"""Tests for encoder/adapters.py -- model-output adapters and canonical geometry validation."""

import gzip
import pickle
import sys
import types

import numpy as np
import pytest

from encoder import adapters


def test_registry_decodes_native_segvggt_dictionary(tmp_path, monkeypatch):
    torch = pytest.importorskip("torch")
    evaluation = types.ModuleType("eval.instance_eval_common")
    evaluation.predict_by_feat_instance = lambda *args, **kwargs: (
        torch.tensor([[1, 0, 0, 0], [0, 1, 0, 0]], dtype=torch.bool),
        torch.tensor([0, 2]),
        torch.ones(2),
    )
    pose = types.ModuleType("segvggt.utils.pose_enc")
    pose.pose_encoding_to_extri_intri = lambda value, size: (
        torch.cat(
            [
                torch.eye(3).reshape(1, 1, 3, 3),
                torch.zeros(1, 1, 3, 1),
            ],
            dim=-1,
        ),
        torch.eye(3).reshape(1, 1, 3, 3),
    )
    monkeypatch.setitem(sys.modules, "eval.instance_eval_common", evaluation)
    monkeypatch.setitem(sys.modules, "segvggt.utils.pose_enc", pose)

    path = tmp_path / "scene.pt"
    torch.save(
        {
            "world_points": torch.zeros(1, 1, 2, 2, 3),
            "instance_maps": torch.zeros(1, 2, 1, 2, 2),
            "instance_labels": torch.zeros(1, 2, 4),
            "pose_enc": torch.zeros(1, 1, 9),
        },
        path,
    )
    result = adapters.adapt("segvggt", path=path)
    assert list(result["instances"]) == ["chair"]
    assert result["instances"]["chair"][0]["n"] == 1


def _scene():
    return {
        "instances": {"chair": [{"pts": [[0, 0, 0]], "best_pts": [[0, 0, 0]]}]},
        "stats": {"chair": {"raw": 1, "merged": 1, "peak": 1}},
        "scene_pts": [[0, 0, 0]],
        "cameras": None,
    }


def test_validate_normalizes_canonical_geometry():
    result = adapters.validate(_scene())
    instance = result["instances"]["chair"][0]
    assert instance["pts"].shape == (1, 3)
    assert instance["frames"] == set()
    assert instance["n"] == 1


@pytest.mark.parametrize(
    ("scene", "error"),
    [
        ([], TypeError),
        ({"instances": {}}, ValueError),
        (
            {"instances": {"chair": [{"pts": [1, 2, 3]}]}, "scene_pts": [[0, 0, 0]]},
            ValueError,
        ),
    ],
)
def test_validate_rejects_invalid_geometry(scene, error):
    with pytest.raises(error):
        adapters.validate(scene)


def test_validate_identifies_empty_scene():
    with pytest.raises(adapters.EmptySceneError, match="no instances"):
        adapters.validate({"instances": {}})


def test_adapt_segvggt_reads_flat_npz(tmp_path):
    path = tmp_path / "scene.npz"
    world = np.array([[[[0, 0, 1], [1, 0, 1]]]], np.float32)
    masks = np.array([[[[True, False]]]])
    np.savez(
        path,
        world_points=world,
        instance_masks=masks,
        labels=np.array(["chair"], dtype=object),
        frame_times=np.array([0], np.float32),
    )

    result = adapters.adapt_segvggt(path=str(path))

    instance = result["instances"]["chair"][0]
    assert list(result["instances"]) == ["chair"]
    assert instance["frames"] == {0}
    assert result["stats"]["chair"] == {"raw": 1, "merged": 1, "peak": 1}


def test_adapt_segvggt_requires_existing_cache(tmp_path):
    with pytest.raises(FileNotFoundError, match="raw cache does not exist"):
        adapters.adapt_segvggt(path=str(tmp_path / "missing.npz"))


