Fixing a regression with `return_all_scores` introduced in #17606 (#17906)

Fixing a regression with `return_all_scores` introduced in #17606

- The legacy test actually tested `return_all_scores=False` (the actual
  default) instead of `return_all_scores=True` (the actual weird case).

This commit adds the correct legacy test and fixes it.

Tmp legacy tests.

Actually fix the regression (also contains lists)

Less diffed code.

src/transformers/pipelines/text_classification.py

@@ -136,7 +136,9 @@ class TextClassificationPipeline(Pipeline):
             If `top_k` is used, one such dictionary is returned per label.
         """
         result = super().__call__(*args, **kwargs)
-        if isinstance(args[0], str) and isinstance(result, dict):
+        # TODO try and retrieve it in a nicer way from _sanitize_parameters.
+        _legacy = "top_k" not in kwargs
+        if isinstance(args[0], str) and _legacy:
             # This pipeline is odd, and return a list when single item is run
             return [result]
         else:

tests/pipelines/test_pipelines_text_classification.py

@@ -60,6 +60,29 @@ class TextClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTestC
         outputs = text_classifier("This is great !", return_all_scores=False)
         self.assertEqual(nested_simplify(outputs), [{"label": "LABEL_0", "score": 0.504}])
 
+        outputs = text_classifier("This is great !", return_all_scores=True)
+        self.assertEqual(
+            nested_simplify(outputs), [[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]]
+        )
+
+        outputs = text_classifier(["This is great !", "Something else"], return_all_scores=True)
+        self.assertEqual(
+            nested_simplify(outputs),
+            [
+                [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
+                [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
+            ],
+        )
+
+        outputs = text_classifier(["This is great !", "Something else"], return_all_scores=False)
+        self.assertEqual(
+            nested_simplify(outputs),
+            [
+                {"label": "LABEL_0", "score": 0.504},
+                {"label": "LABEL_0", "score": 0.504},
+            ],
+        )
+
     @require_torch
     def test_accepts_torch_device(self):
         import torch