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.

Split up TestMinMax::test_uint
8226f64c · Brendan Murphy · Ricardo Vieira · 51430183 · 8226f64c
--- a/tests/tensor/test_math.py
+++ b/tests/tensor/test_math.py
@@ -1403,18 +1403,37 @@ class TestMinMax:
        # 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):
+    def test_uint64_special_value(self):
-        for dtype in ("uint8", "uint16", "uint32", "uint64"):
+        """Example from issue #770"""
-            itype = np.iinfo(dtype)
+        dtype = "uint64"
-            data = np.array([itype.min + 3, itype.min, itype.max - 5, itype.max], dtype)
+        data = np.array([0, 9223372036854775], dtype=dtype)
-            n = as_tensor_variable(data)
+        n = as_tensor_variable(data)
-            assert min(n).dtype == dtype
-            i = eval_outputs(min(n))
+        i_max = eval_outputs(max(n))
-            assert i == itype.min
+        assert i_max == data.max()
-            assert max(n).dtype == dtype
-            i = eval_outputs(max(n))
-            assert i == itype.max
    def test_bool(self):
        data = np.array([True, False], "bool")