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// Copyright 2018 Developers of the Rand project.
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// option. This file may not be copied, modified, or distributed
// except according to those terms.
//! The implementations of the `Standard` distribution for other built-in types.
use core::char;
use core::num::Wrapping;
#[cfg(feature = "alloc")]
use alloc::string::String;
use crate::distributions::{Distribution, Standard, Uniform};
#[cfg(feature = "alloc")]
use crate::distributions::DistString;
use crate::Rng;
#[cfg(feature = "serde1")]
use serde::{Serialize, Deserialize};
#[cfg(feature = "min_const_gen")]
use core::mem::{self, MaybeUninit};
// ----- Sampling distributions -----
/// Sample a `u8`, uniformly distributed over ASCII letters and numbers:
/// a-z, A-Z and 0-9.
///
/// # Example
///
/// ```
/// use rand::{Rng, thread_rng};
/// use rand::distributions::Alphanumeric;
///
/// let mut rng = thread_rng();
/// let chars: String = (0..7).map(|_| rng.sample(Alphanumeric) as char).collect();
/// println!("Random chars: {}", chars);
/// ```
///
/// The [`DistString`] trait provides an easier method of generating
/// a random `String`, and offers more efficient allocation:
/// ```
/// use rand::distributions::{Alphanumeric, DistString};
/// let string = Alphanumeric.sample_string(&mut rand::thread_rng(), 16);
/// println!("Random string: {}", string);
/// ```
///
/// # Passwords
///
/// Users sometimes ask whether it is safe to use a string of random characters
/// as a password. In principle, all RNGs in Rand implementing `CryptoRng` are
/// suitable as a source of randomness for generating passwords (if they are
/// properly seeded), but it is more conservative to only use randomness
/// directly from the operating system via the `getrandom` crate, or the
/// corresponding bindings of a crypto library.
///
/// When generating passwords or keys, it is important to consider the threat
/// model and in some cases the memorability of the password. This is out of
/// scope of the Rand project, and therefore we defer to the following
/// references:
///
#[derive(Debug, Clone, Copy)]
#[cfg_attr(feature = "serde1", derive(Serialize, Deserialize))]
pub struct Alphanumeric;
// ----- Implementations of distributions -----
impl Distribution<char> for Standard {
#[inline]
fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> char {
// A valid `char` is either in the interval `[0, 0xD800)` or
// `(0xDFFF, 0x11_0000)`. All `char`s must therefore be in
// `[0, 0x11_0000)` but not in the "gap" `[0xD800, 0xDFFF]` which is
// reserved for surrogates. This is the size of that gap.
const GAP_SIZE: u32 = 0xDFFF - 0xD800 + 1;
// Uniform::new(0, 0x11_0000 - GAP_SIZE) can also be used but it
// seemed slower.
let range = Uniform::new(GAP_SIZE, 0x11_0000);
let mut n = range.sample(rng);
if n <= 0xDFFF {
n -= GAP_SIZE;
}
unsafe { char::from_u32_unchecked(n) }
}
}
/// Note: the `String` is potentially left with excess capacity; optionally the
/// user may call `string.shrink_to_fit()` afterwards.
#[cfg(feature = "alloc")]
impl DistString for Standard {
fn append_string<R: Rng + ?Sized>(&self, rng: &mut R, s: &mut String, len: usize) {
// A char is encoded with at most four bytes, thus this reservation is
// guaranteed to be sufficient. We do not shrink_to_fit afterwards so
// that repeated usage on the same `String` buffer does not reallocate.
s.reserve(4 * len);
s.extend(Distribution::<char>::sample_iter(self, rng).take(len));
}
}
impl Distribution<u8> for Alphanumeric {
fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> u8 {
const RANGE: u32 = 26 + 26 + 10;
const GEN_ASCII_STR_CHARSET: &[u8] = b"ABCDEFGHIJKLMNOPQRSTUVWXYZ\
abcdefghijklmnopqrstuvwxyz\
0123456789";
// We can pick from 62 characters. This is so close to a power of 2, 64,
// that we can do better than `Uniform`. Use a simple bitshift and
// rejection sampling. We do not use a bitmask, because for small RNGs
// the most significant bits are usually of higher quality.
loop {
let var = rng.next_u32() >> (32 - 6);
if var < RANGE {
return GEN_ASCII_STR_CHARSET[var as usize];
}
}
}
}
#[cfg(feature = "alloc")]
impl DistString for Alphanumeric {
fn append_string<R: Rng + ?Sized>(&self, rng: &mut R, string: &mut String, len: usize) {
unsafe {
let v = string.as_mut_vec();
v.extend(self.sample_iter(rng).take(len));
}
}
}
impl Distribution<bool> for Standard {
#[inline]
fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> bool {
// We can compare against an arbitrary bit of an u32 to get a bool.
// Because the least significant bits of a lower quality RNG can have
// simple patterns, we compare against the most significant bit. This is
// easiest done using a sign test.
