NglddlmZddlmZddlmZmZdZddddZddddZ dddd Z dddd Z dS) ) annotations)conv_sequences)is_none setupPandasctt|t|dz}i}|j}t|dz}|g|z}|g|z}tt |}||d<t dt|dzD]/} ||}}d} |d} | |d<|} t dt|dzD]} || dz || dz || dz kz}|| dz dz}|| dz}t |||}|| dz || dz kr| } || dz || <| } n`||| dz d}| }| |z dkr|| | |z z}t ||}n!| |z dkr| | |z z}t ||}|| } ||| <| ||| dz <1|t|S)Nr)maxlengetlistrangemin)s1s2maxVal last_row_idlast_row_id_getsizeFRR1Ri last_col_id last_i2l1Tjdiagleftuptempkl transposes d/var/www/html/ai-engine/env/lib/python3.11/site-packages/rapidfuzz/distance/DamerauLevenshtein_py.py"_damerau_levenshtein_distance_zhaor' s R#b'' " "Q &FK!oO r77Q;D DB DB U4[[A AbE 1c"ggk " "##A2 aD ! q#b''A+&&  Aa!e91q5 RAY 67DQU8a>> from rapidfuzz.distance import DamerauLevenshtein >>> DamerauLevenshtein.distance("CA", "ABC") 2 Nr)rr')rrr)r*dists r&distancer-7sfL Yr]] Yr]] B # #FB -b" 5 5D (DL,@,@44|VWGWWr(c|||}||}t||\}}tt|t|}t||}||z }|||kr|ndS)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 Nr)rr r r-)rrr)r*maximumr,sims r& similarityr1fs@ Yr]] Yr]] B # #FB#b''3r77##G B  D D.C'3,+>+>33QFr(cRtt|st|rdS|||}||}t||\}}tt |t |}t ||}|r||z nd}|||kr|ndS)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 ?Nrr)rrrr r r-)rrr)r*r/r, norm_dists r&normalized_distancer5s>MMMr{{gbkks Yr]] Yr]] B # #FB#b''3r77##G B  D")0wqI%-l1J1J99QRRr(ctt|st|rdS|||}||}t||\}}t||}d|z }|||kr|ndS)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 gNr3r)rrrr5)rrr)r*r4norm_sims r&normalized_similarityr8s>MMMr{{gbkks Yr]] Yr]] B # #FB#B++IYH$,L0H0H88qPr() __future__rrapidfuzz._common_pyrrapidfuzz._utilsrrr'r-r1r5r8r(r&r=s#"""""//////11111111+++d ,X,X,X,X,Xf (G(G(G(G(G^ +S+S+S+S+Sd *Q*Q*Q*Q*Q*Q*Qr(