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

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

lerobot/common/utils/import_utils.py

@@ -17,7 +17,9 @@ import importlib
 import logging
 
 
-def is_package_available(pkg_name: str, return_version: bool = False) -> tuple[bool, str] | bool:
+def is_package_available(
+    pkg_name: str, return_version: bool = False
+) -> tuple[bool, str] | bool:
     """Copied from https://github.com/huggingface/transformers/blob/main/src/transformers/utils/import_utils.py
     Check if the package spec exists and grab its version to avoid importing a local directory.
     **Note:** this doesn't work for all packages.

lerobot/common/utils/io_utils.py

@@ -28,7 +28,9 @@ def write_video(video_path, stacked_frames, fps):
     # Filter out DeprecationWarnings raised from pkg_resources
     with warnings.catch_warnings():
         warnings.filterwarnings(
-            "ignore", "pkg_resources is deprecated as an API", category=DeprecationWarning
+            "ignore",
+            "pkg_resources is deprecated as an API",
+            category=DeprecationWarning,
         )
         imageio.mimsave(video_path, stacked_frames, fps=fps)
 

lerobot/common/utils/utils.py

@@ -148,7 +148,10 @@ def _relative_path_between(path1: Path, path2: Path) -> Path:
     except ValueError:  # most likely because path1 is not a subpath of path2
         common_parts = Path(osp.commonpath([path1, path2])).parts
         return Path(
-            "/".join([".."] * (len(path2.parts) - len(common_parts)) + list(path1.parts[len(common_parts) :]))
+            "/".join(
+                [".."] * (len(path2.parts) - len(common_parts))
+                + list(path1.parts[len(common_parts) :])
+            )
         )
 
 
@@ -159,10 +162,26 @@ def print_cuda_memory_usage():
     gc.collect()
     # Also clear the cache if you want to fully release the memory
     torch.cuda.empty_cache()
-    print("Current GPU Memory Allocated: {:.2f} MB".format(torch.cuda.memory_allocated(0) / 1024**2))
-    print("Maximum GPU Memory Allocated: {:.2f} MB".format(torch.cuda.max_memory_allocated(0) / 1024**2))
-    print("Current GPU Memory Reserved: {:.2f} MB".format(torch.cuda.memory_reserved(0) / 1024**2))
-    print("Maximum GPU Memory Reserved: {:.2f} MB".format(torch.cuda.max_memory_reserved(0) / 1024**2))
+    print(
+        "Current GPU Memory Allocated: {:.2f} MB".format(
+            torch.cuda.memory_allocated(0) / 1024**2
+        )
+    )
+    print(
+        "Maximum GPU Memory Allocated: {:.2f} MB".format(
+            torch.cuda.max_memory_allocated(0) / 1024**2
+        )
+    )
+    print(
+        "Current GPU Memory Reserved: {:.2f} MB".format(
+            torch.cuda.memory_reserved(0) / 1024**2
+        )
+    )
+    print(
+        "Maximum GPU Memory Reserved: {:.2f} MB".format(
+            torch.cuda.max_memory_reserved(0) / 1024**2
+        )
+    )
 
 
 def capture_timestamp_utc():
@@ -232,7 +251,12 @@ def is_valid_numpy_dtype_string(dtype_str: str) -> bool:
 
 
 class TimerManager:
-    def __init__(self, elapsed_time_list: list[float] | None = None, label="Elapsed time", log=True):
+    def __init__(
+        self,
+        elapsed_time_list: list[float] | None = None,
+        label="Elapsed time",
+        log=True,
+    ):
         self.label = label
         self.elapsed_time_list = elapsed_time_list
         self.log = log