TST: refactor test_utils

tests/test_helpers.py

@@ -0,0 +1,150 @@
+from types import ModuleType
+
+import numpy as np
+import pytest
+
+from array_api_extra._lib import Backend
+from array_api_extra._lib._testing import xp_assert_equal
+from array_api_extra._lib._utils._compat import device as get_device
+from array_api_extra._lib._utils._helpers import asarrays, in1d
+from array_api_extra._lib._utils._typing import Device
+from array_api_extra.testing import lazy_xp_function
+
+# mypy: disable-error-code=no-untyped-usage
+
+# FIXME calls xp.unique_values without size
+lazy_xp_function(in1d, jax_jit=False, static_argnames=("assume_unique", "invert", "xp"))
+
+
+class TestIn1D:
+    @pytest.mark.xfail_xp_backend(
+        Backend.SPARSE, reason="no unique_inverse, no device kwarg in asarray()"
+    )
+    # cover both code paths
+    @pytest.mark.parametrize(
+        "n",
+        [
+            pytest.param(9, id="fast path"),
+            pytest.param(
+                15,
+                id="slow path",
+                marks=pytest.mark.xfail_xp_backend(
+                    Backend.DASK, reason="NaN-shaped array"
+                ),
+            ),
+        ],
+    )
+    def test_no_invert_assume_unique(self, xp: ModuleType, n: int):
+        x1 = xp.asarray([3, 8, 20])
+        x2 = xp.arange(n)
+        expected = xp.asarray([True, True, False])
+        actual = in1d(x1, x2)
+        xp_assert_equal(actual, expected)
+
+    @pytest.mark.xfail_xp_backend(Backend.SPARSE, reason="no device kwarg in asarray")
+    def test_device(self, xp: ModuleType, device: Device):
+        x1 = xp.asarray([3, 8, 20], device=device)
+        x2 = xp.asarray([2, 3, 4], device=device)
+        assert get_device(in1d(x1, x2)) == device
+
+    @pytest.mark.skip_xp_backend(Backend.NUMPY_READONLY, reason="xp=xp")
+    @pytest.mark.xfail_xp_backend(
+        Backend.SPARSE, reason="no arange, no device kwarg in asarray"
+    )
+    def test_xp(self, xp: ModuleType):
+        x1 = xp.asarray([1, 6])
+        x2 = xp.arange(5)
+        expected = xp.asarray([True, False])
+        actual = in1d(x1, x2, xp=xp)
+        xp_assert_equal(actual, expected)
+
+
+class TestAsArrays:
+    @pytest.mark.xfail_xp_backend(Backend.SPARSE, reason="no isdtype")
+    @pytest.mark.parametrize(
+        ("dtype", "b", "defined"),
+        [
+            # Well-defined cases of dtype promotion from Python scalar to Array
+            # bool vs. bool
+            ("bool", True, True),
+            # int vs. xp.*int*, xp.float*, xp.complex*
+            ("int16", 1, True),
+            ("uint8", 1, True),
+            ("float32", 1, True),
+            ("float64", 1, True),
+            ("complex64", 1, True),
+            ("complex128", 1, True),
+            # float vs. xp.float, xp.complex
+            ("float32", 1.0, True),
+            ("float64", 1.0, True),
+            ("complex64", 1.0, True),
+            ("complex128", 1.0, True),
+            # complex vs. xp.complex
+            ("complex64", 1.0j, True),
+            ("complex128", 1.0j, True),
+            # Undefined cases
+            ("bool", 1, False),
+            ("int64", 1.0, False),
+            ("float64", 1.0j, False),
+        ],
+    )
+    def test_array_vs_scalar(
+        self, dtype: str, b: int | float | complex, defined: bool, xp: ModuleType
+    ):
+        a = xp.asarray(1, dtype=getattr(xp, dtype))
+
+        xa, xb = asarrays(a, b, xp)
+        assert xa.dtype == a.dtype
+        if defined:
+            assert xb.dtype == a.dtype
+        else:
+            assert xb.dtype == xp.asarray(b).dtype
+
+        xbr, xar = asarrays(b, a, xp)
+        assert xar.dtype == xa.dtype
+        assert xbr.dtype == xb.dtype
+
+    def test_scalar_vs_scalar(self, xp: ModuleType):
+        a, b = asarrays(1, 2.2, xp=xp)
+        assert a.dtype == xp.asarray(1).dtype  # Default dtype
+        assert b.dtype == xp.asarray(2.2).dtype  # Default dtype; not broadcasted
+
+    ALL_TYPES: tuple[str, ...] = (
+        "int8",
+        "int16",
+        "int32",
+        "int64",
+        "uint8",
+        "uint16",
+        "uint32",
+        "uint64",
+        "float32",
+        "float64",
+        "complex64",
+        "complex128",
+        "bool",
+    )
+
+    @pytest.mark.parametrize("a_type", ALL_TYPES)
+    @pytest.mark.parametrize("b_type", ALL_TYPES)
+    def test_array_vs_array(self, a_type: str, b_type: str, xp: ModuleType):
+        """
+        Test that when both inputs of asarray are already Array API objects,
+        they are returned unchanged.
+        """
+        a = xp.asarray(1, dtype=getattr(xp, a_type))
+        b = xp.asarray(1, dtype=getattr(xp, b_type))
+        xa, xb = asarrays(a, b, xp)
+        assert xa.dtype == a.dtype
+        assert xb.dtype == b.dtype
+
+    @pytest.mark.parametrize("dtype", [np.float64, np.complex128])
+    def test_numpy_generics(self, dtype: type):
+        """
+        Test special case of np.float64 and np.complex128,
+        which are subclasses of float and complex.
+        """
+        a = dtype(0)
+        xa, xb = asarrays(a, 0, xp=np)
+        assert xa.dtype == dtype
+        assert xb.dtype == dtype