"""make train: Ameisen-Optimierung (ACO) der Pipeline-Parameter — anytime, multi-fidelity.

Prinzip: Pheromon-Gewichte je (Parameter, Stufe) steuern, welche Kandidaten („Ameisen")
als Nächstes getestet werden. Gute Kandidaten verstärken ihre Stufen, Verdunstung hält
die Suche offen — je länger der Trainer läuft, desto gezielter werden die Tests.
Jederzeit stoppbar; der Stand (beste_params.json/report.md) ist immer aktuell.

Fidelity-Kaskade pro Kandidat:
  F0  Fake-E2E (train_f0.py, Sekunden, 0 Tokens): Invarianten + Struktur-Proxy — Filter.
  F1  Frozen-Inventar (train_lauf.py --board2, ~5–8 min): misst Board-2/Guide-Parameter.
  F2  Volllauf inkl. Soll-Abgleich: alle N Runden für den Besten + Inventar-Parameter.

CLI: python3 train.py [--stunden 8] [--trials 60] [--ameisen 3] [--seed 0]
     [--sitzung NAME] [--f2-intervall 5]
     python3 train.py --init   (baut das Frozen-Inventar-Vorlage-Topic, einmalig)
Ergebnis: storage/train/<sitzung>/{trials.jsonl, pheromon.json, report.md, beste_params.json}
"""

import argparse
import asyncio
import hashlib
import json
import os
import random
import sys
import time
from datetime import datetime, timezone
from pathlib import Path

from config import STORAGE_DIR
from train_params import PARAMS

VORLAGE_TOPIC = "train-vorlage"
BENCHMARK = "benchmarks/pruefstand"
# Score-Gewichte: Qualität + Auswahl dominieren (Entwicklungsphase), Kosten ziehen ab.
W_NOTE, W_AUSWAHL, W_ZEIT, W_TOKEN = 4.0, 4.0, 1.0, 1.0
RHO = 0.2            # Pheromon-Verdunstung je Runde
SPARSITY = 0.5       # Wahrscheinlichkeit, dass eine Ameise einen Parameter auf Default lässt
F0_CALL_FAKTOR = 1.5  # Struktur-Proxy: mehr als 1.5× Baseline-Calls → Kandidat verworfen
# Trainings-Fixa (Speed, kein Suchraum): Abschluss-QA ohne Judges, kurze Nachzügler-Gnade
TRAIN_FIXA = {"ABSCHLUSS_QA_LLM": 0, "CONSENSUS_GRACE": 60}


def stufen(name: str) -> list[float]:
    p = PARAMS[name]
    out, w = [], p["min"]
    while w <= p["max"] + 1e-9:
        out.append(round(w, 4))
        w += p["step"]
    return out


def score(m: dict, basis: dict) -> float:
    """Skalarer Vergleichswert. note 0–10; auswahl aus Soll-Abgleich (F2) oder MECE-Quoten;
    Zeit/Tokens normiert auf die Baseline derselben Fidelity."""
    if m.get("soll"):
        auswahl = 10.0 * m["soll"]["f1"]
    else:
        q = m.get("quoten") or {}
        qa_ = m.get("quoten_artefakte") or {}
        auswahl = 10.0 * max(0.0, 1.0 - min(1.0, (
            q.get("dubletten_verdacht", 0) + q.get("luecken", 0) + q.get("fremd", 0)
            + qa_.get("sub_dubletten_verdacht", 0) + qa_.get("verwaiste", 0))))
    zeit = (m.get("dauer_min") or 0) / max(basis.get("dauer_min") or 1, 0.1)
    tok = _tokens(m) / max(_tokens(basis), 1)
    return round(W_NOTE * (m.get("note") or 0) + W_AUSWAHL * auswahl
                 - W_ZEIT * 10 * zeit - W_TOKEN * 10 * tok, 2)


def _tokens(m: dict) -> int:
    t = m.get("tokens") or {}
    return int(t.get("input") or 0) + int(t.get("output") or 0)


