fix(pre_commit): 🎨 auto format pre-commit hooks

test/metrics/test_mean_average_precision_area.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
-import pytest
 import numpy as np
+import pytest
 
 from supervision.detection.core import Detections
 from supervision.metrics.mean_average_precision import MeanAveragePrecision
@@ -14,32 +14,37 @@ class TestMeanAveragePrecisionArea:
         "xyxy, expected_areas, expected_size_maps",
         [
             (
-                np.array([
-                    [10, 10, 40, 40],  # Small: 900
-                    [100, 100, 200, 150],  # Medium: 5000
-                    [300, 300, 500, 400],  # Large: 20000
-                ], dtype=np.float32),
+                np.array(
+                    [
+                        [10, 10, 40, 40],  # Small: 900
+                        [100, 100, 200, 150],  # Medium: 5000
+                        [300, 300, 500, 400],  # Large: 20000
+                    ],
+                    dtype=np.float32,
+                ),
                 [900.0, 5000.0, 20000.0],
-                {"small": True, "medium": True, "large": True}
+                {"small": True, "medium": True, "large": True},
             ),
             (
                 np.array([[0, 0, 10, 10]], dtype=np.float32),  # Small: 100
                 [100.0],
-                {"small": True, "medium": False, "large": False}
+                {"small": True, "medium": False, "large": False},
             ),
             (
                 np.array([[0, 0, 50, 50]], dtype=np.float32),  # Medium: 2500
                 [2500.0],
-                {"small": False, "medium": True, "large": False}
+                {"small": False, "medium": True, "large": False},
             ),
             (
                 np.array([[0, 0, 100, 100]], dtype=np.float32),  # Large: 10000
                 [10000.0],
-                {"small": False, "medium": False, "large": True}
+                {"small": False, "medium": False, "large": True},
             ),
-        ]
+        ],
     )
-    def test_area_calculation_and_size_specific_map(self, xyxy, expected_areas, expected_size_maps):
+    def test_area_calculation_and_size_specific_map(
+        self, xyxy, expected_areas, expected_size_maps
+    ):
         """Test area calculation and size-specific mAP functionality."""
         gt = Detections(
             xyxy=xyxy,
@@ -57,50 +62,70 @@ class TestMeanAveragePrecisionArea:
         # Test area calculation
         prepared_targets = map_metric._prepare_targets(map_metric._targets_list)
         areas = [ann["area"] for ann in prepared_targets["annotations"]]
-        assert np.allclose(areas, expected_areas), f"Expected {expected_areas}, got {areas}"
+        assert np.allclose(areas, expected_areas), (
+            f"Expected {expected_areas}, got {areas}"
+        )
 
         # Test size-specific mAP
         result = map_metric.compute()
 
         if expected_size_maps["small"]:
-            assert result.small_objects.map50 > 0.9, "Small objects should have high mAP"
+            assert result.small_objects.map50 > 0.9, (
+                "Small objects should have high mAP"
+            )
         else:
-            assert result.small_objects.map50 == -1.0, "Small objects should have no data"
+            assert result.small_objects.map50 == -1.0, (
+                "Small objects should have no data"
+            )
 
         if expected_size_maps["medium"]:
-            assert result.medium_objects.map50 > 0.9, "Medium objects should have high mAP"
+            assert result.medium_objects.map50 > 0.9, (
+                "Medium objects should have high mAP"
+            )
         else:
-            assert result.medium_objects.map50 == -1.0, "Medium objects should have no data"
+            assert result.medium_objects.map50 == -1.0, (
+                "Medium objects should have no data"
+            )
 
         if expected_size_maps["large"]:
-            assert result.large_objects.map50 > 0.9, "Large objects should have high mAP"
+            assert result.large_objects.map50 > 0.9, (
+                "Large objects should have high mAP"
+            )
         else:
-            assert result.large_objects.map50 == -1.0, "Large objects should have no data"
+            assert result.large_objects.map50 == -1.0, (
+                "Large objects should have no data"
+            )
 
     def test_area_preserved_from_data(self):
         """Test that area from data field is preserved (COCO case)."""
         gt = Detections(
-            xyxy=np.array([[100, 100, 200, 150]], dtype=np.float32),  # Would calculate to 5000
+            xyxy=np.array(
+                [[100, 100, 200, 150]], dtype=np.float32
+            ),  # Would calculate to 5000
             class_id=np.array([0]),
         )
         # Override with custom area
         gt.data = {"area": np.array([3000.0])}
-        
+
         pred = Detections(
             xyxy=gt.xyxy.copy(),
             class_id=gt.class_id.copy(),
             confidence=np.array([0.9]),
         )
         pred.data = {"area": np.array([3000.0])}
-        
+
         map_metric = MeanAveragePrecision()
         map_metric.update([pred], [gt])
-        
+
         prepared_targets = map_metric._prepare_targets(map_metric._targets_list)
         used_area = prepared_targets["annotations"][0]["area"]
-        
-        assert np.allclose(used_area, 3000.0), f"Should use provided area 3000.0, got {used_area}"
-        
+
+        assert np.allclose(used_area, 3000.0), (
+            f"Should use provided area 3000.0, got {used_area}"
+        )
+
         # Verify it's different from what would be calculated
         calculated_area = (200 - 100) * (150 - 100)  # 100 * 50 = 5000
-        assert not np.allclose(used_area, calculated_area), "Should use provided area, not calculated"
+        assert not np.allclose(used_area, calculated_area), (
+            "Should use provided area, not calculated"
+        )