black formatting

src/transformers/pipelines.py

@@ -705,14 +705,17 @@ class QuestionAnsweringPipeline(Pipeline):
 
         # Convert inputs to features
         examples = self._args_parser(*texts, **kwargs)
-        features_list = [ squad_convert_examples_to_features(
+        features_list = [
+            squad_convert_examples_to_features(
                 [example],
                 self.tokenizer,
                 kwargs["max_seq_len"],
                 kwargs["doc_stride"],
                 kwargs["max_question_len"],
-                            False
-                            ) for example in examples ]
+                False,
+            )
+            for example in examples
+        ]
         all_answers = []
         for features, example in zip(features_list, examples):
             fw_args = self.inputs_for_model([f.__dict__ for f in features])
@@ -737,7 +740,10 @@ class QuestionAnsweringPipeline(Pipeline):
                 end_ = np.exp(end_) / np.sum(np.exp(end_))
 
                 # Mask padding and question
-                start_, end_ = start_ * np.abs(np.array(feature.p_mask) - 1), end_ * np.abs(np.array(feature.p_mask) - 1)
+                start_, end_ = (
+                    start_ * np.abs(np.array(feature.p_mask) - 1),
+                    end_ * np.abs(np.array(feature.p_mask) - 1),
+                )
 
                 # TODO : What happens if not possible
                 # Mask CLS
@@ -758,8 +764,8 @@ class QuestionAnsweringPipeline(Pipeline):
                     }
                     for s, e, score in zip(starts, ends, scores)
                 ]
-            answers = sorted(answers, key = lambda x:x['score'], reverse=True)[:kwargs["topk"]]    
-            all_answers+=answers
+            answers = sorted(answers, key=lambda x: x["score"], reverse=True)[: kwargs["topk"]]
+            all_answers += answers
 
         if len(all_answers) == 1:
             return all_answers[0]