o e²ã@sŽddlmZddlmZmZmZddlmZddlm Z dd d „Z d d d œddd„Z d d d œddd„Z d d d œddd„Z d d d œddd„Zd S)é)Ú annotations)ÚCallableÚHashableÚSequence)Úconv_sequences)Úis_noneÚs1úSequence[Hashable]Ús2ÚreturnÚintcCs°tt|ƒt|ƒƒd}i}|j}t|ƒd}|g|}|g|}tt|ƒƒ}||d<tdt|ƒdƒD]ž} ||}}d} |d} | |d<|} tdt|ƒdƒD]y} || d|| d|| dk}|| dd}|| d}t|||ƒ}|| d|| dkr| } || d|| <| } n0||| ddƒ}| }| |dkr¯|| | |}t||ƒ}n| |dkrÀ| | |}t||ƒ}|| } ||| <qO| ||| d<q3|t|ƒS)Néééÿÿÿÿr)ÚmaxÚlenÚgetÚlistÚrangeÚmin)rr ZmaxValZ last_row_idZlast_row_id_getÚsizeZFRZR1ÚRÚiZ last_col_idZ last_i2l1ÚTÚjZdiagÚleftÚupÚtempÚkÚlZ transpose©r úXD:\Projects\ConvertPro\env\Lib\site-packages\rapidfuzz/distance/DamerauLevenshtein_py.pyÚ"_damerau_levenshtein_distance_zhao sF     $         r"N)Ú processorÚ score_cutoffr#ú(Callable[..., Sequence[Hashable]] | Noner$ú int | NonecCsL|dur ||ƒ}||ƒ}t||ƒ\}}t||ƒ}|dus ||kr"|S|dS)a« Calculates the Damerau-Levenshtein distance. Parameters ---------- s1 : Sequence[Hashable] First string to compare. s2 : Sequence[Hashable] Second string to compare. processor: callable, optional Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour. score_cutoff : int, optional Maximum distance between s1 and s2, that is considered as a result. If the distance is bigger than score_cutoff, score_cutoff + 1 is returned instead. Default is None, which deactivates this behaviour. Returns ------- distance : int distance between s1 and s2 Examples -------- Find the Damerau-Levenshtein distance between two strings: >>> from rapidfuzz.distance import DamerauLevenshtein >>> DamerauLevenshtein.distance("CA", "ABC") 2 Nr )rr")rr r#r$Údistr r r!Údistance:s & r(cCsb|dur ||ƒ}||ƒ}t||ƒ\}}tt|ƒt|ƒƒ}t||ƒ}||}|dus-||kr/|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 Nr)rrrr()rr r#r$Úmaximumr'Úsimr r r!Ú similarityis  r+ú float | NoneÚfloatcCs~t|ƒst|ƒr dS|dur||ƒ}||ƒ}t||ƒ\}}tt|ƒt|ƒƒ}t||ƒ}|r1||nd}|dus;||kr=|SdS)aB Calculates a normalized Damerau-Levenshtein similarity in the range [1, 0]. This is calculated as ``distance / max(len1, len2)``. Parameters ---------- s1 : Sequence[Hashable] First string to compare. s2 : Sequence[Hashable] Second string to compare. processor: callable, optional Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour. score_cutoff : float, optional Optional argument for a score threshold as a float between 0 and 1.0. For norm_dist > score_cutoff 1.0 is returned instead. Default is 1.0, which deactivates this behaviour. Returns ------- norm_dist : float normalized distance between s1 and s2 as a float between 0 and 1.0 çð?Nrr )rrrrr()rr r#r$r)r'Ú norm_distr r r!Únormalized_distance”s r0cCsdt|ƒst|ƒr dS|dur||ƒ}||ƒ}t||ƒ\}}t||ƒ}d|}|dus.||kr0|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 gNr.r)rrr0)rr r#r$r/Znorm_simr r r!Únormalized_similarityÁs r1)rr r r r r ) rr r r r#r%r$r&r r ) rr r r r#r%r$r,r r-)Ú __future__rÚtypingrrrZrapidfuzz._common_pyrZrapidfuzz._utilsrr"r(r+r0r1r r r r!Ús"    2û3û/û1û