

import torch

from transformers import AutoModel






class 						__magic_name__					(							torch.nn.Module				):






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

         UpperCamelCase_			  =						AutoModel.from_pretrained(_lowercase		,						return_dict=_lowercase	)
         UpperCamelCase_			  =						torch.nn.CosineSimilarity(3		,						1e-0_8	)
         UpperCamelCase_			  =						torch.nn.Softmax(dim=1	)






     def 	UpperCAmelCase_				(				self		,						**_lowercase	)->     Optional[int]:
         return self.bert(**_lowercase	).last_hidden_state






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     Tuple:
         return token_embeddings.sum(2		,						keepdim=_lowercase	)






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase		,						_lowercase=1	)->     Any:
         return self.softmax(T * self.cos(_lowercase		,						_lowercase	)	)






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase	)->     Optional[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(**_lowercase	)
         UpperCamelCase_			  =						self.BERT(**_lowercase	)

         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(_lowercase	):
             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



def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						int:



    """simple docstring"""






    if exponent == 1:
        return base
    if exponent % 2 == 0:
        UpperCamelCase_			  =						_modexpt(SCREAMING_SNAKE_CASE_			,						exponent // 2			,						SCREAMING_SNAKE_CASE_			) % modulo_value
        return (x * x) % modulo_value
    else:
        return (base * _modexpt(SCREAMING_SNAKE_CASE_			,						exponent - 1			,						SCREAMING_SNAKE_CASE_			)) % modulo_value




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_ = 1_7_7_7			,						SCREAMING_SNAKE_CASE_ = 1_8_5_5			,						SCREAMING_SNAKE_CASE_ = 8			)->						int:



    """simple docstring"""






    UpperCamelCase_			  =						base
    for _ in range(1			,						SCREAMING_SNAKE_CASE_			):
        UpperCamelCase_			  =						_modexpt(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						1_0**digits			)

    return result


if __name__ == "__main__":
 print(F'''{solution() = }''')
