcgSf36YpQbE4P3R+3aEYAUs2jcWI47fnyU98ejR1HeHwYiI0VIXblSjIgVCoUCwMrKiuzZs6f0ZSgUig+Ap0/h669h+3aZGPT2lrHDx+qNa8DcHEqUkKV/fxkTnTsXJcTt3g3378v4zODPZmYmWR4GIa5ChSixKzoGC5bUpo8ZRtHOP+HHDB48gPnzZTLW0RET4a1aNRkvfv+9LCAprUuiPWZ06ACdO0PfvtJWr4dJk+T+wsLeppcUCoVCAUp4e2citcgEt+t1CWfxzj4xm4jICPqU7hNrW/f13fE56YNXRS+q56rO9WfXGb5rOHUW12F7m+3YWtgy/9R89t7Yy9keZxN1H8mNpkn66KBBUV/6tWrJzFr+/Cl6aclGnjzivdGnj/iD7NwZFQ1344ZEqW3ZIoOZ3Lll1rFePRHkYvp1JERKen8AnD4NvXvD0aNvd90KhUKhUCg+Dy5dkknCixcl4n/JEmjQIKWvKnnQ66XYgpubZERomkSIGYS4PXvg2jUZNx09KpYkIAW6DEJcxYqQObOIeK87V0LMng0RETIejEn37uDjI2mo1atLgYvhw2UcuX27pJR26iTi3tChMqms00l2Q5cuMk5UKBQKxduhPN7eEQcrKefzIsTUc80Q6WbYHh8rzq2glkutWMUW7vjf4c/jfzKo/CBGVRtFlRxVaFe0HRtabuDQ7UP4nPThtv9t+mzqw8RaE3GycyI8MpyISIlWioiMMP6e0hw7JpU/W7cW0S1XLlizBjZt+nRFt5jY2cmA87ffZLB17pyE9levLgLW5csSRVa3rvhr1K8f1TYhUtr7A6BNGxnUHTokVb4UCoVCoVAoDOzaJZ5gFy9Kwar9+z9d0S0udDqZYO3QQaw4rl6VqqCLF4uAZfC2O3NGxn7Nm8MXX0h6qUGUu3LF1CvYEOn2uqqiK1bIRHfMCdc7d+DPP2VCfNQoqFIF2rWTTI1Dh0SQM9C/v0Qrnj8vY8Zly8TTLq4IPYVCoVAkjIp4e0dc0rpgpjPj8pPLJusNr/M7xa8s3fG/w8n7J+lbpi/hkeE8D39OhpwZeB7+nKvPrqKhUd65vMk+BZwKkM4mHWcfnMXe0p7nIc/puLYjHdd2NGlXY2ENsjtk53rf60lzo++An594X8ydK4MFOzsRePr1k1TLzxWdTgTH/Pkl9P/FC5lZ3LhRBjy3b0dVTQUZkBmi4SpWNO27lPb+AIm0e4PKyQqFQqFQKD4zZs+WyKrwcPE4W7NGPGM/d5ydoUULWUBSQvfujYqI+/dfiRK8dEm2d+smVi2GiDiDPUtCE9h37sDJk5JZEZObN2VsXt70MYMCBWSS9eyrRJpjx6Rt48amxS9OnJBUWYVCoVC8HUp4e0esza2plL0Sqy6sYmC5geheGVWsPLcSBysHSmUpFe++h+8cBiBf+nxMPTyVyYcmc9v/NllTZ6VTsU6Y6czYe3MvdfPUNe7j+8iXx0GPyZUmFx55PTja+ajJMY/fPU639d2YWX8m5ZzLJcMdv57QUIneGjkyakauVSuYMEFm8BSmpEoFDRvKomnw339R3nD79ongdeGCeGrY2UGNGiKI1a2b8t4foEQ3hUKhUCgUpkREwODBURFbzZvLRKyypIibDBmgSRNZQCLK9u8XEW7mTBnP3bolUXKLF0sbnU4mRE+fFjGuUCHTLIXD8pgRS1wDmaA1MxOxr27UYwa+vvD4sWSngEQr/vSTjA8NYt/WrSLMGcaGCoVCoXhzlPCWCLwqeVFjQQ2arWhGh6IdOHDrAN4HvBlfYzy2Frb4h/hz7uE5XNK4mKSUnvE7g5WZFd3Xd+fEvRPG9bf8bzFs1zAy2mXE+4A3ADVz1eTG8xuM2D2C7A7Z6Vy8M47WjqSzTWdyLS9DXwLgmt6VQhnfvyKycaNEtPn6yusSJcQ/rGzZ934pHyU6nQycChWSAeuzZ1IRyyDE3b8vs8Vr1kh7w8Do33/FG87gu2Ygub0/FAqFQqFQKKLz4gW0bAn//COvhw2T5WMvovA+cXQU25H69aF2bZl0rVgRsmSBAwfEK1jTTMeEqVNHRcRVqiSVVq2swMUl9vGdnCQSzlseM6hZU445YgRkzy4FFUAmzseNg2bNRGi7eVNST8uXl20KhUKheDuUx1siqJazGiubrcT3kS8N/27I4jOL8a7pzaDygwA4ce8EZeeUZf2l9Sb73Xt5D0szSxPRLTp+AX7Uz1OfVedXUWdxHYbvGk7NXDU52vkojtaOyX1bb4XBNLdePRHdMmSAOXNktk2Jbu+Oo6P4tfn4SMrAiRMwerSUodfro0q5DxwoJd+bNpV+Nwif78P7Q6FQKBQKhQJEmKlQQUQ3KytYulQm7ZTo9u5UqyZV458+lZ96vVSbN1RILVlS2vn7w7p1MnYrU0YEM02Tcdzu3RAUZHpcb29ZVq2SSdXhw0WAO3o0KrotUyYpBBYSIummw4dD+/YyGRw9uk6hUCgUb4aKeEskjfI3olH+RnFuq5KjCtowLdb6EVVGsOHSBl6EvohjL+Hk/ZMc6XSEjPYZ3+g64jtXcvHihQhBkyZJJU1zc4me+umn14s+irdDr5dUT3d3GDIEnjyRAVa7dhJ95u8vA7KVK6P22b9foufKlJG/TXSSwvtDofgc8PT05M6dOyw0lA5+A3bu3ImzszO5DYaLyYSrqyvjxo3jq6++inO7p6cn//vf/6hatSozZ86MtX39+vX079+fUqVKGe/v3r17eHt7c/jwYUJDQylcuDCenp7kyZMn3uvw8vJi+fLlJuuyZMnCjh073ug+WrduzZEjR+LcNmHCBBo2bEi1atVo1KgRvXv3fqNjvsl5dDodNjY2uLi40LZtW+rVqwdAZGQkPj4+LF++HD8/P7JkyUK7du34+uuv3/ncCsWnzuHD8NVX4vGbMSOsXi3jD0XiadQodiEtkHHaDz+Ih96//0pq6u7dkkL65Im0GTpUflpa2lCoUF4aNjSnYUMoWFDGgHGNA6NTvLgcU6FQKBSJRwlvKYBOp+OW/60E29x6fsvoG/chERkppvqenpL+COIRMWkSuLqm7LV9LqRNKxVF582TWcwpU6IKNBieKw1eII6OkqpQt67MambMmDTeHwqFIjZ37tyhW7duLFiwINmFtzfBwsKC/fv38/LlS+zt7U22bdiwweQ7JjQ0lC5duuDo6MjMmTOxtrZm2rRptG3blnXr1pE2njJ2vr6+dOvWjVbRco/M3iIcYtq0aYSFhRlfa5pGv379eP78OTVfVyHmLalbty5DhgwxnicgIIBZs2YxaNAgsmbNiru7O3/88Qc+Pj6MGDECNzc3Dh48yPDhw7GwsKBhw4ZJej0KxafAX3/JRGBICBQuLBFv2bKl9FV9Ppibi0BWvLhYvkRGwrlzUcUa9uyBe/d0HD+eiuPHZYLc2TnKM7h69Sh/YIVCoVAkH0p4SwE0TcM5tXOC4puzgzOa9v4i2N6EI0egd+8ocSdPHhHc6tdP2ev6XPHyEu8Pb28pVR8eLmkCzZpJlNymTZKe8PffsoB479nZgaUl5MgR+5hv6v2hUChi86F9Zru5uXHlyhV27NhBgwYNjOtfvnzJ3r17KV68uHHdsWPHuHjxInv27CFjRom09vb2pnTp0uzYsYOmTZvGOr6maVy+fJkuXbrgFDNv/Q1xNOQ1vWLRokWcPn2aNWvWYGdn907HjA9ra+tY1/nTTz+xceNGNmzYgLu7O0uXLqVDhw7GCLhs2bLx77//snz5ciW8KRTR0DQZG4wYIa89PGTCT4k4KYteD25usvToIX+nM2eCWLToIf/+m4U9e8y4dUtsRf78UzyCK1YUy5i6daVa6gc4769QKBQfPUp4SwHS2aajb5m+DNgyIN42fUv3Jb1t+vd4VfFz756Es8+fL6/t7WXGrE8f8fFQpAwG749hw6QyapYsIpgNePW22r5dhDkPjyifuGPHovbPmFGi4KpVM8PFJcru0dsbsmaValq//AKZM4sf3JgxUd4fCsXbomkQGBh7fUQEBAfLz+TyjbG1TZoHiWrVqtGyZUtOnTrFvn37sLS0xMPDA09PT+7fv0/16tUBaNOmDb169aJ3795cuXKF8ePHc+zYMezs7ChdujSenp5GAah169bkyJGDCxcucO3aNYYOHcqBAwcASJMmDatXryYwMJAyZcowcuRIoyj2JlhYWFC9enU2bdpkIrxt27YNV1dXnJ2duXPnDgB58uThzz//NDm+/lWVFv94yiTfvHmTwMBAciVRKOyTJ0+YPHky3bt3j3XMhw8f0qlTJw4fPoyTkxMdO3akZcuWxu2urq7GPn8bzF/l4ltaWhIZGcmECRPImTOnSRu9Xh9vHygUnyNBQeL3ZZjUGzhQvMeU99eHh04HuXNrNG/+kOHD06PT2bJrV1SmxJUrsGOHLAMHyiRr3boixFWrJpO1CoVCoUg8SnhLAcz15rQu3JolZ5Zw/N7xWNuLZy5O68KtMdOn7AgmJETSGEeNgpdSNJW2bcW0NXPmpDnHlS1X2DFkBw/OPsA+oz0le5ak7ICycabZnpp3ijXt18R7rIg/IshbKy9Tck6Jt03RdkX5aq54It06eIvtP2zn3vF7WNpbUuDrAlQbUw2rVB+Pmhif9wdI+kD0AJz79yUKbsMGMcx9/NiQkmqFjU1hWreOpG9fme18E++P6Fy//u73oPj00TQx3X6lJ8XADEjekX358pI+nRTi25QpUxg4cCCDBg3iyJEjDBkyBDc3Nzw8PFi+fDlff/0106ZNo3z58vj5+dGiRQujOBcUFMS0adNo3rw569atw/ZVieDly5fj7e2Nq6srTk5OHDhwgHXr1uHh4cGiRYt4/Pgx/fv3Z/LkyYwbN+6trrdu3br07t3bJN10w4YN1K9fnwsXLhjbOTk5UblyZZN9Fy5cSHBwMOXjyksHLl68aGy3Z88e9Ho9lSpVol+/fqR6h7CXWbNmYW1tTceOHWNtW7ZsGX379mXIkCHs27ePMWPGkCFDBmM66r59+4z9+aY8ffqUadOmERwcTM2aNdHr9ZSNURXo7t27rF+/nm+++eat70eh+BS5f1/83I4ckTTHmTMhjn9ZxQeKjU1UmunUqVIkbcMGEeJ27ZIsh5kzZbG0hMqVo4S4vHlVNJxCoVC8K6qqaQrhZOfExpYb+bXWr6S1Ee8cKzMrfq31K5tabiK9XcpGu61fL2HqgweL6FaqlFS1nDcv6US324dus+TLJaTPl57mq5pTqGUhtg7ayv4J++Nsn6d+Hjoe7BhrSZ8/PdYZrclVOxf2me3jbFO4VWH0FnrcO7oD4HfajwXVF2CVyopmK5tRfVx1zi47y/Kmy+M896dApkziw7JsGTx8KL4fnp7g6hpJUJAZf/5pQYECEt22bp34hCgUScWnMlivUKECbdq0wdnZmSZNmpAvXz5OnDiBmZmZ0QfNwcEBOzs7li5dSqZMmfDy8sLFxQU3NzcmT57M48eP2bRpk/GY+fPnx8PDg7x585ImTRoAUqVKxciRI3FxcaFUqVLUq1ePEyfiroSdEOXKlcPGxoadO3cC8Pz5cw4ePEjd6CaOcbB161Z++eUX2rVrh2s8Bp4XL15Er9eTIUMGZs6ciaenJ/v27aNHjx5EvuUHyMuXL1m2bBkdO3bEKo5Q6ho1atCtWzdy5sxJ69atqVu3Lj7Ryiw7OTm9NjX1n3/+wd3dHXd3dwoXLkyZMmXYuXMnw4YNo1ChQrHaP3r0iM6dO5MuXTq6d+/+VvejUHyK/PuvjAePHBG/2a1b30B027JFym/a2kLOnDBxoumsYHTmzZMvixiLrZ0dxUuUwGzRoqi2q1eLsZm9vRjUjhgBoaGmx/Pzg5YtpTqUgwN8+62kcSiM5MkjGSybNsmE7D//SIpqjhzSnVu3Qv/+kC8fuLhAr17yjBBXBLtCoVAo4kdFvKUgTnZOfFf6O0pkLkGl+ZUA6Fq8K7aWbzdrn5T4+oo568aN8jpTJkkfaN1afCOSkl3DdpHZPTONFkrIVu46uYkIi2Dv2L2U7lMaCxsLk/Z2TnbYOZk+WB2eepjHvo8p51MOWydbzK3MyVomq0mbu8fv8t/f/1F9bHWyVRDH30OTDmGT1oZmK5thZhkVWbim/Roe+T4iveuHkeabXBg8PSpWBC+vYBYuvM2GDblYt86crVtloOXiAj17SjqJSjFVJAadTiLO4k41jSA4OBhra+u3MuV/G5Iq1RTAxcXF5HWqVKlMigNE59y5c1y6dAl3d3eT9SEhIVy5csX4Onv27LH2zZYtGxYWUZ+BCZ0nIQzppps3b8bDw4MtW7ZQtGjRBFNWly5dyqhRo2jQoAGDBg2Kt1337t1p0aKFUSzMmzcvTk5ONGvWjDNnzlCkSJE3vs5t27YRGhpKkyZN4twe3Y8OoEiRIux+y3J71apVY+DAgYCkj9rb25M6dWqCg4Njtb169SpdunQhIiKCBQsWkDp16rc6l0LxqbF2LbRoAQEBEvm0bp2INgly6BB8+SU0by7pE/v2waBBYkrr6Rm7ff36cPBgrNWRHToQ/uQJEbVry4qtW6FxYznu+PFScv2HH+DRI5g2TdqEh0uolr+/hG+Fhck5a9US7w0Li1jn+dyxs5M/15dfijbq6xuVkrpnD1y7Br/9JouVFVSpEuUN99r3gkKhUHzmKOEthTHTm1HMqRhpLdPyJPQJJ+6foEK2Cu/9Op4/lzHRlCkyVrGwEAFuyBBIjueN8JBwru+6TpURVUzWF2hagAM/H+Dmvpu41HSJc18DL/1essNrB0U7FyWNW5o422iaxoaeG3Aq4ESZflG17auOrkqZfmVMRDfD7+HB4e92Ux8pOh2ULPmCNm1C8fMzZ8YMmD1bfD/69xc/v7ZtZZYzf/6UvlrFx4pOF7dXjMHbzdr64/AHsrS0jLUuvqIKkZGRlClThmHDhsXaFj0V09ra+o3O867Uq1ePHj16EBAQwMaNG42FA+LC29ub2bNn0759ewYPHpxgdW29Xm8U3QzkefX0df/+/bcW3ipXrhyvwKWPMfMTGRn51n1kZ2cXS+SMiIiI1e748eN0796djBkzMnv27Lfy1VMoPjU0TfxeBw2S36tXh+XLIU3cwy5Thg0Dd3dYuFBe16kjAtjYsRJmZWNj2t7JSZboTJ2KzteXKz4+ZDdsmztXSqcuWiRfHDVrSnTbr7/KYmEhF3nypIhyBQrIfkWLSjrHsmUSCaeIF51Ootzy5ZPngZcvxQfOIMTdvAmbN8vSp48EHRpSWKtUif2nVSgUis8dlWr6AaDT6XBPJxERe27sea/njowEHx+ZvfzlFxHdvvxSxikTJiSP6Abw9OpTIkIjSJc3ncn6tLklVeux7+PXHmPXsF3o9DoqDI1fqDz791nuHL5Dncl10JtFvd1TZ0lNxsLyMBUaEMrVbVfZ/uN2nMs7k6lIpne5pU+CnDmluMLt2zJBXLCgzG7PmCHj1tq1VRqqQhEfMUWqPHnycOXKFTJnzkz27NnJnj07Dg4OjB071uiP9j4oU6YMtra2rF69mmPHjlHbEDUSA4PoNnjwYDw9PRMU3QAGDRpEu3btTNadOXMGgNy5c7/VNR47diyWv1p0zp49a/L6+PHjRpEvKTl9+jSdOnUiT548LF68WIluis+a0FDo1Am+/15Et65dRXh5I9EtJERMw2Ia0TZtCi9eSPTb6/DzAy8vwjt3JtDNLWp9cLDM5ESfrUmXTi74xQt5vXkzuLpGiW4gv+fPL8qR4q2wt4cGDeD338XX97//ZLxYrZronJcvS7BhvXqShly/PkyfDlevpvSVKxQKxYeBEt4+ENzTvn/h7eBBKF1a/DkePBDxbcMG8XdI7pDxkOchAFilNvXyMRQ2CPEPSXD/gAcB/Dv/X0r1KoW1Y+xoEQMHvA/gXN6ZHFVyxLld0zS803uzsOZCQl+GUndawr5Hnwt2djLAPnNGqqN+9ZXMfm7ZIlVS8+aFyZMlUlKhUAgGc/+LFy/y4sULWrRowYsXLxg4cCAXLlzgwoUL9OvXjzNnzpA3b95En+/ixYvs3buX/fv3s3fvXvbs2cPp06djtTM3N6dmzZr8+uuvlCxZ0uhFF53Dhw8ze/ZsWrdujYeHBw8fPjQuAQEBAAQHB/Pw4UNjlFjt2rU5ePAg06dP5+bNm+zevZsff/yRL7/8MlZKbkLcu3ePp0+fki9fvnjbrF+/Hh8fH65evcqff/7J1q1b6dGjh3F79Ot8V8LDwxk4cCDp0qVj/PjxhISEGPvgyZMniTq2QvGx8fixZGX6+IjVyOTJIrq8cYbm1asihMX8rDOI8r6+rz/GsGGg1xM2dKjp+p49pSrAxInw7JmktE6eHKX6AJw/H/vchvO/ybkV8aLTycTswIEyRnz8GP73P+jcGbJmFV10wwbo3VtsS1xdJWpuyxbZplAoFJ8jSnj7QCiWrhgA+2/tJzwyeVMd794Vz7Zy5eDYMUiVSsYuZ85IiPj7QIuMx1j3FTp9wpEWJ2afIDIiktJ9Ssfb5taBW9w7cY9y35eLt01keCTfrP2Gb9Z+Q7q86ZhXaR73/72f8MV/Ruh0Mpu5erWkng4cKH5vV67IICpLFklBjVYcUaH4bEmTJg1NmjTh559/ZsqUKTg7O7No0SICAgL49ttvadWqFRYWFixYsCBO8ettmTt3Ll27dqV379507dqVzp074+3tHWfbevXq8fLlS+rXrx/n9nXr1gFSobRChQomi6GIwYYNG6hQoQL3XpmTV69encmTJ7N9+3Y8PDwYMmQItWrVYuzYsW91Hw8fPgTAMQEzyY4dO7Jz504aNGjAypUr+eWXXyhdOurzP/p1viunT5/mxo0b3Lp1ixo1apj0QdOmTRN1bIXiY+LCBZmY3b1bxojr1kk64Vt5ZRpm5mKmThjS7P39E97/wQOYP18GGTE/G6pVk9zX77+X8LuyZSFDBliyxPT8caVtpEr1+nMr3opUqaBhQ/jzT0lBPX1asmYqV5bKtxcvii5au7YEJnp4SCbF9espfOEKhULxPtEUr+X06dPa6dOnk+34AQEB2pGjRzTHcY4aw9GO3jmaLOcJDta0ceM0zc5O00DTdDpN69BB0+7fT5bTJYjff37acIZr51adM1kf+DhQG85w7ciMIwnuP9N9pra4/mJN06T/jh07pgUEBJi02dRvkzY+zXgtPDT8ja4p6FmQNt5xvLa6/eq3uJOPn/j6Lz5evtS0mTM1rUABeR8Zllq1NG3dOk2LiEjmC/7AeNv++9QJCgrSzp07pwUFBb1R+/DwcO3ly5daePib/Z8qTFH9lzgS23+ve78n9/hBkTS8j3Hex/I9sXWrpjk4yPd69uyadubMOx5o/345yNatpuvDwmT9uHEJ7z9mjKZZWGjagwex+69rV9nm5aVpO3dq2ty5crFlymiaoU2ePJrWsmXs47ZsqWmuru94Ux8nKfn+e/ZM01as0LSOHTUtc2bTcSNoWr58mta/v7xNgoOT/vybN2taiRKaZmOjaTlyaJq3t6ZFRsbddu7c2NcXffnjj2Dt2rWE27RrJ8fKnj3+NjlyJP19fsh8TJ9/HyKq/xJHcvff24wfVHGFDwS9Tk+5rOXYcGUDe27socQXJZLs2Jom6aP9+0ukEsjk4NSpUCLpTvNWpHVJi85Mx5PLpuk7htdO+Z3i2g0A/zv+3D95nzJ9y8TbBuDiuovka5gPM4vYju2+//hi7WBN9kpRRtvWDtakcUnDi7sv3uZWPjsMaahduojR7rRpUu1syxZZcueWCep27cDBIaWvVqFQKBQKxZswc6Z8f0dESFbE//4ngWTvhGEA8CLGmMoQbfa6AcKKFZLr6uRkWhL7zh0JrfrxR6kKZqBUKcl/9PGRm3BwiH1uw/nV4OS94eAATZrIomkSDbdhg3gFHjgg0ZUXLkhNDDs7Kd5hqJSaLVvizp1ERXXp0CGSJ0/CqV07gkyZ4m7z22/w999i3wPyvxMSwzXn4EF5FuvWLXH3pVAoPk6U8PYBUT5reaPw1r9s/yQ55vnz0LevCCIAmTPDzz9LSXh9CiYam1ubk71Sdi6sukC5geWMJt7nVp7DysGKLKWyxLvvncN3AHAu70xkeCThz8PJmSEn4c/DibSMRG+uJ+hJEE8uPaH84PJxHuPQpEME+AXQ7XQ3Y9EF/9v+PDz3kFK9SiXx3X6a6HQyQKpeXaxcDNVQL1+W95yXV1Q11ASsmxQKxSfIyZMn6dChQ4Jtateuzfjx49/TFSkUivgID4cBA2RCFqBVK5g1S6pNvzMuLlL84PJl0/WG1wmVSb9zRyqS9u0be9vNm6LglI8xvitQQPIYDYVYXF3lGDG5fFlEOsV7R6eDIkVk+eEHsefbulWEuE2b4P59mchdu1baFywoAly9evLnftti30lUVBdfXx0+PldwcsqOlRWUiTHvf/y4iG5jx0KFV/Xe3N1N2/j7w7ffirg3ePDb3YdCofg0UB5vHxAVnOXTeu/NvURqiSsb+eyZeHAVLiyim6WlzO74+sqAKiVFNwOVvCpx+/BtVjRbwaWNl9jx0w4OeB+g4o8VsbC1IMQ/hNuHbhPw0NQw2++MH2ZWZliltuLw1MPMKT2HadmnMaf0HA5PPUzAwwD8zvgB4FQg7si5Sj9V4pHvI1Y0X8GVLVc4vfg0C2oswCaNDWUHxF9ZTxE3uXKJT+Dt22K+nD+/lJ7/7Tf5vU4dGVipaqgKxedBgQIFWL16dYLLwIEDU/oyFYrPnufPpVqlQXQbPRoWLEik6AZygEqVYNUqEcoMrFwpYVAJiV+HD8vP8uUhPByr589xy5ABq+fPIUcOEfT27jXdx9dXXP5z5ZLXtWrJ7PO5c1Ftzp2TdbVqJfLmFEmBoyN8/TXMnSta6/Hj8v4rV06eU86elbFltWqQPj00biyC8O3brz+2saiu20UoWRJsbSFnTpoGL4q/qO68eaIOvlr8dBnx6uNP98jfaNuuAGaLFokxXbQ2mk5HzxKHKBB2in7fm0H79lHHe/YMevSATJkYnX4SD24G8VvLA0nRdQqF4iPkA5BfPm62bDH5PGfiRNPxRXRifJ4bFzs7W0qUKM7ZrSWws7DjSdAT1h24TIMG4gubNq1UY49Zkjva5zn29pI+unWrfCkZqk6Gh8uA6uxZGDcuytP2QyBntZw0W9mMR76P+Lvh35xZfIaa3jUpP0hmMe+duMecsnO4tP6SyX4BfgFYpbZicZ3FbBmwBf9b/qCB/y1/tgzYwuK6i3l86TEANmlsYp0XIGfVnLTe2poAvwCWNV3Gxt4byVwsMx0PdcQ+o33y3vgnjL29hNCfPQvbtsl7T6eDzZtlls/VFaZMUdVQFYpPHSsrK7Jnz57gkj59+pS+TIXis+baNdG2Nm6U6J/ly2HIkLcsopAQXl4iojVrJif56Sfw9pY0UVtbCQM6dAheFVgxcuYMWFnJIHjqVMxKl8Yqe3bMSpeGpUvF68JwnJ07ZYBdpw5kzy6lNUHyC/PmlZCppUtlqVsXChWS61F8UOj1UKyYvP/275e3xNKl0KaNRKG9eCHpm126gLOzBBZ4ekoBkLCw2MczFtWd7yVpF6tWQcuW5J76HRBPYVtDrumrZVjD0+jtbRmZdyGhGTMSUbu2pA5Fa/P3yIscpgyTa6wXaxtDrmlEhLzf1qzh5uDfmEIfvs+1iuztq0m+rUKh+OxQqaaJIKm9A+rVhf/tKcfWk+dpUS8bhQvKl05goIxdatWSsYiNTdTn+c2bkjqaMSMMHy4VgwzCX/78Ir59yBN7+RvlJ3+juNMNclTJwTBtWKz1dafWJW3utGwZsCXO/e4dv0foi1B+Cv/JmEYaFzmr5iTn3pzvduGKBImZhvrbbzBnjmkaart2kobq6prSV6tQKBQKxefF/v1SifLRI9ES1q5NBt/fatUkwm3YMDlZliwimA0YINtPnICqVSXkqV27qP38/ER0q1NH2hi4dUv2LVZMjrloEfzyi9xArVowZkxUBVQrK5mN7tNH1BoLC2kzaZKU2lR80KRNC998I0tkpLwNDN5whw/L89CZM1I9NXVqqFlTUlLr1IEvvohWVDd3Blg4XV7UqUOqkAiYCP6PwwAL05NGyzV98ADmb4IBlY6QZtsxLvj4kN3JiZi5pt49oXyRl1TZPcI013TJEjh2DE6cYPLcQlhZQ589TaDyCInaKFw4mXtQoVB8aKhvnkSQHN4BlbJXYuuk5mD9nG3brLG1lXY5c0r00LFjULGiyec5adKI4HfokLS1tpbotp49ZZzxqRH4OJBDkw8l2Obw5MMUalFIRa99AOTKJePiESNkjDx1qmR6TJ8uS+3a8N138v/zIaRAKxQKhULxKbNwIXTqJBFB7u4iumXNmkwna9RIlrioUiXuNJGpU6VSk0Ggi8mJE+KbcvaspJ3Gh7OzRDopPmr0ehGFS5SAoUNFLN6yRYS4zZvl9cqVsgAULQpFCoQClkSUi+EF2LgxTAT9zetAnnjPOXs2RERo9NnfnPDOnQl0c4vV5sABeSuuzjNCPAb79YvauGIFVK5McJ5CzJkjgXBpMlvDxYuJ7Q6FQvGRoh5z3xGjd0CMsUTTpsTvHRADPz+J/OncORw3N6nYVDFbJTjXBF2xedjYRA1GSpSAu3dFdAP5PK9YUQZLrq4SGafTSYT9jRsSVfQpim4AOp1O0ksT4Pmt58aCDYoPg+hpqFu3godH7DTUqVOjCp4pFAqFQqFIOiIjJZWvTRsR3Ro1Equ0ZBPd3pXHj6XMZUKMHatEjM+U9OmlSNyiRVKQ4dAhCYYoWVLGladOwZ4ltwAYuzgnzZtLNvL9++CfUcQ2h5d3EjzHihVQK/MZnMyfEjZ0aLxt0tiFUO/SZEkxii4CnzoFBQuypdda/P2h5bQy8jAX05tQoVB8Nijh7R0xegfkNV2fO7f8jNM7IAbDhsksztChUeYEGcNKQ4gjL23+o3Wn56RNKxFsX30VZSaqaTLLcvy4CHeBgfJFky8ftG6diNLvHwnXdlyD12hqDs4OaPGZ7SlSFJ0OatQQ0fjyZSmt7uAgv/fpI5kovXu/2f+QQqFQKBSK1xMQINZmY8fK6x9+EOHAzi5lrysW4eEym3wnYWGER4+k7GWJEuLvsm0bBAW9n2tUfDCYmUHp0mK3c+SIiGsLFsA31R5hRjh3Q9OxbJnUPMicGSrUlUyYNIG3iYiI+5iGorrN7k0RTxRD+nIM1q2DhtabsChfWqI3o/PwISxfzrqVIeTM8JISa4eKr2GtWsrjTaH4TFHC2zti9A5IbbreULzgdVE7Dx7A/PmxP8/9n1rJL1sncPbqc/76S8KdDTYYR4+Kj8GjR3IOc3NJ0/vnH5kB+pQ/zzVN49DkQ6xquQpeo6mV7lsa2/S27+fCFO+MIQ319m2YMSOqGur06SIk160rfh6qGqpCoVAoFO/GnTtSYHTlSsmGmDdPBLgPyt5B08Q9v3Bh05S9+LCwkH2OHxejr5o1xXulRg0YP178WOJTVhSfLBkySBDC2FERVGIPdjmcGDIEiheX7Rcu6nHgGUfX3SdDBvj2W0m9fvAg6hjGorrafpkRjoMnT+DSJSj/eC18/33sBqGh8OwZhzJ9Rfla9mJAt369PCiOH5/Ed61QKD4GPqSv3I+K1wkBrxvMiHdA7M/z0NBXv9j7Ubj3cGrVEhsLgzF96dKwfbs0MTeXtL0pUyRV71P+PA8PCWdtp7Vs7rcZLVLD7Vs3MrlnirOt3kJP7rq5EyysoPiwsLeH7t1jp6Fu2iRjlXz5VBqqQqFQKBRvy/HjUKqUTOCmTw87dkDbtil9VTHYvRvKlRP/rfPnxVnfwyPhfSZMkApjCxZI7uwXX4gPzPbtEs5XsqQYKzdtCjNnyiBaZUJ8Pjg44MVoTt9wwNdXiuD17QvodPzIWLCx48kT+OsveftkyiRvmWHDZMLXSheCS+3csc25X3HmjPwskOq2DFRjkioVEUWKcf6yJQUKRK2jXDkJp1MoFJ8dSpl4Rxwc5OeLF6brDcKAYXt8rFgh0WkxP88NEXPk3sjeW7uJiIA//hAvA5AxQ6NGEmlXsqRpqutH+Xm+ZYvciK2tVJCYODHWwCjgQQALayxE85nLMIYzjOE0WdqUrie7GV8PYzhl0vhil8GOyLBIDnT0QfPwkI5Km1Y67epV03OfOCFflk5OkC6d/EGiV89SvHeip6FeuiST3g4O8rshDfW775Sti+L9UK1aNaZNmxbvdk9PT1q3bv0eryhuNm/eTMOGDSlcuDA1atRg1qxZb5Rqv2DBAmrWrEnRokVp3Lgxu3fvNm5btWoVroksOXz48GFcXV1NloIFC1KxYkWGDBnCc0PoeDQiIyPp1KlTgv2uUCjejFWrxA/47l2JKD98OKro4gfBv//KOKxKFTHqsrUVE7qrV2XG2RCmFJPixSWsydlZfs6fL6Hz587JLF2DBjIofvpUwvy6d4c8eWSc2amTqC3RQ5wUnx4uLlQz28PK1mvw9ZWiumvWgHfvWwzCm5//yc/UqdLU2VkePY4dg5EjYcnslzhqT/n9cTMWL5Yso5j4+cnPNDWKx22qnScPjwOsCQ+XQEwjYWGxq+8pFIrPAiW8vSMuLuIrcPmy6XrD6/z549/X6B3QLO7j6nQauggbrj27RuGKt+jWTXxm9Xpo0kQGUq6uMrEXk4/q8/zQIfjySwlnWrUKWraU8qwTJhib3P/3PrNKzuLmvpvcsHfj1q/L4OBB4xK5bz8RrvmJ+CIL5faMptXWVqS1DqDmgRH4n7omPiF//CGDsVq1ovw/Ll+GypXFIG/OHMm7CAmREakyF/sgcHERb2VDGmq+fJKGOm2avP/r1VNpqArFvn378PLyomnTpqxfv55BgwYxY8YMFixYkOB+q1atYtKkSQwYMIB//vmHypUr07NnTy5cuJDk17h8+XL27dvHvn372L59O6NHj2bHjh0MGjTIpF1oaCg//vgje5X59EdFZGQkU6dOpWLFihQtWpTOnTtz69atONtOmzYtlhhrWH744QdABO/42hw9ehQAPz+/OLevUhUsARERxo2TMWNQkFQPP3hQ7B0+CK5dk3QOd3f5Ijc3F3Hs8mUYPVpm3JycZNuvv0K2bDIzly2bvN60ScL3oqPTyeC7d29RWJ48EUPkkSMlz9bCQqqPzZkj+YUZM0r5y4ED5XgBASnSFYpkwtoaKlWi0cUJnP5XIyRE9NwB1r+BgwNmZUvRu7f8r9y8Kc9mc+ZIgGQjmy3cJzMTD5WnVSvIkcOG777Lja9vlMF0sxpP0NCRr75L3OevV48MZ3einTtPt26v1j1+DPv3R1XKUygUnxVKeHtHXn2es2qVaYDWypUyXihVKv59jd4B5WNvs7eH0qV16M41h3BLzr3ci6Mj9OwpAkPLltKuXj0pmHP+fNS+H93n+bBhMuhauBDq1JHB1vffi/FIUBDn/3cen/I+PL/5nLS509LyaF+c+30NZcoYF/3xY+gv+XJx7BjMcmUiU+FMtCh1iRCsmH77K65Y5oOvv4bFi0VkO3ZMzj11qsysrl8vM6MeHlKX3M5OlB3FB4MhDfXcOQmQNKShbtwYlYY6bZpKQ1V8njx8+JD27dvTqlUrnJ2dqVWrFuXKlWP//v0J7rdt2zYqVKhAnTp1cHZ2pk+fPtja2nLw4MEkv8a0adPi5OSEk5MTmTJlonLlyrRt25bdu3fj/+of98SJEzRu3Jhjx46ROqZ5quKDZsaMGSxZsoRRo0bx119/GaMWQ43eGVF06NDBKMIalo4dO2Jra0u7du0AWLFihcn2HTt2kCNHDsqUKYO7uzsAFy5cwMrKir1795q0rRdXytdnRkgItGsHP/4or3v3FhP412VivBcePJCwdVdXGZdpGjRvLoPZGTPE/T46Tk7w3XdEHDpEyI0bRBw6JPvHFN3iwtwcypaFn36SVNYnT2Sc17+/+MiBRNz98osYyqZJI5F3o0fLxHB4eJLfvuI94+UlD13Nmsmg8aefwNtb/jlsbWXgeOgQPHzIF19Ahw6wfDnMH3iGSEsrvh7sQuHCoGk6DhxwoFQpawYNepXtZMw1LRD3ufv0kXLB9etLEMDatfI+0+lE7FUoFJ8dSnhLBPJ5rtEs3TY2WjXkJ8dpeHtr/PiDFvPzXJg3D3Q6zjQZhhXBuOTWgU6HrZ0dxUuUIHLuIkaOlGxHzd+Jwr9sYOX5pfwS0pPls59RumgwDRrIofr0gayZI6hf8gFL7Tuz1vYb6ua9jI7Ij+PzPCQEdu2SFNDoNG0KL15wuvsMljVeRlhAGLlq5KLT4U6kzxdjoOXnB15ehHfuTKCbm6zTNNL9uxO/4vUJx5JVLVbhf9tfql7dvRulSubPL1980ct52dnJl+SVK8l224p3R6cT7+ToaaipU8vv330nfzqVhvrhomkaAaEBKbK8rwrHx44do02bNhQrVgw3Nzfq1q3LmjVrjNs9PT0ZNGgQo0ePpkSJEpQqVYqpU6dy5coVWrRoQeHChfHw8ODff/817nPx4kW6du1KyZIlcXNzo3r16vj4+Bi3N2rUiB49egASeXTgwAGOHj1K+bhmdqKRLl06jh49yoULF9A0jQ0bNvDixQsKFSpk0m7ZsmVUrFiRIkWK0K1bN+5EqzRoiF56F8zMzNDpdFi8StHZvXs3FStWZPXq1aQyei4oPnRCQ0Px8fHhu+++o0qVKuTLl49JkyZx//59tmzZEqu9nZ2dUYR1cnLi4cOHLFiwgKFDhxrfS9GFWicnJxYtWoS/vz+TJk3C3NwckP+LHDlykCFDBpO21tbW7/X+PzQePoTq1cX2zMwMfvtN5hlfdVvK4e8vk625cslMWViYfKEfOyZpn7lzx7+vmRkhDg789+ABIQ4OcmPvgr29CB+//CKCm5+fCCIdO0okXViYCHQ//SSCXbp0kp84fTpcuKD84T5GqlWTiAhDrunixSK8GaKtT5yQv/X69Sa7mT3yQ5/GkfHj5a1y6lQQFSs+Izxch7e3TPjuW2XINU1DnKRJI9EQZctK9ESLFmJ9s2+f5LYqFIrPjpT+Kv6oqWZ7iJX6Xxim/ULDyFVk0T3DW/ueAVp6wNNYiXTuXJl9pH59OHgQP+8cOO7Sw3qJLIjs0IGXd59RedxXnLoDWbnFbLP2/BA5hOYsw8FcT0PdWiY+G4tZ6AGwsSFNqnD2O37J4Ofd6an9RmikjgqB+9mXdSDOmZYDcfgNfEhcvSqVJKKb1AFhX2THArg9fztQmlK9S1H719rozePQiIcNA72esKFD4d49WXf9Ojx/Tp4eNWg6eB65Hh3GItuPaPXrovt9hqgzICFUMbl8Gf77T0atig8aQxrqyJHygDFtmoyLp02TpW5dEeFq1Uq40MmWLWInc/asZJ307AkDBojIF5N586QcfWxsgeL88UcIXbrImqxZJW0hJg8fRk3Ur1sn13/6tKz7+msx/7W3f7u++BjQNI0Kcytw4NaBFDl/eefy7G2/F11cf9gkws/Pj44dO9KqVStGjRpFWFgYs2bNYsiQIZQvX570r/7wGzZsoGXLlqxatYp169YxZcoU/vnnHzw9PcmaNStDhgxhxIgRrFq1iqCgIDp06ED58uX566+/MDMzY/ny5UyYMIGyZcuSP5qnwd27d6lTpw7h4eFUqFCBb7/9NsHr7d27N5cvX+arr77CzMyMyMhIhg8fTokSJUzaLVy4kClTpmBpacmoUaPo2bMn//vf/9DpdHTo0IFvvvnmrfopPDycU6dOsWDBAipXrozNK2+Efm9SxVDxwXHhwgUCAgIoW7ascV3q1KkpUKAAR48e5csvv0xw/5EjR1KiRAkaxZyEe8Xly5dZsGABY8eOJW3atMb1vr6+uLjEk+L1mXL2rESEX7sm0W3Llsl3YIoSEiKFDUaPjjLKKlFCqoCl9FgrQwb45htZNE0mXbdtk2XHDvGHW7NGFhCT2Ro1ZKlePXZ0XnKRBAMVGaVAyB9/yJsiZ874z9eunTy4gIiT/ftLKm54uKQZ/Prr+7v3pKBRo9iT/AaqVIlbUJ04Ebp0EVH4wgUKnj/PUrd0HOo2nkE/pebqVag4tRmVKzdjeji4xXfurFlF7FMoFAqU8JY4hg2jUfFnNDqc7dWKtDDYTFIl+/ShShUb089zJydwcmLGSpgBQBmCf56KxXlfanKAU88z4uwMu1yGk/36ZRq1qEa4JZwb6EeG8zmhgZ/MDlasCMuXk/XsZhaf/RUKWMrxz2UAt7Uy2jLkpH6oGEy1o6UU+d/x5+8GK+kMWOtD+fL3LyneJR5j3QcPxEx3wABwdIwS3l6FF5p5/UieokVZdeBbLAOfUW/XXqyrVpX83OhRbgaCgqTMl7W15GUoPgrs7aFHD9FRt22Tmf316yWjYONG0XV79ZI/bczsNYPFYPPmInjt2yeToOHh4OkZ+1yvdPNYdOgQyZMn4dSuHQHIs8WdOzKpGtPE2tFRfv7vf+K9U6WK/LuGhso1HDggE6QpHp2QDOhIPtHrQyAkJITevXvTsWNHo8DXpUsXVq9ezfXr143Cm6OjI4MHD0av19OuXTumTJlCvXr1qP7qIbRx48aMHTsWgKCgINq0aUPLli2xe/W59d133zF79mx8fX1NhLfUqVOzfPlybty4wejRoxk0aBCTJ0+O93pv3rxJZGQkP//8M3ny5GHLli2MGTOGLFmyUDGaX4G3tzf58uUDYMKECdSuXZuDBw9Srlw57OzsjNeVEF9++aWxT4KDgzEzM6Ny5cqMHDnyTbtX8YFy//59ADLHeBDPkCGDcVt87Ny5k5MnT7J69ep420ydOpW8efPy1Vdfmay/ePEiadKkoWXLlly7do3s2bPTvXt3KlWq9G438pGzaZN8l/n7S1DZunUJew0nOxERsGQJDB0qE6IgX8hjxsiXXzJOgrwTOp1E3eXODd26yfWfPBklxO3bJ1/s8+fLAlCwYJQQV7lytOpoSUgSDVQiO3Qg/MkTImrXlvKdcQ1mfvsN/v5bIgBBzlG3rrypZs6UiEBPz6hCZHEVFPiY0DR5drhwQSLiLlyIWm7eNGlqDmQBGm3ZQv0Zc5h4tDJjx0qAZNGiMs4cMeIDSedWKBQfLJ/g4917wpAqOWKE6fqmTeHnn+XLsWbNhI/h54fZcC9+pzv/WpXkh/5heA0xxzbLShg4kNzplnHmwRn23dxH4xKNJVXSwObN4pER3VugQAEZaW3Y8OELbzEc8W8fvs3fDf8m4L4Icu4di5MmPtENYPZsGRj16WO63uApkzEjlpvXUWS1L8uaLOPJy7R0ujxHZp4MYUkGXryQEPQjR6TcbPbsibw5xfvGkIZas6YELv72G/j4SNrpd9/JZHH79jI4ypNH9oluMQhiMxgWZtTNYxUpeaWbmzB1Kvj66vDxuYKTk7xvTp2SbY0aSWReXAwfLv+qmzaB5SvdvGJFaT93LnTunOgu+aDQ6XTsbb+XwLDAWNsiIiIIDg7G2toas3dNIXoNtha2yRrtBpAtWzYaN27MggULuHjxIjdv3jQWKoiIiDC2y5o1K/pXYZi2trYAOEdLO7G2tiYsLAyQlLsWLVqwbt06zp07Z3LMyBifofb29hQoUIACBQoQERHBgAED+P777/nnn3/4448/jO08PDzw9PSkZ8+e/PDDD0ZBo0CBAty5c4eJEycahTc7Ozuj6AaQI0cOHBwcuHjxIuXKlXvjvvnzzz/JmDEjAJaWlqRLlw5Lwxtf8VET9KpgUcy/p5WVVZxVa6Mzd+5cqlataiIgR+fWrVts3bqVKVOmmKwPDw/n6tWr5M6dG09PT+zt7Vm/fj1dunRh7ty5JtF3b4OmaQQGxv6MSgoM/WT4mVRoGsycac6gQRZERuooXz6CJUtCSJ9ebG3fO5qGfvNmLIcORX/2LACRmTIRNmQIEa1bi1jzDn2QXP2XIAUKyPLddxAUhP7gQcx27kS/cyf6U6fQnT0rUWhTpqCZmxNZsiSRVasSUbUqkSVLJokwZeXlBUWKSKQaQKVKWAQGYj52LEGdO8ceqNjZRfnXvcJ8xgwsfH254uNDBnt7GTvHaKM7eRLrv/8mbPhwwosVg8BAzJYtw+rkSYKOHUN79T+qc3XFumRJQhcuJOIto51TjJAQdFeuoL94Ed3Fi+h9fdFduiSvX7yIdzctbVoi8+ZFy5uX0IwZMZ83D8srV7CuUwXPdu1ounMcP4xPz+rV5kyZAkuWaIweHUqLFhEJZlp8jqTI/+8nhOq/xJHc/adp2hs/Yyjh7V2JJ1XS6FPh6/t64W3YMMwt9WT57SdWZviPqlVzY+t3W6LBsmdn+t8abtsh1ZhmULe+qAmGVMnz52Of23D+j6Eqp2Fa6MULTi8+zdqOa4kIicA5X2q4AGnccyS8/4oVMuvm5GQ6ujTMONatC3o9+Rvnp0y/MhyaBMFYo9t9CKvowtutWzKb6OsrM30xZtUVHx+5c8OkSZLGuXChQRyTn1OnSqZEt25Jopvj5QWdO4fj5hb1Hjx1St6GCVWPO39eRMDoz6oZM4oYt379pye8gYhvdpaxo6MiIiIwizTD2jL5hLf3weXLl2nRogUFCxakXLly1KpVizRp0vD111+btLOI42FMH88o/eHDhzRv3py0adNSrVo1KlSoQKFChahcubKxzfHjx9E0zSRF1OCV9eDBA7755hvq1q1r3GZvb8+VK1d49uxZLD+3okWLsnXrVuPruP4ekZGRby2affHFF2Q1fHcpPikMnmqhoaEm/mohISHGNOK4uHv3LocPH+bPP/+Mt83atWtJly4dNWrUMFlvbm7O4cOHMTMzM57Tzc2NS5cuMWfOnHcW3sLCwjgfvWJVMnDdEP2VBISHw8SJzqxYkQEAD49H/PDDTR4+1KK8hd8jdv/+S5bp07E9eVKuz96e++3a8eCbb9CsrWVWLJEkZf+9NZkzi09XixaYPXtGqmPHSH3kCKmOHMH69m3MDh7E7OBBLMaOJcLWlhfFivGiVCn8S5Ui2MXlraP8dKGhFN2zh7tdu+IX7X1pW7Qo+V+84PZff/GiTJkEj2H++DFuw4fzsEkTAt3c4u4/TcO1WzeCcubkfI0axqpt2VeuRMuenXNgUsmtQM6cBC5fzvUiRd7qfpIVTcP82TOsr183LlY3bsjPu3fRxZioMu6m1xOSJQvBOXLIkj278fcIQ4rCK/T165N12jScVq7EfN48XP75h4mDBlGjekO8J2bjxg1runa14rffXjJo0E3y5VMiSUxS9P/3E0D1X+JIzv5703GxEt7elThSJYEo4ed1JRZfpUrqBgygdvNUnD9/W9YbRkuDB+OSPwvfNIVSFlkZvfWVYZwhVfL586jQnZjn/xjKO7q4oJmZcXnaRv63MwsAeT3y0qSfM1Qj4RyJO3ckBaBvXwgPx+r5c9wyZMD8lWCJTicRia+oMaEGdw7fQX8gggtbb1IgOBxza3OpSFS7tsy+btkiZWoVnwypUkkaarduUWmoGzZELSDPAS9eRP3bvqVujl4PQ4eGGTOdQf5F06YVEW/bNplcrl8fJk+OskVJnx5u3DA9XliYZDdEe+sqPiL++usv0qVLx1yDNw6wY8cOgHcu7rBu3TqePXvG5s2bjYKd76uJFcMxFy5cyP379/nrr7+M+/3777+Ym5uTI0cOHB0dcYzxAGGIlovpk+Xr60uOHDmMr/39/bl58ybZsmUzbn/x4gV545r0UXyWGFJMHzx4YHyfGF4nVHhj27ZtpE2bNsEiINu2baN+/fpxCtNxpTjnyZOHffv2vc3lm2BhYUHuOEz+t13bxoh9Izj/6DwZ7DLQpWgX+pTsE+cM98L/FtJtY7d4zzG9+nTaFxMPLt/Hvnjt9mLvrb2Y682pkLUC46qOI6djlP/Wfw//w2u3F8fuHcPSzJLqOaozuvJorMIy0qqVFTt3mqHTaYwcGUa/frbodPniO3WyoTt3DovhwzF/ZVCvWVsT3q0bYQMGkDZtWtK+Zv83ISgoiOvXr5MjR44EBd33yiuBNxIIun4d/a5dmO3cidmuXZg9eoTjvn04vno/ahkyEGGIhqtaFe0NJiJ0Fy6gDwvDqVw50kYfE2fKBECOkBDCX5NPbPHdd+jNzTEfPx6ePo2z/8yWL8fqv/8I3riR/IZCZYDV/ftoBQvGiki1yJ8fx7t3441UTVbCwtBduxYVvRb959On8e6mpU5tjF4z+ZkrF1hZYQlYAvHV0za8//R//EFw165Y9uqFxcWLuAweTFePfbTa8CvTVmVj/HgLTp+2p3Xr/HTqFM7QoWGkTYp/gI+cD/L/9yNC9V/iSO7+u/wWk0pKeHtX4pk9MfK6OOM3SJXUr1rN1slZ2cZNPBdtwb5KzahUyYTO/xHEOIeE6Xjm6Ir1zo1ARyr8UJFqo6uh+8FTouFKlYp/58OH5We+fDB1KmaTJ2N2+7ZEA/btK4OhVaskZ9DKCjMLM5r3To/lgTDOP0zPzb6b+HJIEfHlMDcXU634yoErPnr0egmOrFUrKg31zz8lUHLOHPFY69BBItAMmsMb6uaxLAZBhLc7d+TftG9fmSgeOlQsYE6eFN28QwexupkwQX4PCpLouefPP83iCh8zN27cYM+ePSbrrK2tKRXjMypTpkzcv3+f3bt3kzt3bs6ePcvo0aMBiQZ6FzJlykRQUBCbNm2iePHiXL16lXHjxpkcs23btrRp04YpU6bQpEkTzp07h7e3N23atCFNPNXWnJyc+PLLLxk7dixWVlbkzZuXnTt3snLlSn755RdjO71eT9++fRk6dCgAw4YNo1SpUsbouoCAAAIDA3GKmYOt+GzIly8f9vb2HD582Ci8+fv7c+7cOVq1ahXvfseOHaNUqVLGKqUxefnyJefPn6dv376xtl26dInmzZvz+++/U7p0aeP6//77L07h7E3R6XTG9G8Dh24foumqpjR3a86Y6mPYd3MfXru90Jvr8awQ22OrccHGFPkidiRQhzUdePLyCV/m+xJbW1tuPb9FjaU1cE3nytImSwkMC8RrpxdfrfiKM93PYGNhg99LP+ovq49zamfmNZxHUFgQg7cN5sulTYiYeRjf82bY2sLixToaNjRIB++RmzdlBmrBAhmT6vXQoQO6YcOwyJo1WUp82djYxPobfRAY0lJ79JC+OH06yh9uzx50Dx5g/vffmP/9t7R3dY3yh6tSJcoANjqvPuOtnJwg+j2/iqywDA7GMqG+ePBAnhkGDMA6UyZ4+jTu/ps6FcqXx7pOHdP1L16AqyvmMds7OsLFi8n7d3j6NLb3mq+vDOLCw+PeR6eTyfd8+aR/8+UzLrqMGTFLArsJGxsbrGvWlFKnY8bA+PGY//MPDrt34/Xzz7Q/35nvB+tYulTHrFkWrFplwbhxMs77iAP6k4wP9v/3I0H1X+JIrv57GysbJby9K9FSJU0wPLG/zmHzDVIlMztkIU/aPFx6cok9X4RSz8FBntwNx4/Lm8Df/4N393x69SlLGyzF7nEJ2rCQPqUO4FixNAwbKo7048fLIMPfH86dE+Or6A92Z86AlZU46p84EbX+1i1RQlxdxQ+vXj0YOBD8/LAbPJjgfEXwveCK9sdxKh77FYcHD8Qw1t9fDGwNpE6thLhPFEMaav36EtFWO/0xRj3qTsEpZ/GbkhEf117AgLgzQqJVC5vND0QwnD5jsmA75lFUtbAuXZg1C8y/qk/JIRJWVxEoSDkq+O1nwYyXdP/enuHDIXzeIn7ybIanpyUWhNKZWXxFes7pPJAaZIoPgX/++Yd//vnHZF2WLFmM0WwG2rRpw9WrVxk0aBChoaHkyJGD/v37M3XqVM6cOfNOpu916tTh7NmzjB8/npcvX5IlSxa+/vprtm/fzpkzZ/j2229xd3dnypQpzJw5k/nz55M2bVo6dOhA59fkK48ZM4bff/+d8ePH8+jRI3LmzMmvv/5K7dq1jW3Spk3LV199RY8ePQgKCqJq1ap4eXkZt/v4+DB9+nRjFJ7i88PS0pJWrVoxceJE0qZNS5YsWfD29iZTpkzUqlWLiIgInjx5QqpUqUxSUc+dO0eTJk3iPe6FCxfQNM3EY9CAi4sLuXLlYuTIkYwYMYI0adKwbNkyTp06xcqVK5P0/obtGoZ7ZncWNhIz0Dq56xAWEcbYvWPpU7oPNhams+dOdk442ZkK0VMPT8X3sS8+5XxwspVtw3cNx8HKgW1ttmFrIZ/3OdPkpMHSBhy7e4yK2SuyxncNjwIfcajjIVzSSmTqzUuODDpdBwIPkDVrZdauFa/S98rjxzKx+dtvUSHajRuLEBHH3+uzQ68Xx/2iRWUMGhIixQwMQtzRoyIi+fpKH+r1ULJklBBXtqyMcZNrgj86Bw7IODquAifJPcEfESGh/9GLGhiEtgcP4t/P1ja2uObqKllA70uUsLaWYhfNmkGnTuIR3a0bWSotYcmff9Kliyu9eokFYJcuMtn7228JxxQoFIrPAE3xWk6fPq2dPn3adGVQkKaZmWnazz+brj98WNNA03bujP+At29Lm/nzNU3TtICAAO3YsWNaQECApr14oWk6nab1769pmqZ1XNNRYzja4K2DNc3OTtP69ZNjtGypaQUKxD52/vya1rbtu93oe+DarmvahHQTtOEM1yZmnqg9mjBL0woV0jRLS03LmVPTJk6Marxzp/TT3LmmB+naVdNSpZJt8S29e2ta5cqaZmOjaenSaVrHjpr29Km2Y+gObSReWjj6+PetXPn9dcgHgMn77zPhv//kT73SrKl2p3or7aeSG7VRDNEeklYDTcuQQdOmT9c0f/9oOz14oGkHD2rawYOae96XWv1yTzTt4EEtIn9+LSRjRi3g+nVp9/ChHNzb29heO3hQc7AP07p2jpA2kZGaliqVFvTdIO3sklPa0y1HNO3gQa1i0edapfLh770/kpKgoCDt3LlzWlBQ0Bu1Dw8P116+fKmFh3/c951SqP5LHIntv9e93+McP3xihIeHaz///LNWpkwZrWjRolrnzp21W7duaZqmabdu3dLy5s2rrVy50mSfwoULa0uWLIn3mOvXr9fy5s2rBQcHx7n94cOHmqenp1a+fHmtUKFCWvPmzbWjR4++8z3E9XcKDgvWLEdZauP2jjNZf+T2EY3haFsub3ntce+/uK+lGptK67qmq/F7NjIyUnMY56CN2j0qwX2nHJqiMRztwcsHmqZp2pw5mmaW7ajGcDQXj5Xa3btveZOJ5eVLTRs9WtNSp44aL1WpommHDiX7qT+pccrTp5r2v/9pWs+emubqGnsMamOjabVra9qAAfJ6xQrT/R8/lvUzZiR8Hnd3TatfX9O0BPqvXz9NS5NG00JDY+9fooSmNWgQe72Hh6aVKvXGt6v5+2va0aOatnChpnl5aVrTpprm5qZpVlYJj+OzZNG06tWln6ZN07QtWzTt5k1Ni4h483MnEQm+/8LDNW3SJE2ztZXrtrLStDFjtNCAUG3yZNN/l44dZSj5ufFJ/f+mAKr/Ekdy99/bjPNUxNu7Ym0tnmCrVsmMliFEZuXKN0+VjMvbxN4+6rhjx1IpeyXmnJxDwKZ/ICBASh+CRMstWSIRYYborHPnJK8tWkRCiqJpMtMXGAiBgRyfe5oNI48RGa7xRV57mv+Qg9Q24dCvn7QJCpJ79PQ07sO338KaNbB0adS6Fy9eX65rzRqZgXpVSc9A5aGVuX3gNqO3DSV9/vR0PtIZS3tVXe9zw8UFzHQRXPmiEo239WYkcOlSHf5ukBEuwNMHIfTqZcWPP0ZVQ82dW8qa3rkDJy9C3yF2cOQIulfVwrI7OfH8Oawc/4BSFMQtWlnTyEgIjQCnV2/HXUvuEvKiLLUb1KJAdUlLCg+HM9ehXbsU6RKFQqF4J8zMzPj+++/5/vvvY23LmjVrnBGR//77b4LHrFevHvXq1Yt3e/r06Y1p18nF1adXCY0IJW86U0/D3GklndX3sS81XRI2Ax22axh6nZ6hFYZy75p4Elx/dp3nIc/J7pCdnut7svQ/STWtnbs2v9X7jaypxf+rWcFmjN83np4bepHu6GRmzgqGr77HOiwz++bXIFPcmeRJT1gYzJolFYv8/GRdkSKSnVC79lsXDfjscXSEhg1lAcnW2L49KiLOzw82b5YFoE0bWL48KiLOEA32pl7ICbFunVxHXBVYXV2jsmyic/ly7GecyEi4fds0NdQQwXbnTvznt7KSQnHRI9fy5ZN1hgygDx0zM+nnhg3FVHjzZhgyBIu//qLPnDl8c7EkgweLPcmcOfKYOHo0dO0qbjcKheLzQf3LJwYvL/kSbNZMEvgPHHi7VMloptYmjBsnng/16lG7W2vangSvbeeIKFUSswYNpE3z5hLqX7eunA9EsCpUSK4nITQNgoNF6DKIWdGXpFyvaUSiZxO1OYp4sbhxhgYX12DRPh6fhqTg5s04V+vN9DRe3Jg/3P/g0flHrOu6jkaLGr1Vfrbi48daF0IlDrCKxgzU5LkhTx6YXbIpDhee8WeX4wzdXR1fX5gyJaoaau/eURne5V0fQU0vwjt3JvCVIbGVFfSamodG5kNZHK2s6dq18u9Qtaq8XrEwiLXM4kpBO6MPjo8PPHsWNRZXKBQKRcrxPESKaKW2MrVcT2UlgoB/SMJmoA8CHjD/3/kMKDsAR2tH7iHC28NAKaI1eNtgSmUpxV9N/+JBwAN+2P4DVedX5VTXU9hZ2pHJPhOTqs+k5f++IcJuGfQFay0NB7rsJFOa+Gzgk5DISDFB9fKCK1dkXa5ckmL3zTcfhZ/wR4Gzs8y4tWsn4/OzZ6NEuI0bZTz999+ygAh3lpYiaD1+DOnSxT5mQhP8Bp48gUuXYPDguLfHNcF//LhM8FevLmXhDUKbr2/CE+IZM8bpvUa2bJ+O+VmOHPL3WrxYhLgzZ6BMGTL26cO830bRpYsdvXqJltmrl2jZ06dDhQopfeEKheJ9oYS3xFCtmkxdDBsmT8tZsojwNmCAbD/xqhLp3LmmYSx+fnEbqWqaPJ3nzQt//QXjx5OhTXd+1etY5aqRt2NNKi1fHiVqNWkikXFt28oA6IsvRMxr2jRhYeyVIPY+CMKG5XzNNUSEqGp/hIoZfNHZ5hNxMuZiY/P6dcHBMquUkAcEyMCgWzdo2RKimYzbZbCj6d9NmVdlHmeWnCFbxWyU6FYiObtB8aFx9Spe2ihq3N5uqpsvysR4BtOsUDZqTajOvHkyIbx9O6xfL0u6dDJL6fynF+j1hA0daqyuYG0NnvnXMOx0MzLm/Yd6d2dzJjwfw7VhfFVbT7Vq4nHULdsGZtGVduVP0OHRz/wbkAfPyDE0rxdE5crv4YFKoVAoFAkSqYnH1bFj8ENz0UMyZoRuPWS7XhdbeIpmBQoVZ0OVCMZ49GFMoC1QnD/+CKFAHTHNdzTPSMSSVTTtosfcHArVzc2evGVZfGYxXYp3YdquJXy3szWc+xpOdgDzYILLTaTYpFpMLrqbPi2TyU9N02DrVpnMNUQ8ZcggVYI6dzaa+yuSAZ0O3Nxk6dtXoqfq1o2Kbjt/XmboAAzFS1xdRSRr0ECENhub10/wg7SB2J7Gmgb378ubPUMGKFMGcuYU7+RHj6TN9Omxj2duLjOYMcU1V9e4n3k+RXQ6+bvUri3ZPIsXi7Hw//5HuT/+4OjRWvz5JwwZIvUZKlaU5j//HFX1XqFQfLoo4S2xNGokS1xUqRK3wDVjhiwAly9jXb8+RW/exCw4OFZTHfBdY1hcGIYtHUulXQlcy7VrsrwNFhZvLoC95fpHd0JY2nYzT64+w8LOgkYLG5G/0bC3u764CA+X+zQInHFhbi6j5N69JRW4aVMxQK1cGXQ6slXIRvVx1dk2aBub+mzii5Jf8EXxLxJ/bYqPg+fPqcZOVg49zbBVRaJ08wmRDBjkDf5jOHVKxk1z58Lvv4sxro+PTDDriCDCZz5bSg0g8+M0QFRZU6/wETjpd/Gb/zB+D69HOtsguoX6MPzyHxBwCOzscLu7hXX8ww8vffAIWUGmtIEMiZjBj0cmwN1jIqIrFAqFIsVwsJJCVV6jXvBtUQn02rcPPIf5w+Co7dGpX1989AHaHlhBeuta/LLdiQ4dInnyJJzatSN4ZikRc9e21sXxid7opOHlVQZ9DgeO3j5J0Qjot2Y4PC+H1T9/MW2aJDQER9Tk23352a550YcVSX/TR4+K4GYoHpMqFXz/vXwZvmPJ7StbrrBjyA4enH2AfUZ7SvYsSdkBZePMNDg17xRr2q+J91gRf0RQqoukOT668Iitg7Zyfdd19OZ6clTOQa1fapEmV9w5uBfWXODvhn/TdmdbclTJ8U738t6pXTtqgt/XVyLEqleXv8u2bTLONRRqmDZNxLby5cW2xc5OChjEF1F2+7b8PHoUdu40TQ+NWdr99Omo3x0dRayLmR6aM2fcKaufI05OsGiRTPx36wbXr0Pt2pi1aUP3X3/l66/T8eOPUv9i0SJxxxk+XB5ZVBcqFJ8uSnhLaR48QHf5MvqY1YMsLY2CVqXAcBbzkD2FUoOueJIJY9jYJJvBwKWNl1j5zWpC/ENwyO7At2u/JWPhjK/f8U0wN4fWrSUE/vjx2NuLFxdPuE2bJJb7zBmZdVq8WMpaduwIbdtSbmA5bu27he9aX5Z/vZwux7tgk8Ym9vEUnx6v/t8aVXhIo+HR1odrMAjQ62Pp5pMny4PX/PkQOnwCZo8jaHmkD4+LWFO1ai5+/VVHkSKgn/0n3c3N6V7SkF5uD/vdocJ/sGCBVOMdM4aag55Ts5Lzqzap4KoH5B8kua0TJiR3DygUCoUiAVzSuqDTzPii4GUWSlFT6tSBmxGXWQjkSh3bY8tJrEC543+Hi5tPMqR6Xw7uisDXV8//Nj7FMk0k2S2zg6bD0jaEbcuiCjHmzAmlVofz30kbKn8DEQNvwH+NGDlSAs0EG8rfKcHZh2eT9mZ9fSWldMUrMc/SEnr0gB9/NLVKeUtuH7rNki+X4NbcjaqjqnJz3022DtpKZHgkFTxj59jlqZ+Hjgc7xlq/psMaXj55Sa7akj3x/NZzfMr7kM41HU2WNiEsMIydXjtZWGsh3c90x8LGVL0IfBzIuq7r3vk+UpSEJvjv3TP1h7tzJ0o0BUifHqpVw7xSJdI9eoTFggVw9aoIbFevSpu4qp7q9ZJWHFNcy5dPjql4M+rWFXF0yBARRhcsgI0bST9lCn/+8Q2dO+vo1UssqQcMEA+46dOjbEkUCsWnhRLeUppy5Qi6coUrp0/jUqgQtunTxxLEKj26AL/l52DGUEInbcLS7MMN89c0jYO/HmTboG1okRrZKmSj2cpm2GWwS9oTOTmJl8KiRaKI3LolPhl9+4oolz69hLz36iV5IrNnixh3+TL88AN4eaGrX5+vvmnHn6cdeHbtGWvar6H5/5orv7fPAYdXkQoGwzYDhlleh9iRDCCTzL16geazgocutSiV3okNG2DHjjSUKKHRtSsMG1Y29nNK+fJyTIOheJEisQ+eK5ekk7zGdFyhUCgUyY8uwhpuVIICq9C0gVFjgwIr4ZwDoddKgWvc+x6+Ix5bTuSny4/h2JZdSsODHch6Nit9SvfF7HYl9AVXYWY5FrAC4Fma7WgWARz6qyIEg21QPgKz7adaNQ3Jf4Dg8GBO3DtBfqcEjPXfhjt3xKvLx0eio3Q6GUONGCGeVYlk17BdZHbPTKOFIhzlrpObiLAI9o7dS+k+pWMJZHZOdtg5mY4XD089zGPfx5TzKYetk6iUu4bvwsrBijbb2mBhK8dIkzMNSxss5e6xu2SvmN3kGBt6bMDM4hPxEotO5sySq9iqlcwU+vpGiXA7d0pa6qpVWK5aRY649k+dOm5xzcVFoucUicfeXiZUv/1WMm/OnoUWLWDRIkr+/jsHD2Zj7lwJND13TlyMmjWDiRPlsUahUHw6KGfUD4EMGQjNkkX8FFKlihWF5prOFSdbJ4LDgzl291gKXeTrCQ8JZ22HtWwduBUtUsO9oztttrdJetHNgJMTfPcdEYcOEXLjBhGHDsF335nOxul0ULIk/PGHzAzOnSsiSEQErF2LTYvGfP3SBzMzDd81vhz85WDyXKviw8LFRdIvLl82XW94/ZpqYbqTJ8nQsxnr18PRo0FUqPCM8HAdC397zrBsPszq85+pz3BkJISGyns2PFzC5g7G8V4LCkpUdIFCoVAokoarV0Hb7cVt7TDNVjRj46WN/LTjJxZd9Ya9P3Ljsi3+If4cun2IhwEPTfY943cGKzMr2nx3g5CIIF5W6IuGxi3/WwxcNo2IzeN4yV1chtUjlftGLErOo/afLeB2afBtQL9+0C7bKHA+QPOVzUhbahPmbmvIOLAOt5/f4adKPyXu5p4+lSf93LklMyAiAjw8ZOJn/vwkEd3CQ8K5vus6+RqZetEVaFqA0Beh3NwXdxGs6Lz0e8kOrx0U7VyUNG6SQqppGudXnse9g7tRdAP4osQXDLg7IJbo9t/f/3Fl6xVq/Fwj0ff0QaPTiWjWqxesXi2+GAcPwujRRFStyvOyZQnr0UO8M3buFM+2Z8+kEMP8+RLd2LixpJEq0S3pKVNGvL9HjpSI0g0boGBB9DOm07FdBBcvyp9Or5eaJvnySe28kJCUvnCFQpFUKOHtI0Cn01EpeyUA9tzYk8JXEzcv/V6yoNoCTs07hU6vo/bk2njM8sDMMplnGM3MCHFw4L8HDwhxcEi4OpKdnRS52LdPppUGDID06fni0RnqRKwHYNugLdwc5iMFHBSfLtbWUKmSFCeJnk+6cqVEppUqFf++MaqFFSigMXnyFdavD6ZAUSsmBvfCduo48uYVnTciAtOypubmEk3w/femxz1xQoQ/lWOgUCgUKc7z58C1agzNtxLfR740/Lshi88sZkJ1b9g/CH9/OHHvBGXnlGX9pfUm+957eQ9znRWPDtSHUtPB5nnUxgAnuF0WyxXrCQ6JJLRREyJrDCTyggcs2sS0qWb8+ivoLjaAxRt4od0lwKMRdt90Rhdqj37OUWyflHm3mwoKEif3XLnE0iA4GMqVg7175XuqUKF377AYPL36lIjQCNLlNa26mTZ3WgAe+z5+7TF2DduFTq+jwtCotNRn158R8lxsTNb3XM+EtBMYbT2av776C//bpt5kL/1esqHnBupMqUOqzKmS4K4+IszNRewZMoSQdeu4PG0aYd7e4jlWpYpEy6kMj/eLpSX89BOcOiVjyJcvxditYkXS3D3LtGnioFO+vPg+/vCD/Etu2pTSF65QKJICJbx9JHzIwtu9k/eYVXIWtw7cwsrBihYbWlCmT5kPO2Uzf36J475zB5Yvp3itdBTiNJqmY8XIswRkchHfC0PVJ8Wnh5eXiGjNmkna8k8/SVXiH38U0x1/fzh0CB6aRjLEVy2sSpVI9h+35lITT1qyhP53+rO4wzZ+dZ5EWMu28NVXkkMA4qK7fz+0aSPV42bPhi+/hKJFpUqxQqFQKFIUg/VuhXSNON39NCFeIVztc5V+ZaSwk14PVXJUQRum0a5oO5N9R1QZgfmBHyHSDMpMMT1whNiFhD3NzN+NV5BxdiCR4x9hv2M2BDsai4b27g2bptfhwfj9hPwUxHOvB1wfu45UgYUZM+YtbyY8XCLbcueGwYMl0qlgQRHb9u2DCrH91hJLyHMJ1bFKbRo9ZZVKXof4JxzKE/AggH/n/0upXqWwdrQ2rg98KOHk2wZv48WdFzT9qykNZjfg3ol7zK86n9CAUGPbdV3W4VzWmSKt47B3UChSivz5Yc8eKbSXKpVEJrq7w/DhFM0fwt69YgeXMSNcuiRWcY0aSY0GhULx8aKEt48Eg/C27+Y+IiIjUvhqoji38hxzK8zF/5Y/afOkpdPhTuSunTulL+vNsbSEpk3Rbd7El2cnkD69xgtSs+p5dSKnToPChaF0aRmwxvQDU3zcVKsmEW6+vtCwoRTf8PaGQYNk+4kTULYsrDeNZMDPT6p6xYFeD0WWeRE2ZQatM27hHzz49t4v/BrYjS9fLI2yb2vTBv7+WyIvGzYUsc/DQ0S4hKI2Fe+VatWqMW3atHi3e3p60rp16/d4RXGzefNmGjZsSOHChalRowazZs1Ci6uidgwWLFhAzZo1KVq0KI0bN2b37t3GbatWrcLVNR4Dqzfk8OHDuLq6miwFCxakYsWKDBkyhOfPo6KALl26RJcuXShdujRly5blu+++4+7du4k6v0KRGN7RChSQTIXnJ2qCyxawe2S60UoOGOZwjga10nDrlljSHj8uxzx5Upq5ukpRy+g4Oko0zBtbgWqafM+5uUGXLpJemC0bzJsnB/HwSLaoJy0y4c8gnT7h856YfYLIiEhK9yltsj4iVMbA9hntab6qOS61XCjcqjBfL/+aJ5efcGaxTJiemn+KG3tv8OWfXybiLhSKZEKvl2JbZ8/K/2FYmGRDuLujO3iA1q3h4kXo31+GhatXi143cqQErioUio8PJbx9JBTKUAgHKwdehL7gX7+UN1/XNI3dI3ezvOlywgLDyFUzF50OdyK968db7ciyQG6a7e6Jha0FV3FhT/5uEqp/5IgMWDNnloqoBw+apicqPl4aNYLTp8VE4+pVST82YChr2q6d6T4zZohf4JkzMHkylm3akHHePBk0Aej1WHzXHaf7/xH4KIhf+t7mJ4sJrN9hg7u7HO72bSTS7tgxCAiABw/EhzBt2vdy24pPh3379uHl5UXTpk1Zv349gwYNYsaMGSxYsCDB/VatWsWkSZMYMGAA//zzD5UrV6Znz55cuHAhya9x+fLl7Nu3j3379rF9+3ZGjx7Njh07GPRK5H769Cnt27fH2tqahQsXMmvWLJ48eUKnTp0IUQY3ihQiMVagd+/o4H4xKLgs9sY0V4BIONeUwAA9VatKcHXevBKYZvOquPrff8OWLbF3f2Mr0J07JdWwaVOZYEqXDn79VX5v2zbZJ3msHF5Ftr0w/R82RLoZtsfHuRXncKnlEqvYgmUqCQnMXTe3iXiXtUxWrBysuHfyHv63/dnUZxO1JtbCzsmOyPBIIiMkhDEyIup3hSLFcXaGNWvkHz5DBjh/XiJQe/Uite4Fv/wiGnnVqpIZPmxYVLCqehRRKD4ulPD2kWCmN6NCNkkFSOl007DAMFY0X8GuYbsAKN2nNC03tMQmjU2KXldS4FTAiS//kNnR3RcycmXxIYmCcnUVgcTHR/xQ3Nxg0iR49Og1R1R8Mly/Limh334LmTJJNGS/fpivXEnW6dOxrlJFItiikS6dvE0uXIDmzWWQNH++RDf8+OMrDyGFIhE8fPiQ9u3b06pVK5ydnalVqxblypVj//79Ce63bds2KlSoQJ06dXB2dqZPnz7Y2tpyMK6iH4kkbdq0ODk54eTkRKZMmahcuTJt27Zl9+7d+Pv7s23bNgIDA/n555/Jmzcvbm5ueHt7c+XKFU6cOJHk16NQvAmJsQK9dPrVJEq2GP+HkXrYPAkZfuto3z6SzZtlzmX7dhlmVKwoTf/4Q+y4QqMyJ7lzR1wKErQCPXkS6tSRqO4jR8Q6wcsLrlyBfv3kxt4DaV3SojPT8eTyE5P1htdO+eNXD/3v+HP/5H0KNitIZHgk4c/DyZkhJ+HPw3HM7gg6Kd4Qk8jwSCxsLLi67Sohz0NY23EtoyxGMcpiFAtrLARgYY2FTHWZmnQ3qlAkFp1OJmPPn4f27eUD57ffpNDFunUULCifD3/9BVmywLVr4l7y5ZeSiqpQKD4OlPD2EfEh+Lw9v/Ucnwo+nFt+Dr2FHo9ZHtSZXAe9+afzVircqjDFuhQDDVb13IH/N13ky3DvXpkltrERgaV/f/jiC1FUtm6NMoRRfBo8fCgzkF26SOhDzpzQubOMfB48kIeZ2rUJGziQcAcH9KdOQbFiMHlyrPdCrlyy26FD8lAVHAzjxondzvTpUcFyio+bY8eO0aZNG4oVK4abmxt169ZlzZo1xu2enp4MGjSI0aNHU6JECUqVKsXUqVO5cuUKLVq0oHDhwnh4ePBvtDyyixcv0rVrV0qWLImbmxvVq1fHx8fHuL1Ro0b06NEDgMjISA4cOMDRo0cp/6r4R3ykS5eOo0ePcuHCBTRNY8OGDbx48YJCMczVly1bRsWKFSlSpAjdunXjzp07xm3Tpk1753RUMzMzdDodFhYWlC1blhkzZmAdTRDQ6+U7xd+Q16dQpADvagV67qwZVlYaxQumMd3wJA+c6AxEYmamceOGnm3bJPOzRQtxtmjQQJr+9BPcuCEP2Js2wZIloqWl+z975x0W1dHF4XeX3hQLYkUUKyp2FBTsXWPX2GI3RmP7LGlGjSZq1BSNscXee+y9F4pdURAUC4JSRAVB+u73x2QpAhaKS5n3ee6DO3vv3HNnr7tzz5zzO0VSBmcn4usrOqlTB44eFRH7o0eL9lmz3p0bmw3oGupS1rksd3ffTZH67rnLE4OCBpSyL5XusQHu4numaJWiuC9yZ1WDVfxV9i9WNVjF9dXXKe1Qmru776Zwvj04+YC4yDisnKyo1KkSwy8PT7F1WNYBgA7LOtBnf59sumqJJBMULiwW+I8fFxNHf3+RhtqnD4qQYHr3Fgu5334LenqiMGr16vDDD8JpL5FIcjZ5x1uSD0juePsQ/Z6sxt/Nn3/q/0Pg9UCMixrzxYkvqDOszie341PQbmE7itcuzpvnb9jZeycJ8SoR+r12rUgzXLYM6tUTHpPt26F1a/EjOWsWPHmibfMlGSEiQsxiJk6EmjVFyP/nnwt9vwcPRFqOo6N4Gjp7Fl68gCNHiPvpJ+5s20ZC69YiZXXCBGjVCvz8Up2iQQNx6J49Iojy+XMhoF2tWuqoiryGWq0mNjJWK9un+L4MCgpi6NCh1KhRg3///Zc9e/ZgZ2fHDz/8wPNkkbGHDh1CR0eH3bt3M2jQIP7++29GjhzJ0KFD2bFjBwYGBvz0008AREVFMWTIEMzNzdm6dSsHDhygbdu2/Prrr3h5eaU4/9OnT6lRowaDBw+mRo0a9Onz7gfLMWPGUK5cOTp37ky1atX43//+x/Tp06lXr16K/TZs2MDChQvZtGkTL1++ZPTo0YnjOWTIEC5cuPBR4xQfH8+VK1dYv349TZo0wcjIiNKlS9OwYcoqjStWrMDQ0JD69et/VP8SSVaSOSlQBYf7Heb31r9TLMZBvFHEB/0iT9m04zXnzilQqaB7d5g0STxfHzmSlAHarJlINY2IEOt7o0cLn9r582/50AID4euvoUoV2LJFtPXpI57QFy8WEdpawnmqM/7u/uzstZN7h+9x6sdTuMx3wel7J/SM9YgJj8HfzZ/IkJRegyCPIHQMdDj41UGOTTxG+JNwUEP4k3COTTxGVGgUr5++ZnP7zdw7fI8ba2+wu+9uSjUoReXPKmNcxJiS9Uqm2DRSKEUrF8WyhqU2hkMi+TBathRyJpMnCy24rVtFbvu6dZiaqJkzB27fFhqQsbEwe7Z4e+fOvD2PlEhyO7raNkClUrF48WJ27NjB69evqV+/PtOmTaNMmTKp9v3rr79YvHhxmv1069aNOXPmADB48GBcXFxSvG9vb8+GDRuy/gI+IXVK1MFYz5jQqFC8nntha2H7yc59c/1N9g/fT0JsAsVqFKPPvj6YW5t/svN/anQNdem5oycr6q7gicsTTnx7gja//adyXLAgfPml2G7cgFWrYONGsTQ9bZqoWNmmDQwbJmbSenravBRJesTGilCFkyfF5u4uBHaSY2cHLVqIzdlZVJ9Kg/iiRYnZvRvjDRuE4+7UKVEDfvFi6N8/hXi1QiEiGNq3F5mrM2aIVIHu3YVfb8EC8SCXl1Cr1axpvIYnLtpxSpdpVIbB5wdna6XlmJgYxowZw9ChQxPPM2LECPbs2cOjR48oWlQ89Jmbm/PNN9+gVCoZNGgQCxcupH379rRo0QIQv2WzZ88GhOPtiy++oF+/fpiYCJ2jsWPHsnLlSry9vamaTGSqQIEC7Nixg8ePH/Pzzz8zZcoU/vzzz3Tt9fPzQ6VSMW/ePCpWrMixY8f45ZdfKFWqFE6aXDdg/vz5VKlSBYBff/2VNm3a4OrqiqOjIyYmJol2vYuOHTsmjkl0dDQ6Ojo0adKEmTNnprn/hg0b2LhxI1OnTqWw1D2UaJmuJsfoavAD6NwBtSWoR4N6IigUiVKgiaxdC4MHswRYAmAKExAbwMAuaqqsPU3fjv2gdGlOayJIo4BV/20hIfDf90WLqAO0iJkJMbdEW/GeUHiW6Dg8XHgB//gjKdylTRsRTl27djaPyodRrnk5eu3qxZnpZ9jWZRtmpcxoNb8VjhMdARIrkXZe05lag2olHhfxLAIdfR2eXXuWZr+h3qHYj7En6FYQ27tvR89YjypdqtB6QWuUOjKmQJIHMDaGefOE133YMPG8MWiQeN5YvpxKlcpz+LCQhxs/XjyC9OwpfHaLFr1bg1IikWgHrTvelixZwubNm5k7dy7Fixdn/vz5DBs2jP3796Ovqan+H0OGDOHzzz9P0bZmzRq2bNnCoGQC6N7e3syYMYOWLVsmtunlAeeHvo4+DqUdOPnwJOcen/skjjdVgoqT353EZb5wZFbuXJmuG7omloPPyxS2KUznNZ3Z3m07br+7YdXYiqpd3/olq1UL/vpL/Dju3i08KWfOiJyUw4dF1NTAgaIoQyYrBEoyiUolFGo1jrZz5+DNm5T7lCuX5Ghr3lx8fh+KQiEEeVq0EFVL3dzE3717RYRk0ZSFR/T0REGr/v3F7fPbb+DiIpxv3bvD3LkiFTXPkH0+rxyBlZUV3bp1Y/369fj4+ODn55dYqCAhIakSdenSpRPTKI2NjQFSLDQZGhoS91/uceHChenbty8HDhzA09MzRZ+qt9KZTU1NsbW1xdbWloSEBCZOnMjkyZPZv38/y5cvT9yvU6dOfPvtt4wePZrvvvuOzp07A2Bra0tAQAALFixIdLyZmJgkOt0ArK2tKViwID4+Pjg6On7w2KxYsQJLSxFhoq+vT5EiRVL9voNw0C5cuJClS5fy1Vdf5YiKsZJ8jpubEFLq3VtEtF+4IMLd4uNFvtfbdOggCjAl48kTCO89jIJGjzlUMQJF/HF43kYIts2fL6Lpk6Opmv3vv+LHoGlTEVkfG5tkQ+/e4kciNFTsa28vXr9T/E07VO1aNfXc6T+sm1ozXT09VXvTn5py79A9Yl/Hpj7oP7z3ejPs0jBMLU0/yI70ziWR5Gjq1hVajb//LlZqT5wQ+aWzZqEYN44uXXRp3Rp+/VVsJ06INePx40UsQDrrxRKJRAto1fEWGxvL6tWrmTRpEk2bNgXgjz/+wMnJiWPHjtGxY8oS4G+vrnt6erJ+/XpmzZqVqDMTGhpKaGgoNWvWxOKDyj7lLpzLOic63kbWG5mt54oJj2FX313cOyiUO51+cKLZzGbvLQGfl6jatSoN/9cQt9/d2DtoL5Z2lhS2SSMCw8gI+vUT2717QqNh7VqRAjJ/vticnMSqVY8eYiVLkr2o1ULbRuNoO3Uq6SFFg4WFcLC1bCkcZuXKZf68FSuKXKB580T5qV27xIPSqlXioewtzMzEs9TIkWL3NWvEIXv3CsfctGmpfHa5DoVCweDzg4l7k1rMLiEhgejoaAwNDdHJpip7esZ62RrtBnD//n369u1LtWrVcHR0pHXr1hQqVIiePXumtCWNRSCNI+5tQkJC6N27N4ULF6Z58+Y0btyYGjVq0KRJk8R9rl69ilqtTpEiqvk9DA4O5vPPP6ddu3aJ75mamuLr68urV69S6bnVqlWL48ePJ75O6/NQqVRpOs3eRcmSJSlduvQ794mLi+O7777jwIEDfPfddykW0yQSrTF9uoge02RMtG0rJCZmz4Zx45JKkGqwsEhVcrTMpUWoFV7s+uV/PA9bwDHfY6hNrou1iK5dhYZoWsyYIcJWjhwBfX1ISBDOugkTREVsEAt6v/wC3bqliKzOC4Q/ebe+Y9iTsGz/XpdIcgR6evDNN+L/+YgRYoF/0iSRgrpyJcY1a/LTT2Kdf/x42L9fZE9s2iT+9umT574eJJJciVbjse/evUtkZCQOyfKqChQogK2tLZcvX37v8TNnzqRevXp07do1sc3b2xuFQkG5rHiAzoF8Kp23F74vWOWwinsH76FrqEu3zd1o/nPzfOV009BybkvKOJYhJjyGHT13EB+dupJWCipWFKkefn5CzKtjR6HRoCnOUKIEjBolxGEkWUtgoFCgHjIErK3FZzFyJOzYIZxupqbC+fX77yL6LTBQTFyGDcsap5sGXV2hvu3uLqpSBQWJ++DLL4VgTxqUKiUCJm/cgHbtREDFX3+JZ7K5cyEqKuvM0wYKhQJ9E32tbJ/i4Wzr1q0UKVKENWvWMHz4cJo0aZKo7ZbR7+oDBw7w6tUrtmzZwqhRo2jVqhVh/5XC1fS5YcMGFixYkOK4mzdvoquri7W1Nebm5pQtWzZxK1KkCMX/03zy9vZOcZy3tzfW1taJr8PDw/FLplXo7e3N69evqVSpUoau511MmTKFI0eO8Ntvv0mnmyRnEBMjHnCTzTEBsXj2+rVYUHkfQUEwdSrxw4dT0rErRrpGPIt4xrMLR8SqS/ny6R/r5SVSR/X0YN8+oT06frxYVDI0FPqjt2+LqLg88lStilfhscWDDa3fLw1jYGaQShtOIsnTVKwoFpFXrhSyN1euiIi477+HqCjKlxdfFQcOiLnjs2ciHqBpU7h1S9vGSyQSrUa8BQYGAlCiRIkU7cWKFUt8Lz1Onz7N9evX2bNnT4p2Hx8fzMzMmDlzJhcvXsTY2Ji2bdsyatSoj16lT45arebN22lpWUTUf0/UUR/wZF2jUA30lHoEvA7A85kn5cyz3sHod9aPvf33Ev0iGtMSpnTZ1oUSdUtk2/Vnlo8Zv4zSYW0H1jmuI/B6IPtH76fNX20+7MBWraBVKxRPn6KzcSO669ahfPQIli6FpUtR1axJ/KBBxPfqlZRe8on5FOOXbYSFoXPhAsrTp9E5cwblW4Lzaj09VA0akNC0KaqmTVHVq5dScy86OtMmvHP8qlSB8+fRmzED3cWLUaxYgerECWL/+QfVW2LyGmxshEDu6dNKvv9en1u3lHz3Hfz9t4pp0+Lo0yeBdAKkcgQxMTGoVCoSEhJSpFimh8aBpFarP2j/T4larebRo0ecOXMmRbtG9F+tVifaXaxYMZ49e8bp06exsbHB09MzUastOjqahISEFPsnRzNemn8DiX1GRUVx6NAh6tSpw8OHD5k7d26KPr/44gsGDhzIn3/+SdeuXfHy8mL+/Pn079+fAgUKpDmmhQsXpkOHDsyePRs9PT0qVqzImTNn2LVrF/PnzychIQGVSoVSqWT8+PFMnToVgBkzZmBvb0/t2rVJSEggMjKSN2/epBtdrrmW5NeXFv/++y+HDh1i0qRJ1K1bN8Xvv5mZWYpqp1lNZu8/zVhFRUWlSv/V9CujcnIpDx6I9M63Hc0aDQBvb/Eb/y6mTwelkrhp0zB49gynMk4ce3iMV26nKVm4sHDinTghotk6dBBVsTVz4qJFxQKdkxNcvCjaChYU+1pbiwWjPELM6xiur7qO2x9uhPmJxQWlnhJVXPrV4mPCY1hpv5I6I+rgONGRAqULfCpzJRLtoVAI+Zr27UV1rl27xGL/zp3CGd+kCR06iCSO334TAbHnzonCLKNHw08/ae1xQyLJ92jV8aZ5UH3bIWZgYJC4qp8ea9asoVmzZinEpUE43mJiYrCzs2Pw4MF4eXkxb948nj59yrx58zJsa1xcXKoqclnNo0ePPmg/24K23Hx5kx2XdtCpTKestWHnI+7Mv4M6QU1B24LU/60+r4xf8crrVZaeJzv40PHLKHYz7HAf486t1bdQWisp3f7dqVOp6NQJOnTA7MoViu7Zg/np0yhv3kR/wgR0v/2Wly1a8LxLFyJq1/7g1WszNzdKLVmCka8vcUWKENKzJ0FviflrKLJ/P9b/VUtMjjFQBHg4YwaP3krvBij9229YbtnCVU1qSxqUnzKFBGNjHs+Y8UF2ZxRFTAwmHh4UuHQJs8uXMfH0RJHsYVmtUBBVqRLh9va8rl+fiNq1USVPBbp/P9tse+f9N3AgptWqYT1jBgYPHmDQqhWBAwfybMQI1OnoTxYvLhY1Dx8uzJIlpfD312fECAMWLHjDuHH+NGjwOnsuJAvQ1dUlJibmo4752P0/BWq1mgMHDnDgwIEU7SVKlODgwYOJTpfo6Gh69uzJ/fv3+eabb4iLi8PKyorRo0ezbNkybty4Qf369VPsn5y4uLjENo2+W3R0NE2aNOGLL77g119/JTIykhIlStC1a1fOnDnDzZs36dKlC7a2tixcuJAlS5awfv16zM3N6d+/P4MGDUp1nuT88MMPrFq1irlz5xIaGkrZsmWZM2cOTZo0ITo6mri4OAoVKkS7du0YPXo0UVFRODs7M2XKlMR+//nnH1asWMG1dKJ3Y2OFPlNMTMw7bdm/fz8ACxYsSBW9N2PGDD777LN0j80qMnr/xcTEEB8fz4MHD9LdJzOLfhItopmHFnjLoaMRTQp/dyokwcGwbp0ouGNuDs+e0bJcS449PIbRHR8IihFpY+PHi+i2adOgSRO4fl04/UxMRBlsAAMDEcn94oWIpM+hC6Efy+unr3H/y52ry64S/Up8RxhbGGM/xp5qvaqxu99unl1NXWChUPlC6JvpE3QzCPc/3bn892VqDapFoymNKFxBFmSR5ANKlBDOtj17hEft3j0R2jZ8OMybh6G5OT/8AAMGiK+gnTtF0YUtW4QW3MCB5OhFXIkkL6JQZ2e+4ns4evQoY8eO5ebNmylWtMeNG0dsbCxLly5N87inT5/SrFkzVqxYkULrBiA+Pp7IyEgKJqu1fujQISZMmMDFixcTK8t9DB4eHqjVaipkk9J5VFQUjx49wtraGqO39ULSYPq56SxwX8CA6gNY1m5ZltiQEJfAqSmnuLHiBgBVe1WlzZI26Bnl/KIUHzt+meHiLxdxme2CnrEe/c/2p6htJsS3QkPR3bYN3TVrUHp6JjarKlQgfuBA4vv2Fd6XdFBeuoRB69YkdO9OfO/e6Li6ojt/PnEzZhA/aVLqA0JCUD58mKpZ96uvUL18ScTZsxi+VU1YeeECBm3bolCreROZRkqHSoXeN9+gt2QJ8f36EbtixYdf/4eQkIDy5k0R0Xb6NEpXVxRvPcCrbGxIaNYMVdOmJDg7Q5EiWWvDe/io+y8sDP3Jk9HdtAkAlZ0dMStXoq5W7T3ngCVLdFmwQI/wcOFUbdUqgVmzYqlRI2fVjo+JieHp06dYW1t/UKSSWq0mJiYGAwMDGRmUAeT4ZY7Mjl90dDSPHj2iZMmSGBikLjp0//59FApFKj09Sc7Cw8MDIOXn5OICjRrB8eNCB1RDfLyInJ4zJ+0CCxpmzxY6bQEBvDExwcvLC2UxJXVW18EpQJejg05i5OictP/Fi6LQQoMGQkxdrRaLaAqFKA6kpyceqp8/B09P+M/m3EjwnWBcf3Pl1sZbiVFtRSoVwWGiA3YD7BLnnpEhkdzaeAv3P90JexJGwTIFaTC+AXYD7DAuYsyD4w84/8t5Hp97DIBCqaD659Vp/F1jilX/iOJIeZw3b97g5eVF1apVE4v6SD6cHD9+YWFCA05TSKlECfj77xRp8sePw9ix8F99Jho2hMWLRaZqdpPjxy+HI8cvc2T3+KU5f0gHrUa8aVJMg4ODsbKySmwPDg5OFIdOixMnTlC4cGEaNWqU6j1dXd0UTjeAihUrAiK1NSOONxD6RNl9sxsZGX3QOVpUaMEC9wW4BLhkiU1vQt+wu+duHp1+BApoMbsFjb5plOse4j50/DJDi5ktCLwUyIMTD9g/YD/DLw9H3zSD0QzGxkIcdeJEuHxZhDdt2YLy/n30f/wR/Z9+ElFyw4YJnZe3hc7nzIHatdHdskX8R+7SBQD9BQvQnzw5tehz2bJiS86iRah9fLi3ejVly5RJOX4REULdv1Qp8PdPPba3bokw98uXwcgIXV1ddDM7/mo1+PiI1JuTJ4W+zsuXKfcpXjyp8miLFiitrLQrVvkfH3T/GRuLUvDdusGXX6K8dQujxo3FA9r48ak/42SH/fij+DhmzYIlS+D4cR1OnDBi0CDRVqpUll9ShlAqlSiVSnR0dD6oWIImvU+hUGRbcYW8jBy/zJHZ8dPR0UGpVGJkZJSmozm3/Y5KkqGZS75+K7pYE+n21lwzFTt3QuvWotjCfxFqlQpXwqqgFefx47RFBO01+z5/LvYHoQ0KIg3155/F7/aDB1CypIicc3aGwrkvqkutVvPozCNcF7hy79C9xPYyjcrgONmRyp0qp9IRNrEwocHYBlT7vBrx8fHo6upiWswUpY741bdpbYNNaxv8LvhxfvZ57h++j8dmDzw2e1C5c2WcfnCiVP0c8uMokWQXBQvCsmXQt69wzvv4iHlmt25CLLhkSVq1EtLGixaJdFM3N6hfX0gP//zzJ1+zlkjyJVp9Xq1SpQqmpqa4ayYZCDFnT09P6tevn+5xV65cwd7eHl3d1H7DAQMG8N1336Vo8/DwQE9PL4VodG7GsYwjSoUS35e+BIQHZKqvEM8QVjZYyaPTj9A31efzPZ/T+NvG8mEhHZQ6Srpt6oZZSTOe333O/hH7M1/kQqEAe3tYsUIooa5aBQ4OYlX933+F7kvZsiINRROxlsWiz2+qV0/9/uTJwsk1eHDax3/xhdCacXODYplYWQ4IgPXrRdx7mTJCF+3rr8W1v3wp0nw++wwWLoQ7d+DpU+G8GjwYkjnscxXduolohY4dhYbQpEmiuup70qWLFhXD4OUlPmq1WlRBrVgRpk59f+aTRCKRSD4QGxuxGPK2RIHm9VtSJykICBApo716pWhWKBR0Kd6Mwdfg1onNYoFr1ixRZOHPP8VO1tYi4m30aPGbYGgoivSYm4t5gYeHEGzKJajiVdzeept/6v/D+ubrhdNNAVW7V2WIyxCGXBhClc5V0i3epdRRoltQl4fBD9EtqJvodEuOVWMr+h3qx4irI7DtYQsK8N7rzUr7lWxotYFHZx5la0EyiSRH4OwsvGs//CCKfO3eLb47/vkHVCr09cV08+5d4aNTq4W/rlIlESyXw2R2JZI8h1Ydb/r6+vTv358FCxZw8uRJ7t69y4QJEyhevDitW7cmISGBkJCQVNownp6eVKlSJc0+27Rpw969e9myZQtPnjzh0KFDzJs3j6FDh2JqavopLivbKWBQgNrFawNw3u98hvu5d+geKxuu5KXvS8ytzRniMoTKn6UfaSgRmBQzoce2Hih0FNzecpsry9LXPvtoTE2FjouLi6hWNmGCWIYKCEianLdqJULI3yf6/D6SiT6n4vhx4QxbsyZ9EYgNG4SDz87u467x5UvhVPv6a/HgUrq0cLqtXy+u08AAmjUTirBubqIa6d69Ikbe1jbPVG+jeHFRfuqff4SWz7lzYizXrhWzoXdQoYIo1OriAo6OIhX1l19E+5Il8J9MmEQikUgyiqGheJDdvTvld/KuXSLCxN4+/WM1C8ppZGY0rdyGxYeg2rKdwrk3bZpYMNNU1l65UoSi7NwpoleSf6GvXg2vXiVGuOdkYiNicVvoxqIKi9jVZxfPrj5D11CXel/VY4zPGHrt7EUZhzLv7+gjKFGnBD139GTUnVHUHFgThY6CBycesK7ZOlY3Wo3PQR/pgJPkbQwNRQjb1avieyQsTGhJNm8uIuEQGRKbNon1++rVhXTkyJEiy93NTbvmSyR5Ga1naI0dO5YePXowdepU+vTpg46ODqtWrUJPT49nz57RuHFjDh06lOKYkJAQzNMpydK/f39++OEHNmzYQPv27VmwYAGDBg1i3Lhxn+BqPh3OZYUuyLnH5z76WLVazcX5F9nccTOxr2Mp61yWYZeGYVnDMqvNzLNYNbai5Vyh+XJ0/FGeXnma9SepVg1+/104o7ZtS6qeduKESE8F8ct5507SMR8r+vz116nLG4WFiYpJM2emduwl50M1i6KihM3ffismAUWLioivv/8Wy24KhWj/9lvh8Hv5UpRL//57MQtII7I1z6BQiFTiW7fEA9rr1yKSr1s38Rm9BwcH4fvcvVtEvYWEiCCJ6tWF3q58vpBIJJJMMHWqcKL16gWHD4uc//nzxe+TsbH4rXVzE1++yfHwEItINjYp21Uqmt2OYF4j6HQrhvBXwUKPacAAscjUubOQUQDxJBwUBIMGCemF338Xv9m9e4siDDmUiMAITv5wkj+s/uDo+KOEPQ7DuKgxTWY0YbzfeDos6ZDtBRAsqlrQZW0Xxt4fS71R9dAx0MHf1Z8tHbewvPZy7my/gyoh/YqpEkmux84OXF3F94axsSjUYmcnZGr+c+ZrarksXCiSS65eFfPKIUOSpqDHjokpurGxWBtYsCD9ueXatUmylMk3ExNj6tWry8aNSXIOpUunve/z52n3vXCheD+b6+hlOXL8JMnR+hOtjo4OkydPZvLkyaneK126NN5pRO7cvHnznX3269ePfv36ZZmNORHnss784fbHRzve4qPj2T9iP7c23AKgzvA6tF/cHh19qQ30sThMdMDvgh/ee73Z0XMHI66NwKhQNhR3MDAQk/5evUSq6Zo1sHSp+HbdvVtsDRsKB0737uKY95UqWrlSxJSn5ZAeP16kfE6YkDF74+PhyhXxoHDypAjLertiYJUqSTptTZtCoUIZO1deoXx5MSlasEA82O3ZI8btn39Emu07UChExnHHjiJb+aefxKJm165Cp3vBAuG/lEgkEslH0ry5iHCbPl1EmZUqJRxvmsWva9dEhPaaNcJBpiEoKOWillpNgYsXMRw8GGMPD04MhiBT+OVKwaTFppEjRTEGDdWrw4ED8N13Qu+1eHGRQvb999l/3RkgxCtEFEzYcIuEWJGzVrhCYRwmOlBzYE2tFOsytzanw98dcJ7qjNsfblxZeoWgm0Hs7L2TIpWK0OjbRtj1s5NzYEneREdHzOW7dBHfL8eOie+PbdvEc0C9eujqioSS3r3F+vfateLrbPdu4YBbvFi8N2uWWOidMkVM89OqK9Ohg/D1vc2QISpevIinTRvxvfD8uYgpmD9fzFOTk1ZcjY+P+BrMbbi5ibm5HD+JBq073iQZo7GV+J92J+QOz988p6jx+4tGRARGsK3rNvzd/FHoKGjzRxvsv7aXem4ZRKFQ0GVtF5bXWc6rh6/YO2gvvff0zt7xLFdORKL16AE1awqPytWr4tvdzS3JkfbiRVJFtLRIQ/QZEJP8rVuF40ylStpA/FIolamdemq1WD3z8hKr9WfOpI64K1UqRUGEHFMJICehoyOqUrVtK6IfPDzEeA4ZIrR/NNGM6aCnJ6LdBgwQpeJ//138yDdsKHy2s2enDr7ITmQ6jyQ/IO/zfEDXrqn1VDU0bZp2+MKSJWIDiIzE4LPPqHjqlHhdoACty9VjetlTBH/Rgl29dqV/7latkqLdcyBqtZrH5x7jusAVnwM+ie2lHUqLggmfVU5Tk+1TY1bCjFbzWtH428a4/+WO+0J3Qn1C2TdkH2emn6HRlEbUHlpbK85BiSTbKVcOjhwR+sjjxwsduAYNhFPup5/AxARLS+FwGzFCBNZeuwZ//CHqtI0YAU5OYnoaFyfmk+PGpa7hZmEhtuQsWgTe3gpWr/bFwkIUeLtxQ7zXtev756UJCWJNo0gR8PfPisH4dEyfDrVrC2UekOMnyQGpppKMUdS4KNUsqgFwwe/9QvrPrj3jn/r/4O/mj6G5If0O96PBmAbS6ZZJDM0N6bmjJzr6Onjv88ZlgcunOXGlSsJR0727+CadN0+0RUaK9+fPFyHlCxeK9JXkpCP6DAiHXHS0WGnX0xPbrFniPT094QQC8PMTv9D9+4tKa0+fCjHoffuE083cPGU66ZMnIrX1iy+k0+191KwpKsVOniwcp6tXi8/y/IfpORYoIPTe7t0TP7YKBWzfLuT0xo9PfTtkNXp64sHlTXKHrkSSR9Hc55r7XiJJhbc3OqdOodLTI27MGPD1pc3g2QCcfHCSeFW8lg38eFTxKu5sv8PKBitZ13SdcLopoErXKgy5OIShLkOp2rVqjnC6JceosBFNpzdl/OPxtJrfCtPipoQ/CefwmMMstF7IhV8vEBMe8/6OJJLchkIhVma9vERlBZUKfvtNyMYcP564m4ODmM4vWiReR0UJqcv+/UX9twzUcGP48HiqV0+aE964IdaSy5d/fx8LFoh+clvEVhbWwMuX45dXkRFvuRjnss7cCbnDucfn6FKlS7r73dlxhz0D9xAfFU+RykXos68PRSrJutFZRcm6JWm7sC0HvzrIye9OUrphaco6lc3ekyYXfZ40SThpJk2Cfv2E80ypFMUZxo8Xcc3duolU1GbN3in6zIwZYqkrOStWiJTHOXNEFFbFiqmrvIHQqRk3TkS01a4tHIOSjGFgIJypHTuKwhOPHgkxjkmThCPUwOC9XZQuLXyjEyaIW+DoUeGHXbtWZBqMHStuo6xGR0cHc3Nzgv8TCDE2Nn6ngz8hIYGY/1KRdeQ989HI8cscGR0/tVrNmzdvCA4OxtzcXI69JH3q1CH61Cl8oqKo0LQpesbG1FMVorBRYV5EvcDd351GVmn8HudAYiNjub76Om5/uPHq4SsAdA11qTmoJg4THHLN3NLAzADHSY7Yf23P9TXXufjrRcIeh3Hy25NcnHsR+zH2NBjXAOMixto2VSLJWooVE/rQ/fqJ9NOHD0UGzMCBwhFXpAg6OtBSyFjTqpWQad60SdQ5mzJFtHt7vz8Y978abkybFsezZ0ntN25A4cLCCXXihIjK6tBBJHeUKJG035074rHkyBFhZm7iwYP318CT45f/kI63XIxzWWeWXlmars6bWqXmzE9nODdTvG/TxoYeW3tgaJ4NT9v5nLpf1sXvgh8emzzY9fkuvrz+JSbFTLL3pFOnil/GXr2SKqFu3Qpz54q48NWrhdPM21u0b90qws3LlUtb9BnA2lpskZEiwurkSdi/X7yXfLlER0eohbZoIWwYOFA49b75JnuvOb+hKQ0/YYL4POfPT0oX+MBqsnZ24pDjx4V/9uZN8TH9/beIjOvb9/2SgB9L8eLFARKdb+9CpVIRHx+Prq4uyqw2JB8gxy9zZHb8zM3NE+93iSQ9VA0aEOfllfhaR6lDy/It2X5nO0d9j+Z4x1tEUASX/rrE5SWXiX4ZDYBRESPqj66P/Wj77J/vZBO6hrrU/6o+dYbV4faW21yYc4Hnd59zbtY5XH93pe6XdXGc6IhZyXdLPUgkuY727YVn5ocfhJDbunWigMyiRdCrF2FhYsF0yhSRGvn112Ldfto0cfj16+/uXlPDbeJEkQTztuMoIEA8qowfL4Lwpk1LKvZgYiLUbb74QsQMNGmS+xxHYWHib4ECKds/tgZefh2/vIp0vOVinKycALgeeJ3wmHAKGCT9746NjGXPwD147RITvYYTGtJqXiuUuvLBLDtQKBR0XNaRZ9ee8dzrObv67qL/0f7Zm2bxPtHnOnWE0236dFFxbdMm8c2r+fbt2BGGDxcaNfHxKF1d4eJF4WxzdU2sepRItWrCydaihXAIFSyYfACy7zrzOwUKwKpVosjC8OEi6rBePRH5NmnSB0cWtmol5AA3bhQ+Wz8/kXXwxx/itmnePOtMVigUlChRgmLFihH39n30FlFRUTx48AArKyuM3ha8kLwXOX6ZIzPjp6enJyPdJBmmjU2bRMfbzGYztW1Omjy/+xzX3125uf4mCTFC2LuQTSEc/udArUG10DPOGynWOno61PyiJjX61eDunruc/+U8gdcDcfvdjcuLL1NrcC0aTWlEofL5vBCUJG9hZiYcbX36CA+Npyd8/jls3IhqyErAEhBTThcXkTExZYqQLFm9WqRM/vabqMf2Nu+q4fbPP6CrK9bvQejHVasmCgWsXw9ffSUWhl+9ErEEuRGNPHZ6ZKYGXn4Yv7yKdLzlYkoVKIVNIRt8X/ri8sSFthXaAhDmF8bWzlsJvBGIUk9Jx2UdqT2ktpatzfvom+rTa2cv/qn/Dw9PPuTszLM0+6lZ9p70Y0Sf588XjrqVK+HcOTh4EA4exKhoUWq9eYPO25pcVlZJjrbmzUVFtfSQ9amzn86dhfjGiBEi3v/bb0UxjHXrPkzoAeGjGzhQBEn++afIHr52TXzE7duL7NZq1bLOZB0dnfc6JlT/zU4MDAwwzI7c1zyOHL/MIcdPoi1a27QG4HLAZULfhFLEOGekaarVavwu+OEy3wWf/UkFE0o1KIXjZEeqdKmS47TbsgqljhLb7rZU7VYV36O+nP/lPH4X/Li6/CrXVl6jRp8aNP6uMRa2Fu/vTCLJLTg4iFCpuXPh55/hwAEKnuwIXOb12Wvw3Zco79xhiKUlrYdNpMyvX6NQwI4d4lHihx/Emr/BlrUweDAAO7lKa55iUawTAHWBmOXLYcQIHBwQeigBAYkmNAIK8pKb7gZcb2jE7Nlw6J8ADHqNIv7CBVQJ/YBFJLx6TUKCWY5Xs9HEJrx+nbJdE+mWPHYhLdKrgQfi43qbRo1Enzdvio9y9mw4dEgkOMXHJzkCExLEltPHL6+SN3858xHOZZ0BEtNNn7g84Z/6/xB4IxBjC2MGnhoonW6fEAtbCzqu6AjAuVnnuH80DS00bWFsLEKczp4VBQ+mTIFixVA8f47OmzeoixSBnj1h2TKhzP/okYi06tv33U43yaejWDH4918h3mZmJtRZa9YUztSPqK5oZCQyh319RfqArq74gbazE0F1yUPaJRKJRJL1lC5QmmoW1VCj5sSDE9o2B1WCCs+dnqxyWMVa57WJTrfKn1Vm8PnBDHUdim132zzrdEuOQqGgQtsKDD4/mEHnBmHTxgZ1gppbG2+xpNoStnXbxtMrT7VtpkSSdejri3zFGzfA0RGbKA90iOf+z1uFcNju3dCvH09/FSU6V6wQEVZv3gjHW/XqcNKwA7i6ErD3CtepQ68fq4CrK6qqVYm1tCShTRvCwmD1wtfcDjAXAQGurmKfi67EGphhUcqAvXuFPlrLgaXQO7QXvfBQhkaKag8VapvRooX2hulDsbERzq23JbE1r6tWTf/Yd9XACwsT0Ya3b6dsV6nEmFlYkDR+LZPq5A0dKvarUIFcMX55lbz/65nHSe54u7H2BuuarSMyOBLLmpYMvzwcq8ZWWrYw/2HXz466X9YFNezut5uwJ2HaNik1lSvDr7+Cvz/RBw/iuXEjUY8eifKXX34pvpll+mjORKEQ5Upv3RIpvxERwlvWubMoXfQRWFjAX38JmY/u3cUP98qV4uOfPj31Sp1EIpFIso42Nm0AOOZ7TGs2xL2J49Lfl1hcaTE7eu4gwD0AHQMd6oyow+i7o/l87+dYNbZ6Z5GcvExZp7L0P9Kf4VeGU7WbeFq+++9d/qn/DxvbbOTxucdatlAiyUJsbeH8eQz//h1nzrObrqiPHBUCbz/+yC77XynIK/p2jeLcOSFfUry4cCi17GNBl7kN2RdQF4BGAyvApUsovL3x/fVXsLDAwAC+/saYOXwnMnYaNoSGDdkX3JCoGB2atVAyYgRcHrWGywaNuXz8FZcvizkpwD46sfzLa1ocoA8jeQ285Oviu3aJyDR7+/SPfVcNPAMDsWA+Z07K9n37RAXaZs1EYszlyym3xPHbB8uXZ+7aJBlHOt5yOc5lnVGoFJitMmPv4L0kxCaIcu4XhmBe1lzb5uVb2v7ZlhJ1ShAVGsXO3jtJiEvQtklpo6eHqmlToqpUyXqFfUn2Ym0Np0+LWuH6+qIIRvXqIiLuI6lUSYS1X7woQtjfvIGZM0UB22XLRJi6RCKRSLKWNhWE4+2o71HUHxG1nBVEBkdyetpp/rD6g8NfH+blg5cYFTbCaaoT4x+Pp9PyThStXPST2pSTKVm3JL129WLUnVHYDbBDoaPA95gva5usZY3TGu4dvvfJP0OJJFtQKmHoUKbqzsWdBvSK28jhGW78WHoN8y834XtmY3ztAq9fi8iuixdFqqmuroi2GjtWRHuV1AmCqVOJHz6cN9WrA8Ih9W0TNzbTj/8tLs+JE0JreOBAsX7cvDmULAn17qyjXlNT6rU0p149MeUFqGHykMq3d2lvbD6CqVOFE61XL1G34scfRZDf99+LJKTwcHBzEzLcyfHwSL8GnqGhUJrZvBn+9z/SH796KbfE8ashYi8k2kE+aedySihKMHj7YBq6NATA+Udneu3shb6pvpYty9/oGurSc0dPDAoa4O/qz4lvtJ9GIsmDKJVitnP1qkg5ff4cunUTEXFhHx9p6egoJlA7d4qot6AgIdJao4ZYJZPPFBKJRJJ1OFk5YahrSMDrADxDPD/JOUN9Qtn/5X7+sPqDc7POERUaRaHyhWi3uB3j/cbTfFZzTC1NP4ktuRELWwu6ru/KmHtjqDuyLjr6Ovhd8GNz+82sqLsCz52eqBLeo6wukeR0Hjygefwxdk12x9uqFV3Yw6bnrZmvnsQU5sPNm1y7JhZrz50Ta8A3bwrHT3y80BHbXWM6cQlKYn+clqLrqUWXsaTwVI6tfkKnVlH8NjGAkSX2suX3ZDonXl5iVfhtypQRheNyAZoaeN7eogbepk3C8TZlinhfM34HD6Y8LihIVDJNj6lTYckSOHYMOnUSBS5GjoQtW7LrSiRZhSyukEl8j/ly6odTBN8JxtTSlPqj6+Mw0SHNkPwba2+wd/DedPtKWJ6A/Qj7xH1dFrjw0vclBa0KipLtY+xT9Ot7wpdtXbZhFWmFGjWKugocJzmiUObPdICcRqHyheiytgvbum7D7Q83rBpbJaYpSCRZSvXqcOkSzJghUojXrRPRcOvWiSIbH4FCIdJOO3US4eg//SQkATt3FmHzCxYkVVI6dkxoe9y5A5aWMHq08AOmlZG0dm2i5u5bGAN1Wb48hhEjkvZdsEBo0FlZiX7HjEnZ7/37MGECnD8vVll79hSX/nbp9pyMHD+JJH9jpGdEk7JNOOp7lKO+R6lWLAur27yF30VRMMF7nzf8t4hSsn5JHCc7UrVb1Xyh3ZaVFCpXiI5LO9Lkxya4/u7KlWVXCLweyI6eOyhapSiNvm1Ejb410NGTKuaSXMh/i7ddW0fSdZ65KGU6aZaYYAD8/DNNN9uiVrdPPMTWVkRg7dwJv4wLptuzdfzKRM4NtqRTp0iCgpQYGoK9y3W+DNtKq4HGhNVwwvixF+XXTSPeuQCnll5HZWRCs5dh+IUW4P5x0XepUmLOVHDCa54/TOBasuz8tBaFP6TtUxynVMKsWSn32bMnaZ/du8Xf5MkqLVuKTfNeTIwOERGmlC0rIuWUSrEo/tVXqc+ZHoMGiU2iXaTjLRP4u/mzueNmqveuTrNZzfC74MfxKcdRxato/G3jVPtX7FCRoa5DU7XvHbKXiBcRlG8jKhNeW3mN/cP34zjFEZvWNgS4B3D0f0eJjYjF6XsnAO7uvcu2rttADYqCCs7ancXxqiM7eu2g/5H+2Xvhkg+mSpcqOEx0wPU3V/YO3oulnSWFKxTWtlmSvIi+vihj1KEDfPEFPHggxB7+9z9RV/wjKzbq6wtnzRdfiEJXf/4pVjXt7UW1+Z49xd/evcWk4sIFsYoXHy/C4N+mg9DcTcWQISpevIinTRuRjr1ypZCsmzJFVHRydxeXEBEhwvNBlEjXFNpdtw6Cg8X+Dx/CkSMfN2zaws0NOnaU4yeR5Hfa2LRJdLz9z+F/Wdq3KkGF915vXBa44O/qn9heqVMlHCc5YuWUf7Xbsgqzkma0XtCaxt81xn2RO5cWXeL53efsHbSXM9PP0GhKI2oNroWekZ62TZVIPhzVW1GbRYqIwl6ffw5t2wrHXIcO0KePmCAWKwaIBb6ePeGz2yvR+TmBJcpxPDuuw/HjFRO7asg/xKPLldX/reLihCPVuPiyMTs+W88yviIGFZs2w4+bU5pxHohASbs22XblORADoDJffaWmVi2RneLoKKLlrKykJHduQjreMsGZ6WcoUbsEXTd0BaBC2wokxCVwfvZ5GoxrkOpH1sTCBBMLkxRt7ovcCfUOxXG1I8YWxgCcn30e2x62tPq1FQDlW5Qn1CeUS39dovF3jbm85DKHxxwGNRSvXZx66+sxfdd0gssF03N9T/wu+mHVSBZVyCm0mNMCfzd/nlx8wo6eOxjiMkROwCTZR6NGIt5/4kRRdur33+HoUdiwAWp/fIXjggWFiOuoUSK8fcMG2LoVtm0TUVoLF0LhwmIeFhcnfH/jxonKqcmxsBBbchYtAm9vBatX+2JhURYQx/foIaKvQFRf8vERRSA0jqOlS8Xi67VrUPQ/CaLSpaF9e5Eqm5YgbU5j+nTxcWwQBcLk+Ekk+ZQ2FdrAMVEkKyouCiM9o/cf9B7i3sRxY90N3H5348X9FwDo6Otg94UdDv9zwKKqxXt6kHwsxkWMafZTMxwnOnJl2RVcf3Ml7HEYh0Yf4uzMszhMdKDeyHoYmBlo21SJ5P0ULCj+vl1lS5Py0LIlnDol8huPHhVCYwMGJHqBDPbvhHatOfO7Bd9+G8/Nm3EYGBigVCp5jQMA1ZM5jMJpRLhnQZqZ3+RiKYjwLEg589fYlUp5+mI+4UQalKKmdVLb246ntBxR79snI8d8qn0SEhLw9U0gOFifq1eFusxff4n3SpZMcsQ5Oop5pb5Um8qxSMdbBomPiefRmUc0/alpinbbHra4zHPB74IfNq3SUEVMRkRQBKemnqLW8FoUql4osb3vwb6pHDM6+jrER8dz8KuDXF1+FQDTEqYMdRmKjoEORQ8XxdPaEx1THe4duicdbzkIHT0demzrwfLaywm8EciRcUfotKKTts2S5GVMTUWe6GefiRrid+5AgwYiFXXKFJFb+JGUKSOioyZMED69U6cgMFCIv06dKtIZe/SAefNE9FarVu/uL0ho7jJ8eDzVq79JbD94MLXTSV8foqOTXh89Ck5OSU4jENFdZmZw6FDOdxzFxMCZMyKNNzly/CSS/EfVolUpXaA0/uH+nHt8LrHgQkaIDInk8t+Xufz3Zd48F98LhoUMqT+qPvZf22NaXGq3ZTcGBQxoNKUR9mPsub76Oi7zXAjzC+PElBNcmHOBBmMbYD/GHuMixto2VSJJHxsbUSHh/v2U7ZrXP/wgVmWHDROLvQMHChGzZcvEpOP6dRg/nkqVYOPGWLy8vKhatSrGcXFC+MzeXsikaFCpwDSWXqMs6DULaFyZfgXv0y+5/llCApg/pNL4brSZkc3Xn4N48yYGLy8vzMxsuX7dCBcXcHERQ/z0qUjt3blT7GtgIIopJI+Ks7TUrv2SJKTjLYO8fPCShNgEilQqkqJdk0YY6h36XsfbmelnUCgVNJ7WmIfPHia2a1Yi1Wo10S+j8drtxc31NzGxNBFONwXomehh290WXUPxETpZOfHv3X9RWaoI9Q7NykuVZAEFShWg++bubGi9gWv/XMPKyYqaA2pq2yxJXqdDB7h9W6iu7tolJkoHDsD69aJ6QgaoVUustFWrJkLc/fxg0iRYvDgposrb+/2Oo+nThU7FtGlxPEump1v1PxlEtRpevhQaF+vXC2efBi8vkaKZHB0dKFcud2juPngAsbGpdYM1H4kcP0luQqVSsXjxYnbs2MHr16+pX78+06ZNo0yZMqn2/euvv1i8eHGa/XTr1o05c+YAMHjwYFxcXFK8b29vz4b/QkRjYmKYO3cuR44cITo6mubNm/PDDz9QuHDWSzlkl5avBoVCQe/TvTHbb8bRhkdTON6iX0Vz8vuTeO32IjYiFssaljT/pTnlmpdL0UfovVBcf3fl5tqbxEeLMtTm1uY0/F9Dag+uLQtuaQE9Iz3sR9tTd3hdPDZ7cGHOBUJ9Qjn701lcFrhQ76t6OPzPAbMSZto2VSJJjaGhEPbdvVtM8jTfd7t2iWg4e3shOHb5slD3nzFDiLBVry7KeELaq3gGBvD119C1q3DUadi3D6KihEQKiNXAefNEyU9NuP+xY0I3o3XrbLvsnEzp0moqVUqav715A1eukOiIc3ER2QwXL4pNQ/nyKR1xNWqIOZ/k0yMdbxkkJiwGECtbydGEkMeEx7zz+MjgSG6uu4nDRAcMzQ3hWep9/N38We24GhARb+F+4eib6tNtczd29NyBQcGkczuXdebfu/8SrhP+3nNLtEP5luVpMr0JZ2ec5eDIg5SoXYJi1Ytp2yxJXqdoUdixAzZuFJMdV1dRAfX332HEiAyJQ4SHi78rVojVtqlT4dEjEsX9b9x49/HBwSJ6buJEUbnpWRrff25uYpIAYvUuueMoLCztIgBmZkm25WQ0BWffvgaz/56/3ncN+X38JDmLJUuWsHnzZubOnUvx4sWZP38+w4YNY//+/ei/lfMyZMgQPv/88xRta9asYcuWLQxKpvzs7e3NjBkzaNmyZWKbnl5SJsCMGTO4cuUKf/31F/r6+kyfPp2xY8eycePGLL227NLyTc7jc48xOyD+8x/1PZrYrkpQsandJsL8wmg1rxUmlia4L3RnU/tNDL80HEs7S564PsFlvgt399xNKphQL1nBBF1ZMEHb6OjrUGtQLewG2OG124vzv5wn6GYQrgtcufTXJWoPqU2jKY0wtzZP8/jsdvwCHJlwBPc/3Zmunp6ifXf/3Xhs8ki1f88dPbHtYfueK5fkeqZOFSmlvXrBkCHCszN/vhD+NTYWEwZPT5FZ0b27EJg9e1YUYFAoIDIydZ+GhkLIdvp0EYrVvj14eAjHXefOQoAWROWAv/4Sq5DTpwuP0pQp0K5d0uQmn2NsLHyjzs7itVotAhKTO+Lu3BGLvQ8eiMcAEEkxDRokOeIaNoRChdI/jyTrkI63DKJWpVG+JBnvqyx6beU1VAkqGoxrkO4+5mXNaT67OWd/OktCTAJKPSVfnP6CUvVKpTq/c1nxvy48JhykyGKOxXmqM08uPuHB8Qds77Gd4ZeHS70PSfajUAjtjSZNRFmj06dFFNzevbBqFZQo8VHdaTR3dXREpc3evYW8x9y5YjFy1SqRCvnrr6LK1dusXCkyBsaNS/8cZcuKdMyHD8Xcz9FRaJIZG6fW/E2OMhc8Z77Lfnj/NeT38ZPkHGJjY1m9ejWTJk2i6X8VlP/44w+cnJw4duwYHTt2TLG/iYkJJiZJWreenp6sX7+eWbNmUblyZQBCQ0MJDQ2lZs2aWLwtbAgEBQWxZ88eli1bRr169QD4/fffadu2LdevX6d2BrQs0yO7tHw1xEbEsnfwXkxLmhIREIFniCdPwp5QpmAZPDZ78PTKU0ZcG4FlDZErZN3EmqV2S3H93ZUX917wxOVJYl8VO1TEcZIjZZuUlQUTciBKHSXVelbDtoct9w/f5/wv53ni8oQrS69wdcVV7PrZ0fi7xhStkqQB8Kkcv+4L3dO0OfBGINX7VKfB2JTPKm9n+0jyKM2biwi36dOhSxdRWnT+/KSVvGvXRITamjVibnnqFKxeLbRHYmOhbl3hLEu+8gdiUmJhAX//LURnixQRc9IZM5L2sbAQc9Xx46FfP7Ey2LOnKNkuSROFAipWFNvAgaItLEwU2dI44tzchGzfyZNi02BrmzIqrnLlnFG04dgxkaxz547w044eLW6ntGxbu1Y8k6TGGKjL8uUxiQECyZkwQdQHebsybf/+KYMyNezYIaRhMoKcYmcQTbRZzOuU0WWaaLPk0Whp4bnTE5vWNqkmaBrUajU3N9zk1A+nSIhJoHit4qjiVDz3fA6AYUHDFOeuaVkTM30zdKN0iTaITrNPifZR6ijptqkbZqXMCPUO5cCIA6jTqkEtkWQHVlai1vsff4hw/8OHRVrAjh0f1c3bmrvGxuKH8coV8VqhEBmtNWrAl18KLbjk7NwpMgXSeKZOpGTJJD/h5s0iBVKjYVGwYGq9XxCLrxrbcjLpaRZros3edw35ffwkOYe7d+8SGRmJg4NDYluBAgWwtbXl8uXL7z1+5syZ1KtXj65duya2eXt7o1AoKFeuXJrHXL0qdG4bNmyY2FauXDksLS0/6JwfikbLt0rXKinabXvYEvs6Fr8Lfu/tIz0tXw3HJh/DtLgpdYbUSWrzPQaA104vyjYpm+h0i4sSBRMAbq67yROXJyKaakgtvrr9FX0P9MW6qbV0uuVwFAoFFdtXZPCFwQw8PZDyrcqjTlBzc/1N/rb9m+09tvPsmghjTu74rdC2As1/bo7jZEfOzz5PXFRcqr5NLEwo3bB0ii3gUgCh3qHU/bVuuo7fAqVSh0DHR8cT6h1KuRblUvVpVDjzBUAkuYSuXeHWLSFO++BBSida06bCW6GJVlYqhebbo0ciAi4+HmbPxrBhQ0xu3Uo6TqkUEW23b4v0Un9/sVL7tkBt9epizvrmjVjNXb48KTVA8kEULCjmi5pM4Jcvxce5bBl88YVw0oEIXFy5UgQ2Vq0qkmU6dhQFu06fTjt4MbtxcxM2VKkiMp779RN+XE3xsLfp0EEk9by9Va2qwtIyljZtElIdc+6cKBKXFjduiKK9b/enCcrMCDLiLYMUtimMQkeRWC1Kg+b1uypGhQeEE3g9kIbjG6Z6LzYiFs+dntzdcxfvvUJsp+7IurSY3YJ5hefx+ql4WipSuQgv779MPE5HqUPj0o0xf2VOaDGp8ZaTMbEwoce2HqxtspbbW29j5WRF/VH133+gRJIVKJViBbF1axEFd+2aSCPo108ItZmbv7eL9DR3NSmUa9fCnj3w778iHXXTJpg8WczXwsISNXdTEREhZD7s7VNK0NX575n06VPxt3Ll1OdOSBDRXd26vX8ItM37NIs1Om1pERAgx0+Scwj8z6te4q2o2WLFiiW+lx6nT5/m+vXr7NmzJ0W7j48PZmZmzJw5k4sXL2JsbEzbtm0ZNWoU+vr6BAUFUahQIQwMUi5wfsg534VarebNm6RCJaF3Q0mITcC0rGmKdqOS4uHwmcczSjR6d7TwiR9OoFAqqDelHs9ePiMqKirxvUcnH3Fr/S2+cPkCr+1eie2HfA7Rp0ofnl1/RoWOFTg5/SSXF15OlDgBMDA3oNawWtQZWQfTEqJgQnIb8xqacUs+fnmBYvbF6L6nO8+uPMNtgRv399/Ha5cXXru8sG5hjd85PxpPbZzisy3fsTwu81y4d+Ie1i2s39l/ZFAkp6aeovrg6hSqXijV+B2bcAyjYkaUbVoW17muKc4TeC0QVbwK8yrmefre+hDy6v2XbRQsCOvXo9OjB3oTJqC8dw+byZMJ/+wzbVuWK8nq+8/GRmwDBojXISFw6ZIObm5K3N2VXL2q5MULBQcPioJdADo6amrUUNOwYQL29ioaNlRhZaXO1qi4qVMNqFkTli8Xv33OzvDmjR6zZ+syfHhUKj+tiQnY2aVsW7JEF29vPVav9sXUtBjJv8oiImDQIENKloSAAGWK77noaPD2NmL06Fjs7FI77JL3o1arP3jBSzreMoiuoS5lnctyd/ddHCc5Jg645y5PDAoaUMq+VLrHBrgHAFCmUWrh4ciQSPYN3YdapUaho6DdonbUH1Ufr91iUmZpJ1Y+bVrbcHHeRSJDIhOj5hoFNSI+Np47Ze9k6bVKsh6rRla0/LUlxycd5+iEo5SyL0XJeiW1bZYkP2FrK5ZuZs0SS1qbNiVpc7Ro8c5D36e526OHWEm7cEG87+4uVtuWLRPZCpC25q6urlgsHTBALGxqOCYCQBJ/UHO75u6HaBanh/t/GUH5efwkOQfNg8DbWm4GBgaEaTzx6bBmzRqaNWtG1bc8zT4+PsTExGBnZ8fgwYPx8vJi3rx5PH36lHnz5hEVFZXqfJpzxsRkXOM2Li4OL68kB9hLD7G4GfgykASvpIm3Kl7kavv7+mPklX7kT8yLGDw2elC+X3mevRQRTI8ePRLniojj7PCzVBhRgaD4IEJCQhKPO/HgBLc9bxMRFMHNNTdJiElI1G9T6itRq9TU+70ehewK8eTVE3iV4UvOdWjGL89hAlWmV6Fkv5L4rvMl4GgAj04+AuDa2mtEF47GoqEFCoWC2PhYADzPexJV8t0P4rdm30KlVlGyv5hfJh+/ELcQbm+8jdMmJwKOiOeS5Pe/31ER0Xnuj3MEngkkNiyWQtULUXV81TSjN/MDefb+yy4qVkRnyxYs168nvmBBguX4ZYrsvP/Klxdb374QF6fAx8eIW7dMuXXLhFu3TAkK0ufGDQU3bihZtkwcU7RoLHZ2kdjZRWBnF0mVKm/Q18+aLK7YWAXnztXiyy+f4uUVlNheq5Yxr19XZetWfxo2TCN1IxmhobrMmFGd7t1DqF79TarxmzPHCjMzFfb24axcWTLF95+npzHx8VUxM3uAl9f7Fx7SmpOkhXS8ZQLnqc6sb7menb12UmtILZ64CJHblnNbomesR0x4DCGeIRSyKZQipTTIIwgdAx0K26SsvhV4LZA9n+9J1G+r/nl1ilYpiusfrpz96SzlW5anQjsRxlDvq3pc+usSG1ptoMn0JkSFRsHPcK/CPY4aHv0o76tEOzj8z4EnF55wd89ddvTcwYhrIzAqJMP3JZ8QfX3heOvQQXhr7t8XQrpjxwrBtreXk5LxIZq7uroiAuvsWaGl++CBcL4pFHD3rviRT46hIXz3nZATKVZMSIfcvAk//STO1a6d2C8vaO5+qGaxjU3KlFIPD5ElbJNG0ez8NH6SnIGhoSEgtN40/wZRddToHd8fT58+xd3dnRUrVqR6b+bMmXzzzTcU/C/vuVKlSujp6TFhwgSmTJmCoaEhsbGxqY573znfh56eHhWShYoGhAVwkYtYWVlhXdU6sV0Vr+IQh7C0tEzlNEyO23w3UEGbaW1QmCp49OgR1tbWGBkZcfjLwxQuV5gOszqgUCp4YfGCe9zD3MAcE18TLk+9jCo2SYzRspYl9cfXp1zLcqyqtYoXh1/g2Dv//GeNiopKMX55lqrQoFMDXj54yenvTuN7wJeIhxFcGnMJy9qWNJzckOrtqnOMYxQ2KfzO+y8yOJIjh45Qb2w9KtaomGL8YsJiODv3LE7TnKjftj6xV2O5x70U/QX8I5xxJvomdNnYhagXUVz67RLuX7nT73Q/itXIP8XB8s39l01E1ajBUzl+GUYb95+dXXIds3j8/RNwd1fi5qbk0iUlN24oef5cn1On9Dl1Sjji9fXV1KmjokEDzZZA8eIZO//duwri4pQ4OlpQtWqSv0TTX0yMNVWrxr+zj7Fj9dDVVTJ3ri4vX5Ji/E6eVHL4sAEuLtFs3y4m2cm//9zdRdnXs2crMGmSLi9eQP36KmbPjqN+/ZRCyfffTiF5B9LxlgnKNS9Hr129ODP9DNu6bMOslBmt5rfCcaKYDD279ox1zdbReU1nag2qlXhcZFCkqGSajKfHnnJk5hHio+MpUrkINfrW4M72O2zusBljC2PqjaxH0xlNE51pJhYmDDw9kCPjj7C7324MzAyo3qs684rPI/xNOHef36WqxTvylSRaR6FQ0HlNZ4JuBfHywUv2DNzD53s+f29hDokky2nYUIgZTJ4shG4XLRIhUBs2iJKYafCxmrudO4uuv/lGSIV07CjenzVLQbLn9UTN3cWLhYauhUWS5q5mLSEvaO5+7PhpCAp6dzZwfhk/Sc5Ak2IaHByMlZVVYntwcHBisYS0OHHiBIULF6ZRGqGburq6iU43DRX/E6IJDAykePHivHr1itjY2BSrzMHBwVhaWmb4WhQKBcbGSRpYBS2FDYq4lO1RL0SUkamFaYr2t7m39x42rW0oWrZoYgqLkZER/qf8ubvzLsOvDMfIUDwE6CjFJH/42uGYeJvwnP/0fM0N6bW7VwrttjKNyhDiEfLOc+dVjIyM8sV1G1c3psl3TfA94EvVblW5f+Q+QdeD2Nt3L0WqiMIGenp67xyLq5uvokpQ0XhSYxRG4t7RjN/x0ccxtzLH+RtnFEpFYsXg5P05TnDEtqstFdokOaOrtq/KXxX/4srvV+ixLYPq4rmY/HL/ZRdy/DKHNsevUiWxadJTo6KErrOLi0iecXGBkBAFbm46uLnpJB5XrlxSwQZHR6H9rPsB3ifN2pqFhQHJL1nzkx8drY+xcfpRZsHBIpFn4kQoXtyQly+Txi8sTBRpmDkTatY04t9/xTHJx9bTU/yNidFj61axQD13rg7t2ung5pYypfVjAp2k4y2TVO1alapd03ZwWTe1TlWaG6DDkg50WNIBEHnBF2Ze4Nqv1wCo0K4C3bd0x7CgIU2mNXnnuYtVL8YXJ75I0VZ3XV1OPzrNucfnpOMtF2BobkjPHT1Z5bgKn/0+uCxwodGUNHLIJJLsxsQEliyBzz4TIVh374pfyh9/hO+/T/OXsmtXsaWFRnNXg76+qMI5cCDMmSPETE+fhsaNjShXzhZDQ4NUFTU1UV0HDogtLTRRcxcvCnNzIxpx2zVrxKahWjXhDHvbIVa0qNAcBlCpDClUqAKffaZLx44ig3jkSLG9C41msUSSGapUqYKpqSnu7u6Jjrfw8HA8PT3p379/usdduXIFe3t7dNP4XhkwYAClS5dmzpw5iW0eHh7o6elhbW2NhYUFKpWKq1evJhZ1ePjwIUFBQdSvn3V6qdml5eu505P46HiWVl+a6j0TbxNUqHjm+Iza4bXR0dehXLOURSZUcSp0jeT0Pa+jKdKW0DKBXS12cSf4DsbhxjS80JAGNMD1d1eMChtRa1AtdA3F/bD2xloG7xVl/b5c9iWvrV9jusQ0sc/lCctpGtyU21tvM/zKcNQqNWqVmqM+RzHEEFW8CoVSgUKpoGjlovgV8KP9pvZcfnoZlVpF3RJ16Va3G4E3M66lKJFIcj9GRuDkJDYQ831f35SOOA8PoR388GFSdVATE2jQIMkR17AhFC6cun+VKnVbct5+XniblSuFdvG4canfGz8eypQR1UzTY8wY6NQJ2rRJamvRQszXf/kFtm179/nTQ/5ya5kHJx7g+qsrAPXH1aftb21R6mS82KxzWWfhePM7x5f1vswqMyXZSIk6JWi7sC0HRx7k5PcnKd2wNGWdy2rbLEl+pW1b8Ws5ahRs3y5Csg4eFNFvlSpluntzc1GRaNQoEZ21cSM8fChTDzKOEiiIi4tI5y1VSui0tWkj0kuLFNG2fZK8jL6+Pv3792fBggUULlyYUqVKMX/+fIoXL07r1q1JSEjgxYsXmJmZpUhF9fT0pHv37mn22aZNG2bPno2dnR2NGzfGw8ODefPmMXToUExNTTE1NaVDhw5MnTqV2bNnY2RkxPTp07G3t6dWrVpZdm3ZpeXbdEZT7Prb4bnLkzvb7hD9MqkSvU0fG741/pZnVs84EHOAy3MvE+IVkujkexP6Br+LftQcWDPLrlOSM9E4fpfuWYrtSFt+af0LZ+6dYePrjTS43ICo0CgOfnWQszPP4jDRgXpf1qNDxQ64DnUl+lk0Z2ecpdU3rRjTYwxD9g7hRcQL2pRvw/Ul11M5fg0R/zdn6c2i5sCadFnbhROrTjD+4niKOhdl1WerUKBggesC3HzdqF9EFgSTSCRJKBSiqFeFCkLjGYRkirt7kiPO1VW0nTolNg1Vq6aMiqtcWegdA7x+S8YtPFz8fSsoPhU7d4q5sIVFykIIBw7A1q0iWk+lStpAFOFVKsVWubLYkmNuLvSVb978qKFJgXS8aRnLGpZU6VEFo1pGNJ3QNFNONxCON4Czj85KnbdcRN0RdXly4Qm3Nt5i5+c7+fL6l5hamr7/QIkkOyhSRPwyde4s4rEvXYJatUQu5KhRZEUZo7JlhS9vypQoXFz8sLIqm+LBXPJhvHkTzcmTIXh4lOTCBR0CApIi5xQKqFtXOOFatxaTmv8yiiSSLGPs2LHEx8czdepUoqOjqV+/PqtWrUJPTw9/f39atGjBnDlz6JasZG5ISAjm6eRM9+/fH4VCwYYNG5g9ezYWFhYMGjSIESNGJO4za9YsZs+ezddffw2As7MzU6dOzfJry2ot31cPX3Fj2Q1urL5B3Js4AAqUKUDRqkV5cOwB/Tf355e/fyHgeQDhHcMpsLEAmztspvkvzdE30efcz+dQKBQ4Tso/+m75FV1DXV5VekWd+3X4o8sfKBQK2lZoi+5aXaINo2k1sxU3F90k3D+c45OOc2H2BRqMa4D9GHsePXkEQLNOzdgYsBHvUG9WO67GwtiCpjOaYv+1qODzJu4NfXf1xeaiDVXcqjD88nCMi4p0qzOLztDavzUzFs2ggGkBAOrr1+evL//icb3H2hgSiUSSiyhQQGgJt2olXqtUIoUzeVScjw94eYlt1SqxX6FCosiYUglHjojjTf97JNbIqb1D3pKAALh+XUS2vc3OnaJiqSZrJDl6eiIrZ+1aEdFWqFDqgmNRUSl1lz8W6XjTMqbFTem0rlOKShqZoWHphugqdQl4HcCjV48oV6jc+w+SaB2FQkGHZR14du0ZIZ4h7O67m/7H+mfaESuRZBiFQpQ3cnYWqafHj8PXX4tqCatXi9CqLMDGRk1sbARVq6qQ0h8fz5s3KooXD6Zq1SIolcacPy/k+Y4eFYGLV66I7ZdfhJZbs2ZJjrhkOvISSYbR0dFh8uTJTJ48OdV7pUuXxtvbO1X7zfcsGffr149+/fql+76xsTE///wzP//888cb/BFklZZv4LVArv50lcBTgYkFtIrXKo7jZEdse9py/pfzPDj2AIA2Nm24+/wuJ0JO8NvF3zjxzQkOjT5EQmwCVo2tGHxhMAXLvGe5X5LriYmP4UDDA/Rb2y+F41dvhx5nWpyhebfmfDn4S1x/d+X21tu8eviKM9PP4DLfBcualugY6BBnGcfU7VMZXms41QuJJ01za3PMrc0B+OrAV+jZ6lEppBK4Qcl6JRPPbzHSgldfv2J/z/00GNeAqBdRnP3pLPGm8Xg299TCiEgkktyMUikcXtWrg2Yd7flz4YTTOOIuXYKXL8UcFmDFCvjnH7H27+AAfn5iLvsuVQl3d/E3DQlZZswQjzLJ0Zzj8mUh5QKwfDk8eiRUdzS6cgEBQtbmf//L4AAgHW95DmM9Y+qXrI+rvyvnHp+TjrdchL6JPj139uSf+v/w8NRDzv50lmYzm2nbLEl+p3RpseT099+i9OWxY0IddckS+PxzbVsnSYahYdLq4vz58PSp8JceOya258+F33TfPrF/+fJJaanNmr0/dF8iyY9kVss3xCuEjU03ok4QDjebNjY4TnakXPNyiVkJTWc0pemMpoBwvC10X8hR36Os6LSCbpu6pepfkvd58PIB98rew2qhFc//eZ7o+G08uzEzYmbgHepNxScVuTDnAp+t+gw9Yz3Ozz5PsEcwTy4+AWDwz4NRFFAwrfE0nj18lqL/477HWX9rPde/vM7mc5tTnX/cV+N4UOkBZ2ecZWfvnSiUCiybW7LSeiWDyw7+JGMgkUjyNkWLCi21Tp3E67g4kcrp6gr//iu0oNVqEcF2/XrScRUqiNpv1tZCIadFCxILtXl4gIFBkk50cqytxZYcjYZ08lpyP/4o5Fo6dxY6cS9ewE8/iYQgTRG0jCDDafIgmnTTc4/PadkSycdiUdWCTivEt8+5Wee4f+TDSxRLJNmGUimURq9fF8tML19Cnz6igkLt2mBsLEoXLViQsqJCctauFVF0b23GJibUrVcPnY0bUx/z+rXod+3a1O+5ugpvkbExFCsmovKCgrLwonM/JUuKsPlNm8TQXL0qCls0bSpC6h88gGXLRIGMIkWESO6sWWLFMSFB29ZLJHkD46LGlGlchtIdSzPQbSD9j/SnfIvy6UqBNLFugoGOAU/Cn3D3+d1PbK0kpxAWEwZAmQ5l+OrWV0yNmcq4B+NoMkUUXguPCU90/NYeUpvqn1dn5M2RfL7vc0o1KEWESQTHdY9T63QtXMe5EuEXkdR3dBhD9w1lZtOZVCpSCQbBjBkzUtlQvkV5Bp8fzLdh3zI2eCz/dPiH2KKxjLEf8ymGQCKR5DP09IQDbMwYoQO3ezdUqSLquxUoAFZWYp/AQOEwW7wYOnYUC8eOjsIp5uoq9s0MzZqJBeuICOjdW6ju1KkD589nbpFaOt7yIImONz/peMuN1Ohbg7oj6wKwu/9uwp6EadkiieQ/qlQRcdYzZghn3Nmz4O0NP/wA/fqJiLhff0372A4dkuLJk22qqlWJtbQkIXnpIBDOvQ4dRKz321y6JLxHL1/CunUi9fXBAxGHHib/v6SFUikmDd9+K1YQX7yA/fvF5KZSJeFou3ABpk0TFaeKFROTjVWr4MkTbVsvkeReTCxM6H2oN7Vm1KJYjWLv3d9YzxinsqJU3FHfo9ltniSHolK/u6yfUpH6EU6hUFC5U2WGug5FvViNWkdNA5cG3N54m7M9z3J09FHCnoQx/uh4yhQswwSHd5T1S8brmNd03NKRSwGX2Nh1I2XNZQEwiUSS/XTtKvTf4uLE9P7xY/H3wgWYNw+6dBHz1dhY8Vjx++8iTTUkRKzb9+0Ly5bpEhCgn+45ZsxIO2agRQvhaAsLE48bW7YIx19mkI63PEijMo1QoOD+i/s8ff1U2+ZIMkDbP9pSok4JokKj2NlrJwmxMvxEkkPQ0xOVTuvXF3HdUVGiPOmrV0LJdPZs0fY2Fhaibnjy7dIlFN7e+P76a0q10n37RCTdnTtp2/DLL2LJ6fRp6NlTLHcdOiRKEs2blx1XnecwNRXDtmiR8J0+fCg0Lbp3F0P74oUoajtsmJho2NqKj/fwYYiM1Lb1Eknepo2NWIiQjrf8S0EDEVbxOiZlWb/wmPAU76eFQqHgVMwp2lZuy9iTYynftjzqBDW31t7i67Zfs/n6Zv50/BOVWkW8Kj7RyZf83xqehD2h8ZrGXPS7yLYe2+hcpXNWXqZEIpF8FEZGQr9t8mSRjhoYKIoubNgAX30FNWuKxeZHj4SzbOJEfXr1qpYj5q7S8ZYHKWhYkFrFawFw/vF57RojyRC6hrr03NETg4IG+Lv5c/yb49o2SSJJIiZGpJ1+950ImQKhAbdrl0gPvXDh/X0EBcHUqcQPH86b5OWFXr0SS1xNmiSpq76Nlxc0bixKDmkwNhahWgcPZviy8jPW1kLsdudOoQXn4iJWAR0cxATGywsWLoT27aFwYaF9MW+e0OJIL7tYIpFkDI3j7eyjs0THR2vZGok2sClsg45Ch/svUkqOaF5XtUi/rF9AeADXA6/Tq1ovSjcsTfdd3XFc5UgZpzJ4VPIgVhGL/TZ79GbpoTdLj1nnZgGgN0uPIXuHJPbjEeRBg5UN8Avz49iAY3SrKvUGJRJJzkKhEHpu/fsL+ekbN8SjxIkTMHMmtGqVQKtWLzAw0LalsrhCnsW5rDPXA69z7vE5elfvrW1zJBmgUPlCdFnXhW1dtuH+pztWja2w7W6rbbMkEpHWGRsrShN16yZUUQcPFuWGQMR6a4TE0mP6dFAqiZs2DZ4lE302Nhb1xitXTjvNFIQa6+PHqdt9fYVtkkyhqyscbg4O4mN69QpOnkyqlvr4sXh98iR88w1YWoqCDm3aiL+Wltq+Aokkd1O9WHVKmpXk6eunnH98nlY2rbRtkuQTY6hriHNZZ3bf3c0kx0mJmoC7PHdR0KAg9qXs0z3WPUCU9WtUJqmsX+GahXE87IjNKRuOLTxGiFcIAAZmBjzu/5j9Cfu5PPwyRY1FWb8nYU9ouaElukpdLg65iK2FnH9KJJLcgZmZSBVt0QLevInBy+sxurrpL1Z8KmTEWx5F6rzlDap0roLDJAcA9g3Zx4v7L7RskURCko6aRr20VStRRkhT5fTIEeG18fJK+/jgYKHN9vXXYG6e8j19feF0exdDhsC1ayL38elTEWf+zTfCYZcTYsnzGObmIgV1+XKRkurtLVJUO3YEExMRvLhxIwwYAMWLiyzhb74RwrgxMdln17FjIuM5E7U9MDExpl69umzcqJPqmHfV9nj1CkaNEtdraipu91OnsvLqJPkZhUJBa5vWgEw3zc9MdZ6Ku787vXb24vC9w/x46kfmu8zne6fvMdYzJjwmHDd/N0IiQ1Ic5xHkgYGOATaFU5b1UygUNOnUhJ+P/8yEvyZQs2BNinoXJfykSF9V71VTxrgMAGOPjCU4MphpztMSz6PZPEM8P80ASCQSSR5COt7yKE5WQpj3dvBtQt+EatkaSWZoMbsFVo2tiAmPYXuP7cRFxWnbJEl+R5WG6HOhQkJgAYQAw9WrQs1/0aLU+69cKdT8x43L2PmHDYPffhP9lColync+fChyJY2NM9an5INQKEQxhjFjRHGG0FAhtfftt+LjBhHmP2+eWGksXFjUyFi4EO7ezbq0VDc34firUkVUvcpgbQ+qVlVhaRlLmzYpdTTfVdsjIQHatYO9e8V17tolbv/27eHWray5PolE6rxJmpdrzq5eu/B+7k2XbV3Y5LGJ+a3mM6XRFACuPbuGwyoHDt5LKbEQFBmEuaF5uv0qFAqqdqvKVx5f0XltZwwLGgJwaNQhFldZzJX1VzjgcwCAkQdH4rDKIcU26uCo7LlgiUQiycPIVNM8ioWJBVWLVsXruRcX/C5IMdRcjI6eDt23dmd57eUE3Qzi8NjDfPbPZ9o2S5Kf0dTSfp1S9JlwsWrOjz+KiqdHjwrn2r59sGYNlBEr6ezcCa1bi4IKb95kzIb//U94f3x9oUgR0dcXXwhPj+STYWAgsoqbNoU5c0Qw44kTSWmpgYGi7sWhQ2L/MmVESmrr1kmOuYwwfbqIrNP4etu2FVWvZs8Wt5yRUcr9LSxS1u8ATWEJBatX+2JhkVSlb98+GDs29e2tYfNmuHJFBF3WqCHamjQBOztx3XZ2GbsmiSQ5rcq3QoGC28G3CQgPoFSBUto2SaIFulbtSteqXdN8r6l1U9TTU69mLOmwhCUdlgDwJu4NXXd2pYxuGRZVXZRiP6WOkloDa3Hg8wNcW3mNc5bnePXwFQcHHmRhtYU0/7k5lTtXTkxzlUgkEknGkRFveZjEdNPHMt00t1OgVAG6b+4OCri+8jo319/UtkmS/IyNDejoiDJCydG8dnAQ5S+XLBERaCdPCg/Fxo3g7y8KM/TqlfHzX7kiwpz09ETIk8ajcu1aUtiVRCsUKybKt69dK7KAb92C+fNFNrKBATx5IgIVe/VKKnQ7bRpcvCiK0n4IMTFw5oyowZGcHj0+urYHw4fHU716kvP3Q2p77Nwp3tc43UAU+PXxgUmTPuwaJJL3UcS4CPVK1gPgmO8xLVsjya3s8tzFsYfHcA12TXcfXQNd7EfbM9Z3LC3mtMDQ3JCQOyFs67qNlQ1W8uCE1E6VSCSSzCIdb3kYqfOWtyjfsjxNZzQF4MDIAwR5BGnXIEn+xdAQnJ2F8yt57uCuXSIazt5e5CR+9ZXIO2zQQOjCDRiQ5HBr1CjNrj+IM2dEbuGrV0ltx4/DnTvQpUvG+5VkKQqFcE5NmiQiwV68EPJ/EyZAtWoiA9ndHWbNEkVqixQRtTqWLROZw+mhqe1RqVLK9goVxF9v7/fb9l9tD6ZNS5m6r6ntsW6dqOGRFjduCPv//FNUg9XTg3r14LwsIi7JYmS6qSSzbLuzDYDGlo3fu6++iT6Nv23MuIfjcPrBCT0TPZ5efsqGVhtY32I9/m7+2W2uRCKR5Fmk4y0Po9F5u/bsGq9j0smZkeQqnKc6Y9PahvioeHb03EHM62xULpdI3sXUqcJr0quXiG778UcR2vT998J7ER4uhLjMzUUI0qxZolym63+r7j4+GT93//7iHL16CYfbqlUi3KlRI/GeJEdibCzSTH//HW7fFtFvq1dD794i5TQ8HP79V/hry5eHihVh9GiR+pk87fPt2h4azMzEX03Gc3pktrZHSAjs2AH//CMKOuzdK66tdWup8SbJWtpUEI634w+Ok6BKeM/eEklKXka9TIyWbFXiwyvjGpob0vzn5oz1HUuDcQ3Q0dfh4amHrHJYxdbOW+XCr0QikWQA6XjLw5QpWIZy5uVQqVW4PHHRtjmSLEChVNB1Y1fMSpkR6h3K/uH7UWeVWrlE8jE0by4i3Ly9RZTZpk3C8TZFiD5z7ZpIOT14UDjcpk5NcsSBUKIfORIiIj7+3MWLixCqmBgRIjVjBgweLByAOqmrU0pyJqVLi49t61bhDLt0CX7+GZycxC1z/77IVu7cWTjmmjSBX34RgY3vQvmemU1ma3vExopgy6NHhb+3fXtxm5uZwdy5GetTIkmLBqUaUMCgAC+iXnD12VVtmyPJZey5u4c4VRzVilajnFm5jz7e1NKUtn+25Wufr6k1pBYKpQLvfd4sq7mM3f1388L3RTZYLZFIJHkT6XjL40idt7yHiYUJPbf3RKmr5M62O1xeclnbJknyK127ihCfmBiR/zdxYtJ7TZuKNNRBg5La6tYVwl8TJojXy5dj6OCAsadn2v1bW6fuI3lfZ8+KUKgnT0TenybkSZLr0NGB+vXhhx/g3DlRLXXvXhg1SqSQxseL9qlTRVFbgJ9+EjU7AgLEa02km6b2R3okr+2REczMoFYt4ThM3uboKOQLJZKsQk9HjxblWgBw9L5MN5V8HJo00+5VumeqH/Oy5nRe1ZlRd0Zh29MW1OCxyYO/q/zNgZEHCA94T5ixRCKRSKTjLa8jdd7yJmUcy9Dy15YAHJ1wlIDLAVq2SCL5QIyMRK7hyZNQpgzKBw8oO3Omtq2S5DAKFIDPPoO//4Z790Tx2qVLRXClqanY58IFGDJEOMCqVxepoyDSVNMjICDztT0qVhS+5reJi0tdTVUiySxS502SEULfhHLiwQkg8443DUWrFKXn9p6MuDqCCu0qoIpXcXX5Vf6q8BfHJh/jzfMMVimXSCSSfIB0vOVxNI63SwGXiIqL0rI1kqyk4YSGVOlaBVWcih09dxD1Qn6+klxE8+bg4UHclCkEDRyobWskOZzy5UVm8r//iiINdepAqVIiSk6hEOmnW7aIfT/7TES0LVgAHh4p63+4u4u/mant0b69KLDg5ZXUFhoqKrM6OWW8X4kkLTQ6b27+boRFh2nZGkluYbfXbhLUCdQuXpsKhSpkad8l6pSg36F+DDo3CKvGVsRHx+O6wJWF5Rdy5qczUn9YIpFI0kA63vI4NoVsKGFagtiEWC4FXNK2OZIsRKFQ0Hl1ZwqVL0TY4zD2DNyDWiX13iS5iIIFiZs+nRft2mnbEkkuQk9PyAk+fQplywqNuG7dxHsFCwoNtuPHYfJksLMTDrpBg2DzZiEzaGAANjYZP/+4cSLKrkMH4ezbtw/atRMOwEmTsuQSJZJErM2tqVSkEgnqBE4+PKltcyS5hO2e2wHoVS0T4b3voaxTWQadG0TfQ30pXqs4sa9jOTvjLIvKL8L1d1fiouLe34lEIpHkE6TjLY+jUCikzlsextDckJ47e6JjoIPPAR8uzr+obZMkEokk20le22PAAJE+umABvHwJnp6iGioIJ92zZ6KKab9+wmGXkCCK7545I5x0H0uhQiK6zcFBnKdvX1H84cIFKFMmSy9TIgGSpZtKnTfJBxAcGcyph6eA7HW8gXjOqNiuIiOujqDHth4UqVSEN8/fcGziMf6q+BdX/7lKQpysyCuRSCTS8ZYPkDpveZsStUvQbpGIGDr1wyken3usZYskEokk+0mrtodCAVWrwuLFIsU0PBxOnBDFdmvWFMfFx8OcOdCsGZQubcRvv5VOs/931fYoXVoU8n3xQhTmPXIEqlXLtkuV5HOS67zJSuaS97HLcxcqtYr6JetTvtA7RC+zEIVSQbVe1Rh1ZxSfrfqMAmUK8DrgNQdGHGCJ7RI8tnjIrAyJRJKvkY63fIDG8ebyxIW4BBn2nRepM7wOdv3tUCeo2fn5TiKCIrRtkkQikWgdQ0No0QJ+/VXosj17BuvXQ//+UKwYREYqOHKkMNKXIcnJNLVuir6OPo/DHuMT6qNtcyQ5nE+RZpoeSl0ltYfUZozPGNr82QZjC2Ne3H/B7r67WV57OT4HfKTzWCKR5Euk4y0fYGthS2GjwryJe8O1Z9e0bY4kG1AoFHRY1gGLahZEPItgV59dqBJU2jZLIpFIchTFi4vU1A0bhBPO3T2KjRu9UCi0bZlEkj4m+iY0tmoMwDHfY1q2RpKTefb6GWcfnQWgp21Prdmha6hLw3ENGfdgHM1+boZBAQOCbgWxpdMWVjdazaMzj7Rmm0QikWgD6XjLBygVSpysRKk1qfOWd9E30afnjp7omejx6PQjzkw/o22TJBKJJMeiVEL16mosLWUkuCTnkzzdVCJJj52eO1GjpmHphpQ1L6ttc9A31cf5B2fGPRxHo28aoWuki7+rP+uarWND6w0EXA7QtokSiUTySZCOt3yC1HnLH1hUtaDTP50AOP/Lee4dvqdliyQSiUQikWQWjePt9KPTxMTHaNkaSU5Fk2bau1pvLVuSEqPCRrSc25KxvmOpP7o+Sj0lD44/YKX9SrZ3306IZ4i2TZRIJJJsRVfbBkg+DRrH2/nH50lQJaCj1NGyRZKswPeYL6d+OEXwnWBMLU2pP7o+DhMd8Dvvx5WlV/i3/798ef1LCloV5MbaG+wdvDfdvhKWJ2A/wh6Ap1eecmzSMZ5eeYpBAQNqDapF0xlN0dFPum9299+NxyaPVP303NET2x62WX+xEolEIpHkU+ws7ShuWpzAiEAuPrlI83LNtW2SJIfhH+7PBb8LgHbTTN+FWQkz2i9uj8NEB87OOMvNDTfx2u3F3T13setvR5MZTShUrpC2zZRIJJIsRzre8gm1itfCVN+UsJgwbgffpmbxmto2SZJJ/N382dRxE0/qPMGlpwuVn1Xm1ZRXJMQl0OaPNgRcCuDZ1Wfs6LWDwecGc9XqKiuHrkzVz2f7PsMgxoCrZa9ijz0vH7xkTYs1BJUN4nT305R6UYrXC14TGRrJZ8s/Szwu8EYg1ftUp8HYBpx7dI5vTnzD3x3+plyTcp9yGCQSiUQiyfMoFApa27Rm/c31HL1/VDreJKnY6bkTgMZWjSlVoJSWrXk3hcoVosu6LjhOceTMtDN47fbi5vqbeGzxoO6Iujj94IRZCTNtmymRSCRZhkw1zSfoKnVpVKYRIHXe8gp7vtmDfzF/or+J5rcff8P2W1suOl7k1M+nUKvU9NzRE0NzQwLcAzg+5TifNfiMHTN2pNgWWC2g6POinOh/gvZ12wNweOZhXipecn/ifX6d+iutvmvF0bZHubbyGmF+YQDER8cT6h1KuRblMLIz4n/+/8O/jD8FahfAqLCRNodFIpFIJJI8idR5k7yLbXe2ATkvzfRdFKtWjF67ejHs0jDKtyqPKk7F5b8vs8hmESe+O0HUiyhtmyiRSCRZgnS85SOkzlveIT4mnhCXEF43eM2GrhtoW6EtPzf/mbqf14U3cP/M/cTVRAD3he6EHA6hYemGiVt1verc/fUuV+pfYWLXiVgYWwDgddgLf1t/9vTfQ/uK7ZnoOJH+X/dHoVJwac8lAIJvB6OKV1G8VnFGHRqFno6etoZCIpFIJJJ8QavyrVCg4GbQTQIjArVtjiQH8fjVY9z83VCgoIdtD22b89GUql+KAccG8MWpLyjdsDTxUfFcnHuRheUXcu6Xc8RGxGrbRIlEIskU0vGWj0h0vD0+h1qt1rI1kswQ5BOEMl5J9TrVU7R3aiYKK1x2uwxA5c8q4zjZEYC9Q/YSei80cd/D3x8mWhWN1TgrqhcS/cRFxaEbrEsp21Lo6+gn7turUS+iDaK5efkmINJMAXbM20HZIWUZNmUYg1cPJvx6eDZdsUQikUgk+RsLEwvqlKgDwDHfY1q2RpKT2OG5A4Am1k0oblpcy9ZknHLNyjHEZQif7/ucYjWKERMWw+mpp1lkswj3Re7Ex8Rr20SJRCLJENLxlo+oX7I+BjoGBEcG4xPqo21zJJngof9DAMoUL5OivbJVZQACQ5JWwpv/0hyrxlbEvo5lR48dxEXFERkcyZ2Nd7jpcJMf2/6YuO+r568AsLCwSNGvhYkFcQZxvAx9Kfr/z/Hmdt+NCr9XwHaRLbrxutzse5OgW0FZe7ESiUQikUgAmW4qSZvcmGaaHgqFgsqdKjPyxki6be5GIZtCRAZHcmTcERZXWsz11ddRxau0baZEIpF8FNLxlo8w0DWgYemGgNR5y+28jnkNgLG+cYp2MwMhRBuTEJPYpqOnQ/et3TG2MCboVhCHxxzm3JJzqBJU1Pm6DuaG5on7hkeLiDUj3dQ6bQqFgjh1HAD2Y+y59+09IiZFMGLYCCzaWbBhwAZ0jHU4/8v5LL1WiUQikUgkgjYVhOPtmO8xVGrpfJCA7wtfrjy9glKhpFvVbto2J8tQKBXU6FOD0V6j6bi8I2alzAjzC2Pf0H0sqb6EOzvuoFbJDB6JRJI7kI63fIbUecsb6JjqAJAQkZCiPSZcONyUpin/axcoVYDuW7qDAq6vuo7LKhce2DxgXJtxKfbTNRWFjtVvUk9k9KP1UZuI9oNRBzlkfogVHVckvh9tFE2BugUIvCl1ZyQSiUQiyQ4cSjtgpm/G8zfPuf7surbNkeQANGmmzcs1p5hJMS1bk/Xo6OlQd0RdxtwbQ6sFrTAqYkSodyg7e+1kRb0V3Dt8T0roSCSSHI+utg2QfFqS67xJci/FKxVHpVDxyvdVivYnnk8AMK1omuqY8i3K0/SnppyZdgZdf13Ug9RYmFjw5s2bxH2KFClCuFk4eo9SFkuIDI5EP0YfdTk1/uH+/D3vb35u/DMWJhbEq+JJUAkHYEJ0AmYWsvy7RCKRSCTZgZ6OHs3LNWev916O+h6lbsm62jZJomXyUprpu9Az0sNxoiN1h9fF9Q9XXH9zJfB6IJvbb8bKyYoWs1tg1dgqxTG+x3w59cMpgu8EY2ppSv3R9XGY6IBCoUjV/421N9g7eG+6509YnoD9CHsA7uy4g8s8F57fuI2jJgAAqZNJREFUfY6huSHlW5anxdwWmFomzb9399+NxyaPVP303NET2x62GR0GiUSSS5GOt3yGQ2kHdJW6+IX58fjVY8qal9W2SZIMUKlkJR5bP0b3iC7q+erECcSVrVeINoimepPqaR7n/IMzl/ddJvJKJPq++iJCLtm3gKm+Kc+qPMPkvAnxMfHoGog33Te7o1KoKN+8PCcenMDW1Rafwz4YPjMkQVc43czCzQh2C+ZMszMMYUj2DoBEIpFIJPmUNjZtEh1v3zt9r21zJFrEJ9SHG4E30FXq0rVKV22b80kwKGBA0+lNsR9tz4W5F7i0+BJ+5/1Y47SGCu0q0PyX5pSoXQJ/N382d9xM9d7VaTarGX4X/Dg+5TiqeBWNv22cqt+KHSoy1HVoqva9Q/YS8SKC8m3KA3B762129dlF3S/r0vyX5kQERnD6x9Osb76eEVdHoGso5s6BNwKp3qc6DcY2SNFfkUpFsmFUJBJJTkc63vIZJvom1C1RF/cAd849PscA8wHaNkmSAQx1DQnvGU7C/AR29NpB7SG1eeLyhAfLHnC5zWV+rPAjMeExhHiGUMimECYWJoDQyzCsZUjY9TAK3C7A/uH7abe6XYq+TQeaop6gZkPbDTSa2IhQn1DOf3eea/WuMdRxKCZ6JhSfVxz3z91ZdHER1kOt8Xnog+86XwyLGPLj4h/TMlkikUgkEkkWoNF5c3niQnhMOAUMCmjZIom22H5nOwAty7ekiHH+cugYFzWm9YLWNBzfkHM/n+PaymvcP3yf+4fvU61XNcIDwilRuwRdNwiHZIW2FUiIS+D87PM0GNcAPaOU2R0mFiaJ82UN7ovcCfUOxXG1I8YWQlf5/OzzVGxfkY7LOibuV6RyEVY1XIXPAR9se9gSHx1PqHcoDSc0pHTD0tk8EhKJJDcgNd7yITLdNG/w9civ2d5rO9cvXWdL5y2cW3WO462O03pqa4z1jLnvdp9VDqu4tvtaiuNCg0OJNoqm6Oui3Nl+h+vLU2rE/K/3/9g6cCu3/W6ztftWjs89jksDF8r8UAarglYUMS5C255tGXBsAEZxRtwadYvI+ZE8K/GM+jvrU6dinU85DBKJRCKR5CvKFypPhcIViFfFc/rhaW2bI9Ei+SXN9F0UKF2Ajss68vXdr6nRrwYo4M72Ozy5+ARVgoowv7DEfW172BL7Oha/C37v7TciKIJTU09Ra3gtClUvBIBapaZ8q/LUGZFyrlu0SlEAXvi+ACD4djCqeBXFaxXPqsuUSCS5HOl4y4fIAgt5g+blmvPL9F84+O1Bfpn2C+u/WU+36d2Y0mgKACGVQpgxYwa+DXxTHBc0IogN0zfQcl5LAE5/e5oXN18kvl+laBVWT13Nxe8uMvvH2az4dgV1ptVhYceFKfop36I8g88P5tuwb2l0sxG7euzCsJRhNl+1RCKRSCSSNjYi6u2o71EtWyLRFp4hntwOvo2eUo/OlTtr2xytU7hCYbpt7MbIGyOxbmYNwLOrz/ir4l8cGX+EyOBIClcoDECod+h7+zsz/QwKpYLG05LSUhVKBW1+a0OVzlVS7Ht3z10AilUTxS0Cb4hCY9dWXuO3Er8xS38Wa5zW4O/un+nrlEgkuROZapoPaVSmEQoU+IT6EBgRSHFTuRqTW+latStdq6at6dHUuinq6amrPC3psIQlHZagVqvxPeuL715fLg6/yMujL2k6rSnFqhXDqawTbsPcPtiO9M4lkUgkEokk62lj04a/L/8tHW/5GE2aaZsKbShkVEjL1uQcLO0saTG7BascVmFZ05Kgm0G4L3Tn2spriXprMeEx7+wjMjiSm+tu4jDRAUNzQ3iW/r4vfF9wfNJxitcqTsX2FYEkx1tcZBzdt3TnTegbLs69yLpm6xjmNgxLO8usuViJRJJrkBFv+ZBCRoWws7QD4Pzj81q2RqItFAoFT0Y+4W7luyhUCry2e7G0+lK299jOs+vvmGFIJBKJRCLRKs3KNUNPqceDlw+4/+K+ts2RfGLUanVimmkv215atibnoVaJxeBW81sx4PgAStYvSVxkHBfmXADg8fnHxL2JS/f4ayuvoUpQ0WBcg3T3AXh+9znrmq1Dqauk586eKJSi2Jn9GHv6HelH1/VdsW5qjW13WwYcH4C+iT7nf5HPXhJJfkTrjjeVSsWiRYtwcnKiVq1aDB8+nCdPnqS5719//UXlypXT3L777rvE/VxdXenWrRs1a9akbdu2HDx48FNdTq5B6rxJ1Go1a+6tYWufrah/U1Oxs1il89rlxYo6K9jSaYsMiZdIJBKJJAdiqm9KI6tGABy9L6Pe8hu3g29z9/ldDHQM6FxFppm+jUFBAwBiI2Ip37I8w9yH0fvf3hSpLApQ+B7xZZHNIi79fYmE2IRUx3vu9MSmtU2qYgvJeXTmEascVwEw8PRACtsUTnyvaOWiVGhTIcX+huaGlGlUhsCbgZm+PolEkvvIkOMtKCgoywxYsmQJmzdvZtasWWzduhWVSsWwYcOIjY1Nte+QIUO4cOFCim3o0KEYGxszaNAgAHx9ffnyyy9xcnJi9+7d9OzZkylTpuDq6pplNucFpM6b5ILfBe69uIepniktGrWgy+YufHX7K2r0rYFCqcDngA+rGq5iQ+sNPD73WNvmSiQSiSQHkh0LqLt27aJTp07UqlWL1q1bs2LFChISkh6O9+3bl2Yf/v75a7FI6rzlXzTRbu0qtpNVbdOgsE1hFDoKXtwXGsYKhYIqXarQeY1wUpoUNyEiMILDXx9mceXF3Fx/E1WCCoDwgHACrwdSrVe1dPv32OLBhtYbKFC6AENdhyYWV9Bwe9ttfI/5pjouPir+nc48iUSSd8mQ461Zs2YMGzaMQ4cOpekg+1BiY2NZvXo1Y8eOpWnTplSpUoU//viDwMBAjh07lmp/ExMTLCwsEreQkBDWr1/PtGnTqFy5MgDr1q2jcuXKTJgwARsbG4YOHUrbtm1ZuXJlhu3MizhZOQHgEeTBi6gX79lbkhdZfWM1AN2rdMdYV5RIL1atGN02dWO012hqDa6FUlfJg+MPWNtkLWubrOXBiQeo1VLLTSKRSCSCrF5A3bdvH9OnT6d///7s27eP8ePHs3z5cpYuXZrYj7e3N/b29qn6KlGixKe67BxBa5vWAJx+dJrYhIzPxyW5C7VanajvJtNM00bXUJeyzmW5u/tuinnr3T13MShowNdeX9P+7/aYFjfl1aNX7Bm4h2V2y/D61wt/N+HAL9OoTJp93zt0j38H/EsZxzIMuTCEAqVSOz6vLr/KgZEHUkTThQeE43fRL7Hwg0QiyV9kyPE2Z84cVCoVkyZNonHjxvz00094eHh8dD93794lMjISBweHxLYCBQpga2vL5cuX33v8zJkzqVevHl27JonLX7lyJUV/AA0bNuTq1avSYZAMS1NLKhepjBo1F/0uatscySfmdczrxEnbwBoDU71fpFIROq/uzJh7Y6j7ZV109HV4fO4xG1ptYJXDKnwO+sj/TxKJRJLPyY4F1C1bttClSxd69+6NlZUV7du3Z8iQIezcuTOxHx8fHypXrpyiLwsLC3R0dD7ZtecEahWvhYWxBRGxEbg8cdG2OZJPxI3AG9x7cQ8jXSM6Ve6kbXNyLM5TnfF392dnr53cO3yPUz+ewmW+C07fO2Fobohdfzu6b+mO84/OGBYyJMQzhO3dtnNk7BGUekoKlU9dsCI+Op59w/ZhYGaA0w9OhHiG4O/mn7iF+4eLc//oTNjjMLZ23sr9I/fx2OzB+ubrMS5ijMNEh1T9SiSSvE+GHG+dO3dm9erVnD59miFDhuDm5kbPnj3p2LEjq1ev5vnz5x/UT2CgyHF/e4WyWLFiie+lx+nTp7l+/TrffPNNqj6LF09ZpbNYsWJERUXx8uXLD7IrvyB13vIv2+5s403cG6oUrYJ9Sft09zO3Nqfjso6M9R2L/Vh7dA11CXAPYEvHLayouwKv3V6JArYSiUQiyV9kxwLqpEmTGDp0aIr9lEolYWFhia+9vb2xsbHJgivI3SgVysSoN6nzln/QpJl2qNQBU31TLVuTcynXvBy9dvXiufdztnXZhscmD1rNb0WjKUIb8dm1Z6xrto5C5Qsx7sE4nKY6oWeix+unr1HFqVjffD1PXFOmzT9xeULEswiiX0WzsfVGVjmsSrFdW3lNnLtZOfof609sRCw7e+/k0OhDlKhTgsHnB2NY0PCTj4VEItE+upk52NLSkpEjRzJy5Eju3LnD3LlzmT9/Pr///ntiOmrNmjXTPT4qKgoAfX39FO0GBgYpJlhpsWbNGpo1a0bVqlVTtEdHR6fqT/M6M2mxarWaN2/eZPj4d6EZB83fT0WD4g34h3848+hMtl3bp0Bb45ebWXlVpF73r9af6Oho4N3jp1tYlyZzmlBvfD0uL7rMjX9uEHg9kO3dt1O0alEaTmlI5e6VUepovV7LJ0fef5lDjl/mkOOXObJ7/NRqNQqFIlv6zglkxQLqnj17UrTXrVs3xevXr1+zZcsWnJyEREZYWBhBQUFcuXKFzZs38/LlS+zs7Jg8eTLlypXL8LXk1nle0zJN2eSxicP3DvOj449Z3n9OQH7PJaFWq9l2WzjeOlfo/EH3bH4ev7JtyjKwTcrMDs2YFbMvxuTIyQCoUNHwu4bUGFoD9wXu3PjnBo/OPGK142qs21hjPdyaKOsoLBtaJh6THpr+izsUp/fR3um+n1/Iz/dfViDHL3PkpHlephxvIFI79+7dy/HjxwkPD6dRo0Y0bdqUM2fO0KdPH6ZMmZKo2/E2hobC4x8bG5v4b4CYmBiMjIzSPefTp09xd3dnxYoVqd4zMDBI5WDTvH5Xn+8jLi4OLy+vDB//ITx69Chb+3+b4jEiMvB64HWuelxN1PnKrXzq8cutPHz9EPen7ugodKinVy9x3D50/Cz7W9KsYzMebnnIw60Pee71nAODD3B6+mkqDK5AqXalUOrmPwecvP8yhxy/zCHHL3Nk5/i9vRiYl8iOBdTkREZGMmrUKGJiYpgyZQoA9+7dA8Rkd86cOURHR7N06VL69u3L/v37KVq0aLr9vYvcOs+zirMC4GbwTS7cuEARgyJZfo6cgvyegzuv7vAo7BGGOoaUiyv3UfesHL8Po8TgEpi3M8fnHx+e7H/Co6OPeO7zHNMdMrowM8j7L3PI8cscOWGelyHH2+PHj9m7dy/79u0jICCAUqVKMWDAALp165a46tm/f38mTZrE0qVL03W8afYNDg7GysoqsT04ODhR6yMtTpw4QeHChWnUqFGafQYHB6doCw4OxtjYGDMzs4+91ET09PSoUKHC+3fMAFFRUTx69Ahra+tMOQc/lqpUxeqyFX7hfrwye0Vd67rvPygHoq3xy61sPLMRgHY27XCq7ZTh8avpUJPomdFcX36dK4uvEOkXyc2fbvJo7SPsJ9pTvX91dA0y7dvP8cj7L3PI8csccvwyR3aP3/3797O8z5xEdiygaggJCeHLL7/E39+fVatWUbp0aQDq1auHq6srhQoVSlxlXrx4MU2bNmX37t2MGDEiQ9eSm+d5djftuBV8iyd6T2hctXGW969t5PdcEutPrwegY8WO1K5e+4OOkeOXAapCnWZ1eOHzAveF7qiKqOT4ZRB5/2UOOX6ZIyfN8zL0VNymTRsMDAxo2bIls2bNSlXMQEP58uXf6V2sUqUKpqamuLu7JzrewsPD8fT0pH///uked+XKFezt7dHVTW1+vXr1uHTpUoo2Nzc36tSpg1KZ8SgchUKBsXH2RoQZGRll+znepol1Ezbc2sClwEt0ss3dAq3aGL/cRlxCHJs9NwMwvN7wFOOVkfEzNjamxU8tcJrsxOWll3Fd4ErY4zCOjz2O269uNJrSiDrD6qBnrJel15ETkfdf5pDjlznk+GWO7Bq/vJxmCtmzgArg6+vLsGHDUKlUbNq0iYoVK6Z4v3DhwileGxkZUbp0aYKCgjJ6Kbl6nteuYjtuBd/ijP8ZhtYf+v4Dcin5/XtOrVbzr8+/APS16/vRY5Hfxy8jGNcypvDfhfHy8pLjl0nk+GUOOX6ZIyfM8zLkifrxxx+5cOECv/32W7pON4BRo0alqEL1Nvr6+vTv358FCxZw8uRJ7t69y4QJEyhevDitW7cmISGBkJCQRA0qDZ6enlSpUiXNPgcMGMCtW7dYsGABvr6+rF69miNHjjBs2LCMXGqeJ7HAgp8ssJAfOHTvEMGRwViaWNKuQrss61ffVJ9Gkxsx7tE42i5si1kpM14HvObIuCMsLLeQi/MvEhuRcY1FiUQikeQ8ki+gatAsoNavXz/d4961gPrkyRMGDhyIkZERW7duTeV027ZtGw0aNEihkxQREcGjR4+yLWItp9PGpg0Ax3yPoVKrtGyNJLtw83fjSfgTTPVNaVcx6+ZwEolEIsl+MuR469evH+fPn2fatGmJbdeuXaNHjx6cOnXqo/oaO3YsPXr0YOrUqfTp0wcdHR1WrVqFnp4ez549o3Hjxhw6dCjFMSEhIZibm6fZX8WKFVmyZAlnz56lS5cu7Nixg/nz57/TQZif0Tje3P3diY6Pfs/ektzO6hurARhYcyB6OlkfhaZnpEeDsQ0Y6zuWDss6YG5tTmRwJCemnODPsn9y7udzRL+S95lEIpHkBbJjAfX7778nNjaW33//HV1dXUJCQhI3AGdnZ1QqFVOmTOHevXt4eHgwZswYChcuTLdu3bL9mnMijawaYaJnQnBkMDcDb2rbHEk2sf3OdgA6V+6Moa6sjCmRSCS5iQylmu7Zs4dvv/2W1q1bJ7aZm5tjYWHB119/zaJFi2jZsuUH9aWjo8PkyZOZPDl1hZjSpUvj7e2dqv3mzXdPKpydnXF2dv6g8+d3KhauiKWJJUGRQVwOuIxTWSdtmyTJJgIjAjnocxCAwbUHZ+u5dA10qfdlPWoPqY3HJg/Ozz7Pi3svOP3jaVzmu2A/1p6G4xtiXESGTEskEkluZuzYscTHxzN16lSio6OpX79+4gKqv78/LVq0YM6cOSmcYuktoAYFBSXKhXTu3DnV+97e3pQoUYK1a9fy22+/0adPH9RqNY0aNWL9+vUYGBhk23XmZPR19GlWrhkHfA5w1PcotUt8mPaXJPegUqvY4bkDgN7VUlfKlEgkEknOJkOOt1WrVjF48GC++eabxLby5cuzdOlSfv31V5YsWfLBjjeJdlEoFDiXdWaH5w7OPT4nHW95mPU315OgTsCxjCNViqYdaZDV6OjpUGtQLewG2HFn+x3O/3KekDshnP/5PG5/uFF/VH0cJjpgaikrRUkkEkluJCsXUC0tLdPc/22qVavG6tWrP97YPEwbmzaJjrdvG3+rbXMkWcxFv4sEvA6goEFBWtu0fv8BEolEIslRZCjV1M/PjyZNmqT5nrOzMw8ePMiUUZJPi9R5y/uo1WpWXxcPKUNqDfnk51fqKKnRpwZf3fqKXrt6Ubx2ceIi43CZ78JC64UcHneY8IDwT26XRCKRSCR5AY3O20W/i0TERmjZGklWo0kz7VKlCwa6+TOyUyKRSHIzGXK8WVhYcOvWrTTfu3v3LoUKFcqUUZJPi8bxdtHvIvGqeC1bI8kOXP1d8Q71xkTPhF7VemnNDoVSQdVuVRlxdQR9DvShVINSxEfHc2nRJRaVX8SBkQd49eiV1uyTSCQSiSQ3UqFwBcqZlyNOFcfph6e1bY4kC0lQJbDTSxSrk2mmEolEkjvJkOOtY8eOLF26lI0bNxIUFERcXBxBQUFs3bqVv/76i88++yyr7ZRkI9WLVcfc0JzIuEiuP7uubXMk2cCqa6sA6FWtF2YGZlq2RqQ4V+pQiaGuQxlwfABlncuSEJvA1eVXWVRhEXsH7yX0Xqi2zZRIJBKJJFegUCgSo96O+h7VsjWSrOTc43MERgRSyLAQLcq30LY5EolEIskAGXK8jR49GicnJ37++WeaNm2KnZ0dTZs2ZcaMGTg7OzNmzJistlOSjSgVSpyshLbbuccy3TSvEREbwbY72wAYUvvTp5m+C4VCQfmW5Rl0dhCDzg6ifKvyqBPU3Fh7g7+r/M2uvrsIvhOsbTMlEolEIsnxtKkgHW95EU2aabeq3dDX0deyNRKJRCLJCBkqrqCnp8eiRYvw8fHh6tWrhIWFYWZmRt26ddMtDy/J2TiXdWa/z37O+Z1jouNEbZsjyUJ23NlBZFwklYpUolGZRto2J13KOpdlwLEB+Lv7c/7n8/gc8OH2ltvc3nKbqt2q4jTViRK1S2jbTIlEIpFIciTNyzVHV6nL/Rf3efDyAeULlde2SZJMEq+KZ5fXLkCmmUokEkluJkOONw2VKlWiUqVKqdojIiIwNZVVCnMTGp2384/Po1KrUCoyFAwpyYGsvpFUVEGhUGjZmvdTukFp+uzvw7Przzj/y3m8dnnhtVtsFTtUxPlHZ0o3KK1tMyUSiUQiyVEUMCiAQ2kHzvud5+j9o3xV/yttmyTJJKcfnibkTQhFjYvSrFwzbZsjkUgkkgySIcdbbGws69at49KlS8TGxqJWqwFROfHNmzfcv38/3VLxkpxJ7eK1MdEz4WX0S+4E36GGZQ1tmyTJAryfe3PB7wI6Ch2+qPmFts35KErULkGvnb0IvhPMhdkXuL31NvcO3uPewXuUb1ke5x+dKetcVttmSiQSiUSSY2hj00Y43nyl4y0voEkz7V61O7rKTMVLSCQSiUSLZCisad68efz2228EBQXh6+tLQEAAUVFR3Lp1Cy8vL7788sustlOSzejp6OFYxhGQOm95iTU31gDQrmI7SpjlzjTNYtWK0W1TN0bfHU2twbVQ6ip5cOIBa5usZY3zGnyP+yY6/yUSiUQiyc9odN5OPTxFXEKclq2RZIa4hDh2390NyDRTiUQiye1kyPF27NgxBg8ezL59++jfvz/Vq1dnx44dHDt2jFKlSqFSqbLaTsknQJNues5POt7yAvGqeNbdXAeINNPcTpGKRei8ujNj7o2h7si66Ojr4Hfej42tN7LKYRU+B3ykA04ikUgk+Zo6JepQ1Lgor2Nf4+bvpm1zJJng5MOTvIh6gaWJZeIcXSKRSCS5kww53l68eIGzs/gBqFSpEh4eHgBYWloyYsQIDh06lHUWSj4ZiY63x+ekAyMPcPjeYQIjAilmUoyOlTpq25wsw9zanI5LOzLWdywNxjVA11CXAPcAtnTawoo6K/Dc5YlaJe9fiUQikeQ/lAolrcq3AmR109yOpiJ9D9se6Ch1tGyNRCKRSDJDhhxvZmZmxMbGAlC2bFmePXtGREQEANbW1jx79izrLJR8MuxL2aOvo09gRCD3X9zXtjmSTKIpqjDAbgB6OnpatibrKVC6AG3/bMu4R+NwnOKInokegTcC2dFjB0trLMVjsweqBBl9K5FIJJL8RRsbkW4qHW+5l5j4GP71+heAXtV6adkaiUQikWSWDKl01qtXjw0bNmBvb0/ZsmUxMjLixIkTdOnShevXr8uKprkUQ11DGpRqwHm/85x7fI6KRSpq2yRJBgmKCOKAzwEAhtTO/Wmm78LU0pRWv7ai0ZRGuC90x32ROyGeIezut5szM87Q+LvG2PW3Q0cv9Wqx7zFfTv1wiuA7wZhamlJ/dH0cJjq8s/qrz0Efzv50lmCPYIyKGFHxs4oU61sMgFePXrGw3MJ0j601qBad13QG4InrE059fwp/d3/0TfWp1LESLea0wNRSfn9KJBKJJOO0tmkNwNWnV3n+5jlFjYtq2SLJx3L8wXHCYsIoaVaSxlaNtW3O/9m787iqqv3/46/DDCIiTjgAKiqIE5iaI5oTZpppptd5LrO0a+XQzW5W2qCWqX2tW6k365ZlmqlZjuWUmfMMKCriCDkzD+f8/uDHKQIHxs3wfj4e56Hsvfben708wuZz1mctERHJo1yNeHvmmWc4ePAgTz75JHZ2dgwcOJBXXnmFPn36MG/ePEJCQvI7TikkmuetZPji8BekmlNpWaMlAZUCjA6nULhUcOGh1x/in2f/yUMzHsLZw5lrJ6+xeuRqFtRdwN6P9pKalGptf/6383zZ40sq+lek/8r+NBrUiI2TN7LznZ13vEbYmjCWPbqMyg0qM/CHgbSd2pajXxzl8IzDALhWdWXUrlFZXo0HN8bG3oagUUEAXPj9Ap91+IyE6wk89tlj9Frci+unr7Oo1SISbyYWbEeJiEiJVrVsVRpXaYwFCxsjNhodjuRCRpnpEwFPYGPK1a9rIiJShORqxJu/vz8//vgj4eHhALzwwgu4urqyf/9+OnbsyJNPPpmvQUrhCfYJZub2mVrZtBizWCwsOrAIKBmLKuSUk7sTwS8H0/K5luz9aC+/zvmVm5E3+eHpH9j2xjZaT27NA2Me4JdXf6FqUFV6f94bgDrd6pCWksb2N7fz4HMPYu+ctTx3/cT1BPQNsI5aq9WxFkkJSfw27zdS4lNwqehCjZY1Mh1zcd9Fjn59lE5vdsK7rTcA22dux7GcI8N+HoZzeWfruT7w/4Cds3bSaWanguwiEREp4UJ8Qzh85TDrI9YzoNEAo8ORHEhMTeT70O8BlZmKiJQUufoI5ZVXXuHixYu0adMGAJPJxNixY/n444959tlncXBwyNcgpfC0qtEKW5MtZ2+c5dzNc0aHI7mw+8JuTvxxAmc7Z/o3LL3Lzzu4OtD6xdY8d+Y5us3vRtnqZbl98Tbr/7me92u+z+nNp6nTvU6mYwL6BpB8O5lzO7K+9y8duMT1iOu0GN8i0/YHnnmAjt93xN4la6LOYrGw7pl1VAqoRMuJLa3bY07E4N3W25p0A7B3safGgzU4+cPJvN66iIiUchnzvG2I2KAFs4qZn079xO3k23i5edGyRst7HyAiIkVerhJvq1evJi4uLr9jkSKgrGNZmlZtCsD2yO0GRyO5sfhA+qIKTzR4AjdHN4OjMZ69sz0Pjn+QCRET6PGfHrjXdCc+Jh5LmoVdc3ax9Y2tJN5IL+/0qOMBwNWwq1nOc/ngZQDsnOz4sseXzHSeyTse77Bl0hbSktOyvfaxr49xYfcFur3fDRvbP7/dulR04WbkzSztr0Vc4/rp63m+ZxERKd3aerfFxd6FS7GXOBJ9xOhwJAcyykz7NeinMlMRkRIiV9/Ng4KC2L17d37HIkWEdZ43lZsWO3HJcSw7ugyAUUGjDI6maLFztOOBJx/g2fBnaTetHQDJscn88u9feN/nfbZM20JaUnoCLelWUpbj42PiAfi699dUalCJgevS53g7tPgQh147lO01f539K15tvKjZoWam7UEjg7i0/xI//fMnbl+8TezlWDZO2UjM8RhS4lLy8a5FRKQ0crRzpEPNDgCsP6XVTYuL+JR41oStAVRmKiJSkuRqjjc/Pz8WLVrETz/9hL+/Py4uLpn2m0wm3nzzzXwJUApfe5/2vLvrXS2wUAx9e/xbbiffpo5HHdp5tzM6nCLJ1t6Wug/XZfuM7bR9qS1hq8OIORbD9pnb2TV3FwDJcclZjssY1ebf258u73QBoNZDtUhOTGbbq9u4dvIaLk3+/F4Y9WsUl/Zfov+qrOW+TUc3JelWEj//+2d2z9sNpvQy1weefICDSw4WwF2LiEhpE+IbwrqT61gfsZ5JbSYZHY7ch3Un1xGXEkdN95o0r9bc6HBERCSf5CrxtnHjRipXrkxKSgpHjmQdvm4ymfIcmBinrXdbTJgI/SOU6LhoKpepbHRIcp8WH0wvMx0ROEL/D+/CsZwjANWaV6PjjI6Ergpl24xtXD6QXk7666xfSY5Nps2kNrjVSC/XdSibPndlvR71Mp2rZpeabHt1G9GHoqnR5M+FFY5/exyn8k7U7V432xhaPd+KFuNbcD3iOs4VnClTqQzfDf0OZw/nbNuLiORWx44d7/tngslkYtOmTQUckRSGjHnetp/bTlxyHGUcyhgckdyLtcw0oJ+e40RESpBcJd62bNmS33FIEVLeuTyNqjTi8JXDbI/czuMBjxsdktyHk1dPsi1yGzYmG4Y1GWZ0OEWah68HJlsT105dw2Rjon6f+vj39ue3ub+x4YUNmFPN/D7/d/Z+uJfAEYG0ndqWCnUrAJCalJrpXOZUMwB2zpm/nYavDcf/MX9s7W2zXP/i3ovcPHeT+n3qU9G/onX7pf2XqNq0an7froiUciNGjOCdd97B1dWVhx56yOhwpJDUq1APn3I+RN6MZGvkVrrX7W50SHIXscmx/BD+A0CpXhxLRKQkylXiTUq+YO9gDl85zLbIbUq8FRNLDi4BoFudblR3q25wNEWbnZMdPsE+hK4MpfWLrTGZTJhMJmKvxOJYzpE+X/Th19m/Erktkv0f7+fAogM0/EdD7F3sOfrVUfx6+lnPFfFDBCZbE9VaVLNuS7iWwLWT12gzpU221z/7y1l+fuVnXrj0Ak7uTunn2RhBzLEYWk9qXbA3LyKlzpAhQ/Dw8OCFF16gU6dOdO7c2eiQpBCYTCZCfEP4eP/HrD+1Xom3Im5t+FoSUhOo41GHIM8go8MREZF8lKvE29ChQ+/ZZunSpbk5tRQRwT7BfLDnA83zVkykmlP57NBnAIwMHGlwNMVD8LRglnZeyrf9viVwZCBRv0bx6+xf6fx2Z+r1qIdPsA+H/3eY498c5+wvZznyvyNggqNfHcVittB0TFOifo1i93u7qTWgFi6V/pzf7cqRKwBUCqiU7bUbD27Mjrd2sLzfclpPas3NczfZ8PwGvNp40Xhw40K5fxEpXR555BF2797NW2+9xUMPPYStbdbRuFLyhNT5/4m3CC2wUNSpzFREpOTK1aqmFoslyysuLo7Dhw9z6tQpateund9xSiFr55M+Mf+hy4e4kXjD2GDknjZEbODi7YtUdKlIT7+eRodTLNTqWIt+K/rxR9gffP3Y1xz53xG6zO5Cm8npo9Qu7b/EunHraDKsCaN+G0W9nvXAkn7ssa+P8UXXL9j30T7avNyG+hPqZzp33JU4AJzLZz9fm6unK4M3DCYtKY1v+nzD1ulbCRwRyKAfB2Fjm6tvyyIid5SUlL5S8z//+U86derEuXPnDI5ICkunWp2wNdkSdjWMyBuRRocjd3Ar6RY/nvwRUJmpiEhJlKsRb59//nm222/evMmYMWOUeCsBPF09qVehHuFXw9l5bieP1HvE6JDkLhYfSF9UYXCjwTjYOhgcTfFRv3d96veun+2+mh1q8qrlVevXA1YP4PLBy2ybsY0TK05gMVu4ffE2F367gE0DG2jw57EN+jWgQb8G2Zz1T9UeqMbwrcPz4zZERO6qY8eOfPDBBwQFBeHm5pZlNXopuco5laNljZbsjNrJ+oj1PPnAk0aHJNlYHbaapLQk/Cv606hyI6PDERGRfJavQyvKlSvHk08+yX//+9/8PK0YJNg7GIBtkSo3Lcpi4mJYHbYagFFNRxkcTcnmGehJv2/78fTRp2k0qBEmGxOnfzrN4RmHjQ5NROSObt++TXR0NAD/93//x5UrVwyOSApTxuqmKjctur459g2gMlMRkZKqQBZXuHr1akGcVgpZsE8wnx74VPO8FXFfHP6CFHMKzas1p2HlhkaHUypUblCZPl/0of2r7fn9o98xVzcbHZKIyB01atSIF154gXfeeQeLxcIzzzyDg0P2o6NNJhObNm0q5AilIIXUCeHfv/ybzac3k2pOxc5Ga6sVJTcSb/DTqZ8A6Negn8HRiIhIQcjVT949e/Zk2ZaWlsbly5dZuHAhDRrcvcRKiodgn/QRb3sv7iUuOY4yDmUMjkj+zmKxsOjAIgBGBmlRhcJWoW4F2r/RnhMnThgdiojIHb333nv897//5caNG6xatYqAgAA8PDyMDksKyQNVH8DD2YNrCdfYfX43bbyzX3FbjLEqdBUp5hQaVGpAg8r6HUpEpCTKVeJtyJAhmEwmLBaLdTi0xZI+63jVqlX517/+lX8RimF83H3wLufNuZvn+O38b3Sq3cnokORv9l7cy7GYYzjZOTGg4QCjwxERkSKoSpUqTJkyBYDdu3czceJE/P39DY5KCoutjS1danfh62Nfsz5ivRJvRUxGmWn/BlpUQUSkpMpV4m3p0qVZtplMJlxdXfHz88PGRqvylRTBPsF8cfgLtkVuU+KtCMoY7dY3oC/lnMoZHI2IiBR1W7ZsMToEMUCIb4g18fb6Q68bHY78f1fjr7Lx9EZAZaYiIiVZrjJkLVq0wN/fn8TERFq0aEGLFi2oWrUq+/fvJy4uLr9jFANZF1jQPG9FTnxKPF8d/QqAkYEqMxUREZHsdfXtCsCeC3u4Gq+5mIuK70K/I9WcSpMqTfCr6Gd0OCIiUkBylXiLiIjgkUceYfr06dZtUVFRvPXWWzz++ONcvHgxv+ITg2XM8/bb+d9ISk0yOBr5q5UnVnIr6Ra13GvRvmZ7o8MRERGRIqq6W3UaVm6IBQubTmvxjKJCZaYiIqVDrhJvs2fPpkqVKnz11VfWba1atWLr1q24u7sza9asfAtQjFWvQj0ql6lMYmoiey/uNToc+Yu/LqpgY1J5t4iIiNxZ19rpo97WR6w3OBIBiImLYcuZ9NJvlZmKiJRsufptff/+/YwfP54qVapk2l6hQgXGjh3Lb7/9li/BifFMJpN11Nu2SJWbFhUR1yL45ewvmDAxrMkwo8MRERGRIi6kTgiQnnjLWBRNjLPyxErSLGk8UPUBfD18jQ5HREQKUK4SbyaTiYSEhGz3paamkpKSkqegpGjRPG9Fz38P/hdIn7PFq5yXscGIiIhIkdfOux1Odk5cvH2RYzHHjA6n1Pv62NeAykxFREqDXCXemjdvzv/93/9x7dq1TNtv3LjBRx99RIsWLfIlOCkaMka87Ty3k1RzqsHRSJo5jf8e+i+QXmYqIiIici/O9s6090mfE3b9KZWbGuly7GW2Rm4FVGYqIlIa2OXmoBdeeIF+/frRqVMnAgMD8fDw4Pr16xw8eBAHBwfefffd/I5TDNSwckPcndy5kXiDQ5cP8UC1B4wOqVTbeHoj52+dx8PZg15+vYwOR0RERIqJEN8Q1kesZ33Eel5o/YLR4ZRaK46vwGwx82D1B/Fx9zE6HBERKWC5GvFWq1Yt1q5dyz/+8Q/i4+M5evQot27dol+/fqxatYpatWrld5xiIFsbW9p6twU0z1tRsPjAYgAGNxqMo52jwdGIiIhIcZExz9u2yG3Ep8QbHE3ppTJTEZHSJVcj3gCqVKnCmDFj8PDwAODmzZvExMTg6emZb8FJ0RHsHcza8LVsO7eNia0mGh1OqfVH/B+sCl0FqMxUREREcqZ+xfrUcKvB+Vvn2Ra5jW51uhkdUqlz4dYFdpzbAUDfgL4GRyMiIoUhVyPebt++zejRoxk0aJB126FDh+jRowcTJkwgMTEx3wKUoiFjnrftkdsxW8wGR1N6fXnkS1LMKTSt2pQmnk2MDkdEREo5s9nM/PnzadeuHYGBgYwZM4aoqKhs2y5YsAA/P79sXy+99JK13a5du+jTpw9NmjShW7du/PDDD5nOk5SUxGuvvUarVq0ICgrihRdeyDLvsGTPZDIR4vv/VzfVPG+G+Pb4t1iw0MarjRbIEhEpJXKVeJszZw4nTpxg/Pjx1m0tW7ZkwYIF7N+/nwULFuRbgFI0NK3aFBd7F64mXOVEzAmjwymVLBYLiw4sAmBU0CiDoxEREYGFCxfy5Zdf8sYbb7Bs2TLMZjOjR48mOTk5S9uRI0eyY8eOTK9Ro0bh4uLC8OHDAYiIiOCpp56iXbt2rFy5kieeeILJkyeza9cu63mmT5/Ojh07WLBgAZ999hmnT59mwoQJhXXLxZ418RahxJsRVGYqIlL65CrxtmXLFqZMmUL37t2t2xwcHOjSpQvPP/8869aty7cApWiwt7WntVdrQPO8GWX/pf0cvnIYR1tHBjQcYHQ4IiJSyiUnJ7N48WImTJhAhw4d8Pf3Z+7cuVy+fJkNGzZkaV+mTBkqVapkfcXExLB06VL+/e9/4+fnB8Bnn32Gn58fEydOxNfXl1GjRtGtWzc+/fRTAK5cucKqVauYNm0azZo1o3Hjxrz33nvs2bOHAwcOFOr9F1eda3fGxmTDiT9OEHUz+9GJUjDO3TzHrvO7MGHi8YDHjQ5HREQKSa4Sb7GxsZQrVy7bfZUqVdJw/xIq2Du93HTbOSXejJCxqEKf+n0o71ze4GhERKS0Cw0NJS4ujlatWlm3ubm5ERAQwJ49e+55/Ouvv06zZs3o3bu3ddvevXsznQ/Sqyr27duHxWJh37591m0ZatWqRZUqVe7rmgLlncvTonoLQKPeCtvyY8uB9ClcqpWtZnA0IiJSWHKVePP392fFihXZ7lu1apX1U0spWTLmedsWuQ2LxWJwNKVLQkoC/zvyP0BlpiIiUjRcvnwZgKpVq2baXrlyZeu+O/n55585cOAAU6ZMyXLOvy/UVblyZRISErh+/TpXrlyhfPnyODo6Zmlzr2vKn1RuagyVmYqIlE65WtV07NixjB07lj59+tClSxcqVKjAtWvX+Pnnnzly5AgffvhhfscpRUCL6i1wsHXg4u2LnL5+Gl8PX6NDKjW+C/2Om0k38Snnw0O1HjI6HBERERISEoD06Ub+ytHRkZs3b9712CVLlvDQQw9Rv379TNsTExOznC/j6+TkZBISErLsz7hmUlJSju8hg8ViIT4+PtfH301GP2X8WRS0r94egE0Rm7gVews7m1z9SlAoimL/5cbZG2fZc3EPNiYbutXsVmDvt78rKf1nFPVf3qj/8kb9lzcF3X8WiwWTyXRfbXP1U7Z9+/YsXLiQBQsWMH/+fOsF69evz8KFC2nfvn1uTitFnLO9My2qt2DHuR1si9ymxFshyigzHRE4AhtTrgaqioiI5CsnJycgPSGW8XdIX3XU2dn5jsddvHiR3bt38/HHH2fZ5+jomGVhhoyvnZ2dcXJyynbhhntd815SUlI4caJgF486e/ZsgZ4/J1zMLpS1L8uNpBus+G0Fjcs3NjqkeypK/Zcb/z31XwCaVWjGtXPXuEbhTs1T3PvPaOq/vFH/5Y36L28Ksv+y+zAwO7n+eOuhhx6icePGJCUlcfnyZdzc3HByciIhIYGvvvqKAQM0+XtJFOwdnJ54O7eNEUEjjA6nVDh74yybz2zGhInhgcONDkdERAT4s8Q0Ojoab29v6/bo6Oi7TjuyadMmPDw8aNOmTbbnjI6OzrQtOjoaFxcXypYti6enJzdu3CA5OTnTw250dDRVqlTJ9b3Y29tTp06dXB9/NwkJCZw9e5aaNWvmKTmY3zqf6sx34d9xynKK/vWLbuljUe2/nNr++3YAhjQdkmWkZ0EqKf1nFPVf3qj/8kb9lzcF3X+nTp2677a5SryFhoby4osvEhERke1+k8mkxFsJFewTzJs73tTKpoVoyYElQPoqZD7uPgZHIyIiks7f3x9XV1d2795tTbzdunWL48ePM3jw4Dset3fvXlq0aIGdXdbH0GbNmvH7779n2vbbb7/RtGlTbGxseOCBBzCbzezbt8+6CMOZM2e4cuUKzZs3z/W9mEwmXFxccn38/XB2di7wa+REd7/ufBf+HVvObWFml5lGh3NPRa3/cuLk1ZMcij6ErcmWfzT5hyH3UZz7ryhQ/+WN+i9v1H95U1D9d79lppDLxRVmzZrFzZs3mTJlCi1atKBt27a88sortG/fHpPJxNKlS3NzWikGWnu1xsZkw+nrpzl/67zR4ZR4aeY0lhxMT7yNDBppcDQiIiJ/cnBwYPDgwcyZM4fNmzcTGhrKxIkT8fT0pGvXrqSlpRETE0NiYmKm444fP46/v3+25xwyZAiHDx9mzpw5REREsHjxYn766SdGjx4NQJUqVXjkkUeYNm0au3fv5vDhwzz//PO0aNGCwMDAgr7lEiVjgYXfL/zO9YTrBkdTsn1z7Bsg/UPUii4VDY5GREQKW64Sb4cOHeK5555j+PDhdO/enYSEBAYOHMhHH31E586d+fzzz/M7TikiyjqWpWnVpgBsj9xucDQl35YzW4i6FYW7kzuP+T9mdDgiIiKZTJgwgb59+zJt2jQGDBiAra0tixYtwt7enkuXLtG2bVvWrVuX6ZiYmBjc3d2zPV/dunVZuHAhW7du5bHHHmP58uXMnj3bOroN4I033qBVq1Y8++yzjBo1itq1azN//vyCvM0SyaucF/Ur1sdsMbP5zGajwynRvjmennjr16CfwZGIiIgRclVqmpycTM2aNQGoWbMmoaGh1n19+vTh1VdfzZfgpGgK9g5m78W9bIvcxoBGKikuSIsPpi+qMKjRIJzsnO7RWkREpHDZ2toyadIkJk2alGVfjRo1CAsLy7L90KFDdz1ncHAwwcHBd9zv4uLCjBkzmDFjRs4DlkxCfEM48ccJ1p9aT9+AvkaHUyKF/hHK4SuHsbexp7d/b6PDERERA+RqxFu1atWIiooC0hNvsbGxnD+fXnbo4OBwzyXkpXgL9kl/GN52TvO8FaRrCdf47sR3AIwKGmVwNCIiIlLShNRJLzddH7Eei8VicDQl09dHvwagq29XyjuXNzgaERExQq4Sb127duXdd99l/fr1VKlShdq1a/P+++8TFhbG4sWL8fLyyu84pQhp690WgOMxx4mJizE4mpLryyNfkpSWRKBnIEFVg4wOR0REREqYYJ9gHG0diboVRegfofc+QHJMZaYiIpKrxNuzzz5L06ZN+fbbbwF46aWX2LhxI4899hi//fYb48ePz9cgpWip4FKBhpUbArDj3A6Doym5Fh9ILzMdGahFFURERCT/udi7WCsZ1kesNziakudo9FGOxxzHwdaBXn69jA5HREQMkqvEm6OjI/Pnz2fhwoUAtGvXjrVr1/Lee++xbt06unbtmq9BStET7P3/y00jVW5aEA5cOsCBywdwsHVgYKOBRocjIiIiJVTG6qZKvOW/jDLTbnW6Uc6pnMHRiIiIUXKVeMtgb29v/buXlxcPP/ww3t7eeQ5Kij7N81awMka79fbvTQWXCgZHIyIiIiVVxjxvW89uJTE10eBoSg6LxWItM+3foL/B0YiIiJHylHiT0qudTzsADl4+yM1ELaaRnxJTE/nfkf8BMDJIZaYiIiJScBpUakD1stVJSE1ge+R2o8MpMQ5dOUT41XCc7JzoWa+n0eGIiIiBlHiTXKlWthp1POpgtpj5NepXo8MpUb4P/Z7ridfxcvOiU61ORocjIiIiJZjJZKKrb/o0MSo3zT8ZZabd63anrGNZg6MREREjKfEmuaZ53grGogOLABgeOBxbG1uDoxEREZGSTvO85S+VmYqIyF8p8Sa5pnne8l/kjUg2nd4EwIjAEQZHIyIiIqVB59qdMWHiaPRRLty6YHQ4xd6+S/s4ff00LvYuPFL3EaPDERERgynxJrmWkXjbc2EP8SnxBkdTMnx26DMsWOhYqyO1ytcyOhwREREpBSq4VKB59eYAbIjYYHA0xd83x9JHu/Wo14MyDmUMjkZERIymxJvkWk33mtRwq0GKOYXd53cbHU6xZ7aYWXJwCQAjA7WogoiIiBQelZvmD4vFYk28qcxURERAiTfJA5PJZB31tjVyq8HRFH8/n/mZszfOUs6xHH3q9zE6HBERESlFMhJvG09vJM2cZnA0xdfuC7uJvBmJq4MrD9d52OhwRESkCFDiTfJECyzkn8UHFwMwsNFAnO2dDY5GRERESpMHazxIOcdyXEu4xr5L+4wOp9jKGO32qN+jep4TERFAiTfJo4wRb7vO7yI5LdngaIqv6wnXWXF8BQAjg1RmKiIiIoXLzsaOTrU7AbD+lMpNc8NsMavMVEREsjA88WY2m5k/fz7t2rUjMDCQMWPGEBUVdcf2KSkpvPvuu9b2gwcP5sSJE5najBgxAj8/v0yvIUOGFPStlEr+Ff2p6FKRxNRE9l7ca3Q4xdZXR78iKS2JRpUb8UDVB4wOR0REREohzfOWN79G/cqF2xdwc3Sz9qWIiIjhibeFCxfy5Zdf8sYbb7Bs2TLMZjOjR48mOTn70VPTp09n5cqVvPnmm6xYsQIPDw/GjBnD7du3rW3CwsKYPn06O3bssL4WLFhQWLdUqvx1njeVm+be4gPpZaajgkZhMpkMjkZERERKo4xk0W/nf+Nm4k2Doyl+Mka7Peb/GI52jgZHIyIiRYWhibfk5GQWL17MhAkT6NChA/7+/sydO5fLly+zYUPWpcyjoqJYsWIFM2fOpF27dvj6+jJjxgwcHBw4evQoAFevXuXq1as0adKESpUqWV/u7u6FfHelh+Z5y5tDlw+x79I+7G3sGdR4kNHhiIiISCnl4+6DXwU/0ixpbD6z2ehwipU0cxrLjy8HVGYqIiKZGZp4Cw0NJS4ujlatWlm3ubm5ERAQwJ49e7K037lzJ2XLliU4ODhT+y1btljPERYWhslkolatWgV/AwL8Oc/bjnM7tApWLiw5uASAXv69qOhS0eBoREREpDSzlptqnrcc2XFuB5djL1PeqTyda3c2OhwRESlCDE28Xb58GYCqVatm2l65cmXrvr86c+YMXl5ebNiwgT59+tCmTRvGjBlDRESEtU14eDhly5bl9ddfJzg4mG7duvH+++/fsXRV8q5xlca4ObpxO/k2h64cMjqcYiUpNYnPD38OwMhALaogIiIixgqp8+c8bxaLxeBoio+vj30NQG//3jjYOhgcjYiIFCV2Rl48ISEBAAeHzD+cHB0duXkz67wSsbGxREZGsnDhQiZPnoybmxsffvghAwcOZN26dVSoUIHw8HCSkpJo3LgxI0aM4MSJE8yaNYuLFy8ya9asXMdqsViIj4/P9fF3k9EPGX8WR62qt2L96fVsOrkJ/3L+hXrt4tx/K8NWci