'''simple docstring'''

import torch

from transformers import AutoModel


class     __UpperCAmelCase       ( torch.nn.Module		):





  def __init__(	self    ,     _lowerCamelCase="sayef/fsner-bert-base-uncased" ):
     super(lowercase_    ,     self ).__init__()

     lowerCAmelCase_			=      AutoModel.from_pretrained(lowercase_    ,     return_dict=lowercase_ )
     lowerCAmelCase_			=      torch.nn.CosineSimilarity(3    ,     1E-08 )
     lowerCAmelCase_			=      torch.nn.Softmax(dim=1 )





  def 						UpperCAmelCase_	(	self    ,     **_lowerCamelCase ):
     return self.bert(**lowercase_ ).last_hidden_state





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     return token_embeddings.sum(2    ,     keepdim=lowercase_ )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase=1 ):
     return self.softmax(T * self.cos(lowercase_    ,     lowercase_ ) )







  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      W_supports['''sizes'''].tolist()
     lowerCAmelCase_			=      W_supports['''start_token_id'''].item()
     lowerCAmelCase_			=      W_supports['''end_token_id'''].item()

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

     lowerCAmelCase_			=      self.BERT(**lowercase_ )
     lowerCAmelCase_			=      self.BERT(**lowercase_ )

     lowerCAmelCase_			=      None
     lowerCAmelCase_			=      None

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

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

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

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

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

     return p_starts, p_ends







"""simple docstring"""

from __future__ import annotations

from collections.abc import Iterator
class   _UpperCAmelCase				:




      def __init__(				self ,							lowercase_				)    ->       None:
          UpperCAmelCase          =       value
          UpperCAmelCase          =       None
          UpperCAmelCase          =       None

class   _UpperCAmelCase				:




      def __init__(				self ,							lowercase_				)    ->       None:
          UpperCAmelCase          =       tree




      def      a_   (				self ,							lowercase_				)    ->       int:
          if node is None:
              return 0
          return node.value + (
              self.depth_first_search(node.left				) + self.depth_first_search(node.right				)
          )




      def __iter__(				self				)    ->       Iterator[int]:
          yield self.depth_first_search(self.tree				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



