ÿØÿàJFIFÿþ ÿÛC       ÿÛC ÿÀÿÄÿÄ"#QrÿÄÿÄ&1!A"2qQaáÿÚ ?Øy,æ/3JæݹÈ߲؋5êXw²±ÉyˆR”¾I0ó2—PI¾IÌÚiMö¯–þrìN&"KgX:Šíµ•nTJnLK„…@!‰-ý ùúmë;ºgµŒ&ó±hw’¯Õ@”Ü— 9ñ-ë.²1<yà‚¹ïQÐU„ہ?.’¦èûbß±©Ö«Âw*VŒ) `$‰bØԟ’ëXÖ-ËTÜíGÚ3ð«g Ÿ§¯—Jx„–’U/ÂÅv_s(Hÿ@TñJÑãõçn­‚!ÈgfbÓc­:él[ðQe 9ÀPLbÃãCµm[5¿ç'ªjglå‡Ûí_§Úõl-;"PkÞÞÁQâ¼_Ñ^¢SŸx?"¸¦ùY騐ÒOÈ q’`~~ÚtËU¹CڒêV  I1Áß_ÿÙfrom 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]: ...