Initial vendor packages

Signed-off-by: Valentin Popov <valentin@popov.link>

1 files changed, 1220 insertions, 0 deletions

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+// Copyright 2013-2017 The Rust Project Developers. See the COPYRIGHT
+// file at the top-level directory of this distribution and at
+// http://rust-lang.org/COPYRIGHT.
+//
+// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
+// http://www.apache.org/licenses/LICENSE-2.0> or the MIT license
+// <LICENSE-MIT or http://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
+//!
+//! The key functions are `random()` and `Rng::gen()`. These are polymorphic and
+//! so can be used to generate any type that implements `Rand`. Type inference
+//! means that often a simple call to `rand::random()` or `rng.gen()` will
+//! suffice, but sometimes an annotation is required, e.g.
+//! `rand::random::<f64>()`.
+//!
+//! See the `distributions` submodule for sampling random numbers from
+//! distributions like normal and exponential.
+//!
+//! # Usage
+//!
+//! This crate is [on crates.io](https://crates.io/crates/rand) and can be
+//! used by adding `rand` to the dependencies in your project's `Cargo.toml`.
+//!
+//! ```toml
+//! [dependencies]
+//! rand = "0.4"
+//! ```
+//!
+//! and this to your crate root:
+//!
+//! ```rust
+//! extern crate rand;
+//! ```
+//!
+//! # Thread-local RNG
+//!
+//! There is built-in support for a RNG associated with each thread stored
+//! in thread-local storage. This RNG can be accessed via `thread_rng`, or
+//! used implicitly via `random`. This RNG is normally randomly seeded
+//! from an operating-system source of randomness, e.g. `/dev/urandom` on
+//! Unix systems, and will automatically reseed itself from this source
+//! after generating 32 KiB of random data.
+//!
+//! # Cryptographic security
+//!
+//! An application that requires an entropy source for cryptographic purposes
+//! must use `OsRng`, which reads randomness from the source that the operating
+//! system provides (e.g. `/dev/urandom` on Unixes or `CryptGenRandom()` on
+//! Windows).
+//! The other random number generators provided by this module are not suitable
+//! for such purposes.
+//!
+//! *Note*: many Unix systems provide `/dev/random` as well as `/dev/urandom`.
+//! This module uses `/dev/urandom` for the following reasons:
+//!
+//! -   On Linux, `/dev/random` may block if entropy pool is empty;
+//!     `/dev/urandom` will not block.  This does not mean that `/dev/random`
+//!     provides better output than `/dev/urandom`; the kernel internally runs a
+//!     cryptographically secure pseudorandom number generator (CSPRNG) based on
+//!     entropy pool for random number generation, so the "quality" of
+//!     `/dev/random` is not better than `/dev/urandom` in most cases.  However,
+//!     this means that `/dev/urandom` can yield somewhat predictable randomness
+//!     if the entropy pool is very small, such as immediately after first
+//!     booting.  Linux 3.17 added the `getrandom(2)` system call which solves
+//!     the issue: it blocks if entropy pool is not initialized yet, but it does
+//!     not block once initialized.  `OsRng` tries to use `getrandom(2)` if
+//!     available, and use `/dev/urandom` fallback if not.  If an application
+//!     does not have `getrandom` and likely to be run soon after first booting,
+//!     or on a system with very few entropy sources, one should consider using
+//!     `/dev/random` via `ReadRng`.
+//! -   On some systems (e.g. FreeBSD, OpenBSD and Mac OS X) there is no
+//!     difference between the two sources. (Also note that, on some systems
+//!     e.g.  FreeBSD, both `/dev/random` and `/dev/urandom` may block once if
+//!     the CSPRNG has not seeded yet.)
+//!
+//! # Examples
+//!
+//! ```rust
+//! use rand::Rng;
+//!
+//! let mut rng = rand::thread_rng();
+//! if rng.gen() { // random bool
+//!     println!("i32: {}, u32: {}", rng.gen::<i32>(), rng.gen::<u32>())
+//! }
+//! ```
+//!
+//! ```rust
+//! let tuple = rand::random::<(f64, char)>();
+//! println!("{:?}", tuple)
+//! ```
+//!
+//! ## Monte Carlo estimation of π
+//!
+//! For this example, imagine we have a square with sides of length 2 and a unit
+//! circle, both centered at the origin. Since the area of a unit circle is π,
+//! we have:
+//!
+//! ```text
+//!     (area of unit circle) / (area of square) = π / 4
+//! ```
+//!
+//! So if we sample many points randomly from the square, roughly π / 4 of them
+//! should be inside the circle.
+//!
+//! We can use the above fact to estimate the value of π: pick many points in
+//! the square at random, calculate the fraction that fall within the circle,
+//! and multiply this fraction by 4.
+//!
+//! ```
+//! use rand::distributions::{IndependentSample, Range};
+//!
+//! fn main() {
+//!    let between = Range::new(-1f64, 1.);
+//!    let mut rng = rand::thread_rng();
+//!
+//!    let total = 1_000_000;
+//!    let mut in_circle = 0;
+//!
+//!    for _ in 0..total {
+//!        let a = between.ind_sample(&mut rng);
+//!        let b = between.ind_sample(&mut rng);
+//!        if a*a + b*b <= 1. {
+//!            in_circle += 1;
+//!        }
+//!    }
+//!
+//!    // prints something close to 3.14159...
+//!    println!("{}", 4. * (in_circle as f64) / (total as f64));
+//! }
+//! ```
+//!
+//! ## Monty Hall Problem
+//!
