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

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

src/lerobot/envs/utils.py

@@ -16,10 +16,8 @@
 import warnings
 from typing import Any
 
-import einops
 import gymnasium as gym
 import numpy as np
-import torch
 from torch import Tensor
 
 from lerobot.configs.types import FeatureType, PolicyFeature
@@ -38,8 +36,8 @@ def preprocess_observation(observations: dict[str, np.ndarray]) -> dict[str, Ten
     Returns:
         Dictionary of observation batches with keys renamed to LeRobot format and values as tensors.
     """
-    from lerobot.processor.pipeline import RobotPipeline, TransitionIndex
     from lerobot.processor.observation_processor import ObservationProcessor
+    from lerobot.processor.pipeline import RobotPipeline, TransitionIndex
 
     # Create pipeline with observation processor
     pipeline = RobotPipeline([ObservationProcessor()])
@@ -52,9 +50,6 @@ def preprocess_observation(observations: dict[str, np.ndarray]) -> dict[str, Ten
     return processed_transition[TransitionIndex.OBSERVATION]
 
 
-
-
-
 def env_to_policy_features(env_cfg: EnvConfig) -> dict[str, PolicyFeature]:
     # TODO(aliberts, rcadene): remove this hardcoding of keys and just use the nested keys as is
     # (need to externalize normalization from policies)

src/lerobot/processor/__init__.py

@@ -13,12 +13,12 @@
 # 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 .pipeline import RobotPipeline, PipelineStep, EnvTransition
 from .observation_processor import (
     ImageProcessor,
-    StateProcessor, 
     ObservationProcessor,
+    StateProcessor,
 )
+from .pipeline import EnvTransition, PipelineStep, RobotPipeline
 
 __all__ = [
     "RobotPipeline",

src/lerobot/processor/observation_processor.py

@@ -15,14 +15,15 @@
 # limitations under the License.
 from __future__ import annotations
 
-from typing import Any
 from dataclasses import dataclass, field
+from typing import Any
+
+import einops
 import numpy as np
 import torch
-import einops
 from torch import Tensor
 
-from lerobot.processor.pipeline import EnvTransition, PipelineStep, TransitionIndex
+from lerobot.processor.pipeline import EnvTransition, TransitionIndex
 
 
 @dataclass
@@ -212,8 +213,12 @@ class ObservationProcessor:
 
     def load_state_dict(self, state: dict[str, torch.Tensor]) -> None:
         """Load state dictionary."""
-        image_state = {k.replace("image_processor.", ""): v for k, v in state.items() if k.startswith("image_processor.")}
-        state_state = {k.replace("state_processor.", ""): v for k, v in state.items() if k.startswith("state_processor.")}
+        image_state = {
+            k.replace("image_processor.", ""): v for k, v in state.items() if k.startswith("image_processor.")
+        }
+        state_state = {
+            k.replace("state_processor.", ""): v for k, v in state.items() if k.startswith("state_processor.")
+        }
 
         self.image_processor.load_state_dict(image_state)
         self.state_processor.load_state_dict(state_state)

src/lerobot/processor/pipeline.py

@@ -14,19 +14,22 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from __future__ import annotations
-import os, json
-from typing import Any, Dict, Sequence, Iterable, Protocol, Optional, Tuple, Callable, Union
+
+import json
+import os
 from dataclasses import dataclass, field
-from pathlib import Path
 from enum import IntEnum
-import numpy as np
+from pathlib import Path
+from typing import Any, Callable, Dict, Iterable, Protocol, Sequence, Tuple
+
 import torch
-from huggingface_hub import  hf_hub_download, ModelHubMixin
-from safetensors.torch import save_file, load_file
+from huggingface_hub import ModelHubMixin, hf_hub_download
+from safetensors.torch import load_file, save_file
 
 
 class TransitionIndex(IntEnum):
     """Explicit indices for EnvTransition tuple components."""
+
     OBSERVATION = 0
     ACTION = 1
     REWARD = 2
@@ -38,17 +41,16 @@ class TransitionIndex(IntEnum):
 
 # (observation, action, reward, done, truncated, info, complementary_data)
 EnvTransition = Tuple[
-    Any| None,                # observation
-    Any| None,                # action
-    float| None,              # reward
-    bool| None,               # done
-    bool| None,               # truncated
-    Dict[str, Any]| None,     # info
-    Dict[str, Any]| None,     # complementary_data
+    Any | None,  # observation
+    Any | None,  # action
+    float | None,  # reward
+    bool | None,  # done
+    bool | None,  # truncated
+    Dict[str, Any] | None,  # info
+    Dict[str, Any] | None,  # complementary_data
 ]
 
 
-
 class PipelineStep(Protocol):
     """Structural typing interface for a single pipeline step.
 
