chore: enable pyugrade ruff lint (#2084)

pyproject.toml

@@ -201,7 +201,7 @@ exclude = ["tests/artifacts/**/*.safetensors", "*_pb2.py", "*_pb2_grpc.py"]
 # N: pep8-naming
 # TODO: Uncomment rules when ready to use
 select = [
-    "E", "W", "F", "I", "B", "C4", "T20", "N" # "SIM", "A", "S", "D", "RUF", "UP"
+    "E", "W", "F", "I", "B", "C4", "T20", "N", "UP" # "SIM", "A", "S", "D", "RUF"
 ]
 ignore = [
     "E501", # Line too long

src/lerobot/datasets/lerobot_dataset.py

@@ -1421,7 +1421,7 @@ class MultiLeRobotDataset(torch.utils.data.Dataset):
         """Keys to access image and video stream from cameras."""
         keys = []
         for key, feats in self.features.items():
-            if isinstance(feats, (datasets.Image, VideoFrame)):
+            if isinstance(feats, (datasets.Image | VideoFrame)):
                 keys.append(key)
         return keys
 

src/lerobot/datasets/transforms.py

@@ -120,7 +120,7 @@ class SharpnessJitter(Transform):
         self.sharpness = self._check_input(sharpness)
 
     def _check_input(self, sharpness):
-        if isinstance(sharpness, (int, float)):
+        if isinstance(sharpness, (int | float)):
             if sharpness < 0:
                 raise ValueError("If sharpness is a single number, it must be non negative.")
             sharpness = [1.0 - sharpness, 1.0 + sharpness]

src/lerobot/datasets/utils.py

@@ -21,7 +21,7 @@ from collections import deque
 from collections.abc import Iterable, Iterator
 from pathlib import Path
 from pprint import pformat
-from typing import Any, Deque, Generic, TypeVar
+from typing import Any, Generic, TypeVar
 
 import datasets
 import numpy as np
@@ -207,13 +207,13 @@ def serialize_dict(stats: dict[str, torch.Tensor | np.ndarray | dict]) -> dict:
     """
     serialized_dict = {}
     for key, value in flatten_dict(stats).items():
-        if isinstance(value, (torch.Tensor, np.ndarray)):
+        if isinstance(value, (torch.Tensor | np.ndarray)):
             serialized_dict[key] = value.tolist()
-        elif isinstance(value, list) and isinstance(value[0], (int, float, list)):
+        elif isinstance(value, list) and isinstance(value[0], (int | float | list)):
             serialized_dict[key] = value
         elif isinstance(value, np.generic):
             serialized_dict[key] = value.item()
-        elif isinstance(value, (int, float)):
+        elif isinstance(value, (int | float)):
             serialized_dict[key] = value
         else:
             raise NotImplementedError(f"The value '{value}' of type '{type(value)}' is not supported.")
@@ -1179,7 +1179,7 @@ def item_to_torch(item: dict) -> dict:
         dict: Dictionary with all tensor-like items converted to torch.Tensor.
     """
     for key, val in item.items():
-        if isinstance(val, (np.ndarray, list)) and key not in ["task"]:
+        if isinstance(val, (np.ndarray | list)) and key not in ["task"]:
             # Convert numpy arrays and lists to torch tensors
             item[key] = torch.tensor(val)
     return item
@@ -1253,8 +1253,8 @@ class Backtrackable(Generic[T]):
             raise ValueError("lookahead must be > 0")
 
         self._source: Iterator[T] = iter(iterable)
-        self._back_buf: Deque[T] = deque(maxlen=history)
+        self._back_buf: deque[T] = deque(maxlen=history)
-        self._ahead_buf: Deque[T] = deque(maxlen=lookahead) if lookahead > 0 else deque()
+        self._ahead_buf: deque[T] = deque(maxlen=lookahead) if lookahead > 0 else deque()
         self._cursor: int = 0
         self._history = history
         self._lookahead = lookahead

src/lerobot/envs/libero.py

@@ -35,7 +35,7 @@ def _parse_camera_names(camera_name: str | Sequence[str]) -> list[str]:
     """Normalize camera_name into a non-empty list of strings."""
     if isinstance(camera_name, str):
         cams = [c.strip() for c in camera_name.split(",") if c.strip()]
-    elif isinstance(camera_name, (list, tuple)):
+    elif isinstance(camera_name, (list | tuple)):
         cams = [str(c).strip() for c in camera_name if str(c).strip()]
     else:
         raise TypeError(f"camera_name must be str or sequence[str], got {type(camera_name).__name__}")

src/lerobot/envs/utils.py

@@ -183,10 +183,10 @@ def _(env: Mapping) -> None:
 
