#!/usr/bin/env python3
"""Broad public-benchmark suite for LOREA-cyber.

Loads the model once and runs 22 benchmarks of the kind frontier models report.
Results are written after EVERY benchmark, and completed benchmarks are skipped on
re-run, so an interrupted session (or an OOM) costs one benchmark, not the whole suite.

  python3 eval/bench_suite.py --model <path> [--adapter <path>] --tag v5.9 \
      --output v59/eval/suite_v59.json [--only mmlu,gsm8k] [--limit 150]
"""
import argparse
import json
import os
import random
import re
import subprocess
import sys
import tempfile
import time
import warnings

warnings.filterwarnings("ignore")
random.seed(20260802)

SYS = "You are a helpful assistant. Answer accurately and concisely."
CODE_SYS = "You are an expert Python programmer. Write correct, complete, runnable code."
LETTERS = "ABCDEFGHIJKLMNOP"

_THINK_CLOSE = re.compile(r"</think>", re.I)


def visible(text):
    """Return the answer, dropping any reasoning block.

    The chat template can open <think> in the prompt, so a completion may contain only
    the closing tag. Everything before it is reasoning.
    """
    text = text or ""
    m = list(_THINK_CLOSE.finditer(text))
    return text[m[-1].end():].strip() if m else text.strip()


def ds(repo, cfg=None, split="test"):
    from datasets import load_dataset
    return load_dataset(repo, cfg, split=split) if cfg else load_dataset(repo, split=split)


def mc(q, options, answer_idx, meta=None):
    return {"q": q, "options": [str(o) for o in options], "answer_idx": answer_idx}


# ---------------------------------------------------------------- MCQ loaders

def l_mmlu():
    return [mc(r["question"], r["choices"], r["answer"]) for r in ds("cais/mmlu", "all", "test")]

def l_mmlu_pro():
    out = []
    for r in ds("TIGER-Lab/MMLU-Pro", split="test"):
        if r["options"] and r["answer_index"] is not None and r["answer_index"] < len(r["options"]):
            out.append(mc(r["question"], r["options"], r["answer_index"]))
    return out

def _arc(cfg):
    out = []
    for r in ds("allenai/ai2_arc", cfg, "test"):
        labels = list(r["choices"]["label"]); texts = list(r["choices"]["text"])
        if r["answerKey"] in labels:
            out.append(mc(r["question"], texts, labels.index(r["answerKey"])))
    return out

def l_arc_challenge(): return _arc("ARC-Challenge")
def l_arc_easy():      return _arc("ARC-Easy")

def l_hellaswag():
    out = []
    for r in ds("Rowan/hellaswag", split="validation"):
        try: idx = int(r["label"])
        except (TypeError, ValueError): continue
        out.append(mc(r["ctx"], r["endings"], idx))
    return out

def l_winogrande():
    out = []
    for r in ds("allenai/winogrande", "winogrande_xl", "validation"):
        if r["answer"] in ("1", "2"):
            out.append(mc(r["sentence"].replace("_", "____"),
                          [r["option1"], r["option2"]], int(r["answer"]) - 1))
    return out

def l_piqa():
    return [mc(r["goal"], [r["sol1"], r["sol2"]], int(r["label"]))
            for r in ds("baber/piqa", split="validation") if r["label"] in (0, 1, "0", "1")]

def l_siqa():
    out = []
    for r in ds("lighteval/siqa", split="validation"):
        try: idx = int(r["label"]) - 1
        except (TypeError, ValueError): continue
        if 0 <= idx < 3:
            out.append(mc(f"{r['context']} {r['question']}",
                          [r["answerA"], r["answerB"], r["answerC"]], idx))
    return out

def l_openbookqa():
    out = []
    for r in ds("allenai/openbookqa", "main", "test"):
        labels = list(r["choices"]["label"]); texts = list(r["choices"]["text"])
        if r["answerKey"] in labels:
            out.append(mc(r["question_stem"], texts, labels.index(r["answerKey"])))
    return out

def l_commonsense_qa():
    out = []
    for r in ds("tau/commonsense_qa", split="validation"):
        labels = list(r["choices"]["label"]); texts = list(r["choices"]["text"])
        if r["answerKey"] in labels:
            out.append(mc(r["question"], texts, labels.index(r["answerKey"])))
    return out