3hGtVcq9G2atsCe4/dTXHuv6JA/Zc36r+8Uf/lTUH3n8Vi0bydUuy092mPg60DkTcjCb8ajl9FP6NDKvJSzal8e/xbAPo16GdwNCIiUtQYmnhzcnIC0ud6y/g7QFJSEs7Ozlna29nZERsby9y5c/H19QVg7ty5tG/fnu+++47Ro0fz+uuvM2XKFMqVKwdAvXr1sLe3Z+LEiUyePJmKFXNXypeSkpJl9dT8dvbs2QI9f0Gq61CX9aznpxM/0alMJ0NiKI799+HuDwHo5tmN8LBwQ2Mpjv1XlKj/8kb9lzfqv7wpyP77+4eLIkVdGYcytPNux+Yzm1kfsV6Jt/uw9exWYuJjqOBcgY61OhodjoiIFDGGJt4ySkyjo6Px9va2bo+OjsbPL+sPeU9PT+zs7KxJN0hP3nl5eXH+/HkgPTmXkXTLULduXSC9tDW3iTd7e3vq1KmTq2PvJSEhgbNnz1KzZs1sE47FQR+3PnwQ+gGHbx7G39+/UD/hL679d/7WeXbF7ALgnx3+iW9533scUTCKa/8VFeq/vFH/5Y36L28Kuv9OnTqV7+cUKQwhviHWxNuEBycYHU6Rl1Fm+nj9x7G3tTc4GhERKWoMTbz5+/vj6urK7t27rYm3W7ducfz4cQYPHpylffPmzUlNTeXIkSM0atQIgMTERKKionjkkUcAGDJkCDVq1OCtt96yHnfkyBHs7e2pWbNmrmM1mUy4uLjk+vj74ezsXODXKChtarfB2c6ZqwlXiYyPJKBSQKHHUNz675u932DBQnuf9jSq3sjocIpd/xU16r+8Uf/ljfovbwqq/1RmKsVVSJ0QJm+azC9nfyEpNQlHO0ejQyqyUtJSWHFiBaAyUxERyZ6hiys4ODgwePBg5syZw+bNmwkNDWXixIl4enrStWtX0tLSiImJITExEYBmzZrRunVrpkyZwt69ezl16hSTJ0/G1taWXr16ARASEsL333/PV199RVRUFOvWrWPWrFmMGjUKV1dXI2+3RHOwdaCVV/rKstsitxkcTdFntpitq5mOChplcDQiIiIif2pUuRGerp7Ep8Sz49wOo8Mp0rac2cK1hGtULlOZ9jXbGx2OiIgUQYYm3gAmTJhA3759mTZtGgMGDMDW1pZFixZhb2/PpUuXaNu2LevWrbO2X7BgAS1atODZZ5+lb9++xMbGsnTpUjw8PAAYPHgwL7/8Mp9//jndu3dnzpw5DB8+nOeee86oWyw1gr2DASXe7se2yG2cvn6asg5leTzgcaPDEREREbEymUx09e0KpK9uKneWUWbat35f7GwMLSYSEZEiyvCfDra2tkyaNIlJkyZl2VejRg3CwsIybXN1dWX69OlMnz79juccNGgQgwYNyu9Q5R6Cff5MvGklt7tbdGARAAMaDsDFXuVhIiIiUrSE+Iaw9NBS1kesZ1aXWUaHUyQlpyXzXeh3gMpMRUTkzgwf8SYlx4M1HsTexp4Lty9w5sYZo8Mpsm4m3rQuOT+qqcpMRUREpOjpUrsLJkwcvnKYS7cvGR1OkbQxYiM3Em9Q1bUqbb3bGh2OiIgUUUq8Sb5xsXehefXmgMpN72bZ0WUkpibSoFIDmldrbnQ4IiIiIllUKlOJplWbArAhYoPB0RRN1jLTgL7Y2tgaHI2IiBRVSrxJvtI8b/e2+OBiAEYGjVQ5roiIiBRZIb4hgOZ5y05iaiLfh30PQP8G/Q2ORkREijIl3iRf/XWeN8nqaPRRfr/wO3Y2dgxuPNjocERERETuKKROeuJt4+mNmC1mg6MpWtafWs+tpFvUcKtBK69WRocjIiJFmBJvkq9ae7XGxmRDxPUILty6YHQ4Rc7iA+mj3R71e5TKZSobHI2IiIjInbWq0YqyDmX5I/4P9l/ab3Q4RUpGmekTAU9gY9KvVCIicmf6KSH5qpxTOQI9AwHYfm67scEUMclpyXx++HMARgaONDgaERERkbuzt7WnY62OQPoIL0mXkJLA6rDVgMpMRUTk3pR4k3yned6ytyZsDX/E/0FV16rW0g0RERGRokzzvGW17uQ64lLi8CnnQ4vqLYwOR0REijgl3iTfaZ637GUsqjCsyTDsbOwMjkZERETk3jI+LNx1fhe3km4ZHE3R8M3xbwDo16CfFsoSEZF7UuJN8l1b77YAHIs5xh/xfxgcTdFw4dYFfjr1E5C+mqmIiIhIcVC7fG3qeNQh1ZzKljNbjA7HcHHJcawNXwuozFRERO6PEm+S7yqVqURApQAAdpzbYXA0RcPSQ0sxW8y0825H3Qp1jQ5HRERE5L5Zy001zxtrw9cSnxJP7fK1aVq1qdHhiIhIMaDEmxQIzfP2J4vFYi0z1Wg3ERERKW4yEm8bTm8wOBLjZZSZ9m/QX2WmIiJyX5R4kwKhed7+tP3cdk5dO4Wrgyt9A/oaHY6IiIhIjjxU6yHsbew5ff00p66dMjocw9xOus26k+sAlZmKiMj9U+JNCkQ7n3YAHLh8oNRPxLv4QPpot380+AeuDq4GRyMiIiKSM64OrrTxbgOU7nLT1WGrSUxNpF6FejSu0tjocEREpJhQ4k0KRA23GtQuXxuzxcyvUb8aHY5hbiXdYvnx5YDKTEVEpOQxm83Mnz+fdu3aERgYyJgxY4iKirpj+5SUFN59911r+8GDB3PixAnr/o4dO+Ln55fta8+ePQBcuXIl2/0rV64s8PstzazzvEWU3sSbykxFRCQ37IwOQEquYJ9gTl8/zbbIbXSr083ocAzx9dGviU+Jx7+iPy1rtDQ6HBERkXy1cOFCvvzyS95++208PT2ZPXs2o0ePZs2aNTg4OGRpP336dH755RfefvttqlWrxrx58xgzZgw//vgjZcuW5dtvvyUtLc3aPjk5mZEjR+Lp6UlQUBAAoaGhODo6smnTpkzJj7Jlyxb8DZdiIb4hvLT5JX4++zPJack42Gb99y3JbiTesK5QrzJTERHJCY14kwKjBRawLqowKmiUPhkVEZESJTk5mcWLFzNhwgQ6dOiAv78/c+fO5fLly2zYkHUS/qioKFasWMHMmTNp164dvr6+zJgxAwcHB44ePQqAh4cHlSpVsr6++OILbt26xdy5c7GzS/+8ODw8nJo1a1K5cuVMbZ2cnAr1/kubJp5NqFymMrHJsaWymmF12GqS05IJqBRAg8oNjA5HRESKESXepMBkLLDw+4XfSUhJMDiawnc85ji/nf8NW5MtQxoPMTocERGRfBUaGkpcXBytWrWybnNzcyMgIMBaFvpXO3fupGzZsgQHB2dqv2XLlkznyHDq1CmWLl3K1KlT8fDwsG4PCwvD19c3n+9G7sXGZENX365A6Zzn7etjXwMa7SYiIjmnUlMpMLXL16Za2WpcvH2R3Rd206FmB6NDKlQZiyr0qNeDKq5VDI5GREQkf12+fBmAqlWrZtpeuXJl676/OnPmDF5eXmzYsIGPP/6YK1euEBAQwNSpU7NNpM2fP5969erRq1evTNvDw8MpX748gwYN4syZM/j4+PD0009nSujllMViIT4+PtfH301CQkKmP4uzDjU68MXhL/jx5I+80vqVQrlmUei/awnX2BCRPoqzZ+2eBfZeKQhFof+KM/Vf3qj/8kb9lzcF3X8Wi+W+q9qUeJMCYzKZCPYJZtnRZWyL3FaqEm8paSksPbQU0KIKIiJSMmU8yP59LjdHR0du3ryZpX1sbCyRkZEsXLiQyZMn4+bmxocffsjAgQNZt24dFSpUsLaNiopi48aNzJs3L9M5UlNTOX36NHXq1GHq1Km4urryww8/8OSTT7JkyZJsR87dj5SUlEyLPBSEs2fPFuj5C4N3ijcAh6IPsePgDio4VrjHEfnHyP77/tz3pJpTqVu2LuZoMyeiC/a9UhBKwvvPSOq/vFH/5Y36L28Ksv+ym882O0q8SYEK9v4z8Vaa/HDyB2LiY/B09aR73e5GhyMiIpLvMuZUS05OzjS/WlJSEs7Ozlna29nZERsby9y5c60j3ObOnUv79u357rvvGD16tLXt6tWrqVChAp07d85yjt27d2Nra2u9ZsOGDTl58iSLFi3KdeLN3t6eOnXq5OrYe0lISODs2bPUrFkz234pbpocasKh6ENE2UfRtn7bAr9eUei/KUenADAocBD169c3JIbcKgr9V5yp//JG/Zc36r+8Kej+O3Xq1H23VeJNClTGPG+/Rv1aqlbAyigzHdp4KHY2+m8mIiIlT0aJaXR0NN7e3tbt0dHR+Pn5ZWnv6emJnZ1dprJSJycnvLy8OH/+fKa2mzZt4pFHHsHGJut0xGXKlMmyrW7duuzYsSPX92IymXBxccn18ffD2dm5wK9RGB6u+zCHog/xy/lfGNV8VKFd16j+i4mL4ZfIX4D0xFtx/TcsKe8/o6j/8kb9lzfqv7wpqP7LyeKJWlxBClT9SvWp4FyBhNQE9l/ab3Q4heLS7UusO7kOgBFBIwyORkREpGD4+/vj6urK7t27rdtu3brF8ePHad68eZb2zZs3JzU1lSNHjli3JSYmEhUVhY+Pj3VbbGwsJ06coHXr1lnOcfLkSZo2bZrpmgBHjx4tsBFrkllInRAANkRswGwxGxxNwfsu9DvSLGk0rdqUOh56j4mISM4p8SYFysZkQzufdgClptx06aGlpFnSaO3VGv+K/kaHIyIiUiAcHBwYPHgwc+bMYfPmzYSGhjJx4kQ8PT3p2rUraWlpxMTEkJiYCECzZs1o3bo1U6ZMYe/evZw6dYrJkydja2ubaQGF0NBQLBYL/v5Zf4b6+vpSu3ZtXn/9dfbu3UtERARvvfUWBw8e5Omnny60ey/NWnu1xtXBlei4aA5dPmR0OAVOq5mKiEheKfEmBS7YO73ctDQk3iwWC4sPppeZjgoqvPILERERI0yYMIG+ffsybdo0BgwYgK2tLYsWLcLe3p5Lly7Rtm1b1q1bZ22/YMECWrRowbPPPkvfvn2JjY1l6dKleHh4WNtER0cD4O7unuV6NjY2fPTRRzRu3Jh//vOf9O7dm0OHDrFkyRLq1atX4Pcr4GDrwEM1HwJgfcR6g6MpWFdir/DL2V8AeCLgCWODERGRYkuTT0mBy5jnbce5HaSZ07C1sTU4ooLza9SvhF8Np4x9GT2giYhIiWdra8ukSZOYNGlSln01atQgLCws0zZXV1emT5/O9OnT73jO7t270737nRcmqlixIm+99VauY5a8C/ENYU34GtZHrGdq26lGh1NgVpxYgdlipkX1FtQqX8vocEREpJjSiDcpcE08m1DWoSw3k25yJPrIvQ8oxhYdWARAvwb9KOtY1uBoRERERPJfxjxvO8/tJDY51uBoCk5GmWm/gH4GRyIiIsWZEm9S4Oxs7Gjj3QYo2eWmt5Nu882xbwAYGTTS4GhERERECkYdjzrULl+bFHMKP5/52ehwCsTF2xfZHrkdSP9AVUREJLeUeJNCURrmeVt+fDlxKXHUq1CPNl5tjA5HREREpMCE+KaPeiup87x9e/xbLFho7dUar3JeRocjIiLFmBJvUigy5nnbFrkNi8VicDQFI6PMdGTgSEwmk8HRiIiIiBSckp54U5mpiIjkFyXepFA0q9YMJzsnYuJjCLsadu8DipnQP0L5NepXbE22DG0y1OhwRERERArUQ7Uews7GjlPXTnH6+mmjw8lXUTej+DXqV0yYeKKBFssSEZG8UeJNCoWjnSMta7QESma56ZIDSwDoXrc7VctWNTgaERERkYLl5uhGa6/WAKw/VbJGvS0/vhyAdj7tqFa2msHRiIhIcafEmxSakjrPW0paCp8d+gzQogoiIiJSepTUctOMxbJUZioiIvlBiTcpNBnzvG2N3Fqi5nn78dSPXIm7QuUylXmk7iNGhyMiIiJSKDISb1vObCElLcXgaPLH2Rtn2X1hNzYmG/oG9DU6HBERKQGUeJNC07JGS+xs7Dh/6zyRNyONDiffLD6wGIAhjYdgb2tvcDQiIiIihSOoahCVXCpxO/k2u87vMjqcfJEx2q1DzQ5Uca1icDQiIlISKPEmhaaMQxmaVWsGlJxy08uxl1kbvhZQmamIiIiULjYmG7r4dgFKzjxvKjMVEZH8psSbFKqSNs/bF4e/IM2SRssaLQmoFGB0OCIiIiKFqiTN83bq2in2XdqHrcmWPvX7GB2OiIiUEHZGByClS7BPMLN+nVUiEm8Wi4VFBxYBMDJQo91ERESk9Onq2xWA/Zf2ExMXQ6UylQyOKPcyRrt1rNWxWN+HSGFIS0sjJeXuczsmJSVZ/7Sx0ZifnFL/5U1e+s/e3h5bW9t8i0WJNylUbbzbYMLEyWsnuXT7ElXLVjU6pFz77fxvhP4Riou9C/0b9jc6HBEREZFC5+nqSZMqTTh05RAbT29kYKOBRoeUaxmJt/4N9FwncicWi4XLly9z48aNe7Y1m83Y2dlx8eJFJY5yQf2XN3ntP3d3dzw9PTGZTHmORYk3KVTuTu408WzCwcsH2X5uO/0aFN/5MzIWVXgi4AncHN0MjkZERETEGF19u3LoyiHWR6wvtom3sD/COHTlEHY2dvSu39vocESKrIykW+XKlXFxcblrUiItLY2kpCQcHR3zdfRQaaH+y5vc9p/FYiE+Pp7o6GgAqlbN+2AhJd6k0AV7B3Pw8kG2RW4rtom32ORYlh1bBmhRBRERESndQnxDmP3rbDZEbMBiseTL6IDC9vWxrwHoUrsLHs4eBkcjUjSlpaVZk24VKlS4r/YATk5OShzlgvovb/LSf87OzgBER0dTuXLlPPe/xitKoQv2Kf4LLHx7/Ftik2Op41GHdt7tjA5HRERExDBtvdviYu/C5djLHL5y2OhwckVlpiL3ljGnm4uLi8GRiBS8jPf5veYyvB9KvEmha+eTnqg6En2EawnXDI4mdzLKTEcEjiiWn+qKiIiI5BdHO0c61OwAFM/VTY9FH+NYzDEcbB3o5d/L6HBEijz9/iOlQX6+z5V4k0JXuUxl/Cv6A7Dj3A6Do8m58KvhbD+3HRuTDcOaDDM6HBERERHDhfiGAMUz8ZYx2i3ENwR3J3djgxERkRJHiTcxRLB38S03XXJgCQDd6nSjult1g6MRERERMV5G4m3HuR3EJccZHM39s1gs1vndVGYqIiIFQYk3MURxnect1ZzKZ4c+A2BkoBZVEBEREQGoV6EePuV8SE5L5pezvxgdzn07fOUwYVfDcLR1pKdfT6PDEZFCMnXqVIYMGZKjY37++WdOnTpVQBH9yc/Pj5UrV95x/9SpU/Hz82Ps2LHZ7v/hhx/w8/PLdH+XLl3i+eefp02bNjRv3pxRo0Zx8uTJu8Yxbdo0/Pz8Mr06dux43/cxZMiQLMdnvFatWgVAx44dWbBgwX2f836u4+/vT1BQEP3792fLli3ZHrNv3z7q16+fp+vmhFY1FUNkJN72X9rP7aTblHUsa3BE92f9qfVcir1ERZeKejgTERER+f9MJhMhviF8vP9j1kes55F6jxgd0n3JKDPtXrc7bo5uBkcjIkXVhQsXGDt2LEuXLqVOnTpGh4O9vT07d+4kNjYWV1fXTPvWrVuXaX6y5ORknnzySdzd3fnoo49wcnJiwYIFDBs2jLVr1+Lhkf1KzmFhYYwdO5bBgwdbt+Vkdc8FCxZkWpjAYrEwceJEbt68SZcuXe77PPfj4Ycf5uWXX7ZeJy4ujk8++YTJkydTo0YNgoKCrG337dvHuHHjMJvN+RrD3WjEmxjCq5wXNd1rkmZJY9f5XUaHc98WHVgEwJDGQ3CwdTA4GhEREZGiI6RO8ZrnTWWmInK/LBaL0SFk0rBhQ5ycnLKM6IqNjWX79u088MAD1m179+4lPDycOXPm0KhRI+rWrcvs2bOJj4+/44gwi8XCqVOnaNiwIZUqVbK+7pSky467u3umYzds2MDhw4eZP38+ZcqUyd2N34GTk5P1OpUrV6ZWrVq88sorODk5sW7dOgBSU1N56623GDZsGNWrF+6UUUq8iWGKW7lpdFw0a8LXADAySGWmIiIiIn/VqVYnbE22hF8N5+yNs0aHc0/7L+0n4noEznbOxWaEnkiRZLFAXJwxr3xKiHXs2JFFixYxfvx4goKCePDBB5kxYwapqamcP3+eTp06ATB06FBraWRERARjxowhKCiItm3b8sILLxATE2M955AhQ3jllVd44oknaNasGatXr2bq1KlMnTqVd955h1atWtGkSROeeuoprly5kqN47e3t6dSpEz/99FOm7Zs2bcLPzw8vLy/rtrp16/Lxxx9TpUoV6zYbm/RU0K1bt7I9/7lz54iPj6d27do5iutOrl27xvvvv8/TTz+d5ZwxMTGMHj2aRo0a0bFjR/73v/9l2u/n55erclQ7u/QCTweH9AEz8fHx7Nmzh08//TTTKL7CoMSbGKa4LbDwxeEvSDWn0rxacxpWbmh0OCIiIiJFSjmncrSs0RJIn56jqMsoM+1RrweuDq73aC0i2bJYoG1bcHXN8rItV44yVapgW65ctvvz5dWuXb4l3+bNm0fz5s1ZvXo1kydP5osvvmDt2rVUrVqV5cuXA+nlkyNHjuTKlSsMHDgQHx8fvv32Wz766CNiY2Pp378/8fHx1nMuX76coUOH8uWXX9KuXTsA1q5dy40bN/jiiy/45JNPOHbsGO+//36O43344YfZsWMHsbGx1m3r1q3jkUcyf5BQqVIl2rdvn2nb559/TmJiIm3atMn23OHh4dZ2HTt2pHPnzrz++uvcvn07x3ECfPLJJzg5OTFq1Kgs+7755htrYnLEiBHMnDmTjRs3Wvfv2LGDkSNzNvDl+vXrvPXWWyQmJlrLWt3c3Fi5ciUtW7bM1T3khRJvYpiMEW+7L+wmMTXR4GjuzmKxWMtMRwVl/WYhIiIiIn+ubrrh9AaDI7k7i8XCN8fTE28qMxXJo7/MJ1actW3blqFDh+Ll5cXjjz+Ov78/+/fvx9bW1lpiWa5cOcqUKcNXX32Fp6cn06ZNw9fXl4YNG/L+++9z9erVTKPQ6tevT8+ePalXrx7ly5cHoGzZsrz++uv4+vrSokULunfvzv79+3Mcb+vWrXF2dubnn38G4ObNm+zatYuHH374rsdt3LiRd999l+HDh+Pn55dtm/DwcGxsbKhcuTIfffQRU6dOZceOHbmaGy02NpZvvvmGUaNG4ejomGV/586dGTt2LLVq1WLIkCE8/PDDLF682Lq/UqVK9yxNXbNmDUFBQQQFBdG4cWNatmzJzz//zKuvvkqjRo1yFG9B0OIKYpg6HnXwdPXkcuxlfr/wuzURVxT9fuF3jsccx8nOiX80/IfR4YiIiIgUSSF1Qvj3L/9m8+nNpJpTsbMpmr9u7Lm4h7M3zlLGvgwP1737L6kichcmE2zfDn8Z5ZUhLS2NxMREnJyccjQpf464uORb4s/X1zfT12XLls20OMBfHT9+nJMnT2aatB8gKSmJiIgI69c+Pj5ZjvX29sbe3v6+rnM3GeWm69evp2fPnmzYsIHAwMBMJaV/99VXX/HGG2/w6KOPMnny5Du2e/rppxk4cKA1WVivXj0qVapEv379OHLkCE2aNLnvODdt2kRycjKPP/54tvv/Oh8dQJMmTdi6det9nx/SS4VffPFFIL2M1tXVFTc3NxITi8YAn6L5k1BKBZPJRLBPMN8c+4ZtkduKdOJt8YH0jHvfgL6UcypncDQiIiIiRdMDVR/Aw9mDawnX2H1+N228sy9jMtrXR9MXVXjU71Fc7F0MjkakmDOZILsRSWlpYGsLTk7pfxZxGXOB/dWdFlUwm820bNmSV199Ncu+smXLWv/u5OR0X9fJre7duzNu3Dji4uL48ccf6d69+x3bzp49m08//ZQRI0YwZcqUTCuf/p2NjY016Zahbt26AFy+fDnHibf27dvj5pb9ytEZ881lMJvNOe6jMmXKZElypqWl5egcBUmlpmKo4jDPW3xKPF8d/QqAkYFaVEFERETkTmxtbOlSO30+naK6uqnZYraWmfZr0M/gaESkOPh7kqpu3bpERERQtWpVfHx88PHxoVy5crz55pvW+dEKQ8uWLXFxcWHVqlXs3buXkJCQbNtlJN2mTJnC1KlT75p0A5g8eTLDhw/PtO3IkSMA1KlTJ0cx7t27l1atWt1x/7FjxzJ9vW/fPmuSr6RQ4k0MlTHK7deoX0lJy/nw2sKw4vgKbiffpnb52rSv2f7eB4iIiIiUYhnzvBXVxNtv53/j/K3zuDm60a1ON6PDEZFiwMUlfWRseHg4t2/fZuDAgdy+fZsXX3yR0NBQQkNDmThxIkeOHKFevXp5vl54eDjbt29n586dbN++nW3btnH48OEs7ezs7OjSpQvvvfcezZs3t85F91e7d+/m008/ZciQIfTs2ZOYmBjrKy4uDoDExERiYmKso8RCQkLYtWsXH3zwAefOnWPr1q3861//okePHllKcu/m0qVLXL9+HX9//zu2+eGHH1i8eDGnT5/m448/ZuPGjYwbN866/69xFldKvImhGlRuQHmn8sSlxHHg8gGjw8lWxqIKIwJHYGPSfxkRERGRu+nq2xWAPRf2cDX+qsHRZJVRZtrLrxdOdlnLwERE/q58+fI8/vjjzJo1i3nz5uHl5cUXX3xBXFwcAwYMYPDgwdjb27N06dJsk185tWTJEp566inGjx/PU089xZgxY5g9e3a2bbt3705sbGyW1UwzrF27FkhfobRt27aZXhmLGKxbt462bdty6dIlADp16sT777/P5s2b6dmzJy+//DJdu3blzTffzNF9xMTEAODu7n7HNqNGjeLnn3/m0UcfZcWKFbz77rs8+OCD1v1/jbO4MlnuVLQsVhlDKgtqNYz4+HhOnDhB/fr1rZn00qTXsl6sDlvN7C6zebH1izk+viD779S1U9RdUBcTJiL/GYlXOa98PX9RUNrff3ml/ssb9V/eqP/ypqD7r6CfHyR/6DmvYDT6sBFHo4+y7PFl9G+Y+1VD87v/0sxpeM314lLsJdYMWEOPej3yfM6irLS+//KL+i+zxMREzpw5Q61atbKdu+zvCmVxhRJM/Zc3ee2/e73fc/L8oOE7YriiPM/bfw/+F0j/5LYkJt1ERERECkJRLTfdGbWTS7GXcHdyt47MExERKUha1VQMlzHP2/Zz2zFbzEWmnDPNnGZNvI0KGmVsMCIiIiLFSIhvCO/uepf1EeuxWCz3nMi7sGSUmfb2742Dbf6tLCgiUlocOHCAkSPvvuhgSEgIb7/9diFFVPQp8SaGC6oaRBn7MtxIvMHR6KM0rtLY6JAA2BCxgQu3L+Dh7MGjfo8aHY6IiIhIsdHOpx3Ods5cvH2RYzHHaFi5odEhkWZO49sT3wJazVREJLcCAgJYtWrVXduUKVOmcIIpJpR4E8PZ2djRxrsNGyI2sC1yW5FJvC0+mD6B4+BGg3G0czQ4GhEREZHiw8nOifY12/PTqZ9Yf2p9kUi8bY3cSnRcNB7OHnSq1cnocEREiiVHR0d8fHyMDqNYKRo1fVLqFbV53v6I/4PvQ78HYGTQ3YfRioiIiEhWRW2et4wy0z7+fbC3tTc4GhERKS2UeJMiIWOet22R2ygKC+1+cfgLUswpPFD1AZp4NjE6HBEREZFiJyPxti1yG/Ep8YbGkmpOZcWJFQB5WmVVREQkp5R4kyKhefXmONo6ciXuCievnTQ0FovFwuID6WWmGu0mIiIikjv+Ff3xcvMiKS3J8KqGLWe2cDXhKpVcKtGhZgdDYxERkdJFiTcpEpzsnHiwxoOA8eWm+y7t40j0ERxtHRnQcIChsYiIiBRlZrOZ+fPn065dOwIDAxkzZgxRUVF3bJ+SksK7775rbT948GBOnDiRqc2IESPw8/PL9BoyZIh1f1JSEq+99hqtWrUiKCiIF154gWvXrhXYPUrumUymP8tNTxlbbppRZvp4/cexs9E01yIiUniUeJMio6jM85Yx2u3xgMcp71ze0FhERESKsoULF/Lll1/yxhtvsGzZMsxmM6NHjyY5OTnb9tOnT2flypW8+eabrFixAg8PD8aMGcPt27etbcLCwpg+fTo7duywvhYsWJDpHBnbPvvsM06fPs2ECRMK/F4ld0LqGD/PW3JaMt+FfgeozFRERAqf4Ym3gvikdNeuXfTp04cmTZrQrVs3fvjhh4K+DckHf53nzSgJKQl8eeRLAEYGqsxURETkTpKTk1m8eDETJkygQ4cO+Pv7M3fuXC5fvsyGDRuytI+KimLFihXMnDmTdu3a4evry4wZM3BwcODo0aMAXL16latXr9KkSRMqVapkfbm7uwNw5coVVq1axbRp02jWrBmNGzfmvffeY8+ePRw4cKAwb1/uU6danbAx2XDijxNE3bzzM35B2nR6E9cTr+Pp6kk773aGxCAiRcvUqVMzjaa+Hz///DOnTp0qoIj+5Ofnx8qVK++4f+rUqfj5+TF27Nhs9//www9ZRotfunSJ559/njZt2tC8eXNGjRrFyZN3n+Jp2rRpWUagd+zY8b7vY8iQIVmOz3itWrUKgI4dO2b6cC03/n4df39/goKC6N+/P1u2bLG2M5vNfPrpp4SEhBAYGMgjjzzC8uXL83Tt+2V44i2/PymNiIjgqaeeol27dqxcuZInnniCyZMns2vXrsK8LcmFVl6tsDXZEnkzksgbkYbEsPLESm4m3cSnnA8P1XrIkBhERESKg9DQUOLi4mjVqpV1m5ubGwEBAezZsydL+507d1K2bFmCg4Mztd+yZYv1HGFhYZhMJmrVqpXtNfft2wdAy5Ytrdtq1apFlSpVsr2mGK+8c3kerJ4+nYhRo96+OfYNAH3r98XWxtaQGESkeLtw4QJjx47l6tWrRocCgL29PTt37iQ2NjbLvnXr1mEymaxfJycn8+STTxITE8NHH33El19+SZkyZRg2bNhdp2oICwtj7NixmUagf/vtt/cd44IFCzIdu337dpo1a0bdunXp0qVLzm74Hh5++GHrdbZt28bKlSupV68ekydP5vDhwwD85z//4T//+Q/PPfccq1evZujQoUyfPt2aBCxIhibeCuKT0s8++ww/Pz8mTpyIr68vo0aNolu3bnz66aeFfXuSQ64OrjxQ7QEAtp/bbkgMiw+ml5mOCByBjcnwvLSIiEiRdfnyZQCqVq2aaXvlypWt+/7qzJkzeHl5sWHDBvr06UObNm0YM2YMERER1jbh4eGULVuW119/neDgYLp168b7779v/UD2ypUrlC9fHkdHx/u6phQN1nneDEi8JaUmsSp0FaAyUxHJPYvFYnQImTRs2BAnJ6dMI7oAYmNj2b59Ow888IB12969ewkPD2fOnDk0atSIunXrMnv2bOLj47Mcn8FisXDq1CkaNmyYaQS6h4fHfcfo7u6e6dgNGzZw+PBh5s+fT5kyZXJ343fg5ORkvU7lypWpVasWr7zyCk5OTqxbtw6Ar776ipEjR9K9e3e8vb3p378/vXr1KpRRb4bOLHqvT0p79OiRqf3dPinNsHfvXjp37pzpuJYtWzJz5kwsFkumzK8UPcHewfx+4Xe2RW5jcOPBhXrt09dPs+XMFkyYGB44vFCvLSIiUtwkJCQA4ODgkGm7o6MjN2/ezNI+NjaWyMhIFi5cyOTJk3Fzc+PDDz9k4MCBrFu3jgoVKhAeHk5SUhKNGzdmxIgRnDhxglmzZnHx4kVmzZpFQkJClutlXDMpKSnX92KxWIiPj8/18XeT0U8Zf5ZG7Wu0B2BTxCZuxd7K0eIGee2/H079wM2km1RzrUZghcAC+3cuqvT+yxv1X2ZJSUmYzWbS0tJIS0u7Z/uMZJXFYrmv9oXJYrFkiqtz584MHDiQQ4cOsWPHDhwcHOjRowdTpkzhypUr1hFaQ4cOZdy4cTz77LNEREQwa9Ys9u7dS5kyZXjwwQeZPHkylSpVAmDYsGHUrFmT0NBQzp49y7Rp06yVeO7u7qxevZr4+HgefPBBXnvtNSpXrmyNz2w237H/LBYLdnZ2dOzYkR9//JFHHnnEum/jxo3Uq1ePGjVqcOHCBdLS0qhduzYfffQRFStWtJ4n49w3btzI9t8mMjKS+Ph4atasmS//dteuXeP9999n7Nix+Pj4ZIrjypUrjBo1it9//51KlSoxYsQIBg4caD02ICDA2ufZ+fu/ZQZb2/QRzvb29qSkpPDmm29Sq1atTO1MJhM3b97M9h7T0tIwm80kJCRgNpuzve795pcMTbzl5ZPSjz/+mCtXrhAQEMDUqVPx9fW1ntPT0zPL+RISErh+/XqOMrRS+IJ9gpmza44h87z99+B/AehcuzM+7j6Ffn0REZHixMnJCUivYMj4O6T/Yubs7JylvZ2dHbGxscydO9f63DZ37lzat2/Pd999x+jRo3n99deZMmUK5cqVA6BevXrY29szceJEJk+ejJOTU7bTkdzpmvcrJSUly5zB+e3s2bMFev6izMXigpu9GzeSbrDitxU0Lt84x+fIbf8tObAEgPaV2hMWGparc5QEpfn9lx/Uf3+ys7PL8kGHxQJ3y2nHxeX+g5F7cXGB3IytyUiqJCYmAulJlAULFjBhwgTGjx/Pvn37eP311/Hz8+Phhx/m888/Z8iQIcyePZtWrVoRFRXFkCFDePjhh/nnP/9JQkICH330EQMGDGD58uU4OztjNpv59ttvmTFjBnXr1qVixYrs2LGDn376iYcffphPPvmEa9eu8dJLL/Hee+8xffp0a3wpKSnWfv57f2fE3qlTJ1588UWuXr1qHUG2du1aunTpwsmTJ633V7ZsWVq0aGG9V4DPP/+cxMREmjVrlml7hmPHjgHpFYU7d+7ExsaG1q1b88wzz1C2bNkc9/d//vMfHB0dGTBgQKbrWSwWvv32W8aNG8cLL7zArl27eOutt3B3d7fOJ7dhwwZcXFyyjROwJoP/uv/GjRt8/PHHJCYm0qFDB5KTkwkKCgKwtrt06RI//PADffv2zfbcSUlJpKamcvr06TveV3YfBmbH0MRbQXxSmpiYmOV8GV/fad64+6FPQgtH00pNMWEi7GoYZ2LOUKVMlXsekx/9l2ZOsz6YDQoYVKo+DdX7L2/Uf3mj/ssb9V/eFHT/lfSR9hkfnEZHR+Pt7W3dHh0djZ+fX5b2np6e2NnZWZNukJ688/Ly4vz580D6L3QZSbcMdevWBf78cPXGjRskJydnet6Ljo6mSpV7PzPcib29PXXq1Mn18XeTkJDA2bNnqVmzZp6Sg8Vd51OdWRm2klOWU/Svf/8ln3npv4SUBLavT5++5MnWT1K/Wv0cHV8S6P2XN+q/zJKSkrh48SKOjo7WD1wsFggOtmHXLmN+3rVubWHrVnOOk2+2trbY2NhY78NkMtGmTRtGjBgBQJ06dfj66685cuQIffv2tQ7uySi3/OKLL6hSpQqvvPKK9Zzz5s2jTZs2/PLLL/Tu3RsbGxv8/f3p3bt3puuWLVuWN954A3t7ewC6d+/Otm3bMn2IZW9vbx3N7ejomOl5IiP24OBgnJyc2LVrFz169ODmzZv8/vvvvPHGG8ybNy/T/f3Vpk2bWLBgAUOHDqVRo0bZ9k9kZCQ2NjZUrVqVDz/8kHPnzjF79mzOnDnDkiVLsLG5/2mZ4uLi+O677xg3blyWn/Emk4lOnTrxzDPPAOkLSxw7dowvv/yS7t27A1CjRo27nt/GxoYff/yRTZs2AemJuKSkJKpVq8arr75K06ZNszyP/fHHH0yYMIGKFSvyzDPPZNtPkP5c4u3tnWWKCyBHC20YmngriE9KHR0dsyTYMr7WJ6HFQ52ydTh5+yTf/PYNnat1vvcB/19e+u+3mN84f/s8bvZu1E2rW+D/1kWR3n95o/7LG/Vf3qj/8qYg++9+Pwktjvz9/XF1dWX37t3WxNutW7c4fvw4gwdnnS6iefPmpKamcuTIEeuDfmJiIlFRUdYymSFDhlCjRg3eeust63FHjhzB3t6emjVrUqlSJcxmM/v27bNOVXLmzBmuXLlC8+bNc30vJpMJFxeXXB9/P5ydnQv8GkVZ93rdWRm2ki3ntjCzy8wcH5+b/vvpxE/EpsTiXc6b9r7tS3Qi/F5K+/svr9R/6WxsbLCxscHW1tZaxmexQA5yMPnOZDJha2ub48SbyWSyHpvxdZ06daxfQ/q0VmlpadZEF2C9/xMnTnDq1CmaNWuW6bxJSUmcOXPm/8dkombNmpnOaTKZ8Pb2zpT/cHNzIyUlJVM7Gxsb6/esv8b519idnJzo3LkzGzdupFevXmzevJnAwECqVauW5f4yfPXVV7zxxhs8+uijTJ069Y4JtHHjxjFo0CDKly8PpP/Mr1KlCv369eP48eM0adLkvvt6y5YtJCcn88QTT2SJx2Qy0axZs0zbAwMD2bZtW5a2d2IymejYsSMvvvgikN53rq6uuLm5kZiYmKUfTp8+zZNPPklaWhpLly613uPfZfy7Ozs7Z5uYy8nPFEMTbwXxSWnVqlWJjo7OdFx0dDQuLi65GhKZQZ+EFp5OFzpx8sBJIomkfv17fzKZH/331sn0B/yBDQcS2DAwV+corvT+yxv1X96o//JG/Zc3Bd1/OfkktDhycHBg8ODBzJkzBw8PD6pXr87s2bPx9PSka9eupKWlce3aNcqWLYuTkxPNmjWjdevWTJkyhddffx13d3fmz5+Pra0tvXr1AiAkJIQ333yTxo0b07ZtW44cOcKsWbMYNWoUrq6uuLq68sgjjzBt2jTefPNNnJ2defXVV2nRogWBgYHGdojcVUid9AUWfr/wO9cTrlPeOftfdPJTxmqm/QL6leqkm0hBMplg+/bsS00zyv+cnJzuO4mSU7ktNc1Odh+W3WlRBbPZTMuWLXn11Vez7Ptr3iG7hE1+fijXvXt3xo0bR1xcHD/++KN1lFh2Zs+ezaeffsqIESOYMmXKXb8v2tjYZElI/XUEek4Sb5s2baJ9+/a4ubnd8Vp/ZTabc9xHZcqUwccn83RR2c3btm/fPp5++mmqVKnCp59+mqfR8jlhaOKtID4pbdasGb///num43777TeaNm2ao+GQf6dPQgtPR9+OfHTgI3698GuO+iO3/Xc1/iprTq0B4MnmT5bafwO9//JG/Zc36r+8Uf/lTUH1X2n4RX/ChAmkpqYybdo0EhMTad68OYsWLcLe3p7z58/TqVMn3nrrLfr06QPAggULmDNnDs8++yyJiYk0bdqUpUuXWufgHTx4MCaTic8//5w333yTSpUqMXz4cJ588knrNd944w3efPNN6yTLwcHBTJs2rfBvXnKkhlsNAioFcDzmOJtOb+KJBk8U6PXikuNYE57+fKfVTEUKlskE2S1SmZYGtrbg5JT+Z3H295/pdevWZd26dVStWtWaJLpx4wZTpkxhxIgRtGzZslDiatmyJS4uLqxatYq9e/cyZ86cbNtlJN2mTJnCyJEj73neyZMnEx0dzX//+1/rtiNHjgDkeEDS3r17GT9+/B33Z8wnl2Hfvn3WJF9+Onz4MKNHjyYgIIAPP/zwjonAgmBo4q0gPikdMmQIvXv3Zs6cOfTu3ZutW7fy008/8emnnxp5q5ID7XzaAXD4yuFC+UT0yyNfkpyWTKBnIEFVgwr0WiIiIiWJra0tkyZNYtKkSVn21ahRg7CwzJPZu7q6Mn369EwTSP/doEGDGDRo0B33u7i4MGPGDGbMmJHruMUYIb4hHI85zvqI9QWeeFt3ch3xKfHULl+bB6o+UKDXEpGSL+MDuvDwcAICAhg4cCBff/01L774IuPGjQPgnXfeISwsjHr16uX5euHh4Wzfvt06p6mNjQ3u7u40bpx5cRo7Ozu6dOnCe++9R/PmzbNdTHL37t18+umnDBkyhJ49exITE5PpvsqUKUNiYiK3b9/Gw8MDW1tbQkJCGDduHB988AGPPvooZ86c4fXXX6dHjx6ZKhDv5dKlS1y/fh1/f/87tvnhhx/w9/enQ4cObNq0iY0bN/LZZ59Z98fExFjjzK3U1FRefPFFKlSowNtvv01SUpK1H2xtbQt8EU4Dq7HTTZgwgb59+zJt2jQGDBiAra2t9ZPSS5cu0bZtW9atW2dtv2DBAlq0aMGzzz5L3759iY2NzfRJad26dVm4cCFbt27lscceY/ny5daVR6R48HT1pF6FeliwsDNqZ4Ffb/HBxQCMChpV4NcSERERKa1CfNPLTddHrL9j+VZ++frY14DKTEUkf5QvX57HH3+cWbNmMW/ePLy8vPjiiy+Ii4tjwIABDB48GHt7+0y5ibxYsmQJTz31FOPHj+epp55izJgxzJ49O9u23bt3JzY21loF+Hdr164F0lcybdu2babX4sXpvwuvW7eOtm3bcunSJQA6derE+++/z+bNm+nZsycvv/wyXbt25c0338zRfWQkt9zd3e/YZtSoUfz88888+uijrFixgnfffZcHH3zQuv+vcebW4cOHiYyMJCoqis6dO2fqg759++bp3PfDZCnon3olQMaQyjut+JFX8fHxnDhxgvr166tU6P8bs3oMnx74lEmtJzGry6y7ts1L/+2/tJ8HPn4AB1sHLr1wCQ/ngs10F0V6/+WN+i9v1H95o/7Lm4Luv4J+fpD8oee8wpOQkoDHLA8SUxM5Nu4YAZUC7nlMbvrvdtJtKs+pTGJqIvuf3F+qKxr0/ssb9V9miYmJnDlzhlq1at1xFci/Kow53koy9V/e5LX/7vV+z8nzg+Ej3kSyE+wTDMC2yG0Fep3FB9Iz5739e5fKpJuIiIhIYXG2d7Y+460/tb7ArrM2fC2JqYnU9ahLoGdggV