diff --git a/pixi.lock b/pixi.lock
index f94d86a8..217da68f 100644
--- a/pixi.lock
+++ b/pixi.lock
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- python >=3.12,<3.13.0a0
- python >=3.12,<3.13.0a0 *_cpython
- python_abi 3.12.* *_cp312
constrains:
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license: Apache-2.0
license_family: APACHE
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+ - libarrow 19.0.0.* *cpu
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- python_abi 3.10.* *_cp310
@@ -11856,20 +12020,20 @@ packages:
- vc >=14.2,<15
- vc14_runtime >=14.29.30139
constrains:
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- numpy >=1.21,<3
+ - apache-arrow-proc =*=cpu
license: Apache-2.0
license_family: APACHE
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+ size: 3463874
+ timestamp: 1737128591552
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+ md5: 235c1ddd76763dbfade3170d93d15ef6
depends:
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- - libarrow 18.1.0.* *cuda
+ - libarrow 19.0.0.* *cuda
- libzlib >=1.3.1,<2.0a0
- python >=3.10,<3.11.0a0
- python_abi 3.10.* *_cp310
@@ -11877,20 +12041,20 @@ packages:
- vc >=14.2,<15
- vc14_runtime >=14.29.30139
constrains:
- - apache-arrow-proc =*=cuda
- numpy >=1.21,<3
+ - apache-arrow-proc =*=cuda
license: Apache-2.0
license_family: APACHE
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+ size: 3514276
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+ md5: 7f28d61c0af46c21ab16bef8a3e2c21e
depends:
- __cuda >=11.8
- - libarrow 18.1.0.* *cuda
+ - libarrow 19.0.0.* *cuda
- libzlib >=1.3.1,<2.0a0
- python >=3.12,<3.13.0a0
- python_abi 3.12.* *_cp312
@@ -11898,19 +12062,19 @@ packages:
- vc >=14.2,<15
- vc14_runtime >=14.29.30139
constrains:
- - apache-arrow-proc =*=cuda
- numpy >=1.21,<3
+ - apache-arrow-proc =*=cuda
license: Apache-2.0
license_family: APACHE
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+ size: 3537440
+ timestamp: 1737129453444
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+ md5: 847470f1fbc5bdd9bb14d0646f681ea5
depends:
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+ - libarrow 19.0.0.* *cpu
- libzlib >=1.3.1,<2.0a0
- python >=3.12,<3.13.0a0
- python_abi 3.12.* *_cp312
@@ -11924,8 +12088,8 @@ packages:
license_family: APACHE
purls:
- pkg:pypi/pyarrow?source=hash-mapping
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- timestamp: 1732651918640
+ size: 3478814
+ timestamp: 1737128361782
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sha256: 79db7928d13fab2d892592223d7570f5061c192f27b9febd1a418427b719acc6
md5: 12c566707c80111f9799308d9e265aef
@@ -12969,9 +13133,9 @@ packages:
- pkg:pypi/requests?source=hash-mapping
size: 58723
timestamp: 1733217126197
-- conda: https://prefix.dev/conda-forge/linux-64/s2n-1.5.10-hb5b8611_0.conda
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- md5: 999f3673f2a011f59287f2969e3749e4
+- conda: https://prefix.dev/conda-forge/linux-64/s2n-1.5.11-h072c03f_0.conda
+ sha256: cfdd98c8f9a1e5b6f9abce5dac6d590cc9fe541a08466c9e4a26f90e00b569e3
+ md5: 5e8060d52f676a40edef0006a75c718f
depends:
- __glibc >=2.17,<3.0.a0
- libgcc >=13
@@ -12979,8 +13143,8 @@ packages:
license: Apache-2.0
license_family: Apache
purls: []
- size: 355142
- timestamp: 1734415467047
+ size: 356213
+ timestamp: 1737146304079
- conda: https://prefix.dev/conda-forge/linux-64/scipy-1.15.1-py310hfa6ec8c_0.conda
sha256: 9941f3bc9af712e60ce7b3910f9da0298f6b6f4c0b4fbc85f43b3db6342e21e4
md5: a24baa04ee53ee3078ac1856887c3dea
@@ -13123,7 +13287,7 @@ packages:
license: MIT
license_family: MIT
purls:
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+ - pkg:pypi/setuptools?source=hash-mapping
size: 775598
timestamp: 1736512753595
- conda: https://prefix.dev/conda-forge/noarch/six-1.17.0-pyhd8ed1ab_0.conda
@@ -13249,6 +13413,7 @@ packages:
- numba >=0.49
- python
license: BSD-3-Clause
+ license_family: BSD
purls: []
size: 98482
timestamp: 1736951003899
@@ -13280,9 +13445,9 @@ packages:
- pkg:pypi/sphinx?source=hash-mapping
size: 1387076
timestamp: 1733754175386
-- conda: https://prefix.dev/conda-forge/noarch/sphinx-autodoc-typehints-3.0.0-pyhd8ed1ab_0.conda
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- md5: 243019ab35941dc825817a5ee4fd7ef4
+- conda: https://prefix.dev/conda-forge/noarch/sphinx-autodoc-typehints-3.0.1-pyhd8ed1ab_0.conda
+ sha256: 0f93bb75a41918433abc8d8d80ef99d7fd8658d5ba34da3c5d8f707cb6bb3f46
+ md5: 6ad405d62c8de3792608a27b7e085e15
depends:
- python >=3.10
- sphinx >=8.1.3
@@ -13290,8 +13455,8 @@ packages:
license_family: MIT
purls:
- pkg:pypi/sphinx-autodoc-typehints?source=hash-mapping
- size: 23860
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+ size: 24055
+ timestamp: 1737099757820
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sha256: 091293964075ed1905731d09ff2691e053cd9d5335d99501f05683da29de0ee7
md5: 463d989a8f1506bcf51cc37d7beebdf1
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+- conda: https://prefix.dev/conda-forge/noarch/tzdata-2025a-h78e105d_0.conda
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license: LicenseRef-Public-Domain
purls: []
- size: 122354
- timestamp: 1728047496079
+ size: 122921
+ timestamp: 1737119101255
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sha256: db8dead3dd30fb1a032737554ce91e2819b43496a0db09927edf01c32b577450
