space_bert_demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-22T20:56:30.349927Z",
     "start_time": "2023-10-22T20:56:28.451433Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ebevoe42UgLK",
    "outputId": "1dc11050-1428-46b9-e600-4c6ac486981d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: transformers in /Users/stepantytarenko/opt/anaconda3/envs/space-model/lib/python3.10/site-packages (4.34.0)\r\n",
      "Requirement already satisfied: filelock in /Users/stepantytarenko/opt/anaconda3/envs/space-model/lib/python3.10/site-packages (from transformers) (3.9.0)\r\n",
      "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /Users/stepantytarenko/opt/anaconda3/envs/space-model/lib/python3.10/site-packages (from transformers) (0.17.3)\r\n",
      "Requirement already satisfied: numpy>=1.17 in /Users/stepantytarenko/opt/anaconda3/envs/space-model/lib/python3.10/site-packages (from transformers) (1.26.1)\r\n",
      "Requirement already satisfied: packaging>=20.0 in /Users/stepantytarenko/opt/anaconda3/envs/space-model/lib/python3.10/site-packages (from transformers) (23.2)\r\n",
      "Requirement already satisfied: pyyaml>=5.1 in /Users/stepantytarenko/opt/anaconda3/envs/space-model/lib/python3.10/site-packages (from transformers) (6.0.1)\r\n",
      "Requirement already satisfied: regex!=2019.12.17 in /Users/stepantytarenko/opt/anaconda3/envs/space-model/lib/python3.10/site-packages (from transformers) (2023.10.3)\r\n",
      "Requirement already satisfied: requests in /Users/stepantytarenko/opt/anaconda3/envs/space-model/lib/python3.10/site-packages (from transformers) (2.31.0)\r\n",
      "Requirement already satisfied: tokenizers<0.15,>=0.14 in /Users/stepantytarenko/opt/anaconda3/envs/space-model/lib/python3.10/site-packages (from transformers) (0.14.1)\r\n",
      "Requirement already satisfied: safetensors>=0.3.1 in /Users/stepantytarenko/opt/anaconda3/envs/space-model/lib/python3.10/site-packages (from transformers) (0.4.0)\r\n",
      "Requirement already satisfied: tqdm>=4.27 in /Users/stepantytarenko/opt/anaconda3/envs/space-model/lib/python3.10/site-packages (from transformers) (4.66.1)\r\n",
      "Requirement already satisfied: fsspec in /Users/stepantytarenko/opt/anaconda3/envs/space-model/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.16.4->transformers) (2023.9.2)\r\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /Users/stepantytarenko/opt/anaconda3/envs/space-model/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.16.4->transformers) (4.7.1)\r\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/stepantytarenko/opt/anaconda3/envs/space-model/lib/python3.10/site-packages (from requests->transformers) (3.3.0)\r\n",
      "Requirement already satisfied: idna<4,>=2.5 in /Users/stepantytarenko/opt/anaconda3/envs/space-model/lib/python3.10/site-packages (from requests->transformers) (3.4)\r\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/stepantytarenko/opt/anaconda3/envs/space-model/lib/python3.10/site-packages (from requests->transformers) (2.0.6)\r\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /Users/stepantytarenko/opt/anaconda3/envs/space-model/lib/python3.10/site-packages (from requests->transformers) (2023.7.22)\r\n"
     ]
    }
   ],
   "source": [
    "! pip install transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5Ud9mPBrMnep",
    "outputId": "693e04c9-5abf-4743-a84a-cf43ae978dfb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'space-model'...\n",
      "remote: Enumerating objects: 25, done.\u001B[K\n",
      "remote: Counting objects: 100% (25/25), done.\u001B[K\n",
      "remote: Compressing objects: 100% (18/18), done.\u001B[K\n",
      "remote: Total 25 (delta 7), reused 19 (delta 4), pack-reused 0\u001B[K\n",
      "Unpacking objects: 100% (25/25), 2.10 MiB | 6.89 MiB/s, done.\n",
      "Cloning into 'nano-BERT'...\n",
      "remote: Enumerating objects: 56, done.\u001B[K\n",
      "remote: Counting objects: 100% (56/56), done.\u001B[K\n",
      "remote: Compressing objects: 100% (46/46), done.\u001B[K\n",
      "remote: Total 56 (delta 25), reused 30 (delta 8), pack-reused 0\u001B[K\n",
      "Unpacking objects: 100% (56/56), 39.04 MiB | 9.88 MiB/s, done.\n"
     ]
    }
   ],
   "source": [
    "! rm -rf space-model\n",
    "! git clone https://github.com/StepanTita/space-model.git\n",
    "! git clone https://github.com/StepanTita/nano-BERT.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "ppjscAk9Mwxc"
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "sys.path.append('space-model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "E-jG_7erM1jL"
   },
   "outputs": [],
   "source": [
    "import math\n",
    "import json\n",
    "from collections import Counter\n",
    "import random\n",
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from tqdm import tqdm\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from transformers import AutoModel, AutoTokenizer, AutoModelForSequenceClassification\n",
    "\n",
    "from space_model.model import SpaceModelForClassification, SpaceModel\n",
    "from space_model.loss import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "DgD4mX6CPqAx"
   },
   "outputs": [],
   "source": [
    "SEED = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "287PDnJQPm4K"
   },
   "outputs": [],
   "source": [
    "def seed_everything(seed=42):\n",
    "    random.seed(seed)\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed(seed)\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "\n",
    "seed_everything(seed=SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "gdyFsHsxPtFR"
   },
   "outputs": [],
   "source": [
    "def on_gpu(f):\n",
    "    def wrapper():\n",
    "        if torch.cuda.is_available():\n",
    "            return f()\n",
    "        else:\n",
    "            print('cuda unavailable')\n",
    "    return wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QdNbXp-oPw7W",
    "outputId": "9519a866-424c-44c6-a93a-f54ebfffba11"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pynvml in /home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages (11.4.1)\n"
     ]
    }
   ],
   "source": [
    "if torch.cuda.is_available():\n",
    "    ! pip install pynvml\n",
    "    from pynvml import *\n",
    "    from numba import cuda\n",
    "\n",
    "@on_gpu\n",
    "def print_gpu_utilization():\n",
    "    try:\n",
    "        nvmlInit()\n",
    "        handle = nvmlDeviceGetHandleByIndex(0)\n",
    "        info = nvmlDeviceGetMemoryInfo(handle)\n",
    "        print(f\"GPU memory occupied: {info.used//1024**2} MB.\")\n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "\n",
    "@on_gpu\n",
    "def free_gpu_cache():\n",
    "    print(\"Initial GPU Usage\")\n",
    "    print_gpu_utilization()\n",
    "\n",
    "    torch.cuda.empty_cache()\n",
    "\n",
    "    print(\"GPU Usage after emptying the cache\")\n",
    "    print_gpu_utilization()\n",
    "\n",
    "def print_summary(result):\n",
    "    print(f\"Time: {result.metrics['train_runtime']:.2f}\")\n",
    "    print(f\"Samples/second: {result.metrics['train_samples_per_second']:.2f}\")\n",
    "    print_gpu_utilization()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "VxNX0IVTNAM5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "TfSktUJrNB2H",
    "outputId": "940e3fc3-473d-47e9-e128-07ce215e7c7f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[\", Ko, to, tamo, peva, \", is, one, of, the, b...</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[This, was, a, fine, example, of, how, an, int...</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[\", World, 's, Finest, \", is, an, unique, proj...</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[The, second, alternate, Gundam, universe, tal...</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[maybe, i, identify, with, this, film, cause, ...</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>24995</th>\n",
       "      <td>[Yeah, ,, that, about, sums, it, up, ., This, ...</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24996</th>\n",
       "      <td>[It, was, everything, this, is, n't, :, it, ha...</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24997</th>\n",
       "      <td>[This, show, comes, up, with, interesting, loc...</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24998</th>\n",
       "      <td>[and, a, 30,000, $, budget, and, this, movie, ...</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24999</th>\n",
       "      <td>[Wow, -, this, one, sucks, ., I, 'm, gon, na, ...</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    text label\n",
       "0      [\", Ko, to, tamo, peva, \", is, one, of, the, b...   pos\n",
       "1      [This, was, a, fine, example, of, how, an, int...   pos\n",
       "2      [\", World, 's, Finest, \", is, an, unique, proj...   pos\n",
       "3      [The, second, alternate, Gundam, universe, tal...   pos\n",
       "4      [maybe, i, identify, with, this, film, cause, ...   pos\n",
       "...                                                  ...   ...\n",
       "24995  [Yeah, ,, that, about, sums, it, up, ., This, ...   neg\n",
       "24996  [It, was, everything, this, is, n't, :, it, ha...   neg\n",
       "24997  [This, show, comes, up, with, interesting, loc...   neg\n",
       "24998  [and, a, 30,000, $, budget, and, this, movie, ...   neg\n",
       "24999  [Wow, -, this, one, sucks, ., I, 'm, gon, na, ...   neg\n",
       "\n",
       "[25000 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = None\n",
    "with open('nano-BERT/data/imdb_train.json') as f:\n",
    "    data = pd.DataFrame([json.loads(l) for l in f.readlines()])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "wRywCPRlNEDD"
   },
   "outputs": [],
   "source": [
    "test_data = None\n",
    "with open('nano-BERT/data/imdb_test.json') as f:\n",
    "   test_data = pd.DataFrame([json.loads(l) for l in f.readlines()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "ExLCdYtWN7Np"
   },
   "outputs": [],
   "source": [
    "def encode_label(label):\n",
    "    if label == 'pos':\n",
    "        return 1\n",
    "    elif label == 'neg':\n",
    "        return 0\n",
    "    raise Exception(f'Unknown Label: {label}!')\n",
    "\n",
    "class IMDBDataset(torch.utils.data.Dataset):\n",
    "    def __init__(self, dataframe, tokenizer, label_encoder, max_seq_len, inference=False):\n",
    "        self.len = len(dataframe)\n",
    "        self.data = dataframe\n",
    "        self.tokenizer = tokenizer\n",
    "        self.label_encoder = label_encoder\n",
    "        self.max_seq_len = max_seq_len\n",
    "        self.inference = inference\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        sentence = ' '.join(self.data.text[index])\n",
    "\n",
    "        if not self.inference:\n",
    "            labels = self.label_encoder(self.data.label[index])\n",
    "\n",
    "        inputs = tokenizer(sentence, max_length=self.max_seq_len, padding='max_length', truncation=True, return_tensors='pt')\n",
    "\n",
    "        return {\n",
    "            **{k: v.squeeze(0) for k, v in inputs.items()},\n",
    "            'label_ids': torch.tensor(labels, dtype=torch.long) if not self.inference else []\n",
    "        }\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "XH_wO_V2O0dh"
   },
   "outputs": [],
   "source": [
    "MODEL_NAME = 'bert-base-uncased'\n",
    "\n",
    "NUM_EPOCHS = 5\n",
    "BATCH_SIZE = 256\n",
    "MAX_SEQ_LEN = 256\n",
    "LEARNING_RATE = 2e-4\n",
    "MAX_GRAD_NORM = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 306,
     "referenced_widgets": [
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     ]
    },
    "id": "ku5ofLQBO0Ib",
    "outputId": "c24177b4-8177-45b5-f570-c8c7c112aae2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PreTrainedTokenizerFast(name_or_path='bert-base-uncased', vocab_size=30522, model_max_len=512, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'unk_token': '[UNK]', 'sep_token': '[SEP]', 'pad_token': '[PAD]', 'cls_token': '[CLS]', 'mask_token': '[MASK]'})"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n",
    "tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "5OsegzW0O8fs"
   },
   "outputs": [],
   "source": [
    "train_dataset = IMDBDataset(data, tokenizer, encode_label, MAX_SEQ_LEN)\n",
    "test_dataset = IMDBDataset(test_data, tokenizer, encode_label, MAX_SEQ_LEN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 105,
     "referenced_widgets": [
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      "cbd1b5e52b524be49b5f9fa0fea52ab5",
      "ba82a35d13704fe2965eb9350c419cd1",
      "9a8917f0f1ae4379a4faa0333e8fd7c0",
      "9b9f85d12cfc4419a39b4382ffebf4d3",
      "bc628dda81c940d088118ae072946bff",
      "e934b7cffbff4f59a2bfd320dfe91d8f",
      "4ca0e17d8e574703808d0b5a97df35b1",
      "94168e6b15584fc08efef0dbca6ca916",
      "12dfaa3511024cabb551cf8f1a32db22",
      "09f3785e65754b14936ad64c38c2b5b8"
     ]
    },
    "id": "fL_dWSNhP_4p",
    "outputId": "8ef871ff-0acf-44ea-f456-f2ab7d72fc0a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight']\n",
      "- This IS expected if you are initializing BertForSequenceClassification 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",
      "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "cls_bert = AutoModelForSequenceClassification.from_pretrained(\n",
    "    MODEL_NAME,\n",
    ").to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "vrl6sP8fQcQt"
   },
   "outputs": [],
   "source": [
    "for param in cls_bert.bert.parameters():\n",
    "    param.requires_grad_(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "1NzT1ZxdQiQj"
   },
   "outputs": [],
