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edit//Qwen3-TTS-test//.venv//Lib//site-packages//sklearn//externals//array_api_compat//common//_helpers.py

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+"""
+Various helper functions which are not part of the spec.
+
+Functions which start with an underscore are for internal use only but helpers
+that are in __all__ are intended as additional helper functions for use by end
+users of the compat library.
+"""
+
+from __future__ import annotations
+
+import inspect
+import math
+import sys
+import warnings
+from collections.abc import Collection, Hashable
+from functools import lru_cache
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Final,
+    Literal,
+    SupportsIndex,
+    TypeAlias,
+    TypeGuard,
+    TypeVar,
+    cast,
+    overload,
+)
+
+from ._typing import Array, Device, HasShape, Namespace, SupportsArrayNamespace
+
+if TYPE_CHECKING:
+
+    import dask.array as da
+    import jax
+    import ndonnx as ndx
+    import numpy as np
+    import numpy.typing as npt
+    import sparse  # pyright: ignore[reportMissingTypeStubs]
+    import torch
+
+    # TODO: import from typing (requires Python >=3.13)
+    from typing_extensions import TypeIs, TypeVar
+
+    _SizeT = TypeVar("_SizeT", bound = int | None)
+
+    _ZeroGradientArray: TypeAlias = npt.NDArray[np.void]
+    _CupyArray: TypeAlias = Any  # cupy has no py.typed
+
+    _ArrayApiObj: TypeAlias = (
+        npt.NDArray[Any]
+        | da.Array
+        | jax.Array
+        | ndx.Array
+        | sparse.SparseArray
+        | torch.Tensor
+        | SupportsArrayNamespace[Any]
+        | _CupyArray
+    )
+
+_API_VERSIONS_OLD: Final = frozenset({"2021.12", "2022.12", "2023.12"})
+_API_VERSIONS: Final = _API_VERSIONS_OLD | frozenset({"2024.12"})
+
+
+@lru_cache(100)
+def _issubclass_fast(cls: type, modname: str, clsname: str) -> bool:
+    try:
+        mod = sys.modules[modname]
+    except KeyError:
+        return False
+    parent_cls = getattr(mod, clsname)
+    return issubclass(cls, parent_cls)
+
+
+def _is_jax_zero_gradient_array(x: object) -> TypeGuard[_ZeroGradientArray]:
+    """Return True if `x` is a zero-gradient array.
+
+    These arrays are a design quirk of Jax that may one day be removed.
+    See https://github.com/google/jax/issues/20620.
+    """
+    # Fast exit
+    try:
+        dtype = x.dtype  # type: ignore[attr-defined]
+    except AttributeError:
+        return False
+    cls = cast(Hashable, type(dtype))
+    if not _issubclass_fast(cls, "numpy.dtypes", "VoidDType"):
+        return False
+
+    if "jax" not in sys.modules:
+        return False
+
+    import jax
+    # jax.float0 is a np.dtype([('float0', 'V')])
+    return dtype == jax.float0
+
+
+def is_numpy_array(x: object) -> TypeGuard[npt.NDArray[Any]]:
+    """
+    Return True if `x` is a NumPy array.
+
+    This function does not import NumPy if it has not already been imported
+    and is therefore cheap to use.
+
+    This also returns True for `ndarray` subclasses and NumPy scalar objects.
+
+    See Also
+    --------
+
+    array_namespace
+    is_array_api_obj
+    is_cupy_array
+    is_torch_array
+    is_ndonnx_array
+    is_dask_array
+    is_jax_array
+    is_pydata_sparse_array
+    """
+    # TODO: Should we reject ndarray subclasses?
+    cls = cast(Hashable, type(x))
+    return (
+        _issubclass_fast(cls, "numpy", "ndarray") 
+        or _issubclass_fast(cls, "numpy", "generic")
+    ) and not _is_jax_zero_gradient_array(x)
+
+
+def is_cupy_array(x: object) -> bool:
+    """
+    Return True if `x` is a CuPy array.
+
+    This function does not import CuPy if it has not already been imported
+    and is therefore cheap to use.
+
+    This also returns True for `cupy.ndarray` subclasses and CuPy scalar objects.
