#!/usr/bin/env python3
"""Acceptance (alpha/tau) benchmark via llama-server (requires gaps G1/G3/G4).

Why a server and not llama-cli:
- llama-cli does NOT expose speculative-decoding statistics; llama-server DOES:
  every /completion response returns timings.draft_n and timings.draft_n_accepted
  (server-task.cpp result_timings::to_json), plus predicted_per_second.
- Additionally, with --verbose it writes per-request log lines:
    "draft acceptance = 0.xxxxx (N accepted / M generated), mean len = X.XX"
  and at TRC level "acc per pos = (r1, r2, ...)" (per-position curve, optional:
  parse <out>/server.log after the run).

Protocol parity with F1/F2/F3 (llama-cli, default n_max = 3 in common.h):
- --spec-draft-n-max 3, -c 2048, -t 8, seed 42, n_predict 256.
- Explicit sampling in the body: temperature 0.0 (pure greedy argmax; ignores
  top_k/top_p), top_k 40, top_p 0.95 — the final run corrects F1/F2/F3 which
  ran at T=0.8 (llama-cli defaults). Non-thinking: --reasoning off in the
  server command (if raw mode rejects it, the documented escape is in --help).
- The numbers from this script are ONLY comparable among themselves
  (server-consistent methodology: each config starts its own server). DO NOT
  mix with the F1/F2/F3 tok/s (fresh process per prompt vs persistent server).

Anti-rerun design (identical to bench_spec.py):
- <out>/results.jsonl (one line per OK prompt), <out>/errors.jsonl (failures,
  retried with --resume), exclusive lock (exit 3 if another runner writes the
  same --out), vram.json (max_gpu_mib + max_power_w sampled during the run),
  config.json (args + sampling + reproducibility: llama.cpp commit, version,
  sha256/size of the GGUFs), metrics.json (total and per-domain aggregates)
  and results.csv (without text column) at the end.
- exit 0 = clean; 2 = failures; 3 = lock busy; 1 = server did not start or
  died mid-run (abort after 2 consecutive connection failures).

Usage (example):
    # 1) create a per-domain stratified subset (seed 42):
    python scripts/bench_accept.py --make-subset-from experiments/prompts/f1-sample.jsonl \
        --prompts experiments/prompts/acc-sample.jsonl --subset-per-domain 60

    # 2) run one config (target + drafter):
    python scripts/bench_accept.py --model models/Qwen3-8B-Q4_K_M.gguf \
        --draft models/drafts/dflash_qwen3_8b_block7.gguf --spec-type draft-dflash \
        --config-name q4-dflash-q4 --prompts experiments/prompts/acc-sample.jsonl \
        --out experiments/runs/acc-q4-dflash-q4 --resume

    # 3) target-solo (no drafter): omit --draft.
    # 4) smoke/mini-runs: --max-prompts N (first N pending prompts).
    RUN ONLY WITH THE GPU FREE (never while the F3 chain is measuring).

Per-record output: {id, domain, text, config, tok_per_s, alpha, tau, draft_n,
prompt_ms, predicted_ms, elapsed_s, attempts, ts} with alpha = draft_n_accepted /
draft_n (None if no draft), tau = draft_n_accepted (accepted tokens),
prompt_ms = TTFT (prefill), predicted_ms = total generation time.
"""

from __future__ import annotations

import argparse
import csv
import fcntl
import hashlib
import json
import math
import os
import random
import re
import shlex
import socket
import subprocess
import sys
import threading
import time
import urllib.request
from pathlib import Path

LLAMA_BIN = Path(os.environ.get("LLAMA_CPP_BIN", os.path.expanduser("~/llama.cpp/build/bin")))
SERVER_BIN = LLAMA_BIN / "llama-server"

# llama-cli default n_max in this build (common.h) — parity with F1/F2/F3.
DEFAULT_N_MAX = 3

# Sampling of the final run: true greedy (T=0 = pure argmax, ignores top_k/top_p),
# fixed seed 42. F1/F2/F3 ran at T=0.8 (llama-cli defaults) — not greedy.
DEFAULT_SAMPLING = {"temperature": 0.0, "top_k": 40, "top_p": 0.95}