   "source": [
    "def count_parameters(model):\n",
    "    return sum(p.numel() for p in model.parameters() if p.requires_grad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2ObZDAUaQipi",
    "outputId": "fe2d6f83-8ead-4551-d005-d865a2f0ad6b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1538"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_parameters(cls_bert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QMTmk2ThQMIC",
    "outputId": "2266a718-d1a3-49f2-a3fc-2ac9beabdb59"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.6425, device='cuda:0', grad_fn=<NllLossBackward0>)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ids = train_dataset[0]['input_ids'].unsqueeze(0)\n",
    "mask = train_dataset[0]['attention_mask'].unsqueeze(0)\n",
    "targets = train_dataset[0]['label_ids'].unsqueeze(0)\n",
    "\n",
    "ids = ids.to(device)\n",
    "mask = mask.to(device)\n",
    "targets = targets.to(device)\n",
    "\n",
    "outputs = cls_bert(input_ids=ids, attention_mask=mask, labels=targets)\n",
    "initial_loss = outputs[0]\n",
    "initial_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "id": "Tru9Ev77QseN"
   },
   "outputs": [],
   "source": [
    "def eval(f):\n",
    "    def wrapper(model, *args, **kwargs):\n",
    "        model.eval()\n",
    "        return f(model, *args, **kwargs)\n",
    "    return wrapper\n",
    "\n",
    "def train(f):\n",
    "    def wrapper(model, *args, **kwargs):\n",
    "        model.train()\n",
    "        return f(model, *args, **kwargs)\n",
    "    return wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "id": "OF6FIFmxTFy4"
   },
   "outputs": [],
   "source": [
    "@train\n",
    "def train_epoch(model, train_dataloader, optimizer):\n",
    "    train_loss = 0.0\n",
    "    train_preds = []\n",
    "    train_labels = []\n",
    "\n",
    "    for step, batch in enumerate(tqdm(train_dataloader, total=len(train_dataloader))):\n",
    "        ids = batch['input_ids'].to(device, dtype = torch.long)\n",
    "        mask = batch['attention_mask'].to(device, dtype = torch.long)\n",
    "        targets = batch['label_ids'].to(device, dtype = torch.long)\n",
    "\n",
    "        outputs = model(input_ids=ids, attention_mask=mask, labels=targets) # (B, Seq_Len, 2)\n",
    "\n",
    "        loss, logits = outputs.loss, outputs.logits\n",
    "\n",
    "        probs = F.softmax(logits, dim=-1).cpu()\n",
    "        pred = torch.argmax(probs, dim=-1) # (B)\n",
    "        train_preds += pred.detach().tolist()\n",
    "        train_labels += [l.item() for l in targets.cpu()]\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        train_loss += loss.item()\n",
    "    return train_loss, train_preds, train_labels\n",
    "\n",
    "@eval\n",
    "def eval_epoch(model, val_dataloader):\n",
    "    val_loss = 0.0\n",
    "    val_preds = []\n",
    "    val_labels = []\n",
    "\n",
    "    with torch.no_grad():\n",
    "\n",
    "        for step, batch in enumerate(tqdm(val_dataloader, total=len(val_dataloader))):\n",
    "            ids = batch['input_ids'].to(device, dtype = torch.long)\n",
    "            mask = batch['attention_mask'].to(device, dtype = torch.long)\n",
    "            targets = batch['label_ids'].to(device, dtype = torch.long)\n",
    "\n",
    "            outputs = model(input_ids=ids, attention_mask=mask, labels=targets)\n",
    "\n",
    "            loss, logits = outputs.loss, outputs.logits\n",
    "\n",
    "            probs = F.softmax(logits, dim=-1).cpu()\n",
    "            pred = torch.argmax(probs, dim=-1) # (B)\n",
    "            val_preds += pred.detach().tolist()\n",
    "            val_labels += [l.item() for l in targets.cpu()]\n",
    "\n",
    "            val_loss += loss.item()\n",
    "    return val_loss, val_preds, val_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "id": "COJTR-VlQzGz"
   },
   "outputs": [],
   "source": [
    "def training(model, train_data, val_data, config):\n",
    "    model = model.to(device)\n",
    "\n",
    "    optimizer = torch.optim.Adam(\n",
    "        params=model.parameters(),\n",
    "        lr=config['lr'],\n",
    "        weight_decay=config['weight_decay']\n",
    "    )\n",
    "\n",
    "    num_train_steps = int(len(train_data) / config['batch_size'] * config['epochs'])\n",
    "\n",
    "    print(f'Train steps: {num_train_steps}')\n",
    "\n",
    "    train_dataloader = torch.utils.data.DataLoader(train_data, batch_size=config['batch_size'], shuffle=True)\n",
    "    val_dataloader = torch.utils.data.DataLoader(val_data, batch_size=2 * config['batch_size'])\n",
    "\n",
    "    history = {\n",
    "        'train_losses': [],\n",
    "        'val_losses': [],\n",
    "        'train_acc': [],\n",
    "        'val_acc': [],\n",
    "        'train_f1': [],\n",
    "        'val_f1': [],\n",
    "        'train_precision': [],\n",
    "        'val_precision': [],\n",
    "        'train_recall': [],\n",
    "        'val_recall': [],\n",
    "    }\n",
    "\n",
    "    for epoch_num in range(config['epochs']):\n",
    "        # train stage\n",
    "        train_loss, train_preds, train_labels = train_epoch(model, train_dataloader, optimizer)\n",
    "\n",
    "        # eval stage\n",
    "        val_loss, val_preds, val_labels = eval_epoch(model, val_dataloader)\n",
    "        \n",
    "        # metrics\n",
    "        train_acc = accuracy_score(train_labels, train_preds)\n",
    "        val_acc = accuracy_score(val_labels, val_preds)\n",
    "        train_f1 = f1_score(train_labels, train_preds, average='macro')\n",
    "        val_f1 = f1_score(val_labels, val_preds, average='macro')\n",
    "        train_precision = precision_score(train_labels, train_preds)\n",
    "        val_precision = precision_score(val_labels, val_preds)\n",
    "        train_recall = recall_score(train_labels, train_preds)\n",
    "        val_recall = recall_score(val_labels, val_preds)\n",
    "\n",
    "        history['train_losses'].append(train_loss)\n",
    "        history['val_losses'].append(val_loss)\n",
    "        history['train_acc'].append(train_acc)\n",
    "        history['val_acc'].append(val_acc)\n",
    "        history['train_f1'].append(train_f1)\n",
    "        history['val_f1'].append(val_f1)\n",
    "        history['train_precision'].append(train_precision)\n",
    "        history['val_precision'].append(val_precision)\n",
    "        history['train_recall'].append(train_recall)\n",
    "        history['val_recall'].append(val_recall)\n",
    "\n",
    "        print()\n",
    "        print(f'Train loss: {train_loss / len(train_dataloader)} | Val loss: {val_loss / len(val_dataloader)}')\n",
    "        print(f'Train acc: {train_acc} | Val acc: {val_acc}')\n",
    "        print(f'Train f1: {train_f1} | Val f1: {val_f1}')\n",
    "        print(f'Train precision: {train_precision} | Val precision: {val_precision}')\n",
    "        print(f'Train recall: {train_recall} | Val recall: {val_recall}')\n",
    "    return history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "id": "XWHQmrInXyaJ"
   },
   "outputs": [],
   "source": [
    "config = {\n",
    "    'epochs': NUM_EPOCHS,\n",
    "    'batch_size': BATCH_SIZE,\n",
    "    'gradient_accumulation_steps': 0,\n",
    "    'fp16': False,\n",
    "    'lr': LEARNING_RATE,\n",
    "    'max_grad_norm': MAX_GRAD_NORM,\n",
    "    'weight_decay': 0.01,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cIb53Le6Xkre",
    "outputId": "934c7561-1803-4905-f2d0-d104df7a7be4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train steps: 488\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 98/98 [06:39<00:00,  4.08s/it]\n",
      "100%|██████████| 49/49 [06:32<00:00,  8.00s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train loss: 0.6799552160866407 | Val loss: 0.6545614344733102\n",
      "Train acc: 0.56896 | Val acc: 0.64816\n",
      "Train f1: 0.5689587834292703 | Val f1: 0.635741732798343\n",
      "Train precision: 0.5687290703237123 | Val precision: 0.6082028511334424\n",
      "Train recall: 0.57064 | Val recall: 0.8328\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 98/98 [06:15<00:00,  3.83s/it]\n",
      "100%|██████████| 49/49 [05:47<00:00,  7.09s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train loss: 0.6458353844224191 | Val loss: 0.6252157420528178\n",
      "Train acc: 0.65332 | Val acc: 0.7052\n",
      "Train f1: 0.6533026213085167 | Val f1: 0.7038626912958614\n",
      "Train precision: 0.6555221942708755 | Val precision: 0.6808885754583921\n",
      "Train recall: 0.64624 | Val recall: 0.7724\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 98/98 [06:31<00:00,  4.00s/it]\n",
      "100%|██████████| 49/49 [06:23<00:00,  7.83s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train loss: 0.6219088739278366 | Val loss: 0.6116622960080906\n",
      "Train acc: 0.68364 | Val acc: 0.68608\n",
      "Train f1: 0.6835641723560474 | Val f1: 0.6743846013351267\n",
      "Train precision: 0.6895071410880872 | Val precision: 0.6349344471516417\n",
      "Train recall: 0.66816 | Val recall: 0.8756\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 98/98 [06:30<00:00,  3.99s/it]\n",
      "100%|██████████| 49/49 [05:54<00:00,  7.24s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train loss: 0.6082114358337558 | Val loss: 0.5945583521103373\n",
      "Train acc: 0.696 | Val acc: 0.70224\n",
      "Train f1: 0.6959676198218332 | Val f1: 0.6949140936777078\n",
      "Train precision: 0.7001306975984316 | Val precision: 0.6543911078539147\n",
      "Train recall: 0.68568 | Val recall: 0.8572\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 98/98 [06:15<00:00,  3.83s/it]\n",
      "100%|██████████| 49/49 [06:23<00:00,  7.82s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train loss: 0.5945276131435316 | Val loss: 0.579324368919645\n",
      "Train acc: 0.7054 | Val acc: 0.72472\n",
      "Train f1: 0.7053264376919054 | Val f1: 0.7213334045475901\n",
      "Train precision: 0.7121024370095002 | Val precision: 0.6841242789722076\n",
      "Train recall: 0.6896 | Val recall: 0.83496\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "history = training(cls_bert, train_dataset, test_dataset, config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial GPU Usage\n",
      "GPU memory occupied: 8630 MB.\n",
      "GPU Usage after emptying the cache\n",
      "GPU memory occupied: 1856 MB.\n"
     ]
    }
   ],
   "source": [
    "free_gpu_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_results(history, do_val=True):\n",
    "    fig, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "    x = list(range(0, len(history['train_losses'])))\n",
    "\n",
    "    # loss\n",
    "\n",
    "    ax.plot(x, history['train_losses'], label='train_loss')\n",
    "\n",
    "    if do_val:\n",
    "        ax.plot(x, history['val_losses'], label='val_loss')\n",
    "\n",
    "    plt.title('Train / Validation Loss')\n",
    "    plt.legend(loc='upper right')\n",
    "\n",
    "    # accuracy\n",
    "    \n",
    "    if 'train_acc' in history:\n",
    "        fig, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "        ax.plot(x, history['train_acc'], label='train_acc')\n",
    "\n",
    "        if do_val:\n",
    "            ax.plot(x, history['val_acc'], label='val_acc')\n",
    "\n",
    "    plt.title('Train / Validation Accuracy')\n",
    "    plt.legend(loc='upper right')\n",
    "\n",
    "    # f1-score\n",
    "    \n",
    "    if 'train_f1' in history:\n",
    "        fig, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "        ax.plot(x, history['train_f1'], label='train_f1')\n",
    "\n",
    "        if do_val:\n",
    "            ax.plot(x, history['val_f1'], label='val_f1')\n",
    "\n",
    "        plt.title('Train / Validation F1')\n",
    "        plt.legend(loc='upper right')\n",
    "    \n",
    "    # precision\n",
    "    \n",
    "    if 'train_precision' in history:\n",
    "        fig, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "        ax.plot(x, history['train_precision'], label='train_precision')\n",
    "\n",
    "        if do_val:\n",
    "            ax.plot(x, history['val_precision'], label='val_precision')\n",
    "\n",
    "        plt.title('Train / Validation Precision')\n",
    "        plt.legend(loc='upper right')\n",
    "    \n",
    "    # recall\n",
    "    \n",
    "    if 'train_recall' in history:\n",
    "        fig, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "        ax.plot(x, history['train_recall'], label='train_recall')\n",
    "\n",
    "        if do_val:\n",
    "            ax.plot(x, history['val_recall'], label='val_recall')\n",
    "\n",
    "        plt.title('Train / Validation Recall')\n",
    "        plt.legend(loc='upper right')\n",
    "\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_results(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "id": "uwdCs_G0b2qJ"
   },
   "outputs": [],
   "source": [
    "class SpaceModelForSequenceClassificationOutput:\n",
    "    def __init__(self, loss=None, logits=None, concept_spaces=None):\n",
    "        self.loss = loss\n",
    "        self.logits = logits\n",
    "        self.concept_spaces = concept_spaces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "id": "bOJHvbQIX68D"
   },
   "outputs": [],
   "source": [
    "class SpaceBertForSequenceClassification(torch.nn.Module):\n",
    "    def __init__(self, base_model, n_embed=3, n_latent=3, n_concept_spaces=2, dropout=0.1, l1=1e-3, l2=1e-4, fine_tune=True):\n",
    "        super().__init__()\n",
    "\n",
    "        if fine_tune:\n",
