name : _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 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))