(rng.next_u32() as i32) < 0
}
}
macro_rules! tuple_impl {
// use variables to indicate the arity of the tuple
($($tyvar:ident),* ) => {
// the trailing commas are for the 1 tuple
impl< $( $tyvar ),* >
Distribution<( $( $tyvar ),* , )>
for Standard
where $( Standard: Distribution<$tyvar> ),*
{
#[inline]
fn sample<R: Rng + ?Sized>(&self, _rng: &mut R) -> ( $( $tyvar ),* , ) {
(
// use the $tyvar's to get the appropriate number of
// repeats (they're not actually needed)
$(
_rng.gen::<$tyvar>()
),*
,
)
}
}
}
}
impl Distribution<()> for Standard {
#[allow(clippy::unused_unit)]
#[inline]
fn sample<R: Rng + ?Sized>(&self, _: &mut R) -> () {
()
}
}
tuple_impl! {A}
tuple_impl! {A, B}
tuple_impl! {A, B, C}
tuple_impl! {A, B, C, D}
tuple_impl! {A, B, C, D, E}
tuple_impl! {A, B, C, D, E, F}
tuple_impl! {A, B, C, D, E, F, G}
tuple_impl! {A, B, C, D, E, F, G, H}
tuple_impl! {A, B, C, D, E, F, G, H, I}
tuple_impl! {A, B, C, D, E, F, G, H, I, J}
tuple_impl! {A, B, C, D, E, F, G, H, I, J, K}
tuple_impl! {A, B, C, D, E, F, G, H, I, J, K, L}
#[cfg(feature = "min_const_gen")]
#[cfg_attr(doc_cfg, doc(cfg(feature = "min_const_gen")))]
impl<T, const N: usize> Distribution<[T; N]> for Standard
where Standard: Distribution<T>
{
#[inline]
fn sample<R: Rng + ?Sized>(&self, _rng: &mut R) -> [T; N] {
let mut buff: [MaybeUninit<T>; N] = unsafe { MaybeUninit::uninit().assume_init() };
for elem in &mut buff {
*elem = MaybeUninit::new(_rng.gen());
}
unsafe { mem::transmute_copy::<_, _>(&buff) }
}
}
#[cfg(not(feature = "min_const_gen"))]
macro_rules! array_impl {
// recursive, given at least one type parameter:
{$n:expr, $t:ident, $($ts:ident,)*} => {
array_impl!{($n - 1), $($ts,)*}
impl<T> Distribution<[T; $n]> for Standard where Standard: Distribution<T> {
#[inline]
fn sample<R: Rng + ?Sized>(&self, _rng: &mut R) -> [T; $n] {
[_rng.gen::<$t>(), $(_rng.gen::<$ts>()),*]
}
}
};
// empty case:
{$n:expr,} => {
impl<T> Distribution<[T; $n]> for Standard {
fn sample<R: Rng + ?Sized>(&self, _rng: &mut R) -> [T; $n] { [] }
}
};
}
#[cfg(not(feature = "min_const_gen"))]
array_impl! {32, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T,}
impl<T> Distribution<Option<T>> for Standard
where Standard: Distribution<T>
{
#[inline]
fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Option<T> {
if rng.gen::<bool>() {
Some(rng.gen())
} else {
None
}
}
}
impl<T> Distribution<Wrapping<T>> for Standard
where Standard: Distribution<T>
{
#[inline]
fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Wrapping<T> {
Wrapping(rng.gen())
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::RngCore;
#[cfg(feature = "alloc")] use alloc::string::String;
#[test]
fn test_misc() {
let rng: &mut dyn RngCore = &mut crate::test::rng(820);
rng.sample::<char, _>(Standard);
rng.sample::<bool, _>(Standard);
}
#[cfg(feature = "alloc")]
#[test]
fn test_chars() {
use core::iter;
let mut rng = crate::test::rng(805);
// Test by generating a relatively large number of chars, so we also
// take the rejection sampling path.
let word: String = iter::repeat(())
.map(|()| rng.gen::<char>())
.take(1000)
.collect();
assert!(!word.is_empty());
}
#[test]
fn test_alphanumeric() {
let mut rng = crate::test::rng(806);
// Test by generating a relatively large number of chars, so we also
// take the rejection sampling path.
let mut incorrect = false;
for _ in 0..100 {
let c: char = rng.sample(Alphanumeric).into();
incorrect |= !(('0'..='9').contains(&c) ||
('A'..='Z').contains(&c) ||
('a'..='z').contains(&c) );
}
assert!(!incorrect);
}
#[test]
fn value_stability() {
fn test_samples<T: Copy + core::fmt::Debug + PartialEq, D: Distribution<T>>(
distr: &D, zero: T, expected: &[T],
) {
let mut rng = crate::test::rng(807);
let mut buf = [zero; 5];
for x in &mut buf {
*x = rng.sample(&distr);
}
assert_eq!(&buf, expected);
}
test_samples(&Standard, 'a', &[
'\u{8cdac}',
'\u{a346a}',
'\u{80120}',
'\u{ed692}',
'\u{35888}',
]);
test_samples(&Alphanumeric, 0, &[104, 109, 101, 51, 77]);
test_samples(&Standard, false, &[true, true, false, true, false]);
test_samples(&Standard, None as Option<bool>, &[
Some(true),
None,
Some(false),
None,
Some(false),
]);
test_samples(&Standard, Wrapping(0i32), &[
Wrapping(-2074640887),
Wrapping(-1719949321),
Wrapping(2018088303),
Wrapping(-547181756),
Wrapping(838957336),
]);
// We test only sub-sets of tuple and array impls
test_samples(&Standard, (), &[(), (), (), (), ()]);
test_samples(&Standard, (false,), &[
(true,),
(true,),
(false,),
(true,),
(false,),
]);
test_samples(&Standard, (false, false), &[
(true, true),
(false, true),
(false, false),
(true, false),
(false, false),
]);
test_samples(&Standard, [0u8; 0], &[[], [], [], [], []]);
test_samples(&Standard, [0u8; 3], &[
[9, 247, 111],
[68, 24, 13],
[174, 19, 194],
[172, 69, 213],
[149, 207, 29],
]);
}
}