/*
    * Licensed to the Apache Software Foundation (ASF) under one or more
    * contributor license agreements.  See the NOTICE file distributed with
    * this work for additional information regarding copyright ownership.
    * The ASF licenses this file to You under the Apache License, Version 2.0
    * (the "License"); you may not use this file except in compliance with
    * the License.  You may obtain a copy of the License at
    *
    *      http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   */
  
  package org.apache.commons.numbers.arrays;
  
  /**
   * Partition array data.
   *
   * <p>Note: Requires that the floating-point data contains no NaN values; sorting does not
   * respect the order of signed zeros imposed by {@link Double#compare(double, double)};
   * mixed signed zeros may be destroyed (the mixture updated during partitioning). The
   * caller is responsible for counting a mixture of signed zeros and restoring them if
   * required.
   *
   * @see Selection
   * @since 1.2
   */
  final class QuickSelect {
      // Implementation Notes
      //
      // Selection is performed using a quickselect variant to recursively divide the range
      // to select the target index, or indices. Partition sizes or recursion are monitored
      // will fall-backs on poor convergence of a linearselect (single index) or heapselect.
      //
      // Many implementations were tested, each with strengths and weaknesses on different
      // input data containing random elements, repeat elements, elements with repeat
      // patterns, and constant elements. The final implementation performs well across data
      // types for single and multiple indices with no obvious weakness.
      // See: o.a.c.numbers.examples.jmh.arrays for benchmarking implementations.
      //
      // Single indices are selected using a quickselect adaptive method based on Alexandrescu.
      // The algorithm is a quickselect around a pivot identified using a
      // sample-of-sample-of-samples created from the entire range data. This pivot will
      // have known lower and upper margins and ensures elimination of a minimum fraction of
      // data at each step. To increase speed the pivot can be identified using less of the data
      // but without margin guarantees (sampling mode). The algorithm monitors partition sizes
      // against the known margins. If the reduction in the partition size is not large enough
      // then the algorithm can disable sampling mode and ensure linear performance by removing
      // a set fraction of the data each iteration.
      //
      // Modifications from Alexandrescu are:
      // 1. Initialise sampling mode using the Floyd-Rivest (FR) SELECT algorithm.
      // 2. Adaption is adjusted to force use of the lower margin in the far-step method when
      //    sampling is disabled.
      // 3. Change the far-step method to a min-of-4 then median-of-3 into the 2nd 12th-tile.
      //    The original method uses a lower-median-of-4, min-of-3 into the 4th 12th-tile.
      // 4. Position the sample around the target k when in sampling mode for the non-far-step
      //    methods.
      //
      // The far step method is used when target k is within 1/12 of the end of the data A.
      // The differences in the far-step method are:
      // - The upper margin when not sampling is 8/24 vs. 9/24; the lower margin remains at 1/12.
      // - The position of the sample is closer to the expected location of k < |A|/12.
      // - Sampling mode uses a median-of-3 with adaptive k, matching the other step methods.
      //   Note the original min-of-3 sample is more likely to create a pivot too small if used
      //   with adaption leaving k in the larger partition and a wasted iteration.
      //
      // The Floyd-Rivest (FR) SELECT algorithm is preferred for sampling over using quickselect
      // adaptive sampling. It uses a smaller sample and has improved heuristics to place the sample
      // pivot. However the FR sample is a small range of the data and pivot selection can be poor
      // if the sample is not representative. This can be mitigated by creating a random sample
      // of the entire range for the pivot selection. This implementation does not use random
      // sampling for the FR mode. Performance is identical on random data (randomisation is a
      // negligible overhead) and faster on sorted data. Any data not suitable for the FR algorithm
      // are immediately switched to the quickselect adaptive algorithm with sampling. Performance
      // across a range of data shows this strategy is approximately mid-way in performance between
      // FR with random sampling, and quickselect adaptive in sampling mode. The benefit is that
      // sorted or partially partitioned data are not intentionally unordered as the method will
      // only move elements known to be incorrectly placed in the array.
      //
      // Multiple indices are selected using a dual-pivot partition method by
      // Yaroslavskiy to divide the interval containing the indices. Recursion is performed for
      // regions containing target indices. The method is thus a partial quicksort into regions of
      // interest. Excess recursion triggers use of a heapselect. When indices are effectively
      // a single index the method can switch to the single index selection to use the FR algorithm.
      //
      // Alternative schemes to partition multiple indices are to repeat call single index select
      // with cached pivots, or without cached pivots if processing indices in order as the previous
      // index brackets the range for the next search. Caching pivots is the most effective
      // alternative. It requires storing all pivots during select, and using the cache to look-up
      // the search bounds (sub-range) for each target index. This requires 2n searches for n indices.
      // All pivots must be stored to avoid destroying previously partitioned data on repeat entry
      // to the array. The current scheme inverts this by requiring at most n-1 divides of the
      // indices during recursion and has the advantage of tracking recursion depth during selection
      // for each sub-range. Division of indices is a small overhead for the common case where
     // the number of indices is far smaller than the size of the data.
     //
     // Dual-pivot partitioning adapted from Yaroslavskiy
     // http://codeblab.com/wp-content/uploads/2009/09/DualPivotQuicksort.pdf
     //
     // Modified to allow partial sorting (partitioning):
     // - Ignore insertion sort for tiny array (handled by calling code).
     // - Ignore recursive calls for a full sort (handled by calling code).
     // - Change to fast-forward over initial ascending / descending runs.
     // - Change to fast-forward great when v > v2 and either break the sorting
     //   loop, or move a[great] direct to the correct location.
     // - Change to use the 2nd and 4th of 5 elements for the pivots.
     // - Identify a large central region using ~5/8 of the length to trigger search for
     //   equal values.
     //
     // For some indices and data a full sort of the data will be faster; this is impossible to
     // predict on unknown data input and attempts to analyse the indices and data impact
     // performance for the majority of use cases where sorting is not a suitable choice.
     // Use of the sortselect finisher allows the current multiple indices method to degrade
     // to a (non-optimised) dual-pivot quicksort (see below).
     //
     // heapselect vs sortselect
     //
     // Quickselect can switch to an alternative when: the range is very small
     // (e.g. insertion sort); or the target index is close to the end (e.g. heapselect).
     // Small ranges and a target index close to the end are handled using a hybrid of insertion
     // sort and selection (sortselect). This is faster than heapselect for small distance from
     // the edge (m) for a single index and has the advantage of sorting all upstream values from
     // the target index (heapselect requires push-down of each successive value to sort). This
     // allows the dual-pivot quickselect on multiple indices that saturate the range to degrade
     // to a (non-optimised) dual-pivot quicksort. However sortselect is worst case Order(m * (r-l))
     // for range [l, r] so cannot be used when quickselect fails to converge as m may be very large.
     // Thus heapselect is used as the stopper algorithm when quickselect progress is slow on
     // multiple indices. If heapselect is used for small range handling the performance on
     // saturated indices is significantly slower. Hence the presence of two final selection
     // methods for different purposes.
 
     /** Sampling mode using Floyd-Rivest sampling. */
     static final int MODE_FR_SAMPLING = -1;
     /** Sampling mode. */
     static final int MODE_SAMPLING = 0;
     /** No sampling but use adaption of the target k. */
     static final int MODE_ADAPTION = 1;
     /** No sampling and no adaption of target k (strict margins). */
     static final int MODE_STRICT = 2;
 
     /** Minimum size for sortselect.
      * Below this perform a sort rather than selection. This is used to avoid
      * sort select on tiny data. */
     private static final int MIN_SORTSELECT_SIZE = 4;
     /** Single-pivot sortselect size for quickselect adaptive. Note that quickselect adaptive
      * recursively calls quickselect so very small lengths are included with an initial medium
      * length. Using lengths of 1023-5 and 2043-53 indicate optimum performance around 20-30.
      * Note: The expand partition function assumes a sample of at least length 2 as each end
      * of the sample is used as a sentinel; this imposes a minimum length of 24 on the range
      * to ensure it contains a 12-th tile of length 2. Thus the absolute minimum for the
      * distance from the edge is 12. */
     private static final int LINEAR_SORTSELECT_SIZE = 24;
     /** Dual-pivot sortselect size for the distance of a single k from the edge of the
      * range length n. Benchmarking in range [81+81, 243+243] suggests a value of ~20 (or
      * higher on some hardware). Ranges are chosen based on third interval spacing between
      * powers of 3.
      *
      * <p>Sortselect is faster at this small size than heapselect. A second advantage is
      * that all indices closer to the edge than the target index are also sorted. This
      * allows selection of multiple close indices to be performed with effectively the
      * same speed. High density indices will result in recursion to very short fragments
      * which also trigger use of sort select. The threshold for sorting short lengths is
      * configured in {@link #dualPivotSortSelectSize(int, int)}. */
     private static final int DP_SORTSELECT_SIZE = 20;
     /** Threshold to use Floyd-Rivest sub-sampling. This partitions a sample of the data to
      * identify a pivot so that the target element is in the smaller set after partitioning.
      * The original FR paper used 600 otherwise reverted to the target index as the pivot.
      * This implementation reverts to quickselect adaptive which increases robustness
      * at small size on a variety of data and allows raising the original FR threshold. */
     private static final int FR_SAMPLING_SIZE = 1200;
 
     /** Increment used for the recursion counter. The counter will overflow to negative when
      * recursion has exceeded the maximum level. The counter is maintained in the upper bits
      * of the dual-pivot control flags. */
     private static final int RECURSION_INCREMENT = 1 << 20;
     /** Mask to extract the sort select size from the dual-pivot control flags. Currently
      * the bits below those used for the recursion counter are only used for the sort select size
      * so this can use a mask with all bits below the increment. */
     private static final int SORTSELECT_MASK = RECURSION_INCREMENT - 1;
 
     /** Threshold to use repeated step left: 7 / 16. */
     private static final double STEP_LEFT = 0.4375;
     /** Threshold to use repeated step right: 9 / 16. */
     private static final double STEP_RIGHT = 0.5625;
     /** Threshold to use repeated step far-left: 1 / 12. */
     private static final double STEP_FAR_LEFT = 0.08333333333333333;
     /** Threshold to use repeated step far-right: 11 / 12. */
     private static final double STEP_FAR_RIGHT = 0.9166666666666666;
 
     /** No instances. */
     private QuickSelect() {}
 
     /**
      * Partition the elements between {@code ka} and {@code kb} using a heap select
      * algorithm. It is assumed {@code left <= ka <= kb <= right}.
      *
      * @param a Data array to use to find out the K<sup>th</sup> value.
      * @param left Lower bound (inclusive).
      * @param right Upper bound (inclusive).
      * @param ka Lower index to select.
      * @param kb Upper index to select.
      */
     static void heapSelect(double[] a, int left, int right, int ka, int kb) {
         if (right <= left) {
             return;
         }
         // Use the smallest heap
         if (kb - left < right - ka) {
             heapSelectLeft(a, left, right, ka, kb);
         } else {
             heapSelectRight(a, left, right, ka, kb);
         }
     }
 
     /**
      * Partition the elements between {@code ka} and {@code kb} using a heap select
      * algorithm. It is assumed {@code left <= ka <= kb <= right}.
      *
      * <p>For best performance this should be called with {@code k} in the lower
      * half of the range.
      *
      * @param a Data array to use to find out the K<sup>th</sup> value.
      * @param left Lower bound (inclusive).
      * @param right Upper bound (inclusive).
      * @param ka Lower index to select.
      * @param kb Upper index to select.
      */
     static void heapSelectLeft(double[] a, int left, int right, int ka, int kb) {
         // Create a max heap in-place in [left, k], rooted at a[left] = max
         // |l|-max-heap-|k|--------------|
         // Build the heap using Floyd's heap-construction algorithm for heap size n.
         // Start at parent of the last element in the heap (k),
         // i.e. start = parent(n-1) : parent(c) = floor((c - 1) / 2) : c = k - left
         int end = kb + 1;
         for (int p = left + ((kb - left - 1) >> 1); p >= left; p--) {
             maxHeapSiftDown(a, a[p], p, left, end);
         }
         // Scan the remaining data and insert
         // Mitigate worst case performance on descending data by backward sweep
         double max = a[left];
         for (int i = right + 1; --i > kb;) {
             final double v = a[i];
             if (v < max) {
                 a[i] = max;
                 maxHeapSiftDown(a, v, left, left, end);
                 max = a[left];
             }
         }
         // Partition [ka, kb]
         // |l|-max-heap-|k|--------------|
         //  |  <-swap->  |   then sift down reduced size heap
         // Avoid sifting heap of size 1
         final int last = Math.max(left, ka - 1);
         while (--end > last) {
             maxHeapSiftDown(a, a[end], left, left, end);
             a[end] = max;
             max = a[left];
         }
     }
 
     /**
      * Sift the element down the max heap.
      *
      * <p>Assumes {@code root <= p < end}, i.e. the max heap is above root.
      *
      * @param a Heap data.
      * @param v Value to sift.
      * @param p Start position.
      * @param root Root of the heap.
      * @param end End of the heap (exclusive).
      */
     private static void maxHeapSiftDown(double[] a, double v, int p, int root, int end) {
         // child2 = root + 2 * (parent - root) + 2
         //        = 2 * parent - root + 2
         while (true) {
             // Right child
             int c = (p << 1) - root + 2;
             if (c > end) {
                 // No left child
                 break;
             }
             // Use the left child if right doesn't exist, or it is greater
             if (c == end || a[c] < a[c - 1]) {
                 --c;
             }
             if (v >= a[c]) {
                 // Parent greater than largest child - done
                 break;
             }
             // Swap and descend
             a[p] = a[c];
             p = c;
         }
         a[p] = v;
     }
 
