// Copyright (c) the JPEG XL Project Authors. All rights reserved.
//
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

#include "lib/jxl/modular/encoding/enc_ma.h"

#include <algorithm>
#include <limits>
#include <numeric>
#include <queue>
#include <unordered_map>
#include <unordered_set>

#include "lib/jxl/modular/encoding/ma_common.h"

#undef HWY_TARGET_INCLUDE
#define HWY_TARGET_INCLUDE "lib/jxl/modular/encoding/enc_ma.cc"
#include <hwy/foreach_target.h>
#include <hwy/highway.h>

#include "lib/jxl/base/fast_math-inl.h"
#include "lib/jxl/base/random.h"
#include "lib/jxl/enc_ans.h"
#include "lib/jxl/modular/encoding/context_predict.h"
#include "lib/jxl/modular/options.h"
#include "lib/jxl/pack_signed.h"
HWY_BEFORE_NAMESPACE();
namespace jxl {
namespace HWY_NAMESPACE {

// These templates are not found via ADL.
using hwy::HWY_NAMESPACE::Eq;
using hwy::HWY_NAMESPACE::IfThenElse;
using hwy::HWY_NAMESPACE::Lt;
using hwy::HWY_NAMESPACE::Max;

const HWY_FULL(float) df;
const HWY_FULL(int32_t) di;
size_t Padded(size_t x) { return RoundUpTo(x, Lanes(df)); }

// Compute entropy of the histogram, taking into account the minimum probability
// for symbols with non-zero counts.
float EstimateBits(const int32_t *counts, size_t num_symbols) {
  int32_t total = std::accumulate(counts, counts + num_symbols, 0);
  const auto zero = Zero(df);
  const auto minprob = Set(df, 1.0f / ANS_TAB_SIZE);
  const auto inv_total = Set(df, 1.0f / total);
  auto bits_lanes = Zero(df);
  auto total_v = Set(di, total);
  for (size_t i = 0; i < num_symbols; i += Lanes(df)) {
    const auto counts_iv = LoadU(di, &counts[i]);
    const auto counts_fv = ConvertTo(df, counts_iv);
    const auto probs = Mul(counts_fv, inv_total);
    const auto mprobs = Max(probs, minprob);
    const auto nbps = IfThenElse(Eq(counts_iv, total_v), BitCast(di, zero),
                                 BitCast(di, FastLog2f(df, mprobs)));
    bits_lanes = Sub(bits_lanes, Mul(counts_fv, BitCast(df, nbps)));
  }
  return GetLane(SumOfLanes(df, bits_lanes));
}

void MakeSplitNode(size_t pos, int property, int splitval, Predictor lpred,
                   int64_t loff, Predictor rpred, int64_t roff, Tree *tree) {
  // Note that the tree splits on *strictly greater*.
  (*tree)[pos].lchild = tree->size();
  (*tree)[pos].rchild = tree->size() + 1;
  (*tree)[pos].splitval = splitval;
  (*tree)[pos].property = property;
  tree->emplace_back();
  tree->back().property = -1;
  tree->back().predictor = rpred;
  tree->back().predictor_offset = roff;
  tree->back().multiplier = 1;
  tree->emplace_back();
  tree->back().property = -1;
  tree->back().predictor = lpred;
  tree->back().predictor_offset = loff;
  tree->back().multiplier = 1;
}

enum class IntersectionType { kNone, kPartial, kInside };
IntersectionType BoxIntersects(StaticPropRange needle, StaticPropRange haystack,
                               uint32_t &partial_axis, uint32_t &partial_val) {
  bool partial = false;
  for (size_t i = 0; i < kNumStaticProperties; i++) {
    if (haystack[i][0] >= needle[i][1]) {
      return IntersectionType::kNone;
    }
    if (haystack[i][1] <= needle[i][0]) {
      return IntersectionType::kNone;
    }
    if (haystack[i][0] <= needle[i][0] && haystack[i][1] >= needle[i][1]) {
      continue;
    }
    partial = true;
    partial_axis = i;
    if (haystack[i][0] > needle[i][0] && haystack[i][0] < needle[i][1]) {
      partial_val = haystack[i][0] - 1;
    } else {
      JXL_DASSERT(haystack[i][1] > needle[i][0] &&
                  haystack[i][1] < needle[i][1]);
      partial_val = haystack[i][1] - 1;
    }
  }
  return partial ? IntersectionType::kPartial : IntersectionType::kInside;
}

