


import torch

from transformers import AutoModel







class       lowerCamelCase__   (       torch.nn.Module):





     def __init__(self		,     UpperCAmelCase="sayef/fsner-bert-base-uncased"       )     ->   Optional[int]:
      super(__lowerCamelCase		,     self       ).__init__()

      _lowercase							      =AutoModel.from_pretrained(__lowerCamelCase		,     return_dict=__lowerCamelCase       )
      _lowercase							      =torch.nn.CosineSimilarity(3		,     1e-08       )
      _lowercase							      =torch.nn.Softmax(dim=1       )





     def      __A					(self		,     **UpperCAmelCase       )     ->   Optional[int]:
      return self.bert(**__lowerCamelCase       ).last_hidden_state





     def      __A					(self		,     UpperCAmelCase       )     ->   Optional[int]:
      return token_embeddings.sum(2		,     keepdim=__lowerCamelCase       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=1       )     ->   Dict:
      return self.softmax(T * self.cos(__lowerCamelCase		,     __lowerCamelCase       )       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Any:
      _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(**__lowerCamelCase       )
      _lowercase							      =self.BERT(**__lowerCamelCase       )

      _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(__lowerCamelCase       ):
       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





from __future__ import annotations

A      :    Union[str, Any]		=					{
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}







class 			_lowercase :


       """simple docstring"""




       def __init__(   self							:			Tuple					,      __lowerCamelCase							:			dict[str, list[str]]					,      __lowerCamelCase							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  graph
             # mapping node to its parent in resulting breadth first tree
             lowerCamelCase__		:				dict[str, str | None]    =  {}
             lowerCamelCase__		:				Dict    =  source_vertex





       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  {self.source_vertex}
             lowerCamelCase__		:				Optional[int]    =  None
             lowerCamelCase__		:				Dict    =  [self.source_vertex]  # first in first out queue

             while queue:
                   lowerCamelCase__		:				Optional[int]    =  queue.pop(0       )
                   for adjacent_vertex in self.graph[vertex]:
                         if adjacent_vertex not in visited:
                               visited.add(__lowerCamelCase       )
                               lowerCamelCase__		:				List[str]    =  vertex
                               queue.append(__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Optional[Any]					,      __lowerCamelCase							:			str       ):


             '''simple docstring'''



             if target_vertex == self.source_vertex:
                   return self.source_vertex

             lowerCamelCase__		:				Tuple    =  self.parent.get(__lowerCamelCase       )
             if target_vertex_parent is None:
                   lowerCamelCase__		:				Tuple    =  (
                       f"No path from vertex: {self.source_vertex} to vertex: {target_vertex}"
                   )
                   raise ValueError(__lowerCamelCase       )

             return self.shortest_path(__lowerCamelCase       ) + f"->{target_vertex}"


if __name__ == "__main__":
    A      :    List[str]		=					Graph(graph, "G")
    g.breath_first_search()
    print(g.shortest_path("D"))
    print(g.shortest_path("G"))
    print(g.shortest_path("Foo"))


