





'''simple docstring'''
import torch

from transformers import AutoModel

class      snake_case     (    torch.nn.Module       ):




 """simple docstring"""



 def __init__(       self       :							Optional[int]							,	__A       :							List[Any]="sayef/fsner-bert-base-uncased"	):
        super(__A							,	self	).__init__()

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






 def        _lowerCamelCase					(       self       :							Tuple							,	**__A       :							Optional[int]	):
        return self.bert(**__A	).last_hidden_state






 def        _lowerCamelCase					(       self       :							Tuple							,	__A       :							Tuple	):
        return token_embeddings.sum(2							,	keepdim=__A	)






 def        _lowerCamelCase					(       self       :							List[Any]							,	__A       :							str							,	__A       :							int							,	__A       :							str=1	):
        return self.softmax(T * self.cos(__A							,	__A	)	)






 def        _lowerCamelCase					(       self       :							Optional[int]							,	__A       :							str							,	__A       :							Any	):
        __UpperCamelCase       =  W_supports['sizes'].tolist()
        __UpperCamelCase       =  W_supports['start_token_id'].item()
        __UpperCamelCase       =  W_supports['end_token_id'].item()

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

        __UpperCamelCase       =  self.BERT(**__A	)
        __UpperCamelCase       =  self.BERT(**__A	)

        __UpperCamelCase       =  None
        __UpperCamelCase       =  None

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

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

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

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

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

        return p_starts, p_ends


import functools






def    lowerCamelCase__				(				__lowerCAmelCase  :  str    ,							__lowerCAmelCase  :  str				):




			"""simple docstring"""


			lowerCAmelCase_						=	len(__lowerCAmelCase				)
			lowerCAmelCase_						=	len(__lowerCAmelCase				)

			@functools.cache
			def min_distance(__lowerCAmelCase  :  int    ,							__lowerCAmelCase  :  int				) -> int:
						# if first word index is overflow - delete all from the second word
						if indexa >= len_worda:
									return len_worda - indexa
						# if second word index is overflow - delete all from the first word
						if indexa >= len_worda:
									return len_worda - indexa
						lowerCAmelCase_						=	int(worda[indexa] != worda[indexa]				)  # current letters not identical
						return min(
						    1 + min_distance(indexa + 1    ,							__lowerCAmelCase				)    ,							1 + min_distance(__lowerCAmelCase    ,							indexa + 1				)    ,							diff + min_distance(indexa + 1    ,							indexa + 1				)    ,							)

			return min_distance(0    ,							0				)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



