_helpers.py

"""
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 sys
import math
import inspect
import warnings
from typing import Optional, Union, Any

from ._typing import Array, Device, Namespace


def _is_jax_zero_gradient_array(x: object) -> bool:
    """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.
    """
    if 'numpy' not in sys.modules or 'jax' not in sys.modules:
        return False

    import numpy as np
    import jax

    return isinstance(x, np.ndarray) and x.dtype == jax.float0


def is_numpy_array(x: object) -> bool:
    """
    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
    """
    # Avoid importing NumPy if it isn't already
    if 'numpy' not in sys.modules:
        return False

    import numpy as np

    # TODO: Should we reject ndarray subclasses?
    return (isinstance(x, (np.ndarray, np.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
    """
    # Avoid importing CuPy if it isn't already
    if 'cupy' not in sys.modules:
        return False

    import cupy as cp

    # TODO: Should we reject ndarray subclasses?
    return isinstance(x, cp.ndarray)


def is_torch_array(x: object) -> bool:
    """
    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
    """
    # Avoid importing torch if it isn't already
    if 'torch' not in sys.modules:
        return False

    import torch

    # TODO: Should we reject ndarray subclasses?
    return isinstance(x, torch.Tensor)


def is_ndonnx_array(x: object) -> bool:
    """
    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
    """
    # Avoid importing torch if it isn't already
    if 'ndonnx' not in sys.modules:
        return False

    import ndonnx as ndx

    return isinstance(x, ndx.Array)


def is_dask_array(x: object) -> bool:
    """
    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
    """
    # Avoid importing dask if it isn't already
    if 'dask.array' not in sys.modules:
        return False

    import dask.array

    return isinstance(x, dask.array.Array)


def is_jax_array(x: object) -> bool:
    """
    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
    """
    # Avoid importing jax if it isn't already
    if 'jax' not in sys.modules:
        return False

    import jax

    return isinstance(x, jax.Array) or _is_jax_zero_gradient_array(x)


def is_pydata_sparse_array(x) -> bool:
    """
    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
    """
    # Avoid importing jax if it isn't already
    if 'sparse' not in sys.modules:
        return False

    import sparse

    # TODO: Account for other backends.
    return isinstance(x, sparse.SparseArray)


def is_array_api_obj(x: object) -> bool:
    """
    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 is_numpy_array(x) \
        or is_cupy_array(x) \
        or is_torch_array(x) \
        or is_dask_array(x) \
        or is_jax_array(x) \
        or is_pydata_sparse_array(x) \
        or hasattr(x, '__array_namespace__')


def _compat_module_name() -> str:
    assert __name__.endswith('.common._helpers')
    return __name__.removesuffix('.common._helpers')


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'}


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'}


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'


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:
    if api_version in ['2021.12', '2022.12', '2023.12']:
        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 ['2021.12', '2022.12',
                                                         '2023.12', '2024.12']:
        raise ValueError("Only the 2024.12 version of the array API specification is currently supported")


def array_namespace(
    *xs: Union[Array, bool, int, float, complex, None],
    api_version: Optional[str] = None,
    use_compat: Optional[bool] = 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()
    for x in xs:
        if is_numpy_array(x):
            from .. import numpy as numpy_namespace
            import numpy as np
            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
                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
            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
            # `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")
            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(xp, device):
    if xp == sys.modules.get('numpy'):
        if device not in ["cpu", None]:
            raise ValueError(f"Unsupported device for NumPy: {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):
        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: Array, /) -> 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):
            # 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
        except AttributeError:
            return "cpu"
        # Return the device of the constituent array
        return device(inner)
    return x.device

# Prevent shadowing, used below
_device = device

# Based on cupy.array_api.Array.to_device
def _cupy_to_device(x, device, /, stream=None):
    import cupy as cp
    from cupy.cuda import Device as _Device
    from cupy.cuda import stream as stream_module
    from cupy_backends.cuda.api import runtime

    if device == x.device:
        return x
    elif device == "cpu":
        # allowing us to use `to_device(x, "cpu")`
        # is useful for portable test swapping between
        # host and device backends
        return x.get()
    elif not isinstance(device, _Device):
        raise ValueError(f"Unsupported device {device!r}")
    else:
        # see cupy/cupy#5985 for the reason how we handle device/stream here
        prev_device = runtime.getDevice()
        prev_stream: stream_module.Stream = None
        if stream is not None:
            prev_stream = stream_module.get_current_stream()
            # stream can be an int as specified in __dlpack__, or a CuPy stream
            if isinstance(stream, int):
                stream = cp.cuda.ExternalStream(stream)
            elif isinstance(stream, cp.cuda.Stream):
                pass
            else:
                raise ValueError('the input stream is not recognized')
            stream.use()
        try:
            runtime.setDevice(device.id)
            arr = x.copy()
        finally:
            runtime.setDevice(prev_device)
            if stream is not None:
                prev_stream.use()
        return arr

def _torch_to_device(x, device, /, stream=None):
    if stream is not None:
        raise NotImplementedError
    return x.to(device)

def to_device(x: Array, device: Device, /, *, stream: Optional[Union[int, Any]] = 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: Optional[Union[int, Any]]
        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)
    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
            # ... 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)


def size(x: Array) -> 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(x.shape)
    # dask.array.Array.shape can contain NaN
    return None if math.isnan(out) else out


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.
    """
    if is_numpy_array(x):
        return x.flags.writeable
    if is_jax_array(x) or is_pydata_sparse_array(x):
        return False
    return is_array_api_obj(x)


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.
    """
    if (
        is_numpy_array(x)
        or is_cupy_array(x)
        or is_torch_array(x)
        or is_pydata_sparse_array(x)
    ):
        return False

    # **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.

    if is_jax_array(x) or is_dask_array(x) or is_ndonnx_array(x):
        return True

    if not is_array_api_obj(x):
        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(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 = ['sys', 'math', 'inspect', 'warnings']