black format

evaluation/metrics/similarity/models/RawNetModel.py

@@ -121,9 +121,7 @@ def forward(self, x):
         w = self.attention(global_x)
 
         mu = torch.sum(x * w, dim=2)
-        sg = torch.sqrt(
-            (torch.sum((x**2) * w, dim=2) - mu**2).clamp(min=1e-4, max=1e4)
-        )
+        sg = torch.sqrt((torch.sum((x**2) * w, dim=2) - mu**2).clamp(min=1e-4, max=1e4))
 
         x = torch.cat((mu, sg), 1)
 

models/tta/autoencoder/autoencoder.py

@@ -250,8 +250,7 @@ def forward(self, x):
 
 
 # TODO: Encoder1d
-class Encoder1d(Encoder2d):
-    ...
+class Encoder1d(Encoder2d): ...
 
 
 class Decoder2d(nn.Module):
@@ -351,8 +350,7 @@ def forward(self, z):
 
 
 # TODO: decoder1d
-class Decoder1d(Decoder2d):
-    ...
+class Decoder1d(Decoder2d): ...
 
 
 class AutoencoderKL(nn.Module):

models/tta/autoencoder/autoencoder_dataset.py

@@ -106,9 +106,7 @@ def __call__(self, batch):
         return packed_batch_features
 
 
-class AutoencoderKLTestDataset(BaseTestDataset):
-    ...
+class AutoencoderKLTestDataset(BaseTestDataset): ...
 
 
-class AutoencoderKLTestCollator(BaseTestCollator):
-    ...
+class AutoencoderKLTestCollator(BaseTestCollator): ...

models/tta/ldm/audioldm_dataset.py

@@ -145,9 +145,7 @@ def __call__(self, batch):
         return packed_batch_features
 
 
-class AudioLDMTestDataset(BaseTestDataset):
-    ...
+class AudioLDMTestDataset(BaseTestDataset): ...
 
 
-class AudioLDMTestCollator(BaseTestCollator):
-    ...
+class AudioLDMTestCollator(BaseTestCollator): ...

modules/diffusion/karras/karras_diffusion.py

@@ -465,9 +465,7 @@ def to_d(x, sigma, denoised):
 def get_ancestral_step(sigma_from, sigma_to):
     """Calculates the noise level (sigma_down) to step down to and the amount
     of noise to add (sigma_up) when doing an ancestral sampling step."""
-    sigma_up = (
-        sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2
-    ) ** 0.5
+    sigma_up = (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
     sigma_down = (sigma_to**2 - sigma_up**2) ** 0.5
     return sigma_down, sigma_up
 

modules/transformer/mh_attention.py

@@ -344,9 +344,7 @@ def forward(
                     (
                         1
                         if key_padding_mask is not None
-                        else 0
-                        if attn_mask is not None
-                        else None
+                        else 0 if attn_mask is not None else None
                     ),
                 )
 

optimizer/optimizers.py

@@ -697,9 +697,7 @@ def _size_update(
 
         denom = scale_exp_avg_sq.sqrt() + eps
 
-        scale_step = (
-            -size_lr * (bias_correction2**0.5) * scale_grads.sum(dim=0) / denom
-        )
+        scale_step = -size_lr * (bias_correction2**0.5) * scale_grads.sum(dim=0) / denom
 
         is_too_small = param_rms < param_min_rms
         is_too_large = param_rms > param_max_rms