
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 		SCREAMING_SNAKE_CASE__			(					__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase							):
    # Initialise PyTorch model
    snake_case__  = ElectraConfig.from_json_file(__lowerCAmelCase							)
    print(F"""Building PyTorch model from configuration: {config}"""							)

    if discriminator_or_generator == "discriminator":
        snake_case__  = ElectraForPreTraining(__lowerCAmelCase							)
    elif discriminator_or_generator == "generator":
        snake_case__  = 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__":
     __magic_name__   				=     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\'.'''
         ),
     )
     __magic_name__   				=     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'''






def 					_snake_case   (     A       )						->    str:
							return " ".join(sorted(set(sentence.split()       )       )       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()