diff --git a/pixi.lock b/pixi.lock
index 00c8feef..f74068dc 100644
--- a/pixi.lock
+++ b/pixi.lock
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- conda: https://prefix.dev/conda-forge/linux-64/zlib-1.3.1-hb9d3cd8_2.conda
- conda: https://prefix.dev/conda-forge/linux-64/zstandard-0.23.0-py310ha39cb0e_1.conda
- conda: https://prefix.dev/conda-forge/linux-64/zstd-1.5.6-ha6fb4c9_0.conda
+ - pypi: git+https://github.com/data-apis/array-api-compat#fa558f21884dbccc0102f6c9a2a34d0b149100b5
- pypi: .
osx-arm64:
- - conda: https://prefix.dev/conda-forge/noarch/array-api-compat-1.10.0-pyhd8ed1ab_0.conda
- conda: https://prefix.dev/conda-forge/noarch/array-api-strict-2.2-pyhd8ed1ab_1.conda
- conda: https://prefix.dev/conda-forge/osx-arm64/aws-c-auth-0.8.1-hfc2798a_0.conda
- conda: https://prefix.dev/conda-forge/osx-arm64/aws-c-cal-0.8.1-hc8a0bd2_3.conda
@@ -2674,10 +2673,10 @@ environments:
- conda: https://prefix.dev/conda-forge/osx-arm64/zlib-1.3.1-h8359307_2.conda
- conda: https://prefix.dev/conda-forge/osx-arm64/zstandard-0.23.0-py310h2665a74_1.conda
- conda: https://prefix.dev/conda-forge/osx-arm64/zstd-1.5.6-hb46c0d2_0.conda
+ - pypi: git+https://github.com/data-apis/array-api-compat#fa558f21884dbccc0102f6c9a2a34d0b149100b5
- pypi: .
win-64:
- conda: https://prefix.dev/conda-forge/win-64/_openmp_mutex-4.5-2_gnu.conda
- - conda: https://prefix.dev/conda-forge/noarch/array-api-compat-1.10.0-pyhd8ed1ab_0.conda
- conda: https://prefix.dev/conda-forge/noarch/array-api-strict-2.2-pyhd8ed1ab_1.conda
- conda: https://prefix.dev/conda-forge/win-64/aws-c-auth-0.8.1-hd11252f_0.conda
- conda: https://prefix.dev/conda-forge/win-64/aws-c-cal-0.8.1-h099ea23_3.conda
@@ -2828,6 +2827,7 @@ environments:
- conda: https://prefix.dev/conda-forge/noarch/zipp-3.21.0-pyhd8ed1ab_1.conda
- conda: https://prefix.dev/conda-forge/win-64/zstandard-0.23.0-py310he5e10e1_1.conda
- conda: https://prefix.dev/conda-forge/win-64/zstd-1.5.6-h0ea2cb4_0.conda
+ - pypi: git+https://github.com/data-apis/array-api-compat#fa558f21884dbccc0102f6c9a2a34d0b149100b5
- pypi: .
tests-cuda:
channels:
@@ -2838,7 +2838,6 @@ environments:
linux-64:
- conda: https://prefix.dev/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2
- conda: https://prefix.dev/conda-forge/linux-64/_openmp_mutex-4.5-2_kmp_llvm.tar.bz2
- - conda: https://prefix.dev/conda-forge/noarch/array-api-compat-1.10.0-pyhd8ed1ab_0.conda
- conda: https://prefix.dev/conda-forge/noarch/array-api-strict-2.2-pyhd8ed1ab_1.conda
- conda: https://prefix.dev/conda-forge/linux-64/attr-2.5.1-h166bdaf_1.tar.bz2
- conda: https://prefix.dev/conda-forge/linux-64/aws-c-auth-0.8.1-h205f482_0.conda
@@ -3088,9 +3087,9 @@ environments:
- conda: https://prefix.dev/conda-forge/linux-64/zlib-1.3.1-hb9d3cd8_2.conda
- conda: https://prefix.dev/conda-forge/linux-64/zstandard-0.23.0-py310ha39cb0e_1.conda
- conda: https://prefix.dev/conda-forge/linux-64/zstd-1.5.6-ha6fb4c9_0.conda
+ - pypi: git+https://github.com/data-apis/array-api-compat#fa558f21884dbccc0102f6c9a2a34d0b149100b5
- pypi: .