+//! This is a simulation of the [Monty Hall Problem][]:
+//!
+//! > Suppose you're on a game show, and you're given the choice of three doors:
+//! > Behind one door is a car; behind the others, goats. You pick a door, say
+//! > No. 1, and the host, who knows what's behind the doors, opens another
+//! > door, say No. 3, which has a goat. He then says to you, "Do you want to
+//! > pick door No. 2?" Is it to your advantage to switch your choice?
+//!
+//! The rather unintuitive answer is that you will have a 2/3 chance of winning
+//! if you switch and a 1/3 chance of winning if you don't, so it's better to
+//! switch.
+//!
+//! This program will simulate the game show and with large enough simulation
+//! steps it will indeed confirm that it is better to switch.
+//!
+//! [Monty Hall Problem]: http://en.wikipedia.org/wiki/Monty_Hall_problem
+//!
+//! ```
+//! use rand::Rng;
+//! use rand::distributions::{IndependentSample, Range};
+//!
+//! struct SimulationResult {
+//!     win: bool,
+//!     switch: bool,
+//! }
+//!
+//! // Run a single simulation of the Monty Hall problem.
+//! fn simulate<R: Rng>(random_door: &Range<u32>, rng: &mut R)
+//!                     -> SimulationResult {
+//!     let car = random_door.ind_sample(rng);
+//!
+//!     // This is our initial choice
+//!     let mut choice = random_door.ind_sample(rng);
+//!
+//!     // The game host opens a door
+//!     let open = game_host_open(car, choice, rng);
+//!
+//!     // Shall we switch?
+//!     let switch = rng.gen();
+//!     if switch {
+//!         choice = switch_door(choice, open);
+//!     }
+//!
+//!     SimulationResult { win: choice == car, switch: switch }
+//! }
+//!
+//! // Returns the door the game host opens given our choice and knowledge of
+//! // where the car is. The game host will never open the door with the car.
+//! fn game_host_open<R: Rng>(car: u32, choice: u32, rng: &mut R) -> u32 {
+//!     let choices = free_doors(&[car, choice]);
+//!     rand::seq::sample_slice(rng, &choices, 1)[0]
+//! }
+//!
+//! // Returns the door we switch to, given our current choice and
+//! // the open door. There will only be one valid door.
+//! fn switch_door(choice: u32, open: u32) -> u32 {
+//!     free_doors(&[choice, open])[0]
+//! }
+//!
+//! fn free_doors(blocked: &[u32]) -> Vec<u32> {
+//!     (0..3).filter(|x| !blocked.contains(x)).collect()
+//! }
+//!
+//! fn main() {
+//!     // The estimation will be more accurate with more simulations
+//!     let num_simulations = 10000;
+//!
+//!     let mut rng = rand::thread_rng();
+//!     let random_door = Range::new(0, 3);
+//!
+//!     let (mut switch_wins, mut switch_losses) = (0, 0);
+//!     let (mut keep_wins, mut keep_losses) = (0, 0);
+//!
+//!     println!("Running {} simulations...", num_simulations);
+//!     for _ in 0..num_simulations {
+//!         let result = simulate(&random_door, &mut rng);
+//!
+//!         match (result.win, result.switch) {
+//!             (true, true) => switch_wins += 1,
+//!             (true, false) => keep_wins += 1,
+//!             (false, true) => switch_losses += 1,
+//!             (false, false) => keep_losses += 1,
+//!         }
+//!     }
+//!
+//!     let total_switches = switch_wins + switch_losses;
+//!     let total_keeps = keep_wins + keep_losses;
+//!
+//!     println!("Switched door {} times with {} wins and {} losses",
+//!              total_switches, switch_wins, switch_losses);
+//!
+//!     println!("Kept our choice {} times with {} wins and {} losses",
+//!              total_keeps, keep_wins, keep_losses);
+//!
+//!     // With a large number of simulations, the values should converge to
+//!     // 0.667 and 0.333 respectively.
+//!     println!("Estimated chance to win if we switch: {}",
+//!              switch_wins as f32 / total_switches as f32);
+//!     println!("Estimated chance to win if we don't: {}",
+//!              keep_wins as f32 / total_keeps as f32);
+//! }
+//! ```
+
+#![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://docs.rs/rand/0.4")]
+
+#![deny(missing_debug_implementations)]
+
+#![cfg_attr(not(feature="std"), no_std)]
+#![cfg_attr(all(feature="alloc", not(feature="std")), feature(alloc))]
+#![cfg_attr(feature = "i128_support", feature(i128_type, i128))]
+
+#[cfg(feature="std")] extern crate std as core;
+#[cfg(all(feature = "alloc", not(feature="std")))] extern crate alloc;
+
+#[cfg(target_env = "sgx")]
+extern crate rdrand;
+
+#[cfg(target_env = "sgx")]
+extern crate rand_core;
+
+use core::marker;
+use core::mem;
+#[cfg(feature="std")] use std::cell::RefCell;
+#[cfg(feature="std")] use std::io;
+#[cfg(feature="std")] use std::rc::Rc;
+
+// external rngs
+pub use jitter::JitterRng;
+#[cfg(feature="std")] pub use os::OsRng;
+
+// pseudo rngs
+pub use isaac::{IsaacRng, Isaac64Rng};
+pub use chacha::ChaChaRng;
+pub use prng::XorShiftRng;
+
+// local use declarations
+#[cfg(target_pointer_width = "32")]
+use prng::IsaacRng as IsaacWordRng;
+#[cfg(target_pointer_width = "64")]
+use prng::Isaac64Rng as IsaacWordRng;
+
+use distributions::{Range, IndependentSample};
+use distributions::range::SampleRange;
+
+// public modules
+pub mod distributions;
+pub mod jitter;
+#[cfg(feature="std")] pub mod os;
+#[cfg(feature="std")] pub mod read;
+pub mod reseeding;
+#[cfg(any(feature="std", feature = "alloc"))] pub mod seq;
+
+// These tiny modules are here to avoid API breakage, probably only temporarily
+pub mod chacha {
+    //! The ChaCha random number generator.