    "            for p in base_model.parameters():\n",
    "                p.requires_grad_(False)\n",
    "\n",
    "        self.bert = base_model\n",
    "\n",
    "        self.space_model = SpaceModel(n_embed, n_latent, n_concept_spaces, output_concept_spaces=True)\n",
    "\n",
    "        # self.ffwd = torch.nn.Sequential(\n",
    "        #     torch.nn.Linear(n_concept_spaces * n_latent, 2 * n_concept_spaces * n_latent),\n",
    "        #     torch.nn.GELU(),\n",
    "        #     torch.nn.Linear(2 * n_concept_spaces * n_latent, n_concept_spaces * n_latent),\n",
    "        #     torch.nn.Dropout(dropout),\n",
    "        # )\n",
    "\n",
    "        self.classifier = torch.nn.Linear(n_concept_spaces * n_latent, n_concept_spaces)\n",
    "\n",
    "        self.l1 = l1\n",
    "        self.l2 = l2\n",
    "\n",
    "    def forward(self, input_ids, attention_mask, labels=None):\n",
    "        embed = self.bert(input_ids, attention_mask).last_hidden_state\n",
    "\n",
    "        out = self.space_model(embed)\n",
    "\n",
    "        concept_hidden = out.logits\n",
    "\n",
    "        logits = self.classifier(concept_hidden)\n",
    "\n",
    "        loss = 0.0\n",
    "        if labels is not None:\n",
    "            loss = F.cross_entropy(logits, labels)\n",
    "        loss += self.l1 * inter_space_loss(out.concept_spaces) + self.l2 * intra_space_loss(out.concept_spaces)\n",
    "\n",
    "        return SpaceModelForSequenceClassificationOutput(loss, logits, out.concept_spaces)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WS9YV-Hljbbf",
    "outputId": "03a4b80d-cd0b-4019-93b2-ec8d4d5fbb4c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight']\n",
      "- This IS expected if you are initializing BertModel 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",
      "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "BertModel(\n",
       "  (embeddings): BertEmbeddings(\n",
       "    (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
       "    (position_embeddings): Embedding(512, 768)\n",
       "    (token_type_embeddings): Embedding(2, 768)\n",
       "    (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "    (dropout): Dropout(p=0.1, inplace=False)\n",
       "  )\n",
       "  (encoder): BertEncoder(\n",
       "    (layer): ModuleList(\n",
       "      (0): BertLayer(\n",
       "        (attention): BertAttention(\n",
       "          (self): BertSelfAttention(\n",
       "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (output): BertSelfOutput(\n",
       "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (intermediate): BertIntermediate(\n",
       "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          (intermediate_act_fn): GELUActivation()\n",
       "        )\n",
       "        (output): BertOutput(\n",
       "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (1): BertLayer(\n",
       "        (attention): BertAttention(\n",
       "          (self): BertSelfAttention(\n",
       "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (output): BertSelfOutput(\n",
       "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (intermediate): BertIntermediate(\n",
       "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          (intermediate_act_fn): GELUActivation()\n",
       "        )\n",
       "        (output): BertOutput(\n",
       "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (2): BertLayer(\n",
       "        (attention): BertAttention(\n",
       "          (self): BertSelfAttention(\n",
       "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (output): BertSelfOutput(\n",
       "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (intermediate): BertIntermediate(\n",
       "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          (intermediate_act_fn): GELUActivation()\n",
       "        )\n",
       "        (output): BertOutput(\n",
       "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (3): BertLayer(\n",
       "        (attention): BertAttention(\n",
       "          (self): BertSelfAttention(\n",
       "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (output): BertSelfOutput(\n",
       "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (intermediate): BertIntermediate(\n",
       "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          (intermediate_act_fn): GELUActivation()\n",
       "        )\n",
       "        (output): BertOutput(\n",
       "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (4): BertLayer(\n",
       "        (attention): BertAttention(\n",
       "          (self): BertSelfAttention(\n",
       "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (output): BertSelfOutput(\n",
       "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (intermediate): BertIntermediate(\n",
       "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          (intermediate_act_fn): GELUActivation()\n",
       "        )\n",
       "        (output): BertOutput(\n",
       "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (5): BertLayer(\n",
       "        (attention): BertAttention(\n",
       "          (self): BertSelfAttention(\n",
       "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (output): BertSelfOutput(\n",
       "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (intermediate): BertIntermediate(\n",
       "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          (intermediate_act_fn): GELUActivation()\n",
       "        )\n",
       "        (output): BertOutput(\n",
       "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (6): BertLayer(\n",
       "        (attention): BertAttention(\n",
       "          (self): BertSelfAttention(\n",
       "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (output): BertSelfOutput(\n",
       "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (intermediate): BertIntermediate(\n",
       "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          (intermediate_act_fn): GELUActivation()\n",
       "        )\n",
       "        (output): BertOutput(\n",
       "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (7): BertLayer(\n",
       "        (attention): BertAttention(\n",
       "          (self): BertSelfAttention(\n",
       "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (output): BertSelfOutput(\n",
       "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (intermediate): BertIntermediate(\n",
       "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          (intermediate_act_fn): GELUActivation()\n",
       "        )\n",
       "        (output): BertOutput(\n",
       "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (8): BertLayer(\n",
       "        (attention): BertAttention(\n",
       "          (self): BertSelfAttention(\n",
       "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (output): BertSelfOutput(\n",
       "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (intermediate): BertIntermediate(\n",
       "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          (intermediate_act_fn): GELUActivation()\n",
       "        )\n",
       "        (output): BertOutput(\n",
       "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (9): BertLayer(\n",
       "        (attention): BertAttention(\n",
       "          (self): BertSelfAttention(\n",
       "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (output): BertSelfOutput(\n",
       "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (intermediate): BertIntermediate(\n",
       "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          (intermediate_act_fn): GELUActivation()\n",
       "        )\n",
       "        (output): BertOutput(\n",
       "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (10): BertLayer(\n",
       "        (attention): BertAttention(\n",
       "          (self): BertSelfAttention(\n",
       "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (output): BertSelfOutput(\n",
       "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (intermediate): BertIntermediate(\n",
       "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          (intermediate_act_fn): GELUActivation()\n",
       "        )\n",
       "        (output): BertOutput(\n",
       "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (11): BertLayer(\n",
       "        (attention): BertAttention(\n",
       "          (self): BertSelfAttention(\n",
       "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (output): BertSelfOutput(\n",
       "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (intermediate): BertIntermediate(\n",
       "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          (intermediate_act_fn): GELUActivation()\n",
       "        )\n",
       "        (output): BertOutput(\n",
       "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (pooler): BertPooler(\n",
       "    (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "    (activation): Tanh()\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bert = AutoModel.from_pretrained(MODEL_NAME)\n",
    "bert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_gSGah9kbqmg",
    "outputId": "bbcdb2d3-1a1e-40ba-8ceb-783e4dbed426"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SpaceBertForSequenceClassification(\n",
       "  (bert): BertModel(\n",
       "    (embeddings): BertEmbeddings(\n",
       "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
       "      (position_embeddings): Embedding(512, 768)\n",
       "      (token_type_embeddings): Embedding(2, 768)\n",
       "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "      (dropout): Dropout(p=0.1, inplace=False)\n",
       "    )\n",
       "    (encoder): BertEncoder(\n",
       "      (layer): ModuleList(\n",
       "        (0): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (1): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (2): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (3): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (4): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (5): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (6): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (7): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (8): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (9): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (10): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (11): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (pooler): BertPooler(\n",
       "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "      (activation): Tanh()\n",
       "    )\n",
       "  )\n",
       "  (space_model): SpaceModel(\n",
       "    (concept_spaces): ModuleList(\n",
       "      (0): Sequential(\n",
       "        (0): Linear(in_features=768, out_features=3, bias=False)\n",
       "        (1): Tanh()\n",
       "      )\n",
       "      (1): Sequential(\n",
       "        (0): Linear(in_features=768, out_features=3, bias=False)\n",
       "        (1): Tanh()\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (classifier): Linear(in_features=6, out_features=2, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "space_bert = SpaceBertForSequenceClassification(bert, n_embed=768, n_latent=3, n_concept_spaces=2)\n",
    "space_bert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zoqfK9Y-jphw",
    "outputId": "433f9ee3-b4c4-4d85-8baf-9d96af86db80"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4622"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_parameters(space_bert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "id": "z_lJuPF4mAGF",
    "outputId": "74bb147c-ef50-4096-ae32-04827064358d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train steps: 488\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/98 [00:00<?, ?it/s]/home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages/torch/nn/functional.py:1956: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n",
      "  warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n",
      "100%|██████████| 98/98 [07:08<00:00,  4.37s/it]\n",
      "100%|██████████| 49/49 [06:30<00:00,  7.97s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train loss: 0.8552391675053811 | Val loss: 0.9462040528959158\n",
      "Train acc: 0.61264 | Val acc: 0.7432\n",
      "Train f1: 0.6101449275362318 | Val f1: 0.7412177928855421\n",
      "Train precision: 0.597103448275862 | Val precision: 0.7069716775599129\n",
      "Train recall: 0.69264 | Val recall: 0.83072\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/98 [00:00<?, ?it/s]/home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages/torch/nn/functional.py:1956: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n",
      "  warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n",
      "100%|██████████| 98/98 [06:25<00:00,  3.93s/it]\n",
      "100%|██████████| 49/49 [06:30<00:00,  7.98s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train loss: 0.7854433734806217 | Val loss: 0.8997797005030573\n",
      "Train acc: 0.77492 | Val acc: 0.77264\n",
      "Train f1: 0.7740881785766215 | Val f1: 0.7692685580773513\n",
      "Train precision: 0.7451665834344011 | Val precision: 0.7195593351372246\n",
      "Train recall: 0.8356 | Val recall: 0.89352\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/98 [00:00<?, ?it/s]/home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages/torch/nn/functional.py:1956: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n",
      "  warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n",
      "100%|██████████| 98/98 [06:56<00:00,  4.25s/it]\n",
