Add more tests to cover more use cases

Author: antonwolfy

dpnp/dpnp_array.py

@@ -1990,14 +1990,15 @@ def view(self, dtype=None, *, type=None):
         ----------
         dtype : {None, str, dtype object}, optional
             The desired data type of the returned view, e.g. :obj:`dpnp.float32`
-            or :obj:`dpnp.int16`. Omitting it results in the view having the
+            or :obj:`dpnp.int16`. By default, it results in the view having the
             same data type.
 
+            Default: ``None``.
+
         Notes
         -----
-        Passing ``None`` for `dtype` is different from omitting the parameter,
-        since the former invokes ``dtype(None)`` which is an alias for the
-        default floating point data type.
+        Passing ``None`` for `dtype` is the same as omitting the parameter,
+        opposite to NumPy where they have different meaning.
 
         ``view(some_dtype)`` or ``view(dtype=some_dtype)`` constructs a view of
         the array's memory with a different data type. This can cause a

dpnp/tests/test_ndarray.py

@@ -74,6 +74,34 @@ def test_attributes(self):
         assert_equal(self.two.itemsize, self.two.dtype.itemsize)
 
 
+class TestView:
+    def test_none_dtype(self):
+        a = numpy.ones((1, 2, 4), dtype=numpy.int32)
+        ia = dpnp.array(a)
+
+        expected = a.view()
+        result = ia.view()
+        assert_allclose(result, expected)
+
+        expected = a.view()  # numpy returns dtype(None) otherwise
+        result = ia.view(None)
+        assert_allclose(result, expected)
+
+    @pytest.mark.parametrize("dt", [bool, int, float, complex])
+    def test_python_types(self, dt):
+        a = numpy.ones((8, 4), dtype=numpy.complex64)
+        ia = dpnp.array(a)
+
+        expected = a.view(dt)
+        result = ia.view(dt)
+        assert_allclose(result, expected)
+
+    def test_type_error(self):
+        x = dpnp.ones(4, dtype="i4")
+        with pytest.raises(NotImplementedError):
+            x.view("i2", type=dpnp.ndarray)
+
+
 @pytest.mark.parametrize(
     "arr",
     [