void SplitTreeSamples(TreeSamples &tree_samples, size_t begin, size_t pos,
                      size_t end, size_t prop) {
  auto cmp = [&](size_t a, size_t b) {
    return int32_t(tree_samples.Property(prop, a)) -
           int32_t(tree_samples.Property(prop, b));
  };
  Rng rng(0);
  while (end > begin + 1) {
    {
      size_t pivot = rng.UniformU(begin, end);
      tree_samples.Swap(begin, pivot);
    }
    size_t pivot_begin = begin;
    size_t pivot_end = pivot_begin + 1;
    for (size_t i = begin + 1; i < end; i++) {
      JXL_DASSERT(i >= pivot_end);
      JXL_DASSERT(pivot_end > pivot_begin);
      int32_t cmp_result = cmp(i, pivot_begin);
      if (cmp_result < 0) {  // i < pivot, move pivot forward and put i before
                             // the pivot.
        tree_samples.ThreeShuffle(pivot_begin, pivot_end, i);
        pivot_begin++;
        pivot_end++;
      } else if (cmp_result == 0) {
        tree_samples.Swap(pivot_end, i);
        pivot_end++;
      }
    }
    JXL_DASSERT(pivot_begin >= begin);
    JXL_DASSERT(pivot_end > pivot_begin);
    JXL_DASSERT(pivot_end <= end);
    for (size_t i = begin; i < pivot_begin; i++) {
      JXL_DASSERT(cmp(i, pivot_begin) < 0);
    }
    for (size_t i = pivot_end; i < end; i++) {
      JXL_DASSERT(cmp(i, pivot_begin) > 0);
    }
    for (size_t i = pivot_begin; i < pivot_end; i++) {
      JXL_DASSERT(cmp(i, pivot_begin) == 0);
    }
    // We now have that [begin, pivot_begin) is < pivot, [pivot_begin,
    // pivot_end) is = pivot, and [pivot_end, end) is > pivot.
    // If pos falls in the first or the last interval, we continue in that
    // interval; otherwise, we are done.
    if (pivot_begin > pos) {
      end = pivot_begin;
    } else if (pivot_end < pos) {
      begin = pivot_end;
    } else {
      break;
    }
  }
}

void FindBestSplit(TreeSamples &tree_samples, float threshold,
                   const std::vector<ModularMultiplierInfo> &mul_info,
                   StaticPropRange initial_static_prop_range,
                   float fast_decode_multiplier, Tree *tree) {
  struct NodeInfo {
    size_t pos;
    size_t begin;
    size_t end;
    uint64_t used_properties;
    StaticPropRange static_prop_range;
  };
  std::vector<NodeInfo> nodes;
  nodes.push_back(NodeInfo{0, 0, tree_samples.NumDistinctSamples(), 0,
                           initial_static_prop_range});

  size_t num_predictors = tree_samples.NumPredictors();
  size_t num_properties = tree_samples.NumProperties();

  // TODO(veluca): consider parallelizing the search (processing multiple nodes
  // at a time).
  while (!nodes.empty()) {
    size_t pos = nodes.back().pos;
    size_t begin = nodes.back().begin;
    size_t end = nodes.back().end;
    uint64_t used_properties = nodes.back().used_properties;
    StaticPropRange static_prop_range = nodes.back().static_prop_range;
    nodes.pop_back();
    if (begin == end) continue;

    struct SplitInfo {
      size_t prop = 0;
      uint32_t val = 0;
      size_t pos = 0;
      float lcost = std::numeric_limits<float>::max();
      float rcost = std::numeric_limits<float>::max();
      Predictor lpred = Predictor::Zero;
      Predictor rpred = Predictor::Zero;
      float Cost() { return lcost + rcost; }
    };

    SplitInfo best_split_static_constant;
    SplitInfo best_split_static;
    SplitInfo best_split_nonstatic;
    SplitInfo best_split_nowp;

    JXL_DASSERT(begin <= end);
    JXL_DASSERT(end <= tree_samples.NumDistinctSamples());

    // Compute the maximum token in the range.
    size_t max_symbols = 0;
    for (size_t pred = 0; pred < num_predictors; pred++) {
      for (size_t i = begin; i < end; i++) {
        uint32_t tok = tree_samples.Token(pred, i);
        max_symbols = max_symbols > tok + 1 ? max_symbols : tok + 1;
      }
    }
    max_symbols = Padded(max_symbols);
    std::vector<int32_t> counts(max_symbols * num_predictors);
    std::vector<uint32_t> tot_extra_bits(num_predictors);
    for (size_t pred = 0; pred < num_predictors; pred++) {
      for (size_t i = begin; i < end; i++) {
        counts[pred * max_symbols + tree_samples.Token(pred, i)] +=
            tree_samples.Count(i);
        tot_extra_bits[pred] +=
            tree_samples.NBits(pred, i) * tree_samples.Count(i);
      }
    }

    float base_bits;
    {
      size_t pred = tree_samples.PredictorIndex((*tree)[pos].predictor);
      base_bits =
          EstimateBits(counts.data() + pred * max_symbols, max_symbols) +
          tot_extra_bits[pred];
    }

    SplitInfo *best = &best_split_nonstatic;

    SplitInfo forced_split;
    // The multiplier ranges cut halfway through the current ranges of static
    // properties. We do this even if the current node is not a leaf, to
    // minimize the number of nodes in the resulting tree.
    for (size_t i = 0; i < mul_info.size(); i++) {
      uint32_t axis, val;
      IntersectionType t =
          BoxIntersects(static_prop_range, mul_info[i].range, axis, val);
      if (t == IntersectionType::kNone) continue;
      if (t == IntersectionType::kInside) {
        (*tree)[pos].multiplier = mul_info[i].multiplier;
        break;
      }
      if (t == IntersectionType::kPartial) {
        forced_split.val = tree_samples.QuantizeProperty(axis, val);
        forced_split.prop = axis;
        forced_split.lcost = forced_split.rcost = base_bits / 2 - threshold;
        forced_split.lpred = forced_split.rpred = (*tree)[pos].predictor;
        best = &forced_split;
        best->pos = begin;
        JXL_ASSERT(best->prop == tree_samples.PropertyFromIndex(best->prop));
        for (size_t x = begin; x < end; x++) {
          if (tree_samples.Property(best->prop, x) <= best->val) {
            best->pos++;
          }
        }
        break;
      }
    }

