/tests/benchmarks/cloudopsbench/adapter.py

"""CloudOpsBench adapter — implements ``BenchmarkAdapter`` for the framework.

Wraps the existing CloudOpsBench machinery (HF dataset loader, State Snapshot
replay backend, 15-metric scorer) behind the framework's adapter interface
defined in ``tests/benchmarks/_framework/adapters.py``.

This module preserves the paper's protocol (Wang et al, arXiv:2603.00468v1)
by re-using the existing files unchanged:
  - ``case_loader.py`` — HF dataset access
  - ``replay_backend.py`` — State Snapshot via mocked tool interface
  - ``scoring.py`` — 15 paper metrics

The adapter adds:
  - Framework-compatible types (BenchmarkCase, AlertPayload, etc.)
  - Filter mapping (CaseFilters → case_loader's flat args)
  - Seeded random selection (integrity Mechanism 6)
  - Per-case backend lifecycle (build → run → score)

Validity metrics (citation_grounding, entity_existence, kubectl_actionability)
are NOT yet declared by this adapter — they ship in a follow-up commit (Phase C
of the task scope). The framework's IntegrityGuard will refuse to start a full
benchmark run until they are present.
"""

from __future__ import annotations

import random
from collections.abc import Iterator
from dataclasses import asdict, replace
from pathlib import Path
from typing import Any

from tests.benchmarks._framework.adapters import (
    AdapterCapabilities,
    AlertPayload,
    BenchmarkAdapter,
    BenchmarkCase,
    CaseFilters,
    CaseScore,
    MetricSchema,
    RunContext,
    RunResult,
)
from tests.benchmarks.cloudopsbench.bench_agent import (
    BaselineLLMAloneAgent,
    BenchInvestigationAgent,
    PureBaselineAgent,
)
from tests.benchmarks.cloudopsbench.case_loader import (
    BENCHMARK_DIR,
    CloudOpsCase,
)
from tests.benchmarks.cloudopsbench.case_loader import (
    build_alert as _legacy_build_alert,
)
from tests.benchmarks.cloudopsbench.case_loader import (
    load_cases as _legacy_load_cases,
)
from tests.benchmarks.cloudopsbench.held_out_split import compute_held_out_set
from tests.benchmarks.cloudopsbench.performance_alert_localization import (
    performance_context_for_case_dir,
)
from tests.benchmarks.cloudopsbench.predictor import (
    emit_paper_predictions,
)
from tests.benchmarks.cloudopsbench.replay_backend import CloudOpsBenchReplayBackend
from tests.benchmarks.cloudopsbench.scoring import score_case as _legacy_score_case
from tests.benchmarks.cloudopsbench.tags import ALL_LABELED_SHAPES, seen_shape_for
from tests.benchmarks.cloudopsbench.validity_scoring import (
    compute_citation_grounding,
    compute_entity_existence,
    compute_kubectl_actionability,
)

# Adapter identity string — single source of truth for the benchmark name.
# Referenced by the adapter's ``name`` class attribute below, by the
# framework's CLI and config lint (which conditionalize cloudopsbench-only
# knobs on this string), and by anything else that needs to distinguish a
# cloudopsbench config from another adapter's config. Keeping it as a
# module-level constant avoids the magic-string drift the greptile review
# flagged on 2026-06-09.
BENCHMARK_NAME = "cloudopsbench"


# --------------------------------------------------------------------------- #
# Metric inventory — the paper's 15 metrics                                   #
# Validity metrics are added in a follow-up (Phase C).                        #
# --------------------------------------------------------------------------- #

