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

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

lerobot/scripts/server/buffer.py

@@ -14,7 +14,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import functools
-import random
 from typing import Any, Callable, Optional, Sequence, TypedDict
 
 import io
@@ -737,7 +736,6 @@ def concatenate_batch_transitions(
 
 
 if __name__ == "__main__":
-    import numpy as np
     from tempfile import TemporaryDirectory
 
     # ===== Test 1: Create and use a synthetic ReplayBuffer =====
@@ -1139,7 +1137,7 @@ if __name__ == "__main__":
 
     savings_percent = (std_mem - opt_mem) / std_mem * 100
 
-    print(f"\nMemory optimization result:")
+    print("\nMemory optimization result:")
     print(f"- Standard buffer state memory: {std_mem / (1024 * 1024):.2f} MB")
     print(f"- Optimized buffer state memory: {opt_mem / (1024 * 1024):.2f} MB")
     print(f"- Memory savings for state tensors: {savings_percent:.1f}%")

lerobot/scripts/server/crop_dataset_roi.py

@@ -225,7 +225,9 @@ def convert_lerobot_dataset_to_cropper_lerobot_dataset(
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Crop rectangular ROIs from a LeRobot dataset.")
+    parser = argparse.ArgumentParser(
+        description="Crop rectangular ROIs from a LeRobot dataset."
+    )
     parser.add_argument(
         "--repo-id",
         type=str,
@@ -247,7 +249,9 @@ if __name__ == "__main__":
     args = parser.parse_args()
 
     local_files_only = args.root is not None
-    dataset = LeRobotDataset(repo_id=args.repo_id, root=args.root, local_files_only=local_files_only)
+    dataset = LeRobotDataset(
+        repo_id=args.repo_id, root=args.root, local_files_only=local_files_only
+    )
 
     images = get_image_from_lerobot_dataset(dataset)
     images = {k: v.cpu().permute(1, 2, 0).numpy() for k, v in images.items()}
@@ -256,7 +260,7 @@ if __name__ == "__main__":
     if args.crop_params_path is None:
         rois = select_square_roi_for_images(images)
     else:
-        with open(args.crop_params_path, "r") as f:
+        with open(args.crop_params_path) as f:
             rois = json.load(f)
 
     # rois = {

lerobot/scripts/server/find_joint_limits.py

@@ -31,7 +31,9 @@ def find_joint_bounds(
         if display_cameras and not is_headless():
             image_keys = [key for key in observation if "image" in key]
             for key in image_keys:
-                cv2.imshow(key, cv2.cvtColor(observation[key].numpy(), cv2.COLOR_RGB2BGR))
+                cv2.imshow(
+                    key, cv2.cvtColor(observation[key].numpy(), cv2.COLOR_RGB2BGR)
+                )
             cv2.waitKey(1)
 
         timestamp = time.perf_counter() - start_episode_t
@@ -57,7 +59,12 @@ if __name__ == "__main__":
         nargs="*",
         help="Any key=value arguments to override config values (use dots for.nested=overrides)",
     )
-    parser.add_argument("--control-time-s", type=float, default=20, help="Maximum episode length in seconds")
+    parser.add_argument(
+        "--control-time-s",
+        type=float,
+        default=20,
+        help="Maximum episode length in seconds",
+    )
     args = parser.parse_args()
     robot_cfg = init_hydra_config(args.robot_path, args.robot_overrides)
 

lerobot/scripts/server/learner_server.py

@@ -146,7 +146,7 @@ def log_training_info(cfg: DictConfig, out_dir: str, policy: nn.Module) -> None:
 
 
 def initialize_replay_buffer(
-    cfg: DictConfig, logger: Logger, device: str, storage_device:str
+    cfg: DictConfig, logger: Logger, device: str, storage_device: str
 ) -> ReplayBuffer:
     if not cfg.resume:
         return ReplayBuffer(

lerobot/scripts/server/maniskill_manipulator.py

@@ -10,7 +10,9 @@ from typing import Any
 from mani_skill.vector.wrappers.gymnasium import ManiSkillVectorEnv
 
 
-def preprocess_maniskill_observation(observations: dict[str, np.ndarray]) -> dict[str, torch.Tensor]:
+def preprocess_maniskill_observation(
+    observations: dict[str, np.ndarray],
+) -> dict[str, torch.Tensor]:
     """Convert environment observation to LeRobot format observation.
     Args:
         observation: Dictionary of observation batches from a Gym vector environment.
@@ -62,7 +64,9 @@ class ManiSkillCompat(gym.Wrapper):
         new_action_space_shape = env.action_space.shape[-1]
         new_low = np.squeeze(env.action_space.low, axis=0)
         new_high = np.squeeze(env.action_space.high, axis=0)
-        self.action_space = gym.spaces.Box(low=new_low, high=new_high, shape=(new_action_space_shape,))
+        self.action_space = gym.spaces.Box(
+            low=new_low, high=new_high, shape=(new_action_space_shape,)
+        )
 
     def reset(
         self, *, seed: int | None = None, options: dict[str, Any] | None = None
@@ -81,7 +85,9 @@ class ManiSkillCompat(gym.Wrapper):
 class ManiSkillActionWrapper(gym.ActionWrapper):
     def __init__(self, env):
         super().__init__(env)
-        self.action_space = gym.spaces.Tuple(spaces=(env.action_space, gym.spaces.Discrete(2)))
+        self.action_space = gym.spaces.Tuple(
+            spaces=(env.action_space, gym.spaces.Discrete(2))
+        )
 
     def action(self, action):
         action, telop = action
@@ -95,7 +101,9 @@ class ManiSkillMultiplyActionWrapper(gym.Wrapper):
         action_space_agent: gym.spaces.Box = env.action_space[0]
         action_space_agent.low = action_space_agent.low * multiply_factor
         action_space_agent.high = action_space_agent.high * multiply_factor
-        self.action_space = gym.spaces.Tuple(spaces=(action_space_agent, gym.spaces.Discrete(2)))
+        self.action_space = gym.spaces.Tuple(
+            spaces=(action_space_agent, gym.spaces.Discrete(2))
+        )
 
     def step(self, action):
         if isinstance(action, tuple):
@@ -137,7 +145,9 @@ def make_maniskill(
 
     env = ManiSkillObservationWrapper(env, device=cfg.env.device)
     env = ManiSkillVectorEnv(env, ignore_terminations=True, auto_reset=False)
-    env._max_episode_steps = env.max_episode_steps = 50  # gym_utils.find_max_episode_steps_value(env)
+    env._max_episode_steps = env.max_episode_steps = (
+        50  # gym_utils.find_max_episode_steps_value(env)
+    )
     env.unwrapped.metadata["render_fps"] = 20
     env = ManiSkillCompat(env)
     env = ManiSkillActionWrapper(env)
@@ -149,10 +159,11 @@ def make_maniskill(
 if __name__ == "__main__":
     import argparse
     import hydra
-    from omegaconf import OmegaConf
 
     parser = argparse.ArgumentParser()
-    parser.add_argument("--config", type=str, default="lerobot/configs/env/maniskill_example.yaml")
+    parser.add_argument(
+        "--config", type=str, default="lerobot/configs/env/maniskill_example.yaml"
+    )
     args = parser.parse_args()
 
     # Initialize config