Resolve PYI041 (Lightning-AI#2151)

Co-authored-by: Nicki Skafte Detlefsen <skaftenicki@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

pyproject.toml

@@ -122,7 +122,6 @@ ignore = [
     "S310",   # todo: Audit URL open for permitted schemes. Allowing use of `file:` or custom schemes is often unexpected.  # todo
     "B905",   # todo: `zip()` without an explicit `strict=` parameter
     "PYI024",  # todo: Use `typing.NamedTuple` instead of `collections.namedtuple`
-    "PYI041",  # todo: Use `float` instead of `int | float``
 ]
 # Exclude a variety of commonly ignored directories.
 exclude = [

src/torchmetrics/functional/image/_deprecated.py

@@ -42,7 +42,7 @@ def _spectral_distortion_index(
 def _error_relative_global_dimensionless_synthesis(
     preds: Tensor,
     target: Tensor,
-    ratio: Union[int, float] = 4,
+    ratio: float = 4,
     reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
 ) -> Tensor:
     """Wrapper for deprecated import.

src/torchmetrics/functional/image/ergas.py

@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Tuple, Union
+from typing import Tuple
 
 import torch
 from torch import Tensor
@@ -46,7 +46,7 @@ def _ergas_update(preds: Tensor, target: Tensor) -> Tuple[Tensor, Tensor]:
 def _ergas_compute(
     preds: Tensor,
     target: Tensor,
-    ratio: Union[int, float] = 4,
+    ratio: float = 4,
     reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
 ) -> Tensor:
     """Erreur Relative Globale Adimensionnelle de Synthèse.
@@ -86,7 +86,7 @@ def _ergas_compute(
 def error_relative_global_dimensionless_synthesis(
     preds: Tensor,
     target: Tensor,
-    ratio: Union[int, float] = 4,
+    ratio: float = 4,
     reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
 ) -> Tensor:
     """Erreur Relative Globale Adimensionnelle de Synthèse.

src/torchmetrics/functional/multimodal/clip_iqa.py

@@ -178,7 +178,7 @@ def _clip_iqa_update(
     images: Tensor,
     model: _CLIPModel,
     processor: _CLIPProcessor,
-    data_range: Union[int, float],
+    data_range: float,
     device: Union[str, torch.device],
 ) -> Tensor:
     images = images / float(data_range)
@@ -221,7 +221,7 @@ def clip_image_quality_assessment(
         "openai/clip-vit-large-patch14-336",
         "openai/clip-vit-large-patch14",
     ] = "clip_iqa",
-    data_range: Union[int, float] = 1.0,
+    data_range: float = 1.0,
     prompts: Tuple[Union[str, Tuple[str, str]]] = ("quality",),
 ) -> Union[Tensor, Dict[str, Tensor]]:
     """Calculates `CLIP-IQA`_, that can be used to measure the visual content of images.

src/torchmetrics/functional/nominal/cramers.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import itertools
-from typing import Optional, Union
+from typing import Optional
 
 import torch
 from torch import Tensor
@@ -34,7 +34,7 @@ def _cramers_v_update(
     target: Tensor,
     num_classes: int,
     nan_strategy: Literal["replace", "drop"] = "replace",
-    nan_replace_value: Optional[Union[int, float]] = 0.0,
+    nan_replace_value: Optional[float] = 0.0,
 ) -> Tensor:
     """Compute the bins to update the confusion matrix with for Cramer's V calculation.
 
@@ -90,7 +90,7 @@ def cramers_v(
     target: Tensor,
     bias_correction: bool = True,
     nan_strategy: Literal["replace", "drop"] = "replace",
-    nan_replace_value: Optional[Union[int, float]] = 0.0,
+    nan_replace_value: Optional[float] = 0.0,
 ) -> Tensor:
     r"""Compute `Cramer's V`_ statistic measuring the association between two categorical (nominal) data series.
 
@@ -142,7 +142,7 @@ def cramers_v_matrix(
     matrix: Tensor,
     bias_correction: bool = True,
     nan_strategy: Literal["replace", "drop"] = "replace",
-    nan_replace_value: Optional[Union[int, float]] = 0.0,
+    nan_replace_value: Optional[float] = 0.0,
 ) -> Tensor:
     r"""Compute `Cramer's V`_ statistic between a set of multiple variables.
 

src/torchmetrics/functional/nominal/pearson.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import itertools
-from typing import Optional, Union
+from typing import Optional
 
 import torch
 from torch import Tensor
@@ -32,7 +32,7 @@ def _pearsons_contingency_coefficient_update(
     target: Tensor,
     num_classes: int,
     nan_strategy: Literal["replace", "drop"] = "replace",
-    nan_replace_value: Optional[Union[int, float]] = 0.0,
+    nan_replace_value: Optional[float] = 0.0,
 ) -> Tensor:
     """Compute the bins to update the confusion matrix with for Pearson's Contingency Coefficient calculation.
 
@@ -76,7 +76,7 @@ def pearsons_contingency_coefficient(
     preds: Tensor,
     target: Tensor,
     nan_strategy: Literal["replace", "drop"] = "replace",
-    nan_replace_value: Optional[Union[int, float]] = 0.0,
+    nan_replace_value: Optional[float] = 0.0,
 ) -> Tensor:
     r"""Compute `Pearson's Contingency Coefficient`_ for measuring the association between two categorical data series.
 
