"""AgentEval — the unit test BFCL/AIME missed.

The 2026-06-29 reality check showed our models pass BFCL multi_turn (backend-state correct) while
FAILING as real agents: they make tool calls but never state the answer and loop. BFCL scores
state; users need a delivered answer. This harness scores what matters end-to-end:

  - did the agent state the CORRECT final answer? (regex/value on the final natural-language msg)
  - did it TERMINATE cleanly within a turn budget (no looping)?
  - (optional) did it leave the correct artifact?

A faithful-but-light agent loop over the Ollama OpenAI-compatible endpoint with a real sandboxed
toolset (write_file/read_file/run_python). Model-agnostic: works for our Hermes models AND Qwen
(ollama parses both into OpenAI tool_calls; we also fall back to parsing raw <tool_call>).

task success = answer_correct AND terminated. The aggregate success rate is the new gate canary.

Usage: python agent_eval.py <ollama_model> [--tasks agent_tasks.json] [--max-turns 8] [--out x.json]
"""
import sys, os, json, re, argparse, tempfile, subprocess, shutil, requests

OLLAMA = os.environ.get("AGENTEVAL_URL", "http://127.0.0.1:11434/v1/chat/completions")

TOOLS = [
    {"type": "function", "function": {"name": "write_file", "description": "Write text to a file (creates parent dirs).",
        "parameters": {"type": "object", "properties": {"path": {"type": "string"}, "content": {"type": "string"}}, "required": ["path", "content"]}}},
    {"type": "function", "function": {"name": "read_file", "description": "Read a file's contents.",
        "parameters": {"type": "object", "properties": {"path": {"type": "string"}}, "required": ["path"]}}},
    {"type": "function", "function": {"name": "run_python", "description": "Run a Python3 script string; returns stdout+stderr.",
        "parameters": {"type": "object", "properties": {"code": {"type": "string"}}, "required": ["code"]}}},
]
SYS = ("You are a helpful agent with tools (write_file, read_file, run_python). Use them to complete "
       "the user's task, then give a FINAL natural-language answer that explicitly states the result. "
       "Do not repeat tool calls once you have what you need — stop and answer.")


def exec_tool(name, args, sandbox):
    try:
        if name == "write_file":
            p = os.path.join(sandbox, args["path"].lstrip("/"))
            os.makedirs(os.path.dirname(p) or sandbox, exist_ok=True)
            open(p, "w").write(args.get("content", ""))
            return f"wrote {len(args.get('content',''))} bytes to {args['path']}"
        if name == "read_file":
            p = os.path.join(sandbox, args["path"].lstrip("/"))
            return open(p).read()
        if name == "run_python":
            r = subprocess.run([sys.executable, "-c", args["code"]], cwd=sandbox,
                               capture_output=True, text=True, timeout=20)
            return (r.stdout + r.stderr)[:2000] or "(no output)"
    except Exception as e:
        return f"ERROR: {e}"
    return "ERROR: unknown tool"


def parse_raw_toolcalls(content):
    """Fallback: parse Hermes <tool_call>{...}</tool_call> if model didn't use native tool_calls."""
    out = []
    for m in re.finditer(r"<tool_call>\s*(\{.*?\})\s*</tool_call>", content or "", re.S):
        try:
            o = json.loads(m.group(1))
            out.append((o["name"], o.get("arguments", {}) or {}))
        except Exception:
            pass
    return out