@@ -78,11 +80,11 @@ class PipelineStep(Protocol):
 
     def __call__(self, transition: EnvTransition) -> EnvTransition: ...
 
-    def get_config(self) -> Dict[str, Any]: ...
+    def get_config(self) -> dict[str, Any]: ...
 
-    def state_dict(self) -> Dict[str, torch.Tensor]: ...
+    def state_dict(self) -> dict[str, torch.Tensor]: ...
 
-    def load_state_dict(self, state: Dict[str, torch.Tensor]) -> None: ...
+    def load_state_dict(self, state: dict[str, torch.Tensor]) -> None: ...
 
     def reset(self) -> None: ...
 
@@ -120,17 +122,18 @@ class RobotPipeline(ModelHubMixin):
         pipe.push_to_hub("my-org/cartpole_pipe")
         loaded = RobotPipeline.from_pretrained("my-org/cartpole_pipe")
     """
+
     steps: Sequence[PipelineStep] = field(default_factory=list)
     name: str = "RobotPipeline"
-    seed: Optional[int] = None
+    seed: int | None = None
 
     # Pipeline-level hooks
     # A hook can optionally return a modified transition.  If it returns
     # ``None`` the current value is left untouched.
-    before_step_hooks: list[Callable[[int, EnvTransition], Optional[EnvTransition]]] = field(
+    before_step_hooks: list[Callable[[int, EnvTransition], EnvTransition | None]] = field(
         default_factory=list, repr=False
     )
-    after_step_hooks: list[Callable[[int, EnvTransition], Optional[EnvTransition]]] = field(
+    after_step_hooks: list[Callable[[int, EnvTransition], EnvTransition | None]] = field(
         default_factory=list, repr=False
     )
     reset_hooks: list[Callable[[], None]] = field(default_factory=list, repr=False)
@@ -177,14 +180,14 @@ class RobotPipeline(ModelHubMixin):
         """Serialize the pipeline definition and parameters to *destination_path*."""
         os.makedirs(destination_path, exist_ok=True)
 
-        config: Dict[str, Any] = {
+        config: dict[str, Any] = {
             "name": self.name,
             "seed": self.seed,
             "steps": [],
         }
 
         for step_index, pipeline_step in enumerate(self.steps):
-            step_entry: Dict[str, Any] = {
+            step_entry: dict[str, Any] = {
                 "class": f"{pipeline_step.__class__.__module__}.{pipeline_step.__class__.__name__}",
             }
 
@@ -204,19 +207,19 @@ class RobotPipeline(ModelHubMixin):
             json.dump(config, file_pointer, indent=2)
 
     @classmethod
-    def from_pretrained(cls, source: str) -> "RobotPipeline":
+    def from_pretrained(cls, source: str) -> RobotPipeline:
         """Load a serialized pipeline from *source* (local path or Hugging Face Hub identifier)."""
         if Path(source).is_dir():
             # Local path - use it directly
             base_path = Path(source)
             with open(base_path / cls._CFG_NAME) as file_pointer:
-                config: Dict[str, Any] = json.load(file_pointer)
+                config: dict[str, Any] = json.load(file_pointer)
         else:
             # Hugging Face Hub - download all required files
             # First download the config file
             config_path = hf_hub_download(source, cls._CFG_NAME, repo_type="model")
             with open(config_path) as file_pointer:
-                config: Dict[str, Any] = json.load(file_pointer)
+                config: dict[str, Any] = json.load(file_pointer)
 
             # Store downloaded files in the same directory as the config
             base_path = Path(config_path).parent
@@ -254,11 +257,11 @@ class RobotPipeline(ModelHubMixin):
             return RobotPipeline(self.steps[idx], self.name, self.seed)
         return self.steps[idx]
 
-    def register_before_step_hook(self, fn: Callable[[int, EnvTransition], Optional[EnvTransition]]):
+    def register_before_step_hook(self, fn: Callable[[int, EnvTransition], EnvTransition | None]):
         """Attach fn to be executed before every pipeline step."""
         self.before_step_hooks.append(fn)
 
-    def register_after_step_hook(self, fn: Callable[[int, EnvTransition], Optional[EnvTransition]]):
+    def register_after_step_hook(self, fn: Callable[[int, EnvTransition], EnvTransition | None]):
         """Attach fn to be executed after every pipeline step."""
         self.after_step_hooks.append(fn)
 
