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

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

lerobot/common/envs/factory.py

@@ -14,7 +14,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import importlib
-from collections import deque
 
 import gymnasium as gym
 

lerobot/common/optim/optimizers.py

@@ -108,6 +108,7 @@ class MultiAdamConfig(OptimizerConfig):
         optimizer_groups: Dictionary mapping parameter group names to their hyperparameters
         grad_clip_norm: Gradient clipping norm
     """
+
     lr: float = 1e-3
     weight_decay: float = 0.0
     grad_clip_norm: float = 10.0
@@ -142,7 +143,9 @@ class MultiAdamConfig(OptimizerConfig):
         return optimizers
 
 
-def save_optimizer_state(optimizer: torch.optim.Optimizer | dict[str, torch.optim.Optimizer], save_dir: Path) -> None:
+def save_optimizer_state(
+    optimizer: torch.optim.Optimizer | dict[str, torch.optim.Optimizer], save_dir: Path
+) -> None:
     """Save optimizer state to disk.
 
     Args:

lerobot/common/policies/factory.py

@@ -24,11 +24,11 @@ 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.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

@@ -82,7 +82,9 @@ 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)
@@ -96,11 +98,15 @@ 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)
@@ -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
 

lerobot/common/utils/wandb_utils.py

@@ -115,11 +115,13 @@ class WandBLogger:
         artifact.add_file(checkpoint_dir / PRETRAINED_MODEL_DIR / SAFETENSORS_SINGLE_FILE)
         self._wandb.log_artifact(artifact)
 
-    def log_dict(self, d: dict, step: int | None = None, mode: str = "train", custom_step_key: str | None = None):
+    def log_dict(
+        self, d: dict, step: int | None = None, mode: str = "train", custom_step_key: str | None = None
+    ):
         if mode not in {"train", "eval"}:
             raise ValueError(mode)
         if step is None and custom_step_key is None:
-                    raise ValueError("Either step or custom_step_key must be provided.")
+            raise ValueError("Either step or custom_step_key must be provided.")
 
         # NOTE: This is not simple. Wandb step is it must always monotonically increase and it
         # increases with each wandb.log call, but in the case of asynchronous RL for example,
@@ -142,10 +144,7 @@ class WandBLogger:
                 continue
 
             # Do not log the custom step key itself.
-            if (
-                self._wandb_custom_step_key is not None
-                and k in self._wandb_custom_step_key
-            ):
+            if self._wandb_custom_step_key is not None and k in self._wandb_custom_step_key:
                 continue
 
             if custom_step_key is not None:
@@ -160,7 +159,6 @@ class WandBLogger:
 
             self._wandb.log(data={f"{mode}/{k}": v}, step=step)
 
-
     def log_video(self, video_path: str, step: int, mode: str = "train"):
         if mode not in {"train", "eval"}:
             raise ValueError(mode)

lerobot/configs/train.py

@@ -34,7 +34,7 @@ TRAIN_CONFIG_NAME = "train_config.json"
 
 @dataclass
 class TrainPipelineConfig(HubMixin):
-    dataset: DatasetConfig | None = None # NOTE: In RL, we don't need a dataset
+    dataset: DatasetConfig | None = None  # NOTE: In RL, we don't need a dataset
     env: envs.EnvConfig | None = None
     policy: PreTrainedConfig | None = None
     # Set `dir` to where you would like to save all of the run outputs. If you run another training session # with the same value for `dir` its contents will be overwritten unless you set `resume` to true.

lerobot/scripts/server/actor_server.py

@@ -47,7 +47,7 @@ from lerobot.scripts.server.buffer import (
     python_object_to_bytes,
     transitions_to_bytes,
 )
-from lerobot.scripts.server.gym_manipulator import get_classifier, make_robot_env
+from lerobot.scripts.server.gym_manipulator import make_robot_env
 from lerobot.scripts.server.network_utils import (
     receive_bytes_in_chunks,
     send_bytes_in_chunks,
@@ -444,7 +444,7 @@ def receive_policy(
 
         # Initialize logging with explicit log file
         init_logging(log_file=log_file)
-        logging.info(f"Actor receive policy process logging initialized")
+        logging.info("Actor receive policy process logging initialized")
 
         # Setup process handlers to handle shutdown signal
         # But use shutdown event from the main process

lerobot/scripts/server/end_effector_control_utils.py

@@ -701,10 +701,10 @@ def teleoperate_gym_env(env, controller, fps: int = 30):
 
 
 if __name__ == "__main__":
+    from lerobot.common.envs.configs import EEActionSpaceConfig, EnvWrapperConfig, HILSerlRobotEnvConfig
     from lerobot.common.robot_devices.robots.configs import RobotConfig
     from lerobot.common.robot_devices.robots.utils import make_robot_from_config
     from lerobot.scripts.server.gym_manipulator import make_robot_env
-    from lerobot.common.envs.configs import HILSerlRobotEnvConfig, EEActionSpaceConfig, EnvWrapperConfig
 
