"""Indexation FAISS des documents de référence CIM-10 / Guide métho / CCAM."""

from __future__ import annotations

import json
import logging
import re
from dataclasses import dataclass, asdict
from pathlib import Path
from typing import Optional

import pdfplumber

from ..config import RAG_INDEX_DIR, CIM10_PDF, GUIDE_METHODO_PDF, CCAM_PDF

logger = logging.getLogger(__name__)

# Singleton pour l'index chargé en mémoire
_faiss_index = None
_metadata: list[dict] = []


@dataclass
class Chunk:
    text: str
    document: str  # "cim10", "guide_methodo", "ccam"
    page: Optional[int] = None
    code: Optional[str] = None


# ---------------------------------------------------------------------------
# Chunking CIM-10
# ---------------------------------------------------------------------------

def _chunk_cim10(pdf_path: Path) -> list[Chunk]:
    """Découpe le PDF CIM-10 en chunks par code 3 caractères (ex: K80, K85)."""
    chunks: list[Chunk] = []
    current_code: str | None = None
    current_text: list[str] = []
    current_page: int | None = None

    # Pattern pour détecter un code CIM-10 à 3 caractères en début de ligne
    code3_pattern = re.compile(r"^([A-Z]\d{2})\s+(.+)")
    # Pattern pour les sous-codes (ex: K80.0, K80.1)
    subcode_pattern = re.compile(r"^([A-Z]\d{2}\.\d+)\s+(.+)")

    logger.info("Extraction des chunks CIM-10 depuis %s", pdf_path.name)

    with pdfplumber.open(pdf_path) as pdf:
        for page_num, page in enumerate(pdf.pages, start=1):
            text = page.extract_text()
            if not text:
                continue

            for line in text.split("\n"):
                line = line.strip()
                if not line:
                    continue

                m = code3_pattern.match(line)
                if m and not subcode_pattern.match(line):
                    # Nouveau code 3-char → sauvegarder le chunk précédent
                    if current_code and current_text:
                        chunk_text = "\n".join(current_text)
                        if len(chunk_text.split()) >= 5:
                            chunks.append(Chunk(
                                text=chunk_text,
                                document="cim10",
                                page=current_page,
                                code=current_code,
                            ))
                    current_code = m.group(1)
                    current_text = [line]
                    current_page = page_num
                else:
                    if current_code:
                        current_text.append(line)

    # Dernier chunk
    if current_code and current_text:
        chunk_text = "\n".join(current_text)
        if len(chunk_text.split()) >= 5:
            chunks.append(Chunk(
                text=chunk_text,
                document="cim10",
                page=current_page,
                code=current_code,
            ))

    logger.info("CIM-10 : %d chunks extraits", len(chunks))
    return chunks


# ---------------------------------------------------------------------------
# Chunking Guide Méthodologique MCO
# ---------------------------------------------------------------------------

def _chunk_guide_methodo(pdf_path: Path) -> list[Chunk]:
    """Découpe le Guide Méthodologique MCO par sections/titres."""
    chunks: list[Chunk] = []
    current_title: str | None = None
    current_text: list[str] = []
    current_page: int | None = None

    # Patterns de titres de sections (chapitres, sous-chapitres)
    title_patterns = [
        re.compile(r"^((?:CHAPITRE|TITRE|PARTIE)\s+[IVXLCDM0-9]+.*)$", re.IGNORECASE),
        re.compile(r"^(\d+\.\d*\s+[A-ZÉÈÊÀÂÔÙÛÜ].{5,})$"),
        re.compile(r"^([A-ZÉÈÊÀÂÔÙÛÜ][A-ZÉÈÊÀÂÔÙÛÜ\s]{10,})$"),
    ]

    logger.info("Extraction des chunks Guide Métho depuis %s", pdf_path.name)

    with pdfplumber.open(pdf_path) as pdf:
        for page_num, page in enumerate(pdf.pages, start=1):
            text = page.extract_text()
            if not text:
                continue

