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

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

lerobot/common/envs/utils.py

@@ -13,13 +13,15 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Any
 import warnings
+from typing import Any
+
 import einops
+import gym
 import numpy as np
 import torch
 from torch import Tensor
-import gym
+
 from lerobot.common.envs.configs import EnvConfig
 from lerobot.common.utils.utils import get_channel_first_image_shape
 from lerobot.configs.types import FeatureType, PolicyFeature
@@ -89,27 +91,30 @@ def env_to_policy_features(env_cfg: EnvConfig) -> dict[str, PolicyFeature]:
 
     return policy_features
 
+
 def check_all_envs_same_type(env: gym.vector.VectorEnv) -> bool:
     first_type = type(env.envs[0])  # Get type of first env
     return all(type(e) is first_type for e in env.envs)  # Fast type check
 
+
 def check_env_attributes_and_types(env: gym.vector.VectorEnv) -> None:
     with warnings.catch_warnings():
         warnings.simplefilter("once", UserWarning)  # Apply filter only in this function
-        
+
         if not (hasattr(env.envs[0], "task_description") and hasattr(env.envs[0], "task")):
             warnings.warn(
                 "The environment does not have 'task_description' and 'task'. Some policies require these features.",
                 UserWarning,
-                stacklevel=2
+                stacklevel=2,
             )
         if not check_all_envs_same_type(env):
             warnings.warn(
                 "The environments have different types. Make sure you infer the right task from each environment. Empty task will be passed instead.",
                 UserWarning,
-                stacklevel=2
+                stacklevel=2,
             )
 
+
 def infer_envs_task(env: gym.vector.VectorEnv, observation: dict[str, Any]) -> dict[str, Any]:
     if hasattr(env.envs[0], "task_description"):
         observation["task"] = env.call("task_description")
@@ -118,4 +123,4 @@ def infer_envs_task(env: gym.vector.VectorEnv, observation: dict[str, Any]) -> d
     else:  #  For envs without language instructions, e.g. aloha transfer cube and etc.
         num_envs = observation[list(observation.keys())[0]].shape[0]
         observation["task"] = ["" for _ in range(num_envs)]
-    return observation
+    return observation

lerobot/scripts/eval.py

@@ -66,7 +66,7 @@ from torch import Tensor, nn
 from tqdm import trange
 
 from lerobot.common.envs.factory import make_env
-from lerobot.common.envs.utils import preprocess_observation, infer_envs_task, check_env_attributes_and_types
+from lerobot.common.envs.utils import check_env_attributes_and_types, infer_envs_task, preprocess_observation
 from lerobot.common.policies.factory import make_policy
 from lerobot.common.policies.pretrained import PreTrainedPolicy
 from lerobot.common.policies.utils import get_device_from_parameters
@@ -158,7 +158,7 @@ def rollout(
 
         # Infer "task" from envs. Works with AsyncVectorEnv.
         observation = infer_envs_task(env, observation)
-        
+
         with torch.inference_mode():
             action = policy.select_action(observation)