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os\nimport gc\nimport re\nimport ast\nimport sys\nimport copy\nimport json\nimport time\nimport datetime\nimport math\nimport string\nimport pickle\nimport random\nimport joblib\nimport itertools\nfrom distutils.util import strtobool\nimport warnings\nwarnings.filterwarnings('ignore')\n\nimport scipy as sp\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n%matplotlib inline\nfrom tqdm.auto import tqdm\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.model_selection import StratifiedKFold, GroupKFold, KFold\n\nos.system('pip install iterative-stratification==0.1.7')\nfrom iterstrat.ml_stratifiers import MultilabelStratifiedKFold\n\nimport torch\nimport torch.nn as nn\nfrom torch.nn import Parameter\nimport torch.nn.functional as F\nfrom torch.optim import Adam, SGD, AdamW\nfrom torch.utils.data import DataLoader, Dataset\nfrom torch.utils.checkpoint import checkpoint\n\nos.system('pip uninstall -y transformers')\nos.system('pip uninstall -y tokenizers')\nos.system('python -m pip install --no-index --find-links=../input/fb3-my-pip-wheels transformers')\nos.system('python -m pip install --no-index --find-links=../input/fb3-my-pip-wheels tokenizers')\n\nimport transformers\nimport tokenizers\nprint(f'transformers.__version__: {transformers.__version__}')\nprint(f'tokenizers.__version__: {tokenizers.__version__}')\n\nfrom transformers import AutoTokenizer, AutoModel, AutoConfig\nfrom transformers import get_linear_schedule_with_warmup, get_cosine_schedule_with_warmup\nos.environ['TOKENIZERS_PARALLELISM']='true'","metadata":{"papermill":{"duration":46.795557,"end_time":"2022-11-14T19:29:42.954216","exception":false,"start_time":"2022-11-14T19:28:56.158659","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-11-21T17:36:55.808656Z","iopub.execute_input":"2022-11-21T17:36:55.809532Z","iopub.status.idle":"2022-11-21T17:37:39.593874Z","shell.execute_reply.started":"2022-11-21T17:36:55.809434Z","shell.execute_reply":"2022-11-21T17:37:39.592907Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"Collecting iterative-stratification==0.1.7\n  Downloading iterative_stratification-0.1.7-py3-none-any.whl (8.5 kB)\nRequirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (from iterative-stratification==0.1.7) (1.21.6)\nRequirement already satisfied: scipy in /opt/conda/lib/python3.7/site-packages (from iterative-stratification==0.1.7) (1.7.3)\nRequirement already satisfied: scikit-learn in /opt/conda/lib/python3.7/site-packages (from iterative-stratification==0.1.7) (1.0.2)\nRequirement already satisfied: joblib>=0.11 in /opt/conda/lib/python3.7/site-packages (from scikit-learn->iterative-stratification==0.1.7) (1.0.1)\nRequirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.7/site-packages (from scikit-learn->iterative-stratification==0.1.7) (3.1.0)\nInstalling collected packages: iterative-stratification\nSuccessfully installed iterative-stratification-0.1.7\n","output_type":"stream"},{"name":"stderr","text":"WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\n","output_type":"stream"},{"name":"stdout","text":"Found existing installation: transformers 4.20.1\nUninstalling transformers-4.20.1:\n  Successfully uninstalled transformers-4.20.1\n","output_type":"stream"},{"name":"stderr","text":"WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\n","output_type":"stream"},{"name":"stdout","text":"Found existing installation: tokenizers 0.12.1\nUninstalling tokenizers-0.12.1:\n  Successfully uninstalled tokenizers-0.12.1\n","output_type":"stream"},{"name":"stderr","text":"WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\n","output_type":"stream"},{"name":"stdout","text":"Looking in links: ../input/fb3-my-pip-wheels\nProcessing /kaggle/input/fb3-my-pip-wheels/transformers-4.20.1-py3-none-any.whl\nRequirement already satisfied: tqdm>=4.27 in /opt/conda/lib/python3.7/site-packages (from transformers) (4.64.0)\nRequirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.7/site-packages (from transformers) (2021.11.10)\nRequirement already satisfied: filelock in /opt/conda/lib/python3.7/site-packages (from transformers) (3.7.1)\nProcessing /kaggle/input/fb3-my-pip-wheels/tokenizers-0.12.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl\nRequirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.7/site-packages (from transformers) (1.21.6)\nRequirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.7/site-packages (from transformers) (6.0)\nRequirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.7/site-packages (from transformers) (21.3)\nRequirement already satisfied: importlib-metadata in /opt/conda/lib/python3.7/site-packages (from transformers) (4.13.0)\nRequirement already satisfied: huggingface-hub<1.0,>=0.1.0 in /opt/conda/lib/python3.7/site-packages (from transformers) (0.10.1)\nRequirement already satisfied: requests in /opt/conda/lib/python3.7/site-packages (from transformers) (2.28.1)\nRequirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.7/site-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (4.1.1)\nRequirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.7/site-packages (from packaging>=20.0->transformers) (3.0.9)\nRequirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata->transformers) (3.8.0)\nRequirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests->transformers) (3.3)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests->transformers) (2022.9.24)\nRequirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.7/site-packages (from requests->transformers) (2.1.0)\nRequirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests->transformers) (1.26.12)\nInstalling collected packages: tokenizers, transformers\nSuccessfully installed tokenizers-0.12.1 transformers-4.20.1\n","output_type":"stream"},{"name":"stderr","text":"WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\n","output_type":"stream"},{"name":"stdout","text":"Looking in links: ../input/fb3-my-pip-wheels\nRequirement already satisfied: tokenizers in /opt/conda/lib/python3.7/site-packages (0.12.1)\n","output_type":"stream"},{"name":"stderr","text":"WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\n","output_type":"stream"},{"name":"stdout","text":"transformers.__version__: 4.20.1\ntokenizers.__version__: 0.12.1\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Config","metadata":{"papermill":{"duration":0.008559,"end_time":"2022-11-14T19:29:42.971642","exception":false,"start_time":"2022-11-14T19:29:42.963083","status":"completed"},"tags":[]}},{"cell_type":"code","source":"class Config(dict):\n    __getattr__ = dict.__getitem__\n    __setattr__ = dict.__setitem__\n    __delattr__ = dict.__delitem__\n    \n    def init(self, kwargs):\n        super().init(kwargs)\n\n        for k, v in kwargs.items():\n            setattr(self, k, v)\n\n    def set(self, key, val):\n        self[key] = val\n        setattr(self, key, val)","metadata":{"papermill":{"duration":0.01913,"end_time":"2022-11-14T19:29:42.998966","exception":false,"start_time":"2022-11-14T19:29:42.979836","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-11-21T17:37:39.595983Z","iopub.execute_input":"2022-11-21T17:37:39.596628Z","iopub.status.idle":"2022-11-21T17:37:39.603723Z","shell.execute_reply.started":"2022-11-21T17:37:39.596583Z","shell.execute_reply":"2022-11-21T17:37:39.602791Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"CFG = Config(\n    str_now = datetime.datetime.now().strftime('%Y%m%d-%H%M%S'),\n    train = True,\n    debug = False,\n    offline = False,\n    \n    save_all_models = False,\n    competition = 'FB3',\n    apex = True,\n    print_freq = 20,\n    num_workers = 0,\n    model = 'roberta-base', #If you want to train on the kaggle platform, v3-base is realistic. v3-large will time out.\n    loss_func = 'SmoothL1', # 'SmoothL1', 'RMSE'\n    max_len = 512,\n    \n    epochs = 3,\n    n_fold = 4,\n    trn_fold = (0,1,2,3),\n    batch_size = 8,\n    \n    gradient_checkpointing = True,\n    scheduler = 'cosine',\n    batch_scheduler = True,\n    num_cycles = 0.5,\n    num_warmup_steps = 0,\n    encoder_lr = 2e-5,\n    decoder_lr = 2e-5,\n    min_lr = 1e-6,\n    \n    # llrd\n    llrd = True,\n    layerwise_lr = 1e-5,\n    layerwise_lr_decay = 0.9,\n    layerwise_weight_decay = 0.01,\n    layerwise_adam_epsilon = 1e-6,\n    layerwise_use_bertadam = False,\n    weight_decay = 0.01,\n    eps = 1e-6,\n    betas = (0.9, 0.999),\n    \n    # pooling\n    pooling = 'attention4', # mean, max, min, attention, weightedlayer\n    layer_start = 4,\n    \n    # weight init\n    init_weight = 'normal', # normal, xavier_uniform, xavier_normal, kaiming_uniform, kaiming_normal, orthogonal\n    \n    # reinit layers\n    reinit = True,\n    reinit_n = 1,\n    \n    # adversarial\n    fgm = True,\n    awp = False,\n    adv_lr = 1,\n    adv_eps = 0.2,\n    \n    # gradient\n    unscale = False,\n    gradient_accumulation_steps = 1,\n    max_grad_norm = 1000,\n    target_cols = ['cohesion', 'syntax', 'vocabulary', 'phraseology', 'grammar', 'conventions'],\n    \n    seed = 42,\n    cv_seed = 42,\n    n_targets = 6,\n    \n    #gpu_id = 0,\n    #device = f'cuda:{gpu_id}',\n    device = str(torch.device('cuda' if torch.cuda.is_available() else 'cpu')),\n    train_file = '../input/feedback-prize-english-language-learning/train.csv',\n    test_file = '../input/feedback-prize-english-language-learning/test.csv',\n    submission_file = '../input/feedback-prize-english-language-learning/sample_submission.csv')","metadata":{"papermill":{"duration":0.09556,"end_time":"2022-11-14T19:29:43.102809","exception":false,"start_time":"2022-11-14T19:29:43.007249","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-11-21T17:37:39.605433Z","iopub.execute_input":"2022-11-21T17:37:39.606075Z","iopub.status.idle":"2022-11-21T17:37:39.686247Z","shell.execute_reply.started":"2022-11-21T17:37:39.606040Z","shell.execute_reply":"2022-11-21T17:37:39.685064Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"#Unique model name\nif len(CFG.model.split(\"/\")) == 2:\n    CFG.identifier = f'{CFG.str_now}-{CFG.model.split(\"/\")[1]}'\nelse:\n    CFG.identifier = f'{CFG.str_now}-{CFG.model}'\n    \nprint(CFG.identifier)","metadata":{"papermill":{"duration":0.01903,"end_time":"2022-11-14T19:29:43.130152","exception":false,"start_time":"2022-11-14T19:29:43.111122","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-11-21T17:37:39.689544Z","iopub.execute_input":"2022-11-21T17:37:39.691621Z","iopub.status.idle":"2022-11-21T17:37:39.701525Z","shell.execute_reply.started":"2022-11-21T17:37:39.691585Z","shell.execute_reply":"2022-11-21T17:37:39.700519Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"20221121-173739-roberta-base\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Data splits","metadata":{"papermill":{"duration":0.008334,"end_time":"2022-11-14T19:29:43.146820","exception":false,"start_time":"2022-11-14T19:29:43.138486","status":"completed"},"tags":[]}},{"cell_type":"code","source":"CFG.output_dir = f'./{CFG.identifier}/'\nCFG.log_filename = CFG.output_dir + 'train'\n\nos.makedirs(CFG.output_dir, exist_ok=True)    \n\ncfg_json = json.dumps(CFG)\nwith open(os.path.join(CFG.output_dir, 'CFG.json'), 'w') as f:\n    f.write(cfg_json)","metadata":{"papermill":{"duration":0.021442,"end_time":"2022-11-14T19:29:43.176430","exception":false,"start_time":"2022-11-14T19:29:43.154988","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-11-21T17:37:39.702713Z","iopub.execute_input":"2022-11-21T17:37:39.703062Z","iopub.status.idle":"2022-11-21T17:37:39.712333Z","shell.execute_reply.started":"2022-11-21T17:37:39.703026Z","shell.execute_reply":"2022-11-21T17:37:39.711440Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"CFG.df_train = pd.read_csv(CFG.train_file)\n\nmskfold = MultilabelStratifiedKFold(n_splits = CFG.n_fold, shuffle = True, random_state = CFG.cv_seed)\nfor n, (train_index, val_index) in enumerate(mskfold.split(CFG.df_train, CFG.df_train[CFG.target_cols])):\n    CFG.df_train.loc[val_index, 'fold'] = int(n)\nCFG.df_train['fold'] = CFG.df_train['fold'].astype(int)\n\nif CFG.debug:\n    CFG.epochs = 2\n    CFG.trn_fold = [0]\n    if CFG.train:\n        CFG.df_train = CFG.df_train.sample(n = 100, random_state = CFG.seed).reset_index(drop=True)","metadata":{"papermill":{"duration":0.400875,"end_time":"2022-11-14T19:29:43.585603","exception":false,"start_time":"2022-11-14T19:29:43.184728","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-11-21T17:37:39.715543Z","iopub.execute_input":"2022-11-21T17:37:39.715824Z","iopub.status.idle":"2022-11-21T17:37:40.063406Z","shell.execute_reply.started":"2022-11-21T17:37:39.715796Z","shell.execute_reply":"2022-11-21T17:37:40.062435Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"markdown","source":"# Helper function","metadata":{"papermill":{"duration":0.008728,"end_time":"2022-11-14T19:29:43.603900","exception":false,"start_time":"2022-11-14T19:29:43.595172","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def mcrmse(y_trues, y_preds):\n    scores = []\n    idxes = y_trues.shape[1]\n    for i in range(idxes):\n        y_true = y_trues[:, i]\n        y_pred = y_preds[:, i]\n        score = mean_squared_error(y_true, y_pred, squared = False)\n        scores.append(score)\n    mcrmse_score = np.mean(scores)\n    return mcrmse_score, scores\n\ndef get_score(y_trues, y_preds):\n    mcrmse_score, scores = mcrmse(y_trues, y_preds)\n    return mcrmse_score, scores\n\ndef get_logger(filename = CFG.log_filename):\n    from logging import getLogger, INFO, StreamHandler, FileHandler, Formatter\n    logger = getLogger(__name__)\n    logger.setLevel(INFO)\n    handler1 = StreamHandler()\n    handler1.setFormatter(Formatter('%(message)s'))\n    handler2 = FileHandler(filename = f'{filename}.log')\n    handler2.setFormatter(Formatter('%(message)s'))\n    logger.addHandler(handler1)\n    logger.addHandler(handler2)\n    return logger\n\ndef prepare_input(cfg, text):\n    inputs = cfg.tokenizer.encode_plus(\n        text,\n        return_tensors = None,\n        add_special_tokens = True,\n        max_length = cfg.max_len,\n        pad_to_max_length = True,\n        truncation = True\n    )\n    for k, v in inputs.items():\n        inputs[k] = torch.tensor(v, dtype = torch.long)\n    return inputs    \n\ndef collate(inputs):\n    mask_len = int(inputs['attention_mask'].sum(axis = 1).max())\n        \n    for k, v in inputs.items():\n        inputs[k] = inputs[k][:, :mask_len]\n    return inputs\n\nclass AverageMeter(object):\n    def __init__(self):\n        self.reset()\n\n    def reset(self):\n        self.val = 0\n        self.avg = 0\n        self.sum = 0\n        self.count = 0\n        self.history = []\n\n    def update(self, val, n=1):\n        self.val = val\n        self.history.append(val)\n        self.sum += val * n\n        self.count += n\n        self.avg = self.sum / self.count\n\ndef asMinutes(s):\n    m = math.floor(s / 60)\n    s -= m * 60\n    return f'{str(int(m)).zfill(2)}:{str(int(s)).zfill(2)}'\n\ndef timeSince(since, percent):\n    now = time.time()\n    s = now - since\n    es = s / (percent)\n    rs = es - s\n    return f'{str(asMinutes(s))}<{str(asMinutes(rs))}'\n\ndef 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    torch.backends.cudnn.benchmark = False\n    \nclass RMSELoss(nn.Module):\n    def __init__(self, reduction = 'mean', eps = 1e-9):\n        super().