# Fixed results.csv header (no text column; None → empty cell).
CSV_HEADER = [
    "id",
    "domain",
    "config",
    "tok_per_s",
    "alpha",
    "tau",
    "draft_n",
    "prompt_ms",
    "predicted_ms",
    "elapsed_s",
    "attempts",
    "ts",
]

# Shared sha256 cache of GGUFs (keyed path|size_bytes|mtime_ns) — avoids
# re-hashing ~12 GB per config (30-60 s) across the 25-config chain.
SHARED_HASH_CACHE = Path("experiments/runs/model-hashes.json")


class VramSampler:
    """Samples VRAM (memory.used) and power draw (power.draw) every 3 s in a daemon thread."""

    def __init__(self, out_dir: Path) -> None:
        self.out_dir = out_dir
        self._max_mib = 0
        self._max_power = 0.0
        self._lock = threading.Lock()
        self._stop = threading.Event()
        self._thread = threading.Thread(target=self._run, daemon=True)

    def start(self) -> None:
        self._thread.start()

    def stop(self) -> None:
        self._stop.set()
        self._thread.join(timeout=5)
        with self._lock:
            max_mib = self._max_mib
            max_power = self._max_power
        (self.out_dir / "vram.json").write_text(
            json.dumps(
                {
                    "max_gpu_mib": max_mib,
                    "max_power_w": max_power,
                    "sample_interval_s": 3,
                    "timestamp": time.strftime("%Y-%m-%dT%H:%M:%S"),
                }
            )
        )

    def _run(self) -> None:
        while not self._stop.is_set():
            try:
                out = subprocess.run(  # noqa: S603
                    [
                        "/usr/bin/nvidia-smi",
                        "--query-gpu=memory.used,power.draw",
                        "--format=csv,noheader,nounits",
                    ],
                    capture_output=True,
                    text=True,
                    timeout=10,
                    check=True,
                )
                parts = [p.strip() for p in out.stdout.split(",")]
                used = int(parts[0])
                pwr_raw = parts[1] if len(parts) > 1 else ""
                pwr = float(pwr_raw) if pwr_raw not in ("", "[N/A]") else 0.0
                with self._lock:
                    self._max_mib = max(self._max_mib, used)
                    self._max_power = max(self._max_power, pwr)
            except (subprocess.SubprocessError, ValueError, IndexError):
                pass
            self._stop.wait(3)


def _free_port() -> int:
    with socket.socket() as s:
        s.bind(("127.0.0.1", 0))
        return s.getsockname()[1]


def _http_json(url: str, payload: dict | None = None, timeout: float = 30.0) -> tuple[int, dict]:
    """GET (payload=None) or POST JSON; returns (status, json)."""
    if payload is None:
        req = urllib.request.Request(url)  # noqa: S310 — localhost
    else:
        req = urllib.request.Request(  # noqa: S310 — localhost
            url,
            data=json.dumps(payload).encode("utf-8"),
            headers={"Content-Type": "application/json"},
        )
    with urllib.request.urlopen(req, timeout=timeout) as resp:  # noqa: S310 — localhost
        return resp.status, json.loads(resp.read().decode("utf-8"))


class Server:
    """llama-server lifecycle: start → wait_health → stop (always)."""

    def __init__(self, cmd: list[str]) -> None:
        self.cmd = cmd
        self.proc: subprocess.Popen | None = None
        self.url = ""

    def start(self, port: int) -> None:
        self.url = f"http://127.0.0.1:{port}"
        self.proc = subprocess.Popen(  # noqa: S603 — internally controlled command
            self.cmd,
            stdout=subprocess.DEVNULL,
            stderr=subprocess.DEVNULL,
        )

    def wait_health(self, timeout: float = 300.0) -> str:
        """Wait for /health == 200. Returns "" if OK, otherwise the reason."""
        deadline = time.time() + timeout
        while time.time() < deadline:
            if self.proc is not None and self.proc.poll() is not None:
                return f"server died on startup (rc={self.proc.returncode})"
            try:
                status, _ = _http_json(self.url + "/health", timeout=5)
                if status == 200:
                    return ""
            except Exception:  # noqa: BLE001, S110 — still loading
                pass
            time.sleep(2)
        return f"timeout waiting for /health ({int(timeout)}s)"

    def stop(self) -> None:
        if self.proc is not None and self.proc.poll() is None:
            self.proc.terminate()
            try:
                self.proc.wait(timeout=10)
            except subprocess.TimeoutExpired:
                self.proc.kill()
        self.proc = None


def _tail(text: str, n: int = 300) -> str:
    return text[-n:] if text else ""


def _parse_completion(
    data: dict,
) -> tuple[float | None, float | None, int | None, int | None, float | None, float | None]:
    """(tok_per_s, alpha, tau, draft_n, prompt_ms, predicted_ms) from /completion.