 @close_envs.register
 def _(envs: Sequence) -> None:
-    if isinstance(envs, (str, bytes)):
+    if isinstance(envs, (str | bytes)):
         return
     for v in envs:
-        if isinstance(v, Mapping) or isinstance(v, Sequence) and not isinstance(v, (str, bytes)):
+        if isinstance(v, Mapping) or isinstance(v, Sequence) and not isinstance(v, (str | bytes)):
             close_envs(v)
         elif hasattr(v, "close"):
             _close_single_env(v)

src/lerobot/motors/motors_bus.py

@@ -342,7 +342,7 @@ class MotorsBus(abc.ABC):
             raise TypeError(motors)
 
     def _get_ids_values_dict(self, values: Value | dict[str, Value] | None) -> list[str]:
-        if isinstance(values, (int, float)):
+        if isinstance(values, (int | float)):
             return dict.fromkeys(self.ids, values)
         elif isinstance(values, dict):
             return {self.motors[motor].id: val for motor, val in values.items()}
@@ -669,7 +669,7 @@ class MotorsBus(abc.ABC):
         """
         if motors is None:
             motors = list(self.motors)
-        elif isinstance(motors, (str, int)):
+        elif isinstance(motors, (str | int)):
             motors = [motors]
         elif not isinstance(motors, list):
             raise TypeError(motors)
@@ -697,7 +697,7 @@ class MotorsBus(abc.ABC):
         """
         if motors is None:
             motors = list(self.motors)
-        elif isinstance(motors, (str, int)):
+        elif isinstance(motors, (str | int)):
             motors = [motors]
         elif not isinstance(motors, list):
             raise TypeError(motors)
@@ -733,7 +733,7 @@ class MotorsBus(abc.ABC):
         """
         if motors is None:
             motors = list(self.motors)
-        elif isinstance(motors, (str, int)):
+        elif isinstance(motors, (str | int)):
             motors = [motors]
         elif not isinstance(motors, list):
             raise TypeError(motors)

src/lerobot/policies/vqbet/vqbet_utils.py

@@ -260,13 +260,11 @@ class GPT(nn.Module):
         param_dict = dict(self.named_parameters())
         inter_params = decay & no_decay
         union_params = decay | no_decay
-        assert len(inter_params) == 0, "parameters {} made it into both decay/no_decay sets!".format(
+        assert len(inter_params) == 0, (
-            str(inter_params)
+            f"parameters {str(inter_params)} made it into both decay/no_decay sets!"
         )
         assert len(param_dict.keys() - union_params) == 0, (
-            "parameters {} were not separated into either decay/no_decay set!".format(
+            f"parameters {str(param_dict.keys() - union_params)} were not separated into either decay/no_decay set!"
-                str(param_dict.keys() - union_params),
-            )
         )
 
         decay = [param_dict[pn] for pn in sorted(decay)]

src/lerobot/rl/buffer.py

@@ -176,7 +176,7 @@ class ReplayBuffer:
                     self.complementary_info[key] = torch.empty(
                         (self.capacity, *value_shape), device=self.storage_device
                     )
-                elif isinstance(value, (int, float)):
+                elif isinstance(value, (int | float)):
                     # Handle scalar values similar to reward
                     self.complementary_info[key] = torch.empty((self.capacity,), device=self.storage_device)
                 else:
@@ -223,7 +223,7 @@ class ReplayBuffer:
                     value = complementary_info[key]
                     if isinstance(value, torch.Tensor):
                         self.complementary_info[key][self.position].copy_(value.squeeze(dim=0))
-                    elif isinstance(value, (int, float)):
+                    elif isinstance(value, (int | float)):
                         self.complementary_info[key][self.position] = value
 
         self.position = (self.position + 1) % self.capacity

src/lerobot/rl/wandb_utils.py

@@ -137,7 +137,7 @@ class WandBLogger:
                 self._wandb.define_metric(new_custom_key, hidden=True)
 
         for k, v in d.items():
-            if not isinstance(v, (int, float, str)):
+            if not isinstance(v, (int | float | str)):
                 logging.warning(
                     f'WandB logging of key "{k}" was ignored as its type "{type(v)}" is not handled by this wrapper.'
                 )

src/lerobot/scripts/lerobot_record.py

@@ -267,11 +267,7 @@ def record_loop(
                 for t in teleop
                 if isinstance(
                     t,
-                    (
+                    (so100_leader.SO100Leader | so101_leader.SO101Leader | koch_leader.KochLeader),
-                        so100_leader.SO100Leader,
-                        so101_leader.SO101Leader,
-                        koch_leader.KochLeader,
-                    ),
                 )
             ),
             None,

src/lerobot/teleoperators/homunculus/homunculus_arm.py

@@ -18,7 +18,6 @@ import logging
 import threading
 from collections import deque
 from pprint import pformat
-from typing import Deque
 
