


'''simple docstring'''







import argparse

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


logging.set_verbosity_info()






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

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

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


if __name__ == "__main__":
   A   :  Optional[int]       		=				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=50358, type=int, help='''Vocab size of model''')
   A   :  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'''







A   :  Optional[Any]       		=				{
    '''0000''': '''0''',
    '''0001''': '''1''',
    '''0010''': '''2''',
    '''0011''': '''3''',
    '''0100''': '''4''',
    '''0101''': '''5''',
    '''0110''': '''6''',
    '''0111''': '''7''',
    '''1000''': '''8''',
    '''1001''': '''9''',
    '''1010''': '''a''',
    '''1011''': '''b''',
    '''1100''': '''c''',
    '''1101''': '''d''',
    '''1110''': '''e''',
    '''1111''': '''f''',
}






def    lowerCAmelCase__ (       lowerCamelCase  :    str     ):
       _A  :			Union[str, Any]											=      str(lowerCamelCase     ).strip()

       # Exceptions
       if not binary_str:
              raise ValueError('Empty string was passed to the function'     )
       _A  :			Any											=      binary_str[0] == '-'
       _A  :			str											=      binary_str[1:] if is_negative else binary_str
       if not all(char in '01' for char in binary_str     ):
              raise ValueError('Non-binary value was passed to the function'     )

       _A  :			Optional[Any]											=      (
           '0' * (4 * (divmod(len(lowerCamelCase     )						,4     )[0] + 1) - len(lowerCamelCase     )) + binary_str
       )

       _A  :			Optional[int]											=      []
       for x in range(0						,len(lowerCamelCase     )						,4     ):
              hexadecimal.append(BITS_TO_HEX[binary_str[x : x + 4]]     )
       _A  :			int											=      '0x' + ''.join(lowerCamelCase     )

       return "-" + hexadecimal_str if is_negative else hexadecimal_str


if __name__ == "__main__":
   from doctest import testmod

   testmod()