    alpha = draft_n_accepted / draft_n; tau = draft_n_accepted. With draft_n 0
    or missing (target-solo configs) alpha/tau/draft_n are None (spec). prompt_ms
    (TTFT) and predicted_ms are draft-independent and always present.
    """
    tim = data.get("timings", {})
    tok_s = tim.get("predicted_per_second")
    draft_n = tim.get("draft_n")
    draft_acc = tim.get("draft_n_accepted")
    alpha: float | None = None
    tau: int | None = None
    if draft_n:
        if draft_acc is not None:
            alpha = round(draft_acc / draft_n, 4)
            tau = draft_acc
    else:
        draft_n = None
    prompt_ms = tim.get("prompt_ms")
    predicted_ms = tim.get("predicted_ms")
    return tok_s, alpha, tau, draft_n, prompt_ms, predicted_ms


def read_git_commit(repo: str) -> str | None:
    """HEAD commit of the pinned repo (fixed -C); None non-fatal if it fails.

    The run proceeds without commit reproducibility (threat matrix: git).
    """
    try:
        out = subprocess.run(  # noqa: S603 — fixed internal repo
            ["git", "-C", str(repo), "rev-parse", "HEAD"],  # noqa: S607 — git from the env PATH
            capture_output=True,
            text=True,
            timeout=10,
            check=True,
        )
    except (subprocess.SubprocessError, FileNotFoundError):
        return None
    return out.stdout.strip() or None


def parse_llama_version(bin_path: Path) -> str | None:
    """Binary version: 'version: 22 (0713275)' → '22'; 'build: 10249' → '10249'.

    None if the binary does not answer or the format is not recognized.
    """
    try:
        out = subprocess.run(  # noqa: S603, S607 — internally controlled binary
            [str(bin_path), "--version"],
            capture_output=True,
            text=True,
            timeout=15,
            check=True,
        )
    except (subprocess.SubprocessError, FileNotFoundError):
        return None
    combined = (out.stdout or "") + "\n" + (out.stderr or "")
    m = re.search(r"version:\s*(\S+)", combined)
    if m:
        return m.group(1)
    m = re.search(r"build:\s*(\d+)", combined)
    return m.group(1) if m else None


def file_sha256(path: Path, cache: dict[tuple[str, int, int], str] | None = None) -> str | None:
    """SHA-256 of a file; cache keyed (path, size_bytes, mtime_ns) (hit → no re-hash).

    None if the file does not exist or is not readable.
    """
    try:
        st = path.stat()
    except OSError:
        return None
    key = (str(path), st.st_size, st.st_mtime_ns)
    if cache is not None and key in cache:
        return cache[key]
    h = hashlib.sha256()
    try:
        with path.open("rb") as f:
            for chunk in iter(lambda: f.read(1 << 20), b""):
                h.update(chunk)
    except OSError:
        return None
    digest = h.hexdigest()
    if cache is not None:
        cache[key] = digest
    return digest


def _cache_key_str(key: tuple[str, int, int]) -> str:
    return f"{key[0]}|{key[1]}|{key[2]}"


def load_hash_cache(path: Path) -> dict[tuple[str, int, int], str]:
    """Load the shared cache (JSON with 'path|size|mtime_ns' keys) → dict of tuples."""
    cache: dict[tuple[str, int, int], str] = {}
    try:
        if path.exists():
            raw = json.loads(path.read_text())
            for k, v in raw.items():
                p, s, m = k.rsplit("|", 2)
                cache[(p, int(s), int(m))] = v
    except (OSError, ValueError):
        pass
    return cache


