import importlib.util
from pathlib import Path


def _load_runtime_hook(case_id: str):
    path = (
        Path(__file__).resolve().parents[2]
        / "evaluation"
        / "regression"
        / "cases"
        / case_id
        / "runtime_hook.py"
    )
    spec = importlib.util.spec_from_file_location(f"runtime_hook_{case_id}", path)
    assert spec is not None
    assert spec.loader is not None
    module = importlib.util.module_from_spec(spec)
    spec.loader.exec_module(module)
    return module


def test_read_pool_cpu_runtime_hook_uses_thread_cpu_queries(monkeypatch):
    module = _load_runtime_hook("read-pool-cpu-scope-compare-in-premium-cluster-during-latest-window")
    calls: list[dict] = []

    def _payload(series):
        return {
            "status": "success",
            "data": {
                "result": series,
            },
        }

    per_instance_series = [
        {
            "metric": {"instance": "db-125d9e7b-tikv-a"},
            "values": [[1784531400, "2.5"], [1784531460, "6.1"]],
        },
        {
            "metric": {"instance": "db-125d9e7b-tikv-b"},
            "values": [[1784531400, "1.2"], [1784531460, "4.3"]],
        },
    ]
    cluster_total_series = [
        {
            "metric": {},
            "values": [[1784531400, "3.7"], [1784531460, "10.4"]],
        }
    ]

    class FakeClient:
        def __init__(self, *, api_key: str, base_url: str, timeout_s: int = 60) -> None:
            self.api_key = api_key
            self.base_url = base_url
            self.timeout_s = timeout_s

        def query_range(
            self,
            *,
            cluster_id: str,
            promql: str,
            start: int,
            end: int,
            step_seconds: int,
        ) -> dict:
            calls.append(
                {
                    "cluster_id": cluster_id,
                    "promql": promql,
                    "start": start,
                    "end": end,
                    "step_seconds": step_seconds,
                }
            )
            if promql == module.PER_INSTANCE_QUERY:
                return _payload(per_instance_series)
            if promql == module.CLUSTER_TOTAL_QUERY:
                return _payload(cluster_total_series)
            raise AssertionError(f"unexpected promql: {promql}")

    monkeypatch.setattr(module, "_env_api_key", lambda: "token")
    monkeypatch.setattr(module, "ClinicClient", FakeClient)

    payload = module._build_reference(
        cluster_id="10748116073341772391",
        metrics_cluster_id="2026948324023603200",
        start="2026-07-20T07:10:00Z",
        end="2026-07-20T08:00:00Z",
    )

    assert [call["promql"] for call in calls] == [
        'sum(rate(tikv_thread_cpu_seconds_total{name=~"unified_read.*"}[1m])) by (instance)',
        'sum(sum by (instance) (rate(tikv_thread_cpu_seconds_total{name=~"unified_read.*"}[1m])))',
    ]
    assert all(call["cluster_id"] == "2026948324023603200" for call in calls)
    assert payload == {
        "cluster_id": "10748116073341772391",
        "metrics_cluster_id": "2026948324023603200",
        "window": {
            "start": "2026-07-20T07:10:00Z",
            "end": "2026-07-20T08:00:00Z",
        },
        "evidence_sufficient": True,
        "peak_instance_node": "db-125d9e7b-tikv-a",
        "peak_instance_read_pool_cpu_cores": 6.1,
        "cluster_total_peak_read_pool_cpu_cores": 10.4,
    }