"""simple docstring"""







import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Any  ,       _SCREAMING_SNAKE_CASE				:				Tuple  ,       _SCREAMING_SNAKE_CASE				:				Union[str, Any]  ,       _SCREAMING_SNAKE_CASE				:				Dict  ,       _SCREAMING_SNAKE_CASE				:				Dict						)    ->     Optional[int]:







     '''simple docstring'''


     __A					:					Union[str, Any]								=		BertConfig.from_pretrained(
         'bert-large-cased'  ,       vocab_size=_SCREAMING_SNAKE_CASE  ,       max_position_embeddings=512  ,       is_decoder=_SCREAMING_SNAKE_CASE  ,       add_cross_attention=_SCREAMING_SNAKE_CASE  ,       )
     __A					:					Tuple								=		bert_config.to_dict()
     del bert_config_dict["type_vocab_size"]
     __A					:					Dict								=		BertGenerationConfig(**_SCREAMING_SNAKE_CASE						)
     if is_encoder:
          __A					:					Optional[int]								=		BertGenerationEncoder(_SCREAMING_SNAKE_CASE						)
     else:
          __A					:					List[str]								=		BertGenerationDecoder(_SCREAMING_SNAKE_CASE						)
     print(F'Building PyTorch model from configuration: {config}'						)

     # Load weights from tf checkpoint
     load_tf_weights_in_bert_generation(
         _SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE  ,       model_class='bert'  ,       is_encoder_named_decoder=_SCREAMING_SNAKE_CASE  ,       is_encoder=_SCREAMING_SNAKE_CASE  ,       )

     # Save pytorch-model
     print(F'Save PyTorch model and config to {pytorch_dump_path}'						)
     model.save_pretrained(_SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
       lowerCamelCase      : Tuple       =argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--tf_hub_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
       )
       parser.add_argument(
           '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
       )
       parser.add_argument(
           '''--is_encoder_named_decoder''',
           action='''store_true''',
           help='''If decoder has to be renamed to encoder in PyTorch model.''',
       )
       parser.add_argument('''--is_encoder''', action='''store_true''', help='''If model is an encoder.''')
       parser.add_argument('''--vocab_size''', default=5_03_58, type=int, help='''Vocab size of model''')
       lowerCamelCase      : Optional[Any]       =parser.parse_args()
       convert_tf_checkpoint_to_pytorch(
           args.tf_hub_path,
           args.pytorch_dump_path,
           args.is_encoder_named_decoder,
           args.vocab_size,
           is_encoder=args.is_encoder,
       )




"""simple docstring"""







def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				list[int]						)    ->     tuple[int, int]:







     '''simple docstring'''


     if len(_SCREAMING_SNAKE_CASE						) == 1:
          return a[0], a[0]
     else:
          # split A into half.
          __A					:					List[str]								=		a[: len(_SCREAMING_SNAKE_CASE						) // 2]
          __A					:					Optional[Any]								=		a[len(_SCREAMING_SNAKE_CASE						) // 2 :]

          # 2 sub problems, 1/2 of original size.
          __A						,		__A					:					int								=		max_difference(_SCREAMING_SNAKE_CASE						)
          __A						,		__A					:					str								=		max_difference(_SCREAMING_SNAKE_CASE						)

          # get min of first and max of second
          # linear time
          __A					:					str								=		min(_SCREAMING_SNAKE_CASE						)
          __A					:					Optional[int]								=		max(_SCREAMING_SNAKE_CASE						)

          # 3 cases, either (small1, big1),
          # (min_first, max_second), (small2, big2)
          # constant comparisons
          if biga - smalla > max_second - min_first and biga - smalla > biga - smalla:
               return smalla, biga
          elif biga - smalla > max_second - min_first:
               return smalla, biga
          else:
               return min_first, max_second


if __name__ == "__main__":
       import doctest

       doctest.testmod()



