




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

import torch_xla.distributed.xla_multiprocessing as xmp

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					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     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					parse_args()

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

      # Patch sys.argv
      lowerCamelCase_	  =					[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()







def     _SCREAMING_SNAKE_CASE    (							lowercase			: list[int]			,							lowercase			: list[int]  ):


      '''simple docstring'''



      lowerCamelCase_	  =					len(lowercase  )
      print('The following activities are selected:'  )

      # The first activity is always selected
      lowerCamelCase_	  =					0
      print(lowercase			,							end=','  )

      # Consider rest of the activities
      for j in range(lowercase  ):
            # If this activity has start time greater than
            # or equal to the finish time of previously
            # selected activity, then select it
            if start[j] >= finish[i]:
                  print(lowercase			,							end=','  )
                  lowerCamelCase_	  =					j


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 lowerCamelCase       :	Tuple									=     [1, 3, 0, 5, 8, 5]
 lowerCamelCase       :	int									=     [2, 4, 6, 7, 9, 9]
 print_max_activities(start, finish)



