Bump ruff from 0.11.13 to 0.12.2, clang-format from 19.1.7 to 20.1.7 (microsoft#25301)

Author: dependabot[bot]

.vscode/settings.json

@@ -8,15 +8,7 @@
     ],
     "[python]": {
         "editor.tabSize": 4,
-        // Auto sort imports
-        "editor.formatOnSave": true,
-        "editor.codeActionsOnSave": {
-            "source.organizeImports": "explicit"
-        },
-        "editor.defaultFormatter": "ms-python.black-formatter"
     },
-    // Enable Python linting and Pylance type checking
-    "python.analysis.typeCheckingMode": "basic",
     "cpplint.lineLength": 120,
     "cpplint.filters": [
         "-build/include_subdir",

csharp/tools/MauiModelTester/create_test_data.py

@@ -84,7 +84,7 @@ def parse_args():
 
 
 def create_existing_data_map(pb_files: list[Path]):
-    import onnx_test_data_utils as data_utils
+    import onnx_test_data_utils as data_utils  # noqa: PLC0415
 
     data_map = {}
     for file in pb_files:
@@ -101,7 +101,7 @@ def add_model_and_test_data_to_app(
     input_map: dict[str, np.ndarray] | None = None,
     output_map: dict[str, np.ndarray] | None = None,
 ):
-    import ort_test_dir_utils as utils
+    import ort_test_dir_utils as utils  # noqa: PLC0415
 
     output_path = SOLUTION_DIR / "Resources" / "Raw"
     test_name = "test_data"

docs/python/_common/onnx_sphinx.py

@@ -23,7 +23,7 @@
 
 def get_template():
     try:
-        from jinja2 import Template
+        from jinja2 import Template  # noqa: PLC0415
     except ImportError:  # pragma no cover
 
         class Template:
@@ -251,7 +251,7 @@ def _clean_unicode(text):
 
 
 def _populate__get_all_schemas_with_history():
-    import onnxruntime.capi.onnxruntime_pybind11_state as rtpy
+    import onnxruntime.capi.onnxruntime_pybind11_state as rtpy  # noqa: PLC0415
 
     get_schemas = rtpy.get_all_operator_schema or rtpy.get_all_opkernel_def
 
@@ -885,7 +885,7 @@ def setup(app):
     Sphinx extension `onnx_sphinx` displays documentation
     of all ONNX Operators.
     """
-    import sphinx
+    import sphinx  # noqa: PLC0415
 
     app.add_config_value("onnx_doc_folder", "operators", "env")
     app.add_config_value("max_opsets", {}, "env")

docs/python/conf.py

@@ -119,7 +119,7 @@ def setup(app):
     this = os.path.abspath(os.path.dirname(__file__))
     dest = os.path.join(this, "model.onnx")
     if not os.path.exists(dest):
-        import urllib.request
+        import urllib.request  # noqa: PLC0415
 
         url = "https://raw.githubusercontent.com/onnx/onnx/master/onnx/backend/test/data/node/test_sigmoid/model.onnx"
         urllib.request.urlretrieve(url, dest)

onnxruntime/__init__.py

@@ -95,7 +95,7 @@
 
 
 def _get_package_version(package_name: str):
-    from importlib.metadata import PackageNotFoundError, version
+    from importlib.metadata import PackageNotFoundError, version  # noqa: PLC0415
 
     try:
         package_version = version(package_name)
@@ -105,7 +105,7 @@ def _get_package_version(package_name: str):
 
 
 def _get_package_root(package_name: str, directory_name: str | None = None):
-    from importlib.metadata import PackageNotFoundError, distribution
+    from importlib.metadata import PackageNotFoundError, distribution  # noqa: PLC0415
 
     root_directory_name = directory_name or package_name
     try:
@@ -167,10 +167,10 @@ def _get_nvidia_dll_paths(is_windows: bool, cuda: bool = True, cudnn: bool = Tru
 
 def print_debug_info():
     """Print information to help debugging."""
-    import importlib.util
-    import os
-    import platform
-    from importlib.metadata import distributions
+    import importlib.util  # noqa: PLC0415
+    import os  # noqa: PLC0415
+    import platform  # noqa: PLC0415
+    from importlib.metadata import distributions  # noqa: PLC0415
 
     print(f"{package_name} version: {__version__}")
     if cuda_version:
@@ -227,7 +227,7 @@ def is_target_dll(path: str):
                 target_keywords = ["cufft", "cublas", "cudart", "nvrtc", "curand", "cudnn", *target_keywords]
             return any(keyword in path for keyword in target_keywords)
 
-        import psutil
+        import psutil  # noqa: PLC0415
 
         p = psutil.Process(os.getpid())
 
@@ -238,7 +238,7 @@ def is_target_dll(path: str):
 
         if cuda_version:
             if importlib.util.find_spec("cpuinfo") and importlib.util.find_spec("py3nvml"):
-                from .transformers.machine_info import get_device_info
+                from .transformers.machine_info import get_device_info  # noqa: PLC0415
 
                 print("\nDevice information:")
                 print(get_device_info())
@@ -265,10 +265,10 @@ def preload_dlls(cuda: bool = True, cudnn: bool = True, msvc: bool = True, direc
            If directory is empty string (""), the search order: nvidia site packages, default DLL loading paths.
            If directory is a path, the search order: the directory, default DLL loading paths.
     """
-    import ctypes
-    import os
-    import platform
-    import sys
+    import ctypes  # noqa: PLC0415
+    import os  # noqa: PLC0415
+    import platform  # noqa: PLC0415
+    import sys  # noqa: PLC0415
 
     if platform.system() not in ["Windows", "Linux"]:
         return

onnxruntime/contrib_ops/cuda/bert/attention_softmax.cu

@@ -29,16 +29,16 @@ namespace onnxruntime {
 namespace contrib {
 namespace attention_softmax_cuda {
 
-#define DISPATCH_BIAS(attn_bias, HAS_BIAS, ...)                 \
-  [&] {                                                         \
-    const dim3 grid(num_heads* sequence_length, batch_size, 1); \
-    if (attn_bias != nullptr) {                                 \
-      constexpr static bool HAS_BIAS = true;                    \
-      return __VA_ARGS__();                                     \
-    } else {                                                    \
-      constexpr static bool HAS_BIAS = false;                   \
-      return __VA_ARGS__();                                     \
-    }                                                           \
+#define DISPATCH_BIAS(attn_bias, HAS_BIAS, ...)                  \
+  [&] {                                                          \
+    const dim3 grid(num_heads * sequence_length, batch_size, 1); \
+    if (attn_bias != nullptr) {                                  \
+      constexpr static bool HAS_BIAS = true;                     \
+      return __VA_ARGS__();                                      \
+    } else {                                                     \
+      constexpr static bool HAS_BIAS = false;                    \
+      return __VA_ARGS__();                                      \
+    }                                                            \
   }()
 
 // Macro to declare variables:

onnxruntime/core/providers/coreml/mlprogram_test_scripts/div_test.py

@@ -76,7 +76,7 @@ def nn_scale():
         mode="MULTIPLY",
     )
 
-    from coremltools.models.utils import save_spec
+    from coremltools.models.utils import save_spec  # noqa: PLC0415
 
     save_spec(builder.spec, "network.mlmodel")
     m = ct.models.MLModel("network.mlmodel")

onnxruntime/core/providers/webgpu/program.h

@@ -401,18 +401,18 @@ class ProgramWrapper : public ProgramBase {
 #define ORT_WEBGPU_REGISTER_DERIVED_PROGRAM_CLASS_TYPE_CHECK(identifier, element_type)                                                   \
  private:                                                                                                                                \
   template <typename U>                                                                                                                  \
-  static auto test_has_##identifier(int)->decltype(U::identifier, std::true_type{}); /* checks if member exists */                       \
+  static auto test_has_##identifier(int) -> decltype(U::identifier, std::true_type{}); /* checks if member exists */                     \
   template <typename...>                                                                                                                 \
-  static auto test_has_##identifier(...)->std::false_type;                                                                               \
+  static auto test_has_##identifier(...) -> std::false_type;                                                                             \
                                                                                                                                          \
   template <typename U,                                                                       /* The following type check uses SFINAE */ \
             typename = std::enable_if_t<                                                      /* to ensure the specific member:       */ \
                                         is_const_std_array<decltype(U::identifier)>::value && /*  - is a const std::array             */ \
                                         std::is_const_v<decltype(U::identifier)> &&           /*  - has "const" modifier              */ \
                                         !std::is_member_pointer_v<decltype(&U::identifier)>>> /*  - is static                         */ \
-  static auto test_has_##identifier##_with_correct_type(int)->std::true_type;                                                            \
+  static auto test_has_##identifier##_with_correct_type(int) -> std::true_type;                                                          \
   template <typename...>                                                                                                                 \
-  static auto test_has_##identifier##_with_correct_type(...)->std::false_type;                                                           \
+  static auto test_has_##identifier##_with_correct_type(...) -> std::false_type;                                                         \
                                                                                                                                          \
  public:                                                                                                                                 \
   static constexpr bool has_##identifier = decltype(test_has_##identifier<T>(0))::value;                                                 \

onnxruntime/python/onnxruntime_validation.py

@@ -57,7 +57,7 @@ def check_distro_info():
                 f"Unsupported macOS version ({__my_distro_ver__}). ONNX Runtime supports macOS 11.0 or later."
             )
     elif __my_system__ == "aix":
-        import subprocess
+        import subprocess  # noqa: PLC0415
 
         returned_output = subprocess.check_output("oslevel")
         __my_distro_ver__str = returned_output.decode("utf-8")
@@ -74,11 +74,11 @@ def get_package_name_and_version_info():
     cuda_version = ""
 
     try:
-        from .build_and_package_info import __version__ as version
-        from .build_and_package_info import package_name
+        from .build_and_package_info import __version__ as version  # noqa: PLC0415
+        from .build_and_package_info import package_name  # noqa: PLC0415
 
         try:  # noqa: SIM105
-            from .build_and_package_info import cuda_version
+            from .build_and_package_info import cuda_version  # noqa: PLC0415
         except ImportError:
             # cuda_version is optional. For example, cpu only package does not have the attribute.
             pass
@@ -94,7 +94,7 @@ def check_training_module():
 
     has_ortmodule = False
     try:
-        from onnxruntime.training.ortmodule import ORTModule  # noqa: F401
+        from onnxruntime.training.ortmodule import ORTModule  # noqa: F401, PLC0415
 
         has_ortmodule = True
     except ImportError:
@@ -105,7 +105,7 @@ def check_training_module():
         # for any exception other than not having ortmodule, we want to continue
         # device version validation and raise the exception after.
         try:
-            from onnxruntime.training.ortmodule._fallback import ORTModuleInitException
+            from onnxruntime.training.ortmodule._fallback import ORTModuleInitException  # noqa: PLC0415
 
             if isinstance(e, ORTModuleInitException):
                 # ORTModule is present but not ready to run yet
@@ -125,7 +125,7 @@ def check_training_module():
             # collect cuda library build info. the library info may not be available
             # when the build environment has none or multiple libraries installed
             try:
-                from .build_and_package_info import cudart_version
+                from .build_and_package_info import cudart_version  # noqa: PLC0415
             except ImportError:
                 warnings.warn("WARNING: failed to get cudart_version from onnxruntime build info.")
                 cudart_version = None
@@ -137,7 +137,7 @@ def print_build_package_info():
                 warnings.warn(f"onnxruntime build info: cudart_version: {cudart_version}")
 
             # collection cuda library info from current environment.
-            from onnxruntime.capi.onnxruntime_collect_build_info import find_cudart_versions
+            from onnxruntime.capi.onnxruntime_collect_build_info import find_cudart_versions  # noqa: PLC0415
 
             local_cudart_versions = find_cudart_versions(build_env=False, build_cuda_version=cuda_version)
             if cudart_version and local_cudart_versions and cudart_version not in local_cudart_versions:

onnxruntime/python/tools/pytorch_export_contrib_ops.py

@@ -121,7 +121,7 @@ def unregister():
             # The symbolic_registry module was removed in PyTorch 1.13.
             # We are importing it here for backwards compatibility
             # because unregister_custom_op_symbolic is not available before PyTorch 1.12
-            from torch.onnx import symbolic_registry
+            from torch.onnx import symbolic_registry  # noqa: PLC0415
 
             namespace, kind = name.split("::")
             for version in symbolic_helper._onnx_stable_opsets: