from __future__ import annotations

from dataclasses import dataclass

from orchestration.ara_first_run_pipeline import AraFirstRunCommand, AraFirstRunPipeline


@dataclass
class _FakeCommand:
    """A stand-in for AraFirstRunTriggerBody — structurally a AraFirstRunCommand."""

    portfolio_id: int
    property_ids: list[int]
    scenario_ids: list[int]


class _SpyIngestion:
    def __init__(self, log: list[tuple[object, ...]]) -> None:
        self._log = log

    def run(self, property_ids: list[int]) -> None:
        self._log.append(("ingestion", property_ids))


class _SpyBaseline:
    def __init__(self, log: list[tuple[object, ...]]) -> None:
        self._log = log

    def run(self, property_ids: list[int]) -> None:
        self._log.append(("baseline", property_ids))


class _SpyModelling:
    def __init__(self, log: list[tuple[object, ...]]) -> None:
        self._log = log

    def run(self, property_ids: list[int], scenario_ids: list[int]) -> None:
        self._log.append(("modelling", property_ids, scenario_ids))


def test_run_sequences_the_three_stages_threading_only_property_ids() -> None:
    # Arrange
    log: list[tuple[object, ...]] = []
    command: AraFirstRunCommand = _FakeCommand(
        portfolio_id=1, property_ids=[10, 11], scenario_ids=[7]
    )
    pipeline = AraFirstRunPipeline(
        ingestion=_SpyIngestion(log),
        baseline=_SpyBaseline(log),
        modelling=_SpyModelling(log),
    )

    # Act
    pipeline.run(command)

    # Assert — Ingestion -> Baseline -> Modelling, in order. Ingestion and
    # Baseline receive only property_ids; Modelling additionally gets the
    # scenario_ids (off the command, not a prior stage). Nothing else is
    # threaded between stages — they communicate through repos (ADR-0011).
    assert log == [
        ("ingestion", [10, 11]),
        ("baseline", [10, 11]),
        ("modelling", [10, 11], [7]),
    ]