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

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

lerobot/common/optim/factory.py

@@ -34,13 +34,7 @@ def make_optimizer_and_scheduler(
     Returns:
         tuple[Optimizer, LRScheduler | None]: The couple (Optimizer, Scheduler). Scheduler can be `None`.
     """
-    params = (
-        policy.get_optim_params()
-        if cfg.use_policy_training_preset
-        else policy.parameters()
-    )
+    params = policy.get_optim_params() if cfg.use_policy_training_preset else policy.parameters()
     optimizer = cfg.optimizer.build(params)
-    lr_scheduler = (
-        cfg.scheduler.build(optimizer, cfg.steps) if cfg.scheduler is not None else None
-    )
+    lr_scheduler = cfg.scheduler.build(optimizer, cfg.steps) if cfg.scheduler is not None else None
     return optimizer, lr_scheduler

lerobot/common/optim/optimizers.py

@@ -102,9 +102,7 @@ def save_optimizer_state(optimizer: torch.optim.Optimizer, save_dir: Path) -> No
     write_json(param_groups, save_dir / OPTIMIZER_PARAM_GROUPS)
 
 
-def load_optimizer_state(
-    optimizer: torch.optim.Optimizer, save_dir: Path
-) -> torch.optim.Optimizer:
+def load_optimizer_state(optimizer: torch.optim.Optimizer, save_dir: Path) -> torch.optim.Optimizer:
     current_state_dict = optimizer.state_dict()
     flat_state = load_file(save_dir / OPTIMIZER_STATE)
     state = unflatten_dict(flat_state)

lerobot/common/optim/schedulers.py

@@ -36,9 +36,7 @@ class LRSchedulerConfig(draccus.ChoiceRegistry, abc.ABC):
         return self.get_choice_name(self.__class__)
 
     @abc.abstractmethod
-    def build(
-        self, optimizer: Optimizer, num_training_steps: int
-    ) -> LRScheduler | None:
+    def build(self, optimizer: Optimizer, num_training_steps: int) -> LRScheduler | None:
         raise NotImplementedError
 
 
@@ -79,11 +77,7 @@ class VQBeTSchedulerConfig(LRSchedulerConfig):
                 )
                 return max(
                     0.0,
-                    0.5
-                    * (
-                        1.0
-                        + math.cos(math.pi * float(self.num_cycles) * 2.0 * progress)
-                    ),
+                    0.5 * (1.0 + math.cos(math.pi * float(self.num_cycles) * 2.0 * progress)),
                 )
 
         return LambdaLR(optimizer, lr_lambda, -1)
@@ -111,9 +105,7 @@ class CosineDecayWithWarmupSchedulerConfig(LRSchedulerConfig):
 
             def cosine_decay_schedule(current_step):
                 step = min(current_step, self.num_decay_steps)
-                cosine_decay = 0.5 * (
-                    1 + math.cos(math.pi * step / self.num_decay_steps)
-                )
+                cosine_decay = 0.5 * (1 + math.cos(math.pi * step / self.num_decay_steps))
                 alpha = self.decay_lr / self.peak_lr
                 decayed = (1 - alpha) * cosine_decay + alpha
                 return decayed
@@ -132,8 +124,6 @@ def save_scheduler_state(scheduler: LRScheduler, save_dir: Path) -> None:
 
 
 def load_scheduler_state(scheduler: LRScheduler, save_dir: Path) -> LRScheduler:
-    state_dict = deserialize_json_into_object(
-        save_dir / SCHEDULER_STATE, scheduler.state_dict()
-    )
+    state_dict = deserialize_json_into_object(save_dir / SCHEDULER_STATE, scheduler.state_dict())
     scheduler.load_state_dict(state_dict)
     return scheduler