def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ):
       if len(str(lowerCAmelCase__      )      ) == 1 or len(str(lowerCAmelCase__      )      ) == 1:
              return a * b

       lowerCAmelCase__	      =   max(len(str(lowerCAmelCase__      )      )      ,    len(str(lowerCAmelCase__      )      )      )
       lowerCAmelCase__	      =   ma // 2

       lowerCAmelCase__      ,   lowerCAmelCase__	      =   divmod(lowerCAmelCase__      ,    1_0**ma      )
       lowerCAmelCase__      ,   lowerCAmelCase__	      =   divmod(lowerCAmelCase__      ,    1_0**ma      )

       lowerCAmelCase__	      =   karatsuba(lowerCAmelCase__      ,    lowerCAmelCase__      )
       lowerCAmelCase__	      =   karatsuba((aa + aa)      ,    (ba + ba)      )
       lowerCAmelCase__	      =   karatsuba(lowerCAmelCase__      ,    lowerCAmelCase__      )

       return (z * 1_0 ** (2 * ma)) + ((y - z - x) * 1_0 ** (ma)) + (x)






def  __lowerCamelCase						(		):
       print(karatsuba(1_5_4_6_3      ,    2_3_4_8_9      )      )


if __name__ == "__main__":
    main()

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

import torch_xla.distributed.xla_multiprocessing as xmp






def  __lowerCamelCase						(		):
       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  __lowerCamelCase						(		):
       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()
