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

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src/lerobot/processor/normalize_processor.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 from dataclasses import dataclass, field
-from typing import Any, Mapping, Optional, Set
+from typing import Any, Mapping
 
 import numpy as np
 import torch
@@ -51,12 +51,12 @@ class NormalizerProcessor:
     norm_map: dict[FeatureType, NormalizationMode]
 
     # Pre-computed statistics coming from dataset.meta.stats for instance.
-    stats: Optional[dict[str, dict[str, Any]]] = None
+    stats: dict[str, dict[str, Any]] | None = None
 
     # Explicit subset of keys to normalise. If ``None`` every key (except
     # "action") found in ``stats`` will be normalised. Using a ``set`` makes
     # membership checks O(1).
-    normalize_keys: Optional[Set[str]] = None
+    normalize_keys: set[str] | None = None
 
     eps: float = 1e-8
 
@@ -69,9 +69,9 @@ class NormalizerProcessor:
         features: dict[str, PolicyFeature],
         norm_map: dict[FeatureType, NormalizationMode],
         *,
-        normalize_keys: Optional[Set[str]] = None,
+        normalize_keys: set[str] | None = None,
         eps: float = 1e-8,
-    ) -> "NormalizerProcessor":
+    ) -> NormalizerProcessor:
         """Factory helper that pulls statistics from a :class:`LeRobotDataset`.
 
         The features and norm_map parameters are mandatory to match the design
@@ -195,7 +195,7 @@ class UnnormalizerProcessor:
 
     features: dict[str, PolicyFeature]
     norm_map: dict[FeatureType, NormalizationMode]
-    stats: Optional[dict[str, dict[str, Any]]] = None
+    stats: dict[str, dict[str, Any]] | None = None
     eps: float = 1e-8
 
     _tensor_stats: dict[str, dict[str, Tensor]] = field(default_factory=dict, init=False, repr=False)
@@ -208,7 +208,7 @@ class UnnormalizerProcessor:
         norm_map: dict[FeatureType, NormalizationMode],
         *,
         eps: float = 1e-8,
-    ) -> "UnnormalizerProcessor":
+    ) -> UnnormalizerProcessor:
         return cls(features=features, norm_map=norm_map, stats=dataset.meta.stats, eps=eps)
 
     def __post_init__(self):

tests/processor/test_batch_conversion.py

@@ -223,7 +223,7 @@ def test_complex_nested_observation():
     reconstructed_batch = _default_transition_to_batch(transition)
 
     # Check that all observation keys are preserved
-    original_obs_keys = {k for k in batch.keys() if k.startswith("observation.")}
+    original_obs_keys = {k for k in batch if k.startswith("observation.")}
     reconstructed_obs_keys = {k for k in reconstructed_batch.keys() if k.startswith("observation.")}
 
     assert original_obs_keys == reconstructed_obs_keys