






"""simple docstring"""


import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel


class 					snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   A_						:	str						 = "M-CLIP"


   def __init__(	self :     Optional[Any],       _lowerCamelCase :     str=10_24,       _lowerCamelCase :     List[str]=7_68,       **_lowerCamelCase :     Union[str, Any]  ):

     '''simple docstring'''
     __A									= transformerDimSize
     __A									= imageDimSize
     super().__init__(**_lowerCamelCase  )





class 					snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   A_						:	int						 = MCLIPConfig


   def __init__(	self :     Optional[Any],       _lowerCamelCase :     Optional[int],       *_lowerCamelCase :     List[Any],       **_lowerCamelCase :     int  ):

     '''simple docstring'''
     super().__init__(_lowerCamelCase,       *_lowerCamelCase,       **_lowerCamelCase  )
     __A									= XLMRobertaModel(_lowerCamelCase  )
     __A									= torch.nn.Linear(
         in_features=config.transformerDimensions,       out_features=config.numDims  )






   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple,       _lowerCamelCase :     str,       _lowerCamelCase :     List[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"""


from __future__ import annotations


class 					snake_case		:


   '''simple docstring'''

   def __init__(	self :     int,       _lowerCamelCase :     List[Any]=None  ):

     '''simple docstring'''
     __A									= data
     __A									= None






   def __repr__(	self :     Union[str, Any]  ):

     '''simple docstring'''
     __A									= []
     __A									= self
     while temp:
       string_rep.append(f'{temp.data}'  )
       __A									= temp.next
     return "->".join(_lowerCamelCase  )





def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  if not elements_list:
    raise Exception('''The Elements List is empty''' )

  __A									= __A									= Node(elements_list[0] )
  for i in range(1		,       len(__UpperCamelCase ) ):
    __A									= Node(elements_list[i] )
    __A									= current.next
  return head



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  if head_node is not None and isinstance(__UpperCamelCase		,       __UpperCamelCase ):
    print_reverse(head_node.next )
    print(head_node.data )



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  from doctest import testmod

  testmod()

  __A									= make_linked_list([1_4, 5_2, 1_4, 1_2, 4_3] )
  print('''Linked List:''' )
  print(__UpperCamelCase )
  print('''Elements in Reverse:''' )
  print_reverse(__UpperCamelCase )


if __name__ == "__main__":
       main()




