"""Analyze a CloudOpsBench validation run (read-only over cases/*.json).

Compares bench arms per shape stratum and reports:

  - per-stratum a1 / object_a1 / false-healthy rate per arm
  - L0 vs L1 panel: ``investigation_a1`` (opensre prose) vs ``a1`` (predictor
    rank-1) and translation-loss rate
  - paired ``opensre+llm − control`` contrasts on **a1** and **investigation_a1**
    (scenario-clustered bootstrap CI) — use investigation_a1 to answer whether
    opensre's investigation improved, not just the LLM formalizer
  - translation-loss proxy: among failures, how often opensre's report NAMED
    the correct fault_object but the predictor's top-3 dropped it

Usage:
    uv run python -m tests.benchmarks.cloudopsbench.analyze_validation \
        .bench-results/cloudopsbench_fixa_validation_openai/<run-id>

Pass a run directory (the one containing ``cases/``). Exploratory only — this
is a dev-pilot analyzer, not a publication report generator.
"""

from __future__ import annotations

import glob
import json
import random
import sys
from collections.abc import Callable
from pathlib import Path

from tests.benchmarks.cloudopsbench.scoring import (
    infer_final_answer_from_opensre_text,
)

_DEFAULT_ARMS = ("opensre+llm", "llm_alone", "llm_alone_pure")

# Minimum service-name length for the seen-shape translation-loss substring
# proxy. All seen-shape ground-truth objects are ``app/<service>`` with
# specific multi-token names (e.g. ``ts-voucher-service``), so a substring
# match in the report is a reliable "the investigation named this service"
# signal. We do NOT gate on a hard-coded service list — the corpus has more
# services (esp. trainticket) than any short allowlist, and an incomplete list
# silently undercounts the leak.
_MIN_SERVICE_NAME_LEN = 4


def _norm(s: object) -> str:
    return str(s or "").strip().lower()


def _load(run_dir: Path) -> list[dict]:
    rows: list[dict] = []
    for fp in sorted(glob.glob(str(run_dir / "cases" / "*.json"))):
        try:
            rows.append(json.loads(Path(fp).read_text(encoding="utf-8")))
        except (json.JSONDecodeError, OSError):
            continue
    return rows


def _gt(case: dict) -> tuple[str, str, str]:
    g = case.get("metadata", {}).get("ground_truth", {})
    return _norm(g.get("fault_object")), _norm(g.get("root_cause")), _norm(g.get("fault_taxonomy"))


def _top(run: dict) -> list[dict]:
    return (run.get("final_diagnosis") or {}).get("top_3_predictions") or []


def _is_a1(pred: dict, gt: tuple[str, str, str]) -> bool:
    go, gr, gtax = gt
    return (
        _norm(pred.get("fault_object")) == go
        and _norm(pred.get("root_cause")) == gr
        and _norm(pred.get("fault_taxonomy")) == gtax
    )


def _bootstrap_ci(deltas: list[float], iters: int = 2000) -> tuple[float, float, float]:
    if not deltas:
        return float("nan"), float("nan"), float("nan")
    pt = sum(deltas) / len(deltas)
    random.seed(42)
    boots = []
    for _ in range(iters):
        samp = [deltas[random.randrange(len(deltas))] for _ in deltas]
        boots.append(sum(samp) / len(samp))
    boots.sort()
    return pt, boots[int(0.025 * len(boots))], boots[int(0.975 * len(boots))]


def _arms_in_run(rows: list[dict]) -> tuple[str, ...]:
    seen = sorted({str(r["run"]["mode"]) for r in rows if r.get("run")})
    # Prefer canonical order; append any extra modes present in the run.
    ordered = [a for a in _DEFAULT_ARMS if a in seen]
    extras = [a for a in seen if a not in ordered]
    return tuple(ordered + extras)


def _metric(row: dict, name: str) -> float | None:
    """Read a scored metric from the cell artifact when the runner recorded it."""
    score = row.get("score") or {}
    metrics = score.get("metrics") if isinstance(score, dict) else None
    if isinstance(metrics, dict) and name in metrics:
        return float(metrics[name])
    return None


def analyze(run_dir: Path) -> int:
    rows = _load(run_dir)
    if not rows:
        print(f"No case files found under {run_dir}/cases/")
        return 1

    arms = _arms_in_run(rows)
    control_arm = "llm_alone_pure" if "llm_alone_pure" in arms else "llm_alone"

    print(f"Validation analysis: {run_dir.name}  ({len(rows)} cells)\n")

