from collections import namedtuple

lowerCAmelCase__      		=					namedtuple('from_to', 'from_ to')

lowerCAmelCase__      		=					{
    'atm': from_to(1, 1),
    'pascal': from_to(0.0_000_098, 101325),
    'bar': from_to(0.986_923, 1.01_325),
    'kilopascal': from_to(0.00_986_923, 101.325),
    'megapascal': from_to(9.86_923, 0.101_325),
    'psi': from_to(0.068_046, 14.6_959),
    'inHg': from_to(0.0_334_211, 29.9_213),
    'torr': from_to(0.00_131_579, 760),
}






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ):
       if from_type not in PRESSURE_CONVERSION:
              raise ValueError(
                  F"""Invalid 'from_type' value: {from_type!r}  Supported values are:\n"""
                  + ', '.join(lowerCAmelCase__      )      )
       if to_type not in PRESSURE_CONVERSION:
              raise ValueError(
                  F"""Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"""
                  + ', '.join(lowerCAmelCase__      )      )
       return (
           value * PRESSURE_CONVERSION[from_type].from_ * PRESSURE_CONVERSION[to_type].to
       )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

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()