    if (best != &forced_split) {
      std::vector<int> prop_value_used_count;
      std::vector<int> count_increase;
      std::vector<size_t> extra_bits_increase;
      // For each property, compute which of its values are used, and what
      // tokens correspond to those usages. Then, iterate through the values,
      // and compute the entropy of each side of the split (of the form `prop >
      // threshold`). Finally, find the split that minimizes the cost.
      struct CostInfo {
        float cost = std::numeric_limits<float>::max();
        float extra_cost = 0;
        float Cost() const { return cost + extra_cost; }
        Predictor pred;  // will be uninitialized in some cases, but never used.
      };
      std::vector<CostInfo> costs_l;
      std::vector<CostInfo> costs_r;

      std::vector<int32_t> counts_above(max_symbols);
      std::vector<int32_t> counts_below(max_symbols);

      // The lower the threshold, the higher the expected noisiness of the
      // estimate. Thus, discourage changing predictors.
      float change_pred_penalty = 800.0f / (100.0f + threshold);
      for (size_t prop = 0; prop < num_properties && base_bits > threshold;
           prop++) {
        costs_l.clear();
        costs_r.clear();
        size_t prop_size = tree_samples.NumPropertyValues(prop);
        if (extra_bits_increase.size() < prop_size) {
          count_increase.resize(prop_size * max_symbols);
          extra_bits_increase.resize(prop_size);
        }
        // Clear prop_value_used_count (which cannot be cleared "on the go")
        prop_value_used_count.clear();
        prop_value_used_count.resize(prop_size);

        size_t first_used = prop_size;
        size_t last_used = 0;

        // TODO(veluca): consider finding multiple splits along a single
        // property at the same time, possibly with a bottom-up approach.
        for (size_t i = begin; i < end; i++) {
          size_t p = tree_samples.Property(prop, i);
          prop_value_used_count[p]++;
          last_used = std::max(last_used, p);
          first_used = std::min(first_used, p);
        }
        costs_l.resize(last_used - first_used);
        costs_r.resize(last_used - first_used);
        // For all predictors, compute the right and left costs of each split.
        for (size_t pred = 0; pred < num_predictors; pred++) {
          // Compute cost and histogram increments for each property value.
          for (size_t i = begin; i < end; i++) {
            size_t p = tree_samples.Property(prop, i);
            size_t cnt = tree_samples.Count(i);
            size_t sym = tree_samples.Token(pred, i);
            count_increase[p * max_symbols + sym] += cnt;
            extra_bits_increase[p] += tree_samples.NBits(pred, i) * cnt;
          }
          memcpy(counts_above.data(), counts.data() + pred * max_symbols,
                 max_symbols * sizeof counts_above[0]);
          memset(counts_below.data(), 0, max_symbols * sizeof counts_below[0]);
          size_t extra_bits_below = 0;
          // Exclude last used: this ensures neither counts_above nor
          // counts_below is empty.
          for (size_t i = first_used; i < last_used; i++) {
            if (!prop_value_used_count[i]) continue;
            extra_bits_below += extra_bits_increase[i];
            // The increase for this property value has been used, and will not
            // be used again: clear it. Also below.
            extra_bits_increase[i] = 0;
            for (size_t sym = 0; sym < max_symbols; sym++) {
              counts_above[sym] -= count_increase[i * max_symbols + sym];
              counts_below[sym] += count_increase[i * max_symbols + sym];
              count_increase[i * max_symbols + sym] = 0;
            }
            float rcost = EstimateBits(counts_above.data(), max_symbols) +
                          tot_extra_bits[pred] - extra_bits_below;
            float lcost = EstimateBits(counts_below.data(), max_symbols) +
                          extra_bits_below;
            JXL_DASSERT(extra_bits_below <= tot_extra_bits[pred]);
            float penalty = 0;
            // Never discourage moving away from the Weighted predictor.
            if (tree_samples.PredictorFromIndex(pred) !=
                    (*tree)[pos].predictor &&
                (*tree)[pos].predictor != Predictor::Weighted) {
              penalty = change_pred_penalty;
            }
            // If everything else is equal, disfavour Weighted (slower) and
            // favour Zero (faster if it's the only predictor used in a
            // group+channel combination)
            if (tree_samples.PredictorFromIndex(pred) == Predictor::Weighted) {
              penalty += 1e-8;
            }
            if (tree_samples.PredictorFromIndex(pred) == Predictor::Zero) {
              penalty -= 1e-8;
            }
            if (rcost + penalty < costs_r[i - first_used].Cost()) {
              costs_r[i - first_used].cost = rcost;
              costs_r[i - first_used].extra_cost = penalty;
              costs_r[i - first_used].pred =
                  tree_samples.PredictorFromIndex(pred);
            }
            if (lcost + penalty < costs_l[i - first_used].Cost()) {
              costs_l[i - first_used].cost = lcost;
              costs_l[i - first_used].extra_cost = penalty;
              costs_l[i - first_used].pred =
                  tree_samples.PredictorFromIndex(pred);
            }
          }
        }
        // Iterate through the possible splits and find the one with minimum sum
        // of costs of the two sides.
        size_t split = begin;
        for (size_t i = first_used; i < last_used; i++) {
          if (!prop_value_used_count[i]) continue;
          split += prop_value_used_count[i];
          float rcost = costs_r[i - first_used].cost;
          float lcost = costs_l[i - first_used].cost;
          // WP was not used + we would use the WP property or predictor
          bool adds_wp =
              (tree_samples.PropertyFromIndex(prop) == kWPProp &&
               (used_properties & (1LU << prop)) == 0) ||
              ((costs_l[i - first_used].pred == Predictor::Weighted ||
                costs_r[i - first_used].pred == Predictor::Weighted) &&
               (*tree)[pos].predictor != Predictor::Weighted);
          bool zero_entropy_side = rcost == 0 || lcost == 0;