      "100%|██████████| 49/49 [06:25<00:00,  7.88s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train loss: 0.7438294127279398 | Val loss: 0.86251805753124\n",
      "Train acc: 0.795 | Val acc: 0.79896\n",
      "Train f1: 0.7947333829680429 | Val f1: 0.7983074571779845\n",
      "Train precision: 0.7751660323856429 | Val precision: 0.768424077000431\n",
      "Train recall: 0.83104 | Val recall: 0.85584\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/98 [00:00<?, ?it/s]/home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages/torch/nn/functional.py:1956: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n",
      "  warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n",
      "100%|██████████| 98/98 [06:59<00:00,  4.28s/it]\n",
      "100%|██████████| 49/49 [06:24<00:00,  7.85s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train loss: 0.7093264527466833 | Val loss: 0.8325603908422042\n",
      "Train acc: 0.80268 | Val acc: 0.8128\n",
      "Train f1: 0.8026659431847304 | Val f1: 0.8127950925356738\n",
      "Train precision: 0.7976555739123594 | Val precision: 0.8160362107985775\n",
      "Train recall: 0.81112 | Val recall: 0.80768\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/98 [00:00<?, ?it/s]/home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages/torch/nn/functional.py:1956: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n",
      "  warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n",
      "100%|██████████| 98/98 [06:57<00:00,  4.26s/it]\n",
      "100%|██████████| 49/49 [06:26<00:00,  7.88s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train loss: 0.6806782319837686 | Val loss: 0.8092017696828259\n",
      "Train acc: 0.81288 | Val acc: 0.81752\n",
      "Train f1: 0.8128721424264094 | Val f1: 0.8175192700770804\n",
      "Train precision: 0.8169881666396499 | Val precision: 0.8162549800796812\n",
      "Train recall: 0.8064 | Val recall: 0.81952\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "space_history = training(space_bert, train_dataset, test_dataset, config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial GPU Usage\n",
      "GPU memory occupied: 9034 MB.\n",
      "GPU Usage after emptying the cache\n",
      "GPU memory occupied: 2116 MB.\n"
     ]
    }
   ],
   "source": [
    "free_gpu_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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F+utf/6qKigqlpKRo/vz5dbb+P/vss5o/f75Gjx6t22+/XX379tWxY8f08ccfa8WKFYqJiXHve+ONN+qVV17RkiVL9Je//KVxFxTAGRFQAZxRXFyc/ve//+l3v/udHnnkEbVr107XX3+9xo0bp4kTJ7ba+y5evFhlZWXuVsrGuO6667Ry5UoNHz5cPXr0qPHaQw89pKKiIn3wwQf68MMPdc455+irr75q8i1M33rrLXeX7xdffKELL7xQX331lVJTU2vs9/LLL8tms2n27NkqLS3Vueeeq4ULF9a6hsnJyZo1a5ZmzJihW2+9VXa7XUuWLKkzoIaGhmrp0qWaPn263n33XeXn56t37956++23dfPNNzfp85zKNWN98uTJ9e4zefJkPfHEE/rxxx81aNAgff3113rmmWf0wQcf6NNPP1VcXJzOO+88DRw40H3M22+/rUGDBunNN9/U73//e0VHR2vo0KEaNWqUe5/HHntMmZmZ+uSTT/TRRx/p4osv1jfffFPntajPBx98oF//+td67bXXZBiGJkyYoG+++abWercpKSlavXq1Hn30Uc2ePVv5+flKSUnRxRdfXOtuakOGDFH//v21ffv2Zt+dDEBtFqO1m0AAoInuvvturVu3TmvWrDG7FKCWs88+W7GxsVq0aJHZpQA+hxZUAB5r8ODBp221A8yybt06bdq0qc7bsAJoPlpQAQBooC1btmj9+vV64YUXlJWVpX379ikkJMTssgCfwyx+AAAa6JNPPtEtt9yiiooK/fvf/yacAq2EFlQAAAB4FFpQAQAA4FEIqAAAAPAoPjGL3+Fw6OjRo4qMjGzULQIBAADQNgzDUEFBgTp06FDj1sx18YmAevTo0VoLYgMAAMDzpKWlqWPHjqfdxycCqutWfmlpaYqKijK5GgAAAJwqPz9fqampDboFs08EVFe3flRUFAEVAADAgzVkOCaTpAAAAOBRCKgAAADwKARUAAAAeBSfGIMKAAB8i91uV0VFhdlloJECAwNls9mafR4CKgAA8BiGYej48ePKzc01uxQ0UUxMjJKTk5u1Nj0BFQAAeAxXOE1MTFRYWBg34PEihmGouLhYGRkZkqT27ds3+VwEVAAA4BHsdrs7nMbFxZldDpogNDRUkpSRkaHExMQmd/czSQoAAHgE15jTsLAwkytBc7h+fs0ZQ0xABQAAHoVufe/WEj8/AioAAAA8CgEVAADAg3Tp0kUzZ840uwxTMUkKAACgmcaMGaPBgwe3SLBcu3atwsPDm1+UFyOgAgAAtDLDMGS32xUQcObolZCQ0AYVeTa6+AEAAJrh5ptv1rJly/Tyyy/LYrHIYrHonXfekcVi0TfffKMhQ4YoODhYK1as0N69e3XZZZcpKSlJERERGjZsmBYuXFjjfKd28VssFv3f//2frrjiCoWFhalnz576z3/+06Da7Ha7br31VnXt2lWhoaHq3bu3Xn755Vr7vfXWW+rfv7+Cg4PVvn173Xvvve7XcnNzdccddygpKUkhISEaMGCA/ve//zXtYjUQLagAAMBjGYahkgp7m79vaKCtwbPRX375Ze3atUsDBgzQU089JUnaunWrJGn69Ol6/vnn1a1bN7Vr105paWn66U9/qmeeeUbBwcF67733NHnyZO3cuVOdOnWq9z2efPJJ/fWvf9Vzzz2nV199Vdddd50OHjyo2NjY09bmcDjUsWNHffzxx4qLi9PKlSt1++23q3379rr66qslSX//+981bdo0/fnPf9bFF1+svLw8fffdd+7jL774YhUUFOj9999X9+7dtW3btha5nenpEFABAIDHKqmwq99j89r8fbc9NVFhQQ2LSdHR0QoKClJYWJiSk5MlSTt27JAkPfXUU7rooovc+8bGxuqss85yP3/66af1+eef6z//+U+NVstT3XzzzbrmmmskSc8++6xeeeUVrVmzRpMmTTptbYGBgXryySfdz7t27apVq1bpo48+cgfUP/3pT/rd736n++67z73fsGHDJEkLFy7UmjVrtH37dvXq1UuS1K1btzNflGYioAIAALSSoUOH1nheWFioJ554Ql999ZWOHTumyspKlZSU6NChQ6c9z6BBg9x/Dg8PV1RUlPuWomfy2muv6a233tKhQ4dUUlKi8vJyDR48WJLzjk9Hjx7VuHHj6jx206ZN6tixozucthUCKgAA8FihgTZte2qiKe/bEk6djf/AAw9owYIFev7559WjRw+FhobqF7/4hcrLy097nsDAwBrPLRaLHA7HGd9/zpw5euCBB/TCCy9o5MiRioyM1HPPPafVq1dLqr41aX3O9HprIaACAACPZbFYGtzVbqagoCDZ7WceK/vdd9/p5ptv1hVXXCHJ2aJ64MCBVqvru+++06hRo3T33Xe7t+3du9f958jISHXp0kWLFi3S2LFjax0/aNAgHT58WLt27WrTVlRm8QMAADRTly5dtHr1ah04cEBZWVn1tm727NlTn332mTZt2qQffvhB1157bYNaQpuqZ8+eWrdunebNm6ddu3bp0Ucf1dq1a2vs88QTT+iFF17QK6+8ot27d2vDhg169dVXJUmjR4/WBRdcoJ///OdasGCB9u/fr2+++UZz585ttZolAioAAECzPfDAA7LZbOrXr58SEhLqHVP64osvql27dho1apQmT56siRMn6pxzzmm1uu644w5deeWVmjJlikaMGKHs7OwaramSdNNNN2nmzJl6/fXX1b9/f1166aXavXu3+/VPP/1Uw4YN0zXXXKN+/frpD3/4Q4Nai5vDYhiG0arv0Aby8/MVHR2tvLw8RUVFmV0OAABogtLSUu3fv19du3ZVSEiI2eWgier7OTYmr9GCCgAAAI9CQAUAAPBSd955pyIiIup83HnnnWaX12SePy0OAAAAdXrqqaf0wAMP1PlaVGSk5KiU7JXO/zoqqv5cUbW96r9RKVJwRBtXfnoEVAAAAG9hGJLhcIfNxMhAJYZHV4fNGgG0UDp+9MzntJ9+DVYzEFABAADM5rDXbNU8ubWzRuislNTIZaksNskaINkCJGtg1Z9P+m9gWKt8pOYgoAIAALQGV+hsSPA0Ghs6rc6AaQ2sDp51BVBroGT1vilHBFQAAICGcjhqtmbWGM95yvaWCJ31tXxaW+ZWrJ6KgAoAAPyb4ah78lCt1s5KyWjsAvWWmq2ZdbZ2Vv3ZYpUsllb5iN6GgAoAAHyPO3SeZva6a3uzQmdA3QHUtZ3Q2SQEVAAA4B0Mo2ZrpqOintnrFU0LnSe3ZtY3ntMW4Jx01MKhs0uXLrr//vt1//33t+h5vRUBFQAAmMdhl4qypKIMKS9TKo9yPi+z1AygrkejWGq3ctZq+TxpTCctnR6DgAoAAFqWwy4V50iF6c7gWVj1OPnPrudFWZIM53ERqdK5L0hFVingNGGxwbPXCZ3eioAKAADOzOGQSk7UDp2F6VJR5imhM7ORM9gtUni8FNdTCgiRgqOlkJB6gmeAx4XOf/zjH3riiSd0+PBhWU9a0umyyy5TXFycHn74YU2bNk3ff/+9ioqK1LdvX82YMUPjx49v0vu9+OKLevvtt7Vv3z7FxsZq8uTJ+utf/6qIiOq7QX333Xd6+OGHtWbNGgUHB2v48OGaM2eO2rVrJ4fDoeeff17/+Mc/lJaWpqSkJN1xxx16+OGHm30tWgoBFQAAf2UYVaHz1NZNV+hMr3qtKoA2alynRQqLkyISpfAEKSLJ+eeIRCk8sfrPEUnO/aw2qbRU2r9fik5xBlRXjRXFVZOeytvurkeBYQ0OwldddZV+/etfa8mSJRo3bpwkKScnR3PnztXXX3+twsJC/fSnP9Uzzzyj4OBgvffee5o8ebJ27typTp06Nbo0q9WqV155RV27dtW+fft099136w9/+INef/11SdKmTZs0btw4/fKXv9TLL7+sgIAALVmyRHa78+f34IMP6p///KdeeuklnXfeeTp27Jh27NjR6Dpak8UwDMPsIporPz9f0dHRysvLU1RUlNnlAABgHsOQSnOlwsyTWjsz62j5rAqejorGnT80tu6gGV4VNiOqwmhYvLMFtBFKS0u1f/9+de3aVSGugFpeJD3boXE1toSHjkpB4Q3e/fLLL1dcXJzefPNNSc5W1SeffFJpaWk1WlVdBgwYoDvvvFP33nuvpOZNkvrkk0905513KisrS5J07bXX6tChQ1qxYkWtfQsKCpSQkKC//e1vuvXWW1Ve6VCgzSqrteVapev8OapxeY0WVAAAvFnOfmnfUmnfEunIBmcQbWwrY0hMdWumu7Wz6r/hiTVDZ0BQa3wKr3fdddfpV7/6lV5//XUFBwdr9uzZmjp1qqxWqwoLC/XEE0/oq6++0rFjx1RZWamSkhIdOnSoSe+1cOFCzZgxQzt27FB+fr4qKytVWlqq4uJihYWFadOmTbrqqqtqHGMYhsoqHVq98UeVlZWpx+CfaNvRfNkNQ93iwxUREtgSl6HFEFABAPAmxTnSgW+lvUucofTEgbr3C44+KWTWFToTq7vfA4Lb9CM0SmCYszXTjPdthMmTJ8swDH311VcaNmyYvv32W7300kuSpAceeEALFizQ888/rx49eig0NFS/+MUvVF7e+OEKBw4c0KWXXqq77rpLzzzzjGJjY7VixQpna2h5ucLCwhQaGqpKu0MnispVUmFXSbldJRV2OQxDeWXO8xSX22U3DFksFpXbPa8znYAKAIAnqyyT0lY7W0n3LpGObpR71rvknDTUcZjUbazU5TwpJtUZQAND6jujd7FYGtXVbpaQkBBdeeWVmj17tvbs2aPevXvrnHPOkeScsHTzzTfriiuukCQVFhbqwIEDTXqf9evXy+Fw6IUXXpDVapXDMDT733MkSUfzSpRVHqDUHn309fwFmnLn72oca7VY1Kd3b4WEhmr3xlUaO3SAggOtsnrYpDOJgAoAgGcxDCl9a3W3/cGVzklCJ4vvLXUfWxVKz5WCI00pFTVdd911uvTSS7V161Zdf/317u09e/bUZ599psmTJ8tisejRRx+Vw9GYVQ6cHIahjp27qKKiQk//5QWdP26SVq9aqVmz3pAk5RSWK8pWqV/e81v94qJz9ZdHf69bbv2VIsNCtHrlt7pmytVKSEjQ9D/+UU8++pDaRYbp3HPPVWZmprZu3apbb721xa5FcxFQAQAwW/7R6hbSfUudk5lOFp4odRtTFUrHSFEmTBrCGV144YWKjY3Vzp07de2117q3v/jii/rlL3+pUaNGKT4+Xn/84x+Vn59/2nM5DEOlJ3XPl5TbVVrpUGhydz3w2DN6beaL+vNTj+ucEaN0//TH9ND9dyouIkjJsWHqlXS25s+bp4cffliXjr9AoaGhGjFihG68/jpJ0qOPPqqAgAA99thjOnr0qNq3b68777yzVa9NYzGLHwCAtlZWIB1YUR1Ks3bWfD0g1Nky2m2sM5Qm9vO4tT9bQ32zv32dw2GotLIqjFYF0tJKh+qKaDarRaGBNoUG2Zz/DbQpKMAqiwd9P5jFDwCAN7BXSkfWV3fbH157ym07LVKHs6u77VOHe/bEJTSZw2E4W0RPah0tq3DIUAPCaJBNQTbPCqOthYAKAEBLMwwpe091l/2Bb6WyU7p023Wt7rbvcr4UFmtGpWhFdscp3fQVdpVV2OuIolKA1arQIJu+/vwj/fG3v67zfJ07d9bWrVtbt2gPQUAFAKAlFGVVt5DuXSrlH675ekiM1G10dbd9uy5tXyNajd3hUEmFo8aY0bLKuu+85QqjJ7eOBtosslgsunHqLzRx7Pl1HhcY6FlrlbYmAioAAE1RUeKcYe8Kpcc313zdFiSljqjutm9/lvN2nvB6lQ6HSt1B1OFsGa0njAbarLXGjAZUhdG6REZGKjKSVRkIqAAANITDIR3/oXpi06HvJXtZzX2SBlR323caJQU1brF3eJ5Ku6PWmNHyyrqXiKoVRoNsCrTVvs0pzoyACgBAfU4cdLaO7lsq7VsmleTUfD2yQ3ULabfRzjszodnMWmDIHUZP6qYvt9cdRoNsNbvpQwIJoy4t8fMjoAIA4FKSK+1fXh1Kc/bVfD0oUup6vrOVtNtYKb6nXyz/1FZcYyyLi4sVGhraqu9VYa85XrSkwq6K+sJoQF3d9ITR+hQXO28s0ZwxswRUAID/qiyXDq+pnm1/dINknBRSLDap49DqiU0pQySb/0xUaWs2m00xMTHKyHDeqCAsLKxFllSqsDtUVrW2aFmFXaUVDlXWcyenIJtNwQFWhQRZFRxgVXDAqWHUrsoKuyorml2WzzEMQ8XFxcrIyFBMTIxstqaPuSagAgD8h2FIGdurW0gPfCdVFNXcJ75XdQtpl/OkEG4A05aSk5MlyR1SG8vuMFRud6ii0qEKu0PldkN2R+0uZ4ukAJtFgTarAm1WBQU4/yyLRWWSymodgYaKiYlx/xybioAKAPBt+ceqxpBWPQqP13w9LL7mbUSjO7Z5iahmsVjUvn17JSYmqqKi/mZKwzCUnl+q3ekF2p1RpF3pBdqdXqDcktrHWCxSp3Zh6pUUqZ5JkeqZFKHuiREKDyIGtbTAwMBmtZy68JMBAPiWskLp4HfV3faZ22u+HhAqdR5VHUoT+0tWxhN6GpvN5g46hmEoLadEm4/kacvRPG054nycKK4dRm1Wi3omRmhASrQGdIjSwI7R6ts+SmGEUa/CTwsA4N3sldLRjdXd9mlrJMfJwcXiXIPUfRvREVKg/9zn3ds4HIYO5hS7Q+jmqv/ml1bW2jfAalGvpEgNSInSwJRoDUhxhtGQQNab9XYEVACAdzEM5+z6vYudgXT/t1JZXs19YjpVT2zqOprbiHooh8PQ/uwiZxA97Gwd3XokXwVltcNooM2iPslRGpASpQEp0RqYEq1eSZGEUR9FQAUAeL6ibGn/0qpu+2VS3qGar4dEO4Ooq9s+tpsZVeI07A5D+zILq1pE87XlSJ62Hs1TUXntOzAFBVjVNznSHUQHVIXRoACGYvgLAioAwPNUlEqHVlV32x/7UdJJM7GtgVKnn1Td2/5CqcNgbiPqQSrtDu3JLHQH0c1H8rTtaL5KKmqH0ZBAq/q2r+qi7+AMoz2TIlj03s8RUAEA5nM4pPTNVS2kVbcRrSytuU9i/+oW0s6jpKBwU0pFTRV2h3alF2jrkXz3JKbtx/JVWlF7ndHQQJv6d3B20btaR7snhLPoPWohoAIAzJGbVvM2osVZNV+PbF+9Hmm3MVJkkglF4mTllc4wurmqVXTrkTxtP15Q573pw4Ns6l/VKjqwo7OFtGt8hGxW7ryFMyOgAgDaRmmec0KTK5Rm76n5elCEc2F8VyhN6M1tRE1UWmHXzuPOMLr1qDOQ7jxeoAp77UXvI4MD1P+kmfQDUqLVNS5cVsIomoiACgBoHfYK6fDa6vVIj6yXjJPGIFpszluHuhbITxkqBQSZVa1fK62wa9uxfG2tahndfCRfu9MLVFnHHZiiQwNrzKQf0CFanWLDCKNoUQRUAEDLMAwpc6ezhXTvEudi+eWFNfeJ61HdZd/1fOfse7Sp4vJKbT+Wr82HnUF0y5E87cksrPN2oO3CAmvMpB+YEq2O7UJloWUbrYyACgBouoL0qjGkVa2kBcdqvh4WV9VlP8YZTGNS275GP1ZYVqltR/Pd40U3H8nT3sxC1ZFFFR8RVHX3paow2jFaHaJDCKMwBQEVANBw5UXSwZXV3fYZW2u+HhAidRpZPds+aSC3EW0DhmEou6hcu9MLnXdgqhozuj+rSEYdYTQxMlgDU6LV3906GqXkKMIoPAcBFQBQP4ddOrpJ2rdY2rtUSltdx21EB1V323f6iRQYak6tfqCorFL7s4pqPPZlFmpfVpEK6rgVqCQlR4W4u+cHdozSgA7RSoziVq/wbARUAEBNOfuq1yPdv9w5+/5k0Z2k7mOqxpGOkcLj2r5GH1Zhdygtp7g6gFaF0P1ZRUrPL6v3OItFSokJVf8OUe7W0QEdopUQGdyG1QMtg4AKAP6uOEfav6y62z73YM3Xg6OdE5q6j3W2lMZ2Y/mnZjIMQ+n5ZdqX5Qye+zOdQXR/VpEO5RTXOWHJJTY8SF3jw9UtPlxdE6r+Gx+hznFh3JcePoOACgD+prLMeacm18Smo5tU6zaiqcOru+07nC3Z+OuiKfJKKqpaQgu1zxVCM4t0ILtIxXXcg94lNNCmrjUCaPUjJoyluOD7mvQb57XXXtNzzz2n48eP66yzztKrr76q4cOH17v/zJkz9fe//12HDh1SfHy8fvGLX2jGjBkKCQlp8jkBAA3kcDgnM7m67Q+ukipLau6T0Ld6PdLO50rBEaaU6o1KK+w6lFNcFUALtT+zenxodlF5vcfZrBZ1ig2rET5draJJkSGsKwq/1uiA+uGHH2ratGmaNWuWRowYoZkzZ2rixInauXOnEhMTa+3/wQcfaPr06Xrrrbc0atQo7dq1SzfffLMsFotefPHFJp0TAHAGeUeq1yPdv0wqyqz5ekRSdQtptzFSVHszqvQadoeho7kl7klJrrGh+7OKdCS3pM6Z8i5JUcFVATSiujU0IVyp7cIUFMAKB0BdLIZxuv+tahsxYoSGDRumv/3tb5Ikh8Oh1NRU/frXv9b06dNr7X/vvfdq+/btWrRokXvb7373O61evVorVqxo0jlPlZ+fr+joaOXl5SkqKqoxHwcAfENpvnRgRXUozd5d8/XAcKnLudWhNLEv40hPYRiGcorKq0KoK4A6w+iB7OI67zfvEhkcoG4J4e4g6uqa7xIfrohghkcAUuPyWqP+rykvL9f69ev14IMPurdZrVaNHz9eq1atqvOYUaNG6f3339eaNWs0fPhw7du3T19//bVuuOGGJp+zrKxMZWXVMxnz8/Mb8zEAwPvZK5y3DnV12x9ed8ptRK1Sh3Oqu+07Duc2olWKyyu1L/OUpZqyirQ/s1D59SzVJElBNqs6x4WdMjY0Qt0SwhUXHsQaokALalRAzcrKkt1uV1JSUo3tSUlJ2rFjR53HXHvttcrKytJ5550nwzBUWVmpO++8Uw899FCTzzljxgw9+eSTjSkdALybYUhZu6tbSA+skMoLau4T283ZQtp9rNTlPCm0nTm1eoC6lmpyjQ09nl9a73EWi9QhOvSk1tBwdUtwds13iAmVjXGhQJto9X6HpUuX6tlnn9Xrr7+uESNGaM+ePbrvvvv09NNP69FHH23SOR988EFNmzbN/Tw/P1+pqdw+D4CPKcyseRvR/CM1Xw+NlbqNru62b9fZhCLNU9dSTftPWqqpsgFLNbke3RNYqgnwJI0KqPHx8bLZbEpPT6+xPT09XcnJyXUe8+ijj+qGG27QbbfdJkkaOHCgioqKdPvtt+vhhx9u0jmDg4MVHMzCwwB8VMZ2aemfpW1fqsbyT7Zg552aXN32yWf5xW1ET16q6eT1QvdnnX6pppBAa42JSSe3irJUE+DZGhVQg4KCNGTIEC1atEiXX365JOeEpkWLFunee++t85ji4mJZT/kFarM5/3VqGEaTzgkAPilrj7Tsz9LmT+QOpskDq7vtO4302duInrxU0/6TJiftzypSVuHpl2pKbRfq7opnqSbANzS6i3/atGm66aabNHToUA0fPlwzZ85UUVGRbrnlFknSjTfeqJSUFM2YMUOSNHnyZL344os6++yz3V38jz76qCZPnuwOqmc6JwD4tJz90rK/Sj/OkYyqmeJ9fyaNeVBK6mdubS3o5KWaaowNzSrU4ROnX6opMTK4xnhQlmoCfFujA+qUKVOUmZmpxx57TMePH9fgwYM1d+5c9ySnQ4cO1WgxfeSRR2SxWPTII4/oyJEjSkhI0OTJk/XMM880+JwA4JNyD0nLn5M2fSA5qmaP9/6pM5i2H2RubU1UY6kmVxCtWsC+IUs1nTw7nqWaAP/V6HVQPRHroALwKvlHpW9fkNa/KzkqnNt6XCSNfVBKGWJubQ1UXF5Z3RJa1S2/txlLNXWND1d8BEs1Ab6s1dZBBQA0Q0G6tOIlad1bkr1qLeeuo6WxD0udRphbWx0q7A4dPlFS6z7yTVmqyTk2NEIp7ViqCcCZEVABoLUVZUnfvSyt+adUWeLc1mmUdOHDzvVKTWQYhjIKympMTnL9ubFLNbkmJ3WJC2epJgDNQkAFgNZSnCOt+pv0/Syposi5reMwZ4tptzFteqvRvJIKHXBNTMosbPJSTSd3zbNUE4DWQkAFgJZWmietel36/nWprOpWzO0HSxc+IvUY32rBtKzSrkPZxdpXdS/5pizVdPLkpG4s1QTAJARUAGgpZQXS6jekla9KpbnObUkDpLEPOWfnt0AwdTgMHc0rqWoJrblU05ETJTpNj/xJSzWFu8NoN5ZqAuCBCKgA0FzlxdLafzrHmRZnO7cl9HEuF9X3Z02621Ol3aEfDudpb2bN23geyC5SWQOWajr1PvIs1QTAm/DbCgCaqqJUWv+29O2LUlGGc1tsd2cwHXClZG38RKEDWUX6aF2aPll/WBkFZXXuE2izqHNc9cSkbgks1QTAtxBQAaCxKsukDe851zItOObc1q6LNPqP0sCrJVvjfrWWVtg1d8txfbg2Tav2Zbu3x4QFakCH6Brd8t3iI9QhJkQBNrrkAfguAioANJS9Qto0W1r+vJSX5twW1VEa/Xtp8HWSLbBRp9t6NE8frU3T5xuPuBe3t1ikC3omaOqwVI3rm8TYUAB+iYAKAGdir5Q2fyQt+4t04oBzW2R76fzfSefcKAUEN/hU+aUV+s+mo/pwbZo2H8lzb0+JCdVVQzvqqqGpSokJbeEPAADehYAKAPVx2KUtn0nL/ixl73FuC0+Uzp8mDblFCgxp0GkMw9DaAyc0Z+0hfb35mEornJOcAm0WTeiXrCnDUnVuj3jusAQAVQioAHAqh0Pa/qW09M9S5g7nttBY6bz7pWG3SUHhDTpNZkGZPt1wWB+tTdO+rCL39p6JEZoyLFVXnJ2iuIiGt74CgL8goAKAi2FIO7+WlsyQ0jc7t4XESKN+LY24QwqOPOMpKu0OLd+dqQ/XpmnR9gz3rULDgmyaPKiDrh6WqnM6xTDTHgBOg4AKAIYh7V4gLXlGOrbJuS04SvrJ3dLIu6WQ6DOeIi2nWB+tS9PH6w7reH6pe/vg1BhNHZaqS8/qwDqkANBA/LYE4L8MQ9q31BlMD691bgsMl35ypzTyXiks9rSHl1bYNX9buj5ce0jf7aleHqpdWKCuOLujpgxLVe/kM7e6AgBqIqAC8E8HVkhLnpUOfud8HhAqDf+VdO59Unj8aQ/dcTxfH1YtD5VbXOHefn7PeE0ZlqqL+iUpOKDxi/QDAJwIqAD8y6HVzhbT/cucz23B0tBfSuf9VopMqvewwrJK/feHo5qzNk0/pOW6t7ePDtFVQ1N11ZCOSo0Na+XiAcA/EFAB+Icj650tpnsWOp9bA