1HRETkfhg6x5vInWQ8lO27tI/Y5FhcHVzz/RqJqYn878j/ABgZdO8JJkVEREQkb0J8Q9gQsYH1EeuZ2GpigVwjo8y0f4P+KjMVEclnBw4cuOcCDSEhIbz99tuFFFHRp8SbFEk+7j54l/Pm3M1z/Hb+NzrX7pzv1/juxHfcSLyBdzlvOtXqlO/nFxEREZHMQnxDeIEX2Bq5lYSUBJztnfP1/DcTb/LjqR8B6NegX76eW0REICAggFWrVt21TV4WQiiJlHiTIivYJ5gvDn/BtshtBZJ4y1hUYXiT4djaqGZeREREpKAFVAqgetnqXLh9ge3nttPVt2u+nn912GqS05KpX7E+DSs3zNdzi4gIODo64uPjY3QYxYrmeJMiK9i74OZ5O3vjLJtPbwZgeODwfD+/iIiIiGRlMpmsybaCmOdNZaYiIlLUKPEmRVbGPG+/nf+NpNSkfD33Zwc/w4KFjrU6Uqt8rXw9t4iIiIjcWYhvCADrI/I38XY94TobIjYAKjMVEZGiQ4k3KbLqVahH5TKVSUpLYs/FPfl2XrPFzJKDSwAYFTQq384rIiIiIvfWuXZnTJg4FnOM87fO59t5V4WuIsWcQqPKjahfqX6+nVdERCQvlHiTIstkMllHveVnuemWM1uIvBlJOcdy9PbvnW/nFREREZF7q+BSgebVmwNYR6jlh4wyU412ExGRokSJNynS2vu0B/I38bb4QPqiCgMbDcz3lbRERERE5N7yu9z0avxVNp3eBKTP7yYiIlJUKPEmRVrGiLedUTtJNafm+XzXE66z8sRKAEYGjczz+UREREQk5zISbxsjNpJmTsvz+VaeWEmaJY0gzyDqVqib5/OJiIjkFyXepEhrWLkh7k7uxCbHcvDywTyf78sjX5KUlkTjKo15oOoDeQ9QRERERHLswRoPUs6xHNcTr7Pv0r48n09lpiKSEx07dmTBggV33D916lSGDBlSiBFlb/369Tz22GM0btyYzp0788knn2CxWO553NKlS+nSpQuBgYH06dOHrVu3WvetXLkSPz+/PMW1e/du/Pz8Mr0aNGhAu3btePnll7l582aWY8xmM6NHj75rv5dUSrxJkWZjsqGddzsgf8pNFx9MLzMdGThSS8yLiIiIGMTOxo5OtTsBsP5U3spNo+Oi+fnsz4ASbyJScuzYsYNp06bRt29ffvjhByZPnszChQtZunTpXY9buXIlc+fO5YUXXmDNmjW0b9+eZ555htDQ0HyPcfny5ezYsYMdO3awefNmZsyYwZYtW5g8eXKmdsnJyfzrX/9i+/bt+R5DcaDEmxR5+bXAwsHLB9l/aT/2NvYMajwoP0ITERERkVzKr3neVhxfgdlipnm15tQuXzs/QhMRMVxMTAwjRoxg8ODBeHl50bVrV1q3bs3OnTvvetymTZto27Yt3bp1w8vLi+eeew4XFxd27dqV7zF6eHhQqVIlKlWqhKenJ+3bt2fYsGFs3bqVW7duAbB//3769OnD3r17cXNzy/cYigMl3qTIy0i8bT+3HbPFnOvzZCyq8Jj/Y1R0qZgvsYmIiIhI7mQk3n47/xs3E7OWJd0vlZmKSEHbu3cvQ4cOpWnTpjRs2JCHH36Y77//3rp/6tSpTJ48mRkzZtCsWTNatGjB/PnziYiIYODAgTRu3JiePXty6NAh6zHh4eE89dRTNG/enIYNG9KpUycWL15s3d+7d2/GjRsHpJdp/vrrr+zZs4c2bdrcNdYKFSqwZ88eQkNDsVgsrFu3jtu3b9OoUaNM7b755hvatWtHkyZNGDt2LBcuXLDuW7BgQa7LUW1tbTGZTNjb2wOwdetW2rVrx6pVqyhbtmyuzlncKfEmRV6QZxBl7MtwLeEax2OO5+ociamJfHH4C0CLKoiIiIgUBT7uPvhV8CPNksbmM5tzdY5Lty9ZqyKeCHgiP8MTkftksViIS44z5HU/853l1ZUrVxg1ahSNGjXiu+++Y9WqVTRu3JiXX36ZP/74w9pu3bp12NrasnLlSoYPH87//d//MXbsWEaNGsXy5ctxdHTktddeAyAhIYGRI0fi7u7OsmXLWLt2Ld26deOdd97hxIkTma5/8eJFGjVqxIgRI2jUqBEDBgy4a7zjx4+nVq1a9OrViwYNGvD888/z6quv0qxZs0ztPv/8c+bNm8f//vc/rl+/zjPPPGPtz5EjR7Jjx44c9VNqaip79+5l6dKltG/fHmdnZwAmTpzIlClTcHV1zdH5ShI7owMQuRd7W3tae7Vm4+mNbIvcRsPKDXN8jtVhq7meeJ3qZavTpXaXAohSRERERHIqxDeEsKthrD+1nj71++T4+G+Pf4sFCy1rtMTH3acAIhSRu7FYLLRd0pZfo3415PptvNqwfcT2Ap2/OykpifHjxzNq1CjrdZ588klWrVrF2bNnqVgxvZrK3d2dKVOmYGNjw/Dhw5k3bx7du3enU6f0+Sz79OnDm2++CaQn3oYOHcqgQYMoU6YMABMmTODTTz8lLCyM+vXrW6/v5ubG8uXLiYyMZMaMGUyePJn333//jvGeO3cOs9nMrFmzqFu3Lhs2bGDmzJlUr16ddu3aWdvNnj0bf39/AN555x1CQkLYtWsXrVu3pkyZMta47qZHjx7WPklMTMTW1pb27dvz+uuv32/3lgpKvEmxEOwTbE28jWs+LsfHZ5SZDg8cjq2NbX6HJyIiIiK5EFInhPm/z2d9xHosFkuOf3n+5vg3APRv0L8gwhOR+2CiZC9a5+3tTZ8+fVi6dCnh4eGcO3fOulBBWlqatV2NGjWwsUkvKnRxcQHAy8vLut/JyYmUlBQgfW60gQMHsnbtWo4fP57pnGZz5umVXF1dCQgIICAggLS0NF544QUmTZrEmjVr+M9//mNt17NnT6ZOncozzzzDSy+9RK9evQAICAjgwoULzJkzx5p4K1OmjDXpBlCzZk3KlStHeHg4rVu3vu+++fjjj6lSpQoADg4OVKhQAQcHh/s+vrRQ4k2Khb8usJDTh7JzN8+xIWIDACMCRxRIfCIiIiKSc+192uNg60DkzUjCr4bjV/H+5xQ6f+s8O86ll0KpzFTEGCaTie0jthOfEp9lX1paGomJiTg5OWFrWzCDH1zsXQp0tBvAqVOnGDhwIA0aNKB169Z07dqV8uXL88QTmb/vZMxp9lcZibi/i4mJoX///nh4eNCxY0fatm1Lo0aNaN++vbXNvn37sFgsmUpEM+Zdi46O5h//+AcPP/ywdZ+rqysRERHcuHEjy3xugYGBbNy40fp1dv8eZrM5x0mzatWqUaNGjRwdUxop8SbFQovqLXCwdeBS7CUirkdQx6POfR/72cHPsGChQ80O+Hr4FmCUIiIiIpITZRzK0M67HZvPbGZ9xPocJd6WH1sOQFvvtlR3q15QIYrIPZhMJso4ZC1LTEtLw9Zsi5NDwSXeCsOyZcuoUKECS5YssW7bsmULQK7nmFu7di03btxg/fr11oRdWFhYpnN+/vnnXL58mWXLllmPO3ToEHZ2dtSsWRN3d3fc3d0znTdjtFxYWBi+vn/+7hsWFkbNmjWtX9+6dYtz587h7e1t3X/79m3q1auXq/uRu9PiClIsONk58WD1BwGsE+jeD7PFzJKD6d8gRwZqUQURERGRoiZjddP1EetzdJzKTEUkLyIjI9m2bVum1++//56lnaenJ5cvX2br1q1cuHCBDRs2MH36dACSk5NzdW1PT08SEhL46aefuHjxIjt27OD555/PdM5hw4Zx7Ngx5s2bR2RkJD/++COzZ89m6NChlC9fPtvzVqpUiR49evDmm2+yefNmoqKiWLp0KStWrGDs2LHWdjY2Nvzzn//k4MGDHDx4kMmTJ9OiRQvr6Lq4uDhiYmJydW+SlUa8SbER7BPM9nPb2Ra57b5XJv3l7C+cuXEGN0c3Hg94vIAjFBEREZGcCqkTwuRNk/nl7C8kpSbhaOd4z2Mib0Ty2/nfMGGib0DfQohSREqaNWvWsGbNmkzbqlevbh3NlmHo0KGcPn2ayZMnk5ycTM2aNXn++eeZP38+R44cITg4OMfX7tatG8eOHePtt98mNjaW6tWr88QTT7B582aOHDnCgAEDCAoKYt68eXz00Ud89tlneHh4MHLkSMaMGXPXc8+cOZMPP/yQt99+mz/++INatWrx3nvvERISYm3j4eFBr169GDduHAkJCTz00ENMmzbNun/x4sV88MEH1lF4kjdKvEmxEewTzMztM3M04i1jUYUBDQfgYu9SUKGJiIiISC41qtyIqq5VuRR7iR3ndtCpdqd7HvPNsfTRbu1rtsfT1bOgQxSREubvybW/e/vtt61/d3BwsK5G+lePPfZYtu0z/D1p1adPH/r0SV+92WQy8eKLL/Liiy9majNiROY5yVu3bk3Hjh1zVKrr5OTExIkTmThxYrb7/xrHsGHDsm0zfvx4xo8ff8drPPjgg7lKyt2r30sqlZpKsdGqRitsTbacuXGGqJtR92x/I/EGK06sALjvEXIiIiIiUrhMJhNdfbsC919uqjJTEREpLpR4k2KjrGNZmlZtCsD2c9vv2X7Z0WUkpibSoFIDmldrXtDhiYiIiEgu5WSet4hrEey9uBcbkw196vcp6NBERETyRIk3KVaCfdLr5++n3HTRgUVA+mi3gl5iWkRERERyr4tvF0yYOHzlMJduX7pr2+XH01cz7VirI5XLVC6M8ERERHJNiTcpVu438Xb4ymH2XtyLnY0dQxoPKYzQRERERCSXKrpU5IFqDwCwIWLDXdt+fexrQGWmIiJSPCjxJsVKW++2AJz44wTRcdF3bLfkwBIAHvV7lEplKhVKbCIiIiKSe/dTbhp+NZyDlw9iZ2NHb//ehRWaiIhIrinxJsWKh7MHjSo3AmDHuR3ZtklOS+bzw58DMDJQiyqIiIiIFAcZibeNpzditpizbZOxmmnn2p2p4FKh0GITERHJLSXexFAbIjbQ/JPmuMx0oda8Wsz5dQ4Wi+WO7U9dO8WR6CMAPP7N45heM1Fmdhn6be1nbbM6bDVXE67iYOtAv2/7UWVOFYatGpZlhNza8LW0+KQFTjOcqPFeDSb+NJHY5NiCuVERERGRUianz3kta7TE2c6ZP+L/wGWmCzXeq8GLm18kITXB2iajzDT0j1DKvlUWr7lejPh+BFdir2Q61+CVgzG9Zsry+vb4twVzsyIiIndgZ3QAUnr9dv43enzZg/4N+/PGQ2+w49wOJm+cTKo5laltp2Z7zMHLB61/r+dRj896f0ZiYiKXoy5bt8/dNRcAbzdv/u+R/+Pi7Yu8tPklTl49ya+jfgXguxPf8fg3j9OhZge+eeIbktOSeWPbG/y69Fd2jtyJnY3+a4iIiIjkVm6e83469ZM1yTaw0UCaVm3Kvzb/i9MVT7Oy0UqOxxznaPRRADr4dGBAowFcjr3MKz+/QselHdn35D6c7JyA9GfGAQ0HMOHBCZmuUa9CvQK8axERkayUXRDDvPrLqwRVDeLz3ullod3qdCMlLYU3t7/Jcw8+h7O9c5ZjDl4+SFXXqlyKvcTJayepX7E+9mZ7Ttw8AcD5W+f59Xx6cm3twLX4VfQDwM3Rjed+eo4z189Qq3wtpm+dTv1K9flp8E842DoA0M67Hb7zfVlyYAljHhhTGF0gIiIiUiLl5jlv4vqJPFD1AfZd2sepa6dY3GsxCUkJzPttHvEp8dYy08plKrPksSXW4/wq+NFyUUvWhq+lb0BfElMTCbsaxsSWE2lZo2Xh3LCIiMgdqNRUDJGUmsQvZ3/JMilu34C+3E6+fcf52w5ePsgD1R6grkddLFjYGbUz0/4P93wIpD+AZSTdAPrU70PUxChqla8FwImYE4T4hliTbgBVXKtQv1J9fjj5Q77co4iIiEhplJvnvAOXDhBxPYLJbSYDsOv8Lm4l3eKZB57h+47f42znzLKjywAY2HBgpmP9K/oDEHEtAoCj0UdJNacS6BmY37cmIiKSY0q8iSFOXz9NclpyluH+dTzqABB2NSzb4w5ePsjtpNvcTLoJQL/l/Xhl6yukmlMxW8wsOZj+6WePej0YtHIQZd8qi+ubrgz9big3Em9Yz1PRpSKRNyMznTslLYVzN89x+vrp/LpNERERkVInN895GdOJ1C5fGxd7F1LNqVR/rzqTtkwiOS2ZY38cI+xqGI62jrz20GuZjl0VugqABpUbZDrXp/s/peq7VXF4w4F2S9qx+/zufLxLESnOOnbsyIIFC+64f+rUqQwZMqQQI8re+vXreeyxx2jcuDGdO3fmk08+uetcmRmWLl1Kly5dCAwMpE+fPmzdutW6b+XKlfj5+d3l6HvbvXs3fn5+mV4NGjSgXbt2vPzyy9y8edPa9uTJkzz55JM8+OCDtGrVigkTJnDx4sU8Xb+4UeJNDJGROHNzdMu0vaxjWQBuJd3Kcswf8X9w4fYFQv8IpZdfLwDcndz5YN8HTD84nZ3nd3Ip9hIAXx75Emc7Z1b1X8WcrnNYE76GHl/2sH6TGhk0kpUnVvLOjneIiYvh3M1zjFo9ipuJN4lLiSuw+xYREREp6XLznBcTHwNA7697418hfQRbg0oNWHxoMa8deo0VoSsAeLjuw5nOG3Etghc3vkigZyDd63YH/ky8xaXE8dXjX/HV41+RmJrIQ589xOErh/PxTkVECs6OHTuYNm0affv25YcffmDy5MksXLiQpUuX3vW4lStXMnfuXF544QXWrFlD+/bteeaZZwgNDc33GJcvX86OHTvYsWMHmzdvZsaMGWzZsoXJk9NHL1+/fp0RI0bg5OTE559/zieffMK1a9cYPXo0SUlJ+R5PUaU53sQQd1oiPoONKWtOuIx9GTYM3kDdCnUB+GT/J1yJu8KklpN469e3sN1ra23brFozPn30UwA61e6Eu5M7A1YMYOPpjXT17cr0DtNJNafyys+vMHXzVOxt7BnTdAy9/HtxPOZ4Pt6piIiISOmSm+e85LRkAHr796arb1d6ftWT6LhoXmr1Eq9uf5Xjt9Ofz/oF/LmSfegfoXT9vCt2NnZ8+8S31vOObzGenvV6ElInxNq2U+1O1F1Ql5nbZ/J136/zfI8iIgUtJiaGESNGMHjwYGxtbfHy8uL7779n586dDBs27I7Hbdq0ibZt29KtWzcAnnvuOf73v/+xa9cu/P398zVGDw8PKlWqZP3a09OTYcOG8f7773Pr1i02bdpEfHw8s2bNwskpffGb2bNn06FDB/bv30+rVq3yNZ6iSok3MUQ5x3IA3E66nWl7xiegGfv/ytnemS6+XQCwWCx4uXkRdSuKqmWqArDh9AZr2x71emQ6tlud9G86By4doKtv+gPa253fZnqH6Zy+fppqZavh7uRO8JJgPJw98ukuRUREREqf3DznlXVIHw3Xo14PWnu1xt7GnjM3zljnb4u6HYWznTM9/XoC8MvZX+jzdR9cHVz5edjP+Hr4Ws/lVzHzXL+QXiXRxqsNhy4fyqe7FJEMFouFlPiULNvT0tJITkzGJs0GW1vbbI7MO3sXe0wmU4Gc+6/27t3L/PnzOXr0KMnJyXh5eTF27Fh69UqvxJo6dSpmsxk3NzdWrVqFjY0NgwcP5pFHHuGVV17h6NGj+Pj4MGPGDJo0aQJAeHg47777Lvv37ychIYEqVaowaNAgRo4cCUDv3r1JTEwEwGw289tvv7Fnzx6eeeaZu8ZaoUIFNm7cSGhoKH5+fvz444/cvn2bRo0aZWr3zTffsGDBAm7dukWrVq145ZVXqF69OgALFizggw8+ICws+ymg7sbW1haTyYS9vT2tWrVi4cKF1qQbgI1N+ockt25lHf1cUinxJobw9fDF1mTLqWunMm3P+Lp+pfpZjjl59SRbzmyhf8P+6Ukyn2D+d+R/7Lm0B4AUcwq13Wtz+sZpklIzD1tNSUv/QZCxgtYvZ38hKTWJkDohBFQKACDVnMqR6CMMbzI8X+9VREREpDTJzXNeRkVDUmoSrg6utPVuy89nf7auVg/wSL1HcHVw5asjXzFs1TD8K/rz46Afqe5WPdO5vj76NeWdy9PVt2um7QmpCVQqUwkRyT8Wi4UlbZcQ9WuUIdf3auPFiO0jCjT5duXKFUaNGsXgwYN54403SElJ4ZNPPuHll1+mTZs2VKxYEYB169YxaNAgVq5cydq1a5k3bx5r1qxh6tSp1KhRg5dffpnXXnuNlStXkpCQwMiRI2nTpg3Lli3D1taW5cuX884779CqVSvq1//z++TFixfp1q0bqamptG3blgEDBtw13vHjx3Pq1Cl69eqFra0tZrOZ6dOn06xZs0ztPv/8c+bNm4eDgwNvvPEGzzzzDN999x0mk4mRI0fyj3/8I0f9lJqaysGDB1m6dCnt27fH2dmZGjVqUKNGjUztPv74Y5ycnGjevHmOzl+caY43MYSTnRPBPsGsDF2ZaXLIFcdXUM6xHC2qt8hyzKXYS4z9YSzLjy0HINgnGIAtZ7dg8//fymObjaWme02WHVuW6byrw1YD0M67HQDfHv+WMWvGWBNyAIsPLOZG4g0e838sf29WRESkhDKbzcyfP5927doRGBjImDFjiIq68y9fKSkpvPvuu9b2gwcP5sSJE5narFixgp49exIYGEjXrl35+OOPSUtLs+5fvXp1lgmd/fz8OH/+fIHdp+RMbp7zgn2CKWNfhq+OfgVAiG96mejak2utbfoF9GPdyXUM+W4Irb1as2PkjixJN4D/7PsPY9eOtZavAly4dYGd53byUM2H8u0+ReT/K/gBZ4ZKSkpi/PjxvPjii/j4+FCnTh2efPJJUlJSOHv2rLWdu7s7U6ZMwdvbm+HDhwPQvXt3OnXqhJ+fH3369CE8PByAhIQEhg4dyr///W98fX2pWbMmEyZMAMgyyszNzY3ly5fz/vvvExoaap0/7U7OnTuH2Wxm1qxZfPvtt4wdO5aZM2eyffv2TO1mz55N06ZNadiwIe+88w4nTpxg165dAJQpUyZTCemd9OjRg6CgIIKCgmjUqBHDhw+ncePGzJw5M9v2n3/+OV988QUvvvgiHh6lp9JMI97EMNOCp9F5aWf6fduPkYEj+TXqV2b/Opu3O7+Ni70Lt5JucTzmOL7lfalUphJtvdvSqVYnXtjwAgmpCbg6uAJwIfYCALYmW4Y2GUqt8rXot7wf/1jxD8Y0HcPxmOO8vOVlHq//OEFVg4D0BN0n+z9h+PfDGRk4kkNXDjF101T6N+hP+5rtDesTERGR4mThwoV8+eWXvP3223h6ejJ79mxGjx7NmjVrcHBwyNJ++vTp/PLLL7z99ttUq1aNefPmMWbMGH788UfKli3L6tWrefXVV3nllVdo1aoVR48e5ZVXXiE5OZlnn30WSP+FpEWLFrz33nuZzl2aHuCLg5w+57k6uPL6Q6/zwoYXKO9UnsZVGgMQcSMCSJ/rt1PtTjRc2JCyjmV5ud3LWeblreFWgxpuNXgl+BU6f96ZXst68dyDz3Et4RqvbX2NCi4VeKHVC4XeFyIlmclkYsT2EXcsNU1MTMTJyalYl5p6e3vTp08fli5dSnh4OOfOnbMuVPDXD4Zq1KhhLaN0cXEBwMvLy7rfycmJlJT0fvLw8GDgwIGsXbuW48ePZzqn2Zx5nkxXV1cCAgIICAggLS2NF154gUmTJrFmzRr+85//WNv17NmTqVOn8swzz/DSSy9Zy2ADAgK4cOECc+bMoV279IEoZcqUyTTfW82aNSlXrhzh4eG0bt36vvvm448/pkqVKgA4ODhQoUKFbH/+WywW5s2bx4cffsjTTz9dJFaMLUxKvIlhOtbqyIp+K3j1l1d57OvHqF62OrO7zOaF1ukPRPsv7eehzx5iSa8lDA8cjo3JhpX9V/LaL68x97e5XLp9CVuTLWmW9G92D/s+TBXXKvQN6MvqAat5fevr9PiyBx7OHox9YCwzOs6wXrth5YasHbCWlza/RM+veuLp6snL7V7mX+3+ZUhfiIiIFDfJycksXryYF198kQ4dOgAwd+5c2rVrx4YNG+jRI/N8q1FRUaxYsYKPPvrI+uA/Y8YMHnvsMY4ePUqrVq346quveOyxx+jfvz+Q/svOmTNnWL58uTXxFh4ejp+f3319Ei/GyelzHsDzrZ6nvFN53t31Lp8e+BQbk411oYaHfR/m4OWD1hXsu37RNcs1X23/KtM7TOehWg+xYfAGpm+dTv9v+2NjsqFbnW680/kdyjllnV9ORPLGZDLhUCZrsiUtLQ2zrRkHJ4cCS7wVhlOnTjFw4EAaNGhA69at6dq1K+XLl+eJJ57I1M7e3j7LsRmJuL+LiYmhf//+eHh40LFjR9q2bUujRo1o3/7PQSD79u3DYrFkKhH180ufvzI6Opp//OMfPPzww9Z9rq6uREREcOPGjSzzuQUGBrJx40br19n9e5jN5myTZndTrVq1LKWkf5eSksJLL73E2rVreemll6yjAUsTJd7EUL3r96Z3/d7Z7utQswOWVy2Ztrk5uvFuyLu8G/IuAH2/6cuKE+nLyw9r9OfKLj3q9ciywMLfdfHtYl2sQURERHImNDSUuLi4TCuSubm5ERAQwJ49e7Ik3nbu3EnZsmUJDg7O1H7Lli3Wr7MrPbGxseHmzZvWr8PCwujYsWN+344UgJw+5wGMCBrBiKARAAz9biifH/4cgMf9HqdjrY7ZHpOdTrU70al2p1xGLiLyp2XLllGhQgWWLFli3Zbxs+uv5fQ5sXbtWm7cuMH69eutCbuMEtOMc37++edcvnyZZcuWWY87dOgQdnZ21KxZE3d3d9zd3TOdN2O0XFhYGL6+fy46ExYWRs2aNa1f37p1i3PnzuHt7W3df/v2berVq5er+7mbyZMns3HjRt59910eeeSRfD9/caA53qRYy5jnrYJjBbrWzvrJp4iIiBSMy5cvA1C1atVM2ytXrmzd91dnzpzBy8uLDRs20KdPH9q0acOYMWOIiIiwtnnggQeoVauW9evbt2/z1VdfWUfI3bx5kytXrrB371569uxJ27ZtGTduHGfOnCmIWxSDZczz5mLrouc8Ecl3kZGRbNu2LdPr999/z9LO09OTy5cvs3XrVi5cuMCGDRuYPn06kD76Ozc8PT1JSEjgp59+4uLFi+zYsYPnn38+0zmHDRvGsWPHmDdvHpGRkfz444/Mnj2boUOHUr58+WzPW6lSJXr06MGbb77J5s2biYqKYunSpaxYsYKxY8da29nY2PDPf/6TgwcPcvDgQSZPnkyLFi2so+vi4uKIiYnJ1b391cqVK1m3bh0TJ06kRYsWxMTEWF8ZK7aWBhrxJsXa0CZD2XZmGy1dW2Jno7eziIhIYUlISADIUpbi6OiYaYRahtjYWCIjI1m4cCGTJ0/Gzc2NDz/8kIEDB7Ju3ToqVKiQqX1cXBzjxo0jKSnJOpH0yZMngfTRAG+99RaJiYnWc6xZs8a6slxOWSwW4uPjc3XsvWT0U8afcv8ervkw//D/B36OflhSCu7fqCTT+y9v1H+ZJSUlYTabSUtLyzS32Z1kjNyyWCz31b4wWSwW1qxZw5o1azJtr1atGps2bcJisVjjHjRoEBEREUyaNImUlBR8fHz45z//yQcffMDhw4dp06ZNpvZ/ldFfGX+H9BLcLl26MHLkSN566y3i4uKoVq0affv2ZcuWLRw+fJh+/foRGBhonRfts88+o3z58gwfPpzRo0fftT9ff/11/vOf//DWW2/xxx9/UKtWLebMmUOXLl3Sy3/NZsqXL8+jjz7KuHHjiI+P56GHHuLll1+2nvfTTz9l4cKFHD9+PNtrZNzLX+8vOxn9O2vWLGbNmpVp38yZM+ndO/tR0fkhr++/jL5KSEjIMu9exnnvd35BkyW3YyNLkSNHjgBkqZPOL/Hx8Zw4cYL69etbJ2GU+6f+yxv1X96o//JG/Zc36r+8Kej+K+jnB6OtX7+eCRMmcOjQIZycnKzbn3vuOZKTk/nwww8ztX/11VdZtmwZ69ats5a/JCYm0r59e8aMGcPo0aOtbWNiYnjqqac4f/48ixYtytSH165do3z58taH3YSEBDp06MCoUaN48sknc3wfR44cyfWIBRGR0sbOzg4vLy8cHR2NDkWkQCUlJREVFUVqauod2zg4ONzXc56GCImIiIhIjmWUmEZHR1vniMn4OmPy57/y9PTEzs4u05wzTk5OeHl5cf78eeu2iIgIRo8ejdls5n//+x9169bNdJ6/zwHn7OxMjRo1uHLlSq7vxd7enjp16uT6+LtJSEjg7Nmz1KxZE2dn5wK5Rkmm/ssb9V/eqP8yS0pK4uLFizg6Omb6wOVOLBYLSUlJODo6FvjKoyWR+i9v8qP/7Ozs8Pb2zjbRfOrUqfs/T66uLiIiIiKlmr+/P66uruzevduaeLt16xbHjx9n8ODBWdo3b96c1NRUjhw5Yv10ODExkaioKOtky1FRUQwbNgw3NzcWLVqUZf64r7/+mvfee4+ff/7ZOkoxNjaWs2fP0rdv31zfi8lkKvBRo87OzhqZmgfqv7xR/+WN+i+djY0NNjY22Nra3tcqpRnlfSaTqVivamoU9V/e5LX/bG1tsbGxwdnZOdtEc06SeVpcQURERERyzMHBgcGDBzNnzhw2b95MaGgoEydOxNPTk65du5KWlpZp8uRmzZrRunVrpkyZwt69ezl16hSTJ0/G1taWXr16AfCvf/2L5ORk3nvvPezs7DJNwgwQHByM2Wxm8uTJnDx5kiNHjjB+/Hg8PDzo06ePYX0hIiIicica8SYiIiIiuTJhwgRSU1OZNm0aiYmJNG/enEWLFmFvb8/58+fp1KkTb731ljUptmDBAubMmcOzzz5LYmIiTZs2ZenSpXh4eHDlyhXranIZibi/CgsLo2rVqvz3v//l3XffZcCAAVgsFtq0acPSpUs135CIiIgUSUq8iYiIiEiu2NraMmnSJCZNmpRlX40aNQgLC8u0zdXVlenTpzN9+vQs7atUqZKlfXYaNGjA4sWLcx2ziIiISGFSqamIiIiIiIiI3BeLxWJ0CCIFLj/f50q8iYiIiIiIiMhd2dvbAxAfH29wJCIFL+N9nvG+zwuVmoqIiIiIiIjIXdna2uLu7k50dDQALi4ud13ZMS0tjaSkJOuxkjPqv7zJbf9ZLBbi4+OJjo7G3d09X/peiTcRERERERERuSdPT08Aa/LtbsxmM6mpqdjZ2WFjo2K7nFL/5U1e+8/d3d36fs8rJd5ERERERERE5J5MJhNVq1alcuXKpKSk3LVtQkICp0+fxtvbG2dn50KKsORQ/+VNXvrP3t4+X0cZKvEmIiIiIiIiIvfN1tb2nokJs9kMgKOjI05OToURVomi/subotR/Gq8oIiIiIiIiIiJSAJR4ExERERERERERKQBKvImIiIiIiIiIiBQAk8VisRgdRFG3f/9+LBYLDg4OBXJ+i8VCSkoK9vb2d12OWbKn/ssb9V/eqP/yRv2XN+q/vCno/ktOTsZkMtG0adN8P7fkHz3nFW3qv7xR/+WN+i9v1H95o/7Lm6L0nKfFFe5DQb/JTSZTgT3slQbqv7xR/+WN+i9v1H95o/7Lm4LuP5PJpAflYkDPeUWb+i9v1H95o/7LG/Vf3qj/8qYoPedpxJuIiIiIiIiIiEgB0BxvIiIiIiIiIiIiBUCJNxERERERERERkQKgxJuIiIiIiIiIiEgBUOJNRERERERERESkACjxJiIiIiIiIiIiUgCUeBMRERERERERESkASryJiIiIiIiIiIgUACXeRERERERERERECoASbyIiIiIiIiIiIgVAiTcREREREREREZECoMSbiIiIiIiIiIhIAVDiTUREREREREREpAAo8WYgs9nM/PnzadeuHYGBgYwZM4aoqCijwyqW/vOf/zBkyBCjwyhWbty4wb///W+Cg4Np2rQpAwYMYO/evUaHVWxcvXqVSZMm0bJlS4KCgnjyySeJiIgwOqxi6cyZMwQFBbFy5UqjQyk2rly5gp+fX5aX+vD+rVq1iu7du9OoUSMeeeQRfvzxR6NDkhJGz3n5R895OafnvLzRc17+0XNezuk5L++K2nOeEm8GWrhwIV9++SVvvPEGy5Ytw2w2M3r0aJKTk40OrVj53//+x/vvv290GMXO888/z4EDB3jvvfdYsWIF9evXZ9SoUZw+fdro0IqFZ555hsjISD7++GO+/fZbnJycGD58OAkJCUaHVqykpKTw4osvEh8fb3QoxUpoaCiOjo5s376dHTt2WF/du3c3OrRi4fvvv+fll19m0KBB/PDDD/To0cP6PVEkv+g5L3/oOS939JyXN3rOyx96zssdPeflTVF8zlPizSDJycksXryYCRMm0KFDB/z9/Zk7dy6XL19mw4YNRodXLFy5coWxY8cyZ84catasaXQ4xUpkZCQ7d+5k+vTpNGvWjFq1avHKK69QuXJl1qxZY3R4Rd7NmzepXr06M2bMoHHjxvj6+jJu3Diio6M5efKk0eEVKwsWLMDV1dXoMIqd8PBwatasSeXKlalUqZL15eTkZHRoRZ7FYmHevHkMHTqUQYMG4e3tzdNPP03r1q35/fffjQ5PSgg95+WdnvNyT895eaPnvPyj57zc0XNe7hXV5zwl3gwSGhpKXFwcrVq1sm5zc3MjICCAPXv2GBhZ8XHs2DHs7e1ZvXo1TZo0MTqcYqV8+fJ8/PHHNGrUyLrNZDJhMpm4deuWgZEVD+XKlePdd9+lXr16AFy7do3//ve/eHp6UqdOHYOjKz727NnD119/zdtvv210KMVOWFgYvr6+RodRLJ05c4YLFy7Qs2fPTNsXLVrEU089ZVBUUtLoOS/v9JyXe3rOyxs95+UPPeflnp7zcq+oPufZGXblUu7y5csAVK1aNdP2ypUrW/fJ3XXs2JGOHTsaHUax5ObmRvv27TNtW79+PZGRkfzrX/8yKKri6ZVXXuGbb77BwcGBDz/8EBcXF6NDKhZu3brF5MmTmTZtWpbvg3Jv4eHhlC9fnkGDBnHmzBl8fHx4+umnCQ4ONjq0Iu/MmTMAxMfHM2rUKI4fP06NGjV4+umn9TNF8o2e8/JOz3m5p+e8/KPnvNzRc17e6Dkv94rqc55GvBkkY34ABweHTNsdHR1JSkoyIiQpxfbv389LL71E165d6dChg9HhFCvDhg1jxYoV9OjRg2eeeYZjx44ZHVKxMH36dIKCgrJ8GiX3lpqayunTp7l58ybjx4/n448/JjAwkCeffJJdu3YZHV6RFxsbC8CUKVPo0aMHixcvpk2bNowbN079J/lGz3lSlOg5L/f0nJc7es7LPT3n5U1Rfc7TiDeDZNRnJycnZ6rVTkpKwtnZ2aiwpBTatGkTL774Ik2bNmXOnDlGh1PsZJQczJw5k0OHDvHFF1/w1ltvGRxV0bZq1Sr27t2reWZyyc7Ojt27d2Nra2v9+dGwYUNOnjzJokWLMpW2SVb29vYAjBo1it69ewNQv359jh8/zpIlS9R/ki/0nCdFhZ7z8kbPeTmn57y80XNe3hTV5zyNeDNIxpDb6OjoTNujo6OpUqWKESFJKfTFF18wfvx4HnroIT766CMcHR2NDqlYuHbtGj/88AOpqanWbTY2NtSpUyfL/2nJasWKFVy9epUOHToQFBREUFAQAK+++iqjR482OLrioUyZMlkm2K1bty5XrlwxKKLiI+NnbMbcPRnq1KnD+fPnjQhJSiA950lRoOe83NFzXt7oOS/v9JyXe0X1OU+JN4P4+/vj6urK7t27rdtu3brF8ePHad68uYGRSWnx5Zdf8sYbbzBo0CDee++9LOUwcmd//PEHzz//fKbhyikpKRw/flwTod6HOXPmsG7dOlatWmV9AUyYMIGZM2caG1wxcPLkSZo2bZrp5wfA0aNHNenzfWjQoAFlypTh0KFDmbaHh4fj7e1tUFRS0ug5T4ym57zc03Ne3ug5L2/0nJc3RfU5T6WmBnFwcGDw4MHMmTMHDw8PqlevzuzZs/H09KRr165Ghycl3JkzZ/5fe/cSEtX/h3H8GXXEIhtFS5CCULpQOSkoZjdUbGWbatGizBLRKBEnWowwEqVImBcG72gXpCTKFhEGIVQIQlhiQTcoIdHNlKDphILa+S+iAfH3j8BOM+j7BS70+/Wcz5nVw8M5Z1RZWamDBw+qsLBQY2NjvrWwsDCFh4f7cbrAt2XLFh04cEAVFRWqqKiQzWZTa2urJicnderUKX+PF/D+390eUVFR3AnyB+Lj4xUXF6fLly/r0qVLioyM1N27d/Xq1Svdv3/f3+MFvLCwMOXn56uxsVExMTGy2+3q7u5WX1+fbt686e/xsEyQ8+BP5LylIectDTlvach5SxOoOY/izY+Ki4s1Nzcnl8ulmZkZpaSk6Nq1a77nkgGzPH78WLOzs+rp6VFPT8+CtcOHD/O133+gtrZWNTU1cjgcmpqaUnJysm7fvq3Y2Fh/j4ZlLigoSC0tLaqpqVFJSYkmJye1fft23bhxY9Ft9fhvZ8+e1apVq1RXVyePx6P4+HjV19crNTXV36NhGSHnwV/IeUtHzoO/kPOWLhBznsUwDMNvZwcAAAAAAACWKd7xBgAAAAAAAJiA4g0AAAAAAAAwAcUbAAAAAAAAYAKKNwAAAAAAAMAEFG8AAAAAAACACSjeAAAAAAAAABNQvAEAAAAAAAAmoHgDAD/JzMyU0+n09xgAAAD4y8h5AH6heAMAAAAAAABMQPEGAAAAAAAAmIDiDcCKc+/ePWVnZ2vnzp1KT09XfX295ufnJUlOp1M5OTnq6upSRkaGkpKSlJubqw8fPiw4xufPn1VcXKy9e/cqMTFROTk5GhgYWLDH6/WqvLxc+/fvV2Jioo4ePapnz54t2DM7O6uqqirfcfLy8jQ8PGzq9QMAACxX5DwAgYbiDcCK0traqrKyMqWlpamlpUXHjx9XW1ubysrKfHvev3+vuro6FRUV6erVqxofH9eJEyf05csXSdKnT5905MgRjY6OyuVyqbq6WhaLRbm5uerv75ckzc/PKy8vTw8fPlRhYaGampoUFxenc+fO6eXLl75zPXr0SB8/ftSVK1d08eJFvXnzRg6H499+KAAAAMsAOQ9AIArx9wAA8K9MTU2pqalJx44dk8vlkiTt27dPERERcrlcOn36tG9fS0uLkpOTJUl2u11ZWVnq6OjQhQsX1NDQoNDQUHV0dGjNmjWSpPT0dB06dEhVVVXq6upSb2+vXr9+rcbGRmVlZUmSdu/erZGRET1//tx37JiYGDU1NclqtUqShoeH1dzcLK/X6zs2AAAAfo+cByBQUbwBWDEGBwc1MzOjzMxMzc3N+f6emZkpSerr65MkbdiwwReYJGn9+vVKSkrSixcvJEn9/f3KyMhYEJhCQkKUnZ2txsZGff/+XQMDA7Jarb5jS1JQUJDu3LmzYCa73e4LY7/OLUmTk5MEMgAAgD9EzgMQqCjeAKwYExMTkqSCgoL/XP/1iEFMTMyitaioKL19+1aS9O3bN0VHRy/aEx0dLcMw5PV6NTExoYiICAUF/f6J/tWrVy/4/df+Hz9+/P5iAAAA4EPOAxCoKN4ArBhr166VJFVXV2vTpk2L1qOjo+V2uzU+Pr5obWxsTFFRUZIkm82msbGxRXu+fv0qSYqMjFR4eLgmJiZkGIYsFotvz7t372QYhnbs2PE3LgkAAAAi5wEIXHy5AoAVY9euXbJarfJ4PEpISPD9hISEqLa2VqOjo5J+fpPV0NCQ7/88Ho8GBweVlpYmSUpJSdHTp0/l9Xp9e+bn59Xd3a2EhASFhoYqOTlZs7Oz6u3t9e0xDEOlpaVqbW39R1cMAACwMpDzAAQq7ngDsGJERkYqPz9fbrdbXq9Xqamp8ng8crvdslgs2rZtm6SfwenMmTNyOBwKDg5WQ0ODbDabcnJyJElFRUXq7e3VyZMnVVBQIKvVqlu3bmlkZETt7e2Sfr6ENykpSU6nUyUlJdq4caMePHigoaEhlZeX++0zAAAAWI7IeQACFcUbgBWlpKRE69atU2dnp9rb22Wz2ZSWlqbz588rPDxckhQbG6u8vDxVVlZqenpae/bsUXNzsyIiIiRJmzdvVmdnp2pra1VaWiqLxSK73a6Ojg7fy3qDg4PV1tam6upqud1uTU9Pa+vWrbp+/brsdru/Lh8AAGDZIucBCEQWwzAMfw8BAIHC6XSqv79fT5488fcoAAAA+IvIeQD8gXe8AQAAAAAAACageAMAAAAAAABMwKOmAAAAAAAAgAm44w0AAAAAAAAwAcUbAAAAAAAAYAKKNwAAAAAAAMAEFG8AAAAAAACACSjeAAAAAAAAABNQvAEAAAAAAAAmoHgDAAAAAAAATEDxBgAAAAAAAJiA4g0AAAAAAAAwwf8A155Yaluc+HcAAAAASUVORK5CYII=","text/plain":["<Figure size 1500x500 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# plot the results\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import matplotlib.ticker as ticker\n","\n","sns.set_theme(style=\"whitegrid\")\n","\n","# print the best results\n","best_p1 = df_p1[df_p1[\"accuracy\"] == df_p1[\"accuracy\"].max()]\n","best_p2 = df_p2[df_p2[\"accuracy\"] == df_p2[\"accuracy\"].max()]\n","best_llama3_p1 = df_p1_llama3[df_p1_llama3[\"accuracy\"] == df_p1_llama3[\"accuracy\"].max()]\n","best_llama3_p2 = df_p2_llama3[df_p2_llama3[\"accuracy\"] == df_p2_llama3[\"accuracy\"].max()]\n","\n","print(\"Best P1 accuracy:\")\n","print(best_p1[\"accuracy\"].values[0])\n","print(\"Best P2 accuracy:\")\n","print(best_p2[\"accuracy\"].values[0])\n","print(\"Best P1 accuracy llama3:\")\n","print(best_llama3_p1[\"accuracy\"].values[0])\n","print(\"Best P2 accuracy llama3:\")\n","print(best_llama3_p2[\"accuracy\"].values[0])\n","\n","# plot the best results\n","fig, ax = plt.subplots(1, 2, figsize=(15, 5))\n","\n","plot_results(df_p1, df_p2, best_p1, best_p2)\n","plot_results(df_p1_llama3, df_p2_llama3, best_llama3_p1, best_llama3_p2, color_p1=\"green\", color_p2=\"purple\", model_name=\"Llama3-8b\")\n","\n","for a in ax:\n","    for line_index, line in enumerate(a.lines):\n","        # Get the data\n","        line_color = line.get_color()\n","        xdata, ydata = line.get_data()\n","        for index in range(xdata.size):\n","            a.annotate(  # Use 'a' instead of 'ax' to refer to the current subplot\n","                f\"{ydata[index]:.3f}\",\n","                xy=(xdata[index], ydata[index]),\n","                textcoords=\"offset points\",\n","                xytext=(\n","                    0,\n","                    1,\n","                    # 10 if line_index % 2 == 0 else -10,\n","                ),  # Adjusted for better visibility\n","                ha=\"center\",\n","                color=line_color,\n","            )\n","\n","ax[0].set_title(\"Accuracy\")\n","ax[1].set_title(\"F1\")\n","\n","# After plotting your data and before plt.show(), add these lines\n","ax[0].xaxis.set_major_locator(ticker.MaxNLocator(integer=True))\n","ax[1].xaxis.set_major_locator(ticker.MaxNLocator(integer=True))\n","\n","plt.show()"]}],"metadata":{"accelerator":"GPU","application/vnd.databricks.v1+notebook":{"dashboards":[],"environmentMetadata":null,"language":"python","notebookMetadata":{"pythonIndentUnit":4},"notebookName":"07_MAC_+_Qwen2-7B-Instructi_Unsloth_train","widgets":{}},"colab":{"gpuType":"T4","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.9"}},"nbformat":4,"nbformat_minor":0}
