ENH: generate numpy scalars or 0D arrays

array_api_tests/hypothesis_helpers.py

@@ -64,7 +64,7 @@ def from_dtype(dtype, **kwargs) -> SearchStrategy[Scalar]:
 
 
 @wraps(xps.arrays)
-def arrays(dtype, *args, elements=None, **kwargs) -> SearchStrategy[Array]:
+def arrays_no_scalars(dtype, *args, elements=None, **kwargs) -> SearchStrategy[Array]:
     """xps.arrays() without the crazy large numbers."""
     if isinstance(dtype, SearchStrategy):
         return dtype.flatmap(lambda d: arrays(d, *args, elements=elements, **kwargs))
@@ -77,6 +77,19 @@ def arrays(dtype, *args, elements=None, **kwargs) -> SearchStrategy[Array]:
     return xps.arrays(dtype, *args, elements=elements, **kwargs)
 
 
+def _f(a, flag):
+    return a[()] if a.ndim==0 and flag else a
+
+
+@wraps(xps.arrays)
+def arrays(dtype, *args, elements=None, **kwargs) -> SearchStrategy[Array]:
+    """xps.arrays() without the crazy large numbers. Also draw 0D arrays or numpy scalars.
+
+    Is only relevant for numpy: on all other libraries, array[()] is no-op.
+    """
+    return builds(_f, arrays_no_scalars(dtype, *args, elements=elements, **kwargs), booleans())
+
+
 _dtype_categories = [(xp.bool,), dh.uint_dtypes, dh.int_dtypes, dh.real_float_dtypes, dh.complex_dtypes]
 _sorted_dtypes = [d for category in _dtype_categories for d in category]
 

array_api_tests/test_creation_functions.py

@@ -263,7 +263,8 @@ def scalar_eq(s1: Scalar, s2: Scalar) -> bool:
     data=st.data(),
 )
 def test_asarray_arrays(shape, dtypes, data):
-    x = data.draw(hh.arrays(dtype=dtypes.input_dtype, shape=shape), label="x")
+    # generate arrays only since we draw the copy= kwd below (and np.asarray(scalar, copy=False) error out)
+    x = data.draw(hh.arrays_no_scalars(dtype=dtypes.input_dtype, shape=shape), label="x")
     dtypes_strat = st.just(dtypes.input_dtype)
     if dtypes.input_dtype == dtypes.result_dtype:
         dtypes_strat |= st.none()