Fix quality due to ruff release

src/transformers/pipelines/document_question_answering.py

@@ -418,7 +418,7 @@ class DocumentQuestionAnsweringPipeline(ChunkPipeline):
         else:
             model_outputs = self.model(**model_inputs)
 
-        model_outputs = {k: v for (k, v) in model_outputs.items()}
+        model_outputs = dict(model_outputs.items())
         model_outputs["p_mask"] = p_mask
         model_outputs["word_ids"] = word_ids
         model_outputs["words"] = words

src/transformers/utils/generic.py

@@ -282,7 +282,7 @@ class ModelOutput(OrderedDict):
 
     def __getitem__(self, k):
         if isinstance(k, str):
-            inner_dict = {k: v for (k, v) in self.items()}
+            inner_dict = dict(self.items())
             return inner_dict[k]
         else:
             return self.to_tuple()[k]

tests/models/mask2former/test_image_processing_mask2former.py

@@ -298,9 +298,7 @@ class Mask2FormerImageProcessingTest(ImageProcessingSavingTestMixin, unittest.Te
             high = num_labels
             if is_instance_map:
                 labels_expanded = list(range(num_labels)) * 2
-                instance_id_to_semantic_id = {
-                    instance_id: label_id for instance_id, label_id in enumerate(labels_expanded)
-                }
+                instance_id_to_semantic_id = dict(enumerate(labels_expanded))
             annotations = [
                 np.random.randint(0, high * 2, (img.size[1], img.size[0])).astype(np.uint8) for img in image_inputs
             ]

tests/models/maskformer/test_image_processing_maskformer.py

@@ -298,9 +298,7 @@ class MaskFormerImageProcessingTest(ImageProcessingSavingTestMixin, unittest.Tes
             high = num_labels
             if is_instance_map:
                 labels_expanded = list(range(num_labels)) * 2
-                instance_id_to_semantic_id = {
-                    instance_id: label_id for instance_id, label_id in enumerate(labels_expanded)
-                }
+                instance_id_to_semantic_id = dict(enumerate(labels_expanded))
             annotations = [
                 np.random.randint(0, high * 2, (img.size[1], img.size[0])).astype(np.uint8) for img in image_inputs
             ]

tests/models/oneformer/test_image_processing_oneformer.py

@@ -329,9 +329,7 @@ class OneFormerImageProcessingTest(ImageProcessingSavingTestMixin, unittest.Test
             high = num_labels
             if is_instance_map:
                 labels_expanded = list(range(num_labels)) * 2
-                instance_id_to_semantic_id = {
-                    instance_id: label_id for instance_id, label_id in enumerate(labels_expanded)
-                }
+                instance_id_to_semantic_id = dict(enumerate(labels_expanded))
             annotations = [
                 np.random.randint(0, high * 2, (img.size[1], img.size[0])).astype(np.uint8) for img in image_inputs
             ]

tests/models/oneformer/test_processor_oneformer.py

@@ -401,9 +401,7 @@ class OneFormerProcessingTest(unittest.TestCase):
             high = num_labels
             if is_instance_map:
                 labels_expanded = list(range(num_labels)) * 2
-                instance_id_to_semantic_id = {
-                    instance_id: label_id for instance_id, label_id in enumerate(labels_expanded)
-                }
+                instance_id_to_semantic_id = dict(enumerate(labels_expanded))
             annotations = [
                 np.random.randint(0, high * 2, (img.size[1], img.size[0])).astype(np.uint8) for img in image_inputs
             ]

tests/pipelines/test_pipelines_automatic_speech_recognition.py

@@ -56,11 +56,8 @@ if is_torch_available():
 
 @is_pipeline_test
 class AutomaticSpeechRecognitionPipelineTests(unittest.TestCase):
-    model_mapping = {
-        k: v
-        for k, v in (list(MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING.items()) if MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING else [])
-        + (MODEL_FOR_CTC_MAPPING.items() if MODEL_FOR_CTC_MAPPING else [])
-    }
+    model_mapping = dict((list(MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING.items()) if MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING else [])
+        + (MODEL_FOR_CTC_MAPPING.items() if MODEL_FOR_CTC_MAPPING else []))
 
     def get_test_pipeline(self, model, tokenizer, processor):
         if tokenizer is None:

tests/pipelines/test_pipelines_image_segmentation.py

@@ -80,14 +80,11 @@ def mask_to_test_readable_only_shape(mask: Image) -> Dict:
 @require_timm
 @require_torch
 class ImageSegmentationPipelineTests(unittest.TestCase):
-    model_mapping = {
-        k: v
-        for k, v in (
+    model_mapping = dict((
             list(MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()) if MODEL_FOR_IMAGE_SEGMENTATION_MAPPING else []
         )
         + (MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items() if MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING else [])
-        + (MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items() if MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING else [])
-    }
+        + (MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items() if MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING else []))
 
     def get_test_pipeline(self, model, tokenizer, processor):
         image_segmenter = ImageSegmentationPipeline(model=model, image_processor=processor)