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    // 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.

    //! Sequence-related functionality
    //!
    //! This module provides:
    //!
    //! *   [`seq::SliceRandom`] slice sampling and mutation
    //! *   [`seq::IteratorRandom`] iterator sampling
    //! *   [`seq::index::sample`] low-level API to choose multiple indices from
    //!     `0..length`
    //!
    //! Also see:
    //!
    //! *   [`distributions::weighted`] module which provides implementations of
    //!     weighted index sampling.
    //!
    //! In order to make results reproducible across 32-64 bit architectures, all
    //! `usize` indices are sampled as a `u32` where possible (also providing a
    //! small performance boost in some cases).

    #[cfg(feature="alloc")] pub mod index;

    #[cfg(feature="alloc")] use core::ops::Index;

    #[cfg(all(feature="alloc", not(feature="std")))] use crate::alloc::vec::Vec;

    use crate::Rng;
    #[cfg(feature="alloc")] use crate::distributions::WeightedError;
    #[cfg(feature="alloc")] use crate::distributions::uniform::{SampleUniform, SampleBorrow};

    /// Extension trait on slices, providing random mutation and sampling methods.
    ///
    /// This trait is implemented on all `[T]` slice types, providing several
    /// methods for choosing and shuffling elements. You must `use` this trait:
    ///
    /// ```
    /// use rand::seq::SliceRandom;
    ///
    /// fn main() {
    ///     let mut rng = rand::thread_rng();
    ///     let mut bytes = "Hello, random!".to_string().into_bytes();
    ///     bytes.shuffle(&mut rng);
    ///     let str = String::from_utf8(bytes).unwrap();
    ///     println!("{}", str);
    /// }
    /// ```
    /// Example output (non-deterministic):
    /// ```none
    /// l,nmroHado !le
    /// ```
    pub trait SliceRandom {
        /// The element type.
        type Item;

        /// Returns a reference to one random element of the slice, or `None` if the
        /// slice is empty.
        ///
        /// For slices, complexity is `O(1)`.
        ///
        /// # Example
        ///
        /// ```
        /// use rand::thread_rng;
        /// use rand::seq::SliceRandom;
        ///
        /// let choices = [1, 2, 4, 8, 16, 32];
        /// let mut rng = thread_rng();
        /// println!("{:?}", choices.choose(&mut rng));
        /// assert_eq!(choices[..0].choose(&mut rng), None);
        /// ```
        fn choose<R>(&self, rng: &mut R) -> Option<&Self::Item>
        where R: Rng + ?Sized;

        /// Returns a mutable reference to one random element of the slice, or
        /// `None` if the slice is empty.
        ///
        /// For slices, complexity is `O(1)`.
        fn choose_mut<R>(&mut self, rng: &mut R) -> Option<&mut Self::Item>
        where R: Rng + ?Sized;

        /// Chooses `amount` elements from the slice at random, without repetition,
        /// and in random order. The returned iterator is appropriate both for
        /// collection into a `Vec` and filling an existing buffer (see example).
        ///
        /// In case this API is not sufficiently flexible, use [`index::sample`].
        ///
        /// For slices, complexity is the same as [`index::sample`].
        ///
        /// # Example
        /// ```
        /// use rand::seq::SliceRandom;
        ///
        /// let mut rng = &mut rand::thread_rng();
        /// let sample = "Hello, audience!".as_bytes();
        ///
        /// // collect the results into a vector:
        /// let v: Vec<u8> = sample.choose_multiple(&mut rng, 3).cloned().collect();
        ///
        /// // store in a buffer:
        /// let mut buf = [0u8; 5];
        /// for (b, slot) in sample.choose_multiple(&mut rng, buf.len()).zip(buf.iter_mut()) {
        ///     *slot = *b;
        /// }
        /// ```
        #[cfg(feature = "alloc")]
        fn choose_multiple<R>(&self, rng: &mut R, amount: usize) -> SliceChooseIter<Self, Self::Item>
        where R: Rng + ?Sized;

