TST: undo (some) test skips of test with scalars

Author: ev-br

torch_np/_helpers.py

@@ -125,3 +125,9 @@ def allow_only_single_axis(axis):
     if len(axis) != 1:
         raise NotImplementedError("does not handle tuple axis")
     return axis[0]
+
+
+def to_tensors(*inputs):
+    """Convert all ndarrays from `inputs` to tensors."""
+    return tuple([value.get() if isinstance(value, ndarray) else value
+            for value in inputs])

torch_np/_wrapper.py

@@ -193,8 +193,9 @@ def arange(start=None, stop=None, step=1, dtype=None, *, like=None):
     if dtype is None:
         dtype = _dtypes.default_int_type()
         dtype = result_type(start, stop, step, dtype)
-
     torch_dtype = _dtypes.torch_dtype_from(dtype)
+    start, stop, step = _helpers.to_tensors(start, stop, step)
+
     try:
         return asarray(torch.arange(start, stop, step, dtype=torch_dtype))
     except RuntimeError:

torch_np/tests/test_function_base.py

@@ -96,16 +96,15 @@ def test_arange_booleans(self):
         with pytest.raises(TypeError):
             np.arange(3, dtype="bool")
 
-    @pytest.mark.skip(reason='XXX: python scalars from array scalars')
     @pytest.mark.parametrize("which", [0, 1, 2])
     def test_error_paths_and_promotion(self, which):
-        args = [0, 1, 2]  # start, stop, and step
+        args = [0, 10, 2]  # start, stop, and step
         args[which] = np.float64(2.)  # should ensure float64 output
         assert np.arange(*args).dtype == np.float64
 
         # Cover stranger error path, test only to achieve code coverage!
         args[which] = [None, []]
-        with pytest.raises(ValueError):
+        with pytest.raises((ValueError, RuntimeError)):
             # Fails discovering start dtype
             np.arange(*args)
 

torch_np/tests/test_shape_base.py

@@ -464,7 +464,7 @@ def test_stack():
 
     # 0d input
     for input_ in [(1, 2, 3),
-###                   [np.int32(1), np.int32(2), np.int32(3)],    # XXX: numpy scalars?
+                   [np.int32(1), np.int32(2), np.int32(3)],
                    [np.array(1), np.array(2), np.array(3)]]:
         assert_array_equal(stack(input_), [1, 2, 3])
     # 1d input examples