osx-arm64:
- - conda: https://prefix.dev/conda-forge/noarch/array-api-compat-1.10.0-pyhd8ed1ab_0.conda
- conda: https://prefix.dev/conda-forge/noarch/array-api-strict-2.2-pyhd8ed1ab_1.conda
- conda: https://prefix.dev/conda-forge/osx-arm64/aws-c-auth-0.8.1-hfc2798a_0.conda
- conda: https://prefix.dev/conda-forge/osx-arm64/aws-c-cal-0.8.1-hc8a0bd2_3.conda
@@ -3264,10 +3263,10 @@ environments:
- conda: https://prefix.dev/conda-forge/osx-arm64/zlib-1.3.1-h8359307_2.conda
- conda: https://prefix.dev/conda-forge/osx-arm64/zstandard-0.23.0-py310h2665a74_1.conda
- conda: https://prefix.dev/conda-forge/osx-arm64/zstd-1.5.6-hb46c0d2_0.conda
+ - pypi: git+https://github.com/data-apis/array-api-compat#fa558f21884dbccc0102f6c9a2a34d0b149100b5
- pypi: .
win-64:
- conda: https://prefix.dev/conda-forge/win-64/_openmp_mutex-4.5-2_gnu.conda
- - conda: https://prefix.dev/conda-forge/noarch/array-api-compat-1.10.0-pyhd8ed1ab_0.conda
- conda: https://prefix.dev/conda-forge/noarch/array-api-strict-2.2-pyhd8ed1ab_1.conda
- conda: https://prefix.dev/conda-forge/win-64/aws-c-auth-0.8.1-hd11252f_0.conda
- conda: https://prefix.dev/conda-forge/win-64/aws-c-cal-0.8.1-h099ea23_3.conda
@@ -3436,6 +3435,7 @@ environments:
- conda: https://prefix.dev/conda-forge/noarch/zipp-3.21.0-pyhd8ed1ab_1.conda
- conda: https://prefix.dev/conda-forge/win-64/zstandard-0.23.0-py310he5e10e1_1.conda
- conda: https://prefix.dev/conda-forge/win-64/zstd-1.5.6-h0ea2cb4_0.conda
+ - pypi: git+https://github.com/data-apis/array-api-compat#fa558f21884dbccc0102f6c9a2a34d0b149100b5
- pypi: .
tests-py310:
channels:
@@ -3446,7 +3446,6 @@ environments:
linux-64:
- conda: https://prefix.dev/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2
- conda: https://prefix.dev/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2
- - conda: https://prefix.dev/conda-forge/noarch/array-api-compat-1.10.0-pyhd8ed1ab_0.conda
- conda: https://prefix.dev/conda-forge/noarch/array-api-strict-2.2-pyhd8ed1ab_1.conda
- conda: https://prefix.dev/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda
- conda: https://prefix.dev/conda-forge/linux-64/ca-certificates-2024.12.14-hbcca054_0.conda
@@ -3486,9 +3485,9 @@ environments:
- conda: https://prefix.dev/conda-forge/noarch/toml-0.10.2-pyhd8ed1ab_1.conda
- conda: https://prefix.dev/conda-forge/noarch/tomli-2.2.1-pyhd8ed1ab_1.conda
- conda: https://prefix.dev/conda-forge/noarch/tzdata-2025a-h78e105d_0.conda
+ - pypi: git+https://github.com/data-apis/array-api-compat#fa558f21884dbccc0102f6c9a2a34d0b149100b5
- pypi: .
osx-arm64:
- - conda: https://prefix.dev/conda-forge/noarch/array-api-compat-1.10.0-pyhd8ed1ab_0.conda
- conda: https://prefix.dev/conda-forge/noarch/array-api-strict-2.2-pyhd8ed1ab_1.conda
- conda: https://prefix.dev/conda-forge/osx-arm64/bzip2-1.0.8-h99b78c6_7.conda
- conda: https://prefix.dev/conda-forge/osx-arm64/ca-certificates-2024.12.14-hf0a4a13_0.conda
@@ -3522,9 +3521,9 @@ environments:
- conda: https://prefix.dev/conda-forge/noarch/toml-0.10.2-pyhd8ed1ab_1.conda
- conda: https://prefix.dev/conda-forge/noarch/tomli-2.2.1-pyhd8ed1ab_1.conda
- conda: https://prefix.dev/conda-forge/noarch/tzdata-2025a-h78e105d_0.conda
+ - pypi: git+https://github.com/data-apis/array-api-compat#fa558f21884dbccc0102f6c9a2a34d0b149100b5
- pypi: .
