
def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				return " ".join(input_str.split()[::-1]						)


if __name__ == "__main__":
						import doctest

						doctest.testmod()


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

import torch_xla.distributed.xla_multiprocessing as xmp

def  __snake_case							(	):







				'''simple docstring'''


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

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

				return parser.parse_args()

def  __snake_case							(	):







				'''simple docstring'''


				lowercase         =      parse_args()

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

				# Patch sys.argv
				lowercase         =      [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()
