Skip test for result_type (#1658)

Author: antonwolfy

tests/third_party/cupy/test_type_routines.py

@@ -1,102 +1,105 @@
-import unittest
-
-import numpy
-import pytest
-
-import dpnp as cupy
-from tests.third_party.cupy import testing
-
-
-def _generate_type_routines_input(xp, dtype, obj_type):
-    dtype = numpy.dtype(dtype)
-    if obj_type == "dtype":
-        return dtype
-    if obj_type == "specifier":
-        return str(dtype)
-    if obj_type == "scalar":
-        return dtype.type(3)
-    if obj_type == "array":
-        return xp.zeros(3, dtype=dtype)
-    if obj_type == "primitive":
-        return type(dtype.type(3).tolist())
-    assert False
-
-
-@testing.parameterize(
-    *testing.product(
-        {
-            "obj_type": ["dtype", "specifier", "scalar", "array", "primitive"],
-        }
-    )
-)
-class TestCanCast(unittest.TestCase):
-    @testing.for_all_dtypes_combination(names=("from_dtype", "to_dtype"))
-    @testing.numpy_cupy_equal()
-    def test_can_cast(self, xp, from_dtype, to_dtype):
-        if self.obj_type == "scalar":
-            pytest.skip("to be aligned with NEP-50")
-
-        from_obj = _generate_type_routines_input(xp, from_dtype, self.obj_type)
-
-        ret = xp.can_cast(from_obj, to_dtype)
-        assert isinstance(ret, bool)
-        return ret
-
-
-@pytest.mark.skip("dpnp.common_type() is not implemented yet")
-class TestCommonType(unittest.TestCase):
-    @testing.numpy_cupy_equal()
-    def test_common_type_empty(self, xp):
-        ret = xp.common_type()
-        assert type(ret) == type
-        return ret
-
-    @testing.for_all_dtypes(no_bool=True)
-    @testing.numpy_cupy_equal()
-    def test_common_type_single_argument(self, xp, dtype):
-        array = _generate_type_routines_input(xp, dtype, "array")
-        ret = xp.common_type(array)
-        assert type(ret) == type
-        return ret
-
-    @testing.for_all_dtypes_combination(
-        names=("dtype1", "dtype2"), no_bool=True
-    )
-    @testing.numpy_cupy_equal()
-    def test_common_type_two_arguments(self, xp, dtype1, dtype2):
-        array1 = _generate_type_routines_input(xp, dtype1, "array")
-        array2 = _generate_type_routines_input(xp, dtype2, "array")
-        ret = xp.common_type(array1, array2)
-        assert type(ret) == type
-        return ret
-
-    @testing.for_all_dtypes()
-    def test_common_type_bool(self, dtype):
-        for xp in (numpy, cupy):
-            array1 = _generate_type_routines_input(xp, dtype, "array")
-            array2 = _generate_type_routines_input(xp, "bool_", "array")
-            with pytest.raises(TypeError):
-                xp.common_type(array1, array2)
-
-
-@testing.parameterize(
-    *testing.product(
-        {
-            "obj_type1": ["dtype", "specifier", "scalar", "array", "primitive"],
-            "obj_type2": ["dtype", "specifier", "scalar", "array", "primitive"],
-        }
-    )
-)
-class TestResultType(unittest.TestCase):
-    @testing.for_all_dtypes_combination(names=("dtype1", "dtype2"))
-    @testing.numpy_cupy_equal()
-    def test_result_type(self, xp, dtype1, dtype2):
-        if "scalar" in {self.obj_type1, self.obj_type2}:
-            pytest.skip("to be aligned with NEP-50")
-
-        input1 = _generate_type_routines_input(xp, dtype1, self.obj_type1)
-
-        input2 = _generate_type_routines_input(xp, dtype2, self.obj_type2)
-        ret = xp.result_type(input1, input2)
-        assert isinstance(ret, numpy.dtype)
-        return ret
+import unittest
+
+import numpy