@@ -274,7 +277,7 @@ class RobotPipeline(ModelHubMixin):
         for fn in self.reset_hooks:
             fn()
 
-    def profile_steps(self, transition: EnvTransition, num_runs: int = 100) -> Dict[str, float]:
+    def profile_steps(self, transition: EnvTransition, num_runs: int = 100) -> dict[str, float]:
         """Profile the execution time of each step for performance optimization."""
         import time
 

src/lerobot/scripts/eval.py

@@ -69,12 +69,11 @@ from lerobot.configs import parser
 from lerobot.configs.eval import EvalPipelineConfig
 from lerobot.envs.factory import make_env
 from lerobot.envs.utils import add_envs_task, check_env_attributes_and_types
-
-from lerobot.processor.pipeline import RobotPipeline, TransitionIndex
-from lerobot.processor.observation_processor import ObservationProcessor
 from lerobot.policies.factory import make_policy
 from lerobot.policies.pretrained import PreTrainedPolicy
 from lerobot.policies.utils import get_device_from_parameters
+from lerobot.processor.observation_processor import ObservationProcessor
+from lerobot.processor.pipeline import RobotPipeline, TransitionIndex
 from lerobot.utils.io_utils import write_video
 from lerobot.utils.random_utils import set_seed
 from lerobot.utils.utils import (

tests/envs/test_envs.py

@@ -22,9 +22,8 @@ from gymnasium.utils.env_checker import check_env
 
 import lerobot
 from lerobot.envs.factory import make_env, make_env_config
-
-from lerobot.processor.pipeline import RobotPipeline, TransitionIndex
 from lerobot.processor.observation_processor import ObservationProcessor
+from lerobot.processor.pipeline import RobotPipeline, TransitionIndex
 from tests.utils import require_env
 
 OBS_TYPES = ["state", "pixels", "pixels_agent_pos"]

tests/policies/test_policies.py

@@ -30,8 +30,6 @@ from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
 from lerobot.datasets.factory import make_dataset
 from lerobot.datasets.utils import cycle, dataset_to_policy_features
 from lerobot.envs.factory import make_env, make_env_config
-from lerobot.processor.pipeline import RobotPipeline, TransitionIndex
-from lerobot.processor.observation_processor import ObservationProcessor
 from lerobot.optim.factory import make_optimizer_and_scheduler
 from lerobot.policies.act.modeling_act import ACTTemporalEnsembler
 from lerobot.policies.factory import (
@@ -41,6 +39,8 @@ from lerobot.policies.factory import (
 )
 from lerobot.policies.normalize import Normalize, Unnormalize
 from lerobot.policies.pretrained import PreTrainedPolicy
+from lerobot.processor.observation_processor import ObservationProcessor
+from lerobot.processor.pipeline import RobotPipeline, TransitionIndex
 from lerobot.utils.random_utils import seeded_context
 from tests.artifacts.policies.save_policy_to_safetensors import get_policy_stats
 from tests.utils import DEVICE, require_cpu, require_env, require_x86_64_kernel

tests/processor/test_observation_processor.py

@@ -20,10 +20,9 @@ import torch
 
 from lerobot.processor.observation_processor import (
     ImageProcessor,
-    StateProcessor,
     ObservationProcessor,
+    StateProcessor,
 )
-from lerobot.processor.pipeline import EnvTransition
 
 
 def test_process_single_image():
@@ -51,6 +50,7 @@ def test_process_single_image():
     assert processed_img.min() >= 0.0
     assert processed_img.max() <= 1.0
 
+
 def test_process_image_dict():
     """Test processing multiple images in a dictionary."""
     processor = ImageProcessor()
@@ -59,12 +59,7 @@ def test_process_image_dict():
     image1 = np.random.randint(0, 256, size=(32, 32, 3), dtype=np.uint8)
     image2 = np.random.randint(0, 256, size=(48, 48, 3), dtype=np.uint8)
 
-    observation = {
-        "pixels": {
-            "camera1": image1,
-            "camera2": image2
-        }
-    }
+    observation = {"pixels": {"camera1": image1, "camera2": image2}}
     transition = (observation, None, None, None, None, None, None)
 
     result = processor(transition)
@@ -78,6 +73,7 @@ def test_process_image_dict():
     assert processed_obs["observation.images.camera1"].shape == (1, 3, 32, 32)
     assert processed_obs["observation.images.camera2"].shape == (1, 3, 48, 48)
 