        /// Similar to [`choose`], but where the likelihood of each outcome may be
        /// specified.
        ///
        /// The specified function `weight` maps each item `x` to a relative
        /// likelihood `weight(x)`. The probability of each item being selected is
        /// therefore `weight(x) / s`, where `s` is the sum of all `weight(x)`.
        ///
        /// For slices of length `n`, complexity is `O(n)`.
        /// See also [`choose_weighted_mut`], [`distributions::weighted`].
        ///
        /// # Example
        ///
        /// ```
        /// use rand::prelude::*;
        ///
        /// let choices = [('a', 2), ('b', 1), ('c', 1)];
        /// let mut rng = thread_rng();
        /// // 50% chance to print 'a', 25% chance to print 'b', 25% chance to print 'c'
        /// println!("{:?}", choices.choose_weighted(&mut rng, |item| item.1).unwrap().0);
        /// ```
        /// [`choose`]: SliceRandom::choose
        /// [`choose_weighted_mut`]: SliceRandom::choose_weighted_mut
        /// [`distributions::weighted`]: crate::distributions::weighted
        #[cfg(feature = "alloc")]
        fn choose_weighted<R, F, B, X>(
            &self, rng: &mut R, weight: F,
        ) -> Result<&Self::Item, WeightedError>
        where
            R: Rng + ?Sized,
            F: Fn(&Self::Item) -> B,
            B: SampleBorrow<X>,
            X: SampleUniform
                + for<'a> ::core::ops::AddAssign<&'a X>
                + ::core::cmp::PartialOrd<X>
                + Clone
                + Default;

        /// Similar to [`choose_mut`], but where the likelihood of each outcome may
        /// be specified.
        ///
        /// The specified function `weight` maps each item `x` to a relative
        /// likelihood `weight(x)`. The probability of each item being selected is
        /// therefore `weight(x) / s`, where `s` is the sum of all `weight(x)`.
        ///
        /// For slices of length `n`, complexity is `O(n)`.
        /// See also [`choose_weighted`], [`distributions::weighted`].
        ///
        /// [`choose_mut`]: SliceRandom::choose_mut
        /// [`choose_weighted`]: SliceRandom::choose_weighted
        /// [`distributions::weighted`]: crate::distributions::weighted
        #[cfg(feature = "alloc")]
        fn choose_weighted_mut<R, F, B, X>(
            &mut self, rng: &mut R, weight: F,
        ) -> Result<&mut Self::Item, WeightedError>
        where
            R: Rng + ?Sized,
            F: Fn(&Self::Item) -> B,
            B: SampleBorrow<X>,
            X: SampleUniform
                + for<'a> ::core::ops::AddAssign<&'a X>
                + ::core::cmp::PartialOrd<X>
                + Clone
                + Default;

        /// Shuffle a mutable slice in place.
        ///
        /// For slices of length `n`, complexity is `O(n)`.
        ///
        /// # Example
        ///
        /// ```
        /// use rand::seq::SliceRandom;
        /// use rand::thread_rng;
        ///
        /// let mut rng = thread_rng();
        /// let mut y = [1, 2, 3, 4, 5];
        /// println!("Unshuffled: {:?}", y);
        /// y.shuffle(&mut rng);
        /// println!("Shuffled:   {:?}", y);
        /// ```
        fn shuffle<R>(&mut self, rng: &mut R)
        where R: Rng + ?Sized;

        /// Shuffle a slice in place, but exit early.
        ///
        /// Returns two mutable slices from the source slice. The first contains
        /// `amount` elements randomly permuted. The second has the remaining
        /// elements that are not fully shuffled.
        ///
        /// This is an efficient method to select `amount` elements at random from
        /// the slice, provided the slice may be mutated.
        ///
        /// If you only need to choose elements randomly and `amount > self.len()/2`
        /// then you may improve performance by taking
        /// `amount = values.len() - amount` and using only the second slice.
        ///
        /// If `amount` is greater than the number of elements in the slice, this
        /// will perform a full shuffle.
        ///
        /// For slices, complexity is `O(m)` where `m = amount`.
        fn partial_shuffle<R>(
            &mut self, rng: &mut R, amount: usize,
        ) -> (&mut [Self::Item], &mut [Self::Item])
        where R: Rng + ?Sized;
    }

