
def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      UpperCamelCase__    :   Dict					=				[0 for i in range(len(UpperCamelCase__		)		)]

      # initialize interval's left pointer and right pointer
      UpperCamelCase__				,UpperCamelCase__    :   Any					=				0, 0

      for i in range(1 ,		len(UpperCamelCase__		)		):
            # case when current index is inside the interval
            if i <= right_pointer:
                  UpperCamelCase__    :   Optional[int]					=				min(right_pointer - i + 1 ,		z_result[i - left_pointer]		)
                  UpperCamelCase__    :   List[Any]					=				min_edge

            while go_next(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
                  z_result[i] += 1

            # if new index's result gives us more right interval,
            # we've to update left_pointer and right_pointer
            if i + z_result[i] - 1 > right_pointer:
                  UpperCamelCase__				,UpperCamelCase__    :   Tuple					=				i, i + z_result[i] - 1

      return z_result


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      return i + z_result[i] < len(UpperCamelCase__		) and s[z_result[i]] == s[i + z_result[i]]


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   Union[str, Any]					=				0
      # concatenate 'pattern' and 'input_str' and call z_function
      # with concatenated string
      UpperCamelCase__    :   Optional[Any]					=				z_function(pattern + input_str		)

      for val in z_result:
            # if value is greater then length of the pattern string
            # that means this index is starting position of substring
            # which is equal to pattern string
            if val >= len(UpperCamelCase__		):
                  answer += 1

      return answer


if __name__ == "__main__":
    import doctest

    doctest.testmod()




import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  '''M-CLIP'''






     def __init__(							self						,						__SCREAMING_SNAKE_CASE=1_0_2_4						,						__SCREAMING_SNAKE_CASE=7_6_8						,						**__SCREAMING_SNAKE_CASE  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				transformerDimSize
           UpperCamelCase__    :   Any					=				imageDimSize
           super().__init__(**__SCREAMING_SNAKE_CASE  )




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  MCLIPConfig






     def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						*__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )  ->   Optional[int]:




           """simple docstring"""







           super().__init__(__SCREAMING_SNAKE_CASE						,						*__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Union[str, Any]					=				XLMRobertaModel(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Tuple					=				torch.nn.Linear(
               in_features=config.transformerDimensions						,						out_features=config.numDims  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   List[str]:




           """simple docstring"""







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