 import serial
 
@@ -60,7 +59,7 @@ class HomunculusArm(Teleoperator):
         self.n: int = n
         self.alpha: float = 2 / (n + 1)
         # one deque *per joint* so we can inspect raw history if needed
-        self._buffers: dict[str, Deque[int]] = {
+        self._buffers: dict[str, deque[int]] = {
             joint: deque(maxlen=n)
             for joint in (
                 "shoulder_pitch",

src/lerobot/teleoperators/homunculus/homunculus_glove.py

@@ -18,7 +18,6 @@ import logging
 import threading
 from collections import deque
 from pprint import pformat
-from typing import Deque
 
 import serial
 
@@ -97,7 +96,7 @@ class HomunculusGlove(Teleoperator):
         self.n: int = n
         self.alpha: float = 2 / (n + 1)
         # one deque *per joint* so we can inspect raw history if needed
-        self._buffers: dict[str, Deque[int]] = {joint: deque(maxlen=n) for joint in self.joints}
+        self._buffers: dict[str, deque[int]] = {joint: deque(maxlen=n) for joint in self.joints}
         # running EMA value per joint – lazily initialised on first read
         self._ema: dict[str, float | None] = dict.fromkeys(self._buffers)
 

src/lerobot/utils/transition.py

@@ -63,7 +63,7 @@ def move_transition_to_device(transition: Transition, device: str = "cpu") -> Tr
         for key, val in transition["complementary_info"].items():
             if isinstance(val, torch.Tensor):
                 transition["complementary_info"][key] = val.to(device, non_blocking=non_blocking)
-            elif isinstance(val, (int, float, bool)):
+            elif isinstance(val, (int | float | bool)):
                 transition["complementary_info"][key] = torch.tensor(val, device=device)
             else:
                 raise ValueError(f"Unsupported type {type(val)} for complementary_info[{key}]")

src/lerobot/utils/visualization_utils.py

@@ -35,7 +35,7 @@ def _is_scalar(x):
     return (
         isinstance(x, float)
         or isinstance(x, numbers.Real)
-        or isinstance(x, (np.integer, np.floating))
+        or isinstance(x, (np.integer | np.floating))
         or (isinstance(x, np.ndarray) and x.ndim == 0)
     )
 

tests/artifacts/policies/save_policy_to_safetensors.py

@@ -66,15 +66,13 @@ def get_policy_stats(ds_repo_id: str, policy_name: str, policy_kwargs: dict):
     for key, param in policy.named_parameters():
         if param.requires_grad:
             grad_stats[f"{key}_mean"] = param.grad.mean()
-            grad_stats[f"{key}_std"] = (
+            grad_stats[f"{key}_std"] = param.grad.std() if param.grad.numel() > 1 else torch.tensor(0.0)
-                param.grad.std() if param.grad.numel() > 1 else torch.tensor(float(0.0))
-            )
 
     optimizer.step()
     param_stats = {}
     for key, param in policy.named_parameters():
         param_stats[f"{key}_mean"] = param.mean()
-        param_stats[f"{key}_std"] = param.std() if param.numel() > 1 else torch.tensor(float(0.0))
+        param_stats[f"{key}_std"] = param.std() if param.numel() > 1 else torch.tensor(0.0)
 
     optimizer.zero_grad()
     policy.reset()

tests/processor/test_pipeline.py

@@ -770,7 +770,7 @@ class MockStepWithNonSerializableParam(ProcessorStep):
         # Add type validation for multiplier
         if isinstance(multiplier, str):
             raise ValueError(f"multiplier must be a number, got string '{multiplier}'")
-        if not isinstance(multiplier, (int, float)):
+        if not isinstance(multiplier, (int | float)):
             raise TypeError(f"multiplier must be a number, got {type(multiplier).__name__}")
         self.multiplier = float(multiplier)
         self.env = env  # Non-serializable parameter (like gym.Env)
@@ -1623,7 +1623,7 @@ def test_override_with_callables():
 
             # Define a transform function
             def double_values(x):
-                if isinstance(x, (int, float)):
+                if isinstance(x, (int | float)):
                     return x * 2
                 elif isinstance(x, torch.Tensor):
                     return x * 2

tests/utils/test_replay_buffer.py

@@ -121,7 +121,7 @@ def get_tensors_memory_consumption(obj, visited_addresses):
 
     if isinstance(obj, torch.Tensor):
         return get_tensor_memory_consumption(obj)
-    elif isinstance(obj, (list, tuple)):
+    elif isinstance(obj, (list | tuple)):
         for item in obj:
             total_size += get_tensors_memory_consumption(item, visited_addresses)
     elif isinstance(obj, dict):