import re

import itertools

def preprocess(text):

	text = re.sub(r"[\W\d_]+", " ", " "+text+" ")

	return text.lower()

def extract_kgram_freqs(text, k):

	n = len(text)

	d = dict()

	for i in range(0, n-k+1):

		key = text[i:i+k]

		if key.isspace():

			continue

		d[key] = d.get(key, 0) + 1

	count = sum(d.values())

	return {key: val/count for (key, val) in d.items()}

def extract_ngram_freqs(text):

	frequencies = {}

	for k in range(1, 4):

		frequencies.update(extract_kgram_freqs(text, k))

	return frequencies

def rank_ngram_freqs(frequencies):

	ordered_ngrams = sorted(frequencies.items(), key=lambda kv: -kv[1])[:TOP_NGRAM_COUNT]

	return dict(zip([key for (key, freq) in ordered_ngrams], itertools.count(0)))

def extract_ranked_ngrams(text):

	frequencies = extract_ngram_freqs(text)

	return rank_ngram_freqs(frequencies)

class Sample:

	def __init__(self, language, ranked_ngrams):

		self.language = language

		self.ranked_ngrams = ranked_ngrams

	@classmethod

	def extract(cls, text, language="??"):

		return cls(language, extract_ranked_ngrams(preprocess(text)))

	@classmethod

	def load(cls, exported):

		ranked_ngrams = {key: order for (order, key) in enumerate(exported["ngrams"])}

		return cls(exported["language"], ranked_ngrams)

	def export(self):

		return {

			"language": self.language,

			"ngrams": [key for (key, order) in sorted(self.ranked_ngrams.items(), key=lambda key_order: key_order[1])]

		}

	def compare(self, other):

		"""take k most common

		use frequencies x order

		use letter, digrams, trigrams

		use absolute x square"""

		return res

def identify(text, models):

	sample = Sample.extract(text)