__init__()\n        self.mse = nn.MSELoss(reduction = 'none')\n        self.reduction = reduction\n        self.eps = eps\n        \n    def forward(self, y_pred, y_true):\n        loss = torch.sqrt(self.mse(y_pred, y_true) + self.eps)\n        if self.reduction == 'none':\n            loss = loss\n        elif self.reduction == 'sum':\n            loss = loss.sum()\n        elif self.reduction == 'mean':\n            loss = loss.mean()\n        return loss\n    \nseed_everything(CFG.seed)","metadata":{"papermill":{"duration":0.03552,"end_time":"2022-11-14T19:29:43.648022","exception":false,"start_time":"2022-11-14T19:29:43.612502","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-11-21T17:37:40.065092Z","iopub.execute_input":"2022-11-21T17:37:40.065511Z","iopub.status.idle":"2022-11-21T17:37:40.089214Z","shell.execute_reply.started":"2022-11-21T17:37:40.065472Z","shell.execute_reply":"2022-11-21T17:37:40.088206Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"markdown","source":"# Pooling","metadata":{"papermill":{"duration":0.00838,"end_time":"2022-11-14T19:29:43.664794","exception":false,"start_time":"2022-11-14T19:29:43.656414","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"# Fast Gradient Method (FGM)\nReference :\n\nhttps://www.kaggle.com/c/tweet-sentiment-extraction/discussion/143764","metadata":{"papermill":{"duration":0.008107,"end_time":"2022-11-14T19:29:43.719628","exception":false,"start_time":"2022-11-14T19:29:43.711521","status":"completed"},"tags":[]}},{"cell_type":"code","source":"class FGM():\n    def __init__(self, model):\n        self.model = model\n        self.backup = {}\n\n    def attack(self, epsilon = 1., emb_name = 'word_embeddings'):\n        for name, param in self.model.named_parameters():\n            if param.requires_grad and emb_name in name:\n                self.backup[name] = param.data.clone()\n                norm = torch.norm(param.grad)\n                if norm != 0:\n                    r_at = epsilon * param.grad / norm\n                    param.data.add_(r_at)\n\n    def restore(self, emb_name = 'word_embeddings'):\n        for name, param in self.model.named_parameters():\n            if param.requires_grad and emb_name in name:\n                assert name in self.backup\n                param.data = self.backup[name]\n            self.backup = {}\n","metadata":{"papermill":{"duration":0.020405,"end_time":"2022-11-14T19:29:43.748293","exception":false,"start_time":"2022-11-14T19:29:43.727888","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-11-21T17:37:40.090750Z","iopub.execute_input":"2022-11-21T17:37:40.091225Z","iopub.status.idle":"2022-11-21T17:37:40.102078Z","shell.execute_reply.started":"2022-11-21T17:37:40.091190Z","shell.execute_reply":"2022-11-21T17:37:40.101173Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"markdown","source":"# Adversarial Weight Perturbation (AWP)\nThere may be a bug in my implementation because it does not work well.\n\nReference : \n\nhttps://www.kaggle.com/code/wht1996/feedback-nn-train/notebook","metadata":{"papermill":{"duration":0.008443,"end_time":"2022-11-14T19:29:43.765735","exception":false,"start_time":"2022-11-14T19:29:43.757292","status":"completed"},"tags":[]}},{"cell_type":"code","source":"class AWP:\n    def __init__(\n        self,\n        model,\n        optimizer,\n        adv_param=\"weight\",\n        adv_lr=1,\n        adv_eps=0.2,\n        start_epoch=0,\n        adv_step=1,\n        scaler=None\n    ):\n        self.model = model\n        self.optimizer = optimizer\n        self.adv_param = adv_param\n        self.adv_lr = adv_lr\n        self.adv_eps = adv_eps\n        self.start_epoch = start_epoch\n        self.adv_step = adv_step\n        self.backup = {}\n        self.backup_eps = {}\n        self.scaler = scaler\n        \n    def attack_backward(self, x, y, attention_mask,epoch):\n        if (self.adv_lr == 0) or (epoch < self.start_epoch):\n            return None\n\n        self._save() \n        for i in range(self.adv_step):\n            self._attack_step() \n            with torch.cuda.amp.autocast():\n                adv_loss, tr_logits = self.model(input_ids=x, attention_mask=attention_mask, labels=y)\n                adv_loss = adv_loss.mean()\n            self.optimizer.zero_grad()\n            self.scaler.scale(adv_loss).backward()\n            \n        self._restore()\n        \n    def _attack_step(self):\n        e = 1e-6\n        for name, param in self.model.named_parameters():\n            if param.requires_grad and param.grad is not None and self.adv_param in name:\n                norm1 = torch.norm(param.grad)\n                norm2 = torch.norm(param.data.detach())\n                if norm1 != 0 and not torch.isnan(norm1):\n                    r_at = self.adv_lr * param.grad / (norm1 + e) * (norm2 + e)\n                    param.data.add_(r_at)\n                    param.data = torch.min(\n                        torch.max(param.data, self.backup_eps[name][0]), self.backup_eps[name][1]\n                    )\n                # param.data.clamp_(*self.backup_eps[name])\n\n    def _save(self):\n        for name, param in self.model.named_parameters():\n            if param.requires_grad and param.grad is not None and self.adv_param in name:\n                if name not in self.backup:\n                    self.backup[name] = param.data.clone()\n                    grad_eps = self.adv_eps * param.abs().detach()\n                    self.backup_eps[name] = (\n                        self.backup[name] - grad_eps,\n                        self.backup[name] + grad_eps,\n                    )\n\n    def _restore(self,):\n        for name, param in self.model.named_parameters():\n            if name in self.backup:\n                param.data = self.backup[name]\n        self.backup = {}\n        self.backup_eps = {}","metadata":{"papermill":{"duration":0.028282,"end_time":"2022-11-14T19:29:43.802588","exception":false,"start_time":"2022-11-14T19:29:43.774306","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-11-21T17:37:40.105260Z","iopub.execute_input":"2022-11-21T17:37:40.105528Z","iopub.status.idle":"2022-11-21T17:37:40.122742Z","shell.execute_reply.started":"2022-11-21T17:37:40.105505Z","shell.execute_reply":"2022-11-21T17:37:40.121769Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"markdown","source":"# Train function","metadata":{"papermill":{"duration":0.008381,"end_time":"2022-11-14T19:29:43.820006","exception":false,"start_time":"2022-11-14T19:29:43.811625","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def train_one_epoch(fold, train_loader, model, criterion, optimizer, epoch, scheduler, device):\n    losses = AverageMeter()\n    model.train()\n    scaler = torch.cuda.amp.GradScaler(enabled = CFG.apex)\n    start = end = time.time()\n    global_step = 0\n    \n    if CFG.fgm:\n        fgm = FGM(model)\n    if CFG.awp:\n        awp = AWP(\n            model,\n            optimizer,\n            adv_lr = CFG.adv_lr, \n            adv_eps = CFG.adv_eps, \n            scaler = scaler)\n        \n    for step, (inputs, labels) in enumerate(train_loader):\n        attention_mask = inputs['attention_mask'].to(device)\n          #inputs = collate(inputs)\n        for k, v in inputs.items():\n            inputs[k] = v.to(device)\n        labels = labels.to(device)\n        batch_size = labels.size(0)\n        \n        with torch.cuda.amp.autocast(enabled = CFG.apex):\n            y_preds = model(inputs)\n            loss = criterion(y_preds, labels)\n        if CFG.gradient_accumulation_steps > 1:\n            loss = loss / CFG.gradient_accumulation_steps\n        losses.update(loss.item(), batch_size)\n        scaler.scale(loss).backward()\n        \n        #Fast Gradient Method (FGM)\n        if CFG.fgm:\n            fgm.attack()\n            with torch.cuda.amp.autocast(enabled = CFG.apex):\n                y_preds = model(inputs)\n                loss_adv = criterion(y_preds, labels)\n                loss_adv.backward()\n            fgm.restore()\n            \n        #Adversarial Weight Perturbation (AWP)\n        if CFG.awp:\n            loss_awp = awp.attack_backward(inputs, labels, attention_mask, step + 1)\n            loss_awp.backward()\n            awp._restore()\n        \n        if (step + 1) % CFG.gradient_accumulation_steps == 0:\n            if CFG.unscale:\n                scaler.unscale_(optimizer)\n            grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), CFG.max_grad_norm)\n            scaler.step(optimizer)\n            scaler.update()\n            optimizer.zero_grad()\n            global_step += 1\n            if CFG.batch_scheduler:\n                scheduler.step()\n        end = time.time()\n        if step % CFG.print_freq == 0 or step == (len(train_loader) - 1):\n            print('>>> [TRAIN] Epoch {0}: {1}/{2} [{remain:s}]  loss:{loss.val:.4f}({loss.avg:.4f});    grad:{grad_norm:.4f};    lr:{lr:.8f}'.format(\n                epoch+1, \n                step,\n                len(train_loader),\n                remain = timeSince(start, float(step + 1)/len(train_loader)),\n                loss = losses,\n                grad_norm = grad_norm,\n                lr = scheduler.get_lr()[0]))\n        del inputs, labels, y_preds; gc.collect(); torch.cuda.empty_cache();\n        \n        #print(torch.cuda.memory_allocated())\n    \n    ax = pd.DataFrame(losses.history, columns=['train_loss']).plot()\n    ax.set_title(f'fold{fold}  epoch{epoch}')\n    plt.show()\n    \n    return losses","metadata":{"papermill":{"duration":0.028737,"end_time":"2022-11-14T19:29:43.857579","exception":false,"start_time":"2022-11-14T19:29:43.828842","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-11-21T17:37:40.127040Z","iopub.execute_input":"2022-11-21T17:37:40.127343Z","iopub.status.idle":"2022-11-21T17:37:40.143458Z","shell.execute_reply.started":"2022-11-21T17:37:40.127316Z","shell.execute_reply":"2022-11-21T17:37:40.142562Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"markdown","source":"# Valid function","metadata":{"papermill":{"duration":0.008153,"end_time":"2022-11-14T19:29:43.875210","exception":false,"start_time":"2022-11-14T19:29:43.867057","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def validate(valid_loader, model, criterion, device):\n    losses = AverageMeter()\n    model.eval()\n    preds = []\n    start = end = time.time()\n    for step, (inputs, labels) in enumerate(valid_loader):\n        inputs = collate(inputs)\n        for k, v in inputs.items():\n            inputs[k] = v.to(device)\n        labels = labels.to(device)\n        batch_size = labels.size(0)\n        \n        with torch.no_grad():\n            y_preds = model(inputs)\n            loss = criterion(y_preds, labels)\n        if CFG.gradient_accumulation_steps > 1:\n            loss = loss / CFG.gradient_accumulation_steps\n            \n        losses.update(loss.item(), batch_size)\n        preds.append(y_preds.to('cpu').numpy())\n        \n        end = time.time()\n        if step % CFG.print_freq == 0 or step == (len(valid_loader) - 1):\n            print('>>> [EVAL] {0}/{1} [{remain:s}]   loss: {loss.val:.4f}({loss.avg:.4f})'.format(\n                step+1, \n                len(valid_loader),\n                loss = losses,\n                remain = timeSince(start, float(step + 1) / len(valid_loader))))\n    return losses.avg, np.concatenate(preds)","metadata":{"papermill":{"duration":0.023702,"end_time":"2022-11-14T19:29:43.907590","exception":false,"start_time":"2022-11-14T19:29:43.883888","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-11-21T17:37:40.144890Z","iopub.execute_input":"2022-11-21T17:37:40.145525Z","iopub.status.idle":"2022-11-21T17:37:40.156912Z","shell.execute_reply.started":"2022-11-21T17:37:40.145491Z","shell.execute_reply":"2022-11-21T17:37:40.155925Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"markdown","source":"# Plot metrics","metadata":{"papermill":{"duration":0.008643,"end_time":"2022-11-14T19:29:43.925253","exception":false,"start_time":"2022-11-14T19:29:43.916610","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def plot_metrics(df):\n    f, ax = plt.subplots(1, 2, figsize=(20, 5))\n    ax = ax.ravel()\n    \n    df[['val_loss', 'train_loss']].plot(ax=ax[0])\n    df[['mcrmse'] + CFG.target_cols].plot(ax=ax[1])\n\n    plt.show()\n    return f","metadata":{"papermill":{"duration":0.019614,"end_time":"2022-11-14T19:29:43.953700","exception":false,"start_time":"2022-11-14T19:29:43.934086","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-11-21T17:37:40.158730Z","iopub.execute_input":"2022-11-21T17:37:40.159178Z","iopub.status.idle":"2022-11-21T17:37:40.170273Z","shell.execute_reply.started":"2022-11-21T17:37:40.159134Z","shell.execute_reply":"2022-11-21T17:37:40.169204Z"},"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"markdown","source":"# Logger","metadata":{"papermill":{"duration":0.00895,"end_time":"2022-11-14T19:29:43.971770","exception":false,"start_time":"2022-11-14T19:29:43.962820","status":"completed"},"tags":[]}},{"cell_type":"code","source":"logger = get_logger()\nlogger.info(f'output_dir: {CFG.output_dir}')","metadata":{"papermill":{"duration":0.021368,"end_time":"2022-11-14T19:29:44.001989","exception":false,"start_time":"2022-11-14T19:29:43.980621","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-11-21T17:37:40.172009Z","iopub.execute_input":"2022-11-21T17:37:40.172416Z","iopub.status.idle":"2022-11-21T17:37:40.182146Z","shell.execute_reply.started":"2022-11-21T17:37:40.172366Z","shell.execute_reply":"2022-11-21T17:37:40.181339Z"},"trusted":true},"execution_count":13,"outputs":[{"name":"stderr","text":"output_dir: ./20221121-173739-roberta-base/\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Tokenizer","metadata":{"papermill":{"duration":0.008656,"end_time":"2022-11-14T19:29:44.019563","exception":false,"start_time":"2022-11-14T19:29:44.010907","status":"completed"},"tags":[]}},{"cell_type":"code","source":"CFG.tokenizer = AutoTokenizer.from_pretrained(CFG.model)\nCFG.tokenizer.save_pretrained(CFG.output_dir + 'tokenizer')\n\n#max_len\nlengths = []\ntk0 = tqdm(CFG.df_train['full_text'].fillna('').values, total = len(CFG.df_train))\nfor text in tk0:\n    length = len(CFG.tokenizer(text, add_special_tokens = False)['input_ids'])\n    lengths.append(length)\n# CFG.max_len = max(lengths) + 2\nCFG.max_len = 512\nlogger.info(f'max_len: {CFG.max_len}')","metadata":{"papermill":{"duration":11.374799,"end_time":"2022-11-14T19:29:55.403347","exception":false,"start_time":"2022-11-14T19:29:44.028548","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-11-21T17:37:40.183828Z","iopub.execute_input":"2022-11-21T17:37:40.184581Z","iopub.status.idle":"2022-11-21T17:37:51.757136Z","shell.execute_reply.started":"2022-11-21T17:37:40.184540Z","shell.execute_reply":"2022-11-21T17:37:51.756067Z"},"trusted":true},"execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/plain":"Downloading:   0%|          | 0.00/481 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"aa298277f5b84db0932eb53898ef60e0"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading:   0%|          | 0.00/878k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"91d56221a5c348fca067097766358376"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading:   0%|          | 0.00/446k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1fa8a802c8f74c7fbe0e2c38b1eb1391"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading:   0%|          | 0.00/1.29M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d1525c5ac00e4f83ba638bdcb8d8b296"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"  0%|          | 0/3911 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1fda7a3db2414b4f86337738e1454f1f"}},"metadata":{}},{"name":"stderr","text":"Token indices sequence length is longer than the specified maximum sequence length for this model (569 > 512). Running this sequence through the model will result in indexing errors\nmax_len: 512\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Dataset","metadata":{"papermill":{"duration":0.009442,"end_time":"2022-11-14T19:29:55.424410","exception":false,"start_time":"2022-11-14T19:29:55.414968","status":"completed"},"tags":[]}},{"cell_type":"code","source":"class FB3TrainDataset(Dataset):\n    def __init__(self, cfg, df):\n        self.cfg = cfg\n        self.texts = df['full_text'].values\n        self.labels = df[cfg.target_cols].values\n        \n    def __len__(self):\n        return len(self.texts)\n    \n    def __getitem__(self, item):\n        inputs = prepare_input(self.cfg, self.texts[item])\n        label = torch.tensor(self.labels[item], dtype = torch.float)\n        return inputs, label","metadata":{"papermill":{"duration":0.02095,"end_time":"2022-11-14T19:29:55.455902","exception":false,"start_time":"2022-11-14T19:29:55.434952","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-11-21T17:37:51.759071Z","iopub.execute_input":"2022-11-21T17:37:51.759755Z","iopub.status.idle":"2022-11-21T17:37:51.767199Z","shell.execute_reply.started":"2022-11-21T17:37:51.759717Z","shell.execute_reply":"2022-11-21T17:37:51.766081Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"markdown","source":"# Model\n\n* Initializing module (normal, xavier_uniform, xavier_normal, kaiming_uniform, kaiming_normal, orthogonal) \n* Freeze lower layer when you use large model (v2-xlarge, funnnel, etc.)","metadata":{"papermill":{"duration":0.009116,"end_time":"2022-11-14T19:29:55.474948","exception":false,"start_time":"2022-11-14T19:29:55.465832","status":"completed"},"tags":[]}},{"cell_type":"code","source":"class MeanPooling(nn.Module):\n    def __init__(self):\n        super(MeanPooling, self).