//! Parallel iterator types for [slices]

//!

//! You will rarely need to interact with this module directly unless you need

//! to name one of the iterator types.

//!

//! [slices]: std::slice

mod chunk_by;

mod chunks;

mod rchunks;

mod sort;

mod test;

use self::sort::par_mergesort;

use self::sort::par_quicksort;

use crate::iter::plumbing::*;

use crate::iter::*;

use crate::split_producer::*;

use std::cmp::Ordering;

use std::fmt::{self, Debug};

pub use self::chunk_by::{ChunkBy, ChunkByMut};

pub use self::chunks::{Chunks, ChunksExact, ChunksExactMut, ChunksMut};

pub use self::rchunks::{RChunks, RChunksExact, RChunksExactMut, RChunksMut};

/// Parallel extensions for slices.

pub trait ParallelSlice<T: Sync> {

    /// Returns a plain slice, which is used to implement the rest of the

    /// parallel methods.

    fn as_parallel_slice(&self) -> &[T];

    /// Returns a parallel iterator over subslices separated by elements that

    /// match the separator.

    ///

    /// # Examples

    ///

    /// ```

    /// use rayon::prelude::*;

    /// let products: Vec<_> = [1, 2, 3, 0, 2, 4, 8, 0, 3, 6, 9]

    ///     .par_split(|i| *i == 0)

    ///     .map(|numbers| numbers.iter().product::<i32>())

    ///     .collect();

    /// assert_eq!(products, [6, 64, 162]);

    /// ```

    fn par_split<P>(&self, separator: P) -> Split<'_, T, P>

    where

        P: Fn(&T) -> bool + Sync + Send,

    {

        Split {

            slice: self.as_parallel_slice(),

            separator,

        }

    }

    /// Returns a parallel iterator over subslices separated by elements that

    /// match the separator, including the matched part as a terminator.

    ///

    /// # Examples

    ///

    /// ```

    /// use rayon::prelude::*;

    /// let lengths: Vec<_> = [1, 2, 3, 0, 2, 4, 8, 0, 3, 6, 9]

    ///     .par_split_inclusive(|i| *i == 0)

    ///     .map(|numbers| numbers.len())

    ///     .collect();

    /// assert_eq!(lengths, [4, 4, 3]);

    /// ```

    fn par_split_inclusive<P>(&self, separator: P) -> SplitInclusive<'_, T, P>

    where

        P: Fn(&T) -> bool + Sync + Send,

    {

        SplitInclusive {

            slice: self.as_parallel_slice(),

            separator,

        }

    }

    /// Returns a parallel iterator over all contiguous windows of length

    /// `window_size`. The windows overlap.

    ///

    /// # Examples

    ///

    /// ```

    /// use rayon::prelude::*;

    /// let windows: Vec<_> = [1, 2, 3].par_windows(2).collect();

    /// assert_eq!(vec![[1, 2], [2, 3]], windows);

    /// ```

    fn par_windows(&self, window_size: usize) -> Windows<'_, T> {

        Windows {

            window_size,

            slice: self.as_parallel_slice(),

        }

    }

    /// Returns a parallel iterator over at most `chunk_size` elements of

    /// `self` at a time. The chunks do not overlap.

    ///

    /// If the number of elements in the iterator is not divisible by

    /// `chunk_size`, the last chunk may be shorter than `chunk_size`.  All

    /// other chunks will have that exact length.

    ///

    /// # Examples

    ///

    /// ```

    /// use rayon::prelude::*;

    /// let chunks: Vec<_> = [1, 2, 3, 4, 5].par_chunks(2).collect();

    /// assert_eq!(chunks, vec![&[1, 2][..], &[3, 4], &[5]]);

    /// ```

    #[track_caller]

    fn par_chunks(&self, chunk_size: usize) -> Chunks<'_, T> {

        assert!(chunk_size != 0, "chunk_size must not be zero");

        Chunks::new(chunk_size, self.as_parallel_slice())

    }

    /// Returns a parallel iterator over `chunk_size` elements of

    /// `self` at a time. The chunks do not overlap.

    ///

    /// If `chunk_size` does not divide the length of the slice, then the

    /// last up to `chunk_size-1` elements will be omitted and can be

    /// retrieved from the remainder function of the iterator.

    ///

    /// # Examples

    ///

    /// ```

    /// use rayon::prelude::*;

    /// let chunks: Vec<_> = [1, 2, 3, 4, 5].par_chunks_exact(2).collect();

    /// assert_eq!(chunks, vec![&[1, 2][..], &[3, 4]]);

    /// ```

    #[track_caller]

    fn par_chunks_exact(&self, chunk_size: usize) -> ChunksExact<'_, T> {

        assert!(chunk_size != 0, "chunk_size must not be zero");

        ChunksExact::new(chunk_size, self.as_parallel_slice())

    }

    /// Returns a parallel iterator over at most `chunk_size` elements of `self` at a time,

    /// starting at the end. The chunks do not overlap.

    ///

    /// If the number of elements in the iterator is not divisible by

    /// `chunk_size`, the last chunk may be shorter than `chunk_size`.  All

    /// other chunks will have that exact length.