md5: 6797b005cd0f439c4c5c9ac565783700
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purls: []
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- md5: 680b1c287b10cefc8bda0530b217229f
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+ md5: de06336c9833cffd2a4bd6f27c4cf8ea
depends:
- distlib >=0.3.7,<1
- filelock >=3.12.2,<4
@@ -13843,9 +14008,9 @@ packages:
license: MIT
license_family: MIT
purls:
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- size: 3350367
- timestamp: 1735929107438
+ - pkg:pypi/virtualenv?source=compressed-mapping
+ size: 3501167
+ timestamp: 1737145224475
- conda: https://prefix.dev/conda-forge/win-64/vs2015_runtime-14.42.34433-hdffcdeb_23.conda
sha256: 568ce8151eaae256f1cef752fc78651ad7a86ff05153cc7a4740b52ae6536118
md5: 5c176975ca2b8366abad3c97b3cd1e83
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purls: []
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- md5: c79cea50b258f652010cb6c8d81591b5
+- conda: https://prefix.dev/conda-forge/noarch/xyzservices-2025.1.0-pyhd8ed1ab_0.conda
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+ md5: fdf07e281a9e5e10fc75b2dd444136e9
depends:
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license: BSD-3-Clause
license_family: BSD
purls:
- pkg:pypi/xyzservices?source=hash-mapping
- size: 46860
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+ size: 48641
+ timestamp: 1737234992057
- conda: https://prefix.dev/conda-forge/linux-64/yaml-0.2.5-h7f98852_2.tar.bz2
sha256: a4e34c710eeb26945bdbdaba82d3d74f60a78f54a874ec10d373811a5d217535
md5: 4cb3ad778ec2d5a7acbdf254eb1c42ae
diff --git a/pyproject.toml b/pyproject.toml
index 1ad1ca1c..d6ea9805 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -167,7 +167,7 @@ cupy = "*"
[tool.pixi.environments]
default = { solve-group = "default" }
-lint = { features = ["lint"], solve-group = "default" }
+lint = { features = ["lint", "backends"], solve-group = "default" }
tests = { features = ["tests"], solve-group = "default" }
docs = { features = ["docs"], solve-group = "default" }
dev = { features = ["lint", "tests", "docs", "dev", "backends"], solve-group = "default" }
diff --git a/src/array_api_extra/__init__.py b/src/array_api_extra/__init__.py
index 360097f7..65d2f738 100644
--- a/src/array_api_extra/__init__.py
+++ b/src/array_api_extra/__init__.py
@@ -1,8 +1,8 @@
"""Extra array functions built on top of the array API standard."""
from ._delegation import pad
+from ._lib._at import at
from ._lib._funcs import (
- at,
atleast_nd,
cov,
create_diagonal,
diff --git a/src/array_api_extra/_lib/_at.py b/src/array_api_extra/_lib/_at.py
new file mode 100644
index 00000000..f2dd7219
--- /dev/null
+++ b/src/array_api_extra/_lib/_at.py
@@ -0,0 +1,373 @@
+"""Update operations for read-only arrays."""
+
+# https://github.com/scikit-learn/scikit-learn/pull/27910#issuecomment-2568023972
+from __future__ import annotations
+
+import operator
+from collections.abc import Callable
+from enum import Enum
+from types import ModuleType
+from typing import ClassVar, cast
+
+from ._utils._compat import array_namespace, is_jax_array, is_writeable_array
+from ._utils._typing import Array, Index
+
+
+class _AtOp(Enum):
+ """Operations for use in `xpx.at`."""
+
+ SET = "set"
+ ADD = "add"
+ SUBTRACT = "subtract"
+ MULTIPLY = "multiply"
+ DIVIDE = "divide"
+ POWER = "power"
+ MIN = "min"
+ MAX = "max"
+
+ # @override from Python 3.12
+ def __str__(self) -> str: # type: ignore[explicit-override] # pyright: ignore[reportImplicitOverride]
+ """
+ Return string representation (useful for pytest logs).
+
+ Returns
+ -------
+ str
+ The operation's name.
+ """
+ return self.value
+
+
+_undef = object()
+
+
+class at: # pylint: disable=invalid-name # numpydoc ignore=PR02
+ """
+ Update operations for read-only arrays.
+
+ This implements ``jax.numpy.ndarray.at`` for all writeable
+ backends (those that support ``__setitem__``) and routes
+ to the ``.at[]`` method for JAX arrays.
+
+ Parameters
+ ----------
+ x : array
+ Input array.
+ idx : index, optional
+ Only `array API standard compliant indices
+ <https://data-apis.org/array-api/latest/API_specification/indexing.html>`_
+ are supported.
+
+ You may use two alternate syntaxes::
+
+ >>> import array_api_extra as xpx
+ >>> xpx.at(x, idx).set(value) # or add(value), etc.
+ >>> xpx.at(x)[idx].set(value)
+
+ copy : bool, optional
+ None (default)
+ The array parameter *may* be modified in place if it is
+ possible and beneficial for performance.
+ You should not reuse it after calling this function.
+ True
+ Ensure that the inputs are not modified.
+ False
+ Ensure that the update operation writes back to the input.
+ Raise ``ValueError`` if a copy cannot be avoided.
+
+ xp : array_namespace, optional
+ The standard-compatible namespace for `x`. Default: infer.
+
+ Returns
+ -------
+ Updated input array.
+
+ Warnings
+ --------
+ (a) When you omit the ``copy`` parameter, you should never reuse the parameter
+ array later on; ideally, you should reassign it immediately::
+
+ >>> import array_api_extra as xpx
+ >>> x = xpx.at(x, 0).set(2)
+
+ The above best practice pattern ensures that the behaviour won't change depending
+ on whether ``x`` is writeable or not, as the original ``x`` object is dereferenced
+ as soon as ``xpx.at`` returns; this way there is no risk to accidentally update it
+ twice.