     /**
      * Partition the elements between {@code ka} and {@code kb} using a heap select
      * algorithm. It is assumed {@code left <= ka <= kb <= right}.
      *
      * <p>For best performance this should be called with {@code k} in the upper
      * half of the range.
      *
      * @param a Data array to use to find out the K<sup>th</sup> value.
      * @param left Lower bound (inclusive).
      * @param right Upper bound (inclusive).
      * @param ka Lower index to select.
      * @param kb Upper index to select.
      */
     static void heapSelectRight(double[] a, int left, int right, int ka, int kb) {
         // Create a min heap in-place in [k, right], rooted at a[right] = min
         // |--------------|k|-min-heap-|r|
         // Build the heap using Floyd's heap-construction algorithm for heap size n.
         // Start at parent of the last element in the heap (k),
         // i.e. start = parent(n-1) : parent(c) = floor((c - 1) / 2) : c = right - k
         int end = ka - 1;
         for (int p = right - ((right - ka - 1) >> 1); p <= right; p++) {
             minHeapSiftDown(a, a[p], p, right, end);
         }
         // Scan the remaining data and insert
         // Mitigate worst case performance on descending data by backward sweep
         double min = a[right];
         for (int i = left - 1; ++i < ka;) {
             final double v = a[i];
             if (v > min) {
                 a[i] = min;
                 minHeapSiftDown(a, v, right, right, end);
                 min = a[right];
             }
         }
         // Partition [ka, kb]
         // |--------------|k|-min-heap-|r|
         //                 |  <-swap->  |   then sift down reduced size heap
         // Avoid sifting heap of size 1
         final int last = Math.min(right, kb + 1);
         while (++end < last) {
             minHeapSiftDown(a, a[end], right, right, end);
             a[end] = min;
             min = a[right];
         }
     }
 
     /**
      * Sift the element down the min heap.
      *
      * <p>Assumes {@code root >= p > end}, i.e. the max heap is below root.
      *
      * @param a Heap data.
      * @param v Value to sift.
      * @param p Start position.
      * @param root Root of the heap.
      * @param end End of the heap (exclusive).
      */
     private static void minHeapSiftDown(double[] a, double v, int p, int root, int end) {
         // child2 = root - 2 * (root - parent) - 2
         //        = 2 * parent - root - 2
         while (true) {
             // Right child
             int c = (p << 1) - root - 2;
             if (c < end) {
                 // No left child
                 break;
             }
             // Use the left child if right doesn't exist, or it is less
             if (c == end || a[c] > a[c + 1]) {
                 ++c;
             }
             if (v <= a[c]) {
                 // Parent less than smallest child - done
                 break;
             }
             // Swap and descend
             a[p] = a[c];
             p = c;
         }
         a[p] = v;
     }
 
     /**
      * Partition the elements between {@code ka} and {@code kb} using a sort select
      * algorithm. It is assumed {@code left <= ka <= kb <= right}.
      *
      * @param a Data array to use to find out the K<sup>th</sup> value.
      * @param left Lower bound (inclusive).
      * @param right Upper bound (inclusive).
      * @param ka Lower index to select.
      * @param kb Upper index to select.
      */
     static void sortSelect(double[] a, int left, int right, int ka, int kb) {
         // Combine the test for right <= left with
         // avoiding the overhead of sort select on tiny data.
         if (right - left <= MIN_SORTSELECT_SIZE) {
             Sorting.sort(a, left, right);
             return;
         }
         // Sort the smallest side
         if (kb - left < right - ka) {
             sortSelectLeft(a, left, right, kb);
         } else {
             sortSelectRight(a, left, right, ka);
         }
     }
 
     /**
      * Partition the minimum {@code n} elements below {@code k} where
      * {@code n = k - left + 1}. Uses an insertion sort algorithm.
      *
      * <p>Works with any {@code k} in the range {@code left <= k <= right}
      * and performs a full sort of the range below {@code k}.
      *
      * <p>For best performance this should be called with
      * {@code k - left < right - k}, i.e.
      * to partition a value in the lower half of the range.
      *
      * @param a Data array to use to find out the K<sup>th</sup> value.
      * @param left Lower bound (inclusive).
      * @param right Upper bound (inclusive).
      * @param k Index to select.
      */
     static void sortSelectLeft(double[] a, int left, int right, int k) {
         // Sort
         for (int i = left; ++i <= k;) {
             final double v = a[i];
             // Move preceding higher elements above (if required)
             if (v < a[i - 1]) {
                 int j = i;
                 while (--j >= left && v < a[j]) {
                     a[j + 1] = a[j];
                 }
                 a[j + 1] = v;
             }
         }
         // Scan the remaining data and insert
         // Mitigate worst case performance on descending data by backward sweep
         double m = a[k];
         for (int i = right + 1; --i > k;) {
             final double v = a[i];
             if (v < m) {
                 a[i] = m;
                 int j = k;
                 while (--j >= left && v < a[j]) {
                     a[j + 1] = a[j];
                 }
                 a[j + 1] = v;
                 m = a[k];
             }
         }
     }
 
     /**
      * Partition the maximum {@code n} elements above {@code k} where
      * {@code n = right - k + 1}. Uses an insertion sort algorithm.
      *
      * <p>Works with any {@code k} in the range {@code left <= k <= right}
      * and can be used to perform a full sort of the range above {@code k}.
      *
      * <p>For best performance this should be called with
      * {@code k - left > right - k}, i.e.
      * to partition a value in the upper half of the range.
      *
      * @param a Data array to use to find out the K<sup>th</sup> value.
      * @param left Lower bound (inclusive).
      * @param right Upper bound (inclusive).
      * @param k Index to select.
      */
     static void sortSelectRight(double[] a, int left, int right, int k) {
         // Sort
         for (int i = right; --i >= k;) {
             final double v = a[i];
             // Move succeeding lower elements below (if required)
             if (v > a[i + 1]) {
                 int j = i;
                 while (++j <= right && v > a[j]) {
                     a[j - 1] = a[j];
                 }
                 a[j - 1] = v;
             }
         }
         // Scan the remaining data and insert
         // Mitigate worst case performance on descending data by backward sweep
         double m = a[k];
         for (int i = left - 1; ++i < k;) {
             final double v = a[i];
             if (v > m) {
                 a[i] = m;
                 int j = k;
                 while (++j <= right && v > a[j]) {
                     a[j - 1] = a[j];
                 }
                 a[j - 1] = v;
                 m = a[k];
             }
         }
     }
 
     /**
      * Partition the array such that index {@code k} corresponds to its correctly
      * sorted value in the equivalent fully sorted array.
      *
      * <p>Assumes {@code k} is a valid index into [left, right].
      *
      * @param a Values.
      * @param left Lower bound of data (inclusive).
      * @param right Upper bound of data (inclusive).
      * @param k Index.
      */
     static void select(double[] a, int left, int right, int k) {
         quickSelectAdaptive(a, left, right, k, k, new int[1], MODE_FR_SAMPLING);
     }
 
     /**
      * Partition the array such that indices {@code k} correspond to their correctly
      * sorted value in the equivalent fully sorted array.
      *
      * <p>The count of the number of used indices is returned. If the keys are sorted in-place,
      * the count is returned as a negative.
      *
      * @param a Values.
      * @param left Lower bound of data (inclusive).
      * @param right Upper bound of data (inclusive).
      * @param k Indices (may be destructively modified).
      * @param n Count of indices.
      * @return the count of used indices
      * @throws IndexOutOfBoundsException if any index {@code k} is not within the
      * sub-range {@code [left, right]}
      */
     static int select(double[] a, int left, int right, int[] k, int n) {
         if (n < 1) {
             return 0;
         }
         if (n == 1) {
             quickSelectAdaptive(a, left, right, k[0], k[0], new int[1], MODE_FR_SAMPLING);
             return -1;
         }
 
         // Interval creation validates the indices are in [left, right]
         final UpdatingInterval keys = IndexSupport.createUpdatingInterval(left, right, k, n);
 
         // Save number of used indices
         final int count = IndexSupport.countIndices(keys, n);
 
         // Note: If the keys are not separated then they are effectively a single key.
         // Any split of keys separated by the sort select size
         // will be finished on the next iteration.
         final int k1 = keys.left();
         final int kn = keys.right();
         if (kn - k1 < DP_SORTSELECT_SIZE) {
             quickSelectAdaptive(a, left, right, k1, kn, new int[1], MODE_FR_SAMPLING);
         } else {
             // Dual-pivot mode with small range sort length configured using index density
             dualPivotQuickSelect(a, left, right, keys, dualPivotFlags(left, right, k1, kn));
         }
         return count;
     }
 
     /**
      * Partition the array such that indices {@code k} correspond to their
      * correctly sorted value in the equivalent fully sorted array.
      *
      * <p>For all indices {@code [ka, kb]} and any index {@code i}:
      *
      * <pre>{@code
      * data[i < ka] <= data[ka] <= data[kb] <= data[kb < i]
      * }</pre>
      *
      * <p>This function accepts indices {@code [ka, kb]} that define the
      * range of indices to partition. It is expected that the range is small.
      *
      * <p>The {@code flags} are used to control the sampling mode and adaption of
      * the index within the sample.
      *
      * <p>Returns the bounds containing {@code [ka, kb]}. These may be lower/higher
      * than the keys if equal values are present in the data.
      *
      * @param a Values.
      * @param left Lower bound of data (inclusive, assumed to be strictly positive).
      * @param right Upper bound of data (inclusive, assumed to be strictly positive).
      * @param ka First key of interest.
      * @param kb Last key of interest.
      * @param bounds Upper bound of the range containing {@code [ka, kb]} (inclusive).
      * @param flags Adaption flags.
      * @return Lower bound of the range containing {@code [ka, kb]} (inclusive).
      */
     static int quickSelectAdaptive(double[] a, int left, int right, int ka, int kb,
             int[] bounds, int flags) {
         int l = left;
         int r = right;
         int m = flags;
         while (true) {
             // Select when ka and kb are close to the same end
             // |l|-----|ka|kkkkkkkk|kb|------|r|
             if (Math.min(kb - l, r - ka) < LINEAR_SORTSELECT_SIZE) {
                 sortSelect(a, l, r, ka, kb);
                 bounds[0] = kb;
                 return ka;
             }
 
             // Only target ka; kb is assumed to be close
             int p0;
             final int n = r - l;
             // f in [0, 1]
             final double f = (double) (ka - l) / n;
             // Record the larger margin (start at 1/4) to create the estimated size.
             // step        L     R
             // far left    1/12  1/3   (use 1/4 + 1/32 + 1/64 ~ 0.328)
             // left        1/6   1/4
             // middle      2/9   2/9   (use 1/4 - 1/32 ~ 0.219)
             int margin = n >> 2;
             if (m < MODE_SAMPLING && r - l > FR_SAMPLING_SIZE) {
                 // Floyd-Rivest sample step uses the same margins
                 p0 = sampleStep(a, l, r, ka, bounds);
                 if (f <= STEP_FAR_LEFT || f >= STEP_FAR_RIGHT) {
                     margin += (n >> 5) + (n >> 6);
                 } else if (f > STEP_LEFT && f < STEP_RIGHT) {
                     margin -= n >> 5;
                 }
             } else if (f <= STEP_LEFT) {
                 if (f <= STEP_FAR_LEFT) {
                     margin += (n >> 5) + (n >> 6);
                     p0 = repeatedStepFarLeft(a, l, r, ka, bounds, m);
                 } else {
                     p0 = repeatedStepLeft(a, l, r, ka, bounds, m);
                 }
             } else if (f >= STEP_RIGHT) {
                 if (f >= STEP_FAR_RIGHT) {
                     margin += (n >> 5) + (n >> 6);
                     p0 = repeatedStepFarRight(a, l, r, ka, bounds, m);
                 } else {
                     p0 = repeatedStepRight(a, l, r, ka, bounds, m);
                 }
             } else {
                 margin -= n >> 5;
                 p0 = repeatedStep(a, l, r, ka, bounds, m);
             }
 
             // Note: Here we expect [ka, kb] to be small and splitting is unlikely.
             //                   p0 p1
             // |l|--|ka|kkkk|kb|--|P|-------------------|r|
             // |l|----------------|P|--|ka|kkk|kb|------|r|
             // |l|-----------|ka|k|P|k|kb|--------------|r|
             final int p1 = bounds[0];
             if (kb < p0) {
                 // Entirely on left side
                 r = p0 - 1;
             } else if (ka > p1) {
                 // Entirely on right side
                 l = p1 + 1;
             } else {
                 // Pivot splits [ka, kb]. Expect ends to be close to the pivot and finish.
                 // Here we set the bounds for use after median-of-medians pivot selection.
                 // In the event there are many equal values this allows collecting those
                 // known to be equal together when moving around the medians sample.
                 if (kb > p1) {
                     sortSelectLeft(a, p1 + 1, r, kb);
                     bounds[0] = kb;
                 }
                 if (ka < p0) {
                     sortSelectRight(a, l, p0 - 1, ka);
                     p0 = ka;
                 }
                 return p0;
             }
             // Update mode based on target partition size
             if (r - l > n - margin) {
                 m++;
             }
         }
     }
 
