


import torch

from transformers import AutoModel







class 	snake_case_       (torch.nn.Module				):
					def __init__(				self       :List[Any]      ,__snake_case       :List[str]="sayef/fsner-bert-base-uncased"				) ->   str:
												super(__snake_case      ,self				).__init__()

												a__					=					AutoModel.from_pretrained(__snake_case      ,return_dict=__snake_case				)
												a__					=					torch.nn.CosineSimilarity(3      ,1E-08				)
												a__					=					torch.nn.Softmax(dim=1				)
					def 			lowerCamelCase__(				self       :Dict      ,**__snake_case       :Any				) ->   List[Any]:
												return self.bert(**__snake_case				).last_hidden_state
					def 			lowerCamelCase__(				self       :Any      ,__snake_case       :List[str]				) ->   Any:
												return token_embeddings.sum(2      ,keepdim=__snake_case				)
					def 			lowerCamelCase__(				self       :Union[str, Any]      ,__snake_case       :Optional[int]      ,__snake_case       :List[Any]      ,__snake_case       :str=1				) ->   Optional[Any]:
												return self.softmax(T * self.cos(__snake_case      ,__snake_case				)				)



					def 			lowerCamelCase__(				self       :Any      ,__snake_case       :Union[str, Any]      ,__snake_case       :Union[str, Any]				) ->   Any:
												a__					=					W_supports['sizes'].tolist()
												a__					=					W_supports['start_token_id'].item()
												a__					=					W_supports['end_token_id'].item()

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

												a__					=					self.BERT(**__snake_case				)
												a__					=					self.BERT(**__snake_case				)

												a__					=					None
												a__					=					None

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

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

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

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

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

												return p_starts, p_ends








from __future__ import annotations






def 							__lowercase       (			__lowerCAmelCase							:    float ,   __lowerCAmelCase							:    float ,   __lowerCAmelCase							:    float       ):
							if (voltage, current, resistance).count(0       ) != 1:
														raise ValueError('One and only one argument must be 0'       )
							if resistance < 0:
														raise ValueError('Resistance cannot be negative'       )
							if voltage == 0:
														return {"voltage": float(current * resistance       )}
							elif current == 0:
														return {"current": voltage / resistance}
							elif resistance == 0:
														return {"resistance": voltage / current}
							else:
														raise ValueError('Exactly one argument must be 0'       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()