+
+ On the reverse, the anti-pattern below must be avoided, as it will result in
+ different behaviour on read-only versus writeable arrays::
+
+ >>> x = xp.asarray([0, 0, 0])
+ >>> y = xpx.at(x, 0).set(2)
+ >>> z = xpx.at(x, 1).set(3)
+
+ In the above example, both calls to ``xpx.at`` update ``x`` in place *if possible*.
+ This causes the behaviour to diverge depending on whether ``x`` is writeable or not:
+
+ - If ``x`` is writeable, then after the snippet above you'll have
+ ``x == y == z == [2, 3, 0]``
+ - If ``x`` is read-only, then you'll end up with
+ ``x == [0, 0, 0]``, ``y == [2, 0, 0]`` and ``z == [0, 3, 0]``.
+
+ The correct pattern to use if you want diverging outputs from the same input is
+ to enforce copies::
+
+ >>> x = xp.asarray([0, 0, 0])
+ >>> y = xpx.at(x, 0).set(2, copy=True) # Never updates x
+ >>> z = xpx.at(x, 1).set(3) # May or may not update x in place
+ >>> del x # avoid accidental reuse of x as we don't know its state anymore
+
+ (b) The array API standard does not support integer array indices.
+ The behaviour of update methods when the index is an array of integers is
+ undefined and will vary between backends; this is particularly true when the
+ index contains multiple occurrences of the same index, e.g.::
+
+ >>> import numpy as np
+ >>> import jax.numpy as jnp
+ >>> import array_api_extra as xpx
+ >>> xpx.at(np.asarray([123]), np.asarray([0, 0])).add(1)
+ array([124])
+ >>> xpx.at(jnp.asarray([123]), jnp.asarray([0, 0])).add(1)
+ Array([125], dtype=int32)
+
+ See Also
+ --------
+ jax.numpy.ndarray.at : Equivalent array method in JAX.
+
+ Notes
+ -----
+ `sparse <https://sparse.pydata.org/>`_, as well as read-only arrays from libraries
+ not explicitly covered by ``array-api-compat``, are not supported by update
+ methods.
+
+ Examples
+ --------
+ Given either of these equivalent expressions::
+
+ >>> import array_api_extra as xpx
+ >>> x = xpx.at(x)[1].add(2)
+ >>> x = xpx.at(x, 1).add(2)
+
+ If x is a JAX array, they are the same as::
+
+ >>> x = x.at[1].add(2)
+
+ If x is a read-only numpy array, they are the same as::
+
+ >>> x = x.copy()
+ >>> x[1] += 2
+
+ For other known backends, they are the same as::
+
+ >>> x[1] += 2
+ """
+
+ _x: Array
+ _idx: Index
+ __slots__: ClassVar[tuple[str, ...]] = ("_idx", "_x")
+
+ def __init__(
+ self, x: Array, idx: Index = _undef, /
+ ) -> None: # numpydoc ignore=GL08
+ self._x = x
+ self._idx = idx
+
+ def __getitem__(self, idx: Index, /) -> at: # numpydoc ignore=PR01,RT01
+ """
+ Allow for the alternate syntax ``at(x)[start:stop:step]``.
+
+ It looks prettier than ``at(x, slice(start, stop, step))``
+ and feels more intuitive coming from the JAX documentation.
+ """
+ if self._idx is not _undef:
+ msg = "Index has already been set"
+ raise ValueError(msg)
+ return at(self._x, idx)
+
+ def _update_common(
+ self,
+ at_op: _AtOp,
+ y: Array,
+ /,
+ copy: bool | None,
+ xp: ModuleType | None,
+ ) -> tuple[Array, None] | tuple[None, Array]: # numpydoc ignore=PR01
+ """
+ Perform common prepocessing to all update operations.
+
+ Returns
+ -------
+ tuple
+ If the operation can be resolved by ``at[]``, ``(return value, None)``
+ Otherwise, ``(None, preprocessed x)``.
+ """
+ x, idx = self._x, self._idx
+
+ if idx is _undef:
+ msg = (
+ "Index has not been set.\n"
+ "Usage: either\n"
+ " at(x, idx).set(value)\n"
+ "or\n"
+ " at(x)[idx].set(value)\n"
+ "(same for all other methods)."
+ )
+ raise ValueError(msg)
+
+ if copy not in (True, False, None):
+ msg = f"copy must be True, False, or None; got {copy!r}"
+ raise ValueError(msg)
+
+ if copy is None:
+ writeable = is_writeable_array(x)
+ copy = not writeable
+ elif copy:
+ writeable = None
+ else:
+ writeable = is_writeable_array(x)
+
+ if copy:
+ if is_jax_array(x):
+ # Use JAX's at[]
+ func = cast(Callable[[Array], Array], getattr(x.at[idx], at_op.value))
+ return func(y), None
+ # Emulate at[] behaviour for non-JAX arrays
+ # with a copy followed by an update
+ if xp is None:
+ xp = array_namespace(x)
+ x = xp.asarray(x, copy=True)
+ if writeable is False:
+ # A copy of a read-only numpy array is writeable
+ # Note: this assumes that a copy of a writeable array is writeable
+ writeable = None
+
+ if writeable is None:
+ writeable = is_writeable_array(x)
+ if not writeable:
+ # sparse crashes here
+ msg = f"Can't update read-only array {x}"
+ raise ValueError(msg)
+
+ return None, x
+
+ def set(
+ self,
+ y: Array,
+ /,
+ copy: bool | None = None,
+ xp: ModuleType | None = None,
+ ) -> Array: # numpydoc ignore=PR01,RT01
+ """Apply ``x[idx] = y`` and return the update array."""
+ res, x = self._update_common(_AtOp.SET, y, copy=copy, xp=xp)
+ if res is not None:
+ return res
+ assert x is not None
+ x[self._idx] = y
+ return x
+
+ def _iop(
+ self,
+ at_op: _AtOp,
+ elwise_op: Callable[[Array, Array], Array],
+ y: Array,
+ /,
+ copy: bool | None,
+ xp: ModuleType | None,
+ ) -> Array: # numpydoc ignore=PR01,RT01
+ """
+ ``x[idx] += y`` or equivalent in-place operation on a subset of x.
