/*

 * Copyright (c) 2016, Alliance for Open Media. All rights reserved.

 *

 * This source code is subject to the terms of the BSD 2 Clause License and

 * the Alliance for Open Media Patent License 1.0. If the BSD 2 Clause License

 * was not distributed with this source code in the LICENSE file, you can

 * obtain it at www.aomedia.org/license/software. If the Alliance for Open

 * Media Patent License 1.0 was not distributed with this source code in the

 * PATENTS file, you can obtain it at www.aomedia.org/license/patent.

 */

#include <math.h>

#include <stdlib.h>

#include "av1/common/pred_common.h"

#include "aom_ports/bitops.h"

#include "av1/encoder/block.h"

#include "av1/encoder/cost.h"

#include "av1/encoder/encoder.h"

#include "av1/encoder/intra_mode_search.h"

#include "av1/encoder/intra_mode_search_utils.h"

#include "av1/encoder/palette.h"

#include "av1/encoder/random.h"

#include "av1/encoder/rdopt_utils.h"

#include "av1/encoder/tx_search.h"

#define AV1_K_MEANS_DIM 1

#include "av1/encoder/k_means_template.h"

#undef AV1_K_MEANS_DIM

#define AV1_K_MEANS_DIM 2

#include "av1/encoder/k_means_template.h"

#undef AV1_K_MEANS_DIM

static int int16_comparer(const void *a, const void *b) {

  return (*(int16_t *)a - *(int16_t *)b);

}

/*!\brief Removes duplicated centroid indices.

 *

 * \ingroup palette_mode_search

 * \param[in]    centroids          A list of centroids index.

 * \param[in]    num_centroids      Number of centroids.

 *

 * \return Returns the number of unique centroids and saves the unique centroids

 * in beginning of the centroids array.

 *

 * \attention The centroids should be rounded to integers before calling this

 * method.

 */

static int remove_duplicates(int16_t *centroids, int num_centroids) {

  int num_unique;  // number of unique centroids

  int i;

  qsort(centroids, num_centroids, sizeof(*centroids), int16_comparer);

  // Remove duplicates.

  num_unique = 1;

  for (i = 1; i < num_centroids; ++i) {

    if (centroids[i] != centroids[i - 1]) {  // found a new unique centroid

      centroids[num_unique++] = centroids[i];

    }

  }

  return num_unique;

}

static int delta_encode_cost(const int *colors, int num, int bit_depth,

                             int min_val) {

  if (num <= 0) return 0;

  int bits_cost = bit_depth;

  if (num == 1) return bits_cost;

  bits_cost += 2;

  int max_delta = 0;

  int deltas[PALETTE_MAX_SIZE];

  const int min_bits = bit_depth - 3;

  for (int i = 1; i < num; ++i) {

    const int delta = colors[i] - colors[i - 1];

    deltas[i - 1] = delta;

    assert(delta >= min_val);

    if (delta > max_delta) max_delta = delta;

  }

  int bits_per_delta = AOMMAX(aom_ceil_log2(max_delta + 1 - min_val), min_bits);

  assert(bits_per_delta <= bit_depth);

  int range = (1 << bit_depth) - colors[0] - min_val;

  for (int i = 0; i < num - 1; ++i) {

    bits_cost += bits_per_delta;

    range -= deltas[i];

    bits_per_delta = AOMMIN(bits_per_delta, aom_ceil_log2(range));

  }

  return bits_cost;

}

int av1_index_color_cache(const uint16_t *color_cache, int n_cache,

                          const uint16_t *colors, int n_colors,

                          uint8_t *cache_color_found, int *out_cache_colors) {

  if (n_cache <= 0) {

    for (int i = 0; i < n_colors; ++i) out_cache_colors[i] = colors[i];

    return n_colors;

  }

  memset(cache_color_found, 0, n_cache * sizeof(*cache_color_found));

  int n_in_cache = 0;

  int in_cache_flags[PALETTE_MAX_SIZE];

  memset(in_cache_flags, 0, sizeof(in_cache_flags));

  for (int i = 0; i < n_cache && n_in_cache < n_colors; ++i) {

    for (int j = 0; j < n_colors; ++j) {

      if (colors[j] == color_cache[i]) {

        in_cache_flags[j] = 1;

        cache_color_found[i] = 1;

        ++n_in_cache;

        break;

      }

    }

  }

  int j = 0;

  for (int i = 0; i < n_colors; ++i)

    if (!in_cache_flags[i]) out_cache_colors[j++] = colors[i];

  assert(j == n_colors - n_in_cache);

  return j;

}

int av1_get_palette_delta_bits_v(const PALETTE_MODE_INFO *const pmi,

                                 int bit_depth, int *zero_count,

                                 int *min_bits) {

  const int n = pmi->palette_size[1];

  const int max_val = 1 << bit_depth;

  int max_d = 0;

  *min_bits = bit_depth - 4;

  *zero_count = 0;

  for (int i = 1; i < n; ++i) {

    const int delta = pmi->palette_colors[2 * PALETTE_MAX_SIZE + i] -

                      pmi->palette_colors[2 * PALETTE_MAX_SIZE + i - 1];

    const int v = abs(delta);

    const int d = AOMMIN(v, max_val - v);

    if (d > max_d) max_d = d;

    if (d == 0) ++(*zero_count);

