+ ʇi|^RIHt^RIHt^RIHtHtRtRRRR/RltRRRR/R lt RRRR/R lt RRRR/R lt R#) ) annotations)conv_sequences)is_none setupPandasc\\V4\V44^,p/pVPp\V4^,pV.V,pV.V,p\\ V44pW(R&\ ^\V4^,4EFfp YxrxRp V^,p W^&Tp \ ^\V4^,4EFp W}^, ,W ^, ,W^, ,8g,pW^, ,^,pW},^,p\ WV4pW ^, ,W^, ,8XdT p W}^, ,Wm&T p MrV!W^, ,R4pT pV V, ^8Xd$Wm,V V, ,p\ VV4pM)V V, ^8XdWV, ,p\ VV4pW,p VW&EK WW ^, ,&EKi V\V4,#))maxlengetlistrangemin)s1s2maxVal last_row_idlast_row_id_getsizeFRR1Ri last_col_id last_i2l1Tjdiagleftuptempkl transposes&& }/Users/max/.openclaw/workspace/postharvest/merge_env/lib/python3.14/site-packages/rapidfuzz/distance/DamerauLevenshtein_py.py"_damerau_levenshtein_distance_zhaor& s R#b' "Q &FK!ooO r7Q;D DB DB U4[A bE 1c"gk "2 aD ! q#b'A+&A!e9q5 RAY 67DU8a>> from rapidfuzz.distance import DamerauLevenshtein >>> DamerauLevenshtein.distance("CA", "ABC") 2 )rr&)rrr(r)dists&&$$ r%distancer,7sNL r] r] B #FB -b 5D (D,@4W|VWGWWr'cVeV!V4pV!V4p\W4wr\\V4\V44p\W4pWE, pVeWc8dV#^#)a Calculates the Damerau-Levenshtein similarity in the range [max, 0]. This is calculated as ``max(len1, len2) - distance``. Parameters ---------- s1 : Sequence[Hashable] First string to compare. s2 : Sequence[Hashable] Second string to compare. processor: callable, optional Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour. score_cutoff : int, optional Maximum distance between s1 and s2, that is considered as a result. If the similarity is smaller than score_cutoff, 0 is returned instead. Default is None, which deactivates this behaviour. Returns ------- similarity : int similarity between s1 and s2 )rr r r,)rrr(r)maximumr+sims&&$$ r% similarityr0fsc@ r] r] B #FB#b'3r7#G B D .C'3+>3FQFr'c2\4\V4'g\V4'dR#VeV!V4pV!V4p\W4wr\\ V4\ V44p\ W4pV'd WT, M^pVeWc8:dV#^#)a Calculates a normalized Damerau-Levenshtein distance in the range [1, 0]. This is calculated as ``distance / max(len1, len2)``. Parameters ---------- s1 : Sequence[Hashable] First string to compare. s2 : Sequence[Hashable] Second string to compare. processor: callable, optional Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour. score_cutoff : float, optional Optional argument for a score threshold as a float between 0 and 1.0. For norm_dist > score_cutoff 1.0 is returned instead. Default is 1.0, which deactivates this behaviour. Returns ------- norm_dist : float normalized distance between s1 and s2 as a float between 0 and 1.0 ?)rrrr r r,)rrr(r)r.r+ norm_dists&&$$ r%normalized_distancer4s>Mr{{gbkk r] r] B #FB#b'3r7#G B D")qI%-1J9RQRRr'c\4\V4'g\V4'dR#VeV!V4pV!V4p\W4wr\W4pRV, pVeWS8dV#^#)a Calculates a normalized Damerau-Levenshtein similarity in the range [0, 1]. This is calculated as ``1 - normalized_distance`` Parameters ---------- s1 : Sequence[Hashable] First string to compare. s2 : Sequence[Hashable] Second string to compare. processor: callable, optional Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour. score_cutoff : float, optional Optional argument for a score threshold as a float between 0 and 1.0. For norm_sim < score_cutoff 0 is returned instead. Default is 0, which deactivates this behaviour. Returns ------- norm_sim : float normalized similarity between s1 and s2 as a float between 0 and 1.0 gr2)rrrr4)rrr(r)r3norm_sims&&$$ r%normalized_similarityr7si>Mr{{gbkk r] r] B #FB#B+IYH$,0H8PqPr') __future__rrapidfuzz._common_pyrrapidfuzz._utilsrrr&r,r0r4r7r'r%r<s#/1+\,X ,X  ,X^(G (G  (GV+S +S  +S\*Q *Q  *Qr'