dh SrSSKrSSKJrJrJrJr SSKr\R"\ 5r \\\\ \\R\R4rS\S\S\R4SjrS S\S\S\\S \\ S\\\\\4\\ 44 S jjrg) z Math utils.N)ListOptionalTupleUnionXYreturnc *[U5S:Xd[U5S:Xa[R"/5$[R"U5n[R"U5nURSURS:wa&[ SURSURS35eSSKn[R"U[R S9n[R"U[R S9nS[R"URXSS 95- nU$![a [RS 5 [RRUSS 9n[RRUSS 9n[R"S S S 9 [R"XR5[R "XE5- nSSS5 O!,(df  O=fSW[R""U5[R$"U5-'Us$f=f)z>? s,(A2DA0H 8G  H G 9HHtop_kscore_thresholdc [U5S:Xd[U5S:Xa//4$[X5nU=(d SnSXDU:'[U=(d [U5[R"U55n[R "XB*SS9U*SnU[R "UR5U5SSS2n[R"XTR5nUR5UR5n[[U65U4$)a}Row-wise cosine similarity with optional top-k and score threshold filtering. Args: X: Matrix. Y: Matrix, same width as X. top_k: Max number of results to return. score_threshold: Minimum cosine similarity of results. Returns: Tuple of two lists. First contains two-tuples of indices (X_idx, Y_idx), second contains corresponding cosine similarities. rgNr) rr.minr count_nonzero argpartitionargsortravel unravel_indexrtolistlistzip)rrr/r0 score_array top_k_idxsret_idxsscoress r-cosine_similarity_top_kr@.s$ 1v{c!fk2v #A)K%-O12Ko-. )[)2+;+;K+H IEf4@%IJBJJ{'8'8'::'FGH2NJ ,=,=>H     , 3 3 5F X  '')N)__doc__loggingtypingrrrrnumpyr getLogger__name__rfloatndarrayMatrixr.intr@rAr-rNs//   8 $ tDK $rzz"2BJJ> ?FrzzH'+ ( ( ( C=(e_ (  4c3h $u+ -. (rA