import os

import re

import itertools

import json

import gzip

TOP_NGRAM_COUNT = 3000

MODEL_PATH = os.path.join(os.path.dirname(__file__), "models.json.gz")

def preprocess(text: str) -> str:

	"""Preprocess text by stripping non-letter characters, collapsing whitespace and converting to lowercase."""

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

	return text.lower()

	n = len(text)

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

		key = text[i:i+k]

		if key.isspace():

			continue

	for k in range(1, 4):

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

def extract_ranked_ngrams(text):

class Sample:

	def __init__(self, language: str, ranked_ngrams: dict[str, float]):

		"""Create a new Sample from language and ngrams.

		This is usually impractical and Sample.extract or Sample.load are preferred."""

		self.language = language

		self.ranked_ngrams = ranked_ngrams

	@classmethod

	def extract(cls, text: str, language="??") -> "Sample":

		"""Create a new Sample by extracting it from text.

		:param text: a string, from which to extract the ngrams into a Sample

		:param language: a two letter language code if it is known (cs|de|en|...)"""

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

	@classmethod

	def load(cls, exported: dict) -> "Sample":

		"""Load a previously exported dict and create a new Sample.

		:param exported: {"language": str, "ngrams": [str, ...]}"""

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

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

	def export(self) -> dict:

		"""Export to a dict. Complement to Sample.load()

		:return: {"language": str, "ngrams": [str, ...]}"""

		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: "Sample") -> int:

		"""Compute a similarity score between self and other.

		The method is asymmetric. You are supposed to use sample.compare(model), not model.compare(sample).

		:param other: a reference model in known language"""

		m = len(other.ranked_ngrams)

		res = sum(

			(abs(v - other.ranked_ngrams[k]) if k in other.ranked_ngrams else m)

			for (k, v) in self.ranked_ngrams.items()

		)

		return res

def load_models(model_path: str) -> list[Sample]:

	"""Load language models from path and return as a list."""

	with gzip.open(model_path, mode="rt", encoding="utf-8") as f:

		return [Sample.load(obj) for obj in json.load(f)]

def identify(text: str, models=[]) -> str:

	"""Return the language closest to text among the models.

	:param text: the text to identify

	:param models: list of models to choose from. The default is loaded from MODEL_PATH

	:return: best matching language (cs, de, en, ...)"""

	if not models:

		models = load_models(MODEL_PATH)

	sample = Sample.extract(text)

	return sorted(models, key=lambda m: sample.compare(m))[0].language

import os

import random

import itertools

import json

import gzip

random.seed(19181028)

CROSSVALIDATION_SOURCE_COUNT = 5

TEST_LENS = [8, 16, 32, 64]

	res = dict()

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

			res.setdefault(key, 0)

	return res

class SampleSet:

	def __init__(self, language):

		self.language = language

		self.texts = []

	def add(self, text):

		self.texts.append(text)

	def create_model(self):

		return res

	def generate_tests(self, n):

			if i >= n:

				break

			yield (text, Sample(self.language, ranked_ngrams))

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(CROSSVALIDATION_SOURCE_COUNT):

			real_lang = partial_model.language

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

			for k in TEST_LENS:

				for i in range(10):

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

					t = test_text[j:j+k]

					predicted_lang = identify(t, test_models)

					if predicted_lang == real_lang:

						score += 1

					else:

						print(real_lang, predicted_lang, t)

					max_score += 1

	return score / max_score, (score, max_score)

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

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

MODEL_PATH = os.path.join(os.path.dirname(__file__), "models.json.gz")

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 sorted(os.scandir(d), key=lambda f: f.name):

			with open(file) as f:

				text = f.read()

				text = preprocess(text)

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

				lang_samples.add(text)

	with gzip.open(MODEL_PATH, mode="wt", encoding="utf-8") as f:

		json.dump([sample_set.create_model().export() for sample_set in samples], f, ensure_ascii=False)

	print(cross_validate(samples))

from unittest import TestCase

class TestPredict(TestCase):

	def test_preprocess(self):

		self.assertEqual(preprocess("abc"), " abc ")

		self.assertEqual(preprocess("A  b.c"), " a b c ")

		self.assertEqual(preprocess("1% "), " ")

		self.assertEqual(preprocess("Глава ĚŠČŘŽ"), " глава ěščřž ")

		freqs = {"a": 3, "aa": 1, "b": 4, "bb": 1, "c": 1}

		expected = {"b": 0, "a": 1, "c": 2, "aa": 3, "bb": 4}

class TestSample(TestCase):

	def test_extract(self):

		a = Sample.extract("aaaaaa", "a")

		self.assertEqual(a.language, "a")

		b = Sample.extract("aa aa aa", "b")

		c = Sample.extract("aa")

		self.assertEqual(c.language, "??")

class TestIdentify(TestCase):

	def test_identify(self):

		samples = [

			("cs", "Severní ledový oceán je nejmenší světový oceán."),

			("de", "Der Arktische Ozean ist der kleinste Ozean der Erde."),

			("en", "The Arctic Ocean is the smallest of the world's oceans."),

			("es", "Océano Ártico más pequeña y más septentrional del planeta"),

			("fr", "L'océan Arctique ce qui en fait le plus petit des océans."),

			("it", "Il Mar Glaciale Artico è una massa d'acqua..."),

			("ru", "Се́верный Ледови́тый океа́н — наименьший по площади океан Земли")

		]

		for (lang, sample) in samples:

			self.assertEqual(lang, identify(sample))