# Stubs for random
# Ron Murawski <ron@horizonchess.com>
# Updated by Jukka Lehtosalo

# based on http://docs.python.org/3.2/library/random.html

# ----- random classes -----

import _random
import sys
from typing import (
    Any, TypeVar, Sequence, List, Callable, AbstractSet, Union, Optional
)

_T = TypeVar('_T')

class Random(_random.Random):
    def __init__(self, x: Any = ...) -> None: ...
    def seed(self, a: Any = ..., version: int = ...) -> None: ...
    def getstate(self) -> tuple: ...
    def setstate(self, state: tuple) -> None: ...
    def getrandbits(self, k: int) -> int: ...
    def randrange(self, start: int, stop: Union[int, None] = ..., step: int = ...) -> int: ...
    def randint(self, a: int, b: int) -> int: ...
    def choice(self, seq: Sequence[_T]) -> _T: ...
    if sys.version_info >= (3, 6):
        def choices(self, population: Sequence[_T], weights: Optional[Sequence[float]] = ..., *, cum_weights: Optional[Sequence[float]] = ..., k: int = ...) -> List[_T]: ...
    def shuffle(self, x: List[Any], random: Union[Callable[[], float], None] = ...) -> None: ...
    def sample(self, population: Union[Sequence[_T], AbstractSet[_T]], k: int) -> List[_T]: ...
    def random(self) -> float: ...
    def uniform(self, a: float, b: float) -> float: ...
    def triangular(self, low: float = ..., high: float = ..., mode: float = ...) -> float: ...
    def betavariate(self, alpha: float, beta: float) -> float: ...
    def expovariate(self, lambd: float) -> float: ...
    def gammavariate(self, alpha: float, beta: float) -> float: ...
    def gauss(self, mu: float, sigma: float) -> float: ...
    def lognormvariate(self, mu: float, sigma: float) -> float: ...
    def normalvariate(self, mu: float, sigma: float) -> float: ...
    def vonmisesvariate(self, mu: float, kappa: float) -> float: ...
    def paretovariate(self, alpha: float) -> float: ...
    def weibullvariate(self, alpha: float, beta: float) -> float: ...

# SystemRandom is not implemented for all OS's; good on Windows & Linux
class SystemRandom(Random):
    ...

# ----- random function stubs -----
def seed(a: Any = ..., version: int = ...) -> None: ...
def getstate() -> object: ...
def setstate(state: object) -> None: ...
def getrandbits(k: int) -> int: ...
def randrange(start: int, stop: Union[None, int] = ..., step: int = ...) -> int: ...
def randint(a: int, b: int) -> int: ...
def choice(seq: Sequence[_T]) -> _T: ...
if sys.version_info >= (3, 6):
    def choices(population: Sequence[_T], weights: Optional[Sequence[float]] = ..., *, cum_weights: Optional[Sequence[float]] = ..., k: int = ...) -> List[_T]: ...
def shuffle(x: List[Any], random: Union[Callable[[], float], None] = ...) -> None: ...
def sample(population: Union[Sequence[_T], AbstractSet[_T]], k: int) -> List[_T]: ...
def random() -> float: ...
def uniform(a: float, b: float) -> float: ...
def triangular(low: float = ..., high: float = ...,
               mode: float = ...) -> float: ...
def betavariate(alpha: float, beta: float) -> float: ...
def expovariate(lambd: float) -> float: ...
def gammavariate(alpha: float, beta: float) -> float: ...
def gauss(mu: float, sigma: float) -> float: ...
def lognormvariate(mu: float, sigma: float) -> float: ...
def normalvariate(mu: float, sigma: float) -> float: ...
def vonmisesvariate(mu: float, kappa: float) -> float: ...
def paretovariate(alpha: float) -> float: ...
def weibullvariate(alpha: float, beta: float) -> float: ...