_PAPER_METRIC_SCHEMA = MetricSchema(
    outcome_metrics=[
        "a1",
        "a3",
        "partial_a1",
        "partial_a3",
        "object_a1",
        "object_a3",
        "investigation_a1",
        "investigation_partial_a1",
        "investigation_object_a1",
        "translation_loss",
        "tcr",
    ],
    process_metrics=["exact", "in_order", "any_order", "rel", "cov"],
    efficiency_metrics=["steps", "mtti"],
    robustness_metrics=["iac", "rar", "ztdr"],
    # Phase C — heuristic validity metrics computed against the State Snapshot.
    # See validity_scoring.py for the heuristic limitations.
    validity_metrics=[
        "citation_grounding_rate",
        "entity_existence_rate",
        "kubectl_actionability_rate",
    ],
    higher_is_better={
        # Outcome (higher is better)
        "a1": True,
        "a3": True,
        "partial_a1": True,
        "partial_a3": True,
        "object_a1": True,
        "object_a3": True,
        "investigation_a1": True,
        "investigation_partial_a1": True,
        "investigation_object_a1": True,
        "translation_loss": False,
        "tcr": True,
        # Process — trajectory alignment + tool usage (higher better)
        "exact": True,
        "in_order": True,
        "any_order": True,
        "rel": True,
        "cov": True,
        # Efficiency (lower better — fewer steps, faster MTTI)
        "steps": False,
        "mtti": False,
        # Robustness (lower better — fewer invalid/redundant/zero-tool actions)
        "iac": False,
        "rar": False,
        "ztdr": False,
        # Validity (higher better — more grounded, less hallucinated)
        "citation_grounding_rate": True,
        "entity_existence_rate": True,
        "kubectl_actionability_rate": True,
    },
)


# --------------------------------------------------------------------------- #
# Adapter                                                                     #
# --------------------------------------------------------------------------- #


class CloudOpsBenchAdapter(BenchmarkAdapter):
    """The first ``BenchmarkAdapter`` — CloudOpsBench K8s scenarios.

    Usage::

        adapter = CloudOpsBenchAdapter()
        for case in adapter.load_cases(CaseFilters(limit=5, seed=42)):
            alert = adapter.build_alert(case)
            integrations = adapter.build_opensre_integrations(case)
            # ... runner invokes opensre, builds RunResult ...
            score = adapter.score_case(case, run_result)
    """

    name = BENCHMARK_NAME
    version = "1.0.0"
    # Framework features this adapter opts into. Replaces the hardcoded
    # ``if config.benchmark != "cloudopsbench"`` guards that previously
    # lived in ``_framework/config.py``. The framework now validates
    # config knobs against this declaration; a new adapter that wants to
    # use ``agent_variant`` or ``predictor_variant`` opts in the same way.
    capabilities = AdapterCapabilities(
        supports_agent_variant=True,
        supports_predictor_variant=True,
    )

    # M7 (IntegrityGuard.pre_flight) — a documented data-contamination review
    # has been performed: Cloud-OpsBench was published 2026-02 and every model
    # in the grid has a training cutoff PRIOR to that date, so none could have
    # seen the corpus. Full declaration + caveats live in the pre-registration
    # (preregistrations/cloudopsbench_v1.yml::contamination_check). This flag is
    # what the integrity gate reads to allow a non-dev (promotable) run.
    data_contamination_checked = True

    # Dataset pinning surfaced into provenance.json (_dataset_section reads these
    # by attribute). Must match the pre-reg target_corpus so a reviewer can
    # reproduce against the exact corpus revision.
    hf_dataset = "tracer-cloud/cloud-ops-bench-dataset"
    hf_revision = "ce0ded4f196f01e176cf1d69ec15c2db42b2a677"

    def __init__(self, benchmark_dir: Path = BENCHMARK_DIR) -> None:
        self._benchmark_dir = benchmark_dir
        # CloudOpsCase cache so we don't re-load case files between
        # build_alert / build_opensre_integrations / score_case for the same case.
        # Mutated only from load_cases (single-threaded before parallel runs
        # start); read-only during cell execution → safe for the framework
        # runner's ThreadPoolExecutor.
        self._cases_by_id: dict[str, CloudOpsCase] = {}
        # Predictor variant — set via apply_config_overrides at run start;
        # checked at score_case time to dispatch between the text-emit
        # predictor (default) and the OpenAI structured-outputs variant.
        self._predictor_variant: str = "default"

    @property
    def benchmark_dir(self) -> Path:
        """Local corpus path, surfaced into provenance.json (_dataset_section
        reads ``benchmark_dir`` by attribute). Read-only view of the private
        field so provenance records where the cases were loaded from."""
        return self._benchmark_dir

    # ----------------------------------------------------------------------- #
    # BenchmarkAdapter interface                                              #
    # ----------------------------------------------------------------------- #

    def apply_config_overrides(self, config: Any) -> None:
        """Honor cloudopsbench-specific config knobs before the runner starts.

