
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__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )     ->		Union[str, Any]:
				# Initialise PyTorch model
				UpperCamelCase_:							str		=  ElectraConfig.from_json_file(lowerCamelCase  )
				print(F'''Building PyTorch model from configuration: {config}'''  )

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

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

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


if __name__ == "__main__":
							lowerCamelCase_  :       Optional[int]					      =     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_  :       Optional[Any]					      =     parser.parse_args()
							convert_tf_checkpoint_to_pytorch(
							    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
							)












import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


lowercase__      :			Union[str, Any]           =					logging.get_logger(__name__)




class 			a__       (					UpperCamelCase__      ):







     def __init__(	self  ,		*A  ,		**A )  ->    None:



          '''simple docstring'''



          warnings.warn(
              "The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
              " Please use MobileNetV2ImageProcessor instead."  ,		A  ,		)
          super().__init__(*A  ,		**A )

