Update random tests

IntelPython · Jan 14, 2025 · 0420fb2 · 0420fb2
1 parent d79dcb0
commit 0420fb2
Show file tree

Hide file tree

Showing 2 changed files with 5 additions and 5 deletions.
diff --git a/dpnp/tests/third_party/cupy/random_tests/test_permutations.py b/dpnp/tests/third_party/cupy/random_tests/test_permutations.py
@@ -129,7 +129,7 @@ class TestPermutationSoundness(unittest.TestCase):
 
     def setUp(self):
         a = cupy.random.permutation(self.num)
-        self.a = a
+        self.a = a.asnumpy()
 
     # Test soundness
 

diff --git a/dpnp/tests/third_party/cupy/random_tests/test_sample.py b/dpnp/tests/third_party/cupy/random_tests/test_sample.py
@@ -89,15 +89,15 @@ def test_bound_float2(self):
     def test_goodness_of_fit(self):
         mx = 5
         trial = 100
-        vals = [random.randint(mx) for _ in range(trial)]
+        vals = [random.randint(mx).asnumpy() for _ in range(trial)]
         counts = numpy.histogram(vals, bins=numpy.arange(mx + 1))[0]
         expected = numpy.array([float(trial) / mx] * mx)
         assert _hypothesis.chi_square_test(counts, expected)
 
     @_condition.repeat(3, 10)
     def test_goodness_of_fit_2(self):
         mx = 5
-        vals = random.randint(mx, size=(5, 20))
+        vals = random.randint(mx, size=(5, 20)).asnumpy()
         counts = numpy.histogram(vals, bins=numpy.arange(mx + 1))[0]
         expected = numpy.array([float(vals.size) / mx] * mx)
         assert _hypothesis.chi_square_test(counts, expected)
@@ -191,15 +191,15 @@ def test_bound_2(self):
     def test_goodness_of_fit(self):
         mx = 5
         trial = 100
-        vals = [random.randint(0, mx) for _ in range(trial)]
+        vals = [random.randint(0, mx).asnumpy() for _ in range(trial)]
         counts = numpy.histogram(vals, bins=numpy.arange(mx + 1))[0]
         expected = numpy.array([float(trial) / mx] * mx)
         assert _hypothesis.chi_square_test(counts, expected)
 
     @_condition.repeat(3, 10)
     def test_goodness_of_fit_2(self):
         mx = 5
-        vals = random.randint(0, mx, (5, 20))
+        vals = random.randint(0, mx, (5, 20)).asnumpy()
         counts = numpy.histogram(vals, bins=numpy.arange(mx + 1))[0]
         expected = numpy.array([float(vals.size) / mx] * mx)
         assert _hypothesis.chi_square_test(counts, expected)