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install transformers","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nzlH8aaV2ZGS","outputId":"f56bcb3a-85b2-44d7-8d42-b9137b89be3f"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n\nCollecting transformers\n\n  Downloading transformers-4.23.1-py3-none-any.whl (5.3 MB)\n\n\u001b[K     |████████████████████████████████| 5.3 MB 32.9 MB/s \n\n\u001b[?25hCollecting huggingface-hub<1.0,>=0.10.0\n\n  Downloading huggingface_hub-0.10.1-py3-none-any.whl (163 kB)\n\n\u001b[K     |████████████████████████████████| 163 kB 71.5 MB/s \n\n\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.64.1)\n\nRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.21.6)\n\nRequirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.8.0)\n\nCollecting tokenizers!=0.11.3,<0.14,>=0.11.1\n\n  Downloading tokenizers-0.13.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.6 MB)\n\n\u001b[K     |████████████████████████████████| 7.6 MB 61.6 MB/s \n\n\u001b[?25hRequirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (6.0)\n\nRequirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.13.0)\n\nRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2022.6.2)\n\nRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n\nRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n\nRequirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.10.0->transformers) (4.1.1)\n\nRequirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n\nRequirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.9.0)\n\nRequirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2022.9.24)\n\nRequirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n\nRequirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n\nRequirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n\nInstalling collected packages: tokenizers, huggingface-hub, transformers\n\nSuccessfully installed huggingface-hub-0.10.1 tokenizers-0.13.1 transformers-4.23.1\n"}]},{"cell_type":"markdown","source":"#Import Packages","metadata":{"id":"JLJ-oMRyehJI"}},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport os\nimport string\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nimport nltk\nfrom nltk.corpus import stopwords","metadata":{"id":"mtLoz1UzDLw1"},"execution_count":7,"outputs":[]},{"cell_type":"markdown","source":"#Preprocessing Dataset","metadata":{"id":"FNUCPftwelfq"}},{"cell_type":"code","source":"train_dataset = pd.read_csv(\"/kaggle/input/feedback-prize-english-language-learning/train.csv\")\ntrain_dataset.head()","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":302},"id":"yLThkzRuDLYy","outputId":"2c265307-c871-49db-f62e-41b1576290f0"},"execution_count":8,"outputs":[{"output_type":"execute_result","execution_count":8,"data":{"text/plain":["        text_id                                          full_text  cohesion  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     <td>3.500000</td>\n","      <td>3.500000</td>\n","      <td>3.500000</td>\n","      <td>3.500000</td>\n","      <td>3.500000</td>\n","      <td>3.500000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>5.000000</td>\n","      <td>5.000000</td>\n","      <td>5.000000</td>\n","      <td>5.000000</td>\n","      <td>5.000000</td>\n","      <td>5.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-959e21f1-3232-43bd-b02b-c0cff3258155')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 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Column       Non-Null Count  Dtype  \n\n---  ------       --------------  -----  \n\n 0   text_id      3911 non-null   object \n\n 1   full_text    3911 non-null   object \n\n 2   cohesion     3911 non-null   float64\n\n 3   syntax       3911 non-null   float64\n\n 4   vocabulary   3911 non-null   float64\n\n 5   phraseology  3911 non-null   float64\n\n 6   grammar      3911 non-null   float64\n\n 7   conventions  3911 non-null   float64\n\ndtypes: float64(6), object(2)\n\nmemory usage: 244.6+ KB\n"}]},{"cell_type":"code","source":"# for test\ntest_dataset = pd.read_csv(\"/kaggle/input/feedback-prize-english-language-learning/test.csv\")\ntest_dataset.head()","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"APET964vq_-m","outputId":"35d50b7b-daff-494b-907c-55ec193b30c3"},"execution_count":150,"outputs":[{"output_type":"execute_result","execution_count":150,"data":{"text/plain":["        text_id                                          full_text\n","0  0000C359D63E  when a person has no experience on a job their...\n","1  000BAD50D026  Do you think students would benefit from being...\n","2  00367BB2546B  Thomas Jefferson once states that \"it is wonde..."],"text/html":["\n","  <div id=\"df-33204a4e-57d0-4f18-9b32-18f822df9486\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>text_id</th>\n","      <th>full_text</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0000C359D63E</td>\n","      <td>when a person has no experience on a job their...</td>\n","    </tr>\n","    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train_dataset['conventions']","metadata":{"id":"ddqN03sqtxtG"},"execution_count":11,"outputs":[]},{"cell_type":"code","source":"# for test\nfull_text_test = test_dataset['full_text']","metadata":{"id":"B9midTNHrHim"},"execution_count":151,"outputs":[]},{"cell_type":"code","source":"full_text.shape","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PLVeUJnOx-dQ","outputId":"cfe1a616-5a14-45c8-bf8f-01cce80fad8b"},"execution_count":12,"outputs":[{"output_type":"execute_result","execution_count":12,"data":{"text/plain":["(3911,)"]},"metadata":{}}]},{"cell_type":"code","source":"cohesion","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"C0kzZaFtDANt","outputId":"04a46ba9-15af-4489-b2dc-472052ca50e6"},"execution_count":13,"outputs":[{"output_type":"execute_result","execution_count":13,"data":{"text/plain":["0       3.5\n","1       2.5\n","2       3.0\n","3       4.5\n","4       2.5\n","       ... \n","3906    2.5\n","3907    4.0\n","3908    2.5\n","3909    4.0\n","3910    3.5\n","Name: cohesion, Length: 3911, dtype: float64"]},"metadata":{}}]},{"cell_type":"code","source":"np.unique(cohesion)","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JOxGfUbDArO2","outputId":"0cbbb865-2c36-43c8-8b25-571dbf70b419"},"execution_count":14,"outputs":[{"output_type":"execute_result","execution_count":14,"data":{"text/plain":["array([1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ])"]},"metadata":{}}]},{"cell_type":"code","source":"len(np.unique(cohesion))","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"blBewRT-vddL","outputId":"45d5b181-a27a-4dfe-cee4-e88b523af4bc"},"execution_count":15,"outputs":[{"output_type":"execute_result","execution_count":15,"data":{"text/plain":["9"]},"metadata":{}}]},{"cell_type":"code","source":"x = cohesion - 1\nx","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"N2DZKJqHv705","outputId":"3916f3e9-427e-4ad8-d7d5-1a4365d501df"},"execution_count":16,"outputs":[{"output_type":"execute_result","execution_count":16,"data":{"text/plain":["0       2.5\n","1       1.5\n","2       2.0\n","3       3.5\n","4       1.5\n","       ... \n","3906    1.5\n","3907    3.0\n","3908    1.5\n","3909    3.0\n","3910    2.5\n","Name: cohesion, Length: 3911, dtype: float64"]},"metadata":{}}]},{"cell_type":"code","source":"cohesion","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"D492k7BtxiXX","outputId":"bd6f0ae0-adcb-4464-cf6d-d860ef3f3209"},"execution_count":20,"outputs":[{"output_type":"execute_result","execution_count":20,"data":{"text/plain":["0       3.5\n","1       2.5\n","2       3.0\n","3       4.5\n","4       2.5\n","       ... \n","3906    2.5\n","3907    4.0\n","3908    2.5\n","3909    4.0\n","3910    3.5\n","Name: cohesion, Length: 3911, dtype: float64"]},"metadata":{}}]},{"cell_type":"code","source":"from nltk.corpus import stopwords\nnltk.download('stopwords')\nstop_words = stopwords.words('english')","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iTJRTt5oSn_v","outputId":"d20513ae-4807-4092-dfc4-c6b58b13c876"},"execution_count":21,"outputs":[{"output_type":"stream","name":"stderr","text":"[nltk_data] Downloading package stopwords to /root/nltk_data...\n\n[nltk_data]   Unzipping corpora/stopwords.zip.\n"}]},{"cell_type":"code","source":"def CleanFeatures(sentences):\n  sentences = sentences.apply(lambda sequence:\n                                            [ltrs for ltrs in sequence if ltrs not in string.punctuation])\n  sentences = sentences.apply(lambda wrd: ''.join(wrd))\n  sentences = sentences.apply(lambda sequence:\n                                            [word for word in sequence.split() if word not in stop_words])\n  sentences = sentences.apply(lambda wrd: ' '.join(wrd))\n  return sentences","metadata":{"id":"q9BuoCMBEa7m"},"execution_count":22,"outputs":[]},{"cell_type":"code","source":"full_text = CleanFeatures(full_text)","metadata":{"id":"4LBClh5fEcRN"},"execution_count":23,"outputs":[]},{"cell_type":"code","source":"# for test\nfull_text_test = CleanFeatures(full_text_test)","metadata":{"id":"-j63EyE9rPB_"},"execution_count":152,"outputs":[]},{"cell_type":"code","source":"full_text","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6UYUxNuOm1I_","outputId":"6d9d684e-7b50-4d0f-a786-2eab122e9f0b"},"execution_count":24,"outputs":[{"output_type":"execute_result","execution_count":24,"data":{"text/plain":["0       I think students would benefit learning homebe...