+
+ which is the same as saying
+ x[idx] = x[idx] + y
+ Note that this is not the same as
+ operator.iadd(x[idx], y)
+ Consider for example when x is a numpy array and idx is a fancy index, which
+ triggers a deep copy on __getitem__.
+ """
+ res, x = self._update_common(at_op, y, copy=copy, xp=xp)
+ if res is not None:
+ return res
+ assert x is not None
+ x[self._idx] = elwise_op(x[self._idx], y)
+ return x
+
+ def add(
+ self,
+ y: Array,
+ /,
+ copy: bool | None = None,
+ xp: ModuleType | None = None,
+ ) -> Array: # numpydoc ignore=PR01,RT01
+ """Apply ``x[idx] += y`` and return the updated array."""
+
+ # Note for this and all other methods based on _iop:
+ # operator.iadd and operator.add subtly differ in behaviour, as
+ # only iadd will trigger exceptions when y has an incompatible dtype.
+ return self._iop(_AtOp.ADD, operator.iadd, y, copy=copy, xp=xp)
+
+ def subtract(
+ self,
+ y: Array,
+ /,
+ copy: bool | None = None,
+ xp: ModuleType | None = None,
+ ) -> Array: # numpydoc ignore=PR01,RT01
+ """Apply ``x[idx] -= y`` and return the updated array."""
+ return self._iop(_AtOp.SUBTRACT, operator.isub, y, copy=copy, xp=xp)
+
+ def multiply(
+ self,
+ y: Array,
+ /,
+ copy: bool | None = None,
+ xp: ModuleType | None = None,
+ ) -> Array: # numpydoc ignore=PR01,RT01
+ """Apply ``x[idx] *= y`` and return the updated array."""
+ return self._iop(_AtOp.MULTIPLY, operator.imul, y, copy=copy, xp=xp)
+
+ def divide(
+ self,
+ y: Array,
+ /,
+ copy: bool | None = None,
+ xp: ModuleType | None = None,
+ ) -> Array: # numpydoc ignore=PR01,RT01
+ """Apply ``x[idx] /= y`` and return the updated array."""
+ return self._iop(_AtOp.DIVIDE, operator.itruediv, y, copy=copy, xp=xp)
+
+ def power(
+ self,
+ y: Array,
+ /,
+ copy: bool | None = None,
+ xp: ModuleType | None = None,
+ ) -> Array: # numpydoc ignore=PR01,RT01
+ """Apply ``x[idx] **= y`` and return the updated array."""
+ return self._iop(_AtOp.POWER, operator.ipow, y, copy=copy, xp=xp)
+
+ def min(
+ self,
+ y: Array,
+ /,
+ copy: bool | None = None,
+ xp: ModuleType | None = None,
+ ) -> Array: # numpydoc ignore=PR01,RT01
+ """Apply ``x[idx] = minimum(x[idx], y)`` and return the updated array."""
+ if xp is None:
+ xp = array_namespace(self._x)
+ y = xp.asarray(y)
+ return self._iop(_AtOp.MIN, xp.minimum, y, copy=copy, xp=xp)
+
+ def max(
+ self,
+ y: Array,
+ /,
+ copy: bool | None = None,
+ xp: ModuleType | None = None,
+ ) -> Array: # numpydoc ignore=PR01,RT01
+ """Apply ``x[idx] = maximum(x[idx], y)`` and return the updated array."""
+ if xp is None:
+ xp = array_namespace(self._x)
+ y = xp.asarray(y)
+ return self._iop(_AtOp.MAX, xp.maximum, y, copy=copy, xp=xp)
diff --git a/src/array_api_extra/_lib/_funcs.py b/src/array_api_extra/_lib/_funcs.py
index 320e5455..85778356 100644
--- a/src/array_api_extra/_lib/_funcs.py
+++ b/src/array_api_extra/_lib/_funcs.py
@@ -4,19 +4,16 @@
from __future__ import annotations
import math
-import operator
import warnings
-from collections.abc import Callable
-from enum import Enum
from types import ModuleType
-from typing import ClassVar, cast
+from typing import cast
+from ._at import at
from ._utils import _compat, _helpers
-from ._utils._compat import array_namespace, is_jax_array, is_writeable_array
-from ._utils._typing import Array, Index
+from ._utils._compat import array_namespace, is_jax_array
+from ._utils._typing import Array
__all__ = [
- "at",
"atleast_nd",
"cov",
"create_diagonal",
@@ -413,6 +410,87 @@ def kron(a: Array, b: Array, /, *, xp: ModuleType | None = None) -> Array:
return xp.reshape(result, res_shape)
+def nunique(x: Array, /, *, xp: ModuleType | None = None) -> Array:
+ """
+ Count the number of unique elements in an array.
+
+ Compatible with JAX and Dask, whose laziness would be otherwise
+ problematic.
+
+ Parameters
+ ----------
+ x : Array
+ Input array.
+ xp : array_namespace, optional
+ The standard-compatible namespace for `x`. Default: infer.
+
+ Returns
+ -------
+ array: 0-dimensional integer array
+ The number of unique elements in `x`. It can be lazy.
+ """
+ if xp is None:
+ xp = array_namespace(x)
+
+ if is_jax_array(x):
+ # size= is JAX-specific
+ # https://github.com/data-apis/array-api/issues/883
+ _, counts = xp.unique_counts(x, size=_compat.size(x))
+ return xp.astype(counts, xp.bool).sum()
+
+ _, counts = xp.unique_counts(x)
+ n = _compat.size(counts)
+ # FIXME https://github.com/data-apis/array-api-compat/pull/231
+ if n is None or math.isnan(n): # e.g. Dask, ndonnx
+ return xp.astype(counts, xp.bool).sum()
+ return xp.asarray(n, device=_compat.device(x))
+
+
+def pad(
+ x: Array,
+ pad_width: int | tuple[int, int] | list[tuple[int, int]],
+ *,
+ constant_values: bool | int | float | complex = 0,
+ xp: ModuleType,
+) -> Array: # numpydoc ignore=PR01,RT01
+ """See docstring in `array_api_extra._delegation.py`."""
