

'''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     a			(				_snake_case       ):






						"""simple docstring"""




						@slow
						@require_torch
						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



								snake_case__						:       List[str]       						=  EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny'''			,			'''prajjwal1/bert-tiny'''  )
								snake_case__						:       List[str]       						=  BertTokenizer.from_pretrained('''bert-base-uncased'''  )

								snake_case__						:       Dict       						=  bertabert.config.encoder.vocab_size
								snake_case__						:       str       						=  tokenizer.sep_token_id
								snake_case__						:       List[str]       						=  tokenizer.cls_token_id
								snake_case__						:       Any       						=  1_2_8

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

								snake_case__						:       Any       						=  train_dataset.select(range(3_2  )  )
								snake_case__						:       int       						=  val_dataset.select(range(1_6  )  )

								snake_case__						:       Tuple       						=  4

								def _map_to_encoder_decoder_inputs(snake_case_					:      int  ):
										# Tokenizer will automatically set [BOS] <text> [EOS]
										snake_case__						:       List[str]       						=  tokenizer(batch['''article''']			,			padding='''max_length'''			,			truncation=lowerCAmelCase__			,			max_length=5_1_2  )
										snake_case__						:       Optional[Any]       						=  tokenizer(batch['''highlights''']			,			padding='''max_length'''			,			truncation=lowerCAmelCase__			,			max_length=1_2_8  )
										snake_case__						:       Optional[Any]       						=  inputs.input_ids
										snake_case__						:       Optional[Any]       						=  inputs.attention_mask

										snake_case__						:       Dict       						=  outputs.input_ids
										snake_case__						:       str       						=  outputs.input_ids.copy()
										snake_case__						:       str       						=  [
										    [-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
										]
										snake_case__						:       Optional[int]       						=  outputs.attention_mask

										assert all(len(lowerCAmelCase__  ) == 5_1_2 for x in inputs.input_ids  )
										assert all(len(lowerCAmelCase__  ) == 1_2_8 for x in outputs.input_ids  )

										return batch

								def _compute_metrics(snake_case_					:      Optional[Any]  ):
										snake_case__						:       Union[str, Any]       						=  pred.label_ids
										snake_case__						:       List[str]       						=  pred.predictions

										# all unnecessary tokens are removed
										snake_case__						:       int       						=  tokenizer.batch_decode(lowerCAmelCase__			,			skip_special_tokens=lowerCAmelCase__  )
										snake_case__						:       List[str]       						=  tokenizer.batch_decode(lowerCAmelCase__			,			skip_special_tokens=lowerCAmelCase__  )

										snake_case__						:       Optional[Any]       						=  sum([int(pred_str[i] == label_str[i]  ) for i in range(len(lowerCAmelCase__  )  )]  ) / len(lowerCAmelCase__  )

										return {"accuracy": accuracy}

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

								snake_case__						:       Union[str, Any]       						=  self.get_auto_remove_tmp_dir()

								snake_case__						:       Optional[int]       						=  SeqaSeqTrainingArguments(
								    output_dir=lowerCAmelCase__			,			per_device_train_batch_size=lowerCAmelCase__			,			per_device_eval_batch_size=lowerCAmelCase__			,			predict_with_generate=lowerCAmelCase__			,			evaluation_strategy='''steps'''			,			do_train=lowerCAmelCase__			,			do_eval=lowerCAmelCase__			,			warmup_steps=0			,			eval_steps=2			,			logging_steps=2			,			)

								# instantiate trainer
								snake_case__						:       Tuple       						=  SeqaSeqTrainer(
								    model=lowerCAmelCase__			,			args=lowerCAmelCase__			,			compute_metrics=_compute_metrics			,			train_dataset=lowerCAmelCase__			,			eval_dataset=lowerCAmelCase__			,			tokenizer=lowerCAmelCase__			,			)

								# start training
								trainer.train()






'''simple docstring'''


import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    """The `image_to_image.py` script is outdated. Please use directly `from diffusers import"""
    """ StableDiffusionImg2ImgPipeline` instead."""
)



