



'''simple docstring'''




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     UpperCamelCase_				(							lowercase__    ):





	@slow
	@require_torch
	def 				_lowercase(						self					)    ->    Tuple:
								UpperCAmelCase  :	Tuple							  =	EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny"""	,	"""prajjwal1/bert-tiny"""					)
								UpperCAmelCase  :	Optional[int]							  =	BertTokenizer.from_pretrained("""bert-base-uncased"""					)

								UpperCAmelCase  :	int							  =	bertabert.config.encoder.vocab_size
								UpperCAmelCase  :	Union[str, Any]							  =	tokenizer.sep_token_id
								UpperCAmelCase  :	List[str]							  =	tokenizer.cls_token_id
								UpperCAmelCase  :	str							  =	128

								UpperCAmelCase  :	Optional[int]							  =	datasets.load_dataset("""cnn_dailymail"""	,	"""3.0.0"""	,	split="""train[:1%]"""					)
								UpperCAmelCase  :	str							  =	datasets.load_dataset("""cnn_dailymail"""	,	"""3.0.0"""	,	split="""validation[:1%]"""					)

								UpperCAmelCase  :	List[Any]							  =	train_dataset.select(range(32					)					)
								UpperCAmelCase  :	Any							  =	val_dataset.select(range(16					)					)

								UpperCAmelCase  :	Any							  =	4

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

															UpperCAmelCase  :	List[Any]							  =	outputs.input_ids
															UpperCAmelCase  :	Any							  =	outputs.input_ids.copy()
															UpperCAmelCase  :	Any							  =	[
															    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
															]
															UpperCAmelCase  :	List[Any]							  =	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					):
															UpperCAmelCase  :	Tuple							  =	pred.label_ids
															UpperCAmelCase  :	Any							  =	pred.predictions

															# all unnecessary tokens are removed
															UpperCAmelCase  :	List[Any]							  =	tokenizer.batch_decode(A	,	skip_special_tokens=A					)
															UpperCAmelCase  :	int							  =	tokenizer.batch_decode(A	,	skip_special_tokens=A					)

															UpperCAmelCase  :	List[str]							  =	sum([int(pred_str[i] == label_str[i]					) for i in range(len(A					)					)]					) / len(A					)

															return {"accuracy": accuracy}

								# map train dataset
								UpperCAmelCase  :	Union[str, Any]							  =	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
								UpperCAmelCase  :	str							  =	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"""]	,	)

								UpperCAmelCase  :	Optional[Any]							  =	self.get_auto_remove_tmp_dir()

								UpperCAmelCase  :	Optional[Any]							  =	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
								UpperCAmelCase  :	Optional[Any]							  =	SeqaSeqTrainer(
								    model=A	,	args=A	,	compute_metrics=_compute_metrics	,	train_dataset=A	,	eval_dataset=A	,	tokenizer=A	,	)

								# start training
								trainer.train()






'''simple docstring'''




def    __lowerCamelCase						(      _lowercase = "The quick brown fox jumps over the lazy dog"			,       )   ->    bool:
							UpperCAmelCase  :	Union[str, Any]							  =	set()

							# Replace all the whitespace in our sentence
							UpperCAmelCase  :	List[str]							  =	input_str.replace(""" """			,       """"""	)
							for alpha in input_str:
														if "a" <= alpha.lower() <= "z":
																					frequency.add(alpha.lower()	)
							return len(_lowercase	) == 2_6



def    __lowerCamelCase						(      _lowercase = "The quick brown fox jumps over the lazy dog"			,       )   ->    bool:
							UpperCAmelCase  :	Tuple							  =	[False] * 2_6
							for char in input_str:
														if char.islower():
																					UpperCAmelCase  :	Any							  =	True
														elif char.isupper():
																					UpperCAmelCase  :	Union[str, Any]							  =	True
							return all(_lowercase	)



def    __lowerCamelCase						(      _lowercase = "The quick brown fox jumps over the lazy dog"			,       )   ->    bool:
							return len({char for char in input_str.lower() if char.isalpha()}	) == 2_6



def    __lowerCamelCase						(      )   ->    None:
							from timeit import timeit

							UpperCAmelCase  :	str							  =	"""from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"""
							print(timeit("""is_pangram()"""			,       setup=_lowercase	)	)
							print(timeit("""is_pangram_faster()"""			,       setup=_lowercase	)	)
							print(timeit("""is_pangram_fastest()"""			,       setup=_lowercase	)	)
							# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
							# 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							benchmark()