+
+    See Also
+    --------
+
+    array_namespace
+    is_array_api_obj
+    is_numpy_array
+    is_torch_array
+    is_ndonnx_array
+    is_dask_array
+    is_jax_array
+    is_pydata_sparse_array
+    """
+    cls = cast(Hashable, type(x))
+    return _issubclass_fast(cls, "cupy", "ndarray")
+
+
+def is_torch_array(x: object) -> TypeIs[torch.Tensor]:
+    """
+    Return True if `x` is a PyTorch tensor.
+
+    This function does not import PyTorch if it has not already been imported
+    and is therefore cheap to use.
+
+    See Also
+    --------
+
+    array_namespace
+    is_array_api_obj
+    is_numpy_array
+    is_cupy_array
+    is_dask_array
+    is_jax_array
+    is_pydata_sparse_array
+    """
+    cls = cast(Hashable, type(x))
+    return _issubclass_fast(cls, "torch", "Tensor")
+
+
+def is_ndonnx_array(x: object) -> TypeIs[ndx.Array]:
+    """
+    Return True if `x` is a ndonnx Array.
+
+    This function does not import ndonnx if it has not already been imported
+    and is therefore cheap to use.
+
+    See Also
+    --------
+
+    array_namespace
+    is_array_api_obj
+    is_numpy_array
+    is_cupy_array
+    is_ndonnx_array
+    is_dask_array
+    is_jax_array
+    is_pydata_sparse_array
+    """
+    cls = cast(Hashable, type(x))
+    return _issubclass_fast(cls, "ndonnx", "Array")
+
+
+def is_dask_array(x: object) -> TypeIs[da.Array]:
+    """
+    Return True if `x` is a dask.array Array.
+
+    This function does not import dask if it has not already been imported
+    and is therefore cheap to use.
+
+    See Also
+    --------
+
+    array_namespace
+    is_array_api_obj
+    is_numpy_array
+    is_cupy_array
+    is_torch_array
+    is_ndonnx_array
+    is_jax_array
+    is_pydata_sparse_array
+    """
+    cls = cast(Hashable, type(x))
+    return _issubclass_fast(cls, "dask.array", "Array")
+
+
+def is_jax_array(x: object) -> TypeIs[jax.Array]:
+    """
+    Return True if `x` is a JAX array.
+
+    This function does not import JAX if it has not already been imported
+    and is therefore cheap to use.
+
+
+    See Also
+    --------
+
+    array_namespace
+    is_array_api_obj
+    is_numpy_array
+    is_cupy_array
+    is_torch_array
+    is_ndonnx_array
+    is_dask_array
+    is_pydata_sparse_array
+    """
+    cls = cast(Hashable, type(x))
+    return _issubclass_fast(cls, "jax", "Array") or _is_jax_zero_gradient_array(x)
+
+
+def is_pydata_sparse_array(x: object) -> TypeIs[sparse.SparseArray]:
+    """
+    Return True if `x` is an array from the `sparse` package.
+
+    This function does not import `sparse` if it has not already been imported
+    and is therefore cheap to use.
+
+
+    See Also
+    --------
+
+    array_namespace
+    is_array_api_obj
+    is_numpy_array
+    is_cupy_array
+    is_torch_array
+    is_ndonnx_array
+    is_dask_array
+    is_jax_array
+    """
+    # TODO: Account for other backends.
+    cls = cast(Hashable, type(x))
+    return _issubclass_fast(cls, "sparse", "SparseArray")
+
+
+def is_array_api_obj(x: object) -> TypeIs[_ArrayApiObj]:  # pyright: ignore[reportUnknownParameterType]
+    """
+    Return True if `x` is an array API compatible array object.
