chore: lint

Author: rishisurana-labelbox

examples/annotation_import/audio.ipynb

@@ -67,7 +67,7 @@
 __all__ = [
     # Geometry
     "Line",
-    "Point", 
+    "Point",
     "Mask",
     "Polygon",
     "Rectangle",

libs/labelbox/src/labelbox/data/annotation_types/__init__.py

@@ -74,4 +74,3 @@ class ClassificationAnnotation(
 
     value: Union[Text, Checklist, Radio]
     message_id: Optional[str] = None
-

libs/labelbox/src/labelbox/data/annotation_types/classification/classification.py

@@ -69,8 +69,22 @@ def validate_data(cls, data):
     def object_annotations(self) -> List[ObjectAnnotation]:
         return self._get_annotations_by_type(ObjectAnnotation)
 
-    def classification_annotations(self) -> List[Union[ClassificationAnnotation, TemporalClassificationText, TemporalClassificationQuestion]]:
-        return self._get_annotations_by_type((ClassificationAnnotation, TemporalClassificationText, TemporalClassificationQuestion))
+    def classification_annotations(
+        self,
+    ) -> List[
+        Union[
+            ClassificationAnnotation,
+            TemporalClassificationText,
+            TemporalClassificationQuestion,
+        ]
+    ]:
+        return self._get_annotations_by_type(
+            (
+                ClassificationAnnotation,
+                TemporalClassificationText,
+                TemporalClassificationQuestion,
+            )
+        )
 
     def _get_annotations_by_type(self, annotation_type):
         return [
@@ -112,13 +126,26 @@ def frame_annotations(
                 (VideoObjectAnnotation, VideoClassificationAnnotation),
             ):
                 frame_dict[annotation.frame].append(annotation)
-            elif isinstance(annotation, (TemporalClassificationText, TemporalClassificationQuestion)):
+            elif isinstance(
+                annotation,
+                (TemporalClassificationText, TemporalClassificationQuestion),
+            ):
                 # For temporal annotations with multiple values/answers, use first frame
-                if isinstance(annotation, TemporalClassificationText) and annotation.value:
-                    frame_dict[annotation.value[0][0]].append(annotation)  # value[0][0] is start_frame
-                elif isinstance(annotation, TemporalClassificationQuestion) and annotation.value:
+                if (
+                    isinstance(annotation, TemporalClassificationText)
+                    and annotation.value
+                ):
+                    frame_dict[annotation.value[0][0]].append(
+                        annotation
+                    )  # value[0][0] is start_frame
+                elif (
+                    isinstance(annotation, TemporalClassificationQuestion)
+                    and annotation.value
+                ):
                     if annotation.value[0].frames:
-                        frame_dict[annotation.value[0].frames[0][0]].append(annotation)  # frames[0][0] is start_frame
+                        frame_dict[annotation.value[0].frames[0][0]].append(
+                            annotation
+                        )  # frames[0][0] is start_frame
         return dict(frame_dict)
 
     def add_url_to_masks(self, signer) -> "Label":

libs/labelbox/src/labelbox/data/annotation_types/label.py

@@ -47,7 +47,11 @@ class TemporalClassificationAnswer(BaseModel):
         description="List of (start_frame, end_frame) tuples in milliseconds",
     )
     classifications: Optional[
-        List[Union["TemporalClassificationText", "TemporalClassificationQuestion"]]
+        List[
+            Union[
+                "TemporalClassificationText", "TemporalClassificationQuestion"
+            ]
+        ]
     ] = None
 
 
@@ -97,7 +101,11 @@ class TemporalClassificationText(BaseModel):
         description="List of (start_frame, end_frame, text_value) tuples",
     )
     classifications: Optional[
-        List[Union["TemporalClassificationText", "TemporalClassificationQuestion"]]
+        List[
+            Union[
+                "TemporalClassificationText", "TemporalClassificationQuestion"
+            ]
+        ]
     ] = None
 
 
@@ -175,7 +183,11 @@ class TemporalClassificationQuestion(BaseModel):
         description="List of temporal answer options",
     )
     classifications: Optional[
-        List[Union["TemporalClassificationText", "TemporalClassificationQuestion"]]
+        List[
+            Union[
+                "TemporalClassificationText", "TemporalClassificationQuestion"
+            ]
+        ]
     ] = None
 
 

libs/labelbox/src/labelbox/data/annotation_types/temporal.py

@@ -175,7 +175,10 @@ def _create_temporal_classifications(
         temporal_annotations = [
             annot
             for annot in label.annotations
-            if isinstance(annot, (TemporalClassificationText, TemporalClassificationQuestion))
+            if isinstance(
+                annot,
+                (TemporalClassificationText, TemporalClassificationQuestion),
+            )
         ]
 
         if not temporal_annotations:

libs/labelbox/src/labelbox/data/serialization/ndjson/label.py

@@ -63,7 +63,9 @@ def create_temporal_ndjson_classifications(
         elif isinstance(first_ann, TemporalClassificationQuestion):
             answers = _process_question_group(group_anns, parent_frames=None)
         else:
-            logger.warning(f"Unknown temporal annotation type: {type(first_ann)}")
+            logger.warning(
+                f"Unknown temporal annotation type: {type(first_ann)}"
+            )
             continue
 
         if answers:  # Only add if we have valid answers
@@ -99,7 +101,9 @@ def _process_text_group(
     for ann in annotations:
         for start, end, text_value in ann.value:
             # Validate frames against parent if provided
-            if parent_frames and not _is_frame_subset([(start, end)], parent_frames):
+            if parent_frames and not _is_frame_subset(
+                [(start, end)], parent_frames
+            ):
                 logger.warning(
                     f"Text value frames ({start}, {end}) not subset of parent frames {parent_frames}. Discarding."
                 )
@@ -127,7 +131,9 @@ def _process_text_group(
 