def test_adapter_owned_raw_cache_locations(tmp_path, monkeypatch):
    seen = {}
    raw_path = tmp_path / "segvggt" / "scene1.pt"
    raw_path.parent.mkdir()
    raw_path.touch()

    def fake_segvggt(path):
        seen["segvggt"] = str(path)
        return {
            "world_points": np.zeros((1, 1, 1, 3), np.float32),
            "instance_masks": np.ones((1, 1, 1, 1), bool),
            "labels": np.array(["chair"], dtype=object),
            "camera_positions": np.zeros((1, 3), np.float32),
        }

    monkeypatch.setattr(adapters, "_decode_segvggt_raw", fake_segvggt)
    adapters.adapt_segvggt(root=str(tmp_path), scene="scene1")
    assert seen["segvggt"] == str(raw_path)


def test_fusion_adapter_resolves_two_native_model_directories(tmp_path, monkeypatch):
    seen = {}
    depth = np.ones((1, 1, 1), np.float32)
    intr = np.eye(3, dtype=np.float32)[None]
    c2w = np.eye(4, dtype=np.float32)[None]

    def fake_da3(path):
        seen["da3"] = str(path)
        return depth, intr, c2w, None

    def fake_sam3(path):
        seen["sam3"] = str(path)
        return {"object": {0: {0: np.ones((1, 1), bool)}}}

    monkeypatch.setattr(adapters, "_load_native_da3", fake_da3)
    monkeypatch.setattr(adapters, "_load_native_sam3", fake_sam3)
    adapters.adapt_sam3_depth_anything_3(root=str(tmp_path), scene="scene1")
    assert seen == {
        "da3": str(tmp_path / "depth-anything-3" / "scene1.pkl"),
        "sam3": str(tmp_path / "sam3" / "scene1.pt"),
    }


def test_adapters_default_to_root_data_caches(monkeypatch, tmp_path):
    monkeypatch.delenv("VSI_CACHE_ROOT", raising=False)
    seen = {}

    def fake_da3(path):
        seen["da3"] = str(path)
        return (
            np.ones((1, 1, 1), np.float32),
            np.eye(3, dtype=np.float32)[None],
            np.eye(4, dtype=np.float32)[None],
            None,
        )

    def fake_sam3(path):
        seen["sam3"] = str(path)
        return {"object": {0: {0: np.ones((1, 1), bool)}}}

    monkeypatch.setattr(adapters, "_load_native_da3", fake_da3)
    monkeypatch.setattr(adapters, "_load_native_sam3", fake_sam3)
    adapters.adapt_sam3_depth_anything_3(scene="scene1")
    assert seen == {
        "da3": "/root/data/caches/depth-anything-3/scene1.pkl",
        "sam3": "/root/data/caches/sam3/scene1.pt",
    }


def test_adapt_segvggt_rejects_missing_npz_fields(tmp_path):
    path = tmp_path / "broken.npz"
    np.savez(path, labels=np.array(["chair"], dtype=object))
    with pytest.raises(KeyError):
        adapters.adapt_segvggt(path=str(path))


def test_adapt_sam3_depth_anything_3_decodes_masks_and_backprojects(tmp_path):
    da3_path = tmp_path / "scene.da3.npz"
    depth = np.full((1, 2, 2), 2.0, np.float32)
    intrinsics = np.eye(3, dtype=np.float32)[None]
    poses = np.eye(4, dtype=np.float32)[None]
    np.savez(
        da3_path,
        depth=depth,
        intr=intrinsics,
        c2w=poses,
        frame_times=np.array([1.5], np.float32),
    )
    mask = np.array([[True, False], [False, True]])
    packed = {"chair": {0: {7: (np.packbits(mask), mask.shape)}}}
    mask_path = tmp_path / "scene.sam3.pkl.gz"
    with gzip.open(mask_path, "wb") as cache:
        pickle.dump(packed, cache)

