


"""simple docstring"""







import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp




def    UpperCAmelCase__       ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				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=lowerCAmelCase_  ,						default=1  ,						help="Number of TPU cores to use (1 or 8)."   )

					# positional
					parser.add_argument(
					    "training_script"  ,						type=lowerCAmelCase_  ,						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=lowerCAmelCase_   )

					return parser.parse_args()




def    UpperCAmelCase__       ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				parse_args()

					# Import training_script as a module.
					__SCREAMING_SNAKE_CASE						   =				Path(args.training_script   )
					sys.path.append(str(script_fpath.parent.resolve()   )   )
					__SCREAMING_SNAKE_CASE						   =				script_fpath.stem
					__SCREAMING_SNAKE_CASE						   =				importlib.import_module(lowerCAmelCase_   )

					# Patch sys.argv
					__SCREAMING_SNAKE_CASE						   =				[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()






import random

from .binary_exp_mod import bin_exp_mod

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=1000    )	-> List[str]:
       if n < 2:
              return False

       if n % 2 == 0:
              return n == 2

       # this means n is odd
       lowerCamelCase   :      List[Any]			 =					n - 1
       lowerCamelCase   :      Dict			 =					0
       while d % 2 == 0:
              d /= 2
              exp += 1

       # n - 1=d*(2**exp)
       lowerCamelCase   :      Optional[Any]			 =					0
       while count < prec:
              lowerCamelCase   :      str			 =					random.randint(2 ,n - 1    )
              lowerCamelCase   :      Dict			 =					bin_exp_mod(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
              if b != 1:
                     lowerCamelCase   :      str			 =					True
                     for _ in range(_SCREAMING_SNAKE_CASE    ):
                            if b == n - 1:
                                   lowerCamelCase   :      Tuple			 =					False
                                   break
                            lowerCamelCase   :      int			 =					b * b
                            b %= n
                     if flag:
                            return False
                     count += 1
       return True


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      Optional[int]  =   abs(int(input('Enter bound : ').strip()))
       print('Here\'s the list of primes:')
       print(', '.join(str(i) for i in range(n + 1) if is_prime_big(i)))