    # Per-stratum per-arm summary.
    for seen in (True, False):
        label = "SEEN-shape" if seen else "UNSEEN-shape"
        print(f"=== {label} ===")
        print(f"{'arm':<14}{'n':>5}{'a1':>8}{'object_a1':>11}{'healthy%':>10}")
        for arm in arms:
            cells = [
                r for r in rows if r["run"]["mode"] == arm and r["case"].get("seen_shape") is seen
            ]
            if not cells:
                continue
            a1 = obj = healthy = 0
            for r in cells:
                gt = _gt(r["case"])
                preds = _top(r["run"])
                p1 = preds[0] if preds else {}
                if preds and _is_a1(p1, gt):
                    a1 += 1
                if preds and _norm(p1.get("fault_object")) == gt[0]:
                    obj += 1
                if _norm((r["run"].get("final_diagnosis") or {}).get("stage")) == "healthy":
                    healthy += 1
            n = len(cells)
            print(f"{arm:<14}{n:>5}{a1 / n:>8.3f}{obj / n:>11.3f}{100 * healthy / n:>9.1f}%")
        print()

    # L0 (investigation) vs L1 (predictor) side-by-side per arm.
    #
    # This is the "are we benchmarking opensre or the LLM wrapping its text?"
    # panel. ``investigation_a1`` rebuilds a paper triple from opensre's
    # prose using the same keyword parser the legacy bridge uses (with
    # ``include_predictor_output=False`` so the predictor's structured JSON
    # doesn't feed back through). The gap a1 − investigation_a1 is the
    # predictor's contribution; ``translation_loss`` is the wrong-direction
    # half (opensre right, predictor wrong).
    #
    # Read the panel as:
    #   - inv_a1 column = opensre's own ability, conservative lower bound
    #   - a1 column = full pipeline (investigate → predictor → rank-1)
    #   - tl% column = how often the predictor LOST what opensre named
    print("=== L0 (investigation) vs L1 (predictor) ===")
    print("    inv_a1 = opensre prose alone (lower bound on investigation quality)")
    print("    a1     = top_3_predictions[0] (paper-compatible headline)")
    print("    tl%    = translation loss: inv_a1 right but a1 wrong")
    print(f"{'arm':<14}{'n':>5}{'inv_a1':>9}{'a1':>8}{'gap':>8}{'tl%':>7}")
    for arm in arms:
        cells = [r for r in rows if r["run"]["mode"] == arm]
        if not cells:
            continue
        n = len(cells)
        inv_a1 = a1 = tl = 0
        for r in cells:
            gt = _gt(r["case"])
            inv_hit = _investigation_a1_hit(r, gt)
            a1_hit = _cell_a1(r)
            inv_a1 += inv_hit
            a1 += a1_hit
            if inv_hit and not a1_hit:
                tl += 1
        gap = (a1 - inv_a1) / n
        print(f"{arm:<14}{n:>5}{inv_a1 / n:>9.3f}{a1 / n:>8.3f}{gap:>+8.3f}{100 * tl / n:>6.1f}%")
    print()

    # Paired contrasts per stratum — L1 (a1) and L0 (investigation_a1).
    if "opensre+llm" in arms and control_arm in arms:
        for metric_label, hit_fn in (
            ("a1 (predictor rank-1)", _cell_a1),
            (
                "investigation_a1 (opensre prose)",
                lambda r: _investigation_a1_hit(r, _gt(r["case"])),
            ),
        ):
            print(f"=== paired {metric_label}: (opensre+llm) − ({control_arm}) ===")
            for seen in (True, False, None):
                label = {True: "seen", False: "unseen", None: "all"}[seen]

                def scen_hit(
                    arm: str,
                    seen: bool | None = seen,
                    hit_fn: Callable[[dict], int] = hit_fn,
                ) -> dict[str, float]:
                    by: dict[str, list[int]] = {}
                    for r in rows:
                        if r["run"]["mode"] != arm:
                            continue
                        if seen is not None and r["case"].get("seen_shape") is not seen:
                            continue
                        hit = hit_fn(r)
                        by.setdefault(r["case"]["case_id"], []).append(hit)
                    return {k: sum(v) / len(v) for k, v in by.items()}

                a = scen_hit("opensre+llm")
                b = scen_hit(control_arm)
                shared = sorted(set(a) & set(b))
                deltas = [a[k] - b[k] for k in shared]
                pt, lo, hi = _bootstrap_ci(deltas)
                verdict = (
                    "ns (incl 0)"
                    if (lo <= 0 <= hi)
                    else ("opensre+ SIG" if pt > 0 else "control+ SIG")
                )
                print(
                    f"  {label:<7} d={pt:+.4f}  95%CI[{lo:+.4f},{hi:+.4f}]  "
                    f"n_scen={len(shared):>3}  {verdict}"
                )
            print()

