from _typeshed import Incomplete from builtins import bool as py_bool from collections.abc import Callable, Iterable, Sequence from typing import ( Any, Final, Literal as L, SupportsAbs, SupportsIndex, TypeAlias, TypeGuard, TypeVar, overload, ) import numpy as np from numpy import ( False_, True_, _OrderCF, _OrderKACF, bitwise_not, inf, little_endian, nan, newaxis, ufunc, ) from numpy._typing import ( ArrayLike, DTypeLike, NDArray, _ArrayLike, _ArrayLikeBool_co, _ArrayLikeComplex_co, _ArrayLikeFloat_co, _ArrayLikeInt_co, _ArrayLikeNumber_co, _ArrayLikeTD64_co, _CDoubleCodes, _Complex128Codes, _DoubleCodes, _DTypeLike, _DTypeLikeBool, _Float64Codes, _IntCodes, _NestedSequence, _NumberLike_co, _ScalarLike_co, _Shape, _ShapeLike, _SupportsArray, _SupportsArrayFunc, _SupportsDType, ) from ._asarray import require from ._ufunc_config import ( errstate, getbufsize, geterr, geterrcall, setbufsize, seterr, seterrcall, ) from .arrayprint import ( array2string, array_repr, array_str, format_float_positional, format_float_scientific, get_printoptions, printoptions, set_printoptions, ) from .fromnumeric import ( all, amax, amin, any, argmax, argmin, argpartition, argsort, around, choose, clip, compress, cumprod, cumsum, cumulative_prod, cumulative_sum, diagonal, matrix_transpose, max, mean, min, ndim, nonzero, partition, prod, ptp, put, ravel, repeat, reshape, resize, round, searchsorted, shape, size, sort, squeeze, std, sum, swapaxes, take, trace, transpose, var, ) from .multiarray import ( ALLOW_THREADS as ALLOW_THREADS, BUFSIZE as BUFSIZE, CLIP as CLIP, MAXDIMS as MAXDIMS, MAY_SHARE_BOUNDS as MAY_SHARE_BOUNDS, MAY_SHARE_EXACT as MAY_SHARE_EXACT, RAISE as RAISE, WRAP as WRAP, _Array, _ConstructorEmpty, arange, array, asanyarray, asarray, ascontiguousarray, asfortranarray, broadcast, can_cast, concatenate, copyto, dot, dtype, empty, empty_like, flatiter, from_dlpack, frombuffer, fromfile, fromiter, fromstring, inner, lexsort, matmul, may_share_memory, min_scalar_type, ndarray, nditer, nested_iters, normalize_axis_index as normalize_axis_index, promote_types, putmask, result_type, shares_memory, vdot, where, zeros, ) from .numerictypes import ( ScalarType, bool, bool_, busday_count, busday_offset, busdaycalendar, byte, bytes_, cdouble, character, clongdouble, complex64, complex128, complex192, complex256, complexfloating, csingle, datetime64, datetime_as_string, datetime_data, double, flexible, float16, float32, float64, float96, float128, floating, generic, half, inexact, int8, int16, int32, int64, int_, intc, integer, intp, is_busday, isdtype, issubdtype, long, longdouble, longlong, number, object_, short, signedinteger, single, str_, timedelta64, typecodes, ubyte, uint, uint8, uint16, uint32, uint64, uintc, uintp, ulong, ulonglong, unsignedinteger, ushort, void, ) from .umath import ( absolute, add, arccos, arccosh, arcsin, arcsinh, arctan, arctan2, arctanh, bitwise_and, bitwise_count, bitwise_or, bitwise_xor, cbrt, ceil, conj, conjugate, copysign, cos, cosh, deg2rad, degrees, divide, divmod, e, equal, euler_gamma, exp, exp2, expm1, fabs, float_power, floor, floor_divide, fmax, fmin, fmod, frexp, frompyfunc, gcd, greater, greater_equal, heaviside, hypot, invert, isfinite, isinf, isnan, isnat, lcm, ldexp, left_shift, less, less_equal, log, log1p, log2, log10, logaddexp, logaddexp2, logical_and, logical_not, logical_or, logical_xor, matvec, maximum, minimum, mod, modf, multiply, negative, nextafter, not_equal, pi, positive, power, rad2deg, radians, reciprocal, remainder, right_shift, rint, sign, signbit, sin, sinh, spacing, sqrt, square, subtract, tan, tanh, true_divide, trunc, vecdot, vecmat, ) __all__ = [ "False_", "ScalarType", "True_", "absolute", "add", "all", "allclose", "amax", "amin", "any", "arange", "arccos", "arccosh", "arcsin", "arcsinh", "arctan", "arctan2", "arctanh", "argmax", "argmin", "argpartition", "argsort", "argwhere", "around", "array", "array2string", "array_equal", "array_equiv", "array_repr", "array_str", "asanyarray", "asarray", "ascontiguousarray", "asfortranarray", "astype", "base_repr", "binary_repr", "bitwise_and", "bitwise_count", "bitwise_not", "bitwise_or", "bitwise_xor", "bool", "bool_", "broadcast", "busday_count", "busday_offset", "busdaycalendar", "byte", "bytes_", "can_cast", "cbrt", "cdouble", "ceil", "character", "choose", "clip", "clongdouble", "complex64", "complex128", "complex192", "complex256", "complexfloating", "compress", "concatenate", "conj", "conjugate", "convolve", "copysign", "copyto", "correlate", "cos", "cosh", "count_nonzero", "cross", "csingle", "cumprod", "cumsum", "cumulative_prod", "cumulative_sum", "datetime64", "datetime_as_string", "datetime_data", "deg2rad", "degrees", "diagonal", "divide", "divmod", "dot", "double", "dtype", "e", "empty", "empty_like", "equal", "errstate", "euler_gamma", "exp", "exp2", "expm1", "fabs", "flatiter", "flatnonzero", "flexible", "float16", "float32", "float64", "float96", "float128", "float_power", "floating", "floor", "floor_divide", "fmax", "fmin", "fmod", "format_float_positional", "format_float_scientific", "frexp", "from_dlpack", "frombuffer", "fromfile", "fromfunction", "fromiter", "frompyfunc", "fromstring", "full", "full_like", "gcd", "generic", "get_printoptions", "getbufsize", "geterr", "geterrcall", "greater", "greater_equal", "half", "heaviside", "hypot", "identity", "indices", "inexact", "inf", "inner", "int8", "int16", "int32", "int64", "int_", "intc", "integer", "intp", "invert", "is_busday", "isclose", "isdtype", "isfinite", "isfortran", "isinf", "isnan", "isnat", "isscalar", "issubdtype", "lcm", "ldexp", "left_shift", "less", "less_equal", "lexsort", "little_endian", "log", "log1p", "log2", "log10", "logaddexp", "logaddexp2", "logical_and", "logical_not", "logical_or", "logical_xor", "long", "longdouble", "longlong", "matmul", "matrix_transpose", "matvec", "max", "maximum", "may_share_memory", "mean", "min", "min_scalar_type", "minimum", "mod", "modf", "moveaxis", "multiply", "nan", "ndarray", "ndim", "nditer", "negative", "nested_iters", "newaxis", "nextafter", "nonzero", "not_equal", "number", "object_", "ones", "ones_like", "outer", "partition", "pi", "positive", "power", "printoptions", "prod", "promote_types", "ptp", "put", "putmask", "rad2deg", "radians", "ravel", "reciprocal", "remainder", "repeat", "require", "reshape", "resize", "result_type", "right_shift", "rint", "roll", "rollaxis", "round", "searchsorted", "set_printoptions", "setbufsize", "seterr", "seterrcall", "shape", "shares_memory", "short", "sign", "signbit", "signedinteger", "sin", "single", "sinh", "size", "sort", "spacing", "sqrt", "square", "squeeze", "std", "str_", "subtract", "sum", "swapaxes", "take", "tan", "tanh", "tensordot", "timedelta64", "trace", "transpose", "true_divide", "trunc", "typecodes", "ubyte", "ufunc", "uint", "uint8", "uint16", "uint32", "uint64", "uintc", "uintp", "ulong", "ulonglong", "unsignedinteger", "ushort", "var", "vdot", "vecdot", "vecmat", "void", "where", "zeros", "zeros_like", ] _T = TypeVar("_T") _ScalarT = TypeVar("_ScalarT", bound=generic) _NumberObjectT = TypeVar("_NumberObjectT", bound=number | object_) _NumericScalarT = TypeVar("_NumericScalarT", bound=number | timedelta64 | object_) _DTypeT = TypeVar("_DTypeT", bound=dtype) _ArrayT = TypeVar("_ArrayT", bound=np.ndarray[Any, Any]) _ShapeT = TypeVar("_ShapeT", bound=_Shape) _AnyShapeT = TypeVar( "_AnyShapeT", tuple[()], tuple[int], tuple[int, int], tuple[int, int, int], tuple[int, int, int, int], tuple[int, ...], ) _AnyNumericScalarT = TypeVar( "_AnyNumericScalarT", np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint16, np.uint32, np.uint64, np.float16, np.float32, np.float64, np.longdouble, np.complex64, np.complex128, np.clongdouble, np.timedelta64, np.object_, ) _CorrelateMode: TypeAlias = L["valid", "same", "full"] _Array1D: TypeAlias = np.ndarray[tuple[int], np.dtype[_ScalarT]] _Array2D: TypeAlias = np.ndarray[tuple[int, int], np.dtype[_ScalarT]] _Array3D: TypeAlias = np.ndarray[tuple[int, int, int], np.dtype[_ScalarT]] _Array4D: TypeAlias = np.ndarray[tuple[int, int, int, int], np.dtype[_ScalarT]] _Int_co: TypeAlias = np.integer | np.bool _Float_co: TypeAlias = np.floating | _Int_co _Number_co: TypeAlias = np.number | np.bool _TD64_co: TypeAlias = np.timedelta64 | _Int_co _ArrayLike1D: TypeAlias = _SupportsArray[np.dtype[_ScalarT]] | Sequence[_ScalarT] _ArrayLike1DBool_co: TypeAlias = _SupportsArray[np.dtype[np.bool]] | Sequence[py_bool | np.bool] _ArrayLike1DInt_co: TypeAlias = _SupportsArray[np.dtype[_Int_co]] | Sequence[int | _Int_co] _ArrayLike1DFloat_co: TypeAlias = _SupportsArray[np.dtype[_Float_co]] | Sequence[float | _Float_co] _ArrayLike1DNumber_co: TypeAlias = _SupportsArray[np.dtype[_Number_co]] | Sequence[complex | _Number_co] _ArrayLike1DTD64_co: TypeAlias = _ArrayLike1D[_TD64_co] _ArrayLike1DObject_co: TypeAlias = _ArrayLike1D[np.object_] _DTypeLikeInt: TypeAlias = type[int] | _IntCodes _DTypeLikeFloat64: TypeAlias = type[float] | _Float64Codes | _DoubleCodes _DTypeLikeComplex128: TypeAlias = type[complex] | _Complex128Codes | _CDoubleCodes ### # keep in sync with `ones_like` @overload def zeros_like( a: _ArrayT, dtype: None = None, order: _OrderKACF = "K", subok: L[True] = True, shape: None = None, *, device: L["cpu"] | None = None, ) -> _ArrayT: ... @overload def zeros_like( a: _ArrayLike[_ScalarT], dtype: None = None, order: _OrderKACF = "K", subok: py_bool = True, shape: _ShapeLike | None = None, *, device: L["cpu"] | None = None, ) -> NDArray[_ScalarT]: ... @overload def zeros_like( a: object, dtype: _DTypeLike[_ScalarT], order: _OrderKACF = "K", subok: py_bool = True, shape: _ShapeLike | None = None, *, device: L["cpu"] | None = None, ) -> NDArray[_ScalarT]: ... @overload def zeros_like( a: object, dtype: DTypeLike | None = None, order: _OrderKACF = "K", subok: py_bool = True, shape: _ShapeLike | None = None, *, device: L["cpu"] | None = None, ) -> NDArray[Any]: ... ones: Final[_ConstructorEmpty] # keep in sync with `zeros_like` @overload def ones_like( a: _ArrayT, dtype: None = None, order: _OrderKACF = "K", subok: L[True] = True, shape: None = None, *, device: L["cpu"] | None = None, ) -> _ArrayT: ... @overload def ones_like( a: _ArrayLike[_ScalarT], dtype: None = None, order: _OrderKACF = "K", subok: py_bool = True, shape: _ShapeLike | None = None, *, device: L["cpu"] | None = None, ) -> NDArray[_ScalarT]: ... @overload def ones_like( a: object, dtype: _DTypeLike[_ScalarT], order: _OrderKACF = "K", subok: py_bool = True, shape: _ShapeLike | None = None, *, device: L["cpu"] | None = None, ) -> NDArray[_ScalarT]: ... @overload def ones_like( a: object, dtype: DTypeLike | None = None, order: _OrderKACF = "K", subok: py_bool = True, shape: _ShapeLike | None = None, *, device: L["cpu"] | None = None, ) -> NDArray[Any]: ... # TODO: Add overloads for bool, int, float, complex, str, bytes, and memoryview # 1-D shape @overload def full( shape: SupportsIndex, fill_value: _ScalarT, dtype: None = None, order: _OrderCF = "C", *, device: L["cpu"] | None = None, like: _SupportsArrayFunc | None = None, ) -> _Array[tuple[int], _ScalarT]: ... @overload def full( shape: SupportsIndex, fill_value: Any, dtype: _DTypeT | _SupportsDType[_DTypeT], order: _OrderCF = "C", *, device: L["cpu"] | None = None, like: _SupportsArrayFunc | None = None, ) -> np.ndarray[tuple[int], _DTypeT]: ... @overload def full( shape: SupportsIndex, fill_value: Any, dtype: type[_ScalarT], order: _OrderCF = "C", *, device: L["cpu"] | None = None, like: _SupportsArrayFunc | None = None, ) -> _Array[tuple[int], _ScalarT]: ... @overload def full( shape: SupportsIndex, fill_value: Any, dtype: DTypeLike | None = None, order: _OrderCF = "C", *, device: L["cpu"] | None = None, like: _SupportsArrayFunc | None = None, ) -> _Array[tuple[int], Any]: ... # known shape @overload def full( shape: _AnyShapeT, fill_value: _ScalarT, dtype: None = None, order: _OrderCF = "C", *, device: L["cpu"] | None = None, like: _SupportsArrayFunc | None = None, ) -> _Array[_AnyShapeT, _ScalarT]: ... @overload def full( shape: _AnyShapeT, fill_value: Any, dtype: _DTypeT | _SupportsDType[_DTypeT], order: _OrderCF = "C", *, device: L["cpu"] | None = None, like: _SupportsArrayFunc | None = None, ) -> np.ndarray[_AnyShapeT, _DTypeT]: ... @overload def full( shape: _AnyShapeT, fill_value: Any, dtype: type[_ScalarT], order: _OrderCF = "C", *, device: L["cpu"] | None = None, like: _SupportsArrayFunc | None = None, ) -> _Array[_AnyShapeT, _ScalarT]: ... @overload def full( shape: _AnyShapeT, fill_value: Any, dtype: DTypeLike | None = None, order: _OrderCF = "C", *, device: L["cpu"] | None = None, like: _SupportsArrayFunc | None = None, ) -> _Array[_AnyShapeT, Any]: ... # unknown shape @overload def full( shape: _ShapeLike, fill_value: _ScalarT, dtype: None = None, order: _OrderCF = "C", *, device: L["cpu"] | None = None, like: _SupportsArrayFunc | None = None, ) -> NDArray[_ScalarT]: ... @overload def full( shape: _ShapeLike, fill_value: Any, dtype: _DTypeT | _SupportsDType[_DTypeT], order: _OrderCF = "C", *, device: L["cpu"] | None = None, like: _SupportsArrayFunc | None = None, ) -> np.ndarray[Any, _DTypeT]: ... @overload def full( shape: _ShapeLike, fill_value: Any, dtype: type[_ScalarT], order: _OrderCF = "C", *, device: L["cpu"] | None = None, like: _SupportsArrayFunc | None = None, ) -> NDArray[_ScalarT]: ... @overload def full( shape: _ShapeLike, fill_value: Any, dtype: DTypeLike | None = None, order: _OrderCF = "C", *, device: L["cpu"] | None = None, like: _SupportsArrayFunc | None = None, ) -> NDArray[Any]: ... @overload def full_like( a: _ArrayT, fill_value: object, dtype: None = None, order: _OrderKACF = "K", subok: L[True] = True, shape: None = None, *, device: L["cpu"] | None = None, ) -> _ArrayT: ... @overload def full_like( a: _ArrayLike[_ScalarT], fill_value: object, dtype: None = None, order: _OrderKACF = "K", subok: py_bool = True, shape: _ShapeLike | None = None, *, device: L["cpu"] | None = None, ) -> NDArray[_ScalarT]: ... @overload def full_like( a: object, fill_value: object, dtype: _DTypeLike[_ScalarT], order: _OrderKACF = "K", subok: py_bool = True, shape: _ShapeLike | None = None, *, device: L["cpu"] | None = None, ) -> NDArray[_ScalarT]: ... @overload def full_like( a: object, fill_value: object, dtype: DTypeLike | None = None, order: _OrderKACF = "K", subok: py_bool = True, shape: _ShapeLike | None = None, *, device: L["cpu"] | None = None, ) -> NDArray[Any]: ... # @overload def count_nonzero(a: ArrayLike, axis: None = None, *, keepdims: L[False] = False) -> np.intp: ... @overload def count_nonzero(a: _ScalarLike_co, axis: _ShapeLike | None = None, *, keepdims: L[True]) -> np.intp: ... @overload def count_nonzero( a: NDArray[Any] | _NestedSequence[ArrayLike], axis: _ShapeLike | None = None, *, keepdims: L[True] ) -> NDArray[np.intp]: ... @overload def count_nonzero(a: ArrayLike, axis: _ShapeLike | None = None, *, keepdims: py_bool = False) -> Any: ... # def isfortran(a: ndarray | generic) -> py_bool: ... # def argwhere(a: ArrayLike) -> _Array2D[np.intp]: ... def flatnonzero(a: ArrayLike) -> _Array1D[np.intp]: ... # keep in sync with `convolve` @overload def correlate( a: _ArrayLike1D[_AnyNumericScalarT], v: _ArrayLike1D[_AnyNumericScalarT], mode: _CorrelateMode = "valid" ) -> _Array1D[_AnyNumericScalarT]: ... @overload def correlate(a: _ArrayLike1DBool_co, v: _ArrayLike1DBool_co, mode: _CorrelateMode = "valid") -> _Array1D[np.bool]: ... @overload def correlate(a: _ArrayLike1DInt_co, v: _ArrayLike1DInt_co, mode: _CorrelateMode = "valid") -> _Array1D[np.int_ | Any]: ... @overload def correlate(a: _ArrayLike1DFloat_co, v: _ArrayLike1DFloat_co, mode: _CorrelateMode = "valid") -> _Array1D[np.float64 | Any]: ... @overload def correlate( a: _ArrayLike1DNumber_co, v: _ArrayLike1DNumber_co, mode: _CorrelateMode = "valid" ) -> _Array1D[np.complex128 | Any]: ... @overload def correlate( a: _ArrayLike1DTD64_co, v: _ArrayLike1DTD64_co, mode: _CorrelateMode = "valid" ) -> _Array1D[np.timedelta64 | Any]: ... # keep in sync with `correlate` @overload def convolve( a: _ArrayLike1D[_AnyNumericScalarT], v: _ArrayLike1D[_AnyNumericScalarT], mode: _CorrelateMode = "valid" ) -> _Array1D[_AnyNumericScalarT]: ... @overload def convolve(a: _ArrayLike1DBool_co, v: _ArrayLike1DBool_co, mode: _CorrelateMode = "valid") -> _Array1D[np.bool]: ... @overload def convolve(a: _ArrayLike1DInt_co, v: _ArrayLike1DInt_co, mode: _CorrelateMode = "valid") -> _Array1D[np.int_ | Any]: ... @overload def convolve(a: _ArrayLike1DFloat_co, v: _ArrayLike1DFloat_co, mode: _CorrelateMode = "valid") -> _Array1D[np.float64 | Any]: ... @overload def convolve( a: _ArrayLike1DNumber_co, v: _ArrayLike1DNumber_co, mode: _CorrelateMode = "valid" ) -> _Array1D[np.complex128 | Any]: ... @overload def convolve( a: _ArrayLike1DTD64_co, v: _ArrayLike1DTD64_co, mode: _CorrelateMode = "valid" ) -> _Array1D[np.timedelta64 | Any]: ... # keep roughly in sync with `convolve` and `correlate`, but for 2-D output and an additional `out` overload @overload def outer( a: _ArrayLike[_AnyNumericScalarT], b: _ArrayLike[_AnyNumericScalarT], out: None = None ) -> _Array2D[_AnyNumericScalarT]: ... @overload def outer(a: _ArrayLikeBool_co, b: _ArrayLikeBool_co, out: None = None) -> _Array2D[np.bool]: ... @overload def outer(a: _ArrayLikeInt_co, b: _ArrayLikeInt_co, out: None = None) -> _Array2D[np.int_ | Any]: ... @overload def outer(a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, out: None = None) -> _Array2D[np.float64 | Any]: ... @overload def outer(a: _ArrayLikeComplex_co, b: _ArrayLikeComplex_co, out: None = None) -> _Array2D[np.complex128 | Any]: ... @overload def outer(a: _ArrayLikeTD64_co, b: _ArrayLikeTD64_co, out: None = None) -> _Array2D[np.timedelta64 | Any]: ... @overload def outer(a: _ArrayLikeNumber_co | _ArrayLikeTD64_co, b: _ArrayLikeNumber_co | _ArrayLikeTD64_co, out: _ArrayT) -> _ArrayT: ... # keep in sync with numpy.linalg._linalg.tensordot (ignoring `/, *`) @overload def tensordot( a: _ArrayLike[_AnyNumericScalarT], b: _ArrayLike[_AnyNumericScalarT], axes: int | tuple[_ShapeLike, _ShapeLike] = 2 ) -> NDArray[_AnyNumericScalarT]: ... @overload def tensordot(a: _ArrayLikeBool_co, b: _ArrayLikeBool_co, axes: int | tuple[_ShapeLike, _ShapeLike] = 2) -> NDArray[np.bool]: ... @overload def tensordot( a: _ArrayLikeInt_co, b: _ArrayLikeInt_co, axes: int | tuple[_ShapeLike, _ShapeLike] = 2 ) -> NDArray[np.int_ | Any]: ... @overload def tensordot( a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, axes: int | tuple[_ShapeLike, _ShapeLike] = 2 ) -> NDArray[np.float64 | Any]: ... @overload def tensordot( a: _ArrayLikeComplex_co, b: _ArrayLikeComplex_co, axes: int | tuple[_ShapeLike, _ShapeLike] = 2 ) -> NDArray[np.complex128 | Any]: ... # @overload def cross( a: _ArrayLike[_AnyNumericScalarT], b: _ArrayLike[_AnyNumericScalarT], axisa: int = -1, axisb: int = -1, axisc: int = -1, axis: int | None = None, ) -> NDArray[_AnyNumericScalarT]: ... @overload def cross( a: _ArrayLikeInt_co, b: _ArrayLikeInt_co, axisa: int = -1, axisb: int = -1, axisc: int = -1, axis: int | None = None, ) -> NDArray[np.int_ | Any]: ... @overload def cross( a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, axisa: int = -1, axisb: int = -1, axisc: int = -1, axis: int | None = None, ) -> NDArray[np.float64 | Any]: ... @overload def cross( a: _ArrayLikeComplex_co, b: _ArrayLikeComplex_co, axisa: int = -1, axisb: int = -1, axisc: int = -1, axis: int | None = None, ) -> NDArray[np.complex128 | Any]: ... # @overload def roll(a: _ArrayT, shift: _ShapeLike, axis: _ShapeLike | None = None) -> _ArrayT: ... @overload def roll(a: _ArrayLike[_ScalarT], shift: _ShapeLike, axis: _ShapeLike | None = None) -> NDArray[_ScalarT]: ... @overload def roll(a: ArrayLike, shift: _ShapeLike, axis: _ShapeLike | None = None) -> NDArray[Any]: ... # def rollaxis(a: _ArrayT, axis: int, start: int = 0) -> _ArrayT: ... def moveaxis(a: _ArrayT, source: _ShapeLike, destination: _ShapeLike) -> _ArrayT: ... def normalize_axis_tuple( axis: int | Iterable[int], ndim: int, argname: str | None = None, allow_duplicate: py_bool | None = False, ) -> tuple[int, ...]: ... # @overload # 0d, dtype=int (default), sparse=False (default) def indices(dimensions: tuple[()], dtype: type[int] = int, sparse: L[False] = False) -> _Array1D[np.intp]: ... @overload # 0d, dtype=, sparse=True def indices(dimensions: tuple[()], dtype: DTypeLike | None = int, *, sparse: L[True]) -> tuple[()]: ... @overload # 0d, dtype=, sparse=False (default) def indices(dimensions: tuple[()], dtype: _DTypeLike[_ScalarT], sparse: L[False] = False) -> _Array1D[_ScalarT]: ... @overload # 0d, dtype=, sparse=False (default) def indices(dimensions: tuple[()], dtype: DTypeLike, sparse: L[False] = False) -> _Array1D[Any]: ... @overload # 1d, dtype=int (default), sparse=False (default) def indices(dimensions: tuple[int], dtype: type[int] = int, sparse: L[False] = False) -> _Array2D[np.intp]: ... @overload # 1d, dtype=int (default), sparse=True def indices(dimensions: tuple[int], dtype: type[int] = int, *, sparse: L[True]) -> tuple[_Array1D[np.intp]]: ... @overload # 1d, dtype=, sparse=False (default) def indices(dimensions: tuple[int], dtype: _DTypeLike[_ScalarT], sparse: L[False] = False) -> _Array2D[_ScalarT]: ... @overload # 1d, dtype=, sparse=True def indices(dimensions: tuple[int], dtype: _DTypeLike[_ScalarT], sparse: L[True]) -> tuple[_Array1D[_ScalarT]]: ... @overload # 1d, dtype=, sparse=False (default) def indices(dimensions: tuple[int], dtype: DTypeLike, sparse: L[False] = False) -> _Array2D[Any]: ... @overload # 1d, dtype=, sparse=True def indices(dimensions: tuple[int], dtype: DTypeLike, sparse: L[True]) -> tuple[_Array1D[Any]]: ... @overload # 2d, dtype=int (default), sparse=False (default) def indices(dimensions: tuple[int, int], dtype: type[int] = int, sparse: L[False] = False) -> _Array3D[np.intp]: ... @overload # 2d, dtype=int (default), sparse=True def indices( dimensions: tuple[int, int], dtype: type[int] = int, *, sparse: L[True] ) -> tuple[_Array2D[np.intp], _Array2D[np.intp]]: ... @overload # 2d, dtype=, sparse=False (default) def indices(dimensions: tuple[int, int], dtype: _DTypeLike[_ScalarT], sparse: L[False] = False) -> _Array3D[_ScalarT]: ... @overload # 2d, dtype=, sparse=True def indices( dimensions: tuple[int, int], dtype: _DTypeLike[_ScalarT], sparse: L[True] ) -> tuple[_Array2D[_ScalarT], _Array2D[_ScalarT]]: ... @overload # 2d, dtype=, sparse=False (default) def indices(dimensions: tuple[int, int], dtype: DTypeLike, sparse: L[False] = False) -> _Array3D[Any]: ... @overload # 2d, dtype=, sparse=True def indices(dimensions: tuple[int, int], dtype: DTypeLike, sparse: L[True]) -> tuple[_Array2D[Any], _Array2D[Any]]: ... @overload # ?d, dtype=int (default), sparse=False (default) def indices(dimensions: Sequence[int], dtype: type[int] = int, sparse: L[False] = False) -> NDArray[np.intp]: ... @overload # ?d, dtype=int (default), sparse=True def indices(dimensions: Sequence[int], dtype: type[int] = int, *, sparse: L[True]) -> tuple[NDArray[np.intp], ...]: ... @overload # ?d, dtype=, sparse=False (default) def indices(dimensions: Sequence[int], dtype: _DTypeLike[_ScalarT], sparse: L[False] = False) -> NDArray[_ScalarT]: ... @overload # ?d, dtype=, sparse=True def indices(dimensions: Sequence[int], dtype: _DTypeLike[_ScalarT], sparse: L[True]) -> tuple[NDArray[_ScalarT], ...]: ... @overload # ?d, dtype=, sparse=False (default) def indices(dimensions: Sequence[int], dtype: DTypeLike, sparse: L[False] = False) -> ndarray: ... @overload # ?d, dtype=, sparse=True def indices(dimensions: Sequence[int], dtype: DTypeLike, sparse: L[True]) -> tuple[ndarray, ...]: ... # def fromfunction( function: Callable[..., _T], shape: Sequence[int], *, dtype: DTypeLike | None = float, like: _SupportsArrayFunc | None = None, **kwargs: object, ) -> _T: ... # def isscalar(element: object) -> TypeGuard[generic | complex | str | bytes | memoryview]: ... # def binary_repr(num: SupportsIndex, width: int | None = None) -> str: ... def base_repr(number: SupportsAbs[float], base: float = 2, padding: SupportsIndex | None = 0) -> str: ... # @overload # dtype: None (default) def identity(n: int, dtype: None = None, *, like: _SupportsArrayFunc | None = None) -> _Array2D[np.float64]: ... @overload # dtype: known scalar type def identity(n: int, dtype: _DTypeLike[_ScalarT], *, like: _SupportsArrayFunc | None = None) -> _Array2D[_ScalarT]: ... @overload # dtype: like bool def identity(n: int, dtype: _DTypeLikeBool, *, like: _SupportsArrayFunc | None = None) -> _Array2D[np.bool]: ... @overload # dtype: like int_ def identity(n: int, dtype: _DTypeLikeInt, *, like: _SupportsArrayFunc | None = None) -> _Array2D[np.int_ | Any]: ... @overload # dtype: like float64 def identity(n: int, dtype: _DTypeLikeFloat64, *, like: _SupportsArrayFunc | None = None) -> _Array2D[np.float64 | Any]: ... @overload # dtype: like complex128 def identity(n: int, dtype: _DTypeLikeComplex128, *, like: _SupportsArrayFunc | None = None) -> _Array2D[np.complex128 | Any]: ... @overload # dtype: unknown def identity(n: int, dtype: DTypeLike, *, like: _SupportsArrayFunc | None = None) -> _Array2D[Incomplete]: ... # def allclose( a: ArrayLike, b: ArrayLike, rtol: ArrayLike = 1e-5, atol: ArrayLike = 1e-8, equal_nan: py_bool = False, ) -> py_bool: ... # @overload # scalar, scalar def isclose( a: _NumberLike_co, b: _NumberLike_co, rtol: ArrayLike = 1e-5, atol: ArrayLike = 1e-8, equal_nan: py_bool = False, ) -> np.bool: ... @overload # known shape, same shape or scalar def isclose( a: np.ndarray[_ShapeT], b: np.ndarray[_ShapeT] | _NumberLike_co, rtol: ArrayLike = 1e-5, atol: ArrayLike = 1e-8, equal_nan: py_bool = False, ) -> np.ndarray[_ShapeT, np.dtype[np.bool]]: ... @overload # same shape or scalar, known shape def isclose( a: np.ndarray[_ShapeT] | _NumberLike_co, b: np.ndarray[_ShapeT], rtol: ArrayLike = 1e-5, atol: ArrayLike = 1e-8, equal_nan: py_bool = False, ) -> np.ndarray[_ShapeT, np.dtype[np.bool]]: ... @overload # 1d sequence, <=1d array-like def isclose( a: Sequence[_NumberLike_co], b: Sequence[_NumberLike_co] | _NumberLike_co | np.ndarray[tuple[int]], rtol: ArrayLike = 1e-5, atol: ArrayLike = 1e-8, equal_nan: py_bool = False, ) -> np.ndarray[tuple[int], np.dtype[np.bool]]: ... @overload # <=1d array-like, 1d sequence def isclose( a: Sequence[_NumberLike_co] | _NumberLike_co | np.ndarray[tuple[int]], b: Sequence[_NumberLike_co], rtol: ArrayLike = 1e-5, atol: ArrayLike = 1e-8, equal_nan: py_bool = False, ) -> np.ndarray[tuple[int], np.dtype[np.bool]]: ... @overload # 2d sequence, <=2d array-like def isclose( a: Sequence[Sequence[_NumberLike_co]], b: Sequence[Sequence[_NumberLike_co]] | Sequence[_NumberLike_co] | _NumberLike_co | np.ndarray[tuple[int] | tuple[int, int]], rtol: ArrayLike = 1e-5, atol: ArrayLike = 1e-8, equal_nan: py_bool = False, ) -> np.ndarray[tuple[int], np.dtype[np.bool]]: ... @overload # <=2d array-like, 2d sequence def isclose( b: Sequence[Sequence[_NumberLike_co]] | Sequence[_NumberLike_co] | _NumberLike_co | np.ndarray[tuple[int] | tuple[int, int]], a: Sequence[Sequence[_NumberLike_co]], rtol: ArrayLike = 1e-5, atol: ArrayLike = 1e-8, equal_nan: py_bool = False, ) -> np.ndarray[tuple[int], np.dtype[np.bool]]: ... @overload # unknown shape, unknown shape def isclose( a: ArrayLike, b: ArrayLike, rtol: ArrayLike = 1e-5, atol: ArrayLike = 1e-8, equal_nan: py_bool = False, ) -> NDArray[np.bool] | Any: ... # def array_equal(a1: ArrayLike, a2: ArrayLike, equal_nan: py_bool = False) -> py_bool: ... def array_equiv(a1: ArrayLike, a2: ArrayLike) -> py_bool: ... # @overload def astype( x: ndarray[_ShapeT], dtype: _DTypeLike[_ScalarT], /, *, copy: py_bool = True, device: L["cpu"] | None = None, ) -> ndarray[_ShapeT, dtype[_ScalarT]]: ... @overload def astype( x: ndarray[_ShapeT], dtype: DTypeLike | None, /, *, copy: py_bool = True, device: L["cpu"] | None = None, ) -> ndarray[_ShapeT]: ...