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✎ Editing: _creation_functions.py ← Back

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 numpy as np

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[Union[bool, np._CopyMode]] = 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 not in ["cpu", None]:
        raise ValueError(f"Unsupported device {device!r}")
    if copy in (False, np._CopyMode.IF_NEEDED):
        # 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 in (True, np._CopyMode.ALWAYS):
            return Array._new(np.array(obj._array, copy=True, dtype=dtype))
        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")
    res = np.asarray(obj, dtype=dtype)
    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 not in ["cpu", None]:
        raise ValueError(f"Unsupported device {device!r}")
    return Array._new(np.arange(start, stop=stop, step=step, dtype=dtype))

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 not in ["cpu", None]:
        raise ValueError(f"Unsupported device {device!r}")
    return Array._new(np.empty(shape, dtype=dtype))

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 not in ["cpu", None]:
        raise ValueError(f"Unsupported device {device!r}")
    return Array._new(np.empty_like(x._array, dtype=dtype))

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 not in ["cpu", None]:
        raise ValueError(f"Unsupported device {device!r}")
    return Array._new(np.eye(n_rows, M=n_cols, k=k, dtype=dtype))

def from_dlpack(x: object, /) -> Array:
    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 not in ["cpu", None]:
        raise ValueError(f"Unsupported device {device!r}")
    if isinstance(fill_value, Array) and fill_value.ndim == 0:
        fill_value = fill_value._array
    res = np.full(shape, fill_value, dtype=dtype)
    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 not in ["cpu", None]:
        raise ValueError(f"Unsupported device {device!r}")
    res = np.full_like(x._array, fill_value, dtype=dtype)
    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 not in ["cpu", None]:
        raise ValueError(f"Unsupported device {device!r}")
    return Array._new(np.linspace(start, stop, num, dtype=dtype, endpoint=endpoint))

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 not in ["cpu", None]:
        raise ValueError(f"Unsupported device {device!r}")
    return Array._new(np.ones(shape, dtype=dtype))

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 not in ["cpu", None]:
        raise ValueError(f"Unsupported device {device!r}")
    return Array._new(np.ones_like(x._array, dtype=dtype))

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 not in ["cpu", None]:
        raise ValueError(f"Unsupported device {device!r}")
    return Array._new(np.zeros(shape, dtype=dtype))

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 not in ["cpu", None]:
        raise ValueError(f"Unsupported device {device!r}")
    return Array._new(np.zeros_like(x._array, dtype=dtype))

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