Update ruff to 0.11.2 (#36962)

* update

* update

* update

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

tests/models/fuyu/test_image_processing_fuyu.py

@@ -42,9 +42,9 @@ class TestFuyuImageProcessor(unittest.TestCase):
         expected_num_patches = self.processor.get_num_patches(image_height=self.height, image_width=self.width)
 
         patches_final = self.processor.patchify_image(image=self.image_input)
-        assert (
-            patches_final.shape[1] == expected_num_patches
-        ), f"Expected {expected_num_patches} patches, got {patches_final.shape[1]}."
+        assert patches_final.shape[1] == expected_num_patches, (
+            f"Expected {expected_num_patches} patches, got {patches_final.shape[1]}."
+        )
 
     def test_scale_to_target_aspect_ratio(self):
         # (h:450, w:210) fitting (160, 320) -> (160, 210*160/450)

tests/models/gpt2/test_modeling_gpt2.py

@@ -431,9 +431,9 @@ class GPT2ModelTester:
         model.eval()
 
         # We want this for SDPA, eager works with a `None` attention mask
-        assert (
-            model.config._attn_implementation == "sdpa"
-        ), "This test assumes the model to have the SDPA implementation for its attention calculations."
+        assert model.config._attn_implementation == "sdpa", (
+            "This test assumes the model to have the SDPA implementation for its attention calculations."
+        )
 
         # Prepare cache and non_cache input, needs a full attention mask
         cached_len = input_ids.shape[-1] // 2

tests/models/gpt_neox/test_modeling_gpt_neox.py

@@ -222,9 +222,9 @@ class GPTNeoXModelTester:
         model.eval()
 
         # We want this for SDPA, eager works with a `None` attention mask
-        assert (
-            model.config._attn_implementation == "sdpa"
-        ), "This test assumes the model to have the SDPA implementation for its attention calculations."
+        assert model.config._attn_implementation == "sdpa", (
+            "This test assumes the model to have the SDPA implementation for its attention calculations."
+        )
 
         # Prepare cache and non_cache input, needs a full attention mask
         cached_len = input_ids.shape[-1] // 2

tests/models/mask2former/test_image_processing_mask2former.py

@@ -315,7 +315,7 @@ class Mask2FormerImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase
             inst2class = {}
             for label in class_labels:
                 instance_ids = np.unique(instance_seg[class_id_map == label])
-                inst2class.update({i: label for i in instance_ids})
+                inst2class.update(dict.fromkeys(instance_ids, label))
 
             return instance_seg, inst2class
 

tests/models/maskformer/test_image_processing_maskformer.py

@@ -269,7 +269,7 @@ class MaskFormerImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase)
             inst2class = {}
             for label in class_labels:
                 instance_ids = np.unique(instance_seg[class_id_map == label])
-                inst2class.update({i: label for i in instance_ids})
+                inst2class.update(dict.fromkeys(instance_ids, label))
 
             return instance_seg, inst2class