from __future__ import annotations

import hmac
import logging
import pickle
import re
import warnings
from hashlib import sha256
from pathlib import Path
from typing import TYPE_CHECKING

if TYPE_CHECKING:
    from collections.abc import Callable
    from collections.abc import Iterator
    from datetime import datetime

    from numpy import ndarray

from django.conf import settings
from django.core.cache import cache
from django.core.cache import caches

from documents.caching import CACHE_5_MINUTES
from documents.caching import CACHE_50_MINUTES
from documents.caching import CLASSIFIER_HASH_KEY
from documents.caching import CLASSIFIER_MODIFIED_KEY
from documents.caching import CLASSIFIER_VERSION_KEY
from documents.caching import StoredLRUCache
from documents.models import Document
from documents.models import MatchingModel
from paperless.signed_pickle import SignedPickleError
from paperless.signed_pickle import signed_pickle_dumps
from paperless.signed_pickle import signed_pickle_loads

logger = logging.getLogger("paperless.classifier")

ADVANCED_TEXT_PROCESSING_ENABLED = (
    settings.NLTK_LANGUAGE is not None and settings.NLTK_ENABLED
)

read_cache = caches["read-cache"]


RE_DIGIT = re.compile(r"\d")
RE_WORD = re.compile(r"\b[\w]+\b")  # words that may contain digits


class IncompatibleClassifierVersionError(Exception):
    def __init__(self, message: str, *args: object) -> None:
        self.message: str = message
        super().__init__(*args)


class ClassifierModelCorruptError(Exception):
    pass


def load_classifier(*, raise_exception: bool = False) -> DocumentClassifier | None:
    if not settings.MODEL_FILE.is_file():
        logger.debug(
            "Document classification model does not exist (yet), not "
            "performing automatic matching.",
        )
        return None

    classifier = DocumentClassifier()
    try:
        classifier.load()

    except IncompatibleClassifierVersionError as e:
        logger.info(f"Classifier version incompatible: {e.message}, will re-train")
        Path(settings.MODEL_FILE).unlink()
        classifier = None
        if raise_exception:
            raise e
    except ClassifierModelCorruptError as e:
        # there's something wrong with the model file.
        logger.exception(
            "Unrecoverable error while loading document "
            "classification model, deleting model file.",
        )
        Path(settings.MODEL_FILE).unlink()
        classifier = None
        if raise_exception:
            raise e
    except OSError as e:
        logger.exception("IO error while loading document classification model")
        classifier = None
        if raise_exception:
            raise e
    except Exception as e:  # pragma: no cover
        logger.exception("Unknown error while loading document classification model")
        classifier = None
        if raise_exception:
            raise e

    return classifier


class DocumentClassifier:
    # v7 - Updated scikit-learn package version
    # v8 - Added storage path classifier
    # v9 - Changed from hashing to time/ids for re-train check
    # v10 - HMAC-signed model file
    FORMAT_VERSION = 10

    HMAC_SIZE = 32  # SHA-256 digest length

    def __init__(self) -> None:
        # last time a document changed and therefore training might be required
        self.last_doc_change_time: datetime | None = None
        # Hash of primary keys of AUTO matching values last used in training
        self.last_auto_type_hash: bytes | None = None

        self.data_vectorizer = None
        self.data_vectorizer_hash = None
        self.tags_binarizer = None
        self.tags_classifier = None
        self.correspondent_classifier = None
        self.document_type_classifier = None
        self.storage_path_classifier = None
        self._stemmer = None
        # 10,000 elements roughly use 200 to 500 KB per worker,
        # and also in the shared Redis cache,
        # Keep this cache small to minimize lookup and I/O latency.
        if ADVANCED_TEXT_PROCESSING_ENABLED:
            self._stem_cache = StoredLRUCache(
                f"stem_cache_v{self.FORMAT_VERSION}",
                capacity=10000,
            )
        self._stop_words = None

    def _update_data_vectorizer_hash(self) -> None:
        self.data_vectorizer_hash = sha256(
            pickle.dumps(self.data_vectorizer),
        ).hexdigest()

