ChSSKJr SSKrSSKrSSKJr SSKJr SSKJ r SSK J r J r J r Jr \ R"\5rSSjrSS jrSS jrSS jrSS jrSS jrSSjrSSjrSSjrSSjrg)) annotationsN)pairwise_distances)Tensor)logging)_convert_to_batch_tensor_convert_to_tensornormalize_embeddings to_scipy_cooc[X5$) Computes the cosine similarity between two tensors. Args: a (Union[list, np.ndarray, Tensor]): The first tensor. b (Union[list, np.ndarray, Tensor]): The second tensor. Returns: Tensor: Matrix with res[i][j] = cos_sim(a[i], b[j]) )cos_simabs ]/var/www/html/shao/venv/lib/python3.13/site-packages/sentence_transformers/util/similarity.pypytorch_cos_simrs 1=c[U5n[U5n[U5n[U5n[R"X#R SS55R 5$)r rr)rr torchmm transposeto_denserra_normb_norms rrrsQ !#A #A !! $F !! $F 88F,,Q2 3 < < >>rc4[U5n[U5nUR(dUR(a5[U5n[U5nX#-RSS9R 5$[ [U5[U55R 5$)z Computes the pairwise cosine similarity cos_sim(a[i], b[i]). Args: a (Union[list, np.ndarray, Tensor]): The first tensor. b (Union[list, np.ndarray, Tensor]): The second tensor. Returns: Tensor: Vector with res[i] = cos_sim(a[i], b[i]) dim)r is_sparser sumrpairwise_dot_scorers rpairwise_cos_simr$0s} 1A1A {{akk%a(%a($$$,5577!"6q"9;OPQ;RS\\^^rc[U5n[U5n[R"XRSS55R 5$)a Computes the dot-product dot_prod(a[i], b[j]) for all i and j. Args: a (Union[list, np.ndarray, Tensor]): The first tensor. b (Union[list, np.ndarray, Tensor]): The second tensor. Returns: Tensor: Matrix with res[i][j] = dot_prod(a[i], b[j]) rr)rrrrrrs r dot_scorer&Gs; !#A #A 88A{{1a( ) 2 2 44rcl[U5n[U5nX-RSS9R5$)z Computes the pairwise dot-product dot_prod(a[i], b[i]). Args: a (Union[list, np.ndarray, Tensor]): The first tensor. b (Union[list, np.ndarray, Tensor]): The second tensor. Returns: Tensor: Vector with res[i] = dot_prod(a[i], b[i]) rr)r r"rrs rr#r#Xs4 1A1A E;;2;  ' ' ))rc[U5n[U5nUR(dUR(a[RS5 [ U5n[ U5n[ X#SS9n[ R"U*5R5RUR5R5$[ R"XSS9R5*$)a` Computes the manhattan similarity (i.e., negative distance) between two tensors. Handles sparse tensors without converting to dense when possible. Args: a (Union[list, np.ndarray, Tensor]): The first tensor. b (Union[list, np.ndarray, Tensor]): The second tensor. Returns: Tensor: Matrix with res[i][j] = -manhattan_distance(a[i], b[j]) z8Using scipy for sparse Manhattan similarity computation. manhattan)metricg?p) rr!logger warning_oncer rr from_numpyfloattodevicercdist)rra_coob_coodists r manhattan_simr7is !#A #A{{akkVWQQ!%{C&,,.11!((;DDFF AC(11333rc[U5n[U5n[R"[R"X- 5SS9R 5*$)a  Computes the manhattan similarity (i.e., negative distance) between pairs of tensors. Args: a (Union[list, np.ndarray, Tensor]): The first tensor. b (Union[list, np.ndarray, Tensor]): The second tensor. Returns: Tensor: Vector with res[i] = -manhattan_distance(a[i], b[i]) rr)r rr"absrrs rpairwise_manhattan_simr:s@ 1A1A IIeii&B / 8 8 : ::rc^[U5n[U5nUR(a[RR X-SS9R 5R S5n[RR X-SS9R 5R S5n[R"XR55R 5nUSU-- U-n[R"USS9n[R"U5R 5*$[R"XSS9*$) a` Computes the euclidean similarity (i.e., negative distance) between two tensors. Handles sparse tensors without converting to dense when possible. Args: a (Union[list, np.ndarray, Tensor]): The first tensor. b (Union[list, np.ndarray, Tensor]): The second tensor. Returns: Tensor: Matrix with res[i][j] = -euclidean_distance(a[i], b[j]) rrrg)ming@r+) rr!rsparser"r unsqueezematmultclampsqrtr3)rr a_norm_sq b_norm_sq dot_product squared_dists r euclidean_simrHs !#A #A{{LL$$QU$2;;=GGJ LL$$QU$2;;=GGJ ll1cce,557 !1{?2Y> {{rfsc" .&dd   H % ?&_.5"*"46;")>C"#!r