


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 	_lowerCamelCase  (		A_  :	List[Any]							,  A_  :	int							,  A_  :	str							,  A_  :	List[str]      )			->			Optional[Any]:

   '''simple docstring'''






   UpperCamelCase__ :			Union[str, Any] 						=ElectraConfig.from_json_file(A_      )
   print(f'''Building PyTorch model from configuration: {config}'''      )

   if discriminator_or_generator == "discriminator":
      UpperCamelCase__ :			str 						=ElectraForPreTraining(A_      )
   elif discriminator_or_generator == "generator":
      UpperCamelCase__ :			List[Any] 						=ElectraForMaskedLM(A_      )
   else:
      raise ValueError("The discriminator_or_generator argument should be either 'discriminator' or 'generator'"      )

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

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


if __name__ == "__main__":
     __UpperCAmelCase      			=  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'."""
         ),
     )
     __UpperCAmelCase      			=  parser.parse_args()
     convert_tf_checkpoint_to_pytorch(
         args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
     )





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase      			=  {"""configuration_sew_d""": ["""SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWDConfig"""]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __UpperCAmelCase      			=  [
         """SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """SEWDForCTC""",
         """SEWDForSequenceClassification""",
         """SEWDModel""",
         """SEWDPreTrainedModel""",
     ]

if TYPE_CHECKING:
     from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_sew_d import (
              SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
              SEWDForCTC,
              SEWDForSequenceClassification,
              SEWDModel,
              SEWDPreTrainedModel,
          )


else:
     import sys

     __UpperCAmelCase      			=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