def save_hash_cache(path: Path, cache: dict[tuple[str, int, int], str]) -> bool:
    """Save the shared cache; True if OK, False if it failed (per-out fallback)."""
    try:
        path.parent.mkdir(parents=True, exist_ok=True)
        path.write_text(json.dumps({_cache_key_str(k): v for k, v in cache.items()}, indent=2))
        return True
    except OSError:
        return False


def _gguf_meta(path: str, cache: dict[tuple[str, int, int], str]) -> dict:
    p = Path(path)
    st = p.stat() if p.exists() else None
    return {"path": path, "size_bytes": st.st_size if st else None, "sha256": file_sha256(p, cache)}


def read_reproducibility(
    model: str, draft: str | None, cache: dict[tuple[str, int, int], str]
) -> dict:
    """Reproducibility metadata: llama.cpp commit + version + sha256/size of GGUFs.

    Failed git → commit None + warn (the run continues; null reproducibility).
    """
    repo = os.path.expanduser("~/llama.cpp")
    commit = read_git_commit(repo)
    if commit is None:
        print(
            f"[bench] WARN: could not read the commit of {repo} → incomplete reproducibility",
            file=sys.stderr,
        )
    return {
        "llama_cpp_commit": commit,
        "llama_cpp_version": parse_llama_version(SERVER_BIN),
        "host": socket.gethostname(),
        "ts": time.strftime("%Y-%m-%dT%H:%M:%S"),
        "model": _gguf_meta(model, cache),
        "draft": _gguf_meta(draft, cache) if draft else None,
    }


def build_server_cmd(
    args: argparse.Namespace, port: int, out_dir: Path, log_name: str = "server.log"
) -> list[str]:
    """llama-server command: target (± drafter), non-thinking (--reasoning off).

    --reasoning off goes after --verbose and before the extras; no auto-retry
    (if raw mode rejects it, the escape is --extra reasoning_effort:"none",
    see --help of --extra).
    """
    cmd = [
        str(SERVER_BIN),
        "-m",
        args.model,
        "-ngl",
        str(args.n_gpu_layers),
        "-c",
        str(args.ctx),
        "-t",
        str(args.threads),
        "-np",
        "1",
        "--host",
        "127.0.0.1",
        "--port",
        str(port),
        "--log-file",
        str(out_dir / log_name),
        "--verbose",
    ]
    if args.draft:
        cmd += [
            "-md",
            args.draft,
            "--spec-type",
            args.spec_type,
            "--spec-draft-n-max",
            str(args.spec_draft_n_max),
            "-ngld",
            str(args.draft_ngl),
        ]
        if args.spec_draft_p_min is not None:
            cmd += ["--spec-draft-p-min", str(args.spec_draft_p_min)]
    cmd += ["--reasoning", "off"]
    cmd += [a for pair in args.extra for a in shlex.split(pair)]
    return cmd


def resume_done(results_path: Path, errors_path: Path) -> set[str]:
    """Ids already processed (results.jsonl) or known failures (errors.jsonl) for resume.

    Includes failures so prompts that always fail are not retried (e.g. the 8
    arena-hard-v2 ones > ctx 2048) and errors.jsonl entries are not duplicated.
    Tolerates corrupt lines (e.g. power loss) and missing files.
    """
    done: set[str] = set()
    for path in (results_path, errors_path):
        if not path.exists():
            continue
        for line in path.read_text(encoding="utf-8", errors="replace").splitlines():
            try:
                done.add(json.loads(line)["id"])
            except (json.JSONDecodeError, KeyError):
                continue
    return done


def pending_prompts(records: list[dict], done: set[str], max_prompts: int | None) -> list[dict]:
    """Pending = records not completed (resume), truncated to max_prompts (N>0)."""
    pending = [p for p in records if p["id"] not in done]
    if max_prompts is not None and max_prompts > 0:
        pending = pending[:max_prompts]
    return pending


def read_results(results_path: Path) -> list[dict]:
    """Read results.jsonl tolerating corrupt lines (power loss) and a missing file."""
    records: list[dict] = []
    if results_path.exists():
        for line in results_path.read_text(encoding="utf-8", errors="replace").splitlines():
            try:
                records.append(json.loads(line))
            except json.JSONDecodeError:
                continue
    return records


