


import argparse

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


logging.set_verbosity_info()





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

       # Load weights from tf checkpoint
       load_tf_weights_in_bert_generation(
           UpperCamelCase__    ,							UpperCamelCase__    ,							model_class="bert"    ,							is_encoder_named_decoder=UpperCamelCase__    ,							is_encoder=UpperCamelCase__    ,							)

       # Save pytorch-model
       print(f"Save PyTorch model and config to {pytorch_dump_path}"						)
       model.save_pretrained(UpperCamelCase__						)


if __name__ == "__main__":
    lowerCAmelCase__						=			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__						=			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,
    )




def 							_UpperCAmelCase    (UpperCamelCase__					:  str						):
       _A				:	Tuple       =			str(UpperCamelCase__						).strip()
       if not bin_string:
              raise ValueError("Empty string was passed to the function"						)
       _A				:	Dict       =			bin_string[0] == "-"
       if is_negative:
              _A				:	Any       =			bin_string[1:]
       if not all(char in "01" for char in bin_string						):
              raise ValueError("Non-binary value was passed to the function"						)
       _A				:	Union[str, Any]       =			0
       for char in bin_string:
              _A				:	Union[str, Any]       =			2 * decimal_number + int(UpperCamelCase__						)
       return -decimal_number if is_negative else decimal_number


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

    testmod()