  }

  return AOMMAX(aom_ceil_log2(max_d + 1), *min_bits);

}

int av1_palette_color_cost_y(const PALETTE_MODE_INFO *const pmi,

                             const uint16_t *color_cache, int n_cache,

                             int bit_depth) {

  const int n = pmi->palette_size[0];

  int out_cache_colors[PALETTE_MAX_SIZE];

  uint8_t cache_color_found[2 * PALETTE_MAX_SIZE];

  const int n_out_cache =

      av1_index_color_cache(color_cache, n_cache, pmi->palette_colors, n,

                            cache_color_found, out_cache_colors);

  const int total_bits =

      n_cache + delta_encode_cost(out_cache_colors, n_out_cache, bit_depth, 1);

  return av1_cost_literal(total_bits);

}

int av1_palette_color_cost_uv(const PALETTE_MODE_INFO *const pmi,

                              const uint16_t *color_cache, int n_cache,

                              int bit_depth) {

  const int n = pmi->palette_size[1];

  int total_bits = 0;

  // U channel palette color cost.

  int out_cache_colors[PALETTE_MAX_SIZE];

  uint8_t cache_color_found[2 * PALETTE_MAX_SIZE];

  const int n_out_cache = av1_index_color_cache(

      color_cache, n_cache, pmi->palette_colors + PALETTE_MAX_SIZE, n,

      cache_color_found, out_cache_colors);

  total_bits +=

      n_cache + delta_encode_cost(out_cache_colors, n_out_cache, bit_depth, 0);

  // V channel palette color cost.

  int zero_count = 0, min_bits_v = 0;

  const int bits_v =

      av1_get_palette_delta_bits_v(pmi, bit_depth, &zero_count, &min_bits_v);

  const int bits_using_delta =

      2 + bit_depth + (bits_v + 1) * (n - 1) - zero_count;

  const int bits_using_raw = bit_depth * n;

  total_bits += 1 + AOMMIN(bits_using_delta, bits_using_raw);

  return av1_cost_literal(total_bits);

}

// Extends 'color_map' array from 'orig_width x orig_height' to 'new_width x

// new_height'. Extra rows and columns are filled in by copying last valid

// row/column.

static inline void extend_palette_color_map(uint8_t *const color_map,

                                            int orig_width, int orig_height,

                                            int new_width, int new_height) {

  int j;

  assert(new_width >= orig_width);

  assert(new_height >= orig_height);

  if (new_width == orig_width && new_height == orig_height) return;

  for (j = orig_height - 1; j >= 0; --j) {

    memmove(color_map + j * new_width, color_map + j * orig_width, orig_width);

    // Copy last column to extra columns.

    memset(color_map + j * new_width + orig_width,

           color_map[j * new_width + orig_width - 1], new_width - orig_width);

  }

  // Copy last row to extra rows.

  for (j = orig_height; j < new_height; ++j) {

    memcpy(color_map + j * new_width, color_map + (orig_height - 1) * new_width,

           new_width);

  }

}

// Bias toward using colors in the cache.

// TODO(huisu): Try other schemes to improve compression.

static inline void optimize_palette_colors(uint16_t *color_cache, int n_cache,

                                           int n_colors, int stride,

                                           int16_t *centroids, int bit_depth) {

  if (n_cache <= 0) return;

  for (int i = 0; i < n_colors * stride; i += stride) {

    int min_diff = abs((int)centroids[i] - (int)color_cache[0]);

    int idx = 0;

    for (int j = 1; j < n_cache; ++j) {

      const int this_diff = abs((int)centroids[i] - (int)color_cache[j]);

      if (this_diff < min_diff) {

        min_diff = this_diff;

        idx = j;

      }

    }

    const int min_threshold = 4 << (bit_depth - 8);

    if (min_diff <= min_threshold) centroids[i] = color_cache[idx];

  }

}

/*!\brief Calculate the luma palette cost from a given color palette

 *

 * \ingroup palette_mode_search

 * \callergraph

 * Given the base colors as specified in centroids[], calculate the RD cost

 * of palette mode.

 */

static inline void palette_rd_y(

    const AV1_COMP *const cpi, MACROBLOCK *x, MB_MODE_INFO *mbmi,

    BLOCK_SIZE bsize, int dc_mode_cost, const int16_t *data, int16_t *centroids,

    int n, uint16_t *color_cache, int n_cache, bool do_header_rd_based_gating,

    MB_MODE_INFO *best_mbmi, uint8_t *best_palette_color_map, int64_t *best_rd,

    int *rate, int *rate_tokenonly, int64_t *distortion, uint8_t *skippable,

    int *beat_best_rd, PICK_MODE_CONTEXT *ctx, uint8_t *tx_type_map,

    int *beat_best_palette_rd, bool *do_header_rd_based_breakout,

    int discount_color_cost) {

  if (do_header_rd_based_breakout != NULL) *do_header_rd_based_breakout = false;

  optimize_palette_colors(color_cache, n_cache, n, 1, centroids,

                          cpi->common.seq_params->bit_depth);

  const int num_unique_colors = remove_duplicates(centroids, n);

  if (num_unique_colors < PALETTE_MIN_SIZE) {

    // Too few unique colors to create a palette. And DC_PRED will work

    // well for that case anyway. So skip.

