diff --git "a/competition/00a_InternLM_2.5_and_Llama_3_Results.ipynb" "b/competition/00a_InternLM_2.5_and_Llama_3_Results.ipynb"
--- "a/competition/00a_InternLM_2.5_and_Llama_3_Results.ipynb"
+++ "b/competition/00a_InternLM_2.5_and_Llama_3_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\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","    <tr>\n","      <th>7</th>\n","      <td>7</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>0.290667</td>\n","      <td>0.696844</td>\n","      <td>0.290667</td>\n","      <td>0.396871</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>8</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>0.574667</td>\n","      <td>0.749095</td>\n","      <td>0.574667</td>\n","      <td>0.626323</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>9</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>10</th>\n","      <td>10</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","  </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","7       7  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.290667   \n","8       8  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.574667   \n","9       9  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...       NaN   \n","10     10  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...       NaN   \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  \n","7    0.696844  0.290667  0.396871  \n","8    0.749095  0.574667  0.626323  \n","9         NaN       NaN       NaN  \n","10        NaN       NaN       NaN  "]},"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>0.730333</td>\n","      <td>0.771041</td>\n","      <td>0.730333</td>\n","      <td>0.746484</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","    <tr>\n","      <th>7</th>\n","      <td>7</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>0.642333</td>\n","      <td>0.764460</td>\n","      <td>0.642333</td>\n","      <td>0.678280</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>8</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","  </tbody>\n","</table>\n","</div>"],"text/plain":["   epoch                                              model  accuracy  \\\n","0      0                shenzhi-wang/Llama3-8B-Chinese-Chat  0.730333   \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","7      7  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.642333   \n","8      8  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...       NaN   \n","\n","   precision    recall        f1  \n","0   0.771041  0.730333  0.746484  \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  \n","7   0.764460  0.642333  0.678280  \n","8        NaN       NaN       NaN  "]},"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":24,"metadata":{},"outputs":[],"source":["# plot the results\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import matplotlib.ticker as ticker\n","\n","def plot_model_results(model_name, df_p1, df_p2, ax):\n","    print(f\"Model: {model_name}\")\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","\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","\n","    plot_results(df_p1, df_p2, best_p1, best_p2, model_name=model_name)\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()"]},{"cell_type":"code","execution_count":25,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: InternLM_2_5-7b\n","Best P1 accuracy:\n","0.7616666666666667\n","Best P2 accuracy:\n","0.7963333333333333\n"]},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABN4AAAHZCAYAAABZzPY5AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3iT1RfA8W+6aYGyWkD2LAVEkL1kyF6WLVPZe8jeU2ZREBBQpshShiwBEQUFZIPKz0Jly5AhCAW62/z+OKZt6KA0bdNxPs+Tp8mb931zc0npzXnvPcdgNBqNKKWUUkoppZRSSimlEpWNtRuglFJKKaWUUkoppVRapIE3pZRSSimllFJKKaWSgAbelFJKKaWUUkoppZRKAhp4U0oppZRSSimllFIqCWjgTSmllFJKKaWUUkqpJKCBN6WUUkoppZRSSimlkoAG3pRSSimllFJKKaWUSgIaeFNKKaWUUkoppZRSKglo4E0ppZRSSimllFJKqSSggTelVJoyfPhwPDw8WLVqlbWbopRSSimlUrgxY8bg4eER623fvn1m+z979oy6deuybds2K7VYKZXa2Fm7AUoplViePn3KgQMHKF68OF999RXdunXDYDBYu1lKKaWUUioFc3NzY/HixTE+V7BgwYj7T548oX///ty+fTuZWqaUSgs08KaUSjN2794NwPjx43nvvfc4fvw4VatWtXKrlFJKKaVUSubg4EDZsmXj3OeHH35gxowZPH/+PHkapZRKM3SpqVIqzdi6dStVq1alSpUqFChQgE2bNkXbZ/v27bRs2ZI33niD2rVr89FHHxEcHBzx/K+//kr37t158803qVKlCsOGDePevXsAbNu2DQ8PD27dumV2zrp16zJmzJiIxx4eHixevJhWrVpRpkyZiCuop06dokePHlSsWJHSpUtTt25dFi1aRHh4eMSxz549Y/r06dSsWZOyZcvSunVrDh06BMCcOXMoU6YMT58+NXv9JUuWUL58eQICAizrQKWUUkopFY2fnx8DBw6kYsWKrFixwtrNUUqlMhp4U0qlCZcuXeL8+fN4eXkB4OXlxQ8//MA///wTsc/69esZPXo0pUqVYvHixfTu3Zsvv/ySDz/8EAAfHx86d+5MUFAQc+fOZerUqfzvf/+jR48ehIaGvlJ7li1bRvPmzVm4cCENGzbk4sWLvP/++2TJkoX58+ezdOlSKlSowOLFi9m7dy8AYWFhdO/enV27dtGnTx+WLFlC4cKFGTBgAKdPn6ZNmzYEBQVFyzWyY8cOmjRpQoYMGSzoQaWUUkqp9Cs0NDTazWg0AuDk5MS3337LnDlzyJo1q5VbqpRKbXSpqVIqTdi6dStZsmShbt26ALRs2ZJFixaxZcsW+vbtS3h4OJ9++in16tWLCLQBBAQE8O233xISEsKyZcvIkiULq1atwtHREQB3d3eGDx/OpUuXXqk9FSpUoFu3bhGPt2/fTrVq1fD29sbGRq55VK9enR9//JETJ07QtGlTfv75Z3777beIdgJUqVKFmzdvcvz4cQYOHEi5cuXYsWMHbdu2BeDs2bNcv36d2bNnJ7zzlFJKKaXSsdu3b1OqVKlo24cPH07v3r1xcHCgcOHCVmiZUiot0MCbUirVCwkJYefOndSrV4/AwEACAwNxcXGhfPnyfP311/Tu3Ztr167x8OFD6tevb3Zsjx496NGjBwBnzpyhVq1aEUE3gHLlyvHjjz8CcOHChXi3ydPT0+yxl5cXXl5eBAUFce3aNW7cuMGFCxcICwsjJCQk4vXt7e0jgocANjY2ZktmW7duzcSJE7l9+zZ58uThm2++oVChQpQrVy7ebVNKKaWUUpHc3NxYunRptO25cuWyQmuUUmmNBt6UUqneoUOHePjwIVu2bGHLli3Rnj98+DAZM2YEIHv27LGe5/Hjx3E+/yqcnZ3NHgcGBjJ9+nR27NhBaGgoefPmpVy5ctjZ2UUsY3j8+DFZsmSJmBEXkyZNmjBz5kx27NhBjx492Lt3L717906UNiullFJKpUcODg68/vrr1m6GUiqN0sCbUirV27p1K/ny5WPGjBlm241GIwMHDmTTpk0MGzYMgEePHpnt8++//+Lj40O5cuXIlClTtOcBfvrpJzw9PTEYDABmxRCAeFW3mjFjBt999x0LFiygWrVqEYG5qFVXM2XKxOPHjzEajRGvBZJ7zmg0UqpUKVxcXGjUqBF79+6lePHi+Pv7884777z09ZVSSimllFJKJT8trqCUStUePHjA4cOHadq0KZUrVza7ValShUaNGvHTTz+ROXNmsmbNysGDB82O37FjB7179yYkJIQKFSpw9OhRsyqnPj4+9O7dmz/++CNi1tzdu3cjnr9y5QqPHz9+aTvPnDlD5cqVqVevXkTQ7X//+x+PHj2KCORVqFCBkJAQfv7554jjjEYjY8eO5bPPPovY1qZNG/7880+++OILqlWrRs6cOV+945RSSimllFJKJTmd8aaUStW2b99OaGgoTZs2jfF5Ly8vNm/ezNdff82gQYOYNm0a2bNnp27duly7do2FCxfSqVMnXF1d6d+/P+3bt6dPnz507dqVwMBAFixYQJkyZahevTqBgYE4OTkxe/ZshgwZwvPnz1m4cCFZsmR5aTvLlCnD3r172bhxI0WKFOHixYssXboUg8FAQEAAALVr16ZcuXKMGTOGoUOHki9fPnbs2MGVK1eYPn16xLnKly9PoUKFOHnyJPPnz0+UflRKKaWUUkoplfg08KaUStW2bdtGsWLFKF68eIzPly9fnrx587J582YOHjyIs7MzK1eu5KuvviJXrlz06tWLXr16AVCyZEm+/PJLPvroI4YOHUrGjBmpVasWI0aMwMHBAQcHBxYtWsRHH33EgAEDyJMnDwMHDmT79u0vbeeYMWMICQlhwYIFBAcHkzdvXvr168fly5f58ccfCQsLw9bWluXLlzNv3jw++eQTAgIC8PDwYNWqVZQpU8bsfLVr1+bRo0cR1U+VUkoppZRSSqU8BqMpq7dSSqlUwWg00rRpU2rUqMG4ceOs3RyllFJKKaWUUrHQGW9KKZVKPHv2jDVr1nD+/Hlu3rxJly5drN0kpZRSSimllFJx0MCbUkqlEk5OTmzatInw8HBmzpxJvnz5rN0kpZRSSimllFJx0KWmSimllFJKKaWUUkolARtrNyA8PJyFCxdSs2ZNypYtS69evbh582as+z98+JDhw4dTpUoVKleuzAcffMC9e/fM9tm7dy9NmjShTJkyeHl5cezYsaR+G0oppZRSSimllFJKmbF64G3JkiVs2LCB6dOnRyyh6tmzJ8HBwTHuP3ToUO7cucPq1atZvXo1d+7cYcCAARHPHz9+nJEjR/Luu+/yzTffULVqVXr37s2VK1eS6y0ppZRSSimllFJKKWXdwFtwcDCrVq1i8ODB1K5dmxIlSjB//nzu3r3L/v37o+3v5+fHyZMn6dWrF56enpQsWZLevXtz/vx5Hj9+DMDy5cupV68eXbt2pUiRIowePZpSpUrxxRdfJPO7U0oppZRSSimllFLpmVWLK1y8eJHnz59TtWrViG2ZM2emZMmSnDp1imbNmpnt7+TkhIuLC9u3b6dSpUoA7Nixg0KFCpE5c2bCw8M5e/YsY8aMMTuucuXKMQby4uvcuXMYjUbs7e0TfA6llFJKpS8hISEYDAbKlStn7aaoOOg4TymllFKv6lXGeVad8Xb37l0AcufObbbd3d094rmoHBwcmD17NidPnqRChQpUrFiR3377jeXLl2NjY4Ofnx/+/v7kypUrXueLL6PRSFLWoDAajQQHByfpa6Rl2n+W0f6zjPafZbT/LKP9Z5mk7r+kHj+kFik9n6+O81I27T/LaP9ZRvvPMtp/ltH+s0xKGudZdcZbQEAAIAG1qBwdHXny5Em0/Y1GIxcuXKBcuXL07NmTsLAw5s+fT//+/dm4cSOBgYGxni8oKCjB7bS3t8doNFKkSJEEnyMuAQEBXL9+nTx58pAhQ4YkeY20TPvPMtp/ltH+s4z2n2W0/yyT1P13+fJlDAZDop83tTHl8509eza5cuXC29ubnj17smvXrmhjNpB8vqGhoaxevRqj0cjUqVMZMGAAW7ZsASLz+Y4aNYrq1auzZcsWevfuzfbt2xM0VjPNdHv99dcte6Ox8Pf358KFCxQtWhRnZ+ckeY20TPvPMtp/ltH+s4z2n2W0/yyT1P13/vz5eO9r1cCbk5MTILneTPcBgoKCYhwA7927l3Xr1nHw4EEyZswIwLJly6hTpw5btmzhnXfeiThfVLGd71WEhIRw4cIFi87xMtevX0/S86d12n+W0f6zjPafZbT/LKP9Z5mk7L+YAkvpiSmf74gRI6hduzYA8+fPp2bNmuzfvz9aWhFTPt+lS5fi6ekJQO/evenfvz+PHz8mS5YsZvl8AUaPHs25c+f44osvmDZtWrK+P6WUUkqpl7Fq4M20xPT+/fvkz58/Yvv9+/fx8PCItv/p06cpVKhQRNANwNXVlUKFCnHjxg2yZMmCs7Mz9+/fNzvu/v375MyZ06K22tvbU7RoUYvOERvTFfeCBQvqjIUE0P6zjPafZbT/LKP9ZxntP8skdf9dvnw50c+Z2qSWfL5KKaWUUknFqoG3EiVKkDFjRk6cOBERePPz88PHx4fOnTtH2z9Xrlx8++23BAUF4ejoCMj0wVu3btGiRQsMBgNvvvkmJ0+epG3bthHHnThxggoVKljUVoPBkOTTOzNkyJDuppDu3w/jx8Mff0DOnDBgAAwfDjGtzFmzBrp1i+kszkB2PvssiN69HSP2nTcPLl+G116D99+HcePALsonPigIpk6FdevgwQMoXlza0q5dor/NVCE9fv4Sk/afZbT/LKP9Z5mk6j9dZprwfL6TJk2iQoUKGAwG3N3dWbduHTY2Njx+/DhJ8vmqpJF447zy/43zIvd92Tjvxg0YORIOHYLwcKhRAz76CJIoc4xSSikVK6sG3hwcHOjcuTPz5s0jW7Zs5MmTB29vb3LlykWDBg0ICwvj0aNHZMqUCScnJ7y8vFi5ciVDhw5lyJAhACxYsABHR0datWoFQLdu3ejduzclS5bkrbfeYuvWrVy4cIEZM2ZY862qGBw/Ds2aQfv2MH06HDkCo0ZBaCi8cCEbgKZNIabcyd27h/PoUSgNG4YB8MknMHQotGkD3t4SVJs0CX77DbZujTyuc2cZEM6eDcWKwZdfwrvvQubM0KhR0rxnpZRSKj1JLfl8jUYj/v7+CT4+LqY+MP1ML06etKFZM0datw5j/PhQjh2zZdQoO/z9QxgxIjTa/nXqwMGD0eu+9etnx7//hlOr1nP8/cP49FM7Ro1yoGXLUKZPD+Wffwx8+KE9Z8+Gs2GDpJsJCIB69ZwIDYV580LIkAGmT7enVi04eTKQLFmS+t2nHOn185dYtP8so/1nGe0/yyR1/xmNxnhfZLVq4A1g8ODBhIaGMmHCBAIDA6lYsSIrV67E3t6eW7du8fbbbzNr1ixatWqFu7s7GzZswNvbm/feew8bGxsqVKjAhg0byJQpEwA1atRg5syZLFmyhPnz51O0aFGWLVuWZIURVMJNngzlyknACyTYFRICM2fCkCHw4qofNze5RbVwIfj6Gli16gpubgUIC4Np06B+fdi8OXK/N9+E11+H77+X5w4fhi1bYM8eaNxY9nn7bblyunevBt6UUkqpxJBa8vlqLt/EN3ZsUYoXD2PEiIsA5MsHd+/mYe5cN95++384OUWvBPffcD7Cpk1u/PlnPlatusSzZ/78738wY8YbVK7sx/jxlwDInx/mznXi3XdLsXr1VapUecrx45m4fLk4S5b8SalSTwGYPNmRNm1Ks3z5PZo1e5S0bz4FSm+fv8Sm/WcZ7T/LaP9ZJiXk8rV64M3W1paRI0cycuTIaM/lzZsXX19fs21FihRh2bJlcZ7Ty8sLLy+vxGymSmRBQTL1f+pU8+1t2sDcuTL7rX79uM9x7x5MmAC9eoVSurR/xLZHj2QmXVSlS0OOHPDtt3LeLVtkqYEp6Aay7OHoUcvfm1JpXVhYGCEhIYlyLtMMlaCgIGxsos90UHHT/rOMJf1nZ2eHra2tLid9idSSz1dz+SauoCA4ezYD48eHRBTJAOjZ04a1a2159Kgkb78dHuc57t2Dzz7LQLduQZQu7U/BggV5/NiZJ0/saNPGyey8np6QI4cRH59CdOsWwtWrtgCULp0fT095HdPFW0fHPHh6Wpb7OTVJj5+/xJQe+89oNBIWFkZYWJjF5woMDOTOnTu89tprZhdfVPxo/1nG0v4zjfVi8yq5fK0eeFPp09WrEBwsedWiMo15fX1fHnibPBlsbGDSpBD+/lu2Zcki+T1u3DDf999/5Xb1qjz+9VcJxm3YIMtcL12S5aazZoHGbJWKmdFo5O7duzx+/DjRzhkeHo6dnR137tzRwFECaP9ZxtL+s7W1xd3dHVdXVw3AxSK15PPVXL6J68YNGeeVLu2As3PkbIDSpU3PO/GyrpgzR8Z506aF8/ff0n8ZMmTAzg7+/tv8vKZx3s2b9jg729O8uQTjJk1yYuVKcHaWdCYZM0K7dubHphfp6fOXFNJD/xmNRh4/fsyDBw8SJegGkX9nHz16pOOUBND+s0xi9F+WLFnIlStXjOO8Vxn7aeBNWYUprUvmzObbTUsM/PziPv7+ffjiC0nQmyULEYE3Z2fJGbd4MZQqBS1byr5DhkhA7vlz2e/BAwm2nTkDM2ZA7tywZAm0aiXLT3WpqVLRmYJu7u7uODs7J0qgISwsLOILdlxXlFTMtP8sk9D+MxqNhIaG4ufnx99//01AQEC04gFKaD7f9Mna4zwnJ1i5Epo3jyym4OgIu3ZB4cKJ8haVSnNM47zMmTOTOXNm7OzsLB7r6TjFMtp/lrGk/0y5X00z7C0d52ngTVlFeNyrC3hZQHrFCggLk4HWi5Ytk8FVz57Qo4fkihszBp4+JeLqanCwDOLOnJH8bwB168Ibb0iOOA28KWUuLCwsIuiWPXv2RD0vSB4oHVC8Ou0/y1jaf5kyZcLR0ZF//vkHd3d3/TeIhebzTX+sPc776Sdo2BCqV4dhw8DWFj77TAJ1e/dCzZoJe19KpVVhYWE8efIENzc3cuTIkajnBR2nJJT2n2Us7T/T8vL79+9bPM7TwJuyCldX+fn0qfl20xVQ0/Ox2bIFGjSQfB0vFiHLmFGucn7yiSx1KFBAtq1YEbmUNVMmmeVmCrqBDMrq1ZOBmVLKnCmnW1pfZqHUq3JxceHBgweEhITooDgWms83/bH2OG/GDMiTR1Yx/LdimQYNoFo1+OADOH3asvenVFoTEhKC0WjExcXF2k1RKkUxffexdJynC4WVVRQpIoGuF/MRmh5HyZcbze3bcO4ctGsX8/O7d0uRhIwZZRlCxoyyDOHWrchAW7FiMuvN+EJBrZCQ6NVUlVKRNI+VUub0d0Kp6Kw9zrtxAypUiAy6gcyyq1ED/vgj4e9LqbRO/6YpZS6xfic08KaswskJ3noLtm0zD35t3SpXQStViv3YEyfkZ/XqEBoKT5444u5emidPHAkNlSUII0aYH7NggQwATdVOmzSBhw/h++8j9wkOhn37dPmBUkoppZQlrD3OK1ECTp6U6qomRiMcO6Y53pRSSiU/Dbwpq5kwQQZX7dpJvo2JE8HbG8aNkxwdfn5w/LgUQojq/Hm5gpk5MyxcCJUr21KggCOVK9uycCF07SrHffABHDworzNrliToNaV/6dRJrop26iRLE/btg3fekaulkycnf18opZLPmDFj6NKlyysdc/DgwVcqGZ5QHh4ebNu2Ldbnx4wZg4eHB3379o3x+W+//RYPD49Xfn9bt26lefPmlC1blgYNGvD555+/ckWzBg0a4OHhYXYbM2ZMrPtv27Yt2v6mW9euXQFYtGgRdevWfaV2xOd1SpcuTd26dZk7dy5BUb+Z/ycoKIgWLVrE+W+hlIqbNcd5EydKLt/GjWHnTlly2ratBN6mT0/+vlBKJR8d50Wn4zxz1hjnaY43ZTV168qVz8mTwctLcnF4e8vACeDsWahTB1avhvffjzzu3j0ZjDVqJPuY3Lwpx5YvL1dDFy6UfG0FCsj9QYMi97W3hwMHZPA3frzkIHnzTdkWNe+bUkrdvn2bvn37snbtWoqaEghZkb29PUePHuXZs2cRSV9N9uzZ88pT4nfu3MnkyZOZOHEiVatW5X//+x8TJ04kODiYgQMHxusc/v7+3Lx5k88++4xSpUpFbHdycor1mCZNmlDzhSnG+/btY9asWbEOOC1x5MiRiPshISGcO3eOcePG4e/vz5QpUyKee/r0KUOHDo2Wd0wp9WqsOc6rUEEKLEycCB07goODFNA6eBBq1UqOd6+USi10nPdyOs6znAbelFW1bCm3mNSuHT0HG8jgqmjRyIHbi86ckZlsv/8uyw5ikzUrLF0qN6WUio0xpv+IrKh06dJcuXKFH3/8kaZNm0Zsf/bsGYcPH6Z8+fKvdL6NGzfi5eVF+/btAcifPz/Xrl1j8+bN8R6QXb58mfDwcMqVK4fry7Km/8fJyclswHb37l0++eQT+vfvT7Vq1V7pPcSHm5ub2ePXXnuNY8eOsXfv3ogB2Y8//sj06dPJmjVror++UumRNcd5lSvD/v2v3GSlVDqj47yX03Ge5XSpqUp1Hj6UXB5xWbAA/vknOVqjlErt6taty8qVKxk0aBDlypWjcuXKfPjhh4SGhnLr1i3efvttALp27cqiRYsAuHLlCr169aJcuXLUqFGD4cOH8yDKeqkuXbowceJE2rZtS4UKFdi5cydjxoxhzJgxzJkzh6pVq/LGG2/Qp08f7t2790rttbe35+2332bfvn1m2w8cOICHhwf58uV7pfONGDGCHj16mG2zsbHhyZMn8T6Hr68vOXLkiPdgLCbe3t64u7vTu3fvaM99+umnVK5cmTfffJMRI0bw+PHjiOfGjBmT4GUKtra2ODg4RDw+cOAA7777Lps2bUrQ+ZRSltNxnlIqMek4T8d5JtYc52ngTaU6BoMsN4jLzZuyn1IqGRiN8Py5dW6JdJXyk08+oWLFiuzcuZNRo0axbt06du/eTe7cudm8eTMgeSi6d+/OvXv36NixIwUKFGDLli0sW7aMZ8+e0b59e/z9/SPOuXnzZrp27cqGDRsiptrv3r2bx48fs27dOpYvX84ff/zBgpd9w4xB48aNOXLkCM+fP4/YtmfPHrMro/FVvnx5ChUqFPH46dOnbNy4MdrygLj4+vri7OzM4MGDqVGjBs2bN2fNmjWEh4fH+/jdu3czbNgwswESyBKQ48ePs3r1apYtW8b58+cZO3ZsxPPjx49ny5Yt8W4rQHBwMIcOHWLnzp3Ur18/YvvMmTPp06dPtDYopZKPjvOUSmF0nKfjPB3nWUyXmqpUx2iEfPniHpTlzZto/08rpeJiNEKNGvDLLwk63BZwseT1q1eHw4ct/gZWo0aNiESv+fLl48svv+Ts2bN4eXmRLVs2AFxdXXFxcWH58uXkypWLCRMmRBy/YMECqlSpwr59+2jVqhUAnp6eNG/e3Ox1MmXKxLRp07C3t6dIkSI0adKEn3766ZXbW61aNTJkyMDBgwepV68eT5484dixY0yfPp2LFy8mtBt4/vw5/fv3JygoiFGjRsX7uEuXLuHn50fDhg0ZMGAAZ86cwdvbmydPnjBkyJCXHr9mzRo8PDwirjpH5ejoyPz588mRIwcAkyZNonv37ty4cYMCBQqQKVOmeLWxXLlyEfcDAgJwdHSkcePGDIqaGEopZXXxGee99pqO85RKFjrOA3Scp+M8y2ngTaU62bNDt24wbVrs+9jZQTwD8EopS6WBaQdFTKXw/pMpUyZCQkJi3NfHx4dLly6Z/YEHqZB05cqViMcFChSIdmz+/Pmxt7eP1+vExbQMYf/+/dSrV48DBw5QtmxZcubM+crnMnnw4AF9+vTh1q1brFy5krx588b72OXLlxMUFBQxOPLw8ODZs2csXbqUQYMG0bt3b86cOROx/9SpU2nRogUAgYGB7Nu3j5EjR8aYMLhAgQIRgzGAN954A5BBYEx9HJvt27cDYDAYcHR0jDhnYGBgvM+hlEp62bPD0KGx53gDKYp1+TJY8F+eUiq+dJwH6DhPx3mW0cCbSnXs7OC332J/3sYGrl2TpLo7dsAL/2cqpRKTwSBXIqNMvX8VYWFhBAYG4uTkhG1cWbJj4+ycKAPCmKacx5ZsNzw8nCpVqjB58uRoz0W9KhdTpafEnNrepEkT+vfvj7+/P3v37qVJkyYJPteVK1fo2bMn4eHhrF+/nmLFir3S8Q4ODtHeW