
import math
from collections.abc import Iterator
from itertools import takewhile







def     A__   (  lowerCamelCase  )					->		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     A__   (  )					->		Iterator[int]:
				UpperCamelCase_:							List[Any]		=  2
				while True:
								if is_prime(lowerCamelCase  ):
												yield num
								num += 1







def     A__   (  lowerCamelCase = 2_00_00_00  )					->		int:
				return sum(takewhile(lambda lowerCamelCase  : x < n       ,      prime_generator()  )  )


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








import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Union[str, Any]:
				# Construct model
				if gpta_config_file == "":
								UpperCamelCase_:							Union[str, Any]		=  GPTaConfig()
				else:
								UpperCamelCase_:							Optional[Any]		=  GPTaConfig.from_json_file(lowerCamelCase  )
				UpperCamelCase_:							List[Any]		=  GPTaModel(lowerCamelCase  )

				# Load weights from numpy
				load_tf_weights_in_gpta(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )

				# Save pytorch-model
				UpperCamelCase_:							int		=  pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
				UpperCamelCase_:							Any		=  pytorch_dump_folder_path + """/""" + CONFIG_NAME
				print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''  )
				torch.save(model.state_dict()       ,      lowerCamelCase  )
				print(F'''Save configuration file to {pytorch_config_dump_path}'''  )
				with open(lowerCamelCase       ,      """w"""       ,      encoding="""utf-8"""  ) as f:
								f.write(config.to_json_string()  )


if __name__ == "__main__":
							lowerCamelCase_  :	int 				=					argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--gpt2_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
							)
							parser.add_argument(
							    """--gpt2_config_file""",
							    default="""""",
							    type=str,
							    help=(
							        """An optional config json file corresponding to the pre-trained OpenAI model. \n"""
							        """This specifies the model architecture."""
							    ),
							)
							lowerCamelCase_  :	List[str] 				=					parser.parse_args()
							convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)






