'''simple docstring'''

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			,      __a			,      __a			,      __a      )  ->      Dict:
     '''simple docstring'''







     _UpperCamelCase  :Optional[Any]       =ElectraConfig.from_json_file(__a      )
     print(F'''Building PyTorch model from configuration: {config}'''      )

     if discriminator_or_generator == "discriminator":
          _UpperCamelCase  :Optional[Any]       =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__":
      _lowerCamelCase :				List[str]														= 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 :				Any														= parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
      )







# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__UpperCAmelCase      =				abspath(join(dirname(dirname(dirname(__file__))), "src"))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="ignore", category=FutureWarning)





def 						A_					(						lowercase_			)				->str:







 """simple docstring"""

 from transformers.testing_utils import pytest_addoption_shared

 pytest_addoption_shared(lowercase_			)





def 						A_					(						lowercase_			)				->Tuple:







 """simple docstring"""

 from transformers.testing_utils import pytest_terminal_summary_main

 SCREAMING_SNAKE_CASE				    =		terminalreporter.config.getoption('--make-reports'			)
 if make_reports:
  pytest_terminal_summary_main(lowercase_	,  id=lowercase_			)




