"""
Matching-Orchestrator:
- Erkennt deutsche Hörbuch-Serien (die drei ???, TKKG, ...)
- Versucht MusicBrainz → OpenLibrary → Google Books
- Lädt Cover herunter
- Bewertet Konfidenz und entscheidet über Auto-Accept
"""
import re
import os
import logging
import httpx
import asyncio
from pathlib import Path
from datetime import datetime
from sqlalchemy.ext.asyncio import AsyncSession
from sqlalchemy import select

from ..config import get_settings
from ..models.media_item import LibraryItem, BookFile, Chapter
from ..models.library import Library
from ..models.session import ServerSetting
from ..database import AsyncSessionLocal
from .matching.base import MatchResult
from .matching.musicbrainz import search_musicbrainz, get_release_details
from .matching.open_library import search_open_library, get_work_details
from .matching.google_books import search_google_books
from .matching.dnb import search_dnb

logger = logging.getLogger(__name__)

AUTO_ACCEPT_THRESHOLD = 0.75
UNCERTAIN_THRESHOLD = 0.50

# Bekannte deutsche Hörbuch-Serien: (regex, kanonischer Name)
SERIES_PATTERNS = [
    (r"(?i)^(die drei \?\?\?|die drei fragezeichen|drei fragezeichen)\s*[-–]?\s*(?:folge\s*)?(\d+)", "Die drei ???"),
    (r"(?i)^(tkkg)\s*[-–]?\s*(?:folge\s*)?(\d+)", "TKKG"),
    (r"(?i)^(fünf freunde|funf freunde)\s*[-–]?\s*(?:band\s*)?(\d+)", "Fünf Freunde"),
    (r"(?i)^(bibi blocksberg)\s*[-–]?\s*(?:folge\s*)?(\d+)", "Bibi Blocksberg"),
    (r"(?i)^(benjamin blümchen|benjamin blumchen)\s*[-–]?\s*(?:folge\s*)?(\d+)", "Benjamin Blümchen"),
    (r"(?i)^(bibi und tina)\s*[-–]?\s*(?:folge\s*)?(\d+)", "Bibi und Tina"),
    (r"(?i)^(der kleine vampir)\s*[-–]?\s*(?:band\s*)?(\d+)", "Der kleine Vampir"),
    # Generisch: "Serie - Folge/Band/Teil N - Titel"
    (r"(?i)^(.+?)\s*[-–]\s*(?:folge|band|teil|nr\.?|#)\s*(\d+)", None),
    # Generisch: "Serie (Folge N)"
    (r"(?i)^(.+?)\s*\((?:folge|band|teil|nr\.?|#|episode)\s*(\d+)\)", None),
]


def detect_series(title: str) -> tuple[str | None, str | None]:
    """Gibt (Serienname, Folgennummer) zurück oder (None, None)."""
    for pattern, canonical_name in SERIES_PATTERNS:
        m = re.match(pattern, title.strip())
        if m:
            series = canonical_name or m.group(1).strip()
            episode = m.group(2)
            return series, episode
    return None, None


def _title_similarity(a: str, b: str) -> float:
    """Einfache Ähnlichkeit: Wort-Überlapp."""
    if not a or not b:
        return 0.0
    wa = set(re.findall(r'\w+', a.lower()))
    wb = set(re.findall(r'\w+', b.lower()))
    if not wa or not wb:
        return 0.0
    return len(wa & wb) / max(len(wa), len(wb))


def _score_result(result: MatchResult, query_title: str, query_author: str | None) -> float:
    score = result.confidence
    title_sim = _title_similarity(result.title, query_title)
    score = score * 0.4 + title_sim * 0.6
    if query_author and result.author:
        author_sim = _title_similarity(result.author, query_author)
        score = score * 0.7 + author_sim * 0.3
    return min(score, 1.0)


async def _download_cover(url: str, item_id: str) -> str | None:
    """Lädt Cover herunter und speichert es lokal."""
    settings = get_settings()
    ext = ".jpg"
    if ".png" in url:
        ext = ".png"
    dest = os.path.join(settings.covers_dir, f"{item_id}{ext}")
    logger.info(f"Cover-Download: {url}")
    try:
        async with httpx.AsyncClient(timeout=20, follow_redirects=True) as client:
            r = await client.get(url)
            if r.status_code == 200:
                os.makedirs(settings.covers_dir, exist_ok=True)
                with open(dest, "wb") as f:
                    f.write(r.content)
                logger.info(f"Cover gespeichert: {dest} ({len(r.content)} Bytes)")
                return dest
            else:
                logger.warning(f"Cover-Download HTTP {r.status_code}: {url}")
    except Exception as e:
        logger.warning(f"Cover-Download Fehler ({url}): {e}")
    return None


async def _apply_match(db: AsyncSession, item: LibraryItem, result: MatchResult, confidence: float):
    """Schreibt Metadaten aus MatchResult in die DB."""
    if result.title:
        item.title = result.title
    if result.subtitle and not item.subtitle:
        item.subtitle = result.subtitle
    if result.author:
        item.author = result.author
    if result.narrator:
        item.narrator = result.narrator
    if result.description:
        item.description = result.description
    if result.publisher:
        item.publisher = result.publisher
    if result.publish_year:
        item.publish_year = result.publish_year
    if result.language:
        item.language = result.language
    if result.genres:
        item.genres = result.genres
    if result.series:
        item.series = result.series
    if result.series_sequence:
        item.series_sequence = result.series_sequence

    item.matched_source = result.source
    item.matched_id = result.source_id
    item.match_confidence = confidence
    item.updated_at = datetime.utcnow()

