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__init__.py
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__init__.pyi
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arraypad.py
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arraypad.pyi
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arraysetops.py
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arraysetops.pyi
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arrayterator.py
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arrayterator.pyi
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format.py
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function_base.pyi
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histograms.py
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histograms.pyi
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index_tricks.py
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index_tricks.pyi
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mixins.py
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nanfunctions.py
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nanfunctions.pyi
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recfunctions.py
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scimath.py
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scimath.pyi
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setup.py
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shape_base.py
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shape_base.pyi
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stride_tricks.py
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stride_tricks.pyi
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tests
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twodim_base.py
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twodim_base.pyi
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type_check.py
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type_check.pyi
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ufunclike.py
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ufunclike.pyi
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user_array.py
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utils.py
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utils.pyi
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Editing: arrayterator.pyi
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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]: ...