6UhN0nnTZOiU+o8xDAMbTh0QnPWpOmrzcdUXG6XJAVYLRrfN0lThqfqgp4JLA8FAC2MgArAtx370RlMd33jfG6xSWdfJ13weymmU52HZBeW6bMNRzRn7SHtzaxeHqpbQrimDkvVFWd3VEIky0MBQGshoALwTenbpKUzpO3/cT63WKVBU523JY3tVmt3u8PQt7sz9dG6NC3Ylq4Ku3N5qNBAmy4Z1F5Th6VqSOd2LA8FAG2AgArAt2TtdgbTLZ9JMiRZpIG/kEb/UYrvWWv3wyeK9fG6w/p4XZqO5lUvD3VWx2hNGdZJk89qr8iQwLarHwBAQAXgI7L3Ssufk378UDKctxJVv8ukMQ9KiX1r7FpWadfCbRmas/aQVuzJkuFsLFV0aKCuODtFVw9NVb8OUW38AQAALgRUAN4t95C07K/Spg8kwzmJSb0vkcZMl9oPqrHrrvQC9/JQOUXl7u2jusdpyrBUTeyfrJBAlocCALMRUAF4p7wj0rfPSxv+JTmq1iLtcZE09iEp5Rz3bkVllfrqx2Oas/aQNhzKdW9PigrWVUNSddXQjuocF97GxQMAToeACsC7FByXVrwkrXtbspc5t3UbI419WEodLsm5PNSmtFx9uDZN//3hqIqqloeyWS0a1ydRU4alanSvBAXYrCZ9CADA6RBQAXiHoixnMF37plRZ4tzW+VxnMO1yriQpp6hcn288oo/WpmlneoH70C5xYZoyrJN+PiRFiZEhZlQPAGgEAioAz1acI618VVr9hlRRtSZpx+HShQ9LXUfLYUjf7c7Uh2vTNH9rusrtzglSwQFWXTKwvaYMS9XwrrEsDwUAXoSACsAzleRK378urXpdKq9qDe1wtrPFtMd4Hc0r1SeL9+ijdWk6fKLEfdiAlChNGZqqnw1OUXQoy0MBgDcioALwLGUF0vezpFWvSqV5zm1JA6WxD6m8+0Qt3pmhOe+s1fJdmXJULQ8VGRKgywenaMqwVA1IiTavdgBAiyCgAvAM5UXSmn9K370sleQ4tyX0lcY+qD1xY/XR+iP67JPFyiqsXh5qRNdYTR2eqosHtGd5KADwIQRUAOaqKJHWveWcAFWU6dwW10Nl5/1B/6kcoY+WH9XaA9+6d0+IDNYvhnTU1UNT1TWe5aEAwBcRUAGYo7JMWv+u9O0LUuFxSZLRrovSBv1G/zhxjr74IkOFZVslSVaLdGGfRF09NFVj+yQqkOWhAMCnEVABtC17hbTxfWn581L+YUmSI6qjVqX8Us8ePVtb55VIOipJ6hQbpinDUvXzczoqOZrloQDAXxBQAbQNe6X04xznbUlzD0qSysKS9WXkNXryyBAVZVgllSgowKqLByRryrBU/aRrnKxWlocCAH9DQAXQuhx2acun0tI/Szl7JUnFgXH6p67Q6znnqSwnSJLUJzlS1wzvpMsGd1BMWJCZFQMATEZABdA6HA5p2xfOYJq1U5JUYI3Wq+WX6L3Si1SqYEUEB+jngzto6rBUDUyJZjF9AIAkAiqAlmYY0o7/SUtmSBnOSU55itAbFT/Vu/aJKlKohnVppynDOumnA5MVFsSvIQBATfzNAKBlGIa0e74ci5+R9fgPkqR8I1RvVv5Ub9kvVnBEjK4/p6OuGpqqHokRJhcLAPBkBFQAzWMY0t7FKp7/tMIyNsoqqdAI0dv2SXrT/lOd3aurnhvWSeP6sjwUAKBhCKgAmqxwx2IVzX1KSbkbFSapxAjSu/aJ+m/ELzRxWH99PaSjOsSEml0mAMDLEFABNIphGNq6er6Cl89Qz+KNipBUZgTqA8d47ep5my4ZOVi3d2d5KABA0xFQATRIRkGpvl0yT6k/vKTh9o2SpHLDprnBE1U8/D5dPvIctQtneSgAQPMRUAHUq9Lu0LJdmfpuxWKdm/YP/dy6QZJUYdi0IfanCr9ouib37c/yUACAFkVABVDLwewifbQuTRvXfqcby/6tx2xrJatkl1WHOv5MSZc+qhHJPcwuEwDgowioACRJpRV2zdt6XHPWpClj/4+6P+BT/c66WlabIUMWFfS8XFETH1HXeIIpAKB1EVABP7ftaL4+WpemzzceUUxpmu4L+EyXBX0nm8WQJNn7Xibb2IcUldjH5EoBAP6CgAr4oYLSCv3nh6P6cG2afjycp46WDD1k+0I/D16uADmcO/W+RBr7oGzJA80tFgDgdwiogJ8wDEPrDp7QnDVp+nrzMZVU2NVe2ZoR+IWuti2VTXbnjj0nSGMfkjqcbWq9AAD/RUAFfFxmQZk+23BYH65L077MIklSgk5oRuTXmlw5Xzajwrljt7HOYJo63MRqAQAgoAI+ye4wtHx3pj5ck6aF29NV6XCOJ00JLNSMxIU6L/dLWSvKnDt3Ps8ZTLuca2LFAABUI6ACPiQtp1gfr0vTx+sP61heqXv7eSlWPRQ9X33T5siSXezcmDpCGvuw1PUCiXVMAQAehIAKeLmySrvmb03XR+vStGJPlgxnY6liwgI1dWCUbrN9pfgtb0nZhc4XOpwtjX1E6jGOYAoA8EgEVMBL7TxeoA/XpunzjYd1orjCvf28HvG6bnA7jc//TIGrX5fK8pwvJA90tpj2mkQwBQB4NAIq4EUKyyr1vx+Oas7aNG1Ky3VvT44K0VVDO2rKoFh13P0vadErUskJ54sJfaWxD0p9JktWqzmFAwDQCARUwMMZhqENh3L14dpD+t+Px1Rc7lwOKsBq0bi+iZo6rJMu6Boh2/q3pPdekoqznAfG9ZTGTJf6X0kwBQB4FQIq4KGyC8v0+cYj+nBtmnZnFLq3d4sP15RhqbrynI5KCDGkDe9Kr74gFaY7d2jX1RlMB/xCsvG/OADA+/C3F+BBHA5DK/Zk6cO1aZq/7bgq7M4ZTyGBVl0ysIOmDEvVsC7tZLFXSBv/JX37gpR/xHlwdCdp9O+ls66RbIEmfgoAAJqHgAp4gCO5Jc7lodYd1pHcEvf2QR2jdfXQVP1scAdFhQRK9gpp4/vS8r9KuYecO0V2kC54QDr7BikgyKRPAABAyyGgAiYpr3Ro4fZ0zVmbpm93Z7qXh4oKCdAVZ6fo6mGp6t8h2rnRYZd+mCMt/bN0Yr9zW3iidP7vpCE3S4EhpnwGAABaAwEVaGO7053LQ3228Yhyisrd20d2i9PU4ama2D9ZIYE250aHQ9r2uTOYZu1ybguLk877rTT0VikozIRPAABA6yKgAm3AMAx9tuGIPlhzSOsPnnBvT4wM1i+GdNTVQ1PVJT785AOk7f+Vls6QMrY5t4XESOf+Rhp+hxQc0bYfAACANkRABdrA+98f1KNfbpUk2awWje2dqKnDUjWmd4ICbCctAWUY0q650pJnpeM/OrcFR0kj75V+cpcUEmVC9QAAtC0CKtAGPt3gnGl/zfBU/XZ8LyVGnTJm1DCkvYucwfTIeue2oAhnKB15jxTaro0rBgDAPARUoJUdzyvVprRcWSyqO5zuW+YMpmnfO58HhknDfyWNuk8Kj2v7ggEAMBkBFWhl87cdlySdnRpTM5weXCUteUY68K3zuS1YGnabdN79UkRi2xcKAICHIKACrWzeVmdAnTQg2bnh8Dpp8Z+kfUucz21B0jk3OZeMimpvUpUAAHgOAirQinKLy/X9vhxJ0uSEDGn2g9Luec4XrQHS2ddL5z8gxaSaWCUAAJ6FgAq0ooXbM5TkyNQzkZ+o/YfLnRstNuftSC94QIrtam6BAAB4IAIq0FrKixT87QwtDv5QIRUVkizSwKukMdOluO5mVwcAgMcioAItzeGQNn8kx4LHNbnwuGSRipKHK/xnz0kdBptdHQAAHo+ACrSkQ6uludOloxtklZTmSNAbIbfo6dsflKzWMx4OAAAIqEDLyE2TFj4ubfnU+TwoQv+NvkYPpJ2rG4b3loVwCgBAgxFQgeYoK5S+e1la+YpUWSrJIp19vcpHP6yHZv6oMlVWLy8FAAAahIAKNIXDIf34obToSangmHNb53Olic9KHQZr1a5MFZRWKj4iWOd04jalAAA0BgEVaKyTxplKkmI6SxOelvr+TLJYJFUvzn9RvyRZrRazKgUAwCsRUIGGyj0kLXyixjhTXfCANOIuKbD6FqZ2h6H5W9Mlie59AACagIAKnElZofTdTGnlqzXGmerCR6XIpFq7bzx0QlmFZYoMCdDIbnFtXi4AAN6OgArUx+GQfpwjLXxSKnR22avzedKkZ6X2Z9V7mKt7/8I+iQoKYPY+AACNRUAF6nLo+6pxphudz2M6SxP+JPWd7B5nWhfDMDTP1b3fn+59AACagoAKnCz3kLTgcWnrZ87nQZHSBb+rNc60PtuPFehQTrGCA6wa3TuhlYsFAMA3EVABqe5xpufcII19pM5xpvVxde+f3zNBYUH87wUAQFPwNyj8W73jTGdI7Qc1+nSugMrsfQAAmo6ACv91cJVznOmxTc7n7bo4x5n2ufS040zrPV12kXYcL5DNatH4voktWioAAP6EgAr/k3tIWvCYtPVz5/OgSOd6pj+5SwoIbvJpXa2nI7rGKiYsqCUqBQDALxFQ4T/KCqUVLznHmdrL5BxneqN04SNSRPNbPOexOD8AAC2CgArf53BIP/xbWvSkVOgMkepyvjTx2SaNM61LRn6p1h88IUma0I+ACgBAcxBQ4dtaeJxpfeZvcwbfs1JjlBx95uWoAABA/Qio8E0nDkoLH685znT076URdzZrnGl93LP3WZwfAIBmI6DCt5QVVI0z/Vv1ONMhN0ljH26RcaZ1ySuu0Kq92ZKkif0bvmYqAACoGwEVvsHhkH74QFr0VM1xppNmSMkDW/WtF+9MV6XDUM/ECHVLiGjV9wIAwB8QUOH9Dq6sGmf6g/N5u65V40wvadFxpvWZt4XZ+wAAtCQCKrzXiQPSgselbV84nwdHSRf8XhpxR6uMM61LSbldS3dlSJImMv4UAIAWQUCF9ykrkL59UVr1mnOcqcXqXM907CNSREKblrJ8d6ZKKxxKiQlV/w5RbfreAAD4KgIqvIeJ40zr45q9P7F/sixtMJwAAAB/QECFd6hrnOnEZ6TeP22TcaZ1qbA7tGi7q3uf2fsAALQUAio8mweMM63P6n05yiupUFx4kIZ2iTW1FgAAfAkBFZ6pznGmrvVM23acaX1c3fsX9UuSzUr3PgAALYWACs/icEibZjvHmRY5u8+d40z/LCUPMLe2kzgchuZvqx5/CgAAWg4BFZ7jwHfOcabHf3Q+94BxpvXZdDhX6flliggO0KgecWaXAwCATyGgwnwnDkgLHpO2fel8Hhwljf6DNPx208eZ1sfVvT+2T6KCA2wmVwMAgG8hoMI8ZQXSty9UjTMt98hxpnUxDEPztri695m9DwBASyOgou057NKmD2qOM+06Wpr4rEeNM63PrvRCHcguVlCAVWN6J5pdDgAAPoeAirZ1YIU098Hqcaax3aQJz0i9L/a4cab1cXXvn98jXhHB/C8EAEBL429XtI2c/c5xptv/43zuHmd6hxQQZG5tjXTy3aMAAEDLI6CidZXmSyterDnOdMjNznGm4fFmV9doaTnF2no0X1aLNK4v3fsAALQGAipah8NetZ7p0zXHmU6aISX1N7e2ZnC1ng7vGqu4CM9cYQAAAG9HQEXLO7Ciaj3Tzc7nsd2d65n2muQ140zrM39ruiS69wEAaE0EVLScnP3Sgkel7f91Pg+OPmk9U+8aZ1qXzIIyrT2YI0maQEAFAKDVEFDRfKX5zvVMv3/9pHGmt0hjH/LKcab1Wbg9XYYhDeoYrZSYULPLAQDAZxFQ0XQOu7TxfWnx01JRpnNbtzHSxBlSUj9TS2sNzN4HAKBtEFDRNPu/leY96JPjTOuSX1qh7/ZkSeLuUQAAtDYCKhqnrnGmY/4oDfuVT4wzrc+SHRmqsBvqlhCuHomRZpcDAIBPI6CiYUrzpW+fl77/e/U406G/lMY86FPjTOvjmr0/ie59AABaHQEVp+dn40zrUlph15KdzrVcGX8KAEDrI6Cifvu/leY+KKWfPM70WanXRJ8cZ1qfFbuzVFxuV/voEA3qGG12OQAA+DwCKmrL2SfNf1Ta8T/n85BoafR0adhtPj3OtD4nz963+FEwBwDALARUVKt3nOlDUnic2dWZotLu0MLtzvGnE5i9DwBAmyCgomqc6b+kxX86aZzpWGd3vp+MM63PmgM5OlFcoXZhgRreJdbscgAA8AsEVH+3f3nVONMtzudxPaQJz/jdONP6uGbvj++bpACb1eRqAADwDwRUf8U40zMyDIO7RwEAYAICqr8pzZOWPy+tnlU1ztR20nqm/jnOtD4/Hs7TsbxShQXZdF5P31/rFQAAT0FA9RcOu7ThPec402LnLTvVbaw0aYaU2Nfc2jyUq/V0bO9EhQTaTK4GAAD/QUD1B3WNM534rNRzAuNMT2NuVUBl9j4AAG2LgOrLsvdKCx6rOc50zIPOcaa2QHNr83B7Mgq0L7NIgTaLxvZJNLscAAD8CgHVF5XmScufk76fJTkqqseZjn1ICmOppIaYVzV7/9we8YoKIcwDANCWmrRuzmuvvaYuXbooJCREI0aM0Jo1a+rdd8yYMbJYLLUel1xyiXufm2++udbrkyZNakpp/s1hl9a9Lb1yjrTyVWc47X6hdNd30iXPE04bYe4WZu8DAGCWRregfvjhh5o2bZpmzZqlESNGaObMmZo4caJ27typxMTaXaGfffaZysvL3c+zs7N11lln6aqrrqqx36RJk/T222+7nwcHBze2NP+2b5k076GTxpn2rBpnehHjTBvpSG6JNh/Jk8XiXP8UAAC0rUYH1BdffFG/+tWvdMstt0iSZs2apa+++kpvvfWWpk+fXmv/2NiarXZz5sxRWFhYrYAaHBys5GRaqxote69zPdOdXzmfM8602eZXTY4a1jlWCZH8QwkAgLbWqC7+8vJyrV+/XuPHj68+gdWq8ePHa9WqVQ06x5tvvqmpU6cqPDy8xvalS5cqMTFRvXv31l133aXs7OzGlOZ/SvOk+Y9Ir41whlOLTRp+u/SbTdJP7iKcNsM8Zu8DAGCqRrWgZmVlyW63Kymp5l/cSUlJ2rFjxxmPX7NmjbZs2aI333yzxvZJkybpyiuvVNeuXbV371499NBDuvjii7Vq1SrZbLXXnywrK1NZWZn7eX5+fmM+hndz2KUN70qLn6lez7T7OGd3fmIfc2vzAdmFZVqzP0cS408BADBLm87if/PNNzVw4EANHz68xvapU6e6/zxw4EANGjRI3bt319KlSzVu3Lha55kxY4aefPLJVq/X4+xb5lzPNGOr83l8r+pxpmgRi7ZnyGFI/TtEKTU2zOxyAADwS43q4o+Pj5fNZlN6enqN7enp6WccP1pUVKQ5c+bo1ltvPeP7dOvWTfHx8dqzZ0+drz/44IPKy8tzP9LS0hr+IbxR9l7p39dK7/3MGU5DYqRJf5HuWkk4bWGu7n1aTwEAME+jAmpQUJCGDBmiRYsWubc5HA4tWrRII0eOPO2xH3/8scrKynT99def8X0OHz6s7OxstW/fvs7Xg4ODFRUVVePhk0pypXkP1zHOdKP0kzsZZ9rCCssq9e1u57AJAioAAOZpdBf/tGnTdNNNN2no0KEaPny4Zs6cqaKiIves/htvvFEpKSmaMWNGjePefPNNXX755YqLi6uxvbCwUE8++aR+/vOfKzk5WXv37tUf/vAH9ejRQxMnTmzGR/Ni9kpp43vS4j9JxVWTxXqMlyY8wzjTVrR0Z4bK7Q51jQ9Xr6QIs8sBAMBvNTqgTpkyRZmZmXrsscd0/PhxDR48WHPnznVPnDp06JCs1poNszt37tSKFSs0f/78Wuez2Wz68ccf9e677yo3N1cdOnTQhAkT9PTTT/vnWqj7lkpzH2KcqQlcd4+a0D9JFtaOBQDANBbDMAyzi2iu/Px8RUdHKy8vz3u7+7P3OpeN2vm183lIjPPWpEN/SVd+GyirtGvI0wtVWFapz+4epXM6tTO7JAAAfEpj8lqbzuJHHUpypeXPSavfcN6a1GKThv9KGv1Hbk3ahlbuyVZhWaWSooI1uGOM2eUAAODXCKhmsVc61zNd8sxJ40wvkiY+IyX0Nrc2P+RenL9fsqxWuvcBADATAdUMe5dI8x6SMrY5nzPO1FR2h6EF25zjT5m9DwCA+QiobSl7r3PZqF3fOJ8zztQjrDuQo+yickWHBmpEN4ZVAABgNgJqW2CcqUdzzd4f1zdRgbZGLQ0MAABaAQG1NdkrpQ3vSEuerR5n2nOCNOFPjDP1EIZhcPcoAAA8DAG1texd7FzPNHO783l876pxpuPNrQs1bD2aryO5JQoNtOmCnglmlwMAAERAbXlZe5zrmbrGmYa2k8Y8JA29hXGmHsjVejq6V4JCg2wmVwMAACQCasspyZWW/VVa84bkqJSsAdKwX0mj/8A4Uw82d0tV9/6AJJMrAQAALgTU5nKNM138jFSS49zWc4I04RkpoZeppeH09mUWandGoQKsFl3Yh4AKAICnIKA2B+NMvZpr9v7I7nGKDmX4BQAAnoKA2hRZu6vGmc51Pg9tJ419WBpyM+NMvchcZu8DAOCRCKhNsfkTZzh1jTMd80dnSIXXOJ5Xqh/ScmWxSBP60b0PAIAnIaA2xbm/kXIPSef9lnGmXmr+Nmfr6Tmd2ikxKsTkagAAwMkIqE0RFC5d8Xezq0AzuGfv96f1FAAAT8N9HeF3ThSVa/V+54oLjD8FAMDzEFDhdxbtyJDdYahPcqQ6x4WbXQ4AADgFARV+p7p7n9ZTAAA8EQEVfqW4vFLf7s6UREAFAMBTEVDhV5btzFRZpUOdYsPUt32k2eUAAIA6EFDhV6oX50+SxWIxuRoAAFAXAir8RnmlQ4t3ZEiiex8AAE9GQIXfWLUvWwWllUqIDNY5nbjzFwAAnoqACr/hmr1/Ub8kWa107wMA4KkIqPALdoehBdvSJdG9DwCApyOgwi9sPHRCWYVligwJ0MhucWaXAwAAToOACr/g6t4f1ydRQQF87QEA8GT8TQ2fZxiG5m3j7lEAAHgLAip83vZjBUrLKVFwgFWjeyeYXQ4AADgDAip83ryqxfkv6JWgsKAAk6sBAABnQkCFz5u3le59AAC8CQEVPu1gdpF2HC+QzWrR+L6JZpcDAAAagIAKn+ZqPf1Jt1jFhAWZXA0AAGgIAip8mmt5Kbr3AQDwHgRU+KyM/FJtOJQrSZrQj4AKAIC3IKDCZ82vurXp4NQYJUeHmFwNAABoKAIqfBaz9wEA8E4EVPikvOIKrdqbLUma2D/J5GoAAEBjEFDhkxbvTFelw1CvpAh1S4gwuxwAANAIBFT4JGbvAwDgvQio8Dkl5XYt25UpiYAKAIA3IqDC5yzfnanSCodSYkLVv0OU2eUAAIBGIqDC58w7qXvfYrGYXA0AAGgsAip8SoXdoYXbneufThpA9z4AAN6IgAqfsnpfjvJLKxUXHqQhnduZXQ4AAGgCAip8ytytxyRJF/VLks1K9z4AAN6IgAqf4XAYmr/V2b0/ke59AAC8FgEVPmPT4VxlFJQpIjhAo7rHmV0OAABoIgIqfIZr9v7YPokKDrCZXA0AAGgqAip8gmEYmrfVGVAnsTg/AABejYAKn7ArvVAHsosVFGDVmN4JZpcDAACagYAKnzC3qnv//B7xCg8OMLkaAADQHARU+ARX9z6z9wEA8H4EVHi9tJxibTuWL6tFGt83yexyAABAMxFQ4fVcrafDu8YqNjzI5GoAAEBzEVDh9Zi9DwCAbyGgwqtlFpRp3cETkqQJBFQAAHwCARVebcG2dBmGNKhjtDrEhJpdDgAAaAEEVHg19+x9Wk8BAPAZBFR4rfzSCq3cmyWJgAoAgC8hoMJrLdmRoQq7oe4J4eqRGGF2OQAAoIUQUOG13LP3WZwfAACfQkCFVyqtsGvpzkxJdO8DAOBrCKjwSt/uzlJxuV0dokM0MCXa7HIAAEALIqDCK7m69yf0T5bFYjG5GgAA0JIIqPA6lXaHFm1Pl0T3PgAAvoiACq+z5kCOThRXqF1YoIZ1aWd2OQAAoIURUOF15m1xdu+P75ukABtfYQAAfA1/u8OrGIah+duc3fssLwUAgG8ioMKr/Hg4T8fyShUeZNO5PeLNLgcAALQCAiq8ytyq2ftjeicqJNBmcjUAAKA1EFDhVVzLS02kex8AAJ9FQIXX2JNRoH2ZRQqyWTW2d4LZ5QAAgFZCQIXXmFs1e39UjzhFhgSaXA0AAGgtBFR4jXlbq2bvszg/AAA+jYAKr3Akt0Sbj+TJapHG90syuxwAANCKCKjwCq7F+Yd2jlV8RLDJ1QAAgNZEQIVXYPY+AAD+g4AKj5ddWKa1B3IkSRPo3gcAwOcRUOHxFm5Pl8OQ+neIUmpsmNnlAACAVkZAhcdj9j4AAP6FgAqPVlhWqRW7syQx/hQAAH9BQIVHW7IjQ+V2h7rGh6tnYoTZ5QAAgDZAQIVHc8/e758si8VicjUAAKAtEFDhsUor7FqyI0OSNLE/s/cBAPAXBFR4rJV7s1RUbldSVLDO6hhjdjkAAKCNEFDhseZtcc7en9g/WVYr3fsAAPgLAio8kt1haMH26oAKAAD8BwEVHmntgRzlFJUrOjRQw7vGml0OAABoQwRUeCTX7P3xfZMUaONrCgCAP+FvfngcwzA0f6ure5/Z+wAA+BsCKjzOliP5OpJbotBAmy7olWB2OQAAoI0RUOFxXN37Y3onKCTQZnI1AACgrRFQ4XFOvnsUAADwPwRUeJS9mYXanVGoAKtFY/skml0OAAAwAQEVHsXVejqqR7yiQwNNrgYAAJiBgAqPMo/Z+wAA+D0CKjzGsbwS/ZCWK4tFuqgfARUAAH9FQIXHcK19OqRTOyVGhphcDQAAMAsBFR6D2fsAAEAioMJDnCgq1+r9OZIIqAAA+DsCKjzCwu3psjsM9W0fpU5xYWaXAwAATERAhUdg9j4AAHAhoMJ0RWWV+nZ3piS69wEAAAEVHmDZrkyVVTrUOS5MfZIjzS4HAACYjIAK0508e99isZhcDQAAMBsBFaYqr3Ro8fYMSYw/BQAATgRUmGrl3iwVlFUqITJYZ6e2M7scAADgAQioMJVr9v6EfkmyWuneBwAABFSYyO4wtGCba3kpZu8DAAAnAipMs+HQCWUVlikyJEA/6RZndjkAAMBDEFBhmnlbnLP3x/dNUlAAX0UAAOBEKoApDMPQvG2u5aWYvQ8AAKoRUGGKbcfylZZTouAAqy7olWB2OQAAwIMQUGEK1+z90b0SFBYUYHI1AADAkxBQYYr5J909CgAA4GQEVLS5A1lF2nG8QDarReP6JppdDgAA8DAEVLS5eVWtpyO7xSkmLMjkagAAgKchoKLNzdvK7H0AAFA/AiraVHp+qTYcypUkXdSP8acAAKA2Aira1PyqW5ue3SlGydEhJlcDAAA8EQEVbYrZ+wAA4EwIqGgzecUVWrU3WxIBFQAA1I+AijazaEe6Kh2GeidFqmt8uNnlAAAAD0VARZth9j4AAGgIAiraREm5Xct2ZUqSJtC9DwAATqNJAfW1115Tly5dFBISohEjRmjNmjX17jtmzBhZLJZaj0suucS9j2EYeuyxx9S+fXuFhoZq/Pjx2r17d1NKg4datitTpRUOdWwXqv4doswuBwAAeLBGB9QPP/xQ06ZN0+OPP64NGzborLPO0sSJE5WRkVHn/p999pmOHTvmfmzZskU2m01XXXWVe5+//vWveuWVVzRr1iytXr1a4eHhmjhxokpLS5v+yeBRTp69b7FYTK4GAAB4skYH1BdffFG/+tWvdMstt6hfv36aNWuWwsLC9NZbb9W5f2xsrJKTk92PBQsWKCwszB1QDcPQzJkz9cgjj+iyyy7ToEGD9N577+no0aP64osvmvXh4Bkq7A4t3O5c/5TZ+wAA4EwaFVDLy8u1fv16jR8/vvoEVqvGjx+vVatWNegcb775pqZOnarwcOcs7v379+v48eM1zhkdHa0RI0bUe86ysjLl5+fXeMBzfb8vW/mllYqPCNKQzu3MLgcAAHi4RgXUrKws2e12JSXVnIWdlJSk48ePn/H4NWvWaMuWLbrtttvc21zHNeacM2bMUHR0tPuRmpramI+BNuaavX9RvyTZrHTvAwCA02vTWfxvvvmmBg4cqOHDhzfrPA8++KDy8vLcj7S0tBaqEC3N4TA0f6uze5/Z+wAAoCEaFVDj4+Nls9mUnp5eY3t6erqSk08fPoqKijRnzhzdeuutNba7jmvMOYODgxUVFVXjAc+0MS1XGQVligwO0KjucWaXAwAAvECjAmpQUJCGDBmiRYsWubc5HA4tWrRII0eOPO2xH3/8scrKynT99dfX2N61a1clJyfXOGd+fr5Wr159xnPC87lm74/tk6jgAJvJ1QAAAG8Q0NgDpk2bpptuuklDhw7V8OHDNXPmTBUVFemWW26RJN14441KSUnRjBkzahz35ptv6vLLL1dcXM1WNIvFovvvv19/+tOf1LNnT3Xt2lWPPvqoOnTooMsvv7zpnwymMwxDc09aXgoAAKAhGh1Qp0yZoszMTD322GM6fvy4Bg8erLlz57onOR06dEhWa82G2Z07d2rFihWaP39+nef8wx/+oKKiIt1+++3Kzc3Veeedp7lz5yokJKQJHwmeYmd6gQ5mFysowKoxvRPMLgcAAHgJi2EYhtlFNFd+fr6io6OVl5fHeFQP8vLC3Xpp4S6N75uo/7tpmNnlAAAAEzUmr7XpLH74F9fyUszeBwAAjUFARatIyynWtmP5slktGt836cwHAAAAVCGgolW4Wk+Hd4lVbHiQydUAAABvQkBFq5jnnr1P6ykAAGgcAipaXGZBmdYdPCGJ8acAAKDxCKhocQu2pcswpLM6RqtDTKjZ5QAAAC9DQEWLY/Y+AABoDgIqWlR+aYVW7s2SJE0aQEAFAACNR0BFi1qyI0MVdkM9EiPUPSHC7HIAAIAXIqCiRTF7HwAANBcBFS2mtMKuJTsyJUmT+rc3uRoAAOCtCKhoMd/uzlJJhV0pMaEakHL6e+wCAADUh4CKFuPq3r+oX5IsFovJ1QAAAG9FQEWLqLQ7tHB7uiRm7wMAgOYhoKJFrNmfo9ziCsWGB2lYl1izywEAAF6MgIoW4ereH983UTYr3fsAAKDpCKhoNofD0Lytzu79idw9CgAANBMBFc3245E8Hc8vVXiQTef2iDe7HAAA4OUIqGg2V/f+mD6JCgm0mVwNAADwdgRUNIthGJq3xXX3KLr3AQBA8xFQ0Sx7Mgq1L6tIQTarxvZOMLscAADgAwioaBZX9/65PeIUGRJocjUAAMAXEFDRLHO30r0PAABaFgEVTXb4RLG2HMmX1SKN75dkdjkAAMBHEFDRZPOr1j4d2iVW8RHBJlcDAAB8BQEVTUb3PgAAaA0EVDRJdmGZ1h3IkSRN7E/3PgAAaDkEVDTJwu3pchjSgJQodWwXZnY5AADAhxBQ0SRzXYvz96N7HwAAtCwCKhqtoLRC3+3JliRNGkBABQAALYuAikZbujNT5XaHusWHq0dihNnlAAAAH0NARaO5Zu9P6J8si8VicjUAAMDXEFDRKKUVdi3dkSGJ7n0AANA6CKholJV7s1RUbldyVIgGpUSbXQ4AAPBBBFQ0imv2/oT+SbJa6d4HAAAtj4CKBqu0O7Rwe1X3PnePAgAArYSAigZbd/CEcorKFRMWqOFdY80uBwAA+CgCKhpsXtXs/XF9khRg46sDAABaBykDDWIYhuZvTZfE7H0AANC6CKhokC1H8nUkt0RhQTad3