    return;

  }

  PALETTE_MODE_INFO *const pmi = &mbmi->palette_mode_info;

  if (cpi->common.seq_params->use_highbitdepth) {

    for (int i = 0; i < num_unique_colors; ++i) {

      pmi->palette_colors[i] = clip_pixel_highbd(

          (int)centroids[i], cpi->common.seq_params->bit_depth);

    }

  } else {

    for (int i = 0; i < num_unique_colors; ++i) {

      pmi->palette_colors[i] = clip_pixel(centroids[i]);

    }

  }

  pmi->palette_size[0] = num_unique_colors;

  MACROBLOCKD *const xd = &x->e_mbd;

  uint8_t *const color_map = xd->plane[0].color_index_map;

  int block_width, block_height, rows, cols;

  av1_get_block_dimensions(bsize, 0, xd, &block_width, &block_height, &rows,

                           &cols);

  av1_calc_indices(data, centroids, color_map, rows * cols, num_unique_colors,

                   1);

  extend_palette_color_map(color_map, cols, rows, block_width, block_height);

  RD_STATS tokenonly_rd_stats;

  int this_rate;

  if (do_header_rd_based_gating) {

    assert(do_header_rd_based_breakout != NULL);

    const int palette_mode_rate = intra_mode_info_cost_y(

        cpi, x, mbmi, bsize, dc_mode_cost, discount_color_cost);

    const int64_t header_rd = RDCOST(x->rdmult, palette_mode_rate, 0);

    // Less aggressive pruning when prune_luma_palette_size_search_level == 1.

    const int header_rd_shift =

        (cpi->sf.intra_sf.prune_luma_palette_size_search_level == 1) ? 1 : 0;

    // Terminate further palette_size search, if the header cost corresponding

    // to lower palette_size is more than *best_rd << header_rd_shift. This

    // logic is implemented with a right shift in the LHS to prevent a possible

    // overflow with the left shift in RHS.

    if ((header_rd >> header_rd_shift) > *best_rd) {

      *do_header_rd_based_breakout = true;

      return;

    }

    av1_pick_uniform_tx_size_type_yrd(cpi, x, &tokenonly_rd_stats, bsize,

                                      *best_rd);

    if (tokenonly_rd_stats.rate == INT_MAX) return;

    this_rate = tokenonly_rd_stats.rate + palette_mode_rate;

  } else {

    av1_pick_uniform_tx_size_type_yrd(cpi, x, &tokenonly_rd_stats, bsize,

                                      *best_rd);

    if (tokenonly_rd_stats.rate == INT_MAX) return;

    this_rate = tokenonly_rd_stats.rate +

                intra_mode_info_cost_y(cpi, x, mbmi, bsize, dc_mode_cost,

                                       discount_color_cost);

  }

  int64_t this_rd = RDCOST(x->rdmult, this_rate, tokenonly_rd_stats.dist);

  if (!xd->lossless[mbmi->segment_id] && block_signals_txsize(mbmi->bsize)) {

    tokenonly_rd_stats.rate -= tx_size_cost(x, bsize, mbmi->tx_size);

  }

  // Collect mode stats for multiwinner mode processing

  const int txfm_search_done = 1;

  store_winner_mode_stats(

      &cpi->common, x, mbmi, NULL, NULL, NULL, THR_DC, color_map, bsize,

      this_rd, cpi->sf.winner_mode_sf.multi_winner_mode_type, txfm_search_done);

  if (this_rd < *best_rd) {

    *best_rd = this_rd;

    // Setting beat_best_rd flag because current mode rd is better than best_rd.

    // This flag need to be updated only for palette evaluation in key frames

    if (beat_best_rd) *beat_best_rd = 1;

    memcpy(best_palette_color_map, color_map,

           block_width * block_height * sizeof(color_map[0]));

    *best_mbmi = *mbmi;

    av1_copy_array(tx_type_map, xd->tx_type_map, ctx->num_4x4_blk);

    if (rate) *rate = this_rate;

    if (rate_tokenonly) *rate_tokenonly = tokenonly_rd_stats.rate;

    if (distortion) *distortion = tokenonly_rd_stats.dist;

    if (skippable) *skippable = tokenonly_rd_stats.skip_txfm;

    if (beat_best_palette_rd) *beat_best_palette_rd = 1;

  }

}

static inline int is_iter_over(int curr_idx, int end_idx, int step_size) {

  assert(step_size != 0);

  return (step_size > 0) ? curr_idx >= end_idx : curr_idx <= end_idx;

}

// Performs count-based palette search with number of colors in interval

// [start_n, end_n) with step size step_size. If step_size < 0, then end_n can

// be less than start_n. Saves the last numbers searched in last_n_searched and

// returns the best number of colors found.

static inline int perform_top_color_palette_search(

    const AV1_COMP *const cpi, MACROBLOCK *x, MB_MODE_INFO *mbmi,

    BLOCK_SIZE bsize, int dc_mode_cost, const int16_t *data,

    int16_t *top_colors, int start_n, int end_n, int step_size,

    bool do_header_rd_based_gating, int *last_n_searched, uint16_t *color_cache,

    int n_cache, MB_MODE_INFO *best_mbmi, uint8_t *best_palette_color_map,

    int64_t *best_rd, int *rate, int *rate_tokenonly, int64_t *distortion,

    uint8_t *skippable, int *beat_best_rd, PICK_MODE_CONTEXT *ctx,

    uint8_t *tx_type_map, int discount_color_cost) {

  int16_t centroids[PALETTE_MAX_SIZE];

  int n = start_n;

  int top_color_winner = end_n;

