






import pytest

from datasets.utils.version import Version






@pytest.mark.parametrize(
    '''other, expected_equality'''    ,	[
        (Version('''1.0.0'''     ), True),
        ('''1.0.0''', True),
        (Version('''2.0.0'''     ), False),
        ('''2.0.0''', False),
        ('''1''', False),
        ('''a''', False),
        (1, False),
        (None, False),
    ]    ,	)
def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase     )     ->		Optional[Any]:







							"""simple docstring"""





							_a  :    str			=						Version('''1.0.0'''     )
							assert (version == other) is expected_equality
							assert (version != other) is not expected_equality
							assert (hash(UpperCAmelCase     ) == hash(UpperCAmelCase     )) is expected_equality







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    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     )     ->		str:







							"""simple docstring"""





							_a  :    Union[str, Any]			=						ElectraConfig.from_json_file(UpperCAmelCase     )
							print(F'Building PyTorch model from configuration: {config}'     )

							if discriminator_or_generator == "discriminator":
														_a  :    int			=						ElectraForPreTraining(UpperCAmelCase     )
							elif discriminator_or_generator == "generator":
														_a  :    Any			=						ElectraForMaskedLM(UpperCAmelCase     )
							else:
														raise ValueError('''The discriminator_or_generator argument should be either \'discriminator\' or \'generator\''''     )

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

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


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
						)