run precommit and fix issues

docs/source/peft_training.mdx

@@ -41,7 +41,7 @@ There are more complex methods that have more parameters. These are not yet supp
 if you want to see a specific PEFT method supported.
 
 By default, PEFT will target the `q_proj` and `v_proj` layers of the LM expert in SmolVLA. It will also target the
-state and action projection matrices as they are most likely taks-dependent. If you need to target different layers
+state and action projection matrices as they are most likely task-dependent. If you need to target different layers
 you can use `--peft.target_modules` to specify which layers to target. You can refer to the respective PEFT method's
 documentation to see what inputs are supported, (e.g., [LoRA's target_modules documentation](https://huggingface.co/docs/peft/main/en/package_reference/lora#peft.LoraConfig.target_modules)).
 Usually a list of suffixes or a regex are supported. For example, to target the MLPs of the `lm_expert` instead of

src/lerobot/configs/policies.py

@@ -129,6 +129,8 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):  # type: igno
 
     @property
     def robot_state_feature(self) -> PolicyFeature | None:
+        if self.input_features is None:
+            return None
         for ft_name, ft in self.input_features.items():
             if ft.type is FeatureType.STATE and ft_name == OBS_STATE:
                 return ft
@@ -136,6 +138,8 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):  # type: igno
 
     @property
     def env_state_feature(self) -> PolicyFeature | None:
+        if self.input_features is None:
+            return None
         for _, ft in self.input_features.items():
             if ft.type is FeatureType.ENV:
                 return ft
@@ -143,10 +147,14 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):  # type: igno
 
     @property
     def image_features(self) -> dict[str, PolicyFeature]:
+        if self.input_features is None:
+            return {}
         return {key: ft for key, ft in self.input_features.items() if ft.type is FeatureType.VISUAL}
 
     @property
     def action_feature(self) -> PolicyFeature | None:
+        if self.output_features is None:
+            return None
         for ft_name, ft in self.output_features.items():
             if ft.type is FeatureType.ACTION and ft_name == ACTION:
                 return ft

src/lerobot/policies/factory.py

@@ -474,7 +474,7 @@ def make_policy(
     if not cfg.pretrained_path and cfg.use_peft:
         raise ValueError(
             "Instantiating a policy with `use_peft=True` without a checkpoint is not supported since that requires "
-            "the PEFT config parameters to be set. For traning with PEFT, see `lerobot_train.py` on how to do that."
+            "the PEFT config parameters to be set. For training with PEFT, see `lerobot_train.py` on how to do that."
         )
 
     if cfg.pretrained_path and not cfg.use_peft:

src/lerobot/scripts/lerobot_eval.py

@@ -278,16 +278,17 @@ def eval_policy(
         raise ValueError("If max_episodes_rendered > 0, videos_dir must be provided.")
 
     if not isinstance(policy, PreTrainedPolicy):
-        exc = ValueError(
-            f"Policy of type 'PreTrainedPolicy' is expected, but type '{type(policy)}' was provided."
-        )
         try:
             from peft import PeftModel
 
             if not isinstance(policy, PeftModel):
-                raise exc
+                raise ValueError(
+                    f"Policy of type 'PreTrainedPolicy' is expected, but type '{type(policy)}' was provided."
+                )
         except ImportError:
-            raise exc
+            raise ValueError(
+                "PEFT is required to evaluate a PEFT-trained policy. Please install PEFT using `pip install peft`."
+            ) from None
 
     start = time.time()
     policy.eval()

src/lerobot/scripts/lerobot_train.py

@@ -183,7 +183,7 @@ def wrap_policy_in_peft_model(cfg, policy):
 
     if cfg.policy.type == "smolvla" and not cfg.policy.load_vlm_weights:
         logging.warning(
-            "Traning SmolVLA from scratch using PEFT. This is unlikely to yield good results. Set "
+            "Training SmolVLA from scratch using PEFT. This is unlikely to yield good results. Set "
             "`load_vlm_weights=True` to fine-tune the existing policy."
         )