    ///

    /// # Examples

    ///

    /// ```

    /// use rayon::prelude::*;

    /// let chunks: Vec<_> = [1, 2, 3, 4, 5].par_rchunks(2).collect();

    /// assert_eq!(chunks, vec![&[4, 5][..], &[2, 3], &[1]]);

    /// ```

    #[track_caller]

    fn par_rchunks(&self, chunk_size: usize) -> RChunks<'_, T> {

        assert!(chunk_size != 0, "chunk_size must not be zero");

        RChunks::new(chunk_size, self.as_parallel_slice())

    }

    /// Returns a parallel iterator over `chunk_size` elements of `self` at a time,

    /// starting at the end. The chunks do not overlap.

    ///

    /// If `chunk_size` does not divide the length of the slice, then the

    /// last up to `chunk_size-1` elements will be omitted and can be

    /// retrieved from the remainder function of the iterator.

    ///

    /// # Examples

    ///

    /// ```

    /// use rayon::prelude::*;

    /// let chunks: Vec<_> = [1, 2, 3, 4, 5].par_rchunks_exact(2).collect();

    /// assert_eq!(chunks, vec![&[4, 5][..], &[2, 3]]);

    /// ```

    #[track_caller]

    fn par_rchunks_exact(&self, chunk_size: usize) -> RChunksExact<'_, T> {

        assert!(chunk_size != 0, "chunk_size must not be zero");

        RChunksExact::new(chunk_size, self.as_parallel_slice())

    }

    /// Returns a parallel iterator over the slice producing non-overlapping runs

    /// of elements using the predicate to separate them.

    ///

    /// The predicate is called on two elements following themselves,

    /// it means the predicate is called on `slice[0]` and `slice[1]`

    /// then on `slice[1]` and `slice[2]` and so on.

    ///

    /// # Examples

    ///

    /// ```

    /// use rayon::prelude::*;

    /// let chunks: Vec<_> = [1, 2, 2, 3, 3, 3].par_chunk_by(|&x, &y| x == y).collect();

    /// assert_eq!(chunks[0], &[1]);

    /// assert_eq!(chunks[1], &[2, 2]);

    /// assert_eq!(chunks[2], &[3, 3, 3]);

    /// ```

    fn par_chunk_by<F>(&self, pred: F) -> ChunkBy<'_, T, F>

    where

        F: Fn(&T, &T) -> bool + Send + Sync,

    {

        ChunkBy::new(self.as_parallel_slice(), pred)

    }

}

impl<T: Sync> ParallelSlice<T> for [T] {

    #[inline]

    fn as_parallel_slice(&self) -> &[T] {

        self

    }

}

/// Parallel extensions for mutable slices.

pub trait ParallelSliceMut<T: Send> {

    /// Returns a plain mutable slice, which is used to implement the rest of

    /// the parallel methods.

    fn as_parallel_slice_mut(&mut self) -> &mut [T];

    /// Returns a parallel iterator over mutable subslices separated by

    /// elements that match the separator.

    ///

    /// # Examples

    ///

    /// ```

    /// use rayon::prelude::*;

    /// let mut array = [1, 2, 3, 0, 2, 4, 8, 0, 3, 6, 9];

    /// array.par_split_mut(|i| *i == 0)

    ///      .for_each(|slice| slice.reverse());

    /// assert_eq!(array, [3, 2, 1, 0, 8, 4, 2, 0, 9, 6, 3]);

    /// ```

    fn par_split_mut<P>(&mut self, separator: P) -> SplitMut<'_, T, P>

    where

        P: Fn(&T) -> bool + Sync + Send,

    {

        SplitMut {

            slice: self.as_parallel_slice_mut(),

            separator,

        }

    }

    /// Returns a parallel iterator over mutable subslices separated by elements

    /// that match the separator, including the matched part as a terminator.

    ///

    /// # Examples

    ///

    /// ```

    /// use rayon::prelude::*;

    /// let mut array = [1, 2, 3, 0, 2, 4, 8, 0, 3, 6, 9];

    /// array.par_split_inclusive_mut(|i| *i == 0)

    ///      .for_each(|slice| slice.reverse());

    /// assert_eq!(array, [0, 3, 2, 1, 0, 8, 4, 2, 9, 6, 3]);

    /// ```

    fn par_split_inclusive_mut<P>(&mut self, separator: P) -> SplitInclusiveMut<'_, T, P>

    where

        P: Fn(&T) -> bool + Sync + Send,

    {

        SplitInclusiveMut {

            slice: self.as_parallel_slice_mut(),

            separator,

        }

    }

    /// Returns a parallel iterator over at most `chunk_size` elements of

    /// `self` at a time. The chunks are mutable and do not overlap.

    ///

    /// If the number of elements in the iterator is not divisible by

    /// `chunk_size`, the last chunk may be shorter than `chunk_size`.  All

    /// other chunks will have that exact length.

    ///

    /// # Examples

    ///

    /// ```

    /// use rayon::prelude::*;

    /// let mut array = [1, 2, 3, 4, 5];

    /// array.par_chunks_mut(2)

    ///      .for_each(|slice| slice.reverse());

    /// assert_eq!(array, [2, 1, 4, 3, 5]);

    /// ```

    #[track_caller]

    fn par_chunks_mut(&mut self, chunk_size: usize) -> ChunksMut<'_, T> {

        assert!(chunk_size != 0, "chunk_size must not be zero");

        ChunksMut::new(chunk_size, self.as_parallel_slice_mut())

    }

    /// Returns a parallel iterator over `chunk_size` elements of

    /// `self` at a time. The chunks are mutable and do not overlap.

