"""
Benchmark Steward routing quality against model and thinking settings.

Talks to Ollama directly. No Tatlock server, no agents, no tools, nothing is
executed — the mutating fixtures ("turn on the lights", "update the wiki") only
ever produce a routing decision. That makes this cheap and repeatable, and it
isolates the question: does the Steward still pick the right capabilities when
the model reasons less?

The request body is byte-identical to StewardAgent._call_ollama, plus the
`think` flag under test, so a cell labelled `unset` is exactly what production
sends today.

Three thinking settings, because "on vs off" hides the interesting case:

    unset   what production sends now. gemma4 reasons by default, and the
            response carries no `thinking` field, so those tokens are generated
            and discarded.
    true    reasoning requested explicitly and returned in `thinking`.
    false   reasoning suppressed.

Scoring is deliberately asymmetric. A missing capability under-routes and the
Butler answers without a tool it needed; a spurious one over-routes, and that is
a real agent call — a stray librarian is a multi-second web search on a query
that asked for arithmetic. Over-routing is the predicted failure when thinking
is off, so `forbid` violations are reported separately rather than folded into
one accuracy number.

Usage:
    .venv/bin/python scripts/benchmark_routing.py
    .venv/bin/python scripts/benchmark_routing.py --models gemma4:e2b
    .venv/bin/python scripts/benchmark_routing.py --think false --repeats 3
"""
from __future__ import annotations

import argparse
import json
import signal
import statistics
import sys
import time
from datetime import UTC, datetime
from pathlib import Path
from typing import Any

import httpx

PROJECT_ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(PROJECT_ROOT))

from scripts.fixtures.routing_fixtures import FIXTURES  # noqa: E402
from src.agents.steward.agent import build_steward_prompt  # noqa: E402
from src.agents.steward.service import _DELEGATE_LINE_RE, _extract_capabilities  # noqa: E402
from src.core.startup import register_household_members  # noqa: E402

OLLAMA_URL = "http://localhost:11434"
DEFAULT_MODELS = ["gemma4:e2b", "gemma4:e4b"]
DEFAULT_THINK = ["unset", "true", "false"]
RESULTS_DIR = PROJECT_ROOT / "logs"


def build_body(model: str, prompt: str, think: str) -> dict[str, Any]:
    """Mirror StewardAgent._call_ollama exactly, then add the flag under test."""
    body: dict[str, Any] = {
        "model": model,
        "prompt": prompt,
        "stream": False,
        "options": {
            "temperature": 0.3,  # Lower = more consistent
            "top_p": 0.9,
        },
    }
    if think != "unset":
        body["think"] = think == "true"
    return body


def call(client: httpx.Client, body: dict[str, Any]) -> dict[str, Any] | None:
    try:
        response = client.post(f"{OLLAMA_URL}/api/generate", json=body)
        response.raise_for_status()
        return response.json()
    except Exception as exc:  # noqa: BLE001 - a failed cell must not abort the run
        print(f"    ! {exc}", file=sys.stderr)
        return None


PINNED_THRESHOLD_SECONDS = 86400


def snapshot_residency(client: httpx.Client) -> dict[str, bool]:
    """Which models are resident, and which of those are pinned.

    Benchmarking swaps models on a GPU that production is using. Ollama evicts to
    make room, so a run silently unpins the model Tatlock serves from and leaves
    its own behind: the next voice turn then pays a ~36s cold load. Snapshot
    before, restore after.
    """
    try:
        data = client.get(f"{OLLAMA_URL}/api/ps", timeout=10).json()
    except Exception:  # noqa: BLE001
        return {}
    resident: dict[str, bool] = {}
    now = datetime.now(UTC)
    for model in data.get("models", []):
        expires = model.get("expires_at", "")
        pinned = False
        try:
            delta = datetime.fromisoformat(expires.replace("Z", "+00:00")) - now
            pinned = delta.total_seconds() > PINNED_THRESHOLD_SECONDS
        except ValueError:
            pass
        resident[model["name"]] = pinned
    return resident


