diff --git "a/notebooks/00_Data Analysis.ipynb" "b/notebooks/00_Data Analysis.ipynb" --- "a/notebooks/00_Data Analysis.ipynb" +++ "b/notebooks/00_Data Analysis.ipynb" @@ -1 +1 @@ -{"cells":[{"cell_type":"code","execution_count":210,"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":211,"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":212,"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":212,"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":213,"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\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","\n","print(model_name, adapter_name_or_path, load_in_4bit, data_path, results_path)"]},{"cell_type":"code","execution_count":214,"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 9.65 ms, sys: 19.5 ms, total: 29.1 ms\n","Wall time: 1.87 s\n"]}],"source":["%%time\n","os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n","\n","!python --version\n","!pip show torch transformers"]},{"cell_type":"code","execution_count":215,"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":216,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","RangeIndex: 1133 entries, 0 to 1132\n","Data columns (total 55 columns):\n"," # Column Non-Null Count Dtype \n","--- ------ -------------- ----- \n"," 0 chinese 1133 non-null object\n"," 1 english 1133 non-null object\n"," 2 Qwen/Qwen2-7B-Instruct/rpp-1.00 1133 non-null object\n"," 3 Qwen/Qwen2-7B-Instruct/rpp-1.02 1133 non-null object\n"," 4 Qwen/Qwen2-7B-Instruct/rpp-1.04 1133 non-null object\n"," 5 Qwen/Qwen2-7B-Instruct/rpp-1.06 1133 non-null object\n"," 6 Qwen/Qwen2-7B-Instruct/rpp-1.08 1133 non-null object\n"," 7 Qwen/Qwen2-7B-Instruct/rpp-1.10 1133 non-null object\n"," 8 Qwen/Qwen2-72B-Instruct/rpp-1.00 1133 non-null object\n"," 9 Qwen/Qwen2-7B-Instruct/rpp-1.12 1133 non-null object\n"," 10 Qwen/Qwen2-7B-Instruct/rpp-1.14 1133 non-null object\n"," 11 Qwen/Qwen2-7B-Instruct/rpp-1.16 1133 non-null object\n"," 12 Qwen/Qwen2-7B-Instruct/rpp-1.18 1133 non-null object\n"," 13 Qwen/Qwen2-7B-Instruct/rpp-1.20 1133 non-null object\n"," 14 Qwen/Qwen2-7B-Instruct/rpp-1.22 1133 non-null object\n"," 15 Qwen/Qwen2-7B-Instruct/rpp-1.24 1133 non-null object\n"," 16 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.00 1133 non-null object\n"," 17 Qwen/Qwen2-7B-Instruct/rpp-1.26 1133 non-null object\n"," 18 Qwen/Qwen2-72B-Instruct/rpp-1.02 1133 non-null object\n"," 19 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.02 1133 non-null object\n"," 20 Qwen/Qwen2-7B-Instruct/rpp-1.28 1133 non-null object\n"," 21 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.04 1133 non-null object\n"," 22 Qwen/Qwen2-7B-Instruct/rpp-1.30 1133 non-null object\n"," 23 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.06 1133 non-null object\n"," 24 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.00 1133 non-null object\n"," 25 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.02 1133 non-null object\n"," 26 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.08 1133 non-null object\n"," 27 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.04 1133 non-null object\n"," 28 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.10 1133 non-null object\n"," 29 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.06 1133 non-null object\n"," 30 Qwen/Qwen2-72B-Instruct/rpp-1.04 1133 non-null object\n"," 31 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12 1133 non-null object\n"," 32 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.08 1133 non-null object\n"," 33 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14 1133 non-null object\n"," 34 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.10 1133 non-null object\n"," 35 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16 1133 non-null object\n"," 36 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.12 1133 non-null object\n"," 37 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18 1133 non-null object\n"," 38 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.14 1133 non-null object\n"," 39 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.16 1133 non-null object\n"," 40 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20 1133 non-null object\n"," 41 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.18 1133 non-null object\n"," 42 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22 1133 non-null object\n"," 43 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.20 1133 non-null object\n"," 44 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24 1133 non-null object\n"," 45 Qwen/Qwen2-72B-Instruct/rpp-1.06 1133 non-null object\n"," 46 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.22 1133 non-null object\n"," 47 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26 1133 non-null object\n"," 48 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28 1133 non-null object\n"," 49 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.24 1133 non-null object\n"," 50 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30 1133 non-null object\n"," 51 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.26 1133 non-null object\n"," 52 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.28 1133 non-null object\n"," 53 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.30 1133 non-null object\n"," 54 Qwen/Qwen2-72B-Instruct/rpp-1.08 1133 non-null object\n","dtypes: object(55)\n","memory usage: 487.0+ KB\n"]}],"source":["import pandas as pd\n","\n","df = pd.read_csv(results_path)\n","df.info()"]},{"cell_type":"code","execution_count":217,"metadata":{},"outputs":[{"data":{"text/plain":["['chinese',\n"," 'english',\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/Qwen2-7B-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"," 'Qwen/Qwen2-7B-Instruct/rpp-1.18',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.20',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.22',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.24',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.26',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.28',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.30',\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"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.30',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.00',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.02',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.04',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.06',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.08',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.10',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30']"]},"execution_count":217,"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":218,"metadata":{},"outputs":[],"source":["df = df[columns]"]},{"cell_type":"code","execution_count":219,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Qwen/Qwen2-72B-Instruct/rpp-1.00: {'meteor': 0.3931693232556192, 'bleu_scores': {'bleu': 0.12273151341458781, 'precisions': [0.4199273774494459, 0.16226917210268393, 0.07941374663072777, 0.04192938209331652], 'brevity_penalty': 1.0, 'length_ratio': 1.0581649552832064, 'translation_length': 31946, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.4422424373632814, 'rouge2': 0.19255208879947344, 'rougeL': 0.38436072285817197, 'rougeLsum': 0.384629860342585}, 'accuracy': 0.0, 'correct_ids': []}\n","Qwen/Qwen2-72B-Instruct/rpp-1.02: {'meteor': 0.3925672197170406, 'bleu_scores': {'bleu': 0.12421056155279153, 'precisions': [0.4254972181364712, 0.16363093460734549, 0.08028819635962493, 0.042581432056249105], 'brevity_penalty': 1.0, 'length_ratio': 1.0359059291156012, 'translation_length': 31274, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.4427056080159416, 'rouge2': 0.19219660001604671, 'rougeL': 0.38353574009053226, 'rougeLsum': 0.38400128515398857}, 'accuracy': 0.0, 'correct_ids': []}\n","Qwen/Qwen2-72B-Instruct/rpp-1.04: {'meteor': 0.39235866930301305, 'bleu_scores': {'bleu': 0.12402693297052149, 'precisions': [0.4284005689164727, 0.16380901251551858, 0.07997907220090687, 0.04215992446800784], 'brevity_penalty': 1.0, 'length_ratio': 1.0247101689301092, 'translation_length': 30936, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.4426208198446371, 'rouge2': 0.19199681779764224, 'rougeL': 0.3839514694136028, 'rougeLsum': 0.3841982412661236}, 'accuracy': 0.0, 'correct_ids': []}\n","Qwen/Qwen2-72B-Instruct/rpp-1.06: {'meteor': 0.39099278006036825, 'bleu_scores': {'bleu': 0.1232450878300488, 'precisions': [0.4272606426093441, 0.16253786603837092, 0.07929176289453425, 0.04189893248806791], 'brevity_penalty': 1.0, 'length_ratio': 1.0216296787015569, 'translation_length': 30843, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.44144425317154196, 'rouge2': 0.19133012055570608, 'rougeL': 0.38314456527389706, 'rougeLsum': 0.3834154006635245}, 'accuracy': 0.0, 'correct_ids': []}\n","Qwen/Qwen2-72B-Instruct/rpp-1.08: {'meteor': 0.3919843215003691, 'bleu_scores': {'bleu': 0.12201600208223494, 'precisions': [0.4260587376277787, 0.16168047975203828, 0.07821366024518389, 0.04113935592107663], 'brevity_penalty': 1.0, 'length_ratio': 1.0207022192779065, 'translation_length': 30815, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.44234072135783076, 'rouge2': 0.19220259288979116, 'rougeL': 0.3836061734813752, 'rougeLsum': 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modelrppmeteorbleu_1rouge_lews_scorerepetition_scoretotal_repetitionsnum_entries_with_max_output_tokens
0Qwen/Qwen2-72B-Instruct1.000.3931690.1227320.3843610.00.3636360.3636360
1Qwen/Qwen2-72B-Instruct1.020.3925670.1242110.3835360.00.3459840.3459840
2Qwen/Qwen2-72B-Instruct1.040.3923590.1240270.3839510.00.3565750.3565750
3Qwen/Qwen2-72B-Instruct1.060.3909930.1232450.3831450.00.3565750.3565750
4Qwen/Qwen2-72B-Instruct1.080.3919840.1220160.3836060.00.3459840.3459840
5Qwen/Qwen2-7B-Instruct1.000.3773140.1174820.3687770.00.2639010.2639010
6Qwen/Qwen2-7B-Instruct1.020.3776790.1164320.3693180.00.2639010.2639010
7Qwen/Qwen2-7B-Instruct1.040.3778170.1154580.3689630.00.2550750.2550750
8Qwen/Qwen2-7B-Instruct1.060.3773530.1150990.3671350.00.2497790.2497790
9Qwen/Qwen2-7B-Instruct1.080.3747070.1116490.3641090.00.2418360.2418360
10Qwen/Qwen2-7B-Instruct1.100.3726930.1092540.3601940.00.2506620.2506620
11Qwen/Qwen2-7B-Instruct1.120.3709820.1064750.3585560.00.2506620.2506620
12Qwen/Qwen2-7B-Instruct1.140.3680160.1043740.3564450.00.2506620.2506620
13Qwen/Qwen2-7B-Instruct1.160.3673920.1020630.3542770.00.2850840.2850840
14Qwen/Qwen2-7B-Instruct1.180.3639670.0987850.3500970.00.2753750.2753750
15Qwen/Qwen2-7B-Instruct1.200.3597350.0951480.3464210.00.2859660.2859660
16Qwen/Qwen2-7B-Instruct1.220.3574080.0919950.3451150.00.2056490.2056490
17Qwen/Qwen2-7B-Instruct1.240.3534400.0864390.3394490.00.1791700.1791700
18Qwen/Qwen2-7B-Instruct1.260.3479600.0814270.3347510.00.2135920.2135920
19Qwen/Qwen2-7B-Instruct1.280.3438110.0734160.3301920.00.2197710.2197710
20Qwen/Qwen2-7B-Instruct1.300.3405230.0723060.3268720.00.2127100.2127101
21shenzhi-wang/Llama3.1-8B-Chinese-Chat1.000.3576980.1015240.3456620.00.3706970.3706970
22shenzhi-wang/Llama3.1-8B-Chinese-Chat1.020.3581070.1010700.3454840.00.3398060.3398060
23shenzhi-wang/Llama3.1-8B-Chinese-Chat1.040.3565930.1007710.3448640.00.3601060.3601060
24shenzhi-wang/Llama3.1-8B-Chinese-Chat1.060.3561110.0993820.3432290.00.3309800.3309800
25shenzhi-wang/Llama3.1-8B-Chinese-Chat1.080.3546270.0969250.3429150.00.3556930.3556930
26shenzhi-wang/Llama3.1-8B-Chinese-Chat1.100.3528660.0967160.3412880.00.3186230.3186230
27shenzhi-wang/Llama3.1-8B-Chinese-Chat1.120.3514960.0947580.3406650.00.3389230.3389230
28shenzhi-wang/Llama3.1-8B-Chinese-Chat1.140.3520470.0946390.3400980.00.3601060.3601060
29shenzhi-wang/Llama3.1-8B-Chinese-Chat1.160.3505260.0935260.3384690.00.3759930.3759930
30shenzhi-wang/Llama3.1-8B-Chinese-Chat1.180.3489000.0928940.3376210.00.3830540.3830540
31shenzhi-wang/Llama3.1-8B-Chinese-Chat1.200.3477430.0913330.3357230.00.3830540.3830540
32shenzhi-wang/Llama3.1-8B-Chinese-Chat1.220.3460870.0902440.3346420.00.4033540.4033540
33shenzhi-wang/Llama3.1-8B-Chinese-Chat1.240.3440970.0832330.3325460.00.4880850.4880850
34shenzhi-wang/Llama3.1-8B-Chinese-Chat1.260.3426440.0851370.3306300.00.3495150.3495150
35shenzhi-wang/Llama3.1-8B-Chinese-Chat1.280.3403420.0837870.3281820.00.2974400.2974400
36shenzhi-wang/Llama3.1-8B-Chinese-Chat1.300.3393500.0819880.3272860.00.2806710.2806710
37shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.000.3256020.0834010.3158220.00.1721090.1721090
38shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.020.3255640.0839780.3158770.00.1862310.1862310
39shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.040.3259200.0808490.3166050.05.8464255.8464251
40shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.060.3256450.0811270.3150510.05.8367175.8367171
41shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.080.3255420.0817670.3149460.05.8420125.8420121
42shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.100.3259690.0855630.3152080.00.3009710.3009710
43shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.120.3245180.0852720.3139130.00.4466020.4466020
44shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.140.3227100.0845800.3135310.00.2771400.2771400
45shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.160.3226900.0832600.3131360.00.2824360.2824360
46shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.180.3216390.0817910.3111010.00.1562220.1562220
47shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.200.3206160.0803960.3104100.00.1562220.1562220
48shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.220.3190950.0793960.3080240.00.1535750.1535750
49shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.240.3189440.0796320.3072170.00.1006180.1006180
50shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.260.3179100.0772310.3069260.00.2356580.2356580
51shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.280.3154750.0753160.3047500.00.0847310.0847310
52shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.300.3142370.0745430.3034690.00.1253310.1253310
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"],"text/plain":[" model rpp meteor bleu_1 \\\n","0 Qwen/Qwen2-72B-Instruct 1.00 0.393169 0.122732 \n","1 Qwen/Qwen2-72B-Instruct 1.02 0.392567 0.124211 \n","2 Qwen/Qwen2-72B-Instruct 1.04 0.392359 0.124027 \n","3 Qwen/Qwen2-72B-Instruct 1.06 0.390993 0.123245 \n","4 Qwen/Qwen2-72B-Instruct 1.08 0.391984 0.122016 \n","5 Qwen/Qwen2-7B-Instruct 1.00 0.377314 0.117482 \n","6 Qwen/Qwen2-7B-Instruct 1.02 0.377679 0.116432 \n","7 Qwen/Qwen2-7B-Instruct 1.04 0.377817 0.115458 \n","8 Qwen/Qwen2-7B-Instruct 1.06 0.377353 0.115099 \n","9 Qwen/Qwen2-7B-Instruct 1.08 0.374707 0.111649 \n","10 Qwen/Qwen2-7B-Instruct 1.10 0.372693 0.109254 \n","11 Qwen/Qwen2-7B-Instruct 1.12 0.370982 0.106475 \n","12 Qwen/Qwen2-7B-Instruct 1.14 0.368016 0.104374 \n","13 Qwen/Qwen2-7B-Instruct 1.16 0.367392 0.102063 \n","14 Qwen/Qwen2-7B-Instruct 1.18 0.363967 0.098785 \n","15 Qwen/Qwen2-7B-Instruct 1.20 0.359735 0.095148 \n","16 Qwen/Qwen2-7B-Instruct 1.22 0.357408 0.091995 \n","17 Qwen/Qwen2-7B-Instruct 1.24 0.353440 0.086439 \n","18 Qwen/Qwen2-7B-Instruct 1.26 0.347960 0.081427 \n","19 Qwen/Qwen2-7B-Instruct 1.28 0.343811 0.073416 \n","20 Qwen/Qwen2-7B-Instruct 1.30 0.340523 0.072306 \n","21 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.00 0.357698 0.101524 \n","22 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.02 0.358107 0.101070 \n","23 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.04 0.356593 0.100771 \n","24 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.06 0.356111 0.099382 \n","25 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.08 0.354627 0.096925 \n","26 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.10 0.352866 0.096716 \n","27 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.12 0.351496 0.094758 \n","28 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.14 0.352047 0.094639 \n","29 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.16 0.350526 0.093526 \n","30 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.18 0.348900 0.092894 \n","31 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.20 0.347743 0.091333 \n","32 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.22 0.346087 0.090244 \n","33 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.24 0.344097 0.083233 \n","34 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.26 0.342644 0.085137 \n","35 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.28 0.340342 0.083787 \n","36 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.30 0.339350 0.081988 \n","37 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.00 0.325602 0.083401 \n","38 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.02 0.325564 0.083978 \n","39 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.04 0.325920 0.080849 \n","40 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.06 0.325645 0.081127 \n","41 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.08 0.325542 0.081767 \n","42 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.10 0.325969 0.085563 \n","43 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.12 0.324518 0.085272 \n","44 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.14 0.322710 0.084580 \n","45 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.16 0.322690 0.083260 \n","46 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.18 0.321639 0.081791 \n","47 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.20 0.320616 0.080396 \n","48 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.22 0.319095 0.079396 \n","49 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.24 0.318944 0.079632 \n","50 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.26 0.317910 0.077231 \n","51 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.28 0.315475 0.075316 \n","52 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.30 0.314237 0.074543 \n","\n"," rouge_l ews_score repetition_score total_repetitions \\\n","0 0.384361 0.0 0.363636 0.363636 \n","1 0.383536 0.0 0.345984 0.345984 \n","2 0.383951 0.0 0.356575 0.356575 \n","3 0.383145 0.0 0.356575 0.356575 \n","4 0.383606 0.0 0.345984 0.345984 \n","5 0.368777 0.0 0.263901 0.263901 \n","6 0.369318 0.0 0.263901 0.263901 \n","7 0.368963 0.0 0.255075 0.255075 \n","8 0.367135 0.0 0.249779 0.249779 \n","9 0.364109 0.0 0.241836 0.241836 \n","10 0.360194 0.0 0.250662 0.250662 \n","11 0.358556 0.0 0.250662 0.250662 \n","12 0.356445 0.0 0.250662 0.250662 \n","13 0.354277 0.0 0.285084 0.285084 \n","14 0.350097 0.0 0.275375 0.275375 \n","15 0.346421 0.0 0.285966 0.285966 \n","16 0.345115 0.0 0.205649 0.205649 \n","17 0.339449 0.0 0.179170 0.179170 \n","18 0.334751 0.0 0.213592 0.213592 \n","19 0.330192 0.0 0.219771 0.219771 \n","20 0.326872 0.0 0.212710 0.212710 \n","21 0.345662 0.0 0.370697 0.370697 \n","22 0.345484 0.0 0.339806 0.339806 \n","23 0.344864 0.0 0.360106 0.360106 \n","24 0.343229 0.0 0.330980 0.330980 \n","25 0.342915 0.0 0.355693 0.355693 \n","26 0.341288 0.0 0.318623 0.318623 \n","27 0.340665 0.0 0.338923 0.338923 \n","28 0.340098 0.0 0.360106 0.360106 \n","29 0.338469 0.0 0.375993 0.375993 \n","30 0.337621 0.0 0.383054 0.383054 \n","31 0.335723 0.0 0.383054 0.383054 \n","32 0.334642 0.0 0.403354 0.403354 \n","33 0.332546 0.0 0.488085 0.488085 \n","34 0.330630 0.0 0.349515 0.349515 \n","35 0.328182 0.0 0.297440 0.297440 \n","36 0.327286 0.0 0.280671 0.280671 \n","37 0.315822 0.0 0.172109 0.172109 \n","38 0.315877 0.0 0.186231 0.186231 \n","39 0.316605 0.0 5.846425 5.846425 \n","40 0.315051 0.0 5.836717 5.836717 \n","41 0.314946 0.0 5.842012 5.842012 \n","42 0.315208 0.0 0.300971 0.300971 \n","43 0.313913 0.0 0.446602 0.446602 \n","44 0.313531 0.0 0.277140 0.277140 \n","45 0.313136 0.0 0.282436 0.282436 \n","46 0.311101 0.0 0.156222 0.156222 \n","47 0.310410 0.0 0.156222 0.156222 \n","48 0.308024 0.0 0.153575 0.153575 \n","49 0.307217 0.0 0.100618 0.100618 \n","50 0.306926 0.0 0.235658 0.235658 \n","51 0.304750 0.0 0.084731 0.084731 \n","52 0.303469 0.0 0.125331 0.125331 \n","\n"," num_entries_with_max_output_tokens \n","0 0 \n","1 0 \n","2 0 \n","3 0 \n","4 0 \n","5 0 \n","6 0 \n","7 0 \n","8 0 \n","9 0 \n","10 0 \n","11 0 \n","12 0 \n","13 0 \n","14 0 \n","15 0 \n","16 0 \n","17 0 \n","18 0 \n","19 0 \n","20 1 \n","21 0 \n","22 0 \n","23 0 \n","24 0 \n","25 0 \n","26 0 \n","27 0 \n","28 0 \n","29 0 \n","30 0 \n","31 0 \n","32 0 \n","33 0 \n","34 0 \n","35 0 \n","36 0 \n","37 0 \n","38 0 \n","39 1 \n","40 1 \n","41 1 \n","42 0 \n","43 0 \n","44 0 \n","45 0 \n","46 0 \n","47 0 \n","48 0 \n","49 0 \n","50 0 \n","51 0 \n","52 0 "]},"execution_count":219,"metadata":{},"output_type":"execute_result"}],"source":["metrics_df = get_metrics(df)\n","metrics_df"]},{"cell_type":"code","execution_count":220,"metadata":{},"outputs":[],"source":["metrics_df[\"rap\"] = metrics_df.apply(\n"," lambda x: x[\"meteor\"] / math.log10(10 + x[\"total_repetitions\"]), axis=1\n",")"]},{"cell_type":"code","execution_count":221,"metadata":{},"outputs":[{"data":{"text/html":["
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modelrppmeteorbleu_1rouge_lews_scorerepetition_scoretotal_repetitionsnum_entries_with_max_output_tokensrap
0Qwen/Qwen2-72B-Instruct1.000.3931690.1227320.3843610.00.3636360.36363600.387164
1Qwen/Qwen2-72B-Instruct1.020.3925670.1242110.3835360.00.3459840.34598400.386853
2Qwen/Qwen2-72B-Instruct1.040.3923590.1240270.3839510.00.3565750.35657500.386478
3Qwen/Qwen2-72B-Instruct1.060.3909930.1232450.3831450.00.3565750.35657500.385133
4Qwen/Qwen2-72B-Instruct1.080.3919840.1220160.3836060.00.3459840.34598400.386278
5Qwen/Qwen2-7B-Instruct1.000.3773140.1174820.3687770.00.2639010.26390100.373093
6Qwen/Qwen2-7B-Instruct1.020.3776790.1164320.3693180.00.2639010.26390100.373454
7Qwen/Qwen2-7B-Instruct1.040.3778170.1154580.3689630.00.2550750.25507500.373729
8Qwen/Qwen2-7B-Instruct1.060.3773530.1150990.3671350.00.2497790.24977900.373352
9Qwen/Qwen2-7B-Instruct1.080.3747070.1116490.3641090.00.2418360.24183600.370858
10Qwen/Qwen2-7B-Instruct1.100.3726930.1092540.3601940.00.2506620.25066200.368729
11Qwen/Qwen2-7B-Instruct1.120.3709820.1064750.3585560.00.2506620.25066200.367036
12Qwen/Qwen2-7B-Instruct1.140.3680160.1043740.3564450.00.2506620.25066200.364101
13Qwen/Qwen2-7B-Instruct1.160.3673920.1020630.3542770.00.2850840.28508400.362961
14Qwen/Qwen2-7B-Instruct1.180.3639670.0987850.3500970.00.2753750.27537500.359723
15Qwen/Qwen2-7B-Instruct1.200.3597350.0951480.3464210.00.2859660.28596600.355384
16Qwen/Qwen2-7B-Instruct1.220.3574080.0919950.3451150.00.2056490.20564900.354276
17Qwen/Qwen2-7B-Instruct1.240.3534400.0864390.3394490.00.1791700.17917000.350735
18Qwen/Qwen2-7B-Instruct1.260.3479600.0814270.3347510.00.2135920.21359200.344795
19Qwen/Qwen2-7B-Instruct1.280.3438110.0734160.3301920.00.2197710.21977100.340595
20Qwen/Qwen2-7B-Instruct1.300.3405230.0723060.3268720.00.2127100.21271010.337439
21shenzhi-wang/Llama3.1-8B-Chinese-Chat1.000.3576980.1015240.3456620.00.3706970.37069700.352132
22shenzhi-wang/Llama3.1-8B-Chinese-Chat1.020.3581070.1010700.3454840.00.3398060.33980600.352985
23shenzhi-wang/Llama3.1-8B-Chinese-Chat1.040.3565930.1007710.3448640.00.3601060.36010600.351197
24shenzhi-wang/Llama3.1-8B-Chinese-Chat1.060.3561110.0993820.3432290.00.3309800.33098000.351146
25shenzhi-wang/Llama3.1-8B-Chinese-Chat1.080.3546270.0969250.3429150.00.3556930.35569300.349324
26shenzhi-wang/Llama3.1-8B-Chinese-Chat1.100.3528660.0967160.3412880.00.3186230.31862300.348124
27shenzhi-wang/Llama3.1-8B-Chinese-Chat1.120.3514960.0947580.3406650.00.3389230.33892300.346480
28shenzhi-wang/Llama3.1-8B-Chinese-Chat1.140.3520470.0946390.3400980.00.3601060.36010600.346720
29shenzhi-wang/Llama3.1-8B-Chinese-Chat1.160.3505260.0935260.3384690.00.3759930.37599300.344995
30shenzhi-wang/Llama3.1-8B-Chinese-Chat1.180.3489000.0928940.3376210.00.3830540.38305400.343295
31shenzhi-wang/Llama3.1-8B-Chinese-Chat1.200.3477430.0913330.3357230.00.3830540.38305400.342157
32shenzhi-wang/Llama3.1-8B-Chinese-Chat1.220.3460870.0902440.3346420.00.4033540.40335400.340244
33shenzhi-wang/Llama3.1-8B-Chinese-Chat1.240.3440970.0832330.3325460.00.4880850.48808500.337120
34shenzhi-wang/Llama3.1-8B-Chinese-Chat1.260.3426440.0851370.3306300.00.3495150.34951500.337607
35shenzhi-wang/Llama3.1-8B-Chinese-Chat1.280.3403420.0837870.3281820.00.2974400.29744000.336064
36shenzhi-wang/Llama3.1-8B-Chinese-Chat1.300.3393500.0819880.3272860.00.2806710.28067100.335319
37shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.000.3256020.0834010.3158220.00.1721090.17210900.323207
38shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.020.3255640.0839780.3158770.00.1862310.18623100.322975
39shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.040.3259200.0808490.3166050.05.8464255.84642510.271615
40shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.060.3256450.0811270.3150510.05.8367175.83671710.271447
41shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.080.3255420.0817670.3149460.05.8420125.84201210.271328
42shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.100.3259690.0855630.3152080.00.3009710.30097100.321824
43shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.120.3245180.0852720.3139130.00.4466020.44660200.318475
44shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.140.3227100.0845800.3135310.00.2771400.27714000.318923
45shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.160.3226900.0832600.3131360.00.2824360.28243600.318833
46shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.180.3216390.0817910.3111010.00.1562220.15622200.319488
47shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.200.3206160.0803960.3104100.00.1562220.15622200.318472
48shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.220.3190950.0793960.3080240.00.1535750.15357500.316997
49shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.240.3189440.0796320.3072170.00.1006180.10061800.317564
50shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.260.3179100.0772310.3069260.00.2356580.23565800.314726
51shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.280.3154750.0753160.3047500.00.0847310.08473100.314323
52shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.300.3142370.0745430.3034690.00.1253310.12533100.312547
