Ignore E704 after update black

monai/apps/detection/utils/anchor_utils.py

@@ -369,8 +369,12 @@ class AnchorGeneratorWithAnchorShape(AnchorGenerator):
     def __init__(
         self,
         feature_map_scales: Sequence[int] | Sequence[float] = (1, 2, 4, 8),
-        base_anchor_shapes: Sequence[Sequence[int]]
-        | Sequence[Sequence[float]] = ((32, 32, 32), (48, 20, 20), (20, 48, 20), (20, 20, 48)),
+        base_anchor_shapes: Sequence[Sequence[int]] | Sequence[Sequence[float]] = (
+            (32, 32, 32),
+            (48, 20, 20),
+            (20, 48, 20),
+            (20, 20, 48),
+        ),
         indexing: str = "ij",
     ) -> None:
         nn.Module.__init__(self)

monai/data/decathlon_datalist.py

@@ -24,13 +24,11 @@
 
 
 @overload
-def _compute_path(base_dir: PathLike, element: PathLike, check_path: bool = False) -> str:
-    ...
+def _compute_path(base_dir: PathLike, element: PathLike, check_path: bool = False) -> str: ...
 
 
 @overload
-def _compute_path(base_dir: PathLike, element: list[PathLike], check_path: bool = False) -> list[str]:
-    ...
+def _compute_path(base_dir: PathLike, element: list[PathLike], check_path: bool = False) -> list[str]: ...
 
 
 def _compute_path(base_dir, element, check_path=False):

monai/losses/image_dissimilarity.py

@@ -277,9 +277,7 @@ def parzen_windowing_b_spline(self, img: torch.Tensor, order: int) -> tuple[torc
         if order == 0:
             weight = weight + (sample_bin_matrix < 0.5) + (sample_bin_matrix == 0.5) * 0.5
         elif order == 3:
-            weight = (
-                weight + (4 - 6 * sample_bin_matrix**2 + 3 * sample_bin_matrix**3) * (sample_bin_matrix < 1) / 6
-            )
+            weight = weight + (4 - 6 * sample_bin_matrix**2 + 3 * sample_bin_matrix**3) * (sample_bin_matrix < 1) / 6
             weight = weight + (2 - sample_bin_matrix) ** 3 * (sample_bin_matrix >= 1) * (sample_bin_matrix < 2) / 6
         else:
             raise ValueError(f"Do not support b-spline {order}-order parzen windowing")

monai/transforms/utility/dictionary.py

@@ -1765,9 +1765,9 @@ def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> dict[Hashable, N
 LabelToMaskD = LabelToMaskDict = LabelToMaskd
 FgBgToIndicesD = FgBgToIndicesDict = FgBgToIndicesd
 ClassesToIndicesD = ClassesToIndicesDict = ClassesToIndicesd
-ConvertToMultiChannelBasedOnBratsClassesD = (
-    ConvertToMultiChannelBasedOnBratsClassesDict
-) = ConvertToMultiChannelBasedOnBratsClassesd
+ConvertToMultiChannelBasedOnBratsClassesD = ConvertToMultiChannelBasedOnBratsClassesDict = (
+    ConvertToMultiChannelBasedOnBratsClassesd
+)
 AddExtremePointsChannelD = AddExtremePointsChannelDict = AddExtremePointsChanneld
 TorchVisionD = TorchVisionDict = TorchVisiond
 RandTorchVisionD = RandTorchVisionDict = RandTorchVisiond

monai/utils/dist.py

@@ -50,18 +50,15 @@ def get_dist_device():
 
 
 @overload
-def evenly_divisible_all_gather(data: torch.Tensor, concat: Literal[True]) -> torch.Tensor:
-    ...
+def evenly_divisible_all_gather(data: torch.Tensor, concat: Literal[True]) -> torch.Tensor: ...
 