+    pub use prng::ChaChaRng;
+}
+pub mod isaac {
+    //! The ISAAC random number generator.
+    pub use prng::{IsaacRng, Isaac64Rng};
+}
+
+// private modules
+mod rand_impls;
+mod prng;
+
+
+/// A type that can be randomly generated using an `Rng`.
+///
+/// ## Built-in Implementations
+///
+/// This crate implements `Rand` for various primitive types.  Assuming the
+/// provided `Rng` is well-behaved, these implementations 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)`.  (The [`Open01`], [`Closed01`], [`Exp1`], and
+///   [`StandardNormal`] wrapper types produce floating point numbers with
+///   alternative ranges or distributions.)
+///
+/// [`Open01`]: struct.Open01.html
+/// [`Closed01`]: struct.Closed01.html
+/// [`Exp1`]: distributions/exponential/struct.Exp1.html
+/// [`StandardNormal`]: distributions/normal/struct.StandardNormal.html
+///
+/// The following aggregate types also implement `Rand` as long as their
+/// component types implement it:
+///
+/// * Tuples and arrays: Each element of the tuple or array is generated
+///   independently, using its own `Rand` implementation.
+/// * `Option<T>`: Returns `None` with probability 0.5; otherwise generates a
+///   random `T` and returns `Some(T)`.
+pub trait Rand : Sized {
+    /// Generates a random instance of this type using the specified source of
+    /// randomness.
+    fn rand<R: Rng>(rng: &mut R) -> Self;
+}
+
+/// A random number generator.
+pub trait Rng {
+    /// Return the next random u32.
+    ///
+    /// This rarely needs to be called directly, prefer `r.gen()` to
+    /// `r.next_u32()`.
+    // FIXME #rust-lang/rfcs#628: Should be implemented in terms of next_u64
+    fn next_u32(&mut self) -> u32;
+
+    /// Return the next random u64.
+    ///
+    /// By default this is implemented in terms of `next_u32`. An
+    /// implementation of this trait must provide at least one of
+    /// these two methods. Similarly to `next_u32`, this rarely needs
+    /// to be called directly, prefer `r.gen()` to `r.next_u64()`.
+    fn next_u64(&mut self) -> u64 {
+        ((self.next_u32() as u64) << 32) | (self.next_u32() as u64)
+    }
+
+    /// Return the next random f32 selected from the half-open
+    /// interval `[0, 1)`.
+    ///
+    /// This uses a technique described by Saito and Matsumoto at
+    /// MCQMC'08. Given that the IEEE floating point numbers are
+    /// uniformly distributed over [1,2), we generate a number in
+    /// this range and then offset it onto the range [0,1). Our
+    /// choice of bits (masking v. shifting) is arbitrary and
+    /// should be immaterial for high quality generators. For low
+    /// quality generators (ex. LCG), prefer bitshifting due to
+    /// correlation between sequential low order bits.
+    ///
+    /// See:
+    /// A PRNG specialized in double precision floating point numbers using
+    /// an affine transition
+    ///
+    /// * <http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/ARTICLES/dSFMT.pdf>
+    /// * <http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/SFMT/dSFMT-slide-e.pdf>
+    ///
+    /// By default this is implemented in terms of `next_u32`, but a
+    /// random number generator which can generate numbers satisfying
+    /// the requirements directly can overload this for performance.
+    /// It is required that the return value lies in `[0, 1)`.
+    ///
+    /// See `Closed01` for the closed interval `[0,1]`, and
+    /// `Open01` for the open interval `(0,1)`.
+    fn next_f32(&mut self) -> f32 {
+        const UPPER_MASK: u32 = 0x3F800000;
+        const LOWER_MASK: u32 = 0x7FFFFF;
+        let tmp = UPPER_MASK | (self.next_u32() & LOWER_MASK);
+        let result: f32 = unsafe { mem::transmute(tmp) };
+        result - 1.0
+    }
+
+    /// Return the next random f64 selected from the half-open
+    /// interval `[0, 1)`.
+    ///
+    /// By default this is implemented in terms of `next_u64`, but a
+    /// random number generator which can generate numbers satisfying
+    /// the requirements directly can overload this for performance.
+    /// It is required that the return value lies in `[0, 1)`.
+    ///
+    /// See `Closed01` for the closed interval `[0,1]`, and
+    /// `Open01` for the open interval `(0,1)`.
+    fn next_f64(&mut self) -> f64 {
+        const UPPER_MASK: u64 = 0x3FF0000000000000;
+        const LOWER_MASK: u64 = 0xFFFFFFFFFFFFF;
+        let tmp = UPPER_MASK | (self.next_u64() & LOWER_MASK);
+        let result: f64 = unsafe { mem::transmute(tmp) };
+        result - 1.0
+    }
+
+    /// Fill `dest` with random data.
+    ///
+    /// This has a default implementation in terms of `next_u64` and
+    /// `next_u32`, but should be overridden by implementations that
+    /// offer a more efficient solution than just calling those
+    /// methods repeatedly.
