// 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/enc_cluster.h"

#include <algorithm>

#include <cstddef>

#include <cstdint>

#include <limits>

#include <map>

#include <numeric>

#include <queue>

#include <tuple>

#include <vector>

#include "lib/jxl/base/status.h"

#include "lib/jxl/enc_ans_params.h"

#undef HWY_TARGET_INCLUDE

#define HWY_TARGET_INCLUDE "lib/jxl/enc_cluster.cc"

#include <hwy/foreach_target.h>

#include <hwy/highway.h>

#include "lib/jxl/base/fast_math-inl.h"

HWY_BEFORE_NAMESPACE();

namespace jxl {

namespace HWY_NAMESPACE {

// These templates are not found via ADL.

using hwy::HWY_NAMESPACE::AllTrue;

using hwy::HWY_NAMESPACE::Eq;

using hwy::HWY_NAMESPACE::GetLane;

using hwy::HWY_NAMESPACE::IfThenZeroElse;

using hwy::HWY_NAMESPACE::SumOfLanes;

using hwy::HWY_NAMESPACE::Zero;

template <class V>

V Entropy(V count, V inv_total, V total) {

  const HWY_CAPPED(float, Histogram::kRounding) d;

  const auto zero = Set(d, 0.0f);

  // TODO(eustas): why (0 - x) instead of Neg(x)?

  return IfThenZeroElse(

      Eq(count, total),

      Sub(zero, Mul(count, FastLog2f(d, Mul(inv_total, count)))));

}

void HistogramCondition(Histogram& a) {

  const HWY_CAPPED(int32_t, Histogram::kRounding) di;

  const auto kZero = Zero(di);

  auto total = kZero;

  int nz_pos = -static_cast<int>(Lanes(di));

  for (size_t i = 0; i < a.counts.size(); i += Lanes(di)) {

    const auto counts = LoadU(di, &a.counts[i]);

    const bool nz = !AllTrue(di, Eq(counts, kZero));

    total = Add(total, counts);

    if (nz) nz_pos = i;

  }

  a.counts.resize(nz_pos + Lanes(di));

  a.total_count = GetLane(SumOfLanes(di, total));

}

void HistogramEntropy(const Histogram& a) {

  a.entropy = 0.0f;

  if (a.total_count == 0) return;

  const HWY_CAPPED(float, Histogram::kRounding) df;

  const HWY_CAPPED(int32_t, Histogram::kRounding) di;

  const auto inv_tot = Set(df, 1.0f / a.total_count);

  auto entropy_lanes = Zero(df);

  auto total = Set(df, a.total_count);

  for (size_t i = 0; i < a.counts.size(); i += Lanes(di)) {

    const auto counts = LoadU(di, &a.counts[i]);

    entropy_lanes =

        Add(entropy_lanes, Entropy(ConvertTo(df, counts), inv_tot, total));

  }

  a.entropy += GetLane(SumOfLanes(df, entropy_lanes));

}

float HistogramDistance(const Histogram& a, const Histogram& b) {

  if (a.total_count == 0 || b.total_count == 0) return 0;

  const HWY_CAPPED(float, Histogram::kRounding) df;

  const HWY_CAPPED(int32_t, Histogram::kRounding) di;

  const auto inv_tot = Set(df, 1.0f / (a.total_count + b.total_count));

  auto distance_lanes = Zero(df);

  auto total = Set(df, a.total_count + b.total_count);

  for (size_t i = 0; i < std::max(a.counts.size(), b.counts.size());

       i += Lanes(di)) {

    const auto a_counts =

        a.counts.size() > i ? LoadU(di, &a.counts[i]) : Zero(di);

    const auto b_counts =

        b.counts.size() > i ? LoadU(di, &b.counts[i]) : Zero(di);

    const auto counts = ConvertTo(df, Add(a_counts, b_counts));

    distance_lanes = Add(distance_lanes, Entropy(counts, inv_tot, total));

  }

  const float total_distance = GetLane(SumOfLanes(df, distance_lanes));

  return total_distance - a.entropy - b.entropy;

}

constexpr const float kInfinity = std::numeric_limits<float>::infinity();

float HistogramKLDivergence(const Histogram& actual, const Histogram& coding) {

  if (actual.total_count == 0) return 0;

  if (coding.total_count == 0) return kInfinity;

  const HWY_CAPPED(float, Histogram::kRounding) df;

  const HWY_CAPPED(int32_t, Histogram::kRounding) di;

  const auto coding_inv = Set(df, 1.0f / coding.total_count);

  auto cost_lanes = Zero(df);

  for (size_t i = 0; i < actual.counts.size(); i += Lanes(di)) {

    const auto counts = LoadU(di, &actual.counts[i]);

    const auto coding_counts =

        coding.counts.size() > i ? LoadU(di, &coding.counts[i]) : Zero(di);

    const auto coding_probs = Mul(ConvertTo(df, coding_counts), coding_inv);

    const auto neg_coding_cost = BitCast(

        df,

        IfThenZeroElse(Eq(counts, Zero(di)),

                       IfThenElse(Eq(coding_counts, Zero(di)),

                                  BitCast(di, Set(df, -kInfinity)),

                                  BitCast(di, FastLog2f(df, coding_probs)))));

    cost_lanes = NegMulAdd(ConvertTo(df, counts), neg_coding_cost, cost_lanes);

  }

  const float total_cost = GetLane(SumOfLanes(df, cost_lanes));

  return total_cost - actual.entropy;

}

// First step of a k-means clustering with a fancy distance metric.