 
 @overload
-def evenly_divisible_all_gather(data: torch.Tensor, concat: Literal[False]) -> list[torch.Tensor]:
-    ...
+def evenly_divisible_all_gather(data: torch.Tensor, concat: Literal[False]) -> list[torch.Tensor]: ...
 
 
 @overload
-def evenly_divisible_all_gather(data: torch.Tensor, concat: bool) -> torch.Tensor | list[torch.Tensor]:
-    ...
+def evenly_divisible_all_gather(data: torch.Tensor, concat: bool) -> torch.Tensor | list[torch.Tensor]: ...
 
 
 def evenly_divisible_all_gather(data: torch.Tensor, concat: bool = True) -> torch.Tensor | list[torch.Tensor]:

monai/utils/misc.py

@@ -103,13 +103,11 @@ def star_zip_with(op, *vals):
 
 
 @overload
-def first(iterable: Iterable[T], default: T) -> T:
-    ...
+def first(iterable: Iterable[T], default: T) -> T: ...
 
 
 @overload
-def first(iterable: Iterable[T]) -> T | None:
-    ...
+def first(iterable: Iterable[T]) -> T | None: ...
 
 
 def first(iterable: Iterable[T], default: T | None = None) -> T | None:

setup.cfg

@@ -174,6 +174,7 @@ max_line_length = 120
 # B907 https://github.com/Project-MONAI/MONAI/issues/5868
 # B908 https://github.com/Project-MONAI/MONAI/issues/6503
 # B036 https://github.com/Project-MONAI/MONAI/issues/7396
+# E704 https://github.com/Project-MONAI/MONAI/issues/7421
 ignore =
     E203
     E501
@@ -188,6 +189,7 @@ ignore =
     B907
     B908
     B036
+    E704
 per_file_ignores = __init__.py: F401, __main__.py: F401
 exclude = *.pyi,.git,.eggs,monai/_version.py,versioneer.py,venv,.venv,_version.py
 

tests/test_hilbert_transform.py

@@ -180,15 +180,17 @@ def test_value(self, arguments, image, expected_data, atol):
 @SkipIfNoModule("torch.fft")
 class TestHilbertTransformGPU(unittest.TestCase):
     @parameterized.expand(
-        []
-        if not torch.cuda.is_available()
-        else [
-            TEST_CASE_1D_SINE_GPU,
-            TEST_CASE_2D_SINE_GPU,
-            TEST_CASE_3D_SINE_GPU,
-            TEST_CASE_1D_2CH_SINE_GPU,
-            TEST_CASE_2D_2CH_SINE_GPU,
-        ],
+        (
+            []
+            if not torch.cuda.is_available()
+            else [
+                TEST_CASE_1D_SINE_GPU,
+                TEST_CASE_2D_SINE_GPU,
+                TEST_CASE_3D_SINE_GPU,
+                TEST_CASE_1D_2CH_SINE_GPU,
+                TEST_CASE_2D_2CH_SINE_GPU,
+            ]
+        ),
         skip_on_empty=True,
     )
     def test_value(self, arguments, image, expected_data, atol):

tests/test_spacing.py

@@ -74,9 +74,11 @@
             torch.ones((1, 2, 1, 2)),  # data
             torch.tensor([[2, 1, 0, 4], [-1, -3, 0, 5], [0, 0, 2.0, 5], [0, 0, 0, 1]]),
             {},
-            torch.tensor([[[[0.75, 0.75]], [[0.75, 0.75]], [[0.75, 0.75]]]])
-            if USE_COMPILED
-            else torch.tensor([[[[0.95527864, 0.95527864]], [[1.0, 1.0]], [[1.0, 1.0]]]]),
+            (
+                torch.tensor([[[[0.75, 0.75]], [[0.75, 0.75]], [[0.75, 0.75]]]])
+                if USE_COMPILED
+                else torch.tensor([[[[0.95527864, 0.95527864]], [[1.0, 1.0]], [[1.0, 1.0]]]])
+            ),
             *device,
         ]
     )