




from __future__ import annotations





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase    )      ->       int:
	if len(__lowerCamelCase    ) < k or k < 0:
		raise ValueError("""Invalid Input"""    )
	a										=							a										=							sum(array[:k]    )
	for i in range(len(__lowerCamelCase    ) - k    ):
		a										=							current_sum - array[i] + array[i + k]
		a										=							max(__lowerCamelCase							,			__lowerCamelCase    )
	return max_sum


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

		testmod()
		__UpperCamelCase					:       Union[str, Any]    					=      [randint(-1_000, 1_000) for i in range(100)]
		__UpperCamelCase					:       Optional[int]    					=      randint(0, 110)
		print(F'The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}')







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

import torch_xla.distributed.xla_multiprocessing as xmp





def 		__A		(	)      ->       Any:
	a										=							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 		__A		(	)      ->       Union[str, Any]:
	a										=							parse_args()

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

	# Patch sys.argv
	a										=							[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()

