"""
Tests for the Visualization Module — ActionVisualizer.
"""

import numpy as np
import pytest

from src.visualization.visualizer import ActionVisualizer


class TestActionVisualizer:
    """Tests for ActionVisualizer methods."""

    @pytest.fixture
    def visualizer(self) -> ActionVisualizer:
        return ActionVisualizer()

    @pytest.fixture
    def test_image(self) -> np.ndarray:
        """A 480x640 synthetic test image."""
        return np.zeros((480, 640, 3), dtype=np.uint8)

    @pytest.fixture
    def sample_detections(self) -> list[dict]:
        return [
            {"label": "bottle", "confidence": 0.92, "bbox": [100, 150, 200, 350]},
            {"label": "box", "confidence": 0.85, "bbox": [400, 300, 550, 500]},
        ]

    @pytest.fixture
    def sample_action_plan(self) -> dict:
        return {
            "action": "move",
            "steps": [
                "Step 1: Locate 'bottle'",
                "Step 2: Locate 'box'",
                "Step 3: Move 'bottle' → 'box'",
                "Step 4: Verify action completion",
            ],
            "source_bbox": [100, 150, 200, 350],
            "dest_bbox": [400, 300, 550, 500],
            "status": "success",
        }

    def test_draw_detections_returns_image(
        self, visualizer, test_image, sample_detections
    ):
        """draw_detections should return an image of the same shape."""
        result = visualizer.draw_detections(test_image, sample_detections)

        assert isinstance(result, np.ndarray)
        assert result.shape == test_image.shape

    def test_draw_detections_modifies_image(
        self, visualizer, test_image, sample_detections
    ):
        """draw_detections should modify the image (not return a blank copy)."""
        result = visualizer.draw_detections(test_image, sample_detections)

        # The annotated image should differ from the original
        assert not np.array_equal(result, test_image)

    def test_draw_detections_empty_list(self, visualizer, test_image):
        """draw_detections with empty list should still return an image."""
        result = visualizer.draw_detections(test_image, [])

        assert isinstance(result, np.ndarray)
        assert result.shape == test_image.shape

    def test_visualize_returns_image(
        self, visualizer, test_image, sample_action_plan
    ):
        """visualize should return an image of the same shape."""
        result = visualizer.visualize(test_image, sample_action_plan)

        assert isinstance(result, np.ndarray)
        assert result.shape == test_image.shape

    def test_visualize_modifies_image(
        self, visualizer, test_image, sample_action_plan
    ):
        """visualize should modify the image."""
        result = visualizer.visualize(test_image, sample_action_plan)

        assert not np.array_equal(result, test_image)

    def test_visualize_with_no_bboxes(self, visualizer, test_image):
        """visualize should handle missing bounding boxes gracefully."""
        plan = {
            "action": "inspect",
            "steps": ["Step 1: Inspect object"],
            "source_bbox": None,
            "dest_bbox": None,
            "status": "failed",
        }

        result = visualizer.visualize(test_image, plan)

        assert isinstance(result, np.ndarray)
        assert result.shape == test_image.shape

    def test_draw_detections_invalid_image(self, visualizer):
        """draw_detections with None should return a fallback image."""
        result = visualizer.draw_detections(None, [])

        assert isinstance(result, np.ndarray)