

lowerCAmelCase   :      Dict     =     "Alexander Joslin"

import operator as op

from .stack import Stack

def       A_     (							a     ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_ :     List[Any]							=   {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}

      SCREAMING_SNAKE_CASE_ :     int							=   Stack()
      SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   Stack()

      for i in equation:
            if i.isdigit():
                  # RULE 1
                  operand_stack.push(int(_UpperCamelCase     )     )
            elif i in operators:
                  # RULE 2
                  operator_stack.push(_UpperCamelCase     )
            elif i == ")":
                  # RULE 4
                  SCREAMING_SNAKE_CASE_ :     List[Any]							=   operator_stack.peek()
                  operator_stack.pop()
                  SCREAMING_SNAKE_CASE_ :     int							=   operand_stack.peek()
                  operand_stack.pop()
                  SCREAMING_SNAKE_CASE_ :     int							=   operand_stack.peek()
                  operand_stack.pop()

                  SCREAMING_SNAKE_CASE_ :     Dict							=   operators[opr](_UpperCamelCase    ,      _UpperCamelCase     )
                  operand_stack.push(_UpperCamelCase     )

    # RULE 5
      return operand_stack.peek()


if __name__ == "__main__":
    lowerCAmelCase   :      Optional[Any]     =     "(5 + ((4 * 2) * (2 + 3)))"
    # answer = 45
    print(F'{equation} = {dijkstras_two_stack_algorithm(equation)}')









'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp


def        _lowerCAmelCase					(						)      ->			Any:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =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=_UpperCamelCase							, default=1							, help='Number of TPU cores to use (1 or 8).'				)

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

     return parser.parse_args()


def        _lowerCAmelCase					(						)      ->			Optional[int]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =parse_args()

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

     # Patch sys.argv
     _SCREAMING_SNAKE_CASE							    =[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()