  /* clang-format off */

  assert(IMPLIES(step_size < 0, start_n > end_n));

  /* clang-format on */

  assert(IMPLIES(step_size > 0, start_n < end_n));

  while (!is_iter_over(n, end_n, step_size)) {

    int beat_best_palette_rd = 0;

    bool do_header_rd_based_breakout = false;

    memcpy(centroids, top_colors, n * sizeof(top_colors[0]));

    palette_rd_y(cpi, x, mbmi, bsize, dc_mode_cost, data, centroids, n,

                 color_cache, n_cache, do_header_rd_based_gating, best_mbmi,

                 best_palette_color_map, best_rd, rate, rate_tokenonly,

                 distortion, skippable, beat_best_rd, ctx, tx_type_map,

                 &beat_best_palette_rd, &do_header_rd_based_breakout,

                 discount_color_cost);

    *last_n_searched = n;

    if (do_header_rd_based_breakout) {

      // Terminate palette_size search by setting last_n_searched to end_n.

      *last_n_searched = end_n;

      break;

    }

    if (beat_best_palette_rd) {

      top_color_winner = n;

    } else if (cpi->sf.intra_sf.prune_palette_search_level == 2) {

      // At search level 2, we return immediately if we don't see an improvement

      return top_color_winner;

    }

    n += step_size;

  }

  return top_color_winner;

}

// Performs k-means based palette search with number of colors in interval

// [start_n, end_n) with step size step_size. If step_size < 0, then end_n can

// be less than start_n. Saves the last numbers searched in last_n_searched and

// returns the best number of colors found.

static inline int perform_k_means_palette_search(

    const AV1_COMP *const cpi, MACROBLOCK *x, MB_MODE_INFO *mbmi,

    BLOCK_SIZE bsize, int dc_mode_cost, const int16_t *data, int lower_bound,

    int upper_bound, int start_n, int end_n, int step_size,

    bool do_header_rd_based_gating, int *last_n_searched, uint16_t *color_cache,

    int n_cache, MB_MODE_INFO *best_mbmi, uint8_t *best_palette_color_map,

    int64_t *best_rd, int *rate, int *rate_tokenonly, int64_t *distortion,

    uint8_t *skippable, int *beat_best_rd, PICK_MODE_CONTEXT *ctx,

    uint8_t *tx_type_map, uint8_t *color_map, int data_points,

    int discount_color_cost) {

  int16_t centroids[PALETTE_MAX_SIZE];

  const int max_itr = 50;

  int n = start_n;

  int top_color_winner = end_n;

  /* clang-format off */

  assert(IMPLIES(step_size < 0, start_n > end_n));

  /* clang-format on */

  assert(IMPLIES(step_size > 0, start_n < end_n));

  while (!is_iter_over(n, end_n, step_size)) {

    int beat_best_palette_rd = 0;

    bool do_header_rd_based_breakout = false;

    for (int i = 0; i < n; ++i) {

      centroids[i] =

          lower_bound + (2 * i + 1) * (upper_bound - lower_bound) / n / 2;

    }

    av1_k_means(data, centroids, color_map, data_points, n, 1, max_itr);

    palette_rd_y(cpi, x, mbmi, bsize, dc_mode_cost, data, centroids, n,

                 color_cache, n_cache, do_header_rd_based_gating, best_mbmi,

                 best_palette_color_map, best_rd, rate, rate_tokenonly,

                 distortion, skippable, beat_best_rd, ctx, tx_type_map,

                 &beat_best_palette_rd, &do_header_rd_based_breakout,

                 discount_color_cost);

    *last_n_searched = n;

    if (do_header_rd_based_breakout) {

      // Terminate palette_size search by setting last_n_searched to end_n.

      *last_n_searched = end_n;

      break;

    }

    if (beat_best_palette_rd) {

      top_color_winner = n;

    } else if (cpi->sf.intra_sf.prune_palette_search_level == 2) {

      // At search level 2, we return immediately if we don't see an improvement

      return top_color_winner;

    }

    n += step_size;

  }

  return top_color_winner;

}

// Sets the parameters to search the current number of colors +- 1

static inline void set_stage2_params(int *min_n, int *max_n, int *step_size,

                                     int winner, int end_n) {

  // Set min to winner - 1 unless we are already at the border, then we set it

  // to winner + 1

  *min_n = (winner == PALETTE_MIN_SIZE) ? (PALETTE_MIN_SIZE + 1)

                                        : AOMMAX(winner - 1, PALETTE_MIN_SIZE);

  // Set max to winner + 1 unless we are already at the border, then we set it

  // to winner - 1

  *max_n =

      (winner == end_n) ? (winner - 1) : AOMMIN(winner + 1, PALETTE_MAX_SIZE);

  // Set the step size to max_n - min_n so we only search those two values.

  // If max_n == min_n, then set step_size to 1 to avoid infinite loop later.

