[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

src/neurosis/models/text_encoder/clip_t5.py

@@ -22,8 +22,8 @@ def __init__(
         self.clip_encoder = FrozenCLIPEmbedder(clip_version, device, max_length=clip_max_length)
         self.t5_encoder = FrozenT5Embedder(t5_version, device, max_length=t5_max_length)
         logger.info(
-            f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder) * 1.e-6:.2f} M parameters, "
-            f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder) * 1.e-6:.2f} M params."
+            f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder) * 1.0e-6:.2f} M parameters, "
+            f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder) * 1.0e-6:.2f} M params."
         )
 
     def encode(self, text):

src/neurosis/modules/autoencoding/regularizers/quantize.py

@@ -101,9 +101,9 @@ def __init__(
             self.unknown_index = self.re_embed
             self.re_embed = self.re_embed + 1
         else:
-            assert unknown_index == "random" or isinstance(
-                unknown_index, int
-            ), "unknown index needs to be 'random', 'extra' or any integer"
+            assert unknown_index == "random" or isinstance(unknown_index, int), (
+                "unknown index needs to be 'random', 'extra' or any integer"
+            )
             self.unknown_index = unknown_index  # "random" or "extra" or integer
         if self.remap is not None:
             logpy.info(
@@ -206,9 +206,9 @@ def __init__(
             self.unknown_index = self.re_embed
             self.re_embed = self.re_embed + 1
         else:
-            assert unknown_index == "random" or isinstance(
-                unknown_index, int
-            ), "unknown index needs to be 'random', 'extra' or any integer"
+            assert unknown_index == "random" or isinstance(unknown_index, int), (
+                "unknown index needs to be 'random', 'extra' or any integer"
+            )
             self.unknown_index = unknown_index  # "random" or "extra" or integer
         if self.remap is not None:
             logpy.info(
@@ -349,9 +349,9 @@ def __init__(
             self.unknown_index = self.re_embed
             self.re_embed = self.re_embed + 1
         else:
-            assert unknown_index == "random" or isinstance(
-                unknown_index, int
-            ), "unknown index needs to be 'random', 'extra' or any integer"
+            assert unknown_index == "random" or isinstance(unknown_index, int), (
+                "unknown index needs to be 'random', 'extra' or any integer"
+            )
             self.unknown_index = unknown_index  # "random" or "extra" or integer
         if self.remap is not None:
             logpy.info(

src/neurosis/modules/diffusion/openaimodel.py

@@ -362,9 +362,9 @@ def __init__(
         if num_head_channels == -1:
             self.num_heads = num_heads
         else:
-            assert (
-                channels % num_head_channels == 0
-            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
+            assert channels % num_head_channels == 0, (
+                f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
+            )
             self.num_heads = channels // num_head_channels
         self.use_checkpoint = use_checkpoint
         self.norm = nn.GroupNorm(channels)

src/neurosis/schedulers/cosine.py

@@ -90,7 +90,7 @@ def get_lr(self):
 
         if not self._get_lr_called_within_step:
             warnings.warn(
-                "To get the last learning rate computed by the scheduler, " "please use `get_last_lr()`."
+                "To get the last learning rate computed by the scheduler, please use `get_last_lr()`."
             )
 
         if n < 0:

src/neurosis/schedulers/linear.py

@@ -47,7 +47,7 @@ def schedule(self, n, **kwargs):
         cycle = self.find_in_interval(n)
         n = n - self.total_cycles[cycle]
         if (self.verbosity_interval > 0) and (n % self.verbosity_interval == 0):
-            logger.info(f"current step: {n}, last lr-multiplier: {self.last_f}, " f"current cycle {cycle}")
+            logger.info(f"current step: {n}, last lr-multiplier: {self.last_f}, current cycle {cycle}")
 
         f_min = self.f_min[cycle]
         f_max = self.f_max[cycle]
@@ -74,7 +74,7 @@ def schedule(self, n, **kwargs):
         cycle = self.find_in_interval(n)
         n = n - self.total_cycles[cycle]
         if (self.verbosity_interval > 0) and (n % self.verbosity_interval == 0):
-            logger.info(f"current step: {n}, last lr-multiplier: {self.last_f}, " f"current cycle {cycle}")
+            logger.info(f"current step: {n}, last lr-multiplier: {self.last_f}, current cycle {cycle}")
 
         f_min = self.f_min[cycle]
         f_max = self.f_max[cycle]

src/neurosis/utils/sgm.py

@@ -134,7 +134,7 @@ def mean_flat(tensor: Tensor) -> Tensor:
 def count_params(model: Module, verbose: bool = False) -> int:
     total_params = sum(p.numel() for p in model.parameters())
     if verbose:
-        logger.info(f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.")
+        logger.info(f"{model.__class__.__name__} has {total_params * 1.0e-6:.2f} M params.")
     return total_params