+
+    See Also
+    --------
+
+    array_namespace
+    is_numpy_array
+    is_cupy_array
+    is_torch_array
+    is_ndonnx_array
+    is_dask_array
+    is_jax_array
+    """
+    return (
+        hasattr(x, '__array_namespace__') 
+        or _is_array_api_cls(cast(Hashable, type(x)))
+    )
+
+
+@lru_cache(100)
+def _is_array_api_cls(cls: type) -> bool:
+    return (
+        # TODO: drop support for numpy<2 which didn't have __array_namespace__
+        _issubclass_fast(cls, "numpy", "ndarray")
+        or _issubclass_fast(cls, "numpy", "generic")
+        or _issubclass_fast(cls, "cupy", "ndarray")
+        or _issubclass_fast(cls, "torch", "Tensor")
+        or _issubclass_fast(cls, "dask.array", "Array")
+        or _issubclass_fast(cls, "sparse", "SparseArray")
+        # TODO: drop support for jax<0.4.32 which didn't have __array_namespace__
+        or _issubclass_fast(cls, "jax", "Array")
+    )
+
+
+def _compat_module_name() -> str:
+    assert __name__.endswith(".common._helpers")
+    return __name__.removesuffix(".common._helpers")
+
+
+@lru_cache(100)
+def is_numpy_namespace(xp: Namespace) -> bool:
+    """
+    Returns True if `xp` is a NumPy namespace.
+
+    This includes both NumPy itself and the version wrapped by array-api-compat.
+
+    See Also
+    --------
+
+    array_namespace
+    is_cupy_namespace
+    is_torch_namespace
+    is_ndonnx_namespace
+    is_dask_namespace
+    is_jax_namespace
+    is_pydata_sparse_namespace
+    is_array_api_strict_namespace
+    """
+    return xp.__name__ in {"numpy", _compat_module_name() + ".numpy"}
+
+
+@lru_cache(100)
+def is_cupy_namespace(xp: Namespace) -> bool:
+    """
+    Returns True if `xp` is a CuPy namespace.
+
+    This includes both CuPy itself and the version wrapped by array-api-compat.
+
+    See Also
+    --------
+
+    array_namespace
+    is_numpy_namespace
+    is_torch_namespace
+    is_ndonnx_namespace
+    is_dask_namespace
+    is_jax_namespace
+    is_pydata_sparse_namespace
+    is_array_api_strict_namespace
+    """
+    return xp.__name__ in {"cupy", _compat_module_name() + ".cupy"}
+
+
+@lru_cache(100)
+def is_torch_namespace(xp: Namespace) -> bool:
+    """
+    Returns True if `xp` is a PyTorch namespace.
+
+    This includes both PyTorch itself and the version wrapped by array-api-compat.
+
+    See Also
+    --------
+
+    array_namespace
+    is_numpy_namespace
+    is_cupy_namespace
+    is_ndonnx_namespace
+    is_dask_namespace
+    is_jax_namespace
+    is_pydata_sparse_namespace
+    is_array_api_strict_namespace
+    """
+    return xp.__name__ in {"torch", _compat_module_name() + ".torch"}
+
+
+def is_ndonnx_namespace(xp: Namespace) -> bool:
+    """
+    Returns True if `xp` is an NDONNX namespace.
+
+    See Also
+    --------
+
+    array_namespace
+    is_numpy_namespace
+    is_cupy_namespace
+    is_torch_namespace
+    is_dask_namespace
+    is_jax_namespace
+    is_pydata_sparse_namespace
+    is_array_api_strict_namespace
+    """
+    return xp.__name__ == "ndonnx"
+
+
+@lru_cache(100)
+def is_dask_namespace(xp: Namespace) -> bool:
+    """
+    Returns True if `xp` is a Dask namespace.
+
+    This includes both ``dask.array`` itself and the version wrapped by array-api-compat.
+
+    See Also
+    --------
+
+    array_namespace
+    is_numpy_namespace
+    is_cupy_namespace
+    is_torch_namespace
+    is_ndonnx_namespace
+    is_jax_namespace
+    is_pydata_sparse_namespace
+    is_array_api_strict_namespace
+    """
+    return xp.__name__ in {"dask.array", _compat_module_name() + ".dask.array"}
+
+
+def is_jax_namespace(xp: Namespace) -> bool:
+    """
+    Returns True if `xp` is a JAX namespace.
+
+    This includes ``jax.numpy`` and ``jax.experimental.array_api`` which existed in
+    older versions of JAX.
+
+    See Also
+    --------
+
+    array_namespace
+    is_numpy_namespace
+    is_cupy_namespace
+    is_torch_namespace
+    is_ndonnx_namespace
+    is_dask_namespace
+    is_pydata_sparse_namespace
+    is_array_api_strict_namespace
+    """
+    return xp.__name__ in {"jax.numpy", "jax.experimental.array_api"}
+
+
+def is_pydata_sparse_namespace(xp: Namespace) -> bool:
+    """
+    Returns True if `xp` is a pydata/sparse namespace.