  *step_size = AOMMAX(1, *max_n - *min_n);

}

static inline void fill_data_and_get_bounds(const uint8_t *src,

                                            const int src_stride,

                                            const int rows, const int cols,

                                            const int is_high_bitdepth,

                                            int16_t *data, int *lower_bound,

                                            int *upper_bound) {

  if (is_high_bitdepth) {

    const uint16_t *src_ptr = CONVERT_TO_SHORTPTR(src);

    *lower_bound = *upper_bound = src_ptr[0];

    for (int r = 0; r < rows; ++r) {

      for (int c = 0; c < cols; ++c) {

        const int val = src_ptr[c];

        data[c] = (int16_t)val;

        *lower_bound = AOMMIN(*lower_bound, val);

        *upper_bound = AOMMAX(*upper_bound, val);

      }

      src_ptr += src_stride;

      data += cols;

    }

    return;

  }

  // low bit depth

  *lower_bound = *upper_bound = src[0];

  for (int r = 0; r < rows; ++r) {

    for (int c = 0; c < cols; ++c) {

      const int val = src[c];

      data[c] = (int16_t)val;

      *lower_bound = AOMMIN(*lower_bound, val);

      *upper_bound = AOMMAX(*upper_bound, val);

    }

    src += src_stride;

    data += cols;

  }

}

/*! \brief Colors are sorted by their count: the higher the better.

 */

struct ColorCount {

  //! Color index in the histogram.

  int index;

  //! Histogram count.

  int count;

};

static int color_count_comp(const void *c1, const void *c2) {

  const struct ColorCount *color_count1 = (const struct ColorCount *)c1;

  const struct ColorCount *color_count2 = (const struct ColorCount *)c2;

  if (color_count1->count > color_count2->count) return -1;

  if (color_count1->count < color_count2->count) return 1;

  if (color_count1->index < color_count2->index) return -1;

  return 1;

}

static void find_top_colors(const int *const count_buf, int bit_depth,

                            int n_colors, int16_t *top_colors) {

  // Top color array, serving as a priority queue if more than n_colors are

  // found.

  struct ColorCount top_color_counts[PALETTE_MAX_SIZE] = { { 0 } };

  int n_color_count = 0;

  for (int i = 0; i < (1 << bit_depth); ++i) {

    if (count_buf[i] > 0) {

      if (n_color_count < n_colors) {

        // Keep adding to the top colors.

        top_color_counts[n_color_count].index = i;

        top_color_counts[n_color_count].count = count_buf[i];

        ++n_color_count;

        if (n_color_count == n_colors) {

          qsort(top_color_counts, n_colors, sizeof(top_color_counts[0]),

                color_count_comp);

        }

      } else {

        // Check the worst in the sorted top.

        if (count_buf[i] > top_color_counts[n_colors - 1].count) {

          int j = n_colors - 1;

          // Move up to the best one.

          while (j >= 1 && count_buf[i] > top_color_counts[j - 1].count) --j;

          memmove(top_color_counts + j + 1, top_color_counts + j,

                  (n_colors - j - 1) * sizeof(top_color_counts[0]));

          top_color_counts[j].index = i;

          top_color_counts[j].count = count_buf[i];

        }

      }

    }

  }

  assert(n_color_count == n_colors);

  for (int i = 0; i < n_colors; ++i) {

    top_colors[i] = top_color_counts[i].index;

  }

}

void av1_rd_pick_palette_intra_sby(

    const AV1_COMP *cpi, MACROBLOCK *x, BLOCK_SIZE bsize, int dc_mode_cost,

    MB_MODE_INFO *best_mbmi, uint8_t *best_palette_color_map, int64_t *best_rd,

    int *rate, int *rate_tokenonly, int64_t *distortion, uint8_t *skippable,

    int *beat_best_rd, PICK_MODE_CONTEXT *ctx, uint8_t *tx_type_map) {

  MACROBLOCKD *const xd = &x->e_mbd;

  MB_MODE_INFO *const mbmi = xd->mi[0];

  assert(!is_inter_block(mbmi));

  assert(av1_allow_palette(cpi->common.features.allow_screen_content_tools,

                           bsize));

  assert(PALETTE_MAX_SIZE == 8);

  assert(PALETTE_MIN_SIZE == 2);

  const int src_stride = x->plane[0].src.stride;

  const uint8_t *const src = x->plane[0].src.buf;

  int block_width, block_height, rows, cols;

  av1_get_block_dimensions(bsize, 0, xd, &block_width, &block_height, &rows,

                           &cols);

  const SequenceHeader *const seq_params = cpi->common.seq_params;

  const int is_hbd = seq_params->use_highbitdepth;

  const int bit_depth = seq_params->bit_depth;

  const int discount_color_cost = cpi->sf.rt_sf.discount_color_cost;

  int unused;

  int count_buf[1 << 12];  // Maximum (1 << 12) color levels.

  int colors, colors_threshold = 0;

  if (is_hbd) {

    int count_buf_8bit[1 << 8];  // Maximum (1 << 8) bins for hbd path.

    av1_count_colors_highbd(src, src_stride, rows, cols, bit_depth, count_buf,

                            count_buf_8bit, &colors_threshold, &colors);

  } else {

    av1_count_colors(src, src_stride, rows, cols, count_buf, &colors);

    colors_threshold = colors;

  }

  uint8_t *const color_map = xd->plane[0].color_index_map;

  int color_thresh_palette = x->color_palette_thresh;

  // Allow for larger color_threshold for palette search, based on color,

  // scene_change, and block source variance.