__init__()\n        \n    def forward(self, last_hidden_state, attention_mask):\n        input_mask_expanded = attention_mask.unsqueeze(-1).expand(last_hidden_state.size()).float()\n        sum_embeddings = torch.sum(last_hidden_state * input_mask_expanded, 1)\n        sum_mask = input_mask_expanded.sum(1)\n        sum_mask = torch.clamp(sum_mask, min = 1e-9)\n        mean_embeddings = sum_embeddings/sum_mask\n        return mean_embeddings\n\nclass MaxPooling(nn.Module):\n    def __init__(self):\n        super(MaxPooling, self).__init__()\n        \n    def forward(self, last_hidden_state, attention_mask):\n        input_mask_expanded = attention_mask.unsqueeze(-1).expand(last_hidden_state.size()).float()\n        embeddings = last_hidden_state.clone()\n        embeddings[input_mask_expanded == 0] = -1e4\n        max_embeddings, _ = torch.max(embeddings, dim = 1)\n        return max_embeddings\n    \nclass MinPooling(nn.Module):\n    def __init__(self):\n        super(MinPooling, self).__init__()\n        \n    def forward(self, last_hidden_state, attention_mask):\n        input_mask_expanded = attention_mask.unsqueeze(-1).expand(last_hidden_state.size()).float()\n        embeddings = last_hidden_state.clone()\n        embeddings[input_mask_expanded == 0] = 1e-4\n        min_embeddings, _ = torch.min(embeddings, dim = 1)\n        return min_embeddings\n\n#Attention pooling\nclass AttentionPooling(nn.Module):\n    def __init__(self, in_dim):\n        super().__init__()\n        self.attention = nn.Sequential(\n        nn.Linear(in_dim, in_dim),\n        nn.LayerNorm(in_dim),\n        nn.GELU(),\n        nn.Linear(in_dim, 1),\n        )\n\n    def forward(self, last_hidden_state, attention_mask):\n        w = self.attention(last_hidden_state).float()\n        w[attention_mask==0]=float('-inf')\n        w = torch.softmax(w,1)\n        attention_embeddings = torch.sum(w * last_hidden_state, dim=1)\n        return attention_embeddings\n    \nclass AttentionHead(nn.Module):\n    def __init__(self, h_size, hidden_dim=512):\n        super().__init__()\n        self.W = nn.Linear(h_size, hidden_dim)\n        self.V = nn.Linear(hidden_dim, 1)\n        \n    def forward(self, features):\n        att = torch.tanh(self.W(features))\n        score = self.V(att)\n        attention_weights = torch.softmax(score, dim=1)\n        context_vector = attention_weights * features\n        context_vector = torch.sum(context_vector, dim=1)\n\n        return context_vector\nclass WeightedLayerPooling(nn.Module):\n    def __init__(self, num_hidden_layers, layer_start: int = 4, layer_weights = None):\n        super(WeightedLayerPooling, self).__init__()\n        self.layer_start = layer_start\n        self.num_hidden_layers = num_hidden_layers\n        self.layer_weights = layer_weights if layer_weights is not None \\\n            else nn.Parameter(\n                torch.tensor([1] * (num_hidden_layers+1 - layer_start), dtype=torch.float)\n            )\n\n    def forward(self, ft_all_layers):\n        all_layer_embedding = torch.stack(ft_all_layers)\n        all_layer_embedding = all_layer_embedding[self.layer_start:, :, :, :]\n\n        weight_factor = self.layer_weights.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).expand(all_layer_embedding.size())\n        weighted_average = (weight_factor*all_layer_embedding).sum(dim=0) / self.layer_weights.sum()\n\n        return weighted_average","metadata":{"papermill":{"duration":0.029713,"end_time":"2022-11-14T19:29:43.702465","exception":false,"start_time":"2022-11-14T19:29:43.672752","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-11-21T17:37:51.769051Z","iopub.execute_input":"2022-11-21T17:37:51.769746Z","iopub.status.idle":"2022-11-21T17:37:51.789991Z","shell.execute_reply.started":"2022-11-21T17:37:51.769711Z","shell.execute_reply":"2022-11-21T17:37:51.789357Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"code","source":"class FB3Model(nn.Module):\n    def __init__(self, CFG, config_path = None, pretrained = False):\n        super().__init__()\n        self.CFG = CFG\n        if config_path is None:\n            self.config = AutoConfig.from_pretrained(CFG.model, output_hidden_states = True)\n            self.config.save_pretrained(CFG.output_dir + 'config')\n            self.config.hidden_dropout = 0.\n            self.config.hidden_dropout_prob = 0.\n            self.config.attention_dropout = 0.\n            self.config.attention_probs_dropout_prob = 0.\n            self.config.layer_norm_eps = 1e-7\n        else:\n            self.config = torch.load(config_path)\n            \n        logger.info(self.config)\n        \n        if pretrained:\n            self.model = AutoModel.from_pretrained(CFG.model, config=self.config)\n            self.model.save_pretrained(CFG.output_dir + 'model')\n        else:\n            self.model = AutoModel(self.config)\n            \n        if self.CFG.gradient_checkpointing:\n            self.model.gradient_checkpointing_enable()\n            \n        if CFG.pooling == 'mean':\n            self.pool = MeanPooling()\n        elif CFG.pooling == 'max':\n            self.pool = MaxPooling()\n        elif CFG.pooling == 'min':\n            self.pool = MinPooling()\n        elif CFG.pooling == 'attention':\n            self.pool = AttentionPooling(self.config.hidden_size)\n        elif CFG.pooling == 'attention4':\n            self.pool = AttentionHead(self.config.hidden_size*4)\n        elif CFG.pooling == 'weightedlayer':\n            self.pool = WeightedLayerPooling(self.config.num_hidden_layers, layer_start = CFG.layer_start, layer_weights = None)\n        \n        if CFG.pooling == 'attention4':\n            self.fc = nn.Linear(self.config.hidden_size*8, self.CFG.n_targets)\n        else:\n            self.fc = nn.Linear(self.config.hidden_size, self.CFG.n_targets)\n        self._init_weights(self.fc)\n        \n        if 'deberta-v2-xxlarge' in CFG.model:\n            self.model.embeddings.requires_grad_(False)\n            self.model.encoder.layer[:24].requires_grad_(False)\n        if 'deberta-v2-xlarge' in CFG.model:\n            self.model.embeddings.requires_grad_(False)\n            self.model.encoder.layer[:12].requires_grad_(False)\n        if 'funnel-transformer-xlarge' in CFG.model:\n            self.model.embeddings.requires_grad_(False)\n            self.model.encoder.blocks[:1].requires_grad_(False)\n        if 'funnel-transformer-large' in CFG.model:\n            self.model.embeddings.requires_grad_(False)\n            self.model.encoder.blocks[:1].requires_grad_(False)\n        if 'deberta-large' in CFG.model:\n            self.model.embeddings.requires_grad_(False)\n            self.model.encoder.layer[:16].requires_grad_(False)\n        if 'deberta-xlarge' in CFG.model:\n            self.model.embeddings.requires_grad_(False)\n            self.model.encoder.layer[:36].requires_grad_(False)\n            \n        \n    def _init_weights(self, module):\n        if isinstance(module, nn.Linear):\n            if CFG.init_weight == 'normal':\n                module.weight.data.normal_(mean = 0.0, std = self.config.initializer_range)\n            elif CFG.init_weight == 'xavier_uniform':\n                module.weight.data = nn.init.xavier_uniform_(module.weight.data)\n            elif CFG.init_weight == 'xavier_normal':\n                module.weight.data = nn.init.xavier_normal_(module.weight.data)\n            elif CFG.init_weight == 'kaiming_uniform':\n                module.weight.data = nn.init.kaiming_uniform_(module.weight.data)\n            elif CFG.init_weight == 'kaiming_normal':\n                module.weight.data = nn.init.kaiming_normal_(module.weight.data)\n            elif CFG.init_weight == 'orthogonal':\n                module.weight.data = nn.init.orthogonal_(module.weight.data)\n                \n            if module.bias is not None:\n                module.bias.data.zero_()\n        elif isinstance(module, nn.Embedding):\n            if CFG.init_weight == 'normal':\n                module.weight.data.normal_(mean = 0.0, std = self.config.initializer_range)\n            elif CFG.init_weight == 'xavier_uniform':\n                module.weight.data = nn.init.xavier_uniform_(module.weight.data)\n            elif CFG.init_weight == 'xavier_normal':\n                module.weight.data = nn.init.xavier_normal_(module.weight.data)\n            elif CFG.init_weight == 'kaiming_uniform':\n                module.weight.data = nn.init.kaiming_uniform_(module.weight.data)\n            elif CFG.init_weight == 'kaiming_normal':\n                module.weight.data = nn.init.kaiming_normal_(module.weight.data)\n            elif CFG.init_weight == 'orthogonal':\n                module.weight.data = nn.init.orthogonal_(module.weight.data)\n                \n            if module.padding_idx is not None:\n                module.weight.data[module.padding_idx].zero_()\n        elif isinstance(module, nn.LayerNorm):\n            module.bias.data.zero_()\n            module.weight.data.fill_(1.0)\n    \n    def feature(self, inputs):\n        outputs = self.model(input_ids=inputs['input_ids'], attention_mask=inputs['attention_mask'])\n        if CFG.pooling == 'attention4':\n            all_layer_embeddings = torch.stack(outputs.hidden_states)\n            cat_over_last_layers = torch.cat((all_layer_embeddings[-1], all_layer_embeddings[-2], all_layer_embeddings[-3], all_layer_embeddings[-4]), -1)\n            cls_pooling = cat_over_last_layers[:, 0]\n            head_logits = self.pool(cat_over_last_layers)\n            feature = torch.cat([head_logits, cls_pooling], -1)\n        elif CFG.pooling != 'weightedlayer':\n            last_hidden_states = outputs[0]\n            feature = self.pool(last_hidden_states, inputs['attention_mask'])\n        \n        else:\n            all_layer_embeddings = outputs[1]\n            feature = self.pool(all_layer_embeddings)\n            \n        return feature\n    \n    def forward(self, inputs):\n        feature = self.feature(inputs)\n        outout = self.fc(feature)\n        return outout","metadata":{"papermill":{"duration":0.039596,"end_time":"2022-11-14T19:29:55.523956","exception":false,"start_time":"2022-11-14T19:29:55.484360","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-11-21T17:37:51.792579Z","iopub.execute_input":"2022-11-21T17:37:51.793187Z","iopub.status.idle":"2022-11-21T17:37:51.820188Z","shell.execute_reply.started":"2022-11-21T17:37:51.793149Z","shell.execute_reply":"2022-11-21T17:37:51.819231Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"markdown","source":"# Train","metadata":{"papermill":{"duration":0.009759,"end_time":"2022-11-14T19:29:55.543885","exception":false,"start_time":"2022-11-14T19:29:55.534126","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def re_initializing_layer(model, config, layer_num):\n    for module in model.model.encoder.layer[-layer_num:].modules():\n        if isinstance(module, nn.Linear):\n\n            if CFG.init_weight == 'normal':\n                module.weight.data.normal_(mean=0.0, std=config.initializer_range)\n            elif CFG.init_weight == 'xavier_uniform':\n                module.weight.data = nn.init.xavier_uniform_(module.weight.data)\n            elif CFG.init_weight == 'xavier_normal':\n                module.weight.data = nn.init.xavier_normal_(module.weight.data)\n            elif CFG.init_weight == 'kaiming_uniform':\n                module.weight.data = nn.init.kaiming_uniform_(module.weight.data)\n            elif CFG.init_weight == 'kaiming_normal':\n                module.weight.data = nn.init.kaiming_normal_(module.weight.data)\n            elif CFG.init_weight == 'orthogonal':\n                module.weight.data = nn.init.orthogonal_(module.weight.data) \n                \n            if module.bias is not None:\n                module.bias.data.zero_()\n        elif isinstance(module, nn.Embedding):\n            if CFG.init_weight == 'normal':\n                module.weight.data.normal_(mean=0.0, std=config.initializer_range)\n            elif CFG.init_weight == 'xavier_uniform':\n                module.weight.data = nn.init.xavier_uniform_(module.weight.data)\n            elif CFG.init_weight == 'xavier_normal':\n                module.weight.data = nn.init.xavier_normal_(module.weight.data)\n            elif CFG.init_weight == 'kaiming_uniform':\n                module.weight.data = nn.init.kaiming_uniform_(module.weight.data)\n            elif CFG.init_weight == 'kaiming_normal':\n                module.weight.data = nn.init.kaiming_normal_(module.weight.data)\n            elif CFG.init_weight == 'orthogonal':\n                module.weight.data = nn.init.orthogonal_(module.weight.data)\n                \n            if module.padding_idx is not None:\n                module.weight.data[module.padding_idx].zero_()\n        elif isinstance(module, nn.LayerNorm):\n            module.bias.data.zero_()\n            module.weight.data.fill_(1.0)\n    return model   \n\ndef train_loop(folds, fold):\n    logger.info(f\"========== fold: {fold} training ==========\")\n    \n    train_folds = folds[folds['fold'] != fold].reset_index(drop = True)\n    valid_folds = folds[folds['fold'] == fold].reset_index(drop = True)\n    valid_labels = valid_folds[CFG.target_cols].values\n    \n    train_dataset = FB3TrainDataset(CFG, train_folds)\n    valid_dataset = FB3TrainDataset(CFG, valid_folds)\n    \n    train_loader = DataLoader(train_dataset,\n                              batch_size = CFG.batch_size,\n                              shuffle = True, \n                              num_workers = CFG.num_workers,\n                              pin_memory = True, \n                              drop_last = True\n                             )\n    valid_loader = DataLoader(valid_dataset,\n                              batch_size = CFG.batch_size * 2,\n                              shuffle=False,\n                              num_workers=CFG.num_workers,\n                              pin_memory=True, \n                              drop_last=False)\n\n    model = FB3Model(CFG, config_path = None, pretrained = True)\n    if CFG.reinit:\n        model = re_initializing_layer(model, model.config, CFG.reinit_n)\n        \n    #os.makedirs(CFG.output_dir + 'config/', exist_ok = True)\n    #torch.save(model.config, CFG.output_dir + 'config/config.pth')\n    model.to(CFG.device)\n    \n    def get_optimizer_params(model,\n                             encoder_lr,\n                             decoder_lr,\n                             weight_decay=0.0):\n        param_optimizer = list(model.named_parameters())\n        no_decay = [\"bias\", \"LayerNorm.bias\", \"LayerNorm.weight\"]\n        optimizer_parameters = [\n            {'params': [p for n, p in model.model.named_parameters() if not any(nd in n for nd in no_decay)],\n             'lr': encoder_lr,\n             'weight_decay': weight_decay},\n            {'params': [p for n, p in model.model.named_parameters() if any(nd in n for nd in no_decay)],\n             'lr': encoder_lr,\n             'weight_decay': 0.0},\n            {'params': [p for n, p in model.named_parameters() if \"model\" not in n],\n             'lr': decoder_lr,\n             'weight_decay': 0.0}\n        ]\n        return optimizer_parameters\n    \n    #llrd\n    def get_optimizer_grouped_parameters(model):\n        named_parameters = list(model.named_parameters())    \n    \n        if CFG.model == 'roberta-large':\n            roberta_parameters = named_parameters[:389]    \n            attention_parameters = named_parameters[391:395]\n            regressor_parameters = named_parameters[395:]\n        elif CFG.model == 'roberta-base':\n            roberta_parameters = named_parameters[:197]    \n            attention_parameters = named_parameters[199:203]\n            regressor_parameters = named_parameters[203:]\n\n        attention_group = [params for (name, params) in attention_parameters]\n        regressor_group = [params for (name, params) in regressor_parameters]\n\n        parameters = []\n        parameters.append({\"params\": attention_group})\n        parameters.append({\"params\": regressor_group})\n        # increase lr every second layer\n        increase_lr_every_k_layer = 1\n        lrs = np.linspace(1, 5, 24 // increase_lr_every_k_layer)\n        for layer_num, (name, params) in enumerate(roberta_parameters):\n            weight_decay = 0.0 if \"bias\" in name else 0.01\n            splitted_name = name.split('.')