/HixfH39+fJkyfMmDHDbOCTPXv2iPtHjx4lJCSExo0bx3juFz8XYWFhAGYD2/iIafBmOpdSKuWws4MuXWDOHLh/P/rzTk7g5ycVUT/5BPr2TRNxAaVSJh3nmdFxXiQd570azfGmUp0DBySgZmMjV0Pz55f/j/Pnh48/hiNHoHhxWaJQvTp89ZW1W6xUGmcwgIuLdW7J8G3rxatzxYoV48qVK+TOnZsCBQpQoEABXF1dmTlzJn/++WeSt8ekSpUqODs7s2vXLs6cOUPDhg0TdJ6bN2/y3nvvkSFDBjZt2vTKgzGj0Ui9evVYvHix2fbz58/j5uZG1qxZyZkzZ0RfFShQgIwZM0bsd/r0aUqUKBFrhanr16/z7NmziMdnzpzBYDBQtGjRV2qnUir1yJIFTN+X3NzMx3kXLkCbNhASAv37yyqIgACrNleptE3HeTrO03GexXTGm0pVgoJgwAC5P3CgXA0dPjyM0NBQ7OzscHe3xdYWTpyADh1g3z54910pOT99ugTrlFLqVTg7OwPw559/UrJkSTp27MhXX33FiBEj6N+/PwBz5szB19eX4sWLW/x6f/75Jz///LPZtixZslCmTBmzbXZ2dtSrV49PP/2UihUrRuQoeVXjxo0jODiYjz/+GDs7O7OqXS+WZo+JwWCgfv36rFy5ksKFC1O6dGmOHTvGihUrGD9+/EuP9/HxoUSJErE+HxQUxNChQxk2bBj//vsv06dPx8vLizx58gCSJDgkJCTB718plfLs2yfVTXPmhFOnwgDzcd7XX8NHH8Ho0fDFFzLO27oVouQPV0qpeNFxXtx0nJc4NPCmUpWPPoI//4RcuSTHm60tuLoGceHCBTw9PbG1lf84s2SB3bth7Fjw9oaZM+H8eVi3DjJntu57UEqlLlmzZqV169bMnTuXGzduMGHCBNatW8dHH31Ehw4dsLW15c0332Tt2rWJMihYvXo1q1evNttWqVIlvvzyy2j7NmrUiM2bN8c6ff9l7t27x8mTJwF45513oj3v6+sbr/MMHz6cjBkz8vHHH3P37l3y5s3L+PHjadeu3UuPffDgAa+//nqsz5cuXRpPT0+6du2KwWCgSZMmjBkzJuL5GTNmcPLkSX788cd4tVUplfKZ/rvr1AmyZ48+zjMYYMQIKF8e2reHc+fk/saNkMBJIUqpdErHeS+n4zzLGYyxLW5WEc6fPw8Q5wfGEv7+/hEDClPEXUV3/TqULCnLCdatk8EYvLz/1q2Dnj1ltpynJ+zcCWls5qpF9PNnmfTSf4GBgVy7do1ChQrFmNMioSzO/ZHOaf9ZJjH6L67fjaQeP6jEoeO8lOXffyF3bhm3/forFCsWd//dvClLT0+elIDctGkwbpyucjDRz59l0kv/6TgvZdL+s0xKGufpnySVagwZIkG32rWhY8f4H9e5s+QEfe01yQtSqRJ8/32SNVMppZRSSiXQ5s0SdCtTBv4rbhenfPng55+hTx8wGmHiRPDygsePk7qlSimlVPzoUlOVKuzeLTPV7Ozg009fPc9mxYpw+jS0agXHj0OjRjBvnpSr10pYSqm0aM+ePS/NvdGtWzcGDx4c6/PLly9nyZIlcZ5j3LhxtG3bNkFtVEqpF61dKz+7do3/MY6OsGyZVLTv1w927ZKx3zffQOnSSdNOpZSyJh3npS4aeFMpXkAAmP6/+OADWW6aELlzw8GDMiBbswaGDZNkvMuWyYBNKaXSklq1arF9+/Y498n8kqSX7dq1o0GDBnHuE7VkvFJKWeLKFTh6VJaJvsrqBpNu3WSmXOvWcPmyBOJWrpRCW0oplZboOC910cCbSvFmzYJr1yBvXpg0ybJzOTnBqlWydGH4cAnAXbwI27ZJYE4ppdIKFxcXXFxcLDqHq6srrq6uidQipZSKmym3eP36CR+XlS8vqxw6dpTUIh06SLX7uXPB3j7x2qqUUtak47zURXO8qRTt0iWYM0fuz58PGTNafk6DQZaY7tsHWbPK0tMKFeDUKcvPrZRSSimlXp3RmLBlpjHJkQP27pUiCwALFkC9enD3rmXnVUoppRJCA28qxTIaYdAgCA6W0vCtWyfu+evXlwpYnp5w5w7UrCkVUJVSSimlVPI6elRWOGTMKMURLGVrCzNmSJ63TJmkAEP58vDLL5afWymllHoVGnhTKda2bfDdd+DgAIsWJU0RhKJFZcZb8+ZSQatLFxg5EsLCEv+1lFJKKaVUzEyz3dq2BWfnxDuvl5esaihZUi601q4thbqMxsR7DaWUUiouGnhTKdKzZ7IcFGD0aChWLOleK3Nm2L49cjnCvHnQrJmWoVdKKaWUSg4BAfD113Lf0mWmMfHwkDxvbdtCSAgMHAjvvQf+/on/WkoppdSLNPCmUqTp0+HWLShUCMaOTfrXs7GR5QibNkGGDJL/rXJl8PVN+tdWSimllErPdu2CJ08gf354662keY2MGeGrr+QCq62tFHKoVg2uXk2a11NKKaVMNPCmUhwfH/j4Y7m/cKEEwpJL+/Zw5Ajkywd//gmVKklyXqWUUkoplTRMy0y7dJGLoUnFYJCq9t9/D25u8NtvUmBLx3pKKaWSkgbeVIpiNMKAARAaCi1ayJLP5Pbmm5ILpHp18PODpk3B21tzgSiVVowZM4YuXbq80jEHDx7k8uXLSdSiSB4eHmzbti3W58eMGYOHhwd9+/aN8flvv/0WDw+PV35/W7dupXnz5pQtW5YGDRrw+eefE/aKyS4bNGiAh4eH2W3MmDGx7r9t27Zo+5tuXf9ba7Zo0SLq1q37Su2Iz+uULl2aunXrMnfuXIKCgiL2PXv2LF26dKF8+fLUrFmT8ePH81jzDiiVpO7dk5UGIIG35FCnDpw9KxdY//1XxnrTpkF4ePK8vlIq6eg4Lzod5wlrjvPskuVVlIqnDRvg0CGZ5fbJJ9ZrR86c8OOPkgNk+XIYNUquii5fnrwz8JRS1nf79m369u3L2rVrKVq0qLWbg729PUePHuXZs2dkeOE/pD179mB4xUo0O3fuZPLkyUycOJGqVavyv//9j4kTJxIcHMzAgQPjdQ5/f39u3rzJZ599RqlSpSK2Ozk5xXpMkyZNqFmzptm2ffv2MWvWrFgHnJY4cuRIxP2QkBDOnTvHuHHj8Pf3Z8qUKVy7do0ePXrQunVrpkyZwr///svUqVMZMmQIX3zxRaK3RyklNm6UolaVK0sutuSSN69UOh0yBD77DCZPlguvX34JWbIkXzuUUtal47yX03Ge5XTGm0oxnjyR6f8A48dDwYJWbQ4ODjIQW7xYcoGsXy95R27dsm67lFLJy5jCpruWLl0aJycnfvzxR7Ptz5494/Dhw5QvX/6Vzrdx40a8vLxo3749+fPnp0mTJnTv3p0tW7bE+xyXL18mPDyccuXK4ebmFnHLlClTrMc4OTmZ7RsWFsYnn3xC//79qVat2iu9h/iI+lqvvfYaTZs2pXnz5uz9b43Z9u3bcXd3Z/z48RQpUoQKFSowefJkjh8/zs2bNxO9PUopYVpmmhRFFV7G0RGWLYNVq+T+7t2y9PT335O/LUop69Bx3svpOM9yOuNNpRiTJslyg+LFYcQIa7dGGAyy9LVkSamEdfo0VKwI27ZB1arWbp1SKYPRmPDKcGFhEBgoP21tX/14Z2f5PbVE3bp16dSpE7/++itHjhzBwcGB5s2bM2bMGO7evcvbb78NQNeuXRk4cCCDBg3iypUrzJ49m9OnT+Pi4kLlypUZM2YMbm5uAHTp0oWCBQty8eJFrl27xqRJk/jll18AyJo1K9u3b8ff358qVaowbdo0cubMGe/22tvb8/bbb7Nv3z6aNm0asf3AgQN4eHiQL18+bt++He/zjRgxgmzZsplts7Gx4cmTJ/E+h6+vLzly5MDV1TXex7zI29sbd3d3evfuHe25Tz/9lLVr1xISEkLdunWZMGECWf6bkjJmzBhOnjwZbYAaH7a2tjg4OADQokUL6tSpY3Yl2XT/yZMn5MuXLwHvSikVl/Pn4dw5sLeXPLvW0q0blCkDrVvDlStQpQqsWAEdO1qvTUqlFDrO03GejvMsZ/UZb+Hh4SxcuJCaNWtStmxZevXqFWvEcdGiRbGuEx4bpfTljh07ItYwt23blqNHjybX21EJ9OuvMrMM5Kejo1WbE02dOrL84PXX4e5dqF0bVq+2dquUsj6jEWrUkGpxCbm5utqSM6cLrq62CTq+Zs3Eyb/4ySefULFiRXbu3MmoUaNYt24du3fvJnfu3GzevBmQv0Hdu3fn3r17dOzYkQIFCrBlyxaWLVvGs2fPaN++Pf5RRqabN2+ma9eubNiwIWKq/e7du3n8+DHr1q1j+fLl/PHHHyxYsOCV29u4cWOOHDnC8+fPI7bt2bPHbIAWX+XLl6dQoUIRj58+fcrGjRujLQ+Ii6+vL87OzgwePJgaNWrQvHlz1qxZQ3g8Eyb5+vqye/duhg0bFjFAMrl9+zbHjx9n9erVLFu2jPPnz5v9zR8/fvwrXbUFCA4O5tChQ+zcuZP69esDUKRIEcqWLWu23/Lly3Fzc8MjOde/KZWOfPml/GzWDLJnt25bypeHM2egfn0ICIBOnWQZakiIddullDXpOE/HeaDjvMRg9cDbkiVL2LBhA9OnT2fTpk2Eh4fTs2dPgoODo+3bvXt3jhw5Ynbr0aMHzs7OvP/++4B82EePHk2TJk345ptv8PLyol+/fpw4cSKZ35mKr/Bw6N9ffrZtKwOelKhQIfjlF2jZEoKDoXt3GDpUCkEolZ5ZeiUyJahRowZdu3YlX758tG7dmhIlSnD27FlsbW0jrhK6urri4uLCxo0byZUrFxMmTKBIkSKULl2aBQsW8PDhQ/aZMoQDnp6eNG/enOLFi5M1a1YAMmXKxLRp0yhSpAiVKlWiSZMmnD179pXbW61aNTJkyMDBgwcBuVJ37NgxGjdubFE/PH/+nP79+xMUFMSoUaPifdylS5fw8/OjYcOGrFy5kg4dOvDJJ5+waNGieB2/Zs0aPDw8Iq46R+Xo6Mj8+fMpWbIklSpVYtKkSfz444/cuHEDkD598UpuTMqVKxdxK1OmDEOGDKFhw4YMGjQoxv3nzJnDoUOHmDJlCvb29vF6H0qp+AsLg3Xr5L41lpnGJHt2qXA6bpw8XrgQ6taFv/+2bruUsiYd5+k4T8d5lrPqUtPg4GBWrVrFiBEjqF27NgDz58+nZs2a7N+/n2YvlLR0cXHBxcUl4rGPjw9r165l+vTpEVHK5cuX07hxY/r16wdAoUKFuHDhAosXL6Zy5crJ88bUK1mzBo4dk6sa8+dbuzVxy5gRtmyB6dNhyhQpAPHHH/DVVxCP/w+USnMMBjh82JIlCGEEBgbi5OSEbQLWICTGEgSQq2BRZcqUiZBYpjn4+Phw6dIlypUrZ7Y9KCiIK1euRDwuUKBAtGPz589v9sc9rteJi2kZwv79+6lXrx4HDhygbNmyr7SU4UUPHjygT58+3Lp1i5UrV5I3b954H7t8+XKCgoIicn14eHjw7Nkzli5dyqBBg+jduzdnzpyJ2H/q1Km0aNECgMDAQPbt28fIkSNjTBhcoEABcuTIEfH4jTfeAGQQGFMfx2b79u2ALCtwdHSMOGdgYKDZfiEhIUyaNInt27czffp06tWrF+/XUErF3w8/SEArWzZo0sTarYlkawszZkjF065d4cgRmQ23ebNUvFcqPdFxXiQd5+k4zxJWDbxdvHiR58+fUzVKsqzMmTNTsmRJTp06FS3w9qJp06ZRoUIFWrZsGbHtxo0btGvXzmw/T09Ptm/fTmhoKHZ2mtYuJXn0CEaPlvtTpkCePFZtTrzY2Ejlq9Kl4b334MABGZzt2AFRirwolW4YDBDlmsgrMeX8cHJKWO6PxPLitHeIPdlueHg4VapUYfLkydGei5pkNqZKTzG9TkI1adKE/v374+/vz969e2liwTfXK1eu0LNnT8LDw1m/fj3FihV7peMdHByivbfixYvj7+/PkydPmDFjhtnAJ3uUNWVHjx4lJCQk1qu4Lw7Uw8LCAF756mRMgzfTuUyePXvGwIEDOX36NB9//LHFV5aVUrEzFVXo0EEKWqU077wjaUZatgQfH0kz8vHHUvE+LcwAUiq+dJwXScd5kXSc92qsGoW6e/cuALlz5zbb7u7uHvFcbA4ePMi5c+ciIptRj71z547Zttu3bxMSEoKfn1+8pinGxGg0mq3pTkwBAQFmP9OTUaPs+ecfezw9w+nZMzBBV1Os1X+NG8MPPxho396RK1dsqFLFyKpVwTRtGvbyg1OQ9Pz5Swzppf+CgoIIDw8nLCws2h8xS5gGPUajMVHP+7LXjPp6RqMx4r3FtI8pf4Vpn6JFi7J3717c3d0jBiGPHz9m7NixvP/++1SuXDnaa8T0uqZzxrQttr6Ieo6KFSvi7OzMrl27OHPmDN7e3oSFhcX4OnG5desWXbt2JXPmzCxfvpzcuXO/0r+F0WikUaNGtGjRggEDBkRs/+2338iRIweZM2eO8TjTa5w8eZISJUqQOXPmaK8bHh7O9evX8fPzi5jxfurUKQwGA4UKFYpXO03/fjHtG/XzFxAQQO/evbl48SLLly+nUqVK8Tq/6TMSEBAQLdeJ0WiM8equUund06dSqApSzjLTmBQvDidOQI8e8PXXMHgwnDwpVe+dna3dOqVUYnjx73SxYsXYs2cPuXPnNhvnjR49mm7dulGlSpVkaVeVKlXMxnkfffRRgs5z8+ZN3nvvPTJnzszKlSujxV5exmg0Ur9+fby8vBg4cGDE9vPnz+Pm5haxzDY2p0+fpkSJErHud/36dZ49e0bGjBkBOHPmDAaDgaJFi75SO18mODiYPn36cOHCBVauXJnsqyGtGngzfVF9MXrq6Oj40iobq1evpk6dOnh6epptb9GiBStXrqRKlSpUq1aNU6dOsXXrVoAETfM0CQkJ4cKFCwk+Pj6uX7+epOdPaf74w5lVq0oA8MEHl7h8+ZlF57NG/9nZwcqVtoweXYQzZzLRvr0DffveoXv3u6nuamh6+/wltvTQf3Z2dgQFBSXJuZPqvDExBUpMV+aMRiOhoaFmV+pMwa/AwMCIK3E+Pj4UKlSIVq1a8fXXXzN8+HB69eoFSJqES5cukT9/fgIDA82Oj+11AUJDQzEajWbbLly4EC1Y5erqSunSpaOdo3bt2nz66ae8+eabZMiQgcDAwBhfJy5jx44lODiYmTNnEhYWxq1btyKeizr1Py61a9dm9erV5MuXD09PT06ePBmRSuJl7fjjjz8oWrRojPuFhoYSFBTEkCFDGDhwII8fP2b69Ok0a9aM7NmzExgYyNOnTwkNDY11QGf62x9XO4KCgli2bBlnz55l5syZvPbaa2b94OrqGuuV16CgIEJDQ7l69WqMzyfm1W+l0opt26SAgYeHVItPyTJmhE2boHJlGDVK8tL9/ru8hxdWrymlUiHn/6Lof/75JyVLlqRjx4589dVXjBgxgv79+wOSD8zX15fixYtb/Hp//vknP//8s9m2LFmyUKZMGbNtdnZ21KtXj08//ZSKFSsmeALRuHHjCA4O5uOPP8bOzo4HDx5EPGeq0hoXg8FA/fr1WblyJYULF6Z06dIcO3aMFStWMH78+Jce7+PjQ4kSJWJ9PigoiKFDhzJs2DD+/fdfpk+fjpeXF3n+Wwr39OlTQkJCEvz+TT777LOIAGbhwoXN+sHV1TXJx2tWDbyZpmcGBwebTdUMCgoiQ4YMsR53584dTpw4weeffx7tud69e/Pvv//Sr1+/iJkJvXr1wtvb22xq6Kuyt7dP9KirSUBAANevX6dgwYJxvu+0JCwMevVyxGg00KFDKJ06Jbx8b0rovx9+gNGjQ/jsM3uWLs3D/fs5Wbo0OMHTspNTSui/1Cy99F9QUBB37tzB0dExxqn1CWU0GgkKCsLR0THZZgbZ2tpiY2MT8T4MBgN2dnZm78vGxgZbW1ucnJzIlSsXrVq1YsGCBdy+fZvx48fzxRdfMH/+fLp164adnR3lypVjzZo1EVcRox4f2+uCDKoMBoPZtnXr1rHOlHH8PxUrVuSLL76Ido5mzZrxzTff0LRp04htMb1ObO7fvx+Rk+Pdd9+N9ryPj89LzwEwcuRIsmTJwqeffsrdu3fJmzcvY8eOpW3bti899uHDh7zxxhsxttfOzo5SpUpRsmRJ+vTpg8FgoHHjxowaNSpi/2nTpnHy5EkOHDgQ4/lNAbOYzh/18/fdd99hNBrNKmmZrFmzhkqVKsX6Huzs7MifPz+OL5Tkvnz5cuxvXKl0zLTMtGvX1LFs02CAYcPgzTehfXsJvFWoAOvXp6z8dEqpV5c1a1Zat27N3LlzuXHjBhMmTGDdunV89NFHdOjQAVtbW958803Wrl1rcfAHZALR6tWrzbZVqlSJL01lnqNo1KgRmzdvTvCSyHv37nHy5EkA3nnnnWjP+/r6xus8w4cPJ2PGjHz88ccR47zx48dHS/EVkwcPHvD666/H+nzp0qXx9PSka9euGAwGmjRpwpgxYyKenzFjBidPnuTHH3+MV1tjs3v3boxGI8OGDYv23Nq1a5N8BpzBGNvi5mTw+++/07ZtW77//nvy588fsb1Dhw54eHgwZcqUGI9bu3YtS5cu5fDhw7HmbAsODubx48e4u7uzfv16lixZwtGjRxPUzvPnzwPE+YGxhL+/PxcuXMDT0zMi4p7WLV0qlUxdXcHXFyzIE5mi+u/zz2HAAKl0Wras5H2L8tFOkVJS/6VG6aX/AgMDuXbtGoUKFUrUwJulSXfTO+0/yyRG/8X1u5HU4weVOHScl7z++gsKFgSjEW7cePk4KaX1361b0LYtHD8uAbnJk2HiRMkBnBKltP5LbdJL/+k4L2XS/rNMShrnWfVPRIkSJciYMSMnTpyI2Obn54ePjw8V45h3fvr0aSpVqhRj0G3+/PksXboUBwcH3N3dAdi/fz/VtQxRinH/fmSZ9g8/tCzoltL07g0//ghubvDrr3I19PBha7dKKaWUUiplWL9egm516qT8i5MxyZsXDh2Cfv3kfUyZAi1awL//WrtlSimlUiqrLjV1cHCgc+fOzJs3j2zZspEnTx68vb3JlSsXDRo0ICwsjEePHpEpUyaz6KKPjw+tW7eO8Zz58uVjxowZlChRgqJFi7J27Vp+//33iDxvyvpGjYLHj6FcORm0pDU1a8Lp01IN69df4e23YfFiCcoppVRy2bNnz0tzb3Tr1o3BgwfH+vzy5ctZsmRJnOcYN25cvJaUKqWU0Ri5zLRLF+u2xRKOjrBkieR969sXvv1WLrZ+8w28kKZJKaWShI7zUherBt4ABg8eTGhoKBMmTCAwMJCKFSuycuVK7O3tuXXrFm+//TazZs2iVatWEcc8ePCALFmyxHi+Nm3a8PDhQ6ZOncqTJ08oXbo0X3zxBYULF06md6TicuQIfPGF3F+yxLplpZNS/vzyXrt3lypYffrAb7/BggXwipWRlVIqQWrVqhWt8veLYqs4atKuXTsaNGgQ5z5RS8YrpVRcTp+GixchQwaI5Rp6qvLeexJoa9UKrl6FKlVg+XLo1MnaLVNKpXU6zktdrB54s7W1ZeTIkYwcOTLac3nz5o0x4d9vv/0W5zn79OlDnz59Eq2NKnGEhkpeN4CePWVwkpa5uEgVrDfegAkTJNDo4wObN0M8CwUqpVSCubi44GJhhRdXV1dcXV0TqUVKqfTONNutZUt4yffBVKNcOQkoduoE330HnTvDiRMwbx5oUWOlVFLRcV7qkkLTgKq0aNEiOH8esmWDWbOs3ZrkYTBIPrsdO6Qc/aFDULGiVMNSSimllEovgoNh40a537WrdduS2LJnl+WmEybI40WLoG5d+Ptv67ZLKaVUyqCBN5Us7tyRqk8As2envxlfzZtL9asiReD6dahWDbZts3arlHp1ViyErVSKpL8TSsXP3r3w8CHkzi35b9MaW1uYPl0utmbODEePwptvSuoRpVIL/ZumlLnE+p3QwJtKFsOHw9OnkoS2Rw9rt8Y6SpWCkyehXj14/lxym0yZAuHh1m6ZUi9n/19yQn9/fyu3RKmU5fnz5xgMhojfEaVUzEzLTDt1AjurJ7tJOi1ayNLT0qXh7l2p3rpwoRSWUCqlsre3x2Aw8Pz5c2s3RakUxfTdx9JxXhr+s6dSih9+kFxnNjawdKn8TK+yZZMrviNHSqGFqVNl2enatbIUVamUytbWlixZsnD//n0AnJ2dMRgMFp83LCyMoKCgiNdQr0b7zzIJ7T+j0UhoaCh+fn74+fmRJUsW7X+l4vDoEezaJffT2jLTmBQrJisdevaUMfCQIZL37fPPJQewUimNra0trq6uPHjwgKCgIDJnzoydnZ3FYz0dp1hG+88ylvSf0WjE39+f+/fvJ8o4TwNvKkkFB8PAgXK/f39JQJve2dnB/PlSBatvXyk9X62aLE0oVMjarVMqdrly5QKICL4lhvDwcEJDQ7Gzs8MmPUflE0j7zzKW9p+trS25c+fWxMRKvcRXX0FICJQtC6+/bu3WJA8XF9iwQVZ7jBgh98+fl1QjRYtau3VKRZcrVy4yZMjA/fv38fPzS5Rz6jjFMtp/lkmM/suSJUvEdyBLaOBNJamPP5ay8TlzSt4LFalbNyhRQkrQnz8vRRc2b5YlCUqlRAaDgdy5c+Pu7k5ISEiinDMgIICrV6+SP39+MmTIkCjnTE+0/yxjSf/Z2dlha2ubKDM/lUrrTMtM08Nst6gMBhg6VHK9tWsn470KFWDdOmjWzNqtU8qcwWAgS5YsuLq6EhYWRmhoqMXn1HGKZbT/LGNp/9nb2yfaTEMNvKkkc+NGZLDN2xuyZLFqc1KkqlXh1Clo2VLygdSvD598IrMD9bucSqlsbW0T7Y9Q+H9JDh0dHXFyckqUc6Yn2n+W0f5TKun9+acsu7S1hQ4drN0a63jrLThzBtq2hWPHpOjWpElSeEwnsaiUxmAwYGdnh10iJGPUv7OW0f6zTErqP/2vXiWZoUPB318GG507W7s1KVfevPDzz5JsOCxMlub26SPLdJVSSimlUrMvv5SfDRtCIqzWSbXy5IFDh2DAAHk8bZrMenv0yKrNUkoplQw08KaSxJ49sH27XN389FOdvfUyGTLIwHTuXOmr5cuhbl24d8/aLVNKKaWUSpjw8MjAW3pbZhoTBwdYvBi++AKcnKTgVoUK8Ouv1m6ZUkqppKSBN5XoAgJg0CC5P3SolFNXL2cwSLXTb78FV1c4elTyvp09a+2WKaWUUkq9usOHJfVI5szQooW1W5NydO0qS04LFYJr1yT1iClAqZRSKu3RwJtKdHPmwNWr8NprkrtCvZrGjaXkfPHicPMm1Kgh1cCUUkoppVITU1GFdu1kdr+KVLas5Pdt1AgCAyUYN3CgphpRSqm0SANvKlFduQKzZ8v9+fMhUybrtie18vCQ4FujRjKD8N13Yfx4WbKhlFJKKZXS+ftLtXbQZaaxyZYNdu+GiRPl8aefSnX7O3es2y6llFKJSwNvKtEYjbLENCgI6tWTyk0q4bJkkcHYyJHyeOZM8PICPz9rtkoppZRS6uV27ICnT2U5ZfXq1m5NymVrK4UWdu6UVCO//AJvvimFt5RSSqUNGnhTiWb7dkkS6+CgBRUSi62tFFz48ktwdIRdu6BKFbh0ydotU0oppZSKnWmZaZcuYKPfOF6qeXNZelq6tBTXqlsXFiyQC9tKKaVSN/0zqBLF8+cwZIjcHzlS8pOpxNO5syQofu01uHABKlWC77+3dquUUkqplwsPD2fhwoXUrFmTsmXL0qtXL27evBnjvosWLcLDwyPG29ixYyP227FjB82bN6ds2bK0bduWo0ePJtfbUfFw9y7s3y/3u3SxbltSk6JF4fhx6NABwsLggw+gUycZZyullEq9NPCmEsWHH0ohgAIFYNw4a7cmbapYUa6EVqkCjx9L/rf58/VKqFJKqZRtyZIlbNiwgenTp7Np0ybCw8Pp2bMnwTFkke/evTtHjhwxu/Xo0QNnZ2fef/99AHbv3s3o0aNp0qQJ33zzDV5eXvTr148TJ04k8ztTsdmwQfLSVqsmwSQVfy4usH69zHazsYGNGyVncr58MG9e7OO+NWtktcmLNxcXZypUKM+6dbYR+27fDuXLQ8aM8u8zdWr0og7Hjkm+OWdncHeH7t1lJp5SSqlXp4E3ZbGLF+Gjj+T+woXyB1oljdy54dAheP99GdAOGyYDoaAga7dMKaWUii44OJhVq1YxePBgateuTYkSJZg/fz53795lv2lKVBQuLi64ublF3B48eMDatWuZNGkSHh4eACxfvpzGjRvTr18/ChUqRKdOnWjRogWLFy9O7renYmFaZqpFFRLGYIDKlSXw5uQkwbYHD2DUKJgzJ+ZjmjaVYNmLN0/PcHLmDKZhwzBAVky0aiWrU775Riqpzp4Nw4dHnuvkSahdG/79F774AlatgqtXoWpVePIk6d+/UkqlNRp4UxYxGmHAAAgJgWbNoEULa7co7XN0lAHQ/PkyIFuzRgZHf/9t7ZYppZRS5i5evMjz58+pWrVqxLbMmTNTsmRJTp069dLjp02bRoUKFWjZsmXEths3blChQgWz/Tw9PTl37hyhoaGJ13iVIL/9JjcHB2jXztqtSb0mT5YiC1euyMzBoCAZd0+eDM+eRd/fzU1WRUS9nTwJvr4G5sy5gpub7Ld6NeTPD+vWQf36MHSo3D7/XMbzADNmSKGHgwelWFqzZrBnD4SGSu5hpZRSr8bO2g1QqdumTfDjj3I1buFCa7cm/TAYZJAUECAl6I8flyUIAwbI0oSYClusWQPdusV0NmegPJ99FkTv3rJl+3aYPh18fSFXLsnPMnasDKJj0qaNLFdYs8by96aUUirtuHv3LgC5c+c22+7u7h7xXGwOHjzIuXPn2L59e7Rj79y5Y7bt9u3bhISE4OfnR7Zs2V65nUajEX9//1c+Lj4CAgLMfqZ1q1bZA/Y0bRqKo2MwlnZreus/kCDboUMZGD8+hCxZQvn2Wxg71p5ly+wJDoZatcLYsSOIuD7q9+7BhAkZ6NYtiNKl/SP67/lzB5ydbQgKCozYN1MmO4KDHbh3z59s2cDHx4mqVcOj/ftVqODArl02jB8f+OLLpVnp8fOXmLT/LKP9Z5mk7j+j0YghnhUlNfCmEszPL3Ja+rhxUi5eJZ/jx+WqZ7NmcPas5NhbuFASGn/1VfT9TUsQXtS9eziPHoVGW4LQvr0sPfjjDwm6/fMPLFpkfqxpuevWrfDee0nwJpVSSqVqpsGuwwtXbhwdHXnykjVrq1evpk6dOnh6epptb9GiBStXrqRKlSpUq1aNU6dOsXXrVgBCTFN2XlFISAgXLlxI0LHxdf369SQ9f0oQGgrr15cBoEaN61y4kHjrEtND/5lcu+ZEcHApMmS4yYULjwHo2ROyZs3BrFkFOHvWlipVbJkz5wolSsT8hXLmzPwYjQ507nwRiOy/Ro0ysWdPUcaNe4SX1z9cv+7EJ58Upnr1J9y7d5l798DZ2YM//zRw4cJFs3P6+Hhy65Zjkv+upETp6fOXFLT/LKP9Z5mk7L8Xxzex0cCbSrDJk2V5Y9GiUslUJa/Jk6FcOZmd5ucnlU937YKvv5ZccB99BLaReXRxcyNimYHJwoWyBGHVqiu4uRUAzJcg2NrKMoR79+Djj+Vmby/H/v47DBoEp05BhgzJ856VUkqlLk5OToDkejPdBwgKCiJDHH887ty5w4kTJ/j888+jPde7