import numpy as np
import cv2


def _create_project_and_frame(client):
    project = client.post("/api/projects", json={"name": "AI Project"}).json()
    frame = client.post(f"/api/projects/{project['id']}/frames", json={
        "project_id": project["id"],
        "frame_index": 0,
        "image_url": "frames/0.jpg",
        "width": 640,
        "height": 360,
    }).json()
    template = client.post("/api/templates", json={
        "name": "Template",
        "color": "#06b6d4",
        "z_index": 0,
        "classes": [],
        "rules": [],
    }).json()
    return project, frame, template


def test_predict_accepts_point_object_with_labels(client, monkeypatch):
    _, frame, _ = _create_project_and_frame(client)
    calls = {}

    monkeypatch.setattr("routers.ai._load_frame_image", lambda frame: np.zeros((10, 10, 3), dtype=np.uint8))

    def fake_predict_points(image, points, labels):
        calls["args"] = (points, labels)
        return (
            [[[0.1, 0.1], [0.9, 0.1], [0.9, 0.9]]],
            [0.95],
        )

    monkeypatch.setattr("routers.ai.sam_registry.predict_points", lambda model, image, points, labels: fake_predict_points(image, points, labels))

    response = client.post("/api/ai/predict", json={
        "image_id": frame["id"],
        "prompt_type": "point",
        "prompt_data": {"points": [[0.5, 0.5], [0.1, 0.1]], "labels": [1, 0]},
    })

    assert response.status_code == 200
    assert response.json()["scores"] == [0.95]
    assert calls["args"] == ([[0.5, 0.5], [0.1, 0.1]], [1, 0])


def test_predict_applies_crop_and_background_filter_options(client, monkeypatch):
    _, frame, _ = _create_project_and_frame(client)
    calls = {}
    monkeypatch.setattr("routers.ai._load_frame_image", lambda frame: np.zeros((100, 200, 3), dtype=np.uint8))

    def fake_predict_points(model, image, points, labels):
        calls["shape"] = image.shape
        calls["points"] = points
        calls["labels"] = labels
        return (
            [
                [[0.0, 0.0], [0.2, 0.0], [0.2, 0.2]],
                [[0.45, 0.45], [0.55, 0.45], [0.55, 0.55]],
            ],
            [0.9, 0.01],
        )

    monkeypatch.setattr("routers.ai.sam_registry.predict_points", fake_predict_points)

    response = client.post("/api/ai/predict", json={
        "image_id": frame["id"],
        "prompt_type": "point",
        "prompt_data": {"points": [[0.5, 0.5], [0.52, 0.52]], "labels": [1, 0]},
        "options": {
            "crop_to_prompt": True,
            "crop_margin": 0.1,
            "auto_filter_background": True,
            "min_score": 0.05,
        },
    })

    assert response.status_code == 200
    assert calls["shape"][0] < 100
    assert calls["shape"][1] < 200
    assert calls["labels"] == [1, 0]
    assert response.json()["scores"] == [0.9]
    polygon = response.json()["polygons"][0]
    assert all(0.0 <= coord <= 1.0 for point in polygon for coord in point)


def test_predict_box_and_semantic_fallback(client, monkeypatch):
    _, frame, _ = _create_project_and_frame(client)
    calls = {}
    monkeypatch.setattr("routers.ai._load_frame_image", lambda frame: np.zeros((10, 10, 3), dtype=np.uint8))
    monkeypatch.setattr("routers.ai.sam_registry.predict_box", lambda model, image, box: (
        [[[0.2, 0.2], [0.8, 0.2], [0.8, 0.8]]],
        [0.8],
    ))

    def fake_predict_semantic(model, image, text, confidence_threshold=None):
        calls["semantic"] = {
            "model": model,
            "text": text,
            "confidence_threshold": confidence_threshold,
        }
        return (
            [[[0.0, 0.0], [1.0, 0.0], [1.0, 1.0]]],
            [0.5],
        )

    monkeypatch.setattr("routers.ai.sam_registry.predict_semantic", fake_predict_semantic)

    box_response = client.post("/api/ai/predict", json={
        "image_id": frame["id"],
        "prompt_type": "box",
        "prompt_data": [0.2, 0.2, 0.8, 0.8],
    })
    semantic_response = client.post("/api/ai/predict", json={
        "image_id": frame["id"],
        "prompt_type": "semantic",
        "prompt_data": "胆囊",
        "model": "sam3",
        "options": {"min_score": 0.05},
    })

