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74 lines
1.9 KiB
74 lines
1.9 KiB
from collections.abc import Iterable
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from typing import Any, SupportsIndex, TypeVar, overload
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from numpy import generic
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from numpy._typing import ArrayLike, NDArray, _AnyShape, _ArrayLike, _ShapeLike
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__all__ = ["broadcast_to", "broadcast_arrays", "broadcast_shapes"]
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_ScalarT = TypeVar("_ScalarT", bound=generic)
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class DummyArray:
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__array_interface__: dict[str, Any]
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base: NDArray[Any] | None
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def __init__(
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self,
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interface: dict[str, Any],
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base: NDArray[Any] | None = None,
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) -> None: ...
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@overload
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def as_strided(
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x: _ArrayLike[_ScalarT],
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shape: Iterable[int] | None = None,
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strides: Iterable[int] | None = None,
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subok: bool = False,
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writeable: bool = True,
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) -> NDArray[_ScalarT]: ...
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@overload
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def as_strided(
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x: ArrayLike,
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shape: Iterable[int] | None = None,
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strides: Iterable[int] | None = None,
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subok: bool = False,
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writeable: bool = True,
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) -> NDArray[Any]: ...
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@overload
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def sliding_window_view(
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x: _ArrayLike[_ScalarT],
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window_shape: int | Iterable[int],
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axis: SupportsIndex | None = None,
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*,
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subok: bool = False,
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writeable: bool = False,
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) -> NDArray[_ScalarT]: ...
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@overload
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def sliding_window_view(
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x: ArrayLike,
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window_shape: int | Iterable[int],
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axis: SupportsIndex | None = None,
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*,
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subok: bool = False,
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writeable: bool = False,
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) -> NDArray[Any]: ...
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@overload
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def broadcast_to(
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array: _ArrayLike[_ScalarT],
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shape: int | Iterable[int],
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subok: bool = False,
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) -> NDArray[_ScalarT]: ...
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@overload
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def broadcast_to(
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array: ArrayLike,
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shape: int | Iterable[int],
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subok: bool = False,
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) -> NDArray[Any]: ...
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def broadcast_shapes(*args: _ShapeLike) -> _AnyShape: ...
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def broadcast_arrays(*args: ArrayLike, subok: bool = False) -> tuple[NDArray[Any], ...]: ...
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# used internally by `lib._function_base_impl._parse_input_dimensions`
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def _broadcast_shape(*args: ArrayLike) -> _AnyShape: ...
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