





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

import torch_xla.distributed.xla_multiprocessing as xmp






def      lowerCamelCase_						(				)		->						Dict:






				"""simple docstring"""


				snake_case_						:    Optional[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      lowerCamelCase_						(				)		->						Optional[int]:






				"""simple docstring"""


				snake_case_						:    Tuple				=  parse_args()

				# Import training_script as a module.
				snake_case_						:    Dict				=  Path(args.training_script )
				sys.path.append(str(script_fpath.parent.resolve() ) )
				snake_case_						:    Tuple				=  script_fpath.stem
				snake_case_						:    List[str]				=  importlib.import_module(_UpperCamelCase )

				# Patch sys.argv
				snake_case_						:    Dict				=  [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()







from __future__ import annotations






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						tuple[float, list[float]]:






				"""simple docstring"""


				snake_case_						:    Dict				=  list(range(len(_UpperCamelCase ) ) )
				snake_case_						:    Dict				=  [v / w for v, w in zip(_UpperCamelCase ,				_UpperCamelCase )]
				index.sort(key=lambda _UpperCamelCase : ratio[i] ,				reverse=_UpperCamelCase )

				snake_case_						:    float				=  0
				snake_case_						:    list[float]				=  [0] * len(_UpperCamelCase )
				for i in index:
								if weight[i] <= capacity:
												snake_case_						:    Dict				=  1
												max_value += value[i]
												capacity -= weight[i]
								else:
												snake_case_						:    Union[str, Any]				=  capacity / weight[i]
												max_value += value[i] * capacity / weight[i]
												break

				return max_value, fractions


if __name__ == "__main__":
	import doctest

	doctest.testmod()
