





import sys






def 							_lowerCAmelCase (  _a   :					int			)	->			List[Any]:
     lowerCAmelCase_					:       Dict    =		len(_a			)
     lowerCAmelCase_					:       int    =		[[0 for x in range(_a			)] for x in range(_a			)]
     lowerCAmelCase_					:       List[str]    =		[[0 for x in range(_a			)] for x in range(_a			)]

     for chain_length in range(2					,   _a			):
          for a in range(1					,   n - chain_length + 1			):
               lowerCAmelCase_					:       Optional[int]    =		a + chain_length - 1

               lowerCAmelCase_					:       Union[str, Any]    =		sys.maxsize
               for c in range(_a					,   _a			):
                    lowerCAmelCase_					:       int    =		(
                        matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
                    )
                    if cost < matrix[a][b]:
                         lowerCAmelCase_					:       Any    =		cost
                         lowerCAmelCase_					:       List[str]    =		c
     return matrix, sol






def 							_lowerCAmelCase (  _a   :					int					,   _a   :					str					,   _a   :					Optional[Any]			)	->			Optional[int]:
     if i == j:
          print("""A""" + str(_a			)					,   end=""" """			)
     else:
          print("""("""					,   end=""" """			)
          print_optiomal_solution(_a					,   _a					,   optimal_solution[i][j]			)
          print_optiomal_solution(_a					,   optimal_solution[i][j] + 1					,   _a			)
          print(""")"""					,   end=""" """			)






def 							_lowerCAmelCase (  )	->			List[str]:
     lowerCAmelCase_					:       List[str]    =		[30, 35, 15, 5, 10, 20, 25]
     lowerCAmelCase_					:       int    =		len(_a			)
     # Size of matrix created from above array will be
     # 30*35 35*15 15*5 5*10 10*20 20*25
     lowerCAmelCase_   ,  lowerCAmelCase_					:       Union[str, Any]    =		matrix_chain_order(_a			)

     print("""No. of Operation required: """ + str(matrix[1][n - 1]			)			)
     print_optiomal_solution(_a					,   1					,   n - 1			)


if __name__ == "__main__":
      main()







import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel





class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  """M-CLIP"""







  def __init__(	self    ,						_lowercase=1_024    ,						_lowercase=768    ,						**_lowercase						):
       lowerCAmelCase_					:       Tuple    =		transformerDimSize
       lowerCAmelCase_					:       Optional[Any]    =		imageDimSize
       super().__init__(**_lowercase						)





class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  MCLIPConfig







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







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