[smolvlm] skip the test (#38099)

skip the test

tests/models/smolvlm/test_processor_smolvlm.py

@@ -22,7 +22,7 @@ import requests
 
 from transformers import SmolVLMProcessor
 from transformers.models.auto.processing_auto import AutoProcessor
-from transformers.testing_utils import is_flaky, require_av, require_torch, require_vision
+from transformers.testing_utils import require_av, require_torch, require_vision
 from transformers.utils import is_vision_available
 
 from ...test_processing_common import ProcessorTesterMixin
@@ -118,10 +118,6 @@ class SmolVLMProcessorTest(ProcessorTesterMixin, unittest.TestCase):
     def tearDownClass(cls):
         shutil.rmtree(cls.tmpdirname, ignore_errors=True)
 
-    @is_flaky  # fails 15 out of 100, FIXME @raushan
-    def test_structured_kwargs_nested_from_dict_video(self):
-        super().test_structured_kwargs_nested_from_dict_video()
-
     def test_process_interleaved_images_prompts_no_image_splitting(self):
         processor_components = self.prepare_components()
         processor_components["tokenizer"] = self.get_component("tokenizer", padding_side="left")
@@ -467,6 +463,31 @@ class SmolVLMProcessorTest(ProcessorTesterMixin, unittest.TestCase):
         self.assertEqual(inputs["pixel_values"].shape[3], 300)
         self.assertEqual(len(inputs["input_ids"][0]), 76)
 
+    @require_torch
+    @require_vision
+    def test_unstructured_kwargs_batched_video(self):
+        if "video_processor" not in self.processor_class.attributes:
+            self.skipTest(f"video_processor attribute not present in {self.processor_class}")
+        processor_components = self.prepare_components()
+        processor_kwargs = self.prepare_processor_dict()
+        processor = self.processor_class(**processor_components, **processor_kwargs)
+        self.skip_processor_without_typed_kwargs(processor)
+
+        input_str = self.prepare_text_inputs(batch_size=2, modality="video")
+        video_input = self.prepare_video_inputs(batch_size=2)
+        inputs = processor(
+            text=input_str,
+            videos=video_input,
+            return_tensors="pt",
+            do_rescale=True,
+            rescale_factor=-1,
+            padding="max_length",
+            max_length=76,
+        )
+
+        self.assertLessEqual(inputs[self.videos_input_name][0].mean(), 0)
+        self.assertEqual(len(inputs["input_ids"][0]), 76)
+
     @require_torch
     @require_vision
     def test_text_only_inference(self):