\n","
"],"text/plain":[" model rpp meteor bleu_1 \\\n","0 Qwen/Qwen2-72B-Instruct 1.00 0.393169 0.122732 \n","1 Qwen/Qwen2-72B-Instruct 1.02 0.392567 0.124211 \n","2 Qwen/Qwen2-72B-Instruct 1.04 0.392359 0.124027 \n","3 Qwen/Qwen2-72B-Instruct 1.06 0.390993 0.123245 \n","4 Qwen/Qwen2-72B-Instruct 1.08 0.391984 0.122016 \n","5 Qwen/Qwen2-7B-Instruct 1.00 0.377314 0.117482 \n","6 Qwen/Qwen2-7B-Instruct 1.02 0.377679 0.116432 \n","7 Qwen/Qwen2-7B-Instruct 1.04 0.377817 0.115458 \n","8 Qwen/Qwen2-7B-Instruct 1.06 0.377353 0.115099 \n","9 Qwen/Qwen2-7B-Instruct 1.08 0.374707 0.111649 \n","10 Qwen/Qwen2-7B-Instruct 1.10 0.372693 0.109254 \n","11 Qwen/Qwen2-7B-Instruct 1.12 0.370982 0.106475 \n","12 Qwen/Qwen2-7B-Instruct 1.14 0.368016 0.104374 \n","13 Qwen/Qwen2-7B-Instruct 1.16 0.367392 0.102063 \n","14 Qwen/Qwen2-7B-Instruct 1.18 0.363967 0.098785 \n","15 Qwen/Qwen2-7B-Instruct 1.20 0.359735 0.095148 \n","16 Qwen/Qwen2-7B-Instruct 1.22 0.357408 0.091995 \n","17 Qwen/Qwen2-7B-Instruct 1.24 0.353440 0.086439 \n","18 Qwen/Qwen2-7B-Instruct 1.26 0.347960 0.081427 \n","19 Qwen/Qwen2-7B-Instruct 1.28 0.343811 0.073416 \n","20 Qwen/Qwen2-7B-Instruct 1.30 0.340523 0.072306 \n","21 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.00 0.357698 0.101524 \n","22 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.02 0.358107 0.101070 \n","23 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.04 0.356593 0.100771 \n","24 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.06 0.356111 0.099382 \n","25 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.08 0.354627 0.096925 \n","26 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.10 0.352866 0.096716 \n","27 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.12 0.351496 0.094758 \n","28 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.14 0.352047 0.094639 \n","29 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.16 0.350526 0.093526 \n","30 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.18 0.348900 0.092894 \n","31 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.20 0.347743 0.091333 \n","32 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.22 0.346087 0.090244 \n","33 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.24 0.344097 0.083233 \n","34 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.26 0.342644 0.085137 \n","35 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.28 0.340342 0.083787 \n","36 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.30 0.339350 0.081988 \n","37 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.00 0.325602 0.083401 \n","38 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.02 0.325564 0.083978 \n","39 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.04 0.325920 0.080849 \n","40 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.06 0.325645 0.081127 \n","41 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.08 0.325542 0.081767 \n","42 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.10 0.325969 0.085563 \n","43 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.12 0.324518 0.085272 \n","44 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.14 0.322710 0.084580 \n","45 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.16 0.322690 0.083260 \n","46 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.18 0.321639 0.081791 \n","47 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.20 0.320616 0.080396 \n","48 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.22 0.319095 0.079396 \n","49 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.24 0.318944 0.079632 \n","50 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.26 0.317910 0.077231 \n","51 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.28 0.315475 0.075316 \n","52 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.30 0.314237 0.074543 \n","\n"," rouge_l ews_score repetition_score total_repetitions \\\n","0 0.384361 0.0 0.363636 0.363636 \n","1 0.383536 0.0 0.345984 0.345984 \n","2 0.383951 0.0 0.356575 0.356575 \n","3 0.383145 0.0 0.356575 0.356575 \n","4 0.383606 0.0 0.345984 0.345984 \n","5 0.368777 0.0 0.263901 0.263901 \n","6 0.369318 0.0 0.263901 0.263901 \n","7 0.368963 0.0 0.255075 0.255075 \n","8 0.367135 0.0 0.249779 0.249779 \n","9 0.364109 0.0 0.241836 0.241836 \n","10 0.360194 0.0 0.250662 0.250662 \n","11 0.358556 0.0 0.250662 0.250662 \n","12 0.356445 0.0 0.250662 0.250662 \n","13 0.354277 0.0 0.285084 0.285084 \n","14 0.350097 0.0 0.275375 0.275375 \n","15 0.346421 0.0 0.285966 0.285966 \n","16 0.345115 0.0 0.205649 0.205649 \n","17 0.339449 0.0 0.179170 0.179170 \n","18 0.334751 0.0 0.213592 0.213592 \n","19 0.330192 0.0 0.219771 0.219771 \n","20 0.326872 0.0 0.212710 0.212710 \n","21 0.345662 0.0 0.370697 0.370697 \n","22 0.345484 0.0 0.339806 0.339806 \n","23 0.344864 0.0 0.360106 0.360106 \n","24 0.343229 0.0 0.330980 0.330980 \n","25 0.342915 0.0 0.355693 0.355693 \n","26 0.341288 0.0 0.318623 0.318623 \n","27 0.340665 0.0 0.338923 0.338923 \n","28 0.340098 0.0 0.360106 0.360106 \n","29 0.338469 0.0 0.375993 0.375993 \n","30 0.337621 0.0 0.383054 0.383054 \n","31 0.335723 0.0 0.383054 0.383054 \n","32 0.334642 0.0 0.403354 0.403354 \n","33 0.332546 0.0 0.488085 0.488085 \n","34 0.330630 0.0 0.349515 0.349515 \n","35 0.328182 0.0 0.297440 0.297440 \n","36 0.327286 0.0 0.280671 0.280671 \n","37 0.315822 0.0 0.172109 0.172109 \n","38 0.315877 0.0 0.186231 0.186231 \n","39 0.316605 0.0 5.846425 5.846425 \n","40 0.315051 0.0 5.836717 5.836717 \n","41 0.314946 0.0 5.842012 5.842012 \n","42 0.315208 0.0 0.300971 0.300971 \n","43 0.313913 0.0 0.446602 0.446602 \n","44 0.313531 0.0 0.277140 0.277140 \n","45 0.313136 0.0 0.282436 0.282436 \n","46 0.311101 0.0 0.156222 0.156222 \n","47 0.310410 0.0 0.156222 0.156222 \n","48 0.308024 0.0 0.153575 0.153575 \n","49 0.307217 0.0 0.100618 0.100618 \n","50 0.306926 0.0 0.235658 0.235658 \n","51 0.304750 0.0 0.084731 0.084731 \n","52 0.303469 0.0 0.125331 0.125331 \n","\n"," num_entries_with_max_output_tokens rap \n","0 0 0.387164 \n","1 0 0.386853 \n","2 0 0.386478 \n","3 0 0.385133 \n","4 0 0.386278 \n","5 0 0.373093 \n","6 0 0.373454 \n","7 0 0.373729 \n","8 0 0.373352 \n","9 0 0.370858 \n","10 0 0.368729 \n","11 0 0.367036 \n","12 0 0.364101 \n","13 0 0.362961 \n","14 0 0.359723 \n","15 0 0.355384 \n","16 0 0.354276 \n","17 0 0.350735 \n","18 0 0.344795 \n","19 0 0.340595 \n","20 1 0.337439 \n","21 0 0.352132 \n","22 0 0.352985 \n","23 0 0.351197 \n","24 0 0.351146 \n","25 0 0.349324 \n","26 0 0.348124 \n","27 0 0.346480 \n","28 0 0.346720 \n","29 0 0.344995 \n","30 0 0.343295 \n","31 0 0.342157 \n","32 0 0.340244 \n","33 0 0.337120 \n","34 0 0.337607 \n","35 0 0.336064 \n","36 0 0.335319 \n","37 0 0.323207 \n","38 0 0.322975 \n","39 1 0.271615 \n","40 1 0.271447 \n","41 1 0.271328 \n","42 0 0.321824 \n","43 0 0.318475 \n","44 0 0.318923 \n","45 0 0.318833 \n","46 0 0.319488 \n","47 0 0.318472 \n","48 0 0.316997 \n","49 0 0.317564 \n","50 0 0.314726 \n","51 0 0.314323 \n","52 0 0.312547 "]},"execution_count":221,"metadata":{},"output_type":"execute_result"}],"source":["metrics_df"]},{"cell_type":"code","execution_count":222,"metadata":{},"outputs":[],"source":["models = metrics_df[\"model\"].unique()"]},{"cell_type":"code","execution_count":223,"metadata":{},"outputs":[{"data":{"text/plain":["array(['Qwen/Qwen2-72B-Instruct', 'Qwen/Qwen2-7B-Instruct',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat'], dtype=object)"]},"execution_count":223,"metadata":{},"output_type":"execute_result"}],"source":["models"]},{"cell_type":"code","execution_count":224,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"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(model_df[\"rpp\"], model_df[\"meteor\"], label=model + \" (METEOR)\")\n"," ax.plot(\n"," model_df[\"rpp\"], model_df[\"rap\"], label=model + \" (RAP-METEOR)\", linestyle=\"--\"\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, -0.5))\n","plt.show()"]},{"cell_type":"code","execution_count":234,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"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[\"total_repetitions\"], label=model)\n","\n","\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, -0.3))\n","plt.show()"]},{"cell_type":"code","execution_count":225,"metadata":{},"outputs":[],"source":["tokenizers = {model: load_tokenizer(model) for model in models}"]},{"cell_type":"code","execution_count":226,"metadata":{},"outputs":[],"source":["col = \"shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.04\"\n","df[[\"ews_score\", \"repetition_score\", \"total_repetitions\"]] = df[col].apply(\n"," detect_scores\n",")\n","df[\"output_tokens\"] = df[col].apply(\n"," lambda x: len(tokenizers[col.split(\"/rpp\")[0]](x)[\"input_ids\"])\n",")"]},{"cell_type":"code","execution_count":227,"metadata":{},"outputs":[{"data":{"text/html":["
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chineseenglishQwen/Qwen2-72B-Instruct/rpp-1.00Qwen/Qwen2-72B-Instruct/rpp-1.02Qwen/Qwen2-72B-Instruct/rpp-1.04Qwen/Qwen2-72B-Instruct/rpp-1.06Qwen/Qwen2-72B-Instruct/rpp-1.08Qwen/Qwen2-7B-Instruct/rpp-1.00Qwen/Qwen2-7B-Instruct/rpp-1.02Qwen/Qwen2-7B-Instruct/rpp-1.04...shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30ews_scorerepetition_scoretotal_repetitionsoutput_tokens
193“有…… 没有…… 有…… 没有……'Yes . . . no . . . yes . . . no . . .\"There is... There isn't... There is... There ...\"There is... There isn't... There is... There ...\"There is... There isn't... There is... There ...\"There is... There isn't... There is... There ...\"There is... There isn't... There is... There ...\"Yes... No... Yes... No...\"\"Yes... No... Yes... No...\"\"Yes... No... Yes... No...\"...Yes... No... Yes... No...Yes... No... Yes... No...Yes... No... Yes... No...Yes... No... Yes... No...Yes... No... Yes... No...Yes... No... Yes... No...0649664962049
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1 rows × 59 columns

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"],"text/plain":[" chinese english \\\n","193 “有…… 没有…… 有…… 没有…… 'Yes . . . no . . . yes . . . no . . . \n","\n"," Qwen/Qwen2-72B-Instruct/rpp-1.00 \\\n","193 \"There is... There isn't... There is... There ... \n","\n"," Qwen/Qwen2-72B-Instruct/rpp-1.02 \\\n","193 \"There is... There isn't... There is... There ... \n","\n"," Qwen/Qwen2-72B-Instruct/rpp-1.04 \\\n","193 \"There is... There isn't... There is... There ... \n","\n"," Qwen/Qwen2-72B-Instruct/rpp-1.06 \\\n","193 \"There is... There isn't... There is... There ... \n","\n"," Qwen/Qwen2-72B-Instruct/rpp-1.08 \\\n","193 \"There is... There isn't... There is... There ... \n","\n"," Qwen/Qwen2-7B-Instruct/rpp-1.00 Qwen/Qwen2-7B-Instruct/rpp-1.02 \\\n","193 \"Yes... No... Yes... No...\" \"Yes... No... Yes... No...\" \n","\n"," Qwen/Qwen2-7B-Instruct/rpp-1.04 ... \\\n","193 \"Yes... No... Yes... No...\" ... \n","\n"," shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20 \\\n","193 Yes... No... Yes... No... \n","\n"," shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22 \\\n","193 Yes... No... Yes... No... \n","\n"," shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24 \\\n","193 Yes... No... Yes... No... \n","\n"," shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26 \\\n","193 Yes... No... Yes... No... \n","\n"," shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28 \\\n","193 Yes... No... Yes... No... \n","\n"," shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30 ews_score \\\n","193 Yes... No... Yes... No... 0 \n","\n"," repetition_score total_repetitions output_tokens \n","193 6496 6496 2049 \n","\n","[1 rows x 59 columns]"]},"execution_count":227,"metadata":{},"output_type":"execute_result"}],"source":["rows = df.query(\"total_repetitions > 1000\")\n","rows"]},{"cell_type":"code","execution_count":228,"metadata":{},"outputs":[],"source":["row = rows.iloc[0]"]},{"cell_type":"code","execution_count":229,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["“有…… 没有…… 有…… 没有……\n"]}],"source":["print(row[\"chinese\"])"]},{"cell_type":"code","execution_count":230,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["'Yes . . . no . . . yes . . . no . . .\n"]}],"source":["print(row[\"english\"])"]},{"cell_type":"code","execution_count":231,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["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! 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! 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! 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! 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! 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! 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! 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! 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\n","----detect excessive whitespaces----\n","----detect text repetitions----\n","\n","Group 1 found at 159-3407: `:\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! 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! 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! 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! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation`\n","Group 2 found at 3407-6655: `:\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! 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! 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! 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! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation`\n","Group 3 found at 3407-6655: `:\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! 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! 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! 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! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation`\n","(0, 6496, 6496)\n"]},{"data":{"text/plain":["(0, 6496, 6496)"]},"execution_count":231,"metadata":{},"output_type":"execute_result"}],"source":["output = row[col]\n","print(output)\n","detect_repetitions(output, debug=True)"]},{"cell_type":"code","execution_count":232,"metadata":{},"outputs":[{"data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
ews_scorerepetition_scoretotal_repetitionsoutput_tokens
count1133.01133.0000001133.0000001133.000000
mean0.05.8464255.84642533.958517
std0.0192.990061192.99006163.822891
min0.00.0000000.0000003.000000
25%0.00.0000000.00000017.000000
50%0.00.0000000.00000027.000000
75%0.00.0000000.00000042.000000
max0.06496.0000006496.0000002049.000000
\n","
"],"text/plain":[" ews_score repetition_score total_repetitions output_tokens\n","count 1133.0 1133.000000 1133.000000 1133.000000\n","mean 0.0 5.846425 5.846425 33.958517\n","std 0.0 192.990061 192.990061 63.822891\n","min 0.0 0.000000 0.000000 3.000000\n","25% 0.0 0.000000 0.000000 17.000000\n","50% 0.0 0.000000 0.000000 27.000000\n","75% 0.0 0.000000 0.000000 42.000000\n","max 0.0 6496.000000 6496.000000 2049.000000"]},"execution_count":232,"metadata":{},"output_type":"execute_result"}],"source":["df.describe()"]}],"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} +{"cells":[{"cell_type":"code","execution_count":16,"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":17,"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":18,"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":18,"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":19,"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":20,"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 8.99 ms, sys: 15.6 ms, total: 24.6 ms\n","Wall time: 1.82 s\n"]}],"source":["%%time\n","os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n","\n","!python --version\n","!pip show torch transformers"]},{"cell_type":"code","execution_count":21,"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":22,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","RangeIndex: 1133 entries, 0 to 1132\n","Data columns (total 58 columns):\n"," # Column Non-Null Count Dtype \n","--- ------ -------------- ----- \n"," 0 chinese 1133 non-null object\n"," 1 english 1133 non-null object\n"," 2 Qwen/Qwen2-7B-Instruct/rpp-1.00 1133 non-null object\n"," 3 Qwen/Qwen2-7B-Instruct/rpp-1.02 1133 non-null object\n"," 4 Qwen/Qwen2-7B-Instruct/rpp-1.04 1133 non-null object\n"," 5 Qwen/Qwen2-7B-Instruct/rpp-1.06 1133 non-null object\n"," 6 Qwen/Qwen2-7B-Instruct/rpp-1.08 1133 non-null object\n"," 7 Qwen/Qwen2-7B-Instruct/rpp-1.10 1133 non-null object\n"," 8 Qwen/Qwen2-72B-Instruct/rpp-1.00 1133 non-null object\n"," 9 Qwen/Qwen2-7B-Instruct/rpp-1.12 1133 non-null object\n"," 10 Qwen/Qwen2-7B-Instruct/rpp-1.14 1133 non-null object\n"," 11 Qwen/Qwen2-7B-Instruct/rpp-1.16 1133 non-null object\n"," 12 Qwen/Qwen2-7B-Instruct/rpp-1.18 1133 non-null object\n"," 13 Qwen/Qwen2-7B-Instruct/rpp-1.20 1133 non-null object\n"," 14 Qwen/Qwen2-7B-Instruct/rpp-1.22 1133 non-null object\n"," 15 Qwen/Qwen2-7B-Instruct/rpp-1.24 1133 non-null object\n"," 16 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.00 1133 non-null object\n"," 17 Qwen/Qwen2-7B-Instruct/rpp-1.26 1133 non-null object\n"," 18 Qwen/Qwen2-72B-Instruct/rpp-1.02 1133 non-null object\n"," 19 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.02 1133 non-null object\n"," 20 Qwen/Qwen2-7B-Instruct/rpp-1.28 1133 non-null object\n"," 21 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.04 1133 non-null object\n"," 22 Qwen/Qwen2-7B-Instruct/rpp-1.30 1133 non-null object\n"," 23 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.06 1133 non-null object\n"," 24 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.00 1133 non-null object\n"," 25 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.02 1133 non-null object\n"," 26 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.08 1133 non-null object\n"," 27 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.04 1133 non-null object\n"," 28 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.10 1133 non-null object\n"," 29 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.06 1133 non-null object\n"," 30 Qwen/Qwen2-72B-Instruct/rpp-1.04 1133 non-null object\n"," 31 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12 1133 non-null object\n"," 32 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.08 1133 non-null object\n"," 33 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14 1133 non-null object\n"," 34 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.10 1133 non-null object\n"," 35 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16 1133 non-null object\n"," 36 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.12 1133 non-null object\n"," 37 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18 1133 non-null object\n"," 38 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.14 1133 non-null object\n"," 39 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.16 1133 non-null object\n"," 40 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20 1133 non-null object\n"," 41 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.18 1133 non-null object\n"," 42 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22 1133 non-null object\n"," 43 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.20 1133 non-null object\n"," 44 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24 1133 non-null object\n"," 45 Qwen/Qwen2-72B-Instruct/rpp-1.06 1133 non-null object\n"," 46 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.22 1133 non-null object\n"," 47 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26 1133 non-null object\n"," 48 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28 1133 non-null object\n"," 49 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.24 1133 non-null object\n"," 50 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30 1133 non-null object\n"," 51 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.26 1133 non-null object\n"," 52 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.28 1133 non-null object\n"," 53 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.30 1133 non-null object\n"," 54 Qwen/Qwen2-72B-Instruct/rpp-1.08 1133 non-null object\n"," 55 Qwen/Qwen2-72B-Instruct/rpp-1.10 1133 non-null object\n"," 56 Qwen/Qwen2-72B-Instruct/rpp-1.12 1133 non-null object\n"," 57 internlm/internlm2_5-7b-chat/rpp-1.00 1133 non-null object\n","dtypes: object(58)\n","memory usage: 513.5+ KB\n"]}],"source":["import pandas as pd\n","\n","df = pd.read_csv(results_path)\n","df.info()"]},{"cell_type":"code","execution_count":23,"metadata":{},"outputs":[{"data":{"text/plain":["['chinese',\n"," 'english',\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/Qwen2-7B-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"," 'Qwen/Qwen2-7B-Instruct/rpp-1.18',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.20',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.22',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.24',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.26',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.28',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.30',\n"," 'internlm/internlm2_5-7b-chat/rpp-1.00',\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"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.30',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.00',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.02',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.04',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.06',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.08',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.10',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30']"]},"execution_count":23,"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":24,"metadata":{},"outputs":[],"source":["df = df[columns]"]},{"cell_type":"code","execution_count":25,"metadata":{},"outputs":[{"ename":"TypeError","evalue":"get_metrics() got an unexpected keyword argument 'max_new_tokens'","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)","Cell \u001b[0;32mIn[25], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m metrics_df \u001b[38;5;241m=\u001b[39m \u001b[43mget_metrics\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_new_tokens\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_new_tokens\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m metrics_df\n","\u001b[0;31mTypeError\u001b[0m: get_metrics() got an unexpected keyword argument 'max_new_tokens'"]}],"source":["metrics_df = get_metrics(df, max_new_tokens=max_new_tokens)\n","metrics_df"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["metrics_df[\"rap\"] = metrics_df.apply(\n"," lambda x: x[\"meteor\"] / math.log10(10 + x[\"total_repetitions\"]), axis=1\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/html":["
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modelrppmeteorbleu_1rouge_lews_scorerepetition_scoretotal_repetitionsnum_entries_with_max_output_tokensrap
0Qwen/Qwen2-72B-Instruct1.000.3931690.1227320.3843610.00.3636360.36363600.387164
1Qwen/Qwen2-72B-Instruct1.020.3925670.1242110.3835360.00.3459840.34598400.386853
2Qwen/Qwen2-72B-Instruct1.040.3923590.1240270.3839510.00.3565750.35657500.386478
3Qwen/Qwen2-72B-Instruct1.060.3909930.1232450.3831450.00.3565750.35657500.385133
4Qwen/Qwen2-72B-Instruct1.080.3919840.1220160.3836060.00.3459840.34598400.386278
5Qwen/Qwen2-72B-Instruct1.100.3910100.1206100.3819630.00.3768760.37687600.384827
6Qwen/Qwen2-72B-Instruct1.120.3899890.1183830.3817370.00.4068840.40688400.383349
7Qwen/Qwen2-7B-Instruct1.000.3773140.1174820.3687770.00.2639010.26390100.373093
8Qwen/Qwen2-7B-Instruct1.020.3776790.1164320.3693180.00.2639010.26390100.373454
9Qwen/Qwen2-7B-Instruct1.040.3778170.1154580.3689630.00.2550750.25507500.373729
10Qwen/Qwen2-7B-Instruct1.060.3773530.1150990.3671350.00.2497790.24977900.373352
11Qwen/Qwen2-7B-Instruct1.080.3747070.1116490.3641090.00.2418360.24183600.370858
12Qwen/Qwen2-7B-Instruct1.100.3726930.1092540.3601940.00.2506620.25066200.368729
13Qwen/Qwen2-7B-Instruct1.120.3709820.1064750.3585560.00.2506620.25066200.367036
14Qwen/Qwen2-7B-Instruct1.140.3680160.1043740.3564450.00.2506620.25066200.364101
15Qwen/Qwen2-7B-Instruct1.160.3673920.1020630.3542770.00.2850840.28508400.362961
16Qwen/Qwen2-7B-Instruct1.180.3639670.0987850.3500970.00.2753750.27537500.359723
17Qwen/Qwen2-7B-Instruct1.200.3597350.0951480.3464210.00.2859660.28596600.355384
18Qwen/Qwen2-7B-Instruct1.220.3574080.0919950.3451150.00.2056490.20564900.354276
19Qwen/Qwen2-7B-Instruct1.240.3534400.0864390.3394490.00.1791700.17917000.350735
20Qwen/Qwen2-7B-Instruct1.260.3479600.0814270.3347510.00.2135920.21359200.344795
21Qwen/Qwen2-7B-Instruct1.280.3438110.0734160.3301920.00.2197710.21977100.340595
22Qwen/Qwen2-7B-Instruct1.300.3405230.0723060.3268720.00.2127100.21271010.337439
23shenzhi-wang/Llama3.1-8B-Chinese-Chat1.000.3576980.1015240.3456620.00.3706970.37069700.352132
24shenzhi-wang/Llama3.1-8B-Chinese-Chat1.020.3581070.1010700.3454840.00.3398060.33980600.352985
25shenzhi-wang/Llama3.1-8B-Chinese-Chat1.040.3565930.1007710.3448640.00.3601060.36010600.351197
26shenzhi-wang/Llama3.1-8B-Chinese-Chat1.060.3561110.0993820.3432290.00.3309800.33098000.351146
27shenzhi-wang/Llama3.1-8B-Chinese-Chat1.080.3546270.0969250.3429150.00.3556930.35569300.349324
28shenzhi-wang/Llama3.1-8B-Chinese-Chat1.100.3528660.0967160.3412880.00.3186230.31862300.348124
29shenzhi-wang/Llama3.1-8B-Chinese-Chat1.120.3514960.0947580.3406650.00.3389230.33892300.346480
30shenzhi-wang/Llama3.1-8B-Chinese-Chat1.140.3520470.0946390.3400980.00.3601060.36010600.346720
31shenzhi-wang/Llama3.1-8B-Chinese-Chat1.160.3505260.0935260.3384690.00.3759930.37599300.344995
32shenzhi-wang/Llama3.1-8B-Chinese-Chat1.180.3489000.0928940.3376210.00.3830540.38305400.343295
33shenzhi-wang/Llama3.1-8B-Chinese-Chat1.200.3477430.0913330.3357230.00.3830540.38305400.342157
34shenzhi-wang/Llama3.1-8B-Chinese-Chat1.220.3460870.0902440.3346420.00.4033540.40335400.340244
35shenzhi-wang/Llama3.1-8B-Chinese-Chat1.240.3440970.0832330.3325460.00.4880850.48808500.337120
36shenzhi-wang/Llama3.1-8B-Chinese-Chat1.260.3426440.0851370.3306300.00.3495150.34951500.337607
37shenzhi-wang/Llama3.1-8B-Chinese-Chat1.280.3403420.0837870.3281820.00.2974400.29744000.336064
