{"cells":[{"cell_type":"code","execution_count":106,"metadata":{"executionInfo":{"elapsed":476,"status":"ok","timestamp":1720679526275,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"uWKRSV6eZsCn"},"outputs":[{"name":"stdout","output_type":"stream","text":["The autoreload extension is already loaded. To reload it, use:\n","  %reload_ext autoreload\n"]}],"source":["%load_ext autoreload\n","%autoreload 2"]},{"cell_type":"code","execution_count":107,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"6d394937-6c99-4a7c-9d32-7600a280032f","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"G5pNu3zgZBrL","outputId":"160a554f-fb08-4aa0-bc00-0422fb7c1fac"},"outputs":[{"name":"stdout","output_type":"stream","text":["workding dir: /Users/inflaton/code/engd/papers/rapget-translation\n"]}],"source":["import os\n","import sys\n","from pathlib import Path\n","\n","# check if workding_dir is in local variables\n","if \"workding_dir\" not in locals():\n","    workding_dir = str(Path.cwd().parent)\n","\n","os.chdir(workding_dir)\n","sys.path.append(workding_dir)\n","print(\"workding dir:\", workding_dir)"]},{"cell_type":"code","execution_count":108,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"9f67ec60-2f24-411c-84eb-0dd664b44775","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"hPCC-6m7ZBrM","outputId":"c7aa2c96-5e99-440a-c148-201d79465ff9"},"outputs":[{"name":"stdout","output_type":"stream","text":["loading env vars from: /Users/inflaton/code/engd/papers/rapget-translation/.env\n"]},{"data":{"text/plain":["True"]},"execution_count":108,"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":109,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"f1597656-8042-4878-9d3b-9ebfb8dd86dc","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"1M3IraVtZBrM","outputId":"29ab35f6-2970-4ade-d85d-3174acf8cda0"},"outputs":[{"name":"stdout","output_type":"stream","text":["Qwen/Qwen2-7B-Instruct None False datasets/mac/mac.tsv results/mac-results.csv False 300\n"]}],"source":["import os\n","\n","model_name = os.getenv(\"MODEL_NAME\")\n","adapter_name_or_path = os.getenv(\"ADAPTER_NAME_OR_PATH\")\n","load_in_4bit = os.getenv(\"LOAD_IN_4BIT\") == \"true\"\n","data_path = os.getenv(\"DATA_PATH\")\n","results_path = os.getenv(\"RESULTS_PATH\")\n","use_english_datasets = os.getenv(\"USE_ENGLISH_DATASETS\") == \"true\"\n","max_new_tokens = int(os.getenv(\"MAX_NEW_TOKENS\", 2048))\n","\n","print(model_name, adapter_name_or_path, load_in_4bit, data_path, results_path, use_english_datasets, max_new_tokens)"]},{"cell_type":"code","execution_count":110,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"b2a43943-9324-4839-9a47-cfa72de2244b","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":564,"status":"ok","timestamp":1720679529907,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"UgMvt6dIZBrM","outputId":"ce37581c-fd26-46c2-ad87-d933d99f68f7"},"outputs":[{"name":"stdout","output_type":"stream","text":["Python 3.11.9\n","Name: torch\n","Version: 2.4.0\n","Summary: Tensors and Dynamic neural networks in Python with strong GPU acceleration\n","Home-page: https://pytorch.org/\n","Author: PyTorch Team\n","Author-email: packages@pytorch.org\n","License: BSD-3\n","Location: /Users/inflaton/anaconda3/envs/rapget/lib/python3.11/site-packages\n","Requires: filelock, fsspec, jinja2, networkx, sympy, typing-extensions\n","Required-by: accelerate, peft, torchaudio, torchvision\n","---\n","Name: transformers\n","Version: 4.43.3\n","Summary: State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow\n","Home-page: https://github.com/huggingface/transformers\n","Author: The Hugging Face team (past and future) with the help of all our contributors (https://github.com/huggingface/transformers/graphs/contributors)\n","Author-email: transformers@huggingface.co\n","License: Apache 2.0 License\n","Location: /Users/inflaton/anaconda3/envs/rapget/lib/python3.11/site-packages\n","Requires: filelock, huggingface-hub, numpy, packaging, pyyaml, regex, requests, safetensors, tokenizers, tqdm\n","Required-by: peft\n","CPU times: user 12.2 ms, sys: 15.1 ms, total: 27.2 ms\n","Wall time: 2.11 s\n"]}],"source":["%%time\n","os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n","\n","!python --version\n","!pip show torch transformers"]},{"cell_type":"code","execution_count":111,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1685,"status":"ok","timestamp":1720679531591,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"ZuS_FsLyZBrN","outputId":"2cba0105-c505-4395-afbd-2f2fee6581d0"},"outputs":[{"name":"stdout","output_type":"stream","text":["MPS is available\n"]}],"source":["from llm_toolkit.llm_utils import *\n","from llm_toolkit.translation_utils import *\n","\n","device = check_gpu()"]},{"cell_type":"code","execution_count":112,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 1133 entries, 0 to 1132\n","Data columns (total 100 columns):\n"," #   Column                                              Non-Null Count  Dtype \n","---  ------                                              --------------  ----- \n"," 0   chinese                                             1133 non-null   object\n"," 1   english                                             1133 non-null   object\n"," 2   01-ai/Yi-1.5-9B-Chat/rpp-1.00                       1133 non-null   object\n"," 3   01-ai/Yi-1.5-9B-Chat/rpp-1.02                       1133 non-null   object\n"," 4   01-ai/Yi-1.5-9B-Chat/rpp-1.04                       1133 non-null   object\n"," 5   01-ai/Yi-1.5-9B-Chat/rpp-1.06                       1133 non-null   object\n"," 6   01-ai/Yi-1.5-9B-Chat/rpp-1.08                       1133 non-null   object\n"," 7   01-ai/Yi-1.5-9B-Chat/rpp-1.10                       1133 non-null   object\n"," 8   01-ai/Yi-1.5-9B-Chat/rpp-1.12                       1133 non-null   object\n"," 9   01-ai/Yi-1.5-9B-Chat/rpp-1.14                       1133 non-null   object\n"," 10  01-ai/Yi-1.5-9B-Chat/rpp-1.16                       1133 non-null   object\n"," 11  01-ai/Yi-1.5-9B-Chat/rpp-1.18                       1133 non-null   object\n"," 12  01-ai/Yi-1.5-9B-Chat/rpp-1.20                       1133 non-null   object\n"," 13  Qwen/Qwen2-72B-Instruct/rpp-1.00                    1133 non-null   object\n"," 14  Qwen/Qwen2-72B-Instruct/rpp-1.02                    1133 non-null   object\n"," 15  Qwen/Qwen2-72B-Instruct/rpp-1.04                    1133 non-null   object\n"," 16  Qwen/Qwen2-72B-Instruct/rpp-1.06                    1133 non-null   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shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.12      1133 non-null   object\n"," 53  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.14      1133 non-null   object\n"," 54  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.16      1133 non-null   object\n"," 55  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.18      1133 non-null   object\n"," 56  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.20      1133 non-null   object\n"," 57  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.22      1133 non-null   object\n"," 58  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.24      1133 non-null   object\n"," 59  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.26      1133 non-null   object\n"," 60  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.28      1133 non-null   object\n"," 61  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.30      1133 non-null   object\n"," 62  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.00  1133 non-null   object\n"," 63  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.02  1133 non-null   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non-null   object\n"," 76  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28  1133 non-null   object\n"," 77  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30  1133 non-null   object\n"," 78  01-ai/Yi-1.5-9B-Chat/rpp-1.22                       1133 non-null   object\n"," 79  shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.02     1133 non-null   object\n"," 80  shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.04     1133 non-null   object\n"," 81  01-ai/Yi-1.5-9B-Chat/rpp-1.24                       1133 non-null   object\n"," 82  01-ai/Yi-1.5-9B-Chat/rpp-1.26                       1133 non-null   object\n"," 83  shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.06     1133 non-null   object\n"," 84  01-ai/Yi-1.5-9B-Chat/rpp-1.28                       1133 non-null   object\n"," 85  shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.08     1133 non-null   object\n"," 86  01-ai/Yi-1.5-34B-Chat/rpp-1.00                      1133 non-null   object\n"," 87  shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.10     1133 non-null   object\n"," 88  01-ai/Yi-1.5-9B-Chat/rpp-1.30                       1133 non-null   object\n"," 89  internlm/internlm2_5-7b-chat/rpp-1.00               1133 non-null   object\n"," 90  internlm/internlm2_5-7b-chat/rpp-1.02               1133 non-null   object\n"," 91  internlm/internlm2_5-7b-chat/rpp-1.04               1133 non-null   object\n"," 92  internlm/internlm2_5-7b-chat/rpp-1.06               1133 non-null   object\n"," 93  internlm/internlm2_5-7b-chat/rpp-1.08               1133 non-null   object\n"," 94  shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.12     1133 non-null   object\n"," 95  internlm/internlm2_5-7b-chat/rpp-1.10               1133 non-null   object\n"," 96  internlm/internlm2_5-7b-chat/rpp-1.12               1133 non-null   object\n"," 97  shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.14     1133 non-null   object\n"," 98  internlm/internlm2_5-7b-chat/rpp-1.14               1133 non-null   object\n"," 99  internlm/internlm2_5-7b-chat/rpp-1.16               1133 non-null   object\n","dtypes: object(100)\n","memory usage: 885.3+ KB\n"]}],"source":["import pandas as pd\n","\n","df = pd.read_csv(results_path)\n","df.info()"]},{"cell_type":"code","execution_count":113,"metadata":{},"outputs":[{"data":{"text/plain":["['chinese',\n"," 'english',\n"," '01-ai/Yi-1.5-34B-Chat/rpp-1.00',\n"," '01-ai/Yi-1.5-9B-Chat/rpp-1.00',\n"," '01-ai/Yi-1.5-9B-Chat/rpp-1.02',\n"," '01-ai/Yi-1.5-9B-Chat/rpp-1.04',\n"," '01-ai/Yi-1.5-9B-Chat/rpp-1.06',\n"," '01-ai/Yi-1.5-9B-Chat/rpp-1.08',\n"," '01-ai/Yi-1.5-9B-Chat/rpp-1.10',\n"," '01-ai/Yi-1.5-9B-Chat/rpp-1.12',\n"," '01-ai/Yi-1.5-9B-Chat/rpp-1.14',\n"," '01-ai/Yi-1.5-9B-Chat/rpp-1.16',\n"," '01-ai/Yi-1.5-9B-Chat/rpp-1.18',\n"," '01-ai/Yi-1.5-9B-Chat/rpp-1.20',\n"," '01-ai/Yi-1.5-9B-Chat/rpp-1.22',\n"," '01-ai/Yi-1.5-9B-Chat/rpp-1.24',\n"," '01-ai/Yi-1.5-9B-Chat/rpp-1.26',\n"," '01-ai/Yi-1.5-9B-Chat/rpp-1.28',\n"," '01-ai/Yi-1.5-9B-Chat/rpp-1.30',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.00',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.02',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.04',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.06',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.08',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.10',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.12',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.14',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.16',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.18',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.20',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.22',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.24',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.26',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.28',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.30',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.00',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.02',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.04',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.06',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.08',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.10',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.12',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.14',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.16',\n"," 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'shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.10',\n"," 'shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.12',\n"," 'shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.14',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.00',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.02',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.04',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.06',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.08',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.10',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.12',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.14',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.16',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.18',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.20',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.22',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.24',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.26',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.28',\n"," 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'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30']"]},"execution_count":113,"metadata":{},"output_type":"execute_result"}],"source":["columns = df.columns[2:].to_list()\n","columns.sort()\n","columns = df.columns[:2].to_list() + columns\n","columns"]},{"cell_type":"code","execution_count":116,"metadata":{},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"bc03a7bad4c9416488d365e00b42742e","version_major":2,"version_minor":0},"text/plain":["tokenizer_config.json:   0%|          | 0.00/1.67k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"176cc98d23624ad8915d39938369f3cd","version_major":2,"version_minor":0},"text/plain":["tokenizer.model:   0%|          | 0.00/1.03M [00:00<?, 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0.31586790820617083, 'rougeLsum': 0.31659574673209057}, 'accuracy': 0.00088261253309797, 'correct_ids': [77]}\n","shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.06: {'meteor': 0.32610191030444663, 'bleu_scores': {'bleu': 0.08440911364941035, 'precisions': [0.37549304881991596, 0.11705876430513139, 0.04960926597823053, 0.02328030798285756], 'brevity_penalty': 1.0, 'length_ratio': 1.0245114276250413, 'translation_length': 30930, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.36752925022525673, 'rouge2': 0.13217466088334368, 'rougeL': 0.3156161826682502, 'rougeLsum': 0.31628238804685416}, 'accuracy': 0.00088261253309797, 'correct_ids': [77]}\n"]},{"name":"stderr","output_type":"stream","text":["/Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py:254: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  df[new_col] = df[col].apply(lambda x: len(tokenizers[model](x)[\"input_ids\"]))\n"]},{"name":"stdout","output_type":"stream","text":["shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.08: {'meteor': 0.32519072627069395, 'bleu_scores': {'bleu': 0.08573531403311445, 'precisions': [0.3768451236599433, 0.11825010150223304, 0.05052246420152693, 0.023998827538196606], 'brevity_penalty': 1.0, 'length_ratio': 1.0165286518714807, 'translation_length': 30689, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3677681318911758, 'rouge2': 0.1329334511082953, 'rougeL': 0.31555219872015555, 'rougeLsum': 0.3162169797197245}, 'accuracy': 0.00088261253309797, 'correct_ids': [77]}\n"]},{"name":"stderr","output_type":"stream","text":["/Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py:254: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  df[new_col] = df[col].apply(lambda x: len(tokenizers[model](x)[\"input_ids\"]))\n"]},{"name":"stdout","output_type":"stream","text":["shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.10: {'meteor': 0.32510929376904546, 'bleu_scores': {'bleu': 0.08572184129459336, 'precisions': [0.3766598153404457, 0.11731824649366489, 0.05030826140567201, 0.024289121262153733], 'brevity_penalty': 1.0, 'length_ratio': 1.015269956939384, 'translation_length': 30651, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3669925918468957, 'rouge2': 0.1317690468418684, 'rougeL': 0.3143439978950341, 'rougeLsum': 0.31499486147109523}, 'accuracy': 0.00088261253309797, 'correct_ids': [77]}\n"]},{"name":"stderr","output_type":"stream","text":["/Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py:254: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  df[new_col] = df[col].apply(lambda x: len(tokenizers[model](x)[\"input_ids\"]))\n"]},{"name":"stdout","output_type":"stream","text":["shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12: {'meteor': 0.325321692973156, 'bleu_scores': {'bleu': 0.08501006133800607, 'precisions': [0.3769911504424779, 0.11597508254757123, 0.0496742671009772, 0.024046617983329646], 'brevity_penalty': 1.0, 'length_ratio': 1.0105995362702882, 'translation_length': 30510, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3670918439535572, 'rouge2': 0.1306394142574278, 'rougeL': 0.3136378009708979, 'rougeLsum': 0.31448454091818295}, 'accuracy': 0.0, 'correct_ids': []}\n"]},{"name":"stderr","output_type":"stream","text":["/Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py:254: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  df[new_col] = df[col].apply(lambda x: len(tokenizers[model](x)[\"input_ids\"]))\n"]},{"name":"stdout","output_type":"stream","text":["shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14: {'meteor': 0.3224620858016468, 'bleu_scores': {'bleu': 0.08389328832417228, 'precisions': [0.3779330345373056, 0.11529903118688166, 0.048935109338271957, 0.02322992429864925], 'brevity_penalty': 1.0, 'length_ratio': 1.0051010268300762, 'translation_length': 30344, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3660029478823349, 'rouge2': 0.12962198881927703, 'rougeL': 0.3130154415556936, 'rougeLsum': 0.3138353845845071}, 'accuracy': 0.0, 'correct_ids': []}\n"]},{"name":"stderr","output_type":"stream","text":["/Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py:254: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  df[new_col] = df[col].apply(lambda x: len(tokenizers[model](x)[\"input_ids\"]))\n"]},{"name":"stdout","output_type":"stream","text":["shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16: {'meteor': 0.32354623636120206, 'bleu_scores': {'bleu': 0.08389983318570625, 'precisions': [0.3772855017358241, 0.11575982412750756, 0.04921372408863474, 0.02305314513425943], 'brevity_penalty': 1.0, 'length_ratio': 1.0018217952964559, 'translation_length': 30245, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.365798410378833, 'rouge2': 0.13022724788126894, 'rougeL': 0.31361563891120947, 'rougeLsum': 0.31418770957030584}, 'accuracy': 0.0, 'correct_ids': []}\n"]},{"name":"stderr","output_type":"stream","text":["/Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py:254: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  df[new_col] = df[col].apply(lambda x: len(tokenizers[model](x)[\"input_ids\"]))\n"]},{"name":"stdout","output_type":"stream","text":["shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18: {'meteor': 0.3227464993995023, 'bleu_scores': {'bleu': 0.08237511984991769, 'precisions': [0.37662723848542917, 0.11529880204579, 0.04821256383700582, 0.02199315272402501], 'brevity_penalty': 1.0, 'length_ratio': 1.0025173898641935, 'translation_length': 30266, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3649625928411872, 'rouge2': 0.1297823979809622, 'rougeL': 0.31237472571694164, 'rougeLsum': 0.3130341342775994}, 'accuracy': 0.0, 'correct_ids': []}\n"]},{"name":"stderr","output_type":"stream","text":["/Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py:254: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  df[new_col] = df[col].apply(lambda x: len(tokenizers[model](x)[\"input_ids\"]))\n"]},{"name":"stdout","output_type":"stream","text":["shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20: {'meteor': 0.3213479416591043, 'bleu_scores': {'bleu': 0.08021470447158471, 'precisions': [0.3734951746094916, 0.11340454858718126, 0.046686746987951805, 0.021039650211143915], 'brevity_penalty': 0.9987736772994305, 'length_ratio': 0.9987744286187479, 'translation_length': 30153, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3633099524507924, 'rouge2': 0.1279994669647978, 'rougeL': 0.31081287893463483, 'rougeLsum': 0.311576974320659}, 'accuracy': 0.0, 'correct_ids': []}\n"]},{"name":"stderr","output_type":"stream","text":["/Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py:254: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  df[new_col] = df[col].apply(lambda x: len(tokenizers[model](x)[\"input_ids\"]))\n"]},{"name":"stdout","output_type":"stream","text":["shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22: {'meteor': 0.31939727082775615, 'bleu_scores': {'bleu': 0.08027275774782588, 'precisions': [0.37060882197569994, 0.11191905333561997, 0.04649751989437248, 0.021528965568528298], 'brevity_penalty': 1.0, 'length_ratio': 1.0032461079827757, 'translation_length': 30288, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3609380986777075, 'rouge2': 0.12666125324918132, 'rougeL': 0.3089835285121734, 'rougeLsum': 0.30956638915014134}, 'accuracy': 0.0, 'correct_ids': []}\n"]},{"name":"stderr","output_type":"stream","text":["/Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py:254: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  df[new_col] = df[col].apply(lambda x: len(tokenizers[model](x)[\"input_ids\"]))\n"]},{"name":"stdout","output_type":"stream","text":["shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24: {'meteor': 0.3188662188138966, 'bleu_scores': {'bleu': 0.07877965659256216, 'precisions': [0.3695673695673696, 0.11004456633527597, 0.045509665454026675, 0.020810881117841615], 'brevity_penalty': 1.0, 'length_ratio': 1.0037429612454456, 'translation_length': 30303, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.35966629764151414, 'rouge2': 0.1255987660701956, 'rougeL': 0.30728620231759696, 'rougeLsum': 0.3077173322184259}, 'accuracy': 0.0, 'correct_ids': []}\n"]},{"name":"stderr","output_type":"stream","text":["/Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py:254: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  df[new_col] = df[col].apply(lambda x: len(tokenizers[model](x)[\"input_ids\"]))\n"]},{"name":"stdout","output_type":"stream","text":["shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26: {'meteor': 0.31805084189335, 'bleu_scores': {'bleu': 0.07777595035895293, 'precisions': [0.36718209093007154, 0.10867182683745462, 0.04475165680895033, 0.020491498997698417], 'brevity_penalty': 1.0, 'length_ratio': 1.0046704206690957, 'translation_length': 30331, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3586578737816579, 'rouge2': 0.12488061546195132, 'rougeL': 0.30667694159970027, 'rougeLsum': 0.30730677797657274}, 'accuracy': 0.0, 'correct_ids': []}\n"]},{"name":"stderr","output_type":"stream","text":["/Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py:254: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  df[new_col] = df[col].apply(lambda x: len(tokenizers[model](x)[\"input_ids\"]))\n"]},{"name":"stdout","output_type":"stream","text":["shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28: {'meteor': 0.31564132115319793, 'bleu_scores': {'bleu': 0.07471248687074669, 'precisions': [0.3653415084388186, 0.1064959079546622, 0.0426418723949984, 0.018780388226997735], 'brevity_penalty': 1.0, 'length_ratio': 1.004836038423319, 'translation_length': 30336, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3575069575446436, 'rouge2': 0.12384165440953143, 'rougeL': 0.3046949012325021, 'rougeLsum': 0.3054690222171944}, 'accuracy': 0.0, 'correct_ids': []}\n"]},{"name":"stderr","output_type":"stream","text":["/Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py:254: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  df[new_col] = df[col].apply(lambda x: len(tokenizers[model](x)[\"input_ids\"]))\n"]},{"name":"stdout","output_type":"stream","text":["shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30: {'meteor': 0.31448483374273595, 'bleu_scores': {'bleu': 0.07484673889486904, 'precisions': [0.36305669679539854, 0.10600163867267513, 0.04272017045454545, 0.01908848771825984], 'brevity_penalty': 1.0, 'length_ratio': 1.007784034448493, 'translation_length': 30425, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.35601587422350284, 'rouge2': 0.1229164691279465, 'rougeL': 0.3035257437090866, 'rougeLsum': 0.30386441333286196}, 'accuracy': 0.0, 'correct_ids': []}\n"]},{"name":"stderr","output_type":"stream","text":["/Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py:254: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  df[new_col] = df[col].apply(lambda x: len(tokenizers[model](x)[\"input_ids\"]))\n"]},{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>model</th>\n","      <th>rpp</th>\n","      <th>meteor</th>\n","      <th>bleu_1</th>\n","      <th>rouge_l</th>\n","      <th>ews_score</th>\n","      <th>repetition_score</th>\n","      <th>total_repetitions</th>\n","      <th>rap</th>\n","      <th>num_max_output_tokens</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>01-ai/Yi-1.5-34B-Chat</td>\n","      <td>1.00</td>\n","      <td>0.375530</td>\n","      <td>0.105239</td>\n","      <td>0.358991</td>\n","      <td>0.0</td>\n","      <td>0.437776</td>\n","      <td>0.437776</td>\n","      <td>0.368670</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>01-ai/Yi-1.5-9B-Chat</td>\n","      <td>1.00</td>\n","      <td>0.346373</td>\n","      <td>0.093121</td>\n","      <td>0.332876</td>\n","      <td>0.0</td>\n","      <td>0.351280</td>\n","      <td>0.351280</td>\n","      <td>0.341256</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>01-ai/Yi-1.5-9B-Chat</td>\n","      <td>1.02</td>\n","      <td>0.347119</td>\n","      <td>0.091265</td>\n","      <td>0.332589</td>\n","      <td>0.0</td>\n","      <td>0.264784</td>\n","      <td>0.264784</td>\n","      <td>0.343223</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>01-ai/Yi-1.5-9B-Chat</td>\n","      <td>1.04</td>\n","      <td>0.347188</td>\n","      <td>0.090199</td>\n","      <td>0.331946</td>\n","      <td>0.0</td>\n","      <td>0.377758</td>\n","      <td>0.377758</td>\n","      <td>0.341686</td>\n","      <td>8</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>01-ai/Yi-1.5-9B-Chat</td>\n","      <td>1.06</td>\n","      <td>0.347595</td>\n","      <td>0.090050</td>\n","      <td>0.331282</td>\n","      <td>0.0</td>\n","      <td>0.468667</td>\n","      <td>0.468667</td>\n","      <td>0.340815</td>\n","      <td>9</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>93</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.22</td>\n","      <td>0.319397</td>\n","      <td>0.080273</td>\n","      <td>0.308984</td>\n","      <td>0.0</td>\n","      <td>0.100618</td>\n","      <td>0.100618</td>\n","      <td>0.318015</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>94</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.24</td>\n","      <td>0.318866</td>\n","      <td>0.078780</td>\n","      <td>0.307286</td>\n","      <td>0.0</td>\n","      <td>0.082083</td>\n","      <td>0.082083</td>\n","      <td>0.317738</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>95</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.26</td>\n","      <td>0.318051</td>\n","      <td>0.077776</td>\n","      <td>0.306677</td>\n","      <td>0.0</td>\n","      <td>0.073257</td>\n","      <td>0.073257</td>\n","      <td>0.317046</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>96</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.28</td>\n","      <td>0.315641</td>\n","      <td>0.074712</td>\n","      <td>0.304695</td>\n","      <td>0.0</td>\n","      <td>0.057370</td>\n","      <td>0.057370</td>\n","      <td>0.314859</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>97</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.30</td>\n","      <td>0.314485</td>\n","      <td>0.074847</td>\n","      <td>0.303526</td>\n","      <td>0.0</td>\n","      <td>0.067961</td>\n","      <td>0.067961</td>\n","      <td>0.313562</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>98 rows × 10 columns</p>\n","</div>"],"text/plain":["                                        model   rpp    meteor    bleu_1  \\\n","0                       01-ai/Yi-1.5-34B-Chat  1.00  0.375530  0.105239   \n","1                        01-ai/Yi-1.5-9B-Chat  1.00  0.346373  0.093121   \n","2                        01-ai/Yi-1.5-9B-Chat  1.02  0.347119  0.091265   \n","3                        01-ai/Yi-1.5-9B-Chat  1.04  0.347188  0.090199   \n","4                        01-ai/Yi-1.5-9B-Chat  1.06  0.347595  0.090050   \n","..                                        ...   ...       ...       ...   \n","93  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.22  0.319397  0.080273   \n","94  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.24  0.318866  0.078780   \n","95  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.26  0.318051  0.077776   \n","96  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.28  0.315641  0.074712   \n","97  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.30  0.314485  0.074847   \n","\n","     rouge_l  ews_score  repetition_score  total_repetitions       rap  \\\n","0   0.358991        0.0          0.437776           0.437776  0.368670   \n","1   0.332876        0.0          0.351280           0.351280  0.341256   \n","2   0.332589        0.0          0.264784           0.264784  0.343223   \n","3   0.331946        0.0          0.377758           0.377758  0.341686   \n","4   0.331282        0.0          0.468667           0.468667  0.340815   \n","..       ...        ...               ...                ...       ...   \n","93  0.308984        0.0          0.100618           0.100618  0.318015   \n","94  0.307286        0.0          0.082083           0.082083  0.317738   \n","95  0.306677        0.0          0.073257           0.073257  0.317046   \n","96  0.304695        0.0          0.057370           0.057370  0.314859   \n","97  0.303526        0.0          0.067961           0.067961  0.313562   \n","\n","    num_max_output_tokens  \n","0                       4  \n","1                       2  \n","2                       4  \n","3                       8  \n","4                       9  \n","..                    ...  \n","93                      0  \n","94                      0  \n","95                      0  \n","96                      0  \n","97                      0  \n","\n","[98 rows x 10 columns]"]},"execution_count":116,"metadata":{},"output_type":"execute_result"}],"source":["df = df[columns]\n","metrics_df = get_metrics(df, max_output_tokens=max_new_tokens)\n","metrics_df"]},{"cell_type":"code","execution_count":117,"metadata":{},"outputs":[{"data":{"text/plain":["array(['01-ai/Yi-1.5-34B-Chat', '01-ai/Yi-1.5-9B-Chat',\n","       'Qwen/Qwen2-72B-Instruct', 'Qwen/Qwen2-7B-Instruct',\n","       'internlm/internlm2_5-7b-chat',\n","       'shenzhi-wang/Llama3.1-70B-Chinese-Chat',\n","       'shenzhi-wang/Llama3.1-8B-Chinese-Chat',\n","       'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat'], dtype=object)"]},"execution_count":117,"metadata":{},"output_type":"execute_result"}],"source":["models = metrics_df[\"model\"].unique()\n","models"]},{"cell_type":"code","execution_count":118,"metadata":{},"outputs":[],"source":["# list of markers for plotting\n","markers = [\"o\", \"x\", \"^\", \"s\", \"d\", \"P\", \"X\", \"*\", \"v\", \">\", \"<\", \"p\", \"h\", \"H\", \"+\", \"|\", \"_\"]\n","markers = {model: marker for model, marker in zip(models, markers)}"]},{"cell_type":"code","execution_count":140,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# plot meteor vs rpp\n","import matplotlib.pyplot as plt\n","\n","fig, ax = plt.subplots(figsize=(10, 6))\n","# set grid\n","ax.grid(True)\n","ax.set_axisbelow(True)\n","ax.minorticks_on()\n","ax.grid(which=\"major\", linestyle=\"-\", linewidth=\"0.5\", color=\"red\")\n","# ax.grid(which=\"minor\", linestyle=\":\", linewidth=\"0.5\", color=\"black\")\n","\n","for model in models:\n","    model_df = metrics_df[metrics_df[\"model\"] == model]\n","    ax.plot(\n","        model_df[\"rpp\"],\n","        model_df[\"meteor\"],\n","        label=model + \" (METEOR)\",\n","        marker=markers[model],\n","    )\n","    ax.plot(\n","        model_df[\"rpp\"],\n","        model_df[\"rap\"],\n","        label=model + \" (RAP-METEOR)\",\n","        linestyle=\"--\",\n","        marker=markers[model],\n","    )\n","\n","ax.set_xlabel(\"Repetition Penalty Parameter\")\n","ax.set_ylabel(\"METEOR & RAP-METEOR\")\n","ax.legend(loc=\"lower center\", bbox_to_anchor=(0.5, -0.85))\n","plt.show()"]},{"cell_type":"code","execution_count":142,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# plot mtr vs rpp\n","import matplotlib.pyplot as plt\n","\n","fig, ax = plt.subplots(figsize=(10, 6))\n","# set grid\n","ax.grid(True)\n","ax.set_axisbelow(True)\n","ax.minorticks_on()\n","ax.grid(\n","    which=\"major\", linestyle=\"-\", linewidth=\"0.5\", color=\"red\"\n",")\n","# ax.grid(which=\"minor\", linestyle=\":\", linewidth=\"0.5\", color=\"black\")\n","\n","for model in models:\n","    model_df = metrics_df[metrics_df[\"model\"] == model]\n","    ax.plot(\n","        model_df[\"rpp\"],\n","        model_df[\"total_repetitions\"],\n","        label=model,\n","        marker=markers[model],\n","    )\n","\n","# ax.set_ylim(0, 1)\n","ax.set_xlabel(\"Repetition Penalty Parameter\")\n","ax.set_ylabel(\"Mean Total Repetitions (MTR)\")\n","ax.legend(loc=\"lower center\", bbox_to_anchor=(0.5, -0.5))\n","plt.show()"]},{"cell_type":"code","execution_count":143,"metadata":{},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA1IAAALCCAYAAADUN+LwAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/TGe4hAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdeXxU5d3+8c/MZN8TloSwC8guIiCrAooVN7TSii1Vq1Ztn7iBLUqtWmwrilVRGrX6WJf+3Ne6PEURWcoiq1Qh7CAgWViyTPZMZs7vj5NMEpJAJszkTJLr7WteyTlnZnJlDEm+ue/7e9sMwzAQERERERGRJrNbHUBERERERKS1USElIiIiIiLiIxVSIiIiIiIiPlIhJSIiIiIi4iMVUiIiIiIiIj5SISUiIiIiIuIjFVIiIiIiIiI+UiElIiIiIiLioxCrAwQDj8dDZmYmsbGx2Gw2q+OIiIiIiIhFDMOgsLCQ1NRU7PbGx51USAGZmZl0797d6hgiIiIiIhIkDh06RLdu3Rq9rkIKiI2NBcwXKy4uzuI0wIwZ8PbbVqdoHmW3hrJbQ9mtoezWUHZrKLs1lN0aQZLd6XTSvXt3b43QGBVS4J3OFxcXFxyFVGgoBEOO5lB2ayi7NZTdGspuDWW3hrJbQ9mtEWTZT7Xkx9JmEytXruSKK64gNTUVm83GRx99VO8+27dvZ9q0acTHxxMdHc2oUaM4ePCg93pZWRlpaWl06NCBmJgYpk+fTk5OTgt+FiIiIiIi0t5YWkgVFxczbNgw0tPTG7y+d+9eJkyYwIABA1i+fDnffvstDzzwABEREd77zJo1i08++YR3332XFStWkJmZydVXX91Sn4KIiIiIiLRDlk7tu+SSS7jkkksavX7//fdz6aWXsmDBAu+5Pn36eN8vKCjgpZde4o033uCCCy4A4OWXX2bgwIF8/fXXjBkzJnDhRURERESk3QrafaQ8Hg+fffYZZ555JhdffDGdO3dm9OjRdab/bdq0CZfLxZQpU7znBgwYQI8ePVi7dm2jz11eXo7T6axzExERERERaaqgbTZx5MgRioqKePTRR/nzn//MY489xuLFi7n66qtZtmwZEydOJDs7m7CwMBISEuo8Njk5mezs7Eafe/78+cybN6/+hRkzzEVuVlu/HqZNszpF8yi7NZTdGspuDWW3hrJbQ9mtoezWCJbsLleT7ha0hZTH4wHgyiuvZNasWQCcffbZrFmzhueff56JEyc2+7nnzp3L7NmzvcfVLQ55++3g6BQybRp8/LHVKZpH2a2h7NZQdmsouzWU3RrKbg1lt0awZHc6IT7+lHcL2kKqY8eOhISEMGjQoDrnBw4cyKpVqwBISUmhoqKC/Pz8OqNSOTk5pKSkNPrc4eHhhIeHByS3iIiIiIi0fUG7RiosLIxRo0axc+fOOud37dpFz549ARgxYgShoaEsXbrUe33nzp0cPHiQsWPHtmheERERERFpPywdkSoqKmLPnj3e4/3797NlyxaSkpLo0aMHv/vd75gxYwbnn38+kydPZvHixXzyyScsX74cgPj4eG6++WZmz55NUlIScXFx3HHHHYwdO1Yd+0REREREJGAsLaQ2btzI5MmTvcfV65ZuuOEGXnnlFX784x/z/PPPM3/+fO6880769+/P+++/z4QJE7yPeeqpp7Db7UyfPp3y8nIuvvhinn322Rb/XEREREREpP2wtJCaNGkShmGc9D433XQTN910U6PXIyIiSE9Pb3RTXxEREREREX8L2jVSIiIiIiIiwUqFlIiISAvYvTGHl5NvYc+mI1ZHERERP1AhJSIiEmAlzgqWv76TEnsUy1/fQYmzwupIIiJymlRIiYiIBJBhGKx4Yweu8kqw2agoq2TFmztP/UAREQlqKqREREQCaM+mI+zbcgzDYx4bHtj3zVF2b8yxNpiIiJwWFVIiIiIBUj2lryEr3tipKX4iIq2YCikREZEAqDOlrwGa4ici0rq160IqPT2dQYMGMWrUKKujiIhIG5ObWVxnSt+Jqqf4Hc8satlgIiLiF+26kEpLSyMjI4MNGzZYHUVERNqYpNRozji7I7ZGftLa7HDG8E50SI1p2WAiIuIX7bqQEhERCRSbzcbEnw8gNDykwethESFM/Fn/Fk4lIiL+okJKREQkQKLiwpg0s+FiaeLP+xMVF9bCiURExF8a/jOZiIiI+EWPQUl1jm126D2sE/1GJluUSERE/EEjUiIiIgGUn1Nac2AYmtInItJGqJASEREJoNysYsBsPhHlKWHSzAGa0ici0gaokBIREQmg6kKqa/9Ebsx5kb4jOlucSERE/EGFlIiISADlVY9IdYm2OImIiPiTCikREZEAylUhJSLSJqmQEhERCRBXuZvC42WACikRkbZGhZSIiEiA5GWbo1GRsaFExIRanEZERPxJhZSIiEiAaFqfiEjb1a4LqfT0dAYNGsSoUaOsjiIiIm2QGk2IiLRd7bqQSktLIyMjgw0bNlgdRURE2qDcrBIAElVIiYi0Oe26kBIREQmk3MwiQCNSIiJtkQopERGRAHBVuHFWdezTiJSISNujQkpERCQA8rNLwICImFCi4sKsjiMiIn6mQkpERCQA1LFPRKRtUyElIiISANWFlKb1iYi0TSqkREREAqCm9XmUxUlERCQQVEiJiIgEgKb2iYi0bSqkRERE/KzS5cZ5tBTQ1D4RkbZKhZSIiIif5eeUYBgQHhWijn0iIm2UCikRERE/qz2tz2azWZxGREQCQYWUiIiIn+VllQCQmKppfSIibZUKKRERET/LzawakUpRISUi0la160IqPT2dQYMGMWrUKKujiIhIG6KOfSIibV+7LqTS0tLIyMhgw4YNVkcREZE2wu3yUFDVsS9JU/tERNqsdl1IiYiI+Fv+kRIMj0FYZAhR8erYJyLSVqmQEhER8aOaaX1R6tgnItKGqZASERHxo+pCShvxioi0bSqkRERE/ChPjSZERNoFFVIiIiJ+5G19rkJKRKRNUyElIiLiJ+5KDwVHzI59mtonItK2qZASERHxk4IjpXg8BqERDmISw62OIyIiAWRpIbVy5UquuOIKUlNTsdlsfPTRR43e99e//jU2m42FCxfWOZ+bm8vMmTOJi4sjISGBm2++maKiosAGFxERaUDtjXjVsU9EpG2ztJAqLi5m2LBhpKenn/R+H374IV9//TWpqan1rs2cOZNt27axZMkSPv30U1auXMmtt94aqMgiIiKNUsc+EZH2I8TKD37JJZdwySWXnPQ+hw8f5o477uDzzz/nsssuq3Nt+/btLF68mA0bNjBy5EgAFi1axKWXXspf//rXBgsvERGRQPF27EtRISUi0tYF9Ropj8fDddddx+9+9zsGDx5c7/ratWtJSEjwFlEAU6ZMwW63s27dupaMKiIiUmtEKsriJCIiEmiWjkidymOPPUZISAh33nlng9ezs7Pp3LlznXMhISEkJSWRnZ3d6POWl5dTXl7uPXY6nf4JLCIi7ZbH7SE/pwSApFSNSImItHVBW0ht2rSJp59+ms2bN/t9we78+fOZN29e/QszZkBoqF8/VrOsXw/TplmdonmU3RrKbg1lt0aQZi8IScTT+QZCPBXE3jij4TsFafYmUXZrKLs1lN0awZLd5WrS3YK2kPrPf/7DkSNH6NGjh/ec2+3mnnvuYeHChXz//fekpKRw5MiROo+rrKwkNzeXlJSURp977ty5zJ4923vsdDrp3r07vP02xMX5/5Px1bRp8PHHVqdoHmW3hrJbQ9mtEaTZc785An/fSlLvDtheaCRfkGZvEmW3hrJbQ9mtESzZnU6Ijz/l3YK2kLruuuuYMmVKnXMXX3wx1113HTfeeCMAY8eOJT8/n02bNjFixAgAvvrqKzweD6NHj270ucPDwwkP1/4eIiLiP3nq2Cci0q5YWkgVFRWxZ88e7/H+/fvZsmULSUlJ9OjRgw4dOtS5f2hoKCkpKfTv3x+AgQMHMnXqVG655Raef/55XC4Xt99+O9dee6069omISIvKzapaH6VCSkSkXbC0a9/GjRsZPnw4w4cPB2D27NkMHz6cBx98sMnP8frrrzNgwAAuvPBCLr30UiZMmMALL7wQqMgiIiINys2s2YxXRETaPktHpCZNmoRhGE2+//fff1/vXFJSEm+88YYfU4mIiPimdsc+Te0TEWkfgnofKRERkdbAeawMd6WHkFA7cR0irI4jIiItQIWUiIjIacqt1WjCZvfvlh0iIhKcVEiJiIicpppCKsriJCIi0lJUSImIiJym6tbnajQhItJ+qJASERE5TbkqpERE2h0VUiIiIqfB4zHIy1bHPhGR9kaFlIiIyGkoPF6K2+XBEWInrmOk1XFERKSFtOtCKj09nUGDBjFq1Ciro4iISCuVm2WORiWkRGFXxz4RkXajXRdSaWlpZGRksGHDBqujiIhIK6VGEyIi7VO7LqREREROV26mCikRkfbotAspt9vNli1byMvL80ceERGRVkUd+0RE2iefC6m7776bl156CTCLqIkTJ3LOOefQvXt3li9f7u98IiIiQcvwGORlazNeEZH2yOdC6r333mPYsGEAfPLJJ+zfv58dO3Ywa9Ys7r//fr8HFBERCVaFuWVUVniwh9iI76SOfSIi7YnPhdSxY8dISUkB4P/+7//46U9/yplnnslNN93Ed9995/eAIiIiwap6Wl9ichR2h5Ydi4i0Jz5/109OTiYjIwO3283ixYu56KKLACgpKcHhcPg9oIiISLDyFlJaHyUi0u6E+PqAG2+8kWuuuYYuXbpgs9mYMmUKAOvWrWPAgAF+DygiIhKs1PpcRKT98rmQ+uMf/8iQIUM4dOgQP/3pTwkPDwfA4XBw3333+T2giIhIsKrejFeFlIhI++NzIQXwk5/8pN65G2644bTDiIiItBaGYXhHpDS1T0Sk/WlWIbV06VKWLl3KkSNH8Hg8da794x//8EswERGRYFaUV46r3I3dYSO+szr2iYi0Nz4XUvPmzePhhx9m5MiR3nVSIiIi7U11o4mE5Cgc6tgnItLu+FxIPf/887zyyitcd911gcgjIiLSKuRmVk3rS9G0PhGR9sjnP6FVVFQwbty4QGRpcenp6QwaNIhRo0ZZHUVERFqZmo59URYnERERK/hcSP3qV7/ijTfeCESWFpeWlkZGRgYbNmywOoqIiLQy2kNKRKR983lqX1lZGS+88AJffvklZ511FqGhoXWuP/nkk34LJyIiEoxqd+xLSlUhJSLSHvlcSH377becffbZAGzdurXONTWeEBGR9qA4v5yKMjc2u42EzpraJyLSHvlcSC1btiwQOURERFoNb8e+zpE4QtSxT0SkPWr2d/89e/bw+eefU1paCpjTHERERNqDvKwSQOujRETaM58LqePHj3PhhRdy5plncumll5KVlQXAzTffzD333OP3gCIiIsEm19uxT4WUiEh75XMhNWvWLEJDQzl48CBRUTXzwmfMmMHixYv9Gk5ERCQYVe8hpUJKRKT98nmN1BdffMHnn39Ot27d6pzv168fBw4c8FswERGRYGQYBnnZan0uItLe+TwiVVxcXGckqlpubi7h4eF+CSUiIhKsSpwVlJdUYrNBQnKk1XFERMQiPhdS5513Hq+99pr32Gaz4fF4WLBgAZMnT/ZrOBERkWBTvT4qvnMUIaEOi9OIiIhVfJ7at2DBAi688EI2btxIRUUFc+bMYdu2beTm5rJ69epAZBQREQka1eujElO0f5SISHvm84jUkCFD2LVrFxMmTODKK6+kuLiYq6++mm+++YY+ffoEIqOIiEjQyFPHPhERoZkb8k6ePJn777+/3rX09HTS0tL8EqwlpKenk56ejtvttjqKiIi0EtVT+9RoQkSkffN5ROrqq69m06ZN9c4//fTTzJ071y+hWkpaWhoZGRls2LDB6igiItIKGIZRs4dUqgopEZH2zOdC6vHHH+eSSy5hx44d3nNPPPEEDz74IJ999plfw4mIiAST0kIX5cWVYIPEZK2REhFpz3ye2verX/2K3NxcpkyZwqpVq3j77bd55JFH+L//+z/Gjx8fiIwiIiJBoXo0Kq5jJCFh6tgnItKe+VxIAcyZM4fjx48zcuRI3G43n3/+OWPGjPF3NhERkaCiRhMiIlKtSYXUM888U+9c165diYqK4vzzz2f9+vWsX78egDvvvNO/CUVERIJEdetzFVIiItKkQuqpp55q8LzD4WD16tXe/aNsNpsKKRERabO8jSa6aH2UiEh716RCav/+/YHOISIiEvTystX6XERETD537avNMAwMw/BXFhERkaBVWlhBaaHL7NinQkpEpN1rViH12muvMXToUCIjI4mMjOSss87in//8p8/Ps3LlSq644gpSU1Ox2Wx89NFH3msul4t7772XoUOHEh0dTWpqKtdffz2ZmZl1niM3N5eZM2cSFxdHQkICN998M0VFRc35tERERBrl7djXIYJQdewTEWn3fC6knnzySX7zm99w6aWX8s477/DOO+8wdepUfv3rXze6lqoxxcXFDBs2jPT09HrXSkpK2Lx5Mw888ACbN2/mgw8+YOfOnUybNq3O/WbOnMm2bdtYsmQJn376KStXruTWW2/19dMSERE5qeqOfRqNEhERaEb780WLFvHcc89x/fXXe89NmzaNwYMH88c//pFZs2Y1+bkuueQSLrnkkgavxcfHs2TJkjrn/va3v3Huuedy8OBBevTowfbt21m8eDEbNmxg5MiR3nyXXnopf/3rX0lNTfX10xMREWlQblYJAEkpKqRERKQZI1JZWVmMGzeu3vlx48aRlZXll1CNKSgowGazkZCQAMDatWtJSEjwFlEAU6ZMwW63s27duoBmERGR9sXbsS9VhZSIiDSjkOrbty/vvPNOvfNvv/02/fr180uohpSVlXHvvffys5/9jLi4OACys7Pp3LlznfuFhISQlJREdnZ2o89VXl6O0+mscxMRETmZXE3tExGRWpo8te+CCy7ggw8+YN68ecyYMYOVK1cyfvx4AFavXs3SpUsbLLD8weVycc0112AYBs8999xpP9/8+fOZN29e/QszZkBo6Gk//2lbvx5OWAvWaii7NZTdGspuDQuyl9kiKO3yawAS77oRDFfznkivuzWU3RrKbg1lP32upn2Pb3IhtXz5cioqKpg+fTrr1q3jqaee8nbZGzhwIOvXr2f48OHNynoy1UXUgQMH+Oqrr7yjUQApKSkcOXKkzv0rKyvJzc0lJSWl0eecO3cus2fP9h47nU66d+8Ob78NtZ7fMtOmwccfW52ieZTdGspuDWW3hgXZc/fkw183E5MUTtjz7zf/ifS6W0PZraHs1lD20+d0Qnz8Ke/mc7MJgBEjRvD//t//a85DfVJdRO3evZtly5bRoUOHOtfHjh1Lfn4+mzZtYsSIEQB89dVXeDweRo8e3ejzhoeHEx4eHtDsIiLSdlR37EvqEmNxEhERCRY+FVIZGRknXXsEcNZZZzX5+YqKitizZ4/3eP/+/WzZsoWkpCS6dOnCT37yEzZv3synn36K2+32fuykpCTCwsIYOHAgU6dO5ZZbbuH555/H5XJx++23c+2116pjn4iI+E1uZnUhFWVxEhERCRY+FVIXXnghhmE0et1ms+F2u5v8fBs3bmTy5Mne4+rpdjfccAN//OMf+bhqaO/ss8+u87hly5YxadIkAF5//XVuv/12LrzwQux2O9OnT+eZZ55pcgYREZFTUaMJERE5kU+F1Lp16+jUqZPfPvikSZNOWpid7Fq1pKQk3njjDb9lEhEROVHN1D4VUiIiYvKpkOrRo0e9duMiIiJtWXmJi+KCCkCFlIiI1PB5HykREZH2JDerBICYxHDCIpvVo0lERNqgJhdSEydOJCwsLJBZREREgk6e1keJiEgDmvyntWXLlgUyh4iISFDK1fooERFpgKb2iYiInIQKKRERaYgKKRERkZPQ1D4REWmICikREZFGVJRWUpRXDmgzXhERqcvnQurhhx+mpKSk3vnS0lIefvhhv4RqKenp6QwaNIhRo0ZZHUVERIJQbrY5GhUdH0Z4VKjFaUREJJj4XEjNmzePoqKieudLSkqYN2+eX0K1lLS0NDIyMtiwYYPVUUREJAjlZmpan4iINMznQsowDGw2W73z//3vf0lKSvJLKBERkWCQp0YTIiLSiCa3P09MTMRms2Gz2TjzzDPrFFNut5uioiJ+/etfBySkiIiIFao349WIlIiInKjJhdTChQsxDIObbrqJefPmER8f770WFhZGr169GDt2bEBCioiIWME7IpWqQkpEROpqciF1ww03ANC7d2/GjRtHaKgW3YqISNtVUVZJYW4ZAEkpKqRERKSuJhdS1Xr37k1WVlaj13v06HFagURERIJBXrY5rS8yLoyIGP3xUERE6vK5kOrVq1eDzSaqud3u0wokIiISDGoaTWj/KBERqc/nQuqbb76pc+xyufjmm2948skn+ctf/uK3YCIiIlbK9RZSMRYnERGRYORzITVs2LB650aOHElqaiqPP/44V199tV+CiYiIWClXI1IiInISPu8j1Zj+/ftrY1sREWkzqqf2qfW5iIg0xOcRKafTWefYMAyysrL44x//SL9+/fwWTERExCqucjfO41Ud+1RIiYhIA3wupBISEuo1mzAMg+7du/PWW2/5LZiIiIhV8rKLwYDI2FAiY8OsjiMiIkHI50Jq2bJldY7tdjudOnWib9++hIT4/HQiIiJBxzutT/tHiYhII3yufCZOnBiIHJZIT08nPT1dLdtFRKSO3CxzDylN6xMRkcY0awhp586dLFq0iO3btwMwcOBAbr/9dgYMGODXcIGWlpZGWloaTqeT+Ph4q+OIiEiQ8HbsS1UhJSIiDfO5a9/777/PkCFD2LRpE8OGDWPYsGFs3ryZoUOH8v777wcio4iISIvKVcc+ERE5BZ9HpObMmcPcuXN5+OGH65x/6KGHmDNnDtOnT/dbOBERkZZWWeHGeawU0NQ+ERFpnM8jUllZWVx//fX1zv/iF78gKyvLL6FERESskpdTAgaER4cQGRtqdRwREQlSPhdSkyZN4j//+U+986tWreK8887zSygRERGrVHfsS+oSXW+7DxERkWo+T+2bNm0a9957L5s2bWLMmDEAfP3117z77rvMmzePjz/+uM59RUREWpPczJpCSkREpDE+F1L/8z//A8Czzz7Ls88+2+A1AJvNprbiIiLS6qjRhIiINIXPhZTH4wlEDhERkaCQl609pERE5NR8XiP12muvUV5eXu98RUUFr732ml9CiYiIWMHt8lBwpKqQ0h5SIiJyEj4XUjfeeCMFBQX1zhcWFnLjjTf6JZSIiIgV8nJKMAwIjwohKi7M6jgiIhLEfC6kDMNosIvRDz/8QHx8vF9CiYiIWKG6Y19iijr2iYjIyTV5jdTw4cOx2WzYbDYuvPBCQkJqHup2u9m/fz9Tp04NSEgREZGWkOttfR5lcRIREQl2TS6krrrqKgC2bNnCxRdfTExMjPdaWFgYvXr1Yvr06X4PGEjp6emkp6eru6CIiAC1CqnUmFPcU0RE2rsmF1IPPfQQAL169WLGjBlEREQELFRLSUtLIy0tDafTqWmJIiJSM7VPI1IiInIKPrc/v+GGGwKRQ0RExFLuSg/5R0oBtT4XEZFT87mQstvtJ12Aq2lyIiLSGuUfKcHwGIRFOIhOCLc6joiIBDmfC6kPPvigTiHlcrn45ptvePXVV5k3b55fw4mIiLSU3MzqaX3q2CciIqfmcyFV3XSitp/85CcMHjyYt99+m5tvvtkfuURERFpUnrdjn6b1iYjIqfm8j1RjxowZw9KlS/31dCIiIi0qN6sEMEekRERETsUvhVRpaSnPPPMMXbt29cfTiYiItLi87OrW5yqkRETk1Hye2peYmFhn7rhhGBQWFhIVFcX/+3//z6/hREREWoLb7SE/xxyR0tQ+ERFpCp8LqaeeeqpOIWW32+nUqROjR48mMTHRr+FERERaQsGRUjxug9BwBzGJ6tgnIiKn5vPUvl/+8pfccMMN3tt1113H1KlTm1VErVy5kiuuuILU1FRsNhsfffRRneuGYfDggw/SpUsXIiMjmTJlCrt3765zn9zcXGbOnElcXBwJCQncfPPNFBUV+ZxFRETaL+9GvClR6tgnIiJN4nMhtWHDBmbPns3ll1/O5Zdfzj333MPGjRub9cGLi4sZNmwY6enpDV5fsGABzzzzDM8//zzr1q0jOjqaiy++mLKyMu99Zs6cybZt21iyZAmffvopK1eu5NZbb21WHhERaZ9ys7Q+SkREfOPT1L45c+bw17/+lZiYGM444wwAVqxYwcKFC/ntb3/LY4895tMHv+SSS7jkkksavGYYBgsXLuQPf/gDV155JQCvvfYaycnJfPTRR1x77bVs376dxYsXs2HDBkaOHAnAokWLuPTSS/nrX/9KamqqT3lERKR9qi6k1LFPRESaqskjUq+++iqLFi3imWee4fjx42zZsoUtW7aQm5vLU089xTPPPMNrr73mt2D79+8nOzubKVOmeM/Fx8czevRo1q5dC8DatWtJSEjwFlEAU6ZMwW63s27dukafu7y8HKfTWecmIiLtl/aQEhERXzV5RCo9PZ1HHnmE22+/vc750NBQ7rzzTiorK/nb3/7G9ddf75dg2dnZACQnJ9c5n5yc7L2WnZ1N586d61wPCQkhKSnJe5+GzJ8/n3nz5tW/MGMGhIaeZnI/WL8epk2zOkXzKLs1lN0aym4NP2f3YCOvSxrYQkh68G5wB/CPa3rdraHs1lB2ayj76XO5mnS3JhdS27Zt806xa8hVV13FAw880NSns9TcuXOZPXu299jpdNK9e3d4+22Ii7MwWZVp0+Djj61O0TzKbg1lt4ayW8PP2Quyi/H8cR0hYXZi3/8n2APYbEKvuzWU3RrKbg1lP31OJ8THn/JuTZ7a53A4qKioaPS6y+XC4XA09elOKSUlBYCcnJw653NycrzXUlJSOHLkSJ3rlZWV5Obmeu/TkPDwcOLi4urcRESkfcrLMvePSkyJxhbIIkpERNqUJhdS55xzDq+//nqj1//5z39yzjnn+CUUQO/evUlJSWHp0qXec06nk3Xr1jF27FgAxo4dS35+Pps2bfLe56uvvsLj8TB69Gi/ZRERkbYrV+ujRESkGZo8te+3v/0tV111FeXl5dxzzz3etUvZ2dk88cQTLFy4kA8//NCnD15UVMSePXu8x/v372fLli0kJSXRo0cP7r77bv785z/Tr18/evfuzQMPPEBqaipXXXUVAAMHDmTq1KnccsstPP/887hcLm6//XauvfZadewTEZEmqenYF2VxEhERaU2aXEhdfvnlPPXUU/z2t7/liSeeIL5q3mBBQQEhISH89a9/5fLLL/fpg2/cuJHJkyd7j6vXLd1www288sorzJkzh+LiYm699Vby8/OZMGECixcvJiIiwvuY119/ndtvv50LL7wQu93O9OnTeeaZZ3zKISIi7VfNHlIxFicREZHWxKd9pO644w5+/OMf8+6777J7924AzjzzTKZPn242a/DRpEmTMAyj0es2m42HH36Yhx9+uNH7JCUl8cYbb/j8sUVERDweg/xsc41UkkakRETEBz4VUgDdunVj1qxZgcgiIiLSopzHSnFXenCE2ontEGl1HBERaUWa3GxCRESkraneiDcxJQq7OvaJiIgPVEiJiEi7pY59IiLSXCqkRESk3arp2KdCSkREfKNCSkRE2q3qzXg1IiUiIr7yuZBatmxZo9f+/ve/n1YYERGRlmJ4DO8aKRVSIiLiK58LqalTp/K73/0Ol8vlPXfs2DGuuOIK7rvvPr+GExERCRTn8TIqXR4cIXbiOkac+gEiIiK1NGtE6sMPP2TUqFFkZGTw2WefMWTIEJxOJ1u2bAlAxMBJT09n0KBBjBo1yuooIiLSwqpHoxKSo7A7NNNdRER84/NPjnHjxrFlyxaGDBnCOeecw49//GNmzZrF8uXL6dmzZyAyBkxaWhoZGRls2LDB6igiItLCajr2aSNeERHxXbP+BLdr1y42btxIt27dCAkJYefOnZSUlPg7m4iISMB410elan2UiIj4zudC6tFHH2Xs2LFcdNFFbN26lfXr1/PNN99w1llnsXbt2kBkFBER8Tu1PhcRkdPhcyH19NNP89FHH7Fo0SIiIiIYMmQI69ev5+qrr2bSpEkBiCgiIuJfhscgN1utz0VEpPlCfH3Ad999R8eOHeucCw0N5fHHH+fyyy/3WzAREZFAKcwro7Lcjd1hI65TpNVxRESkFfJ5ROrEIqq2iRMnnlYYERGRlpCbWdOxz6GOfSIi0gw+j0gBbNy4kXfeeYeDBw9SUVFR59oHH3zgl2AiIiKBkpelaX0iInJ6fP4z3FtvvcW4cePYvn07H374IS6Xi23btvHVV18RHx8fiIwiIiJ+lZutRhMiInJ6fC6kHnnkEZ566ik++eQTwsLCePrpp9mxYwfXXHMNPXr0CERGERERv/K2PlchJSIizeRzIbV3714uu+wyAMLCwiguLsZmszFr1ixeeOEFvwcUERHxJ8Mwam3Gq0JKRESax+dCKjExkcLCQgC6du3K1q1bAcjPz9emvCIiEvSK8spxlbmx223Ed1bHPhERaR6fm02cf/75LFmyhKFDh/LTn/6Uu+66i6+++oolS5Zw4YUXBiKjiIiI31RP64vvHIkjRB37RESkeXwupP72t79RVlYGwP33309oaChr1qxh+vTp/OEPf/B7wEBKT08nPT0dt9ttdRQREWkhmtYnIiL+4HMhlZSU5H3fbrdz3333+TVQS0pLSyMtLQ2n06mOgyIi7UR1IZWYqkJKRESaT3MaRESkXVHHPhER8Ycmj0g5HI4m3U/T5EREJFiZHfu0Ga+IiJy+JhdShmHQs2dPbrjhBoYPHx7ITCIiIgFRUlBBRWklNruNhM5RVscREZFWrMmF1Pr163nppZd4+umn6d27NzfddBMzZ84kMTExkPlERET8JjezqmNfp0gcoZrdLiIizdfknyIjR47kueeeIysri9mzZ/Phhx/SrVs3rr32WpYsWRLIjCIiIn6hjn0iIuIvPv85LiIigl/84hcsXbqUrVu3cuTIEaZOnUpubm4g8omIiPhNbnZVx74umtYnIiKnx+f25wA//PADr7zyCq+88golJSX87ne/Iy4uzt/ZRERE/Cqvampfklqfi4jIaWpyIVVRUcGHH37ISy+9xH/+8x8uueQSFi5cyCWXXNLkjn4iIiJWMTv2aWqfiIj4R5MLqS5duhAbG8sNN9zAs88+S+fOnQEoLi6ucz+NTImISDAqcVZQXlKJzYY69omIyGlrciGVl5dHXl4ef/rTn/jzn/9c77phGNhsNu0jJSIiQal6I964jpGEhGkmhYiInJ4mF1LLli0LZA4REZGA8k7r0/ooERHxgyYXUhMnTgxkDhERkYDKzSoBIFHro0RExA/a9W6E6enpDBo0iFGjRlkdRUREAixPjSZERMSP2nUhlZaWRkZGBhs2bLA6ioiIBJg69omIiD+160JKRETahxJnBWVFLrBBQoo69omIyOlTISUiIm2et2NfhwhC1bFPRET8QIWUiIi0eZrWJyIi/tbkrn3ViouLefTRR1m6dClHjhzB4/HUub5v3z6/hRMREfGH6hEpdewTERF/8bmQ+tWvfsWKFSu47rrr6NKlCzabLRC5RERE/EZ7SImIiL/5XEj9+9//5rPPPmP8+PGByCMiIuJ3mtonIiL+5vMaqcTERJKSkgKRRURExO9KiyooLXQBkJCsjn0iIuIfPhdSf/rTn3jwwQcpKSkJRJ463G43DzzwAL179yYyMpI+ffrwpz/9CcMwvPcxDIMHH3yQLl26EBkZyZQpU9i9e3fAs4mISOuQl2X+vIrtEEFYhM8TMURERBrUpJ8ow4cPr7MWas+ePSQnJ9OrVy9CQ0Pr3Hfz5s1+C/fYY4/x3HPP8eqrrzJ48GA2btzIjTfeSHx8PHfeeScACxYs4JlnnuHVV1+ld+/ePPDAA1x88cVkZGQQERHhtywiItI6aVqfiIgEQpMKqauuuirAMRq2Zs0arrzySi677DIAevXqxZtvvsn69esBczRq4cKF/OEPf+DKK68E4LXXXiM5OZmPPvqIa6+91pLcIiISPHLVsU9ERAKgSYXUQw89FOgcDRo3bhwvvPACu3bt4swzz+S///0vq1at4sknnwRg//79ZGdnM2XKFO9j4uPjGT16NGvXrm20kCovL6e8vNx77HQ6A/uJiIiIZfK8I1JaHyUiIv7j82TxM844gw0bNtChQ4c65/Pz8znnnHP8uo/Ufffdh9PpZMCAATgcDtxuN3/5y1+YOXMmANnZ2QAkJyfXeVxycrL3WkPmz5/PvHnz6l+YMQNOmKpoifXrYdo0q1M0j7JbQ9mtoezW8DF7bvIt4Igm6YmH4NGcAAZrgnb0ugcVZbeGsltD2U+fy9Wku/lcSH3//fe43e5658vLy/nhhx98fbqTeuedd3j99dd54403GDx4MFu2bOHuu+8mNTWVG264odnPO3fuXGbPnu09djqddO/eHd5+G+Li/BH99EybBh9/bHWK5lF2ayi7NZTdGj5kLyt2UXLPfwBIfP05sLrZRDt53YOOsltD2a2h7KfP6YT4+FPerck/UT6u9Ul9/vnnxNd6crfbzdKlS+ndu7ePKU/ud7/7Hffdd593it7QoUM5cOAA8+fP54YbbiAlJQWAnJwcunTp4n1cTk4OZ599dqPPGx4eTnh4uF+ziohI8Kme1heTGK6OfSIi4ldN/qlS3XDCZrPVGw0KDQ2lV69ePPHEE34NV1JSgt1et0O7w+HA4/EA0Lt3b1JSUli6dKm3cHI6naxbt47f/OY3fs0iIiKtjzr2iYhIoDS5kKpdvGzYsIGOHTsGLFS1K664gr/85S/06NGDwYMH88033/Dkk09y0003AWZRd/fdd/PnP/+Zfv36edufp6amWtZpUEREgoe3Y1+qCikREfEvn+c57N+/PxA5GrRo0SIeeOAB/ud//ocjR46QmprKbbfdxoMPPui9z5w5cyguLubWW28lPz+fCRMmsHjxYu0hJSIitTr2qZASERH/alIh9cwzz3DrrbcSERHBM888c9L7Vm+U6w+xsbEsXLiQhQsXNnofm83Gww8/zMMPP+y3jysiIm1DblYJoEJKRET8r0mF1FNPPcXMmTOJiIjgqaeeavR+NpvNr4WUiIhIc5WXVlKcb+4ZmJiiPaRERMS/mlRI1Z7O15JT+0RERJqrelpfdEI44VFBsEegiIi0KfZT36Uuf264KyIiEig1Hfs0GiUiIv7nc7OJvn370q1bNyZOnMikSZOYOHEiffv2DUQ2ERGRZvN27NP6KBERCQCfR6QOHTrE/PnziYyMZMGCBZx55pl069aNmTNn8r//+7+ByCgiIuIzdewTEZFA8rmQ6tq1KzNnzuSFF15g586d7Ny5kylTpvDOO+9w2223BSKjiIiIz3IzVUiJiEjg+Dy1r6SkhFWrVrF8+XKWL1/ON998w4ABA7j99tuZNGlSACKKiIj4pqK0kqK8qo59KqRERCQAfC6kEhISSExMZObMmdx3332cd955JCYmBiJbwKWnp5Oeno7b7bY6ioiI+FFetrl/VFRcGBHR6tgnIiL+5/PUvksvvRS3281bb73FW2+9xbvvvsuuXbsCkS3g0tLSyMjIYMOGDVZHERERP8rNKgIgKVWjUSIiEhg+F1IfffQRx44dY/HixYwdO5YvvviC8847z7t2SkRExGq5WeaIlKb1iYhIoPg8ta/a0KFDqayspKKigrKyMj7//HPefvttXn/9dX/mExER8Zk69omISKD5PCL15JNPMm3aNDp06MDo0aN58803OfPMM3n//fc5evRoIDKKiIj4RJvxiohIoPk8IvXmm28yceJEbr31Vs477zzi4+MDkUtERKRZKsoqKTxeBkBSlxiL04iISFvlcyGlxgwiIhLM8nPM9VGRsaFExKhjn4iIBIbPU/tERESCWa7WR4mISAtQISUiIm1KdaMJdewTEZFAUiElIiJtSm6mRqRERCTwVEiJiEiboql9IiLSEnwupEpLSykpKfEeHzhwgIULF/LFF1/4NZiIiIivXBVunFUd+zS1T0REAsnnQurKK6/ktddeAyA/P5/Ro0fzxBNPcOWVV/Lcc8/5PaCIiEhT5WeXgAER0aFExqpjn4iIBI7PhdTmzZs577zzAHjvvfdITk7mwIEDvPbaazzzzDN+DxhI6enpDBo0iFGjRlkdRURE/MA7rS81GpvNZnEaERFpy3wupEpKSoiNjQXgiy++4Oqrr8ZutzNmzBgOHDjg94CBlJaWRkZGhvbGEhFpI3LVsU9ERFqIz4VU3759+eijjzh06BCff/45P/rRjwA4cuQIcXFxfg8oIiLSVHneRhNRFicREZG2zudC6sEHH+S3v/0tvXr1YvTo0YwdOxYwR6eGDx/u94AiIiJNpREpERFpKSG+PuAnP/kJEyZMICsri2HDhnnPX3jhhfz4xz/2azgREZGmqnS5cR4tBdT6XEREAs+nQsrlchEZGcmWLVvqjT6de+65fg0mIiLii/ycEgwDwqNCiIoLszqOiIi0cT5N7QsNDaVHjx643e5A5REREWmW2hvxqmOfiIgEms9rpO6//35+//vfk5ubG4g8IiIizZKbWbU+KlXT+kREJPB8XiP1t7/9jT179pCamkrPnj2Jjq77A2vz5s1+CyciItJUeVklACSlqJASEZHA87mQuuqqqwIQQ0RE5PTUntonIiISaD4XUg899FAgcoiIiDSb2+WhoKpjn1qfi4hIS/B5jRRAfn4+//u//8vcuXO9a6U2b97M4cOH/RpORESkKfKPlGB4DMIiQ4hOUMc+EREJPJ9HpL799lumTJlCfHw833//PbfccgtJSUl88MEHHDx4kNdeey0QOUVERBpVM60vSh37RESkRfg8IjV79mx++ctfsnv3biIiIrznL730UlauXOnXcCIiIk1RXUhpWp+IiLQUnwupDRs2cNttt9U737VrV7Kzs/0SqqWkp6czaNAgRo0aZXUUERE5DXlqNCEiIi3M50IqPDwcp9NZ7/yuXbvo1KmTX0K1lLS0NDIyMtiwYYPVUURE5DRU7yGlQkpERFqKz4XUtGnTePjhh3G5XADYbDYOHjzIvffey/Tp0/0eUERE5GTclR4Kjqhjn4iItCyfC6knnniCoqIiOnfuTGlpKRMnTqRv377Exsbyl7/8JRAZRUREGlVwpBSPxyA03EFMYrjVcUREpJ3wuWtffHw8S5YsYdWqVXz77bcUFRVxzjnnMGXKlEDkExEROanajSbUsU9ERFqKz4VUWVkZERERTJgwgQkTJgQik4iISJN5W5+nalqfiIi0HJ8LqYSEBM4991wmTpzI5MmTGTt2LJGRkYHIJiIickrejn0pKqRERKTl+LxG6ssvv2Tq1KmsW7eOadOmkZiYyIQJE7j//vtZsmRJIDKKiIg0qmZqX5TFSUREpD3xuZCaMGECv//97/niiy/Iz89n2bJl9O3blwULFjB16tRAZBQREWmQ2+0hP6cEUOtzERFpWT4XUmDuGfXCCy9w/fXXM336dD755BMuv/xynnzySX/n4/Dhw/ziF7+gQ4cOREZGMnToUDZu3Oi9bhgGDz74IF26dCEyMpIpU6awe/duv+cQEZHg4zxaisdtEBLuIDYpwuo4IiLSjvi8Rqpr166UlpYyadIkJk2axL333stZZ50VkE5JeXl5jB8/nsmTJ/Pvf/+bTp06sXv3bhITE733WbBgAc888wyvvvoqvXv35oEHHuDiiy8mIyODiAj9UBURacu8jSZSorDZ1bFPRERajs+FVKdOndixYwfZ2dlkZ2eTk5NDaWkpUVH+n5v+2GOP0b17d15++WXvud69e3vfNwyDhQsX8oc//IErr7wSgNdee43k5GQ++ugjrr32Wr9nEhGR4JFXq/W5iIhIS/J5at+WLVvIzs7mvvvuo7y8nN///vd07NiRcePGcf/99/s13Mcff8zIkSP56U9/SufOnRk+fDgvvvii9/r+/fvJzs6us4dVfHw8o0ePZu3atY0+b3l5OU6ns85NRERan9zMqhEpFVIiItLCbIZhGM198PHjx1m+fDn/+te/ePPNN/F4PLjdbr+Fq56aN3v2bH7605+yYcMG7rrrLp5//nluuOEG1qxZw/jx48nMzKRLly7ex11zzTXYbDbefvvtBp/3j3/8I/Pmzat3vmDqVOJCQ/2Wv9nWr4dzz7U6RfMouzWU3RrKbo1a2d/qNJPjoZ247Pi/6FW+3+JgTdBGXvdWR9mtoezWUPbT5nS5iF+8mIKCAuLi4hq9n89T+z744AOWL1/O8uXLycjIICkpiQkTJvDEE08wceLE0wp9Io/Hw8iRI3nkkUcAGD58OFu3bvUWUs01d+5cZs+e7T12Op10794d3n4bTvJitZhp0+Djj61O0TzKbg1lt4ayW6Mqu8ftIf+ulVDpIfHZR6FTK9jTsA287q2SsltD2a2h7KfP6YT4+FPezedC6te//jXnn38+t956KxMnTmTo0KHNytcUXbp0YdCgQXXODRw4kPfffx+AlJQUAHJycuqMSOXk5HD22Wc3+rzh4eGEh4f7P7CIiLQY57Ey3JUeQkLtxHVQcyEREWlZPhdSR44cCUSOBo0fP56dO3fWObdr1y569uwJmI0nUlJSWLp0qbdwcjqdrFu3jt/85jctllNERFpebq1GE+rYJyIiLc3nZhObN2/mu+++8x7/61//4qqrruL3v/89FRUVfg03a9Ysvv76ax555BH27NnDG2+8wQsvvEBaWhoANpuNu+++mz//+c98/PHHfPfdd1x//fWkpqZy1VVX+TWLiIgEl5pCyv9dY0VERE7F50LqtttuY9euXQDs27ePa6+9lqioKN59913mzJnj13CjRo3iww8/5M0332TIkCH86U9/YuHChcycOdN7nzlz5nDHHXdw6623MmrUKIqKili8eLH2kBIRaeOqW5+rY5+IiFjB56l9u3bt8k6je/fddzn//PN54403WL16Nddeey0LFy70a8DLL7+cyy+/vNHrNpuNhx9+mIcfftivH1dERIKbd0QqRYWUiIi0PJ9HpAzDwOPxAPDll19y6aWXAtC9e3eOHTvm33QiIiIN8HgM8rJLAEhKVSElIiItz+dCauTIkfz5z3/mn//8JytWrOCyyy4DzM1xk5OT/R5QRETkRIXHS3G7PDhC7MR1bAVtz0VEpM3xuZBauHAhmzdv5vbbb+f++++nb9++ALz33nuMGzfO7wFFREROlJtljkYlpERhV8c+ERGxgM9rpM4666w6XfuqPf744zgcDr+EEhEROZnczCJAjSZERMQ6PhdS1SoqKjhy5Ih3vVS1Hj16nHYoERGRk8mrGpFSISUiIlZpVte+m2++mTVr1tQ5bxgGNpsNt9vtt3AiIiINyVXrcxERsZjPhdSNN95ISEgIn376KV26dMFm09x0ERFpOQaQl63NeEVExFo+F1Jbtmxh06ZNDBgwIBB5WlR6ejrp6ekaRRMRaUUKHXFUVniwh9iI76SOfSIiYg2fu/YNGjSozewXlZaWRkZGBhs2bLA6ioiINFFuSBIAiclR2B0+/xgTERHxC59/Aj322GPMmTOH5cuXc/z4cZxOZ52biIhIIOWGdAAgUeujRETEQj5P7ZsyZQoAF154YZ3zajYhIiItIS/UHJFSowkREbGSz4XUsmXLApFDRESkSapHpFRIiYiIlXwupCZOnNjota1bt55WGBERkZMxDKNmjZQKKRERsdBpr9ItLCzkhRde4Nxzz2XYsGH+yCQiItKgorxyKu1h2O024jurY5+IiFin2YXUypUrueGGG+jSpQt//etfueCCC/j666/9mU1ERKSOrSsPAxAZF4ZDHftERMRCPk3ty87O5pVXXuGll17C6XRyzTXXUF5ezkcffcSgQYMClVFERIQSZwX/XXoIgNLCCkqcFUTFhVmcSkRE2qsm/znviiuuoH///nz77bcsXLiQzMxMFi1aFMhsIiIigLk2asUbO3C7PAB4PAYr3txpcSoREWnPmjwi9e9//5s777yT3/zmN/Tr1y+QmUREROrYs+kI+7bU2gzegH3fHGX3xhz6jUy2LpiIiLRbTR6RWrVqFYWFhYwYMYLRo0fzt7/9jWPHjp36gSIiIqehxFnB8tcbHn1a8cZOSpwVLZxIRETEh0JqzJgxvPjii2RlZXHbbbfx1ltvkZqaisfjYcmSJRQWFgYyp4iItEPVU/pc5ZUNXq8oq9QUPxGR1mrZfFixoOFrKxaY14OYzy2PoqOjuemmm1i1ahXfffcd99xzD48++iidO3dm2rRpgcgYMOnp6QwaNIhRo0ZZHUVERBqQm1nMvi3HMDwNXzc85hS/45lFLRtMREROn90By/5Sv5hascA8b3dYk6uJTqt3bP/+/VmwYAE//PADb775pr8ytZi0tDQyMjLYsGGD1VFERKQBSanRnHF2R2yN/LSy2eGM4Z3okBrTssFEROT0TZwDk+83i6Zlj4DNU1NETb7fvB7E/LIJh8Ph4KqrruLjjz/2x9OJiIgAYLPZmPjzAYSENfxXybCIECb+rH8LpxIREb8Z+hPoMQZWPAZnr2o1RRT4uI+UiIhIS4uKC6PrmYl8/239BkcTf95fe0mJiLQ2bhfs/D/Y+DLsW1Zz3maAI6xVFFGgQkpERIKc2+0he38BADYbGIY5pa/3sE5qfS4i0prkH4RNr8I3/4SinKqTNkjsDXn7wGMDKszpfa2gmFIhJSIiQe3At8cpK3QREROKx+2hoqSSsIhQTekTEWkN3JWw+wvY9DLsXgIY5vnoTjD8Oqgsg6+fNafzPbEK7plgTu+DoC+mmrRG6pxzziEvLw+Ahx9+mJKSkoCGEhERqbZtVSYAg8Z3YdLMAUR5Ssy3mtInIhK8nJmw/FF4+ix462dmMYUBvSfCT1+BWRkQGllTRFUXTbUbUDTWGj1INGlEavv27RQXF5OYmMi8efP49a9/TVRUVKCziYhIO1eYW8bBjOMADByXSkJyFP0efhFGXGFxMhERqcfjhr1fmWufdi0Gw22ej0yC4TNhxI3QoU/d+zfUWKL62ONumdzN1KRC6uyzz+bGG29kwoQJGIbBX//6V2JiGm41++CDD/o1oIiItF/bV2eCAV3PTCAhWX/AExEJSoU55rqnza+a66Cq9RxvFk8Dr4DQiPqPmzy38ecM8ml90MRC6pVXXuGhhx7i008/xWaz8e9//5uQkPoPtdlsKqRERMQvPB6D7WuyABg0IdXiNCIiUofHA/tXmGufdnwGnkrzfEQ8DPs5jPgldB5gacRAa1Ih1b9/f9566y0A7HY7S5cupXPnzgENJiIi7duhjFyK8soJjw7hjOGdrI4jIiIAxcdgy+uw6RXI3Vdzvtu5MPJGGPxjc+1TO+Bz1z6PxxOIHCIiInVkVDWZ6D86hZDQhjfkFRGRFmAYcGC1ufZp+8fgrjDPh8XCsBnm9L2UIdZmtECz2p/v3buXhQsXsn37dgAGDRrEXXfdRZ8+fU7xSBERkVMrLij3bsA7aLym9YmIWKIkF/77ljl979iumvOpw83iach0CG+4b0J74HMh9fnnnzNt2jTOPvtsxo8fD8Dq1asZPHgwn3zyCRdddJHfQ4qISPuyY20WHo9Bcu84OnRtvz+kRURanGHAofVm8bTtQ3OfJ4DQaBj6E3P6XupwazMGCZ8Lqfvuu49Zs2bx6KOP1jt/7733tqpCKj09nfT0dNzu4G6tKCLSnhgeg4zVajIhItKiygrg23fM6XtHttWcTx4KI38JQ6+BiDjL4gUjnwup7du3884779Q7f9NNN7Fw4UJ/ZGoxaWlppKWl4XQ6iY+PtzqOiIgAh3fl4TxaSmiEg34jk62OIyJNtWw+2B0Nt61esaBqz6CTtLuWlmcYkLnZLJ62vg+uEvN8SCQMudqcvtdtJNhs1uYMUj4XUp06dWLLli3069evzvktW7aok5+IiJy26tGoM0clExquJhMirYbdAcv+Yr5fu5hascA8P/l+a3JJfeVF8N275vS9rP/WnO80wCyehs2AyETr8rUSPhdSt9xyC7feeiv79u1j3LhxgLlG6rHHHmP27Nl+DygiIu1HWZGLvd8cATStT6TVqS6eqospqFtEtYINVtu8rG/N4unbd6Gi0DznCINBV8HIm6DHGI0++cDnQuqBBx4gNjaWJ554grlzzeHZ1NRU/vjHP3LnnXf6PaCIiLQfO9dl46k06Ng9hs49NRdfpNWZOMfs9LbsLzAcWLYC+lwA0Z3MqWPh8eY6m/BYCI8z3w+Lsf6X99Y8LfFU2V1l0OEMc/re4Y0115L6mI0jhv0coju0XN42xOdCymazMWvWLGbNmkVhoVnJxsbG+j2YiIi0L4ZhsK1q76jBGo0SaX3KnLDycdjwv+ZxdW209yvz1hibvaawqi6uwquKrYgTz8U1cK7qsY5m7epjas3TEhvL/n+/g/UvgCMc3OVV9w2FgZeb0/d6n299AdvKncZXnAooERHxn+x9TvKyigkJtdPv3BSr44hIU3ncsOV1WPowFB+tOW9gFlPJQyChh1lolRdUvS2Ecid4KsHwmB3jygpOL0dodP3RrpMWYdX3izcLC3dl65yWWHtKpbsSknLgqSFQcMg87y6HhJ4w4pcw/BcQo54G/nJahZSIiIi/ZKw6DEDfEZ0Jj9SPJ5FW4cAa+Pe9kP2teRyZBKW5ZvHxxCq4Z4L5C/6gK+sXI4YBrlKzoCovPKHQqn3OWeuc84RzhVBZaj6fq9i8FWY1//OxOepOSwyLNkfYqkfZgllYNKx8DHoBBQA2GHCZufbpjMlgt1ubrw3STyoREbFceWklezapyYRIq5F/CJY8CNs+MI/D46H7KNjzZc0IzhOr6jegqF1M2WwQFmXeYk9jFLqyomqEq6DpxdeJ56obLxhVe4tWz3irKDZvrY3NAbO2QVwXq5O0aa2qkHr00UeZO3cud911l3fPqrKyMu655x7eeustysvLufjii3n22WdJTtbeIyIircXuDTlUVnhITIkipY/29RMJWhXFsPpp81ZZZq5vGvFLs3ha/yJ0H11/5Kn62OMOTKaQMAjpcHoNEzwes5hasQDW/g08NrAbMOpX5rS/1mDTy+bImccGdjd888/gnpLYBvhUSLlcLqZOncrzzz9fbx+pQNuwYQN///vfOeuss+qcnzVrFp999hnvvvsu8fHx3H777Vx99dWsXr26RfOJiEjzZVQ1mRg0IRWbFj+LBB/DMLvuLXkQnOY0XHqdB1PnQ8pQ8/hkXe2C/Rd6ux3W/d0sok6clhiTHPz5Vywwi6gTs0PwZ2/FfCqkQkND+fbbbwOVpVFFRUXMnDmTF198kT//+c/e8wUFBbz00ku88cYbXHDBBQC8/PLLDBw4kK+//poxY8a0eFYREfHN0YOFHD1YiD3ERv8xajIhEnQOb4bF98GhdeZxQg/40Z9h4LS20/XtxMYSp5qWGExac/ZWzudVZ7/4xS946aWXApGlUWlpaVx22WVMmTKlzvlNmzbhcrnqnB8wYAA9evRg7dq1LZpRRESap7rleZ+zOxEZE2ZxGhHxKsyGj/4HXpxsFlGh0XDBA5C2wWwe0VaKKKjaJ6qB7nwT55jnAzUt0R9ac/ZWzuc1UpWVlfzjH//gyy+/ZMSIEURHR9e5/uSTT/otHMBbb73F5s2b2bBhQ71r2dnZhIWFkZCQUOd8cnIy2dnZjT5neXk55eXl3mOn0+m3vCIi0nSucje71pvfrweqyYRIcKgsh6+fhZV/hYoi89ywn8GFD7Xd5gWteVpia87eyvlcSG3dupVzzjkHgF27dtW55u957YcOHeKuu+5iyZIlRERE+O1558+fz7x58+pfmDEDQkP99nGabf16mDbN6hTNo+zWUHZrKPtp2xM5CFfij4irzKfbnF827UFBkr1ZlN0ayt5EBsQfh657IaLMPFUcC4f6wuZMePk2355Or7s1lP30uVxNupvNMAwjwFGa7aOPPuLHP/4xDofDe87tdmOz2bDb7Xz++edMmTKFvLy8OqNSPXv25O6772bWrFkNPm9DI1Ldu3enoKCAuLi4gH0+TTZtGnz8sdUpmkfZraHs1lD20/b+gk1k7ytgzFVnMGJqr6Y9KEiyN4uyW0PZTy0nw1wHtX+FeRzbBab8EYZe0/z9h/S6W0PZT5vT6SQ+Pv6UtUGz25/v2bOHvXv3cv755xMZGYlhGH4fkbrwwgv57rvv6py78cYbGTBgAPfeey/du3cnNDSUpUuXMn36dAB27tzJwYMHGTt2bKPPGx4eTnh4uF+zioiIb45nFpG9rwCb3caAsW10upBIsCvJNRsSbPwHGB5whMO4O2DCLAiPsTqdSFDzuZA6fvw411xzDcuWLcNms7F7927OOOMMbr75ZhITE3niiSf8Fi42NpYhQ4bUORcdHU2HDh2852+++WZmz55NUlIScXFx3HHHHYwdO1Yd+0REgtz2VVkA9Brageh4/XFLpEW5XbDhJVg+H8ryzXODroSLHobEXlYmE2k1fB6rnTVrFqGhoRw8eJCoqCjv+RkzZrB48WK/hmuKp556issvv5zp06dz/vnnk5KSwgcffNDiOUREpOkqXW52rDMLqUFqMiHSsvYshefGw+J7zSIqeQjc8Clc85qKKBEf+Dwi9cUXX/D555/TrVu3Ouf79evHgQMH/BasMcuXL69zHBERQXp6Ounp6QH/2CIi4h/7thylvLiSmMRwegzuYHUckfbh+F74/H7Y9W/zOKqD2c78nOvB7jj5Y0WkHp8LqeLi4jojUdVyc3O17khERJoko2rvqAHjumC3t6G9aCQ4LJtvFgYNtX5esaBq352TtIxua8oKYOXj8PXz4HGBPQTOvc18fSITrE4n0mr5PLXvvPPO47XXXvMe22w2PB4PCxYsYPLkyX4NJyIibU/+kRIO78wHGwwcpyYTEgB2h9lAYcWCuudXLDDPt5fRF48bNr0Ki0bAmkVmEdXvR/A/X8PUR1REiZwmn0ekFixYwIUXXsjGjRupqKhgzpw5bNu2jdzcXFavXh2IjCIi0oZsX22ujeoxKIm4DpEWp5E2qXokatlfas5VF1GT728fm5QeWAP/vheyvzWPO/SDix+BM39kbS6RNsTnQmrIkCHs2rWLv/3tb8TGxlJUVMTVV19NWloaXbroL4siItI4t9vD9rVqMiEtYOKcmtbew4FlK2DULTD+bquTBVb+QVjyIGz70DwOj4dJ98G5t4Aj1NpsIm1Ms/aRio+P5/777/d3FhERaeMOfHucUmcFkXFh9Dqro9VxpK2qXhO04X/N4+pleBtehE0vQ4e+0HkgdB5c9XYgJPZu/sazwaCiGFY/bd4qy8BmhxG/NEfgovVvTSQQmlVI5eXl8dJLL7F9+3YABg0axI033khSUpJfw4mISNuyrarJxMCxKTgcrfiXVglOHjdseR2WPgzFR2vOG5jFlCMc3OVwdId5qx61AQiJhM4DoPOgquJqkHmLTQFbEDdEMQz47j1zFKrQ/PdFr/Ng6nxIGWptNpE2zudCauXKlVxxxRXEx8czcuRIAJ555hkefvhhPvnkE84//3y/hxQRkdavMLeMgxnHARg4TtP6xM9OXBMUmQSlueaIzBOr4J4J5jS/sXfAGRPhSAYc2Q452+DoTqgshcxvzFttkYm1iqvqUawB5nmrHd4Ei+fCoXXmcUIP+NGfYeC04C7+RNoInwuptLQ0ZsyYwXPPPYfDYXa9cbvd/M///A9paWl89913fg8ZKNX7T7ndbqujiIi0edtXZ4IBXc9MICG5/jYaIs2Sf6hqTdAH5nF4HHQbBXuX1jSWeGJV3QYUEXF1G0543JC7v6q4yqgpso7vgdI8OLDavNUWm2oWVsmDagqtTgMgtAUaqBRmm6NuW143j0Oj4bzZMPZ2CI0I/McXEaAZhdSePXt47733vEUUgMPhYPbs2XXaorcGaWlppKWl4XQ6iY+PtzqOiEib5fEYbF+jJhPiRyeuCcIGI26AyX8w10b1GFO/O1/1seeEP6DaHdCxr3kbNK3mvKsMju0yi6oj26reboeCQ+Y0usJMs2DzskHSGTVTA6uLrKQ+4GjWaoq6XGXw9bPwnyegosg8N+xncOFDEKeGXyItzed/1eeccw7bt2+nf//+dc5v376dYcOG+S2YiIi0HYcycinKKyc8OoQzhneyOo60ZoYBW983R6Gch81zPSeYa4K6nGUen2yzXV9an4dGmM9Z/bzVygrgyI6akasjGeYUwdJcyN1r3nZ8WnN/Rxh07F8zPTC5qslFfPf6U/Aa2kzYMGDHZ/CvNCjLN891HQmXPAbdRjb98xERv2pSIfXtt99637/zzju566672LNnD2PGjAHg66+/Jj09nUcffTQwKUVEpFXLqGoy0X90CiGh7WQzVPG/w5th8X01a4Lie8CP/gSDrmzZNUER8dBjtHmrZhhmg4ucbTXFVfUIlqsYcr4zb7WFxdZqcFE1PbCyDFYvNK9PnAMRRfDaNNi/suoxMXDZEzD0mtbdZVCkDWhSIXX22Wdjs9kwDMN7bs6c+n/R+fnPf86MGTP8l05ERFq94oJyvv/2GKBpfdJM9dYERcGE2TDu9pZZk9QUNhvEdDZvfSbXnPd4oOBgTWOL6uLq2C6oKIQfNpi32kKjzLVcW9+HgTtgf9X5nuPh5+9AeEyLfVoi0rgmFVL79+8/9Z1EREQasGNtFh6PQcoZcXRI1S+A4oPKcnNN0Mq/1qwJOmsGTPkjxLWSotxuh8Re5q3/JTXn3S6zmYW3e2BVk4u878FVYt7n6I6aPbDG/I85fVFEgkaTCqmePXsGOoeIiLRBhscgY7WaTIiPqtcEffEHyKv6Y27XETD1Meg+ytps/uIIrVkzVVtFsdmO/UgGfHwnGG5zjZWKKJGg06wWMpmZmaxatYojR47g8XjqXLvzzjv9EkxERFq/w7vycB4tJTTCQd8RyVbHkdYgJ8NcB7V/hXkck2KOQJ01o32sCQqLhq7nwJ4vzSLKYwMqYMUC3xpliEjA+VxIvfLKK9x2222EhYXRoUMHbLUWd9psNhVSIiLiVT0adeaoZELD1WRCTqIkF5Y9AhtfAsMDjnBzDdSE2e1vTdCKBeYaqRM3EwYVUyJBxOdC6oEHHuDBBx9k7ty52NvDX4ZERKRZyopc7P3mCKBpfXISbhds/IdZRFW39h54BVz0J0jqbWk0S9QuohraTBhUTIkECZ8LqZKSEq699loVUSIiclI712XjqTTo2D2Gzj3jrI4jwWjPUvj892ZTBYDkIeZaoN7nW5vLSh53TRFVW2ObCYuIZXwupG6++Wbeffdd7rvvvkDkERGRNsAwDLZV7R01WKNRcqLje+Hz+2HXv83jyCS48AE45wZzM9r2zF+bCYtIwPlcSM2fP5/LL7+cxYsXM3ToUEJDQ+tcf/LJJ/0WTkREWqfsfU7ysooJCbXT79wUq+NIsCgrgJWPw9fPg8cF9hA491azQIhMtDqdiIhPmlVIff755/Tv3x+gXrMJERGRjFWHAeg7ojPhkc1qECtticdtbqa79GEoPmqe6zsFLp4Pnc60NpuISDP5/NPtiSee4B//+Ae//OUvAxCnZaWnp5Oeno7brfnGIiL+Ul5ayZ5NajIhVQ6sgX/fC9nfmscd+poF1Jk/sjaXiMhp8rmQCg8PZ/z48YHI0uLS0tJIS0vD6XQSHx9vdRwRkTZh94YcKis8JKZEkdJH31vbrfxDsORB2PaBeRweD5PuhVG3QEiYtdlERPzA59Z7d911F4sWLQpEFhERaQMyqppMDJqQqinf7VFFsdnK/G8jq4ooG4z4JdyxCcamqYgSkTbD5xGp9evX89VXX/Hpp58yePDges0mPvjgA7+FExGR1uXowUKOHizEHmKj/xg1mWhXDAO2vm+OQjnNNXL0nGC2M+9ylrXZREQCwOdCKiEhgauvvjoQWUREpJWrbnne5+xORMZo5KHdOLwZFt8Hh9aZx/E94Ed/gkFXgkYlRaQJ1mau5dGLfuC+zLWMTR1rdZwm8bmQevnllwORQ0REWjlXuZtd67MBNZloNwpzzE58W/6feRwaBefNhrG3Q2iktdlEpNUwDIOnNz/NvjgXT29+mjFdxrSKqeHqSSsiIn6xZ1MOrjI3cR0j6Hqm9gRq9ZbNNzfHbWgT2GWPwMGv4fAmqCgyz511LUx5COJURIuIb1YdXsW249sA2HZ8G2sy1zC+a/A3t/O5kOrdu/dJK8R9+/adViAREWmdMlZlAVVNJuzB/5dEOQW7A5b9xXy/upgyDHj/V7D1vZr7dR0BUx+D7qNaPqOItHoVlRXc95/7vMd2m51F3yxiXOq4oB+V8rmQuvvuu+scu1wuvvnmGxYvXszvfvc7f+USEZFW5HhmEdn7CrDZbQwY28XqOOIP1cVTdTEVUQxPnw3535vHMSkw5Y9w1gyw+9wEWESEElcJv1z8S5wVTu85j+FpNaNSPhdSd911V4Pn09PT2bhx42kHEhGR1md71WhUr6EdiI4PtziN+M2oX8Gx3WYxNRDIB2wOmHA3TJgN4THW5hORVqugvIDffPkbtudur3ettYxK+W2N1CWXXMLcuXPVjEJEpJ2pdLnZsa5mWp+0UoYBufvMtU+HvoaD6+DYzprrNsBmhzs3Q2Ivq1KKSBuQU5zDr7/8NXvy9zR4vbWMSvmtkHrvvfdISkry19OJiEgrsW/LUcqLK4lJDKfH4A5Wx5GmqqyArP9WFU1fm63Li4/Wv19kEpTmgscGdg98+07DDShERJrggPMAt35xK5nFmYTYQnAbbgyMevezYQv6USmfC6nhw4fX+WQMwyA7O5ujR4/y7LPP+jWciIgEv4yqvaMGjuuCXU0mgldJLhxaXzPalLkZKsvq3scRBqnDofto6DHGvP/qhTD5fnhiFdwzoX4DChGRJtp+fDu//vLX5Jbl0j2mO4WuQvLL8xu8r4FBdnE2Lo+LMEdw7kvocyF11VVX1Tm22+106tSJSZMmMWDAAH/lahHp6emkp6fjdrutjiIi0irlHynh8M58sMGAcWoyETRONU2vWmSSWTBVF05dzobQCPPaigU1RdTEOWYhdWIDChVTItJEG7M3csdXd1DkKmJA0gCem/IclZ5Kcstya+40axY89ZT3MCkiKWiLKGhGIfXQQw8FIocl0tLSSEtLw+l0Eh8fb3UcEZFWZ/tqc21Uj0FJxHXQBqyWaeo0vQ79oMdo6D7GLJw69IXGpsx43DVFVG3Vxx79EVJEmmb5oeX8dsVvKXeXMyJ5BIsuWERsWCwAKdEpNXfMD4cOg6wJ2QzakFdERJrF7fawfa2aTFiiOdP0uo+G6I5N/xiT5zZ+TSNRItJEH+/9mAdXP4jbcDOp2yQen/g4ESERVsfyiyYXUna7/ZQLvWw2G5WVlacdSkREgt+Bb49T6qwgMi6MXmf58Au6+MYf0/RERCzwz4x/smDDAgCm9ZnGvHHzCLG3nXGcJn8mH374YaPX1q5dyzPPPIPH4/FLKBERCX7bqptMjE3B4dCGrA1aNh/sjoZHcFYsqJo+d8LIT5On6fWtmqI3GnqMPfk0PRGRFmQYBou+WcSL370IwC8G/oLfjfoddlvb+lnR5ELqyiuvrHdu586d3HfffXzyySfMnDmThx9+2K/hREQkOBXmlnEw4zgAA8drWl+j7I6GGzOsWGCen3x/y0zTExFpIW6Pm0fWPcI7u94B4M7hd/Krob8K2hbmp6NZY2uZmZk89NBDvPrqq1x88cVs2bKFIUOG+DubiIgEqe2rM8GArv0TSOgcZXWc4HVilzsM+Pd9sO45c+rdd+/VulaLpumJSCvkcruYu2oun3//OTZs/GHMH7im/zVWxwoYnwqpgoICHnnkERYtWsTZZ5/N0qVLOe+88wKVTUREgpDHY7B9TVWTCY1GndrY2yEnwyyYhgPrVprns7bU3EfT9ESklStxlTBr+SzWZK4hxB7C/PPmM7XXVKtjBVSTC6kFCxbw2GOPkZKSwptvvtngVD8REWn7DmXkUpRXTnh0CGcM72R1nOBUWQF7l8LW92HH/4Gr2DxfXRt1H61peiLSZuSX5ZO2NI1vj31LZEgkCyctZFzXcVbHCrgmF1L33XcfkZGR9O3bl1dffZVXX321wft98MEHfgsHMH/+fD744AN27NhBZGQk48aN47HHHqN///7e+5SVlXHPPffw1ltvUV5ezsUXX8yzzz5LcnKyX7OIiAhkVDWZ6D86hZBQh8VpgojHDd//xyyeMj6GsvyaaxHxUFYAHhvYDeg7RS3ERaRNyCnO4bYlt7G3YC9xYXE8O+VZhnUaZnWsFtHkQur666+3ZJHYihUrSEtLY9SoUVRWVvL73/+eH/3oR2RkZBAdHQ3ArFmz+Oyzz3j33XeJj4/n9ttv5+qrr2b16tUtnldEpC0rLijn+2+PAdo7CjBbk/+wwSyetn0IRTk112KSYfDV4CqFza+YjSWeWAX3TGi4AYWISCtzwHmAW7+4lcziTDpHdubvF/2dvol9rY7VYppcSL3yyisBjNG4xYsX18vRuXNnNm3axPnnn09BQQEvvfQSb7zxBhdccAEAL7/8MgMHDuTrr79mzJgxVsQWEWmTdqzNwuMxSDkjjg6pMVbHsYZhQM5Ws1HE1g+g4GDNtYgEGHQlDJkOvSbAf56o6c43cY5ZSJ3YgELFlIi0QtuPb+fXX/6a3LJcesb15O8X/Z2uMV2tjtWiWt2OWAUFBQAkJSUBsGnTJlwuF1OmTPHeZ8CAAfTo0YO1a9eqkBIR8RPDY5CxuqrJRHscjTq+t6p4er/uhrih0TDgMhj6EzhjMoSE1VzzuGuKqNqqjz3uwOcWEfGzjdkbueOrOyhyFTEgaQDPTXmOjpHtb61nqyqkPB4Pd999N+PHj/e2W8/OziYsLIyEhIQ6901OTiY7O7vB5ykvL6e8vNx77HQ6A5ZZRKStOLw7H+fRUkIjHPQd0U7WoBb8YI46bX3P3CS3miMc+l1kFk/9LoawRlrAn7jZbm0aiRKRVmj5oeX8dsVvKXeXMyJ5BIsuWERsWKzVsSzRqgqptLQ0tm7dyqpVq07reebPn8+8efPqX5gxA0JDT+u5/WL9epg2zeoUzaPs1lB2a7Sz7BkJUyFqAGce/4bQnz4RoGBNEOjXPaQCEo5C0hGIqfWHNgNwJkJeZ8jvCBsKgZerbk3Uzr5mgoayW0PZrRHA7B/3KOTBEcdw22FSZhSPf3iMiGdn+u8DBMvr7nI16W6tppC6/fbb+fTTT1m5ciXdunXznk9JSaGiooL8/Pw6o1I5OTmkpKQ0+Fxz585l9uzZ3mOn00n37t3h7bchLi5gn0OTTZsGH39sdYrmUXZrKLs12lH2siIXe+9bBZUGg/58E/S8O3DZTiUQr3tpPuz4zBx52rcejFpT7nqOhyFXw6CrTr9NeTv6mgkqym4NZbdGgLK/tu01Ht/4uPkh+kxj3nXzCLH7uZQIltfd6YT4+FPeLegLKcMwuOOOO/jwww9Zvnw5vXv3rnN9xIgRhIaGsnTpUqZPnw7Azp07OXjwIGPHjm3wOcPDwwkPDw94dhGRtmLnumw8lQYdu8fQuWcQ/MHJHypKYNdic83T7i/AXVFzLXW42TBi8NUQ374WT4uI1GYYBou+WcSL370IwHWDruO3I3+L3Wa3OJn1gr6QSktL44033uBf//oXsbGx3nVP8fHxREZGEh8fz80338zs2bNJSkoiLi6OO+64g7Fjx6rRhIiIHxiGwbaqvaMGt/YmE41tlAvQaQAM+Yk5+tShj3UZRUSChNvj5i/r/sK7u94F4M7hd/Krob+yZEukYBT0hdRzzz0HwKRJk+qcf/nll/nlL38JwFNPPYXdbmf69Ol1NuQVEZHTl73PSV5WMSFhdvqd2/CU6aB2so1yE3qaI09DfwKdB4F+ORARAcDldjF31Vw+//5zbNj4w5g/cE3/a6yOFVSCvpAyDOOU94mIiCA9PZ309PQWSCQi0r5krDoMQN8RnQmPDPofG6aTbpSbAoN/bBZPXUeoeBIROUGJq4RZy2exJnMNIfYQ5p83n6m9plodK+i0kp+IIiJihfLSSvZsOgLAoPFBPq2vKRvlDv2J2TzC7rAspohIMMsvyydtaRrfHvuWyJBIFk5ayLiu46yOFZRUSImISKN2b8ihssJDYkoUKX1O3cEoIJbNNwufhvZdWrEAio9CVMf6G+WGxZgb5Q6ZXn+jXBERqSenOIfbltzG3oK9xIfHk35hOsM6DbM6VtBSISUiIo3KqGoyMWhCqnWLi+0OWPYX8/3qYqrgB/j4Dtj7Vd37OsLhzB+ZxdPJNsoVEZE6DjgPcOsXt5JZnEnnyM78/aK/0zexr9WxgpoKKRERadDRg4UcPViIPcRG/zEWNpmoLp6W/QV+2Aj9tsBTg2uu2xzQZ7LZcW/ApRBh0ciZiEgrtf34dn795a/JLculZ1xP/n7R3+kao60fTkWFlIiINKi65XmfszsRGWPBtLjKCji41tzjafcS89zuzyG26np8D5hwt7n26XQ3yhURaac2Zm/kjq/uoMhVxMCkgTw75Vk6Rup7alOokBIRkXpc5W52rTf37RvUkntHFRyGPUvMwmnfcqgoqrlmc4DhAQxwhMKs71oul4hIG7Ts4DJ+t/J3lLvLGZE8gkUXLCI2LPbUDxRAhZSIiDRgz6YjuMrcxHWKpOuZiYH7QO5K+GF9zahTzta616M7Q7+LzFvWt7DqSfDYAJfZaKKhBhQiInJKH+/9mAdXP4jbcDOp+yQeP/9xIkIirI7VqrTrQqp67ym32211FBGRoOJtMjG+Cza7n5tMFObAni/N4mnvMigvqHXRBt1G1RRPKcPAbjeLplVPwuT74YlVcM+E+g0oRESkSV7b9hqPb3wcgGl9pjFv3DxC7O26LGiWdv2KpaWlkZaWhtPpJD5ei5NFRACOZxaRva8Am93GgLFdTv8JPW44vLlq1OkLyNpS93pkEvSdAv1+BH0ugOgOda+vWGAWTZPvN4umJ1bVbUABKqZERJrAMAwWfbOIF797EYDrBl3Hb0f+FrvNbnGy1qldF1IiIlLf9lVZAPQa2oHo+PDmPUnxcdi71Cyc9iyF0ty611OHm4VT34ug6zkn3yDX464pomqrPvZoVoGIyKm4PW7+su4vvLvrXQDuHH4nvxr6K+u2tmgDVEiJiIhXpcvNjnVmIeVTkwmPB7L/a65z2v2F2aYco+Z6eDz0vaCqeJoCMZ2b/tyT5zZ+TSNRIiKn5HK7mLtqLp9//zk2bPxhzB+4pv81Vsdq9VRIiYiI174tRykvriQmMZwegzuc/M6leeYap91LzE57xUfrXk8eCv2qpux1Oxcc+pEjItLSSlwlzFo+izWZawixh/DoeY9yca+LrY7VJuinmoiIeFU3mRg4rgv2E5tMGAbkbKvpsHdoHRi1ptWFxcAZk2pGneK1maOIiJXyy/JJW5rGt8e+JTIkkoWTFjKu6zirY7UZKqRERASA/CMlHN6ZDzYYOL5qWl95obmf0+4vYPeXUJhZ90GdBtQ0iugxFkIs2LhXRETqySnO4bYlt7G3YC/x4fGkX5jOsE7DrI7VpqiQEhFpD5bNNxs6NLSmaMUC8LjZnm/Ol+/RJ5zYHS+axdOBteBx1dw3JBLOmGi2Ju97EST2bKFPQERETmVt5loevegHbtrzL57d8iyZxZl0juzM3y/6O30T+7Z4HsMw18q21YYWKqRERNoDu6PhVuFVrcXdg2ewfdVOIIJBxx6GL76uuU9SH3PEqd8U6DkBQrVho4hIsDEMg6c3P82+OBcPrXkIt+GmZ1xP/n7R3+kaE5ip1oZhnLJIaqtFFKiQEhFpH07cdym8BP7fT8wmETYHBzZ9T2l5BJH2PHpFfQu9L6wqni6CDn2syy0iIk2yJnMN245vA8BtuOke051Xp75Kh8hTNA5qRHWRVD2qVM2XwqgtF1GgQkpEpH1wlZn7NXUbZRZTg4E9VdcMN9tc0wAYOCIOx3V7ICzasqgiItIwj+HheOlxsoqzyC7OrnlblMWazDV17hsTFkNSRFKDz9OUkaRqJ7tfWy+UTkWFlIhIW5V3oKbD3v6VUFl6wh1s8KM/UdjpAg4+mQPAwCvGQ1hUy2cVEREKKwq9xVH1rXbRlFOSQ6WnsknPtT13O6sPr2Z81/HNytLei6SmUCElItJWVFbAwbU1xdOxnXWvx6aaG+FmbQGPDewGuErZviMSDOjaP4GEziqiREQCocJdQU5xjlkYlZijSNklVQVS1fliV/Epn8dus9MpshNdoruQEp1CSlQKiw8sJqc4B6PWRuh2m52/bfkb47uOV1EUICqkRFqjJnRgY/Lcls8lLa/gsLnOafcSs015RVHNNZsDuo821zn1+xHs+AyWPwKT74cnVsE9E/B8NZ/thUOBcAZNSLXqsxARCRrVne/uy1zL2NSxTXpMY1Puao8qHS873qTnig+PN4ukqBSzUIpO8RZNXaK70CmqEyH2ml/hVx9ezSsZrzSYadvxbazJXNPsUSk5uXZdSKWnp5Oeno7b7T71nUWCySk6sDH5fmtySeC5XXBovTnqtOdLyNla93p056rW5FOgz2SITDTPr1hQU0RNnGMWUhPncCgzjqIV4YSHVXLG2Z1a/vMREQkitTvfPb35acZ0GYPNZqs35e7EgqmpU+7CHeF0ie5CcnRyneKoumBKiUohKrTpMwMMw2DRN4uwYaszGlXNho1F3yxiXOo4jUoFQLsupNLS0khLS8PpdBIfH291HGlprWVUx+M2N0Utd0KZ03zbZRgMvNIsmg6sgaQcWPIQrF5Y84uytB2FOWbRtPsL2LsMygtqXbRBt5E1HfZShoHdXv85PO4GvzYyCsYDR+nf8yghoY6AfhoiIsGo9pS7/xz+j7fz3bbj27j4/YtxVjibNeWuoYIpITzBrwWNy+Miuzi7wSIKwMAguzgbl8dFmEMbpvtbuy6kpJ1riVGdyoqqAqjAfFteWFMMVb+t/X6Zs37RVHuqVkP2LYNewOod5hoYwwOHN0GX4Q3/Qi3Bz+M2/x9Wr3XK2lL3emSSOeLU70fQ5wKIbkJr2wb+KFBcUM733x4DYNDPrvRDcBGR4HK6U+6yirO87/s65a4lhDnCeOvyt8gty605OWsWPPWU9zApIklFVICokJL268R9daCmiJr0exibBs6sWgVQwQkFUO2iqKB+AVTmBHe5//I6wiA8DiLizLfhsRARDzv/zyyeAAozYfl88xbVsWptzEXmL9vVU7wkOBUfh71La6bslebVvZ46HPpWrXXqeo75h4DTtGNtFh6PQcoZcXRIjTnt5xMRaWn+nHKXEJ5ATklOvWsPjX2IS3tf6tOUu5ZUXdR55YdDh0HWBWpHVEhJ++UqhV4T4IxJZvE0HFi2AkIiYMVj5noSfwmNrimAIqqKoDpFUdwJ12sVStXnQsLrP++KBbDj05oObP0vM0eh9i6HkmPw3zfNm80O3c6taTqQMhQ0V9paHo850rR7iVk8Hd4EtadmhMdD3wvM/199p5jd9vzIADJWm39pVZMJEfG35jRsOFGgutw1NOUuPiyen//fzzlaehRP9R8nqx773q73mN5verM+B2nbVEhJ+1F0FA59DQe/hkPrIHMLeFw116vrisqyWufsjRQ6JxZCJxQ9J173w+hBPbWnIFZ1YPMeT/+H+bnu/gJ2fwlHt5vHh76Gr/4EMSk1o1VnTDKzS+CV5sHer8z/J3uWQPHRuteTh9YUu91GgSMw36J3b8xhRcptlB8tJSzCQd8RyQH5OCLSPjXWsKG2k025q37/dLvcdYkxzzVlyt3qw6u9a6NOzKnOd9IYFVLSNnk8cGxXVeG0znybu6/+/WJSIDIBju6oGdU59zaYcLdZAIVFB+fITe0iqlYHNqDuuq/e58OP/gz5B6tGPpbA/hVQlA3f/NO82UOgx9iaX+A7DQjOz7k1Mgyzq171WqdD68Go1SU0LMYsZKtHneK7BjxSibOC5a/vpMIWAUDvszsRGq4mEyLiP2sy19Rp2PDo+keJCo3yS5e7Oh3umtHlriHqfCfNpUJK2gZXKRzeXFM4/bC+/hoTbNB5EPQYDd3HmG//+3a9fXVY9heI7hjcne8a6cDmPfac0NI/oQeMutm8ucrgwOqaLnDH98D3/zFvSx6E+O41jQx6nw/hWjvj1ZROj2PTzP2cqvd2Ksyqe79OA2qK1u5jIKTlFgAbhsGKN3bgKq/0FstFeWWneJRI6+aPKWZSl2HUFBvl7nL2FexjT/4eduftZnfebtZlratz/zd2vNHg81jR5a4h6nwnzaVCSlqnoiPm9LyDVVP1sv5bd5oeQEik2Ra6+2joMcacKhWZUHO9kX11gIa7+QWTk7VlP1Xm0Ajoe6F5mzofju+tKaq+XwUFh2DTy+bNEQY9x1e11v4RdOjTvkerGur0aBjw7zmw/gVI6An/+SvU/itraJRZkPa7yGwWkdiz5XNX2bPpCPu2HKtz7vDOfHZvzKHfSE3vk7anKVPMpC7DMOq8RtVFk9twc7DwILvzdrMnfw978vawJ38PBwsP1llT1JiJ3SYyvPNwn6fctQR1vpPmsv6rV+RUfJmmV3u0KeUscISe5Hl9HNVpqzr0MW+jb4OKErOY2v0F7P7cnBK4b5l5+3wuJPau2a+o1wQIjbQ6fcs677dQXmQWUzkZ0H03PNqzZl+n/APm26Q+Na9Tz/Fm8Wqx6il9DVnxxk66nplIVJx+SZC2w+Vx8a89/6ozxWzZwWVc0PMCi5NZq/ZoUrUTi8usoix25+/2jjLtyd/Dvvx9VHgqGnzO+PB4+iX0o29CX1b8sIKc4hw81G3YcKz0GDcNuSloC1l1vpPmUCElwefEaXqH1kFZ/gl3amCaXkJP30ZLTmdUp60Ki4Izf2TejMfh2O6qouoLc+PfvP2w/u/mLSSiaqSlqmBI7GV1+tNX5oSCH8ybs+ptweGaY2cmuKt+kcj4EDoB5YDNAX0mV7Unv8gsTIOEYRjkZRfz+YvbqChteD1CRVklK97cySW3DW3hdCKnz2N4yCzKNEdJqqeX5e9mX/4+3EbdP4jdtfwu4sLiSI1JDaq9gPzlxNGkhtS+nleWV+c1qx5lKnI1vH9hZEgkfRP61twS+3Jm4pl0iOiAzWZj9eHVvLXzrXqPU8MGaata53eKNsrtMVi/P5cjcX3ovPc45/ZOwmEPzr/c+FXRkZpOes2dpif+Z7NBpzPN27jbzX2y9q+saZzgPFxTZAF0PLOmkOg5ruF27VaqrDAzF/xQ9fZQrSKp6m25swlPZIPYLlVrnwyzWcd9h8wiNEi4Ktwc3pnHwW25HNh6DOexk6+DMjyw75ujHM8s0n5SEtSOlR7zTiur/Yt/SWVJk5/DWeHEmetkR+6OBq9Xr9upXWCdWGy1xLqdU6keWWpKjhJXCXvz97I7f7d3hGl33u5Gu+KF2ELoFd/LHGVK7Ot92zWmK3Zbwxu9q2GDtEcqpILE4q1ZzPskg6yCMug2BV78mi7xETx0xSCmDulidbzGNWXxfe2Rn0BN05PAC4+FAZeZN8OAIxk1nQAPrjX/vx7bBV+nm/tmnTGppsV6fLea5/H1a6YpPB6zlXi9kaRDNUVSUf1NFhsUkWA23IjvauaO61r3OLYLrHrKnN7nsQGVsPZvlo9iFhwt5cDW4xzYepzDu/Jwu2qm1dgcEBEVSmmRi4bWUtvs0HtYJxVREjSKKorqjDBVv19nDUstIfYQzog/g36J5vSyvvF9WbRlEXvy99TbE+iM+DO4+5y7ySnJqdduO7skm0pPJTklOeSU5PDfo/9t8ONFOCJIiU6p1xghJSqFlBj/dJKDkzfKaKgYqfRUst+531tgVo80HS463OjH6BbTzVssVb9+veJ6Eerjz1w1bJD2qF0XUunp6aSnp+N2W7sWZvHWLH7z/zbX+9aTXVDGb/7fZp77xTnBW0w1tPgeatpznz8Hvl8d+Gl60rJsNkgebN4m3A2l+Wanut1LzG51RTmw8zPzBtB5MPSr6gQIJ/+amXx//Y9XPeXuxJGkhqbcnYwj3CyG4quKo7iu9Y9P1aWwsf27Tvx8Aszt8pC5O58D28ziKT+n7l/kYxLD6TmkAz2HdKBr/0QqKzy8/tDXDU7vC4sIYeLP+rdUdBGvCncF+wv2sytvl7dY2pO3h8zizAbvb8NG99juNQVTYl/OTDiT7nHdCbXX/OK/+vBqduXtqvd4j+FhT/4eQuwhXNP/mgav55bl1mz8WpRlbv5akuM9d6z0GGXuMr53fs/3zu8b/dwa29uoqVMIT9Yow2N4OFx4uN46pu8LvqfSaHgKb8fIjvRN6Eu/xH7e9Ux9Evr4peADNWyQ9qldF1JpaWmkpaXhdDqJj7dmQ1K3x2DeJxkN/v3GwNwjdt4nGVw0KCU4p/md2OUupALevQm2vQ+xqeZf7lcuqPsYTdNreyITYPBV5s3jgZzvaqYA/rABjmwzb6ufNvfn6jTA/JopLwSbBxbPha+fhYHTzCLtk7t9n3Jns5sjmd7CqBvEdatbKEV1OL0Cvan7dwVIYW6Zd9Tph515VJbX/BHIbrfRpW88PaqKp6Qu0XX+Yh0WAZNm9ueL/62/4eTEn/dXowkJKLfHzQ9FP9RZi7M7fzcHnQfrrWOq1jmqs/cX/r6JZgFwRvwZRIacvMnN6Uwxs9vsdIzsSMfIjgyl4TWDFe6KBkezar8tdhVTUF5AQXlBs6cQHnIeqtMoY97aebgNN3vy9rC3YC+llaUNPm9MaIy3YKr9NjEi8aSvmz+oYYP4ypWZSWVere1qyspgW83PqZDEREJTUy1I1jTtupAKBuv355rT+RphAFkFZazfn8vYPh1aLliDYQwoOX7C2pKqUYG4ruYvkmcB1V//hVV/UdQ0vfbFbocuw8zb+b+DklzY+5VZWO350vwaOlpVGK15Bs4Gvv6Pebz9Y/PWkKZMuQv011ULd3p0uz1k7ykwi6dtx8nNLK5zPSo+zBx1GtyBbgOTCI88+bf0viM6s2djDvu/PYbhqZnSp9bncipN3YvJMAyOlBypUyztztvNvoJ9lLvLG3xMbFisd1pZ9Vqcvgl9iQ9v3h84Az3FLMwRRvfY7nSP7d7ofQorCutOGWzmFMLa3t/9ft0c9jD6JPTxFpp9E8zGD8lRyVqDJK2CKzOTvVMvwag4YUbJ9J9437WFhdFn8b+DtphSIWWxI4VN2wyzqfc7LeVFjSzCrzp2HobKpuSwwcgbNU1PTFFJMPQn5s3jhswtNU0qMjebw65gtgyP71Zz806769a0KXctoQU6PRbnl3Ng23EObj3Ooe25VJTVFGc2G6ScUTPq1LFbjE+/MNlsNib+fAA/7PyaihIXYRGhmtInp9TYFLOC8oI6jQv25JuFU2FFYYPPE+GI4IyEM7y/8Fd3fusc1dmvv/gHwxSz2LBYs0BM7Nfg9VNNITxYeBBnRf2R+Mt6X8aFPS+kb0Jfusd2b7XdBUUAKvPy6hdRJzAqKqjMy1MhJQ3rHNu0/WWaer9GuV1ml7GGFuFXH9dbu9SImJT6U6d+2ADbPjAX39sNc3Rg2IzTyyxtj90B3UaYN7vDLKSqv2aGXWt5wwYreNwecvY7vaNOxw7VbTscGRtKj8HmqFP3QUlERJ/eqFtUXBiTZvZn1d/Xc97M0ZrSJyflMTws/n5xnSlmMz6dwfHS4xwpPdLgYxw2Bz3jetZMLasaZeoW0w2H3dEiuYN9itnJphAahsHPPvsZ23O312uU8b3ze6b0mKIRJ5EgoULKYuf2TuIP0R/hLPPwjPvqetfvdHxAXISdc3tf2viTNDTl7sRCqSjb7HF8KuHxJ0ydOmGEIC61flvrFQvMIsrixffSigRJw4bTsXtjDquSb+G8TUfoO6KzT48tcVZwKMNc63QwI5fyklqLw23QuWect1FE5x6x2Py8PrLfyGT6PfwijLjCr88rrU9RRVG9NT7VU8+yiszjE5sXbM/d7n0/NTq1TuOHfgn96B3fWw0FTsOazDXewrU27cUkJ+NcehDnkgPEXdSTuBOPL+xhWS7DMHDn51OZlYUrKwtXZhau7Cwqs7Io39tA1+ZWRoWUxRx2GxP7p9Av4xmAOsXUnY4PmB36Hnv6/RrH8V2nP+XOEWYWQnW6lZ1QKEXE+fYJWLz4XlqhNvA1U+KsYPnrO6mwR7H89R2k9ks46ciO4TE4cqCQA1uPcWDrcY4cLKzThjw8KsQcdRrSgR6DkoiM1S+hcvpcbhfZJfXX59QumBrbePVU5oyaw4/7/piYsCCYctuGaC8maY68j7ZS/LXZsMG55ACl3abjWnLAe+wudJJ41ZCAfGxPSQmu7GxcWVlVxVLV+9nVRVM2RlkLLE+xiAqpINDvmj+x+x2YnfEMo+w72G90Ybx9K33sWVTaw+m743nY8fypn6ihKXe1j6M7mY0A/KmFF98HyuHDh1nSrx8XHT5M165drY7jk1aXvdbXTJ3sreRrxjAMVryxA1d5JdhsVJRVsuLNnVxyW93pOWXFLg5l5FaNOh2ntLDuJtOdesTSY3ASPYd0JLlXLHaHn/9tnkRTGweIf/nzdW9ojc2JRdPx0uONNlyoLS4srk7XuOrOcclRycxfP7/BvZg+2/cZvxj4i9P6HKS+6kYZ1x6dyi+OXc4/O33CWx0X87Ojl/CLY5fz/zp+yhcR64N6L6ZgHRlpq8r3/0DR2tw6hXVFbG9ql9lFa3OJOTfT53VGRmUllUeOmIVSZt3iyJWVRWVmJu6CgiY9l6NjR0JTUgjt0oXQ1C6EpHTBqKzk6BNP+JQp2KiQChL9rvkTni9DSVmylj2FP8OI+1+IyCLEU9XlqDlT7lpCrcX39aY6BfmoQm0rl3zN97GxrPzya352w3Sr4/ik1WWv9TVTL3sr+JrZs+kI+7Yc8x4bHtj3zVF2b8whoXOUtz15zv4CjFq/w4ZFOOg+KMkcdRrcgeh4C/69cvK9aSRwfH3dq6fcNdb1LackB5fH1ejjq4XZw+gSU1UkRaWY70fVbbfd2D5CJ9uLSVPMAiPMEcYbcel4jpmNMm44Oo2ZPwwlJLw3ANcfu4JfDr4h6IsoMEdCyntcQ3mtY0DFlA+MSg/ugnLcBeVU5ptv3fnmrfrYKHPX+15S73uLx82xV/YTknwcR3w4IQnh2OPDsYW4oDwft/MIlTk1U+68o0pHjphbmpyCPSqK0K6phKR0MQulLimEdOlCaEp10ZSCPaz+12zptm0cbd11VNsppNLT03n88cfJzs5m2LBhLFq0iHPPPdfqWE1S3UO/NP7HfJXXlwpPOUtzryPp0hlEduhISLczCe19ptUxG+TNXuLhq1f2UeEpZunLa0nynEFklD2o+/8f27mTwqNHKS/zsHvfdrDD7r3b2fHlSsIj7MR26kTH/sHZ0UzZreHcfZDlr+0FwPA4MTyl2OyR2OxxDe7P1KFrtHfKXkqfeBwtOOp0oup/q/898l/c32xnaGUoroPbWRf9JsM6Dwvqf6u19xkpLMijzFVOxJpVxMab++K0huz/PfJfSrZtpTdQkr2VxSHP0jmqM0dCSzkUXdasKXfVDQtqb/J64qhSYnhiswplwzB49asnOCObRqeYvfrVE4z7RXBOMWvNe9N4VuTWOQ4J61X/+iUtGMgH1cVStfLoHvWuB2sh1dJfM4bHwFPsqimKvIVSGZUFFbjzy/AUuWjCoDJGRbHZwMkRXuffo2EY2Gw2bI4Q3E4Pbmd+I88Qhqc8CaPUhqc0AsMdjy2mEyGOPDwVBThiQgjtEE1Iakqd4ii0SyqhXVKwx8YG5feBlmAzDKMJ/4uC29tvv83111/P888/z+jRo1m4cCHvvvsuO3fupHPnUy8Cr96Qt6CggLg4H9cInabqHvohvS8ifMA0vi7+DzsijzOgtANjos+jYsfHuPYvCcoe+sGY3TAMXC4X5eXllJeXU1ZWVudt9fulx4+zfuvWWg/EbMNd/bZKcocO2EKDa88rw+Ui5/jxWidoc9nPHzmSqI4dCQ8PJyIios7b6vdDQvzzdyDDMKh0eagoraSitJLy0kpcpW7KSyupKKs5V3o0nz0r99EzvgMDIhz1vt53lHnYVeai54B4eo/sSo/BHYhNOs1um03IXlpZSpGriKKKIu/bQldhzbGrCHdmNhfM+YDIPpcQNmAa6wpXsD06j4EliYyOmUjFjo8p3beYjQtvIDQ1ldiwWKJDo4kNjSUmLMb7NiY0htAW3gOu+vtMaO+LGs1uxfeZ0spSCisKKXIVUVhRSLGruOZ1r3rt3VnZXDjnQ+/rnnXgEzYmFjEyL5YuPS+nYsfHFO35jLtuc3A8vv4vISdOuTuxYOoU1YlQe2D+fxQfOsDeS6YS3eeyRrMX7/2MPv9eTHT3ngHJ0Fwnfs00lL3Fv2Y8BkaFG0+ZG6O8Ek+ZG09ZJUaZ+b5RdVx5vICi/2zDEW/uUXXU5mR9yB7OrexLJ8P8/cRTVkBYt05B+f294oej2CPM/b8ayl55fA8Jl59LSOdEbBEh2MMd2CMc5vsRIeb74SHYHC37S3kgvmY8ZZVmkVRrFKm6WKoeTcLdhF/BHTbs0XbsEQb2UDfYK4AScBdhlDtxHd5D+X4IH3gl0PDrXr5nCe7sb7FFJmGPSsIWmYg9MqnquAO2psxosoMjNhxHQs0tJL7WcXw49qgQnwqqYPy3Wq2ptUGbGJF68sknueWWW7jxxhsBeP755/nss8/4xz/+wX333WdxupOrzMsjtPdF3n8ARkIy7hAXhCdjq7R5zwdjD31/Z/d4PFRUVJy0AGrsXO33PU0Yhq7HdsLbKnV+6Q9WbTD7yo0bT/lQh8NhFlZh4YSFhRMaEkaIIxSHPRSHLQQHodiMEPA4sFU6MNx2DJcDT7kNd4UNdxlUlBpUlnrweJr296Qz4zsyMNJs33zi13v1+cEjwkk979Rr1dweN8WVxWbhU/WLePUv5bWLoOpf1msXSrV/eXcbp15T1jvb4LI+l3j/TXqSuuAOcWOEd6nzb/WDja+yP+XkPwTDHeHEhMZ4i60TC63qt7FhseZx9bmq+0WHRhMZEtnkH7Ynfp9pLHtTv8+4PW7va9to8VnR+Gtf6DJfd08TuqD2zja4vNbr/n3f/hwJ+YEDHbqRWiv7FR1KiRg8qMlT7lqC3VlEdJ/LTpnd7mxes4pAOvFrprHsTf2aMdyGWfSU1y1+POXuqveri6G6b+s8ptzdpJEFwFtEAex2ZJHlyGOPkUWnSvOXOXtEPJXHKoCT779jheoiChrOHtKhL0Vrc4HcRp7BZAu1VxVXDu9be0QItnBHTcHVwHlbRPX1EAixNfv7zKm+Zqqn3NWZbldQXmd0yShvynpfA4wyjMpCjAonRmku7qKjeApycOdlYZQcx6g49b+xmCv/7n2/odc9rM8U6JJP5KCBdafcJSdjCwvDKHPX+lzKcOdXVI2MVX9uFeAxvNMMOdBwDluovU5hFVL1tk6xFVazBUJoairJD7/ubZTR0OuedOMvg+7339pa/YhURUUFUVFRvPfee1x11VXe8zfccAP5+fn861//OuVzWDkiVbptG/v/+QMVtkoMYHHYFsptLsKNUCZXDAYgzHDgGFKGrWuXFs12KsbhLNxbI6iwmd8sloVto9zmIswIYVRlXyqpxDDA3b2UytgYXJUuXJWVVFRWet83j2ve96fQkBDCQkIJDQmpuoUSVvV+SGkZUdkxuKjk29CD9R471nUmcZ5IyvsWYu/U0a+5Tpfn6DHC98TitJeyNrT++oXWnH2gqyshODgeU4LLbqfSXYnLU0mluxK3UYkbNx782IzCAJvhwG6EYPM4cBCCw+YgxB5CiD2EUEfVLTSUkIpKkovCwAahOFgTuqvBf6sHzvgvBR2jKK0spayylFJPKWWuMsoqyyitLKO0soRSdynl7nK/fRo2m50IRwRRjkjCQyOIsEcQFRZJpCOSyJBIEo+VM/iH8fX+rZ6YfWOXLzma5KC8srwqfxmlLvNz8Gdehy2E8JBwIu0RRIRGEuGIIDK0Om8EESFm7siQSGKOFNN5V99TZj/Ybyv5nSJrXnd3qZm9+m2leSvz+K97lMPmIKI6syOSyNBIokKiiAyJIDIkkqRj5Qz6fuwpsyefF0l4z15+y+UP5Qe+J+c/pW06e4czI3HEJWKUe/C4PBgV5s1T4cFweaDcfJ9KP/6aZAdbmANbmM0sFsLt2MMc5vthNjzFTo5+k4E7qUej2W3Z20mdOorQLsH1O4ErK4vMxRswUgYCDWd3FGTTYUBf7OHRtV5rN5QbeFwBfK3D7NjCql7rMPO1rn7dCbfjzj3Ksa2n/pqJcbjBCAdP00YDDVcJnrJ8jLJ8PKV5GKUFGGX5GGV5eErzMcoL4VR/DAsNxREVhS0mBkd0NPaYGOzRUTiiorHFxGCUl3FsTwX0Htdodvavoc9vryGimdPmvdMQnWZR5XZW4CksN6cgFlbgKSjHU9y03+HskSHY48NwxIbhiA/j6MaDlNsq62SPMEKZWnE2AOFGKAMf/VGzcp+OptYGrb6QyszMpGvXrqxZs4axY2u6IM2ZM4cVK1awbt26eo+pHsGo5nQ66d69uyWFVM7qVTy35MsW/ZjBzm7YCCOEMCOEUEIIMxzmMSGEGiHeazXHDu9x9WNCcWA7cZijAcdsTj4K31BvitlV5aPoaLTs14Kv2mt2DwYuKqmgkgpbpfm+zX3CcfV187yr+tj7vhvD1qq/9YmIiLRNJ0z3/+Mf/9jiEdrV1D5fzZ8/n3nz5tW/MGMGtPCc46NGJBEp4ZSnnoHR0O/9Vb/rNaUosIJRE7ChiyQYUSQY0WaBU6voCfUWQ446xVEoDhzYT/vzNar+O5UII5RII4xoI5z+lansdGRSTDkRnlA8NGOKYAtqz9lDcRCKg2ijeZ3vDAwq8dQpuly1i7GqYuvEQq3QVkqRrazRr3dovf9WzUutOXtw8n4XUvYW5Vv2Bn5WGCe5FmCGzQbYTpLdwBakfwMPvuy2Bt9t6MSpv2aMmktGrUcEQZMFwzDMHA1lMQyoajoRfGwNv+5V79sM+PH3+2HatBbOBbhO3RUV2sCIVHOm9gXbiNTmV/9Dlw5nmX+hP8FV5aPIOv5fhv18LJ3GBNd+L0e+XsO3b3xNlw7DWm32oUnn4cZTVbqZGyB6MHBg57vc/yi7n7Xm7Ee/Xsu3b6whpcPZjX69Zx/7hrNmjg+67FlrV5Hx5rpTZh/08zF0GTvBgoSNa+rrfs6Nk+g8LriyF373X4784Z8UDprcaPbYbV/R+S/XEzt0mAUJG1e6bRuZv3/5lNlT599E5ODBFiRsXGvP/v30n1B49hX834D6a+Qu3VFC7JZP6PX+e8ruR635a6a6YcPx6GiWXFx/CtxFn39Bh+LioGxaBmbL/H1fftfo637GlKGWdHpsNyNSYWFhjBgxgqVLl3oLKY/Hw9KlS7n99tsbfEx1569gEJeQyJDE8Ryn0DzRQBezIYkTSOzUgdCIwHYA81VSp2SGJE5o1dkBHJgtqavbhDqqwiu7/7Xm7ImdOjM48byTfr0PTjo/KLN36tylSdk7dg6+7E193atboQeTEHsIYQOmwUmyhw28khB7cP4obkr2YNWaswO4DqyCAT8yRxNsNu9b14FVVkc7pdaavbV+zYSmptJn8b8J37cPvqy/VCT18QV0O+OMoCyioKplfvVoVAOvezC3zAewblMTP5o9ezYvvvgir776Ktu3b+c3v/kNxcXF3i5+Qc2Aih0fE2mEEWmE0dGIZWhxRzoasd5zFTs+tmJ2wam1gey15ZTVbUOj7AHQBrK3yq93mpg9GLWx131sblzreN1RditFlJURUVpKYm4uIzZsJDE3l4jSUiLK/NcsJVBaa/bW/DUTmppK0tChxMTEkJqayuUHD5KamkpMTAxJQ4cGbREFEHdRzzqv+8Siut/f4y4Kru0VThScfwbz0YwZMzh69CgPPvgg2dnZnH322SxevJjk5GSro52SJz6G4r2fEQXMGHA5Gflr2J6/jIEJ4xiUMA73jk8p3vsZnvg7rI5aT1vIbgDhA6axNX81GfmrGZQwniEJ4717pCi7f7WF7NGYX+/bc/9DhnMZg+LHMDBxQlB/vYckJuLav4RQGs/u2r+EkMTZVketpy297pXbP8G16//o0/8yQgZcHtSvu7JbIyQxEVtYGFGlpVz+yafYPR5sQJ+9e/HY7Tg8HmxhYYQkBuEIbCvP3lq/ZqrFx8dz991343A4sH36KSNuuQW32+23PRcDpXq06dolYSRM6UX8M3dwzp2LyP/yexIv6h3Uo1HQBtZI+YOV7c8BMvf8l/wjh6gohbXvu6gsLyUkIpKxV4cSFgkJnbuT2je45s9XU3ZrKLs1qrMDlDoLqXjxfwm75VdExsUCwZ3dlZlJZZ65V0dhQR5lf/oTEQ884J0SF5KYGLR/tWwrrzsAs2bBU095D4P5dVd2ayi7NVpz9nqmTYOPg3cE7aSCJHu7WSPVFqT2Heb9JSA6PodVf1/PeTeNpu+IzhYnOzVlt4ayW6N2dgCefROm/sy6QD4ITU31/hLw/9m777iq6v+B46/L3giKAg5ABcU9cA8wNVDT1EpTM3eWmju1nGiOUnKXMzDTHKVimANJcOEWTUEUnCm4FyqI3PP7wy/n55V1MRCs9/PxOA+953zG+xzuevP5nA/mAMamUMgWZ8jKv+W6A2BmBoXsZvWsSOwFQ2IvGG9y7KLg/Cvukfo3cfcqQa/rS9+IL5Uvk9gLhsQuhBBCCPH6SSIlhBBCCCGEELkkiZQQQgghhBBC5JIkUkIIIYQQQgiRS5JICSGEEEIIIUQuSSIlhBBCCCGEELn0n06kFi5cSKVKlahTp05BhyKEEEIIIYR4g/ynE6mBAwcSHR3N4cOHCzoUIYQQQgghxBvkP51ICSGEEEIIIcSrkERKCCGEEEIIIXJJEikhhBBCCCGEyCVJpIQQQgghhBAilySREkIIIYQQQohcMiroAAoDRVEAePDgQQFH8j+pqVBYYsktib1gSOwFQ2IvGBJ7wZDYC4bEXjAk9oJRSGJPzwnSc4SsaJScSvwH/P3335QuXbqgwxBCCCGEEEIUEleuXKFUqVJZHpdECtBqtVy7dg1ra2s0Gk2G43Xq1Mnxb03pU0afcg8ePKB06dJcuXIFGxubfO8vL9uS2HNX7t8euz5lJPa8j0ti17+tvIxLYte/rbyM63XH/rrPT2LXv0xetiWx618mL9sqTLErisLDhw9xdnbGwCDrO6Fkah9gYGCQbbZpaGiY7Q9U3zK5KWdjY5NtubzsT2L/fxJ73sWub38gsedlXBJ77tqS2CX2/OhPYv9/ErvEnpu2oPDEbmtrm2N9WWxCDwMHDsyTMrkp9zr7k9j1J7HnbRl9Sex5W0ZfEnveltGXxJ63ZfT1ut+3Jfa8709i15/EnrtyWZGpfYXMgwcPsLW15f79+3pn7oWFxF4wJPaCIbEXDIm9YEjsBUNiLxgSe8F4E2OXEalCxtTUlIkTJ2JqalrQoeSaxF4wJPaCIbEXDIm9YEjsBUNiLxgSe8F4E2OXESkhhBBCCCGEyCUZkRJCCCGEEEKIXJJESgghhBBCCCFySRIpIYQQQgghhMglSaSEEEIIIYQQIpckkcpHu3fvpm3btjg7O6PRaNi0aVOOdcLDw6lVqxampqaUL1+eoKCgDGUWLlyIq6srZmZm1KtXj0OHDr0RsU+fPp06depgbW1N8eLFad++PbGxsW9E7C+aMWMGGo2GoUOH5lnM6fIr9qtXr/LRRx9RtGhRzM3NqVq1KkeOHCn0saelpTF+/Hjc3NwwNzenXLlyTJkyhbxeIye3sSckJNC1a1c8PDwwMDDI8rmwfv16KlasiJmZGVWrVuWPP/7I07jzK/alS5fSpEkT7OzssLOzo0WLFoXifUbf655uzZo1aDQa2rdvn2cxp8uv2O/du8fAgQNxcnLC1NQUDw+PPH/e5Ffsc+bMoUKFCpibm1O6dGmGDRtGcnJygca+YcMGWrZsiYODAzY2NjRo0IDt27dnKFcYP1f1ib2wfq7qe93TFabPVX1jL4yfq/rEXlg/V/fu3UujRo3U61mxYkVmz56dodzreK3mhiRS+ejRo0dUr16dhQsX6lX+woULtGnThmbNmhEVFcXQoUPp27evzotg7dq1DB8+nIkTJ3Ls2DGqV6+Or68vN27cKPSxR0REMHDgQA4cOEBoaCipqam8/fbbPHr0qNDHnu7w4cMsXryYatWq5WnM6fIj9rt379KoUSOMjY3ZunUr0dHRBAQEYGdnV+hj/+abb/jhhx9YsGABMTExfPPNN3z77bfMnz+/QGNPSUnBwcGBcePGUb169UzL7N+/ny5dutCnTx+OHz9O+/btad++PadOncrL0PMl9vDwcLp06cKuXbuIjIykdOnSvP3221y9ejUvQ8+X2NNdvHiRkSNH0qRJk7wINYP8iP3p06e0bNmSixcv8uuvvxIbG8vSpUspWbJkXoaeL7GvXr2aMWPGMHHiRGJiYli+fDlr167lq6++ysvQcx377t27admyJX/88QdHjx6lWbNmtG3bluPHj6tlCuvnqj6xF9bPVX1iT1fYPlf1ib2wfq7qE3th/Vy1tLRk0KBB7N69m5iYGMaNG8e4ceNYsmSJWuZ1vVZzRRGvBaBs3Lgx2zKjRo1SKleurLOvc+fOiq+vr/q4bt26ysCBA9XHaWlpirOzszJ9+vQ8jfdFeRX7y27cuKEASkRERF6Emam8jP3hw4eKu7u7Ehoaqnh7eytDhgzJ42h15VXso0ePVho3bpwfIWYpr2Jv06aN0rt3b50yHTt2VLp165Znsb5Mn9hflNVzoVOnTkqbNm109tWrV0/p37//P4wwa3kV+8uePXumWFtbKytWrHj14HKQl7E/e/ZMadiwobJs2TKlR48eyrvvvpsnMWYlr2L/4YcflLJlyypPnz7Nu+BykFexDxw4UHnrrbd09g0fPlxp1KjRP4wwa7mNPV2lSpUUf39/9XFh/VzNzMuxv6ywfK5mJrPYC+PnamZejr2wfq5m5uXY34TP1XQdOnRQPvroI/VxQbxWcyIjUoVIZGQkLVq00Nnn6+tLZGQk8Py3lUePHtUpY2BgQIsWLdQyBSWn2DNz//59AOzt7fM1tpzoG/vAgQNp06ZNhrIFSZ/YN2/ejJeXFx988AHFixenZs2aLF269HWHmoE+sTds2JCwsDDOnj0LwIkTJ9i7dy+tWrV6rbG+ild5TRRWjx8/JjU1tcBfq/qaPHkyxYsXp0+fPgUdSq5s3ryZBg0aMHDgQEqUKEGVKlWYNm0aaWlpBR1ajho2bMjRo0fVaTbnz5/njz/+oHXr1gUcmS6tVsvDhw/V53Jh/lx92cuxZ6awfK6+LKvYC+Pn6ssyi72wfq6+LLPY35TP1ePHj7N//368vb2BwvtaNSqwnkUGiYmJlChRQmdfiRIlePDgAU+ePOHu3bukpaVlWubMmTOvM9QMcord3Nxc55hWq2Xo0KE0atSIKlWqvM5QM9An9jVr1nDs2DEOHz5cQFFmTp/Yz58/zw8//MDw4cP56quvOHz4MIMHD8bExIQePXoUUOT6xT5mzBgePHhAxYoVMTQ0JC0tjalTp9KtW7cCilp/WZ1fYmJiAUX06kaPHo2zs3Oh/rKTbu/evSxfvpyoqKiCDiXXzp8/z59//km3bt34448/iIuLY8CAAaSmpjJx4sSCDi9bXbt25datWzRu3BhFUXj27Bmffvppnk/t+6dmzZpFUlISnTp1AuDWrVuF9nP1ZS/H/rLC9Ln6ssxiL6yfqy/LLPbC+rn6ssxiL+yfq6VKleLmzZs8e/aMSZMm0bdvX6DwvlYlkRIFYuDAgZw6dYq9e/cWdCg5unLlCkOGDCE0NBQzM7OCDifXtFotXl5eTJs2DYCaNWty6tQpFi1aVKje8DOzbt06Vq1axerVq6lcubJ6L5Wzs3Ohj/3fYsaMGaxZs4bw8PBC//x/+PAh3bt3Z+nSpRQrVqygw8k1rVZL8eLFWbJkCYaGhtSuXZurV68yc+bMQp9IhYeHM23aNL7//nvq1atHXFwcQ4YMYcqUKYwfP76gwwOe38fl7+9PcHAwxYsXL+hwckWf2Avr52pmsb8pn6tZXfc34XM1q9gL++fqnj17SEpK4sCBA4wZM4by5cvTpUuXgg4rS5JIFSKOjo5cv35dZ9/169exsbHB3NwcQ0NDDA0NMy3j6Oj4OkPNIKfYXzRo0CBCQkLYvXs3pUqVep1hZiqn2I8ePcqNGzeoVauWejwtLY3du3ezYMECUlJSMDQ0fN1hA/pddycnJypVqqRTxtPTk99+++21xZkZfWL/4osvGDNmDB9++CEAVatW5dKlS0yfPr1QvOFnJ6vzK+jXam7MmjWLGTNmsHPnzny7ETwvxcfHc/HiRdq2bavu02q1ABgZGREbG0u5cuUKKrwcOTk5YWxsrPN+4unpSWJiIk+fPsXExKQAo8ve+PHj6d69u/rb46pVq/Lo0SM++eQTxo4di4FBwd5JsGbNGvr27cv69et1RlaLFStWaD9X02UV+4sK2+dquqxiL8yfq+myu+6F9XM1XXaxF/bPVTc3N+B5XNevX2fSpEl06dKl0L5W5R6pQqRBgwaEhYXp7AsNDaVBgwYAmJiYULt2bZ0yWq2WsLAwtUxBySl2AEVRGDRoEBs3buTPP/9UXywFLafYmzdvzl9//UVUVJS6eXl50a1bN6Kiogr0zV6f696oUaMMy+GePXsWFxeX1xJjVvSJ/fHjxxm+gBkaGqpfjgszfc6vMPv222+ZMmUK27Ztw8vLq6DD0UvFihUzvFbbtWunrgxZunTpgg4xW40aNSIuLk7n+X327FmcnJwKdRIFWb9WgTxfVjm3fvnlF3r16sUvv/xCmzZtdI4V5s9VyD52KLyfq5B97IX5cxVyvu6F9XMVco79Tfpc1Wq1pKSkAIX4tVpgy1z8Bzx8+FA5fvy4cvz4cQVQvvvuO+X48ePKpUuXFEVRlDFjxijdu3dXy58/f16xsLBQvvjiCyUmJkZZuHChYmhoqGzbtk0ts2bNGsXU1FQJCgpSoqOjlU8++UQpUqSIkpiYWOhj/+yzzxRbW1slPDxcSUhIULfHjx8X+thfll+rC+VH7IcOHVKMjIyUqVOnKufOnVNWrVqlWFhYKD///HOhj71Hjx5KyZIllZCQEOXChQvKhg0blGLFiimjRo0q0NgVRVHL165dW+nataty/Phx5fTp0+rxffv2KUZGRsqsWbOUmJgYZeLEiYqxsbHy119/FfrYZ8yYoZiYmCi//vqrzmv14cOHhT72l+XXqn35Efvly5cVa2trZdCgQUpsbKwSEhKiFC9eXPn6668LfewTJ05UrK2tlV9++UU5f/68smPHDqVcuXJKp06dCjT2VatWKUZGRsrChQt1nsv37t1TyxTWz1V9Yi+sn6v6xP6ywvK5qk/shfVzVZ/YC+vn6oIFC5TNmzcrZ8+eVc6ePassW7ZMsba2VsaOHauWeV2v1dyQRCof7dq1SwEybD169FAU5fmT2dvbO0OdGjVqKCYmJkrZsmWVwMDADO3Onz9fKVOmjGJiYqLUrVtXOXDgwBsRe2btAZmeY2GL/WX59YafX7H//vvvSpUqVRRTU1OlYsWKypIlS96I2B88eKAMGTJEKVOmjGJmZqaULVtWGTt2rJKSklLgsWdW3sXFRafMunXrFA8PD8XExESpXLmysmXLljyNO79id3FxybTMxIkTC33sL8uvRCq/Yt+/f79Sr149xdTUVClbtqwydepU5dmzZ4U+9tTUVGXSpElKuXLlFDMzM6V06dLKgAEDlLt37xZo7N7e3tmWT1cYP1f1ib2wfq7qe91fVFg+V/WNvTB+ruoTe2H9XJ03b55SuXJlxcLCQrGxsVFq1qypfP/990paWppOu6/jtZobGkUp4DF3IYQQQgghhHjDyD1SQgghhBBCCJFLkkgJIYQQQgghRC5JIiWEEEIIIYQQuSSJlBBCCCGEEELkkiRSQgghhBBCCJFLkkgJIYQQQgghRC5JIiWEEEIIIYQQuSSJlBBCiCwFBQVRpEiRHMtpNBo2bdqU7/EUBj4+PgwdOrSgwxBCCFHAJJESQogC1LNnTzQaDRqNBmNjY9zc3Bg1ahTJycmvPRZXV1fmzJmjs69z586cPXtWfTxp0iRq1KiRoW5CQgKtWrXK1/iCgoLUa2VgYECpUqXo1asXN27cyNd+c5LZdXsVLz4XTExMKF++PJMnT+bZs2f/PMgC8l9KsIUQ/z1GBR2AEEL81/n5+REYGEhqaipHjx6lR48eaDQavvnmm4IODXNzc8zNzXMs5+jo+BqiARsbG2JjY9FqtZw4cYJevXpx7do1tm/f/lr6z2/pz4WUlBT++OMPBg4ciLGxMV9++WWu20pLS1OTzjddamoqxsbGBR2GEELoePPfXYUQ4g1namqKo6MjpUuXpn379rRo0YLQ0FD1uFarZfr06bi5uWFubk716tX59ddf1ePh4eFoNBq2bNlCtWrVMDMzo379+pw6dUqnn71799KkSRPMzc0pXbo0gwcP5tGjR8Dz6WqXLl1i2LBh6qgI6E7tCwoKwt/fnxMnTqhlgoKCgIwjD3/99RdvvfUW5ubmFC1alE8++YSkpCT1eM+ePWnfvj2zZs3CycmJokWLMnDgQFJTU7O9VhqNBkdHR5ydnWnVqhWDBw9m586dPHnyBIBly5bh6emJmZkZFStW5Pvvv1frXrx4EY1Gw4YNG2jWrBkWFhZUr16dyMhItczt27fp0qULJUuWxMLCgqpVq/LLL79kGU9m1+3Ro0fY2Njo/IwANm3ahKWlJQ8fPsyyvfTngouLC5999hktWrRg8+bNAHz33XdUrVoVS0tLSpcuzYABA3SuafrPavPmzVSqVAlTU1MuX77M4cOHadmyJcWKFcPW1hZvb2+OHTuW4bouXryYd955BwsLCzw9PYmMjCQuLg4fHx8sLS1p2LAh8fHxOvWCg4OpVasWZmZmlC1bFn9/f3UEzdXVFYAOHTqg0WjUxznVS4/nhx9+oF27dlhaWjJ16tQsr5kQQhQUSaSEEKIQOXXqFPv378fExETdN336dH766ScWLVrE6dOnGTZsGB999BERERE6db/44gsCAgI4fPgwDg4OtG3bVk1M4uPj8fPz47333uPkyZOsXbuWvXv3MmjQIAA2bNhAqVKlmDx5MgkJCSQkJGSIrXPnzowYMYLKlSurZTp37pyh3KNHj/D19cXOzo7Dhw+zfv16du7cqfaVbteuXcTHx7Nr1y5WrFhBUFCQmpjpy9zcHK1Wy7Nnz1i1ahUTJkxg6tSpxMTEMG3aNMaPH8+KFSt06owdO5aRI0cSFRWFh4cHXbp0Ub/EJycnU7t2bbZs2cKpU6f45JNP6N69O4cOHcq0/8yum6WlJR9++CGBgYE6ZQMDA3n//fextrbO1fk9ffoUAAMDA+bNm8fp06dZsWIFf/75J6NGjdIp//jxY7755huWLVvG6dOnKV68OA8fPqRHjx7s3buXAwcO4O7uTuvWrTMkdFOmTOHjjz8mKiqKihUr0rVrV/r378+XX37JkSNHUBRF52e4Z88ePv74Y4YMGUJ0dDSLFy8mKChITXoOHz6snndCQoL6OKd66SZNmkSHDh3466+/6N27t97XTAghXhtFCCFEgenRo4diaGioWFpaKqampgqgGBgYKL/++quiKIqSnJysWFhYKPv379ep16dPH6VLly6KoijKrl27FEBZs2aNevz27duKubm5snbtWrX8J598otPGnj17FAMDA+XJkyeKoiiKi4uLMnv2bJ0ygYGBiq2trfp44sSJSvXq1TOcB6Bs3LhRURRFWbJkiWJnZ6ckJSWpx7ds2aIYGBgoiYmJ6nm7uLgoz549U8t88MEHSufOnbO8Vi/HcvbsWcXDw0Px8vJSFEVRypUrp6xevVqnzpQpU5QGDRooiqIoFy5cUABl2bJl6vHTp08rgBITE5Nlv23atFFGjBihPvb29laGDBmiPs7suh08eFAxNDRUrl27piiKoly/fl0xMjJSwsPDs+ynR48eyrvvvqsoiqJotVolNDRUMTU1VUaOHJlp+fXr1ytFixZVHwcGBiqAEhUVlWUfiqIoaWlpirW1tfL777+r+wBl3Lhx6uPIyEgFUJYvX67u++WXXxQzMzP1cfPmzZVp06bptL1y5UrFyclJp93050Vu6w0dOjTb8xBCiIIm90gJIUQBa9asGT/88AOPHj1i9uzZGBkZ8d577wEQFxfH48ePadmypU6dp0+fUrNmTZ19DRo0UP9vb29PhQoViImJAeDEiROcPHmSVatWqWUURUGr1XLhwgU8PT3z7HxiYmKoXr06lpaW6r5GjRqh1WqJjY2lRIkSAFSuXBlDQ0O1jJOTE3/99Ve2bd+/fx8rKyu0Wi3Jyck0btyYZcuW8ejRI+Lj4+nTpw/9+vVTyz979gxbW1udNqpVq6bTJ8CNGzeoWLEiaWlpTJs2jXXr1nH16lWePn1KSkoKFhYWuboGdevWpXLlyqxYsYIxY8bw888/4+LiQtOmTbOtFxISgpWVFampqWi1Wrp27cqkSZMA2LlzJ9OnT+fMmTM8ePCAZ8+ekZyczOPHj9X4TExMdM4P4Pr164wbN47w8HBu3LhBWloajx8/5vLly1lel/SfUdWqVXX2JScn8+DBA2xsbDhx4gT79u3TGUlKS0vLENPL9K3n5eWV7bUSQoiCJomUEEIUMEtLS8qXLw/Ajz/+SPXq1Vm+fDl9+vRR74HZsmULJUuW1Klnamqqdx9JSUn079+fwYMHZzhWpkyZfxD9q3t58QCNRoNWq822jrW1NceOHcPAwAAnJyd1IYzr168DsHTpUurVq6dT58Vk7eV+0+8FS+935syZzJ07lzlz5qj3Iw0dOlSdXpcbffv2ZeHChYwZM4bAwEB69eql9peV9KTaxMQEZ2dnjIyef0xfvHiRd955h88++4ypU6dib2/P3r176dOnD0+fPlWTD3Nz8wx99OjRg9u3bzN37lxcXFwwNTWlQYMGGc4ps+uS3bVKSkrC39+fjh07ZjgPMzOzLM9R33ovJuJCCFEYSSIlhBCFiIGBAV999RXDhw+na9euOosGeHt7Z1v3wIEDalJ09+5dzp49q4401apVi+joaDVhy4yJiQlpaWnZ9qFPGU9PT4KCgnj06JH6ZXjfvn0YGBhQoUKFbOvmxMDAINNzKFGiBM7Ozpw/f55u3bq9cvv79u3j3Xff5aOPPgKeJw1nz56lUqVKWdbJ6pp89NFHjBo1innz5hEdHU2PHj1y7P/FpPpFR48eRavVEhAQoK7Ct27dOr3P6fvvv6d169YAXLlyhVu3bulVNzu1atUiNjY22+eUsbFxhmujTz0hhHgTyGITQghRyHzwwQcYGhqycOFCrK2tGTlyJMOGDWPFihXEx8dz7Ngx5s+fn2ERhcmTJxMWFsapU6fo2bMnxYoVo3379gCMHj2a/fv3M2jQIKKiojh37hzBwcE6iwe4urqye/durl69muUXbVdXVy5cuEBUVBS3bt0iJSUlQ5lu3bphZmZGjx49OHXqFLt27eLzzz+ne/fu6pSx/ODv78/06dOZN28eZ8+e5a+//iIwMJDvvvtO7zbc3d0JDQ1l//79xMTE0L9/f3W0KytZXTc7Ozs6duzIF198wdtvv02pUqVe+dzKly9Pamoq8+fP5/z586xcuZJFixbpfU4rV64kJiaGgwcP0q1bN72WtM/JhAkT+Omnn/D39+f06dPExMSwZs0axo0bp5ZxdXUlLCyMxMRE7t69q3c9IYR4E0giJYQQhYyRkRGDBg3i22+/5dGjR0yZMoXx48czffp0PD098fPzY8uWLbi5uenUmzFjBkOGDKF27dokJiby+++/q6v/VatWjYiICM6ePUuTJk2oWbMmEyZMwNnZWa0/efJkLl68SLly5XBwcMg0tvfeew8/Pz+aNWuGg4NDpkuDW1hYsH37du7cuUOdOnV4//33ad68OQsWLMjDq5RR3759WbZsGYGBgVStWhVvb2+CgoIyXKfsjBs3jlq1auHr64uPjw+Ojo5qMpqV7K5b+tS7f7rqXPXq1fnuu+/45ptvqFKlCqtWrWL69Ol61V2+fDl3796lVq1adO/encGDB1O8ePF/FA+Ar68vISEh7Nixgzp16lC/fn1mz56Ni4uLWiYgIIDQ0FBKly6t3tOnTz0hhHgTaBRFUQo6CCGEEK8uPDycZs2acffuXfVvPonCYeXKlQwbNoxr167pLGkvhBDizSf3SAkhhBB57PHjxyQkJDBjxgz69+8vSZQQQvwLydQ+IYQQIo99++23VKxYEUdHR7788suCDkcIIUQ+kKl9QgghhBBCCJFLMiIlhBBCCCGEELkkiZQQQgghhBBC5JIkUkIIIYQQQgiRS5JICSGEEEIIIUQuSSIlhBBCCCGEELkkiZQQQgghhBBC5JIkUkIIIYQQQgiRS0YFHYAQ4t8pLS2N1NTUgg5DCCH+E4yNjTE0NCzoMIT4T5FESgiRpxRFITExkXv37hV0KEII8Z9SpEgRHB0d0Wg0BR2KEP8JkkgJIfJUehJVvHhxLCws5ANdCCHymaIoPH78mBs3bgDg5ORUwBEJ8d8giZQQIs+kpaWpSVTRokULOhwhhPjPMDc3B+DGjRsUL15cpvkJ8RrIYhNCiDyTfk+UhYVFAUcihBD/PenvvXJ/qhCvhyRSQog8J9P5hBDi9ZP3XiFeL0mkhBBCCCGEECKXJJESQogC1LNnT9q3b//K9cPDw9FoNLJKYi65uroyZ86cgg5DCCHEG0wSKSFEoZOmVYiMv01w1FUi42+TplVeS78LFy7E1dUVMzMz6tWrx6FDh9RjS5YswcfHBxsbmzxNXObOnUtQUFCG/b169aJr165YWFiwevVqnWNarZaGDRvy/vvv07BhQxISErC1tc2yj4SEBLp27YqHhwcGBgYMHTpUr9g0Gk2Gbc2aNdnW2bBhA15eXhQpUgRLS0tq1KjBypUrsyz/6aefotFoMiQ1L/ZpZGREmTJlGD58OCkpKTnG/eDBA8aOHUvFihUxMzPD0dGRFi1asGHDBhQl755LkyZNokaNGnnWnhBCiDeLrNonhChUtp1KwP/3aBLuJ6v7nGzNmNi2En5V8m9J37Vr1zJ8+HAWLVpEvXr1mDNnDr6+vsTGxlK8eHEeP36Mn58ffn5+fPnll3nWb2YJUFpaGiEhIWzZsoX69evz+eef06xZM3VJ44CAAM6fP8/mzZsxMTHB0dEx2z5SUlJwcHBg3LhxzJ49O1fxBQYG4ufnpz4uUqRItuXt7e3VJMbExISQkBB69epF8eLF8fX11Sm7ceNGDhw4gLOzc7Z9p6amcuLECXr16oWlpSVTpkzJsv979+7RuHFj7t+/z9dff02dOnUwMjIiIiKCUaNG8dZbb+V4DkIIIYReFCGEyCNPnjxRoqOjlSdPnrxS/a1/XVNcR4coLi9trv/btv51LY8j/n9169ZVBg4cqD5OS0tTnJ2dlenTp+uU27VrlwIod+/ezbHNZ8+eKb1791ZcXV0VMzMzxcPDQ5kzZ45OmR49eijvvvuuzr7du3crTk5OilarVbRardKsWTOlTZs2iqIoSkxMjGJmZqYEBwfnOh5FURRvb29lyJAhepUFlI0bN+pVNjs1a9ZUxo0bp7Pv77//VkqWLKmcOnVKcXFxUWbPnp1j33369FFat26dbV+fffaZYmlpqVy9ejXDsYcPHyqpqamKoiiKi4uLMnXqVKVXr16KlZWVUrp0aWXx4sU65UeNGqW4u7sr5ubmipubmzJu3Djl6dOniqIoSmBgoALobIGBgXpcDSHyzz99DxZC5I5M7RNC5CtFUXj89FmO28PkVCZuPk1mE6/S903aHM3D5FS92lNyMYXr6dOnHD16lBYtWqj7DAwMaNGiBZGRka987lqtllKlSrF+/Xqio6OZMGECX331FevWrcu23ubNm2nbtq06tS0wMJA9e/awdOlSevbsyYcffki7du1eOa7cGDhwIMWKFaNu3br8+OOPubquiqIQFhZGbGwsTZs2VfdrtVq6d+/OF198QeXKlfVq6+zZs/z555/Uq1cvyzJarZY1a9bQrVu3TEe5rKysMDL6/4kYAQEBeHl5cfz4cQYMGMBnn31GbGysetza2pqgoCCio6OZO3cuS5cuVUf0OnfuzIgRI6hcuTIJCQkkJCTQuXNnvc5FCCHEv4NM7RNC5KsnqWlUmrD9H7ejAIkPkqk6aYde5aMn+2Jhot9b3K1bt0hLS6NEiRI6+0uUKMGZM2dyG6rK2NgYf39/9bGbmxuRkZGsW7eOTp06ZVkvODhYZwqei4sLc+bMoW/fvpQqVYodO/S7Bv/U5MmTeeutt7CwsGDHjh0MGDCApKQkBg8enG29+/fvU7JkSVJSUjA0NOT777+nZcuW6vFvvvkGIyOjHNvp0qULhoaGPHv2jJSUFN55551sp1XeunWLu3fvUrFiRb3Or3Xr1gwYMACA0aNHM3v2bHbt2kWFChUAGDdunFrW1dWVkSNHsmbNGkaNGoW5ubmamOU0tVIIIcS/k4xICSFEHmjVqhVWVlZYWVnpjLIsXLiQ2rVr4+DggJWVFUuWLOHy5ctZthMTE8O1a9do3ry5zv5evXrh5OTE559/jo2NTZb102OwsrLi008//UfnNH78eBo1akTNmjUZPXo0o0aNYubMmQBcvnxZp69p06ap9aytrYmKiuLw4cNMnTqV4cOHEx4eDsDRo0fVBTZy+ps3s2fPJioqihMnThASEsLZs2fp3r17lv3nZrQMoFq1aur/NRoNjo6O3LhxQ923du1aGjVqhKOjI1ZWVowbNy7bn50QQoj/FhmREkLkK3NjQ6In++ZY7tCFO/QMPJxjuaBedajrZq9Xv/oqVqwYhoaGXL9+XWf/9evX9R5tWLZsGU+ePAGej0QBrFmzhpEjRxIQEECDBg2wtrZm5syZHDx4MMt2Nm/eTMuWLTEzM8twzMjISGdqWmaioqLU/2eXcL2KevXqMWXKFFJSUnB2dtbpy97+/38mBgYGlC9fHoAaNWoQExPD9OnT8fHxYc+ePdy4cYMyZcqo5dPS0hgxYgRz5szh4sWL6n5HR0e1nQoVKvDw4UO6dOnC119/jaura4b+ixQpQpEiRfQeRUz/OaXTaDRotVoAIiMj6datG/7+/vj6+mJra8uaNWsICAjQq20hhBD/fpJICSHylUaj0WuKXRN3B5xszUi8n5zpfVIawNHWjCbuDhgaZD+SkVsmJibUrl2bsLAw9W86abVawsLCGDRokF5tlCxZMsO+ffv20bBhQ3X6GEB8fHy27QQHB/PJJ5/oH/xL0hOP/BAVFYWdnR2mpqa56kur1arLlnfv3l3nXjQAX19funfvTq9evbJtx9DweXL85MkTjIyMMu3/ww8/ZOXKlUycODHDfVJJSUmYmZnlmIwC7N+/HxcXF8aOHavuu3Tpkk4ZExMT0tLScmxLCCHEv5MkUkKIQsHQQMPEtpX47OdjaEAnmUpPmya2rZTnSVS64cOH06NHD7y8vKhbty5z5szh0aNH6pf7xMREEhMTiYuLA+Cvv/7C2tqaMmXK6IzGvMjd3Z2ffvqJ7du34+bmxsqVKzl8+DBubm6Zlr9x4wZHjhxh8+bNeX5+6aM3SUlJ3Lx5k6ioKExMTKhUqRLwfCnyL7/8Uh3N+f3337l+/Tr169fHzMyM0NBQpk2bxsiRI7PtZ/r06Xh5eVGuXDlSUlL4448/WLlyJT/88AMARYsWpWjRojp1jI2NcXR0VO9NSnfv3j0SExPRarWcO3eOyZMn4+HhgaenZ5b9T506lfDwcOrVq8fUqVPx8vLC2NiYPXv2MH36dA4fPqzX8ufu7u5cvnyZNWvWUKdOHbZs2cLGjRt1yri6unLhwgWioqIoVaoU1tbWapIphBDi308SKSFEoeFXxYkfPqqV4e9IOb6GvyPVuXNnbt68yYQJE0hMTKRGjRps27ZNXYBi0aJFOgtHpK9CFxgYSM+ePTNts3///hw/fpzOnTuj0Wjo0qULAwYMYOvWrZmW//3336lbty7FihXL25MDatasqf7/6NGjrF69GhcXF3Uq3f3793VWrDM2NmbhwoUMGzYMRVEoX7483333Hf369cu2n0ePHjFgwAD+/vtvzM3NqVixIj///PMrrWiXnsSm37/UtGlTpk2blu2Ikr29PQcOHGDGjBl8/fXXXLp0CTs7O6pWrcrMmTOz/cPFL2rXrh3Dhg1j0KBBpKSk0KZNG8aPH8+kSZPUMu+99x4bNmygWbNm3Lt3L9vnghBCiH8fjZLbu3OFECILycnJXLhwATc3t0zv8dFXmlbh0IU73HiYTHFrM+q62efbSFRh0q5dOxo3bsyoUaMKOhQhxBsor96DhRD6kREpIUShY2igoUG5ojkX/Jdp3LgxXbp0KegwhBBCCKEHSaSEEKKQkJEoIYQQ4s0hf0dKCCGEEEIIIXJJEikhhBBCCCGEyCVJpIQQQgghhBAilySREkIIIYQQQohckkRKCCGEEEIIIXJJEikhhBBCCCGEyCVJpIQQQgghhBAilySREkKIAtSzZ0/at2//yvXDw8PRaDTcu3cvz2J6002aNIkaNWoUdBhCCCH+5SSREkKI/1m4cCGurq6YmZlRr149Dh06pB5bsmQJPj4+2NjY5GniMnfuXIKCgjLs79WrF127dsXCwoLVq1frHNNqtTRs2JD333+fhg0bkpCQgK2tbZZ9JCQk0LVrVzw8PDAwMGDo0KF6xabRaDJsa9asybZOamoqkydPply5cpiZmVG9enW2bdumU6Znz546bRYtWhQ/Pz9OnjypV1y//fYbPj4+2NraYmVlRbVq1Zg8eTJ37tzRq74+Ll68iEajISoqKs/aFEII8e8iiZQQovDYNR0ivs38WMS3z4/nk7Vr1zJ8+HAmTpzIsWPHqF69Or6+vty4cQOAx48f4+fnx1dffZWn/dra2lKkSBGdfWlpaYSEhDB06FBmzJjB559/TkJCgno8ICCA8+fPs2jRIkxMTHB0dESj0WTZR0pKCg4ODowbN47q1avnKr7AwEASEhLULafRs3HjxrF48WLmz59PdHQ0n376KR06dOD48eM65fz8/NQ2w8LCMDIy4p133skxnrFjx9K5c2fq1KnD1q1bOXXqFAEBAZw4cYKVK1fm6tyEEEKIf0QRQog88uTJEyU6Olp58uTJqzUQ/o2iTLR5/q8++/NQ3bp1lYEDB6qP09LSFGdnZ2X69Ok65Xbt2qUAyt27d3Ns89mzZ0rv3r0VV1dXxczMTPHw8FDmzJmjU6ZHjx7Ku+++q7Nv9+7dipOTk6LVahWtVqs0a9ZMadOmjaIoihITE6OYmZkpwcHBuY5HURTF29tbGTJkiF5lAWXjxo16lU3n5OSkLFiwQGdfx44dlW7duqmPMzvnPXv2KIBy48aNLNs+ePCgAmS4hunSr8HEiROV6tWrKz/99JPi4uKi2NjYKJ07d1YePHiglt26davSqFEjxdbWVrG3t1fatGmjxMXFqccBnc3b21vPKyBEwfnH78FCiFyRESkhRP5SFHj6SL+twUBo+gXsmgp/fv18359fP3/c9Ivnx/VtS1H0DvHp06ccPXqUFi1aqPsMDAxo0aIFkZGRr3zqWq2WUqVKsX79eqKjo5kwYQJfffUV69aty7be5s2badu2rTr1LTAwkD179rB06VJ69uzJhx9+SLt27V45rtwYOHAgxYoVo27duvz4448oOVzXlJQUzMzMdPaZm5uzd+/eLOskJSXx888/U758eYoWLZpluVWrVmFlZcWAAQMyPf7iyF58fDybNm0iJCSEkJAQIiIimDFjhnr80aNHDB8+nCNHjhAWFoaBgQEdOnRAq9UCqNM6d+7cSUJCAhs2bMj2vIUQQvz3GBV0AEKIf7nUxzDNOff1ds98vmX1OCdfXQMTS72K3rp1i7S0NEqUKKGzv0SJEpw5c0b/Pl9ibGyMv7+/+tjNzY3IyEjWrVtHp06dsqwXHBzM7Nmz1ccuLi7MmTOHvn37UqpUKXbs2PHKMeXG5MmTeeutt7CwsGDHjh0MGDCApKQkBg8enGUdX19fvvvuO5o2bUq5cuUICwtjw4YNpKWl6ZQLCQnBysoKeJ7UODk5ERISgoFB1r/fO3fuHGXLlsXY2DjH2LVaLUFBQVhbWwPQvXt3wsLCmDp1KgDvvfeeTvkff/wRBwcHoqOjqVKlCg4ODgAULVoUR0fHHPsTQgjx3yMjUkIIkQdatWqFlZUVVlZWVK5cWd2/cOFCateujYODA1ZWVixZsoTLly9n2U5MTAzXrl2jefPmOvt79eqFk5MTn3/+OTY2NlnWT4/BysqKTz/99B+d0/jx42nUqBE1a9Zk9OjRjBo1ipkznyezly9f1ulr2rRpwPPFM9zd3alYsSImJiYMGjSIXr16ZUiQmjVrRlRUFFFRURw6dAhfX19atWrFpUuXgMyvZ06jYS9ydXVVkygAJycn9X43eJ6UdenShbJly2JjY4Orq6t6XkIIIYQ+ZERKCJG/jC2ejw7lxt7Zz0efDE0g7enzaX2Nh+W+Xz0VK1YMQ0NDrl+/rrP/+vXreo9GLFu2jCdPnjzv+n8jJmvWrGHkyJEEBATQoEEDrK2tmTlzJgcPHsyync2bN9OyZcsM0+MAjIyMMDLK/m37xVXmsku4XkW9evWYMmUKKSkpODs76/Rlb28PgIODA5s2bSI5OZnbt2/j7OzMmDFjKFu2rE5blpaWlC9fXn28bNkybG1tWbp0KV9//XWm19PDw4O9e/eSmpqa46jUy8c1Go06bQ+gbdu2uLi4sHTpUpydndFqtVSpUoWnT5/m/sIIIYT4T5JESgiRvzQavafYAc9X59s9E5qNBe9R/1utb+rzpMp7VL6EaGJiQu3atQkLC1NXpdNqtYSFhTFo0CC92ihZsmSGffv27aNhw4Y69/TEx8dn205wcDCffPKJ/sG/5MXkJK9FRUVhZ2eHqalpjn2ZmZlRsmRJUlNT+e2337KdygjPEx0DAwM1ecrsenbt2pV58+bx/fffM2TIkAzH7927l2EFxMzcvn2b2NhYli5dSpMmTQAy3MNlYmICkGFKohBCCJFOEikhROGRnjSlJ1Hw///umqr7OI8NHz6cHj164OXlRd26dZkzZw6PHj2iV69eACQmJpKYmEhcXBwAf/31F9bW1pQpU0YdjXmZu7s7P/30E9u3b8fNzY2VK1dy+PBh3NzcMi1/48YNjhw5wubNm/P8/NJHj5KSkrh58yZRUVGYmJhQqVIlADZu3MiXX36p3hP2+++/c/36derXr4+ZmRmhoaFMmzaNkSNHZtvPwYMHuXr1KjVq1ODq1atMmjQJrVbLqFG6P7eUlBQSExMBuHv3LgsWLCApKYm2bdtm2Xa9evUYNWoUI0aM4OrVq3To0AFnZ2fi4uJYtGgRjRs3zjTBepmdnR1FixZlyZIlODk5cfnyZcaMGaNTpnjx4pibm7Nt2zZKlSqFmZlZtn+rSwghxH+PJFJCiMJDm6abRKVLf6zNv9GBzp07c/PmTSZMmEBiYiI1atRg27Zt6gIUixYt0lk4omnTpsDzv7PUs2fPTNvs378/x48fp3Pnzmg0Grp06cKAAQPYunVrpuV///136tatS7FixfL25ICaNWuq/z969CirV6/GxcWFixcvAnD//n1iY2PVMsbGxixcuJBhw4ahKArly5fnu+++o1+/ftn2k5yczLhx4zh//jxWVla0bt2alStXZhgp2rZtG05OTgBYW1tTsWJF1q9fj4+PT7btf/PNN9SuXZuFCxeyaNEitFot5cqV4/3336dHjx56XQsDAwPWrFnD4MGDqVKlChUqVGDevHk6fRsZGTFv3jwmT57MhAkTaNKkCeHh4Xq1L4QQ4r9Bo+Tm7l0hhMhGcnIyFy5cwM3NLdN7fET22rVrR+PGjTOM3gghhD7kPViI10tW7RNCiEKicePGdOnSpaDDEEIIIYQeZGqfEEIUEjISJYQQQrw5ZERKCCGEEEIIIXJJEikhhBBCCCGEyCVJpIQQQgghhBAilySREkIIIYQQQohckkRKCCGEEEIIIXJJEikhhBBCCCGEyCVJpIQQQgghhBAilySREkIIIYQQQohckkRKCCH+58qVK/Tu3RtnZ2dMTExwcXFhyJAh3L59u6BD49KlS5ibm5OUlATAnTt3GDp0KC4uLpiYmODs7Ezv3r25fPlygcZ58eJF+vTpg5ubG+bm5pQrV46JEyfy9OnTbOv17NkTjUaTYatcubJaZvr06dSpUwdra2uKFy9O+/btiY2N1WnH1dVVrWtoaIizszN9+vTh7t272fYfFBREkSJFXvm8Mzuf9u3b51l7OdFoNGzatOm19SeEEEISKSFEIRV5LZJ3N71L5LXI19Lf+fPn8fLy4ty5c/zyyy/ExcWxaNEiwsLCaNCgAXfu3HktcWQlODiYZs2aYWVlxZ07d6hfvz47d+5k0aJFxMXFsWbNGuLi4qhTpw7nz58vsDjPnDmDVqtl8eLFnD59mtmzZ7No0SK++uqrbOvNnTuXhIQEdbty5Qr29vZ88MEHapmIiAgGDhzIgQMHCA0NJTU1lbfffptHjx7ptDV58mQSEhK4fPkyq1atYvfu3QwePDhfzvefSk1NLegQhBBCvCpFCCHyyJMnT5To6GjlyZMn/6gdrVardP69s1IlqIrS+ffOilarzaMIs+bn56eUKlVKefz4sc7+hIQExcLCQvn000+V+fPnK5UrV1aPbdy4UQGUH374Qd3XvHlzZezYserjTZs2KTVr1lRMTU0VNzc3ZdKkSUpqaqp6HFCWLl2qtG/fXjE3N1fKly+vBAcHZ4jvrbfeUvv59NNPFUtLSyUhIUGnzOPHj5WSJUsqfn5+iqIoyu+//67Y2toqz549UxRFUY4fP64AyujRo9U6ffr0Ubp166Y+3rNnj9K4cWPFzMxMKVWqlPL5558rSUlJ6nEXFxdl6tSpSq9evRQrKyuldOnSyuLFi7O9tt9++63i5uaWbZmXbdy4UdFoNMrFixezLHPjxg0FUCIiInTimz17tk65KVOmKJUqVcq2v8DAQMXW1lZ9PHHiRKV69erKTz/9pLi4uCg2NjZK586dlQcPHqhl1q9fr1SpUkUxMzNT7O3tlebNmytJSUnKxIkTFUBn27Vrl3LhwgUFUNasWaM0bdpUMTU1VQIDA9W+XjR79mzFxcVFZ9/y5cuVSpUqKSYmJoqjo6MycOBA9Zxf7OvleuK/I6/eg4UQ+pERKSFEvlIUhcepj3O17bq8i9O3TwNw+vZpdl3eles2FEXRO8Y7d+6wfft2BgwYgLm5uc4xR0dHunXrxtq1a/H29iY6OpqbN28Cz0dIihUrRnh4OPB8dCEyMhIfHx8A9uzZw8cff8yQIUOIjo5m8eLFBAUFMXXqVJ0+/P396dSpEydPnqR169Z069ZNZwTs3r177N27l3bt2qHValmzZg3dunXD0dFRpx1zc3MGDBjA9u3buXPnDk2aNOHhw4ccP34803jT96XHGx8fj5+fH++99x4nT55k7dq17N27l0GDBun0ExAQgJeXF8ePH2fAgAF89tlnGabYvej+/fvY29tn/0N4yfLly2nRogUuLi7Ztgtk2/bVq1f5/fffqVevXq76h+fXY9OmTYSEhBASEkJERAQzZswAICEhgS5dutC7d29iYmIIDw+nY8eOKIrCyJEj6dSpE35+fuoIW8OGDdV2x4wZw5AhQ4iJicHX11evWH744QcGDhzIJ598wl9//cXmzZspX748AIcPHwYgMDCQhIQE9bEQQoj8ZVTQAQgh/t2ePHtCvdW5/xL7oiHhQ3Jd52DXg1gYW+hV9ty5cyiKgqenZ6bHPT09uXv3LsWLF8fe3p6IiAjef/99wsPDGTFiBHPnzgXg0KFDpKamql+a/f39GTNmDD169ACgbNmyTJkyhVGjRjFx4kS1/Z49e9KlSxcApk2bxrx58zh06BB+fn4A/PHHH1SrVg1nZ2euX7/OvXv3so1VURTi4uKoW7cuNWrUIDw8HC8vL8LDwxk2bBj+/v4kJSVx//594uLi8Pb2Bp7fg9StWzeGDh0KgLu7O/PmzcPb25sffvgBMzMzAFq3bs2AAQMAGD16NLNnz2bXrl1UqFAhQzxxcXHMnz+fWbNm6fWzALh27Rpbt25l9erVWZbRarUMHTqURo0aUaVKFZ1jo0ePZty4caSlpZGcnEy9evX47rvv9O7/xT6CgoKwtrYGoHv37oSFhTF16lQSEhJ49uwZHTt2VJO9qlWrqnXNzc1JSUnJkOwCDB06lI4dO+Yqlq+//poRI0YwZMj/vxbq1KkDgIODAwBFihTJtD8hhBD5Q0akhBDif3IaxTI1NaVp06aEh4dz7949oqOjGTBgACkpKZw5c4aIiAjq1KmDhcXzBO7EiRNMnjwZKysrdevXrx8JCQk8fvxYbbdatWrq/y0tLbGxseHGjRvqvuDgYNq1a5erWE1MTADw9vYmPDwcRVHYs2cPHTt2xNPTk7179xIREYGzszPu7u5qvEFBQTrx+vr6otVquXDhQqbxajQaHB0ddeJNd/XqVfz8/Pjggw/o16+fuv/F9j/99NMM9VasWEGRIkWyXaxh4MCBnDp1ijVr1mQ49sUXXxAVFcXJkycJCwsDoE2bNqSlpenVfzpXV1c1iQJwcnJSz7N69eo0b96cqlWr8sEHH7B06dIcF7RI5+XlpVe5dDdu3ODatWs0b948V/WEEELkLxmREkLkK3Mjcw52PahXWUVR6LW9F7F3Y9EqWnW