def server_log_path(out_dir: Path) -> Path:
    """llama-server session log: server.log the 1st time, server-2.log/3.log on
    resumes. llama-server TRUNCATES --log-file (fopen "w", log.cpp:322) → a resume
    of a partial config must not erase the "acc per pos" curves of its previous session."""
    first = out_dir / "server.log"
    if not first.exists():
        return first
    n = 2
    while (out_dir / f"server-{n}.log").exists():
        n += 1
    return out_dir / f"server-{n}.log"


def _server_alive(url: str) -> bool:
    """Probe /health: True if the server answers 200 (not down)."""
    try:
        status, _ = _http_json(url + "/health", timeout=5)
        return status == 200
    except Exception:  # noqa: BLE001, S110 — server down
        return False


def _mean(xs: list[float]) -> float | None:
    return round(sum(xs) / len(xs), 4) if xs else None


def _median(xs: list[float]) -> float | None:
    if not xs:
        return None
    s = sorted(xs)
    n = len(s)
    mid = n // 2
    if n % 2 == 1:
        return round(s[mid], 4)
    return round((s[mid - 1] + s[mid]) / 2, 4)


def _pct(xs: list[float], p: float) -> float | None:
    if not xs:
        return None
    s = sorted(xs)
    idx = min(len(s) - 1, max(0, math.ceil(p / 100 * len(s)) - 1))
    return round(s[idx], 4)


def write_metrics(out_dir: Path, records: list[dict], failed: int, duration_s: float) -> Path:
    """Write metrics.json with total and per-domain aggregates (math/code/chat).

    Resume-safe: the caller passes ALL records (this run + those re-read from
    results.jsonl, which include the previous ones). Aggregates ignore None
    (alpha/tau/draft_n of target-solo configs). sampling/spec/reproducibility
    are copied from config.json; vram/power from vram.json.
    """
    cfg: dict = {}
    vram: dict = {}
    try:
        cfg = json.loads((out_dir / "config.json").read_text())
    except (OSError, ValueError):
        pass
    try:
        vram = json.loads((out_dir / "vram.json").read_text())
    except (OSError, ValueError):
        pass

    toks = [r["tok_per_s"] for r in records if r.get("tok_per_s") is not None]
    alphas = [r["alpha"] for r in records if r.get("alpha") is not None]
    taus = [r["tau"] for r in records if r.get("tau") is not None]
    draft_ns = [r["draft_n"] for r in records if r.get("draft_n") is not None]
    ttfts = [r["prompt_ms"] for r in records if r.get("prompt_ms") is not None]

    per_domain: dict[str, dict] = {}
    for dom in ("math", "code", "chat"):
        dr = [r for r in records if r.get("domain") == dom]
        dtoks = [r["tok_per_s"] for r in dr if r.get("tok_per_s") is not None]
        dalphas = [r["alpha"] for r in dr if r.get("alpha") is not None]
        dtaus = [r["tau"] for r in dr if r.get("tau") is not None]
        dttfts = [r["prompt_ms"] for r in dr if r.get("prompt_ms") is not None]
        per_domain[dom] = {
            "n": len(dr),
            "tok_per_s.mean": _mean(dtoks),
            "alpha.mean": _mean(dalphas),
            "tau.mean": _mean(dtaus),
            "ttft.mean": _mean(dttfts),
        }

    metrics = {
        "prompts": {"total": len(records) + failed, "ok": len(records), "failed": failed},
        "tok_per_s": {
            "mean": _mean(toks),
            "median": _median(toks),
            "p50": _median(toks),
            "p95": _pct(toks, 95),
            "min": round(min(toks), 4) if toks else None,
            "max": round(max(toks), 4) if toks else None,
        },
        "alpha": {"mean": _mean(alphas), "median": _median(alphas)},
        "tau": {"mean": _mean(taus), "median": _median(taus)},
        "draft_n": {"total": sum(draft_ns), "mean": _mean(draft_ns), "median": _median(draft_ns)},
        "ttft": {
            "prompt_ms.mean": _mean(ttfts),
            "prompt_ms.median": _median(ttfts),
            "prompt_ms.p95": _pct(ttfts, 95),
        },
        "vram": {"max_gpu_mib": vram.get("max_gpu_mib")},
        "power": {"max_power_w": vram.get("max_power_w")},
        "duration_s": round(duration_s, 2),
        "errors": failed,
        "per_domain": per_domain,
        "sampling": cfg.get("sampling", {}),
        "spec": cfg.get("spec", {}),
        "reproducibility": cfg.get("reproducibility", {}),
    }
    path = out_dir / "metrics.json"
    path.write_text(json.dumps(metrics, indent=2, ensure_ascii=False) + "\n")
    return path