        Two knobs today:
          - ``config.min_tool_calls`` (Optional[int]) — overrides
            ``BenchInvestigationAgent.MIN_TOOL_CALLS`` so the floor is
            reproducible from the YAML rather than a launch-time env var.
          - ``config.agent_variant`` (Literal["default", "trimmed_prompt"])
            — when ``"trimmed_prompt"``, swaps this adapter's
            ``investigation_agent_class`` to
            ``BenchInvestigationAgentTrimmedPrompt`` for this run only.

        Both overrides print a "✓ ..." confirmation line so the run log
        records which knobs fired (or didn't).

        Late imports — keeps the adapter importable even if bench_agent
        has unmet deps in some other context.
        """
        from tests.benchmarks.cloudopsbench.bench_agent import (
            BenchInvestigationAgent,
            BenchInvestigationAgentTrimmedPrompt,
        )

        min_tool_calls = getattr(config, "min_tool_calls", None)
        if min_tool_calls is not None:
            BenchInvestigationAgent.MIN_TOOL_CALLS = min_tool_calls
            print(
                f"  ✓ BenchInvestigationAgent.MIN_TOOL_CALLS = {min_tool_calls} "
                f"(from config.min_tool_calls)"
            )

        agent_variant = getattr(config, "agent_variant", "default")
        if agent_variant == "trimmed_prompt":

            def _trimmed_investigation_agent_class() -> type[BenchInvestigationAgentTrimmedPrompt]:
                return BenchInvestigationAgentTrimmedPrompt

            # type: ignore[method-assign] — strategy-pattern instance attr
            # shadowing of the method dispatch lookup. Documented; the
            # named wrapper makes the override survive base-method
            # signature changes.
            self.investigation_agent_class = _trimmed_investigation_agent_class  # type: ignore[method-assign]
            print(
                "  ✓ adapter.investigation_agent_class = "
                "BenchInvestigationAgentTrimmedPrompt "
                "(from config.agent_variant=trimmed_prompt)"
            )

        predictor_variant = getattr(config, "predictor_variant", "default")
        if predictor_variant in ("default", "structured"):
            self._predictor_variant = predictor_variant
            if predictor_variant == "structured":
                print(
                    "  ✓ adapter._predictor_variant = structured "
                    "(from config.predictor_variant=structured) — "
                    "OpenAI grammar-constrained sampling will be used "
                    "in score_case"
                )

    def extend_provenance(self, provenance: dict[str, Any]) -> dict[str, Any]:
        """Inject CloudOpsBench-specific knob values into ``run_inputs``.

        Phase 4 of the framework decoupling moved this capture out of
        ``_framework/provenance.py`` (which used to import
        ``cloudopsbench.bench_agent._resolve_min_tool_calls`` directly)
        into the adapter that owns the knob. The framework still calls
        ``capture_provenance`` once per run; the adapter decides what
        adapter-specific keys belong in the artifact.

        ``min_tool_calls`` is the effective ``BenchInvestigationAgent.
        MIN_TOOL_CALLS`` floor for the opensre+llm arm. Recording it
        means a sweep over ``BENCH_MIN_TOOL_CALLS`` is self-documenting:
        the report no longer has to be cross-referenced with the shell
        that launched it. Best-effort — when ``bench_agent`` cannot be
        imported (e.g. opensre deps absent in a unit-test sandbox), the
        field falls back to ``None`` rather than raising, so the
        provenance artifact remains valid.
        """
        try:
            from tests.benchmarks.cloudopsbench.bench_agent import _resolve_min_tool_calls

            min_tool_calls: int | None = _resolve_min_tool_calls()
        except Exception:
            min_tool_calls = None
        run_inputs = provenance.get("run_inputs")
        if isinstance(run_inputs, dict):
            run_inputs["min_tool_calls"] = min_tool_calls
        return provenance

    def load_cases(self, filters: CaseFilters) -> Iterator[BenchmarkCase]:
        """Stream cases matching the filter, with seeded random selection
        when ``filters.seed`` is set.

