




'''simple docstring'''



import doctest
from collections import deque

import numpy as np







class 		lowercase_   :


   """simple docstring"""

   def __init__(   self :     Any   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				[2, 1, 2, -1]
      _SCREAMING_SNAKE_CASE	=				[1, 2, 3, 4]



   def 							lowerCAmelCase_      (   self :     Dict   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				len(self.first_signal   )
      _SCREAMING_SNAKE_CASE	=				len(self.second_signal   )

      _SCREAMING_SNAKE_CASE	=				max(__lowerCamelCase  ,				__lowerCamelCase   )

      # create a zero matrix of max_length x max_length
      _SCREAMING_SNAKE_CASE	=				[[0] * max_length for i in range(__lowerCamelCase   )]

      # fills the smaller signal with zeros to make both signals of same length
      if length_first_signal < length_second_signal:
         self.first_signal += [0] * (max_length - length_first_signal)
      elif length_first_signal > length_second_signal:
         self.second_signal += [0] * (max_length - length_second_signal)
      for i in range(__lowerCamelCase   ):
         _SCREAMING_SNAKE_CASE	=				deque(self.second_signal   )
         rotated_signal.rotate(__lowerCamelCase   )
         for j, item in enumerate(__lowerCamelCase   ):
            matrix[i][j] += item

        # multiply the matrix with the first signal
      _SCREAMING_SNAKE_CASE	=				np.matmul(np.transpose(__lowerCamelCase   )  ,				np.transpose(self.first_signal   )   )

      # rounding-off to two decimal places
      return [round(__lowerCamelCase  ,				2   ) for i in final_signal]


if __name__ == "__main__":
 doctest.testmod()







'''simple docstring'''



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"   ):

      """simple docstring"""
      super(__lowerCamelCase  ,				self   ).__init__()

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



   def 							lowerCAmelCase_      (   self :     Dict  ,				**__lowerCamelCase :     Any   ):

      """simple docstring"""
      return self.bert(**__lowerCamelCase   ).last_hidden_state



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

      """simple docstring"""
      return token_embeddings.sum(2  ,				keepdim=__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Optional[Any]  ,				__lowerCamelCase :     List[str]  ,				__lowerCamelCase :     Any  ,				__lowerCamelCase :     Tuple=1   ):

      """simple docstring"""
      return self.softmax(T * self.cos(__lowerCamelCase  ,				__lowerCamelCase   )   )



   def 							lowerCAmelCase_      (   self :     int  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     str   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				W_supports["sizes"].tolist()
      _SCREAMING_SNAKE_CASE	=				W_supports["start_token_id"].item()
      _SCREAMING_SNAKE_CASE	=				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	=				self.BERT(**__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				self.BERT(**__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				None
      _SCREAMING_SNAKE_CASE	=				None

      _SCREAMING_SNAKE_CASE	=				W_supports["input_ids"] == start_token_id
      _SCREAMING_SNAKE_CASE	=				W_supports["input_ids"] == end_token_id

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

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

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

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

      return p_starts, p_ends