+{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{},"inputWidgets":{},"nuid":"0ea8b46b-839b-445b-8043-ccdf4e920ace","showTitle":false,"title":""},"id":"YLH80COBzi_F"},"outputs":[],"source":["%load_ext autoreload\n","%autoreload 2"]},{"cell_type":"code","execution_count":2,"metadata":{"id":"63B5exAuzq4M"},"outputs":[],"source":["from pathlib import Path\n","\n","try:\n","    from google.colab import drive\n","    drive.mount('/content/drive')\n","    workding_dir = \"/content/drive/MyDrive/logical-reasoning/\"\n","except ModuleNotFoundError:\n","    workding_dir = str(Path.cwd().parent)"]},{"cell_type":"code","execution_count":3,"metadata":{"executionInfo":{"elapsed":368,"status":"ok","timestamp":1719461634865,"user":{"displayName":"Donghao Huang","userId":"00463591218503521679"},"user_tz":-480},"id":"zFulf0bg0H-9","outputId":"debdd535-c828-40b9-efc0-8a180e5830dd"},"outputs":[{"name":"stdout","output_type":"stream","text":["workding dir: /Users/inflaton/code/engd/projects/logical-reasoning\n"]}],"source":["import os\n","import sys\n","\n","os.chdir(workding_dir)\n","sys.path.append(workding_dir)\n","print(\"workding dir:\", workding_dir)"]},{"cell_type":"code","execution_count":4,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{},"inputWidgets":{},"nuid":"9f67ec60-2f24-411c-84eb-0dd664b44775","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":589,"status":"ok","timestamp":1719462011879,"user":{"displayName":"Donghao Huang","userId":"00463591218503521679"},"user_tz":-480},"id":"DIUiweYYzi_I","outputId":"e16e9247-9077-4b0c-f8ea-17059f05a1c4"},"outputs":[{"name":"stdout","output_type":"stream","text":["loading env vars from: /Users/inflaton/code/engd/projects/logical-reasoning/.env\n"]},{"data":{"text/plain":["True"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["from dotenv import find_dotenv, load_dotenv\n","\n","found_dotenv = find_dotenv(\".env\")\n","\n","if len(found_dotenv) == 0:\n","    found_dotenv = find_dotenv(\".env.example\")\n","print(f\"loading env vars from: {found_dotenv}\")\n","load_dotenv(found_dotenv, override=True)"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[],"source":["P1 = \"\"\"你是一个逻辑游戏的主持人。游戏规则如下：\n","\n","1. 参与者会得到一个谜题。\n","2. 参与者可以通过提问来获取线索，尝试解开谜题。\n","3. 对于每个问题，主持人将根据实际情况回答以下五个选项之一：是、不是、不重要、回答正确、问法错误。\n","4. 回答中不能添加任何其它信息，也不能省略选项中的任何一个字。例如，不可以把“不是”省略成“不”。\n","5. 参与者需要根据回答来推理，并最终找出谜题的正确答案。\n","\n","请严格按照这些规则回答参与者提出的问题。\n","\n","谜题: {}\n","\n","实际情况: {}\n","\n","参与者提出的问题: {}\n","\"\"\""]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[],"source":["P2 = \"\"\"你是一个情景猜谜游戏的主持人。游戏规则如下：\n","\n","1. 参与者会得到一个谜面，谜面会描述一个简单又难以理解的事件。\n","2. 主持人知道谜底，谜底是谜面的答案。\n","3. 参与者可以询问任何封闭式问题来找寻事件的真相。\n","4. 对于每个问题，主持人将根据实际情况回答以下五个选项之一：是、不是、不重要、回答正确、问法错误。各回答的判断标准如下：\n","   - 若谜面和谜底能找到问题的答案，回答：是或者不是\n","   - 若谜面和谜底不能直接或者间接推断出问题的答案，回答：不重要\n","   - 若参与者提问不是一个封闭式问题或者问题难以理解，回答：问法错误\n","   - 若参与者提问基本还原了谜底真相，回答：回答正确\n","5. 回答中不能添加任何其它信息，也不能省略选项中的任何一个字。例如，不可以把“不是”省略成“不”。\n","\n","请严格按照这些规则回答参与者提出的问题。\n","\n","**谜面:** {}\n","\n","**谜底:** {}\n","\n","**参与者提出的问题:** {}\n","\"\"\""]},{"cell_type":"code","execution_count":7,"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>epoch</th>\n","      <th>model</th>\n","      <th>accuracy</th>\n","      <th>precision</th>\n","      <th>recall</th>\n","      <th>f1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>internlm/internlm2_5-7b-chat-1m</td>\n","      <td>0.759667</td>\n","      <td>0.741854</td>\n","      <td>0.781014</td>\n","      <td>0.758887</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-44</td>\n","      <td>0.761667</td>\n","      <td>0.810873</td>\n","      <td>0.761667</td>\n","      <td>0.780018</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-88</td>\n","      <td>0.741333</td>\n","      <td>0.816182</td>\n","      <td>0.741333</td>\n","      <td>0.769524</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-132</td>\n","      <td>0.755000</td>\n","      <td>0.809829</td>\n","      <td>0.755000</td>\n","      <td>0.775657</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-176</td>\n","      <td>0.719000</td>\n","      <td>0.803307</td>\n","      <td>0.719000</td>\n","      <td>0.750319</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   epoch                                           model  accuracy  precision  \\\n","0      0                 internlm/internlm2_5-7b-chat-1m  0.759667   0.741854   \n","1      1   internlm/internlm2_5-7b-chat-1m_checkpoint-44  0.761667   0.810873   \n","2      2   internlm/internlm2_5-7b-chat-1m_checkpoint-88  0.741333   0.816182   \n","3      3  internlm/internlm2_5-7b-chat-1m_checkpoint-132  0.755000   0.809829   \n","4      4  internlm/internlm2_5-7b-chat-1m_checkpoint-176  0.719000   0.803307   \n","\n","     recall        f1  \n","0  0.781014  0.758887  \n","1  0.761667  0.780018  \n","2  0.741333  0.769524  \n","3  0.755000  0.775657  \n","4  0.719000  0.750319  "]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["import pandas as pd\n","\n","df_p1 = pd.read_csv(\"results/mgtv-results_p1_full_metrics.csv\")\n","df_p1"]},{"cell_type":"code","execution_count":8,"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>epoch</th>\n","      <th>model</th>\n","      <th>accuracy</th>\n","      <th>precision</th>\n","      <th>recall</th>\n","      <th>f1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>internlm/internlm2_5-7b-chat-1m</td>\n","      <td>0.766000</td>\n","      <td>0.747969</td>\n","      <td>0.787526</td>\n","      <td>0.764922</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-88</td>\n","      <td>0.796333</td>\n","      <td>0.808232</td>\n","      <td>0.796333</td>\n","      <td>0.798160</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-176</td>\n","      <td>0.781333</td>\n","      <td>0.804716</td>\n","      <td>0.781333</td>\n","      <td>0.788581</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-264</td>\n","      <td>0.759000</td>\n","      <td>0.805502</td>\n","      <td>0.759000</td>\n","      <td>0.777237</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-352</td>\n","      <td>0.730333</td>\n","      <td>0.790676</td>\n","      <td>0.730333</td>\n","      <td>0.753716</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>5</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-440</td>\n","      <td>0.730333</td>\n","      <td>0.790420</td>\n","      <td>0.730333</td>\n","      <td>0.753750</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>6</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-528</td>\n","      <td>0.716000</td>\n","      <td>0.789892</td>\n","      <td>0.716000</td>\n","      <td>0.744833</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   epoch                                           model  accuracy  precision  \\\n","0      0                 internlm/internlm2_5-7b-chat-1m  0.766000   0.747969   \n","1      1   internlm/internlm2_5-7b-chat-1m_checkpoint-88  0.796333   0.808232   \n","2      2  internlm/internlm2_5-7b-chat-1m_checkpoint-176  0.781333   0.804716   \n","3      3  internlm/internlm2_5-7b-chat-1m_checkpoint-264  0.759000   0.805502   \n","4      4  internlm/internlm2_5-7b-chat-1m_checkpoint-352  0.730333   0.790676   \n","5      5  internlm/internlm2_5-7b-chat-1m_checkpoint-440  0.730333   0.790420   \n","6      6  internlm/internlm2_5-7b-chat-1m_checkpoint-528  0.716000   0.789892   \n","\n","     recall        f1  \n","0  0.787526  0.764922  \n","1  0.796333  0.798160  \n","2  0.781333  0.788581  \n","3  0.759000  0.777237  \n","4  0.730333  0.753716  \n","5  0.730333  0.753750  \n","6  0.716000  0.744833  "]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["df_p2 = pd.read_csv(\"results/mgtv-results_p2_full_metrics.csv\")\n","df_p2"]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>epoch</th>\n","      <th>model</th>\n","      <th>accuracy</th>\n","      <th>precision</th>\n","      <th>recall</th>\n","      <th>f1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat</td>\n","      <td>0.783667</td>\n","      <td>0.766712</td>\n","      <td>0.792917</td>\n","      <td>0.767940</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>0.292000</td>\n","      <td>0.724450</td>\n","      <td>0.292000</td>\n","      <td>0.396036</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>0.314000</td>\n","      <td>0.823098</td>\n","      <td>0.314000</td>\n","      <td>0.444607</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>5</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>6</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>0.550667</td>\n","      <td>0.720997</td>\n","      <td>0.550667</td>\n","      <td>0.601762</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   epoch                                              model  accuracy  \\\n","0      0                shenzhi-wang/Llama3-8B-Chinese-Chat  0.783667   \n","1      1  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.292000   \n","2      2  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.314000   \n","3      3  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...       NaN   \n","4      4  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.000000   \n","5      5  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...       NaN   \n","6      6  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.550667   \n","\n","   precision    recall        f1  \n","0   0.766712  0.792917  0.767940  \n","1   0.724450  0.292000  0.396036  \n","2   0.823098  0.314000  0.444607  \n","3        NaN       NaN       NaN  \n","4   0.000000  0.000000  0.000000  \n","5        NaN       NaN       NaN  \n","6   0.720997  0.550667  0.601762  "]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["df_p1_llama3 = pd.read_csv(\"results/mgtv-llama3_p1_full_metrics.csv\")\n","df_p1_llama3"]},{"cell_type":"code","execution_count":10,"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>epoch</th>\n","      <th>model</th>\n","      <th>accuracy</th>\n","      <th>precision</th>\n","      <th>recall</th>\n","      <th>f1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat</td>\n","      <td>0.730000</td>\n","      <td>0.770974</td>\n","      <td>0.730000</td>\n","      <td>0.746291</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>5</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>6</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>0.665667</td>\n","      <td>0.792412</td>\n","      <td>0.665667</td>\n","      <td>0.712431</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   epoch                                              model  accuracy  \\\n","0      0                shenzhi-wang/Llama3-8B-Chinese-Chat  0.730000   \n","1      1  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...       NaN   \n","2      2  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...       NaN   \n","3      3  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...       NaN   \n","4      4  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...       NaN   \n","5      5  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...       NaN   \n","6      6  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.665667   \n","\n","   precision    recall        f1  \n","0   0.770974  0.730000  0.746291  \n","1        NaN       NaN       NaN  \n","2        NaN       NaN       NaN  \n","3        NaN       NaN       NaN  \n","4        NaN       NaN       NaN  \n","5        NaN       NaN       NaN  \n","6   0.792412  0.665667  0.712431  "]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["df_p2_llama3 = pd.read_csv(\"results/mgtv-llama3_p2_full_metrics.csv\")\n","df_p2_llama3"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[{"data":{"text/plain":["5"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["new_len = min(len(df_p1), len(df_p2), len(df_p1_llama3), len(df_p2_llama3))\n","new_len"]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[],"source":["# df_p1 = df_p1[:new_len]\n","# df_p2 = df_p2[:new_len]\n","# df_p1_llama3 = df_p1_llama3[:new_len]\n","# df_p2_llama3 = df_p2_llama3[:new_len]"]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[],"source":["def plot_results(df_p1, df_p2, best_p1, best_p2, color_p1=\"red\", color_p2=\"blue\", model_name=\"InternLM2.5_7b\"):\n","    sns.lineplot(\n","        x=\"epoch\",\n","        y=\"accuracy\",\n","        data=df_p1,\n","        ax=ax[0],\n","        color=color_p1,\n","        label=f\"{model_name}: P1\",\n","    )\n","    sns.lineplot(\n","        x=\"epoch\",\n","        y=\"accuracy\",\n","        data=df_p2,\n","        ax=ax[0],\n","        color=color_p2,\n","        label=f\"{model_name}: P2\",\n","    )\n","    sns.scatterplot(\n","        x=\"epoch\", y=\"accuracy\", data=best_p1, ax=ax[0], color=color_p1, s=50\n","    )\n","    sns.scatterplot(\n","        x=\"epoch\", y=\"accuracy\", data=best_p2, ax=ax[0], color=color_p2, s=50\n","    )\n","\n","    sns.lineplot(\n","        x=\"epoch\",\n","        y=\"f1\",\n","        data=df_p1,\n","        ax=ax[1],\n","        color=color_p1,\n","        label=f\"{model_name}: P1\",\n","    )\n","    sns.lineplot(\n","        x=\"epoch\",\n","        y=\"f1\",\n","        data=df_p2,\n","        ax=ax[1],\n","        color=color_p2,\n","        label=f\"{model_name}: P2\",\n","    )\n","    sns.scatterplot(x=\"epoch\", y=\"f1\", data=best_p1, ax=ax[1], color=color_p1, s=50)\n","    sns.scatterplot(x=\"epoch\", y=\"f1\", data=best_p2, ax=ax[1], color=color_p2, s=50)"]},{"cell_type":"code","execution_count":14,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Best P1 accuracy:\n","0.7616666666666667\n","Best P2 accuracy:\n","0.7963333333333333\n","Best P1 accuracy llama3:\n","0.7836666666666666\n","Best P2 accuracy llama3:\n","0.73\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 1500x500 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# plot the results\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import matplotlib.ticker as ticker\n","\n","sns.set_theme(style=\"whitegrid\")\n","\n","# print the best results\n","best_p1 = df_p1[df_p1[\"accuracy\"] == df_p1[\"accuracy\"].max()]\n","best_p2 = df_p2[df_p2[\"accuracy\"] == df_p2[\"accuracy\"].max()]\n","best_llama3_p1 = df_p1_llama3[df_p1_llama3[\"accuracy\"] == df_p1_llama3[\"accuracy\"].max()]\n","best_llama3_p2 = df_p2_llama3[df_p2_llama3[\"accuracy\"] == df_p2_llama3[\"accuracy\"].max()]\n","\n","print(\"Best P1 accuracy:\")\n","print(best_p1[\"accuracy\"].values[0])\n","print(\"Best P2 accuracy:\")\n","print(best_p2[\"accuracy\"].values[0])\n","print(\"Best P1 accuracy llama3:\")\n","print(best_llama3_p1[\"accuracy\"].values[0])\n","print(\"Best P2 accuracy llama3:\")\n","print(best_llama3_p2[\"accuracy\"].values[0])\n","\n","# plot the best results\n","fig, ax = plt.subplots(1, 2, figsize=(15, 5))\n","\n","plot_results(df_p1, df_p2, best_p1, best_p2)\n","plot_results(df_p1_llama3, df_p2_llama3, best_llama3_p1, best_llama3_p2, color_p1=\"green\", color_p2=\"purple\", model_name=\"Llama3-8b\")\n","\n","for a in ax:\n","    for line_index, line in enumerate(a.lines):\n","        # Get the data\n","        line_color = line.get_color()\n","        xdata, ydata = line.get_data()\n","        for index in range(xdata.size):\n","            a.annotate(  # Use 'a' instead of 'ax' to refer to the current subplot\n","                f\"{ydata[index]:.3f}\",\n","                xy=(xdata[index], ydata[index]),\n","                textcoords=\"offset points\",\n","                xytext=(\n","                    0,\n","                    1,\n","                    # 10 if line_index % 2 == 0 else -10,\n","                ),  # Adjusted for better visibility\n","                ha=\"center\",\n","                color=line_color,\n","            )\n","\n","ax[0].set_title(\"Accuracy\")\n","ax[1].set_title(\"F1\")\n","\n","# After plotting your data and before plt.show(), add these lines\n","ax[0].xaxis.set_major_locator(ticker.MaxNLocator(integer=True))\n","ax[1].xaxis.set_major_locator(ticker.MaxNLocator(integer=True))\n","\n","plt.show()"]}],"metadata":{"accelerator":"GPU","application/vnd.databricks.v1+notebook":{"dashboards":[],"environmentMetadata":null,"language":"python","notebookMetadata":{"pythonIndentUnit":4},"notebookName":"07_MAC_+_Qwen2-7B-Instructi_Unsloth_train","widgets":{}},"colab":{"gpuType":"T4","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.9"}},"nbformat":4,"nbformat_minor":0}