'''simple docstring'''




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

import torch_xla.distributed.xla_multiprocessing as xmp

def _UpperCamelCase   (       ):
     lowercase_					:       str     				=					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=SCREAMING_SNAKE_CASE_  ,       default=1  ,       help='Number of TPU cores to use (1 or 8).'							)

     # positional
     parser.add_argument(
         'training_script'  ,       type=SCREAMING_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=SCREAMING_SNAKE_CASE_							)

     return parser.parse_args()

def _UpperCamelCase   (       ):
     lowercase_					:       Optional[int]     				=					parse_args()

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

     # Patch sys.argv
     lowercase_					:       List[str]     				=					[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 _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):

     # preprocessing the first row
     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(SCREAMING_SNAKE_CASE_							)							):
          matrix[i][0] += matrix[i - 1][0]

     # updating the path cost for current position
     for i in range(1  ,       len(SCREAMING_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()




