Merge branch 'main' into release

array_api_strict/_array_object.py

@@ -66,7 +66,9 @@ def __hash__(self):
 
 _default = object()
 
-_allow_array = False
+# See https://github.com/data-apis/array-api-strict/issues/67 and the comment
+# on __array__ below.
+_allow_array = True
 
 class Array:
     """
@@ -147,15 +149,18 @@ def __repr__(self: Array, /) -> str:
             mid = np.array2string(self._array, separator=', ', prefix=prefix, suffix=suffix)
         return prefix + mid + suffix
 
-    # Disallow __array__, meaning calling `np.func()` on an array_api_strict
-    # array will give an error. If we don't explicitly disallow it, NumPy
-    # defaults to creating an object dtype array, which would lead to
-    # confusing error messages at best and surprising bugs at worst.
-    #
-    # The alternative of course is to just support __array__, which is what we
-    # used to do. But this isn't actually supported by the standard, so it can
+    # In the future, _allow_array will be set to False, which will disallow
+    # __array__. This means calling `np.func()` on an array_api_strict array
+    # will give an error. If we don't explicitly disallow it, NumPy defaults
+    # to creating an object dtype array, which would lead to confusing error
+    # messages at best and surprising bugs at worst. The reason for doing this
+    # is that __array__ is not actually supported by the standard, so it can
     # lead to code assuming np.asarray(other_array) would always work in the
     # standard.
+    #
+    # This was implemented historically for compatibility, and removing it has
+    # caused issues for some libraries (see
+    # https://github.com/data-apis/array-api-strict/issues/67).
     def __array__(self, dtype: None | np.dtype[Any] = None, copy: None | bool = None) -> npt.NDArray[Any]:
         # We have to allow this to be internally enabled as there's no other
         # easy way to parse a list of Array objects in asarray().

array_api_strict/tests/test_array_object.py

@@ -364,6 +364,23 @@ def test_array_conversion():
         with pytest.raises(RuntimeError, match="Can not convert array"):
             asarray([a])
 
+def test__array__():
+    # __array__ should work for now
+    a = ones((2, 3))
+    np.array(a)
+
+    # Test the _allow_array private global flag for disabling it in the
+    # future.
+    from .. import _array_object
+    original_value = _array_object._allow_array
+    try:
+        _array_object._allow_array = False
+        a = ones((2, 3))
+        with pytest.raises(ValueError, match="Conversion from an array_api_strict array to a NumPy ndarray is not supported"):
+            np.array(a)
+    finally:
+        _array_object._allow_array = original_value
+
 def test_allow_newaxis():
     a = ones(5)
     indexed_a = a[None, :]