




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

import torch_xla.distributed.xla_multiprocessing as xmp







def 	_a      (						):
					__lowerCAmelCase				   =       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      (						):
					__lowerCAmelCase				   =       parse_args()

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

					# Patch sys.argv
					__lowerCAmelCase				   =       [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()










from diffusers.utils.testing_utils import require_onnxruntime







@require_onnxruntime
class __snake_case			:
   pass






