
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
				import datasets





class       _snake_case					(     snake_case				):







			@slow
			@require_torch
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[str]	   =     EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny"						,   "prajjwal1/bert-tiny"		)
				__magic_name__		:		Dict	   =     BertTokenizer.from_pretrained("bert-base-uncased"		)

				__magic_name__		:		Optional[Any]	   =     bertabert.config.encoder.vocab_size
				__magic_name__		:		int	   =     tokenizer.sep_token_id
				__magic_name__		:		Any	   =     tokenizer.cls_token_id
				__magic_name__		:		Tuple	   =     128

				__magic_name__		:		Dict	   =     datasets.load_dataset("cnn_dailymail"						,   "3.0.0"						,   split="train[:1%]"		)
				__magic_name__		:		List[str]	   =     datasets.load_dataset("cnn_dailymail"						,   "3.0.0"						,   split="validation[:1%]"		)

				__magic_name__		:		int	   =     train_dataset.select(range(32		)		)
				__magic_name__		:		int	   =     val_dataset.select(range(16		)		)

				__magic_name__		:		Dict	   =     4

				def _map_to_encoder_decoder_inputs(_a		):
					# Tokenizer will automatically set [BOS] <text> [EOS]
					__magic_name__		:		Optional[int]	   =     tokenizer(batch["article"]						,   padding="max_length"						,   truncation=_a						,   max_length=512		)
					__magic_name__		:		Union[str, Any]	   =     tokenizer(batch["highlights"]						,   padding="max_length"						,   truncation=_a						,   max_length=128		)
					__magic_name__		:		Union[str, Any]	   =     inputs.input_ids
					__magic_name__		:		List[Any]	   =     inputs.attention_mask

					__magic_name__		:		List[Any]	   =     outputs.input_ids
					__magic_name__		:		List[Any]	   =     outputs.input_ids.copy()
					__magic_name__		:		List[Any]	   =     [
					    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
					]
					__magic_name__		:		int	   =     outputs.attention_mask

					assert all(len(_a		) == 512 for x in inputs.input_ids		)
					assert all(len(_a		) == 128 for x in outputs.input_ids		)

					return batch

				def _compute_metrics(_a		):
					__magic_name__		:		List[Any]	   =     pred.label_ids
					__magic_name__		:		List[Any]	   =     pred.predictions

					# all unnecessary tokens are removed
					__magic_name__		:		Any	   =     tokenizer.batch_decode(_a						,   skip_special_tokens=_a		)
					__magic_name__		:		Any	   =     tokenizer.batch_decode(_a						,   skip_special_tokens=_a		)

					__magic_name__		:		str	   =     sum([int(pred_str[i] == label_str[i]		) for i in range(len(_a		)		)]		) / len(_a		)

					return {"accuracy": accuracy}

				# map train dataset
				__magic_name__		:		Tuple	   =     train_dataset.map(
				    _map_to_encoder_decoder_inputs						,   batched=_a						,   batch_size=_a						,   remove_columns=["article", "highlights"]						,   )
				train_dataset.set_format(
				    type="torch"						,   columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]						,   )

				# same for validation dataset
				__magic_name__		:		Union[str, Any]	   =     val_dataset.map(
				    _map_to_encoder_decoder_inputs						,   batched=_a						,   batch_size=_a						,   remove_columns=["article", "highlights"]						,   )
				val_dataset.set_format(
				    type="torch"						,   columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]						,   )

				__magic_name__		:		List[Any]	   =     self.get_auto_remove_tmp_dir()

				__magic_name__		:		str	   =     SeqaSeqTrainingArguments(
				    output_dir=_a						,   per_device_train_batch_size=_a						,   per_device_eval_batch_size=_a						,   predict_with_generate=_a						,   evaluation_strategy="steps"						,   do_train=_a						,   do_eval=_a						,   warmup_steps=0						,   eval_steps=2						,   logging_steps=2						,   )

				# instantiate trainer
				__magic_name__		:		List[Any]	   =     SeqaSeqTrainer(
				    model=_a						,   args=_a						,   compute_metrics=_compute_metrics						,   train_dataset=_a						,   eval_dataset=_a						,   tokenizer=_a						,   )

				# start training
				trainer.train()


def   lowerCAmelCase_						(					_snake_case      :  str      ,   _snake_case      :  str   )				->		bool:







	'''simple docstring'''


	__magic_name__		:		Union[str, Any]	   =     len(_snake_case   ) + 1
	__magic_name__		:		List[str]	   =     len(_snake_case   ) + 1

	# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
	# length i of input_string matches with prefix string of length j of
	# given pattern.
	# "dp" stands for dynamic programming.
	__magic_name__		:		str	   =     [[0 for i in range(_snake_case   )] for j in range(_snake_case   )]

	# since string of zero length match pattern of zero length
	__magic_name__		:		Optional[int]	   =     1

	# since pattern of zero length will never match with string of non-zero length
	for i in range(1      ,   _snake_case   ):
		__magic_name__		:		Optional[int]	   =     0

	# since string of zero length will match with pattern where there
	# is at least one * alternatively
	for j in range(1      ,   _snake_case   ):
		__magic_name__		:		Union[str, Any]	   =     dp[0][j - 2] if pattern[j - 1] == "*" else 0

	# now using bottom-up approach to find for all remaining lengths
	for i in range(1      ,   _snake_case   ):
		for j in range(1      ,   _snake_case   ):
			if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
				__magic_name__		:		Optional[int]	   =     dp[i - 1][j - 1]

			elif pattern[j - 1] == "*":
				if dp[i][j - 2] == 1:
					__magic_name__		:		Optional[Any]	   =     1
				elif pattern[j - 2] in (input_string[i - 1], "."):
					__magic_name__		:		List[Any]	   =     dp[i - 1][j]
				else:
					__magic_name__		:		Union[str, Any]	   =     0
			else:
				__magic_name__		:		Dict	   =     0

	return bool(dp[-1][-1]   )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				# inputing the strings
				# input_string = input("input a string :")
				# pattern = input("input a pattern :")

				snake_case :		Optional[Any]							      =  "aab"
				snake_case :		List[str]							      =  "c*a*b"

				# using function to check whether given string matches the given pattern
				if match_pattern(input_string, pattern):
								print(F"{input_string} matches the given pattern {pattern}")
				else:
								print(F"{input_string} does not match with the given pattern {pattern}")