+
 def test_process_batched_image():
     """Test processing already batched images."""
     processor = ImageProcessor()
@@ -94,6 +90,7 @@ def test_process_batched_image():
     # Check that batch dimension is preserved
     assert processed_obs["observation.image"].shape == (2, 3, 64, 64)
 
+
 def test_invalid_image_format():
     """Test error handling for invalid image formats."""
     processor = ImageProcessor()
@@ -106,6 +103,7 @@ def test_invalid_image_format():
     with pytest.raises(ValueError, match="Expected channel-last images"):
         processor(transition)
 
+
 def test_invalid_image_dtype():
     """Test error handling for invalid image dtype."""
     processor = ImageProcessor()
@@ -118,6 +116,7 @@ def test_invalid_image_dtype():
     with pytest.raises(ValueError, match="Expected torch.uint8 images"):
         processor(transition)
 
+
 def test_no_pixels_in_observation():
     """Test processor when no pixels are in observation."""
     processor = ImageProcessor()
@@ -132,6 +131,7 @@ def test_no_pixels_in_observation():
     assert "other_data" in processed_obs
     np.testing.assert_array_equal(processed_obs["other_data"], np.array([1, 2, 3]))
 
+
 def test_none_observation():
     """Test processor with None observation."""
     processor = ImageProcessor()
@@ -141,6 +141,7 @@ def test_none_observation():
 
     assert result == transition
 
+
 def test_serialization_methods():
     """Test serialization methods."""
     processor = ImageProcessor()
@@ -161,64 +162,64 @@ def test_serialization_methods():
 
 
 def test_process_environment_state():
-        """Test processing environment_state."""
-        processor = StateProcessor()
+    """Test processing environment_state."""
+    processor = StateProcessor()
 
-        env_state = np.array([1.0, 2.0, 3.0], dtype=np.float32)
-        observation = {"environment_state": env_state}
-        transition = (observation, None, None, None, None, None, None)
+    env_state = np.array([1.0, 2.0, 3.0], dtype=np.float32)
+    observation = {"environment_state": env_state}
+    transition = (observation, None, None, None, None, None, None)
 
-        result = processor(transition)
-        processed_obs = result[0]
+    result = processor(transition)
+    processed_obs = result[0]
 
-        # Check that environment_state was renamed and processed
-        assert "observation.environment_state" in processed_obs
-        assert "environment_state" not in processed_obs
+    # Check that environment_state was renamed and processed
+    assert "observation.environment_state" in processed_obs
+    assert "environment_state" not in processed_obs
+
+    processed_state = processed_obs["observation.environment_state"]
+    assert processed_state.shape == (1, 3)  # Batch dimension added
+    assert processed_state.dtype == torch.float32
+    torch.testing.assert_close(processed_state, torch.tensor([[1.0, 2.0, 3.0]]))
 
-        processed_state = processed_obs["observation.environment_state"]
-        assert processed_state.shape == (1, 3)  # Batch dimension added
-        assert processed_state.dtype == torch.float32
-        torch.testing.assert_close(processed_state, torch.tensor([[1.0, 2.0, 3.0]]))
 
 def test_process_agent_pos():
-        """Test processing agent_pos."""
-        processor = StateProcessor()
+    """Test processing agent_pos."""
+    processor = StateProcessor()
 
-        agent_pos = np.array([0.5, -0.5, 1.0], dtype=np.float32)
-        observation = {"agent_pos": agent_pos}
-        transition = (observation, None, None, None, None, None, None)
+    agent_pos = np.array([0.5, -0.5, 1.0], dtype=np.float32)
+    observation = {"agent_pos": agent_pos}
+    transition = (observation, None, None, None, None, None, None)
 
-        result = processor(transition)
-        processed_obs = result[0]
+    result = processor(transition)
+    processed_obs = result[0]
 
-        # Check that agent_pos was renamed and processed
-        assert "observation.state" in processed_obs
-        assert "agent_pos" not in processed_obs
+    # Check that agent_pos was renamed and processed
+    assert "observation.state" in processed_obs
+    assert "agent_pos" not in processed_obs
+
+    processed_state = processed_obs["observation.state"]
+    assert processed_state.shape == (1, 3)  # Batch dimension added
+    assert processed_state.dtype == torch.float32
+    torch.testing.assert_close(processed_state, torch.tensor([[0.5, -0.5, 1.0]]))
 
