






"""simple docstring"""







import argparse

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


logging.set_verbosity_info()

def _a (		_snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case							):







      """simple docstring"""






      UpperCAmelCase							=       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_      ,       )
      UpperCAmelCase							=       bert_config.to_dict()
      del bert_config_dict["type_vocab_size"]
      UpperCAmelCase							=       BertGenerationConfig(**SCREAMING_SNAKE_CASE_							)
      if is_encoder:
            UpperCAmelCase							=       BertGenerationEncoder(SCREAMING_SNAKE_CASE_							)
      else:
            UpperCAmelCase							=       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__":
  _UpperCamelCase     			=  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""")
  _UpperCamelCase     			=  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'''



from ..utils import DummyObject, requires_backends

class       _a (metaclass=_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     ['torch', 'librosa']






       def __init__(							self				,							*A__				,							**A__					)			->	str:
          requires_backends(self				,							["""torch""", """librosa"""]					)






       @classmethod
       def 				UpperCamelCase (							cls				,							*A__				,							**A__					)			->	Tuple:
          requires_backends(cls				,							["""torch""", """librosa"""]					)






       @classmethod
       def 				UpperCamelCase (							cls				,							*A__				,							**A__					)			->	Optional[Any]:
          requires_backends(cls				,							["""torch""", """librosa"""]					)






class       _a (metaclass=_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     ['torch', 'librosa']






       def __init__(							self				,							*A__				,							**A__					)			->	Dict:
          requires_backends(self				,							["""torch""", """librosa"""]					)






       @classmethod
       def 				UpperCamelCase (							cls				,							*A__				,							**A__					)			->	Optional[Any]:
          requires_backends(cls				,							["""torch""", """librosa"""]					)






       @classmethod
       def 				UpperCamelCase (							cls				,							*A__				,							**A__					)			->	Union[str, Any]:
          requires_backends(cls				,							["""torch""", """librosa"""]					)