def l_boolq():
    return [mc(f"{r['passage']}\n\nQuestion: {r['question']}?", ["yes", "no"],
               0 if r["answer"] else 1) for r in ds("google/boolq", split="validation")]

def l_truthfulqa():
    out = []
    for r in ds("truthfulqa/truthful_qa", "multiple_choice", "validation"):
        t = r["mc1_targets"]
        ch, lb = list(t["choices"]), list(t["labels"])
        if 1 in lb:
            out.append(mc(r["question"], ch, lb.index(1)))
    return out

def l_race():
    out = []
    for r in ds("ehovy/race", "high", "test"):
        if r["answer"] in "ABCD" and len(r["options"]) == 4:
            out.append(mc(f"{r['article'][:1800]}\n\nQuestion: {r['question']}",
                          r["options"], "ABCD".index(r["answer"])))
    return out

def l_sciq():
    out = []
    for r in ds("allenai/sciq", split="test"):
        opts = [r["correct_answer"], r["distractor1"], r["distractor2"], r["distractor3"]]
        order = list(range(4)); random.shuffle(order)
        out.append(mc(r["question"], [opts[i] for i in order], order.index(0)))
    return out

def l_medmcqa():
    out = []
    for r in ds("openlifescienceai/medmcqa", split="validation"):
        opts = [r["opa"], r["opb"], r["opc"], r["opd"]]
        if r["cop"] is not None and 0 <= r["cop"] < 4 and all(opts):
            out.append(mc(r["question"], opts, r["cop"]))
    return out

def l_secqa():
    out = []
    for cfg in ("secqa_v1", "secqa_v2"):
        try: rows = ds("zefang-liu/secqa", cfg, "test")
        except Exception: continue
        for r in rows:
            opts = [r.get("A"), r.get("B"), r.get("C"), r.get("D")]
            a = str(r.get("Answer", "")).strip().upper()
            if all(opts) and a in "ABCD":
                out.append(mc(r["Question"], opts, "ABCD".index(a)))
    return out

def l_cybermetric():
    import urllib.request
    for size in ("500", "2000", "80"):
        url = (f"https://raw.githubusercontent.com/cybermetric/CyberMetric/main/"
               f"CyberMetric-{size}-v1.json")
        try:
            with urllib.request.urlopen(url, timeout=45) as f:
                data = json.load(f)
        except Exception:
            continue
        qs = data.get("questions", data) if isinstance(data, dict) else data
        out = []
        for r in qs:
            a = r.get("answers", {}); keys = sorted(a.keys())
            sol = str(r.get("solution", "")).strip().upper()
            if sol in keys:
                out.append(mc(r["question"], [a[k] for k in keys], keys.index(sol)))
        if out:
            random.shuffle(out); return out
    return []

def l_cyber_mcq_local():
    p = os.path.join(os.path.dirname(os.path.abspath(__file__)), "cyber_mcq_eval.jsonl")
    if not os.path.isfile(p): return []
    out = []
    for line in open(p):
        if not line.strip(): continue
        r = json.loads(line)
        a = r["answer"].strip().upper()
        if a in "ABCD":
            out.append(mc(r["question"], [r["A"], r["B"], r["C"], r["D"]], "ABCD".index(a)))
    return out

def l_bbh():
    subs = ["boolean_expressions", "causal_judgement", "date_understanding",
            "disambiguation_qa", "formal_fallacies", "logical_deduction_three_objects",
            "navigate", "sports_understanding"]
    out = []
    for s in subs:
        try: rows = ds("lukaemon/bbh", s, "test")
        except Exception: continue
        for r in rows:
            out.append({"q": r["input"], "options": None, "answer_idx": None,
                        "free_target": str(r["target"]).strip()})
    return out


# ------------------------------------------------------- generative loaders

def l_gsm8k():
    return [{"q": r["question"], "free_target": r["answer"].split("####")[-1].strip()}
            for r in ds("openai/gsm8k", "main", "test")]

def l_humaneval():
    return list(ds("openai/openai_humaneval", split="test"))

def l_mbpp():
    return list(ds("google-research-datasets/mbpp", "full", "test"))