d2/+/fdf+vXrR1hYGEWLFqVXr154e3uTKVOmBLXT3t6eoklUBSAgIIDr169TsGDBON9zWrB/vw0PH9qTI4eR7t1fw8HhNYvPmZ76z+TpU8kG5OmZB0/PyNmiY8bArFmQNWs4t2870rOnJwsXBtOpU5jZ8ffvw549GRg8OJTXX89n1n8lSsD162HMnZuXTz7JC8Abb4SzebM9rq4S5O7Tx5YBAxxZtaoMw4aFYjAYWbzYnuvX7QgJIVowPC1Lj5+/xKT9ZxntP8skdf9dvnw53vtq4E0lyO+/R85+WrxYlpqq5CNLEKQKFUDmzBKA69lTAmeffCLLRDduhCxZYj6HLEGAXr1CKV06ch1BYKBU1IoatMueXapdPX1KxLKGrl1leenx45rbTymlVMxMS0zv379P/vz5I7bfv38/olhCTA4cOEC2bNmoXr16tOccHByYOHEio0eP5vHjx7i7u7N+/Xpy5MiBcwIrPBkMhgQfG18ZMmRI8tewtq+/lp8dOhjIkiVx32t66D8T0wQKJycns6JlphSGgwbZcPo07NljoHdvR379VXL/mo7bsAHCwmDECPuIL5um/uvbV3IFT5gAb78N16/DlCk2tGrlzA8/SJG0/v1lPDhpkj1LlthjMEhakd69ZZyZXv4dokpPn7+koP1nGe0/yyRV/8V3mSlocQWVAOHh8gc5LAxat4aGDa3dovTn6lUJhBUvHrnNxiayuqy9PezbJxWxfH1jPsfkyXLMpEnmy3IGDIBLl6Rk/ePHElhbsACaNMEsl8iXX8KRI1CmTKK+NaWUUmlIiRIlyJgxIydOnIjY5ufnh4+PDxUrVoz1uNOnT1OpUiXs7KJfI54/fz5Lly7FwcEBd3d3APbv3x9jkE4lHz8/qZIJWs3UUq6u8vPpU/Ptfn7yM1cuWeUwebI8XrJECm3dvi2Pt2yBBg2ir3S4fVuKKIwaJbl8a9eG99+XwgnHj0tAzmTYMKlqeuGCXKz9+msZFyYghaJSSqV7GnhTr2ztWjh6VGZFzZ9v7dakT6a0OJkzm283rbDp3VuKLfz5J1SqJAOqqO7fl/LwAwdGnxFXt64MyEaOhKxZpXS8u7tcPY3q9dcT7e0opZRKoxwcHOjcuTPz5s3jhx9+4OLFi3zwwQfkypWLBg0aEBYWxoMHDwgMNE/W7uPjQ4kSJWI8Z758+fj88885ePAgN2/eZMaMGfz+++/07ds3Od6SisWWLTJLytMType3dmtStyJFZOXBi6uYTI89PeXi6ZQpsHu3jOWOHZMqqFu2wLlzMVeU/esvqYz6Yoy6ZElZ3fDHH/L49GnYtk0u5JYoERnAO3tWXkMppdSr0cCbeiX//itBGYBJkyS4o5JfeHjcz+fNK4OmGjXk6mizZlL+3WiU51eskBmLQ4ZEP7ZfP9l3wgQpI796NTx6BI0aYXFlMqWUUunP4MGDadOmDRMmTKBDhw7Y2tqycuVK7O3t+fvvv6lRowZ7XrhC9ODBA7LEkiuhTZs29O3bl6lTp9KiRQsuXrzIF198QeHChZPh3ajYrF0rP7t2jbm6uoo/Jyd46y0JfpnGbiDFrFxd5aKqSdOmMuYrU0YurLZvL9urVYt+3qJFJaB3+LD5dl9fePgQTL9Chw5Bp04yw83k++8lMOfllQhvUCml0hnN8aZeyfjx8OCBXBkbOtTarUm/XrYEwdVVZqn98IMUQPj8cxg9WnLzLV9uvgQhajDNtARh3DhZgmBSqRKUKiVLEAYOTNr3ppRSKm2xtbVl5MiRjIyhElPevHnxjSEnwm+//RbnOfv06UOfPn0SrY3KMtevw08/ScCtUydrtyZtmDAB6tWTmWvdu8Mvv4C3t1Scd3aWMZ+Pj8yOK1JEZrz17g3r10cev2KFzIwzcXOT8bu3tzyuXx9u3JCcwQUKQK9esr1zZyni0K6drID46y9Zelq9ujynlFLq1eiMNxVvp0/DsmVy/9NPIxO4quQXnyUIIP9Gy5bJv5etrQzGqlSxfAmCUkoppZTJunXys25dXQ2RWOrWlRluvr4yy2z9egmYmVaenD0r6UC+/VYeOztL/t0aNeTxV1/JmO/yZfPph97ectu2TVYzTJkiAbhTpyLTj+TKBfv3SzGvVq1kn27dYO9e8+JbSiml4kdnvKl4CQuTggpGo1zJrF3b2i1K36IuQRgxInJJR0xLEAwG+bcrWVIqUv3+u2yPqaJy1CUIjRtHbn9xCYJSSimlFMjYMOoyU5V4WraUW0xq1zZfhgoy5jt8WIpftW0rF0xr1nRiyhTXiIuyBoPMenvZypXy5WUWo1JKKcvpjDcVLytWyJWwzJml2qWyvgkT4MQJmbm2dy9MnChXMMeNi1yCcPy4LA0GGaCdOiVLUEGWCqxebX7OqEsQxo2THG9r1sgV0ahLEJRSSimlQMYily7J2KNVK2u3RoHMejt71pTr18Dw4UVYvVqnqimllLVo4E291IMHMHas3J8+XaafK+t71SUIAIUKQYsW4OgIwcGSM2TkSHtCQyP3ic8SBKWUUkopiJzt1ro1ZMxo3baoSLlzw48/QrduoRiNBgYOdGTuXGu3Siml0iddaqpeavRoqWZatqwsWVQpx6suQQAprvDZZxJEnTIFliyx5/ffi0QE6OK7BCGq69dfqdlKKaWUSgOCgmDTJrmvy0xTHnt7WLQoGKPxAWvW5Gb0aKlUP2uWVp5VSqnkZPUZb+Hh4SxcuJCaNWtStmxZevXqxc2bN2Pcd9GiRXh4eMR4G2uakgX88ssvtG7dmrJly1KvXj1WrlyZXG8nzTl6NHI54pIlYKeh2jTBxgYmT5YZc87ORo4cycKYMfbWbpZSSimlUpFvv5WLs3nyQJ061m6NionBAAMH3mH69GAA5syBPn0kf7NSSqnkYfXA25IlS9iwYQPTp09n06ZNhIeH07NnT4KDg6Pt2717d44cOWJ269GjB87Ozrz//vsAXL16lT59+lCnTh127drFsGHDWLhwIetNtbVVvIWGRs5w695dli2qtKVVK1i5Un7Xli61Z8UKKzdIKaWUUqmGaZlp585a7TKlGzYslOXL5eLr8uXQoYOkHVFKKZX0rBp4Cw4OZtWqVQwePJjatWtTokQJ5s+fz927d9m/f3+0/V1cXHBzc4u4PXjwgLVr1zJp0iQ8PDwA+Pnnn3F2dmbgwIHky5ePJk2aULNmTQ4fPpzcby/V+/RTqYCZNSvMnm3t1qik0qJFGH373gYk0HrkiJUbpJRSSqkU759/IvPIduli3bao+OnZE776Spagbt4seX+fP7d2q5RSKu2zauDt4sWLPH/+nKpRplJlzpyZkiVLcurUqZceP23aNCpUqEDLKEmusmfPzuPHj9m9ezdGoxFfX1/OnDnDG2+8kSTvIa36+2+pkgmSB8LNzbrtUUmrR4+7tGoVSkiIzIK7ccPaLVJKKaVUSrZpk6yOKF8eSpWydmtUfLVpA7t3SxXa776DBg1kubBSSqmkY9XA2927dwHInTu32XZ3d/eI52Jz8OBBzp07x+jRo822N27cmLZt2zJy5EhKlSpFixYtqF69On379k3cxqdxI0bA06dQsaJcHVNpm8EAn30WTLlyUsW2RQt49szarVJKKaVUSvXll/JTiyqkPg0awIEDUq3+l1+gVi14yVcvpZRSFrBqqvyAgAAAHBwczLY7Ojry5MmTOI9dvXo1derUwdPT02z7w4cPuX37NoMHD6ZWrVr4+PgwZ84cFi1axODBgxPcVqPRiL+/f4KPj4upH0w/re2nn2zYsMEJg8HI/PmBBAXFUBozBUlp/ZfamPrNYAhg0yYDNWs68fvvBjp3DmXdumBsrJ4JMmXTz59ltP8so/1nmaTuP6PRiEFLB6o06OJFOHlSim69+661W6MSompV+OknaNgQzp+HGjXg+++hUCFrt0wppdIeqwbenJycAMn1ZroPEBQURIYMGWI97s6dO5w4cYLPP/882nPjx48nd+7c9OvXD4CSJUtiNBqZMmUKnTt3Jlu2bAlqa0hICBcuXEjQsfF1/fr1JD1/fISEGBgwQIKZbdo8wMnpJkn8thNNSui/1MzUf7Nnu9CnT3F27LBj2LD79Onzt3Ublkro588y2n+W0f6zTFL234sXF5VKC0yz3Ro3Bnd367ZFJVyZMpLbt359uHJFgm/79+vSYaWUSmxWDbyZlpjev3+f/PnzR2y/f/9+RLGEmBw4cIBs2bJRvXr1aM+dOXOGUaNGmW0rW7YsoaGh3Lp1K8GBN3t7e4oWLZqgY18mICCA69evU7BgwTgDjsnh44/tuHbNgRw5jMyfn5GsWT1ffpCVpaT+S41e7D9PTwgODqFvX0eWL3+Nt97KTqtWWnM+Nvr5s4z2n2W0/yyT1P13+fLlRD+nUtYWHq7LTNOSIkUk+NagAfzxB7z1FuzdC5UqWbtlSimVdlg18FaiRAkyZszIiRMnIgJvfn5++Pj40Llz51iPO336NJUqVcLOLnrzc+bMia+vr9k2X19fDAYDBQoUSHBbDQYDzs7OCT4+PjJkyJDkrxGXmzelkALAvHkG8uSxXlsSwtr9l9pF7b8+feDPP+Hjj6F3b0dKlYJy5azcwBROP3+W0f6zjPafZZKq/3SZqUqLfvpJxoxZskCzZtZujUoMr70GP/8MTZrAiRNQty7s2AFvv23tlimlVNpg1exNDg4OdO7cmXnz5vHDDz9w8eJFPvjgA3LlykWDBg0ICwvjwYMHBAYGmh3n4+NDiRIlYjxnt27d2Lx5M2vXruXmzZscOHCA2bNn07FjR1xdXZPjbaVaH3wA/v4yzVyvYKq5c6FRIwgIgHfegXv3rN0ipZRSSlnb2rXys317iJIpRqVy2bJJwYV69eD5cwnCffONtVullFJpg9XTpg8ePJg2bdowYcIEOnTogK2tLStXrsTe3p6///6bGjVqsGfPHrNjHjx4QJYsWWI8X/v27ZkxYwZbt26lefPmeHt707FjR8aOHZsM7yb12rcPtm4FW1tYskSqXKr0zdYWNm4EDw+5st2qFQQFWbtVSimllLKW589hyxa536WLdduiEl/GjLB7t4z5goOhTRtYvdrarVJKqdTPqktNAWxtbRk5ciQjR46M9lzevHmjLRsF+O233+I8p5eXF15eXonVxDQvMBAGDpT7gwfD669btz0q5ciSBXbulDwfv/wC/frBypUamFVKKaXSo+3b4dkzKFwYqlWzdmtUUnB0hK++gt69JejWvTs8fiwrY5RSSiWM1We8KeubO1cqGeXODVOmWLs1KqUpXhy+/hpsbGQAtmCBtVuklFJKKWswLTPt2lUvwqVldnZyoXX4cHk8bBhMnAhGo3XbpZRSqZUG3tK5q1cjCyp8/DFkzmzd9qiUqUED+OgjuT9iBHz3nXXbo5RSSqnkdfu25AADXWaaHhgM4O0NM2bI4w8/hEGDpKqtUkqpV6OBt3TMaJSlpYGBUr2ofXtrt0ilZEOGyHKD8HD5rMSwClwppZRSadSGDTIGqFFDlpqqtM9ggHHjIvM/f/qpBF1DQqzdMqWUSl008JaO7dwJ334L9vbyh1SXDKi4GAwy8KpWDZ48gRYtJOeHUkoppdI2oxG++ELud+1q3bao5NevH6xfL0tQN2yAli3B39/arVJKqdRDA2/plL+/zGACyd9QooR126NSB0dH2LYN8uWDP/+Ed9+F0FBrt0oppZRSSenXX+GPP2Qc0LattVujrKFDB9ixA5yc5MJ9o0ZyIVYppdTLaeAtnZoxA27cgPz5YcIEa7dGpSY5c8psSWdnyfU2apS1W6SUUkqppGQqqvDOO1LxXKVPTZrA/v2SE/rwYahTB+7ft3arlFIq5dPAWzrk6yvJUgE++QRcXKzbHpX6lC0bueRk/nypdqqUUkqptCckRJYXgi4zVVCzJhw6BG5ucO6cPP7rL2u3SimlUjYNvKUzRiMMHCiDqCZN5MqlUgnRpg1Mniz3+/aFX36xbnuUUkoplfj275dZTe7uUuVcqXLl4MgRWTnz559QvTpcvGjtVimlVMqlgbd0ZvNmKQXv6AgLF2pBBWWZSZOgdWsIDpZEu3rFUymllEpbTMtMO3aUglxKARQvDkePSp7oW7dk5tuZM9ZulVJKpUwaeEtHnj6FDz6Q+2PHQpEi1m2PSv1sbGTJ6RtvyNXwd96B58+t3SqllFJKJYbHjyWhPugyUxVd3ryS6618efjnH8n59tNP1m6VUkqlPBp4S0emTIE7dyTgNnq0tVuj0goXFxmUu7lJ1bNu3WRJs1JKKaVSt82bISgISpeW/K5KvShHDvjxR6hVSy7yN2oEu3ZZu1VKKZWyaOAtnTh/XgopACxaJKXAlUosBQrAtm2yBGXzZpg+3dotUkoppZSlTMtMu3bV9CQqdpkzw9690KIFBAZK+pF166zdKqWUSjk08JYOGI0wYACEhckfwsaNrd0ilRbVqAFLl8r9yZMlEKeUUkqp1OnqVUmgb2MDnTpZuzUqpcuQAbZuhS5d5DtHly6weLG1W6WUUimDBt7SgS+/lPwLzs6wYIG1W6PSsh49YMgQud+lC/z2m3Xbo5RSSqmEMc1YqlcPXnvNum1RqYOdHaxZA4MGyeNBg2QVhKYgUUqldxp4S+MeP4aRI+X+xIlS9luppDRvHtSvD/7+suTg/n1rt0gppZRSr8JoNF9mqlR82dhIepspU+TxpElS3C083KrNUkopq9LAWxo3YYIEPkqUgGHDrN0alR7Y2cFXX0GxYvDXX9C6NQQHW7tVSimllIqvY8fgyhXImBG8vKzdGpXaGAySdsSUX/qTT6B7dwgNtW67lFLKWjTwloadPRuZc+vTT8HBwbrtUelH1qywc6ck2z1yRHIM6jIDpZRSKnUwzXZr00aqlyuVEIMHy2fJ1ha++EI+T4GB1m6VUkolPw28pVHh4dC/v/zs0AHq1rV2i1R6U6IEbNokSw5WrJBqukoppZRK2QIDZeY66DJTZbkuXaTglqMj7NgBTZrA06fWbpVSSiUvDbylUStXwokTkCkTfPSRtVuj0qvGjWHuXLn/wQfw/ffWbY9SSiml4rZ7t+QIzpcPatWydmtUWtCiBezdK0uXDx6UCQH//GPtVimlVPLRwFsa9M8/MGaM3J82DXLntm57VPo2bBi8957MvmzXDi5dsnaLlFJKKRUb0zLTzp1l1rpSiaFOHQm6Zc8Op0/DW2/BrVvWbpVSSiUP/XOaBo0ZA48eQZkyMHCgtVuj0juDAZYtgypV5Ap6ixbw5Im1W6WUUkqpF92/LzOTQJYIKpWYKlSAw4chb164cAFq1NALskqp9EEDb2nMsWOyzBRgyRKpMKmUtTk5wTffyEDr4kXJOxgWZu1WKaWUUiqqTZuk8mTFiuDpae3WqLTI01MKbxUrBjduSPDt11+t3SqllEpaGnhLQ0JDpaACwPvvQ/XqVm2OUmZy5YLt2yFDBrmabloOrZRSSqmUwbTMVIsqqKRUoIDMfCtbVmZZ1q4twTillEqrNPCWhixdKleMsmSBOXOs3RqloitfHlavlvvz5kUO8JVSSillXX/8AWfOyGqJd9+1dmtUWpczJxw6JDPenjyBBg0ilzkrpVRao4G3NOLuXZgwQe7PnAnu7tZtj1Kxad8+8rPaqxccP27d9iillFIKvvxSfjZtCjlyWLctKn1wdYXvvoPGjSEgQPIAf/WVtVullFKJTwNvacTIkeDnJ0lLe/e2dmuUitvUqeDlBcHB8lOrWimllFLWExYG69bJfV1mqpKTs7OkInn3XUmb06EDfPaZtVullFKJSwNvacBPP8lgyWCQggq2ttZukVJxs7GRK+uvvw737knwzd/f2q1SSiml0qeDB+H2bciaVWa8KZWcHBzku0y/fmA0Qt++MGuW3FdKqbRAA2+pXEgIDBgg93v3lipUSqUGGTPCzp2ynOXMGejRQwdYSimllDWYcq6++y44Olq3LSp9srWFTz+F8ePl8bhxMHq0jg2VUmmDBt5SuU8+kWS4OXJIbjelUpOCBWHLFknkvGmTfoaVUkqp5PbsGWzdKvd1mamyJoMBPvxQCnABeHtLPuCwMOu2SymlLKWBt1Ts1i2YMkXuz50L2bJZtTlKJUitWnKFE6Towo4d1m2PUkoplZ5s2ybpHooVg8qVE/nk+/fLcgxnZyhUSCIqsU1hWrNGIi8v3JxdXChfoQK2piR0IEnBypeX6fNFi0ry2OBg8/PduwedOkH27JLFv0MH+PvvRH6DKikMHw4rV0pqkpUrZSZmUJC1W6WUUgmngbdUbNgweP4cqlWD996zdmuUSrjevWHgQLnfqROcP2/d9iillFLphWmZadeuEutKNMePQ7NmUKKERPc6dYJRo2DOnJj3b9oUjh2Ldgv39CQ4Z07CGjaU/b7/Hlq1guLF4ZtvZAAxe7ZEa0xCQ6VU5okTsGwZLF0KR49CgwaSp0WleN27w+bNkv9tyxZo3lxmZyqlVGpkZ+0GqITZv1/+GNnYSEEFGw2hqlTu44/Bxwd+/FHKyZ88CW5u1m6VUkoplXbdvCl/dwE6d07kk0+eDOXKSTUlgEaNJOg1cyYMGQIZMpjv7+YW/Q//woUYfH25smoVBUzPrV4N+fNLNn5bW6hfX2a3ffyx3OztZZB87pzkYylZUo4rWxZKl4avv5YgoErxWrWCb7+VIlzffy//1N9+q6t8lFKpj9XDNeHh4SxcuJCaNWtStmxZevXqxc2bN2Pcd9GiRXh4eMR4Gzt2LAB169aNdZ9Tp04l51tLMkFBkbODBg2CN96wbnuUSgymcXKRInD9OrRpE33ViFJKKaUSz/r1svKzVi3Ju5pogoLg0CFo2dJ8e5s28PQpHDny8nPcuwcTJhDaqxf+pUtHbg8MBBcXCbqZZM8ug4anT+Xxd9+Bh0dk0A3kvqcn7NmT4Lelkl+9evDDD1Jx9/hx+azqimGlVGpj9cDbkiVL2LBhA9OnT2fTpk2Eh4fTs2dPgmP4xt29e3eOHDliduvRowfOzs68//77AGzZssXs+R9//JGCBQtSpUoVypUrl8zvLml4e8OlS5Arl6S0UCqtyJZNKp1mygQ//wyDB2s1K6WUUiopGI3my0wT1dWrEggrXtx8e9Gi8tPX9+XnmDwZbGwImTTJfPuAATIQnjcPHj+WaMyCBdCkSeRUqAsXor+26fXj89oqRalcWcaFuXPD//4HNWrIR0wppVILqwbegoODWbVqFYMHD6Z27dqUKFGC+fPnc/fuXfbv3x9tfxcXF9zc3CJuDx48YO3atUyaNAkPDw8AsmXLZrbPunXr8PPzY/78+djZpf6VtdeuwYwZcv+jjyRXrFJpScmSsHGj5Jn57DNZSq2UUkqpxHXmjMSnnJxkIlqievJEfmbObL49Uyb56ecX9/H378MXX8gSjyxZzJ+rW1dyxY0cKdOgqlYFd3fYsMH89V98bdPrv+y1VYpUurSk6StcWIJuNWpIEE4ppVIDqwbeLl68yPPnz6latWrEtsyZM1OyZMl4LQudNm0aFSpUoOWL09j/c/nyZdauXcuYMWPIlkaSAQwZIjPs69SR4kxKpUVNm0qeZJDPvCn/jFJKKaUShyn1WsuWMceoLBIeHvfzL0tOvGIFhIXJIOBF/frB3LlSCv3gQcn59uiR5JDz93/562ti5FSrUCFZpfz667Lc9K23ZMKjUkqldFadAnb37l0AcufObbbd3d094rnYHDx4kHPnzrF9+/ZY91m4cCHFixfnnXfesbitRqMRf9Mf80QWEBBg9jM2e/bYsmuXI3Z2Rry9AwkI0DV4EP/+UzFLqf03YACcO+fApk12tG1r5KefAilcOOV95lNq/6UW2n+W0f6zTFL3n9FoxJCoZSKVShwhIZETxBJ9mSlELskw5VwzMc02e9mSjS1bpAKpm1tkMA3g9m34/HMYNw6mT4/cXqkSlCoFq1bJLDlX1+ivbXp9XS6SquXOLekDmzaVoFu9elLctn59a7dMKaViZ9XAm2mg6+DgYLbd0dGRJ6Yp6rFYvXo1derUwdPTM8bnb968yffff88nn3ySKG0NCQnhwoULiXKu2Fy/fj3W5wIDDQwZUgqAjh3vAbdJ4uakOnH1n3q5lNh/gwYZOH/egz/+cOGdd2DVKl8yZnzJVXQrSYn9l5po/1lG+88ySdl/L45x0pvw8HAWL17M5s2befr0KRUrVmTSpEnky5cv2r6LFi1i8eLFMZ6nVatWzJo1C4BffvmFjz76iCtXrpAjRw46dOhAjx49kvR9pDX79sE//0i+4Hr1kuAFihSR4geXL5tvNz2OZfwOSHDt3DkYOhRCQ3F88oTS7u7YPXkiawyNRqhe3fyYkiWlwMIff8hjDw85x4suX5YgnUrVsmWDAwek6un+/RKE27gRWre2dsuUUipmVg28OTk5AZLrzXQfICgoiAwvlhiP4s6dO5w4cYLPP/881n127txJ9uzZqZdIowl7e3uKmhLCJrKAgACuX79OwYIFY33f06bZc+eOPXnyhOPtnZmMGRN7TUDqFZ/+U7FL6f23Y4eBmjXDuXo1A3PmlOGrr4LMCplZW0rvv5RO+88y2n+WSer+u/xi0CEdMhXRmj17Nrly5cLb25uePXuya9euaEHJ7t278+6775ptW716NRs3bowoonX16lX69OlDnz59WLBgAefPn2fs2LE4OTnRqVOn5HpbqZ6pqEKnTpAkKZCdnGQd4LZtMGKEJG4F2LpVZpzFFfw6cUJ+ligBCxdiu2ABtrduQd680LOnBPQOH4bGjSOP8fWFhw8lARjIbLkNG8DHJ7KyqY+PJLWbMCHx369Kdi4uUpCrSxfYvBnatYPly6F7d2u3TCmlorNq4M20xPT+/fvkz58/Yvv9+/cjiiXE5MCBA2TLlo3qL17temGfpk2bYpNIeRwMBgPOzs6Jcq7YZMiQIcbXuHQJ5s+X+wsX2uDunrTtSK1i6z8VPym1/4oUgR07ZPy+d68tM2Y4R+R/S0lSav+lFtp/ltH+s0xS9V96X2ZqKqI1YsQIateuDcD8+fOpWbMm+/fvp1mzZmb7u7i44OLiEvHYx8eHtWvXMn369Ihx4c8//4yzszMDBw4EIF++fOzZs4fDhw9r4C2e/v1XAhaQRMtMTSZMkOl07dpJNOSXX8DbW5K4OjvLsk8fH/lD7+YWedz58+DoKLnczp6N3H7zplQ6zZlTzgOyvvDGDZg6FQoUgF69ZHv79jBzpgTnTIOGMWMkOVi7dkn4plVycnSUmW6urpIWsEcP+XwPH27tlimllDmrZhctUaIEGTNm5ITpyhbg5+eHj48PFStWjPW406dPU6lSpVirlD579owLFy5QrVq1RG9zcjMaJVVFcLDkjI2ljoRSaVrFirBypdyfMwfWr7due5RSSr1cUhTRyp49O48fP2b37t0YjUZ8fX05c+YMb7zxRpK8h7To669lXPnGG1CmTBK+UN26MsPN1xe8vOSPt7e3VCQFCapVrQrffmt+3N9/g4ODedAtqnv3ZG3htm0yOJ4yRQJwp05FVkB1dITvv4fy5aF3b0kcW7UqfPddEk3xU9Ziaytp/0aOlMcjRsD48fIdSimlUgqr/uVxcHCgc+fOzJs3j2zZspEnTx68vb3JlSsXDRo0ICwsjEePHpEpUyazpag+Pj60jmMR/8WLFzEajZQoUSI53kaS2rpVchc4OsKiRZEz9ZVKbzp2lLLxs2bJFc1ixTRNi1JKpWRJUUSrcePGnDhxgpEjRzJq1CjCwsJo3rw5ffv2taitKaGIVnJZs8YRsKV9+2D8/UOT9sUaNpRbVKZ+rlQJnj833wY4Tp6M7Z49MRdHMDl3jrDjxwl6sVBC1H/D7Nlh3broxybRv3NKldI+f0llyhTIlMmOSZMcmDkT7t8P4eOPQyxOT5Je+i+paP9ZRvvPMimpiJbVL/kMHjyY0NBQJkyYQGBgIBUrVmTlypXY29tz69Yt3n77bWbNmkWrVq0ijnnw4AFZTFe0YnD//n2AOPdJLPv3y1WVP/6Qme8DBsj05pj6f80a6NYtprM4A+X57LMgeveWLRcvwrBhkvwWoGDB6NXPHz+Wok7btsGzZzJ7fsYMucCoVFr04YcSfNu1Sy6enzoFefJYu1VKKaVikhRFtB4+fMjt27cZPHgwtWrVwsfHhzlz5rBo0SIGDx6c4LZau4hWcrl505Hjx0tjY2PkzTcvcOFCEgfeEqC0uzu2N2/GvdPNm4SGhib5v1lakhI+f0mtSRMIDMzBrFn5WbHCnr/+esrUqdext7d8+lt66L+kpP1nGe0/y6SEIlpWD7zZ2toycuRIRprmB0eRN29efH19o23/7bff4jxnkyZNaNKkSaK1MTbHj0OzZpJGYvp0OHJEZs+HhkoaiRc1bQrHjkXf3r17OI8ehdKwYRggKSyqV5e8tEYjuLtDeLjkiT1/HjJkgLAwSVvx118wd64E/T75RP7gnDyZxEsHlLISGxtZqVK1qgS7W7aEn36S3wmllFIpS1IU0Ro/fjy5c+emX79+AJQsWRKj0ciUKVPo3Lkz2bJlS1BbrV1EK7ls3WoPwNtvh1OzZjGrtiU2dk+eQL58MiCOTd682NnZRQvMquhS0ucvOYwfDx4ewfTs6cD+/dkAV9avDyKhaTzTW/8lNu0/y2j/WSYlFdGyeuAtNZs8GcqVgy+/lMeNGkFIiORyHTIkejDAzc08dyzAwoXg62tg1aoruLkVAGSqtLOzpLgAWLVKAmstWsDp01CzphRqOn1a0l+8/rrsV6uWBNz279fAm0q7MmWSpNAVK8qMt549ZSWJLsNWSqmUJSmKaJ05c4ZRphxh/ylbtiyhoaHcunUrwYE3axbRSi7h4ZKIHqBbN9uUW5DFwQEGDYrMBReT3LmxzZoV5ygBXRU3a3/+klPXrjJxoVUr2L/flpYtndm1KzIFYEKkp/5LCtp/ltH+s0xKKKJl1eIKqVlQEBw6FL3YQZs2kpLiyJGXn+PePSn41KtXKKVLS74Jo1Hyutnayqy2d96RmXIVKsCdOxJ0A9iyRQJtpqAbyAy5P/+UpKJKpWWFC8vvgJ2dBKHnzLF2i5RSSr0oKYpo5cyZM9pqCF9fXwwGAwUKFEi8xqdBR4/C9etyAeudd6zdmjjEp/jByZOy7OTx4yRvjkqdGjWS+hqurvK9rHZt+e6llFLWoIG3BLp6VSpCFS9uvt20SiGGFbLRTJ4sS+cmTQqJ2Hb9Ojx5IpXRbW0luOfkJAOkW7cij/31VyhVChYskPxv9vYSnDt82LL3pVRqUaeOzBgFyXW4a5d126OUUspc1CJaP/zwAxcvXuSDDz4wK6L14MEDAgMDzY7z8fGJtUBWt27d2Lx5M2vXruXmzZscOHCA2bNn07FjR1xfTLSvzKxdKz/btiXBy+6Sxb//Rl5Ra9kS8ueXae3588PHH0vOCRcX+OEHqFYNrl2zbntVilW9uqQkyZkTfvsNatSQ71hKKZXcNPCWQKacwJkzm2/PlEl++vnFffz9+/DFFzBwoPm056tXI+8XLy4l31eskCWldepEFn968AA2b4bly2HePNixQwZRDRrA779b9NaUSjX69YO+fWWmaMeOkvdNKaVUyjF48GDatGnDhAkT6NChA7a2thFFtP7++29q1KjBnj17zI6Jq4hW+/btmTFjBlu3bqV58+Z4e3vTsWNHxo4dmwzvJvUKCJAxJcgyvBRt6lR4+BBKloSNG6V66Y0bhB0/DoMHyx/8I0fgtdfgwgWoUgWizKpUymT/fklJ8uSJTGi4fFlitTHV5FizRuK7L95cXJypUKE869bZcv16zPuYbqYiegULxr5PoULJ2AFKqRRDc7wlUHh43M+/WIH0RStWyFLSIUPMty9dKj8dHeHMmcg8cUWLSkL59euhd2+Zbff4scy0z5tX9qlZE4oUgdmzZfmdUunBwoVSBfjQIcmDePIkZM9u7VYppZSCpCmi5eXlhZeXV2I1MV3YuVMuChcoEJm2JEW6cAEWL5b7CxaAoyNBrq5cuHABT09PnG1t5bmyZSXY1qyZTGWqXVsSvrZubaWGq5TmxSJ4e/fKmNGUumffPlktZBKfIni5csW8z6efwldfQY8e8vibbyQtUVTHjsGwYXLBWCmV/mjgLYFMqxmePjXfbprp9rLVDlu2yOw0Nzfwl/Ru/PqrgW3b5H7LlubFGapUkXOeOyePM2UCT8/IoJtpW7VqkfsolR7Y28vsz0qVZMZo27bw3XeyXSmllFKRy0y7dHn5xWGrMRrhgw/kynSLFlC/ftz7580rOVbefRf27JEBwNy5MHy4VlxSMRbBMxolSPbwoawk2rlTfkL8iuA5Osp3sqjOnJGg28yZspQV5HWj8vODDh0kuDd6dOK/V6VUypdS//SmeEWKRE5Zjsr0OK7q5rdvS3CsXbvIbeHh8MEHDhiN8vi116IfFxoaGYwrViz6lRSQqqpaaVilNzlyyOApY0Y4eBCGDrV2i5RSSqmU4e5duSAFEnhLsfbsibxy9tFH8TsmUybJtzJggERVRo6UPBShoUnbVpWixVYEr0sX+c5Vtiw8ewaNG8vHJyYxFcF7kdEoH72SJSVmHJsPP5Q0Q59+mqC3o5RKAzTwlkBOTvDWW7BtGxHBMpCKpK6uMvsmNqY0FNWrR27buTM7J0/akjGjXEnZts08sPbDD5LfzbQ8oEkTKbAQNUfBw4dSsSpFLyFQKomULi1LsQ0GWLIEli2zdouUUkop69u4USaRVakSvShYihEcHBm5GDo0slpZfNjZwaJFMH++DAI++0zWGL4s4bJKs15WBK9rV/Dyku9arVtHzgiNKqYieC/66iv5XrdggUzIiMlff8Enn0hMWAsvK5V+aeDNAhMmyH+27dpJ3oCJE8HbWyosOjvL3/vjx6UQQlTnz0sOtyJF5PHDh7BokawZnTpVCjbduSPBtb17Jdlnx45QubLMvAfJDZc3r0xZ3rhRZvs0bizjjREjkq8PlEpJWrSAGTPk/qBBcrVTKaWUSs9MQYUUXVRh0SK4dAnc3WWA/aoMBgnYbdsmSz+++07W/d28mehNVSnfy4rgBQRImpL335eg9HvvSXDMJLYieC/y9paJFLVrx77Pf6kKo+X1VkqlLxp4s0DdujLDzddXrpqsXy//AY8aJc+fPSsFEb791vy4e/fM/xOfMsWeJ0/sKFkynEGD5JiDB2UqdOvWEkhr3lySgJqupmTNKrPbqlaVKc4dO0K2bFLkKV++5Hj3SqVMY8ZIHo3QUGjTxrxSsFJKKZWe/P67rJCwt5ck8ynS/fswbZrcnzUrerTkVXh5wc8/Q65ccqW7cmVJwqXSlfgUwbOzg5UrI9OTDB0KU6bISqbYiuBF9csv8l0vhroxEQID5TV69JDvbkqp9EuLK1ioZcvo+QNMatc2X4ZqsmSJ3ABu3IDVq+WfYcGCYOztnQApknDwYNyvnTevBPuUUpEMBhnkXLoEp0/DO+/I4Mh0lVMppZRKL0yJ5Zs3lwu0KdKECbJMpHx5mYJkqQoVZMlJ06bwxx+SG2bjxshlIyrNi28RPBsbWWmUPbusXJo6FR49kokMLxbBe9GWLRJMa9Ik9nbs3y+v2amTZe9HKZX66Yw3K3NxgddfN9K9+99Ur/6SyzNKqXjJkAG2b5cL3v/7X2QyXaWUUiq9CAuLvECbYpeZnjsn04tA1volVsnVAgVkaUj9+hI58fKSEpUqXXiVIngGg8R+Fy+Wx4sWyceyVau4X2P3bvlY2dvHvU+hQhILVkqlbxp4s7IcOeDYsUD6979j7aYolabkySPBN0dHqVg1caK1W6SUUkolnx9+gL//ltk8jRtbuzUxMBplLZ/RCO++a151LDG4ukq+l169Il9r8GCJSKo0LSFF8AYMgHXrImO/69dLLriYPHokKyte9pE9fjzxP9ZKqdRJA29KqTSrcmVYvlzuz5wpK02UUkqp9MBUVKFDB3BwsG5bYrR5Mxw+LNPU58xJmtewt5cqp6bzL1ok05SePUua11MphhTBM9Iu+wH2OnoxMcsivL2NjBtrjLkI3po1dOpsYFL4ZBwJ5IcfDWRwNuDs4kL5ChWwXbcOrl8Hg4Hz2WsBULJnVZkyZ7p16xbx+mEPH3PhjzBKbp8BGTNKYu4ff0z+jlBKpQgaeFNKpWldukQmvu3eXfK+KaWUUmnZ06cy2wdS6DLTgIDIP86jR0P+/En3WgaDVD7bvFmmQu3eLdOh7uhqk7SsrvNxttq0w9dYHK/wbaw3dMLbOJJRRgnCRiuC17QpHDvGvVb9yJjJhredj1GFY1x2KkWgWy7CGjaE3Llln+lyVTfrxqVw7Bh07ixB3h495FxhYTxs2JHQcFuytn5bptqZEsL9/rsVekMpZW0aeFNKpXmzZsl4KjBQLnT//be1W6SUUkolna1bJbZVokQKzS81bx789Rfkyxd3WcjE1KaNVC5zc5MkXpUrw2+/Jc9rq+Q3eTIty//F7//mJyjEhqv/ZmP4KFtZAhEQEFEEL6Keh5sbVKnCkq25+MfPAe/DVajrcpJCgRfoaL+JwExukr+kShXaTSiO0Qgl3i0rAbevvpLz1qgh59qwAfffvsf4+3n6rqkCDRtKJDx/fqm4oJRKdzTwpqxr/36oWBGcnSX76Lx5MZeCBVizxnw69383syngJhcvSvWqzJmljFfLlnD1qvn5zp6VK09ubpIApUED2abSHFtb2LBBkunevi0fh8BAa7dKKaWUShqmZaZdu8pwKUW5eVOuiAF4e8sYMLlUqSLrC0uUgFu3JFCyd2/yvb5KHkFBcOiQDPiiatNGpoMeOfLSU7yZ5x7TmcCaDH355k4tVqywi76T0SjJ4UqWhA8+iNy+ZQvUqgWvvx65zckJ/vwTRoxI2HtSSqVqGnhT1nP8ODRrJoOfbduk1vaoUbHn+fhvCviLt3BPT4Jz5pQp4CADuurV4Z9/JKnXZ5+Bj48E1kxZUi9flj+I/v6wcqUE9YKCZADm65ssb18lr8yZYedOmel/4gT07h17jFcppZRKrW7ckIldBoMMrVKcMWNkPFajBrRrl/yvX7gw/PIL1Kkjud6aN4elS5O/HSrpXL0KwcFQvLj59qJF5Wd8xvqTJ2NrZ0P41EkAzJtnHz014FdfyaBywQK5ymvy669QqpRsL1hQZsVVqCA5DZVS6ZIG3pT1TJ4M5crBl19Co0bw4Yey3OC/KeDR/DcF3Ox28iQGX1+uzJkjzwNMmSIliw4ckGBd27ZSmsjfPzLB18KFcoX1229lZlzz5rBnD7i4SOJdlSYVLSopXmxt5WM3b561W6SUUkolrvXr5WedOkmbOi1BfvlFpqAbDBKUsNZ0vKxZYd8+WWcYFgb9+8Pw4VrxNK148kR+Zs5svj1TJvnp5xf38ffvwxdfwMCBtOudibx5A3nwwBD9K4K3t1zsr13bfPuDBzLgXL5cBps7dsj3jgYNNMebUumUBt6UdSTCFHDu3YMJEwjt1Qv/0qVlm9EoiU26dzdfulChgiTRrVlTHnt6ylRvF5fIfVxcIG9euHLForemUra335axPkg+5z17rNocpZRSKtEYjZHLTLt0sW5bogkPhyFD5H737lC+vHXb4+AAq1bJhV+Ajz+Wi7X+/tZtl7JceHjcz9u85CvwihUShB0yBHt76N1bkgPPnQuPH/+3zy+/SIqamHIUBgfLjt99J99tmjSRi/2ZMsHs2a/6bpRSaYAG3pR1JNIUcGxsCJk0KXLb9etylatAAcm5kC2b5FR45x3J5WHSr1/0P5SXL8P//idTw1WaNmAA9OolX1A6dIALF6zdIqWUUspyp07JECpDBmjd2tqtecHatbLyIFMmmDHD2q0RBgOMHy+z8Bwc4JtvZPbS3bvWbpmyhKur/Hz61Hy7aaab6fnYbNkis9P+W03TsOEjPD3DefwYPvooyj6mSqUvypQJypaVC/pRt1WrJoU9lFLpjgbelHUk4hRwsmSJ3P7ggfwcPVqy6G/aJFetzp6VNRfPn8d8voAAeO89CdINGvTKb0elLgYDLF4Mb70lH7XmzeHRI2u3SimllLKMabZbq1aRQ6oUwc9PcrsBTJwIOXNatz0v6tABfvhBim2dOiUVT//4w9qtUglVpIjkFbl82Xy76bGnZ+zH3r4twbEo+QdtbWHixBBAVk08eADs3g1eXpK/7UXFisnqnheFhEhUXCmV7iQo8Hbv3r3EbodKbxJxCriZ4GD5mTOnFGxo0AA6d5Y8C5cvRyY+ierpUynycPIkrFsns+VUmufgIBcrCxSQ1cXt2kFoqLVbpZRSSiVMcLDUlAKpZpqizJwpKUKKFoXBg63dmpjVqCGFu4oVg7/+ktlJBw5Yu1UqIZyc5Orqtm3mlbS2bpXZbpUqxX7siRPys3p1s80tWoTx5ptSj2PhlEdw6VK0fSI0aSIFFqIuqXj4EI4ejUx7o5RKVxIUeKtTpw49e/Zkz549BJsCHUq9ikSeAh7BdHm3cWPz4F2VKnLOF6d337wpA62jR6Uy0TvvvNr7UKmam5tUOnVxkQvdw4ZZu0VKKaVUwuzZI7O3c+eWfKYpxpUrMH++3P/4Y3B0tG574lKsmATfatSQMWnjxrBypbVbpRJiwgQJorVrB3v3ykxLb28YN07yQPv5wfHjkatlTM6fl89okSJmmw2GyHSAx1eclzslS8b82kOGyDLTpk0lGr5zp3yWDAbJMa2USncSFHibNWsW4eHhjBgxgho1ajB16lTOnz+f2G1TaVkiTwE3O6/BEPP07tBQ8+nd58/LUoK//oL9+2Vdhkp3ypSRCqcgBW2XL7due5RSSqmEMC0z7dxZhlgpxogRMh2vQQNZYZDSZc8uM906dZKxY8+eMHbsy1drqJSlbl2Z4ebrK0tC16+XwNuoUfL82bNQtaoUPYjq3j3zNDZRNGokk9yyBv+3+itr1phfO2tWuahftaokFu7YUfJOHzkC+fIlyttTSqUuCQq8vfPOO6xatYqDBw/SvXt3jh8/Ttu2bWnWrBmrVq3in3/+Sex2plz790PFinLlpFAhKRkddUpzVGvWSFDohZuziwvlK1TAdt26yH0vXoQWLSQHWrZsUv3z6lXz8927J4OC7NllNleHDvD330n2VhNVYk0BDw3F8ckTSru74/jkifl5owbffvhB8ruZpnffvAn16sm/wdGjcoxKt1q2hOnT5f6AAXD4sHXbo5RSSr2Khw8l5RSksGWmBw7A9u0SCZw/X8ZdqYGjo1yVMxXwmj0b3n1XcgKr1KNlS/j9d/lOcPUqDB8e+Vzt2vId5P33zY9ZsiSyuMbz59h9/jnZ/vvlMhikLshm2mFvZ+SaY4nYXztvXgn2PXok61P37dMCbkqlYxYVV8iZMyd9+/Zl7969bN26laxZs+Lt7U3t2rUZNGgQv/32W2K1M2U6flyu3JUoIYGeTp3kKsqcOTHv37SpTF9/4Rbu6UlwzpyENWwo+928KUGlf/6R6cmffQY+PnKl0PQHPzRUpiyfOAHLlsHSpRJAatBAEnemBpZOAc+cGRYuxLZyZRwLFMC2cmVYuFD+De7ckfwKe/dKwLNjR5nd1qKFnGPwYCnQMGlS5OuYbj4+yd4VyvrGj5ePYkiITH68ft3aLVJKKaXi56uv5O9XuXJQurS1W/Of0FAYOlTu9+8f+7K8lMpggKlTpZiXvb3kC3777ejjUpX2+PtL+dLChXH44AMKTZmCzbFjANSqBfXry8d72jQrt1MplWrYWXqC06dPs2PHDr7//nv8/PyoXr06tWvX5tChQ3To0IFRo0bx/otXEtKKyZNlhGNap9aokYx6Zs6Utf0vVq1xc4uek2zhQgy+vlxZtYoCpuemTJFZXwcOSAAKZDZdixZShr1mTfnjf+6cVFwyDWTKlpXR1tdfSxAwpTNNAZ88WaaA58kjgTfT1ShTJdLVq82vRt27J0G3Ro1kH5ObN+XY8uUlEDpvHrRuLX3o5SWPbW1luYPpsnDfvtHbVasWHDqUNO9ZpVgGg3zULl2SX6133pFYdsaM1m6ZUkopFTfTMtMUNdvts89knJotm4xtU6uuXSF/fpk9deyY5A3+9lu58K7SloAAmdAwZ4583wCM9vYYQkKwnzs3Innihx/C99/L793o0fpRUEq9XIJmvN24cYOFCxdSr149unTpwrFjx+jSpQs//PADK1asoHPnzqxYsYImTZqwdOnSxG5zyhAUJMGZli3Nt7dpIwUDjhx5+Tnu3YMJEwjt1Qt/0+VJo1GCUd27RwbdACpUkFlcpqWS330HHh7mVw9LlpTcaHv2WPTWklVCpoAvXCgl6aMG3aI6cwb+/FMCl/7+MnNwxYrIfA0ODhIgNRpjvmnQLd14caX4kiWyIiZnTvlYdu0amdIllpXiuLg4U6FCedati0yokzdvzPtGXYW/e7esqHZykv0/+EBWIiillFKvwtdXFhDY2krWkRTh0aPIZZrTp0vwLTWrXVuCboUKyXi1alUdL6YlAQGwYAEULiyVtu7dg4IFYcUKAk+cwGhjg+3+/RHfPSpVkvkQ4eEyf0AppV4mQTPeGjZsiKOjI/Xq1WP69OlUrVo1xv0KFy7M9bS6XuvqVZk5Vby4+faiReWnr6/MQ47L5MlgY0PIpEmRudmuX4cnT6BAAUk2tXGjBI8aNoRPP5Vv6CDlqV98bdPr+/pa9NZShIAAKaJw65bcot6/dk2Wm8ZlwQJZXpozZ7I0V6U+ppXi7dvLd4IjR2SVcmgofPONjLG/+UYu0k+bFrlS/EXdu4fz6FEoDRuGARJcu31bJm/WqGG+ryn2+803Mhmzdm2ZoBocLG345ReZZWdn8VxkpZRS6UXUhRcpZtgzZYoE30qXht69rd2axFGihEQ433lHBgQNGsiF3RQ1zVC9koAAqao1e3bkd7ECBSQdTteu4OCA0d+fRw0akH3fPlnVtGULIOO2XbtkHDd2rCw8Ukqp2CTo693EiRNp0aIFmTJlinO//v37079//wQ1LMV78kR+Zs5svt3UJ35+cR9//77kjBg+XL6Nm/6zN+WNGD1aLqds2iT7jh0ryy5//RVcXOT1ixWLft5MmV7+2tb29GlkEC3qLWpw7eFDy17j5s3Uk8BXWcXLVop//rlMtpw+Xb43tGsX40pxfH0NrFp1BTe3AoD8ioJM5nyhEn2EKVNkcuq+fTIBE2Qya5Eisty1V6/EfrdKKaXSovDwyL9jKSb+88cfMoUc5EJoWrqa5OYmBbvee0/Svrz3Hly5In/YddyZegQGSsBt1qzI72D580vC3/ffjxyc/edut24SeNu2TXJBlyxJmTJy8XbTJklTvWtX8r8NpVTqkaC/hJ06dWLPnj0cP36caf9llTx79iwzZ86kf//+1K1bN1EbmSK9rKS4zUtW8a5YAWFh8g0/quBg+Zkzp/znbjpP0aIyrX39erlyGNfrv+y1k4rRCP/+G3NQLWpgLb6BQWdnmeH34s3FRaYm/Zd7IUb58sVeXVale6aV4lOnmm9v0wbmzpXZb++9J8tNP/5YxmBFi8Kbb0bu+99KcXr1CqV0af+I7b/+KvHvwoVjf/0LF2DgQPNxXc6cEoz79lsNvCmllIqfn3+Gv/6S1MDNm1u7NcjY64MPZIzr5RWREytNyZBBoi1Fi0rgZto0Cb6tXCnFv1TKFRgo38FmzZIUPiDfGcaPh27dogXcIg4rUoTQFi2w27lTjv0v2j11qsx4271bVlJUqZJcb0QpldokKPC2fft2xowZQ4MGDSK2ZcmSBTc3NwYOHBiR/y0+wsPDWbx4MZs3b+bp06dUrFiRSZMmkS9fvmj7Llq0iMWLF8d4nlatWjFr1iwArl27xqxZszh16hTOzs40bNiQkSNHkuHFYgeWcHWVn0+fmm83BZVMz8dmyxaZou7mJktJTUwz5ho3Ng+gVaki5zx3LvL8L7626fVf9toJER4uM+9enJn24i0wMH7nc3WNOaiWN68UWcibV2YCxnT1MDRU1vNFzQf3osGDIUeOBL1VlfbFd6X43LlyYXPfPllZcuoU5Mol+/y3UpxJk0IiLpaCBN6yZZMg3oED8t2jaVO56J87t+yTIwfcuGH+2iEh8uUpKCgp3rFSSqm0yFRUoV276DW9rGLXLsk67+AgRa3SKhsbmSJfuLAU6lq/Xv6If/MNZM9u7dapFwUFSWB05kz5HgPyXcMUcItHwDRk1CgJvG3cKBG3woUpXlwu1K5eLRdjDxxI4vehlEq1EhR4W7lyJd26dWP06NER2woXLszSpUuZM2cOS5YsiXfgbcmSJWzYsIHZs2eTK1cuvL296dmzJ7t27cLhhasO3bt359133zXbtnr1ajZu3BhROfXff/+lc+fOvPHGG2zevJn79+8zevRowsPDmZKYFZWKFJEstpcvm283Pfb0jP3Y27clgGYqsf7ieQ2GmL99h4ZGjqo8PCKDcC++fqVK8XoLZuf9+++Yl3yabnfuSGQgPtzczANoMQXWLCkVaWcHXbrAhg1SSCEm//uf9Wb+qRQvvivFbW1lfFWligTjWrWCgwfl+JhWioME3m7flompQ4fK7LZJk6RY7rlzMmGze3eYMUOKZnXvLilGJkyQ82oVVaWUUvHh7x+RbiplLDMNCoq8KDpsWOz5FtKSnj0lJ1ibNnD4sKxO2bMn8kqesq6gIImKzZwpaWhAvoeMGwc9erzSDEVjuXKSc/u772QA99lngIzx1q2TFcgHD0pmIKWUelGCAm9//fUXtWrVivG5t956i40bN8brPMHBwaxatYoRI0ZQu3ZtAObPn0/NmjXZv38/zZo1M9vfxcUFFxeXiMc+Pj6sXbuW6dOn4+HhAcC6deuws7Nj/vz5ODo6UrRoUQYPHszGjRsxGo0YEiv/gpMTvPWWLAcdMSJyZtbWrTKbK67g14kT8rN69ejPZcwYed6ZMyP/IPzwAzx/HlnVtEEDCTz9l2cAkPsXLsg3eJPAQAmaxZVP7e7dly+dBXmPuXPHPDvNdHvtNembpObmBnv3yl+6BQvkj2m+fPLXbu1aKUFZqFBkRS2loniVleJZssDOnVC5suRS7ttXxtMxrRQHSRliZyfVUkF+ZUuVkkILa9dCv36SCiY0VHKCjBkD9vayvPSdd+TXWCmllHqZHTtk8UOhQjEPKZPdwoVyAThXLglspBf160t1pKZN4dIluVq3fXv0Cksq+QQHS8BtxozIgNtrr0nO7J49E/5dZcIECbytWSODuLx5KVhQLrZ++qlMoDt6VNP9KaWiS1Dgzc3Njd9//50qMSxkv3jxIlmzZo3XeS5evMjz58/NqqJmzpyZkiVLcurUqWiBtxdNmzaNChUq0LJly4htR44coX79+jhGuYLRtm1b2rZtG682vZIJE6BePZnf3727/NH19pbKOM7OMm3Gx0eu+EXNyn7+vATUYrsSOGuWlDts0kSCevfuSbGFypWldjVINs8PP5Q/9u+/L5WjNm6UNW7r18v0/lu3Ios1vIydXdwz1PLmlYGUvb0lPZa43Nxg8GDC3n2X0NBQ7OzssHV3l6DngAGyFjBXrrRTTUslmlddKV68OHz1lawAX7NGfiViWikOcrH7RdWryzl/+00e29nJfxNTpsiy19dekwDfW2/Jr7BSSin1MqZlpl27poAv+nfvSjUikD9wLynAluaUKiVJvlq0kLwUb78tA4YOHazdsvQlOFiWJMyYEZnTI3duCbj16mX55IAaNWSw9vPP8l1rwQJAAm6rVskF2r175SucUkpFlaDAW7NmzVi6dCnOzs7Ur1+fbNmy8ejRIw4ePMiiRYvo0qVLvM5z9+5dAHKbEh/9x93dPeK52Bw8eJBz586xfft2s+3Xrl3j7bffZtasWXz33XfY29tTv359hgwZYhaMe1VGoxH/F79hV6mC7YYN2H/4IQYvL4yvvUbojBmEDhwI/v7Y/PILTo0bE7RsGWFR+sT+9m3sXF0J+O98AQEBZj8pUwabr7/Gfvp0bLy8wN4eY9GihBcujKFRIwy3b2P4+28Mjx/L/jNnmrdrzx7ztjs5YcyTR26vvRZ5P08ewvPkwZg7N7i7v3xpZkhI/JebJqMABweu37lDwYIFyRAUBO+/j/3Nm9jPno2xXz+CXV0JSxEZh1OmaJ+/dCB3brC1zYCPTwgNG4ZGbP/f/2wAJwoVCsTf33xaXI0aMHu2HaNGOXD7NrRuHYy/f6hZ/z15Atu321KhQjilSkUW9wgPh+DgDGTJEoq/fwg//2xDUBDUrx9OwYKyj58fnD+fgU6dZJ/0Ij1+/hKT9p9lkrr/EnWmvVJR/P037N8v9+M57E5a48fL1ayKFVNIg6wgVy6p3NS5s+R669hRrq6NG5cCIqNpXEhIZMDt+nXZlitXZMAtMRMgjh8vgbfPP5d/W3d3cueWolne3jIvo1EjzXijlDKXoMDbgAEDuHr1Kh9++CEzZsyI2G40GmnUqBGDBg2K13lMA90Xc7k5OjryxJSEKRarV6+mTp06eL6QS+3Zs2csX76cpk2bsnjxYu7cucP06dN58OAB3t7e8WpXTEJCQrhw4UL0J4oVk//oozLt5+YGp0+bbwPo00duFy5AWBhumzdT8MIF7O/fx+bePezv38c2apGC4GAMv/2GjWm6TBRhLi4E58xJsLs7IW5uBOfMSYi7uzz+b3tY5sxx/8H/91+5pXLXTX9oAVq3Jr+vL27ffIN9165c//RTnpUrZ7W2pQZm/ZcOlCtXjK++sqFhQ9+IX4+VK/OQMWMOMmXy4cKF6FVx69SBChWKcfp0ZlassOGtt65SsKDkY7x+/TpBQQY++KAsder8y4cfXo847tAhVwICilKw4DUuXHjKqlX5+PnnLOzY8Qd2//0vvG1bDh4/LkCZMle5cOFZUr/9FCe9ff4Sm/afZZKy/14c4yiVGDZskIs61aungFRqZ87Isj6ATz5J3xEHZ2fYvFlWqnz0kURhLl+WfGD6f0HiCwmRqZ8ffhgZcMuZU/J49OmTNBVH6teHChXkO96CBRETIEaNgmXLJJ/vtm2S9k8ppUwSFHizt7dn4cKF/Pnnn5w5c4YnT56QKVMmypcvT4kSJeJ9Hqf/pvsGBwdH3AcICgqKswLpnTt3OHHiBJ9//nm05+zs7ChUqFBEIYXSpUsTFhbG0KFDGTNmDNkTWGnI3t6eokmQKNXwxx9kiKXqkzF79ogZauGm2WpRZ6y99lpEdnj7/27Oid7ClC8gIIDr16/LjLeon5s1awgNDsbu228pPnIkgfv3YyxVynoNTaFi7b80bupUG5o1c2TmzDfo0iWUEyds+fJLO6ZNC6FcuRL4+cHFizYUKhRutlL87bftOHPGiL+/HWPGlGTfvsc8fnwtov9Gjgzlww+zU7RoZho2DOOPP2yYOdOeZs1C6do1LwAjRxrYscOejz9+g65dQzl/3gZvb3vatAmlc+foFZ3TsvT6+Uss2n+WSer+u/xiASalEknUZaZWZTRKwlOjETp1ijnfQnpjayvLEIsUkWlQa9bIssetWyGe6XjUS4SESJ7nDz+UWYUgAbfRoyXg5pyE34gMBgmoennB4sUwciRkzUqOHPDBBzBtmqSYbtlSPgpKKQUJDLyZFC9enOLFi0fb/uzZMzLGozSfaYnp/fv3yZ8/f8T2+/fvRxRLiMmBAwfIli0b1WPIJJsrVy6KFStmts30+Pbt2wkOvBkMBpyT4j/xChUInj+f+1eu4FauHI6FC0fkVTNkyIBpnpr+v/1yGTJkiP5vtHkzNGiA4cgRMnh5SR6+AgWs0r6ULsb+S8OaNJEx8OTJdrz7rh158sgSgeHDHQAHTp6UGW6rV0saRRM/P8iRQ9KEXL5sQ+/ersyc+X/27jzO5uqP4/jrzj5j34lElCWKQskSCoWSJSqUvZTse3YieyhFWUKoaFEUkYqKyFrDyPpDWUrZmfX3x8edfSwz986d5f18PO7j3vne7/3ec48xc+Zzzvl8YvpvxAjL2fbWW768954vefJYQYbhw30IDLQfuZUrw5dfwsCBPjz1lA8FC9rOhUGDfPD1TdGP5XQrs33/uZr6L2Xc1X/aZirusGMH7Nxp6YLdkcL4pnz4oWWTDwqy3G4So0sXKFbMckGvWwcPPmjpYIoX93TL0q/w8JiA2/79dix/fltu1qWLewNusT3+OJQrB7/9ZsG3IUMAK+Y7fbptalq0KPPuuhaRhJL1F15oaCjvv/8+v/zyC6GhoURF2ZYsZx60ffv2sSORbZHxlS5dmqxZs7Jp06bowNvZs2cJDg6mdevWSb5uy5YtVKlSBR+fhM2vXLkyO3fujJNXZe/evXh7e1OkSJHkfFz3cjgI79yZE7t3k7tMmdT7hZFZBAZaScoaNeD3360M+I8/QjIDsJKxNGlit8TUqmUT+PHNmGG37dtti8+333ozbVoR3n3XnvfysrFfly7Xfu+6de0mIiJyM5yr3R5/3MMLqC5etNU+YFv70uI429Meeww2bLCKp3v2WKG05cut8qncuPBwi2SNGmVbd8FS+jgDblmypG57vLwsv9uzz9p20549IWtWcuSwRXcDBlgBraefTlt16UTEc5KVhGH8+PFMmjSJEydOsH//fo4dO8alS5fYuXMnu3fv5oUXXrih6/j5+dG6dWsmTpzI2rVr2bNnDz179qRgwYLUq1ePiIgITp06xeXY+c6A4ODgJLe0dujQgSNHjjBs2DAOHjzI+vXrGTduHI0bNya3ygVmTrlywddfw623QkgINGoEFy54ulWSzlWoEPPHz6JFBfjuu0yc00ZERFJFeLgVr4c0sM10/Hg4ehSKFoU+fTzcmDTsnntg0yaoWBFOnbLl9EuXerpV6UN4OCxYAGXLwvPPW9Atb14YNw4OHrTvu9QOujm1aAElS8Lp05bD76quXW3X64EDVulURASSGXhbvXo17dq1Y/ny5bRu3Zpy5crx8ccfs3r1agoXLkxkZOT1L3JVt27daN68OYMHD+aZZ57B29ub2bNn4+vry19//UX16tVZGa9K56lTp8iZM2ei17v99tuZP38+Bw4coHHjxgwYMIAGDRowYsSI5HxUySiKFIFVqyB3biv33rJlmqzQKulLs2bQqZN9H73yih8qLCkiIu70zTdw4oTFHh591IMN+d//LPAGls9MOSavrXBhq4TZqBFcvmx7hMePT3xpvUBEhEWY77rLIsx//GG7VV5/3QJu/fp5LuDm5O1tS9vA/g9cXSiSJYsthgNboBdv/YiIZFLJCrydPn2amjVrApbnbdeuXQAUKFCAzp07JwiUXYu3tzd9+/bl559/Ztu2bcyaNSt6S2iRIkUICQmhadOmcV6zY8cOnnnmmSSveffdd7Nw4UJ27tzJjz/+SP/+/VVVTKBMGUusFRgIK1ZYeXENeCSFRo4MI3/+UA4c8ELxfRERcSfnSutnn/XwFrb+/eHSJahZU+Ubb1TWrPDZZ/DKK/Z1//6WBFYTwTEiImxL6V13QevWsHevTZqPHWsBt/79rR/TijZtbEfN8eNxlre98IIdPnYM3n7bg+0TkTQjWYG3bNmyERoaCsBtt93GX3/9xfnz5wEoVqwYf/31l+taKOJKVavCRx/ZLNX778dMSYkkU/bs0L///wCb8Ny61cMNEhGRDOnMGYvbgIe3ma5fD0uWWHXHqVPtXm6MtzdMmxbTb7Nm2Sq4M2c83TLPioiw76ny5a06bkiIBdxeew0OHbKVZdmyebqVCfn52eo7sBWMV4Oo/v5W2RQsZnj1z2QRycSSFXirVKkSCxYs4NKlS9x2220EBgayZs0aALZt23ZDFU1FPKZRI6Iz4b/+ug1+RFLgoYfO0KxZOBER0LGjpSQRERFxpaVLbdta2bJw770eakREBHTvbo87drSEp3LzunWzKGpQEKxeDdWr2/bdzCYy0irj3n03PPOMlQPNlcuqlh48aBPkaTHgFluHDpbU7fDhmASMWEq6kiUtrd+0aR5sn4ikCckKvL388sts376dzp074+Pjw7PPPsuQIUNo2rQpU6dOpX79+q5up4hrtWsHY8bY4x49bJZNMp/Vq6FyZRv4Fi9uS9aS2n48b57NTse7BWXJwn2VKvH2A3PIlQu2bYNLeYskei5//x1zverVEz9ny5ZU+egiIpK+OLeZPvecBxeZzZtnv+iyZ7fgiCTfE09Y3rdCheC336zi6a+/erpVqSMy0naglC9vpT+DgyFnThg50gJur75q32PpQWAg9Oplj8eOteA0thV8+HA7PGEC/PefR1onImmET3JeVLp0ab766iv27t0LQO/evcmaNStbt26lTp06dO7c2aWNFHGLAQMsJ8O0aTaKzZsXHnnE062S1LJxo61+bNnSst9u2GDbBcLDY5LlxtawIfz8c4LDke3bE376NFmeqs+kPNC3/d9kO3OMv/tPIO+T1eOe7CwKExUFO3faQO2pp+KeU6aMaz6fiIhkGAcPWozG4bCdeB5x9mxMio5hwyB/fg81JAO57z6reNqwIezaZTnzFi2Cxo093TL3iIyETz6BESMs2AiQI4eNh7p3t8fpUZcutotm715bmtqyJWAxxbFj4fffbW5XsWqRzCtZgbchQ4bQvHlzqlWrBoDD4eDFF190acNE3M7hgClTrDzYhx9Ckybw3Xc2CJKMb9gwqFjRytSDlYcLC7OVkN27J6zQli+f3WKbNg1HSAj758zhtnz5aNsWgqdth+3Q4/smLBhbIvFVCfv3w7lz0KABPPCA6z+biIhkKAsX2v3DD1uhdo8YPRpOnoQ774SuXT3UiAzo1ltt8q9FC1i1ysajkyfbWCSj5M+LjIRPP7WA29WifGTPDj172s4T58RkepUtm/17DR9u48gWLcDhwNvb5nabNoU33rBT4g8lRSRzSNZW0+XLl3PhwgVXt0Uk9Xl5WZGFhx+2zKcNGlhQRDK2K1csyNqkSdzjzZtbQGzDhutf48QJGDyY8E6duFiuHGDj4/6Pbucs2fhg4+3Mm5fEa7dvt3vlxhERkeuIioq7zdQj/vjDIgdgk5Z+fh5qSAaVPTt8+aWVw4yKsoBUt27pP2msM+BWsaKNsXbtss86dKgVTRg+PP0H3ZxeecUqru7caf+WVz35pM3pX7hgi+JEJHNKVuCtYsWKbNq0ydVtEfEMf39b9l6xos3k1qtnQRXJuA4cgNBQm7WPrWRJuw8Juf41hg0DLy/CnGWrrsp7ZDtRuXKzlOY0a5+DyCxZbctB7GrP27fb4KxPH9viHBBgQd8beV8REclUNm6EffsgS5aE80WppndvWxX+6KP2+0pcz8cH3n7bEoI5HPDmm7bl9Nw5T7fs5kVFWfGI++6z5V47d9qqsCFDLOA2YoQVUchIcueGl16yx6+9Fp0z2OGI2WL61ltw7JiH2iciHpWsraalSpVi9uzZfP3115QuXZqgoKA4zzscDsY4E9eLpAfZs8NXX8GDD1pQ5rHHbEVUeknsKjfnzBm7j//v66ycdfbstV9/8qStlOzd22Zq4wXVsp87xl+FOtPwrx60KbObzt8PhYcesoTUWbJY4O38eRt0fvqpVcIaMQJq1LDnbrnFRR9URETSO+dqt2bNbM4m1a1eDV98YYGhyZM90IBMxOGwSbnixaF1a1i50vK+ffklFC7s6dZdX1SUfa8MH25jHrBv2u7dLY9b7twebZ7b9epluaM3bYK1a6NzR9evbzW1NmywINzbb3u4nSKS6pIVePvmm2/Inz8/YWFh7HLu04/FkVHyEUjmUqCA5daoVs0GC02bwooVtiJOMpbIyGs/73WdxcDvvWdVq7p3T/jcu+/i8PHhQe/K9KgCG36tQdnX76L6gOr211OXLjYT2q+fDabBAm4PPmiFFaZOhXHjkve5RESSoU6dOjc8dnM4HKxZs8bNLRKnK1diCq97ZJtpWJhtewTL66YCQKmjWTNL5vfEEzYhd//9FnxLqykqoqKsfcOHw9atdixrVtsu26sX5Mnj0ealmgIFoFMnmD7dxnpXA28Oh3350EM2hOzXz2KrIpJ5JCvw9u2337q6HSJpQ8mSNrtYq5bNVD3/vFWXul4gRtIXZ9Ws+Ns3nCvdrldVa+lS25KcLx9cvBj3uapVAbgXWxA3fjw8Pb0aR3LkwLFjh51zzz0Jr3n77fYHjfMcEZFU0q5dO8aNG0fWrFmpXbu2p5sjsXz5Jfz3n8VgatXyQAPeeQeCgy0tQrzUCuJm999v205feMH2J957L7RrZ5GbxALl8+bZ8/EEAfcBV2bOtLHLtSI+bdvC3Ln2+MQJC5p9/bXlmmvQwFY8FioUc35UlI2bhw+HLVvsWJYslu+sd2/7vsls+va1/zfffQc//WQTq9hca716toB0xAiSzgMsIhlSsgJvIhnafffZ9r8GDazaaYECllBYKzkzjhIlwNvbkubE5vz6WjP6x47ZisgePRI+d+YMLFsGVapAuXIMH25fHtgfSZhPKH758tng9YMPLL/c1SBdtEuXVO5KRFJdmzZtyJ07N7179+bhhx/mkaurNMTznIW3W7e2X1up6u+/Y4Jto0ZlvJxcad3GjdCxoyX227vXVr7NmQP//GP50+Jr2BB+/jnB4cj27Qk/fZqI+vWhYMFEz+Gtt2zM26GDfR0ebmlXzp61IFJYGAwYYJGjrVtt2/FXX1nAbfNme02WLLYq0pm/NrO69VZbnjp7ti1zW7Ei+qnRoy3wtmAB9O+vBaQimUmyAm/P3cBa9/nOhBQi6dEjj9i2wGeesVwNhQrZgEMyhoAAm3r85BMbIDqDqsuW2Wq3KlWSfq2zsEy1agmf8/e3QWeTJvDBBwQGwrvvwtQ6y/ELv8SuvLUp7+NjU5233BK3eurWrRb469/fdZ9TROQGNWzYkE2bNjF27Fhq166Nd6pHeSS+v/+O+Zu9TRsPNGDYMFtud/fdtn1OUtewYVb468MPrSBUly4WePv8c9vCOWVK3GhsvnwJJ++mTcMREsL+OXO4LV8+G6c88EDcc3791d5jzBhLRAbw8cc2yfj771C2rB2rUAHKlYNXX4Xvv4dffrHjQUExATdNHpoBA2zl4MqVNr67914AKle2ehmff27/vB995OF2ikiqSdb+uaioqAS3CxcusHPnTvbt28ftt9/u6naKpL6nn7aVbgADB8YsvZeMYfBgC6K1aGGztkOG2JaOQYNsEHn2rM02nzoV93W7dtnAtUSJhNcMCLDB1qJFtj1jzRpqb5/CIt/n+YzGtHinDpcvYzPEP/5oM6LffGPbRho1skHt88+nwocXEYlx5coVAHr06MHDDz/M//73Pw+3SMByu4WHQ6VKMbGPVLNrl610AhsLKRCbuq5csa2KzjK2fn42VnBWzZw+3fLAXbiQ9DVOnIDBgwnv1ImL5colfk5UFLz8sn2DOXP5geU8LlUq5hsvKgqOHLFxzoQJFnQLDLRg28GDlptWQbcYJUva3xFgAc1YRo2y+V5nbFNEModkBd4WLFiQ4LZs2TK+//57ihQposCbZBzdu8esQOrUyZKtSMZQp46tcAsJgSeftO2fEyZYxluwGcqqVeNsEQBsIJszZ8zX587hdf58zNeDB8OMGbaX4PHHYdIkvLq8SI/8i9mz5+r467nnbHY5ONjee9AgO/ebb/THjYikujp16rBt2zZy585N9uzZE1SrF89wbh5J9aIKUVGWTiEy0oI7yvuX+g4csFVud94Zc8zhsL2KYFs9P//csvXHrqwe27Bh4OVF2LVy8334oU1Cxg+u7t5t7x0VZeOZatXg0UctJYbDYfnbDh60cVP+/Cn+uBnSwIF2/8kn1p9XlS8fE5NT2kSRzMOlGeNz5MhB586dmadskZKRjB1rq5AiImx1VGK5MSR9atIEdu60meUDB2wg6VSrlg0427aN+5oZM+D4cXvuvfcILFaMsq1axcw6e3nZdpDffrMB6tGjBEwdx8S3AgH7dtq1C/te2rLFXnfyJMycCblzp8anFhGJ49y5c5w8eRKAt956ixMnTrjs2pGRkUybNo0aNWpQoUIFOnXqxJEjRxI9d/r06ZQqVSrR28Crf8TWqVMnyXM2O3NNZQC7d1vqLB+fmD/SU83nn8O339rq7gkTUvnNBbCcsQDZs8c9ni2b3bdvb3nUfv3Vto7+9lvc806ehPffty2gsScL45swwYJq8St3nDljxaOqV4f69W3sGxAApUtbcYaJEy0HsiStXDmbXI2KssFfLMOHW5zzyy9tc4WIZHxuKdX4zz//uOOyIp7hcFiirgYNLJDSqFGcmSvJhM6ds0zXnTrhuHwZ/2PH8HFuyUlCs2aW1yM83BZPRkSkUltFRK6jfPny9O7dmzp16hAVFcXLL7/Mww8/nOjtZgsvzJgxg0WLFjFq1CiWLFlCZGQkHTt2JDQ0NMG57du3Z8OGDXFuHTp0ICgoiLZXJ0GWLl0a5/lvv/2WYsWK8cADD1CxYkVXdEea4Cyq0KBBKu/gu3w5ZhKqd+9rV8AU94mMvPbzxYtbxObOO+F//7Pg2erVMc+/954NNLp3T/oaP/1kq/v79o05FhUFa9fC4cN2/9NPFnDr0cNWuN13H/j6puijZSqvvmr3ixbZBO9Vd94Zk1nEeYqIZGzJKq6Q2IxiREQEx48fZ8aMGdx1110pbphImuLraxlQH37YluTXr2+DkSJFPN0ySW3bt9tqtT/+AG9vwh99FJ8VK/B94w0b4Mafnb7K4bCiYevW2bfQW29ZbmQREU+bPHky8+bN47///uOzzz6jbNmy5HbBCtzQ0FDmzJlDnz59qHV1Rc2UKVOoUaMGq1evplGjRnHOz5IlC1myZIn+Ojg4mPnz5zNq1ChKlSoFkKBd48aN4+zZsyxevBgfn2QNa9OcyMiYwFuqbzN94w0LEBQqFLNVTlJfjhx2f+5c3ONnz8Y8X6KErURr0gR++MGitG+/bbN7S5daBdJ8+WzlWmKWLrVKtQ0a2Nfr1tn21PXr7WsvL1sxN2CAfT8439/ZNrm+SpXsb4ZVqywP3syZ0U8NHWr/z7/91m516niwnSLidskaobRp0waHw0FUVBSOq9UAo6KiAChUqBCDBg1yXQtF0oosWSzfV/XqsGeP5bpYv94GLZLxRUXZgLZXL9uaWqQILFlCaPnyhJcvT8D//mcVcAcPTvIShQvbuKtLF0vr1rgx3HZbKn4GEZFEFChQgP5X85lu2rSJnj17Urp06RRfd8+ePVy4cIGqVatGH8uePTtly5Zl8+bNCQJv8Y0cOZJKlSrRxJlgPp59+/Yxf/58xowZ45JAYVrx3Xdw9KjtELxOF7nWX3/Ba6/Z43HjIGvWVHxziaNECduLuG9f3OPOr8uUsfvcuW2lW6dOFsXp3NkmCLdts1Vq1/Lll7YV8scfbe/j99/bcX9/KFbMxj1TpyZ8/2tVfpeEXn3VAm/z5lm0rXBhwMZ/L7wAb75pQ8cff7RJWhHJmJK11XT+/Pm8//770ffvv/8+CxYs4NNPP2Xt2rWUS6pyjkh6lyeP/fK85RYrsf7EE7b9VDK2//6zVW4vv2xBt0aNbGBbrRr4+PBn58523qRJdu41dO4MNWpYarcuXWxcKyKSVnz77bcuCboBHD9+HLBJ2djy588f/VxS1q1bx7Zt26IDgomZNm0ad955J40bN055Y9MQZ1GFli0tBpJqBg2C8+fh/vuhVatUfGNJICAAata0xPyxBwrLltmKs9jBL39/y+c2fLh9PWOG3d93X9LXP33aVu7/9JMVz/j+e6uc2rUr7N9v3wt//GFFoJyCgy3VSr16LvuYmUKNGnYLDbXceLG8+qoVh/35Z1i50kPtE5FUkawVb1WqVOHs2bNs376dmjVrAnD06NHoqqbZnIk/RTKiokXh66/tl+iGDZb1eNkyy4AsGc/mzfbXz8GDtuV43DibRY41Lflv3bpEfvABXrt3w+TJMHJkkpfz8rKUgXffDV99BYsXw7PPpsLnEBFJZZeuTkz5+fnFOe7v788ZZ/L4JMydO5fatWtTxrmyJ54jR47wzTffMDX+ipxkioqK4mJSW/JSyNkPl25gou7CBVi6NBBw0KLFZS5evE6uLxfx+vVXAq4WR7s8bhyRly+nyvveiJvpv4zEq08f/Bs1IqJZM8LbtMF70yZ8JkwgbORIwgGOH8drzx4iixe3LaV9++JdpAh+nTvjiIwkon17rnz4IZeu/l3m7D+vH3/Et08fvAFCQojy8yO8bVvC+/Qh6upqLB5/nIA77oBHHyXs6pjGd+hQuOsuLjdqlPT21QzIFd9/Xn36ELB+PVEzZ3Kpe/foSrDZs8OLL/oyZYovgwZF8tBDl/FySwZ2z8ms/39dRf2XMu7uv9g7QK8nWZGC/fv307ZtW3x9ffn2228BGwCNHTuW999/n3nz5nHLLbck59Ii6UP58vDFF1C3LixfbkuXZs3SGvGMxLnFol8/CAuzbRcffpj4Fgtvb8JefRX/1q0tP0737rY6MgmlStlug8GD7dR69aw4mYhIRhIQEABYrjfnY4ArV64QGBiY5Ov+/PNPNm3axKxZs5I8Z/ny5eTJk+emiz0kJSwsjN1uLpx06NCh656zcmVuLlwoTpEil8me/ffUqeUUFUWprl0B+KdBAw5lzZomi0jdSP9lKAUKkHP8eArNnElAy5aE5c/P0e7dOfnYY7B7N1m3bKHUiy9yaNgw/nn8cXtNhQrcXqMGOX/4Ae9ffsG7enWOv/EGFC/Oqc8+45ZZs8j+yy/Rb/FPvXoc69aNsIIFLX+bM4cc4Dt5MrdOmkT2l18myseH/+6/nyO9exP+xx+p3BFpQ4q+/265hdJly5IlOJgzI0fy58svRz/VsKE3s2aVZ+dOb9566y8eeeS/FLc1Lcp0/39dTP2XMu7sv/iTi0lJVuBtwoQJFChQgLfeeiv6WNWqVfn+++/p0qUL48eP54033kjOpUXSjxo1YMkSK1f53nuWePYaK50kHTl9Gtq1s6AqQNOmMHu2JdxJQkTjxnDPPbBjh20liFc6Pr6+fS2Ot2sX9OwZk0hbRCSjcG4xPXnyJEWLFo0+fvLkyehiCYlZs2YNuXPnplq1atc8p2HDhni5aHmIr68vJUuWdMm14rt06RKHDh2iWLFi1ww4AvTrZ3tL27b1omzZxFf7uZr3hx/iv3MnUVmyEDR1KmXS2OT5zfRfhlOmDLz0Es71h3mu3pzPXWzThvxA/tivWbmSy3v34t+sGf4HDlCmQwfO33kn2bdsASDK15fw554jvG9fAm+9lSS/68uUgdq1uXL1ywDgDld+tnTCVd9/3kOHwtNPU3DZMnKMHh1nTNm9eyRjxngzd25xXnrpMt7eLmh4GpGp//+6gPovZdzdf/vi5+G8hmQF3rZu3RodfIstT548vPjiiyquIJnHk09awv0XXoBRo6BgQXjpJU+3SlLip59s+/CRI5bvZPJk+ze93mpGLy8YMcK+J6ZNs2ha/vxJnu7nZ/HaBx6AhQstnc6jj7r2o4iIeFLp0qXJmjUrmzZtig68nT17luDgYFq3bp3k67Zs2UKVKlWSrFJ6/vx5du/eTY/rJY+/CQ6Hg6CgIJddLzGBgYHXfI9jx6ywJEC7dn4EBd3YLHqKXLgAQ4YA4Bg0iEA3BR9d4Xr9J7FUqGAl1Bs3xuunn8i+ZQtRPj442rfHMWgQvrfdhq+n25jOpPj776mnYNQoHL//TtCcOXGKcfXvD++8AyEhXnz6aVDqVzNOBfr/mzLqv5RxV//d6DZTSGZxBYfDkeQ+2fDwcMLCwpJzWZH0qXNnC7iAJaVdutSz7ZHkiYy0/G01a1rQrWRJ2LjRCirc6A/VJ56w0vEXL8L48dc9vUoV22oK8OKLltNaRCSj8PPzo3Xr1kycOJG1a9eyZ88eevbsScGCBalXrx4RERGcOnWKy/HyiQUHB1+zwMOePXuIiopyWRGItOKDDyzLQY0acPvtqfSm48ZZxK9YMavaLRlH3rywdi1hr7zCyZYtubxzJ8ycqXLqnuLlZdUUAKZMiTPoy57dgm9gNTJCQ1O/eSLiXskKvFWuXJm33nqL06dPxzn+33//8c4771BFZaYlsxkyxCInUVG2dOm77zzdIrkZp05Bw4YwYABERNiKt19/hYoVb+46DkfMduO33oK//rruS0aNsjHw4cPRiw5ERDKMbt260bx5cwYPHswzzzyDt7c3s2fPxtfXl7/++ovq1auzMl45v1OnTpHzGlv7T548CXDNc9KbqCgrTAmk3mqXQ4dgwgR7PHGiVdKUjCUggLDXX+dI375EKeDmeS1a2MTu6dMWBI3l5ZehQAGr5TVnjofaJyJuk6zAW+/evTl+/DgPP/ww7dq1o3fv3rRv3546derw559/0q9fP1e3UyRtczjgzTctF1hoKDRubLm+JO37/nvbkvH11/ZHx7vvwqJFNv2YHI8+ClWrwuXL183zBpA1q20vAKvlsGlT8t5WRCQt8vb2pm/fvvz8889s27aNWbNmUaRIEQCKFClCSEgITZs2jfOaHTt28MwzzyR5zQYNGhASEoK/v79b256atm2D4GDw97cdaamiXz/7XVWrlo1fRMS9vL1tkhcs2B1rtW+WLDEL4kaNAhWxFMlYkhV4K168OF9++SVPP/00Fy9e5LfffuPs2bO0aNGCzz77jOLFi7u6nSJpn7e37RN56CGrCvXoozZtJWlTRISNbOrUgT//hNKl4ZdfoGPHlFWnjb3qbeZMOHr0ui959FFo3dpWPHTqpC0GIiKZzfz5dv/kk5AjRyq84Q8/wMcf2/a3N95QVXaR1NKmDdx6Kxw/DnPnxnmqc2coWtSGpc5JWRHJGJJdCqpAgQJ06tSJDz/8kFWrVjF79myaN29OwYIFXdk+kfQlIAA++wzKl7dfqPXr2zZGSVuc/zZDh1put+efhy1b7N/NFR5+2HLFhYbCa6/d0EumTLF0LLt2xez8ERGRjC8szBZaQyptM42IiEkw2rmzVeQWkdTh52erTcFyLMbKje7vb0NTsE0Tyv0rknEkK/B27tw5OnbsSKtWraKP7dixg0aNGtGtW7cESXJFMpWcOW3b4m23wR9/WO4w/eZMO9assT8y1q6FoCCYN89uWbK47j1ir3qbPdvy6FxH3ry21RTspXv2uK45IiKSdq1aZXN0+fNDvXqp8IZz5sD27TZecf6uEpHU06GDJXQ7fNh2y8Ty/POWBu7UqZhxoYikf8kKvE2cOJHdu3fzyiuvRB974IEHmD59Olu3bmX69Okua6BIunTLLTaSzpMHNm+G5s21f9DTwsOtdHu9enDypK1u+/VXG+G4w0MP2cq3sDAYPfqGXvLMM/DYY/at0rmzLcYTEZGMzbnNtFUr8PFx85v9919MIqlhwyBfPje/oYgkEBgYU0V47FhbhXqVjw+MGGGPJ0yAf//1QPtExOWSFXj79ttv6d+/Pw0aNIg+5ufnR926denVq1eC6lQimVKpUrBiha2qWrXKZrcUSfGMo0ctl9trr1kitc6drYpB6dLufd9Ro+x+3jzYt++6pzsc8Pbbtvhu/XqYNcu9zRMREc/67z9Yvtwep8o201GjbClN6dJWRlFEPKNLF8iVC/buhWXL4jz19NNQrhycOQOTJnmofSLiUskKvJ0/f54cSWR+zZcvH6dPn77ha0VGRjJt2jRq1KhBhQoV6NSpE0eOHEn03OnTp1OqVKlEbwMHDow+r127dgmeb9Omzc19SBFXuP9++2Xq4wMLF8bkdJDUs3KlVS1dv95KiC5ebEUPAgPd/95Vq9oStoiIG97Oc9ttMGaMPe7XD44dc2P7RETEoz7+GK5csUXYbk+1FhIC06bZ4ylTwNfXzW8oIknKli0m16JzYvgqL6+Yuds33rCNGiKSviUr8Fa6dGmWxYvMO3322WeUKlXqhq81Y8YMFi1axKhRo1iyZAmRkZF07NiR0ES25bVv354NGzbEuXXo0IGgoCDatm0bfV5ISAjDhw+Pc562v4rHPPqo5VMBm7bS1FXqCAuzyFXDhvDPP1CxImzdatOIqcm5X+CDD244cdvLL1vM9tw5eOmlOGMxERHJQJzbTJ97LhUKi/bubWkXGja0sYmIeNYrr9ik8M6d8OWXcZ5q3BgqVYILF+D11z3UPhFxmWQF3l588UW++eYbmjZtyttvv81HH33EO++8Q8uWLfnqq694+QaXroeGhjJnzhy6detGrVq1KF26NFOmTOH48eOsXr06wflZsmQhX7580bdTp04xf/58hg4dGh3s++eff/jnn3+455574pybM2fO5HxUEddo0yamVGWfPrb6Tdzn8GGrKurs81degZ9/hjvuSP22VK4MTzxh24ydQbjr8PaG996zhZLLlyfYgSAiIhnA/v2wYYOtbnn2WTe/2VdfWfoLHx+YPNnNbyYiNyR3bpthhQSr3hyOmBTBM2ZY1hQRSb+SFXh76KGHmDFjBgDTpk1j6NChTJ06lbCwMGbMmMFDDz10Q9fZs2cPFy5coGrVqtHHsmfPTtmyZdm8efN1Xz9y5EgqVapEkyZNoo+FhITgcDgoXrz4TX4qETfr0ycmkWq7dlb5VFzvs89sa+nGjZAjh0Wtpk2zGu2e4gy4ffgh/PbbDb2kXDlw7qDv2lXJdUVEMhrnHFzdulaTyW3CwmLGH926wZ13uvHNROSm9OoFAQGWe/jbb+M8Va8e1Khh29FvsE6XiKRRyQq8AdSuXZt3332XtWvXsmjRIr744gumTZtG4cKFWbx48Q1d4/jx4wAUKlQozvH8+fNHP5eUdevWsW3bNvr37x/n+N69e8mWLRsjR46kZs2aPProo7zxxhuJbl0VSXUTJljZsvBwq3R6AwFmuUFXrkCPHtCkiWWrrlIFtm2Dpk093TILBDZrZjOZw4ff8MtefdXyX584AX37uq11IiKSyqKi4m4zdasZMyzVQb58MGSIm99MRG5KgQLQqZM9jhddczhsIRzA7Nlw4EAqt01EXCZZRcv37NlDnz592L9/f6LPOxwOnnnmmete59KlS4BVRI3N39+fM2fOXPO1c+fOpXbt2pQpUybO8b1793LlyhXuvvtu2rVrx+7duxk/fjx//vkn48ePv26bkhIVFcXFixeT/fprcfaD815uTrrrvzffxP/4cbzXriWqQQMur1lDlCe2QF6V7vovEY4DB/B77jm8t20DIKxbN8JGjAA/P3DT/1unG+0/x4ABBHzyCY5ly7j0889E3WAW7enTvahbN4DZs6Fp08vUqpWxKuNmhO8/T1L/pYy7+y8qKgqH2xN3SXr000/2R3TWrPDkk258o1OnYNgwe/zaa6DUKyJpT9++8M478N139sPhwQejn6pRA+rXh1WrbAPF++97rpkiknzJCryNHz+eM2fO0L9/f9atW4efnx+1a9fmhx9+4IcffmC+cwrvOgICAgDL9eZ8DHDlyhUCr1Fx8M8//2TTpk3MmjUrwXMjR46kf//+0VVX77zzTnx9fenZsyf9+vUjb968N/NRo4WFhbF79+5kvfZGHTp0yK3Xz+jSU/95DRvGnX/9RZbgYLwaNGDPnDmEJ/N701XSU//Fluubb7ht9Gi8L1wgPEcODg0fzpkaNSx5Tiq6bv85HBSvV4/cq1ZxpX9/9k+ZckPXzZULnnrqVj7+OD8vvABLluwhICDjVVtIr99/aYX6L2Xc2X/xJxdFIGa121NPQVCQG99o6FA4c8ZWXrdv78Y3EpFku/VWW/o6e7YFyFesiPP06NEWeFu4EAYMgHjrTkQkHUhW4G3Hjh0MHDiQ5s2bExgYyBdffMGzzz7Ls88+S7du3ViwYAGVKlW67nWcW0xPnjxJ0aJFo4+fPHnympVR16xZQ+7cualWrVqC53x8fKKDbk53XF1NdPz48WQH3nx9fSlZsmSyXns9ly5d4tChQxQrVuyaAUdJXLrtvxUriHzkEfz376d8375cXrXKcpKlsnTbf5cu4TtgAL7vvQdARNWqhM2bxy1FiuDOVDkJm3Hj/ed4/XWivvmGnOvXc9fFi0Ted98Nvce0afDjj5EcPRrAJ5+UZ9SoMFc0PU1It99/aYT6L2Xc3X/79u1z+TUl/bt82VJ+gpu3me7YAc5J6qlTrXKPiKRNAwbA3LmwcqWlSqlYMfqpSpVsZexnn1ks/eOPPdZKEUmmZAXeQkNDKVasGADFihVjz5490c81bdqUYc4l7ddRunRpsmbNyqZNm6IDb2fPniU4OJjWrVsn+botW7ZQpUoVfHwSNr9NmzYUKVKEsWPHRh/btWsXvr6+0W1ODofDQZBbpyQhMDDQ7e+RkaW7/itWDL75Bh58EK9duwh69lmrOhZr9WdqSlf9FxICLVpY+XWAgQPxHjmSwER+JqSWG+q/ChWgdWuYP5+AMWPs3/sGBAXB229bafmpU31p3do39ngsQ0hX339pkPovZdzVf9pmKon54gtbhFa0qBXgdouoKMt7Ghlpy+rc9kYi4hIlS8LTT8OiRbbqbenSOE+PGgWff26H48XlRCQdSFZxhVtuuYUjR44AFng7f/48R6/WOPbz87tufjYnPz8/WrduzcSJE1m7di179uyhZ8+eFCxYkHr16hEREcGpU6e4fPlynNcFBwdTunTpRK9Zv359Pv/8cxYvXsyRI0dYuXIl48ePp0OHDmTNmjU5H1fEfYoXt+BLtmyW16F1a4iI8HSr0raFC+G++yzoli+fVYcdMwY8GHS7KUOH2qqDr7+2PB436IknLNYYEQEdO1p9DhERca/Vq6FyZZsAKV4cJk60mFZi5s2zZOjxb1myBFGp0n0sXGgrzpzbTP39LeVa7txWFyh+4vTWrRO/Xry/xxP36ac2rggIgBTkOBaRVOQsZ//JJxAvxVG5cuBMoa4aKSLpT7ICb/Xq1WPSpEmsWrWKAgUKcPvtt/PGG28QEhLCnDlzuPXWW2/4Wt26daN58+YMHjyYZ555Bm9vb2bPno2vry9//fUX1atXZ+XKlXFec+rUKXImkRy2devWvPrqqyxYsIAGDRowceJE2rZtS/fu3ZPzUUXcr0IFm8Ly84Nly6Bbt6RH9ZnZxYvQoQO0aQMXLkCtWrB9u2WcTU9KlIC2be3x0KE39dJp0yzn29atcIMp4kREJJk2boRGjay69CefWFHyfv1g3LjEz2/YEH7+OeGtTJlIChQIpX79CE6etJ1kAFmywOLFMHMmBAdDvXoQu87H9u32h3b869Wpc52GX74MvXvb4759bYW9iKR95crZntKoKIi1e8tp+HCbu12xwn4WiEj6kawlIl27duXw4cMsXbqU+vXrM3DgQLp27cqKFSvw9vZm8uTJN3wtb29v+vbtS9++fRM8V6RIEUJCQhIc37FjxzWv2apVK1q1anXDbRDxuNq1bSVXy5YwYwYULKjprNh+/92WewUH23T/sGEweHD6zVczZIgteVi7Fr7/Hh566IZeVqAATJpk+bGHDrUVEm5KPSkikukNG2bbuRYssK8ffRTCwmyRdffuED8tYL58dott2jQICXEwZ85+8uW7jXfftd2f/v7w448xhRWKF7eVzVu2WBXDy5ctq0LPnvDAAzfZ8MmT4dAhKFwY+vdPzkcXEU959VVL5rZokUXabr89+qk77rC529mz7bRvv/VUI0XkZiVrxZu/vz/Tpk1jxowZANSoUYMvv/ySyZMns3LlSurVq+fSRopkCk89BdOn2+OhQ+Hddz3bnrQgKgrmzLF9PsHBFpBcu9b+GkqvQTeA226z/aJgQbibWOHYtq2tdrh8GV54QYsjRUTc4coV26nZpEnc482bw7lzsGHD9a9x4oTNEXXqFE65chcBeP99e65u3bjVTCtVgj//tKAbwG+/WUqBChVusuHHjllkEGxpXpYsN3kBEfGoSpVsN0dERKLLa4cOtU0y69bZkFhE0odkBd6cfH19ox/feuutPPbYY3Gqk4rITXr5ZZvCAnjxRduCmlmdO2fbSjt0sL039epZhbbatT3dMtcYNMiWPKxff1MjJ4fDitQFBNhM57x57muiiEhmdeAAhIbCnXfGPe5cZZzIhowEhg0DLy8YOtQqUf/+u4Nt2+y5xx6zX/m5c9vP88aN4Wq6ZMC2mQK89x4UKmR/aNeoAZs2XedNBw60dAxVq8Kzz16/kSKS9jj/Fpg3z4LpsRQtahOvYIF9TcCKpA8pCryJiBuMGmXBpshIq250I9PqGc2OHTbj98EHtrLNWQE0f35Pt8x1ihSJGTnd5Kq3EiVg5Eh73Ls3HD/uhvaJiGRizjph2bPHPZ4tm92fPXvt1588aavbuna1AgoAixfHZHgZPdr+nl6yxIJrW7favNKFC/a8M/B24YLlgVu82FY6164dU9A7gU2bYvbFTp1qMzUikv7UqGG30FCr6BLPoEG21X3jRsv3JiJpnwJvImmNwwHvvGPJXi5fhscftz0nmUFUlH32+++HvXstOPXddzaD75UBf1wNHBgzcvrqq5t6ac+ecO+98O+/lmtIRERcJzLy2s9f71fSe+/ZTjHnz+eICFiyJCZFQoECVrChXj2rXvrxx7Bvn803AbzyihW/nj/fagk1awbffGM7R197LYkGO9+sbVtL0SAi6Zdz1dvMmXDqVJynCha0nxFgq96u9/NKRDwvA/4lK5IB+PjY9Ha1avDff5bR+X//83Sr3OvMGSsu0aWLJddp1Mim/KtX93TL3KdgQdtrBJa04yZWvfn42B923t7w0UewfLmb2igikgnlyGH3587FPe5c6eZ8PilLl1pQzVlsYfPmbPz1l1f0CrrHHosbvHvgAbumcytqqVIJi3bnzGnDgkRrjH3wga14y5o1JsebiKRf9erZ7o9LlxItZd+vn63I3bHDft6ISNqmwJtIWhUUZNGUsmVtP0r9+vDPP55ulXts2WLLtz7+2CJKkybZZ8+Tx9Mtc79+/WwJw6+/3nT0rGJF6NPHHr/00vW3PomIyI0pUcImNvbti3vc+XWZMkm/9tgxC6C1aBFzbMUK+33WsqUtbL9yJeHrwsNjKqV++CGsXp3wnEuXElZO5fz5mOqlr75qSeFEJH1zOGJWvb31lk3Ex5InD/TqZY+HDrWfHyKSdinwJpKW5c5te02KFIE9e2wVmDMBTEYQFWV5aB580DJZ33ab5bTr1Svz5KbJlw+6dbPHQ4fe9H6BYcPsD8Rjx2znqoiIpFxAANSsadtBYy9GXrbMVqZVqZL0a50FEKpVs/tz52DdupyApXB1Xjd28G3tWvv17qxqOnOm1VgKDY0559gx+PHHRGoMvf46/PUX3H479OiRnI8rImnRE0/AXXfZzOqbbyZ4umdPC8CFhMRsUxeRtEmBN5G07tZbYdUqyJXLcoG1bAlhYZ5uVcqdPg1NmtgfCWFh0LSpLRG4/35Ptyz19eljGbt37rS/xm5CYCC8+649njEjc9biEBFxh8GDLYjWooWl4RwyBCZMsMTmQUH2t/DGjQnSL7FrlxWtLlHCvv78c28uX/bmjjsiqVIFxo6FP/+EBg3suvPmWQHS+++3v7PB3uvwYat2+vXXsGgR1Kljf2T37h3rzQ4ejEm+PmmSRQxFJGPw8rIfOABvvGGrW2PJnj1msevw4XED9SKStijwJpIelC0LX35pUZYVK6waZnquH75xo+2T/Pxz8POD6dMtQUWuXJ5umWfkzm3TlmBL2CIiburltWvbKgqATp2sJoeIiKRMnTq2wi0kBJ580laUTJhgGQLAKpFWrZqwquCJEzGVTAEWLbJqps8+G47DYa9Zt84WODdrZnMvjz9uATbvq/UXate2rabnz9t828svW0aG9evj5Zfr29eWzj38sEXpRCRjadECSpa0dDMzZyZ4+uWXLWXwoUMwe3bqN09EbowCbyLpxYMPWtIXb2+YOzcm70N6Ehlpf7XUqGHFIkqUgJ9/hq5dM8/W0qT07Gl/qQUH27/zTZowwark7dmjvNoiIq7SpIktRr5yxTIixF5tVquWzYG1bRv3NTNmwPHj9vj0afjhBxtuP/10zKTKgw9a8O3iRfj7byuWEztYBxZLW7/eag/9+6/VXCpaNNYJ69ZZZNDLy5KvZ/bfoyIZkY8PDBhgjydOTDC7GhQU8yfB6NGWB1JE0h4F3kTSk8cfh1mz7PHYsTBtmmfbczNOnbIcdf36WQbYli1tucC993q6ZWlDzpwxlRKGD7/pLLm5csWk/xg71rY6iYiIZwUEwIMPRvL00ycoWtSFK9UjImLyub34IpQv77pri0ja0qaNpZ45ftwm3+Pp1MmC8n/+CW+/7YH2ich1KfAmkt60bw+vvWaPe/RI1uqoVPfDD1ChgiWzCQiwpfKLF1tyConRrZsl8Pnjj2RlyW3WzHYahYfbIOwmd6yKiIiLBQXB6tVX6NPnqGsv/N57thQvVy4YOdK11xaRtMXPz7aVA4wblyDXs7+/ZSoBm3w9dy6V2yci16XAm0h6NHCgbc+MirJZsDVrPN2ixEVE2Lr32rVtGq50actU3bmztsQkJlu2mORBI0fedBENh8MqzmfPbt381ltuaKOIiHjWv//G7C0bMcImbEQkY+vYEfLnt6oriUzOPvcc3HGHbV2fOtUD7RORa1LgTSQ9cjisulGLFhacadLEtm2mJSdOwKOPWmm2yEgbEWzeDHff7emWpW0vv2wDqwMHrNTdTSpcGMaPt8eDBtn4TEREMpCRIy3Retmyts1URDK+wMCYJJNjxybY1uDjY3F4sFRw//6byu0TkWtS4E0kvfL2hvnzreza+fPw2GOwf7+nW2XWroV77rGVeEFBFkB6/33ImtXTLUv7smSJSaI7erRl9L5JnTpZ/YoLF6BLl/RdAFdERGLZvTsmoeeUKeDr69n2iEjq6dLFtpfv3WuFVeJp2dLSPZ45Y8E3EUk7FHgTSc/8/eHTTy1/2smTUL++rTTzlPBwGDoU6ta1dpQrZ6vcnn/ec21Kj158EW65xSq/JqM2vJcXvPuupQT56itLpyciIhlAr172u/bxx6FePU+3RkRSU7Zslg8YLN9zvJlVLy8YNcoeT51qfxqISNqgwJtIepc9u0VXihe3FW8NGngmq+qxY/Dww/YbPyrKll1t2mRbYeTmBAbaPlGwgVUyasOXKmUxUIDu3S3nh4iIpGMrV8LXX9sqt0mTPN0aEfGEbt1sB8nOnfDllwmefuIJqFzZdj2MHeuB9olIohR4E8kIChaE1ashXz7L9dakSbK2KCbb11/bqrsffrDBwKJFMGuWbTOV5OnY0UrH//mn9WUy9O1rWw7+/ht69nRx+0REJPWEhsb8IO/Rw7Koi0jmkzs3vPSSPU5k1ZvDYZlKAN5+G466uKCyiCSPAm8i6dnq1TatFRRk2zuffdZyhK1da9s7IyNjzp03z34bx7sFZcnCfZUq4b1wYeLv0bNn0hVIw8IsH9ljj1l0p0IFC/w984yrP2nm4+8Pgwfb47Fj4eLFm76Enx+895798y1caPFRERFJh9580/I65c8f87tBRDKnXr0gIMB2lnz7bYKn69aFmjVtDt4ZhBMRz1LgTSS92rgRGjWC0qXhk0+gVSuYNs0yq/r6wocf2i9m50xYw4bw888JbpFlyhBaoAAR9esnfI8ffki6Jvn//me/1ceNs69Ll7Zrahbeddq1sy3EJ07AW28l6xJVqthWU7DUcefPu7B9IiLifidPxpQrHDPGUkyISOZVoIDtjABb9RaPwxFzePZsOHAgFdsmIolS4E0kvRo2DCpWhAUL4NFHbUqrb1/4+GPLrA8WNBs/3h7nywcPPBD39ssvOEJC2D9unD0f2/nzFvgpXDjhey9fboUTNm60r/384P77bfZNXMfXF4YMscfjxiU7d9+oUXDbbXD4cMzlREQknRgyBM6ehXvvhbZtPd0aEUkL+vYFHx9Ytw5++inB09Wr258H4eEwfHjqN09E4lLgTSQ9unIFvvvOcrnF1ry5BWduuQWmTLFjAwbYNtP4TpyAwYMJ79SJi+XKJXy+b1/LHdeuXcwxZ46Zxo3tfbJmhRUroFAhV30yia9NG1tF+M8/MH16si6RNSvMnGmPp061nQkiIpIObN8edzLN29ujzRGRNKJoUUsrA4mueoOYCqcLF0JwcCq1S0QSpcCbSHp04IAFwe68M+7xkiXtPiTEki/362dfd+xoAbLYhg0DLy/CnKUvY/vmG5g/H+bOtdrkzvesVg3eeMO+btPGgkENGrjqU0lifHzs3wpg4kQ4cyZZl6lf3/7JnAVnQ0Nd2EYREXG9qCjLFRAVBU8/bUtYREScBgywcfrKlbBtW4KnK1WyOfqoqJhK9yLiGQq8iaRHzuBL/Dwv2bLZ/dmzdv/66/DccxARAU89FbM19ORJeP996NoVcuZMeO0OHWDkyLiBvYoVYcsWyJXLtprOn29bTMX9nn4aypSBf/+NCXwmw+TJkDcv7NoFEya4rnkiIuIGS5dartXAwJh8qiIiTiVLWm5nsPyPiRg1ynK+LVtm9c9ExDMUeBNJj2JXK02Mc5Waw2FlLR97DC5dsgILu3fbsYiImKz7sfXoAbfealtKL1+OWSl39iw8+KBte3n8cVd+Grkeb++YBB2TJ8Pp08m6TN68MbUyRo6EPXtc0zwREXGxS5egTx973K+fbSsTEYlv0CC7X7bMxvjx3HUXPPusPVaeXxHPUeBNJD3KkcPu4yfbd650cz4PlqD/44+t+MHp07bncPFiqFcvYUGFL7+EJUtg1iyLytx/v61yAxv4f/utBv+e0rw5lC9v/8aTJiX7Ms88Y3HY0FDo3Pn6MVwREfGASZOseniRIjFpI0RE4itXDp580vaTjh2b6CnDh9sc7sqVidZhEJFUoMCbSHpUooT9Bt23L+5x59dlysQ9niWLBdVKlYIjR+C332z1W3xLl9oqt3LlbIps586Y58aPhxdecO3nkBvn5QUjRtjjqVPh77+TdRmHA95+274l1q+PydktIiJpxNGjMX9AT5gAQUGebY+IpG2vvmr3ixZZTuZ4SpaMqZX26qsWoxOR1KXAm0h6FBAANWvCJ5/E/e25bJmtdqtSJeFr8uaFVassRxvA7Nm2lSW2/v3hiSdivr73XmjVyh5v3qx65J725JP2b3LhggVCk+m222JSgfTrB8eOuaZ5IiLiAgMGwMWLVtDImb9JRCQplSrZTpaIiCTHh0OGWGrm776zDSwikroUeBNJrwYPhk2boEUL+Oor+406YYLleggKsi2JGzfCqVMxr7ntNkvUD/Drr5b0ITwcAMfu3VaAYflyWxY1dCj88ktMpdRKlaBYsdT9jBKXw2HJ2QDefBOOH0/2pV5+2XYSnz0LL72k2U8RkTThp5/ggw/s5/3UqXYvInI9zlVvc+cmOqNatCi8+GLMqRr3iaQuBd5E0qs6dWyFW0iIrYT64AMLvDlzwWzdClWrxhRHiC13bvD3h88+w69HD/J88QUBNWvC779DwYKwZo1ta/T2TtWPJDegQQOLmF26lKIqd97eVmPDx8dircuWubCNIiJy8yIjY4oetWsH993n2faISPpRsybUqGFJfCdOTPSUgQNtbn7TJstAIyKpR4E3kfSsSRPLw3bliuV06N075rlatWw6q23buK+ZMQP++ccKLHh54TN3LsVGjMBx8SLUrWtVS+vUiTl/+PDrT4sdOgTz5rniE8n1xF719vbbKdonWq6cDcIAunaFf/91QftERCR5FiywgkbZssFrr3m6NSKS3jhXvc2cGXfHy1UFC8Irr9jjwYNVYEskNSnwJpJZNWliQTggysuL0OHD4euvoUABz7ZLrq9uXahe3QKuzmRtyfTqq1C6NJw4AX37uqh9IiJyc86ds9xuYKkjChb0bHtEJP2pV89Wyl66BG+8kegp/fpB9uw2b790aeo2TyQz83jgLTIykmnTplGjRg0qVKhAp06dOHLkSKLnTp8+nVKlSiV6G+hcthFLVFQUHTp0oE2bNu7+GCLp0wsvcPmrrwhetIjwvn2tcqakfQ4HjBplj999F/73v2Rfyt/ftpyC1dtQwl0REQ8YM8bydpYsCd26ebo1IpIeORwxq97efBP++y/BKblzx2yQGTo0OtWziLiZx//KnjFjBosWLWLUqFEsWbKEyMhIOnbsSGhoaIJz27dvz4YNG+LcOnToQFBQEG3jb6cD3n//fTZs2JAKn0Ik/YqsWZPLzgIKkn7UqgW1a0NYGIwenaJLVatmBRYAOndOWOxWRETcaP9+mDzZHk+aZDMiIiLJ0bgx3HWXVc96881ET+nRA/LksTTRCxembvNEMiuPBt5CQ0OZM2cO3bp1o1atWpQuXZopU6Zw/PhxVq9eneD8LFmykC9fvujbqVOnmD9/PkOHDqVUqVJxzg0JCeGtt96iQoUKqfRpRERSmXPV29y5luMvBcaOhcKF7e+/ESNc0DYREbkxfftaQvS6deHxxz3dGhFJz7y8YNAge/zGG3D+fIJTsmeP2dk+YoT9+BER9/Jo4G3Pnj1cuHCBqlWrRh/Lnj07ZcuWZfPmzdd9/ciRI6lUqRJNmjSJc/zKlSv06dOHbt26Ubx4cZe3W0QkTahWDerXt30CziBcMmXPHp3yj4kTYds2F7RPRESube1a+PRTKzU9ZYptFRMRSYkWLaBECSumNmtWoqe89JKlkjx0KCbliIi4j48n3/z48eMAFCpUKM7x/PnzRz+XlHXr1rFt2zY+++yzBM9NmDCB/Pnz07p160RzvyVHVFQUFy9edMm14rt0dV/XJe3vShb1X8qo/1LG0/3nNWgQAatWETV/Ppd79CDqjjuSfa1HHoFmzfxYtsyH9u0j+f77y/i4+beEp/svvVP/pYy7+y8qKgqHAinp2+rVljPp99+t+NDLL1uCpMT+XefNg3btEhwOAu4Drsycafv5AYoUiVuVOiLCSk2fOgV589qxL7+0KtY7d9qxp56ySZasWV39KUUkI/HxsSVtnTrBhAkWZQsIiHNKUJBVNu3a1TKWtGsHgYEeaq9IJuDRwJtzoOvn5xfnuL+/P2fOnLnma+fOnUvt2rUpU6ZMnOM//PADX3zxBcuXL3fpYDcsLIzdu3e77HqJOXTokFuvn9Gp/1JG/ZcyHuu/LFkoUaMGOdev5+KAARxKYb63zp19+Oabu9i+3YchQ07z3HMnXNTQa9P3X8qo/1LGnf0Xf4yT2URGRvLmm2/y8ccfc+7cOSpXrszQoUO59dZbE5w7ffp03kwiJ1HTpk0ZO3YsAAcPHmTs2LFs3ryZoKAg6tevT9++fQl09V+NGzdCo0bQsqUFvDZssJKA4eEx+7Ria9gQfv45weHI9u0JP32aiPr17cDff1vQrUkTW+2WPTt89BHkyAE5c9o5n34KzZpZPs+PPrK9YKNGwU8/wY8/4vZZERFJ3557zvaRHj1qKUm6dElwijMud/iw7XpwFl0QEdfz6G/tgKuR99DQ0OjHYFtFrzV4+vPPP9m0aROz4i2dPX36NIMGDWL48OEUKFDApW319fWlpJsS0F+6dIlDhw5RrFgx1w8aMwH1X8qo/1ImLfSf4/XXoVo1cq9aRdDo0UTFm5C4WePGRdCliw+zZhWmY8c8lCgR5aKWJpQW+i89U/+ljLv7b9++fS6/ZnrjLKL1+uuvU7BgQSZMmEDHjh354osvEgQl27dvz9NPPx3n2Ny5c1m8eHF0Ea1///2X1q1bc8899/Dxxx9z8uRJ+vfvT2RkJMOHD3dt44cNg4oVYcEC+/rRR62gzZgx0L17wuUh+fLZLbZp03CEhLB/zhxucz63fbvdO8tIjxljaQNiGz4cypSBr78GZz/VqGHbx+bOtb+YRUSS4udnEwXdusG4cdCxI/j6Jjhl2DBo3x5ef90W5GbL5qH2imRwHg28ObeYnjx5kqJFi0YfP3nyZIJiCbGtWbOG3LlzU61atTjHv//+e06dOsWgQYMYdDWpZGhoKJGRkVSsWJEVK1Zwyy23JKutDoeDoKCgZL32RgUGBrr9PTIy9V/KqP9SxqP99+CD0KQJjk8/JXDcOFsdkQIvvABLl8LatQ569AhkzRr3px3S91/KqP9Sxl39l9m3mTqLaPXp04datWoBMGXKFGrUqMHq1atp1KhRnPOzZMlClixZor8ODg5m/vz5jBo1KnpcuHDhQnx8fJgyZQr+/v6ULFmSbt26sXjxYtdu7b1yBb77LmG1mebNYfx4W/1Wt+61r3HiBAweTHinTlwsVy7m+Pbt9gfwmTNWffCFFxK+dvdu2wMWOzhZoIAF41asUOBNRK6vY0fbR3r4MCxaBM8/n+CUNm0s6LZ3r9ViGDIk9Zspkhl4tLhC6dKlyZo1K5s2bYo+dvbsWYKDg6lcuXKSr9uyZQtVqlTBJ94y+7p167J69Wo+++yz6FudOnUoV64cn332Gfnz53fbZxER8agRIyw69vHHsGNHii7lcMDMmbaY49tvLW2RiMjNckcRrQ0bNlC3bl38/f2jjz311FN88sknrg10Hjhg2zvvvDPucefuh5CQ619j2DDw8iJs6NC4x7//3lbOARw8aNtLW7aEv/6KOSdvXvtjObawMPjf/1JcxVpEMonAQOjVyx6PGWO5JOPx8YmZX5g4EU6fTsX2iWQiHl3x5ufnR+vWrZk4cSK5c+emcOHCTJgwgYIFC1KvXj0iIiI4ffo02bJli7MVNTg4mGbNmiW4XtasWckaL+FslixZCAgI4LbbbnP75xER8Zjy5a2K1Ycf2halTz9N0eVKlLCc3n37Ws6Pxx6z6lciIjfKHUW0Dh48yMMPP8zYsWNZtWoVvr6+1K1bl+7du8cJxt2s+EW0vE6cIAC47OdHZOziWt7eBAGh//xD+LWKbp08SeD77xPerRuXrrbr0qVLEBVF4Lp1OICI0qUJu7oV1W/UKKJq1uTyTz9Bliz4tmmD7/jxhI4aRfhzz+G4fBnfESPwPnOGqCxZuOymgl9pkYrIpIz6L2XSff89/zyB48bh2LuXK4sWEZHI39CNGkG5cgH89psXY8eGMWJEmMvePt33n4ep/1ImLRXR8nhm1m7duhEeHs7gwYO5fPkylStXZvbs2fj6+nL06NHowVXTpk2jX3Pq1ClyOpPPioiIGTbMVrx99hn8+ivcd1+KLtejByxeDFu3WjqjDz90SStFJJNwRxGt8+fP8+6779KwYUPefPNN/vzzT0aNGsWpU6eYMGFCstsav4hWlgMHKA3873//41zs4lrh4dwHnPz7b05co+hWwTlzCAgPJ7huXcKvFu84dOgQOX74gZIXLhDp7U3w668Tmjs3VK1KljFjKN2xI6cmT+bv5s2haVMKnzhB/pEj8Rs6lEgfH/5u0gSfmjUJPHDA7QW/0iIVkUkZ9V/KpOf+K9S8Obe8+y4Ro0ezu0yZRPOHtGuXg969S/Lmm17UrfsHefKEu7QN6bn/0gL1X8qkhSJaHg+8eXt707dvX/r27ZvguSJFihCSyFL+HTexjer1119PUftERNKNMmXg2Wdh4UIYOtTyAKWAjw+89x5Urmxp41q1gieecFFbRSTDc3URLQAfHx+KFy8eXUihXLlyRERE0KNHDwYMGECePHmS1db4RbQcUVZU5rbcuYmIHfy7ug8rf8mS5L5GIZuAH38k6pFHuOPBB2OKeBQqRK633gIgont3SsTOEVemDFG9e3PLqVPkc153xgwuT56M4+BBogoVImvOnPjXqweFCiUISGZkKiKTMuq/lMkQ/Td0KFGLFxO0dy93HT5M5GOPJTildGlYvDiCLVu8+fzzskyY4JpVbxmi/zxI/ZcyaamIlscDbyIi4kJDh9oytZUrYeNGeOCBFF2uYkXo08cKYr30EtSqBdmzu6apIpKxubqIFkDBggW544474hxzfn3s2LFkB94SFNEqVw68vfE/cgRiH//tNwD87rkHv6QKchw7Zrk2e/WKc81s8+bhtX8/ZM+Ob4sW+MZ+fWQkhIbiW6iQHf/uOyvwUL8+5M5t54SHw++/Q9u2mbKYiorIpIz6L2XSdf8FBUGXLjBhAgETJkDTpomuehszBurVg/fe82XAAF9uvdV1TUjX/ZcGqP9SJi0U0fJocQUREXGxO+6A556zx/ETeifTsGGW8+3YMRg40CWXFJFMwNVFtAAqV67Mzp07ibq6Ig1g7969eHt7U6RIEdc1PiAAataETz6BWO/FsmWQIwdUqZL0a52fN1bg0Oeff/B17sK4fBmmTYv7muXL4dIlqF3bvl661CqXhsVadTJnDvz3Hzz5ZLI/lohkUr162c+1TZusclYiHnkEHnrI6sqMHp3K7RPJ4BR4ExHJaIYMsX2i33wD69en+HKBgfDuu/Z4xgz48ccUX1JEMoHYRbTWrl3Lnj176NmzZ5wiWqdOneLy5ctxXhccHEzp0qUTvWaHDh04cuQIw4YN4+DBg6xfv55x48bRuHFjcjtXhrnK4MH2R2qLFvDVV/azdcIEGDTIVpCcPWsri0+divu6XbvA399mLK4qPGMGjnPnoFIle/2iRfaH8Jo1MGUKPP88NG4MderYC158EU6cgLZtYe1amDwZuna16qcPPeTazykiGV/BgtCxoz1+7bVET3E4Yp6aMwf270+ltolkAgq8iYhkNMWLQ4cO9thFq95q1465ZMeOtmBDROR6unXrRvPmzRk8eDDPPPMM3t7e0UW0/vrrL6pXr87KlSvjvOZaRbRuv/125s+fz4EDB2jcuDEDBgygQYMGjBgxwvWNr1PHVriFhNgqsw8+sMBbv372/NatULVqwnyaJ05ArPY7tm0jz/Ll9sXUqRbAmzEDVq+Gxx+HSZMs0LZ4ccw1ypWDL7+09378cXjzTXj1VViwwPWfU0Qyh759bWJ23Tr46adET6lWzSrZh4fD1VSaIuICyvEmIpIRvfoqzJ1reYK+/TZmFUUKTJhgf1/u2WN5QEaOTHkzRSRjc0cRrbvvvpuFCxe6rI3X1KSJ3RJTq1bcbahOM2bYDSAqCr9+/XBERRHeogU+Dz5ox7t0sdu11K1rNxERVyha1NKRzJljS9uSKMI1apQt8v3gAxgwAO66K5XbKZIBacWbiEhGdOut0LmzPR4yJPE/Dm9Srly26AJg7FjbTSUiItdw4gTeP/1EREAAYUqaJCKeNmAAeHlZEa5t2xI95b77rP5CVJTl+RWRlFPgTUQkoxo0yBLp/vQTrFrlkks2bWo7rsLDLe93RIRLLisikjEVLMiVWbPYN20aUYULe7o1IpLZ3XGH5YoE276QhJEjLefbsmW2q15EUkaBNxGRjKpQIXjpJXs8dKhLVr05HLbqLXt2yzn+1lspvqSISIYW0aoV5++919PNEBExgwbZ/bJlsHt3oqfcdRe0amWPBw9OpXaJZGAKvImIZGT9+1v1vc2bLVG3CxQuDOPH2+NBg+DwYZdcVkRERETcrVw5q6IcFWW5Q5IwfDh4e1u+N1W0F0kZBd5ERDKy/PnhlVfs8dChEBnpkst26gQ1asCFC5Yf3AWL6UREREQkNbz6qt0vWgQHDiR6SokS0L59zOka64kknwJvIiIZXd++kDUrbN8On33mkkt6ecG774Kfn82ELl7sksuKiIiIiLtVrgz16lmyXuc2hkQMGWJjve+/h7VrU7F9IhmMAm8iIhldnjzQo4c9HjbMZaveSpWyRXQA3bvD33+75LIiIiIi4m7OVW9z58KxY4mecuuttrPBebpWvYkkjwJvIiKZQa9ekCMH/PYbfPSRyy7bty+UL29Bt169XHZZEREREXGnmjWhenUIDYWJE5M8beBASxf8yy/wxRep2D6RDESBNxGRzCBXLujd2x4PH25bC1zAzw/ee8+2ni5YAF9/7ZLLioiIiIi7OUuWzpwJp04lekqBAtCtmz0eMsRlGydEMhUF3kREMovu3SF3bggJsWS6LlKlil0a4MUX4fx5l11aRERERNylXj247z64dAneeCPJ0/r2hezZYedO+Pjj1GueSEahwJuISGaRPbuNnABGjICwMJddetQoKFYMDh+22VARERERSeMcjphcb2++Cf/9l+hpuXNDnz72eOhQCA9PneaJZBQKvImIZCZdu0K+fLB/v+0NdZEsWeCdd+zx1KmwaZPLLi0iIiIi7tK4Mdx1F5w9a8G3JPToAXnzwt69Lh1CimQKCryJiGQmWbNC//72eORIS6jrIvXrQ5s2VvGqUyeXXlpERERE3MHLCwYNssdvvJFkzpBs2WDAAHs8YgRcuZI6zRPJCBR4ExHJbLp0gYIFbV/onDkuvfTkyTYbumsXTJjg0kuLiIiIiDu0aAElSsA//8CsWUme9tJLUKiQDSFnz07F9omkcwq8iYhkNkFBMTObr70Gly+77NJ589pWU7AFdXv2uOzSIiIiIuIOPj4xy9kmTkxybBgYGFMIdfRouHgxldonks4p8CYikhl16gRFisDRo/Duuy699DPPwGOP2VbTzp1Vdl5EREQkzXvuORsb/vUXzJ2b5GkdO1pBrb/+ghkzUq95IumZAm8iIplRQEBMFasxY1w6ZelwwNtvW8GF9etdHtcTEREREVfz84N+/ezxuHEQFpbkacOG2ePXX7eaDCJybQq8iYhkVu3bw223wfHjMSVJb8Lq1VC5su1cLV7cdiZERdlzt91m8TywMdyUKRaQi3/LkiWISpXuY+FC7+jrzpsH5crZdoZSpWDatJjrOu3bB48/Djlz2vbWLl3S38DvWv0X37x56j8RERFxs44dIX9+S+K2aFGSp7VubWOMf/6JSTEiIklT4E1EJLPy84MhQ+zx668nWcUqMRs3QqNGULo0fPIJtGplAbZx42LOeflleOABC+isXg0//QQ//xz3VqZMJAUKhFK/fgQA770H7dpBw4bw5Zfw/PPQqxeMHRtz3f/+gzp14MQJeP99e27JEssLnF7cSP/F1rBhwr7LzP0nIiIibhAYaAMHsAFCRESip/n4WGVTsInD06dTqX0i6ZSPpxsgIiIe9NxzNrDavx/efDMmse51DBsGFSvCggX29aOP2o6EMWOge3cbt3l7WyCoYkX4+mvo0AGaN4+5xrRpEBLiYM6c/eTLdxtgr2/ePCYA9fDDsHcvTJ8eUw/i7bdthnXrVlutBZaSpEED+PFHqFbNFR3jXjfSf7Hly