[;jjddlmZddlmZddlmZmZdZddddZddddZ dddd Z dddd Z y) ) 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 k7z}|| dz dz}|| dz}t |||}|| dz || dz k(r| } || dz || <| } nM||| dz d}| }| |z dk(r|| | |z z}t ||}n| |z dk(r| | |z z}t ||}|| } ||| <| ||| dz <|t|S)Nr)maxlengetlistrangemin)s1s2maxVal last_row_idlast_row_id_getsizeFRR1Ri last_col_id last_i2l1Tjdiagleftuptempkl transposes qG:\00. PROJECTS\API\Inventory\templateJSON\kerjaOCR\Lib\site-packages\rapidfuzz/distance/DamerauLevenshtein_py.py"_damerau_levenshtein_distance_zhaor' s R#b' "Q &FK!ooO r7Q;D DB DB U4[A AbE 1c"gk "A2 aD ! q#b'A+&Aa!e91q5 RAY 67DQU8a>> from rapidfuzz.distance import DamerauLevenshtein >>> DamerauLevenshtein.distance("CA", "ABC") 2 r)rr')rrr)r*dists r&distancer-7sVL r] r] B #FB -b" 5D (DL,@4W|VWGWWr(c|||}||}t||\}}tt|t|}t||}||z }|||k\r|SdS)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 r)rr r r-)rrr)r*maximumr,sims r& similarityr1fsm@ r] r] B #FB#b'3r7#G B D D.C'3,+>3FQFr(ctt|s t|ry|||}||}t||\}}tt |t |}t ||}|r||z nd}|||kr|SdS)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 ?rr)rrrr r r-)rrr)r*r/r, norm_dists r&normalized_distancer5s>Mr{gbk r] r] B #FB#b'3r7#G B D")wqI%-l1J9RQRRr(ctt|s t|ry|||}||}t||\}}t||}d|z }|||k\r|SdS)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 gr3r)rrrr5)rrr)r*r4norm_sims r&normalized_similarityr8sm>Mr{gbk r] r] B #FB#B+IYH$,L0H8PqPr() __future__rrapidfuzz._common_pyrrapidfuzz._utilsrrr'r-r1r5r8r(r&r=sQ#/1+d ,Xf (G^ +Sd *Qr(