

lowercase_       		=  "Alexander Joslin"

import operator as op

from .stack import Stack
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	str      )							->      int:





    '''simple docstring'''






    A__          =   {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}

    A__          =   Stack()
    A__          =   Stack()

    for i in equation:
        if i.isdigit():
            # RULE 1
            operand_stack.push(int(SCREAMING_SNAKE_CASE__      )      )
        elif i in operators:
            # RULE 2
            operator_stack.push(SCREAMING_SNAKE_CASE__      )
        elif i == ")":
            # RULE 4
            A__          =   operator_stack.peek()
            operator_stack.pop()
            A__          =   operand_stack.peek()
            operand_stack.pop()
            A__          =   operand_stack.peek()
            operand_stack.pop()

            A__          =   operators[opr](SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
            operand_stack.push(SCREAMING_SNAKE_CASE__      )

    # RULE 5
    return operand_stack.peek()


if __name__ == "__main__":
     lowercase_       		=  "(5 + ((4 * 2) * (2 + 3)))"
     # answer = 45
     print(f"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE_:str             =						{
    """configuration_transfo_xl""": ["""TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TransfoXLConfig"""],
    """tokenization_transfo_xl""": ["""TransfoXLCorpus""", """TransfoXLTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:Union[str, Any]             =						[
         """TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """AdaptiveEmbedding""",
         """TransfoXLForSequenceClassification""",
         """TransfoXLLMHeadModel""",
         """TransfoXLModel""",
         """TransfoXLPreTrainedModel""",
         """load_tf_weights_in_transfo_xl""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:Any             =						[
         """TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFAdaptiveEmbedding""",
         """TFTransfoXLForSequenceClassification""",
         """TFTransfoXLLMHeadModel""",
         """TFTransfoXLMainLayer""",
         """TFTransfoXLModel""",
         """TFTransfoXLPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
     from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_transfo_xl import (
              TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
              AdaptiveEmbedding,
              TransfoXLForSequenceClassification,
              TransfoXLLMHeadModel,
              TransfoXLModel,
              TransfoXLPreTrainedModel,
              load_tf_weights_in_transfo_xl,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_transfo_xl import (
              TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFAdaptiveEmbedding,
              TFTransfoXLForSequenceClassification,
              TFTransfoXLLMHeadModel,
              TFTransfoXLMainLayer,
              TFTransfoXLModel,
              TFTransfoXLPreTrainedModel,
          )

else:
     import sys

     SCREAMING_SNAKE_CASE_:Dict             =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