    # Cover herunterladen
    if result.cover_url and not item.cover_path:
        cover_path = await _download_cover(result.cover_url, item.id)
        if cover_path:
            item.cover_path = cover_path
    elif not result.cover_url:
        logger.info(f"Kein Cover-URL in Match-Ergebnis ({result.source}: {result.source_id})")

    # Kapitel aus MusicBrainz-Tracklisting
    if result.chapters:
        from sqlalchemy import delete
        from ..models.media_item import Chapter
        await db.execute(delete(Chapter).where(Chapter.library_item_id == item.id))
        for idx, ch in enumerate(result.chapters):
            chapter = Chapter(
                library_item_id=item.id,
                chapter_index=idx,
                title=ch.get("title", f"Kapitel {idx + 1}"),
                start_seconds=ch.get("start", 0.0),
                end_seconds=ch.get("end", 0.0),
            )
            db.add(chapter)

    # zu_prüfen entfernen wenn Konfidenz hoch genug
    if confidence >= AUTO_ACCEPT_THRESHOLD:
        tags = item.tags or []
        item.tags = [t for t in tags if t != "zu_prüfen"]


_SOURCE_FUNCS = {
    "musicbrainz": (search_musicbrainz, get_release_details),
    "open_library": (search_open_library, get_work_details),
    "google_books": (search_google_books, None),
    "dnb": (search_dnb, None),
}


async def match_audiobook(item_id: str):
    """
    Haupt-Matching-Funktion. Wird nach dem Scan als Hintergrund-Task gestartet.
    Quellen und Reihenfolge werden aus den Library-Settings gelesen.
    """
    async with AsyncSessionLocal() as db:
        result_row = await db.execute(select(LibraryItem).where(LibraryItem.id == item_id))
        item = result_row.scalar_one_or_none()
        if not item or item.match_locked:
            return

        # Globale Auto-Match Einstellung prüfen
        setting = await db.execute(
            select(ServerSetting).where(ServerSetting.key == "autoMatchBooks")
        )
        s = setting.scalar_one_or_none()
        if s and s.value is False:
            return

        # Matching-Quellen aus Library-Settings lesen
        lib_row = await db.execute(select(Library).where(Library.id == item.library_id))
        lib = lib_row.scalar_one_or_none()
        sources: list[str] = (
            (lib.settings or {}).get("match_sources", list(_SOURCE_FUNCS.keys()))
            if lib else list(_SOURCE_FUNCS.keys())
        )

        title = item.title or ""
        author = item.author

        series, episode = detect_series(title)
        search_title = title
        if series:
            search_title = f"{series} {episode}" if episode else series
            if not item.series:
                item.series = series
            if not item.series_sequence and episode:
                item.series_sequence = episode

        logger.info(f"Matche: '{title}' | Quellen: {sources}")

        best: MatchResult | None = None
        best_score = 0.0

        for source_name in sources:
            if best_score >= UNCERTAIN_THRESHOLD:
                break
            funcs = _SOURCE_FUNCS.get(source_name)
            if not funcs:
                continue
            search_func, details_func = funcs
            try:
                results = await search_func(search_title, author)
                for r in results:
                    score = _score_result(r, title, author)
                    if score > best_score:
                        best_score = score
                        best = r
                # Details holen wenn Treffer gut genug (z.B. MB Tracklist)
                if best and best.source == source_name and best_score >= UNCERTAIN_THRESHOLD and details_func:
                    try:
                        details = await details_func(best.source_id)
                        if details:
                            details.confidence = best_score
                            best = details
                    except Exception as e:
                        logger.warning(f"{source_name} Details Fehler: {e}")
            except Exception as e:
                logger.warning(f"{source_name} Fehler: {e}")

        if best and best_score >= UNCERTAIN_THRESHOLD:
            try:
                await _apply_match(db, item, best, best_score)
                await db.commit()
                logger.info(f"Match angewendet: '{item.title}' ← {best.source} ({best_score:.2f})")
            except Exception as e:
                logger.error(f"_apply_match fehlgeschlagen für '{title}': {e}", exc_info=True)
        else:
            logger.info(f"Kein Match für '{title}' (beste Konfidenz: {best_score:.2f})")
            await db.commit()


async def search_for_item(title: str, author: str | None = None) -> list[dict]:
    """Suche über alle Quellen – für manuelles Matching."""
    results = []

    async def _search_source(coro):
        try:
            return await coro
        except Exception:
            return []

    mb, ol, gb, dnb = await asyncio.gather(
        _search_source(search_musicbrainz(title, author)),
        _search_source(search_open_library(title, author)),
        _search_source(search_google_books(title, author)),
        _search_source(search_dnb(title, author)),
    )

    for r in mb + ol + gb + dnb:
        results.append({
            "source": r.source,
            "id": r.source_id,
            "title": r.title,
            "author": r.author,
            "publishYear": r.publish_year,
            "cover": r.cover_url,
            "confidence": r.confidence,
        })

    results.sort(key=lambda x: x["confidence"], reverse=True)
    return results