black formatting upgraded

secml2/manipulations/manipulation.py

@@ -1,4 +1,5 @@
 from abc import ABC
+from typing import Tuple
 
 import torch
 
@@ -24,12 +25,13 @@ def _apply_perturbation_constraints(self, delta: torch.Tensor) -> torch.Tensor:
             delta = constraint(delta)
         return delta
 
-    def _apply_manipulation(self, x: torch.Tensor, delta: torch.Tensor) -> torch.Tensor:
-        ...
+    def _apply_manipulation(
+        self, x: torch.Tensor, delta: torch.Tensor
+    ) -> torch.Tensor: ...
 
     def __call__(
         self, x: torch.Tensor, delta: torch.Tensor
-    ) -> (torch.Tensor, torch.Tensor):
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
         delta.data = self._apply_perturbation_constraints(delta.data)
         x_adv, delta = self._apply_manipulation(x, delta)
         x_adv.data = self._apply_domain_constraints(x_adv.data)
@@ -39,5 +41,5 @@ def __call__(
 class AdditiveManipulation(Manipulation):
     def _apply_manipulation(
         self, x: torch.Tensor, delta: torch.Tensor
-    ) -> (torch.Tensor, torch.Tensor):
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
         return x + delta, delta

secml2/models/base_model.py

@@ -32,8 +32,7 @@ def __init__(
         )
 
     @abstractmethod
-    def predict(self, x: torch.Tensor) -> torch.Tensor:
-        ...
+    def predict(self, x: torch.Tensor) -> torch.Tensor: ...
 
     def decision_function(self, x: torch.Tensor) -> torch.Tensor:
         x = self._preprocessing(x)
@@ -42,16 +41,13 @@ def decision_function(self, x: torch.Tensor) -> torch.Tensor:
         return x
 
     @abstractmethod
-    def _decision_function(self, x: torch.Tensor) -> torch.Tensor:
-        ...
+    def _decision_function(self, x: torch.Tensor) -> torch.Tensor: ...
 
     @abstractmethod
-    def gradient(self, x: torch.Tensor, y: int) -> torch.Tensor:
-        ...
+    def gradient(self, x: torch.Tensor, y: int) -> torch.Tensor: ...
 
     @abstractmethod
-    def train(self, dataloader: DataLoader):
-        ...
+    def train(self, dataloader: DataLoader): ...
 
     def __call__(self, x: torch.Tensor) -> torch.Tensor:
         return self.decision_function(x)

secml2/models/data_processing/data_processing.py

@@ -5,11 +5,9 @@
 
 class DataProcessing(ABC):
     @abstractmethod
-    def process(self, x: torch.Tensor) -> torch.Tensor:
-        ...
+    def process(self, x: torch.Tensor) -> torch.Tensor: ...
 
-    def invert(self, x: torch.Tensor) -> torch.Tensor:
-        ...
+    def invert(self, x: torch.Tensor) -> torch.Tensor: ...
 
     def __call__(self, x: torch.Tensor) -> torch.Tensor:
         return self.process(x)

secml2/optimization/constraints.py

@@ -1,12 +1,10 @@
-import math
 from abc import abstractmethod
 
 import torch
 
 
 class Constraint:
-    def __call__(self, x: torch.Tensor, *args, **kwargs):
-        ...
+    def __call__(self, x: torch.Tensor, *args, **kwargs): ...
 
 
 class ClipConstraint(Constraint):
@@ -25,8 +23,7 @@ def __init__(self, radius=0, center=0, p=torch.inf):
         self.radius = radius
 
     @abstractmethod
-    def project(self, x: torch.Tensor) -> torch.Tensor:
-        ...
+    def project(self, x: torch.Tensor) -> torch.Tensor: ...
 
     def __call__(self, x: torch.Tensor, *args, **kwargs) -> torch.Tensor:
         x = x + self.center

secml2/optimization/gradient_processing.py

@@ -1,14 +1,11 @@
-import math
-
 import torch.linalg
 from torch.nn.functional import normalize
 
 from secml2.adv.evasion.perturbation_models import PerturbationModels
 
 
 class GradientProcessing:
-    def __call__(self, grad: torch.Tensor) -> torch.Tensor:
-        ...
+    def __call__(self, grad: torch.Tensor) -> torch.Tensor: ...
 
 
 class LinearProjectionGradientProcessing(GradientProcessing):