          SplitInfo &best =
              prop < kNumStaticProperties
                  ? (zero_entropy_side ? best_split_static_constant
                                       : best_split_static)
                  : (adds_wp ? best_split_nonstatic : best_split_nowp);
          if (lcost + rcost < best.Cost()) {
            best.prop = prop;
            best.val = i;
            best.pos = split;
            best.lcost = lcost;
            best.lpred = costs_l[i - first_used].pred;
            best.rcost = rcost;
            best.rpred = costs_r[i - first_used].pred;
          }
        }
        // Clear extra_bits_increase and cost_increase for last_used.
        extra_bits_increase[last_used] = 0;
        for (size_t sym = 0; sym < max_symbols; sym++) {
          count_increase[last_used * max_symbols + sym] = 0;
        }
      }

      // Try to avoid introducing WP.
      if (best_split_nowp.Cost() + threshold < base_bits &&
          best_split_nowp.Cost() <= fast_decode_multiplier * best->Cost()) {
        best = &best_split_nowp;
      }
      // Split along static props if possible and not significantly more
      // expensive.
      if (best_split_static.Cost() + threshold < base_bits &&
          best_split_static.Cost() <= fast_decode_multiplier * best->Cost()) {
        best = &best_split_static;
      }
      // Split along static props to create constant nodes if possible.
      if (best_split_static_constant.Cost() + threshold < base_bits) {
        best = &best_split_static_constant;
      }
    }

    if (best->Cost() + threshold < base_bits) {
      uint32_t p = tree_samples.PropertyFromIndex(best->prop);
      pixel_type dequant =
          tree_samples.UnquantizeProperty(best->prop, best->val);
      // Split node and try to split children.
      MakeSplitNode(pos, p, dequant, best->lpred, 0, best->rpred, 0, tree);
      // "Sort" according to winning property
      SplitTreeSamples(tree_samples, begin, best->pos, end, best->prop);
      if (p >= kNumStaticProperties) {
        used_properties |= 1 << best->prop;
      }
      auto new_sp_range = static_prop_range;
      if (p < kNumStaticProperties) {
        JXL_ASSERT(static_cast<uint32_t>(dequant + 1) <= new_sp_range[p][1]);
        new_sp_range[p][1] = dequant + 1;
        JXL_ASSERT(new_sp_range[p][0] < new_sp_range[p][1]);
      }
      nodes.push_back(NodeInfo{(*tree)[pos].rchild, begin, best->pos,
                               used_properties, new_sp_range});
      new_sp_range = static_prop_range;
      if (p < kNumStaticProperties) {
        JXL_ASSERT(new_sp_range[p][0] <= static_cast<uint32_t>(dequant + 1));
        new_sp_range[p][0] = dequant + 1;
        JXL_ASSERT(new_sp_range[p][0] < new_sp_range[p][1]);
      }
      nodes.push_back(NodeInfo{(*tree)[pos].lchild, best->pos, end,
                               used_properties, new_sp_range});
    }
  }
}

// NOLINTNEXTLINE(google-readability-namespace-comments)
}  // namespace HWY_NAMESPACE
}  // namespace jxl
HWY_AFTER_NAMESPACE();

#if HWY_ONCE
namespace jxl {

HWY_EXPORT(FindBestSplit);  // Local function.

void ComputeBestTree(TreeSamples &tree_samples, float threshold,
                     const std::vector<ModularMultiplierInfo> &mul_info,
                     StaticPropRange static_prop_range,
                     float fast_decode_multiplier, Tree *tree) {
  // TODO(veluca): take into account that different contexts can have different
  // uint configs.
  //
  // Initialize tree.
  tree->emplace_back();
  tree->back().property = -1;
  tree->back().predictor = tree_samples.PredictorFromIndex(0);
  tree->back().predictor_offset = 0;
  tree->back().multiplier = 1;
  JXL_ASSERT(tree_samples.NumProperties() < 64);

  JXL_ASSERT(tree_samples.NumDistinctSamples() <=
             std::numeric_limits<uint32_t>::max());
  HWY_DYNAMIC_DISPATCH(FindBestSplit)
  (tree_samples, threshold, mul_info, static_prop_range, fast_decode_multiplier,
   tree);
}

constexpr int32_t TreeSamples::kPropertyRange;
constexpr uint32_t TreeSamples::kDedupEntryUnused;

