



"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp





def      __magic_name__				(							):
			SCREAMING_SNAKE_CASE_: List[Any]     			=ArgumentParser(
			    description=(
			        """PyTorch TPU distributed training launch """
			        """helper utility that will spawn up """
			        """multiple distributed processes"""
			    ) )

			# Optional arguments for the launch helper
			parser.add_argument("""--num_cores"""							,			type=lowercase							,			default=1							,			help="""Number of TPU cores to use (1 or 8).""" )

			# positional
			parser.add_argument(
			    """training_script"""							,			type=lowercase							,			help=(
			        """The full path to the single TPU training """
			        """program/script to be launched in parallel, """
			        """followed by all the arguments for the """
			        """training script"""
			    )							,			)

			# rest from the training program
			parser.add_argument("""training_script_args"""							,			nargs=lowercase )

			return parser.parse_args()





def      __magic_name__				(							):
			SCREAMING_SNAKE_CASE_: int     			=parse_args()

			# Import training_script as a module.
			SCREAMING_SNAKE_CASE_: Optional[int]     			=Path(args.training_script )
			sys.path.append(str(script_fpath.parent.resolve() ) )
			SCREAMING_SNAKE_CASE_: Optional[int]     			=script_fpath.stem
			SCREAMING_SNAKE_CASE_: Optional[Any]     			=importlib.import_module(lowercase )

			# Patch sys.argv
			SCREAMING_SNAKE_CASE_: Any     			=[args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores )]
			xmp.spawn(mod._mp_fn							,			args=()							,			nprocs=args.num_cores )


if __name__ == "__main__":
			main()








"""simple docstring"""
def      __magic_name__				(							lowercase = 200_0000 ):
   SCREAMING_SNAKE_CASE_: List[Any]     			=[0 for i in range(n + 1 )]
   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=1
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=1

   for i in range(2							,			int(n**0.5 ) + 1 ):
      if primality_list[i] == 0:
         for j in range(i * i							,			n + 1							,			lowercase ):
            SCREAMING_SNAKE_CASE_: Optional[int]     			=1
   SCREAMING_SNAKE_CASE_: Dict     			=0
   for i in range(lowercase ):
      if primality_list[i] == 0:
         sum_of_primes += i
   return sum_of_primes


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