+
+    See Also
+    --------
+
+    array_namespace
+    is_numpy_namespace
+    is_cupy_namespace
+    is_torch_namespace
+    is_ndonnx_namespace
+    is_dask_namespace
+    is_jax_namespace
+    is_array_api_strict_namespace
+    """
+    return xp.__name__ == "sparse"
+
+
+def is_array_api_strict_namespace(xp: Namespace) -> bool:
+    """
+    Returns True if `xp` is an array-api-strict namespace.
+
+    See Also
+    --------
+
+    array_namespace
+    is_numpy_namespace
+    is_cupy_namespace
+    is_torch_namespace
+    is_ndonnx_namespace
+    is_dask_namespace
+    is_jax_namespace
+    is_pydata_sparse_namespace
+    """
+    return xp.__name__ == "array_api_strict"
+
+
+def _check_api_version(api_version: str | None) -> None:
+    if api_version in _API_VERSIONS_OLD:
+        warnings.warn(
+            f"The {api_version} version of the array API specification was requested but the returned namespace is actually version 2024.12"
+        )
+    elif api_version is not None and api_version not in _API_VERSIONS:
+        raise ValueError(
+            "Only the 2024.12 version of the array API specification is currently supported"
+        )
+
+
+def array_namespace(
+    *xs: Array | complex | None,
+    api_version: str | None = None,
+    use_compat: bool | None = None,
+) -> Namespace:
+    """
+    Get the array API compatible namespace for the arrays `xs`.
+
+    Parameters
+    ----------
+    xs: arrays
+        one or more arrays. xs can also be Python scalars (bool, int, float,
+        complex, or None), which are ignored.
+
+    api_version: str
+        The newest version of the spec that you need support for (currently
+        the compat library wrapped APIs support v2024.12).
+
+    use_compat: bool or None
+        If None (the default), the native namespace will be returned if it is
+        already array API compatible, otherwise a compat wrapper is used. If
+        True, the compat library wrapped library will be returned. If False,
+        the native library namespace is returned.
+
+    Returns
+    -------
+
+    out: namespace
+        The array API compatible namespace corresponding to the arrays in `xs`.
+
+    Raises
+    ------
+    TypeError
+        If `xs` contains arrays from different array libraries or contains a
+        non-array.
+
+
+    Typical usage is to pass the arguments of a function to
+    `array_namespace()` at the top of a function to get the corresponding
+    array API namespace:
+
+    .. code:: python
+
+       def your_function(x, y):
+           xp = array_api_compat.array_namespace(x, y)
+           # Now use xp as the array library namespace
+           return xp.mean(x, axis=0) + 2*xp.std(y, axis=0)
+
+
+    Wrapped array namespaces can also be imported directly. For example,
+    `array_namespace(np.array(...))` will return `array_api_compat.numpy`.
+    This function will also work for any array library not wrapped by
+    array-api-compat if it explicitly defines `__array_namespace__
+    <https://data-apis.org/array-api/latest/API_specification/generated/array_api.array.__array_namespace__.html>`__
+    (the wrapped namespace is always preferred if it exists).
+
+    See Also
+    --------
+
+    is_array_api_obj
+    is_numpy_array
+    is_cupy_array
+    is_torch_array
+    is_dask_array
+    is_jax_array
+    is_pydata_sparse_array
+
+    """
+    if use_compat not in [None, True, False]:
+        raise ValueError("use_compat must be None, True, or False")
+
+    _use_compat = use_compat in [None, True]
+
+    namespaces: set[Namespace] = set()
+    for x in xs:
+        if is_numpy_array(x):
+            import numpy as np
+
+            from .. import numpy as numpy_namespace
+
+            if use_compat is True:
+                _check_api_version(api_version)
+                namespaces.add(numpy_namespace)
+            elif use_compat is False:
+                namespaces.add(np)
+            else:
+                # numpy 2.0+ have __array_namespace__, however, they are not yet fully array API
+                # compatible.
