build(deps): bump mypy from 1.17.1 to 1.18.2 in /requirements

requirements/typing.txt

@@ -1,4 +1,4 @@
-mypy ==1.17.1
+mypy ==1.18.2
 torch ==2.8.0
 
 types-PyYAML

src/torchmetrics/functional/image/ssim.py

@@ -111,14 +111,14 @@ def _ssim_update(
         raise ValueError(f"Expected `sigma` to have positive number. Got {sigma}.")
 
     if data_range is None:
-        data_range = max(preds.max() - preds.min(), target.max() - target.min())  # type: ignore[call-overload]
+        data_range = max(preds.max() - preds.min(), target.max() - target.min()).item()
     elif isinstance(data_range, tuple):
         preds = torch.clamp(preds, min=data_range[0], max=data_range[1])
         target = torch.clamp(target, min=data_range[0], max=data_range[1])
         data_range = data_range[1] - data_range[0]
 
-    c1 = pow(k1 * data_range, 2)  # type: ignore[operator]
-    c2 = pow(k2 * data_range, 2)  # type: ignore[operator]
+    c1 = pow(k1 * data_range, 2)
+    c2 = pow(k2 * data_range, 2)
     device = preds.device
 
     channel = preds.size(1)

src/torchmetrics/functional/text/squad.py

@@ -87,7 +87,7 @@ def _metric_max_over_ground_truths(
     metric_fn: Callable[[str, str], Tensor], prediction: str, ground_truths: list[str]
 ) -> Tensor:
     """Calculate maximum score for a predicted answer with all reference answers."""
-    return max(metric_fn(prediction, truth) for truth in ground_truths)  # type: ignore[type-var]
+    return max(metric_fn(prediction, truth) for truth in ground_truths)
 
 
 def _squad_input_check(