    @staticmethod
    def _compute_hmac(data: bytes) -> bytes:
        return hmac.new(
            settings.SECRET_KEY.encode(),
            data,
            sha256,
        ).digest()

    def load(self) -> None:
        from sklearn.exceptions import InconsistentVersionWarning

        raw = Path(settings.MODEL_FILE).read_bytes()

        if len(raw) <= self.HMAC_SIZE:
            raise ClassifierModelCorruptError

        signature = raw[: self.HMAC_SIZE]
        data = raw[self.HMAC_SIZE :]

        if not hmac.compare_digest(signature, self._compute_hmac(data)):
            raise ClassifierModelCorruptError

        # Catch warnings for processing
        with warnings.catch_warnings(record=True) as w:
            try:
                (
                    schema_version,
                    self.last_doc_change_time,
                    self.last_auto_type_hash,
                    self.data_vectorizer,
                    self.tags_binarizer,
                    self.tags_classifier,
                    self.correspondent_classifier,
                    self.document_type_classifier,
                    self.storage_path_classifier,
                ) = pickle.loads(data)
            except Exception as err:
                raise ClassifierModelCorruptError from err

        if schema_version != self.FORMAT_VERSION:
            raise IncompatibleClassifierVersionError(
                "Cannot load classifier, incompatible versions.",
            )

        self._update_data_vectorizer_hash()

        # Check for the warning about unpickling from differing versions
        # and consider it incompatible
        sk_learn_warning_url = (
            "https://scikit-learn.org/stable/"
            "model_persistence.html"
            "#security-maintainability-limitations"
        )
        for warning in w:
            # The warning is inconsistent, the MLPClassifier is a specific warning, others have not updated yet
            if issubclass(warning.category, InconsistentVersionWarning) or (
                issubclass(warning.category, UserWarning)
                and sk_learn_warning_url in str(warning.message)
            ):
                raise IncompatibleClassifierVersionError("sklearn version update")

    def save(self) -> None:
        target_file: Path = settings.MODEL_FILE
        target_file_temp: Path = target_file.with_suffix(".pickle.part")

        data = pickle.dumps(
            (
                self.FORMAT_VERSION,
                self.last_doc_change_time,
                self.last_auto_type_hash,
                self.data_vectorizer,
                self.tags_binarizer,
                self.tags_classifier,
                self.correspondent_classifier,
                self.document_type_classifier,
                self.storage_path_classifier,
            ),
        )

        signature = self._compute_hmac(data)

        with target_file_temp.open("wb") as f:
            f.write(signature + data)

        target_file_temp.rename(target_file)

    def train(
        self,
        status_callback: Callable[[str], None] | None = None,
    ) -> bool:
        notify = status_callback if status_callback is not None else lambda _: None

        # Get non-inbox documents
        docs_queryset = (
            Document.objects.exclude(
                tags__is_inbox_tag=True,
            )
            .select_related("document_type", "correspondent", "storage_path")
            .prefetch_related("tags")
            .order_by("pk")
        )

        # No documents exit to train against
        if docs_queryset.count() == 0:
            raise ValueError("No training data available.")

        labels_tags = []
        labels_correspondent = []
        labels_document_type = []
        labels_storage_path = []

