





'''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


_SCREAMING_SNAKE_CASE   =      logging.getLogger(__name__)






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			Any     =    "token-classification"


	def __init__(self       ,_lowerCamelCase	)							->							Tuple:







				'''simple docstring'''





				if type(_lowerCamelCase	) == dict:
							__lowercase			  =							Namespace(**_lowerCamelCase	)
				__lowercase			  =							import_module('''tasks'''	)
				try:
							__lowercase			  =							getattr(_lowerCamelCase       ,hparams.task_type	)
							__lowercase			  =							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__()}"	)
				__lowercase			  =							self.token_classification_task.get_labels(hparams.labels	)
				__lowercase			  =							CrossEntropyLoss().ignore_index
				super().__init__(_lowerCamelCase       ,len(self.labels	)       ,self.mode	)


	def   _UpperCAmelCase       (self       ,**_lowerCamelCase	)							->							Dict:







				'''simple docstring'''





				return self.model(**_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase	)							->							List[str]:







				'''simple docstring'''





				__lowercase			  =							{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
				if self.config.model_type != "distilbert":
							__lowercase			  =							(
							    batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
							)  # XLM and RoBERTa don"t use token_type_ids

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


	def   _UpperCAmelCase       (self	)							->							str:







				'''simple docstring'''





				__lowercase			  =							self.hparams
				for mode in ["train", "dev", "test"]:
							__lowercase			  =							self._feature_file(_lowerCamelCase	)
							if os.path.exists(_lowerCamelCase	) and not args.overwrite_cache:
										logger.info('''Loading features from cached file %s'''       ,_lowerCamelCase	)
										__lowercase			  =							torch.load(_lowerCamelCase	)
							else:
										logger.info('''Creating features from dataset file at %s'''       ,args.data_dir	)
										__lowercase			  =							self.token_classification_task.read_examples_from_file(args.data_dir       ,_lowerCamelCase	)
										__lowercase			  =							self.token_classification_task.convert_examples_to_features(
										    _lowerCamelCase       ,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=_lowerCamelCase       ,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'''       ,_lowerCamelCase	)
										torch.save(_lowerCamelCase       ,_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase = False	)							->							DataLoader:







				'''simple docstring'''





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


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase	)							->							int:







				'''simple docstring'''





				"""Compute validation""" ""
				__lowercase			  =							{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
				if self.config.model_type != "distilbert":
							__lowercase			  =							(
							    batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
							)  # XLM and RoBERTa don"t use token_type_ids
				__lowercase			  =							self(**_lowerCamelCase	)
				__lowercase  ,						__lowercase			  =							outputs[:2]
				__lowercase			  =							logits.detach().cpu().numpy()
				__lowercase			  =							inputs['''labels'''].detach().cpu().numpy()
				return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}


	def   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							int:







				'''simple docstring'''





				__lowercase			  =							torch.stack([x['''val_loss'''] for x in outputs]	).mean()
				__lowercase			  =							np.concatenate([x['''pred'''] for x in outputs]       ,axis=0	)
				__lowercase			  =							np.argmax(_lowerCamelCase       ,axis=2	)
				__lowercase			  =							np.concatenate([x['''target'''] for x in outputs]       ,axis=0	)

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

				__lowercase			  =							{
				    '''val_loss''': val_loss_mean,
				    '''accuracy_score''': accuracy_score(_lowerCamelCase       ,_lowerCamelCase	),
				    '''precision''': precision_score(_lowerCamelCase       ,_lowerCamelCase	),
				    '''recall''': recall_score(_lowerCamelCase       ,_lowerCamelCase	),
				    '''f1''': fa_score(_lowerCamelCase       ,_lowerCamelCase	),
				}

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


	def   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							Tuple:







				'''simple docstring'''





				__lowercase  ,						__lowercase  ,						__lowercase			  =							self._eval_end(_lowerCamelCase	)
				__lowercase			  =							ret['''log''']
				return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}


	def   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							Dict:







				'''simple docstring'''





				__lowercase  ,						__lowercase  ,						__lowercase			  =							self._eval_end(_lowerCamelCase	)

				# Converting to the dict required by pl
				# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
				# pytorch_lightning/trainer/logging.py#L139
				__lowercase			  =							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   _UpperCAmelCase       (_lowerCamelCase       ,_lowerCamelCase	)							->							Optional[int]:







				'''simple docstring'''





				BaseTransformer.add_model_specific_args(_lowerCamelCase       ,_lowerCamelCase	)
				parser.add_argument(
				    '''--task_type'''       ,default='''NER'''       ,type=_lowerCamelCase       ,help='''Task type to fine tune in training (e.g. NER, POS, etc)'''	)
				parser.add_argument(
				    '''--max_seq_length'''       ,default=128       ,type=_lowerCamelCase       ,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=_lowerCamelCase       ,help='''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''       ,)
				parser.add_argument(
				    '''--gpus'''       ,default=0       ,type=_lowerCamelCase       ,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__":
							_SCREAMING_SNAKE_CASE   =      argparse.ArgumentParser()
							add_generic_args(parser, os.getcwd())
							_SCREAMING_SNAKE_CASE   =      NERTransformer.add_model_specific_args(parser, os.getcwd())
							_SCREAMING_SNAKE_CASE   =      parser.parse_args()
							_SCREAMING_SNAKE_CASE   =      NERTransformer(args)
							_SCREAMING_SNAKE_CASE   =      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
														_SCREAMING_SNAKE_CASE   =      sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True))
														_SCREAMING_SNAKE_CASE   =      model.load_from_checkpoint(checkpoints[-1])
														trainer.test(model)












'''simple docstring'''



def 				_lowerCAmelCase  (			lowerCamelCase_     :					Union[str, Any]			):
			__lowercase			  =							1
			__lowercase			  =							2
			while i * i <= n:
						__lowercase			  =							0
						while n % i == 0:
									n //= i
									multiplicity += 1
						n_divisors *= multiplicity + 1
						i += 1
			if n > 1:
						n_divisors *= 2
			return n_divisors




def 				_lowerCAmelCase  (			):
			__lowercase			  =							1
			__lowercase			  =							1

			while True:
						i += 1
						t_num += i

						if count_divisors(lowerCamelCase_			) > 5_0_0:
									break

			return t_num


if __name__ == "__main__":
	print(solution())