+                namespaces.add(numpy_namespace)
+        elif is_cupy_array(x):
+            if _use_compat:
+                _check_api_version(api_version)
+                from .. import cupy as cupy_namespace
+
+                namespaces.add(cupy_namespace)
+            else:
+                import cupy as cp  # pyright: ignore[reportMissingTypeStubs]
+
+                namespaces.add(cp)
+        elif is_torch_array(x):
+            if _use_compat:
+                _check_api_version(api_version)
+                from .. import torch as torch_namespace
+
+                namespaces.add(torch_namespace)
+            else:
+                import torch
+
+                namespaces.add(torch)
+        elif is_dask_array(x):
+            if _use_compat:
+                _check_api_version(api_version)
+                from ..dask import array as dask_namespace
+
+                namespaces.add(dask_namespace)
+            else:
+                import dask.array as da
+
+                namespaces.add(da)
+        elif is_jax_array(x):
+            if use_compat is True:
+                _check_api_version(api_version)
+                raise ValueError("JAX does not have an array-api-compat wrapper")
+            elif use_compat is False:
+                import jax.numpy as jnp
+            else:
+                # JAX v0.4.32 and newer implements the array API directly in jax.numpy.
+                # For older JAX versions, it is available via jax.experimental.array_api.
+                import jax.numpy
+
+                if hasattr(jax.numpy, "__array_api_version__"):
+                    jnp = jax.numpy
+                else:
+                    import jax.experimental.array_api as jnp  # pyright: ignore[reportMissingImports]
+            namespaces.add(jnp)
+        elif is_pydata_sparse_array(x):
+            if use_compat is True:
+                _check_api_version(api_version)
+                raise ValueError("`sparse` does not have an array-api-compat wrapper")
+            else:
+                import sparse  # pyright: ignore[reportMissingTypeStubs]
+            # `sparse` is already an array namespace. We do not have a wrapper
+            # submodule for it.
+            namespaces.add(sparse)
+        elif hasattr(x, "__array_namespace__"):
+            if use_compat is True:
+                raise ValueError(
+                    "The given array does not have an array-api-compat wrapper"
+                )
+            x = cast("SupportsArrayNamespace[Any]", x)
+            namespaces.add(x.__array_namespace__(api_version=api_version))
+        elif isinstance(x, (bool, int, float, complex, type(None))):
+            continue
+        else:
+            # TODO: Support Python scalars?
+            raise TypeError(f"{type(x).__name__} is not a supported array type")
+
+    if not namespaces:
+        raise TypeError("Unrecognized array input")
+
+    if len(namespaces) != 1:
+        raise TypeError(f"Multiple namespaces for array inputs: {namespaces}")
+
+    (xp,) = namespaces
+
+    return xp
+
+
+# backwards compatibility alias
+get_namespace = array_namespace
+
+
+def _check_device(bare_xp: Namespace, device: Device) -> None:  # pyright: ignore[reportUnusedFunction]
+    """
+    Validate dummy device on device-less array backends.
+
+    Notes
+    -----
+    This function is also invoked by CuPy, which does have multiple devices
+    if there are multiple GPUs available.
+    However, CuPy multi-device support is currently impossible
+    without using the global device or a context manager:
+
+    https://github.com/data-apis/array-api-compat/pull/293
+    """
+    if bare_xp is sys.modules.get("numpy"):
+        if device not in ("cpu", None):
+            raise ValueError(f"Unsupported device for NumPy: {device!r}")
+
+    elif bare_xp is sys.modules.get("dask.array"):
+        if device not in ("cpu", _DASK_DEVICE, None):
+            raise ValueError(f"Unsupported device for Dask: {device!r}")
+
+
+# Placeholder object to represent the dask device
+# when the array backend is not the CPU.
+# (since it is not easy to tell which device a dask array is on)
+class _dask_device:
+    def __repr__(self) -> Literal["DASK_DEVICE"]:
+        return "DASK_DEVICE"
+
+
+_DASK_DEVICE = _dask_device()
+
+
+# device() is not on numpy.ndarray or dask.array and to_device() is not on numpy.ndarray
+# or cupy.ndarray. They are not included in array objects of this library
+# because this library just reuses the respective ndarray classes without
+# wrapping or subclassing them. These helper functions can be used instead of
+# the wrapper functions for libraries that need to support both NumPy/CuPy and
+# other libraries that use devices.