win-64:
- - conda: https://prefix.dev/conda-forge/noarch/array-api-compat-1.10.0-pyhd8ed1ab_0.conda
- conda: https://prefix.dev/conda-forge/noarch/array-api-strict-2.2-pyhd8ed1ab_1.conda
- conda: https://prefix.dev/conda-forge/win-64/bzip2-1.0.8-h2466b09_7.conda
- conda: https://prefix.dev/conda-forge/win-64/ca-certificates-2024.12.14-h56e8100_0.conda
@@ -3562,6 +3561,7 @@ environments:
- conda: https://prefix.dev/conda-forge/win-64/vc-14.3-h5fd82a7_24.conda
- conda: https://prefix.dev/conda-forge/win-64/vc14_runtime-14.42.34433-h6356254_24.conda
- conda: https://prefix.dev/conda-forge/win-64/vs2015_runtime-14.42.34433-hfef2bbc_24.conda
+ - pypi: git+https://github.com/data-apis/array-api-compat#fa558f21884dbccc0102f6c9a2a34d0b149100b5
- pypi: .
tests-py313:
channels:
@@ -3572,7 +3572,6 @@ environments:
linux-64:
- conda: https://prefix.dev/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2
- conda: https://prefix.dev/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2
- - conda: https://prefix.dev/conda-forge/noarch/array-api-compat-1.10.0-pyhd8ed1ab_0.conda
- conda: https://prefix.dev/conda-forge/noarch/array-api-strict-2.2-pyhd8ed1ab_1.conda
- conda: https://prefix.dev/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda
- conda: https://prefix.dev/conda-forge/linux-64/ca-certificates-2024.12.14-hbcca054_0.conda
@@ -3612,9 +3611,9 @@ environments:
- conda: https://prefix.dev/conda-forge/noarch/toml-0.10.2-pyhd8ed1ab_1.conda
- conda: https://prefix.dev/conda-forge/noarch/tomli-2.2.1-pyhd8ed1ab_1.conda
- conda: https://prefix.dev/conda-forge/noarch/tzdata-2025a-h78e105d_0.conda
+ - pypi: git+https://github.com/data-apis/array-api-compat#fa558f21884dbccc0102f6c9a2a34d0b149100b5
- pypi: .
osx-arm64:
- - conda: https://prefix.dev/conda-forge/noarch/array-api-compat-1.10.0-pyhd8ed1ab_0.conda
- conda: https://prefix.dev/conda-forge/noarch/array-api-strict-2.2-pyhd8ed1ab_1.conda
- conda: https://prefix.dev/conda-forge/osx-arm64/bzip2-1.0.8-h99b78c6_7.conda
- conda: https://prefix.dev/conda-forge/osx-arm64/ca-certificates-2024.12.14-hf0a4a13_0.conda
@@ -3650,9 +3649,9 @@ environments:
- conda: https://prefix.dev/conda-forge/noarch/toml-0.10.2-pyhd8ed1ab_1.conda
- conda: https://prefix.dev/conda-forge/noarch/tomli-2.2.1-pyhd8ed1ab_1.conda
- conda: https://prefix.dev/conda-forge/noarch/tzdata-2025a-h78e105d_0.conda
+ - pypi: git+https://github.com/data-apis/array-api-compat#fa558f21884dbccc0102f6c9a2a34d0b149100b5
- pypi: .
win-64:
- - conda: https://prefix.dev/conda-forge/noarch/array-api-compat-1.10.0-pyhd8ed1ab_0.conda
- conda: https://prefix.dev/conda-forge/noarch/array-api-strict-2.2-pyhd8ed1ab_1.conda
- conda: https://prefix.dev/conda-forge/win-64/bzip2-1.0.8-h2466b09_7.conda
- conda: https://prefix.dev/conda-forge/win-64/ca-certificates-2024.12.14-h56e8100_0.conda
@@ -3692,6 +3691,7 @@ environments:
- conda: https://prefix.dev/conda-forge/win-64/vc-14.3-h5fd82a7_24.conda
- conda: https://prefix.dev/conda-forge/win-64/vc14_runtime-14.42.34433-h6356254_24.conda
- conda: https://prefix.dev/conda-forge/win-64/vs2015_runtime-14.42.34433-hfef2bbc_24.conda
+ - pypi: git+https://github.com/data-apis/array-api-compat#fa558f21884dbccc0102f6c9a2a34d0b149100b5
- pypi: .