  // Since palette is Y based, only allow larger threshold if block

  // color_dist is below threshold.

  if (cpi->sf.rt_sf.use_nonrd_pick_mode &&

      cpi->sf.rt_sf.increase_color_thresh_palette && cpi->rc.high_source_sad &&

      x->source_variance > 50) {

    int64_t norm_color_dist = 0;

    if (x->color_sensitivity[0] || x->color_sensitivity[1]) {

      norm_color_dist = x->min_dist_inter_uv >>

                        (mi_size_wide_log2[bsize] + mi_size_high_log2[bsize]);

      if (x->color_sensitivity[0] && x->color_sensitivity[1])

        norm_color_dist = norm_color_dist >> 1;

    }

    if (norm_color_dist < 8000) color_thresh_palette += 20;

  }

  if (colors_threshold > 1 && colors_threshold <= color_thresh_palette) {

    int16_t *const data = x->palette_buffer->kmeans_data_buf;

    int16_t centroids[PALETTE_MAX_SIZE];

    int lower_bound, upper_bound;

    fill_data_and_get_bounds(src, src_stride, rows, cols, is_hbd, data,

                             &lower_bound, &upper_bound);

    mbmi->mode = DC_PRED;

    mbmi->filter_intra_mode_info.use_filter_intra = 0;

    uint16_t color_cache[2 * PALETTE_MAX_SIZE];

    const int n_cache = av1_get_palette_cache(xd, 0, color_cache);

    // Find the dominant colors, stored in top_colors[].

    int16_t top_colors[PALETTE_MAX_SIZE] = { 0 };

    find_top_colors(count_buf, bit_depth, AOMMIN(colors, PALETTE_MAX_SIZE),

                    top_colors);

    // The following are the approaches used for header rdcost based gating

    // for early termination for different values of prune_palette_search_level.

    // 0: Pruning based on header rdcost for ascending order palette_size

    // search.

    // 1: When colors > PALETTE_MIN_SIZE, enabled only for coarse palette_size

    // search and for finer search do_header_rd_based_gating parameter is

    // explicitly passed as 'false'.

    // 2: Enabled only for ascending order palette_size search and for

    // descending order search do_header_rd_based_gating parameter is explicitly

    // passed as 'false'.

    const bool do_header_rd_based_gating =

        cpi->sf.intra_sf.prune_luma_palette_size_search_level != 0;

    // TODO(huisu@google.com): Try to avoid duplicate computation in cases

    // where the dominant colors and the k-means results are similar.

    if ((cpi->sf.intra_sf.prune_palette_search_level == 1) &&

        (colors > PALETTE_MIN_SIZE)) {

      // Start index and step size below are chosen to evaluate unique

      // candidates in neighbor search, in case a winner candidate is found in

      // coarse search. Example,

      // 1) 8 colors (end_n = 8): 2,3,4,5,6,7,8. start_n is chosen as 2 and step

      // size is chosen as 3. Therefore, coarse search will evaluate 2, 5 and 8.

      // If winner is found at 5, then 4 and 6 are evaluated. Similarly, for 2

      // (3) and 8 (7).

      // 2) 7 colors (end_n = 7): 2,3,4,5,6,7. If start_n is chosen as 2 (same

      // as for 8 colors) then step size should also be 2, to cover all

      // candidates. Coarse search will evaluate 2, 4 and 6. If winner is either

      // 2 or 4, 3 will be evaluated. Instead, if start_n=3 and step_size=3,

      // coarse search will evaluate 3 and 6. For the winner, unique neighbors

      // (3: 2,4 or 6: 5,7) would be evaluated.

      // Start index for coarse palette search for dominant colors and k-means

      const uint8_t start_n_lookup_table[PALETTE_MAX_SIZE + 1] = { 0, 0, 0,

                                                                   3, 3, 2,

                                                                   3, 3, 2 };

      // Step size for coarse palette search for dominant colors and k-means

      const uint8_t step_size_lookup_table[PALETTE_MAX_SIZE + 1] = { 0, 0, 0,

                                                                     3, 3, 3,

                                                                     3, 3, 3 };

      // Choose the start index and step size for coarse search based on number

      // of colors

      const int max_n = AOMMIN(colors, PALETTE_MAX_SIZE);

      const int min_n = start_n_lookup_table[max_n];

      const int step_size = step_size_lookup_table[max_n];

      assert(min_n >= PALETTE_MIN_SIZE);

      // Perform top color coarse palette search to find the winner candidate

      const int top_color_winner = perform_top_color_palette_search(

          cpi, x, mbmi, bsize, dc_mode_cost, data, top_colors, min_n, max_n + 1,

          step_size, do_header_rd_based_gating, &unused, color_cache, n_cache,

          best_mbmi, best_palette_color_map, best_rd, rate, rate_tokenonly,

          distortion, skippable, beat_best_rd, ctx, tx_type_map,

          discount_color_cost);

      // Evaluate neighbors for the winner color (if winner is found) in the

      // above coarse search for dominant colors

      if (top_color_winner <= max_n) {

        int stage2_min_n, stage2_max_n, stage2_step_size;

        set_stage2_params(&stage2_min_n, &stage2_max_n, &stage2_step_size,

                          top_color_winner, max_n);

        // perform finer search for the winner candidate

        perform_top_color_palette_search(

            cpi, x, mbmi, bsize, dc_mode_cost, data, top_colors, stage2_min_n,

            stage2_max_n + 1, stage2_step_size,

            /*do_header_rd_based_gating=*/false, &unused, color_cache, n_cache,

            best_mbmi, best_palette_color_map, best_rd, rate, rate_tokenonly,

            distortion, skippable, beat_best_rd, ctx, tx_type_map,

            discount_color_cost);

      }

      // K-means clustering.

