






"""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	(  )						->    List[str]:

		"""simple docstring"""







		lowerCAmelCase_   :							List[Any]									=			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=_lowercase      ,       default=1      ,       help="Number of TPU cores to use (1 or 8)."       )

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

		return parser.parse_args()

def 		__lowerCamelCase	(  )						->    List[str]:

		"""simple docstring"""







		lowerCAmelCase_   :							Optional[int]									=			parse_args()

		# Import training_script as a module.
		lowerCAmelCase_   :							Dict									=			Path(args.training_script       )
		sys.path.append(str(script_fpath.parent.resolve()       )       )
		lowerCAmelCase_   :							List[str]									=			script_fpath.stem
		lowerCAmelCase_   :							Optional[Any]									=			importlib.import_module(_lowercase       )

		# Patch sys.argv
		lowerCAmelCase_   :							Optional[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()







"""simple docstring"""




from __future__ import annotations






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    list[int]    ,       _lowercase						:    int					) ->int:






	'''simple docstring'''






	if len(_lowercase					) < k or k < 0:
		raise ValueError("Invalid Input"					)
	a      :		Optional[Any]	=				sum(array[:k]					)
	for i in range(len(_lowercase					) - k					):
		a      :		Optional[Any]	=				current_sum - array[i] + array[i + k]
		a      :		Union[str, Any]	=				max(_lowercase    ,       _lowercase					)
	return max_sum


if __name__ == "__main__":
		from doctest import testmod
		from random import randint

		testmod()
		a						: Any     	=			[randint(-1000, 1000) for i in range(100)]
		a						: List[str]     	=			randint(0, 110)
		print(F'''The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}''')


