"""Empirical filter (step F, second layer): run accepted eval tasks with real models.

A task where a strong model fails although the reference passes can have an unclear spec (flag it).
A task that every model passes with ease does not separate models (candidate for removal).

  python3 -m harness.empirical --model deepseek-v4.1-flash:cloud --run-base 10000 [G0100 ...]

Results: runs/emp/results.jsonl and <task>/empirical.json (one entry per model). Resumable: a task that
already has a result for the model is skipped. Tasks with review decision "reject" are skipped.
"""
import argparse
import glob
import json
import os

from .adt_client import load_env
from .agents import LlmAgent
from .ledger import BudgetExceeded
from .runner import Runner

ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
POOL = os.path.join(ROOT, "tasks_gen", "eval")


def _json(path, default):
    try:
        return json.load(open(path))
    except (OSError, ValueError):
        return default


def candidates():
    out = []
    for d in sorted(glob.glob(os.path.join(POOL, "G*"))):
        if not _json(os.path.join(d, "generation.json"), {}).get("accepted"):
            continue
        if _json(os.path.join(d, "review.json"), {}).get("decision") == "reject":
            continue
        out.append(os.path.basename(d))
    return out


def decision(score, calls):
    """The filter decisions of step F: an easy candidate (DeepSeek >= 95 and <= 20 tool calls, tasks_gen/eval/easy_candidates.json),
    a flag (a strong model fails, score under 50, although the reference passes: the spec may be unclear), else normal."""
    if score is None:
        return "no result"
    if score >= 95 and (calls or 99) <= 20:
        return "easy candidate"
    return "flag: strong model fails" if score < 50 else "normal"


def rerun(a):
    """Rerun some tasks (after the proxy fix of 2026-10-06) WITHOUT touching empirical.json or the eval set: results go to runs/emp_rerun/,
    then the old and the new filter decision are printed. A changed decision is reported to Kral and Opus before anything in the eval set changes."""
    out_dir = os.path.join(ROOT, "runs", "emp_rerun")
    os.makedirs(out_dir, exist_ok=True)
    runner = Runner(POOL, out_dir)
    changed = []
    for i, tid in enumerate(a.tasks):
        old = _json(os.path.join(POOL, tid, "empirical.json"), {}).get(a.model, {})
        try:
            rep, run_dir = runner.run(tid, LlmAgent(a.model, a.base_url), a.run_base + i)
        except BudgetExceeded as e:
            print("BUDGET", e, flush=True)
            break
        h = rep.get("hidden_tests") or {}
        new = {"score": (rep.get("score") or {}).get("total"), "hidden": f"{h.get('passed')}/{h.get('total')}", "tool_calls": rep.get("tool_calls"),
               "end_reason": rep.get("end_reason"), "run_dir": os.path.relpath(run_dir, ROOT)}
        d_old, d_new = decision(old.get("score"), old.get("tool_calls")), decision(new["score"], new["tool_calls"])
        line = {"task": tid, "old": {k: old.get(k) for k in ("score", "hidden", "tool_calls")}, "new": new, "decision_old": d_old, "decision_new": d_new,
                "decision_changed": d_old != d_new}
        open(os.path.join(out_dir, "results.jsonl"), "a").write(json.dumps(line) + "\n")
        print(json.dumps(line), flush=True)
        if line["decision_changed"]:
            changed.append(tid)
    print("DECISION CHANGED for:", changed or "none", "(nothing in tasks_gen/eval was changed)", flush=True)


def main():
    load_env(os.path.join(ROOT, ".env"))
    ap = argparse.ArgumentParser()
    ap.add_argument("tasks", nargs="*")
    ap.add_argument("--model", required=True)
    ap.add_argument("--base-url")
    ap.add_argument("--run-base", type=int, required=True)
    ap.add_argument("--rerun", action="store_true", help="rerun the named tasks into runs/emp_rerun/ and compare the filter decision; the eval set is not changed")
    a = ap.parse_args()
    if a.rerun:
        if not a.tasks:
            raise SystemExit("--rerun needs task ids")
        return rerun(a)
    runs_root = os.path.join(ROOT, "runs", "emp")
    os.makedirs(runs_root, exist_ok=True)
    runner = Runner(POOL, runs_root)
    tasks = a.tasks or candidates()
    for i, tid in enumerate(tasks):
        path = os.path.join(POOL, tid, "empirical.json")
        res = _json(path, {})
        if a.model in res:
            continue
        try:
            rep, run_dir = runner.run(tid, LlmAgent(a.model, a.base_url), a.run_base + i)
        except BudgetExceeded as e:
            print("BUDGET", e, flush=True)
            break
        except Exception as e:  # noqa: BLE001  one broken run must not stop the series
            print(json.dumps({"task": tid, "error": str(e)[:300]}), flush=True)
            continue
        h = rep.get("hidden_tests") or {}
        entry = {"score": (rep.get("score") or {}).get("total"), "parts": rep.get("score"),
                 "hidden": f"{h.get('passed')}/{h.get('total')}", "tool_calls": rep.get("tool_calls"),
                 "seconds": rep.get("seconds"), "run_dir": os.path.relpath(run_dir, ROOT)}
        res[a.model] = entry
        json.dump(res, open(path, "w"), indent=1)
        line = dict(task=tid, model=a.model, **{k: entry[k] for k in ("score", "hidden", "tool_calls")})
        open(os.path.join(runs_root, "results.jsonl"), "a").write(json.dumps(line) + "\n")
        print(json.dumps(line), flush=True)


if __name__ == "__main__":
    main()