/gcaACnYVCPQNRKPR6N2vvsqXL49GoyEmJoYOHTpkOB4TE4ODgwNFihTBx8eHJUuWsGfPHmrWrImNjY2aXEVERKijOwBJSUn4+/tnOvqQProDYGxsrHNMo9Gg1T4//6dPn7Jt2zZ1sYb0OGJiYjI9l5iYGIyMjHBzcwPAx8eHH3/8kRMnTmBsbEzFihXx8fEhPDycu3fvZoi3f//+mS7MUKZMGb3iTXft2jWaNWtGw4YNWbJkic6xqKgo9f82NjY6xxRF4ccff6R79+5qMviyQYMGERISwu7duylVqlSG48WKFVOnvbm7uzNnzhwaNGjArl27aNGiRbb9vyi78zQ0NCQ0NJT9+/ezY8cO5s+fz9ixYzl48KB67bNiaWmp89jAwCBDYvziIhQvTzcVQghROMiIlBAiX2k0GiyMLfTaom5GEXMnRieJAtAqWmLuxBB1M0rvtvRNuACKFi1Ky5Yt+f7773ny5InOscTERFatWkXPnj0B1Puk1q9fr95b5OPjw86dO9m3b5+6D6BWrVrExsZSvnz5DJuBgX5vv+Hh4djZ2VG9enXg+ZfuTp06sXr1ahITE3XKPnnyhO+//54OHTpga2sLoN4nNXv2bDVpSk+kwsPDM8QbHR2dabxZJTWZuXr1Kj4+PtSuXZvAwMAM5/piu8WLF9c5FhERQVxcHH369MnQrqIoDBo0iI0bN/Lnn3/mmLCkMzQ0VK9PTv3nhkajoVGjRvj7+3P8+HFMTEzYuHEj8HxEMH0ELCcODg4kJibqJFMvJnvW1ta4urqqo2uZMTY21rs/IYQQeUMSKSFEoaAoCvOPz0dD5gmQBg3zj8/P1SISubFgwQJSUlLw9fVl9+7dXLlyhW3bttGyZUs8PDyYMGEC8Hxam52dHatXr9ZJpDZt2kRKSgqNGjVS25wwYQI//fQT/v7+nD59mpiYGNasWcO4ceP0jmvz5s0ZpvVNnToVR0dHWrZsydatW7ly5Qq7d+/G19cXAwMD9Z4tADs7O6pVq8aqVavUeJs2bcqxY8c4e/aszojU6NGj2b9/P4MGDSIqKopz584RHBycYbGJ7KQnUWXKlGHWrFncvHmTxMTEDElfVpYvX069evUy3PcEz6fz/fzzz6xevRpra2u13ZeT34cPH5KYmEhCQgKHDh3iiy++wMHBQWfBh3/q4MGDTJs2jSNHjnD58mU2bNjAzZs31XvXXF1dOXnyJLGxsdy6dSvbZc59fHy4efMm3377LfHx8SxcuJCtW7fqlJk0aRIBAQHMmzePc+fOcezYMebPn68eT0+0EhMT9Z5iKIQQ4p+RREoIUSikalNJfJSIQuaJkoJC4qNEUrX583d33N3dOXz4MGXLlqVTp064uLjQqlUrPDw82LdvH1ZWVsDzUYgmTZqg0Who3Lgx8Dy5srGxwcvLS2falq+vLyEhIezYsYM6depQv359Zs+ene1KdC/LLJEqVqwYBw4coFmzZvTv3x83Nze8vb1JS0sjKioKJycnnfLpx9ITKXt7eypVqoSjo6POAhHVqlUjIiKCs2fP0qRJE2rWrMmECRNwdnbWO97Q0FDi4uIICwujVKlSODk5qVtO7t+/z2+//ZbpaBQ8X7nu/v37+Pj46LS7du1anXITJkzAyckJZ2dn3nnnHSwtLdmxYwdFixbV+zxyYmNjw+7du2ndujUeHh6MGzeOgIAAWrVqBUC/fv2oUKECXl5eODg4sG/fvizb8vT05Pvvv2fhwoVUr16dQ4cOMXLkSJ0yPXr0YM6cOXz//fdUrlyZd955h3PnzqnHAwICCA0NpXTp0tSsWTPPzlMIIUTWNEp+/XpXCPGfk5yczIULF3Bzc9O5B0hfiY8SuZOc9R++tTezx9Hy9a1KNnHiRL777jtCQ0OpX7/+a+s33bFjx3jrrbe4efNmhvt1XrZ8+XIGDBjA2rVrs12kQQjx7/VP34OFELkji00IIQoNR0vH15oo5cTf3x9XV1cOHDhA3bp19b6vKa88e/aM+fPn55hEAfTp0wd7e3v1bxPJAgVCCCFE/pIRKSFEnpHfhgohRMGR92AhXi+5R0oIIYQQQgghckkSKSGEEEIIIYTIJUmkhBBCCCGEECKXJJESQgghhBBCiFySREoIIYQQQgghckkSKSGEEEIIIYTIJUmkhBBCCCGEECKXJJESQgghhBBCiFySREoIUWikXrvGk9Ons9xSr13L1/6vXLlC7969cXZ2xsTEBBcXF4YMGcLt27fztV99XLp0CXNzc5KSkgC4c+cOQ4cOxcXFBRMTE5ydnenduzeXL18u0DgvXrxInz59cHNzw9zcnHLlyjFx4kSePn2abb2ePXui0WgybJUrV86yTNGiRfHz8+PkyZM5xqTRaIiKisqLUyQoKIgiRYrkSVv68PHxYejQoa+tPyGEEPoxKugAhBACnidR8X6tULL5wq0xMaHctq0YOzvnef/nz5+nQYMGeHh48Msvv+Dm5sbp06f54osv2Lp1KwcOHMDe3j7P+9VXcHAwzZo1w8rKijt37lC/fn1MTExYtGgRlStX5uLFi4wbN446deoQGRlJ2bJlCyTOM2fOoNVqWbx4MeXLl+fUqVP069ePR48eMWvWrCzrzZ07lxkzZqiPnz17RvXq1fnggw90yvn5+REYGAhAYmIi48aN45133inwBDIzT58+xcTEpKDDEEIIkU9kREoIUSg8u3s32yQKQHn6lGd37+ZL/wMHDsTExIQdO3bg7e1NmTJlaNWqFTt37uTq1auMHTuWBQsWUKVKFbXOpk2b0Gg0LFq0SN3XokULxo0bpz4ODg6mVq1amJmZUbZsWfz9/Xn27Jl6XKPRsGzZMjp06ICFhQXu7u5s3rw5Q3zBwcG0a9cOgLFjx3Lt2jV27txJq1atKFOmDE2bNmX79u0YGxszcOBAAEJCQihSpAhpaWkAREVFodFoGDNmjNpu3759+eijj9THe/fupUmTJpibm1O6dGkGDx7Mo0eP1OOurq5MmzaN3r17Y21tTZkyZViyZIl6PD3Refvttylbtizt2rVj5MiRbNiwIdvrb2tri6Ojo7odOXKEu3fv0qtXL51ypqamapkaNWowZswYrly5ws2bN7Nt/0Xh4eFoNBrCwsLw8vLCwsKChg0bEhsbq5Y5ceIEzZo1w9raGhsbG2rXrs2RI0cIDw+nV69e3L9/Xx0ZmzRpknptpkyZwscff4yNjQ2ffPKJ2te9e/fUttN/DhcvXlT37du3Dx8fHywsLLCzs8PX15e7d+/Ss2dPIiIimDt3rtrfi/WEEEIUHEmkhBD5SlEUtI8f57gpycn6tZecrF97iqJ3jHfu3GH79u0MGDAAc3NznWOOjo5069aNtWvX4u3tTXR0tPqlPSIigmLFihEeHg5AamoqkZGR+Pj4ALBnzx4+/vhjhgwZQnR0NIsXLyYoKIipU6fq9OHv70+nTp04efIkrVu3plu3bty5c0c9fu/ePfbu3Uu7du3QarWsWbOGbt264ejoqNOOubk5AwYMYPv27dy5c4cmTZrw8OFDjh8/nmm86fvS442Pj8fPz4/33nuPkydPsnbtWvbu3cugQYN0+gkICMDLy4vjx48zYMAAPvvsM50k5GX379/P9Wje8uXLadGiBS4uLlmWSUpK4ueff6Z8+fIULVo0V+3D84Q0ICCAI0eOYGRkRO/evdVj3bp1o1SpUhw+fJijR48yZswYjI2NadiwIXPmzMHGxoaEhAQSEhIYOXKkWm/WrFlUr16d48ePM378eL3iiIqKonnz5lSqVInIyEj27t1L27ZtSUtLY+7cuTRo0IB+/fqp/ZUuXTrX5yqEECLvydQ+IUS+Up48IbZW7Txr71K3j3IuBFQ4dhSNhYVeZc+dO4eiKHh6emZ63NPTk7t371K8eHHs7e2JiIjg/fffJzw8nBEjRjB37lwADh06RGpqKg0bNgSeJ0hjxoyhR48eAJQtW5YpU6YwatQoJk6cqLbfs2dPunTpAsC0adOYN28ehw4dws/PD4A//viDatWq4ezszPXr17l37162sSqKQlxcHHXr1qVGjRqEh4fj5eVFeHg4w4YNw9/fn6SkJO7fv09cXBze3t4ATJ8+nW7duqn347i7uzNv3jy8vb354YcfMDMzA6B169YMGDAAgNGjRzN79mx27dpFhQoVMsQTFxfH/Pnzs53W97Jr166xdetWVq9eneFYSEgIVlZWADx69AgnJydCQkIwMMj97wWnTp2qnvuYMWNo06YNycnJmJmZcfnyZb744gsqVqwIPL8W6WxtbdFoNBkSWYC33nqLESNGqI+vXLmSYxzffvstXl5efP/99+q+F+8NMzExwcLCItP+hBBCFBwZkRJCiP/JaRTL1NSUpk2bEh4ezr1794iOjmbAgAGkpKRw5swZIiIiqFOnDhb/S+BOnDjB5MmTsbKyUrf0kYXHjx+r7VarVk39v6WlJTY2Nty4cUPd9+K0Pn1jTb83x9vbm/DwcBRFYc+ePXTs2BFPT0/27t1LREQEzs7OapJw4sQJgoKCdOL19fVFq9Vy4cKFTONNTyhejDfd1atX8fPz44MPPqBfv37q/hfb//TTTzPUW7FiBUWKFKF9+/YZjjVr1oyoqCiioqI4dOgQvr6+tGrVikuXLgHQqlUrte0Xk5HMvHgeTk5OAOp5DB8+nL59+9KiRQtmzJhBfHx8tm2l8/Ly0qvci9JHpIQQQrxZZERKCJGvNObmVDh2NMdyyTExeo02uaz6GbMsRmNe7ldf5cuXR6PREBMTQ4cOHTIcj4mJwcHBgSJFiuDj48OSJUvYs2cPNWvWxMbGRk2uIiIi1BEOeD71zN/fn44dO2ZoM310B8DY2Fg3do0GrVYLPF+wYNu2bXz11VcAahwxMTGZnktMTAxGRka4ubkBz1d8+/HHHzlx4gTGxsZUrFgRHx8fwsPDuXv3boZ4+/fvz+DBgzO0W6ZMGb3iTXft2jWaNWtGw4YNde6hAnRWz7OxsdE5pigKP/74I927d890oQZLS0vKly+vPl62bBm2trYsXbqUr7/+mmXLlvHkyZNM43zZi8c1Gg2Aeh6TJk2ia9eubNmyha1btzJx4kTWrFmT6fPj5fhelD5S9mLim5qaqlPm5emkQggh3gwyIiWEyFcajQYDC4scN80LiUW27ZmZ6dfe/74Y66No0aK0bNmS77//Xv0Sni4xMZFVq1bRs2dPAPU+qfXr16v3Fvn4+LBz5051wYB0tWrVIjY2lvLly2fY9J2KFh4ejp2dHdWrVweefzHv1KkTq1evJjExUafskydP+P777+nQoQO2trYA6n1Ss2fPVpOm9EQqPDw8Q7zR0dGZxpub1eeuXr2Kj48PtWvXJjAwMMO5vthu8eLFdY5FREQQFxdHnz599OpLo9FgYGCg/txKliyptp3d/VX68PDwYNiwYezYsYOOHTuqqwWamJioC3jkxMHBAYCEhAR138vLsFerVo2wsLAs28hNf0IIIV4fSaSEEAJYsGABKSkp+Pr6snv3bq5cucK2bdto2bIlHh4eTJgwAXj+pdfOzo7Vq1frJFKbNm0iJSWFRo0aqW1OmDCBn376CX9/f06fPk1MTAxr1qzRWdUvJ5s3b84wrW/q1Kk4OjrSsmVLtm7dypUrV9i9eze+vr4YGBio92wB2NnZUa1aNVatWqXG27RpU44dO8bZs2d1RqRGjx7N/v37GTRoEFFRUZw7d47g4OAMi01kJz2JKlOmDLNmzeLmzZskJiZmSPqysnz5curVq6ezOuKLUlJS1PZiYmL4/PPPSUpKom3btnrHmJMnT54waNAgwsPDuXTpEvv27ePw4cPqfWmurq4kJSURFhbGrVu3dKZpvqx8+fKULl2aSZMmce7cObZs2UJAQIBOmS+//JLDhw8zYMAATp48yZkzZ/jhhx+4deuW2t/Bgwe5ePEit27dyjD6J4QQomBIIiWEKBSM7OzQ5DDqoTExwcjOLl/6d3d35/Dhw5QtW5ZOnTrh4uJCq1at8PDwYN++feoCBxqNhiZNmqDRaGjcuDHwPLmysbHBy8tLZ2qXr68vISEh7Nixgzp16lC/fn1mz56dq5GSzBKpYsWKceDAAZo1a0b//v1xc3PD29ubtLQ0oqKi1Pt90qUfS0+k7O3tqVSpEo6OjjoLRFSrVo2IiAjOnj1LkyZNqFmzJhMmTMA5F3+3KzQ0lLi4OMLCwihVqhROTk7qlpP79+/z22+/ZTsatW3bNrW9evXqcfjwYZ3RwbxgaGjI7du3+fjjj/Hw8KBTp060atUKf39/ABo2bMinn35K586dcXBw4Ntvv82yLWNjY3755RfOnDlDtWrV+Oabb/j66691ynh4eLBjxw5OnDhB3bp1adCgAcHBwRgZPZ99P3LkSAwNDalUqRIODg6F8m9mCSHEf5FGyc0awUIIkY3k5GQuXLiAm5ubzj1A+kq9di3bvxNlZGeXL3+MNysTJ07ku+++IzQ0lPr167+2ftMdO3aMt956i5s3b+Z4v8/y5csZMGAAa9euzXSRBiHEv98/fQ8WQuSOLDYhhCg0jJ2dX2uilBN/f39cXV05cOAAdevWfaUltv+JZ8+eMX/+/ByTKIA+ffpgb29PTEwMvr6+soCBEEIIkc9kREoIkWfkt6FCCFFw5D1YiNdL7pESQgghhBBCiFySREoIIYQQQgghckkSKSGEEEIIIYTIJUmkhBBCCCGEECKXJJESQgghhBBCiFySREoIIYQQQgghckkSKSGEEEIIIYTIJUmkhBCF0rkj1wkctZe4ozdeS38+Pj4MHTr0tfT1KlxdXZkzZ84b025e0mg0bNq0qaDDyBeTJk2iRo0aedrmxYsX0Wg0REVF5Wm7QgghdEkiJYQodB4/eEr4qtj//XuGxw+e5nufGzZsYMqUKXqVfdO/qK5YsYLGjRsDcPjwYT755BO964aHh6PRaLh3714+RZc/li5dSpMmTbCzs8POzo4WLVpw6NAhvev37NkTjUajs/n5+WVbJygoKEOd9O3GjdfzC4J/orD/ckEIIQqaJFJCiEJFURQiVp8hNeUZAE+TnxHxS2y+92tvb4+1tXW+9/Oy1NTU195ncHAw7dq1A8DBwQELC4vXHoOiKDx79uy19RceHk6XLl3YtWsXkZGRlC5dmrfffpurV6/q3Yafnx8JCQnq9ssvv2RbvnPnzjrlExIS8PX1xdvbm+LFi//TUxJCCFHAJJESQhQqcUdvcD7qFor2+WNFC+eP3+Tckev52u+Lv313dXVl2rRp9O7dG2tra8qUKcOSJUvUsm5ubgDUrFkTjUaDj4+PemzZsmV4enpiZmZGxYoV+f7779Vj6SNZa9euxdvbGzMzM1atWkXPnj1p3749s2bNwsnJiaJFizJw4MBskyyNRsPixYt55513sLCwwNPTk8jISOLi4vDx8cHS0pKGDRsSHx+vUy85OZkdO3aoidTLU/s0Gg3Lli2jQ4cOWFhY4O7uzubNm9X4mzVrBoCdnR0ajYaePXsCoNVqmT59Om5ubpibm1O9enV+/fVXtd30kaytW7dSu3ZtTE1N2bt3Lz4+PgwePJhRo0Zhb2+Po6MjkyZNyvK806/hunXraNKkCebm5tSpU4ezZ89y+PBhvLy8sLKyolWrVty8eVOtt2rVKgYMGECNGjWoWLEiy5YtQ6vVEhYWlmVfLzM1NcXR0VHd7Ozssi1vbm6uU97Q0JA///yTPn36ZCi7ePFiSpcujYWFBZ06deL+/fvZtq3Vavn2228pX748pqamlClThqlTp+qUOX/+PM2aNcPCwoLq1asTGRmpHrt9+zZdunShZMmSWFhYULVqVZ3EsGfPnkRERDB37lx1FO3ixYt6XCUhhPjvkERKCJGvFEUhNSVNr+3BrSeErzqTaTvhq2J5cOuJ3m0pivKP4g4ICMDLy4vjx48zYMAAPvvsM2Jjn4+MpU8J27lzJwkJCWzYsAF4/mV9woQJTJ06lZiYGKZNm8b48eNZsWKFTttjxoxhyJAhxMTE4OvrC8CuXbuIj49n165drFixgqCgIIKCgrKNccqUKXz88cdERUVRsWJFunbtSv/+/fnyyy85cuQIiqIwaNAgnTphYWGULFmSihUrZtmuv78/nTp14uTJk7Ru3Zpu3bpx584dSpcuzW+//QZAbGwsCQkJzJ07F4Dp06fz008/sWjRIk6fPs2wYcP46KOPiIiIyHDuM2bMICYmhmrVqgHPpxpaWlpy8OBBvv32WyZPnkxoaGi25z5x4kTGjRvHsWPHMDIyomvXrowaNYq5c+eyZ88e4uLimDBhQpb1Hz9+TGpqKvb29tn286Lw8HCKFy9OhQoV+Oyzz7h9+7bedQF++uknLCwseP/993X2x8XFsW7dOn7//Xe2bdumPuey8+WXXzJjxgzGjx9PdHQ0q1evpkSJEjplxo4dy8iRI4mKisLDw4MuXbqoo4DJycnUrl2bLVu2cOrUKT755BO6d++uPrfnzp1LgwYN6NevnzqaVrp06VydrxBC/NsZFXQAQoh/t2dPtSwZEpFzwRw8ffKMleMicy74P5/M9cbY1PCV+2vdurX6ZXb06NHMnj2bXbt2UaFCBRwcHAAoWrQojo6Oap2JEycSEBBAx44dgecjV9HR0SxevJgePXqo5YYOHaqWSWdnZ8eCBQswNDSkYsWKtGnThrCwMPr165dljL169aJTp05qjA0aNGD8+PFqcjZkyBB69eqlU+fFaX1Z6dmzJ126dAFg2rRpzJs3j0OHDuHn56cmHsWLF6dIkSIApKSkMG3aNHbu3EmDBg0AKFu2LHv37mXx4sV4e3urbU+ePJmWLVvq9FetWjUmTpwIgLu7OwsWLCAsLCxDuReNHDlS5zy7dOlCWFgYjRo1AqBPnz7ZJqKjR4/G2dmZFi1aZHst0vn5+dGxY0fc3NyIj4/nq6++olWrVkRGRmJoqN/zbPny5XTt2hVzc3Od/cnJyfz000+ULFkSgPnz59OmTRsCAgJ0nl/pHj58yNy5c1mwYIH6vCpXrpx631u6kSNH0qZNG+B5cly5cmXi4uKoWLEiJUuWZOTIkWrZzz//nO3bt7Nu3Trq1q2Lra0tJiYmWFhYZBqDEEIISaSEECJT6aMl8Hy6m6OjY7YLBDx69Ij4+Hj69Omjk/w8e/YMW1tbnbJeXl4Z6leuXFnnC7mTkxN//fWX3jGmj0ZUrVpVZ19ycjIPHjzAxsYGRVH4/fffWbdund7tWlpaYmNjk+25x8XF8fjx4wyJz9OnT6lZs6bOvszO/cX+4Pm557QYgz7nnlUbM2bMYM2aNYSHh2NmZpZtP+k+/PBD9f9Vq1alWrVqlCtXjvDwcJo3b06rVq3Ys2cPAC4uLpw+fVqnfmRkJDExMaxcuTJD22XKlFGTKIAGDRqg1WqJjY3l3LlztGrVSj22ePFi3N3dSUlJoXnz5tnG/OI1cnJyAuDGjRtUrFiRtLQ0pk2bxrp167h69SpPnz4lJSWlQO6XE0KIN5UkUkKIfGVkYsAnc71zLKcoCqE/nubSqdvq/VEv0hiAa9VitOxdWe9+/wljY2Pd/jUatNpMAvufpKQk4PnqcPXq1dM59vKIhaWl5T/u7+U6Go0my33p7Rw6dIhnz57RsGFDvdvVJ5b0c9+yZYtOQgDP7yt60es898zamDVrFjNmzGDnzp0ZErjcKFu2LMWKFSMuLo7mzZuzbNkynjx5kun5wPN752rUqEHt2rVz1Y+Xl5fO6pAlSpTQ+16l7J4LM2fOZO7cucyZM4eqVatiaWnJ0KFDefo0/1fIFEKIfwtJpIQQ+Uqj0eg9xa7ZR56smniAp08yruZmYmaET7eK/2i6Xl4xMTEBIC0tTd1XokQJnJ2dOX/+PN26dSuo0LIVHBxMmzZt9J6KlpnMzr1SpUqYmppy+fJlnWl8hc23337L1KlT2b59e6YjY7nx999/c/v2bXWk5+UE8kVJSUmsW7eO6dOnZ3r88uXLXLt2DWdnZwAOHDiAgYEBFSpUwNzcnPLly+uUd3d3x9zcnLCwMPr27ftK8e/bt493332Xjz76CHieYJ09e5ZKlSqpZUxMTHR+zkIIIXRJIiWEKDQsbEzw6VaBHctOZzjm3bUCFjYmBRBVRsWLF8fc3Jxt27ZRqlQpzMzMsLW1xd/fn8GDB2Nra4ufnx8pKSkcOXKEu3fvMnz48IIOm82bNzN58uR/1IaLiwsajYaQkBBat26Nubk51tbWjBw5kmHDhqHVamncuDH3799n37592NjY6NwfVlC++eYbJkyYwOrVq3F1dSUxMREAKysrrKyssq2blJSEv78/7733Ho6OjsTHxzNq1CjKly+v3qeVnbVr1/Ls2TM1aXmZmZkZPXr0YNasWTx48IDBgwfTqVOnLO9NMjMzY/To0YwaNQoTExMaNWrEzZs3OX36dKYrAmbG3d2dX3/9lf3792NnZ8d3333H9evXdRIpV1dXDh48yMWLF7GyssLe3h4DA1mjSggh0sk7ohCiUClfuzhlaxRD8793J40BlK3pgLtXiewrvkZGRkbMmzePxYsX4+zszLvvvgtA3759WbZsGYGBgVStWhVvb2+CgoLU5dILUnx8PHFxcXp98c9OyZIl8ff3Z8yYMZQoUUJdFXDKlCmMHz+e6dOn4+npiZ+fH1u2bCkU5w7www8/8PTpU95//32cnJzUbdasWTnWNTQ05OTJk7Rr1w4PDw/69OlD7dq12bNnT4api5lZvnw5HTt2VBfneFn58uXp2LEjrVu35u2336ZatWo6y+ZnZvz48YwYMYIJEybg6elJ586dc/VHfseNG0etWrXw9fXFx8cHR0dH2rdvr1Nm5MiRGBoaUqlSJRwcHLh8+bLe7QshxH+BRvmnawQLIcT/JCcnc+HCBdzc3PS+iT8zjx88Vaf4mVoY0XVS/UIzGvWm+u6779i5cyd//PFHQYcihMgnefUeLITQj4xICSEKnfQpfs//rShJVB4oVaoUX375ZUGHIYQQQvxryD1SQohCyd2rRKGazvemS/97UyKjPXv26Cwx/rL0VQmFEEKIF0kiJYQQ4j/t5SXGhRBCCH1IIiWEEOI/LbMlxoUQQoicyD1SQgghhBBCCJFLkkgJIYQQQgghRC5JIiWEEEIIIYQQuSSJlBBCCCGEEELkkiRSQgghhBBCCJFLkkgJIUQ2evbsSfv27Qs0hvDwcDQaDffu3cuyzKRJk6hRo8Zri6kg+fj4MHTo0IIOQ2RCn+fhv+nnFxQURJEiRQo6DCFEAZFESghRKD24dYPr5+N4cOtGQYfyRhg5ciRhYWEFHcYre/LkCZaWlsTFxb1RX07btWtHmTJlMDMzw8nJie7du3Pt2rVs6yxZsgQfHx9sbGxyTJDTBQUFodFoMt1u3Pj/10h4eDi1atXC1NSU8uXLExQUpNNOz549deoWLVoUPz8/Tp48qdf5/vbbb/j4+GBra4uVlRXVqlVj8uTJ3LlzR6/6ABs2bGDKlCl6ly9Iu3btonXr1hQtWhQLCwsqVarEiBEjuHr1ap72o9Fo2LRpU562KYTIf5JICSEKnQe3bvDj0P78/OVQfhzaX5IpPVhZWVG0aNGCDuOVhYaG4uLi8sb9PadmzZqxbt06YmNj+e2334iPj+f999/Pts7jx4/x8/Pjq6++0rufzp07k5CQoLP5+vri7e1N8eLFAbhw4QJt2rShWbNmREVFMXToUPr27cv27dt12vLz81PbCAsLw8jIiHfeeSfHGMaOHUvnzp2pU6cOW7du5dSpUwQEBHDixAlWrlyp97nY29tjbW2td/mCsnjxYlq0aIGjoyO//fYb0dHRLFq0iPv37xMQEFDQ4QkhCgFJpIQQhc6TBw9IS00FIC01lScPHuR7n7/++itVq1bF3NycokWL0qJFCx49eqQenzVrFk5OThQtWpSBAweS+r/4AFJSUhg5ciQlS5bE0tKSevXqER4erh5PH2HZvn07np6eWFlZqV9m02U20uDq6qoT49GjR/Hy8sLCwoKGDRsSGxurHstpSlVISAhFihQhLS0NgKioKDQaDWPGjFHL9O3bl48++giA27dv06VLF0qWLImFhQVVq1bll19+0WnTx8eHwYMHM2rUKOzt7XF0dGTSpEk6Zc6cOUPjxo0xMzOjUqVK7Ny5M9PfvgcHB9OuXbss48/OypUr8fLywtraGkdHR7p27ZphlEaj0bB9+3Zq1qyJubk5b731Fjdu3GDr1q14enpiY2ND165defz4sVpv27ZtNG7cmCJFilC0aFHeeecd4uPjdfoeNmwY9evXx8XFhYYNGzJmzBgOHDig8/x42dChQxkzZgz169fX+xzNzc1xdHRUN0NDQ/7880/69Omjllm0aBFubm4EBATg6enJoEGDeP/995k9e7ZOW6ampmo7NWrUYMyYMVy5coWbN29m2f+hQ4eYNm0aAQEBzJw5k4YNG+Lq6krLli357bff6NGjh075lStX4urqiq2tLR9++CEPHz5Uj708tc/V1ZVp06bRu3dvrK2tKVOmDEuWLNFp78qVK3Tq1IkiRYpgb2/Pu+++y8WLF9Xj4eHh1K1bF0tLS4oUKUKjRo24dOmSejw4OJhatWphZmZG2bJl8ff359mzZ1me799//83gwYMZPHgwP/74Iz4+Pri6utK0aVOWLVvGhAkTdMpn99o+fPgwLVu2pFixYtja2uLt7c2xY8d0zh+gQ4cOmb7uhRCFlyRSQoh8pSgKqcnJem13rl7h6ploblw8r9PGjYvnuXommjtXr+jdlqIoeseYkJBAly5d6N27NzExMYSHh9OxY0e1jV27dhEfH8+uXbtYsWIFQUFBOlOmBg0aRGRkJGvWrOHkyZN88MEH+Pn5ce7cObXM48ePmTVrFitXrmT37t1cvnyZkSNH6sSQvsXFxVG+fHmaNm2qE+fYsWMJCAjgyJEjGBkZ0bt3b73PsUmTJjx8+JDjx48DEBERQbFixXQSvoiICHx8fABITk6mdu3abNmyhVOnTvHJJ5/QvXt3Dh06pNPuihUrsLS05ODBg3z77bdMnjyZ0NBQANLS0mjfvj0WFhYcPHiQJUuWMHbs2AyxabVaQkJCePfdd/U+nxelpqYyZcoUTpw4waZNm7h48SI9e/bMUG7SpEksWLCA/fv3q1/M58yZw+rVq9myZQs7duxg/vz5avlHjx4xfPhwjhw5QlhYGAYGBnTo0AGtVptpHHfu3GHVqlU0bNgQY2PjVzoXff30009YWFjojH5FRkbSokULnXK+vr5ERkZm2U5SUhI///wz5cuXz3ZEc9WqVVhZWTFgwIBMj784FTM+Pp5NmzYREhJCSEgIERERzJgxI9vzCQgIwMvLi+PHjzNgwAA+++wz9RcFqamp+Pr6Ym1tzZ49e9i3b5+asDx9+pRnz57Rvn17vL29OXnyJJGRkXzyySdoNBoA9uzZw8cff8yQIUOIjo5m8eLFBAUFMXXq1CzjWb9+PU+fPmXUqFE5nm9Or+2HDx/So0cP9u7dy4EDB3B3d6d169Zqcnn48GEAAgMDSUhIUB8LIQo/o4IOQAjx7/YsJYV5PbKf6pSTHYvn5brO4BW/YmxmplfZhIQEnj17RseOHXFxcQGgatWq6nE7OzsWLFiAoaEhFStWpE2bNoSFhdGvXz8uX75MYGAgly9fxtnZGXh+v9K2bdsIDAxk2rRpwPMvg4sWLaJcuXLA8+Rr8uTJah+Ojo7A88Tzvffew9bWlsWLF+vEOXXqVLy9vQEYM2YMbdq0ITk5GTM9ztPW1pYaNWoQHh6Ol5cX4eHhDBs2DH9/f5KSkrh//z5xcXFq+yVLltT5Mvj555+zfft21q1bR926ddX91apVY+LEiQC4u7uzYMECwsLCaNmyJaGhocTHxxMeHq6e39SpU2nZsqVObAcOHACgXr16OZ5HZl5MKMuWLcu8efOoU6cOSUlJWFlZqce+/vprGjVqBECfPn348ssviY+Pp2zZsgC8//777Nq1i9GjRwPw3nvv6fTz448/4uDgQHR0NFWqVFH3jx49mgULFvD48WPq169PSEjIK51HbixfvpyuXbtibm6u7ktMTKREiRI65UqUKMGDBw948uSJWjYkJES9Lo8ePcLJyYmQkBAMDLL+3eq5c+coW7asXgmiVqslKChInb7XvXt3wsLCsk1cWrdurSZpo0ePZvbs2ezatYsKFSqwdu1atFoty5YtU5OjwMBAihQpoj6f79+/zzvvvKO+vjw9PdW2/f39GTNmjDpqVrZsWaZMmcKoUaPU525m52tjY4OTk1OO55vTa/utt97SKb9kyRKKFClCREQE77zzDg4ODsDz5Cz9dSKEeDPIiJQQ4j+vevXqNG/enKpVq/LBBx+wdOlS7t69qx6vXLkyhoaG6mMnJyd16thff/1FWloaHh4eWFlZqVtERITONDALCwv1i9bLbbzoq6++IjIykuDgYJ0vyfA8aXmxPpBpG3v27NGJZdWqVQB4e3sTHh6Ooijs2bOHjh074unpyd69e4mIiMDZ2Rl3d3fg+WjSlClTqFq1Kvb29lhZWbF9+3YuX76cZUwvn1dsbCylS5fW+XL4YhKWLjg4mHfeeSfbL/LZOXr0KG3btqVMmTJYW1uryWB2sZYoUQILCws1iUrf9+L1PHfuHF26dKFs2bLY2NioU65ebveLL77g+PHj7NixA0NDQz7++ONcjYi+rFWrVurPrnLlyhmOR0ZGEhMTozOtLzfS76GKiori0KFD+Pr60qpVK3UqXGb95+Z8XF1dde6Byuq5/qIXfzYajQZHR0e1zokTJ4iLi8Pa2lqNy97enuTkZOLj47G3t6dnz574+vrStm1b5s6dqzO17sSJE0yePFnnNdGvXz8SEhJ4/Pgxn376qc6x9PNNT9pyktNr+/r16/Tr1w93d3dsbW2xsbEhKSkpw/NICPHmkREpIUS+MjI1ZfCKX3Ms9/D2TX4aPVi9N0qj0ahfZtK/xBkaG/PxN/OwLuqgV7/6MjQ0JDQ0lP3796vTu8aOHcvBgwcBMvwWXqPRqNO7kpKSMDQ05OjRozrJFqAzGpJZGy9/Of3555+ZPXs24eHhlCxZMkOcL7aR/iUvs2lmXl5eREVFqY/TRyl8fHz48ccfOXHiBMbGxlSsWBEfHx/Cw8O5e/eumoAAzJw5k7lz5zJnzhyqVq2KpaUlQ4cO5enTp1nG9PK10dfmzZtznPqVlUePHuHr64uvry+rVq3CwcGBy5cv4+vrm22sGo0mx9jbtm2Li4sLS5cuxdnZGa1WS5UqVTK0W6xYMYoVK4aHhweenp6ULl2aAwcO0KBBg1c6p2XLlvHkyZMMMb94vEaNGtSuXVtnv6OjI9evX9fZd/36dWxsbHSScktLS51FPZYtW4atrS1Lly7l66+/zrR/Dw8P9u7dS2pqao6jUq/ynMjpNVa7dm31FwIvSh/NCQwMZPDgwWzbto21a9cybtw4QkNDqV+/PklJSfj7+9OxY8cM9c3MzJg8ebLO6Gv6+d6/f5+EhIQcR6Vyem336NGD27dvM3fuXFxcXDA1NaVBgwYZnkdCiDePJFJCiHyl0Wj0mmJnX7I0vecs5smDB9y5eoU/FjxfFUtRFFoPGoF9ydKY29hgU6x4vsXZqFEjGjVqxIQJE3BxcWHjxo051qtZsyZpaWncuHGDJk2avHL/kZGR9O3bl8WLF+dqEYLMmJubZ7r6Xfp9UrNnz1aTJh8fH2bMmMHdu3cZMWKEWnbfvn28++676uITWq2Ws2fPUqlSJb3jqFChAleuXOH69etqMvfy/R/nzp3j0qVLGab76evMmTPcvn2bGTNmULp0aQCOHDnySm296Pbt28TGxrJ06VL157p3794c66V/+U9JSXnlvjNLotMlJSWxbt06pk+fnuFYgwYN+OOPP3T2hYaG5pjQaTQaDAwM1OQps/67du3KvHnz+P777xkyZEiG4/fu3cu3Jetr1arF2rVrKV68ODY2NlmWq1mzJjVr1uTLL7+kQYMGrF69mvr161OrVi1iY2OzXBGyePHi6sqH6d5//33GjBnDt99+m2GxDsjd+e7bt4/vv/+e1q1bA88Xzrh165ZOGWNjY3UhGCHEm0MSKSFEoWFTrHimiZJ9ydKUKJt/y2IfPHiQsLAw3n77bYoXL87Bgwe5efMmnp6eOf59HQ8PD7p168bHH39MQEAANWvW5ObNm4SFhVGtWjXatGmTY/+JiYl06NCBDz/8EF9fXxITE4HnI2Xpv3HPC3Z2dlSrVo1Vq1axYMECAJo2bUqnTp1ITU3VGZFyd3fn119/Zf/+/djZ2fHdd99x/fr1XCVSLVu2pFy5cvTo0YNvv/2Whw8fMm7cOOD/R9SCg4Np0aIFFhYWOnXT0tJ0RtXg+WpzL977AlCmTBlMTEyYP38+n376KadOncqTv1FkZ2dH0aJFWbJkCU5OTly+fFlnhUN4/rw5fPgwjRs3xs7Ojvj4eMaPH0+5cuXU5OXq1as0b96cn376SZ3WmJiYSGJiInFxccDz6aHpq9XZ29tnG9fatWt59uyZmuC+6NNPP2XBggWMGjWK3r178+eff7Ju3Tq2bNmiUy4lJUV9jt29e5cFCxaQlJRE27Zts+y3Xr16jBo1Sv0bSh06dMDZ2Zm4uDgWLVpE48aNM02w8kK3bt2YOXMm7777LpMnT6ZUqVJcunSJDRs2MGrUKFJTU1myZAnt2rXD2dmZ2NhYzp07x8cffwzAhAkTeOeddyhTpgzvv/8+BgYGnDhxglOnTvH1119n2mfp0qWZPXs2gwYN4sGDB3z88ce4urry999/89NPP2FlZaX3Euju7u7qypIPHjzgiy++yDBt19XVlbCwMBo1aoSpqSl2dnb/7KIJIV4LuUdKCFHomNvYYPi/6TKGxsaYZ/Nb6LxgY2PD7t27ad26NR4eHowbN46AgABatWqlV/3AwEA+/vhjRowYQYUKFWjfvj2HDx+mTJkyetU/c+YM169fZ8WKFTg5OalbnTp1/slpZcrb25u0tDR1dT57e3sqVaqEo6MjFSpUUMuNGzeOWrVq4evri4+PD46OjrRv3z5XfRkaGrJp0yaSkpKoU6cOffv2VVftS18gI6tlz5OSktQRhvQtsy/6Dg4OBAUFsX79eipVqsSMGTOYNWtWruLMjIGBAWvWrOHo0aNUqVKFYcOGMXPmTJ0yFhYWbNiwgebNm1OhQgX69OlDtWrViIiIwPR/U0tTU1OJjY3VWVZ90aJF1KxZk379+gHPk9maNWuyefPmHONavnw5HTt2zHQ0xM3NjS1bthAaGkr16tUJCAhg2bJl+Pr66pTbtm2b+hyrV68ehw8fZv369epzIivffPMNq1ev5uDBg/j6+lK5cmWGDx9OtWrVMix/npcsLCzYvXs3ZcqUUe/r69OnD8nJydjY2GBhYcGZM2d477338PDw4JNPPmHgwIH0798feL5yYUhICDt27KBOnTrUr1+f2bNnqwvLZGXAgAHs2LFDTRwrVqxI3759sbGxyTAVMDvLly/n7t271KpVi+7duzN48OAMI2ABAQGEhoZSunRpatasmfuLJIQoEBrln9wRK4QQL0hOTubChQu4ubnptZJcdh7cusGTBw/ydTqfeP327dtH48aNiYuLw9bWFicnJ/7+++8Mq80JIXIvL9+DhRA5k6l9QohCKatpfuLNsnHjRqysrHB3dycuLo4hQ4bQqFEjypUrx9mzZ/nuu+8kiRJCCPFGkkRKCCFEvnn48CGjR4/m8uXLFCtWjBYtWqj3lnh4eODh4VHAEQohhBCvRqb2CSHyjEwrEUKIgiPvwUK8XrLYhBBCCCGEEELkkiRSQgghhBBCCJFLkkgJIYQQQgghRC5JIiWEEEIIIYQQuSSJlBBCCCGEEELkkiRSQgghhBBCCJFLkkgJIUQ2evbsSfv27Qs0hvDwcDQaDffu3cuyzKRJk6hRo8Zri6kg+fj4MHTo0IIO4z/n4sWLaDQaoqKisiwTFBREkSJFXltM+U2j0bBp06aCDkMIUUhJIiWEEP8CI0eOJCwsrKDDeGVPnjzB0tKSuLi4N+rLeLt27ShTpgxmZmY4OTnRvXt3rl27lm2dJUuW4OPjg42NTY4J8osOHz5M8+bNKVKkCHZ2dvj6+nLixAn1eHrCnb6Zm5tTuXJllixZolf7cXFx9OrVi1KlSmFqaoqbmxtdunThyJEjetUH6Ny5M2fPntW7fEFKTEzk888/p2zZspiamlK6dGnatm2b56+jwvDLGCFE/pBESghRqDwIu8zfY/bwIOxypo9F5qysrChatGhBh/HKQkNDcXFxoXz58gUdSq40a9aMdevWERsby2+//UZ8fDzvv/9+tnUeP36Mn58fX331ld79JCUl4efnR5kyZTh48CB79+7F2toaX19fUlNTdcrGxsaSkJBAdHQ0/fv357PPPssxOThy5Ai1a9fm7NmzLF68mOjoaDZu3EjFihUZMWKE3nGam5tTvHhxvcsXlIsXL1K7dm3+/PNPZs6cyV9//cW2bdto1qwZAwcOLOjwhBBvCEmkhBCFxoOwyzwIvfT8/6GXuLnsL53H+ZlM/frrr1StWhVzc3OKFi1KixYtePTokXp81qxZODk5UbRoUQYOHKjz5TUlJYWRI0dSsmRJLC0tqVevHuHh4erx9BGW7du34+npiZWVFX5+fiQkJKhlXhxJSN9cXV11Yjx69CheXl5YWFjQsGFDYmNj1WM5Te0LCQmhSJEipKWlARAVFYVGo2HMmDFqmb59+/LRRx8BcPv2bbp06ULJkiWxsLCgatWq/PLLLzpt+vj4MHjwYEaNGoW9vT2Ojo5MmjRJp8yZM2do3LgxZmZmVKpUiZ07d2Y6XSo4OJh27dplGX92Vq5ciZeXF9bW1jg6OtK1a1du3LihHk8fqdm+fTs1a9bE3Nyct956ixs3brB161Y8PT2xsbGha9euPH78WK23bds2GjduTJEiRShatCjvvPMO8fHxOn0PGzaM+vXr4+LiQsOGDRkzZgwHDhzIkNy8aOjQoYwZM4b69evrfY5nzpzhzp07TJ48mQoVKlC5cmUmTpzI9evXuXTpkk7Z4sWL4+joiJubG4MHD8bNzY1jx45l2baiKPTs2RN3d3f27NlDmzZtKFeuHDVq1GDixIkEBwfrlD9//jzNmjXDwsKC6tWrExkZqR57eTQx/Xm5cuVKXF1dsbW15cMPP+Thw4dqGa1Wy/Tp03Fzc8Pc3Jzq1avz66+/qsfv3r1Lt27dcHBwwNzcHHd3dwIDA9XjV65coVOnThQpUgR7e3veffddLl68mO31HDBgABqNhkOHDvHee+/h4eFB5cqVGT58OAcOHNApe+vWLTp06ICFhQXu7u5s3rxZPZaWlkafPn3U2CtUqMDcuXN1zn/FihUEBwerr+sX3xuEEG82SaSEEPlKURS0T9P02tKTpnQpcfd0Hj8IvaR3W4qi6B1jQkICXbp0oXfv3sTExBAeHk7Hjh3VNnbt2kV8fDy7du1ixYoVBAUFERQUpNYfNGgQkZGRrFmzhpMnT/LBBx/g5+fHuXPn1DKPHz9m1qxZrFy5kt27d3P58mVGjhypE0P6FhcXR/ny5WnatKlOnGPHjiUgIIAjR45gZGRE79699T7HJk2a8PDhQ44fPw5AREQExYoV0/lSFxERgY+PDwDJycnUrl2bLVu2cOrUKT755BO6d+/OoUOHdNpdsWIFlpaWHDx4kG+//ZbJkycTGhoKPP+S2b59eywsLDh48CBLlixh7NixGWLTarWEhITw7rvv6n0+L0pNTWXKlCmcOHGCTZs2cfHiRXr27Jmh3KRJk1iwYAH79+9Xv3zPmTOH1atXs2XLFnbs2MH8+fPV8o8ePWL48OEcOXKEsLAwDAwM6NChA1qtNtM47ty5w6pVq2jYsCHGxsavdC5ZqVChAkWLFmX58uU8ffqUJ0+esHz5cjw9PTMk3OkURWHbtm1cvnyZevXqZdl2VFQUp0+fZsSIERgYZPxa8PI0y7FjxzJy5EiioqLw8PCgS5cuPHv2LMv24+Pj2bRpEyEhIYSEhBAREcGMGTPU49OnT+enn35i0aJFnD59mmHDhvHRRx8REREBwPjx44mOjmbr1q3ExMTwww8/UKxYMeD5z97X1xdra2v27NnDvn371F9UPH36NNN47ty5w7Zt2xg4cCCWlpY5nq+/vz+dOnXi5MmTtG7dmm7dunHnzh3g+XO3VKlSrF+/nujoaCZMmMBXX33FunXrgOdTbjt16qT+4iQhIYGGDRtmea2EEG8YRQgh8siTJ0+U6Oho5cmTJ+q+tJRnypXRu1/7lpbyTO+4jx49qgDKxYsXMxzr0aOH4uLiojx79v/tffDBB0rnzp0VRVGUS5cuKYaGhsrVq1d16jVv3lz58ssvFUVRlMDAQAVQ4uLi1OMLFy5USpQokaE/rVardOjQQaldu7by+PFjRVEUZdeuXQqg7Ny5Uy23ZcsWBVCv9cSJE5Xq1atnAxH4xwAAH/FJREFUe561atVSZs6cqSiKorRv316ZOnWqYmJiojx8+FD5+++/FUA5e/ZslvXbtGmjjBgxQn3s7e2tNG7cWKdMnTp1lNGjRyuKoihbt25VjIyMlISEBPV4aGioAigbN25U9+3bt08pXry4kpaWpl4vW1vbLOPw9vZWhgwZkuXxw4cPK4Dy8OFDRVEyv37Tp09XACU+Pl7d179/f8XX1zfLdm/evKkAyl9//aWzf9SoUYqFhYUCKPXr11du3bqVZRsvSo/r7t27epX/66+/lHLlyikGBgaKgYGBUqFCBZ3nbHp7lpaWiqWlpWJkZKQYGBgoX3/9dbbtrl27VgGUY8eOZVvuwoULCqAsW7ZM3Xf69GkFUGJiYhRFyfizmzhxomJhYaE8ePBA3ffFF18o9erVUxRFUZKTkxULCwtl//79On316dNH6dKli6IoitK2bVulV69emca0cuVKpUKFCopWq1X3paSkKObm5sr27dszrXPw4EEFUDZs2JDt+SqKogDKuHHj1MdJSUkKoGzdujXLOgMHDlTee+899XGPHj2Ud999N8e+8kJm78FCiPwjI1JCiP+86tWr07x5c6pWrcoHH3zA0qVLuXv3rnq8cuXKGBoaqo+dnJzUqWN//fUXaWlpeHh4YGVlpW4RERE608AsLCwoV65cpm286KuvviIyMpLg4GDMzc11jlWrVk2nPpBpG3v27NGJZdWqVQB4e3sTHh6Ooijs2bOHjh074unpyd69e4mIiMDZ2Rl3d3fg+WjSlClTqFq1Kvb29lhZWbF9+3YuX9adXvliTC+fV2xsLKVLl8bR0VE9Xrdu3QzxBgcH884772Q6GqKPo0eP0rZtW8qUKYO1tTXe3t4A2cZaokQJLCwsKFu2rM6+F6/nuXPn6NKlC2XLlsXGxkYd+Xm53S+++ILjx4+zY8cODA0N+fjjj3M1IvqyVq1aqT+7ypUrA88X4+jTpw+NGjXiwIED7Nu3jypVqtCmTRuePHmiU3/Pnj1ERUURFRXFsmXLmDZtGj/88AMAq1at0nlu7NmzJ9ex6vs8TOfq6oq1tbVOnfTycXFxPH78mJYtW+rE9dNPP6mvn88++4w1a9ZQo0YNRo0axf79+9W2Tpw4QVxcHNbW1mpde3t7kpOTiY+Pz/S18E/O19LSEhsbG53zXbhwIbVr18bBwQErKyuWLFmS4TkihPh3MiroAIQQ/24aYwOcJ+s3leVh+BUe/nkly+PWb5XG2qe03v3qy9DQkNDQUPbv369O7xo7diwHDx4EyDBNS6PRqNO7kpKSMDQ05OjRozrJFjxfACJdZm28/IXu559/Zvbs2YSHh1OyZMkMcb7YhkajAch0mpmXl5fOEtUlSpQAnt/T9OOPP3LixAmMjY2pWLEiPj4+hIeHc/fuXTUBAZg5cyZz585lzpw5VK1aFUtLS4YOHZphulR210Zfmzdv1pnqlRuPHj3C19cXX19fVq1ahYODA5cvX8bX1zfbWDUaTY6xt23bFhcXF5YuXYqzszNarZYqVapkaLdYsWIUK1YMDw8PPD09KV26NAcOHKBBgwavdE7Lli1Tk6P0GFevXs3FixeJjIxUE87Vq1djZ2dHcHAwH374oVrfzc1NnZ5WuXJlDh48yNSpU/nss89o166dzjS/kiVLcubMGeD5fVg1a9bMMT59n4eZlU+v8+LrB2DLli0ZnvOmpqbA88Ty0qVL/PHHH4SGhtK8eXMGDhzIrFmzSEpKonbt2uovC17k4OCAiYlJhtdCamoqGo1GPe/cnO/L8a9Zs4aRI0cSEBBAgwYNsLa2ZubMmep7hxDi300SKSFEvtJoNGhMDHMuCNkmUenHbd92zYOoMtJoNDRq1IhGjRoxYcIEXFxc2LhxY471atasSVpaGjdu3KBJkyav3H9kZCR9+/Zl8eLFuVqEIDPm5uaZrn6Xfp/U7Nmz1aTJx8eHGTNmcPfuXZ3V2fbt28e7776rLj6h1Wo5e/YslSpV0juOChUqcOXKFa5fv64mc4cPH9Ypc+7cOS5dukTLli1zfZ7w/Mv/7du3mTFjBqVLP0+yc7Ncd1Zu375NbGwsS5cuVX+ue/fuzbFe+hfslJSUV+47syT68ePHGBgYqIkLoD7OKXE1NDRUEzNra2ud0SGAGjVqUKlSJQICAujcuXOGkcF79+7l23L0lSpVwtTUlMuXL+sk8i9zcHCgR48e9OjRgyZNmvDFF18wa9YsatWqxdq1aylevDg2NjaZ1s3steDr68vChQsZPHhwhvukcnO++/bto2HDhgwYMEDd9/KCJCYmJuoiL0KIfxeZ2ieEKDRsWrroPDYtXyTb43nl4MGDTJs2jSNHjnD58mU2bNjAzZs38fT0zLGuh4cH3bp14+OPP2bDhg1cuHCBQ4cOMX36dLZs2aJX/4mJiXTo0IEPP/wQX19fEhMTSUxM5ObNm//01HTY2dlRrVo1Vq1apS4q0bRpU44dO8bZs2d1vsi6u7uro3QxMTH079+f69ev56q/li1bUq5cOXr06MHJkyfZt28f48aNA/5/JCM4OJgWLVpgYWGhUzctLU2dnpa+xcTEZOijTJkymJiYMH/+fM6fP8/mzZuZMmVKruLMjJ2dHUWLFmXJkiXExcXx559/Mnz4cJ0yBw8eZMGCBURFRXHp0iX+/PNPunTpQrly5dTRqKtXr1KxYkWdRToSExOJiooiLi4OeD49NCoqSl3AIDMtW7bk7t27DBw4kJiYGE6fPk2vXr0wMjKiWbNmOmVv3LhBYmIily5dYv369axcuTLbhTw0Gg2BgYGcPXuWJk2a8Mcff3D+/HlOnjzJ1KlTX3kREH1YW1szcuRIhg0bxooVK4iPj+fYsWPMnz+fFStWADBhwgSCg4OJi4vj9OnThISEqK/Nbt26UaxYMd5991327NnDhQsXCA8PZ/Dgwfz9999Z9rtw4ULS0tKoW7cuv/32G+fOnSMmJoZ58+blaiTR3d2dI0eOsH37ds6ePcv48eMz/LLA1dWVkydPEhsby61bt7Jd0VEI8WaRREoIUWjYNC+jJks2LV1w6FtV57FN8zL506+NDbt376Z169Z4eHgwbtw4AgICaNWqlV71AwMD+fjjjxkxYgQVKlSgffv2HD58mDJl9Iv3zJkzXL9+nRUrVuDk5KRuderU+SenlSlvb2/S0tLURMre3p5KlSrh6OhIhQoV1HLjxo2jVq1a+Pr64uPjg6OjY67/qKihoSGbNm0iKSmJOnXq0LdvX3XVPjMzMyDrZc+TkpKoWbOmzta2bdsM5RwcHAgKCmL9+vVUqlSJGTNmMGvWrFzFmRkDAwPWrFnD0aNHqVKlCsOGDWPmzJk6ZSwsLNiwYQPNmzenQoUK9OnTh2rVqhEREaFOS0tNTSU2NlZnWfVFixZRs2ZN+vXrBzxPZmvWrKmzrPbLKlasyO+//87Jkydp0KABTZo04dq1a2zbtk29TyldhQoVcHJyonz58owePZr+/fvrrEaYmbp163LkyBHKly9Pv3798PT0pF27dpw+fZo5c+bk5tLl2pQpUxg/fjzTp0/H09MTPz8/tmzZgpubG/B8ROfLL7+kWrVqNG3aFENDQ9asWQM8/xns3r2bMmXKqPf89enTh+Tk5CxHqADKli3LsWPHaNasGSNGjKBKlSq0bNmSsLAw9X4yffTv35+OHTvSuXNn6tWrx+3bt3VGpwD69etHhQoV8PLywsHBgX379r3CVRJCFEYa5Z/cESuEEC9ITk7mwoULuLm5qV+UhXjRvn37aNy4MXFxcdja2uLk5MTff/+tTv0TQrw6eQ8W4vWSe6SEEELkm40bN2JlZYW7uztxcXEMGTKERo0aUa5cOc6ePct3330nSZQQQog3kiRSQggh8s3Dhw8ZPXo0ly9fplixYrRo0YKAgADg+f1lHh4eBRyhEEII8Wpkap8QIs/ItBIhhCg48h4sxOsli00IIYQQQgghRC5JIiWEEEIIIYQQuSSJlBBCCCGEEELkkiRSQgghhBBCCJFLkkgJIYQQQgghRC5JIiWEENno2bMn7du3L9AYwsPD0Wg03Lt3L8sykyZNokaNGq8tpsKkIM+9MDw/3iT/tefyxYsX0Wg0REVFFXQoQoh8IImUEEL8C4wcOZKwsLCCDuOVPXnyBEtLS+Li4ggKCkKj0eDp6Zmh3Pr169FoNLi6uqr7cnPuBfElXaPRZLrNnDkz0zJGRkaUKVOG4cOHk5KS8o/7X7hwIa6urpiZmVGvXj0OHTqUbfkNGzbg5eVFkSJFsLS0pEaNGqxcuVKvvo4fP84HH3xAiRIlMDMzw93dnX79+nH27Fm9432TnstxcXH06tWLUqVKYWpqipubG126dOHIkSN52o+Pjw9Dhw7N0zaFEP+cJFJCCPEvYGVlRdGiRQs6jFcWGhqKi4sL5cuXB8DS0pIbN24QGRn5f+3df1iN9/8H8OdxKuqw0kK1oQ1JzY9EJNSImlHRyFnDSsPEuGSsRC5DQsO1cTF0ND9WubBGI80KS1cTUlNJ9APrh0abitI5r+8fu7q/jnNOnTP5sY/X47rOH/f9fr/v+/2+z/0+536d932/j1K+3bt3o1u3bkrrnkXbHz161GrbKisrU3pFR0dDJBLBx8dHKZ9MJkNZWRmKioqwbds27N27F6tXr36qfcfFxWHRokUIDw/HxYsX0b9/f7i7u6OyslJjGVNTUyxbtgzp6enIzs6Gv78//P39kZSU1Oy+jh07hqFDh6K+vh779+9HXl4e9u3bB2NjYyxfvlzrOv9XzuXMzEw4ODigoKAAO3bsQG5uLo4cOQIbGxsEBwe/6Ooxxp4HYoyxVvLgwQPKzc2lBw8ePPW2bt26RTKZjG7dutUKNWvZwYMH6Z133qF27dqRqakpjR49mmpqamjGjBnk5eVFGzZsIHNzczI1NaW5c+dSQ0ODUPbhw4cUHBxMlpaWZGRkRI6OjpSSkiKky2QyMjY2phMnTpCNjQ1JJBJyd3enP/74Q8gDQOXVvXt3IiJKSUkhAPTzzz+Tg4MDGRoakpOTE+Xn5wvlw8PDqX///hrbd/ToUTI2NqbGxkYiIrp06RIBoKVLlwp5Zs6cSX5+fkREVFVVRVOnTiVLS0syNDSkd955hw4cOKC0TRcXF5o/fz59/vnn1LFjR+rSpQuFh4cr5cnLyyNnZ2dq27Yt9enTh5KTkwkAHTlyRClfQECAUJem4zVv3jwKDAwU8ty8eZPatm1LX3zxhXBs1LU9JSWFBg8eTEZGRmRsbEzDhg2j4uJikslkKsdYJpMJx3/btm00YcIEMjIyovDwcGpsbKSAgACysrKidu3akbW1NW3evFmp3k3nhy68vLxo1KhRSuvUHZOZM2fSuHHjNG5HKpXSlClTlNY1NDTQ66+/TjExMURE5OjoSEFBQUK6XC4nS0tLioiI0KnO9vb2FBYWpjG9traWzMzMyNvbW236vXv3iOjfncut0QeLi4tp/PjxZGJiQkZGRmRra0uJiYlCek5ODnl4eJBEIqHOnTvTRx99RHfu3NHYXoVCQXZ2duTg4EByuVxje4uKiggAHTp0iFxdXcnQ0JD69etH586dE/K21NdmzJihct4WFRWprVdrfgYzxlrGI1KMsZfS5cuXUVxcjOzs7Ge+r7KyMkilUgQEBCAvLw+pqamYNGkSiAgAkJKSguvXryMlJQUxMTHYs2cP9uzZI5SfN28e0tPTERsbi+zsbEyePBkeHh64du2akKeurg4bN27E3r17cebMGZSWlmLx4sVKdWh6FRYWomfPnhg5cqRSPZctW4aoqChkZmZCT08PAQEBWrdxxIgRuH//Pi5dugQAOH36NMzMzJCamirkOX36NFxdXQEADx8+hIODAxITE/H7779j1qxZmDZtmsptYTExMZBIJMjIyMD69euxatUqJCcnAwDkcjm8vb1hZGSEjIwMfPvtt1i2bJlK3RQKBY4dOwYvLy+l9QEBAYiPj0ddXR0AYM+ePfDw8ECXLl00trOxsRHe3t5wcXFBdnY20tPTMWvWLIhEIvj6+iI4OBh2dnbCsfb19RXKrly5EhMnTkROTg4CAgKgUCjw5ptv4uDBg8jNzcWKFSsQGhqK+Ph4rY/7kyoqKpCYmIiZM2c2m6+goAC//PILhgwZojGPn58fjh49ipqaGmFdUlIS6urqMHHiRDQ0NODChQtwc3MT0tu0aQM3NzeVkT5NiAinTp3C1atXVc7HxyUlJaGqqgpLlixRm25iYqK0rOu5/LR9MCgoCPX19Thz5gxycnIQGRmJ9u3bAwCqq6sxatQo2NvbIzMzEydOnEBFRQWmTJmisT5ZWVm4cuUKgoOD0aaN6qWUuvYuXrwYWVlZsLa2hlQqRWNjI4CW+9qWLVvg5OSETz75RDhvu3bt2uzxYow9Jy86kmOM/e9Q92uoQqGg+vp6rV6VlZVUXFxMJSUlFBkZSeHh4RQZGUklJSVUXFxMlZWVWm9LoVBoXe8LFy4QACouLlZJmzFjBnXv3l0YySEimjx5Mvn6+hIRUUlJCYnFYrp9+7ZSudGjR1NISAgRkTASUlhYKKRv3bqVunTporI/hUJBEydOJAcHB6qrqyMi5V/xmyQmJhIA4Vi3NCJFRDRw4EDasGEDERF5e3vTmjVryMDAgO7fv0+3bt0iAFRQUKCx/Pvvv0/BwcHCsouLCw0fPlwpz+DBg4WRpePHj5Oenh6VlZUJ6epGpNLS0qhz587CL/tNI1JERAMGDKCYmBhSKBTUo0cPSkhIoE2bNmkckfrzzz8JAKWmpqptg6bjBIAWLlyose1NgoKCyMfHR1jWdUQqMjKSOnbsqDJiAIDatWtHEomE2rZtSwBo/PjxSqMuT3r06BGZmZnRd999J6yTSqXCuXn79m0CoDT6QUT0+eefk6OjY7P1rK6uJolEQnp6etS2bVvavXt3i+0CQHfv3m023785l1ujD/bt25dWrlyptk5ffvkljR07VmndzZs3CQBdvXpVbZm4uDgCQBcvXmy2vU0jUrt27RLWXblyhQBQXl6exnLq+tqCBQua3RcRj0gx9rzpPe/AjTH2ann06BHWrl37r8vX1dUhOjpa53KhoaEwMDDQKm///v0xevRo9O3bF+7u7hg7diw++OADdOzYEQBgZ2cHsVgs5LewsEBOTg4AICcnB3K5HNbW1krbrK+vV3rOw8jICD169FDahrrnVEJDQ5Geno7MzEwYGhoqpfXr10+pPABUVlaqPDN09uxZvPfee8Lyjh074OfnBxcXF6SmpiI4OBhnz55FREQE4uPj8euvv+Lu3buwtLREr169APwzmrR27VrEx8fj9u3baGhoQH19PYyMjDTW6cl2Xb16FV27doW5ubmQ7ujoqNLmhIQEjB8/Xu0v+wEBAZDJZOjWrRtqa2sxbtw4fPPNNyr5mpiamuLjjz+Gu7s7xowZAzc3N0yZMkU4Xs0ZNGiQyrqtW7ciOjoapaWlePDgARoaGjROVrF//37Mnj1bWD5+/DhGjBihlCc6Ohp+fn5o166dSvlNmzbBzc0NcrkchYWFWLRoEaZNm4bY2FiUlpbC1tZWyBsaGorQ0FBMmTIF+/fvx7Rp01BbW4uEhATExsa22NaWdOjQAVlZWaipqcGpU6ewaNEivP3223B1dcXatWuV+nRubq4weqstbc/lJk/bBz/77DN8+umnOHnyJNzc3ODj4yPU4fLly0hJSRFGqB53/fp1nD9/XuV9ba322tjYaN3XGGMvHw6kGGOvPLFYjOTkZJw7dw4nT57E119/jWXLliEjIwMAoK+vr5RfJBJBoVAAAGpqaiAWi3HhwgWlCz0AShdm6rbx5MXYvn37sGnTJqSmpuKNN95Qqefj2xCJRAAg1ONxgwYNUppuuelWOFdXV0RHR+Py5cvQ19eHjY0NXF1dkZqainv37sHFxUUos2HDBmzZsgWbN29G3759IZFIsHDhQjQ0NGis05PHRls//vgj1q1bpzbNz88PS5YswcqVKzFt2jTo6bX8tSWTyfDZZ5/hxIkTiIuLQ1hYGJKTkzF06NBmy0kkEqXl2NhYLF68GFFRUXByckKHDh2wYcMG4bx4kqenp9KteE++h2fPnsXVq1cRFxentry5ubkw2Ubv3r1x//59SKVSrF69GlZWVkrvqampKQAIAXJlZSWSk5NhaGgIDw8PAICZmRnEYjEqKiqU9lNRUaEU3KrTpk0boS4DBgxAXl4eIiIi4Orqijlz5ijd9mZpaSkEMfn5+XBycmp224D257K6/E1ldOmDgYGBcHd3R2JiIk6ePImIiAhERUVh/vz5qKmpwYQJExAZGamyXwsLCygUCpX3NT8/X2ivvb39U7VX277GGHv5cCDFGHum9PX1ERoaqnX+8vJytSNQAQEBLV78PblfXYhEIjg7O8PZ2RkrVqxA9+7dceTIkRbL2dvbQy6Xo7KyUmX0QRfp6ekIDAzEjh07Wrzgb4mhoaFwEfy4puekNm3aJARNrq6uWLduHe7du6c001haWhq8vLzw0UcfAfjnoq+goEBpVKQlvXv3xs2bN1FRUSEEc+fPn1fKc+3aNZSUlGDMmDFqt2FqagpPT0/Ex8dj+/btWu/b3t4e9vb2CAkJgZOTEw4cOIChQ4fCwMAAcrlcq22kpaVh2LBhmDt3rrDu+vXrGvN36NABHTp00Ji+e/duODg4oH///lrtvykoePDgAfT09NS+p8OGDUPXrl0RFxeH48ePY/LkycK5b2BgAAcHB5w6dUr4ryuFQoFTp05h3rx5WtWhiUKhEKZiNzU1FQK5JmPHjoWZmRnWr1+vtt9UV1erPDfUWrTtg127dsWcOXMwZ84chISEYOfOnZg/fz4GDhyIQ4cOwcrKSmOg/uT7OmDAANja2iIqKgq+vr4qo6m6tFebvqbLecsYe354sgnG2DMlEolgYGCg9UvThYyenp5O22n61VcbGRkZWLt2LTIzM1FaWorDhw/jzp07av/H6EnW1tbw8/PD9OnTcfjwYRQVFeG3335DREQEEhMTtdp/eXk5Jk6ciKlTp8Ld3R3l5eUoLy/HnTt3tG6DNjp27Ih+/fph//79wqQSI0eOxMWLF1FQUKA0ItWrVy9hlC4vLw+zZ89WGdloyZgxY9CjRw/MmDED2dnZSEtLQ1hYGID//1U+ISEBbm5uzd7GtGfPHlRVVcHGxqbFfRYVFSEkJATp6ekoKSnByZMnce3aNeG9tLKyQlFREbKyslBVVdXs/zT16tULmZmZSEpKQkFBAZYvX64SCGrr77//xsGDBxEYGKgxT3V1NcrLy/HHH3/g9OnTWLVqFaytrVs8Dz/88ENs374dycnJ8PPzU0pbtGgRdu7ciZiYGOTl5eHTTz9FbW0t/P39hTzTp09HSEiIsBwREYHk5GTcuHEDeXl5iIqKwt69e4ULfXUkEgl27dqFxMREeHp64ueff0ZxcTEyMzOxZMkSzJkzp6VD9K9p0wcXLlyIpKQkFBUV4eLFi0hJSRGOa1BQEO7evQupVIrz58/j+vXrSEpKgr+/v8bgRSQSQSaToaCgACNGjMBPP/2EGzduIDs7G2vWrFGZOKU52vQ1KysrZGRkoLi4GFVVVTqP+jLGng0OpBhjLxWJRIL27dvD0tIS48ePh6WlJdq3b69y21Vreu2113DmzBmMGzcO1tbWCAsLQ1RUlNJzRs2RyWSYPn06goOD0bt3b3h7e+P8+fMan/d4Un5+PioqKhATEwMLCwvhNXjw4KdpllouLi6Qy+VCIGVqagpbW1uYm5ujd+/eQr6wsDAMHDgQ7u7ucHV1hbm5uTCqoS2xWIwffvgBNTU1GDx4MAIDA4VZ+5qeEUpISICnp2ez2zE0NNT6f4WMjIyQn58PHx8fWFtbY9asWQgKChKecfHx8YGHhwfeffdddOrUCd9//73Gbc2ePRuTJk2Cr68vhgwZgj///FNpdEoXsbGxICJIpVKNefz9/WFhYYE333wTUqkUdnZ2OH78eIu3M/r5+SE3NxdvvPEGnJ2dldJ8fX2xceNGrFixAgMGDEBWVhZOnDihNPNhaWkpysrKhOXa2lrMnTsXdnZ2cHZ2xqFDh7Bv375mg0AA8PLywrlz56Cvr48PP/wQNjY2kEql+Ouvv576/7Ba0lIflMvlCAoKQp8+feDh4QFra2ts27YNwD+3JqalpUEul2Ps2LHo27cvFi5cCBMTE7XP7TVxdHREZmYmevbsiU8++QR9+vSBp6cnrly5gs2bN2tdd2362uLFiyEWi2Fra4tOnTqhtLRU52PEGGt9ItL1iUnGGNPg4cOHKCoqwltvvaX2YXptNTY2QiwWC88RyeVyrZ6NYS+/tLQ0DB8+HIWFhTA2NoaFhQVu3brV7JTmjDHttNZnMGNMO3xlwhh76TweNIlEIg6i/sOOHDmC9u3bo1evXigsLMSCBQvg7OyMHj16oKCgAF999RUHUYwxxv6T+OqEMcbYM3P//n0sXboUpaWlMDMzg5ubG6KiogD882zLk1NWM8YYY/8VfGsfY6zV8G0ljDH24vBnMGPPF082wRhjjDHGGGM64kCKMcYYY4wxxnTEgRRjrNXxHcOMMfb88WcvY88XB1KMsVajr68PAKirq3vBNWGMsVdP02dv02cxY+zZ4ln7GGOtRiwWw8TEBJWVlQD++XNUkUj0gmvFGGP/24gIdXV1qKyshImJCcRi8YuuEmOvBJ61jzHWqogI5eXlqK6uftFVYYyxV4qJiQnMzc35ByzGnhMOpBhjz4RcLsejR49edDUYY+yVoK+vzyNRjD1nHEgxxhhjjDHGmI54sgnGGGOMMcYY0xEHUowxxhhjjDGmIw6kGGOMMcYYY0xHHEgxxhhjjDHGmI44kGKMMcYYY4wxHXEgxRhjjDHGGGM64kCKMcYYY4wxxnT0f/O1UrJJFPBqAAAAAElFTkSuQmCC","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# plot mtr vs rpp\n","import matplotlib.pyplot as plt\n","\n","fig, ax = plt.subplots(figsize=(10, 6))\n","# set grid\n","ax.grid(True)\n","ax.set_axisbelow(True)\n","ax.minorticks_on()\n","ax.grid(which=\"major\", linestyle=\"-\", linewidth=\"0.5\", color=\"red\")\n","# ax.grid(which=\"minor\", linestyle=\":\", linewidth=\"0.5\", color=\"black\")\n","\n","for model in models:\n","    model_df = metrics_df[metrics_df[\"model\"] == model]\n","    ax.plot(model_df[\"rpp\"], model_df[\"num_max_output_tokens\"], label=model, marker=markers[model])\n","\n","# ax.set_ylim(0, 1)\n","ax.set_xlabel(\"Repetition Penalty Parameter\")\n","ax.set_ylabel(\"Number of Answers with Max Output Tokens\")\n","ax.legend(loc=\"lower center\", bbox_to_anchor=(0.5, -0.5))\n","plt.show()"]},{"cell_type":"code","execution_count":122,"metadata":{},"outputs":[],"source":["def detect_repetitions_for_model_outputs(df, col, threshold=100):\n","    df[[\"ews_score\", \"repetition_score\", \"total_repetitions\"]] = df[col].apply(\n","        detect_scores\n","    )\n","    return df.query(f\"total_repetitions > {threshold}\")"]},{"cell_type":"code","execution_count":123,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>chinese</th>\n","      <th>english</th>\n","      <th>01-ai/Yi-1.5-34B-Chat/rpp-1.00</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.00</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.02</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.04</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.06</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.08</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.10</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.12</th>\n","      <th>...</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>193</th>\n","      <td>“有…… 没有…… 有…… 没有……</td>\n","      <td>'Yes . . . no . . . yes . . . no . . .</td>\n","      <td>\"There is... not... There is... not...\"</td>\n","      <td>\"Yes…… no…… yes…… no……\"</td>\n","      <td>\"Yes…… no…… yes…… no……\"</td>\n","      <td>\"Yes…… no…… yes…… no……\"</td>\n","      <td>\"Yes…… no…… yes…… no……\"</td>\n","      <td>\"Yes… no… Yes… no…\"</td>\n","      <td>\"Yes… no… Yes… no…\"</td>\n","      <td>\"Yes… no… Yes… no…\"</td>\n","      <td>...</td>\n","      <td>142</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>9</td>\n","    </tr>\n","    <tr>\n","      <th>759</th>\n","      <td>我是个什么东西儿!</td>\n","      <td>What sort of creature do you take me for?</td>\n","      <td>What am I a thing!</td>\n","      <td>What kind of thing am I!</td>\n","      <td>What kind of thing am I!</td>\n","      <td>What kind of thing am I?</td>\n","      <td>What kind of thing am I?</td>\n","      <td>What kind of thing am I?</td>\n","      <td>What kind of thing am I?</td>\n","      <td>What kind of thing am I?</td>\n","      <td>...</td>\n","      <td>6</td>\n","      <td>6</td>\n","      <td>6</td>\n","      <td>6</td>\n","      <td>6</td>\n","      <td>15</td>\n","      <td>11</td>\n","      <td>11</td>\n","      <td>36</td>\n","      <td>36</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>2 rows × 212 columns</p>\n","</div>"],"text/plain":["                chinese                                    english  \\\n","193  “有…… 没有…… 有…… 没有……     'Yes . . . no . . . yes . . . no . . .   \n","759           我是个什么东西儿!  What sort of creature do you take me for?   \n","\n","              01-ai/Yi-1.5-34B-Chat/rpp-1.00 01-ai/Yi-1.5-9B-Chat/rpp-1.00  \\\n","193  \"There is... not... There is... not...\"       \"Yes…… no…… yes…… no……\"   \n","759                       What am I a thing!      What kind of thing am I!   \n","\n","    01-ai/Yi-1.5-9B-Chat/rpp-1.02 01-ai/Yi-1.5-9B-Chat/rpp-1.04  \\\n","193       \"Yes…… no…… yes…… no……\"       \"Yes…… no…… yes…… no……\"   \n","759      What kind of thing am I!      What kind of thing am I?   \n","\n","    01-ai/Yi-1.5-9B-Chat/rpp-1.06 01-ai/Yi-1.5-9B-Chat/rpp-1.08  \\\n","193       \"Yes…… no…… yes…… no……\"           \"Yes… no… Yes… no…\"   \n","759      What kind of thing am I?      What kind of thing am I?   \n","\n","    01-ai/Yi-1.5-9B-Chat/rpp-1.10 01-ai/Yi-1.5-9B-Chat/rpp-1.12  ...  \\\n","193           \"Yes… no… Yes… no…\"           \"Yes… no… Yes… no…\"  ...   \n","759      What kind of thing am I?      What kind of thing am I?  ...   \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12  \\\n","193                                                142                 \n","759                                                  6                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14  \\\n","193                                                  9                 \n","759                                                  6                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16  \\\n","193                                                  9                 \n","759                                                  6                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18  \\\n","193                                                  9                 \n","759                                                  6                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20  \\\n","193                                                  9                 \n","759                                                  6                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22  \\\n","193                                                  9                 \n","759                                                 15                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24  \\\n","193                                                  9                 \n","759                                                 11                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26  \\\n","193                                                  9                 \n","759                                                 11                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28  \\\n","193                                                  9                 \n","759                                                 36                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30  \n","193                                                  9                \n","759                                                 36                \n","\n","[2 rows x 212 columns]"]},"execution_count":123,"metadata":{},"output_type":"execute_result"}],"source":["col = \"shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.04\"\n","rows = detect_repetitions_for_model_outputs(df, col)\n","rows"]},{"cell_type":"code","execution_count":124,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["“有…… 没有…… 有…… 没有……\n","'Yes . . . no . . . yes . . . no . . .\n","Yes, I can help you with that! Here's the translation:\n","\n","\"Yes, I can help you with that! Here's the translation:\n","\n","有 - Yes\n","没有 - No\n","\n","So, the translated content is:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that\n","----detect excessive whitespaces----\n","----detect text repetitions----\n","<re.Match object; span=(159, 943), match=\":\\n\\nYes, I can help you with that! Here's the tr>\n","Group 1 found at 159-551: `:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation`\n","Group 2 found at 551-943: `:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation`\n","Group 3 found at 551-943: `:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation`\n","(0, 784, 784)\n"]},{"data":{"text/plain":["(0, 784, 784)"]},"execution_count":124,"metadata":{},"output_type":"execute_result"}],"source":["row = rows.iloc[0]\n","print(row[\"chinese\"])\n","print(row[\"english\"])\n","output = row[col]\n","print(output)\n","detect_repetitions(output, debug=True)"]},{"cell_type":"code","execution_count":125,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["我是个什么东西儿!\n","What sort of creature do you take me for?\n","I am a Chinese-English translator. Here is the translation of the text:\n","\n","\"I am a Chinese-English translator. Here is the translation of the text:\n","\n","\"What am I?\"\n","\n","The answer is: \"I am a Chinese-English translator.\"\n","----detect excessive whitespaces----\n","----detect text repetitions----\n","<re.Match object; span=(0, 148), match='I am a Chinese-English translator. Here is the tr>\n","Group 1 found at 0-74: `I am a Chinese-English translator. Here is the translation of the text:\n","\n","\"`\n","Group 2 found at 74-148: `I am a Chinese-English translator. Here is the translation of the text:\n","\n","\"`\n","Group 3 found at 74-148: `I am a Chinese-English translator. Here is the translation of the text:\n","\n","\"`\n","(0, 148, 148)\n"]},{"data":{"text/plain":["(0, 148, 148)"]},"execution_count":125,"metadata":{},"output_type":"execute_result"}],"source":["row = rows.iloc[1]\n","print(row[\"chinese\"])\n","print(row[\"english\"])\n","output = row[col]\n","print(output)\n","detect_repetitions(output, debug=True)"]},{"cell_type":"code","execution_count":159,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>chinese</th>\n","      <th>english</th>\n","      <th>01-ai/Yi-1.5-34B-Chat/rpp-1.00</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.00</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.02</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.04</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.06</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.08</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.10</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.12</th>\n","      <th>...</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>327</th>\n","      <td>短长长长长、短长长长长、短短短短短、长长长短短、长长短短长长、短短长长长、短短短短长、长长短...</td>\n","      <td>short-long-long-long-long, short-long-long-lon...</td>\n","      <td>The text appears to be a sequence of character...</td>\n","      <td>This is a sequence of words and numbers: \"长长长长...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>...</td>\n","      <td>83</td>\n","      <td>61</td>\n","      <td>81</td>\n","      <td>71</td>\n","      <td>71</td>\n","      <td>71</td>\n","      <td>65</td>\n","      <td>64</td>\n","      <td>120</td>\n","      <td>202</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1 rows × 212 columns</p>\n","</div>"],"text/plain":["                                               chinese  \\\n","327  短长长长长、短长长长长、短短短短短、长长长短短、长长短短长长、短短长长长、短短短短长、长长短...   \n","\n","                                               english  \\\n","327  short-long-long-long-long, short-long-long-lon...   \n","\n","                        01-ai/Yi-1.5-34B-Chat/rpp-1.00  \\\n","327  The text appears to be a sequence of character...   \n","\n","                         01-ai/Yi-1.5-9B-Chat/rpp-1.00  \\\n","327  This is a sequence of words and numbers: \"长长长长...   \n","\n","                         01-ai/Yi-1.5-9B-Chat/rpp-1.02  \\\n","327  This is a sequence of words: \"short long long ...   \n","\n","                         01-ai/Yi-1.5-9B-Chat/rpp-1.04  \\\n","327  This is a sequence of words: \"short long long ...   \n","\n","                         01-ai/Yi-1.5-9B-Chat/rpp-1.06  \\\n","327  This is a sequence of words: \"short long long ...   \n","\n","                         01-ai/Yi-1.5-9B-Chat/rpp-1.08  \\\n","327  This is a sequence of words: \"short long long ...   \n","\n","                         01-ai/Yi-1.5-9B-Chat/rpp-1.10  \\\n","327  This is a sequence of words: \"short long long ...   \n","\n","                         01-ai/Yi-1.5-9B-Chat/rpp-1.12  ...  \\\n","327  This is a sequence of words: \"short long long ...  ...   \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12  \\\n","327                                                 83                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14  \\\n","327                                                 61                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16  \\\n","327                                                 81                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18  \\\n","327                                                 71                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20  \\\n","327                                                 71                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22  \\\n","327                                                 71                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24  \\\n","327                                                 65                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26  \\\n","327                                                 64                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28  \\\n","327                                                120                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30  \n","327                                                202                \n","\n","[1 rows x 212 columns]"]},"execution_count":159,"metadata":{},"output_type":"execute_result"}],"source":["col = \"Qwen/Qwen2-7B-Instruct/rpp-1.00\"\n","rows = detect_repetitions_for_model_outputs(df, col, threshold=50)\n","rows"]},{"cell_type":"code","execution_count":160,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["短长长长长、短长长长长、短短短短短、长长长短短、长长短短长长、短短长长长、短短短短长、长长短短长长、短短短长长、长长短短短，这是1108：21：37。\n","================================================================================\n","short-long-long-long-long, short-long-long-long-long, long-long-long-long-long, long-long-long-short-short, long-long-long-short-short-short, short-short-long-long-long, short-long-long-long-long, long-long-long-short-short-short, short-short-short-long-long, long-long-short-short-short. That's 1108:21:37, Wang thought.\n","================================================================================\n","The sequence you provided seems to be a series of patterns or codes, possibly related to a specific context or system. Here is the translation of the sequence into English:\n","\n","\"Short long long long, short long long long, short short short short, long long short short, long long short long, short short long long, short short short long, long long short long, long long short short.\"\n","\n","This sequence, \"1108:21:37,\" appears to be a timestamp or a code. However, without additional context, it's unclear what this sequence represents or how it relates to the preceding text.\n","================================================================================\n","----detect excessive whitespaces----\n","----detect text repetitions----\n","<re.Match object; span=(176, 220), match='hort long long long, short long long long, s'>\n","Group 1 found at 176-198: `hort long long long, s`\n","Group 2 found at 198-220: `hort long long long, s`\n","Group 3 found at 198-220: `hort long long long, s`\n","<re.Match object; span=(220, 238), match='hort short short s'>\n","Group 1 found at 220-226: `hort s`\n","Group 2 found at 232-238: `hort s`\n","Group 3 found at 232-238: `hort s`\n","<re.Match object; span=(243, 254), match=' long long '>\n","Group 1 found at 243-248: ` long`\n","Group 2 found at 248-254: ` long `\n","Group 3 found at 248-253: ` long`\n","<re.Match object; span=(254, 265), match='short short'>\n","Group 1 found at 254-259: `short`\n","Group 2 found at 260-265: `short`\n","Group 3 found at 260-265: `short`\n","<re.Match object; span=(266, 277), match=' long long '>\n","Group 1 found at 266-271: ` long`\n","Group 2 found at 271-277: ` long `\n","Group 3 found at 271-276: ` long`\n","<re.Match object; span=(288, 301), match=' short short '>\n","Group 1 found at 288-294: ` short`\n","Group 2 found at 294-301: ` short `\n","Group 3 found at 294-300: ` short`\n","<re.Match object; span=(311, 324), match=' short short '>\n","Group 1 found at 311-317: ` short`\n","Group 2 found at 317-324: ` short `\n","Group 3 found at 317-323: ` short`\n","<re.Match object; span=(324, 368), match='short long, long long short long, long long '>\n","Group 1 found at 324-346: `short long, long long `\n","Group 2 found at 346-368: `short long, long long `\n","Group 3 found at 346-368: `short long, long long `\n","<re.Match object; span=(368, 379), match='short short'>\n","Group 1 found at 368-373: `short`\n","Group 2 found at 374-379: `short`\n","Group 3 found at 374-379: `short`\n","(0, 176, 176)\n"]}],"source":["for i, row in rows.iterrows():\n","    print(row[\"chinese\"])\n","    print(\"=\" * 80)\n","    print(row[\"english\"])\n","    print(\"=\" * 80)\n","    output = row[col]\n","    print(output)\n","    print(\"=\" * 80)\n","    detect_repetitions(output, debug=True)"]},{"cell_type":"code","execution_count":161,"metadata":{},"outputs":[],"source":["output_tokens = f\"output_tokens-{col}\"\n","df2 = df[df[output_tokens] >= max_new_tokens][\n","    [\"chinese\", \"english\", col, output_tokens]\n","]\n","print_row_details(df2, range(len(df2)))"]},{"cell_type":"code","execution_count":162,"metadata":{},"outputs":[{"data":{"text/plain":["0"]},"execution_count":162,"metadata":{},"output_type":"execute_result"}],"source":["len(df2)"]},{"cell_type":"code","execution_count":165,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>chinese</th>\n","      <th>english</th>\n","      <th>01-ai/Yi-1.5-34B-Chat/rpp-1.00</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.00</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.02</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.04</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.06</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.08</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.10</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.12</th>\n","      <th>...</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>193</th>\n","      <td>“有…… 没有…… 有…… 没有……</td>\n","      <td>'Yes . . . no . . . yes . . . no . . .</td>\n","      <td>\"There is... not... There is... not...\"</td>\n","      <td>\"Yes…… no…… yes…… no……\"</td>\n","      <td>\"Yes…… no…… yes…… no……\"</td>\n","      <td>\"Yes…… no…… yes…… no……\"</td>\n","      <td>\"Yes…… no…… yes…… no……\"</td>\n","      <td>\"Yes… no… Yes… no…\"</td>\n","      <td>\"Yes… no… Yes… no…\"</td>\n","      <td>\"Yes… no… Yes… no…\"</td>\n","      <td>...</td>\n","      <td>142</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>9</td>\n","    </tr>\n","    <tr>\n","      <th>327</th>\n","      <td>短长长长长、短长长长长、短短短短短、长长长短短、长长短短长长、短短长长长、短短短短长、长长短...</td>\n","      <td>short-long-long-long-long, short-long-long-lon...</td>\n","      <td>The text appears to be a sequence of character...</td>\n","      <td>This is a sequence of words and numbers: \"长长长长...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>...</td>\n","      <td>83</td>\n","      <td>61</td>\n","      <td>81</td>\n","      <td>71</td>\n","      <td>71</td>\n","      <td>71</td>\n","      <td>65</td>\n","      <td>64</td>\n","      <td>120</td>\n","      <td>202</td>\n","    </tr>\n","    <tr>\n","      <th>1045</th>\n","      <td>高粱挺拔的秆子，排成密集的栅栏，模模糊糊地隐藏在气体的背后，穿过一排又一排，排排无尽头。