        Filter mapping:
            ``filters.systems[0]`` → ``system_filter``  (only first used; legacy limit)
            ``filters.fault_categories[0]`` → ``fault_category_filter``
            ``filters.case_ids[0]`` → ``case_filter``
            ``filters.limit`` → applied AFTER seeded sample so randomization is fair
            ``filters.seen_shape`` → applied AFTER tagging (Phase D); each case
                gets ``seen_shape`` from :func:`tags.seen_shape_for`

        For multi-value filters (e.g., multiple systems), call this method
        once per value and merge — current case_loader doesn't support OR.
        """
        legacy_cases = list(
            _legacy_load_cases(
                benchmark_dir=self._benchmark_dir,
                system=filters.systems[0] if filters.systems else None,
                fault_category=(filters.fault_categories[0] if filters.fault_categories else None),
                case_name=filters.case_ids[0] if filters.case_ids else None,
                limit=None,  # we apply limit below after random sample
            )
        )

        # Held-out 20% set — computed against the FULL filter-loaded corpus
        # so the split is stable regardless of seen-shape / limit filtering
        # applied later. Integrity Mechanism 8 (generalization gate).
        held_out_ids = compute_held_out_set(c.case_id for c in legacy_cases)

        # Seeded random selection — integrity Mechanism 6 (no cherry-picking)
        if filters.seed is not None:
            rng = random.Random(filters.seed)
            rng.shuffle(legacy_cases)

        # Shape filter runs BEFORE the limit so ``limit=N`` means
        # "N matching cases", not "N candidates, some of which match."
        #
        # ``seen_shape_for`` is tri-valued: SHAPE_SEEN / SHAPE_UNSEEN /
        # SHAPE_MID. A naive ``tag in {SHAPE_SEEN, SHAPE_UNSEEN}`` check
        # drops every SHAPE_MID case (scheduling, service, infra — 22%
        # of the corpus). The ``ALL_LABELED_SHAPES`` short-circuit
        # treats ``[SHAPE_SEEN, SHAPE_UNSEEN]`` (the standard "give me
        # everything" config) as "no filter" so SHAPE_MID also passes.
        #
        # Single-bucket filters (``[SHAPE_SEEN]`` only or ``[SHAPE_UNSEEN]``
        # only) still narrow the result as expected.
        wanted_seen_shape: set[bool] | None = (
            set(filters.seen_shape) if filters.seen_shape else None
        )
        if wanted_seen_shape is not None and wanted_seen_shape == ALL_LABELED_SHAPES:
            wanted_seen_shape = None
        if wanted_seen_shape is not None:
            legacy_cases = [
                c for c in legacy_cases if seen_shape_for(c.fault_category) in wanted_seen_shape
            ]

        # Apply limit after shape filtering so the sample is uniform random
        # over the filtered subset
        if filters.limit is not None and filters.limit > 0:
            legacy_cases = legacy_cases[: filters.limit]

        for legacy in legacy_cases:
            seen_shape = seen_shape_for(legacy.fault_category)
            self._cases_by_id[legacy.case_id] = legacy
            yield BenchmarkCase(
                case_id=legacy.case_id,
                benchmark_name=self.name,
                metadata={
                    "system": legacy.system,
                    "fault_category": legacy.fault_category,
                    "case_name": legacy.case_name,
                    "namespace": legacy.namespace,
                    "query": legacy.query,
                    "ground_truth": asdict(legacy.result),
                    "process": legacy.process,
                    "is_held_out": legacy.case_id in held_out_ids,
                },
                seen_shape=seen_shape,
            )

    def build_alert(self, case: BenchmarkCase) -> AlertPayload:
        """Wrap the legacy build_alert in the framework's AlertPayload shape."""
        legacy = self._require_case(case)
        raw = _legacy_build_alert(legacy)
        return AlertPayload(
            raw=raw,
            normalized={
                "system": legacy.system,
                "fault_category": legacy.fault_category,
                "namespace": legacy.namespace,
                "query": legacy.query,
            },
        )

    def build_opensre_integrations(self, case: BenchmarkCase) -> dict[str, Any]:
        """Construct a fresh State Snapshot replay backend per case and
        wire it under the ``eks`` integration key the bench's replay
        tools (``tests/benchmarks/cloudopsbench/tools/k8s``) read from.

        The returned dict is the only place this cell's backend lives;
        the runner passes it back via ``RunContext`` to ``score_case``.
        Stateless on the adapter — safe for parallel execution.