2e32DJz/4mIiIibdOliE7IhIbBsWZIzc089ZUPIHTuskn3sST4RiUsr3kREMjNf35hEHRMm3NB+wytX4LvvoEmTuMebN4dz52DDhphjd90FAwfa465d4d9/7fGJE1YVq1OncMqVi8kvt2KFNSM2P7+4xbVWrYIaNWKCRgD16kG2bLBy5XWb73E3039Jycz9JyIiIm6UPbvNAoLN6CWRB8PLC0aNssfTplnmEhFJnAJvIiKZ3bPPWqKO06dvKFHHgQNWsfTOO+MeL1nS7kNC4h4fNMi2VJ44AX372rFhw2zANnRo3MS9ZcpYpayoKGvOe+/B/Pnw0ksx5+zenfC9vb0tT1r8906Lbrb/EpOZ+09ERETcrFs3Vgc8QeUd7xIUEJlkLtpGjaBKFavRVajQjeWiLVIk8by1f/+deFOmTrXnDx1y38d1B3fl8s0s/ZfRaKupiEhm5+0Nw4fDM8/ApEm2NC1XriRPP3PG7rNnj3s8Wza7j79ozt/fAkDVq8Ps2fDYY5ZbrHdvS+7/118J32PjRnjwQXtcqZKdG/v947+38/3TQ4GAm+2/+E6ezNz9JyIiIu61cW9uGoUuoyWLGVX8AzY0m0K/fg7Cw+NmJXE44LXXoG5dy/u2dCkUKBDzfPv2kZw+HR6di/bvv+HYMVudX7163PfMmTNhO/bujdk5kZ44c/m2bGmrAjdssFy+8fvPyZnLN77M2n8ZkQJvIiJi+TtGj4bff7cSpCNHJnlqZOS1L+WVyFrqatVs1dWMGdC5s+Xqde5iSMxtt9l2zIMHbUvlgw9aTrKgoGu/f2LvndYkp/9ie++9zN1/IiIi4l7DhkHFeyJZENwJQq7waJ3HCQt/ONFctA8/DLVq2bhjxQqYNcuOJ5aLdvt2e65JEyhR4tptiIiAtm0hTx44etTFH9DN3JXLN7P0X0akIbaIiFjExVme6o03LPt+EnLksPtz5+Ied66Wcj4f39ixULiwbYEsWjThACO2W26Bhx6yAcOiRbYFcunSmOvHf2/n+yf13mlJcvvPaelSy8mWWftPRERE3Cc6F20LP+jUyQ6+9lqSuWidq94A5syBffuSzkW7fbutsL/99uu3Y+JEu056W7Hlzly+maH/MioF3kRExDRpAhUq2Kggfob+WEqUsN2p+/bFPe78ukyZxF+XPXtMEt4DB2DbtrjPnz9vQaL41733Xrv/80+7L1Uq4TkREba6K6n3TkuS239g2wu2bUu8wFhm6T8RERFxnzi5aPv2tT2k69ZR8vQvQOL5YB980KqjR0RY9pKkctFu3w65c1sQKkcOyJrVtmPGT5vx++92nTlzbLV+euLOXL6Zof8yKo8H3iIjI5k2bRo1atSgQoUKdOrUiSNHjiR67vTp0ylVqlSit4GxQrkLFiygXr16lC9fnoYNG7Js2bLU+jgiIumXl1fMFtPp0y2ZWCICAqBmTfjkk7hJYpcts0FAlSpJv4VzRVVUFHTsaLkunHx87Fj8mN/q1XZ/9912X68efP89nDoV95zz5+25tC4l/bdpk91Xq5bwuczSfyIiIuI+cXLRFi0Kzz0HQLapo4Gk88E6J1c/+MCKBXTtmjDv2PbtNol4333w5ZcwebKNSR56CC5csHPCw+0tO3a04+mNq3L5Ztb+y6g8HnibMWMGixYtYtSoUSxZsoTIyEg6duxIaGhognPbt2/Phg0b4tw6dOhAUFAQbdu2BeDDDz9k4sSJvPLKK6xcuZLnn3+eIUOGsGbNmlT+ZCIi6VCjRlaC6eJFGDcuydMGD7YgUIsW8NVXMGSIBXwGDbKZtbNnLbFs7OAOwK5dVmwhVy7LOfbmmzGpRgMCbDn8u+/a9b791tLNtW8PjzxiRRkAunSx3Bh168Knn1rOs1at7HlnQYG0LqX9l1hej8zUfyIiIuIeCXLBDhhgk7NfrQSSzgd7773QrJk9Dg1NPBftu+/CTz/ZeKdGDcv7u2wZ/PGHVWEH27b633/w+usu+Tipzp25fDND/2VUHg28hYaGMmfOHLp160atWrUoXbo0U6ZM4fjx46x2TtHHkiVLFvLlyxd9O3XqFPPnz2fo0KGUKlUKgHPnztG7d28ef/xxbr31Vlq0aMGdd97Jjz/+mNofT0Qk/XE4Yla9zZgRsz8xnjp17Bd9SAg8+aTNbk6YYBWbwIJqVatakt3YTpyw2bvJk+3r0aN9OXrUL/r5wYPtbT/91Co8TZkCL74IX3xhTQPLbbZuHeTNawGjV1+Fp56CDz90XTe4W0r7LymZpf9ERETEPRLkor3jDmjZkrNkj/N8YpxDyKgo+N//Ej5ftarN78ZWrZpdc8cOS6cxZowVaPD3t9VbzkBWRITd0jp35vLNDP2XUXm0qumePXu4cOECVatWjT6WPXt2ypYty+bNm2nUqNE1Xz9y5EgqVapEk1iZCzt27Bj9OCwsjG+++Yb9+/fTtWtX138AEZGMqH59W/r00082XTZtWqKnNWmSMHGsU61acbdROs2YYbeoKFi4ENaudfDSS3dyxx0+eHvHnFegQEw5+s2bLYiUGOevj337LICV3sSuYrVypd2c6tSxaljOilhOd91lFcQAIiL8yZ//VqZOtZVyDocF2l588drvW64caCG4iIiIxJdoLtpBg9i32A6UCToM3Jboa2MHlQYPtklGpzNn7OsqVWwc4hQZaSvk8uWDzz+3x488kvDaJUva1snvvkv2R0sVrsjl26NHwucyS/9lVB4NvB0/fhyAQoUKxTmeP3/+6OeSsm7dOrZt28Znn32W6PNbtmyhTZs2REZG0qxZMx52/pUiIiLX5nBYoo6HH4aZMy2x7q23uvwtZs2C8uWj+PNP/6QW1sl1eQP52bo1kk8/hfLlPd0eERERSc9i56Lt0+fqivly5VhWcgA59v1HldWj4fl3E32tMxettzd8/TX89JMXuXLZMX9/y1vWpImt9HdavhwuXYLataF0act6EtuXX8KIEXZe/IIFaVGi/UfKc/lmlv7LqDwaeLt06RIAfn5+cY77+/tzxpmVMAlz586ldu3alEkiZFy8eHE+/fRTdu3axZgxY8iVKxd9+/ZNdlujoqK4ePHi9U9MBmc/OO/l5qj/Ukb9lzIZtv/uvx//GjXwXr+esBEjCEti1VtKFCwIa9aEsm7dafLly5fgd4Fc37lzYbz2mi/79/vzwANRvPlmKC1bah/BjXL3/9+oqCgczhG3iIhIOjF4sK2aatHCcsX+9BNM2N+E1+lP0IdzOdtvEMGXilOiRNwtkc5ctM8/bxOsI0b4MmWKPRcQYOnihg2zXQ0NGtj5w4dD48a20h/gllvituW33+y+fHkoVszdn9w1Eu2/CbaRxJnLNziYJPsvqVy+maX/MiKPBt4CAgIAy/XmfAxw5coVAgMDk3zdn3/+yaZNm5g1a1aS5+TJk4c8efJQunRpTp8+zZtvvkn37t2T/YddWFgYu3fvTtZrb9ShQ4fcev2MTv2XMuq/lMmI/Zf1uecotX49PvPmsadxY0Lj/yZ3AT8/29kK/7r82pnFwoXevPrq7WzalJ327f1ZteokPXocxdc3kb2+kih3/v9VQFlERNIbZy7aYcMslUfhwjBhgoPeq3fA6gi2DvmU2l/0Yu5cuFrjEIjJRTtkiFXm3LDBm02bslG2rD0/eLAFmt56C95+G/LksfQYw4en/md0p8T7D3r3tue3brUVakn1X1IyS/9lRI6oqMSy8KSOnTt38tRTT/HNN99QtGjR6OPPPPMMpUqVYngS30Hz58/n7bffZv369fj4xI0d/vDDD9xyyy2ULFky+th3333HCy+8wPr168mfP/9Nt3PXrl1ERUXFuaYrXbp0iUOHDlGsWLFrBhwlceq/lFH/pUxG7z//xx/H+9tvCX/uOULfftvl18/o/eduzv679dZiTJqUnfHjfQG4//4IFi4M5ZZbFHy7Fnd//+3btw+Hw0F57QFO03bt2gXgtn+nixcvsnv3bsqUKUNQUJBb3iMjU/+ljPovZdR/8fzwgyUK8/ODAwcsopSEnj3hjTfg1lsv88gjPnh7+xAVFZMD+HqPXXFeer9GZGQkuXKdZfp0f+65R+Pkm+Xu/783M37w6Iq30qVLkzVrVjZt2hQdeDt79izBwcG0bt06yddt2bKFKlWqJAi6AbzxxhsUK1aMyc6SecCOHTvImTMnefPmTXZbHQ6H23/YBgYG6gd6Cqj/Ukb9lzIZtv9eew2qVsXngw/wGTLEMrO6QYbtv1SSNWsg48b5Ur06tGkDmzZ5U61aIB9+aIUu5Nrc9f2nbaYiIpKh1KwJ1avDhg0waVJMmfpEDBwI774bxZEjAcydm4ptzFC8gJxUqxbFjBnw3HOebo8kl0cDb35+frRu3ZqJEyeSO3duChcuzIQJEyhYsCD16tUjIiKC06dPky1btjhbUYODg2nWrFmi1+zYsSO9evXi3nvvpUaNGmzatInZs2fTr18/vLy8UuujiYhkDA88YEkkVq60GvHz53u6RXINjz8OW7ZAs2awc6flFxk3Dnr1iknuKyIiIpJsr74Kjz0G77xj0bXYScpiyZ8fli+/wscf/0P+/JbL1zkWcThI8FjPJXx88eJlRo0K49dfs/H88/DNNzBjBmTLlsS/jaRZHg28AXTr1o3w8HAGDx7M5cuXqVy5MrNnz8bX15ejR4/y8MMPM3bsWJo2bRr9mlOnTpEzic3PDRo0ICwsjHfffZdx48Zxyy23MGTIEJ566qlU+kQiIhnMyJEWePvgAxg0yEomSZpVsiT8/DO88AIsXGgVtTZuhDlzNFATERGRFKpfH+67D3791faSvvZakqc+8EAkOXIcp0yZXAQFKefpzbp4MZJChfayYkV5Ro/2Y+FCG9MtWWL/BJJ+eDzw5u3tTd++fROtOFqkSBFCQkISHN+xY8c1r9m4cWMaN27ssjaKiGRq991n5ZI+/9yyty5Z4ukWyXUEBdnixKpVoUcPWLrUqlp98gkkUQxcRERE5PocDlv11rQpvPkm9O177YoAkiLe3tC/fzh16/rx7LOwb5+N78aNszGedjSkD9p7KSIi1zdypN1/9JHVLpc0z+GAl16yPMiFC8OePVC5sv0TioiIiCRb48Zw111w9qyV2BS3q14dtm+HJk0gLMzSiDRqBKdOebplciMUeBMRkeu7+2546ikrsaSa5enKAw/ElK2/cAFatrRy9mFhnm6ZiIiIpEteXpbfDWDKFDh/3rPtySRy54ZlyyzPm7+/ZYK55x749ltPt0yuR4E3ERG5McOG2TKqTz6Bbds83Rq5Cfnzw+rV0L+/fT15shVeOH7cs+0SERGRdKplS7j9dvjnH5g1y9OtyTQcDujSBTZvtvQhf/1lY7pXX4XwcE+3TpKiwJuIiNyYu+6CZ56xx8OGebYtctN8fOD1122mNFs224J6773w44+ebpmIiIikOz4+MaveJk6Ey5c9255Mpnx5q2TfqZNtSBkzBh56CA4f9nTLJDEKvImIyI0bNsy2F3zxBfzyi6dbI8nQtKnNkpYta7OktWrB1Kk2aBMRERG5Yc89B0WK2IBi3jxPtybTCQqyxYYffgjZs8NPP0GFCjbJKmmLAm8iInLj7rwT2rSxx0OHerYtkmylSsGmTfD007YtoUcPePZZpWgRERGRq1avtqpMQUFQvLitaos/S+fnZ1VNwfY/OhxxbkFZsnBfpUp4L1wY85oiRRKch8MBf/8dc86+ffD441YtNW9eu/bZs27/yOlVixZWeOH+++G//6B5c3jxRbh0ydMtEycF3kRE5OYMHWrbC1at0j7FdCxrVli0yFa7+fjAkiVWiGHvXk+3TERERDxq40YrmVm6tOX2bdUK+vWDceMSntuxI+TJY48HD4aff46+RZYpQ2iBAkTUr2/P//03HDsGEybEOY+ff7YgG1jkqE4dOHEC3n8fxo61QUqLFqnxydOt4sVh/XoYMMDimDNnWtz099893TIBBd5ERORm3X47tGtnj7XqLV1zOKBbN1i3DgoWtMFZpUrw6aeebpmIiIh4zLBhULEiLFgAjz4Ko0fbyrYxYxIuowoKiln19vHHFu154AH45RccISHsHzcO8uWz57dvt/smTeyc2DcfH3vu7betYMPKldC4sSUxW7RIE743wNfX4pSrVsUd182cqZQinqbAm4iI3LxXX7Xf7t9+C9995+nWSApVrw5bt0KNGnDunOWBGzBA1bFEREQynStXbGzXpEnc482b2yBhw4aEr+nSxVashYTYCrkTJ2DwYMI7deJiuXIx523fbhWebr896fdftcoGJHnzxhyrV89et3JlCj5Y5lG3LuzYYTHTy5dt22mLFraYUDxDgTcREbl5t91mM5AAQ4ZoGi0DKFQI1q6Fnj3t63HjoH59OHnSs+0SERGRVHTgAISGWl7f2EqWtPuQkISvyZ7dltADvPaa7Yjw8iIs/s6I7dshd24L4uXIYXkvWra04gxOu3cnfG9vb9tLmdh7S6Ly54cVKyw1n48PLF1qhRd+/tnTLcucFHgTEZHkGTQI/P1t5nPNGk+3RlzA1xcmT7bqWFmy2ILGe++1VC8iIiKSCZw5Y/fZs8c9ni2b3SdV5KBbNxs87NhhFU67do3J2+a0fbvleLvvPvjySxt0fP89PPQQXLgQ8/7x39v5/iqwcFO8vKB3b6t2evvtcPiwLSYcMwYiIjzdusxFgTcREUmewoVt7Tpo1VsG06IF/PKLVT89dgxq1rSUK/onFhERyeAiI6/9vFcSIYQ8eeCll+xxWFjMCrjY3n3XokCDBlkEqHNnWLYM/vgD5s+//vsn9d5yTZUrw7ZtVsE+IsIyxtSrB3/+6emWZR76zhURkeQbMAACA2HTJuXdyGDKlrXgW7NmNn5+6SVo2xYuXvR0y0RERMRtcuSw+3Pn4h53rjZzPp+YXr2sclNUFPz2W8Lnq1a1KFBs1arZNXfsiLl+/Pd2vv+13luuKXt2WLgQ5s61ehjffgv33KPhe2pR4E1ERJKvYEHbSgCWz0NLojKU7NmtQNmECTbJPH++jZn37/d0y0RERMQtSpSwnGr79sU97vy6TJmkXxsRETMWHD067nNnzsCcOQkDcpGRllPOWfm0VKmE7x0RAQcPXvu95bocDptE3brV8r39/Tc0bGjbUUNDPd26jE2BNxERSZl+/Sw57tat8Pnnnm6NuJjDAX36WOGF/Plh586Y1CwiIiKSwQQEWI6JTz6JO6G6bJmtOKtSJenXbtpk997esG4dXs6vwfICd+0KY8fGfc3y5XDpEtSubV/Xq2d5306dijln9Wo4f96ekxQrVcqKLDh3A0+eDA8+mDDeKa6jwJuIiKRM3rwxv7mHDr1+bhBJl2rVsthq1ao2af3445baT8l55VoiIyOZNm0aNWrUoEKFCnTq1IkjR44keu706dMpVapUoreBAwdGn9euXbsEz7dp0ya1PpKISMY3eLAF0Vq0gK++sl/4EyZYbragINv2uXFj3OAYwK5dFmB7/nkAfMePj3kuIMBSlCxaZFtS16yBKVPs3MaNoU4dO69LF0tjUrcufPopvPcetGoFjz1m0SFxiYAAmDrV5sxz54Zff4WKFW07qrieAm8iIpJyvXvbvsRdu2xGVDKkwoXhu+/glVfs69GjoUED+OcfjzZL0rAZM2awaNEiRo0axZIlS4iMjKRjx46EJrKnpX379mzYsCHOrUOHDgQFBdG2bdvo80JCQhg+fHic86ZPn56Kn0pEJIOrU8fGcyEh8OST8MEHFnjr18+ed87ErVgR93UnTlgl0wEDwMsL76+/JnDPnpjnBw+GGTNsBdvjj8OkSVaoa/HimHPy5YN162xit1UrqwTw1FNWcl1c7oknLL1ezZq2qLBNG4uFnj/v6ZZlLAq8iYhIyuXObbOXAMOGaRlUBubnB9Om2YxoYKCNne+7D7Zs8XTLJK0JDQ1lzpw5dOvWjVq1alG6dGmmTJnC8ePHWb16dYLzs2TJQr58+aJvp06dYv78+QwdOpRSpUoB8M8///DPP/9wzz33xDk3Z86cqfzpREQyuCZNLL/ElStw4IBNsjrVqmXbUGNNigAWVDt+HO64w1bLAYXmzo153svLVrT99pttLz16FMaNswFFbOXK2Yq4ixctmDdzJmTL5paPKVCkiBVbGDEiJqfvvfdafFVcQ4E3ERFxjR49IFcu2L0blizxdGvEzVq1sl0mJUvC4cNWlOy99zzdKklL9uzZw4ULF6hatWr0sezZs1O2bFk2b9583dePHDmSSpUq0aRJk+hjISEhOBwOihcv7pY2i4iIiwwaBEDOb79NvEqppCne3pYx5rvvLBD3xx+2qHHqVNVOcwUF3kRExDVy5LAs/GBTZuHhnm2PuN3dd8PmzZaaJTQUOnWCDh1sElvk+PHjABQqVCjO8fz580c/l5R169axbds2+vfvH+f4dqtJ8gAAJaBJREFU3r17yZYtGyNHjqRmzZo8+uijvPHGG4luXRUREQ8qX57Q0aM51bx5whVtkmbVqGFbT5980sZ2PXrYdtS///Z0y9I3H083QEREMpBXXrFEuX/8YXsR429BkAwnZ04rfDZunKVumTMHtm+31DDFinm4ceJRl65GYP38/OIc9/f358yZM9d87dy5c6lduzZlypSJc3zv3r1cuXKFu+++m3bt2rF7927Gjx/Pn3/+yfjYSbxvUlRUFBcvXkz266/F2Q+XFJFOFvVfyqj/Ukb9lzKXXnyRI4cOUSwszLaNyk3x1PdfQIAN42fN8mHgQF++/NLB3XdHMnt2KA89lH6KqLm7/6KionA4HDd0rgJvIiLiOtmyWeLdfv1g5Ejbj+jr6+lWiZt5ecHAgVC5MjzzjOUEue8+y8X86KOebp14SkBAAGC53pyPAa5cuULgNVY//Pnnn2zatIlZs2YleG7kyJH079+fHDlyAHDnnXfi6+tLz5496devH3nz5k1WW8PCwti9e3eyXnujDh065NbrZ3Tqv5RR/6WM+i9l1H8p46n+q1kT5s4NZNCg4hw6FEjDhv60b3+cTp3+xCcdRZLc2X/xJxeTko66S0RE0oWXX7YqVQcPwrx5tv9QMoVHHrFy9M2b2xbUBg1s1/Grr1pwTjIX5xbTkydPUrRo0ejjJ0+ejC6WkJg1a9aQO3duqlWrluA5Hx+f6KCb0x133AHY1tbkBt58fX0pWbJksl57PZcuXeLQoUMUK1bsmgFHSZz6L2XUfymj/ksZ9V/KpIX+K1MGHnkkir59w3n/fR9mzy5EcHB+5s4N5dZb03byN3f33759+274XAXeRETEtYKCbPlTjx4wahQ89xz4+3u6VZJKihaF9euhe3crQjZ0KGzaBAsWWO0NyTxKly5N1qxZ2bRpU3Tg7ezZswQHB9O6deskX7dlyxaqVKmCTyLT6W3atKFIkSKMHTs2+tiuXbvw9fWlWAr2NjscDoKCgpL9+hsRGBjo9vfIyNR/KaP+Sxn1X8qo/1LG0/0XFGRz6Y89Bp07w88/e/PAA4HMng1Nm3qsWTfMXf13o9tMQcUVRETEHV54AW65BY4cUanLTMjfH955B+bOtTwhK1ZApUqW+00yDz8/P1q3bs3EiRNZu3Yte/bsoWfPnhQsWJB69eoRERHBqVOnuHz5cpzXBQcHU7p06USvWb9+fT7//HMWL17MkSNHWLlyJePHj6dDhw5kzZo1NT6WiIhIptSyJWzbBlWqwH//QbNm8NJLKqp1IxR4ExER1wsIsP2FAGPG6DdyJtW2Lfz8MxQvDgcOWFn699/3dKskNXXr1o3mzZszePBgnnnmGby9vZk9eza+vr789ddfVK9enZUrV8Z5zalTp8iZM2ei12vdujWvvvoqCxYsoEGDBkycOJG2bdvSvXv3VPg0IiIimdvtt8OGDZbOGeDtt+H++yE42LPtSuu01VRERNyjQwcrdfm//9mewx49PN0i8YAKFSzvW+vWsHKlBeM2boQ33tAO5MzA29ubvn370rdv3wTPFSlShJCQkATHd+zYcc1rtmrVilatWrmsjSIiInLjfH1tiP/ww9CmDezaZTsbpk6Fjh3hJnZgZhpa8SYiIu7h7w+DB9vjsWPhwgXPtkc8Jlcu+OILK7TgcNg21Jo1bSeyiIiIiKQ/9erBzp12f+mS5X9r2dK2oUpcCryJiEjyrF4NlStbxtXixWHiRIiKV92obVtbk37yJGTNalGXWLegLFm4r1IlvBcuTHj9c+fsuvPmJXzu55+hdm177/z5oX17OHHCHZ9SXMTLywotrFxpgbhffoF774U1azzdMhERERFJjgIF4KuvYPx48PGBjz+GihVtd4PEUOBNRERu3saN0KgRlC4Nn3wCrVpZsodx4+Ke5+tr0RaA7NktyvLzz9G3yDJlCC1QgIj69eO+7t9/oWFDOHQo4Xv/8gvUqmXnvP8+zJkTk0DszBl3fFpxoUcfta2n994Lf/8N9evbgsjISE+3TERERERulpcX9O0LP/5o8+2HDkH16vD66xrfOSnwJiIiN2/YMJvOWrDAIimjR9tv3MQKKbRqBXfeCWfPWsDugQfs9ssvOEJC2D9uHOTLF3P+8uV27d9/T/y9X3sNcuSAdevgqacsALhyJYSH23SbpHnFi9vgrH17G5ANGmTl6BU3FREREUmfqlSBrVvh6achIgIGDrQJ1uPHPd0yz1PgTUREbs6VK/Ddd9CkSdzjzZvb9tANG+Ie9/GxQB3YdtQzZ2xb6ODBhHfqxMVy5WLO/e8/u+5DD8GqVYm//+7dNo2WK1fMsaAgK6m0YkVKP52kkoAAmD0b3n0X/Pzg888tMe+uXZ5umYiIiIgkR44csGiRjfGCgmyzy913w9dfe7plnuXxwFtkZCTTpk2jRo0aVKhQgU6dOnEkiWzL06dPp1SpUoneBg4cGH3esmXLePzxx6lQoQL16tVj1qxZREREpNZHEhHJ2A4cgNBQW8UWW8mSdp9IlUJatoSyZS2wNmWKBeK8vAhzbkN1CgqyeuTvvw958yb+/nnzwuHDCY/v329tk3SlY0eL1RYtCvv22WLIRYs83SoRERERSQ6Hw3Y1bNliQbdTp+Cxx6BPH/sTIjPyeOBtxowZLFq0iFGjRrFkyRIiIyPp2LEjoYn8i7Rv354NGzbEuXXo0IGgoCDatm0LwPLlyxk2bBitW7dm+fLl9OjRg5kzZ/L222+n8icTEcmgnPsBs2ePezxbNrs/ezbha7y9YfhwezxpkgXWunaFnDnjnufnB6VKXfv927e3dew9esCff9r69f79LWCnyqnpUuXKlvetXj24eNF2J3frlnkHZyIiIiLpXZkysGmTDfnB/gSoVs0mWjMbjwbeQkNDmTNnDt26daNWrVqULl2aKVOmcPz4cVavXp3g/CxZspAvX77o26lTp5g/fz5Dhw6l1NU/1BYvXsyTTz5Jy5YtKVq0KA0aNKB9+/YsXbo0tT+eiEjGdL0sqV5J/Gpp1symvc6ft4hK9+7Je/+OHe0393vvQeHCcMstcPCg1TAPCkreNcXj8ua1VH2DB9vX06dbDY1jxzzaLBERERFJpoAAG9N99hnkzm2r4O69N/PtbvBo4G3Pnj1cuHCBqlWrRh/Lnj07ZcuWZfPmzdd9/ciRI6lUqRJNYuUZ6tOnDx06dIhznpeXF2eUsVlExDVy5LD7c+fiHneudHM+H5+XF4wcaY8djpS1oVcvq2q6e7fli/voI9vGmjt3yq4rHuXtDaNGWX2NHDms+O2991pKQRERERFJnxo3hu3boUYN+xOiVSto187m4zMDH0+++fGr5S0KFSoU53j+/Pmjn0vKunXr2LZtG5999lmc4/fdd1+cr8+dO8fixYupUaNGitoaFRXFxYsXU3SNpFy6WgHwUvxKgHJD1H8po/5LmUzZf4UKEejtTVhwMOH160cf9vrtNwKAy8WLE5nEz0tH2bIEAkREEPbaa1waMgRIvP8cly4RCFy5coWIWNfz2roVx5EjRDRubInBAC5eJGDLFiLvuYdQN/2sTosy6vffww/D+vUOnn3Wn99+8+KRR6IYNSqMbt3CUxyzjc3d/RcVFYXDlQ0WERERSaduvRW+/RZGj7aJ1nnzbJJ1yRKoUMHTrXMvjwbenANdPz+/OMf9/f2vu0Jt7ty51K5dmzJlyiR5zoULF3jppZe4cuUK/fr1S1Fbw8LC2L17d4qucT2HDh1y6/UzOvVfyqj/Uiaz9d8dFSvi9eGHhNSvH716rfDs2eTNmpXgbNmISuLnZc5vv6XE1cfe77zD0cceg7x5E+0/vz//pDzw119/8U+s6xVYupRb3nmH3V9/TcTVvHLZNm7kzt27OdyyJafd/LM6Lcqo33/vvONgzJjbWLkyD4MG+bF27QWGDj1ElizX2e58k9zZf/HHOCIiIiKZlY+PpX2uXdtWvYWEwP33w4QJ8MorKd8Uk1Z5NPAWEBAAWK4352Ow1Q2BgYFJvu7PP/9k06ZNzJo1K8lzTp06xQsvvMDRo0eZPXs2RYoUSVFbfX19Kems2Odily5d4tChQxQrVuyan1sSp/5LGfVfymTW/vMaMQL/Ro24Z8wYwtu0wXvTJnwWLCBs5EhKV6wIZ8/itWcPkcWLQ7580a/z+fRTovz9ibznHrx/+YU7P/2U4E6dEu0/x9V8bYUKFSJ/7EmWbt1wLFhA+VGjCOvRA8fRo/gNGkRE1aoU6NWLAt7eqdIHaUFm+P776CN4771Q+vb1Ze3aXBw9moNFi65QunRUiq/t7v7blxmzB4uIiIhcx0MPwY4dVjNt+XJL/bxmDcyZY3l/MxqPBt6cW0xPnjxJUed2oatfl7pGVbs1a9aQO3duqlWrlujz+/fvp2PHjkRGRvLBBx9wxx13pLitDoeDIDcn7Q4MDHT7e2Rk6r+UUf+lTKbrvwYNYNkyfIYNw+fpp63IwYQJ+PXujR/AL7/YVNbcuXC16jQAp09Dzpx4v/Ya1K1LwLx5+D7xBIFlyiTsv6uBEH9//7hFE26/HVavxrtXL7yffdYqo7Zrh/eoUQQ5K6tmMhn9+697d5sNbd4cQkK8qFkzkDlzoEUL11zfXf2nbaYiIiIiicuTx4ouvPUW9O4NX3xhW04/+MACcxmJR4srlC5dmqxZs7Jp06boY2fPniU4OJjKlSsn+botW7ZQpUoVfHwSxg2PHDnC888/T2BgIEuWLHFJ0E1ERBLRpAns3AlXrsCBA/Yb06lWLYiKiht0A5gxA44ftyReNWrguHKFgvPmJX79YsUSvwbAfffB999bdtYjR+CNNyCTBt0yiwcegK1bLZ574QK0bGnfcmFhnm6ZiIiIiCSHwwFdu8KmTVCqlFWzr1MHhg2D8HBPt851PBp48/Pzo3Xr1kycOJG1a9eyZ88eevbsScGCBalXrx4RERGcOnWKy5cvx3ldcHAwpUuXTvSagwYNIjQ0lMmTJ+Pj48OpU6eibyIikkY4HJZVFci7fLkF2ESuI39+WL0a+ve3rydPtjL1RYvCxIlJfxvNm2ffcvFvWbIEUanSfSxcmHB78rlzULy4vTa+//6Dl16CggUha1aoWtWSBYuIiIjIzatQAX791SqdRkbCyJGQK5dtgCle3PXjPICePRPPKde6deLXW7o0+Z/Po1tNAbp160Z4eDiDBw/m8uXLVK5cmdmzZ+Pr68vRo0d5+OGHGTt2LE2bNo1+zalTp8iZM2eCa504cYJffvkFgMaNGyd4PiQkxG2fQ0REbtJDDxE6ejQnjx8nt6fbIumGjw88+SRMmmRfh4fDv/9Cv372eMCAhK9p2NCqZsXXvn0kp0+HU79+RJzj//5rZe8Tq7kQEQGPPQb/+x+MHw8FCsDUqbb7+pdf4O67U/wRRURERDKdLFksx1uxYrbi7fx5m+CsVMm14zyAH36w8Vtitm+HZ56Bbt3iHr/zzpv+SNE8Hnjz9vamb9++9O3bN8FzRYoUSTRYtmPHjkSvVaBAAQXXRETSkfCePTmxeze5lQtLbsKwYXDvvbBgATRtCr//bjORw4fbICl+urZ8+eLU+ABg2jQICXEwZ85+8uW7Lfr48uV2jXPnEn/vRYtgyxbb9lq+vB176CELuK1ercCbiIiISEr8+CPccw/4+cHmzbbSrGJFGDPG8v7Gr4d1M+M8sIBeu3aWovro0bivu3zZKq327GlpTlzFo1tNRURERG7GlSvw3XeWYvDOO2HjRnj6adt+cOWKzXqeP3/ta5w4AYMHQ6dO4ZQrdzH6+H//2XUfeghWrUr8tUuX2vPOoBvYdte9e6FPnxR/PBEREZFMyznOe/pp2LABnOuztm2zSdFFi65/jaTGeU59+1q6kHbtEr72t99sZV2FCin6GAko8CYiIiLpxoEDEBoas9w/a1YbhI0da19/953NUO7dm/Q1hg0DLy8YOjRuZYagIAgOhvffT7qU/fbtcNddVs+jWDHw9bUtEOvXp/CDiYiIiGRyscd5fn6W1uPrr2PGZV26wOzZ104PndQ4D+Cbb2D+fJg7186Jb/t2u3/vPShUyNpQo4YVf0gJBd5EREQk3Thzxu6zZ4855nDErDbLls22nlaqBJ9+mvD1J09aYK1rV4ifLtbPzypqXcupU/Dxx/Duu5bo9/PPLWBXr54V+RURERGR5ElsnFe/vq14A6tm37Gj5WBznhvbtcZ5Z85Ahw5WuCGpfG3OwNuFC7B4sd0uX4batVM2zlPgTURERNKNyMhrP//KK1Czpm1HaNrUkvDGLkf/3ntWIKF79+S9f2iobUldtQqaN7eiCitWWMDv9deTd00RERERSXqcV7Cg3T/6qBXa+vBDy/sWfyXatcZ5PXrArbda/rakvPKKrbCbPx9q1YJmzWyVXJYs8NpryflERoE3ERERSTdy5LD7+MUPzp61+yJFYM0a6NXLvh43zmZKT560r5cutdVp8ZPw3qhs2SzvR5EicY89+GDMbKyIiIiI3LzrjfOeeMJyvxUrBgcPQvXqNtZzBuySGud9+SUsWQKzZtm54eExr4n9uFQpGzfGljMnVKsGSdT4vCEKvImIiEi6UaIEeHvDvn1xjzu/LlPG8q5NmmSzoVmywLffWhXU5cstONaiRfLf/447LPFvfGFhCatsiYiIiMiNu5Fx3v3325bQli0taDZggK2E27Yt6XHe0qW2ZbRcORsn+vrCqFH2nK8vtG9vjz/80KrUx3fpUvInbUGBNxEREUlHAgJsK+knn8RNrLtsmc2SVqkSc6xFC/jlF5u9PHbMtp6CrU5LrgYNbLC3e3fMsX/+gR9/tOS7IiIiIpI8NzrOy5HD8q+9955NfH7zjW0NBVudFt/w4bB5c9xbp0723ObN9jzAzJnw4ouWWsTp2DEb59WunfzPpcCbiIiIpCuDB1tOjxYt4KuvYMgQmDABBg2yQgdnz8LGjVYIoWxZC741a2Y5P8BmOC8mrC5/Q7p3t22mDRvagG/5cnjssbgFHkREREQkeW50nPf331YsYcsWKF8+ZjvqzJlxA2dgW1MrVYp7u+UWe65SJXse7L0OH4bGjS3X26JFUKcO5MkDvXsn/zMp8CYiIiLpSp06NvMZEgJPPgkffGADsn797PmtW6FqVSt6AFYZ6+OP7RhYwtyqVeHAAcdNv3euXDbrWbUqvPwyPPss5M5t+UZuvdU1n09EREQks7rZcV7Zshaou+su+3rCBMv9dvDgzY/zate2rabnz9tW1pdftnQl69fH5J9LDp/kv1RERETEM5o0sVtiatWKuz0BbEXaTz/Bd9/ZQGrnTqhePYDhw3NQpkzCaxQrlvAaTkWK2CBQRERERFzvZsd5gYHw22/w6ae2Cm7zZqhaNYABA3IlOs4D217q3GIa28MP282VtOJNREREMo1atWJmSs+ccdCv3+3884+nWyUiIiIiKdWkieXirV4dzp1zMHhwcfbtu/mVb66mFW8iIiKSqRQubCvfhg0L4/ffz5AzZ5CnmyQiIiIiLlC0KKxbB2PGhPLTT+cpWDDA001S4E1EREQyHz8/GDIkjN27D+PtncQeBBERERFJd3x8oE+fcHbvPkjWrJ4f52mrqYiIiIiIiIiIiBso8CYiIiIiIiIiIuIGCryJiIiIiIiIiIi4gQJvIiIiIiIiIiIibqDAm4iIiIiIiIiIiBso8CYiIiIiIiIiIuIGCryJiIiIiIiIiIi4gQJvIiIiIiIiIiIibqDAm4iIiIiIiIiIiBso8CYiIiIiIiIiIuIGCryJiIiIiIiIiIi4gQJvIiIiIiIiIiIibqDAm4iIiIiIiIiIiBso8CYiIiIiIiIiIuIGjqioqChPNyKt27p1K1FRUfj5+bnl+lFRUYSFheHr64vD4XDLe2Rk6r+UUf+ljPovZdR/KaP+Sxl3919oaCgOh4N7773X5dcW19E4L21T/6WM+i9l1H8po/5LGfVfyqSlcZ6Py989A3L3N7nD4XDbYC8zUP+ljPovZdR/KaP+Sxn1X8q4u/8cDocGyumAxnlpm/ovZdR/KaP+Sxn1X8qo/1ImLY3ztOJNRERERERERETEDZTjTURERERERERExA0UeBMREREREREREXEDBd5ERERERERERETcQIE3ERERERERERERN1DgTURERERERERExA0UeBMREREREREREXEDBd5ERERERERERETcQIE3ERERERERERERN1DgTURERERERERExA0UeBMREREREREREXEDBd5ERERERERERETcQIE3ERERERERERERN1DgzYMiIyOZNm0aNWrUoEKFCnTq1IkjR454ulnp0syZM2nTpo2nm5Gu/PfffwwdOpSaNWty77338swzz7BlyxZPNyvd+Oeff+jbty8PPPAAFStWpHPnzuzfv9/TzUqXDh48SMWKFfnkk0883ZR05cSJE5QqVSrBTf14Yz777DMaNGhA+fLladiwIV999ZWnmyQZjMZ5rqNx3s3TOC9lNM5zHY3zkkfjvJRLS2M9Bd48aMaMGSxatIhRo0axZMkSIiMj6dixI6GhoZ5uWrrywQcf8MYbb3i6GelOr1692LZtG5MnT2bZsmWUKVOGDh06cODAAU83LV14+eWXOXz4MLNmzWLp0qUEBATQtm1bLl265OmmpSthYWH06dOHixcverop6c6ePXvw9/dn/fr1bNiwIfrWoEEDTzctzfv888959dVXadWqFStWrKBRo0bRPxNFXEXjPNfQOC95NM5LGY3zXEPjvOTTOC9l0tpYT4E3DwkNDWXOnDl069aNWrVqUbp0aaZMmcLx48dZvXq1p5uXLpw4cYIXX3yRiRMnUqxYMU83J105fPgwP/74I8OHD6dSpUoUL16cIUOGkD9/fr744gtPNy/NO3PmDIULF2b06NHcfffdlChRgpdeeomTJ0/yxx9/eLp56cr06dPJmjWrp5uRLu3du5dixYqRP39+8uXLF30LCAjwdNPStKioKKZOncpzzz1Hq1atKFq0KF26dOHBBx/kl19+8XTzJIPQOC/lNM5LPo3zUkbjPNfROC/5NM5LvrQ41lPgzUP27NnDhQsXqFq1avSx7NmzU7ZsWTZv3uzBlqUfv//+O76+vixfvpx77rnH081JV3LlysWsWbMoX7589DGHw4HD4eDs2bMebFn6kCNHDiZNmsSdd94JwOnTp5k3bx4FCxakZMmSHm5d+rF582Y+/PBDXn/9dU83JV0KCQmhRIkSnm5GunPw4EGOHTvG448/Huf47NmzeeGFFzzUKsloNM5LOY3zkk/jvJTROM81NM5LGY3zki8tjvV8PPKuwvHjxwEoVKhQnOP58+ePfk6urU6dOtSpU8fTzUiXsmfPzkMPPRTn2KpVqzh8+DCDBg3yUKvSpyFDhvDRRx/h5+fH22+/TVBQkKeblC6cPXuWfv36MXjw4AQ/B+XG7N27l1y5ctGqVSsOHjzIbbfdRpcuXahZs6anm5amHTx4EICLFy/SoUMHgoODKVKkCF26dNHvFHEZjfNSTuO85NM4z3U0zksejfNSTuO85EuLYz2tePMQZ34APz+/OMf9/f25cuWKJ5okmdjWrVsZOHAg9erVo1atWp5uTrry/PPPs2zZMho1asTLL7/M77//7ukmpQvDhw+nYsWKCWai5MaEh4dz4MABzpw5wyuvvMKsWbOoUKECnTt35ueff/Z089K08+fPA9C/f38aNWrEnDlzqFatGi+99JL6TlxG4zxJSzTOSz6N85JH47yU0TgvZdLiWE8r3jzEuTc7NDQ0zj7tK1euEBgY6KlmSSa0Zs0a+vTpw7333svEiRM93Zx0x7nl4LXXXmPHjh0sXLiQsWPHerhVadtnn33Gli1blGcmBXx8fNi0aRPe3t7Rv0PKlSvHH3/8wezZs+Nsb5O4fH19AejQoQNNmjQBoEyZMgQHBzN37lz1nbiExnmSVmiclzIa5908jfNSTuO8lEmLYz2tePMQ55LbkydPxjl+8uRJChQo4IkmSSa0cOFCXnnlFWrXrs0777yDv7+/p5uULpw+fZoVK1YQHh4efczLy4uSJUsm+D8tCS1btox//vmHWrVqUbFiRSpWrAjAsGHD6Nixo4dbl35kyZIlQYLdO+64gxMnTnioRemD83esM3ePU8mSJTl69KgnmiQZkMZ5khZonJc8GueljMZ5rqFxXvKlxbGeAm8eUrp0abJmzcqmTZuij509e5bg4GAqV67swZZJZrFo0SJGjRpFq1atmDx5coLtMJK0v//+m169esVZqhwWFkZwcLCSoN6AiRMnsnLlSj777LPoG0C3bt147bXXPNu4dOKPP/7g3nvvjfM7BOC3335T4ufruOuuu8iSJQs7duyIc3zv3r0ULVrUQ62SjEbjPPE0jfOST+O8lNE4L+U0zkuZtDjW01ZTD/Hz86N169ZMnDiR3LlzU7hwYSZMmEDBggWpV6+ep5snGdzBgwcZM2YMdevW5YUXXuDvv/+Ofi4gIIBs2bJ5sHVp35133knNmjUZPXo0o0ePJkeOHMycOZOzZ8/Stm1bTzcvzUtqtUeePHm0EuQGlShRgttvv52RI0cyYsQIcuXKxUcffcT27dtZtmyZp5uXpgUEBNCxY0feeustChQowN13382KFSv48ccfmTdvnqebJxmExnniSRrnpYzGeSmjcV7KaZyXMmlxrKfAmwd169aN8PBwBg8ezOXLl6lcuTKzZ8+O3pMs4i6rVq0iLCyMb775hm+++SbOc02aNFHZ7xswefJkJk2aRM+ePTl37hyVKlXigw8+4JZbbvF00yQT8PLy4p133mHSpEn06NGDs2fPUrZsWebOnZtgWb0k9NJLLxEYGMiUKVM4ceIEJUqUYPr06dx///2ebppkIBrniadonJdyGueJJ2mcl3JpbazniIqKivLIO4uIiIiIiIiIiGRgyvEmIiIiIiIiIiLiBgq8iYiIiIiIiIiIuIECbyIiIiIiIiIiIm6gwJuIiIiIiIiIiIgbKPAmIiIiIiIiIiLiBgq8iYiIiIiIiIiIuIECbyIiIiIiIiIiIm6gwJuIiIfUqVOHAQMGeLoZIiIiIuJiGueJiJMCbyIiIiIiIiIiIm6gwJuIiIiIiIiIiIgbKPAmIpnOxx9/TMOGDSlXrhy1atVi+vTpREREADBgwADatGnD0qVLqV27NhUrVuT5559nz549ca5x6NAhunXrRrVq1ahQoQJt2rTh119/jXPO+fPnGTVqFDVq1KBChQo0a9aM7777Ls45YWFhjB8/Pvo67du35/Dhw279/CIiIiIZlcZ5IpLWKPAmIpnKzJkzGTJkCFWrVuWdd96hVatWvPvuuwwZMiT6nN27dzNlyhS6du3KhAkT+Pfff2ndujUnT54EYN++fTRt2pSjR48yePBgJk6ciMPh4Pnnn+eXX34BICIigvbt2/PFF1/wwgsvMGPGDG6//XZefvlltmzZEv1eK1eu5I8//uD1119n2LBh/Pbbb/Ts2TN1O0VEREQkA9A4T0TSIh9PN0BEJLWcO3eOGTNm0LJlSwYPHgxA9erVyZkzJ4MHD6Zdu3bR573zzjtUqlQJgLvvvptHHnmE+fPn06dPH9588038/PyYP38+WbNmBaBWrVo0atSI8ePHs3TpUn744Qd27NjBW2+9xSOPPALAAw88wJEjR9i4cWP0tQsUKMCMGTPw9fUF4PDhw7z99tucP38++toiIiIicm0a54lIWqXAm4hkGtu2bePy5cvUqVOH8PDw6ON16tQB4McffwSgSJEi0QMmgPz581OxYkU2b94MwC+//ELt2rXjDJh8fHxo2LAhb731FhcuXODXX3/F19c3+toAXl5eLFmyJE6b7r777ujBmPO9Ac6ePasBmYiIiMgN0jhPRNIqBd5EJNP477//AOjcuXOizzu3GBQoUCDBc3ny5OH3338H4MyZM+TNmzfBOXnz5iUqKorz58/z33//kTNnTry8rr2jPygoKM7XzvMjIyOv/WFEREREJJrGeSKSVinwJiKZRvbs2QGYOHEixYoVS/B83rx5mTp1Kv/++2+C5/7++2/y5MkDQI4cOfj7778TnHPq1CkAcuXKRbZs2fjvv/+IiorC4XBEnxMcHExUVBR33XWXKz6SiIiIiKBxnoikXSquICKZxj333IOvry8nTpygfPny0TcfHx8mT57M0aNHAatktX///ujXnThxgm3btlG1alUAKleuzLp16zh//nz0OREREaxYsYLy5cvj5+dHpUqVCAsL44cffog+JyoqioEDBzJz5sxU+sQiIiIimYPGeSKSVmnFm4hkGrly5aJjx45MnTqV8+fPc//993PixAmmTp2Kw+GgdOnSgA2cXnzxRXr27Im3tzdvvvkmOXLkoE2bNgB07dr1/+3cIapqURiG4U/EKFosglFsglEHYzCYxaRZMIlgMigGEbsTMBidg8lBGD3hwh3B3SjnPs8I1trp52XtP7fbLcPhMOPxOJVKJafTKc/nM/v9PsmfJby9Xi+z2SyTySStViuXyyWPxyOLxeJj3wAA4Dcy5wHfSngD/iuTySSNRiPn8zn7/T61Wi39fj/T6TTVajVJ0mw2MxqNslwu83q9MhgMst1uU6/XkyTtdjvn8znr9Trz+TylUindbjfH4/Hvst5yuZzdbpfVapXNZpPX65VOp5PD4ZBut/up6wMA/FrmPOAbld7v9/vThwD4FrPZLPf7Pdfr9dNHAQDgHzLnAZ9gxxsAAAAAFEB4AwAAAIAC+NUUAAAAAArgxRsAAAAAFEB4AwAAAIACCG8AAAAAUADhDQAAAAAKILwBAAAAQAGENwAAAAAogPAGAAAAAAUQ3gAAAACgAMIbAAAAABTgB6omOu8asjW9AAAAAElFTkSuQmCC","text/plain":["<Figure size 1500x500 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["_, ax = plt.subplots(1, 2, figsize=(15, 5))\n","plot_model_results(\"InternLM_2_5-7b\", df_p1, df_p2, ax)"]},{"cell_type":"code","execution_count":26,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: Llama3-8b\n","Best P1 accuracy:\n","0.7836666666666666\n","Best P2 accuracy:\n","0.7303333333333333\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 1500x500 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["_, ax = plt.subplots(1, 2, figsize=(15, 5))\n","plot_model_results(\"Llama3-8b\", df_p1_llama3, df_p2_llama3, ax)"]}],"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>0.013000</td>\n","      <td>0.922640</td>\n","      <td>0.026621</td>\n","      <td>0.015316</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>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>7</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>0.134333</td>\n","      <td>0.678404</td>\n","      <td>0.134333</td>\n","      <td>0.197460</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>8</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>9</th>\n","      <td>9</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>0.445333</td>\n","      <td>0.737958</td>\n","      <td>0.445333</td>\n","      <td>0.548035</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>10</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>0.485000</td>\n","      <td>0.743099</td>\n","      <td>0.485000</td>\n","      <td>0.573525</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...  0.013000   \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...       NaN   \n","7       7  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.134333   \n","8       8  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...       NaN   \n","9       9  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.445333   \n","10     10  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.485000   \n","\n","    precision    recall        f1  \n","0    0.766712  0.792917  0.767940  \n","1    0.922640  0.026621  0.015316  \n","2         NaN       NaN       NaN  \n","3         NaN       NaN       NaN  \n","4         NaN       NaN       NaN  \n","5         NaN       NaN       NaN  \n","6         NaN       NaN       NaN  \n","7    0.678404  0.134333  0.197460  \n","8         NaN       NaN       NaN  \n","9    0.737958  0.445333  0.548035  \n","10   0.743099  0.485000  0.573525  "]},"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>0.730333</td>\n","      <td>0.771041</td>\n","      <td>0.730333</td>\n","      <td>0.746484</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>0.707000</td>\n","      <td>0.800984</td>\n","      <td>0.707000</td>\n","      <td>0.741300</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>0.702000</td>\n","      <td>0.776710</td>\n","      <td>0.702000</td>\n","      <td>0.728534</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>0.623333</td>\n","      <td>0.768108</td>\n","      <td>0.623333</td>\n","      <td>0.676031</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>7</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>0.642333</td>\n","      <td>0.764460</td>\n","      <td>0.642333</td>\n","      <td>0.678280</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>8</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>9</th>\n","      <td>9</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>0.556667</td>\n","      <td>0.755559</td>\n","      <td>0.556667</td>\n","      <td>0.628456</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   epoch                                              model  accuracy  \\\n","0      0                shenzhi-wang/Llama3-8B-Chinese-Chat  0.730333   \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...  0.707000   \n","4      4  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.702000   \n","5      5  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...       NaN   \n","6      6  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.623333   \n","7      7  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.642333   \n","8      8  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...       NaN   \n","9      9  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.556667   \n","\n","   precision    recall        f1  \n","0   0.771041  0.730333  0.746484  \n","1        NaN       NaN       NaN  \n","2        NaN       NaN       NaN  \n","3   0.800984  0.707000  0.741300  \n","4   0.776710  0.702000  0.728534  \n","5        NaN       NaN       NaN  \n","6   0.768108  0.623333  0.676031  \n","7   0.764460  0.642333  0.678280  \n","8        NaN       NaN       NaN  \n","9   0.755559  0.556667  0.628456  "]},"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":[],"source":["# plot the results\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import matplotlib.ticker as ticker\n","\n","def plot_model_results(model_name, df_p1, df_p2, ax):\n","    print(f\"Model: {model_name}\")\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","\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","\n","    plot_results(df_p1, df_p2, best_p1, best_p2, model_name=model_name)\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()"]},{"cell_type":"code","execution_count":15,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: InternLM_2_5-7b\n","Best P1 accuracy:\n","0.7616666666666667\n","Best P2 accuracy:\n","0.7963333333333333\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 1500x500 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["_, ax = plt.subplots(1, 2, figsize=(15, 5))\n","plot_model_results(\"InternLM_2_5-7b\", df_p1, df_p2, ax)"]},{"cell_type":"code","execution_count":16,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: Llama3-8b\n","Best P1 accuracy:\n","0.7836666666666666\n","Best P2 accuracy:\n","0.7303333333333333\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 1500x500 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["_, ax = plt.subplots(1, 2, figsize=(15, 5))\n","plot_model_results(\"Llama3-8b\", df_p1_llama3, df_p2_llama3, ax)"]}],"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}