
"""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     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )				-> List[str]:
	# Initialise PyTorch model
	_lowerCAmelCase								=ElectraConfig.from_json_file(__UpperCamelCase       )
	print(F'''Building PyTorch model from configuration: {config}'''       )

	if discriminator_or_generator == "discriminator":
		_lowerCAmelCase								=ElectraForPreTraining(__UpperCamelCase       )
	elif discriminator_or_generator == "generator":
		_lowerCAmelCase								=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__":
			__A       						=       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\'.'
			    ),
			)
			__A       						=       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     _lowerCamelCase(__UpperCamelCase = 4000000       )				-> int:
	_lowerCAmelCase								=1
	_lowerCAmelCase								=2
	_lowerCAmelCase								=0
	while j <= n:
		if j % 2 == 0:
			total += j
		_lowerCAmelCase						,							_lowerCAmelCase								=j, i + j

	return total


if __name__ == "__main__":
			print(F"""{solution() = }""")