def run_task(model, task, max_turns, temp=0.3):
    sandbox = tempfile.mkdtemp(prefix="ageval_")
    msgs = [{"role": "system", "content": SYS}, {"role": "user", "content": task["prompt"]}]
    seen_calls, looped, final = set(), False, ""
    try:
        for turn in range(max_turns):
            r = requests.post(OLLAMA, json={"model": model, "messages": msgs, "tools": TOOLS,
                "temperature": temp, "stream": False}, timeout=180).json()
            m = r["choices"][0]["message"]
            calls = []
            for tc in (m.get("tool_calls") or []):
                fn = tc["function"]; a = fn["arguments"]
                a = json.loads(a) if isinstance(a, str) else a
                calls.append((fn["name"], a))
            if not calls:
                calls_raw = parse_raw_toolcalls(m.get("content"))
                calls = calls_raw
            if not calls:
                final = m.get("content") or ""
                break  # agent terminated with a final answer
            # loop detection: identical (name,args) seen before
            msgs.append({"role": "assistant", "content": m.get("content") or "", "tool_calls": m.get("tool_calls")})
            for name, a in calls:
                sig = name + json.dumps(a, sort_keys=True)[:200]
                if sig in seen_calls:
                    looped = True
                seen_calls.add(sig)
                res = exec_tool(name, a, sandbox)
                msgs.append({"role": "tool", "content": str(res)[:2000]})
        else:
            looped = True  # hit max_turns without a final answer
        # score
        want = task["answer"]
        ans_ok = bool(re.search(rf"(?<![\w]){re.escape(str(want))}(?![\w])", final.replace(",", ""), re.I)) if final else False
        # avoid trivially-correct from echoing the task: require it's in the FINAL msg only (already)
        artifact_ok = True
        if "artifact" in task:
            ap = os.path.join(sandbox, task["artifact"]["path"].lstrip("/"))
            artifact_ok = os.path.exists(ap) and (task["artifact"].get("contains", "") in (open(ap).read() if os.path.exists(ap) else ""))
        success = ans_ok and not looped
        return dict(id=task["id"], difficulty=task.get("difficulty", "?"), answer_ok=ans_ok,
                    terminated=not looped, artifact_ok=artifact_ok,
                    success=success, turns=turn + 1, final=final[:200])
    except Exception as e:  # a bad task must never kill the whole suite
        return dict(id=task["id"], difficulty=task.get("difficulty", "?"), answer_ok=False,
                    terminated=False, artifact_ok=False, success=False, turns=-1,
                    final=f"HARNESS_ERROR: {str(e)[:150]}")
    finally:
        shutil.rmtree(sandbox, ignore_errors=True)


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("model")
    ap.add_argument("--tasks", default="agent_tasks.json")
    ap.add_argument("--max-turns", type=int, default=8)
    ap.add_argument("--out", default=None)
    a = ap.parse_args()
    tasks = json.load(open(a.tasks))
    rows = [run_task(a.model, t, a.max_turns) for t in tasks]
    n = len(rows)
    succ = sum(r["success"] for r in rows)
    ans = sum(r["answer_ok"] for r in rows)
    term = sum(r["terminated"] for r in rows)
    print(f"=== AgentEval — {a.model} ({n} tasks) ===")
    order = {"easy": 0, "medium": 1, "hard": 2, "really_hard": 3, "expert": 4}
    for r in sorted(rows, key=lambda r: order.get(r["difficulty"], 9)):
        flag = "PASS" if r["success"] else "FAIL"
        print(f"  [{flag}] {r['difficulty']:11s} {r['id']:14s} ans={int(r['answer_ok'])} "
              f"term={int(r['terminated'])} turns={r['turns']} | {r['final'][:55].replace(chr(10),' ')}")
    # per-difficulty breakdown
    print("  --- by difficulty (success rate) ---")
    by = {}
    for r in rows:
        by.setdefault(r["difficulty"], []).append(r["success"])
    for d in ["easy", "medium", "hard", "really_hard", "expert"]:
        if d in by:
            v = by[d]; print(f"    {d:11s}: {sum(v)}/{len(v)} = {sum(v)/len(v):.2f}")
    print(f"SUCCESS {succ}/{n}={succ/n:.3f} | answer_ok {ans}/{n} | terminated {term}/{n}")
    if a.out:
        json.dump(dict(model=a.model, success=succ / n, rows=rows), open(a.out, "w"))
    return succ / n


if __name__ == "__main__":
    main()