

"""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__										=   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=snake_case__		,					default=1		,					help="""Number of TPU cores to use (1 or 8)."""     )

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

			return parser.parse_args()

def 						A							(  ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   parse_args()

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

			# Patch sys.argv
			SCREAMING_SNAKE_CASE__										=   [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							(  snake_case__     ):




			'''simple docstring'''


			for i in range(1		,					len(matrix[0]     )     ):
						matrix[0][i] += matrix[0][i - 1]

			# preprocessing the first column
			for i in range(1		,					len(snake_case__     )     ):
						matrix[i][0] += matrix[i - 1][0]

			# updating the path cost for current position
			for i in range(1		,					len(snake_case__     )     ):
						for j in range(1		,					len(matrix[0]     )     ):
									matrix[i][j] += min(matrix[i - 1][j]		,					matrix[i][j - 1]     )

			return matrix[-1][-1]


if __name__ == "__main__":
						import doctest

						doctest.testmod()