    assert box_response.status_code == 200
    assert box_response.json()["scores"] == [0.8]
    assert semantic_response.status_code == 200
    assert semantic_response.json()["scores"] == [0.5]
    assert calls["semantic"] == {
        "model": "sam3",
        "text": "胆囊",
        "confidence_threshold": 0.05,
    }


def test_predict_interactive_combines_box_and_points(client, monkeypatch):
    _, frame, _ = _create_project_and_frame(client)
    calls = {}
    monkeypatch.setattr("routers.ai._load_frame_image", lambda frame: np.zeros((10, 10, 3), dtype=np.uint8))

    def fake_predict_interactive(model, image, box, points, labels):
        calls["model"] = model
        calls["box"] = box
        calls["points"] = points
        calls["labels"] = labels
        return (
            [[[0.2, 0.2], [0.8, 0.2], [0.8, 0.8]]],
            [0.88],
        )

    monkeypatch.setattr("routers.ai.sam_registry.predict_interactive", fake_predict_interactive)

    response = client.post("/api/ai/predict", json={
        "image_id": frame["id"],
        "prompt_type": "interactive",
        "prompt_data": {
            "box": [0.1, 0.1, 0.9, 0.9],
            "points": [[0.5, 0.5], [0.2, 0.2]],
            "labels": [1, 0],
        },
        "model": "sam2",
    })

    assert response.status_code == 200
    assert response.json()["scores"] == [0.88]
    assert calls == {
        "model": "sam2",
        "box": [0.1, 0.1, 0.9, 0.9],
        "points": [[0.5, 0.5], [0.2, 0.2]],
        "labels": [1, 0],
    }


def test_model_status_reports_runtime(client, monkeypatch):
    monkeypatch.setattr("routers.ai.sam_registry.runtime_status", lambda selected_model=None: {
        "selected_model": selected_model or "sam2",
        "gpu": {
            "available": False,
            "device": "cpu",
            "name": None,
            "torch_available": True,
            "torch_version": "2.x",
            "cuda_version": None,
        },
        "models": [
            {
                "id": "sam2",
                "label": "SAM 2",
                "available": True,
                "loaded": False,
                "device": "cpu",
                "supports": ["point", "box", "auto"],
                "message": "ready",
                "package_available": True,
                "checkpoint_exists": True,
                "checkpoint_path": "model.pt",
                "python_ok": True,
                "torch_ok": True,
                "cuda_required": False,
            },
            {
                "id": "sam3",
                "label": "SAM 3",
                "available": False,
                "loaded": False,
                "device": "unavailable",
                "supports": ["semantic"],
                "message": "missing Python 3.12+ runtime",
                "package_available": False,
                "checkpoint_exists": False,
                "checkpoint_path": None,
                "python_ok": False,
                "torch_ok": True,
                "cuda_required": True,
            },
        ],
    })

    response = client.get("/api/ai/models/status?selected_model=sam3")

    assert response.status_code == 200
    body = response.json()
    assert body["selected_model"] == "sam3"
    assert body["models"][1]["id"] == "sam3"
    assert body["models"][1]["available"] is False


def test_propagate_saves_tracked_annotations(client, monkeypatch):
    project = client.post("/api/projects", json={"name": "Video Project"}).json()
    frames = [
        client.post(f"/api/projects/{project['id']}/frames", json={
            "project_id": project["id"],
            "frame_index": idx,
            "image_url": f"frames/{idx}.jpg",
            "width": 640,
            "height": 360,
        }).json()
        for idx in range(3)
    ]
    calls = {}
    monkeypatch.setattr("routers.ai.download_file", lambda object_name: b"jpeg")

    def fake_propagate_video(model, frame_paths, source_frame_index, seed, direction, max_frames):
        calls["model"] = model
        calls["source_frame_index"] = source_frame_index
        calls["seed"] = seed
        calls["direction"] = direction
        calls["max_frames"] = max_frames
        calls["frame_count"] = len(frame_paths)
        return [
            {
                "frame_index": 0,
                "polygons": [[[0.1, 0.1], [0.2, 0.1], [0.2, 0.2]]],
                "scores": [0.9],
                "object_ids": [1],
            },
            {
                "frame_index": 1,
                "polygons": [[[0.15, 0.15], [0.25, 0.15], [0.25, 0.25]]],
                "scores": [0.8],
                "object_ids": [1],
            },
        ]

