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							(						__SCREAMING_SNAKE_CASE    : List[Any]						,				__SCREAMING_SNAKE_CASE    : Union[str, Any]						,				__SCREAMING_SNAKE_CASE    : Optional[int]						,				__SCREAMING_SNAKE_CASE    : Tuple					):







    '''simple docstring'''
    __snake_case    :							List[str]             =  ElectraConfig.from_json_file(_SCREAMING_SNAKE_CASE					)
    print(F'''Building PyTorch model from configuration: {config}'''					)

    if discriminator_or_generator == "discriminator":
        __snake_case    :							Tuple             =  ElectraForPreTraining(_SCREAMING_SNAKE_CASE					)
    elif discriminator_or_generator == "generator":
        __snake_case    :							Tuple             =  ElectraForMaskedLM(_SCREAMING_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(
        _SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				discriminator_or_generator=_SCREAMING_SNAKE_CASE					)

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


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






'''simple docstring'''
from __future__ import annotations

import re


def       a__    (  _SCREAMING_SNAKE_CASE					:    list[str]							)			-> list[str]:


	"""simple docstring"""





	def alphanum_key(_SCREAMING_SNAKE_CASE					:    Optional[Any]							):
		return [int(_SCREAMING_SNAKE_CASE							) if s.isdigit() else s.lower() for s in re.split("([0-9]+)"     ,							_SCREAMING_SNAKE_CASE							)]

	return sorted(_SCREAMING_SNAKE_CASE     ,							key=_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
					import doctest

					doctest.testmod()