    ///

    /// If `chunk_size` does not divide the length of the slice, then the

    /// last up to `chunk_size-1` elements will be omitted and can be

    /// retrieved from the remainder function of the iterator.

    ///

    /// # Examples

    ///

    /// ```

    /// use rayon::prelude::*;

    /// let mut array = [1, 2, 3, 4, 5];

    /// array.par_chunks_exact_mut(3)

    ///      .for_each(|slice| slice.reverse());

    /// assert_eq!(array, [3, 2, 1, 4, 5]);

    /// ```

    #[track_caller]

    fn par_chunks_exact_mut(&mut self, chunk_size: usize) -> ChunksExactMut<'_, T> {

        assert!(chunk_size != 0, "chunk_size must not be zero");

        ChunksExactMut::new(chunk_size, self.as_parallel_slice_mut())

    }

    /// Returns a parallel iterator over at most `chunk_size` elements of `self` at a time,

    /// starting at the end. The chunks are mutable and do not overlap.

    ///

    /// If the number of elements in the iterator is not divisible by

    /// `chunk_size`, the last chunk may be shorter than `chunk_size`.  All

    /// other chunks will have that exact length.

    ///

    /// # Examples

    ///

    /// ```

    /// use rayon::prelude::*;

    /// let mut array = [1, 2, 3, 4, 5];

    /// array.par_rchunks_mut(2)

    ///      .for_each(|slice| slice.reverse());

    /// assert_eq!(array, [1, 3, 2, 5, 4]);

    /// ```

    #[track_caller]

    fn par_rchunks_mut(&mut self, chunk_size: usize) -> RChunksMut<'_, T> {

        assert!(chunk_size != 0, "chunk_size must not be zero");

        RChunksMut::new(chunk_size, self.as_parallel_slice_mut())

    }

    /// Returns a parallel iterator over `chunk_size` elements of `self` at a time,

    /// starting at the end. The chunks are mutable and do not overlap.

    ///

    /// If `chunk_size` does not divide the length of the slice, then the

    /// last up to `chunk_size-1` elements will be omitted and can be

    /// retrieved from the remainder function of the iterator.

    ///

    /// # Examples

    ///

    /// ```

    /// use rayon::prelude::*;

    /// let mut array = [1, 2, 3, 4, 5];

    /// array.par_rchunks_exact_mut(3)

    ///      .for_each(|slice| slice.reverse());

    /// assert_eq!(array, [1, 2, 5, 4, 3]);

    /// ```

    #[track_caller]

    fn par_rchunks_exact_mut(&mut self, chunk_size: usize) -> RChunksExactMut<'_, T> {

        assert!(chunk_size != 0, "chunk_size must not be zero");

        RChunksExactMut::new(chunk_size, self.as_parallel_slice_mut())

    }

    /// Sorts the slice in parallel.

    ///

    /// This sort is stable (i.e., does not reorder equal elements) and *O*(*n* \* log(*n*)) worst-case.

    ///

    /// When applicable, unstable sorting is preferred because it is generally faster than stable

    /// sorting and it doesn't allocate auxiliary memory.

    /// See [`par_sort_unstable`](#method.par_sort_unstable).

    ///

    /// # Current implementation

    ///

    /// The current algorithm is an adaptive merge sort inspired by

    /// [timsort](https://en.wikipedia.org/wiki/Timsort).

    /// It is designed to be very fast in cases where the slice is nearly sorted, or consists of

    /// two or more sorted sequences concatenated one after another.

    ///

    /// Also, it allocates temporary storage the same size as `self`, but for very short slices a

    /// non-allocating insertion sort is used instead.

    ///

    /// In order to sort the slice in parallel, the slice is first divided into smaller chunks and

    /// all chunks are sorted in parallel. Then, adjacent chunks that together form non-descending

    /// or descending runs are concatenated. Finally, the remaining chunks are merged together using

    /// parallel subdivision of chunks and parallel merge operation.

    ///

    /// # Examples

    ///

    /// ```

    /// use rayon::prelude::*;

    ///

    /// let mut v = [-5, 4, 1, -3, 2];

    ///

    /// v.par_sort();

    /// assert_eq!(v, [-5, -3, 1, 2, 4]);

    /// ```

    fn par_sort(&mut self)

    where

        T: Ord,

    {

        par_mergesort(self.as_parallel_slice_mut(), T::lt);

    }

    /// Sorts the slice in parallel with a comparator function.

    ///

    /// This sort is stable (i.e., does not reorder equal elements) and *O*(*n* \* log(*n*)) worst-case.

    ///

    /// The comparator function must define a total ordering for the elements in the slice. If

    /// the ordering is not total, the order of the elements is unspecified. An order is a

    /// total order if it is (for all `a`, `b` and `c`):

    ///

    /// * total and antisymmetric: exactly one of `a < b`, `a == b` or `a > b` is true, and

    /// * transitive, `a < b` and `b < c` implies `a < c`. The same must hold for both `==` and `>`.

