"""
Migration aus aria-data/brain-import/ → Vector-DB.

Parst die mitgelieferten Markdown-Dateien (AGENT.md, USER.md, TOOLING.md)
und zerlegt sie in atomare Memory-Punkte. Jeder Punkt bekommt:

  source       = "import"
  migration_key = stabiler Identifier (z.B. "agent.md/rule-1") fuer Idempotenz
  pinned       = True

Beim Re-Run werden vorhandene Punkte mit gleicher migration_key entfernt
und neu geschrieben.

Mapping pro Datei:

  AGENT.md
    "Identitaet"                    → 1 Punkt type=identity
    "Persoenlichkeit" (Intro)       → 1 Punkt type=identity
    "Kern-Eigenschaften" (Liste)    → 1 Punkt pro Bullet  type=identity
    "Tool-Freigaben"                → 1 Punkt type=tool
    "Sicherheitsregeln" (Liste)     → 1 Punkt pro Bullet  type=rule
    "Arbeitsprinzipien" (Liste)     → 1 Punkt pro Bullet  type=rule
    "Dateien an Stefan zurueckgeben"→ 1 Punkt type=skill
    "Stimme"                        → 1 Punkt type=tool

  USER.md
    "Allgemein" (Liste)             → 1 Punkt pro Bullet  type=preference
    "Bestaetigung erforderlich"     → 1 Punkt type=preference
    "Autonomes Arbeiten OK fuer"    → 1 Punkt type=preference
    "Tools & Infrastruktur"         → 1 Punkt type=preference

  TOOLING.md
    gesamter Inhalt                 → 1 Punkt type=tool, title="Tooling-Stack"

BOOTSTRAP.md ist eine Variante von AGENT.md — wird (vorerst) ignoriert
damit keine doppelten Punkte landen.
"""

from __future__ import annotations

import logging
import re
from dataclasses import dataclass
from pathlib import Path
from typing import List, Optional

from memory import Embedder, VectorStore, MemoryPoint
from memory.vector_store import COLLECTION
from qdrant_client.http import models as qm

logger = logging.getLogger(__name__)


@dataclass
class _Block:
    title: str
    content: str


def _split_h2(md: str) -> List[_Block]:
    """Zerlegt Markdown in H2-Bloecke. Inhalt vor dem ersten H2 wird verworfen."""
    blocks: List[_Block] = []
    current: Optional[_Block] = None
    for line in md.splitlines():
        m = re.match(r"^##\s+(.+?)\s*$", line)
        if m and not line.startswith("### "):
            if current:
                blocks.append(current)
            current = _Block(title=m.group(1).strip(), content="")
            continue
        if current is not None:
            current.content += line + "\n"
    if current:
        blocks.append(current)
    return blocks


def _split_h3(content: str) -> List[_Block]:
    """Zerlegt einen H2-Block in H3-Untersektionen + 'header'-Block davor."""
    blocks: List[_Block] = []
    header_lines: List[str] = []
    current: Optional[_Block] = None
    for line in content.splitlines():
        m = re.match(r"^###\s+(.+?)\s*$", line)
        if m:
            if current is None and header_lines:
                blocks.append(_Block(title="_intro", content="\n".join(header_lines).strip()))
            if current:
                blocks.append(current)
            current = _Block(title=m.group(1).strip(), content="")
            continue
        if current is None:
            header_lines.append(line)
        else:
            current.content += line + "\n"
    if current:
        blocks.append(current)
    elif header_lines:
        blocks.append(_Block(title="_intro", content="\n".join(header_lines).strip()))
    return blocks


def _extract_bullets(content: str) -> List[tuple[str, str]]:
    """Findet "- **Title** — Body" oder "N. **Title** — Body" Bullets.

    Returns: Liste von (title, full_bullet_text).
    """
    bullets: List[tuple[str, str]] = []
    current_lines: List[str] = []
    current_title: Optional[str] = None

    def flush():
        if current_title and current_lines:
            bullets.append((current_title, "\n".join(current_lines).strip()))

    for line in content.splitlines():
        m = re.match(r"^\s*(?:[-*]|\d+\.)\s+\*\*([^*]+?)\*\*\s*[—\-:]?\s*(.*)$", line)
        if m:
            flush()
            current_title = m.group(1).strip()
            current_lines = [line]
            continue
        # Folge-Zeilen mit Einrueckung gehoeren zum aktuellen Bullet
        if current_title and (line.startswith(" ") or line.startswith("\t") or not line.strip()):
            current_lines.append(line)
            continue
        if current_title and not re.match(r"^\s*(?:[-*]|\d+\.)\s+", line):
            current_lines.append(line)
            continue
        # Neuer Bullet ohne **Title** Format
        if re.match(r"^\s*(?:[-*]|\d+\.)\s+", line):
            flush()
            text = re.sub(r"^\s*(?:[-*]|\d+\.)\s+", "", line).strip()
            short_title = (text[:60] + "…") if len(text) > 60 else text
            bullets.append((short_title, line.strip()))
            current_title = None
            current_lines = []
    flush()
    return bullets


