U ÿÌ-e²ã@sÆddlmZddlmZmZmZddlmZddlm Z ddddœdd „Z d d d œddd d ddœdd„Z d d d œddd d ddœdd„Z d d d œddd dddœdd„Z d d d œddd dddœdd„Zd S)é)Ú annotations)ÚCallableÚHashableÚSequence)Úconv_sequences)Úis_nonezSequence[Hashable]Úint)Ús1Ús2ÚreturncCs¸tt|ƒt|ƒƒd}i}|j}t|ƒd}|g|}|g|}tt|ƒƒ}||d<tdt|ƒdƒD]B} ||}}d} |d} | |d<|} tdt|ƒdƒD]ø} || d|| d|| dk}|| dd}|| d}t|||ƒ}|| d|| dkr$| } || d|| <| } nd||| ddƒ}| }| |dkrd|| | |}t||ƒ}n$| |dkrˆ| | |}t||ƒ}|| } ||| <q | ||| d<qf|t|ƒS)Néééÿÿÿÿr)ÚmaxÚlenÚgetÚlistÚrangeÚmin)r r ZmaxValZ last_row_idZlast_row_id_getÚsizeZFRZR1ÚRÚiZ last_col_idZ last_i2l1ÚTÚjZdiagÚleftÚupÚtempÚkÚlZ transpose©rúi/var/www/html/Darija-Ai-Train/env/lib/python3.8/site-packages/rapidfuzz/distance/DamerauLevenshtein_py.pyÚ"_damerau_levenshtein_distance_zhao sF     $      r!N)Ú processorÚ score_cutoffz(Callable[..., Sequence[Hashable]] | Nonez int | None)r r r"r#r cCsL|dk r||ƒ}||ƒ}t||ƒ\}}t||ƒ}|dks@||krD|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!)r r r"r#Údistrrr Údistance:s & r%cCsb|dk r||ƒ}||ƒ}t||ƒ\}}tt|ƒt|ƒƒ}t||ƒ}||}|dksZ||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%)r r r"r#Úmaximumr$Úsimrrr Ú similarityis  r(z float | NoneÚfloatcCs~t|ƒst|ƒrdS|dk r,||ƒ}||ƒ}t||ƒ\}}tt|ƒt|ƒƒ}t||ƒ}|rb||nd}|dksv||krz|SdS)aB Calculates a normalized Damerau-Levenshtein similarity in the range [1, 0]. This is calculated as ``distance / max(len1, len2)``. Parameters ---------- s1 : Sequence[Hashable] First string to compare. s2 : Sequence[Hashable] Second string to compare. processor: callable, optional Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour. score_cutoff : float, optional Optional argument for a score threshold as a float between 0 and 1.0. For norm_dist > score_cutoff 1.0 is returned instead. Default is 1.0, which deactivates this behaviour. Returns ------- norm_dist : float normalized distance between s1 and s2 as a float between 0 and 1.0 çð?Nrr )rrrrr%)r r r"r#r&r$Ú norm_distrrr Únormalized_distance”s r,cCsdt|ƒst|ƒrdS|dk r,||ƒ}||ƒ}t||ƒ\}}t||ƒ}d|}|dks\||kr`|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)rrr,)r r r"r#r+Znorm_simrrr Únormalized_similarityÁs r-)Ú __future__rÚtypingrrrZrapidfuzz._common_pyrZrapidfuzz._utilsrr!r%r(r,r-rrrr Ús   2û3û/û1û