from py_stringmatching import utils
from py_stringmatching.similarity_measure.cython.cython_levenshtein import levenshtein
from py_stringmatching.similarity_measure.sequence_similarity_measure import \
    SequenceSimilarityMeasure


class Levenshtein(SequenceSimilarityMeasure):
    """Computes Levenshtein measure (also known as edit distance).

    Levenshtein distance computes the minimum cost of transforming one string into the other. Transforming a string
    is carried out using a sequence of the following operators: delete a character, insert a character, and
    substitute one character for another.
    """

    def __init__(self):
        super(Levenshtein, self).__init__()

    def get_raw_score(self, string1, string2):
        """Computes the raw Levenshtein distance between two strings.

        Args:
            string1,string2 (str): Input strings.

        Returns:
            Levenshtein distance (int).

        Raises:
            TypeError : If the inputs are not strings.

        Examples:
            >>> lev = Levenshtein()
            >>> lev.get_raw_score('a', '')
            1
            >>> lev.get_raw_score('example', 'samples')
            3
            >>> lev.get_raw_score('levenshtein', 'frankenstein')
            6
        """
        
        # input validations
        utils.sim_check_for_none(string1, string2)

        # convert input to unicode.
        string1 = utils.convert_to_unicode(string1)
        string2 = utils.convert_to_unicode(string2)

        utils.tok_check_for_string_input(string1, string2)

        if utils.sim_check_for_exact_match(string1, string2):
            return 0.0

        return levenshtein(string1, string2)

    def get_sim_score(self, string1, string2):
        """Computes the normalized Levenshtein similarity score between two strings.

        Args:
            string1,string2 (str): Input strings.

        Returns:
            Normalized Levenshtein similarity (float).

        Raises:
            TypeError : If the inputs are not strings.

        Examples:
            >>> lev = Levenshtein()
            >>> lev.get_sim_score('a', '')
            0.0
            >>> lev.get_sim_score('example', 'samples')
            0.5714285714285714
            >>> lev.get_sim_score('levenshtein', 'frankenstein')
            0.5

        """

        # convert input strings to unicode.
        string1 = utils.convert_to_unicode(string1)
        string2 = utils.convert_to_unicode(string2)

        raw_score = self.get_raw_score(string1, string2)
        max_len = max(len(string1), len(string2))
        if max_len == 0:
            return 1.0
        return 1 - (raw_score / max_len)