





'''simple docstring'''




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

import torch_xla.distributed.xla_multiprocessing as xmp



def 						__A						(       ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Union[str, 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=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						(       ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Tuple						=	parse_args()

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

		# Patch sys.argv
		SCREAMING_SNAKE_CASE				:							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



def 						__A						(       lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							List[Any]						=	m - 1, 0
		while i >= 0 and j < n:
				if key == mat[i][j]:
						print(f'''Key {key} found at row- {i + 1} column- {j + 1}'''					)
						return
				if key < mat[i][j]:
						i -= 1
				else:
						j += 1
		print(f'''Key {key} not found'''					)



def 						__A						(       ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Optional[Any]						=	[[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
		SCREAMING_SNAKE_CASE				:							Dict						=	int(input("""Enter the element to be searched:"""					)					)
		print(lowerCamelCase_					)
		search_in_a_sorted_matrix(lowerCamelCase_	,	len(lowerCamelCase_					)	,	len(mat[0]					)	,	lowerCamelCase_					)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()