        NOTE: ``run_suite._build_resolved_integrations`` placed the backend
        under the ``aws`` key, which doesn't match what the CloudOpsBench
        tools look for. As a result the legacy benchmark agent has been
        completing investigations without ever calling the State Snapshot
        tools. This adapter fixes the key (uses ``eks``); the legacy
        ``run_suite.py`` will be removed by the framework rollout.
        """
        legacy = self._require_case(case)
        backend = CloudOpsBenchReplayBackend(legacy)
        cluster_name = f"cloudopsbench-{legacy.system}"
        return {
            # Useful for AWS-region-aware tools; not where the backend lives.
            "aws": {
                "role_arn": "",
                "external_id": "",
                "region": "us-east-1",
                "cluster_names": [cluster_name],
            },
            # CloudOpsBenchK8sTools read from sources["eks"]["_bench_backend"].
            # Deliberately distinct from the ``_backend`` slot used by synthetic
            # tests — production tool availability checks (_eks_available,
            # eks_available_or_backend) read only ``_backend`` and
            # ``connection_verified``, so this key stays invisible to them and
            # they correctly skip activation for bench cells.
            "eks": {
                "namespace": legacy.namespace,
                "cluster_name": cluster_name,
                "_bench_backend": backend,
            },
        }

    def build_baseline_tools(self, case: BenchmarkCase) -> dict[str, Any]:
        """Tool surface for the LLM-alone control arm.

        Same replay backend, same per-case integrations, same bench-tool
        registration the opensre+llm path uses — fairness in tool surface
        is the entire point of the in-harness baseline. The only difference
        between the two modes is the agent class (see
        :meth:`baseline_agent_class`), which carries the policy delta.
        """
        return self.build_opensre_integrations(case)

    def score_case(self, case: BenchmarkCase, run: RunResult, context: RunContext) -> CaseScore:
        """Score the case using CloudOpsBench's 15 paper metrics.

        Reads the replay backend out of ``context.integrations`` — the same
        dict ``build_opensre_integrations`` returned for THIS cell. No
        per-cell state on the adapter (thread-safe).
        """
        legacy = self._require_case(case)
        backend = (context.integrations.get("eks") or {}).get("_bench_backend")
        if not isinstance(backend, CloudOpsBenchReplayBackend):
            return CaseScore(
                case_id=case.case_id,
                metrics={},
                failure_reason=(
                    "context.integrations missing 'eks._bench_backend' of type "
                    "CloudOpsBenchReplayBackend — runner must pass the same "
                    "integrations dict to score_case as it passed to run_investigation"
                ),
            )

        case_data = _build_case_data(legacy, backend, run)
        legacy_score = _legacy_score_case(legacy, case_data)

        # Combine paper metrics + new validity metrics (Phase C)
        metrics: dict[str, float] = dict(asdict(legacy_score.metrics))
        finding_text = (
            str(run.final_diagnosis.get("report") or "")
            + "\n"
            + str(run.final_diagnosis.get("root_cause") or "")
        )
        metrics["citation_grounding_rate"] = compute_citation_grounding(
            finding_text, run.evidence_entries
        )
        metrics["entity_existence_rate"] = compute_entity_existence(
            finding_text, backend, legacy.namespace
        )
        metrics["kubectl_actionability_rate"] = compute_kubectl_actionability(finding_text)

        return CaseScore(case_id=case.case_id, metrics=metrics)

    def metric_schema(self) -> MetricSchema:
        """The paper's 15 metrics. Validity metrics arrive in Phase C."""
        return _PAPER_METRIC_SCHEMA

    def investigation_agent_class(self) -> type[BenchInvestigationAgent]:
        """CloudOpsBench uses a stricter agent: minimum-tool-call floor.

        See :class:`BenchInvestigationAgent` for the rationale (June-3 bench
        showed median 4-7 tool calls vs the paper's expected 15-20 winning
        trajectory). Production code is unaffected — the runner injects this
        class via the ``agent_class`` parameter on ``run_investigation``.
        """
        return BenchInvestigationAgent

    def baseline_agent_class(self) -> type[BaselineLLMAloneAgent]:
        """Agent class for the llm_alone control arm.

        Returns :class:`BaselineLLMAloneAgent` — same bench-package tool
        filter as :class:`BenchInvestigationAgent` (so the comparison is
        fair on tool surface) but without the MIN_TOOL_CALLS=8 floor (so
        the comparison isolates the lever).
        """
        return BaselineLLMAloneAgent

    def pure_baseline_agent_class(self) -> type[PureBaselineAgent]:
        """Agent class for the llm_alone_pure control arm.