+ # make pad_width a list of length-2 tuples of ints
+ x_ndim = cast(int, x.ndim)
+ if isinstance(pad_width, int):
+ pad_width = [(pad_width, pad_width)] * x_ndim
+ if isinstance(pad_width, tuple):
+ pad_width = [pad_width] * x_ndim
+
+ # https://github.com/python/typeshed/issues/13376
+ slices: list[slice] = [] # type: ignore[no-any-explicit]
+ newshape: list[int] = []
+ for ax, w_tpl in enumerate(pad_width):
+ if len(w_tpl) != 2:
+ msg = f"expect a 2-tuple (before, after), got {w_tpl}."
+ raise ValueError(msg)
+
+ sh = x.shape[ax]
+ if w_tpl[0] == 0 and w_tpl[1] == 0:
+ sl = slice(None, None, None)
+ else:
+ start, stop = w_tpl
+ stop = None if stop == 0 else -stop
+
+ sl = slice(start, stop, None)
+ sh += w_tpl[0] + w_tpl[1]
+
+ newshape.append(sh)
+ slices.append(sl)
+
+ padded = xp.full(
+ tuple(newshape),
+ fill_value=constant_values,
+ dtype=x.dtype,
+ device=_compat.device(x),
+ )
+ return at(padded, tuple(slices)).set(x)
+
+
def setdiff1d(
x1: Array,
x2: Array,
@@ -553,444 +631,3 @@ def sinc(x: Array, /, *, xp: ModuleType | None = None) -> Array:
xp.asarray(xp.finfo(x.dtype).eps, dtype=x.dtype, device=_compat.device(x)),
)
return xp.sin(y) / y
-
-
-def pad(
- x: Array,
- pad_width: int | tuple[int, int] | list[tuple[int, int]],
- *,
- constant_values: bool | int | float | complex = 0,
- xp: ModuleType,
-) -> Array: # numpydoc ignore=PR01,RT01
- """See docstring in `array_api_extra._delegation.py`."""
- # make pad_width a list of length-2 tuples of ints
- x_ndim = cast(int, x.ndim)
- if isinstance(pad_width, int):
- pad_width = [(pad_width, pad_width)] * x_ndim
- if isinstance(pad_width, tuple):
- pad_width = [pad_width] * x_ndim
-
- # https://github.com/python/typeshed/issues/13376
- slices: list[slice] = [] # type: ignore[no-any-explicit]
- newshape: list[int] = []
- for ax, w_tpl in enumerate(pad_width):
- if len(w_tpl) != 2:
- msg = f"expect a 2-tuple (before, after), got {w_tpl}."
- raise ValueError(msg)
-
- sh = x.shape[ax]
- if w_tpl[0] == 0 and w_tpl[1] == 0:
- sl = slice(None, None, None)
- else:
- start, stop = w_tpl
- stop = None if stop == 0 else -stop
-
- sl = slice(start, stop, None)
- sh += w_tpl[0] + w_tpl[1]
-
- newshape.append(sh)
- slices.append(sl)
-
- padded = xp.full(
- tuple(newshape),
- fill_value=constant_values,
- dtype=x.dtype,
- device=_compat.device(x),
- )
- return at(padded, tuple(slices)).set(x)
-
-
-def nunique(x: Array, /, *, xp: ModuleType | None = None) -> Array:
- """
- Count the number of unique elements in an array.
-
- Compatible with JAX and Dask, whose laziness would be otherwise
- problematic.
-
- Parameters
- ----------
- x : Array
- Input array.
- xp : array_namespace, optional
- The standard-compatible namespace for `x`. Default: infer.
-
- Returns
- -------
- array: 0-dimensional integer array
- The number of unique elements in `x`. It can be lazy.
- """
- if xp is None:
- xp = array_namespace(x)
-
- if is_jax_array(x):
- # size= is JAX-specific
- # https://github.com/data-apis/array-api/issues/883
- _, counts = xp.unique_counts(x, size=_compat.size(x))
- return xp.astype(counts, xp.bool).sum()
-
- _, counts = xp.unique_counts(x)
- n = _compat.size(counts)
- # FIXME https://github.com/data-apis/array-api-compat/pull/231
- if n is None or math.isnan(n): # e.g. Dask, ndonnx
- return xp.astype(counts, xp.bool).sum()
- return xp.asarray(n, device=_compat.device(x))
-
-
-class _AtOp(Enum):
- """Operations for use in `xpx.at`."""
-
- SET = "set"
- ADD = "add"
- SUBTRACT = "subtract"
- MULTIPLY = "multiply"
- DIVIDE = "divide"
- POWER = "power"
- MIN = "min"
- MAX = "max"
-
- # @override from Python 3.12
- def __str__(self) -> str: # type: ignore[explicit-override] # pyright: ignore[reportImplicitOverride]
- """
- Return string representation (useful for pytest logs).
-
- Returns
- -------
- str
- The operation's name.
- """
- return self.value
-
-
-_undef = object()
-
-
-class at: # pylint: disable=invalid-name # numpydoc ignore=PR02
- """
- Update operations for read-only arrays.
-
- This implements ``jax.numpy.ndarray.at`` for all writeable
- backends (those that support ``__setitem__``) and routes
- to the ``.at[]`` method for JAX arrays.
-
- Parameters
- ----------
- x : array
- Input array.
- idx : index, optional
- Only `array API standard compliant indices
- <https://data-apis.org/array-api/latest/API_specification/indexing.html>`_
- are supported.
-
- You may use two alternate syntaxes::
-
- >>> import array_api_extra as xpx
- >>> xpx.at(x, idx).set(value) # or add(value), etc.
- >>> xpx.at(x)[idx].set(value)
-
- copy : bool, optional
- None (default)
- The array parameter *may* be modified in place if it is
- possible and beneficial for performance.
- You should not reuse it after calling this function.
- True
- Ensure that the inputs are not modified.
- False
- Ensure that the update operation writes back to the input.
- Raise ``ValueError`` if a copy cannot be avoided.
-
- xp : array_namespace, optional
- The standard-compatible namespace for `x`. Default: infer.
-
- Returns
- -------
- Updated input array.
-
- Warnings
- --------
- (a) When you omit the ``copy`` parameter, you should never reuse the parameter
- array later on; ideally, you should reassign it immediately::
-
- >>> import array_api_extra as xpx
- >>> x = xpx.at(x, 0).set(2)
-
- The above best practice pattern ensures that the behaviour won't change depending
- on whether ``x`` is writeable or not, as the original ``x`` object is dereferenced
- as soon as ``xpx.at`` returns; this way there is no risk to accidentally update it
- twice.
-
- On the reverse, the anti-pattern below must be avoided, as it will result in
- different behaviour on read-only versus writeable arrays::
-
- >>> x = xp.asarray([0, 0, 0])
- >>> y = xpx.at(x, 0).set(2)
- >>> z = xpx.at(x, 1).set(3)
-
- In the above example, both calls to ``xpx.at`` update ``x`` in place *if possible*.
- This causes the behaviour to diverge depending on whether ``x`` is writeable or not:
-
- - If ``x`` is writeable, then after the snippet above you'll have
- ``x == y == z == [2, 3, 0]``
- - If ``x`` is read-only, then you'll end up with
- ``x == [0, 0, 0]``, ``y == [2, 0, 0]`` and ``z == [0, 3, 0]``.
-
- The correct pattern to use if you want diverging outputs from the same input is
- to enforce copies::
-
- >>> x = xp.asarray([0, 0, 0])
- >>> y = xpx.at(x, 0).set(2, copy=True) # Never updates x
- >>> z = xpx.at(x, 1).set(3) # May or may not update x in place
- >>> del x # avoid accidental reuse of x as we don't know its state anymore
-
- (b) The array API standard does not support integer array indices.
- The behaviour of update methods when the index is an array of integers is
- undefined and will vary between backends; this is particularly true when the
- index contains multiple occurrences of the same index, e.g.::
-
- >>> import numpy as np
- >>> import jax.numpy as jnp
- >>> import array_api_extra as xpx
- >>> xpx.at(np.asarray([123]), np.asarray([0, 0])).add(1)
- array([124])
- >>> xpx.at(jnp.asarray([123]), jnp.asarray([0, 0])).add(1)
- Array([125], dtype=int32)
-
- See Also
- --------
- jax.numpy.ndarray.at : Equivalent array method in JAX.
-
- Notes
- -----
- `sparse <https://sparse.pydata.org/>`_, as well as read-only arrays from libraries
- not explicitly covered by ``array-api-compat``, are not supported by update
- methods.
-
- Examples
- --------
- Given either of these equivalent expressions::
-
- >>> import array_api_extra as xpx
- >>> x = xpx.at(x)[1].add(2)
- >>> x = xpx.at(x, 1).add(2)
-
- If x is a JAX array, they are the same as::
-
- >>> x = x.at[1].add(2)
-
- If x is a read-only numpy array, they are the same as::
-
- >>> x = x.copy()
- >>> x[1] += 2
-
- For other known backends, they are the same as::
-
- >>> x[1] += 2
- """
-
- _x: Array
- _idx: Index
- __slots__: ClassVar[tuple[str, ...]] = ("_idx", "_x")
-
- def __init__(
- self, x: Array, idx: Index = _undef, /
- ) -> None: # numpydoc ignore=GL08
- self._x = x
- self._idx = idx
-
- def __getitem__(self, idx: Index, /) -> at: # numpydoc ignore=PR01,RT01
- """
- Allow for the alternate syntax ``at(x)[start:stop:step]``.
-
- It looks prettier than ``at(x, slice(start, stop, step))``
- and feels more intuitive coming from the JAX documentation.
- """
- if self._idx is not _undef:
- msg = "Index has already been set"
- raise ValueError(msg)
- return at(self._x, idx)
-
- def _update_common(
- self,
- at_op: _AtOp,
- y: Array,
- /,
- copy: bool | None,
- xp: ModuleType | None,
- ) -> tuple[Array, None] | tuple[None, Array]: # numpydoc ignore=PR01
- """
- Perform common prepocessing to all update operations.
-
- Returns
- -------
- tuple
- If the operation can be resolved by ``at[]``, ``(return value, None)``
- Otherwise, ``(None, preprocessed x)``.
- """
- x, idx = self._x, self._idx
-
- if idx is _undef:
- msg = (
- "Index has not been set.\n"
- "Usage: either\n"
- " at(x, idx).set(value)\n"
- "or\n"
- " at(x)[idx].set(value)\n"
- "(same for all other methods)."
- )
- raise ValueError(msg)
-
- if copy not in (True, False, None):
- msg = f"copy must be True, False, or None; got {copy!r}"
- raise ValueError(msg)
-
- if copy is None:
- writeable = is_writeable_array(x)
- copy = not writeable
- elif copy:
- writeable = None
- else:
- writeable = is_writeable_array(x)
-
- if copy:
- if is_jax_array(x):
- # Use JAX's at[]
- func = cast(Callable[[Array], Array], getattr(x.at[idx], at_op.value))
- return func(y), None
- # Emulate at[] behaviour for non-JAX arrays
- # with a copy followed by an update
- if xp is None:
- xp = array_namespace(x)
- x = xp.asarray(x, copy=True)
- if writeable is False:
- # A copy of a read-only numpy array is writeable
- # Note: this assumes that a copy of a writeable array is writeable
- writeable = None
-
- if writeable is None:
- writeable = is_writeable_array(x)
- if not writeable:
- # sparse crashes here
- msg = f"Can't update read-only array {x}"
- raise ValueError(msg)
-
- return None, x
-
- def set(
- self,
- y: Array,
- /,
- copy: bool | None = None,
- xp: ModuleType | None = None,
- ) -> Array: # numpydoc ignore=PR01,RT01
- """Apply ``x[idx] = y`` and return the update array."""
