Accept changes suggested by linter.

src/dalle_mini/model/modeling.py

@@ -569,14 +569,18 @@ class DalleBart(FlaxBartPreTrainedModel, FlaxBartForConditionalGeneration):
         """
         Initializes from a wandb artifact, or delegates loading to the superclass.
         """
-        if ':' in pretrained_model_name_or_path and not os.path.isdir(pretrained_model_name_or_path):
+        if ":" in pretrained_model_name_or_path and not os.path.isdir(
+            pretrained_model_name_or_path
+        ):
             # wandb artifact
             artifact = wandb.Api().artifact(pretrained_model_name_or_path)
-            
+
             # we download everything, including opt_state, so we can resume training if needed
             # see also: #120
             pretrained_model_name_or_path = artifact.download()
 
-        model = super(DalleBart, cls).from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
+        model = super(DalleBart, cls).from_pretrained(
+            pretrained_model_name_or_path, *model_args, **kwargs
+        )
         model.config.resolved_name_or_path = pretrained_model_name_or_path
         return model

tools/train/train.py

@@ -437,7 +437,9 @@ def main():
     if training_args.resume_from_checkpoint is not None:
         # load model
         model = DalleBart.from_pretrained(
-            training_args.resume_from_checkpoint, dtype=getattr(jnp, model_args.dtype), abstract_init=True
+            training_args.resume_from_checkpoint,
+            dtype=getattr(jnp, model_args.dtype),
+            abstract_init=True,
         )
         # avoid OOM on TPU: see https://github.com/google/flax/issues/1658
         print(model.params)