+    ///
+    /// This method does *not* have a requirement to bear any fixed
+    /// relationship to the other methods, for example, it does *not*
+    /// have to result in the same output as progressively filling
+    /// `dest` with `self.gen::<u8>()`, and any such behaviour should
+    /// not be relied upon.
+    ///
+    /// This method should guarantee that `dest` is entirely filled
+    /// with new data, and may panic if this is impossible
+    /// (e.g. reading past the end of a file that is being used as the
+    /// source of randomness).
+    ///
+    /// # Example
+    ///
+    /// ```rust
+    /// use rand::{thread_rng, Rng};
+    ///
+    /// let mut v = [0u8; 13579];
+    /// thread_rng().fill_bytes(&mut v);
+    /// println!("{:?}", &v[..]);
+    /// ```
+    fn fill_bytes(&mut self, dest: &mut [u8]) {
+        // this could, in theory, be done by transmuting dest to a
+        // [u64], but this is (1) likely to be undefined behaviour for
+        // LLVM, (2) has to be very careful about alignment concerns,
+        // (3) adds more `unsafe` that needs to be checked, (4)
+        // probably doesn't give much performance gain if
+        // optimisations are on.
+        let mut count = 0;
+        let mut num = 0;
+        for byte in dest.iter_mut() {
+            if count == 0 {
+                // we could micro-optimise here by generating a u32 if
+                // we only need a few more bytes to fill the vector
+                // (i.e. at most 4).
+                num = self.next_u64();
+                count = 8;
+            }
+
+            *byte = (num & 0xff) as u8;
+            num >>= 8;
+            count -= 1;
+        }
+    }
+
+    /// Return a random value of a `Rand` type.
+    ///
+    /// # Example
+    ///
+    /// ```rust
+    /// use rand::{thread_rng, Rng};
+    ///
+    /// let mut rng = thread_rng();
+    /// let x: u32 = rng.gen();
+    /// println!("{}", x);
+    /// println!("{:?}", rng.gen::<(f64, bool)>());
+    /// ```
+    #[inline(always)]
+    fn gen<T: Rand>(&mut self) -> T where Self: Sized {
+        Rand::rand(self)
+    }
+
+    /// Return an iterator that will yield an infinite number of randomly
+    /// generated items.
+    ///
+    /// # Example
+    ///
+    /// ```
+    /// use rand::{thread_rng, Rng};
+    ///
+    /// let mut rng = thread_rng();
+    /// let x = rng.gen_iter::<u32>().take(10).collect::<Vec<u32>>();
+    /// println!("{:?}", x);
+    /// println!("{:?}", rng.gen_iter::<(f64, bool)>().take(5)
+    ///                     .collect::<Vec<(f64, bool)>>());
+    /// ```
+    fn gen_iter<'a, T: Rand>(&'a mut self) -> Generator<'a, T, Self> where Self: Sized {
+        Generator { rng: self, _marker: marker::PhantomData }
+    }
+
+    /// Generate a random value in the range [`low`, `high`).
+    ///
+    /// This is a convenience wrapper around
+    /// `distributions::Range`. If this function will be called
+    /// repeatedly with the same arguments, one should use `Range`, as
+    /// that will amortize the computations that allow for perfect
+    /// uniformity, as they only happen on initialization.
+    ///
+    /// # Panics
+    ///
+    /// Panics if `low >= high`.
+    ///
+    /// # Example
+    ///
+    /// ```rust
+    /// use rand::{thread_rng, Rng};
+    ///
+    /// let mut rng = thread_rng();
+    /// let n: u32 = rng.gen_range(0, 10);
+    /// println!("{}", n);
+    /// let m: f64 = rng.gen_range(-40.0f64, 1.3e5f64);
+    /// println!("{}", m);
+    /// ```
+    fn gen_range<T: PartialOrd + SampleRange>(&mut self, low: T, high: T) -> T where Self: Sized {
+        assert!(low < high, "Rng.gen_range called with low >= high");
+        Range::new(low, high).ind_sample(self)
+    }
+
+    /// Return a bool with a 1 in n chance of true
+    ///
+    /// # Example
+    ///
+    /// ```rust
+    /// use rand::{thread_rng, Rng};
+    ///
+    /// let mut rng = thread_rng();
+    /// println!("{}", rng.gen_weighted_bool(3));
+    /// ```
+    fn gen_weighted_bool(&mut self, n: u32) -> bool where Self: Sized {
+        n <= 1 || self.gen_range(0, n) == 0
+    }
+
+    /// Return an iterator of random characters from the set A-Z,a-z,0-9.
+    ///
+    /// # Example
+    ///
+    /// ```rust
+    /// use rand::{thread_rng, Rng};
+    ///
+    /// let s: String = thread_rng().gen_ascii_chars().take(10).collect();
+    /// println!("{}", s);
+    /// ```
+    fn gen_ascii_chars<'a>(&'a mut self) -> AsciiGenerator<'a, Self> where Self: Sized {
+        AsciiGenerator { rng: self }
+    }
+
+    /// Return a random element from `values`.
+    ///
+    /// Return `None` if `values` is empty.
