add a workaround

Author: vtavana

dpnp/tests/test_linalg.py

@@ -2075,9 +2075,6 @@ def test_matrix_transpose():
 
 
 class TestNorm:
-    def setup_method(self):
-        numpy.random.seed(42)
-
     @pytest.mark.usefixtures("suppress_divide_numpy_warnings")
     @pytest.mark.parametrize(
         "shape", [(0,), (5, 0), (2, 0, 3)], ids=["(0,)", "(5, 0)", "(2, 0, 3)"]
@@ -2099,15 +2096,16 @@ def test_empty(self, shape, ord, axis, keepdims):
             # Improper number of dimensions to norm
             assert_raises(ValueError, dpnp.linalg.norm, ia, **kwarg)
             assert_raises(ValueError, numpy.linalg.norm, a, **kwarg)
-        elif (
-            axis is None
-            and ord in [-2, -1, 0, 3]
-            and a.ndim != 1
-            and a.shape[-1] == 0
-        ):
-            # reduction cannot be performed over zero-size axes
-            assert_raises(ValueError, dpnp.linalg.norm, ia, **kwarg)
-            assert_raises(ValueError, numpy.linalg.norm, a, **kwarg)
+        elif axis is None and a.ndim != 1 and a.shape[-1] == 0:
+            # TODO: when similar changes in numpy are available,
+            # instead of assert_equal with zero, we should compare with numpy
+            if ord in [-2, -1, 0, 3]:
+                # reduction cannot be performed over zero-size axes
+                assert_raises(ValueError, dpnp.linalg.norm, ia, **kwarg)
+                assert_raises(ValueError, numpy.linalg.norm, a, **kwarg)
+            else:
+                # ord in [None, 1, 2]
+                assert_equal(dpnp.linalg.norm(ia, **kwarg), 0)
         else:
             result = dpnp.linalg.norm(ia, **kwarg)
             expected = numpy.linalg.norm(a, **kwarg)
@@ -2303,6 +2301,8 @@ def test_matrix_norm_empty(self, dtype, shape_axis):
         shape, axis = shape_axis[0], shape_axis[1]
         x = dpnp.zeros(shape, dtype=dtype)
 
+        # TODO: when similar changes in numpy are available,
+        # instead of assert_equal with zero, we should compare with numpy
         assert_equal(dpnp.linalg.norm(x, axis=axis, ord="fro"), 0)
         assert_equal(dpnp.linalg.norm(x, axis=axis, ord="nuc"), 0)
         assert_equal(dpnp.linalg.norm(x, axis=axis, ord=2), 0)
@@ -2313,6 +2313,8 @@ def test_matrix_norm_empty(self, dtype, shape_axis):
     @pytest.mark.parametrize("axis", [None, 0])
     def test_vector_norm_empty(self, dtype, axis):
         x = dpnp.zeros(0, dtype=dtype)
+        # TODO: when similar changes in numpy are available,
+        # instead of assert_equal with zero, we should compare with numpy
         assert_equal(dpnp.linalg.vector_norm(x, axis=axis, ord=1), 0)
         assert_equal(dpnp.linalg.vector_norm(x, axis=axis, ord=2), 0)
         assert_equal(dpnp.linalg.vector_norm(x, axis=axis, ord=dpnp.inf), 0)