    ///

    /// For example, while [`f64`] doesn't implement [`Ord`] because `NaN != NaN`, we can use

    /// `partial_cmp` as our sort function when we know the slice doesn't contain a `NaN`.

    ///

    /// ```

    /// use rayon::prelude::*;

    ///

    /// let mut floats = [5f64, 4.0, 1.0, 3.0, 2.0];

    /// floats.par_sort_by(|a, b| a.partial_cmp(b).unwrap());

    /// assert_eq!(floats, [1.0, 2.0, 3.0, 4.0, 5.0]);

    /// ```

    ///

    /// When applicable, unstable sorting is preferred because it is generally faster than stable

    /// sorting and it doesn't allocate auxiliary memory.

    /// See [`par_sort_unstable_by`](#method.par_sort_unstable_by).

    ///

    /// # Current implementation

    ///

    /// The current algorithm is an adaptive merge sort inspired by

    /// [timsort](https://en.wikipedia.org/wiki/Timsort).

    /// It is designed to be very fast in cases where the slice is nearly sorted, or consists of

    /// two or more sorted sequences concatenated one after another.

    ///

    /// Also, it allocates temporary storage the same size as `self`, but for very short slices a

    /// non-allocating insertion sort is used instead.

    ///

    /// In order to sort the slice in parallel, the slice is first divided into smaller chunks and

    /// all chunks are sorted in parallel. Then, adjacent chunks that together form non-descending

    /// or descending runs are concatenated. Finally, the remaining chunks are merged together using

    /// parallel subdivision of chunks and parallel merge operation.

    ///

    /// # Examples

    ///

    /// ```

    /// use rayon::prelude::*;

    ///

    /// let mut v = [5, 4, 1, 3, 2];

    /// v.par_sort_by(|a, b| a.cmp(b));

    /// assert_eq!(v, [1, 2, 3, 4, 5]);

    ///

    /// // reverse sorting

    /// v.par_sort_by(|a, b| b.cmp(a));

    /// assert_eq!(v, [5, 4, 3, 2, 1]);

    /// ```

    fn par_sort_by<F>(&mut self, compare: F)

    where

        F: Fn(&T, &T) -> Ordering + Sync,

    {

        par_mergesort(self.as_parallel_slice_mut(), |a, b| {

            compare(a, b) == Ordering::Less

        });

    }

    /// Sorts the slice in parallel with a key extraction function.

    ///

    /// This sort is stable (i.e., does not reorder equal elements) and *O*(*m* \* *n* \* log(*n*))

    /// worst-case, where the key function is *O*(*m*).

    ///

    /// For expensive key functions (e.g. functions that are not simple property accesses or

    /// basic operations), [`par_sort_by_cached_key`](#method.par_sort_by_cached_key) is likely to

    /// be significantly faster, as it does not recompute element keys.

    ///

    /// When applicable, unstable sorting is preferred because it is generally faster than stable

    /// sorting and it doesn't allocate auxiliary memory.

    /// See [`par_sort_unstable_by_key`](#method.par_sort_unstable_by_key).

    ///

    /// # Current implementation

    ///

    /// The current algorithm is an adaptive merge sort inspired by

    /// [timsort](https://en.wikipedia.org/wiki/Timsort).

    /// It is designed to be very fast in cases where the slice is nearly sorted, or consists of

    /// two or more sorted sequences concatenated one after another.

    ///

    /// Also, it allocates temporary storage the same size as `self`, but for very short slices a

    /// non-allocating insertion sort is used instead.

    ///

    /// In order to sort the slice in parallel, the slice is first divided into smaller chunks and

    /// all chunks are sorted in parallel. Then, adjacent chunks that together form non-descending

    /// or descending runs are concatenated. Finally, the remaining chunks are merged together using

    /// parallel subdivision of chunks and parallel merge operation.

    ///

    /// # Examples

    ///

    /// ```

    /// use rayon::prelude::*;

    ///

    /// let mut v = [-5i32, 4, 1, -3, 2];

    ///

    /// v.par_sort_by_key(|k| k.abs());

    /// assert_eq!(v, [1, 2, -3, 4, -5]);

    /// ```

    fn par_sort_by_key<K, F>(&mut self, f: F)

    where

        K: Ord,

        F: Fn(&T) -> K + Sync,

    {

        par_mergesort(self.as_parallel_slice_mut(), |a, b| f(a).lt(&f(b)));

    }

    /// Sorts the slice in parallel with a key extraction function.

    ///

    /// During sorting, the key function is called at most once per element, by using

    /// temporary storage to remember the results of key evaluation.

    /// The key function is called in parallel, so the order of calls is completely unspecified.

    ///

    /// This sort is stable (i.e., does not reorder equal elements) and *O*(*m* \* *n* + *n* \* log(*n*))

    /// worst-case, where the key function is *O*(*m*).

    ///

    /// For simple key functions (e.g., functions that are property accesses or

    /// basic operations), [`par_sort_by_key`](#method.par_sort_by_key) is likely to be

    /// faster.

    ///

    /// # Current implementation

    ///

    /// The current algorithm is based on [pattern-defeating quicksort][pdqsort] by Orson Peters,

    /// which combines the fast average case of randomized quicksort with the fast worst case of

    /// heapsort, while achieving linear time on slices with certain patterns. It uses some

    /// randomization to avoid degenerate cases, but with a fixed seed to always provide

    /// deterministic behavior.