- res, x = self._update_common(_AtOp.SET, y, copy=copy, xp=xp)
- if res is not None:
- return res
- assert x is not None
- x[self._idx] = y
- return x
-
- def _iop(
- self,
- at_op: _AtOp,
- elwise_op: Callable[[Array, Array], Array],
- y: Array,
- /,
- copy: bool | None,
- xp: ModuleType | None,
- ) -> Array: # numpydoc ignore=PR01,RT01
- """
- ``x[idx] += y`` or equivalent in-place operation on a subset of x.
-
- which is the same as saying
- x[idx] = x[idx] + y
- Note that this is not the same as
- operator.iadd(x[idx], y)
- Consider for example when x is a numpy array and idx is a fancy index, which
- triggers a deep copy on __getitem__.
- """
- res, x = self._update_common(at_op, y, copy=copy, xp=xp)
- if res is not None:
- return res
- assert x is not None
- x[self._idx] = elwise_op(x[self._idx], y)
- return x
-
- def add(
- self,
- y: Array,
- /,
- copy: bool | None = None,
- xp: ModuleType | None = None,
- ) -> Array: # numpydoc ignore=PR01,RT01
- """Apply ``x[idx] += y`` and return the updated array."""
-
- # Note for this and all other methods based on _iop:
- # operator.iadd and operator.add subtly differ in behaviour, as
- # only iadd will trigger exceptions when y has an incompatible dtype.
- return self._iop(_AtOp.ADD, operator.iadd, y, copy=copy, xp=xp)
-
- def subtract(
- self,
- y: Array,
- /,
- copy: bool | None = None,
- xp: ModuleType | None = None,
- ) -> Array: # numpydoc ignore=PR01,RT01
- """Apply ``x[idx] -= y`` and return the updated array."""
- return self._iop(_AtOp.SUBTRACT, operator.isub, y, copy=copy, xp=xp)
-
- def multiply(
- self,
- y: Array,
- /,
- copy: bool | None = None,
- xp: ModuleType | None = None,
- ) -> Array: # numpydoc ignore=PR01,RT01
- """Apply ``x[idx] *= y`` and return the updated array."""
- return self._iop(_AtOp.MULTIPLY, operator.imul, y, copy=copy, xp=xp)
-
- def divide(
- self,
- y: Array,
- /,
- copy: bool | None = None,
- xp: ModuleType | None = None,
- ) -> Array: # numpydoc ignore=PR01,RT01
- """Apply ``x[idx] /= y`` and return the updated array."""
- return self._iop(_AtOp.DIVIDE, operator.itruediv, y, copy=copy, xp=xp)
-
- def power(
- self,
- y: Array,
- /,
- copy: bool | None = None,
- xp: ModuleType | None = None,
- ) -> Array: # numpydoc ignore=PR01,RT01
- """Apply ``x[idx] **= y`` and return the updated array."""
- return self._iop(_AtOp.POWER, operator.ipow, y, copy=copy, xp=xp)
-
- def min(
- self,
- y: Array,
- /,
- copy: bool | None = None,
- xp: ModuleType | None = None,
- ) -> Array: # numpydoc ignore=PR01,RT01
- """Apply ``x[idx] = minimum(x[idx], y)`` and return the updated array."""
- if xp is None:
- xp = array_namespace(self._x)
- y = xp.asarray(y)
- return self._iop(_AtOp.MIN, xp.minimum, y, copy=copy, xp=xp)
-
- def max(
- self,
- y: Array,
- /,
- copy: bool | None = None,
- xp: ModuleType | None = None,
- ) -> Array: # numpydoc ignore=PR01,RT01
- """Apply ``x[idx] = maximum(x[idx], y)`` and return the updated array."""
- if xp is None:
- xp = array_namespace(self._x)
- y = xp.asarray(y)
- return self._iop(_AtOp.MAX, xp.maximum, y, copy=copy, xp=xp)
diff --git a/tests/conftest.py b/tests/conftest.py
index c588d802..fa555018 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -105,9 +105,9 @@ def xp(library: Backend) -> ModuleType: # numpydoc ignore=PR01,RT03
return NumPyReadOnly() # type: ignore[return-value] # pyright: ignore[reportReturnType]
xp = pytest.importorskip(library.value)
if library == Backend.JAX_NUMPY:
- import jax # type: ignore[import-not-found] # pyright: ignore[reportMissingImports]
+ import jax
- jax.config.update("jax_enable_x64", True)
+ jax.config.update("jax_enable_x64", True) # type: ignore[no-untyped-call]
# Possibly wrap module with array_api_compat
return array_namespace(xp.empty(0))
diff --git a/tests/test_at.py b/tests/test_at.py
index 749c1b55..c65a4a0d 100644
--- a/tests/test_at.py
+++ b/tests/test_at.py
@@ -12,7 +12,7 @@
from array_api_extra import at
from array_api_extra._lib import Backend
-from array_api_extra._lib._funcs import _AtOp
+from array_api_extra._lib._at import _AtOp
from array_api_extra._lib._testing import xp_assert_equal
from array_api_extra._lib._utils._typing import Array
diff --git a/tests/test_funcs.py b/tests/test_funcs.py
index 897b7811..3557642b 100644
--- a/tests/test_funcs.py
+++ b/tests/test_funcs.py
@@ -22,6 +22,7 @@
from array_api_extra._lib._utils._compat import device as get_device
from array_api_extra._lib._utils._typing import Array, Device
+# some xp backends are untyped
# mypy: disable-error-code=no-untyped-usage
@@ -330,6 +331,74 @@ def test_xp(self, xp: ModuleType):
xp_assert_equal(kron(a, b, xp=xp), k)
+class TestNUnique:
+ def test_simple(self, xp: ModuleType):
+ a = xp.asarray([[1, 1], [0, 2], [2, 2]])
+ xp_assert_equal(nunique(a), xp.asarray(3))
+
+ def test_empty(self, xp: ModuleType):
+ a = xp.asarray([])
+ xp_assert_equal(nunique(a), xp.asarray(0))
+
+ def test_device(self, xp: ModuleType, device: Device):
+ a = xp.asarray(0.0, device=device)
+ assert get_device(nunique(a)) == device
+
+ def test_xp(self, xp: ModuleType):
+ a = xp.asarray([[1, 1], [0, 2], [2, 2]])
+ xp_assert_equal(nunique(a, xp=xp), xp.asarray(3))
+
+
+@pytest.mark.skip_xp_backend(Backend.SPARSE, reason="no arange, no device")
+class TestPad:
+ def test_simple(self, xp: ModuleType):
+ a = xp.arange(1, 4)
+ padded = pad(a, 2)
+ xp_assert_equal(padded, xp.asarray([0, 0, 1, 2, 3, 0, 0]))
+
+ def test_fill_value(self, xp: ModuleType):
+ a = xp.arange(1, 4)
+ padded = pad(a, 2, constant_values=42)
+ xp_assert_equal(padded, xp.asarray([42, 42, 1, 2, 3, 42, 42]))
+
+ def test_ndim(self, xp: ModuleType):
+ a = xp.reshape(xp.arange(2 * 3 * 4), (2, 3, 4))
+ padded = pad(a, 2)
+ assert padded.shape == (6, 7, 8)
+
+ def test_mode_not_implemented(self, xp: ModuleType):
+ a = xp.arange(3)
+ with pytest.raises(NotImplementedError, match="Only `'constant'`"):
+ pad(a, 2, mode="edge") # type: ignore[arg-type] # pyright: ignore[reportArgumentType]
+
+ def test_device(self, xp: ModuleType, device: Device):
+ a = xp.asarray(0.0, device=device)
+ assert get_device(pad(a, 2)) == device
+
+ def test_xp(self, xp: ModuleType):
+ padded = pad(xp.asarray(0), 1, xp=xp)
+ xp_assert_equal(padded, xp.asarray(0))
+
+ def test_tuple_width(self, xp: ModuleType):
+ a = xp.reshape(xp.arange(12), (3, 4))
+ padded = pad(a, (1, 0))
+ assert padded.shape == (4, 5)
+
+ padded = pad(a, (1, 2))
+ assert padded.shape == (6, 7)
+
+ with pytest.raises((ValueError, RuntimeError)):
+ pad(a, [(1, 2, 3)]) # type: ignore[list-item] # pyright: ignore[reportArgumentType]
+
+ def test_list_of_tuples_width(self, xp: ModuleType):
+ a = xp.reshape(xp.arange(12), (3, 4))
+ padded = pad(a, [(1, 0), (0, 2)])
+ assert padded.shape == (4, 6)
+
+ padded = pad(a, [(1, 0), (0, 0)])
+ assert padded.shape == (4, 4)
+
+
@pytest.mark.skip_xp_backend(Backend.DASK_ARRAY, reason="no argsort")
@pytest.mark.skip_xp_backend(Backend.SPARSE, reason="no device")
class TestSetDiff1D:
@@ -401,71 +470,3 @@ def test_device(self, xp: ModuleType, device: Device):
def test_xp(self, xp: ModuleType):
xp_assert_equal(sinc(xp.asarray(0.0), xp=xp), xp.asarray(1.0))
-
-
-@pytest.mark.skip_xp_backend(Backend.SPARSE, reason="no arange, no device")
-class TestPad:
- def test_simple(self, xp: ModuleType):
- a = xp.arange(1, 4)
- padded = pad(a, 2)
- xp_assert_equal(padded, xp.asarray([0, 0, 1, 2, 3, 0, 0]))
-
- def test_fill_value(self, xp: ModuleType):
- a = xp.arange(1, 4)
- padded = pad(a, 2, constant_values=42)
- xp_assert_equal(padded, xp.asarray([42, 42, 1, 2, 3, 42, 42]))
-
- def test_ndim(self, xp: ModuleType):
- a = xp.reshape(xp.arange(2 * 3 * 4), (2, 3, 4))
- padded = pad(a, 2)
- assert padded.shape == (6, 7, 8)
-
- def test_mode_not_implemented(self, xp: ModuleType):
- a = xp.arange(3)
- with pytest.raises(NotImplementedError, match="Only `'constant'`"):
- pad(a, 2, mode="edge") # type: ignore[arg-type] # pyright: ignore[reportArgumentType]
-
- def test_device(self, xp: ModuleType, device: Device):
- a = xp.asarray(0.0, device=device)
- assert get_device(pad(a, 2)) == device
-
- def test_xp(self, xp: ModuleType):
- padded = pad(xp.asarray(0), 1, xp=xp)
- xp_assert_equal(padded, xp.asarray(0))
-
- def test_tuple_width(self, xp: ModuleType):
- a = xp.reshape(xp.arange(12), (3, 4))
- padded = pad(a, (1, 0))
- assert padded.shape == (4, 5)
-
- padded = pad(a, (1, 2))
- assert padded.shape == (6, 7)
-
- with pytest.raises((ValueError, RuntimeError)):
- pad(a, [(1, 2, 3)]) # type: ignore[list-item] # pyright: ignore[reportArgumentType]
-
- def test_list_of_tuples_width(self, xp: ModuleType):
- a = xp.reshape(xp.arange(12), (3, 4))
- padded = pad(a, [(1, 0), (0, 2)])
- assert padded.shape == (4, 6)
-
- padded = pad(a, [(1, 0), (0, 0)])
- assert padded.shape == (4, 4)
-
-
-class TestNUnique:
- def test_simple(self, xp: ModuleType):
- a = xp.asarray([[1, 1], [0, 2], [2, 2]])
- xp_assert_equal(nunique(a), xp.asarray(3))
-
- def test_empty(self, xp: ModuleType):
- a = xp.asarray([])
- xp_assert_equal(nunique(a), xp.asarray(0))
-
- def test_device(self, xp: ModuleType, device: Device):
- a = xp.asarray(0.0, device=device)
- assert get_device(nunique(a)) == device
-
- def test_xp(self, xp: ModuleType):
- a = xp.asarray([[1, 1], [0, 2], [2, 2]])
- xp_assert_equal(nunique(a, xp=xp), xp.asarray(3))