38shenzhi-wang/Llama3.1-8B-Chinese-Chat1.300.3393500.0819880.3272860.00.2806710.28067100.335319
39shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.000.3256020.0834010.3158220.00.1721090.17210900.323207
40shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.020.3255640.0839780.3158770.00.1862310.18623100.322975
41shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.040.3259200.0808490.3166050.05.8464255.84642510.271615
42shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.060.3256450.0811270.3150510.05.8367175.83671710.271447
43shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.080.3255420.0817670.3149460.05.8420125.84201210.271328
44shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.100.3259690.0855630.3152080.00.3009710.30097100.321824
45shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.120.3245180.0852720.3139130.00.4466020.44660200.318475
46shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.140.3227100.0845800.3135310.00.2771400.27714000.318923
47shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.160.3226900.0832600.3131360.00.2824360.28243600.318833
48shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.180.3216390.0817910.3111010.00.1562220.15622200.319488
49shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.200.3206160.0803960.3104100.00.1562220.15622200.318472
50shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.220.3190950.0793960.3080240.00.1535750.15357500.316997
51shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.240.3189440.0796320.3072170.00.1006180.10061800.317564
52shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.260.3179100.0772310.3069260.00.2356580.23565800.314726
53shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.280.3154750.0753160.3047500.00.0847310.08473100.314323
54shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.300.3142370.0745430.3034690.00.1253310.12533100.312547
\n","
"],"text/plain":[" model rpp meteor bleu_1 \\\n","0 Qwen/Qwen2-72B-Instruct 1.00 0.393169 0.122732 \n","1 Qwen/Qwen2-72B-Instruct 1.02 0.392567 0.124211 \n","2 Qwen/Qwen2-72B-Instruct 1.04 0.392359 0.124027 \n","3 Qwen/Qwen2-72B-Instruct 1.06 0.390993 0.123245 \n","4 Qwen/Qwen2-72B-Instruct 1.08 0.391984 0.122016 \n","5 Qwen/Qwen2-72B-Instruct 1.10 0.391010 0.120610 \n","6 Qwen/Qwen2-72B-Instruct 1.12 0.389989 0.118383 \n","7 Qwen/Qwen2-7B-Instruct 1.00 0.377314 0.117482 \n","8 Qwen/Qwen2-7B-Instruct 1.02 0.377679 0.116432 \n","9 Qwen/Qwen2-7B-Instruct 1.04 0.377817 0.115458 \n","10 Qwen/Qwen2-7B-Instruct 1.06 0.377353 0.115099 \n","11 Qwen/Qwen2-7B-Instruct 1.08 0.374707 0.111649 \n","12 Qwen/Qwen2-7B-Instruct 1.10 0.372693 0.109254 \n","13 Qwen/Qwen2-7B-Instruct 1.12 0.370982 0.106475 \n","14 Qwen/Qwen2-7B-Instruct 1.14 0.368016 0.104374 \n","15 Qwen/Qwen2-7B-Instruct 1.16 0.367392 0.102063 \n","16 Qwen/Qwen2-7B-Instruct 1.18 0.363967 0.098785 \n","17 Qwen/Qwen2-7B-Instruct 1.20 0.359735 0.095148 \n","18 Qwen/Qwen2-7B-Instruct 1.22 0.357408 0.091995 \n","19 Qwen/Qwen2-7B-Instruct 1.24 0.353440 0.086439 \n","20 Qwen/Qwen2-7B-Instruct 1.26 0.347960 0.081427 \n","21 Qwen/Qwen2-7B-Instruct 1.28 0.343811 0.073416 \n","22 Qwen/Qwen2-7B-Instruct 1.30 0.340523 0.072306 \n","23 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.00 0.357698 0.101524 \n","24 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.02 0.358107 0.101070 \n","25 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.04 0.356593 0.100771 \n","26 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.06 0.356111 0.099382 \n","27 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.08 0.354627 0.096925 \n","28 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.10 0.352866 0.096716 \n","29 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.12 0.351496 0.094758 \n","30 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.14 0.352047 0.094639 \n","31 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.16 0.350526 0.093526 \n","32 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.18 0.348900 0.092894 \n","33 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.20 0.347743 0.091333 \n","34 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.22 0.346087 0.090244 \n","35 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.24 0.344097 0.083233 \n","36 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.26 0.342644 0.085137 \n","37 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.28 0.340342 0.083787 \n","38 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.30 0.339350 0.081988 \n","39 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.00 0.325602 0.083401 \n","40 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.02 0.325564 0.083978 \n","41 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.04 0.325920 0.080849 \n","42 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.06 0.325645 0.081127 \n","43 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.08 0.325542 0.081767 \n","44 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.10 0.325969 0.085563 \n","45 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.12 0.324518 0.085272 \n","46 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.14 0.322710 0.084580 \n","47 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.16 0.322690 0.083260 \n","48 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.18 0.321639 0.081791 \n","49 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.20 0.320616 0.080396 \n","50 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.22 0.319095 0.079396 \n","51 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.24 0.318944 0.079632 \n","52 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.26 0.317910 0.077231 \n","53 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.28 0.315475 0.075316 \n","54 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.30 0.314237 0.074543 \n","\n"," rouge_l ews_score repetition_score total_repetitions \\\n","0 0.384361 0.0 0.363636 0.363636 \n","1 0.383536 0.0 0.345984 0.345984 \n","2 0.383951 0.0 0.356575 0.356575 \n","3 0.383145 0.0 0.356575 0.356575 \n","4 0.383606 0.0 0.345984 0.345984 \n","5 0.381963 0.0 0.376876 0.376876 \n","6 0.381737 0.0 0.406884 0.406884 \n","7 0.368777 0.0 0.263901 0.263901 \n","8 0.369318 0.0 0.263901 0.263901 \n","9 0.368963 0.0 0.255075 0.255075 \n","10 0.367135 0.0 0.249779 0.249779 \n","11 0.364109 0.0 0.241836 0.241836 \n","12 0.360194 0.0 0.250662 0.250662 \n","13 0.358556 0.0 0.250662 0.250662 \n","14 0.356445 0.0 0.250662 0.250662 \n","15 0.354277 0.0 0.285084 0.285084 \n","16 0.350097 0.0 0.275375 0.275375 \n","17 0.346421 0.0 0.285966 0.285966 \n","18 0.345115 0.0 0.205649 0.205649 \n","19 0.339449 0.0 0.179170 0.179170 \n","20 0.334751 0.0 0.213592 0.213592 \n","21 0.330192 0.0 0.219771 0.219771 \n","22 0.326872 0.0 0.212710 0.212710 \n","23 0.345662 0.0 0.370697 0.370697 \n","24 0.345484 0.0 0.339806 0.339806 \n","25 0.344864 0.0 0.360106 0.360106 \n","26 0.343229 0.0 0.330980 0.330980 \n","27 0.342915 0.0 0.355693 0.355693 \n","28 0.341288 0.0 0.318623 0.318623 \n","29 0.340665 0.0 0.338923 0.338923 \n","30 0.340098 0.0 0.360106 0.360106 \n","31 0.338469 0.0 0.375993 0.375993 \n","32 0.337621 0.0 0.383054 0.383054 \n","33 0.335723 0.0 0.383054 0.383054 \n","34 0.334642 0.0 0.403354 0.403354 \n","35 0.332546 0.0 0.488085 0.488085 \n","36 0.330630 0.0 0.349515 0.349515 \n","37 0.328182 0.0 0.297440 0.297440 \n","38 0.327286 0.0 0.280671 0.280671 \n","39 0.315822 0.0 0.172109 0.172109 \n","40 0.315877 0.0 0.186231 0.186231 \n","41 0.316605 0.0 5.846425 5.846425 \n","42 0.315051 0.0 5.836717 5.836717 \n","43 0.314946 0.0 5.842012 5.842012 \n","44 0.315208 0.0 0.300971 0.300971 \n","45 0.313913 0.0 0.446602 0.446602 \n","46 0.313531 0.0 0.277140 0.277140 \n","47 0.313136 0.0 0.282436 0.282436 \n","48 0.311101 0.0 0.156222 0.156222 \n","49 0.310410 0.0 0.156222 0.156222 \n","50 0.308024 0.0 0.153575 0.153575 \n","51 0.307217 0.0 0.100618 0.100618 \n","52 0.306926 0.0 0.235658 0.235658 \n","53 0.304750 0.0 0.084731 0.084731 \n","54 0.303469 0.0 0.125331 0.125331 \n","\n"," num_entries_with_max_output_tokens rap \n","0 0 0.387164 \n","1 0 0.386853 \n","2 0 0.386478 \n","3 0 0.385133 \n","4 0 0.386278 \n","5 0 0.384827 \n","6 0 0.383349 \n","7 0 0.373093 \n","8 0 0.373454 \n","9 0 0.373729 \n","10 0 0.373352 \n","11 0 0.370858 \n","12 0 0.368729 \n","13 0 0.367036 \n","14 0 0.364101 \n","15 0 0.362961 \n","16 0 0.359723 \n","17 0 0.355384 \n","18 0 0.354276 \n","19 0 0.350735 \n","20 0 0.344795 \n","21 0 0.340595 \n","22 1 0.337439 \n","23 0 0.352132 \n","24 0 0.352985 \n","25 0 0.351197 \n","26 0 0.351146 \n","27 0 0.349324 \n","28 0 0.348124 \n","29 0 0.346480 \n","30 0 0.346720 \n","31 0 0.344995 \n","32 0 0.343295 \n","33 0 0.342157 \n","34 0 0.340244 \n","35 0 0.337120 \n","36 0 0.337607 \n","37 0 0.336064 \n","38 0 0.335319 \n","39 0 0.323207 \n","40 0 0.322975 \n","41 1 0.271615 \n","42 1 0.271447 \n","43 1 0.271328 \n","44 0 0.321824 \n","45 0 0.318475 \n","46 0 0.318923 \n","47 0 0.318833 \n","48 0 0.319488 \n","49 0 0.318472 \n","50 0 0.316997 \n","51 0 0.317564 \n","52 0 0.314726 \n","53 0 0.314323 \n","54 0 0.312547 "]},"execution_count":288,"metadata":{},"output_type":"execute_result"}],"source":["metrics_df"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["models = metrics_df[\"model\"].unique()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/plain":["array(['Qwen/Qwen2-72B-Instruct', 'Qwen/Qwen2-7B-Instruct',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat'], dtype=object)"]},"execution_count":290,"metadata":{},"output_type":"execute_result"}],"source":["models"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA1cAAALCCAYAAAAyHim0AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/TGe4hAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd5xU9b3/8dc50/v2vpSlgwJSLYAgKhoNaqIxmthjiagxtlzTbrwxMZpEzS/BmJtcjSWJNUbsBUQBFRGkSO9le5/Z6e33x5k9O8MusODCLvB5Ph7zmDPnfOfMd0aQec/3ez5fJZlMJhFCCCGEEEII8ZWovd0BIYQQQgghhDgWSLgSQgghhBBCiB4g4UoIIYQQQggheoCEKyGEEEIIIYToARKuhBBCCCGEEKIHSLgSQgghhBBCiB4g4UoIIYQQQggheoCEKyGEEEIIIYToAcbe7kBflEgkqKqqwuVyoShKb3dHCCGEEEII0UuSySQ+n4+SkhJUdf9jUxKuulBVVUV5eXlvd0MIIYQQQgjRR+zevZuysrL9tpFw1QWXywVoH6Db7e7l3gCXXgrPP9/bvTg00vfeIX3vHdL33iF97x3S994hfe8d0vfe0Uf67vV6KS8v1zPC/ki46kL7VEC32903wpXJBH2hH4dC+t47pO+9Q/reO6TvvUP63juk771D+t47+ljfu3O5kBS0EEIIIYQQQogeIOFKCCGEEEIIIXqAhCshhBBCCCGE6AESroQQQgghhBCiB0i4EkIIIYQQQogeIOFKCCGEEEIIIXqAhCshhBBCCCGE6AESroQQQgghhBCiB0i4EkIIIYQQQogeIOFKCCGEEEIIIXqAhCshhBBCCCGE6AESroQQQgghhBCiB0i4SjN37lxGjhzJxIkTe7srQgghhBBCiKOMhKs0c+bMYd26dSxbtqy3uyKEEEIIIYQ4yki4EkIIIYQQQogeIOFKCCGEEEIIIXqAsbc7IPavzhdCMdhwRGJYjQZUVentLgkhhBBCCCG6IOGqj/v+sytYPuxK+Pk7ANhMBuxmA3aLAbvJiM1swGExYDMZse+1rbUxYDcbtW3zXu3MRu24xYDZoKIoEtyEEEIIIYQ4VBKu+rhEMomSTJJMBZ9gNE4wGqfR37OvY1AVPWjZzUZsps4BzG5OBTVzKqiljuntzJ3bWFExJpMS3IQQQgghxDFPwlUf98rNp5GcPZvQS6/gj8QIRuIEIvGM7UAklrqPEwjHCETjBCNx/HttB6Nam2Dq+YFInEgsAUA8kcQXjuELx4Bwz72BkdfDvW+iKmBUVQyqglFVMBhS96qi7zfoj/e+Tz3PkLlfu6ldtE/t37u9kv66XTzPkLnf6iijoNpLodtKtt0kAVEIIYQQQuyXhKujgALYzAZsZkOPnzsWT+gBLLBXCAuEU6Et2rEdjKba7CfktYe3RLLjdRJJiMQTEO/xt3D49D8P/rAIALNBJd9locBtodBl1e7dVgpcFgrcVgpT+7MkhH0lyaT2h0Y+QSGEEEIcjSRcHeeMBhW3QcVtNfXoeZPJJOFYgvAllxJ79h/EE0niySSxeJJ4Ikks0X6fyHwcz9wfT+zdPkk8keiivbY/niDz+N6v0+Xrd/0c/8Yt1OUW0+SPEIknqGwJUtkS3O/7bg9hhW4LBS4tdBWkQlih26oHsuMhhEViCVoCEZoDUf2+NajdNwcitKbumwNRfbslGCU+4no8v3yPLJuJLLuJLLs5tW1OPTbhsZnIbn9sM5PlMOGyGI/5z1QIIYQQfZuEK3FYKIqC1WTAmoiA09Lb3Tk0s38Dj84jEktQ3xam1huizhumzhfSt2t9Yeq8Iep84UMOYZkjYOlBzILH1vshLJ5I0hpMhZ+0oNSSetweilr049q+QOQQhykVlSZ/hCZ/5KCeZlAVPDaThDIhhBBC9BoJV0IcgNmoUpplozTLtt924Vicel+YulTgqtWDmBbM6n3afXMg2v0QZlT1sKXfp42Kte/vTghLJpN4Q7GMUaKWQIRmf/t2ZoBqCUZp9kfwhmIH/Zm1UxXSQo4WbDyp+yybiSyHmez2oJMKQKbrrqXlsb/qfdBDW6qPmY+17WA0TjyRlFAmhBBCiF4l4UqIHmIxGijLtlOWbd9vu/YQVuvtGPWqTQtj2ohYiJZAlEgswZ7mIHuaDxzC0qciWkum431qWcYUvJZglHj6hXAHyWU1dgQMe3so6gghnY+ZcVmNB782WyxAYZHroJ4SisZpTQ9fnUJZ5qhae9seD2UlM8h6bR05jvbPIfVZ2M3kOLTPx2rq+WsnhRBCCNE3SLgS4gjrbggLRdtHwlKBSw9iXYew3U1BdjelQljWMFhf1+V5bSaD/oU/czQptZ0apcl2mPDYtHDgsZkwGtSe/ih6jNVkwGoyUOi2HtTz9g5l7deFaSGsY7vboSxrKCzZvt/XTP/8sx2pzzothGXuk1EyIYQQ4mgi4UqIPspqMlCeY6c8p/shrH00LPi3J8j+/vVpI0lmfXqbjJx06OlQ1vzYX2n+1uW0+KM0BSKdCnrEE0ltrbrWOFWtoW6/nlFV9CCcnT5S6DCRk77P0RHSsvp4IBZCCCGORRKuhDjKdRnCHlwFk/r1XqeOcfsMZY2r4NxfdvmcZFJbS67Z31ExUbvmrb1qYloQS9sXimqVLBvaIjS0HdzURbfVSLbDrE/VzLF3bLdf76aPkhkd5McTEsiEEEKIr0DClRBCHAGKouC2mnBbTfTP7f7zQtG4FrT8WvBqag9h/o4w1pQRzDqKkHhDMbyhGDsbAwd+oaHfZUFTgIp85yG+QyGEEEJIuEozd+5c5s6dSzx+NK10K4Q4lllNBoo9Noo9+69WmS4WT+jl85sDWtXH9uvGmgIRWvwdlSHbR8hafEGy7ebD+E6EEEKIY5+EqzRz5sxhzpw5eL1ePB5Pb3dHCCEOidGgkuu0kHsQa8wlZ88G+9cPY6+EEEKIY5+EKyGEECgAUpFQCCGE+ErkymUhhBBCCCGE6AESroQQQgghhBCiB0i4EkIIIYQQQogeIOFKCCGEEEIIIXqAhCshhBBCCCGE6AFSLfBo4G6Elf8Cgyl1M6fdW6Df5I62vhqIR1LH0tqpJlAlSwshhBBCCHG4SLg6GhTuhv/c1PUxoxV+Wtvx+LUfwKa3u26rGOBn9aAatMdv3Amb39sriKWFt2//C8x2re3yp2D3Z3uFu7Ttyd8Hi1Nru3sZNG7R9mfVw6Z3tHZGixYGi07QtgHCPohHO44Z5I+kEEIIIYQ4Osk32aOB3w0nTdRGpOLRtPto5zCiGLSQEo8Ayb1OlOwIVqCNcrXs3Pfrpq95s/NjWP3cvtuOv7YjXK36J3z+hLZdAfzzW5ltf7Aasvtr2x8+CB//Me01Va3/xtSo3DVvQt4Q7djyp2DlPzKDWns7oxmm3tVx3t2fwc4le7WxdDy3fDLYc7S2/kbw16WOWTvadfr8hBBCCCGE2DcJV0eDqgr47svda3vZPzu2E/FUEEsLZenO+h847XZIRPcKbqltg6Wj7QnfgPxhndvEI9rzTbaOtvnDYdBM7diXq2BIhbYdC2v36W3j0cw+JRMQC2o30MJWu+YdsHvpvt/7xO8BqXC1/UNYcP++217zNvQ/Rdte8wK8/V+d25wEPDQIvvlXGHSGtq92nRbanIXgKgZXobZttHR+vjiwZCrAtgf5uvXgaYDK5eAqAWdB5g8CQgghhBB9mISrY5lqANWWGWbS5Q7Sbt0xdJZ2647JN2o3gNmz4Xfz9t323Afh7F9BPNwRvtLvPWUdbcd8G0rHdd0uHta+jLcrPAHGfqfjWCySeW/L6mirGsGW3XEsEdP2K0CgAUz2jrY7FsFb93R+H7ZsLWyd93vof6q2r3Er1KzW9jsLwVW07/8Wx5JoMHWdXyoUVS6HHUvAXw/+Bu0z9denRgzr4fr5UDhKa7v+dRi0Fv6aCrOKQfvcXMXgLobp93a09TdAqBXcJcfH5yqEEEKIPk/Cleh9BqN2Mzv23y5/mHbrjmHnarfumHS9dmuXSGgh69KL4LHfQk5FxzFPOQw/H9pqwVcLbakCIsFm7aakjbJsfg/e/lHma1k8qbBQCGf+AkrHa/tb90DzTu2Ys7BjimVfEAtrQcaRr02xBNi+CLbO7whM/oaO7agfvv9xRwjaumD/o4j+ho7tguHgd0GRS/tsk3HwVmq3SmDKHR1tV/0L3v2ptm3NAnepFsBcxVrgOum7kNVPOx6PaiE6faqrEEIIIUQPk3AlxN5UVRvxi5mh6MTMY8O/pt3aJZNaqPLVgK8aCkZ0HLPnQvnJWkjw1UAsBOFW7dawUQtx7da/ljk10exMBa1UEJt6Z0dYaauHYJMWwqyegw8MsQgEGrURpNwhYLJq+ze9CxteywxLgUYIe7XjNy3RipGANj1z8SP7fg1/fcd28VgYfakWzuy54MhLbedp2+60EccRX4eNf4XfzoN4TLsWzlsNvirwVmUG3VhYG1WMBiDUot3q1nYcH3ZuR7ha+jjM/2UqfJWkhbBUIBt4esc1eEIIIYQQh0jClRBfhaJoX8rtOVA4MvPY6Eu0G2ghLNSaGvGq1ka92gt1gHbNVu5gLYRF2rRb4xbtBnDyzR1t17wI79ybep4tdd1XKoS5imHSDR1t174Ca17KnI4Xau04ftPijgBZuwZWPN31+1SNWnhpVz5JqxDpyOsclhx5YHF3tB1ylnY7WAajFrzcJcD4zsen3aWFzlCr9pl6UwGsfTurf0dbb5U2Gtm8Q7vt7YYPO8LV509oxVPcJR3TEdsDmbsUsgd2jOAJIYQQQqSRcCXEkaAo2nVetqyupzZOuFa7AYTb0kJYjbadPmKTiGnTC8OtWuGPvQPDiZd0bDdthw2vd9EfVQtDkUDHvgFTYcZP9gpL+eDI1abdpY+QDZym3Xpb+ueaPmq4tzN/oV0HqI+CpQJY+3b6tX31m6B6pXbryg0fQslYbXvtK9pSA+1BzNMAVV9oIcyeJ2vLCSGEEMcZCVdp5s6dy9y5c4nH473dFXE8szi1276KjZx2m3aLBFIhrCY19TAVyLIHdLQdPBMsrlRIyu8ITtaszl/8yydpt2OR0aJ9Lumfzb5MvhEqTk8bBavWrvlq306fxrhrqXbtV7tBwP9O17ZVE9z4UceI5vZFULOmYzTOXaKNOMrabkIIIcQxQ/5VTzNnzhzmzJmD1+vF4/H0dneE2D+zHXIGard9KR6j3UT3HegzTTfi61q5+PapiMs/hEKHFnoTUe1Yuw1vwNI/Zz5fUbVr59wlcPETHeGvfqM2hdNdok1JbL8uTgghhBB9moQrIYQ4VANO027t/jk7VYwjqgUse27HseIxMOobadeGVWlTPH3V2s2cViHy8ye0Ihzt7LkdQctdok3fdOZrxwJNYDBpI5RCCCGE6FUSroQQoqcZTJnXcQGMvUy7tUsktCIj3kotbKUHMasHcgZp+2PBVHXHRm1aIcAZP+1ou/A38NlftCIi7WXo26sguku0a/AkeAkhhBBHhIQrIYToDaqqTRt0FkDJSZnHZvxYuyWTWpXG9tEub6V23Vd6EAs2a/dhr3Zr2Jh5rhEXgCW1/e7PtLL/7tJUCCvu2FblWlMhhBDiq5JwJYQQfZWigC1bu7Wvc7a3b/4Vzn8kdd1XZWYIa6vNXL+raRs0b9duezOcfHjegxBCCHEckXAlhBBHO4sTLEMy107ryrkPwSlzMgNYeyXEFbJ2lxBCCPFVSbgSQojjhadUu3Vl3uwj2xchhBDiGCQrXAohhBBCCCFED5BwJYQQQgghhBA9QMKVEEIIIYQQQvQACVdCCCGEEEII0QMkXAkhhBBCCCFED+j1cDV37lwGDBiA1Wpl8uTJfPbZZ/ts++9//5sJEyaQlZWFw+Fg7NixPPPMMxlt2trauOWWWygrK8NmszFy5Egef/zxw/02hBBCCCGEEMe5Xi3F/vzzz3PHHXfw+OOPM3nyZB599FFmzZrFxo0bKSgo6NQ+JyeHn/zkJwwfPhyz2czrr7/ONddcQ0FBAbNmzQLgjjvuYMGCBTz77LMMGDCAd999l5tvvpmSkhJmz5ZSw0IIIYQQQojDo1dHrh5++GGuv/56rrnmGn2EyW6388QTT3TZfvr06Vx00UWMGDGCQYMG8YMf/IDRo0ezePFivc3HH3/MVVddxfTp0xkwYAA33HADY8aM2e+ImBBCCCGEEEJ8Vb0WriKRCMuXL+fMM8/s6IyqcuaZZ/LJJ58c8PnJZJL58+ezceNGpk2bpu8/9dRTmTdvHpWVlSSTST744AM2bdrE2Wefvc9zhcNhvF5vxk0IIYQQQgghDkavTQtsaGggHo9TWFiYsb+wsJANGzbs83mtra2UlpYSDocxGAw89thjnHXWWfrxP/7xj9xwww2UlZVhNBpRVZW//vWvGQFsbw888AD33Xdf5wOXXgom08G/uZ722WdwtE5plL73Dul775C+9w7pe++QvvcO6XvvkL73jr7S92i020179ZqrQ+FyuVi5ciVtbW3Mnz+fO+64g4qKCqZPnw5o4erTTz9l3rx59O/fn48++og5c+ZQUlKSMUqW7t577+WOO+7QH3u9XsrLy+H558HtPhJva/9mz4Z583q7F4dG+t47pO+9Q/reO6TvvUP63juk771D+t47+krfvV7weLrVtNfCVV5eHgaDgdra2oz9tbW1FBUV7fN5qqoyePBgAMaOHcv69et54IEHmD59OsFgkB//+Me88sornHfeeQCMHj2alStX8rvf/W6f4cpisWCxWHronQkhhBBCCCGOR712zZXZbGb8+PHMnz9f35dIJJg/fz6nnHJKt8+TSCQIh8MARKNRotEoqpr5tgwGA4lEomc6LoQQQgghhBBd6NVpgXfccQdXXXUVEyZMYNKkSTz66KP4/X6uueYaAK688kpKS0t54IEHAO3aqAkTJjBo0CDC4TBvvvkmzzzzDH/+858BcLvdnH766dx9993YbDb69+/Phx9+yNNPP83DDz/ca+9TCCGEEEIIcezr1XB16aWXUl9fz89//nNqamoYO3Ysb7/9tl7kYteuXRmjUH6/n5tvvpk9e/Zgs9kYPnw4zz77LJdeeqne5rnnnuPee+/lO9/5Dk1NTfTv359f/epX3HTTTUf8/QkhhBBCCCGOH71e0OKWW27hlltu6fLYwoULMx7ff//93H///fs9X1FREU8++WRPdU8chZLJJEmSxJNxEslEp1tX++PJOMlkMuNecUWweHdjMVqwGCxYjVbMqhlFUXr7LQohhBBCiD6o18OV6B2xRIxIPEI0ESWaiBKJR7RbIkI0Hs24z9i/vzZdnC96ag2J928ikUiQIBVkEnE9/LQHma5C0L6CUKcwRJJ4ItUu9Ro94mzgla912m0xpMKWwYrZYMZqtOr7LAYLFmPaMYNVD2ftz9nfY33/Xuc0qIaeeU9CCCGEEOKwkXDVx7257U3qhrQSWf2/enhJv28PNtF4KtTsFXT2FYZ6LIAcSDFQueTIvNZBUhUVVVExKAZ9W0VFVbX7pLeVsMNCOB7O+LzC8TDheBgvR26xaaNq1IJYNwKcxWAha1QTw7e/zYjcEZS7ylGVXqtdI4QQQghx3JBw1cc9ve5p1o5ugi/+eNheQ0HBbDBjVs2YDCbMBjMm1YRZNWvbho7t9jYm1aQ/Tm+j70+dw/SnP6P+4PbOIWavm0ExoChKRpv0ffo9qTapANRVm67O3dVrHlBqbYVkMkksESMcDxOKh7RwFQvrISsUDxGJRwjFQl0+1tvFUvu7eY5YIqZ3JZaI0ZZooy3a1r3/qMOBj+4GwGFyMCx7GCNzRzIidwTDc4ZT4anAqMpffyGEEEKIniTfrvq4qWVTqVixE/OZszCqxoxAo4egtHs9IO3VJj306IFINWEymDAqxsN3HdHOZ2HwBYfn3EeIoihaoDSYcOI8Yq8bT8QzwtnBBLTafz/FhkkD2NS8CX/Uz4q6FayoW6Gf22KwMDR7KMNzhjMidwQjc0YyOHswFoOs9yaEEEIIcagkXPVxc8bOgZ+/Az//RW93RRxhBtWAXbVjN9kP/sk/ewPu+yexRIztrdtZ37Se9Y3rWd+0ng1NG/BH/axpWMOahjX6U4yKkYqsCkbkjNACV+5IhmUPO7TXF0IIIYQ4Dkm4EuIYZlSNDMkewpDsIcweNBuARDLBbt9uPWy137eEW9jUvIlNzZt4deurgDZltL+7PyNyR+iha0TOCDwWT2++LSGEEEKIPknClRDHGVVR6e/uT393f84ZeA6gla+v8ddoYSstcNUF6tjh3cEO7w7e2v6Wfo4SR0mnwJVvz++ttySEEEII0SdIuBJCoCgKxc5iip3FnNHvDH1/Q7CBDU0bMka59rTtocpfRZW/ivm75utt82x5jMjRCma0F88ocZTIumBCCCGEOG5IuBJC7FOeLY8ppVOYUjpF3+eNeNnYtJF1jeu0a7gaN7Ddu52GYAOLKhexqHKR3tZtdmeMbo3IHUF/d38pDS+EEEKIY5KEKyHEQXGb3UwsmsjEoon6vkA0wKbmTXrBjPWN69ncshlvxMvSmqUsrVmqt7UZbVqVwrRRroqsCkyqqTfejhBCCCFEj5FwJYT4yuwmO2MLxjK2YKy+LxKPsKVlCxuaNuijXJuaNhGMBfmi7gu+qPtCb2tSTXpp+GEVXgZUfUJ/d3+KHEUyyiWEEEKIo4aEqzRz585l7ty5xOPx3u6KEEc9s8HMyNyRjMwdyTeGfAPQ1u7a4d3BusZ12ghX6jqutmgbaxvXsrZxLZwEvHcDoK3HVe4qZ4B7gF6Eo/2WY82R67mEEEII0adIuEozZ84c5syZg9frxeORUtNC9DSDamBQ1iAGZQ3i64O+Dmil4St9laxvWs+6xnVsffdf7BxRyG7fbsLxMFtatrClZUunc7lMLi1oeVKBy5XadvXHaT5yiz0LIYQQQrSTcCVESjIeJxmLkYzGIBYlGY+jtPlRbVYUg6G3u3fMUhWVcnc55e5yzh5wNty3AO6dRywRo7qtmp2+nez07mRH6w52erXtan81vqiPLxu/5MvGLzudM8+WR393f33Eq5+7HwPcAyh3lWM2mHvhXQohhBDieCDhSvS4hN9PIhAgGYlot82btdCSCi62E0ahmLUvuKGNG4ls35E6FiUZi0KqXTIWI+vib2JwuwFoW7QI/8efpNpGSMZikGqXjMUovOduTKWlALS88h9aXngh7XUjGW3L5s7FdsIoAJqeeora3zwIyWTnNzNhAuV//V+cU6cC0DpvHrW/eRDFbEaxmFHNZhSTWXtsNpN/263YJ0wAILhqFc0vvpjZxmJJtTXhnDYNS0UFANG6OkJffpl2LhOKOXV+iwVDTi4GpwOAZCIBinLMT4kzqkY9dKVXKwQIx8Ps9u7WQpe3I3Tt8O6gKdREQ7CBhmADy2uXZzxPVVSKHcWdQld/d3+KHcUYVAnRQgghhDh0Eq6OQsl4XAsiES1gGHNy9GORHTuINTeTjKSOt7eLRkjG4mRddKHe1vvWW4Q2bUqFoGhHGEo9r/SRh/URm/r/9//wLVwI0SiJ9Pap8w9Z9BEGlwuAmgceoPWllzs6/PXZGf0f/OFCTIWFALS8/DLNTz+zz/fqnDFdD1eBz5fT9OST+2ybe8P1eriK1dUR/OKLfbZNhoIdDxS162DVftjUUcUu3tZGvKlpn23jXp++Hd6+PfNz2IsxL18PV8GVK6m87Qf7bFv0P/eR/a1vAeD/+BN2f+97KCaTHurSb3k33kD7pNZEIEB423YsQwajWiz7PP/RxmKwMDh7MIOzB3c65ov42OXdpYeu9PDlj/qpbKuksq2SJVVLMp5nUk30c/XrdG3XAM8Acq25x3yYFUIIIcRXJ+HqKLC7sorA5JP14ENawQ3VbmfYio5f52vu/xX+xYv3eS7PhRfoXxK9b72N791399k2GYmg2GwARCurCK9bv9+2ep/MZm1kxWxGiUZR3G4toJiMKMbMctvmfv2xjR+vBQWjUbuZjGA0ophMqHa73tY+YTzJ2LUoRlNHe5P2HIxGTEVFelvXWWdhrhiYOmeqfVpby4ABetusb34D97nn6K+ppO658EKSL76oPaf98zvvPOzjJ3QE1kjmzTpqlN7WOmIE+bffnjoWToXSjmBqLi/T2xpcLqxjRmeG3HBY31ZT/x3SP+tkNEoyGgW/P+MzTaQ9Dq5ew66rrwaDAfPAAViHDccyfBjW4cOxDBuGMT//mAsNLrOLUXmjGJU3KmN/MpmkMdSYMcq1y7uLnd6d7PLuIpKIsLV1K1tbt3Y6p8PkyLyuKzXlsJ+7H26z+0i9NSGEEEL0cRKujgKJRIJEa2uXx5LRaMZjY0EBprKyvUYzTFrgMZm0YJYKC46pU7Qv12bzXqMgqTCSdp1RztVX4T7/vI527dPXUo8NWVl628Kf/pTCn/1M+9I+ezbMm7fP95bz3e+Q893vdOtzcE6dqk/POxBLxUAsFQO71VZ1OFAdjs4HFAXFas3YZfB4MHSz2Il12DCsw4Z1q63jlFMYeMop3WrrnHIaQ5Yszgh1enALRzAP6A//+hcA8ZYWDB4P8dZWIlu2EtmyFd54Qz9X8f2/JOviiwGINTURq6vDUlGhT9s8liiKQp4tjzxbHuMLx2cciyfi1ARq2NmaCl2+1MhX606q/FX4o37WNa5jXeO6TufNseboo1z9hrVQuu1NSpwllDpLybXlSil5IYQQ4jgi4eooUFxUSHLu3LRQY0IxmVHNJi0wpSn59a+6fd7sSy7pdlvriBHdbquo8mXycFLMZoy5ud1q6z5nFq5ZZxOrrSW8cSOhDRsJb9xAaMNGIjt2YB40SG/re/99an7+32AyYamowDp8GJZhw7X74cMzpp8eawyqgVJnKaXOUk4tPTXjWCQeYY9vjz7SlT7NsD5YT1OoiaZQk7Zu1wnAoh/pzzWrZj1olThLMrZLnaUy3VAIIYQ4xki4OgqYTSZIXZsjxMFSFAVTURGmoiKcp5+u708EgxnXkyUCAVSXi4TPR3jjRsIbNwIdo479nn4Kx6RJAET2VJII+LEMHJhxjmOR2WCmIquCiqzOfwf9Ub8+rXC7dzt7/vM0VZOHU9VWRU2ghkgiwg7vDnZ4d3R5bovB0hG6HB2hqz2AyVpeQgghxNFFwpUQx6n067gAcq++mpyrriJWVUVo40ZCGzYQ3qCFrMiuXViGDNHbtjz3Lxr/9n8oZjPmwYOwto9wpe7Tp4keyxwmh75QMgA/ewv+Wyu6Ek1EqfXXUtVWRWVbJVX+qo7ttipqA7WE42G2t25ne+v2Ls9vNVi7HPFq3862ZEv4EkIIIfoQCVdCCJ2iKJhKSzGVluI64wx9fyIQyCgukozFUe12rRrhuvWE160n/arAwQvmYyopASC8ZQuoKub+/Y+r9cJMqokyVxllrrIuj0fjUWoCNVS1ZYau9mqGdYE6QvEQ21q3sa11W5fnsBltlDhKOoWu9vssS5aELyGEEOIIknAlhDig9GAFUPhfP6LgnruJ7tlDaONGwhs2EtqojXTFvV6MxcV62/o//D98772HYrViGTIEy7ChaSNdw/RS+8cbk8FEuauccld5l8ej8Sg1/hoq/Zmhq327PlBPMBbcZ4VDALvR3mnUK30aosfikfAlhBBC9CAJV0KIQ6KoKuZ+/TD36wdnnaXvT4RCmV/YDQYUm41kMEhozRpCa9boo1yKxcKwFcv1Ea3gmjWokQjmWCyjBP7xyGQw6YsodyUSj2jhq61z+Kpqq6IuWEcgFmBLyxa2tGzp8hwOk0MPWgNObOLEHe8yOn80RY6iLtsLIYQQYv+O728vQogep+5Vvr7s0UdIxuNEdu1KVSzURrhCmzZizMnNmCpY/dOfEd6xE04ah7lfPywVAzEPrMBcMRDL4CHYThi198sdt8wGM/3c/ejn7tfl8XA8rIUvX2XG6Fd7+KoP1uOP+tncvJnNzZthKPDhnQAU2AoYnT+a0fmjOTHvREbmjsRusnf5OkIIIYToIOFKCHHYKQYDloEDsQwciPucc/T9ibTFp5PJJKrNhqIoJKNRIlu3EtnaMd3NMmQwFa+9pj9u+Mv/otrtWvCqqMBYVCRT3NJYDBZ9/a2uhONhqtuq9RGvTf94lNUTy9jUvIm6YB3v73qf93e9D4BBMTA0eygn5p2oh67+7v6yhpcQQgixFwlXQoheo6YtVqwoCgOe+xfJr3+d2F/+QnjbdiLbthHevo3Itu2YBwzQ2yYTCRoef5xkMNjxfLsdy4ABmCsqsE8YT/a3v30k38pRx2KwMMAzgAGeAdqOlc/C/7xAMBZkfeN6VtevZnXDalbVr6IuUMf6pvWsb1rPC5teAMBtdmeErRPzTsRj6d4C20IIIcSxSsKVEKJPURQFU0mJVm1wymldtklGo+R897t68Irs2kUyECC0bh2hdetIBIN6uEomk2y/6BsYC/KxDKzAPKgCS0UF5ooKDNlSynxvNqONcYXjGFc4Tt9X469hTcMaLXDVr2Zd4zq8ES9LqpawpGqJ3m6Ae4AetEbnj2ZI9hBM6rG9DpoQQgiRTsJVmrlz5zJ37lzi8Xhvd0UIsR+qxULBnXfoj5PRKJHde4hs30Z42zbM5R1FIOINDYQ3bCC8YQP+jxZlnMfg8eD5xjco/NE9+r7Izp2YSkuP+4Ia6YocRRQ5ijirv1a4JJqIsrl5sx621jSs0RdL3uHdwbyt2uLTVoOVkbkj9dGt0XmjKXQU9uZbEUIIIQ4r+faQZs6cOcyZMwev14vHI9NbhDhaKCYTloqBWCoG4po5M+OY6nbT/5mnM6cZbt1GtKqKeGsrJBJ621hzM1tnnQMmE+b+/bSRrooKrbBGRQXmgRUYnI4j/fb6HJNq0hdP/vZwbYSwJdSijW41rGZNvXbvi/hYUbeCFXUr9OcW2AsYkz+G0Xla4BqROwKb0bavlxJCCCGOKhKuhBDHNNViwT5xIvaJEzP2J4JBIjt3ZqzhFauuRrFaSYZCRLZsJbIlc/2o7O98h6Kf/VR7vt9Py79f6SioUVjI8TzBMMuaxdSyqUwtmwpAIplgh3eHFrRS129tat5EXaCO93a+x3s73wM6imWkj271d/eX6ZpCCCGOShKuhBDHJdVmwzp8eMY+68iRDFuxnFh1tTbSlZpmGNm+g8i2bZgHVehtw1u3UvurX+mPFbsdczyGac4tmEpLcJ15Jo5JkwDtui/guAoMqqJS4amgwlPBBYMvACAQDbCucR2rG1brUwrrg/V6sYznNz4PgMfi0a7bSo1unZB3ghTLEEIIcVSQcCWEEGkUVcVUWoqptBSmTsk41h6SABSjEeeZMzMKaoSB8Pz5AJjLyvVwFVqzhl3XXqefV7uVaPclJZj7DzguphvaTXYmFE1gQtEEQPs8awO1rKpfpV+7ta5xHa3hVhZXLmZx5WL9ue3FMsbkj+HEvBMZkj0Eoyr/hAkhhOhb5F8mIYTopvSRJ+vIkZT/6U9Ae0GN3US+dz3R664lWlWF7aSxettoZSWJtjbCGzcS3rix03kLf/xjcq68AoDw9u00P/uPTkHMkJV1zI18KYqiF8uYNWAWANF4lE3Nm1hVv0qvULjLt6tTsQyb0aYVy8gbzcD+Phw73sVusmMz2rAb7dhNduxG7bHNaMOgGvbXFSGEEKJHSLgSQoivSCuoUYHF6YDvfKfTcecZZ1DxxutEKys7blVVRCoriVZWYSor1duGN26k+R//6Pwadjvm0hLyb79dL9oRb2khsnuPFr6OkbLyJoOJUXmjGJU3St/XHGrOKAW/pmENbdE2ltcuZ3ntcpgAfHjnfs9rNVi14NUewPYRxNq37UY7NpNtv9tWo1UWUhZCCJFBwpUQQhxmqsWCZdAgLIMGHbCtuX9/cq+/PiOExerrtWmHm7dktPV/+imVt/8QAMVmy5xqWFqKc+ZMLAMHHpb3dCRlW7OZVjaNaWXTgFSxjNYd2nTChtVUf/QGwdEjCMaCBGIBglHtPhALkEhq1SBD8RCheIjmcHOP9q09oKUHs4yQtleIax9Jaz9e7IzSP5k8JoKxEEIICVdCCNGnWEeMwDpiRMa+RDhMtKqKaGUV1pEdx5KRCMaCAmJ1dSSDwU4VDs0DBujhyjd/PnW/f7jz9V6pqYeGZPKoqXaoKioVWRVUZFVw0ZCL4IHl8KOnOrVLJpOE42E9dAWigYztQCz1eK/tjPsunheMBUmiXX8XjAUJxoKH/mZmQf6LM5lQNIFJRZOYWDSRfq5+EraEEOIoJeFKCCH6ONViwTJwYKdRKM/s2XhmzyYRiRCrqkqbaqhNNzSnjZRFduwksm0bkW3bunyNkqJC2uvxxVtaSMbjGHNzD9dbOiIURcFqtGI1Wskmu8fOm0wmCcVDXQe01KhZVwFt77aBWICdDVuoD9bz1va3eGv7W4C2FtjEoolMLJzIxKKJlLvKJWwJIcRRQsKVEEIc5VSzGfOAAZgHDGBfNQc9F8zGOnIE0crKjgCWGg2L1dRgtVr1ti3/+Q91v3kQU2kptjFjsI0ZjXX0aKwjR6JaLEfmTfVhiqLo0/ty+WoBNHzh+ax+/L9ZVrOMZTXLWFW/irpAHW9se4M3tr0BQKG9UAtbqVuZs0zClhBC9FESroQQ4jhgzMvDmJfX5bFkJALf/Kb+OFZdA6Bf9+V9803tgMmEddgwSh99BHNZ2WHv8/HAklD10AQQioVYXb+aZbXL+Kz6M1Y3rKY2UMvr217n9W2vA1DkKNJHtSYWTaTUWSphSwgh+ggJV0IIcZxTzGZI+3JeeO9/kXfLHEJr1hBcvZrgqtUEV68m3thIaMOGjJBW98ijhNau7RjhOvFEjNk9NwXveGM1WplUPIlJxZOYM3YOwViQ1fWr+azmMz6v+ZzVDaup8dfw2rbXeG3bawAUO4ozRrZKnaUHeBUhhBCHi4QrIYQQnRhcLhynnorj1FMB7TqjaGUVke3bUNOmEPqXLCH05Zf4F3cs+Gvq3w/b6DHYRo8m+zuXo6hSrvxQ2Yw2JhdPZnLxZEAroLGybqU+jfDLhi+p9lczb+s8fQ2wUmcpEwonMLFoIpOKJlHsLO7NtyCEEMcVCVdCCCEOSFEUzGWlmMsyR0WK7vsFwZUrCaVGuCI7dhDduYvozl0Eln9OzhXf1ds2P/ccqtOFbcxoTGVy3dChsBltnFJyCqeUnAJAIBpgZX1H2FrbsJbKtkoq2yp5deurgBa22oPWxKKJFDmKevMtCCHEMU3ClRBCiENmGzUK26hR+uLJ8ZYWgmvWEFy1WptumJJMJql/5FHira0AGHJysJ14IraxY7COHo3txBMxuN298h6OZnaTnVNLTuXUEm2EMRAN8EXdFx1hqzEVtrZU8p8t/wGg3FXOxKKJ+uiWhC0hhOg5Eq6EEEL0GENWFs6pU3FOnZqxPxkK4f761wmuXk1o/XriTU20ffghbR9+CIB90iT6P92xVlV482bMAweiGOWfqYNhN9k5rfQ0Tis9DQB/1M8XdV/o12yta1zHbt9udvt28+/N/wagn6ufFraKJjCxcCKFjsLefAtCCHFUk3+10sydO5e5c+cSj8d7uytCCHFMUW02in76E0BbFDm8fn1GsQzbmDF623hLC9u+PhvFasU6ahS20aOxjRmNbfRojMXFMp3wIDhMDqaUTmFK6RQA2iJtrKhbwec1n7OsZhnrmtaxy7eLXb5dvLz5ZQD6u/szobBjUeN8e35vvgUhhDiqSLhKM2fOHObMmYPX68Xj8Rz4CUIIIQ6aarFgGzsW29ix+r5k2o9akd27UV0uEj4fweXLCS5frh8z5OeRd9NN5KSmIYqD4zQ7mVY2jWll0wDwRXzayFb1ZyyrXcaGpg3s9O5kp3enHrYGuAdkVCPMs3Vd0l8IIYSEKyGEEH2AYjDo27YTT2To0k+J7NiRGtlaRWjVakKbNhGvb0AxmfS2wbVrqdy+HcOll2LMzsGQm4MxJwdDTi7GnGxs48ZhLi8HtOu+ZNQrk8vsyghb3oiXFbUr9Gu2NjRtYId3Bzu8O3hx04sADPQMZGLhRE4u8XNaNIDdZO/NtyCEEH2KhCshhBB9jqKqWCoqsFRUkHXRhQAkQiFC69Zh7tdPbxdcuZJoNEZ01eouz1P861/r4cq/eDGVt/8QQ24uxuxsDLm5GHKy9VDmnDIFy+DBACSjUUgmM4pyHA/cZjfTy6czvXw6AK3hVlbUrtCu2ar9nI1NG9neup3trdt54RQwPzeVk0tO5ozyMzi9/HQZ1RJCHPckXAkhhDgqqFYr9nHjMva5v/Y1rH//O/Ef/YhYUxPxpibtvrGJeHMT5n7lett4UxMJv5+E3090165O5zf82qOHK/8nn7D7hhtRXa60AKaNhhlycnGddRa2E0YBkAgGiXt9GLOzjrkw5rF4mNFvBjP6zQC0sPV5rXa91ofLnmePM8JHez7ioz0foXyiMCZ/DDP6zeCM8jMY4BnQu50XQoheIOFKCCHEUcuYnY3RZoMzzzxgW9fZZ1MxenRHAGtqJt7USKypmXhTE5ZBFXrbWFMTAAmfj4TPR3RnZhgzDxygh6vAZ5+x+8abAFDdbm1ULCc1RTE7B8+FF2AfPx6AuM9HtLISQ04OxuxsjrZJih6Lh5n9ZjKz30x+dP8Stjz1MAt2LeCD3R+wtnEtK+tXsrJ+JY8sf4SBnoGcUX4GM/rN4MS8E1EVWUxaCHHsk3AlhBDiuKDabFgGDoSBAw/Y1jN7Nq7p0zNHw9JCmXXYML1tvK0NDAaIx0l4vUS8Xti5Uz9uGz9OD1eB5cvZc9P39WNGoxH7XXdjnzAe+/jxmAcNQlGPjhCioDAkewhDsodw45gbqfHXsHD3QhbsWsCymmVsb93O/7X+H//35f+RZ8tjevl0zig/g8nFkzEbjq0RPiGEaCfhSgghhNiLoqoYsrIwZGVBRcV+23rOOw/3uecSb20l3txMvLF9NKyRWFOTtshyu1gMQ24u8eZmSCSIxWJ4X38d7+uvA1D0y/8h+5JLAO0aM0VVj5qphkWOIr49/Nt8e/i38Ua8LN6zmA92f8CiykU0BBt4adNLvLTpJexGO1NKpzCj3wymlk7FY5HqvEKIY4eEKyGEEOIrUlRVm6KYnb3fMOY680xcZ55JMpEg3tJC+FvfInDBhQSWLye4ahX2k07S27a+8gq1Dz6EbcwY7OPHYRs/HvvYsagOx5F4S1+J2+zmaxVf42sVXyMSj7CsZhkLdi1g4e6F1AXreHfnu7y7812MipHxReO16YPlMyh2Fvd214UQ4iuRcCWEEEIcYYqqYszJwWi347j1FiBVodDY8c9y8MsvSYZCBJYuJbB0qbbTYMA6YgT28ePJveF6jLm5vdH9g2I2mDmt9DROKz2Nn5z8E9Y1rmPBrgUs2LWAra1bWVq9lKXVS3ngswcYkTNCL4gxNHuolM4XQhx1JFwJIYQQfUD6+l0Axb/8JbnXXEPg8+UEli8nsPxzYlXVhL78ktC6deSlQhmA9513SQQC2CeMx1RW1mdDiaqonJB3AifkncBt425jp3cnH+z6gA92f8AXdV+wvmk965vW89jKxyh1ljKjfAZn9DuDkwpOwqjKVxYhRN8n/6cSQggh+iBFVbEMHoxl8GCyv30pANGqKgLLVxCt3IPB6dTbNj3xBMFVqwAwFhRgnzBem0Y4YQKWIUP6bJGM/u7+XH3C1Vx9wtU0Bhv5aM9HLNi9gE+qPqGyrZJn1z/Ls+ufxWPxcHrZ6ZxRfganlJwiCxcLIfosCVdCCCHEUcJUUoKnpKTTfvspJwMQXLuWWF0d3jffwvvmWwCYBw5k0Ftv6m2TiUSfDFu5tlwuGnIRFw25iEA0wCdVn7Bg9wI+3PMhreFW5m2dx7yt87AYLJxSfAoz+s3g9LLTybX1/amRQojjR6//33Xu3LkMGDAAq9XK5MmT+eyzz/bZ9t///jcTJkwgKysLh8PB2LFjeeaZZzq1W79+PbNnz8bj8eBwOJg4cSK7ulgwUgghhDgWFNx+OwOef45hyz6j39//Tt6tt+A49RQUu11fGBkgmUyyddY57PzuFdQ9+ihtixZrpeT7GLvJzsz+M/nVlF+x8FsLeWLWE3x3xHcpdZYSjodZuGch//3xfzPjhRlc+daV/P3Lv7PTu/PAJxZCiMOsV0eunn/+ee644w4ef/xxJk+ezKOPPsqsWbPYuHEjBQUFndrn5OTwk5/8hOHDh2M2m3n99de55pprKCgoYNasWQBs3bqVKVOmcN1113HffffhdrtZu3YtVqv1SL89IYQQ4ohSbTYcJ0/GcfJkAJKxGHGvVz8e3bOH6O7dRHfvJvD55zTyF1BVLMOHYR8/AdfMmfpz+wqjamRi0UQmFk3knon3sLlls75w8brGdXxR9wVf1H3B75f/nkGeQXpBjFF5o2ThYiHEEder4erhhx/m+uuv55prrgHg8ccf54033uCJJ57gv/7rvzq1nz59esbjH/zgBzz11FMsXrxYD1c/+clP+NrXvsZDDz2ktxs0aNDhexNCCCFEH6UYjRhzcvTHprIyKt58k8DyzwmmCmVE9+whvG494XXrUQwGPVwlAgG8b7+jFckoL+8TRTIURWFo9lCGZg/lpjE3UeOv4YPdH7Bg1wI+r/mcra1b2bpmK39b8zfybfnMKJ/BjH4zmFQ0SRYuFkIcEb0WriKRCMuXL+fee+/V96mqyplnnsknn3xywOcnk0kWLFjAxo0befDBBwFIJBK88cYb3HPPPcyaNYsvvviCgQMHcu+993LhhRfu81zhcJhwOKw/9qb9yieEEEIcKxRFwVIxEEvFQH2x4mhtLYHPPye4fAXOGTP0tsFVq6j+8Y8BMObnawUyxo/HPmE8lqFD6f2opS1cfNnwy7hs+GV4I14W7VmkLVy8ZxH1wXpe2PQCL2x6AYfJwZTSKZxRfgZTyqbg7u2OCyGOWUoymUz2xgtXVVVRWlrKxx9/zCmnnKLvv+eee/jwww9Z2r6mx15aW1spLS0lHA5jMBh47LHHuPbaawGoqamhuLgYu93O/fffz4wZM3j77bf58Y9/zAcffMDpp5/e5Tl/8YtfcN9993V+rXPOwb1Xadxe8dlnMGlSb/fi0Ejfe4f0vXdI33uH9P2w8PsD1Dc2EgqH2furgqqqFPm8eNIWPe5LImqSz/KDLCgJ8EFxgAZbXD9mTMCo3VEGJ3IY0mpisNfMYK+Z3LChF3t8EPrwn5kDkr73Dun7V+aNRvG8/Tatra243fv/eeaoqxbocrlYuXIlbW1tzJ8/nzvuuIOKigqmT59OIpEA4IILLuCHP/whAGPHjuXjjz/m8ccf32e4uvfee7njjjv0x16vl/Lycnj+eTjAB3hEzJ4N8+b1di8OjfS9d0jfe4f0vXdI3w8LR+qWCIUIrVmjrbX1+XKCX3xBwu/HMnKk3nf/Z58R3rwZ15lnYios7NV+A5iBKanbT5MJvmz4Up8+uK11G6v6m1iFL+M52ZZsBmcPZnBWx21Q1iA8Fk9vvIV968N/Zg5I+t47pO9fndcLnu79v6DXwlVeXh4Gg4Ha2tqM/bW1tRQVFe3zeaqqMjhV+Wjs2LGsX7+eBx54gOnTp5OXl4fRaGTkyJEZzxkxYgSLFy/e5zktFgsWi+UrvBshhBDi2KRardgnTsQ+cSKgFckIb96M5Z579DYtzz2P9803qf3l/VjHjMZ15pm4zzoL84ABvdTrDqqiMjp/NKPzR/ODcT9gl3cXa//rajZ/70K2tGxha8tWdvt20xxuZlnNMpbVLMt4foG9ICNwDckeQoWnQtbaEkJ0qdfCldlsZvz48cyfP1+/HiqRSDB//nxuueWW/T85TSKR0K+XMpvNTJw4kY0bN2a02bRpE/379++xvgshhBDHK8VoxDpiBKQVuLBPnEC0uprgypWEVq0mtGo19b9/GMuQIbjOOou8W+b0mbW1+rn70W+Pk3PH3abvC8aCbGvdxpZmLWxtbtnMlpYt1PhrqAvUUReo4+OqjzPOU+osZUjWEAZnayNcQ7KGMNAzUApnCHGc69VpgXfccQdXXXUVEyZMYNKkSTz66KP4/X69euCVV15JaWkpDzzwAAAPPPAAEyZMYNCgQYTDYd58802eeeYZ/vznP+vnvPvuu7n00kuZNm2afs3Va6+9xsKFC3vjLQohhBDHvOzLLiP7ssuI1tXRtmABvnff06cKKiYT+bfdqrcNb9mCeeBAFEPfucbJZrQxKncUo3JHZez3RXxsbdnKlpYtHbfmLTSGGqlsq6SyrZKFexbq7Q2KgX7ufhkjXYOzBtPP3Q+jetRdiSGEOAS9+jf90ksvpb6+np///OfU1NQwduxY3n77bQpT87V37dqFmvZLl9/v5+abb2bPnj3YbDaGDx/Os88+y6WXXqq3ueiii3j88cd54IEHuO222xg2bBgvv/wyU6ZMOeLv73iQTCSJxxLEognisQTx1H37Y8VUiKM5jN1jRlX7Qm0pIYQQh4upoIDsb3+b7G9/m3hrK20LF6JYbfrxeJuf7Rd9A9XjwXXGGbjOOgvH5Eko5r452uMyuxhbMJaxBWMz9jeFmjpCV7MWuja3bMYX8bG9dTvbW7fz3s739PYm1cRAz8DM0JU9mFJnqazFJcQxptd/Rrnlllv2OQ1w79Gm+++/n/vvv/+A57z22mv1CoLHskRcCzGJWDIVZuLEo12Enb0CT8bjtH2xWOZjff/e+9Kem4gfoNhk/mVw7xIUVcHhMePMtuLMtqRu2rYj24IzyyoBTAghjiEGjwfPBRdk7Its2YxitRJvaKDlhRdoeeEFVJcL5+mn4zrrLJxTp6Da+/61TDnWHHKKcphYNFHfl0wmqQ/Ws6VZC1rpI17BWJBNzZvY1Lwp4zw2o40KT4V+LdegrEEMzhpMob2wT6wrJoQ4eL0ersT+vfu3L2nIv4L4Tz/eKwAlSSZ6pYr+vilgNKoYTCqG1H2irp6A0UUykaStOUxbc3jfT5cAJoQQxzTb2LEMXbIY/2fL8L33Hr7584k3NOB9/XW8r79O4Y/vJefKK3u7m4dEURQK7AUU2As4tfRUfX8imaCqrSrjWq6tLVvZ1rKNYCzI2sa1rG1cm3Eul8mlX8s1OGuwfm1XjjVn75cVQvQxEq76OG9jiGZTLjSE9ttOVZWMUNO+bdT3KRiMBu2xScVgVDCYDKkwpHQ8r/056edK3e8dnIx7vZ6qKp1/aZs9m8Qr/yHgjejhqq05RFtLmLamMP6WEG3NYfwtYQlgQghxHFDMZpxTTsM55TSKfv4zgqtW4XvvfXzvv49r5ky9Xct//kPrq6/iOussXDPPxFRY0Iu9PnSqolLmKqPMVcbp5R1LwsQSMXb7dmdcy7WlZQs7vTvxRX18UfcFX9R9kXGuHGsOJ54aZOqG55lWNo1iZ/GRfjtCiAOQcNXHTf3WUGJ3/xeGhx7YZ6gxGNU+HSZUg5oKQNZ9tknEExLAhBDiOKMYDNjHjcM+bhwF99yd8QOd7623CXzyKYFPPqX2f36JbcwYXGefhevMMzEfAxWAjaqRgZ6BDPQM5Kz+Z+n7I/EIO7w79LDVftvj20NTqIkPi+HDpffDUhicNZipZVOZWjqVsQVjMammXnxHQgiQcNXnFQ50Q2QPVPSxRQx7WJ8MYCjIZcZCCHFk7D3zofDH92KfNBHfu+8RXLVKv9X99ndYRoxg4PPP9dlCGF+F2WBmaPZQhmYPzdgfiAbY2rKVpQ/OYdHMgaysX6kHrye/fBKXycUpJacwrWwap5WeRp4tr5fegRDHNwlX4qhxpAMYJT9AnfOBNlJo1qZUGvX79umTBn3baDJgSNvu1Kb9uanzGNK2tddQMRgkzgkhBIC5f39yr7uO3OuuI1pbi2/+fNrefx//0s8wOJ0Zwarp2X9gHTkS29gxfWY9rZ5mN9k5Mf9ETtyUxfd+9xSt4VY+rvqYj/Z8xJLKJTSHm3l357u8u/NdAE7IPYFpZdOYWjaVkbkjpSqhEEeIhCtxTOnRAJaERDxJJB6HUPyI9F9RFT28GTJCWhdhbR+BzmBSsVgH49jeqo3AuU2oEtqEEEcxU2EhOZdfTs7llxNvaSHW1KQfizU2UvurX0EyiSE/D9fMmbjOPAvHpInH5MhWO4/Fw7kDz+XcgecST8T5svFLFu1ZxEd7PmJ903q+bPySLxu/5LFVj5FjzWFK6RSmlU3jlJJTcJvdvd19IY5ZPRauQqEQf/rTn7jrrrt66pRCHBbdDWDhb36b2BNPE4vE9dLz7duxSIJ4tGM7ltqOR7Qy9bFoPNWmY1s7R5xoJEG8/Typ87ZLJpJEw3Gi4a8Y5nLOhweXA6AoYPekpj5mdUx77JgCacGRZcFglAAmhOj7DFlZGLKy9MeJYBD3+efTtnAh8foGWp57npbnnkd1u3FOP53sb1+GfdxJvdfhI8CgGhiTP4Yx+WO45aRbqA/Us7hyMYsqF/Fx1cc0hZqYt3Ue87bOw6AYOKngJKaWTWVa6TQGZQ2Ssu9C9KCDClf19fUsXboUs9nMzJkzMRgMRKNRHnvsMR544AFisZiEK3FMUA0qtkQIcvYdwHpKMpnU1xNrD2rxLkNbfL9tYpG4Hu7CK9bQVjSQQEuERCKJv0UbjavdTz9sLhPObCuOLEvHdWhZFhzZVj2UmcyGw/55CCHEwTCXlVH624dIRiL4l37WUeK9sRHvvNdwTJ6sh6u4zweJBMf6/8ny7flcNOQiLhpyEdF4lC/qvuCjPR+xqHIR21q38Xnt53xe+zmPLH+EEkeJFrTKpjGxaCI2o+3ALyCE2Kduh6vFixdz/vnn4/V6URSFCRMm8OSTT3LhhRdiNBr5xS9+wVVXXXU4+yrEMUlRFO26K7MBHD100tk/gyfmkUgkCfq0KZD+5jBtLR1TH9vaHzeHiccSBH1Rgr4o9bt8+zytxW7URryyOgqBtIcx7d6K2WqQX0GFEEecYjbjnDoF59QpFP33zwmuXInv3fdwnnGG3qbl5Zep+93vsRgMGK+9DkNODobsbIw52Riyc3B/7VwMbm3KXCISQTEaj/pruEwGE5OKJzGpeBJ3TbyL3b7dLNqziEWVi1hWs4wqfxXPb3ye5zc+j1k1M7F4ItNKpzGtbBplrrLe7r4QR51uh6uf/vSnfO1rX+PHP/4xTz31FL///e+56KKL+PWvf83FF198OPsohDhEqqrg8FhweCwwoOs2yWSSkD+6VwBLXYuWuv7M1xwmFo4TDsQIB2I0Vvr3+ZomiyEjdOmjYe1TErMtWB0mCWBCiMNGMRiwjx+Pffz4jP3hDRshFiMcixH++ONOz3NOnaKHq/o//IGmJ/+uTUPMycaYrQUxQ042xpwcsr/7XYw52qK+seZmktEoxqysPn+dV7mrnMtHXM7lIy4nGAuyrGYZH+35iI/2fES1v5ollUtYUrmEBz57gIGegUwr1YpijCsYh8kgpd6FOJBuh6s1a9bw2GOPMXLkSP7nf/6Hhx9+mIceeogLLrjgcPZPCHGYKYqCzWnG5jSTX+7qsk0ymSQSitPWHNIDWHvw0u61IBYOxIiG4zTXBGiuCezzNQ1GVb/eK2MELMuK01SAuy2KxWGUACaE6FElv3mAvJu/T+Saa4jdeivxpmbizc3EmpuINzVjSIUlgHhzCyQSxJuaiDc1EWFrxrmy0n5Ybvzb32j6vycAUF2ujjCWk4MhJ5v8W2/TF0GO7N5NvKUFQ3YOxpxsVLv98L/xfbAZbUwr00apkskkW1u2sqhSK4rxRd0XbG/dzvbW7Ty17ikcJgenFGul3qeUTiHfnt9r/RaiL+t2uGpubiYvT1szwWazYbfbOeGEEw5bx3rD3LlzmTt3LvH4kakMJ8TRQlEULDYjFpuT3BLnPttFw/GOUa9OAUzbH/RFiccSeOuDeOuDnU+SfznctQiT1YA714Y7z4o7L3Wfa8OVujdZjvWrJoQQh4O5Xz/MDgdceOF+2xXf9wvyb/8B8eZmLWA1NxNr0rZjzU0ZQSwZjoCqQiJBwucj4fMR3blLP54/Z46+3fyv52h64gn9sWK1ZoyMFd/3C0ylpQCENmwgumdP2vTFHNRkksPxs5OiKAzOHszg7MFcc8I1eCNePqn6RJ9C2BRq4v1d7/P+rvcBGJEzQi/1fkLuCRhU+X+yEHCQBS3WrVtHTU0NoP2SvXHjRvz+zOlBo0eP7rneHWFz5sxhzpw5eL1ePJ5je9FeIQ4Hk8VAVqGdrMJ9/xIbjybwt7Zf89Ux9bB9RMy3pZqAwUE0FKexso3GyrYuz2NzmbTQlWvFlbrXQpgNZ45F1gwTQnwlismEqaAAU0HBAdsW/fQnFP74XuKtrVoYa24m1tSUGhlrwpCbq7dVrVaMRUXEm5pIRiIkQyFiVdXEqqpTDTr+39X66jyannwy47UMBgOOO+7EfvJk3Oeco09j7Glus5tZA2Yxa8AsEskE6xvX69MHv2z8kvVN61nftJ6/rP4L2ZZsTis9jWll0zi15FQ8FvkOJY5fBxWuZs6cSTKZ1B+ff/75gPZrRzKZRFEUGfURQuyXwaTqIahLs2cTe+kVvI0hvA1BfKl7b0MIb6N2HwnG9AIctdu9nU6hKODMtuLO2yt4pe7tbjOKKlMOhRA9R1FVjNnZGLOz99su/7Zbyb/tVpLJJMlAgFhqZKw9jBnTgpixsADrmNFaSGtqIuH3E4/H8b75Jt4338R52ml6uApv24bqdHYrDB4sVVEZlTeKUXmj+P7Y79MQbGBJ5RKt1HvlxzSHm3l92+u8vu11VEVlbP5YppZNZWrpVIZmD5Up3uK40u1wtX379sPZDyGE0BnNBnKKHeQUd10+MeSPdhm6fI1BvI0h4tEEvqYQvqYQbGrp9HyDScWVkzndsH36oSvXitUhF20LIQ4vRVFQHA5timJZ11X5cq++mtyrr9YfJ0IhQud/Hf8FFxDZvk2fPghQ99BvaVu4EPOgQThOPhn7yZNxTJqE4TDMxMmz5XHB4Au4YPAFRBNRVtWt4qPKj1i0ZxFbWrawom4FK+pW8IcVf6DQXqgHrVMMCaTQuzjWdTtc9e/f/3D2Qwghus3qMGF1mMjv17kARzKRJOCLaKGrIagFroaOINbWrIWvltoALbVdF90w24ydrvHSr/3KtWpl84UQ4ghTrVbsdhv2W2/pdCwRDIKiENm6lcjWrTT/4x+gqlhHjsQ5bSr5t912WPpkUk1MKJrAhKIJ3DH+DqraqlhcuZiP9nzE0uql1AZqeWnTS7y06SWc5ymc9+n9XDL0EoblDDss/RGitx3UtECAZcuW8a9//YtNmzYBMHToUC6//HImTJjQ450TfVcymSQWjRD2+4kEA+SUdPzqtnX5Uhp27SQc8JM0JnC/8zqegkI8BUV48gsx9vEyteLopqSVny8e1PkX23g8QVtTGG9jEF976GrsuA96I0SCMRp2t9Gwu+vrvexuszblcK/Q5TJ4cETiEr6EEEdc/6f+TrylBf+yZQQ++RT/p58S2baN0JdfdqpI2PTPf2IdOhTb6NE9Xjq+xFnCt4Z9i28N+xbheJhlNctYtGcRH+75kMq2Sn1NrdH5o7lk6CXMGjBLFi4Wx5SDClf33HMPv/vd73A6nVRUVADw4Ycf8oc//IG77rqLBx988LB0Uhwe0UiYsN+v3QJthP1+QgHtcTwaYfx5F+ptP/rHk+xetybV1k/Y30Y8FgO0eeY//Oer+pzqtQvns/mz1PohJuCJxzNe9+b/+xc2pzbisP2Lzwl4W/HkF+IpLMKZnXPUL9go+jaDQcWTb8OT3/U/5tFIXAtdjR1TDn0NIVobgvgagkRCcQLeCAFvhJpte13vVXgN3PYhFrsRu8eCw2PWgl6WOfU4tS/Lgt1jxmiSECaE6DmGrCzcZ52F+6yzAIjW1hFY+imqs2OUP9bURO3//BIAxWbDPn48jlNOxj75ZKwjhqMYeu7/SxaDhSmlU5hSOoUfTfoRn31vFi9eMZoFuxawun41q+tX89BnD/H1QV/n4qEXMyR7SI+9thC9pdvh6qmnnuKPf/wj/+///T9uvPFGTCbtmoRoNMqf//xnfvSjHzFq1CiuvPLKw9ZZkSkWiRAJBbG7O36d37FqBa11NYTSQlB7IEomk1z8k1/qbf/z4H3s+nJ1l+dWVJVxX7tAD0wtNdXUbNnUuZ2iYrHZiYXDmKxWAPqdMAaLw4HF7iD56n/wnnIyrXW1tNZplSatjo5S3ivfe5Ntyz/THxuMRtz5hfpI1/SrrseY+rMWj0UxGOVaGHF4mcwGckoc5JR0vt4rmUwSDsQyrvVKD2Jt1a3EVJO+2HJz9b4XWwaw2I04sjpCmD0VxLRAZsHu1rYNJvnBQQhx8EyFBXhmz87YlwgEcH/tXPyfLiXe1IR/8WL8ixcDoHo85M+ZQ86VV/R4X1RF5eR6GydP/z0NwQb+s+U/vLTpJSrbKvnnhn/yzw3/ZGz+WC4Zdgln9z8bq9Ha430Q4kjodriaO3cuv/71r7nllsx5viaTidtuu41YLMaf/vQnCVeHwSJjgrpf/zxtZKmNcMBPPBrF5nJz89/+qbddNu+l/Qam9qqOAGabAxQFq92RCkNOPRRZnU4S8ZgeZsaffxEjT5/Z0TbV3my1dhppGjvrvI4HL82Du34KtH8x9WdUDSqqGEIsEqG1rgZfQz3xWIzm6kqaqysx22zMvO77etvXHvkNlevX4i4oJKugCE9hUcd0w8IisgqLpSKROKwURdGv9yro37n8cXL2bCLP/5tAa6q8fGsEf0uYQGsEf2tYu6X2x6MJPYQ1Ve0/hFkdprTRr7Tw5ckMYgbjsRfCkokksWiCWCRONBwnFkkQi6ZtR+Kolgqcu3w4sy1YnSb5/4AQ+2EuK6P04YdJJhKEN28m8Omn+D/5lMCyZSRaW1GdHT+AhjdvpuGvf8Ux+WQcp5yMqaSkR/qQZ8vjeyd+j2tPuJZPqz7lxU0v8sHuD1hZv5KV9St58LMHmT1oNpcMvYSKrIoeeU0hjpRuh6u1a9dywQUX7PP4hRdeyM9+9rMe6ZTIVK3C7lUrujwWCQUzAlPJsFGYbVr4se4VmCwOJySTWp1q4Lzb7sZgNHZrGl7psBFf+X1oX0wzF6A95eLL9O1EPI6vsUEf5YqGQxlfklprawj52whtb6Nu+9aM85gsVm596kX98Yq3XiMS8Gvhq7AIT0ERdk+WfOkSh5UCqcWWjWQXdV3pELQfGiLBGP4WLXQFWjuCmPY4FcZaIsRjCUL+KCF/lMbKA4Qwp0mfhqiNhHWEr/bHdo+5x9YASyaTxGMJYpFEKuxogScaSdvuan84QTTasR2LxFPH9gpRkTixaOLAHcmdDb9eBoDBqOLItuDKtuDMturbjmwrzmyLFsAcEsCEUFQV67BhWIcNI+eqq0jGYoS+/BJTWgGztsVL8M57De+81wAw9euH4+STcZw8GfvkyRll4w+FqqicWnoqp5aeSn2gnle2vMLLm16myl/Fs+uf5dn1zzKuYByXDLuEs/qfhcVg+UqvJ8SR0O1wZTAYiEQi+zwejUYx9OA8XdFhfExh1K23Y3E4ux45SvuScNq3vtPt8/a1whKqwZAaiSoEOi9Gfdn9v8NbV0tLXS2ttTW01tdo93W1mK22jM9hzYJ3aNi1I+P5RosFT34heeX9Of/2H+n7vQ312JwufVqjEIeboihY7CYsdlOX0w/btU9D9LeGCbSkj35FUoEsrAe0RDxJqC1KqC1KY+X+XhxsTlNqtKsjiJmcE4i+to1YWrDpFHj2HkGKxElb+vCwM5hUTGYDRrOK0WzAZNG2Y+s20ZZXTtCrhVFvfRBvfXCf5zGatADmTAUwZ5YFZ05H+HJmW7HYjRLAxHFFMRqxjR2bsc9x8mRyb7qRwKdLCa5ZQ3TXLlp27aLlhRcA6P/Pf2IfdxJAxg+9hyLfns8No2/guhOu4+Oqj3lx04t8tOcjvax7+2jWxUMvZqBn4CG/jhCHW7fD1bhx4/jHP/7BL3/5yy6PP/PMM4wbN67HOiY6DEoocPrM3u5GrzNbbeT1G0BevwEHbDtq2hk07NlFa50WvnyNDcTCYRr37OrU9j8P/Q/1O7dj92TphTU8BUU4DEnsnyxm2ClT9LYblnxIJBgERbveDAUU7QEWh4MhE0/R225bsYywv00bKVQUbU0TRQEUTBYLFeMm6m13r1tDyN+mn0v790lrrxqNDBh9kt62dtsWgm2+VFv0cyqK9ktk2YgTDuHTFX1R+jTE3P3Mxkkmk4T9sbRph9pIWKB9amJrx4hYIp7UF2CGtGqI7inwxo5D7qtqVLTgY1IxWgxa+OkiCO1zv+kAbUzqvhd+nn0fPDGPeDRBW0sYf0sIX5P2WbQ1hfA1p7abQwR9UWLRBK11QVrr9hPAzKoWvLItevhyZGnhy5XalgAmjnXWESOwjtBmrsTb2gh8/rlWiXDpUiLbt2Md2TGrpe53vyPw+ec4Tj4Fx8mTsZ10Euoh/GhpUA3aulhlU6n11/LvLf/m35v/TY2/hqfXPc3T655mYtFELhl6CTP7zcRs6Fs/FAvR7XB11113ceGFFxIOh7nzzjspLCwEoKamht///vc8+uijvPLKK4eto0IcjAlf/0bG43gsire+jta6WhKJeMaxcECbahVobSHQ2kL1lo3aATMUvPpiRrha/NzTtNbVdvma2SVlGeHqo3882WWYA3Dm5nHjY3/PaNtVwRAAq9PFnP/7l/74w2efYPfarq+rM5hM3P5sx9/D9/82l+rNm8gqLNJDY1ZhMZ7CIly5eRiMB70ag+iDFEXB6jRhdZrILXXus10ykSTkj+rhS7seTBv9ir39LqZzz8bYHmosewWcA+xXe2iq4VdhMO2/EiRALBpPBa32W6jTdqgtSiyy/7XQAIwWgxa80kfBMu4tmG0SwMSxweB04po+Hdf06YAWttLDk3/Jx4Q3bCC0ajWNf/kLitmM7aSTUpUIJ2MbO5aD/ZtQ6Cjk+2O+zw0n3sCSqiW8uPFFPqr8iGU1y1hWs4xsSzYXDr6Qi4deTD93v557s0J8Bd3+ZnX++efzyCOPcNddd/H73/8eT2rF79bWVoxGI7/73e84//zzD1tHhfgqDEYT2cWlZBeXdjp2/Z+eIORv06/1ak1NOwy+/SaeE8dmtO1/4kn4W5tJJpO0z4dq33bm5mW0LRk6HEd2TqpdEq1ZApJkVHgEyO83QLv2LZmEJCTRzplMJrHstT5JVmERIZ+XpPbi2uun+mEwZVZTrNu5nbodW6nbkXmNGmhB7LanXkJNTefduvwzYpGIHsT2vj5OHP0UVcHmMmNzmckr2+vgc3fDZbf2Sr+OJKPJgCffjiffvs82sUictlQA8zenRr5S4at9O+SPEgvHDxjATBZDRvjSrgHTwpcj24JLMSG/u4ujkcGZ+W9E+WNz8X+6lMBSrUBGrK6OwNKlBJYuxVhYyOCFHxz6a6kGppVNY1rZNGr8Nfx78795efPL1AXqeHLtkzy59kkmF0/mkqGXcEb5GZgMUllY9J6D+tn61ltv5aKLLuLFF19k8+bNgLaI8De/+U3Ky8sPSweFOBKsDifWgU4KBw7q2DnvbfjONRntzrrhFrrr7Btv6/W253z/dpqrK2mtraGlTrtGraW2Bm9dLfasbD1YAXz26ktUbVynP7Y6nB2jXUXFTPn2lfov8F91br0QfZnRbCCrwE5Wwb4DWDQS1wNXW0uYtqa9tltChP0xouE4zTUBmmv2EcCK52C54yOcOVZcqZszx6Jvu3Kt2F3mfU+JFKKPMJWUkPWNi8j6xkVa0Z7tO/B/+gmBT7Vwpf/7kUhQddfdOM84A/ess1FMBxeEihxF3Dz2Zm4YfQOL9izixU0vsrhyMUurl7K0eik51hx9NKvcJd9NxZF30HOCysrK+OEPf3g4+iKE6GE5JWXklOw9RKH94xYKZFaeK6oYDMkkrXU1+FuatcqM27ZQu20Lrrx8pl52ld72xf/5Md7Gem2KYaocvjbiVUxWYREW+76LNAhxLDCZDWQV2skq3E8AC8czApe/JXMUrK05rJfjDwfaaNzT1uV5VKOCM9uKKxW6nGnBy5WthTFZkFr0JYqiYKkYiKViIDmXX55xzL94Md4338T75pvU/baQ7MsvJ+tbl2DMzj6o1zCqRmb0m8GMfjOoaqvi5c0v88rmV6gP1vPEl0/wxJdPcErxKVwy7BKml0/HpMpoljgyuh2ubr75Zh566CGcqWHgf/3rX8yePRuHQ/sS1dLSwuWXX86bb755eHoqhOgxiqpic7oy9s24+gZ9OxoK0VpXo1dm3PtX8+bqStqam2itrel07r2vJ1v13lsoqqqFrwLtWi9VKouK44DJYiC7yLHfsvyRC76J789P0dYUxtcUwtcYwtcU0gpxNIXwt4RJxJIHrIBoc5tTo117BbBUCJPiG6KvsI4aRd6tt9D8r+eI1dZS/8gjNDz2GJ7Zs8m58gosQ4Yc9DlLnCXcetKt3DTmJj7a/REvbnqRj6s+5pPqT/ik+hPybHlcNPgivjn0m5Q6O18eIERP6na4+stf/sIvfvELPVzdeOONTJ48mYoKbXG3cDjMO++8c3h6eYTMnTuXuXPnEo/HD9xYiGOYyWrdb2XGy3/9MK01qamG7dMNU/dZhUUZbT/993O0NTXqj1WDAXd+AZ6CIooGDWXKt6/Qj0UjYeS3RXE8MSej5JY4yS3p+hrHeDzRUfWwKayFr+YQbakQ5msKEYskCHojBL0R6nZ0/TomiyEtcFlw5Vq10bBcbZ/DY+4TRUnEsc+Ym0v+nDnkXn89vrfeoumppwmtW0fLiy/S8uKLDHj5JWyjRh3SuU2qiZn9ZzKz/0z2+Pboo1kNwQb+uuav/G3N3zi19FQuGXoJp5edjlGVok6i53X7T1Vyr8VM9n58LJgzZw5z5szB6/XqBTuEEJ25cvJw5eRRNrJz2fd4LKZvJ5NJhkw6lZaaKlrqavHW1RCPxWipqaalppp4NJrx3Cdvv4mINYH99huxe7KwezzYPdnY3R5yy8oZdspUvW00HMJotsiv8eKYZjCouHNtuHO7roCYTGoVINvag1dT5siXr0krPx8Nx2mu9tNc3fVC1Iqq4Mgyd4x2tY9+5XY8NllkxFn0HNVsxnPBBbhnzya4YgVNTz1NZM9urCNH6m0CK1ZgGToMg/Pgp5qXucr4wbgfcPOYm/lg9we8uOlFPq3+lCWVS1hSuYQCWwEXDbmIbw75JsXO4p58a+I4J5FdCNGj0su7K4rCGdfcqD9OJOK0NTXpo13p12bFY1F8TQ2gQLi6kubqzJVwy0eemBGu/nbr94gEAtg8HhyerFQYy9KCWHl/Rk6dobcNtbVhtttQVflyKI4tiqJgc5qxOc3k93N12SYWidPWnBm+0gNYW1OYRCKpFeJoClNNa5fnsTpMGcU2HM7x2D6uxuYyYXebsTpN2F1mjGb5eya6T1EU7OPHYx8/nmQkov9glvD72X3jTZBMknXxxWR/9zuYyzpfQ3wgJoOJsweczdkDzmaXdxcvbX6JV7e8Sl2wjr+s/gt/XfNXppRO4ZKhlzCldIqMZomvTP4ECSGOGFU14M7Lx52XT/nIEzOOGYwmbn3yeXzfvpTAA78m0NqCv6WFoLcFf2tLRhn9ZCJB0OslmUzga6jH11Cfca7yUaMzwtUTP7yRkM+Hze3WA1h7GMvr158TZ5ytt/W3NGOxOzCapUC2ODYYD1B8I5FIEmiNaKXmuwpgjSEioTghf5SQP0rD7lThDfdUeHp9p/OZLAZsLpNe9t/mMmkBMLXP7jJjdZn0e4NMRxQpStr/dyN7KjHm5hLZsYOmv/+dpqefxjXzDLKvuAL7xImHNGuhn7sfd4y/g1vG3sKCXQt4cdOLfFbzGR/t+YiP9nxEob2Qbw75JhcNuYgiR9GBTyhEFw4qXP385z/HnlpzJxKJ8Ktf/UqfPhcI7HudDyGE6A6zzU5uUiF3r+C1N0VVufXvLxDwagEs4G0l0NpMILWdft1XIhEn1OYjmUzoC0WnKx81OiNcPXX3LQS9rZhtdhxZWdjcWamRMQ95/QYy9uyv6W299XVYHA7MNrtMTxRHLVVV9LW4iiq6nhIfDsbwNaZNN2wMEXj1bYITphDwRQi1RQn4IiRiSaLhONFwHG9DqFuvb7EbO0JYeyBzmvR96WHM4jChSln644J12FAq3nwD/6JFND39DP4lS/C99z6+997HMmIExff9Atvo0Yd0brPBzDkDz+Gcgeewo3UHL216iVe3vkptoJbHVj3G46sfZ1rZNC4ZegmnlZyGQWY9iIPQ7XA1bdo0Nm7cqD8+9dRT2bZtW6c2QghxJJisVjxWrQLh/qiqgduffSUVwFoybv7WloznJxJxokGtIlskGCASDNBcXaUf73fC6Ixw9ey9txP0eTGYTNjdWTgsCdyP/AZ3fgEF/QcyIm30TIijmcVmxFLmJK8srfDGU+/CbR1r/yWTSSKhuFZcoy1K0BdJu2mPA6n7YFuUkC9CMolejr6l9sD9UBSwpgUvLYh1PSpmc5kw26RK4tFMUVWcp5+O8/TTCW/ZQtMzz9L66quEN2zAkFa6/ausvTjAM4C7Jt7FreNu5f2d7/PiphdZXruchbsXsnD3QoodxXxjyDe4wBZDrswS3dHtcLVw4cLD2A0hhDh8VIMBZ3YOzuyc/bdTDdz2zMuE/X4C3hYCLVoAC3i1MObKzdfbJhJxEqnKovFoFF9jPT4Vaj5dDGhBLD1cPXH7jRhMJm1aZH4B7rwC/d5TUIjdk9Xzb1yII0hRFC2E2YxkFR64fTKRJBSIEvRGCbalB7AIofTt1KhY2B8jmSTVLnrgFwBUg6KNgrkzR8QcjnG4ltfppeptLpOEsD7OMngwxff9goIf3o7/06WYyzsWCK66804Uk5nsK6845EqDFoOF8yrO47yK89jWso0XN73IvK3zqPZXM3flXOZ+DYa/pq2ZNb18OiNzRsqfGdGlboeriooKli1bRm5u7uHsjxBC9CpFUbA6nVidzi4XYG6nqgZuefJ5opEwwdZW/K3NtN11F97rv4e3oY6soo7fOKORsF6go2HXjk7n6nfCaC752a/1xx/8/X+xOl2p8JWPK68AV24uBqMUqhfHDkXtKMYBB64GF48nCLVF9RCWPiK296hY0BchGoqTiCfxt0bwt0YyT+aZBn/9Un9oMKkdCzOnL9KcundkWWQ6Yh9hyMrCfc4s/XG0pgbv2+9AIkHrq69inzCB7CuvwDVzJsohrqlYkVXBjyb9iB+M+wHv7XyPlze/zIqa5Wxo2sCGpg08vupxCuwFTC/Tgtak4klYDJaeeoviKNftcLVjxw5Z/0kIIfZiMlsw5WujUCQUOO+CTm0MRiNX//7PeBvq8NbXpd1rxTjSpyZGI2FWvDWv8wspCs7sHIZMPpUzru6owLhj9Rc4s3Nw5xdgtnZdrluIY4HBoOLwWHB4uvclNhaJp01P7LgP+CL4X30X3wmT8TUG8XsjxKMJWmoDtNR2ff24kroube/QlV663mCSwhy9wVRUxIDn/kXT08/gffttAp9/TuDzzzGVlpL9ne+QdfE3Mbjdh3Ruq9HK1wd9na8P+jpNF5/Hot/cwMLdC1lStYS6QB0vbHqBFza9gM1o47SS05hePp1pZdPItmYf8Nzi2CXVAoUQ4jBTVQO5ZeXklpV3eTx93cBkPM6p3/oO3vp6vA11+Bq0EBaPRmlraiSSuiYMIBaJ8PKvfqY/tjpdqemG+bjzCigdMYqhk0/LeB2ZxiKOF0azAVeOAVeOtfPBp+6Ae24GIB5LZFZKTK+Y2NhRqt7XqD1mc9evZ/eY9zv6ZbbKV67DxTZ6NKW/+y0Fd99F8z//RcvzzxOtrKTuoYdQzGZyvvudr/waOREDFwy+gAsGX0A4Huaz6s/067LqgnW8v+t93t/1PqqiMjZ/rD59cKBn4Fd+bXF0Oai/6e+8884BF9edPXv2V+qQEEIcb9IDj9lm55RvXpZxPJlIEPC24m2oyxidCvnbyO83AG9DPeGAn1Cbj1Cbj7odWwEIBwJ6uIpFIjz2vctx5ebtdc1XPq78ArJJ6hOzYtEoNVs2kognSMRj2vVlsXjqPoanoIjiIcP0866e/zaJWIxEIkEiHteek3pufv+BjJgyHdAWmH7vf/+UOq5ds9Z+zkQiQfGQ4Zz2rY4vQU//6Dbi0SjJRJx4LLNt+YgTmH3nj/W2rzz0P6iqiju/EE9BoX7vyS/AbOu6BLkQAAajiiffjid/f6Xqw51CV/p9LJIg0Boh0Bqhdru3y/NYHMYuQ1f7vdUh1319VabCQgp+eDt537+J1tdeo/WV/+C58EL9uP/jjwGwn3LKV/qsLQYLU8umMrVsKj89+aesa1qnB60NTRtYUbeCFXUreHj5wwxwD9CD1pj8MbKO1nHgoP4LX3XVVfs9riiKTB0UQogepqgqjqxsHFmZU02c2Tlc+ds/ARAO+PE21GdMOywePFRv622oJxoO0VS1h6aqPZ1eY5wJ2stvBH2tPP+L/9pnf0484+yOcBWN8MHf/3efbYefdroergDWfvj+PtvuvbZY4+5dJOKxLtuGA236doIkO1au2Gfb/qNP4uKf/DKtD/OxudxaCMsrwGTtYmRDiBStVL0VZ7a1y2pxyWSSkD+qj2x1NfoVDsQI+2OE/W0d64TtxWgx7BW+LKl7G+5cK3a3GUWu++oW1Wol+5JLyL7kEn1fMpmk9sGHCG/ciGXIYLKvuALP7NmoX/Hvv6IojModxajcUcwZO4fqtmoW7tGC1mc1n7HDu4O/r/07f1/7d7IsWUwrm8b08umcWnIqDtOBrzUUR5+DClc1NTUUFBQcrr4IIYQ4RBa7g/x+DvL7DejyeFZhEdf+4X/Twle9NuUw9Vit7KiDbTCayC4uRTUYOm6qAdWo3acv6Gw0mRl2ytRUOyOqQU3da88rGDhIb6saDEy9/Oq0th3nNxgMGdUYAb754/tQFAXFYMCQ0d7YKRBdcNdPaK2robW+Dm99La11tXjr6wi1+bA6OsqHJxJx3v3LHzOCmN2ThTu/AE9+IWUjTmDsrPP0Y/FYVAqJiP1SlI7CHAX9u762JxKMdTnipa8Z5o0QC8dprvbTXO3v8hyqUQt5npyL8PxrI1kFdjwFNrIK7LjyrLIY8wEkw2HsEycS2b2b8OYt1Pz8v6l/+BGyvvUtsr9zOabCbpS47IZiZzGXDb+My4ZfRlukjSVVS1i4eyEf7fmIlnAL87bOY97WeZhUE5OKJ+lFMWTR4mNHt8OVDFULIcTRSzUYyC4qIbuopOsGaVO67W4P1z76l26d12g2c/7tP+pWW0VRmHTBxd1qC9DvhDHdaqeiUDFuYpfHwoEAsUhYfxwNhRl40gQ9gEWCAX3ds5otm0gmk3q4SiYS/PHqb+mVGz3pUw7zC8kuKcGdJz84igMz24zkljrJLXV2eTwWjdPWFM6cdpgWxNpawiRiSbz1QbzW/uz+sDLj+aqq4MqzklVgzwhdnkIbrmyrjHihjWYV/fQn5N92Ky0v/5vmZ58lWllJ4//+L41PPEHB7T8g93vf69HXdJqdzBowi1kDZhFLxPii7gs+3P0hH+z+gF2+XSypXMKSyiX8aumvGJEzQp8+OCJnhHzvPop1O1ylX3AthBBCHA0sdjsWuz3j8YV3/1R/HGpro7W+Fm+9NtLlKeyY+NXW3EQ8GsXf3IS/uYnqTRsyzj305Cl8/Yfa9MlkIsE7j/8BV14+nvyOa75cuXmoh1gOWhw/jCYDWYV2sgr3cd1XPIG/NYKvMUjrTx+k5bLraa0L0FIbpLUuQCyaoLUuSGtdkJ00ZjzXYFTxFNjw5GuBK6uwI3zZPebj7ku8we0m95qrybnyCnwLFtD89DMEli3DMrRjGnXC70cxm1FMPTdqbVSNTCyayMSiidw54U62e7fr12mtrFvJ+qb1rG9az59X/ZlCe6EetCYVTcJsMB/o9KIP6Xa4uuqqq7DZpMyvEEKIY0f7mmaFadMX2zlzcrn5b/9MTTHURrpa6+vwpqYf5pR2rIPW1tLE2g/ndzqHoqq4cvMZdfoZnHqJVqwjmUhQuWEd7oJCnCSRyVziQFSDql+PVRJcBxd2/HlNJpL4W8O01AVTgSugb7fWB4nHEjRV+Wmq6jzd0GgxkFVgw5NvJ6swM3wd6wU2FIMB91ln4T7rLEIbN2IZMkQ/1vC3v9H671fIvvxysr51Ccbsni2trigKFZ4KKjwVXHvCtTQGG1lUuYiFuxfycdXH1AZqeX7j8zy/8XnsRjunlWpl3qeWTpUy70eBboerc889F4ulY22JPXv2UFJSgqpq/ywEAgH+9Kc/cc899/R8L4UQQogjTFEUbC43NpebokFD9tvWYDQx5bKrMq738tbXEo/F8NbXEg13TE30tzTz/H3aiJdqBfdt12vl8/MLcecXUD7iRMpGnnBY35s4dihpBTfKhmV+8U4kkrQ1hfTA1VIX0AJYXRBfQ5BYOE7D7q6LbFjsRjwFdi18FXSEL0+BHYvt2Kp4Zx02TN9OJpO0vT+fWG0t9Y88QsNjj+GZPZusUAjrYVrOIteWy4WDL+TCwRcSjodZWr1UH9WqD9bz3s73eG/ne3qZ9xnlM5hePp0BngE93hfx1XX7b8dll11GdXW1XtBi5MiRrFy5koqKCgB8Ph/33nvvUR2u5s6dy9y5c6XioRBCiINid3uYfOElGfuSiQT+lmZa6+uwuToKHYT8bXgKi/A11JOIx2mpraaltlo/PuHr39DDVVtzE8/99z148gv08OXO067/chcU4MzJRVVl2qHomqoquPNsuPNs9BuVeSweS+BtCHaMeNUFaanVwldbc5hwIEbdDi91OzqXlre5TKlruuxpI1/aiJfJfHT/eVQUhQEvv4T3zTdpevppwuvW0/Lii7QAxukzyPrWJeTPmXPYXt9isDCtbBrTyqbx05N/yvrG9Xyw+wMW7l7IxuaNepn33y//PQPcA/SgNSZ/DAb5f0GfcMjXXB2L12DNmTOHOXPm4PV6D7ielxBCCLE/iqrizMnFmZObsT+vvD/f+39/I5GI03bRhXh/8wDe+rrUtV91lA4bqbf11tfSWltDa21Nl68x4evf4PTvXgtAsM3Hyrdf14tvSPgS+2MwqmQXOcgu6lwOPBqJ460Ppka8ArSmRr1a6oIEvRGCvihBXyvVW1s7PdeZbdGu8UoV12gf+fLk2Tha/iSqZjNZF16I54ILCC5fTtM//kHb2+8Qq60l3tLxnpPRKN533sU5bSoGd9eVIr9SPxSVUXmjGJU3iltOuoWqtip9RGtZ7TJ2eHfw5NoneXLtk3qZ9xnlMzi15FTsJlnfr7ccW+O6QgghxFFCVQ24kwruESfAiK7b5PUbwKX3PahNM2y/5isVwrwNdbjzO6oVNldV8vGL/8h8DYMhtXB0IaPPPIfhp04DtIWiA63NOLNzpeCG6MRkNuyzumEkGNsrcKW2awOEAzHamsO0NYep3NiS8TxFAUfhdTge/BxnlgVH6ubMTtvOsmDsQyNfiqJgnzAB+4QJJDafj/+uuzCXdVxrGVi+nKq77gKjEfvECbjOmInrjBmYSkv3c9ZDV+Is4fIRl3P5iMvxRXx6mfdFexZ1WeZ9RtkMTi8/HSnyfmRJuBJCCCH6KLPVRtnwUTB8VKdjiUScRDzR0dZm44QZZ6eFr3oS8ZhWiKOuliGTTtHb1m3fyr9+dpdecCN92qGnoJCSYSP2XbZfHNfMNiMF/d1drukVaot2ClztI16xcJw2g4u27V5quzhvO4vDmApfVpxZ5lQAs2YEMIvDeMSLbaiqimv69Ix9iWAQ8+BBRLZsJfDJpwQ++ZTaX/0Ky4gRuM44g6yLv4mpuKulp786l9nFOQPO4ZwB5+hl3hfuXsgHuz9gt2+3Xub9/qX3M/hME+M/vZ/xheMZXzieArssIXE4HVS4euedd/TpcolEgvnz5/Pll18C0NLS0uOdE0IIIUTXVNWQMeUvr7w/s266TX+cSMTxNzfr0w2LBnWUmg76WlENRhLxWCqM1QJr9OMzrr6R7HO1cFW3YxsLn/prRvhy5xfgVJLYA37MNvsxXVVOdJ/VaaLI6aGoIvPSimQyScAbwXftrfh/ej9tzWH8LR239sexaIKwP0bYH6OxsuvFlAEMJlUPWhn3aaNgDo8Z9TAvrOyaMQPXjBlEdu7EN38BbQsWEFixgvD69YTXr8c5Y4YermLNzRgcDhRzz5dVTy/zfteEu9jeul2/TmtV/Sq2eKJsSVUfBCh3letBa3zBeMpcZfJ3uAcdVLi66qqrMh7feOONGY/lP4wQQgjRN6iqNiXQlZvXaeRr0PjJ3P7sv2lracJbp001bJ9y2FpfR155P71tU9Uedq9bQ3r4AsAKXHMpZ35vDmPOOhfQgtjnr7+C3e3G5s7C7vZgc3uwp26OnBxMZgvi+KIoCg6PBUe0Bk7qetQkmUwSDsS0sNVF8GprCeNvDhPyR4lHE9qCyvXB/bwm2NzmzACWnRnGHFkWzNavPonL3L8/uddeQ+611xBrbqZt4YcEPl+GdVTH9ZN1v3kQ34IFOKdOxTnzDJzTpmFwub7ya+9NURQqsiqoyKrguhOvoynUxIrbL2H5tWezvHY5G5s3stu3m92+3fxny38AKLAV6GFrXOE4BmUNQlVkkYhD1e0/UYlE4sCNhBBCCHFUUFQVV04erpw8SoeP3Ge7kqEj+Nqtd2UU3Witq8FfXU1U0SoltmuurmT9og/2ea70IFazZRNLXng2Fb46wpjd48Hm8pBdUorN2fNfPkXfpCgKVocJq8PU5bVe7WLROP6WSCpwhfA3R9ICWYi2ljCBlgiJRJJAa4RAawR2+vZ5PrPNmApcZhzZ1k6jYY4sCzanie4OHxizs8m66EKyLrpQ35dMJgmtW0vC58P75pt433wTTCYcEyfiPOMMXDPPOGzTB3OsOZxZ5eDMST8CwBfxsbJuJctrl7O8djlfNn5JXbCOt3a8xVs73gLAY/EwrmAc4wvHM6FwAsNyhmFU5Uqi7pJPSgghhBD75M7Lxz1leucDs2cTffEFlPSpif0GMO271xJobSHo9RL0tRJobSHg9RL0tmJPq8TbUlfDjlUr9vm6Z91wC6NnngNA5cb1LHjy8YyRMH1EzOOhYMAgbYROHPOMJgOefBuefNs+2yQTSQK+SJdTD9tHxdpawkRDcSLBGJFgjObqfU9DVA0KnvwryfnLGrJLHOQUO8gtceIptGHoxtRDRVEY+OqrBFetom3BAnzzFxDZtg3/xx/j//hjWp5/norX5h3S53GwXGYXU8umMrVsKgChWIg1DWv4vPZzVtSuYFX9KlrDrXyw+wM+2K39UGI32hlbMFYf3Toh7wQsBhmB3pduh6uPPvqoW+2mTZt2yJ0RQgghxNHDZLFmPM4tLSe3tHyf7dOXcSkePIxzbv5hKny1EvS2EvC2EmhtJehrxZndUcLe11BH3fat+zxvehDbvW4Nb819uOsg5vZQOmIU2fs8kzgWKGpqGqLHAv333S4SinU59TB9WmLAFyERT9JsyqH5i3r4ol5/vmpQyCq0k1PsIKckdSt24Mm3dbreS1FV7CedhP2kkyi4807C27frQctx8mS9XSIYZPvFl+A45RRcM8/APmECisnU459RO6vRql+vBRBNRFnfuJ7ltctZUbuC5XXL8UV8fFz1MR9XfQyASTVxYt6J+sjWmIIxOEydS/ofr7odrqZPn65fU7WvNa4URZEFeIUQQgjRpfRrsz0FhXgKCrv1vPJRo7noR/+dCl8tBH3aSFj7qJg7v+M8bc1N+Brq8TXUd3mus264VQ9XNVs28cHTfyO7qJiswmKyiorJKiohq7AYq3PfU9PEscFsNWIuMna51le7eDyBvyVMy01303TjPTRV+2mq8tNU7ScaimvbVX5Y3vEcg1HVQlda4MopceDOs6Gq2t8By8CBWK67jtzrrsv4Xu3/5BMiW7cS2bqV5mefRXW7cU6bhmvmGTimTsVwmP9cmlQTo/NHMzp/NNeccA2JZILNzZu1sFW3guW1y2kINuiLGf91zV8xKAaG5wzvuG6rYBxZ1qzD2s++rNvhKjs7G5fLxdVXX80VV1xBXp4MvwshhBDi8HNkZVMxbmK32g4cO57Lf/V7gl5vx6iYz5uaqthKTnHHGkSNlbup2riOqo3rOp3H6nRx1vVzGHryFAACrS201FaTVVSCzeWWIl7HCYNBxZ1rwx3eRb8zOwq9JJNJ2prDerhqqmrTgle1n1gkQWNlG42VbRnnMppUsosdnUa6XDlW2i/qcpxyCmWPzcU3fz5tHywk3tSE9/XX8b7+OphMlP72IdznnHPE3r+qqAzLGcawnGFcPuJykskku3y79Gu2ltcup7KtkrWNa1nbuJan1z0NwOCswRlhq9DRvR9SjgXdDlfV1dW88sorPPHEEzz00EN87Wtf47rrruOcc86R/8EIIYQQok+wOpwUDx7Wrbb9ThjD+bf/iJaaapprqmipqaalthp/cxOhNh9me8eIxo5VK3hr7sMAmG12fZSrfdSr/5iTcOXID8/HC0VRcOVYceVY6X9CxxTWZCKJrylEY3rgqvLTXB0gFk1Qv8tH/a7MAhtGi4GcIjs5pc5U8BpDzp2nUnTffYRWr9aC1vwFRHbswDp8uP68to8+Ivjll7hmzsQydOgR+T6uKAr93f3p7+7PN4Z8A4Aaf40etFbUrmBr61a2tGxhS8sWvfx7mbNMD1sTCicc0+Xfux2uzGYzl156KZdeeim7du3i73//O7fccgvhcJirrrqK++67D6NR6mMIIYQQ4ujgys1j2ClTO+2PhIK01tZkTFtMxOO4cvPxNdYTCQao27414zqwb9x7nx6utn/xOWsWvIunsIjsopJUECvGlZOHokqJ62OZoiq482y482wMHN0RthOJJN76YMe0wlTwaq4NEAvHqdvpo26vqoZmqyE1unU+OXd/G5ehjWh2MaZkEkVRaHnxJXzvvUfD//sjptJSnDPPwHXGTOwTxqMcwe/kRY4izqs4j/MqzgOgKdTEF7VfsLxOC1wbmjawp20Pe9r28OrWVwHIt+V3rLVVOP6YKv9+SJ98v379+PnPf84VV1zBddddx29+8xvuvPNOcnJyDqkTc+fO5be//S01NTWMGTOGP/7xj0yaNKnLtv/+97/59a9/zZYtW4hGowwZMoQ777yTK664osv2N910E3/5y1945JFHuP322w+pf0IIIYQ4fpitNvL7D8zYd8KMszhhxllEI2G8dbW01FanRryqaampIqekTG9bvWUTmz/7uNN5DSYTnoIizp1zB0WDhgDgb2kmGg7jzstHNRg6PUccG1RVK36RVWinYmy+vj8RT9BaH6Sx0p9xPVdrbYBIKE7NNi8127wdJ3quGovdSE6JA2fxeVin98e49hMcdTuJPv0MzU8/g+rx4Jo+neJf/wqlF/5M5VhzmNl/JjP7zwSgLdLGyvqV+sjWmoY11AfreXvH27y9421AK/9+UsFJTCicwPjC8QzPGX7Uln8/6F6Hw2FefvllnnjiCT755BPOO+883njjjUMOVs8//zx33HEHjz/+OJMnT+bRRx9l1qxZbNy4kYKCzgvN5eTk8JOf/IThw4djNpt5/fXXueaaaygoKGDWrFkZbV955RU+/fRTSkpKDqlvQgghhBDpTGYLuWX9yC3rt882gyeejNXpoqU2NdWwpprWulri0ShNlbsx2+x62zXz32HJC8+iGgx4CgrJKizGU1isTTcsKqFsxKiM9uLYohpUsoscnYpqxGMJWmoDaSNdqdBVFyAciFG9pRUwASfBqJMAMCsRHL5K7K27yK5SYZuXnGInVqDpmWcxD+iPbezYw7J48f44zU6mlE5hSql2/WJ7+ff2sLWyfiWt4VYW7l7Iwt0LAbAZbYzNH8v44c1cn0wcVaNa3Q5Xn332GU8++STPPfccAwYM4JprruGFF1445FDV7uGHH+b666/nmmuuAeDxxx/njTfe4IknnuC//uu/OrWfPn16xuMf/OAHPPXUUyxevDgjXFVWVnLrrbfyzjvvcN55532lPgohhBBCdFfBgAoKBlRk7EvE4/ga62muqc6YbhgNhzCYTMSjUZqrq2iursp43tW//zO5ZVq42rDkQ2pMCUqWLqF02EgcWVJU/lhlMKrkljo7Lagci8ZprgnoYav93tsQJJI0E3EOpNk5kErgy99/AYCxaA6G+T5M0W2YYl9isarYcp04ivNwVpRgL87F6jSnFnE2YnWYsNiNncrJ95Suyr9vaNygXbdVpwUub8TLJ9WfUNXPyI1HUbCCgwhXJ598Mv369eO2225j/PjxACxevLhTu9mzZ3f7xSORCMuXL+fee+/V96mqyplnnsknn3xywOcnk0kWLFjAxo0befDBB/X9iUSCK664grvvvptRo0Yd8DzhcJhwOKw/9nq9+2kthBBCCHFwtJGpIjwFRRn7p15+NVO+fSVtzU201FRp0wxrtamGLXsFsZ1rVvKlEZY//ACglbMvGTaSkqEjKB02grzy/nJN1zHOaDKQX+4ivzxz9CkaidNc7c8c6ary42sKEVNNxKw5hK1pAyIBYCuwtR7oetkCs82ohy2rw4TFYcLqNGG1G7E6TVjsqcdpocxsMx50oQqTauLE/BM5Mf9EruZqEskEW1q2sLx2OYY//+XgPqA+4KCmBe7atYtf/vKX+zx+sOtcNTQ0EI/HKSzMLM9YWFjIhg0b9vm81tZWSktLCYfDGAwGHnvsMc466yz9+IMPPojRaOS2227rVj8eeOAB7rvvvs4HLr0UDuPCbd322WdwEKG1T5G+9w7pe++QvvcO6XvvkL73GAVwpW6dlmC++GJ9c6iaxNBQR1VxAfUKtNbV0lpXy/pFH0ASbgkpWFI1vVuUJPYkmOlDFdn62Od+UPp4301AQeqWLqKYCK7ZTOikyYRUK4GkGV/cQFvSRAAzIcWK6sgmbnYSUq2EFCsRg7Y4eCQYIxKM4W0IdbsfSjKBJRHCmghhTabuEyEsiWDadsd+rU0QYzKm/0lVgaGpG59tgO194HOPRrvdtNvhKpFIHFJfDgeXy8XKlStpa2tj/vz53HHHHVRUVDB9+nSWL1/OH/7wB1asWNHt5Hzvvfdyxx136I+9Xi/l5eXw/PPgdh+ut9F9s2fDvHm93YtDI33vHdL33iF97x3S994hfT/iBgIDZ8+GF+cRDgSo3ryByo3rqdq0nlg4jOWXv9XbvvXze6jevIH8/gP1ka2SYSNx5+Xv+wUOt6P0cweO2r6bAfPs2Xhe/muXxxPBICQSqA7tmq/m51+g6hd3EzPaiZoc2r3ZRbJ8EJQPQh1+IjGLm5A/RsgfJeSPEk7dxyIJkopKyGAnZDi46wQNRhVL2ihZ+2iYy/kSE/75s6/6MXx1Xi94PN1q2qtlOPLy8jAYDNTW1mbsr62tpaioaB/P0qYODh48GICxY8eyfv16HnjgAaZPn86iRYuoq6ujX7+OC03j8Th33nknjz76KDt27Oh0PovFgsVi6Zk3JYQQQghxmFnsdgaMGceAMeMA7VKJdslEgqDPSzKR0EvGr3zndQCcuXkMGj+ZM6/7fq/0W/Qtqs2W8Tjr4m9iGzuW4IrlBFZ8QXDFCqKVq6FhNXwB5V//K86p2lpbwS/XEly1Dvv48ViGDCGegHBa6Ar5o4TaooQDMUJt0Yz96fsS8STxWIJAa4RAaySjP9m2EUw4Yp9Gz/hK4crtdrNy5UoqKioO3LgLZrOZ8ePHM3/+fC688EJAGyGbP38+t9xyS7fPk0gk9GumrrjiCs4888yM47NmzeKKK67Qi2YIIYQQQhxL0mfrKKrKtY88jq+pgaqN66nauJ7Kjeup27GVtsYG2poa9bbJZJLXH/kNOaVllAwdQfHQ4Vgdzq5eQhwHFIMB67ChWIcNJfuyywCI1tYSXLGCwIovsI0do7f1vf0WjX/7PwBUpxPbmDHYxp2Effx4skePRrUfuOBKMpkkGo6nRsBie4WyKKZnFgIXH+AsfctXClfpv5IcqjvuuIOrrrqKCRMmMGnSJB599FH8fr8ehK688kpKS0t54AHt4s0HHniACRMmMGjQIMLhMG+++SbPPPMMf/7znwHIzc0lNzc34zVMJhNFRUUMG9a9FduFEEIIIY52rhxtkeT2hZKjoRA1WzdhSLue3Ftfx6alSzqepCjklfWjZNgISoaOoHzUibjzOi+NI44fpsJCTOeei/vcczP2mwcNxnHqqQRXriTR1oZ/yRL8S1J/lgwGBr/7DqbSUgASkQiq2dzp3IqiYLYaMVuNkNvpMDy+soffzeHX66tzXXrppdTX1/Pzn/+cmpoaxo4dy9tvv60Xudi1axdqWuUbv9/PzTffzJ49e7DZbAwfPpxnn32WSy+9tLfeghBCCCFEn2eyWikfNTpjn8Xu4Kzrb6Fy4zqqNq2npaaaht07adi9k9Xvv8348y5g+pXXAxCNhKnfsZ3CikEYjH2g4JfoVVkXXUjWRReSjMcJb9pEYPkKbYTriy9IhsMY09aZrbr7HkJr12ojW+PGYRs3DsvgwcdkdcuvFK6++93v4u6Bgg+33HLLPqcBLly4MOPx/fffz/33339Q5+/qOishhBBCiOOd1elk9JnnMPrMcwDwtzRTtWk9VZs2ULlxHWUjTtTbVm/ayIu//DFGk5nCQUMoGZYqlDF0BDZXHygAJnqFYjBgHTEC64gR8N3vABBrbs6YqhpcvZpYdTXRPXvwznsNANXtxjZ2DI5Jk8j93vd6pe+Hw0GFqx07dvDee+8RiUQ4/fTT9al4QgghhBDi6OfIymbIpFMZMunUTscC3hasLjchn5fKDWup3LCWZalj2SVlzLz2JvqfOPaI9rc7kskkiXiMWCRCNBwmFokQi4TJKirBmJoi2bBrBw27dxKNtB/X2rRvj1aS+qy1SDBAMqkVFRFdM2ZnXm9V8do8gitXpUa2VhBctZqE14v/o0XEm1sywlXjk3/HVFqCfdy43p9idwi63ecPPviA888/n2AwqD3RaOSJJ57gu9/97mHrnBBCCCGE6BuG/3/27ju+qar/A/jnZnaXTtrS0sGm7CEisucjiogKIioCikpRFLeiiANwgYoVVJYiAj+fBwFxICJlCCiyFCizzNKyWrqbZpzfHyGXpGlLCik3bT/v1yuQ3Hty7ydp2uSbc+65t3RDk85dkZ2RLk+ScebgfmSdOY3sM6cdJsI4sGUjUjenyNPA123QCLaBhLZj9m09G4U5l1CUl3u58LlS0FgLHQOa3dpTLoIO/7kFpw/ss7azFUpGayFkLCnB4OcmwSewDgBg89JF2PnTSphKSiCE8ymFRs2cg+Co6Mt5N+HP75eV+9ib250v7J/ffsGGb+ZD5+0D/5BQ+IeGWf8PCYV/SBgS2nWET4Br03bXFmo/P/jd2gV+t3YBAAiTCcUHDqJo506oAq6cENlSUIBzH3wAmM1QBwaiUXiYJ52pzSUuF1evvfYa+vbti9mzZ8PLywuTJk3CCy+8wOKKiIiIqJaQJAnBUdEIjopGi559AQBFebk4c8h6Ti2b43t2Im3HX0jb8RcAQKXWQO9lgemhe2AqKcGTC/8PWi/ryWo3Ll6AfRvWlbvPhLYdoalj7Qk5uW8Pdq/5sdy2hqJCubgSFjOMhlInwJUkaHQ6aHV6WMxmeXFQZBRiEltBo9PJ6zU6PTQ6HdQ6HQKX/k9uW5ibA8Dag3Xx9ElcPH3SYRcPTPtILq72rP0Ze377WS68/ENCEWC7Hmr9X6VWl/t4aipJo4F3i0R4t0h0WG4pLkadofeiaMdOaGNiIJ06Wc4WPJfLxdXevXuxZcsWREZGAgDef/99fP7557h48aLT7HxEREREVDt4+wegQfubHJa1v+1OhNWPs06UcTAVBZeyUSQBuFzsGEsMcnGl9/WDl3/A5aJGJxc1tv/tJz2IbdUOOi9vuzY6aPRXCiHfy4UVALS//S607NX/8nrr9tQajcOxQDaJ3XsjsXvv8h/k0uXy1W4jRuHmu+9D3oULyLt4HnkX7f+/AH+7EzVnpZ/C+eNpOH88rczNPjDtI9RNsJ679cjff+LU3j2lesPC4BsUBJWqdhRgmpAQRE6eDAAQZjNw110KJ6o8l4ur3NxchIaGyrd9fHzg7e2NnJwcFldEREREJAuLjUdYbDzaDxwMIQRyz5+D8dFHoPn8C2j0enj7XRkK1nPko+g58lGXttuwQyc07NDJpbY+AYFVNjxP5+WNkOgYhETHVNiu3W13IrZV2yvF14UrRVhelmMhdnLvbuz6+QenbUgqFfyCQnDPpLfkYYxn044g98I5uTfMJyCwxs28J1XTHr1KHSe2Zs0aBAZeeZHaTvi7d+9eedmgQYPcl46IiIiIqjVJkhAYXhcQEhARqXScGyowvK71sZeh9Pli41q3g1qjvVJ8XTyP/KyLEBYL8i6eh5ddQbpv4zqHQkyt0cDP7rivbiNGwS8oGACQKwkYT5+EztvHevHyqnGFmCepVHE1cuRIp2WPPfaYfF2SJJjtxq8SEREREZGz0sMTE9p2RELbjg7LLBYzCi5lI+/CBYfp7v1DwhDRsDHyLl5AwaVsmE0m5JzNRM7ZTABAj4euzL73l0Zgz7PjHLar8/aWi617Jr0F/2Dr6LRDf/6B9AP7L6/zhv7y/zofa9u6CQ2h1ekBWIvDsoZY1nYuF1cWi/MsK0REREREVDVUKjX8g0Pl4sem4x1D0PGOIQAAs8mEguws5F4eeph/0bEQUwPw8vNHSVGhPIlHSVERSoqKAFx0OCH0yb3/YM+v5U8YMvrjLxAUYT058OalX2PnT6usRZiPL3TePtD7XCnaugx7EAGXhz2eTTuCi+mnrG1shZ2Pz+XizQdqrbbGFGpumz7eYrHgp59+wu233+6uTRIRERERUQXUGg0CwsIREBZe5vqeRhV6zlsCIQTMRiNKigphKCpESWEhSooKHabQj2/TDnofH5RcXm8oKkJJYYG1fVEh9D6+ctuSosLLU+cbUJhzyWm/N999n3z98F9b8Of3/1fuYxgxdSYiGjQCAOzbsA57169FSHQM+lT2yfAA111cHTlyBPPnz8fChQtx/vx5GI1Gd+QiIiIiIiI3kS5PQ6/R6eTp6ktr0L4TGrR3bcKQrsNHosPtQ64Ua3LBVgRDUaHDzI0BYXVRv2UbuzbWws1YbD1/rs77ygmZszPO4HTqXphKDNf8WJV0TcVVUVERvvvuO8ydOxd//PEHunbtitdffx13VcPpEu0lJycjOTmZx40REREREVXANvzPFa1690er3v2dllssZhiLi+Vp+QGg2a09EB4XD63eC9j7ptvy3iiVKq62b9+OuXPnYunSpWjQoAFGjBiBLVu24LPPPkPz5s2rKuMNk5SUhKSkJOTm5jrMikhERERERO6lUqkdhhoCcGmKe0/mcnHVqlUr5Obm4v7778eWLVuQmGg9o/JLL71UZeGIiIiIiIiqC5cnuT948CC6deuGnj171oheKiIiIiIiIndyubhKS0tDkyZN8MQTTyA6OhrPPfccdu3aVWOmTSQiIiIiIroeLhdX9erVw6uvvoojR45g0aJFyMzMRJcuXWAymbBw4UIcOnSoKnMSERERERF5NJeLK3u9evXCN998g4yMDHz66af4/fff0bRpU7Rq1crd+YiIiIiIiKqFayqubAIDAzFu3Dj8/fff2LlzJzp37uyuXERERERERNXKdRVXNgaDAb///jtWrlzpjs0RERERERFVOy4XVwaDAS+//DI6dOiAW265BStWrAAALFiwAPHx8Zg5cyaeeeaZqspJRERERETk0Vw+z9Xrr7+Ozz//HH369MGWLVtw7733YtSoUdi2bRtmzJiBe++9F2q1uiqzEhEREREReSyXi6vvvvsOX3/9NQYNGoS9e/eiVatWMJlM2LNnD6djJyIiIiKiWs/lYYGnT59G+/btAQAtWrSAXq/HM888w8KKiIiIiIgIlSiuzGYzdDqdfFuj0cDPz69KQhEREREREVU3Lg8LFELg4Ycfhl6vBwAUFxfj8ccfh6+vr0O75cuXuzchERERERFRNeBycTVy5EiH2w888IDbwxAREREREVVXLhdXCxYsqMocRERERERE1ZpbTiJMRERERERU27G4spOcnIzmzZujY8eOSkchIiIiIqJqhsWVnaSkJOzfvx/bt29XOgoREREREVUzLK6IiIiIiIjcwK3FlcFgcOfmiIiIiIiIqo1rLq4GDRqEDz74ABkZGQCA8+fPo2fPnm4LRkREREREVJ1cc3EVFxeHH3/8EQ0aNMAzzzyDm2++GUVFRe7MRkREREREVG24fJ6r0j755BMAwNKlS3H//ffDz88Pf//9t9uCERERERERVScu91yNHz8ec+fOdVh29OhRPPPMMxg9ejTat2+PWbNmuT0gERERERFRdeBycbVy5UrcdNNN8u2MjAz07dsX9913H+bOnYtXX30VixYtqpKQREREREREns7l4urixYvw8/MDAGRnZ6N///548MEHMXPmTABAQkIC0tPTqyYlERERERGRh3O5uGratCnefvtt/Pbbb+jduzfuvPNOTJkyRV7/xx9/IDY2tkpCEhEREREReTqXi6upU6di2bJluPvuu9GgQQMsXboUa9aswdmzZ/Hdd9/h2WefxciRI6syKxERERERkcdyebbAAQMGICsrCwCg1+sxZcoUDB48GCUlJRBC4L777sNzzz1XZUGJ3EFYBGAWEGYLhMkCYRbA5f+FyboMJut6+MYBRy5BUkuAWoKkkgC1CpJaslumsv6vlgCVBEmtAlSAJElKP1S6RkJcfo2YLBBGi/y6sF2HyQJhEo7rLq+XgtpAve8C1AF6qAP1UPlpra8bIiIiqhUqNRW7Xq+Xr0+ePBkTJkzAwYMHUa9ePURHR7s9HAHG84UQXuHAmfwrCyUJDp/dHa5L5S6TrtbWvoFUur1U6r6ltuF41XpDAqDSQ+SVXC5myihkbNfNdtdtxY3pyrrSRZDDdsoqkMrZLiyinGe6DLH3AnP/db29PbkYsxViKkiqcooyta0os96+cr8yCrfL6+VtyuuubFfSSEBAE+Df89YiT7L7edheO6WWS5Jk7ceWrvycS99XUl19W4637ZapymgDx7a2glQAlwuY0oXN5Z9zGUXNlcL4StuK19tf7Aolo7XNNYvsCyxKvXJbBaj9dZeLrfL/l7Tqa98nEREReYxrPs8VANSpUwedOnVyVxYqQ9aygzAmjAQ+2aV0lGvT9CngnT+VTlE+lQRJI0HSqOSCRr5+4jhE/TjAIq4UaRZhLeYu937BIi5XA6WYL7czlr26ykUPAhYfUGLP185WkDV7Dpj0h9JprtBYXxOS1vo6kS/ay68Vu9ti0xaYm7eFObcElrwSwAKYc0pgzikBTpW/C8lbA02gDqoAPdQBOqgD7Yqvy7dVPhr2iBIREXm46yquqOqpfbUwG/OB4CDrAvtP6uLyP3bLhLBvI8pub/9fJdpeV5Wgtn0wvdzjorENr1MBl4sbSW0rcqzt5EJHvi45r7PbrnVb9tux+zCslhzW2fcClWvQFOCjVVd9aPJQQ4tFLqqE5XIvizwMUchFmlycWQRgu21ftFmu9LJZlzlux1bUVbQdsecfoEUL689MABDi8mvj8s/5clEobLft/ndoV+Yy59tlb6uSrxHbfUoXEJqyixrbdTgtl8otfpy3I5W9Xmv3+qpMQbPkBeCz0daHYxYw55fAnGOAJdf6vym3BJYcA8y5JdZLjsHaa1ZkgrHIBGQWlr9tjeRQbJUuvtSBOqj9ddbXNhERESmCxZWHCx3VAhj0CrDq6h/ybyRhq8qcCq9SRdqQIcDK72v0N+6S6vLQPNfnh6l6g14FZo1QNIIoVaA5FWCAtRgsXZCNHAnp20VXiptqesySpJagCdRDE6gvt40QAqLI5FBsmUsVX+ZcAywFJsAkYM4qhjmruIKdAipfrbXYshVdAXbDEC/fVnnxTz8REVFV4DusneTkZCQnJ8NsNisdxeNJpY/XurKm1G2Lc08E1QqS3fFbtisuvRLMBYB37fjTJEkSJB8tVD5aaCN8y20nTJbLBdfl4iunxO725SIsrwQwC1jyjbDkG2Gs4LSDkk7tUGypA/RQB7WG5lA2NMFeUNfRW4tbIiIiqpTa8QnGRUlJSUhKSkJubi4CAwOVjkNEBACQNCpogr2gCfYqt42wCFgKjVeKLfviy+5/UWyGKDHDdL4IpvNFVzYQ2Q+Yv/fyDgF1oN5aaAV7QRPidXn/3lAHe/H4LyIionK4rbhavnw53njjDfzzzz/u2iQREblIUklQ++mg9tMB9fzKbWcxmB2LLtvww183wdSwFcxZxRBGC8yXDDBfMgBpOc770quvFF52F3WwFzRBXuz1IiKiWqtSxdXnn3+OtWvXQqfTYcKECejUqRN+//13PPvsszh06BAeeuihqspJRERuoNKroQrzgTbMx3HFvIlA8igIYR1aaMoqhimrGOaLRVeuZxXDnFsCYTDDmFEAY0aB8w4kWIcZ2hdeIVcKMZWvlr1eRERUY7lcXE2fPh2vv/46WrVqhQMHDmDlypV49dVXMWvWLEyYMAGPPfYYgoKCqjIrERFVMUmSrOfm8tdBHxvgtF4YLTBlXym2rhRe1iJMlFjkiTlKjpXR66VTXe7l8nbs8bL1emnZ60VERNWXy8XVggUL8OWXX2LkyJHYtGkTunfvji1btuDIkSPw9S3/QGy6fksTEnDq/fehUqmcLlqtFmPHjpXbrlu3Dunp6VCr1WW2v/POO6FSWT+87NmzB2fPni23bceOHaHRWF8ip06dQnZ2NlQqVZntY2Ji5La5ubkoLi6GSqWCn0qF8o8SIaLqRtKqoA33gTbcx2mdEAKWAuOVwuticaleLwNEiQXGzEIYy5l2Xh2gc+j1UodcKcJUfuz1IiIiz+ZycXXy5En06tULANC1a1dotVpMmTKFhdUNUKRWo6CgjOE3ALRarcPtjIwMpKWllbutwYMHy9cPHjyI/fv3l9u2ffv28vUdO3Zg9+7d5bZ97rnn4OdnPc5j06ZN2L59OwBA06oVnrh4ESEhIeXel4hqBkmyO+6rfhm9XiZrr5d9j5d9ISZKzPI09CXHc523r1U5Fl62oss7EjiZK2coO1yZga+pbbn13TXsQ9L4Q1VihkqnLmejRERUnbhcXBkMBnh5XemD0Ol0CA4OrpJQ5OiuEydgSE6GxWKBxWKB2WyWr5fWpUsXtGzZUl5fur39B48mTZogMDCw3LZq9ZU3+7CwMMTHxzu0tW9v31ar1cLb2xslJSUwAThw4AC6dOlSpc8REXk+SaOCtqzjvXC516vQdLnwunyc18UrhZjthMums4UwnS3V6xX/APDZnhv0KNys8ePA61sgaVVQ+Wig8tFC5Xv5cvm22lcLle/ldZfXq301kLQsyIiIPE2lJrR47bXX4ONjfVMsKSnB22+/7TRl+YwZM9yXjgAAdUpKgLp1XWobHx/v8nZbt26N1q1bu9S2S5cuLhdI/fr1Q79+/bBt2zb88ssvSEtLY3FFRBWSJAlqX2shoYvxd1ovTNYZDE1lHOdlOZ4ORERcOTk14Hgy8zKXCadloqJ2V91mxe3so9nfRxSVAJLaOkNjTgnMOSVl7KRs1oLscuHle7nw8tFYizEfu4Ls8m0WZEREVc/l4qpbt244ePCgfPuWW25xGn7GsfBkLyEhAQBw4sQJGI1GpyGMRDWJyWSCiccYVhlJo4Im1BuaUG/nlYPeAGasuuGZ3EEMGgTxf8thKTBae+4KjfJ16/9Xbpvl2ybAIi4XZNbJQ1wlaVVXesXkosvxtn1RpvbRcpIRIqJKcLm4SklJqcIYVBOFhYXBz2hEPoDTp09XqleNqDoRQmDBggVoGxyMDpeXFRYWoqSkBHXq1FEyGnk4CYDkpYHKSwO4eGiqEALCYC6z6LIUXinIzA637Qoy2znMXM2oU9n1gGnkoksV2hmqrWeuDGe0K8wkrYpfuBJRrXTNJxG+cOECACA0NNRtYahmkSQJLbKyUNi3L/R6vdJxiKrMxYsXkZ6ejjBfX7Q1m6FWq7Fv3z78+OOPqFOnDmJjYxEXF4fY2FgEBQXxQyddF0mS3FOQ2XrIbL1jBUaY7W8XGgELrNPrl5RRkIXfCqw8WvYONZK1V8zHvlfMrgizW2ZrI3lpIKn4u0FE1VuliqtLly7h1VdfxbJly5CdnQ0ACAoKwn333Ye3336b39CSkwHp6cCQIUrHIKpStiHSl3Q6eXKX3NxcSJKES5cu4dKlS9izxzrhQkBAAGJjY9G3b18EBDjPqEdUFa63IDM7DVU0wfL9alhu7XWlILv8P8wCMAlYcktgyXX9GDJIcC7A7HrD1GUsU/loIKk5bJGIPIfLxVVWVhY6d+6M9PR0jBgxAs2aNQMA7N+/HwsXLsS6deuwZcuWazqRcHJyMt5//31kZmaidevWmDVrFm666aYy2y5fvhxTp07FkSNHYDQa0ahRIzz77LN48MEHAQBGoxGTJk3CTz/9hLS0NAQGBqJPnz6YPn06oqKiKp2NiOhqjh61fnvfIPfK9OG9e/fGrbfeilOnTuH48eM4ceIE0tPTkZubi7179+L222+X2/77778oKipCXFwcwsLC2LNFHsG+INOElHGs22e/Ag+Od1gkhIAoMdsNUbzSE1a6CLtSrJkgSsyAgPV+BSYARa7n1KsdZ1f00ZTqHSujKLvO54aIqDwuF1dvvvkmdDodjh49irqlZq5788030a9fP7z55puYOXNmpQIsW7YMEydOxJw5c9CpUyd89NFH6N+/Pw4ePIjw8HCn9sHBwXj11VfRtGlT6HQ6rF69GqNGjUJ4eDj69++PwsJC7Ny5E6+99hpat26N7OxsTJgwAYMGDcLff/9dqWzkHhaLBefOnYNWq+X5rqjGMZvNOH78OAAgIS/PYZ1er0fDhg3RsGFDANZZVk+fPo0LFy44DJX9888/cfr0aQCAj48PYmNj5aGE4eHh8om/iTydJEmQ9Bqo9Bog2PXpXYTJIhdcZvvhig7HkpX6v8gECEAYzDAbzDBnVSJnkwlQz/gb6kC9fNEE6qGuo4c6UAd1oN7ay0dEVEku/+VYsWIFPv/8c6fCCgAiIiLw3nvv4fHHH690cTVjxgw8+uijGDVqFABgzpw5+PHHHzF//ny89NJLTu179OjhcHvChAn46quvsHnzZvTv3x+BgYFYu3atQ5tPP/0UN910E06ePIn69etXKh9dv99++w1btmxBx44dMXDgQKXjELlVeno6DAYDvL29EVlYWGFbnU6HhIQEeSZNmyZNmkCr1eLUqVMoLCxEamoqUlNTAQAhISF48skn5bZCCPZsUY0jaVRQB+ihDtDD1XllhUXAUuTcCyYXZZeXmUstg1lAqHUwnSuC6Vz5PWSSXm0tvOrooQ7QQVNHb1eM6aCuo7cWkUREdlz+q5CRkYHExMRy17do0QKZmZmV2nlJSQl27NiBl19+WV6mUqnQp08fbN269ar3F0Lg999/x8GDB/Huu++W2y4nJweSJJV7TJjBYIDBcOVA3Vy7oT10/WJiYgDAaep+oprA9rqOj4+H6o8/rmkbXbt2RdeuXWEymXDmzBmcOHECJ06cwMmTJx2+0BJCIDk5GXXq1JEnyIiKinI4iTdRbSGprpwbzVW248jMD4yBefpH1nOLXTLIU9qbcwww5ZRAFJkgDGaYzhXCdK78L00kL7Vjz9flXi+1XSGm0vP3k6g2cbm4Cg0NxfHjxxEdHV3m+mPHjiE4OLhSO79w4QLMZrNTb1jdunVx4MCBcu+Xk5ODevXqwWAwQK1W47PPPkPfvn3LbFtcXIwXX3wRw4cPL/fg8WnTpmHKlCnOK4YNAzzh3Ex//QUMGqR0imvz11+Ie+UVSK1a4eLFi8i5+24EGo1Kp3JNNX/emf3GONWwIRAQgAY//3zd2TUA6l++dAVgBlC8fTvwzTcAgCy9HhcSE3HhwgUcOXIEAKA1mxFTUIC4/Hw0yslBZJHrx6o4qGbPuwNmv2EEAAsANVDtsgOXp74HoPrrL2gnjiy3nUXSwqz1t140/g7XTZevC7UXRLEZpuJCmM4WorzJ7SVzMdTGPGiMeVCb8qA2Xr6Y8qA25kJtzIdKVOJ9sRo+7zJmVwazX79KfHZ1ubjq378/Xn31VaxduxY6nc5hncFgwGuvvYYBAwa4HvI6+Pv7Y/fu3cjPz8e6deswceJEJCQkOA0ZNBqNGDp0KIQQmD17drnbe/nllzFx4kT5dm5urrW3ZdkywBNm8xo0CFhVPU+QiUGD4P3994j68kukp6cjbdIktG3bVulUrqnmzzuz3xj3m804c+aM9cul4cPdml0NwNfudpDFgsfOnpUnyDhx4gSKioqQFhCAtIAAFN19NyL79wdgHRmQnp6O6Oho107gXc2edwfMflUlJSXIyspCSUmJw8VgMKCkpAT169dHbGwsAOsEVj///LPDevtLt27d0KtXL2DQIOQtXozk5GT4+PjA29vb6f+YmBh5GKzFYkFeXh58fHyUP6n8VZ531eVLRSktBpO15yvHYNf7VQKTrRfskgHCYIZQe8Gk9oLJK6zcbUneGmhsvV62i93xX+pAPVQ6tUvZPRqzK4PZr19uLhAY6FLTSk1o0aFDBzRq1AhJSUlo2rQphBBITU3FZ599BoPBgEWLFlUqZ2hoKNRqNc6ePeuw/OzZs4iIiCj3fiqVSj5AvE2bNkhNTcW0adMciitbYXXixAn8/vvvFU55rNfreR6mKpaQkGAtrtLSqk9xReQCtVotD32taiqVCpGRkYiMjETnzp1hsVhw/vx5udiy/V0EgFOnTmHRokVQq9WoV6+ePEFGTEyM0xdk1Y3RaERJSQmMRiNMJhMsXl6Qzp+3TqYgSfD395cfo8FgQHFxsbzOdgGsky/o9Xp5WKXZbIbFYpHXlXWfqmYymZCfn+9U/NguMTEx8vvj+fPnsWHDhjKLpZKSEvTo0QOdO3cGAJw7dw5z584td7/du3eXiyuz2YzDhw+X27ak5Mr06oWFhSguLkZxcXGZbTt16iQXVwUFBfJx2RqNxqkQa9y4Mdq0aSNnOHr0qLzex8cHer3eoyZ3Uek1UIVroA33KbeNpdgkF13mHANMpYYgmi+VQJSYIYpMMBaZYMwsfwiiykcDdYAeUux9kL74B1BJgGQdHglJAlQSJAnycuttqVS7UstV1te69f4AJMnazv66VN5yu+2WXl76/pf3KXmFQ3WpmCeaphrN5eIqOjoaW7duxbhx4/Dyyy9DCAHA+kvZt29ffPrpp5X+gKHT6dC+fXusW7cOgwcPBmD9ZmvdunUYP358xXe2Y7FYHI6ZshVWhw8fxvr16zlDnQdISEjApk2bkJaWxgPyidxEpVKhbt26qFu3Ljp16uSwrqioCH5+fsjPz8fJkydx8uRJbNq0CSqVClFRUejXr981T/AjhIDJZJIvtiLHZDIhPDwcGo31rSUjIwPnzp0rs53JZEKXLl3g7+8PANizZw/++eefMtuZTCY8+OCDiIyMBABs27YN69atuxKoeXMgOVm++dBDD8kf6P/55x/8+OOP5T6W4cOHo0mTJgCsU+KvWLGi3LZ33303WrZsCQBITU3F//73PwBlF2L/+c9/0KpVKwDWYfPLly8vs505MRG99uxB69atAQDHjx/HN5eHgZalX79+cnFVXFyMvXv3ltvW/n1Rr9fD19cXOp1Ovuj1evm6/ReaAQEBuPPOOx3a2re3/zIyJCQE48aNQ1FREQoLC53+t3+NFRcXQ6VSwWKxwGQyITc31+EYZ/svQQsKCvDtt986PB5JkuDt7Q1vb2+0atUK3bt3B2AtSLdu3Vpu75nt9agE1eWp7LV1fcttIxdgl+x6vi4ZYM690iMmSiyXJ+swAb4xQFrODXwUbpQwEpi+3XpdI10+d5ndFPn2J5sudZJpla8Wkl7Nzw/k8Sr1Fyc+Ph4///wzsrOz5W+1GjZsWOljrexNnDgRI0eORIcOHXDTTTfho48+QkFBgTx74EMPPYR69eph2rRpAKzHR3Xo0AENGjSAwWDATz/9hEWLFsnD/oxGI+655x7s3LkTq1evhtlslifaCA4Orvbf2FZXMTEx0Gg0KCgowLlz58qcdZKoulm0aBHq1KmD7t27e9wJgVu0aIHExERkZWXJPVvHjx9Hbm4uTp8+7fC38MCBA9gXFwfTsmVORY3RaMSoUaPg62v9cPjzzz/jzz//LHe/Tz31lPyesHfvXvxRwSQfbdq0kYurS5cuyecLK4vRbry7bUiZWq2GRqOBqqAA8Pe3TlYghFPvhlqtltfZvhi8FvYf6mwFQnnMZrND9rxS0/TL9HoU2s0yqdPpoNFonAob28X+XJJBQUHo379/mcWSTqeTf2YAEBYWhueff96lx6nX610eYaDRaMo8bUpZwsLC8Nprr8FgMJRZiNkXeGazGZGRkfL6kpISCCFQWFgoL7MpLCx0LLZLadeuHQZdPmbDaDRi5cqV8KtXDwFbtsDf3x8BAQEICAiAv7+/IoXY1QowIQREsfnyZBsGiDenAi+8CAgBYRHWg+Bs14WwTk9f1nKH2wAsl38fLHbXbctLt7l83Xr78vXytlVqufW2dbnlzDlYvALkE02bc0tgrsyJplWS3fnLLhde5Z3LzNd6zjPJS2PtQSO6Qa7pr0hQUJDTSX6FEDh//rzLf2Rthg0bhvPnz+P1119HZmYm2rRpg19++UX+8H3y5EmHN8qCggKMGzcOp0+fhre3N5o2bYpvvvkGw4YNA2CdFnnV5bGZtuEFNuvXr3c6LotuDI1Gg9tuuw2BgYHXVYwTeQpbMSBJEvr06aN0nDJJkoSQkBCEhISgffv2AIDs7GycPHnS4W/1/v378W9wMHB5+vfS7Aub0oWLJEnQaDTQaDTQarXysDrAOvS7QYMG8vrSF2/vKyembdKkCQIDA6HVap3aabVah9leb7rpJtx0001XslQwJr9jx47o2LGjw7LyiqyWLVuiWbNmDuvtizL7HptGjRrh6aefdlhvfx8/Pz+5bf369fHYY485tRVCQP3CC6hjV/TUr18fkyZNKvOxlObn5ycP+6suJEmCl5cXvLwqPgdWUFAQHnvsMfm2yWRyKMTsn19JktCmTRungq2oqAhCCIcvEoqKiqy9fXXrAr/+6rTf9u3b44477gBgLfD++OMPufiyXW70l7SSJEHy1kDlrYE2whfIOwS0Lv/4LY82aBDEypXWnrgCo8N5yxzOb1bgPL2+MFoAi4Al3whLfiUmAJFwpfAq4+TSat8yTjjtrYWkZkFG18bl4srHxwcnTpxAWJj1F3rgwIGYO3euPEzj3LlziIqKcvi2zlXjx48vdxhgSkqKw+23334bb7/9drnbiouLu65vJqnqtGvXTukIRG5jm4K9Xr16DkWCpwsKCnLo/QCAxMRERPz0EzRjx8rFjH1xY98D0r17d3Tp0kVup1KVf9xE27ZtXe4BiYiIqPBYW3vXe9xNecdQqdVql6e1t/UOucLLy0t+r3RSWAj4lj9kjKw0Go1c3JTm7+8vH1pgz2KxyMfb2Wi1WvTr1w/5ixYhr08feWhibm4uzGazQ9GXn5+P33//3Wm7er0eAQEBaN26NW699VYA1kIsLS1Nzujl5cXha+WwnmhabZ2ivjInmjaarecsKyh1cmm7AsxcqjgTBjMgYG1bYALg+myqkpcGavtesIje0P6VAW2EL7R1fTnFPpXL5eKquLjYoWjZuHGjQ7c8ABY1RFRr2IawlT4hcHXUpEkT4Nw5oNSIhLJcrceByFOoVCr4+DhONuHt7Y1bbrkFmD4duPtuebltyGFpbdq0cSjAbJOGnD9/3uEzUF5eHhYvXizfti8GAwIC0LhxY7Ro0QKAtegrKCiAr6+vR03QcS0sFgssFos8nNJkMiEzM1OecMZ+8hmj0YiQwEA0u8Z9SVo1NIFqIND1CciEyVJ2z5h9cWZXsJkLTBDF1uG+otgEU7EJuHh5spbgdsDyI5fDAOpgL2uhFeELXaQvNBG+0AR7cQgiXduwwPLwWxq6mrS0NBw6dAitWrVCVFSU0nGIronFYsGxY8cAAA0aNFA4DRFdL0mSHHpoASAwMNCpR6y4uBi5ubnIy8uTjxcErENnIyIikJubi8LCQphMJmRlZSErKwuAtXfNVlzl5ubio48+gkqlcjrmKyAgADExMW6bgdRiscjFjf1QXIPBgOPHj5dbBNWvXx/NmlnLoLy8PHz33XdltjOZTOjYsSMGDhwoPz8VzUqZGBwsF1dCCPz++++oW7cu6tWrhzp16rj9c6SkUUEdoIM6QFfhtPr2hFnAUuQ4RNFcYITpi29h7NwPxsxCWPJKYL5YDPPFYhTvu3hlf1oVNBG+0Nb1gTbSVy6+KnOia6r+lJtCh2qlHTt2YN++fdDr9SyuqNrKzMxEYWEhdDpduSdWJ6Kax3a8WOnjy8PCwvD4448DuDKBiX2Pl/3fifz8fADWwicnJwc5OY4z/3Xp0kUuri5duoS5c+c6FF/qevVg/OEHGI1GNGvWTC6Czp8/j6VLlzoUQPaHanTt2hW9e/cGYC2YlixZUu7jtFgs8nYB6/Hv5bE/JlOn06FOnTrQarXQarXQ6XTyda1Wi0bffy+3zcrKwqZNm+TbPj4+iIqKQr169eRL6YL3RpDUEtR+Oqj9Sg37fXsDMOZZAIA5vwTGzEIYMwvsLoUQRguMp/JgPOU4gY0qQGcttCJ9oYuw9nJpw7whaap3zyWVzeXiqvQY9Rt53g+qORISErBv3z6kpaWhZ8+eSschuia2463i4uJcPkaHiGoHrVaL4ODgcidvio6OxmuvvYb8/HyHAsxWkNWrV09um5ubi/z8fLkgA2CdjGPHDgBAnTp1HIqgixev9KKUZj/ZjJeXF6KiohyKH/vr9lPoe3t7495773UqlGy37Y891Ol0ePrpp8t/chYskK9KkoQOHTogPT0dZ8+eRWFhIY4cOYIjR6xD72699VZ5sqDi4mJkZGQgMjLSI4Ymq/10UDfUwathHXmZsAiYLhZZC62MArn4MmcVw5JbAkNuCQyHsq9sRCVBG+5tLbTsCi9VgI6fr6s5l4srIQQaN24s/8Dz8/PRtm1bebwwj7ciV9iOTzl9+jSKi4s94o8kUWXpdDqEhITUiOOtiOjGU6vVCAwMRGBgYIXtIiIiMHbsWIeeMMt330F7333QarUOwwfr1KmDhx9+2KlQsk3vb/+B3c/PD2PHjnUpq0ajQWJi4rU90AoEBwfj9ttvB2Dt/Tp79izS09Nx5swZpKenO/T2nTp1Sj6mLTQ0VO7ZioqKQkREhKLnMrORVBK0YT7QhvkALa/M5mgxmBx7uTKs/4ti8+XlhSjC+SvbuTwrpO7ysEJNhA+0Eb5Q6fhFXnXh8qtxgd23DUTXyjZTWXZ2Nk6cOCGfvJOoOrFNBW7/TTARkbvpdDrnIfQffwyUcVoZrVaLuLi4G5LL3bRaLaKjo8sdZl1SUoLAwEDk5OTgwoULuHDhAvbs2QPAOnHJvffeK/fgGY1GqNVqj5ksRKXXQB8bAH3slZkuhRAw5xisxVXGlaLLdKEQosiEkmM5KDlmN1xUAjTBXnIvl63wUnMCDY/kcnEVHx+PW265xSO+HaDqLSEhATt27EBaWhqLK6rWPOXNm4ioJktMTERiYiLy8/MderfS09NRVFTkMARz586dWLduHSIjI+XeraqaMONaSZIETR0vaOp4wbvplezCaIHxvF3BdfliyTPCdLEYpnIm0NBF+EIb4SMXX5xAQ1kuV0o9e/ZERkZGpU8STFSafXFFVN1kZ2fD39+fXzQREd1gfn5+aNKkifzFrBACly5dchheaZsK/sSJEzhx4oS83DZhxu233+5wUnJPImlV0EX5QRfl57DcOoFGgWNP19nyJ9BQB+ishdbl47jU3lFQXyyCyl/H4YU3QKWOuSJyB9uwheLiYhgMBuj1rp+zgkhpS5cuRXZ2NoYPH474+Hil4xAR1VqSJDmdFP2OO+7AzTffLPdunTlzRp7h9ciRIw4nfd+4cSMyMjLkY7g8ZcKM0q5MoHHlscoTaGRcma3QNoGGObcEZvsJNOJHAO//DQCQdCqo/K2zIar8tFD7aaHy00Htr7Net/ufhdi1qdRXr57SnUrVm6+vL5566ikEBQXxNUXVSn5+Ps6ePQvAOvUyERF5FpVKhbp166Ju3bpo27YtgCsnN7548aLDF7qHDx/GqVOnkJqaKi+zTZgRFRWFjh07euzwb4cJNFrZTaBRbILxbKHD5BnmQydh8Q2GMFogSizyObquug+dylp42Rddfjqo/bXW4sxumUrPQsymUsXVww8/fNVehuXLl19XICUlJycjOTnZ4bwQVDXKm6KWyJPZhrJGRETAz8/vKq2JiMgTaDSaMifM6NOnD06dOiX3ctlPmHH06FHcdNNNctsfY2JQ9N//QqPRQK1WQ6PRyBc/Pz907NhRbnv48GH5xM2l29qm6rcxmUxQq9Vu+7JZ5eU8gQYGvQaxciVEiRmWPCPM+SUw5xlhyS+BOd9oPSlyvuNtuRDLKoY5q5KF2OUCrLYWYpUqrvz9/R26U2uapKQkJCUlITc396rTo5J7WCwWnjONqg1bccUp2ImIqr/Y2FjExsbKt/Pz8+VCS6VSOXw2ORgYiNy9e8vcTlhYmENxtWbNGly4cKHMtoGBgXjmmWfk2/Pnz8eZM2ecijC1Wg0/Pz+MHj1abvvbb7/h4sWLDm1s13U6Hbp16ya3PXr0KAoLC6HRaODr64s6eXnw8/ODJtQbmtCKP8sLIcovxMoozCpdiGlVTj1hDoWYnxZqf521ELvq1jxPpYqrTz75hBNakFsIIbBixQocPnwYo0ePRmhoqNKRiCokhMDRo0cBAA0aNFA4DRERuZufnx8aN26Mxo0bO63rdeYMih9/HCaTyelSeiRDVFQUfHx8HNqYzWaYTCb4+Pg4tDWZTAAAs9kMs9kMg8EgrzMajQ5tjx8/jtOnT5eZXa/XOxRXf/zxx5WJw5o0AWbMgEajQZ06dVCnTh0MHz4carW1Byk3Nxc6nU4+3kySJEh6DVR6TaULMUu+Eea8qxRiRtcLMU2DRxBx1VaexeXiij0L5E6SJCE3NxeFhYVIS0tjcUUe78KFC8jLy4NarUb9+vWVjkNERDdQm6ws4OabXWo7ZMgQl7c7ZswYGI1GhwLMdint1ltvRV5enlNxZzabnY4Ni4yMhBACJpMJeUeOIMfLCyaTCRcuXEBBQYFcWAHAypUrcfToUXh5ecnnIw0KCkKdOnUQFBSEBg0alFsHVKYQAwCLwXx5GOLlQsy+ACujEJMshqtu09NwtkBSTEJCAo4dO4a0tDSHcc1EnsjWaxUbGwutlucQISKi66fX612eNblp06Yub7dv375XbgwaBPP33yMnJweXLl1CcbFjj5Gtt6y4uBgZGRnIyMiQ13l7e+PFF1+Ub69duxb5+flORZifn59Lk3+o9Gqo9FcfmghYCzFx3wMA7r1qW0/icnG1fv16TkJAbpWQkIB169bh2LFjsFgsHjsjDxFgHQrYq1cv/h0kIqJqR61WIzg4uMz3sEceeQQGgwGXLl1Cdna2/H92drbTl4kHDhzAxYsXnbahVqsRGRmJRx55RF528uRJaLVa1KlT55rmbFDp1YC5oNL3U5rLxdW7776LJUuWyBM9TJ8+HY8//rh8IraLFy+ia9eu2L9/f5UEpZrHdj6J4uJinDlzxmkWHyJPEhYWxunXiYioRtLr9fIU9hXp1asXLl68KBdfly5dQk5OjnzMmL1Vq1bJE3vYDzmsU6cOwsPD0aZNm6p6OIpyubhas2aNw0F2U6dOxdChQ+XiymQy4eDBg24PSDWXSqVCXFwcDhw4gLS0NBZXRERERB4sMTHRaZnZbEZOTo7TBBy+vr4oLCxEYWGh05DDiIgIh+Lqq6++gsVikY/zqlOnDkJCQhBTpY+malzzMVc8BovcISEhQS6u7Ge5IfIkqampMJlMaNCggdNMT0RERLWZbchhaaNGjQKAMocc2s+wKITAqVOnYDKZcOLECXl5SEgInqz6+G5XqanYidwtISEB9erVQ1xcnNJRiMq1efNmpKen484770Tbtm2VjkNERFRtXG3IoRACDz/8sMNQw+zsbHl0XHVTqanYS0/DyOnZ6XqFhobi0UcfVToGUbmKiopw5swZADx5MBERkbupVCpER0eXfXjIvHk3PtB1qtSwwIcfflieLrK4uBiPP/44fH19AcDheCwiopri2LFjEEIgNDRUntCHiIiIqCwuF1cjR450uP3AAw84tXnooYeuPxHVSgaDAWfOnEF8fLzSUYgc2M5vxV4rIiIiuhqXi6sFCxZUZQ6qxYqKivD+++/DYrHg+eefl3tDiTxBWloaAOt5roiIiIgqwrO2kuK8vb0RGhoKADh+/LiyYYjsZGVlITs7Wz5tABEREVFFXO65Gj16tEvt5s+ff81hqPZKSEjAuXPnkJaWVuY5FIiUkJ6eDgCIjo6WjzclIiIiKo/LxdXChQsRGxuLtm3b8hxX5HYJCQnYtm2bPASLyBO0bNkSsbGxKCwsVDoKERERVQMuF1dPPPEElixZgmPHjmHUqFF44IEHyjxhGNG1iI2NhUqlQnZ2NrKysvjaIo8REBCAgIAApWMQERFRNeDyMVfJycnIyMjACy+8gB9++AExMTEYOnQo1qxZU2N6spKTk9G8eXN07NhR6Si1jl6vl89vcOzYMYXTEBERERFVXqUmtNDr9Rg+fDjWrl2L/fv3IzExEePGjUNcXBzy8/OrKuMNk5SUhP3792P79u1KR6mVbFNdc2ggeYKtW7di0aJFOHDggNJRiIiIqJpweVhgaSqVCpIkQQgBs9nszkxUSzVv3hw+Pj6c8po8woEDB3DixAk0adJE6ShERERUTVSq58pgMGDJkiXo27cvGjdujH///ReffvopTp48CT8/v6rKSLVEeHg4brrpJoSEhCgdhWo5g8GAU6dOAeD5rYiIiMh1LvdcjRs3DkuXLkVMTAxGjx6NJUuWyOcmIiKqSU6cOAGLxYI6depwchUiIiJymcvF1Zw5c1C/fn0kJCRgw4YN2LBhQ5ntli9f7rZwVPsUFhZi//79KCgoQPfu3ZWOQ7WU7bi/hIQESJKkcBoiIiKqLlwurh566CF+yKAql5+fj9WrV0Oj0eCWW26BVqtVOhLVQkePHgXAIYFERERUOZU6iTBRVQsLC4Ofnx/y8/Nx+vRpxMfHKx2Japnc3FycP38eAPj6IyIiokqp1IQWRFVNkiROyU6KKi4uRkJCAurXrw8fHx+l4xAREVE1wuKKPA6LK1JSeHg4HnroIYwaNUrpKERERFTNsLgij2MbinXmzBkUFRUpnIZqKx5jSkRERJXF4oo8TmBgIEJDQyGEwPHjx5WOQ7VIYWEh8vLylI5BRERE1RSLK/JItimws7KylI5Ctcju3bvx4YcfYvXq1UpHISIiomrI5dkCiW6krl27olevXvDy8lI6CtUitinYQ0JCFE5CRERE1RGLK/JI/v7+SkegWsZoNOLEiRMArkyqQkRERFQZHBZIHk8IoXQEqgVOnToFk8kEPz8/hIeHKx2HiIiIqiH2XJHHOnr0KNavX4+QkBDcddddSsehGs429b/teD8iIiKiymJxZSc5ORnJyckwm81KRyEAKpUKp0+fRnZ2NoQQ/MBLVcp2vFWDBg0UTkJERETVFYcF2klKSsL+/fuxfft2paMQgJiYGGg0GhQUFODcuXNKx6EarKCgABkZGQB4vBURERFdOxZX5LE0Gg1iY2MBXBmyRVQVtFothgwZgltvvZWTqRAREdE1Y3FFHs3Wi8DiiqqSTqdDq1at0KdPH6WjEBERUTXG4oo8mq24On78OI+FIyIiIiKPxuKKPFrdunXh7e0No9GI06dPKx2HaqBLly5h8+bN8jFXRERERNeKxRV5NJVKhebNm6N58+bQaDi5JbnfoUOH8Ntvv2HNmjVKRyEiIqJqjp9WyePdcccdSkegGsw2BTtnCSQiIqLrxZ4rIqq1zGYzjh8/DoDntyIiIqLrx+KKqgUhBC5cuIDs7Gylo1ANkp6eDoPBAC8vL0RGRiodh4iIiKo5FldULaxduxaffvop/vzzT6WjUA1im+I/ISEBKhX/HBIREdH14acJqhbq1asHgOe7IveyHW/FIYFERETkDiyuqFqIi4sDAJw7dw55eXnKhqEawWg04ty5cwA4mQURERG5h0cUV8nJyYiLi4OXlxc6deqEv/76q9y2y5cvR4cOHVCnTh34+vqiTZs2WLRokUMbIQRef/11REZGwtvbG3369MHhw4er+mFQFfL19ZWPiTl27JjCaagm0Gq1eP755zF69GgEBQUpHYeIiIhqAMWLq2XLlmHixImYPHkydu7cidatW6N///7yN8qlBQcH49VXX8XWrVvxzz//YNSoURg1apTDOWree+89fPLJJ5gzZw7+/PNP+Pr6on///iguLr5RD4uqgK13gUMDyV00Gg3q16+vdAwiIiKqIRQvrmbMmIFHH30Uo0aNQvPmzTFnzhz4+Phg/vz5Zbbv0aMH7rrrLjRr1gwNGjTAhAkT0KpVK2zevBmAtdfqo48+wqRJk3DnnXeiVatW+Prrr3HmzBmsWLHiBj4ycjf74koIoXAaIiIiIiJHihZXJSUl2LFjB/r06SMvU6lU6NOnD7Zu3XrV+wshsG7dOhw8eBDdunUDYB0ylpmZ6bDNwMBAdOrUqdxtGgwG5ObmOlzI89SvXx9qtRq5ubm4ePGi0nGoGsvJycGnn36KNWvWsFAnIiIit9EoufMLFy7AbDajbt26Dsvr1q2LAwcOlHu/nJwc1KtXDwaDAWq1Gp999hn69u0LAMjMzJS3UXqbtnWlTZs2DVOmTHFeMWwYoNVW5iFVjb/+AgYNUjrFtXFjdi2AfmFhqGMwIGDMGKCqPxTzeVfGDch+NCQEF2Jj4XX8OKTkZPdtmM+7MphdGcyuDGZXBrMrw1OyG40uN1W0uLpW/v7+2L17N/Lz87Fu3TpMnDgRCQkJ6NGjxzVt7+WXX8bEiRPl27m5uYiJiQGWLQMCAtyU+joMGgSsWqV0imvj5uyd3LYlF/B5V8YNyJ723/8Ce/ci4bbbgA8+cN+G+bwrg9mVwezKYHZlMLsyPCV7bi4QGOhSU0WLq9DQUKjVapw9e9Zh+dmzZxEREVHu/VQqFRo2bAgAaNOmDVJTUzFt2jT06NFDvt/Zs2fl2eVst9u0aVPm9vR6PfR6/XU+GiKqDiwWizwpCs9vRURERO6k6DFXOp0O7du3x7p16+RlFosF69atQ+fOnV3ejsVigcFgAADEx8cjIiLCYZu5ubn4888/K7VN8lwnT57EunXrnIpyIlecPXsWhYWF0Ol0iI6OVjoOERER1SCKDwucOHEiRo4ciQ4dOuCmm27CRx99hIKCAowaNQoA8NBDD6FevXqYNm0aAOvxUR06dECDBg1gMBjw008/YdGiRZg9ezYAQJIkPP3003j77bfRqFEjxMfH47XXXkNUVBQGDx6s1MMkN9qyZQsOHDgArVbrdGwd0dUcPXoUgPXE1Gq1WuE0REREVJMoXlwNGzYM58+fx+uvv47MzEy0adMGv/zyi/yh+eTJk1CprnSwFRQUYNy4cTh9+jS8vb3RtGlTfPPNNxg2bJjc5oUXXkBBQQHGjh2LS5cu4dZbb8Uvv/wCLy+vG/74yP0SEhJw4MABpKWlybNEErnKVlxxSCARERG5m+LFFQCMHz8e48ePL3NdSkqKw+23334bb7/9doXbkyQJb775Jt588013RSQPYjvf1alTp1BSUgKdTqdwIqpOQkNDkZWVJb+OiIiIiNzFI4orosoICQlBQEAAcnNzcfLkSXlyEyJXDBw4ELfddpvSMYiIiKgGUnRCC6JrIUmS3Otgm/WNqDIkSYIkSUrHICIiohqGxRVVSyyu6FpkZmbCYrEoHYOIiIhqKBZXVC3Ziqvc3FyUlJQonIaqg/z8fMyZMwfvv/++fOoGIiIiInfiMVdULfn5+WHcuHEIDQ11mE2SqDy2Xs7AwECeNJyIiIiqBIsrqrbCw8OVjkDViG0Kds4SSERERFWFX/lTtSeEUDoCeTghhNxzxfNbERERUVVhcUXVlhACq1atwowZM5CVlaV0HPJg58+fR15eHtRqNerXr690HCIiIqqhWFxRtSVJEi5cuIC8vDwcO3ZM6TjkwWy9VrGxsdBqtQqnISIiopqKxRVVa5ySnVxhO96KQwKJiIioKrG4spOcnIzmzZujY8eOSkchF9kXVzx/EZWnW7du6NatGxo3bqx0FCIiIqrBWFzZSUpKwv79+7F9+3alo5CL6tWrB51Oh6KiIpw9e1bpOOShYmJi0KtXL4SFhSkdhYiIiGowFldUranVasTFxQHg0EAiIiIiUhaLK6r2eNwVVWTz5s1ITU1FSUmJ0lGIiIiohmNxRdVeQkICwsPDERUVpXQU8jBFRUVYt24dli1bhqKiIqXjEBERUQ2nUToA0fUKDw/HuHHjlI5BHujYsWMQQiA0NBSBgYFKxyEiIqIajj1XRFRj2YaK2oaOEhEREVUlFldUYxiNRpw+fVrpGORBeH4rIiIiupFYXFGNUFhYiOnTp2PevHk8toYAANnZ2cjOzoZKpZJnlCQiIiKqSiyuqEbw8fFBUFAQhBA4fvy40nHIA9h6raKjo6HX6xVOQ0RERLUBiyuqMTglO9nLzMwEwOOtiIiI6MbhbIFUYyQkJOCvv/5icUUAgNtvvx233HILtFqt0lGIiIiolmDPFdUYsbGxkCQJFy9eRE5OjtJxyAMEBwfD399f6RhERERUS7C4ohrD29tbPpEwe6+IiIiI6EZjcUU1Co+7IgBYsmQJlixZgrNnzyodhYiIiGoRHnNFNUpiYiK8vb3RsGFDpaOQQgwGAw4fPgyLxYJ+/fopHYeIiIhqERZXVKNEREQgIiJC6RikoBMnTsBisaBOnToIDg5WOg4RERHVIhwWSEQ1iu38VgkJCZAkSeE0REREVJuwuLKTnJyM5s2bo2PHjkpHoetQVFSE3bt3Y8uWLUpHIQXYjrdr0KCBwkmIiIiotmFxZScpKQn79+/H9u3blY5C1+HSpUtYsWIFUlJSYDablY5DN1Bubi7Onz8PAIiPj1c4DREREdU2LK6oxqlbty58fHxQUlKC9PR0pePQDWTrtYqKioKPj4/CaYiIiKi2YXFFNY5KpZJ7LTgle+2i1+tRv359NGrUSOkoREREVAtxtkCqkRISErBv3z6kpaWhR48eSsehG6RZs2Zo1qyZ0jGIiIiolmLPFdVItpMJnz59GgaDQeE0RERERFQbsLiiGikoKAhBQUGwWCw4ceKE0nHoBrh48SIKCwuVjkFERES1GIsrqrFsvVfnzp1TOAndCD/99BPee+897NmzR+koREREVEvxmCuqsbp164ZevXrB19dX6ShUxYxGo9xDGRERoXAaIiIiqq1YXFGNFRgYqHQEukFOnToFk8kEPz8/hIeHKx2HiIiIaikOC6RaQQihdASqQrYp9xMSEiBJksJpiIiIqLZizxXVaGlpadi4cSNCQkJwxx13KB2HqsjRo0cBAA0aNFA4CREREdVmLK6oRrNYLDh+/DiysrIghGCvRg1UWFiIjIwMAFcmMSEiIiJSAocFUo1Wv359qNVq5Obm4uLFi0rHoSpgGxIYHh4Of39/hdMQERFRbcaeK6rRdDodYmJicPz4caSlpSE0NFTpSORmsbGxGDRoENRqtdJRiIiIqJZjzxXVeLahYrYeDqpZ/P390a5dO7Ru3VrpKERERFTLsbiiGs9WXB07dgwWi0XhNERERERUU7G4spOcnIzmzZujY8eOSkchN4qKioJer4fBYMCZM2eUjkNudOjQIWzbtg1ZWVlKRyEiIiJicWUvKSkJ+/fvx/bt25WOQm6kUqnQtGlTNG7cWOko5GY7d+7EL7/8gr179yodhYiIiIgTWlDtcNdddykdgdzMbDbj2LFjAHh+KyIiIvIM7LkiomopPT0dBoMB3t7eiIyMVDoOEREREYsrql0uXbqEnJwcpWOQG9hmf4yPj4dKxT9lREREpDx+IqFa49dff8VHH32EP//8U+ko5AZHjx4FwCGBRERE5DlYXFGtERERAYDnu6oJiouLcfr0aQBXptonIiIiUhqLK6o14uPjAQCZmZkoKChQOA1dj3PnzkGSJAQHByMoKEjpOEREREQAOFsg1SL+/v4IDw/HuXPncOzYMbRo0ULpSHSN6tevj5deegmXLl1SOgoRERGRjD1XVKvYhpBxaGD1p9PpEB4ernQMIiIiIhmLK6pVWFxVf0IIpSMQERERlYnFFdUqsbGxUKlUuHTpErKyspSOQ9dg9+7dmDNnDrZv3650FCIiIiIHPOaKahW9Xo9evXohKCgIvr6+Sseha3DkyBFkZmYiLy9P6ShEREREDhTvuUpOTkZcXBy8vLzQqVMn/PXXX+W2/fLLL9G1a1cEBQUhKCgIffr0cWqfn5+P8ePHIzo6Gt7e3mjevDnmzJlT1Q+DqpFbb70ViYmJ0Ov1SkehSrJYLDh27BgAnt+KiIiIPI+ixdWyZcswceJETJ48GTt37kTr1q3Rv39/nDt3rsz2KSkpGD58ONavX4+tW7ciJiYG/fr1Q3p6utxm4sSJ+OWXX/DNN98gNTUVTz/9NMaPH49Vq1bdqIdFRFUkMzMThYWF0Ol0iI6OVjoOERERkQNFi6sZM2bg0UcfxahRo+QeJh8fH8yfP7/M9osXL8a4cePQpk0bNG3aFHPnzoXFYsG6devkNlu2bMHIkSPRo0cPxMXFYezYsWjdunWFPWJU+6Snp2PDhg04f/680lGoEmwTkcTFxUGtViuchoiIiMiRYsVVSUkJduzYgT59+lwJo1KhT58+2Lp1q0vbKCwshNFoRHBwsLzslltuwapVq5Ceng4hBNavX49Dhw6hX79+5W7HYDAgNzfX4UI124YNG+TXBlUfR48eBcAhgUREROSZFJvQ4sKFCzCbzahbt67D8rp16+LAgQMubePFF19EVFSUQ4E2a9YsjB07FtHR0dBoNFCpVPjyyy/RrVu3crczbdo0TJkyxXnFsGGAVuvaA6pKf/0FDBqkdIpr46HZE8LCcCgmBmn/+x+6