class AmeisenTrainer:
    def __init__(self, sitzung: Path, *, max_trials: int, max_stunden: float, ameisen: int = 3,
                 seed: int = 0, f2_intervall: int = 5, runner=None, runner_f0=None):
        self.dir = sitzung
        self.dir.mkdir(parents=True, exist_ok=True)
        self.rng = random.Random(seed)
        self.ameisen = ameisen
        self.f2_intervall = max(f2_intervall, 1)
        self.max_trials = max_trials
        self.deadline = time.monotonic() + max_stunden * 3600
        self.gezahlt = 0
        self.runner = runner or self._subprozess       # (params, fidelity) -> metrics|None
        self.runner_f0 = runner_f0 or self._subprozess_f0  # (params) -> {"ok","calls",…}|None
        self.log: list[str] = []
        self.basis: dict[str, dict] = {}               # Fidelity → Baseline-Metriken
        self.f0_basis: int | None = None
        self.best_params: dict = {}
        self.best_score: float | None = None
        self.rauschen = 0.5
        # Pheromon + Trial-Cache (Resume)
        self.pheromon: dict[str, dict[str, float]] = {
            n: {str(s): 1.0 for s in stufen(n)} for n in PARAMS}
        ph = self.dir / "pheromon.json"
        if ph.exists():
            gespeichert = json.loads(ph.read_text(encoding="utf-8"))
            for n, taus in gespeichert.get("pheromon", {}).items():
                if n in self.pheromon:
                    self.pheromon[n].update({k: float(v) for k, v in taus.items()})
            self.best_params = gespeichert.get("best_params", {})
            self.best_score = gespeichert.get("best_score")
        self.cache_pfad = self.dir / "trials.jsonl"
        self.cache: dict[str, dict] = {}
        if self.cache_pfad.exists():
            for line in self.cache_pfad.read_text(encoding="utf-8").splitlines():
                e = json.loads(line)
                self.cache[e["key"]] = e["metrics"]

    # ── Kandidaten ──────────────────────────────────────────────────────────────────
    def kandidat(self, fidelity: str) -> dict:
        """Eine Ameise: je Parameter der Fidelity mit SPARSITY auf Default, sonst
        Pheromon-gewichtete Stufe. Sparsame Kandidaten → saubere Attribution."""
        params = {}
        for name, p in PARAMS.items():
            if fidelity == "board2" and p["fidelity"] != "board2":
                continue
            if self.rng.random() < SPARSITY:
                continue
            st = stufen(name)
            taus = [self.pheromon[name][str(s)] for s in st]
            wert = self.rng.choices(st, weights=taus)[0]
            if wert != p["default"]:
                params[name] = wert
        return params

    # ── Trial-Ausführung ────────────────────────────────────────────────────────────
    def _key(self, params: dict, fidelity: str, tag: str = "") -> str:
        raw = json.dumps({"p": params, "f": fidelity, "tag": tag}, sort_keys=True)
        return hashlib.md5(raw.encode()).hexdigest()[:12]

    async def trial(self, params: dict, fidelity: str, tag: str = "") -> dict | None:
        key = self._key(params, fidelity, tag)
        if key in self.cache:
            return self.cache[key]
        if self.gezahlt >= self.max_trials or time.monotonic() > self.deadline:
            return None
        self.gezahlt += 1
        metrics = await self.runner(params, fidelity, "0")
        if metrics is not None:
            with open(self.cache_pfad, "a", encoding="utf-8") as f:
                f.write(json.dumps({"key": key, "params": params, "fidelity": fidelity,
                                    "tag": tag, "metrics": metrics}, ensure_ascii=False) + "\n")
            self.cache[key] = metrics
        return metrics

    async def _subprozess(self, params: dict, fidelity: str, topic_suffix: str = "0") -> dict | None:
        out = self.dir / f"metrics-{self._key(params, fidelity)}{topic_suffix}.json"
        topic = f"train-t{topic_suffix}"
        args = ([topic, VORLAGE_TOPIC, str(out), "--board2"] if fidelity == "board2"
                else [topic, BENCHMARK, str(out)])
        env = {**os.environ, "CREATOR_PARAMS": json.dumps({**TRAIN_FIXA, **params})}
        proc = await asyncio.create_subprocess_exec(sys.executable, "train_lauf.py", *args, env=env)
        rc = await proc.wait()
        if rc != 0 or not out.exists():
            self._log(f"Trial fehlgeschlagen (rc={rc}, {fidelity}, params={params})")
            return None
        return json.loads(out.read_text(encoding="utf-8"))

    async def _subprozess_f0(self, params: dict) -> dict | None:
        out = self.dir / f"f0-{self._key(params, 'f0')}.json"
        env = {**os.environ, "CREATOR_PARAMS": json.dumps(params)}
        proc = await asyncio.create_subprocess_exec(sys.executable, "train_f0.py", str(out), env=env)
        rc = await proc.wait()
        return json.loads(out.read_text(encoding="utf-8")) if rc == 0 and out.exists() else None

    def _log(self, msg: str) -> None:
        line = f"{datetime.now(timezone.utc).isoformat()[11:19]} {msg}"
        print(line, flush=True)
        self.log.append(line)

