

import torch

from transformers import AutoModel



class   __lowercase				( torch.nn.Module	):




			"""simple docstring"""







			def __init__( self  :     List[Any]   ,						lowerCAmelCase__  :     Union[str, Any]="sayef/fsner-bert-base-uncased"):
									super(_a   ,						self).__init__()

									SCREAMING_SNAKE_CASE_: str  		=       AutoModel.from_pretrained(_a   ,						return_dict=_a)
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       torch.nn.CosineSimilarity(3   ,						1E-08)
									SCREAMING_SNAKE_CASE_: List[str]  		=       torch.nn.Softmax(dim=1)



			def        _SCREAMING_SNAKE_CASE			( self  :     int   ,						**lowerCAmelCase__  :     str):
									return self.bert(**_a).last_hidden_state



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     int):
									return token_embeddings.sum(2   ,						keepdim=_a)



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     List[str]   ,						lowerCAmelCase__  :     Dict   ,						lowerCAmelCase__  :     Dict=1):
									return self.softmax(T * self.cos(_a   ,						_a))




			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     List[str]   ,						lowerCAmelCase__  :     List[Any]):
									SCREAMING_SNAKE_CASE_: str  		=       W_supports["sizes"].tolist()
									SCREAMING_SNAKE_CASE_: Any  		=       W_supports["start_token_id"].item()
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       W_supports["end_token_id"].item()

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

									SCREAMING_SNAKE_CASE_: str  		=       self.BERT(**_a)
									SCREAMING_SNAKE_CASE_: List[Any]  		=       self.BERT(**_a)

									SCREAMING_SNAKE_CASE_: str  		=       None
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       None

									SCREAMING_SNAKE_CASE_: int  		=       W_supports["input_ids"] == start_token_id
									SCREAMING_SNAKE_CASE_: List[Any]  		=       W_supports["input_ids"] == end_token_id

									for i, size in enumerate(_a):
															if i == 0:
																					SCREAMING_SNAKE_CASE_: Any  		=       0
															else:
																					SCREAMING_SNAKE_CASE_: List[Any]  		=       support_sizes[i - 1]

															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       S[s : s + size][start_token_masks[s : s + size]]
															SCREAMING_SNAKE_CASE_: str  		=       S[s : s + size][end_token_masks[s : s + size]]

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

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

									return p_starts, p_ends









'''simple docstring'''
class 							A__    :





  def __init__(		self      :	Union[str, Any]							,					_a      :	int )	->    None:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =size
       _SCREAMING_SNAKE_CASE							    =[0] * size
       _SCREAMING_SNAKE_CASE							    =[0] * size





  @staticmethod
  def 						A  (		_a      :	int )	->    int:





       '''simple docstring'''






       return index | (index + 1)





  @staticmethod
  def 						A  (		_a      :	int )	->    int:





       '''simple docstring'''






       return (index & (index + 1)) - 1





  def 						A  (		self      :	Tuple							,					_a      :	int							,					_a      :	int )	->    None:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =value
       while index < self.size:
            _SCREAMING_SNAKE_CASE							    =self.get_prev(_a ) + 1
            if current_left_border == index:
                 _SCREAMING_SNAKE_CASE							    =value
            else:
                 _SCREAMING_SNAKE_CASE							    =max(_a							,					_a							,					_a )
            _SCREAMING_SNAKE_CASE							    =self.get_next(_a )





  def 						A  (		self      :	int							,					_a      :	int							,					_a      :	int )	->    int:





       '''simple docstring'''






       right -= 1  # Because of right is exclusive
       _SCREAMING_SNAKE_CASE							    =0
       while left <= right:
            _SCREAMING_SNAKE_CASE							    =self.get_prev(_a )
            if left <= current_left:
                 _SCREAMING_SNAKE_CASE							    =max(_a							,					self.tree[right] )
                 _SCREAMING_SNAKE_CASE							    =current_left
            else:
                 _SCREAMING_SNAKE_CASE							    =max(_a							,					self.arr[right] )
                 right -= 1
       return result


if __name__ == "__main__":
     import doctest

     doctest.testmod()