packages:
- conda: https://prefix.dev/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2
@@ -3753,23 +3753,21 @@ packages:
- pkg:pypi/alabaster?source=hash-mapping
size: 18684
timestamp: 1733750512696
-- conda: https://prefix.dev/conda-forge/noarch/array-api-compat-1.10.0-pyhd8ed1ab_0.conda
- sha256: c98308dcf035a413a635317c69b48143cdf4c5895853457062780395e5ea4633
- md5: e399bc184553ca13cb068d272a995f48
- depends:
- - python >=3.9
- license: MIT
- license_family: MIT
- purls:
- - pkg:pypi/array-api-compat?source=hash-mapping
- size: 38442
- timestamp: 1735201429468
+- pypi: git+https://github.com/data-apis/array-api-compat#fa558f21884dbccc0102f6c9a2a34d0b149100b5
+ name: array-api-compat
+ version: 1.10.1.dev0
+ requires_dist:
+ - cupy ; extra == 'cupy'
+ - dask ; extra == 'dask'
+ - jax ; extra == 'jax'
+ - numpy ; extra == 'numpy'
+ - pytorch ; extra == 'pytorch'
+ - sparse>=0.15.1 ; extra == 'sparse'
+ requires_python: '>=3.9'
- pypi: .
name: array-api-extra
version: 0.6.1.dev0
- sha256: bb6cd89a7f100a73d3f853de571b2f4fff0e70de8df0d113f2f5c1559744e6b6
- requires_dist:
- - array-api-compat>=1.10.0,<2
+ sha256: c806efb7ff2f1885f9194e51fbc67f00cc07a32927570d7e630459a6930b395e
requires_python: '>=3.10'
editable: true
- conda: https://prefix.dev/conda-forge/noarch/array-api-strict-2.2-pyhd8ed1ab_1.conda
diff --git a/pyproject.toml b/pyproject.toml
index d15aba84..33bbb3cd 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -26,7 +26,7 @@ classifiers = [
"Typing :: Typed",
]
dynamic = ["version"]
-dependencies = ["array-api-compat>=1.10.0,<2"]
+# dependencies = ["array-api-compat>=1.11.0,<2"] # DNM
[project.urls]
Homepage = "https://github.com/data-apis/array-api-extra"
@@ -48,10 +48,11 @@ platforms = ["linux-64", "osx-arm64", "win-64"]
[tool.pixi.dependencies]
python = ">=3.10,<3.14"
-array-api-compat = ">=1.10.0,<2"
+# array-api-compat = ">=1.11.0,<2" # DNM
[tool.pixi.pypi-dependencies]
array-api-extra = { path = ".", editable = true }
+array-api-compat = { git = "https://github.com/data-apis/array-api-compat" } # DNM
[tool.pixi.feature.lint.dependencies]
typing-extensions = "*"
diff --git a/src/array_api_extra/_lib/_funcs.py b/src/array_api_extra/_lib/_funcs.py
index f7eb8c88..ee3064b0 100644
--- a/src/array_api_extra/_lib/_funcs.py
+++ b/src/array_api_extra/_lib/_funcs.py
@@ -11,7 +11,12 @@
from ._at import at
from ._utils import _compat, _helpers
-from ._utils._compat import array_namespace, is_jax_array
+from ._utils._compat import (
+ array_namespace,
+ is_dask_namespace,
+ is_jax_array,
+ is_jax_namespace,
+)
from ._utils._helpers import asarrays
from ._utils._typing import Array
@@ -547,6 +552,8 @@ def setdiff1d(
/,
*,
assume_unique: bool = False,
+ size: int | None = None,
+ fill_value: object | None = None,
xp: ModuleType | None = None,
) -> Array:
"""
@@ -563,6 +570,16 @@ def setdiff1d(
assume_unique : bool
If ``True``, the input arrays are both assumed to be unique, which
can speed up the calculation. Default is ``False``.
+ size : int, optional
+ The size of the output array. This is exclusively used inside the JAX JIT, and
+ only for as long as JAX does not support arrays of unknown size inside it. In
+ all other cases, it is disregarded.
+ Returned elements will be clipped if they are more than size, and padded with
+ `fill_value` if they are less. Default: raise if inside ``jax.jit``.
+
+ fill_value : object, optional
+ Pad the output array with this value. This is exclusively used for JAX arrays
+ when running inside ``jax.jit``. Default: 0.
xp : array_namespace, optional
The standard-compatible namespace for `x1` and `x2`. Default: infer.
