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

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

lerobot/common/policies/factory.py

@@ -24,12 +24,12 @@ from lerobot.common.envs.configs import EnvConfig
 from lerobot.common.envs.utils import env_to_policy_features
 from lerobot.common.policies.act.configuration_act import ACTConfig
 from lerobot.common.policies.diffusion.configuration_diffusion import DiffusionConfig
+from lerobot.common.policies.hilserl.classifier.configuration_classifier import ClassifierConfig
 from lerobot.common.policies.pi0.configuration_pi0 import PI0Config
 from lerobot.common.policies.pi0fast.configuration_pi0fast import PI0FASTConfig
 from lerobot.common.policies.pretrained import PreTrainedPolicy
 from lerobot.common.policies.tdmpc.configuration_tdmpc import TDMPCConfig
 from lerobot.common.policies.vqbet.configuration_vqbet import VQBeTConfig
-from lerobot.common.policies.hilserl.classifier.configuration_classifier import ClassifierConfig
 from lerobot.configs.policies import PreTrainedConfig
 from lerobot.configs.types import FeatureType
 

lerobot/common/policies/hilserl/classifier/configuration_classifier.py

@@ -1,10 +1,9 @@
-from dataclasses import dataclass, field
-from typing import Dict, List
+from dataclasses import dataclass
+from typing import List
 
 from lerobot.common.optim.optimizers import AdamWConfig, OptimizerConfig
 from lerobot.common.optim.schedulers import LRSchedulerConfig
 from lerobot.configs.policies import PreTrainedConfig
-from lerobot.configs.types import FeatureType, PolicyFeature
 
 
 @PreTrainedConfig.register_subclass(name="hilserl_classifier")

lerobot/common/policies/normalize.py

@@ -82,8 +82,10 @@ def create_stats_buffers(
         if stats and key in stats:
             if norm_mode is NormalizationMode.MEAN_STD:
                 if "mean" not in stats[key] or "std" not in stats[key]:
-                    raise ValueError(f"Missing 'mean' or 'std' in stats for key {key} with MEAN_STD normalization")
-                
+                    raise ValueError(
+                        f"Missing 'mean' or 'std' in stats for key {key} with MEAN_STD normalization"
+                    )
+
                 if isinstance(stats[key]["mean"], np.ndarray):
                     buffer["mean"].data = torch.from_numpy(stats[key]["mean"]).to(dtype=torch.float32)
                     buffer["std"].data = torch.from_numpy(stats[key]["std"]).to(dtype=torch.float32)
@@ -96,12 +98,16 @@ def create_stats_buffers(
                     buffer["std"].data = stats[key]["std"].clone().to(dtype=torch.float32)
                 else:
                     type_ = type(stats[key]["mean"])
-                    raise ValueError(f"np.ndarray or torch.Tensor expected for 'mean', but type is '{type_}' instead.")
-                    
+                    raise ValueError(
+                        f"np.ndarray or torch.Tensor expected for 'mean', but type is '{type_}' instead."
+                    )
+
             elif norm_mode is NormalizationMode.MIN_MAX:
                 if "min" not in stats[key] or "max" not in stats[key]:
-                    raise ValueError(f"Missing 'min' or 'max' in stats for key {key} with MIN_MAX normalization")
-                
+                    raise ValueError(
+                        f"Missing 'min' or 'max' in stats for key {key} with MIN_MAX normalization"
+                    )
+
                 if isinstance(stats[key]["min"], np.ndarray):
                     buffer["min"].data = torch.from_numpy(stats[key]["min"]).to(dtype=torch.float32)
                     buffer["max"].data = torch.from_numpy(stats[key]["max"]).to(dtype=torch.float32)
@@ -110,7 +116,9 @@ def create_stats_buffers(
                     buffer["max"].data = stats[key]["max"].clone().to(dtype=torch.float32)
                 else:
                     type_ = type(stats[key]["min"])
-                    raise ValueError(f"np.ndarray or torch.Tensor expected for 'min', but type is '{type_}' instead.")
+                    raise ValueError(
+                        f"np.ndarray or torch.Tensor expected for 'min', but type is '{type_}' instead."
+                    )
 
         stats_buffers[key] = buffer
     return stats_buffers

lerobot/common/policies/sac/configuration_sac.py

@@ -19,7 +19,7 @@ from dataclasses import dataclass, field
 
 from lerobot.common.optim.optimizers import MultiAdamConfig
 from lerobot.configs.policies import PreTrainedConfig
-from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
+from lerobot.configs.types import NormalizationMode
 
 
 @dataclass

lerobot/common/policies/sac/modeling_sac.py

@@ -897,7 +897,6 @@ if __name__ == "__main__":
     #     for j in range(i + 1, num_critics):
     #         diff = torch.abs(q_values[i] - q_values[j]).mean().item()
     #         print(f"Mean difference between critic {i} and {j}: {diff:.6f}")
-    import draccus
 
     from lerobot.configs import parser