Fixes NoneType exception when topk is larger than one coupled with a small context in the Question-Answering pipeline (#11628)

* added fix to decode function. added test to qa pipeline tests

* completed topk docstring

* fixed formatting with black

* applied style_doc to fix line length

tests/test_pipelines_question_answering.py

@@ -15,7 +15,8 @@
 import unittest
 
 from transformers.data.processors.squad import SquadExample
-from transformers.pipelines import Pipeline, QuestionAnsweringArgumentHandler
+from transformers.pipelines import Pipeline, QuestionAnsweringArgumentHandler, pipeline
+from transformers.testing_utils import slow
 
 from .test_pipelines_common import CustomInputPipelineCommonMixin
 
@@ -50,6 +51,34 @@ class QAPipelineTests(CustomInputPipelineCommonMixin, unittest.TestCase):
         },
     ]
 
+    def get_pipelines(self):
+        question_answering_pipelines = [
+            pipeline(
+                task=self.pipeline_task,
+                model=model,
+                tokenizer=model,
+                framework="pt",
+                **self.pipeline_loading_kwargs,
+            )
+            for model in self.small_models
+        ]
+        return question_answering_pipelines
+
+    @slow
+    def test_high_topk_small_context(self):
+        self.pipeline_running_kwargs.update({"topk": 20})
+        valid_inputs = [
+            {"question": "Where was HuggingFace founded ?", "context": "Paris"},
+        ]
+        nlps = self.get_pipelines()
+        output_keys = {"score", "answer", "start", "end"}
+        for nlp in nlps:
+            result = nlp(valid_inputs, **self.pipeline_running_kwargs)
+            self.assertIsInstance(result, dict)
+
+            for key in output_keys:
+                self.assertIn(key, result)
+
     def _test_pipeline(self, nlp: Pipeline):
         output_keys = {"score", "answer", "start", "end"}
         valid_inputs = [