def set_keep_alive(client: httpx.Client, model: str, keep_alive: Any) -> bool:
    """Load/unload/pin a model. Embedding models reject /api/generate."""
    payload = {"model": model, "keep_alive": keep_alive}
    for endpoint in ("generate", "embed"):
        try:
            response = client.post(f"{OLLAMA_URL}/api/{endpoint}", json=payload, timeout=180)
            if response.status_code == 200:
                return True
            if response.status_code == 400 and "does not support generate" in response.text:
                continue  # embedding-only model; try /api/embed
            return False
        except Exception:  # noqa: BLE001
            return False
    return False


def restore_residency(client: httpx.Client, before: dict[str, bool], used: list[str]) -> None:
    """Evict what the benchmark loaded, then re-pin what production had pinned."""
    base = {name.split(":")[0] for name in before}
    for model in used:
        if model not in before and model.split(":")[0] not in base:
            print(f"  restoring: unloading benchmark model {model}")
            set_keep_alive(client, model, 0)
    for name, pinned in before.items():
        if pinned:
            ok = set_keep_alive(client, name, -1)
            print(f"  restoring: re-pinned {name}" if ok
                  else f"  restoring: FAILED to re-pin {name} -- run warmup-ollama.sh")


def score(fixture: dict, found: list[str]) -> dict[str, Any]:
    expected = set(fixture["expect"])
    forbidden = set(fixture["forbid"])
    got = set(found)
    missing = sorted(expected - got)
    spurious = sorted(got & forbidden)
    return {
        "found": found,
        "missing": missing,
        "spurious": spurious,
        # Exact only when everything expected arrived and nothing forbidden did.
        "exact": not missing and not spurious,
        "under_routed": bool(missing),
        "over_routed": bool(spurious),
    }


def run_cell(client: httpx.Client, model: str, think: str, repeats: int) -> list[dict[str, Any]]:
    rows: list[dict[str, Any]] = []
    for fixture in FIXTURES:
        prompt = build_steward_prompt(fixture["query"], [])
        body = build_body(model, prompt, think)
        for rep in range(repeats):
            started = time.perf_counter()
            data = call(client, body)
            elapsed_ms = (time.perf_counter() - started) * 1000
            if data is None:
                rows.append({
                    "id": fixture["id"], "group": fixture["group"], "rep": rep,
                    "error": True, "exact": False, "under_routed": False, "over_routed": False,
                })
                continue

            text = data.get("response", "") or ""
            found = _extract_capabilities(text)
            rows.append({
                "id": fixture["id"],
                "group": fixture["group"],
                "rep": rep,
                "error": False,
                "latency_ms": round(elapsed_ms, 1),
                "eval_tokens": data.get("eval_count"),
                "prompt_tokens": data.get("prompt_eval_count"),
                # Did the model obey the documented output shape at all?
                "has_delegate_line": bool(_DELEGATE_LINE_RE.search(text)),
                # Whether reasoning came back, as opposed to being generated and dropped.
                "thinking_returned": bool(data.get("thinking")),
                "response_chars": len(text),
                **score(fixture, found),
            })
    return rows


def summarise(rows: list[dict[str, Any]]) -> dict[str, Any]:
    ok = [r for r in rows if not r["error"]]
    if not ok:
        return {"n": 0, "errors": len(rows)}
    latencies = [r["latency_ms"] for r in ok]
    tokens = [r["eval_tokens"] for r in ok if r["eval_tokens"] is not None]
    return {
        "n": len(ok),
        "errors": len(rows) - len(ok),
        "exact_pct": round(100 * sum(r["exact"] for r in ok) / len(ok), 1),
        "under_routed_pct": round(100 * sum(r["under_routed"] for r in ok) / len(ok), 1),
        "over_routed_pct": round(100 * sum(r["over_routed"] for r in ok) / len(ok), 1),
        "format_ok_pct": round(100 * sum(r["has_delegate_line"] for r in ok) / len(ok), 1),
        "thinking_returned_pct": round(100 * sum(r["thinking_returned"] for r in ok) / len(ok), 1),
        "latency_ms_median": round(statistics.median(latencies), 1),
        "latency_ms_mean": round(statistics.fmean(latencies), 1),
        "eval_tokens_median": round(statistics.median(tokens), 1) if tokens else None,
        "eval_tokens_total": sum(tokens) if tokens else None,
    }


def _raise_on_term(signum, _frame):
    """Turn SIGTERM into an exception so the restore guard actually runs.

