






"""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 					_UpperCAmelCase       ( _A							):




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

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

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

							lowercase_ : Tuple							       =   train_dataset.select(range(32   )   )
							lowercase_ : List[str]							       =   val_dataset.select(range(16   )   )

							lowercase_ : Dict							       =   4

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

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

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

									return batch

							def _compute_metrics(A     :						Union[str, Any]   ):
									lowercase_ : Optional[int]							       =   pred.label_ids
									lowercase_ : Dict							       =   pred.predictions

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

									lowercase_ : Union[str, Any]							       =   sum([int(pred_str[i] == label_str[i]   ) for i in range(len(A   )   )]   ) / len(A   )

									return {"accuracy": accuracy}

							# map train dataset
							lowercase_ : Optional[int]							       =   train_dataset.map(
							    _map_to_encoder_decoder_inputs    ,						batched=A    ,						batch_size=A    ,						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_ : str							       =   val_dataset.map(
							    _map_to_encoder_decoder_inputs    ,						batched=A    ,						batch_size=A    ,						remove_columns=['''article''', '''highlights''']    ,						)
							val_dataset.set_format(
							    type='''torch'''    ,						columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels''']    ,						)

							lowercase_ : Optional[Any]							       =   self.get_auto_remove_tmp_dir()

							lowercase_ : Union[str, Any]							       =   SeqaSeqTrainingArguments(
							    output_dir=A    ,						per_device_train_batch_size=A    ,						per_device_eval_batch_size=A    ,						predict_with_generate=A    ,						evaluation_strategy='''steps'''    ,						do_train=A    ,						do_eval=A    ,						warmup_steps=0    ,						eval_steps=2    ,						logging_steps=2    ,						)

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

							# start training
							trainer.train()











"""simple docstring"""



from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version



def 						lowercase    (				__snake_case				: str		,					__snake_case				: str		,					__snake_case				: Optional[str] = None	):
		if version.parse(hfh.__version__	).release < version.parse('''0.11.0'''	).release:
				# old versions of hfh don't url-encode the file path
				lowercase_ : Union[str, Any]							       =   quote(__snake_case	)
		return hfh.hf_hub_url(__snake_case		,					__snake_case		,					repo_type='''dataset'''		,					revision=__snake_case	)