            for line in text.split("\n"):
                line = line.strip()
                if not line:
                    continue

                is_title = False
                for pat in title_patterns:
                    if pat.match(line):
                        is_title = True
                        break

                if is_title and len(line) > 8:
                    # Sauvegarder le chunk précédent
                    if current_title and current_text:
                        chunk_text = current_title + "\n" + "\n".join(current_text)
                        if len(chunk_text.split()) >= 20:
                            chunks.append(Chunk(
                                text=chunk_text,
                                document="guide_methodo",
                                page=current_page,
                            ))
                    current_title = line
                    current_text = []
                    current_page = page_num
                else:
                    current_text.append(line)

    # Dernier chunk
    if current_title and current_text:
        chunk_text = current_title + "\n" + "\n".join(current_text)
        if len(chunk_text.split()) >= 20:
            chunks.append(Chunk(
                text=chunk_text,
                document="guide_methodo",
                page=current_page,
            ))

    # Si trop peu de chunks (le PDF ne suit pas les patterns de titre),
    # fallback : découper par pages groupées par 3
    if len(chunks) < 10:
        logger.info("Guide Métho : fallback découpe par pages (peu de titres détectés)")
        chunks = []
        with pdfplumber.open(pdf_path) as pdf:
            page_texts: list[str] = []
            start_page = 1
            for page_num, page in enumerate(pdf.pages, start=1):
                text = page.extract_text()
                if text:
                    page_texts.append(text)
                if len(page_texts) >= 3:
                    combined = "\n".join(page_texts)
                    if len(combined.split()) >= 20:
                        chunks.append(Chunk(
                            text=combined,
                            document="guide_methodo",
                            page=start_page,
                        ))
                    page_texts = []
                    start_page = page_num + 1
            if page_texts:
                combined = "\n".join(page_texts)
                if len(combined.split()) >= 20:
                    chunks.append(Chunk(
                        text=combined,
                        document="guide_methodo",
                        page=start_page,
                    ))

    logger.info("Guide Métho : %d chunks extraits", len(chunks))
    return chunks


# ---------------------------------------------------------------------------
# Chunking CCAM
# ---------------------------------------------------------------------------

def _chunk_ccam(pdf_path: Path) -> list[Chunk]:
    """Découpe le PDF CCAM en chunks par code d'acte."""
    chunks: list[Chunk] = []
    ccam_pattern = re.compile(r"([A-Z]{4}\d{3})\s+(.*)")

    logger.info("Extraction des chunks CCAM depuis %s", pdf_path.name)

    with pdfplumber.open(pdf_path) as pdf:
        for page_num, page in enumerate(pdf.pages, start=1):
            text = page.extract_text()
            if not text:
                continue

            current_code: str | None = None
            current_lines: list[str] = []

            for line in text.split("\n"):
                line = line.strip()
                if not line:
                    continue

                m = ccam_pattern.match(line)
                if m:
                    if current_code and current_lines:
                        chunks.append(Chunk(
                            text="\n".join(current_lines),
                            document="ccam",
                            page=page_num,
                            code=current_code,
                        ))
                    current_code = m.group(1)
                    current_lines = [line]
                elif current_code:
                    current_lines.append(line)

            if current_code and current_lines:
                chunks.append(Chunk(
                    text="\n".join(current_lines),
                    document="ccam",
                    page=page_num,
                    code=current_code,
                ))

    # Fallback : si aucun code CCAM détecté, indexer par page
    if not chunks:
        logger.info("CCAM : aucun code détecté, fallback par page")
        with pdfplumber.open(pdf_path) as pdf:
            for page_num, page in enumerate(pdf.pages, start=1):
                text = page.extract_text()
                if text and len(text.split()) >= 10:
                    chunks.append(Chunk(
                        text=text,
                        document="ccam",
                        page=page_num,
                    ))

    logger.info("CCAM : %d chunks extraits", len(chunks))
    return chunks


# ---------------------------------------------------------------------------
# Construction de l'index FAISS
# ---------------------------------------------------------------------------

def build_index(force: bool = False) -> None:
    """Construit l'index FAISS à partir des 3 PDFs de référence.

    Args:
        force: Si True, reconstruit même si l'index existe déjà.
    """
    import faiss
    import numpy as np
    from sentence_transformers import SentenceTransformer

    index_path = RAG_INDEX_DIR / "faiss.index"
    meta_path = RAG_INDEX_DIR / "metadata.json"

    if not force and index_path.exists() and meta_path.exists():
        logger.info("Index FAISS déjà existant dans %s (use force=True pour reconstruire)", RAG_INDEX_DIR)
        return

    # Collecter tous les chunks
    all_chunks: list[Chunk] = []

    for pdf_path, chunk_fn in [
        (CIM10_PDF, _chunk_cim10),
        (GUIDE_METHODO_PDF, _chunk_guide_methodo),
        (CCAM_PDF, _chunk_ccam),
    ]:
        if pdf_path.exists():
            all_chunks.extend(chunk_fn(pdf_path))
        else:
            logger.warning("PDF non trouvé : %s", pdf_path)

    if not all_chunks:
        logger.error("Aucun chunk extrait — vérifiez les chemins des PDFs")
        return

    logger.info("Total : %d chunks à indexer", len(all_chunks))

    # Embeddings — forcer CPU pour éviter les bugs CUDA avec ce modèle
    logger.info("Chargement du modèle d'embedding dangvantuan/sentence-camembert-large (CPU)...")
    model = SentenceTransformer("dangvantuan/sentence-camembert-large", device="cpu")
    model.max_seq_length = 512  # CamemBERT max position embeddings

    texts = [c.text[:2000] for c in all_chunks]  # Tronquer les chunks trop longs
    logger.info("Calcul des embeddings pour %d chunks...", len(texts))
    embeddings = model.encode(
        texts, show_progress_bar=True, normalize_embeddings=True, batch_size=64,
    )
    embeddings = np.array(embeddings, dtype=np.float32)

    # Index FAISS (IndexFlatIP = cosine similarity avec vecteurs normalisés)
    dim = embeddings.shape[1]
    index = faiss.IndexFlatIP(dim)
    index.add(embeddings)

    # Sauvegarder
    RAG_INDEX_DIR.mkdir(parents=True, exist_ok=True)
    faiss.write_index(index, str(index_path))

    metadata = [asdict(c) for c in all_chunks]
    # Ne pas sauvegarder le texte complet dans metadata (trop lourd),
    # garder un extrait de 500 chars
    for m in metadata:
        m["extrait"] = m.pop("text")[:500]

    meta_path.write_text(json.dumps(metadata, ensure_ascii=False, indent=2), encoding="utf-8")

    logger.info("Index FAISS sauvegardé : %s (%d vecteurs, dim=%d)", index_path, len(all_chunks), dim)


def get_index() -> tuple | None:
    """Charge l'index FAISS et les métadonnées (singleton lazy-loaded).

    Returns:
        Tuple (faiss_index, metadata_list) ou None si l'index n'existe pas.
    """
    global _faiss_index, _metadata

    if _faiss_index is not None:
        return _faiss_index, _metadata

    index_path = RAG_INDEX_DIR / "faiss.index"
    meta_path = RAG_INDEX_DIR / "metadata.json"

    if not index_path.exists() or not meta_path.exists():
        logger.warning("Index FAISS non trouvé dans %s — lancez build_index() d'abord", RAG_INDEX_DIR)
        return None

    import faiss

    _faiss_index = faiss.read_index(str(index_path))
    _metadata = json.loads(meta_path.read_text(encoding="utf-8"))

    logger.info("Index FAISS chargé : %d vecteurs", _faiss_index.ntotal)
    return _faiss_index, _metadata