



from ..utils import DummyObject, requires_backends



class 		_snake_case      (			metaclass=lowerCamelCase				):





 """simple docstring"""



 lowerCamelCase_          =			['''torch''', '''transformers''', '''k_diffusion''']
 def __init__(						self	,    *a	,    **a					)	->							Union[str, Any]:




   """simple docstring"""







   requires_backends(self	,    ['''torch''', '''transformers''', '''k_diffusion''']					)
 @classmethod
 def lowercase_		(						cls	,    *a	,    **a					)	->							Tuple:




   """simple docstring"""







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



 @classmethod
 def lowercase_		(						cls	,    *a	,    **a					)	->							Optional[Any]:




   """simple docstring"""







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




import argparse

import torch

from transformers import ElectraConfig, ElectraForMaskedLM, ElectraForPreTraining, load_tf_weights_in_electra
from transformers.utils import logging


logging.set_verbosity_info()




def     UpperCAmelCase__ (							__snake_case						,				__snake_case						,				__snake_case						,				__snake_case							)   -> Tuple:
  # Initialise PyTorch model
  _A           =    ElectraConfig.from_json_file(__snake_case							)
  print(F'''Building PyTorch model from configuration: {config}'''							)

  if discriminator_or_generator == "discriminator":
    _A           =    ElectraForPreTraining(__snake_case							)
  elif discriminator_or_generator == "generator":
    _A           =    ElectraForMaskedLM(__snake_case							)
  else:
    raise ValueError('''The discriminator_or_generator argument should be either \'discriminator\' or \'generator\''''							)

  # Load weights from tf checkpoint
  load_tf_weights_in_electra(
      __snake_case						,				__snake_case						,				__snake_case						,				discriminator_or_generator=__snake_case							)

  # Save pytorch-model
  print(F'''Save PyTorch model to {pytorch_dump_path}'''							)
  torch.save(model.state_dict()						,				__snake_case							)


if __name__ == "__main__":
    __lowerCamelCase  				=					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(
        """--config_file""",
        default=None,
        type=str,
        required=True,
        help="""The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.""",
    )
    parser.add_argument(
        """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    parser.add_argument(
        """--discriminator_or_generator""",
        default=None,
        type=str,
        required=True,
        help=(
            """Whether to export the generator or the discriminator. Should be a string, either 'discriminator' or """
            """'generator'."""
        ),
    )
    __lowerCamelCase  				=					parser.parse_args()
    convert_tf_checkpoint_to_pytorch(
        args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
    )