     /**
      * Partition an array slice around a pivot. Partitioning exchanges array elements such
      * that all elements smaller than pivot are before it and all elements larger than
      * pivot are after it.
      *
      * <p>Partitions a Floyd-Rivest sample around a pivot offset so that the input {@code k} will
      * fall in the smaller partition when the entire range is partitioned.
      *
      * <p>Assumes the range {@code r - l} is large.
      *
      * @param a Data array.
      * @param l Lower bound (inclusive).
      * @param r Upper bound (inclusive).
      * @param k Target index.
      * @param upper Upper bound (inclusive) of the pivot range.
      * @return Lower bound (inclusive) of the pivot range.
      */
     private static int sampleStep(double[] a, int l, int r, int k, int[] upper) {
         // Floyd-Rivest: use SELECT recursively on a sample of size S to get an estimate
         // for the (k-l+1)-th smallest element into a[k], biased slightly so that the
         // (k-l+1)-th element is expected to lie in the smaller set after partitioning.
         final int n = r - l + 1;
         final int ith = k - l + 1;
         final double z = Math.log(n);
         // sample size = 0.5 * n^(2/3)
         final double s = 0.5 * Math.exp(0.6666666666666666 * z);
         final double sd = 0.5 * Math.sqrt(z * s * (n - s) / n) * Integer.signum(ith - (n >> 1));
         final int ll = Math.max(l, (int) (k - ith * s / n + sd));
         final int rr = Math.min(r, (int) (k + (n - ith) * s / n + sd));
         // Sample recursion restarts from [ll, rr]
         final int p = quickSelectAdaptive(a, ll, rr, k, k, upper, MODE_FR_SAMPLING);
         return expandPartition(a, l, r, ll, rr, p, upper[0], upper);
     }
 
     /**
      * Partition an array slice around a pivot. Partitioning exchanges array elements such
      * that all elements smaller than pivot are before it and all elements larger than
      * pivot are after it.
      *
      * <p>Assumes the range {@code r - l >= 8}; the caller is responsible for selection on a smaller
      * range. If using a 12th-tile for sampling then assumes {@code r - l >= 11}.
      *
      * <p>Uses the Chen and Dumitrescu repeated step median-of-medians-of-medians algorithm
      * with the median of 3 then median of 3; the final sample is placed in the
      * 5th 9th-tile; the pivot chosen from the sample is adaptive using the input {@code k}.
      *
      * <p>Given a pivot in the middle of the sample this has margins of 2/9 and 2/9.
      *
      * @param a Data array.
      * @param l Lower bound (inclusive).
      * @param r Upper bound (inclusive).
      * @param k Target index.
      * @param upper Upper bound (inclusive) of the pivot range.
      * @param flags Control flags.
      * @return Lower bound (inclusive) of the pivot range.
      */
     private static int repeatedStep(double[] a, int l, int r, int k, int[] upper, int flags) {
         // Adapted from Alexandrescu (2016), algorithm 8.
         final int fp;
         final int s;
         int p;
         if (flags <= MODE_SAMPLING) {
             // Median into a 12th-tile
             fp = (r - l + 1) / 12;
             // Position the sample around the target k
             s = k - mapDistance(k - l, l, r, fp);
             p = k;
         } else {
             // i in tertile [3f':6f')
             fp = (r - l + 1) / 9;
             final int f3 = 3 * fp;
             final int end = l + (f3 << 1);
             for (int i = l + f3; i < end; i++) {
                 Sorting.sort3(a, i - f3, i, i + f3);
             }
             // 5th 9th-tile: [4f':5f')
             s = l + (fp << 2);
             // No adaption uses the middle to enforce strict margins
             p = s + (flags == MODE_ADAPTION ? mapDistance(k - l, l, r, fp) : (fp >>> 1));
         }
         final int e = s + fp - 1;
         for (int i = s; i <= e; i++) {
             Sorting.sort3(a, i - fp, i, i + fp);
         }
         p = quickSelectAdaptive(a, s, e, p, p, upper, MODE_FR_SAMPLING);
         return expandPartition(a, l, r, s, e, p, upper[0], upper);
     }
 
     /**
      * Partition an array slice around a pivot. Partitioning exchanges array elements such
      * that all elements smaller than pivot are before it and all elements larger than
      * pivot are after it.
      *
      * <p>Assumes the range {@code r - l >= 11}; the caller is responsible for selection on a smaller
      * range.
      *
      * <p>Uses the Chen and Dumitrescu repeated step median-of-medians-of-medians algorithm
      * with the lower median of 4 then either median of 3 with the final sample placed in the
      * 5th 12th-tile, or min of 3 with the final sample in the 4th 12th-tile;
      * the pivot chosen from the sample is adaptive using the input {@code k}.
      *
      * <p>Given a pivot in the middle of the sample this has margins of 1/6 and 1/4.
      *
      * @param a Data array.
      * @param l Lower bound (inclusive).
      * @param r Upper bound (inclusive).
      * @param k Target index.
      * @param upper Upper bound (inclusive) of the pivot range.
      * @param flags Control flags.
      * @return Lower bound (inclusive) of the pivot range.
      */
     private static int repeatedStepLeft(double[] a, int l, int r, int k, int[] upper, int flags) {
         // Adapted from Alexandrescu (2016), algorithm 9.
         final int fp;
         final int s;
         int p;
         if (flags <= MODE_SAMPLING) {
             // Median into a 12th-tile
             fp = (r - l + 1) / 12;
             // Position the sample around the target k
             // Avoid bounds error due to rounding as (k-l)/(r-l) -> 1/12
             s = Math.max(k - mapDistance(k - l, l, r, fp), l + fp);
             p = k;
         } else {
             // i in 2nd quartile
             final int f = (r - l + 1) >> 2;
             final int f2 = f + f;
             final int end = l + f2;
             for (int i = l + f; i < end; i++) {
                 Sorting.lowerMedian4(a, i - f, i, i + f, i + f2);
             }
             // i in 5th 12th-tile
             fp = f / 3;
             s = l + f + fp;
             // No adaption uses the middle to enforce strict margins
             p = s + (flags == MODE_ADAPTION ? mapDistance(k - l, l, r, fp) : (fp >>> 1));
         }
         final int e = s + fp - 1;
         for (int i = s; i <= e; i++) {
             Sorting.sort3(a, i - fp, i, i + fp);
         }
         p = quickSelectAdaptive(a, s, e, p, p, upper, MODE_FR_SAMPLING);
         return expandPartition(a, l, r, s, e, p, upper[0], upper);
     }
 
     /**
      * Partition an array slice around a pivot. Partitioning exchanges array elements such
      * that all elements smaller than pivot are before it and all elements larger than
      * pivot are after it.
      *
      * <p>Assumes the range {@code r - l >= 11}; the caller is responsible for selection on a smaller
      * range.
      *
      * <p>Uses the Chen and Dumitrescu repeated step median-of-medians-of-medians algorithm
      * with the upper median of 4 then either median of 3 with the final sample placed in the
      * 8th 12th-tile, or max of 3 with the final sample in the 9th 12th-tile;
      * the pivot chosen from the sample is adaptive using the input {@code k}.
      *
      * <p>Given a pivot in the middle of the sample this has margins of 1/4 and 1/6.
      *
      * @param a Data array.
      * @param l Lower bound (inclusive).
      * @param r Upper bound (inclusive).
      * @param k Target index.
      * @param upper Upper bound (inclusive) of the pivot range.
      * @param flags Control flags.
      * @return Lower bound (inclusive) of the pivot range.
      */
     private static int repeatedStepRight(double[] a, int l, int r, int k, int[] upper, int flags) {
         // Mirror image repeatedStepLeft using upper median into 3rd quartile
         final int fp;
         final int e;
         int p;
         if (flags <= MODE_SAMPLING) {
             // Median into a 12th-tile
             fp = (r - l + 1) / 12;
             // Position the sample around the target k
             // Avoid bounds error due to rounding as (r-k)/(r-l) -> 11/12
             e = Math.min(k + mapDistance(r - k, l, r, fp), r - fp);
             p = k;
         } else {
             // i in 3rd quartile
             final int f = (r - l + 1) >> 2;
             final int f2 = f + f;
             final int end = r - f2;
             for (int i = r - f; i > end; i--) {
                 Sorting.upperMedian4(a, i - f2, i - f, i, i + f);
             }
             // i in 8th 12th-tile
             fp = f / 3;
             e = r - f - fp;
             // No adaption uses the middle to enforce strict margins
             p = e - (flags == MODE_ADAPTION ? mapDistance(r - k, l, r, fp) : (fp >>> 1));
         }
         final int s = e - fp + 1;
         for (int i = s; i <= e; i++) {
             Sorting.sort3(a, i - fp, i, i + fp);
         }
         p = quickSelectAdaptive(a, s, e, p, p, upper, MODE_FR_SAMPLING);
         return expandPartition(a, l, r, s, e, p, upper[0], upper);
     }
 
     /**
      * Partition an array slice around a pivot. Partitioning exchanges array elements such
      * that all elements smaller than pivot are before it and all elements larger than
      * pivot are after it.
      *
      * <p>Assumes the range {@code r - l >= 11}; the caller is responsible for selection on a smaller
      * range.
      *
      * <p>Uses the Chen and Dumitrescu repeated step median-of-medians-of-medians algorithm
      * with the minimum of 4 then median of 3; the final sample is placed in the
      * 2nd 12th-tile; the pivot chosen from the sample is adaptive using the input {@code k}.
      *
      * <p>Given a pivot in the middle of the sample this has margins of 1/12 and 1/3.
      *
      * @param a Data array.
      * @param l Lower bound (inclusive).
      * @param r Upper bound (inclusive).
      * @param k Target index.
      * @param upper Upper bound (inclusive) of the pivot range.
      * @param flags Control flags.
      * @return Lower bound (inclusive) of the pivot range.
      */
     private static int repeatedStepFarLeft(double[] a, int l, int r, int k, int[] upper, int flags) {
         // Far step has been changed from the Alexandrescu (2016) step of lower-median-of-4, min-of-3
         // into the 4th 12th-tile to a min-of-4, median-of-3 into the 2nd 12th-tile.
         // The differences are:
         // - The upper margin when not sampling is 8/24 vs. 9/24; the lower margin remains at 1/12.
         // - The position of the sample is closer to the expected location of k < |A| / 12.
         // - Sampling mode uses a median-of-3 with adaptive k, matching the other step methods.
         //   A min-of-3 sample can create a pivot too small if used with adaption of k leaving
         //   k in the larger parition and a wasted iteration.
         // - Adaption is adjusted to force use of the lower margin when not sampling.
         final int fp;
         final int s;
         int p;
         if (flags <= MODE_SAMPLING) {
             // 2nd 12th-tile
             fp = (r - l + 1) / 12;
             s = l + fp;
             // Use adaption
             p = s + mapDistance(k - l, l, r, fp);
         } else {
             // i in 2nd quartile; min into i-f (1st quartile)
             final int f = (r - l + 1) >> 2;
             final int f2 = f + f;
             final int end = l + f2;
             for (int i = l + f; i < end; i++) {
                 if (a[i + f] < a[i - f]) {
                     final double u = a[i + f];
                     a[i + f] = a[i - f];
                     a[i - f] = u;
                 }
                 if (a[i + f2] < a[i]) {
                     final double v = a[i + f2];
                     a[i + f2] = a[i];
                     a[i] = v;
                 }
                 if (a[i] < a[i - f]) {
                     final double u = a[i];
                     a[i] = a[i - f];
                     a[i - f] = u;
                 }
             }
             // 2nd 12th-tile
             fp = f / 3;
             s = l + fp;
             // Lower margin has 2(d+1) elements; d == (position in sample) - s
             // Force k into the lower margin
             p = s + ((k - l) >>> 1);
         }
         final int e = s + fp - 1;
         for (int i = s; i <= e; i++) {
             Sorting.sort3(a, i - fp, i, i + fp);
         }
         p = quickSelectAdaptive(a, s, e, p, p, upper, MODE_FR_SAMPLING);
         return expandPartition(a, l, r, s, e, p, upper[0], upper);
     }
 
     /**
      * Partition an array slice around a pivot. Partitioning exchanges array elements such
      * that all elements smaller than pivot are before it and all elements larger than
      * pivot are after it.
      *
      * <p>Assumes the range {@code r - l >= 11}; the caller is responsible for selection on a smaller
      * range.
      *
      * <p>Uses the Chen and Dumitrescu repeated step median-of-medians-of-medians algorithm
      * with the maximum of 4 then median of 3; the final sample is placed in the
      * 11th 12th-tile; the pivot chosen from the sample is adaptive using the input {@code k}.
      *
      * <p>Given a pivot in the middle of the sample this has margins of 1/3 and 1/12.
      *
      * @param a Data array.
      * @param l Lower bound (inclusive).
      * @param r Upper bound (inclusive).
      * @param k Target index.
      * @param upper Upper bound (inclusive) of the pivot range.
      * @param flags Control flags.
      * @return Lower bound (inclusive) of the pivot range.
      */
     private static int repeatedStepFarRight(double[] a, int l, int r, int k, int[] upper, int flags) {
         // Mirror image repeatedStepFarLeft
         final int fp;
         final int e;
         int p;
         if (flags <= MODE_SAMPLING) {
             // 11th 12th-tile
             fp = (r - l + 1) / 12;
             e = r - fp;
             // Use adaption
             p = e - mapDistance(r - k, l, r, fp);
         } else {
             // i in 3rd quartile; max into i+f (4th quartile)
             final int f = (r - l + 1) >> 2;
             final int f2 = f + f;
             final int end = r - f2;
             for (int i = r - f; i > end; i--) {
                 if (a[i - f] > a[i + f]) {
                     final double u = a[i - f];
                     a[i - f] = a[i + f];
                     a[i + f] = u;
                 }
                 if (a[i - f2] > a[i]) {
                     final double v = a[i - f2];
                     a[i - f2] = a[i];
                     a[i] = v;
                 }
                 if (a[i] > a[i + f]) {
                     final double u = a[i];
                     a[i] = a[i + f];
                     a[i + f] = u;
                 }
             }
             // 11th 12th-tile
             fp = f / 3;
             e = r - fp;
             // Upper margin has 2(d+1) elements; d == e - (position in sample)
             // Force k into the upper margin
             p = e - ((r - k) >>> 1);
         }
         final int s = e - fp + 1;
         for (int i = s; i <= e; i++) {
             Sorting.sort3(a, i - fp, i, i + fp);
         }
         p = quickSelectAdaptive(a, s, e, p, p, upper, MODE_FR_SAMPLING);
         return expandPartition(a, l, r, s, e, p, upper[0], upper);
     }
 
     /**
      * Expand a partition around a single pivot. Partitioning exchanges array
      * elements such that all elements smaller than pivot are before it and all
      * elements larger than pivot are after it. The central region is already
      * partitioned.
      *
      * <pre>{@code
      * |l             |s   |p0 p1|   e|                r|
      * |    ???       | <P | ==P | >P |        ???      |
      * }</pre>
      *
      * <p>This requires that {@code start != end}. However it handles
      * {@code left == start} and/or {@code end == right}.
      *
      * @param a Data array.
      * @param left Lower bound (inclusive).
      * @param right Upper bound (inclusive).
      * @param start Start of the partition range (inclusive).
      * @param end End of the partitioned range (inclusive).
      * @param pivot0 Lower pivot location (inclusive).
      * @param pivot1 Upper pivot location (inclusive).
      * @param upper Upper bound (inclusive) of the pivot range [k1].
      * @return Lower bound (inclusive) of the pivot range [k0].
      */
     // package-private for testing
     static int expandPartition(double[] a, int left, int right, int start, int end,
         int pivot0, int pivot1, int[] upper) {
         // 3-way partition of the data using a pivot value into
         // less-than, equal or greater-than.
         // Based on Sedgewick's Bentley-McIroy partitioning: always swap i<->j then
         // check for equal to the pivot and move again.
         //
         // Move sentinels from start and end to left and right. Scan towards the
         // sentinels until >=,<=. Swap then move == to the pivot region.
         //           <-i                           j->
         // |l |        |            |p0  p1|       |             | r|
         // |>=|   ???  |     <      |  ==  |   >   |     ???     |<=|
         //
         // When either i or j reach the edge perform finishing loop.
         // Finish loop for a[j] <= v replaces j with p1+1, optionally moves value
         // to p0 for < and updates the pivot range p1 (and optionally p0):
         //                                             j->
         // |l                       |p0  p1|           |         | r|
         // |         <              |  ==  |       >   |   ???   |<=|
 
         final double v = a[pivot0];
         // Use start/end as sentinels (requires start != end)
         double vi = a[start];
         double vj = a[end];
         a[start] = a[left];
         a[end] = a[right];
         a[left] = vj;
         a[right] = vi;
 
         int i = start + 1;
         int j = end - 1;
 
         // Positioned for pre-in/decrement to write to pivot region
         int p0 = pivot0 == start ? i : pivot0;
         int p1 = pivot1 == end ? j : pivot1;
 
         while (true) {
             do {
                 --i;
             } while (a[i] < v);
             do {
                 ++j;
             } while (a[j] > v);
             vj = a[i];
             vi = a[j];
             a[i] = vi;
             a[j] = vj;
             // Move the equal values to pivot region
             if (vi == v) {
                 a[i] = a[--p0];
                 a[p0] = v;
             }
             if (vj == v) {
                 a[j] = a[++p1];
                 a[p1] = v;
             }
             // Termination check and finishing loops.
             // Note: This works even if pivot region is zero length (p1 == p0-1 due to
             // length 1 pivot region at either start/end) because we pre-inc/decrement
             // one side and post-inc/decrement the other side.
             if (i == left) {
                 while (j < right) {
                     do {
                         ++j;
                     } while (a[j] > v);
                     final double w = a[j];
                     // Move upper bound of pivot region
                     a[j] = a[++p1];
                     a[p1] = v;
                     // Move lower bound of pivot region
                     if (w != v) {
                         a[p0] = w;
                         p0++;
                     }
                 }
                 break;
             }
             if (j == right) {
                 while (i > left) {
                     do {
                         --i;
                     } while (a[i] < v);
                     final double w = a[i];
                     // Move lower bound of pivot region
                     a[i] = a[--p0];
                     a[p0] = v;
                     // Move upper bound of pivot region
                     if (w != v) {
                         a[p1] = w;
                         p1--;
                     }
                 }
                 break;
             }
         }
 
         upper[0] = p1;
         return p0;
     }
 
     /**
      * Partition the array such that indices {@code k} correspond to their
      * correctly sorted value in the equivalent fully sorted array.
      *
      * <p>For all indices {@code k} and any index {@code i}:
      *
      * <pre>{@code
      * data[i < k] <= data[k] <= data[k < i]
      * }</pre>
      *
      * <p>This function accepts a {@link UpdatingInterval} of indices {@code k} that define the
      * range of indices to partition. The {@link UpdatingInterval} can be narrowed or split as
      * partitioning divides the range.
      *
      * <p>Uses an introselect variant. The quickselect is a dual-pivot quicksort
      * partition method by Vladimir Yaroslavskiy; the fall-back on poor convergence of
      * the quickselect is a heapselect.
      *
      * <p>The {@code flags} contain the the current recursion count and the configured
      * length threshold for {@code r - l} to perform sort select. The count is in the upper
      * bits and the threshold is in the lower bits.
      *
      * @param a Values.
      * @param left Lower bound of data (inclusive, assumed to be strictly positive).
      * @param right Upper bound of data (inclusive, assumed to be strictly positive).
      * @param k Interval of indices to partition (ordered).
      * @param flags Control flags.
      */
     // package-private for testing
     static void dualPivotQuickSelect(double[] a, int left, int right, UpdatingInterval k, int flags) {
         // If partitioning splits the interval then recursion is used for the left-most side(s)
         // and the right-most side remains within this function. If partitioning does
         // not split the interval then it remains within this function.
         int l = left;
         int r = right;
         int f = flags;
         int ka = k.left();
         int kb = k.right();
         final int[] upper = {0, 0, 0};
         while (true) {
             // Select when ka and kb are close to the same end,
             // or the entire range is small
             // |l|-----|ka|--------|kb|------|r|
             final int n = r - l;
             if (Math.min(kb - l, r - ka) < DP_SORTSELECT_SIZE ||
                 n < (f & SORTSELECT_MASK)) {
                 sortSelect(a, l, r, ka, kb);
                 return;
             }
             if (kb - ka < DP_SORTSELECT_SIZE) {
                 // Switch to single-pivot mode with Floyd-Rivest sub-sampling
                 quickSelectAdaptive(a, l, r, ka, kb, upper, MODE_FR_SAMPLING);
                 return;
             }
             if (f < 0) {
                 // Excess recursion, switch to heap select
                 heapSelect(a, l, r, ka, kb);
                 return;
             }
 
             // Dual-pivot partitioning
             final int p0 = partition(a, l, r, upper);
             final int p1 = upper[0];
 
             // Recursion to max depth
             // Note: Here we possibly branch left, middle and right with multiple keys.
             // It is possible that the partition has split the keys
             // and the recursion proceeds with a reduced set in each region.
             //                   p0 p1               p2 p3
             // |l|--|ka|--k----k--|P|------k--|kb|----|P|----|r|
             //                 kb  |      ka
             f += RECURSION_INCREMENT;
             // Recurse left side if required
             if (ka < p0) {
                 if (kb <= p1) {
                     // Entirely on left side
                     r = p0 - 1;
                     if (r < kb) {
                         kb = k.updateRight(r);
                     }
                     continue;
                 }
                 dualPivotQuickSelect(a, l, p0 - 1, k.splitLeft(p0, p1), f);
                 // Here we must process middle and/or right
                 ka = k.left();
             } else if (kb <= p1) {
                 // No middle/right side
                 return;
             } else if (ka <= p1) {
                 // Advance lower bound
                 ka = k.updateLeft(p1 + 1);
             }
             // Recurse middle if required
             final int p2 = upper[1];
             final int p3 = upper[2];
             if (ka < p2) {
                 l = p1 + 1;
                 if (kb <= p3) {
                     // Entirely in middle
                     r = p2 - 1;
                     if (r < kb) {
                         kb = k.updateRight(r);
                     }
                     continue;
                 }
                 dualPivotQuickSelect(a, l, p2 - 1, k.splitLeft(p2, p3), f);
                 ka = k.left();
             } else if (kb <= p3) {
                 // No right side
                 return;
             } else if (ka <= p3) {
                 ka = k.updateLeft(p3 + 1);
             }
             // Continue right
             l = p3 + 1;
         }
     }
 
     /**
      * Partition an array slice around 2 pivots. Partitioning exchanges array elements
      * such that all elements smaller than pivot are before it and all elements larger
      * than pivot are after it.
      *
      * <p>This method returns 4 points describing the pivot ranges of equal values.
      *
      * <pre>{@code
      *         |k0  k1|                |k2  k3|
      * |   <P  | ==P1 |  <P1 && <P2    | ==P2 |   >P   |
      * }</pre>
      *
      * <ul>
      * <li>k0: lower pivot1 point
      * <li>k1: upper pivot1 point (inclusive)
      * <li>k2: lower pivot2 point
      * <li>k3: upper pivot2 point (inclusive)
      * </ul>
      *
      * <p>Bounds are set so {@code i < k0}, {@code i > k3} and {@code k1 < i < k2} are
      * unsorted. When the range {@code [k0, k3]} contains fully sorted elements the result
      * is set to {@code k1 = k3; k2 == k0}. This can occur if
      * {@code P1 == P2} or there are zero or one value between the pivots
      * {@code P1 < v < P2}. Any sort/partition of ranges [left, k0-1], [k1+1, k2-1] and
      * [k3+1, right] must check the length is {@code > 1}.
      *
      * @param a Data array.
      * @param left Lower bound (inclusive).
      * @param right Upper bound (inclusive).
      * @param bounds Points [k1, k2, k3].
      * @return Lower bound (inclusive) of the pivot range [k0].
      */
     private static int partition(double[] a, int left, int right, int[] bounds) {
         // Pick 2 pivots from 5 approximately uniform through the range.
         // Spacing is ~ 1/7 made using shifts. Other strategies are equal or much
         // worse. 1/7 = 5/35 ~ 1/8 + 1/64 : 0.1429 ~ 0.1406
         // Ensure the value is above zero to choose different points!
         final int n = right - left;
         final int step = 1 + (n >>> 3) + (n >>> 6);
         final int i3 = left + (n >>> 1);
         final int i2 = i3 - step;
         final int i1 = i2 - step;
         final int i4 = i3 + step;
         final int i5 = i4 + step;
         Sorting.sort5(a, i1, i2, i3, i4, i5);
 
         // Partition data using pivots P1 and P2 into less-than, greater-than or between.
         // Pivot values P1 & P2 are placed at the end. If P1 < P2, P2 acts as a sentinel.
         // k traverses the unknown region ??? and values moved if less-than or
         // greater-than:
         //
         // left        less              k       great         right
         // |P1|  <P1   |   P1 <= & <= P2 |    ???    |    >P2   |P2|
         //
         // <P1            (left, lt)
         // P1 <= & <= P2  [lt, k)
         // >P2            (gt, right)
         //
         // At the end pivots are swapped back to behind the less and great pointers.
         //
         // |  <P1        |P1|     P1<= & <= P2    |P2|      >P2    |
 
         // Swap ends to the pivot locations.
         final double v1 = a[i2];
         a[i2] = a[left];
         a[left] = v1;
         final double v2 = a[i4];
         a[i4] = a[right];
         a[right] = v2;
 
         // pointers
         int less = left;
         int great = right;
 
         // Fast-forward ascending / descending runs to reduce swaps.
         // Cannot overrun as end pivots (v1 <= v2) act as sentinels.
         do {
             ++less;
         } while (a[less] < v1);
         do {
             --great;
         } while (a[great] > v2);
 
         // a[less - 1] < P1 : a[great + 1] > P2
         // unvisited in [less, great]
         SORTING:
         for (int k = less - 1; ++k <= great;) {
             final double v = a[k];
             if (v < v1) {
                 // swap(a, k, less++)
                 a[k] = a[less];
                 a[less] = v;
                 less++;
             } else if (v > v2) {
                 // while k < great and a[great] > v2:
                 //   great--
                 while (a[great] > v2) {
                     if (great-- == k) {
                         // Done
                         break SORTING;
                     }
                 }
                 // swap(a, k, great--)
                 // if a[k] < v1:
                 //   swap(a, k, less++)
                 final double w = a[great];
                 a[great] = v;
                 great--;
                 // delay a[k] = w
                 if (w < v1) {
                     a[k] = a[less];
                     a[less] = w;
                     less++;
                 } else {
                     a[k] = w;
                 }
             }
         }
 
         // Change to inclusive ends : a[less] < P1 : a[great] > P2
         less--;
         great++;
         // Move the pivots to correct locations
         a[left] = a[less];
         a[less] = v1;
         a[right] = a[great];
         a[great] = v2;
 
         // Record the pivot locations
         final int lower = less;
         bounds[2] = great;
 
         // equal elements
         // Original paper: If middle partition is bigger than a threshold
         // then check for equal elements.
 
         // Note: This is extra work. When performing partitioning the region of interest
         // may be entirely above or below the central region and this can be skipped.
 