# ------------------------------------------------------------------ runners

def mcq_prompt(q, options):
    lines = [q.strip(), ""]
    L = LETTERS[:len(options)]
    for i, o in enumerate(options):
        lines.append(f"{L[i]}) {o}")
    lines.append("\nRespond with ONLY the single letter of the correct answer.")
    return "\n".join(lines)


def parse_letter(out, n):
    t = visible(out).upper()
    L = LETTERS[:n]
    m = re.search(rf"\b([{L}])\b", t) or re.search(rf"([{L}])", t)
    return m.group(1) if m else "?"


def run_mcq(gen, items):
    ok = 0
    for r in items:
        pred = parse_letter(gen(mcq_prompt(r["q"], r["options"]), 12), len(r["options"]))
        if pred == LETTERS[r["answer_idx"]]:
            ok += 1
    return {"n": len(items), "correct": ok, "acc": round(ok / max(1, len(items)), 4)}


def run_bbh(gen, items):
    ok = 0
    for r in items:
        out = visible(gen(r["q"] + "\n\nAnswer with the final answer only.", 24)).strip()
        tgt = r["free_target"].strip()
        first = out.splitlines()[0].strip() if out else ""
        if tgt.lower() in out.lower()[:120] or first.lower() == tgt.lower():
            ok += 1
    return {"n": len(items), "correct": ok, "acc": round(ok / max(1, len(items)), 4)}


def run_gsm8k(gen, items):
    ok = 0
    for r in items:
        out = visible(gen(r["q"] + "\n\nSolve it, then give the final number on its own "
                                   "last line after '####'.", 400))
        nums = re.findall(r"-?\d[\d,]*\.?\d*", out.replace("$", ""))
        tgt = r["free_target"].replace(",", "").strip()
        if nums and nums[-1].replace(",", "").strip() == tgt:
            ok += 1
    return {"n": len(items), "correct": ok, "acc": round(ok / max(1, len(items)), 4)}


def _exec(program, timeout=12):
    path = None
    try:
        with tempfile.NamedTemporaryFile("w", suffix=".py", delete=False) as f:
            f.write(program); path = f.name
        return subprocess.run([sys.executable, path], capture_output=True,
                              timeout=timeout).returncode == 0
    except Exception:
        return False
    finally:
        if path:
            try: os.unlink(path)
            except OSError: pass


def _code_from(out):
    out = visible(out)
    m = re.search(r"```(?:python)?\n(.*?)```", out, re.S)
    return m.group(1) if m else out


def run_humaneval(gen, items):
    ok = 0
    for r in items:
        body = _code_from(gen(r["prompt"] + "\n\nComplete the function above. Give the full "
                                            "function in a ```python block.", 512, CODE_SYS))
        prog = body if f"def {r['entry_point']}" in body else r["prompt"] + "\n" + body
        prog += "\n" + r["test"] + f"\ncheck({r['entry_point']})\n"
        ok += _exec(prog)
    return {"n": len(items), "correct": ok, "acc": round(ok / max(1, len(items)), 4)}


def run_mbpp(gen, items):
    ok = 0
    for r in items:
        tests = "\n".join(r["test_list"])
        body = _code_from(gen(f"{r['text']}\n\nYour solution must satisfy:\n{tests}\n\n"
                              f"Give the full function in a ```python block.", 512, CODE_SYS))
        ok += _exec(body + "\n" + (r.get("test_setup_code") or "") + "\n" + tests + "\n")
    return {"n": len(items), "correct": ok, "acc": round(ok / max(1, len(items)), 4)}


