





def   _A (	SCREAMING_SNAKE_CASE  :     int		):





  """simple docstring"""



  if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		) or number < 0:
    raise ValueError("Input must be a non-negative integer"		)

  a__			:      List[str]            =number & ~(number - 1)
  a__			:      Optional[int]            =0
  while intermediate:
    intermediate >>= 1
    index += 1
  return index - 1


if __name__ == "__main__":
       import doctest

       doctest.testmod(verbose=True)













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"""



  a__			:      Optional[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   _A (	):





  """simple docstring"""



  a__			:      Optional[Any]            =parse_args()

  # Import training_script as a module.
  a__			:      Tuple            =Path(args.training_script		)
  sys.path.append(str(script_fpath.parent.resolve()		)		)
  a__			:      Tuple            =script_fpath.stem
  a__			:      Any            =importlib.import_module(SCREAMING_SNAKE_CASE		)

  # Patch sys.argv
  a__			:      List[str]            =[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()






