


"""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_					(					)     ->     Any:
   a   :	Union[str, 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=_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        A_					(					)     ->     Dict:
   a   :	Union[str, Any]						=    parse_args()

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

   # Patch sys.argv
   a   :	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()












"""simple docstring"""





def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		UpperCamelCase =						sorted(_SCREAMING_SNAKE_CASE	,						key=lambda _SCREAMING_SNAKE_CASE     : value[2]	,						reverse=_SCREAMING_SNAKE_CASE     )

		# Create a list of size equal to the maximum deadline
		# and initialize it with -1
		UpperCamelCase =						max(_SCREAMING_SNAKE_CASE	,						key=lambda _SCREAMING_SNAKE_CASE     : value[1]     )[1]
		UpperCamelCase =						[-1] * max_deadline

		# Finding the maximum profit and the count of jobs
		UpperCamelCase =						0
		UpperCamelCase =						0
		for job in jobs:
				# Find a free time slot for this job
				# (Note that we start from the last possible slot)
				for i in range(job[1] - 1	,						-1	,						-1     ):
						if time_slots[i] == -1:
								UpperCamelCase =						job[0]
								count += 1
								max_profit += job[2]
								break
		return [count, max_profit]


if __name__ == "__main__":
						import doctest

						doctest.testmod()