vPtu2Y08NLtLamB2oyThZOvWgEqFhA8+AAwGBcJdRQ183qsFZlcGsyuD2ZXB7MrwlOxGo8tNq+1sgdOnT8fSpUuRkpICLy8vefmsWbOwbds2rFq1CrGxsdi4cSOSkpKcijB7L7/8MiZOnCjfzs3NRUxMDLBsGRAQUOWP5aoGDQKq6zFjHpo94dw54LPPcCI4GMb//Q/asopoD83ukhqYvTgvD81//RUXL15E6OTJgCQpEO4qauDzXi0wuzKYXRnMrgxmV4anZM/NBQIDXWqqWHEVGhoKtVqNs2fPOiw/e/YsIiIiKrzvBx98gOnTp+O3335Dq1at5OVFRUV45ZVX8P3332PgwIEAgFatWmH37t344IMPyi2u9Ho9Z46rZcLCwuDn54f8/HycPn0a8fHxSkeiq/D398fdd9+tdAwiIiKicil2zJVOp0P79u0dJqOwTU7RuXPncu/33nvv4a233sIvv/yCDh06OKwzGo0wGo1QqRwfllqthsVice8DoGpNkiQkJCQAuDJJAhERERHR9VB0tsCJEyfiyy+/xFdffYXU1FQ88cQTKCgowKhRowAADz30EF5++WW5/bvvvovXXnsN8+fPR1xcHDIzM5GZmYn8/HwAQEBAALp3747nn38eKSkpOHbsGBYuXIivv/4ad911lyKPkTwXi6vqo7i4GJmZmRBCKB2FiIiIqFyKHnM1bNgwnD9/Hq+//joyMzPRpk0b/PLLL/IkFydPnnTohZo9ezZKSkpwzz33OGxn8uTJeOONNwAAS5cuxcsvv4wRI0YgKysLsbGxeOedd/D444/fsMdF1YNtKODFixdhNBrLPu6KPMKhQ4ewfPlyJCQk4KGHHlI6DhEREVGZFJ/QYvz48Rg/fnyZ61JSUhxuHz9+/Krbi4iIwIIFC9yQjGq6wMBAjB07FnXr1uU5kzycrXfxasdjEhERESlJ8eKKSElRUVFKR6CrEELw/FZERERULSh6zBUR0dVcuHABeXl5UKvVqF+/vtJxiIiIiMrF4opqNSEEVq9ejY8//hg5OTlKx6Ey2HqtYmNjeVwcEREReTQWV1SrSZKEjIwMZGdnc9ZAD2X7udhmdyQiIiLyVCyuqNazfWg/duyYwkmoNLPZLE9kw+OtiIiIyNOxuKJaz/58VzyPkucZNmwYunXrJp+igYiIiMhTcbZAqvViYmKg0WiQn5+P8+fPIzw8XOlIdJlarUaDBg3Ya0VERETVAnuuqNbTaDSIjY0FAB53RURERETXjMUVERyHBpJnKCoqwpo1a3D48GEO1yQiIqJqgcUVEazFVXBwMEJDQ5WOQpcdO3YMW7duxZo1ayBJktJxiIiIiK6Kx1zZSU5ORnJyMsxms9JR6AaLjIzEU089pXQMsmM7vxWnYCciIqLqgj1XdpKSkrB//35s375d6ShEtZ5tiCYnsyAiIqLqgsUVkR2z2YzMzEylY9R6WVlZyM7OhkqlQlxcnNJxiIiIiFzC4orosoKCArz77rv44osvYDAYlI5Tq9l6raKjo6HX6xVOQ0REROQaFldEl/n6+sLX1xcWiwUnTpxQOk6txuOtiIiIqDpicUVkh1OyK08AuHDhAgAeb0VERETVC2cLJLKTkJCAHTt2sLhSkARg3LhxOH/+PEJCQpSOQ0REROQy9lwR2bFNnnDu3Dnkafjdg1IkSUJ4eDjUarXSUYiIiIhcxuKKyI6vry8iIyMBAMf8/RVOUzsJpQMQERERXSMWV0SlyMddsbi64QwGA2a0aIHvvvsOJSUlSschIiIiqhSOeyIqpXnz5tDr9WiUnAyLxQKDwQCVSgVJkpz+J/c6ceIE8nQ6pKenQ6vVKh2HiIiIqFJYXBGVUq9ePdSrVw/44AMUFhfjvffeK7dt69atcddddwEATCYTPvzwwzKLMJVKhYYNG+K2226T7zt37lwAKLN9vXr10KtXL7ntihUrYDaby9xuSEgIbr75Zrntpk2bYIyMhLR+vdzOdvH390fr1q3ltrt370ZJSYlTO0mS4OXlhaZNm8ptjx49ipKSEqd2kiRBq9UiNjZWbpuZmQmj0eiQ1XZRqVQICwuT2+bl5cFkMkGSJBw8eBCAtfeQxSsRERFVNyyuiCpgsVgq1baoqKjc9fn5+fJ1IQROnz5dbtvShcW+fftgNBrLbBsbG+tQXG3duhWFkZHAhg1ObaOiohyKq5SUFFy6dKnM7YaGhjoUV7/88gvOnz9fZtvAwEA888wz8u1Vq1bhzJkzZbb19vbGiy++KN/+3//+h+PHjzu04RTsREREVB2xuCKqgK+vLyZNmgQhBCwWCywWi3xdCOEwm51Go0FSUpJTG9v/3t7eDtsePnx4me0sFgv8Sx3v1bdvX5hMpjLbBwYGOrRt164dDMuXQ9x2G4QQDpc6deo4tG3YsCEKCgqc2lksFqftRkZGwsvLy6GN7XrpvAEBASgqKnJoY7uUfh7UajU0Go28PqSgAA0bNqzUz4mIiIjIE7C4IqqAJEnQuDgle+nhblfbbpMmTVzOcdNNN7nctk+fPsAnnwC3337Vtre70MZmyJAhLre97777XG774IMPOi4YNAjQ612+PxEREZGn4GyBREREREREbsDiioiIiIiIyA1YXBEREREREbkBiys7ycnJaN68OTp27Kh0FCIiIiIiqmZYXNlJSkrC/v37sX37dqWjEBERERFRNcPiioiIiIiIyA1YXBEREREREbkBiysiIiIiIiI3YHFFRERERETkBiyuiIiIiIiI3IDFFRERERERkRuwuCIiIiIiInIDFldERERERERuwOKKiIiIiIjIDVhcERERERERuQGLKyIiIiIiIjdgcUVEREREROQGLK6IiIiIiIjcQKN0AE8khAAA5ObmKpzkMqMR8JQslcXsymB2ZTC7MphdGcyuDGZXBrMrw0Oy22oCW41QEUm40qqWOX36NGJiYpSOQUREREREHuLUqVOIjo6usA2LqzJYLBacOXMG/v7+kCTJaX3Hjh2xffv2CrfhShtX2uXm5iImJganTp1CQEDAde+T2V1rx+zub+NKu9qQ3V37c+e2mL1y7Zid2SvbrqZnd6UNs7s/F7O7vq3rzSWEQF5eHqKioqBSVXxUFYcFlkGlUlVYlarV6qt+8HOlTWXaBQQEuGWfzF65dszO7JVtd7Xs7twfs1/B7Mxe2XbMfuP/jgLM7s5czF65bV1vrsDAwKvuA+CEFtckKSnJLW0q085d22L2yrVz17aYvXLt3LUtT8zuzv0xu+uY3f3bcud2mL1y7W7k/tz5+FzB7O5t4ypmr9y2KsJhgR4uNzcXgYGByMnJcbnC9xTMrgxmVwazK4PZlcHsymB2ZTC7MqprdvZceTi9Xo/JkydDr9crHaXSmF0ZzK4MZlcGsyuD2ZXB7MpgdmVU1+zsuSIiIiIiInID9lwRERERERG5AYsrIiIiIiIiN2BxRURERERE5AYsroiIiIiIiNyAxdUNtHHjRtxxxx2IioqCJElYsWLFVe+TkpKCdu3aQa/Xo2HDhli4cKFTm+TkZMTFxcHLywudOnXCX3/9VS2yT5s2DR07doS/vz/Cw8MxePBgHDx4sFpktzd9+nRIkoSnn37abZltqip7eno6HnjgAYSEhMDb2xstW7bE33//7fHZzWYzXnvtNcTHx8Pb2xsNGjTAW2+9BXfPy1PZ7BkZGbj//vvRuHFjqFSqcl8L3333HZo2bQovLy+0bNkSP/30k1tzV1X2L7/8El27dkVQUBCCgoLQp08fj/g74+rzbrN06VJIkoTBgwe7LbNNVWW/dOkSkpKSEBkZCb1ej8aNG7v9dVNV2T/66CM0adIE3t7eiImJwTPPPIPi4mJFsy9fvhx9+/ZFWFgYAgIC0LlzZ6xZs8apnSe+r7qS3VPfV1193m086X3V1eye+L7qSnZPfV/dvHkzunTpIj+fTZs2xcyZM53a3Yjf1cpicXUDFRQUoHXr1khOTnap/bFjxzBw4ED07NkTu3fvxtNPP41HHnnE4Rdj2bJlmDhxIiZPnoydO3eidevW6N+/P86dO+fx2Tds2ICkpCRs27YNa9euhdFoRL9+/VBQUODx2W22b9+Ozz//HK1atXJrZpuqyJ6dnY0uXbpAq9Xi559/xv79+/Hhhx8iKCjI47O/++67mD17Nj799FOkpqbi3XffxXvvvYdZs2Ypmt1gMCAsLAyTJk1C69aty2yzZcsWDB8+HGPGjMGuXbswePBgDB48GHv37nVn9CrJnpKSguHDh2P9+vXYunUrYmJi0K9fP6Snp7szepVktzl+/Diee+45dO3a1R1RnVRF9pKSEvTt2xfHjx/Hf//7Xxw8eBBffvkl6tWr587oVZL922+/xUsvvYTJkycjNTUV8+bNw7Jly/DKK6+4M3qls2/cuBF9+/bFTz/9hB07dqBnz5644447sGvXLrmNp76vupLdU99XXclu42nvq65k99T3VVeye+r7qq+vL8aPH4+NGzciNTUVkyZNwqRJk/DFF1/IbW7U72qlCVIEAPH9999X2OaFF14QiYmJDsuGDRsm+vfvL9++6aabRFJSknzbbDaLqKgoMW3aNLfmteeu7KWdO3dOABAbNmxwR8wyuTN7Xl6eaNSokVi7dq3o3r27mDBhgpvTOnJX9hdffFHceuutVRGxXO7KPnDgQDF69GiHNkOGDBEjRoxwW9bSXMlur7zXwtChQ8XAgQMdlnXq1Ek89thj15mwfO7KXprJZBL+/v7iq6++uvZwV+HO7CaTSdxyyy1i7ty5YuTIkeLOO+90S8byuCv77NmzRUJCgigpKXFfuKtwV/akpCTRq1cvh2UTJ04UXbp0uc6E5atsdpvmzZuLKVOmyLc99X21LKWzl+Yp76tlKSu7J76vlqV0dk99Xy1L6ezV4X3V5q677hIPPPCAfFuJ31VXsOfKg23duhV9+vRxWNa/f39s3boVgPVbzR07dji0UalU6NOnj9xGKVfLXpacnBwAQHBwcJVmuxpXsyclJWHgwIFObZXkSvZVq1ahQ4cOuPfeexEeHo62bdviyy+/vNFRnbiS/ZZbbsG6detw6NAhAMCePXuwefNm/Oc//7mhWa/FtfxOeKrCwkIYjUbFf1dd9eabbyI8PBxjxoxROkqlrFq1Cp07d0ZSUhLq1q2LFi1aYOrUqTCbzUpHu6pbbrkFO3bskIfopKWl4aeffsJtt92mcDJHFosFeXl58mvZk99XSyudvSye8r5aWnnZPfF9tbSysnvq+2ppZWWvLu+ru3btwpYtW9C9e3cAnv27qlF071ShzMxM1K1b12FZ3bp1kZubi6KiImRnZ8NsNpfZ5sCBAzcyqpOrZff29nZYZ7FY8PTTT6NLly5o0aLFjYzqxJXsS5cuxc6dO7F9+3aFUpbNlexpaWmYPXs2Jk6ciFdeeQXbt2/HU089BZ1Oh5EjRyqU3LXsL730EnJzc9G0aVOo1WqYzWa88847GDFihEKpXVfe48vMzFQo0bV78cUXERUV5dEfgGw2b96MefPmYffu3UpHqbS0tDT8/vvvGDFiBH766SccOXIE48aNg9FoxOTJk5WOV6H7778fFy5cwK233gohBEwmEx5//HG3Dwu8Xh988AHy8/MxdOhQAMCFCxc89n21tNLZS/Ok99XSysruqe+rpZWV3VPfV0srK7unv69GR0fj/PnzMJlMeOONN/DII48A8OzfVRZX5BGSkpKwd+9ebN68WekoV3Xq1ClMmDABa9euhZeXl9JxKs1isaBDhw6YOnUqAKBt27bYu3cv5syZ41FvAmX5v//7PyxevBjffvstEhMT5WOzoqKiPD57TTF9+nQsXboUKSkpHv/6z8vLw4MPPogvv/wSoaGhSsepNIvFgvDwcHzxxRdQq9Vo37490tPT8f7773t8cZWSkoKpU6fis88+Q6dOnXDkyBFMmDABb731Fl577TWl4wGwHhc2ZcoUrFy5EuHh4UrHqRRXsnvq+2pZ2avL+2p5z3t1eF8tL7unv69u2rQJ+fn52LZtG1566SU0bNgQw4cPVzpWhVhcebCIiAicPXvWYdnZs2cREBAAb29vqNVqqNXqMttERETcyKhOrpbd3vjx47F69Wps3LgR0dHRNzJmma6WfceOHTh37hzatWsnrzebzdi4cSM+/fRTGAwGqNXqGx0bgGvPe2RkJJo3b+7QplmzZvjf//53w3KWxZXszz//PF566SXcd999AICWLVvixIkTmDZtmke8CVSkvMen9O9qZXzwwQeYPn06fvvttyo72Nydjh49iuPHj+OOO+6Ql1ksFgCARqPBwYMH0aBBA6XiXVVkZCS0Wq3D35NmzZohMzMTJSUl0Ol0Cqar2GuvvYYHH3xQ/pa5ZcuWKCgowNixY/Hqq69CpVL2qISlS5fikUcewXfffefQAxsaGuqx76s25WW352nvqzblZffk91Wbip53T31ftakou6e/r8bHxwOw5jp79izeeOMNDB8+3KN/V3nMlQfr3Lkz1q1b57Bs7dq16Ny5MwBAp9Ohffv2Dm0sFgvWrVsnt1HK1bIDgBAC48ePx/fff4/ff/9d/gVS2tWy9+7dG//++y92794tXzp06IARI0Zg9+7dir4BuPK8d+nSxWlq3kOHDiE2NvaGZCyPK9kLCwudPpSp1Wr5A7Mnc+XxebL33nsPb731Fn755Rd06NBB6Tguadq0qdPv6qBBg+QZKWNiYpSOWKEuXbrgyJEjDq/vQ4cOITIy0qMLK6D831UAbp/iubKWLFmCUaNGYcmSJRg4cKDDOk9+XwUqzg547vsqUHF2T35fBa7+vHvq+ypw9ezV6X3VYrHAYDAA8PDfVUWn06hl8vLyxK5du8SuXbsEADFjxgyxa9cuceLECSGEEC+99JJ48MEH5fZpaWnCx8dHPP/88yI1NVUkJycLtVotfvnlF7nN0qVLhV6vFwsXLhT79+8XY8eOFXXq1BGZmZken/2JJ54QgYGBIiUlRWRkZMiXwsJCj89eWlXNalQV2f/66y+h0WjEO++8Iw4fPiwWL14sfHx8xDfffOPx2UeOHCnq1asnVq9eLY4dOyaWL18uQkNDxQsvvKBodiGE3L59+/bi/vvvF7t27RL79u2T1//xxx9Co9GIDz74QKSmporJkycLrVYr/v33X4/PPn36dKHT6cR///tfh9/VvLw8j89eWlXNFlgV2U+ePCn8/f3F+PHjxcGDB8Xq1atFeHi4ePvttz0+++TJk4W/v79YsmSJSEtLE7/++qto0KCBGDp0qKLZFy9eLDQajUhOTnZ4LV+6dElu46nvq65k99T3VVeyl+Yp76uuZPfU91VXsnvq++qnn34qVq1aJQ4dOiQOHTok5s6dK/z9/cWrr74qt7lRv6uVxeLqBlq/fr0A4HQZOXKkEML6Au/evbvTfdq0aSN0Op1ISEgQCxYscNrurFmzRP369YVOpxM33XST2LZtW7XIXtb2AJT5GD0te2lV9SZQVdl/+OEH0aJFC6HX60XTpk3FF198US2y5+bmigkTJoj69esLLy8vkZCQIF599VVhMBgUz15W+9jYWIc2//d//ycaN24sdDqdSExMFD/++KNbc1dV9tjY2DLbTJ482eOzl1ZVxVVVZd+yZYvo1KmT0Ov1IiEhQbzzzjvCZDJ5fHaj0SjeeOMN0aBBA+Hl5SViYmLEuHHjRHZ2tqLZu3fvXmF7G098X3Ulu6e+r7r6vNvzlPdVV7N74vuqK9k99X31k08+EYmJicLHx0cEBASItm3bis8++0yYzWaH7d6I39XKkoRQuH+eiIiIiIioBuAxV0RERERERG7A4oqIiIiIiMgNWFwRERERERG5AYsrIiIiIiIiN2BxRURERERE5AYsroiIiIiIiNyAxRUREREREZEbsLgiIiKXLVy4EHXq1LlqO0mSsGLFiirP4wl69OiBp59+WukYRETkAVhcERF5kIcffhiSJEGSJGi1WsTHx+OFF15AcXHxDc8SFxeHjz76yGHZsGHDcOjQIfn2G2+8gTZt2jjdNyMjA//5z3+qNN/ChQvl50qlUiE6OhqjRo3CuXPnqnS/V1PW83Yt7F8LOp0ODRs2xJtvvgmTyXT9IRVSm4puIqqdNEoHICIiRwMGDMCCBQtgNBqxY8cOjBw5EpIk4d1331U6Gry9veHt7X3VdhERETcgDRAQEICDBw/CYrFgz549GDVqFM6cOYM1a9bckP1XNdtrwWAw4KeffkJSUhK0Wi1efvnlSm/LbDbLhWh1ZzQaodVqlY5BROSk+v+FJSKqYfR6PSIiIhATE4PBgwejT58+WLt2rbzeYrFg2rRpiI+Ph7e3N1q3bo3//ve/8vqUlBRIkoQff/wRrVq1gpeXF26++Wbs3bvXYT+bN29G165d4e3tjZiYGDz11FMoKCgAYB3qduLECTzzzDNy7wngOCxw4cKFmDJlCvbs2SO3WbhwIQDnHop///0XvXr1gre3N0JCQjB27Fjk5+fL6x9++GEMHjwYH3zwASIjIxESEoKkpCQYjcYKnytJkhAREYGoqCj85z//wVNPPYXffvsNRUVFAIC5c+eiWbNm8PLyQtOmTfHZZ5/J9z1+/DgkScLy5cvRs2dP+Pj4oHXr1ti6davc5uLFixg+fDjq1asHHx8ftGzZEkuWLCk3T1nPW0FBAQICAhx+RgCwYsUK+Pr6Ii8vr9zt2V4LsbGxeOKJJ9CnTx+sWrUKADBjxgy0bNkSvr6+iImJwbhx4xyeU9vPatWqVWjevDn0ej1OnjyJ7du3o2/fvggNDUVgYCC6d++OnTt3Oj2vn3/+OW6//Xb4+PigWbNm2Lp1K44cOYIePXrA19cXt9xyC44ePepwv5UrV6Jdu3bw8vJCQkICpkyZIve0xcXFAQDuuusuSJIk377a/Wx5Zs+ejUGDBsHX1xfvvPNOuc8ZEZGSWFwREXmwvXv3YsuWLdDpdPKyadOm4euvv8acOXOwb98+PPPMM3jggQewYcMGh/s+//zz+PDDD7F9+3aEhYXhjjvukIuVo0ePYsCAAbj77rvxzz//YNmyZdi8eTPGjx8PAFi+fDmio6Px5ptvIiMjAxkZGU7Zhg0bhmeffRaJiYlym2HDhjm1KygoQP/+/REUFITt27fju+++w2+//Sbvy2b9+vU4evQo1q9fj6+++goLFy6UizVXeXt7w2KxwGQyYfHixXj99dfxzjvvIDU1FVOnTsVrr72Gr776yuE+r776Kp577jns3r0bjRs3xvDhw+UP9sXFxWjfvj1+/PFH7N27F2PHjsWDDz6Iv/76q8z9l/W8+fr64r777sOCBQsc2i5YsAD33HMP/P39K/X4SkpKAAAqlQqffPIJ9u3bh6+++gq///47XnjhBYf2hYWFePfddzF37lzs27cP4eHhyMvLw8iRI7F582Zs27YNjRo1wm233eZU5L311lt46KGHsHv3bjRt2hT3338/HnvsMbz88sv4+++/IYRw+Blu2rQJDz30ECZMmID9+/fj888/x8KFC+VCaPv27fLjzsjIkG9f7X42b7zxBu666y78+++/GD16tMvPGRHRDSWIiMhjjBw5UqjVauHr6yv0er0AIFQqlfjvf/8rhBCiuLhY+Pj4iC1btjjcb8yYMWL48OFCCCHWr18vAIilS5fK6y9evCi8vb3FsmXL5PZjx4512MamTZuESqUSRUVFQgghYmNjxcyZMx3aLFiwQAQGBsq3J0+eLFq3bu30OACI77//XgghxBdffCGCgoJEfn6+vP7HH38UKpVKZGZmyo87NjZWmEwmuc29994rhg0bVu5zVTrLoUOHROPGjUWHDh2EEEI0aNBAfPvttw73eeutt0Tnzp2FEEIcO3ZMABBz586V1+/bt08AEKmpqeXud+DAgeLZZ5+Vb3fv3l1MmDBBvl3W8/bnn38KtVotzpw5I4QQ4uzZs0Kj0YiUlJRy9zNy5Ehx5513CiGEsFgsYu3atUKv14vnnnuuzPbfffedCAkJkW8vWLBAABC7d+8udx9CCGE2m4W/v7/44Ycf5GUAxKRJk+TbW7duFQDEvHnz5GVLliwRXl5e8u3evXuLqVOnOmx70aJFIjIy0mG7ttdFZe/39NNPV/g4iIg8AY+5IiLyMD179sTs2bNRUFCAmTNnQqPR4O677wYAHDlyBIWFhejbt6/DfUpKStC2bVuHZZ07d5avBwcHo0mTJkhNTQUA7NmzB//88w8WL14stxFCwGKx4NixY2jWrJnbHk9qaipat24NX19feVmXLl1gsVhw8OBB1K1bFwCQmJgItVott4mMjMS///5b4bZzcnLg5+cHi8WC4uJi3HrrrZg7dy4KCgpw9OhRjBkzBo8++qjc3mQyITAw0GEbrVq1ctgnAJw7dw5NmzaF2WzG1KlT8X//939IT09HSUkJDAYDfHx8KvUc3HTTTUhMTMRXX32Fl156Cd988w1iY2PRrVu3Cu+3evVq+Pn5wWg0wmKx4P7778cbb7wBAPjtt98wbdo0HDhwALm5uTCZTCguLkZhYaGcT6fTOTw+ADh79iwmTZqElJQUnDt3DmazGYWFhTh58mS5z4vtZ9SyZUuHZcXFxcjNzUVAQAD27NmDP/74w6HHyWw2O2UqzdX7dejQocLniojIE7C4IiLyML6+vmjYsCEAYP78+WjdujXmzZuHMWPGyMfU/Pjjj6hXr57D/fR6vcv7yM/Px2OPPYannnrKaV39+vWvI/21Kz1BgSRJsFgsFd7H398fO3fuhEqlQmRkpDzZxtmzZwEAX375JTp16uRwH/sCrvR+bceW2fb7/vvv4+OPP8ZHH30kH9/09NNPy0PzKuORRx5BcnIyXnrpJSxYsACjRo2S91ceW6Gt0+kQFRUFjcb6tn38+HHcfvvteOKJJ/DOO+8gODgYmzdvxpgxY1BSUiIXJN7e3k77GDlyJC5evIiPP/4YsbGx0Ov16Ny5s9NjKut5qei5ys/Px5QpUzBkyBCnx+Hl5VXuY3T1fvbFORGRp2JxRUTkwVQqFV555RVMnDgR999/v8PEBN27d6/wvtu2bZMLpezsbBw6dEjukWrXrh32798vF3Fl0el0MJvNFe7DlTbNmjXDwoULUVBQIH9A/uOPP6BSqdCkSZMK73s1KpWqzMdQt25dREVFIS0tDSNGjLjm7f/xxx+488478cADDwCwFhKHDh1C8+bNy71Pec/JAw88gBdeeAGffPIJ9u/fj5EjR151//aFtr0dO3bAYrHgww8/lGf/+7//+z+XH9Nnn32G2267DQBw6tQpXLhwwaX7VqRdu3Y4ePBgha8prVbr9Ny4cj8iouqCE1oQEXm4e++9F2q1GsnJyfD398dzzz2HZ555Bl999RWOHj2KnTt3YtasWU4TNbz55ptYt24d9u7di4cffhihoaEYPHgwAODFF1/Eli1bMH78eOzevRuHDx/GypUrHSYoiIuLw8aNG5Genl7uh++4uDgcO3YMu3fvxoULF2AwGJzajBgxAl5eXhg5ciT27t2L9evX48knn8SDDz4oDzerClOmTMG0adPwySef4NChQ/j333+xYMECzJgxw+VtNGrUCGvXrsWWLVuQmpqKxx57TO4VK095z1tQUBCGDBmC559/Hv369UN0dPQ1P7aGDRvCaDRi1qxZSEtLw6JFizBnzhyXH9OiRYuQmpqKP//8EyNGjHBpev2ref311/H1119jypQp2LdvH1JTU7F06VJMmjRJbhMXF4d169YhMzMT2dnZLt+PiKi6YHFFROThNBoNxo8fj/feew8FBQV466238Nprr2HatGlo1qwZBgwYgB9//BHx8fEO95s+fTomTJiA9u3bIzMzEz/88IM862CrVq2wYcMGHDp0CF27dkXbtm3x+uuvIyoqSr7/m2++iePHj6NBgwYICwsrM9vdd9+NAQMGoGfPnggLCytzmnIfHx+sWbMGWVlZ6NixI+655x707t0bn376qRufJWePPPII5s6diwULFqBly5bo3r07Fi5c6PQ8VWTSpElo164d+vfvjx49eiAiIkIuUMtT0fNmG7Z3vbPdtW7dGjNmzMC7776LFi1aYPHixZg2bZpL9503bx6ys7PRrl07PPjgg3jqqacQHh5+XXkAoH///li9ejV+/fVXdOzYETfffDNmzpyJ2NhYuc2HH36ItWvXIiYmRj5G0JX7ERFVF5IQQigdgoiI3CclJQU9e/ZEdna2fE4q8gyLFi3CM888gzNnzjhMr09ERDUDj7kiIiKqYoWFhcjIyMD06dPx2GOPsbAiIqqhOCyQiIioir333nto2rQpIiIi8PLLLysdh4iIqgiHBRIREREREbkBe66IiIiIiIjcgMUVERERERGRG7C4IiIiIiIicgMWV0RERERERG7A4oqIiIiIiMgNWFwRERERERG5AYsrIiIiIiIiN9AoHYCInFksFpSUlCgdg4iIyGPodDqoVOwXIM/G4orIw5SUlODYsWOwWCxKRyEiIvIYKpUK8fHx0Ol0SkchKpckhBBKhyAiKyEETp48CaPRiKioKH5DR0REBOuIjjNnzkCr1aJ+/fqQJEnpSERlYs8VkQcxmUwoLCxEVFQUfHx8lI5DRETkMcLCwnDmzBmYTCZotVql4xCViV+LE3kQs9kMABzyQEREVIrtvdH2XknkiVhcEXkgDncgIiJyxPdGqg5YXBEREREREbkBiysiIqJKmDdvHvr166d0jBvuwoULCA8Px+nTp5WOQkTksVhcEZFbnDp1CqNHj0ZUVBR0Oh1iY2MxYcIEXLx4UeloAIATJ07A29sb+fn5AICsrCw8/fTTiI2NhU6nQ1RUFEaPHo2TJ08qmvP48eMYM2YM4uPj4e3tjQYNGmDy5MlXPe/Zww8/DEmSnC6JiYlym2nTpqFjx47w9/dHeHg4Bg8ejIMHDzpsJy4uTr6vWq1GVFQUxowZg+zs7Ar3v3DhQtSpU+eaH3dZj2fw4MFu297VSJKEFStWXLVdcXExXnvtNUyePFle9sYbb0CSJAwYMMCp/fvvvw9JktCjRw+n9qUvTZs2xfHjx8tcZ39ZuHAhUlJSyl2fmZkp78vV17n960er1SI+Ph4vvPACiouL5TahoaF46KGHHB47ERE5YnFFRNctLS0NHTp0wOHDh7FkyRIcOXIEc+bMwbp169C5c2dkZWUpHRErV65Ez5494efnh6ysLNx888347bffMGfOHBw5cgRLly7FkSNH0LFjR6SlpSmW88CBA7BYLPj888+xb98+zJw5E3PmzMErr7xS4f0+/vhjZGRkyJdTp04hODgY9957r9xmw4YNSEpKwrZt27B27VoYjUb069cPBQUFDtt68803kZGRgZMnT2Lx4sXYuHEjnnrqqSp5vNfLaDTe0P3997//RUBAALp06eKwPDIyEuvXr3fq1Zk/fz7q16/vtJ3ExESHn1dGRgY2b96MmJgYh2XPPvusU9thw4bJ2zl48KDTdsLDwwGg0q/zAQMGICMjA2lpaZg5cyY+//xzp0Jq1KhRWLx4sUf8ThMReSRBRB6jqKhI7N+/XxQVFSkdpVIGDBggoqOjRWFhocPyjIwM4ePjIx5//HEhhBCzZs0SiYmJ8vrvv/9eABCzZ8+Wl/Xu3Vu8+uqr8u0VK1aItm3bCr1eL+Lj48Ubb7whjEajvB6A+PLLL8XgwYOFt7e3aNiwoVi5cqVTxl69esn7efzxx4Wvr6/IyMhwaFNYWCjq1asnBgwYIIQQ4ocffhCBgYHCZDIJIYTYtWuXACBefPFF+T5jxowRI0aMkG9v2rRJ3HrrrcLLy0tER0eLJ598UuTn58vrY2NjxTvvvCNGjRol/Pz8RExMjPj8888rfH7fe+89ER8fX2Gb0r7//nshSZI4fvx4uW3OnTsnAIgNGzY45Js5c6ZDu7feeks0b968wv0tWLBABAYGyrcnT54sWrduLb7++msRGxsrAgICxLBhw0Rubq7c5rvvvhMtWrQQXl5eIjg4WPTu3Vvk5+eLyZMnCwAOl/Xr14tjx44JAGLp0qWiW7duQq/XiwULFsj7sjdz5kwRGxvrsGzevHmiefPmQqfTiYiICJGUlCQ/Zvt9lb6fvYEDB4rnnnvOYZlt/7fffrt4++235eV//PGHCA0NFU888YTo3r27U3tXlNd2/fr1AoDIzs4u976uvs6FEGLkyJHizjvvdGg3ZMgQ0bZtW6ftxsfHi7lz57qUn8idqut7JNUu7Lki8mBCCBSWmBS5CBfPL56VlYU1a9Zg3Lhx8Pb2dlgXERGBESNGYNmyZRBCoHv37ti/fz/Onz8PwNqTEhoaipSUFADWXoitW7fKQ6g2bdqEhx56CBMmTMD+/fvx+eefY+HChXjnnXcc9jNlyhQMHToU//zzD2677TaMGDHC4Zv1S5cuYfPmzRg0aBAsFguWLl2KESNGICIiwmE73t7eGDduHNasWYOsrCx07doVeXl52LVrV5l5bctseY8ePYoBAwbg7rvvxj///INly5Zh8+bNGD9+vMN+PvzwQ3To0AG7du3CuHHj8MQTTzgNz7OXk5OD4ODgin8QpcybNw99+vRBbGxshdsFUOG209PT8cMPP6BTp06V2j9gfT5WrFiB1atXY/Xq1diwYQOmT58OAMjIyMDw4cMxevRopKamIiUlBUOGDIEQAs899xyGDh0q96RkZGTglltukbf70ksvYcKECUhNTUX//v1dyjJ79mwkJSVh7Nix+Pfff7Fq1So0bNgQALB9+3YAwIIFC5CRkSHfLsvmzZvRoUOHMteNHj0aCxculG/Pnz8fI0aMUOTUCpV5nZdl79692LJlS5nZb7rpJmzatKlKchMRVXc8iTCRBysymtH89TWK7Hv/m/3ho7v6n4jDhw9DCIFmzZqVub5Zs2bIzs7G+fPn0aJFCwQHB2PDhg245557kJKSgmeffRYff/wxAOCvv/6C0WiUP0hPmTIFL730EkaOHAkASEhIwFtvvYUXXnjBYbjSww8/jOHDhwMApk6dik8++QR//fWXfAzMTz/9hFatWiEqKgpnz57FpUuXKswrhMCRI0dw0003oU2bNkhJSUGHDh2QkpKCZ555BlOmTEF+fj5ycnJw5MgRdO/eHYD1mKYRI0bg6aefBgA0atQIn3zyCbp3747Zs2fDy8sLAHDbbbdh3LhxAIAXX3wRM2fOxPr169GkSROnPEeOHMGsWbPwwQcfXPVnYXPmzBn8/PPP+Pbbb8ttY7FY8PTTT6NLly5o0aKFw7oXX3wRkyZNgtlsRnFxMTp16oQZM2a4vH/7fSxcuBD+/v4AgAcffBDr1q3DO++8g4yMDJhMJgwZMkQuAFu2bCnf19vbGwaDwakwAICnn34aQ4YMqVSWt99+G88++ywmTJggL+vYsSMA64lJAaBOnTpl7s/m0qVLyMnJQVRUVJnrb7/9djz++OPYuHEj2rdvj//7v//D5s2bMX/+fKe2//77L/z8/ByWPfDAA5gzZ06lHld0dLTD7djYWOzbtw/nz5+v1OscAFavXg0/Pz+YTCYYDAaoVCp8+umnTveNioqSv3AgIiJHLK6IyC2u1tOl0+kgSRK6deuGlJQU9OnTB/v378e4cePw3nvv4cCBA9iwYQM6duwIHx8fAMCePXvwxx9/OPRU2T7wFxYWyu1atWolr/f19UVAQADOnTsnL1u5ciUGDRpU6bwA0L17d7kI3LRpE6ZNmyZ/aM7KykJUVBQaNWok5/3nn3+wePFih/1YLBYcO3ZM/qBrn1eSJERERDjktUlPT8eAAQNw77334tFHH5WX238oL+sD+VdffYU6depUOCFEUlIS9u7di82bNzute/755/Hwww9DCIFTp07hlVdewcCBA7Fx40ao1eqr7t8mLi5OLqwA63FJtsfZunVr9O7dGy1btkT//v3Rr18/3HPPPQgKCio3s015PUflOXfuHM6cOYPevXtX6n6lFRUVAYBcJJem1WrxwAMPYMGCBUhLS0Pjxo0dftb2mjRpglWrVjksCwgIqHSmTZs2OTzHWq3WYb2rPdAA0LNnT8yePRsFBQWYOXMmNBoN7r77bqd23t7eKCwsrHRWIqLagMUVkQfz1qqx/03Xhj1Vxb5d0bBhQ0iShNTUVNx1111O61NTUxEWFibPJNejRw988cUX2LRpE9q2bYuAgAC54NqwYYPcCwQA+fn5mDJlSpm9FPYfcEt/oJQkCRaLBQBQUlKCX375RZ4QwpYlNTW1zMeTmpoKjUaD+Ph4Oe/8+fOxZ88eaLVaNG3aFD169EBKSgqys7Od8j722GNlTv5gP6lBRXltzpw5g549e+KWW27BF1984bBu9+7d8vXSH8iFEJg/fz4efPDBcoejjR8/HqtXr8bGjRudej4A66xwtiFzjRo1wkcffYTOnTtj/fr16NOnT4X7t1fR41Sr1Vi7di22bNmCX3/9FbNmzcKrr76KP//8U37uy+Pr6+twW6VSORUR9hNdlB6ueq1CQkIgSVKFMyeOHj0anTp1wt69ezF69Ohy2+l0Ovk5vh7x8fFlztLoyutckiSHDL6+vvLt+fPno3Xr1pg3bx7GjBnjcN+srCy5t4+IiBzxmCsiDyZJEnx0GkUukiS5lDEkJAR9+/bFZ599Jn+zb5OZmYnFixfj4YcflpfZjrv67rvv5GOVevTogd9++w1//PGHw5TV7dq1w8GDB9GwYUOni0rl2p+vlJQUBAUFoXXr1gCsH8SHDh2Kb7/91mHKasDaM/HZZ5/hrrvuQmBgIADIx13NnDlTLqRsxVVKSopT3v3795eZtzLH3aSnp6NHjx5o3749FixY4PRY7bdrmxnOZsOGDThy5IjTB2LAWniNHz8e33//PX7//ferFjE2arVafn6utv/KkCQJXbp0wZQpU7Br1y7odDp8//33AKzFh9lsdmk7YWFhyMzMdCiw7AtAf39/xMXFYd26deVuQ6vVXnV/Op0OzZs3x/79+8ttk5iYiMTEROzduxf333+/S/mrgiuv8/79+5d7vJ1KpcIrr7yCSZMmOf1e7927F23btq2y7ERE1RmLKyK6bp9++ikMBgP69++PjRs34tSpU/jll1/Qt29fNG7cGK+//rrctlWrVggKCsK3337rUFytWLECBoPBYYrr119/HV9//TWmTJmCffv2ITU1FUuXLsWkSZNczrZq1SqnIYHvvPMOIiIi0LdvX/z88884deoUNm7ciP79+0OlUsnHgAFAUFAQWrVqhcWLF8t5u3Xrhp07d+LQoUMOPVcvvvgitmzZgvHjx2P37t04fPgwVq5c6TShRUVshVX9+vXxwQcf4Pz588jMzHT6gFyeefPmoVOnTk7HUQHWoYDffPMNvv32W/j7+8vbLf3hOS8vD5mZmcjIyMBff/2F559/HmFhYQ6TSlyvP//8E1OnTsXff/+NkydPYvny5Th//rw8dDIuLg7//PMPDh48iAsXLlQ45XqPHj1w/vx5vPfeezh69CiSk5Px888/O7R544038OGHH+KTTz7B4cOHsXPnTsyaNUtebyu+MjMzK+yZ6t+/f5lDKe39/vvvyMjIqPC8XyaTSX7+bZezZ89WuN2ynDt3zmk7tudq6tSp5b7OjUYjkpOTK9z2vffeC7Va7dCusLAQO3bsqJUnUSYicgWLKyK6bo0aNcL27duRkJCAoUOHIjY2Fv/5z3/QuHFj/PHHHw7H6EiShK5du0KSJNx6660ArAVXQEAAOnTo4DDkq3///li9ejV+/fVXdOzYETfffDNmzpxZ4Qx4pZVVXIWGhmLbtm3o2bMnHnvsMcTHx6N79+4wm83YvXs3IiMjHdrb1tmKq+DgYDRv3hwREREOk1C0atUKGzZswKFDh9C1a1e0bdsWr7/+erkTIJRl7dq1OHLkCNatW4fo6GhERkbKl6vJycnB//73vzJ7rQDrjHk5OTno0aOHw3aXLVvm0O71119HZGQkoqKicPvtt8PX1xe//vorQkJCXH4cVxMQEICNGzfitttuQ+PGjTFp0iR8+OGH+M9//gMAePTRR9GkSRN06NABYWFh+OOPP8rdVrNmzfDZZ58hOTkZrVu3xl9//YXnnnvOoc3IkSPx0Ucf4bPPPkNiYiJuv/12HD58WF7/4YcfYu3atYiJiamwV2bMmDH46aef5JkWy+Lr63vVEyrv27fP4WcQGRlZqde1TZMmTZy2s2PHDgDWXmX713mDBg0wdOhQNGjQQP59rYhGo8H48ePx3nvvyedCW7lyJerXr4+uXbtWOisRUW0gicoc7UpEVaq4uBjHjh1DmWkn+QAAl/dJREFUfHx8uQfNVxeTJ0/GjBkzsHbtWtx8882KZNi5cyd69eqF8+fPOx3/U9q8efMwbtw4LFu2rMKJIIjuvfdetGvXDi+//LLSUW64m2++GU899ZSiQx6p9qpJ75FUc7HnioiqxJQpU/DJJ59g27ZtTpM13CgmkwmzZs26amEFWHskli5ditTUVKdhckT23n//fadp1GuDCxcuYMiQIfJpD4iIyBl7rog8CL+VIyIiKhvfI6k6YM8VERERERGRG7C4IiIiIiIicgMWV0RERERERG7A4oqIiIiIiMgNWFwRERERERG5AYsrIiIiIiIiN2BxRURERERE5AYsroiIiCph3rx56Nevn9Ixap05c+bgjjvuUDoGEVGFWFwRkVucOnUKo0ePRlRUFHQ6HWJjYzFhwgRcvHhR6WgAgBMnTsDb2xv5+fkAgKysLDz99NOIjY2FTqdDVFQURo8ejZMnTyqa8/jx4xgzZgzi4+Ph7e2NBg0aYPLkySgpKanwfg8//DAkSXK6JCYmym2mTZuGjh07wt/fH+Hh4Rg8eDAOHjzosJ24uDj5vmq1GlFRURgzZgyys7Mr3P/ChQtRp06da37cZT2ewYMHu217VyNJElasWHHVdsXFxXjttdcwefJkedkbb7zh8JzFxMRg7NixyMrKcrp/UVERgoODERoaCoPB4LTe/vn39fVFu3bt8N1331WYyXafpUuXOq1LTEyEJElYuHBhmfuwv0yfPt3hsZR3Acp/vQ0YMMBh/1u2bMFtt92GoKAgeHl5oWXLlpgxYwbMZrNDO/ttBAQEoGPHjli5cqVDm9GjR2Pnzp3YtGlThc8HEZGSWFwR0XVLS0tDhw4dcPjwYSxZsgRHjhzBnDlzsG7dOnTu3LnMD5k32sqVK9GzZ0/4+fkhKysLN998M3777TfMmTMHR44cwdKlS3HkyBF07NgRaWlpiuU8cOAALBYLPv/8c+zbtw8zZ87EnDlz8Morr1R4v48//hgZGRny5dSpUwgODsa9994rt9mwYQOSkpKwbds2rF27FkajEf369UNBQYHDtt58801kZGTg5MmTWLx4MTZu3IinnnqqSh7v9TIajTd0f//9738REBCALl26OCxPTEyUn7MFCxbgl19+wRNPPOF0///9739ITExE06ZNyy3mbM//rl270LFjRwwbNgxbtmypMFdMTAwWLFjgsGzbtm3IzMyEr69vufuwvzz55JN47rnnHJZFR0c7tbUZMGCA0zaWLFkir//+++/RvXt3REdHY/369Thw4AAmTJiAt99+G/fddx+EEA6ZFixYgIyMDPz999/o0qUL7rnnHvz777/yep1Oh/vvvx+ffPJJhc8FEZGiBBF5jKKiIrF//35RVFSkdJRKGTBggIiOjhaFhYUOyzMyMoSPj494/PHHhRBCzJo1SyQmJsrrv//+ewFAzJ49W17Wu3dv8eqrr8q3V6xYIdq2bSv0er2Ij48Xb7zxhjAajfJ6AOLLL78UgwcPFt7e3qJhw4Zi5cqVThl79eol7+fxxx8Xvr6+IiMjw6FNYWGhqFevnhgwYIAQQogffvhBBAYGCpPJJIQQYteuXQKAePHFF+X7jBkzRowYMUK+vWnTJnHrrbcKLy8vER0dLZ588kmRn58vr4+NjRXvvPOOGDVqlPDz8xMxMTHi888/r/D5fe+990R8fHyFbUr7/vvvhSRJ4vjx4+W2OXfunAAgNmzY4JBv5syZDu3eeust0bx58wr3t2DBAhEYGCjfnjx5smjdurX4+uuvRWxsrAgICBDDhg0Tubm5cpvvvvtOtGjRQnh5eYng4GDRu3dvkZ+fLyZPniwAOFzWr18vjh07JgCIpUuXim7dugm9Xi8WLFgg78vezJkzRWxsrMOyefPmiebNmwudTiciIiJEUlKS/Jjt91X6fvYGDhwonnvuOYdlZe1/4sSJIigoyOn+PXr0EHPmzBGzZ88Wffv2dVpf+vk3Go3Cx8dHvPTSS+Vmio2NFS+99JLQ6/Xi5MmT8vJHH31UPPnkkyIwMFAsWLCg3H1UpLy2I0eOFHfeeWe598vPzxchISFiyJAhTutWrVol/xxtAIjvv/9evp2bmysAiI8//tjhvhs2bBA6nc7pbw3VDtX1PZJqF/ZcEVUHJQXlX4zFlWhb5FrbSsjKysKaNWswbtw4eHt7O6yLiIjAiBEjsGzZMggh0L17d+zfvx/nz58HYO1JCQ0NRUpKCgBrL8TWrVvRo0cPAMCmTZvw0EMPYcKECdi/fz8+//xzLFy4EO+8847DfqZMmYKhQ4fin3/+wW233YYRI0Y49JZdunQJmzdvxqBBg2CxWLB06VKMGDECERERDtvx9vbGuHHjsGbNGmRlZaFr167Iy8vDrl27ysxrW2bLe/ToUQwYMAB33303/vnnHyxbtgybN2/G+PHjHfbz4YcfokOHDti1axfGjRuHJ554wml4nr2cnBwEBwdX/IMoZd68eejTpw9iY2Mr3C6ACrednp6OH374AZ06darU/gHr87FixQqsXr0aq1evxoYNGzB9+nQAQEZGBoYPH47Ro0cjNTUVKSkpGDJkCIQQeO655zB06FCHnpFbbrlF3u5LL72ECRMmIDU1Ff3793cpy+zZs5GUlISxY8fi33//xapVq9CwYUMAwPbt2wFc6Tmx3S7L5s2b0aFDhwr3dfz4caxZswY6nc7p+di6dSuGDh2KoUOHYtOmTThx4kSF29JoNNBqtVcdFlq3bl30798fX331FQCgsLAQy5Ytw+jRoyu8X1X59ddfcfHiRTz33HNO6+644w40btzYoZfLnslkwrx58wDA6Tns0KEDTCYT/vzzT/eHJiJyB6WrOyK6otxv5SYHlH/55h7Htm9HlN92/m2Obd+NL7tdJWzbts3pW2d7M2bMEADE2bNnhcViESEhIeK7774TQgjRpk0bMW3aNBERESGEEGLz5s1Cq9WKgoICIYS1F2vq1KkO21u0aJGIjIyUbwMQkyZNkm/n5+cLAOLnn3+Wly1evFh06NBBCCFEZmamAFDuN/fLly8XAMSff/4phBCiXbt24v333xdCCDF48GDxzjvvCJ1OJ/Ly8sTp06cFAHHo0CEhhLUXa+zYsQ7b27Rpk1CpVPLPNDY2VjzwwAPyeovFIsLDwx167+wdPnxYBAQEiC+++KLM9WVJT08XarVaLFu2rNw2ZrNZDBw4UHTp0sVheWxsrNDpdMLX11d4eXkJAKJTp04iOzu7wn2W1XPl4+Pj0FP1/PPPi06dOgkhhNixY4cAUG7PWlk9I7aeq48++shhuSs9V1FRUQ49oqVV9Bq2yc7OFgDExo0bnfavUqkcnjMAYsaMGQ7tXnnlFTF48GD59p133ikmT57s0Ma+p8hgMIipU6cKAGL16tXl5rLdZ8WKFaJBgwbCYrGIr776SrRt21YIIcrsubL9jO0vpR9X6Tz2Ro4cKdRqtdM23nnnHSGEENOnTxcAyn3dDBo0SDRr1ky+DUB4eXkJX19foVKpBAARFxcnLl686HTfoKAgsXDhwnKfD6q52HNF1QF7rojILUSp4ydK0+l0kCQJ3bp1Q0pKCi5duoT9+/dj3LhxMBgMOHDgADZs2ICOHTvCx8cHALBnzx68+eab8PPzky+PPvooMjIyUFhYKG+7VatW8nVfX18EBATg3Llz8rKVK1di0KBBlc4LAN27d0dKSgqEENi0aROGDBmCZs2aYfPmzdiwYQOioqLQqFEjOe/ChQsd8vbv3x8WiwXHjh0rM68kSYiIiHDIa5Oeno4BAwbg3nvvxaOPPiovt9/+448/7nS/r776CnXq1KlwQoikpCTs3bu3zEkQnn/+eezevRv//PMP1q1bBwAYOHCgPAnB1fZvExcXB39/f/l2ZGSk/Dhbt26N3r17o2XLlrj33nvx5ZdfXnXSDJur9RyVdu7cOZw5cwa9e/eu1P1KKyqy9vx6eXk5rWvSpAl2796N7du348UXX0T//v3x5JNPyuvNZjO++uorPPDAA/KyBx54AAsXLoTFYnHY1osvvgg/Pz/4+Pjg3XffxfTp0zFw4EBMnTrV4bkvPfnKwIEDkZ+fj40bN2L+/PkV9lrZfsb2l8o+rz179nTaRunXw9V+z+zNnDkTu3fvxs8//4zmzZtj7ty5Zfaqent7O/z+ExF5Eo3SAYjIBa+cKX+dpHa8/fyRCtqW+j7l6X/LblcJDRs2hCRJSE1NxV133eW0PjU1FWFhYfJMcj169MAXX3yBTZs2oW3btggICJALrg0bNqB79+7yffPz8zFlyhQMGTLEabv2H3C1Wq3DOkmS5A+sJSUl+OWXX+QJIWxZUlNTy3w8qamp0Gg0iI+Pl/POnz8fe/bsgVarRdOmTdGjRw+kpKQgOzvbKe9jjz1W5uQP9evXdymvzZkzZ9CzZ0/ccsst+OKLLxzW7d69W74eEBDgsE4Igfnz5+PBBx90GlJlM378eKxevRobN25EdHS00/rQ0FB5yFyjRo3w0UcfoXPnzli/fj369OlT4f7tVfQ41Wo11q5diy1btuDXX3/FrFmz8Oqrr+LPP/+Un/vylJ6gQaVSOX2It5/oovRw1WsVEhICSZLKLAJ1Op38nNmKoSlTpuCtt94CAKxZswbp6ekYNmyYw/3MZjPWrVuHvn37ysuef/55PPzww/Dz80PdunXlGfoef/xxDB06VG4XFRXlsC2NRoMHH3wQkydPxp//z969x9WU/f8Df51uOl2VoiIV6aJECqWhDM0JQ2RIE6KYjDtljGsY4YMG437p4pKJPiSjUdI4SZoUEjpdlQxFKUa6UK3fH/3O/rQ759RpMI3vrOfjsR8P7bX22u+9z97n7Pdeey+pqYiKipK4Lc0/479KWVlZYhsmJiYAms6n5o90CgkEAvTt25c1T0dHB8bGxjA2NkZoaCjGjBmDrKwsdO3alVWvoqIC2tra7xU7RVHUx0J7rijqU6CgLHmSV2xHXa50dduhS5cucHZ2xv79+5k7+0KlpaUIDw/HzJkzmXnC964iIyOZd5WcnJxw5coVJCcnM/MAYODAgcjJyWEuuJpPMjLSfX3x+XxoaGigf//+AJouxKdMmYJTp06htLSUVbempgb79+/HxIkToa6uDgDMe1c7d+5kEilhcsXn80XizcrKEhuvpERHnCdPnsDJyQk2NjYIDQ0V2dbm7ba88ExMTER+fj58fHxE2iWEYMGCBYiKisJvv/3WZhIjJCsry+yfttbfHhwOBw4ODtiwYQPu3LkDBQUFJiFQUFAQGa5bEm1tbZSWlrISrOYJoKqqKgwNDZleOHHk5eXbXJ+CggL69u2LrKysNmNas2YNduzYgadPm26MBAcHY+rUqSI9PVOnTmXeLxISJj46OjpMYgU0vRvXfN/LyYneH/X29kZiYiJcXV2hoaHRZpwfyxdffAFNTU0EBQWJlF24cAF5eXnw8PCQuPzgwYNhY2Mj8n5lQUEBamtrYW1t/cFjpiiK+hBockVR1Hvbu3cv6urqwOPxcO3aNTx+/BixsbFwdnaGiYkJ1q1bx9S1srKChoYGTp06xUquzp8/j7q6OtYQ1+vWrcPx48exYcMGPHjwAAKBABEREVizZo3UsV24cEHkkcDAwEDo6OjA2dkZly5dwuPHj3Ht2jXweDzIyMhg9+7dTF0NDQ1YWVkhPDyciXf48OG4ffs2cnNzWT1XK1aswI0bN7BgwQJkZGQgLy8P0dHRIgNatEaYWPXs2RM7duxAWVkZSktLRRJBSYKDgzFkyBBYWlqKlM2fPx8nT57EqVOnoKqqyrTbMil+/fo1SktLUVJSgps3b2L58uXQ1tYW2wPxV6WmpmLz5s1IT09HcXExzp07h7KyMpibmwNoeqQwMzMTOTk5KC8vb3XIdScnJ5SVlWHbtm0oKCjAvn37cOnSJVad9evXIygoCD/99BPy8vJw+/Zt7NmzhykXJl+lpaWtPp7I4/Fw/fr1NrfP3t4eVlZW2Lx5M8rKyvDLL7/Ay8sLlpaWrGnGjBk4f/78B/vvCszNzVFeXi4yLHtLws+4+fTnn3+2a111dXUibZSXlwNo6tU6dOgQoqOj8c033yAzMxNFRUUIDg7GzJkz8dVXX7F64cRZsmQJDh06hCdPnjDzkpKS0KtXL/Tu3btdsVIURf1tOu51L4qiWvqUX9YtLCwkXl5epFu3boTD4RAAxM3NjRmcojlXV1ciJydHXr9+TQhpGlxBQ0OD2NnZidSNjY0lQ4cOJVwul6ipqZHBgwezBneAmIEImr/Ar6+vT+Lj40XaLSsrIwsXLiT6+vpEVlaWACBDhw4V+wL94sWLCQAiEAiYef3792cG4mju5s2bxNnZmaioqBBlZWViZWXFvORPiPgBAvr3788MbBAaGioyDLlwasvLly8Jl8uVOPiFpHZbDnbQvExbW5uMGTOG3Llzp9V1SxqKvbnmg0xkZWURHo9HtLW1SadOnYiJiQnZs2cPU/f58+fMfkSLodjFxXLgwAGir69PlJWVyYwZM0hgYKDIkOoHDx4kpqamRF5enujq6pKFCxcyZRcuXCDGxsZETk6u1aHYHzx4QLhcLnn58mWr20oIIT///DPp1KkTWb9+PencuTN5+/atSJ26ujrSuXNnZsjx9gyTLtTWMuIGtBB3HPj6+krdtpeXl9g2TE1NWfWuXbtGeDweUVNTIwoKCsTCwoLs2LGD+e8NhMSdx42NjcTMzIx8++23zLwvvviCbNmyRfLOoP5P+5R/I6l/Dw4h7XjblKKoj6q2thaFhYUwMjIS+9L8pyQgIAA//vgj4uPjYWdn1yEx3L59G59//jnKyspE3v9pKTg4GPPmzcPp06dbHQiCoiZPnoyBAwdi5cqVHR3Kv8qDBw/w+eefIzc3l3lsl/p3+b/0G0n930UfC6Qo6qPYsGEDfvrpJ/z+++8igzX8Xerr67Fnz542EysA8PHxQUREBAQCgchjchTV3Pbt26GiotLRYfzrlJSU4Pjx4zSxoijqH432XFHUPwi9K0dRFEVR4tHfSOpTQHuuKIqiKIqiKIqiPgCaXFEURVEURVEURX0ANLmiKIqiKIqiKIr6AGhyRVEURVEURVEU9QHQ5IqiKIqiKIqiKOoDoMkVRVEURVEURVHUB0CTK4qiKIqiKIqiqA+AJlcURVEU9f/l5ORAR0cHr1+/7uhQ/nZ2dnY4e/ZsR4dBURT1SaPJFUVRH8Tjx4/h7e0NPT09KCgowMDAAIsXL8aLFy86OjQAwKNHj8DlclFVVQUAqKiowJIlS2BgYAAFBQXo6enB29sbxcXFHRpnUVERfHx8YGRkBC6Xi969eyMgIABv375tdbmZM2eCw+GITBYWFhLrdOnSBS4uLsjMzGwzJg6Hg4yMjA+xiQgLC0Pnzp0/SFvScHJywpIlS6Squ3LlSixcuBCqqqoAAD6fDw6HAw0NDdTW1rLqpqWlMftSSFhf3FRaWgpDQ0OJ5RwOBzNnzgQAieURERHMuhoaGrBz507069cPioqK0NDQwOjRo5GcnMyKMywsjFleRkYGurq6cHd3FznW16xZg++//x6NjY3S7lqKoiiqBZpcURT13h4+fAhbW1vk5eXh559/Rn5+Pg4ePIiEhATY29ujoqKio0NEdHQ0RowYARUVFVRUVMDOzg5XrlzBwYMHkZ+fj4iICOTn52PQoEF4+PBhh8WZnZ2NxsZGHDp0CA8ePMDOnTtx8OBBrFq1qtXldu/ejZKSEmZ6/PgxNDU1MXnyZFY9FxcXpk5CQgLk5OTw5ZdffsxN+svaSig/tOLiYly8eJFJcJpTVVVFVFQUa15wcDB69uwptq2cnBzW51FSUoKuXbsiLS2N+VvYS9S87u7du5k2QkNDRdqYMGECAIAQgqlTp2Ljxo1YvHgxBAIB+Hw+9PX14eTkhPPnz7PiUVNTQ0lJCZ48eYKzZ88iJydH5NgYPXo0Xr9+jUuXLrVzz1EURVEMQlHUP0ZNTQ3JysoiNTU1HR1Ku7i4uJAePXqQ6upq