






from __future__ import annotations





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       list(range(len(UpperCamelCase__							)							)							)
     __lowerCAmelCase             =       [v / w for v, w in zip(UpperCamelCase__       ,			UpperCamelCase__							)]
     index.sort(key=lambda __snake_case							: ratio[i]       ,			reverse=UpperCamelCase__							)

     __lowerCAmelCase             =       0
     __lowerCAmelCase             =       [0] * len(UpperCamelCase__							)
     for i in index:
          if weight[i] <= capacity:
               __lowerCAmelCase             =       1
               max_value += value[i]
               capacity -= weight[i]
          else:
               __lowerCAmelCase             =       capacity / weight[i]
               max_value += value[i] * capacity / weight[i]
               break

     return max_value, fractions


if __name__ == "__main__":
 import doctest

 doctest.testmod()










'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp


def       _lowercase							(					):
 __A	:		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=UpperCamelCase__,					default=1,					help='Number of TPU cores to use (1 or 8).'      )

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

 return parser.parse_args()


def       _lowercase							(					):
 __A	:		str	  =      parse_args()

 # Import training_script as a module.
 __A	:		Tuple	  =      Path(args.training_script      )
 sys.path.append(str(script_fpath.parent.resolve()      )      )
 __A	:		str	  =      script_fpath.stem
 __A	:		Dict	  =      importlib.import_module(UpperCamelCase__      )

 # Patch sys.argv
 __A	:		Optional[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()






