[pre-commit.ci] pre-commit suggestions

.pre-commit-config.yaml

@@ -23,7 +23,7 @@ ci:
 
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v5.0.0
+    rev: v6.0.0
     hooks:
       - id: end-of-file-fixer
       - id: trailing-whitespace
@@ -80,7 +80,7 @@ repos:
           )$
 
   - repo: https://github.com/pre-commit/mirrors-prettier
-    rev: v3.1.0
+    rev: v4.0.0-alpha.8
     hooks:
       - id: prettier
         files: \.(json|yml|yaml|toml)
@@ -112,7 +112,7 @@ repos:
       - id: text-unicode-replacement-char
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.12.2
+    rev: v0.13.3
     hooks:
       # try to fix what is possible
       - id: ruff
@@ -123,7 +123,7 @@ repos:
       - id: ruff
 
   - repo: https://github.com/tox-dev/pyproject-fmt
-    rev: v2.6.0
+    rev: v2.7.0
     hooks:
       - id: pyproject-fmt
         additional_dependencies: [tox]

README.md

@@ -39,13 +39,15 @@ ______________________________________________________________________
 </div>
 
 # Looking for GPUs?
-Over 340,000 developers use [Lightning Cloud](https://lightning.ai/?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme) - purpose-built for PyTorch and PyTorch Lightning. 
-- [GPUs](https://lightning.ai/pricing?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme) from $0.19.   
-- [Clusters](https://lightning.ai/clusters?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme): frontier-grade training/inference clusters.   
+
+Over 340,000 developers use [Lightning Cloud](https://lightning.ai/?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme) - purpose-built for PyTorch and PyTorch Lightning.
+
+- [GPUs](https://lightning.ai/pricing?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme) from $0.19.
+- [Clusters](https://lightning.ai/clusters?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme): frontier-grade training/inference clusters.
 - [AI Studio (vibe train)](https://lightning.ai/studios?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme): workspaces where AI helps you debug, tune and vibe train.
-- [AI Studio (vibe deploy)](https://lightning.ai/studios?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme): workspaces where AI helps you optimize, and deploy models.     
+- [AI Studio (vibe deploy)](https://lightning.ai/studios?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme): workspaces where AI helps you optimize, and deploy models.
 - [Notebooks](https://lightning.ai/notebooks?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme): Persistent GPU workspaces where AI helps you code and analyze.
-- [Inference](https://lightning.ai/deploy?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme): Deploy models as inference APIs.   
+- [Inference](https://lightning.ai/deploy?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme): Deploy models as inference APIs.
 
 # Installation
 

src/torchmetrics/functional/image/_deprecated.py

@@ -43,7 +43,7 @@ def _error_relative_global_dimensionless_synthesis(
     preds: Tensor,
     target: Tensor,
     ratio: float = 4,
-    reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
+    reduction: Optional[Literal["elementwise_mean", "sum", "none"]] = "elementwise_mean",
 ) -> Tensor:
     """Wrapper for deprecated import.
 
@@ -82,7 +82,7 @@ def _peak_signal_noise_ratio(
     target: Tensor,
     data_range: Union[float, tuple[float, float]] = 3.0,
     base: float = 10.0,
-    reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
+    reduction: Optional[Literal["elementwise_mean", "sum", "none"]] = "elementwise_mean",
     dim: Optional[Union[int, tuple[int, ...]]] = None,
 ) -> Tensor:
     """Wrapper for deprecated import.
@@ -135,7 +135,7 @@ def _root_mean_squared_error_using_sliding_window(
 def _spectral_angle_mapper(
     preds: Tensor,
     target: Tensor,
-    reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
+    reduction: Optional[Literal["elementwise_mean", "sum", "none"]] = "elementwise_mean",
 ) -> Tensor:
     """Wrapper for deprecated import.
 
@@ -156,7 +156,7 @@ def _multiscale_structural_similarity_index_measure(
     gaussian_kernel: bool = True,
     sigma: Union[float, Sequence[float]] = 1.5,
     kernel_size: Union[int, Sequence[int]] = 11,
-    reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
+    reduction: Optional[Literal["elementwise_mean", "sum", "none"]] = "elementwise_mean",
     data_range: Optional[Union[float, tuple[float, float]]] = None,
     k1: float = 0.01,
     k2: float = 0.03,
@@ -194,7 +194,7 @@ def _structural_similarity_index_measure(
     gaussian_kernel: bool = True,
     sigma: Union[float, Sequence[float]] = 1.5,
     kernel_size: Union[int, Sequence[int]] = 11,
-    reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
+    reduction: Optional[Literal["elementwise_mean", "sum", "none"]] = "elementwise_mean",
     data_range: Optional[Union[float, tuple[float, float]]] = None,
     k1: float = 0.01,
     k2: float = 0.03,
@@ -226,7 +226,7 @@ def _structural_similarity_index_measure(
     )
 
 
-def _total_variation(img: Tensor, reduction: Literal["mean", "sum", "none", None] = "sum") -> Tensor:
+def _total_variation(img: Tensor, reduction: Optional[Literal["mean", "sum", "none"]] = "sum") -> Tensor:
     """Wrapper for deprecated import.
 