@@ -587,13 +604,90 @@ def setdiff1d(
xp = array_namespace(x1, x2)
x1, x2 = asarrays(x1, x2, xp=xp)
- if assume_unique:
- x1 = xp.reshape(x1, (-1,))
- x2 = xp.reshape(x2, (-1,))
- else:
- x1 = xp.unique_values(x1)
- x2 = xp.unique_values(x2)
- return x1[_helpers.in1d(x1, x2, assume_unique=True, invert=True, xp=xp)]
+ x1 = xp.reshape(x1, (-1,))
+ x2 = xp.reshape(x2, (-1,))
+ if x1.shape == (0,) or x2.shape == (0,):
+ return x1
+
+ def _x1_not_in_x2(x1: Array, x2: Array) -> Array: # numpydoc ignore=PR01,RT01
+ """For each element of x1, return True if it is not also in x2."""
+ # Even when assume_unique=True, there is no provision for x to be sorted
+ x2 = xp.sort(x2)
+ idx = xp.searchsorted(x2, x1)
+
+ # FIXME at() is faster but needs JAX jit support for bool mask
+ # idx = at(idx, idx == x2.shape[0]).set(0)
+ idx = xp.where(idx == x2.shape[0], xp.zeros_like(idx), idx)
+
+ return xp.take(x2, idx, axis=0) != x1
+
+ def _generic_impl(x1: Array, x2: Array) -> Array: # numpydoc ignore=PR01,RT01
+ """Generic implementation (including eager JAX)."""
+ # Note: there is no provision in the Array API for xp.unique_values to sort
+ if not assume_unique:
+ # Call unique_values early to speed up the algorithm
+ x1 = xp.unique_values(x1)
+ x2 = xp.unique_values(x2)
+ mask = _x1_not_in_x2(x1, x2)
+ x1 = x1[mask]
+ return x1 if assume_unique else xp.sort(x1)
+
+ def _dask_impl(x1: Array, x2: Array) -> Array: # numpydoc ignore=PR01,RT01
+ """
+ Dask implementation.
+
+ Works around unique_values returning unknown shapes.
+ """
+ # Do not call unique_values yet, as it would make array shapes unknown
+ mask = _x1_not_in_x2(x1, x2)
+ x1 = x1[mask]
+ # Note: da.unique_values sorts
+ return x1 if assume_unique else xp.unique_values(x1)
+
+ def _jax_jit_impl(
+ x1: Array, x2: Array, size: int | None, fill_value: object | None
+ ) -> Array: # numpydoc ignore=PR01,RT01
+ """
+ JAX implementation inside jax.jit.
+
+ Works around unique_values requiring a size= parameter
+ and not being able to filter by a boolean mask.
+ Returns array the same size as x1, padded with fill_value.
+ """
+ if size is None:
+ msg = "`size` is mandatory when running inside `jax.jit`."
+ raise ValueError(msg)
+ if fill_value is None:
+ fill_value = xp.zeros((), dtype=x1.dtype)
+ else:
+ fill_value = xp.asarray(fill_value, dtype=x1.dtype)
+ if cast(Array, fill_value).ndim != 0:
+ msg = "`fill_value` must be a scalar."
+ raise ValueError(msg)
+
+ # unique_values inside jax.jit is not supported unless it's got a fixed size
+ mask = _x1_not_in_x2(x1, x2)
+ x1 = xp.where(mask, x1, fill_value)
+ # Move fill_value to the right
+ x1 = xp.take(x1, xp.argsort(~mask, stable=True))
+ x1 = x1[:size]
+ x1 = xp.unique_values(x1, size=size, fill_value=fill_value)
+
+ if is_dask_namespace(xp):
+ return _dask_impl(x1, x2)
+
+ if is_jax_namespace(xp):
+ import jax
+
+ try:
+ return _generic_impl(x1, x2) # eager mode
+ except (
+ jax.errors.ConcretizationTypeError,
+ jax.errors.NonConcreteBooleanIndexError,
+ ):
+ return _jax_jit_impl(x1, x2, size, fill_value) # inside jax.jit
+
+ return _generic_impl(x1, x2)
def sinc(x: Array, /, *, xp: ModuleType | None = None) -> Array:
diff --git a/src/array_api_extra/_lib/_utils/_helpers.py b/src/array_api_extra/_lib/_utils/_helpers.py
index b32a1081..62ff0116 100644
--- a/src/array_api_extra/_lib/_utils/_helpers.py
+++ b/src/array_api_extra/_lib/_utils/_helpers.py
@@ -10,66 +10,7 @@
from ._compat import is_array_api_obj, is_numpy_array
from ._typing import Array
-__all__ = ["in1d", "mean"]
-
-
-def in1d(
- x1: Array,
- x2: Array,
- /,
- *,
- assume_unique: bool = False,
- invert: bool = False,
- xp: ModuleType | None = None,
-) -> Array: # numpydoc ignore=PR01,RT01
- """
- Check whether each element of an array is also present in a second array.
