fix typehints in 3.13

pyproject.toml

@@ -242,6 +242,7 @@ warn_unused_configs = true
 strict = true
 enable_error_code = ["ignore-without-code", "redundant-expr", "truthy-bool"]
 warn_unreachable = true
+plugins = "numpy.typing.mypy_plugin"
 
 [[tool.mypy.overrides]]
 module = [

src/pyhf/readxml.py

@@ -216,7 +216,7 @@ def process_sample(
                     modtag.attrib.get('HistoPath', ''),
                     modtag.attrib['HistoName'],
                 )
-                staterr = np.multiply(extstat, data).tolist()
+                staterr = cast(list[float], np.multiply(extstat, data).tolist())
             if not staterr:
                 raise RuntimeError('cannot determine stat error.')
             modifier_staterror: StatError = {

src/pyhf/tensor/numpy_backend.py

@@ -3,7 +3,7 @@
 from __future__ import annotations
 
 import logging
-from typing import TYPE_CHECKING, Callable, Generic, TypeVar, Union
+from typing import TYPE_CHECKING, Any, Callable, Generic, TypeVar, Union
 from collections.abc import Mapping, Sequence
 
 import numpy as np
@@ -27,7 +27,7 @@
 log = logging.getLogger(__name__)
 
 
-class _BasicPoisson:
+class _BasicPoisson(Generic[T]):
     def __init__(self, rate: Tensor[T]):
         self.rate = rate
 
@@ -39,7 +39,7 @@ def log_prob(self, value: NDArray[np.number[T]]) -> ArrayLike:
         return tensorlib.poisson_logpdf(value, self.rate)
 
 
-class _BasicNormal:
+class _BasicNormal(Generic[T]):
     def __init__(self, loc: Tensor[T], scale: Tensor[T]):
         self.loc = loc
         self.scale = scale
@@ -199,9 +199,12 @@ def conditional(
         """
         return true_callable() if predicate else false_callable()
 
-    def tolist(self, tensor_in: Tensor[T] | list[T]) -> list[T]:
+    def tolist(
+        self, tensor_in: Tensor[T] | list[T]
+    ) -> int | float | complex | list[T] | list[Any]:
         try:
-            return tensor_in.tolist()  # type: ignore[union-attr,no-any-return]
+            # unused-ignore for [no-any-return] in python 3.9
+            return tensor_in.tolist()  # type: ignore[union-attr,no-any-return,unused-ignore]
         except AttributeError:
             if isinstance(tensor_in, list):
                 return tensor_in
@@ -551,7 +554,7 @@ def normal_cdf(
         """
         return norm.cdf(x, loc=mu, scale=sigma)  # type: ignore[no-any-return]
 
-    def poisson_dist(self, rate: Tensor[T]) -> _BasicPoisson:
+    def poisson_dist(self, rate: Tensor[T]) -> _BasicPoisson[T]:
         r"""
         The Poisson distribution with rate parameter :code:`rate`.
 
@@ -572,7 +575,7 @@ def poisson_dist(self, rate: Tensor[T]) -> _BasicPoisson:
         """
         return _BasicPoisson(rate)
 
-    def normal_dist(self, mu: Tensor[T], sigma: Tensor[T]) -> _BasicNormal:
+    def normal_dist(self, mu: Tensor[T], sigma: Tensor[T]) -> _BasicNormal[T]:
         r"""
         The Normal distribution with mean :code:`mu` and standard deviation :code:`sigma`.