     >>> from torch import rand

src/torchmetrics/functional/image/ergas.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from typing import Optional
+
 import torch
 from torch import Tensor
 from typing_extensions import Literal
@@ -45,7 +47,7 @@ def _ergas_compute(
     preds: Tensor,
     target: Tensor,
     ratio: float = 4,
-    reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
+    reduction: Optional[Literal["elementwise_mean", "sum", "none"]] = "elementwise_mean",
 ) -> Tensor:
     """Erreur Relative Globale Adimensionnelle de Synthèse.
 
@@ -85,7 +87,7 @@ def error_relative_global_dimensionless_synthesis(
     preds: Tensor,
     target: Tensor,
     ratio: float = 4,
-    reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
+    reduction: Optional[Literal["elementwise_mean", "sum", "none"]] = "elementwise_mean",
 ) -> Tensor:
     """Calculates `Error relative global dimensionless synthesis`_ (ERGAS) metric.
 

src/torchmetrics/functional/image/psnr.py

@@ -25,7 +25,7 @@ def _psnr_compute(
     num_obs: Tensor,
     data_range: Tensor,
     base: float = 10.0,
-    reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
+    reduction: Optional[Literal["elementwise_mean", "sum", "none"]] = "elementwise_mean",
 ) -> Tensor:
     """Compute peak signal-to-noise ratio.
 
@@ -97,7 +97,7 @@ def peak_signal_noise_ratio(
     target: Tensor,
     data_range: Union[float, tuple[float, float]],
     base: float = 10.0,
-    reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
+    reduction: Optional[Literal["elementwise_mean", "sum", "none"]] = "elementwise_mean",
     dim: Optional[Union[int, tuple[int, ...]]] = None,
 ) -> Tensor:
     """Compute the peak signal-to-noise ratio.

src/torchmetrics/functional/image/sam.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from typing import Optional
+
 import torch
 from torch import Tensor
 from typing_extensions import Literal
@@ -49,7 +51,7 @@ def _sam_update(preds: Tensor, target: Tensor) -> tuple[Tensor, Tensor]:
 def _sam_compute(
     preds: Tensor,
     target: Tensor,
-    reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
+    reduction: Optional[Literal["elementwise_mean", "sum", "none"]] = "elementwise_mean",
 ) -> Tensor:
     """Compute Spectral Angle Mapper.
 
@@ -81,7 +83,7 @@ def _sam_compute(
 def spectral_angle_mapper(
     preds: Tensor,
     target: Tensor,
-    reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
+    reduction: Optional[Literal["elementwise_mean", "sum", "none"]] = "elementwise_mean",
 ) -> Tensor:
     """Universal Spectral Angle Mapper.
 

src/torchmetrics/functional/image/scc.py

@@ -169,7 +169,7 @@ def spatial_correlation_coefficient(
     target: Tensor,
     hp_filter: Optional[Tensor] = None,
     window_size: int = 8,
-    reduction: Optional[Literal["mean", "none", None]] = "mean",
+    reduction: Optional[Optional[Literal["mean", "none"]]] = "mean",
 ) -> Tensor:
     """Compute Spatial Correlation Coefficient (SCC_).
 

src/torchmetrics/functional/image/ssim.py

@@ -188,7 +188,7 @@ def _ssim_update(
 
 def _ssim_compute(
     similarities: Tensor,
-    reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
+    reduction: Optional[Literal["elementwise_mean", "sum", "none"]] = "elementwise_mean",
 ) -> Tensor:
     """Apply the specified reduction to pre-computed structural similarity.
 
@@ -213,7 +213,7 @@ def structural_similarity_index_measure(
     gaussian_kernel: bool = True,
     sigma: Union[float, Sequence[float]] = 1.5,
     kernel_size: Union[int, Sequence[int]] = 11,
-    reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
+    reduction: Optional[Literal["elementwise_mean", "sum", "none"]] = "elementwise_mean",
     data_range: Optional[Union[float, tuple[float, float]]] = None,
     k1: float = 0.01,
     k2: float = 0.03,
@@ -427,7 +427,7 @@ def _multiscale_ssim_update(
 
 def _multiscale_ssim_compute(
     mcs_per_image: Tensor,
-    reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
+    reduction: Optional[Literal["elementwise_mean", "sum", "none"]] = "elementwise_mean",
 ) -> Tensor:
     """Apply the specified reduction to pre-computed multi-scale structural similarity.
 
@@ -452,7 +452,7 @@ def multiscale_structural_similarity_index_measure(
     gaussian_kernel: bool = True,
     sigma: Union[float, Sequence[float]] = 1.5,
     kernel_size: Union[int, Sequence[int]] = 11,
-    reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
+    reduction: Optional[Literal["elementwise_mean", "sum", "none"]] = "elementwise_mean",
     data_range: Optional[Union[float, tuple[float, float]]] = None,
     k1: float = 0.01,
     k2: float = 0.03,

src/torchmetrics/functional/pairwise/cosine.py

@@ -48,7 +48,7 @@ def _pairwise_cosine_similarity_update(
 def pairwise_cosine_similarity(
     x: Tensor,
     y: Optional[Tensor] = None,
-    reduction: Literal["mean", "sum", "none", None] = None,
+    reduction: Optional[Literal["mean", "sum", "none"]] = None,
     zero_diagonal: Optional[bool] = None,
 ) -> Tensor:
     r"""Calculate pairwise cosine similarity.

src/torchmetrics/functional/pairwise/euclidean.py

@@ -47,7 +47,7 @@ def _pairwise_euclidean_distance_update(
 def pairwise_euclidean_distance(
     x: Tensor,
     y: Optional[Tensor] = None,
-    reduction: Literal["mean", "sum", "none", None] = None,
+    reduction: Optional[Literal["mean", "sum", "none"]] = None,
     zero_diagonal: Optional[bool] = None,
 ) -> Tensor:
     r"""Calculate pairwise euclidean distances.

src/torchmetrics/functional/pairwise/linear.py

@@ -42,7 +42,7 @@ def _pairwise_linear_similarity_update(
 def pairwise_linear_similarity(
     x: Tensor,
     y: Optional[Tensor] = None,
-    reduction: Literal["mean", "sum", "none", None] = None,
+    reduction: Optional[Literal["mean", "sum", "none"]] = None,
     zero_diagonal: Optional[bool] = None,
 ) -> Tensor:
     r"""Calculate pairwise linear similarity.

src/torchmetrics/functional/pairwise/manhattan.py

@@ -41,7 +41,7 @@ def _pairwise_manhattan_distance_update(
 def pairwise_manhattan_distance(
     x: Tensor,
     y: Optional[Tensor] = None,
-    reduction: Literal["mean", "sum", "none", None] = None,
+    reduction: Optional[Literal["mean", "sum", "none"]] = None,
     zero_diagonal: Optional[bool] = None,
 ) -> Tensor:
     r"""Calculate pairwise manhattan distance.

src/torchmetrics/functional/pairwise/minkowski.py

@@ -50,7 +50,7 @@ def pairwise_minkowski_distance(
     x: Tensor,
     y: Optional[Tensor] = None,
     exponent: float = 2,
-    reduction: Literal["mean", "sum", "none", None] = None,
+    reduction: Optional[Literal["mean", "sum", "none"]] = None,
     zero_diagonal: Optional[bool] = None,
 ) -> Tensor:
     r"""Calculate pairwise minkowski distances.

src/torchmetrics/functional/regression/js_divergence.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Union
+from typing import Optional, Union
 
 import torch
 from torch import Tensor
@@ -53,7 +53,7 @@ def _jsd_update(p: Tensor, q: Tensor, log_prob: bool) -> tuple[Tensor, int]:
 
 
 def _jsd_compute(
-    measures: Tensor, total: Union[int, Tensor], reduction: Literal["mean", "sum", "none", None] = "mean"
+    measures: Tensor, total: Union[int, Tensor], reduction: Optional[Literal["mean", "sum", "none"]] = "mean"
 ) -> Tensor:
     """Compute and reduce the Jensen-Shannon divergence based on the type of reduction."""
     if reduction == "sum":
@@ -66,7 +66,7 @@ def _jsd_compute(
 
 
 def jensen_shannon_divergence(
-    p: Tensor, q: Tensor, log_prob: bool = False, reduction: Literal["mean", "sum", "none", None] = "mean"
+    p: Tensor, q: Tensor, log_prob: bool = False, reduction: Optional[Literal["mean", "sum", "none"]] = "mean"
 ) -> Tensor:
     r"""Compute `Jensen-Shannon divergence`_.
 

src/torchmetrics/functional/regression/kl_divergence.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Union
+from typing import Optional, Union
 
 import torch
 from torch import Tensor
@@ -48,7 +48,7 @@ def _kld_update(p: Tensor, q: Tensor, log_prob: bool) -> tuple[Tensor, int]:
 
 
 def _kld_compute(
-    measures: Tensor, total: Union[int, Tensor], reduction: Literal["mean", "sum", "none", None] = "mean"
+    measures: Tensor, total: Union[int, Tensor], reduction: Optional[Literal["mean", "sum", "none"]] = "mean"
 ) -> Tensor:
     """Compute the KL divergenece based on the type of reduction.
 
