


'''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__					(					a_   ):





			'''simple docstring'''

			@slow
			@require_torch
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		Tuple:
						__A=	EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'			,    'prajjwal1/bert-tiny'	)
						__A=	BertTokenizer.from_pretrained('bert-base-uncased'	)

						__A=	bertabert.config.encoder.vocab_size
						__A=	tokenizer.sep_token_id
						__A=	tokenizer.cls_token_id
						__A=	128

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

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

						__A=	4

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

									__A=	outputs.input_ids
									__A=	outputs.input_ids.copy()
									__A=	[
									    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
									]
									__A=	outputs.attention_mask

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

									return batch

						def _compute_metrics(lowerCAmelCase_							:			Tuple	):
									__A=	pred.label_ids
									__A=	pred.predictions

									# all unnecessary tokens are removed
									__A=	tokenizer.batch_decode(lowerCAmelCase_			,    skip_special_tokens=lowerCAmelCase_	)
									__A=	tokenizer.batch_decode(lowerCAmelCase_			,    skip_special_tokens=lowerCAmelCase_	)

									__A=	sum([int(pred_str[i] == label_str[i]	) for i in range(len(lowerCAmelCase_	)	)]	) / len(lowerCAmelCase_	)

									return {"accuracy": accuracy}

						# map train dataset
						__A=	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
						__A=	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']			,    )

						__A=	self.get_auto_remove_tmp_dir()

						__A=	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
						__A=	SeqaSeqTrainer(
						    model=lowerCAmelCase_			,    args=lowerCAmelCase_			,    compute_metrics=_compute_metrics			,    train_dataset=lowerCAmelCase_			,    eval_dataset=lowerCAmelCase_			,    tokenizer=lowerCAmelCase_			,    )

						# start training
						trainer.train()







'''simple docstring'''





import numpy as np

from transformers import Pipeline






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Any ):






			"""simple docstring"""




			__A=	np.max(_SCREAMING_SNAKE_CASE,axis=-1,keepdims=_SCREAMING_SNAKE_CASE )
			__A=	np.exp(outputs - maxes )
			return shifted_exp / shifted_exp.sum(axis=-1,keepdims=_SCREAMING_SNAKE_CASE )






class 						a__					(					a_   ):





			'''simple docstring'''

			def 					lowerCAmelCase     (  self							:			Optional[Any]			,    **lowerCAmelCase_							:			List[str]	)      ->		List[str]:
						__A=	{}
						if "second_text" in kwargs:
									__A=	kwargs['second_text']
						return preprocess_kwargs, {}, {}
			def 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			Union[str, Any]			,    lowerCAmelCase_							:			Optional[int]=None	)      ->		int:
						return self.tokenizer(lowerCAmelCase_			,    text_pair=lowerCAmelCase_			,    return_tensors=self.framework	)
			def 					lowerCAmelCase     (  self							:			Dict			,    lowerCAmelCase_							:			List[str]	)      ->		Tuple:
						return self.model(**lowerCAmelCase_	)

			def 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			Dict	)      ->		Optional[int]:
						__A=	model_outputs.logits[0].numpy()
						__A=	softmax(lowerCAmelCase_	)

						__A=	np.argmax(lowerCAmelCase_	)
						__A=	self.model.config.idalabel[best_class]
						__A=	probabilities[best_class].item()
						__A=	logits.tolist()
						return {"label": label, "score": score, "logits": logits}



