Split up TestMinMax::test_uint

This is expected to fail, but has started to pass.

I split out the example from the issue cited in the xfail
to a new test, which fails as expected.

However, the original `test_uint` (which isn't exactly the same as the
example on Issue pymc-devs#770) is passing, while it is marked xfail.

Author: brendan-m-murphy

tests/tensor/test_math.py

@@ -1398,17 +1398,28 @@ def _grad_list(self):
         # axis=1)[0], n)),axis=1)
 
     @pytest.mark.xfail(reason="Fails due to #770")
-    def test_uint(self):
-        for dtype in ("uint8", "uint16", "uint32", "uint64"):
-            itype = np.iinfo(dtype)
-            data = np.array([itype.min + 3, itype.min, itype.max - 5, itype.max], dtype)
-            n = as_tensor_variable(data)
-            assert min(n).dtype == dtype
-            i = eval_outputs(min(n))
-            assert i == itype.min
-            assert max(n).dtype == dtype
-            i = eval_outputs(max(n))
-            assert i == itype.max
+    @pytest.mark.parametrize("dtype", ("uint8", "uint16", "uint32", "uint64"))
+    def test_uint(self, dtype):
+        itype = np.iinfo(dtype)
+        data = np.array([itype.min + 3, itype.min, itype.max - 5, itype.max], dtype)
+        n = as_tensor_variable(data)
+
+        assert min(n).dtype == dtype
+        i_min = eval_outputs(min(n))
+        assert i_min == itype.min
+
+        assert max(n).dtype == dtype
+        i_max = eval_outputs(max(n))
+        assert i_max == itype.max
+
+    @pytest.mark.xfail(reason="Fails due to #770")
+    def test_uint64_special_value(self):
+        dtype = "uint64"
+        data = np.array([0, 9223372036854775], dtype=dtype)
+        n = as_tensor_variable(data)
+
+        i_max = eval_outputs(max(n))
+        assert i_max == data.max()
 
     def test_bool(self):
         data = np.array([True, False], "bool")