    monkeypatch.setattr("routers.ai.sam_registry.propagate_video", fake_propagate_video)

    response = client.post("/api/ai/propagate", json={
        "project_id": project["id"],
        "frame_id": frames[0]["id"],
        "model": "sam2",
        "direction": "forward",
        "max_frames": 2,
        "include_source": False,
        "seed": {
            "polygons": [[[0.1, 0.1], [0.2, 0.1], [0.2, 0.2]]],
            "bbox": [0.1, 0.1, 0.1, 0.1],
            "label": "胆囊",
            "color": "#ff0000",
            "class_metadata": {"id": "c1", "name": "胆囊", "color": "#ff0000", "zIndex": 20},
            "template_id": None,
        },
    })

    assert response.status_code == 200
    body = response.json()
    assert body["created_annotation_count"] == 1
    assert body["processed_frame_count"] == 2
    assert calls["model"] == "sam2"
    assert calls["source_frame_index"] == 0
    assert calls["direction"] == "forward"
    assert calls["frame_count"] == 2
    saved = body["annotations"][0]
    assert saved["frame_id"] == frames[1]["id"]
    assert saved["mask_data"]["source"] == "sam2_propagation"
    assert saved["mask_data"]["class"]["name"] == "胆囊"
    assert saved["mask_data"]["score"] == 0.8

    listing = client.get(f"/api/ai/annotations?project_id={project['id']}")
    assert len(listing.json()) == 1


def test_predict_validation_errors(client, monkeypatch):
    project, _, _ = _create_project_and_frame(client)

    assert client.post("/api/ai/predict", json={
        "image_id": 999,
        "prompt_type": "point",
        "prompt_data": [[0.5, 0.5]],
    }).status_code == 404

    frame = client.post(f"/api/projects/{project['id']}/frames", json={
        "project_id": project["id"],
        "frame_index": 1,
        "image_url": "frames/1.jpg",
    }).json()
    monkeypatch.setattr("routers.ai._load_frame_image", lambda frame: np.zeros((10, 10, 3), dtype=np.uint8))
    assert client.post("/api/ai/predict", json={
        "image_id": frame["id"],
        "prompt_type": "box",
        "prompt_data": [0.1, 0.2],
    }).status_code == 400


def test_save_annotation_validates_project_and_frame(client):
    project, frame, template = _create_project_and_frame(client)

    saved = client.post("/api/ai/annotate", json={
        "project_id": project["id"],
        "frame_id": frame["id"],
        "template_id": template["id"],
        "mask_data": {"polygons": [[[0.1, 0.1], [0.9, 0.1], [0.9, 0.9]]]},
        "points": [[0.5, 0.5]],
        "bbox": [0.1, 0.1, 0.8, 0.8],
    })
    assert saved.status_code == 201
    assert saved.json()["project_id"] == project["id"]

    listing = client.get(f"/api/ai/annotations?project_id={project['id']}")
    assert listing.status_code == 200
    assert listing.json()[0]["id"] == saved.json()["id"]

    frame_listing = client.get(f"/api/ai/annotations?project_id={project['id']}&frame_id={frame['id']}")
    assert frame_listing.status_code == 200
    assert len(frame_listing.json()) == 1

    missing_project = client.post("/api/ai/annotate", json={"project_id": 999})
    assert missing_project.status_code == 404

    missing_frame = client.post("/api/ai/annotate", json={
        "project_id": project["id"],
        "frame_id": 999,
    })
    assert missing_frame.status_code == 404

    missing_project_list = client.get("/api/ai/annotations?project_id=999")
    assert missing_project_list.status_code == 404


def test_update_and_delete_annotation(client):
    project, frame, template = _create_project_and_frame(client)
    saved = client.post("/api/ai/annotate", json={
        "project_id": project["id"],
        "frame_id": frame["id"],
        "template_id": template["id"],
        "mask_data": {
            "polygons": [[[0.1, 0.1], [0.9, 0.1], [0.9, 0.9]]],
            "label": "AI Mask",
            "color": "#06b6d4",
        },
        "points": [[0.5, 0.5]],
        "bbox": [0.1, 0.1, 0.8, 0.8],
    }).json()

