
"""simple docstring"""

from __future__ import annotations






def 							A		(       snake_case   :int	)   ->			bool:
 __UpperCamelCase       						= str(snake_case	)
 return len(snake_case	) == 9 and set(snake_case	) == set('123456789'	)






def 							A		(       )   ->			int | None:
 for base_num in range(9_9_9_9       ,      4_9_9_9       ,      -1	):
  __UpperCamelCase       						= 1_0_0_0_0_2 * base_num
  if is_9_pandigital(snake_case	):
   return candidate

 for base_num in range(3_3_3       ,      9_9       ,      -1	):
  __UpperCamelCase       						= 1_0_0_2_0_0_3 * base_num
  if is_9_pandigital(snake_case	):
   return candidate

 return None


if __name__ == "__main__":
      print(f'''{solution() = }''')





"""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		(       )   ->			List[Any]:
 __UpperCamelCase       						= 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=snake_case       ,      default=1       ,      help='Number of TPU cores to use (1 or 8).'	)

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

 return parser.parse_args()






def 							A		(       )   ->			Optional[int]:
 __UpperCamelCase       						= parse_args()

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

 # Patch sys.argv
 __UpperCamelCase       						= [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()



