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()

def extract_kgram_counts(text: str, k: int) -> dict[str, int]:

	"""Extract k-gram counts from the text for a provided k.

	:param text: the source text

	:param k: length of the kgrams to extract. 1 for letters, 2 for bigrams, ...

	:return: a dict mapping kgrams to their counts in the text"""

	n = len(text)

	counts = dict()

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

		key = text[i:i+k]

		if key.isspace():

			continue

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

	return counts

def extract_ngram_counts(text: str) -> dict[str, int]:

	"""Extract counts of 1- to 3-grams from the text.

	:param text: the source text

	:return: a dict mapping ngrams to their counts in the text"""

	counts = dict()

	for k in range(1, 4):

		counts.update(extract_kgram_counts(text, k))

	return counts

	"""Order supplied ngrams by their counts (then length, then alphabetically) and return their ranking.

	:param counts: a dict mapping ngrams to their counts

	:return: a dict mapping ngrams to their rank (the most frequent: 0, the second: 1, ...)"""

	ordered_ngrams = sorted(counts.items(), key=lambda kv: (-kv[1], len(kv[0]), kv[0]))[:TOP_NGRAM_COUNT]

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

def extract_ranked_ngrams(text: str) -> dict[str, int]:

	"""Extract ngrams from the text and rank them from the most common.

	:param text: the source text

	:return: a dict mapping ngrams to their ranks {most_common_ngram: 0, second: 1, ...}"""

	counts = extract_ngram_counts(text)

	return rank_ngram_counts(counts)

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])]

import os

import random

import itertools

import json

import gzip

from languedoc.predict import preprocess, identify, extract_ngram_counts, rank_ngram_counts, Sample

random.seed(19181028)

CROSSVALIDATION_SOURCE_COUNT = 5

TEST_LENS = [8, 16, 32, 64]

	n = len(counts)

	res = dict()

	for d in counts:

		k = sum(d.values())

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

			res.setdefault(key, 0)

			res[key] += val/k/n

	return res

class SampleSet:

	def __init__(self, language):

		self.language = language

		self.texts = []

		self.counts = []

		self.texts.append(text)

		self.counts.append(extract_ngram_counts(text))

		merged_frequencies = merge_ngram_freqs(self.counts)

		res = Sample(self.language, rank_ngram_counts(merged_frequencies))

		return res

		for (i, (text, freqs)) in enumerate(itertools.cycle(zip(self.texts, self.counts))):

			if i >= n:

				break

			ranked_ngrams = rank_ngram_counts(merge_ngram_freqs([f for f in self.counts if f is not freqs]))

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

	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

		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)

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

	print(cross_validate(samples))