         // Here we look for equal elements if the centre is more than 5/8 the length.
         // 5/8 = 1/2 + 1/8. Pivots must be different.
         if ((great - less) > (n >>> 1) + (n >>> 3) && v1 != v2) {
 
             // Fast-forward to reduce swaps. Changes inclusive ends to exclusive ends.
             // Since v1 != v2 these act as sentinels to prevent overrun.
             do {
                 ++less;
             } while (a[less] == v1);
             do {
                 --great;
             } while (a[great] == v2);
 
             // This copies the logic in the sorting loop using == comparisons
             EQUAL:
             for (int k = less - 1; ++k <= great;) {
                 final double v = a[k];
                 if (v == v1) {
                     a[k] = a[less];
                     a[less] = v;
                     less++;
                 } else if (v == v2) {
                     while (a[great] == v2) {
                         if (great-- == k) {
                             // Done
                             break EQUAL;
                         }
                     }
                     final double w = a[great];
                     a[great] = v;
                     great--;
                     if (w == v1) {
                         a[k] = a[less];
                         a[less] = w;
                         less++;
                     } else {
                         a[k] = w;
                     }
                 }
             }
 
             // Change to inclusive ends
             less--;
             great++;
         }
 
         // Between pivots in (less, great)
         if (v1 != v2 && less < great - 1) {
             // Record the pivot end points
             bounds[0] = less;
             bounds[1] = great;
         } else {
             // No unsorted internal region (set k1 = k3; k2 = k0)
             bounds[0] = bounds[2];
             bounds[1] = lower;
         }
 
         return lower;
     }
 
     /**
      * Partition the elements between {@code ka} and {@code kb} using a heap select
      * algorithm. It is assumed {@code left <= ka <= kb <= right}.
      *
      * @param a Data array to use to find out the K<sup>th</sup> value.
      * @param left Lower bound (inclusive).
      * @param right Upper bound (inclusive).
      * @param ka Lower index to select.
      * @param kb Upper index to select.
      */
     static void heapSelect(int[] a, int left, int right, int ka, int kb) {
         if (right <= left) {
             return;
         }
         // Use the smallest heap
         if (kb - left < right - ka) {
             heapSelectLeft(a, left, right, ka, kb);
         } else {
             heapSelectRight(a, left, right, ka, kb);
         }
     }
 
     /**
      * Partition the elements between {@code ka} and {@code kb} using a heap select
      * algorithm. It is assumed {@code left <= ka <= kb <= right}.
      *
      * <p>For best performance this should be called with {@code k} in the lower
      * half of the range.
      *
      * @param a Data array to use to find out the K<sup>th</sup> value.
      * @param left Lower bound (inclusive).
      * @param right Upper bound (inclusive).
      * @param ka Lower index to select.
      * @param kb Upper index to select.
      */
     static void heapSelectLeft(int[] a, int left, int right, int ka, int kb) {
         // Create a max heap in-place in [left, k], rooted at a[left] = max
         // |l|-max-heap-|k|--------------|
         // Build the heap using Floyd's heap-construction algorithm for heap size n.
         // Start at parent of the last element in the heap (k),
         // i.e. start = parent(n-1) : parent(c) = floor((c - 1) / 2) : c = k - left
         int end = kb + 1;
         for (int p = left + ((kb - left - 1) >> 1); p >= left; p--) {
             maxHeapSiftDown(a, a[p], p, left, end);
         }
         // Scan the remaining data and insert
         // Mitigate worst case performance on descending data by backward sweep
         int max = a[left];
         for (int i = right + 1; --i > kb;) {
             final int v = a[i];
             if (v < max) {
                 a[i] = max;
                 maxHeapSiftDown(a, v, left, left, end);
                 max = a[left];
             }
         }
         // Partition [ka, kb]
         // |l|-max-heap-|k|--------------|
         //  |  <-swap->  |   then sift down reduced size heap
         // Avoid sifting heap of size 1
         final int last = Math.max(left, ka - 1);
         while (--end > last) {
             maxHeapSiftDown(a, a[end], left, left, end);
             a[end] = max;
             max = a[left];
         }
     }
 
     /**
      * Sift the element down the max heap.
      *
      * <p>Assumes {@code root <= p < end}, i.e. the max heap is above root.
      *
      * @param a Heap data.
      * @param v Value to sift.
      * @param p Start position.
      * @param root Root of the heap.
      * @param end End of the heap (exclusive).
      */
     private static void maxHeapSiftDown(int[] a, int v, int p, int root, int end) {
         // child2 = root + 2 * (parent - root) + 2
         //        = 2 * parent - root + 2
         while (true) {
             // Right child
             int c = (p << 1) - root + 2;
             if (c > end) {
                 // No left child
                 break;
             }
             // Use the left child if right doesn't exist, or it is greater
             if (c == end || a[c] < a[c - 1]) {
                 --c;
             }
             if (v >= a[c]) {
                 // Parent greater than largest child - done
                 break;
             }
             // Swap and descend
             a[p] = a[c];
             p = c;
         }
         a[p] = v;
     }
 
     /**
      * Partition the elements between {@code ka} and {@code kb} using a heap select
      * algorithm. It is assumed {@code left <= ka <= kb <= right}.
      *
      * <p>For best performance this should be called with {@code k} in the upper
      * half of the range.
      *
      * @param a Data array to use to find out the K<sup>th</sup> value.
      * @param left Lower bound (inclusive).
      * @param right Upper bound (inclusive).
      * @param ka Lower index to select.
      * @param kb Upper index to select.
      */
     static void heapSelectRight(int[] a, int left, int right, int ka, int kb) {
         // Create a min heap in-place in [k, right], rooted at a[right] = min
         // |--------------|k|-min-heap-|r|
         // Build the heap using Floyd's heap-construction algorithm for heap size n.
         // Start at parent of the last element in the heap (k),
         // i.e. start = parent(n-1) : parent(c) = floor((c - 1) / 2) : c = right - k
         int end = ka - 1;
         for (int p = right - ((right - ka - 1) >> 1); p <= right; p++) {
             minHeapSiftDown(a, a[p], p, right, end);
         }
         // Scan the remaining data and insert
         // Mitigate worst case performance on descending data by backward sweep
         int min = a[right];
         for (int i = left - 1; ++i < ka;) {
             final int v = a[i];
             if (v > min) {
                 a[i] = min;
                 minHeapSiftDown(a, v, right, right, end);
                 min = a[right];
             }
         }
         // Partition [ka, kb]
         // |--------------|k|-min-heap-|r|
         //                 |  <-swap->  |   then sift down reduced size heap
         // Avoid sifting heap of size 1
         final int last = Math.min(right, kb + 1);
         while (++end < last) {
             minHeapSiftDown(a, a[end], right, right, end);
             a[end] = min;
             min = a[right];
         }
     }
 
     /**
      * Sift the element down the min heap.
      *
      * <p>Assumes {@code root >= p > end}, i.e. the max heap is below root.
      *
      * @param a Heap data.
      * @param v Value to sift.
      * @param p Start position.
      * @param root Root of the heap.
      * @param end End of the heap (exclusive).
      */
     private static void minHeapSiftDown(int[] a, int v, int p, int root, int end) {
         // child2 = root - 2 * (root - parent) - 2
         //        = 2 * parent - root - 2
         while (true) {
             // Right child
             int c = (p << 1) - root - 2;
             if (c < end) {
                 // No left child
                 break;
             }
             // Use the left child if right doesn't exist, or it is less
             if (c == end || a[c] > a[c + 1]) {
                 ++c;
             }
             if (v <= a[c]) {
                 // Parent less than smallest child - done
                 break;
             }
             // Swap and descend
             a[p] = a[c];
             p = c;
         }
         a[p] = v;
     }
 
     /**
      * Partition the elements between {@code ka} and {@code kb} using a sort select
      * algorithm. It is assumed {@code left <= ka <= kb <= right}.
      *
      * @param a Data array to use to find out the K<sup>th</sup> value.
      * @param left Lower bound (inclusive).
      * @param right Upper bound (inclusive).
      * @param ka Lower index to select.
      * @param kb Upper index to select.
      */
     static void sortSelect(int[] a, int left, int right, int ka, int kb) {
         // Combine the test for right <= left with
         // avoiding the overhead of sort select on tiny data.
         if (right - left <= MIN_SORTSELECT_SIZE) {
             Sorting.sort(a, left, right);
             return;
         }
         // Sort the smallest side
         if (kb - left < right - ka) {
             sortSelectLeft(a, left, right, kb);
         } else {
             sortSelectRight(a, left, right, ka);
         }
     }
 
     /**
      * Partition the minimum {@code n} elements below {@code k} where
      * {@code n = k - left + 1}. Uses an insertion sort algorithm.
      *
      * <p>Works with any {@code k} in the range {@code left <= k <= right}
      * and performs a full sort of the range below {@code k}.
      *
      * <p>For best performance this should be called with
      * {@code k - left < right - k}, i.e.
      * to partition a value in the lower half of the range.
      *
      * @param a Data array to use to find out the K<sup>th</sup> value.
      * @param left Lower bound (inclusive).
      * @param right Upper bound (inclusive).
      * @param k Index to select.
      */
     static void sortSelectLeft(int[] a, int left, int right, int k) {
         // Sort
         for (int i = left; ++i <= k;) {
             final int v = a[i];
             // Move preceding higher elements above (if required)
             if (v < a[i - 1]) {
                 int j = i;
                 while (--j >= left && v < a[j]) {
                     a[j + 1] = a[j];
                 }
                 a[j + 1] = v;
             }
         }
         // Scan the remaining data and insert
         // Mitigate worst case performance on descending data by backward sweep
         int m = a[k];
         for (int i = right + 1; --i > k;) {
             final int v = a[i];
             if (v < m) {
                 a[i] = m;
                 int j = k;
                 while (--j >= left && v < a[j]) {
                     a[j + 1] = a[j];
                 }
                 a[j + 1] = v;
                 m = a[k];
             }
         }
     }
 
     /**
      * Partition the maximum {@code n} elements above {@code k} where
      * {@code n = right - k + 1}. Uses an insertion sort algorithm.
      *
      * <p>Works with any {@code k} in the range {@code left <= k <= right}
      * and can be used to perform a full sort of the range above {@code k}.
      *
      * <p>For best performance this should be called with
      * {@code k - left > right - k}, i.e.
      * to partition a value in the upper half of the range.
      *
      * @param a Data array to use to find out the K<sup>th</sup> value.
      * @param left Lower bound (inclusive).
      * @param right Upper bound (inclusive).
      * @param k Index to select.
      */
     static void sortSelectRight(int[] a, int left, int right, int k) {
         // Sort
         for (int i = right; --i >= k;) {
             final int v = a[i];
             // Move succeeding lower elements below (if required)
             if (v > a[i + 1]) {
                 int j = i;
                 while (++j <= right && v > a[j]) {
                     a[j - 1] = a[j];
                 }
                 a[j - 1] = v;
             }
         }
         // Scan the remaining data and insert
         // Mitigate worst case performance on descending data by backward sweep
         int m = a[k];
         for (int i = left - 1; ++i < k;) {
             final int v = a[i];
             if (v > m) {
                 a[i] = m;
                 int j = k;
                 while (++j <= right && v > a[j]) {
                     a[j - 1] = a[j];
                 }
                 a[j - 1] = v;
                 m = a[k];
             }
         }
     }
 
     /**
      * Partition the array such that index {@code k} corresponds to its correctly
      * sorted value in the equivalent fully sorted array.
      *
      * <p>Assumes {@code k} is a valid index into [left, right].
      *
      * @param a Values.
      * @param left Lower bound of data (inclusive).
      * @param right Upper bound of data (inclusive).
      * @param k Index.
      */
     static void select(int[] a, int left, int right, int k) {
         quickSelectAdaptive(a, left, right, k, k, new int[1], MODE_FR_SAMPLING);
     }
 
     /**
      * Partition the array such that indices {@code k} correspond to their correctly
      * sorted value in the equivalent fully sorted array.
      *
      * <p>The count of the number of used indices is returned. If the keys are sorted in-place,
      * the count is returned as a negative.
      *
      * @param a Values.
      * @param left Lower bound of data (inclusive).
      * @param right Upper bound of data (inclusive).
      * @param k Indices (may be destructively modified).
      * @param n Count of indices.
      * @throws IndexOutOfBoundsException if any index {@code k} is not within the
      * sub-range {@code [left, right]}
      */
     static void select(int[] a, int left, int right, int[] k, int n) {
         if (n == 1) {
             quickSelectAdaptive(a, left, right, k[0], k[0], new int[1], MODE_FR_SAMPLING);
             return;
         }
 
         // Interval creation validates the indices are in [left, right]
         final UpdatingInterval keys = IndexSupport.createUpdatingInterval(left, right, k, n);
 
         // Note: If the keys are not separated then they are effectively a single key.
         // Any split of keys separated by the sort select size
         // will be finished on the next iteration.
         final int k1 = keys.left();
         final int kn = keys.right();
         if (kn - k1 < DP_SORTSELECT_SIZE) {
             quickSelectAdaptive(a, left, right, k1, kn, new int[1], MODE_FR_SAMPLING);
         } else {
             // Dual-pivot mode with small range sort length configured using index density
             dualPivotQuickSelect(a, left, right, keys, dualPivotFlags(left, right, k1, kn));
         }
     }
 