    updated = client.patch(f"/api/ai/annotations/{saved['id']}", json={
        "template_id": template["id"],
        "mask_data": {
            "polygons": [[[0.2, 0.2], [0.8, 0.2], [0.8, 0.8]]],
            "label": "胆囊",
            "color": "#ff0000",
            "class": {"id": "c1", "name": "胆囊", "color": "#ff0000", "zIndex": 20},
        },
        "points": [[0.4, 0.4]],
        "bbox": [0.2, 0.2, 0.6, 0.6],
    })

    assert updated.status_code == 200
    body = updated.json()
    assert body["mask_data"]["label"] == "胆囊"
    assert body["mask_data"]["class"]["id"] == "c1"
    assert body["points"] == [[0.4, 0.4]]
    assert body["bbox"] == [0.2, 0.2, 0.6, 0.6]

    listing = client.get(f"/api/ai/annotations?project_id={project['id']}")
    assert listing.status_code == 200
    assert listing.json()[0]["mask_data"]["class"]["name"] == "胆囊"

    deleted = client.delete(f"/api/ai/annotations/{saved['id']}")
    assert deleted.status_code == 204

    empty_listing = client.get(f"/api/ai/annotations?project_id={project['id']}")
    assert empty_listing.status_code == 200
    assert empty_listing.json() == []


def test_update_and_delete_annotation_validation(client):
    project, frame, template = _create_project_and_frame(client)
    saved = client.post("/api/ai/annotate", json={
        "project_id": project["id"],
        "frame_id": frame["id"],
        "template_id": template["id"],
    }).json()

    assert client.patch("/api/ai/annotations/999", json={"bbox": [0, 0, 1, 1]}).status_code == 404
    assert client.delete("/api/ai/annotations/999").status_code == 404
    assert client.patch(
        f"/api/ai/annotations/{saved['id']}",
        json={"template_id": 999},
    ).status_code == 404


def test_import_gt_mask_creates_annotations_with_seed_points(client):
    project, frame, template = _create_project_and_frame(client)
    mask = np.zeros((360, 640), dtype=np.uint8)
    cv2.rectangle(mask, (100, 80), (260, 220), 255, thickness=-1)
    ok, encoded = cv2.imencode(".png", mask)
    assert ok

    response = client.post(
        "/api/ai/import-gt-mask",
        data={
            "project_id": str(project["id"]),
            "frame_id": str(frame["id"]),
            "template_id": str(template["id"]),
            "label": "Imported GT",
            "color": "#22c55e",
        },
        files={"file": ("mask.png", encoded.tobytes(), "image/png")},
    )

    assert response.status_code == 201
    body = response.json()
    assert len(body) == 1
    assert body[0]["project_id"] == project["id"]
    assert body[0]["frame_id"] == frame["id"]
    assert body[0]["template_id"] == template["id"]
    assert body[0]["mask_data"]["label"] == "Imported GT"
    assert body[0]["mask_data"]["source"] == "gt_mask"
    assert body[0]["mask_data"]["gt_label_value"] == 255
    assert len(body[0]["mask_data"]["polygons"][0]) >= 3
    assert len(body[0]["points"]) == 1
    assert 0.0 <= body[0]["points"][0][0] <= 1.0
    assert 0.0 <= body[0]["points"][0][1] <= 1.0


def test_import_gt_mask_splits_label_values(client):
    project, frame, _ = _create_project_and_frame(client)
    mask = np.zeros((360, 640), dtype=np.uint8)
    cv2.rectangle(mask, (20, 20), (120, 120), 1, thickness=-1)
    cv2.rectangle(mask, (220, 80), (320, 180), 2, thickness=-1)
    ok, encoded = cv2.imencode(".png", mask)
    assert ok

    response = client.post(
        "/api/ai/import-gt-mask",
        data={
            "project_id": str(project["id"]),
            "frame_id": str(frame["id"]),
            "label": "GT Class",
        },
        files={"file": ("labels.png", encoded.tobytes(), "image/png")},
    )

    assert response.status_code == 201
    body = sorted(response.json(), key=lambda item: item["mask_data"]["gt_label_value"])
    assert [item["mask_data"]["gt_label_value"] for item in body] == [1, 2]
    assert [item["mask_data"]["label"] for item in body] == ["GT Class 1", "GT Class 2"]
    assert all(len(item["points"]) == 1 for item in body)