Status TreeSamples::SetPredictor(Predictor predictor,
                                 ModularOptions::TreeMode wp_tree_mode) {
  if (wp_tree_mode == ModularOptions::TreeMode::kWPOnly) {
    predictors = {Predictor::Weighted};
    residuals.resize(1);
    return true;
  }
  if (wp_tree_mode == ModularOptions::TreeMode::kNoWP &&
      predictor == Predictor::Weighted) {
    return JXL_FAILURE("Invalid predictor settings");
  }
  if (predictor == Predictor::Variable) {
    for (size_t i = 0; i < kNumModularPredictors; i++) {
      predictors.push_back(static_cast<Predictor>(i));
    }
    std::swap(predictors[0], predictors[static_cast<int>(Predictor::Weighted)]);
    std::swap(predictors[1], predictors[static_cast<int>(Predictor::Gradient)]);
  } else if (predictor == Predictor::Best) {
    predictors = {Predictor::Weighted, Predictor::Gradient};
  } else {
    predictors = {predictor};
  }
  if (wp_tree_mode == ModularOptions::TreeMode::kNoWP) {
    auto wp_it =
        std::find(predictors.begin(), predictors.end(), Predictor::Weighted);
    if (wp_it != predictors.end()) {
      predictors.erase(wp_it);
    }
  }
  residuals.resize(predictors.size());
  return true;
}

Status TreeSamples::SetProperties(const std::vector<uint32_t> &properties,
                                  ModularOptions::TreeMode wp_tree_mode) {
  props_to_use = properties;
  if (wp_tree_mode == ModularOptions::TreeMode::kWPOnly) {
    props_to_use = {static_cast<uint32_t>(kWPProp)};
  }
  if (wp_tree_mode == ModularOptions::TreeMode::kGradientOnly) {
    props_to_use = {static_cast<uint32_t>(kGradientProp)};
  }
  if (wp_tree_mode == ModularOptions::TreeMode::kNoWP) {
    auto it = std::find(props_to_use.begin(), props_to_use.end(), kWPProp);
    if (it != props_to_use.end()) {
      props_to_use.erase(it);
    }
  }
  if (props_to_use.empty()) {
    return JXL_FAILURE("Invalid property set configuration");
  }
  props.resize(props_to_use.size());
  return true;
}

void TreeSamples::InitTable(size_t size) {
  JXL_DASSERT((size & (size - 1)) == 0);
  if (dedup_table_.size() == size) return;
  dedup_table_.resize(size, kDedupEntryUnused);
  for (size_t i = 0; i < NumDistinctSamples(); i++) {
    if (sample_counts[i] != std::numeric_limits<uint16_t>::max()) {
      AddToTable(i);
    }
  }
}

bool TreeSamples::AddToTableAndMerge(size_t a) {
  size_t pos1 = Hash1(a);
  size_t pos2 = Hash2(a);
  if (dedup_table_[pos1] != kDedupEntryUnused &&
      IsSameSample(a, dedup_table_[pos1])) {
    JXL_DASSERT(sample_counts[a] == 1);
    sample_counts[dedup_table_[pos1]]++;
    // Remove from hash table samples that are saturated.
    if (sample_counts[dedup_table_[pos1]] ==
        std::numeric_limits<uint16_t>::max()) {
      dedup_table_[pos1] = kDedupEntryUnused;
    }
    return true;
  }
  if (dedup_table_[pos2] != kDedupEntryUnused &&
      IsSameSample(a, dedup_table_[pos2])) {
    JXL_DASSERT(sample_counts[a] == 1);
    sample_counts[dedup_table_[pos2]]++;
    // Remove from hash table samples that are saturated.
    if (sample_counts[dedup_table_[pos2]] ==
        std::numeric_limits<uint16_t>::max()) {
      dedup_table_[pos2] = kDedupEntryUnused;
    }
    return true;
  }
  AddToTable(a);
  return false;
}

void TreeSamples::AddToTable(size_t a) {
  size_t pos1 = Hash1(a);
  size_t pos2 = Hash2(a);
  if (dedup_table_[pos1] == kDedupEntryUnused) {
    dedup_table_[pos1] = a;
  } else if (dedup_table_[pos2] == kDedupEntryUnused) {
    dedup_table_[pos2] = a;
  }
}

void TreeSamples::PrepareForSamples(size_t num_samples) {
  for (auto &res : residuals) {
    res.reserve(res.size() + num_samples);
  }
  for (auto &p : props) {
    p.reserve(p.size() + num_samples);
  }
  size_t total_num_samples = num_samples + sample_counts.size();
  size_t next_pow2 = 1LLU << CeilLog2Nonzero(total_num_samples * 3 / 2);
  InitTable(next_pow2);
}

size_t TreeSamples::Hash1(size_t a) const {
  constexpr uint64_t constant = 0x1e35a7bd;
  uint64_t h = constant;
  for (const auto &r : residuals) {
    h = h * constant + r[a].tok;
    h = h * constant + r[a].nbits;
  }
  for (const auto &p : props) {
    h = h * constant + p[a];
  }
  return (h >> 16) & (dedup_table_.size() - 1);
}
size_t TreeSamples::Hash2(size_t a) const {
  constexpr uint64_t constant = 0x1e35a7bd1e35a7bd;
  uint64_t h = constant;
  for (const auto &p : props) {
    h = h * constant ^ p[a];
  }
  for (const auto &r : residuals) {
    h = h * constant ^ r[a].tok;
    h = h * constant ^ r[a].nbits;
  }
  return (h >> 16) & (dedup_table_.size() - 1);
}

