[Black] Update black (#433)

* Update black

* update table

examples/textual_inversion/textual_inversion.py

@@ -238,7 +238,6 @@ class TextualInversionDataset(Dataset):
         placeholder_token="*",
         center_crop=False,
     ):
-
         self.data_root = data_root
         self.tokenizer = tokenizer
         self.learnable_property = learnable_property

setup.py

@@ -78,7 +78,7 @@ from setuptools import find_packages, setup
 _deps = [
     "Pillow",
     "accelerate>=0.11.0",
-    "black==22.3",
+    "black==22.8",
     "datasets",
     "filelock",
     "flake8>=3.8.3",
@@ -167,7 +167,7 @@ extras = {}
 
 
 extras = {}
-extras["quality"] = ["black==22.3", "isort>=5.5.4", "flake8>=3.8.3", "hf-doc-builder"]
+extras["quality"] = ["black==22.8", "isort>=5.5.4", "flake8>=3.8.3", "hf-doc-builder"]
 extras["docs"] = ["hf-doc-builder"]
 extras["training"] = ["accelerate", "datasets", "tensorboard", "modelcards"]
 extras["test"] = ["datasets", "onnxruntime", "pytest", "pytest-timeout", "pytest-xdist", "scipy", "transformers"]

src/diffusers/dependency_versions_table.py

@@ -4,7 +4,7 @@
 deps = {
     "Pillow": "Pillow",
     "accelerate": "accelerate>=0.11.0",
-    "black": "black==22.3",
+    "black": "black==22.8",
     "datasets": "datasets",
     "filelock": "filelock",
     "flake8": "flake8>=3.8.3",

src/diffusers/models/unet_blocks.py

@@ -979,7 +979,6 @@ class AttnUpBlock2D(nn.Module):
 
     def forward(self, hidden_states, res_hidden_states_tuple, temb=None):
         for resnet, attn in zip(self.resnets, self.attentions):
-
             # pop res hidden states
             res_hidden_states = res_hidden_states_tuple[-1]
             res_hidden_states_tuple = res_hidden_states_tuple[:-1]
@@ -1075,7 +1074,6 @@ class CrossAttnUpBlock2D(nn.Module):
 
     def forward(self, hidden_states, res_hidden_states_tuple, temb=None, encoder_hidden_states=None):
         for resnet, attn in zip(self.resnets, self.attentions):
-
             # pop res hidden states
             res_hidden_states = res_hidden_states_tuple[-1]
             res_hidden_states_tuple = res_hidden_states_tuple[:-1]
@@ -1139,7 +1137,6 @@ class UpBlock2D(nn.Module):
 
     def forward(self, hidden_states, res_hidden_states_tuple, temb=None):
         for resnet in self.resnets:
-
             # pop res hidden states
             res_hidden_states = res_hidden_states_tuple[-1]
             res_hidden_states_tuple = res_hidden_states_tuple[:-1]

src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py

@@ -691,7 +691,6 @@ class LDMBertModel(LDMBertPreTrainedModel):
         output_hidden_states=None,
         return_dict=None,
     ):
-
         outputs = self.model(
             input_ids,
             attention_mask=attention_mask,

src/diffusers/pipelines/latent_diffusion_uncond/pipeline_latent_diffusion_uncond.py

@@ -38,7 +38,6 @@ class LDMPipeline(DiffusionPipeline):
         return_dict: bool = True,
         **kwargs,
     ) -> Union[Tuple, ImagePipelineOutput]:
-
         r"""
         Args:
             batch_size (`int`, *optional*, defaults to 1):

src/diffusers/schedulers/scheduling_ddpm.py

@@ -94,7 +94,6 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
         clip_sample: bool = True,
         tensor_format: str = "pt",
     ):
-
         if trained_betas is not None:
             self.betas = np.asarray(trained_betas)
         elif beta_schedule == "linear":
@@ -251,7 +250,6 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
         noise: Union[torch.FloatTensor, np.ndarray],
         timesteps: Union[torch.IntTensor, np.ndarray],
     ) -> Union[torch.FloatTensor, np.ndarray]:
-
         sqrt_alpha_prod = self.alphas_cumprod[timesteps] ** 0.5
         sqrt_alpha_prod = self.match_shape(sqrt_alpha_prod, original_samples)
         sqrt_one_minus_alpha_prod = (1 - self.alphas_cumprod[timesteps]) ** 0.5

src/diffusers/schedulers/scheduling_sde_vp.py

@@ -40,7 +40,6 @@ class ScoreSdeVpScheduler(SchedulerMixin, ConfigMixin):
 
     @register_to_config
     def __init__(self, num_train_timesteps=2000, beta_min=0.1, beta_max=20, sampling_eps=1e-3, tensor_format="np"):
-
         self.sigmas = None
         self.discrete_sigmas = None
         self.timesteps = None

src/diffusers/utils/logging.py

@@ -65,17 +65,14 @@ def _get_default_logging_level():
 
 
 def _get_library_name() -> str:
-
     return __name__.split(".")[0]
 
 
 def _get_library_root_logger() -> logging.Logger:
-
     return logging.getLogger(_get_library_name())
 
 
 def _configure_library_root_logger() -> None:
-
     global _default_handler
 
     with _lock:
@@ -93,7 +90,6 @@ def _configure_library_root_logger() -> None:
 
 
 def _reset_library_root_logger() -> None:
-
     global _default_handler
 
     with _lock: