Revert "Running formatting with command from CONTRIBUTING.md"

This reverts commit ed00d06.

Reducing diff to keep pull request only for functional change.

Author: alexanderdann

timm/models/_hub.py

@@ -17,7 +17,6 @@
 
 try:
     import safetensors.torch
-
     _has_safetensors = True
 except ImportError:
     _has_safetensors = False
@@ -33,7 +32,6 @@
 try:
     from huggingface_hub import HfApi, hf_hub_download, model_info
     from huggingface_hub.utils import EntryNotFoundError, RepositoryNotFoundError
-
     hf_hub_download = partial(hf_hub_download, library_name="timm", library_version=__version__)
     _has_hf_hub = True
 except ImportError:
@@ -42,16 +40,8 @@
 
 _logger = logging.getLogger(__name__)
 
-__all__ = [
-    'get_cache_dir',
-    'download_cached_file',
-    'has_hf_hub',
-    'hf_split',
-    'load_model_config_from_hf',
-    'load_state_dict_from_hf',
-    'save_for_hf',
-    'push_to_hf_hub',
-]
+__all__ = ['get_cache_dir', 'download_cached_file', 'has_hf_hub', 'hf_split', 'load_model_config_from_hf',
+           'load_state_dict_from_hf', 'save_for_hf', 'push_to_hf_hub']
 
 # Default name for a weights file hosted on the Huggingface Hub.
 HF_WEIGHTS_NAME = "pytorch_model.bin"  # default pytorch pkl
@@ -76,10 +66,10 @@ def get_cache_dir(child_dir: str = ''):
 
 
 def download_cached_file(
-    url: Union[str, List[str], Tuple[str, str]],
-    check_hash: bool = True,
-    progress: bool = False,
-    cache_dir: Optional[Union[str, Path]] = None,
+        url: Union[str, List[str], Tuple[str, str]],
+        check_hash: bool = True,
+        progress: bool = False,
+        cache_dir: Optional[Union[str, Path]] = None,
 ):
     if isinstance(url, (list, tuple)):
         url, filename = url
@@ -102,9 +92,9 @@ def download_cached_file(
 
 
 def check_cached_file(
-    url: Union[str, List[str], Tuple[str, str]],
-    check_hash: bool = True,
-    cache_dir: Optional[Union[str, Path]] = None,
+        url: Union[str, List[str], Tuple[str, str]],
+        check_hash: bool = True,
+        cache_dir: Optional[Union[str, Path]] = None,
 ):
     if isinstance(url, (list, tuple)):
         url, filename = url
@@ -121,7 +111,7 @@ def check_cached_file(
             if hash_prefix:
                 with open(cached_file, 'rb') as f:
                     hd = hashlib.sha256(f.read()).hexdigest()
-                    if hd[: len(hash_prefix)] != hash_prefix:
+                    if hd[:len(hash_prefix)] != hash_prefix:
                         return False
         return True
     return False
@@ -131,8 +121,7 @@ def has_hf_hub(necessary: bool = False):
     if not _has_hf_hub and necessary:
         # if no HF Hub module installed, and it is necessary to continue, raise error
         raise RuntimeError(
-            'Hugging Face hub model specified but package not installed. Run `pip install huggingface_hub`.'
-        )
+            'Hugging Face hub model specified but package not installed. Run `pip install huggingface_hub`.')
     return _has_hf_hub
 
 
@@ -152,9 +141,9 @@ def load_cfg_from_json(json_file: Union[str, Path]):
 
 
 def download_from_hf(
-    model_id: str,
-    filename: str,
-    cache_dir: Optional[Union[str, Path]] = None,
+        model_id: str,
+        filename: str,
+        cache_dir: Optional[Union[str, Path]] = None,
 ):
     hf_model_id, hf_revision = hf_split(model_id)
     return hf_hub_download(
@@ -166,8 +155,8 @@ def download_from_hf(
 
 
 def _parse_model_cfg(
-    cfg: Dict[str, Any],
-    extra_fields: Dict[str, Any],
+        cfg: Dict[str, Any],
+        extra_fields: Dict[str, Any],
 ) -> Tuple[Dict[str, Any], str, Dict[str, Any]]:
     """"""
     # legacy "single‑dict" → split
@@ -178,7 +167,7 @@ def _parse_model_cfg(
             "num_features": pretrained_cfg.pop("num_features", None),
             "pretrained_cfg": pretrained_cfg,
         }
-        if "labels" in pretrained_cfg:  # rename ‑‑> label_names
+        if "labels" in pretrained_cfg:                  # rename ‑‑> label_names
             pretrained_cfg["label_names"] = pretrained_cfg.pop("labels")
 
     pretrained_cfg = cfg["pretrained_cfg"]
@@ -198,8 +187,8 @@ def _parse_model_cfg(
 
 
 def load_model_config_from_hf(
-    model_id: str,
-    cache_dir: Optional[Union[str, Path]] = None,
+        model_id: str,
+        cache_dir: Optional[Union[str, Path]] = None,
 ):
     """Original HF‑Hub loader (unchanged download, shared parsing)."""
     assert has_hf_hub(True)
@@ -209,7 +198,7 @@ def load_model_config_from_hf(
 
 
 def load_model_config_from_path(
-    model_path: Union[str, Path],
+        model_path: Union[str, Path],
 ):
     """Load from ``<model_path>/config.json`` on the local filesystem."""
     model_path = Path(model_path)
@@ -222,10 +211,10 @@ def load_model_config_from_path(
 
 
 def load_state_dict_from_hf(
-    model_id: str,
-    filename: str = HF_WEIGHTS_NAME,
-    weights_only: bool = False,
-    cache_dir: Optional[Union[str, Path]] = None,
+        model_id: str,
+        filename: str = HF_WEIGHTS_NAME,
+        weights_only: bool = False,
+        cache_dir: Optional[Union[str, Path]] = None,
 ):
     assert has_hf_hub(True)
     hf_model_id, hf_revision = hf_split(model_id)
@@ -242,8 +231,7 @@ def load_state_dict_from_hf(
                 )
                 _logger.info(
                     f"[{model_id}] Safe alternative available for '{filename}' "
-                    f"(as '{safe_filename}'). Loading weights using safetensors."
-                )
+                    f"(as '{safe_filename}'). Loading weights using safetensors.")
                 return safetensors.torch.load_file(cached_safe_file, device="cpu")
             except EntryNotFoundError:
                 pass
@@ -275,10 +263,9 @@ def load_state_dict_from_hf(
 )
 _EXT_PRIORITY = ('.safetensors', '.pth', '.pth.tar', '.bin')
 
-
 def load_state_dict_from_path(
-    path: str,
-    weights_only: bool = False,
+        path: str,
+        weights_only: bool = False,
 ):
     found_file = None
     for fname in _PREFERRED_FILES:
@@ -293,7 +280,10 @@ def load_state_dict_from_path(
         files = sorted(path.glob(f"*{ext}"))
         if files:
             if len(files) > 1:
-                logging.warning(f"Multiple {ext} checkpoints in {path}: {names}. " f"Using '{files[0].name}'.")
+                logging.warning(
+                    f"Multiple {ext} checkpoints in {path}: {names}. "
+                    f"Using '{files[0].name}'."
+                )
             found_file = files[0]
 
     if not found_file:
@@ -307,10 +297,10 @@ def load_state_dict_from_path(
 
 
 def load_custom_from_hf(
-    model_id: str,
-    filename: str,
-    model: torch.nn.Module,
-    cache_dir: Optional[Union[str, Path]] = None,
+        model_id: str,
+        filename: str,
+        model: torch.nn.Module,
+        cache_dir: Optional[Union[str, Path]] = None,
 ):
     assert has_hf_hub(True)
     hf_model_id, hf_revision = hf_split(model_id)
@@ -324,7 +314,10 @@ def load_custom_from_hf(
 
 
 def save_config_for_hf(
-    model: torch.nn.Module, config_path: str, model_config: Optional[dict] = None, model_args: Optional[dict] = None
+        model: torch.nn.Module,
+        config_path: str,
+        model_config: Optional[dict] = None,
+        model_args: Optional[dict] = None
 ):
     model_config = model_config or {}
     hf_config = {}
@@ -343,8 +336,7 @@ def save_config_for_hf(
     if 'labels' in model_config:
         _logger.warning(
             "'labels' as a config field for is deprecated. Please use 'label_names' and 'label_descriptions'."
-            " Renaming provided 'labels' field to 'label_names'."
-        )
+            " Renaming provided 'labels' field to 'label_names'.")
         model_config.setdefault('label_names', model_config.pop('labels'))
 
     label_names = model_config.pop('label_names', None)
@@ -371,11 +363,11 @@ def save_config_for_hf(
 
 
 def save_for_hf(
-    model: torch.nn.Module,
-    save_directory: str,
-    model_config: Optional[dict] = None,
-    model_args: Optional[dict] = None,
-    safe_serialization: Union[bool, Literal["both"]] = False,
+        model: torch.nn.Module,
+        save_directory: str,
+        model_config: Optional[dict] = None,
+        model_args: Optional[dict] = None,
+        safe_serialization: Union[bool, Literal["both"]] = False,
 ):
     assert has_hf_hub(True)
     save_directory = Path(save_directory)
@@ -399,18 +391,18 @@ def save_for_hf(
 
 
 def push_to_hf_hub(
-    model: torch.nn.Module,
-    repo_id: str,
-    commit_message: str = 'Add model',
-    token: Optional[str] = None,
-    revision: Optional[str] = None,
-    private: bool = False,
-    create_pr: bool = False,
-    model_config: Optional[dict] = None,
-    model_card: Optional[dict] = None,
-    model_args: Optional[dict] = None,
-    task_name: str = 'image-classification',
-    safe_serialization: Union[bool, Literal["both"]] = 'both',
+        model: torch.nn.Module,
+        repo_id: str,
+        commit_message: str = 'Add model',
+        token: Optional[str] = None,
+        revision: Optional[str] = None,
+        private: bool = False,
+        create_pr: bool = False,
+        model_config: Optional[dict] = None,
+        model_card: Optional[dict] = None,
+        model_args: Optional[dict] = None,
+        task_name: str = 'image-classification',
+        safe_serialization: Union[bool, Literal["both"]] = 'both',
 ):
     """
     Arguments:
@@ -460,9 +452,9 @@ def push_to_hf_hub(
 
 
 def generate_readme(
-    model_card: dict,
-    model_name: str,
-    task_name: str = 'image-classification',
+        model_card: dict,
+        model_name: str,
+        task_name: str = 'image-classification',
 ):
     tags = model_card.get('tags', None) or [task_name, 'timm', 'transformers']
     readme_text = "---\n"