


'''simple docstring'''


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

import torch_xla.distributed.xla_multiprocessing as xmp






def  __lowerCAmelCase ():
		_UpperCAmelCase		:       Dict								=      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  __lowerCAmelCase ():
		_UpperCAmelCase		:       List[str]								=      parse_args()

		# Import training_script as a module.
		_UpperCAmelCase		:       Optional[Any]								=      Path(args.training_script   )
		sys.path.append(str(script_fpath.parent.resolve()   )   )
		_UpperCAmelCase		:       Any								=      script_fpath.stem
		_UpperCAmelCase		:       Tuple								=      importlib.import_module(__lowerCAmelCase   )

		# Patch sys.argv
		_UpperCAmelCase		:       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()







'''simple docstring'''


from __future__ import annotations

import numpy as np






def  __lowerCAmelCase (__lowerCAmelCase   ):
		return np.maximum(0      , __lowerCAmelCase   )


if __name__ == "__main__":
						print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]