    result = adapters.adapt_sam3_depth_anything_3(
        da3_path=str(da3_path), sam3_path=str(mask_path)
    )

    instance = result["instances"]["chair"][0]
    assert instance["frames"] == {0}
    assert instance["first_time"] == pytest.approx(1.5)
    np.testing.assert_allclose(instance["pts"], [[0, 0, 2], [2, 2, 2]])
    assert result["stats"]["chair"] == {"raw": 1, "merged": 1, "peak": 1}
    assert result["raw_inputs"]["per"]["chair"][0][7].dtype == bool


def test_backproject_resizes_sam3_mask_to_da3_depth_shape():
    depth = np.full((2, 2), 2.0, np.float32)
    mask = np.zeros((4, 4), bool)
    mask[0, 0] = True
    mask[2, 2] = True

    points, confidence = adapters._backproject(
        depth,
        np.eye(3, dtype=np.float32),
        np.eye(4, dtype=np.float32),
        mask,
    )

    assert confidence is None
    np.testing.assert_allclose(points, [[0, 0, 2], [2, 2, 2]])


def test_native_sam3_decodes_prompt_keyed_independent_frames(monkeypatch):
    responses = {"chair": [{"masks": np.array([[[1, 0], [0, 0]]], dtype=np.uint8)}, {}]}
    monkeypatch.setitem(
        sys.modules,
        "torch",
        types.SimpleNamespace(load=lambda *args, **kwargs: responses),
    )
    result = adapters._load_native_sam3("scene.pt")
    assert result["chair"][0][0].dtype == bool
    assert result["chair"][1] == {}


def test_native_sam3_preserves_tracked_object_ids(monkeypatch):
    responses = [{"out_obj_ids": np.array([7]), "out_binary_masks": np.ones((1, 2, 2))}]
    monkeypatch.setitem(
        sys.modules,
        "torch",
        types.SimpleNamespace(load=lambda *args, **kwargs: responses),
    )
    monkeypatch.setenv("VSI_SAM3_PROMPT", "chair")
    result = adapters._load_native_sam3("scene.pt")
    assert list(result["chair"][0]) == [7]


def test_native_sam3_decodes_lossless_tracking_cache(monkeypatch):
    responses = {
        "chair": {
            "start_session": {"session_id": "session"},
            "add_prompt": {"is_success": True},
            "stream": [
                {
                    "frame_index": 3,
                    "stream_metadata": "preserved",
                    "outputs": {
                        "out_obj_ids": np.array([7]),
                        "out_binary_masks": np.ones((1, 2, 2), bool),
                    },
                }
            ],
            "close_session": {"is_success": True},
        }
    }
    monkeypatch.setitem(
        sys.modules,
        "torch",
        types.SimpleNamespace(load=lambda *args, **kwargs: responses),
    )

    result = adapters._load_native_sam3("scene.pt")

    assert list(result["chair"][3]) == [7]


def test_spatial_code_format_validation():
    assert adapters.validate_spatial_code_format("compact") == "compact"
    assert adapters.validate_spatial_code_format("explicit") == "explicit"
    with pytest.raises(ValueError, match="unknown spatial-code format"):
        adapters.validate_spatial_code_format("unknown")


def test_native_fusion_times_are_measured_in_seconds(monkeypatch):
    monkeypatch.setattr(adapters, "FPS", 4.0)
    monkeypatch.setattr(
        adapters,
        "_load_native_da3",
        lambda path: (
            np.ones((3, 1, 1), np.float32),
            np.repeat(np.eye(3, dtype=np.float32)[None], 3, axis=0),
            np.repeat(np.eye(4, dtype=np.float32)[None], 3, axis=0),
            None,
        ),
    )
    monkeypatch.setattr(adapters, "_load_native_sam3", lambda path: {})
    *_, frame_times, _ = adapters._load_fusion_inputs("scene.pkl", "scene.pt")
    np.testing.assert_allclose(frame_times, [0.0, 0.25, 0.5])