\n            lr = CFG.layerwise_lr\n            if len(splitted_name) >= 4 and str.isdigit(splitted_name[3]):\n                layer_num = int(splitted_name[3])\n                lr = lrs[layer_num // increase_lr_every_k_layer] * CFG.layerwise_lr\n\n            parameters.append({\"params\": params,\n                               \"weight_decay\": weight_decay,\n                               \"lr\": lr})\n\n        return parameters\n    \n    if CFG.llrd:\n        from transformers import AdamW\n        grouped_optimizer_params = get_optimizer_grouped_parameters(model)\n        optimizer = AdamW(grouped_optimizer_params,\n                          lr = CFG.layerwise_lr,\n                          eps = CFG.layerwise_adam_epsilon,\n                          correct_bias = not CFG.layerwise_use_bertadam)\n    else:\n        from torch.optim import AdamW\n        optimizer_parameters = get_optimizer_params(model,\n                                                    encoder_lr=CFG.encoder_lr, \n                                                    decoder_lr=CFG.decoder_lr,\n                                                    weight_decay=CFG.weight_decay)\n        optimizer = AdamW(optimizer_parameters, \n                          lr=CFG.encoder_lr,\n                          eps=CFG.eps,\n                          betas=CFG.betas)\n    \n    def get_scheduler(cfg, optimizer, num_train_steps):\n        if cfg.scheduler == 'linear':\n            scheduler = get_linear_schedule_with_warmup(\n                optimizer, \n                num_warmup_steps = cfg.num_warmup_steps, \n                num_training_steps = num_train_steps\n            )\n        elif cfg.scheduler == 'cosine':\n            scheduler = get_cosine_schedule_with_warmup(\n                optimizer, \n                num_warmup_steps = cfg.num_warmup_steps, \n                num_training_steps = num_train_steps,\n                num_cycles = cfg.num_cycles\n            )\n        return scheduler\n    \n    num_train_steps = int(len(train_folds) / CFG.batch_size * CFG.epochs)\n    scheduler = get_scheduler(CFG, optimizer, num_train_steps)\n    \n    if CFG.loss_func == 'SmoothL1':\n        criterion = nn.SmoothL1Loss(reduction='mean')\n    elif CFG.loss_func == 'RMSE':\n        criterion = RMSELoss(reduction='mean')\n    \n    best_score = np.inf\n    best_train_loss = np.inf\n    best_val_loss = np.inf\n    \n    epoch_list = []\n    epoch_avg_loss_list = []\n    epoch_avg_val_loss_list = []\n    epoch_score_list = []\n    epoch_scores_list = []\n\n    for epoch in range(CFG.epochs):\n        start_time = time.time()\n\n        # train\n        train_loss = train_one_epoch(fold, train_loader, model, criterion, optimizer, epoch, scheduler, CFG.device)\n        avg_train_loss = train_loss.avg\n        \n        # eval\n        avg_val_loss, predictions = validate(valid_loader, model, criterion, CFG.device)\n        \n        # scoring\n        score, scores = get_score(valid_labels, predictions)\n\n        elapsed = time.time() - start_time\n        \n        logger.info('====================================================')\n        logger.info(f'>>> Epoch {epoch+1} - avg_train_loss: {avg_train_loss:.4f}  avg_val_loss: {avg_val_loss:.4f}  time: {elapsed:.0f}s')\n        logger.info(f'>>> Epoch {epoch+1} - score: {score:.4f}  scores: {scores}')\n        \n        \n        epoch_list.append(epoch+1)\n        epoch_avg_loss_list.append(avg_train_loss)\n        epoch_avg_val_loss_list.append(avg_val_loss)\n        epoch_score_list.append(score)\n        epoch_scores_list.append(scores)\n        \n        if best_score > score:\n            best_score = score\n            best_train_loss = avg_train_loss\n            best_val_loss = avg_val_loss\n            logger.info(f'>>> 🌟🌟🌟🌟🌟 new best_score: {best_score:.4f}')\n            torch.save({'model': model.state_dict(),\n                        'predictions': predictions},\n                        CFG.output_dir + f\"{CFG.model.replace('/', '-')}_fold{fold}_best.pth\")\n        logger.info('====================================================')\n        print('\\n')\n        \n        if CFG.save_all_models:\n            torch.save({'model': model.state_dict(),\n                        'predictions': predictions},\n                        CFG.output_dir + f\"{CFG.model.replace('/', '-')}_fold{fold}_epoch{epoch + 1}.pth\")\n\n    predictions = torch.load(CFG.output_dir + f\"{CFG.model.replace('/', '-')}_fold{fold}_best.pth\", \n                             map_location = torch.device('cpu'))['predictions']\n    valid_folds[[f\"pred_{c}\" for c in CFG.target_cols]] = predictions\n    \n    df_epoch = pd.DataFrame({'epoch' : epoch_list,\n                             'mcrmse' : epoch_score_list,\n                             'train_loss' : epoch_avg_loss_list, \n                             'val_loss' : epoch_avg_val_loss_list})\n    df_scores = pd.DataFrame(epoch_scores_list)\n    df_scores.columns = CFG.target_cols\n    history = pd.concat([df_epoch, df_scores], axis = 1)\n    \n    # Plots.\n    f = plot_metrics(history)\n    f.savefig(os.path.join(CFG.output_dir, f'fold{fold}_metrics.png'), dpi=300)\n    history['fold'] = fold\n    \n    f = pd.DataFrame(train_loss.history, columns=['train_loss']).plot().get_figure()\n    f.savefig(os.path.join(CFG.output_dir, f'fold{fold}_train_loss.png'), dpi=300)\n    plt.show()\n    \n    torch.cuda.empty_cache()\n    gc.collect()\n    \n    return best_train_loss, best_val_loss, valid_folds, history","metadata":{"papermill":{"duration":0.056374,"end_time":"2022-11-14T19:29:55.609969","exception":false,"start_time":"2022-11-14T19:29:55.553595","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-11-21T17:37:51.821961Z","iopub.execute_input":"2022-11-21T17:37:51.822292Z","iopub.status.idle":"2022-11-21T17:37:51.863562Z","shell.execute_reply.started":"2022-11-21T17:37:51.822260Z","shell.execute_reply":"2022-11-21T17:37:51.862655Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"code","source":"def get_result(oof_df, fold, best_train_loss, best_val_loss):\n    labels = oof_df[CFG.target_cols].values\n    preds = oof_df[[f\"pred_{c}\" for c in CFG.target_cols]].values\n    score, scores = get_score(labels, preds)\n    logger.info(f'Score: {score:<.4f}  Scores: {scores}')\n    _output_log = pd.DataFrame([CFG.identifier, CFG.model, CFG.cv_seed, CFG.seed, fold, 'best', score, best_train_loss, best_val_loss] + scores).T\n    _output_log.columns = ['file', 'model', 'cv_seed', 'seed', 'fold', 'epoch', 'mcrmse', 'train_loss', 'val_loss'] + CFG.target_cols\n    return _output_log\n\nif CFG.train:\n    output_log = pd.DataFrame()\n    oof_df = pd.DataFrame()\n    train_loss_list = []\n    val_loss_list = []\n    for fold in range(CFG.n_fold):\n        if fold in CFG.trn_fold:\n            best_train_loss, best_val_loss, _oof_df, train_history = train_loop(CFG.df_train, fold)\n            train_loss_list.append(best_train_loss)\n            val_loss_list.append(best_val_loss)\n            oof_df = pd.concat([oof_df, _oof_df])\n            logger.info(f\"========== Fold {fold} result ==========\")\n\n            train_history['file'] = CFG.identifier\n            train_history['model'] = CFG.model\n            train_history['cv_seed'] = CFG.cv_seed\n            train_history['seed'] = CFG.seed\n            train_history['fold'] = fold\n            train_history = train_history[['file', 'model', 'cv_seed', 'seed', 'fold', 'epoch', 'mcrmse', 'train_loss', 'val_loss'] + CFG.target_cols]\n\n            _output_log = get_result(_oof_df, fold, best_train_loss, best_val_loss)\n            output_log = pd.concat([output_log, train_history, _output_log])\n\n    oof_df = oof_df.reset_index(drop=True)\n    print('\\n')\n    logger.info(f\"========== Out-of-fold result ==========\")\n    \n    _output_log = get_result(oof_df, 'OOF', np.mean(train_loss_list), np.mean(val_loss_list))\n    output_log = pd.concat([output_log, _output_log])\n    \n    output_log.to_csv(os.path.join(CFG.output_dir, f'{CFG.identifier}.csv'), index=False)\n    oof_df.to_pickle(os.path.join(CFG.output_dir, 'oof_df.csv'))","metadata":{"papermill":{"duration":29221.622519,"end_time":"2022-11-15T03:36:57.242768","exception":false,"start_time":"2022-11-14T19:29:55.620249","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-11-21T17:37:51.865170Z","iopub.execute_input":"2022-11-21T17:37:51.865572Z"},"trusted":true},"execution_count":null,"outputs":[{"name":"stderr","text":"========== fold: 0 training ==========\nRobertaConfig {\n  \"_name_or_path\": \"roberta-base\",\n  \"architectures\": [\n    \"RobertaForMaskedLM\"\n  ],\n  \"attention_dropout\": 0.0,\n  \"attention_probs_dropout_prob\": 0.0,\n  \"bos_token_id\": 0,\n  \"classifier_dropout\": null,\n  \"eos_token_id\": 2,\n  \"hidden_act\": \"gelu\",\n  \"hidden_dropout\": 0.0,\n  \"hidden_dropout_prob\": 0.0,\n  \"hidden_size\": 768,\n  \"initializer_range\": 0.02,\n  \"intermediate_size\": 3072,\n  \"layer_norm_eps\": 1e-07,\n  \"max_position_embeddings\": 514,\n  \"model_type\": \"roberta\",\n  \"num_attention_heads\": 12,\n  \"num_hidden_layers\": 12,\n  \"output_hidden_states\": true,\n  \"pad_token_id\": 1,\n  \"position_embedding_type\": \"absolute\",\n  \"transformers_version\": \"4.20.1\",\n  \"type_vocab_size\": 1,\n  \"use_cache\": true,\n  \"vocab_size\": 50265\n}\n\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"Downloading:   0%|          | 0.00/478M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8444e856926d47f493af40ed71dec192"}},"metadata":{}},{"name":"stderr","text":"Some weights of the model checkpoint at roberta-base were not used when initializing RobertaModel: ['lm_head.dense.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.bias', 'lm_head.decoder.weight', 'lm_head.bias', 'lm_head.layer_norm.weight']\n- This IS expected if you are initializing RobertaModel 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 RobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n","output_type":"stream"},{"name":"stdout","text":">>> [TRAIN] Epoch 1: 0/366 [00:02<14:20]  loss:2.2251(2.2251);    grad:inf;    lr:0.00001000\n>>> [TRAIN] Epoch 1: 20/366 [00:30<08:22]  loss:0.3194(1.0548);    grad:290882.3125;    lr:0.00000999\n>>> [TRAIN] Epoch 1: 40/366 [00:59<07:47]  loss:0.1223(0.6586);    grad:95593.0312;    lr:0.00000997\n>>> [TRAIN] Epoch 1: 60/366 [01:27<07:16]  loss:0.2339(0.5041);    grad:85665.9844;    lr:0.00000992\n>>> [TRAIN] Epoch 1: 80/366 [01:55<06:46]  loss:0.1768(0.4136);    grad:59523.5664;    lr:0.00000987\n>>> [TRAIN] Epoch 1: 100/366 [02:23<06:16]  loss:0.1140(0.3605);    grad:88484.7891;    lr:0.00000979\n>>> [TRAIN] Epoch 1: 120/366 [02:51<05:48]  loss:0.1251(0.3236);    grad:77938.7812;    lr:0.00000970\n>>> [TRAIN] Epoch 1: 140/366 [03:20<05:19]  loss:0.1196(0.2958);    grad:52942.3047;    lr:0.00000960\n>>> [TRAIN] Epoch 1: 160/366 [03:48<04:50]  loss:0.1370(0.2788);    grad:53322.9961;    lr:0.00000948\n>>> [TRAIN] Epoch 1: 180/366 [04:16<04:22]  loss:0.1262(0.2642);    grad:101807.3516;    lr:0.00000935\n>>> [TRAIN] Epoch 1: 200/366 [04:45<03:54]  loss:0.1206(0.2500);    grad:66933.7812;    lr:0.00000920\n>>> [TRAIN] Epoch 1: 220/366 [05:13<03:25]  loss:0.1185(0.2386);    grad:68776.6406;    lr:0.00000903\n>>> [TRAIN] Epoch 1: 240/366 [05:41<02:57]  loss:0.1373(0.2297);    grad:78758.4453;    lr:0.00000886\n>>> [TRAIN] Epoch 1: 260/366 [06:09<02:28]  loss:0.2164(0.2213);    grad:90247.5938;    lr:0.00000867\n>>> [TRAIN] Epoch 1: 280/366 [06:37<02:00]  loss:0.1896(0.2147);    grad:56689.4102;    lr:0.00000847\n>>> [TRAIN] Epoch 1: 300/366 [07:06<01:32]  loss:0.1223(0.2079);    grad:68599.9375;    lr:0.00000826\n>>> [TRAIN] Epoch 1: 320/366 [07:34<01:03]  loss:0.0752(0.2031);    grad:134900.5469;    lr:0.00000804\n>>> [TRAIN] Epoch 1: 340/366 [08:02<00:35]  loss:0.1183(0.1977);    grad:90492.1641;    lr:0.00000781\n>>> [TRAIN] Epoch 1: 360/366 [08:30<00:07]  loss:0.1121(0.1936);    grad:45272.0586;    lr:0.00000757\n>>> [TRAIN] Epoch 1: 365/366 [08:37<00:00]  loss:0.2020(0.1928);    grad:76251.8984;    lr:0.00000750\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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obtFAHRgVCkV1kIioUe4rYSuA6NKqXAWEgnd5a/QNZ8rpcJYSCT0CJeV0HVgVCkVzkIiobv8CV2vFK2UCmNtJnQRWSQih0VkayvLzxKREhHZaP/7beeHeXR2C10rdKVUWItoxzpPAw8Bzx5lnU+NMRd1SkTHwO22vpd0UFQpFc7arNCNMZ8AhV0QyzFz66CoUkp1Wg/9VBHZJCLviMhJra0kIgtFZJ2IrMvLy+ukl9ZBUaWUgs5J6BuAYcaYycCDwOutrWiMedwYM90YMz05ObkTXtqig6JKKdUJCd0YU2qMKbdvLwU8IpJ03JF1gN1C1wpdKRXWjjuhi8gAEauJLSIz7ecsON7n7Qi3y3ob9dpEV0qFsTZnuYjIi8BZQJKIZAF3Ah4AY8xjwBXAD0XEB1QBC0wXnyXLY7dc6rTlopQKY20mdGPMt9pY/hDWtMagifVab6O8xhfMMJRSKqhC4kjROK8HgLLquiBHopRSwRMiCd2q0EurtEJXSoWvkEroWqErpcJZSCT0eLvlUlqtFbpSKnyFREKPinAR6XZRpgldKRXGQiKhiwhx3ghtuSilwlpIJHTATuhaoSulwlcIJXQPpVqhK6XCWAgldK3QlVLhLcQSulboSqnwFTIJPd7r0QpdKRXWQiahx2lCV0qFuZBJ6P3joyiv8fHc6v3BDkUppYIiZBL6VbOHMTmlN4tW7gt2KEopFRQhk9BjoiIY0z+O6rr6YIeilFJBETIJHSDK46LGp1ctUkqFp5BK6N4It1boSqmwFVoJ3WMl9C6+Ap5SSnULIZXQoyJcNBjwNWhCV0qFn5BK6F6PG0DbLkqpsBRSCT3KY72d6jodGFVKhZ+QSujeCKtCr/Fpha6UCj8hldC1QldKhbPQSugR2kNXSoWvkEroXrtC14OLlFLhKMQSut1D1wpdKRWGQiqhR0XYPXQdFFVKhaGQSuiNFbq2XJRS4SckE7pW6EqpcBRSCd1puWiFrpQKQyGV0HVQVCkVzkIsofsHRbVCV0qFnzYTuogsEpHDIrK1leUiIg+ISLqIbBaRaZ0fZvvogUVKqXDWngr9aWDeUZZfAIy2/y0EHj3+sI6N2yV43KIHFimlwlKbCd0Y8wlQeJRVLgGeNZbVQIKIDOysADtKr1qklApXndFDHwxkBvycZd93BBFZKCLrRGRdXl5eJ7z0kfS6okqpcNWlg6LGmMeNMdONMdOTk5NPyGtEaYWulApTnZHQDwJDAn5Ose8LCq/HpUeKKqXCUmck9CXANfZsl9lAiTEmpxOe95hERbj1AhdKqbAU0dYKIvIicBaQJCJZwJ2AB8AY8xiwFJgPpAOVwPUnKtj28HpceqSoUiostZnQjTHfamO5AW7stIiOk9ejPXSlVHgKqSNFwTqfi85yUUqFo5BL6FqhK6XCVcgl9KgIl54+VykVlkIuoXs9bp22qJQKSyGZ0LXlopQKRyGX0K2Wi1boSqnwE3oJ3eOm1teANZtSKaXCR8gldP9FLnTqolIq3IRcQteLXCilwlXIJXSt0JVS4Sr0ErpW6EqpMBVyCT3Kf6FonYuulAozIZfQ/RW6nkJXKRVuQi+he/wtF63QlVLhJeQSepQzKKoVulIqvIRcQm8cFNUKXSkVXkIvoTuDolqhK6XCS8gl9ChnUFQrdKVUeAm5hK4VulIqXIVcQo/y6IFFSqnwFHoJPUIP/VdKhaeQTOgiUKMVulIqzIRcQhcRvciFUioshVxCB2umi1boSqlwE5IJ3etx6YFFSqmwE5IJPSrCTbUe+q+UCjMhmdC9Hhc1WqErpcJMiCZ0rdCVUuEnJBN6VIRLDyxSSoWdkEzoXo9bDyxSSoWdkEzoURFuneWilAo7oZnQPS69wIVSKuy0K6GLyDwR2Ski6SJyWwvLrxORPBHZaP/7fueH2n7eCLfOclFKhZ2ItlYQETfwMHAukAWsFZElxpjtzVZ92Rhz0wmIscOsA4u0QldKhZf2VOgzgXRjzF5jTC3wEnDJiQ3r+ERF6KCoUir8tCehDwYyA37Osu9r7nIR2Swir4rIkJaeSEQWisg6EVmXl5d3DOG2j1boSqlw1FmDom8CqcaYk4H3gWdaWskY87gxZroxZnpycnInvfSRvB43vgaDr16rdKVU+GhPQj8IBFbcKfZ9DmNMgTGmxv7xCeCUzgnv2ARe5KKgvIbMwspghqOUUl2iPQl9LTBaRIaLSCSwAFgSuIKIDAz48WvAjs4LseO8AZehm/Wn5Zzx14+CGY5SSnWJNme5GGN8InITsAxwA4uMMdtE5G5gnTFmCfATEfka4AMKgetOYMxtci4U7WvA12CCGYpSSnWZNhM6gDFmKbC02X2/Dbh9O3B754Z27KIirApdL3KhlAonIXmkqFOhBxxcZIxW6kqp0BaSCT3K7qGX1/ic+2p1xotSKsSFZkK3Z7nklFQ591XWaPtFKRXaQjKh+2e5ZBU1JvSKWl9rqyulVEgIyYTur9D//t5O577K2vZV6L76Bv7x/i4KK2pPSGxKKXWihGRCH5IYzeCEXgTOWKyoaV+F/tHOPB5Yvpt73gnqVHqllOqwkEzo8V4PK2/9apP72luhHy6rBqAqYIbMsm25HCyuau0hSinVLYRkQgcQEf7z/VmcNCgeaKzQGxoMZdV1rT5ub14F0DjNscZXzw+fX8+ilftOcMRKKXV8QjahA5w2KokHvzUVaKzQn1mVwaTfvcddb24DrJkwgXPUdx8ut++3KvWc4moaDGQVhc/5YIwxvLjmACWVrX/xKaW6n5BO6AAxUdbBsP6E/sku67S9z3yewed78jn1zx/y/Or9zvrph8oAOGjPkPG3Wnpyy6Wqtp6iDgzyZpdUc/viLby1JfsERqWU6mwhn9CjI60pjJW1PowxbMws5qRB8TQYePijdAA+Sy8AoL7BkG1X5ofKqqnx1XPAPlPjwaKem9AvfeQzpv7+/Xav729JlVV3/VTPqtp6/vJuGlXtHPNQSjUKg4RuVegVNfVkFFRSVFnHVbOHMTihl5PIozwu3tmSw33v7wJgwsB4jIEFj6/m9sVbACiqrGv3TJnuJi23rEPr+99neRAS+gtf7OfRFXt4/JO9Xf7aSvV0IZ/Q3S7B63FRWevjywNFAEwb2oeTU3o767hE+OELG3jIrtinDE0A4MsDxU2eqye3XcDqjWfkVzD37yuc2TwtKbePqi0PwhdYhEsAaw+puZKqOvYXVHR1SEr1GCGf0AFiIiOoqPWx4UARsVERjOoXy5j+cc7ygmb95ZmpiYgc+TyttV3SD5dz9ZNf8Pgne1pc/unuPGq7wTVOa3wNPLFyL3vzKnhnS26r61X6K/QgJHT/Ub4tDche8ejnnPm3FV0ckQqmvy1LI/W2t2nQ02C3S1gk9OgoN5U19Xx5oJgpQxJwu6RJQt+RU9pk/f7xXoYlRjs/f3fOcAAWfbaPn7z45RFnbvznB7v4dHc+r67Pcu7zr7M3r5yrn1zD6182uchTUFTU+Kix59f7j6ZtSXkQWy4Vdu+8uOrIQVz/DKS6IJ9ozVffwK5DHWtjqWPz8EdWkRSM8ZyeKCwSelyUh4PFVaTlljHVbqeM6R/rLM8rq2myfnyvCCbY89ff+vHp/PbiCYzuF8unu/NZsimb0qqmHy7/FMeCcisJ3ff+LibeuYy6+gb25VstguYJ4GBxFdc9tYbiyhN7ioHAL5/K2npq7D0Ft6uFXRBbRRArdP9rFx9lyqR/Ox8LX30DN76wgfX7i475Of723k7Ou+8Tbf90oYKKmrZXCrAnrzwsT5kdFgl9WN9o1mYUUt9gODklAYDUpJhW14/3epiRmki812rPAM4BSgCFdhLOK6vBGOP0owsqaimsqOX+5bupqK0nv7zGmSWzJ6+cihof+eXWB3PRyn2s2JnHf9Yc6PT3G6giYLZIeY2PGp/1c1Wzi39kFVXyxKd7McY4jynr4oS+LbuE/QXW9jpU2nqP/6/vprFmX+ExvUZmURVvb8nh8kc/P6bHA2ywvwyyi1uPsScrrKhlw4Fj/8I7ETpybqXMwkrO+cfHLN9x+ARG1D2FRUJPTYpxzusyboDVavG4Xay/4xxuu2DcEevH9/JwzampfPqruU5Pd8bwRGd5YUUt+wsqmPHHD3j68wwOldYwqLcXgH9/2jg743BpYEKv4I9Ld3D5o59jjKF3Lw8A+WUnpkLfl19BWXVdkz2AihqfU6E334X9+sOf84e3d5BXXuNU5l05q8cYw4UPrOS1DVbbKr+8ttWpi4u/PMiV/1rlxNjeg77e2HiQR+yBb4CtB0sAa0rr1LvfY9m21scVAvk/E6VHOeK4MzQ0mKBM37z+6bVc9sjnQR/3Caywm49zHU1WURXGQEY33YP6Ym/BUSclHI+wSOjD7Wq8l8fN4IRezv19Y6NIjo0C4KrZQ537Y6MicLuE3tEe575vzxzKny+bBEBRRa2TqBd9to9aXwOnpFoJ/4nAhF5WQ6a9XmZRJduzS9lfUMmBwkonabaVjLYeLMFX30B1XT0lVe1LIMYYLn3kMx77eE+T1kVFbb3TFw9sp9Q3GGfPIb+stnFQtBP7ltnFVSx4fFWrM4VKW3itzVnFbT7vRQ+u5PS/tO8i4De/tJFXAsY53t6SA1jTOosq6/jru2nteh7/JQ4Pl3WsDdCSrKJKTv7dMna2MLX0hufXM/637x718aXVdby89kCnthe22Ns98ziOji6pqjvuL6PAqrwjFbq/PdO8ldod+OobuHrRGh7/+MRMyw2rhD66fyyuZr3jS6YM4uWFs/nBV0YCEGcn8+ZEhNNHJQFWy+VwqfVhySy0EtQpdm++rt5w3oT+gNU2OFBYSYRLMAa22BXh6r0F5NsftqPNEc8srOTih1by+sZs/rx0B9+0q1K/uvoGyqrrqK6zzjez2+7Tl1b5KK6sI7OwqmlCr/E5fxiByfrLgN3r/PKaY5q2uGLnYWcef0ve2pzN6r2FrSbN7IBEn9KnFyKwem8hpdV1pN72Nm9uOvKo1cpanzNG4W8ltdf0YX14d2suxhjSD1mDrbFeTxuPskRGWJ+PvKO0hdpr96FySqt9pOU2HZg/XFrNe9sPAUffE3hzUza3vraFPXnlzn0NDea4+vvx9t5jRn7Hn2PrwRIe/iid7zyxmrvf2nbMMUDTllZHErp/3c74wgWo9TVwx+tbmlwwxy+zsJIXvtjfwqNati+/glpfgzNG19nCKqEHzmzxi3C7mDWiL31iIoHGD3NLEu11Citqj/jljh8Yz8jkGAbEe/nZuWMQsX7Z+wsqmWFX7/V232fVngLy7Ir4QGElOSVV3PXmNj5Lz2/ynLsPl1lfBFnFbDlYwq5DZZTX+JwpXL9/azuTfvceK3bm8c7WXCcB5Jb6e/o1TWaLlAf08AOT9Yqdec7t/PKaJoOibU0Xe29bLhU1Pm54fj33L9/dYqUJjVM+1+wrbHGWSuCU0MEJvZgwMJ7VewtIt2e2/HXZkV8EH6Y19kjbqsbqm72PiycPYl9+BQeLq9hpfxFGHGWgOFCF/YXXGQnD//tonrD8v0to/LLLLKzkF//dxOcBn5McO+kFXszlwQ/TOfNvK44pIQNOO3DfMTz+pbUH+NuynWzPLmVbdmnbDziKwL3XjgyE55f7E3rntDVW7y3g+dUH+O0bjV9QuSXVVNfVc9mjn/Ob/21t997zdntG3fiBmtCPWd+YSL42eRAXnTyw1XViIt1EuIQ4b0Sr60RHuomMcHHPO2nc+17TarR/vJcPfn4mq26fy/iB8fSNieT51fup8TXw/TOGO+u57Mozr6yGsfYXzN/f28VTn2Vw3VNrmjyn/8yPO3LLyCiopMHAxDuX8bjd1llqtwxueH49YA28ZhZWOvfnl9VSFFChl1TWOa2NwB76x7vynLGF/PKaJld3qqxrvfLdX1DBwufW88yqDAbZrazA8+IE2pFjJc2ckmoe/LCxj51VVMlDH+5u8scbGxXBjNRENmYWO4k+8ILffmsDBkabJ/RvPb6aSx/5zPm5oLzpcv8fVPrhcudLKKedB475K+aWBm5fXZ/F79/a3uq5c+obTJMZT/7k03z9wHaHP2kv25bLaxuy+PYTXzitK+ckciWNsby6IRNonObZUf7uzb78Ch7/ZA9vb87hgvs/bXHKbnMZ+VbcDcZ6vDEGX31DuxNeoB25ZbgEkmKjKOrAbDD/7/pwqTVpoSPTXKvr6o8oYvyPL7XfQ0WNj9l/Xs5v39jqfO6afxZW7SnglXWZR76nnDI8bmFkcuwRyzpDWCR0EeGBb03lrLH9jrpOQrTnqBW6iBBjnxsGrPaMX7/4KEQEsY9ISo7zUlFbT0qfXnx1bD+ndz9nVBK5pdWk5ZYxbVgfzhid1GT++vbsUv7w1nYaGoxTIa3ZV9ikgvMnMo+76a9vT14F97yTxv3LdwNWcs4qbEwM/pYPWB9K/5GXWw6WcOGkgURGuMgvr21SvR+tj77LblWs2lPgDKCt2HXkzAJjDDtySrlq9lDmTxrAopX7nD+a2xdv4d73dvHO1sYBSY/bxYRB8VTV1Tt7LdUt9GPXZDS2ippXy6v2FvDlgWIq7S+nQ6VNl49MtvbatmWXOtslt7S6xT/+vLKaJm0p/x924Gt+lp7PjD9+wC9f2cSTK/fxt4CrZYHVEjLG8Or6TOb98xNuX7yF2xdvaazQK2ubJMuswip62YOv2SVVHCioZI/9BR/hEv5tnxrBn0g+S89nR04pxhhnoD39cDn1DYaf/3cjazMav/zKa3xccP+n/PSlL1tMtP4vlxU78/jT0jR+9vJGduSUsmRTtlNhtiawqi+r9lFUWcfTn2cw+a73eND+XDbnq29ocU9w28ESRibHMjjB22RQNC23lK/eu4LvPb024LXqnEHuwJbLL17ZxMQ7l1FR4+MX/91EVlElazMKWbIpm7155c7nA6wv23H/713nTKx+/r0D/4Xm/Sf4WxLQBgz8QgV46KPd/OHtHUd8AW7PKWVUvzgij3IcyPFovRwNQ0mxUfSJPnofNbDi7RMTyUe3nMXBoirnnDF+/p7mwq+MwOUSRiTHcLC4ikunDubT3VaSSo6N5OLJA52fAW59bTNbDpYwd1y/Vnd5dx0uo8ZXT25pNb08bmcK4t688iaVXmFlLV9mFnNySm+2Z1t/kNGRbkb1i6WwopZT/7zcOQvlmWOTeWltJvllNU0uqL1ufyEpfaJ5b1suc8f1Y3pq42wffztkXUYR9Q2GCJeQWVjF4dJq+sV7nfUyCiopq/ExYWBvRGDpllzOue9jLp+W4nx5fBFQbdf46hk/wKqg/TNP/FMo/3jpRFwi3L54CztyShmZHMOevAp+8Nx6bp03joRoT5M9sQm/XcaP545ypqsCzByeSF/7d/23ZVbivWzaYBZvOMih0mpS+jQeVAZw3n0fU1RZx94/zcflEmfvZlt2KQufXUeNr4Ev9hU02Yvwj2eUVNZRWFnLj17YQFWtj8F9rCtpvdhsuurzqw/w/OoD7PnTfNwuIauokmnDEli9t5C0nDLuenO71XsdGM+Y/rG8tiGL2+ePc1p/b23O4a3NOYzuF+t8HtIPl7M5q5jFGw5SVu1zWn8vrTnAjpxSduSUcrC4ih/PHc1XxiQ7296/rf17AbX1DcRGRVBZ62PZtkP07uXhT0t38P0zRlBT18CpI/sCVnWb3awVmVFQweYsK9He98EuvjZlEHFeD4s3ZHH9nOE0GMOcez6kuq6e1354GqP6xTpF0bbsUmaPSKSkqo7DZTV8lHaYiYN789iKPezLr2BffoVTIV+7aA3ph8vZdOd5TgIuqapj8QbrgL573knjtQ1ZjO4fyz3vNLbvrpyewl+vmAw0traeWbWfuy6Z6Kzjb48WV9bx8Efpzmcm8Pd9yE7oh0urWbIpmw37i6mqqye/vJbkuChnvR05pZwxOokTRRN6gL9ecbJzut32KKqsJSk2iqTYqCOW/er8sSzfcZjvzBoGwMhk68CkOaOSGBDvJbe0moToSE4bmcTXpwxibUaR1c+1d/+//cQXAEwbmsCGZueUySysYvehcoyB80/qz+sbrUqhrNrXpJVijFXdXzV7qFOFXn3qMLKLq48YZJw4qDdJsZHOtMXkuCjyymq46T9fOut8tqeAN26cYz+3cRK6P4FcMmUQb2zMZt3+IuZPakyqX+y1ToI2c3iiUxHuzatw/jCaq65rsAawpekXqLU9+hAb8DuaMqSPU7n+xR5wXZfRdA71gx+m89WxVsJ6+yenMyLJ2t1NjImkqLKO2SMSuXxaCos3HOTP76QxMjmWr4xOYnpqImXVdU4M2SVVJERHUlJVx7ShCfSL87Juf5FTZftNH9aH9MPl/PODXfzzg6ZVaUbB0WeOPLsqgzPHJJNZVMX5Jw1gX14F727LdfaAhiZGc8nUwby+MZtPduWT26wy3H24nEmDrS/O9Lxyp1j4eFceK3fns35/Ef9dl8ms4YksmDmEn728ie89s5Ztd80jMsLlDKJfOGmgMwsIYM6ovhRV1vH+9kO8v/0QO3JKWWqfPuKur53EtaelklVUSfOOzFOfZXCgsJKRyTFkFlbxoxc2kFdWw+GyGiYN7k3f2EhnT+eyRz4nIcbDomtnkBgTSW5pNScN6s2h0mpWpudz/dNruXr2sCYF0Iw/ftDk9fbklbd4ENJzdiuw+cypwPM17Q0ooArKa9iWXcqghF5szLTW2ZdvfWb7xkQeMY0yt7Sav7+3k0Ur9zU59mNPXjkGw9LNOZx70gDyymqYcIL656AJvYnAKq41gxN6cbC4im/PGsqFk1rvyX//jBF8/4wRzs8XnTyQ0uo6kmOjWHLTHP64dAdn2Unmnwumsj27lPkPfOrs1g3s7WVUv1i+e/pw1u4r5MU1B5okN39/7vyTBjgJvdX3NTiB541VEZ49rj//s09D4H8vI5JjcLmEpNgoNmYWU1bj4/Jpgzk5JYE+0ZFU1fl4cU0m6/cXUVJZR69IN999ei0r0/NJ6dPLGZCbO66f1eddn8XiDVkMSYzms/R8yqt9JMVGMTI5psmH3S8h2kO/uCh+dNYofvryRqp99Xg9boYnxTjJ2q93Lw/9473EeyMorfYxPCn6iOfzz2X3O2lQPB/ZA79j+8cRYbeq/M9/9yUTGZkcy9Wzh/Hq+ize3pzDGxsP8tC3prHg8caZRR/tzOOuJdvwNRjmjuvHTXNHA3Djfzbw9uYcoiKsAfYZw/qwbn/REcl8SGIvZ1YU0GTb+d315vYm6w9M6NXkqNYhib04fVQSfaI9PPRReovb85HvTOPJlft4ZV0mLrFag2U1Pq568gtnnV+cN4ZLp6ZQ5zP86rXN/OXdNE4flUR/e89q3sQBTRK6f8zB/56iI93O3t19H+ziO7OGsjPX+oKfP2kACdGRvLTmgFM4XDk9hZvPGcNf3klzEvjGzGJnb8j/+yyr8fHjF7/kutNSAev4j9ySap6wrxjmT8zXnjqMZ1Y1jtf89qIJ3P3WdnbmllFQUcvY/nHOYLfHLdTVW9806zKKcAnOcSlVdVYrrMbXwL6AmUKn/MH6okiKjWryhX3mmGSe+e5MHl2xh7+8m0a/uCgajGFlej5r9hWSEO2BgN/JgsdXO9vK/3d3IhN6WPTQO9OSm+bwyS1f5U+XTmLOqPbvOk1PTeQfV07B5RL6xXu5f8FURgQMjIwbEIfXY/06/vnNKay6/Wye+94svjq2H7+aN471d5zL2z85naeumwHgfJinDu3DhZMG8uh3pjE08cjkZr12H+f2lCEJzu3Lpg3m6etn8Ox3ZwJWgiuoqGXCwHi+MX0I35o5lHkTB3Dp1BRuv2Ac9Q2GZ1Zl8ItXNrHS7m1fePJAZ0B1SGI05580gOVph/lgx2Ge+iyDXYfKyS6pZvqwPohIk+raf46cey6bxHs/O9M5Ktc/2+Rn545h7rh+TnXtEmvw2e0Spg2z3lPgmMf2u8/nlRtOdX4ekRzDz88dw+PXTOeM0UnMHdfPSeYA91x+MktumsOY/nG4XcLvvz6RHb+fx50XT2B/gTVltHcvD3dcON5af+kOfHYmCHzdyfaZO3927hie/e5MhgUchbz97vOd2/fau/bD+lq/p2/NbDz2IZB/YH5Q715HfMYi3C48bhc/PWcMm+zK0eNunJ2TGBNJSp9eTB2aQEWtdf6ib88e2mTsJzLCxbn21NrR9ikwnly5j+ufXstqe28qOS6Kj285i5cXzkYEpg9LZGZAu+31G+fw+69P5P4FUyiurONXr23mpy9/yaDeXu775hT+dOkkVt4611l/SJ9ovjZ5ECtuOYvXfngqKX2synfnIWvg8/JTUgA4OaU3abllPLB8NyOSY5ic0psZAZ9fv8BiKe3387jm1GFEul08siKd4sq6JnuIP7G/eMHqqzcY+NsVJ/Ozc8aQVVTFPz/YzcQ7l/G7gC9Tv+Z7X7NHWO2lIYnWmFhCtFVgrNlXiAi8e/NXuP2Ccc7fMsApw/owe0Qim+zW04ma4QJaoXdY39go+p6AAWqXS3j1htO44/WtLX5RuFzCSYN6c9Ig+M//zWLrwRKq6xroHx/Fw9+ZBsCc0UlsySph8pAEDhRUMv+BTxkQ72VEciz94qLwNRgiI1zOychmpCY6vVOAW+aN5QdnjmzS8/ObMiSBueP68Q97rvmt88Zx+SmDifd6qKlrIC23jAHxXhbMGMobG7MRsVo+f//GZOrqG5gZcKTtHReOxyXCVbOHcdKgeM4ZbyWXCQPj+eFZI/m2neguOnkQF508iP+uzeSjnXmcNjLJOUbgV+ePY3t2KWeN6cdv5jdQ19BAdKQ1O2bm8ES2Z5ey/OdnOv3Y574364j31Fq7LLDH+fB3pjFlSAIPfpjeZAAxcMzkmlNTqalr4OrZVnst8MRu0ZER/OycMVTV1TNrRF92/eECNmUVs2pPAbNHNG4TgNsuGMcVp6TgFuGxj/fw1bH9mDseHli+m8SYSAorap3teNXsYRSU17Anr4JfXzieqAgXc+9dweSU3ogIs4b3dZ537th+pOWU8fGuPEYkxXD5KSnE2XPuhzc7Bcbdb223t00kw/rGMKxvDF/cfjb94r1U1vqIcAmpSTGM6R/HmP5xzowff6/61gvGOQdeDUro5ewFDrG3icft4pRhiUwb2oclm7J5Z2su/eKi+OV5Yxk3II6pQ/tw3n2fkF1Sza/nj0NE6BsbxUmD4omMcPHlgWJuOX+s83zQeOSuxy3syatg4uB4fnDmCO77wPqsnjW2HwMTehHnjeAHz1kzwkb3j3OmZ97fbLD20e9M483N2U5LCaw9w2/OGOLsOfg/N6l9Y6i222EzUhMZ0NvLD84cycKvjODrj3xOWVUdz353JjW+Br720Eoqa+udKdIngib0bmTi4N68bveoj+a0kUmcNvLIpB/v9ThfBuMHxnHnxRO46ORBAHzyq6866/3k7FHcvnhLk8odrCMgk+PctEREuO/KKTz9eQZnjEli2tDGx157Wipx3ggG9vYysLeX+745mZjICF5am8mFJw90/uD8Aqsrf2UG1pfWrfOOPBXD6aOTGDcgjt99bYJz34RB8az5zTkA/N9XRjRZ/5/fnEJmYaWTzDtqZHIsw/pGM3FQb6ba73N0v1j25JVz2bQUnly5r8lh8V6Pmx+f3VgF+s8TdIX93m4+p3FZZISLGamJzEhNPOLUCqP7xTqJ4vb545371/zmbGIiI6g3hng7Ebtdws/PG9vk8fcvmMoA+xQUA3p7Se0bTV5ZDVOH9mH2iL58vCuPe6+c3OR3lxDdmFxeXjib3NJqymt8TabV+Qe4oyMj+PasoYzu17gs3uvh1nnjiIlyc8HEgUcUA8P6RnOwuOqIL84rpw9hf2ElmzKLmTk8kZioCL45YyjGGKIj3RgD153WON33pYWz8bhdlNf4nOd696dnEBPwxbpg5lDe3ZrLomtn4PW4nVbT0MRoJgVc/wBgeN8Y53QdYH1m3tqczZQhCVwwaSAXTBrINYvWsC6jkMraerweF78O+J1MH9aHH88dxTWnprI1u4SRyTFNPtciwis/OBUR67bX4+Y//zfbmSF1whhjgvLvlFNOMUp1V7W+elNf3+D8vCOnxGzOLDZVtT7zyEfppqrWd9TH7zlcZmp99e16rU935Zmxdyw1RRU1xxVzc6+syzT//mSPMcaY4opa8/Rn+5q8J79ht75lht36lmloOHLZ8TpQUGF+9vKXrW6vjPxyU1De9H3nFFeZ4sraDr9WQ0NDk/e3M7fU/Ovj9CNe7z9f7Hd+3pdXbp5dlWHqWvhd7csrNxv2F5q/L0szXx4o6nA8JwqwzrSSV8UE6RST06dPN+vWrQvKayulGn2YdoiSqjounZrS9soq6ERkvTFmekvLtOWiVJibO65/sENQnaRds1xEZJ6I7BSRdBG5rYXlUSLysr38CxFJ7fRIlVJKHVWbCV1E3MDDwAXABOBbIjKh2WrfA4qMMaOA+4C/dHagSimljq49FfpMIN0Ys9cYUwu8BFzSbJ1LgGfs268CZ8uxTjFQSil1TNqT0AcDgacNy7Lva3EdY4wPKAH6NlsHEVkoIutEZF1eXl7zxUoppY5Dlx4paox53Bgz3RgzPTk5ue0HKKWUarf2JPSDwJCAn1Ps+1pcR0QigN5AQWcEqJRSqn3ak9DXAqNFZLiIRAILgCXN1lkCXGvfvgL40ARrgrtSSoWpNuehG2N8InITsAxwA4uMMdtE5G6sI5aWAE8Cz4lIOlCIlfSVUkp1oaAdKSoieUD7r67aVBKQ3+Zawadxdp6eECNonJ2tJ8TZ1TEOM8a0OAgZtIR+PERkXWuHvnYnGmfn6QkxgsbZ2XpCnN0pRj0fulJKhQhN6EopFSJ6akJ/PNgBtJPG2Xl6QoygcXa2nhBnt4mxR/bQlVJKHamnVuhKKaWa0YSulFIhoscl9LbOzR5MIpIhIltEZKOIrLPvSxSR90Vkt/3/kZcwP7ExLRKRwyKyNeC+FmMSywP2tt0sItOCHOfvROSgvT03isj8gGW323HuFJHzuzDOISLykYhsF5FtInKzfX+32aZHibFbbU8R8YrIGhHZZMd5l33/cPu6Cun2dRYi7fuDct2Fo8T5tIjsC9ieU+z7g/Z3FLRrih7LP6wjVfcAI4BIYBMwIdhxBcSXASQ1u++vwG327duAv3RxTF8BpgFb24oJmA+8AwgwG/giyHH+DvhlC+tOsH/3UcBw+zPh7qI4BwLT7NtxwC47nm6zTY8SY7fanvY2ibVve4Av7G30X2CBff9jwA/t2z8CHrNvLwBe7qLfeWtxPg1c0cL6Qfs76mkVenvOzd7dBJ4r/hng61354saYT7BOx9CemC4BnjWW1UCCiAwMYpytuQR4yRhTY4zZB6RjfTZOOGNMjjFmg327DNiBdfrobrNNjxJja4KyPe1tUm7/6LH/GWAu1nUV4Mht2eXXXThKnK0J2t9RT0vo7Tk3ezAZ4D0RWS8iC+37+htjcuzbuUB3uIBjazF1x+17k73buiigXdUt4rR3+adiVWzdcps2ixG62fYUEbeIbAQOA+9j7R0UG+u6Cs1jadd1F7oiTmOMf3v+0d6e94lIVPM4bV22PXtaQu/uTjfGTMO6XN+NIvKVwIXG2h/rVvNEu2NMAR4FRgJTgBzg70GNJoCIxAKvAT81xpQGLusu27SFGLvd9jTG1BtjpmCdlnsmMC64EbWseZwiMhG4HSveGUAicGvwIrT0tITennOzB40x5qD9/2Hgf1gf0EP+3S37/8PBi9DRWkzdavsaYw7Zf0gNwL9pbAMENU4R8WAlyheMMYvtu7vVNm0pxu66Pe3YioGPgFOxWhT+M8EGxhL06y4ExDnPbm0ZY0wN8BTdYHv2tITennOzB4WIxIhInP82cB6wlabnir8WeCM4ETbRWkxLgGvsUfrZQElAG6HLNes7Xoq1PcGKc4E962E4MBpY00UxCdbponcYY/4RsKjbbNPWYuxu21NEkkUkwb7dCzgXq9//EdZ1FeDIbdnl111oJc60gC9wwerzB27P4PwdddXoa2f9wxpB3oXVa/tNsOMJiGsE1kyBTcA2f2xYPb7lwG7gAyCxi+N6EWv3ug6rl/e91mLCGpV/2N62W4DpQY7zOTuOzVh/JAMD1v+NHedO4IIujPN0rHbKZmCj/W9+d9qmR4mxW21P4GTgSzuercBv7ftHYH2hpAOvAFH2/V7753R7+Yggx/mhvT23As/TOBMmaH9Heui/UkqFiJ7WclFKKdUKTehKKRUiNKErpVSI0ISulFIhQhO6UkqFCE3oSikVIjShK6VUiPj/fdnGWhtd6lkAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}},{"name":"stdout","text":">>> [EVAL] 1/62 [00:00<00:18]   loss: 0.1013(0.1013)\n>>> [EVAL] 21/62 [00:06<00:12]   loss: 0.1014(0.1231)\n>>> [EVAL] 41/62 [00:12<00:06]   loss: 0.0895(0.1227)\n","output_type":"stream"},{"name":"stderr","text":"====================================================\n>>> Epoch 1 - avg_train_loss: 0.1928  avg_val_loss: 0.1232  time: 537s\n>>> Epoch 1 - score: 0.4970  scores: [0.5122660374048454, 0.5678100645574988, 0.44385759041654765, 0.4677380747975239, 0.49310155865453337, 0.49728720326691966]\n>>> 🌟🌟🌟🌟🌟 new best_score: 0.4970\n","output_type":"stream"},{"name":"stdout","text":">>> [EVAL] 61/62 [00:18<00:00]   loss: 0.1133(0.1232)\n>>> [EVAL] 62/62 [00:18<00:00]   loss: 0.1294(0.1232)\n","output_type":"stream"},{"name":"stderr","text":"====================================================\n","output_type":"stream"},{"name":"stdout","text":"\n\n>>> [TRAIN] Epoch 2: 0/366 [00:01<07:11]  loss:0.0870(0.0870);    grad:inf;    lr:0.00000749\n>>> [TRAIN] Epoch 2: 20/366 [00:29<08:02]  loss:0.0920(0.1225);    grad:147333.6250;    lr:0.00000724\n>>> [TRAIN] Epoch 2: 40/366 [00:57<07:36]  loss:0.0868(0.1178);    grad:120604.2109;    lr:0.00000698\n>>> [TRAIN] Epoch 2: 60/366 [01:25<07:09]  loss:0.0488(0.1109);    grad:48336.1523;    lr:0.00000672\n>>> [TRAIN] Epoch 2: 80/366 [01:54<06:41]  loss:0.1248(0.1081);    grad:184683.5000;    lr:0.00000644\n>>> [TRAIN] Epoch 2: 100/366 [02:22<06:13]  loss:0.1146(0.1075);    grad:146720.6562;    lr:0.00000617\n>>> [TRAIN] Epoch 2: 120/366 [02:50<05:45]  loss:0.0581(0.1068);    grad:87277.2422;    lr:0.00000589\n>>> [TRAIN] Epoch 2: 140/366 [03:19<05:18]  loss:0.0822(0.1075);    grad:58088.1562;    lr:0.00000561\n>>> [TRAIN] Epoch 2: 160/366 [03:47<04:49]  loss:0.0650(0.1064);    grad:50674.9688;    lr:0.00000532\n>>> [TRAIN] Epoch 2: 180/366 [04:15<04:21]  loss:0.1238(0.1067);    grad:146812.5469;    lr:0.00000504\n>>> [TRAIN] Epoch 2: 200/366 [04:43<03:53]  loss:0.1280(0.1076);    grad:144725.7344;    lr:0.00000475\n>>> [TRAIN] Epoch 2: 220/366 [05:12<03:24]  loss:0.0913(0.1067);    grad:110870.8906;    lr:0.00000447\n>>> [TRAIN] Epoch 2: 240/366 [05:40<02:56]  loss:0.0826(0.1059);    grad:64715.0391;    lr:0.00000418\n>>> [TRAIN] Epoch 2: 260/366 [06:08<02:28]  loss:0.0724(0.1055);    grad:62771.6953;    lr:0.00000390\n>>> [TRAIN] Epoch 2: 280/366 [06:36<01:59]  loss:0.1266(0.1059);    grad:177787.9219;    lr:0.00000362\n>>> [TRAIN] Epoch 2: 300/366 [07:04<01:31]  loss:0.1020(0.1057);    grad:39901.0703;    lr:0.00000335\n>>> [TRAIN] Epoch 2: 320/366 [07:32<01:03]  loss:0.0926(0.1057);    grad:127494.6641;    lr:0.00000308\n>>> [TRAIN] Epoch 2: 340/366 [08:00<00:35]  loss:0.0983(0.1055);    grad:159661.2656;    lr:0.00000282\n>>> [TRAIN] Epoch 2: 360/366 [08:28<00:07]  loss:0.1281(0.1056);    grad:217423.7812;    lr:0.00000257\n>>> [TRAIN] Epoch 2: 365/366 [08:35<00:00]  loss:0.0849(0.1059);    grad:76643.4609;    