[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, Dict, Mapping
+from typing import Any, Mapping
 
 import numpy as np
 import torch
@@ -11,9 +11,9 @@ from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.processor.pipeline import EnvTransition, ProcessorStepRegistry, TransitionIndex
 
 
-def _convert_stats_to_tensors(stats: Dict[str, Dict[str, Any]]) -> Dict[str, Dict[str, Tensor]]:
+def _convert_stats_to_tensors(stats: dict[str, dict[str, Any]]) -> dict[str, dict[str, Tensor]]:
     """Convert numpy arrays and other types to torch tensors."""
-    tensor_stats: Dict[str, Dict[str, Tensor]] = {}
+    tensor_stats: dict[str, dict[str, Tensor]] = {}
     for key, sub in stats.items():
         tensor_stats[key] = {}
         for stat_name, value in sub.items():
@@ -50,12 +50,12 @@ class ObservationNormalizer:
         Small constant to avoid division by zero.
     """
 
-    stats: Dict[str, Dict[str, Any]]
+    stats: dict[str, dict[str, Any]]
     normalize_keys: set[str] | None = None
     eps: float = 1e-8
 
     # Cached tensors for performance
-    _tensor_stats: Dict[str, Dict[str, Tensor]] = field(default_factory=dict, init=False, repr=False)
+    _tensor_stats: dict[str, dict[str, Tensor]] = field(default_factory=dict, init=False, repr=False)
 
     @classmethod
     def from_lerobot_dataset(
@@ -135,14 +135,14 @@ class ObservationNormalizer:
             transition[TransitionIndex.COMPLEMENTARY_DATA],
         )
 
-    def get_config(self) -> Dict[str, Any]:
+    def get_config(self) -> dict[str, Any]:
         return {
             "normalize_keys": list(self.normalize_keys) if self.normalize_keys is not None else None,
             "eps": self.eps,
         }
 
-    def state_dict(self) -> Dict[str, Tensor]:
-        flat_state: Dict[str, Tensor] = {}
+    def state_dict(self) -> dict[str, Tensor]:
+        flat_state: dict[str, Tensor] = {}
         for key, sub in self._tensor_stats.items():
             for stat_name, tensor in sub.items():
                 flat_state[f"{key}.{stat_name}"] = tensor
@@ -178,11 +178,11 @@ class ActionUnnormalizer:
         Small constant used during normalization (not used in unnormalization).
     """
 
-    action_stats: Dict[str, Any]
+    action_stats: dict[str, Any]
     eps: float = 1e-8  # Kept for consistency, not used in unnormalization
 
     # Cached tensors for performance
-    _tensor_stats: Dict[str, Tensor] = field(default_factory=dict, init=False, repr=False)
+    _tensor_stats: dict[str, Tensor] = field(default_factory=dict, init=False, repr=False)
 
     @classmethod
     def from_lerobot_dataset(
@@ -238,10 +238,10 @@ class ActionUnnormalizer:
             transition[TransitionIndex.COMPLEMENTARY_DATA],
         )
 
-    def get_config(self) -> Dict[str, Any]:
+    def get_config(self) -> dict[str, Any]:
         return {"eps": self.eps}
 
-    def state_dict(self) -> Dict[str, Tensor]:
+    def state_dict(self) -> dict[str, Tensor]:
         return dict(self._tensor_stats.items())
 
     def load_state_dict(self, state: Mapping[str, Tensor]) -> None:
@@ -273,13 +273,13 @@ class NormalizationProcessor:
         Small constant to avoid division by zero.
     """
 
-    stats: Dict[str, Dict[str, Any]]
+    stats: dict[str, dict[str, Any]]
     normalize_keys: set[str] | None = None
     unnormalize_action: bool = True
     eps: float = 1e-8
 
     # Cached tensors for performance
-    _tensor_stats: Dict[str, Dict[str, Tensor]] = field(default_factory=dict, init=False, repr=False)
+    _tensor_stats: dict[str, dict[str, Tensor]] = field(default_factory=dict, init=False, repr=False)
 
     @classmethod
     def from_lerobot_dataset(
@@ -365,15 +365,15 @@ class NormalizationProcessor:
             transition[TransitionIndex.COMPLEMENTARY_DATA],
         )
 
-    def get_config(self) -> Dict[str, Any]:
+    def get_config(self) -> dict[str, Any]:
         return {
             "normalize_keys": list(self.normalize_keys) if self.normalize_keys is not None else None,
             "unnormalize_action": self.unnormalize_action,
             "eps": self.eps,
         }
 
-    def state_dict(self) -> Dict[str, Tensor]:
-        flat_state: Dict[str, Tensor] = {}
+    def state_dict(self) -> dict[str, Tensor]:
+        flat_state: dict[str, Tensor] = {}
         for key, sub in self._tensor_stats.items():
             for stat_name, tensor in sub.items():
                 flat_state[f"{key}.{stat_name}"] = tensor