# ─── Pro Datei eine Parser-Funktion ──────────────────────────────────

def _parse_agent_md(md: str, source_file: str) -> List[MemoryPoint]:
    points: List[MemoryPoint] = []
    h2_blocks = _split_h2(md)
    for h2 in h2_blocks:
        title = h2.title
        content = h2.content.strip()
        if not content:
            continue

        if title.lower() == "identitaet" or title.lower() == "identität":
            points.append(_mk(
                type_="identity", title="ARIA — Identitaet",
                content=f"## {title}\n\n{content}",
                category="persoenlichkeit",
                migration_key=f"{source_file}/identity",
            ))

        elif title.lower() == "persoenlichkeit" or title.lower() == "persönlichkeit":
            # Intro-Absatz + Kern-Eigenschaften-Liste trennen
            sub = _split_h3(content)
            for s in sub:
                if s.title == "_intro" and s.content.strip():
                    points.append(_mk(
                        type_="identity", title="Persoenlichkeit — Grundsatz",
                        content=s.content.strip(),
                        category="persoenlichkeit",
                        migration_key=f"{source_file}/personality-intro",
                    ))
                elif s.title.lower().startswith("kern"):
                    for idx, (btitle, btext) in enumerate(_extract_bullets(s.content), 1):
                        points.append(_mk(
                            type_="identity", title=f"Eigenschaft: {btitle}",
                            content=btext, category="persoenlichkeit",
                            migration_key=f"{source_file}/personality-trait-{idx}",
                        ))

        elif "sicherheitsregel" in title.lower():
            for idx, (btitle, btext) in enumerate(_extract_bullets(content), 1):
                points.append(_mk(
                    type_="rule", title=f"Sicherheit: {btitle}",
                    content=btext, category="sicherheit",
                    migration_key=f"{source_file}/security-{idx}",
                ))

        elif "arbeitsprinzipien" in title.lower() or "arbeitsprinzip" in title.lower():
            for idx, (btitle, btext) in enumerate(_extract_bullets(content), 1):
                points.append(_mk(
                    type_="rule", title=f"Prinzip: {btitle}",
                    content=btext, category="arbeitsweise",
                    migration_key=f"{source_file}/work-principle-{idx}",
                ))

        elif "tool-freigaben" in title.lower() or "tool freigaben" in title.lower():
            points.append(_mk(
                type_="tool", title="Tool-Freigaben — Vollzugriff",
                content=content, category="infrastruktur",
                migration_key=f"{source_file}/tool-access",
            ))

        elif "dateien an stefan" in title.lower() or "dateien zurueckgeben" in title.lower() or "dateien zur" in title.lower():
            points.append(_mk(
                type_="skill", title="Dateien an User zurueckgeben",
                content=content, category="ausgabe",
                migration_key=f"{source_file}/file-return-skill",
            ))

        elif title.lower() == "stimme":
            points.append(_mk(
                type_="tool", title="Stimme (F5-TTS)",
                content=content, category="infrastruktur",
                migration_key=f"{source_file}/voice",
            ))

        # Permanente Freigaben (in BOOTSTRAP) — als rule
        elif "freigaben" in title.lower():
            points.append(_mk(
                type_="rule", title=title,
                content=content, category="freigaben",
                migration_key=f"{source_file}/permissions",
            ))

        else:
            # Unbekannter Block: als generischer fact ablegen, NICHT pinned
            logger.info("Unbekannter H2-Block '%s' in %s — als fact (unpinned)", title, source_file)
            points.append(_mk(
                type_="fact", title=f"{source_file}: {title}",
                content=content, pinned=False,
                migration_key=f"{source_file}/section-{title.lower().replace(' ', '-')}",
            ))
    return points


def _parse_user_md(md: str, source_file: str) -> List[MemoryPoint]:
    points: List[MemoryPoint] = []
    for h2 in _split_h2(md):
        title = h2.title
        content = h2.content.strip()
        if not content:
            continue
        # Template-Platzhalter herausfiltern: Beispiel-Zeilen mit <Tag>
        if "<Beispiel-Tool>" in content or "<Username>" in title:
            continue
        if title.lower() == "allgemein":
            for idx, (btitle, btext) in enumerate(_extract_bullets(content), 1):
                # Template-Platzhalter ueberspringen
                if "<z.B." in btext or "<XYZ>" in btext:
                    continue
                points.append(_mk(
                    type_="preference", title=f"User: {btitle}",
                    content=btext, category="allgemein",
                    migration_key=f"{source_file}/general-{idx}",
                ))
        else:
            cat_key = re.sub(r"[^a-z0-9]+", "-", title.lower()).strip("-") or "allgemein"
            points.append(_mk(
                type_="preference", title=title,
                content=content, category=cat_key,
                migration_key=f"{source_file}/{cat_key}",
            ))
    return points


def _parse_tooling_md(md: str, source_file: str) -> List[MemoryPoint]:
    md = md.strip()
    if not md:
        return []
    return [_mk(
        type_="tool", title="Tooling-Stack (VM)",
        content=md, category="infrastruktur",
        migration_key=f"{source_file}/tooling-full",
    )]


