'''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 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






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

         __UpperCamelCase	:	str		    =  bertabert.config.encoder.vocab_size
         __UpperCamelCase	:	Optional[Any]		    =  tokenizer.sep_token_id
         __UpperCamelCase	:	Any		    =  tokenizer.cls_token_id
         __UpperCamelCase	:	Optional[Any]		    =  1_2_8

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

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

         __UpperCamelCase	:	Optional[int]		    =  4

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

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

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

             return batch

         def _compute_metrics(_lowerCamelCase    ):
             __UpperCamelCase	:	str		    =  pred.label_ids
             __UpperCamelCase	:	Dict		    =  pred.predictions

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

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

             return {"accuracy": accuracy}

         # map train dataset
         __UpperCamelCase	:	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
         __UpperCamelCase	:	Any		    =  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']     ,			)

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

         __UpperCamelCase	:	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
         __UpperCamelCase	:	str		    =  SeqaSeqTrainer(
             model=_lowerCamelCase     ,			args=_lowerCamelCase     ,			compute_metrics=_compute_metrics     ,			train_dataset=_lowerCamelCase     ,			eval_dataset=_lowerCamelCase     ,			tokenizer=_lowerCamelCase     ,			)

         # start training
         trainer.train()




'''simple docstring'''
def  _UpperCamelCase (     _a	:	int			):




    """simple docstring"""



    if bit_count < 0:
        raise ValueError('The given input must be positive'			)

    # get the generated string sequence
    __UpperCamelCase	:	Dict		    =  gray_code_sequence_string(_a			)
    #
    # convert them to integers
    for i in range(len(_a			)			):
        __UpperCamelCase	:	int		    =  int(sequence[i]				,     2			)

    return sequence







def  _UpperCamelCase (     _a	:	int			):




    """simple docstring"""



    if bit_count == 0:
        return ["0"]

    if bit_count == 1:
        return ["0", "1"]

    __UpperCamelCase	:	Dict		    =  1 << bit_count  # defines the length of the sequence
    # 1<< n is equivalent to 2^n

    # recursive answer will generate answer for n-1 bits
    __UpperCamelCase	:	Tuple		    =  gray_code_sequence_string(bit_count - 1			)

    __UpperCamelCase	:	str		    =  []

    # append 0 to first half of the smaller sequence generated
    for i in range(seq_len // 2			):
        __UpperCamelCase	:	Optional[Any]		    =  '0' + smaller_sequence[i]
        sequence.append(_a			)

    # append 1 to second half ... start from the end of the list
    for i in reversed(range(seq_len // 2			)			):
        __UpperCamelCase	:	List[Any]		    =  '1' + smaller_sequence[i]
        sequence.append(_a			)

    return sequence


if __name__ == "__main__":
  import doctest

  doctest.testmod()