-        processed_state = processed_obs["observation.state"]
-        assert processed_state.shape == (1, 3)  # Batch dimension added
-        assert processed_state.dtype == torch.float32
-        torch.testing.assert_close(processed_state, torch.tensor([[0.5, -0.5, 1.0]]))
 
 def test_process_batched_states():
-        """Test processing already batched states."""
-        processor = StateProcessor()
+    """Test processing already batched states."""
+    processor = StateProcessor()
 
-        env_state = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
-        agent_pos = np.array([[0.5, -0.5], [1.0, -1.0]], dtype=np.float32)
+    env_state = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
+    agent_pos = np.array([[0.5, -0.5], [1.0, -1.0]], dtype=np.float32)
 
-        observation = {
-            "environment_state": env_state,
-            "agent_pos": agent_pos
-        }
-        transition = (observation, None, None, None, None, None, None)
+    observation = {"environment_state": env_state, "agent_pos": agent_pos}
+    transition = (observation, None, None, None, None, None, None)
 
-        result = processor(transition)
-        processed_obs = result[0]
+    result = processor(transition)
+    processed_obs = result[0]
+
+    # Check that batch dimensions are preserved
+    assert processed_obs["observation.environment_state"].shape == (2, 2)
+    assert processed_obs["observation.state"].shape == (2, 2)
 
-        # Check that batch dimensions are preserved
-        assert processed_obs["observation.environment_state"].shape == (2, 2)
-        assert processed_obs["observation.state"].shape == (2, 2)
 
 def test_process_both_states():
     """Test processing both environment_state and agent_pos."""
@@ -227,11 +228,7 @@ def test_process_both_states():
     env_state = np.array([1.0, 2.0], dtype=np.float32)
     agent_pos = np.array([0.5, -0.5], dtype=np.float32)
 
-    observation = {
-        "environment_state": env_state,
-        "agent_pos": agent_pos,
-        "other_data": "keep_me"
-    }
+    observation = {"environment_state": env_state, "agent_pos": agent_pos, "other_data": "keep_me"}
     transition = (observation, None, None, None, None, None, None)
 
     result = processor(transition)
@@ -248,6 +245,7 @@ def test_process_both_states():
     # Check that other data was preserved
     assert processed_obs["other_data"] == "keep_me"
 
+
 def test_no_states_in_observation():
     """Test processor when no states are in observation."""
     processor = StateProcessor()
@@ -261,6 +259,7 @@ def test_no_states_in_observation():
     # Should preserve data unchanged
     assert processed_obs == observation
 
+
 def test_none_observation():
     """Test processor with None observation."""
     processor = StateProcessor()
@@ -270,6 +269,7 @@ def test_none_observation():
 
     assert result == transition
 
+
 def test_serialization_methods():
     """Test serialization methods."""
     processor = StateProcessor()
@@ -290,177 +290,177 @@ def test_serialization_methods():
 
 
 def test_complete_observation_processing():
-        """Test processing a complete observation with both images and states."""
-        processor = ObservationProcessor()
+    """Test processing a complete observation with both images and states."""
+    processor = ObservationProcessor()
 
-        # Create mock data
-        image = np.random.randint(0, 256, size=(32, 32, 3), dtype=np.uint8)
-        env_state = np.array([1.0, 2.0, 3.0], dtype=np.float32)
-        agent_pos = np.array([0.5, -0.5, 1.0], dtype=np.float32)
+    # Create mock data
+    image = np.random.randint(0, 256, size=(32, 32, 3), dtype=np.uint8)
+    env_state = np.array([1.0, 2.0, 3.0], dtype=np.float32)
+    agent_pos = np.array([0.5, -0.5, 1.0], dtype=np.float32)
 
-        observation = {
-            "pixels": image,
-            "environment_state": env_state,
-            "agent_pos": agent_pos,
-            "other_data": "preserve_me"
-        }
-        transition = (observation, None, None, None, None, None, None)
+    observation = {
+        "pixels": image,
+        "environment_state": env_state,
+        "agent_pos": agent_pos,
+        "other_data": "preserve_me",
+    }
+    transition = (observation, None, None, None, None, None, None)
 
-        result = processor(transition)
-        processed_obs = result[0]
+    result = processor(transition)
+    processed_obs = result[0]
 
-        # Check that image was processed
-        assert "observation.image" in processed_obs
-        assert processed_obs["observation.image"].shape == (1, 3, 32, 32)
+    # Check that image was processed
+    assert "observation.image" in processed_obs
+    assert processed_obs["observation.image"].shape == (1, 3, 32, 32)
 
