


'''simple docstring'''
UpperCamelCase__    					=      '''Alexander Joslin'''

import operator as op

from .stack import Stack





def 					a__       (					lowerCAmelCase__						)    ->	int:
    UpperCAmelCase__			:		Union[str, Any]													=				{'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}

    UpperCAmelCase__			:		Stack[int]													=				Stack()
    UpperCAmelCase__			:		Stack[str]													=				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__			:		Optional[int]													=				operator_stack.peek()
            operator_stack.pop()
            UpperCAmelCase__			:		List[str]													=				operand_stack.peek()
            operand_stack.pop()
            UpperCAmelCase__			:		int													=				operand_stack.peek()
            operand_stack.pop()

            UpperCAmelCase__			:		List[Any]													=				operators[opr](__a  ,					__a						)
            operand_stack.push(__a						)

    # RULE 5
    return operand_stack.peek()


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



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Union[str, Any]	    =    ["image_processor", "tokenizer"]
							UpperCAmelCase__       :						Dict	    =    "ChineseCLIPImageProcessor"
							UpperCAmelCase__       :						List[str]	    =    ("BertTokenizer", "BertTokenizerFast")


							def __init__(			self       ,							_a=None       ,							_a=None       ,							**_a       )    ->    Any:
												_a	:    Optional[Any]         =		None
												if "feature_extractor" in kwargs:
																	warnings.warn(
																	    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																	    ''' instead.'''       ,							_a       ,							)
																	_a	:    Tuple         =		kwargs.pop('''feature_extractor'''       )

												_a	:    Tuple         =		image_processor if image_processor is not None else feature_extractor
												if image_processor is None:
																	raise ValueError('''You need to specify an `image_processor`.'''       )
												if tokenizer is None:
																	raise ValueError('''You need to specify a `tokenizer`.'''       )

												super().__init__(_a       ,							_a       )
												_a	:    List[str]         =		self.image_processor


							def __call__(			self       ,							_a=None       ,							_a=None       ,							_a=None       ,							**_a       )    ->    int:

												if text is None and images is None:
																	raise ValueError('''You have to specify either text or images. Both cannot be none.'''       )

												if text is not None:
																	_a	:    List[str]         =		self.tokenizer(_a       ,							return_tensors=_a       ,							**_a       )

												if images is not None:
																	_a	:    Optional[Any]         =		self.image_processor(_a       ,							return_tensors=_a       ,							**_a       )

												if text is not None and images is not None:
																	_a	:    Union[str, Any]         =		image_features.pixel_values
																	return encoding
												elif text is not None:
																	return encoding
												else:
																	return BatchEncoding(data=dict(**_a       )       ,							tensor_type=_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Union[str, Any]:
												return self.tokenizer.batch_decode(*_a       ,							**_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Any:
												return self.tokenizer.decode(*_a       ,							**_a       )


							@property
							def 				__lowercase				(			self       )    ->    Optional[Any]:
												_a	:    Any         =		self.tokenizer.model_input_names
												_a	:    Dict         =		self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )




							@property
							def 				__lowercase				(			self       )    ->    Dict:
												warnings.warn(
												    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''       ,							_a       ,							)
												return self.image_processor_class