@@ -80,7 +80,7 @@ def _kld_compute(
 
 
 def kl_divergence(
-    p: Tensor, q: Tensor, log_prob: bool = False, reduction: Literal["mean", "sum", "none", None] = "mean"
+    p: Tensor, q: Tensor, log_prob: bool = False, reduction: Optional[Literal["mean", "sum", "none"]] = "mean"
 ) -> Tensor:
     r"""Compute `KL divergence`_.
 

src/torchmetrics/image/_deprecated.py

@@ -30,7 +30,7 @@ class _ErrorRelativeGlobalDimensionlessSynthesis(ErrorRelativeGlobalDimensionles
     def __init__(
         self,
         ratio: float = 4,
-        reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
+        reduction: Optional[Literal["elementwise_mean", "sum", "none"]] = "elementwise_mean",
         **kwargs: Any,
     ) -> None:
         _deprecated_root_import_class("ErrorRelativeGlobalDimensionlessSynthesis", "image")
@@ -54,12 +54,12 @@ def __init__(
         gaussian_kernel: bool = True,
         kernel_size: Union[int, Sequence[int]] = 11,
         sigma: Union[float, Sequence[float]] = 1.5,
-        reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
+        reduction: Optional[Literal["elementwise_mean", "sum", "none"]] = "elementwise_mean",
         data_range: Optional[Union[float, tuple[float, float]]] = None,
         k1: float = 0.01,
         k2: float = 0.03,
         betas: tuple[float, ...] = (0.0448, 0.2856, 0.3001, 0.2363, 0.1333),
-        normalize: Literal["relu", "simple", None] = "relu",
+        normalize: Optional[Literal["relu", "simple"]] = "relu",
         **kwargs: Any,
     ) -> None:
         _deprecated_root_import_class("MultiScaleStructuralSimilarityIndexMeasure", "image")
@@ -93,7 +93,7 @@ def __init__(
         self,
         data_range: Union[float, tuple[float, float]] = 3.0,
         base: float = 10.0,
-        reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
+        reduction: Optional[Literal["elementwise_mean", "sum", "none"]] = "elementwise_mean",
         dim: Optional[Union[int, tuple[int, ...]]] = None,
         **kwargs: Any,
     ) -> None:
@@ -200,7 +200,7 @@ def __init__(
         gaussian_kernel: bool = True,
         sigma: Union[float, Sequence[float]] = 1.5,
         kernel_size: Union[int, Sequence[int]] = 11,
-        reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
+        reduction: Optional[Literal["elementwise_mean", "sum", "none"]] = "elementwise_mean",
         data_range: Optional[Union[float, tuple[float, float]]] = None,
         k1: float = 0.01,
         k2: float = 0.03,
@@ -234,7 +234,7 @@ class _TotalVariation(TotalVariation):
 
     """
 
-    def __init__(self, reduction: Literal["mean", "sum", "none", None] = "sum", **kwargs: Any) -> None:
+    def __init__(self, reduction: Optional[Literal["mean", "sum", "none"]] = "sum", **kwargs: Any) -> None:
         _deprecated_root_import_class("TotalVariation", "image")
         super().__init__(reduction=reduction, **kwargs)
 
@@ -255,7 +255,7 @@ def __init__(
         self,
         kernel_size: Sequence[int] = (11, 11),
         sigma: Sequence[float] = (1.5, 1.5),
-        reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
+        reduction: Optional[Literal["elementwise_mean", "sum", "none"]] = "elementwise_mean",
         **kwargs: Any,
     ) -> None:
         _deprecated_root_import_class("UniversalImageQualityIndex", "image")

src/torchmetrics/image/ergas.py

@@ -84,7 +84,7 @@ class ErrorRelativeGlobalDimensionlessSynthesis(Metric):
     def __init__(
         self,
         ratio: float = 4,
-        reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
+        reduction: Optional[Literal["elementwise_mean", "sum", "none"]] = "elementwise_mean",
         **kwargs: Any,
     ) -> None:
         super().__init__(**kwargs)