


'''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


__snake_case											=					logging.getLogger(__name__)
class        lowercase     (							A__ ):

		"""simple docstring"""

		_a   			=    'token-classification'




		def __init__(		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							if type(UpperCamelCase_							) == dict:
												UpperCamelCase__       :List[str]							 =			Namespace(**UpperCamelCase_							)
							UpperCamelCase__       :Any							 =			import_module('''tasks'''							)
							try:
												UpperCamelCase__       :int							 =			getattr(UpperCamelCase_   ,       hparams.task_type							)
												UpperCamelCase__       :TokenClassificationTask							 =			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__()}'''							)
							UpperCamelCase__       :Dict							 =			self.token_classification_task.get_labels(hparams.labels							)
							UpperCamelCase__       :Dict							 =			CrossEntropyLoss().ignore_index
							super().__init__(UpperCamelCase_   ,       len(self.labels							)   ,       self.mode							)




		def    lowerCAmelCase__    (		self   ,       **UpperCamelCase_							):

							'''simple docstring'''


							return self.model(**UpperCamelCase_							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_							):

							'''simple docstring'''


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

							UpperCamelCase__       :Union[str, Any]							 =			self(**UpperCamelCase_							)
							UpperCamelCase__       :str							 =			outputs[0]
							# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
							return {"loss": loss}




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_ = False							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :Tuple							 =			torch.stack([x['''val_loss'''] for x in outputs]							).mean()
							UpperCamelCase__       :Optional[int]							 =			np.concatenate([x['''pred'''] for x in outputs]   ,       axis=0							)
							UpperCamelCase__       :str							 =			np.argmax(UpperCamelCase_   ,       axis=2							)
							UpperCamelCase__       :Union[str, Any]							 =			np.concatenate([x['''target'''] for x in outputs]   ,       axis=0							)

							UpperCamelCase__       :Dict							 =			dict(enumerate(self.labels							)							)
							UpperCamelCase__       :Optional[int]							 =			[[] for _ in range(out_label_ids.shape[0]							)]
							UpperCamelCase__       :int							 =			[[] 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]]							)

							UpperCamelCase__       :str							 =			{
							    '''val_loss''': val_loss_mean,
							    '''accuracy_score''': accuracy_score(UpperCamelCase_   ,       UpperCamelCase_							),
							    '''precision''': precision_score(UpperCamelCase_   ,       UpperCamelCase_							),
							    '''recall''': recall_score(UpperCamelCase_   ,       UpperCamelCase_							),
							    '''f1''': fa_score(UpperCamelCase_   ,       UpperCamelCase_							),
							}

							UpperCamelCase__       :Optional[Any]							 =			dict(results.items()							)
							UpperCamelCase__       :Optional[int]							 =			results
							return ret, preds_list, out_label_list




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       :Dict							 =			self._eval_end(UpperCamelCase_							)
							UpperCamelCase__       :Optional[int]							 =			ret['''log''']
							return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       :List[str]							 =			self._eval_end(UpperCamelCase_							)

							# Converting to the dict required by pl
							# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
							# pytorch_lightning/trainer/logging.py#L139
							UpperCamelCase__       :Union[str, Any]							 =			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    lowerCAmelCase__    (		UpperCamelCase_   ,       UpperCamelCase_							):

							'''simple docstring'''


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



'''simple docstring'''
from __future__ import annotations

from typing import Any


def       a				(      __a       )  ->     None:



					'''simple docstring'''






					create_state_space_tree(__a			, []			, 0       )


def       a				(      __a			, __a			, __a       )  ->     None:



					'''simple docstring'''






					if index == len(__a       ):
										print(__a       )
										return

					create_state_space_tree(__a			, __a			, index + 1       )
					current_subsequence.append(sequence[index]       )
					create_state_space_tree(__a			, __a			, index + 1       )
					current_subsequence.pop()


if __name__ == "__main__":
		__snake_case											=					[3, 1, 2, 4]
		generate_all_subsequences(seq)

		seq.clear()
		seq.extend(['''A''', '''B''', '''C'''])
		generate_all_subsequences(seq)