zPe7HIAAIAPI6CiQVzd+6N7JSgk0GZyNQAAwJcRUNEgrrtH0b0PAABaGwEVZ7Q3s1B7MgoVaLNobJ9Es8sBAAA+joCKM3J174/sHq+okECTqwEAAL6OgIozmld19ygW5wcAAG2BgIrTOpZXoh8O58likS7ql2R2OQAAwA8QUHFarrVPh3Rqp4TIYJOrAQAA/oCAitOau4XZ+wAAoG0RUFGvE0XlWnMgR5I0kfGnAACgjRBQUa+F29Nldxjq2z5KqbFhZpcDAAD8BAEV9XItL8XsfQAA0JYIqKhTUVmllu/OkiRNHMDsfQAA0HYIqKjTsl2ZKq90qHNcmHonRZpdDgAA8CMEVNRp7kmL81ssFpOrAQAA/oSAilrKKx1asiNDkjSB8acAAKCNEVBRy8q9WSooq1RiZLDOTo0xuxwAAOBnCKioxTV7f0L/JFmtdO8DAIC2RUBFDXaHoQXbnLc3ZXF+AABgBgIqathw6ISyCssVFRKgn3SLM7scAADghwioqME1e39c3yQF2vh6AACAtkcCgZthGO7xp3TvAwAAsxBQ4bbtWL4OnyhRSKBVo3slmF0OAADwUwRUuM2r6t6/oGeCQoNsJlcDAAD8FQEVbvO2OmfvTxpA9z4AADAPARWSpP1ZRdqZXqAAq0Xj+iSZXQ4AAPBjBFRIql6c/yfd4hQdFmhyNQAAwJ8RUCGpOqBOpHsfAACYjIAKpeeXauOhXEnShH507wMAAHMRUKH5Va2nZ3eKUVJUiMnVAAAAf0dARfXsfRbnBwAAHoCA6ufyiiv0/b5sSdw9CgAAeAYCqp9btCNdlQ5DvZMi1SU+3OxyAAAACKj+bu4WZu8DAADPQkD1YyXldi3fnSlJmtif2fsAAMAzEFD92LJdmSqtcKhju1D1ax9ldjkAAACSCKh+zbU4/6T+ybJYLCZXAwAA4ERA9VMVdocWbXcuL8X4UwAA4EkIqH7q+33Zyi+tVHxEkM7p1M7scgAAANwIqH7KNXv/on7Jslnp3gcAAJ6DgOqHHA5DC7ZVde8zex8AAHgYAqof2piWq4yCMkUGB2hU93izywEAAKiBgOqHXLP3L+ybqKAAvgIAAMCzkE78jGEY7oA6sT+z9wEAgOchoPqZnekFOphdrKAAq0b3SjC7HAAAgFoIqH7GNXv/gp4JCg8OMLkaAACA2giofmbeVmbvAwAAz0ZA9SOHsou1/Vi+bFaLxvcloAIAAM9EQPUjrslRI7rGql14kMnVAAAA1I2A6keYvQ8AALwBAdVPZBSUav2hE5KkCYw/BQAAHoyA6icWbEuXYUhnpcaofXSo2eUAAADUi4DqJ5i9DwAAvAUB1Q/klVRo1d4sSYw/BQAAno+A6geW7MhQhd1Qz8QIdU+IMLscAACA0yKg+gFm7wMAAG9CQPVxpRV2Ld2ZKYmACgAAvAMB1cct35Wpkgq7UmJCNSAlyuxyAAAAzoiA6uNcs/cn9E+SxWIxuRoAAIAzI6D6sAq7Q4t2uJaXonsfAAB4BwKqD1uzP0e5xRWKCw/SsC6xZpcDAADQIARUH+aavT++b5JsVrr3AQCAdyCg+iiHw9B8192jBnD3KAAA4D0IqD7qh8O5Op5fqvAgm0Z1jze7HAAAgAYjoPoo1+z9sX0SFRJoM7kaAACAhiOg+iDDMLh7FAAA8FoEVB+0O6NQ+7OKFGSzakzvBLPLAQAAaBQCqg+at8XZenpez3hFhgSaXA0AAEDjEFB90Lxtru59Zu8DAADvQ0D1MYdPFGvLkXxZLc71TwEAALwNAdXHuGbvD+sSq7iIYJOrAQAAaDwCqo9h9j4AAPB2BFQfklVYpnUHciRJExh/CgAAvBQB1Ycs3JYuhyENTIlWx3ZhZpcDAADQJARUH1LdvU/rKQAA8F4EVB9RUFqh7/ZkS2L8KQAA8G4EVB+xZGemyu0OdUsIV4/ECLPLAQAAaDICqo84efa+xWIxuRoAAICmI6D6gNIKu5buyJBE9z4AAPB+BFQf8N2eLBWV29U+OkSDUqLNLgcAAKBZCKg+wNW9P6FfkqxWuvcBAIB3I6B6uUq7Qwu3070PAAB8BwHVy609cEI5ReWKCQvU8K6xZpcDAADQbARUL+fq3h/fN0kBNn6cAADA+5FovJhhGJp/0vJSAAAAvoCA6sU2H8nT0bxShQXZdH7PeLPLAQAAaBEEVC/m6t4f0ztBIYE2k6sBAABoGU0KqK+99pq6dOmikJAQjRgxQmvWrDnt/rm5ubrnnnvUvn17BQcHq1evXvr666/drz/xxBOyWCw1Hn369GlKaX5l3tZ0SXTvAwAA3xLQ2AM+/PBDTZs2TbNmzdKIESM0c+ZMTZw4UTt37lRiYmKt/cvLy3XRRRcpMTFRn3zyiVJSUnTw4EHFxMTU2K9///5auHBhdWEBjS7Nr+zJKNSejEIF2iwa26f2dQcAAPBWjU6BL774on71q1/plltukSTNmjVLX331ld566y1Nnz691v5vvfWWcnJytHLlSgUGBkqSunTpUruQgAAlJ9MS2FCu7v1R3eMVFRJocjUAAAAtp1Fd/OXl5Vq/fr3Gjx9ffQKrVePHj9eqVavqPOY///mPRo4cqXvuuUdJSUkaMGCAnn32Wdnt9hr77d69Wx06dFC3bt103XXX6dChQ/XWUVZWpvz8/BoPf8PsfQAA4KsaFVCzsrJkt9uVlJRUY3tSUpKOHz9e5zH79u3TJ598Irvdrq+//lqPPvqoXnjhBf3pT39y7zNixAi98847mjt3rv7+979r//79Ov/881VQUFDnOWfMmKHo6Gj3IzU1tTEfw+sdzS3RD4fzZLFIF/VLOvMBAAAAXqTVB3o6HA4lJibqH//4h2w2m4YMGaIjR47oueee0+OPPy5Juvjii937Dxo0SCNGjFDnzp310Ucf6dZbb611zgcffFDTpk1zP8/Pz/erkOpqPR3auZ0SIoNNrgYAAKBlNSqgxsfHy2azKT09vcb29PT0esePtm/fXoGBgbLZqpdB6tu3r44fP67y8nIFBQXVOiYmJka9evXSnj176jxncHCwgoP9N5gxex8AAPiyRnXxBwUFaciQIVq0aJF7m8Ph0KJFizRy5Mg6jzn33HO1Z88eORwO97Zdu3apffv2dYZTSSosLNTevXvVvn37xpTnF3KKyrV6f7YkAioAAPBNjV4Hddq0afrnP/+pd999V9u3b9ddd92loqIi96z+G2+8UQ8++KB7/7vuuks5OTm67777tGvXLn311Vd69tlndc8997j3eeCBB7Rs2TIdOHBAK1eu1BVXXCGbzaZrrrmmBT6ib1m4PV0OQ+rXPkqpsWFmlwMAANDiGj0GdcqUKcrMzNRjjz2m48ePa/DgwZo7d6574tShQ4dktVbn3tTUVM2bN0+//e1vNWjQIKWkpOi+++7TH//4R/c+hw8f1jXXXKPs7GwlJCTovPPO0/fff6+EhIQW+Ii+hdn7AADA11kMwzDMLqK58vPzFR0drby8PEVFRZldTqspLKvUOU8vUHmlQ/Puv0C9kyPNLgkAAKBBGpPXmnSrU5hj2c5MlVc61CUuTL2SIswuBwAAoFUQUL3IvJO69y0Wi8nVAAAAtA4Cqpcoq7Rr8Y4MSdLEAYw/BQAAvouA6iVW7s1WYVmlEiODNbhjjNnlAAAAtBoCqpdwzd6f0D9JVivd+wAAwHcRUL2A3WFoftXdoyb15+YFAADAtxFQvcD6gyeUXVSu6NBAjegWa3Y5AAAArYqA6gVcs/fH9UlUoI0fGQAA8G2kHQ9nGEb18lLM3gcAAH6AgOrhth7N1+ETJQoJtOqCntz6FQAA+D4Cqodzzd4f3StBoUE2k6sBAABofQRUDzevavb+xP507wMAAP9AQPVg+7OKtDO9QAFWi8b1STK7HAAAgDZBQPVgrslRI7vHKTos0ORqAAAA2gYB1YPNc989iu59AADgPwioHup4Xqk2HsqVxSJN7Ef3PgAA8B8EVA+1YJuz9fTs1BglRoWYXA0AAEDbIaB6KGbvAwAAf0VA9UC5xeVatS9bEgEVAAD4HwKqB1q0PUN2h6E+yZHqEh9udjkAAABtioDqgZi9DwAA/BkB1cMUl1dq2a5MSdIkAioAAPBDBFQPs3xXpsoqHUqNDVXf9pFmlwMAANDmCKgexj17v1+yLBaLydUAAAC0PQKqBymvdGjhdmdAnTSA7n0AAOCfCKge5Pt92SoorVR8RLDO6dTO7HIAAABMQUD1IK7Z+xf1S5LVSvc+AADwTwRUD+FwGJq/je59AAAAAqqH2Jh2QpkFZYoMCdDIbnFmlwMAAGAaAqqHcM3ev7BPooIC+LEAAAD/RRLyAIZhaO4W5/hTFucHAAD+joDqAXYcL9ChnGIFB1g1uneC2eUAAACYioDqAVyz98/vmaCwoACTqwEAADAXAdUDuLv3mb0PAABAQDXboexi7TheIJvVovF9E80uBwAAwHQEVJO5uvdHdI1VTFiQydUAAACYj4Bqsrlb6d4HAAA4GQHVRBkFpdpw6IQkaUI/AioAAIBEQDXVgm3pMgzprNQYJUeHmF0OAACARyCgmojF+QEAAGojoJokr6RCq/ZmS5Im9k8yuRoAAADPQUA1yZIdGap0GOqZGKFuCRFmlwMAAOAxCKgmmcfsfQAAgDoRUE1QWmHX0p2ZkqSJjD8FAACogYBqguW7MlVSYVdKTKj6d4gyuxwAAACPQkA1wbyt6ZKcracWi8XkagAAADwLAbWNVdgdWrjdFVCZvQ8AAHAqAmobW7M/R3klFYoLD9LQLrFmlwMAAOBxCKhtzDV7/6J+SbJZ6d4HAAA4FQG1DTkchjugMnsfAACgbgTUNvTD4Vyl55cpIjhAo3rEmV0OAACARyKgtiHX7P2xfRIVHGAzuRoAAADPREBtI4Zxcvc+s/cBAADqQ0BtI7szCrU/q0hBAVaN6Z1odjkAAAAei4DaRuZtcbaentcjXhHBASZXAwAA4LkIqG1kblX3/iRm7wMAAJwWAbUNpOUUa+vRfFkt0ri+dO8DAACcDgG1Dczf5py9P6xLrOIigk2uBgAAwLMRUNuAa/zppAF07wMAAJwJAbWVZRWWae3BHEnSBMafAgAAnBEBtZUt3JYuw5AGpkQrJSbU7HIAAAA8HgG1lbln79O9DwAA0CAE1FZUUFqhlXuyJXH3KAAAgIYioLaiJTszVW53qFtCuHokRppdDgAAgFcgoLYi9+x9JkcBAAA0GAG1lZRW2LV0Z4YkaSIBFQAAoMEIqK3kuz1ZKiq3q310iAZ1jDa7HAAAAK9BQG0lc6u69yf2T5bFYjG5GgAAAO9BQG0FlXaHFm533t50ArP3AQAAGoWA2grWHjihE8UVahcWqOFdYs0uBwAAwKsQUFvBvKrF+cf3TVKAjUsMAADQGKSnFmYYhuZvrR5/CgAAgMYhoLawzUfydDSvVGFBNp3XM97scgAAALwOAbWFuWbvj+2dqJBAm8nVAAAAeB8CagtzjT9l9j4AAEDTEFBb0J6MAu3NLFKgzaKxfRLNLgcAAMArEVBb0LytzrVPz+0Rr6iQQJOrAQAA8E4E1BY0j9n7AAAAzUZAbSFHc0v04+E8WSzO9U8BAADQNATUFuJa+3RY51glRAabXA0AAID3IqC2kLnM3gcAAGgRBNQWkFNUrjX7cyQx/hQAAKC5CKgtYOH2dDkMqX+HKKXGhpldDgAAgFcjoLaAeVuYvQ8AANBSCKjNVFhWqW/3ZEkioAIAALQEAmozLduZqfJKh7rGh6tXUoTZ5QAAAHg9AmoznTx732KxmFwNAACA9yOgNkNZpV1LdmRIonsfAACgpRBQm2Hl3mwVllUqKSpYgzvGmF0OAACATyCgNoNr9v6EfsmyWuneBwAAaAkE1CayOwwt2JYuie59AACAlkRAbaL1B08ou6hc0aGBGtEt1uxyAAAAfAYBtYnmVnXvj+ubqEAblxEAAKClkKyawDAMzdvK3aMAAABaAwG1CbYezdeR3BKFBtp0Qc8Es8sBAADwKQTUJphf1Xo6uleCQoNsJlcDAADgWwLMLsAb3Tmmu/q2j1J8ZLDZpQAAAPgcAmoThAUF6OKB7c0uAwAAwCfRxQ8AAACPQkAFAACARyGgAgAAwKMQUAEAAOBRCKgAAADwKARUAAAAeBQCKgAAADwKARUAAAAehYAKAAAAj0JABQAAgEchoAIAAMCjEFABAADgUQioAAAA8CgEVAAAAHgUAioAAAA8CgEVAAAAHoWACgAAAI9CQAUAAIBHIaACAADAoxBQAQAA4FEIqAAAAPAoBFQAAAB4FAIqAAAAPAoBFQAAAB4lwOwCWoJhGJKk/Px8kysBAABAXVw5zZXbTscnAmpBQYEkKTU11eRKAAAAcDoFBQWKjo4+7T4WoyEx1sM5HA4dPXpUkZGRslgsbfKe+fn5Sk1NVVpamqKiotrkPb0B16V+XJu6cV3qxnWpH9emblyX+nFt6tbW18UwDBUUFKhDhw6yWk8/ytQnWlCtVqs6duxoyntHRUXxZa8D16V+XJu6cV3qxnWpH9emblyX+nFt6taW1+VMLacuTJICAACARyGgAgAAwKMQUJsoODhYjz/+uIKDg80uxaNwXerHtakb16VuXJf6cW3qxnWpH9embp58XXxikhQAAAB8By2oAAAA8CgEVAAAAHgUAioAAAA8CgEVAAAAHoWAehqvvfaaunTpopCQEI0YMUJr1qw57f4ff/yx+vTpo5CQEA0cOFBff/11G1XathpzXd555x1ZLJYaj5CQkDastm0sX75ckydPVocOHWSxWPTFF1+c8ZilS5fqnHPOUXBwsHr06KF33nmn1es0Q2OvzdKlS2t9ZywWi44fP942BbeRGTNmaNiwYYqMjFRiYqIuv/xy7dy584zH+frvmaZcF3/5PfP3v/9dgwYNci+qPnLkSH3zzTenPcbXvy9S46+Lv3xfTvXnP/9ZFotF999//2n385TvDAG1Hh9++KGmTZumxx9/XBs2bNBZZ52liRMnKiMjo879V65cqWuuuUa33nqrNm7cqMsvv1yXX365tmzZ0saVt67GXhfJeYeKY8eOuR8HDx5sw4rbRlFRkc466yy99tprDdp///79uuSSSzR27Fht2rRJ999/v2677TbNmzevlStte429Ni47d+6s8b1JTExspQrNsWzZMt1zzz36/vvvtWDBAlVUVGjChAkqKiqq9xh/+D3TlOsi+cfvmY4dO+rPf/6z1q9fr3Xr1unCCy/UZZddpq1bt9a5vz98X6TGXxfJP74vJ1u7dq3eeOMNDRo06LT7edR3xkCdhg8fbtxzzz3u53a73ejQoYMxY8aMOve/+uqrjUsuuaTGthEjRhh33HFHq9bZ1hp7Xd5++20jOjq6jarzDJKMzz///LT7/OEPfzD69+9fY9uUKVOMiRMntmJl5mvItVmyZIkhyThx4kSb1OQpMjIyDEnGsmXL6t3HX37PnKwh18Uff8+4tGvXzvi///u/Ol/zx++Ly+mui799XwoKCoyePXsaCxYsMEaPHm3cd9999e7rSd8ZWlDrUF5ervXr12v8+PHubVarVePHj9eqVavqPGbVqlU19pekiRMn1ru/N2rKdZGkwsJCde7cWampqWf8V62/8IfvS3MNHjxY7du310UXXaTvvvvO7HJaXV5eniQpNja23n388XvTkOsi+d/vGbvdrjlz5qioqEgjR46scx9//L405LpI/vV9ueeee3TJJZfU+i7UxZO+MwTUOmRlZclutyspKanG9qSkpHrHwR0/frxR+3ujplyX3r1766233tKXX36p999/Xw6HQ6NGjdLhw4fbomSPVd/3JT8/XyUlJSZV5Rnat2+vWbNm6dNPP9Wnn36q1NRUjRkzRhs2bDC7tFbjcDh0//3369xzz9WAAQPq3c8ffs+crKHXxZ9+z2zevFkREREKDg7WnXfeqc8//1z9+vWrc19/+r405rr40/dlzpw52rBhg2bMmNGg/T3pOxPQ5u8IvzJy5Mga/4odNWqU+vbtqzfeeENPP/20iZXBU/Xu3Vu9e/d2Px81apT27t2rl156Sf/6179MrKz13HPPPdqyZYtWrFhhdikepaHXxZ9+z/Tu3VubNm1SXl6ePvnkE910001atmxZvWHMXzTmuvjL9yUtLU333XefFixY4JWTwAiodYiPj5fNZlN6enqN7enp6UpOTq7zmOTk5Ebt742acl1OFRgYqLPPPlt79uxpjRK9Rn3fl6ioKIWGhppUlecaPny4z4a3e++9V//73/+0fPlydezY8bT7+sPvGZfGXJdT+fLvmaCgIPXo0UOSNGTIEK1du1Yvv/yy3njjjVr7+tP3pTHX5VS++n1Zv369MjIydM4557i32e12LV++XH/7299UVlYmm81W4xhP+s7QxV+HoKAgDRkyRIsWLXJvczgcWrRoUb1jWkaOHFljf0lasGDBacfAeJumXJdT2e12bd68We3bt2+tMr2CP3xfWtKmTZt87jtjGIbuvfdeff7551q8eLG6du16xmP84XvTlOtyKn/6PeNwOFRWVlbna/7wfanP6a7LqXz1+zJu3Dht3rxZmzZtcj+GDh2q6667Tps2baoVTiUP+860+bQsLzFnzhwjODjYeOedd4xt27YZt99+uxETE2McP37cMAzDuOGGG4zp06e79//uu++MgIAA4/nnnze2b99uPP7440ZgYKCxefNmsz5Cq2jsdXnyySeNefPmGXv37jXWr19vTJ061QgJCTG2bt1q1kdoFQUFBcbGjRuNjRs3GpKMF1980di4caNx8OBBwzAMY/r06cYNN9zg3n/fvn1GWFiY8fvf/97Yvn278dprrxk2m82YO3euWR+h1TT22rz00kvGF198YezevdvYvHmzcd999xlWq9VYuHChWR+hVdx1111GdHS0sXTpUuPYsWPuR3FxsXsff/w905Tr4i+/Z6ZPn24sW7bM2L9/v/Hjjz8a06dPNywWizF//nzDMPzz+2IYjb8u/vJ9qcups/g9+TtDQD2NV1991ejUqZMRFBRkDB8+3Pj+++/dr40ePdq46aabauz/0UcfGb169TKCgoKM/v37G1999VUbV9w2GnNd7r//fve+SUlJxk9/+lNjw4YNJlTdulxLI536cF2Lm266yRg9enStYwYPHmwEBQUZ3bp1M95+++02r7stNPba/OUvfzG6d+9uhISEGLGxscaYMWOMxYsXm1N8K6rrmkiq8T3wx98zTbku/vJ75pe//KXRuXNnIygoyEhISDDGjRvnDmGG4Z/fF8No/HXxl+9LXU4NqJ78nbEYhmG0XXstAAAAcHqMQQUAAIBHIaACAADAoxBQAQAA4FEIqAAAAPAoBFQAAAB4FAIqAAAAPAoBFQAAAB6FgAoAAACPQkAFAACARyGgAgAAwKMQUAEAAOBRCKgAAADwKP8PBtX2bzqGYlYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_results(space_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "id": "jRIxdZzgATRa"
   },
   "outputs": [],
   "source": [
    "@train\n",
    "def train_unsup_epoch(model, train_dataloader, optimizer):\n",
    "    train_loss = 0.0\n",
    "\n",
    "    for step, batch in enumerate(tqdm(train_dataloader, total=len(train_dataloader))):\n",
    "        ids = batch['input_ids'].to(device, dtype = torch.long)\n",
    "        mask = batch['attention_mask'].to(device, dtype = torch.long)\n",
    "\n",
    "        outputs = model(input_ids=ids, attention_mask=mask) # (B, Seq_Len, 2)\n",
    "\n",
    "        loss, logits = outputs.loss, outputs.logits\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        train_loss += loss.item()\n",
    "    return train_loss\n",
    "\n",
    "@eval\n",
    "def eval_unsup_epoch(model, val_dataloader):\n",
    "    val_loss = 0.0\n",
    "\n",
    "    with torch.no_grad():\n",
    "\n",
    "        for step, batch in enumerate(tqdm(val_dataloader, total=len(val_dataloader))):\n",
    "            ids = batch['input_ids'].to(device, dtype = torch.long)\n",
    "            mask = batch['attention_mask'].to(device, dtype = torch.long)\n",
    "\n",
    "            outputs = model(input_ids=ids, attention_mask=mask)\n",
    "\n",
    "            loss, logits = outputs.loss, outputs.logits\n",
    "\n",
    "            val_loss += loss.item()\n",
    "    return val_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "id": "hEcktDbc_vkS"
   },
   "outputs": [],
   "source": [
    "def unsup_training(model, train_data, val_data, config):\n",
    "    model = model.to(device)\n",
    "\n",
    "    optimizer = torch.optim.Adam(\n",
    "        params=model.parameters(),\n",
    "        lr=config['lr'],\n",
    "        weight_decay=config['weight_decay']\n",
    "    )\n",
    "\n",
    "    num_train_steps = int(len(train_data) / config['batch_size'] * config['epochs'])\n",
    "\n",
    "    print(f'Train steps: {num_train_steps}')\n",
    "\n",
    "    train_dataloader = torch.utils.data.DataLoader(train_data, batch_size=config['batch_size'], shuffle=True)\n",
    "    val_dataloader = torch.utils.data.DataLoader(val_data, batch_size=2 * config['batch_size'])\n",
    "\n",
    "    history = {\n",
    "        'train_losses': [],\n",
    "        'val_losses': [],\n",
    "    }\n",
    "\n",
    "    for epoch_num in range(config['epochs']):\n",
    "        print(f'Epoch: {epoch_num + 1}')\n",
    "        # train stage\n",
    "        train_loss = train_unsup_epoch(model, train_dataloader, optimizer)\n",
    "\n",
    "        # eval stage\n",
    "        val_loss = eval_unsup_epoch(model, val_dataloader)\n",
    "\n",
    "        history['train_losses'].append(train_loss)\n",
    "        history['val_losses'].append(val_loss)\n",
    "\n",
    "        print()\n",
    "        print(f'Train loss: {train_loss / len(train_dataloader)} | Val loss: {val_loss / len(val_dataloader)}')\n",
    "    return history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "id": "szRIPOVV_Gs1"
   },
   "outputs": [],
   "source": [
    "unsup_train, sup_train = train_test_split(data, random_state=SEED, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "id": "A1GRJVrfmSrU"
   },
   "outputs": [],
   "source": [
    "unsup_train_dataset = IMDBDataset(unsup_train.reset_index(drop=True), tokenizer, encode_label, MAX_SEQ_LEN)\n",
    "sup_train_dataset = IMDBDataset(sup_train.reset_index(drop=True), tokenizer, encode_label, MAX_SEQ_LEN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "id": "Xyn6RcSLA6AA"
   },
   "outputs": [],
   "source": [
    "config['epochs'] = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RNzgaggOA3Pz",
    "outputId": "b1b4ab52-6de8-4539-edd6-8bded6fc0570"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train steps: 156\n",
      "Epoch: 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/79 [00:00<?, ?it/s]/home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages/torch/nn/functional.py:1956: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n",
      "  warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n",
      "100%|██████████| 79/79 [05:32<00:00,  4.21s/it]\n",
