

"""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  UpperCamelCase   (     _lowerCAmelCase       :  Any,					_lowerCAmelCase       :  str,					_lowerCAmelCase       :  Optional[Any],					_lowerCAmelCase       :  List[str]	)			->     Union[str, Any]:
 # Initialise PyTorch model
 _UpperCAmelCase      :							Optional[int]       =   ElectraConfig.from_json_file(lowercase__	)
 print(f'''Building PyTorch model from configuration: {config}'''	)

 if discriminator_or_generator == "discriminator":
  _UpperCAmelCase      :							Dict       =   ElectraForPreTraining(lowercase__	)
 elif discriminator_or_generator == "generator":
  _UpperCAmelCase      :							Tuple       =   ElectraForMaskedLM(lowercase__	)
 else:
  raise ValueError("""The discriminator_or_generator argument should be either 'discriminator' or 'generator'"""	)

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

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


if __name__ == "__main__":
      lowerCamelCase__			:					Optional[Any]							  =					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__			:					Any							  =					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  UpperCamelCase   (     _lowerCAmelCase       :  int	)			->     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(_lowerCAmelCase	) + 1	),					6	):
		if number % i == 0 or number % (i + 2) == 0:
			return False
	return True
def  UpperCamelCase   (     _lowerCAmelCase       :  int = 2000000	)			->     int:
	return sum(num for num in range(3,					_lowerCAmelCase,					2	) if is_prime(_lowerCAmelCase	)	) + 2 if n > 2 else 0


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





