update dpnp.ndarray.item (#2138)

* update dpnp.ndarray.item

* address comments

Author: vtavana

dpnp/dpnp_array.py

@@ -989,7 +989,7 @@ def flatten(self, order="C"):
 
         Returns
         -------
-        out: dpnp.ndarray
+        out : dpnp.ndarray
             A copy of the input array, flattened to one dimension.
 
         See Also
@@ -1052,40 +1052,55 @@ def imag(self, value):
         else:
             raise TypeError("array does not have imaginary part to set")
 
-    def item(self, id=None):
+    def item(self, *args):
         """
         Copy an element of an array to a standard Python scalar and return it.
 
         For full documentation refer to :obj:`numpy.ndarray.item`.
 
+        Parameters
+        ----------
+        *args : {none, int, tuple of ints}
+            - none: in this case, the method only works for arrays with
+              one element (``a.size == 1``), which element is copied into a
+              standard Python scalar object and returned.
+            - int: this argument is interpreted as a flat index into the array,
+              specifying which element to copy and return.
+            - tuple of ints: functions as does a single int argument, except
+              that the argument is interpreted as an nd-index into the array.
+
+        Returns
+        -------
+        out : Standard Python scalar object
+            A copy of the specified element of the array as a suitable Python scalar.
+
         Examples
         --------
+        >>> import dpnp as np
         >>> np.random.seed(123)
         >>> x = np.random.randint(9, size=(3, 3))
         >>> x
-        array([[2, 2, 6],
-               [1, 3, 6],
-               [1, 0, 1]])
+        array([[0, 0, 7],
+               [6, 6, 6],
+               [0, 7, 1]])
         >>> x.item(3)
-        1
+        6
         >>> x.item(7)
-        0
+        7
         >>> x.item((0, 1))
-        2
+        0
         >>> x.item((2, 2))
         1
 
-        """
+        >>> x = np.array(5)
+        >>> x.item()
+        5
 
-        if id is None:
-            if self.size != 1:
-                raise ValueError(
-                    "DPNP ndarray::item(): can only convert an array of size 1 to a Python scalar"
-                )
-            else:
-                id = 0
+        """
 
-        return self.flat[id]
+        # TODO: implement a more efficient way to avoid copying to host
+        # for large arrays using `asnumpy()`
+        return self.asnumpy().item(*args)
 
     @property
     def itemsize(self):

tests/test_ndarray.py

@@ -55,7 +55,7 @@ def test_flatten(arr, arr_dtype):
     [(), 0, (0,), (2), (5, 2), (5, 0, 2), (5, 3, 2)],
     ids=["()", "0", "(0,)", "(2)", "(5, 2)", "(5, 0, 2)", "(5, 3, 2)"],
 )
-@pytest.mark.parametrize("order", ["C", "F"], ids=["C", "F"])
+@pytest.mark.parametrize("order", ["C", "F"])
 def test_flags(shape, order):
     usm_array = dpt.usm_ndarray(shape, order=order)
     numpy_array = numpy.ndarray(shape, order=order)
@@ -71,7 +71,7 @@ def test_flags(shape, order):
     ids=["complex64", "float32", "int64", "int32", "bool"],
 )
 @pytest.mark.parametrize("strides", [(1, 4), (4, 1)], ids=["(1, 4)", "(4, 1)"])
-@pytest.mark.parametrize("order", ["C", "F"], ids=["C", "F"])
+@pytest.mark.parametrize("order", ["C", "F"])
 def test_flags_strides(dtype, order, strides):
     itemsize = numpy.dtype(dtype).itemsize
     numpy_strides = tuple([el * itemsize for el in strides])
@@ -104,6 +104,32 @@ def test_flags_writable():
     assert not a.imag.flags.writable
 
 
+class TestItem:
+    @pytest.mark.parametrize("args", [2, 7, (1, 2), (2, 0)])
+    def test_basic(self, args):
+        a = numpy.arange(12).reshape(3, 4)
+        ia = dpnp.array(a)
+
+        expected = a.item(args)
+        result = ia.item(args)
+        assert isinstance(result, int)
+        assert expected == result
+
+    def test_0D(self):
+        a = numpy.array(5)
+        ia = dpnp.array(a)
+
+        expected = a.item()
+        result = ia.item()
+        assert isinstance(result, int)
+        assert expected == result
+
+    def test_error(self):
+        ia = dpnp.arange(12).reshape(3, 4)
+        with pytest.raises(ValueError):
+            ia.item()
+
+
 def test_print_dpnp_int():
     result = repr(dpnp.array([1, 0, 2, -3, -1, 2, 21, -9], dtype="i4"))
     expected = "array([ 1,  0,  2, -3, -1,  2, 21, -9], dtype=int32)"
@@ -165,9 +191,7 @@ def test_print_dpnp_boolean():
     assert result == expected
 
 
-@pytest.mark.parametrize(
-    "character", [dpnp.nan, dpnp.inf], ids=["dpnp.nan", "dpnp.inf"]
-)
+@pytest.mark.parametrize("character", [dpnp.nan, dpnp.inf])
 def test_print_dpnp_special_character(character):
     result = repr(dpnp.array([1.0, 0.0, character, 3.0]))
     expected = f"array([ 1.,  0., {character},  3.])"
@@ -264,7 +288,7 @@ def test_array_as_index(shape, index_dtype):
 @pytest.mark.parametrize(
     "shape",
     [(3, 5), (2, 5, 2), (2, 3, 3, 6)],
-    ids=["(3,5)", "(2,5,2)", "(2,3,3,6)"],
+    ids=["(3, 5)", "(2, 5, 2)", "(2, 3, 3, 6)"],
 )
 def test_matrix_transpose(shape):
     a = numpy.arange(numpy.prod(shape)).reshape(shape)

tests/third_party/cupy/core_tests/test_ndarray_conversion.py

@@ -12,7 +12,6 @@
     {"shape": (1,)},
     {"shape": (1, 1, 1)},
 )
-@pytest.mark.usefixtures("allow_fall_back_on_numpy")
 class TestNdarrayItem(unittest.TestCase):
     @testing.for_all_dtypes()
     @testing.numpy_cupy_equal()