


"""simple docstring"""
import torch

from transformers import AutoModel




class 	lowercase__				(					torch.nn.Module				):





   def __init__(							self      ,   SCREAMING_SNAKE_CASE="sayef/fsner-bert-base-uncased")  ->  str:
      super(SCREAMING_SNAKE_CASE      ,   self).__init__()

      _lowerCamelCase	:					Union[str, Any]   =		AutoModel.from_pretrained(SCREAMING_SNAKE_CASE      ,   return_dict=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					List[str]   =		torch.nn.CosineSimilarity(3      ,   1e-0_8)
      _lowerCamelCase	:					Optional[int]   =		torch.nn.Softmax(dim=1)





   def 		UpperCamelCase_  (							self      ,   **SCREAMING_SNAKE_CASE)  ->  str:
      return self.bert(**SCREAMING_SNAKE_CASE).last_hidden_state





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  Optional[Any]:
      return token_embeddings.sum(2      ,   keepdim=SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=1)  ->  Union[str, Any]:
      return self.softmax(T * self.cos(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE))





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  List[str]:
      _lowerCamelCase	:					str   =		W_supports["""sizes"""].tolist()
      _lowerCamelCase	:					int   =		W_supports["""start_token_id"""].item()
      _lowerCamelCase	:					str   =		W_supports["""end_token_id"""].item()

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

      _lowerCamelCase	:					List[str]   =		self.BERT(**SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Optional[Any]   =		self.BERT(**SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Any   =		None
      _lowerCamelCase	:					List[Any]   =		None

      _lowerCamelCase	:					Any   =		W_supports["""input_ids"""] == start_token_id
      _lowerCamelCase	:					Any   =		W_supports["""input_ids"""] == end_token_id

      for i, size in enumerate(SCREAMING_SNAKE_CASE):
         if i == 0:
            _lowerCamelCase	:					List[str]   =		0
         else:
            _lowerCamelCase	:					Dict   =		support_sizes[i - 1]

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

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

         if p_starts is not None:
            _lowerCamelCase	:					str   =		torch.vstack((p_starts, p_start))
            _lowerCamelCase	:					Optional[Any]   =		torch.vstack((p_ends, p_end))
         else:
            _lowerCamelCase	:					Optional[Any]   =		p_start
            _lowerCamelCase	:					int   =		p_end

      return p_starts, p_ends





"""simple docstring"""
import numpy as np

def 				_snake_case      (   __snake_case							:      np.ndarray   ):




   """simple docstring"""







   return 1 / (1 + np.exp(-vector   ))

def 				_snake_case      (   __snake_case							:      np.ndarray   ):




   """simple docstring"""







   return vector * sigmoid(__snake_case   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()

