import types

from aarecommon.models.models import DAQOperation

from aare.daq.operations.face_detection.models import FaceDetectionResult


def test_execute_face_detection_reports_failure(monkeypatch):
    from aare.daq.daq import AareDAQ

    daq = object.__new__(AareDAQ)

    calls = {"set_state": [], "handle_error": []}

    monkeypatch.setattr(daq, "_set_state", lambda state: calls["set_state"].append(state))
    monkeypatch.setattr(
        daq,
        "_create_face_detection_service",
        lambda: types.SimpleNamespace(
            run=lambda **kwargs: FaceDetectionResult(
                success=False,
                payload={"running": False, "samples": [], "height_fit": {}, "area_fit": {}},
                error=RuntimeError("fd failed"),
                comment="face detection sequence failed",
            )
        ),
    )
    monkeypatch.setattr(
        daq, "_handle_operation_error", lambda **kwargs: calls["handle_error"].append(kwargs)
    )
    monkeypatch.setattr(
        type(daq),
        "sample",
        property(lambda self: types.SimpleNamespace(sample_name="sample-1", db_id=1)),
    )

    result = daq._execute_face_detection(steps=7, step_size=30, report_error=True)

    assert result.success is False
    assert len(calls["set_state"]) == 1
    assert len(calls["handle_error"]) == 1
    assert calls["handle_error"][0]["operation"] == DAQOperation.FACE_CENTERING


def test_execute_face_detection_can_skip_error_reporting(monkeypatch):
    from aare.daq.daq import AareDAQ

    daq = object.__new__(AareDAQ)

    handle_error_calls = []

    monkeypatch.setattr(daq, "_set_state", lambda state: None)
    monkeypatch.setattr(
        daq,
        "_create_face_detection_service",
        lambda: types.SimpleNamespace(
            run=lambda **kwargs: FaceDetectionResult(
                success=False,
                payload={"running": False, "samples": [], "height_fit": {}, "area_fit": {}},
                error=RuntimeError("fd failed"),
                comment="face detection sequence failed",
            )
        ),
    )
    monkeypatch.setattr(
        daq, "_handle_operation_error", lambda **kwargs: handle_error_calls.append(kwargs)
    )

    result = daq._execute_face_detection(report_error=False)

    assert result.success is False
    assert handle_error_calls == []


def test_public_face_detection_uses_execute_face_detection(monkeypatch):
    from aare.daq.daq import AareDAQ

    daq = object.__new__(AareDAQ)
    cfg = types.SimpleNamespace(try_set_busy=lambda timeout=360: None, state_busy=False)
    daq._cfg = cfg

    daq._execute_face_detection = lambda **kwargs: FaceDetectionResult(
        success=True,
        payload={"running": False, "samples": [{"angle": 45}], "height_fit": {}, "area_fit": {}},
    )

    result = daq.face_detection(steps=7, step_size=30)

    assert result["running"] is False
    assert result["samples"] == [{"angle": 45}]
    assert cfg.state_busy is False