    ///

    /// In the worst case, the algorithm allocates temporary storage in a `Vec<(K, usize)>` the

    /// length of the slice.

    ///

    /// All quicksorts work in two stages: partitioning into two halves followed by recursive

    /// calls. The partitioning phase is sequential, but the two recursive calls are performed in

    /// parallel. Finally, after sorting the cached keys, the item positions are updated sequentially.

    ///

    /// [pdqsort]: https://github.com/orlp/pdqsort

    ///

    /// # Examples

    ///

    /// ```

    /// use rayon::prelude::*;

    ///

    /// let mut v = [-5i32, 4, 32, -3, 2];

    ///

    /// v.par_sort_by_cached_key(|k| k.to_string());

    /// assert!(v == [-3, -5, 2, 32, 4]);

    /// ```

    fn par_sort_by_cached_key<K, F>(&mut self, f: F)

    where

        F: Fn(&T) -> K + Sync,

        K: Ord + Send,

    {

        let slice = self.as_parallel_slice_mut();

        let len = slice.len();

        if len < 2 {

            return;

        }

        // Helper macro for indexing our vector by the smallest possible type, to reduce allocation.

        macro_rules! sort_by_key {

            ($t:ty) => {{

                let mut indices: Vec<_> = slice

                    .par_iter_mut()

                    .enumerate()

                    .map(|(i, x)| (f(&*x), i as $t))

Unexecuted instantiation: <_ as rayon::slice::ParallelSliceMut<_>>::par_sort_by_cached_key::<_, _>::{closure#0}

Unexecuted instantiation: <_ as rayon::slice::ParallelSliceMut<_>>::par_sort_by_cached_key::<_, _>::{closure#2}

Unexecuted instantiation: <_ as rayon::slice::ParallelSliceMut<_>>::par_sort_by_cached_key::<_, _>::{closure#3}

Unexecuted instantiation: <_ as rayon::slice::ParallelSliceMut<_>>::par_sort_by_cached_key::<_, _>::{closure#1}

                    .collect();

                // The elements of `indices` are unique, as they are indexed, so any sort will be

                // stable with respect to the original slice. We use `sort_unstable` here because

                // it requires less memory allocation.

                indices.par_sort_unstable();

                for i in 0..len {

                    let mut index = indices[i].1;

                    while (index as usize) < i {

                        index = indices[index as usize].1;

                    }

                    indices[i].1 = index;

                    slice.swap(i, index as usize);

                }

            }};

        }

        let sz_u8 = size_of::<(K, u8)>();

        let sz_u16 = size_of::<(K, u16)>();

        let sz_u32 = size_of::<(K, u32)>();

        let sz_usize = size_of::<(K, usize)>();

        if sz_u8 < sz_u16 && len <= (u8::MAX as usize) {

            return sort_by_key!(u8);

        }

        if sz_u16 < sz_u32 && len <= (u16::MAX as usize) {

            return sort_by_key!(u16);

        }

        if sz_u32 < sz_usize && len <= (u32::MAX as usize) {

            return sort_by_key!(u32);

        }

        sort_by_key!(usize)

    }

    /// Sorts the slice in parallel, but might not preserve the order of equal elements.

    ///

    /// This sort is unstable (i.e., may reorder equal elements), in-place

    /// (i.e., does not allocate), and *O*(*n* \* log(*n*)) worst-case.

    ///

    /// # Current implementation

    ///

    /// The current algorithm is based on [pattern-defeating quicksort][pdqsort] by Orson Peters,

    /// which combines the fast average case of randomized quicksort with the fast worst case of

    /// heapsort, while achieving linear time on slices with certain patterns. It uses some

    /// randomization to avoid degenerate cases, but with a fixed seed to always provide

    /// deterministic behavior.

    ///

    /// It is typically faster than stable sorting, except in a few special cases, e.g., when the

    /// slice consists of several concatenated sorted sequences.

    ///

    /// All quicksorts work in two stages: partitioning into two halves followed by recursive

    /// calls. The partitioning phase is sequential, but the two recursive calls are performed in

    /// parallel.

    ///

    /// [pdqsort]: https://github.com/orlp/pdqsort

    ///

    /// # Examples

    ///

    /// ```

    /// use rayon::prelude::*;

    ///

    /// let mut v = [-5, 4, 1, -3, 2];

    ///

    /// v.par_sort_unstable();

    /// assert_eq!(v, [-5, -3, 1, 2, 4]);

    /// ```

    fn par_sort_unstable(&mut self)

    where

        T: Ord,

    {

        par_quicksort(self.as_parallel_slice_mut(), T::lt);

    }

    /// Sorts the slice in parallel with a comparator function, but might not preserve the order of

    /// equal elements.

    ///

    /// This sort is unstable (i.e., may reorder equal elements), in-place

    /// (i.e., does not allocate), and *O*(*n* \* log(*n*)) worst-case.

    ///

    /// The comparator function must define a total ordering for the elements in the slice. If

    /// the ordering is not total, the order of the elements is unspecified. An order is a

    /// total order if it is (for all `a`, `b` and `c`):

    ///

    /// * total and antisymmetric: exactly one of `a < b`, `a == b` or `a > b` is true, and

    /// * transitive, `a < b` and `b < c` implies `a < c`. The same must hold for both `==` and `>`.

