from .metric import Metric

class Levenshtein(Metric):

    def __init__(self, name='Levenshtein'):
        super().__init__(name=name)

    def distance(self, source, target, cost=(1,1,1)):
        """Levenshtein distance with costs for deletion, insertion and substitution"""
        s_len = len(source)
        t_len = len(target)

        if type(cost) == int or type(cost) == float:
            del_cost = ins_cost = sub_cost = cost
        else:
            del_cost, ins_cost, sub_cost = cost

        rows = s_len + 1
        cols = t_len + 1
        D = [[0 for j in range(cols)] for i in range(rows)]

        # source prefixes can be transformed into empty strings
        # by deletions:
        for i in range(1, rows):
            D[i][0] = i * del_cost
        # target prefixes can be created from an empty source string
        # by inserting the characters
        for j in range(1, cols):
            D[0][j] = j * ins_cost

        for j in range(1, cols):
            for i in range(1, rows):
                deletion = D[i-1][j] + del_cost
                insertion = D[i][j-1] + ins_cost
                substitution_or_equal = D[i-1][j-1]
                if source[i-1] != target[j-1]:
                    substitution_or_equal += sub_cost

                D[i][j] = min(deletion, insertion, substitution_or_equal)

        return D[-1][-1]

    def max_distance(self, source, target, cost=(1,1,1)):
        """Levenshtein maximum distance value"""
        s_len = len(source)
        t_len = len(target)

        if type(cost) == int or type(cost) == float:
            del_cost = ins_cost = sub_cost = cost
        else:
            del_cost, ins_cost, sub_cost = cost

        max_del = max(s_len - t_len, 0)
        max_ins = max(t_len - s_len, 0)
        max_sub = min(s_len, t_len)

        return max_del*del_cost + max_ins*ins_cost + max_sub*sub_cost


if(__name__ == '__main__'):
    print("Levenshtein distance")
