o wi@sdddZdS)FcCsBd\}}}t|t|}}||kr||}}||}}||}|dkr0||||||f} n|dkr=||||f} n |dkrG||f} ndSd} | D]} d} } }| |kr| |kr|| || kr|d}d|krnni|r|dkr| |dkr| |dkr|| d|| kr|| || dkr| d| d} } n1| |d}||kr| d} n"||kr| d} n||ksJ| d| d} } n | d| d} } | |kr| |ks]d|krqM| |kr|| | |d|kr||| }nqM| |kr|| | |d|kr||| }nqM|| kr|} qM| dkrd} | S)aCompute the distance between the two sequences `seq1` and `seq2` up to a maximum of 2 included, and return it. If the edit distance between the two sequences is higher than that, -1 is returned. If `transpositions` is `True`, transpositions will be taken into account for the computation of the distance. This can make a difference, e.g.: >>> fast_comp("abc", "bac", transpositions=False) 2 >>> fast_comp("abc", "bac", transpositions=True) 1 This is faster than `levenshtein` by an order of magnitude, but on the other hand is of limited use. The algorithm comes from `http://writingarchives.sakura.ne.jp/fastcomp`. I've added transpositions support to the original code. )ridN)lencount)seq1seq2transpositionsreplaceinsertdeleteL1L2ldiffmodelsresmodelrjccmdrO/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/distance/_fastcomp.py fast_compsd      (       rN)F)rrrrrs