_creation_functions.py

from __future__ import annotations


from typing import TYPE_CHECKING, List, Optional, Tuple, Union

if TYPE_CHECKING:
    from ._typing import (
        Array,
        Device,
        Dtype,
        NestedSequence,
        SupportsBufferProtocol,
    )
    from collections.abc import Sequence
from ._dtypes import _all_dtypes

import cupy as np
from cupy.cuda import Device as _Device
from cupy_backends.cuda.api import runtime


def _check_valid_dtype(dtype):
    # Note: Only spelling dtypes as the dtype objects is supported.

    # We use this instead of "dtype in _all_dtypes" because the dtype objects
    # define equality with the sorts of things we want to disallow.
    for d in (None,) + _all_dtypes:
        if dtype is d:
            return
    raise ValueError("dtype must be one of the supported dtypes")


def asarray(
    obj: Union[
        Array,
        bool,
        int,
        float,
        NestedSequence[bool | int | float],
        SupportsBufferProtocol,
    ],
    /,
    *,
    dtype: Optional[Dtype] = None,
    device: Optional[Device] = None,
    copy: Optional[bool] = None,
) -> Array:
    """
    Array API compatible wrapper for :py:func:`np.asarray <numpy.asarray>`.

    See its docstring for more information.
    """
    # _array_object imports in this file are inside the functions to avoid
    # circular imports
    from ._array_object import Array

    _check_valid_dtype(dtype)
    if device is not None and not isinstance(device, _Device):
        raise ValueError(f"Unsupported device {device!r}")
    if device is None:
        device = _Device()  # current device
    if copy is False:
        # Note: copy=False is not yet implemented in np.asarray
        raise NotImplementedError("copy=False is not yet implemented")
    if isinstance(obj, Array):
        if dtype is not None and obj.dtype != dtype:
            copy = True
        if copy is True:
            prev_device = runtime.getDevice()
            try:
                runtime.setDevice(device.id)
                obj = Array._new(np.array(obj._array, copy=True, dtype=dtype))
            finally:
                runtime.setDevice(prev_device)
        return obj
    if dtype is None and isinstance(obj, int) and (obj > 2 ** 64 or obj < -(2 ** 63)):
        # Give a better error message in this case. NumPy would convert this
        # to an object array. TODO: This won't handle large integers in lists.
        raise OverflowError("Integer out of bounds for array dtypes")
    prev_device = runtime.getDevice()
    try:
        runtime.setDevice(device.id)
        res = np.asarray(obj, dtype=dtype)
    finally:
        runtime.setDevice(prev_device)
    return Array._new(res)


def arange(
    start: Union[int, float],
    /,
    stop: Optional[Union[int, float]] = None,
    step: Union[int, float] = 1,
    *,
    dtype: Optional[Dtype] = None,
    device: Optional[Device] = None,
) -> Array:
    """
    Array API compatible wrapper for :py:func:`np.arange <numpy.arange>`.

    See its docstring for more information.
    """
    from ._array_object import Array

    _check_valid_dtype(dtype)
    if device is not None and not isinstance(device, _Device):
        raise ValueError(f"Unsupported device {device!r}")
    if device is None:
        device = _Device()  # current device
    prev_device = runtime.getDevice()
    try:
        runtime.setDevice(device.id)
        return Array._new(np.arange(start, stop=stop, step=step, dtype=dtype))
    finally:
        runtime.setDevice(prev_device)


def empty(
    shape: Union[int, Tuple[int, ...]],
    *,
    dtype: Optional[Dtype] = None,
    device: Optional[Device] = None,
) -> Array:
    """
    Array API compatible wrapper for :py:func:`np.empty <numpy.empty>`.

    See its docstring for more information.
    """
    from ._array_object import Array

    _check_valid_dtype(dtype)
    if device is not None and not isinstance(device, _Device):
        raise ValueError(f"Unsupported device {device!r}")
    if device is None:
        device = _Device()  # current device
    prev_device = runtime.getDevice()
    try:
        runtime.setDevice(device.id)
        return Array._new(np.empty(shape, dtype=dtype))
    finally:
        runtime.setDevice(prev_device)


def empty_like(
    x: Array, /, *, dtype: Optional[Dtype] = None, device: Optional[Device] = None
) -> Array:
    """
    Array API compatible wrapper for :py:func:`np.empty_like <numpy.empty_like>`.

