remove allow-untyped-defs from torch/distributions/pareto.py (#144624)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/144624
Approved by: https://github.com/Skylion007

torch/distributions/constraints.py

@@ -1,4 +1,8 @@
 # mypy: allow-untyped-defs
+
+from typing import Any, Callable, Optional
+
+
 r"""
 The following constraints are implemented:
 
@@ -198,13 +202,17 @@ class _DependentProperty(property, _Dependent):
     """
 
     def __init__(
-        self, fn=None, *, is_discrete=NotImplemented, event_dim=NotImplemented
+        self,
-    ):
+        fn: Optional[Callable[..., Any]] = None,
+        *,
+        is_discrete: Optional[bool] = NotImplemented,
+        event_dim: Optional[int] = NotImplemented,
+    ) -> None:
         super().__init__(fn)
         self._is_discrete = is_discrete
         self._event_dim = event_dim
 
-    def __call__(self, fn):  # type: ignore[override]
+    def __call__(self, fn: Callable[..., Any]) -> "_DependentProperty":  # type: ignore[override]
         """
         Support for syntax to customize static attributes::
 

torch/distributions/pareto.py

@@ -1,10 +1,12 @@
-# mypy: allow-untyped-defs
+from typing import Optional
+
 from torch import Tensor
 from torch.distributions import constraints
 from torch.distributions.exponential import Exponential
 from torch.distributions.transformed_distribution import TransformedDistribution
 from torch.distributions.transforms import AffineTransform, ExpTransform
 from torch.distributions.utils import broadcast_all
+from torch.types import _size
 
 
 __all__ = ["Pareto"]
@@ -27,13 +29,17 @@ class Pareto(TransformedDistribution):
     """
     arg_constraints = {"alpha": constraints.positive, "scale": constraints.positive}
 
-    def __init__(self, scale, alpha, validate_args=None):
+    def __init__(
+        self, scale: Tensor, alpha: Tensor, validate_args: Optional[bool] = None
+    ) -> None:
         self.scale, self.alpha = broadcast_all(scale, alpha)
         base_dist = Exponential(self.alpha, validate_args=validate_args)
         transforms = [ExpTransform(), AffineTransform(loc=0, scale=self.scale)]
         super().__init__(base_dist, transforms, validate_args=validate_args)
 
-    def expand(self, batch_shape, _instance=None):
+    def expand(
+        self, batch_shape: _size, _instance: Optional["Pareto"] = None
+    ) -> "Pareto":
         new = self._get_checked_instance(Pareto, _instance)
         new.scale = self.scale.expand(batch_shape)
         new.alpha = self.alpha.expand(batch_shape)
@@ -56,8 +62,8 @@ class Pareto(TransformedDistribution):
         return self.scale.pow(2) * a / ((a - 1).pow(2) * (a - 2))
 
     @constraints.dependent_property(is_discrete=False, event_dim=0)
-    def support(self):
+    def support(self) -> constraints.Constraint:
         return constraints.greater_than_eq(self.scale)
 
-    def entropy(self):
+    def entropy(self) -> Tensor:
         return (self.scale / self.alpha).log() + (1 + self.alpha.reciprocal())