    # Translation-loss proxy (seen-shape, the Fix-A target).
    print("=== translation-loss proxy (seen-shape failures) ===")
    print("    report NAMED correct fault_object but predictor dropped it from top-3")
    for arm in arms:
        fails = dropped = 0
        for r in rows:
            if r["run"]["mode"] != arm or not r["case"].get("seen_shape"):
                continue
            gt = _gt(r["case"])
            preds = _top(r["run"])
            p1 = preds[0] if preds else {}
            if preds and _is_a1(p1, gt):
                continue
            fails += 1
            gt_name = gt[0].split("/")[-1]
            report = _norm((r["run"].get("final_diagnosis") or {}).get("report"))
            named = len(gt_name) >= _MIN_SERVICE_NAME_LEN and gt_name in report
            in_top3 = any(_norm(p.get("fault_object")) == gt[0] for p in preds)
            if named and not in_top3:
                dropped += 1
        if fails:
            print(f"  {arm:<14} {dropped}/{fails} = {100 * dropped / fails:.1f}% of failures")
    print()

    # B2 false-healthy guard activations (Path B, 2026-06-07).
    # The guard rewrites a false-healthy investigation to root_cause_category=unknown
    # with a fixed signature string. Detect fired cells by that signature so the
    # analyzer can split fired vs non-fired a1 per arm — the headline B2 impact.
    print("=== B2 false-healthy guard activations ===")
    print("    cells where the guard downgraded a false-healthy conclusion")
    any_fired = False
    for arm in arms:
        cells = [r for r in rows if r["run"]["mode"] == arm]
        if not cells:
            continue
        fired = [r for r in cells if _b2_fired(r)]
        non_fired = [r for r in cells if not _b2_fired(r)]
        if not fired:
            print(f"  {arm:<14} 0 / {len(cells)} cells fired")
            continue
        any_fired = True
        fired_a1 = sum(1 for r in fired if _cell_a1(r))
        non_fired_a1 = sum(1 for r in non_fired if _cell_a1(r))
        fire_rate = 100 * len(fired) / len(cells)
        non_fired_a1_rate = non_fired_a1 / len(non_fired) if non_fired else 0.0
        print(
            f"  {arm:<14} {len(fired):3d} / {len(cells):3d} = {fire_rate:5.1f}% fired  "
            f"|  fired a1={fired_a1 / len(fired):.3f}  non-fired a1={non_fired_a1_rate:.3f}"
        )
    if not any_fired:
        print(
            "  (no activations detected — either the guard wasn't enabled, "
            "evidence_entries weren't persisted, or no cell matched both conditions)"
        )
    return 0


# Detect a B2 guard activation by the downgrade signature. Keep this marker
# phrase in lockstep with ``false_healthy_guard._DOWNGRADE_ROOT_CAUSE``.
def _b2_fired(row: dict) -> bool:
    diag = row["run"].get("final_diagnosis") or {}
    rc = _norm(diag.get("root_cause"))
    return "tool observations show unhealthy" in rc and "marked unresolved" in rc


def _cell_a1(row: dict) -> int:
    preds = _top(row["run"])
    return 1 if preds and _is_a1(preds[0], _gt(row["case"])) else 0


def _investigation_a1_hit(row: dict, gt: tuple[str, str, str]) -> int:
    """1 when opensre's investigation names the GT triple (L0 metric).

    Primary path: read ``investigation_a1`` from the cell's recorded metrics.
    This is the source of truth — it's what the scorer wrote at run time
    using the full ``case_data`` dict.

    Fallback (legacy artifacts that pre-date this metric): rebuild a
    pseudo-``case_data`` from ``final_diagnosis`` fields and re-run the
    keyword parser. The fallback is **best-effort** and may undercount the
    scorer's value when ``causal_chain`` / ``validated_claims`` were
    captured at a path the synthesized dict doesn't probe — e.g. directly
    on ``run`` rather than nested inside ``final_diagnosis``. We try both
    locations here to cover the most common artifact shapes, but for
    authoritative L0 numbers on legacy data, re-score the run rather than
    rely on this fallback.
    """
    scored = _metric(row, "investigation_a1")
    if scored is not None:
        return 1 if scored >= 1.0 else 0
    run = row.get("run", {})
    diag = run.get("final_diagnosis") or {}
    # ``final_state`` may live nested in ``final_diagnosis`` (current shape)
    # or directly on ``run`` (older artifacts). Prefer the nested form when
    # both are present — that's what the scorer would have read first.
    final_state = diag.get("final_state")
    if not isinstance(final_state, dict):
        final_state = run.get("final_state")
    case_data = {
        "root_cause": diag.get("root_cause"),
        "report": diag.get("report"),
        "final_state": final_state if isinstance(final_state, dict) else None,
    }
    payload = infer_final_answer_from_opensre_text(case_data, include_predictor_output=False)
    if not payload:
        return 0
    preds = payload.get("top_3_predictions") or []
    return 1 if preds and _is_a1(preds[0], gt) else 0


def main() -> int:
    if len(sys.argv) != 2:
        print("usage: analyze_validation.py <run-dir-containing-cases/>")
        return 2
    return analyze(Path(sys.argv[1]))


if __name__ == "__main__":
    raise SystemExit(main())