        # Step 1: Extract and preprocess training data from the database.
        logger.debug("Gathering data from database...")
        notify(f"Gathering data from {docs_queryset.count()} document(s)...")
        hasher = sha256()
        for doc in docs_queryset:
            y = -1
            dt = doc.document_type
            if dt and dt.matching_algorithm == MatchingModel.MATCH_AUTO:
                y = dt.pk
            hasher.update(y.to_bytes(4, "little", signed=True))
            labels_document_type.append(y)

            y = -1
            cor = doc.correspondent
            if cor and cor.matching_algorithm == MatchingModel.MATCH_AUTO:
                y = cor.pk
            hasher.update(y.to_bytes(4, "little", signed=True))
            labels_correspondent.append(y)

            tags: list[int] = list(
                doc.tags.filter(matching_algorithm=MatchingModel.MATCH_AUTO)
                .order_by("pk")
                .values_list("pk", flat=True),
            )
            for tag in tags:
                hasher.update(tag.to_bytes(4, "little", signed=True))
            labels_tags.append(tags)

            y = -1
            sp = doc.storage_path
            if sp and sp.matching_algorithm == MatchingModel.MATCH_AUTO:
                y = sp.pk
            hasher.update(y.to_bytes(4, "little", signed=True))
            labels_storage_path.append(y)

        labels_tags_unique = {tag for tags in labels_tags for tag in tags}

        num_tags = len(labels_tags_unique)

        # Check if retraining is actually required.
        # A document has been updated since the classifier was trained
        # New auto tags, types, correspondent, storage paths exist
        latest_doc_change = docs_queryset.latest("modified").modified
        if (
            self.last_doc_change_time is not None
            and self.last_doc_change_time >= latest_doc_change
        ) and self.last_auto_type_hash == hasher.digest():
            logger.info("No updates since last training")
            # Set the classifier information into the cache
            # Caching for 50 minutes, so slightly less than the normal retrain time
            cache.set(
                CLASSIFIER_MODIFIED_KEY,
                self.last_doc_change_time,
                CACHE_50_MINUTES,
            )
            cache.set(CLASSIFIER_HASH_KEY, hasher.hexdigest(), CACHE_50_MINUTES)
            cache.set(CLASSIFIER_VERSION_KEY, self.FORMAT_VERSION, CACHE_50_MINUTES)
            return False

        # subtract 1 since -1 (null) is also part of the classes.

        # union with {-1} accounts for cases where all documents have
        # correspondents and types assigned, so -1 isn't part of labels_x, which
        # it usually is.
        num_correspondents: int = len(set(labels_correspondent) | {-1}) - 1
        num_document_types: int = len(set(labels_document_type) | {-1}) - 1
        num_storage_paths: int = len(set(labels_storage_path) | {-1}) - 1

        logger.debug(
            f"{docs_queryset.count()} documents, {num_tags} tag(s), {num_correspondents} correspondent(s), "
            f"{num_document_types} document type(s). {num_storage_paths} storage path(s)",
        )

        from sklearn.feature_extraction.text import CountVectorizer
        from sklearn.neural_network import MLPClassifier
        from sklearn.preprocessing import LabelBinarizer
        from sklearn.preprocessing import MultiLabelBinarizer

        # Step 2: vectorize data
        logger.debug("Vectorizing data...")
        notify("Vectorizing document content...")

        def content_generator() -> Iterator[str]:
            """
            Generates the content for documents, but once at a time
            """
            for doc in docs_queryset:
                yield self.preprocess_content(doc.content, shared_cache=False)

        self.data_vectorizer = CountVectorizer(
            analyzer="word",
            ngram_range=(1, 2),
            min_df=0.01,
        )

        data_vectorized: ndarray = self.data_vectorizer.fit_transform(
            content_generator(),
        )

        # See the notes here:
        # https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html
        # This attribute isn't needed to function and can be large
        self.data_vectorizer.stop_words_ = None

        # Step 3: train the classifiers
        if num_tags > 0:
            logger.debug("Training tags classifier...")
            notify(f"Training tags classifier ({num_tags} tag(s))...")