+    ///
+    /// # Example
+    ///
+    /// ```
+    /// use rand::{thread_rng, Rng};
+    ///
+    /// let choices = [1, 2, 4, 8, 16, 32];
+    /// let mut rng = thread_rng();
+    /// println!("{:?}", rng.choose(&choices));
+    /// assert_eq!(rng.choose(&choices[..0]), None);
+    /// ```
+    fn choose<'a, T>(&mut self, values: &'a [T]) -> Option<&'a T> where Self: Sized {
+        if values.is_empty() {
+            None
+        } else {
+            Some(&values[self.gen_range(0, values.len())])
+        }
+    }
+
+    /// Return a mutable pointer to a random element from `values`.
+    ///
+    /// Return `None` if `values` is empty.
+    fn choose_mut<'a, T>(&mut self, values: &'a mut [T]) -> Option<&'a mut T> where Self: Sized {
+        if values.is_empty() {
+            None
+        } else {
+            let len = values.len();
+            Some(&mut values[self.gen_range(0, len)])
+        }
+    }
+
+    /// Shuffle a mutable slice in place.
+    ///
+    /// This applies Durstenfeld's algorithm for the [Fisher–Yates shuffle](https://en.wikipedia.org/wiki/Fisher%E2%80%93Yates_shuffle#The_modern_algorithm)
+    /// which produces an unbiased permutation.
+    ///
+    /// # Example
+    ///
+    /// ```rust
+    /// use rand::{thread_rng, Rng};
+    ///
+    /// let mut rng = thread_rng();
+    /// let mut y = [1, 2, 3];
+    /// rng.shuffle(&mut y);
+    /// println!("{:?}", y);
+    /// rng.shuffle(&mut y);
+    /// println!("{:?}", y);
+    /// ```
+    fn shuffle<T>(&mut self, values: &mut [T]) where Self: Sized {
+        let mut i = values.len();
+        while i >= 2 {
+            // invariant: elements with index >= i have been locked in place.
+            i -= 1;
+            // lock element i in place.
+            values.swap(i, self.gen_range(0, i + 1));
+        }
+    }
+}
+
+impl<'a, R: ?Sized> Rng for &'a mut R where R: Rng {
+    fn next_u32(&mut self) -> u32 {
+        (**self).next_u32()
+    }
+
+    fn next_u64(&mut self) -> u64 {
+        (**self).next_u64()
+    }
+
+    fn next_f32(&mut self) -> f32 {
+        (**self).next_f32()
+    }
+
+    fn next_f64(&mut self) -> f64 {
+        (**self).next_f64()
+    }
+
+    fn fill_bytes(&mut self, dest: &mut [u8]) {
+        (**self).fill_bytes(dest)
+    }
+}
+
+#[cfg(feature="std")]
+impl<R: ?Sized> Rng for Box<R> where R: Rng {
+    fn next_u32(&mut self) -> u32 {
+        (**self).next_u32()
+    }
+
+    fn next_u64(&mut self) -> u64 {
+        (**self).next_u64()
+    }
+
+    fn next_f32(&mut self) -> f32 {
+        (**self).next_f32()
+    }
+
+    fn next_f64(&mut self) -> f64 {
+        (**self).next_f64()
+    }
+
+    fn fill_bytes(&mut self, dest: &mut [u8]) {
+        (**self).fill_bytes(dest)
+    }
+}
+
+/// Iterator which will generate a stream of random items.
+///
+/// This iterator is created via the [`gen_iter`] method on [`Rng`].
+///
+/// [`gen_iter`]: trait.Rng.html#method.gen_iter
+/// [`Rng`]: trait.Rng.html
+#[derive(Debug)]
+pub struct Generator<'a, T, R:'a> {
+    rng: &'a mut R,
+    _marker: marker::PhantomData<fn() -> T>,
+}
+
+impl<'a, T: Rand, R: Rng> Iterator for Generator<'a, T, R> {
+    type Item = T;
+
+    fn next(&mut self) -> Option<T> {
+        Some(self.rng.gen())
+    }
+}
+
+/// Iterator which will continuously generate random ascii characters.
+///
+/// This iterator is created via the [`gen_ascii_chars`] method on [`Rng`].
+///
+/// [`gen_ascii_chars`]: trait.Rng.html#method.gen_ascii_chars
+/// [`Rng`]: trait.Rng.html
+#[derive(Debug)]
+pub struct AsciiGenerator<'a, R:'a> {
+    rng: &'a mut R,
+}
+
+impl<'a, R: Rng> Iterator for AsciiGenerator<'a, R> {
+    type Item = char;
+
+    fn next(&mut self) -> Option<char> {
+        const GEN_ASCII_STR_CHARSET: &'static [u8] =
+            b"ABCDEFGHIJKLMNOPQRSTUVWXYZ\
+              abcdefghijklmnopqrstuvwxyz\
+              0123456789";
+        Some(*self.rng.choose(GEN_ASCII_STR_CHARSET).unwrap() as char)
+    }
+}
+
+/// A random number generator that can be explicitly seeded to produce
+/// the same stream of randomness multiple times.
+pub trait SeedableRng<Seed>: Rng {
+    /// Reseed an RNG with the given seed.
+    ///
+    /// # Example
+    ///
+    /// ```rust
+    /// use rand::{Rng, SeedableRng, StdRng};
+    ///
+    /// let seed: &[_] = &[1, 2, 3, 4];
+    /// let mut rng: StdRng = SeedableRng::from_seed(seed);
+    /// println!("{}", rng.gen::<f64>());
+    /// rng.reseed(&[5, 6, 7, 8]);
+    /// println!("{}", rng.gen::<f64>());
+    /// ```
+    fn reseed(&mut self, Seed);
+
+    /// Create a new RNG with the given seed.