+def device(x: _ArrayApiObj, /) -> Device:
+    """
+    Hardware device the array data resides on.
+
+    This is equivalent to `x.device` according to the `standard
+    <https://data-apis.org/array-api/latest/API_specification/generated/array_api.array.device.html>`__.
+    This helper is included because some array libraries either do not have
+    the `device` attribute or include it with an incompatible API.
+
+    Parameters
+    ----------
+    x: array
+        array instance from an array API compatible library.
+
+    Returns
+    -------
+    out: device
+        a ``device`` object (see the `Device Support <https://data-apis.org/array-api/latest/design_topics/device_support.html>`__
+        section of the array API specification).
+
+    Notes
+    -----
+
+    For NumPy the device is always `"cpu"`. For Dask, the device is always a
+    special `DASK_DEVICE` object.
+
+    See Also
+    --------
+
+    to_device : Move array data to a different device.
+
+    """
+    if is_numpy_array(x):
+        return "cpu"
+    elif is_dask_array(x):
+        # Peek at the metadata of the Dask array to determine type
+        if is_numpy_array(x._meta):  # pyright: ignore
+            # Must be on CPU since backed by numpy
+            return "cpu"
+        return _DASK_DEVICE
+    elif is_jax_array(x):
+        # FIXME Jitted JAX arrays do not have a device attribute
+        #       https://github.com/jax-ml/jax/issues/26000
+        #       Return None in this case. Note that this workaround breaks
+        #       the standard and will result in new arrays being created on the
+        #       default device instead of the same device as the input array(s).
+        x_device = getattr(x, "device", None)
+        # Older JAX releases had .device() as a method, which has been replaced
+        # with a property in accordance with the standard.
+        if inspect.ismethod(x_device):
+            return x_device()
+        else:
+            return x_device
+    elif is_pydata_sparse_array(x):
+        # `sparse` will gain `.device`, so check for this first.
+        x_device = getattr(x, "device", None)
+        if x_device is not None:
+            return x_device
+        # Everything but DOK has this attr.
+        try:
+            inner = x.data  # pyright: ignore
+        except AttributeError:
+            return "cpu"
+        # Return the device of the constituent array
+        return device(inner)  # pyright: ignore
+    return x.device  # pyright: ignore
+
+
+# Prevent shadowing, used below
+_device = device
+
+
+# Based on cupy.array_api.Array.to_device
+def _cupy_to_device(
+    x: _CupyArray,
+    device: Device,
+    /,
+    stream: int | Any | None = None,
+) -> _CupyArray:
+    import cupy as cp
+
+    if device == "cpu":
+        # allowing us to use `to_device(x, "cpu")`
+        # is useful for portable test swapping between
+        # host and device backends
+        return x.get()
+    if not isinstance(device, cp.cuda.Device):
+        raise TypeError(f"Unsupported device type {device!r}")
+
+    if stream is None:
+        with device:
+            return cp.asarray(x)
+
+    # stream can be an int as specified in __dlpack__, or a CuPy stream
+    if isinstance(stream, int):
+        stream = cp.cuda.ExternalStream(stream)
+    elif not isinstance(stream, cp.cuda.Stream):
+        raise TypeError(f"Unsupported stream type {stream!r}")
+
+    with device, stream:
+        return cp.asarray(x)
+
+
+def _torch_to_device(
+    x: torch.Tensor,
+    device: torch.device | str | int,
+    /,
+    stream: None = None,
+) -> torch.Tensor:
+    if stream is not None:
+        raise NotImplementedError
+    return x.to(device)
+
+
+def to_device(x: Array, device: Device, /, *, stream: int | Any | None = None) -> Array:
+    """
+    Copy the array from the device on which it currently resides to the specified ``device``.
+
+    This is equivalent to `x.to_device(device, stream=stream)` according to
+    the `standard
+    <https://data-apis.org/array-api/latest/API_specification/generated/array_api.array.to_device.html>`__.
+    This helper is included because some array libraries do not have the
+    `to_device` method.
+
+    Parameters
+    ----------
+
+    x: array
+        array instance from an array API compatible library.
+
+    device: device
+        a ``device`` object (see the `Device Support <https://data-apis.org/array-api/latest/design_topics/device_support.html>`__
+        section of the array API specification).