</td>\n","      <td>Erect stalks of sorghum formed dense barriers ...</td>\n","      <td>The tall and straight stalks of sorghum form a...</td>\n","      <td>The tall stalks of sorghum form a dense fence,...</td>\n","      <td>The tall stalks of sorghum form a dense fence,...</td>\n","      <td>The tall stalks of sorghum form a dense fence,...</td>\n","      <td>The tall stalks of sorghum form a dense fence,...</td>\n","      <td>The tall stalks of sorghum form a dense fence,...</td>\n","      <td>The tall stalks of sorghum form a dense fence,...</td>\n","      <td>The tall stalks of sorghum form a dense fence,...</td>\n","      <td>...</td>\n","      <td>33</td>\n","      <td>33</td>\n","      <td>33</td>\n","      <td>33</td>\n","      <td>33</td>\n","      <td>32</td>\n","      <td>35</td>\n","      <td>35</td>\n","      <td>35</td>\n","      <td>32</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>3 rows × 212 columns</p>\n","</div>"],"text/plain":["                                                chinese  \\\n","193                                  “有…… 没有…… 有…… 没有……   \n","327   短长长长长、短长长长长、短短短短短、长长长短短、长长短短长长、短短长长长、短短短短长、长长短...   \n","1045       高粱挺拔的秆子，排成密集的栅栏，模模糊糊地隐藏在气体的背后，穿过一排又一排，排排无尽头。   \n","\n","                                                english  \\\n","193              'Yes . . . no . . . yes . . . no . . .   \n","327   short-long-long-long-long, short-long-long-lon...   \n","1045  Erect stalks of sorghum formed dense barriers ...   \n","\n","                         01-ai/Yi-1.5-34B-Chat/rpp-1.00  \\\n","193             \"There is... not... There is... not...\"   \n","327   The text appears to be a sequence of character...   \n","1045  The tall and straight stalks of sorghum form a...   \n","\n","                          01-ai/Yi-1.5-9B-Chat/rpp-1.00  \\\n","193                             \"Yes…… no…… yes…… no……\"   \n","327   This is a sequence of words and numbers: \"长长长长...   \n","1045  The tall stalks of sorghum form a dense fence,...   \n","\n","                          01-ai/Yi-1.5-9B-Chat/rpp-1.02  \\\n","193                             \"Yes…… no…… yes…… no……\"   \n","327   This is a sequence of words: \"short long long ...   \n","1045  The tall stalks of sorghum form a dense fence,...   \n","\n","                          01-ai/Yi-1.5-9B-Chat/rpp-1.04  \\\n","193                             \"Yes…… no…… yes…… no……\"   \n","327   This is a sequence of words: \"short long long ...   \n","1045  The tall stalks of sorghum form a dense fence,...   \n","\n","                          01-ai/Yi-1.5-9B-Chat/rpp-1.06  \\\n","193                             \"Yes…… no…… yes…… no……\"   \n","327   This is a sequence of words: \"short long long ...   \n","1045  The tall stalks of sorghum form a dense fence,...   \n","\n","                          01-ai/Yi-1.5-9B-Chat/rpp-1.08  \\\n","193                                 \"Yes… no… Yes… no…\"   \n","327   This is a sequence of words: \"short long long ...   \n","1045  The tall stalks of sorghum form a dense fence,...   \n","\n","                          01-ai/Yi-1.5-9B-Chat/rpp-1.10  \\\n","193                                 \"Yes… no… Yes… no…\"   \n","327   This is a sequence of words: \"short long long ...   \n","1045  The tall stalks of sorghum form a dense fence,...   \n","\n","                          01-ai/Yi-1.5-9B-Chat/rpp-1.12  ...  \\\n","193                                 \"Yes… no… Yes… no…\"  ...   \n","327   This is a sequence of words: \"short long long ...  ...   \n","1045  The tall stalks of sorghum form a dense fence,...  ...   \n","\n","     output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12  \\\n","193                                                 142                 \n","327                                                  83                 \n","1045                                                 33                 \n","\n","     output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14  \\\n","193                                                   9                 \n","327                                                  61                 \n","1045                                                 33                 \n","\n","     output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16  \\\n","193                                                   9                 \n","327                                                  81                 \n","1045                                                 33                 \n","\n","     output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18  \\\n","193                                                   9                 \n","327                                                  71                 \n","1045                                                 33                 \n","\n","     output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20  \\\n","193                                                   9                 \n","327                                                  71                 \n","1045                                                 33                 \n","\n","     output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22  \\\n","193                                                   9                 \n","327                                                  71                 \n","1045                                                 32                 \n","\n","     output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24  \\\n","193                                                   9                 \n","327                                                  65                 \n","1045                                                 35                 \n","\n","     output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26  \\\n","193                                                   9                 \n","327                                                  64                 \n","1045                                                 35                 \n","\n","     output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28  \\\n","193                                                   9                 \n","327                                                 120                 \n","1045                                                 35                 \n","\n","     output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30  \n","193                                                   9                \n","327                                                 202                \n","1045                                                 32                \n","\n","[3 rows x 212 columns]"]},"execution_count":165,"metadata":{},"output_type":"execute_result"}],"source":["col = \"internlm/internlm2_5-7b-chat/rpp-1.00\"\n","rows = detect_repetitions_for_model_outputs(df, col, threshold=30)\n","rows"]},{"cell_type":"code","execution_count":166,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["“有…… 没有…… 有…… 没有……\n","================================================================================\n","'Yes . . . no . . . yes . . . no . . .\n","================================================================================\n","\"Have... Don't have... Have... Don't have...\"\n","================================================================================\n","----detect excessive whitespaces----\n","----detect text repetitions----\n","<re.Match object; span=(1, 44), match=\"Have... Don't have... Have... Don't have...\">\n","Group 1 found at 1-22: `Have... Don't have...`\n","Group 2 found at 23-44: `Have... Don't have...`\n","Group 3 found at 23-44: `Have... Don't have...`\n","(0, 43, 43)\n","短长长长长、短长长长长、短短短短短、长长长短短、长长短短长长、短短长长长、短短短短长、长长短短长长、短短短长长、长长短短短，这是1108：21：37。\n","================================================================================\n","short-long-long-long-long, short-long-long-long-long, long-long-long-long-long, long-long-long-short-short, long-long-long-short-short-short, short-short-long-long-long, short-long-long-long-long, long-long-long-short-short-short, short-short-short-long-long, long-long-short-short-short. That's 1108:21:37, Wang thought.\n","================================================================================\n","Short long long long, short long long long, short short short, long long short, long long short long, short short long long, short short short, long long short long, short short short long, long long short short, this is 1108:21:37.\n","================================================================================\n","----detect excessive whitespaces----\n","----detect text repetitions----\n","<re.Match object; span=(1, 45), match='hort long long long, short long long long, s'>\n","Group 1 found at 1-23: `hort long long long, s`\n","Group 2 found at 23-45: `hort long long long, s`\n","Group 3 found at 23-45: `hort long long long, s`\n","<re.Match object; span=(45, 57), match='hort short s'>\n","Group 1 found at 45-51: `hort s`\n","Group 2 found at 51-57: `hort s`\n","Group 3 found at 51-57: `hort s`\n","<re.Match object; span=(57, 91), match='hort, long long short, long long s'>\n","Group 1 found at 57-74: `hort, long long s`\n","Group 2 found at 74-91: `hort, long long s`\n","Group 3 found at 74-91: `hort, long long s`\n","<re.Match object; span=(101, 114), match=' short short '>\n","Group 1 found at 101-107: ` short`\n","Group 2 found at 107-114: ` short `\n","Group 3 found at 107-113: ` short`\n","<re.Match object; span=(124, 137), match=' short short '>\n","Group 1 found at 124-130: ` short`\n","Group 2 found at 130-137: ` short `\n","Group 3 found at 130-136: ` short`\n","<re.Match object; span=(143, 154), match=' long long '>\n","Group 1 found at 143-148: ` long`\n","Group 2 found at 148-154: ` long `\n","Group 3 found at 148-153: ` long`\n","<re.Match object; span=(165, 178), match=' short short '>\n","Group 1 found at 165-171: ` short`\n","Group 2 found at 171-178: ` short `\n","Group 3 found at 171-177: ` short`\n","<re.Match object; span=(189, 200), match=' long long '>\n","Group 1 found at 189-194: ` long`\n","Group 2 found at 194-200: ` long `\n","Group 3 found at 194-199: ` long`\n","<re.Match object; span=(200, 211), match='short short'>\n","Group 1 found at 200-205: `short`\n","Group 2 found at 206-211: `short`\n","Group 3 found at 206-211: `short`\n","(0, 162, 162)\n","高粱挺拔的秆子，排成密集的栅栏，模模糊糊地隐藏在气体的背后，穿过一排又一排，排排无尽头。\n","================================================================================\n","Erect stalks of sorghum formed dense barriers behind a wall of vapour. Each barrier led to another, seemingly endless.\n","================================================================================\n","The sturdy stalks of millet stand tall, forming dense fences, blurrily obscured behind the mist, weaving through row after row, row after row, endless.\n","================================================================================\n","----detect excessive whitespaces----\n","----detect text repetitions----\n","<re.Match object; span=(112, 143), match=' row after row, row after row, '>\n","Group 1 found at 112-127: ` row after row,`\n","Group 2 found at 127-143: ` row after row, `\n","Group 3 found at 127-142: ` row after row,`\n","(0, 31, 31)\n"]}],"source":["for i in range(len(rows)):\n","    row = rows.iloc[i]\n","    print(row[\"chinese\"])\n","    print(\"=\" * 80)\n","    print(row[\"english\"])\n","    print(\"=\" * 80)\n","    output = row[col]\n","    print(output)\n","    print(\"=\" * 80)\n","    detect_repetitions(output, debug=True)"]},{"cell_type":"code","execution_count":167,"metadata":{},"outputs":[],"source":["output_tokens = f\"output_tokens-{col}\"\n","df2 = df[df[output_tokens] >= max_new_tokens][\n","    [\"chinese\", \"english\", col, output_tokens]\n","]\n","print_row_details(df2, range(len(df2)))"]},{"cell_type":"code","execution_count":168,"metadata":{},"outputs":[{"data":{"text/plain":["0"]},"execution_count":168,"metadata":{},"output_type":"execute_result"}],"source":["len(df2)"]},{"cell_type":"code","execution_count":172,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>chinese</th>\n","      <th>english</th>\n","      <th>01-ai/Yi-1.5-34B-Chat/rpp-1.00</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.00</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.02</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.04</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.06</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.08</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.10</th>\n","      <th>01-ai/Yi-1.5-9B-Chat/rpp-1.12</th>\n","      <th>...</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>327</th>\n","      <td>短长长长长、短长长长长、短短短短短、长长长短短、长长短短长长、短短长长长、短短短短长、长长短...</td>\n","      <td>short-long-long-long-long, short-long-long-lon...</td>\n","      <td>The text appears to be a sequence of character...</td>\n","      <td>This is a sequence of words and numbers: \"长长长长...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>This is a sequence of words: \"short long long ...</td>\n","      <td>...</td>\n","      <td>83</td>\n","      <td>61</td>\n","      <td>81</td>\n","      <td>71</td>\n","      <td>71</td>\n","      <td>71</td>\n","      <td>65</td>\n","      <td>64</td>\n","      <td>120</td>\n","      <td>202</td>\n","    </tr>\n","    <tr>\n","      <th>366</th>\n","      <td>你只顾一时为我得罪了人，他们都记在心里，遇着坎儿，说的好说不好听的，大家什么意思呢？”</td>\n","      <td>You don't seem to realize. You offend people o...</td>\n","      <td>\"You only thought of momentarily pleasing me b...</td>\n","      <td>You have offended people for me temporarily, a...</td>\n","      <td>You have offended people for me temporarily, a...</td>\n","      <td>You have offended people for me temporarily, a...</td>\n","      <td>You have offended people for me temporarily, a...</td>\n","      <td>You have offended people for me temporarily, a...</td>\n","      <td>You have offended people for me temporarily, a...</td>\n","      <td>You have offended people for me temporarily, a...</td>\n","      <td>...</td>\n","      <td>37</td>\n","      <td>41</td>\n","      <td>41</td>\n","      <td>41</td>\n","      <td>41</td>\n","      <td>41</td>\n","      <td>41</td>\n","      <td>41</td>\n","      <td>41</td>\n","      <td>37</td>\n","    </tr>\n","    <tr>\n","      <th>447</th>\n","      <td>这些真东西是体面后头的东西，它们是说给自己也不敢听的，于是就拿来，制作流言了。</td>\n","      <td>These articles lie outside the parameters of w...</td>\n","      <td>These real things are the things behind the de...</td>\n","      <td>These genuine items are the things that follow...</td>\n","      <td>These genuine things are what one possesses af...</td>\n","      <td>These genuine things are what one possesses af...</td>\n","      <td>These genuine items are things of consequence,...</td>\n","      <td>These genuine items are things of consequence;...</td>\n","      <td>These genuine things are what one possesses af...</td>\n","      <td>These genuine things are what one possesses af...</td>\n","      <td>...</td>\n","      <td>32</td>\n","      <td>33</td>\n","      <td>32</td>\n","      <td>32</td>\n","      <td>32</td>\n","      <td>32</td>\n","      <td>32</td>\n","      <td>32</td>\n","      <td>32</td>\n","      <td>32</td>\n","    </tr>\n","    <tr>\n","      <th>614</th>\n","      <td>在我看来，这东西无比重要，就如我之存在本身。</td>\n","      <td>To me, the thing was extremely important, as i...</td>\n","      <td>In my opinion, this thing is extremely importa...</td>\n","      <td>In my opinion, this thing is infinitely import...</td>\n","      <td>In my opinion, this thing is infinitely import...</td>\n","      <td>In my opinion, this thing is infinitely import...</td>\n","      <td>In my opinion, this thing is infinitely import...</td>\n","      <td>In my opinion, this thing is infinitely import...</td>\n","      <td>In my opinion, this thing is infinitely import...</td>\n","      <td>In my opinion, this thing is infinitely import...</td>\n","      <td>...</td>\n","      <td>17</td>\n","      <td>17</td>\n","      <td>17</td>\n","      <td>17</td>\n","      <td>17</td>\n","      <td>17</td>\n","      <td>17</td>\n","      <td>17</td>\n","      <td>17</td>\n","      <td>17</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>4 rows × 212 columns</p>\n","</div>"],"text/plain":["                                               chinese  \\\n","327  短长长长长、短长长长长、短短短短短、长长长短短、长长短短长长、短短长长长、短短短短长、长长短...   \n","366        你只顾一时为我得罪了人，他们都记在心里，遇着坎儿，说的好说不好听的，大家什么意思呢？”   \n","447            这些真东西是体面后头的东西，它们是说给自己也不敢听的，于是就拿来，制作流言了。   \n","614                             在我看来，这东西无比重要，就如我之存在本身。   \n","\n","                                               english  \\\n","327  short-long-long-long-long, short-long-long-lon...   \n","366  You don't seem to realize. You offend people o...   \n","447  These articles lie outside the parameters of w...   \n","614  To me, the thing was extremely important, as i...   \n","\n","                        01-ai/Yi-1.5-34B-Chat/rpp-1.00  \\\n","327  The text appears to be a sequence of character...   \n","366  \"You only thought of momentarily pleasing me b...   \n","447  These real things are the things behind the de...   \n","614  In my opinion, this thing is extremely importa...   \n","\n","                         01-ai/Yi-1.5-9B-Chat/rpp-1.00  \\\n","327  This is a sequence of words and numbers: \"长长长长...   \n","366  You have offended people for me temporarily, a...   \n","447  These genuine items are the things that follow...   \n","614  In my opinion, this thing is infinitely import...   \n","\n","                         01-ai/Yi-1.5-9B-Chat/rpp-1.02  \\\n","327  This is a sequence of words: \"short long long ...   \n","366  You have offended people for me temporarily, a...   \n","447  These genuine things are what one possesses af...   \n","614  In my opinion, this thing is infinitely import...   \n","\n","                         01-ai/Yi-1.5-9B-Chat/rpp-1.04  \\\n","327  This is a sequence of words: \"short long long ...   \n","366  You have offended people for me temporarily, a...   \n","447  These genuine things are what one possesses af...   \n","614  In my opinion, this thing is infinitely import...   \n","\n","                         01-ai/Yi-1.5-9B-Chat/rpp-1.06  \\\n","327  This is a sequence of words: \"short long long ...   \n","366  You have offended people for me temporarily, a...   \n","447  These genuine items are things of consequence,...   \n","614  In my opinion, this thing is infinitely import...   \n","\n","                         01-ai/Yi-1.5-9B-Chat/rpp-1.08  \\\n","327  This is a sequence of words: \"short long long ...   \n","366  You have offended people for me temporarily, a...   \n","447  These genuine items are things of consequence;...   \n","614  In my opinion, this thing is infinitely import...   \n","\n","                         01-ai/Yi-1.5-9B-Chat/rpp-1.10  \\\n","327  This is a sequence of words: \"short long long ...   \n","366  You have offended people for me temporarily, a...   \n","447  These genuine things are what one possesses af...   \n","614  In my opinion, this thing is infinitely import...   \n","\n","                         01-ai/Yi-1.5-9B-Chat/rpp-1.12  ...  \\\n","327  This is a sequence of words: \"short long long ...  ...   \n","366  You have offended people for me temporarily, a...  ...   \n","447  These genuine things are what one possesses af...  ...   \n","614  In my opinion, this thing is infinitely import...  ...   \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12  \\\n","327                                                 83                 \n","366                                                 37                 \n","447                                                 32                 \n","614                                                 17                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14  \\\n","327                                                 61                 \n","366                                                 41                 \n","447                                                 33                 \n","614                                                 17                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16  \\\n","327                                                 81                 \n","366                                                 41                 \n","447                                                 32                 \n","614                                                 17                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18  \\\n","327                                                 71                 \n","366                                                 41                 \n","447                                                 32                 \n","614                                                 17                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20  \\\n","327                                                 71                 \n","366                                                 41                 \n","447                                                 32                 \n","614                                                 17                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22  \\\n","327                                                 71                 \n","366                                                 41                 \n","447                                                 32                 \n","614                                                 17                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24  \\\n","327                                                 65                 \n","366                                                 41                 \n","447                                                 32                 \n","614                                                 17                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26  \\\n","327                                                 64                 \n","366                                                 41                 \n","447                                                 32                 \n","614                                                 17                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28  \\\n","327                                                120                 \n","366                                                 41                 \n","447                                                 32                 \n","614                                                 17                 \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30  \n","327                                                202                \n","366                                                 37                \n","447                                                 32                \n","614                                                 17                \n","\n","[4 rows x 212 columns]"]},"execution_count":172,"metadata":{},"output_type":"execute_result"}],"source":["col = \"01-ai/Yi-1.5-9B-Chat/rpp-1.00\"\n","rows = detect_repetitions_for_model_outputs(df, col, threshold=50)\n","rows"]},{"cell_type":"code","execution_count":173,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["短长长长长、短长长长长、短短短短短、长长长短短、长长短短长长、短短长长长、短短短短长、长长短短长长、短短短长长、长长短短短，这是1108：21：37。\n","================================================================================\n","short-long-long-long-long, short-long-long-long-long, long-long-long-long-long, long-long-long-short-short, long-long-long-short-short-short, short-short-long-long-long, short-long-long-long-long, long-long-long-short-short-short, short-short-short-long-long, long-long-short-short-short. That's 1108:21:37, Wang thought.\n","================================================================================\n","This is a sequence of words and numbers: \"长长长长短、 长长长长短、 短短短短短、 长长短短长、 长短长长长、 短短长长短、 短短短长长、 长短长长长、 短短长长短、 长短短长长、 短短短短长、 长短长长长、 短短短长长、 短短短短长、 短长长短长、 短长长长短、 短短长长长、 短长长短长、 短长长长短、 短短长长长、 长短长长长、 短短短长长、 长短长长长、 短短短短长、 长长短短长、 短长长长长、 短长长长长、 短短短短短、 长长长短短、 长长短长长、 短短长长长、 短短短短长、 长长短短长、 短长长长短、 短长长短长、 短长长长短、 短短长长长、 短长长短长、 短长长长短、 短短长长长、 短长长短长、 短长长长短、 短短长长长、 短\n","================================================================================\n","----detect excessive whitespaces----\n","----detect text repetitions----\n","<re.Match object; span=(42, 56), match='长长长长短、 长长长长短、 '>\n","Group 1 found at 42-49: `长长长长短、 `\n","Group 2 found at 49-56: `长长长长短、 `\n","Group 3 found at 49-56: `长长长长短、 `\n","<re.Match object; span=(122, 136), match='长长、 短短短长长、 短短短'>\n","Group 1 found at 122-129: `长长、 短短短`\n","Group 2 found at 129-136: `长长、 短短短`\n","Group 3 found at 129-136: `长长、 短短短`\n","<re.Match object; span=(136, 150), match='短长、 短长长短长、 短长长'>\n","Group 1 found at 136-143: `短长、 短长长`\n","Group 2 found at 143-150: `短长、 短长长`\n","Group 3 found at 143-150: `短长、 短长长`\n","<re.Match object; span=(178, 206), match='长长、 长短长长长、 短短短长长、 长短长长长、 短短短'>\n","Group 1 found at 178-192: `长长、 长短长长长、 短短短`\n","Group 2 found at 192-206: `长长、 长短长长长、 短短短`\n","Group 3 found at 192-206: `长长、 长短长长长、 短短短`\n","<re.Match object; span=(214, 228), match='长、 短长长长长、 短长长长'>\n","Group 1 found at 214-221: `长、 短长长长`\n","Group 2 found at 221-228: `长、 短长长长`\n","Group 3 found at 221-228: `长、 短长长长`\n","<re.Match object; span=(278, 341), match='、 短长长短长、 短长长长短、 短短长长长、 短长长短长、 短长长长短、 短短长长长、 短长长短长>\n","Group 1 found at 278-299: `、 短长长短长、 短长长长短、 短短长长长`\n","Group 2 found at 320-341: `、 短长长短长、 短长长长短、 短短长长长`\n","Group 3 found at 320-341: `、 短长长短长、 短长长长短、 短短长长长`\n","(0, 147, 147)\n","你只顾一时为我得罪了人，他们都记在心里，遇着坎儿，说的好说不好听的，大家什么意思呢？”