

from __future__ import annotations







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase					):
						if len(_UpperCAmelCase					) == 0:
												raise ValueError("find_min() arg is an empty sequence"					)
						if (
						    left >= len(_UpperCAmelCase					)
						    or left < -len(_UpperCAmelCase					)
						    or right >= len(_UpperCAmelCase					)
						    or right < -len(_UpperCAmelCase					)
						):
												raise IndexError("list index out of range"					)
						if left == right:
												return nums[left]
						SCREAMING_SNAKE_CASE_:				Dict							= (left + right) >> 1  # the middle
						SCREAMING_SNAKE_CASE_:				Tuple							= find_min(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase					)  # find min in range[left, mid]
						SCREAMING_SNAKE_CASE_:				Optional[int]							= find_min(_UpperCAmelCase       ,			mid + 1       ,			_UpperCAmelCase					)  # find min in range[mid + 1, right]

						return left_min if left_min <= right_min else right_min


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_   (					):
						SCREAMING_SNAKE_CASE_:				Tuple							= 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=_UpperCAmelCase       ,			default=1       ,			help="Number of TPU cores to use (1 or 8)."					)

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

						return parser.parse_args()







def     A_   (					):
						SCREAMING_SNAKE_CASE_:				List[str]							= parse_args()

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

						# Patch sys.argv
						SCREAMING_SNAKE_CASE_:				int							= [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()






