Fix test_pipelines_video_classification that was always failing

src/transformers/pipelines/video_classification.py

@@ -106,9 +106,9 @@ def __call__(self, inputs: Union[str, List[str]] = None, **kwargs):
                 post-processing.
 
         Return:
-            A dictionary or a list of dictionaries containing result. If the input is a single video, will return a
-            dictionary, if the input is a list of several videos, will return a list of dictionaries corresponding to
-            the videos.
+            A list of dictionaries or a list of list of dictionaries containing result. If the input is a single video,
+            will return a list of `top_k` dictionaries, if the input is a list of several videos, will return a list of list of
+            `top_k` dictionaries corresponding to the videos.
 
             The dictionaries contain the following keys:
 

tests/pipelines/test_pipelines_video_classification.py

@@ -91,14 +91,13 @@ def test_small_model_pt(self):
         )
 
         video_file_path = hf_hub_download(repo_id="nateraw/video-demo", filename="archery.mp4", repo_type="dataset")
-        outputs = video_classifier(video_file_path, top_k=2)
+        output = video_classifier(video_file_path, top_k=2)
         self.assertEqual(
-            nested_simplify(outputs, decimals=4),
+            nested_simplify(output, decimals=4),
             [{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}],
         )
-        for output in outputs:
-            for element in output:
-                compare_pipeline_output_to_hub_spec(element, VideoClassificationOutputElement)
+        for element in output:
+            compare_pipeline_output_to_hub_spec(element, VideoClassificationOutputElement)
 
         outputs = video_classifier(
             [