bool TreeSamples::IsSameSample(size_t a, size_t b) const {
  bool ret = true;
  for (const auto &r : residuals) {
    if (r[a].tok != r[b].tok) {
      ret = false;
    }
    if (r[a].nbits != r[b].nbits) {
      ret = false;
    }
  }
  for (const auto &p : props) {
    if (p[a] != p[b]) {
      ret = false;
    }
  }
  return ret;
}

void TreeSamples::AddSample(pixel_type_w pixel, const Properties &properties,
                            const pixel_type_w *predictions) {
  for (size_t i = 0; i < predictors.size(); i++) {
    pixel_type v = pixel - predictions[static_cast<int>(predictors[i])];
    uint32_t tok, nbits, bits;
    HybridUintConfig(4, 1, 2).Encode(PackSigned(v), &tok, &nbits, &bits);
    JXL_DASSERT(tok < 256);
    JXL_DASSERT(nbits < 256);
    residuals[i].emplace_back(
        ResidualToken{static_cast<uint8_t>(tok), static_cast<uint8_t>(nbits)});
  }
  for (size_t i = 0; i < props_to_use.size(); i++) {
    props[i].push_back(QuantizeProperty(i, properties[props_to_use[i]]));
  }
  sample_counts.push_back(1);
  num_samples++;
  if (AddToTableAndMerge(sample_counts.size() - 1)) {
    for (auto &r : residuals) r.pop_back();
    for (auto &p : props) p.pop_back();
    sample_counts.pop_back();
  }
}

void TreeSamples::Swap(size_t a, size_t b) {
  if (a == b) return;
  for (auto &r : residuals) {
    std::swap(r[a], r[b]);
  }
  for (auto &p : props) {
    std::swap(p[a], p[b]);
  }
  std::swap(sample_counts[a], sample_counts[b]);
}

void TreeSamples::ThreeShuffle(size_t a, size_t b, size_t c) {
  if (b == c) return Swap(a, b);
  for (auto &r : residuals) {
    auto tmp = r[a];
    r[a] = r[c];
    r[c] = r[b];
    r[b] = tmp;
  }
  for (auto &p : props) {
    auto tmp = p[a];
    p[a] = p[c];
    p[c] = p[b];
    p[b] = tmp;
  }
  auto tmp = sample_counts[a];
  sample_counts[a] = sample_counts[c];
  sample_counts[c] = sample_counts[b];
  sample_counts[b] = tmp;
}

namespace {
std::vector<int32_t> QuantizeHistogram(const std::vector<uint32_t> &histogram,
                                       size_t num_chunks) {
  if (histogram.empty()) return {};
  // TODO(veluca): selecting distinct quantiles is likely not the best
  // way to go about this.
  std::vector<int32_t> thresholds;
  uint64_t sum = std::accumulate(histogram.begin(), histogram.end(), 0LU);
  uint64_t cumsum = 0;
  uint64_t threshold = 1;
  for (size_t i = 0; i + 1 < histogram.size(); i++) {
    cumsum += histogram[i];
    if (cumsum >= threshold * sum / num_chunks) {
      thresholds.push_back(i);
      while (cumsum > threshold * sum / num_chunks) threshold++;
    }
  }
  return thresholds;
}

std::vector<int32_t> QuantizeSamples(const std::vector<int32_t> &samples,
                                     size_t num_chunks) {
  if (samples.empty()) return {};
  int min = *std::min_element(samples.begin(), samples.end());
  constexpr int kRange = 512;
  min = std::min(std::max(min, -kRange), kRange);
  std::vector<uint32_t> counts(2 * kRange + 1);
  for (int s : samples) {
    uint32_t sample_offset = std::min(std::max(s, -kRange), kRange) - min;
    counts[sample_offset]++;
  }
  std::vector<int32_t> thresholds = QuantizeHistogram(counts, num_chunks);
  for (auto &v : thresholds) v += min;
  return thresholds;
}
}  // namespace

void TreeSamples::PreQuantizeProperties(
    const StaticPropRange &range,
    const std::vector<ModularMultiplierInfo> &multiplier_info,
    const std::vector<uint32_t> &group_pixel_count,
    const std::vector<uint32_t> &channel_pixel_count,
    std::vector<pixel_type> &pixel_samples,
    std::vector<pixel_type> &diff_samples, size_t max_property_values) {
  // If we have forced splits because of multipliers, choose channel and group
  // thresholds accordingly.
  std::vector<int32_t> group_multiplier_thresholds;
  std::vector<int32_t> channel_multiplier_thresholds;
  for (const auto &v : multiplier_info) {
    if (v.range[0][0] != range[0][0]) {
      channel_multiplier_thresholds.push_back(v.range[0][0] - 1);
    }
    if (v.range[0][1] != range[0][1]) {
      channel_multiplier_thresholds.push_back(v.range[0][1] - 1);
    }
    if (v.range[1][0] != range[1][0]) {
      group_multiplier_thresholds.push_back(v.range[1][0] - 1);
    }
    if (v.range[1][1] != range[1][1]) {
      group_multiplier_thresholds.push_back(v.range[1][1] - 1);
    }
  }
  std::sort(channel_multiplier_thresholds.begin(),
            channel_multiplier_thresholds.end());
  channel_multiplier_thresholds.resize(
      std::unique(channel_multiplier_thresholds.begin(),
                  channel_multiplier_thresholds.end()) -
      channel_multiplier_thresholds.begin());
  std::sort(group_multiplier_thresholds.begin(),
            group_multiplier_thresholds.end());
  group_multiplier_thresholds.resize(
      std::unique(group_multiplier_thresholds.begin(),
                  group_multiplier_thresholds.end()) -
      group_multiplier_thresholds.begin());