    /// Extension trait on iterators, providing random sampling methods.
    ///
    /// This trait is implemented on all sized iterators, providing methods for
    /// choosing one or more elements. You must `use` this trait:
    ///
    /// ```
    /// use rand::seq::IteratorRandom;
    ///
    /// fn main() {
    ///     let mut rng = rand::thread_rng();
    ///
    ///     let faces = "😀😎😐😕😠😢";
    ///     println!("I am {}!", faces.chars().choose(&mut rng).unwrap());
    /// }
    /// ```
    /// Example output (non-deterministic):
    /// ```none
    /// I am 😀!
    /// ```
    pub trait IteratorRandom: Iterator + Sized {
        /// Choose one element at random from the iterator.
        ///
        /// Returns `None` if and only if the iterator is empty.
        ///
        /// This method uses [`Iterator::size_hint`] for optimisation. With an
        /// accurate hint and where [`Iterator::nth`] is a constant-time operation
        /// this method can offer `O(1)` performance. Where no size hint is
        /// available, complexity is `O(n)` where `n` is the iterator length.
        /// Partial hints (where `lower > 0`) also improve performance.
        ///
        /// For slices, prefer [`SliceRandom::choose`] which guarantees `O(1)`
        /// performance.
        fn choose<R>(mut self, rng: &mut R) -> Option<Self::Item>
        where R: Rng + ?Sized {
            let (mut lower, mut upper) = self.size_hint();
            let mut consumed = 0;
            let mut result = None;

            if upper == Some(lower) {
                return if lower == 0 { None } else { self.nth(gen_index(rng, lower)) };
            }

            // Continue until the iterator is exhausted
            loop {
                if lower > 1 {
                    let ix = gen_index(rng, lower + consumed);
                    let skip;
                    if ix < lower {
                        result = self.nth(ix);
                        skip = lower - (ix + 1);
                    } else {
                        skip = lower;
                    }
                    if upper == Some(lower) {
                        return result;
                    }
                    consumed += lower;
                    if skip > 0 {
                        self.nth(skip - 1);
                    }
                } else {
                    let elem = self.next();
                    if elem.is_none() {
                        return result;
                    }
                    consumed += 1;
                    let denom = consumed as f64; // accurate to 2^53 elements
                    if rng.gen_bool(1.0 / denom) {
                        result = elem;
                    }
                }

                let hint = self.size_hint();
                lower = hint.0;
                upper = hint.1;
            }
        }

        /// Collects values at random from the iterator into a supplied buffer
        /// until that buffer is filled.
        ///
        /// Although the elements are selected randomly, the order of elements in
        /// the buffer is neither stable nor fully random. If random ordering is
        /// desired, shuffle the result.
        ///
        /// Returns the number of elements added to the buffer. This equals the length
        /// of the buffer unless the iterator contains insufficient elements, in which
        /// case this equals the number of elements available.
        ///
        /// Complexity is `O(n)` where `n` is the length of the iterator.
        /// For slices, prefer [`SliceRandom::choose_multiple`].
        fn choose_multiple_fill<R>(mut self, rng: &mut R, buf: &mut [Self::Item]) -> usize
        where R: Rng + ?Sized {
            let amount = buf.len();
            let mut len = 0;
            while len < amount {
                if let Some(elem) = self.next() {
                    buf[len] = elem;
                    len += 1;
                } else {
                    // Iterator exhausted; stop early
                    return len;
                }
            }

            // Continue, since the iterator was not exhausted
            for (i, elem) in self.enumerate() {
                let k = gen_index(rng, i + 1 + amount);
                if let Some(slot) = buf.get_mut(k) {
                    *slot = elem;
                }
            }
            len
        }

