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from collections.abc import Generator
from typing import (
Any,
TypeVar,
Union,
overload,
)
from numpy import ndarray, dtype, generic
from numpy._typing import DTypeLike
# TODO: Set a shape bound once we've got proper shape support
_Shape = TypeVar("_Shape", bound=Any)
_DType = TypeVar("_DType", bound=dtype[Any])
_ScalarType = TypeVar("_ScalarType", bound=generic)
_Index = Union[
Union[ellipsis, int, slice],
tuple[Union[ellipsis, int, slice], ...],
]
__all__: list[str]
# NOTE: In reality `Arrayterator` does not actually inherit from `ndarray`,
# but its ``__getattr__` method does wrap around the former and thus has
# access to all its methods
class Arrayterator(ndarray[_Shape, _DType]):
var: ndarray[_Shape, _DType] # type: ignore[assignment]
buf_size: None | int
start: list[int]
stop: list[int]
step: list[int]
@property # type: ignore[misc]
def shape(self) -> tuple[int, ...]: ...
@property
def flat( # type: ignore[override]
self: ndarray[Any, dtype[_ScalarType]]
) -> Generator[_ScalarType, None, None]: ...
def __init__(
self, var: ndarray[_Shape, _DType], buf_size: None | int = ...
) -> None: ...
@overload
def __array__(self, dtype: None = ...) -> ndarray[Any, _DType]: ...
@overload
def __array__(self, dtype: DTypeLike) -> ndarray[Any, dtype[Any]]: ...
def __getitem__(self, index: _Index) -> Arrayterator[Any, _DType]: ...
def __iter__(self) -> Generator[ndarray[Any, _DType], None, None]: ...