Update tests for non-0D scalar conversion rules (#2694)

This PR updates dpnp tests for non-0D scalar conversion behavior changes
in [dpctl-2223](IntelPython/dpctl#2223) -
allowing scalar conversion only for 0D `usm_ndarray`

Author: vlad-perevezentsev

CHANGELOG.md

@@ -40,6 +40,7 @@ Also, that release drops support for Python 3.9, making Python 3.10 the minimum
 * Unified public API definitions in `dpnp.linalg` and `dpnp.scipy` submodules [#2663](https://github.com/IntelPython/dpnp/pull/2663)
 * Aligned the signature of `dpnp.reshape` function with Python array API by making `shape` a required argument [#2673](https://github.com/IntelPython/dpnp/pull/2673)
 * Unified `dpnp` public API exports by consolidating function exports in `__init__.py` and removing wildcard imports [#2665](https://github.com/IntelPython/dpnp/pull/2665) [#2666](https://github.com/IntelPython/dpnp/pull/2666)
+* Updated tests to reflect the new scalar conversion rules for non-0D `usm_ndarray` [#2694](https://github.com/IntelPython/dpnp/pull/2694)
 
 ### Deprecated
 

dpnp/tests/test_indexing.py

@@ -766,12 +766,18 @@ def test_axis_as_array(self):
         a = numpy.array([[[1]]])
         ia = dpnp.array(a)
 
-        result = ia.take([0], axis=ia)
-        expected = a.take(
-            [0], axis=1
-        )  # numpy raises an error for axis as array
+        # axis is a 0-D integer array
+        axis_0d = numpy.array(1)
+        iaxis_0d = dpnp.array(1)
+
+        result = ia.take([0], axis=iaxis_0d)
+        expected = a.take([0], axis=axis_0d)
         assert_array_equal(result, expected)
 
+        # axis is not a 0-D integer array
+        assert_raises(TypeError, ia.take, [0], axis=ia)
+        assert_raises(TypeError, a.take, [0], axis=a)
+
     def test_mode_raise(self):
         a = dpnp.array([[1, 2], [3, 4]])
         assert_raises(ValueError, a.take, [-1, 4], mode="raise")

dpnp/tests/test_ndarray.py

@@ -5,6 +5,7 @@
     assert_allclose,
     assert_array_equal,
     assert_equal,
+    assert_raises,
     assert_raises_regex,
 )
 
@@ -530,43 +531,71 @@ def test_print_dpnp_zero_shape():
     assert result == expected
 
 
-# Numpy will raise an error when converting a.ndim > 0 to a scalar
-# TODO: Discuss dpnp behavior according to these future changes
-@pytest.mark.skip("until dpctl-2223")
-@pytest.mark.filterwarnings("ignore::DeprecationWarning")
-@pytest.mark.parametrize("func", [bool, float, int, complex])
+@testing.with_requires("numpy>=2.4")
+@pytest.mark.parametrize("xp", [dpnp, numpy])
 @pytest.mark.parametrize("shape", [tuple(), (1,), (1, 1), (1, 1, 1)])
-@pytest.mark.parametrize(
-    "dtype", get_all_dtypes(no_float16=False, no_complex=True)
-)
-def test_scalar_type_casting(func, shape, dtype):
-    a = numpy.full(shape, 5, dtype=dtype)
-    ia = dpnp.full(shape, 5, dtype=dtype)
-    assert func(a) == func(ia)
-
+class TestPythonScalarConversion:
+    @pytest.mark.parametrize(
+        "dtype", get_all_dtypes(no_none=True, no_float16=False, no_complex=True)
+    )
+    def test_bool_conversion(self, xp, shape, dtype):
+        a = xp.full(shape, 5, dtype=dtype)
+        if xp == dpnp and len(shape) > 0:
+            # dpnp behavior differs from NumPy:
+            # non-0D singe-element arrays are not convertible to
+            # Python bool
+            assert_raises(TypeError, bool, a)
+        else:
+            # NumPy allows conversion to Python bool for
+            # non-0D singe-element arrays
+            assert bool(a) is True
 