            if num_tags == 1:
                # Special case where only one tag has auto:
                # Fallback to binary classification.
                labels_tags = [
                    label[0] if len(label) == 1 else -1 for label in labels_tags
                ]
                self.tags_binarizer = LabelBinarizer()
                labels_tags_vectorized: ndarray = self.tags_binarizer.fit_transform(
                    labels_tags,
                ).ravel()
            else:
                self.tags_binarizer = MultiLabelBinarizer()
                labels_tags_vectorized = self.tags_binarizer.fit_transform(labels_tags)

            self.tags_classifier = MLPClassifier(tol=0.01)
            self.tags_classifier.fit(data_vectorized, labels_tags_vectorized)
        else:
            self.tags_classifier = None
            logger.debug("There are no tags. Not training tags classifier.")

        if num_correspondents > 0:
            logger.debug("Training correspondent classifier...")
            notify(
                f"Training correspondent classifier ({num_correspondents} correspondent(s))...",
            )
            self.correspondent_classifier = MLPClassifier(tol=0.01)
            self.correspondent_classifier.fit(data_vectorized, labels_correspondent)
        else:
            self.correspondent_classifier = None
            logger.debug(
                "There are no correspondents. Not training correspondent classifier.",
            )

        if num_document_types > 0:
            logger.debug("Training document type classifier...")
            notify(
                f"Training document type classifier ({num_document_types} type(s))...",
            )
            self.document_type_classifier = MLPClassifier(tol=0.01)
            self.document_type_classifier.fit(data_vectorized, labels_document_type)
        else:
            self.document_type_classifier = None
            logger.debug(
                "There are no document types. Not training document type classifier.",
            )

        if num_storage_paths > 0:
            logger.debug(
                "Training storage paths classifier...",
            )
            notify(f"Training storage path classifier ({num_storage_paths} path(s))...")
            self.storage_path_classifier = MLPClassifier(tol=0.01)
            self.storage_path_classifier.fit(
                data_vectorized,
                labels_storage_path,
            )
        else:
            self.storage_path_classifier = None
            logger.debug(
                "There are no storage paths. Not training storage path classifier.",
            )

        self.last_doc_change_time = latest_doc_change
        self.last_auto_type_hash = hasher.digest()
        self._update_data_vectorizer_hash()

        # Set the classifier information into the cache
        # Caching for 50 minutes, so slightly less than the normal retrain time
        cache.set(CLASSIFIER_MODIFIED_KEY, self.last_doc_change_time, CACHE_50_MINUTES)
        cache.set(CLASSIFIER_HASH_KEY, hasher.hexdigest(), CACHE_50_MINUTES)
        cache.set(CLASSIFIER_VERSION_KEY, self.FORMAT_VERSION, CACHE_50_MINUTES)

        return True

    def _init_advanced_text_processing(self):
        if self._stop_words is None or self._stemmer is None:
            import nltk
            from nltk.corpus import stopwords
            from nltk.stem import SnowballStemmer

            # Not really hacky, since it isn't private and is documented, but
            # set the search path for NLTK data to the single location it should be in
            nltk.data.path = [settings.NLTK_DIR]
            try:
                # Preload the corpus early, to force the lazy loader to transform
                stopwords.ensure_loaded()

                # Do some one time setup
                # Sometimes, somehow, there's multiple threads loading the corpus
                # and it's not thread safe, raising an AttributeError
                self._stemmer = SnowballStemmer(settings.NLTK_LANGUAGE)
                self._stop_words = frozenset(stopwords.words(settings.NLTK_LANGUAGE))
            except AttributeError:
                logger.debug("Could not initialize NLTK for advanced text processing.")
                return False
        return True

    def stem_and_skip_stop_words(self, words: list[str], *, shared_cache=True):
        """
        Reduce a list of words to their stem. Stop words are converted to empty strings.
        :param words: the list of words to stem
        """

        def _stem_and_skip_stop_word(word: str):
            """
            Reduce a given word to its stem. If it's a stop word, return an empty string.
            E.g. "amazement", "amaze" and "amazed" all return "amaz".
            """
            cached = self._stem_cache.get(word)
            if cached is not None:
                return cached
            elif word in self._stop_words:
                return ""
            # Assumption: words that contain numbers are never stemmed
            elif RE_DIGIT.search(word):
                return word
            else:
                result = self._stemmer.stem(word)
                self._stem_cache.set(word, result)
                return result

        if shared_cache:
            self._stem_cache.load()