\n","1       When problem change let best matter happening ...\n","2       Dear Principal If u change school policy grade...\n","3       The best time life become I agree greatest acc...\n","4       Small act kindness impact people change people...\n","                              ...                        \n","3906    I believe using cellphones class education us ...\n","3907    Working alone students argue decission proyect...\n","3908    A problem chance best What I think quote cant ...\n","3909    Many people disagree Albert Schweitzers quote ...\n","3910    Do think failure main thing people consist goa...\n","Name: full_text, Length: 3911, dtype: object"]},"metadata":{}}]},{"cell_type":"markdown","source":"#Tokenizer Input Text","metadata":{"id":"XdBehtR4e52w"}},{"cell_type":"code","source":"from transformers import AutoTokenizer","metadata":{"id":"9yWRDGWP4ZDw"},"execution_count":25,"outputs":[]},{"cell_type":"code","source":"list_words = [len(text.split()) for text in full_text]","metadata":{"id":"nsR3Zhmf2wmg"},"execution_count":26,"outputs":[]},{"cell_type":"code","source":"seq_len = np.max(list_words)\nseq_len","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hObb8h3E3C2K","outputId":"78deef5d-d4fa-497b-ae6e-3a8859955de8"},"execution_count":27,"outputs":[{"output_type":"execute_result","execution_count":27,"data":{"text/plain":["686"]},"metadata":{}}]},{"cell_type":"code","source":"tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')","metadata":{"id":"RtXtsqTA3L-a","colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["f376cef741ac4a4c9b40670634f65a9a","e3ecf9015b5545eaa333191634c6b720","75b8d87c3f3340a4b5f532f8b3e8d0a8","f3b8a9a8413745ee934af196bac5cc66","6180f1eeeac74480b2f2b68ea02ede9f","860fe561f22d4b9992ca5d015bb1a23f","9f2347ca6ceb4b49b212d46ff5cc2586","08572787a00e46fa83bfa4c2b130bc40","1fd28cda988e40afbf37608b2e41b07d","d7fbb34cb88d4e77a446ef82ff504bfc","65bc2f0b369d4fb1a7b216f943077a1a","1eb036b9b06840e88a53f171344c24d8","06f91e19a14b4cb09890992fedda669d","da76a57849e84124a3739ba574cb294b","0080c86a9d1049efad025c92f2d09637","d4f4fc5b72fe48b885e2ff8bf3001f70","b45f1099b5fc4649be0e51c20701ae79","77d0159be40c41a68f75edc3f21ffa9f","25b1d35aaa584feea09282783b4222b6","b5145a0104e44ce7a3e5957feaf73a02","f6feabe0bd324ff88bdafc5940804370","5f8575805d8740c88ce5981d371a1631","0e08df45773046afaaa9cebb76414f1c","2b5aa63cd0304b1691a91d050fe1557c","51d197173f524050bc0ba33c277c0dc0","1d382155e75b455badab8c4d71c2ed04","7991f644aaba475189f7f78907e27a68","50e20fb7318741c69ef66fc5549c052f","02c97714e1da4976b0934fd07652d9cb","90276d9eae154760a2b8d54ba3a969d0","ccb4bd9d23ea491f85010c6d1ebe1e74","4fa651b5337d46939a5265847bde909b","50b88a47985148f7b2923042d0a2caa3","e9621460e99e43f0a796ff68e8475e95","09dec4e5cb34417c9eb8e06a18199db1","f4311ef5ad1749d294abaaea29603c8d","aa248b1d1a054d4cbf08f6e20b3fcd61","f1b54b60f8004d9db01cea1179af4b69","a311f59f7272491c92454333fa48e13d","888b0cb6d0464c84953b667eb7671aad","b01c5d4c95ed4dba88f9b6c88d54d793","d349575d17174d0aad3430c8ef2d2c41","a38f3638a0a94d099d332faa295953f5","c6fd5d31033f40418f90a4172c9314fa"]},"outputId":"b28d746b-3807-4e83-adb6-f75def66ae09"},"execution_count":28,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f376cef741ac4a4c9b40670634f65a9a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/570 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1eb036b9b06840e88a53f171344c24d8"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/213k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0e08df45773046afaaa9cebb76414f1c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/436k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e9621460e99e43f0a796ff68e8475e95"}},"metadata":{}}]},{"cell_type":"code","source":"input_ids = []\nattention_mask = []","metadata":{"id":"-n69KgDCr-9q"},"execution_count":29,"outputs":[]},{"cell_type":"code","source":"# for test\ninput_ids_test = []\nattention_mask_test = []","metadata":{"id":"xvyKJn_Jrhd3"},"execution_count":153,"outputs":[]},{"cell_type":"code","source":"for index, value in enumerate(full_text):\n  tokens = tokenizer.encode_plus(value, max_length = seq_len,padding = \"max_length\",\n                                 truncation = True, return_token_type_ids = True,\n                                 return_attention_mask = True,\n                                 return_tensors = 'np')\n  input_ids.append(tokens['input_ids'])\n  attention_mask.append(tokens['attention_mask'])","metadata":{"id":"bMIK11tN3kKD"},"execution_count":30,"outputs":[]},{"cell_type":"code","source":"# for test\nfor index, value in enumerate(full_text_test):\n  tokens = tokenizer.encode_plus(value, max_length = seq_len,padding = \"max_length\",\n                                 truncation = True, return_token_type_ids = True,\n                                 return_attention_mask = True,\n                                 return_tensors = 'np')\n  input_ids_test.append(tokens['input_ids'])\n  attention_mask_test.append(tokens['attention_mask'])","metadata":{"id":"rFmFI9XyrmTY"},"execution_count":154,"outputs":[]},{"cell_type":"code","source":"input_ids = np.asarray(input_ids)\nattention_mask = np.asarray(attention_mask)","metadata":{"id":"G48Kkbv8s9_y"},"execution_count":31,"outputs":[]},{"cell_type":"code","source":"# for test\ninput_ids_test = np.asarray(input_ids_test)\nattention_mask_test = np.asarray(attention_mask_test)","metadata":{"id":"lr18o9q6rtuQ"},"execution_count":155,"outputs":[]},{"cell_type":"code","source":"# for test\ninput_ids_test.shape","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hGMh-gJnsWq4","outputId":"9338628f-b85d-459f-80a8-34f34078f1d2"},"execution_count":156,"outputs":[{"output_type":"execute_result","execution_count":156,"data":{"text/plain":["(3, 1, 686)"]},"metadata":{}}]},{"cell_type":"code","source":"input_ids.shape","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"soJ5yg7A4nxr","outputId":"8df6499e-cbdc-48fc-9cb0-cce89e6b2a5e"},"execution_count":32,"outputs":[{"output_type":"execute_result","execution_count":32,"data":{"text/plain":["(3911, 1, 686)"]},"metadata":{}}]},{"cell_type":"code","source":"input_ids = np.reshape(input_ids, (input_ids.shape[0], input_ids.shape[2]))\nattention_mask = np.reshape(attention_mask, (attention_mask.shape[0], attention_mask.shape[2]))","metadata":{"id":"qrSl9Pib4tr6"},"execution_count":33,"outputs":[]},{"cell_type":"code","source":"# for test\ninput_ids_test = np.reshape(input_ids_test, (input_ids_test.shape[0], input_ids_test.shape[2]))\nattention_mask_test = np.reshape(attention_mask_test, (attention_mask_test.shape[0], attention_mask_test.shape[2]))","metadata":{"id":"Cc_9HfFLr3NG"},"execution_count":157,"outputs":[]},{"cell_type":"code","source":"# for test\ninput_ids_test.shape","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UJUtEE7BsadP","outputId":"0cef1e59-6959-4da0-a7bf-8d90e2e7ccfc"},"execution_count":158,"outputs":[{"output_type":"execute_result","execution_count":158,"data":{"text/plain":["(3, 686)"]},"metadata":{}}]},{"cell_type":"code","source":"input_ids.shape","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EA86fPJStSTi","outputId":"8fb44c9c-51ba-4882-8ba8-10d7553ed468"},"execution_count":34,"outputs":[{"output_type":"execute_result","execution_count":34,"data":{"text/plain":["(3911, 686)"]},"metadata":{}}]},{"cell_type":"code","source":"len(syntax)","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2JUb05_rBhEk","outputId":"d624c8d7-51a9-4db6-c583-544c6dae31ed"},"execution_count":35,"outputs":[{"output_type":"execute_result","execution_count":35,"data":{"text/plain":["3911"]},"metadata":{}}]},{"cell_type":"code","source":"syntax = np.asarray(syntax)\ncohesion = np.asarray(cohesion)\nvocabulary = np.asarray(vocabulary)\nphraseology = np.asarray(phraseology)\ngrammar = np.asarray(grammar)\nconventions = np.asarray(conventions)","metadata":{"id":"eIK0p2YHAS5s"},"execution_count":36,"outputs":[]},{"cell_type":"markdown","source":"#TFBertModel","metadata":{"id":"BufP_3KIfBPR"}},{"cell_type":"code","source":"from transformers import TFBertModel\nbert = TFBertModel.from_pretrained('bert-base-cased')","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":156,"referenced_widgets":["68d6a1c16baf485eb7aae7694deda12d","b4ec6bf3289d464cac88d6f4a5c02190","2c97b11343804043b196702d2164bd3c","50739352f3ad4d80b26f42da869a27c3","1e5cf54b97c047ac80cfe3c048590ac8","276fed34425448a295613d1c0c4a0538","d93e599db92d4b6e801c22cafe18a265","65449bcf43344b7eaf69c6064d50060d","cb9e99874d7247e996e309c630fe7437","410fa08b66744411b6e834650e7844a6","4cb1daf7034741238842e709fe8fbc94"]},"id":"GhWRh2Fa6sOr","outputId":"ef283a7f-989a-42c4-c78e-3f31766f8d90"},"execution_count":37,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/527M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"68d6a1c16baf485eb7aae7694deda12d"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":"Some layers from the model checkpoint at bert-base-cased were not used when initializing TFBertModel: ['mlm___cls', 'nsp___cls']\n\n- This IS expected if you are initializing TFBertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n\n- This IS NOT expected if you are initializing TFBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n\nAll the layers of TFBertModel were initialized from the model checkpoint at bert-base-cased.