     /**
      * Partition the array such that indices {@code k} correspond to their
      * correctly sorted value in the equivalent fully sorted array.
      *
      * <p>For all indices {@code [ka, kb]} and any index {@code i}:
      *
      * <pre>{@code
      * data[i < ka] <= data[ka] <= data[kb] <= data[kb < i]
      * }</pre>
      *
      * <p>This function accepts indices {@code [ka, kb]} that define the
      * range of indices to partition. It is expected that the range is small.
      *
      * <p>The {@code flags} are used to control the sampling mode and adaption of
      * the index within the sample.
      *
      * <p>Returns the bounds containing {@code [ka, kb]}. These may be lower/higher
      * than the keys if equal values are present in the data.
      *
      * @param a Values.
      * @param left Lower bound of data (inclusive, assumed to be strictly positive).
      * @param right Upper bound of data (inclusive, assumed to be strictly positive).
      * @param ka First key of interest.
      * @param kb Last key of interest.
      * @param bounds Upper bound of the range containing {@code [ka, kb]} (inclusive).
      * @param flags Adaption flags.
      * @return Lower bound of the range containing {@code [ka, kb]} (inclusive).
      */
     static int quickSelectAdaptive(int[] a, int left, int right, int ka, int kb,
             int[] bounds, int flags) {
         int l = left;
         int r = right;
         int m = flags;
         while (true) {
             // Select when ka and kb are close to the same end
             // |l|-----|ka|kkkkkkkk|kb|------|r|
             if (Math.min(kb - l, r - ka) < LINEAR_SORTSELECT_SIZE) {
                 sortSelect(a, l, r, ka, kb);
                 bounds[0] = kb;
                 return ka;
             }
 
             // Only target ka; kb is assumed to be close
             int p0;
             final int n = r - l;
             // f in [0, 1]
             final double f = (double) (ka - l) / n;
             // Record the larger margin (start at 1/4) to create the estimated size.
             // step        L     R
             // far left    1/12  1/3   (use 1/4 + 1/32 + 1/64 ~ 0.328)
             // left        1/6   1/4
             // middle      2/9   2/9   (use 1/4 - 1/32 ~ 0.219)
             int margin = n >> 2;
             if (m < MODE_SAMPLING && r - l > FR_SAMPLING_SIZE) {
                 // Floyd-Rivest sample step uses the same margins
                 p0 = sampleStep(a, l, r, ka, bounds);
                 if (f <= STEP_FAR_LEFT || f >= STEP_FAR_RIGHT) {
                     margin += (n >> 5) + (n >> 6);
                 } else if (f > STEP_LEFT && f < STEP_RIGHT) {
                     margin -= n >> 5;
                 }
             } else if (f <= STEP_LEFT) {
                 if (f <= STEP_FAR_LEFT) {
                     margin += (n >> 5) + (n >> 6);
                     p0 = repeatedStepFarLeft(a, l, r, ka, bounds, m);
                 } else {
                     p0 = repeatedStepLeft(a, l, r, ka, bounds, m);
                 }
             } else if (f >= STEP_RIGHT) {
                 if (f >= STEP_FAR_RIGHT) {
                     margin += (n >> 5) + (n >> 6);
                     p0 = repeatedStepFarRight(a, l, r, ka, bounds, m);
                 } else {
                     p0 = repeatedStepRight(a, l, r, ka, bounds, m);
                 }
             } else {
                 margin -= n >> 5;
                 p0 = repeatedStep(a, l, r, ka, bounds, m);
             }
 
             // Note: Here we expect [ka, kb] to be small and splitting is unlikely.
             //                   p0 p1
             // |l|--|ka|kkkk|kb|--|P|-------------------|r|
             // |l|----------------|P|--|ka|kkk|kb|------|r|
             // |l|-----------|ka|k|P|k|kb|--------------|r|
             final int p1 = bounds[0];
             if (kb < p0) {
                 // Entirely on left side
                 r = p0 - 1;
             } else if (ka > p1) {
                 // Entirely on right side
                 l = p1 + 1;
             } else {
                 // Pivot splits [ka, kb]. Expect ends to be close to the pivot and finish.
                 // Here we set the bounds for use after median-of-medians pivot selection.
                 // In the event there are many equal values this allows collecting those
                 // known to be equal together when moving around the medians sample.
                 if (kb > p1) {
                     sortSelectLeft(a, p1 + 1, r, kb);
                     bounds[0] = kb;
                 }
                 if (ka < p0) {
                     sortSelectRight(a, l, p0 - 1, ka);
                     p0 = ka;
                 }
                 return p0;
             }
             // Update mode based on target partition size
             if (r - l > n - margin) {
                 m++;
             }
         }
     }
 
     /**
      * Partition an array slice around a pivot. Partitioning exchanges array elements such
      * that all elements smaller than pivot are before it and all elements larger than
      * pivot are after it.
      *
      * <p>Partitions a Floyd-Rivest sample around a pivot offset so that the input {@code k} will
      * fall in the smaller partition when the entire range is partitioned.
      *
      * <p>Assumes the range {@code r - l} is large.
      *
      * @param a Data array.
      * @param l Lower bound (inclusive).
      * @param r Upper bound (inclusive).
      * @param k Target index.
      * @param upper Upper bound (inclusive) of the pivot range.
      * @return Lower bound (inclusive) of the pivot range.
      */
     private static int sampleStep(int[] a, int l, int r, int k, int[] upper) {
         // Floyd-Rivest: use SELECT recursively on a sample of size S to get an estimate
         // for the (k-l+1)-th smallest element into a[k], biased slightly so that the
         // (k-l+1)-th element is expected to lie in the smaller set after partitioning.
         final int n = r - l + 1;
         final int ith = k - l + 1;
         final double z = Math.log(n);
         // sample size = 0.5 * n^(2/3)
         final double s = 0.5 * Math.exp(0.6666666666666666 * z);
         final double sd = 0.5 * Math.sqrt(z * s * (n - s) / n) * Integer.signum(ith - (n >> 1));
         final int ll = Math.max(l, (int) (k - ith * s / n + sd));
         final int rr = Math.min(r, (int) (k + (n - ith) * s / n + sd));
         // Sample recursion restarts from [ll, rr]
         final int p = quickSelectAdaptive(a, ll, rr, k, k, upper, MODE_FR_SAMPLING);
         return expandPartition(a, l, r, ll, rr, p, upper[0], upper);
     }
 
     /**
      * Partition an array slice around a pivot. Partitioning exchanges array elements such
      * that all elements smaller than pivot are before it and all elements larger than
      * pivot are after it.
      *
      * <p>Assumes the range {@code r - l >= 8}; the caller is responsible for selection on a smaller
      * range. If using a 12th-tile for sampling then assumes {@code r - l >= 11}.
      *
      * <p>Uses the Chen and Dumitrescu repeated step median-of-medians-of-medians algorithm
      * with the median of 3 then median of 3; the final sample is placed in the
      * 5th 9th-tile; the pivot chosen from the sample is adaptive using the input {@code k}.
      *
      * <p>Given a pivot in the middle of the sample this has margins of 2/9 and 2/9.
      *
      * @param a Data array.
      * @param l Lower bound (inclusive).
      * @param r Upper bound (inclusive).
      * @param k Target index.
      * @param upper Upper bound (inclusive) of the pivot range.
      * @param flags Control flags.
      * @return Lower bound (inclusive) of the pivot range.
      */
     private static int repeatedStep(int[] a, int l, int r, int k, int[] upper, int flags) {
         // Adapted from Alexandrescu (2016), algorithm 8.
         final int fp;
         final int s;
         int p;
         if (flags <= MODE_SAMPLING) {
             // Median into a 12th-tile
             fp = (r - l + 1) / 12;
             // Position the sample around the target k
             s = k - mapDistance(k - l, l, r, fp);
             p = k;
         } else {
             // i in tertile [3f':6f')
             fp = (r - l + 1) / 9;
             final int f3 = 3 * fp;
             final int end = l + (f3 << 1);
             for (int i = l + f3; i < end; i++) {
                 Sorting.sort3(a, i - f3, i, i + f3);
             }
             // 5th 9th-tile: [4f':5f')
             s = l + (fp << 2);
             // No adaption uses the middle to enforce strict margins
             p = s + (flags == MODE_ADAPTION ? mapDistance(k - l, l, r, fp) : (fp >>> 1));
         }
         final int e = s + fp - 1;
         for (int i = s; i <= e; i++) {
             Sorting.sort3(a, i - fp, i, i + fp);
         }
         p = quickSelectAdaptive(a, s, e, p, p, upper, MODE_FR_SAMPLING);
         return expandPartition(a, l, r, s, e, p, upper[0], upper);
     }
 
     /**
      * Partition an array slice around a pivot. Partitioning exchanges array elements such
      * that all elements smaller than pivot are before it and all elements larger than
      * pivot are after it.
      *
      * <p>Assumes the range {@code r - l >= 11}; the caller is responsible for selection on a smaller
      * range.
      *
      * <p>Uses the Chen and Dumitrescu repeated step median-of-medians-of-medians algorithm
      * with the lower median of 4 then either median of 3 with the final sample placed in the
      * 5th 12th-tile, or min of 3 with the final sample in the 4th 12th-tile;
      * the pivot chosen from the sample is adaptive using the input {@code k}.
      *
      * <p>Given a pivot in the middle of the sample this has margins of 1/6 and 1/4.
      *
      * @param a Data array.
      * @param l Lower bound (inclusive).
      * @param r Upper bound (inclusive).
      * @param k Target index.
      * @param upper Upper bound (inclusive) of the pivot range.
      * @param flags Control flags.
      * @return Lower bound (inclusive) of the pivot range.
      */
     private static int repeatedStepLeft(int[] a, int l, int r, int k, int[] upper, int flags) {
         // Adapted from Alexandrescu (2016), algorithm 9.
         final int fp;
         final int s;
         int p;
         if (flags <= MODE_SAMPLING) {
             // Median into a 12th-tile
             fp = (r - l + 1) / 12;
             // Position the sample around the target k
             // Avoid bounds error due to rounding as (k-l)/(r-l) -> 1/12
             s = Math.max(k - mapDistance(k - l, l, r, fp), l + fp);
             p = k;
         } else {
             // i in 2nd quartile
             final int f = (r - l + 1) >> 2;
             final int f2 = f + f;
             final int end = l + f2;
             for (int i = l + f; i < end; i++) {
                 Sorting.lowerMedian4(a, i - f, i, i + f, i + f2);
             }
             // i in 5th 12th-tile
             fp = f / 3;
             s = l + f + fp;
             // No adaption uses the middle to enforce strict margins
             p = s + (flags == MODE_ADAPTION ? mapDistance(k - l, l, r, fp) : (fp >>> 1));
         }
         final int e = s + fp - 1;
         for (int i = s; i <= e; i++) {
             Sorting.sort3(a, i - fp, i, i + fp);
         }
         p = quickSelectAdaptive(a, s, e, p, p, upper, MODE_FR_SAMPLING);
         return expandPartition(a, l, r, s, e, p, upper[0], upper);
     }
 
     /**
      * Partition an array slice around a pivot. Partitioning exchanges array elements such
      * that all elements smaller than pivot are before it and all elements larger than
      * pivot are after it.
      *
      * <p>Assumes the range {@code r - l >= 11}; the caller is responsible for selection on a smaller
      * range.
      *
      * <p>Uses the Chen and Dumitrescu repeated step median-of-medians-of-medians algorithm
      * with the upper median of 4 then either median of 3 with the final sample placed in the
      * 8th 12th-tile, or max of 3 with the final sample in the 9th 12th-tile;
      * the pivot chosen from the sample is adaptive using the input {@code k}.
      *
      * <p>Given a pivot in the middle of the sample this has margins of 1/4 and 1/6.
      *
      * @param a Data array.
      * @param l Lower bound (inclusive).
      * @param r Upper bound (inclusive).
      * @param k Target index.
      * @param upper Upper bound (inclusive) of the pivot range.
      * @param flags Control flags.
      * @return Lower bound (inclusive) of the pivot range.
      */
     private static int repeatedStepRight(int[] a, int l, int r, int k, int[] upper, int flags) {
         // Mirror image repeatedStepLeft using upper median into 3rd quartile
         final int fp;
         final int e;
         int p;
         if (flags <= MODE_SAMPLING) {
             // Median into a 12th-tile
             fp = (r - l + 1) / 12;
             // Position the sample around the target k
             // Avoid bounds error due to rounding as (r-k)/(r-l) -> 11/12
             e = Math.min(k + mapDistance(r - k, l, r, fp), r - fp);
             p = k;
         } else {
             // i in 3rd quartile
             final int f = (r - l + 1) >> 2;
             final int f2 = f + f;
             final int end = r - f2;
             for (int i = r - f; i > end; i--) {
                 Sorting.upperMedian4(a, i - f2, i - f, i, i + f);
             }
             // i in 8th 12th-tile
             fp = f / 3;
             e = r - f - fp;
             // No adaption uses the middle to enforce strict margins
             p = e - (flags == MODE_ADAPTION ? mapDistance(r - k, l, r, fp) : (fp >>> 1));
         }
         final int s = e - fp + 1;
         for (int i = s; i <= e; i++) {
             Sorting.sort3(a, i - fp, i, i + fp);
         }
         p = quickSelectAdaptive(a, s, e, p, p, upper, MODE_FR_SAMPLING);
         return expandPartition(a, l, r, s, e, p, upper[0], upper);
     }
 
