import numpy as np






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ):
       lowerCAmelCase__	      =   np.array(lowerCAmelCase__      )
       lowerCAmelCase__	      =   np.array(lowerCAmelCase__      )

       lowerCAmelCase__	      =   abs(predict - actual      )
       lowerCAmelCase__	      =   difference.mean()

       return score






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ):
       lowerCAmelCase__	      =   np.array(lowerCAmelCase__      )
       lowerCAmelCase__	      =   np.array(lowerCAmelCase__      )

       lowerCAmelCase__	      =   predict - actual
       lowerCAmelCase__	      =   np.square(lowerCAmelCase__      )

       lowerCAmelCase__	      =   square_diff.mean()
       return score






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ):
       lowerCAmelCase__	      =   np.array(lowerCAmelCase__      )
       lowerCAmelCase__	      =   np.array(lowerCAmelCase__      )

       lowerCAmelCase__	      =   predict - actual
       lowerCAmelCase__	      =   np.square(lowerCAmelCase__      )
       lowerCAmelCase__	      =   square_diff.mean()
       lowerCAmelCase__	      =   np.sqrt(lowerCAmelCase__      )
       return score






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ):
       lowerCAmelCase__	      =   np.array(lowerCAmelCase__      )
       lowerCAmelCase__	      =   np.array(lowerCAmelCase__      )

       lowerCAmelCase__	      =   np.log(predict + 1      )
       lowerCAmelCase__	      =   np.log(actual + 1      )

       lowerCAmelCase__	      =   log_predict - log_actual
       lowerCAmelCase__	      =   np.square(lowerCAmelCase__      )
       lowerCAmelCase__	      =   square_diff.mean()

       lowerCAmelCase__	      =   np.sqrt(lowerCAmelCase__      )

       return score






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ):
       lowerCAmelCase__	      =   np.array(lowerCAmelCase__      )
       lowerCAmelCase__	      =   np.array(lowerCAmelCase__      )

       lowerCAmelCase__	      =   predict - actual
       lowerCAmelCase__	      =   np.sum(lowerCAmelCase__      ) / len(lowerCAmelCase__      )
       lowerCAmelCase__	      =   np.sum(lowerCAmelCase__      ) / len(lowerCAmelCase__      )
       # print(numerator, denumerator)
       lowerCAmelCase__	      =   float(lowerCAmelCase__      ) / denumerator * 1_0_0

       return score






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ):
       return np.mean(np.array(lowerCAmelCase__      ) == np.array(lowerCAmelCase__      )      )

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()