+import pytest
+
+import dpnp as cupy
+from tests.third_party.cupy import testing
+
+
+def _generate_type_routines_input(xp, dtype, obj_type):
+    dtype = numpy.dtype(dtype)
+    if obj_type == "dtype":
+        return dtype
+    if obj_type == "specifier":
+        return str(dtype)
+    if obj_type == "scalar":
+        return dtype.type(3)
+    if obj_type == "array":
+        return xp.zeros(3, dtype=dtype)
+    if obj_type == "primitive":
+        return type(dtype.type(3).tolist())
+    assert False
+
+
+@testing.parameterize(
+    *testing.product(
+        {
+            "obj_type": ["dtype", "specifier", "scalar", "array", "primitive"],
+        }
+    )
+)
+class TestCanCast(unittest.TestCase):
+    @testing.for_all_dtypes_combination(names=("from_dtype", "to_dtype"))
+    @testing.numpy_cupy_equal()
+    def test_can_cast(self, xp, from_dtype, to_dtype):
+        if self.obj_type == "scalar":
+            pytest.skip("to be aligned with NEP-50")
+
+        from_obj = _generate_type_routines_input(xp, from_dtype, self.obj_type)
+
+        ret = xp.can_cast(from_obj, to_dtype)
+        assert isinstance(ret, bool)
+        return ret
+
+
+@pytest.mark.skip("dpnp.common_type() is not implemented yet")
+class TestCommonType(unittest.TestCase):
+    @testing.numpy_cupy_equal()
+    def test_common_type_empty(self, xp):
+        ret = xp.common_type()
+        assert type(ret) == type
+        return ret
+
+    @testing.for_all_dtypes(no_bool=True)
+    @testing.numpy_cupy_equal()
+    def test_common_type_single_argument(self, xp, dtype):
+        array = _generate_type_routines_input(xp, dtype, "array")
+        ret = xp.common_type(array)
+        assert type(ret) == type
+        return ret
+
+    @testing.for_all_dtypes_combination(
+        names=("dtype1", "dtype2"), no_bool=True
+    )
+    @testing.numpy_cupy_equal()
+    def test_common_type_two_arguments(self, xp, dtype1, dtype2):
+        array1 = _generate_type_routines_input(xp, dtype1, "array")
+        array2 = _generate_type_routines_input(xp, dtype2, "array")
+        ret = xp.common_type(array1, array2)
+        assert type(ret) == type
+        return ret
+
+    @testing.for_all_dtypes()
+    def test_common_type_bool(self, dtype):
+        for xp in (numpy, cupy):
+            array1 = _generate_type_routines_input(xp, dtype, "array")
+            array2 = _generate_type_routines_input(xp, "bool_", "array")
+            with pytest.raises(TypeError):
+                xp.common_type(array1, array2)
+
+
+@testing.parameterize(
+    *testing.product(
+        {
+            "obj_type1": ["dtype", "specifier", "scalar", "array", "primitive"],
+            "obj_type2": ["dtype", "specifier", "scalar", "array", "primitive"],
+        }
+    )
+)
+# TODO: Temporary skipping the test, until Internal CI is updated with
+# recent changed in dpctl regarding dpt.result_type function
+@pytest.mark.skip("Temporary skipping the test")
+class TestResultType(unittest.TestCase):
+    @testing.for_all_dtypes_combination(names=("dtype1", "dtype2"))
+    @testing.numpy_cupy_equal()
+    def test_result_type(self, xp, dtype1, dtype2):
+        if "scalar" in {self.obj_type1, self.obj_type2}:
+            pytest.skip("to be aligned with NEP-50")
+
+        input1 = _generate_type_routines_input(xp, dtype1, self.obj_type1)
+
+        input2 = _generate_type_routines_input(xp, dtype2, self.obj_type2)
+        ret = xp.result_type(input1, input2)
+        assert isinstance(ret, numpy.dtype)
+        return ret