





import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			'M-CLIP'




     def __init__(    self				,     snake_case_=1024				,     snake_case_=768				,     **snake_case_							):
         _A     =							transformerDimSize
         _A     =							imageDimSize
         super().__init__(**snake_case_							)







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			MCLIPConfig




     def __init__(    self				,     snake_case_				,     *snake_case_				,     **snake_case_							):
         super().__init__(snake_case_				,     *snake_case_				,     **snake_case_							)
         _A     =							XLMRobertaModel(snake_case_							)
         _A     =							torch.nn.Linear(
             in_features=config.transformerDimensions				,     out_features=config.numDims							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_							):
         _A     =							self.transformer(input_ids=snake_case_				,     attention_mask=snake_case_							)[0]
         _A     =							(embs * attention_mask.unsqueeze(2							)).sum(dim=1							) / attention_mask.sum(dim=1							)[:, None]
         return self.LinearTransformation(snake_case_							), embs












__A							:			Dict         =			"Alexander Joslin"

import operator as op

from .stack import Stack





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> 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__":
 __A							:			Any         =			"(5 + ((4 * 2) * (2 + 3)))"
 # answer = 45
 print(f"{equation} = {dijkstras_two_stack_algorithm(equation)}")





