Remove use of numpy.testing.suppress_warnings in tests (#2529)

NumPy is going to deprecate use of `numpy.testing.suppress_warnings` and
to fully migrate to `warnings` module internally which is tread-safe.
This PR updates the tests and replaces the use of
`numpy.testing.suppress_warnings` with appropriate calls from the
`warnings` module.

Author: antonwolfy

CHANGELOG.md

@@ -27,6 +27,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 * Bumped oneMKL version up to `v0.8` [#2514](https://github.com/IntelPython/dpnp/pull/2514)
 * Removed the use of class template argument deduction for alias template to conform to the C++17 standard [#2517](https://github.com/IntelPython/dpnp/pull/2517)
 * Changed th order of individual FFTs over `axes` for `dpnp.fft.irfftn` to be in forward order [#2524](https://github.com/IntelPython/dpnp/pull/2524)
+* Replaced the use of `numpy.testing.suppress_warnings` with appropriate calls from the warnings module [#2529](https://github.com/IntelPython/dpnp/pull/2529)
 
 ### Deprecated
 

dpnp/tests/conftest.py

@@ -183,9 +183,8 @@ def allow_fall_back_on_numpy(monkeypatch):
 
 @pytest.fixture
 def suppress_complex_warning():
-    sup = numpy.testing.suppress_warnings("always")
-    sup.filter(ComplexWarning)
-    with sup:
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", ComplexWarning)
         yield
 
 

dpnp/tests/test_linalg.py

@@ -1,3 +1,5 @@
+import warnings
+
 import dpctl
 import dpctl.tensor as dpt
 import numpy
@@ -10,7 +12,6 @@
     assert_equal,
     assert_raises,
     assert_raises_regex,
-    suppress_warnings,
 )
 
 import dpnp
@@ -847,8 +848,8 @@ def check_einsum_sums(self, dtype, do_opt=False):
             )
 
         # Suppress the complex warnings for the 'as f8' tests
-        with suppress_warnings() as sup:
-            sup.filter(numpy.exceptions.ComplexWarning)
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", numpy.exceptions.ComplexWarning)
 
             # matvec(a,b) / a.dot(b) where a is matrix, b is vector
             for n in range(1, 17):