    Python runs `finally` for SIGINT, which arrives as KeyboardInterrupt, but the
    default SIGTERM action terminates the process outright — a `timeout`, a
    systemd stop or a plain `kill` would skip the restore and leave production
    unpinned. Verified the hard way: an earlier SIGTERM here bypassed the guard
    entirely and only luck kept the pinned model resident.
    """
    raise KeyboardInterrupt(f"signal {signum}")


def main() -> int:
    signal.signal(signal.SIGTERM, _raise_on_term)

    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--models", default=",".join(DEFAULT_MODELS))
    parser.add_argument("--think", default=",".join(DEFAULT_THINK),
                        help="comma-separated subset of unset,true,false")
    parser.add_argument("--repeats", type=int, default=1)
    parser.add_argument("--timeout", type=float, default=180.0)
    args = parser.parse_args()

    models = [m.strip() for m in args.models.split(",") if m.strip()]
    think_modes = [t.strip() for t in args.think.split(",") if t.strip()]

    # build_steward_prompt reads the registry, and the registry is populated at
    # application startup. Without this the prompt lists no capabilities and every
    # cell scores zero for reasons that have nothing to do with the model.
    register_household_members()

    print(f"{len(FIXTURES)} fixtures x {len(models)} models x {len(think_modes)} think "
          f"x {args.repeats} repeats = {len(FIXTURES) * len(models) * len(think_modes) * args.repeats} calls\n")

    cells: dict[str, Any] = {}
    with httpx.Client(timeout=args.timeout) as client:
        before = snapshot_residency(client)
        pinned_before = [n for n, p in before.items() if p]
        print(f"resident before: {sorted(before)}"
              f"{f' (pinned: {pinned_before})' if pinned_before else ''}\n")
        try:
            for model in models:
                # Absorb the cold load (~36s) outside the measurements.
                print(f"warming {model} ...", flush=True)
                call(client, build_body(model, "hi", "false"))
                for think in think_modes:
                    key = f"{model}|think={think}"
                    print(f"  {key} ...", end=" ", flush=True)
                    started = time.perf_counter()
                    rows = run_cell(client, model, think, args.repeats)
                    summary = summarise(rows)
                    cells[key] = {"summary": summary, "rows": rows}
                    print(f"exact={summary.get('exact_pct')}% "
                          f"over={summary.get('over_routed_pct')}% "
                          f"median={summary.get('latency_ms_median')}ms "
                          f"({time.perf_counter() - started:.0f}s)")
        finally:
            # finally, not a tidy exit path: a Ctrl-C or a failed cell must not
            # leave production unpinned behind us.
            print("\nrestoring GPU residency ...")
            restore_residency(client, before, models)

    RESULTS_DIR.mkdir(parents=True, exist_ok=True)
    stamp = datetime.now(UTC).strftime("%Y%m%dT%H%M%SZ")
    out = RESULTS_DIR / f"routing-bench-{stamp}.json"
    out.write_text(json.dumps({
        "generated_at": datetime.now(UTC).isoformat(),
        "fixtures": len(FIXTURES),
        "repeats": args.repeats,
        "cells": cells,
    }, indent=2))

    print(f"\n{'cell':28} {'exact':>7} {'under':>7} {'over':>7} {'fmt':>6} {'tok':>7} {'ms':>8}")
    print("-" * 76)
    for key, cell in cells.items():
        s = cell["summary"]
        print(f"{key:28} {s.get('exact_pct'):>6}% {s.get('under_routed_pct'):>6}% "
              f"{s.get('over_routed_pct'):>6}% {s.get('format_ok_pct'):>5}% "
              f"{str(s.get('eval_tokens_median')):>7} {s.get('latency_ms_median'):>8}")
    print(f"\nwritten to {out}")
    return 0


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