
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 	A__   (						_A				):





							@slow
							@require_torch
							def 			a__ ( self  :   Tuple				)						->		List[str]:



											"""simple docstring"""


											__lowercase								=					EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'     , 'prajjwal1/bert-tiny'				)
											__lowercase								=					BertTokenizer.from_pretrained('bert-base-uncased'				)

											__lowercase								=					bertabert.config.encoder.vocab_size
											__lowercase								=					tokenizer.sep_token_id
											__lowercase								=					tokenizer.cls_token_id
											__lowercase								=					1_28

											__lowercase								=					datasets.load_dataset('cnn_dailymail'     , '3.0.0'     , split='train[:1%]'				)
											__lowercase								=					datasets.load_dataset('cnn_dailymail'     , '3.0.0'     , split='validation[:1%]'				)

											__lowercase								=					train_dataset.select(range(32				)				)
											__lowercase								=					val_dataset.select(range(16				)				)

											__lowercase								=					4

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

															__lowercase								=					outputs.input_ids
															__lowercase								=					outputs.input_ids.copy()
															__lowercase								=					[
															    [-1_00 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
															]
															__lowercase								=					outputs.attention_mask

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

															return batch

											def _compute_metrics(_UpperCAmelCase  :   Union[str, Any]				):
															__lowercase								=					pred.label_ids
															__lowercase								=					pred.predictions

															# all unnecessary tokens are removed
															__lowercase								=					tokenizer.batch_decode(_UpperCAmelCase     , skip_special_tokens=_UpperCAmelCase				)
															__lowercase								=					tokenizer.batch_decode(_UpperCAmelCase     , skip_special_tokens=_UpperCAmelCase				)

															__lowercase								=					sum([int(pred_str[i] == label_str[i]				) for i in range(len(_UpperCAmelCase				)				)]				) / len(_UpperCAmelCase				)

															return {"accuracy": accuracy}

											# map train dataset
											__lowercase								=					train_dataset.map(
											    _map_to_encoder_decoder_inputs     , batched=_UpperCAmelCase     , batch_size=_UpperCAmelCase     , 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								=					val_dataset.map(
											    _map_to_encoder_decoder_inputs     , batched=_UpperCAmelCase     , batch_size=_UpperCAmelCase     , remove_columns=['article', 'highlights']     , )
											val_dataset.set_format(
											    type='torch'     , columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']     , )

											__lowercase								=					self.get_auto_remove_tmp_dir()

											__lowercase								=					SeqaSeqTrainingArguments(
											    output_dir=_UpperCAmelCase     , per_device_train_batch_size=_UpperCAmelCase     , per_device_eval_batch_size=_UpperCAmelCase     , predict_with_generate=_UpperCAmelCase     , evaluation_strategy='steps'     , do_train=_UpperCAmelCase     , do_eval=_UpperCAmelCase     , warmup_steps=0     , eval_steps=2     , logging_steps=2     , )

											# instantiate trainer
											__lowercase								=					SeqaSeqTrainer(
											    model=_UpperCAmelCase     , args=_UpperCAmelCase     , compute_metrics=_compute_metrics     , train_dataset=_UpperCAmelCase     , eval_dataset=_UpperCAmelCase     , tokenizer=_UpperCAmelCase     , )

											# start training
											trainer.train()













"""simple docstring"""



def 						lowercase    (				__snake_case				: int	):
		if not isinstance(__snake_case		,					__snake_case	):
				raise ValueError('''Input must be an integer'''	)
		if input_num <= 0:
				raise ValueError('''Input must be positive'''	)
		return sum(
		    divisor for divisor in range(1		,					input_num // 2 + 1	) if input_num % divisor == 0	)


if __name__ == "__main__":
						import doctest

						doctest.testmod()