BENCHES = [
    # name,             loader,             runner,        default sample size
    ("mmlu",            l_mmlu,             run_mcq,       200),
    ("mmlu_pro",        l_mmlu_pro,         run_mcq,       200),
    ("arc_challenge",   l_arc_challenge,    run_mcq,       200),
    ("arc_easy",        l_arc_easy,         run_mcq,       200),
    ("hellaswag",       l_hellaswag,        run_mcq,       200),
    ("winogrande",      l_winogrande,       run_mcq,       200),
    ("piqa",            l_piqa,             run_mcq,       200),
    ("siqa",            l_siqa,             run_mcq,       200),
    ("openbookqa",      l_openbookqa,       run_mcq,       200),
    ("commonsense_qa",  l_commonsense_qa,   run_mcq,       200),
    ("boolq",           l_boolq,            run_mcq,       200),
    ("truthfulqa_mc1",  l_truthfulqa,       run_mcq,       200),
    ("race_high",       l_race,             run_mcq,       150),
    ("sciq",            l_sciq,             run_mcq,       200),
    ("medmcqa",         l_medmcqa,          run_mcq,       200),
    ("secqa",           l_secqa,            run_mcq,       200),
    ("cybermetric",     l_cybermetric,      run_mcq,       200),
    ("cyber_mcq_local", l_cyber_mcq_local,  run_mcq,       150),
    ("bbh",             l_bbh,              run_bbh,       200),
    ("gsm8k",           l_gsm8k,            run_gsm8k,     150),
    ("humaneval",       l_humaneval,        run_humaneval, 100),
    ("mbpp",            l_mbpp,             run_mbpp,      100),
]


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--model", required=True)
    ap.add_argument("--adapter", default=None)
    ap.add_argument("--tag", default="model")
    ap.add_argument("--output", required=True)
    ap.add_argument("--only", default="")
    ap.add_argument("--limit", type=int, default=0, help="override every sample size")
    args = ap.parse_args()

    want = [x.strip() for x in args.only.split(",") if x.strip()]
    todo = [b for b in BENCHES if not want or b[0] in want]

    # Resume: keep whatever a previous run already finished.
    results = {}
    if os.path.isfile(args.output):
        try:
            results = json.load(open(args.output)).get("results", {})
            done = [k for k in results if results[k]]
            if done:
                print(f"resuming, already done: {', '.join(sorted(done))}", flush=True)
        except Exception:
            results = {}

    from mlx_lm import load, generate
    try:
        from mlx_lm.sample_utils import make_sampler
        sampler = make_sampler(temp=0.0)
    except Exception:
        sampler = None

    t0 = time.time()
    model, tok = load(args.model, adapter_path=args.adapter)
    print(f"[{args.tag}] model loaded in {time.time()-t0:.0f}s", flush=True)

    def gen(user, max_tokens, system=SYS):
        msgs = [{"role": "system", "content": system}, {"role": "user", "content": user}]
        try:
            p = tok.apply_chat_template(msgs, add_generation_prompt=True, tokenize=False,
                                        enable_thinking=False)
        except TypeError:
            p = tok.apply_chat_template(msgs, add_generation_prompt=True, tokenize=False)
        kw = dict(max_tokens=max_tokens, verbose=False)
        if sampler is not None:
            kw["sampler"] = sampler
        return generate(model, tok, prompt=p, **kw)

    def save():
        os.makedirs(os.path.dirname(os.path.abspath(args.output)), exist_ok=True)
        with open(args.output, "w") as f:
            json.dump({"tag": args.tag, "model": args.model, "adapter": args.adapter,
                       "results": results}, f, indent=2)

    for name, loader, runner, default_n in todo:
        if results.get(name):
            continue
        try:
            items = loader()
        except Exception as e:
            print(f"  {name:16} LOAD FAILED: {str(e)[:70]}", flush=True)
            results[name] = None
            save()
            continue
        if not items:
            print(f"  {name:16} no items", flush=True)
            results[name] = None
            save()
            continue
        n = args.limit or default_n
        random.shuffle(items)
        items = items[:n]
        s = time.time()
        try:
            r = runner(gen, items)
        except Exception as e:
            print(f"  {name:16} RUN FAILED: {str(e)[:70]}", flush=True)
            results[name] = None
            save()
            continue
        r["seconds"] = round(time.time() - s, 1)
        results[name] = r
        print(f"  {name:16} {r['acc']:7.1%}  ({r['correct']}/{r['n']})  {r['seconds']:.0f}s",
              flush=True)
        save()          # after EVERY benchmark, so a crash costs one benchmark

    print(f"\ntotal {time.time()-t0:.0f}s -> {args.output}", flush=True)


if __name__ == "__main__":
    main()