def export_csv(results_path: Path) -> Path:
    """Export results.jsonl → results.csv (fixed header, no text column)."""
    csv_path = results_path.with_suffix(".csv")
    with csv_path.open("w", newline="") as f:
        w = csv.writer(f)
        w.writerow(CSV_HEADER)
        for line in results_path.read_text().splitlines():
            if not line.strip():
                continue
            try:
                r = json.loads(line)
            except ValueError:
                continue
            w.writerow([r.get(h) for h in CSV_HEADER])
    return csv_path


def make_subset(source: Path, dest: Path, per_domain: int, seed: int) -> int:
    """Stratified subset: up to per_domain prompts per domain (shuffle seed)."""
    by_domain: dict[str, list[dict]] = {}
    for line in source.read_text().splitlines():
        if not line.strip():
            continue
        p = json.loads(line)
        by_domain.setdefault(p.get("domain", "unknown"), []).append(p)
    rng = random.Random(seed)  # noqa: S311 — deterministic shuffle with fixed seed
    total = 0
    with dest.open("w") as f:
        for dom in sorted(by_domain):
            chosen = by_domain[dom][:]
            rng.shuffle(chosen)
            chosen = chosen[:per_domain]
            for p in chosen:
                f.write(json.dumps(p, ensure_ascii=False) + "\n")
            total += len(chosen)
            print(f"[subset] {dom}: {len(chosen)}/{len(by_domain[dom])}", file=sys.stderr)
    print(f"[subset] {total} prompts → {dest}", file=sys.stderr)
    return 0


def main() -> int:
    ap = argparse.ArgumentParser(
        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
    )
    ap.add_argument("--model", default=None, help="Target GGUF")
    ap.add_argument("--draft", default=None, help="Drafter GGUF (omit = target-solo)")
    ap.add_argument(
        "--spec-type",
        default="none",
        help="draft-simple|draft-eagle3|draft-mtp|draft-dflash|draft-dspark",
    )
    ap.add_argument(
        "--spec-draft-n-max", type=int, default=DEFAULT_N_MAX, help="llama-cli parity n_max"
    )
    ap.add_argument("--spec-draft-p-min", type=float, default=None, help="DSpark confidence cutoff")
    ap.add_argument("--config-name", default=None, help="Label in every record (default: stems)")
    ap.add_argument("--prompts", required=True, type=Path)
    ap.add_argument("--out", default=None, type=Path)
    ap.add_argument("--n-tokens", type=int, default=256)
    ap.add_argument("--seed", type=int, default=42)
    ap.add_argument("--temperature", type=float, default=DEFAULT_SAMPLING["temperature"])
    ap.add_argument("--top-k", type=int, default=DEFAULT_SAMPLING["top_k"])
    ap.add_argument("--top-p", type=float, default=DEFAULT_SAMPLING["top_p"])
    ap.add_argument("--n-gpu-layers", type=int, default=99)
    ap.add_argument("--draft-ngl", type=int, default=99, help="GPU layers of the drafter (-ngld)")
    ap.add_argument("--ctx", type=int, default=2048)
    ap.add_argument("--threads", type=int, default=8)
    ap.add_argument("--retries", type=int, default=2)
    ap.add_argument("--server-timeout", type=float, default=300.0, help="Wait for /health (s)")
    ap.add_argument(
        "--max-prompts",
        type=int,
        default=None,
        help="Only the first N pending prompts (smoke/mini-runs/OOM-CHECK)",
    )
    ap.add_argument("--resume", action="store_true")
    ap.add_argument(
        "--extra",
        action="append",
        default=[],
        help=(
            "Extra flags for llama-server (shlex). If raw mode rejects --reasoning off, "
            'documented escape: --extra reasoning_effort:"none"'
        ),
    )
    ap.add_argument(
        "--make-subset-from", type=Path, default=None, help="Subset mode: source f1-sample.jsonl"
    )
    ap.add_argument("--subset-per-domain", type=int, default=60)
    args = ap.parse_args()

    if args.make_subset_from is not None:
        return make_subset(args.make_subset_from, args.prompts, args.subset_per_domain, args.seed)

    if args.model is None or args.out is None:
        ap.error("--model and --out are required outside --make-subset-from mode")

    if not SERVER_BIN.exists():
        print(f"[bench] ERROR: {SERVER_BIN} does not exist (did llama.cpp build?)", file=sys.stderr)
        return 1

    out_dir = args.out
    out_dir.mkdir(parents=True, exist_ok=True)
    results_path = out_dir / "results.jsonl"
    errors_path = out_dir / "errors.jsonl"