        /// Collects `amount` values at random from the iterator into a vector.
        ///
        /// This is equivalent to `choose_multiple_fill` except for the result type.
        ///
        /// Although the elements are selected randomly, the order of elements in
        /// the buffer is neither stable nor fully random. If random ordering is
        /// desired, shuffle the result.
        ///
        /// The length of the returned vector equals `amount` unless the iterator
        /// contains insufficient elements, in which case it equals the number of
        /// elements available.
        ///
        /// Complexity is `O(n)` where `n` is the length of the iterator.
        /// For slices, prefer [`SliceRandom::choose_multiple`].
        #[cfg(feature = "alloc")]
        fn choose_multiple<R>(mut self, rng: &mut R, amount: usize) -> Vec<Self::Item>
        where R: Rng + ?Sized {
            let mut reservoir = Vec::with_capacity(amount);
            reservoir.extend(self.by_ref().take(amount));

            // Continue unless the iterator was exhausted
            //
            // note: this prevents iterators that "restart" from causing problems.
            // If the iterator stops once, then so do we.
            if reservoir.len() == amount {
                for (i, elem) in self.enumerate() {
                    let k = gen_index(rng, i + 1 + amount);
                    if let Some(slot) = reservoir.get_mut(k) {
                        *slot = elem;
                    }
                }
            } else {
                // Don't hang onto extra memory. There is a corner case where
                // `amount` was much less than `self.len()`.
                reservoir.shrink_to_fit();
            }
            reservoir
        }
    }

    impl<T> SliceRandom for [T] {
        type Item = T;

        fn choose<R>(&self, rng: &mut R) -> Option<&Self::Item>
        where R: Rng + ?Sized {
            if self.is_empty() {
                None
            } else {
                Some(&self[gen_index(rng, self.len())])
            }
        }

        fn choose_mut<R>(&mut self, rng: &mut R) -> Option<&mut Self::Item>
        where R: Rng + ?Sized {
            if self.is_empty() {
                None
            } else {
                let len = self.len();
                Some(&mut self[gen_index(rng, len)])
            }
        }

        #[cfg(feature = "alloc")]
        fn choose_multiple<R>(&self, rng: &mut R, amount: usize) -> SliceChooseIter<Self, Self::Item>
        where R: Rng + ?Sized {
            let amount = ::core::cmp::min(amount, self.len());
            SliceChooseIter {
                slice: self,
                _phantom: Default::default(),
                indices: index::sample(rng, self.len(), amount).into_iter(),
            }
        }

        #[cfg(feature = "alloc")]
        fn choose_weighted<R, F, B, X>(
            &self, rng: &mut R, weight: F,
        ) -> Result<&Self::Item, WeightedError>
        where
            R: Rng + ?Sized,
            F: Fn(&Self::Item) -> B,
            B: SampleBorrow<X>,
            X: SampleUniform
                + for<'a> ::core::ops::AddAssign<&'a X>
                + ::core::cmp::PartialOrd<X>
                + Clone
                + Default,
        {
            use crate::distributions::{Distribution, WeightedIndex};
            let distr = WeightedIndex::new(self.iter().map(weight))?;
            Ok(&self[distr.sample(rng)])
        }

        #[cfg(feature = "alloc")]
        fn choose_weighted_mut<R, F, B, X>(
            &mut self, rng: &mut R, weight: F,
        ) -> Result<&mut Self::Item, WeightedError>
        where
            R: Rng + ?Sized,
            F: Fn(&Self::Item) -> B,
            B: SampleBorrow<X>,
            X: SampleUniform
                + for<'a> ::core::ops::AddAssign<&'a X>
                + ::core::cmp::PartialOrd<X>
                + Clone
                + Default,
        {
            use crate::distributions::{Distribution, WeightedIndex};
            let distr = WeightedIndex::new(self.iter().map(weight))?;
            Ok(&mut self[distr.sample(rng)])
        }

        fn shuffle<R>(&mut self, rng: &mut R)
        where R: Rng + ?Sized {
            for i in (1..self.len()).rev() {
                // invariant: elements with index > i have been locked in place.
                self.swap(i, gen_index(rng, i + 1));
            }
        }

        fn partial_shuffle<R>(
            &mut self, rng: &mut R, amount: usize,
        ) -> (&mut [Self::Item], &mut [Self::Item])
        where R: Rng + ?Sized {
            // This applies Durstenfeld's algorithm for the
            // [Fisher–Yates shuffle](https://en.wikipedia.org/wiki/Fisher%E2%80%93Yates_shuffle#The_modern_algorithm)
            // for an unbiased permutation, but exits early after choosing `amount`
            // elements.

