o U hk@stddlmZddlmZddlmZmZddZddddd Zdddd d Z dddd d Z dddddZ dS)) annotations)conv_sequences)is_none setupPandascCs |st|Sdt|>d}d}d}d}t|}dt|d>}i}|j} d} |D]} | | d| B|| <| dK} q)|D]R} | | d} || @d>|@}| |@||A| B|B}||B}|||BB}||@}|||@dk7}|||@dk8}|d>dB}|d>}|||BB}||@}| }q;|S)Nr)lenget)s1s2VPVND0PM_j_oldcurrDistmaskblock block_getxch1ch2PM_jTRHPHNrm/var/www/html/construction_image-detection-poc/venv/lib/python3.10/site-packages/rapidfuzz/distance/OSA_py.py_osa_distance_hyrroe2003 s:   rN) processor score_cutoffcCsL|dur ||}||}t||\}}t||}|dus ||kr"|S|dS)a Calculates the optimal string alignment (OSA) 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 OSA distance between two strings: >>> from rapidfuzz.distance import OSA >>> OSA.distance("CA", "AC") 2 >>> OSA.distance("CA", "ABC") 3 Nr)rr)r r rrdistrrrdistance4s ( r cCsb|dur ||}||}t||\}}tt|t|}t||}||}|dus-||kr/|SdS)a5 Calculates the optimal string alignment (OSA) 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)rmaxrr )r r rrmaximumrsimrrr similarityes  r$cCstt|s t|r dS|dur||}||}t||\}}tt|t|}t||}|r4||nd}|dus>||kr@|SdS)aM Calculates a normalized optimal string alignment (OSA) similarity in the range [1, 0]. This is calculated as ``distance / max(len1, len2)``. Parameters ---------- s1 : Sequence[Hashable] First string to compare. s2 : Sequence[Hashable] Second string to compare. processor: callable, optional Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour. score_cutoff : float, optional Optional argument for a score threshold as a float between 0 and 1.0. For norm_dist > score_cutoff 1.0 is returned instead. Default is 1.0, which deactivates this behaviour. Returns ------- norm_dist : float normalized distance between s1 and s2 as a float between 0 and 1.0 ?Nrr)rrrr!rr )r r rrr"r norm_distrrrnormalized_distances r'cCsjtt|s t|r dS|dur||}||}t||\}}t||}d|}|dus1||kr3|SdS)aE Calculates a normalized optimal string alignment (OSA) similarity in the range [0, 1]. This is calculated as ``1 - normalized_distance`` Parameters ---------- s1 : Sequence[Hashable] First string to compare. s2 : Sequence[Hashable] Second string to compare. processor: callable, optional Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour. score_cutoff : float, optional Optional argument for a score threshold as a float between 0 and 1.0. For norm_sim < score_cutoff 0 is returned instead. Default is 0, which deactivates this behaviour. Returns ------- norm_sim : float normalized similarity between s1 and s2 as a float between 0 and 1.0 gNr%r)rrrr')r r rrr&norm_simrrrnormalized_similaritys r)) __future__rrapidfuzz._common_pyrrapidfuzz._utilsrrrr r$r'r)rrrrs  / 5 / 2