      // Perform k-means coarse palette search to find the winner candidate

      const int k_means_winner = perform_k_means_palette_search(

          cpi, x, mbmi, bsize, dc_mode_cost, data, lower_bound, upper_bound,

          min_n, max_n + 1, step_size, do_header_rd_based_gating, &unused,

          color_cache, n_cache, best_mbmi, best_palette_color_map, best_rd,

          rate, rate_tokenonly, distortion, skippable, beat_best_rd, ctx,

          tx_type_map, color_map, rows * cols, discount_color_cost);

      // Evaluate neighbors for the winner color (if winner is found) in the

      // above coarse search for k-means

      if (k_means_winner <= max_n) {

        int start_n_stage2, end_n_stage2, step_size_stage2;

        set_stage2_params(&start_n_stage2, &end_n_stage2, &step_size_stage2,

                          k_means_winner, max_n);

        // perform finer search for the winner candidate

        perform_k_means_palette_search(

            cpi, x, mbmi, bsize, dc_mode_cost, data, lower_bound, upper_bound,

            start_n_stage2, end_n_stage2 + 1, step_size_stage2,

            /*do_header_rd_based_gating=*/false, &unused, color_cache, n_cache,

            best_mbmi, best_palette_color_map, best_rd, rate, rate_tokenonly,

            distortion, skippable, beat_best_rd, ctx, tx_type_map, color_map,

            rows * cols, discount_color_cost);

      }

    } else {

      const int max_n = AOMMIN(colors, PALETTE_MAX_SIZE),

                min_n = PALETTE_MIN_SIZE;

      // Perform top color palette search in ascending order

      int last_n_searched = min_n;

      perform_top_color_palette_search(

          cpi, x, mbmi, bsize, dc_mode_cost, data, top_colors, min_n, max_n + 1,

          1, do_header_rd_based_gating, &last_n_searched, color_cache, n_cache,

          best_mbmi, best_palette_color_map, best_rd, rate, rate_tokenonly,

          distortion, skippable, beat_best_rd, ctx, tx_type_map,

          discount_color_cost);

      if (last_n_searched < max_n) {

        // Search in descending order until we get to the previous best

        perform_top_color_palette_search(

            cpi, x, mbmi, bsize, dc_mode_cost, data, top_colors, max_n,

            last_n_searched, -1, /*do_header_rd_based_gating=*/false, &unused,

            color_cache, n_cache, best_mbmi, best_palette_color_map, best_rd,

            rate, rate_tokenonly, distortion, skippable, beat_best_rd, ctx,

            tx_type_map, discount_color_cost);

      }

      // K-means clustering.

      if (colors == PALETTE_MIN_SIZE) {

        // Special case: These colors automatically become the centroids.

        assert(colors == 2);

        centroids[0] = lower_bound;

        centroids[1] = upper_bound;

        palette_rd_y(cpi, x, mbmi, bsize, dc_mode_cost, data, centroids, colors,

                     color_cache, n_cache, /*do_header_rd_based_gating=*/false,

                     best_mbmi, best_palette_color_map, best_rd, rate,

                     rate_tokenonly, distortion, skippable, beat_best_rd, ctx,

                     tx_type_map, NULL, NULL, discount_color_cost);

      } else {

        // Perform k-means palette search in ascending order

        last_n_searched = min_n;

        perform_k_means_palette_search(

            cpi, x, mbmi, bsize, dc_mode_cost, data, lower_bound, upper_bound,

            min_n, max_n + 1, 1, do_header_rd_based_gating, &last_n_searched,

            color_cache, n_cache, best_mbmi, best_palette_color_map, best_rd,

            rate, rate_tokenonly, distortion, skippable, beat_best_rd, ctx,

            tx_type_map, color_map, rows * cols, discount_color_cost);

        if (last_n_searched < max_n) {

          // Search in descending order until we get to the previous best

          perform_k_means_palette_search(

              cpi, x, mbmi, bsize, dc_mode_cost, data, lower_bound, upper_bound,

              max_n, last_n_searched, -1, /*do_header_rd_based_gating=*/false,

              &unused, color_cache, n_cache, best_mbmi, best_palette_color_map,

              best_rd, rate, rate_tokenonly, distortion, skippable,

              beat_best_rd, ctx, tx_type_map, color_map, rows * cols,

              discount_color_cost);

        }

      }

    }

  }

  if (best_mbmi->palette_mode_info.palette_size[0] > 0) {

    memcpy(color_map, best_palette_color_map,

           block_width * block_height * sizeof(best_palette_color_map[0]));

    // Gather the stats to determine whether to use screen content tools in

    // function av1_determine_sc_tools_with_encoding().

