
SCREAMING_SNAKE_CASE__   :			int												=    "Alexander Joslin"

import operator as op

from .stack import Stack




def  __magic_name__ (   __lowerCAmelCase	:						str							)			->   int:
				__lowerCamelCase    					=	{'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}

				__lowerCamelCase    					=	Stack()
				__lowerCamelCase    					=	Stack()

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

												__lowerCamelCase    					=	operators[opr](__lowerCAmelCase		,  __lowerCAmelCase							)
												operand_stack.push(__lowerCAmelCase							)

    # RULE 5
				return operand_stack.peek()


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :			str												=    "(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_torch_available


SCREAMING_SNAKE_CASE__   :			Optional[Any]												=    {"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			int												=    [
					    "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "WavLMForAudioFrameClassification",
					    "WavLMForCTC",
					    "WavLMForSequenceClassification",
					    "WavLMForXVector",
					    "WavLMModel",
					    "WavLMPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_wavlm import (
										    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    WavLMForAudioFrameClassification,
										    WavLMForCTC,
										    WavLMForSequenceClassification,
										    WavLMForXVector,
										    WavLMModel,
										    WavLMPreTrainedModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE__   :			int												=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



