"""simple docstring"""







import math




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Any						)    ->     Optional[int]:







     '''simple docstring'''


     __A					:					List[Any]								=		0
     if n % 2 == 0:
          count += 1
          while n % 2 == 0:
               __A					:					int								=		int(n / 2						)
    # the n input value must be odd so that
    # we can skip one element (ie i += 2)

     __A					:					List[Any]								=		3

     while i <= int(math.sqrt(_SCREAMING_SNAKE_CASE						)						):
          if n % i == 0:
               count += 1
               while n % i == 0:
                    __A					:					Union[str, Any]								=		int(n / i						)
          __A					:					Any								=		i + 2

     # this condition checks the prime
     # number n is greater than 2

     if n > 2:
          count += 1
     return count


if __name__ == "__main__":
       lowerCamelCase      : Tuple       =51_24_21_83
       print(F'The number of distinct prime factors is/are {exact_prime_factor_count(n)}')
       print(F'The value of log(log(n)) is {math.log(math.log(n)):.4f}')




"""simple docstring"""







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

import torch_xla.distributed.xla_multiprocessing as xmp




def 						_lowercase				(  )    ->     List[str]:







     '''simple docstring'''


     __A					:					int								=		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=_SCREAMING_SNAKE_CASE  ,       default=1  ,       help='Number of TPU cores to use (1 or 8).'						)

     # positional
     parser.add_argument(
         'training_script'  ,       type=_SCREAMING_SNAKE_CASE  ,       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=_SCREAMING_SNAKE_CASE						)

     return parser.parse_args()




def 						_lowercase				(  )    ->     List[str]:







     '''simple docstring'''


     __A					:					List[str]								=		parse_args()

     # Import training_script as a module.
     __A					:					Dict								=		Path(args.training_script						)
     sys.path.append(str(script_fpath.parent.resolve()						)						)
     __A					:					List[Any]								=		script_fpath.stem
     __A					:					Optional[Any]								=		importlib.import_module(_SCREAMING_SNAKE_CASE						)

     # Patch sys.argv
     __A					:					Union[str, 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()