-        # Check that states were processed
-        assert "observation.environment_state" in processed_obs
-        assert "observation.state" in processed_obs
+    # Check that states were processed
+    assert "observation.environment_state" in processed_obs
+    assert "observation.state" in processed_obs
 
-        # Check that original keys were removed
-        assert "pixels" not in processed_obs
-        assert "environment_state" not in processed_obs
-        assert "agent_pos" not in processed_obs
+    # Check that original keys were removed
+    assert "pixels" not in processed_obs
+    assert "environment_state" not in processed_obs
+    assert "agent_pos" not in processed_obs
+
+    # Check that other data was preserved
+    assert processed_obs["other_data"] == "preserve_me"
 
-        # Check that other data was preserved
-        assert processed_obs["other_data"] == "preserve_me"
 
 def test_image_only_processing():
-        """Test processing observation with only images."""
-        processor = ObservationProcessor()
+    """Test processing observation with only images."""
+    processor = ObservationProcessor()
 
-        image = np.random.randint(0, 256, size=(64, 64, 3), dtype=np.uint8)
-        observation = {"pixels": image}
-        transition = (observation, None, None, None, None, None, None)
+    image = np.random.randint(0, 256, size=(64, 64, 3), dtype=np.uint8)
+    observation = {"pixels": image}
+    transition = (observation, None, None, None, None, None, None)
 
-        result = processor(transition)
-        processed_obs = result[0]
+    result = processor(transition)
+    processed_obs = result[0]
+
+    assert "observation.image" in processed_obs
+    assert len(processed_obs) == 1
 
-        assert "observation.image" in processed_obs
-        assert len(processed_obs) == 1
 
 def test_state_only_processing():
-        """Test processing observation with only states."""
-        processor = ObservationProcessor()
+    """Test processing observation with only states."""
+    processor = ObservationProcessor()
 
-        agent_pos = np.array([1.0, 2.0], dtype=np.float32)
-        observation = {"agent_pos": agent_pos}
-        transition = (observation, None, None, None, None, None, None)
+    agent_pos = np.array([1.0, 2.0], dtype=np.float32)
+    observation = {"agent_pos": agent_pos}
+    transition = (observation, None, None, None, None, None, None)
 
-        result = processor(transition)
-        processed_obs = result[0]
+    result = processor(transition)
+    processed_obs = result[0]
+
+    assert "observation.state" in processed_obs
+    assert "agent_pos" not in processed_obs
 
-        assert "observation.state" in processed_obs
-        assert "agent_pos" not in processed_obs
 
 def test_empty_observation():
-        """Test processing empty observation."""
-        processor = ObservationProcessor()
+    """Test processing empty observation."""
+    processor = ObservationProcessor()
 
-        observation = {}
-        transition = (observation, None, None, None, None, None, None)
+    observation = {}
+    transition = (observation, None, None, None, None, None, None)
 
-        result = processor(transition)
-        processed_obs = result[0]
+    result = processor(transition)
+    processed_obs = result[0]
+
+    assert processed_obs == {}
 
-        assert processed_obs == {}
 
 def test_none_observation():
-        """Test processing None observation."""
-        processor = ObservationProcessor()
+    """Test processing None observation."""
+    processor = ObservationProcessor()
 
-        transition = (None, None, None, None, None, None, None)
-        result = processor(transition)
+    transition = (None, None, None, None, None, None, None)
+    result = processor(transition)
+
+    assert result == transition
 
-        assert result == transition
 
 def test_serialization_methods():
-        """Test serialization methods."""
-        processor = ObservationProcessor()
+    """Test serialization methods."""
+    processor = ObservationProcessor()
 
-        # Test get_config
-        config = processor.get_config()
-        assert isinstance(config, dict)
-        assert "image_processor" in config
-        assert "state_processor" in config
+    # Test get_config
+    config = processor.get_config()
+    assert isinstance(config, dict)
+    assert "image_processor" in config
+    assert "state_processor" in config
 
-        # Test state_dict
-        state = processor.state_dict()
-        assert isinstance(state, dict)
+    # Test state_dict
+    state = processor.state_dict()
+    assert isinstance(state, dict)
 
-        # Test load_state_dict (should not raise)
-        processor.load_state_dict(state)
+    # Test load_state_dict (should not raise)
+    processor.load_state_dict(state)
+
+    # Test reset (should not raise)
+    processor.reset()
 