  compact_properties.resize(props_to_use.size());
  auto quantize_channel = [&]() {
    if (!channel_multiplier_thresholds.empty()) {
      return channel_multiplier_thresholds;
    }
    return QuantizeHistogram(channel_pixel_count, max_property_values);
  };
  auto quantize_group_id = [&]() {
    if (!group_multiplier_thresholds.empty()) {
      return group_multiplier_thresholds;
    }
    return QuantizeHistogram(group_pixel_count, max_property_values);
  };
  auto quantize_coordinate = [&]() {
    std::vector<int32_t> quantized;
    quantized.reserve(max_property_values - 1);
    for (size_t i = 0; i + 1 < max_property_values; i++) {
      quantized.push_back((i + 1) * 256 / max_property_values - 1);
    }
    return quantized;
  };
  std::vector<int32_t> abs_pixel_thr;
  std::vector<int32_t> pixel_thr;
  auto quantize_pixel_property = [&]() {
    if (pixel_thr.empty()) {
      pixel_thr = QuantizeSamples(pixel_samples, max_property_values);
    }
    return pixel_thr;
  };
  auto quantize_abs_pixel_property = [&]() {
    if (abs_pixel_thr.empty()) {
      quantize_pixel_property();  // Compute the non-abs thresholds.
      for (auto &v : pixel_samples) v = std::abs(v);
      abs_pixel_thr = QuantizeSamples(pixel_samples, max_property_values);
    }
    return abs_pixel_thr;
  };
  std::vector<int32_t> abs_diff_thr;
  std::vector<int32_t> diff_thr;
  auto quantize_diff_property = [&]() {
    if (diff_thr.empty()) {
      diff_thr = QuantizeSamples(diff_samples, max_property_values);
    }
    return diff_thr;
  };
  auto quantize_abs_diff_property = [&]() {
    if (abs_diff_thr.empty()) {
      quantize_diff_property();  // Compute the non-abs thresholds.
      for (auto &v : diff_samples) v = std::abs(v);
      abs_diff_thr = QuantizeSamples(diff_samples, max_property_values);
    }
    return abs_diff_thr;
  };
  auto quantize_wp = [&]() {
    if (max_property_values < 32) {
      return std::vector<int32_t>{-127, -63, -31, -15, -7, -3, -1, 0,
                                  1,    3,   7,   15,  31, 63, 127};
    }
    if (max_property_values < 64) {
      return std::vector<int32_t>{-255, -191, -127, -95, -63, -47, -31, -23,
                                  -15,  -11,  -7,   -5,  -3,  -1,  0,   1,
                                  3,    5,    7,    11,  15,  23,  31,  47,
                                  63,   95,   127,  191, 255};
    }
    return std::vector<int32_t>{
        -255, -223, -191, -159, -127, -111, -95, -79, -63, -55, -47,
        -39,  -31,  -27,  -23,  -19,  -15,  -13, -11, -9,  -7,  -6,
        -5,   -4,   -3,   -2,   -1,   0,    1,   2,   3,   4,   5,
        6,    7,    9,    11,   13,   15,   19,  23,  27,  31,  39,
        47,   55,   63,   79,   95,   111,  127, 159, 191, 223, 255};
  };

  property_mapping.resize(props_to_use.size());
  for (size_t i = 0; i < props_to_use.size(); i++) {
    if (props_to_use[i] == 0) {
      compact_properties[i] = quantize_channel();
    } else if (props_to_use[i] == 1) {
      compact_properties[i] = quantize_group_id();
    } else if (props_to_use[i] == 2 || props_to_use[i] == 3) {
      compact_properties[i] = quantize_coordinate();
    } else if (props_to_use[i] == 6 || props_to_use[i] == 7 ||
               props_to_use[i] == 8 ||
               (props_to_use[i] >= kNumNonrefProperties &&
                (props_to_use[i] - kNumNonrefProperties) % 4 == 1)) {
      compact_properties[i] = quantize_pixel_property();
    } else if (props_to_use[i] == 4 || props_to_use[i] == 5 ||
               (props_to_use[i] >= kNumNonrefProperties &&
                (props_to_use[i] - kNumNonrefProperties) % 4 == 0)) {
      compact_properties[i] = quantize_abs_pixel_property();
    } else if (props_to_use[i] >= kNumNonrefProperties &&
               (props_to_use[i] - kNumNonrefProperties) % 4 == 2) {
      compact_properties[i] = quantize_abs_diff_property();
    } else if (props_to_use[i] == kWPProp) {
      compact_properties[i] = quantize_wp();
    } else {
      compact_properties[i] = quantize_diff_property();
    }
    property_mapping[i].resize(kPropertyRange * 2 + 1);
    size_t mapped = 0;
    for (size_t j = 0; j < property_mapping[i].size(); j++) {
      while (mapped < compact_properties[i].size() &&
             static_cast<int>(j) - kPropertyRange >
                 compact_properties[i][mapped]) {
        mapped++;
      }
      // property_mapping[i] of a value V is `mapped` if
      // compact_properties[i][mapped] <= j and
      // compact_properties[i][mapped-1] > j
      // This is because the decision node in the tree splits on (property) > j,
      // hence everything that is not > of a threshold should be clustered
      // together.
      property_mapping[i][j] = mapped;
    }
  }
}

