RPM build fix (reverted CI changes which will need to be un-reverted or made conditional) and vendor Rust dependencies to make builds much faster in any CI system.

zeroidc/vendor/rand/src/distributions/bernoulli.rs

@@ -0,0 +1,219 @@
+// Copyright 2018 Developers of the Rand project.
+//
+// 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.
+
+//! The Bernoulli distribution.
+
+use crate::distributions::Distribution;
+use crate::Rng;
+use core::{fmt, u64};
+
+#[cfg(feature = "serde1")]
+use serde::{Serialize, Deserialize};
+/// The Bernoulli distribution.
+///
+/// This is a special case of the Binomial distribution where `n = 1`.
+///
+/// # Example
+///
+/// ```rust
+/// use rand::distributions::{Bernoulli, Distribution};
+///
+/// let d = Bernoulli::new(0.3).unwrap();
+/// let v = d.sample(&mut rand::thread_rng());
+/// println!("{} is from a Bernoulli distribution", v);
+/// ```
+///
+/// # Precision
+///
+/// This `Bernoulli` distribution uses 64 bits from the RNG (a `u64`),
+/// so only probabilities that are multiples of 2<sup>-64</sup> can be
+/// represented.
+#[derive(Clone, Copy, Debug, PartialEq)]
+#[cfg_attr(feature = "serde1", derive(Serialize, Deserialize))]
+pub struct Bernoulli {
+    /// Probability of success, relative to the maximal integer.
+    p_int: u64,
+}
+
+// To sample from the Bernoulli distribution we use a method that compares a
+// random `u64` value `v < (p * 2^64)`.
+//
+// If `p == 1.0`, the integer `v` to compare against can not represented as a
+// `u64`. We manually set it to `u64::MAX` instead (2^64 - 1 instead of 2^64).
+// Note that  value of `p < 1.0` can never result in `u64::MAX`, because an
+// `f64` only has 53 bits of precision, and the next largest value of `p` will
+// result in `2^64 - 2048`.
+//
+// Also there is a 100% theoretical concern: if someone consistently wants to
+// generate `true` using the Bernoulli distribution (i.e. by using a probability
+// of `1.0`), just using `u64::MAX` is not enough. On average it would return
+// false once every 2^64 iterations. Some people apparently care about this
+// case.
+//
+// That is why we special-case `u64::MAX` to always return `true`, without using
+// the RNG, and pay the performance price for all uses that *are* reasonable.
+// Luckily, if `new()` and `sample` are close, the compiler can optimize out the
+// extra check.
+const ALWAYS_TRUE: u64 = u64::MAX;
+
+// This is just `2.0.powi(64)`, but written this way because it is not available
+// in `no_std` mode.
+const SCALE: f64 = 2.0 * (1u64 << 63) as f64;
+
+/// Error type returned from `Bernoulli::new`.
+#[derive(Clone, Copy, Debug, PartialEq, Eq)]
+pub enum BernoulliError {
+    /// `p < 0` or `p > 1`.
+    InvalidProbability,
+}
+
+impl fmt::Display for BernoulliError {
+    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
+        f.write_str(match self {
+            BernoulliError::InvalidProbability => "p is outside [0, 1] in Bernoulli distribution",
+        })
+    }
+}
+
+#[cfg(feature = "std")]
+impl ::std::error::Error for BernoulliError {}
+
+impl Bernoulli {
+    /// Construct a new `Bernoulli` with the given probability of success `p`.
+    ///
+    /// # Precision
+    ///
+    /// For `p = 1.0`, the resulting distribution will always generate true.
+    /// For `p = 0.0`, the resulting distribution will always generate false.
+    ///
+    /// This method is accurate for any input `p` in the range `[0, 1]` which is
+    /// a multiple of 2<sup>-64</sup>. (Note that not all multiples of
+    /// 2<sup>-64</sup> in `[0, 1]` can be represented as a `f64`.)
+    #[inline]
+    pub fn new(p: f64) -> Result<Bernoulli, BernoulliError> {
+        if !(0.0..1.0).contains(&p) {
+            if p == 1.0 {
+                return Ok(Bernoulli { p_int: ALWAYS_TRUE });
+            }
+            return Err(BernoulliError::InvalidProbability);
+        }
+        Ok(Bernoulli {
+            p_int: (p * SCALE) as u64,
+        })
+    }
+
+    /// Construct a new `Bernoulli` with the probability of success of
+    /// `numerator`-in-`denominator`. I.e. `new_ratio(2, 3)` will return
+    /// a `Bernoulli` with a 2-in-3 chance, or about 67%, of returning `true`.
+    ///
+    /// return `true`. If `numerator == 0` it will always return `false`.
+    /// For `numerator > denominator` and `denominator == 0`, this returns an
+    /// error. Otherwise, for `numerator == denominator`, samples are always
+    /// true; for `numerator == 0` samples are always false.
+    #[inline]
+    pub fn from_ratio(numerator: u32, denominator: u32) -> Result<Bernoulli, BernoulliError> {
+        if numerator > denominator || denominator == 0 {
+            return Err(BernoulliError::InvalidProbability);
+        }
+        if numerator == denominator {
+            return Ok(Bernoulli { p_int: ALWAYS_TRUE });
+        }
+        let p_int = ((f64::from(numerator) / f64::from(denominator)) * SCALE) as u64;
+        Ok(Bernoulli { p_int })
+    }
+}
+
+impl Distribution<bool> for Bernoulli {
+    #[inline]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> bool {
+        // Make sure to always return true for p = 1.0.
+        if self.p_int == ALWAYS_TRUE {
+            return true;
+        }
+        let v: u64 = rng.gen();
+        v < self.p_int
+    }
+}
+
+#[cfg(test)]
+mod test {
+    use super::Bernoulli;
+    use crate::distributions::Distribution;
+    use crate::Rng;
+
+    #[test]
+    #[cfg(feature="serde1")]
+    fn test_serializing_deserializing_bernoulli() {
+        let coin_flip = Bernoulli::new(0.5).unwrap();
+        let de_coin_flip : Bernoulli = bincode::deserialize(&bincode::serialize(&coin_flip).unwrap()).unwrap();
+
+        assert_eq!(coin_flip.p_int, de_coin_flip.p_int);
+    }
+
+    #[test]
+    fn test_trivial() {
+        // We prefer to be explicit here.
+        #![allow(clippy::bool_assert_comparison)]
+
+        let mut r = crate::test::rng(1);
+        let always_false = Bernoulli::new(0.0).unwrap();
+        let always_true = Bernoulli::new(1.0).unwrap();
+        for _ in 0..5 {
+            assert_eq!(r.sample::<bool, _>(&always_false), false);
+            assert_eq!(r.sample::<bool, _>(&always_true), true);
+            assert_eq!(Distribution::<bool>::sample(&always_false, &mut r), false);
+            assert_eq!(Distribution::<bool>::sample(&always_true, &mut r), true);
+        }
+    }
+
+    #[test]
+    #[cfg_attr(miri, ignore)] // Miri is too slow
+    fn test_average() {
+        const P: f64 = 0.3;
+        const NUM: u32 = 3;
+        const DENOM: u32 = 10;
+        let d1 = Bernoulli::new(P).unwrap();
+        let d2 = Bernoulli::from_ratio(NUM, DENOM).unwrap();
+        const N: u32 = 100_000;
+
+        let mut sum1: u32 = 0;
+        let mut sum2: u32 = 0;
+        let mut rng = crate::test::rng(2);
+        for _ in 0..N {
+            if d1.sample(&mut rng) {
+                sum1 += 1;
+            }
+            if d2.sample(&mut rng) {
+                sum2 += 1;
+            }
+        }
+        let avg1 = (sum1 as f64) / (N as f64);
+        assert!((avg1 - P).abs() < 5e-3);
+
+        let avg2 = (sum2 as f64) / (N as f64);
+        assert!((avg2 - (NUM as f64) / (DENOM as f64)).abs() < 5e-3);
+    }
+
+    #[test]
+    fn value_stability() {
+        let mut rng = crate::test::rng(3);
+        let distr = Bernoulli::new(0.4532).unwrap();
+        let mut buf = [false; 10];
+        for x in &mut buf {
+            *x = rng.sample(&distr);
+        }
+        assert_eq!(buf, [
+            true, false, false, true, false, false, true, true, true, true
+        ]);
+    }
+
+    #[test]
+    fn bernoulli_distributions_can_be_compared() {
+        assert_eq!(Bernoulli::new(1.0), Bernoulli::new(1.0));
+    }
+}

zeroidc/vendor/rand/src/distributions/distribution.rs

@@ -0,0 +1,272 @@
+// 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.
+
+//! Distribution trait and associates
+
+use crate::Rng;
+use core::iter;
+#[cfg(feature = "alloc")]
+use alloc::string::String;
+
+/// Types (distributions) that can be used to create a random instance of `T`.
+///
+/// It is possible to sample from a distribution through both the
+/// `Distribution` and [`Rng`] traits, via `distr.sample(&mut rng)` and
+/// `rng.sample(distr)`. They also both offer the [`sample_iter`] method, which
+/// produces an iterator that samples from the distribution.
+///
+/// All implementations are expected to be immutable; this has the significant
+/// advantage of not needing to consider thread safety, and for most
+/// distributions efficient state-less sampling algorithms are available.
+///
+/// Implementations are typically expected to be portable with reproducible
+/// results when used with a PRNG with fixed seed; see the
+/// [portability chapter](https://rust-random.github.io/book/portability.html)
+/// of The Rust Rand Book. In some cases this does not apply, e.g. the `usize`
+/// type requires different sampling on 32-bit and 64-bit machines.
+///
+/// [`sample_iter`]: Distribution::sample_iter
+pub trait Distribution<T> {
+    /// Generate a random value of `T`, using `rng` as the source of randomness.
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> T;
+
+    /// Create an iterator that generates random values of `T`, using `rng` as
+    /// the source of randomness.
+    ///
+    /// Note that this function takes `self` by value. This works since
+    /// `Distribution<T>` is impl'd for `&D` where `D: Distribution<T>`,
+    /// however borrowing is not automatic hence `distr.sample_iter(...)` may
+    /// need to be replaced with `(&distr).sample_iter(...)` to borrow or
+    /// `(&*distr).sample_iter(...)` to reborrow an existing reference.
+    ///
+    /// # Example
+    ///
+    /// ```
+    /// use rand::thread_rng;
+    /// use rand::distributions::{Distribution, Alphanumeric, Uniform, Standard};
+    ///
+    /// let mut rng = thread_rng();
+    ///
+    /// // Vec of 16 x f32:
+    /// let v: Vec<f32> = Standard.sample_iter(&mut rng).take(16).collect();
+    ///
+    /// // String:
+    /// let s: String = Alphanumeric
+    ///     .sample_iter(&mut rng)
+    ///     .take(7)
+    ///     .map(char::from)
+    ///     .collect();
+    ///
+    /// // Dice-rolling:
+    /// let die_range = Uniform::new_inclusive(1, 6);
+    /// let mut roll_die = die_range.sample_iter(&mut rng);
+    /// while roll_die.next().unwrap() != 6 {
+    ///     println!("Not a 6; rolling again!");
+    /// }
+    /// ```
+    fn sample_iter<R>(self, rng: R) -> DistIter<Self, R, T>
+    where
+        R: Rng,
+        Self: Sized,
+    {
+        DistIter {
+            distr: self,
+            rng,
+            phantom: ::core::marker::PhantomData,
+        }
+    }
+
+    /// Create a distribution of values of 'S' by mapping the output of `Self`
+    /// through the closure `F`
+    ///
+    /// # Example
+    ///
+    /// ```
+    /// use rand::thread_rng;
+    /// use rand::distributions::{Distribution, Uniform};
+    ///
+    /// let mut rng = thread_rng();
+    ///
+    /// let die = Uniform::new_inclusive(1, 6);
+    /// let even_number = die.map(|num| num % 2 == 0);
+    /// while !even_number.sample(&mut rng) {
+    ///     println!("Still odd; rolling again!");
+    /// }
+    /// ```
+    fn map<F, S>(self, func: F) -> DistMap<Self, F, T, S>
+    where
+        F: Fn(T) -> S,
+        Self: Sized,
+    {
+        DistMap {
+            distr: self,
+            func,
+            phantom: ::core::marker::PhantomData,
+        }
+    }
+}
+
+impl<'a, T, D: Distribution<T>> Distribution<T> for &'a D {
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> T {
+        (*self).sample(rng)
+    }
+}
+
+/// An iterator that generates random values of `T` with distribution `D`,
+/// using `R` as the source of randomness.
+///
+/// This `struct` is created by the [`sample_iter`] method on [`Distribution`].
+/// See its documentation for more.
+///
+/// [`sample_iter`]: Distribution::sample_iter
+#[derive(Debug)]
+pub struct DistIter<D, R, T> {
+    distr: D,
+    rng: R,
+    phantom: ::core::marker::PhantomData<T>,
+}
+
+impl<D, R, T> Iterator for DistIter<D, R, T>
+where
+    D: Distribution<T>,
+    R: Rng,
+{
+    type Item = T;
+
+    #[inline(always)]
+    fn next(&mut self) -> Option<T> {
+        // Here, self.rng may be a reference, but we must take &mut anyway.
+        // Even if sample could take an R: Rng by value, we would need to do this
+        // since Rng is not copyable and we cannot enforce that this is "reborrowable".
+        Some(self.distr.sample(&mut self.rng))
+    }
+
+    fn size_hint(&self) -> (usize, Option<usize>) {
+        (usize::max_value(), None)
+    }
+}
+
+impl<D, R, T> iter::FusedIterator for DistIter<D, R, T>
+where
+    D: Distribution<T>,
+    R: Rng,
+{
+}
+
+#[cfg(features = "nightly")]
+impl<D, R, T> iter::TrustedLen for DistIter<D, R, T>
+where
+    D: Distribution<T>,
+    R: Rng,
+{
+}
+
+/// A distribution of values of type `S` derived from the distribution `D`
+/// by mapping its output of type `T` through the closure `F`.
+///
+/// This `struct` is created by the [`Distribution::map`] method.
+/// See its documentation for more.
+#[derive(Debug)]
+pub struct DistMap<D, F, T, S> {
+    distr: D,
+    func: F,
+    phantom: ::core::marker::PhantomData<fn(T) -> S>,
+}
+
+impl<D, F, T, S> Distribution<S> for DistMap<D, F, T, S>
+where
+    D: Distribution<T>,
+    F: Fn(T) -> S,
+{
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> S {
+        (self.func)(self.distr.sample(rng))
+    }
+}
+
+/// `String` sampler
+///
+/// Sampling a `String` of random characters is not quite the same as collecting
+/// a sequence of chars. This trait contains some helpers.
+#[cfg(feature = "alloc")]
+pub trait DistString {
+    /// Append `len` random chars to `string`
+    fn append_string<R: Rng + ?Sized>(&self, rng: &mut R, string: &mut String, len: usize);
+
+    /// Generate a `String` of `len` random chars
+    #[inline]
+    fn sample_string<R: Rng + ?Sized>(&self, rng: &mut R, len: usize) -> String {
+        let mut s = String::new();
+        self.append_string(rng, &mut s, len);
+        s
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use crate::distributions::{Distribution, Uniform};
+    use crate::Rng;
+
+    #[test]
+    fn test_distributions_iter() {
+        use crate::distributions::Open01;
+        let mut rng = crate::test::rng(210);
+        let distr = Open01;
+        let mut iter = Distribution::<f32>::sample_iter(distr, &mut rng);
+        let mut sum: f32 = 0.;
+        for _ in 0..100 {
+            sum += iter.next().unwrap();
+        }
+        assert!(0. < sum && sum < 100.);
+    }
+
+    #[test]
+    fn test_distributions_map() {
+        let dist = Uniform::new_inclusive(0, 5).map(|val| val + 15);
+
+        let mut rng = crate::test::rng(212);
+        let val = dist.sample(&mut rng);
+        assert!((15..=20).contains(&val));
+    }
+
+    #[test]
+    fn test_make_an_iter() {
+        fn ten_dice_rolls_other_than_five<R: Rng>(
+            rng: &mut R,
+        ) -> impl Iterator<Item = i32> + '_ {
+            Uniform::new_inclusive(1, 6)
+                .sample_iter(rng)
+                .filter(|x| *x != 5)
+                .take(10)
+        }
+
+        let mut rng = crate::test::rng(211);
+        let mut count = 0;
+        for val in ten_dice_rolls_other_than_five(&mut rng) {
+            assert!((1..=6).contains(&val) && val != 5);
+            count += 1;
+        }
+        assert_eq!(count, 10);
+    }
+
+    #[test]
+    #[cfg(feature = "alloc")]
+    fn test_dist_string() {
+        use core::str;
+        use crate::distributions::{Alphanumeric, DistString, Standard};
+        let mut rng = crate::test::rng(213);
+
+        let s1 = Alphanumeric.sample_string(&mut rng, 20);
+        assert_eq!(s1.len(), 20);
+        assert_eq!(str::from_utf8(s1.as_bytes()), Ok(s1.as_str()));
+
+        let s2 = Standard.sample_string(&mut rng, 20);
+        assert_eq!(s2.chars().count(), 20);
+        assert_eq!(str::from_utf8(s2.as_bytes()), Ok(s2.as_str()));
+    }
+}