lr:0.00000251\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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kwk8BhH6AFo6DAMDgyoIGsKYYnAGjYIwJiADgxGBMGLPaAAGDYJxAg8NQgi8tGFXq4dhMAbQDpnABqMMJgx0aHjmje045xt/wSsbd7d6KAajHEL532gYBjAGIZqdXz7KsWugDMDrq2Bg0Ew0u26XYQBjEM3uMzra4TY5NM/AgNEW5aANRheanV042hGU6DUcwKDJcJusrBsGMAZhNIChwTjRDYYL7kgqBmcwMtDsPqOjHaafgsFwoR2KwRmMMgiTCTwkNLs+i4EBQzTZXGsYwBhEs9PLRztMKQ2D4UKzzbWGAYxBBFEsbpUdDbRodp9WAwOGSQQzaDianVwy2mF8AAbDBbfJ2rphAGMQriFgQ4KJAjIYLjQ7adMwgDGIZieXjHaYYnoGwwXRZHNjKgZARAuIaDkRrSSiGzS/f5yIXvQbvD9IRLOl364kohX+35XS9uOI6AX/nDf7ncEMhhGGgNWHMBGsteMwGP1oeTloIrIB3ALgHACHAbiMiA5TdnsWwHwhxFEA7gLwFf/YSQA+B+BEeD1/P0dEE/1jvgXgQwAO9P8WDPluDFKBM1gN+a8PxgRUP+bccC8+duezrR7GiEE7ZAKfAGClEGKVEKII4E4AF8o7CCEeFkL0+1+fgNc4HgDOBvCAEGKbEGI7gAcALPD7AfcKIZ4Qnhj6Q3h9gQ2GAaYa6NBgnMBDw2+XrG/1EEYMmu2vS8MAZgJYI31f629LwjUA7qty7Ez/c9pzGjQQJo59aHBT+ADm3HAvbvjV88M0IoPRihFVDI6IPghgPrwm8Y0657VEtJiIFm/evLlRpx3TaLZjabQjLQO9c9GayjsYGFRBs/10aRjAOgD7SN9n+dsiIKKzAHwGwAVCiEKVY9chNBMlnhMAhBC3CiHmCyHmT506NcVwDarBOH+HhjARrLXjMBj9aIcooEUADiSiuUSUA3ApgHvkHYjoGADfgUf8N0k/3Q/gnUQ00Xf+vhPA/UKIDQB2EdFJfvTPFQDubsD9GKRAswtMjXYYH4DBcKHZdacy1XYQQpSJ6Dp4xNwGcJsQYhkR3QhgsRDiHngmn3EAfulHc74hhLhACLGNiP4NHhMBgBuFENv8z38H4HYAnfB8BvehzVB2XAyWXYzrqPqYRhRMItjQYKKoDIYLQRhok2ZbKsomhFgIYKGy7bPS57MqHHsbgNs02xcDOCL1SFuA6+9cgntf2IDVN53X6qE0FCYRbGio5gQ2JjY9TAOd2tFsc6PJBK6Ae1/Y0OohNAUmDHRoqOYEdgyh06JkMudqRrNLtxsGMAZhEpmGhmqOOccwVi0MY6wd4VQyxeBahlGnuppaNkNCtabwRtDVozza1tEwoNml2w0DSIHRNnFNItjQUM0HYDQAPRzHPJdaEZZuNxpAyzDabOWmKfzQUNUHYAidFqNNkBoOmIYwbYDRNnGb3WRitKNaHoDRAPQoG9tYzTAtIdsAo815xYR/lN3WsKFahcbRNl8ahbLRjGqGaQrfBhhtCzoQUI2kWheqJdKNNpNhozDa1tFwIHhkRgMYPqzfMYA5N9wbfB9tE1dUiWIxqIxqyTmjbb40CqPNlDocaIdaQGMOf34lWnV0tC3oUAEYXfc1XAj7tCb4AEbZfGkUzHOpHc3O2TEMQIOsHX0so82pVy2O3aAyqj0/Q+j0KDnGCVwrTCZwC5Cxo+2JR1tYXyDAtnYYIxZuFbV8tAkMjYJhjLWjyS4AwwB0yI16DcD7b0xA9aFabPZIyBxf+MIGLH9z97Be0/gAakezK/eOrjrHDULMBDTq4pdNJvBQUM0xNxII3d/95BkAGNZKt0YDqB2iySqA0QA0iJmARhn9Ny0hh4agH4DxAdQEkwhWO5qdtGkYgAYWRRnAaJu4phro0FBtURrGqodJBKsdQch2K4vBEdECIlpORCuJ6AbN76cR0TNEVCaii6XtbyeiJdLfIBFd5P92OxG9Jv02r1E3NVSoC3ioEt2O/iLOu/kveG1L35DO0yiYhjBDg4kCqg/8XGyLquxpwBCt1gCIyAZwC4BzABwG4DIiOkzZ7Q0AVwH4qbxRCPGwEGKeEGIegDMA9AP4g7TLJ/l3IcSSem+i0Wg0A/jDso1Ytn4Xbnl45ZDO0yhUi2IxqIxqtYDMc9WDfSM2GQaQFs0uBpfGCXwCgJVCiFUAQER3ArgQwIu8gxBitf9bJUXlYgD3CSH66x7tMEG1+Q+VATD3bpdpPxKLwDmuaBvJsVqjbmPq0IODKRpB/4UQcMXo1yZ4JrWyGNxMAGuk72v9bbXiUgA/U7Z9kYieJ6KvEVGH7iAiupaIFhPR4s2bN+t2aTgarQEw2kXwCZzAbWyq2FMoB5939Bdx5L/ej8de3dLCEYUIpbLw+W3ZU8C1P1yMnQOlIGy4Xd53u4A1ANXHVg/+7y+fw/7/tLD6jiMcbpPDgIbFCUxEMwAcCeB+afOnARwC4HgAkwB8SnesEOJWIcR8IcT8qVOnNn2sQJwwDlkDaDM6K9rcCXzv8xtwxOfux9J1OwEAW/YU0V90sGZbeyiPulpAL6zbiT+8uBGvbNwdOOwM/Y+CNaNGCO2/fmbd0E8yAtBsf10aBrAOwD7S91n+tlpwCYDfCCFKvEEIsUF4KAD4ATxTU1tATfxqVCIYtQlJCNXK9mQBf3plEwAEDIBLCBTbxLSiS88PozVEMF8aIemOJgQawCg32zQS1fxNQ0UaBrAIwIFENJeIcvBMOffUeJ3LoJh/fK0AREQALgKwtMZzNg2qwD/UxJ72IFshqtmwWw0mnDw8ZgClcnuE4+qyM9lv5AgRaJCGAUTBPoDRbrdvJKr1nhgqqjIAIUQZwHXwzDcvAfiFEGIZEd1IRBcAABEdT0RrAbwXwHeIaBkfT0Rz4GkQf1JO/RMiegHACwCmAPhCA+6nIVBNQEO1lTOdaBd6IDQ27HYCPycmsAEDaJOMPF0pDTYTuq4kMLTJ+24XNNIHwGjXOdwoBB6AFkYBQQixEMBCZdtnpc+L4JmGdMeuhsZpLIQ4o5aBDidUm3+jUvvbhwH4/1s7jESQ/6D4sRfLUUbQaugS6XibI0Qwf9rkdcfQKqLpBAygced0BWC364NuAJpdC8hkAmugPuwhawBtRmrDlpDtNS5GsJ4VDaB9fADef1erAYhge7swfBWteu0lh59L4x7MaE+6azazNgxAA5UwDtkHEBzeHhSBo1TalP4HhDPmA2g3DUDEt7kRDaA93reKVjH+wAfQQAbQrkKMioGig7uX1B651A5RQGMOKp1p1CRrF4mw3ZvCM+Hkx95+TuDof++zbwJyRVNMHY1Eq957kAncwAdTbW0OFB1s2DnQsOvVixt/vwzX37kEi1Zvq+k4YwJqAWIawBBND+1GZ5tdXyQJZcfFgy9trKrWBhqAvx+bftpPA4hHAUU0gBZz/DXb+vHXlfHkuZZpAIEJqIHnrMLNrrjtSbzl3x9q3AXrxNrtHhPqLzo1HWc0gBYglgncsDyA9kCrwkC/9ciruOaOxXh4+aaK+/FzCkxA5fbxASx/czd29BcBKE5glzUAtE0m8Pf+sgof+/mS2PaW+QDcxj+XalUyF63e3riLDQFBIbwab77Z5aBNQxgNVKliyI6mtrNTtiYMdM12L5N3065Cxf3UKKB28gF84HtPYsseb/xycAATfTkPoNUMv1B2UdSYzVrtA2jk5UdKt756K6GansAtgErvh14MzkOrJUKGzoY9HFATvJKgmoDaiQHsKQTJ7JHnx3NECCkTuMVOgLIUkSSjVQyg7DaemI2UKKC6GQD/Nwxg+NDoWkCMdokKabZUkQQ1wStxP+U5tZMPQJ4LslqucwK3+m2XHVf7jltFM9kH0EjNc6REAbFQYNdIccOcE+MEHjbEagGNsmJwreoHoJp2kvfz/vPw2IzBCWHDjSdWbcUHvvcEyo4bZQByFJCrYQAtVvmSNIBWJYKxBtBIBpR2bbY6Y7je8iDGCdwCNLocNE++djEBtWopWIppJwmhE7g9TEDPrdmBv67cir6iow39BABHYqrtogHIzEhG68JAXf/6jRtAegbQsEvWhXp9FSYMtAVgbn3i3EkAGlgKoiFnGTpaVQuIpZ9qmdVsO28XJzAvXvX6eg0gXKytZvhlVySYgFrlBG68BpD2XlrtLOZQ8lrvvdllWwwD0IDX+R1/41WormfBrN8xgAde3AigffMAWuUErmoC8v8HJqAWMwAm7mpETVQDCCW1cruYgBw3wQQkfx6+SVBugg8grQbQal9BvZK8CCL2Gj4kAIYBaMEvKet7bKolggkhYpP6olv+ig/9cLH/u7et1QSBEUyqYWZNqZ3AAaPwJW/f9t+qPADmOyoD0kUBuUIuBz0sw0tEWepNIEOeq8MpBASRUtK29TsGhtToJy1BbbkJSKoVVQvCsi3GBDRscIUAUbiAq6mP/3rPMsz9dLQ93abdXqy417u0vXQAN8haHd7rpnWAJYaBtqgUBMevxzUQiZBGnMDetlZHfTm+CUglHvJ7H06tqiwxScbJNz2Et37l4brPmXb4rV6D/MxrNUWFwlpzMKYZwJOrtmLV5j2x7Y4rYBOBiGBbFBCAJNzx+OvBcSpKjt4R10o0W61MApPD6mGgHphgtIsPQI1CkqdFsMATpO5WIDS5RLfrqpgOy3jYCdzAa6Ydf6vXYJgnUttxzc7aH9MM4H23PoEz/lPtU+M9dHZEegwg3fkGS/E6H2XXbZvSAAxdQ5PhAD/Tak51mZgCrfcB8GWLMROQ3gfgBqaOFjsefYKrMiR53EOtc1XTeBIY0lCQVrJvtQzmaLSfNGiHlpAgogVEtJyIVhLRDZrfTyOiZ4ioTEQXK785RLTE/7tH2j6XiJ70z/lzv93ksKESMXGFCMw/NlXXABg6BlByREMlnoagyVJFEpgBViM6PNlDDSD6f7ihdiZjyKNxgwXenHj3epBEdOSv5ZRzu5njacQ5q6HVeQABM65xUrQ8D4CIbAC3ADgHwGEALiOiw5Td3gBwFYCfak4xIISY5/9dIG3/MoCvCSEOALAdwDV1jL9ubN6dXI+GTUAAkKlBAxjQaQCO2zY2YUbLGsJwVE8VWz4vEv4fFoNrlQaQPgrIkZKvWk10Sm1nAmq8PTutua31JiDvf+0moNb7AE4AsFIIsUoIUQRwJ4AL5R2EEKuFEM8DSLVC/UbwZwC4y990B7zG8MOGjbsGE39zhQjMFVYKHwBjsBTfr5U24de39mHODffi/mVvRraHFQaHF6WUJR14sZaV/VtnAoqaohi6PABXSr5qFw1AJX4RJ/AwDrIZGkBa7brV70IuFVILeO9WRgHNBLBG+r4Wmh6/FZAnosVE9AQRXeRvmwxgh99wvuI5ieha//jFmzdvruGyevQVyvjts+sCBqCzy7uuCCJWMhZVJeBc4ElrAnKFZB4Y3lm4ZM0OAMDvn98Q2d5su2ISmJFWk+TDxRLdv1VRQGE4qsoA4pK03BNYt2iHNe4+IfM2ogEMpw8gGE/jzjmcJqDXt/bhup8+g0K5tpr+gGcJAGpfc6PBCTxbCDEfwPsBfJ2I9q/lYCHErUKI+UKI+VOnTh3yYD7zmxfwsZ8vwQMvejXpx3dmY/s4QgRE3dMAqjAAijMAK7B3u9rwt+EAX09tmh3czjBLRWpUTxICghuLAmqNGFdO0ACiZSH8/5IJSJ02m3YNYu6nF9bVGrAeJGkiMjEstcAH0NBEsGF0Aj/52jb8/vkNWLe99g5jYf2tGg8MTECt0wDWAdhH+j7L35YKQoh1/v9VAB4BcAyArQAmEBH3I6jpnEPBG37SCSefdGbt2D6uQFQDqFq6wPs/EGEA3vElRyYIwy1x8/iiHIBHMdzjYZNOdR+A/19x/hYdtyV2dTeBccnPTyb6SQz/sVe3AgD+d2nUJNcs8HNTzSS6BLbhQFNKQaTkX40ww/L8reeZ1Wv+qptxpEQaBrAIwIF+1E4OwKUA7qlyDACAiCYSUYf/eQqAUwC8KLxV/DAAjhi6EsDdtQ6+HvCi2NrnOYF1ZhvPBOR9toiqhi2GGkA4G5kBlF23ZTZhXvhqF6KgFlCKcxTKDtbtaExPVTWqJwk8bp3G0Ki6TLUgdEZHry00+8gNYdS1vmLTbgDAgXuNa85AFSQRHfn7cPpV6tEANu0exH8/uCLxmNQaQAPmTdmtXxN16hQCmx1QUJUB+Hb66wDcD+AlAL8QQiwjohuJ6AIAIKLjiWgtgPcC+A4RLfMPPxTAYiJ6Dh7Bv0kI8aL/26cAfJyIVsLzCXy/kTeWBJ7wOwc894OuR6fjhiagjE2pi5dFooCkkMd608CHiqQm3LXUAvrYnUtwyk0PNURSLKf0AYQRNVwGOty/HoL14vpdOPJf76/o+E8znkLMCSxi+0SdwNFn9spGL+mwV2N2bAaSwlFlqbkVUUC1XPJTdz2P/3zgFTzzxg7t72nXVCPoJxP+ekJn6xUCgzDQmq+YDqlaQgohFgJYqGz7rPR5ETwzjnrcYwCOTDjnKngRRsMKnoQFn1gXyq4n8UtEUjYB2Wk0AI0TmE9XkgpyDbsJyL9e3ASUXqrgCKKy68K24uYywJvcT722DW/Zf3LFcwUSfRUTUCUNoFQWQI0ZI7f++VXsHizj0RVb8J7jYtO0KtyEcUcygaWImyRpb+WmPcE+w4EkJ7BsT35+7U5865FXcfNlxwS1r5qFaC8Fkao2VsF/5gMJzdSHsxhceQi+qLprAQUaQM2XTIUxlwnML7FfItaDilffCwP1PtsWVZ08Oicwx/yXWxgWmGQCCgtMVT9H4C+oQLP/smIzLvvuE9qyGjL42afXAOImo3pyAbb1e20cJ3bXJ3nz5WNhoBIh1UUBqe+bTWnDZcZyHD0jkr8+vmor7lv6Jrb3FZs/Hum+0z6CXMZbiEUngQGkzQNoBANICKutBfUyIlMMrkEoaRw5qhlITgSzLaqauWpV0ADKshN4mDlAUh9SEfzXj+fe5zfgcd9hyfOuktrbV/DuW2dO040ndR6ATgOogwHs6PeIW1culcIbQ6IGIOKfhUi2dQsxdAJSCxJNQNK42Lw2HLkq8jXSEsKcr5UkBQ6kNwEN/f547pWH4Dep9d23QyLYqIKOkKnqpZwIZidEAR1z4x/w9z971tvHZxYDxWpO4OH2AbiRsTCCPICEefyNB1/B7Y+9FtlWaeKmTXMPTUCV9+PHJDOM7pwdfK4VW/d4DKBeBhw6/yokgmkzgaHdf9g0gASzg/yVCetw1ASKagApGYCvARQSGMBwhoEGiYlDOFntmcD1HZcWY44B6Ox3agkHrxaQxAA0T397fwm/e249gHASyqYkijiB4e835OHXhIIflaSadkPHkn5Antkqvi0JAfGrMkt5AanOVBWqBlAou+j0pXeVCO/sL+HRFVsqnm+7rwHUK+WGxeD0Er085qgJKGrzLicQ5GahlCIRjBnAcAgnMpOJladIeCaBCUhhALy+hrMaKD/PoWgAI7IY3GiC/PI4B0DVAFQTULXJU9Q4qijIA2idE5ilpthiq+JY8iJZopO80jPgR1qNsAWSdLU8AE0UUJevAaglma/90WJ88PtPYvdgKfF8bJqqV/JOKganrQYqMU/5cvK1h0MDcN2wHaR6OZlxcVbrsIxJJDOApOt3+Axg0epteOq1bcH2tKXFddeuF+UgCqj+c9XcD8BoAI2F/PJ6Oz2pMq4BhHb9NIlgTBjkFPHAB+BWLg3QTLBPQp2w/C3ptlwR11YqagApndxqbZ9q5ys5Atv6iiiUXcya2AkAsTR8jqxJMhHozlsrkorByWeLNoQJ9+N3Lku/aWtLDQXy+4prAOFndmwPh1ZSaUxJa6wj4zH+Xyxei0u+83iwnQWsSlNJVGA49aDsDN1cVnMYaN1XSoexxwCkl9eb96JCYj4At7ZEMCYMukSwkuPGolqGC4WEKprhwtCPx3HjJawrEYi095e2FIR8Po4sOmLmeADxgntBA/mEa8vb633+TKzi1UClMUtMUKYPvI9ccmE4pG3Z11XpXQY+gGEYk/z8X1i3E5d//8nge1KQAZuAVAQaQEXTpPy5ESYg1gBqY+DyGOsNA22W9aC+sIgRDHkh9uT1GoBcCyhjU2BLZ6gvlBdP1ATk/S87IrWE3GgE6n2C8zJZAxCxSV6JQKR1cpc1iV2Vzld2BV71GcDhe/cCiIfs2hSG2+rA9n/5vLUiKXpJ11tXbgjD322QogEMt7Qd/U3nBB6OMcnX+Kdfv4BVW/rCMSVMiVyV3IRKJhV5DjckkbHOPACZ5tSfCVzTYakx5jQA+UFyRqYavuiKUILXaQCyRC1/jjqBWUVtpQlIr7IG8n9Ser0rYguyktkibanboBZQyoYwjuvi1c19yGUsHOCXTxhU3hUzavUeP/+7ZTj7a3/GnkI52Fa/E5jHnS4KSBftUh5mDUCu8nnbo69h8erQfh71AQwvAwict8q7SJKqrYRcsTROYHlOqLut2rwHL23YVXnACeer1YRXqjCOaqgWsDFUjDkGICMwAak+AMkElNEkghUT4tJlDSDIBHbd1ASy0WANQA1bS6pWKf8eX6DVNYBqBLbmPABH4NVNe7DflG505/TaGjMAlTj/4K+rsXzj7si1Gq0B6BqryFFAgBT6KWsAwxAOJr+vny9eg4u/HdrPtRpASuZYKDt1lUMGvGfE2cYqw056N0nj4kTLShJ1OfIeovud8Z9/wjnf+Ev1QUsIq9PW9v5kDbxWE1A1bX2oGNsMwHcCq1KlXAvItqzYC5dNQrI5Y1D6HOQBOJUzgfcUylixcfcQ7iIZhSDGWy+5Js0pV8QJfiXHlxxv/m+/fxHf+8sq7X5BWeVqpSBEeN4NOwcxc0In8n7EluoD4PeUxFTkqKG6GUCiDyAu6btulHnqGMBw+wBUaBPBUo7pk798Hh+7c0ldY3JcEZh0VMKd9EwSZYUUGkBS5nFSWYlqCJ3AtWkAsnBSbxioMQE1ACqR6MknmYDCPIBchmIvPKoBSOp0pBREeE3eRffyr/7BU3jH1/7cFPMQMyqVgVWrMKhzAqdZaK4A/vTKZjyxaqt2v6SEKhVyRE3RcZHP2mHIboIGkHTORmgAPJ5KJiDZD6IzAZUi9uhhiAKqwLAjDMCpjQGs2d6PtXXUwwe895+x9T6bpOsnEUxeX2kSFNX9nnlje/B550A8fPjHT7yOtdv7Y9tLdYaByutPFg629xXxtQdeSenINiagIUMlHvmMjVzGqpgIlrGsGHGRCb0sFcqTjH0A//PIq/jzK5uD86pYtNqbjM2QCtknkZTBWjkPQFmgFRiUHLVTctxEG78cR61O+kWrt+G3z66LnK/sChTLLnIZC/mcN1XV8t3sBE7SKkpDkL4YPJ5YOWgNA3BcfcbrcGsAlQij/EutDGCw5GpLqFeDEAKu8NYTEDeFJGsACQzA5wAVTUCRxLPw8+LVIQNQm7v0F8v4598uxT1+kmd0jHU6gaW5KQ/3M799Ad94cAUeTxCYgOrm2qFiTDEA1dSTsQmdWRsDxXJku2wCytpxE1CSE1ierFxMbptUZKuS4Jcmjr1WsAaQZA5IWjyOELHfKhEIueVlqewmqsjyIlc7Ub3324/jYz9fEjmf4zOArE3I2RaI4gzASvABMOTt9RLeMBM42QQU2mqFwnS8/43QRGpBJROQLj4+7ZgKJScWiZUGfP6srwGoAkWSVlRtXLrXXnZcnP/ff8GDL20Mtsmn2SUlDaq9LpjJ68qV1OsETgrJ5RIlaqkWGby3KQbXAKimnqxN6MrZmjDQkLDkMqTRAOI+gM6sHSEwhPhLrSRF1yNVVUPgBE40AemPc6TQVkaSSeG5NTuwra8UHFd0ROK+UWdY8rhDDcBFyfE0ACJm1qoJyPufJJVFIjCGaAKKaVKaMatRQELSZhjD4wOoxLDj2xxXQAiBrz3wStAtT4fBkhPzw6QBPx92AquEPWm8ySagZCfwlj1FLF23C/9y97Jgm7z25Hm4TjH1MJPXMdB6W5NG/FDSOHjN57PJZLiW5k31YEzlAaiEPmtbHlFRJrQQchRQ3ASkiwLqzNlVi13puDiRR4hVDWDLngIGSw5mTexKcWd6VHMCJy0uIeLEUieJua7A+259PFJPpuS4iX1m5UVe0aTkhtcsll3kbM/+35m143kAvqqVVF6ilGCiqwVpnMDJPgDvfzlBA7h7yTocOqMXB03rqWtsSUjrA5DHtGVPEd94cAXGd2bxN6fO1R47UHLqYmAxDSClgJHIABKcwP96zzK8sG5nxfOUXIHJ3TnsKZRjGkClpkV837UmgslRU/JwmZGqfRGeW7MDD7y4EZ84++BwrTZJaEilARDRAiJaTkQriegGze+nEdEzRFQmooul7fOI6HEiWkZEzxPR+6Tfbiei14hoif83ryF3VAGqlJ2xLeSTTECUbAKKaACOrAFUDvfSvUO+TkEZ2/wv/BGnfvnhardUEUEeQEIYaNKUcrRhoPFJ31csY7DkRqJ2So6b6JB13JCxpjEplRyBguMim/EO8t6VEgUkNd7RoRHtJJPDQOXPkhlMk/hTSvABfOY3S/HjJ16va1yVULl4X3ybI4RUFyiZwNXrAygHDECvATiu0HZsS4oXSHIC3/7Yajz9+vbY/pHCfY5AxiZMGdcRmGEYbPrRMaS0pSC27Cngq/cvD8ZWiPgAJA1ASdQslB189u6l+MmTr+ObD6+E64qqa3WoqMoAiMgGcAuAcwAcBuAyIjpM2e0NAFcB+KmyvR/AFUKIwwEsAPB1Ipog/f5JIcQ8/29JXXdQA1QJLpdkAnLDctBZjQlIbk7BEqaqAegkXB3RC3sJNMEH4E+wpBo2laKA0sRpy0lWgEdYSo5bwQQkgtoulSMfeBF6zKTDJxr5rBV3AtfgA6jbCZwUvqrLA3BFJM4/CANNiAIaKDl1hyVWQiU7te69O64ICFWSiUMIgcGyg5ITDxKoBn7fHAWkDuGp17bhxC89GHO+Js0T3pq6GJz0OEqui4xlIWPHK/1WqvhZTBkF9Kflm/HNh1fiNT/TWZ6z8nPj7fy8/3fpm/jh46/jF4vX+tcLhatmcYA0GsAJAFYKIVYJIYoA7gRwobyDEGK1EOJ5AK6y/RUhxAr/83oAmwBMbcjI64BKJDKWhc6cHfMNCBEmcmV1JiCJEHBp465c1AegWyC6hccawMZdg+jzCWq1OHkZj67YEkTPqAhMQP5Ytu4pYOELGwLHtG7tyA5dGbr72T2oak4uSo5IlMbLrhvUdkkTVVQouxAirAfTmbPjTuCg5pL+fDJxrbeKb5gJrGpS8X1cV5+AxEyxI2NFiuI5rojkjzQKlU1A8W2OK6Sw4WRmyq+tFi3gkeWbMO/GBwBIGoDy/l/b6hHLP/gtSINxSfvJlhKZ4aZB1AfgaQA2xQs9VspWT5sHENTg0tQI05mAWDhQl8RgyWmLctAzAayRvq/1t9UEIjoBXjfXV6XNX/RNQ18joo6E464losVEtHjz5s21XjYClbBmM5bWsSjXAsrallfgS3pzskrHkUX5rF213ouO6PF1rr59Eb608CUAwBvb+mL7JeGD338yiJ6R8dMn35CafXj/r7ljMf7uJ89UrN2TVNgtDQNQGY4MT50NiXlFDSCwtUbNBvlMXFtjiVImWjKjHYhIX/UR2qRy0Bt2DuD6O5/Fj554PYymEZ4DPcNF6vztfGw+G2qKTEQbEQDwubuX4uB/vi/4XtHElqABFKuYOAYl81stY/7K/y4PPmctvQmI63Jt2lWIjjXCTOOJUWkzmNXeDRmLYGmy/Ct1/QqKGVZhOqx5q/+B6LPnucnPWw0GYgEIGOEdwYhoBoAfAbhaCMFP9tMADgFwPIBJAD6lO1YIcasQYr4QYv7UqUNTHmIMwCKtVOmVgghNQEB08csMoM9nAJ4GUNnhqKM/8ktf7zukXt2cngEk4XuPrsJRs8ZjweHTA+l4R3/U3qmbVEnVB3VEXa3BP6hMaN3xuQQJUIb6m6wBqAxArrrKkN9PlAEkXrIikkxAW/YUcfeS9fjl4jURxll2Qk3HFQL3Pr8Bn//diwA8M1Y5YAAsIQ6dAdzx+OuR+65EpJIYAPuhkpz4sgO+Fq1FXhe8nlSw9rFxd9QP4AiB6b15XH7SbH/s8P+zxpXSBCTtVnJcZG0LGU2714ABaM6btiVkUdEAZJ9hpUqsajjoYMmRzLUVL1k30jCAdQD2kb7P8relAhH1ArgXwGeEEE/wdiHEBuGhAOAH8ExNTYVqAuIooEqZwCyxJDIA32zTmVWjgOLX1y08uV8v29TZdshNUOrBYNHBwdN6ML4zm5jAoh2jf2tpMjVVHwATNJ09no/vCAijfE1lXMrhTEw7MnZiKYiIWU6joQHppUUAWLJmB15cv8sfq14DYHRkrOAehB8FlJVKHjz48sYg2qQj0xwNQEVlH4BufyFFjSVoANI4a/FbyOfjRDAVfL43dyoMwBXoytmYPj7vnUvpcpaWqasRW7ZFsEinAbAJSKMBpPQBqGXYk6KAwvN6+6kMoFAO64i1shjcIgAHEtFcIsoBuBTAPWlO7u//GwA/FELcpfw2w/9PAC4CsLSGcdcFfjFcViBjk1aqjJqA4jZmmdgwERzXkanqA9AyAOmls0ll6x5PDa63iTngSWgdWQvZDEVszhHUYALSTfo9MROQvvw0EEqVOhOQzgkvg4mpTlvLWPH3I0dUpTUBfe7upfjLitDEeNEtf8W5N/8lMp6khV90wqgpRwiUJAYgRJRwyhoAP6+kAIBXNu7Gp3/9Qk0hgLoGNCqSTH9JYcMM+VnWwrRkjYLXk4r+EptL4qG2lkXBelTraqW1jctzquQKZGxL2+2vkp2/7FZmkAxV8h8MWrPGGQ6PB4ibgAZLTuszgYUQZQDXAbgfwEsAfiGEWEZENxLRBQBARMcT0VoA7wXwHSLiDIxLAJwG4CpNuOdPiOgFAC8AmALgC428MR34xXR3eAwgyAOINYSBZALi6oWyhBnuv8uvJTIun4nEz+ukTd1LtDQaAP+vxWatOpgHSw7yGRsZy5KSW6prAHJht+j2+FhUH0BS+WkAQWQMawDywpM1MKEJQeVjOrNW7F3pTEAyQWWiRZQsLbquwB2Pv47Lv/+U9vdqjsZS2Y2VgsjZYaKSPJ6OjB0822omoD+/shk/e+oNbFNMd5VQyQ/D0EqhbhgGmmQ+ku+jUkXQh5dvwk33vRyeW5oP2YT6/nIotmxa5JDsjMQAdL2Yq0GeUmXHRdb3AahTtZIfJKwFVM0J7ETOxe+3K2trGUDYZyB6XtkH0CwnQCoRUwixEMBCZdtnpc+L4JmG1ON+DODHCec8o6aRNgDMADzJuhgwgLIrArsgwCYg7xg2AUXKP0hSyq7BUAMAvIWUsyghD6CyBsCEnwlrLXHrRccNQiwBb/LkszYKZSdRA2C1ctn6nZg7pRtduUzEARvtphW/5u6YCSjZhszbeIwykY9G6njX5QQ5IPQb5DWJYMH9R6qyyqYKvq6VKC3uUfJAVFRlAI4bRk+5vgkoE2oA8v3lsxb6/esFJqCEewoITg2Zp/zeKzMAzdx0RSxoQMVgRANIJoJX/2ARAOBTCw4GEUW0s0wSA5DOvb2vFBRqdFxENIAPfO9JfGrBIcG+0cqr6e657JuABOKCTbBWNM8vbUOYmA+g7MIiT/sNcwPC+w0ijxTtx4sC8u/NFIMbOpiIs22dTUCAaiqQTEAZjYlBelEsrbC5ppK5gInEM29sD+z8csOLPYNlCCECv0Itqr8cocHhhfmshYxtJaquHFVx3s2P4sQvPhiRvtWEJr0GoDqBkxcIPxedCai/FG3a4goRMFRAMgHptDWNfV52ug2WHORsCxnLSiSkrMUldZ+q5juQ47V5jsnCxEDEBCT7ALx91eQ2RpJkWHEsQXnnCsckagDpfQBJWstrUpevXQMsyKQwAUnvtU9iyJyVzxrA82t3Roq5yfOoEmGWmXjZF/Y4DHSg6OB+P/w08JdpnNzMFBxXYNPuwdj8Z8TDQB3ks7YfdeTtI5tPgzLpmpIzTPhbZgIaTQg1AI/o52wrZADSBGS7IxAu5FKCBrB7sIx81gomdtl1Ewk3b373/zyGt3/1EQBRE1DZFVi2flegCdSiAUQiNPzF2ZGxkbU8CUyIeHy+K8Jr7C6U8cSqbZHmNdUqWKo+AB6DqqYDIVHR5QH0KxqA4wr0SAyAj/E0mujzVQmv7llkbYJFyfZiLgnc1aF3ulezxJXKYd9nnhshA4gSy46MHfMByD6LXz29Fhfe8lfc+/wGqfZMegYQ+mGqS8OyzdmVooCSkuoGE3wrMv66ckvweUuf58uSiWm2ihMYUOaD74+zpeNk6Tkanp1sloqYgFwvD8CyvHf7xYUv4v/70dNYsmaHlOylcwKH7+OELz6It/z7Q9prhZJ/6NfoyFiwKNRS5ACKJKYzWHaC+W2KwTUAxbIL26LADJHxM4EBlQFIiWBVGMCuwRI6s3akNWGSxKgzJaiOn/P/+9GgRLQcfvjwy5sq3ps8fpYsWQMAvEmvEnEhokR+7fb+gNi5Il7Bslh28elfv4DVvpQX8wFIY1ClMT6Xzgcgj73sevkC3QkMAIhqYLoyDSqhyma855BkymEG0K1xuut8EiqKUt9nHlsuMAGpGoAV0wBkhnX7Y6vx3JodeHTl5oAY8bN0XYEHX9pYUTOs5AMIk4q87zIxTqcB6P0sO/tLQYixXP2WyyzIY8mk0AD6JQ3AKx8S+gDUa8vvplJF3YgJyPHyAGzLywRes82L0Nqyu5Bo5vEcsuHxgEfE+wpx82FBMv3wsfmsDUtKPJPXTlLkUaEkRwE1B2OLATiuZw6wQ+meI4JUKTSsBaQzAYX7ehqAHUzQspucJq/j4pUkNV44X/3Dclx9+yIskvq6qtBqAFk7YGA6iU2I6KTrK5QjC0pmdGVXYNWWPfjZU2/gbV99BEKIWBiovAD/smJzQJTX7RgImogwYZcFLPnZsw19XD5uAuJ3oSvvIJfvVZ3AWdvS9nZmsAmIgwPUpL80PoBQA/DuJXQCqz6AUAOQSwEw4eEm9nsKTkwDuOXhlbjmjsV4eHmyMFBIqP8k3xc/M5kYu3IUUFIeQIIJ6Ogb/xBk+kYZQCE2lkQnsHS+vkJUG7clHwAQneuum5YBhJ/LfikIJsjMrPuKZa3WVSy7OORf/jdyPOOR5fHkVDUMdDDQAEITkDznk5hOJBHMmICGDm4uEhAUywqkyh0DRWzyi1G5brIJaLDkYJWUqLXb1wBY0nbceC19ho6OpOmOxXZVtXCVjGiEBmsAdkA01V4IgOdYkh1+fUUncUGxBsBYtaUv0QkMeFnHX/2DlwF63U+fwad+9TyAsA+zIwR+uXgNVm3eE5H4yq4nccs+ANYaeKHqmrxE8zSi8eo524JtJftU2FbNzEk+XmVyOhQlJlGImYBELAzUcQUeenkjvnJ/GCnDiVU7+z1m1FcIiRET0Lv9OjmV6sfz2B1dKK5PYFgQkYlx2Qnfb2JZjRQ+gO39xUCo2uIzAyfCAPRjl5nkQCmqAdhEEWYVqa0jDVUtqCgjUgrCFbBtT6uQGcCmXYXg3ldu2oO//9mz2h7IZTecn6s274ldKzTtucG4PB9AOF/7inEfgMrA1GfcDDPQmGIABZ8BZCQHLztv3//dJ3HClx4EoCSCKQzgmw+txGKp2uDuwTI6ZBOQmywx6kwJ1SIKyq6QOgsk7xs1AbEPILxXnQbgmXlkW3xZW6wK8AhEJMO26MSdwMpCec1nlFv2FLDBT/DhlH/HFfjkXc/j7K//OWp+86OAerQaQJwBhHV69OYJ9gFkLCvRlKOagCLmDU3LQBVyFJDOCSyfz3NGu/jts+uxUSp7MFjyJP7dUihwyQmZmxACKzd5xEYn3fN7rmQCKgVJVPDHGM6sNNVAdUKGim19Rey/VzeAUAOQUbMG4HrNlWSml5RZW0kDEIoJKMsmICnabeOuwWBuFcoufvfceqzZNhDT0stO6OPSCXBFnQbgm4CYAUTMnglRevxMmLY0QwsYUwzAqy0fagAZy4pImoxKiWCrt0bLNPQXHXRmrUicMhOlo/1SDAwdB0/SAGRCyZO/0gSQiS8v5LykmajZzjyeUsQE5ES0F9XWKi+8siviTmAlNJClNnmy833JZZL7FR+AI0TEHs8SGkfpyOYeplWVfAAZ24JlJYdzMpFnTUM+flcKBiD7V0RAXDl/RESYE0udW/uixHGw5ESYjawBlBwXWyXTiq5YID/rigxA6tvgjSVc/o7rVq0Gys+FKDkTeHt/EXv15DGhK6vVWJMYgOOK4PlHfAD+Woz6AJKcwBVMQEoUUMa2AoK8w9e6Nu0uxIh9v2QWYng9L5jhx58Vv+8wIcxBR8aLOuJh9GmcwOp75efAtKgZBeHGFgNwPFscL5acbWHmhM7IPp4EirgG4L8ciwhTezrwk789MTimMydrACEDePexs/D3Zx4Qnlvz/pJ8ABO6sv75XKn/afK9DeqcwJkwOknPAKAwgHJkkkWjLVxl4bmaRLDoNThyQ5bo2AQkT3Y1BFcIRHwAzAA4tj7ShlNiJLpxDBSdSMifDkx4+VwRBjBY3QQExIlPLqN/7rbt+SJU4jhYcgNCBKgMIGp+00W7MDFnM4juXgNnsv+TbFZxXMl/kBQFVHaQy1gQAvjmwyvxwIsbY/ts7ythYlcOk7tz2NpX0GR1J5uveM6r/jiLkn0Aug5bOsjLjIv1sQawY4AZwGAsh6Wv4MScs2VXVAzRDUw/nOnt52YQhYyoXxMwoTIAng/dfqBKM9wAY4sB+BOYF0vGJvR2ZiIhhyXXjSaCBVE03svZUyhjWm8HpowLi5dyxi3gawD+pLSkiCP+LdqPVSSG3I3v9BaD64aRQpWSQZI0AB6/bnEIqCYgJ5LwNahI/DKRK5TcmFkppgH4C0zejzWApNo9vKCieQDMsONZvzofgGoOytlhxIcO3COWmbF8H2k0AB1ydlyaBcJnsiXGABzsHPC2zZzQGXECl51ojwWdBmCrJiBtJqv3G89BOe/B0wDYBKR/ToWSi3wmPObh5ZtiWu22viImdWcxubsDW/YUNdVbk0lOPmv7iXJxJ3DUB1CfCajkuLj5wRXYNVjyw0C9ObHTd7xv2l2I9QIeKJVjz7JYDvM+tCagJA1AKgXRJ0n3ScyEGQCbqY0JaIjwGoyHUnHGJhARZk4MtQAO41RNQKzq7R4sYVxHJjIh85IG4NmDve02UaTfp9cyMXyLlaJLmAGUXTfof1ppAgwUo0QPADqkMFCdBqA2MO9TfAARDcARke87NIRRlUxti2IEoNe/r4KGYQHh4umQCE2HHZbuABQG4MalJ3kRFznpx6JIoxYZrAEwk5eZ6a6EZJ9q4LH2qRqA5dUC2t4fZwDb/d7KMyd2oq9QDnrJlhw3YpfXEbpszASkcwIrJiBVA1ASmFQUHc+H9vX3zQPg+b9Uv9BAycHE7hw6c17Oho4BJiFrW+jKZSLmEXYCy3kAUSewNF81Qk5GMp+8sG4n/uuBVzBY8hvCKBrA5l2F2HPrkxhxcJ8lWXrXaABKIybOzpZ9AP0FBxZ50n1ZM4cBLzAFQJCrZExAQwRPYDkKCECk767ncEs2Ae0eLKMnn41IT54GIPkA/BdlW4hoAK4rIhNMtbX+9qOn4JDpPQCkaBlXSBpAMnQhevmMHUjN6kIE4iagfsUHIC9uVQNQCRgQdc4B3uLrlxZzxiJtLL+OeMsJcoEJSOcE5vBLRx5rdBwcBpqoAQyoGoDsA6hsAkqq2Mrmqv6CngCqzH+w5AaEaNaETgyUwuiTkhPN4aisASRL8TETkBUVTtTyxCrKjkc4LzpmJt6y32Ss294fiZLiOTGpK+e1Ui276C9EiXIuk0xycrbldehTc3IUH4AuD0Te/r0r5uMdh00DEBZ+dEV0fmYsryHMYMkJhKOBUtzcM1CMb5PNrcVy/FnJhB/gulwWiAiOC6zZ1o91OwbQncsgK2Xqq9dhgWAoVYGrYWwxgHKYB2D7xaAAYJakAfDLS4oC8hhAJuLM6syFfgW5ho5FFGEUatTNU0pc/7x9JgSmJb6+I0TQNFrbUYxbSkbyAHwfQNYOY5wLeg2AF3s+a6GvWMkHEA1nZPVUXpjqBLYtimgeXZKmlGQC4nabco0klm75mcv765zAKmPN+n6fpDBQ1XEqE4qkdP/wnvTltHIJmpedIAEPlpwgmYrnI2smaptNfi/9xTIO+KeF+N+lb0o+AG/seh9AVAOQ7fFlyQks+wAGSw6uv/NZPLdmR9BJC/C0lHU7BiIEnnMAJnbnkLW9Vqrq/Sc5gQGPOXTnMpEQSa83R/S5yfNQJ7AcPL0He/V46yifC3M75PnsBQZQMOYp43IoK6HOgKcVqyYgVQMoO26EEaqaVMGvzGtb3hp+61cexm+eXYeuDhsZO6zWG/cBeGNjBmBMQEME5wFM6Mxhgm+KAKIMgF8ez9Og45S/oPYUyujpyEQWT8wH4LIGQJHyAq6Ixt1feZtXffKEuZPwuXcdFnwGgL16vQlcdsIwUN0ECBiAJgw0n7UCDYQ1AHncQoSazYTOnJcIFgkDjUraMuHlyTmhKxcflI+sbUUWc3dHJiDs0XpK4T4sUckLns1YOU1dJl0imKqW5/z2f0mSLe+v1ukH4mGg+0/txl0ffgveN38fABU0gATNK8kEMlBysKO/BNsiTPNr37NEXXaimiMTivU7BlB2Bb5y/8sxE5DONBH6APyxRHwAiGgcjN8+uw53L1mPmx9cESlzPXNCJzbtLkQ0QTbdeOvDa6Uqx/RXun8gLM2iKwWRLgqIEyBDLT8kntH5m/XnBN8rC16qr6y/GDUBHTqjN6aN3fbX13DGVx+JmSPlaqD5jB3TQrtzGWQsKzETeLviAzAmoCGC8wCueetc3PWRk4Pt7zxsemBmCZ0zauihG2S/9uSzkcUTiQKS1HXbImRtC6tvOg8fedv+EYlbxkXzZuLqU+YCAK57+wG49x9OxVGzxgOImoBKjovfP78eT70Wag5BaWGNRN2RCTUAllDykklKCBEwtgldWS8RLMGm6jjRMFDWADhyQweL4hoAWx1kaWez1AWKF4Eu2Uk1xwH6UhBqFEvWl/aSFpAcbw9EtSmVAdgWYf6cSUGRQJkByENO9gFU0AAGiujNZwIH+A7fBFB03Mi84T7UOd83Uii5Mc3KK0kdXd5qIphMVOUwUJnZfP/R1wB4Dt+12/uDY2ZO7IQQwIpNYSIUnz/rm1m96CU1CqiKBtARZQDcnc+KMIC4Bihv75BMsrIJSJ5ztkWwJWFooi/IqBpLf7EczMmffegkvGW/yZHfS46LlzfsxqbdhaD2kVwKQviJgB3ZaCYw4NGNrE1Swya9r6HTRAE1BuwDGNeRwdwp3cH2fSd34WuXzAMAvLRhFwAEv8smIC9KxitTEPEBZO1AU5AzgeXFzjHAOslMdsZZFuHwvccHDKgs5QGUHIHrfvosLvnO4wD8Wj7+jEpKBFNNER2SU1ogJKa9nVn0F8qRKKC4DyC8BksnEyswAFeIiEOvuyMTi1YBvOgLRhhuGz8fM7PP/34Zvv2nVwGEPgBZBVdL+XL7vySne1wD8L5nLAoS2Bj8Llht11UtlceqxssnScAbdw2ir+BgnMQAOClMjQJiRszzTG4dKPsAVHt7aAKKjzcaBurtsGZbP1Zs2oNzj5wOV3hrIyNpAACwYtPu2PmztoVcxjMBqfO9GgOIOYGraQAak2VHJgx+yGdDB2pUA7AiZkZm5CoD6CuE5dRzGYqFsRYdN5gja7cPoOxEs8JLjlfbytMAombc7lzGjwKKm4Dk63RLWkyjMbYYQNlFR8IEZCL8zOs7AABH+hJ4GAYa1r7pyWeQz1oBMcsnZALLE4yrUeqSbHRliOXoBT6L6tyU6VnECeyHu1oWST4Ab+wRp7QUBTShM4v+khO5RsQEJEmIAIKQxfGdySagsutGCGBXzg6eiTzZ5UbgPB6dpMzvYs22gaDqJK+JTbsLoc28HCc6ci2gwVK05IVacoGf5QF7jcPrSuKf7OsBQukMiL7HQANQnMDyfX3uXYfhpncfif2nduOZN3agv1hGVzYTKYTH15K1mrDBT2hiUIvROU4lBqCLAnKD8/J+j/rP+MxDpvnXcQPCxFFqcgvHMAuafNOGG5uzScXg+Lgu1QQURAHJ/gp9JF0h0ADCSD+OwnMVAUatL5TPRU2lDDkRLGNZ2mf65q6QAah9Q9iH1JPPRIrB8RiiTuDwt/GSiTo0AaHhSMUAiGgBES0nopVEdIPm99OI6BkiKhPRxcpvVxLRCv/vSmn7cUT0gn/Om4k0On+DwT4AHXjBPvPGdkzqzmFv3w4bhIFGXmYWRIQD9xoHwK+6KVcDZSewNMGIKBZ1w9AtioikSeH4ZciLS3ZMyfHaMQYgawACEROQEFFnMYeh5WwvdFFmMttTmIBKZRExgYzryATPJKneTmAC0jAAmcDyWBxXBGNY/qYnjXLDD0bWd/q7fh7GIf/yv/jHXywJfg97vTJB9f4fsNe4WLw+MzAmuHLGsvwek53A3vaJXVlcfcpcXHrCvjh234l49o3tXlZ5zo5lp8dMQErfhUI5LEb3lxWbsW7HAEquG5NWK0UBOSI0+fG1Hnp5E6b1duCQGT3BfrxOOEpNZgD8TrJ2sgkoqecCAOQyNrpymbgJyKLEXsJyRjWbeInC/TkEWDUBZSyKmBl1RSH5e2Dasq1IeDLgnXO93+957fb+yDWK5TC7e3xXFpZFkfXvZanrNQDZtxaYGVvBAIjIBnALgHMAHAbgMiI6TNntDQBXAfipcuwkAJ8DcCK8pu+fI6KJ/s/fAvAhAAf6fwvqvouUYBOQDrxYlq3fhSNmjg8ib8gvRVtywsxXThw7cFq4MORM4MAEJE0wXfRLeO1kDUAuBVGpUXukI5ZffAoI4+n3FMLQUIYrO4H9CSdHvbCdf2J3Ftw0nG9pR38RtkXaUhqMkhuNA+/KZbQagDxO3m4T4axDp2mlaiBkeI4rcMTenrbG5ruSE01YCvIAhAjCOu9esj5Wv0cNA91/6rjYPVlW9F3ITn6ZSPE8S3ICT5YSCY+dPRHb+0t4cf0udHfYMQ3gyVXbsFiKGCsqJquyn70OAK9s3IP3fefxSJEzhpwI5kXWhL9FS0G4eGLVVjzw4kZcfNysCNHj8XOmtmy+Y+KZtb1e1LVqADnbwrgOG3sKUktIEdcAGAfuNQ5rtw+EndVKTkDI2UdDFPaCiJmApPvXlYUHPEc854LkMtHETgDYvKcQnHft9oFYdFvAADqzsCgqqPUXy56mJPmxDp3Ri1MPmIIjZ44P9uP50IyuYGk0gBMArBRCrBJCFAHcCeBCeQchxGohxPMAVOp2NoAHhBDbhBDbATwAYIHfEL5XCPGE8AxbP4TXGL6p4DBQHeTFO723I/Jb1peAAwbgT36O2d+4qxAQJzUKiMEfKyXxyLADU4MbmIBU00akUb0TnXgs6auESE5MA8LoElY55Yic7f1FEHkRQswAxvkS745+LyGuUmp/yRERjaK7ww6YmfoceAHyPVkW4XtXzscrXzwn2Ee+Fi9UVwhMH5/HxK5shAHIppmAAbgC63xpDUBQXjtmAvJNaPtOCvNDGMzAHI0GkJPGV80JPKk7lPD28/1NW/uK6MxmgrLUjD+9shk3P7Qy+F4MonVkH034ecuegucDiDmBQxOQWl6B36+3n8A9z61HTz6D695+YITx8mceo6wBDAQMwAt/VsNXAWgl+V5/PeUyhAldOQyWXEnDi+cBMA7YaxyECOtz9RXKgb2cc3xs8piAWstKbTLTmeAD+OvKrfh/dz0fjF3WoPNZK+glAHgMIKwGSygqDMC2KMJg+gpeoUI27xXLLg6Z3oMf/+2J2HtCPhxbtrVhoDMBrJG+r/W3pUHSsTP9z1XPSUTXEtFiIlq8eXO89nYtqGQCiqjvyj4Z/2WGPgCPWF518hy8b/4+uPyk2XofgGICAvR1XHQaQGBqECHfV52bSRrAlj0FjOvwxshZtDofgBCh3ZEZQCSxp6/oEfmMRzwLJSeQeLkkrp2gmgOe83JA0QB4d9UUxnZOJm62xiKYld7LoOQItYkwZ0p30HOg7IjIfeYyYS2g9RID4DLbgQnIHxOb0PbRMABLes9AVAOQx8fzqb9YhkXAj685ET+/9qRg+5RxIQPg+eQ9h7gJSEY+a2HDzkG8/OYuxRYe7nPw9N6g7aEM2QRkUdQEosbJb9pVwIzxeXTm7EjEG4+fI8xkiZY/c8FFV8TDKjmUV8bsyd3BcWzOY+3TKwWh9wkd4JtguTx7f8kJCDmP07bId75G155nFgzPFZqAkhP/spmoCUjOAZna04GNOwcDBtCTz6LoqBoARfxq/cVyzAnMQs5EyQQ0povBCSFuFULMF0LMnzp1at3n+ZvbF6EsxTGrkKUlVc1jaYaJI0ur3R0ZfPniozC1p0NbDTQSBVTBBKSTimSfgiupiDJ0seGDJQeLVm/DiX4+QSwMVClNwUSPtZq+SGZnCb35LGzLwoMvb8IfXtwYkXi7cnZMMpOl9JLj+QC6czbOP2oGTt5/cuJz4Ge6I2jOEieCeh+AR5QndGZDJ7ArIvc5rsMONID1O0MGwGWcy5Iphc+dz9qR/BAedyUNQCYOgQ+g4JklTj1wCk7cL7z/yd2hlimXvu7usNGZtbVRUHy959fuxIKv/yUiXcvEYd9JXZD7Wsv3y/sSRSOSSo4XoMDPbdPuwUBLkd+pPFd7lHfExDNjWxIDVKOg4nN938ld/nWsgPBxfgEzeJ3piE10XJO/v1AO5g0zLYu8eH+56T2PI00UkIysHWayAyHTAIC5k7uxp1AO5mVPPoNCyQ2KCfZ2en5DmcGce+QMz1cihYHyepV9APyaWhUGug7APtL3Wf62NEg6dp3/uZ5z1oWH/JaKSaGAspSjagCc1FJQ4nJlRKqBcjE4JQoI0JuAdFKRLTGUgEApDCCiAfi/PfXaNgyWXJx+8NTIvQRhoLIGgJAosFNPXgDb+4sY35mNEIqIzdu2YkRGXhRe6GwZXR0ZfPP9x+LMQ6dpE8GAcAFu9m3KsomEofMBsIQ4XmIAZcUE1OWH26kmoKITbXzPBHXAZwDTevNSFnIoUQKSD0C6jlxSRH7u8lhCH4CsAYSEtDObARFp21MC0blXjow9/MzhwWrhNSaAXOpEdrSzaYKvu3FXyACifpjwGLliK98r78PHqLWgdALYbF/T6iuWgwRNZgBeHgxpNcJxHRlM6+3A61v7/eMlH4AVvi+Ov5fnHBeDY3T6912RAVhRDUA21U0bn8eeQhlb/B4Ie4/vRKHsBGVGevNZ2JIP4N8uPByfv+DwSHiyZ6L2zhkJr05RDr5epGEAiwAcSERziSgH4FIA96Q8//0A3klEE33n7zsB3C+E2ABgFxGd5Ef/XAHg7jrGnwq8OOZO6cYVJ8/W7iMTOdXT76m6YfVLWQoIjw99AEExuIgPQE/45GMj2zivQIR2ejWEVK0QKYTA7Y+tRlfOxklzJwdjyFik1wDcMCx1nEYD2D1YRm9nBvLSU23eqgYgmy/KricBydvCKCC9CagSA5Cf50DJgRAikBBlBuA5gcN31N1hB5Uf1+8YxDTfxyPbqG1pIXoagMfcON6dCRqXGAg0AOne9pNyS0IfQDkyX9hkNlm6P/n5yNqlDvLzj2oAwNmHT8O4jownNPh9b2WwdBqUV5CIan8p2hVt0+6CpAHIJiBZs4qOUY0CAuImFZ3PiH0tm3YVAsk3MAG58ZaQDMvy/FPspB0oOnENwCKvDLNQSkEoGkCnZk2r8ExAUcGCsVdPB/YUytjoh4TOntwFV3hd/DoyVlAMjk1A08d3+ppSNBOYndeyBsCjbEkegBCiDOA6eMT8JQC/EEIsI6IbiegCACCi44loLYD3AvgOES3zj90G4N/gMZFFAG70twHA3wH4HoCVAF4FcF9D70wCE4Yr3zIbe/XktfvokngYc6d0Y8XG3ZE6+yp0GoAs7DAD0JmAdCWh5cgfXccgIUTMB/D4qq146OVN+Pg7DorGp2esqhpAYAJSFmxvPhup/NmtJD7ZyoKWfy+VBdZtH4g4tOwEX0igAfgS1GQNA5AhRNivl3wGsGuwFDA1meh25TLI+GGgG3YMYPakbn98YaJSZ9aOhIHy8SzVsyp/hB+dwe9E1gD2k6KG5D4MMnHRRQHJmlRnwAD0BEnWwGR/zWDJwb6TurDPpC6/eJwbI5qh1oSYE5hr+gQRJwKY5JupIk5g6RiVAUSigII8iOh71oX3zvCZ7KbdBUzsVjSAIBEsvuaICD35TBC40Fcshz4A/zp8n3KxOyCeCZyGAWQsUnwAtj8Or5SE4wq8tsXTRth/tHH3YOBfkyPd+TzsBObS8JynJGsAaYpB1otkb5MEIcRCAAuVbZ+VPi9C1KQj73cbgNs02xcDOKKWwdaLIGu1AlGRJRPVB3DEzF48unILdvSXvCSXCmGbZceNFINj8LzXMQBdd6VAo5DyCqJ9b8PY8JxtoVB2g4gMroTIkBlAXs0DcDxC0ZVlDSBevnmL1NpPtjVzhq2MCANwXazd3o+zDg3HwwRAfQ6dkgnIomgiTBIKJTeQEHs7vTyG3YNllF03pqpzLaDt/UUcMr0XRH5nJ5+Q57M29hTKcP18B9Yg9pkUbRh0pFSiA4hKgXMmx01AjisizJgZ7d5KIyKbCA5EEMWS5AiWNQC5VLWX++AlQDl+IIJqbpHNZkRRYszzY5zEYCbrfADSZ76Xab0d2LirgP6iExRu03WDA/QlPvaf6jHkMw7ZK/AB7Ogv4Tt/ehWDJdcj4rpIOZ8BcK5Gf8EJnl8Yio2gDLOuFhBDV9Pp/KNmYM7kbnzz4ZX+MdEoIH733blM8CxWbtqDyd254PumXSEDkF8Hz08uD758424IEZZLnxjxATTPBJSKAYx0sDQxsUrhMoaqARyx93g4rsBza3dozT9AGLZZzQmsSr5nHLIXTlLqi8j7l92wXo/akIWv05mzUXTCOG51/DIxlMcvRGgqyOeiCWOM3nw20qlq50ApMJdkM1YsCkiWXHcOlLBlTzESTaOWgujM2hgoOREfwMSunFZSVDFQcgIJkRfZzoESSmURIVSdWS8BzXUFdg6U0dvplfMuOmEmdKd//2VXYLDsBqGJsl0fAA6b0evv5x3XLREOWW1Xy4Uzjps9Eb/6yMmYt8+EyHktC4AjEZUEBiATKrUjG0fLcItKvic+jgsGCr/CbCbCAKImICAUmHhfNYiC59n03jw27ir4/ZetyP33lxwQhcRL91ondefw/L++E+Ny3nvKZy0sXbcT9y19M7gvXRioRYRx+WwQzdVfLAfPT07GJK0PIOq/0vn1crYVEURsixQTkB0cywR/1eY9mNabD975pt0FTFUq/AJAh+SrKDkuvnr/cozvzOI9x3pydK90XT5qTEYBNQLb+6ozAJlgqOUiWO1/+vXtiQyA446//+hrwYTUh4FGJd+vXTJPG5oqh345PrGRpakBqWxDV85GsewGUnUsikluriJ9doWfHGdbwX2pceuqJL6zvxQsRp0PQCZ2/BwiDEAxhbFGwgu3UHYramoyBkpOYM6IMADXjWhp3R1etJIjBHYNltDb6dVyKiomIMAviywl0nEk0LuO3hsnzJ0UEEgmMB3ZuHQPRAWKvERciAjHzZ4Yuxd+Lp1VfADynFJLVdtEyFqWV5BQ8QF0d4QZtt4zixIk1g7k68pmuKCHhnRfhcCe7Zn4+otObL+BYjlYG4BeA7CI0JvPBkx/YlcucOzyfWl9AISICai/GAoScjKmRdBEAUXDYHUagG2RkjcDrQkonw17i6/a0ofp4/OBprBpVyGYm7JQw+fJ2J4wtWTNDpxzxPRg7kdph/e/VVFAIx5pKlfKNsYO5aXLfYOTbIX8wjbsHMQXF74U2QZITmClaYpOtZWPlX0AcsXPwZIT1QDKYUu/mBPbZnUzar4S8M6dzViJcdC9nVFCtGMgZABZOx4F1JG18JWLj8JhM3oDqU9OqOLHLLetBKILUOcA1mGg6AQOTZkBlB0RsVV3+ZJlf8FBsexifGcW2YwVMQFxFAiXvOBxnX7QVFx2wr74ynuOwi/+v7cE52Tzm/yskxhAZ7b6MuPn2FXFBCQHAqjNaizfWVp23Vg5jHEdmYDIC8QTwYJgAMnENCnCALx9dY1Zpvd6DMDrvxyNmuJkp3OOmA4gWjFVvXfGhK4cXt0cVhm1LH0UkOwDKPomUX5+bC1lB3LcCUyKlhjOv0N9Le/4uZNiAp/OCZy1rMj7mtbbERw3UHKCeRHRAAIGYGGw5GBrXxHTeuP+yX0ndVXsBzJUjC0TUAXCIqvsagalZRG6czb6ik5MImDoVNRoKQjvPzc8qXScvN2REssiNf/LTiQUsei4kWJYkXvLhI4xebG5vg8gY1FAzGM+gHyUafYVypIDK+4DyNkWLpm/Dx5buQUv+pm5+8jx9KwBOKwBxBlANQcwI2IC6pI0ACUJin0AXF2zN58NGpaEGoBvAnKE5wSW4rH//d1Hxq4tRw8xZCIgh/amcTCGDIBNQPpjZD+QqgEwURsse1pjViFwg5ITmBKkalkDkPte5ySCxeDzsbN4QDIBBR3RSg4ytoVvvv9YOK4InPyRe1eI+6TuLF7a4EZ+15kELfLeZdFxgzUemIBEaALSh4FaEYIsE/q3HjgFt15+HGZN7MTvn98QuaYsHPI7ytpWJCR2Wm9e8UExAwjPE3S5syjwUe6lVCBY8tl3IJexcK8/BuMDqBPbfedtt0bNY0RMQBoi392RicQZq9BOUI0JSHV+JjEAXpwvbdgdOPvUrl+BIzKbCZqvWxRvvC0T7IgKLjzJjxdtPmPFfQCdWXz/yvlYs60fa7cP4Jwjp+NDP3zaG7sdJyJq+0aLopKkmgjGY+vMxW3P1TBYcoKY9ogJyInGwHMegHxPWdtC0YmbgN7134/izV2DiaY+BpvfMpZXs+idh02LXFNXZqASVA0gyQQkS7GqD4BLJrAJSB5DV84ONQAR77LFkJ3AU3tCghSaduJOYI7cGZBNQJZkAvKFC87KVaGunZmKgzzJH2RbFIyBwy+ZKHMghk1+GKgr4i0hLT0DyGftwGyprnddJnDGJvR0hILSvpO6lCg0OxhLeB5/m/SOpo6LMgD2KYUagPYxDAljggHs6C9iQlcuEoalIlrJMb5gx+Uz2LS7ELH5VoNs+0xKgEpqEMLbb/3zqmCb2veXJwQTmN2D5Zj9H5CJcjT9nctB86LuzNmx6pe9+QxOVJzUPDZur6m7FhPDcR2ZyHMnf0EGTWv85xlxpqaIAAJCh7XKAMqK9Cu3ouR7yvnx13IUEIAgUSxJ02NwBK5teTWLVNgJ0mUSYgwgIRFM9gPFnMBEfly5Zw6RhYvOnB08L9cNM2RV8Ps4WCp0CIQMQDaVfuXio/G759bjqFkTAHjmQzV7uF8yCwF6H4AKLg0R3FfCGmETEBDWJGJB4qzDpuGL976E95+4L/68YrMXBippT0TRd5Szo8ySUdkEpNcA5kzpVvwLmWC8wXmycYa6l8YEBMiZwMYJXBe29RUxqYIDGIgSa50GwGnvadR5hkxEdJ2wMn6Egg46zWBAYQCOZAICPJOAbuyyvVGWOATgE0vL3y9+b70aYiz7ANQFzcyTJ3ZPPn68TRQ8h85sVGMAkqVfFWH3Nu+95DIWtvUVUCpHTUCqr4I1ADUPQEa19yzXiNdBfiypTED+AfwOkp6BXEtml+oE9jUAjkRTJdwBqX6SRXrJmt/bBfP21m6XGf7Ung78zalzg/mgNQEVncgxKeg/Zk+ORl4lBYRZhEDy3ugnELIgMXNCJ1754jk4dEZvaAKS1k/ZLzMd3F+CyU4VBOSAis5cONdlk93cyd2R4wINQBMGKmuHe/VENQAGP7OEIgZDwphgAEfvMwFvP2SvivvIk0FXMZQXZDXJUIasLTBh3C2ZWCqVxtVJPfLiH5SigHgS7ZHs8zJygVpOkC/pJceE5hKdqUIXj29LDEBlYB0KQe/JxwmZZVGgAbCEJc/tpD67KthfwaF++03pxqrNfSi5IvZs5efpOYEpkgms3ns1TS8I9U14h1aCeSEJHArMiV7jEnwA37hsHt7ml/mIh4F6GoAXBhr3AXBhvsAHoKHGCw6fgduvPh4fOX3/yHadCUj9TTYnhv0QyhFmnEoDmBTVAJKOsSQNYGOgAcSfW5gJHK4fR9GQMgkmO/Xd6cxGGSuaITyxOxfVFDriTmB+PvNnTwq2TRmXwADQPCfwmGAAH337AbjhnENS76+ToscNUQPgibpDaqKdJD0CCQygmOAD8CfsrsGyNqQ0F9EAwvNyIhiXMdYxN50GEEp5BHWUPLGZAOuiWbgyJxBKQmqrvDTgiCUmZAdN68Hyjbu9SpjKs5WJXW+e8wCSNYBqRPsfzzoIQLLDWn59aebMl959JBb+w1sDIpCkARwyvRfffP+xADRhoOwDcF3fB6AwANkHYOkFkHzWwtsO3iumHehMQAz5PGEUkLffYCn6LlIoAEFxOPm+dPAYgN+Yhn0AmrnDiWDRlotWVANIiAiqNA943WQ1a05eS6yVUKDlhYLT/DkTw/MlVCo2YaDDDJ0GMC7QANIxAKLoedhGuK0vXLQ1awDlqAkoaEuYrewDkNPO1YXdXyyHGoBybxZB6ziXfQCqcMaTmO9dLRim3ptOA0jjNAXCMEiWrA6e3oO12wfginjRMfm+e/IZzwlcDtsgqtespuldcvw+WH3TeYnzYYLUKjOf4n7yWRuH7d0rjTHZDxK8b8Vhb5HkBHZFTKqVeyio5aAZuvkDSIRdQ6RkAp9VBADvmLgPIJ+1cMM5h8TMPUBc60z2ASDmBNbG8xPBdT2/0+UnzcZXLj4KR88aHxEKZE1WPkelucj3IoccczZ4XuMr4CmpJmZOGZeraBqb2tOBk/efnJr21ALDADTQqf9MyNK+BJnLA2HTi6gGUMEprZG05BjwaB6Ad+49hZLeBORv22dSV3BNjt1etHp7sLjVe+MStip4S9a2oOoAgb/BH79eIgs/X+PbkE87MCz1nRQCqYJT9Jm4HyQ5LlXmGvotvJK+uYynAbBjXb33fAIhrAYmADLhqEVrZJy03yR85txDtb/ZltfrWbUIcBho2fcBqE7gsIcC18iJn7tay9SsZs5GNYCoAOCNK/zMQkt3LoMPn74//vTJt2uvd/dHTwmYg8yojvZLcQDec+AwZW7M3qXRnIi86xYdF5PH5XDJ/H38MFjtpaMmoITnAYRmQL7nJZ99BxZe/1bvOKUWlXwfKn350yffjiX/8s7E65y8/xT89EMnxaKjGgHDADRohAag7scNWrb1pTMBVfgJgJcUxuaL7lyoAegWME+8Q6b3Bp9P2m9S4HRKcgKrOQAMzrLMVtAAWOrTmdOC2voW4ahZE7DyS+cG2aSAV7qhFjD94QYh3tj0PoAgece28OwbO3D9nUv8a9ZmAkrC7//hrXjyn84EgKCvdD0MoCNj40On7Zf4O59Tfv7cO7fsm7bUYmdFx/VqVXEtIAo1OUYSA9DlATCi0n7cqS+fnyX2fzjzwMR7Azy/HUcD8btb9vmz8csPnxzer+8DsAh4Y5uXOTxRk+xpEQXhs/L9JfkW0pqAuEQL3/+Erlwwv3QZw5ZkApLR3ZEJ8liGG4YBaKAjWnLlvzRQJUie+LIjKkm1BSozB8ArbxGPAtI7gdds9xbHoTN6Itd8z3Fe3REuwayqu0kF2VjyzNpxH0BgbvLHr1tAPIYkDaiSBvDP5x2K84+agW9cOi/YxhqAXGM/a1u45tS5ga2e7b8X+dEtKoOo1QSUBK9GvUf4Z/oJcLp3khY/uPp4HDGzN7ad33mXXGqaKmgA/n6DZTfInQi0QYn5Js3Jik5g2QQkaVoMmUHkszZW33Qerjx5TsIdh2AbO5tqujsyCgH3mwF15YJufzpma1kIwpvlcjBJJli5wF8lBsBl5nUCo2xyjGkAQ5gPjUb7jKSNoHuhHD6Z1FAGAG6/+vig7IHKRLpy8S5PlfrpVuANAIDn1+4MfQBSASydDfc1v2XeoTN6w0YzArj8JK83wstv7gYQV3fVMhCMNBoAQ2dK4YWQxAAqRQH97Vv3wzfffyzOOWJG7Hxyh6qMbeFfzj8M15/lSZqXnrAPvvR/jsTn3nV4MHYZjdIAZHDFz50DpSp7JuPtB++FT54dD2AIm5+H9xE4gTU+APZDDBQdTwNASOynJ8Sfy8gETLuaEziuKSR14asGnktJiWBsnmSpf1JCro9NhDf9TnByqGUaDaCSkMYa8ixN61AZLNDwqdJUuh0uGAaggY5Dyy0ak/C2g/cKqjyqhI+IYhExlSZXpaS1OZO78PzaHYFUKxNM3dgvOsZrtzx3SneQXTi5O4e9J3Ti8xccju9d4SUyqVJwsgnI+69b2BzuGtrWk01AOnOCdz/VTUAy8+Tzyc9MtVXPGN+J95+4b0BMVCYf8wHUqQHIuOItcwAAx+w7YUjn0QkKat17IAwDLTpu7DfWFJgByB3BZA0gcQxS8bL4+KzYfkldxGoBnyPpaL49Tj5LqvVFJJVb6Kmu7aQNQjjz0L3wzfcfg78/44CK+wUN5/01oav+2yqMiUzgWqGVIlhydt3YbzKYAOsISE8+GzQWAeqXjI6bPQm/emYtXn5zF4DohNXZcD959sG4/qwDkbUtnHbgFPzne4/GeUd5ErSsisecwAkMgDMSvYUdfVZ8/cGy3rkKDE0DYMjvSI7m0JUt1kF9TrE8gDqdwDKOmz0Rq286b8jn0WmkXQEDiGsAuhwFvr8BP4NczoSdkYIB5CqYgGRCqjMBDVUDUBsm9XRksLtQDuYRm3WSqv3K02yaVG9HlwcBpM9DISKcf9TeVffjQIhFr3m9sE49YEqq8w8HUr0ZIlpARMuJaCUR3aD5vYOIfu7//iQRzfG3f4CIlkh/LhHN8397xD8n/1Y5U6vFYMmnXCUdj00/ukgiNSmqkgZQCfN8ifKFdR4DkO3AOsJFFNYxJyK857hZWsIcJrZ440oyAbEPIJeJm4CYAXLdlUoaQNL9p9EAZMhjCAhjFalzOExAjYKOgMp1aBieXV+Ovon7AOQS2vz80zCAsBposnYsj9W2KHgvSZpeNfCclTN4gTA3hc/PGgDXJFLB92lRtBNb0vzTvftTDgil9vmzJyZm7erAz/60g7xIt+Ok2P9Wo+pKIyIbwC0A3gFgLYBFRHSPEOJFabdrAGwXQhxARJcC+DKA9wkhfgLgJ/55jgTwWyHEEum4D/idwdoec/yIhP2ltn868KTVmWJUE1C9C4NDDV/f6tn2ZYKpc2CnBYeqll2B983fB28/WM+ThWQCUpdQOg0gPF6HpEiUJKgVOXcNlqtKndUZQPtYR3X3wnbluAlI1oaieQAA0F8oB4lg/NzSFN/TxfczIg1jMqE5jnMtdKGjaZCoAeQ57NnTptmsOSFBA2Btccq4Dm2TJhXq9lVfOjciZNz1kZNRC9jU9k/nHop/fMdBDdEuG4U0s/wEACuFEKuEEEUAdwK4UNnnQgB3+J/vAnAmxe0ol/nHjkicdtBU/OojJ+OqKtELoQkoOZeAJa5KeQAqZLW0uyODqT0dQZ+DiAmoTqYCRBfQly8+CicnqKqyE1iV1nnRctirrra/VUUDSAs+XD4PZ9BWZQCZ6LVVxtlOizSXiT+n7kADiEr8kfIGmsqduwbLgQ8glIyrvwddQxgZfC1d/+Ahm4CUAooffbtnc+e4+EndoRNYB34karnlNF3neL9KPrm0sK24H7DVSPNmZgJYI31f62/T7uM3kd8JQPV0vA/Az5RtP/DNP/+iYRgAACK6logWE9HizZs3pxhu83Dc7IlVJ02uAgPgiXyUn8xSCwNQG4vLSSERJ/AQJNe0VThDBkA45YDJ+PwFhwclEZgBfeysA/FvFx2Bc6VoHQbbXquZaaqBiXSk8mJH3DSig8ooS4qUmdYOPBzQawD+fUpziBvCMNT6RwCwa6AU1ALi55aGAahlvmNjtOK/V3IcpwG/I5UBvOvovbH6pvMCgWViV2UnMN+f7AAGkn0AYwnDoucS0YkA+oUQS6XNHxBCHAngrf7f5bpjhRC3CiHmCyHmT506VbdLW4GJki6DkNvccfncWhaGurBnTUxgAEOQXCt1TJMhRwEREa48eU7A8GQN6PKTZmsZZrU8gLQIwgSlhTyuI84UdFAJGWfJHjlzPJ7+57NSVyQdDuhMYjpfh+2bXRhyfD7bzXcNloJqoJlAEwPuu/6tWPgPb00cQ6bKOws1gLg/YKgaQMGpHHgR+AASNABuhTknZZnpsYQ0s3wdgH2k77P8bbp91hJRBsB4AFul3y+FIv0LIdb5/3cT0U/hmZp+WNPoG4xDZ/TiDd+uXi+YAGrLMvvbQg0g/cKQCVrGsoJm5V7f0sphoGmRZENVIUQ0BR4IG6SkYUBhFNDQ5A8mEDJ9YdNIv1InR4VKlI6Y2YszDtkL/2/BwRFHYTugkgYgB6XZGg3gkOk9EMJr9WiRl5PAiWCWZALiNojVxpBEzLtyGb/xUvj7tN4ObN5dqDsMlHtxHzq98tg44W7mRH2phCVrdgBAEPnGaDYDePgTb4u1WG03pGEAiwAcSERz4RH6SwG8X9nnHgBXAngcwMUAHhI+lSAiC8Al8KR8+NsyACYIIbYQURbA+QD+OMR7GTLuu/6tQy65ykRelwB125XHY/Hr2wNJpRYJWN7VtsLkl3OOmB4h+kNjAOk0ADkTmMGhh2kcuGEewBA1ADuuATBhVJvbq1CJUkfGxm1XHT+k8TQLWgbgawByhzDbolgG7v9+7LTge08+i50DoQbAJpA0hDBXxZxz7OyJWLdjIGJWmTO5G0vX7apbAzj9oKl46P+ejrlTuivud8j0Xtz/sdNw0DR9gMbUHo8RHavkY8jRQQCw6DNnBYJMI1Bt3O2AqgxACFEmousA3A/ABnCbEGIZEd0IYLEQ4h4A3wfwIyJaCWAbPCbBOA3AGiHEKmlbB4D7feJvwyP+323IHQ0RQ3X2BCYgjQ9gzpRuzJnSjZf8Xrm1EEArItlZOP/ovfHalj58+txDQUTYZ1In1mwbGBIDSJuhyCwyqgGkZwCNcgJ3aExAFx83C795dl2QkDcaoM0D6NCVFom2GFSf7/jOrO8DECBITuAU7yGoBpqgtZ28/2T87rn1QVY5EDe51IP9qkTdMQ6e3pP4271/fypKroitbZ43vH1qDaGdowWpDJ1CiIUAFirbPit9HgTw3oRjHwFwkrKtD8BxNY51RKBSIhijUlq9jFvefywWLt2Ae5/foJiAPCfwTe85Kth2yPRerNk2UHdoKZDeVstOYJnYc4Z0Kg2Aw0CV+7/z2pNS9wKQryUTulMOmJIq+YoJ5zsPmxZk7LYrdCaUcRIDsP3kL4sUDUB5vuM7PQ1gsOSiJ5+pKwooSWg502+4dMLcScE29lNt3BVvBD+cSGq1qGoAYxHtE+w8SsBEqZItPK0J5LyjZuAtftp4tfjlQ3wJaI1fFbGZcN24D2C/qd3+tuqrKSkR7KT9JuNIqdxvNVSrFVMJHPVz6IxenHpg+2Rm6qB73+wELpScMKrKijb8UU2MvZ1ejkR/sYzuXMgA0kTDTO/NI5+1tB3eAI/Ivnjj2bjiLbODbWyTX7ej+XOyHvB9x0sajh0YBtBgpNEAqmXCymCCKq9Rne+AHVzHz5kU+63R0PkAfnDV8bjjb06ozQncIB9APeF8zADqdVAOJ3RmSdYAio4blA63LKU0hHJvrAH0FRx0ddhVwydlnH34dDx2w5kVG9V05TKRsXLSZLvawm3N2hpraJ9Yt1GCIBO4QimBoKduiigYtunKRE6vAfRi5RfPGZIJKC0CE5B0rcnjOnD6QenCdBsdBloPOLa8Xgdlq8EJeIWy65vN3KAWEEOdX72+E7jkuBjXkcFhe/fi4U+8LRWBtizSJvVVwt4TOvH7vz+1avZ8qxBoAGOYAYzM2d/GCKKAKjAAltKSGorLOOvQvXDQtHH4x3ccFGxL0hwaQfxnJYTSyeA8gHqvx+MfarZtWCqgcsSPDhwtlBQ73m44ZHoPPi31teZSEF5Ip7eN+wEwkpzA/QUnYCDNls6PmDk+dXXN4UagOY1hDmA0gAZjrx6v3sjeFQps8cRLUyNlQlcOf/jH07F6S5if0Mz45T9+/PSqBe/kTOB6wAsuTRniSsgmZIqmwYdP3x89+WzQFKfdIYdzAtGm8bJJUTYBqfb63s5s4Pwel7Lt5mgGawCGARg0DLMmdmHxZ86qWGAr0ABqSISSE8uGmkBVCWmqYDJ7qHccnByz94ShMYCg8mgdDCCftXHNqXOHdP1WQo6WijAAiSmrJhs5zLfWiqujEUEfiRaPo5UwJqAmoFp1xcAHUIMELZtLWp3C/vkLDkdXzq475+DNXV4D772H2OQ6qVjYWIAcZCCHc8o+ANW8JX+v1HZzrCCI/hnDHMAwgBagligghuzwHKrzdKi47IR98eKNC+oKvwTCuPChMoCj/FIBQz3PSISuIQ5R1C+j2t7levntVOuoZfAf4ZQ2K/0xnDCzoAUIEsFqcKLK0na9hLddwBL73uOHRrivPHkOjp09MSiuN9bwPx84FgfuNQ5X374IgFcXqJJwIJuEakm4G60Y35nFFy46Am8/pK17UTUVZha0ABm/ZkstJYdbLfU3A0NNvSeiMUv8AeDcI73cD54bjhAVcysmRUxAZukDwAdPml19p1EMYwJqATK2hZ9+6CRcdvy+qY9pREOKdgEnHrXalzFa8KV3H4nD9+7FzAmdFQUFudprO/U7MGgdjBjQIgxHxm674o8fPx27B9u7TO5Iwsn7T8G9fi3/SpFZsh/JaAAGgGEABi3AlHEdY9rx1kywCaha60ETBWQAGBOQgcGoAtdpSiraxjBOYAPAMAADg1EFJvyXVvEvdaZI+DMY/TBigIHBKMKErhyWfv7soGNYEkZ6KLFBY5BKAyCiBUS0nIhWEtENmt87iOjn/u9PEtEcf/scIhogoiX+37elY44johf8Y26m0RTmYmDQQozryCRGjf37u4/E2YdPG+YRGbQrqmoARGQDuAXAOwCsBbCIiO4RQrwo7XYNgO1CiAOI6FIAXwbwPv+3V4UQ8zSn/haADwF4El63sQUA7qv3RgwMDKrjshP2xWUnpA8/NhjdSKMBnABgpRBilRCiCOBOABcq+1wI4A7/810Azqwk0RPRDAC9Qogn/ObxPwRwUa2DNzAwMDCoH2kYwEwAa6Tva/1t2n2EEGUAOwFM9n+bS0TPEtGfiOit0v5rq5wTAEBE1xLRYiJavHnz5hTDNTAwMDBIg2Y7gTcA2FcIsZWIjgPwWyI6vJYTCCFuBXArAMyfP79yofpRjh9fcyK29rW2wbaBgcHoQRoGsA7APtL3Wf423T5riSgDYDyArb55pwAAQoiniehVAAf5+8udOHTnNFDQ7s3LDQwMRhbSmIAWATiQiOYSUQ7ApQDuUfa5B8CV/ueLATwkhBBENNV3IoOI9gNwIIBVQogNAHYR0Um+r+AKAHc34H4MDAwMDFKiqgYghCgT0XUA7gdgA7hNCLGMiG4EsFgIcQ+A7wP4ERGtBLANHpMAgNMA3EhEJQAugA8LIbb5v/0dgNsBdMKL/jERQAYGBgbDCBJi5JjV58+fLxYvXtzqYRgYGBiMKBDR00KI+ep2UwrCwMDAYIzCMAADAwODMQrDAAwMDAzGKAwDMDAwMBijMAzAwMDAYIxiREUBEdFmAK/XefgUAFsaOJxmYSSMcySMETDjbDRGwjhHwhiB4R/nbCHEVHXjiGIAQwERLdaFQbUbRsI4R8IYATPORmMkjHMkjBFon3EaE5CBgYHBGIVhAAYGBgZjFGOJAdza6gGkxEgY50gYI2DG2WiMhHGOhDECbTLOMeMDMDAwMDCIYixpAAYGBgYGEgwDMDAwMBijGBMMgIgWENFyIlpJRDe0ejwMIlpNRC8Q0RIiWuxvm0REDxDRCv//xBaM6zYi2kRES6Vt2nGRh5v9Z/s8ER3b4nH+KxGt85/pEiI6V/rt0/44lxPR2cM0xn2I6GEiepGIlhHR9f72tnqeFcbZbs8zT0RPEdFz/jg/72+fS0RP+uP5ud+7BETU4X9f6f8+p4VjvJ2IXpOe5Tx/e8vWEIQQo/oPXg+DVwHsByAH4DkAh7V6XP7YVgOYomz7CoAb/M83APhyC8Z1GoBjASytNi4A58Lr5UAATgLwZIvH+a8APqHZ9zD/3XcAmOvPCXsYxjgDwLH+5x4Ar/hjaavnWWGc7fY8CcA4/3MWwJP+c/oFgEv97d8G8BH/898B+Lb/+VIAP2/hGG8HcLFm/5atobGgAZwAYKUQYpUQogjgTgAXtnhMlXAhgDv8z3cAuGi4ByCE+DO8xj4yksZ1IYAfCg9PAJhARDNaOM4kXAjgTiFEQQjxGoCV8OZGUyGE2CCEeMb/vBvASwBmos2eZ4VxJqFVz1MIIfb4X7P+nwBwBoC7/O3q8+TnfBeAM/0uhK0YYxJatobGAgOYCWCN9H0tKk/s4YQA8AciepqIrvW3TRNey0wAeBPAtNYMLYakcbXj873OV6Vvk0xoLR+nb344Bp5E2LbPUxkn0GbPk4hsIloCYBOAB+BpHzuEEGXNWIJx+r/vBDB5uMcohOBn+UX/WX6NiDrUMWrG31SMBQbQzjhVCHEsgHMAfJSITpN/FJ5+2HZxuu06Lh/fArA/gHkANgD4z5aOxgcRjQPwKwAfE0Lskn9rp+epGWfbPU8hhCOEmAdgFjyt45DWjigOdYxEdASAT8Mb6/EAJgH4VOtG6GEsMIB1APaRvs/yt7UcQoh1/v9NAH4DbzJvZPXP/7+pdSOMIGlcbfV8hRAb/cXnAvguQrNEy8ZJRFl4RPUnQohf+5vb7nnqxtmOz5MhhNgB4GEAb4FnNuEe5/JYgnH6v48HsLUFY1zgm9mEEKIA4Adog2c5FhjAIgAH+lECOXiOoHtaPCYQUTcR9fBnAO8EsBTe2K70d7sSwN2tGWEMSeO6B8AVfiTDSQB2SqaNYYdiO/0/8J4p4I3zUj8qZC6AAwE8NQzjIQDfB/CSEOK/pJ/a6nkmjbMNn+dUIprgf+4E8A54/oqHAVzs76Y+T37OFwN4yNe4hnuML0sMn+D5KORn2Zo1NFze5lb+wfOyvwLPVviZVo/HH9N+8KIongOwjMcFzz75IIAVAP4IYFILxvYzeOp+CZ498pqkccGLXLjFf7YvAJjf4nH+yB/H8/AW1gxp/8/441wO4JxhGuOp8Mw7zwNY4v+d227Ps8I42+15HgXgWX88SwF81t++HzwGtBLALwF0+Nvz/veV/u/7tXCMD/nPcimAHyOMFGrZGjKlIAwMDAzGKMaCCcjAwMDAQAPDAAwMDAzGKAwDMDAwMBijMAzAwMDAYIzCMAADAwODMQrDAAwMDAzGKAwDMDAwMBij+P8B3YSsVxSX1tAAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}},{"name":"stdout","text":">>> [EVAL] 1/62 [00:00<00:18]   loss: 0.0791(0.0791)\n>>> [EVAL] 21/62 [00:06<00:12]   loss: 0.0792(0.1055)\n>>> [EVAL] 41/62 [00:12<00:06]   loss: 0.0755(0.1061)\n","output_type":"stream"},{"name":"stderr","text":"====================================================\n>>> Epoch 2 - avg_train_loss: 0.1059  avg_val_loss: 0.1071  time: 534s\n>>> Epoch 2 - score: 0.4635  scores: [0.4906004693706389, 0.4560643794744097, 0.4240982238655886, 0.45522071863221436, 0.4836794993818112, 0.47155968251437413]\n>>> 🌟🌟🌟🌟🌟 new best_score: 0.4635\n","output_type":"stream"},{"name":"stdout","text":">>> [EVAL] 61/62 [00:18<00:00]   loss: 0.0910(0.1070)\n>>> [EVAL] 62/62 [00:18<00:00]   loss: 0.1392(0.1071)\n","output_type":"stream"},{"name":"stderr","text":"====================================================\n","output_type":"stream"},{"name":"stdout","text":"\n\n>>> [TRAIN] Epoch 3: 0/366 [00:01<07:17]  loss:0.0908(0.0908);    grad:inf;    lr:0.00000250\n>>> [TRAIN] Epoch 3: 20/366 [00:29<08:03]  loss:0.1107(0.0948);    grad:229866.4062;    lr:0.00000225\n>>> [TRAIN] Epoch 3: 40/366 [00:57<07:35]  loss:0.0739(0.0998);    grad:337128.4062;    lr:0.00000202\n>>> [TRAIN] Epoch 3: 60/366 [01:25<07:08]  loss:0.1365(0.0992);    grad:244737.9844;    lr:0.00000179\n>>> [TRAIN] Epoch 3: 80/366 [01:53<06:40]  loss:0.1288(0.0974);    grad:110750.7969;    lr:0.00000158\n>>> [TRAIN] Epoch 3: 100/366 [02:21<06:12]  loss:0.1002(0.0978);    grad:138876.5312;    lr:0.00000138\n>>> [TRAIN] Epoch 3: 120/366 [02:49<05:43]  loss:0.0909(0.0959);    grad:59067.0664;    lr:0.00000119\n>>> [TRAIN] Epoch 3: 140/366 [03:18<05:15]  loss:0.1017(0.0955);    grad:60974.1641;    lr:0.00000101\n>>> [TRAIN] Epoch 3: 160/366 [03:46<04:47]  loss:0.0730(0.0956);    grad:95599.8750;    lr:0.00000084\n>>> [TRAIN] Epoch 3: 180/366 [04:14<04:19]  loss:0.1138(0.0954);    grad:83229.0078;    lr:0.00000069\n>>> [TRAIN] Epoch 3: 200/366 [04:42<03:51]  loss:0.1382(0.0965);    grad:169713.4062;    lr:0.00000055\n>>> [TRAIN] Epoch 3: 220/366 [05:10<03:23]  loss:0.1160(0.0958);    grad:66738.4531;    lr:0.00000043\n>>> [TRAIN] Epoch 3: 240/366 [05:38<02:55]  loss:0.0858(0.0957);    grad:79352.6328;    lr:0.00000032\n>>> [TRAIN] Epoch 3: 260/366 [06:06<02:27]  loss:0.0835(0.0954);    grad:65069.9570;    lr:0.00000023\n>>> [TRAIN] Epoch 3: 280/366 [06:34<01:59]  loss:0.0597(0.0947);    grad:66984.0469;    lr:0.00000015\n>>> [TRAIN] Epoch 3: 300/366 [07:02<01:31]  loss:0.1053(0.0944);    grad:72944.6250;    lr:0.00000009\n>>> [TRAIN] Epoch 3: 320/366 [07:30<01:03]  loss:0.0864(0.0945);    grad:55546.6406;    lr:0.00000004\n>>> [TRAIN] Epoch 3: 340/366 [07:59<00:35]  loss:0.0869(0.0946);    grad:78000.9844;    lr:0.00000001\n>>> [TRAIN] Epoch 3: 360/366 [08:27<00:07]  loss:0.0751(0.0943);    grad:87666.5078;    lr:0.00000000\n>>> [TRAIN] Epoch 3: 365/366 [08:34<00:00]  loss:0.0878(0.0941);    grad:68920.4062;    lr:0.00000000\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}},{"name":"stdout","text":">>> [EVAL] 1/62 [00:00<00:19]   loss: 0.0664(0.0664)\n>>> [EVAL] 21/62 [00:06<00:12]   loss: 0.0757(0.0983)\n>>> [EVAL] 41/62 [00:12<00:06]   loss: 0.0814(0.1000)\n","output_type":"stream"},{"name":"stderr","text":"====================================================\n>>> Epoch 3 - avg_train_loss: 0.0941  avg_val_loss: 0.1015  time: 533s\n>>> Epoch 3 - score: 0.4508  scores: [0.482026504623507, 0.4410845229127203, 0.41218918058150855, 0.4582273050676872, 0.47180778833734444, 0.4396004998338964]\n>>> 🌟🌟🌟🌟🌟 new best_score: 0.4508\n","output_type":"stream"},{"name":"stdout","text":">>> [EVAL] 61/62 [00:18<00:00]   loss: 0.0869(0.1013)\n>>> [EVAL] 62/62 [00:18<00:00]   loss: 0.1917(0.1015)\n","output_type":"stream"},{"name":"stderr","text":"====================================================\n","output_type":"stream"},{"name":"stdout","text":"\n\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1440x360 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}},{"name":"stderr","text":"========== Fold 0 result ==========\nScore: 0.4508  Scores: [0.482026504623507, 0.4410845229127203, 0.41218918058150855, 0.4582273050676872, 0.47180778833734444, 0.4396004998338964]\n========== fold: 1 training ==========\nRobertaConfig {\n  \"_name_or_path\": \"roberta-base\",\n  \"architectures\": [\n    \"RobertaForMaskedLM\"\n  ],\n  \"attention_dropout\": 0.0,\n  \"attention_probs_dropout_prob\": 0.0,\n  \"bos_token_id\": 0,\n  \"classifier_dropout\": null,\n  \"eos_token_id\": 2,\n  \"hidden_act\": \"gelu\",\n  \"hidden_dropout\": 0.0,\n  \"hidden_dropout_prob\": 0.0,\n  \"hidden_size\": 768,\n  \"initializer_range\": 0.02,\n  \"intermediate_size\": 3072,\n  \"layer_norm_eps\": 1e-07,\n  \"max_position_embeddings\": 514,\n  \"model_type\": \"roberta\",\n  \"num_attention_heads\": 12,\n  \"num_hidden_layers\": 12,\n  \"output_hidden_states\": true,\n  \"pad_token_id\": 1,\n  \"position_embedding_type\": \"absolute\",\n  \"transformers_version\": \"4.20.1\",\n  \"type_vocab_size\": 1,\n  \"use_cache\": true,\n  \"vocab_size\": 50265\n}\n\nSome weights of the model checkpoint at roberta-base were not used when initializing RobertaModel: ['lm_head.dense.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.bias', 'lm_head.decoder.weight', 'lm_head.bias', 'lm_head.layer_norm.weight']\n- This IS expected if you are initializing RobertaModel 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 RobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n","output_type":"stream"},{"name":"stdout","text":">>> [TRAIN] Epoch 1: 0/366 [00:01<07:17]  loss:2.9355(2.9355);    grad:inf;    lr:0.00001000\n>>> [TRAIN] Epoch 1: 20/366 [00:29<08:00]  loss:0.5344(1.6338);    grad:187003.4219;    lr:0.00000999\n>>> [TRAIN] Epoch 1: 40/366 [00:57<07:34]  loss:0.2142(0.9784);    grad:96399.9062;    lr:0.00000997\n>>> [TRAIN] Epoch 1: 60/366 [01:25<07:07]  loss:0.1567(0.7121);    grad:65797.6484;    lr:0.00000992\n>>> [TRAIN] Epoch 1: 80/366 [01:53<06:39]  loss:0.1057(0.5668);    grad:66180.4531;    lr:0.00000987\n>>> [TRAIN] Epoch 1: 100/366 [02:21<06:11]  loss:0.1559(0.4821);    grad:74796.5234;    lr:0.00000979\n>>> [TRAIN] Epoch 1: 120/366 [02:49<05:43]  loss:0.0940(0.4265);    grad:40129.0508;    lr:0.00000970\n>>> [TRAIN] Epoch 1: 140/366 [03:17<05:15]  loss:0.1167(0.3867);    grad:103956.3672;    lr:0.00000960\n>>> [TRAIN] Epoch 1: 160/366 [03:46<04:47]  loss:0.0840(0.3544);    grad:63619.8672;    lr:0.00000948\n>>> [TRAIN] Epoch 1: 180/366 [04:14<04:19]  loss:0.1136(0.3282);    grad:78292.3047;    lr:0.00000935\n>>> [TRAIN] Epoch 1: 200/366 [04:42<03:51]  loss:0.1710(0.3076);    grad:125387.3984;    lr:0.00000920\n>>> [TRAIN] Epoch 1: 220/366 [05:10<03:23]  loss:0.1631(0.2945);    grad:134139.5312;    lr:0.00000904\n>>> [TRAIN] Epoch 1: 240/366 [05:38<02:55]  loss:0.1151(0.2812);    grad:81973.8438;    lr:0.00000886\n>>> [TRAIN] Epoch 1: 260/366 [06:06<02:27]  loss:0.1138(0.2693);    grad:60050.5820;    lr:0.00000867\n>>> [TRAIN] Epoch 1: 280/366 [06:34<01:59]  loss:0.1505(0.2595);    grad:96671.2812;    lr:0.00000847\n>>> [TRAIN] Epoch 1: 300/366 [07:03<01:31]  loss:0.1187(0.2516);    grad:105519.6719;    lr:0.00000826\n>>> [TRAIN] Epoch 1: 320/366 [07:31<01:03]  loss:0.0911(0.2431);    grad:54441.2227;    lr:0.00000804\n>>> [TRAIN] Epoch 1: 340/366 [07:59<00:35]  loss:0.0930(0.2366);    grad:75883.8594;    lr:0.00000781\n>>> [TRAIN] Epoch 1: 360/366 [08:27<00:07]  loss:0.1000(0.2307);    grad:64990.5000;    lr:0.00000757\n>>> [TRAIN] Epoch 1: 365/366 [08:34<00:00]  loss:0.0960(0.2288);    grad:53701.4102;    lr:0.00000751\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}},{"name":"stdout","text":">>> [EVAL] 1/62 [00:00<00:18]   loss: 0.0868(0.0868)\n>>> [EVAL] 21/62 [00:06<00:12]   loss: 0.1536(0.1280)\n>>> [EVAL] 41/62 [00:12<00:06]   loss: 0.1234(0.1225)\n","output_type":"stream"},{"name":"stderr","text":"====================================================\n>>> Epoch 1 - avg_train_loss: 0.2288  avg_val_loss: 0.1190  time: 533s\n>>> Epoch 1 - score: 0.4891  scores: [0.5449953211270169, 0.4927725582330368, 0.4375501270506976, 0.4807140379178113, 0.5023360638880864, 0.4762793724237409]\n>>> 🌟🌟🌟🌟🌟 new best_score: 0.4891\n","output_type":"stream"},{"name":"stdout","text":">>> [EVAL] 61/62 [00:18<00:00]   loss: 0.1010(0.1190)\n>>> [EVAL] 62/62 [00:18<00:00]   loss: 0.0773(0.1190)\n","output_type":"stream"},{"name":"stderr","text":"====================================================\n","output_type":"stream"},{"name":"stdout","text":"\n\n>>> [TRAIN] Epoch 2: 0/366 [00:01<07:15]  loss:0.0675(0.0675);    grad:inf;    lr:0.00000750\n>>> [TRAIN] Epoch 2: 20/366 [00:29<08:04]  loss:0.1235(0.1111);    grad:105476.8281;    lr:0.00000724\n>>> [TRAIN] Epoch 2: 40/366 [00:57<07:36]  loss:0.0904(0.1143);    grad:126904.8047;    lr:0.00000699\n>>> [TRAIN] Epoch 2: 60/366 [01:25<07:08]  loss:0.0901(0.1101);    grad:98824.2891;    lr:0.00000672\n>>> [TRAIN] Epoch 2: 80/366 [01:53<06:41]  loss:0.0988(0.1097);    grad:83797.5781;    lr:0.00000645\n>>> [TRAIN] Epoch 2: 100/366 [02:22<06:12]  loss:0.0859(0.1093);    grad:84955.9219;    lr:0.00000617\n>>> [TRAIN] Epoch 2: 120/366 [02:50<05:44]  loss:0.1075(0.1104);    grad:80274.3047;    lr:0.00000589\n>>> [TRAIN] Epoch 2: 140/366 [03:18<05:16]  loss:0.1246(0.1089);    grad:58600.6875;    lr:0.00000561\n>>> [TRAIN] Epoch 2: 160/366 [03:46<04:48]  loss:0.0524(0.1090);    grad:80658.5312;    lr:0.00000533\n>>> [TRAIN] Epoch 2: 180/366 [04:14<04:20]  loss:0.1059(0.1091);    grad:134937.7500;    lr:0.00000504\n>>> [TRAIN] Epoch 2: 200/366 [04:42<03:52]  loss:0.0574(0.1081);    grad:118766.5469;    lr:0.00000476\n>>> [TRAIN] Epoch 2: 220/366 [05:10<03:23]  loss:0.1643(0.1079);    grad:224039.4531;    lr:0.00000447\n>>> [TRAIN] Epoch 2: 240/366 [05:39<02:55]  loss:0.1037(0.1085);    grad:94825.7812;    lr:0.00000419\n>>> [TRAIN] Epoch 2: 260/366 [06:07<02:27]  loss:0.0867(0.1071);    grad:79518.1406;    lr:0.00000391\n>>> [TRAIN] Epoch 2: 280/366 [06:35<01:59]  loss:0.1302(0.1069);    grad:76227.6719;    lr:0.00000363\n","output_type":"stream"}]},{"cell_type":"code","source":"get_result(oof_df, fold, best_train_loss, best_val_loss)","metadata":{"execution":{"iopub.status.busy":"2022-11-21T19:45:41.812699Z","iopub.execute_input":"2022-11-21T19:45:41.813066Z","iopub.status.idle":"2022-11-21T19:45:41.846624Z","shell.execute_reply.started":"2022-11-21T19:45:41.813036Z","shell.execute_reply":"2022-11-21T19:45:41.845656Z"},"trusted":true},"execution_count":25,"outputs":[{"name":"stderr","text":"Score: 0.4603  Scores: [0.49154900363029463, 0.4508035850468596, 0.41985160436270846, 0.46261502403579335, 0.48686195210368916, 0.45020523765595405]\n","output_type":"stream"},{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"                           file         model cv_seed seed fold epoch  \\\n0  20221121-173739-roberta-base  roberta-base      42   42    3  best   \n\n     mcrmse train_loss  val_loss  cohesion    syntax vocabulary phraseology  \\\n0  0.460314   0.092065  0.102648  0.491549  0.450804   0.419852    0.462615   \n\n    grammar conventions  \n0  0.486862    0.450205  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>file</th>\n      <th>model</th>\n      <th>cv_seed</th>\n      <th>seed</th>\n      <th>fold</th>\n      <th>epoch</th>\n      <th>mcrmse</th>\n      <th>train_loss</th>\n      <th>val_loss</th>\n      <th>cohesion</th>\n      <th>syntax</th>\n      <th>vocabulary</th>\n      <th>phraseology</th>\n      <th>grammar</th>\n      <th>conventions</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>20221121-173739-roberta-base</td>\n      <td>roberta-base</td>\n      <td>42</td>\n      <td>42</td>\n      <td>3</td>\n      <td>best</td>\n      <td>0.460314</td>\n      <td>0.092065</td>\n      <td>0.102648</td>\n      <td>0.491549</td>\n      <td>0.450804</td>\n      <td>0.419852</td>\n      <td>0.462615</td>\n      <td>0.486862</td>\n      <td>0.450205</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!zip -r {CFG.identifier}.zip {CFG.output_dir}","metadata":{"papermill":{"duration":449.347684,"end_time":"2022-11-15T03:44:26.657800","exception":false,"start_time":"2022-11-15T03:36:57.310116","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]}]}