\n\nIf your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertModel for predictions without further training.\n"}]},{"cell_type":"code","source":"from tensorflow.keras.layers import LSTM, Lambda\nfrom tensorflow.keras.layers import add, maximum, subtract, minimum","metadata":{"id":"L8E_avSB7OFq"},"execution_count":38,"outputs":[]},{"cell_type":"code","source":"def resnet(inputs, units):\n  x = tf.keras.layers.LSTM(units,return_sequences = True,)(inputs)\n  x = tf.keras.layers.BatchNormalization()(x)\n  return x","metadata":{"id":"IHvPatIuYfXM"},"execution_count":96,"outputs":[]},{"cell_type":"code","source":"units = 16\ninput_ids_m = tf.keras.layers.Input(shape = (seq_len, ), dtype = 'int32')\nattention_mask_n = tf.keras.layers.Input(shape = (seq_len, ),  dtype = 'int32')\nbert_m = bert(input_ids_m, attention_mask = attention_mask_n)[0]\nx = resnet(bert_m, units)\nfor stack in range(1):\n  for block in range(3):\n    y = resnet(x, units)\n    if stack > 0 and block == 0:\n      x = tf.keras.layers.LSTM(units, return_sequences = True, dropout=0.2, recurrent_dropout=0.2)(x)\n    x = minimum([x, y])\n\n  units *=2\nx1 = tf.keras.layers.GlobalAveragePooling1D()(x)\nx2 = tf.keras.layers.GlobalMaxPooling1D()(x)\nx = tf.keras.layers.concatenate([x1, x2], name=\"our_param\")\ny = tf.keras.layers.Dense(1)(x)\nm = tf.keras.models.Model(inputs = [input_ids_m, attention_mask_n], outputs = [y])      ","metadata":{"id":"IRDyI-a2AQWF"},"execution_count":100,"outputs":[]},{"cell_type":"code","source":"m.layers[2].trainable = False","metadata":{"id":"Lc1UyDgq9yzb"},"execution_count":101,"outputs":[]},{"cell_type":"code","source":"m.summary()","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5eu3K9wt70ah","outputId":"9bc38cdc-1099-4da8-e469-3cddd5f742e9"},"execution_count":102,"outputs":[{"output_type":"stream","name":"stdout","text":"Model: \"model_10\"\n\n__________________________________________________________________________________________________\n\n Layer (type)                   Output Shape         Param #     Connected to                     \n\n==================================================================================================\n\n input_21 (InputLayer)          [(None, 686)]        0           []                               \n\n                                                                                                  \n\n input_22 (InputLayer)          [(None, 686)]        0           []                               \n\n                                                                                                  \n\n tf_bert_model (TFBertModel)    TFBaseModelOutputWi  108310272   ['input_21[0][0]',               \n\n                                thPoolingAndCrossAt               'input_22[0][0]']               \n\n                                tentions(last_hidde                                               \n\n                                n_state=(None, 686,                                               \n\n                                 768),                                                            \n\n                                 pooler_output=(Non                                               \n\n                                e, 768),                                                          \n\n                                 past_key_values=No                                               \n\n                                ne, hidden_states=N                                               \n\n                                one, attentions=Non                                               \n\n                                e, cross_attentions                                               \n\n                                =None)                                                            \n\n                                                                                                  \n\n lstm_18 (LSTM)                 (None, 686, 16)      50240       ['tf_bert_model[10][0]']         \n\n                                                                                                  \n\n batch_normalization_9 (BatchNo  (None, 686, 16)     64          ['lstm_18[0][0]']                \n\n rmalization)                                                                                     \n\n                                                                                                  \n\n lstm_19 (LSTM)                 (None, 686, 16)      2112        ['batch_normalization_9[0][0]']  \n\n                                                                                                  \n\n batch_normalization_10 (BatchN  (None, 686, 16)     64          ['lstm_19[0][0]']                \n\n ormalization)                                                                                    \n\n                                                                                                  \n\n minimum_12 (Minimum)           (None, 686, 16)      0           ['batch_normalization_9[0][0]',  \n\n                                                                  'batch_normalization_10[0][0]'] \n\n                                                                                                  \n\n lstm_20 (LSTM)                 (None, 686, 16)      2112        ['minimum_12[0][0]']             \n\n                                                                                                  \n\n batch_normalization_11 (BatchN  (None, 686, 16)     64          ['lstm_20[0][0]']                \n\n ormalization)                                                                                    \n\n                                                                                                  \n\n minimum_13 (Minimum)           (None, 686, 16)      0           ['minimum_12[0][0]',             \n\n                                                                  'batch_normalization_11[0][0]'] \n\n                                                                                                  \n\n lstm_21 (LSTM)                 (None, 686, 16)      2112        ['minimum_13[0][0]']             \n\n                                                                                                  \n\n batch_normalization_12 (BatchN  (None, 686, 16)     64          ['lstm_21[0][0]']                \n\n ormalization)                                                                                    \n\n                                                                                                  \n\n minimum_14 (Minimum)           (None, 686, 16)      0           ['minimum_13[0][0]',             \n\n                                                                  'batch_normalization_12[0][0]'] \n\n                                                                                                  \n\n global_average_pooling1d_5 (Gl  (None, 16)          0           ['minimum_14[0][0]']             \n\n obalAveragePooling1D)                                                                            \n\n                                                                                                  \n\n global_max_pooling1d_5 (Global  (None, 16)          0           ['minimum_14[0][0]']             \n\n MaxPooling1D)                                                                                    \n\n                                                                                                  \n\n our_param (Concatenate)        (None, 32)           0           ['global_average_pooling1d_5[0][0\n\n                                                                 ]',                              \n\n                                                                  'global_max_pooling1d_5[0][0]'] \n\n                                                                                                  \n\n dense_10 (Dense)               (None, 1)            33          ['our_param[0][0]']              \n\n                                                                                                  \n\n==================================================================================================\n\nTotal params: 108,367,137\n\nTrainable