            let len = self.len();
            let end = if amount >= len { 0 } else { len - amount };

            for i in (end..len).rev() {
                // invariant: elements with index > i have been locked in place.
                self.swap(i, gen_index(rng, i + 1));
            }
            let r = self.split_at_mut(end);
            (r.1, r.0)
        }
    }

    impl<I> IteratorRandom for I where I: Iterator + Sized {}

    /// An iterator over multiple slice elements.
    ///
    /// This struct is created by
    /// [`SliceRandom::choose_multiple`](trait.SliceRandom.html#tymethod.choose_multiple).
    #[cfg(feature = "alloc")]
    #[derive(Debug)]
    pub struct SliceChooseIter<'a, S: ?Sized + 'a, T: 'a> {
        slice: &'a S,
        _phantom: ::core::marker::PhantomData<T>,
        indices: index::IndexVecIntoIter,
    }

    #[cfg(feature = "alloc")]
    impl<'a, S: Index<usize, Output = T> + ?Sized + 'a, T: 'a> Iterator for SliceChooseIter<'a, S, T> {
        type Item = &'a T;

        fn next(&mut self) -> Option<Self::Item> {
            // TODO: investigate using SliceIndex::get_unchecked when stable
            self.indices.next().map(|i| &self.slice[i as usize])
        }

        fn size_hint(&self) -> (usize, Option<usize>) {
            (self.indices.len(), Some(self.indices.len()))
        }
    }

    #[cfg(feature = "alloc")]
    impl<'a, S: Index<usize, Output = T> + ?Sized + 'a, T: 'a> ExactSizeIterator
        for SliceChooseIter<'a, S, T>
    {
        fn len(&self) -> usize {
            self.indices.len()
        }
    }

    // Sample a number uniformly between 0 and `ubound`. Uses 32-bit sampling where
    // possible, primarily in order to produce the same output on 32-bit and 64-bit
    // platforms.
    #[inline]
    fn gen_index<R: Rng + ?Sized>(rng: &mut R, ubound: usize) -> usize {
        if ubound <= (core::u32::MAX as usize) {
            rng.gen_range(0, ubound as u32) as usize
        } else {
            rng.gen_range(0, ubound)
        }
    }

    #[cfg(test)]
    mod test {
        use super::*;
        #[cfg(feature = "alloc")] use crate::Rng;
        #[cfg(all(feature="alloc", not(feature="std")))]
        use alloc::vec::Vec;

        #[test]
        fn test_slice_choose() {
            let mut r = crate::test::rng(107);
            let chars = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n'];
            let mut chosen = [0i32; 14];
            // The below all use a binomial distribution with n=1000, p=1/14.
            // binocdf(40, 1000, 1/14) ~= 2e-5; 1-binocdf(106, ..) ~= 2e-5
            for _ in 0..1000 {
                let picked = *chars.choose(&mut r).unwrap();
                chosen[(picked as usize) - ('a' as usize)] += 1;
            }
            for count in chosen.iter() {
                assert!(40 < *count && *count < 106);
            }

            chosen.iter_mut().for_each(|x| *x = 0);
            for _ in 0..1000 {
                *chosen.choose_mut(&mut r).unwrap() += 1;
            }
            for count in chosen.iter() {
                assert!(40 < *count && *count < 106);
            }

            let mut v: [isize; 0] = [];
            assert_eq!(v.choose(&mut r), None);
            assert_eq!(v.choose_mut(&mut r), None);
        }

        #[derive(Clone)]
        struct UnhintedIterator<I: Iterator + Clone> {
            iter: I,
        }
        impl<I: Iterator + Clone> Iterator for UnhintedIterator<I> {
            type Item = I::Item;
            fn next(&mut self) -> Option<Self::Item> {
                self.iter.next()
            }
        }