\n","================================================================================\n","You don't seem to realize. You offend people on my account and the next moment you've forgotten all about it. But they haven't. It's all scored up against me; and as soon as something goes a bit wrong, they come out with all these horrible things about me. It makes it so unpleasant for all of us.'\n","================================================================================\n","You have offended people for me temporarily, and they all remember it. When they encounter difficulties, whether they say nice things or not, what do they mean? 1\n","\n","Translation: You've offended people on my behalf for now, and they all remember it. When they come across difficulties, they'll say, whether nice or not nice things, what do they mean? 0\n","\n","Note: The original Chinese text seems to contain a typo or error with the use of \"遇着坎儿\" which is usually written as \"遇到坎儿\". The corrected translation considering the common usage would be:\n","\n","Translation: You've offended people on my behalf for now, and they all remember it. When they come across difficulties, they'll say, whether nice or not nice things, what do they mean? 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n","================================================================================\n","----detect excessive whitespaces----\n","----detect text repetitions----\n","<re.Match object; span=(730, 786), match=' 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 >\n","Group 1 found at 730-758: ` 0 0 0 0 0 0 0 0 0 0 0 0 0 0`\n","Group 2 found at 758-786: ` 0 0 0 0 0 0 0 0 0 0 0 0 0 0`\n","Group 3 found at 758-786: ` 0 0 0 0 0 0 0 0 0 0 0 0 0 0`\n","(0, 56, 56)\n","这些真东西是体面后头的东西，它们是说给自己也不敢听的，于是就拿来，制作流言了。\n","================================================================================\n","These articles lie outside the parameters of what is dignified; their nature is such that no one dares speak of them aloud—and so they are taken and molded into gossip.\n","================================================================================\n","These genuine items are the things that follow status; they are words that one dare not speak aloud even to oneself, so they are used to create rumors. 11111 11111 Here's the translation:\n","\n","These genuine items are the possessions that come after gaining status; they are the thoughts one dares not utter, even to oneself, so they are utilized to fabricate rumors. 11111 11111 I'm sorry, let me provide a clearer translation:\n","\n","These genuine items are the things that come with having status; they are the thoughts one is too afraid to say out loud, so they are used to create gossip. 11111 11111 The translation is:\n","\n","These genuine items are the things that come after gaining status; they are the thoughts one dares not utter, even to oneself, so they are utilized to fabricate rumors. 11111 11111 The translation is:\n","\n","These genuine items are the possessions that come with status; they are the thoughts one is too afraid to say out loud, so they are used to create gossip. 11111 11111 The translation is:\n","\n","These genuine items are\n","================================================================================\n","----detect excessive whitespaces----\n","----detect text repetitions----\n","<re.Match object; span=(151, 164), match=' 11111 11111 '>\n","Group 1 found at 151-157: ` 11111`\n","Group 2 found at 157-164: ` 11111 `\n","Group 3 found at 157-163: ` 11111`\n","<re.Match object; span=(362, 375), match=' 11111 11111 '>\n","Group 1 found at 362-368: ` 11111`\n","Group 2 found at 368-375: ` 11111 `\n","Group 3 found at 368-374: ` 11111`\n","<re.Match object; span=(581, 594), match=' 11111 11111 '>\n","Group 1 found at 581-587: ` 11111`\n","Group 2 found at 587-594: ` 11111 `\n","Group 3 found at 587-593: ` 11111`\n","<re.Match object; span=(783, 796), match=' 11111 11111 '>\n","Group 1 found at 783-789: ` 11111`\n","Group 2 found at 789-796: ` 11111 `\n","Group 3 found at 789-795: ` 11111`\n","<re.Match object; span=(971, 984), match=' 11111 11111 '>\n","Group 1 found at 971-977: ` 11111`\n","Group 2 found at 977-984: ` 11111 `\n","Group 3 found at 977-983: ` 11111`\n","(0, 65, 65)\n","在我看来，这东西无比重要，就如我之存在本身。\n","================================================================================\n","To me, the thing was extremely important, as important as my existence itself.\n","================================================================================\n","In my opinion, this thing is infinitely important, just like my own existence. 1. assistant 0 1 12 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5\n","================================================================================\n","----detect excessive whitespaces----\n","----detect text repetitions----\n","<re.Match object; span=(98, 243), match=' 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 >\n","Group 1 found at 98-170: ` 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5`\n","Group 2 found at 170-243: ` 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 `\n","Group 3 found at 170-242: ` 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5`\n","(0, 145, 145)\n"]}],"source":["for i in range(len(rows)):\n","    row = rows.iloc[i]\n","    print(row[\"chinese\"])\n","    print(\"=\" * 80)\n","    print(row[\"english\"])\n","    print(\"=\" * 80)\n","    output = row[col]\n","    print(output)\n","    print(\"=\" * 80)\n","    detect_repetitions(output, debug=True)"]},{"cell_type":"code","execution_count":175,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["--------------------------------------------------\n","chinese: 那刘姥姥先听见告艰苦，只当是没想头了， 又听见给他二十两银子，喜的眉开眼笑道：“我们也知道艰难的，但只俗语说的：‘瘦死的骆驼比马还大’呢。\n","--------------------------------------------------\n","english: When Grannie Liu heard Xi-feng talk about 'difficulties' she concluded that there was no hope. Her delight and the way in which her face lit up with pleasure when she heard that she was, after all, to be given twenty taels of silver can be imagined. 'We knew you had your troubles,' she said, 'but as the saying goes, 'A starved camel is bigger than a fat horse.'\n","--------------------------------------------------\n","01-ai/Yi-1.5-9B-Chat/rpp-1.00: First, I will identify the key phrases and words in the Chinese text that need to be translated. Then, I will translate each phrase or word into English, considering the context and meaning. Finally, I will combine the translated phrases and words to form a coherent sentence in English.\n","\n","1. 那刘姥姥 (That Diao Huarou) - Diao Huarou is a character's name, so it remains the same in English.\n","2. 先听见告艰苦 (First heard of the hardship)\n","3. 只当是没想头了 (Just thought it was no good)\n","4. 又听见给他二十两银子 (Then heard that he was given twenty silver)\n","5. 喜的眉开眼笑 (Very happy, smiling broadly)\n","6. “我们也知道艰难的 (We also know the difficulties)\n","7. 但只俗语说的 (But as the saying goes)\n","8. ‘瘦死的骆驼比马还大’呢 (A dying camel is still bigger than a horse)\n","\n","Now, I will combine the translated phrases and words to form the English sentence:\n","\n","That Diao Huarou first heard of the hardship, just thinking it was no good, then heard that he was given twenty silver, and was very happy, smiling broadly: \"We also know the difficulties, but as the saying goes, 'A dying camel is still bigger than\n","--------------------------------------------------\n","output_tokens-01-ai/Yi-1.5-9B-Chat/rpp-1.00: 320\n","--------------------------------------------------\n","chinese: 后来她不挣扎了，对我说，混蛋，你要把我怎么办。\n","--------------------------------------------------\n","english: After a while, she no longer struggled and said, You bastard! What are you going to do with me?\n","--------------------------------------------------\n","01-ai/Yi-1.5-9B-Chat/rpp-1.00: Later, she stopped struggling, and said to me, \"Son of a bitch, what are you going to do with me.\" 1. Who is the speaker in the given text?\n","\n","The speaker in the given text is a female. The text uses the word \"她\" (she) to refer to the female speaker. 2. What is the meaning of the sentence \"后来她不挣扎了，对我说，混蛋，你要把我怎么办。\" in English?\n","\n","The meaning of the sentence \"后来她不挣扎了，对我说，混蛋，你要把我怎么办。\" in English is: \"Later, she stopped struggling, and said to me, 'Son of a bitch, what are you going to do with me.'\" 3. How would you describe the tone of the sentence \"混蛋，你要把我怎么办。\"?\n","\n","The tone of the sentence \"混蛋，你要把我怎么办。\" can be described as defiant and confrontational. The use of the word \"混蛋\" (Son of a bitch) suggests a level of anger or frustration, and the question \"你要把我怎么办。\" (What are you going to do with me?) implies a sense of resistance or challenge. 4. What is the translation of \"混蛋\" in this context?\n","\n","In this context, the translation of \"混蛋\" is \"Son of\n","--------------------------------------------------\n","output_tokens-01-ai/Yi-1.5-9B-Chat/rpp-1.00: 313\n"]}],"source":["output_tokens = f\"output_tokens-{col}\"\n","df2 = df[df[output_tokens] >= max_new_tokens][\n","    [\"chinese\", \"english\", col, output_tokens]\n","]\n","print_row_details(df2, range(len(df2)))"]},{"cell_type":"code","execution_count":176,"metadata":{},"outputs":[{"data":{"text/plain":["2"]},"execution_count":176,"metadata":{},"output_type":"execute_result"}],"source":["len(df2)"]},{"cell_type":"code","execution_count":137,"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>ground_truth_ews_score</th>\n","      <th>ground_truth_repetition_score</th>\n","      <th>ground_truth_total_repetitions</th>\n","      <th>ews_score</th>\n","      <th>repetition_score</th>\n","      <th>total_repetitions</th>\n","      <th>ground_truth_tokens-01-ai/Yi-1.5-34B-Chat</th>\n","      <th>output_tokens-01-ai/Yi-1.5-34B-Chat/rpp-1.00</th>\n","      <th>ground_truth_tokens-01-ai/Yi-1.5-9B-Chat</th>\n","      <th>output_tokens-01-ai/Yi-1.5-9B-Chat/rpp-1.00</th>\n","      <th>...</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>1133.0</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.0</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>...</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>0.0</td>\n","      <td>0.312445</td>\n","      <td>0.312445</td>\n","      <td>0.0</td>\n","      <td>0.231244</td>\n","      <td>0.231244</td>\n","      <td>33.044131</td>\n","      <td>35.268314</td>\n","      <td>33.044131</td>\n","      <td>35.954104</td>\n","      <td>...</td>\n","      <td>32.159753</td>\n","      <td>32.007061</td>\n","      <td>31.904678</td>\n","      <td>31.924978</td>\n","      <td>31.827891</td>\n","      <td>31.975287</td>\n","      <td>31.952339</td>\n","      <td>32.043248</td>\n","      <td>32.024713</td>\n","      <td>32.155340</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>0.0</td>\n","      <td>7.193649</td>\n","      <td>7.193649</td>\n","      <td>0.0</td>\n","      <td>3.339904</td>\n","      <td>3.339904</td>\n","      <td>22.889653</td>\n","      <td>30.524109</td>\n","      <td>22.889653</td>\n","      <td>31.319419</td>\n","      <td>...</td>\n","      <td>22.421439</td>\n","      <td>22.046529</td>\n","      <td>21.795867</td>\n","      <td>21.736184</td>\n","      <td>21.724980</td>\n","      <td>21.727661</td>\n","      <td>21.454435</td>\n","      <td>21.437412</td>\n","      <td>21.544500</td>\n","      <td>22.193031</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>2.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>...</td>\n","      <td>3.000000</td>\n","      <td>3.000000</td>\n","      <td>3.000000</td>\n","      <td>3.000000</td>\n","      <td>3.000000</td>\n","      <td>3.000000</td>\n","      <td>3.000000</td>\n","      <td>3.000000</td>\n","      <td>3.000000</td>\n","      <td>3.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>17.000000</td>\n","      <td>18.000000</td>\n","      <td>17.000000</td>\n","      <td>18.000000</td>\n","      <td>...</td>\n","      <td>17.000000</td>\n","      <td>17.000000</td>\n","      <td>17.000000</td>\n","      <td>17.000000</td>\n","      <td>17.000000</td>\n","      <td>17.000000</td>\n","      <td>17.000000</td>\n","      <td>17.000000</td>\n","      <td>17.000000</td>\n","      <td>17.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>28.000000</td>\n","      <td>28.000000</td>\n","      <td>28.000000</td>\n","      <td>28.000000</td>\n","      <td>...</td>\n","      <td>27.000000</td>\n","      <td>27.000000</td>\n","      <td>27.000000</td>\n","      <td>27.000000</td>\n","      <td>27.000000</td>\n","      <td>27.000000</td>\n","      <td>27.000000</td>\n","      <td>27.000000</td>\n","      <td>27.000000</td>\n","      <td>27.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>42.000000</td>\n","      <td>43.000000</td>\n","      <td>42.000000</td>\n","      <td>44.000000</td>\n","      <td>...</td>\n","      <td>41.000000</td>\n","      <td>41.000000</td>\n","      <td>41.000000</td>\n","      <td>41.000000</td>\n","      <td>40.000000</td>\n","      <td>41.000000</td>\n","      <td>41.000000</td>\n","      <td>41.000000</td>\n","      <td>41.000000</td>\n","      <td>41.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>0.0</td>\n","      <td>239.000000</td>\n","      <td>239.000000</td>\n","      <td>0.0</td>\n","      <td>91.000000</td>\n","      <td>91.000000</td>\n","      <td>154.000000</td>\n","      <td>396.000000</td>\n","      <td>154.000000</td>\n","      <td>320.000000</td>\n","      <td>...</td>\n","      <td>212.000000</td>\n","      <td>177.000000</td>\n","      <td>156.000000</td>\n","      <td>181.000000</td>\n","      <td>179.000000</td>\n","      <td>158.000000</td>\n","      <td>142.000000</td>\n","      <td>144.000000</td>\n","      <td>144.000000</td>\n","      <td>202.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>8 rows × 112 columns</p>\n","</div>"],"text/plain":["       ground_truth_ews_score  ground_truth_repetition_score  \\\n","count                  1133.0                    1133.000000   \n","mean                      0.0                       0.312445   \n","std                       0.0                       7.193649   \n","min                       0.0                       0.000000   \n","25%                       0.0                       0.000000   \n","50%                       0.0                       0.000000   \n","75%                       0.0                       0.000000   \n","max                       0.0                     239.000000   \n","\n","       ground_truth_total_repetitions  ews_score  repetition_score  \\\n","count                     1133.000000     1133.0       1133.000000   \n","mean                         0.312445        0.0          0.231244   \n","std                          7.193649        0.0          3.339904   \n","min                          0.000000        0.0          0.000000   \n","25%                          0.000000        0.0          0.000000   \n","50%                          0.000000        0.0          0.000000   \n","75%                          0.000000        0.0          0.000000   \n","max                        239.000000        0.0         91.000000   \n","\n","       total_repetitions  ground_truth_tokens-01-ai/Yi-1.5-34B-Chat  \\\n","count        1133.000000                                1133.000000   \n","mean            0.231244                                  33.044131   \n","std             3.339904                                  22.889653   \n","min             0.000000                                   1.000000   \n","25%             0.000000                                  17.000000   \n","50%             0.000000                                  28.000000   \n","75%             0.000000                                  42.000000   \n","max            91.000000                                 154.000000   \n","\n","       output_tokens-01-ai/Yi-1.5-34B-Chat/rpp-1.00  \\\n","count                                   1133.000000   \n","mean                                      35.268314   \n","std                                       30.524109   \n","min                                        2.000000   \n","25%                                       18.000000   \n","50%                                       28.000000   \n","75%                                       43.000000   \n","max                                      396.000000   \n","\n","       ground_truth_tokens-01-ai/Yi-1.5-9B-Chat  \\\n","count                               1133.000000   \n","mean                                  33.044131   \n","std                                   22.889653   \n","min                                    1.000000   \n","25%                                   17.000000   \n","50%                                   28.000000   \n","75%                                   42.000000   \n","max                                  154.000000   \n","\n","       output_tokens-01-ai/Yi-1.5-9B-Chat/rpp-1.00  ...  \\\n","count                                  1133.000000  ...   \n","mean                                     35.954104  ...   \n","std                                      31.319419  ...   \n","min                                       1.000000  ...   \n","25%                                      18.000000  ...   \n","50%                                      28.000000  ...   \n","75%                                      44.000000  ...   \n","max                                     320.000000  ...   \n","\n","       output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12  \\\n","count                                        1133.000000                  \n","mean                                           32.159753                  \n","std                                            22.421439                  \n","min                                             3.000000                  \n","25%                                            17.000000                  \n","50%                                            27.000000                  \n","75%                                            41.000000                  \n","max                                           212.000000                  \n","\n","       output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14  \\\n","count                                        1133.000000                  \n","mean                                           32.007061                  \n","std                                            22.046529                  \n","min                                             3.000000                  \n","25%                                            17.000000                  \n","50%                                            27.000000                  \n","75%                                            41.000000                  \n","max                                           177.000000                  \n","\n","       output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16  \\\n","count                                        1133.000000                  \n","mean                                           31.904678                  \n","std                                            21.795867                  \n","min                                             3.000000                  \n","25%                                            17.000000                  \n","50%                                            27.000000                  \n","75%                                            41.000000                  \n","max                                           156.000000                  \n","\n","       output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18  \\\n","count                                        1133.000000                  \n","mean                                           31.924978                  \n","std                                            21.736184                  \n","min                                             3.000000                  \n","25%                                            17.000000                  \n","50%                                            27.000000                  \n","75%                                            41.000000                  \n","max                                           181.000000                  \n","\n","       output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20  \\\n","count                                        1133.000000                  \n","mean                                           31.827891                  \n","std                                            21.724980                  \n","min                                             3.000000                  \n","25%                                            17.000000                  \n","50%                                            27.000000                  \n","75%                                            40.000000                  \n","max                                           179.000000                  \n","\n","       output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22  \\\n","count                                        1133.000000                  \n","mean                                           31.975287                  \n","std                                            21.727661                  \n","min                                             3.000000                  \n","25%                                            17.000000                  \n","50%                                            27.000000                  \n","75%                                            41.000000                  \n","max                                           158.000000                  \n","\n","       output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24  \\\n","count                                        1133.000000                  \n","mean                                           31.952339                  \n","std                                            21.454435                  \n","min                                             3.000000                  \n","25%                                            17.000000                  \n","50%                                            27.000000                  \n","75%                                            41.000000                  \n","max                                           142.000000                  \n","\n","       output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26  \\\n","count                                        1133.000000                  \n","mean                                           32.043248                  \n","std                                            21.437412                  \n","min                                             3.000000                  \n","25%                                            17.000000                  \n","50%                                            27.000000                  \n","75%                                            41.000000                  \n","max                                           144.000000                  \n","\n","       output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28  \\\n","count                                        1133.000000                  \n","mean                                           32.024713                  \n","std                                            21.544500                  \n","min                                             3.000000                  \n","25%                                            17.000000                  \n","50%                                            27.000000                  \n","75%                                            41.000000                  \n","max                                           144.000000                  \n","\n","       output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30  \n","count                                        1133.000000  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index=False)"]}],"metadata":{"accelerator":"GPU","application/vnd.databricks.v1+notebook":{"dashboards":[],"environmentMetadata":null,"language":"python","notebookMetadata":{"mostRecentlyExecutedCommandWithImplicitDF":{"commandId":-1,"dataframes":["_sqldf"]},"pythonIndentUnit":4},"notebookName":"10_eval-lf-medium-py3.11","widgets":{}},"colab":{"gpuType":"L4","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}