+
+    stream: int | Any | None
+        stream object to use during copy. In addition to the types supported
+        in ``array.__dlpack__``, implementations may choose to support any
+        library-specific stream object with the caveat that any code using
+        such an object would not be portable.
+
+    Returns
+    -------
+
+    out: array
+        an array with the same data and data type as ``x`` and located on the
+        specified ``device``.
+
+    Notes
+    -----
+
+    For NumPy, this function effectively does nothing since the only supported
+    device is the CPU. For CuPy, this method supports CuPy CUDA
+    :external+cupy:class:`Device <cupy.cuda.Device>` and
+    :external+cupy:class:`Stream <cupy.cuda.Stream>` objects. For PyTorch,
+    this is the same as :external+torch:meth:`x.to(device) <torch.Tensor.to>`
+    (the ``stream`` argument is not supported in PyTorch).
+
+    See Also
+    --------
+
+    device : Hardware device the array data resides on.
+
+    """
+    if is_numpy_array(x):
+        if stream is not None:
+            raise ValueError("The stream argument to to_device() is not supported")
+        if device == "cpu":
+            return x
+        raise ValueError(f"Unsupported device {device!r}")
+    elif is_cupy_array(x):
+        # cupy does not yet have to_device
+        return _cupy_to_device(x, device, stream=stream)
+    elif is_torch_array(x):
+        return _torch_to_device(x, device, stream=stream)  # pyright: ignore[reportArgumentType]
+    elif is_dask_array(x):
+        if stream is not None:
+            raise ValueError("The stream argument to to_device() is not supported")
+        # TODO: What if our array is on the GPU already?
+        if device == "cpu":
+            return x
+        raise ValueError(f"Unsupported device {device!r}")
+    elif is_jax_array(x):
+        if not hasattr(x, "__array_namespace__"):
+            # In JAX v0.4.31 and older, this import adds to_device method to x...
+            import jax.experimental.array_api  # noqa: F401  # pyright: ignore
+
+            # ... but only on eager JAX. It won't work inside jax.jit.
+            if not hasattr(x, "to_device"):
+                return x
+        return x.to_device(device, stream=stream)
+    elif is_pydata_sparse_array(x) and device == _device(x):
+        # Perform trivial check to return the same array if
+        # device is same instead of err-ing.
+        return x
+    return x.to_device(device, stream=stream)  # pyright: ignore
+
+
+@overload
+def size(x: HasShape[Collection[SupportsIndex]]) -> int: ...
+@overload
+def size(x: HasShape[Collection[None]]) -> None: ...
+@overload
+def size(x: HasShape[Collection[SupportsIndex | None]]) -> int | None: ...
+def size(x: HasShape[Collection[SupportsIndex | None]]) -> int | None:
+    """
+    Return the total number of elements of x.
+
+    This is equivalent to `x.size` according to the `standard
+    <https://data-apis.org/array-api/latest/API_specification/generated/array_api.array.size.html>`__.
+
+    This helper is included because PyTorch defines `size` in an
+    :external+torch:meth:`incompatible way <torch.Tensor.size>`.
+    It also fixes dask.array's behaviour which returns nan for unknown sizes, whereas
+    the standard requires None.
+    """
+    # Lazy API compliant arrays, such as ndonnx, can contain None in their shape
+    if None in x.shape:
+        return None
+    out = math.prod(cast("Collection[SupportsIndex]", x.shape))
+    # dask.array.Array.shape can contain NaN
+    return None if math.isnan(out) else out
+
+
+@lru_cache(100)
+def _is_writeable_cls(cls: type) -> bool | None:
+    if (
+        _issubclass_fast(cls, "numpy", "generic")
+        or _issubclass_fast(cls, "jax", "Array")
+        or _issubclass_fast(cls, "sparse", "SparseArray")
+    ):
+        return False
+    if _is_array_api_cls(cls):
+        return True
+    return None
+
+
+def is_writeable_array(x: object) -> bool:
+    """
+    Return False if ``x.__setitem__`` is expected to raise; True otherwise.
+    Return False if `x` is not an array API compatible object.
+
+    Warning
+    -------
+    As there is no standard way to check if an array is writeable without actually
+    writing to it, this function blindly returns True for all unknown array types.
