"""Phase C — leaderboard from judged MLflow runs.

Pulls every judged run (``judge_pending=false`` or any run with the
composite metric set) from the experiment, groups by (model, prompt)
cell, and prints a leaderboard sorted by mean composite score.

Also reports the deterministic-only metrics (latency, format_ok) so
cells with great prose but broken JSON are visible.
"""

from __future__ import annotations

import argparse
import os
import statistics
import sys
from collections import defaultdict
from pathlib import Path

_BENCH = Path(__file__).resolve().parent
sys.path.insert(0, str(_BENCH))
from mlflow_client import MLflowClient  # type: ignore


def _params(run: dict) -> dict[str, str]:
    return {p["key"]: p["value"] for p in run["data"].get("params", [])}


def _metrics(run: dict) -> dict[str, float]:
    return {m["key"]: m["value"] for m in run["data"].get("metrics", [])}


def _tags(run: dict) -> dict[str, str]:
    return {t["key"]: t["value"] for t in run["data"].get("tags", [])}


def main() -> int:
    parser = argparse.ArgumentParser(description="oO bench — Phase C (leaderboard)")
    parser.add_argument("--experiment", required=True)
    parser.add_argument("--mlflow-url", default=os.environ.get("MLFLOW_TRACKING_URI", "http://localhost:5000"))
    parser.add_argument("--include-pending", action="store_true",
                        help="Also include rows with no quality scores (latency/format only).")
    args = parser.parse_args()

    client = MLflowClient(
        tracking_uri=args.mlflow_url,
        username=os.environ.get("MLFLOW_TRACKING_USERNAME") or "admin",
        password=os.environ.get("MLFLOW_TRACKING_PASSWORD") or "password",
    )
    exp_id = client.get_or_create_experiment(args.experiment)
    runs = client.search_runs(exp_id, max_results=2000)

    # Group key = (model, prompt_version)
    cells: dict[tuple[str, str], list[dict]] = defaultdict(list)
    for r in runs:
        params = _params(r)
        metrics = _metrics(r)
        tags = _tags(r)
        if r["info"].get("status") != "FINISHED":
            continue
        if not args.include_pending and "composite" not in metrics:
            continue
        cells[(params.get("model", "?"), params.get("prompt_version", "?"))].append({
            "metrics": metrics,
            "scenario": params.get("scenario_id", "?"),
            "judged": tags.get("judge_pending") == "false",
        })

    if not cells:
        print("No judged runs found. Did you run judge_cli.py --apply?")
        return 1

    rows = []
    for (model, prompt), records in cells.items():
        n = len(records)
        comp = [r["metrics"]["composite"] for r in records if "composite" in r["metrics"]]
        rel  = [r["metrics"]["relevance"] for r in records if "relevance" in r["metrics"]]
        act  = [r["metrics"]["actionability"] for r in records if "actionability" in r["metrics"]]
        tone = [r["metrics"]["tone"] for r in records if "tone" in r["metrics"]]
        lat  = [r["metrics"]["latency_ms"] for r in records if "latency_ms" in r["metrics"]]
        fmt  = [r["metrics"]["format_ok"] for r in records if "format_ok" in r["metrics"]]
        div  = [r["metrics"]["mean_diversity"] for r in records if "mean_diversity" in r["metrics"]]

        rows.append({
            "model": model,
            "prompt": prompt,
            "n": n,
            "composite": statistics.mean(comp) if comp else None,
            "relevance": statistics.mean(rel) if rel else None,
            "actionability": statistics.mean(act) if act else None,
            "tone": statistics.mean(tone) if tone else None,
            "format_ok": statistics.mean(fmt) if fmt else None,
            "latency_p50": statistics.median(lat) if lat else None,
            "latency_p95": _p95(lat) if lat else None,
            "diversity": statistics.mean(div) if div else None,
        })

    rows.sort(key=lambda r: r["composite"] if r["composite"] is not None else -1, reverse=True)

    # Width-fitted printer — keeps output legible in a 100-col terminal.
    print()
    print(f"Experiment: {args.experiment}  (id={exp_id})")
    print(f"Cells     : {len(rows)}")
    print()
    header = (
        f"{'#':>2}  {'model':18s} {'prompt':12s} {'n':>3s}  "
        f"{'comp':>5s} {'rel':>4s} {'act':>4s} {'tone':>4s} "
        f"{'fmt':>4s} {'p50':>6s} {'p95':>6s} {'div':>5s}"
    )
    print(header)
    print("─" * len(header))
    for i, r in enumerate(rows, 1):
        comp = f"{r['composite']:.2f}" if r["composite"] is not None else "  -- "
        rel  = f"{r['relevance']:.1f}" if r["relevance"] is not None else " -- "
        act  = f"{r['actionability']:.1f}" if r["actionability"] is not None else " -- "
        tone = f"{r['tone']:.1f}" if r["tone"] is not None else " -- "
        fmt  = f"{r['format_ok']:.2f}" if r["format_ok"] is not None else " -- "
        p50  = f"{r['latency_p50']:.0f}" if r["latency_p50"] is not None else "  --  "
        p95  = f"{r['latency_p95']:.0f}" if r["latency_p95"] is not None else "  --  "
        div  = f"{r['diversity']:.2f}" if r["diversity"] is not None else " -- "
        print(
            f"{i:>2}  {r['model']:18s} {r['prompt']:12s} {r['n']:>3d}  "
            f"{comp:>5s} {rel:>4s} {act:>4s} {tone:>4s} "
            f"{fmt:>4s} {p50:>6s} {p95:>6s} {div:>5s}"
        )

    if rows[0]["composite"] is not None:
        winner = rows[0]
        print()
        print(f"Winner: {winner['model']} × {winner['prompt']}  "
              f"(composite={winner['composite']:.2f}, n={winner['n']})")
    return 0


def _p95(xs: list[float]) -> float:
    if not xs:
        return 0.0
    s = sorted(xs)
    idx = max(0, int(round(0.95 * (len(s) - 1))))
    return s[idx]


if __name__ == "__main__":
    sys.exit(main())