params: 56,737\n\nNon-trainable params: 108,310,400\n\n__________________________________________________________________________________________________\n"}]},{"cell_type":"code","source":"tf.keras.utils.plot_model(m, show_shapes=True, \n                          show_dtype=False, \n                          show_layer_names=True, \n                          expand_nested=True, \n                          show_layer_activations=True)","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":903},"id":"_sp1LY21c-E1","outputId":"bbbc9e88-9a4d-46e2-8fcd-7b939c7a6739"},"execution_count":103,"outputs":[{"output_type":"execute_result","execution_count":103,"data":{"image/png":"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3Tu4sAAAAMCTeDwYAAFBlPXjwoGHDhgWXxyqVytzc3F69en366aedOnWSMRtKoVevXjk5OSdPnlQoFHJnAQAAgGGwfhYAAKDKCgkJ0f1ZWjPbrVu3M2fOHD58mOJsZRQaGnr27NmIiAi5gwAAAMBgWD8LAABQNaWlpdWrVy89Pd3U1FSj0fTp0yc4OLhr165y50KZvPfee/v377969aqVlZXcWQAAAGAArJ8FAAComtatW5eeni6E6N69+4kTJw4ePEhxtgoIDg5OTk4ODQ2VOwgAAAAMg/WzAACUq23btvn5+cmdAkCFFhER4evr+6KzCxcuXLFixY0bN2rUqFGeqQAAAGAMz7/MFwAAlAO2j5SXn59fYGBg1V5MmpiYuGHDBjs7u8mTJ8udBSVT7D/hBAUFrVq1avny5UuXLi2fSAAAADAe6rMAAMigiJVxKAd+fn5du3at8k/hzJkzgi9bJVRsfdbGxmb69Olz586dNGlS/fr1yycVAAAAjIT9ZwEAAIBK5oMPPqhZs+ayZcvkDgIAAICyoj4LAAAAVDIqlWr27NlfffXVzZs35c4CAACAMqE+CwAAAFQ+//M//9OwYcPFixfLHQQAAABlQn0WAAAAqHyUSuW8efO++eabK1euyJ0FAAAApUd9FgAAAKiUhg8f3qJFiwULFsgdBAAAAKVHfRYAgKrps88+q1OnjkKhWLNmjSwBFi5c2KpVKzs7OwsLCzc3t+nTp6enpxdscOLEiW7dullZWTk7O8+YMePZs2ey5NTfnj177O3td+3aJXcQYzlw4MCsWbOioqJcXV0VCoVCoRg5cmTBBv369bO1tTU1NW3duvX58+flypmbm7tkyRI3Nzdzc3MHBwcPD4+EhATd2c2bN3fq1MnW1tbFxWX06NH379/Xv2+x8vPzQ0JC1Gq1/ql27ty5bNkyjUZTqmsthqmp6YIFCyIiImJiYowxPgAAAMoB9VkAAKqmqVOnnjp1SsYAhw4dmjRpUkJCwuPHj5csWRIaGurj46M7GxcX169fv759+z569Gj79u0bNmyYMGGCjGn1odVq5Y5gRB9//HFYWNjs2bO9vb1v3LjRtGnTmjVrbty4cffu3bo2+/fvj4yMHDhwYFxcXPv27eWK6ufn9913323atCkzM/OPP/5o2rSprvQfERExfPhwHx+fO3fuREdHHzt2rH///nl5efr0Lda1a9d69OgRFBSUmZmpf6pBgwapVKq+ffsmJyeX+dIL4e3t3alTp3nz5hljcAAAAJQD6rMAAFRrWVlZhS4GLDsbG5tx48bVqFHD1tbW19fXy8tr3759iYmJ0tlFixY5OTktWLDA2tq6a9euM2bMqPjbaA4YMCAlJWXgwIHGnsh4D+VFPvnkk61bt27bts3W1lZ3MCwszMTEZNy4cSkpKeUZpmhbt27dsWNHZGRkly5dzMzMnJ2do6OjPTw8pLNfffVVvXr1pk2bZm9v365du6CgoJiYmLNnz+rTt2gXL16cOXPmhAkT2rVrV9JUH374Ydu2bd98882ClWJDUSgU8+fP37Vr1+nTpw0+OAAAAMoB9VkAAKq1r7/++uHDh8YY+b///a+pqanuY61atYQQ0sLDvLy83bt39+zZU6FQSGf79++v1Wqjo6ONkaTSMd5DKdT169fnzp27YMEClUpV8LharQ4MDPzrr7+mTp1abmGK9eWXX7Zv397T07PQs4mJic7OzrrvVcOGDYUQt27d0qdv0dq2bRsVFTV8+HALC4uSphJCzJ8/PyYmJjQ0tBRTF6t///49e/acOXOmMQYHAACAsVGfBQCgujh69Gjnzp2trKzs7Ow8PT1TU1MDAwOnTJkSHx+vUCjc3NxCQ0Otra1NTEw6dOhQt25dpVJpbW3dvn37V155pWHDhiqVysHBYfr06aWb/a+//rK0tGzSpIkQ4saNG+np6Y0aNdKdbdq0qRAiNjbWIFdqDCdOnGjUqJFCofjiiy+EEKtXr7a2traysoqOju7fv7+dnV2DBg22bNkiNQ4LC1OpVHXq1Bk/fryzs7NKpVKr1bpVnAEBAebm5k5OTtLHDz74wNraWqFQPH78WAjx3EMRQuzbt8/Ozm7x4sVGurSwsDCtVjto0KB/ngoODm7evPn69esPHDhQaF+tVrtixQp3d3cLCwtHR8chQ4boFkEXfYuEEBqNZt68eY0aNbK0tGzTpk1ERESxUXNycs6cOVPoClaJq6trwdK2tPmsq6urPn1LTZ+RHR0de/bsGRoaaqRdMhYuXHjs2LHDhw8bY3AAAAAYFfVZAACqhYyMjEGDBg0dOvTJkyfXrl1r3rx5Tk5OaGjowIEDmzZtqtVqr1+/HhgYOG3aNK1W++WXX968efP+/fs9evS4cOHCrFmzLly48OTJk3fffXf58uUXL14s6eyZmZmHDh0aO3asubm5+LtqVvBX6VUqlaWl5YMHDwx4yYbVvXv3gvv5Tpw4cfLkyVlZWba2thEREfHx8a6urmPHjs3NzRVCBAQEjBo1KjMz88MPP0xISDh//nxeXt5rr70mbe8QFhbm6+urG2rVqlULFizQfXzuoQghpFdL5efnG+nSdu/e3aJFCysrq3+esrS0/Oabb0xMTMaOHZuRkfHPBvPnz581a9ZHH3308OHDY8eOJSYmvvLKK9JzLPoWCSFmzpz56aefhoSE3Lt3b+DAgcOGDfv111+Ljnr37t2cnJzffvutd+/eUuHb3d191apVuqLn7Nmz79+/v3LlyrS0tLi4uNDQ0Ndff/3ll1/Wp2+p6TnySy+99Ndff5Xix0cfPXr0ePXVV+fOnWuMwQEAAGBU1GcBAKgWEhISUlNTW7durVKp6tatGxUVJW04UKhWrVpZWVnVrFnznXfeEUI0atSoVq1aVlZWI0aMEEKUYpfYJUuWODs7BwcHSx+fPXsmhCi4+4EQQqlUZmVllXRk2anVajs7u9q1a/v7+2dkZNy+fVt3yszMTFpY2qpVq9WrV6elpYWHh5diigEDBqSmphqp9JaRkXHz5k1p/XKhunbtOnny5ISEhH/++nxWVtaKFSvefvvtESNG2Nvbe3p6rlmz5vHjx2vXri3YrNBblJ2dvXr1ai8vL29vbwcHhzlz5iiVymLvj/TGrdq1ay9evDguLu7BgwdDhgyZNGnS5s2bpQY9e/acMWNGQECAnZ2dh4dHWlra+vXr9exbanqO3KxZMyHEpUuXyjjdiyxcuPDkyZM///yzkcYHAACAkVCfBQCgWnB1da1Tp86IESPmz5+fkJCgZy9puavupUZKpVIIoVv/qKft27dv27btp59+0i2YlfY5fe5dSTk5OZaWliUauUKR7tWLbk7Hjh2trKwq4AvQHj58qNVqC108qxMcHNyiRYtVq1adOHGi4PG4uLj09PSOHTvqjnTq1Mnc3Fy3k8NzCt6iq1evZmZm6t6gZWlp6eTkVOz9kfZ+bd26tVqtrlGjhr29/YIFC+zt7XUV4Y8++mjt2rUHDx5MT0+/ceOGWq3u2rWrtGy52L6lpufI0k023iLxrl27vvHGG7NnzzbSFgoAAAAwEuqzAABUC5aWlocOHerevfvixYtdXV39/f3LZ7Hq1q1bP/nkkyNHjjRu3Fh3UNp6NTU1VXckMzMzOzvb2dm5HCLJxcLC4tGjR3KneF52drb4u8L4IiqVKjw8XKFQjBkzpuDXJjk5WQhhY2NTsLGDg0NaWlqx80q7JcyZM0fxt1u3bkmvjyuC9A2RNuqVmJubu7i4xMfHCyHu3bu3bNmy999/v0+fPtbW1k2aNFm3bt3du3eXL19ebN+y0HNk6Z8fpBtuJMHBwb/99tvevXuNNwUAAAAMjvosAADVRevWrXft2nX37t0ZM2ZERER89tlnxp5x5cqVGzduPHToUL169Qoeb9Kkia2t7a1bt3RHpI1W27RpY+xIcsnNzU1OTm7QoIHcQZ4nFQ2lLW6L0LVr16CgoGvXri1atEh30MHBQQjxXDVWz8usXbu2ECIkJERbwOnTp4vuZWNj06xZs8uXLxc8mJeXZ29vL4S4du2aRqMp+GWzs7OrUaNGXFxcsX3LQs+Rc3JyxN833Eg6dOjw1ltvzZs3jyW0AAAAlQj1WQAAqoW7d+9K9aPatWsvXbq0ffv2z5WTDEur1c6YMePSpUs7dux4bn2lEMLMzOzNN988duyY7p1Xe/fuVSgUgwYNMl4keR05ckSr1UovqhJCmJmZlXSbCCOpU6eOQqFISUkptuWiRYtatmx54cIF3REPDw8bG5uCL/U6e/ZsTk5Ohw4dih2tYcOGKpUqJiampIH9/PwuXLhw48YN6WNmZuatW7c8PT2FEFJd+N69e7rGaWlpT548adiwYbF9y0ifkaWbXLdu3bJPV4QFCxacP39+165dRp0FAAAABkR9FgCAauHu3bvjx4+/cuVKTk7OhQsXbt26JdUKa9Socffu3YSEhLS0NANWDC9fvvzpp5+uW7dOqVQqCtAt2p07d+6DBw8+/vjjjIyM06dPL1++fNSoUS1atDBUgIogPz//6dOneXl5sbGxgYGBjRo1GjVqlHTKzc3tyZMnO3bsyM3NffToUcGlxOIfD2Xv3r12dnaLFy82RkgrKytXV9c7d+4U21La5aDgW91UKtWUKVO2b9++cePG1NTUS5cuTZgwwdnZedy4cfqMNnr06C1btqxevTo1NVWj0dy5c0cqrfr7+9etW/f8+fOFdgwKCnJxcaRA1a0AACAASURBVBk1atTt27eTkpJmzJiRlZUlvbusSZMmvXv3Xrdu3bFjx7KyshITE6Uk7733XrF9i523aEWPLJFuskHKwUV46aWXhgwZMmfOHN0/fgAAAKCi0wIAgHIUERFRPv/9/fzzz6WVetbW1m+//XZCQoJarXZ0dDQ1Na1Xr95HH32Ul5en1WrPnz/v4uJiaWnZvXv3WbNmSa8waty48fHjxz/55BPpF7Tr1q27adOmrVu3SgM6Ojpu2bKl6Nlf9JL65cuX69ocPXq0c+fOFhYWzs7O06ZNy87ONu4dKUAIERERUaIuK1eulLbNtbKyGjRo0KpVq6R71axZs/j4+LVr19rZ2QkhXFxc/vzzT61WO27cOKVSWb9+fTMzMzs7uyFDhsTHx+tGS0pK6t27t0qlatKkyb///e9p06YJIdzc3G7fvq39vw/l/v37e/bssbW1DQ4OLullDh06dOjQocU2CwgIUCqVmZmZ0sft27c3bdpUCFGrVq1JkyY913jatGmDBw/WfczPz1++fHmzZs2USqWjo6OXl9fVq1elU8XeomfPns2YMaNRo0ZmZma1a9f29vaOi4vTarVeXl5CCOmX9AuVmJj4zjvvODo6WlhYdO7cee/evbpTjx8/DgwMdHNzs7CwsLGx6dat248//qhn36LnPX36dLdu3XRbJDs5OanV6qNHj+ozsmTAgAH169fPz89/0XXplOL7WdClS5dMTEyioqJKPQIAAADKk0LL7lQAAJSjbdu2+fn58d9feSkUioiICF9fX+NNMX78+MjIyKSkJONNUSwfHx8hRGRkZNHNrl+/7u7uHh4ePmLEiHLJVYz8/PxevXqNGjVqzJgxVWbepKSkBg0aBAcHT5kypdjGZf9++vr6Xr58OTY21sSE35YDAACo6PgbGwAAgFEU+9KtCsLNzW3hwoULFy5MT0+XO4vQaDQ7duxIS0vz9/evSvPOnz+/Xbt2AQEBxhi80On++OOPYkvzAAAAqAiozwIAgBK7cuWK4sXKubKGsps1a5aPj4+/v78+LwozqiNHjkRFRe3du1faG6FqzLtixYqYmJg9e/YolUqDD16oVq1a+fv7z5s3r7L8IwEAAEB1Rn0WAACUWMuWLYvYPmnr1q1yB5TZ7Nmzw8PDU1JSmjRp8sMPP8gdRy+LFy8OCAhYunSpvDH69u27adMmaavfqjFvdHT0s2fPjhw54ujoaPDBizBv3rz4+HiW0AIAAFR87D8LAEC5Yv/ZiqAc9p+tCPTcfxYVjaG+n8OGDbt48aL0ujCDBAMAAIAx8Hc1AAAAoAqaM2fOlStXoqOj5Q4CAACAolCfBQAAAKqgVq1aDR48eNGiRSzYBwAAqMiozwIAAABV07x582JiYvbu3St3EAAAALwQ9VkAAACgamrXrl3//v0XLVokdxAAAAC8kJncAQAAqI62bdsmd4Tq7vTp03JHMLo7d+4IvmzV3rx5815++eVDhw716dNH7iwAAAAohILtqAAAKE/btm3z8/OTOwWACi0iIsLX19dQo7366qv5+fmHDh0y1IAAAAAwINbPAgAggyr876M+Pj5CiMjISLmDFEWhUBi2/lUxVYpngX9SKBSGHXDOnDm9e/c+ceJE9+7dDTsyAAAAyo79ZwEAAICqrFevXq+88srixYvlDgIAAIBCUJ8FAAAAqriPPvpo3759v/zyi9xBAAAA8DzqswAAAEAV9/rrr3fu3Hnp0qVyBwEAAMDzqM8CAAAAVd+0adOio6OvXLkidxAAAAD8H9RnAQAAgKrPy8vL1dU1LCxM7iAAAAD4P6jPAgAAAFWfqalpYGBgeHj4gwcP5M4CAACA/4/6LAAAFU5UVJSrq6tCoVAoFE5OTiNGjHhRy4sXL/r7+zdp0sTCwqJWrVpt27YNDg6WTvn7+yuK9N///rfgRHPnzi10ihUrVigUChMTk5YtWx47dswoF1wqo0ePVqlUCoUiOztb7ixVxIEDB2bNmlXwWzFy5MiCDfr162dra2tqatq6devz58/LlTM3N3fJkiVubm7m5uYODg4eHh4JCQm6s5s3b+7UqZOtra2Li8vo0aPv37+vf99i5efnh4SEqNVq/VPt3Llz2bJlGo2mVNdqYKNHj7axsVmzZo3cQQAAAPD/UZ8FAKDC8fb2vnHjRtOmTe3t7e/fv79x48ZCm126dEmtVjs5OR0+fDglJeXUqVNvvPHGkSNHdA3279+fnJycm5t77949IcSgQYNycnIyMjIePnw4duzYghMJIdavX5+bm/vcFBqNRvpt6D59+ly5cqVHjx7GueLSCA8Pnzp1qtwpqo6PP/44LCxs9uzZum9FzZo1N27cuHv3bl2b/fv3R0ZGDhw4MC4urn379nJF9fPz++677zZt2pSZmfnHH380bdo0PT1dOhURETF8+HAfH587d+5ER0cfO3asf//+eXl5+vQt1rVr13r06BEUFJSZmal/qkGDBqlUqr59+yYnJ5f50svKyspqwoQJq1atysrKkjsLAAAA/hf1WQAAKqvPPvvMwcEhNDS0cePGKpWqefPmixYtsrS0lM4qFIpu3brZ29ubmZnpjiiVSisrq9q1a3fo0KHgUB06dLh///6OHTuemyIqKqp+/frGCJ+VlVXoIsSqwYBXVz436pNPPtm6deu2bdtsbW11B8PCwkxMTMaNG5eSkmLsAPrbunXrjh07IiMju3TpYmZm5uzsHB0d7eHhIZ396quv6tWrN23aNHt7+3bt2gUFBcXExJw9e1afvkW7ePHizJkzJ0yY0K5du5Km+vDDD9u2bfvmm28WrBTLZdKkSenp6d9++63cQQAAAPC/qM8CAFBZJSUlpaSkPHnyRHfE3Nx8165d0p+3bNliZWX1or7jxo176623dB8nTpwohPjyyy+fa7ZixYopU6YYMvTfvv7664cPHxpkKIVCYZBxDMiAV2fAoV7k+vXrc+fOXbBggUqlKnhcrVYHBgb+9ddfFWqd8pdfftm+fXtPT89CzyYmJjo7O+u+Eg0bNhRC3Lp1S5++RWvbtm1UVNTw4cMtLCxKmkoIMX/+/JiYmNDQ0FJMbVh16tQZMWLEihUr8vPz5c4CAAAAIajPAgBQeXXq1CkjI6NPnz4nT54s41B9+vRxd3c/fPjw1atXdQdPnjyZmZnZr1+/Mg7+T4GBgVOmTImPj1coFG5ubkKI48ePt2rVyt7eXqVSeXp6/vTTT0KI1atXW1tbW1lZRUdH9+/f387OrkGDBlu2bCk4lImJye7du/v3729vb+/s7LxhwwZDhdRqtStWrHB3d7ewsHB0dBwyZMiVK1ekUwEBAebm5k5OTtLHDz74wNraWqFQPH78+J9XFxYWplKp6tSpM378eGdnZ5VKpVardSs6SzSUEGLfvn12dnaLFy821GUKIcLCwrRa7aBBg/55Kjg4uHnz5uvXrz9w4EBJ71Kxj0+j0cybN69Ro0aWlpZt2rSJiIgoNmpOTs6ZM2cKXcEqcXV1LVjOljafdXV11advqekzsqOjY8+ePUNDQ7VarcEDlJT0pdq5c6fcQQAAACAE9VkAACqv6dOnd+zY8eLFi927d2/duvWnn35acC1tSY0fP14IUfDFQZ9//nlQUJABgv5DaGjowIEDmzZtqtVqr1+/LoR48OCBn59fQkLC3bt3bWxshg8fLoSYOHHi5MmTs7KybG1tIyIi4uPjXV1dx44dW3Cf3Pz8fAcHh61btyYkJLRv337ixImF7g1aCvPnz581a9ZHH3308OHDY8eOJSYmvvLKK9KL78PCwnx9fXUtV61atWDBghddXUBAwKhRozIzMz/88MOEhITz58/n5eW99tpriYmJJR1KCCG9ZsqwKx93797dokWLQldbW1pafvPNNyYmJmPHjs3IyPhngyLuUrGPb+bMmZ9++mlISMi9e/cGDhw4bNiwX3/9teiod+/ezcnJ+e2333r37i0Vu93d3VetWqUres6ePfv+/fsrV65MS0uLi4sLDQ19/fXXX375ZX36lpqeI7/00kt//fXXxYsXyzhd2bVo0WLAgAGfffaZ3EEAAAAgBPVZAAAqL0tLy1OnTv3nP/9p2bLl5cuXZ8yY4e7ufvTo0dKN9u6771pbW3/77bfSi4Nu3Ljxyy+/DBs2zKCRX2jo0KEff/yxo6NjjRo1Bg0alJSU9OjRI91ZtVptZ2dXu3Ztf3//jIyM27dvF+yrVqvt7e0dHR39/f2fPXt28+bNsufJyspasWLF22+/PWLECHt7e09PzzVr1jx+/Hjt2rWlG9DMzExaZNqqVavVq1enpaWFh4eXYpwBAwakpqbOnTu3dDH+KSMj4+bNm9I74grVtWvXyZMnJyQkzJw587lTet6lQh9fdnb26tWrvby8vL29HRwc5syZo1Qqi70n0hu3ateuvXjx4ri4uAcPHgwZMmTSpEmbN2+WGvTs2XPGjBkBAQF2dnYeHh5paWnr16/Xs2+p6Tlys2bNhBCXLl0q43QGMXXq1JMnT54+fVruIAAAAKA+CwBAZaZUKgMCAv74448zZ84MGTLk4cOHPj4+T58+LcVQ9vb2w4YNe/r06datW4UQISEhEydONDc3N3Tk4imVSvH3QtHnSHkKrp/9Z8cXnS2RuLi49PT0jh076o506tTJ3Nxcty9BWXTs2NHKykq3D4C8Hj58qNVqi9iqWAgRHBzcokWLVatWnThxouDxkt6lgo/v6tWrmZmZujdoWVpaOjk5FXtPpL1fW7durVara9SoYW9vv2DBAnt7e11F+KOPPlq7du3BgwfT09Nv3LihVqu7du0qLVUutm+p6TmydJOlxcWy69GjR8eOHVeuXCl3EAAAAFCfBQCgSujSpcuPP/44YcKER48eHT58uHSDSG8JW7NmTXJycmRkpLTjQfnYvXt3r169ateubWFhMX369HKb90WSk5OFEDY2NgUPOjg4pKWlGWR8CwuLgguEZZSdnS3+rjC+iEqlCg8PVygUY8aMkZZXS8pyl6TdEubMmaP4261bt4rdm8LZ2VkIIW3OKzE3N3dxcYmPjxdC3Lt3b9myZe+//36fPn2sra2bNGmybt26u3fvLl++vNi+ZaHnyJaWluLvG14RTJgwISoqythvnwMAAECxqM8CAFCZHDt2LCQkRPqzt7d3Xl5ewbMjR44UQpR6A9Z27dq9/PLL586dGzdunI+Pj6OjYxnT6un27dteXl5OTk5nz55NSUlZtmxZ+cxbBAcHByHEc3XG5OTkBg0alH3w3NxcQw1VdlLRsNDVygV17do1KCjo2rVrixYt0h0sy12qXbu2ECIkJERbQLG/bm9jY9OsWbPLly8XPJiXl2dvby+EuHbtmkajqVevnu6UnZ1djRo14uLiiu1bFnqOnJOTI/6+4RXBO++8Y2NjU7p9NgAAAGBA1GcBAKhMfvvtN2tra+nPz549e64kdPXqVSFEmzZtSj2+tIT2hx9+mDx5chlilsylS5dyc3MnTpzo6uqqUqkUCkW5Tf0iHh4eNjY2BV9Xdfbs2ZycnA4dOkgfzczMSr2RwpEjR7RarfTSqjIOVXZ16tRRKBQpKSnFtly0aFHLli0vXLigO1LsXSpCw4YNVSpVTExMSQP7+flduHDhxo0b0sfMzMxbt255enoKIaS68L1793SN09LSnjx50rBhw2L7lpE+I0s3uW7dumWfziAsLS1Hjhz51VdfGfZ1cwAAACgp6rMAAFQOubm5Dx48OHLkiK4+K4Tw8vLatm1bcnJySkpKdHT0zJkzBw8eXJb6rK+vb61atby8vFxdXQ2R+oVq1Khx9+7dhISEtLQ06dfDDxw4kJ2dfe3aNYPs8VpGKpVqypQp27dv37hxY2pq6qVLlyZMmODs7Dxu3DipgZub25MnT3bs2JGbm/vo0aNbt24V7F7w6qTaa35+/tOnT/Py8mJjYwMDAxs1ajRq1KhSDLV37147O7vFixcb6kqtrKxcXV3v3LlTbEtplwNTU9OCR4q+S0WPNnr06C1btqxevTo1NVWj0dy5c0cqrfr7+9etW/f8+fOFdgwKCnJxcRk1atTt27eTkpJmzJiRlZUlvbusSZMmvXv3Xrdu3bFjx7KyshITE6Uk7733XrF9i523aEWPLJFuskHKwYYyfvz4hISE/fv3yx0EAACgetMCAIByFBERUex/f7dv3960adMX/bd7+/btUrP9+/f7+fk1bdrUwsLC3Ny8RYsW8+fPz87OLjhUampqjx49atSoIYQwMTFxc3NbvHjxPyeqVavWpEmTpIPTp08/deqU9Oc5c+Y4OTlJfVu1anX8+PFiL3Do0KFDhw4tttn58+ddXFwsLS27d+9+//79GTNm1KhRw8HBwcfH54svvhBCNG3adObMmdIrlZo1axYfH7927Vo7OzshhIuLy59//rls2TLpV8Wlsxs3bpQ2ZGjQoMHvv/9e9OxCiIiIiKLb5OfnL1++vFmzZkql0tHR0cvL6+rVq7qzSUlJvXv3VqlUTZo0+fe//z1t2jQhhJub2+3bt/95dePGjVMqlfXr1zczM7OzsxsyZEh8fHzphtqzZ4+trW1wcHCxd1ir97MICAhQKpWZmZnSx0K/FTrTpk0bPHiwPndp1apVRTw+rVb77NmzGTNmNGrUyMzMrHbt2t7e3nFxcVqt1svLSwgxb968FwVOTEx85513HB0dLSwsOnfuvHfvXt2px48fBwYGurm5WVhY2NjYdOvW7ccff9Szb9Hznj59ulu3btK/JQghnJyc1Gr10aNH9RlZMmDAgPr16+fn57/ounT0+X4aSq9evQo+UAAAAJQ/hVarNU7hFwAAFGLbtm1+fn5V+L+/Pj4+QojIyEi5gxRFoVBERET4+vqWz3Tjx4+PjIxMSkoqn+l09HwW169fd3d3Dw8PHzFiRLnkKkZ+fn6vXr1GjRo1ZsyYKjNvUlJSgwYNgoODp0yZUmzj8vx+RkREDB8+PD4+3sXFpRymAwAAwD+xvwEAAIDRFfsCLhm5ubktXLhw4cKF6enpcmcRGo1mx44daWlp/v7+VWne+fPnt2vXLiAgwBiDl8Xbb79dt27dDRs2yB0EAACg+qI+CwAAUN3NmjXLx8fH399fnxeFGdWRI0eioqL27t0r7Y1QNeZdsWJFTEzMnj17lEqlwQcvI6VSOWrUqHXr1sn4kjoAAIBqjvosAACAEc2ePTs8PDwlJaVJkyY//PCD3HFeaPHixQEBAUuXLpU3Rt++fTdt2iTtelw15o2Ojn727NmRI0ek/ZEroHHjxj18+HDnzp1yBwEAAKimzOQOAAAAUJUtWbJkyZIlcqfQS79+/fr16yd3iqpm8ODBgwcPljtFURo1atS/f/+1a9d6e3vLnQUAAKA6Yv0sAAAAUK2NGjXqwIEDd+7ckTsIAABAdUR9FgAAAKjWBg4c6OjouHnzZrmDAAAAVEfUZwEAAIBqzdzc3NfX99tvv5U7CAAAQHVEfRYAAACo7kaOHHn58uXffvtN7iAAAADVDu8HAwBABj4+PnJHMJYzZ86IynCBISEhkZGRcqcwrsryLFARdO3atXnz5t9//32HDh3kzgIAAFC9KLRardwZAACoRk6fPr1ixQq5U0A29+7d+/333/v27Wtiwq8x4YWCgoK6du1azpPOnz9/7dq1iYmJpqam5Tw1AABAdUZ9FgAAoPwsXLjwu+++u379utxBgOdduXLF3d396NGjPXr0kDsLAABANcLCDQAAgPITGxvbtm1buVMAhWjZsmXr1q23bdsmdxAAAIDqhfosAABA+YmNjfX09JQ7BVA4X1/fH374QaPRyB0EAACgGqE+CwAAUE4yMjLi4+NZP4sKy9fX98GDB8ePH5c7CAAAQDVCfRYAAKCc/P777/n5+W3atJE7CFC4li1buru779q1S+4gAAAA1Qj1WQAAgHJy8eJFa2vrJk2ayB0EeKGBAwdGR0fLnQIAAKAaoT4LAABQTi5dutSmTRsTE/4Chopr4MCB8fHxf/zxh9xBAAAAqgv+9wAAAKCcxMbGsrkBKji1Wl23bt2dO3fKHQQAAKC6oD4LAABQHrRarbR+Vu4gQFFMTEzeeOON3bt3yx0EAACguqA+CwAAUB5u37799OlT6rOo+Pr163f69OnU1FS5gwAAAFQL1GcBAADKQ2xsrEKh8PDwkDsIUIxXX31Vo9EcO3ZM7iAAAADVAvVZAACA8hAbG+vi4uLg4CB3EKAYderUadOmzYEDB+QOAgAAUC1QnwUAACgPsbGxbdu2lTsFoJdXX331559/ljsFAABAtUB9FgAAoDzExsay+Swqi1dfffXy5cv379+XOwgAAEDVR30WAADA6LKysq5du+bp6Sl3EEAvarXa1NT05MmTcgcBAACo+qjPAgAAGF1cXJxGo2F/A1QWdnZ2Hh4ep06dkjsIAABA1Ud9FgAAwOguXrxoaWnZtGlTuYMA+urWrRvrZwEAAMoB9VkAAACju3Tpkqenp6mpqdxBAH2p1erz589nZmbKHQQAAKCKoz4LAABgdLwcDJWOWq3Ozc2NiYmROwgAAEAVR30WAADA6C5dukR9FpVL48aNa9SoceHCBbmDAAAAVHHUZwEAAIzrzp07jx8/pj6LykWhULRt25b1swAAAMZGfRYAAMC4YmNjhRAeHh5yBwFK5qWXXmL9LAAAgLFRnwUAADCu2NjYhg0b1qxZU+4gQMm0a9fu999/z83NlTsIAABAVUZ9FgAAwLhiY2Pbtm0rdwqgxNq0afPs2bNr167JHQQAAKAqoz4LAABgXLGxsWw+i8qoWbNmCoWC+iwAAIBRUZ8FAAAwomfPnv3555+enp5yBwFKzMrKqn79+tRnAQAAjIr6LAAAgBFdvnw5NzeX/Q1QSTVv3pz6LAAAgFFRnwUAADCiixcvWlhYNGvWTO4gQGk0a9bszz//lDsFAABAVUZ9FgAAwIguXbrk4eFhZmYmdxCgNFxdXRMSEuROAQAAUJVRnwUAADAiXg6GSq1+/fp3797VarVyBwEAAKiyqM8CAAAYEfVZVGr16tXLycl5/Pix3EEAAACqLOqzAAAAxnLv3r2HDx9Sn0XlVb9+fSHEX3/9JXcQAACAKov6LAAAgLHExsYKITw9PeUOApRSvXr1hBB3796VOwgAAECVxasqAAAADGb//v3h4eFt27Zt06aNp6dnbGxsvXr1ateuLXcuoJRsbGwsLS0fPXokdxAAAIAqi/osAACAwTg6Om7dujUyMlKj0QghLC0tnZ2dP/zwQ09Pz3bt2rVu3drS0lLujEDJ2Nvbp6SkyJ0CAACgyqI+CwAAYDAtW7ZUKBRScVYIkZWVdePGjTVr1mg0Go1GY2Ji8v333w8bNkzekECJ2NvbJycny50CAACgymL/WQAAAIOxtbX9524GOTk5UnG2cePGPj4+sgQDSo31swAAAEZFfRYAAMCQWrVqVejx/Pz8sLAwpVJZznmAMnJwcKA+CwAAYDzUZwEAAAzJ09PT3Nz8uYNKpbJHjx4DBgyQJRJQFpaWltnZ2XKnAAAAqLKozwIAABhSy5Yt8/Pznzuo0Wi++OILWfIAZWRmZpaXlyd3CgAAgCqL94MBAAAYkru7+3PFLKVSOWrUKE9PT7kiAWVBfRYAAMCoWD8LAABgSO7u7s8dMTMzW7hwoSxhgLKjPgsAAGBU1GcBAAAMycnJydbWVvfR1NR0/vz5Tk5OMkYCysLMzCw3N1fuFAAAAFUW9VkAAAADa968ufQHExMTZ2fngIAAefMAZaHRaMzM2BUNAADAWKjPAgAAGFjbtm2lepZWqw0JCVGpVHInAkovOzub7zAAAIDxUJ8FAAAwMHd3dxMTEzMzsy5dunh7e8sdBygT6rMAAABGxW8qAQAAGFjLli1zcnIUCkVYWJhCoZA7DlAm1GcBAACMivosAKDS27Ztm9wRgP/jwYMHQoju3bvfvHnz5s2bcsdBNeLr62vwMZ89e0Z9FgAAwHgUWq1W7gwAAJQJ6xMBQGKMv9u3adNmyJAhCxcuNPjIAAAAEOw/CwCoGiIiIrSAkUVERAgh9Gy8fft2o4YxKn6mKiPp+2kMSUlJNWvWNNLgAAAAoD4LAABgeF5eXnJHAAyD+iwAAIBRUZ8FAAAAULj09PRnz55RnwUAADAe6rMAAAAACpeUlCSEqFGjhtxBAAAAqizqswAAAAAKd+/ePSGEk5OT3EEAAACqLOqzAAAAAAqXkJBgampav359uYMAAABUWdRnAQAAABTu9u3b9evXNzMzkzsIAABAlUV9FgBQ9XXq1MnU1LRdu3bGGHz06NEqlUqhUGRnZxtj/HL22Wef1alTR6FQrFmzRjqyZ88ee3v7Xbt2GWR8w45WrPz8/JCQELVaXaJThlXOl1z+Dhw4MGvWrKioKFdXV4VCoVAoRo4cWbBBv379bG1tTU1NW7duff78ebly5ubmLlmyxM3Nzdzc3MHBwcPDIyEhQXd28+bNnTp1srW1dXFxGT169P379/XvW6wivmwvGnnnzp3Lli3TaDSlulZDunXrlouLi9wpAAAAqjLqswCAqu+XX37p3bu3kQYPDw+fOnWqkQYvf1OnTj116lTBI1qt1oDjG3a0ol27dq1Hjx5BQf+PvTuNa+pa+wa8AoSEBBLCICCjDIoIlaJWibUOnNqqlUFEsNgetW+LWguI8yzO1D5CEXFsOS1OEVFpBVqrp2h5VKpHqYpDEZBBVAZlBglhvx/2acqDkIRxM/yvb9lrutfaO/3R25W1Q2pqalQv6nTdOeXut3HjxsjIyDVr1nh7e2dnZ9vY2Ojr6x85ciQxMVFe5/z583FxcdOnT8/IyHBxcWEqVF9f3++///7o0aM1NTX379+3sbGpqqqiiyQSib+/v4+PT0FBQUJCwuXLl6dMmdLQ0KBKW6UUP2yt9ezu7s7lct3c3MrKyjo89Q5BfhYAAACgq+GXSgAArWfxLwAAIABJREFU0F+wWKy2NqmtrXVzc2uWr+xvpk2bVl5e3u7mzdawg72p7o8//ti8efPChQurq6ubZUgVFHWFbpty9z+uO3fuPHHixB9//MHlcuUXIyMjP/roo4CAgIyMDKFQ2G3BKHbixImzZ8/+8ccfTk5OhBATE5OEhAR56YEDBwYOHLh8+XIWi+Xs7BwSErJ48eK0tLSxY8cqbauY4odNcc9BQUHZ2dlTp069fPkyg8cLZGVlzZo1i6nRAQAAAPoD7J8FAID+gs1mt7XJN998U1RUpGLlduR/+4M2rWEnGj58eHx8vL+/P4fDUb2oV+vmpX706NH69etDQ0ObJmcJIWKxODg4+MmTJz1qX/m+fftcXFzoNOjr8vPzTUxM5F9hc3NzQkhubq4qbRVT/LAp7XnTpk3p6ekRERHtGLpTSKXSrKysoUOHMhUAAAAAQH+A/CwAAPQXjx49sre35/P5Wlpa48aNS01NlRf99ttvDg4OQqGQy+U6OTn9/PPPhJDg4OClS5dmZWWxWCxbW1u6Zmxs7MiRI7lcLp/Pt7Ky2rJlC31dTU0tMTFxypQpQqHQxMTk22+/VSWk6OhoPp/P4/ESEhKmTJkiEAjMzMyOHz8ur0BR1O7du4cOHcrhcEQikaen54MHD+iiL7/8ksfj6ejoFBUVLV261NTUdOHChXw+X01NbcSIEUZGRmw2m8/nu7i4jBs3ztzcnMvl6urqrlixQvGsm0lNTbWwsGCxWFFRUfQasl7zyy+/qLiGzXpTPEGli9MrNJuy4klFRkZyudwBAwYsWLDAxMSEy+WKxeK0tDS6NDAwUFNT09jYmP74+eef8/l8FotVUlJCWnpcf/rpJ4FAsG3bti6aWmRkJEVR7u7urxdt3bp18ODBhw8fvnDhQottO3LfZTLZhg0bLCwstLS03njjDYlEojTU+vr6a9euKTiB2traumlqmz581traWpW27aZKzyKRaPz48REREUydkvHw4UOpVOrg4MDI6AAAAAD9BQUAANDLEUIkEoniOm5ubtbW1jk5OVKp9O7du6NHj+ZyuX/++SddGhcXt2nTphcvXpSWlo4ZM0ZfX5++7u3tbWNjI+8kPDycELJjx47S0tIXL14cOHDA39+foqi1a9cSQi5evFhWVvbixYupU6dyOBz658xKyduWl5cXFRWNGzeOz+fX19fTpRs2bNDU1IyNjS0rK7t9+7aLi4uBgcGzZ8+atg0KCtqzZ8+MGTPu37+/ceNGQkhaWlp1dXVJScn7779PCElMTCwuLq6urg4MDCSEpKenK551ZmYmIWTfvn30x/z8fELInj176KJVq1bRU3v69KlIJBKLxTKZTPU1bNqbihNsbXFUNHr06OHDh7e1qEV0KrBNo1OvTVnxpAICAvh8/r179+rq6jIyMug3VuXl5dGl/v7+RkZG8p537dpFCCkuLqY/Nlvqc+fO6ejobN68ua0BU6p9p6ytrR0cHJpdtLGxycnJoSjqypUrampqVlZWVVVVFEUlJyd7eHjIq3Xkvi9btozD4Zw6derly5dr1qxRU1O7fv264lBzcnIIIc7OzhMmTDA2NuZwOPb29lFRUY2NjXSFlJQUNpsdGRlZUVFx9+7doUOHvvfeeyq2VdHrD5uKPa9evZoQcuvWLaVDtO/5VOzkyZPq6uq1tbWd2y0AAAAANIX9swAA0F/o6OhYWVlpaGgMGzbs0KFDdXV1Bw8epItmzpy5ceNGkUikp6fn7u5eWlpaXFzcrLlUKg0NDZ04ceKqVav09PREItEnn3wyatQoeQWxWCwUCkUikZ+f36tXr+jki4rEYrFAIDA0NPTz86uurs7LyyOE1NbW7t69e8aMGXPmzBEKhU5OTvv37y8pKZGHTdu5c+fixYvj4+Pt7e3pKw4ODjweT19ff/bs2YQQCwsLAwMDHo83Z84cQoh8o6Iqs27G1tZ2x44dPB6PELJo0aLa2tpvv/1WTU2tfb2pOMEWF6e3UzApDQ0NemOpg4NDdHR0ZWVlTExMO4aYNm1aRUXF+vXrOy/qv1VXV+fk5NjY2LRWwdXVdcmSJY8fP161alWzoo7c97q6uujoaC8vL29vb11d3XXr1rHZbKXrQ79xy9DQcNu2bRkZGc+fP/f09Fy8ePGxY8foCuPHj1+5cmVgYKBAIHB0dKysrDx8+LCKbdtNxZ7t7OwIIXfu3OngcO1z7949a2vrZkdYAAAAAEDnQn4WAAD6IycnJ6FQePv27deL6GNqZTJZs+u3b98uKyt777335FfU1dWDgoJa60EqlbYjME1NTXnbjIyMqqqqkSNHyktHjRqlqakp/8G7ir3JX0OvILDWZt2akydPnjlzJjQ0dMiQIe3ura0TbLo4fYbiSY0cOZLH48lT6j1HUVERRVF0pr41W7duHTJkyN69e5ueJUI6dt8fPnxYU1Pj6OhIF2lpaRkbGytdH/rs12HDhonFYj09PaFQGBoaKhQK5RnhtWvXHjx48OLFi1VVVdnZ2WKx2NXVld74rLRtu6nYM73Iz58/7+Bw7ZORkYHDDQAAAAC6GvKzAADQT7HZbHlSLDExccKECYaGhhwOp+kJrU1VVFQQQnR1dbstwrKyMkKItrZ204u6urqVlZWd0r8qs25RaWnpF198MWrUqKVLl3akt66eYN/A4XCU7kTufnV1deSvDGNruFxuTEwMi8WaP39+bW2t/HpH7nt1dTUhZN26dfLjj3Nzc2tqahS3MjExIYTQB/XSNDU1LS0ts7KyCCFPnz4NCwv77LPPJk2axOfzBw0adOjQocLCQvr4CMVtO0LFnrW0tMhfC979fv/996aZdAAAAADoCsjPAgBAf9TQ0PDixQsLCwtCSF5enpeXl7GxcVpaWnl5eVhYWItNBg4cSP5vMqWr0bngZkmrsrIyMzOzjneu4qxbFBQUVFZWFhMTo66u3pHeunSCfYNUKu2ZC0InDZVukXZ1dQ0JCcnMzJS/SY907L4bGhoSQsLDw5se13X16lXFrbS1te3s7O7du9f0YkNDg1AoJIRkZmbKZDL6C04TCAR6enoZGRlK23aEij3X19eTvxa8mxUXF+fm5r711lvdPzQAAABAv4L8LAAA9Ee//vprY2Oji4sLIeTOnTtSqXTRokX0MYssFqvFJlZWVnp6eufPn++2IB0dHbW1tW/cuCG/kpaWVl9fP2LEiI53ruKsX5eYmHj06NH169cPGzaMvrJ8+fL29dalE+wbUlJSKIoaM2YM/VFDQ6OHHO8wYMAAFotVXl6utOaWLVvs7e1v3bolv9KR+25ubs7lctPT09sasK+v761bt7Kzs+mPNTU1ubm5Tk5OhBA6L/z06VN55crKyhcvXpibmytt20Gq9EwvspGRUceHa6vff/+dxWLh+wgAAADQ1ZCfBQCA/qK+vr68vLyhoeHmzZuBgYGWlpZz584lhNC7aC9cuFBXV5eZmdn0EEw9Pb3CwsLHjx9XVlaqqamtWbPm8uXLgYGBT548aWxsrKysbLb3rXNxudylS5eePn36yJEjFRUVd+7cWbhwoYmJSUBAQMc7VzBrBSoqKhYsWODs7Ey/9Kmuru7GjRvp6ekqrmGz3GKXTrD3amxsfPnyZUNDw+3bt4ODgy0sLOgHlRBia2v74sWLs2fPSqVSem9j04bNljo5OVkgEGzbtq0rguTxeNbW1gUFBUpr0qccyLdak47ddy6XO2/evOPHj0dHR1dUVMhksoKCAjq16ufnZ2RkdPPmzRYbhoSE0F/5vLy80tLSlStX1tbW0o/xoEGDJk6ceOjQocuXL9fW1ubn59ORfPLJJ0rbKh1XMcU90+hF7pR0cFtdv37dxsZGX1+/+4cGAAAA6F8oAACAXo4QIpFIFNeJiYmZOHHigAEDNDQ09PX1Z8+enZubKy9duXKlnp6erq6uj49PVFQUIcTGxiYvL+/mzZuWlpZaWlpvv/32s2fPKIqKiopycnLicrlcLvfNN9/cu3dvWFgY/dNjOzu7rKysI0eOiEQiQoiZmdndu3cVR7V371765T9024MHDwoEAkKIpaXln3/+SVFUY2Pjrl277Ozs2Gy2SCTy8vJ6+PAh3VY+rrm5eWxsLEVRERERdG9WVla//fbbzp076R9KGxkZHT169MSJE/QWPJFIdPz48dZmHRwcTFfj8/kzZszYs2ePsbExIYTH47m7u3/11Vev/y0xdepUFddw3bp1TXtTPEGli6PY1atXx44dSx/xSQgxNjYWi8WXLl1SXKSYRCJp699OzRZQ6aQCAgLYbLapqamGhoZAIPD09MzKypL3VlpaOnHiRC6XO2jQoC+++GL58uWEEFtb27y8PIqimj2uSUlJOjo6W7dubVPANFW+U4GBgWw2u6amhv54+vRpGxsbQoiBgcHixYubVV6+fLmHh4f8Y0fu+6tXr1auXGlhYaGhoWFoaOjt7Z2RkUFRlJeXFyFkw4YNrQWcn58/e/ZskUjE4XDeeuut5ORkeVFJSUlwcLCtrS2Hw9HW1h47duyZM2dUbKt4XKUPm4KeadOmTTM1NW1sbGxtXnLteD4Vmzp1qp+fXyd2CAAAAAAtYlEU1Wm5XgAAACawWCyJRDJr1iymA4E+7uTJk76+vl36t9OCBQvi4uJKS0u7bghVqPKdevTo0dChQ2NiYubMmdNtgSnQ2Ng4YcKEuXPnzp8/v8+MW1paamZmtnXr1qbv4mtN5z6fMpnMwMBgy5Ytixcv7pQOAQAAAKA1ON8AAAAAoAdR+tKtHsLW1nbz5s2bN2+uqqpiOhYik8nOnj1bWVnp5+fXl8bdtGmTs7NzYGBgV3SuWHp6ellZ2YQJE7p/aAAAAID+BvlZAACArvLgwQNW67o5kdSXYGF7iNWrV/v4+Pj5+anyorAulZKSEh8fn5ycTJ+N0DfG3b17d3p6elJSEpvN7vTOlfr1118NDQ3lrwEEAAAAgK6jwXQAAAAAfZa9vT3OEeoKfXVh16xZExMTU19fP2jQoF27ds2cOZPpiJTbtm3b+fPnd+zYsXPnTgbDcHNzc3Nz60vjJiQkvHr1KiUlpemr1brTpUuXxo8fz2KxGBkdAAAAoF9BfhYAAACgR9i+ffv27duZjqLNJk+ePHnyZKaj6Gs8PDw8PDyYGl0mk6Wmpm7ZsoWpAAAAAAD6FZxvAAAAAAAAf7t+/XpZWdnEiROZDgQAAACgX0B+FgAAAAAA/paUlGRubo7DZwEAAAC6B/KzAAAAAADwt6SkpA8++IDpKAAAAAD6C+RnAQAAAADgv549e3bz5s2pU6cyHQgAAABAf4H3gwEAQF8QHh4eFxfHdBTQxxUUFBBCfHx8mA6kO+A71evQz2fHJSUlcTicSZMmdUpvAAAAAKAU9s8CAAAAAMB/JSUlTZw4kcfjMR0IAAAAQH/BoiiK6RgAAAA6hMViSSSSWbNmMR0I9HEnT5709fXtD3874TvVG3XK81lbW2toaPjVV18tWLCgswIDAAAAAMWwfxYAAAAAAAghJCkpqba21sPDg+lAAAAAAPoR5GcBAAAAAIAQQuLj49955x0TExOmAwEAAADoR5CfBQAAAAAA8urVq8TExJkzZzIdCAAAAED/gvwsAAAAAACQn376qaqqysvLi+lAAAAAAPoX5GcBAAAAAICcOnVKLBYPHDiQ6UAAAAAA+hfkZwEAAJqLj4+3trZmNaGhoWFgYPCPf/zj9OnTnTXKvHnzuFwui8Wqq6tTHMNHH33UtGjy5Mk6Ojrq6urDhg27efNmZ8XTJl999dWAAQNYLNb+/fvpK0lJSUKh8Mcff+yU/ju3N+hSFy5cWL16dQ9/YgkhUql0+/bttra2mpqaurq6jo6Ojx8/lpceO3Zs1KhROjo6lpaW8+bNe/bsmeptlWpsbAwPDxeLxapH9cMPP4SFhclksnbNtT2qq6vPnj374YcfdtuIAAAAAEBDfhYAAKA5b2/v7OxsGxsboVBIURRFUcXFxRKJ5MmTJ97e3hKJpFNGiYmJWbZsmdIY9PX1jxw5kpiYKC86f/58XFzc9OnTMzIyXFxcOiWYtlq2bNmVK1eaXqEoqhP779zeoOts3LgxMjJyzZo1PfyJJYT4+vp+//33R48erampuX//vo2NTVVVFV0kkUj8/f19fHwKCgoSEhIuX748ZcqUhoYGVdoqlZmZ+c4774SEhNTU1Kgelbu7O5fLdXNzKysr6/DUVRIXF1dfXz9r1qzuGQ4AAAAA5JCfBQAAUE4kErm5uX399deEkJMnTyqtX1tb2+JeuXaIjIxUU1MLCAgoLy/vlA67yLRp08rLy6dPn96+5s1WrIO99V6d+OR0Ylet2blz54kTJ06ePKmjoyO/2DOf2BMnTpw9ezYuLm706NEaGhomJiYJCQmOjo506YEDBwYOHLh8+XKhUOjs7BwSEpKenp6WlqZKW8X++OOPVatWLVy40NnZua1RBQUFDR8+fOrUqU0zxV0nNjZ2+vTp+vr63TAWAAAAADSF/CwAAICqrKysCCGqbGf75ptvioqKVOyWxWIpKBWLxcHBwU+ePFGw2bYPaNOK9WGduA5dvaSPHj1av359aGgol8tter1nPrH79u1zcXFxcnJqsTQ/P9/ExET+TTQ3NyeE5ObmqtJWseHDh8fHx/v7+3M4nLZGRQjZtGlTenp6REREO4ZukydPnly6dKnZwRQAAAAA0D2QnwUAAFDV7du3CSHjx4+XX/ntt98cHByEQiGXy3Vycvr5558JIcHBwUuXLs3KymKxWLa2tnTN2NjYkSNHcrlcPp9vZWW1ZcsW+rqamlpiYuKUKVOEQqGJicm33377+rhbt24dPHjw4cOHL1y40GJgFEXt3r176NChHA5HJBJ5eno+ePCALvryyy95PJ6Ojk5RUdHSpUtNTU0XLlzI5/PV1NRGjBhhZGTEZrP5fL6Li8u4cePMzc25XK6uru6KFSsUz7GZ1NRUCwsLFosVFRVFCHn06BHrNb/88ouKK9asN8UTjI6O5vP5PB4vISFhypQpAoHAzMzs+PHjqt7ULqAg2sDAQE1NTWNjY/rj559/zufzWSxWSUkJeW0dIiMjuVzugAEDFixYYGJiwuVyxWKxfFNnm7oihPz0008CgWDbtm2dNc3IyEiKotzd3V8v6sgTq/SGymSyDRs2WFhYaGlpvfHGG6qcN1JfX3/t2rUWd7DSrK2tm+ay6cNnra2tVWnbbqr0LBKJxo8fHxER0dUnfnz33Xe6urpTpkzp0lEAAAAAoGUUAABAL0cIkUgknd5t0/Nna2pqkpOTLS0tJ0+eXFVVJa8TFxe3adOmFy9elJaWjhkzRl9fn77u7e1tY2MjrxYeHk4I2bFjR2lp6YsXLw4cOODv709R1Nq1awkhFy9eLCsre/HixdSpUzkcTnV1ddMYcnJyKIq6cuWKmpqalZUVPXpycrKHh4e82oYNGzQ1NWNjY8vKym7fvu3i4mJgYPDs2TO6lB4lKChoz549M2bMuH///saNGwkhaWlp1dXVJSUl77//PiEkMTGxuLi4uro6MDCQEJKenq54jpmZmYSQffv20R/z8/MJIXv27KGLVq1aRU/k6dOnIpFILBbLZDLVV6xpbypO8OLFi+Xl5UVFRePGjePz+fX19e256wrRqUCl1RRH6+/vb2RkJK+8a9cuQkhxcTH9sdk6BAQE8Pn8e/fu1dXVZWRk0C+wysvLa0dX586d09HR2bx5syozVeU7ZW1t7eDg0OxiJz6xrd3QZcuWcTicU6dOvXz5cs2aNWpqatevX1ccak5ODiHE2dl5woQJxsbGHA7H3t4+KiqqsbGRrpCSksJmsyMjIysqKu7evTt06ND33ntPxbYqGj169PDhw9sUFW316tWEkFu3bikdQsXn83WNjY1DhgxZvHhxO9oCAAAAQMdh/ywAAECrysvL6b2fPB6P3srn7+/PZrPlFWbOnLlx40aRSKSnp+fu7l5aWlpcXNysE6lUGhoaOnHixFWrVunp6YlEok8++WTUqFHyCmKxWCgUikQiPz+/V69e0VmbZlxdXZcsWfL48eNVq1Y1K6qtrd29e/eMGTPmzJkjFAqdnJz2799fUlJy8ODBptV27ty5ePHi+Ph4e3t7+oqDgwOPx9PX1589ezYhxMLCwsDAgMfjzZkzhxAi38+oyhybsbW13bFjB4/HI4QsWrSotrb222+/VVNTa19vKk5QLBYLBAJDQ0M/P7/q6uq8vDzF3XYRFaNVnYaGBr3P1MHBITo6urKyMiYmph39TJs2raKiYv369e0Lo5nq6uqcnBwbG5vWKnT8iW3xhtbV1UVHR3t5eXl7e+vq6q5bt47NZitdEPqNW4aGhtu2bcvIyHj+/Lmnp+fixYuPHTtGVxg/fvzKlSsDAwMFAoGjo2NlZeXhw4dVbNtuKvZsZ2dHCLlz504Hh1Pg0qVLDx8+nD9/ftcNAQAAAAAKID8LAADQKvn+WalUWlBQsGTJksDAwDfeeIP+CXkzdN5WJpM1u3779u2ysrL33ntPfkVdXT0oKKi1HqRSaYvBbN26dciQIXv37k1NTW16PSMjo6qqauTIkfIro0aN0tTUlP8QXilNTU1CiPwdRArCaG2OrTl58uSZM2dCQ0OHDBnS7t7aOkF6Oq0tY1fr+O1QYOTIkTweT546Z1BRURFFUXQKvjWd9cQ2vaEPHz6sqamRv0FLS0vL2NhY6YLQZ78OGzZMLBbr6ekJhcLQ0FChUCjPCK9du/bgwYMXL16sqqrKzs4Wi8Wurq70Jm6lbdtNxZ7pRX7+/HkHh1PgwIEDY8aMefPNN7tuCAAAAABQAPlZAAAA5TQ0NExNTefNm/fVV189fPhwx44d9PXExMQJEyYYGhpyOJymZ7Y2VVFRQQjR1dXtYAxcLjcmJobFYs2fP7+2tlZ+nX5fmba2dtPKurq6lZWVHRyRpsocW1RaWvrFF1+MGjVq6dKlHemtqyfYubo6Wg6Ho3THcTeoq6sjf2UYW9MVT2x1dTUhZN26dfJzjXNzc2tqahS3MjExIYQ0/WcVTU1NS0vLrKwsQsjTp0/DwsI+++yzSZMm8fn8QYMGHTp0qLCwkD4vQnHbjlCxZy0tLfLXgneFkpKSM2fOBAQEdFH/AAAAAKAU8rMAAABtQL9p/d69e4SQvLw8Ly8vY2PjtLS08vLysLCwFpsMHDiQ/N8sTLu5urqGhIRkZmbKXy9G/sr8NsttlZWVmZmZdXxEFefYoqCgoLKyspiYGHV19Y701qUT7HRdGq1UKu0hE6eThkr3Pnf6E2toaEgICQ8Pb3pc19WrVxW30tbWtrOzo7+2cg0NDUKhkBCSmZkpk8no7ylNIBDo6ellZGQobdsRKvZcX19P/lrwrhATE8PlcmfNmtVF/QMAAACAUsjPAgAAtMF//vMfQgj9a/07d+5IpdJFixZZW1tzuVwWi9ViEysrKz09vfPnz3dKAFu2bLG3t79165b8iqOjo7a29o0bN+RX0tLS6uvrR4wY0fHhVJzj6xITE48ePbp+/fphw4bRV5YvX96+3rp0gp1OabQaGhrtPnshJSWFoqgxY8Z0vKsOGjBgAIvFKi8vV1qzc59Yc3NzLpebnp7e1oB9fX1v3bqVnZ1Nf6ypqcnNzaX/uYXOCz99+lReubKy8sWLF+bm5krbdpAqPdOLbGRk1PHhXkdR1OHDhz/++GPFR1UAAAAAQJdCfhYAAECR2tpa+nXqhYWFMTEx69atMzAwWLJkCSHEwsKCEHLhwoW6urrMzMymp2fq6ekVFhY+fvy4srJSTU1tzZo1ly9fDgwMfPLkSWNjY2VlZbNNc6qjfzMu35FKX1m6dOnp06ePHDlSUVFx586dhQsXmpiYdMoPlhXMUYGKiooFCxY4OzvT74aqq6u7ceNGenq6iivWLOfYpRPsdEqjtbW1ffHixdmzZ6VSaXFxcW5ubtPmr69DY2Pjy5cvGxoabt++HRwcbGFhMXfu3HZ0lZycLBAItm3b1inT5PF41tbWBQUFqixIJz6xXC533rx5x48fj46OrqiokMlkBQUFdGrVz8/PyMjo5s2bLTYMCQmxtLScO3duXl5eaWnpypUra2tr6edz0KBBEydOPHTo0OXLl2tra/Pz8+lIPvnkE6VtlY6rmOKeafQid0o6+HUXL178888/P/30067oHAAAAABURQEAAPRyhBCJRNKJHZ4+ffr1F9NzOBw7O7tFixbl5eXJa65cuVJPT09XV9fHxycqKooQYmNjk5eXd/PmTUtLSy0trbfffvvZs2cURUVFRTk5OXG5XC6X++abb+7duzcsLIz+zbKdnV1WVtaRI0dEIhEhxMzM7O7du/IYDAwMFi9e3CzC5cuXe3h4yD82Njbu2rXLzs6OzWaLRCIvL6+HDx/SRfJRzM3NY2NjKYqKiIig98pZWVn99ttvO3fupH9PbWRkdPTo0RMnTtA79UQi0fHjx1ubY3BwMF2Nz+fPmDFjz549xsbGhBAej+fu7v7VV1+9/ifH1KlTVVyxdevWNe1N8QT37t1LT4dexoMHDwoEAkKIpaXln3/+2YlPBUVREolElb+dFERLUVRpaenEiRO5XO6gQYO++OKL5cuXE0JsbW3p56rZkxMQEMBms01NTTU0NAQCgaenZ1ZWVvu6SkpK0tHR2bp1qyozVeU7FRgYyGaza2pq6I+d9cQqvaGvXr1auXKlhYWFhoaGoaGht7d3RkYGRVFeXl6EkA0bNrQWcH5+/uzZs0UiEYfDeeutt5KTk+VFJSUlwcHBtra2HA5HW1t77NixZ86cUbGt4nGvXr06duxY+qhZQoixsbFYLL506ZIqPdOmTZtmampK/yuRYio+n0198MEHEyZMaFMTAAAAAOh0LIqiOinTCwAAwAwWiyWRSHB+InS1kydP+vr6duffTgsWLIiLiystLe22EWmqfKcePXo0dOjQmJiYOXNQzy+5AAAgAElEQVTmdFtgCjQ2Nk6YMGHu3Lnz58/vM+OWlpaamZlt3bq16Uv2WtPW5zMzM9Pe3j4+Pt7T07NjYQIAAABAh+B8AwAAAICeS+k7uJhia2u7efPmzZs3V1VVMR0LkclkZ8+erays9PPz60vjbtq0ydnZOTAwsCs6j4yMtLCwmD59eld0DgAAAACqQ34WAAAAANpj9erVPj4+fn5+qrworEulpKTEx8cnJyd383uuunTc3bt3p6enJyUlsdnsTu+8oqLi+++/DwoKano0MAAAAAAwAvlZAAAAgJ5ozZo1MTEx5eXlgwYNOnXqFNPhtGzbtm2BgYE7duxgNgw3N7ejR4/Sxxb3jXETEhJevXqVkpJCH0vd6Q4ePEhR1Lx587qicwAAAABoEw2mAwAAAACAFmzfvn379u1MR6Hc5MmTJ0+ezHQUfY2Hh4eHh0cXdd7Q0LB37965c+fS7wYEAAAAAGZh/ywAAAAAQD8SFxeXn58fFBTEdCAAAAAAQAjyswAAAAAA/cquXbt8fHxsbGyYDgQAAAAACMH5BgAAAAAA/UdycvKtW7cOHz7MdCAAAAAA8F/YPwsAAAAA0F+EhYW99957Li4uTAcCAAAAAP+F/bMAAAAAAP3C9evXL1269O9//5vpQAAAAADgbyyKopiOAQAAoENYLBbTIQAA9AiK/7afMWNGQUHB77//3m3xAAAAAIBS2D8LAAC9nkQiYToEgG7S0NCwatWqmpqaVatWWVhYMB0O9CZ//PFHQkJCXFwc04EAAAAAwP+B/bMAAAAAvUlZWZmXl9eNGzdOnjw5ZcoUpsOBXmPatGnPnz+/fv06fnMAAAAA0KPg/WAAAAAAvYmuru7PP//s6enp7u5+4MABpsOB3iE1NTUpKWnHjh1IzgIAAAD0NNg/CwAAAND7UBQVGhoaGhoaGBgYERGBpBsoNmHCBIqiLl26xHQgAAAAANAczp8FAAAA6H1YLNamTZssLS0DAgLKysoOHTqkqanJdFDQQ507d+7SpUtXrlxhOhAAAAAAaAH2zwIAAAD0Yr/88svMmTNHjhx5+vRpoVDIdDjQ48hkMhcXFxsbm9OnTzMdCwAAAAC0AOfPAgAAAPRi7777bmpqamZm5tixY/Py8pgOB3qc6OjoBw8ebN++nelAAAAAAKBlyM8CAAAA9G5OTk7Xrl1js9ljxoy5efMm0+FAD/Ls2bMNGzYsX77c3t6e6VgAAAAAoGU43wAAAACgL6isrJw1a1ZqauqJEyemTZvGdDjQI8yaNev333/PyMjg8/lMxwIAAAAALcP+WQAAAIC+QEdHJyEhwcvLy8PDY//+/UyHA8z7+eef4+Li9u7di+QsAAAAQE+G/bMAAAAAfQdFUaGhoaGhoYGBgRERESwWi+mIgBm1tbVOTk4jRoyQSCRMxwIAAAAAimgwHQAAAAAAdBoWi7Vp0yYrK6vPPvvs6dOn33//PZfLZTooYMDatWuLi4vDw8OZDgQAAAAAlMD+WQAAAIA+6MKFCzNnznRxcTl9+rSuri7T4UC3+uWXX95///2YmJiPP/6Y6VgAAAAAQAnkZwEAAAD6prt3706dOlVHRycpKcnS0pLpcKCbvHz5cvjw4W+99dapU6eYjgUAAAAAlMP7wQAAAAD6JkdHx2vXrnE4HFdX1//85z9MhwPdJCAggKKogwcPMh0IAAAAAKgE+VkAAACAPmvgwIGXL192dnYeP378uXPnmA4HutyhQ4fi4+O/++47PT09pmMBAAAAAJUgPwsAAADQl2lra//www/+/v6enp779u1jOhzoQvfu3VuyZMny5csnTZrEdCwAAAAAoCqcPwsAAADQL4SFha1evfqLL74IDw9XU8M/0vc15eXlo0eP1tfX//XXXzU1NZkOBwAAAABUpcF0AAAAAADQHVauXGlsbPzpp58WFhbGxsZyuVymI4JOQ1HU/Pnzy8vLL168iOQsAAAAQO+CrRMAAAAA/cU///nP5OTkX375xc3NraSkhOlwoNOEhob++OOPJ0+eNDU1ZToWAAAAAGgbnG8AAAAA0L/cvXt32rRpmpqaycnJtra2TIcDHfXjjz96enru3bt3wYIFTMcCAAAAAG2G/CwAAABAv/P06dMPPvigsLDwxx9/HDlyJNPhQPvdu3dPLBZ7e3t/8803TMcCAAAAAO2B/CwAAABAf1RVVeXr63vp0qXjx49Pnz6d6XCgPQoLC8VisZmZ2YULF3CgMAAAAEAvhfNnAQAAAPojbW3thISEjz76yMvLa+/evUyHA21WWVn5wQcfsNnsM2fOIDkLAAAA0HtpMB0AAAAAADBDQ0Nj37599vb2X3zxxZ9//hkeHq6mhn+87x2kUunMmTOfPXt25coVQ0NDpsMBAAAAgPZDfhYAAACgXwsKChKJRJ9++umTJ09iY2O1tLSYjgiUoCjq008/vXr16qVLl6ysrJgOBwAAAAA6BFskAAAAAPq7jz/+ODk5+eLFi25ubiUlJUyHA0qsWLHi2LFjp06devPNN5mOBQAAAAA6CvlZAAAAACCTJk1KTU0tLCx0dXXNzMxkOhxo1fr168PDw//1r39NnjyZ6VgAAAAAoBMgPwsAAAAAhBAybNiwq1evCoVCV1fXK1euMB0OtOCrr77atm1bdHT0hx9+yHQsAAAAANA5kJ8FAAAAgP8yMTFJSUkZM2bM5MmTf/jhB6bDgf8jPDx8xYoVUVFRn332GdOxAAAAAECnQX4WAAAAAP6mra2dkJDw8ccfz5gxIyoqiulw4L8iIyNDQkLCwsIWLVrEdCwAAAAA0Jk0mA4AAAAAAHoWdXX16OjoIUOGBAUFZWZmhoeHq6nhH/WZ9NVXX61YsWLXrl3Lli1jOhYAAAAA6GQsiqKYjgEAAAAAeqK4uLiPP/546tSpR44c0dLSYjqcfiosLGz16tW7du1aunQp07EAAAAAQOdDfhYAAAAAWnXlyhUPDw9bW9sffvjB0NCQ6XD6F4qili1b9vXXX+/fv////b//x3Q4AAAAANAlkJ8FAAAAAEXu3bs3depUDQ2NpKSkwYMHMx1OfyGTyQICAmJjY2NjY2fNmsV0OAAAAADQVXCUGAAAAAAo4uDgcO3aNV1dXbFYnJqa2qz0yZMnsbGxjATWNxQWFr5+sa6uzs/P79ixY2fOnEFyFgAAAKBvQ34WAAAAAJQwNja+dOmSq6vru+++e/LkSfn1ysrKyZMnL1q0qLi4mMHweq9Xr1794x//+Oabb5peLC0tfffddy9evPjTTz9NnTqVqdgAAAAAoHsgPwsAAAAAyvH5/LNnz86fP9/Pz+/LL78khEil0hkzZmRmZr569WrDhg1MB9gr7dix4/79+wsXLrxx4wZ9JScn5+23387Jyfn111/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object>"]},"metadata":{}}]},{"cell_type":"code","source":"m.compile(loss= \"mse\", optimizer= tf.keras.optimizers.Adam(0.1))","metadata":{"id":"IzjtDcPo0pzS"},"execution_count":104,"outputs":[]},{"cell_type":"markdown","source":"> The following methodology aims at the gradual learning of the neural network in 6 stages, and in each stage the training of the network in the previous stage is taken advantage of, and thus the network itself is trained every time, but what differs here is the target.","metadata":{}},{"cell_type":"code","source":"data = [syntax, cohesion, vocabulary, phraseology, grammar, conventions]\nmodels = []\nhistory = []\nfor s in data:\n    history_training = m.fit([input_ids, attention_mask],\n                         y = s,\n                         batch_size= 128, \n                         epochs= 6,\n                         callbacks = [tf.keras.callbacks.ReduceLROnPlateau(monitor='loss', \n                                                                           factor=0.1, \n                                                                           mode = 'min',\n                                                  patience= 2),\n                             tf.keras.callbacks.EarlyStopping(patience = 2, \n                                                              monitor = 'loss', \n                                                              mode = 'min', \n                                                              restore_best_weights=True)])\n    models.append(m)\n    history.append(history_training)","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EVW2niSV84yJ","outputId":"f867fe43-60de-4357-da51-c26018200801"},"execution_count":106,"outputs":[{"output_type":"stream","name":"stdout","text":"Epoch 1/6\n\n31/31 [==============================] - 302s 9s/step - loss: 0.7619 - lr: 0.1000\n\nEpoch 2/6\n\n31/31 [==============================] - 287s 9s/step - loss: 0.4025 - lr: 0.1000\n\nEpoch 3/6\n\n31/31 [==============================] - 288s 9s/step - loss: 0.3714 - lr: 0.1000\n\nEpoch 4/6\n\n31/31 [==============================] - 288s 9s/step - loss: 0.3295 - lr: 0.1000\n\nEpoch 5/6\n\n31/31 [==============================] - 289s 9s/step - loss: 0.3121 - lr: 0.1000\n\nEpoch 6/6\n\n31/31 [==============================] - 290s 9s/step - loss: 0.3071 - lr: 0.1000\n\nEpoch 1/6\n\n31/31 [==============================] - 286s 9s/step - loss: 0.3393 - lr: 0.1000\n\nEpoch 2/6\n\n31/31 [==============================] - 289s 9s/step - loss: 0.3142 - lr: 0.1000\n\nEpoch 3/6\n\n31/31 [==============================] - 290s 9s/step - loss: 0.2927 - lr: 0.1000\n\nEpoch 4/6\n\n31/31 [==============================] - 290s 9s/step - loss: 0.2946 - lr: 0.1000\n\nEpoch 5/6\n\n31/31 [==============================] - 291s 9s/step - loss: 0.2983 - lr: 0.1000\n\nEpoch 1/6\n\n31/31 [==============================] - 287s 9s/step - loss: 0.2293 - lr: 0.0100\n\nEpoch 2/6\n\n31/31 [==============================] - 290s 9s/step - loss: 0.2185 - lr: 0.0100\n\nEpoch 3/6\n\n31/31 [==============================] - 290s 9s/step - loss: 0.2158 - lr: 0.0100\n\nEpoch 4/6\n\n31/31 [==============================] - 291s 9s/step - loss: 0.2129 - lr: 0.0100\n\nEpoch 5/6\n\n31/31 [==============================] - 291s 9s/step - loss: 0.2110 - lr: 0.0100\n\nEpoch 6/6\n\n31/31 [==============================] - 290s 9s/step - loss: 0.2108 - lr: 0.0100\n\nEpoch 1/6\n\n31/31 [==============================] - 291s 9s/step - loss: 0.2835 - lr: 0.0100\n\nEpoch 2/6\n\n31/31 [==============================] - 290s 9s/step - loss: 0.2778 - lr: 0.0100\n\nEpoch 3/6\n\n31/31 [==============================] - 290s 9s/step - loss: 0.2723 - lr: 0.0100\n\nEpoch 4/6\n\n31/31 [==============================] - 289s 9s/step - loss: 0.2719 - lr: 0.0100\n\nEpoch 5/6\n\n31/31 [==============================] - 289s 9s/step - loss: 0.2718 - lr: 0.0100\n\nEpoch 6/6\n\n31/31 [==============================] - 290s 9s/step - loss: 0.2715 - lr: 0.0100\n\nEpoch 1/6\n\n31/31 [==============================] - 287s 9s/step - loss: 0.3340 - lr: 0.0100\n\nEpoch 2/6\n\n31/31 [==============================] - 290s 9s/step - loss: 0.3330 - lr: 0.0100\n\nEpoch 3/6\n\n31/31 [==============================] - 290s 9s/step - loss: 0.3310 - lr: 0.0100\n\nEpoch 4/6\n\n31/31 [==============================] - 290s 9s/step - loss: 0.3245 - lr: 0.0100\n\nEpoch 5/6\n\n31/31 [==============================] - 290s 9s/step - loss: 0.3151 - lr: 0.0100\n\nEpoch 6/6\n\n31/31 [==============================] - 290s 9s/step - loss: 0.3120 - lr: 0.0100\n\nEpoch 1/6\n\n31/31 [==============================] - 289s 9s/step - loss: 0.2748 - lr: 0.0100\n\nEpoch 2/6\n\n31/31 [==============================] - 290s 9s/step - loss: 0.2626 - lr: 0.0100\n\nEpoch 3/6\n\n31/31 [==============================] - 290s 9s/step - loss: 0.2595 - lr: 0.0100\n\nEpoch 4/6\n\n31/31 [==============================] - 295s 10s/step - loss: 0.2569 - lr: 0.0100\n\nEpoch 5/6\n\n31/31 [==============================] - 295s 10s/step - loss: 0.2521 - lr: 0.0100\n\nEpoch 6/6\n\n31/31 [==============================] - 294s 9s/step - loss: 0.2544 - lr: 0.0100\n"}]},{"cell_type":"markdown","source":"> Previous