        Returns :class:`PureBaselineAgent` — same bench-package tool
        filter as the other two arms, no MIN_TOOL_CALLS floor (like
        BaselineLLMAloneAgent), AND a minimal task-specific system prompt
        instead of opensre's full planner/verifier/stage-gate prompt.
        The contrast (opensre+llm) − (llm_alone_pure) isolates the full
        opensre stack; (llm_alone) − (llm_alone_pure) isolates opensre's
        prompt alone, factoring out the termination policy.
        """
        return PureBaselineAgent

    def format_final_answer(
        self,
        case: BenchmarkCase,
        run: RunResult,
        spec: Any,  # noqa: ARG002 — same LLM the investigation used is already activated
    ) -> RunResult:
        """Emit paper-format ``top_3_predictions`` before scoring.

        opensre produces free-text RCAs that the legacy keyword bridge in
        ``scoring.infer_final_answer_from_opensre_text`` can only match if
        the agent's wording overlaps with hard-coded phrases like
        ``"access denied"`` AND ``"invalid credentials"``. That fails on
        almost every real case.

        This hook runs ONE additional LLM call to translate the
        investigation evidence into the structured
        ``top_3_predictions`` JSON the scorer prefers (see
        ``scoring.extract_final_answer_payload``). The result is stashed
        into ``run.final_diagnosis["top_3_predictions"]`` so the scorer
        picks it up directly via ``parse_json_maybe``.

        If the predictor fails (LLM error, malformed JSON), the run is
        returned unchanged — the keyword bridge still runs as a fallback,
        so there's no regression vs the pre-predictor behavior.

        Mode-agnostic: ``opensre+llm`` passes the investigation summary,
        ``llm_alone`` (Phase B) would pass an empty summary so the model
        reasons from the alert alone. Same predictor, same scoring → the
        honest opensre-vs-pure-LLM comparison.
        """
        # Late import — keeps tests/benchmarks importable without opensre.
        from core.llm.factory import LLMRole, get_llm

        alert = self.build_alert(case)
        legacy = self._require_case(case)
        investigation_summary = _summarize_investigation(run)
        metric_alerts, perf_hint = performance_context_for_case_dir(
            legacy.case_dir, namespace=legacy.namespace
        )
        if legacy.fault_category != "performance":
            perf_hint = None
            metric_alerts = ""

        try:
            llm = get_llm(LLMRole.AGENT)
        except Exception:  # noqa: BLE001 — best-effort hook; never block scoring
            return run

        # Dispatch on predictor_variant — default text-emit (uses opensre's
        # LLM client) vs OpenAI structured-outputs (bypasses opensre's client
        # to use openai.beta.chat.completions.parse for schema enforcement).
        # The structured variant ignores ``llm`` because it talks to OpenAI
        # directly; the cross-field lint in config.py ensures it only fires
        # with OpenAI-model bench configs.
        if self._predictor_variant == "structured":
            from tests.benchmarks.cloudopsbench.predictor.llm_call_structured_openai import (
                emit_paper_predictions_structured,
            )

            # Forward the cell's config-resolved model version so the
            # structured variant doesn't silently fall back to its env-var /
            # default. ``run.model_version`` carries the pinned snapshot
            # the framework resolved from ``config.model_versions[llm]`` —
            # the same value provenance.json records. Without this, the
            # structured variant would use ``OPENSRE_BENCH_PREDICTOR_MODEL``
            # / ``gpt-4o-2024-11-20`` regardless of what the bench config
            # said, breaking reproducibility across model-pin changes.
            payload = emit_paper_predictions_structured(
                alert_text=_alert_text_for_predictor(alert.normalized),
                investigation_summary=investigation_summary,
                metric_alerts=metric_alerts,
                performance_localization_hint=perf_hint,
                model=run.model_version,
            )
        else:
            payload = emit_paper_predictions(
                alert_text=_alert_text_for_predictor(alert.normalized),
                investigation_summary=investigation_summary,
                metric_alerts=metric_alerts,
                performance_localization_hint=perf_hint,
                llm=llm,
            )
        if payload is None:
            return run

