





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        snake_case_		(			__UpperCAmelCase):







		@slow
		@require_torch
		def 			__lowercase   (		self   )				->   Union[str, Any]:
				lowerCamelCase   : Any								=EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny'''			,     '''prajjwal1/bert-tiny'''   )
				lowerCamelCase   : int								=BertTokenizer.from_pretrained('''bert-base-uncased'''   )

				lowerCamelCase   : Any								=bertabert.config.encoder.vocab_size
				lowerCamelCase   : str								=tokenizer.sep_token_id
				lowerCamelCase   : Tuple								=tokenizer.cls_token_id
				lowerCamelCase   : Dict								=1_2_8

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

				lowerCamelCase   : Any								=train_dataset.select(range(3_2   )   )
				lowerCamelCase   : List[str]								=val_dataset.select(range(1_6   )   )

				lowerCamelCase   : int								=4

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

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

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

						return batch

				def _compute_metrics(__lowercase   ):
						lowerCamelCase   : str								=pred.label_ids
						lowerCamelCase   : str								=pred.predictions

						# all unnecessary tokens are removed
						lowerCamelCase   : Dict								=tokenizer.batch_decode(__lowercase			,     skip_special_tokens=__lowercase   )
						lowerCamelCase   : str								=tokenizer.batch_decode(__lowercase			,     skip_special_tokens=__lowercase   )

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

						return {"accuracy": accuracy}

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

				lowerCamelCase   : Any								=self.get_auto_remove_tmp_dir()

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

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

				# start training
				trainer.train()








from __future__ import annotations


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     bool:
		if len(SCREAMING_SNAKE_CASE_		) < 2:
				raise ValueError('''Monogons and Digons are not polygons in the Euclidean space'''		)
		if any(i <= 0 for i in nums		):
				raise ValueError('''All values must be greater than 0'''		)
		lowerCamelCase   : List[Any]								=nums.copy()
		copy_nums.sort()
		return copy_nums[-1] < sum(copy_nums[:-1]		)


if __name__ == "__main__":
				import doctest

				doctest.testmod()

