
"""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			)							->					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"""


import math



def        _lowerCamelCase(__UpperCamelCase			)							->					bool:

	if 1 < number < 4:
		# 2 and 3 are primes
		return True
	elif number < 2 or number % 2 == 0 or number % 3 == 0:
		# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
		return False

	# All primes number are in format of 6k +/- 1
	for i in range(5       ,					int(math.sqrt(__UpperCamelCase			) + 1			)       ,					6			):
		if number % i == 0 or number % (i + 2) == 0:
			return False
	return True



def        _lowerCamelCase(__UpperCamelCase = 600851475143			)							->					int:

	try:
		_lowerCAmelCase								=int(__UpperCamelCase			)
	except (TypeError, ValueError):
		raise TypeError("""Parameter n must be int or castable to int."""			)
	if n <= 0:
		raise ValueError("""Parameter n must be greater than or equal to one."""			)
	_lowerCAmelCase								=0
	if is_prime(__UpperCamelCase			):
		return n
	while n % 2 == 0:
		n //= 2
	if is_prime(__UpperCamelCase			):
		return n
	for i in range(3       ,					int(math.sqrt(__UpperCamelCase			)			) + 1       ,					2			):
		if n % i == 0:
			if is_prime(n // i			):
				_lowerCAmelCase								=n // i
				break
			elif is_prime(__UpperCamelCase			):
				_lowerCAmelCase								=i
	return max_number


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



