Reformat

rasa/nlu/test.py

@@ -1108,7 +1108,9 @@ def do_any_extractors_not_support_overlap(extractors: Optional[Set[Text]]) -> bo
     from rasa.nlu.extractors.crf_entity_extractor import CRFEntityExtractor
     from rasa.nlu.classifiers.diet_classifier import DIETClassifier
 
-    return not extractors.isdisjoint({CRFEntityExtractor.__name__, DIETClassifier.__name__})
+    return not extractors.isdisjoint(
+        {CRFEntityExtractor.__name__, DIETClassifier.__name__}
+    )
 
 
 def align_entity_predictions(

rasa/shared/nlu/training_data/formats/readerwriter.py

@@ -137,7 +137,8 @@ def generate_message(message: Dict[Text, Any]) -> Text:
             for start, entities in sorted(aggregated_entities.items()):
                 md += text[pos:start]
                 md += TrainingDataWriter.generate_entity(
-                    text, entities[0] if len(entities) == 1 else entities,
+                    text,
+                    entities[0] if len(entities) == 1 else entities,
                 )
                 pos = entities[0]["end"]
 
@@ -190,7 +191,6 @@ def generate_entity_attributes(
 
             return f"{json.dumps(entity_dict)}"
 
-
     @staticmethod
     def generate_entity(
         text: Text, entity: Union[Dict[Text, Any], List[Dict[Text, Any]]]

tests/shared/nlu/training_data/formats/test_readerwriter.py

@@ -8,21 +8,31 @@
 @pytest.mark.parametrize(
     "message_text, expected_text, entities",
     [
-        ("I like chocolate", "I like chocolate", [],),
+        (
+            "I like chocolate",
+            "I like chocolate",
+            [],
+        ),
         (
             "I like chocolate",
             "I like [chocolate](food)",
-            [{"entity": "food", "value": "chocolate", "start": 7, "end": 16},],
+            [
+                {"entity": "food", "value": "chocolate", "start": 7, "end": 16},
+            ],
         ),
         (
             "I like chocolate",
             'I like [chocolate]{"entity": "food", "value": "desert"}',
-            [{"entity": "food", "value": "desert", "start": 7, "end": 16},],
+            [
+                {"entity": "food", "value": "desert", "start": 7, "end": 16},
+            ],
         ),
         (
             f"{INTENT_MESSAGE_PREFIX}I like chocolate",
             f"{INTENT_MESSAGE_PREFIX}I like chocolate",
-            [{"entity": "food", "value": "desert", "start": 7, "end": 16},],
+            [
+                {"entity": "food", "value": "desert", "start": 7, "end": 16},
+            ],
         ),
         (
             "I like chocolate",
@@ -89,7 +99,9 @@
     ],
 )
 def test_generate_message(
-    message_text: Text, expected_text: Text, entities: List[Dict[Text, Any]],
+    message_text: Text,
+    expected_text: Text,
+    entities: List[Dict[Text, Any]],
 ):
     message = Message.build(message_text, "dummy_intent", entities=entities)
     message_text = TrainingDataWriter.generate_message(message)
@@ -110,7 +122,8 @@ def test_generate_message(
     ],
 )
 def test_generate_message_raises_on_overlapping_but_not_identical_spans(
-    message_text: Text, entities: List[Dict[Text, Any]],
+    message_text: Text,
+    entities: List[Dict[Text, Any]],
 ):
     message = Message.build(message_text, "dummy_intent", entities=entities)
     with pytest.raises(ValueError):

tests/shared/nlu/training_data/test_entities_parser.py

@@ -117,7 +117,12 @@
                     "entity": "city",
                     "role": "from",
                 },
-                {"start": 29, "end": 31, "value": "Los Angeles", "entity": "location",},
+                {
+                    "start": 29,
+                    "end": 31,
+                    "value": "Los Angeles",
+                    "entity": "location",
+                },
             ],
             "I want to fly from Berlin to LA",
         ),