    See its docstring for more information.
    """
    from ._array_object import Array

    _check_valid_dtype(dtype)
    if device is not None and not isinstance(device, _Device):
        raise ValueError(f"Unsupported device {device!r}")
    if device is None:
        device = _Device()  # current device
    prev_device = runtime.getDevice()
    try:
        runtime.setDevice(device.id)
        return Array._new(np.empty_like(x._array, dtype=dtype))
    finally:
        runtime.setDevice(prev_device)


def eye(
    n_rows: int,
    n_cols: Optional[int] = None,
    /,
    *,
    k: int = 0,
    dtype: Optional[Dtype] = None,
    device: Optional[Device] = None,
) -> Array:
    """
    Array API compatible wrapper for :py:func:`np.eye <numpy.eye>`.

    See its docstring for more information.
    """
    from ._array_object import Array

    _check_valid_dtype(dtype)
    if device is not None and not isinstance(device, _Device):
        raise ValueError(f"Unsupported device {device!r}")
    if device is None:
        device = _Device()  # current device
    prev_device = runtime.getDevice()
    try:
        runtime.setDevice(device.id)
        return Array._new(np.eye(n_rows, M=n_cols, k=k, dtype=dtype))
    finally:
        runtime.setDevice(prev_device)


def from_dlpack(x: object, /) -> Array:
    """
    Array API compatible wrapper for :py:func:`np.from_dlpack <numpy.from_dlpack>`.

    See its docstring for more information.
    """
    from ._array_object import Array
    return Array._new(np.from_dlpack(x))


def full(
    shape: Union[int, Tuple[int, ...]],
    fill_value: Union[int, float],
    *,
    dtype: Optional[Dtype] = None,
    device: Optional[Device] = None,
) -> Array:
    """
    Array API compatible wrapper for :py:func:`np.full <numpy.full>`.

    See its docstring for more information.
    """
    from ._array_object import Array

    _check_valid_dtype(dtype)
    if device is not None and not isinstance(device, _Device):
        raise ValueError(f"Unsupported device {device!r}")
    if device is None:
        device = _Device()  # current device
    if isinstance(fill_value, Array) and fill_value.ndim == 0:
        fill_value = fill_value._array
    prev_device = runtime.getDevice()
    try:
        runtime.setDevice(device.id)
        res = np.full(shape, fill_value, dtype=dtype)
    finally:
        runtime.setDevice(prev_device)
    if res.dtype not in _all_dtypes:
        # This will happen if the fill value is not something that NumPy
        # coerces to one of the acceptable dtypes.
        raise TypeError("Invalid input to full")
    return Array._new(res)


def full_like(
    x: Array,
    /,
    fill_value: Union[int, float],
    *,
    dtype: Optional[Dtype] = None,
    device: Optional[Device] = None,
) -> Array:
    """
    Array API compatible wrapper for :py:func:`np.full_like <numpy.full_like>`.

    See its docstring for more information.
    """
    from ._array_object import Array

    _check_valid_dtype(dtype)
    if device is not None and not isinstance(device, _Device):
        raise ValueError(f"Unsupported device {device!r}")
    if device is None:
        device = _Device()  # current device
    if isinstance(fill_value, Array) and fill_value.ndim == 0:
        fill_value = fill_value._array
    prev_device = runtime.getDevice()
    try:
        runtime.setDevice(device.id)
        res = np.full_like(x._array, fill_value, dtype=dtype)
    finally:
        runtime.setDevice(prev_device)
    if res.dtype not in _all_dtypes:
        # This will happen if the fill value is not something that NumPy
        # coerces to one of the acceptable dtypes.
        raise TypeError("Invalid input to full_like")
    return Array._new(res)


def linspace(
    start: Union[int, float],
    stop: Union[int, float],
    /,
    num: int,
    *,
    dtype: Optional[Dtype] = None,
    device: Optional[Device] = None,
    endpoint: bool = True,
) -> Array:
    """
    Array API compatible wrapper for :py:func:`np.linspace <numpy.linspace>`.

    See its docstring for more information.
    """
    from ._array_object import Array

    _check_valid_dtype(dtype)
    if device is None:
        device = _Device()  # current device
    elif not isinstance(device, _Device):
        raise ValueError(f"Unsupported device {device!r}")
    prev_device = runtime.getDevice()
    try:
        runtime.setDevice(device.id)
        return Array._new(np.linspace(start, stop, num, dtype=dtype, endpoint=endpoint))
    finally:
        runtime.setDevice(prev_device)


def meshgrid(*arrays: Array, indexing: str = "xy") -> List[Array]:
    """
    Array API compatible wrapper for :py:func:`np.meshgrid <numpy.meshgrid>`.

