
"""simple docstring"""




lowerCamelCase_						     =					'''Alexander Joslin'''

import operator as op

from .stack import Stack






def 						__magic_name__			(   __a      :	str						):



     '''simple docstring'''




     UpperCamelCase__				=    {"""*""": op.mul, """/""": op.truediv, """+""": op.add, """-""": op.sub}

     UpperCamelCase__				=    Stack()
     UpperCamelCase__				=    Stack()

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

               UpperCamelCase__				=    operators[opr](__a	,		__a						)
               operand_stack.push(__a						)

    # RULE 5
     return operand_stack.peek()


if __name__ == "__main__":
    lowerCamelCase_						     =					'''(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_tokenizers_available, is_torch_available


lowerCamelCase_						     =					{
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_						     =					[
        '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''CanineForMultipleChoice''',
        '''CanineForQuestionAnswering''',
        '''CanineForSequenceClassification''',
        '''CanineForTokenClassification''',
        '''CanineLayer''',
        '''CanineModel''',
        '''CaninePreTrainedModel''',
        '''load_tf_weights_in_canine''',
    ]


if TYPE_CHECKING:
    from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
    from .tokenization_canine import CanineTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_canine import (
            CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
            CanineForMultipleChoice,
            CanineForQuestionAnswering,
            CanineForSequenceClassification,
            CanineForTokenClassification,
            CanineLayer,
            CanineModel,
            CaninePreTrainedModel,
            load_tf_weights_in_canine,
        )


else:
    import sys

    lowerCamelCase_						     =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