+    ///
+    /// # Example
+    ///
+    /// ```rust
+    /// use rand::{Rng, SeedableRng, StdRng};
+    ///
+    /// let seed: &[_] = &[1, 2, 3, 4];
+    /// let mut rng: StdRng = SeedableRng::from_seed(seed);
+    /// println!("{}", rng.gen::<f64>());
+    /// ```
+    fn from_seed(seed: Seed) -> Self;
+}
+
+/// A wrapper for generating floating point numbers uniformly in the
+/// open interval `(0,1)` (not including either endpoint).
+///
+/// Use `Closed01` for the closed interval `[0,1]`, and the default
+/// `Rand` implementation for `f32` and `f64` for the half-open
+/// `[0,1)`.
+///
+/// # Example
+/// ```rust
+/// use rand::{random, Open01};
+///
+/// let Open01(val) = random::<Open01<f32>>();
+/// println!("f32 from (0,1): {}", val);
+/// ```
+#[derive(Debug)]
+pub struct Open01<F>(pub F);
+
+/// A wrapper for generating floating point numbers uniformly in the
+/// closed interval `[0,1]` (including both endpoints).
+///
+/// Use `Open01` for the closed interval `(0,1)`, and the default
+/// `Rand` implementation of `f32` and `f64` for the half-open
+/// `[0,1)`.
+///
+/// # Example
+///
+/// ```rust
+/// use rand::{random, Closed01};
+///
+/// let Closed01(val) = random::<Closed01<f32>>();
+/// println!("f32 from [0,1]: {}", val);
+/// ```
+#[derive(Debug)]
+pub struct Closed01<F>(pub F);
+
+/// The standard RNG. This is designed to be efficient on the current
+/// platform.
+#[derive(Copy, Clone, Debug)]
+pub struct StdRng {
+    rng: IsaacWordRng,
+}
+
+impl StdRng {
+    /// Create a randomly seeded instance of `StdRng`.
+    ///
+    /// This is a very expensive operation as it has to read
+    /// randomness from the operating system and use this in an
+    /// expensive seeding operation. If one is only generating a small
+    /// number of random numbers, or doesn't need the utmost speed for
+    /// generating each number, `thread_rng` and/or `random` may be more
+    /// appropriate.
+    ///
+    /// Reading the randomness from the OS may fail, and any error is
+    /// propagated via the `io::Result` return value.
+    #[cfg(feature="std")]
+    pub fn new() -> io::Result<StdRng> {
+        match OsRng::new() {
+            Ok(mut r) => Ok(StdRng { rng: r.gen() }),
+            Err(e1) => {
+                match JitterRng::new() {
+                    Ok(mut r) => Ok(StdRng { rng: r.gen() }),
+                    Err(_) => {
+                        Err(e1)
+                    }
+                }
+            }
+        }
+    }
+}
+
+impl Rng for StdRng {
+    #[inline]
+    fn next_u32(&mut self) -> u32 {
+        self.rng.next_u32()
+    }
+
+    #[inline]
+    fn next_u64(&mut self) -> u64 {
+        self.rng.next_u64()
+    }
+}
+
+impl<'a> SeedableRng<&'a [usize]> for StdRng {
+    fn reseed(&mut self, seed: &'a [usize]) {
+        // the internal RNG can just be seeded from the above
+        // randomness.
+        self.rng.reseed(unsafe {mem::transmute(seed)})
+    }
+
+    fn from_seed(seed: &'a [usize]) -> StdRng {
+        StdRng { rng: SeedableRng::from_seed(unsafe {mem::transmute(seed)}) }
+    }
+}
+
+/// Create a weak random number generator with a default algorithm and seed.
+///
+/// It returns the fastest `Rng` algorithm currently available in Rust without
+/// consideration for cryptography or security. If you require a specifically
+/// seeded `Rng` for consistency over time you should pick one algorithm and
+/// create the `Rng` yourself.
+///
+/// This will seed the generator with randomness from thread_rng.
+#[cfg(feature="std")]
+pub fn weak_rng() -> XorShiftRng {
+    thread_rng().gen()
+}
+
+/// Controls how the thread-local RNG is reseeded.
+#[cfg(feature="std")]
+#[derive(Debug)]
+struct ThreadRngReseeder;
+
+#[cfg(feature="std")]
+impl reseeding::Reseeder<StdRng> for ThreadRngReseeder {
+    fn reseed(&mut self, rng: &mut StdRng) {
+        match StdRng::new() {
+            Ok(r) => *rng = r,
+            Err(e) => panic!("No entropy available: {}", e),
+        }
+    }
+}
+#[cfg(feature="std")]
+const THREAD_RNG_RESEED_THRESHOLD: u64 = 32_768;
+#[cfg(feature="std")]
+type ThreadRngInner = reseeding::ReseedingRng<StdRng, ThreadRngReseeder>;
+
+/// The thread-local RNG.
+#[cfg(feature="std")]
+#[derive(Clone, Debug)]
+pub struct ThreadRng {
+    rng: Rc<RefCell<ThreadRngInner>>,
+}
+
+/// Retrieve the lazily-initialized thread-local random number
+/// generator, seeded by the system. Intended to be used in method
+/// chaining style, e.g. `thread_rng().gen::<i32>()`.
+///
+/// After generating a certain amount of randomness, the RNG will reseed itself
+/// from the operating system or, if the operating system RNG returns an error,
+/// a seed based on the current system time.
+///
+/// The internal RNG used is platform and architecture dependent, even
+/// if the operating system random number generator is rigged to give
+/// the same sequence always. If absolute consistency is required,
+/// explicitly select an RNG, e.g. `IsaacRng` or `Isaac64Rng`.
