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from collections.abc import Callable, Sequence from typing import ( Any, Final, TypeAlias, overload, TypeVar, Literal as L, SupportsAbs, SupportsIndex, NoReturn, TypeGuard, ) from typing_extensions import Unpack import numpy as np from numpy import ( # re-exports bitwise_not, False_, True_, broadcast, dtype, flatiter, from_dlpack, inf, little_endian, matmul, vecdot, nan, ndarray, nditer, newaxis, ufunc, # other generic, unsignedinteger, signedinteger, floating, complexfloating, int_, intp, float64, timedelta64, object_, _OrderKACF, _OrderCF, ) from .multiarray import ( # re-exports arange, array, asarray, asanyarray, ascontiguousarray, asfortranarray, can_cast, concatenate, copyto, dot, empty, empty_like, frombuffer, fromfile, fromiter, fromstring, inner, lexsort, may_share_memory, min_scalar_type, nested_iters, putmask, promote_types, result_type, shares_memory, vdot, where, zeros, # other _Array, _ConstructorEmpty, _KwargsEmpty, ) from numpy._typing import ( ArrayLike, NDArray, DTypeLike, _SupportsDType, _ShapeLike, _DTypeLike, _ArrayLike, _SupportsArrayFunc, _ScalarLike_co, _ArrayLikeBool_co, _ArrayLikeUInt_co, _ArrayLikeInt_co, _ArrayLikeFloat_co, _ArrayLikeComplex_co, _ArrayLikeTD64_co, _ArrayLikeObject_co, _ArrayLikeUnknown, ) __all__ = [ "newaxis", "ndarray", "flatiter", "nditer", "nested_iters", "ufunc", "arange", "array", "asarray", "asanyarray", "ascontiguousarray", "asfortranarray", "zeros", "count_nonzero", "empty", "broadcast", "dtype", "fromstring", "fromfile", "frombuffer", "from_dlpack", "where", "argwhere", "copyto", "concatenate", "lexsort", "astype", "can_cast", "promote_types", "min_scalar_type", "result_type", "isfortran", "empty_like", "zeros_like", "ones_like", "correlate", "convolve", "inner", "dot", "outer", "vdot", "roll", "rollaxis", "moveaxis", "cross", "tensordot", "little_endian", "fromiter", "array_equal", "array_equiv", "indices", "fromfunction", "isclose", "isscalar", "binary_repr", "base_repr", "ones", "identity", "allclose", "putmask", "flatnonzero", "inf", "nan", "False_", "True_", "bitwise_not", "full", "full_like", "matmul", "vecdot", "shares_memory", "may_share_memory", ] _T = TypeVar("_T") _SCT = TypeVar("_SCT", bound=generic) _DType = TypeVar("_DType", bound=np.dtype[Any]) _ArrayType = TypeVar("_ArrayType", bound=np.ndarray[Any, Any]) _SizeType = TypeVar("_SizeType", bound=int) _ShapeType = TypeVar("_ShapeType", bound=tuple[int, ...]) _CorrelateMode: TypeAlias = L["valid", "same", "full"] @overload def zeros_like( a: _ArrayType, dtype: None = ..., order: _OrderKACF = ..., subok: L[True] = ..., shape: None = ..., *, device: None | L["cpu"] = ..., ) -> _ArrayType: ... @overload def zeros_like( a: _ArrayLike[_SCT], dtype: None = ..., order: _OrderKACF = ..., subok: bool = ..., shape: None | _ShapeLike = ..., *, device: None | L["cpu"] = ..., ) -> NDArray[_SCT]: ... @overload def zeros_like( a: object, dtype: None = ..., order: _OrderKACF = ..., subok: bool = ..., shape: None | _ShapeLike= ..., *, device: None | L["cpu"] = ..., ) -> NDArray[Any]: ... @overload def zeros_like( a: Any, dtype: _DTypeLike[_SCT], order: _OrderKACF = ..., subok: bool = ..., shape: None | _ShapeLike= ..., *, device: None | L["cpu"] = ..., ) -> NDArray[_SCT]: ... @overload def zeros_like( a: Any, dtype: DTypeLike, order: _OrderKACF = ..., subok: bool = ..., shape: None | _ShapeLike= ..., *, device: None | L["cpu"] = ..., ) -> NDArray[Any]: ... ones: Final[_ConstructorEmpty] @overload def ones_like( a: _ArrayType, dtype: None = ..., order: _OrderKACF = ..., subok: L[True] = ..., shape: None = ..., *, device: None | L["cpu"] = ..., ) -> _ArrayType: ... @overload def ones_like( a: _ArrayLike[_SCT], dtype: None = ..., order: _OrderKACF = ..., subok: bool = ..., shape: None | _ShapeLike = ..., *, device: None | L["cpu"] = ..., ) -> NDArray[_SCT]: ... @overload def ones_like( a: object, dtype: None = ..., order: _OrderKACF = ..., subok: bool = ..., shape: None | _ShapeLike= ..., *, device: None | L["cpu"] = ..., ) -> NDArray[Any]: ... @overload def ones_like( a: Any, dtype: _DTypeLike[_SCT], order: _OrderKACF = ..., subok: bool = ..., shape: None | _ShapeLike= ..., *, device: None | L["cpu"] = ..., ) -> NDArray[_SCT]: ... @overload def ones_like( a: Any, dtype: DTypeLike, order: _OrderKACF = ..., subok: bool = ..., shape: None | _ShapeLike= ..., *, device: None | L["cpu"] = ..., ) -> NDArray[Any]: ... # TODO: Add overloads for bool, int, float, complex, str, bytes, and memoryview # 1-D shape @overload def full( shape: _SizeType, fill_value: _SCT, dtype: None = ..., order: _OrderCF = ..., **kwargs: Unpack[_KwargsEmpty], ) -> _Array[tuple[_SizeType], _SCT]: ... @overload def full( shape: _SizeType, fill_value: Any, dtype: _DType | _SupportsDType[_DType], order: _OrderCF = ..., **kwargs: Unpack[_KwargsEmpty], ) -> np.ndarray[tuple[_SizeType], _DType]: ... @overload def full( shape: _SizeType, fill_value: Any, dtype: type[_SCT], order: _OrderCF = ..., **kwargs: Unpack[_KwargsEmpty], ) -> _Array[tuple[_SizeType], _SCT]: ... @overload def full( shape: _SizeType, fill_value: Any, dtype: None | DTypeLike = ..., order: _OrderCF = ..., **kwargs: Unpack[_KwargsEmpty], ) -> _Array[tuple[_SizeType], Any]: ... # known shape @overload def full( shape: _ShapeType, fill_value: _SCT, dtype: None = ..., order: _OrderCF = ..., **kwargs: Unpack[_KwargsEmpty], ) -> _Array[_ShapeType, _SCT]: ... @overload def full( shape: _ShapeType, fill_value: Any, dtype: _DType | _SupportsDType[_DType], order: _OrderCF = ..., **kwargs: Unpack[_KwargsEmpty], ) -> np.ndarray[_ShapeType, _DType]: ... @overload def full( shape: _ShapeType, fill_value: Any, dtype: type[_SCT], order: _OrderCF = ..., **kwargs: Unpack[_KwargsEmpty], ) -> _Array[_ShapeType, _SCT]: ... @overload def full( shape: _ShapeType, fill_value: Any, dtype: None | DTypeLike = ..., order: _OrderCF = ..., **kwargs: Unpack[_KwargsEmpty], ) -> _Array[_ShapeType, Any]: ... # unknown shape @overload def full( shape: _ShapeLike, fill_value: _SCT, dtype: None = ..., order: _OrderCF = ..., **kwargs: Unpack[_KwargsEmpty], ) -> NDArray[_SCT]: ... @overload def full( shape: _ShapeLike, fill_value: Any, dtype: _DType | _SupportsDType[_DType], order: _OrderCF = ..., **kwargs: Unpack[_KwargsEmpty], ) -> np.ndarray[Any, _DType]: ... @overload def full( shape: _ShapeLike, fill_value: Any, dtype: type[_SCT], order: _OrderCF = ..., **kwargs: Unpack[_KwargsEmpty], ) -> NDArray[_SCT]: ... @overload def full( shape: _ShapeLike, fill_value: Any, dtype: None | DTypeLike = ..., order: _OrderCF = ..., **kwargs: Unpack[_KwargsEmpty], ) -> NDArray[Any]: ... @overload def full_like( a: _ArrayType, fill_value: Any, dtype: None = ..., order: _OrderKACF = ..., subok: L[True] = ..., shape: None = ..., *, device: None | L["cpu"] = ..., ) -> _ArrayType: ... @overload def full_like( a: _ArrayLike[_SCT], fill_value: Any, dtype: None = ..., order: _OrderKACF = ..., subok: bool = ..., shape: None | _ShapeLike = ..., *, device: None | L["cpu"] = ..., ) -> NDArray[_SCT]: ... @overload def full_like( a: object, fill_value: Any, dtype: None = ..., order: _OrderKACF = ..., subok: bool = ..., shape: None | _ShapeLike= ..., *, device: None | L["cpu"] = ..., ) -> NDArray[Any]: ... @overload def full_like( a: Any, fill_value: Any, dtype: _DTypeLike[_SCT], order: _OrderKACF = ..., subok: bool = ..., shape: None | _ShapeLike= ..., *, device: None | L["cpu"] = ..., ) -> NDArray[_SCT]: ... @overload def full_like( a: Any, fill_value: Any, dtype: DTypeLike, order: _OrderKACF = ..., subok: bool = ..., shape: None | _ShapeLike= ..., *, device: None | L["cpu"] = ..., ) -> NDArray[Any]: ... @overload def count_nonzero( a: ArrayLike, axis: None = ..., *, keepdims: L[False] = ..., ) -> int: ... @overload def count_nonzero( a: ArrayLike, axis: _ShapeLike = ..., *, keepdims: bool = ..., ) -> Any: ... # TODO: np.intp or ndarray[np.intp] def isfortran(a: NDArray[Any] | generic) -> bool: ... def argwhere(a: ArrayLike) -> NDArray[intp]: ... def flatnonzero(a: ArrayLike) -> NDArray[intp]: ... @overload def correlate( a: _ArrayLikeUnknown, v: _ArrayLikeUnknown, mode: _CorrelateMode = ..., ) -> NDArray[Any]: ... @overload def correlate( a: _ArrayLikeBool_co, v: _ArrayLikeBool_co, mode: _CorrelateMode = ..., ) -> NDArray[np.bool]: ... @overload def correlate( a: _ArrayLikeUInt_co, v: _ArrayLikeUInt_co, mode: _CorrelateMode = ..., ) -> NDArray[unsignedinteger[Any]]: ... @overload def correlate( a: _ArrayLikeInt_co, v: _ArrayLikeInt_co, mode: _CorrelateMode = ..., ) -> NDArray[signedinteger[Any]]: ... @overload def correlate( a: _ArrayLikeFloat_co, v: _ArrayLikeFloat_co, mode: _CorrelateMode = ..., ) -> NDArray[floating[Any]]: ... @overload def correlate( a: _ArrayLikeComplex_co, v: _ArrayLikeComplex_co, mode: _CorrelateMode = ..., ) -> NDArray[complexfloating[Any, Any]]: ... @overload def correlate( a: _ArrayLikeTD64_co, v: _ArrayLikeTD64_co, mode: _CorrelateMode = ..., ) -> NDArray[timedelta64]: ... @overload def correlate( a: _ArrayLikeObject_co, v: _ArrayLikeObject_co, mode: _CorrelateMode = ..., ) -> NDArray[object_]: ... @overload def convolve( a: _ArrayLikeUnknown, v: _ArrayLikeUnknown, mode: _CorrelateMode = ..., ) -> NDArray[Any]: ... @overload def convolve( a: _ArrayLikeBool_co, v: _ArrayLikeBool_co, mode: _CorrelateMode = ..., ) -> NDArray[np.bool]: ... @overload def convolve( a: _ArrayLikeUInt_co, v: _ArrayLikeUInt_co, mode: _CorrelateMode = ..., ) -> NDArray[unsignedinteger[Any]]: ... @overload def convolve( a: _ArrayLikeInt_co, v: _ArrayLikeInt_co, mode: _CorrelateMode = ..., ) -> NDArray[signedinteger[Any]]: ... @overload def convolve( a: _ArrayLikeFloat_co, v: _ArrayLikeFloat_co, mode: _CorrelateMode = ..., ) -> NDArray[floating[Any]]: ... @overload def convolve( a: _ArrayLikeComplex_co, v: _ArrayLikeComplex_co, mode: _CorrelateMode = ..., ) -> NDArray[complexfloating[Any, Any]]: ... @overload def convolve( a: _ArrayLikeTD64_co, v: _ArrayLikeTD64_co, mode: _CorrelateMode = ..., ) -> NDArray[timedelta64]: ... @overload def convolve( a: _ArrayLikeObject_co, v: _ArrayLikeObject_co, mode: _CorrelateMode = ..., ) -> NDArray[object_]: ... @overload def outer( a: _ArrayLikeUnknown, b: _ArrayLikeUnknown, out: None = ..., ) -> NDArray[Any]: ... @overload def outer( a: _ArrayLikeBool_co, b: _ArrayLikeBool_co, out: None = ..., ) -> NDArray[np.bool]: ... @overload def outer( a: _ArrayLikeUInt_co, b: _ArrayLikeUInt_co, out: None = ..., ) -> NDArray[unsignedinteger[Any]]: ... @overload def outer( a: _ArrayLikeInt_co, b: _ArrayLikeInt_co, out: None = ..., ) -> NDArray[signedinteger[Any]]: ... @overload def outer( a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, out: None = ..., ) -> NDArray[floating[Any]]: ... @overload def outer( a: _ArrayLikeComplex_co, b: _ArrayLikeComplex_co, out: None = ..., ) -> NDArray[complexfloating[Any, Any]]: ... @overload def outer( a: _ArrayLikeTD64_co, b: _ArrayLikeTD64_co, out: None = ..., ) -> NDArray[timedelta64]: ... @overload def outer( a: _ArrayLikeObject_co, b: _ArrayLikeObject_co, out: None = ..., ) -> NDArray[object_]: ... @overload def outer( a: _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeObject_co, b: _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeObject_co, out: _ArrayType, ) -> _ArrayType: ... @overload def tensordot( a: _ArrayLikeUnknown, b: _ArrayLikeUnknown, axes: int | tuple[_ShapeLike, _ShapeLike] = ..., ) -> NDArray[Any]: ... @overload def tensordot( a: _ArrayLikeBool_co, b: _ArrayLikeBool_co, axes: int | tuple[_ShapeLike, _ShapeLike] = ..., ) -> NDArray[np.bool]: ... @overload def tensordot( a: _ArrayLikeUInt_co, b: _ArrayLikeUInt_co, axes: int | tuple[_ShapeLike, _ShapeLike] = ..., ) -> NDArray[unsignedinteger[Any]]: ... @overload def tensordot( a: _ArrayLikeInt_co, b: _ArrayLikeInt_co, axes: int | tuple[_ShapeLike, _ShapeLike] = ..., ) -> NDArray[signedinteger[Any]]: ... @overload def tensordot( a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, axes: int | tuple[_ShapeLike, _ShapeLike] = ..., ) -> NDArray[floating[Any]]: ... @overload def tensordot( a: _ArrayLikeComplex_co, b: _ArrayLikeComplex_co, axes: int | tuple[_ShapeLike, _ShapeLike] = ..., ) -> NDArray[complexfloating[Any, Any]]: ... @overload def tensordot( a: _ArrayLikeTD64_co, b: _ArrayLikeTD64_co, axes: int | tuple[_ShapeLike, _ShapeLike] = ..., ) -> NDArray[timedelta64]: ... @overload def tensordot( a: _ArrayLikeObject_co, b: _ArrayLikeObject_co, axes: int | tuple[_ShapeLike, _ShapeLike] = ..., ) -> NDArray[object_]: ... @overload def roll( a: _ArrayLike[_SCT], shift: _ShapeLike, axis: None | _ShapeLike = ..., ) -> NDArray[_SCT]: ... @overload def roll( a: ArrayLike, shift: _ShapeLike, axis: None | _ShapeLike = ..., ) -> NDArray[Any]: ... def rollaxis( a: NDArray[_SCT], axis: int, start: int = ..., ) -> NDArray[_SCT]: ... def moveaxis( a: NDArray[_SCT], source: _ShapeLike, destination: _ShapeLike, ) -> NDArray[_SCT]: ... @overload def cross( x1: _ArrayLikeUnknown, x2: _ArrayLikeUnknown, axisa: int = ..., axisb: int = ..., axisc: int = ..., axis: None | int = ..., ) -> NDArray[Any]: ... @overload def cross( x1: _ArrayLikeBool_co, x2: _ArrayLikeBool_co, axisa: int = ..., axisb: int = ..., axisc: int = ..., axis: None | int = ..., ) -> NoReturn: ... @overload def cross( x1: _ArrayLikeUInt_co, x2: _ArrayLikeUInt_co, axisa: int = ..., axisb: int = ..., axisc: int = ..., axis: None | int = ..., ) -> NDArray[unsignedinteger[Any]]: ... @overload def cross( x1: _ArrayLikeInt_co, x2: _ArrayLikeInt_co, axisa: int = ..., axisb: int = ..., axisc: int = ..., axis: None | int = ..., ) -> NDArray[signedinteger[Any]]: ... @overload def cross( x1: _ArrayLikeFloat_co, x2: _ArrayLikeFloat_co, axisa: int = ..., axisb: int = ..., axisc: int = ..., axis: None | int = ..., ) -> NDArray[floating[Any]]: ... @overload def cross( x1: _ArrayLikeComplex_co, x2: _ArrayLikeComplex_co, axisa: int = ..., axisb: int = ..., axisc: int = ..., axis: None | int = ..., ) -> NDArray[complexfloating[Any, Any]]: ... @overload def cross( x1: _ArrayLikeObject_co, x2: _ArrayLikeObject_co, axisa: int = ..., axisb: int = ..., axisc: int = ..., axis: None | int = ..., ) -> NDArray[object_]: ... @overload def indices( dimensions: Sequence[int], dtype: type[int] = ..., sparse: L[False] = ..., ) -> NDArray[int_]: ... @overload def indices( dimensions: Sequence[int], dtype: type[int] = ..., sparse: L[True] = ..., ) -> tuple[NDArray[int_], ...]: ... @overload def indices( dimensions: Sequence[int], dtype: _DTypeLike[_SCT], sparse: L[False] = ..., ) -> NDArray[_SCT]: ... @overload def indices( dimensions: Sequence[int], dtype: _DTypeLike[_SCT], sparse: L[True], ) -> tuple[NDArray[_SCT], ...]: ... @overload def indices( dimensions: Sequence[int], dtype: DTypeLike, sparse: L[False] = ..., ) -> NDArray[Any]: ... @overload def indices( dimensions: Sequence[int], dtype: DTypeLike, sparse: L[True], ) -> tuple[NDArray[Any], ...]: ... def fromfunction( function: Callable[..., _T], shape: Sequence[int], *, dtype: DTypeLike = ..., like: _SupportsArrayFunc = ..., **kwargs: Any, ) -> _T: ... def isscalar(element: object) -> TypeGuard[ generic | bool | int | float | complex | str | bytes | memoryview ]: ... def binary_repr(num: SupportsIndex, width: None | int = ...) -> str: ... def base_repr( number: SupportsAbs[float], base: float = ..., padding: SupportsIndex = ..., ) -> str: ... @overload def identity( n: int, dtype: None = ..., *, like: _SupportsArrayFunc = ..., ) -> NDArray[float64]: ... @overload def identity( n: int, dtype: _DTypeLike[_SCT], *, like: _SupportsArrayFunc = ..., ) -> NDArray[_SCT]: ... @overload def identity( n: int, dtype: DTypeLike, *, like: _SupportsArrayFunc = ..., ) -> NDArray[Any]: ... def allclose( a: ArrayLike, b: ArrayLike, rtol: ArrayLike = ..., atol: ArrayLike = ..., equal_nan: bool = ..., ) -> bool: ... @overload def isclose( a: _ScalarLike_co, b: _ScalarLike_co, rtol: ArrayLike = ..., atol: ArrayLike = ..., equal_nan: bool = ..., ) -> np.bool: ... @overload def isclose( a: ArrayLike, b: ArrayLike, rtol: ArrayLike = ..., atol: ArrayLike = ..., equal_nan: bool = ..., ) -> NDArray[np.bool]: ... def array_equal(a1: ArrayLike, a2: ArrayLike, equal_nan: bool = ...) -> bool: ... def array_equiv(a1: ArrayLike, a2: ArrayLike) -> bool: ... @overload def astype( x: ndarray[_ShapeType, dtype[Any]], dtype: _DTypeLike[_SCT], copy: bool = ..., device: None | L["cpu"] = ..., ) -> ndarray[_ShapeType, dtype[_SCT]]: ... @overload def astype( x: ndarray[_ShapeType, dtype[Any]], dtype: DTypeLike, copy: bool = ..., device: None | L["cpu"] = ..., ) -> ndarray[_ShapeType, dtype[Any]]: ...

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