#!/usr/bin/env python3
"""WildTrace — rubric judge harness (3-judge non-contestant panel, averaged).

Scores a model's answers (from run_eval.py) against each task's rubric. Each answer is graded
by THREE non-contestant judges and the three scores are AVERAGED (simple mean, no same-family
exclusion). out_of_context_scope answers stay 0 (the model could not ingest the evidence).

Panel used in the paper (deliberately models NOT on the leaderboard):
  Claude-Sonnet-4.6 · Qwen3.5 · Gemini-2.5-Flash
Supply your own judge endpoints in config.json -> "judges". OpenAI-compatible chat endpoints are
the default; gateways that expose Gemini through native `contents`/`candidates` payloads can set
`"api_type": "gemini_native"` on that judge. Using a single judge is supported (list one) but the
paper headline is the 3-judge average.

Output: results/<model>.scores.json
  { "per_judge": {judge: {qid: score_0_1}}, "average": {qid: mean_score}, "overall": pct }

Usage:
  export API_KEY=sk-...
  python run_judge.py --config config.json --data ../data/wildtrace_strict481.with_answers.json \
                      --responses ../results/mymodel.responses.json --out ../results/mymodel.scores.json
"""
import argparse, ast, json, os, re, time, urllib.request, urllib.error
from concurrent.futures import ThreadPoolExecutor, as_completed
from threading import Lock


def post(url, key, payload, timeout=180):
    for attempt in range(4):
        if attempt:
            time.sleep(3 * attempt)
        try:
            req = urllib.request.Request(url, data=json.dumps(payload).encode(),
                                         headers={"Authorization": f"Bearer {key}", "Content-Type": "application/json"})
            with urllib.request.urlopen(req, timeout=timeout) as r:
                return json.loads(r.read())
        except Exception:
            pass
    return None


def build_payload(judge_cfg, prompt):
    api_type = judge_cfg.get("api_type", "chat")
    max_tokens = judge_cfg.get("max_tokens", 16384)
    temperature = judge_cfg.get("temperature", 0.1)
    if api_type == "gemini_native":
        return {
            "model": judge_cfg["model"],
            "contents": [{"role": "user", "parts": [{"text": prompt}]}],
            "generationConfig": {
                "maxOutputTokens": max_tokens,
                "temperature": temperature,
            },
        }
    return {
        "model": judge_cfg["model"],
        "messages": [{"role": "user", "content": prompt}],
        "max_tokens": max_tokens,
        "temperature": temperature,
    }


def response_content(data):
    content = (data.get("choices", [{}])[0].get("message", {}) or {}).get("content", "")
    if not content and isinstance(data.get("content"), list):  # Anthropic-native content blocks
        content = "".join(b.get("text", "") for b in data["content"] if b.get("type") == "text")
    if not content and isinstance(data.get("candidates"), list):  # Gemini-native candidates
        parts = []
        for cand in data["candidates"]:
            for part in ((cand.get("content") or {}).get("parts") or []):
                if part.get("text"):
                    parts.append(part["text"])
        content = "".join(parts)
    return content


def build_judge_prompt(question, rubric, response):
    if isinstance(rubric, str):
        try: rubric = ast.literal_eval(rubric)
        except Exception: rubric = []
    if not isinstance(rubric, list): rubric = []
    rt = "".join(f'P{i+1} ({p.get("points", 0)}pts): {p.get("correct_criterion", "")[:260]}\n'
                 for i, p in enumerate(rubric) if isinstance(p, dict))
    return ("STRICT grader. Only award points for SPECIFIC details present.\n"
            f"QUESTION: {question[:600]}\nRUBRIC:\n{rt}\nRESPONSE:\n{response[:5000]}\n"
            'Reply JSON: {"points_awarded":[<pts>],"total":<sum>}')


def parse_total(content):
    if not content:
        return None
    t = re.sub(r"^```(?:json)?\s*", "", content.strip()); t = re.sub(r"\s*```$", "", t)
    m = re.search(r'\{.*"total".*\}', t, re.DOTALL)
    if not m:
        return None
    try:
        return float(json.loads(m.group())["total"])
    except Exception:
        return None


def judge_one(judge_cfg, key, question, rubric, response):
    """One judge's score in [0,1], or None on failure. total is a 0-100 sum -> /100, capped at 1."""
    prompt = build_judge_prompt(question, rubric, response)
    data = post(judge_cfg["base_url"], key, build_payload(judge_cfg, prompt))
    if not data:
        return None
    content = response_content(data)
    tot = parse_total(content)
    return None if tot is None else min(tot / 100.0, 1.0)


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--config", default="config.json")
    ap.add_argument("--data", required=True)
    ap.add_argument("--responses", required=True)
    ap.add_argument("--out", required=True)
    ap.add_argument("--workers", type=int, default=8)
    args = ap.parse_args()
    cfg = json.load(open(args.config))
    judges = cfg["judges"]                       # list of {name, base_url, model, api_key_env?, max_tokens?}
    rows = ([json.loads(l) for l in open(args.data) if l.strip()]
            if args.data.endswith(".jsonl") else json.load(open(args.data)))
    rows = {r["question_id"]: r for r in rows}
    responses = {r["question_id"]: r for r in json.load(open(args.responses))}

    out = json.load(open(args.out)) if os.path.exists(args.out) else {"per_judge": {}, "average": {}, "overall": None}
    per = out["per_judge"]
    for j in judges:
        per.setdefault(j["name"], {})

    # work: (judge, qid) for non-oos answers not yet judged; oos -> score 0 directly
    oos = {qid for qid, r in responses.items() if str(r["model_response"]).startswith("[ERROR out_of_context_scope")}
    work = []
    for j in judges:
        for qid, r in responses.items():
            if qid in oos or qid not in rows:
                continue
            if str(r["model_response"]).startswith("[ERROR"):  # transient eval failure: skip, not judged
                continue
            if per[j["name"]].get(qid) is None:
                work.append((j, qid))
    print(f"judges={[j['name'] for j in judges]} | oos(score0)={len(oos)} | to judge={len(work)}", flush=True)
    lock = Lock(); n = [0]

    def run(item):
        j, qid = item
        r = rows[qid]; gt = r.get("ground_truth", {})
        q = r.get("question_text") or gt.get("question_text")
        key = os.environ[j.get("api_key_env", cfg.get("api_key_env", "API_KEY"))]
        sc = judge_one(j, key, q, gt.get("scoring_rubric") or [], responses[qid]["model_response"])
        with lock:
            per[j["name"]][qid] = sc; n[0] += 1
            if n[0] % 100 == 0:
                json.dump(out, open(args.out, "w"), ensure_ascii=False, indent=2)
                print(f"{n[0]}/{len(work)}", flush=True)

    with ThreadPoolExecutor(max_workers=args.workers) as ex:
        list(as_completed([ex.submit(run, it) for it in work]))

    # aggregate: per task, average the available judge scores; oos -> 0
    avg = {}
    all_qids = set(oos) | {qid for jn in per for qid in per[jn]}
    for qid in all_qids:
        if qid in oos:
            avg[qid] = 0.0; continue
        vals = [per[jn][qid] for jn in per if per[jn].get(qid) is not None]
        if vals:
            avg[qid] = sum(vals) / len(vals)
    out["average"] = avg
    out["overall"] = round(100 * sum(avg.values()) / len(avg), 2) if avg else None
    json.dump(out, open(args.out, "w"), ensure_ascii=False, indent=2)
    print(f"DONE: overall={out['overall']} over n={len(avg)} -> {args.out}", flush=True)


if __name__ == "__main__":
    main()