"""simple docstring"""
import torch

from transformers import AutoModel




class        __snake_case	(					torch.nn.Module    ):



      def __init__(						self   ,      lowercase="sayef/fsner-bert-base-uncased")     ->      Union[str, Any]:





        '''simple docstring'''





        super(lowercase   ,      self).__init__()

        a__:					Any      							=     AutoModel.from_pretrained(lowercase   ,      return_dict=lowercase)
        a__:					Dict      							=     torch.nn.CosineSimilarity(3   ,      1e-08)
        a__:					List[Any]      							=     torch.nn.Softmax(dim=1)



      def   lowerCamelCase_       (						self   ,      **lowercase)     ->      Dict:





        '''simple docstring'''





        return self.bert(**lowercase).last_hidden_state



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





        '''simple docstring'''





        return token_embeddings.sum(2   ,      keepdim=lowercase)



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase=1)     ->      int:





        '''simple docstring'''





        return self.softmax(T * self.cos(lowercase   ,      lowercase))



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase)     ->      Any:





        '''simple docstring'''





        a__:					Dict      							=     W_supports['sizes'].tolist()
        a__:					Dict      							=     W_supports['start_token_id'].item()
        a__:					Dict      							=     W_supports['end_token_id'].item()

        del W_supports["sizes"]
        del W_supports["start_token_id"]
        del W_supports["end_token_id"]

        a__:					str      							=     self.BERT(**lowercase)
        a__:					Any      							=     self.BERT(**lowercase)

        a__:					Optional[int]      							=     None
        a__:					Optional[Any]      							=     None

        a__:					Optional[Any]      							=     W_supports['input_ids'] == start_token_id
        a__:					str      							=     W_supports['input_ids'] == end_token_id

        for i, size in enumerate(lowercase):
          if i == 0:
            a__:					Union[str, Any]      							=     0
          else:
            a__:					Union[str, Any]      							=     support_sizes[i - 1]

          a__:					Union[str, Any]      							=     S[s : s + size][start_token_masks[s : s + size]]
          a__:					str      							=     S[s : s + size][end_token_masks[s : s + size]]

          a__:					Any      							=     torch.matmul(q[i]   ,      s_start.T).sum(1).softmax(0)
          a__:					Dict      							=     torch.matmul(q[i]   ,      s_end.T).sum(1).softmax(0)

          if p_starts is not None:
            a__:					str      							=     torch.vstack((p_starts, p_start))
            a__:					Tuple      							=     torch.vstack((p_ends, p_end))
          else:
            a__:					Union[str, Any]      							=     p_start
            a__:					Union[str, Any]      							=     p_end

        return p_starts, p_ends



"""simple docstring"""
from __future__ import annotations




class        __snake_case	:



      def __init__(						self   ,      lowercase=None)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					int      							=     data
        a__:					str      							=     None



      def __repr__(						self)     ->      List[str]:





        '''simple docstring'''





        a__:					Optional[Any]      							=     []
        a__:					Union[str, Any]      							=     self
        while temp:
          string_rep.append(f'{temp.data}')
          a__:					Tuple      							=     temp.next
        return "->".join(lowercase)







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->str:
  if not elements_list:
    raise Exception('The Elements List is empty'       )

  a__:					Any      							=     Node(elements_list[0]       )
  for i in range(1		,					len(_SCREAMING_SNAKE_CASE       )       ):
    a__:					Optional[Any]      							=     Node(elements_list[i]       )
    a__:					Tuple      							=     current.next
  return head







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->None:
  if head_node is not None and isinstance(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       ):
    print_reverse(head_node.next       )
    print(head_node.data       )







def 						__a       (     )     ->Optional[Any]:
  from doctest import testmod

  testmod()

  a__:					Tuple      							=     make_linked_list([14, 52, 14, 12, 43]       )
  print('Linked List:'       )
  print(_SCREAMING_SNAKE_CASE       )
  print('Elements in Reverse:'       )
  print_reverse(_SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
 main()