zeroidc/vendor/rand/src/distributions/float.rs

@@ -0,0 +1,312 @@
+// Copyright 2018 Developers of the Rand project.
+//
+// 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.
+
+//! Basic floating-point number distributions
+
+use crate::distributions::utils::FloatSIMDUtils;
+use crate::distributions::{Distribution, Standard};
+use crate::Rng;
+use core::mem;
+#[cfg(feature = "simd_support")] use packed_simd::*;
+
+#[cfg(feature = "serde1")]
+use serde::{Serialize, Deserialize};
+
+/// A distribution to sample floating point numbers uniformly in the half-open
+/// interval `(0, 1]`, i.e. including 1 but not 0.
+///
+/// All values that can be generated are of the form `n * ε/2`. For `f32`
+/// the 24 most significant random bits of a `u32` are used and for `f64` the
+/// 53 most significant bits of a `u64` are used. The conversion uses the
+/// multiplicative method.
+///
+/// See also: [`Standard`] which samples from `[0, 1)`, [`Open01`]
+/// which samples from `(0, 1)` and [`Uniform`] which samples from arbitrary
+/// ranges.
+///
+/// # Example
+/// ```
+/// use rand::{thread_rng, Rng};
+/// use rand::distributions::OpenClosed01;
+///
+/// let val: f32 = thread_rng().sample(OpenClosed01);
+/// println!("f32 from (0, 1): {}", val);
+/// ```
+///
+/// [`Standard`]: crate::distributions::Standard
+/// [`Open01`]: crate::distributions::Open01
+/// [`Uniform`]: crate::distributions::uniform::Uniform
+#[derive(Clone, Copy, Debug)]
+#[cfg_attr(feature = "serde1", derive(Serialize, Deserialize))]
+pub struct OpenClosed01;
+
+/// A distribution to sample floating point numbers uniformly in the open
+/// interval `(0, 1)`, i.e. not including either endpoint.
+///
+/// All values that can be generated are of the form `n * ε + ε/2`. For `f32`
+/// the 23 most significant random bits of an `u32` are used, for `f64` 52 from
+/// an `u64`. The conversion uses a transmute-based method.
+///
+/// See also: [`Standard`] which samples from `[0, 1)`, [`OpenClosed01`]
+/// which samples from `(0, 1]` and [`Uniform`] which samples from arbitrary
+/// ranges.
+///
+/// # Example
+/// ```
+/// use rand::{thread_rng, Rng};
+/// use rand::distributions::Open01;
+///
+/// let val: f32 = thread_rng().sample(Open01);
+/// println!("f32 from (0, 1): {}", val);
+/// ```
+///
+/// [`Standard`]: crate::distributions::Standard
+/// [`OpenClosed01`]: crate::distributions::OpenClosed01
+/// [`Uniform`]: crate::distributions::uniform::Uniform
+#[derive(Clone, Copy, Debug)]
+#[cfg_attr(feature = "serde1", derive(Serialize, Deserialize))]
+pub struct Open01;
+
+
+// This trait is needed by both this lib and rand_distr hence is a hidden export
+#[doc(hidden)]
+pub trait IntoFloat {
+    type F;
+
+    /// Helper method to combine the fraction and a constant exponent into a
+    /// float.
+    ///
+    /// Only the least significant bits of `self` may be set, 23 for `f32` and
+    /// 52 for `f64`.
+    /// The resulting value will fall in a range that depends on the exponent.
+    /// As an example the range with exponent 0 will be
+    /// [2<sup>0</sup>..2<sup>1</sup>), which is [1..2).
+    fn into_float_with_exponent(self, exponent: i32) -> Self::F;
+}
+
+macro_rules! float_impls {
+    ($ty:ident, $uty:ident, $f_scalar:ident, $u_scalar:ty,
+     $fraction_bits:expr, $exponent_bias:expr) => {
+        impl IntoFloat for $uty {
+            type F = $ty;
+            #[inline(always)]
+            fn into_float_with_exponent(self, exponent: i32) -> $ty {
+                // The exponent is encoded using an offset-binary representation
+                let exponent_bits: $u_scalar =
+                    (($exponent_bias + exponent) as $u_scalar) << $fraction_bits;
+                $ty::from_bits(self | exponent_bits)
+            }
+        }
+
+        impl Distribution<$ty> for Standard {
+            fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $ty {
+                // Multiply-based method; 24/53 random bits; [0, 1) interval.
+                // We use the most significant bits because for simple RNGs
+                // those are usually more random.
+                let float_size = mem::size_of::<$f_scalar>() as u32 * 8;
+                let precision = $fraction_bits + 1;
+                let scale = 1.0 / ((1 as $u_scalar << precision) as $f_scalar);
+
+                let value: $uty = rng.gen();
+                let value = value >> (float_size - precision);
+                scale * $ty::cast_from_int(value)
+            }
+        }
+
+        impl Distribution<$ty> for OpenClosed01 {
+            fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $ty {
+                // Multiply-based method; 24/53 random bits; (0, 1] interval.
+                // We use the most significant bits because for simple RNGs
+                // those are usually more random.
+                let float_size = mem::size_of::<$f_scalar>() as u32 * 8;
+                let precision = $fraction_bits + 1;
+                let scale = 1.0 / ((1 as $u_scalar << precision) as $f_scalar);
+
+                let value: $uty = rng.gen();
+                let value = value >> (float_size - precision);
+                // Add 1 to shift up; will not overflow because of right-shift:
+                scale * $ty::cast_from_int(value + 1)
+            }
+        }
+
+        impl Distribution<$ty> for Open01 {
+            fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $ty {
+                // Transmute-based method; 23/52 random bits; (0, 1) interval.
+                // We use the most significant bits because for simple RNGs
+                // those are usually more random.
+                use core::$f_scalar::EPSILON;
+                let float_size = mem::size_of::<$f_scalar>() as u32 * 8;
+
+                let value: $uty = rng.gen();
+                let fraction = value >> (float_size - $fraction_bits);
+                fraction.into_float_with_exponent(0) - (1.0 - EPSILON / 2.0)
+            }
+        }
+    }
+}
+
+float_impls! { f32, u32, f32, u32, 23, 127 }
+float_impls! { f64, u64, f64, u64, 52, 1023 }
+
+#[cfg(feature = "simd_support")]
+float_impls! { f32x2, u32x2, f32, u32, 23, 127 }
+#[cfg(feature = "simd_support")]
+float_impls! { f32x4, u32x4, f32, u32, 23, 127 }
+#[cfg(feature = "simd_support")]
+float_impls! { f32x8, u32x8, f32, u32, 23, 127 }
+#[cfg(feature = "simd_support")]
+float_impls! { f32x16, u32x16, f32, u32, 23, 127 }
+
+#[cfg(feature = "simd_support")]
+float_impls! { f64x2, u64x2, f64, u64, 52, 1023 }
+#[cfg(feature = "simd_support")]
+float_impls! { f64x4, u64x4, f64, u64, 52, 1023 }
+#[cfg(feature = "simd_support")]
+float_impls! { f64x8, u64x8, f64, u64, 52, 1023 }
+
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::rngs::mock::StepRng;
+
+    const EPSILON32: f32 = ::core::f32::EPSILON;
+    const EPSILON64: f64 = ::core::f64::EPSILON;
+
+    macro_rules! test_f32 {
+        ($fnn:ident, $ty:ident, $ZERO:expr, $EPSILON:expr) => {
+            #[test]
+            fn $fnn() {
+                // Standard
+                let mut zeros = StepRng::new(0, 0);
+                assert_eq!(zeros.gen::<$ty>(), $ZERO);
+                let mut one = StepRng::new(1 << 8 | 1 << (8 + 32), 0);
+                assert_eq!(one.gen::<$ty>(), $EPSILON / 2.0);
+                let mut max = StepRng::new(!0, 0);
+                assert_eq!(max.gen::<$ty>(), 1.0 - $EPSILON / 2.0);
+
+                // OpenClosed01
+                let mut zeros = StepRng::new(0, 0);
+                assert_eq!(zeros.sample::<$ty, _>(OpenClosed01), 0.0 + $EPSILON / 2.0);
+                let mut one = StepRng::new(1 << 8 | 1 << (8 + 32), 0);
+                assert_eq!(one.sample::<$ty, _>(OpenClosed01), $EPSILON);
+                let mut max = StepRng::new(!0, 0);
+                assert_eq!(max.sample::<$ty, _>(OpenClosed01), $ZERO + 1.0);
+
+                // Open01
+                let mut zeros = StepRng::new(0, 0);
+                assert_eq!(zeros.sample::<$ty, _>(Open01), 0.0 + $EPSILON / 2.0);
+                let mut one = StepRng::new(1 << 9 | 1 << (9 + 32), 0);
+                assert_eq!(one.sample::<$ty, _>(Open01), $EPSILON / 2.0 * 3.0);
+                let mut max = StepRng::new(!0, 0);
+                assert_eq!(max.sample::<$ty, _>(Open01), 1.0 - $EPSILON / 2.0);
+            }
+        };
+    }
+    test_f32! { f32_edge_cases, f32, 0.0, EPSILON32 }
+    #[cfg(feature = "simd_support")]
+    test_f32! { f32x2_edge_cases, f32x2, f32x2::splat(0.0), f32x2::splat(EPSILON32) }
+    #[cfg(feature = "simd_support")]
+    test_f32! { f32x4_edge_cases, f32x4, f32x4::splat(0.0), f32x4::splat(EPSILON32) }
+    #[cfg(feature = "simd_support")]
+    test_f32! { f32x8_edge_cases, f32x8, f32x8::splat(0.0), f32x8::splat(EPSILON32) }
+    #[cfg(feature = "simd_support")]
+    test_f32! { f32x16_edge_cases, f32x16, f32x16::splat(0.0), f32x16::splat(EPSILON32) }
+
+    macro_rules! test_f64 {
+        ($fnn:ident, $ty:ident, $ZERO:expr, $EPSILON:expr) => {
+            #[test]
+            fn $fnn() {
+                // Standard
+                let mut zeros = StepRng::new(0, 0);
+                assert_eq!(zeros.gen::<$ty>(), $ZERO);
+                let mut one = StepRng::new(1 << 11, 0);
+                assert_eq!(one.gen::<$ty>(), $EPSILON / 2.0);
+                let mut max = StepRng::new(!0, 0);
+                assert_eq!(max.gen::<$ty>(), 1.0 - $EPSILON / 2.0);
+
+                // OpenClosed01
+                let mut zeros = StepRng::new(0, 0);
+                assert_eq!(zeros.sample::<$ty, _>(OpenClosed01), 0.0 + $EPSILON / 2.0);
+                let mut one = StepRng::new(1 << 11, 0);
+                assert_eq!(one.sample::<$ty, _>(OpenClosed01), $EPSILON);
+                let mut max = StepRng::new(!0, 0);
+                assert_eq!(max.sample::<$ty, _>(OpenClosed01), $ZERO + 1.0);
+
+                // Open01
+                let mut zeros = StepRng::new(0, 0);
+                assert_eq!(zeros.sample::<$ty, _>(Open01), 0.0 + $EPSILON / 2.0);
+                let mut one = StepRng::new(1 << 12, 0);
+                assert_eq!(one.sample::<$ty, _>(Open01), $EPSILON / 2.0 * 3.0);
+                let mut max = StepRng::new(!0, 0);
+                assert_eq!(max.sample::<$ty, _>(Open01), 1.0 - $EPSILON / 2.0);
+            }
+        };
+    }
+    test_f64! { f64_edge_cases, f64, 0.0, EPSILON64 }
+    #[cfg(feature = "simd_support")]
+    test_f64! { f64x2_edge_cases, f64x2, f64x2::splat(0.0), f64x2::splat(EPSILON64) }
+    #[cfg(feature = "simd_support")]
+    test_f64! { f64x4_edge_cases, f64x4, f64x4::splat(0.0), f64x4::splat(EPSILON64) }
+    #[cfg(feature = "simd_support")]
+    test_f64! { f64x8_edge_cases, f64x8, f64x8::splat(0.0), f64x8::splat(EPSILON64) }
+
+    #[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(0x6f44f5646c2a7334);
+            let mut buf = [zero; 3];
+            for x in &mut buf {
+                *x = rng.sample(&distr);
+            }
+            assert_eq!(&buf, expected);
+        }
+
+        test_samples(&Standard, 0f32, &[0.0035963655, 0.7346052, 0.09778172]);
+        test_samples(&Standard, 0f64, &[
+            0.7346051961657583,
+            0.20298547462974248,
+            0.8166436635290655,
+        ]);
+
+        test_samples(&OpenClosed01, 0f32, &[0.003596425, 0.73460525, 0.09778178]);
+        test_samples(&OpenClosed01, 0f64, &[
+            0.7346051961657584,
+            0.2029854746297426,
+            0.8166436635290656,
+        ]);
+
+        test_samples(&Open01, 0f32, &[0.0035963655, 0.73460525, 0.09778172]);
+        test_samples(&Open01, 0f64, &[
+            0.7346051961657584,
+            0.20298547462974248,
+            0.8166436635290656,
+        ]);
+
+        #[cfg(feature = "simd_support")]
+        {
+            // We only test a sub-set of types here. Values are identical to
+            // non-SIMD types; we assume this pattern continues across all
+            // SIMD types.
+
+            test_samples(&Standard, f32x2::new(0.0, 0.0), &[
+                f32x2::new(0.0035963655, 0.7346052),
+                f32x2::new(0.09778172, 0.20298547),
+                f32x2::new(0.34296435, 0.81664366),
+            ]);
+
+            test_samples(&Standard, f64x2::new(0.0, 0.0), &[
+                f64x2::new(0.7346051961657583, 0.20298547462974248),
+                f64x2::new(0.8166436635290655, 0.7423708925400552),
+                f64x2::new(0.16387782224016323, 0.9087068770169618),
+            ]);
+        }
+    }
+}

zeroidc/vendor/rand/src/distributions/integer.rs

@@ -0,0 +1,274 @@
+// Copyright 2018 Developers of the Rand project.
+//
+// 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.
+
+//! The implementations of the `Standard` distribution for integer types.
+
+use crate::distributions::{Distribution, Standard};
+use crate::Rng;
+#[cfg(all(target_arch = "x86", feature = "simd_support"))]
+use core::arch::x86::{__m128i, __m256i};
+#[cfg(all(target_arch = "x86_64", feature = "simd_support"))]
+use core::arch::x86_64::{__m128i, __m256i};
+use core::num::{NonZeroU16, NonZeroU32, NonZeroU64, NonZeroU8, NonZeroUsize,
+    NonZeroU128};
+#[cfg(feature = "simd_support")] use packed_simd::*;
+
+impl Distribution<u8> for Standard {
+    #[inline]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> u8 {
+        rng.next_u32() as u8
+    }
+}
+
+impl Distribution<u16> for Standard {
+    #[inline]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> u16 {
+        rng.next_u32() as u16
+    }
+}
+
+impl Distribution<u32> for Standard {
+    #[inline]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> u32 {
+        rng.next_u32()
+    }
+}
+
+impl Distribution<u64> for Standard {
+    #[inline]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> u64 {
+        rng.next_u64()
+    }
+}
+
+impl Distribution<u128> for Standard {
+    #[inline]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> u128 {
+        // Use LE; we explicitly generate one value before the next.
+        let x = u128::from(rng.next_u64());
+        let y = u128::from(rng.next_u64());
+        (y << 64) | x
+    }
+}
+
+impl Distribution<usize> for Standard {
+    #[inline]
+    #[cfg(any(target_pointer_width = "32", target_pointer_width = "16"))]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> usize {
+        rng.next_u32() as usize
+    }
+
+    #[inline]
+    #[cfg(target_pointer_width = "64")]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> usize {
+        rng.next_u64() as usize
+    }
+}
+
+macro_rules! impl_int_from_uint {
+    ($ty:ty, $uty:ty) => {
+        impl Distribution<$ty> for Standard {
+            #[inline]
+            fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $ty {
+                rng.gen::<$uty>() as $ty
+            }
+        }
+    };
+}
+
+impl_int_from_uint! { i8, u8 }
+impl_int_from_uint! { i16, u16 }
+impl_int_from_uint! { i32, u32 }
+impl_int_from_uint! { i64, u64 }
+impl_int_from_uint! { i128, u128 }
+impl_int_from_uint! { isize, usize }
+
+macro_rules! impl_nzint {
+    ($ty:ty, $new:path) => {
+        impl Distribution<$ty> for Standard {
+            fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $ty {
+                loop {
+                    if let Some(nz) = $new(rng.gen()) {
+                        break nz;
+                    }
+                }
+            }
+        }
+    };
+}
+
+impl_nzint!(NonZeroU8, NonZeroU8::new);
+impl_nzint!(NonZeroU16, NonZeroU16::new);
+impl_nzint!(NonZeroU32, NonZeroU32::new);
+impl_nzint!(NonZeroU64, NonZeroU64::new);
+impl_nzint!(NonZeroU128, NonZeroU128::new);
+impl_nzint!(NonZeroUsize, NonZeroUsize::new);
+
+#[cfg(feature = "simd_support")]
+macro_rules! simd_impl {
+    ($(($intrinsic:ident, $vec:ty),)+) => {$(
+        impl Distribution<$intrinsic> for Standard {
+            #[inline]
+            fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $intrinsic {
+                $intrinsic::from_bits(rng.gen::<$vec>())
+            }
+        }
+    )+};
+
+    ($bits:expr,) => {};
+    ($bits:expr, $ty:ty, $($ty_more:ty,)*) => {
+        simd_impl!($bits, $($ty_more,)*);
+
+        impl Distribution<$ty> for Standard {
+            #[inline]
+            fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $ty {
+                let mut vec: $ty = Default::default();
+                unsafe {
+                    let ptr = &mut vec;
+                    let b_ptr = &mut *(ptr as *mut $ty as *mut [u8; $bits/8]);
+                    rng.fill_bytes(b_ptr);
+                }
+                vec.to_le()
+            }
+        }
+    };
+}
+
+#[cfg(feature = "simd_support")]
+simd_impl!(16, u8x2, i8x2,);
+#[cfg(feature = "simd_support")]
+simd_impl!(32, u8x4, i8x4, u16x2, i16x2,);
+#[cfg(feature = "simd_support")]
+simd_impl!(64, u8x8, i8x8, u16x4, i16x4, u32x2, i32x2,);
+#[cfg(feature = "simd_support")]
+simd_impl!(128, u8x16, i8x16, u16x8, i16x8, u32x4, i32x4, u64x2, i64x2,);
+#[cfg(feature = "simd_support")]
+simd_impl!(256, u8x32, i8x32, u16x16, i16x16, u32x8, i32x8, u64x4, i64x4,);
+#[cfg(feature = "simd_support")]
+simd_impl!(512, u8x64, i8x64, u16x32, i16x32, u32x16, i32x16, u64x8, i64x8,);
+#[cfg(all(
+    feature = "simd_support",
+    any(target_arch = "x86", target_arch = "x86_64")
+))]
+simd_impl!((__m128i, u8x16), (__m256i, u8x32),);
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_integers() {
+        let mut rng = crate::test::rng(806);
+
+        rng.sample::<isize, _>(Standard);
+        rng.sample::<i8, _>(Standard);
+        rng.sample::<i16, _>(Standard);
+        rng.sample::<i32, _>(Standard);
+        rng.sample::<i64, _>(Standard);
+        rng.sample::<i128, _>(Standard);
+
+        rng.sample::<usize, _>(Standard);
+        rng.sample::<u8, _>(Standard);
+        rng.sample::<u16, _>(Standard);
+        rng.sample::<u32, _>(Standard);
+        rng.sample::<u64, _>(Standard);
+        rng.sample::<u128, _>(Standard);
+    }
+
+    #[test]
+    fn value_stability() {
+        fn test_samples<T: Copy + core::fmt::Debug + PartialEq>(zero: T, expected: &[T])
+        where Standard: Distribution<T> {
+            let mut rng = crate::test::rng(807);
+            let mut buf = [zero; 3];
+            for x in &mut buf {
+                *x = rng.sample(Standard);
+            }
+            assert_eq!(&buf, expected);
+        }
+
+        test_samples(0u8, &[9, 247, 111]);
+        test_samples(0u16, &[32265, 42999, 38255]);
+        test_samples(0u32, &[2220326409, 2575017975, 2018088303]);
+        test_samples(0u64, &[
+            11059617991457472009,
+            16096616328739788143,
+            1487364411147516184,
+        ]);
+        test_samples(0u128, &[
+            296930161868957086625409848350820761097,
+            145644820879247630242265036535529306392,
+            111087889832015897993126088499035356354,
+        ]);
+        #[cfg(any(target_pointer_width = "32", target_pointer_width = "16"))]
+        test_samples(0usize, &[2220326409, 2575017975, 2018088303]);
+        #[cfg(target_pointer_width = "64")]
+        test_samples(0usize, &[
+            11059617991457472009,
+            16096616328739788143,
+            1487364411147516184,
+        ]);
+
+        test_samples(0i8, &[9, -9, 111]);
+        // Skip further i* types: they are simple reinterpretation of u* samples
+
+        #[cfg(feature = "simd_support")]
+        {
+            // We only test a sub-set of types here and make assumptions about the rest.
+
+            test_samples(u8x2::default(), &[
+                u8x2::new(9, 126),
+                u8x2::new(247, 167),
+                u8x2::new(111, 149),
+            ]);
+            test_samples(u8x4::default(), &[
+                u8x4::new(9, 126, 87, 132),
+                u8x4::new(247, 167, 123, 153),
+                u8x4::new(111, 149, 73, 120),
+            ]);
+            test_samples(u8x8::default(), &[
+                u8x8::new(9, 126, 87, 132, 247, 167, 123, 153),
+                u8x8::new(111, 149, 73, 120, 68, 171, 98, 223),
+                u8x8::new(24, 121, 1, 50, 13, 46, 164, 20),
+            ]);
+
+            test_samples(i64x8::default(), &[
+                i64x8::new(
+                    -7387126082252079607,
+                    -2350127744969763473,
+                    1487364411147516184,
+                    7895421560427121838,
+                    602190064936008898,
+                    6022086574635100741,
+                    -5080089175222015595,
+                    -4066367846667249123,
+                ),
+                i64x8::new(
+                    9180885022207963908,
+                    3095981199532211089,
+                    6586075293021332726,
+                    419343203796414657,
+                    3186951873057035255,
+                    5287129228749947252,
+                    444726432079249540,
+                    -1587028029513790706,
+                ),
+                i64x8::new(
+                    6075236523189346388,
+                    1351763722368165432,
+                    -6192309979959753740,
+                    -7697775502176768592,
+                    -4482022114172078123,
+                    7522501477800909500,
+                    -1837258847956201231,
+                    -586926753024886735,
+                ),
+            ]);
+        }
+    }
+}

zeroidc/vendor/rand/src/distributions/mod.rs

@@ -0,0 +1,218 @@
+// 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.
+
+//! Generating random samples from probability distributions
+//!
+//! This module is the home of the [`Distribution`] trait and several of its
+//! implementations. It is the workhorse behind some of the convenient
+//! functionality of the [`Rng`] trait, e.g. [`Rng::gen`] and of course
+//! [`Rng::sample`].
+//!
+//! Abstractly, a [probability distribution] describes the probability of
+//! occurrence of each value in its sample space.
+//!
+//! More concretely, an implementation of `Distribution<T>` for type `X` is an
+//! algorithm for choosing values from the sample space (a subset of `T`)
+//! according to the distribution `X` represents, using an external source of
+//! randomness (an RNG supplied to the `sample` function).
+//!
+//! A type `X` may implement `Distribution<T>` for multiple types `T`.
+//! Any type implementing [`Distribution`] is stateless (i.e. immutable),
+//! but it may have internal parameters set at construction time (for example,
+//! [`Uniform`] allows specification of its sample space as a range within `T`).
+//!
+//!
+//! # The `Standard` distribution
+//!
+//! The [`Standard`] distribution is important to mention. This is the
+//! distribution used by [`Rng::gen`] and represents the "default" way to
+//! produce a random value for many different types, including most primitive
+//! types, tuples, arrays, and a few derived types. See the documentation of
+//! [`Standard`] for more details.
+//!
+//! Implementing `Distribution<T>` for [`Standard`] for user types `T` makes it
+//! possible to generate type `T` with [`Rng::gen`], and by extension also
+//! with the [`random`] function.
+//!
+//! ## Random characters
+//!
+//! [`Alphanumeric`] is a simple distribution to sample random letters and
+//! numbers of the `char` type; in contrast [`Standard`] may sample any valid
+//! `char`.
+//!
+//!
+//! # Uniform numeric ranges
+//!
+//! The [`Uniform`] distribution is more flexible than [`Standard`], but also
+//! more specialised: it supports fewer target types, but allows the sample
+//! space to be specified as an arbitrary range within its target type `T`.
+//! Both [`Standard`] and [`Uniform`] are in some sense uniform distributions.
+//!
+//! Values may be sampled from this distribution using [`Rng::sample(Range)`] or
+//! by creating a distribution object with [`Uniform::new`],
+//! [`Uniform::new_inclusive`] or `From<Range>`. When the range limits are not
+//! known at compile time it is typically faster to reuse an existing
+//! `Uniform` object than to call [`Rng::sample(Range)`].
+//!
+//! User types `T` may also implement `Distribution<T>` for [`Uniform`],
+//! although this is less straightforward than for [`Standard`] (see the
+//! documentation in the [`uniform`] module). Doing so enables generation of
+//! values of type `T` with  [`Rng::sample(Range)`].
+//!
+//! ## Open and half-open ranges
+//!
+//! There are surprisingly many ways to uniformly generate random floats. A
+//! range between 0 and 1 is standard, but the exact bounds (open vs closed)
+//! and accuracy differ. In addition to the [`Standard`] distribution Rand offers
+//! [`Open01`] and [`OpenClosed01`]. See "Floating point implementation" section of
+//! [`Standard`] documentation for more details.
+//!
+//! # Non-uniform sampling
+//!
+//! Sampling a simple true/false outcome with a given probability has a name:
+//! the [`Bernoulli`] distribution (this is used by [`Rng::gen_bool`]).
+//!
+//! For weighted sampling from a sequence of discrete values, use the
+//! [`WeightedIndex`] distribution.
+//!
+//! This crate no longer includes other non-uniform distributions; instead
+//! it is recommended that you use either [`rand_distr`] or [`statrs`].
+//!
+//!
+//! [probability distribution]: https://en.wikipedia.org/wiki/Probability_distribution
+//! [`rand_distr`]: https://crates.io/crates/rand_distr
+//! [`statrs`]: https://crates.io/crates/statrs
+
+//! [`random`]: crate::random
+//! [`rand_distr`]: https://crates.io/crates/rand_distr
+//! [`statrs`]: https://crates.io/crates/statrs
+
+mod bernoulli;
+mod distribution;
+mod float;
+mod integer;
+mod other;
+mod slice;
+mod utils;
+#[cfg(feature = "alloc")]
+mod weighted_index;
+
+#[doc(hidden)]
+pub mod hidden_export {
+    pub use super::float::IntoFloat; // used by rand_distr
+}
+pub mod uniform;
+#[deprecated(
+    since = "0.8.0",
+    note = "use rand::distributions::{WeightedIndex, WeightedError} instead"
+)]
+#[cfg(feature = "alloc")]
+#[cfg_attr(doc_cfg, doc(cfg(feature = "alloc")))]
+pub mod weighted;
+
+pub use self::bernoulli::{Bernoulli, BernoulliError};
+pub use self::distribution::{Distribution, DistIter, DistMap};
+#[cfg(feature = "alloc")]
+pub use self::distribution::DistString;
+pub use self::float::{Open01, OpenClosed01};
+pub use self::other::Alphanumeric;
+pub use self::slice::Slice;
+#[doc(inline)]
+pub use self::uniform::Uniform;
+#[cfg(feature = "alloc")]
+pub use self::weighted_index::{WeightedError, WeightedIndex};
+
+#[allow(unused)]
+use crate::Rng;
+
+/// A generic random value distribution, implemented for many primitive types.
+/// Usually generates values with a numerically uniform distribution, and with a
+/// range appropriate to the type.
+///
+/// ## Provided implementations
+///
+/// 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)`. See notes below.
+/// * Wrapping integers (`Wrapping<T>`), besides the type identical to their
+///   normal integer variants.
+///
+/// The `Standard` distribution also supports 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
+///     and float types and tends to be faster for `u32` and smaller types.
+///     When using `rustc` ≥ 1.51, enable the `min_const_gen` feature to support
+///     arrays larger than 32 elements.
+///     Note that [`Rng::fill`] and `Standard`'s array support are *not* equivalent:
+///     the former is optimised for integer types (using fewer RNG calls for
+///     element types smaller than the RNG word size), while the latter supports
+///     any element type supported by `Standard`.
+/// *   `Option<T>` first generates a `bool`, and if true generates and returns
+///     `Some(value)` where `value: T`, otherwise returning `None`.
+///
+/// ## Custom implementations
+///
+/// The [`Standard`] distribution may be implemented for user types as follows:
+///
+/// ```
+/// # #![allow(dead_code)]
+/// use rand::Rng;
+/// use rand::distributions::{Distribution, Standard};
+///
+/// struct MyF32 {
+///     x: f32,
+/// }
+///
+/// impl Distribution<MyF32> for Standard {
+///     fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> MyF32 {
+///         MyF32 { x: rng.gen() }
+///     }
+/// }
+/// ```
+///
+/// ## Example usage
+/// ```
+/// use rand::prelude::*;
+/// use rand::distributions::Standard;
+///
+/// let val: f32 = StdRng::from_entropy().sample(Standard);
+/// println!("f32 from [0, 1): {}", val);
+/// ```
+///
+/// # Floating point implementation
+/// The floating point implementations for `Standard` generate a random value in
+/// the half-open interval `[0, 1)`, i.e. including 0 but not 1.
+///
+/// All values that can be generated are of the form `n * ε/2`. For `f32`
+/// the 24 most significant random bits of a `u32` are used and for `f64` the
+/// 53 most significant bits of a `u64` are used. The conversion uses the
+/// multiplicative method: `(rng.gen::<$uty>() >> N) as $ty * (ε/2)`.
+///
+/// See also: [`Open01`] which samples from `(0, 1)`, [`OpenClosed01`] which
+/// samples from `(0, 1]` and `Rng::gen_range(0..1)` which also samples from
+/// `[0, 1)`. Note that `Open01` uses transmute-based methods which yield 1 bit
+/// less precision but may perform faster on some architectures (on modern Intel
+/// CPUs all methods have approximately equal performance).
+///
+/// [`Uniform`]: uniform::Uniform
+#[derive(Clone, Copy, Debug)]
+#[cfg_attr(feature = "serde1", derive(serde::Serialize, serde::Deserialize))]
+pub struct Standard;

zeroidc/vendor/rand/src/distributions/other.rs

@@ -0,0 +1,365 @@
+// Copyright 2018 Developers of the Rand project.
+//
+// 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.
+
+//! 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:
+///
+/// - [Wikipedia article on Password Strength](https://en.wikipedia.org/wiki/Password_strength)
+/// - [Diceware for generating memorable passwords](https://en.wikipedia.org/wiki/Diceware)
+#[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> {
+        // UFCS is needed here: https://github.com/rust-lang/rust/issues/24066
+        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],
+        ]);
+    }
+}

zeroidc/vendor/rand/src/distributions/slice.rs

@@ -0,0 +1,117 @@
+// Copyright 2021 Developers of the Rand project.
+//
+// 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.
+
+use crate::distributions::{Distribution, Uniform};
+
+/// A distribution to sample items uniformly from a slice.
+///
+/// [`Slice::new`] constructs a distribution referencing a slice and uniformly
+/// samples references from the items in the slice. It may do extra work up
+/// front to make sampling of multiple values faster; if only one sample from
+/// the slice is required, [`SliceRandom::choose`] can be more efficient.
+///
+/// Steps are taken to avoid bias which might be present in naive
+/// implementations; for example `slice[rng.gen() % slice.len()]` samples from
+/// the slice, but may be more likely to select numbers in the low range than
+/// other values.
+///
+/// This distribution samples with replacement; each sample is independent.
+/// Sampling without replacement requires state to be retained, and therefore
+/// cannot be handled by a distribution; you should instead consider methods
+/// on [`SliceRandom`], such as [`SliceRandom::choose_multiple`].
+///
+/// # Example
+///
+/// ```
+/// use rand::Rng;
+/// use rand::distributions::Slice;
+///
+/// let vowels = ['a', 'e', 'i', 'o', 'u'];
+/// let vowels_dist = Slice::new(&vowels).unwrap();
+/// let rng = rand::thread_rng();
+///
+/// // build a string of 10 vowels
+/// let vowel_string: String = rng
+///     .sample_iter(&vowels_dist)
+///     .take(10)
+///     .collect();
+///
+/// println!("{}", vowel_string);
+/// assert_eq!(vowel_string.len(), 10);
+/// assert!(vowel_string.chars().all(|c| vowels.contains(&c)));
+/// ```
+///
+/// For a single sample, [`SliceRandom::choose`][crate::seq::SliceRandom::choose]
+/// may be preferred:
+///
+/// ```
+/// use rand::seq::SliceRandom;
+///
+/// let vowels = ['a', 'e', 'i', 'o', 'u'];
+/// let mut rng = rand::thread_rng();
+///
+/// println!("{}", vowels.choose(&mut rng).unwrap())
+/// ```
+///
+/// [`SliceRandom`]: crate::seq::SliceRandom
+/// [`SliceRandom::choose`]: crate::seq::SliceRandom::choose
+/// [`SliceRandom::choose_multiple`]: crate::seq::SliceRandom::choose_multiple
+#[derive(Debug, Clone, Copy)]
+pub struct Slice<'a, T> {
+    slice: &'a [T],
+    range: Uniform<usize>,
+}
+
+impl<'a, T> Slice<'a, T> {
+    /// Create a new `Slice` instance which samples uniformly from the slice.
+    /// Returns `Err` if the slice is empty.
+    pub fn new(slice: &'a [T]) -> Result<Self, EmptySlice> {
+        match slice.len() {
+            0 => Err(EmptySlice),
+            len => Ok(Self {
+                slice,
+                range: Uniform::new(0, len),
+            }),
+        }
+    }
+}
+
+impl<'a, T> Distribution<&'a T> for Slice<'a, T> {
+    fn sample<R: crate::Rng + ?Sized>(&self, rng: &mut R) -> &'a T {
+        let idx = self.range.sample(rng);
+
+        debug_assert!(
+            idx < self.slice.len(),
+            "Uniform::new(0, {}) somehow returned {}",
+            self.slice.len(),
+            idx
+        );
+
+        // Safety: at construction time, it was ensured that the slice was
+        // non-empty, and that the `Uniform` range produces values in range
+        // for the slice
+        unsafe { self.slice.get_unchecked(idx) }
+    }
+}
+
+/// Error type indicating that a [`Slice`] distribution was improperly
+/// constructed with an empty slice.
+#[derive(Debug, Clone, Copy)]
+pub struct EmptySlice;
+
+impl core::fmt::Display for EmptySlice {
+    fn fmt(&self, f: &mut core::fmt::Formatter<'_>) -> core::fmt::Result {
+        write!(
+            f,
+            "Tried to create a `distributions::Slice` with an empty slice"
+        )
+    }
+}
+
+#[cfg(feature = "std")]
+impl std::error::Error for EmptySlice {}

zeroidc/vendor/rand/src/distributions/utils.rs

@@ -0,0 +1,429 @@
+// Copyright 2018 Developers of the Rand project.
+//
+// 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.
+
+//! Math helper functions
+
+#[cfg(feature = "simd_support")] use packed_simd::*;
+
+
+pub(crate) trait WideningMultiply<RHS = Self> {
+    type Output;
+
+    fn wmul(self, x: RHS) -> Self::Output;
+}
+
+macro_rules! wmul_impl {
+    ($ty:ty, $wide:ty, $shift:expr) => {
+        impl WideningMultiply for $ty {
+            type Output = ($ty, $ty);
+
+            #[inline(always)]
+            fn wmul(self, x: $ty) -> Self::Output {
+                let tmp = (self as $wide) * (x as $wide);
+                ((tmp >> $shift) as $ty, tmp as $ty)
+            }
+        }
+    };
+
+    // simd bulk implementation
+    ($(($ty:ident, $wide:ident),)+, $shift:expr) => {
+        $(
+            impl WideningMultiply for $ty {
+                type Output = ($ty, $ty);
+
+                #[inline(always)]
+                fn wmul(self, x: $ty) -> Self::Output {
+                    // For supported vectors, this should compile to a couple
+                    // supported multiply & swizzle instructions (no actual
+                    // casting).
+                    // TODO: optimize
+                    let y: $wide = self.cast();
+                    let x: $wide = x.cast();
+                    let tmp = y * x;
+                    let hi: $ty = (tmp >> $shift).cast();
+                    let lo: $ty = tmp.cast();
+                    (hi, lo)
+                }
+            }
+        )+
+    };
+}
+wmul_impl! { u8, u16, 8 }
+wmul_impl! { u16, u32, 16 }
+wmul_impl! { u32, u64, 32 }
+wmul_impl! { u64, u128, 64 }
+
+// This code is a translation of the __mulddi3 function in LLVM's
+// compiler-rt. It is an optimised variant of the common method
+// `(a + b) * (c + d) = ac + ad + bc + bd`.
+//
+// For some reason LLVM can optimise the C version very well, but
+// keeps shuffling registers in this Rust translation.
+macro_rules! wmul_impl_large {
+    ($ty:ty, $half:expr) => {
+        impl WideningMultiply for $ty {
+            type Output = ($ty, $ty);
+
+            #[inline(always)]
+            fn wmul(self, b: $ty) -> Self::Output {
+                const LOWER_MASK: $ty = !0 >> $half;
+                let mut low = (self & LOWER_MASK).wrapping_mul(b & LOWER_MASK);
+                let mut t = low >> $half;
+                low &= LOWER_MASK;
+                t += (self >> $half).wrapping_mul(b & LOWER_MASK);
+                low += (t & LOWER_MASK) << $half;
+                let mut high = t >> $half;
+                t = low >> $half;
+                low &= LOWER_MASK;
+                t += (b >> $half).wrapping_mul(self & LOWER_MASK);
+                low += (t & LOWER_MASK) << $half;
+                high += t >> $half;
+                high += (self >> $half).wrapping_mul(b >> $half);
+
+                (high, low)
+            }
+        }
+    };
+
+    // simd bulk implementation
+    (($($ty:ty,)+) $scalar:ty, $half:expr) => {
+        $(
+            impl WideningMultiply for $ty {
+                type Output = ($ty, $ty);
+
+                #[inline(always)]
+                fn wmul(self, b: $ty) -> Self::Output {
+                    // needs wrapping multiplication
+                    const LOWER_MASK: $scalar = !0 >> $half;
+                    let mut low = (self & LOWER_MASK) * (b & LOWER_MASK);
+                    let mut t = low >> $half;
+                    low &= LOWER_MASK;
+                    t += (self >> $half) * (b & LOWER_MASK);
+                    low += (t & LOWER_MASK) << $half;
+                    let mut high = t >> $half;
+                    t = low >> $half;
+                    low &= LOWER_MASK;
+                    t += (b >> $half) * (self & LOWER_MASK);
+                    low += (t & LOWER_MASK) << $half;
+                    high += t >> $half;
+                    high += (self >> $half) * (b >> $half);
+
+                    (high, low)
+                }
+            }
+        )+
+    };
+}
+wmul_impl_large! { u128, 64 }
+
+macro_rules! wmul_impl_usize {
+    ($ty:ty) => {
+        impl WideningMultiply for usize {
+            type Output = (usize, usize);
+
+            #[inline(always)]
+            fn wmul(self, x: usize) -> Self::Output {
+                let (high, low) = (self as $ty).wmul(x as $ty);
+                (high as usize, low as usize)
+            }
+        }
+    };
+}
+#[cfg(target_pointer_width = "16")]
+wmul_impl_usize! { u16 }
+#[cfg(target_pointer_width = "32")]
+wmul_impl_usize! { u32 }
+#[cfg(target_pointer_width = "64")]
+wmul_impl_usize! { u64 }
+
+#[cfg(feature = "simd_support")]
+mod simd_wmul {
+    use super::*;
+    #[cfg(target_arch = "x86")] use core::arch::x86::*;
+    #[cfg(target_arch = "x86_64")] use core::arch::x86_64::*;
+
+    wmul_impl! {
+        (u8x2, u16x2),
+        (u8x4, u16x4),
+        (u8x8, u16x8),
+        (u8x16, u16x16),
+        (u8x32, u16x32),,
+        8
+    }
+
+    wmul_impl! { (u16x2, u32x2),, 16 }
+    wmul_impl! { (u16x4, u32x4),, 16 }
+    #[cfg(not(target_feature = "sse2"))]
+    wmul_impl! { (u16x8, u32x8),, 16 }
+    #[cfg(not(target_feature = "avx2"))]
+    wmul_impl! { (u16x16, u32x16),, 16 }
+
+    // 16-bit lane widths allow use of the x86 `mulhi` instructions, which
+    // means `wmul` can be implemented with only two instructions.
+    #[allow(unused_macros)]
+    macro_rules! wmul_impl_16 {
+        ($ty:ident, $intrinsic:ident, $mulhi:ident, $mullo:ident) => {
+            impl WideningMultiply for $ty {
+                type Output = ($ty, $ty);
+
+                #[inline(always)]
+                fn wmul(self, x: $ty) -> Self::Output {
+                    let b = $intrinsic::from_bits(x);
+                    let a = $intrinsic::from_bits(self);
+                    let hi = $ty::from_bits(unsafe { $mulhi(a, b) });
+                    let lo = $ty::from_bits(unsafe { $mullo(a, b) });
+                    (hi, lo)
+                }
+            }
+        };
+    }
+
+    #[cfg(target_feature = "sse2")]
+    wmul_impl_16! { u16x8, __m128i, _mm_mulhi_epu16, _mm_mullo_epi16 }
+    #[cfg(target_feature = "avx2")]
+    wmul_impl_16! { u16x16, __m256i, _mm256_mulhi_epu16, _mm256_mullo_epi16 }
+    // FIXME: there are no `__m512i` types in stdsimd yet, so `wmul::<u16x32>`
+    // cannot use the same implementation.
+
+    wmul_impl! {
+        (u32x2, u64x2),
+        (u32x4, u64x4),
+        (u32x8, u64x8),,
+        32
+    }
+
+    // TODO: optimize, this seems to seriously slow things down
+    wmul_impl_large! { (u8x64,) u8, 4 }
+    wmul_impl_large! { (u16x32,) u16, 8 }
+    wmul_impl_large! { (u32x16,) u32, 16 }
+    wmul_impl_large! { (u64x2, u64x4, u64x8,) u64, 32 }
+}
+
+/// Helper trait when dealing with scalar and SIMD floating point types.
+pub(crate) trait FloatSIMDUtils {
+    // `PartialOrd` for vectors compares lexicographically. We want to compare all
+    // the individual SIMD lanes instead, and get the combined result over all
+    // lanes. This is possible using something like `a.lt(b).all()`, but we
+    // implement it as a trait so we can write the same code for `f32` and `f64`.
+    // Only the comparison functions we need are implemented.
+    fn all_lt(self, other: Self) -> bool;
+    fn all_le(self, other: Self) -> bool;
+    fn all_finite(self) -> bool;
+
+    type Mask;
+    fn finite_mask(self) -> Self::Mask;
+    fn gt_mask(self, other: Self) -> Self::Mask;
+    fn ge_mask(self, other: Self) -> Self::Mask;
+
+    // Decrease all lanes where the mask is `true` to the next lower value
+    // representable by the floating-point type. At least one of the lanes
+    // must be set.
+    fn decrease_masked(self, mask: Self::Mask) -> Self;
+
+    // Convert from int value. Conversion is done while retaining the numerical
+    // value, not by retaining the binary representation.
+    type UInt;
+    fn cast_from_int(i: Self::UInt) -> Self;
+}
+
+/// Implement functions available in std builds but missing from core primitives
+#[cfg(not(std))]
+// False positive: We are following `std` here.
+#[allow(clippy::wrong_self_convention)]
+pub(crate) trait Float: Sized {
+    fn is_nan(self) -> bool;
+    fn is_infinite(self) -> bool;
+    fn is_finite(self) -> bool;
+}
+
+/// Implement functions on f32/f64 to give them APIs similar to SIMD types
+pub(crate) trait FloatAsSIMD: Sized {
+    #[inline(always)]
+    fn lanes() -> usize {
+        1
+    }
+    #[inline(always)]
+    fn splat(scalar: Self) -> Self {
+        scalar
+    }
+    #[inline(always)]
+    fn extract(self, index: usize) -> Self {
+        debug_assert_eq!(index, 0);
+        self
+    }
+    #[inline(always)]
+    fn replace(self, index: usize, new_value: Self) -> Self {
+        debug_assert_eq!(index, 0);
+        new_value
+    }
+}
+
+pub(crate) trait BoolAsSIMD: Sized {
+    fn any(self) -> bool;
+    fn all(self) -> bool;
+    fn none(self) -> bool;
+}
+
+impl BoolAsSIMD for bool {
+    #[inline(always)]
+    fn any(self) -> bool {
+        self
+    }
+
+    #[inline(always)]
+    fn all(self) -> bool {
+        self
+    }
+
+    #[inline(always)]
+    fn none(self) -> bool {
+        !self
+    }
+}
+
+macro_rules! scalar_float_impl {
+    ($ty:ident, $uty:ident) => {
+        #[cfg(not(std))]
+        impl Float for $ty {
+            #[inline]
+            fn is_nan(self) -> bool {
+                self != self
+            }
+
+            #[inline]
+            fn is_infinite(self) -> bool {
+                self == ::core::$ty::INFINITY || self == ::core::$ty::NEG_INFINITY
+            }
+
+            #[inline]
+            fn is_finite(self) -> bool {
+                !(self.is_nan() || self.is_infinite())
+            }
+        }
+
+        impl FloatSIMDUtils for $ty {
+            type Mask = bool;
+            type UInt = $uty;
+
+            #[inline(always)]
+            fn all_lt(self, other: Self) -> bool {
+                self < other
+            }
+
+            #[inline(always)]
+            fn all_le(self, other: Self) -> bool {
+                self <= other
+            }
+
+            #[inline(always)]
+            fn all_finite(self) -> bool {
+                self.is_finite()
+            }
+
+            #[inline(always)]
+            fn finite_mask(self) -> Self::Mask {
+                self.is_finite()
+            }
+
+            #[inline(always)]
+            fn gt_mask(self, other: Self) -> Self::Mask {
+                self > other
+            }
+
+            #[inline(always)]
+            fn ge_mask(self, other: Self) -> Self::Mask {
+                self >= other
+            }
+
+            #[inline(always)]
+            fn decrease_masked(self, mask: Self::Mask) -> Self {
+                debug_assert!(mask, "At least one lane must be set");
+                <$ty>::from_bits(self.to_bits() - 1)
+            }
+
+            #[inline]
+            fn cast_from_int(i: Self::UInt) -> Self {
+                i as $ty
+            }
+        }
+
+        impl FloatAsSIMD for $ty {}
+    };
+}
+
+scalar_float_impl!(f32, u32);
+scalar_float_impl!(f64, u64);
+
+
+#[cfg(feature = "simd_support")]
+macro_rules! simd_impl {
+    ($ty:ident, $f_scalar:ident, $mty:ident, $uty:ident) => {
+        impl FloatSIMDUtils for $ty {
+            type Mask = $mty;
+            type UInt = $uty;
+
+            #[inline(always)]
+            fn all_lt(self, other: Self) -> bool {
+                self.lt(other).all()
+            }
+
+            #[inline(always)]
+            fn all_le(self, other: Self) -> bool {
+                self.le(other).all()
+            }
+
+            #[inline(always)]
+            fn all_finite(self) -> bool {
+                self.finite_mask().all()
+            }
+
+            #[inline(always)]
+            fn finite_mask(self) -> Self::Mask {
+                // This can possibly be done faster by checking bit patterns
+                let neg_inf = $ty::splat(::core::$f_scalar::NEG_INFINITY);
+                let pos_inf = $ty::splat(::core::$f_scalar::INFINITY);
+                self.gt(neg_inf) & self.lt(pos_inf)
+            }
+
+            #[inline(always)]
+            fn gt_mask(self, other: Self) -> Self::Mask {
+                self.gt(other)
+            }
+
+            #[inline(always)]
+            fn ge_mask(self, other: Self) -> Self::Mask {
+                self.ge(other)
+            }
+
+            #[inline(always)]
+            fn decrease_masked(self, mask: Self::Mask) -> Self {
+                // Casting a mask into ints will produce all bits set for
+                // true, and 0 for false. Adding that to the binary
+                // representation of a float means subtracting one from
+                // the binary representation, resulting in the next lower
+                // value representable by $ty. This works even when the
+                // current value is infinity.
+                debug_assert!(mask.any(), "At least one lane must be set");
+                <$ty>::from_bits(<$uty>::from_bits(self) + <$uty>::from_bits(mask))
+            }
+
+            #[inline]
+            fn cast_from_int(i: Self::UInt) -> Self {
+                i.cast()
+            }
+        }
+    };
+}
+
+#[cfg(feature="simd_support")] simd_impl! { f32x2, f32, m32x2, u32x2 }
+#[cfg(feature="simd_support")] simd_impl! { f32x4, f32, m32x4, u32x4 }
+#[cfg(feature="simd_support")] simd_impl! { f32x8, f32, m32x8, u32x8 }
+#[cfg(feature="simd_support")] simd_impl! { f32x16, f32, m32x16, u32x16 }
+#[cfg(feature="simd_support")] simd_impl! { f64x2, f64, m64x2, u64x2 }
+#[cfg(feature="simd_support")] simd_impl! { f64x4, f64, m64x4, u64x4 }
+#[cfg(feature="simd_support")] simd_impl! { f64x8, f64, m64x8, u64x8 }

zeroidc/vendor/rand/src/distributions/weighted.rs

@@ -0,0 +1,47 @@
+// Copyright 2018 Developers of the Rand project.
+//
+// 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.
+
+//! Weighted index sampling
+//!
+//! This module is deprecated. Use [`crate::distributions::WeightedIndex`] and
+//! [`crate::distributions::WeightedError`] instead.
+
+pub use super::{WeightedIndex, WeightedError};
+
+#[allow(missing_docs)]
+#[deprecated(since = "0.8.0", note = "moved to rand_distr crate")]
+pub mod alias_method {
+    // This module exists to provide a deprecation warning which minimises
+    // compile errors, but still fails to compile if ever used.
+    use core::marker::PhantomData;
+    use alloc::vec::Vec;
+    use super::WeightedError;
+
+    #[derive(Debug)]
+    pub struct WeightedIndex<W: Weight> {
+        _phantom: PhantomData<W>,
+    }
+    impl<W: Weight> WeightedIndex<W> {
+        pub fn new(_weights: Vec<W>) -> Result<Self, WeightedError> {
+            Err(WeightedError::NoItem)
+        }
+    }
+
+    pub trait Weight {}
+    macro_rules! impl_weight {
+        () => {};
+        ($T:ident, $($more:ident,)*) => {
+            impl Weight for $T {}
+            impl_weight!($($more,)*);
+        };
+    }
+    impl_weight!(f64, f32,);
+    impl_weight!(u8, u16, u32, u64, usize,);
+    impl_weight!(i8, i16, i32, i64, isize,);
+    impl_weight!(u128, i128,);
+}

zeroidc/vendor/rand/src/distributions/weighted_index.rs

@@ -0,0 +1,458 @@
+// Copyright 2018 Developers of the Rand project.
+//
+// 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.
+
+//! Weighted index sampling
+
+use crate::distributions::uniform::{SampleBorrow, SampleUniform, UniformSampler};
+use crate::distributions::Distribution;
+use crate::Rng;
+use core::cmp::PartialOrd;
+use core::fmt;
+
+// Note that this whole module is only imported if feature="alloc" is enabled.
+use alloc::vec::Vec;
+
+#[cfg(feature = "serde1")]
+use serde::{Serialize, Deserialize};
+
+/// A distribution using weighted sampling of discrete items
+///
+/// Sampling a `WeightedIndex` distribution returns the index of a randomly
+/// selected element from the iterator used when the `WeightedIndex` was
+/// created. The chance of a given element being picked is proportional to the
+/// value of the element. The weights can use any type `X` for which an
+/// implementation of [`Uniform<X>`] exists.
+///
+/// # Performance
+///
+/// Time complexity of sampling from `WeightedIndex` is `O(log N)` where
+/// `N` is the number of weights. As an alternative,
+/// [`rand_distr::weighted_alias`](https://docs.rs/rand_distr/*/rand_distr/weighted_alias/index.html)
+/// supports `O(1)` sampling, but with much higher initialisation cost.
+///
+/// A `WeightedIndex<X>` contains a `Vec<X>` and a [`Uniform<X>`] and so its
+/// size is the sum of the size of those objects, possibly plus some alignment.
+///
+/// Creating a `WeightedIndex<X>` will allocate enough space to hold `N - 1`
+/// weights of type `X`, where `N` is the number of weights. However, since
+/// `Vec` doesn't guarantee a particular growth strategy, additional memory
+/// might be allocated but not used. Since the `WeightedIndex` object also
+/// contains, this might cause additional allocations, though for primitive
+/// types, [`Uniform<X>`] doesn't allocate any memory.
+///
+/// Sampling from `WeightedIndex` will result in a single call to
+/// `Uniform<X>::sample` (method of the [`Distribution`] trait), which typically
+/// will request a single value from the underlying [`RngCore`], though the
+/// exact number depends on the implementation of `Uniform<X>::sample`.
+///
+/// # Example
+///
+/// ```
+/// use rand::prelude::*;
+/// use rand::distributions::WeightedIndex;
+///
+/// let choices = ['a', 'b', 'c'];
+/// let weights = [2,   1,   1];
+/// let dist = WeightedIndex::new(&weights).unwrap();
+/// let mut rng = thread_rng();
+/// for _ in 0..100 {
+///     // 50% chance to print 'a', 25% chance to print 'b', 25% chance to print 'c'
+///     println!("{}", choices[dist.sample(&mut rng)]);
+/// }
+///
+/// let items = [('a', 0), ('b', 3), ('c', 7)];
+/// let dist2 = WeightedIndex::new(items.iter().map(|item| item.1)).unwrap();
+/// for _ in 0..100 {
+///     // 0% chance to print 'a', 30% chance to print 'b', 70% chance to print 'c'
+///     println!("{}", items[dist2.sample(&mut rng)].0);
+/// }
+/// ```
+///
+/// [`Uniform<X>`]: crate::distributions::Uniform
+/// [`RngCore`]: crate::RngCore
+#[derive(Debug, Clone, PartialEq)]
+#[cfg_attr(feature = "serde1", derive(Serialize, Deserialize))]
+#[cfg_attr(doc_cfg, doc(cfg(feature = "alloc")))]
+pub struct WeightedIndex<X: SampleUniform + PartialOrd> {
+    cumulative_weights: Vec<X>,
+    total_weight: X,
+    weight_distribution: X::Sampler,
+}
+
+impl<X: SampleUniform + PartialOrd> WeightedIndex<X> {
+    /// Creates a new a `WeightedIndex` [`Distribution`] using the values
+    /// in `weights`. The weights can use any type `X` for which an
+    /// implementation of [`Uniform<X>`] exists.
+    ///
+    /// Returns an error if the iterator is empty, if any weight is `< 0`, or
+    /// if its total value is 0.
+    ///
+    /// [`Uniform<X>`]: crate::distributions::uniform::Uniform
+    pub fn new<I>(weights: I) -> Result<WeightedIndex<X>, WeightedError>
+    where
+        I: IntoIterator,
+        I::Item: SampleBorrow<X>,
+        X: for<'a> ::core::ops::AddAssign<&'a X> + Clone + Default,
+    {
+        let mut iter = weights.into_iter();
+        let mut total_weight: X = iter.next().ok_or(WeightedError::NoItem)?.borrow().clone();
+
+        let zero = <X as Default>::default();
+        if !(total_weight >= zero) {
+            return Err(WeightedError::InvalidWeight);
+        }
+
+        let mut weights = Vec::<X>::with_capacity(iter.size_hint().0);
+        for w in iter {
+            // Note that `!(w >= x)` is not equivalent to `w < x` for partially
+            // ordered types due to NaNs which are equal to nothing.
+            if !(w.borrow() >= &zero) {
+                return Err(WeightedError::InvalidWeight);
+            }
+            weights.push(total_weight.clone());
+            total_weight += w.borrow();
+        }
+
+        if total_weight == zero {
+            return Err(WeightedError::AllWeightsZero);
+        }
+        let distr = X::Sampler::new(zero, total_weight.clone());
+
+        Ok(WeightedIndex {
+            cumulative_weights: weights,
+            total_weight,
+            weight_distribution: distr,
+        })
+    }
+
+    /// Update a subset of weights, without changing the number of weights.
+    ///
+    /// `new_weights` must be sorted by the index.
+    ///
+    /// Using this method instead of `new` might be more efficient if only a small number of
+    /// weights is modified. No allocations are performed, unless the weight type `X` uses
+    /// allocation internally.
+    ///
+    /// In case of error, `self` is not modified.
+    pub fn update_weights(&mut self, new_weights: &[(usize, &X)]) -> Result<(), WeightedError>
+    where X: for<'a> ::core::ops::AddAssign<&'a X>
+            + for<'a> ::core::ops::SubAssign<&'a X>
+            + Clone
+            + Default {
+        if new_weights.is_empty() {
+            return Ok(());
+        }
+
+        let zero = <X as Default>::default();
+
+        let mut total_weight = self.total_weight.clone();
+
+        // Check for errors first, so we don't modify `self` in case something
+        // goes wrong.
+        let mut prev_i = None;
+        for &(i, w) in new_weights {
+            if let Some(old_i) = prev_i {
+                if old_i >= i {
+                    return Err(WeightedError::InvalidWeight);
+                }
+            }
+            if !(*w >= zero) {
+                return Err(WeightedError::InvalidWeight);
+            }
+            if i > self.cumulative_weights.len() {
+                return Err(WeightedError::TooMany);
+            }
+
+            let mut old_w = if i < self.cumulative_weights.len() {
+                self.cumulative_weights[i].clone()
+            } else {
+                self.total_weight.clone()
+            };
+            if i > 0 {
+                old_w -= &self.cumulative_weights[i - 1];
+            }
+
+            total_weight -= &old_w;
+            total_weight += w;
+            prev_i = Some(i);
+        }
+        if total_weight <= zero {
+            return Err(WeightedError::AllWeightsZero);
+        }
+
+        // Update the weights. Because we checked all the preconditions in the
+        // previous loop, this should never panic.
+        let mut iter = new_weights.iter();
+
+        let mut prev_weight = zero.clone();
+        let mut next_new_weight = iter.next();
+        let &(first_new_index, _) = next_new_weight.unwrap();
+        let mut cumulative_weight = if first_new_index > 0 {
+            self.cumulative_weights[first_new_index - 1].clone()
+        } else {
+            zero.clone()
+        };
+        for i in first_new_index..self.cumulative_weights.len() {
+            match next_new_weight {
+                Some(&(j, w)) if i == j => {
+                    cumulative_weight += w;
+                    next_new_weight = iter.next();
+                }
+                _ => {
+                    let mut tmp = self.cumulative_weights[i].clone();
+                    tmp -= &prev_weight; // We know this is positive.
+                    cumulative_weight += &tmp;
+                }
+            }
+            prev_weight = cumulative_weight.clone();
+            core::mem::swap(&mut prev_weight, &mut self.cumulative_weights[i]);
+        }
+
+        self.total_weight = total_weight;
+        self.weight_distribution = X::Sampler::new(zero, self.total_weight.clone());
+
+        Ok(())
+    }
+}
+
+impl<X> Distribution<usize> for WeightedIndex<X>
+where X: SampleUniform + PartialOrd
+{
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> usize {
+        use ::core::cmp::Ordering;
+        let chosen_weight = self.weight_distribution.sample(rng);
+        // Find the first item which has a weight *higher* than the chosen weight.
+        self.cumulative_weights
+            .binary_search_by(|w| {
+                if *w <= chosen_weight {
+                    Ordering::Less
+                } else {
+                    Ordering::Greater
+                }
+            })
+            .unwrap_err()
+    }
+}
+
+#[cfg(test)]
+mod test {
+    use super::*;
+
+    #[cfg(feature = "serde1")]
+    #[test]
+    fn test_weightedindex_serde1() {
+        let weighted_index = WeightedIndex::new(&[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]).unwrap();
+
+        let ser_weighted_index = bincode::serialize(&weighted_index).unwrap();
+        let de_weighted_index: WeightedIndex<i32> =
+            bincode::deserialize(&ser_weighted_index).unwrap();
+
+        assert_eq!(
+            de_weighted_index.cumulative_weights,
+            weighted_index.cumulative_weights
+        );
+        assert_eq!(de_weighted_index.total_weight, weighted_index.total_weight);
+    }
+
+    #[test]
+    fn test_accepting_nan(){
+        assert_eq!(
+            WeightedIndex::new(&[core::f32::NAN, 0.5]).unwrap_err(),
+            WeightedError::InvalidWeight,
+        );
+        assert_eq!(
+            WeightedIndex::new(&[core::f32::NAN]).unwrap_err(),
+            WeightedError::InvalidWeight,
+        );
+        assert_eq!(
+            WeightedIndex::new(&[0.5, core::f32::NAN]).unwrap_err(),
+            WeightedError::InvalidWeight,
+        );
+
+        assert_eq!(
+            WeightedIndex::new(&[0.5, 7.0])
+                .unwrap()
+                .update_weights(&[(0, &core::f32::NAN)])
+                .unwrap_err(),
+            WeightedError::InvalidWeight,
+        )
+    }
+
+
+    #[test]
+    #[cfg_attr(miri, ignore)] // Miri is too slow
+    fn test_weightedindex() {
+        let mut r = crate::test::rng(700);
+        const N_REPS: u32 = 5000;
+        let weights = [1u32, 2, 3, 0, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7];
+        let total_weight = weights.iter().sum::<u32>() as f32;
+
+        let verify = |result: [i32; 14]| {
+            for (i, count) in result.iter().enumerate() {
+                let exp = (weights[i] * N_REPS) as f32 / total_weight;
+                let mut err = (*count as f32 - exp).abs();
+                if err != 0.0 {
+                    err /= exp;
+                }
+                assert!(err <= 0.25);
+            }
+        };
+
+        // WeightedIndex from vec
+        let mut chosen = [0i32; 14];
+        let distr = WeightedIndex::new(weights.to_vec()).unwrap();
+        for _ in 0..N_REPS {
+            chosen[distr.sample(&mut r)] += 1;
+        }
+        verify(chosen);
+
+        // WeightedIndex from slice
+        chosen = [0i32; 14];
+        let distr = WeightedIndex::new(&weights[..]).unwrap();
+        for _ in 0..N_REPS {
+            chosen[distr.sample(&mut r)] += 1;
+        }
+        verify(chosen);
+
+        // WeightedIndex from iterator
+        chosen = [0i32; 14];
+        let distr = WeightedIndex::new(weights.iter()).unwrap();
+        for _ in 0..N_REPS {
+            chosen[distr.sample(&mut r)] += 1;
+        }
+        verify(chosen);
+
+        for _ in 0..5 {
+            assert_eq!(WeightedIndex::new(&[0, 1]).unwrap().sample(&mut r), 1);
+            assert_eq!(WeightedIndex::new(&[1, 0]).unwrap().sample(&mut r), 0);
+            assert_eq!(
+                WeightedIndex::new(&[0, 0, 0, 0, 10, 0])
+                    .unwrap()
+                    .sample(&mut r),
+                4
+            );
+        }
+
+        assert_eq!(
+            WeightedIndex::new(&[10][0..0]).unwrap_err(),
+            WeightedError::NoItem
+        );
+        assert_eq!(
+            WeightedIndex::new(&[0]).unwrap_err(),
+            WeightedError::AllWeightsZero
+        );
+        assert_eq!(
+            WeightedIndex::new(&[10, 20, -1, 30]).unwrap_err(),
+            WeightedError::InvalidWeight
+        );
+        assert_eq!(
+            WeightedIndex::new(&[-10, 20, 1, 30]).unwrap_err(),
+            WeightedError::InvalidWeight
+        );
+        assert_eq!(
+            WeightedIndex::new(&[-10]).unwrap_err(),
+            WeightedError::InvalidWeight
+        );
+    }
+
+    #[test]
+    fn test_update_weights() {
+        let data = [
+            (
+                &[10u32, 2, 3, 4][..],
+                &[(1, &100), (2, &4)][..], // positive change
+                &[10, 100, 4, 4][..],
+            ),
+            (
+                &[1u32, 2, 3, 0, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7][..],
+                &[(2, &1), (5, &1), (13, &100)][..], // negative change and last element
+                &[1u32, 2, 1, 0, 5, 1, 7, 1, 2, 3, 4, 5, 6, 100][..],
+            ),
+        ];
+
+        for (weights, update, expected_weights) in data.iter() {
+            let total_weight = weights.iter().sum::<u32>();
+            let mut distr = WeightedIndex::new(weights.to_vec()).unwrap();
+            assert_eq!(distr.total_weight, total_weight);
+
+            distr.update_weights(update).unwrap();
+            let expected_total_weight = expected_weights.iter().sum::<u32>();
+            let expected_distr = WeightedIndex::new(expected_weights.to_vec()).unwrap();
+            assert_eq!(distr.total_weight, expected_total_weight);
+            assert_eq!(distr.total_weight, expected_distr.total_weight);
+            assert_eq!(distr.cumulative_weights, expected_distr.cumulative_weights);
+        }
+    }
+
+    #[test]
+    fn value_stability() {
+        fn test_samples<X: SampleUniform + PartialOrd, I>(
+            weights: I, buf: &mut [usize], expected: &[usize],
+        ) where
+            I: IntoIterator,
+            I::Item: SampleBorrow<X>,
+            X: for<'a> ::core::ops::AddAssign<&'a X> + Clone + Default,
+        {
+            assert_eq!(buf.len(), expected.len());
+            let distr = WeightedIndex::new(weights).unwrap();
+            let mut rng = crate::test::rng(701);
+            for r in buf.iter_mut() {
+                *r = rng.sample(&distr);
+            }
+            assert_eq!(buf, expected);
+        }
+
+        let mut buf = [0; 10];
+        test_samples(&[1i32, 1, 1, 1, 1, 1, 1, 1, 1], &mut buf, &[
+            0, 6, 2, 6, 3, 4, 7, 8, 2, 5,
+        ]);
+        test_samples(&[0.7f32, 0.1, 0.1, 0.1], &mut buf, &[
+            0, 0, 0, 1, 0, 0, 2, 3, 0, 0,
+        ]);
+        test_samples(&[1.0f64, 0.999, 0.998, 0.997], &mut buf, &[
+            2, 2, 1, 3, 2, 1, 3, 3, 2, 1,
+        ]);
+    }
+
+    #[test]
+    fn weighted_index_distributions_can_be_compared() {
+        assert_eq!(WeightedIndex::new(&[1, 2]), WeightedIndex::new(&[1, 2]));
+    }
+}
+
+/// Error type returned from `WeightedIndex::new`.
+#[cfg_attr(doc_cfg, doc(cfg(feature = "alloc")))]
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub enum WeightedError {
+    /// The provided weight collection contains no items.
+    NoItem,
+
+    /// A weight is either less than zero, greater than the supported maximum,
+    /// NaN, or otherwise invalid.
+    InvalidWeight,
+
+    /// All items in the provided weight collection are zero.
+    AllWeightsZero,
+
+    /// Too many weights are provided (length greater than `u32::MAX`)
+    TooMany,
+}
+
+#[cfg(feature = "std")]
+impl std::error::Error for WeightedError {}
+
+impl fmt::Display for WeightedError {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        f.write_str(match *self {
+            WeightedError::NoItem => "No weights provided in distribution",
+            WeightedError::InvalidWeight => "A weight is invalid in distribution",
+            WeightedError::AllWeightsZero => "All weights are zero in distribution",
+            WeightedError::TooMany => "Too many weights (hit u32::MAX) in distribution",
+        })
+    }
+}

zeroidc/vendor/rand/src/lib.rs

@@ -0,0 +1,214 @@
+// 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();
+    }
+}

zeroidc/vendor/rand/src/prelude.rs

@@ -0,0 +1,34 @@
+// Copyright 2018 Developers of the Rand project.
+//
+// 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.
+
+//! Convenience re-export of common members
+//!
+//! Like the standard library's prelude, this module simplifies importing of
+//! common items. Unlike the standard prelude, the contents of this module must
+//! be imported manually:
+//!
+//! ```
+//! use rand::prelude::*;
+//! # let mut r = StdRng::from_rng(thread_rng()).unwrap();
+//! # let _: f32 = r.gen();
+//! ```
+
+#[doc(no_inline)] pub use crate::distributions::Distribution;
+#[cfg(feature = "small_rng")]
+#[doc(no_inline)]
+pub use crate::rngs::SmallRng;
+#[cfg(feature = "std_rng")]
+#[doc(no_inline)] pub use crate::rngs::StdRng;
+#[doc(no_inline)]
+#[cfg(all(feature = "std", feature = "std_rng"))]
+pub use crate::rngs::ThreadRng;
+#[doc(no_inline)] pub use crate::seq::{IteratorRandom, SliceRandom};
+#[doc(no_inline)]
+#[cfg(all(feature = "std", feature = "std_rng"))]
+pub use crate::{random, thread_rng};
+#[doc(no_inline)] pub use crate::{CryptoRng, Rng, RngCore, SeedableRng};

zeroidc/vendor/rand/src/rng.rs

@@ -0,0 +1,600 @@
+// 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.
+
+//! [`Rng`] trait
+
+use rand_core::{Error, RngCore};
+use crate::distributions::uniform::{SampleRange, SampleUniform};
+use crate::distributions::{self, Distribution, Standard};
+use core::num::Wrapping;
+use core::{mem, slice};
+
+/// An automatically-implemented extension trait on [`RngCore`] providing high-level
+/// generic methods for sampling values and other convenience methods.
+///
+/// This is the primary trait to use when generating random values.
+///
+/// # Generic usage
+///
+/// The basic pattern is `fn foo<R: Rng + ?Sized>(rng: &mut R)`. Some
+/// things are worth noting here:
+///
+/// - Since `Rng: RngCore` and every `RngCore` implements `Rng`, it makes no
+///   difference whether we use `R: Rng` or `R: RngCore`.
+/// - The `+ ?Sized` un-bounding allows functions to be called directly on
+///   type-erased references; i.e. `foo(r)` where `r: &mut dyn RngCore`. Without
+///   this it would be necessary to write `foo(&mut r)`.
+///
+/// An alternative pattern is possible: `fn foo<R: Rng>(rng: R)`. This has some
+/// trade-offs. It allows the argument to be consumed directly without a `&mut`
+/// (which is how `from_rng(thread_rng())` works); also it still works directly
+/// on references (including type-erased references). Unfortunately within the
+/// function `foo` it is not known whether `rng` is a reference type or not,
+/// hence many uses of `rng` require an extra reference, either explicitly
+/// (`distr.sample(&mut rng)`) or implicitly (`rng.gen()`); one may hope the
+/// optimiser can remove redundant references later.
+///
+/// Example:
+///
+/// ```
+/// # use rand::thread_rng;
+/// use rand::Rng;
+///
+/// fn foo<R: Rng + ?Sized>(rng: &mut R) -> f32 {
+///     rng.gen()
+/// }
+///
+/// # let v = foo(&mut thread_rng());
+/// ```
+pub trait Rng: RngCore {
+    /// Return a random value supporting the [`Standard`] distribution.
+    ///
+    /// # Example
+    ///
+    /// ```
+    /// use rand::{thread_rng, Rng};
+    ///
+    /// let mut rng = thread_rng();
+    /// let x: u32 = rng.gen();
+    /// println!("{}", x);
+    /// println!("{:?}", rng.gen::<(f64, bool)>());
+    /// ```
+    ///
+    /// # Arrays and tuples
+    ///
+    /// The `rng.gen()` method is able to generate arrays (up to 32 elements)
+    /// and tuples (up to 12 elements), so long as all element types can be
+    /// generated.
+    /// When using `rustc` ≥ 1.51, enable the `min_const_gen` feature to support
+    /// arrays larger than 32 elements.
+    ///
+    /// For arrays of integers, especially for those with small element types
+    /// (< 64 bit), it will likely be faster to instead use [`Rng::fill`].
+    ///
+    /// ```
+    /// use rand::{thread_rng, Rng};
+    ///
+    /// let mut rng = thread_rng();
+    /// let tuple: (u8, i32, char) = rng.gen(); // arbitrary tuple support
+    ///
+    /// let arr1: [f32; 32] = rng.gen();        // array construction
+    /// let mut arr2 = [0u8; 128];
+    /// rng.fill(&mut arr2);                    // array fill
+    /// ```
+    ///
+    /// [`Standard`]: distributions::Standard
+    #[inline]
+    fn gen<T>(&mut self) -> T
+    where Standard: Distribution<T> {
+        Standard.sample(self)
+    }
+
+    /// Generate a random value in the given range.
+    ///
+    /// This function is optimised for the case that only a single sample is
+    /// made from the given range. See also the [`Uniform`] distribution
+    /// type which may be faster if sampling from the same range repeatedly.
+    ///
+    /// Only `gen_range(low..high)` and `gen_range(low..=high)` are supported.
+    ///
+    /// # Panics
+    ///
+    /// Panics if the range is empty.
+    ///
+    /// # Example
+    ///
+    /// ```
+    /// use rand::{thread_rng, Rng};
+    ///
+    /// let mut rng = thread_rng();
+    ///
+    /// // Exclusive range
+    /// let n: u32 = rng.gen_range(0..10);
+    /// println!("{}", n);
+    /// let m: f64 = rng.gen_range(-40.0..1.3e5);
+    /// println!("{}", m);
+    ///
+    /// // Inclusive range
+    /// let n: u32 = rng.gen_range(0..=10);
+    /// println!("{}", n);
+    /// ```
+    ///
+    /// [`Uniform`]: distributions::uniform::Uniform
+    fn gen_range<T, R>(&mut self, range: R) -> T
+    where
+        T: SampleUniform,
+        R: SampleRange<T>
+    {
+        assert!(!range.is_empty(), "cannot sample empty range");
+        range.sample_single(self)
+    }
+
+    /// Sample a new value, using the given distribution.
+    ///
+    /// ### Example
+    ///
+    /// ```
+    /// use rand::{thread_rng, Rng};
+    /// use rand::distributions::Uniform;
+    ///
+    /// let mut rng = thread_rng();
+    /// let x = rng.sample(Uniform::new(10u32, 15));
+    /// // Type annotation requires two types, the type and distribution; the
+    /// // distribution can be inferred.
+    /// let y = rng.sample::<u16, _>(Uniform::new(10, 15));
+    /// ```
+    fn sample<T, D: Distribution<T>>(&mut self, distr: D) -> T {
+        distr.sample(self)
+    }
+
+    /// Create an iterator that generates values using the given distribution.
+    ///
+    /// Note that this function takes its arguments by value. This works since
+    /// `(&mut R): Rng where R: Rng` and
+    /// `(&D): Distribution where D: Distribution`,
+    /// however borrowing is not automatic hence `rng.sample_iter(...)` may
+    /// need to be replaced with `(&mut rng).sample_iter(...)`.
+    ///
+    /// # Example
+    ///
+    /// ```
+    /// use rand::{thread_rng, Rng};
+    /// use rand::distributions::{Alphanumeric, Uniform, Standard};
+    ///
+    /// let mut rng = thread_rng();
+    ///
+    /// // Vec of 16 x f32:
+    /// let v: Vec<f32> = (&mut rng).sample_iter(Standard).take(16).collect();
+    ///
+    /// // String:
+    /// let s: String = (&mut rng).sample_iter(Alphanumeric)
+    ///     .take(7)
+    ///     .map(char::from)
+    ///     .collect();
+    ///
+    /// // Combined values
+    /// println!("{:?}", (&mut rng).sample_iter(Standard).take(5)
+    ///                              .collect::<Vec<(f64, bool)>>());
+    ///
+    /// // Dice-rolling:
+    /// let die_range = Uniform::new_inclusive(1, 6);
+    /// let mut roll_die = (&mut rng).sample_iter(die_range);
+    /// while roll_die.next().unwrap() != 6 {
+    ///     println!("Not a 6; rolling again!");
+    /// }
+    /// ```
+    fn sample_iter<T, D>(self, distr: D) -> distributions::DistIter<D, Self, T>
+    where
+        D: Distribution<T>,
+        Self: Sized,
+    {
+        distr.sample_iter(self)
+    }
+
+    /// Fill any type implementing [`Fill`] with random data
+    ///
+    /// The distribution is expected to be uniform with portable results, but
+    /// this cannot be guaranteed for third-party implementations.
+    ///
+    /// This is identical to [`try_fill`] except that it panics on error.
+    ///
+    /// # Example
+    ///
+    /// ```
+    /// use rand::{thread_rng, Rng};
+    ///
+    /// let mut arr = [0i8; 20];
+    /// thread_rng().fill(&mut arr[..]);
+    /// ```
+    ///
+    /// [`fill_bytes`]: RngCore::fill_bytes
+    /// [`try_fill`]: Rng::try_fill
+    fn fill<T: Fill + ?Sized>(&mut self, dest: &mut T) {
+        dest.try_fill(self).unwrap_or_else(|_| panic!("Rng::fill failed"))
+    }
+
+    /// Fill any type implementing [`Fill`] with random data
+    ///
+    /// The distribution is expected to be uniform with portable results, but
+    /// this cannot be guaranteed for third-party implementations.
+    ///
+    /// This is identical to [`fill`] except that it forwards errors.
+    ///
+    /// # Example
+    ///
+    /// ```
+    /// # use rand::Error;
+    /// use rand::{thread_rng, Rng};
+    ///
+    /// # fn try_inner() -> Result<(), Error> {
+    /// let mut arr = [0u64; 4];
+    /// thread_rng().try_fill(&mut arr[..])?;
+    /// # Ok(())
+    /// # }
+    ///
+    /// # try_inner().unwrap()
+    /// ```
+    ///
+    /// [`try_fill_bytes`]: RngCore::try_fill_bytes
+    /// [`fill`]: Rng::fill
+    fn try_fill<T: Fill + ?Sized>(&mut self, dest: &mut T) -> Result<(), Error> {
+        dest.try_fill(self)
+    }
+
+    /// Return a bool with a probability `p` of being true.
+    ///
+    /// See also the [`Bernoulli`] distribution, which may be faster if
+    /// sampling from the same probability repeatedly.
+    ///
+    /// # Example
+    ///
+    /// ```
+    /// use rand::{thread_rng, Rng};
+    ///
+    /// let mut rng = thread_rng();
+    /// println!("{}", rng.gen_bool(1.0 / 3.0));
+    /// ```
+    ///
+    /// # Panics
+    ///
+    /// If `p < 0` or `p > 1`.
+    ///
+    /// [`Bernoulli`]: distributions::Bernoulli
+    #[inline]
+    fn gen_bool(&mut self, p: f64) -> bool {
+        let d = distributions::Bernoulli::new(p).unwrap();
+        self.sample(d)
+    }
+
+    /// Return a bool with a probability of `numerator/denominator` of being
+    /// true. I.e. `gen_ratio(2, 3)` has chance of 2 in 3, or about 67%, of
+    /// returning true. If `numerator == denominator`, then the returned value
+    /// is guaranteed to be `true`. If `numerator == 0`, then the returned
+    /// value is guaranteed to be `false`.
+    ///
+    /// See also the [`Bernoulli`] distribution, which may be faster if
+    /// sampling from the same `numerator` and `denominator` repeatedly.
+    ///
+    /// # Panics
+    ///
+    /// If `denominator == 0` or `numerator > denominator`.
+    ///
+    /// # Example
+    ///
+    /// ```
+    /// use rand::{thread_rng, Rng};
+    ///
+    /// let mut rng = thread_rng();
+    /// println!("{}", rng.gen_ratio(2, 3));
+    /// ```
+    ///
+    /// [`Bernoulli`]: distributions::Bernoulli
+    #[inline]
+    fn gen_ratio(&mut self, numerator: u32, denominator: u32) -> bool {
+        let d = distributions::Bernoulli::from_ratio(numerator, denominator).unwrap();
+        self.sample(d)
+    }
+}
+
+impl<R: RngCore + ?Sized> Rng for R {}
+
+/// Types which may be filled with random data
+///
+/// This trait allows arrays to be efficiently filled with random data.
+///
+/// Implementations are expected to be portable across machines unless
+/// clearly documented otherwise (see the
+/// [Chapter on Portability](https://rust-random.github.io/book/portability.html)).
+pub trait Fill {
+    /// Fill self with random data
+    fn try_fill<R: Rng + ?Sized>(&mut self, rng: &mut R) -> Result<(), Error>;
+}
+
+macro_rules! impl_fill_each {
+    () => {};
+    ($t:ty) => {
+        impl Fill for [$t] {
+            fn try_fill<R: Rng + ?Sized>(&mut self, rng: &mut R) -> Result<(), Error> {
+                for elt in self.iter_mut() {
+                    *elt = rng.gen();
+                }
+                Ok(())
+            }
+        }
+    };
+    ($t:ty, $($tt:ty,)*) => {
+        impl_fill_each!($t);
+        impl_fill_each!($($tt,)*);
+    };
+}
+
+impl_fill_each!(bool, char, f32, f64,);
+
+impl Fill for [u8] {
+    fn try_fill<R: Rng + ?Sized>(&mut self, rng: &mut R) -> Result<(), Error> {
+        rng.try_fill_bytes(self)
+    }
+}
+
+macro_rules! impl_fill {
+    () => {};
+    ($t:ty) => {
+        impl Fill for [$t] {
+            #[inline(never)] // in micro benchmarks, this improves performance
+            fn try_fill<R: Rng + ?Sized>(&mut self, rng: &mut R) -> Result<(), Error> {
+                if self.len() > 0 {
+                    rng.try_fill_bytes(unsafe {
+                        slice::from_raw_parts_mut(self.as_mut_ptr()
+                            as *mut u8,
+                            self.len() * mem::size_of::<$t>()
+                        )
+                    })?;
+                    for x in self {
+                        *x = x.to_le();
+                    }
+                }
+                Ok(())
+            }
+        }
+
+        impl Fill for [Wrapping<$t>] {
+            #[inline(never)]
+            fn try_fill<R: Rng + ?Sized>(&mut self, rng: &mut R) -> Result<(), Error> {
+                if self.len() > 0 {
+                    rng.try_fill_bytes(unsafe {
+                        slice::from_raw_parts_mut(self.as_mut_ptr()
+                            as *mut u8,
+                            self.len() * mem::size_of::<$t>()
+                        )
+                    })?;
+                    for x in self {
+                    *x = Wrapping(x.0.to_le());
+                    }
+                }
+                Ok(())
+            }
+        }
+    };
+    ($t:ty, $($tt:ty,)*) => {
+        impl_fill!($t);
+        // TODO: this could replace above impl once Rust #32463 is fixed
+        // impl_fill!(Wrapping<$t>);
+        impl_fill!($($tt,)*);
+    }
+}
+
+impl_fill!(u16, u32, u64, usize, u128,);
+impl_fill!(i8, i16, i32, i64, isize, i128,);
+
+#[cfg_attr(doc_cfg, doc(cfg(feature = "min_const_gen")))]
+#[cfg(feature = "min_const_gen")]
+impl<T, const N: usize> Fill for [T; N]
+where [T]: Fill
+{
+    fn try_fill<R: Rng + ?Sized>(&mut self, rng: &mut R) -> Result<(), Error> {
+        self[..].try_fill(rng)
+    }
+}
+
+#[cfg(not(feature = "min_const_gen"))]
+macro_rules! impl_fill_arrays {
+    ($n:expr,) => {};
+    ($n:expr, $N:ident) => {
+        impl<T> Fill for [T; $n] where [T]: Fill {
+            fn try_fill<R: Rng + ?Sized>(&mut self, rng: &mut R) -> Result<(), Error> {
+                self[..].try_fill(rng)
+            }
+        }
+    };
+    ($n:expr, $N:ident, $($NN:ident,)*) => {
+        impl_fill_arrays!($n, $N);
+        impl_fill_arrays!($n - 1, $($NN,)*);
+    };
+    (!div $n:expr,) => {};
+    (!div $n:expr, $N:ident, $($NN:ident,)*) => {
+        impl_fill_arrays!($n, $N);
+        impl_fill_arrays!(!div $n / 2, $($NN,)*);
+    };
+}
+#[cfg(not(feature = "min_const_gen"))]
+#[rustfmt::skip]
+impl_fill_arrays!(32, N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,);
+#[cfg(not(feature = "min_const_gen"))]
+impl_fill_arrays!(!div 4096, N,N,N,N,N,N,N,);
+
+#[cfg(test)]
+mod test {
+    use super::*;
+    use crate::test::rng;
+    use crate::rngs::mock::StepRng;
+    #[cfg(feature = "alloc")] use alloc::boxed::Box;
+
+    #[test]
+    fn test_fill_bytes_default() {
+        let mut r = StepRng::new(0x11_22_33_44_55_66_77_88, 0);
+
+        // 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 buffer = [0u8; 87];
+            let v = &mut buffer[0..n];
+            r.fill_bytes(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_fill() {
+        let x = 9041086907909331047; // a random u64
+        let mut rng = StepRng::new(x, 0);
+
+        // Convert to byte sequence and back to u64; byte-swap twice if BE.
+        let mut array = [0u64; 2];
+        rng.fill(&mut array[..]);
+        assert_eq!(array, [x, x]);
+        assert_eq!(rng.next_u64(), x);
+
+        // Convert to bytes then u32 in LE order
+        let mut array = [0u32; 2];
+        rng.fill(&mut array[..]);
+        assert_eq!(array, [x as u32, (x >> 32) as u32]);
+        assert_eq!(rng.next_u32(), x as u32);
+
+        // Check equivalence using wrapped arrays
+        let mut warray = [Wrapping(0u32); 2];
+        rng.fill(&mut warray[..]);
+        assert_eq!(array[0], warray[0].0);
+        assert_eq!(array[1], warray[1].0);
+
+        // Check equivalence for generated floats
+        let mut array = [0f32; 2];
+        rng.fill(&mut array);
+        let gen: [f32; 2] = rng.gen();
+        assert_eq!(array, gen);
+    }
+
+    #[test]
+    fn test_fill_empty() {
+        let mut array = [0u32; 0];
+        let mut rng = StepRng::new(0, 1);
+        rng.fill(&mut array);
+        rng.fill(&mut array[..]);
+    }
+
+    #[test]
+    fn test_gen_range_int() {
+        let mut r = rng(101);
+        for _ in 0..1000 {
+            let a = r.gen_range(-4711..17);
+            assert!((-4711..17).contains(&a));
+            let a: i8 = r.gen_range(-3..42);
+            assert!((-3..42).contains(&a));
+            let a: u16 = r.gen_range(10..99);
+            assert!((10..99).contains(&a));
+            let a: i32 = r.gen_range(-100..2000);
+            assert!((-100..2000).contains(&a));
+            let a: u32 = r.gen_range(12..=24);
+            assert!((12..=24).contains(&a));
+
+            assert_eq!(r.gen_range(0u32..1), 0u32);
+            assert_eq!(r.gen_range(-12i64..-11), -12i64);
+            assert_eq!(r.gen_range(3_000_000..3_000_001), 3_000_000);
+        }
+    }
+
+    #[test]
+    fn test_gen_range_float() {
+        let mut r = rng(101);
+        for _ in 0..1000 {
+            let a = r.gen_range(-4.5..1.7);
+            assert!((-4.5..1.7).contains(&a));
+            let a = r.gen_range(-1.1..=-0.3);
+            assert!((-1.1..=-0.3).contains(&a));
+
+            assert_eq!(r.gen_range(0.0f32..=0.0), 0.);
+            assert_eq!(r.gen_range(-11.0..=-11.0), -11.);
+            assert_eq!(r.gen_range(3_000_000.0..=3_000_000.0), 3_000_000.);
+        }
+    }
+
+    #[test]
+    #[should_panic]
+    fn test_gen_range_panic_int() {
+        #![allow(clippy::reversed_empty_ranges)]
+        let mut r = rng(102);
+        r.gen_range(5..-2);
+    }
+
+    #[test]
+    #[should_panic]
+    fn test_gen_range_panic_usize() {
+        #![allow(clippy::reversed_empty_ranges)]
+        let mut r = rng(103);
+        r.gen_range(5..2);
+    }
+
+    #[test]
+    fn test_gen_bool() {
+        #![allow(clippy::bool_assert_comparison)]
+
+        let mut r = rng(105);
+        for _ in 0..5 {
+            assert_eq!(r.gen_bool(0.0), false);
+            assert_eq!(r.gen_bool(1.0), true);
+        }
+    }
+
+    #[test]
+    fn test_rng_trait_object() {
+        use crate::distributions::{Distribution, Standard};
+        let mut rng = rng(109);
+        let mut r = &mut rng as &mut dyn RngCore;
+        r.next_u32();
+        r.gen::<i32>();
+        assert_eq!(r.gen_range(0..1), 0);
+        let _c: u8 = Standard.sample(&mut r);
+    }
+
+    #[test]
+    #[cfg(feature = "alloc")]
+    fn test_rng_boxed_trait() {
+        use crate::distributions::{Distribution, Standard};
+        let rng = rng(110);
+        let mut r = Box::new(rng) as Box<dyn RngCore>;
+        r.next_u32();
+        r.gen::<i32>();
+        assert_eq!(r.gen_range(0..1), 0);
+        let _c: u8 = Standard.sample(&mut r);
+    }
+
+    #[test]
+    #[cfg_attr(miri, ignore)] // Miri is too slow
+    fn test_gen_ratio_average() {
+        const NUM: u32 = 3;
+        const DENOM: u32 = 10;
+        const N: u32 = 100_000;
+
+        let mut sum: u32 = 0;
+        let mut rng = rng(111);
+        for _ in 0..N {
+            if rng.gen_ratio(NUM, DENOM) {
+                sum += 1;
+            }
+        }
+        // Have Binomial(N, NUM/DENOM) distribution
+        let expected = (NUM * N) / DENOM; // exact integer
+        assert!(((sum - expected) as i32).abs() < 500);
+    }
+}

zeroidc/vendor/rand/src/rngs/adapter/mod.rs

@@ -0,0 +1,16 @@
+// Copyright 2018 Developers of the Rand project.
+//
+// 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.
+
+//! Wrappers / adapters forming RNGs
+
+mod read;
+mod reseeding;
+
+#[allow(deprecated)]
+pub use self::read::{ReadError, ReadRng};
+pub use self::reseeding::ReseedingRng;

zeroidc/vendor/rand/src/rngs/adapter/read.rs

@@ -0,0 +1,150 @@
+// Copyright 2018 Developers of the Rand project.
+// Copyright 2013 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.
+
+//! A wrapper around any Read to treat it as an RNG.
+
+#![allow(deprecated)]
+
+use std::fmt;
+use std::io::Read;
+
+use rand_core::{impls, Error, RngCore};
+
+
+/// An RNG that reads random bytes straight from any type supporting
+/// [`std::io::Read`], for example files.
+///
+/// This will work best with an infinite reader, but that is not required.
+///
+/// This can be used with `/dev/urandom` on Unix but it is recommended to use
+/// [`OsRng`] instead.
+///
+/// # Panics
+///
+/// `ReadRng` uses [`std::io::Read::read_exact`], which retries on interrupts.
+/// All other errors from the underlying reader, including when it does not
+/// have enough data, will only be reported through [`try_fill_bytes`].
+/// The other [`RngCore`] methods will panic in case of an error.
+///
+/// [`OsRng`]: crate::rngs::OsRng
+/// [`try_fill_bytes`]: RngCore::try_fill_bytes
+#[derive(Debug)]
+#[deprecated(since="0.8.4", note="removal due to lack of usage")]
+pub struct ReadRng<R> {
+    reader: R,
+}
+
+impl<R: Read> ReadRng<R> {
+    /// Create a new `ReadRng` from a `Read`.
+    pub fn new(r: R) -> ReadRng<R> {
+        ReadRng { reader: r }
+    }
+}
+
+impl<R: Read> RngCore for ReadRng<R> {
+    fn next_u32(&mut self) -> u32 {
+        impls::next_u32_via_fill(self)
+    }
+
+    fn next_u64(&mut self) -> u64 {
+        impls::next_u64_via_fill(self)
+    }
+
+    fn fill_bytes(&mut self, dest: &mut [u8]) {
+        self.try_fill_bytes(dest).unwrap_or_else(|err| {
+            panic!(
+                "reading random bytes from Read implementation failed; error: {}",
+                err
+            )
+        });
+    }
+
+    fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
+        if dest.is_empty() {
+            return Ok(());
+        }
+        // Use `std::io::read_exact`, which retries on `ErrorKind::Interrupted`.
+        self.reader
+            .read_exact(dest)
+            .map_err(|e| Error::new(ReadError(e)))
+    }
+}
+
+/// `ReadRng` error type
+#[derive(Debug)]
+#[deprecated(since="0.8.4")]
+pub struct ReadError(std::io::Error);
+
+impl fmt::Display for ReadError {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        write!(f, "ReadError: {}", self.0)
+    }
+}
+
+impl std::error::Error for ReadError {
+    fn source(&self) -> Option<&(dyn std::error::Error + 'static)> {
+        Some(&self.0)
+    }
+}
+
+
+#[cfg(test)]
+mod test {
+    use std::println;
+
+    use super::ReadRng;
+    use crate::RngCore;
+
+    #[test]
+    fn test_reader_rng_u64() {
+        // transmute from the target to avoid endianness concerns.
+        #[rustfmt::skip]
+        let v = [0u8, 0, 0, 0, 0, 0, 0, 1,
+                 0,   4, 0, 0, 3, 0, 0, 2,
+                 5,   0, 0, 0, 0, 0, 0, 0];
+        let mut rng = ReadRng::new(&v[..]);
+
+        assert_eq!(rng.next_u64(), 1 << 56);
+        assert_eq!(rng.next_u64(), (2 << 56) + (3 << 32) + (4 << 8));
+        assert_eq!(rng.next_u64(), 5);
+    }
+
+    #[test]
+    fn test_reader_rng_u32() {
+        let v = [0u8, 0, 0, 1, 0, 0, 2, 0, 3, 0, 0, 0];
+        let mut rng = ReadRng::new(&v[..]);
+
+        assert_eq!(rng.next_u32(), 1 << 24);
+        assert_eq!(rng.next_u32(), 2 << 16);
+        assert_eq!(rng.next_u32(), 3);
+    }
+
+    #[test]
+    fn test_reader_rng_fill_bytes() {
+        let v = [1u8, 2, 3, 4, 5, 6, 7, 8];
+        let mut w = [0u8; 8];
+
+        let mut rng = ReadRng::new(&v[..]);
+        rng.fill_bytes(&mut w);
+
+        assert!(v == w);
+    }
+
+    #[test]
+    fn test_reader_rng_insufficient_bytes() {
+        let v = [1u8, 2, 3, 4, 5, 6, 7, 8];
+        let mut w = [0u8; 9];
+
+        let mut rng = ReadRng::new(&v[..]);
+
+        let result = rng.try_fill_bytes(&mut w);
+        assert!(result.is_err());
+        println!("Error: {}", result.unwrap_err());
+    }
+}

zeroidc/vendor/rand/src/rngs/adapter/reseeding.rs

@@ -0,0 +1,386 @@
+// Copyright 2018 Developers of the Rand project.
+// Copyright 2013 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.
+
+//! A wrapper around another PRNG that reseeds it after it
+//! generates a certain number of random bytes.
+
+use core::mem::size_of;
+
+use rand_core::block::{BlockRng, BlockRngCore};
+use rand_core::{CryptoRng, Error, RngCore, SeedableRng};
+
+/// A wrapper around any PRNG that implements [`BlockRngCore`], that adds the
+/// ability to reseed it.
+///
+/// `ReseedingRng` reseeds the underlying PRNG in the following cases:
+///
+/// - On a manual call to [`reseed()`].
+/// - After `clone()`, the clone will be reseeded on first use.
+/// - When a process is forked on UNIX, the RNGs in both the parent and child
+///   processes will be reseeded just before the next call to
+///   [`BlockRngCore::generate`], i.e. "soon". For ChaCha and Hc128 this is a
+///   maximum of fifteen `u32` values before reseeding.
+/// - After the PRNG has generated a configurable number of random bytes.
+///
+/// # When should reseeding after a fixed number of generated bytes be used?
+///
+/// Reseeding after a fixed number of generated bytes is never strictly
+/// *necessary*. Cryptographic PRNGs don't have a limited number of bytes they
+/// can output, or at least not a limit reachable in any practical way. There is
+/// no such thing as 'running out of entropy'.
+///
+/// Occasionally reseeding can be seen as some form of 'security in depth'. Even
+/// if in the future a cryptographic weakness is found in the CSPRNG being used,
+/// or a flaw in the implementation, occasionally reseeding should make
+/// exploiting it much more difficult or even impossible.
+///
+/// Use [`ReseedingRng::new`] with a `threshold` of `0` to disable reseeding
+/// after a fixed number of generated bytes.
+///
+/// # Limitations
+///
+/// It is recommended that a `ReseedingRng` (including `ThreadRng`) not be used
+/// from a fork handler.
+/// Use `OsRng` or `getrandom`, or defer your use of the RNG until later.
+///
+/// # Error handling
+///
+/// Although unlikely, reseeding the wrapped PRNG can fail. `ReseedingRng` will
+/// never panic but try to handle the error intelligently through some
+/// combination of retrying and delaying reseeding until later.
+/// If handling the source error fails `ReseedingRng` will continue generating
+/// data from the wrapped PRNG without reseeding.
+///
+/// Manually calling [`reseed()`] will not have this retry or delay logic, but
+/// reports the error.
+///
+/// # Example
+///
+/// ```
+/// use rand::prelude::*;
+/// use rand_chacha::ChaCha20Core; // Internal part of ChaChaRng that
+///                              // implements BlockRngCore
+/// use rand::rngs::OsRng;
+/// use rand::rngs::adapter::ReseedingRng;
+///
+/// let prng = ChaCha20Core::from_entropy();
+/// let mut reseeding_rng = ReseedingRng::new(prng, 0, OsRng);
+///
+/// println!("{}", reseeding_rng.gen::<u64>());
+///
+/// let mut cloned_rng = reseeding_rng.clone();
+/// assert!(reseeding_rng.gen::<u64>() != cloned_rng.gen::<u64>());
+/// ```
+///
+/// [`BlockRngCore`]: rand_core::block::BlockRngCore
+/// [`ReseedingRng::new`]: ReseedingRng::new
+/// [`reseed()`]: ReseedingRng::reseed
+#[derive(Debug)]
+pub struct ReseedingRng<R, Rsdr>(BlockRng<ReseedingCore<R, Rsdr>>)
+where
+    R: BlockRngCore + SeedableRng,
+    Rsdr: RngCore;
+
+impl<R, Rsdr> ReseedingRng<R, Rsdr>
+where
+    R: BlockRngCore + SeedableRng,
+    Rsdr: RngCore,
+{
+    /// Create a new `ReseedingRng` from an existing PRNG, combined with a RNG
+    /// to use as reseeder.
+    ///
+    /// `threshold` sets the number of generated bytes after which to reseed the
+    /// PRNG. Set it to zero to never reseed based on the number of generated
+    /// values.
+    pub fn new(rng: R, threshold: u64, reseeder: Rsdr) -> Self {
+        ReseedingRng(BlockRng::new(ReseedingCore::new(rng, threshold, reseeder)))
+    }
+
+    /// Reseed the internal PRNG.
+    pub fn reseed(&mut self) -> Result<(), Error> {
+        self.0.core.reseed()
+    }
+}
+
+// TODO: this should be implemented for any type where the inner type
+// implements RngCore, but we can't specify that because ReseedingCore is private
+impl<R, Rsdr: RngCore> RngCore for ReseedingRng<R, Rsdr>
+where
+    R: BlockRngCore<Item = u32> + SeedableRng,
+    <R as BlockRngCore>::Results: AsRef<[u32]> + AsMut<[u32]>,
+{
+    #[inline(always)]
+    fn next_u32(&mut self) -> u32 {
+        self.0.next_u32()
+    }
+
+    #[inline(always)]
+    fn next_u64(&mut self) -> u64 {
+        self.0.next_u64()
+    }
+
+    fn fill_bytes(&mut self, dest: &mut [u8]) {
+        self.0.fill_bytes(dest)
+    }
+
+    fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
+        self.0.try_fill_bytes(dest)
+    }
+}
+
+impl<R, Rsdr> Clone for ReseedingRng<R, Rsdr>
+where
+    R: BlockRngCore + SeedableRng + Clone,
+    Rsdr: RngCore + Clone,
+{
+    fn clone(&self) -> ReseedingRng<R, Rsdr> {
+        // Recreating `BlockRng` seems easier than cloning it and resetting
+        // the index.
+        ReseedingRng(BlockRng::new(self.0.core.clone()))
+    }
+}
+
+impl<R, Rsdr> CryptoRng for ReseedingRng<R, Rsdr>
+where
+    R: BlockRngCore + SeedableRng + CryptoRng,
+    Rsdr: RngCore + CryptoRng,
+{
+}
+
+#[derive(Debug)]
+struct ReseedingCore<R, Rsdr> {
+    inner: R,
+    reseeder: Rsdr,
+    threshold: i64,
+    bytes_until_reseed: i64,
+    fork_counter: usize,
+}
+
+impl<R, Rsdr> BlockRngCore for ReseedingCore<R, Rsdr>
+where
+    R: BlockRngCore + SeedableRng,
+    Rsdr: RngCore,
+{
+    type Item = <R as BlockRngCore>::Item;
+    type Results = <R as BlockRngCore>::Results;
+
+    fn generate(&mut self, results: &mut Self::Results) {
+        let global_fork_counter = fork::get_fork_counter();
+        if self.bytes_until_reseed <= 0 || self.is_forked(global_fork_counter) {
+            // We get better performance by not calling only `reseed` here
+            // and continuing with the rest of the function, but by directly
+            // returning from a non-inlined function.
+            return self.reseed_and_generate(results, global_fork_counter);
+        }
+        let num_bytes = results.as_ref().len() * size_of::<Self::Item>();
+        self.bytes_until_reseed -= num_bytes as i64;
+        self.inner.generate(results);
+    }
+}
+
+impl<R, Rsdr> ReseedingCore<R, Rsdr>
+where
+    R: BlockRngCore + SeedableRng,
+    Rsdr: RngCore,
+{
+    /// Create a new `ReseedingCore`.
+    fn new(rng: R, threshold: u64, reseeder: Rsdr) -> Self {
+        use ::core::i64::MAX;
+        fork::register_fork_handler();
+
+        // Because generating more values than `i64::MAX` takes centuries on
+        // current hardware, we just clamp to that value.
+        // Also we set a threshold of 0, which indicates no limit, to that
+        // value.
+        let threshold = if threshold == 0 {
+            MAX
+        } else if threshold <= MAX as u64 {
+            threshold as i64
+        } else {
+            MAX
+        };
+
+        ReseedingCore {
+            inner: rng,
+            reseeder,
+            threshold: threshold as i64,
+            bytes_until_reseed: threshold as i64,
+            fork_counter: 0,
+        }
+    }
+
+    /// Reseed the internal PRNG.
+    fn reseed(&mut self) -> Result<(), Error> {
+        R::from_rng(&mut self.reseeder).map(|result| {
+            self.bytes_until_reseed = self.threshold;
+            self.inner = result
+        })
+    }
+
+    fn is_forked(&self, global_fork_counter: usize) -> bool {
+        // In theory, on 32-bit platforms, it is possible for
+        // `global_fork_counter` to wrap around after ~4e9 forks.
+        //
+        // This check will detect a fork in the normal case where
+        // `fork_counter < global_fork_counter`, and also when the difference
+        // between both is greater than `isize::MAX` (wrapped around).
+        //
+        // It will still fail to detect a fork if there have been more than
+        // `isize::MAX` forks, without any reseed in between. Seems unlikely
+        // enough.
+        (self.fork_counter.wrapping_sub(global_fork_counter) as isize) < 0
+    }
+
+    #[inline(never)]
+    fn reseed_and_generate(
+        &mut self, results: &mut <Self as BlockRngCore>::Results, global_fork_counter: usize,
+    ) {
+        #![allow(clippy::if_same_then_else)] // false positive
+        if self.is_forked(global_fork_counter) {
+            info!("Fork detected, reseeding RNG");
+        } else {
+            trace!("Reseeding RNG (periodic reseed)");
+        }
+
+        let num_bytes = results.as_ref().len() * size_of::<<R as BlockRngCore>::Item>();
+
+        if let Err(e) = self.reseed() {
+            warn!("Reseeding RNG failed: {}", e);
+            let _ = e;
+        }
+        self.fork_counter = global_fork_counter;
+
+        self.bytes_until_reseed = self.threshold - num_bytes as i64;
+        self.inner.generate(results);
+    }
+}
+
+impl<R, Rsdr> Clone for ReseedingCore<R, Rsdr>
+where
+    R: BlockRngCore + SeedableRng + Clone,
+    Rsdr: RngCore + Clone,
+{
+    fn clone(&self) -> ReseedingCore<R, Rsdr> {
+        ReseedingCore {
+            inner: self.inner.clone(),
+            reseeder: self.reseeder.clone(),
+            threshold: self.threshold,
+            bytes_until_reseed: 0, // reseed clone on first use
+            fork_counter: self.fork_counter,
+        }
+    }
+}
+
+impl<R, Rsdr> CryptoRng for ReseedingCore<R, Rsdr>
+where
+    R: BlockRngCore + SeedableRng + CryptoRng,
+    Rsdr: RngCore + CryptoRng,
+{
+}
+
+
+#[cfg(all(unix, not(target_os = "emscripten")))]
+mod fork {
+    use core::sync::atomic::{AtomicUsize, Ordering};
+    use std::sync::Once;
+
+    // Fork protection
+    //
+    // We implement fork protection on Unix using `pthread_atfork`.
+    // When the process is forked, we increment `RESEEDING_RNG_FORK_COUNTER`.
+    // Every `ReseedingRng` stores the last known value of the static in
+    // `fork_counter`. If the cached `fork_counter` is less than
+    // `RESEEDING_RNG_FORK_COUNTER`, it is time to reseed this RNG.
+    //
+    // If reseeding fails, we don't deal with this by setting a delay, but just
+    // don't update `fork_counter`, so a reseed is attempted as soon as
+    // possible.
+
+    static RESEEDING_RNG_FORK_COUNTER: AtomicUsize = AtomicUsize::new(0);
+
+    pub fn get_fork_counter() -> usize {
+        RESEEDING_RNG_FORK_COUNTER.load(Ordering::Relaxed)
+    }
+
+    extern "C" fn fork_handler() {
+        // Note: fetch_add is defined to wrap on overflow
+        // (which is what we want).
+        RESEEDING_RNG_FORK_COUNTER.fetch_add(1, Ordering::Relaxed);
+    }
+
+    pub fn register_fork_handler() {
+        static REGISTER: Once = Once::new();
+        REGISTER.call_once(|| {
+            // Bump the counter before and after forking (see #1169):
+            let ret = unsafe { libc::pthread_atfork(
+                Some(fork_handler),
+                Some(fork_handler),
+                Some(fork_handler),
+            ) };
+            if ret != 0 {
+                panic!("libc::pthread_atfork failed with code {}", ret);
+            }
+        });
+    }
+}
+
+#[cfg(not(all(unix, not(target_os = "emscripten"))))]
+mod fork {
+    pub fn get_fork_counter() -> usize {
+        0
+    }
+    pub fn register_fork_handler() {}
+}
+
+
+#[cfg(feature = "std_rng")]
+#[cfg(test)]
+mod test {
+    use super::ReseedingRng;
+    use crate::rngs::mock::StepRng;
+    use crate::rngs::std::Core;
+    use crate::{Rng, SeedableRng};
+
+    #[test]
+    fn test_reseeding() {
+        let mut zero = StepRng::new(0, 0);
+        let rng = Core::from_rng(&mut zero).unwrap();
+        let thresh = 1; // reseed every time the buffer is exhausted
+        let mut reseeding = ReseedingRng::new(rng, thresh, zero);
+
+        // RNG buffer size is [u32; 64]
+        // Debug is only implemented up to length 32 so use two arrays
+        let mut buf = ([0u32; 32], [0u32; 32]);
+        reseeding.fill(&mut buf.0);
+        reseeding.fill(&mut buf.1);
+        let seq = buf;
+        for _ in 0..10 {
+            reseeding.fill(&mut buf.0);
+            reseeding.fill(&mut buf.1);
+            assert_eq!(buf, seq);
+        }
+    }
+
+    #[test]
+    fn test_clone_reseeding() {
+        #![allow(clippy::redundant_clone)]
+
+        let mut zero = StepRng::new(0, 0);
+        let rng = Core::from_rng(&mut zero).unwrap();
+        let mut rng1 = ReseedingRng::new(rng, 32 * 4, zero);
+
+        let first: u32 = rng1.gen();
+        for _ in 0..10 {
+            let _ = rng1.gen::<u32>();
+        }
+
+        let mut rng2 = rng1.clone();
+        assert_eq!(first, rng2.gen::<u32>());
+    }
+}

zeroidc/vendor/rand/src/rngs/mock.rs

@@ -0,0 +1,87 @@
+// Copyright 2018 Developers of the Rand project.
+//
+// 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.
+
+//! Mock random number generator
+
+use rand_core::{impls, Error, RngCore};
+
+#[cfg(feature = "serde1")]
+use serde::{Serialize, Deserialize};
+
+/// A simple implementation of `RngCore` for testing purposes.
+///
+/// This generates an arithmetic sequence (i.e. adds a constant each step)
+/// over a `u64` number, using wrapping arithmetic. If the increment is 0
+/// the generator yields a constant.
+///
+/// ```
+/// use rand::Rng;
+/// use rand::rngs::mock::StepRng;
+///
+/// let mut my_rng = StepRng::new(2, 1);
+/// let sample: [u64; 3] = my_rng.gen();
+/// assert_eq!(sample, [2, 3, 4]);
+/// ```
+#[derive(Debug, Clone, PartialEq, Eq)]
+#[cfg_attr(feature = "serde1", derive(Serialize, Deserialize))]
+pub struct StepRng {
+    v: u64,
+    a: u64,
+}
+
+impl StepRng {
+    /// Create a `StepRng`, yielding an arithmetic sequence starting with
+    /// `initial` and incremented by `increment` each time.
+    pub fn new(initial: u64, increment: u64) -> Self {
+        StepRng {
+            v: initial,
+            a: increment,
+        }
+    }
+}
+
+impl RngCore for StepRng {
+    #[inline]
+    fn next_u32(&mut self) -> u32 {
+        self.next_u64() as u32
+    }
+
+    #[inline]
+    fn next_u64(&mut self) -> u64 {
+        let result = self.v;
+        self.v = self.v.wrapping_add(self.a);
+        result
+    }
+
+    #[inline]
+    fn fill_bytes(&mut self, dest: &mut [u8]) {
+        impls::fill_bytes_via_next(self, dest);
+    }
+
+    #[inline]
+    fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
+        self.fill_bytes(dest);
+        Ok(())
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    #[test]
+    #[cfg(feature = "serde1")]
+    fn test_serialization_step_rng() {
+        use super::StepRng;
+
+        let some_rng = StepRng::new(42, 7);
+        let de_some_rng: StepRng =
+            bincode::deserialize(&bincode::serialize(&some_rng).unwrap()).unwrap();
+        assert_eq!(some_rng.v, de_some_rng.v);
+        assert_eq!(some_rng.a, de_some_rng.a);
+
+    }
+}

zeroidc/vendor/rand/src/rngs/mod.rs

@@ -0,0 +1,119 @@
+// Copyright 2018 Developers of the Rand project.
+//
+// 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.
+
+//! Random number generators and adapters
+//!
+//! ## Background: Random number generators (RNGs)
+//!
+//! Computers cannot produce random numbers from nowhere. We classify
+//! random number generators as follows:
+//!
+//! -   "True" random number generators (TRNGs) use hard-to-predict data sources
+//!     (e.g. the high-resolution parts of event timings and sensor jitter) to
+//!     harvest random bit-sequences, apply algorithms to remove bias and
+//!     estimate available entropy, then combine these bits into a byte-sequence
+//!     or an entropy pool. This job is usually done by the operating system or
+//!     a hardware generator (HRNG).
+//! -   "Pseudo"-random number generators (PRNGs) use algorithms to transform a
+//!     seed into a sequence of pseudo-random numbers. These generators can be
+//!     fast and produce well-distributed unpredictable random numbers (or not).
+//!     They are usually deterministic: given algorithm and seed, the output
+//!     sequence can be reproduced. They have finite period and eventually loop;
+//!     with many algorithms this period is fixed and can be proven sufficiently
+//!     long, while others are chaotic and the period depends on the seed.
+//! -   "Cryptographically secure" pseudo-random number generators (CSPRNGs)
+//!     are the sub-set of PRNGs which are secure. Security of the generator
+//!     relies both on hiding the internal state and using a strong algorithm.
+//!
+//! ## Traits and functionality
+//!
+//! All RNGs implement the [`RngCore`] trait, as a consequence of which the
+//! [`Rng`] extension trait is automatically implemented. Secure RNGs may
+//! additionally implement the [`CryptoRng`] trait.
+//!
+//! All PRNGs require a seed to produce their random number sequence. The
+//! [`SeedableRng`] trait provides three ways of constructing PRNGs:
+//!
+//! -   `from_seed` accepts a type specific to the PRNG
+//! -   `from_rng` allows a PRNG to be seeded from any other RNG
+//! -   `seed_from_u64` allows any PRNG to be seeded from a `u64` insecurely
+//! -   `from_entropy` securely seeds a PRNG from fresh entropy
+//!
+//! Use the [`rand_core`] crate when implementing your own RNGs.
+//!
+//! ## Our generators
+//!
+//! This crate provides several random number generators:
+//!
+//! -   [`OsRng`] is an interface to the operating system's random number
+//!     source. Typically the operating system uses a CSPRNG with entropy
+//!     provided by a TRNG and some type of on-going re-seeding.
+//! -   [`ThreadRng`], provided by the [`thread_rng`] function, is a handle to a
+//!     thread-local CSPRNG with periodic seeding from [`OsRng`]. Because this
+//!     is local, it is typically much faster than [`OsRng`]. It should be
+//!     secure, though the paranoid may prefer [`OsRng`].
+//! -   [`StdRng`] is a CSPRNG chosen for good performance and trust of security
+//!     (based on reviews, maturity and usage). The current algorithm is ChaCha12,
+//!     which is well established and rigorously analysed.
+//!     [`StdRng`] provides the algorithm used by [`ThreadRng`] but without
+//!     periodic reseeding.
+//! -   [`SmallRng`] is an **insecure** PRNG designed to be fast, simple, require
+//!     little memory, and have good output quality.
+//!
+//! The algorithms selected for [`StdRng`] and [`SmallRng`] may change in any
+//! release and may be platform-dependent, therefore they should be considered
+//! **not reproducible**.
+//!
+//! ## Additional generators
+//!
+//! **TRNGs**: The [`rdrand`] crate provides an interface to the RDRAND and
+//! RDSEED instructions available in modern Intel and AMD CPUs.
+//! The [`rand_jitter`] crate provides a user-space implementation of
+//! entropy harvesting from CPU timer jitter, but is very slow and has
+//! [security issues](https://github.com/rust-random/rand/issues/699).
+//!
+//! **PRNGs**: Several companion crates are available, providing individual or
+//! families of PRNG algorithms. These provide the implementations behind
+//! [`StdRng`] and [`SmallRng`] but can also be used directly, indeed *should*
+//! be used directly when **reproducibility** matters.
+//! Some suggestions are: [`rand_chacha`], [`rand_pcg`], [`rand_xoshiro`].
+//! A full list can be found by searching for crates with the [`rng` tag].
+//!
+//! [`Rng`]: crate::Rng
+//! [`RngCore`]: crate::RngCore
+//! [`CryptoRng`]: crate::CryptoRng
+//! [`SeedableRng`]: crate::SeedableRng
+//! [`thread_rng`]: crate::thread_rng
+//! [`rdrand`]: https://crates.io/crates/rdrand
+//! [`rand_jitter`]: https://crates.io/crates/rand_jitter
+//! [`rand_chacha`]: https://crates.io/crates/rand_chacha
+//! [`rand_pcg`]: https://crates.io/crates/rand_pcg
+//! [`rand_xoshiro`]: https://crates.io/crates/rand_xoshiro
+//! [`rng` tag]: https://crates.io/keywords/rng
+
+#[cfg_attr(doc_cfg, doc(cfg(feature = "std")))]
+#[cfg(feature = "std")] pub mod adapter;
+
+pub mod mock; // Public so we don't export `StepRng` directly, making it a bit
+              // more clear it is intended for testing.
+
+#[cfg(all(feature = "small_rng", target_pointer_width = "64"))]
+mod xoshiro256plusplus;
+#[cfg(all(feature = "small_rng", not(target_pointer_width = "64")))]
+mod xoshiro128plusplus;
+#[cfg(feature = "small_rng")] mod small;
+
+#[cfg(feature = "std_rng")] mod std;
+#[cfg(all(feature = "std", feature = "std_rng"))] pub(crate) mod thread;
+
+#[cfg(feature = "small_rng")] pub use self::small::SmallRng;
+#[cfg(feature = "std_rng")] pub use self::std::StdRng;
+#[cfg(all(feature = "std", feature = "std_rng"))] pub use self::thread::ThreadRng;
+
+#[cfg_attr(doc_cfg, doc(cfg(feature = "getrandom")))]
+#[cfg(feature = "getrandom")] pub use rand_core::OsRng;

zeroidc/vendor/rand/src/rngs/small.rs

@@ -0,0 +1,117 @@
+// Copyright 2018 Developers of the Rand project.
+//
+// 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.
+
+//! A small fast RNG
+
+use rand_core::{Error, RngCore, SeedableRng};
+
+#[cfg(target_pointer_width = "64")]
+type Rng = super::xoshiro256plusplus::Xoshiro256PlusPlus;
+#[cfg(not(target_pointer_width = "64"))]
+type Rng = super::xoshiro128plusplus::Xoshiro128PlusPlus;
+
+/// A small-state, fast non-crypto PRNG
+///
+/// `SmallRng` may be a good choice when a PRNG with small state, cheap
+/// initialization, good statistical quality and good performance are required.
+/// Note that depending on the application, [`StdRng`] may be faster on many
+/// modern platforms while providing higher-quality randomness. Furthermore,
+/// `SmallRng` is **not** a good choice when:
+/// - Security against prediction is important. Use [`StdRng`] instead.
+/// - Seeds with many zeros are provided. In such cases, it takes `SmallRng`
+///   about 10 samples to produce 0 and 1 bits with equal probability. Either
+///   provide seeds with an approximately equal number of 0 and 1 (for example
+///   by using [`SeedableRng::from_entropy`] or [`SeedableRng::seed_from_u64`]),
+///   or use [`StdRng`] instead.
+///
+/// The algorithm is deterministic but should not be considered reproducible
+/// due to dependence on platform and possible replacement in future
+/// library versions. For a reproducible generator, use a named PRNG from an
+/// external crate, e.g. [rand_xoshiro] or [rand_chacha].
+/// Refer also to [The Book](https://rust-random.github.io/book/guide-rngs.html).
+///
+/// The PRNG algorithm in `SmallRng` is chosen to be efficient on the current
+/// platform, without consideration for cryptography or security. The size of
+/// its state is much smaller than [`StdRng`]. The current algorithm is
+/// `Xoshiro256PlusPlus` on 64-bit platforms and `Xoshiro128PlusPlus` on 32-bit
+/// platforms. Both are also implemented by the [rand_xoshiro] crate.
+///
+/// # Examples
+///
+/// Initializing `SmallRng` with a random seed can be done using [`SeedableRng::from_entropy`]:
+///
+/// ```
+/// use rand::{Rng, SeedableRng};
+/// use rand::rngs::SmallRng;
+///
+/// // Create small, cheap to initialize and fast RNG with a random seed.
+/// // The randomness is supplied by the operating system.
+/// let mut small_rng = SmallRng::from_entropy();
+/// # let v: u32 = small_rng.gen();
+/// ```
+///
+/// When initializing a lot of `SmallRng`'s, using [`thread_rng`] can be more
+/// efficient:
+///
+/// ```
+/// use rand::{SeedableRng, thread_rng};
+/// use rand::rngs::SmallRng;
+///
+/// // Create a big, expensive to initialize and slower, but unpredictable RNG.
+/// // This is cached and done only once per thread.
+/// let mut thread_rng = thread_rng();
+/// // Create small, cheap to initialize and fast RNGs with random seeds.
+/// // One can generally assume this won't fail.
+/// let rngs: Vec<SmallRng> = (0..10)
+///     .map(|_| SmallRng::from_rng(&mut thread_rng).unwrap())
+///     .collect();
+/// ```
+///
+/// [`StdRng`]: crate::rngs::StdRng
+/// [`thread_rng`]: crate::thread_rng
+/// [rand_chacha]: https://crates.io/crates/rand_chacha
+/// [rand_xoshiro]: https://crates.io/crates/rand_xoshiro
+#[cfg_attr(doc_cfg, doc(cfg(feature = "small_rng")))]
+#[derive(Clone, Debug, PartialEq, Eq)]
+pub struct SmallRng(Rng);
+
+impl RngCore for SmallRng {
+    #[inline(always)]
+    fn next_u32(&mut self) -> u32 {
+        self.0.next_u32()
+    }
+
+    #[inline(always)]
+    fn next_u64(&mut self) -> u64 {
+        self.0.next_u64()
+    }
+
+    #[inline(always)]
+    fn fill_bytes(&mut self, dest: &mut [u8]) {
+        self.0.fill_bytes(dest);
+    }
+
+    #[inline(always)]
+    fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
+        self.0.try_fill_bytes(dest)
+    }
+}
+
+impl SeedableRng for SmallRng {
+    type Seed = <Rng as SeedableRng>::Seed;
+
+    #[inline(always)]
+    fn from_seed(seed: Self::Seed) -> Self {
+        SmallRng(Rng::from_seed(seed))
+    }
+
+    #[inline(always)]
+    fn from_rng<R: RngCore>(rng: R) -> Result<Self, Error> {
+        Rng::from_rng(rng).map(SmallRng)
+    }
+}

zeroidc/vendor/rand/src/rngs/std.rs

@@ -0,0 +1,98 @@
+// Copyright 2018 Developers of the Rand project.
+//
+// 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.
+
+//! The standard RNG
+
+use crate::{CryptoRng, Error, RngCore, SeedableRng};
+
+pub(crate) use rand_chacha::ChaCha12Core as Core;
+
+use rand_chacha::ChaCha12Rng as Rng;
+
+/// The standard RNG. The PRNG algorithm in `StdRng` is chosen to be efficient
+/// on the current platform, to be statistically strong and unpredictable
+/// (meaning a cryptographically secure PRNG).
+///
+/// The current algorithm used is the ChaCha block cipher with 12 rounds. Please
+/// see this relevant [rand issue] for the discussion. This may change as new 
+/// evidence of cipher security and performance becomes available.
+///
+/// The algorithm is deterministic but should not be considered reproducible
+/// due to dependence on configuration and possible replacement in future
+/// library versions. For a secure reproducible generator, we recommend use of
+/// the [rand_chacha] crate directly.
+///
+/// [rand_chacha]: https://crates.io/crates/rand_chacha
+/// [rand issue]: https://github.com/rust-random/rand/issues/932
+#[cfg_attr(doc_cfg, doc(cfg(feature = "std_rng")))]
+#[derive(Clone, Debug, PartialEq, Eq)]
+pub struct StdRng(Rng);
+
+impl RngCore for StdRng {
+    #[inline(always)]
+    fn next_u32(&mut self) -> u32 {
+        self.0.next_u32()
+    }
+
+    #[inline(always)]
+    fn next_u64(&mut self) -> u64 {
+        self.0.next_u64()
+    }
+
+    #[inline(always)]
+    fn fill_bytes(&mut self, dest: &mut [u8]) {
+        self.0.fill_bytes(dest);
+    }
+
+    #[inline(always)]
+    fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
+        self.0.try_fill_bytes(dest)
+    }
+}
+
+impl SeedableRng for StdRng {
+    type Seed = <Rng as SeedableRng>::Seed;
+
+    #[inline(always)]
+    fn from_seed(seed: Self::Seed) -> Self {
+        StdRng(Rng::from_seed(seed))
+    }
+
+    #[inline(always)]
+    fn from_rng<R: RngCore>(rng: R) -> Result<Self, Error> {
+        Rng::from_rng(rng).map(StdRng)
+    }
+}
+
+impl CryptoRng for StdRng {}
+
+
+#[cfg(test)]
+mod test {
+    use crate::rngs::StdRng;
+    use crate::{RngCore, SeedableRng};
+
+    #[test]
+    fn test_stdrng_construction() {
+        // Test value-stability of StdRng. This is expected to break any time
+        // the algorithm is changed.
+        #[rustfmt::skip]
+        let seed = [1,0,0,0, 23,0,0,0, 200,1,0,0, 210,30,0,0,
+                    0,0,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0];
+
+        let target = [10719222850664546238, 14064965282130556830];
+
+        let mut rng0 = StdRng::from_seed(seed);
+        let x0 = rng0.next_u64();
+
+        let mut rng1 = StdRng::from_rng(rng0).unwrap();
+        let x1 = rng1.next_u64();
+
+        assert_eq!([x0, x1], target);
+    }
+}

zeroidc/vendor/rand/src/rngs/thread.rs

@@ -0,0 +1,143 @@
+// Copyright 2018 Developers of the Rand project.
+//
+// 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.
+
+//! Thread-local random number generator
+
+use core::cell::UnsafeCell;
+use std::rc::Rc;
+use std::thread_local;
+
+use super::std::Core;
+use crate::rngs::adapter::ReseedingRng;
+use crate::rngs::OsRng;
+use crate::{CryptoRng, Error, RngCore, SeedableRng};
+
+// Rationale for using `UnsafeCell` in `ThreadRng`:
+//
+// Previously we used a `RefCell`, with an overhead of ~15%. There will only
+// ever be one mutable reference to the interior of the `UnsafeCell`, because
+// we only have such a reference inside `next_u32`, `next_u64`, etc. Within a
+// single thread (which is the definition of `ThreadRng`), there will only ever
+// be one of these methods active at a time.
+//
+// A possible scenario where there could be multiple mutable references is if
+// `ThreadRng` is used inside `next_u32` and co. But the implementation is
+// completely under our control. We just have to ensure none of them use
+// `ThreadRng` internally, which is nonsensical anyway. We should also never run
+// `ThreadRng` in destructors of its implementation, which is also nonsensical.
+
+
+// Number of generated bytes after which to reseed `ThreadRng`.
+// According to benchmarks, reseeding has a noticeable impact with thresholds
+// of 32 kB and less. We choose 64 kB to avoid significant overhead.
+const THREAD_RNG_RESEED_THRESHOLD: u64 = 1024 * 64;
+
+/// A reference to the thread-local generator
+///
+/// An instance can be obtained via [`thread_rng`] or via `ThreadRng::default()`.
+/// This handle is safe to use everywhere (including thread-local destructors),
+/// though it is recommended not to use inside a fork handler.
+/// The handle cannot be passed between threads (is not `Send` or `Sync`).
+///
+/// `ThreadRng` uses the same PRNG as [`StdRng`] for security and performance
+/// and is automatically seeded from [`OsRng`].
+///
+/// Unlike `StdRng`, `ThreadRng` uses the  [`ReseedingRng`] wrapper to reseed
+/// the PRNG from fresh entropy every 64 kiB of random data as well as after a
+/// fork on Unix (though not quite immediately; see documentation of
+/// [`ReseedingRng`]).
+/// Note that the reseeding is done as an extra precaution against side-channel
+/// attacks and mis-use (e.g. if somehow weak entropy were supplied initially).
+/// The PRNG algorithms used are assumed to be secure.
+///
+/// [`ReseedingRng`]: crate::rngs::adapter::ReseedingRng
+/// [`StdRng`]: crate::rngs::StdRng
+#[cfg_attr(doc_cfg, doc(cfg(all(feature = "std", feature = "std_rng"))))]
+#[derive(Clone, Debug)]
+pub struct ThreadRng {
+    // Rc is explicitly !Send and !Sync
+    rng: Rc<UnsafeCell<ReseedingRng<Core, OsRng>>>,
+}
+
+thread_local!(
+    // We require Rc<..> to avoid premature freeing when thread_rng is used
+    // within thread-local destructors. See #968.
+    static THREAD_RNG_KEY: Rc<UnsafeCell<ReseedingRng<Core, OsRng>>> = {
+        let r = Core::from_rng(OsRng).unwrap_or_else(|err|
+                panic!("could not initialize thread_rng: {}", err));
+        let rng = ReseedingRng::new(r,
+                                    THREAD_RNG_RESEED_THRESHOLD,
+                                    OsRng);
+        Rc::new(UnsafeCell::new(rng))
+    }
+);
+
+/// 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>()`, or cached locally, e.g.
+/// `let mut rng = thread_rng();`.  Invoked by the `Default` trait, making
+/// `ThreadRng::default()` equivalent.
+///
+/// For more information see [`ThreadRng`].
+#[cfg_attr(doc_cfg, doc(cfg(all(feature = "std", feature = "std_rng"))))]
+pub fn thread_rng() -> ThreadRng {
+    let rng = THREAD_RNG_KEY.with(|t| t.clone());
+    ThreadRng { rng }
+}
+
+impl Default for ThreadRng {
+    fn default() -> ThreadRng {
+        crate::prelude::thread_rng()
+    }
+}
+
+impl RngCore for ThreadRng {
+    #[inline(always)]
+    fn next_u32(&mut self) -> u32 {
+        // SAFETY: We must make sure to stop using `rng` before anyone else
+        // creates another mutable reference
+        let rng = unsafe { &mut *self.rng.get() };
+        rng.next_u32()
+    }
+
+    #[inline(always)]
+    fn next_u64(&mut self) -> u64 {
+        // SAFETY: We must make sure to stop using `rng` before anyone else
+        // creates another mutable reference
+        let rng = unsafe { &mut *self.rng.get() };
+        rng.next_u64()
+    }
+
+    fn fill_bytes(&mut self, dest: &mut [u8]) {
+        // SAFETY: We must make sure to stop using `rng` before anyone else
+        // creates another mutable reference
+        let rng = unsafe { &mut *self.rng.get() };
+        rng.fill_bytes(dest)
+    }
+
+    fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
+        // SAFETY: We must make sure to stop using `rng` before anyone else
+        // creates another mutable reference
+        let rng = unsafe { &mut *self.rng.get() };
+        rng.try_fill_bytes(dest)
+    }
+}
+
+impl CryptoRng for ThreadRng {}
+
+
+#[cfg(test)]
+mod test {
+    #[test]
+    fn test_thread_rng() {
+        use crate::Rng;
+        let mut r = crate::thread_rng();
+        r.gen::<i32>();
+        assert_eq!(r.gen_range(0..1), 0);
+    }
+}

zeroidc/vendor/rand/src/rngs/xoshiro128plusplus.rs

@@ -0,0 +1,118 @@
+// Copyright 2018 Developers of the Rand project.
+//
+// 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.
+
+#[cfg(feature="serde1")] use serde::{Serialize, Deserialize};
+use rand_core::impls::{next_u64_via_u32, fill_bytes_via_next};
+use rand_core::le::read_u32_into;
+use rand_core::{SeedableRng, RngCore, Error};
+
+/// A xoshiro128++ random number generator.
+///
+/// The xoshiro128++ algorithm is not suitable for cryptographic purposes, but
+/// is very fast and has excellent statistical properties.
+///
+/// The algorithm used here is translated from [the `xoshiro128plusplus.c`
+/// reference source code](http://xoshiro.di.unimi.it/xoshiro128plusplus.c) by
+/// David Blackman and Sebastiano Vigna.
+#[derive(Debug, Clone, PartialEq, Eq)]
+#[cfg_attr(feature="serde1", derive(Serialize, Deserialize))]
+pub struct Xoshiro128PlusPlus {
+    s: [u32; 4],
+}
+
+impl SeedableRng for Xoshiro128PlusPlus {
+    type Seed = [u8; 16];
+
+    /// Create a new `Xoshiro128PlusPlus`.  If `seed` is entirely 0, it will be
+    /// mapped to a different seed.
+    #[inline]
+    fn from_seed(seed: [u8; 16]) -> Xoshiro128PlusPlus {
+        if seed.iter().all(|&x| x == 0) {
+            return Self::seed_from_u64(0);
+        }
+        let mut state = [0; 4];
+        read_u32_into(&seed, &mut state);
+        Xoshiro128PlusPlus { s: state }
+    }
+
+    /// Create a new `Xoshiro128PlusPlus` from a `u64` seed.
+    ///
+    /// This uses the SplitMix64 generator internally.
+    fn seed_from_u64(mut state: u64) -> Self {
+        const PHI: u64 = 0x9e3779b97f4a7c15;
+        let mut seed = Self::Seed::default();
+        for chunk in seed.as_mut().chunks_mut(8) {
+            state = state.wrapping_add(PHI);
+            let mut z = state;
+            z = (z ^ (z >> 30)).wrapping_mul(0xbf58476d1ce4e5b9);
+            z = (z ^ (z >> 27)).wrapping_mul(0x94d049bb133111eb);
+            z = z ^ (z >> 31);
+            chunk.copy_from_slice(&z.to_le_bytes());
+        }
+        Self::from_seed(seed)
+    }
+}
+
+impl RngCore for Xoshiro128PlusPlus {
+    #[inline]
+    fn next_u32(&mut self) -> u32 {
+        let result_starstar = self.s[0]
+            .wrapping_add(self.s[3])
+            .rotate_left(7)
+            .wrapping_add(self.s[0]);
+
+        let t = self.s[1] << 9;
+
+        self.s[2] ^= self.s[0];
+        self.s[3] ^= self.s[1];
+        self.s[1] ^= self.s[2];
+        self.s[0] ^= self.s[3];
+
+        self.s[2] ^= t;
+
+        self.s[3] = self.s[3].rotate_left(11);
+
+        result_starstar
+    }
+
+    #[inline]
+    fn next_u64(&mut self) -> u64 {
+        next_u64_via_u32(self)
+    }
+
+    #[inline]
+    fn fill_bytes(&mut self, dest: &mut [u8]) {
+        fill_bytes_via_next(self, dest);
+    }
+
+    #[inline]
+    fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
+        self.fill_bytes(dest);
+        Ok(())
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn reference() {
+        let mut rng = Xoshiro128PlusPlus::from_seed(
+            [1, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 0, 4, 0, 0, 0]);
+        // These values were produced with the reference implementation:
+        // http://xoshiro.di.unimi.it/xoshiro128plusplus.c
+        let expected = [
+            641, 1573767, 3222811527, 3517856514, 836907274, 4247214768,
+            3867114732, 1355841295, 495546011, 621204420,
+        ];
+        for &e in &expected {
+            assert_eq!(rng.next_u32(), e);
+        }
+    }
+}

zeroidc/vendor/rand/src/rngs/xoshiro256plusplus.rs

@@ -0,0 +1,122 @@
+// Copyright 2018 Developers of the Rand project.
+//
+// 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.
+
+#[cfg(feature="serde1")] use serde::{Serialize, Deserialize};
+use rand_core::impls::fill_bytes_via_next;
+use rand_core::le::read_u64_into;
+use rand_core::{SeedableRng, RngCore, Error};
+
+/// A xoshiro256++ random number generator.
+///
+/// The xoshiro256++ algorithm is not suitable for cryptographic purposes, but
+/// is very fast and has excellent statistical properties.
+///
+/// The algorithm used here is translated from [the `xoshiro256plusplus.c`
+/// reference source code](http://xoshiro.di.unimi.it/xoshiro256plusplus.c) by
+/// David Blackman and Sebastiano Vigna.
+#[derive(Debug, Clone, PartialEq, Eq)]
+#[cfg_attr(feature="serde1", derive(Serialize, Deserialize))]
+pub struct Xoshiro256PlusPlus {
+    s: [u64; 4],
+}
+
+impl SeedableRng for Xoshiro256PlusPlus {
+    type Seed = [u8; 32];
+
+    /// Create a new `Xoshiro256PlusPlus`.  If `seed` is entirely 0, it will be
+    /// mapped to a different seed.
+    #[inline]
+    fn from_seed(seed: [u8; 32]) -> Xoshiro256PlusPlus {
+        if seed.iter().all(|&x| x == 0) {
+            return Self::seed_from_u64(0);
+        }
+        let mut state = [0; 4];
+        read_u64_into(&seed, &mut state);
+        Xoshiro256PlusPlus { s: state }
+    }
+
+    /// Create a new `Xoshiro256PlusPlus` from a `u64` seed.
+    ///
+    /// This uses the SplitMix64 generator internally.
+    fn seed_from_u64(mut state: u64) -> Self {
+        const PHI: u64 = 0x9e3779b97f4a7c15;
+        let mut seed = Self::Seed::default();
+        for chunk in seed.as_mut().chunks_mut(8) {
+            state = state.wrapping_add(PHI);
+            let mut z = state;
+            z = (z ^ (z >> 30)).wrapping_mul(0xbf58476d1ce4e5b9);
+            z = (z ^ (z >> 27)).wrapping_mul(0x94d049bb133111eb);
+            z = z ^ (z >> 31);
+            chunk.copy_from_slice(&z.to_le_bytes());
+        }
+        Self::from_seed(seed)
+    }
+}
+
+impl RngCore for Xoshiro256PlusPlus {
+    #[inline]
+    fn next_u32(&mut self) -> u32 {
+        // The lowest bits have some linear dependencies, so we use the
+        // upper bits instead.
+        (self.next_u64() >> 32) as u32
+    }
+
+    #[inline]
+    fn next_u64(&mut self) -> u64 {
+        let result_plusplus = self.s[0]
+            .wrapping_add(self.s[3])
+            .rotate_left(23)
+            .wrapping_add(self.s[0]);
+
+        let t = self.s[1] << 17;
+
+        self.s[2] ^= self.s[0];
+        self.s[3] ^= self.s[1];
+        self.s[1] ^= self.s[2];
+        self.s[0] ^= self.s[3];
+
+        self.s[2] ^= t;
+
+        self.s[3] = self.s[3].rotate_left(45);
+
+        result_plusplus
+    }
+
+    #[inline]
+    fn fill_bytes(&mut self, dest: &mut [u8]) {
+        fill_bytes_via_next(self, dest);
+    }
+
+    #[inline]
+    fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
+        self.fill_bytes(dest);
+        Ok(())
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn reference() {
+        let mut rng = Xoshiro256PlusPlus::from_seed(
+            [1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0,
+             3, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0]);
+        // These values were produced with the reference implementation:
+        // http://xoshiro.di.unimi.it/xoshiro256plusplus.c
+        let expected = [
+            41943041, 58720359, 3588806011781223, 3591011842654386,
+            9228616714210784205, 9973669472204895162, 14011001112246962877,
+            12406186145184390807, 15849039046786891736, 10450023813501588000,
+        ];
+        for &e in &expected {
+            assert_eq!(rng.next_u64(), e);
+        }
+    }
+}