     /**
      * Partition an array slice around a pivot. Partitioning exchanges array elements such
      * that all elements smaller than pivot are before it and all elements larger than
      * pivot are after it.
      *
      * <p>Assumes the range {@code r - l >= 11}; the caller is responsible for selection on a smaller
      * range.
      *
      * <p>Uses the Chen and Dumitrescu repeated step median-of-medians-of-medians algorithm
      * with the minimum of 4 then median of 3; the final sample is placed in the
      * 2nd 12th-tile; the pivot chosen from the sample is adaptive using the input {@code k}.
      *
      * <p>Given a pivot in the middle of the sample this has margins of 1/12 and 1/3.
      *
      * @param a Data array.
      * @param l Lower bound (inclusive).
      * @param r Upper bound (inclusive).
      * @param k Target index.
      * @param upper Upper bound (inclusive) of the pivot range.
      * @param flags Control flags.
      * @return Lower bound (inclusive) of the pivot range.
      */
     private static int repeatedStepFarLeft(int[] a, int l, int r, int k, int[] upper, int flags) {
         // Far step has been changed from the Alexandrescu (2016) step of lower-median-of-4, min-of-3
         // into the 4th 12th-tile to a min-of-4, median-of-3 into the 2nd 12th-tile.
         // The differences are:
         // - The upper margin when not sampling is 8/24 vs. 9/24; the lower margin remains at 1/12.
         // - The position of the sample is closer to the expected location of k < |A| / 12.
         // - Sampling mode uses a median-of-3 with adaptive k, matching the other step methods.
         //   A min-of-3 sample can create a pivot too small if used with adaption of k leaving
         //   k in the larger parition and a wasted iteration.
         // - Adaption is adjusted to force use of the lower margin when not sampling.
         final int fp;
         final int s;
         int p;
         if (flags <= MODE_SAMPLING) {
             // 2nd 12th-tile
             fp = (r - l + 1) / 12;
             s = l + fp;
             // Use adaption
             p = s + mapDistance(k - l, l, r, fp);
         } else {
             // i in 2nd quartile; min into i-f (1st quartile)
             final int f = (r - l + 1) >> 2;
             final int f2 = f + f;
             final int end = l + f2;
             for (int i = l + f; i < end; i++) {
                 if (a[i + f] < a[i - f]) {
                     final int u = a[i + f];
                     a[i + f] = a[i - f];
                     a[i - f] = u;
                 }
                 if (a[i + f2] < a[i]) {
                     final int v = a[i + f2];
                     a[i + f2] = a[i];
                     a[i] = v;
                 }
                 if (a[i] < a[i - f]) {
                     final int u = a[i];
                     a[i] = a[i - f];
                     a[i - f] = u;
                 }
             }
             // 2nd 12th-tile
             fp = f / 3;
             s = l + fp;
             // Lower margin has 2(d+1) elements; d == (position in sample) - s
             // Force k into the lower margin
             p = s + ((k - l) >>> 1);
         }
         final int e = s + fp - 1;
         for (int i = s; i <= e; i++) {
             Sorting.sort3(a, i - fp, i, i + fp);
         }
         p = quickSelectAdaptive(a, s, e, p, p, upper, MODE_FR_SAMPLING);
         return expandPartition(a, l, r, s, e, p, upper[0], upper);
     }
 
     /**
      * Partition an array slice around a pivot. Partitioning exchanges array elements such
      * that all elements smaller than pivot are before it and all elements larger than
      * pivot are after it.
      *
      * <p>Assumes the range {@code r - l >= 11}; the caller is responsible for selection on a smaller
      * range.
      *
      * <p>Uses the Chen and Dumitrescu repeated step median-of-medians-of-medians algorithm
      * with the maximum of 4 then median of 3; the final sample is placed in the
      * 11th 12th-tile; the pivot chosen from the sample is adaptive using the input {@code k}.
      *
      * <p>Given a pivot in the middle of the sample this has margins of 1/3 and 1/12.
      *
      * @param a Data array.
      * @param l Lower bound (inclusive).
      * @param r Upper bound (inclusive).
      * @param k Target index.
      * @param upper Upper bound (inclusive) of the pivot range.
      * @param flags Control flags.
      * @return Lower bound (inclusive) of the pivot range.
      */
     private static int repeatedStepFarRight(int[] a, int l, int r, int k, int[] upper, int flags) {
         // Mirror image repeatedStepFarLeft
         final int fp;
         final int e;
         int p;
         if (flags <= MODE_SAMPLING) {
             // 11th 12th-tile
             fp = (r - l + 1) / 12;
             e = r - fp;
             // Use adaption
             p = e - mapDistance(r - k, l, r, fp);
         } else {
             // i in 3rd quartile; max into i+f (4th quartile)
             final int f = (r - l + 1) >> 2;
             final int f2 = f + f;
             final int end = r - f2;
             for (int i = r - f; i > end; i--) {
                 if (a[i - f] > a[i + f]) {
                     final int u = a[i - f];
                     a[i - f] = a[i + f];
                     a[i + f] = u;
                 }
                 if (a[i - f2] > a[i]) {
                     final int v = a[i - f2];
                     a[i - f2] = a[i];
                     a[i] = v;
                 }
                 if (a[i] > a[i + f]) {
                     final int u = a[i];
                     a[i] = a[i + f];
                     a[i + f] = u;
                 }
             }
             // 11th 12th-tile
             fp = f / 3;
             e = r - fp;
             // Upper margin has 2(d+1) elements; d == e - (position in sample)
             // Force k into the upper margin
             p = e - ((r - k) >>> 1);
         }
         final int s = e - fp + 1;
         for (int i = s; i <= e; i++) {
             Sorting.sort3(a, i - fp, i, i + fp);
         }
         p = quickSelectAdaptive(a, s, e, p, p, upper, MODE_FR_SAMPLING);
         return expandPartition(a, l, r, s, e, p, upper[0], upper);
     }
 
     /**
      * Expand a partition around a single pivot. Partitioning exchanges array
      * elements such that all elements smaller than pivot are before it and all
      * elements larger than pivot are after it. The central region is already
      * partitioned.
      *
      * <pre>{@code
      * |l             |s   |p0 p1|   e|                r|
      * |    ???       | <P | ==P | >P |        ???      |
      * }</pre>
      *
      * <p>This requires that {@code start != end}. However it handles
      * {@code left == start} and/or {@code end == right}.
      *
      * @param a Data array.
      * @param left Lower bound (inclusive).
      * @param right Upper bound (inclusive).
      * @param start Start of the partition range (inclusive).
      * @param end End of the partitioned range (inclusive).
      * @param pivot0 Lower pivot location (inclusive).
      * @param pivot1 Upper pivot location (inclusive).
      * @param upper Upper bound (inclusive) of the pivot range [k1].
      * @return Lower bound (inclusive) of the pivot range [k0].
      */
     // package-private for testing
     static int expandPartition(int[] a, int left, int right, int start, int end,
         int pivot0, int pivot1, int[] upper) {
         // 3-way partition of the data using a pivot value into
         // less-than, equal or greater-than.
         // Based on Sedgewick's Bentley-McIroy partitioning: always swap i<->j then
         // check for equal to the pivot and move again.
         //
         // Move sentinels from start and end to left and right. Scan towards the
         // sentinels until >=,<=. Swap then move == to the pivot region.
         //           <-i                           j->
         // |l |        |            |p0  p1|       |             | r|
         // |>=|   ???  |     <      |  ==  |   >   |     ???     |<=|
         //
         // When either i or j reach the edge perform finishing loop.
         // Finish loop for a[j] <= v replaces j with p1+1, optionally moves value
         // to p0 for < and updates the pivot range p1 (and optionally p0):
         //                                             j->
         // |l                       |p0  p1|           |         | r|
         // |         <              |  ==  |       >   |   ???   |<=|
 
         final int v = a[pivot0];
         // Use start/end as sentinels (requires start != end)
         int vi = a[start];
         int vj = a[end];
         a[start] = a[left];
         a[end] = a[right];
         a[left] = vj;
         a[right] = vi;
 
         int i = start + 1;
         int j = end - 1;
 
         // Positioned for pre-in/decrement to write to pivot region
         int p0 = pivot0 == start ? i : pivot0;
         int p1 = pivot1 == end ? j : pivot1;
 
         while (true) {
             do {
                 --i;
             } while (a[i] < v);
             do {
                 ++j;
             } while (a[j] > v);
             vj = a[i];
             vi = a[j];
             a[i] = vi;
             a[j] = vj;
             // Move the equal values to pivot region
             if (vi == v) {
                 a[i] = a[--p0];
                 a[p0] = v;
             }
             if (vj == v) {
                 a[j] = a[++p1];
                 a[p1] = v;
             }
             // Termination check and finishing loops.
             // Note: This works even if pivot region is zero length (p1 == p0-1 due to
             // length 1 pivot region at either start/end) because we pre-inc/decrement
             // one side and post-inc/decrement the other side.
             if (i == left) {
                 while (j < right) {
                     do {
                         ++j;
                     } while (a[j] > v);
                     final int w = a[j];
                     // Move upper bound of pivot region
                     a[j] = a[++p1];
                     a[p1] = v;
                     // Move lower bound of pivot region
                     if (w != v) {
                         a[p0] = w;
                         p0++;
                     }
                 }
                 break;
             }
             if (j == right) {
                 while (i > left) {
                     do {
                         --i;
                     } while (a[i] < v);
                     final int w = a[i];
                     // Move lower bound of pivot region
                     a[i] = a[--p0];
                     a[p0] = v;
                     // Move upper bound of pivot region
                     if (w != v) {
                         a[p1] = w;
                         p1--;
                     }
                 }
                 break;
             }
         }
 
         upper[0] = p1;
         return p0;
     }
 
     /**
      * Partition the array such that indices {@code k} correspond to their
      * correctly sorted value in the equivalent fully sorted array.
      *
      * <p>For all indices {@code k} and any index {@code i}:
      *
      * <pre>{@code
      * data[i < k] <= data[k] <= data[k < i]
      * }</pre>
      *
      * <p>This function accepts a {@link UpdatingInterval} of indices {@code k} that define the
      * range of indices to partition. The {@link UpdatingInterval} can be narrowed or split as
      * partitioning divides the range.
      *
      * <p>Uses an introselect variant. The quickselect is a dual-pivot quicksort
      * partition method by Vladimir Yaroslavskiy; the fall-back on poor convergence of
      * the quickselect is a heapselect.
      *
      * <p>The {@code flags} contain the the current recursion count and the configured
      * length threshold for {@code r - l} to perform sort select. The count is in the upper
      * bits and the threshold is in the lower bits.
      *
      * @param a Values.
      * @param left Lower bound of data (inclusive, assumed to be strictly positive).
      * @param right Upper bound of data (inclusive, assumed to be strictly positive).
      * @param k Interval of indices to partition (ordered).
      * @param flags Control flags.
      */
     // package-private for testing
     static void dualPivotQuickSelect(int[] a, int left, int right, UpdatingInterval k, int flags) {
         // If partitioning splits the interval then recursion is used for the left-most side(s)
         // and the right-most side remains within this function. If partitioning does
         // not split the interval then it remains within this function.
         int l = left;
         int r = right;
         int f = flags;
         int ka = k.left();
         int kb = k.right();
         final int[] upper = {0, 0, 0};
         while (true) {
             // Select when ka and kb are close to the same end,
             // or the entire range is small
             // |l|-----|ka|--------|kb|------|r|
             final int n = r - l;
             if (Math.min(kb - l, r - ka) < DP_SORTSELECT_SIZE ||
                 n < (f & SORTSELECT_MASK)) {
                 sortSelect(a, l, r, ka, kb);
                 return;
             }
             if (kb - ka < DP_SORTSELECT_SIZE) {
                 // Switch to single-pivot mode with Floyd-Rivest sub-sampling
                 quickSelectAdaptive(a, l, r, ka, kb, upper, MODE_FR_SAMPLING);
                 return;
             }
             if (f < 0) {
                 // Excess recursion, switch to heap select
                 heapSelect(a, l, r, ka, kb);
                 return;
             }
 
             // Dual-pivot partitioning
             final int p0 = partition(a, l, r, upper);
             final int p1 = upper[0];
 
             // Recursion to max depth
             // Note: Here we possibly branch left, middle and right with multiple keys.
             // It is possible that the partition has split the keys
             // and the recursion proceeds with a reduced set in each region.
             //                   p0 p1               p2 p3
             // |l|--|ka|--k----k--|P|------k--|kb|----|P|----|r|
             //                 kb  |      ka
             f += RECURSION_INCREMENT;
             // Recurse left side if required
             if (ka < p0) {
                 if (kb <= p1) {
                     // Entirely on left side
                     r = p0 - 1;
                     if (r < kb) {
                         kb = k.updateRight(r);
                     }
                     continue;
                 }
                 dualPivotQuickSelect(a, l, p0 - 1, k.splitLeft(p0, p1), f);
                 // Here we must process middle and/or right
                 ka = k.left();
             } else if (kb <= p1) {
                 // No middle/right side
                 return;
             } else if (ka <= p1) {
                 // Advance lower bound
                 ka = k.updateLeft(p1 + 1);
             }
             // Recurse middle if required
             final int p2 = upper[1];
             final int p3 = upper[2];
             if (ka < p2) {
                 l = p1 + 1;
                 if (kb <= p3) {
                     // Entirely in middle
                     r = p2 - 1;
                     if (r < kb) {
                         kb = k.updateRight(r);
                     }
                     continue;
                 }
                 dualPivotQuickSelect(a, l, p2 - 1, k.splitLeft(p2, p3), f);
                 ka = k.left();
             } else if (kb <= p3) {
                 // No right side
                 return;
             } else if (ka <= p3) {
                 ka = k.updateLeft(p3 + 1);
             }
             // Continue right
             l = p3 + 1;
         }
     }
 
