Package the CuPy-based scipy tests

CHANGELOG.md

@@ -123,6 +123,7 @@ This release is compatible with NumPy 2.5.
 * Fixed `dpnp.__all__` missing `ndarray` and `__array_namespace_info__` and listing `mean`, `median` and `min` twice [#3100](https://github.com/IntelPython/dpnp/pull/3100)
 * Fixed `dpnp.repeat` and `dpnp.tensor.repeat` silently returning wrong results for `uint64` repeats above the `int64` maximum [#3115](https://github.com/IntelPython/dpnp/pull/3115)
 * Fixed importing `dpnp.scipy.sparse` re-enabling the GIL on free-threaded Python builds, because its pybind11 extension did not declare free-threading support [#3101](https://github.com/IntelPython/dpnp/pull/3101)
+* Fixed the CuPy-based `dpnp.scipy` tests under `dpnp/tests/third_party/cupyx/scipy_tests/` missing from the `dpnp` package, so they never ran against an installed package [#3102](https://github.com/IntelPython/dpnp/pull/3102)
 
 ### Security
 

dpnp/tests/third_party/cupyx/scipy_tests/sparse_tests/test_linalg.py

@@ -625,7 +625,10 @@ def _make_matrix(self):
         a[mask > self.density] = 0
         m = None
         if self.M == "jacobi":
-            m = numpy.diag(1.0 / numpy.diag(a))
+            # NOTE: at this density diag(a) is mostly zero, so upstream's
+            # 1/diag(a) is inf and both sides return NaN; use 1 there.
+            d = numpy.diag(a)
+            m = numpy.diag(1.0 / numpy.where(d == 0, 1, d))
         return a, m
 
     def _make_normalized_vector(self):

setup.py

@@ -69,6 +69,7 @@
             "tests/third_party/cupy/*/*.py",
             "tests/third_party/cupyx/*.py",
             "tests/third_party/cupyx/*/*.py",
+            "tests/third_party/cupyx/*/*/*.py",
         ],
     },
     include_package_data=False,