zeroidc/vendor/rand/src/seq/index.rs

@@ -0,0 +1,678 @@
+// Copyright 2018 Developers of the Rand project.
+//
+// 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.
+
+//! Low-level API for sampling indices
+
+#[cfg(feature = "alloc")] use core::slice;
+
+#[cfg(feature = "alloc")] use alloc::vec::{self, Vec};
+// BTreeMap is not as fast in tests, but better than nothing.
+#[cfg(all(feature = "alloc", not(feature = "std")))]
+use alloc::collections::BTreeSet;
+#[cfg(feature = "std")] use std::collections::HashSet;
+
+#[cfg(feature = "std")]
+use crate::distributions::WeightedError;
+
+#[cfg(feature = "alloc")]
+use crate::{Rng, distributions::{uniform::SampleUniform, Distribution, Uniform}};
+
+#[cfg(feature = "serde1")]
+use serde::{Serialize, Deserialize};
+
+/// A vector of indices.
+///
+/// Multiple internal representations are possible.
+#[derive(Clone, Debug)]
+#[cfg_attr(feature = "serde1", derive(Serialize, Deserialize))]
+pub enum IndexVec {
+    #[doc(hidden)]
+    U32(Vec<u32>),
+    #[doc(hidden)]
+    USize(Vec<usize>),
+}
+
+impl IndexVec {
+    /// Returns the number of indices
+    #[inline]
+    pub fn len(&self) -> usize {
+        match *self {
+            IndexVec::U32(ref v) => v.len(),
+            IndexVec::USize(ref v) => v.len(),
+        }
+    }
+
+    /// Returns `true` if the length is 0.
+    #[inline]
+    pub fn is_empty(&self) -> bool {
+        match *self {
+            IndexVec::U32(ref v) => v.is_empty(),
+            IndexVec::USize(ref v) => v.is_empty(),
+        }
+    }
+
+    /// Return the value at the given `index`.
+    ///
+    /// (Note: we cannot implement [`std::ops::Index`] because of lifetime
+    /// restrictions.)
+    #[inline]
+    pub fn index(&self, index: usize) -> usize {
+        match *self {
+            IndexVec::U32(ref v) => v[index] as usize,
+            IndexVec::USize(ref v) => v[index],
+        }
+    }
+
+    /// Return result as a `Vec<usize>`. Conversion may or may not be trivial.
+    #[inline]
+    pub fn into_vec(self) -> Vec<usize> {
+        match self {
+            IndexVec::U32(v) => v.into_iter().map(|i| i as usize).collect(),
+            IndexVec::USize(v) => v,
+        }
+    }
+
+    /// Iterate over the indices as a sequence of `usize` values
+    #[inline]
+    pub fn iter(&self) -> IndexVecIter<'_> {
+        match *self {
+            IndexVec::U32(ref v) => IndexVecIter::U32(v.iter()),
+            IndexVec::USize(ref v) => IndexVecIter::USize(v.iter()),
+        }
+    }
+}
+
+impl IntoIterator for IndexVec {
+    type Item = usize;
+    type IntoIter = IndexVecIntoIter;
+
+    /// Convert into an iterator over the indices as a sequence of `usize` values
+    #[inline]
+    fn into_iter(self) -> IndexVecIntoIter {
+        match self {
+            IndexVec::U32(v) => IndexVecIntoIter::U32(v.into_iter()),
+            IndexVec::USize(v) => IndexVecIntoIter::USize(v.into_iter()),
+        }
+    }
+}
+
+impl PartialEq for IndexVec {
+    fn eq(&self, other: &IndexVec) -> bool {
+        use self::IndexVec::*;
+        match (self, other) {
+            (&U32(ref v1), &U32(ref v2)) => v1 == v2,
+            (&USize(ref v1), &USize(ref v2)) => v1 == v2,
+            (&U32(ref v1), &USize(ref v2)) => {
+                (v1.len() == v2.len()) && (v1.iter().zip(v2.iter()).all(|(x, y)| *x as usize == *y))
+            }
+            (&USize(ref v1), &U32(ref v2)) => {
+                (v1.len() == v2.len()) && (v1.iter().zip(v2.iter()).all(|(x, y)| *x == *y as usize))
+            }
+        }
+    }
+}
+
+impl From<Vec<u32>> for IndexVec {
+    #[inline]
+    fn from(v: Vec<u32>) -> Self {
+        IndexVec::U32(v)
+    }
+}
+
+impl From<Vec<usize>> for IndexVec {
+    #[inline]
+    fn from(v: Vec<usize>) -> Self {
+        IndexVec::USize(v)
+    }
+}
+
+/// Return type of `IndexVec::iter`.
+#[derive(Debug)]
+pub enum IndexVecIter<'a> {
+    #[doc(hidden)]
+    U32(slice::Iter<'a, u32>),
+    #[doc(hidden)]
+    USize(slice::Iter<'a, usize>),
+}
+
+impl<'a> Iterator for IndexVecIter<'a> {
+    type Item = usize;
+
+    #[inline]
+    fn next(&mut self) -> Option<usize> {
+        use self::IndexVecIter::*;
+        match *self {
+            U32(ref mut iter) => iter.next().map(|i| *i as usize),
+            USize(ref mut iter) => iter.next().cloned(),
+        }
+    }
+
+    #[inline]
+    fn size_hint(&self) -> (usize, Option<usize>) {
+        match *self {
+            IndexVecIter::U32(ref v) => v.size_hint(),
+            IndexVecIter::USize(ref v) => v.size_hint(),
+        }
+    }
+}
+
+impl<'a> ExactSizeIterator for IndexVecIter<'a> {}
+
+/// Return type of `IndexVec::into_iter`.
+#[derive(Clone, Debug)]
+pub enum IndexVecIntoIter {
+    #[doc(hidden)]
+    U32(vec::IntoIter<u32>),
+    #[doc(hidden)]
+    USize(vec::IntoIter<usize>),
+}
+
+impl Iterator for IndexVecIntoIter {
+    type Item = usize;
+
+    #[inline]
+    fn next(&mut self) -> Option<Self::Item> {
+        use self::IndexVecIntoIter::*;
+        match *self {
+            U32(ref mut v) => v.next().map(|i| i as usize),
+            USize(ref mut v) => v.next(),
+        }
+    }
+
+    #[inline]
+    fn size_hint(&self) -> (usize, Option<usize>) {
+        use self::IndexVecIntoIter::*;
+        match *self {
+            U32(ref v) => v.size_hint(),
+            USize(ref v) => v.size_hint(),
+        }
+    }
+}
+
+impl ExactSizeIterator for IndexVecIntoIter {}
+
+
+/// Randomly sample exactly `amount` distinct indices from `0..length`, and
+/// return them in random order (fully shuffled).
+///
+/// This method is used internally by the slice sampling methods, but it can
+/// sometimes be useful to have the indices themselves so this is provided as
+/// an alternative.
+///
+/// The implementation used is not specified; we automatically select the
+/// fastest available algorithm for the `length` and `amount` parameters
+/// (based on detailed profiling on an Intel Haswell CPU). Roughly speaking,
+/// complexity is `O(amount)`, except that when `amount` is small, performance
+/// is closer to `O(amount^2)`, and when `length` is close to `amount` then
+/// `O(length)`.
+///
+/// Note that performance is significantly better over `u32` indices than over
+/// `u64` indices. Because of this we hide the underlying type behind an
+/// abstraction, `IndexVec`.
+///
+/// If an allocation-free `no_std` function is required, it is suggested
+/// to adapt the internal `sample_floyd` implementation.
+///
+/// Panics if `amount > length`.
+pub fn sample<R>(rng: &mut R, length: usize, amount: usize) -> IndexVec
+where R: Rng + ?Sized {
+    if amount > length {
+        panic!("`amount` of samples must be less than or equal to `length`");
+    }
+    if length > (::core::u32::MAX as usize) {
+        // We never want to use inplace here, but could use floyd's alg
+        // Lazy version: always use the cache alg.
+        return sample_rejection(rng, length, amount);
+    }
+    let amount = amount as u32;
+    let length = length as u32;
+
+    // Choice of algorithm here depends on both length and amount. See:
+    // https://github.com/rust-random/rand/pull/479
+    // We do some calculations with f32. Accuracy is not very important.
+
+    if amount < 163 {
+        const C: [[f32; 2]; 2] = [[1.6, 8.0 / 45.0], [10.0, 70.0 / 9.0]];
+        let j = if length < 500_000 { 0 } else { 1 };
+        let amount_fp = amount as f32;
+        let m4 = C[0][j] * amount_fp;
+        // Short-cut: when amount < 12, floyd's is always faster
+        if amount > 11 && (length as f32) < (C[1][j] + m4) * amount_fp {
+            sample_inplace(rng, length, amount)
+        } else {
+            sample_floyd(rng, length, amount)
+        }
+    } else {
+        const C: [f32; 2] = [270.0, 330.0 / 9.0];
+        let j = if length < 500_000 { 0 } else { 1 };
+        if (length as f32) < C[j] * (amount as f32) {
+            sample_inplace(rng, length, amount)
+        } else {
+            sample_rejection(rng, length, amount)
+        }
+    }
+}
+
+/// Randomly sample exactly `amount` distinct indices from `0..length`, and
+/// return them in an arbitrary order (there is no guarantee of shuffling or
+/// ordering). The weights are to be provided by the input function `weights`,
+/// which will be called once for each index.
+///
+/// This method is used internally by the slice sampling methods, but it can
+/// sometimes be useful to have the indices themselves so this is provided as
+/// an alternative.
+///
+/// This implementation uses `O(length + amount)` space and `O(length)` time
+/// if the "nightly" feature is enabled, or `O(length)` space and
+/// `O(length + amount * log length)` time otherwise.
+///
+/// Panics if `amount > length`.
+#[cfg(feature = "std")]
+#[cfg_attr(doc_cfg, doc(cfg(feature = "std")))]
+pub fn sample_weighted<R, F, X>(
+    rng: &mut R, length: usize, weight: F, amount: usize,
+) -> Result<IndexVec, WeightedError>
+where
+    R: Rng + ?Sized,
+    F: Fn(usize) -> X,
+    X: Into<f64>,
+{
+    if length > (core::u32::MAX as usize) {
+        sample_efraimidis_spirakis(rng, length, weight, amount)
+    } else {
+        assert!(amount <= core::u32::MAX as usize);
+        let amount = amount as u32;
+        let length = length as u32;
+        sample_efraimidis_spirakis(rng, length, weight, amount)
+    }
+}
+
+
+/// Randomly sample exactly `amount` distinct indices from `0..length`, and
+/// return them in an arbitrary order (there is no guarantee of shuffling or
+/// ordering). The weights are to be provided by the input function `weights`,
+/// which will be called once for each index.
+///
+/// This implementation uses the algorithm described by Efraimidis and Spirakis
+/// in this paper: https://doi.org/10.1016/j.ipl.2005.11.003
+/// It uses `O(length + amount)` space and `O(length)` time if the
+/// "nightly" feature is enabled, or `O(length)` space and `O(length
+/// + amount * log length)` time otherwise.
+///
+/// Panics if `amount > length`.
+#[cfg(feature = "std")]
+fn sample_efraimidis_spirakis<R, F, X, N>(
+    rng: &mut R, length: N, weight: F, amount: N,
+) -> Result<IndexVec, WeightedError>
+where
+    R: Rng + ?Sized,
+    F: Fn(usize) -> X,
+    X: Into<f64>,
+    N: UInt,
+    IndexVec: From<Vec<N>>,
+{
+    if amount == N::zero() {
+        return Ok(IndexVec::U32(Vec::new()));
+    }
+
+    if amount > length {
+        panic!("`amount` of samples must be less than or equal to `length`");
+    }
+
+    struct Element<N> {
+        index: N,
+        key: f64,
+    }
+    impl<N> PartialOrd for Element<N> {
+        fn partial_cmp(&self, other: &Self) -> Option<core::cmp::Ordering> {
+            self.key.partial_cmp(&other.key)
+        }
+    }
+    impl<N> Ord for Element<N> {
+        fn cmp(&self, other: &Self) -> core::cmp::Ordering {
+             // partial_cmp will always produce a value,
+             // because we check that the weights are not nan
+            self.partial_cmp(other).unwrap()
+        }
+    }
+    impl<N> PartialEq for Element<N> {
+        fn eq(&self, other: &Self) -> bool {
+            self.key == other.key
+        }
+    }
+    impl<N> Eq for Element<N> {}
+
+    #[cfg(feature = "nightly")]
+    {
+        let mut candidates = Vec::with_capacity(length.as_usize());
+        let mut index = N::zero();
+        while index < length {
+            let weight = weight(index.as_usize()).into();
+            if !(weight >= 0.) {
+                return Err(WeightedError::InvalidWeight);
+            }
+
+            let key = rng.gen::<f64>().powf(1.0 / weight);
+            candidates.push(Element { index, key });
+
+            index += N::one();
+        }
+
+        // Partially sort the array to find the `amount` elements with the greatest
+        // keys. Do this by using `select_nth_unstable` to put the elements with
+        // the *smallest* keys at the beginning of the list in `O(n)` time, which
+        // provides equivalent information about the elements with the *greatest* keys.
+        let (_, mid, greater)
+            = candidates.select_nth_unstable(length.as_usize() - amount.as_usize());
+
+        let mut result: Vec<N> = Vec::with_capacity(amount.as_usize());
+        result.push(mid.index);
+        for element in greater {
+            result.push(element.index);
+        }
+        Ok(IndexVec::from(result))
+    }
+
+    #[cfg(not(feature = "nightly"))]
+    {
+        use alloc::collections::BinaryHeap;
+
+        // Partially sort the array such that the `amount` elements with the largest
+        // keys are first using a binary max heap.
+        let mut candidates = BinaryHeap::with_capacity(length.as_usize());
+        let mut index = N::zero();
+        while index < length {
+            let weight = weight(index.as_usize()).into();
+            if !(weight >= 0.) {
+                return Err(WeightedError::InvalidWeight);
+            }
+
+            let key = rng.gen::<f64>().powf(1.0 / weight);
+            candidates.push(Element { index, key });
+
+            index += N::one();
+        }
+
+        let mut result: Vec<N> = Vec::with_capacity(amount.as_usize());
+        while result.len() < amount.as_usize() {
+            result.push(candidates.pop().unwrap().index);
+        }
+        Ok(IndexVec::from(result))
+    }
+}
+
+/// Randomly sample exactly `amount` indices from `0..length`, using Floyd's
+/// combination algorithm.
+///
+/// The output values are fully shuffled. (Overhead is under 50%.)
+///
+/// This implementation uses `O(amount)` memory and `O(amount^2)` time.
+fn sample_floyd<R>(rng: &mut R, length: u32, amount: u32) -> IndexVec
+where R: Rng + ?Sized {
+    // For small amount we use Floyd's fully-shuffled variant. For larger
+    // amounts this is slow due to Vec::insert performance, so we shuffle
+    // afterwards. Benchmarks show little overhead from extra logic.
+    let floyd_shuffle = amount < 50;
+
+    debug_assert!(amount <= length);
+    let mut indices = Vec::with_capacity(amount as usize);
+    for j in length - amount..length {
+        let t = rng.gen_range(0..=j);
+        if floyd_shuffle {
+            if let Some(pos) = indices.iter().position(|&x| x == t) {
+                indices.insert(pos, j);
+                continue;
+            }
+        } else if indices.contains(&t) {
+            indices.push(j);
+            continue;
+        }
+        indices.push(t);
+    }
+    if !floyd_shuffle {
+        // Reimplement SliceRandom::shuffle with smaller indices
+        for i in (1..amount).rev() {
+            // invariant: elements with index > i have been locked in place.
+            indices.swap(i as usize, rng.gen_range(0..=i) as usize);
+        }
+    }
+    IndexVec::from(indices)
+}
+
+/// Randomly sample exactly `amount` indices from `0..length`, using an inplace
+/// partial Fisher-Yates method.
+/// Sample an amount of indices using an inplace partial fisher yates method.
+///
+/// This allocates the entire `length` of indices and randomizes only the first `amount`.
+/// It then truncates to `amount` and returns.
+///
+/// This method is not appropriate for large `length` and potentially uses a lot
+/// of memory; because of this we only implement for `u32` index (which improves
+/// performance in all cases).
+///
+/// Set-up is `O(length)` time and memory and shuffling is `O(amount)` time.
+fn sample_inplace<R>(rng: &mut R, length: u32, amount: u32) -> IndexVec
+where R: Rng + ?Sized {
+    debug_assert!(amount <= length);
+    let mut indices: Vec<u32> = Vec::with_capacity(length as usize);
+    indices.extend(0..length);
+    for i in 0..amount {
+        let j: u32 = rng.gen_range(i..length);
+        indices.swap(i as usize, j as usize);
+    }
+    indices.truncate(amount as usize);
+    debug_assert_eq!(indices.len(), amount as usize);
+    IndexVec::from(indices)
+}
+
+trait UInt: Copy + PartialOrd + Ord + PartialEq + Eq + SampleUniform
+    + core::hash::Hash + core::ops::AddAssign {
+    fn zero() -> Self;
+    fn one() -> Self;
+    fn as_usize(self) -> usize;
+}
+impl UInt for u32 {
+    #[inline]
+    fn zero() -> Self {
+        0
+    }
+
+    #[inline]
+    fn one() -> Self {
+        1
+    }
+
+    #[inline]
+    fn as_usize(self) -> usize {
+        self as usize
+    }
+}
+impl UInt for usize {
+    #[inline]
+    fn zero() -> Self {
+        0
+    }
+
+    #[inline]
+    fn one() -> Self {
+        1
+    }
+
+    #[inline]
+    fn as_usize(self) -> usize {
+        self
+    }
+}
+
+/// Randomly sample exactly `amount` indices from `0..length`, using rejection
+/// sampling.
+///
+/// Since `amount <<< length` there is a low chance of a random sample in
+/// `0..length` being a duplicate. We test for duplicates and resample where
+/// necessary. The algorithm is `O(amount)` time and memory.
+///
+/// This function  is generic over X primarily so that results are value-stable
+/// over 32-bit and 64-bit platforms.
+fn sample_rejection<X: UInt, R>(rng: &mut R, length: X, amount: X) -> IndexVec
+where
+    R: Rng + ?Sized,
+    IndexVec: From<Vec<X>>,
+{
+    debug_assert!(amount < length);
+    #[cfg(feature = "std")]
+    let mut cache = HashSet::with_capacity(amount.as_usize());
+    #[cfg(not(feature = "std"))]
+    let mut cache = BTreeSet::new();
+    let distr = Uniform::new(X::zero(), length);
+    let mut indices = Vec::with_capacity(amount.as_usize());
+    for _ in 0..amount.as_usize() {
+        let mut pos = distr.sample(rng);
+        while !cache.insert(pos) {
+            pos = distr.sample(rng);
+        }
+        indices.push(pos);
+    }
+
+    debug_assert_eq!(indices.len(), amount.as_usize());
+    IndexVec::from(indices)
+}
+
+#[cfg(test)]
+mod test {
+    use super::*;
+
+    #[test]
+    #[cfg(feature = "serde1")]
+    fn test_serialization_index_vec() {
+        let some_index_vec = IndexVec::from(vec![254_usize, 234, 2, 1]);
+        let de_some_index_vec: IndexVec = bincode::deserialize(&bincode::serialize(&some_index_vec).unwrap()).unwrap();
+        match (some_index_vec, de_some_index_vec) {
+            (IndexVec::U32(a), IndexVec::U32(b)) => {
+                assert_eq!(a, b);
+            },
+            (IndexVec::USize(a), IndexVec::USize(b)) => {
+                assert_eq!(a, b);
+            },
+            _ => {panic!("failed to seralize/deserialize `IndexVec`")}
+        }
+    }
+
+    #[cfg(feature = "alloc")] use alloc::vec;
+
+    #[test]
+    fn test_sample_boundaries() {
+        let mut r = crate::test::rng(404);
+
+        assert_eq!(sample_inplace(&mut r, 0, 0).len(), 0);
+        assert_eq!(sample_inplace(&mut r, 1, 0).len(), 0);
+        assert_eq!(sample_inplace(&mut r, 1, 1).into_vec(), vec![0]);
+
+        assert_eq!(sample_rejection(&mut r, 1u32, 0).len(), 0);
+
+        assert_eq!(sample_floyd(&mut r, 0, 0).len(), 0);
+        assert_eq!(sample_floyd(&mut r, 1, 0).len(), 0);
+        assert_eq!(sample_floyd(&mut r, 1, 1).into_vec(), vec![0]);
+
+        // These algorithms should be fast with big numbers. Test average.
+        let sum: usize = sample_rejection(&mut r, 1 << 25, 10u32).into_iter().sum();
+        assert!(1 << 25 < sum && sum < (1 << 25) * 25);
+
+        let sum: usize = sample_floyd(&mut r, 1 << 25, 10).into_iter().sum();
+        assert!(1 << 25 < sum && sum < (1 << 25) * 25);
+    }
+
+    #[test]
+    #[cfg_attr(miri, ignore)] // Miri is too slow
+    fn test_sample_alg() {
+        let seed_rng = crate::test::rng;
+
+        // We can't test which algorithm is used directly, but Floyd's alg
+        // should produce different results from the others. (Also, `inplace`
+        // and `cached` currently use different sizes thus produce different results.)
+
+        // A small length and relatively large amount should use inplace
+        let (length, amount): (usize, usize) = (100, 50);
+        let v1 = sample(&mut seed_rng(420), length, amount);
+        let v2 = sample_inplace(&mut seed_rng(420), length as u32, amount as u32);
+        assert!(v1.iter().all(|e| e < length));
+        assert_eq!(v1, v2);
+
+        // Test Floyd's alg does produce different results
+        let v3 = sample_floyd(&mut seed_rng(420), length as u32, amount as u32);
+        assert!(v1 != v3);
+
+        // A large length and small amount should use Floyd
+        let (length, amount): (usize, usize) = (1 << 20, 50);
+        let v1 = sample(&mut seed_rng(421), length, amount);
+        let v2 = sample_floyd(&mut seed_rng(421), length as u32, amount as u32);
+        assert!(v1.iter().all(|e| e < length));
+        assert_eq!(v1, v2);
+
+        // A large length and larger amount should use cache
+        let (length, amount): (usize, usize) = (1 << 20, 600);
+        let v1 = sample(&mut seed_rng(422), length, amount);
+        let v2 = sample_rejection(&mut seed_rng(422), length as u32, amount as u32);
+        assert!(v1.iter().all(|e| e < length));
+        assert_eq!(v1, v2);
+    }
+
+    #[cfg(feature = "std")]
+    #[test]
+    fn test_sample_weighted() {
+        let seed_rng = crate::test::rng;
+        for &(amount, len) in &[(0, 10), (5, 10), (10, 10)] {
+            let v = sample_weighted(&mut seed_rng(423), len, |i| i as f64, amount).unwrap();
+            match v {
+                IndexVec::U32(mut indices) => {
+                    assert_eq!(indices.len(), amount);
+                    indices.sort_unstable();
+                    indices.dedup();
+                    assert_eq!(indices.len(), amount);
+                    for &i in &indices {
+                        assert!((i as usize) < len);
+                    }
+                },
+                IndexVec::USize(_) => panic!("expected `IndexVec::U32`"),
+            }
+        }
+    }
+
+    #[test]
+    fn value_stability_sample() {
+        let do_test = |length, amount, values: &[u32]| {
+            let mut buf = [0u32; 8];
+            let mut rng = crate::test::rng(410);
+
+            let res = sample(&mut rng, length, amount);
+            let len = res.len().min(buf.len());
+            for (x, y) in res.into_iter().zip(buf.iter_mut()) {
+                *y = x as u32;
+            }
+            assert_eq!(
+                &buf[0..len],
+                values,
+                "failed sampling {}, {}",
+                length,
+                amount
+            );
+        };
+
+        do_test(10, 6, &[8, 0, 3, 5, 9, 6]); // floyd
+        do_test(25, 10, &[18, 15, 14, 9, 0, 13, 5, 24]); // floyd
+        do_test(300, 8, &[30, 283, 150, 1, 73, 13, 285, 35]); // floyd
+        do_test(300, 80, &[31, 289, 248, 154, 5, 78, 19, 286]); // inplace
+        do_test(300, 180, &[31, 289, 248, 154, 5, 78, 19, 286]); // inplace
+
+        do_test(1_000_000, 8, &[
+            103717, 963485, 826422, 509101, 736394, 807035, 5327, 632573,
+        ]); // floyd
+        do_test(1_000_000, 180, &[
+            103718, 963490, 826426, 509103, 736396, 807036, 5327, 632573,
+        ]); // rejection
+    }
+}