Split up TestMinMax::test_uint

I split this test up to test uint64 separately, since
this is the case discussed in Issue #770. I also added
a test for the exact example used in that issue.

The uint dtypes with lower precision should pass.

The uint64 case started passing for me locally on Mac OSX,
but still fails on CI. I'm not sure why this is, but at
least the test will be more specific now if it fails in the future.

Author: brendan-m-murphy

tests/tensor/test_math.py

@@ -1403,18 +1403,37 @@ def _grad_list(self):
         # check_grad_max(data, eval_outputs(grad(max_and_argmax(n,
         # axis=1)[0], n)),axis=1)
 
+    @pytest.mark.parametrize(
+        "dtype",
+        (
+            "uint8",
+            "uint16",
+            "uint32",
+            pytest.param("uint64", marks=pytest.mark.xfail(reason="Fails due to #770")),
+        ),
+    )
+    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_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
+    def test_uint64_special_value(self):
+        """Example from issue #770"""
+        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")