Use new default_uint=None convention in sum and prod tests

Author: honno

array_api_tests/test_statistical_functions.py

@@ -136,24 +136,27 @@ def test_prod(x, data):
                 default_dtype = dh.default_uint
             else:
                 default_dtype = dh.default_int
-            m, M = dh.dtype_ranges[x.dtype]
-            d_m, d_M = dh.dtype_ranges[default_dtype]
-            if m < d_m or M > d_M:
-                _dtype = x.dtype
+            if default_dtype is None:
+                _dtype = None
             else:
-                _dtype = default_dtype
+                m, M = dh.dtype_ranges[x.dtype]
+                d_m, d_M = dh.dtype_ranges[default_dtype]
+                if m < d_m or M > d_M:
+                    _dtype = x.dtype
+                else:
+                    _dtype = default_dtype
         else:
             if dh.dtype_nbits[x.dtype] > dh.dtype_nbits[dh.default_float]:
                 _dtype = x.dtype
             else:
                 _dtype = dh.default_float
     else:
         _dtype = dtype
-    if isinstance(_dtype, _UndefinedStub):
+    if _dtype is None:
         # If a default uint cannot exist (i.e. in PyTorch which doesn't support
         # uint32 or uint64), we skip testing the output dtype.
         # See https://github.com/data-apis/array-api-tests/issues/106
-        if _dtype in dh.uint_dtypes:
+        if x.dtype in dh.uint_dtypes:
             assert dh.is_int_dtype(out.dtype)  # sanity check
     else:
         ph.assert_dtype("prod", in_dtype=x.dtype, out_dtype=out.dtype, expected=_dtype)
@@ -241,24 +244,27 @@ def test_sum(x, data):
                 default_dtype = dh.default_uint
             else:
                 default_dtype = dh.default_int
-            m, M = dh.dtype_ranges[x.dtype]
-            d_m, d_M = dh.dtype_ranges[default_dtype]
-            if m < d_m or M > d_M:
-                _dtype = x.dtype
+            if default_dtype is None:
+                _dtype = None
             else:
-                _dtype = default_dtype
+                m, M = dh.dtype_ranges[x.dtype]
+                d_m, d_M = dh.dtype_ranges[default_dtype]
+                if m < d_m or M > d_M:
+                    _dtype = x.dtype
+                else:
+                    _dtype = default_dtype
         else:
             if dh.dtype_nbits[x.dtype] > dh.dtype_nbits[dh.default_float]:
                 _dtype = x.dtype
             else:
                 _dtype = dh.default_float
     else:
         _dtype = dtype
-    if isinstance(_dtype, _UndefinedStub):
+    if _dtype is None:
         # If a default uint cannot exist (i.e. in PyTorch which doesn't support
         # uint32 or uint64), we skip testing the output dtype.
         # See https://github.com/data-apis/array-api-tests/issues/160
-        if _dtype in dh.uint_dtypes:
+        if x.dtype in dh.uint_dtypes:
             assert dh.is_int_dtype(out.dtype)  # sanity check
     else:
         ph.assert_dtype("sum", in_dtype=x.dtype, out_dtype=out.dtype, expected=_dtype)