-        # Test reset (should not raise)
-        processor.reset()
 
 def test_custom_sub_processors():
-        """Test ObservationProcessor with custom sub-processors."""
-        image_proc = ImageProcessor()
-        state_proc = StateProcessor()
-        processor = ObservationProcessor(image_processor=image_proc, state_processor=state_proc)
+    """Test ObservationProcessor with custom sub-processors."""
+    image_proc = ImageProcessor()
+    state_proc = StateProcessor()
+    processor = ObservationProcessor(image_processor=image_proc, state_processor=state_proc)
 
-        # Should use the provided processors
-        assert processor.image_processor is image_proc
-        assert processor.state_processor is state_proc
+    # Should use the provided processors
+    assert processor.image_processor is image_proc
+    assert processor.state_processor is state_proc
 
 
 def test_equivalent_to_original_function():
-        """Test that ObservationProcessor produces equivalent results to preprocess_observation."""
-        # Import the original function for comparison
-        from lerobot.envs.utils import preprocess_observation
+    """Test that ObservationProcessor produces equivalent results to preprocess_observation."""
+    # Import the original function for comparison
+    from lerobot.envs.utils import preprocess_observation
 
-        processor = ObservationProcessor()
+    processor = ObservationProcessor()
 
-        # Create test data similar to what the original function expects
-        image = np.random.randint(0, 256, size=(64, 64, 3), dtype=np.uint8)
-        env_state = np.array([1.0, 2.0, 3.0], dtype=np.float32)
-        agent_pos = np.array([0.5, -0.5, 1.0], dtype=np.float32)
+    # Create test data similar to what the original function expects
+    image = np.random.randint(0, 256, size=(64, 64, 3), dtype=np.uint8)
+    env_state = np.array([1.0, 2.0, 3.0], dtype=np.float32)
+    agent_pos = np.array([0.5, -0.5, 1.0], dtype=np.float32)
 
-        observation = {
-            "pixels": image,
-            "environment_state": env_state,
-            "agent_pos": agent_pos
-        }
+    observation = {"pixels": image, "environment_state": env_state, "agent_pos": agent_pos}
 
-        # Process with original function
-        original_result = preprocess_observation(observation)
+    # Process with original function
+    original_result = preprocess_observation(observation)
 
-        # Process with new processor
-        transition = (observation, None, None, None, None, None, None)
-        processor_result = processor(transition)[0]
+    # Process with new processor
+    transition = (observation, None, None, None, None, None, None)
+    processor_result = processor(transition)[0]
 
-        # Compare results
-        assert set(original_result.keys()) == set(processor_result.keys())
+    # Compare results
+    assert set(original_result.keys()) == set(processor_result.keys())
+
+    for key in original_result:
+        torch.testing.assert_close(original_result[key], processor_result[key])
 
-        for key in original_result:
-            torch.testing.assert_close(original_result[key], processor_result[key])
 
 def test_equivalent_with_image_dict():
-        """Test equivalence with dictionary of images."""
-        from lerobot.envs.utils import preprocess_observation
+    """Test equivalence with dictionary of images."""
+    from lerobot.envs.utils import preprocess_observation
 
-        processor = ObservationProcessor()
+    processor = ObservationProcessor()
 
-        # Create test data with multiple cameras
-        image1 = np.random.randint(0, 256, size=(32, 32, 3), dtype=np.uint8)
-        image2 = np.random.randint(0, 256, size=(48, 48, 3), dtype=np.uint8)
-        agent_pos = np.array([1.0, 2.0], dtype=np.float32)
+    # Create test data with multiple cameras
+    image1 = np.random.randint(0, 256, size=(32, 32, 3), dtype=np.uint8)
+    image2 = np.random.randint(0, 256, size=(48, 48, 3), dtype=np.uint8)
+    agent_pos = np.array([1.0, 2.0], dtype=np.float32)
 
-        observation = {
-            "pixels": {"cam1": image1, "cam2": image2},
-            "agent_pos": agent_pos
-        }
+    observation = {"pixels": {"cam1": image1, "cam2": image2}, "agent_pos": agent_pos}
 
-        # Process with original function
-        original_result = preprocess_observation(observation)
+    # Process with original function
+    original_result = preprocess_observation(observation)
 
-        # Process with new processor
-        transition = (observation, None, None, None, None, None, None)
-        processor_result = processor(transition)[0]
+    # Process with new processor
+    transition = (observation, None, None, None, None, None, None)
+    processor_result = processor(transition)[0]
 
