chore: update type hints for efficient_ad init

also pre-commit

src/anomalib/models/image/efficient_ad/lightning_model.py

@@ -61,14 +61,14 @@ class EfficientAd(AnomalyModule):
     """
 
     def __init__(
-            self,
-            imagenet_dir: Path | str = "./datasets/imagenette",
-            teacher_out_channels: int = 384,
-            model_size: EfficientAdModelSize | str = EfficientAdModelSize.S,
-            lr: float = 0.0001,
-            weight_decay: float = 0.00001,
-            padding: bool = False,
-            pad_maps: bool = True,
+        self,
+        imagenet_dir: Path | str = "./datasets/imagenette",
+        teacher_out_channels: int = 384,
+        model_size: EfficientAdModelSize | str = EfficientAdModelSize.S,
+        lr: float = 0.0001,
+        weight_decay: float = 0.00001,
+        padding: bool = False,
+        pad_maps: bool = True,
     ) -> None:
         super().__init__()
 
@@ -82,9 +82,9 @@ def __init__(
             padding=padding,
             pad_maps=pad_maps,
         )
-        self.batch_size = 1  # imagenet dataloader batch_size is 1 according to the paper
-        self.lr = lr
-        self.weight_decay = weight_decay
+        self.batch_size: int = 1  # imagenet dataloader batch_size is 1 according to the paper
+        self.lr: float = lr
+        self.weight_decay: float = weight_decay
 
     def prepare_pretrained_model(self) -> None:
         """Prepare the pretrained teacher model."""
@@ -93,7 +93,7 @@ def prepare_pretrained_model(self) -> None:
             download_and_extract(pretrained_models_dir, WEIGHTS_DOWNLOAD_INFO)
         model_size_str = self.model_size.value if isinstance(self.model_size, EfficientAdModelSize) else self.model_size
         teacher_path = (
-                pretrained_models_dir / "efficientad_pretrained_weights" / f"pretrained_teacher_{model_size_str}.pth"
+            pretrained_models_dir / "efficientad_pretrained_weights" / f"pretrained_teacher_{model_size_str}.pth"
         )
         logger.info(f"Load pretrained teacher model from {teacher_path}")
         self.model.teacher.load_state_dict(torch.load(teacher_path, map_location=torch.device(self.device)))
@@ -147,15 +147,15 @@ def teacher_channel_mean_std(self, dataloader: DataLoader) -> dict[str, torch.Te
 
             n += y[:, 0].numel()
             chanel_sum += torch.sum(y, dim=[0, 2, 3])
-            chanel_sum_sqr += torch.sum(y ** 2, dim=[0, 2, 3])
+            chanel_sum_sqr += torch.sum(y**2, dim=[0, 2, 3])
 
         if n is None:
             msg = "The value of 'n' cannot be None."
             raise ValueError(msg)
 
         channel_mean = chanel_sum / n
 
-        channel_std = (torch.sqrt((chanel_sum_sqr / n) - (channel_mean ** 2))).float()[None, :, None, None]
+        channel_std = (torch.sqrt((chanel_sum_sqr / n) - (channel_mean**2))).float()[None, :, None, None]
         channel_mean = channel_mean.float()[None, :, None, None]
 
         return {"mean": channel_mean, "std": channel_std}