     parser = argparse.ArgumentParser(description="Test end-effector control")
     parser.add_argument(

lerobot/scripts/server/find_joint_limits.py

@@ -5,14 +5,15 @@ import cv2
 import numpy as np
 
 from lerobot.common.robot_devices.control_utils import is_headless
-from lerobot.common.robot_devices.robots.utils import make_robot_from_config
-from lerobot.scripts.server.kinematics import RobotKinematics
-from lerobot.configs import parser
 from lerobot.common.robot_devices.robots.configs import RobotConfig
+from lerobot.common.robot_devices.robots.utils import make_robot_from_config
+from lerobot.configs import parser
+from lerobot.scripts.server.kinematics import RobotKinematics
 
 follower_port = "/dev/tty.usbmodem58760431631"
 leader_port = "/dev/tty.usbmodem58760433331"
 
+
 def find_joint_bounds(
     robot,
     control_time_s=30,
@@ -100,6 +101,7 @@ def make_robot(robot_type="so100"):
 
     return make_robot_from_config(robot_config)
 
+
 if __name__ == "__main__":
     # Create argparse for script-specific arguments
     parser = argparse.ArgumentParser(add_help=False)  # Set add_help=False to avoid conflict

lerobot/scripts/server/maniskill_manipulator.py

@@ -1,7 +1,6 @@
 import logging
 import time
-from dataclasses import dataclass, field
-from typing import Any, Dict, Optional, Tuple
+from typing import Any
 
 import einops
 import gymnasium as gym
@@ -10,10 +9,8 @@ import torch
 from mani_skill.utils.wrappers.record import RecordEpisode
 from mani_skill.vector.wrappers.gymnasium import ManiSkillVectorEnv
 
-from lerobot.common.envs.configs import EnvConfig, ManiskillEnvConfig
+from lerobot.common.envs.configs import ManiskillEnvConfig
 from lerobot.configs import parser
-from lerobot.configs.types import FeatureType, PolicyFeature
-from lerobot.common.constants import ACTION, OBS_IMAGE, OBS_ROBOT
 
 
 def preprocess_maniskill_observation(
@@ -53,9 +50,6 @@ def preprocess_maniskill_observation(
     return return_observations
 
 
-
-
-
 class ManiSkillObservationWrapper(gym.ObservationWrapper):
     def __init__(self, env, device: torch.device = "cuda"):
         super().__init__(env)
@@ -122,6 +116,7 @@ class ManiSkillMultiplyActionWrapper(gym.Wrapper):
 
 class BatchCompatibleWrapper(gym.ObservationWrapper):
     """Ensures observations are batch-compatible by adding a batch dimension if necessary."""
+
     def __init__(self, env):
         super().__init__(env)
 
@@ -136,6 +131,7 @@ class BatchCompatibleWrapper(gym.ObservationWrapper):
 
 class TimeLimitWrapper(gym.Wrapper):
     """Adds a time limit to the environment based on fps and control_time."""
+
     def __init__(self, env, control_time_s, fps):
         super().__init__(env)
         self.control_time_s = control_time_s
@@ -243,6 +239,10 @@ def main(cfg: ManiskillEnvConfig):
 
 if __name__ == "__main__":
     import draccus
+
     config = ManiskillEnvConfig()
     draccus.set_config_type("json")
-    draccus.dump(config=config, stream=open(file='run_config.json', mode='w'), )
+    draccus.dump(
+        config=config,
+        stream=open(file="run_config.json", mode="w"),
+    )

lerobot/scripts/train_hilserl_classifier.py

@@ -32,7 +32,6 @@ from tqdm import tqdm
 from lerobot.common.datasets.factory import resolve_delta_timestamps
 from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.common.policies.hilserl.classifier.configuration_classifier import ClassifierConfig
-
 from lerobot.common.policies.hilserl.classifier.modeling_classifier import Classifier
 from lerobot.common.utils.utils import (
     format_big_number,

tests/optim/test_optimizers.py

@@ -205,10 +205,7 @@ def test_save_and_load_multi_optimizer_state(base_params_dict, multi_optimizers,
 
     # Verify state dictionaries match
     for name in multi_optimizers.keys():
-        torch.testing.assert_close(
-            multi_optimizers[name].state_dict(),
-            loaded_optimizers[name].state_dict()
-        )
+        torch.testing.assert_close(multi_optimizers[name].state_dict(), loaded_optimizers[name].state_dict())
 
 
 def test_save_and_load_empty_multi_optimizer_state(base_params_dict, tmp_path):
@@ -241,7 +238,5 @@ def test_save_and_load_empty_multi_optimizer_state(base_params_dict, tmp_path):
 
         # Verify state dictionaries match (they will be empty)
         torch.testing.assert_close(
-            optimizer.state_dict()["param_groups"],
-            loaded_optimizers[name].state_dict()["param_groups"]
+            optimizer.state_dict()["param_groups"], loaded_optimizers[name].state_dict()["param_groups"]
         )
-