Apply Black formatter

Signed-off-by: Beat Buesser <[email protected]>

Author: Beat Buesser

art/attacks/evasion/imperceptible_asr/imperceptible_asr_pytorch.py

@@ -464,9 +464,7 @@ def _forward_1st_stage(
 
         # Transform data into the model input space
         inputs, targets, input_rates, target_sizes, batch_idx = self.estimator.preprocess_transform_model_input(
-            x=masked_adv_input.to(self.estimator.device),
-            y=original_output,
-            real_lengths=real_lengths,
+            x=masked_adv_input.to(self.estimator.device), y=original_output, real_lengths=real_lengths,
         )
 
         # Compute real input sizes
@@ -606,10 +604,7 @@ class only supports targeted attack.
         return result
 
     def _forward_2nd_stage(
-        self,
-        local_delta_rescale: "torch.Tensor",
-        theta_batch: np.ndarray,
-        original_max_psd_batch: np.ndarray,
+        self, local_delta_rescale: "torch.Tensor", theta_batch: np.ndarray, original_max_psd_batch: np.ndarray,
     ) -> "torch.Tensor":
         """
         The forward pass of the second stage of the attack.
@@ -667,7 +662,7 @@ def _compute_masking_threshold(self, x: np.ndarray) -> Tuple[np.ndarray, np.ndar
 
         psd = abs(transformed_x / win_length)
         original_max_psd = np.max(psd * psd)
-        with np.errstate(divide='ignore'):
+        with np.errstate(divide="ignore"):
             psd = (20 * np.log10(psd)).clip(min=-200)
         psd = 96 - np.max(psd) + psd
 

art/estimators/speech_recognition/pytorch_deep_speech.py

@@ -256,11 +256,7 @@ def __init__(
                 enabled = True
 
             self._model, self._optimizer = amp.initialize(
-                models=self._model,
-                optimizers=self._optimizer,
-                enabled=enabled,
-                opt_level=opt_level,
-                loss_scale=1.0,
+                models=self._model, optimizers=self._optimizer, enabled=enabled, opt_level=opt_level, loss_scale=1.0,
             )
 
     def predict(
@@ -522,10 +518,7 @@ def fit(self, x: np.ndarray, y: np.ndarray, batch_size: int = 128, nb_epochs: in
                 self._optimizer.step()
 
     def preprocess_transform_model_input(
-        self,
-        x: "torch.Tensor",
-        y: np.ndarray,
-        real_lengths: np.ndarray,
+        self, x: "torch.Tensor", y: np.ndarray, real_lengths: np.ndarray,
     ) -> Tuple["torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor", List]:
         """
         Apply preprocessing and then transform the user input space into the model input space. This function is used
@@ -556,11 +549,7 @@ def preprocess_transform_model_input(
 
         # Transform the input space
         inputs, targets, input_rates, target_sizes, batch_idx = self._transform_model_input(
-            x=x,
-            y=y,
-            compute_gradient=False,
-            tensor_input=True,
-            real_lengths=real_lengths,
+            x=x, y=y, compute_gradient=False, tensor_input=True, real_lengths=real_lengths,
         )
 
         return inputs, targets, input_rates, target_sizes, batch_idx

tests/estimators/classification/test_blackbox.py

@@ -90,9 +90,7 @@ def test_repr(self):
         self.assertIn("BlackBoxClassifier", repr_)
         self.assertIn("clip_values=[  0. 255.]", repr_)
         self.assertIn("defences=None", repr_)
-        self.assertIn(
-            "preprocessing=StandardisationMeanStd(mean=0, std=1, apply_fit=True, apply_predict=True)", repr_
-        )
+        self.assertIn("preprocessing=StandardisationMeanStd(mean=0, std=1, apply_fit=True, apply_predict=True)", repr_)
 
 
 if __name__ == "__main__":

tests/estimators/classification/test_classifier.py

@@ -117,9 +117,7 @@ def test_repr(self):
         self.assertIn("ClassifierInstance", repr_)
         self.assertIn("clip_values=None", repr_)
         self.assertIn("defences=None", repr_)
-        self.assertIn(
-            "preprocessing=StandardisationMeanStd(mean=0, std=1, apply_fit=True, apply_predict=True)", repr_
-        )
+        self.assertIn("preprocessing=StandardisationMeanStd(mean=0, std=1, apply_fit=True, apply_predict=True)", repr_)
 
 
 class TestClassifierNeuralNetwork(TestBase):
@@ -146,9 +144,7 @@ def test_repr(self):
         self.assertIn(f"channel_index={Deprecated}, channels_first=True", repr_)
         self.assertIn("clip_values=[0. 1.]", repr_)
         self.assertIn("defences=None", repr_)
-        self.assertIn(
-            "preprocessing=StandardisationMeanStd(mean=0, std=1, apply_fit=True, apply_predict=True)", repr_
-        )
+        self.assertIn("preprocessing=StandardisationMeanStd(mean=0, std=1, apply_fit=True, apply_predict=True)", repr_)
 
 
 if __name__ == "__main__":

tests/estimators/classification/test_detector_classifier.py

@@ -167,9 +167,7 @@ def test_save(self):
     def test_repr(self):
         repr_ = repr(self.detector_classifier)
         self.assertIn("art.estimators.classification.detector_classifier.DetectorClassifier", repr_)
-        self.assertIn(
-            "preprocessing=StandardisationMeanStd(mean=0, std=1, apply_fit=True, apply_predict=True)", repr_
-        )
+        self.assertIn("preprocessing=StandardisationMeanStd(mean=0, std=1, apply_fit=True, apply_predict=True)", repr_)
 
 
 if __name__ == "__main__":