





def      _A       (		SCREAMING_SNAKE_CASE     :   str						):





  """simple docstring"""



  a__	:					Tuple										=0

  # if input_string is "aba" than new_input_string become "a|b|a"
  a__	:					Union[str, Any]										=""
  a__	:					int										=""

  # append each character + "|" in new_string for range(0, length-1)
  for i in input_string[: len(SCREAMING_SNAKE_CASE						) - 1]:
    new_input_string += i + "|"
  # append last character
  new_input_string += input_string[-1]

  # we will store the starting and ending of previous furthest ending palindromic
  # substring
  a__					,							a__	:					List[Any]										=0, 0

  # length[i] shows the length of palindromic substring with center i
  a__	:					Union[str, Any]										=[1 for i in range(len(SCREAMING_SNAKE_CASE						)						)]

  # for each character in new_string find corresponding palindromic string
  a__	:					Optional[int]										=0
  for j in range(len(SCREAMING_SNAKE_CASE						)						):
    a__	:					List[Any]										=1 if j > r else min(length[l + r - j] // 2       ,       r - j + 1						)
    while (
        j - k >= 0
        and j + k < len(SCREAMING_SNAKE_CASE						)
        and new_input_string[k + j] == new_input_string[j - k]
    ):
      k += 1

    a__	:					List[str]										=2 * k - 1

    # does this string is ending after the previously explored end (that is r) ?
    # if yes the update the new r to the last index of this
    if j + k - 1 > r:
      a__	:					Optional[Any]										=j - k + 1  # noqa: E741
      a__	:					Any										=j + k - 1

    # update max_length and start position
    if max_length < length[j]:
      a__	:					Optional[int]										=length[j]
      a__	:					Any										=j

    # create that string
  a__	:					int										=new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
  for i in s:
    if i != "|":
      output_string += i

  return output_string


if __name__ == "__main__":
     import doctest

     doctest.testmod()













import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					Tuple						=			"""M-CLIP"""







      def __init__(    self		,      lowerCAmelCase__=1_0_2_4		,      lowerCAmelCase__=7_6_8		,      **lowerCAmelCase__							)   ->						Any:



        '''simple docstring'''




        a__	:					int										=transformerDimSize
        a__	:					Dict										=imageDimSize
        super().__init__(**lowerCAmelCase__							)







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					Optional[Any]						=			MCLIPConfig







      def __init__(    self		,      lowerCAmelCase__		,      *lowerCAmelCase__		,      **lowerCAmelCase__							)   ->						List[str]:



        '''simple docstring'''




        super().__init__(lowerCAmelCase__		,      *lowerCAmelCase__		,      **lowerCAmelCase__							)
        a__	:					Tuple										=XLMRobertaModel(lowerCAmelCase__							)
        a__	:					List[str]										=torch.nn.Linear(
            in_features=config.transformerDimensions		,      out_features=config.numDims							)







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						Union[str, Any]:



        '''simple docstring'''




        a__	:					Optional[Any]										=self.transformer(input_ids=lowerCAmelCase__		,      attention_mask=lowerCAmelCase__							)[0]
        a__	:					int										=(embs * attention_mask.unsqueeze(2							)).sum(dim=1							) / attention_mask.sum(dim=1							)[:, None]
        return self.LinearTransformation(lowerCAmelCase__							), embs






