9iddlmZddlmZmZmZddlmZddlm Z d dZ ddd d dZ ddd d d Z ddd dd Z ddd dd Zy)) annotations)CallableHashableSequence)conv_sequences)is_nonectt|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 r/var/www/html/backtest/airagagent/rag_env/lib/python3.12/site-packages/rapidfuzz/distance/DamerauLevenshtein_py.py"_damerau_levenshtein_distance_zhaor) s R#b' "Q &F')K!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/:sVL 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 rr/)rrr+r,maximumr.sims r( similarityr3ism@ r] r] B #FB#b'3r7#G B D D.C'3,+>3FQFr*ct|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 )rrr rr/)rrr+r,r1r. norm_dists r(normalized_distancer7s>r{gbk r] r] B #FB#b'3r7#G B D")wqI%-l1J9RQRRr*ct|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 gr5r)rrr7)rrr+r,r6norm_sims r(normalized_similarityr:sh>r{gbk r] r] B #FB#B+IYH$,L0H8PqPr*)rSequence[Hashable]rr;returnint) rr;rr;r+(Callable[..., Sequence[Hashable]] | Noner,z int | Noner<r=) rr;rr;r+r>r,z float | Noner<float) __future__rtypingrrrrapidfuzz._common_pyrrapidfuzz._utilsrr)r/r3r7r:r*r(rEs##///$+d;?# ,X,X,X8 ,X  ,X  ,Xf;?# (G(G(G8 (G  (G  (G^;?!% *S*S*S8 *S  *S  *Sb;?!% )Q)Q)Q8 )Q  )Q  )Qr*