Adding upstream version 124.0.1.upstream/124.0.1

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

1 files changed, 214 insertions, 0 deletions

diff --git a/third_party/rust/rand/src/lib.rs b/third_party/rust/rand/src/lib.rs
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+// Copyright 2018 Developers of the Rand project.
+// Copyright 2013-2017 The Rust Project Developers.
+//
+// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
+// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// 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
+//!
+//! For the user guide and further documentation, please read
+//! [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(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 crate::rngs::thread::thread_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.
+///
+/// This is simply a shortcut for `thread_rng().gen()`. See [`thread_rng`] for
+/// 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;
+///     see also [`Rng::fill`] which supports arbitrary array length for integer
+///     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()
+/// }
+///
+/// // can be made faster by caching thread_rng
+///
+/// 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> {
+    thread_rng().gen()
+}
+
+#[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();
+    }
+}