void CollectPixelSamples(const Image &image, const ModularOptions &options,
                         size_t group_id,
                         std::vector<uint32_t> &group_pixel_count,
                         std::vector<uint32_t> &channel_pixel_count,
                         std::vector<pixel_type> &pixel_samples,
                         std::vector<pixel_type> &diff_samples) {
  if (options.nb_repeats == 0) return;
  if (group_pixel_count.size() <= group_id) {
    group_pixel_count.resize(group_id + 1);
  }
  if (channel_pixel_count.size() < image.channel.size()) {
    channel_pixel_count.resize(image.channel.size());
  }
  Rng rng(group_id);
  // Sample 10% of the final number of samples for property quantization.
  float fraction = std::min(options.nb_repeats * 0.1, 0.99);
  Rng::GeometricDistribution dist = Rng::MakeGeometric(fraction);
  size_t total_pixels = 0;
  std::vector<size_t> channel_ids;
  for (size_t i = 0; i < image.channel.size(); i++) {
    if (image.channel[i].w <= 1 || image.channel[i].h == 0) {
      continue;  // skip empty or width-1 channels.
    }
    if (i >= image.nb_meta_channels &&
        (image.channel[i].w > options.max_chan_size ||
         image.channel[i].h > options.max_chan_size)) {
      break;
    }
    channel_ids.push_back(i);
    group_pixel_count[group_id] += image.channel[i].w * image.channel[i].h;
    channel_pixel_count[i] += image.channel[i].w * image.channel[i].h;
    total_pixels += image.channel[i].w * image.channel[i].h;
  }
  if (channel_ids.empty()) return;
  pixel_samples.reserve(pixel_samples.size() + fraction * total_pixels);
  diff_samples.reserve(diff_samples.size() + fraction * total_pixels);
  size_t i = 0;
  size_t y = 0;
  size_t x = 0;
  auto advance = [&](size_t amount) {
    x += amount;
    // Detect row overflow (rare).
    while (x >= image.channel[channel_ids[i]].w) {
      x -= image.channel[channel_ids[i]].w;
      y++;
      // Detect end-of-channel (even rarer).
      if (y == image.channel[channel_ids[i]].h) {
        i++;
        y = 0;
        if (i >= channel_ids.size()) {
          return;
        }
      }
    }
  };
  advance(rng.Geometric(dist));
  for (; i < channel_ids.size(); advance(rng.Geometric(dist) + 1)) {
    const pixel_type *row = image.channel[channel_ids[i]].Row(y);
    pixel_samples.push_back(row[x]);
    size_t xp = x == 0 ? 1 : x - 1;
    diff_samples.push_back((int64_t)row[x] - row[xp]);
  }
}

// TODO(veluca): very simple encoding scheme. This should be improved.
void TokenizeTree(const Tree &tree, std::vector<Token> *tokens,
                  Tree *decoder_tree) {
  JXL_ASSERT(tree.size() <= kMaxTreeSize);
  std::queue<int> q;
  q.push(0);
  size_t leaf_id = 0;
  decoder_tree->clear();
  while (!q.empty()) {
    int cur = q.front();
    q.pop();
    JXL_ASSERT(tree[cur].property >= -1);
    tokens->emplace_back(kPropertyContext, tree[cur].property + 1);
    if (tree[cur].property == -1) {
      tokens->emplace_back(kPredictorContext,
                           static_cast<int>(tree[cur].predictor));
      tokens->emplace_back(kOffsetContext,
                           PackSigned(tree[cur].predictor_offset));
      uint32_t mul_log = Num0BitsBelowLS1Bit_Nonzero(tree[cur].multiplier);
      uint32_t mul_bits = (tree[cur].multiplier >> mul_log) - 1;
      tokens->emplace_back(kMultiplierLogContext, mul_log);
      tokens->emplace_back(kMultiplierBitsContext, mul_bits);
      JXL_ASSERT(tree[cur].predictor < Predictor::Best);
      decoder_tree->emplace_back(-1, 0, leaf_id++, 0, tree[cur].predictor,
                                 tree[cur].predictor_offset,
                                 tree[cur].multiplier);
      continue;
    }
    decoder_tree->emplace_back(tree[cur].property, tree[cur].splitval,
                               decoder_tree->size() + q.size() + 1,
                               decoder_tree->size() + q.size() + 2,
                               Predictor::Zero, 0, 1);
    q.push(tree[cur].lchild);
    q.push(tree[cur].rchild);
    tokens->emplace_back(kSplitValContext, PackSigned(tree[cur].splitval));
  }
}

}  // namespace jxl
#endif  // HWY_ONCE