chore(format): run black on dev (#144)

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

infer/modules/train/train.py

@@ -32,7 +32,7 @@ try:
 except Exception:
     pass
 finally:
-    if not ('GradScaler' in globals() and 'autocast' in globals()):
+    if not ("GradScaler" in globals() and "autocast" in globals()):
         from torch.amp.grad_scaler import GradScaler
         from torch.amp.autocast_mode import autocast
 

rvc/layers/encoders.py

@@ -213,6 +213,7 @@ class PosteriorEncoder(nn.Module):
 
     def __prepare_scriptable__(self):
         from torch.nn.utils import parametrize
+
         if parametrize.is_parametrized(self.enc, "weight"):
             parametrize.remove_parametrizations(self.enc, "weight")
         return self

rvc/layers/norms.py

@@ -51,7 +51,9 @@ class WN(torch.nn.Module):
             cond_layer = torch.nn.Conv1d(
                 gin_channels, 2 * hidden_channels * n_layers, 1
             )
-            self.cond_layer = torch.nn.utils.parametrizations.weight_norm(cond_layer, name="weight")
+            self.cond_layer = torch.nn.utils.parametrizations.weight_norm(
+                cond_layer, name="weight"
+            )
 
         for i in range(n_layers):
             dilation = dilation_rate**i
@@ -63,7 +65,9 @@ class WN(torch.nn.Module):
                 dilation=dilation,
                 padding=padding,
             )
-            in_layer = torch.nn.utils.parametrizations.weight_norm(in_layer, name="weight")
+            in_layer = torch.nn.utils.parametrizations.weight_norm(
+                in_layer, name="weight"
+            )
             self.in_layers.append(in_layer)
 
             # last one is not necessary
@@ -73,7 +77,9 @@ class WN(torch.nn.Module):
                 res_skip_channels = hidden_channels
 
             res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-            res_skip_layer = torch.nn.utils.parametrizations.weight_norm(res_skip_layer, name="weight")
+            res_skip_layer = torch.nn.utils.parametrizations.weight_norm(
+                res_skip_layer, name="weight"
+            )
             self.res_skip_layers.append(res_skip_layer)
 
     def __call__(