


'''simple docstring'''







import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel


class  lowerCAmelCase_						(       snake_case__      ):





  """simple docstring"""



  a_       :List[str]												="""M-CLIP"""

  def __init__(					self     :							Dict				,							SCREAMING_SNAKE_CASE__     :							Any=1_0_2_4				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]=7_6_8				,							**SCREAMING_SNAKE_CASE__     :							Optional[Any]						):





       '''simple docstring'''

       __a       =   transformerDimSize
       __a       =   imageDimSize
       super().__init__(**__lowerCAmelCase						)


class  lowerCAmelCase_						(       snake_case__      ):





  """simple docstring"""



  a_       :List[str]												=MCLIPConfig

  def __init__(					self     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]				,							*SCREAMING_SNAKE_CASE__     :							Optional[int]				,							**SCREAMING_SNAKE_CASE__     :							Union[str, Any]						):





       '''simple docstring'''

       super().__init__(__lowerCAmelCase				,							*__lowerCAmelCase				,							**__lowerCAmelCase						)
       __a       =   XLMRobertaModel(__lowerCAmelCase						)
       __a       =   torch.nn.Linear(
           in_features=config.transformerDimensions				,							out_features=config.numDims						)


  def __a						(					self     :							Optional[int]				,							SCREAMING_SNAKE_CASE__     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							Any						):





       '''simple docstring'''

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










'''simple docstring'''







def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					Dict:
     """simple docstring"""




     __a       =   []
     __a       =   set({"""(""", """[""", """{"""}		)
     __a       =   set({""")""", """]""", """}"""}		)
     __a       =   {"""{""": """}""", """[""": """]""", """(""": """)"""}

     for i in range(len(__SCREAMING_SNAKE_CASE		)		):
          if s[i] in open_brackets:
               stack.append(s[i]		)

          elif s[i] in closed_brackets and (
              len(__SCREAMING_SNAKE_CASE		) == 0 or (len(__SCREAMING_SNAKE_CASE		) > 0 and open_to_closed[stack.pop()] != s[i])
          ):
               return False

     return len(__SCREAMING_SNAKE_CASE		) == 0




def        __lowercase						(      )				->					List[str]:
     """simple docstring"""




     __a       =   input("""Enter sequence of brackets: """		)
     if is_balanced(__SCREAMING_SNAKE_CASE		):
          print(__SCREAMING_SNAKE_CASE				,  """is balanced"""		)
     else:
          print(__SCREAMING_SNAKE_CASE				,  """is not balanced"""		)


if __name__ == "__main__":
 main()