1vySkhKipKRE5s6dSwghZM+ePcTCwoIpj4qKIgDIgQMHmHkjR44kq1evZv4+f/48sba2Jp06dSJGRkZk/fr15N27d0w5AHLkyBEyYcIEwuVyibGxMYmOjhaJ8fPPP2fWM3fuXKKsrExKSkpYdaqrq0n37t2Ji4sLIYSQX375hairq5P6+npCCCF37twhAMiKFSuYZXx8fIinpyfzd1JSEvnss8+IoqIi6dGjB1m4cCGpqqpiyg0MDEhgYCCZNWsWUVFRIfr6+uTQoUOt7t9t27YRIyOjVuu0FBUVRTgcDikqKmLmeXl5EVdXV1a9pKQkAoA8f/5cYluFhYUEALlz5w4hhJCrV68SAOTKlSvExsaGcLlcYm9vT7Kzs5llMjIyiJOTE1FRUSGqqqpk4MCBJC0tjVm2+RQQEMDsm40bN5Lp06cTVVVV4uXlxdSvrKxk2hZ+DoWFhcy869evE0dHR8Llcknnzp3JF198QSoqKoiXl5fI+pov19z27duJra0ta55w/WvWrCGjRo1i5ldXVxN1dXWydu1a0vynVFy8krRWFwCJioqSuGxERAQBQC5cuCBS5ubmRrp06cIcd6GhoURdXZ1V56effiIAyKtXr1jzZ82aRaZNm9Zm7BTVET7V30jq34X2XFHUPxghBNXvqjtkIoRIFWNFRQXi4uIwb948cLlcVpmOjg48PT1x+vRpEELg6OiIrKwslJWVAQASExOhpaUFPp8PAHj37h1SUlLg5OQEAEhKSsKMGTOwePFiZGVl4dChQwgLC0NgYCBrPRs2bMCUKVOQmZmJMWPGwNPTk9Vb9vLlS1y/fh3jx49HY2MjIiIi4OnpCR0dHVY7XC4X8+bNQ1xcHCoqKjBs2DC8fv0ad+7cERuvcJ4w3oKCAri4uGDSpEnIzMzE6dOncf36dSxYsIC1nqCgINja2uLOnTuYN28evv32W+Tk5Ejcx69evYKmpmbrH0QLwcHBGDVqFAwMDCTWqaqqwsmTJ2FsbIwuXbq0q30AWL16NYKCgpCeng45OTl4e3szZZ6enujRowfS0tJw69YtfP/995CXl8fQoUOxa9cupielpKQE/v7+zHI7duxA//79cefOHaxdu1aqODIyMjBy5Ej07dsXKSkpuH79OsaNG4eGhgbs3r0b9vb2mDNnDrM+fX19se0kJSXB1tZWbNn06dORlJTEPEp39uxZGBoaYuDAgdLurg/q1KlTMDExwbhx40TK/Pz88OLFC8THx4td9vnz54iKioKsrCxkZWVZZYMHD0ZSUtJHiZmiKOrfQK6jA6AoSrKa+hoMOTWkQ9ad+nUqlOSV2qyXl5cHQgjMzc3Flpubm6OyshJlZWWwtLSEpqYmEhMT8dVXX4HP58PPz495FOrmzZt49+4dhg4dCqApafr+++/h5eUFAOjVqxd++OEHfPfddwgICGDWMXPmTHh4eAAANm/ejJ9++gk3b96Ei4sLAODXX3+FlZUV9PT08OzZM7x8+bLVeAkhyM/Px+DBgzFgwADw+XzY2tqCz+dj6dKl2LBhA6qqqvDq1Svk5+fD0dERALBlyxZ4enoy7/f06dMHP/30ExwdHXHgwAEoKioCAMaMGYN58+YBAFasWIGdO3fi6tWrMDU1FYknPz8fe/bswY4dO9r8LISePn2KS5cu4dSpUyJlFy9ehIqKCgDgzZs30NXVxcWLFyEj0/57bYGBgcy2f//99xg7dixqa2uhqKiI4uJiLF++HGZmZgCa9oWQuro6OByOSHILAJ9//jn8/PyYvx8/ftxmHNu2bYOtrS3279/PzGv+rpmCggKUlJTErq+5R48eSUyuunbtitGjRyMsLAzr1q1DSEgIK5lsqUePHqy/DQwM8ODBgza3pTkPDw+R5CcrKws9e/ZEbm5uq8cwAOTm5jLzXr16BRUVlaYbNtXVAIBFixZBWVmZtayenh4eP36MxsbGv3RMUBRF/dvRb06Koj6Itnq6FBQUwOFwMHz4cPD5fLx8+RJZWVmYN28e6urqkJ2djcTERAwaNAhKSk1J3d27d7Fx40aoqKgwk7AHQniBCABWVlbMv5WVlaGmpobnz58z86KjozF+/Ph2xwsAjo6O4PP5IIQgKSkJbm5uMDc3x/Xr15GYmAg9PT0mcbh79y7CwsJY8fJ4PDQ2NqKwsFBsvMIko3m8Qk+ePIGLiwsmT56MOXPmMPObtz937lyR5Y4dO4bOnTsz7+c0N2LECGRkZCAjIwM3b94Ej8fD6NGj8ejRIwBN790I226eoIjTfDt0dXUBgNmOZcuWYfbs2Rg1ahS2bt2KgoKCVtsSkpTctEbYc/W+ampqmARYHG9vb4SFheHhw4dISUmBp6enxLpJSUnMfs7IyMCvv/7a7nh27tzJaiMjIwN6enpMubS9y0DTO2MZGRlIT09HUFAQBg4cKNIDDDT13jY2NqKurq7d8VIURVG054qi/tG4clykfp3aYeuWhrGxMTgcDgQCASZOnChSLhAIoK2tzYwO5+TkhMOHDyMpKQnW1tZQU1NjEq7ExESmJwRoemxtw4YNcHNzE2m3+UWwvLw8q4zD4TAjnr19+xaxsbHMgBDCWAQCgdjtEQgEkJOTg5GRERNvSEgI7t69C3l5eZiZmcHJyQl8Ph+VlZUi8fr6+mLRokUi7TYf+KC1eIWePn2KESNGYOjQoTh8+DCrrPmofWpqaqwyQghCQkIwffp0JkFsTllZGcbGxszfR48ehbq6Oo4cOYJNmzbh6NGjqKmpERtnS83LhSPmCbdj/fr1+PrrrxETE4NLly4hICAAERERYo+RlvE1J+w9aZ5IvHv3jlWn5eOof5WWlhYqKysllo8ePRrffPMNfHx8MG7cuFYfpTQyMnrvERF1dHRYn1VzJiYmrR7DwjpCMjIyTFvm5uYoKCjAt99+ixMnTrCWraiogLKy8gfbpxRFUf82tOeKov7BOBwOlOSVOmRqPrx0a7p06QJnZ2fs37+fuSgXKi0tRXh4OGv0NeF7V5GRkcy7Sk5OTrhy5QqSk5OZeQAwcOBA5OTkwNjYWGSS9pElPp8PDQ0N9O/fH0DTReaUKVNw6tQplJaWsurW1NRg//79mDhxItTV1QGAee9q586dTCIlTK74fL5IvFlZWWLjFZfoSPLkyRM4OTnBxsYGoaGhItvavN2uXbuyyhITE5Gfnw8fHx+p1iUcnlv42XXv3p1pu7X3taRhYmKCpUuX4vLly3Bzc0NoaCiApl7BhoYGqdrQ1tYGAJSUlDDzWg4Jb2VlhYSEBIltSLs+a2trZGVlSSyXk5PDjBkzwOfzW30k8O8wdepU5OXl4ZdffhEpCwoKYs5LSb7//nucPn0at2/fZs2/f/8+rK2tP3i8FEVR/xY0uaIo6r3t3bsXdXV14PF4uHbtGh4/fozY2Fg4OzvDxMQE69atY+paWVlBQ0MDp06dYiVX58+fR11dHRwcHJi669atw/Hjx7FhwwY8ePAAAoEAERERWLNmjdSxXbhwQeSRwMDAQOjo6MDZ2RmXLl3C48ePce3aNfB4PMjIyLCGw9bQ0ICVlRXCw8OZeIcPH47bt28jNzeX1XO1YsUK3LhxAwsWLEBGRgby8vIQHR0tMqBFa4SJVc+ePbFjxw6UlZWhtLRUJBGUJDg4GEOGDIGlpaXY8rq6OqY9gUCAhQsXoqqqSuzACH9VTU0NFixYAD6fj0ePHiE5ORlpaWnMu0CGhoaoqqpCQkICysvLWY94tmRsbAx9fX2sX78eeXl5iImJQVBQEKvOypUrkZaWhnnz5iEzMxPZ2dk4cOAAysvLmfWlpqaiqKgI5eXlEv8fJx6Ph5SUlFYTsR9++AFlZWXg8Xit7oPnz58z+1k4texxa8vLly9F2njz5g2ApuRq4sSJ8PLyQnBwMIqKipCZmQlfX19cuHABR48eFekFbE5fXx8TJ05knZtA0+OMX3zxRbvipCiKoprpmEEKKYoS51MeZrawsJB4eXmRbt26EQ6HQwAQNzc38ubNG5G6rq6uRE5Ojrx+/ZoQQkhDQwPR0NAgdnZ2InVjY2PJ0KFDCZfLJWpqamTw4MHk8OHDTDnEDFmtrq5OQkNDCSGE6Ovrk/j4eJF2y8rKyMKFC4m+vj6RlZUlAMjQoUPJixcvROouXryYACACgYCZ179/f6KjoyNS9+bNm8TZ2ZmoqKgQZWVlYmVlRQIDA5lyAwMDsnPnTtYy/fv3Z4YjDw0NFRk6XDi15eXLl4TL5bL2T3MthyVXVVUlgwYNIv/9739bbVfSUOyShkevq6sjU6dOJfr6+kRBQYHo6emRBQsWsI7ruXPnki5duogMxd5y3xDSNMx6v379iKKiIhk2bBiJjIwUGVKdz+eToUOHkk6dOpHOnTsTHo/HxJeTk0Ps7OwIl8ttdSj2d+/eET09PRIbG8vMa2todeF/J9CyvrgpJSWFtWxbQ7GLm7Zs2cKKd/v27cTCwoIoKCgQNTU1wuPxyPXr11ltiRuKnRBCUlJSCACSmppKCCHkjz/+IPLy8uTx48dit5WiOtqn/BtJ/XtwCGnHG7EURX1UtbW1KCwshJGRUasv1n8KAgIC8OOPPyI+Ph52dnYdEsPt27fx+eefo6ysrM33h4KDgzFv3jycPn1a7EAQ1L/Dvn37cOHCBcTFxXV0KH+7FStWoLKyUuQdP4r6p/i/9BtJ/d9FB7SgKOqj2LBhAwwNDfH7779j8ODBHTKsc319Pfbs2dNmYgUAPj4+0NTUhEAgAI/Hoy/0/0v5+vri5cuXeP36NVRVVTs6nL9V165dsWzZso4Og6Io6pNGe64o6h+E3pWjKIqiKPHobyT1KaADWlAURVEURVEURX0ANLmiKIqiKIqiKIr6AGhyRVEURVEURVEU9QHQ5IqiKIqiKIqiKOoDoMkVRVEURVEURVHUB0CTK4qiKIqiKIqiqA+AJlcURVEURVEURVEfAE2uKIqiKOr/y8nJgY6ODl6/ft3RofyrZGVloUePHnjz5k1Hh0JRFPVeaHJFUdQH8fjxY3h7e0NPTw8KCgowMDDA4sWL8eLFi44ODQDw6NEjcLlcVFVVAQAqKiqwZMkSGBgYQEFBAXp6evD29kZxcXGHxllUVAQfHx8YGRmBy+Wid+/eCAgIwNu3b1tdbubMmeBwOCKThYWFxDpdunSBi4sLMjMz24yJw+EgIyPjQ2wiwsLC0Llz5w/SljScnJywZMkSqequXLkSCxcuhKqqKgCAz+ez9pm2tjbGjBmDe/fuiV2ex+NBVlYWaWlpImXN97+CggKMjY2xceNG1NfXS4xHuMzcuXNFyubPnw8Oh4OZM2eKXUfzycXFRWRbxE18Ph9hYWFiy1r+p63SnvNOTk6sNkxMTLBlyxYQQpg6ffv2hZ2dHX788UeJ+4KiKOpTQJMriqLe28OHD2Fra4u8vDz8/PPPyM/Px8GDB5GQkAB7e3tUVFR0dIiIjo7GiBEjoKKigoqKCtjZ2eHKlSs4ePAg8vPzERERgfz8fAwaNAgPHz7ssDizs7PR2NiIQ4cO4cGDB9i5cycOHjyIVatWtbrc7t27UVJSwkyPHz+GpqYmJk+ezKrn4uLC1ElISICcnBy+/PLLj7lJf1lbCeWHVlxcjIsXL7KSFaGcnByUlJQgLi4OdXV1GDt2rEh8xcXFuHHjBhYsWICQkBCx6xDu/7y8PPj5+WH9+vXYvn17q3Hp6+sjIiICNTU1zLza2lqcOnUKPXv2lLiO5tPPP/+MoUOHsuZNmTJFpO7QoUMBAGpqaiJtPHr0iFlHe8/5OXPmoKSkBDk5OVi5ciXWrVuHgwcPsurMmjULBw4caDXZpCiK+scjFEX9Y9TU1JCsrCxSU1PT0aG0i4uLC+nRoweprq5mzS8pKSFKSkpk7ty5hBBC9uzZQywsLJjyqKgoAoAcOHCAmTdy5EiyevVq5u/z588Ta2tr0qlTJ2JkZETWr19P3r17x5QDIEeOHCETJkwgXC6XGBsbk+joaJEYP//8c2Y9c+fOJcrKyqSkpIRVp7q6mnTv3p24uLgQQgj55ZdfiLq6OqmvryeEEHLnzh0CgKxYsYJZxsfHh3h6ejJ/JyUlkc8++4woKiqSHj16kIULF5Kqqiqm3MDAgAQGBpJZs2YRFRUVoq+vTw4dOtTq/t22bRsxMjJqtU5LUVFRhMPhkKKiImael5cXcXV1ZdVLSkoiAMjz588ltlVYWEgAkDt37hBCCLl69SoBQK5cuUJsbGwIl8sl9vb2JDs7m1kmIyODODk5ERUVFaKqqkoGDhxI0tLSmGWbTwEBAcy+2bhxI5k+fTpRVVUlXl5eTP3KykqmbeHnUFhYyMy7fv06cXR0JFwul3Tu3Jl88cUXpKKignh5eYmsr/lyzW3fvp3Y2tqy5olb/4ULFwgAcvfuXVbd9evXk6lTpxKBQEDU1dVFzgdx+9/Z2ZnY2dlJ3PfCZSwtLcnJkyeZ+eHh4cTKyoq4uroSLy+vVtfRVtsthYaGEnV19VaXlfacJ4QQR0dHsnjxYla9gQMHkokTJ7Lm1dXVkU6dOpErV65IFT/17/Op/kZS/y6054qiPgGN1dWSp7o66evW1kpVtz0qKioQFxeHefPmgcvlssp0dHTg6emJ06dPgxACR0dHZGVloaysDACQmJgILS0t8Pl8AMC7d++QkpICJycnAEBSUhJmzJiBxYsXIysrC4cOHUJYWBgCAwNZ69mwYQOmTJmCzMxMjBkzBp6enqw75y9fvsT169cxfvx4NDY2IiIiAp6entDR0WG1w+VyMW/ePMTFxaGiogLDhg3D69evcefOHbHxCucJ4y0oKICLiwsmTZqEzMxMnD59GtevX8eCBQtY6wkKCoKtrS3u3LmDefPm4dtvv0VOTo7Effzq1Stoamq2/kG0EBwcjFGjRsHAwEBinaqqKpw8eRLGxsbo0qVLu9oHgNWrVyMoKAjp6emQk5ODt7c3U+bp6YkePXogLS0Nt27dwvfffw95eXkMHToUu3btYvWM+Pv7M8vt2LED/fv3x507d7B27Vqp4sjIyMDIkSPRt29fpKSk4Pr16xg3bhwaGhqwe/du2NvbMz0nJSUl0NfXF9tOUlISbG1tW13Xq1evEBERAQBQUFBg5hNCEBoaimnTpsHMzAzGxsb473//22bsXC5Xqh46b29vhIaGMn+HhIRg1qxZbS73MbTnnG+JEIKkpCRkZ2ez9h/QtD8HDBiApKSkjxo/RVHUR9WxuR1FUc1JuiuXZWomcXr0zTesuoIB1hLrFk2bzqqbY2cvtl57/P777wQAiYqKElv+448/EgDk2bNnpLGxkXTp0oVERkYSQggZMGAA2bJlC9HR0SGENPU+yMvLkzdv3hBCmnqxNm/ezGrvxIkTRFdXl/kbAFmzZg3zd1VVFQFALl26xMwLDw9neiRKS0sJALJz506x8Z47d44AIKmpqYSQpjvs27dvJ4QQMmHCBBIYGEgUFBTI69evyR9//EEAkNzcXEJIUy/WNy0+j6SkJCIjI8N8pgYGBmTatGlMeWNjI+natSur9665vLw8oqamRg4fPiy2XJwnT54QWVlZcvr0adZ8Ly8vIisrS5SVlYmysjIBQHR1dcmtW7daba+1niuhmJgYAoDZTlVVVRIWFia2PUk9IwYGBmTChAmsedL0XHl4eBAHBweJ8YvrORGnf//+ZOPGjWLX33yfASDjx49n1bt8+TLR1tZmelV37txJHB0dWXWa9xQ1NjaS+Ph40qlTJ+Lv7y8xJuEyz58/J506dSJFRUWkqKiIKCoqkrKyMrE9V80/Y+EUGBgose2WQkNDWdssnIQ9uu055wlp2v/y8vJEWVmZyMvLEwBEUVGRJCcniyw7ceJEMnPmTIn7g/p3oz1X1KeA9lxRFPVBEDF3qZtTUFAAh8PB8OHDwefz8fLlS2RlZWHevHmoq6tDdnY2EhMTMWjQICgpKQEA7t69i40bN0JFRYWZhD0Q1c162KysrJh/KysrQ01NDc+fP2fmRUdHY/z48e2OFwAcHR3B5/OZO+5ubm4wNzfH9evXkZiYCD09PfTp04eJNywsjBUvj8dDY2MjCgsLxcbL4XCgo6PDilfoyZMncHFxweTJkzFnzhxmfvP2xQ10cOzYMXTu3BkTJkwQKRsxYgQyMjKQkZGBmzdvgsfjYfTo0cz7NKNHj2babj4YhjjNt0NXVxcAmO1YtmwZZs+ejVGjRmHr1q0oKChotS2htnqOxBH2XL2vmpoakUEbhJKSknDr1i2EhYXBxMRE5H2hkJAQuLu7Q05ODgDg4eGB5ORkke2+ePEiVFRUoKioiNGjR8Pd3R3r169HUlIS63MNDw9nLaetrY2xY8ciLCwMoaGhGDt2LLS0tMTG2vwzFk7ijpPWqKqqirRx9OhRVp22zqHmPD09kZGRgeTkZIwePRqrV69m3u9qjsvlss5tiqKoT41cRwdAUVTbTG/fklwoK8v60yT5uuS6Muz7KcYJV94nrKY2jI3B4XAgEAgwceJEkXKBQABtbW1mdDgnJyccPnwYSUlJsLa2hpqaGpNwJSYmwtHRkVm2qqoKGzZsgJubm0i7zS+C5eXlWWUcDgeNjY0AmgZFiI2NZQaEEMYiEAjEbo9AIICcnByMjIyYeENCQnD37l3Iy8vDzMwMTk5O4PP5qKysFInX19cXixYtEmm3+cADrcUr9PTpU4wYMQJDhw7F4cOHWWXNR+1TU1NjlRFCEBISgunTp4s8dgU0JZ/GxsbM30ePHoW6ujqOHDmCTZs24ejRo8zACS3jbKl5OYfDAQBmO9avX4+vv/4aMTExuHTpEgICAhARESH2GGkZX3My//+YbX4h/+7dO1adlo+m/VVaWlqorKwUW2ZkZITOnTvD1NQUz58/h7u7O65duwag6TG5qKgovHv3DgcOHGCWaWhoQEhICOsx1hEjRuDAgQPMCJXCZMzW1pb1uXbr1k0kBm9vb+YR03379kncjpaf8V8hIyMjsQ1pznkNDQ1oa2sz89TV1Zn2zpw5A2NjY9jZ2WHUqFGsZSsqKtC7d+/3ip2iKKoj0Z4rivoEyCgpSZ46dZK+bou78pLqtUeXLl3g7OyM/fv3s0YzA4DS0lKEh4ezRl8TvncVGRnJvKvk5OSEK1euIDk5mZkHAAMHDkROTg6MjY1FJhkZ6b6++Hw+NDQ00L9//6ZtlpHBlClTcOrUKZSWlrLq1tTUYP/+/Zg4cSLU1dUBgHnvaufOnUwiJUyu+Hy+SLxZWVli4xWX6Ejy5MkTODk5wcbGBqGhoSLb2rzdrl27ssoSExORn58PHx8fqdbF4XAgIyPDfHbdu3dn2m7tfS1pmJiYYOnSpbh8+TLc3NyYd4YUFBTQ0NAgVRvCC/SSkhJmXssh4a2srJCQkCCxDWnXZ21tjaysrDbrzZ8/H/fv30dUVBQAIDw8HD169MDdu3dZPT1BQUEICwtjrVuY+PTs2ZNJrICmBLH55yocCr45FxcXvH37Fu/evQOPx2szzo9FmnPe3d2dSbhbUlFRweLFi+Hv7y/S+3X//n1YW1t/tNgpiqI+NppcURT13vbu3Yu6ujrweDxcu3YNjx8/RmxsLJydnWFiYoJ169Yxda2srKChoYFTp06xkqvz58+jrq4ODg4OTN1169bh+PHj2LBhAx48eACBQICIiAisWbNG6tguXLgg8khgYGAgdHR04OzsjEuXLuHx48e4du0aeDweZGRksHv3bqauhoYGrKysEB4ezsQ7fPhw3L59G7m5uayeqxUrVjBDcWdkZCAvLw/R0dEiA1q0RphY9ezZEzt27EBZWRlKS0tFEkFJgoODMWTIEFhaWootr6urY9oTCARYuHAhqqqqMG7cOKljbEtNTQ0WLFgAPp+PR48eITk5GWlpaTA3NwcAGBoaoqqqCgkJCSgvL2/1MTBjY2Po6+tj/fr1yMvLQ0xMDIKCglh1Vq5cibS0NMybNw+ZmZnIzs7GgQMHUF5ezqwvNTUVRUVFKC8vF+klFOLxeEhJSWkzEVNSUsKcOXMQEBAAQgiCg4Px1VdfwdLSkjX5+PigvLwcsbGx7dl9EsnKykIgECArKwuyLXqsm2v+GQsn4b6QFiFEpI3S0lJm37V2znfv3l1k0JmWfH19kZubi7NnzzLzioqK8OTJE5HeLIqiqE8JTa4oinpvffr0QVpaGnr16oUpU6bAwMAAo0ePhomJCZKTk6GiosLU5XA4GDZsGDgcDj777DMATQmXmpoabG1tWY+F8Xg8XLx4EZcvX8agQYNgZ2eHnTt3tqtHRVxypaWlhd9//x0jRoyAr68vjIyM4OjoiIaGBmRkZDDvDwkJy4TJlaamJvr27QsdHR2Ympoy9aysrJCYmIjc3FwMGzYM1tbWWLduHfT09KSONz4+Hvn5+UhISECPHj2gq6vLTG159eoVzp4922qvVWxsLNPekCFDkJaWxupF/BBkZWXx4sULzJgxAyYmJpgyZQpGjx6NDRs2AACGDh2KuXPnwt3dHdra2ti2bZvEtuTl5fHzzz8jOzsbVlZW+M9//oNNmzax6piYmODy5cu4e/cuBg8eDHt7e0RHRzM9Q/7+/pCVlUXfvn2hra0t8T+KHj16NOTk5HDlStuPyy5YsAACgQDbtm3D3bt3MWnSJJE66urqGDlyJIKDg9tsT1pqamoij4K21PwzFk7Cc01af/75p0gburq6zDt1ffr0QXp6OnPO9+7dG9988w1GjBiBlJSUNke31NTUxIwZM7B+/XomYfv555/xxRdfvHePKUVRVEfikPa8kUpR1EdVW1uLwsJCGBkZSXyx/lMREBCAH3/8EfHx8bCzs+uQGG7fvo3PP/8cZWVlbb4/FBwcjHnz5uH06dNiB4Kg/h327duHCxcuIC4urqND+Vd5+/Yt+vTpg1OnTrF6rymquf9Lv5HU/110QAuKoj6KDRs2wNDQEL///jsGDx4s9TtSH1J9fT327NnTZmIFAD4+PtDU1IRAIACPx/tggyRQnxZfX1+8fPkSr1+/FvveE/VxFBcXY9WqVTSxoijqk0d7rijqH4TelaMoiqIo8ehvJPUpoO9cURRFURRFURRFfQA0uaIoiqIoiqIoivoAaHJFURRFURRFURT1AdDkiqIoiqIoiqIo6gOgyRVFURRFURRFUdQHQJMriqIoiqIoiqKoD4AmVxRFURRFURRFUR8ATa4oivqoZs6ciQkTJnRoDHw+HxwOBy9fvpRYZ/369RgwYMDfFlNHcnJywpIlSzo6jH+doqIicDgcZGRkSKwTFhaGzp07/20xfWwcDgfnz5//29b34sULdO3aFUVFRX/bOv8ppk6diqCgoI4Og6L+9WhyRVEUBcDf3x8JCQkdHcZfVlNTA2VlZeTn539SF+jjx49Hz549oaioCF1dXUyfPh1Pnz5tdZnDhw/DyckJampqbSbNzaWlpWHkyJHo3LkzNDQ0wOPxcPfuXaZcmIQLJy6XCwsLCxw+fFiq9vPz8zFr1iz06NEDnTp1gpGRETw8PJCeni7V8gDg7u6O3Nxcqet3pNLSUixcuBC9evVCp06doK+vj3Hjxn3w86g9N2gCAwPh6uoKQ0NDAP9LaGVlZfHkyRNW3ZKSEsjJyYHD4TDJmLC+uOn333+Hk5OTxHIOhwMnJycAgKGhodjyrVu3smI4duwYBg0aBCUlJaiqqsLR0REXL15k1Wl5XGpra2PMmDG4d+8eq96aNWsQGBiIV69eSbdjKYr6KGhyRVEUBUBFRQVdunTp6DD+svj4eBgYGMDY2LijQ2mXESNG4MyZM8jJycHZs2dRUFCAr776qtVlqqur4eLiglWrVkm9nqqqKri4uKBnz55ITU3F9evXoaqqCh6Ph3fv3rHq5uTkoKSkBFlZWfD19cW3337bZsKQnp4OGxsb5Obm4tChQ8jKykJUVBTMzMzg5+cndZxcLhddu3aVun5HKSoqgo2NDX777Tds374d9+7dQ2xsLEaMGIH58+d3SEzV1dUIDg6Gj4+PSFn37t1x/Phx1rxjx46he/fuYtu6cuUKSkpKWJONjQ3OnTvH/H3z5k2RuufOnWPa2Lhxo0gbCxcuZMr9/f3h6+sLd3d3ZGZm4ubNm/jss8/g6uqKvXv3isQkPC7j4uJQV1eHsWPH4u3bt0y5paUlevfujZMnT7Zvx1EU9WERiqL+MWpqakhWVhapqanp6FDaJTIyklhaWhJFRUWiqalJRo4cSaqqqgghhHh5eRFXV1eyfft2oqOjQzQ1Ncm8efPI27dvmeVra2uJn58f0dPTI0pKSmTw4MHk6tWrTHloaChRV1cnsbGxxMzMjCgrKxMej0eePn3K1AEgMhkYGBBCCLl69SoBQK5cuUJsbGwIl8sl9vb2JDs7m1k+ICCA9O/fX+I2/vLLL0RdXZ3U19cTQgi5c+cOAUBWrFjB1PHx8SGenp6EEELKy8vJ1KlTiZ6eHuFyucTS0pKcOnWK1aajoyNZuHAhWb58OdHQ0CDdunUjAQEBrDoCgYA4ODiQTp06EXNzcxIfH08AkKioKFY9b29vJhbh/pLE0dGRLF68mPn7+PHjxMbGhqioqJBu3boRDw8P8uzZM6ZcuP9iY2PJgAEDiKKiIhkxYgR59uwZ+fXXX4mZmRlRVVUlHh4e5M2bN8xyly5dIg4ODkRdXZ1oamqSsWPHkvz8fIlxEUJIdHQ04XA4rONDEmFclZWVbdZNS0sjAEhxcTEzLzMzkwAgeXl5rbbXu3dvsm3bNoltNzY2EgsLC2JjY0MaGhpEyoXtFRYWEgDk7NmzxMnJiXC5XGJlZUVu3LjB1G352QmPy+PHjxMDAwOipqZG3N3dyZ9//snUaWhoIJs3byaGhoZEUVGRWFlZkcjISKa8oqKCfP3110RLS4soKioSY2NjEhISwpQXFxeTyZMnE3V1daKhoUHGjx9PCgsLW92fo0ePJt27d2fOc3HbS0jTeXnkyBEyYcIEwuVyibGxMYmOjmbK6+vribe3NxO7iYkJ2bVrF2v7W57Xzb8bmouMjCTa2tqsecJ9vmbNGtKnTx9WmYmJCVm7di0BwGyvsP6dO3da3f626hoYGJCdO3dKXDYlJYUAID/99JNI2bJly4i8vDxzrIo7Li9cuEAAkLt377KW3bBhA/nss8/ajP1T9an+RlL/LrTniqL+wQgheFfX0CETIUSqGEtKSuDh4QFvb28IBALw+Xy4ubmxlr969SoKCgpw9epVHDt2DGFhYQgLC2PKFyxYgJSUFERERCAzMxOTJ0+Gi4sL8vLymDrV1dXYsWMHTpw4gWvXrqG4uBj+/v6sOIRTfn4+jI2NMXz4cFasq1evRlBQENLT0yEnJwdvb2+pP4thw4bh9evXuHPnDgAgMTERWlpa4PP5TJ3ExETmsaDa2lrY2NggJiYG9+/fxzfffIPp06czd7uFjh07BmVlZaSmpmLbtm3YuHEj4uPjAQANDQ2YMGEClJSUkJqaisOHD2P16tUisTU2NuLixYtwdXWVenuae/fuHX744QfcvXsX58+fR1FREWbOnClSb/369di7dy9u3LiBx48fY8qUKdi1axdOnTqFmJgYXL58GXv27GHqv3nzBsuWLUN6ejoSEhIgIyODiRMnorGxUWwcFRUVCA8Px9ChQyEvL/+XtkUSU1NTdOnSBcHBwXj79i1qamoQHBwMc3Nz5hGylgghiI2NRXFxMYYMGSKx7YyMDDx48AB+fn6QkRH9WW35iObq1avh7++PjIwMmJiYwMPDA/X19RLbLygowPnz53Hx4kVcvHgRiYmJrMfLtmzZguPHj+PgwYN48OABli5dimnTpiExMREAsHbtWmRlZeHSpUsQCAQ4cOAAtLS0ADR99jweD6qqqkhKSkJycjJUVFTg4uLC6hVprqKiArGxsZg/fz6UlZXb3N4NGzZgypQpyMzMxJgxY+Dp6YmKigoATcdujx49EBkZiaysLKxbtw6rVq3CmTNnADT17kyZMgUuLi7M+T106FCxcSUlJcHGxkZs2fjx41FZWYnr168DAK5fv47KykqMGzdO0m7/qH7++WeoqKjA19dXpMzPzw/v3r3D2bNnxS776tUrREREAAAUFBRYZYMHD8bNmzdRV1f34YOmKEoqch0dAEVRktW/bcThxYkdsu5vdjtCvpNsm/VKSkpQX18PNzc3GBgYAAD69evHqqOhoYG9e/dCVlYWZmZmGDt2LBISEjBnzhwUFxcjNDQUxcXF0NPTA9B0QRUbG4vQ0FBs3rwZQNNF4MGDB9G7d28ATQnZxo0bmXXo6OgAaLognjRpEtTV1XHo0CFWHIGBgXB0dAQAfP/99xg7dixqa2uhqKjY5naqq6tjwIAB4PP5sLW1BZ/Px9KlS7FhwwZUVVXh1atXyM/PZ9rv3r07K/lbuHAh4uLicObMGQwePJiZb2VlhYCAAABAnz59sHfvXiQkJMDZ2Rnx8fEoKCgAn89nti8wMBDOzs6s2H7//XcAaDUBaE3zJLNXr1746aefMGjQIFRVVUFFRYUp27RpExwcHAAAPj4+WLlyJQoKCtCrVy8AwFdffYWrV69ixYoVAIBJkyax1hMSEgJtbW1kZWXB0tKSmb9ixQrs3bsX1dXVsLOzE3nn5ENQVVUFn8/HhAkT8MMPPwBo2t9xcXGQk2P/FPbo0QMAUFdXh8bGRmzcuFEkUW9OeBPAzMxMqlj8/f0xduxYAE2Jh4WFBfLz8yUu39jYiLCwMKiqqgIApk+fjoSEBAQGBqKurg6bN2/GlStXYG9vD6DpM7x+/ToOHToER0dHFBcXw9raGra2tgDASiZPnz6NxsZGHD16FBwOBwAQGhqKzp07g8/n44svvhCJJz8/H4QQqbd35syZ8PDwAABs3rwZP/30E27evAkXFxfIy8tjw4YNTF0jIyOkpKTgzJkzmDJlClRUVMDlclFXV8ecA5I8evSI+Q5pSV5eHtOmTUNISAg+++wzhISEYNq0aRKT+KFDh4okylVVVVJtr9CKFSuwZs0a1rxLly5h2LBhyM3NRe/evUWSIwDQ09ODmpqayLt3wuPyzZs3AJoSxpafgZ6eHt6+fYvS0lLm+5iiqL8X7bmiKOq99O/fHyNHjkS/fv0wefJkHDlyBJWVlaw6FhYWkJX9X6Kmq6uL58+fAwDu3buHhoYGmJiYQEVFhZkSExNRUFDALKOkpMQkVi3baG7VqlVISUlBdHQ0uFwuq8zKyoq1PACxbSQlJbFiCQ8PBwA4OjqCz+eDEIKkpCS4ubnB3Nwc169fR2JiIvT09NCnTx8ATb1OP/zwA/r16wdNTU2oqKggLi4OxcXFEmNquV05OTnQ19dnXVQ2T8yEoqOj8eWXX4rtNZHGrVu3MG7cOPTs2ZN5qR5Aq7F269YNSkpKTGIlnNd8f+bl5cHDwwO9evWCmpoac1Hfst3ly5fjzp07uHz5MmRlZTFjxgype07FGT16NPPZWVhYAGga8MPHxwcODg74/fffkZycDEtLS4wdOxY1NTWs5ZOSkpCRkYGMjAwcPXoUmzdvxoEDBwAA4eHhrGMjKSmp3bFKexwKGRoaMomVcBlh/fz8fFRXV8PZ2ZkV1/Hjx5nz59tvv0VERAQGDBiA7777Djdu3GDaunv3LvLz86Gqqsosq6mpidraWhQUFIg9F95ne5WVlaGmpsba3n379sHGxgba2tpQUVHB4cOHRY4RadTU1LR6o8Tb2xuRkZEoLS1FZGRkqz3Xp0+fZo4B4dRey5cvF2lDmOACaPd+TEpKwq1btxAWFgYTExMcPHhQpI7wO6+6urrd8VIU9WHQniuK+geTU5DBN7sdO2zd0pCVlUV8fDxu3LjBPBa2evVqpKamwsjICABE7g5zOBzm0bCqqirIysri1q1brAQMAKvXRFwbLS9OTp48iZ07d4LP54t9Ub15G8K79OIeUbO1tWVdTHXr1g1A0xDmISEhuHv3LuTl5WFmZgYnJyfw+XxUVlYySQkAbN++Hbt378auXbvQr18/KCsrY8mSJSKPWrW2b6R14cIFkVHIpPXmzRvweDzweDyEh4dDW1sbxcXF4PF4rcbK4XDajH3cuHEwMDDAkSNHoKenh8bGRlhaWoq0q6WlBS0tLZiYmMDc3Bz6+vr4/fffmZ6Y9jp69CiTMAljPHXqFIqKipCSksIkoadOnYKGhgaio6MxdepUZnkjIyPm0TYLCwukpqYiMDAQ3377LcaPH8/qIezevTuys7MBANnZ2bC2tm4zPmmPQ3H1hcs0P38AICYmRuSY79SpE4CmZPPRo0f49ddfER8fj5EjR2L+/PnYsWMHqqqqYGNjw9xAaE5bWxsKCgoi58K7d+/A4XCY7W7P9raMPyIiAv7+/ggKCoK9vT1UVVWxfft2pKamStV2c1paWiI3dprr168fzMzM4OHhAXNzc1haWkpMmvT19d97cBgtLS2JbZiYmOD69et4+/atSO/V06dP8eeff8LExIQ1X3hcmpqa4vnz53B3d8e1a9dYdYSPW2pra79X7BRF/XU0uaKofzAOhyPVo3kdjcPhwMHBAQ4ODli3bh0MDAwQFRWFZcuWtbmstbU1Ghoa8Pz5cwwbNuwvx5CSkoLZs2fj0KFDsLOz+8vtAE13f8VdFAnfu9q5cyeTSDk5OWHr1q2orKxkjQqXnJwMV1dXTJs2DUDTxXNubi769u0rdRympqZ4/Pgxnj17xiR4aWlprDp5eXl49OiRyKOC0srOzsaLFy+wdetW6OvrA0C7hg6X5MWLF8jJycGRI0eYz1X4vktrhBfd7/POiLjEurq6GjIyMkwyA4D5u61kVlZWlknWVFVVWb1IADBgwAD07dsXQUFBcHd3F+lBfPny5UcbGr9v377o1KkTiouLWcl9S9ra2vDy8oKXlxeGDRuG5cuXY8eOHRg4cCBOnz6Nrl27Qk1NTeyy4s4FHo+Hffv2YdGiRSLvXbVne5OTkzF06FDMmzePmde8xxpoeq+ooaGhzbasra3bHCnP29sb8+bNY3oiO8rUqVPx008/4dChQ6wRBAFgx44dkJeXF3mstrn58+djy5YtiIqKwsSJE5n59+/fR48ePZh36iiK+vvRxwIpinovqamp2Lx5M9LT01FcXIxz586hrKwM5ubmUi1vYmICT09PzJgxA+fOnUNhYSFu3ryJLVu2ICYmRqo2SktLMXHiREydOhU8Hg+lpaUoLS1FWVnZ+2yaCA0NDVhZWSE8PJwZuGL48OG4ffs2cnNzWRe3ffr0YXr0BAIBfH198ezZs3atz9nZGb1794aXlxcyMzORnJzMvMMhTBKio6MxatQoKCkpsZZtaGgQeSRJIBCIrKNnz55QUFDAnj178PDhQ1y4cIF5J+l9aGhooEuXLjh8+DDy8/Px22+/iSTbqamp2Lt3LzIyMvDo0SP89ttv8PDwQO/evZleqydPnsDMzIw1EEhpaSkyMjKQn58PoOnR0oyMDOauvTjOzs6orKzE/PnzIRAI8ODBA8yaNQtycnIYMWIEq+7z589RWlqKR48eITIyEidOnGh1sBAOh4PQ0FDk5uZi2LBh+PXXX/Hw4UNkZmYy/+/Sx6Kqqgp/f38sXboUx44dQ0FBAW7fvo09e/bg2LFjAIB169YhOjoa+fn5ePDgAS5evMicn56entDS0oKrqyuSkpJQWFgIPp+PRYsW4Y8//pC43n379qGhoQGDBw/G2bNnkZeXB4FAgJ9++qldPY59+vRBeno64uLikJubi7Vr14rcQDA0NERmZiZycnJQXl4uMnS+EI/Hw4MHD1rtvZozZw7Kysowe/bsVuN68eIF8z0inGpra6XeLgB4/fq1SBt//vknAMDe3h6LFy/G8uXLERQUhIKCAmRnZ2PNmjXYvXs3goKCmJsd4igpKWHOnDkICAhg9eAnJSWJfU+Ooqi/D02uKIp6L2pqarh27RrGjBkDExMTrFmzBkFBQRg9erTUbYSGhmLGjBnw8/ODqakpJkyYgLS0NPTs2VOq5bOzs/Hs2TMcO3YMurq6zDRo0KC/ulkSOTo6oqGhgUmuNDU10bdvX+jo6MDU1JSpt2bNGgwcOBA8Hg9OTk7Q0dGR+j9CFZKVlcX58+dRVVWFQYMGYfbs2cxogcJ3S6KjozF+/HiRZauqqmBtbc2axI2Mpq2tjbCwMERGRqJv377YunUrduzY0a44xZGRkUFERARu3boFS0tLLF26FNu3b2fVUVJSwrlz5zBy5EiYmprCx8cHVlZWSExMZB5pe/fuHXJycljvkBw8eBDW1taYM2cOgKYE19raGhcuXJAYj5mZGX755RdkZmbC3t4ew4YNw9OnTxEbG8u89yRkamoKXV1dGBsbY8WKFfD19WWNgijO4MGDkZ6eDmNjY8yZMwfm5uYYP348Hjx4gF27drVn17XbDz/8gLVr12LLli0wNzeHi4sLYmJimMdyFRQUsHLlSlhZWWH48OGQlZVlRptTUlLCtWvX0LNnT+YdQh8fH9TW1krsyQKaBs24ffs2RowYAT8/P1haWsLZ2RkJCQnt6hXy9fWFm5sb3N3dMWTIELx48YLViwU0JUSmpqawtbWFtrY2kpOTxbbVr18/DBw4kBlpUBw5OTloaWmJDGLS0qhRo1jfJbq6ujh//rzU2wU0JbUt2/juu++Y8l27dmH//v34+eefYWlpCVtbW1y7dg3nz58X6c0SZ8GCBRAIBIiMjATQNELp+fPnmfOCoqiOwSHv89YwRVEfVG1tLQoLC2FkZCTVCHbUv09ycjI+++wz5OfnQ11dHbq6uvjjjz+YxwYp6t8sJiYGy5cvx/379//yAC+fqgMHDiAqKgqXL1/u6FA+GvobSX0K6DtXFEVR/2BRUVFQUVFBnz59kJ+fj8WLF8PBwQG9e/dGbm4ufvzxR5pYUdT/N3bsWOTl5eHJkyetPlb3f5G8vHybPawURX18tOeKov5B6F05qqXjx49j06ZNKC4uhpaWFkaNGoWgoCB06dKlo0OjKIr6W9HfSOpTQJMrivoHoT8cFEVRFCUe/Y2kPgX/rgeSKYqiKIqiKIqiPhKaXFEURVEURVEURX0ANLmiKIqiKIqiKIr6AGhyRVEURVEURVEU9QHQ5IqiKIqiKIqiKOoDoMkVRVEURVEURVHUB0CTK4qiPqqZM2diwoQJHRoDn88Hh8PBy5cvJdZZv349BgwY8LfF1JGcnJywZMmSjg7jX6eoqAgcDgcZGRkS64SFhaFz585/W0wfG4fDwfnz5/+29b148QJdu3ZFUVHR37ZOCigvL0fXrl3xxx9/dHQoFNXhaHJFURQFwN/fHwkJCR0dxl9WU1MDZWVl5Ofnf1IX6OPHj0fPnj2hqKgIXV1dTJ8+HU+fPm11mcOHD8PJyQlqamptJs3NpaWlYeTIkejcuTM0NDTA4/Fw9+5dplyYhAsnLpcLCwsLHD58WKr28/PzMWvWLPTo0QOdOnWCkZERPDw8kJ6eLtXyAODu7o7c3Fyp63ek0tJSLFy4EL169UKnTp2gr6+PcePGffDzqD03aAIDA+Hq6gpDQ0MA/0tohZOmpiYcHR2RlJQkdnlfX1/IysoiMjJSpGz9+vVMO3JycjA0NMTSpUtRVVUlMR7hMi4uLiJl27dvB4fDgZOTk9h1NJ/MzMxEtkXcFBYWJnIcN59KS0uZdVVUVGDJkiUwMDCAgoIC9PT04O3tjeLiYlacM2fOZJaXl5eHkZERvvvuO9TW1jJ1tLS0MGPGDAQEBEjcFxT1b0GTK4qiKAAqKiro0qVLR4fxl8XHx8PAwADGxsYdHUq7jBgxAmfOnEFOTg7Onj2LgoICfPXVV60uU11dDRcXF6xatUrq9VRVVcHFxQU9e/ZEamoqrl+/DlVVVfB4PLx7945VNycnByUlJcjKyoKvry++/fbbNhOG9PR02NjYIDc3F4cOHUJWVhaioqJgZmYGPz8/qePkcrno2rWr1PU7SlFREWxsbPDbb79h+/btuHfvHmJjYzFixAjMnz+/Q2Kqrq5GcHAwfHx8RMquXLmCkpISXLt2DXp6evjyyy/x7NkzkeUjIiLw3XffISQkROw6LCwsUFJSgqKiIvznP//B4cOH2/x8dXV1cfXqVZFenZCQEPTs2VPiOppP169fh76+Pmuen5+fSF13d3emHeFx3HwSHlsVFRWws7PDlStXcPDgQeTn5yMiIgL5+fkYNGgQHj58yIrJxcUFJSUlePjwIXbu3IlDhw6JJFKzZs1CeHg4KioqWt0fFPV/HqEo6h+jpqaGZGVlkZqamo4OpV0iIyOJpaUlUVRUJJqammTkyJGkqqqKEEKIl5cXcXV1Jdu3byc6OjpEU1OTzJs3j7x9+5ZZvra2lvj5+RE9PT2ipKREBg8eTK5evcqUh4aGEnV1dRIbG0vMzMyIsrIy4fF45OnTp0wdACKTgYEBIYSQq1evEgDkypUrxMbGhnC5XGJvb0+ys7OZ5QMCAkj//v0lbuMvv/xC1NXVSX19PSGEkDt37hAAZMWKFUwdHx8f4unpSQghpLy8nEydOpXo6ekRLpdLLC0tyalTp1htOjo6koULF5Lly5cTDQ0N0q1bNxIQEMCqIxAIiIODA+nUqRMxNzcn8fHxBACJiopi1fP29mZiEe4vSRwdHcnixYuZv48fP05sbGyIiooK6datG/Hw8CDPnj1jyoX7LzY2lgwYMIAoKiqSESNGkGfPnpFff/2VmJmZEVVVVeLh4UHevHnDLHfp0iXi4OBA1NXViaamJhk7dizJz8+XGBchhERHRxMOh8M6PiQRxlVZWdlm3bS0NAKAFBcXM/MyMzMJAJKXl9dqe7179ybbtm2T2HZjYyOxsLAgNjY2pKGhQaRc2F5hYSEBQM6ePUucnJwIl8slVlZW5MaNG0zdlp+d8Lg8fvw4MTAwIGpqasTd3Z38+eefTJ2GhgayefNmYmhoSBQVFYmVlRWJjIxkyisqKsjXX39NtLS0iKKiIjE2NiYhISFMeXFxMZk8eTJRV1cnGhoaZPz48aSwsLDV/Tl69GjSvXt35jwXt72ENJ2XR44cIRMmTCBcLpcYGxuT6Ohopry+vp54e3szsZuYmJBdu3axtr/led38u6G5yMhIoq2tzZon3Od37txh5gk/9+ZxEEJIWFgYsbOzIy9fviRKSkqsY0UYS8vviDlz5hAdHR2x8TRf5ssvvySbNm1i5icnJxMtLS3y7bffEkdHx1bX0VbbLUlzXsydO5coKyuTkpIS1vzq6mrSvXt34uLiwswTfoc35+bmRqytrUXaNTIyIkePHpUq/r/iU/2NpP5daM8VRX0C3tXWSpzq376Vuu67t3VS1W2PkpISeHh4wNvbGwKBAHw+H25ubiCEMHWuXr2KgoICXL16FceOHUNYWBjCwsKY8gULFiAlJQURERHIzMzE5MmT4eLigry8PKZOdXU1duzYgRMnTuDatWsoLi6Gv78/Kw7hlJ+fD2NjYwwfPpwV6+rVqxEUFIT09HTIycnB29tb6u0cNmwYXr9+jTt37gAAEhMToaWlBT6fz9RJTExkHvGpra2FjY0NYmJicP/+fXzzzTeYPn06bt68yWr32LFjUFZWRmpqKrZt24aNGzciPj4eANDQ0IAJEyZASUkJqampOHz4MFavXi0SW2NjIy5evAhXV1ept6e5d+/e4YcffsDdu3dx/vx5FBUVYebMmSL11q9fj7179+LGjRt4/PgxpkyZgl27duHUqVOIiYnB5cuXsWfPHqb+mzdvsGzZMqSnpyMhIQEyMjKYOHEiGhsbxcZRUVGB8PBwDB06FPLy8n9pWyQxNTVFly5dEBwcjLdv36KmpgbBwcEwNzdnHiFriRCC2NhYFBcXY8iQIRLbzsjIwIMHD+Dn5wcZGdGf1ZaPaK5evRr+/v7IyMiAiYkJPDw8UF9fL7H9goICnD9/HhcvXsTFixeRmJiIrVu3MuVbtmzB8ePHcfDgQTx48ABLly7FtGnTkJiYCABYu3YtsrKycOnSJQgEAhw4cABaWloAmj57Ho8HVVVVJCUlITk5GSoqKnBxccHbFt8tQhUVFYiNjcX8+fOhrKzc5vZu2LABU6ZMQWZmJsaMGQNPT0+md6OxsRE9evRAZGQksrKysG7dOqxatQpnzpwB0PS47pQpU5iek5KSEgwdOlRsXElJSbCxsZG4H4Gmx2ePHz8OAFBQUGCVBQcHY9q0aVBXV8fo0aNZ31GScLlcifupOW9vb1Z7ISEh8PT0FInh79DY2IiIiAh4enpCR0eHVcblcjFv3jzExcVJ7IG6f/8+bty4ITb2wYMHS3zkkqL+NTo6u6Mo6n8k3ZXbMWWsxOnslgBW3V3T3STWjVi/glV3n4+H2HrtcevWLQKAFBUViS338vIiBgYGTI8PIYRMnjyZuLu7E0IIefToEZGVlSVPnjxhLTdy5EiycuVKQkjT3XwArF6Pffv2kW7duomsr7GxkUycOJHY2NiQ6upqQgi750ooJiaGAGD2tTR3jAcOHEi2b99OCCFkwoQJJDAwkCgoKJDXr1+TP/74gwAgubm5EpcfO3Ys8fPzY/52dHQkn332GavOoEGDmB6oS5cuETk5OdbdZXE9V8nJyaRr165Mr0l7e65aEvbyvH79mhAifv9t2bKFACAFBQXMPF9fX8Lj8SS2W1ZWRgCQe/fuseZ/9913RElJiQAgdnZ2pLy8XGIbzbWn54oQQu7du0d69+5NZGRkiIyMDDE1NWUdt8L2lJWVibKyMpGTkyMyMjKsHgdxTp8+TQCQ27dvt1pP2IvS/M7+gwcPCAAiEAgIIeJ7rpSUlFg9VcuXLydDhgwhhDT1+iopKbF6vwhp6kX18PAghBAybtw4MmvWLLExnThxgpiampLGxkZmXl1dHeFyuSQuLk7sMqmpqQQAOXfuXKvbS0hTz9WaNWuYv6uqqggAcunSJYnLzJ8/n0yaNIn5W1zPiTiurq7E29ubNU+4z7lcLlFWViYcDocAIDY2Nqze0dzcXCIvL0/KysoIIYRERUURIyMj1n5p+R2Rnp5OtLS0yFdffSUxJuEyb9++JV27diWJiYmkqqqKqKqqkrt375LFixeL9FzJyMgwx6Bw8vX1ldh2Sy2PY+HUt29fQgghpaWlBADZuXOn2JjPnTtHAJDU1FRCSNP+l5WVJcrKyqRTp04EAJGRkSH//e9/RZZdunQpcXJykrg/3hftuaI+BbTniqKo99K/f3+MHDkS/fr1w+TJk3HkyBFUVlay6lhYWEBWVpb5W1dXF8+fPwcA3Lt3Dw0NDTAxMYGKigozJSYmoqCggFlGSUkJvXv3FttGc6tWrUJKSgqio6PB5XJZZVZWVqzlAYhtIykpiRVLeHg4AMDR0RF8Ph+EECQlJcHNzQ3m5ua4fv06EhMToaenhz59+gBo6nX64Ycf0K9fP2hqakJFRQVxcXEiL4s3j6nlduXk5EBfX591d3nw4MEi8UZHR+PLL78U22sijVu3bmHcuHHo2bMnVFVV4ejoCACtxtqtWzcoKSmhV69erHnN92deXh48PDzQq1cvqKmpMT1ELdtdvnw57ty5g8uXL0NWVhYzZsxg9Xy21+jRo5nPzsLCAkBTj4WPjw8cHBzw+++/Izk5GZaWlhg7dixqampYyyclJSEjIwMZGRk4evQoNm/ejAMHDgAAwsPDWcdGUlJSu2OV9jgUMjQ0hKqqKmsZYf38/HxUV1fD2dmZFdfx48eZ8+fbb79FREQEBgwYgO+++w43btxg2rp79y7y8/OhqqrKLKupqYna2loUFBSIPRfeZ3uVlZWhpqbG2t59+/bBxsYG2traUFFRweHDh0WOEWnU1NRAUVFRbNnp06dx584dnD17FsbGxggLC2P1joaEhIDH4zE9emPGjMGrV6/w22+/sdq5d+8eVFRUwOVyMXjwYNjb22Pv3r0oLi5m7afNmzezlpOXl8e0adMQGhqKyMhImJiYiJz7QqampszxJ5w2btzY7v3R/DjOyMjAr7/+yipvz+c4YsQIZGRkIDU1FV5eXpg1axYmTZokUo/L5aK6urrdsVLU/yVyHR0ARVFtW3TsvxLLOC0uqOcdDpfckAyH9eecveJf2m4PWVlZxMfH48aNG8xjYatXr0ZqaiqMjIwAQOQRLw6HwzwaVlVVBVlZWdy6dYuVgAFNg0wIiWuj5cXByZMnsXPnTvD5fHTv3l0k1uZtcDhN+0LcI2q2tras4bK7desGoGkI85CQENy9exfy8vIwMzODk5MT+Hw+KisrmaQEaBoJbPfu3di1axf69esHZWVlLFmyROQRotb2jbQuXLjAekysPd68eQMejwcej4fw8HBoa2ujuLgYPB6v1ViFI4e1Fvu4ceNgYGCAI0eOQE9PD42NjbC0tBRpV0tLC1paWjAxMYG5uTn09fXx+++/w97e/i9t09GjR5mESRjjqVOnUFRUhJSUFCYJPXXqFDQ0NBAdHY2pU6cyyxsZGTGPtllYWCA1NRWBgYH49ttvMX78eNYjgt27d0d2djYAIDs7G9bW1m3GJ+1xKK6+cJnm5w8AxMTEiBzznTp1AtCUbD569Ai//vor4uPjMXLkSMyfPx87duxAVVUVbGxsmBsIzWlra0NBQUHkXHj37h04HA6z3e3Z3pbxR0REwN/fH0FBQbC3t4eqqiq2b9+O1NRUqdpuTktLS+TGjpC+vj769OmDPn36oL6+HhMnTsT9+/fRqVMnNDQ04NixYygtLYWc3P8uixoaGhASEoKRI0cy80xNTXHhwgXIyclBT0+PeTSuvr6etZ80NTVFYvD29saQIUNw//79Vh9JVlBQ+CAD0zQ/jpvT1tZG586dIRAIxC4nEAjA4XBYMSgrKzN/h4SEoH///mIHD6moqIC2tvZ7x05RnzKaXFHUJ0Bewt3Yv7NuazgcDhwcHODg4IB169bBwMAAUVFRWLZsWZvLWltbo6GhAc+fP8ewYcP+cgwpKSmYPXs2Dh06BDs7u7/cDtB091XcxY3wvaudO3cyiZSTkxO2bt2KyspK1qhhycnJcHV1xbRp0wA0XTzn5uaib9++UsdhamqKx48f49mzZ0yCl5aWxqqTl5eHR48ewdnZud3bCTQlBC9evMDWrVuhr68PAO0aOlySFy9eICcnB0eOHGE+1+vXr7e5nPCiu66uro2akolLrKurqyEjI8MkMwCYv9tKZmVlZZlkTVVVldWLBAADBgxA3759ERQUBHd3d5EexJcvX360ofH79u2LTp06obi4mJXct6StrQ0vLy94eXlh2LBhWL58OXbs2IGBAwfi9OnT6Nq1K9TU1MQuK+5c4PF42LdvHxYtWiTy3lV7tjc5ORlDhw7FvHnzmHnNe6yBpmSjoaGhzbasra1x8uTJNut99dVXWLduHfbv34+lS5fi119/Zd6nbH6D5/79+5g1axZreyQlPnJycm0mRBYWFrCwsEBmZia+/vrrNuP8WGRkZDBlyhSEh4dj48aNrJ7xmpoa7N+/HzweT2yCKFx+1apVWLZsGb7++mvWEwL3799nDS1PUf9G9LFAiqLeS2pqKjZv3oz09HQUFxfj3LlzKCsrg7m5uVTLm5iYwNPTEzNmzMC5c+dQWFiImzdvYsuWLYiJiZGqjdLSUkycOBFTp04Fj8dDaWkpSktLUVZW9j6bJkJDQwNWVlYIDw9nLiCGDx+O27dvIzc3l3Vx26dPH6ZHTyAQwNfXV2To57Y4Ozujd+/e8PLyQmZmJpKTk7FmzRoA/+vxiI6OxqhRo6CkpMRatqGhQeTRInF3qnv27AkFBQXs2bMHDx8+xIULF/DDDz+0K05xNDQ00KVLFxw+fBj5+fn47bffRJLt1NRU7N27FxkZGXj06BF+++03eHh4oHfv3kyv1ZMnT2BmZsYaCKS0tBQZGRnIz88H0PSoVkZGRqtDQDs7O6OyshLz58+HQCDAgwcPMGvWLMjJyWHEiBGsus+fP0dpaSkePXqEyMhInDhxotXBQjgcDkJDQ5Gbm4thw4bh119/xcOHD5GZmcn8v0sfi6qqKvz9/bF06VIcO3YMBQUFuH37Nvbs2YNjx44BANatW4fo6Gjk5+fjwYMHuHjxInN+enp6QktLC66urkhKSkJhYSH4fD4WLVrU6n8Iu2/fPjQ0NGDw4ME4e/Ys8vLyIBAI8NNPP7Wrx7FPnz5IT09HXFwccnNzsXbtWpEbCIaGhsjMzEROTg7Ky8tFhs4X4vF4ePDggcTeKyEOh4NFixZh69atzPDtY8eORf/+/WFpaclMU6ZMQefOncX26v1Vv/32G0pKSlpNPuvr65nvMOHU3u8O4H/HcfNJuO82b94MHR0dODs749KlS3j