        #[derive(Clone)]
        struct ChunkHintedIterator<I: ExactSizeIterator + Iterator + Clone> {
            iter: I,
            chunk_remaining: usize,
            chunk_size: usize,
            hint_total_size: bool,
        }
        impl<I: ExactSizeIterator + Iterator + Clone> Iterator for ChunkHintedIterator<I> {
            type Item = I::Item;
            fn next(&mut self) -> Option<Self::Item> {
                if self.chunk_remaining == 0 {
                    self.chunk_remaining = ::core::cmp::min(self.chunk_size,
                                                            self.iter.len());
                }
                self.chunk_remaining = self.chunk_remaining.saturating_sub(1);

                self.iter.next()
            }
            fn size_hint(&self) -> (usize, Option<usize>) {
                (self.chunk_remaining,
                 if self.hint_total_size { Some(self.iter.len()) } else { None })
            }
        }

        #[derive(Clone)]
        struct WindowHintedIterator<I: ExactSizeIterator + Iterator + Clone> {
            iter: I,
            window_size: usize,
            hint_total_size: bool,
        }
        impl<I: ExactSizeIterator + Iterator + Clone> Iterator for WindowHintedIterator<I> {
            type Item = I::Item;
            fn next(&mut self) -> Option<Self::Item> {
                self.iter.next()
            }
            fn size_hint(&self) -> (usize, Option<usize>) {
                (::core::cmp::min(self.iter.len(), self.window_size),
                 if self.hint_total_size { Some(self.iter.len()) } else { None })
            }
        }

        #[test]
        #[cfg(not(miri))] // Miri is too slow
        fn test_iterator_choose() {
            let r = &mut crate::test::rng(109);
            fn test_iter<R: Rng + ?Sized, Iter: Iterator<Item=usize> + Clone>(r: &mut R, iter: Iter) {
                let mut chosen = [0i32; 9];
                for _ in 0..1000 {
                    let picked = iter.clone().choose(r).unwrap();
                    chosen[picked] += 1;
                }
                for count in chosen.iter() {
                    // Samples should follow Binomial(1000, 1/9)
                    // Octave: binopdf(x, 1000, 1/9) gives the prob of *count == x
                    // Note: have seen 153, which is unlikely but not impossible.
                    assert!(72 < *count && *count < 154, "count not close to 1000/9: {}", count);
                }
            }

            test_iter(r, 0..9);
            test_iter(r, [0, 1, 2, 3, 4, 5, 6, 7, 8].iter().cloned());
            #[cfg(feature = "alloc")]
            test_iter(r, (0..9).collect::<Vec<_>>().into_iter());
            test_iter(r, UnhintedIterator { iter: 0..9 });
            test_iter(r, ChunkHintedIterator { iter: 0..9, chunk_size: 4, chunk_remaining: 4, hint_total_size: false });
            test_iter(r, ChunkHintedIterator { iter: 0..9, chunk_size: 4, chunk_remaining: 4, hint_total_size: true });
            test_iter(r, WindowHintedIterator { iter: 0..9, window_size: 2, hint_total_size: false });
            test_iter(r, WindowHintedIterator { iter: 0..9, window_size: 2, hint_total_size: true });

            assert_eq!((0..0).choose(r), None);
            assert_eq!(UnhintedIterator{ iter: 0..0 }.choose(r), None);
        }

        #[test]
        #[cfg(not(miri))] // Miri is too slow
        fn test_shuffle() {
            let mut r = crate::test::rng(108);
            let empty: &mut [isize] = &mut [];
            empty.shuffle(&mut r);
            let mut one = [1];
            one.shuffle(&mut r);
            let b: &[_] = &[1];
            assert_eq!(one, b);

            let mut two = [1, 2];
            two.shuffle(&mut r);
            assert!(two == [1, 2] || two == [2, 1]);