    # Reproducibility: commit + version + sha256/size of GGUFs with the shared
    # cache (experiments/runs/model-hashes.json); per-out fallback.
    hash_cache = load_hash_cache(SHARED_HASH_CACHE)
    reproducibility = read_reproducibility(args.model, args.draft, hash_cache)
    if not save_hash_cache(SHARED_HASH_CACHE, hash_cache):
        save_hash_cache(out_dir / "model-hashes.json", hash_cache)

    cfg = {k: (str(v) if isinstance(v, Path) else v) for k, v in vars(args).items()}
    cfg["sampling"] = {
        "temperature": args.temperature,
        "top_k": args.top_k,
        "top_p": args.top_p,
        "seed": args.seed,
    }
    cfg["spec"] = {
        "type": args.spec_type,
        "draft_n_max": args.spec_draft_n_max,
        "p_min": args.spec_draft_p_min,
    }
    cfg["reproducibility"] = reproducibility
    (out_dir / "config.json").write_text(json.dumps(cfg, indent=2, ensure_ascii=False))

    done: set[str] = set()
    if args.resume:
        done = resume_done(results_path, errors_path)
        print(
            f"[bench] resume: {len(done)} prompts already processed (results + known errors)",
            file=sys.stderr,
        )

    records_all: list[dict] = []
    for line in args.prompts.read_text().splitlines():
        if not line.strip():
            continue
        try:
            records_all.append(json.loads(line))
        except json.JSONDecodeError:
            continue
    pending = pending_prompts(records_all, done, args.max_prompts)
    if args.max_prompts is not None:
        print(
            f"[bench] max-prompts={args.max_prompts}: {len(pending)} prompts in this run",
            file=sys.stderr,
        )

    if not pending:
        # R3-003: with no pending prompts we do not start a server — protects
        # server.log from truncation ("acc per pos" curves of already-completed
        # configs) and avoids loading model/VRAM during resume walks.
        print(
            "[bench] resume: no pending prompts — not starting the server "
            "(protects server.log, avoids model/VRAM load)",
            file=sys.stderr,
        )
        # Power loss: if the config ended up without metrics/csv (crash before
        # the final aggregation), they are regenerated from results.jsonl — no server/GPU.
        if not (out_dir / "metrics.json").exists() or not (out_dir / "results.csv").exists():
            recs = read_results(results_path)
            export_csv(results_path)
            write_metrics(out_dir, recs, 0, 0.0)
            print(
                "[bench] early-exit: metrics.json/results.csv regenerated (were missing)",
                file=sys.stderr,
            )
        return 0

    port = _free_port()
    cmd = build_server_cmd(args, port, out_dir, log_name=server_log_path(out_dir).name)

    config_name = args.config_name or (
        Path(args.model).stem
        if not args.draft
        else f"{Path(args.model).stem}+{Path(args.draft).stem}"
    )

    server = Server(cmd)
    # Anti-rerun lock BEFORE starting the server (R4-001): if another runner
    # writes this --out, we abort without spawning processes or using the GPU.
    guard = results_path.open("a")
    try:
        fcntl.flock(guard.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB)
    except OSError:
        guard.close()
        print(
            "[bench] ERROR: another runner is writing this --out (lock busy). "
            "Wait for it to finish or check it with ps.",
            file=sys.stderr,
        )
        return 3

    t_start = time.time()
    print(f"[bench] starting llama-server at {server.url} (pid launched)", file=sys.stderr)
    server.start(port)
    try:
        reason = server.wait_health(args.server_timeout)
        if reason:
            server.stop()
            guard.close()
            print(f"[bench] ERROR: {reason} → see {out_dir / 'server.log'}", file=sys.stderr)
            return 1
        print("[bench] server OK", file=sys.stderr)
    except Exception:  # noqa: BLE001
        server.stop()
        guard.close()
        raise

