bi#ddlmZddlZddlZddlmZddlmZddlm Z ddl m Z m Z m Z mZe jeZdd ZddZddZddZddZddZddZddZddZddZdS)) annotationsN)pairwise_distances)Tensor)logging)_convert_to_batch_tensor_convert_to_tensornormalize_embeddings to_scipy_cooarbreturnc"t||S) 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_simr r s `/root/projects/butler/venv/lib/python3.11/site-packages/sentence_transformers/util/similarity.pypytorch_cos_simrs 1a==list | np.ndarray | Tensorct|}t|}t|}t|}tj||ddS)rrr)rr torchmm transposeto_denser r a_normb_norms rrrsf !##A ##A !! $ $F !! $ $F 8FF,,Q22 3 3 < < > >>rcdt|}t|}|js|jrIt|}t|}||zdSt t|t|S)a 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 {_ak_%a((%a(($$$,,55777!"6q"9"9;OPQ;R;RSS\\^^^rct|}t|}tj||ddS)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(GsJ !##A ##A 8Aq{{1a(( ) ) 2 2 4 44rct|}t|}||zdS)a 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]) r r!)r r$rrs rr%r%XsB 1A1A E;;2;   ' ' ) ))rct|}t|}|js|jrtdt |}t |}t ||d}t j|  |j  St j ||d S)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)r r a_coob_coodists r manhattan_simr9is !##A ##A{ 4ak 4VWWWQQ!%{CCC&&,,..11!(;;DDFFF AqC(((113333rct|}t|}tjtj||z d S)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]) r r!)r rr$absrrs rpairwise_manhattan_simr<sP 1A1A IeiA&&B / / / 8 8 : : ::rct|}t|}|jrtj||zdd}tj||zdd}tj||}|d|zz |z}tj |d}tj | Stj ||d S) 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]) rr!rg)ming@r-) rr#rsparser$r unsqueezematmultclampsqrtr5)r r a_norm_sq b_norm_sq dot_product squared_dists r euclidean_simrJs! !##A ##A{ )L$$QU$22;;==GGJJ L$$QU$22;;==GGJJ l1accee,,5577 !1{?2Y> {r r!)r rrEr$rrs rpairwise_euclidean_simrLsT 1A1A Juy!a%A2666 7 7 @ @ B B BBrxyc$|jrftd|}|}t |}t |}t j|dd\}}t j|dd\}}t j|dz|dzzdd}||z||zz|z }||z||zz |z }t j|dz|dzzdddz} t j|dz|dzzdddz} || | z z}|| | z z}t jt j ||fdd} t j | S)aV Computes the absolute normalized angle distance. See :class:`~sentence_transformers.losses.AnglELoss` or https://huggingface.co/papers/2309.12871 for more information. Args: x (Tensor): The first tensor. y (Tensor): The second tensor. Returns: Tensor: Vector with res[i] = angle_sim(a[i], b[i]) zOPairwise angle similarity does not support sparse tensors. Converting to dense.r>rr!T)r"keepdimg?) r#r/r0coalescerr rchunkr$concatr;) rMrNr r cdzreimdzdw norm_angles rpairwise_angle_simr\s {$mnnn JJLL ! ! # # JJLL ! ! # #1A1A ;q! # # #DAq ;q! # # #DAq !Q$A+1d333A a%!a%-1 B a%!a%-1 B 1a4!Q$;At 4 4 4 ;B 1a4!Q$;At 4 4 4 ;B"r'MB"r'MB5<Ra888a@@@J 9Z  r)r rr rrr)r rr rrr)r rr r)rMrrNrrr) __future__rnumpynprsklearn.metricsrrtransformers.utilsrtensorrr r r get_logger__name__r/rrr&r(r%r9r<rJrLr\rrrfst"""""" ......&&&&&&dddddddddddd  H % %    ????&____.5555"****"44446;;;;"))))>CCCC"#!#!#!#!#!#!r