+#[cfg(feature="std")]
+pub fn thread_rng() -> ThreadRng {
+    // used to make space in TLS for a random number generator
+    thread_local!(static THREAD_RNG_KEY: Rc<RefCell<ThreadRngInner>> = {
+        let r = match StdRng::new() {
+            Ok(r) => r,
+            Err(e) => panic!("No entropy available: {}", e),
+        };
+        let rng = reseeding::ReseedingRng::new(r,
+                                               THREAD_RNG_RESEED_THRESHOLD,
+                                               ThreadRngReseeder);
+        Rc::new(RefCell::new(rng))
+    });
+
+    ThreadRng { rng: THREAD_RNG_KEY.with(|t| t.clone()) }
+}
+
+#[cfg(feature="std")]
+impl Rng for ThreadRng {
+    fn next_u32(&mut self) -> u32 {
+        self.rng.borrow_mut().next_u32()
+    }
+
+    fn next_u64(&mut self) -> u64 {
+        self.rng.borrow_mut().next_u64()
+    }
+
+    #[inline]
+    fn fill_bytes(&mut self, bytes: &mut [u8]) {
+        self.rng.borrow_mut().fill_bytes(bytes)
+    }
+}
+
+/// Generates a random value using the thread-local random number generator.
+///
+/// `random()` can generate various types of random things, and so may require
+/// type hinting to generate the specific type you want.
+///
+/// This function uses the thread local random number generator. This means
+/// that if you're calling `random()` in a loop, caching the generator can
+/// increase performance. An example is shown below.
+///
+/// # 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!");
+/// }
+/// ```
+///
+/// Caching the thread local random number generator:
+///
+/// ```
+/// 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();
+/// }
+/// ```
+#[cfg(feature="std")]
+#[inline]
+pub fn random<T: Rand>() -> T {
+    thread_rng().gen()
+}
+
+/// DEPRECATED: use `seq::sample_iter` instead.
+///
+/// Randomly sample up to `amount` elements from a finite iterator.
+/// The order of elements in the sample is not random.
+///
+/// # Example
+///
+/// ```rust
+/// use rand::{thread_rng, sample};
+///
+/// let mut rng = thread_rng();
+/// let sample = sample(&mut rng, 1..100, 5);
+/// println!("{:?}", sample);
+/// ```
+#[cfg(feature="std")]
+#[inline(always)]
+#[deprecated(since="0.4.0", note="renamed to seq::sample_iter")]
+pub fn sample<T, I, R>(rng: &mut R, iterable: I, amount: usize) -> Vec<T>
+    where I: IntoIterator<Item=T>,
+          R: Rng,
+{
+    // the legacy sample didn't care whether amount was met
+    seq::sample_iter(rng, iterable, amount)
+        .unwrap_or_else(|e| e)
+}
+
+#[cfg(test)]
+mod test {
+    use super::{Rng, thread_rng, random, SeedableRng, StdRng, weak_rng};
+    use std::iter::repeat;
+
+    pub struct MyRng<R> { inner: R }
+
+    impl<R: Rng> Rng for MyRng<R> {
+        fn next_u32(&mut self) -> u32 {
+            fn next<T: Rng>(t: &mut T) -> u32 {
+                t.next_u32()
+            }
+            next(&mut self.inner)
+        }
+    }
+
+    pub fn rng() -> MyRng<::ThreadRng> {
+        MyRng { inner: ::thread_rng() }
+    }
+
+    struct ConstRng { i: u64 }
+    impl Rng for ConstRng {
+        fn next_u32(&mut self) -> u32 { self.i as u32 }
+        fn next_u64(&mut self) -> u64 { self.i }
+
+        // no fill_bytes on purpose
+    }
+
+    pub fn iter_eq<I, J>(i: I, j: J) -> bool
+        where I: IntoIterator,
+              J: IntoIterator<Item=I::Item>,
+              I::Item: Eq
+    {
+        // make sure the iterators have equal length
+        let mut i = i.into_iter();
+        let mut j = j.into_iter();
+        loop {
+            match (i.next(), j.next()) {
+                (Some(ref ei), Some(ref ej)) if ei == ej => { }
+                (None, None) => return true,
+                _ => return false,
+            }
+        }
+    }
+
+    #[test]
+    fn test_fill_bytes_default() {
+        let mut r = ConstRng { i: 0x11_22_33_44_55_66_77_88 };
+
+        // check every remainder mod 8, both in small and big vectors.
+        let lengths = [0, 1, 2, 3, 4, 5, 6, 7,
+                       80, 81, 82, 83, 84, 85, 86, 87];
+        for &n in lengths.iter() {
+            let mut v = repeat(0u8).take(n).collect::<Vec<_>>();
+            r.fill_bytes(&mut v);
+
+            // use this to get nicer error messages.