    n_new = 0
    n_failed = 0
    aborted = False
    vram = VramSampler(out_dir)
    vram.start()
    try:
        with results_path.open("a") as fout, errors_path.open("a") as ferr:
            body = {
                "prompt": None,  # per prompt
                "n_predict": args.n_tokens,
                "seed": args.seed,
                "temperature": args.temperature,
                "top_k": args.top_k,
                "top_p": args.top_p,
                "cache_prompt": False,
                "stream": False,
            }
            consec_conn = 0
            for p in pending:
                if p["id"] in done:
                    continue
                n_new += 1
                record: dict | None = None
                last_err = ""
                detail_text = ""
                for attempt in range(1, args.retries + 1):
                    t0 = time.time()
                    try:
                        body["prompt"] = p["text"]
                        status, data = _http_json(server.url + "/completion", body, timeout=900)
                        tok_s, alpha, tau, draft_n, prompt_ms, predicted_ms = _parse_completion(
                            data
                        )
                        if status != 200:
                            detail_text = _tail(str(data), 300)
                    except Exception as e:  # noqa: BLE001 — overnight hardening
                        status = 0
                        tok_s, alpha, tau, draft_n, prompt_ms, predicted_ms = (
                            None,
                            None,
                            None,
                            None,
                            None,
                            None,
                        )
                        last_err = repr(e)
                    elapsed = time.time() - t0
                    if status == 200 and tok_s is not None:
                        consec_conn = 0
                        record = {
                            "id": p["id"],
                            "domain": p.get("domain"),
                            "text": p["text"],
                            "config": config_name,
                            "tok_per_s": round(tok_s, 3),
                            "alpha": alpha,
                            "tau": tau,
                            "draft_n": draft_n,
                            "prompt_ms": round(prompt_ms, 1) if prompt_ms is not None else None,
                            "predicted_ms": round(predicted_ms, 1)
                            if predicted_ms is not None
                            else None,
                            "elapsed_s": round(elapsed, 2),
                            "attempts": attempt,
                            "ts": time.strftime("%Y-%m-%dT%H:%M:%S"),
                        }
                        break
                    if status == 0:
                        consec_conn += 1
                    else:
                        consec_conn = 0
                    if consec_conn >= 2 and not _server_alive(server.url):
                        print(
                            f"[bench] ERROR: server not responding (2 consecutive "
                            f"connection failures: {last_err}) → abort",
                            file=sys.stderr,
                        )
                        aborted = True
                        break
                    last_err = f"status={status} {last_err}"
                    print(
                        f"[bench] {p['id']} attempt {attempt} failed ({last_err})", file=sys.stderr
                    )
                if record is not None:
                    fout.write(json.dumps(record, ensure_ascii=False) + "\n")
                    fout.flush()
                    print(
                        f"[bench] {p['id']}: {record['tok_per_s']} tok/s "
                        f"α={record['alpha']} τ={record['tau']} ({record['elapsed_s']}s)",
                        file=sys.stderr,
                    )
                else:
                    n_failed += 1
                    ferr.write(
                        json.dumps(
                            {
                                "id": p["id"],
                                "domain": p.get("domain"),
                                "error": last_err,
                                "detail": detail_text or last_err,
                                "attempts": args.retries,
                            },
                            ensure_ascii=False,
                        )
                        + "\n"
                    )
                    ferr.flush()
                    print(
                        f"[bench] {p['id']} FAILED after {args.retries} attempts → errors.jsonl",
                        file=sys.stderr,
                    )
                if aborted:
                    break
    finally:
        server.stop()
        vram.stop()
        guard.close()

    # Final metrics: re-read the full results.jsonl (resume-safe) → CSV + JSON.
    records = read_results(results_path)
    export_csv(results_path)
    write_metrics(out_dir, records, n_failed, time.time() - t_start)

    print(
        f"[bench] done: {n_new - n_failed} new valid, {n_failed} failures, "
        f"{len(done)} previous → {results_path}"
    )
    if aborted:
        return 1
    return 0 if n_failed == 0 else 2


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