            fn move_last(slice: &mut [usize], pos: usize) {
                // use slice[pos..].rotate_left(1); once we can use that
                let last_val = slice[pos];
                for i in pos..slice.len() - 1 {
                    slice[i] = slice[i + 1];
                }
                *slice.last_mut().unwrap() = last_val;
            }
            let mut counts = [0i32; 24];
            for _ in 0..10000 {
                let mut arr: [usize; 4] = [0, 1, 2, 3];
                arr.shuffle(&mut r);
                let mut permutation = 0usize;
                let mut pos_value = counts.len();
                for i in 0..4 {
                    pos_value /= 4 - i;
                    let pos = arr.iter().position(|&x| x == i).unwrap();
                    assert!(pos < (4 - i));
                    permutation += pos * pos_value;
                    move_last(&mut arr, pos);
                    assert_eq!(arr[3], i);
                }
                for i in 0..4 {
                    assert_eq!(arr[i], i);
                }
                counts[permutation] += 1;
            }
            for count in counts.iter() {
                // Binomial(10000, 1/24) with average 416.667
                // Octave: binocdf(n, 10000, 1/24)
                // 99.9% chance samples lie within this range:
                assert!(352 <= *count && *count <= 483, "count: {}", count);
            }
        }

        #[test]
        fn test_partial_shuffle() {
            let mut r = crate::test::rng(118);

            let mut empty: [u32; 0] = [];
            let res = empty.partial_shuffle(&mut r, 10);
            assert_eq!((res.0.len(), res.1.len()), (0, 0));

            let mut v = [1, 2, 3, 4, 5];
            let res = v.partial_shuffle(&mut r, 2);
            assert_eq!((res.0.len(), res.1.len()), (2, 3));
            assert!(res.0[0] != res.0[1]);
            // First elements are only modified if selected, so at least one isn't modified:
            assert!(res.1[0] == 1 || res.1[1] == 2 || res.1[2] == 3);
        }

        #[test]
        #[cfg(feature = "alloc")]
        fn test_sample_iter() {
            let min_val = 1;
            let max_val = 100;

            let mut r = crate::test::rng(401);
            let vals = (min_val..max_val).collect::<Vec<i32>>();
            let small_sample = vals.iter().choose_multiple(&mut r, 5);
            let large_sample = vals.iter().choose_multiple(&mut r, vals.len() + 5);

            assert_eq!(small_sample.len(), 5);
            assert_eq!(large_sample.len(), vals.len());
            // no randomization happens when amount >= len
            assert_eq!(large_sample, vals.iter().collect::<Vec<_>>());

            assert!(small_sample.iter().all(|e| {
                **e >= min_val && **e <= max_val
            }));
        }

        #[test]
        #[cfg(feature = "alloc")]
        #[cfg(not(miri))] // Miri is too slow
        fn test_weighted() {
            let mut r = crate::test::rng(406);
            const N_REPS: u32 = 3000;
            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);
                }
            };

            // choose_weighted
            fn get_weight<T>(item: &(u32, T)) -> u32 {
                item.0
            }
            let mut chosen = [0i32; 14];
            let mut items = [(0u32, 0usize); 14]; // (weight, index)
            for (i, item) in items.iter_mut().enumerate() {
                *item = (weights[i], i);
            }
            for _ in 0..N_REPS {
                let item = items.choose_weighted(&mut r, get_weight).unwrap();
                chosen[item.1] += 1;
            }
            verify(chosen);

            // choose_weighted_mut
            let mut items = [(0u32, 0i32); 14]; // (weight, count)
            for (i, item) in items.iter_mut().enumerate() {
                *item = (weights[i], 0);
            }
            for _ in 0..N_REPS {
                items.choose_weighted_mut(&mut r, get_weight).unwrap().1 += 1;
            }
            for (ch, item) in chosen.iter_mut().zip(items.iter()) {
                *ch = item.1;
            }
            verify(chosen);

            // Check error cases
            let empty_slice = &mut [10][0..0];
            assert_eq!(empty_slice.choose_weighted(&mut r, |_| 1), Err(WeightedError::NoItem));
            assert_eq!(empty_slice.choose_weighted_mut(&mut r, |_| 1), Err(WeightedError::NoItem));
            assert_eq!(['x'].choose_weighted_mut(&mut r, |_| 0), Err(WeightedError::AllWeightsZero));
            assert_eq!([0, -1].choose_weighted_mut(&mut r, |x| *x), Err(WeightedError::InvalidWeight));
            assert_eq!([-1, 0].choose_weighted_mut(&mut r, |x| *x), Err(WeightedError::InvalidWeight));
        }
    }

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