revert formatting

dataquality/__init__.py

@@ -30,7 +30,6 @@
         dataquality.get_insights()
 """
 
-
 __version__ = "1.6.1"
 
 import sys

dataquality/analytics.py

@@ -31,6 +31,7 @@ class Analytics(Borg):
     """Analytics is used to track errors and logs in the background"""
 
     _telemetrics_disabled: bool = True
+    _initialized: bool = False
 
     def __init__(self, ApiClient: Type[ApiClient], config: Config) -> None:
         """To initialize the Analytics class you need

dataquality/integrations/setfit.py

@@ -353,7 +353,7 @@ def do_model_eval(
             dq_evaluate(
                 encoded_data[split],
                 split=split,
-                meta=meta_columns
+                meta=meta_columns,
                 # for inference set the split to inference
                 # and pass an inference_name="inference_run_1"
             )

dataquality/integrations/torch.py

@@ -296,10 +296,10 @@ def watch(
 def unwatch(model: Optional[Module] = None, force: bool = True) -> None:
     """Unwatches the model. Run after the run is finished.
     :param force: Force unwatch even if the model is not watched"""
-    torch_helper_data: TorchHelper = (
-        dq.get_model_logger().logger_config.helper_data.get(
-            "torch_helper", TorchHelper()
-        )
+    torch_helper_data: (
+        TorchHelper
+    ) = dq.get_model_logger().logger_config.helper_data.get(
+        "torch_helper", TorchHelper()
     )
 
     model = model or torch_helper_data.model

dataquality/integrations/ultralytics.py

@@ -234,8 +234,8 @@ def _after_pred_step(self, *args: Any, **kwargs: Any) -> None:
             logging_data = process_batch_data(self.bl.batch)
             if not self.nms_fn:
                 raise Exception("NMS function not found")
-            postprocess = (
-                lambda x: x if self.split == Split.validation else self.postprocess
+            postprocess = lambda x: (
+                x if self.split == Split.validation else self.postprocess
             )
             preds = postprocess(preds)
             nms = self.nms_fn(

dataquality/internal.py

@@ -1,4 +1,5 @@
 """Internal functions to help Galileans"""
+
 from typing import Dict
 
 from dataquality import config

dataquality/loggers/base_logger.py

@@ -208,8 +208,7 @@ def non_inference_logged(cls) -> bool:
         )
 
     @abstractmethod
-    def log(self) -> None:
-        ...
+    def log(self) -> None: ...
 
     @staticmethod
     def _convert_tensor_ndarray(
@@ -327,8 +326,7 @@ def _cleanup(cls) -> None:
         pm = PatchManager()
         pm.unpatch()
 
-    def upload(self) -> None:
-        ...
+    def upload(self) -> None: ...
 
     @classmethod
     def get_all_subclasses(cls: Type[T]) -> List[Type[T]]:

dataquality/loggers/data_logger/base_data_logger.py

@@ -578,8 +578,7 @@ def validate_and_format(self) -> None:
 
     @classmethod
     @abstractmethod
-    def validate_labels(cls) -> None:
-        ...
+    def validate_labels(cls) -> None: ...
 
     def validate_metadata(self, batch_size: int) -> None:
         if len(self.meta.keys()) > self.MAX_META_COLS:
@@ -689,17 +688,15 @@ def get_data_logger_attr(cls: object) -> str:
     @abstractmethod
     def separate_dataframe(
         cls, df: DataFrame, prob_only: bool = False, split: Optional[str] = None
-    ) -> BaseLoggerDataFrames:
-        ...
+    ) -> BaseLoggerDataFrames: ...
 
     def validate_kwargs(self, kwargs: Dict) -> None:
         """Raises if a function that shouldn't get kwargs gets any"""
         if kwargs.keys():
             raise GalileoException(f"Unexpected arguments: {tuple(kwargs.keys())}")
 
     @abstractmethod
-    def _get_input_df(self) -> DataFrame:
-        ...
+    def _get_input_df(self) -> DataFrame: ...
 
     @classmethod
     def set_tagging_schema(cls, tagging_schema: TaggingSchema) -> None:

tests/integrations/setfit/test_setfit.py

@@ -191,7 +191,7 @@ def test_setfit_trainer(
     dq_evaluate(
         dataset,
         split="test",
-        column_mapping=column_mapping
+        column_mapping=column_mapping,
         # for inference set the split to inference
         # and pass an inference_name="inference_run_1"
     )

tests/test_telemetrics.py

@@ -26,7 +26,7 @@ def test_mock_log_galileo_import():
     a = Analytics(MockClient, {"api_url": "https://console.dev.rungalileo.io"})
     a.last_log = {}
     a.log_import("test")
-    assert a.last_log["value"] == "test", "No import detected"
+    assert a.last_log.get("value") == "test", "No import detected"
 
 
 def test_log_galileo_exception():
@@ -52,7 +52,7 @@ def test_log_galileo__import():
         assert ac._initialized, "Analytics not initialized"
         ac._telemetrics_disabled = False
         ac.log_import("test")
-        assert ac.last_log["value"] == "test", "No import detected"
+        assert ac.last_log.get("value") == "test", "No import detected"
 
 
 def test_mock_log_galileo_import_disabled():