         # Assign nested classifications based on frame overlap
         if all_nested_classifications:
-            parent_frame_tuples = [(f["start"], f["end"]) for f in unique_frames]
+            parent_frame_tuples = [
+                (f["start"], f["end"]) for f in unique_frames
+            ]
             # Filter nested classifications that overlap with this text value's frames
             relevant_nested = _filter_classifications_by_overlap(
                 all_nested_classifications, parent_frame_tuples
@@ -138,7 +144,9 @@ def _process_text_group(
                     assigned_nested.add(id(cls))
 
                 # Pass ONLY THIS text value's frames so nested answers are filtered correctly
-                nested = _process_nested_classifications(relevant_nested, parent_frame_tuples)
+                nested = _process_nested_classifications(
+                    relevant_nested, parent_frame_tuples
+                )
                 if nested:
                     entry["classifications"] = nested
 
@@ -151,7 +159,11 @@ def _process_text_group(
                 if isinstance(cls, TemporalClassificationText):
                     frames_info = cls.value[0][:2] if cls.value else "no frames"
                 elif isinstance(cls, TemporalClassificationQuestion):
-                    frames_info = cls.value[0].frames if cls.value and cls.value[0].frames else "no frames"
+                    frames_info = (
+                        cls.value[0].frames
+                        if cls.value and cls.value[0].frames
+                        else "no frames"
+                    )
                 else:
                     frames_info = "unknown"
                 logger.warning(
@@ -204,7 +216,9 @@ def _process_question_group(
 
                 # Collect nested classifications at answer level
                 if answer.classifications:
-                    all_nested_by_answer[answer.name].extend(answer.classifications)
+                    all_nested_by_answer[answer.name].extend(
+                        answer.classifications
+                    )
 
     # Track which nested classifications were assigned
     assigned_nested = set()
@@ -225,7 +239,9 @@ def _process_question_group(
 
         # Assign nested classifications based on frame overlap
         if all_nested_by_answer[answer_name]:
-            parent_frame_tuples = [(f["start"], f["end"]) for f in unique_frames]
+            parent_frame_tuples = [
+                (f["start"], f["end"]) for f in unique_frames
+            ]
             # Filter nested classifications that overlap with this answer's frames
             relevant_nested = _filter_classifications_by_overlap(
                 all_nested_by_answer[answer_name], parent_frame_tuples
@@ -235,7 +251,9 @@ def _process_question_group(
                 for cls in relevant_nested:
                     assigned_nested.add(id(cls))
 
-                nested = _process_nested_classifications(relevant_nested, parent_frame_tuples)
+                nested = _process_nested_classifications(
+                    relevant_nested, parent_frame_tuples
+                )
                 if nested:
                     entry["classifications"] = nested
 
@@ -248,7 +266,11 @@ def _process_question_group(
                 if isinstance(cls, TemporalClassificationText):
                     frames_info = cls.value[0][:2] if cls.value else "no frames"
                 elif isinstance(cls, TemporalClassificationQuestion):
-                    frames_info = cls.value[0].frames if cls.value and cls.value[0].frames else "no frames"
+                    frames_info = (
+                        cls.value[0].frames
+                        if cls.value and cls.value[0].frames
+                        else "no frames"
+                    )
                 else:
                     frames_info = "unknown"
                 logger.warning(
@@ -260,7 +282,9 @@ def _process_question_group(
 
 
 def _process_nested_classifications(
-    classifications: List[Union[TemporalClassificationText, TemporalClassificationQuestion]],
+    classifications: List[
+        Union[TemporalClassificationText, TemporalClassificationQuestion]
+    ],
     parent_frames: List[Tuple[int, int]],
 ) -> List[Dict[str, Any]]:
     """
@@ -286,20 +310,26 @@ def _process_nested_classifications(
         elif isinstance(first_item, TemporalClassificationQuestion):
             answers = _process_question_group(group_items, parent_frames)
         else:
-            logger.warning(f"Unknown nested classification type: {type(first_item)}")
+            logger.warning(
+                f"Unknown nested classification type: {type(first_item)}"
+            )
             continue
 
         if answers:  # Only add if we have valid answers
-            results.append({
-                "name": display_name,
-                "answer": answers,
-            })
+            results.append(
+                {
+                    "name": display_name,
+                    "answer": answers,
+                }
+            )
 
     return results
 
 
 def _filter_classifications_by_overlap(
-    classifications: List[Union[TemporalClassificationText, TemporalClassificationQuestion]],
+    classifications: List[
+        Union[TemporalClassificationText, TemporalClassificationQuestion]
+    ],
     parent_frames: List[Tuple[int, int]],
 ) -> List[Union[TemporalClassificationText, TemporalClassificationQuestion]]:
     """