dzddlmZddlmZmZmZddlmZdd Zd d d ddZ d d d ddZ d d d ddZ d d d ddZ d S)) annotations)CallableHashableSequence)is_nones1Sequence[Hashable]s2returnintctt|t|dz}i}|j}t|dz}|g|z}|g|z}dt|D}||d<tdt|dzD]/} ||}}d} |d} | |d<|} tdt|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)Ncg|]}|Sr).0xs l/home/feoh/.local/pipx/venvs/poetry/lib/python3.11/site-packages/rapidfuzz/distance/DamerauLevenshtein_py.py z6_damerau_levenshtein_distance_zhao..s   q   r)maxlengetrangemin)rr maxVal last_row_idlast_row_id_getsizeFRR1Ri last_col_id last_i2l1Tjdiagleftuptempkl transposedists r"_damerau_levenshtein_distance_zhaor1 s#R#b'' " "Q &F')K!oO r77Q;D DB DB  E$KK   A AbE 1c"ggk " "##A2 aD ! q#b''A+&&  Aa!e91q5 RAY 67DQU8a>> from rapidfuzz.distance import DamerauLevenshtein >>> DamerauLevenshtein.distance("CA", "ABC") 2 Nr)r1)rr r2r3r0s rdistancer7<sSL Yr]] Yr]] -b" 5 5D (DL,@,@44|VWGWWrc|||}||}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)rrr7)rr r2r3maximumr0sims r similarityr;jsq@ Yr]] Yr]]#b''3r77##G B  D D.C'3,+>+>33QFr float | Nonefloatct|st|rdS|||}||}tt|t|}t||}|r||z nd}|||kr|ndS)aB Calculates a normalized Damerau-Levenshtein similarity 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)rrrr7)rr r2r3r9r0 norm_dists rnormalized_distancerAs>r{{gbkks Yr]] Yr]]#b''3r77##G B  D")0wqI%-l1J1J99QRRrct|st|rdS|||}||}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 gNr?r)rrA)rr r2r3r@norm_sims rnormalized_similarityrDsu>r{{gbkks Yr]] Yr]]#B++IYH$,L0H0H88qPr)rr r r r r ) rr r r r2r4r3r5r r ) rr r r r2r4r3r<r r=) __future__rtypingrrrrapidfuzz._utilsrr1r7r;rArDrrrrHs#"""""//////////$$$$$$....j;?# +X+X+X+X+X+Xd;?# 'G'G'G'G'G'G\;?!% )S)S)S)S)S)S`;?!% (Q(Q(Q(Q(Q(Q(Q(Qr