





'''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			):





   @slow
   @require_torch
   def      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny"""				,"""prajjwal1/bert-tiny"""    )
      __SCREAMING_SNAKE_CASE      							=       BertTokenizer.from_pretrained("""bert-base-uncased"""    )

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

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

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

      __SCREAMING_SNAKE_CASE      							=       4

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

         __SCREAMING_SNAKE_CASE      							=       outputs.input_ids
         __SCREAMING_SNAKE_CASE      							=       outputs.input_ids.copy()
         __SCREAMING_SNAKE_CASE      							=       [
             [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
         ]
         __SCREAMING_SNAKE_CASE      							=       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    ):
         __SCREAMING_SNAKE_CASE      							=       pred.label_ids
         __SCREAMING_SNAKE_CASE      							=       pred.predictions

         # all unnecessary tokens are removed
         __SCREAMING_SNAKE_CASE      							=       tokenizer.batch_decode(lowerCamelCase				,skip_special_tokens=lowerCamelCase    )
         __SCREAMING_SNAKE_CASE      							=       tokenizer.batch_decode(lowerCamelCase				,skip_special_tokens=lowerCamelCase    )

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

         return {"accuracy": accuracy}

      # map train dataset
      __SCREAMING_SNAKE_CASE      							=       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
      __SCREAMING_SNAKE_CASE      							=       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"""]				,)

      __SCREAMING_SNAKE_CASE      							=       self.get_auto_remove_tmp_dir()

      __SCREAMING_SNAKE_CASE      							=       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
      __SCREAMING_SNAKE_CASE      							=       SeqaSeqTrainer(
          model=lowerCamelCase				,args=lowerCamelCase				,compute_metrics=_compute_metrics				,train_dataset=lowerCamelCase				,eval_dataset=lowerCamelCase				,tokenizer=lowerCamelCase				,)

      # start training
      trainer.train()












'''simple docstring'''

def 						__magic_name__       (							__UpperCAmelCase = 1							,	__UpperCAmelCase = 1000      )						->					int:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       1
   __SCREAMING_SNAKE_CASE      							=       0

   for divide_by_number in range(__UpperCAmelCase							,	digit + 1      ):
      __SCREAMING_SNAKE_CASE      							=       []
      __SCREAMING_SNAKE_CASE      							=       numerator
      for _ in range(1							,	digit + 1      ):
         if now_divide in has_been_divided:
            if longest_list_length < len(__UpperCAmelCase      ):
               __SCREAMING_SNAKE_CASE      							=       len(__UpperCAmelCase      )
               __SCREAMING_SNAKE_CASE      							=       divide_by_number
         else:
            has_been_divided.append(__UpperCAmelCase      )
            __SCREAMING_SNAKE_CASE      							=       now_divide * 10 % divide_by_number

   return the_digit


# Tests
if __name__ == "__main__":
    import doctest

    doctest.testmod()





