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```
```// Copyright 2018 Developers of the Rand project.
// Copyright 2013-2017 The Rust Project Developers.
//
// option. This file may not be copied, modified, or distributed
// except according to those terms.

//! Utilities for random number generation
//!
//! Rand provides utilities to generate random numbers, to convert them to
//! useful types and distributions, and some randomness-related algorithms.
//!
//! # Quick Start
//!
//! To get you started quickly, the easiest and highest-level way to get
//! a random value is to use [`random()`]; alternatively you can use
//! [`thread_rng()`]. The [`Rng`] trait provides a useful API on all RNGs, while
//! the [`distributions`] and [`seq`] modules provide further
//! functionality on top of RNGs.
//!
//! ```
//! use rand::prelude::*;
//!
//! if rand::random() { // generates a boolean
//!     // Try printing a random unicode code point (probably a bad idea)!
//!     println!("char: {}", rand::random::<char>());
//! }
//!
//! let mut rng = rand::thread_rng();
//! let y: f64 = rng.gen(); // generates a float between 0 and 1
//!
//! let mut nums: Vec<i32> = (1..100).collect();
//! nums.shuffle(&mut rng);
//! ```
//!
//! # The Book
//!
//! [The Rust Rand Book](https://rust-random.github.io/book).

#![doc(
html_logo_url = "https://www.rust-lang.org/logos/rust-logo-128x128-blk.png",
html_favicon_url = "https://www.rust-lang.org/favicon.ico",
html_root_url = "https://rust-random.github.io/rand/"
)]
#![deny(missing_docs)]
#![deny(missing_debug_implementations)]
#![doc(test(attr(allow(unused_variables), deny(warnings))))]
#![no_std]
#![cfg_attr(feature = "simd_support", feature(stdsimd))]
#![cfg_attr(feature = "nightly", feature(slice_partition_at_index))]
#![cfg_attr(doc_cfg, feature(doc_cfg))]
#![allow(
clippy::float_cmp,
clippy::neg_cmp_op_on_partial_ord,
)]

#[cfg(feature = "std")] extern crate std;
#[cfg(feature = "alloc")] extern crate alloc;

#[allow(unused)]
macro_rules! trace { (\$(\$x:tt)*) => (
#[cfg(feature = "log")] {
log::trace!(\$(\$x)*)
}
) }
#[allow(unused)]
macro_rules! debug { (\$(\$x:tt)*) => (
#[cfg(feature = "log")] {
log::debug!(\$(\$x)*)
}
) }
#[allow(unused)]
macro_rules! info { (\$(\$x:tt)*) => (
#[cfg(feature = "log")] {
log::info!(\$(\$x)*)
}
) }
#[allow(unused)]
macro_rules! warn { (\$(\$x:tt)*) => (
#[cfg(feature = "log")] {
log::warn!(\$(\$x)*)
}
) }
#[allow(unused)]
macro_rules! error { (\$(\$x:tt)*) => (
#[cfg(feature = "log")] {
log::error!(\$(\$x)*)
}
) }

// Re-exports from rand_core
pub use rand_core::{CryptoRng, Error, RngCore, SeedableRng};

// Public modules
pub mod distributions;
pub mod prelude;
mod rng;
pub mod rngs;
pub mod seq;

// Public exports
#[cfg(all(feature = "std", feature = "std_rng"))]
pub use rng::{Fill, Rng};

#[cfg(all(feature = "std", feature = "std_rng"))]
use crate::distributions::{Distribution, Standard};

/// Generates a random value using the thread-local random number generator.
///
/// documentation of the entropy source and [`Standard`] for documentation of
/// distributions and type-specific generation.
///
/// # Provided implementations
///
/// The following types have provided implementations that
/// generate values with the following ranges and distributions:
///
/// * Integers (`i32`, `u32`, `isize`, `usize`, etc.): Uniformly distributed
///   over all values of the type.
/// * `char`: Uniformly distributed over all Unicode scalar values, i.e. all
///   code points in the range `0...0x10_FFFF`, except for the range
///   `0xD800...0xDFFF` (the surrogate code points). This includes
///   unassigned/reserved code points.
/// * `bool`: Generates `false` or `true`, each with probability 0.5.
/// * Floating point types (`f32` and `f64`): Uniformly distributed in the
///   half-open range `[0, 1)`. See notes below.
/// * Wrapping integers (`Wrapping<T>`), besides the type identical to their
///   normal integer variants.
///
/// Also supported is the generation of the following
/// compound types where all component types are supported:
///
/// *   Tuples (up to 12 elements): each element is generated sequentially.
/// *   Arrays (up to 32 elements): each element is generated sequentially;
///     types and tends to be faster for `u32` and smaller types.
/// *   `Option<T>` first generates a `bool`, and if true generates and returns
///     `Some(value)` where `value: T`, otherwise returning `None`.
///
/// # Examples
///
/// ```
/// let x = rand::random::<u8>();
/// println!("{}", x);
///
/// let y = rand::random::<f64>();
/// println!("{}", y);
///
/// if rand::random() { // generates a boolean
///     println!("Better lucky than good!");
/// }
/// ```
///
/// If you're calling `random()` in a loop, caching the generator as in the
/// following example can increase performance.
///
/// ```
/// use rand::Rng;
///
/// let mut v = vec![1, 2, 3];
///
/// for x in v.iter_mut() {
///     *x = rand::random()
/// }
///
///
/// let mut rng = rand::thread_rng();
///
/// for x in v.iter_mut() {
///     *x = rng.gen();
/// }
/// ```
///
/// [`Standard`]: distributions::Standard
#[cfg(all(feature = "std", feature = "std_rng"))]
#[cfg_attr(doc_cfg, doc(cfg(all(feature = "std", feature = "std_rng"))))]
#[inline]
pub fn random<T>() -> T
where Standard: Distribution<T> {
}

#[cfg(test)]
mod test {
use super::*;

/// Construct a deterministic RNG with the given seed
pub fn rng(seed: u64) -> impl RngCore {
// For tests, we want a statistically good, fast, reproducible RNG.
// PCG32 will do fine, and will be easy to embed if we ever need to.
const INC: u64 = 11634580027462260723;
rand_pcg::Pcg32::new(seed, INC)
}

#[test]
#[cfg(all(feature = "std", feature = "std_rng"))]
fn test_random() {
let _n: usize = random();
let _f: f32 = random();
let _o: Option<Option<i8>> = random();
#[allow(clippy::type_complexity)]
let _many: (
(),
(usize, isize, Option<(u32, (bool,))>),
(u8, i8, u16, i16, u32, i32, u64, i64),
(f32, (f64, (f64,))),
) = random();
}
}
```