





def 	lowerCAmelCase_					(			__a						)	->						Any:







  """simple docstring"""





  return [
      {
          0: [1, 2],
          1: [0, 2],
          2: [0, 1, 3, 5],
          3: [2, 4],
          4: [3],
          5: [2, 6, 8],
          6: [5, 7],
          7: [6, 8],
          8: [5, 7],
      },
      {
          0: [6],
          1: [9],
          2: [4, 5],
          3: [4],
          4: [2, 3],
          5: [2],
          6: [0, 7],
          7: [6],
          8: [],
          9: [1],
      },
      {
          0: [4],
          1: [6],
          2: [],
          3: [5, 6, 7],
          4: [0, 6],
          5: [3, 8, 9],
          6: [1, 3, 4, 7],
          7: [3, 6, 8, 9],
          8: [5, 7],
          9: [5, 7],
      },
      {
          0: [1, 3],
          1: [0, 2, 4],
          2: [1, 3, 4],
          3: [0, 2, 4],
          4: [1, 2, 3],
      },
  ][index]





def 	lowerCAmelCase_					(			__a						)	->						list[tuple[int, int]]:







  """simple docstring"""





  lowerCamelCase__: Dict         =0
  lowerCamelCase__: Union[str, Any]         =len(__a						)  # No of vertices in graph
  lowerCamelCase__: Optional[int]         =[0] * n
  lowerCamelCase__: List[str]         =[False] * n

  def dfs(__a      ,       __a      ,       __a      ,       __a						):
    lowerCamelCase__: Dict         =True
    lowerCamelCase__: List[Any]         =id_
    id_ += 1
    for to in graph[at]:
      if to == parent:
        pass
      elif not visited[to]:
        dfs(__a      ,       __a      ,       __a      ,       id_						)
        lowerCamelCase__: str         =min(low[at]      ,       low[to]						)
        if id_ <= low[to]:
          bridges.append((at, to) if at < to else (to, at)						)
      else:
        # This edge is a back edge and cannot be a bridge
        lowerCamelCase__: str         =min(low[at]      ,       low[to]						)

  lowerCamelCase__: list[tuple[int, int]]         =[]
  for i in range(__a						):
    if not visited[i]:
      dfs(__a      ,       -1      ,       __a      ,       id_						)
  return bridges


if __name__ == "__main__":
     import doctest

     doctest.testmod()









import torch

from transformers import AutoModel






class 					SCREAMING_SNAKE_CASE_  (       torch.nn.Module ):
       """simple docstring"""


       def __init__(				self	:Dict,      snake_case	:str="sayef/fsner-bert-base-uncased"):



          """simple docstring"""

          super(snake_case,      self).__init__()

          _lowercase              =AutoModel.from_pretrained(snake_case,      return_dict=snake_case)
          _lowercase              =torch.nn.CosineSimilarity(3,      1e-0_8)
          _lowercase              =torch.nn.Softmax(dim=1)





       def 				UpperCamelCase__						(				self	:str,      **snake_case	:int):



          """simple docstring"""

          return self.bert(**snake_case).last_hidden_state





       def 				UpperCamelCase__						(				self	:Union[str, Any],      snake_case	:Optional[Any]):



          """simple docstring"""

          return token_embeddings.sum(2,      keepdim=snake_case)





       def 				UpperCamelCase__						(				self	:List[Any],      snake_case	:int,      snake_case	:Dict,      snake_case	:Dict=1):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:List[str],      snake_case	:int,      snake_case	:List[str]):



          """simple docstring"""

          _lowercase              =W_supports['sizes'].tolist()
          _lowercase              =W_supports['start_token_id'].item()
          _lowercase              =W_supports['end_token_id'].item()

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

          _lowercase              =self.BERT(**snake_case)
          _lowercase              =self.BERT(**snake_case)

          _lowercase              =None
          _lowercase              =None

          _lowercase              =W_supports['input_ids'] == start_token_id
          _lowercase              =W_supports['input_ids'] == end_token_id

          for i, size in enumerate(snake_case):
             if i == 0:
                _lowercase              =0
             else:
                _lowercase              =support_sizes[i - 1]

             _lowercase              =S[s : s + size][start_token_masks[s : s + size]]
             _lowercase              =S[s : s + size][end_token_masks[s : s + size]]

             _lowercase              =torch.matmul(q[i],      s_start.T).sum(1).softmax(0)
             _lowercase              =torch.matmul(q[i],      s_end.T).sum(1).softmax(0)

             if p_starts is not None:
                _lowercase              =torch.vstack((p_starts, p_start))
                _lowercase              =torch.vstack((p_ends, p_end))
             else:
                _lowercase              =p_start
                _lowercase              =p_end

          return p_starts, p_ends


