import os

import re

import random

import itertools

random.seed(19181028)

TEST_LENS = [8, 16, 32, 64]

def preprocess(text):

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

	return text.lower()

def extract_ngram_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 merge_ngram_freqs(freqs):

	n = len(freqs)

	res = dict()

	for d in freqs:

		for (key, val) in d.items():

			res.setdefault(key, 0)

			res[key] += val/n

	return res

class Sample:

	def __init__(self, language="??", text=""):

		self.language = language

		self.frequencies = [dict(), dict(), dict()]

		if text:

			self._extract(text)

	def _extract(self, text):

		for k in range(1, 4):

			self.frequencies[k-1] = extract_ngram_freqs(text, k)

	@staticmethod

	def merge(samples):

		assert len({x.language for x in samples}) == 1

		res = Sample(samples[0].language)

		res.frequencies = []

		for freqs in zip(*(x.frequencies for x in samples)):

			res.frequencies.append(merge_ngram_freqs(freqs))

		return res

	def compare(self, other):

		"""take k most common

		use frequencies x order

		use letter, digrams, trigrams

		use absolute x square"""

		ranked_own_trigrams = dict(zip([key for (key, freq) in ordered_own_trigrams], itertools.count(0)))

		ranked_other_trigrams = dict(zip([key for (key, freq) in ordered_other_trigrams], itertools.count(0)))

		return res

	def print_overview(self):

		print(f"Sample({self.language}):")

		for freqs in self.frequencies:

			x = [

				(k, round(v, 3))

				for (k, v) in sorted(freqs.items(), key=lambda kv: -kv[1])

			]

			print("  ", x[:8], "...", x[-8:])

		print()

class SampleSet:

	def __init__(self, language):

		self.language = language

		self.texts = []

		self.samples = []

	def add(self, text):

		self.texts.append(text)

		self.samples.append(Sample(self.language, text))

	def create_model(self):

		return Sample.merge(self.samples)

	def generate_tests(self):

		for (text, sample) in zip(self.texts, self.samples):

			yield (text, Sample.merge([x for x in self.samples if x is not sample]))

def cross_validate(sample_sets):

	models = [s.create_model() for s in sample_sets]

	score = 0

	max_score = 0

	for s in sample_sets:

		for (test_text, partial_model) in s.generate_tests():

			partial_model.print_overview()

			real_lang = partial_model.language

			test_models = [partial_model] + [m for m in models if m.language != real_lang]

			for k in TEST_LENS:

				j = random.randrange(0, len(test_text)-k)

				t = test_text[j:j+k]

				predicted_lang = identify(t, test_models)

				print(real_lang, predicted_lang, t)

				if predicted_lang == real_lang:

					score += 1

				max_score += 1

	return score / max_score, (score, max_score)

def identify(text, models):

	sample = Sample(text=text)

	return min(map(lambda m: (m.compare(sample), m.language), models))[1]

DATA_DIR = os.path.join(os.path.dirname(__file__), "data")

LANG_DIRS = [x.path for x in os.scandir(DATA_DIR)]

if __name__ == "__main__":

	samples = []

	for d in LANG_DIRS:

		lang = os.path.basename(d)

		lang_samples = SampleSet(lang)

		samples.append(lang_samples)

		for file in os.scandir(d):

			with open(file) as f:

				text = f.read()

				text = preprocess(text)

				print(f"{file.name} ({len(text)})")

				print(text[:256])

				print()

				lang_samples.add(text)

	print(cross_validate(samples))