        # B1 investigation handoff — gated to ``predictor_variant == "default"``
        # so the mechanism is independently attributable per variant.
        #
        # WHY this gate: the structured-outputs variant uses grammar-constrained
        # sampling at the OpenAI API layer to prevent off-vocab predictor drift
        # (its own independent mechanism). Layering B1's token-overlap promotion
        # on top would conflate the two:
        #   - couldn't tell whether a lift was from schema enforcement or B1
        #   - could silently mask a structured-variant regression that B1 rescued
        #   - could amplify a spurious structured-variant lift via B1's prose
        #     alignment
        # The structured-outputs variant was REJECTED at full-N (2026-06-10);
        # future runs of that variant (cross-LLM ablations, layer-attribution
        # studies) MUST stay clean for the comparison to be honest.
        #
        # Control arms pass an empty summary — apply_investigation_handoff is a
        # no-op there, so paired contrasts on llm_alone / llm_alone_pure stay valid.
        if self._predictor_variant == "default":
            from tests.benchmarks.cloudopsbench.predictor.investigation_handoff import (
                apply_investigation_handoff,
            )

            predictions = apply_investigation_handoff(
                payload["top_3_predictions"],
                investigation_summary,
            )
        else:
            predictions = payload["top_3_predictions"]
        enriched_diagnosis = dict(run.final_diagnosis)
        enriched_diagnosis["top_3_predictions"] = predictions
        return replace(run, final_diagnosis=enriched_diagnosis)

    def select_best_run(
        self,
        case: BenchmarkCase,  # noqa: ARG002 — interface contract
        runs: list[tuple[RunResult, CaseScore]],
    ) -> int | None:
        """Majority vote on the predicted root-cause taxonomy.

        06-05 run analysis showed median a1=0.43 (gpt-4o) and 0.57 (gpt-5)
        but ORACLE best-of-3=0.83 / 0.80 — a 0.40 / 0.23 consistency gap.
        Majority vote on ``final_diagnosis.top_3_predictions[0].fault_taxonomy``
        closes 60% of the gpt-4o gap and 100% of the gpt-5 gap (gpt-5 hits
        the paper baseline 0.67 exactly). 90% of scenarios had ≥2 of 3
        seeds agreeing on a taxonomy.

        Algorithm:
          1. Extract each run's top-1 predicted taxonomy
            (``final_diagnosis["top_3_predictions"][0]["fault_taxonomy"]``).
          2. Drop runs with no prediction (predictor failed → empty string).
          3. Pick the taxonomy with the most votes. Ties broken by earliest
             run index — deterministic + reproducible.
          4. Return the index of the earliest run that produced that
             taxonomy.

        Returns ``None`` only when no run produced any prediction at all —
        in that case the median ``all`` stratum is the only meaningful view.
        """
        if len(runs) <= 1:
            return 0 if runs else None

        taxonomies: list[str] = []
        for run, _score in runs:
            top = (run.final_diagnosis or {}).get("top_3_predictions") or []
            taxonomies.append(top[0].get("fault_taxonomy", "") if top else "")

        # Tally votes, ignoring blank predictions
        votes: dict[str, int] = {}
        for t in taxonomies:
            if t:
                votes[t] = votes.get(t, 0) + 1
        if not votes:
            return None

        # Highest vote count, tiebreak by first-appearance order (stable)
        winning = max(votes, key=lambda k: (votes[k], -taxonomies.index(k)))
        return taxonomies.index(winning)

    # ----------------------------------------------------------------------- #
    # Internal                                                                #
    # ----------------------------------------------------------------------- #

    def _require_case(self, case: BenchmarkCase) -> CloudOpsCase:
        """Retrieve the cached legacy case; raise if absent.

        The cache is populated by ``load_cases``. Calling other adapter
        methods with a case that wasn't loaded through us is a programming
        error.
        """
        if case.case_id not in self._cases_by_id:
            raise KeyError(
                f"case {case.case_id!r} was not produced by this adapter's "
                f"load_cases — adapter methods can only be called with cases "
                f"this adapter yielded"
            )
        return self._cases_by_id[case.case_id]


# --------------------------------------------------------------------------- #
# Helpers                                                                     #
# --------------------------------------------------------------------------- #


def _build_case_data(
    legacy: CloudOpsCase,
    backend: CloudOpsBenchReplayBackend,
    run: RunResult,
) -> dict[str, Any]:
    """Convert a framework RunResult into the dict the legacy scorer expects.

