


"""simple docstring"""







import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


lowerCamelCase_   :   Optional[Any]									=  logging.getLogger(__name__)






class       __A				(       _lowerCAmelCase			):






 """simple docstring"""




 __lowerCAmelCase 					=  """token-classification"""


 def __init__(		self ,    __A						)					->     List[Any]:
     if type(__A						) == dict:
         a 		=Namespace(**__A						)
     a 		=import_module('''tasks'''						)
     try:
         a 		=getattr(__A ,    hparams.task_type						)
         a 		=token_classification_task_clazz()
     except AttributeError:
         raise ValueError(
             f'''Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
             f'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}'''						)
     a 		=self.token_classification_task.get_labels(hparams.labels						)
     a 		=CrossEntropyLoss().ignore_index
     super().__init__(__A ,    len(self.labels						) ,    self.mode						)


 def       SCREAMING_SNAKE_CASE							(		self ,    **__A						)					->     Any:
     return self.model(**__A						)


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A						)					->     Optional[int]:
     a 		={"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
     if self.config.model_type != "distilbert":
         a 		=(
             batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
         )  # XLM and RoBERTa don"t use token_type_ids

     a 		=self(**__A						)
     a 		=outputs[0]
     # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
     return {"loss": loss}


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Dict:
     a 		=self.hparams
     for mode in ["train", "dev", "test"]:
         a 		=self._feature_file(__A						)
         if os.path.exists(__A						) and not args.overwrite_cache:
             logger.info('''Loading features from cached file %s''' ,    __A						)
             a 		=torch.load(__A						)
         else:
             logger.info('''Creating features from dataset file at %s''' ,    args.data_dir						)
             a 		=self.token_classification_task.read_examples_from_file(args.data_dir ,    __A						)
             a 		=self.token_classification_task.convert_examples_to_features(
                 __A ,    self.labels ,    args.max_seq_length ,    self.tokenizer ,    cls_token_at_end=bool(self.config.model_type in ['''xlnet''']						) ,    cls_token=self.tokenizer.cls_token ,    cls_token_segment_id=2 if self.config.model_type in ['''xlnet'''] else 0 ,    sep_token=self.tokenizer.sep_token ,    sep_token_extra=__A ,    pad_on_left=bool(self.config.model_type in ['''xlnet''']						) ,    pad_token=self.tokenizer.pad_token_id ,    pad_token_segment_id=self.tokenizer.pad_token_type_id ,    pad_token_label_id=self.pad_token_label_id ,    )
             logger.info('''Saving features into cached file %s''' ,    __A						)
             torch.save(__A ,    __A						)


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    __A = False						)					->     DataLoader:
     a 		=self._feature_file(__A						)
     logger.info('''Loading features from cached file %s''' ,    __A						)
     a 		=torch.load(__A						)
     a 		=torch.tensor([f.input_ids for f in features] ,    dtype=torch.long						)
     a 		=torch.tensor([f.attention_mask for f in features] ,    dtype=torch.long						)
     if features[0].token_type_ids is not None:
         a 		=torch.tensor([f.token_type_ids for f in features] ,    dtype=torch.long						)
     else:
         a 		=torch.tensor([0 for f in features] ,    dtype=torch.long						)
         # HACK(we will not use this anymore soon)
     a 		=torch.tensor([f.label_ids for f in features] ,    dtype=torch.long						)
     return DataLoader(
         TensorDataset(__A ,    __A ,    __A ,    __A						) ,    batch_size=__A						)


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A						)					->     List[str]:
     """Compute validation""" ""
     a 		={"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
     if self.config.model_type != "distilbert":
         a 		=(
             batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
         )  # XLM and RoBERTa don"t use token_type_ids
     a 		=self(**__A						)
     a 		=outputs[:2]
     a 		=logits.detach().cpu().numpy()
     a 		=inputs["""labels"""].detach().cpu().numpy()
     return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}


 def       SCREAMING_SNAKE_CASE							(		self ,    __A						)					->     Tuple:
     a 		=torch.stack([x['''val_loss'''] for x in outputs]						).mean()
     a 		=np.concatenate([x['''pred'''] for x in outputs] ,    axis=0						)
     a 		=np.argmax(__A ,    axis=2						)
     a 		=np.concatenate([x['''target'''] for x in outputs] ,    axis=0						)

     a 		=dict(enumerate(self.labels						)						)
     a 		=[[] for _ in range(out_label_ids.shape[0]						)]
     a 		=[[] for _ in range(out_label_ids.shape[0]						)]

     for i in range(out_label_ids.shape[0]						):
         for j in range(out_label_ids.shape[1]						):
             if out_label_ids[i, j] != self.pad_token_label_id:
                 out_label_list[i].append(label_map[out_label_ids[i][j]]						)
                 preds_list[i].append(label_map[preds[i][j]]						)

     a 		={
         """val_loss""": val_loss_mean,
         """accuracy_score""": accuracy_score(__A ,    __A						),
         """precision""": precision_score(__A ,    __A						),
         """recall""": recall_score(__A ,    __A						),
         """f1""": fa_score(__A ,    __A						),
     }

     a 		=dict(results.items()						)
     a 		=results
     return ret, preds_list, out_label_list


 def       SCREAMING_SNAKE_CASE							(		self ,    __A						)					->     Optional[Any]:
     # when stable
     a 		=self._eval_end(__A						)
     a 		=ret["""log"""]
     return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}


 def       SCREAMING_SNAKE_CASE							(		self ,    __A						)					->     Any:
     # updating to test_epoch_end instead of deprecated test_end
     a 		=self._eval_end(__A						)