     /**
      * Partition an array slice around 2 pivots. Partitioning exchanges array elements
      * such that all elements smaller than pivot are before it and all elements larger
      * than pivot are after it.
      *
      * <p>This method returns 4 points describing the pivot ranges of equal values.
      *
      * <pre>{@code
      *         |k0  k1|                |k2  k3|
      * |   <P  | ==P1 |  <P1 && <P2    | ==P2 |   >P   |
      * }</pre>
      *
      * <ul>
      * <li>k0: lower pivot1 point
      * <li>k1: upper pivot1 point (inclusive)
      * <li>k2: lower pivot2 point
      * <li>k3: upper pivot2 point (inclusive)
      * </ul>
      *
      * <p>Bounds are set so {@code i < k0}, {@code i > k3} and {@code k1 < i < k2} are
      * unsorted. When the range {@code [k0, k3]} contains fully sorted elements the result
      * is set to {@code k1 = k3; k2 == k0}. This can occur if
      * {@code P1 == P2} or there are zero or one value between the pivots
      * {@code P1 < v < P2}. Any sort/partition of ranges [left, k0-1], [k1+1, k2-1] and
      * [k3+1, right] must check the length is {@code > 1}.
      *
      * @param a Data array.
      * @param left Lower bound (inclusive).
      * @param right Upper bound (inclusive).
      * @param bounds Points [k1, k2, k3].
      * @return Lower bound (inclusive) of the pivot range [k0].
      */
     private static int partition(int[] a, int left, int right, int[] bounds) {
         // Pick 2 pivots from 5 approximately uniform through the range.
         // Spacing is ~ 1/7 made using shifts. Other strategies are equal or much
         // worse. 1/7 = 5/35 ~ 1/8 + 1/64 : 0.1429 ~ 0.1406
         // Ensure the value is above zero to choose different points!
         final int n = right - left;
         final int step = 1 + (n >>> 3) + (n >>> 6);
         final int i3 = left + (n >>> 1);
         final int i2 = i3 - step;
         final int i1 = i2 - step;
         final int i4 = i3 + step;
         final int i5 = i4 + step;
         Sorting.sort5(a, i1, i2, i3, i4, i5);
 
         // Partition data using pivots P1 and P2 into less-than, greater-than or between.
         // Pivot values P1 & P2 are placed at the end. If P1 < P2, P2 acts as a sentinel.
         // k traverses the unknown region ??? and values moved if less-than or
         // greater-than:
         //
         // left        less              k       great         right
         // |P1|  <P1   |   P1 <= & <= P2 |    ???    |    >P2   |P2|
         //
         // <P1            (left, lt)
         // P1 <= & <= P2  [lt, k)
         // >P2            (gt, right)
         //
         // At the end pivots are swapped back to behind the less and great pointers.
         //
         // |  <P1        |P1|     P1<= & <= P2    |P2|      >P2    |
 
         // Swap ends to the pivot locations.
         final int v1 = a[i2];
         a[i2] = a[left];
         a[left] = v1;
         final int v2 = a[i4];
         a[i4] = a[right];
         a[right] = v2;
 
         // pointers
         int less = left;
         int great = right;
 
         // Fast-forward ascending / descending runs to reduce swaps.
         // Cannot overrun as end pivots (v1 <= v2) act as sentinels.
         do {
             ++less;
         } while (a[less] < v1);
         do {
             --great;
         } while (a[great] > v2);
 
         // a[less - 1] < P1 : a[great + 1] > P2
         // unvisited in [less, great]
         SORTING:
         for (int k = less - 1; ++k <= great;) {
             final int v = a[k];
             if (v < v1) {
                 // swap(a, k, less++)
                 a[k] = a[less];
                 a[less] = v;
                 less++;
             } else if (v > v2) {
                 // while k < great and a[great] > v2:
                 //   great--
                 while (a[great] > v2) {
                     if (great-- == k) {
                         // Done
                         break SORTING;
                     }
                 }
                 // swap(a, k, great--)
                 // if a[k] < v1:
                 //   swap(a, k, less++)
                 final int w = a[great];
                 a[great] = v;
                 great--;
                 // delay a[k] = w
                 if (w < v1) {
                     a[k] = a[less];
                     a[less] = w;
                     less++;
                 } else {
                     a[k] = w;
                 }
             }
         }
 
         // Change to inclusive ends : a[less] < P1 : a[great] > P2
         less--;
         great++;
         // Move the pivots to correct locations
         a[left] = a[less];
         a[less] = v1;
         a[right] = a[great];
         a[great] = v2;
 
         // Record the pivot locations
         final int lower = less;
         bounds[2] = great;
 
         // equal elements
         // Original paper: If middle partition is bigger than a threshold
         // then check for equal elements.
 
         // Note: This is extra work. When performing partitioning the region of interest
         // may be entirely above or below the central region and this can be skipped.
 
         // Here we look for equal elements if the centre is more than 5/8 the length.
         // 5/8 = 1/2 + 1/8. Pivots must be different.
         if ((great - less) > (n >>> 1) + (n >>> 3) && v1 != v2) {
 
             // Fast-forward to reduce swaps. Changes inclusive ends to exclusive ends.
             // Since v1 != v2 these act as sentinels to prevent overrun.
             do {
                 ++less;
             } while (a[less] == v1);
             do {
                 --great;
             } while (a[great] == v2);
 
             // This copies the logic in the sorting loop using == comparisons
             EQUAL:
             for (int k = less - 1; ++k <= great;) {
                 final int v = a[k];
                 if (v == v1) {
                     a[k] = a[less];
                     a[less] = v;
                     less++;
                 } else if (v == v2) {
                     while (a[great] == v2) {
                         if (great-- == k) {
                             // Done
                             break EQUAL;
                         }
                     }
                     final int w = a[great];
                     a[great] = v;
                     great--;
                     if (w == v1) {
                         a[k] = a[less];
                         a[less] = w;
                         less++;
                     } else {
                         a[k] = w;
                     }
                 }
             }
 
             // Change to inclusive ends
             less--;
             great++;
         }
 
         // Between pivots in (less, great)
         if (v1 != v2 && less < great - 1) {
             // Record the pivot end points
             bounds[0] = less;
             bounds[1] = great;
         } else {
             // No unsorted internal region (set k1 = k3; k2 = k0)
             bounds[0] = bounds[2];
             bounds[1] = lower;
         }
 
         return lower;
     }
 
     /**
      * Map the distance from the edge of {@code [l, r]} to a new distance in {@code [0, n)}.
      *
      * <p>The provides the adaption {@code kf'/|A|} from Alexandrescu (2016) where
      * {@code k == d}, {@code f' == n} and {@code |A| == r-l+1}.
      *
      * <p>For convenience this accepts the input range {@code [l, r]}.
      *
      * @param d Distance from the edge in {@code [0, r - l]}.
      * @param l Lower bound (inclusive).
      * @param r Upper bound (inclusive).
      * @param n Size of the new range.
      * @return the mapped distance in [0, n)
      */
     private static int mapDistance(int d, int l, int r, int n) {
         return (int) (d * (n - 1.0) / (r - l));
     }
 
     /**
      * Configure the dual-pivot control flags. This packs the maximum recursion depth and
      * sort select size into a single integer.
      *
      * @param left Lower bound (inclusive).
      * @param right Upper bound (inclusive).
      * @param k1 First key of interest.
      * @param kn Last key of interest.
      * @return the flags
      */
     private static int dualPivotFlags(int left, int right, int k1, int kn) {
         final int maxDepth = dualPivotMaxDepth(right - left);
         final int ss = dualPivotSortSelectSize(k1, kn);
         return dualPivotFlags(maxDepth, ss);
     }
 
     /**
      * Configure the dual-pivot control flags. This packs the maximum recursion depth and
      * sort select size into a single integer.
      *
      * @param maxDepth Maximum recursion depth.
      * @param ss Sort select size.
      * @return the flags
      */
     static int dualPivotFlags(int maxDepth, int ss) {
         // The flags are packed using the upper bits to count back from -1 in
         // step sizes. The lower bits pack the sort select size.
         int flags = Integer.MIN_VALUE - maxDepth * RECURSION_INCREMENT;
         flags &= ~SORTSELECT_MASK;
         return flags | ss;
     }
 
     /**
      * Compute the maximum recursion depth for dual pivot recursion.
      * This is an approximation to {@code 2 * log3 (x)}.
      *
      * <p>The result is between {@code 2*floor(log3(x))} and {@code 2*ceil(log3(x))}.
      * The result is correctly rounded when {@code x +/- 1} is a power of 3.
      *
      * @param x Value.
      * @return maximum recursion depth
      */
     static int dualPivotMaxDepth(int x) {
         // log3(2) ~ 1.5849625
         // log3(x) ~ log2(x) * 0.630929753... ~ log2(x) * 323 / 512 (0.630859375)
         // Use (floor(log2(x))+1) * 323 / 256
         return ((32 - Integer.numberOfLeadingZeros(x)) * 323) >>> 8;
     }
 
     /**
      * Configure the sort select size for dual pivot partitioning.
      *
      * @param k1 First key of interest.
      * @param kn Last key of interest.
      * @return the sort select size.
      */
     private static int dualPivotSortSelectSize(int k1, int kn) {
         // Configure the sort select size based on the index density
         // l---k1---k---k-----k--k------kn----r
         //
         // For a full sort the dual-pivot quicksort can switch to insertion sort
         // when the length is small. The optimum value is dependent on the
         // hardware and the insertion sort implementation. Benchmarks show that
         // insertion sort can be used at length 80-120.
         //
         // During selection the SORTSELECT_SIZE specifies the distance from the edge
         // to use sort select. When keys are not dense there may be a small length
         // that is ignored by sort select due to the presence of another key.
         // Diagram of k-l = SORTSELECT_SIZE and r-k < SORTSELECT_SIZE where a second
         // key b is blocking the use of sort select. The key b is closest it can be to the right
         // key to enable blocking; it could be further away (up to k = left).
         //
         // |--SORTSELECT_SIZE--|
         //    |--SORTSELECT_SIZE--|
         // l--b----------------k--r
         // l----b--------------k----r
         // l------b------------k------r
         // l--------b----------k--------r
         // l----------b--------k----------r
         // l------------b------k------------r
         // l--------------b----k--------------r
         // l----------------b--k----------------r
         // l------------------bk------------------r
         //                    |--SORTSELECT_SIZE--|
         //
         // For all these cases the partitioning method would have to run. Assuming ideal
         // dual-pivot partitioning into thirds, and that the left key is randomly positioned
         // in [left, k) it is more likely that after partitioning 2 partitions will have to
         // be processed rather than 1 partition. In this case the options are:
         // - split the range using partitioning; sort select next iteration
         // - use sort select with a edge distance above the optimum length for single k selection
         //
         // Contrast with a longer length:
         // |--SORTSELECT_SIZE--|
         // l-------------------k-----k-------k-------------------r
         //                                   |--SORTSELECT_SIZE--|
         // Here partitioning has to run and 1, 2, or 3 partitions processed. But all k can
         // be found with a sort. In this case sort select could be used with a much higher
         // length (e.g. 80 - 120).
         //
         // When keys are extremely sparse (never within SORTSELECT_SIZE) then no switch
         // to sort select based on length is *required*. It may still be beneficial to avoid
         // partitioning if the length is very small due to the overhead of partitioning.
         //
         // Benchmarking with different lengths for a switch to sort select show inconsistent
         // behaviour across platforms due to the variable speed of insertion sort at longer
         // lengths. Attempts to transition the length based on various ramps schemes can
         // be incorrect and result is a slowdown rather than speed-up (if the correct threshold
         // is not chosen).
         //
         // Here we use a much simpler scheme based on these observations:
         // - If the average separation is very high then no length will collect extra indices
         // from a sort select over the current trigger of using the distance from the end. But
         // using a length dependence will not effect the work done by sort select as it only
         // performs the minimum sorting required.
         // - If the average separation is within the SORTSELECT_SIZE then a round of
         // partitioning will create multiple regions that all require a sort selection.
         // Thus a partitioning round can be avoided if the length is small.
         // - If the keys are at the end with nothing in between then partitioning will be able
         // to split them but a sort will have to sort the entire range:
         // lk-------------------------------kr
         // After partitioning starts the chance of keys being at the ends is low as keys
         // should be random within the divided range.
         // - Extremely high density keys is rare. It is only expected to saturate the range
         // with short lengths, e.g. 100 quantiles for length 1000 = separation 10 (high density)
         // but for length 10000 = separation 100 (low density).
         // - The density of (non-uniform) keys is hard to predict without complex analysis.
         //
         // Benchmarking using random keys at various density show no performance loss from
         // using a fixed size for the length dependence of sort select, if the size is small.
         // A large length can impact performance with low density keys, and on machines
         // where insertion sort is slower. Extreme performance gains occur when the average
         // separation of random keys is below 8-16, or of uniform keys around 32, by using a
         // sort at lengths up to 90. But this threshold shows performance loss greater than
         // the gains with separation of 64-128 on random keys, and on machines with slow
         // insertion sort. The transition to using an insertion sort of a longer length
         // is difficult to predict for all situations.
 
         // Let partitioning run if the initial length is small.
         // Use kn - k1 as a proxy for the length. If length is actually very large then
         // the final selection is insignificant. This avoids slowdown for small lengths
         // where the keys may only be at the ends. Note ideal dual-pivot partitioning
         // creates thirds so 1 iteration on SORTSELECT_SIZE * 3 should create
         // SORTSELECT_SIZE partitions.
         if (kn - k1 < DP_SORTSELECT_SIZE * 3) {
             return 0;
         }
         // Here partitioning will run at least once.
         // Stable performance across platforms using a modest length dependence.
         return DP_SORTSELECT_SIZE * 2;
     }
 }