    ///

    /// For example, while [`f64`] doesn't implement [`Ord`] because `NaN != NaN`, we can use

    /// `partial_cmp` as our sort function when we know the slice doesn't contain a `NaN`.

    ///

    /// ```

    /// use rayon::prelude::*;

    ///

    /// let mut floats = [5f64, 4.0, 1.0, 3.0, 2.0];

    /// floats.par_sort_unstable_by(|a, b| a.partial_cmp(b).unwrap());

    /// assert_eq!(floats, [1.0, 2.0, 3.0, 4.0, 5.0]);

    /// ```

    ///

    /// # Current implementation

    ///

    /// The current algorithm is based on [pattern-defeating quicksort][pdqsort] by Orson Peters,

    /// which combines the fast average case of randomized quicksort with the fast worst case of

    /// heapsort, while achieving linear time on slices with certain patterns. It uses some

    /// randomization to avoid degenerate cases, but with a fixed seed to always provide

    /// deterministic behavior.

    ///

    /// It is typically faster than stable sorting, except in a few special cases, e.g., when the

    /// slice consists of several concatenated sorted sequences.

    ///

    /// All quicksorts work in two stages: partitioning into two halves followed by recursive

    /// calls. The partitioning phase is sequential, but the two recursive calls are performed in

    /// parallel.

    ///

    /// [pdqsort]: https://github.com/orlp/pdqsort

    ///

    /// # Examples

    ///

    /// ```

    /// use rayon::prelude::*;

    ///

    /// let mut v = [5, 4, 1, 3, 2];

    /// v.par_sort_unstable_by(|a, b| a.cmp(b));

    /// assert_eq!(v, [1, 2, 3, 4, 5]);

    ///

    /// // reverse sorting

    /// v.par_sort_unstable_by(|a, b| b.cmp(a));

    /// assert_eq!(v, [5, 4, 3, 2, 1]);

    /// ```

    fn par_sort_unstable_by<F>(&mut self, compare: F)

    where

        F: Fn(&T, &T) -> Ordering + Sync,

    {

        par_quicksort(self.as_parallel_slice_mut(), |a, b| {

            compare(a, b) == Ordering::Less

        });

    }

    /// Sorts the slice in parallel with a key extraction function, but might not preserve the order

    /// of equal elements.

    ///

    /// This sort is unstable (i.e., may reorder equal elements), in-place

    /// (i.e., does not allocate), and *O*(m \* *n* \* log(*n*)) worst-case,

    /// where the key function is *O*(*m*).

    ///

    /// # Current implementation

    ///

    /// The current algorithm is based on [pattern-defeating quicksort][pdqsort] by Orson Peters,

    /// which combines the fast average case of randomized quicksort with the fast worst case of

    /// heapsort, while achieving linear time on slices with certain patterns. It uses some

    /// randomization to avoid degenerate cases, but with a fixed seed to always provide

    /// deterministic behavior.

    ///

    /// Due to its key calling strategy, `par_sort_unstable_by_key` is likely to be slower than

    /// [`par_sort_by_cached_key`](#method.par_sort_by_cached_key) in cases where the key function

    /// is expensive.

    ///

    /// All quicksorts work in two stages: partitioning into two halves followed by recursive

    /// calls. The partitioning phase is sequential, but the two recursive calls are performed in

    /// parallel.

    ///

    /// [pdqsort]: https://github.com/orlp/pdqsort

    ///

    /// # Examples

    ///

    /// ```

    /// use rayon::prelude::*;

    ///

    /// let mut v = [-5i32, 4, 1, -3, 2];

    ///

    /// v.par_sort_unstable_by_key(|k| k.abs());

    /// assert_eq!(v, [1, 2, -3, 4, -5]);

    /// ```

    fn par_sort_unstable_by_key<K, F>(&mut self, f: F)

    where

        K: Ord,

        F: Fn(&T) -> K + Sync,

    {

        par_quicksort(self.as_parallel_slice_mut(), |a, b| f(a).lt(&f(b)));

    }

    /// Returns a parallel iterator over the slice producing non-overlapping mutable

    /// runs of elements using the predicate to separate them.

    ///

    /// The predicate is called on two elements following themselves,

    /// it means the predicate is called on `slice[0]` and `slice[1]`

    /// then on `slice[1]` and `slice[2]` and so on.

