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import builtins from collections.abc import Callable from typing import Any, Literal, overload import numpy as np from numpy import ( dtype, float64, int8, int16, int32, int64, int_, long, uint, uint8, uint16, uint32, uint64, ulong, ) from numpy._typing import ( ArrayLike, NDArray, _ArrayLikeFloat_co, _ArrayLikeInt_co, _DTypeLikeBool, _Int8Codes, _Int16Codes, _Int32Codes, _Int64Codes, _IntCodes, _LongCodes, _ShapeLike, _SupportsDType, _UInt8Codes, _UInt16Codes, _UInt32Codes, _UInt64Codes, _UIntCodes, _ULongCodes, ) from numpy.random.bit_generator import BitGenerator __all__ = [ "RandomState", "beta", "binomial", "bytes", "chisquare", "choice", "dirichlet", "exponential", "f", "gamma", "geometric", "get_bit_generator", "get_state", "gumbel", "hypergeometric", "laplace", "logistic", "lognormal", "logseries", "multinomial", "multivariate_normal", "negative_binomial", "noncentral_chisquare", "noncentral_f", "normal", "pareto", "permutation", "poisson", "power", "rand", "randint", "randn", "random", "random_integers", "random_sample", "ranf", "rayleigh", "sample", "seed", "set_bit_generator", "set_state", "shuffle", "standard_cauchy", "standard_exponential", "standard_gamma", "standard_normal", "standard_t", "triangular", "uniform", "vonmises", "wald", "weibull", "zipf", ] class RandomState: _bit_generator: BitGenerator def __init__(self, seed: _ArrayLikeInt_co | BitGenerator | None = ...) -> None: ... def __repr__(self) -> str: ... def __str__(self) -> str: ... def __getstate__(self) -> dict[str, Any]: ... def __setstate__(self, state: dict[str, Any]) -> None: ... def __reduce__(self) -> tuple[Callable[[BitGenerator], RandomState], tuple[BitGenerator], dict[str, Any]]: ... # noqa: E501 def seed(self, seed: _ArrayLikeFloat_co | None = None) -> None: ... @overload def get_state(self, legacy: Literal[False] = False) -> dict[str, Any]: ... @overload def get_state( self, legacy: Literal[True] = True ) -> dict[str, Any] | tuple[str, NDArray[uint32], int, int, float]: ... def set_state( self, state: dict[str, Any] | tuple[str, NDArray[uint32], int, int, float] ) -> None: ... @overload def random_sample(self, size: None = None) -> float: ... # type: ignore[misc] @overload def random_sample(self, size: _ShapeLike) -> NDArray[float64]: ... @overload def random(self, size: None = None) -> float: ... # type: ignore[misc] @overload def random(self, size: _ShapeLike) -> NDArray[float64]: ... @overload def beta(self, a: float, b: float, size: None = None) -> float: ... # type: ignore[misc] @overload def beta( self, a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, size: _ShapeLike | None = None ) -> NDArray[float64]: ... @overload def exponential(self, scale: float = 1.0, size: None = None) -> float: ... # type: ignore[misc] @overload def exponential( self, scale: _ArrayLikeFloat_co = 1.0, size: _ShapeLike | None = None ) -> NDArray[float64]: ... @overload def standard_exponential(self, size: None = None) -> float: ... # type: ignore[misc] @overload def standard_exponential(self, size: _ShapeLike) -> NDArray[float64]: ... @overload def tomaxint(self, size: None = None) -> int: ... # type: ignore[misc] @overload # Generates long values, but stores it in a 64bit int: def tomaxint(self, size: _ShapeLike) -> NDArray[int64]: ... @overload def randint( # type: ignore[misc] self, low: int, high: int | None = None, size: None = None, ) -> int: ... @overload def randint( # type: ignore[misc] self, low: int, high: int | None = None, size: None = None, dtype: type[bool] = ..., ) -> bool: ... @overload def randint( # type: ignore[misc] self, low: int, high: int | None = None, size: None = None, dtype: type[np.bool] = ..., ) -> np.bool: ... @overload def randint( # type: ignore[misc] self, low: int, high: int | None = None, size: None = None, dtype: type[int] = ..., ) -> int: ... @overload def randint( # type: ignore[misc] self, low: int, high: int | None = None, size: None = None, dtype: dtype[uint8] | type[uint8] | _UInt8Codes | _SupportsDType[dtype[uint8]] = ..., # noqa: E501 ) -> uint8: ... @overload def randint( # type: ignore[misc] self, low: int, high: int | None = None, size: None = None, dtype: dtype[uint16] | type[uint16] | _UInt16Codes | _SupportsDType[dtype[uint16]] = ..., # noqa: E501 ) -> uint16: ... @overload def randint( # type: ignore[misc] self, low: int, high: int | None = None, size: None = None, dtype: dtype[uint32] | type[uint32] | _UInt32Codes | _SupportsDType[dtype[uint32]] = ..., # noqa: E501 ) -> uint32: ... @overload def randint( # type: ignore[misc] self, low: int, high: int | None = None, size: None = None, dtype: dtype[uint] | type[uint] | _UIntCodes | _SupportsDType[dtype[uint]] = ..., # noqa: E501 ) -> uint: ... @overload def randint( # type: ignore[misc] self, low: int, high: int | None = None, size: None = None, dtype: dtype[ulong] | type[ulong] | _ULongCodes | _SupportsDType[dtype[ulong]] = ..., # noqa: E501 ) -> ulong: ... @overload def randint( # type: ignore[misc] self, low: int, high: int | None = None, size: None = None, dtype: dtype[uint64] | type[uint64] | _UInt64Codes | _SupportsDType[dtype[uint64]] = ..., # noqa: E501 ) -> uint64: ... @overload def randint( # type: ignore[misc] self, low: int, high: int | None = None, size: None = None, dtype: dtype[int8] | type[int8] | _Int8Codes | _SupportsDType[dtype[int8]] = ..., # noqa: E501 ) -> int8: ... @overload def randint( # type: ignore[misc] self, low: int, high: int | None = None, size: None = None, dtype: dtype[int16] | type[int16] | _Int16Codes | _SupportsDType[dtype[int16]] = ..., # noqa: E501 ) -> int16: ... @overload def randint( # type: ignore[misc] self, low: int, high: int | None = None, size: None = None, dtype: dtype[int32] | type[int32] | _Int32Codes | _SupportsDType[dtype[int32]] = ..., # noqa: E501 ) -> int32: ... @overload def randint( # type: ignore[misc] self, low: int, high: int | None = None, size: None = None, dtype: dtype[int_] | type[int_] | _IntCodes | _SupportsDType[dtype[int_]] = ..., # noqa: E501 ) -> int_: ... @overload def randint( # type: ignore[misc] self, low: int, high: int | None = None, size: None = None, dtype: dtype[long] | type[long] | _LongCodes | _SupportsDType[dtype[long]] = ..., # noqa: E501 ) -> long: ... @overload def randint( # type: ignore[misc] self, low: int, high: int | None = None, size: None = None, dtype: dtype[int64] | type[int64] | _Int64Codes | _SupportsDType[dtype[int64]] = ..., # noqa: E501 ) -> int64: ... @overload def randint( # type: ignore[misc] self, low: _ArrayLikeInt_co, high: _ArrayLikeInt_co | None = None, size: _ShapeLike | None = None, ) -> NDArray[long]: ... @overload def randint( # type: ignore[misc] self, low: _ArrayLikeInt_co, high: _ArrayLikeInt_co | None = None, size: _ShapeLike | None = None, dtype: _DTypeLikeBool = ..., ) -> NDArray[np.bool]: ... @overload def randint( # type: ignore[misc] self, low: _ArrayLikeInt_co, high: _ArrayLikeInt_co | None = None, size: _ShapeLike | None = None, dtype: dtype[int8] | type[int8] | _Int8Codes | _SupportsDType[dtype[int8]] = ..., # noqa: E501 ) -> NDArray[int8]: ... @overload def randint( # type: ignore[misc] self, low: _ArrayLikeInt_co, high: _ArrayLikeInt_co | None = None, size: _ShapeLike | None = None, dtype: dtype[int16] | type[int16] | _Int16Codes | _SupportsDType[dtype[int16]] = ..., # noqa: E501 ) -> NDArray[int16]: ... @overload def randint( # type: ignore[misc] self, low: _ArrayLikeInt_co, high: _ArrayLikeInt_co | None = None, size: _ShapeLike | None = None, dtype: dtype[int32] | type[int32] | _Int32Codes | _SupportsDType[dtype[int32]] = ..., # noqa: E501 ) -> NDArray[int32]: ... @overload def randint( # type: ignore[misc] self, low: _ArrayLikeInt_co, high: _ArrayLikeInt_co | None = None, size: _ShapeLike | None = None, dtype: dtype[int64] | type[int64] | _Int64Codes | _SupportsDType[dtype[int64]] | None = ..., # noqa: E501 ) -> NDArray[int64]: ... @overload def randint( # type: ignore[misc] self, low: _ArrayLikeInt_co, high: _ArrayLikeInt_co | None = None, size: _ShapeLike | None = None, dtype: dtype[uint8] | type[uint8] | _UInt8Codes | _SupportsDType[dtype[uint8]] = ..., # noqa: E501 ) -> NDArray[uint8]: ... @overload def randint( # type: ignore[misc] self, low: _ArrayLikeInt_co, high: _ArrayLikeInt_co | None = None, size: _ShapeLike | None = None, dtype: dtype[uint16] | type[uint16] | _UInt16Codes | _SupportsDType[dtype[uint16]] = ..., # noqa: E501 ) -> NDArray[uint16]: ... @overload def randint( # type: ignore[misc] self, low: _ArrayLikeInt_co, high: _ArrayLikeInt_co | None = None, size: _ShapeLike | None = None, dtype: dtype[uint32] | type[uint32] | _UInt32Codes | _SupportsDType[dtype[uint32]] = ..., # noqa: E501 ) -> NDArray[uint32]: ... @overload def randint( # type: ignore[misc] self, low: _ArrayLikeInt_co, high: _ArrayLikeInt_co | None = None, size: _ShapeLike | None = None, dtype: dtype[uint64] | type[uint64] | _UInt64Codes | _SupportsDType[dtype[uint64]] = ..., # noqa: E501 ) -> NDArray[uint64]: ... @overload def randint( # type: ignore[misc] self, low: _ArrayLikeInt_co, high: _ArrayLikeInt_co | None = None, size: _ShapeLike | None = None, dtype: dtype[long] | type[int] | type[long] | _LongCodes | _SupportsDType[dtype[long]] = ..., # noqa: E501 ) -> NDArray[long]: ... @overload def randint( # type: ignore[misc] self, low: _ArrayLikeInt_co, high: _ArrayLikeInt_co | None = None, size: _ShapeLike | None = None, dtype: dtype[ulong] | type[ulong] | _ULongCodes | _SupportsDType[dtype[ulong]] = ..., # noqa: E501 ) -> NDArray[ulong]: ... def bytes(self, length: int) -> builtins.bytes: ... @overload def choice( self, a: int, size: None = None, replace: bool = True, p: _ArrayLikeFloat_co | None = None, ) -> int: ... @overload def choice( self, a: int, size: _ShapeLike | None = None, replace: bool = True, p: _ArrayLikeFloat_co | None = None, ) -> NDArray[long]: ... @overload def choice( self, a: ArrayLike, size: None = None, replace: bool = True, p: _ArrayLikeFloat_co | None = None, ) -> Any: ... @overload def choice( self, a: ArrayLike, size: _ShapeLike | None = None, replace: bool = True, p: _ArrayLikeFloat_co | None = None, ) -> NDArray[Any]: ... @overload def uniform( self, low: float = 0.0, high: float = 1.0, size: None = None ) -> float: ... # type: ignore[misc] @overload def uniform( self, low: _ArrayLikeFloat_co = 0.0, high: _ArrayLikeFloat_co = 1.0, size: _ShapeLike | None = None, ) -> NDArray[float64]: ... @overload def rand(self) -> float: ... @overload def rand(self, *args: int) -> NDArray[float64]: ... @overload def randn(self) -> float: ... @overload def randn(self, *args: int) -> NDArray[float64]: ... @overload def random_integers( self, low: int, high: int | None = None, size: None = None ) -> int: ... # type: ignore[misc] @overload def random_integers( self, low: _ArrayLikeInt_co, high: _ArrayLikeInt_co | None = None, size: _ShapeLike | None = None, ) -> NDArray[long]: ... @overload def standard_normal(self, size: None = None) -> float: ... # type: ignore[misc] @overload def standard_normal( # type: ignore[misc] self, size: _ShapeLike | None = None ) -> NDArray[float64]: ... @overload def normal( self, loc: float = 0.0, scale: float = 1.0, size: None = None ) -> float: ... # type: ignore[misc] @overload def normal( self, loc: _ArrayLikeFloat_co = 0.0, scale: _ArrayLikeFloat_co = 1.0, size: _ShapeLike | None = None, ) -> NDArray[float64]: ... @overload def standard_gamma( # type: ignore[misc] self, shape: float, size: None = None, ) -> float: ... @overload def standard_gamma( self, shape: _ArrayLikeFloat_co, size: _ShapeLike | None = None, ) -> NDArray[float64]: ... @overload def gamma(self, shape: float, scale: float = 1.0, size: None = None) -> float: ... # type: ignore[misc] @overload def gamma( self, shape: _ArrayLikeFloat_co, scale: _ArrayLikeFloat_co = 1.0, size: _ShapeLike | None = None, ) -> NDArray[float64]: ... @overload def f(self, dfnum: float, dfden: float, size: None = None) -> float: ... # type: ignore[misc] @overload def f( self, dfnum: _ArrayLikeFloat_co, dfden: _ArrayLikeFloat_co, size: _ShapeLike | None = None ) -> NDArray[float64]: ... @overload def noncentral_f( self, dfnum: float, dfden: float, nonc: float, size: None = None ) -> float: ... # type: ignore[misc] @overload def noncentral_f( self, dfnum: _ArrayLikeFloat_co, dfden: _ArrayLikeFloat_co, nonc: _ArrayLikeFloat_co, size: _ShapeLike | None = None, ) -> NDArray[float64]: ... @overload def chisquare(self, df: float, size: None = None) -> float: ... # type: ignore[misc] @overload def chisquare( self, df: _ArrayLikeFloat_co, size: _ShapeLike | None = None ) -> NDArray[float64]: ... @overload def noncentral_chisquare( self, df: float, nonc: float, size: None = None ) -> float: ... # type: ignore[misc] @overload def noncentral_chisquare( self, df: _ArrayLikeFloat_co, nonc: _ArrayLikeFloat_co, size: _ShapeLike | None = None ) -> NDArray[float64]: ... @overload def standard_t(self, df: float, size: None = None) -> float: ... # type: ignore[misc] @overload def standard_t( self, df: _ArrayLikeFloat_co, size: None = None ) -> NDArray[float64]: ... @overload def standard_t( self, df: _ArrayLikeFloat_co, size: _ShapeLike | None = None ) -> NDArray[float64]: ... @overload def vonmises(self, mu: float, kappa: float, size: None = None) -> float: ... # type: ignore[misc] @overload def vonmises( self, mu: _ArrayLikeFloat_co, kappa: _ArrayLikeFloat_co, size: _ShapeLike | None = None ) -> NDArray[float64]: ... @overload def pareto(self, a: float, size: None = None) -> float: ... # type: ignore[misc] @overload def pareto( self, a: _ArrayLikeFloat_co, size: _ShapeLike | None = None ) -> NDArray[float64]: ... @overload def weibull(self, a: float, size: None = None) -> float: ... # type: ignore[misc] @overload def weibull( self, a: _ArrayLikeFloat_co, size: _ShapeLike | None = None ) -> NDArray[float64]: ... @overload def power(self, a: float, size: None = None) -> float: ... # type: ignore[misc] @overload def power( self, a: _ArrayLikeFloat_co, size: _ShapeLike | None = None ) -> NDArray[float64]: ... @overload def standard_cauchy(self, size: None = None) -> float: ... # type: ignore[misc] @overload def standard_cauchy(self, size: _ShapeLike | None = None) -> NDArray[float64]: ... @overload def laplace( self, loc: float = 0.0, scale: float = 1.0, size: None = None ) -> float: ... # type: ignore[misc] @overload def laplace( self, loc: _ArrayLikeFloat_co = 0.0, scale: _ArrayLikeFloat_co = 1.0, size: _ShapeLike | None = None, ) -> NDArray[float64]: ... @overload def gumbel( self, loc: float = 0.0, scale: float = 1.0, size: None = None ) -> float: ... # type: ignore[misc] @overload def gumbel( self, loc: _ArrayLikeFloat_co = 0.0, scale: _ArrayLikeFloat_co = 1.0, size: _ShapeLike | None = None, ) -> NDArray[float64]: ... @overload def logistic( self, loc: float = 0.0, scale: float = 1.0, size: None = None ) -> float: ... # type: ignore[misc] @overload def logistic( self, loc: _ArrayLikeFloat_co = 0.0, scale: _ArrayLikeFloat_co = 1.0, size: _ShapeLike | None = None, ) -> NDArray[float64]: ... @overload def lognormal( self, mean: float = 0.0, sigma: float = 1.0, size: None = None ) -> float: ... # type: ignore[misc] @overload def lognormal( self, mean: _ArrayLikeFloat_co = 0.0, sigma: _ArrayLikeFloat_co = 1.0, size: _ShapeLike | None = None, ) -> NDArray[float64]: ... @overload def rayleigh(self, scale: float = 1.0, size: None = None) -> float: ... # type: ignore[misc] @overload def rayleigh( self, scale: _ArrayLikeFloat_co = 1.0, size: _ShapeLike | None = None ) -> NDArray[float64]: ... @overload def wald(self, mean: float, scale: float, size: None = None) -> float: ... # type: ignore[misc] @overload def wald( self, mean: _ArrayLikeFloat_co, scale: _ArrayLikeFloat_co, size: _ShapeLike | None = None ) -> NDArray[float64]: ... @overload def triangular( self, left: float, mode: float, right: float, size: None = None ) -> float: ... # type: ignore[misc] @overload def triangular( self, left: _ArrayLikeFloat_co, mode: _ArrayLikeFloat_co, right: _ArrayLikeFloat_co, size: _ShapeLike | None = None, ) -> NDArray[float64]: ... @overload def binomial( self, n: int, p: float, size: None = None ) -> int: ... # type: ignore[misc] @overload def binomial( self, n: _ArrayLikeInt_co, p: _ArrayLikeFloat_co, size: _ShapeLike | None = None ) -> NDArray[long]: ... @overload def negative_binomial( self, n: float, p: float, size: None = None ) -> int: ... # type: ignore[misc] @overload def negative_binomial( self, n: _ArrayLikeFloat_co, p: _ArrayLikeFloat_co, size: _ShapeLike | None = None ) -> NDArray[long]: ... @overload def poisson( self, lam: float = 1.0, size: None = None ) -> int: ... # type: ignore[misc] @overload def poisson( self, lam: _ArrayLikeFloat_co = 1.0, size: _ShapeLike | None = None ) -> NDArray[long]: ... @overload def zipf(self, a: float, size: None = None) -> int: ... # type: ignore[misc] @overload def zipf( self, a: _ArrayLikeFloat_co, size: _ShapeLike | None = None ) -> NDArray[long]: ... @overload def geometric(self, p: float, size: None = None) -> int: ... # type: ignore[misc] @overload def geometric( self, p: _ArrayLikeFloat_co, size: _ShapeLike | None = None ) -> NDArray[long]: ... @overload def hypergeometric( self, ngood: int, nbad: int, nsample: int, size: None = None ) -> int: ... # type: ignore[misc] @overload def hypergeometric( self, ngood: _ArrayLikeInt_co, nbad: _ArrayLikeInt_co, nsample: _ArrayLikeInt_co, size: _ShapeLike | None = None, ) -> NDArray[long]: ... @overload def logseries(self, p: float, size: None = None) -> int: ... # type: ignore[misc] @overload def logseries( self, p: _ArrayLikeFloat_co, size: _ShapeLike | None = None ) -> NDArray[long]: ... def multivariate_normal( self, mean: _ArrayLikeFloat_co, cov: _ArrayLikeFloat_co, size: _ShapeLike | None = None, check_valid: Literal["warn", "raise", "ignore"] = "warn", tol: float = 1e-8, ) -> NDArray[float64]: ... def multinomial( self, n: _ArrayLikeInt_co, pvals: _ArrayLikeFloat_co, size: _ShapeLike | None = None ) -> NDArray[long]: ... def dirichlet( self, alpha: _ArrayLikeFloat_co, size: _ShapeLike | None = None ) -> NDArray[float64]: ... def shuffle(self, x: ArrayLike) -> None: ... @overload def permutation(self, x: int) -> NDArray[long]: ... @overload def permutation(self, x: ArrayLike) -> NDArray[Any]: ... _rand: RandomState beta = _rand.beta binomial = _rand.binomial bytes = _rand.bytes chisquare = _rand.chisquare choice = _rand.choice dirichlet = _rand.dirichlet exponential = _rand.exponential f = _rand.f gamma = _rand.gamma get_state = _rand.get_state geometric = _rand.geometric gumbel = _rand.gumbel hypergeometric = _rand.hypergeometric laplace = _rand.laplace logistic = _rand.logistic lognormal = _rand.lognormal logseries = _rand.logseries multinomial = _rand.multinomial multivariate_normal = _rand.multivariate_normal negative_binomial = _rand.negative_binomial noncentral_chisquare = _rand.noncentral_chisquare noncentral_f = _rand.noncentral_f normal = _rand.normal pareto = _rand.pareto permutation = _rand.permutation poisson = _rand.poisson power = _rand.power rand = _rand.rand randint = _rand.randint randn = _rand.randn random = _rand.random random_integers = _rand.random_integers random_sample = _rand.random_sample rayleigh = _rand.rayleigh seed = _rand.seed set_state = _rand.set_state shuffle = _rand.shuffle standard_cauchy = _rand.standard_cauchy standard_exponential = _rand.standard_exponential standard_gamma = _rand.standard_gamma standard_normal = _rand.standard_normal standard_t = _rand.standard_t triangular = _rand.triangular uniform = _rand.uniform vonmises = _rand.vonmises wald = _rand.wald weibull = _rand.weibull zipf = _rand.zipf # Two legacy that are trivial wrappers around random_sample sample = _rand.random_sample ranf = _rand.random_sample def set_bit_generator(bitgen: BitGenerator) -> None: ... def get_bit_generator() -> BitGenerator: ...