      "100%|██████████| 49/49 [06:22<00:00,  7.82s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train loss: 0.1434099290378486 | Val loss: 0.27312901737738626\n",
      "Epoch: 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 79/79 [05:32<00:00,  4.20s/it]\n",
      "100%|██████████| 49/49 [05:58<00:00,  7.31s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train loss: 0.1386157336255795 | Val loss: 0.2690245025620169\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "unsup_history = unsup_training(space_bert, unsup_train_dataset, test_dataset, config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial GPU Usage\n",
      "GPU memory occupied: 9034 MB.\n",
      "GPU Usage after emptying the cache\n",
      "GPU memory occupied: 2116 MB.\n"
     ]
    }
   ],
   "source": [
    "free_gpu_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qyspKde/eXZ9++mmD1f+HH35Yn376qUaNGqXrrrtOAwcO1K5du/TOO+/oq6++UlxcnPfYKVOm6Omnn9YXX3yhRx55pGkfKIBGEVABNCoxMVEffPCB/uu//kv333+/4uPj9ac//UljxozRWWed1WrX/fzzz1VRUeGtUjbF5MmTtWrVKp144onq27evz757771XTqdTCxcu1FtvvaXjjjtOS5cubfYUpn//+9+9X/kuWbJEZ5xxhpYuXaq0tDSf4/72t7/JbrdrwYIFKi8v18knn6zPPvus3meYkpKi559/XnPmzNHVV1+t6upqffHFFw0G1PDwcK1YsUJ33323Xn31VRUVFWnAgAF65ZVXNG3atGa9n7pqnlgfP36832PGjx+vjIwM/fDDDzrmmGP04Ycf6qGHHtLChQv1z3/+U4mJiTrllFM0ePBg7zmvvPKKjjnmGL388su68847FRsbqxNOOEEjR470HjNz5kzl5ubq3Xff1dtvv61x48bpo48+avCz8GfhwoW6+eabNW/ePFmWpTPPPFMfffRRvfFuu3fvrjVr1mjGjBlasGCBioqK1L17d40bN67ebGrHH3+8Bg0apF9//fWQZycDUJ/Nau0SCAA00w033KC1a9fq22+/be+mAPUce+yxSkhI0PLly9u7KUCnQwUVgLGGDh160Kod0F7Wrl2rzMzMBqdhBXDoqKACABCgn376SevWrdMTTzyhvLw8ZWdnKywsrL2bBXQ6PMUPAECA3n33XV155ZWqrKzUG2+8QTgFWgkVVAAAABilyRXUlStXavz48UpNTZXNZtOSJUt89mdkZOjII49UZGSk4uPjNXbsWK1Zs+agr5mRkSGbzeazHHnkkU1tGgAAADqBJgdUp9OpIUOGaN68eQ3u79+/v+bOnasff/xRX331lXr16qUzzzxTubm5B33dQYMGadeuXd7lq6++amrTAAAA0Akc0lf8NptNixcv1gUXXOD3mKKiIsXGxuqzzz7TmDFjGjwmIyNDS5YsUWZmZnObIrfbrZ07dyo6OrpJ0wQCAACgbViWpeLiYqWmpvpMz1xXqw4z5XK59OKLLyo2NlZDhgw56LEbN25UamqqwsLCNGLECM2ZM0c9evTwe3xFRYUqKiq86zt27NBRRx3VYm0HAABA69i2bZuOOOIIv/tbJaB+8MEHmjhxokpLS9WtWzctW7ZMSUlJfo8fPny45s+frwEDBmjXrl164IEHdOqpp+qnn37yO03fnDlzGpzDedu2bYqJiWmx9wIAAICWUVRUpLS0tEanYW6Vr/idTqd27dqlvLw8vfTSS/r888+1Zs2agKem279/v3r27Kknn3xSV199dYPH1K2g1rzhwsJCAioAAICBarp+NpbXWmUc1MjISPXt21cnnXSSXn75ZQUHB+vll18O+Py4uDj1799fmzZt8nuMw+FQTEyMzwIAAICOr00G6ne73T7VzsaUlJQoKytL3bp1a8VWAQAAwERNDqglJSXKzMz0PnGfk5OjzMxMbd26VU6nU/fee6+++eYbbdmyRevWrdNVV12lHTt26NJLL/W+xpgxYzR37lzv+h133KF///vf2rx5s1atWqULL7xQdrtdkyZNOvR3CAAAgA6lyQ9JrV27VqNHj/auT58+XZI0depUPf/88/rtt9/06quvKi8vT4mJiRo2bJi+/PJLDRo0yHtOVlaW8vLyvOvbt2/XpEmTlJ+fr+TkZJ1yyin65ptvlJycfCjvDQAAdCBut1sul6u9m4FDEBISIrvdfsiv02mmOg200y0AADCPy+VSTk6O3G53ezcFhyguLk4pKSkNjksfaF5r1XFQAQAAGmNZlnbt2iW73a60tLSDDuAOc1mWpdLSUu3du1eSDulZIgIqAABoV1VVVSotLVVqaqoiIiLauzk4BOHh4ZKkvXv3qkuXLs3+up//ogAAgHZVXV0tSQoNDW3nlqAl1Pwno7KystmvQUAFAABGaKjPIjqelriPBFQAAAAYhYAKAADQznr16qWnnnqqRV5rxYoVstls2r9/f4u8XnvgISkAAIBmOP300zV06NAWCZbfffedIiMjD71RnQQBFQAAoBVYlqXq6moFBzcet5icyBdf8QMAADTRtGnT9O9//1t/+9vfZLPZZLPZNH/+fNlsNn300Uc6/vjj5XA49NVXXykrK0vnn3++unbtqqioKA0bNkyfffaZz+vV/YrfZrPpf//3f3XhhRcqIiJC/fr10/vvv9/s9v7zn//UoEGD5HA41KtXLz3xxBM++5999ln169dPYWFh6tq1qy655BLvvnfffVeDBw9WeHi4EhMTNXbsWDmdzma3JRBUUAEAgFEsy1JZZXW7XDs8xB7QU+h/+9vf9J///EdHH320Zs+eLUn6+eefJUl33323Hn/8cfXp00fx8fHatm2bzjnnHD300ENyOBx67bXXNH78eG3YsEE9evTwe40HHnhAjz76qB577DE988wzmjx5srZs2aKEhIQmvad169ZpwoQJysjI0GWXXaZVq1bphhtuUGJioqZNm6a1a9fqlltu0euvv66RI0eqoKBAX375pSRp165dmjRpkh599FFdeOGFKi4u1pdffqnWnoiUgAoAAIxSVlmto2Z+0i7X/mX2WYoIbTwexcbGKjQ0VBEREUpJSZEk/fbbb5Kk2bNn6w9/+IP32ISEBA0ZMsS7/uCDD2rx4sV6//33ddNNN/m9xrRp0zRp0iRJ0sMPP6ynn35a3377rc4+++wmvacnn3xSY8aM0YwZMyRJ/fv31y+//KLHHntM06ZN09atWxUZGalzzz1X0dHR6tmzp4499lhJnoBaVVWliy66SD179pQkDR48uEnXbw6+4gcAAGhBJ5xwgs96SUmJ7rjjDg0cOFBxcXGKiorSr7/+qq1btx70dY455hjvryMjIxUTE+OdRrQpfv31V5188sk+204++WRt3LhR1dXV+sMf/qCePXuqT58+uuKKK7RgwQKVlpZKkoYMGaIxY8Zo8ODBuvTSS/XSSy9p3759TW5DU1FBBQAARgkPseuX2We127UPVd2n8e+44w4tW7ZMjz/+uPr27avw8HBdcsklcrlcB32dkJAQn3WbzSa3233I7asrOjpa69ev14oVK/Tpp59q5syZysjI0Hfffae4uDgtW7ZMq1at0qeffqpnnnlG9913n9asWaPevXu3eFtqEFABAIBRbDZbQF+zt7fQ0FDvNK0H8/XXX2vatGm68MILJXkqqps3b27l1v1u4MCB+vrrr+u1qX///rLbPYE8ODhYY8eO1dixYzVr1izFxcXp888/10UXXSSbzaaTTz5ZJ598smbOnKmePXtq8eLFmj59equ12fy7DwAAYKBevXppzZo12rx5s6KiovxWN/v166dFixZp/PjxstlsmjFjRqtUQv35r//6Lw0bNkwPPvigLrvsMq1evVpz587Vs88+K0n64IMPlJ2drdNOO03x8fH68MMP5Xa7NWDAAK1Zs0bLly/XmWeeqS5dumjNmjXKzc3VwIEDW7XN9EEFAABohjvuuEN2u11HHXWUkpOT/fYpffLJJxUfH6+RI0dq/PjxOuuss3Tccce1WTuPO+44vf3223rzzTd19NFHa+bMmZo9e7amTZsmSYqLi9OiRYt0xhlnaODAgXr++ef1xhtvaNCgQYqJidHKlSt1zjnnqH///rr//vv1xBNPaNy4ca3aZpvV2uMEtJGioiLFxsaqsLBQMTEx7d0cAAAQoPLycuXk5Kh3794KCwtr7+bgEB3sfgaa16igAgAAwCgEVAAAgA7kz3/+s6Kiohpc/vznP7d381oED0k1x+6fpPdvkiKSpMgkKSLxwM+kWj8TpchkKTRKCmBGCgAAgEDMnj1bd9xxR4P7Oks3RwJqcxTtlHZ+H9ixdoefEOsn1IbFEWgBAIBfXbp0UZcuXdq7Ga2KgNocqcdKk96UnHlSad6Bn/m11vM9PytLpeoKqWiHZwlEULAnvNYE1roB1mc9SQqPl4IOfVBhAAAAUxBQmyMqWRoQwPAKrlI/AbZOkK3ZX1Ekuaukkj2eJRC2IE9I9VuZbWDdHtL46wIAALQTAmprCo2QQntIcT0CO76qon4l1pnrP+SW75cst2d7ab6UtyGw64TFevrHBhpqgx3N/ggAAACaioBqkmCHFJPqWQJRXSmVFhykKltnvazAE2jLCz1L/qbArhMaHUB3g1rroZGNvyYAAIAfBNSOzB4iRXf1LIFwV0tl++sE2Ia6H9Rad1dJrmLPsm9zYNcJDg+8u0FkkuSI4cEwAADgRUA9nATZDwx/lSglD2j8eMvyVFp9AmzuwUNtdYVUVSYVbvMsgbCHBv5QWMSBkQ6CGMIXANCx9erVS7fddptuu+22Ro+12WxavHixLrjgglZvlwkIqPDPZpPC4zxLYnrjx1uW5CoJ8KGwA+uVTqnaJRXv9CwBtcteqxobwPBdEQmMdAAAQAdCQEXLsdkkR7RnSegd2DmVZQcJsA0M31VRKFnVknOvZwmsYZ6RDhqrzHoDbaIUHNrsjwEAABwaAiraV0i4FJfmWQJR5TowakFNd4NGQm3ZPkmW5wGxsgJJ/wnsOo7YWkE2ufHuByFhzf0EAAAd0IsvvqiMjAxt375dQbW6nZ1//vlKTEzUfffdp+nTp+ubb76R0+nUwIEDNWfOHI0dO7ZFrv/jjz/q1ltv1erVqxUREaGLL75YTz75pKKioiRJK1as0H//93/r559/VkhIiAYNGqSFCxeqZ8+e+r//+z/ddtttWrt2rWw2m/r166cXXnhBJ5xwQou0rSUQUNGxBIdKMd08SyCqqzzBNNCHwkrzPSMdVBR6loLswK4TGhX4bGERB0Y64MEwAGiYZXkmu2kPIREB/f186aWX6uabb9YXX3yhMWPGSJIKCgr08ccf68MPP1RJSYnOOeccPfTQQ3I4HHrttdc0fvx4bdiwQT16BDj8pB9Op1NnnXWWRowYoe+++0579+7VNddco5tuuknz589XVVWVLrjgAl177bV644035HK59O2338p24H1NnjxZxx57rJ577jnZ7XZlZmYqJMSsMdIJqOjc7MFSVBfPEgi32zO+bCD9Z2vW3ZWevreuEmn/lsCuExzWxAfDYgm0AA4flaXSwwEOudjS7t0Z0HCJ8fHxGjdunBYuXOgNqO+++66SkpI0evRoBQUFaciQId7jH3zwQS1evFjvv/++brrppkNq4sKFC1VeXq7XXntNkZGets6dO1fjx4/XI488opCQEBUWFurcc89VerrnGZKBAwd6z9+6davuvPNOHXnkkZKkfv36HVJ7WgMBFagtKMjzUFVEgqT+jR/f4EgHjYTaqnLPUrTdswTUrpA6D4YlHzzUMtIBALS6yZMn69prr9Wzzz4rh8OhBQsWaOLEiQoKClJJSYkyMjK0dOlS7dq1S1VVVSorK9PWrVsP+bq//vqrhgwZ4g2nknTyySfL7XZrw4YNOu200zRt2jSdddZZ+sMf/qCxY8dqwoQJ6tbN8+3j9OnTdc011+j111/X2LFjdemll3qDrCkIqMChaNZIB84AHwo7sO4q8VRpS3Z7loDaZT8QtAOcLSw8wVNtBgAThER4Kpntde0AjR8/XpZlaenSpRo2bJi+/PJL/fWvf5Uk3XHHHVq2bJkef/xx9e3bV+Hh4brkkkvkcrlaq+U+XnnlFd1yyy36+OOP9dZbb+n+++/XsmXLdNJJJykjI0OXX365li5dqo8++kizZs3Sm2++qQsvvLBN2hYI/kUC2pLNJjmiPEt8r8DOqSyrP5rBwSq15TUjHeR6ltyAGuYJ2YH0n61ZZ6QDAK3FZusQsxKGhYXpoosu0oIFC7Rp0yYNGDBAxx13nCTp66+/1rRp07yhr6SkRJs3b26R6w4cOFDz58+X0+n0VlG//vprBQUFacCA38c5P/bYY3Xsscfqnnvu0YgRI7Rw4UKddNJJkqT+/furf//+uv322zVp0iS98sorBFQATRASLsUe4VkC4TPSQQBj0npHOtjnWfI3BnYdR0ydIFt71IMGQm1IeLM/AgAw1eTJk3Xuuefq559/1p/+9Cfv9n79+mnRokUaP368bDabZsyYIbfb3WLXnDVrlqZOnaqMjAzl5ubq5ptv1hVXXKGuXbsqJydHL774os477zylpqZqw4YN2rhxo6ZMmaKysjLdeeeduuSSS9S7d29t375d3333nS6++OIWaVtLIaACnU1TRzpwV0ulBU2YAjffU6GtKPIs+3ICu05IZOAPhUUmeUZG4MEwAIY744wzlJCQoA0bNujyyy/3bn/yySd11VVXaeTIkUpKStJdd92loqKiFrlmRESEPvnkE916660aNmyYzzBTNft/++03vfrqq8rPz1e3bt1044036vrrr1dVVZXy8/M1ZcoU7dmzR0lJSbrooov0wAMPtEjbWorNsiyrvRvREoqKihQbG6vCwkLFxMS0d3OAzqtmpIOmPBhW3Yw+V3ZH4LOFRR6YApdAC3RI5eXlysnJUe/evRUWxrjSHd3B7megeY0KKoCmqT3SQVIAQ5NYlqfS2tgYtLXXq8qk6gqpaIdnCahdwZ7wWrcyG5nccKgNj2ekAwAwFAEVQOuy2TzjuIbFBjbSgeQZ6SDQh8Kc+ZKrWHJXSSV7PEtA7QryjF7gt7tBnfWIREY6ANAqFixYoOuvv77BfT179tTPP//cxi1qf/xtC8A8oZGeJb5nYMdXljfyYFj+gVENao904D4we1he4O0Ki2uku0Gd9WBHs94+gMPLeeedp+HDhze4z7QZntoKARVAxxcSJsV29yyBqK4MvLtBaZ7nITJZnr635ful/E2BXSc0OrAHw2p+HRr4+IsAOo/o6GhFR0e3dzOMQkAFcPixh0jRKZ4lEO5qzxBcgT4UVprv6XLgKvYs+zYHdp2QiAZC7EHGpHVE82AYgE6JgAoAjQmy/17pDIR1oNp6sNnCnLn1RzqoLJUKt3qWQNhDA+s/WxN0mQIXhuskAwsd9lriPhJQAaCl2WyeUQLC4yX1bfx4y5IqigOcAvfA/spST6gt3ulZAmqXvVZF1k//2cjkWlPgxnvCOdDK7HbP7zOXy6XwcCb16OhKS0slHVr/WQIqALQ3m00Ki/EsCX0CO8dVGvhsYaX5nqG+rGrJudezBNawA0OKBfhQWESip/sE0ETBwcGKiIhQbm6uQkJCFESlv0OyLEulpaXau3ev4uLivP/xaA4G6geAw0FVRcOVWO/oBnVCbvn+5l0nLLaR7gZ11kMYlB0eLpdLOTk5LTYdKNpPXFycUlJSZGugjzwD9QMAfhfskGJSPUsgqisbmAL3IGPSlhV4hu4qL/QsBVmBXSc0KvDZwiKTPcOPoVMKDQ1Vv3795HI1Y+Y5GCMkJOSQKqc1CKgAgPrsIVJ0V88SCHe1VLa/kf6zddbdVZKrxLPs3xLYdYLDA+9uEJkkOWIY6aADCQoKYqpTSCKgAgBaQpD9QJUzUUoe0PjxluWptDbYf9ZPyK2u8EyDW7jNswTCHtrwFLj+RjtgpAPACARUAEDbs9mk8DjPEsgUuJblqbQG9FDYgfVK54GRDnZ5loDaZW/ag2HhCUyBC7QC/lQBAMxns3kmJnBESwm9AzunsuwgAbaB4bsqCg+MdJDrWXIDaphnOK7GxqCtWY9IlIJDD+WTAA4LBFQAQOcUEi7FpXmWQFS5PKG1wW4GDYTasn2SLM8DYmUFkv4T2HUcsYHPFhaZ5HkfwGGGgAoAgOSpbMZ08yyBqK7yBNNAHworzfeMdFBR6FkKsgO7Tkhk4LOFRSZ5RkbgwTB0cARUAACawx4sRXXxLIFwuw9MgRtA/9madXelpy/tfqe0P8ApcIPDGq/K+kyBG0ughXEIqAAAtIWgoAMPYCVI6t/48ZblmQHMW4nNbTzUVpV7lqIdniWgdoU0PgVu7WAbHs9IB2h1BFQAAExks3mqm2GxTRjpwBngQ2EH1l0lniptyW7PElC7gjyjFwQ6W1hEIiMdoMn4HQMAQGdgs0mOKM8S3yuwcyrLA+9uUJrnGbvWch/oU5sXeNvC4jwzgQXyUFhEEiMdgIAKAMBhKyRMij3CswSi7kgHjY1JWzPSQfl+z5K/MbDrOGLqBNlGhu8KjWjmBwBTEVABAEBgmjrSgbtaKi1owhS4+Z6xaCuKPMu+nMCuExLRQJA9SKXWEc2DYYYjoAIAgNYRZJeikj1LIGpGOgh0trDSPM9sYZWlUuFWzxIIu+NAgK0JtI10PwiLI9C2MQIqAAAwQ+2RDpL6NX58vZEOAqjSVpVJ1RVS8U7PElC7gj3hNZCHwiJrRjqwH9pncZgjoAIAgI6pqSMdSJ6RDgJ9KMyZL7mKJXeVVLLHswTUriBPSPXb3aCBdXtI8z+HToiACgAADh+hkZ4lvmdgx1eWN/JgWJ318v0HRjrI9yx5GwK7Tlhs/UrswUJtsKPZH0FHQEAFAADwJyRMiu3uWQJRXRl4d4PSPM9DZLI8Q3iVF0oFWYFdJzQ6gO4GtdZDI5v9EbQHAioAAEBLsYdI0SmeJRDuas9wXIE+FFaa7+ly4Cr2LPs2B3ad4HD/3Qt6j5KOOL7Zb7k1EFABAADaS5D996/zA2EdGFe20dnCcn1HOqgqkwq3eZa6xmYQUAEAANBMNpvnAazweEl9Gz/esqSK4oNPgZtyTKs3u6kIqAAAAJ2VzSaFxXiWhD7t3ZqABbV3AwAAAIDaCKgAAAAwCgEVAAAARiGgAgAAwCgEVAAAABiFgAoAAACjEFABAABgFAIqAAAAjEJABQAAgFEIqAAAADAKARUAAABGIaACAADAKARUAAAAGIWACgAAAKMQUAEAAGAUAioAAACMQkAFAACAUQioAAAAMAoBFQAAAEYhoAIAAMAoBFQAAAAYhYAKAAAAoxBQAQAAYBQCKgAAAIxCQAUAAIBRCKgAAAAwCgEVAAAARiGgAgAAwCgEVAAAABiFgAoAAACjEFABAABgFAIqAAAAjEJABQAAgFEIqAAAADAKARUAAABGaXJAXblypcaPH6/U1FTZbDYtWbLEZ39GRoaOPPJIRUZGKj4+XmPHjtWaNWsafd158+apV69eCgsL0/Dhw/Xtt982tWkAAADoBJocUJ1Op4YMGaJ58+Y1uL9///6aO3eufvzxR3311Vfq1auXzjzzTOXm5vp9zbfeekvTp0/XrFmztH79eg0ZMkRnnXWW9u7d29TmAQAAoIOzWZZlNftkm02LFy/WBRdc4PeYoqIixcbG6rPPPtOYMWMaPGb48OEaNmyY5s6dK0lyu91KS0vTzTffrLvvvjugttRcp7CwUDExMU1+LwAAAGhdgea1Vu2D6nK59OKLLyo2NlZDhgzxe8y6des0duzY3xsVFKSxY8dq9erVrdk8AAAAGCi4NV70gw8+0MSJE1VaWqpu3bpp2bJlSkpKavDYvLw8VVdXq2vXrj7bu3btqt9++83vNSoqKlRRUeFdLyoqapnGAwAAoF21SgV19OjRyszM1KpVq3T22WdrwoQJLd6fdM6cOYqNjfUuaWlpLfr6AAAAaB+tElAjIyPVt29fnXTSSXr55ZcVHBysl19+ucFjk5KSZLfbtWfPHp/te/bsUUpKit9r3HPPPSosLPQu27Zta9H3AAAAgPbRJuOgut1un6/jawsNDdXxxx+v5cuX+xy/fPlyjRgxwu9rOhwOxcTE+CwAAADo+JrcB7WkpESbNm3yrufk5CgzM1MJCQlKTEzUQw89pPPOO0/dunVTXl6e5s2bpx07dujSSy/1njNmzBhdeOGFuummmyRJ06dP19SpU3XCCSfoxBNP1FNPPSWn06krr7yyBd4iAAAAOpImB9S1a9dq9OjR3vXp06dLkqZOnarnn39ev/32m1599VXl5eUpMTFRw4YN05dffqlBgwZ5z8nKylJeXp53/bLLLlNubq5mzpyp3bt3a+jQofr444/rPTgFAACAzu+QxkE1CeOgAgAAmM2IcVABAACApiKgAgAAwCgEVAAAABiFgAoAAACjEFABAABgFAIqAAAAjEJABQAAgFEIqAAAADAKARUAAABGIaACAADAKARUAAAAGIWACgAAAKMQUAEAAGAUAioAAACMQkAFAACAUQioAAAAMAoBFQAAAEYhoAIAAMAoBFQAAAAYhYAKAAAAoxBQAQAAYBQCKgAAAIxCQAUAAIBRCKgAAAAwCgEVAAAARiGgAgAAwCgEVAAAABiFgAoAAACjEFABAABgFAIqAAAAjEJABQAAgFEIqAAAADAKARUAAABGIaACAADAKARUAAAAGIWACgAAAKMQUAEAAGAUAioAAACMQkAFAACAUQioAAAAMAoBFQAAAEYhoAIAAMAoBFQAAAAYhYAKAAAAoxBQAQAAYBQCKgAAAIxCQAUAAIBRCKgAAAAwCgEVAAAARiGgAgAAwCgEVAAAABiFgAoAAACjEFABAABgFAIqAAAAjEJABQAAgFEIqAAAADAKARUAAABGIaACAADAKARUAAAAGIWACgAAAKMQUAEAAGAUAioAAACMQkAFAACAUQioAAAAMAoBFQAAAEYhoAIAAMAoBFQAAAAYhYAKAAAAoxBQAQAAYBQCKgAAAIxCQAUAAIBRCKgAAAAwCgEVAAAARiGgAgAAwCgEVAAAABiFgAoAAACjEFABAABgFAIqAAAAjEJABQAAgFEIqAAAADAKARUAAABGIaACAADAKARUAAAAGIWACgAAAKMQUAEAAGAUAioAAACMQkAFAACAUQioAAAAMAoBFQAAAEYhoAIAAMAoBFQAAAAYhYAKAAAAoxBQAQAAYBQCKgAAAIxCQAUAAIBRCKgAAAAwCgEVAAAARiGgAgAAwCgEVAAAABiFgAoAAACjEFABAABgFAIqAAAAjEJABQAAgFEIqAAAADBKkwPqypUrNX78eKWmpspms2nJkiXefZWVlbrrrrs0ePBgRUZGKjU1VVOmTNHOnTsP+poZGRmy2Ww+y5FHHtnkNwMAAICOr8kB1el0asiQIZo3b169faWlpVq/fr1mzJih9evXa9GiRdqwYYPOO++8Rl930KBB2rVrl3f56quvmto0AAAAdALBTT1h3LhxGjduXIP7YmNjtWzZMp9tc+fO1YknnqitW7eqR48e/hsSHKyUlJSmNgcAAACdTKv3QS0sLJTNZlNcXNxBj9u4caNSU1PVp08fTZ48WVu3bm3tpgEAAMBATa6gNkV5ebnuuusuTZo0STExMX6PGz58uObPn68BAwZo165deuCBB3Tqqafqp59+UnR0dIPnVFRUqKKiwrteVFTU4u0HAABA22u1gFpZWakJEybIsiw999xzBz22dpeBY445RsOHD1fPnj319ttv6+qrr27wnDlz5uiBBx5o0TYDAACg/bXKV/w14XTLli1atmzZQaunDYmLi1P//v21adMmv8fcc889Kiws9C7btm071GYDAADAAC0eUGvC6caNG/XZZ58pMTGxya9RUlKirKwsdevWze8xDodDMTExPgsAAAA6viYH1JKSEmVmZiozM1OSlJOTo8zMTG3dulWVlZW65JJLtHbtWi1YsEDV1dXavXu3du/eLZfL5X2NMWPGaO7cud71O+64Q//+97+1efNmrVq1ShdeeKHsdrsmTZp06O8QAAAAHUqT+6CuXbtWo0eP9q5Pnz5dkjR16lRlZGTo/ffflyQNHTrU57wvvvhCp59+uiQpKytLeXl53n3bt2/XpEmTlJ+fr+TkZJ1yyin65ptvlJyc3NTmAQAAoIOzWZZltXcjWkJRUZFiY2NVWFjI1/0AAAAGCjSvtfo4qAAAAEBTEFABAABgFAIqAAAAjEJABQAAgFEIqAAAADAKARUAAABGIaACAADAKARUAAAAGIWACgAAAKMQUAEAAGAUAioAAACMQkAFAACAUQioAAAAMAoBFQAAAEYhoAIAAMAoBFQAAAAYhYAKAAAAoxBQAQAAYBQCKgAAAIxCQAUAAIBRCKgAAAAwCgEVAAAARiGgAgAAwCgEVAAAABiFgAoAAACjEFABAABgFAIqAAAAjEJABQAAgFEIqAAAADAKARUAAABGIaACAADAKARUAAAAGIWACgAAAKMQUAEAAGAUAioAAACMQkAFAACAUQioAAAAMAoBFQAAAEYhoAIAAMAoBFQAAAAYhYAKAAAAoxBQAQAAYBQCKgAAAIxCQAUAAIBRCKgAAAAwCgEVAAAARiGgAgAAwCgEVAAAABiFgAoAAACjEFABAABgFAIqAAAAjEJABQAAgFEIqAAAADAKARUAAABGIaACAADAKARUAAAAGIWACgAAAKMQUAEAAGAUAioAAACMQkAFAACAUQioAAAAMAoBFQAAAEYhoAIAAMAoBFQAAAAYhYAKAAAAoxBQAQAAYBQCKgAAAIxCQAUAAIBRCKgAAAAwCgEVAAAARiGgAgAAwCgEVAAAABiFgAoAAACjEFABAABgFAIqAAAAjEJABQAAgFEIqAAAADAKARUAAABGIaACAADAKARUAAAAGIWACgAAAKMQUAEAAGAUAioAAACMQkAFAACAUQioAAAAMAoBFQAAAEYhoAIAAMAoBFQAAAAYhYAKAAAAoxBQAQAAYBQCKgAAAIxCQAUAAIBRCKgAAAAwCgEVAAAARiGgAgAAwCgEVAAAABiFgAoAAACjEFABAABgFAIqAAAAjEJABQAAgFEIqAAAADAKARUAAABGIaACAADAKARUAAAAGKXJAXXlypUaP368UlNTZbPZtGTJEu++yspK3XXXXRo8eLAiIyOVmpqqKVOmaOfOnY2+7rx589SrVy+FhYVp+PDh+vbbb5vaNAAAAHQCTQ6oTqdTQ4YM0bx58+rtKy0t1fr16zVjxgytX79eixYt0oYNG3Teeecd9DXfeustTZ8+XbNmzdL69es1ZMgQnXXWWdq7d29TmwcAAIAOzmZZltXsk202LV68WBdccIHfY7777judeOKJ2rJli3r06NHgMcOHD9ewYcM0d+5cSZLb7VZaWppuvvlm3X333QG1paioSLGxsSosLFRMTEyT3wsAAABaV6B5rdX7oBYWFspmsykuLq7B/S6XS+vWrdPYsWN/b1RQkMaOHavVq1e3dvMAAABgmODWfPHy8nLdddddmjRpkt+UnJeXp+rqanXt2tVne9euXfXbb7/5fe2KigpVVFR414uKilqm0QAAAGhXrVZBrays1IQJE2RZlp577rkWf/05c+YoNjbWu6SlpbX4NQAAAND2WiWg1oTTLVu2aNmyZQftY5CUlCS73a49e/b4bN+zZ49SUlL8nnfPPfeosLDQu2zbtq3F2g8AAID20+IBtSacbty4UZ999pkSExMPenxoaKiOP/54LV++3LvN7XZr+fLlGjFihN/zHA6HYmJifBYAAAB0fE3ug1pSUqJNmzZ513NycpSZmamEhAR169ZNl1xyidavX68PPvhA1dXV2r17tyQpISFBoaGhkqQxY8bowgsv1E033SRJmj59uqZOnaoTTjhBJ554op566ik5nU5deeWVLfEeAQAA0IE0OaCuXbtWo0eP9q5Pnz5dkjR16lRlZGTo/ffflyQNHTrU57wvvvhCp59+uiQpKytLeXl53n2XXXaZcnNzNXPmTO3evVtDhw7Vxx9/XO/BKQAAAHR+hzQOqkkYBxUAAMBsxoyDCgAAADQFARUAAABGIaACAADAKARUAAAAGIWACgAAAKMQUAEAAGAUAioAAACMQkAFAACAUQioAAAAMAoBFQAAAEYhoAIAAMAoBFQAAAAYhYAKAAAAoxBQAQAAYBQCKgAAAIxCQAUAAIBRCKgAAAAwCgEVAAAARiGgAgAAwCgEVAAAABiFgAoAAACjEFABAABgFAIqAAAAjEJABQAAgFEIqAAAADAKARUAAABGIaACAADAKARUAAAAGIWACgAAAKMQUAEAAGAUAioAAACMQkAFAACAUQioAAAAMAoBFQAAAEYhoAIAAMAoBFQAAAAYhYAKAAAAoxBQAQAAYBQCKgAAAIxCQAUAAIBRCKgAAAAwCgEVAAAARiGgAgAAwCgEVAAAABiFgAoAAACjEFABAABgFAIqAAAAjEJABQAAgFEIqAAAADAKARUAAABGIaACAADAKARUAAAAGIWACgAAAKMQUAEAAGAUAioAAACMQkAFAACAUQioAAAAMAoBFQAAAEYhoAIAAMAoBFQAAAAYhYAKAAAAoxBQAQAAYBQCKgAAAIxCQAUAAIBRCKgAAAAwCgEVAAAARiGgAgAAwCgEVAAAABiFgAoAAACjEFABAABgFAIqAAAAjEJABQAAgFEIqAAAADAKARUAAABGIaACAADAKARUAAAAGIWACgAAAKMQUAEAAGAUAioAAACMQkAFAACAUQioAAAAMAoBFQAAAEYhoAIAAMAoBFQAAAAYhYAKAAAAoxBQAQAAYBQCKgAAAIxCQAUAAIBRCKgAAAAwCgEVAAAARiGgAgAAwCgEVAAAABiFgAoAAACjEFABAABgFAIqAAAAjEJABQAAgFEIqAAAADAKARUAAABGIaACAADAKARUAAAAGIWACgAAAKMQUAEAAGAUAioAAACMQkAFAACAUZocUFeuXKnx48crNTVVNptNS5Ys8dm/aNEinXnmmUpMTJTNZlNmZmajrzl//nzZbDafJSwsrKlNAwAAQCfQ5IDqdDo1ZMgQzZs3z+/+U045RY888kiTXjcmJka7du3yLlu2bGlq0wAAANAJBDf1hHHjxmncuHF+919xxRWSpM2bNzfpdW02m1JSUpraHAAAAHQyxvRBLSkpUc+ePZWWlqbzzz9fP//8c3s3CQAAAO3AiIA6YMAA/f3vf9d7772nf/zjH3K73Ro5cqS2b9/u95yKigoVFRX5LAAAAOj4jAioI0aM0JQpUzR06FCNGjVKixYtUnJysl544QW/58yZM0exsbHeJS0trQ1bDAAAgNZiRECtKyQkRMcee6w2bdrk95h77rlHhYWF3mXbtm1t2EIAAAC0FiMDanV1tX788Ud169bN7zEOh0MxMTE+CwAAADq+Jj/FX1JS4lPZzMnJUWZmphISEtSjRw8VFBRo69at2rlzpyRpw4YNkqSUlBTvU/pTpkxR9+7dNWfOHEnS7NmzddJJJ6lv377av3+/HnvsMW3ZskXXXHPNIb9BAAAAdCxNDqhr167V6NGjvevTp0+XJE2dOlXz58/X+++/ryuvvNK7f+LEiZKkWbNmKSMjQ5K0detWBQX9Xrzdt2+frr32Wu3evVvx8fE6/vjjtWrVKh111FHNelMAAADouGyWZVnt3YiWUFRUpNjYWBUWFvJ1PwAAgIECzWtG9kEFAADA4YuACgAAAKMQUAEAAGAUAioAAACMQkAFAACAUQioAAAAMAoBFQAAAEYhoAIAAMAoBFQAAAAYhYAKAAAAoxBQAQAAYBQCKgAAAIxCQAUAAIBRCKgAAAAwCgEVAAAARiGgAgAAwCgEVAAAABiFgAoAAACjEFABAABgFAIqAAAAjEJABQAAgFEIqAAAADAKARUAAABGIaACAADAKARUAAAAGIWACgAAAKMQUAEAAGAUAioAAACMQkAFAACAUQioAAAAMAoBFQAAAEYhoDZDtduSq8rd3s0AAADolILbuwEd0YbdxRo/9yulxYcrPTlK6V2i1Ccp0vszITJUNputvZsJAADQIRFQmyE7r0TVbkub80u1Ob9Uy3/b67M/LiJE6cm+oTW9S5R6JEQoxE7RGgAA4GBslmVZ7d2IllBUVKTY2FgVFhYqJiamVa9lWZb2FlcoK7dEWblOZe0tUXae5+eO/WV+zwsOsqlHYoQnvCZHeqqvyVFKT45UXERoq7YZAACgvQWa16igNoPNZlPXmDB1jQnTyPQkn31lrmrl5DmVlVui7NwDP/NKlLXXqbLKamXnOpWd66z3momRoT6htebXR8SHK5iqKwAAOIxQQW0jbrel3UXlv4fWmuprbol2FZb7PS/EblOvxEif0NonOVJ9kqMUGx7Shu8AAADg0ASa1wioBnBWVHmrrlm5v1dfs3NLVHGQ0QKSox31+rn2TY5Saly47EE8pAUAAMxCQO0E3G5LOwvLavVz9XQVyM4r0Z6iCr/nhQYHqU9SZL0uA32SoxTloFcHAABoHwTUTq64vNJTZa0VWrP2OpWT55Sr2n/VtWuMo95DWn2SI5UaG64gqq4AAKAVEVAPU9VuSzv2lR3oLuDbZSCvxH/VNSwkSL2TPCMK1O3vGhFK1RUAABw6AirqKSyr9Hk4q+bXW/Kdqqz2/9sgNTaszmQEUUrvEqmUmDAmJAAAAAEjoCJgVdVubdtXVq+fa1auUwVOl9/zIkPt6l1TaT0QWtOTo9Q7KVJhIfY2fAcAAKAjYBxUBCzYHqTeSZHqnRQpqavPvn1Olze0ZtWE19wSbSkoldNVrZ92FOmnHUU+59hsUve4cPVJ9u0y0Dc5SsnRDqquAADgoKigollcVW5tLShtsMtAYVml3/OiHcE+/Vs9P6PUKylCjmCqrgAAdGZ8xY92YVmWCpyueqE1O7dEWwtK5fbzuy3IJqUlRHj6uR4IrenJnj6viZGhVF0BAOgECKgwTkVVtbbk+1Zds3Kdyt5bouKKKr/nxYQF+zyclX4gvPZIiFRoMNPAAgDQURBQ0WFYlqXckgqf8Vyzcj0PbG3fVyZ/v0PtQTb1SIhoYGisKCVEhrbtmwAAAI0ioKJTKK+s1uZ85++htVaXAaer2u958REh9UKrp+oaoWA7VVcAANoDARWdmmVZ2lNU4RNaayYk2LG/zO95IfaaqqtvP9f0pCjFRoS04TsAAODwQ0DFYavUVaWcPKe30pqV61TW3hLl5DlVVum/6poUFerTz7Wm+npEfITsTAMLAMAhI6ACdbjdlnYVlXtC694DXQUO9HndXVTu97xQe5B6JUXU6zLQJzlSMWFUXQEACBQBFWiCkooq5XgD6+9dBnLynKqocvs9r0u0o14/1/TkKHWPC1cQVVcAAHwQUIEW4HZb2rG/7PchsXJLvH1d9xZX+D3PEeyZnSu9Vj/XPkmeqmukgwncAACHJwIq0MqKyiuVXSe0ZuWWaHNeqVzV/quuKTFhv/dzTToQXpOj1C0mjKorAKBTI6AC7aTabWn7vlKf0FozxmteicvveeEhdvVJjvR2FfD+TIpSeCjTwAIAOj4CKmCgwtJKZdXq51pTfd2SX6oqf/PASuoeF+7t65peq89r1xgH08ACADoMAirQgVRWu7WtoLReP9dNuSXaX1rp97zIULvPw1l9kj3DZPVKjFRYCFVXAIBZCKhAJ1HgdNXr55qV69TWglJV+6m62mzSEfHhB/q5ekJrzc/kKKquAID2QUAFOjlXlVtbC5zeIbFq+rlm7S1RUXmV3/Oiw4J9qq41P3skRsgRTNUVANB6CKjAYcqyLOU7XfX6uWbnObWtoFT+uroG2aQeCRH1uwwkRyohMpSqKwDgkBFQAdRTXlmtLfmlDXYZKKnwX3WNDQ+pF1r7JEepZ2KEQuxBbfgOAAAdGQEVQMAsy1JucYU21Qmt2bkl2rG/TP7+lggOsqlHYoS3f2t6rf6u8ZGhbfsmAADGI6ACaBHlldXKyavTz/VAkC11Vfs9LyEy1DuOq3diguQopcWHK5iqKwAclgLNa8y5COCgwkLsGtgtRgO7+f5FYlmWdheV+zyclZ3nVNbeEu0sLFeB06UCp0vfbd7nc16I3aaeiZG1JiP4vctAbHhIW741AIChqKACaHGlripvV4HaXQZy8kpUXul/GtikKEe9CQnSk6PUPT5cdqaBBYAOj6/4ARjH7ba0s7CsVmj9PcDuKarwe15ocJB6J0b6jOfaJylKfZIjFR1G1RUAOgoCKoAOpbi80tvXtXb1NTvPKVeV/6pr1xhHnckIPNXX1NhwBVF1BQCjEFABdArVbks79pUpq04/16xcp/JK/Fddw0KC1PtAlbV2l4HeSZGKdND9HgDaAwEVQKdXWFapbJ9+rp5fb853qrLa/19t3WLDfB7O8owwEKlusWFMSAAArYiACuCwVVXt1vZ9ZfX6uWbnOpXvdPk9LyLUrj41Q2Ml/1597Z0UqfBQpoEFgENFQAWABuxzug6M5erb33Vrfqmq/MwDa7NJqbHhSu8SpT5Jkd5+runJUeoS7aDqCgABIqACQBNUVru1taC0Tj9XT5AtLKv0e16UI9inn2tNl4GeiREKC6HqCgC1MVA/ADRBiD3IO+5qbZZlqcDp8gmt3qprQalKKqr0w/ZC/bC90Oe8IJt0RHxEvX6u6clRSooKpeoKAAdBBRUAmqmiqlpb80u9ldba4bW4vMrveTFhwfVCa98ukeqREKnQYKaBBdB58RU/ALQTy7KUW1LRwGxaJdq+r0z+/ta1B9nUIyHCp59rTZBNiAxt2zcBAK2AgAoABiqvrNbmfKcntNZ0GTjQfcDpqvZ7XlxESINDY/VIiFCInaorgI6BgAoAHYhlWdpbXOHzcFZN9XXH/jK/5wUH2dQzMaJel4H05EjFRVB1BWAWAioAdBJlrmrvNLB1x3Utq/RfdU2MDPUNrQemgz0iPlzBVF0BtAMCKgB0cm63pd1F5fVCa1ZuiXYVlvs9L9QepJ6JET6hNb2LJ8jGhIW04TsAcLghoALAYcxZUVWr6nrg594S5eQ5VVHl9ntecrSjXj/XvslRSo0Llz2IobEAHBoCKgCgHrfb0o79ZQ2O67q3uMLveY7gIPVOiqzTzzVKvZMjFeVgSG0AgSGgAgCapLi8sl5Xgexcp3LynHJV+6+6psSE1Z9Nq0uUusWEKYiqK4BaCKgAgBZR7ba0fV9prfFca8JrifJKXH7PCwsJ+r1/64GxXfskRapPcqQiQqm6AocjAioAoNUVllYqK6/WZAR7PeO6bsl3qrLa/z8v3ePCvVXX2l0GusY4mAYW6MQIqACAdlNV7da2fWX1+rlm5zlV4PRfdY0MtatPndDaJzlSvZMiFRZib8N3AKA1EFABAEYqcLqU7TMFrFPZuSXaUlCqanfD/yTZbJ6qa3oDExIkR1N1BToKAioAoENxVbm1taDUp+Ja022gqLzK73nRjmD16RKl9Fr9XNO7RKlnYoQcwVRdAZMQUAEAnYJlWcp3uur1c83KLdG2glL5KboqyCalJXgmJKgJrTXV18TIUKquQDsgoAIAOr2KqmptyS/9PbTuLVFWnlPZe0tUXOG/6hobHlKvn2t6sqfqGsI0sECrIaACAA5blmUpt7ii1pBYv3cZ2LG/TP7+5bMH2dQzIeLATFq+owzER4a27ZsAOiECKgAADSivrFZOntMntNb8utRV7fe8hMhQT1eB2g9pdYlSWny4gqm6AgEJNK8xUjIA4LASFmLXwG4xGtjN9x9Hy7K0p6iiXmjN2luinYXlKnC6VOB0ae2WfT7nhdht6pkYWa+fa3pSlGIjQtryrQGdBhVUAAAaUeqqUnau8/d+rgcCbHZeicor/U8DmxQVeqC7gG+XgSPiI2RnGlgchviKHwCAVuZ2W9pVVF5/QoJcp3YXlfs9LzQ4SL0TI+vNptUnOVLRYVRd0XkRUAEAaEclFVU+ExLUnk3LVeW/6tol2lGvn2ufpEh1jwtXEFVXdHAEVAAADFTttrRzf5k21ennmp3nVG5xhd/zwkKC1Cvx936uNV0GeidFKtLBIyXoGAioAAB0MEXllZ7QWqfLwJb8Urmq/Vddu8WG1esqkJ4cpW6xYUxIAKMQUAEA6CSqqt3avq9M2Xklytrr22Ug3+nye15EqF296w6NdaDqGh7KNLBoewRUAAAOA/tLXfUmJMg+UHWt8jcPrKTuceE+/VzTDwyT1SXaQdUVrYaACgDAYayy2q2tBaU+obUmyO4vrfR7XpQj+PeuAgdCa5/kSPVKjFRYCFVXHBoCKgAAaFCB0+UbWg88pLW1oFTVfqquNpuUFh9Rr59renKUkqJCqboiIARUAADQJK4qt7YWOLWpTj/XrNwSFZdX+T0vOiy4XmhNT45Uz8RIhQYzDSx+R0AFAAAtwrIs5ZW46vVzzcp1atu+UvlLEvYgm9Liw33Gc635mRBJ1fVw1GoBdeXKlXrssce0bt067dq1S4sXL9YFF1zg3b9o0SI9//zzWrdunQoKCvT9999r6NChjb7uO++8oxkzZmjz5s3q16+fHnnkEZ1zzjkBt4uACgBA2yuvrNaW/NJ6/Vyzc50qqfBfdY2LCPHt53rgZ4+ECIXYqbp2VoHmtSaP7Ot0OjVkyBBdddVVuuiiixrcf8opp2jChAm69tprA3rNVatWadKkSZozZ47OPfdcLVy4UBdccIHWr1+vo48+uqlNBAAAbSQsxK4BKdEakBLts92yLO0trjjQReD3fq5Ze0u0Y3+Z9pdWat2WfVq3ZZ/PecFBNvVIjGiwy0BcRGhbvjW0o0P6it9ms9WroNbYvHmzevfuHVAF9bLLLpPT6dQHH3zg3XbSSSdp6NChev755wNqCxVUAAA6hjJXtXLy6k4B6xnjtayy2u95iZGhPqG15tdHxIcrmKprh9BqFdTWsHr1ak2fPt1n21lnnaUlS5a0T4MAAECrCQ+166jUGB2V6htQ3G5Lu4vKGxwaa1dhufKdLuU7Xfpus2/VNcRu80wDW2c2rT7JUYoND2nLt4YWYkRA3b17t7p27eqzrWvXrtq9e7ffcyoqKlRR8fucxUVFRa3WPgAA0PqCgmxKjQtXaly4TumX5LPPWVHlrbrW7ueanVuiiiq3Nu4t0ca9JfVeMznaUa+fa9/kKKXGhcsexENapjIioDbHnDlz9MADD7R3MwAAQBuIdATr6O6xOrp7rM92t9vSzsKyWv1cPV0FsvNKtKeoQrnFnmVNToHPeaHBQeqTFFmvy0Cf5ChFOTpsPOo0jLgDKSkp2rNnj8+2PXv2KCUlxe8599xzj0+3gKKiIqWlpbVaGwEAgHmCgmw6Ij5CR8RHaFT/ZJ99xeWVniprrdCatdepnDynXFVu/ba7WL/tLq73ml1jHPUe0uqTHKnU2HAFUXVtE0YE1BEjRmj58uW67bbbvNuWLVumESNG+D3H4XDI4XC0QesAAEBHFB0WoiFpcRqSFuezvdptace+Mu8kBLW7DOSVVGhPkWdZlZXvc15YSJB6J3lGFKjb3zUi1IhI1Wk0+dMsKSnRpk2bvOs5OTnKzMxUQkKCevTooYKCAm3dulU7d+6UJG3YsEGSp0paUxGdMmWKunfvrjlz5kiSbr31Vo0aNUpPPPGE/vjHP+rNN9/U2rVr9eKLLx7yGwQAAKjNfmAoqx6JERp9ZBeffYVllXXGc/X8eku+U+WVbv26q0i/7qr/3EtqbFidyQiilN4lUikxYUxI0AxNHmZqxYoVGj16dL3tU6dO1fz58zV//nxdeeWV9fbPmjVLGRkZkqTTTz9dvXr10vz5873733nnHd1///3egfofffRRBuoHAABGqKp2a9u+snr9XLNynSpwuvyeFxlqV++aSuuB0JqeHKXeSZEKC7G34TswA1OdAgAAtIF9Tpc3tGbVhNfcEm0pKFW1u+GYZbNJ3ePC1SfZt8tA3+QoJUc7Om3VlYAKAADQjlxVbm0tKG2wy0BhWaXf86IdwT79Wz0/o9QrKUKO4I5ddSWgAgAAGMiyLBU4XfVCa3ZuibYWlMpP0VVBNiktIcLTz/VAaE1P9vR5TYwM7RBVVwIqAABAB1NRVa0t+b5V16xcp7L3lqi4osrveTFhwT4PZ6UfCK89EiIVGmzONLAEVAAAgE7CsizlllT4jOealet5YGv7vjL5S3P2IJt6JEQ0MDRWlBIiQ9v2TYiA2t7NAQAAaBPlldXanO/8PbTW6jLgdFX7PS8+IkTpyVG6fHgPXXTcEW3S1kDzGqPKAgAAdGBhIXYdmRKjI1N8A59lWdpTVOETWmsmJNixv0z7Siu1dss+nTO4Wzu13D8CKgAAQCdks9mUEhumlNgwndw3yWdfqatKOXlOZeU6Nbh7bDu10D8CKgAAwGEmIjRYg1JjNSjVvHAqSeY81gUAAACIgAoAAADDEFABAABgFAIqAAAAjEJABQAAgFEIqAAAADAKARUAAABGIaACAADAKARUAAAAGIWACgAAAKMQUAEAAGAUAioAAACMQkAFAACAUQioAAAAMAoBFQAAAEYhoAIAAMAoBFQAAAAYhYAKAAAAoxBQAQAAYBQCKgAAAIxCQAUAAIBRCKgAAAAwCgEVAAAARiGgAgAAwCjB7d2AlmJZliSpqKionVsCAACAhtTktJrc5k+nCajFxcWSpLS0tHZuCQAAAA6muLhYsbGxfvfbrMYibAfhdru1c+dORUdHy2aztfr1ioqKlJaWpm3btikmJqbVr4eWxz3s2Lh/HR/3sOPjHnZs7XH/LMtScXGxUlNTFRTkv6dpp6mgBgUF6Ygjjmjz68bExPCHsoPjHnZs3L+Oj3vY8XEPO7a2vn8Hq5zW4CEpAAAAGIWACgAAAKMQUJvJ4XBo1qxZcjgc7d0UNBP3sGPj/nV83MOOj3vYsZl8/zrNQ1IAAADoHKigAgAAwCgEVAAAABiFgAoAAACjEFABAABgFALqQcybN0+9evVSWFiYhg8frm+//fagx7/zzjs68sgjFRYWpsGDB+vDDz9so5aiIU25fy+99JJOPfVUxcfHKz4+XmPHjm30fqP1NfXPYI0333xTNptNF1xwQes2EI1q6j3cv3+/brzxRnXr1k0Oh0P9+/fn79J21tR7+NRTT2nAgAEKDw9XWlqabr/9dpWXl7dRa1HbypUrNX78eKWmpspms2nJkiWNnrNixQodd9xxcjgc6tu3r+bPn9/q7WyQhQa9+eabVmhoqPX3v//d+vnnn61rr73WiouLs/bs2dPg8V9//bVlt9utRx991Prll1+s+++/3woJCbF+/PHHNm45LKvp9+/yyy+35s2bZ33//ffWr7/+ak2bNs2KjY21tm/f3sYtR42m3sMaOTk5Vvfu3a1TTz3VOv/889umsWhQU+9hRUWFdcIJJ1jnnHOO9dVXX1k5OTnWihUrrMzMzDZuOWo09R4uWLDAcjgc1oIFC6ycnBzrk08+sbp162bdfvvtbdxyWJZlffjhh9Z9991nLVq0yJJkLV68+KDHZ2dnWxEREdb06dOtX375xXrmmWcsu91uffzxx23T4FoIqH6ceOKJ1o033uhdr66utlJTU605c+Y0ePyECROsP/7xjz7bhg8fbl1//fWt2k40rKn3r66qqiorOjraevXVV1uriWhEc+5hVVWVNXLkSOt///d/ralTpxJQ21lT7+Fzzz1n9enTx3K5XG3VRDSiqffwxhtvtM444wyfbdOnT7dOPvnkVm0nGhdIQP3v//5va9CgQT7bLrvsMuuss85qxZY1jK/4G+ByubRu3TqNHTvWuy0oKEhjx47V6tWrGzxn9erVPsdL0llnneX3eLSe5ty/ukpLS1VZWamEhITWaiYOorn3cPbs2erSpYuuvvrqtmgmDqI59/D999/XiBEjdOONN6pr1646+uij9fDDD6u6urqtmo1amnMPR44cqXXr1nm7AWRnZ+vDDz/UOeec0yZtxqExKcsEt/kVO4C8vDxVV1era9euPtu7du2q3377rcFzdu/e3eDxu3fvbrV2omHNuX913XXXXUpNTa33BxVtozn38KuvvtLLL7+szMzMNmghGtOce5idna3PP/9ckydP1ocffqhNmzbphhtuUGVlpWbNmtUWzUYtzbmHl19+ufLy8nTKKafIsixVVVXpz3/+s+699962aDIOkb8sU1RUpLKyMoWHh7dZW6igAnX85S9/0ZtvvqnFixcrLCysvZuDABQXF+uKK67QSy+9pKSkpPZuDprJ7XarS5cuevHFF3X88cfrsssu03333afnn3++vZuGAK1YsUIPP/ywnn32Wa1fv16LFi3S0qVL9eCDD7Z309DBUEFtQFJSkux2u/bs2eOzfc+ePUpJSWnwnJSUlCYdj9bTnPtX4/HHH9df/vIXffbZZzrmmGNas5k4iKbew6ysLG3evFnjx4/3bnO73ZKk4OBgbdiwQenp6a3baPhozp/Dbt26KSQkRHa73btt4MCB2r17t1wul0JDQ1u1zfDVnHs4Y8YMXXHFFbrmmmskSYMHD5bT6dR1112n++67T0FB1MVM5i/LxMTEtGn1VKKC2qDQ0FAdf/zxWr58uXeb2+3W8uXLNWLEiAbPGTFihM/xkrRs2TK/x6P1NOf+SdKjjz6qBx98UB9//LFOOOGEtmgq/GjqPTzyyCP1448/KjMz07ucd955Gj16tDIzM5WWltaWzYea9+fw5JNP1qZNm7z/uZCk//znP+rWrRvhtB005x6WlpbWC6E1/+GwLKv1GosWYVSWafPHsjqIN99803I4HNb8+fOtX375xbruuuusuLg4a/fu3ZZlWdYVV1xh3X333d7jv/76ays4ONh6/PHHrV9//dWaNWsWw0y1o6bev7/85S9WaGio9e6771q7du3yLsXFxe31Fg57Tb2HdfEUf/tr6j3cunWrFR0dbd10003Whg0brA8++MDq0qWL9T//8z/t9RYOe029h7NmzbKio6OtN954w8rOzrY+/fRTKz093ZowYUJ7vYXDWnFxsfX9999b33//vSXJevLJJ63vv//e2rJli2VZlnX33XdbV1xxhff4mmGm7rzzTuvXX3+15s2bxzBTJnrmmWesHj16WKGhodaJJ55offPNN959o0aNsqZOnepz/Ntvv23179/fCg0NtQYNGmQtXbq0jVuM2ppy/3r27GlJqrfMmjWr7RsOr6b+GayNgGqGpt7DVatWWcOHD7ccDofVp08f66GHHrKqqqrauNWorSn3sLKy0srIyLDS09OtsLAwKy0tzbrhhhusffv2tX3DYX3xxRcN/ttWc8+mTp1qjRo1qt45Q4cOtUJDQ60+ffpYr7zySpu327Isy2ZZ1NwBAABgDvqgAgAAwCgEVAAAABiFgAoAAACjEFABAABgFAIqAAAAjEJABQAAgFEIqAAAADAKARUAAABGIaACAADAKARUAAAAGIWACgAAAKMQUAEAAGCU/w/3BShsFt2gRgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_results(unsup_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "config['epochs'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "id": "ViP8kbbaC-07"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train steps: 19\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 20/20 [01:17<00:00,  3.87s/it]\n",
      "100%|██████████| 49/49 [06:29<00:00,  7.94s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train loss: 0.8032501250505447 | Val loss: 0.895375699413066\n",
      "Train acc: 0.5472 | Val acc: 0.63872\n",
      "Train f1: 0.43526849989553473 | Val f1: 0.5885471797715361\n",
      "Train precision: 0.9845559845559846 | Val precision: 0.959946949602122\n",
      "Train recall: 0.10139165009940358 | Val recall: 0.28952\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "sup_history = training(space_bert, sup_train_dataset, test_dataset, config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial GPU Usage\n",
      "GPU memory occupied: 9034 MB.\n",
      "GPU Usage after emptying the cache\n",
      "GPU memory occupied: 2116 MB.\n"
     ]
    }
   ],
   "source": [
    "free_gpu_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_results(sup_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "gpuType": "T4",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "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": "3.10.13"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "09f3785e65754b14936ad64c38c2b5b8": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
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