        # Stem the words and skip stop words
        result = " ".join(
            filter(None, (_stem_and_skip_stop_word(w) for w in words)),
        )
        if shared_cache:
            self._stem_cache.save()
        return result

    def preprocess_content(
        self,
        content: str,
        *,
        shared_cache=True,
    ) -> str:
        """
        Process the contents of a document, distilling it down into
        words which are meaningful to the content.

        A stemmer cache is shared across workers with the parameter "shared_cache".
        This is unnecessary when training the classifier.
        """

        # Lower case the document, reduce space,
        # and keep only letters and digits.
        content = " ".join(match.group().lower() for match in RE_WORD.finditer(content))

        if ADVANCED_TEXT_PROCESSING_ENABLED:
            from nltk.tokenize import word_tokenize

            if not self._init_advanced_text_processing():
                return content
            # Tokenize
            # This splits the content into tokens, roughly words
            words = word_tokenize(content, language=settings.NLTK_LANGUAGE)
            # Stem the words and skip stop words
            content = self.stem_and_skip_stop_words(words, shared_cache=shared_cache)

        return content

    def _get_vectorizer_cache_key(self, content: str):
        hash = sha256(content.encode())
        hash.update(
            f"|{self.FORMAT_VERSION}|{settings.NLTK_LANGUAGE}|{settings.NLTK_ENABLED}|{self.data_vectorizer_hash}".encode(),
        )
        return f"vectorized_content_{hash.hexdigest()}"

    def _vectorize(self, content: str):
        key = self._get_vectorizer_cache_key(content)
        serialized_result = read_cache.get(key)
        if serialized_result is None:
            result = self.data_vectorizer.transform([self.preprocess_content(content)])
            read_cache.set(key, signed_pickle_dumps(result), CACHE_5_MINUTES)
        else:
            try:
                result = signed_pickle_loads(serialized_result)
            except SignedPickleError:
                result = self.data_vectorizer.transform(
                    [self.preprocess_content(content)],
                )
                read_cache.set(key, signed_pickle_dumps(result), CACHE_5_MINUTES)
            else:
                read_cache.touch(key, CACHE_5_MINUTES)
        return result

    def predict_correspondent(self, content: str) -> int | None:
        if self.correspondent_classifier:
            X = self._vectorize(content)
            correspondent_id = self.correspondent_classifier.predict(X)
            if correspondent_id != -1:
                return correspondent_id
            else:
                return None
        else:
            return None

    def predict_document_type(self, content: str) -> int | None:
        if self.document_type_classifier:
            X = self._vectorize(content)
            document_type_id = self.document_type_classifier.predict(X)
            if document_type_id != -1:
                return document_type_id
            else:
                return None
        else:
            return None

    def predict_tags(self, content: str) -> list[int]:
        from sklearn.utils.multiclass import type_of_target

        if self.tags_classifier:
            X = self._vectorize(content)
            y = self.tags_classifier.predict(X)
            tags_ids = self.tags_binarizer.inverse_transform(y)[0]
            if type_of_target(y).startswith("multilabel"):
                # the usual case when there are multiple tags.
                return list(tags_ids)
            elif type_of_target(y) == "binary" and tags_ids != -1:
                # This is for when we have binary classification with only one
                # tag and the result is to assign this tag.
                return [tags_ids]
            else:
                # Usually binary as well with -1 as the result, but we're
                # going to catch everything else here as well.
                return []
        else:
            return []

    def predict_storage_path(self, content: str) -> int | None:
        if self.storage_path_classifier:
            X = self._vectorize(content)
            storage_path_id = self.storage_path_classifier.predict(X)
            if storage_path_id != -1:
                return storage_path_id
            else:
                return None
        else:
            return None
