from __future__ import division

import unicodedata

import uniseg.wordbreak

from .edit_distance import levenshtein


def words(s):
    # Patch uniseg.wordbreak.word_break to deal with our private use characters. See also
    # https://www.unicode.org/Public/UCD/latest/ucd/auxiliary/WordBreakProperty.txt
    old_word_break = uniseg.wordbreak.word_break

    def new_word_break(c, index=0):
        if 0xE000 <= ord(c) <= 0xF8FF:  # Private Use Area
            return 'ALetter'
        else:
            return old_word_break(c, index)
    uniseg.wordbreak.word_break = new_word_break

    # Check if c is an unwanted character, i.e. whitespace, punctuation, or similar
    def unwanted(c):

        # See https://www.fileformat.info/info/unicode/category/index.htm
        # and https://unicodebook.readthedocs.io/unicode.html#categories
        unwanted_categories = 'O', 'M', 'P', 'Z', 'S'
        unwanted_subcategories = 'Cc', 'Cf'

        subcat = unicodedata.category(c)
        cat = subcat[0]
        return cat in unwanted_categories or subcat in unwanted_subcategories

    # We follow Unicode Standard Annex #29 on Unicode Text Segmentation here: Split on word boundaries using
    # uniseg.wordbreak.words() and ignore all "words" that contain only whitespace, punctation "or similar characters."
    for word in uniseg.wordbreak.words(s):
        if all(unwanted(c) for c in word):
            pass
        else:
            yield word


def words_normalized(s):
    return words(unicodedata.normalize('NFC', s))


def word_error_rate(reference, compared):
    if isinstance(reference, str):
        reference_seq = list(words_normalized(reference))
        compared_seq = list(words_normalized(compared))
    else:
        reference_seq = list(reference)
        compared_seq = list(compared)

    d = levenshtein(reference_seq, compared_seq)
    if d == 0:
        return 0

    n = len(reference_seq)
    if n == 0:
        return float('inf')

    return d / n