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61 changes: 61 additions & 0 deletions array_api_tests/test_operators_and_elementwise_functions.py
Original file line number Diff line number Diff line change
Expand Up @@ -2104,6 +2104,67 @@ def test_subtract(ctx, data):
raise


def make_reflected_scalar_params():
cases = (
("__radd__", "add", operator.add, dh.numeric_dtypes),
("__rsub__", "subtract", operator.sub, dh.numeric_dtypes),
("__rmul__", "multiply", operator.mul, dh.numeric_dtypes),
("__rtruediv__", "divide", operator.truediv, dh.all_float_dtypes),
("__rfloordiv__", "floor_divide", operator.floordiv, dh.real_dtypes),
("__rpow__", "pow", operator.pow, dh.numeric_dtypes),
("__rmod__", "remainder", operator.mod, dh.real_dtypes),
("__rand__", "bitwise_and", operator.and_, dh.bool_and_all_int_dtypes),
("__ror__", "bitwise_or", operator.or_, dh.bool_and_all_int_dtypes),
("__rxor__", "bitwise_xor", operator.xor, dh.bool_and_all_int_dtypes),
("__rlshift__", "bitwise_left_shift", operator.lshift, dh.all_int_dtypes),
("__rrshift__", "bitwise_right_shift", operator.rshift, dh.all_int_dtypes),
)
for method, func_name, op, dtypes in cases:
for dtype in dtypes:
if isinstance(dtype, xp._UndefinedStub):
continue
yield pytest.param(
method, func_name, op, dtype,
id=f"{method}-{dh.dtype_to_name[dtype]}",
)


# XXX: this test is parametrized over lists of dtypes not dtype strategies.
# Therefore adding `--max-examples` to the test run does not improve coverage.
@pytest.mark.parametrize(
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"method, func_name, op, dtype", list(make_reflected_scalar_params())

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parametrizing over the full lists of dtypes from dtype_helpers is a bit unorthodox: the rest of the test suite uses the matching strategies from hypothesis_helpers. The difference seems minor, but where it bites is that with the rest of the test suite we typically use --max-examples= to control the coverage [1]. Using a large value of examples makes it instead draw many values and still loop over all dtypes. Which does not really increase the coverage and just makes it slower.

[1] so that a typical test incantation for a thorough test is

$ ARRAY_API_TESTS_SKIP_DTYPES=uint16,uint32,uint64 ARRAY_API_TESTS_MODULE=array_api_compat.cupy pytest array_api_tests/test_operators_and_elementwise_functions.py -kreflected -v --max-examples=10_000

)
@given(data=st.data())
def test_reflected_scalar_operator(method, func_name, op, dtype, data):
# Small positive values avoid division by zero, invalid powers, and overflow.
if dtype == xp.bool:
elements = st.booleans()
else:
elements = st.integers(1, 3).map(dh.get_scalar_type(dtype))
scalar_strategy = elements
if func_name == "subtract" and dtype in dh.uint_dtypes:
scalar_strategy = st.integers(3, 7)
scalar = data.draw(scalar_strategy, label="scalar")
x = data.draw(
hh.arrays(
dtype=dtype, shape=hh.shapes(min_dims=1, min_side=1), elements=elements
),
label="x",
)
op_sym = dh.binary_op_to_symbol[func_to_op[func_name]]
repro_snippet = ph.format_snippet(f"{scalar!r} {op_sym} {x!r} # {method}")
try:
out = op(scalar, x)
expected = getattr(xp, func_name)(xp.asarray(scalar, dtype=dtype), x)

ph.assert_dtype(method, in_dtype=dtype, out_dtype=out.dtype)
ph.assert_result_shape(method, in_shapes=[x.shape], out_shape=out.shape)
ph.assert_array_elements(method, out=out, expected=expected)
except Exception as exc:
ph.add_note(exc, repro_snippet)
raise


@given(hh.arrays(dtype=hh.all_floating_dtypes(), shape=hh.shapes()))
def test_tan(x):
repro_snippet = ph.format_snippet(f"xp.tan({x!r})")
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