results can be improved by increasing a larger number of epochs","metadata":{}},{"cell_type":"markdown","source":"# XGBOOST WITH RESNET","metadata":{"id":"zm4Vy_UnfGhX"}},{"cell_type":"markdown","source":"> XGBRegressor helped improve the results further, but with longer training of the neural network (graduated in its learning) better results could be achieved.","metadata":{}},{"cell_type":"code","source":"import xgboost as xg","metadata":{"id":"l0VaNcm9Ywsm"},"execution_count":111,"outputs":[]},{"cell_type":"code","source":"x_train = [input_ids[:3300], attention_mask[:3300]]\nx_test = [input_ids[3300:], attention_mask[3300:]]\nx_sub = [input_ids_test, attention_mask_test]","metadata":{"id":"V5uupzgdtUPO"},"execution_count":161,"outputs":[]},{"cell_type":"code","source":"from sklearn.metrics import mean_squared_error as MSE","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def xgboost_syntax(data, x_train, x_test, x_sub, m):\n  model = tf.keras.models.Model(\n    m.input, \n    m.get_layer('our_param').output\n  )\n  data_y_train = data[:3300]\n  data_y_test = data[3300:]\n  X_train_features = model.predict(x_train)\n  X_test_features = model.predict(x_test)\n  X_sub_features = model.predict(x_sub)\n  xgb = xg.XGBRegressor(objective ='reg:squarederror',\n                  n_estimators = 100)\n  xgb.fit(X_train_features, data_y_train)\n  y_pred = xgb.predict(X_test_features)\n  loss_metrics = MSE(data_y_test, y_pred)\n  return [y_pred, data_y_test, loss_metrics, xgb.predict(X_sub_features)]","metadata":{"id":"40bPZh9rkgWc"},"execution_count":163,"outputs":[]},{"cell_type":"code","source":"[y_pred, y_true, loss_metrics, res] = xgboost_syntax(syntax, x_train, x_test, x_sub, models[0])","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-ySCtDv_tjFW","outputId":"94ef6c2d-ad6d-42c4-f3a0-deabab42f124"},"execution_count":164,"outputs":[{"output_type":"stream","name":"stdout","text":"104/104 [==============================] - 194s 2s/step\n\n20/20 [==============================] - 37s 2s/step\n\n1/1 [==============================] - 0s 307ms/step\n"}]},{"cell_type":"code","source":"loss_metrics","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RkrEEvmZuuE8","outputId":"c7e8ba9b-38e3-45b3-c70e-63c21941c996"},"execution_count":166,"outputs":[{"output_type":"execute_result","execution_count":166,"data":{"text/plain":["0.2628825783601268"]},"metadata":{}}]},{"cell_type":"code","source":"res","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YBQIxRw8uqOm","outputId":"1dd2e74b-58cb-4dc3-e4b3-91c3caee7aec"},"execution_count":165,"outputs":[{"output_type":"execute_result","execution_count":165,"data":{"text/plain":["array([2.7652476, 2.572466 , 3.1476033], dtype=float32)"]},"metadata":{}}]},{"cell_type":"code","source":"[y_pred, y_true, loss_metrics, res2] = xgboost_syntax(cohesion, x_train, x_test, x_sub, models[1])","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9n8Z19SityTG","outputId":"d4045bab-8847-4998-a45d-36fc181a8854"},"execution_count":167,"outputs":[{"output_type":"stream","name":"stdout","text":"104/104 [==============================] - 201s 2s/step\n\n20/20 [==============================] - 37s 2s/step\n\n1/1 [==============================] - 0s 216ms/step\n"}]},{"cell_type":"code","source":"loss_metrics","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vuA6RVUYvyvV","outputId":"c5497cba-e47e-4ad4-8dfc-ebc478f2f692"},"execution_count":169,"outputs":[{"output_type":"execute_result","execution_count":169,"data":{"text/plain":["0.26670361592075215"]},"metadata":{}}]},{"cell_type":"code","source":"res2","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Rv7GAK6kvtCN","outputId":"2ec65c4a-81b3-4164-d5c2-6194218f956a"},"execution_count":168,"outputs":[{"output_type":"execute_result","execution_count":168,"data":{"text/plain":["array([2.9157226, 2.8101494, 3.3038282], dtype=float32)"]},"metadata":{}}]},{"cell_type":"code","source":"[y_pred, y_true, loss_metrics, res3] = xgboost_syntax(vocabulary, x_train, x_test, x_sub, models[2])","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"whmaQEJAtzGV","outputId":"11c7a472-09e9-42ce-a154-a92570f0edb7"},"execution_count":170,"outputs":[{"output_type":"stream","name":"stdout","text":"104/104 [==============================] - 201s 2s/step\n\n20/20 [==============================] - 37s 2s/step\n\n1/1 [==============================] - 0s 311ms/step\n"}]},{"cell_type":"code","source":"loss_metrics","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WkNtA7BAwPbM","outputId":"5b16afc2-e2b9-4bf9-d2e9-a59839d957ba"},"execution_count":171,"outputs":[{"output_type":"execute_result","execution_count":171,"data":{"text/plain":["0.19673316096102522"]},"metadata":{}}]},{"cell_type":"code","source":"res3","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3kqx4rhqwQs8","outputId":"5c38b63e-9ae5-43ad-ff31-638100f99944"},"execution_count":172,"outputs":[{"output_type":"execute_result","execution_count":172,"data":{"text/plain":["array([3.0715544, 2.9414926, 3.3097732], dtype=float32)"]},"metadata":{}}]},{"cell_type":"code","source":"[y_pred, y_true, loss_metrics, res4] = xgboost_syntax(phraseology, x_train, x_test, x_sub, models[3])","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"q0P7FHqNtz91","outputId":"05fa45f9-8174-42d5-f9b7-98c507c93676"},"execution_count":173,"outputs":[{"output_type":"stream","name":"stdout","text":"104/104 [==============================] - 202s 2s/step\n\n20/20 [==============================] - 37s 2s/step\n\n1/1 [==============================] - 0s 300ms/step\n"}]},{"cell_type":"code","source":"loss_metrics","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_dsRxlPRwSQl","outputId":"f8257b1e-be74-4dd3-91c1-c10b8d8d76b1"},"execution_count":174,"outputs":[{"output_type":"execute_result","execution_count":174,"data":{"text/plain":["0.2543783047818731"]},"metadata":{}}]},{"cell_type":"code","source":"res4","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cYHxFyHPwS5U","outputId":"83c21c62-4659-4610-fc08-ce9d84ff5fbc"},"execution_count":175,"outputs":[{"output_type":"execute_result","execution_count":175,"data":{"text/plain":["array([2.8998566, 2.8286774, 3.2767384], dtype=float32)"]},"metadata":{}}]},{"cell_type":"code","source":"[y_pred, y_true, loss_metrics, res5] = xgboost_syntax(grammar, x_train, x_test, x_sub, models[4])","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JtXF-HR9t0o-","outputId":"bb6b4f0f-b8dd-4b08-f4f9-125a17e1aa21"},"execution_count":176,"outputs":[{"output_type":"stream","name":"stdout","text":"104/104 [==============================] - 204s 2s/step\n\n20/20 [==============================] - 37s 2s/step\n\n1/1 [==============================] - 0s 292ms/step\n"}]},{"cell_type":"code","source":"res5","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JgkbV_Rwx1qs","outputId":"5fbce49d-a2b5-4d47-9a4a-a3f5f922108a"},"execution_count":177,"outputs":[{"output_type":"execute_result","execution_count":177,"data":{"text/plain":["array([2.6547122, 2.7881646, 3.0478206], dtype=float32)"]},"metadata":{}}]},{"cell_type":"code","source":"[y_pred, y_true, loss_metrics, res6] = xgboost_syntax(conventions, x_train, x_test, x_sub, models[5])","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kSbLQfRpt1Q9","outputId":"e02a123f-d3d3-48bc-8bb3-163c218b9087"},"execution_count":178,"outputs":[{"output_type":"stream","name":"stdout","text":"104/104 [==============================] - 204s 2s/step\n\n20/20 [==============================] - 37s 2s/step\n\n1/1 [==============================] - 0s 309ms/step\n"}]},{"cell_type":"code","source":"res6","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JV2ZaAPTx3bj","outputId":"153ff3f7-33f7-40f7-fc13-b2ada0176687"},"execution_count":179,"outputs":[{"output_type":"execute_result","execution_count":179,"data":{"text/plain":["array([2.723715 , 2.8658981, 3.2375422], dtype=float32)"]},"metadata":{}}]},{"cell_type":"code","source":"csv = pd.DataFrame({\n    \"text_id\": test_dataset['text_id'],\n    \"cohesion\": res2,\n    \"syntax\": res,\n    \"vocabulary\": res3,\n    \"phraseology\": res4,\n    \"grammar\": res5,\n    \"conventions\": res6\n\n})\ncsv.head()","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"491xtFvDzyGj","outputId":"7b71c3b0-11b5-40ab-c96c-e5b0ae7cfda5"},"execution_count":180,"outputs":[{"output_type":"execute_result","execution_count":180,"data":{"text/plain":["        text_id  cohesion    syntax  vocabulary  phraseology   grammar  \\\n","0  0000C359D63E  2.915723  2.765248    3.071554     2.899857  2.654712   \n","1  000BAD50D026  2.810149  2.572466    2.941493     2.828677  2.788165   \n","2  00367BB2546B  3.303828  3.147603    3.309773     3.276738  3.047821   \n","\n","   conventions  \n","0     2.723715  \n","1     2.865898  \n","2     3.237542  "],"text/html":["\n","  <div id=\"df-171ac5df-3146-4927-9496-63666ee57c7e\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    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