




"""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 lowerCAmelCase__			(  __lowerCAmelCase		):






   '''simple docstring'''



   @slow
   @require_torch
   def       A_					(						self						):
       _lowerCamelCase  :    Any					=					EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'						,   'prajjwal1/bert-tiny'						)
       _lowerCamelCase  :    int					=					BertTokenizer.from_pretrained('bert-base-uncased'						)

       _lowerCamelCase  :    Optional[int]					=					bertabert.config.encoder.vocab_size
       _lowerCamelCase  :    Tuple					=					tokenizer.sep_token_id
       _lowerCamelCase  :    str					=					tokenizer.cls_token_id
       _lowerCamelCase  :    List[Any]					=					128

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

       _lowerCamelCase  :    Optional[int]					=					train_dataset.select(range(32						)						)
       _lowerCamelCase  :    str					=					val_dataset.select(range(16						)						)

       _lowerCamelCase  :    Dict					=					4

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

           _lowerCamelCase  :    Any					=					outputs.input_ids
           _lowerCamelCase  :    Tuple					=					outputs.input_ids.copy()
           _lowerCamelCase  :    Optional[int]					=					[
               [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
           ]
           _lowerCamelCase  :    List[Any]					=					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(lowercase						):
           _lowerCamelCase  :    str					=					pred.label_ids
           _lowerCamelCase  :    Optional[Any]					=					pred.predictions

           # all unnecessary tokens are removed
           _lowerCamelCase  :    Tuple					=					tokenizer.batch_decode(lowerCAmelCase_						,   skip_special_tokens=lowerCAmelCase_						)
           _lowerCamelCase  :    Union[str, Any]					=					tokenizer.batch_decode(lowerCAmelCase_						,   skip_special_tokens=lowerCAmelCase_						)

           _lowerCamelCase  :    List[str]					=					sum([int(pred_str[i] == label_str[i]						) for i in range(len(lowerCAmelCase_						)						)]						) / len(lowerCAmelCase_						)

           return {"accuracy": accuracy}

       # map train dataset
       _lowerCamelCase  :    str					=					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
       _lowerCamelCase  :    int					=					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']						,   )

       _lowerCamelCase  :    Optional[int]					=					self.get_auto_remove_tmp_dir()

       _lowerCamelCase  :    List[Any]					=					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
       _lowerCamelCase  :    Any					=					SeqaSeqTrainer(
           model=lowerCAmelCase_						,   args=lowerCAmelCase_						,   compute_metrics=_compute_metrics						,   train_dataset=lowerCAmelCase_						,   eval_dataset=lowerCAmelCase_						,   tokenizer=lowerCAmelCase_						,   )

       # start training
       trainer.train()





"""simple docstring"""






def   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    List[str]					=					abs(lowercase__	)
				_lowerCamelCase  :    Optional[int]					=					0
				while n > 0:
								res += n % 10
								n //= 10
				return res







def   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    List[str]					=					abs(lowercase__	)
				return n if n < 10 else n % 10 + sum_of_digits(n // 10	)







def   _snake_case    (       lowercase__	):
				return sum(int(lowercase__	) for c in str(abs(lowercase__	)	)	)







def   _snake_case    (       ):
				from collections.abc import Callable
				from timeit import timeit

				def benchmark_a_function(lowercase__							,					lowercase__	) -> None:
								_lowerCamelCase  :    int					=					f'''{func.__name__}({value})'''
								_lowerCamelCase  :    Optional[Any]					=					timeit(f'''__main__.{call}'''							,					setup='import __main__'	)
								print(f'''{call:56} = {func(lowercase__	)} -- {timing:.4f} seconds'''	)

				for value in (262144, 1125899906842624, 1267650600228229401496703205376):
								for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
												benchmark_a_function(lowercase__							,					lowercase__	)
								print()


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			benchmark()