# ─── Helper ─────────────────────────────────────────────────────────

def _mk(
    type_: str,
    title: str,
    content: str,
    migration_key: str,
    pinned: bool = True,
    category: str = "",
) -> MemoryPoint:
    p = MemoryPoint(
        id="",
        type=type_,
        title=title,
        content=content.strip(),
        pinned=pinned,
        category=category,
        source="import",
        tags=[],
    )
    # migration_key wird ueber Payload-Index angesprochen — in to_payload manuell anhaengen
    setattr(p, "_migration_key", migration_key)
    return p


# ─── Eintrittspunkt ─────────────────────────────────────────────────

def run_migration(
    import_dir: Path,
    store: VectorStore,
    embedder: Embedder,
) -> dict:
    """Liest alle .md-Dateien aus import_dir, parst sie, schreibt in DB.

    Idempotent: vorhandene Punkte mit gleicher migration_key werden geloescht
    und neu geschrieben.

    Returns: {"created": int, "updated": int, "skipped": int, "files": [...]}
    """
    if not import_dir.exists():
        return {"created": 0, "updated": 0, "skipped": 0, "files": [], "error": f"{import_dir} nicht gefunden"}

    parsers = {
        "AGENT.md": _parse_agent_md,
        "BOOTSTRAP.md": _parse_agent_md,  # gleicher Parser, ggf. ueberlappende Eintraege
        "USER.md": _parse_user_md,
        "USER.md.example": _parse_user_md,
        "TOOLING.md": _parse_tooling_md,
        "TOOLING.md.example": _parse_tooling_md,
    }

    # USER.md hat Vorrang vor USER.md.example
    file_priority = ["AGENT.md", "BOOTSTRAP.md", "USER.md", "USER.md.example",
                     "TOOLING.md", "TOOLING.md.example"]
    seen_kinds: set[str] = set()  # "USER" / "TOOLING" — nur einmal

    points: List[MemoryPoint] = []
    processed_files: List[str] = []

    for fname in file_priority:
        fp = import_dir / fname
        if not fp.exists():
            continue
        kind = fname.split(".")[0]  # "AGENT", "BOOTSTRAP", "USER", "TOOLING"
        # USER.md.example nur wenn USER.md fehlt
        if kind in ("USER", "TOOLING") and kind in seen_kinds:
            continue
        seen_kinds.add(kind)
        parser = parsers.get(fname)
        if not parser:
            continue
        try:
            md = fp.read_text(encoding="utf-8")
            file_points = parser(md, fname)
            points.extend(file_points)
            processed_files.append(f"{fname} ({len(file_points)})")
            logger.info("Migration: %s → %d Punkte", fname, len(file_points))
        except Exception as exc:
            logger.exception("Migration: %s fehlgeschlagen", fname)
            processed_files.append(f"{fname} (FEHLER: {exc})")

    if not points:
        return {"created": 0, "updated": 0, "skipped": 0, "files": processed_files}

    # Erst alte Migration-Punkte mit gleicher migration_key loeschen
    migration_keys = [getattr(p, "_migration_key", None) for p in points]
    migration_keys = [k for k in migration_keys if k]
    if migration_keys:
        store.client.delete(
            collection_name=COLLECTION,
            points_selector=qm.FilterSelector(filter=qm.Filter(must=[
                qm.FieldCondition(key="migration_key", match=qm.MatchAny(any=migration_keys))
            ])),
        )
        logger.info("Migration: %d alte Punkte mit gleicher migration_key entfernt", len(migration_keys))

    # Embed in Batches
    texts = [p.content for p in points]
    vectors = embedder.embed_batch(texts)

    created = 0
    for p, vec in zip(points, vectors):
        payload = p.to_payload()
        mkey = getattr(p, "_migration_key", None)
        if mkey:
            payload["migration_key"] = mkey
        from datetime import datetime, timezone
        import uuid as _uuid
        pid = str(_uuid.uuid4())
        now = datetime.now(timezone.utc).isoformat()
        payload["created_at"] = now
        payload["updated_at"] = now
        store.client.upsert(
            collection_name=COLLECTION,
            points=[qm.PointStruct(id=pid, vector=vec, payload=payload)],
        )
        created += 1

    return {
        "created": created,
        "files": processed_files,
        "import_dir": str(import_dir),
    }