@@ -131,7 +131,7 @@ def pearsons_contingency_coefficient(
 def pearsons_contingency_coefficient_matrix(
     matrix: Tensor,
     nan_strategy: Literal["replace", "drop"] = "replace",
-    nan_replace_value: Optional[Union[int, float]] = 0.0,
+    nan_replace_value: Optional[float] = 0.0,
 ) -> Tensor:
     r"""Compute `Pearson's Contingency Coefficient`_ statistic between a set of multiple variables.
 

src/torchmetrics/functional/nominal/theils_u.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import itertools
-from typing import Optional, Union
+from typing import Optional
 
 import torch
 from torch import Tensor
@@ -57,7 +57,7 @@ def _theils_u_update(
     target: Tensor,
     num_classes: int,
     nan_strategy: Literal["replace", "drop"] = "replace",
-    nan_replace_value: Optional[Union[int, float]] = 0.0,
+    nan_replace_value: Optional[float] = 0.0,
 ) -> Tensor:
     """Compute the bins to update the confusion matrix with for Theil's U calculation.
 
@@ -109,7 +109,7 @@ def theils_u(
     preds: Tensor,
     target: Tensor,
     nan_strategy: Literal["replace", "drop"] = "replace",
-    nan_replace_value: Optional[Union[int, float]] = 0.0,
+    nan_replace_value: Optional[float] = 0.0,
 ) -> Tensor:
     r"""Compute `Theils Uncertainty coefficient`_ statistic measuring the association between two nominal data series.
 
@@ -154,7 +154,7 @@ def theils_u(
 def theils_u_matrix(
     matrix: Tensor,
     nan_strategy: Literal["replace", "drop"] = "replace",
-    nan_replace_value: Optional[Union[int, float]] = 0.0,
+    nan_replace_value: Optional[float] = 0.0,
 ) -> Tensor:
     r"""Compute `Theil's U`_ statistic between a set of multiple variables.
 

src/torchmetrics/functional/nominal/tschuprows.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import itertools
-from typing import Optional, Union
+from typing import Optional
 
 import torch
 from torch import Tensor
@@ -34,7 +34,7 @@ def _tschuprows_t_update(
     target: Tensor,
     num_classes: int,
     nan_strategy: Literal["replace", "drop"] = "replace",
-    nan_replace_value: Optional[Union[int, float]] = 0.0,
+    nan_replace_value: Optional[float] = 0.0,
 ) -> Tensor:
     """Compute the bins to update the confusion matrix with for Tschuprow's T calculation.
 
@@ -92,7 +92,7 @@ def tschuprows_t(
     target: Tensor,
     bias_correction: bool = True,
     nan_strategy: Literal["replace", "drop"] = "replace",
-    nan_replace_value: Optional[Union[int, float]] = 0.0,
+    nan_replace_value: Optional[float] = 0.0,
 ) -> Tensor:
     r"""Compute `Tschuprow's T`_ statistic measuring the association between two categorical (nominal) data series.
 
@@ -148,7 +148,7 @@ def tschuprows_t_matrix(
     matrix: Tensor,
     bias_correction: bool = True,
     nan_strategy: Literal["replace", "drop"] = "replace",
-    nan_replace_value: Optional[Union[int, float]] = 0.0,
+    nan_replace_value: Optional[float] = 0.0,
 ) -> Tensor:
     r"""Compute `Tschuprow's T`_ statistic between a set of multiple variables.
 

src/torchmetrics/functional/nominal/utils.py

@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Optional, Tuple, Union
+from typing import Optional, Tuple
 
 import torch
 from torch import Tensor
@@ -20,12 +20,12 @@
 from torchmetrics.utilities.prints import rank_zero_warn
 
 
-def _nominal_input_validation(nan_strategy: str, nan_replace_value: Optional[Union[int, float]]) -> None:
+def _nominal_input_validation(nan_strategy: str, nan_replace_value: Optional[float]) -> None:
     if nan_strategy not in ["replace", "drop"]:
         raise ValueError(
             f"Argument `nan_strategy` is expected to be one of `['replace', 'drop']`, but got {nan_strategy}"
         )
-    if nan_strategy == "replace" and not isinstance(nan_replace_value, (int, float)):
+    if nan_strategy == "replace" and not isinstance(nan_replace_value, (float, int)):
         raise ValueError(
             "Argument `nan_replace` is expected to be of a type `int` or `float` when `nan_strategy = 'replace`, "
             f"but got {nan_replace_value}"

src/torchmetrics/functional/pairwise/minkowski.py

@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Optional, Union
+from typing import Optional
 
 import torch
 from torch import Tensor
@@ -22,7 +22,7 @@
 
 
 def _pairwise_minkowski_distance_update(
-    x: Tensor, y: Optional[Tensor] = None, exponent: Union[int, float] = 2, zero_diagonal: Optional[bool] = None
+    x: Tensor, y: Optional[Tensor] = None, exponent: float = 2, zero_diagonal: Optional[bool] = None
 ) -> Tensor:
     """Calculate the pairwise minkowski distance matrix.
 
@@ -49,7 +49,7 @@ def _pairwise_minkowski_distance_update(
 def pairwise_minkowski_distance(
     x: Tensor,
     y: Optional[Tensor] = None,
-    exponent: Union[int, float] = 2,
+    exponent: float = 2,
     reduction: Literal["mean", "sum", "none", None] = None,
     zero_diagonal: Optional[bool] = None,
 ) -> Tensor:

src/torchmetrics/image/_deprecated.py

@@ -28,7 +28,7 @@ class _ErrorRelativeGlobalDimensionlessSynthesis(ErrorRelativeGlobalDimensionles
 
     def __init__(
         self,
-        ratio: Union[int, float] = 4,
+        ratio: float = 4,
         reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
         **kwargs: Any,
     ) -> None:

src/torchmetrics/image/ergas.py

@@ -75,7 +75,7 @@ class ErrorRelativeGlobalDimensionlessSynthesis(Metric):
 
     def __init__(
         self,
-        ratio: Union[int, float] = 4,
+        ratio: float = 4,
         reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
         **kwargs: Any,
     ) -> None: