




"""simple docstring"""







from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments





def     __snake_case   (							):


				"""simple docstring"""







				UpperCamelCase										=  HfArgumentParser(_lowercase						)
				try:
								UpperCamelCase										=  parser.parse_args_into_dataclasses()[0]
				except ValueError as e:
								UpperCamelCase										=  '''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
								UpperCamelCase										=  ''' '''.join(str(_lowercase						).split(''' '''						)[:-1]						)
								UpperCamelCase										=  ''''''
								UpperCamelCase										=  eval(str(_lowercase						).split(''' '''						)[-1]						)
								UpperCamelCase										=  []
								for arg in depreciated_args:
												# arg[2:] removes '--'
												if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
																# arg[5:] removes '--no_'
																full_error_msg += arg_error_msg.format(arg[5:]						)
												else:
																wrong_args.append(_lowercase						)
								if len(_lowercase						) > 0:
												UpperCamelCase										=  full_error_msg + begin_error_msg + str(_lowercase						)
								raise ValueError(_lowercase						)

				UpperCamelCase										=  PyTorchBenchmark(args=_lowercase						)
				benchmark.run()


if __name__ == "__main__":
		main()





"""simple docstring"""







import argparse

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


logging.set_verbosity_info()





def     __snake_case   (							_lowercase ,_lowercase ,_lowercase ,_lowercase ,_lowercase						):


    """simple docstring"""







    UpperCamelCase										=  BertConfig.from_pretrained(
        '''bert-large-cased''' ,vocab_size=_lowercase ,max_position_embeddings=512 ,is_decoder=_lowercase ,add_cross_attention=_lowercase ,)
    UpperCamelCase										=  bert_config.to_dict()
    del bert_config_dict["type_vocab_size"]
    UpperCamelCase										=  BertGenerationConfig(**_lowercase						)
    if is_encoder:
        UpperCamelCase										=  BertGenerationEncoder(_lowercase						)
    else:
        UpperCamelCase										=  BertGenerationDecoder(_lowercase						)
    print(f'Building PyTorch model from configuration: {config}'						)

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

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


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE_      =					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')
  SCREAMING_SNAKE_CASE_      =					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,
  )