    See its docstring for more information.
    """
    from ._array_object import Array

    # Note: unlike np.meshgrid, only inputs with all the same dtype are
    # allowed

    if len({a.dtype for a in arrays}) > 1:
        raise ValueError("meshgrid inputs must all have the same dtype")

    return [
        Array._new(array)
        for array in np.meshgrid(*[a._array for a in arrays], indexing=indexing)
    ]


def ones(
    shape: Union[int, Tuple[int, ...]],
    *,
    dtype: Optional[Dtype] = None,
    device: Optional[Device] = None,
) -> Array:
    """
    Array API compatible wrapper for :py:func:`np.ones <numpy.ones>`.

    See its docstring for more information.
    """
    from ._array_object import Array

    _check_valid_dtype(dtype)
    if device is not None and not isinstance(device, _Device):
        raise ValueError(f"Unsupported device {device!r}")
    if device is None:
        device = _Device()  # current device
    prev_device = runtime.getDevice()
    try:
        runtime.setDevice(device.id)
        return Array._new(np.ones(shape, dtype=dtype))
    finally:
        runtime.setDevice(prev_device)


def ones_like(
    x: Array, /, *, dtype: Optional[Dtype] = None, device: Optional[Device] = None
) -> Array:
    """
    Array API compatible wrapper for :py:func:`np.ones_like <numpy.ones_like>`.

    See its docstring for more information.
    """
    from ._array_object import Array

    _check_valid_dtype(dtype)
    if device is not None and not isinstance(device, _Device):
        raise ValueError(f"Unsupported device {device!r}")
    if device is None:
        device = _Device()  # current device
    prev_device = runtime.getDevice()
    try:
        runtime.setDevice(device.id)
        return Array._new(np.ones_like(x._array, dtype=dtype))
    finally:
        runtime.setDevice(prev_device)


def tril(x: Array, /, *, k: int = 0) -> Array:
    """
    Array API compatible wrapper for :py:func:`np.tril <numpy.tril>`.

    See its docstring for more information.
    """
    from ._array_object import Array

    if x.ndim < 2:
        # Note: Unlike np.tril, x must be at least 2-D
        raise ValueError("x must be at least 2-dimensional for tril")
    return Array._new(np.tril(x._array, k=k))


def triu(x: Array, /, *, k: int = 0) -> Array:
    """
    Array API compatible wrapper for :py:func:`np.triu <numpy.triu>`.

    See its docstring for more information.
    """
    from ._array_object import Array

    if x.ndim < 2:
        # Note: Unlike np.triu, x must be at least 2-D
        raise ValueError("x must be at least 2-dimensional for triu")
    return Array._new(np.triu(x._array, k=k))


def zeros(
    shape: Union[int, Tuple[int, ...]],
    *,
    dtype: Optional[Dtype] = None,
    device: Optional[Device] = None,
) -> Array:
    """
    Array API compatible wrapper for :py:func:`np.zeros <numpy.zeros>`.

    See its docstring for more information.
    """
    from ._array_object import Array

    _check_valid_dtype(dtype)
    if device is not None and not isinstance(device, _Device):
        raise ValueError(f"Unsupported device {device!r}")
    if device is None:
        device = _Device()  # current device
    prev_device = runtime.getDevice()
    try:
        runtime.setDevice(device.id)
        return Array._new(np.zeros(shape, dtype=dtype))
    finally:
        runtime.setDevice(prev_device)


def zeros_like(
    x: Array, /, *, dtype: Optional[Dtype] = None, device: Optional[Device] = None
) -> Array:
    """
    Array API compatible wrapper for :py:func:`np.zeros_like <numpy.zeros_like>`.

    See its docstring for more information.
    """
    from ._array_object import Array

    _check_valid_dtype(dtype)
    if device is not None and not isinstance(device, _Device):
        raise ValueError(f"Unsupported device {device!r}")
    if device is None:
        device = _Device()  # current device
    prev_device = runtime.getDevice()
    try:
        runtime.setDevice(device.id)
        return Array._new(np.zeros_like(x._array, dtype=dtype))
    finally:
        runtime.setDevice(prev_device)