     # Converting to the dict required by pl
     # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
     # pytorch_lightning/trainer/logging.py#L139
     a 		=ret["""log"""]
     # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
     return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





 @staticmethod
 def       SCREAMING_SNAKE_CASE							(		__A ,    __A						)					->     Dict:
     # Add NER specific options
     BaseTransformer.add_model_specific_args(__A ,    __A						)
     parser.add_argument(
         '''--task_type''' ,    default='''NER''' ,    type=__A ,    help='''Task type to fine tune in training (e.g. NER, POS, etc)'''						)
     parser.add_argument(
         '''--max_seq_length''' ,    default=128 ,    type=__A ,    help=(
             '''The maximum total input sequence length after tokenization. Sequences longer '''
             '''than this will be truncated, sequences shorter will be padded.'''
         ) ,    )

     parser.add_argument(
         '''--labels''' ,    default='''''' ,    type=__A ,    help='''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.''' ,    )
     parser.add_argument(
         '''--gpus''' ,    default=0 ,    type=__A ,    help='''The number of GPUs allocated for this, it is by default 0 meaning none''' ,    )

     parser.add_argument(
         '''--overwrite_cache''' ,    action='''store_true''' ,    help='''Overwrite the cached training and evaluation sets'''						)

     return parser


if __name__ == "__main__":
       lowerCamelCase_   :   Optional[Any]									=  argparse.ArgumentParser()
       add_generic_args(parser, os.getcwd())
       lowerCamelCase_   :   List[Any]									=  NERTransformer.add_model_specific_args(parser, os.getcwd())
       lowerCamelCase_   :   Any									=  parser.parse_args()
       lowerCamelCase_   :   Optional[Any]									=  NERTransformer(args)
       lowerCamelCase_   :   int									=  generic_train(model, args)

       if args.do_predict:
              # See https://github.com/huggingface/transformers/issues/3159
              # pl use this default format to create a checkpoint:
              # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
              # /pytorch_lightning/callbacks/model_checkpoint.py#L322
              lowerCamelCase_   :   Optional[Any]									=  sorted(glob.glob(os.path.join(args.output_dir, """checkpoint-epoch=*.ckpt"""), recursive=True))
              lowerCamelCase_   :   Tuple									=  model.load_from_checkpoint(checkpoints[-1])
              trainer.test(model)




"""simple docstring"""
import math
class 				lowerCamelCase  :






					'''simple docstring'''





					def    lowerCAmelCase_				(       self:							Tuple						,     snake_case:							list[list[float]]						,     snake_case:							list[int]						)   ->	int:
											snake_case_ :Any						=			0.0
											snake_case_ :Tuple						=			0.0
											for i in range(len(snake_case						)						):
																	da += math.pow((sample[i] - weights[0][i])						,     2						)
																	da += math.pow((sample[i] - weights[1][i])						,     2						)
																	return 0 if da > da else 1
											return 0






					def    lowerCAmelCase_				(       self:							Optional[int]						,     snake_case:							list[list[int | float]]						,     snake_case:							list[int]						,     snake_case:							int						,     snake_case:							float						)   ->	list[list[int | float]]:
											for i in range(len(snake_case						)						):
																	weights[j][i] += alpha * (sample[i] - weights[j][i])
											return weights

def    A_  (			):







						'''simple docstring'''


						snake_case_ :Dict						=			[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

						# weight initialization ( n, C )
						snake_case_ :List[Any]						=			[[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

						# training
						snake_case_ :Optional[Any]						=			SelfOrganizingMap()
						snake_case_ :Dict						=			3
						snake_case_ :Dict						=			0.5

						for _ in range(_lowercase	):
												for j in range(len(_lowercase	)	):
																		# training sample
																		snake_case_ :List[Any]						=			training_samples[j]

																		# Compute the winning vector
																		snake_case_ :Optional[int]						=			self_organizing_map.get_winner(_lowercase,					_lowercase	)

																		# Update the winning vector
																		snake_case_ :List[str]						=			self_organizing_map.update(_lowercase,					_lowercase,					_lowercase,					_lowercase	)

    # classify test sample
						snake_case_ :str						=			[0, 0, 0, 1]
						snake_case_ :List[Any]						=			self_organizing_map.get_winner(_lowercase,					_lowercase	)

						# results
						print(f"""Clusters that the test sample belongs to : {winner}"""	)
						print(f"""Weights that have been trained : {weights}"""	)


# running the main() function
if __name__ == "__main__":
						main()