    # ── Pheromon ────────────────────────────────────────────────────────────────────
    def verstaerke(self, params: dict, delta: float) -> None:
        for name, wert in params.items():
            taus = self.pheromon[name]
            key = str(wert)
            if key in taus:
                taus[key] += delta

    def verdunste(self) -> None:
        for taus in self.pheromon.values():
            for k in taus:
                taus[k] = max(0.1, (1 - RHO) * taus[k] + RHO * 1.0)  # Drift zurück zu uniform

    # ── Hauptschleife ───────────────────────────────────────────────────────────────
    async def run(self) -> dict:
        # Baseline F1 ×2 → Score-Basis + Rausch-Schwelle; F0-Basis für den Struktur-Proxy
        f0 = await self.runner_f0({})
        self.f0_basis = (f0 or {}).get("calls")
        b1 = await self.trial({}, "board2", tag="baseline-1")
        b2 = await self.trial({}, "board2", tag="baseline-2")
        if not b1 or not b2:
            self._log("Baseline unvollständig — Abbruch.")
            return self.best_params
        self.basis["board2"] = b1
        s1, s2 = score(b1, b1), score(b2, b1)
        self.rauschen = max(abs(s1 - s2), 0.5)
        if self.best_score is None:
            self.best_score = max(s1, s2)
        self._log(f"Baseline {s1}/{s2}, Rauschen {self.rauschen}, F0-Basis {self.f0_basis} Calls")

        runde, stagnation, gezahlt_vorher = 0, 0, self.gezahlt
        while (self.gezahlt < self.max_trials and time.monotonic() < self.deadline
               and stagnation < 20):  # konvergiert: nur noch Cache-Treffer → fertig
            if runde > 0:
                stagnation = stagnation + 1 if self.gezahlt == gezahlt_vorher else 0
            gezahlt_vorher = self.gezahlt
            runde += 1
            fidelity = "voll" if runde % self.f2_intervall == 0 else "board2"
            if fidelity == "voll" and "voll" not in self.basis:
                base = await self.trial({}, "voll", tag="baseline-voll")
                if base is None:
                    break
                self.basis["voll"] = base
            kandidaten = []
            for _ in range(self.ameisen * 3):  # ziehen bis K einzigartige nicht-leere da sind
                k = self.kandidat(fidelity)
                if k and k not in kandidaten and k != self.best_params:
                    kandidaten.append(k)
                if len(kandidaten) >= self.ameisen:
                    break
            if not kandidaten:
                continue
            # F0-Filter: Invarianten + Struktur-Proxy, parallel, kostenlos
            f0s = await asyncio.gather(*[self.runner_f0(k) for k in kandidaten])
            ueberlebende = []
            for k, f in zip(kandidaten, f0s):
                if f is None or not f.get("ok") or f.get("invarianten_fehler"):
                    self._log(f"F0 verwirft {k} (Invarianten)")
                elif self.f0_basis and f.get("calls", 0) > self.f0_basis * F0_CALL_FAKTOR:
                    self._log(f"F0 verwirft {k} (Calls {f['calls']} > {self.f0_basis}×{F0_CALL_FAKTOR})")
                else:
                    ueberlebende.append(k)
            if not ueberlebende:
                self.verdunste()
                continue
            # F1/F2 parallel (eigene Topic-Namen)
            ergebnisse = await asyncio.gather(*[
                self._bewertet(k, fidelity, str(i + 1)) for i, k in enumerate(ueberlebende)])
            bewertet = [(k, m, score(m, self.basis[fidelity]))
                        for k, m in ergebnisse if m is not None]
            if not bewertet:
                continue
            bewertet.sort(key=lambda x: -x[2])
            self.verdunste()
            top_k, _top_m, top_s = bewertet[0]
            self._log(f"Runde {runde} ({fidelity}): top {top_s} {top_k} "
                      f"(best {self.best_score})")
            if top_s > (self.best_score or 0):
                self.verstaerke(top_k, delta=1.0)
            if top_s > (self.best_score or 0) + self.rauschen:
                m2 = await self.trial(top_k, fidelity, tag="bestaetigung")
                if m2 is not None and score(m2, self.basis[fidelity]) > self.best_score + self.rauschen:
                    self.best_params = top_k
                    self.best_score = min(top_s, score(m2, self.basis[fidelity]))
                    self._log(f"NEUER BESTER {self.best_params} → {self.best_score}")
                else:
                    self._log(f"{top_k}: nicht bestätigt")
            self.verstaerke(self.best_params, delta=0.5)  # Elite hält die Spur warm
            self._speichern()
        self._speichern()
        return self.best_params

