


_lowerCAmelCase:						Tuple   				=       'Alexander Joslin'

import operator as op

from .stack import Stack






def   _lowercase(					__a				:						str     ):
							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(__a     )     )
														elif i in operators:
																					# RULE 2
																					operator_stack.push(__a     )
														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](__a    ,     __a     )
																					operand_stack.push(__a     )

    # RULE 5
							return operand_stack.peek()


if __name__ == "__main__":
	_lowerCAmelCase:						Dict   				=       '(5 + ((4 * 2) * (2 + 3)))'
	# answer = 45
	print(F"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")









# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE   =  {
    "configuration_xmod": [
        "XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "XmodConfig",
        "XmodOnnxConfig",
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE   =  [
        "XMOD_PRETRAINED_MODEL_ARCHIVE_LIST",
        "XmodForCausalLM",
        "XmodForMaskedLM",
        "XmodForMultipleChoice",
        "XmodForQuestionAnswering",
        "XmodForSequenceClassification",
        "XmodForTokenClassification",
        "XmodModel",
        "XmodPreTrainedModel",
    ]

if TYPE_CHECKING:
    from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_xmod import (
            XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
            XmodForCausalLM,
            XmodForMaskedLM,
            XmodForMultipleChoice,
            XmodForQuestionAnswering,
            XmodForSequenceClassification,
            XmodForTokenClassification,
            XmodModel,
            XmodPreTrainedModel,
        )

else:
    import sys

    _SCREAMING_SNAKE_CASE   =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