+            for (i, &byte) in v.iter().enumerate() {
+                if byte == 0 {
+                    panic!("byte {} of {} is zero", i, n)
+                }
+            }
+        }
+    }
+
+    #[test]
+    fn test_gen_range() {
+        let mut r = thread_rng();
+        for _ in 0..1000 {
+            let a = r.gen_range(-3, 42);
+            assert!(a >= -3 && a < 42);
+            assert_eq!(r.gen_range(0, 1), 0);
+            assert_eq!(r.gen_range(-12, -11), -12);
+        }
+
+        for _ in 0..1000 {
+            let a = r.gen_range(10, 42);
+            assert!(a >= 10 && a < 42);
+            assert_eq!(r.gen_range(0, 1), 0);
+            assert_eq!(r.gen_range(3_000_000, 3_000_001), 3_000_000);
+        }
+
+    }
+
+    #[test]
+    #[should_panic]
+    fn test_gen_range_panic_int() {
+        let mut r = thread_rng();
+        r.gen_range(5, -2);
+    }
+
+    #[test]
+    #[should_panic]
+    fn test_gen_range_panic_usize() {
+        let mut r = thread_rng();
+        r.gen_range(5, 2);
+    }
+
+    #[test]
+    fn test_gen_weighted_bool() {
+        let mut r = thread_rng();
+        assert_eq!(r.gen_weighted_bool(0), true);
+        assert_eq!(r.gen_weighted_bool(1), true);
+    }
+
+    #[test]
+    fn test_gen_ascii_str() {
+        let mut r = thread_rng();
+        assert_eq!(r.gen_ascii_chars().take(0).count(), 0);
+        assert_eq!(r.gen_ascii_chars().take(10).count(), 10);
+        assert_eq!(r.gen_ascii_chars().take(16).count(), 16);
+    }
+
+    #[test]
+    fn test_gen_vec() {
+        let mut r = thread_rng();
+        assert_eq!(r.gen_iter::<u8>().take(0).count(), 0);
+        assert_eq!(r.gen_iter::<u8>().take(10).count(), 10);
+        assert_eq!(r.gen_iter::<f64>().take(16).count(), 16);
+    }
+
+    #[test]
+    fn test_choose() {
+        let mut r = thread_rng();
+        assert_eq!(r.choose(&[1, 1, 1]).map(|&x|x), Some(1));
+
+        let v: &[isize] = &[];
+        assert_eq!(r.choose(v), None);
+    }
+
+    #[test]
+    fn test_shuffle() {
+        let mut r = thread_rng();
+        let empty: &mut [isize] = &mut [];
+        r.shuffle(empty);
+        let mut one = [1];
+        r.shuffle(&mut one);
+        let b: &[_] = &[1];
+        assert_eq!(one, b);
+
+        let mut two = [1, 2];
+        r.shuffle(&mut two);
+        assert!(two == [1, 2] || two == [2, 1]);
+
+        let mut x = [1, 1, 1];
+        r.shuffle(&mut x);
+        let b: &[_] = &[1, 1, 1];
+        assert_eq!(x, b);
+    }
+
+    #[test]
+    fn test_thread_rng() {
+        let mut r = thread_rng();
+        r.gen::<i32>();
+        let mut v = [1, 1, 1];
+        r.shuffle(&mut v);
+        let b: &[_] = &[1, 1, 1];
+        assert_eq!(v, b);
+        assert_eq!(r.gen_range(0, 1), 0);
+    }
+
+    #[test]
+    fn test_rng_trait_object() {
+        let mut rng = thread_rng();
+        {
+            let mut r = &mut rng as &mut Rng;
+            r.next_u32();
+            (&mut r).gen::<i32>();
+            let mut v = [1, 1, 1];
+            (&mut r).shuffle(&mut v);
+            let b: &[_] = &[1, 1, 1];
+            assert_eq!(v, b);
+            assert_eq!((&mut r).gen_range(0, 1), 0);
+        }
+        {
+            let mut r = Box::new(rng) as Box<Rng>;
+            r.next_u32();
+            r.gen::<i32>();
+            let mut v = [1, 1, 1];
+            r.shuffle(&mut v);
+            let b: &[_] = &[1, 1, 1];
+            assert_eq!(v, b);
+            assert_eq!(r.gen_range(0, 1), 0);
+        }
+    }
+
+    #[test]
+    fn test_random() {
+        // not sure how to test this aside from just getting some values
+        let _n : usize = random();
+        let _f : f32 = random();
+        let _o : Option<Option<i8>> = random();
+        let _many : ((),
+                     (usize,
+                      isize,
+                      Option<(u32, (bool,))>),
+                     (u8, i8, u16, i16, u32, i32, u64, i64),
+                     (f32, (f64, (f64,)))) = random();
+    }
+
+    #[test]
+    fn test_std_rng_seeded() {
+        let s = thread_rng().gen_iter::<usize>().take(256).collect::<Vec<usize>>();
+        let mut ra: StdRng = SeedableRng::from_seed(&s[..]);
+        let mut rb: StdRng = SeedableRng::from_seed(&s[..]);
+        assert!(iter_eq(ra.gen_ascii_chars().take(100),
+                        rb.gen_ascii_chars().take(100)));
+    }
+
+    #[test]
+    fn test_std_rng_reseed() {
+        let s = thread_rng().gen_iter::<usize>().take(256).collect::<Vec<usize>>();
+        let mut r: StdRng = SeedableRng::from_seed(&s[..]);
+        let string1 = r.gen_ascii_chars().take(100).collect::<String>();
+
+        r.reseed(&s);
+
+        let string2 = r.gen_ascii_chars().take(100).collect::<String>();
+        assert_eq!(string1, string2);
+    }
+
+    #[test]
+    fn test_weak_rng() {
+        let s = weak_rng().gen_iter::<usize>().take(256).collect::<Vec<usize>>();
+        let mut ra: StdRng = SeedableRng::from_seed(&s[..]);
+        let mut rb: StdRng = SeedableRng::from_seed(&s[..]);
+        assert!(iter_eq(ra.gen_ascii_chars().take(100),
+                        rb.gen_ascii_chars().take(100)));
+    }
+}