
'''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 			SCREAMING_SNAKE_CASE__       (  lowerCamelCase_					):






					@slow
					@require_torch
					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Optional[int]													=	EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny'''    ,							'''prajjwal1/bert-tiny'''		)
											lowercase_     :					Union[str, Any]													=	BertTokenizer.from_pretrained('''bert-base-uncased'''		)

											lowercase_     :					Tuple													=	bertabert.config.encoder.vocab_size
											lowercase_     :					Any													=	tokenizer.sep_token_id
											lowercase_     :					Optional[Any]													=	tokenizer.cls_token_id
											lowercase_     :					str													=	1_28

											lowercase_     :					str													=	datasets.load_dataset('''cnn_dailymail'''    ,							'''3.0.0'''    ,							split='''train[:1%]'''		)
											lowercase_     :					Optional[Any]													=	datasets.load_dataset('''cnn_dailymail'''    ,							'''3.0.0'''    ,							split='''validation[:1%]'''		)

											lowercase_     :					str													=	train_dataset.select(range(32		)		)
											lowercase_     :					Any													=	val_dataset.select(range(16		)		)

											lowercase_     :					int													=	4

											def _map_to_encoder_decoder_inputs(__SCREAMING_SNAKE_CASE		):
																	# Tokenizer will automatically set [BOS] <text> [EOS]
																	lowercase_     :					List[Any]													=	tokenizer(batch['''article''']    ,							padding='''max_length'''    ,							truncation=__SCREAMING_SNAKE_CASE    ,							max_length=5_12		)
																	lowercase_     :					Optional[Any]													=	tokenizer(batch['''highlights''']    ,							padding='''max_length'''    ,							truncation=__SCREAMING_SNAKE_CASE    ,							max_length=1_28		)
																	lowercase_     :					Tuple													=	inputs.input_ids
																	lowercase_     :					str													=	inputs.attention_mask

																	lowercase_     :					str													=	outputs.input_ids
																	lowercase_     :					Dict													=	outputs.input_ids.copy()
																	lowercase_     :					List[str]													=	[
																	    [-1_00 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
																	]
																	lowercase_     :					List[Any]													=	outputs.attention_mask

																	assert all(len(__SCREAMING_SNAKE_CASE		) == 5_12 for x in inputs.input_ids		)
																	assert all(len(__SCREAMING_SNAKE_CASE		) == 1_28 for x in outputs.input_ids		)

																	return batch

											def _compute_metrics(__SCREAMING_SNAKE_CASE		):
																	lowercase_     :					Any													=	pred.label_ids
																	lowercase_     :					Any													=	pred.predictions

																	# all unnecessary tokens are removed
																	lowercase_     :					List[str]													=	tokenizer.batch_decode(__SCREAMING_SNAKE_CASE    ,							skip_special_tokens=__SCREAMING_SNAKE_CASE		)
																	lowercase_     :					Dict													=	tokenizer.batch_decode(__SCREAMING_SNAKE_CASE    ,							skip_special_tokens=__SCREAMING_SNAKE_CASE		)

																	lowercase_     :					Dict													=	sum([int(pred_str[i] == label_str[i]		) for i in range(len(__SCREAMING_SNAKE_CASE		)		)]		) / len(__SCREAMING_SNAKE_CASE		)

																	return {"accuracy": accuracy}

											# map train dataset
											lowercase_     :					List[Any]													=	train_dataset.map(
											    _map_to_encoder_decoder_inputs    ,							batched=__SCREAMING_SNAKE_CASE    ,							batch_size=__SCREAMING_SNAKE_CASE    ,							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
											lowercase_     :					Dict													=	val_dataset.map(
											    _map_to_encoder_decoder_inputs    ,							batched=__SCREAMING_SNAKE_CASE    ,							batch_size=__SCREAMING_SNAKE_CASE    ,							remove_columns=['''article''', '''highlights''']    ,							)
											val_dataset.set_format(
											    type='''torch'''    ,							columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels''']    ,							)

											lowercase_     :					Tuple													=	self.get_auto_remove_tmp_dir()

											lowercase_     :					int													=	SeqaSeqTrainingArguments(
											    output_dir=__SCREAMING_SNAKE_CASE    ,							per_device_train_batch_size=__SCREAMING_SNAKE_CASE    ,							per_device_eval_batch_size=__SCREAMING_SNAKE_CASE    ,							predict_with_generate=__SCREAMING_SNAKE_CASE    ,							evaluation_strategy='''steps'''    ,							do_train=__SCREAMING_SNAKE_CASE    ,							do_eval=__SCREAMING_SNAKE_CASE    ,							warmup_steps=0    ,							eval_steps=2    ,							logging_steps=2    ,							)

											# instantiate trainer
											lowercase_     :					Union[str, Any]													=	SeqaSeqTrainer(
											    model=__SCREAMING_SNAKE_CASE    ,							args=__SCREAMING_SNAKE_CASE    ,							compute_metrics=_compute_metrics    ,							train_dataset=__SCREAMING_SNAKE_CASE    ,							eval_dataset=__SCREAMING_SNAKE_CASE    ,							tokenizer=__SCREAMING_SNAKE_CASE    ,							)

											# start training
											trainer.train()





'''simple docstring'''







def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str							):



						"""simple docstring"""





						assert column_title.isupper()
						lowercase_     :					Dict													=	0
						lowercase_     :					Tuple													=	len(__SCREAMING_SNAKE_CASE							) - 1
						lowercase_     :					Optional[int]													=	0

						while index >= 0:
												lowercase_     :					Optional[Any]													=	(ord(column_title[index]							) - 64) * pow(26				,				__SCREAMING_SNAKE_CASE							)
												answer += value
												power += 1
												index -= 1

						return answer


if __name__ == "__main__":
							from doctest import testmod

							testmod()



