import os

import re

import random

random.seed(19181028)

def preprocess(text):

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

	return text.lower()

def extract_ngram_freqs(text, k):

	n = len(text)

	d = dict()

		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):

	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()

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

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

models = dict()

for d in LANG_DIRS:

	lang = os.path.basename(d)

	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()

			samples.append(Sample(lang, text))

			samples[-1].print_overview()

	models[lang] = Sample.merge(samples)

	models[lang].print_overview()