



"""simple docstring"""
def   A_    (     _lowercase      ):







						'''simple docstring'''


						if number != int(_lowercase      ):
												raise ValueError("""factorial() only accepts integral values"""      )
						if number < 0:
												raise ValueError("""factorial() not defined for negative values"""      )
						snake_case_					:Any		=     1
						for i in range(1,   number + 1      ):
												value *= i
						return value
def   A_    (     _lowercase      ):







						'''simple docstring'''


						if not isinstance(_lowercase,   _lowercase      ):
												raise ValueError("""factorial() only accepts integral values"""      )
						if n < 0:
												raise ValueError("""factorial() not defined for negative values"""      )
						return 1 if n == 0 or n == 1 else n * factorial(n - 1      )


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						__a           =							int(input("Enter a positive integer: ").strip() or 0)
						print(F"""factorial{n} is {factorial(n)}""")





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

import torch_xla.distributed.xla_multiprocessing as xmp
def   A_    (     ):







						'''simple docstring'''


						snake_case_					:str		=     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   A_    (     ):







						'''simple docstring'''


						snake_case_					:Any		=     parse_args()

						# Import training_script as a module.
						snake_case_					:str		=     Path(args.training_script      )
						sys.path.append(str(script_fpath.parent.resolve()      )      )
						snake_case_					:Dict		=     script_fpath.stem
						snake_case_					:int		=     importlib.import_module(_lowercase      )

						# Patch sys.argv
						snake_case_					:Optional[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()
