



import argparse

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


logging.set_verbosity_info()






def 						_lowerCAmelCase    (					__lowerCamelCase :  Optional[int]		,						__lowerCamelCase :  str		,						__lowerCamelCase :  Tuple		,						__lowerCamelCase :  Optional[int]		,						__lowerCamelCase :  Dict  ):



 """simple docstring"""





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

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

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


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=50358, 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,
      )










from ..utils import DummyObject, requires_backends

class      __snake_case				(   metaclass=_UpperCAmelCase):
					SCREAMING_SNAKE_CASE_		:       str								=			["torch", "transformers", "k_diffusion"]
					def __init__(						self       :						Tuple	,      *_lowerCAmelCase       :						Union[str, Any]	,      **_lowerCAmelCase       :						Union[str, Any]  )							->					Dict:



											'''simple docstring'''





											requires_backends(self	,      ['torch', 'transformers', 'k_diffusion']  )
					@classmethod
					def 	a			(						cls       :						Optional[int]	,      *_lowerCAmelCase       :						Any	,      **_lowerCAmelCase       :						Optional[Any]  )							->					Optional[Any]:



											'''simple docstring'''





											requires_backends(cls	,      ['torch', 'transformers', 'k_diffusion']  )







					@classmethod
					def 	a			(						cls       :						Union[str, Any]	,      *_lowerCAmelCase       :						Tuple	,      **_lowerCAmelCase       :						str  )							->					Tuple:



											'''simple docstring'''





											requires_backends(cls	,      ['torch', 'transformers', 'k_diffusion']  )