-        # Compare results
-        assert set(original_result.keys()) == set(processor_result.keys())
+    # Compare results
+    assert set(original_result.keys()) == set(processor_result.keys())
 
-        for key in original_result:
-            torch.testing.assert_close(original_result[key], processor_result[key]) 
+    for key in original_result:
+        torch.testing.assert_close(original_result[key], processor_result[key])

tests/processor/test_pipeline.py

@@ -16,15 +16,14 @@
 
 import json
 import tempfile
+from dataclasses import dataclass
 from pathlib import Path
 from typing import Any
-from dataclasses import dataclass
 
-import numpy as np
 import pytest
 import torch
 
-from lerobot.processor.pipeline import RobotPipeline, EnvTransition, PipelineStep
+from lerobot.processor.pipeline import EnvTransition, RobotPipeline
 
 
 @dataclass
@@ -151,6 +150,7 @@ def test_empty_pipeline():
     assert result == transition
     assert len(pipeline) == 0
 
+
 def test_single_step_pipeline():
     """Test pipeline with a single step."""
     step = MockStep("test_step")
@@ -166,6 +166,7 @@ def test_single_step_pipeline():
     result = pipeline(transition)
     assert result[6]["test_step_counter"] == 1
 
+
 def test_multiple_steps_pipeline():
     """Test pipeline with multiple steps."""
     step1 = MockStep("step1")
@@ -179,6 +180,7 @@ def test_multiple_steps_pipeline():
     assert result[6]["step1_counter"] == 0
     assert result[6]["step2_counter"] == 0
 
+
 def test_invalid_transition_format():
     """Test pipeline with invalid transition format."""
     pipeline = RobotPipeline([MockStep()])
@@ -191,6 +193,7 @@ def test_invalid_transition_format():
     with pytest.raises(ValueError, match="EnvTransition must be a 7-tuple"):
         pipeline("not a tuple")
 
+
 def test_step_through():
     """Test step_through method."""
     step1 = MockStep("step1")
@@ -206,6 +209,7 @@ def test_step_through():
     assert "step1_counter" in results[1][6]  # After step1
     assert "step2_counter" in results[2][6]  # After step2
 
+
 def test_indexing():
     """Test pipeline indexing."""
     step1 = MockStep("step1")
@@ -222,6 +226,7 @@ def test_indexing():
     assert len(sub_pipeline) == 1
     assert sub_pipeline[0] is step1
 
+
 def test_hooks():
     """Test before/after step hooks."""
     step = MockStep("test_step")
@@ -247,6 +252,7 @@ def test_hooks():
     assert before_calls == [0]
     assert after_calls == [0]
 
+
 def test_hook_modification():
     """Test that hooks can modify transitions."""
     step = MockStep("test_step")
@@ -263,6 +269,7 @@ def test_hook_modification():
 
     assert result[2] == 42.0  # reward modified by hook
 
+
 def test_reset():
     """Test pipeline reset functionality."""
     step = MockStep("test_step")
@@ -288,6 +295,7 @@ def test_reset():
     assert step.counter == 0
     assert len(reset_called) == 1
 
+
 def test_profile_steps():
     """Test step profiling functionality."""
     step1 = MockStep("step1")
@@ -303,6 +311,7 @@ def test_profile_steps():
     assert "step_1_MockStep" in profile_results
     assert all(isinstance(time, float) and time >= 0 for time in profile_results.values())
 
+
 def test_save_and_load_pretrained():
     """Test saving and loading pipeline.
 
@@ -349,6 +358,7 @@ def test_save_and_load_pretrained():
         assert loaded_pipeline.steps[0].counter == 5
         assert loaded_pipeline.steps[1].counter == 10
 
+
 def test_step_without_optional_methods():
     """Test pipeline with steps that don't implement optional methods."""
     step = MockStepWithoutOptionalMethods(multiplier=3.0)
@@ -368,6 +378,7 @@ def test_step_without_optional_methods():
         loaded_pipeline = RobotPipeline.from_pretrained(tmp_dir)
         assert len(loaded_pipeline) == 1
 
+
 def test_mixed_json_and_tensor_state():
     """Test step with both JSON attributes and tensor state."""
     step = MockStepWithTensorState(name="stats", learning_rate=0.05, window_size=5)
@@ -404,5 +415,3 @@ def test_mixed_json_and_tensor_state():
         # Check tensor state was restored
         assert loaded_step.running_count.item() == 10
         assert torch.allclose(loaded_step.running_mean, step.running_mean)
-
-