    The legacy ``score_case(case, case_data)`` reads case_data from the
    payload that ``run_suite.run_case`` builds. We replicate that shape
    here so the legacy scorer works unchanged.
    """
    return {
        "case_id": legacy.case_id,
        "system": legacy.system,
        "fault_category": legacy.fault_category,
        "case_name": legacy.case_name,
        "ground_truth": {
            "fault_taxonomy": legacy.result.fault_taxonomy,
            "fault_object": legacy.result.fault_object,
            "root_cause": legacy.result.root_cause,
        },
        "final_answer": run.final_diagnosis,
        "root_cause": run.final_diagnosis.get("root_cause"),
        "report": run.final_diagnosis.get("report"),
        "expert_steps": {
            "path1": list(legacy.process.get("path1") or []),
            "path2": list(legacy.process.get("path2") or []),
        },
        "steps": _steps_from_backend(backend),
        # Real measured wall-clock of the investigation (runner's monotonic
        # timer around run_investigation). The scorer's calculate_total_latency
        # reads this for MTTI — without it, MTTI is structurally 0 because the
        # replay backend has no per-step latency to sum.
        "latency_ms": run.latency_ms,
        # The legacy scorer doesn't require final_state, but pass it through
        # for forward-compat with future scoring extensions.
        "final_state": {"evidence_entries": run.evidence_entries},
    }


def _steps_from_backend(backend: CloudOpsBenchReplayBackend) -> list[dict[str, Any]]:
    """Convert backend.action_log into the step list shape legacy scoring expects.

    Mirrors ``run_suite._steps_from_backend`` so legacy scoring works on
    framework-produced runs without changes.
    """
    steps: list[dict[str, Any]] = []
    for idx, entry in enumerate(backend.action_log, start=1):
        steps.append(
            {
                "step_id": idx,
                "action_type": "tool",
                "action_name": entry.get("action_name"),
                "action_input": entry.get("action_input", {}),
                "error": entry.get("error"),
                "tool_latency": 0.0,
            }
        )
    return steps


def _alert_text_for_predictor(normalized: dict[str, Any]) -> str:
    """Compact alert representation for the paper-format predictor.

    Pulls the fields the predictor cares about (cluster, namespace, alert
    name, message) from the adapter's normalized alert dict. Avoids
    forwarding huge nested payloads — the predictor only needs context
    to disambiguate which system + namespace it is reasoning about.
    """
    parts: list[str] = []
    for field in ("alert_name", "severity", "cluster_name", "namespace", "message"):
        value = normalized.get(field)
        if value:
            parts.append(f"{field}: {value}")
    return "\n".join(parts) if parts else ""


def _summarize_investigation(run: RunResult) -> str:
    """Render opensre's free-text RCA as input to the paper-format predictor.

    Pulls the human-readable report + root_cause out of the investigation
    output. The predictor sees this as evidence, not as the answer — its
    job is to translate to the paper's structured taxonomy.
    """
    parts: list[str] = []
    diagnosis = run.final_diagnosis
    # Lead with opensre's own conclusion so the predictor anchors rank-1 on it
    # rather than re-deriving from the (hedge-heavy) report body. The
    # 2026-06-06 run showed the predictor dropped the correct component named
    # in opensre's report from its top-3 on 15% of failures (3x the
    # no-investigation arm) — a translation-loss leak this framing closes.
    component = diagnosis.get("component")
    if component:
        parts.append(f"Identified component: {component}")
    root_cause = diagnosis.get("root_cause")
    if root_cause:
        parts.append(f"Investigation conclusion (root cause): {root_cause}")
    report = diagnosis.get("report")
    if report:
        parts.append(f"Supporting RCA report:\n{report}")
    return "\n\n".join(parts) if parts else ""


# --------------------------------------------------------------------------- #
# Registration                                                                 #
#                                                                              #
# Self-register into the framework's adapter registry on module import. The   #
# CLI's bootstrap (``ensure_known_adapters_registered``) imports this module  #
# at startup; this side-effect makes the framework dispatch CloudOpsBench by  #
# name without an if/elif chain in the framework itself.                       #
# --------------------------------------------------------------------------- #


from tests.benchmarks._framework.adapters import register_adapter  # noqa: E402

register_adapter(BENCHMARK_NAME, CloudOpsBenchAdapter)