8+DGuXbvG/NcE+/bta7XtyZMnQ1ZWllWvuroat27dwhdffNHuWCnq/xKaXFEU9V7U1NRw7do1jBkzBiYmJlizZg2CgoIwevRoqdsIDQ3FjBkz4OfnB1NTU0yYMAFpaWli/w8YcbKzs/Hs2TMcO3YMurq6zDRo0KC/ulkSOTo6oqGhgUmuNDU10bdvX+jo6MDU1JSpt2bNGgwcOBA8Hg9OTk7Q0dGR+j9CFZKVlcX58+dRVVWFQYMGYfbs2cxogcJ3S6KjozF+/HiRZauqqmBtbc2axo0bJ1JPW1sbYWFhiIyMRN++fbF161bs2LGjXXGKIyMjg4iICNy6dQuWlpZYunQptm/fzqqjpKSEc+fOYeTIkTA1NYWPjw+srKyQmJjIPNL27t075OTksN7jOHjwIKytrTFnzhwATQmutbU1Lly4IDEeMzMz/PLLL8jMzIS9vT2GDRuGp0+fIjY2lnnvScjU1BS6urowNjbGihUr4OvryxoFUZzBgwcjPT0dxsbGmDNnDszNzTF+/Hg8ePAAu3btas+ua7cffvgBa9euxZYtW2Bubg4XFxfExMQwj+UqKChg5cqVsLKywvDhwyErK4uIiAgATZ/BtWvX0LNnT+YdQh8fH9TW1krsyQKAXr164fbt2xgxYgT8/PxgaWkJZ2dnJCQkMO+nScPX1xdubm5wd3fHkCFD8OLFC1YvFgDMmTMHpqamsLW1hba2NpKTk8W21a9fPwwcOJAZabA1Xl5eePfuHfbs2YOYmBix7w8JR7cMDg6Wenvaoqys3Gav3oMHD1jfY7q6ujAwMGj3uoTHcfPp1q1bAIAuXbrg999/x4gRI+Dr64vevXtjypQp6N27N9LS0ljvUoojJyeHBQsWYNu2bXjz5g2Apu+inj17vtcTCBT1fwGHvM9bwxRFfVC1tbUoLCyEkZGRxBezqX+35ORkfPbZZ8jPz4e6ujp0dXXxxx9/MI8NUtS/WUxMDJYvX4779+//5QFeqL/Gzs4OixYt+qiPPNLfSOpTQN+5oiiK+geLioqCiooK+vTpg/z8fCxevBgODg7o3bs3cnNz8eOPP9LEiqL+v7FjxyIvLw9Pnjxh3iGkPr7y8nK4ubnBw8Ojo0OhqA5He64o6h+E3pWjWjp+/Dg2bdqE4uJiaGlpYdSoUQgKCkKXLl06OjSKoqi/Ff2NpD4FNLmiqH8Q+sNBURRFUeLR30jqU0AfSKYoiqIoiqIoivoAaHJFURRFURRFURT1AdDkiqIoiqIoiqIo6gOgyRVFURRFURRFUdQHQJMriqIoiqIoiqKoD4AmVxRFURRFURRFUR8ATa4oivqoZs6ciQkTJnRoDHw+HxwOBy9fvpRYZ/369RgwYMDfFtM/SUdu+z/h+PiU/NuO5aKiInA4HGRkZPxt68zJyYGOjg5ev379t63zn8LOzg5nz57t6DAo6pNGkyuKoigA/v7+SEhI6Ogw/rKamhooKysjPz8fYWFh4HA4MDc3F6kXGRkJDocDQ0NDZl57tr0jLtw5HI7Yafv27WLryMnJoWfPnli2bBnq6uree/379u2DoaEhFBUVMWTIENy8ebPV+ufOnYOtrS06d+4MZWVlDBgwACdOnJBqXXfu3MHkyZPRrVs3KCoqok+fPpgzZw5yc3OljvdTOpbz8/Mxa9Ys9OjRA506dYKRkRE8PDyQnp7+Qdfj5OSEJUuWSFV35cqVWLhwIVRVVQH8L6HV0NBAbW0tq25aWhpz3AkJ64ubSktLYWhoKLGcw+Fg5syZACQf9xEREcy6GhoasHPnTvTr1w+KiorQ0NDA6NGjkZyczIpT+J3A4XAgIyMDXV1duLu7o7i4mFVvzZo1+P7779HY2CjtrqUoqgWaXFEURQFQUVFBly5dOjqMvyw+Ph4GBgYwNjYGACgrK+P58+dISUlh1QsODkbPnj1Z8z7Gtr979+6DtVVSUsKaQkJCwOFwMGnSJFa90NBQlJSUoLCwEPv378eJEyewadOm91r36dOnsWzZMgQEBOD27dvo378/eDwenj9/LnEZTU1NrF69GikpKcjMzMSsWbMwa9YsxMXFtbquixcvws7ODnV1dQgPD4dAIMDJkyehrq6OtWvXSh3zp3Isp6enw8bGBrm5uTh06BCysrIQFRUFMzMz+Pn5dUhMxcXFuHjxIpPgNKeqqoqoqCjWPHHnk1BOTo7Isdu1a1ekpaUxfwt7iZrX3b17N9OG8JhuPgl7egkhmDp1KjZu3IjFixdDIBCAz+dDX18fTk5OOH/+PCseNTU1lJSU4MmTJzh79ixycnIwefJkVp3Ro0fj9evXuHTpUjv3HEVRDEJR1D9GTU0NycrKIjU1NR0dSrtERkYSS0tLoqioSDQ1NcnIkSNJVVUVIYQQLy8v4urqSrZv3050dHSIpqYmmTdvHnn79i2zfG1tLfHz8yN6enpESUmJDB48mFy9epUpDw0NJerq6iQ2NpaYmZkRZWVlwuPxyNOnT5k6AEQmAwMDQgghV69eJQDIlStXiI2NDeFyucTe3p5kZ2czywcEBJD+/ftL3MZffvmFqKurk/r6ekIIIXfu3CEAyIoVK5g6Pj4+xNPTkxBCSHl5OZk6dSrR09MjXC6XWFpaklOnTrHadHR0JAsXLiTLly8nGhoapFu3biQgIIBVRyAQEAcHB9KpUydibm5O4uPjCQASFRXFquft7c3EItxfCxYsILNnz2bqPH78mHTq1Il8//33zL4Rt+1Xr14lgwYNIkpKSkRdXZ0MHTqUFBUVkdDQUJF9HBoayuz//fv3k3HjxhElJSUSEBBA6uvribe3NzE0NCSKiorExMSE7Nq1ixW38PhoD1dXV/L555+z5onbJz4+PmTMmDES2/Hw8CBTpkxhzXv79i3p0qULOXbsGCGEkMGDB5P58+cz5Q0NDURPT49s2bKlXTFbW1uTNWvWSCx/8+YN0dLSIhMmTBBbXllZSQj5a8fyhzgHi4qKyJdffkk6d+5MlJSUSN++fUlMTAxTfu/ePeLi4kKUlZVJ165dybRp00hZWZnE7W1sbCQWFhbExsaGNDQ0SNzewsJCAoCcPXuWODk5ES6XS6ysrMiNGzeYum2da15eXiLHbWFhodi4tm/fTmxtbVnzhPt8zZo1ZNSoUcz86upqoq6uTtauXUuaX04J6wu3oTWt1RV3TDcXERFBAJALFy6IlLm5uZEuXbow38PC74TmfvrpJwKAvHr1ijV/1qxZZNq0aW3G3hE+1d9I6t+F9lxR1D8YIQSNbxs6ZCKESBVjSUkJPDw84O3tzdw5dXNzYy1/9epVFBQU4OrVqzh27BjCwsIQFhbGlC9YsAApKSmIiIhAZmYmJk+eDBcXF+Tl5TF1qqursWPHDpw4cQLXrl1DcXEx/P39WXEIp/z8fBgbG2P48OGsWFevXo2goCCkp6dDTk4O3t7eUn8Ww4YNw+vXr3Hnzh0AQGJiIrS0tMDn85k6iYmJcHJyAgDU1tbCxsYGMTExuH//Pr755htMnz5d5JGyY8eOQVlZGampqdi2bRs2btyI+Ph4AE2P/EyYMAFKSkpITU3F4cOHsXr1apHYGhsbcfHiRbi6urLme3t748yZM6iurgbQ9GiQi4sLunXrJnE76+vrMWHCBDg6OiIzMxMpKSn45ptvwOFw4O7uDj8/P1hYWDD72t3dnVl2/fr1mDhxIu7duwdvb280NjaiR48eiIyMRFZWFtatW4dVq1bhzJkzUu/3lp49e4aYmBj4+Pi0Wi83Nxe//fYbhgwZIrGOp6cnfvnlF1RVVTHz4uLiUF1djYkTJ+Lt27e4desWRo0axZTLyMhg1KhRIj2CkhBCkJCQgJycHJHjsbm4uDiUl5fju+++E1veuXNn1t/tPZbf9xycP38+6urqcO3aNdy7dw//+c9/oKKiAgB4+fIlPv/8c1hbWyM9PR2xsbF49uwZpkyZIjGejIwMPHjwAH5+fpCREb0UEbe9/v7+yMjIgImJCTw8PFBfXw+g7XNt9+7dsLe3x5w5c5jjVl9fX2xcSUlJsLW1FVs2ffp0JCUlMY/SnT17FoaGhhg4cKDE7fyYTp06BRMTE4wbN06kzM/PDy9evGC+S1p6/vw5oqKiICsrC1lZWVbZ4MGDkZSU9FFipqh/A7mODoCiKMnIu0Y8XXejQ9att3EoOAqybdYrKSlBfX093NzcYGBgAADo168fq46Ghgb27t0LWVlZmJmZYezYsUhISMCcOXNQXFyM0NBQFBcXQ09PD0DTOyOxsbEIDQ3F5s2bATQ9Znbw4EH07t0bQNPF4MaNG5l16OjoAGi6mJ00aRLU1dVx6NAhVhyBgYFwdHQEAHz//fcYO3Ysamtroaio2OZ2qqurY8CAAeDz+bC1tQWfz8fSpUuxYcMGVFVV4dWrV8jPz2fa7969Oyv5W7hwIeLi4nDmzBkMHjyYmW9lZYWAgAAAQJ8+fbB3714kJCTA2dkZ8fHxKCgoAJ/PZ7YvMDAQzs7OrNh+//13ABBJJKytrdGrVy/897//xfTp0xEWFoYff/wRDx8+lLidf/75J169eoUvv/yS2dfN391SUVGBnJwcE09zX3/9NWbNmsWat2HDBubfRkZGSElJwZkzZ1q98G7NsWPHoKqqCjc3N5EyDw8PyMrKor6+HnV1dfjyyy+xcuVKiW3xeDwoKysjKioK06dPB9B0wTp+/Hioqqri6dOnaGhoEElGu3Xrhuzs7FbjfPXqFbp37466ujrIyspi//79Ip9bc8IkxszMrNV2hdp7LL/vOVhcXIxJkyYx53avXr2Ytvfu3Qtra2vmXAWAkJAQ6OvrIzc3FyYmJu+9vf7+/hg7diyApmPKwsIC+fn5MDMza/NcU1dXh4KCApSUlMQet809evRIYnLVtWtXjB49GmFhYVi3bh1CQkJaTWp79OjB+tvAwAAPHjyQanuFhMd0c1lZWejZsydyc3PFvlcJ/O+cbf6u3qtXr6CiogJCCHPDZdGiRVBWVmYtq6enh8ePH6OxsVFs4ktRVOvoWUNR1Hvp378/Ro4ciX79+mHy5Mk4cuQIKisrWXUsLCxYFwi6urrMOyv37t1DQ0MDTExMoKKiwkyJiYkoKChgllFSUmIu9lu20dyqVauQkpKC6OhocLlcVpmVlRVreQBi20hKSmLFEh4eDgBwdHQEn88HIQRJSUlwc3ODubk5rl+/jsTEROjp6aFPnz4AmnqdfvjhB/Tr1w+amppQUVFBXFycyAvkzWNquV05OTnQ19dnXRA2T8yEoqOj8eWXX4q9EPL29kZoaCgSExPx5s0bjBkzRqROc5qampg5cyZ4PB7GjRuH3bt3o6SkpNVlhMRdlO7btw82NjbQ1taGiooKDh8+LLIPhMLDw1n7Xdzd85CQEHh6eopNInbu3ImMjAzcvXsXFy9eRG5uLpM0FRcXs9revHkz5OTkMGXKFObzffPmDaKjo+Hp6SnV9rZGVVUVGRkZSEtLQ2BgIJYtW8b0cm7evJkVS3FxsdQ9xULSHstC73sOLlq0CJs2bYKDgwMCAgKQmZnJtHX37l1cvXqVtawwaSooKBD7uX7I7ZX2XJNGTU1NqzdbvL29ERYWhocPHyIlJaXVYyUpKQkZGRnM9Ouvv7Y7HuEx3XwSJsAA2rUfhcdkeno6goKCMHDgQAQGBorU43K5aGxs/CCDwVDUvxHtuaKofzCOvAz0Ng7tsHVLQ1ZWFvHx8bhx4wYuX76MPXv2YPXq1UhNTYWRkREAQF5ent02h8OMRlVVVQVZWVncunVL5A6t8LEjSW20vLA4efIkdu7cCT6fj+7du4vE2rwN4ehe4kbFsrW1ZQ39LOy5cHJyQkhICO7evQt5eXmYmZnByckJfD4flZWVTE8CAGzfvh27d+/Grl270K9fPygrK2PJkiV4+/atxJha7htpXbhwAVu3bhVb5unpie+++w7r16/H9OnTISfX9td+aGgoFi1ahNjYWJw+fRpr1qxBfHw87OzsWl2u5R3wiIgI+Pv7IygoCPb29lBVVcX27duRmpoqdvnx48ezet9afoZJSUnIycnB6dOnxS6vo6PDDOhhamqK169fw8PDA5s2bYKhoSHrM9XU1ATQtH8cHR3x/PlzxMfHg8vlwsXFBQCgpaUFWVlZPHv2jLWeZ8+etdkDIiMjw8QyYMAACAQCbNmyBU5OTpg7dy6r505PT4/p3cnOzoa9vX2rbQPSH8vi6guXac85OHv2bPB4PMTExODy5cvYsmULgoKCsHDhQlRVVWHcuHH4z3/+I7JeXV1dNDY2inyuwp6/7OxsWFtbv9f2SnuuSUNLS0vk5lBzo0ePxjfffAMfHx+MGzeu1YFDjIyMRB5vbK/mx3RLJiYmEAgEYsuE85v3GjY/Js3NzVFQUIBvv/1WZCTLiooKKCsri9ycoihKOjS5oqh/MA6HI9WjeR2Nw+HAwcEBDg4OWLduHQwMDBAVFYVly5a1uay1tTUaGhrw/PlzDBs27C/HkJKSgtmzZ+PQoUNtJgFt4XK5Yi9ohO9d7dy5k0mknJycsHXrVlRWVrJGOEtOToarqyumTZsGoOlCMDc3F3379pU6DlNTUzx+/BjPnj1jEry0tDRWnby8PDx69EjiI2eampoYP348zpw5g4MHD0q9bmtra1hbW2PlypWwt7fHqVOnYGdnBwUFBTQ0NEjVRnJyMoYOHYp58+Yx85r3RrakqqrKDH8tTnBwMGxsbNC/f3+p1i9MFGpqaiAnJyf2Mx06dCj09fVx+vRpXLp0CZMnT2Yu5BUUFGBjY4OEhARmhLbGxkYkJCRgwYIFUsUg1LwnQFNTk0nuhL744gtoaWlh27ZtIiPSAU3vNb3vhbok0p6D+vr6mDt3LubOnYuVK1fiyJEjWLhwIQYOHMi8fyQpeW/5uQ4YMAB9+/ZFUFAQ3N3dRXpd27O90pxr0h631tbWyMrKklguJyeHGTNmYNu2bR0+ot7UqVPx9ddf45dffhF57yooKAhdunRp9VHU77//Hr1798bSpUtZ743dv39fqoSXoijx6GOBFEW9l9TUVGzevBnp6ekoLi7GuXPnUFZWJvFdgJZMTEzg6emJGTNm4Ny5cygsLMTNmzexZcsWxMTESNVGaWkpJk6ciKlTp4LH46G0tBSlpaUoKyt7n00ToaGhASsrK4SHhzMDVwwfPhy3b99Gbm4uq+eqT58+TI+eQCCAr6+vSA9IW5ydndG7d294eXkhMzMTycnJWLNmDYD/3b2Pjo7GqFGjoKSkJLGdsLAwlJeXS/V+S2FhIVauXImUlBQ8evQIly9fRl5eHvN5GhoaorCwEBkZGSgvL2/10aE+ffogPT0dcXFxyM3Nxdq1a0WSQ2n9+eefiIyMxOzZsyXWefnyJUpLS/H06VMkJiZi48aNMDExafNY/Prrr3Hw4EHEx8eLPOa1bNkyHDlyBMeOHYNAIMC3336LN2/esN4tmzFjBuvdri1btiA+Ph4PHz6EQCBAUFAQTpw4wVz8i6OsrIyjR48iJiYG48ePx5UrV1BUVIT09HR89913mDt3blu76C+T5hxcsmQJ4uLiUFhYiNu3b+Pq1avMfp0/fz4qKirg4eGBtLQ0FBQUIC4uDrNmzZKY0HA4HISGhiI3NxfDhg3Dr7/+iocPHyIzMxOBgYEig7O0RppzzdDQEKmpqSgqKkJ5ebnEXj4ej4eUlJRWE7EffvgBZWVl4PF4rcb1/Plz5rtIOLX3vygQHtPNpzdv3gBoSq4mTpwILy8vBAcHo6ioCJmZmfD19cWFCxdw9OhRkd7k5vT19TFx4kSsW7eONT8pKQlffPFFu+KkKOp/aHJFUdR7UVNTw7Vr1zBmzBiYmJhgzZo1CAoKwujRo6VuIzQ0FDNmzICfnx9MTU0xYcIEpKWlSfz/Y1rKzs7Gs2fPcOzYMejq6jLToEGD/upmSeTo6IiGhgYmudLU1ETfvn2ho6MDU1NTpt6aNWswcOBA8Hg8ODk5QUdHh+n9kJasrCzOnz+PqqoqDBo0CLNnz2ZGCxS+FxIdHY3x48e32g6Xy5X6/z1SUlJCdnY2Jk2aBBMTE3zzzTeYP38+fH19AQCTJk2Ci4sLRowYAW1tbfz8888S2/L19YWbmxvc3d0xZMgQvHjxgtWL1R4REREghMDDw0NinVmzZkFXVxc9evSAh4cHLCwscOnSpTYfhfT09ERWVha6d+8OBwcHVpm7uzt27NiBdevWYcCAAcjIyEBsbCxrkIvi4mLWe2lv3rzBvHnzYGFhAQcHB5w9exYnT55sNTEEAFdXV9y4cQPy8vL4+uuvYWZmBg8PD7x69eq9/7+utrR1DjY0NGD+/PkwNzeHi4sLTExMsH//fgBNjzUmJyejoaEBX3zxBfr164clS5agc+fOrQ6IMHjwYKSnp8PY2Bhz5syBubk5xo8fjwcPHmDXrl1Sxy7Nuebv7w9ZWVn07dsX2traEt/HGj16NOTk5HDlyhWJ61NQUICWlhbrPw4Wx9TUlPV9pKuri1u3bkm9XcD/junm0549ewA0JahnzpzBqlWrsHPnTpiammLYsGF49OgR+Hy+VN83S5cuRUxMDDOy4pMnT3Djxg2RgWkoipIeh7T3rVKKoj6a2tpaFBYWwsjISKoR7Kh/n+TkZHz22WfIz8+Huro6dHV18ccff7Q6vDpFUdLbt28fLly40OZ/+vx/0YoVK1BZWYnDhw93dChi0d9I6lNA37miKIr6B4uKioKKigr69OmD/Px8LF68GA4ODujduzdyc3Px448/0sSKoj4gX19fvHz5Eq9fv271HcD/i7p27SrVu7IURUlGe64o6h+E3pWjWjp+/Dg2bdqE4uJiaGlpYdSoUczL6hRFUf8m9DeS+hTQ5Iqi/kHoDwdFURRFiUd/I6lPAR3QgqIoiqIoiqIo6gOgyRVFURRFURRFUdQHQJMriqIoiqIoiqKoD4AmVxRFURRFURRFUR8ATa4oiqIoiqIoiqI+AJpcURRFURRFURRFfQA0uaIo6qOaOXMmJkyY0KEx8Pl8cDgcvHz5UmKd9evXY8CAAX9bTP8kHbnt/4Tj41PybzuWi4qKwOFwkJGR8betMycnBzo6Onj9+vXftk4KyMrKQo8ePfDmzZuODoWi3gtNriiKogD4+/sjISGho8P4y2pqaqCsrIz8/HyEhYWBw+HA3NxcpF5kZCQ4HA4MDQ2Zee3Z9o64cOdwOGKn7du3i60jJyeHnj17YtmyZairq3vv9e/btw+GhoZQVFTEkCFDcPPmzVbrnzt3Dra2tujcuTOUlZUxYMAAnDhxQqp13blzB5MnT0a3bt2gqKiIPn36YM6cOcjNzZU63k/pWM7Pz8esWbPQo0cPdOrUCUZGRvDw8EB6evoHXY+TkxOWLFkiVd2VK1di4cKFUFVVBfC/hFY4aWtrY8yYMbh3757Y5Xk8HmRlZZGWliZSNnPmTKYdBQUFGBsbY+PGjaivr5cYj3CZuXPnipTNnz8fHA4HM2fOFLuO5pOLi4vItoib+Hw+8x3Scmr5f0s9fvwY3t7e0NPTg4KCAgwMDLB48WK8ePGCVc/JyYnVhomJCbZs2YLm/9Vq3759YWdnhx9//FHivqCoTwFNriiKogCoqKigS5cuHR3GXxYfHw8DAwMYGxsDAJSVlfH8+XOkpKSw6gUHB6Nnz56seR9j29+9e/fB2iopKWFNISEh4HA4mDRpEqteaGgoSkpKUFhYiP379+PEiRPYtGnTe6379OnTWLZsGQICAnD79m30798fPB4Pz58/l7iMpqYmVq9ejZSUFGRmZmLWrFmYNWsW4uLiWl3XxYsXYWdnh7q6OoSHh0MgEODkyZNQV1fH2rVrpY75UzmW09PTYWNjg9zcXBw6dAhZWVmIioqCmZkZ/Pz8OiSm4uJiXLx4kZWsCOXk5KCkpARxcXGoq6vD2LFj8fbtW5Hlb9y4gQULFiAkJETsOlxcXFBSUoK8vDz4+flh/fr1rBsF4ujr6yMiIgI1NTXMvNraWpw6dUrkfG6+jubTzz//jKFDh7LmTZkyRaTu0KFDAQBqamoibTx69IhZx8OHD2Fra4u8vDz8/PPPyM/Px8GDB5GQkAB7e3tUVFSwYpozZw5KSkqQk5ODlStXYt26dTh48CCrzqxZs3DgwIFWk02K+scjFEX9Y9TU1JCsrCxSU1PT0aG0S2RkJLG0tCSKiopEU1OTjBw5klRVVRFCCPHy8iKurq5k+/btREdHh2hqapJ58+aRt2/fMsvX1tYSPz8/oqenR5SUlMjgwYPJ1atXmfLQ0FCirq5OYmNjiZmZGVFWViY8Ho88ffqUqQNAZDIwMCCEEHL16lUCgFy5coXY2NgQLpdL7O3tSXZ2NrN8QEAA6d+/v8Rt/OWXX4i6ujqpr68nhBBy584dAoCsWLGCqePj40M8PT0JIYSUl5eTqVOnEj09PcLlcomlpSU5deoUq01HR0eycOFCsnz5cqKhoUG6detGAgICWHUEAgFxcHAgnTp1Iubm5iQ+Pp4AIFFRUax63t7eTCzC/bVgwQIye/Zsps7jx49Jp06dyPfff8/sG3HbfvXqVTJo0CCipKRE1NXVydChQ0lRUREJDQ0V2cehoaHM/t+/fz8ZN24cUVJSIgEBAaS+vp54e3sTQ0NDoqioSExMTMiuXbtYcQuPj/ZwdXUln3/+OWueuH3i4+NDxowZI7EdDw8PMmXKFNa8t2/fki5dupBjx44RQggZPHgwmT9/PlPe0NBA9PT0yJYtW9oVs7W1NVmzZo3E8jdv3hAtLS0yYcIEseWVlZWEkL92LH+Ic7CoqIh8+eWXpHPnzkRJSYn07duXxMTEMOX37t0jLi4uRFlZmXTt2pVMmzaNlJWVSdzexsZGYmFhQWxsbEhDQ4PE7S0sLCQAyNmzZ4mTkxPhcrnEysqK3Lhxg6nb1rnm5eUlctwWFhaKjWv79u3E1taWNU+4z4UxEULIhQsXCABy9+5dVt3169eTqVOnEoFAQNTV1Ul1dTWrXNzx7uzsTOzs7CTtKmYZS0tLcvLkSWZ+eHg4sbKyIq6ursTLy6vVdbTVdkvC75DWuLi4kB49eohsY0lJCVFSUiJz585l5jk6OpLFixez6g0cOJBMnDiRNa+uro506tSJXLlyRew6P9XfSOrfhfZcUdQn4O3btxKnlj0EH6Jue5SUlMDDwwPe3t4QCATg8/lwc3NjPe5x9epVFBQU4OrVqzh27BjCwsIQFhbGlC9YsAApKSmIiIhAZmYmJk+eDBcXF+Tl5TF1qqursWPHDpw4cQLXrl1DcXEx/P39WXEIp/z8fBgbG2P48OGsWFevXo2goCCkp6dDTk4O3t7eUm/nsGHD8Pr1a9y5cwcAkJiYCC0tLfD5fKZOYmIinJycADTdVbaxsUFMTAzu37+Pb775BtOnTxd5pOzYsWNQVlZGamoqtm3bho0bNyI+Ph4A0NDQgAkTJkBJSQmpqak4fPgwVq9eLRJbY2MjLl68CFdXV9Z8b29vnDlzBtXV1QCAsLAwuLi4oFu3bhK3s76+HhMmTICjoyMyMzORkpKCb775BhwOB+7u7vDz84OFhQWzr93d3Zll169fj4kTJ+LevXvw9vZGY2MjevTogcjISGRlZWHdunVYtWoVzpw5I/V+b+nZs2eIiYmBj49Pq/Vyc3Px22+/YciQIRLreHp64pdffkFVVRUzLy4uDtXV1Zg4cSLevn2LW7duYdSoUUy5jIwMRo0aJdIjKAkhBAkJCcjJyRE5HpuLi4tDeXk5vvvuO7HlnTt3Zv3d3mP5fc/B+fPno66uDteuXcO9e/fwn//8ByoqKgCAly9f4vPPP4e1tTXS09MRGxuLZ8+eYcqUKRLjycjIwIMHD+Dn5wcZGdFLEXHb6+/vj4yMDJiYmMDDw4Pp3WjrXNu9ezfs7e2ZnpOSkhLo6+uLjSspKQm2trat7stXr14hIiICAKCgoMDMJ4QgNDQU06ZNg5mZGYyNjfHf//631bYAgMvlSvW96+3tjdDQUObvkJAQzJo1q83lPoaKigrExcVh3rx54HK5rDIdHR14enri9OnTrN8BIUIIkpKSkJ2dzdp/QNP+HDBgAJKSkj5q/BT1Mcl1dAAURbVt8+bNEsv69OkDT09P5u/t27dLfCTLwMCA9WO8a9cu5sK7ufXr10sdW0lJCerr6+Hm5gYDAwMAQL9+/Vh1NDQ0sHfvXsjKysLMzAxjx45FQkIC5syZg+LiYoSGhqK4uBh6enoAmt4ZiY2NRWhoKLPt7969w8GDB9G7d28ATReDGzduZNaho6MDoOmHe9KkSVBXV8ehQ4dYcQQGBsLR0REA8P3332Ps2LGora0VeY9AHHV1dQwYMAB8Ph+2trbg8/lYunQpNmzYgKqqKrx69Qr5+flM+927d2clfwsXLkRcXBzOnDmDwYMHM/OtrKwQEBAAoOmz3Lt3LxISEuDs7Iz4+HgUFBSAz+cz2xcYGAhnZ2dWbL///jsAiCQS1tbW6NWrF/773/9i+vTpCAsLw48//oiHDx9K3M4///wTr169wpdffsns6+bvbqmoqEBOTo6Jp7mvv/5a5GJvw4YNzL+NjIyQkpKCM2fOtHrh3Zpjx45BVVUVbm5uImUeHh6QlZVFfX096urq8OWXX2LlypUS2+LxeFBWVkZUVBSmT58OADh16hTGjx8PVVVVPH36FA0NDSLJaLdu3ZCdnd1qnK9evUL37t1RV1cHWVlZ7N+/X+Rza06YxJiZmbXarlB7j+X3PQeLi4sxadIk5tzu1asX0/bevXthbW3N+p4KCQmBvr4+cnNzYWJi8t7b6+/vj7FjxwJoOqYsLCyQn58PMzOzNs81dXV1KCgoQElJSexx29yjR48kJlc9evQAAGbAhfHjx7Piv3LlCqqrq8Hj8QAA06ZNQ3BwMHNstSRMvOPi4rBw4cI298G0adOwcuVK5tG85ORkREREsG7wCF28eJFJfoVWrVqFVatWtbkeoVevXom0MWzYMFy6dAl5eXkghIh9rxNo+s6orKxEWVkZunbtCgDYv38/jh49ytzoU1RUxKJFi0SW1dPTYz1+SFGfGtpzRVHUe+nfvz9GjhyJfv36YfLkyThy5AgqKytZdSwsLCArK8v8raury7yzcu/ePTQ0NMDExAQqKirMlJiYiIKCAmYZJSUl5mK/ZRvNrVq1CikpKYiOjha5o2plZcVaHoDYNpKSklixhIeHAwAcHR3B5/OZO69ubm4wNzfH9evXkZiYCD09PfTp0wdAU6/TDz/8gH79+kFTUxMqKiqIi4tDcXGxxJhabldOTg709fVZF4TNEzOh6OhofPnll2J7AIR3uxMTE/HmzRuMGTNGpE5zmpqamDlzJng8HsaNG4fdu3ejpKSk1WWExF2U7tu3DzY2NtDW1oaKigoOHz4ssg+EwsPDWftd3N3rkJAQeHp6ik0idu7ciYyMDNy9excXL15Ebm4uc2FbXFzManvz5s2Qk5PDlClTmM/3zZs3iI6OZt2s+KtUVVWRkZGBtLQ0BAYGYtmyZcxF8ObNm1mxFBcXi73D3xppj2Wh9z0HFy1ahE2bNsHBwQEBAQHIzMxk2rp79y6uXr3KWlaYdBQUFIj9XD/k9kp7rkmjpqZGYoKalJSEW7duISwsDCYmJiLvC4WEhMDd3R1yck33rT08PJCcnMz6HgP+l/goKipi9OjRcHd3x/r16yV+7whpa2tj7NixCAsLQ2hoKMaOHQstLS2xsY4YMQIZGRmsSdyAGK0RHsPNp6NHj7LqtOdz9PT0REZGBpKTkzF69GisXr2aeb+rOS6XK/amH0V9KmjPFUV9Alq728jhcFh/L1++XOq60o6e1RpZWVnEx8fjxo0buHz5Mvbs2YPVq1cjNTUVRkZGAAB5eXmROBobGwEAVVVVkJWVxa1bt1gXfwBYd03FtdHyh/3kyZPYuXMn+Hw+unfvLhJr8zaE+0IYR3O2trasoZ+FPRdOTk4ICQnB3bt3IS8vDzMzMzg5OYHP56OyspLpSQCaehB3796NXbt2oV+/flBWVsaSJUtEHv9pbd9I68KFC9i6davYMk9PT3z33XdYv349pk+fzlz4tSY0NBSLFi1CbGwsTp8+jTVr1iA+Ph52dnatLqesrMz6OyIiAv7+/ggKCoK9vT1UVVWxfft2pKamil1+/PjxrN63lp9hUlIScnJycPr0abHL6+joMAN6mJqa4vXr1/Dw8MCmTZtgaGjI+kw1NTUBNO0fR0dHPH/+HPHx8eByuXBxcQEAaGlpQVZWFs+ePWOt59mzZ232gMjIyDCxDBgwAAKBAFu2bIGTkxPmzp3L6rnT09Njeneys7Nhb2/fatuA9MeyuPrCZdpzDs6ePRs8Hg8xMTG4fPkytmzZgqCgICxcuBBVVVUYN24c/vOf/4isV1dXF42NjSKfq7DnLzs7G9bW1u+1vdKea9LQ0tISuTkkZGRkhM6dO8PU1BTPnz+Hu7s7rl27BqDpMbmoqCi8e/cOBw4cYJZpaGhASEgIAgMDmXkjRozAgQMHoKCgAD09PeaclPS905y3tzcWLFgAoOnGhSTKysrM8fdXNT+GWzI2NgaHw4FAIMDEiRNFygUCATQ0NKCtrc3MU1dXZ9o7c+YMjI2NYWdnx3rsFmjal81vpFHUp4YmVxT1CWj5XHpH1G0Nh8OBg4MDHBwcsG7dOhgYGCAqKgrLli1rc1lra2s0NDTg+fPnGDZs2F+OISUlBbNnz8ahQ4faTALawuVyxV5UCN+72rlzJ5NIOTk5YevWraisrGSNcJacnAxXV1dMmzYNQNOFYG5uLvr27St1HKampnj8+DGePXvGXGi1HN45Ly8Pjx49kvjImaamJsaPH48zZ86I3GlvjbW1NaytrbFy5UrY29vj1KlTsLOzg4KCAhoaGqRqIzk5GUOHDsW8efOYeS3v4jenqqrKDH8tTnBwMGxsbNC/f3+p1i9MFGpqaiAnJyf2Mx06dCj09fVx+vRpXLp0CZMnT2Yu5BUUFGBjY4OEhATm/+JqbGxEQkICc4ErrcbGRmZYeE1NTSa5E/riiy+gpaWFbdu2ISoqSmT5ly9firyH9KFIew7q6+tj7ty5mDt3LlauXIkjR45g4cKFGDhwIM6ePQtDQ0OJyXvLz3XAgAHo27cvgoKC4O7uLtLr2p7tleZck/a4tba2RlZWVpv15s+fjy1btiAqKgoTJ05EeHg4evTogfPnz7PqXb58GUFBQdi4cSNzPEpKfCR97zTn4uKCt2/fgsPhMI8fdoQuXbrA2dkZ+/fvx9KlS1lPCZSWliI8PBwzZswQuaEnpKKigsWLF8Pf3x937txh1bt//z6++uqrj74NFPWx0McCKYp6L6mpqdi8eTPS09NRXFyMc+fOoaysTOKz+C2ZmJjA09MTM2bMwLlz51BYWIibN29iy5YtiImJkaqN0tJSTJw4EVOnTgWPx0NpaSlKS0tRVlb2PpsmQkNDA1ZWVggPD2cGrhg+fDhu376N3NxcVs9Vnz59mB49gUAAX19fkR6Qtjg7O6N3797w8vJCZmYmkpOTsWbNGgD/u3sfHR2NUaNGQUlJSWI7YWFhKC8vl+r9lsLCQqxcuRIpKSl49OgRLl++jLy8PObzNDQ0RGFhITIyMlBeXt7q/yPVp08fpKenIy4uDrm5uVi7dq3Y//tHGn/++SciIyMxe/ZsiXVevnyJ0tJSPH36FImJidi4cSNMTEzaPBa//vprHDx4EPHx8SKPBC5btgxHjhzBsWPHIBAI8O233+LNmzesd8tmzJjBerdry5YtiI+Px8OHDyEQCBAUFIQTJ04wF//iKCsr4+jRo4iJicH48eNx5coVFBUVIT09Hd999127H+lqD2nOwSVLliAuLg6FhYW4ffs2rl69yuzX+fPno6KiAh4eHkhLS0NBQQHi4uIwa9YsiQkNh8NBaGgocnNzMWzYMPz66694+PAhMjMzERgYKDI4S2ukOdcMDQ2RmpqKoqIilJeXS+zl4/F4SElJaTMRU1JSwpw5cxAQEABCCIKDg/HVV1/B0tKSNfn4+KC8vByxsbFSb09rZGVlIRAIkJWVJdLL2FxdXR3zPSicysvL27UuQohIG6Wlpcy+27t3L+rq6sDj8XDt2jU8fvwYsbGxcHZ2Rvfu3Vm9deL4+voiNzcXZ8+eZeYVFRXhyZMnIr1ZFPUpockVRVHvRU1NDdeuXcOYMWNgYmKCNWvWICgoCKNHj5a6jdDQUMyYMQN+fn4wNTXFhAkTkJaWJvb/bxEnOzsbz549w7Fjx6Crq8tMgwYN+qubJZGjoyMaGhqY5EpTUxN9+/aFjo4OTE1NmXpr1qzBwIEDwePx4OTkBB0dHab3Q1qysrI4f/48qqqqMGjQIMyePZsZLVD4Xkh0dDTGjx/fajtcLlfq//dISUkJ2dnZmDRpEkxMTPDNN99g/vz58PX1BQBMmjQJLi4uGDFiBLS1tfHzzz9LbMvX1xdubm5wd3fHkCFD8OLFC1YvVntERESAEAIPDw+JdWbNmgVdXV306NEDHh4esLCwwKVLl9p8FNLT0xNZWVno3r07HBwcWGXu7u7YsWMH1q1bhwEDBiAjIwOxsbGsR7aKi4tZ76W9efMG8+bNg4WFBRwcHHD27FmcPHmy1cQQAFxdXXHj/7V3/yCphWEcx383/wwiFmGDo6mhQWuLLmJILo6CDm3ZYFCDU5NDU1C01NbSoGPQ1NBqLS0NEYQgSJuL1RIH9N4hjMpS456Lx9v3M4vndTg+/N5/z8WFHA6HcrmcwuGwstmsHh4e/rpf1yCD3sF2u61CoaBIJKLl5WXNzc3p8PBQ0su2xmq1qna7rWQyqYWFBW1ubmpqaurTc4Bdi4uLurq6UjAY1OrqqiKRiNLptG5ubrS/vz/02Id514rFomw2m+bn5zUzM/PleaxUKiW73a7z8/OBz11fX9ft7a12dnZ0fX3d03dNetkKl0gkdHR0NPTvGcTj8cjj8fT9zNnZ2bv/Qp/Pp1gs9q3nPD4+9nzH27N63cmT2dlZZTIZBQIB5fN5xeNxXV5e9qzOfjQ9Pa2VlRWVSqXXwFapVJRMJl8vRwLG0a/f3z1VCuCfeX5+Vr1el9/vH+oGO/w81WpVsVhMtVpNk5OT8vl8ur+/73u9OoDhHRwc6PT0dGDTZ5jLMAyFQiGVy+WeSY4uaiTGAWeuAMDCTk5O5Ha7FQqFVKvVtLGxoWg0qkAgoLu7O+3t7RGsABOtra2p1Wrp6emp7xlAmKvRaGhra+vLYAWMC1auAAthVg4fHR8fa3t7W41GQ16vV0tLS9rd3R16mx8A/C+okRgHhCvAQigcAAB8jhqJccCFFgAAAABgAsIVYEEsKAMA8B61EeOAcAVYSLdviWEYIx4JAADW0q2N/Xp8AaPGbYGAhdjtdrlcLjWbTTkcjr49YgAA+Ck6nY6azaZcLtfA3nXAKHGhBWAxhmGoXq+/NlUEAADSxMSE/H6/nE7nqIcCfIlwBVhQp9NhayAAAG84nU52dMDyCFcAAAAAYALiPwAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAn+APa6Gt8kcIzVAAAAAElFTkSuQmCC","text/plain":["
"]},"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(model_df[\"rpp\"], model_df[\"meteor\"], label=model + \" (METEOR)\")\n"," ax.plot(\n"," model_df[\"rpp\"], model_df[\"rap\"], label=model + \" (RAP-METEOR)\", linestyle=\"--\"\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, -0.5))\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"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[\"total_repetitions\"], label=model)\n","\n","\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, -0.3))\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["tokenizers = {model: load_tokenizer(model) for model in models}"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["col = \"shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.04\"\n","df[[\"ews_score\", \"repetition_score\", \"total_repetitions\"]] = df[col].apply(\n"," detect_scores\n",")\n","df[\"output_tokens\"] = df[col].apply(\n"," lambda x: len(tokenizers[col.split(\"/rpp\")[0]](x)[\"input_ids\"])\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/html":["
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chineseenglishQwen/Qwen2-72B-Instruct/rpp-1.00Qwen/Qwen2-72B-Instruct/rpp-1.02Qwen/Qwen2-72B-Instruct/rpp-1.04Qwen/Qwen2-72B-Instruct/rpp-1.06Qwen/Qwen2-72B-Instruct/rpp-1.08Qwen/Qwen2-72B-Instruct/rpp-1.10Qwen/Qwen2-72B-Instruct/rpp-1.12Qwen/Qwen2-7B-Instruct/rpp-1.00...shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30ews_scorerepetition_scoretotal_repetitionsoutput_tokens
193“有…… 没有…… 有…… 没有……'Yes . . . no . . . yes . . . no . . .\"There is... There isn't... There is... There ...\"There is... There isn't... There is... There ...\"There is... There isn't... There is... There ...\"There is... There isn't... There is... There ...\"There is... There isn't... There is... There ...\"There is... There isn't... There is... There ...\"There is... There isn't... There is... There ...\"Yes... No... Yes... No...\"...Yes... No... Yes... No...Yes... No... Yes... No...Yes... No... Yes... No...Yes... No... Yes... No...Yes... No... Yes... No...Yes... No... Yes... No...0649664962049
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1 rows × 61 columns

\n","
"],"text/plain":[" chinese english \\\n","193 “有…… 没有…… 有…… 没有…… 'Yes . . . no . . . yes . . . no . . . \n","\n"," Qwen/Qwen2-72B-Instruct/rpp-1.00 \\\n","193 \"There is... There isn't... There is... There ... \n","\n"," Qwen/Qwen2-72B-Instruct/rpp-1.02 \\\n","193 \"There is... There isn't... There is... There ... \n","\n"," Qwen/Qwen2-72B-Instruct/rpp-1.04 \\\n","193 \"There is... There isn't... There is... There ... \n","\n"," Qwen/Qwen2-72B-Instruct/rpp-1.06 \\\n","193 \"There is... There isn't... There is... There ... \n","\n"," Qwen/Qwen2-72B-Instruct/rpp-1.08 \\\n","193 \"There is... There isn't... There is... There ... \n","\n"," Qwen/Qwen2-72B-Instruct/rpp-1.10 \\\n","193 \"There is... There isn't... There is... There ... \n","\n"," Qwen/Qwen2-72B-Instruct/rpp-1.12 \\\n","193 \"There is... There isn't... There is... There ... \n","\n"," Qwen/Qwen2-7B-Instruct/rpp-1.00 ... \\\n","193 \"Yes... No... Yes... No...\" ... \n","\n"," shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20 \\\n","193 Yes... No... Yes... No... \n","\n"," shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22 \\\n","193 Yes... No... Yes... No... \n","\n"," shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24 \\\n","193 Yes... No... Yes... No... \n","\n"," shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26 \\\n","193 Yes... No... Yes... No... \n","\n"," shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28 \\\n","193 Yes... No... Yes... No... \n","\n"," shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30 ews_score \\\n","193 Yes... No... Yes... No... 0 \n","\n"," repetition_score total_repetitions output_tokens \n","193 6496 6496 2049 \n","\n","[1 rows x 61 columns]"]},"execution_count":295,"metadata":{},"output_type":"execute_result"}],"source":["rows = df.query(\"total_repetitions > 1000\")\n","rows"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["row = rows.iloc[0]"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["“有…… 没有…… 有…… 没有……\n"]}],"source":["print(row[\"chinese\"])"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["'Yes . . . no . . . yes . . . no . . .\n"]}],"source":["print(row[\"english\"])"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["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! 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! 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! 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! 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! 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! 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! 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! 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\n","----detect excessive whitespaces----\n","----detect text repetitions----\n","\n","Group 1 found at 159-3407: `:\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! 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! 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! 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! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation`\n","Group 2 found at 3407-6655: `:\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! 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! 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! 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! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation`\n","Group 3 found at 3407-6655: `:\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! 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! 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! 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! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation`\n","(0, 6496, 6496)\n"]},{"data":{"text/plain":["(0, 6496, 6496)"]},"execution_count":299,"metadata":{},"output_type":"execute_result"}],"source":["output = row[col]\n","print(output)\n","detect_repetitions(output, debug=True)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
ews_scorerepetition_scoretotal_repetitionsoutput_tokensground_truth_tokens-Qwen/Qwen2-72B-Instructground_truth_tokens-Qwen/Qwen2-7B-Instructground_truth_tokens-shenzhi-wang/Llama3.1-8B-Chinese-Chatground_truth_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat
count1133.01133.0000001133.0000001133.0000001133.0000001133.0000001133.0000001133.000000
mean0.05.8464255.84642533.95851729.45807629.45807629.43248032.805825
std0.0192.990061192.99006163.82289120.12665920.12665920.07666621.906509
min0.00.0000000.0000003.0000001.0000001.0000001.0000002.000000
25%0.00.0000000.00000017.00000016.00000016.00000016.00000018.000000
50%0.00.0000000.00000027.00000025.00000025.00000025.00000027.000000
75%0.00.0000000.00000042.00000038.00000038.00000038.00000042.000000
max0.06496.0000006496.0000002049.000000135.000000135.000000135.000000149.000000
\n","
"],"text/plain":[" ews_score repetition_score total_repetitions output_tokens \\\n","count 1133.0 1133.000000 1133.000000 1133.000000 \n","mean 0.0 5.846425 5.846425 33.958517 \n","std 0.0 192.990061 192.990061 63.822891 \n","min 0.0 0.000000 0.000000 3.000000 \n","25% 0.0 0.000000 0.000000 17.000000 \n","50% 0.0 0.000000 0.000000 27.000000 \n","75% 0.0 0.000000 0.000000 42.000000 \n","max 0.0 6496.000000 6496.000000 2049.000000 \n","\n"," ground_truth_tokens-Qwen/Qwen2-72B-Instruct \\\n","count 1133.000000 \n","mean 29.458076 \n","std 20.126659 \n","min 1.000000 \n","25% 16.000000 \n","50% 25.000000 \n","75% 38.000000 \n","max 135.000000 \n","\n"," ground_truth_tokens-Qwen/Qwen2-7B-Instruct \\\n","count 1133.000000 \n","mean 29.458076 \n","std 20.126659 \n","min 1.000000 \n","25% 16.000000 \n","50% 25.000000 \n","75% 38.000000 \n","max 135.000000 \n","\n"," ground_truth_tokens-shenzhi-wang/Llama3.1-8B-Chinese-Chat \\\n","count 1133.000000 \n","mean 29.432480 \n","std 20.076666 \n","min 1.000000 \n","25% 16.000000 \n","50% 25.000000 \n","75% 38.000000 \n","max 135.000000 \n","\n"," ground_truth_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat \n","count 1133.000000 \n","mean 32.805825 \n","std 21.906509 \n","min 2.000000 \n","25% 18.000000 \n","50% 27.000000 \n","75% 42.000000 \n","max 149.000000 "]},"execution_count":300,"metadata":{},"output_type":"execute_result"}],"source":["for model in models:\n"," df[f\"ground_truth_tokens-{model}\"] = df[\"english\"].apply(\n"," lambda x: len(tokenizers[model](x)[\"input_ids\"])\n"," )\n","\n","df.describe()"]}],"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}