    ///

    /// # Examples

    ///

    /// ```

    /// use rayon::prelude::*;

    /// let mut xs = [1, 2, 2, 3, 3, 3];

    /// let chunks: Vec<_> = xs.par_chunk_by_mut(|&x, &y| x == y).collect();

    /// assert_eq!(chunks[0], &mut [1]);

    /// assert_eq!(chunks[1], &mut [2, 2]);

    /// assert_eq!(chunks[2], &mut [3, 3, 3]);

    /// ```

    fn par_chunk_by_mut<F>(&mut self, pred: F) -> ChunkByMut<'_, T, F>

    where

        F: Fn(&T, &T) -> bool + Send + Sync,

    {

        ChunkByMut::new(self.as_parallel_slice_mut(), pred)

    }

}

impl<T: Send> ParallelSliceMut<T> for [T] {

    #[inline]

    fn as_parallel_slice_mut(&mut self) -> &mut [T] {

        self

    }

}

impl<'data, T: Sync> IntoParallelIterator for &'data [T] {

    type Item = &'data T;

    type Iter = Iter<'data, T>;

    fn into_par_iter(self) -> Self::Iter {

        Iter { slice: self }

    }

}

impl<'data, T: Sync> IntoParallelIterator for &'data Box<[T]> {

    type Item = &'data T;

    type Iter = Iter<'data, T>;

    fn into_par_iter(self) -> Self::Iter {

        Iter { slice: self }

    }

}

impl<'data, T: Send> IntoParallelIterator for &'data mut [T] {

    type Item = &'data mut T;

    type Iter = IterMut<'data, T>;

    fn into_par_iter(self) -> Self::Iter {

        IterMut { slice: self }

    }

Unexecuted instantiation: <&mut [rav1e::tiling::plane_region::PlaneRegionMut<u16>] as rayon::iter::IntoParallelIterator>::into_par_iter

Unexecuted instantiation: <&mut [rav1e::tiling::plane_region::PlaneRegionMut<u8>] as rayon::iter::IntoParallelIterator>::into_par_iter

Unexecuted instantiation: <&mut [_] as rayon::iter::IntoParallelIterator>::into_par_iter

}

impl<'data, T: Send> IntoParallelIterator for &'data mut Box<[T]> {

    type Item = &'data mut T;

    type Iter = IterMut<'data, T>;

    fn into_par_iter(self) -> Self::Iter {

        IterMut { slice: self }

    }

}

/// Parallel iterator over immutable items in a slice

#[derive(Debug)]

pub struct Iter<'data, T> {

    slice: &'data [T],

}

impl<T> Clone for Iter<'_, T> {

    fn clone(&self) -> Self {

        Iter { ..*self }

    }

}

impl<'data, T: Sync> ParallelIterator for Iter<'data, T> {

    type Item = &'data T;

    fn drive_unindexed<C>(self, consumer: C) -> C::Result

    where

        C: UnindexedConsumer<Self::Item>,

    {

        bridge(self, consumer)

    }

    fn opt_len(&self) -> Option<usize> {

        Some(self.len())

    }

}

impl<T: Sync> IndexedParallelIterator for Iter<'_, T> {

    fn drive<C>(self, consumer: C) -> C::Result

    where

        C: Consumer<Self::Item>,

    {

        bridge(self, consumer)

    }

    fn len(&self) -> usize {

        self.slice.len()

    }

    fn with_producer<CB>(self, callback: CB) -> CB::Output

    where

        CB: ProducerCallback<Self::Item>,

    {

        callback.callback(IterProducer { slice: self.slice })

    }

}

struct IterProducer<'data, T: Sync> {

    slice: &'data [T],

}

impl<'data, T: 'data + Sync> Producer for IterProducer<'data, T> {

    type Item = &'data T;

    type IntoIter = ::std::slice::Iter<'data, T>;

    fn into_iter(self) -> Self::IntoIter {

        self.slice.iter()

    }

    fn split_at(self, index: usize) -> (Self, Self) {

        let (left, right) = self.slice.split_at(index);

        (IterProducer { slice: left }, IterProducer { slice: right })

    }

}

/// Parallel iterator over immutable overlapping windows of a slice

#[derive(Debug)]

pub struct Windows<'data, T> {

    window_size: usize,

    slice: &'data [T],

}

impl<T> Clone for Windows<'_, T> {

    fn clone(&self) -> Self {

        Windows { ..*self }

    }

}

impl<'data, T: Sync> ParallelIterator for Windows<'data, T> {

    type Item = &'data [T];

    fn drive_unindexed<C>(self, consumer: C) -> C::Result

    where

        C: UnindexedConsumer<Self::Item>,

    {

        bridge(self, consumer)

    }

    fn opt_len(&self) -> Option<usize> {

        Some(self.len())

    }

}

impl<T: Sync> IndexedParallelIterator for Windows<'_, T> {

    fn drive<C>(self, consumer: C) -> C::Result

    where

        C: Consumer<Self::Item>,

    {

        bridge(self, consumer)

    }

    fn len(&self) -> usize {

        assert!(self.window_size >= 1);

        self.slice.len().saturating_sub(self.window_size - 1)

    }

    fn with_producer<CB>(self, callback: CB) -> CB::Output

    where

        CB: ProducerCallback<Self::Item>,

    {

        callback.callback(WindowsProducer {

            window_size: self.window_size,

            slice: self.slice,

        })

    }

}

struct WindowsProducer<'data, T: Sync> {

    window_size: usize,

    slice: &'data [T],

}

impl<'data, T: 'data + Sync> Producer for WindowsProducer<'data, T> {

    type Item = &'data [T];

    type IntoIter = ::std::slice::Windows<'data, T>;

    fn into_iter(self) -> Self::IntoIter {

        self.slice.windows(self.window_size)

    }

    fn split_at(self, index: usize) -> (Self, Self) {

        let left_index = Ord::min(self.slice.len(), index + (self.window_size - 1));

        let left = &self.slice[..left_index];

        let right = &self.slice[index..];