+    """
+    cls = cast(Hashable, type(x))
+    if _issubclass_fast(cls, "numpy", "ndarray"):
+        return cast("npt.NDArray", x).flags.writeable
+    res = _is_writeable_cls(cls)
+    if res is not None:
+        return res
+    return hasattr(x, '__array_namespace__')
+
+
+@lru_cache(100)
+def _is_lazy_cls(cls: type) -> bool | None:
+    if (
+        _issubclass_fast(cls, "numpy", "ndarray")
+        or _issubclass_fast(cls, "numpy", "generic")
+        or _issubclass_fast(cls, "cupy", "ndarray")
+        or _issubclass_fast(cls, "torch", "Tensor")
+        or _issubclass_fast(cls, "sparse", "SparseArray")
+    ):
+        return False
+    if (
+        _issubclass_fast(cls, "jax", "Array")
+        or _issubclass_fast(cls, "dask.array", "Array")
+        or _issubclass_fast(cls, "ndonnx", "Array")
+    ):
+        return True
+    return  None
+
+
+def is_lazy_array(x: object) -> bool:
+    """Return True if x is potentially a future or it may be otherwise impossible or
+    expensive to eagerly read its contents, regardless of their size, e.g. by
+    calling ``bool(x)`` or ``float(x)``.
+
+    Return False otherwise; e.g. ``bool(x)`` etc. is guaranteed to succeed and to be
+    cheap as long as the array has the right dtype and size.
+
+    Note
+    ----
+    This function errs on the side of caution for array types that may or may not be
+    lazy, e.g. JAX arrays, by always returning True for them.
+    """
+    # **JAX note:** while it is possible to determine if you're inside or outside
+    # jax.jit by testing the subclass of a jax.Array object, as well as testing bool()
+    # as we do below for unknown arrays, this is not recommended by JAX best practices.
+
+    # **Dask note:** Dask eagerly computes the graph on __bool__, __float__, and so on.
+    # This behaviour, while impossible to change without breaking backwards
+    # compatibility, is highly detrimental to performance as the whole graph will end
+    # up being computed multiple times.
+
+    # Note: skipping reclassification of JAX zero gradient arrays, as one will
+    # exclusively get them once they leave a jax.grad JIT context.
+    cls = cast(Hashable, type(x))
+    res = _is_lazy_cls(cls)
+    if res is not None:
+        return res
+
+    if not hasattr(x, "__array_namespace__"):
+        return False
+
+    # Unknown Array API compatible object. Note that this test may have dire consequences
+    # in terms of performance, e.g. for a lazy object that eagerly computes the graph
+    # on __bool__ (dask is one such example, which however is special-cased above).
+
+    # Select a single point of the array
+    s = size(cast("HasShape[Collection[SupportsIndex | None]]", x))
+    if s is None:
+        return True
+    xp = array_namespace(x)
+    if s > 1:
+        x = xp.reshape(x, (-1,))[0]
+    # Cast to dtype=bool and deal with size 0 arrays
+    x = xp.any(x)
+
+    try:
+        bool(x)
+        return False
+    # The Array API standard dictactes that __bool__ should raise TypeError if the
+    # output cannot be defined.
+    # Here we allow for it to raise arbitrary exceptions, e.g. like Dask does.
+    except Exception:
+        return True
+
+
+__all__ = [
+    "array_namespace",
+    "device",
+    "get_namespace",
+    "is_array_api_obj",
+    "is_array_api_strict_namespace",
+    "is_cupy_array",
+    "is_cupy_namespace",
+    "is_dask_array",
+    "is_dask_namespace",
+    "is_jax_array",
+    "is_jax_namespace",
+    "is_numpy_array",
+    "is_numpy_namespace",
+    "is_torch_array",
+    "is_torch_namespace",
+    "is_ndonnx_array",
+    "is_ndonnx_namespace",
+    "is_pydata_sparse_array",
+    "is_pydata_sparse_namespace",
+    "is_writeable_array",
+    "is_lazy_array",
+    "size",
+    "to_device",
+]
+
+_all_ignore = ['lru_cache', 'sys', 'math', 'inspect', 'warnings']
+
+def __dir__() -> list[str]:
+    return __all__