    async def _bewertet(self, params: dict, fidelity: str, suffix: str):
        if self.gezahlt >= self.max_trials or time.monotonic() > self.deadline:
            return params, None
        key = self._key(params, fidelity)
        if key in self.cache:
            return params, self.cache[key]
        self.gezahlt += 1
        m = await self.runner(params, fidelity, suffix)
        if m is not None:
            with open(self.cache_pfad, "a", encoding="utf-8") as f:
                f.write(json.dumps({"key": key, "params": params, "fidelity": fidelity,
                                    "tag": "", "metrics": m}, ensure_ascii=False) + "\n")
            self.cache[key] = m
        return params, m

    def _speichern(self) -> None:
        from fsutil import atomic_write_json, atomic_write_text
        atomic_write_json(self.dir / "pheromon.json",
                          {"pheromon": self.pheromon, "best_params": self.best_params,
                           "best_score": self.best_score}, indent=1)
        atomic_write_json(self.dir / "beste_params.json", self.best_params, indent=1)
        staerkste = sorted(((n, max(t.items(), key=lambda x: x[1]))
                            for n, t in self.pheromon.items()),
                           key=lambda x: -x[1][1])[:10]
        report = ["# Trainings-Report (Ameisen)", "",
                  f"Bezahlte Läufe: {self.gezahlt}/{self.max_trials}",
                  f"Bester Score: {self.best_score} (Rauschband {self.rauschen})",
                  f"Beste Parameter: `{json.dumps(self.best_params, ensure_ascii=False)}`",
                  "", "Stärkste Pheromon-Spuren:",
                  *[f"- {n}={s} (τ={t:.1f})" for n, (s, t) in staerkste],
                  "", "Nutzung: `CREATOR_PARAMS=$(cat beste_params.json)` —",
                  "Übernahme nach config.py bleibt eine manuelle Entscheidung.", "", "## Log", ""]
        report += [f"- {l}" for l in self.log[-200:]]
        atomic_write_text(self.dir / "report.md", "\n".join(report))


async def init_vorlage() -> None:
    """Einmalig: Prüfstand-Volllauf mit Defaults, Ergebnis bleibt als Frozen-Inventar-Vorlage
    liegen (Topic train-vorlage). Nach Korpus-/Prompt-Änderungen neu ausführen."""
    import agents
    import database
    from blocks import generate_blocks
    from fsutil import atomic_write_json as awj
    from paths import source_path
    await database.init_db()
    agents.on_event = database.add_event
    await database.create_topic(VORLAGE_TOPIC)
    qp = source_path(VORLAGE_TOPIC)
    qp.parent.mkdir(parents=True, exist_ok=True)
    awj(qp, {"type": "uni", "location": BENCHMARK, "spec": ""})
    await generate_blocks(VORLAGE_TOPIC, provider="minimax", research=True, qa_force=True)
    await database.close_db()
    print(f"Vorlage {VORLAGE_TOPIC} steht — Training kann starten (make train).")


def main() -> None:
    ap = argparse.ArgumentParser()
    ap.add_argument("--init", action="store_true", help="Frozen-Inventar-Vorlage bauen")
    ap.add_argument("--trials", type=int, default=60)
    ap.add_argument("--stunden", type=float, default=8.0)
    ap.add_argument("--ameisen", type=int, default=3)
    ap.add_argument("--seed", type=int, default=0)
    ap.add_argument("--f2-intervall", type=int, default=5)
    ap.add_argument("--sitzung", default="aco")  # fester Default: Resume über Sitzungen hinweg
    args = ap.parse_args()
    if args.init:
        asyncio.run(init_vorlage())
        return
    trainer = AmeisenTrainer(STORAGE_DIR / "train" / args.sitzung,
                             max_trials=args.trials, max_stunden=args.stunden,
                             ameisen=args.ameisen, seed=args.seed,
                             f2_intervall=args.f2_intervall)
    asyncio.run(trainer.run())


if __name__ == "__main__":
    main()