Fix style

src/transformers/trainer_callback.py

@@ -416,7 +416,11 @@ class DefaultFlowCallback(TrainerCallback):
             control.should_log = True
 
         # Evaluate
-        if args.evaluation_strategy == IntervalStrategy.STEPS and state.global_step % args.eval_steps == 0 and args.eval_delay > state.global_step:
+        if (
+            args.evaluation_strategy == IntervalStrategy.STEPS
+            and state.global_step % args.eval_steps == 0
+            and args.eval_delay > state.global_step
+        ):
             control.should_evaluate = True
 
         # Save

src/transformers/training_args.py

@@ -139,7 +139,8 @@ class TrainingArguments:
             left unset, the whole predictions are accumulated on GPU/TPU before being moved to the CPU (faster but
             requires more memory).
         eval_delay (`float`, *optional*):
-            Number of epochs or steps to wait for before the first evaluation can be performed, depending on the evaluation_strategy.
+            Number of epochs or steps to wait for before the first evaluation can be performed, depending on the
+            evaluation_strategy.
         learning_rate (`float`, *optional*, defaults to 5e-5):
             The initial learning rate for [`AdamW`] optimizer.
         weight_decay (`float`, *optional*, defaults to 0):
@@ -476,8 +477,10 @@ class TrainingArguments:
 
     eval_delay: Optional[float] = field(
         default=0,
-        metadata={"help": "Number of epochs or steps to wait for before the first evaluation can be performed, depending on the evaluation_strategy."},
-    )            
+        metadata={
+            "help": "Number of epochs or steps to wait for before the first evaluation can be performed, depending on the evaluation_strategy."
+        },
+    )
 
     learning_rate: float = field(default=5e-5, metadata={"help": "The initial learning rate for AdamW."})
     weight_decay: float = field(default=0.0, metadata={"help": "Weight decay for AdamW if we apply some."})