





import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()
def  __UpperCamelCase    (						snake_case							,			snake_case							,			snake_case      )    ->		Optional[int]:


   '''simple docstring'''







   __A    =  ConvBertConfig.from_json_file(lowercase__      )
   __A    =  ConvBertModel(lowercase__      )

   __A    =  load_tf_weights_in_convbert(lowercase__							,			lowercase__							,			lowercase__      )
   model.save_pretrained(lowercase__      )

   __A    =  TFConvBertModel.from_pretrained(lowercase__							,			from_pt=lowercase__      )
   tf_model.save_pretrained(lowercase__      )


if __name__ == "__main__":
    _UpperCamelCase       :				Tuple           =			argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
    )
    parser.add_argument(
        """--convbert_config_file""",
        default=None,
        type=str,
        required=True,
        help=(
            """The config json file corresponding to the pre-trained ConvBERT model. \n"""
            """This specifies the model architecture."""
        ),
    )
    parser.add_argument(
        """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    _UpperCamelCase       :				Optional[Any]           =			parser.parse_args()
    convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)









'''simple docstring'''





from ..utils import DummyObject, requires_backends
class 					lowercase							(				metaclass=_lowerCamelCase   ):


    '''simple docstring'''

    lowerCAmelCase__   					=				["torch", "transformers", "invisible_watermark"]

    def __init__(       self  :  Optional[Any]					,  *__lowerCamelCase  :  List[Any]					,  **__lowerCamelCase  :  Union[str, Any]  )				->				Dict:

     '''simple docstring'''




     requires_backends(self					,  ["torch", "transformers", "invisible_watermark"]  )

    @classmethod
    def  a__					(       cls  :  Any					,  *__lowerCamelCase  :  List[Any]					,  **__lowerCamelCase  :  Tuple  )				->				List[str]:

     '''simple docstring'''




     requires_backends(cls					,  ["torch", "transformers", "invisible_watermark"]  )




    @classmethod
    def  a__					(       cls  :  Union[str, Any]					,  *__lowerCamelCase  :  Tuple					,  **__lowerCamelCase  :  Tuple  )				->				List[Any]:

     '''simple docstring'''




     requires_backends(cls					,  ["torch", "transformers", "invisible_watermark"]  )
class 					lowercase							(				metaclass=_lowerCamelCase   ):


    '''simple docstring'''

    lowerCAmelCase__   					=				["torch", "transformers", "invisible_watermark"]

    def __init__(       self  :  Optional[Any]					,  *__lowerCamelCase  :  str					,  **__lowerCamelCase  :  Optional[Any]  )				->				str:

     '''simple docstring'''




     requires_backends(self					,  ["torch", "transformers", "invisible_watermark"]  )

    @classmethod
    def  a__					(       cls  :  Union[str, Any]					,  *__lowerCamelCase  :  List[str]					,  **__lowerCamelCase  :  Dict  )				->				Any:

     '''simple docstring'''




     requires_backends(cls					,  ["torch", "transformers", "invisible_watermark"]  )




    @classmethod
    def  a__					(       cls  :  List[Any]					,  *__lowerCamelCase  :  Union[str, Any]					,  **__lowerCamelCase  :  Any  )				->				Any:

     '''simple docstring'''




     requires_backends(cls					,  ["torch", "transformers", "invisible_watermark"]  )