        (

            WindowsProducer {

                window_size: self.window_size,

                slice: left,

            },

            WindowsProducer {

                window_size: self.window_size,

                slice: right,

            },

        )

    }

}

/// Parallel iterator over mutable items in a slice

#[derive(Debug)]

pub struct IterMut<'data, T> {

    slice: &'data mut [T],

}

impl<'data, T: Send> ParallelIterator for IterMut<'data, T> {

    type Item = &'data mut T;

    fn drive_unindexed<C>(self, consumer: C) -> C::Result

    where

        C: UnindexedConsumer<Self::Item>,

    {

        bridge(self, consumer)

    }

    fn opt_len(&self) -> Option<usize> {

        Some(self.len())

    }

}

impl<T: Send> IndexedParallelIterator for IterMut<'_, T> {

    fn drive<C>(self, consumer: C) -> C::Result

    where

        C: Consumer<Self::Item>,

    {

        bridge(self, consumer)

    }

    fn len(&self) -> usize {

        self.slice.len()

    }

Unexecuted instantiation: <rayon::slice::IterMut<rav1e::tiling::plane_region::PlaneRegionMut<u16>> as rayon::iter::IndexedParallelIterator>::len

Unexecuted instantiation: <rayon::slice::IterMut<rav1e::tiling::plane_region::PlaneRegionMut<u8>> as rayon::iter::IndexedParallelIterator>::len

Unexecuted instantiation: <rayon::slice::IterMut<_> as rayon::iter::IndexedParallelIterator>::len

    fn with_producer<CB>(self, callback: CB) -> CB::Output

    where

        CB: ProducerCallback<Self::Item>,

    {

        callback.callback(IterMutProducer { slice: self.slice })

    }

Unexecuted instantiation: <rayon::slice::IterMut<rav1e::tiling::plane_region::PlaneRegionMut<u16>> as rayon::iter::IndexedParallelIterator>::with_producer::<<rayon::iter::enumerate::Enumerate<_> as rayon::iter::IndexedParallelIterator>::with_producer::Callback<rayon::iter::plumbing::bridge::Callback<rayon::iter::for_each::ForEachConsumer<rav1e::deblock::deblock_filter_frame<u16>::{closure#0}>>>>

Unexecuted instantiation: <rayon::slice::IterMut<rav1e::tiling::plane_region::PlaneRegionMut<u8>> as rayon::iter::IndexedParallelIterator>::with_producer::<<rayon::iter::enumerate::Enumerate<_> as rayon::iter::IndexedParallelIterator>::with_producer::Callback<rayon::iter::plumbing::bridge::Callback<rayon::iter::for_each::ForEachConsumer<rav1e::deblock::deblock_filter_frame<u8>::{closure#0}>>>>

Unexecuted instantiation: <rayon::slice::IterMut<_> as rayon::iter::IndexedParallelIterator>::with_producer::<_>

}

struct IterMutProducer<'data, T: Send> {

    slice: &'data mut [T],

}

impl<'data, T: 'data + Send> Producer for IterMutProducer<'data, T> {

    type Item = &'data mut T;

    type IntoIter = ::std::slice::IterMut<'data, T>;

    fn into_iter(self) -> Self::IntoIter {

        self.slice.iter_mut()

    }

Unexecuted instantiation: <rayon::slice::IterMutProducer<rav1e::tiling::plane_region::PlaneRegionMut<u16>> as rayon::iter::plumbing::Producer>::into_iter

Unexecuted instantiation: <rayon::slice::IterMutProducer<rav1e::tiling::plane_region::PlaneRegionMut<u8>> as rayon::iter::plumbing::Producer>::into_iter

Unexecuted instantiation: <rayon::slice::IterMutProducer<_> as rayon::iter::plumbing::Producer>::into_iter

    fn split_at(self, index: usize) -> (Self, Self) {

        let (left, right) = self.slice.split_at_mut(index);

        (

            IterMutProducer { slice: left },

            IterMutProducer { slice: right },

        )

    }

Unexecuted instantiation: <rayon::slice::IterMutProducer<rav1e::tiling::plane_region::PlaneRegionMut<u16>> as rayon::iter::plumbing::Producer>::split_at

Unexecuted instantiation: <rayon::slice::IterMutProducer<rav1e::tiling::plane_region::PlaneRegionMut<u8>> as rayon::iter::plumbing::Producer>::split_at

Unexecuted instantiation: <rayon::slice::IterMutProducer<_> as rayon::iter::plumbing::Producer>::split_at

}

/// Parallel iterator over slices separated by a predicate

pub struct Split<'data, T, P> {

    slice: &'data [T],

    separator: P,

}

impl<T, P: Clone> Clone for Split<'_, T, P> {

    fn clone(&self) -> Self {

        Split {

            separator: self.separator.clone(),

            ..*self

        }

    }

}

impl<T: Debug, P> Debug for Split<'_, T, P> {

    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {

        f.debug_struct("Split").field("slice", &self.slice).finish()

    }

}

impl<'data, T, P> ParallelIterator for Split<'data, T, P>

where

    P: Fn(&T) -> bool + Sync + Send,

    T: Sync,

{

    type Item = &'data [T];

    fn drive_unindexed<C>(self, consumer: C) -> C::Result