    x->palette_pixels += (block_width * block_height);

  }

  *mbmi = *best_mbmi;

}

void av1_rd_pick_palette_intra_sbuv(const AV1_COMP *cpi, MACROBLOCK *x,

                                    int dc_mode_cost,

                                    uint8_t *best_palette_color_map,

                                    MB_MODE_INFO *const best_mbmi,

                                    int64_t *best_rd, int *rate,

                                    int *rate_tokenonly, int64_t *distortion,

                                    uint8_t *skippable) {

  MACROBLOCKD *const xd = &x->e_mbd;

  MB_MODE_INFO *const mbmi = xd->mi[0];

  assert(!is_inter_block(mbmi));

  assert(av1_allow_palette(cpi->common.features.allow_screen_content_tools,

                           mbmi->bsize));

  PALETTE_MODE_INFO *const pmi = &mbmi->palette_mode_info;

  const BLOCK_SIZE bsize = mbmi->bsize;

  const SequenceHeader *const seq_params = cpi->common.seq_params;

  int this_rate;

  int64_t this_rd;

  int colors_u, colors_v;

  int colors_threshold_u = 0, colors_threshold_v = 0, colors_threshold = 0;

  const int src_stride = x->plane[1].src.stride;

  const uint8_t *const src_u = x->plane[1].src.buf;

  const uint8_t *const src_v = x->plane[2].src.buf;

  uint8_t *const color_map = xd->plane[1].color_index_map;

  RD_STATS tokenonly_rd_stats;

  int plane_block_width, plane_block_height, rows, cols;

  av1_get_block_dimensions(bsize, 1, xd, &plane_block_width,

                           &plane_block_height, &rows, &cols);

  mbmi->uv_mode = UV_DC_PRED;

  if (seq_params->use_highbitdepth) {

    int count_buf[1 << 12];      // Maximum (1 << 12) color levels.

    int count_buf_8bit[1 << 8];  // Maximum (1 << 8) bins for hbd path.

    av1_count_colors_highbd(src_u, src_stride, rows, cols,

                            seq_params->bit_depth, count_buf, count_buf_8bit,

                            &colors_threshold_u, &colors_u);

    av1_count_colors_highbd(src_v, src_stride, rows, cols,

                            seq_params->bit_depth, count_buf, count_buf_8bit,

                            &colors_threshold_v, &colors_v);

  } else {

    int count_buf[1 << 8];

    av1_count_colors(src_u, src_stride, rows, cols, count_buf, &colors_u);

    av1_count_colors(src_v, src_stride, rows, cols, count_buf, &colors_v);

    colors_threshold_u = colors_u;

    colors_threshold_v = colors_v;

  }

  uint16_t color_cache[2 * PALETTE_MAX_SIZE];

  const int n_cache = av1_get_palette_cache(xd, 1, color_cache);

  colors_threshold = colors_threshold_u > colors_threshold_v

                         ? colors_threshold_u

                         : colors_threshold_v;

  if (colors_threshold > 1 && colors_threshold <= 64) {

    int r, c, n, i, j;

    const int max_itr = 50;

    int lb_u, ub_u, val_u;

    int lb_v, ub_v, val_v;

    int16_t *const data = x->palette_buffer->kmeans_data_buf;

    int16_t centroids[2 * PALETTE_MAX_SIZE];

    uint16_t *src_u16 = CONVERT_TO_SHORTPTR(src_u);

    uint16_t *src_v16 = CONVERT_TO_SHORTPTR(src_v);

    if (seq_params->use_highbitdepth) {

      lb_u = src_u16[0];

      ub_u = src_u16[0];

      lb_v = src_v16[0];

      ub_v = src_v16[0];

    } else {

      lb_u = src_u[0];

      ub_u = src_u[0];

      lb_v = src_v[0];

      ub_v = src_v[0];

    }

    for (r = 0; r < rows; ++r) {

      for (c = 0; c < cols; ++c) {

        if (seq_params->use_highbitdepth) {

          val_u = src_u16[r * src_stride + c];

          val_v = src_v16[r * src_stride + c];

          data[(r * cols + c) * 2] = val_u;

          data[(r * cols + c) * 2 + 1] = val_v;

        } else {

          val_u = src_u[r * src_stride + c];

          val_v = src_v[r * src_stride + c];

          data[(r * cols + c) * 2] = val_u;

          data[(r * cols + c) * 2 + 1] = val_v;

        }

        if (val_u < lb_u)

          lb_u = val_u;

        else if (val_u > ub_u)

          ub_u = val_u;

        if (val_v < lb_v)

          lb_v = val_v;

        else if (val_v > ub_v)

          ub_v = val_v;

      }

    }

    const int colors = colors_u > colors_v ? colors_u : colors_v;

    const int max_colors =

        colors > PALETTE_MAX_SIZE ? PALETTE_MAX_SIZE : colors;

    for (n = PALETTE_MIN_SIZE; n <= max_colors; ++n) {

      for (i = 0; i < n; ++i) {

        centroids[i * 2] = lb_u + (2 * i + 1) * (ub_u - lb_u) / n / 2;

        centroids[i * 2 + 1] = lb_v + (2 * i + 1) * (ub_v - lb_v) / n / 2;

      }

      av1_k_means(data, centroids, color_map, rows * cols, n, 2, max_itr);