-# Numpy will raise an error when converting a.ndim > 0 to a scalar
-# TODO: Discuss dpnp behavior according to these future changes
-@pytest.mark.filterwarnings("ignore::DeprecationWarning")
-@pytest.mark.skip("until dpctl-2223")
-@pytest.mark.parametrize(
-    "method", ["__bool__", "__float__", "__int__", "__complex__"]
-)
-@pytest.mark.parametrize("shape", [tuple(), (1,), (1, 1), (1, 1, 1)])
-@pytest.mark.parametrize(
-    "dtype", get_all_dtypes(no_float16=False, no_complex=True)
-)
-def test_scalar_type_casting_by_method(method, shape, dtype):
-    a = numpy.full(shape, 4.7, dtype=dtype)
-    ia = dpnp.full(shape, 4.7, dtype=dtype)
-    assert_allclose(getattr(a, method)(), getattr(ia, method)(), rtol=1e-06)
+    @pytest.mark.parametrize(
+        "dtype", get_all_dtypes(no_none=True, no_float16=False, no_complex=True)
+    )
+    def test_bool_method_conversion(self, xp, shape, dtype):
+        a = xp.full(shape, 5, dtype=dtype)
+        if xp == dpnp and len(shape) > 0:
+            assert_raises(TypeError, getattr(a, "__bool__"))
+        else:
+            assert a.__bool__() is True
+
+    @pytest.mark.parametrize("func", [float, int, complex])
+    @pytest.mark.parametrize(
+        "dtype", get_all_dtypes(no_none=True, no_float16=False, no_complex=True)
+    )
+    def test_non_bool_conversion(self, xp, func, shape, dtype):
+        a = xp.full(shape, 5, dtype=dtype)
+        if len(shape) > 0:
+            # Non-0D arrays are not allowed to be converted to
+            # Python numeric scalars
+            assert_raises(TypeError, func, a)
+        else:
+            # 0D arrays are allowed to be converted to
+            # Python numeric scalars
+            expected = 1 if dtype == xp.bool else 5
+            assert func(a) == func(expected)
+
+    @pytest.mark.parametrize("method", ["__float__", "__int__", "__complex__"])
+    @pytest.mark.parametrize(
+        "dtype", get_all_dtypes(no_none=True, no_float16=False, no_complex=True)
+    )
+    def test_non_bool_method_conversion(self, xp, method, shape, dtype):
+        a = xp.full(shape, 5, dtype=dtype)
+        if len(shape) > 0:
+            assert_raises(TypeError, getattr(a, method))
+        else:
+            expected = 1 if dtype == xp.bool else 5
+            func = {"__float__": float, "__int__": int, "__complex__": complex}[
+                method
+            ]
+            assert getattr(a, method)() == func(expected)
 
 
-@pytest.mark.parametrize("shape", [(1,), (1, 1), (1, 1, 1)])
 @pytest.mark.parametrize("index_dtype", [dpnp.int32, dpnp.int64])
-def test_array_as_index(shape, index_dtype):
-    ind_arr = dpnp.ones(shape, dtype=index_dtype)
-    a = numpy.arange(ind_arr.size + 1)
+def test_array_as_index(index_dtype):
+    ind_arr = dpnp.ones((1,), dtype=index_dtype)
+    a = dpnp.arange(ind_arr.size + 1)
     assert a[tuple(ind_arr)] == a[1]
 
 

dpnp/tests/third_party/cupy/core_tests/test_ndarray_unary_op.py

@@ -25,13 +25,17 @@ def test_bool_scalar(self, dtype):
         assert not bool(cupy.array(0, dtype=dtype))
 
     def test_bool_one_element_bool(self):
-        assert bool(cupy.array([True], dtype=numpy.bool_))
-        assert not bool(cupy.array([False], dtype=numpy.bool_))
+        # Different behavior from CuPy:
+        # CuPy as NumPy allows conversion to Python bool for
+        # non-0D singe-element arrays
+        with self.assertRaises(TypeError):
+            bool(cupy.array([True], dtype=numpy.bool_))
 
     @testing.for_all_dtypes()
     def test_bool_one_element(self, dtype):
-        assert bool(cupy.array([1], dtype=dtype))
-        assert not bool(cupy.array([0], dtype=dtype))
+        # Different behavior from CuPy
+        with self.assertRaises(TypeError):
+            bool(cupy.array([1], dtype=dtype))
 
     @testing.for_all_dtypes()
     def test_bool_two_elements(self, dtype):

dpnp/tests/third_party/cupyx/scipy_tests/special_tests/test_erf.py

@@ -80,10 +80,10 @@ def test_erfinv_behavior(self, dtype):
 
         a[:] = 1.0 + 1e-6
         a = cupy.scipy.special.erfinv(a)
-        assert cupy.isnan(a)
+        assert cupy.isnan(a).item()
         a[:] = -1.0 - 1e-6
         a = cupy.scipy.special.erfinv(a)
-        assert cupy.isnan(a)
+        assert cupy.isnan(a).item()
         a[:] = 1.0
         a = cupy.scipy.special.erfinv(a)
         assert numpy.isposinf(cupy.asnumpy(a))
@@ -98,10 +98,10 @@ def test_erfcinv_behavior(self, dtype):
 
         a[:] = 2.0 + 1e-6
         a = cupy.scipy.special.erfcinv(a)
-        assert cupy.isnan(a)
+        assert cupy.isnan(a).item()
         a[:] = 0.0 - 1e-6
         a = cupy.scipy.special.erfcinv(a)
-        assert cupy.isnan(a)
+        assert cupy.isnan(a).item()
         a[:] = 0.0
         a = cupy.scipy.special.erfcinv(a)
         assert numpy.isposinf(cupy.asnumpy(a))