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()

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


DATA_DIR = os.path.join(os.path.dirname(__file__), "data")
LANG_DIRS = [x.path for x in os.scandir(DATA_DIR)]

for d in LANG_DIRS:
	models = [[], [], []]

	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()

			for k in range(1, 4):
				models[k-1].append(extract_ngram_freqs(text, k))

	models = [merge_ngram_freqs(sources) for sources in models]
	print(sorted(((key, round(val, 3)) for (key, val) in models[0].items()), key=lambda kv: -kv[1]))
	print(sorted(((key, round(val, 3)) for (key, val) in models[1].items()), key=lambda kv: -kv[1]))