style(examples): Reformat code using black

examples/hf_serialization.py

@@ -40,7 +40,7 @@
 
 
 def check_file_exists(
-        file: str,
+    file: str,
 ):
     """
     Check if file exists and is not empty. If the file is found locally,
@@ -55,18 +55,18 @@ def check_file_exists(
         return True
     else:
         with stream_io.open_stream(file, "rb") as f:
-            if f.read(1) == b'':
+            if f.read(1) == b"":
                 return False
             else:
                 return True
 
 
 def serialize_model(
-        model: torch.nn.Module,
-        config: Optional[Union[ConfigMixin, AutoConfig, dict]],
-        model_directory: str,
-        model_prefix: str = None,
-        force: bool = False,
+    model: torch.nn.Module,
+    config: Optional[Union[ConfigMixin, AutoConfig, dict]],
+    model_directory: str,
+    model_prefix: str = None,
+    force: bool = False,
 ):
     """
     Remove the tensors from a PyTorch model, convert them to NumPy
@@ -91,8 +91,10 @@ def serialize_model(
         model_prefix = "model"
 
     dir_prefix = f"{model_directory}/{model_prefix}"
-    config_file_exists, weights_file_exists = (check_file_exists(f"{dir_prefix}-config.json"),
-                                               check_file_exists(f"{dir_prefix}.tensors"))
+    config_file_exists, weights_file_exists = (
+        check_file_exists(f"{dir_prefix}-config.json"),
+        check_file_exists(f"{dir_prefix}.tensors"),
+    )
     if config is None:
         config = model
     if config is not None:
@@ -114,16 +116,16 @@ def serialize_model(
 
 
 def load_model(
-        path_uri: str,
-        model_class: Union[
-            Type[PreTrainedModel], Type[ModelMixin], Type[ConfigMixin]
-        ],
-        config_class: Optional[
-            Union[Type[PretrainedConfig], Type[ConfigMixin], Type[AutoConfig]]
-        ] = None,
-        model_prefix: Optional[str] = "model",
-        device: torch.device = utils.get_device(),
-        dtype: Optional[str] = None,
+    path_uri: str,
+    model_class: Union[
+        Type[PreTrainedModel], Type[ModelMixin], Type[ConfigMixin]
+    ],
+    config_class: Optional[
+        Union[Type[PretrainedConfig], Type[ConfigMixin], Type[AutoConfig]]
+    ] = None,
+    model_prefix: Optional[str] = "model",
+    device: torch.device = utils.get_device(),
+    dtype: Optional[str] = None,
 ) -> torch.nn.Module:
     """
     Given a path prefix, load the model with a custom extension
@@ -212,7 +214,12 @@ def df_main(args: argparse.Namespace) -> None:
     logger.info("GPU: " + utils.get_gpu_name())
     logger.info("PYTHON USED RAM: " + utils.get_mem_usage())
 
-    serialize_model(pipeline.text_encoder.eval(), pipeline.text_encoder.config, output_prefix, "encoder")
+    serialize_model(
+        pipeline.text_encoder.eval(),
+        pipeline.text_encoder.config,
+        output_prefix,
+        "encoder",
+    )
     serialize_model(pipeline.vae.eval(), None, output_prefix, "vae")
     serialize_model(pipeline.unet.eval(), None, output_prefix, "unet")
 
@@ -258,7 +265,9 @@ def hf_main(args):
 
     # May not be necessary if users can be assumed won't accidentally
     # include trailing slashes in their output_prefix
-    output_prefix = output_prefix[:-1] if output_prefix[-1] == "/" else output_prefix
+    output_prefix = (
+        output_prefix[:-1] if output_prefix[-1] == "/" else output_prefix
+    )
 
     print("MODEL PATH:", args.input_directory)
     print("OUTPUT PREFIX:", output_prefix)
@@ -276,11 +285,7 @@ def hf_main(args):
     logger.info("GPU: " + utils.get_gpu_name())
     logger.info("PYTHON USED RAM: " + utils.get_mem_usage())
 
-    serialize_model(model,
-                    model_config,
-                    output_prefix,
-                    None,
-                    args.force)
+    serialize_model(model, model_config, output_prefix, None, args.force)
 
     if args.validate:
         # Not sure if this part is needed as, although I doubt it,