-
- Returns a boolean array the same length as `x1` that is True
- where an element of `x1` is in `x2` and False otherwise.
-
- This function has been adapted using the original implementation
- present in numpy:
- https://github.com/numpy/numpy/blob/v1.26.0/numpy/lib/arraysetops.py#L524-L758
- """
- if xp is None:
- xp = _compat.array_namespace(x1, x2)
-
- # This code is run to make the code significantly faster
- if x2.shape[0] < 10 * x1.shape[0] ** 0.145:
- if invert:
- mask = xp.ones(x1.shape[0], dtype=xp.bool, device=_compat.device(x1))
- for a in x2:
- mask &= x1 != a
- else:
- mask = xp.zeros(x1.shape[0], dtype=xp.bool, device=_compat.device(x1))
- for a in x2:
- mask |= x1 == a
- return mask
-
- rev_idx = xp.empty(0) # placeholder
- if not assume_unique:
- x1, rev_idx = xp.unique_inverse(x1)
- x2 = xp.unique_values(x2)
-
- ar = xp.concat((x1, x2))
- device_ = _compat.device(ar)
- # We need this to be a stable sort.
- order = xp.argsort(ar, stable=True)
- reverse_order = xp.argsort(order, stable=True)
- sar = xp.take(ar, order, axis=0)
- ar_size = _compat.size(sar)
- assert ar_size is not None, "xp.unique*() on lazy backends raises"
- if ar_size >= 1:
- bool_ar = sar[1:] != sar[:-1] if invert else sar[1:] == sar[:-1]
- else:
- bool_ar = xp.asarray([False]) if invert else xp.asarray([True])
- flag = xp.concat((bool_ar, xp.asarray([invert], device=device_)))
- ret = xp.take(flag, reverse_order, axis=0)
-
- if assume_unique:
- return ret[: x1.shape[0]]
- return xp.take(ret, rev_idx, axis=0)
+__all__ = ["mean"]
def mean(
diff --git a/tests/test_funcs.py b/tests/test_funcs.py
index 2c265b23..de8a6fd0 100644
--- a/tests/test_funcs.py
+++ b/tests/test_funcs.py
@@ -35,8 +35,7 @@
lazy_xp_function(kron, static_argnames="xp")
lazy_xp_function(nunique, static_argnames="xp")
lazy_xp_function(pad, static_argnames=("pad_width", "mode", "constant_values", "xp"))
-# FIXME calls in1d which calls xp.unique_values without size
-lazy_xp_function(setdiff1d, jax_jit=False, static_argnames=("assume_unique", "xp"))
+lazy_xp_function(setdiff1d, static_argnames=("assume_unique", "xp"))
# FIXME .device attribute https://github.com/data-apis/array-api-compat/pull/238
lazy_xp_function(sinc, jax_jit=False, static_argnames="xp")
@@ -576,8 +575,7 @@ def test_sequence_of_tuples_width(self, xp: ModuleType):
assert padded.shape == (4, 4)
-@pytest.mark.skip_xp_backend(Backend.DASK, reason="no argsort")
-@pytest.mark.skip_xp_backend(Backend.SPARSE, reason="no device kwarg in asarray")
+@pytest.mark.skip_xp_backend(Backend.SPARSE, reason="no sort")
class TestSetDiff1D:
@pytest.mark.skip_xp_backend(
Backend.TORCH, reason="index_select not implemented for uint32"
diff --git a/tests/test_utils.py b/tests/test_utils.py
index d9f50362..ab5f3589 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -4,49 +4,10 @@
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
+from array_api_extra._lib._utils._helpers import asarrays
# 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.skip_xp_backend(Backend.DASK, reason="no argsort")
- @pytest.mark.skip_xp_backend(
- Backend.SPARSE, reason="no unique_inverse, no device kwarg in asarray"
- )
- # cover both code paths
- @pytest.mark.parametrize("n", [9, 15])
- 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.skip_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="explicit xp")
- @pytest.mark.skip_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)
-
@pytest.mark.skip_xp_backend(Backend.SPARSE, reason="no isdtype")
@pytest.mark.parametrize(
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