

'''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       a__    (  _SCREAMING_SNAKE_CASE					:    List[Any]     ,							_SCREAMING_SNAKE_CASE					:    Union[str, Any]     ,							_SCREAMING_SNAKE_CASE					:    Optional[int]     ,							_SCREAMING_SNAKE_CASE					:    Tuple							)			-> Any:


	"""simple docstring"""





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

	if discriminator_or_generator == "discriminator":
		UpperCAmelCase_			:	Tuple      =   ElectraForPreTraining(_SCREAMING_SNAKE_CASE							)
	elif discriminator_or_generator == "generator":
		UpperCAmelCase_			:	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__":
					_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
					)







'''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()




