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from __future__ import annotations
from ._types import (Optional, SupportsDLPack, SupportsBufferProtocol, Tuple,
Union, array, device, dtype)
import numpy as np
def asarray(obj: Union[float, NestedSequence[bool|int|float], SupportsDLPack, 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.
"""
if device is not None:
# Note: Device support is not yet implemented on ndarray
raise NotImplementedError("Device support is not yet implemented")
return np.asarray(obj, dtype=dtype, copy=copy)
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.
"""
if device is not None:
# Note: Device support is not yet implemented on ndarray
raise NotImplementedError("Device support is not yet implemented")
return 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.
"""
if device is not None:
# Note: Device support is not yet implemented on ndarray
raise NotImplementedError("Device support is not yet implemented")
return 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.
"""
if device is not None:
# Note: Device support is not yet implemented on ndarray
raise NotImplementedError("Device support is not yet implemented")
return np.empty_like._implementation(x, dtype=dtype)
def eye(N: int, /, *, M: Optional[int] = None, k: Optional[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.
"""
if device is not None:
# Note: Device support is not yet implemented on ndarray
raise NotImplementedError("Device support is not yet implemented")
return np.eye(N, M=M, k=k, dtype=dtype)
def from_dlpack(x: object, /) -> array:
# Note: dlpack support is not yet implemented on ndarray
raise NotImplementedError("DLPack support is not yet implemented")
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.
"""
if device is not None:
# Note: Device support is not yet implemented on ndarray
raise NotImplementedError("Device support is not yet implemented")
return np.full(shape, fill_value, dtype=dtype)
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.
"""
if device is not None:
# Note: Device support is not yet implemented on ndarray
raise NotImplementedError("Device support is not yet implemented")
return np.full_like._implementation(x, fill_value, dtype=dtype)
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.
"""
if device is not None:
# Note: Device support is not yet implemented on ndarray
raise NotImplementedError("Device support is not yet implemented")
return np.linspace(start, stop, num, dtype=dtype, endpoint=endpoint)
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.
"""
if device is not None:
# Note: Device support is not yet implemented on ndarray
raise NotImplementedError("Device support is not yet implemented")
return 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.
"""
if device is not None:
# Note: Device support is not yet implemented on ndarray
raise NotImplementedError("Device support is not yet implemented")
return np.ones_like._implementation(x, dtype=dtype)
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.
"""
if device is not None:
# Note: Device support is not yet implemented on ndarray
raise NotImplementedError("Device support is not yet implemented")
return 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.
"""
if device is not None:
# Note: Device support is not yet implemented on ndarray
raise NotImplementedError("Device support is not yet implemented")
return np.zeros_like._implementation(x, dtype=dtype)
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