# Path: search/utils.py
# def _dec(fl):
# def normalize(iterable):
# def scale_to_one(iterable):
# def weighted_average(values, weights):
# def tokenize(string):
# def max_distance(phrase, word_index):
# def position_similarity(token1, token2, phrase1, phrase2):
# 
# Path: search/config.py
# MATCH_WEIGHT = 0.2
# DIST_WEIGHT = 0.8
# THRESHOLD = 0.75
# MIN_WORD_LENGTH = 3
# STR_SPLIT_REGEX = r'\W+'


import jellyfish as jf
from search import utils, config
    query = utils.tokenize(query.lower())
    string = utils.tokenize(string.lower())

    # if one of the two strings is falsy (no content, or was passed with items
    # short enough to be trimmed out), return 0 here to avoid ZeroDivisionError
    # later on while processing.
    if len(query) == 0 or len(string) == 0:
        return 0

    shortest, longest = sorted((query, string), key=lambda x: len(x))

    # matrix of tuples for each segment of both query and string
    matrix = [(s1, s2) for s1 in longest for s2 in shortest]

    matches = {}
    for string1, string2 in matrix:
        # get the jaro winkler equality between the two strings
        match = jf.jaro_winkler(string1, string2)
        # calculate the distance factor for the position of the segments
        # on their respective lists
        positional = utils.position_similarity(
            string1, string2, longest, shortest)

        # get them together and append to the matches dictionary
        match = (match, positional)
        matches.setdefault(string1, []).append(match)

    # get the highest value for each list, the apply the word-distance factor
    # the key takes the jaro winkler distance value to get the max value
    matches = [max(m, key=lambda x: x[0]) for m in matches.values()]

# Path: search/utils.py
# def _dec(fl):
# def normalize(iterable):
# def scale_to_one(iterable):
# def weighted_average(values, weights):
# def tokenize(string):
# def max_distance(phrase, word_index):
# def position_similarity(token1, token2, phrase1, phrase2):
# 
# Path: search/config.py
# MATCH_WEIGHT = 0.2
# DIST_WEIGHT = 0.8
# THRESHOLD = 0.75
# MIN_WORD_LENGTH = 3
# STR_SPLIT_REGEX = r'\W+'


# Path: search/core.py
import jellyfish as jf
from search import utils, config

    query = utils.tokenize(query.lower())
    string = utils.tokenize(string.lower())

    # if one of the two strings is falsy (no content, or was passed with items
    # short enough to be trimmed out), return 0 here to avoid ZeroDivisionError
    # later on while processing.
    if len(query) == 0 or len(string) == 0:
        return 0

    shortest, longest = sorted((query, string), key=lambda x: len(x))

    # matrix of tuples for each segment of both query and string
    matrix = [(s1, s2) for s1 in longest for s2 in shortest]

    matches = {}
    for string1, string2 in matrix:
        # get the jaro winkler equality between the two strings
        match = jf.jaro_winkler(string1, string2)
        # calculate the distance factor for the position of the segments
        # on their respective lists
        positional = utils.position_similarity(
            string1, string2, longest, shortest)

        # get them together and append to the matches dictionary
        match = (match, positional)
        matches.setdefault(string1, []).append(match)

    # get the highest value for each list, the apply the word-distance factor
    # the key takes the jaro winkler distance value to get the max value
    matches = [max(m, key=lambda x: x[0]) for m in matches.values()]

    _weights = (config.MATCH_WEIGHT, config.DIST_WEIGHT)