Status FastClusterHistograms(const std::vector<Histogram>& in,

                             size_t max_histograms, std::vector<Histogram>* out,

                             std::vector<uint32_t>* histogram_symbols) {

  const size_t prev_histograms = out->size();

  out->reserve(max_histograms);

  histogram_symbols->clear();

  histogram_symbols->resize(in.size(), max_histograms);

  std::vector<float> dists(in.size(), std::numeric_limits<float>::max());

  size_t largest_idx = 0;

  for (size_t i = 0; i < in.size(); i++) {

    if (in[i].total_count == 0) {

      (*histogram_symbols)[i] = 0;

      dists[i] = 0.0f;

      continue;

    }

    HistogramEntropy(in[i]);

    if (in[i].total_count > in[largest_idx].total_count) {

      largest_idx = i;

    }

  }

  if (prev_histograms > 0) {

    for (size_t j = 0; j < prev_histograms; ++j) {

      HistogramEntropy((*out)[j]);

    }

    for (size_t i = 0; i < in.size(); i++) {

      if (dists[i] == 0.0f) continue;

      for (size_t j = 0; j < prev_histograms; ++j) {

        dists[i] = std::min(HistogramKLDivergence(in[i], (*out)[j]), dists[i]);

      }

    }

    auto max_dist = std::max_element(dists.begin(), dists.end());

    if (*max_dist > 0.0f) {

      largest_idx = max_dist - dists.begin();

    }

  }

  constexpr float kMinDistanceForDistinct = 48.0f;

  while (out->size() < max_histograms) {

    (*histogram_symbols)[largest_idx] = out->size();

    out->push_back(in[largest_idx]);

    dists[largest_idx] = 0.0f;

    largest_idx = 0;

    for (size_t i = 0; i < in.size(); i++) {

      if (dists[i] == 0.0f) continue;

      dists[i] = std::min(HistogramDistance(in[i], out->back()), dists[i]);

      if (dists[i] > dists[largest_idx]) largest_idx = i;

    }

    if (dists[largest_idx] < kMinDistanceForDistinct) break;

  }

  for (size_t i = 0; i < in.size(); i++) {

    if ((*histogram_symbols)[i] != max_histograms) continue;

    size_t best = 0;

    float best_dist = std::numeric_limits<float>::max();

    for (size_t j = 0; j < out->size(); j++) {

      float dist = j < prev_histograms ? HistogramKLDivergence(in[i], (*out)[j])

                                       : HistogramDistance(in[i], (*out)[j]);

      if (dist < best_dist) {

        best = j;

        best_dist = dist;

      }

    }

    JXL_ENSURE(best_dist < std::numeric_limits<float>::max());

    if (best >= prev_histograms) {

      (*out)[best].AddHistogram(in[i]);

      HistogramEntropy((*out)[best]);

    }

    (*histogram_symbols)[i] = best;

  }

  return true;

}

// NOLINTNEXTLINE(google-readability-namespace-comments)

}  // namespace HWY_NAMESPACE

}  // namespace jxl

HWY_AFTER_NAMESPACE();

#if HWY_ONCE

namespace jxl {

HWY_EXPORT(FastClusterHistograms);  // Local function

HWY_EXPORT(HistogramEntropy);       // Local function

HWY_EXPORT(HistogramCondition);     // Local function

void Histogram::Condition() { HWY_DYNAMIC_DISPATCH(HistogramCondition)(*this); }

float Histogram::ShannonEntropy() const {

  HWY_DYNAMIC_DISPATCH(HistogramEntropy)(*this);

  return entropy;

}

namespace {

// -----------------------------------------------------------------------------

// Histogram refinement

// Reorder histograms in *out so that the new symbols in *symbols come in

// increasing order.

void HistogramReindex(std::vector<Histogram>* out, size_t prev_histograms,

                      std::vector<uint32_t>* symbols) {

  std::vector<Histogram> tmp(*out);

  std::map<int, int> new_index;

  for (size_t i = 0; i < prev_histograms; ++i) {

    new_index[i] = i;

  }

  int next_index = prev_histograms;

  for (uint32_t symbol : *symbols) {

    if (new_index.find(symbol) == new_index.end()) {

      new_index[symbol] = next_index;

      (*out)[next_index] = tmp[symbol];

      ++next_index;

    }

  }

  out->resize(next_index);

  for (uint32_t& symbol : *symbols) {

    symbol = new_index[symbol];

  }

}

}  // namespace

// Clusters similar histograms in 'in' together, the selected histograms are

// placed in 'out', and for each index in 'in', *histogram_symbols will

// indicate which of the 'out' histograms is the best approximation.

Status ClusterHistograms(const HistogramParams& params,

                         const std::vector<Histogram>& in,

                         size_t max_histograms, std::vector<Histogram>* out,

                         std::vector<uint32_t>* histogram_symbols) {

  size_t prev_histograms = out->size();

  max_histograms = std::min(max_histograms, params.max_histograms);

  max_histograms = std::min(max_histograms, in.size());

  if (params.clustering == HistogramParams::ClusteringType::kFastest) {

    max_histograms = std::min(max_histograms, static_cast<size_t>(4));

  }

  JXL_RETURN_IF_ERROR(HWY_DYNAMIC_DISPATCH(FastClusterHistograms)(

      in, prev_histograms + max_histograms, out, histogram_symbols));

  if (prev_histograms == 0 &&

      params.clustering == HistogramParams::ClusteringType::kBest) {

    for (auto& histo : *out) {

      JXL_ASSIGN_OR_RETURN(histo.entropy, histo.ANSPopulationCost());

    }

    uint32_t next_version = 2;

    std::vector<uint32_t> version(out->size(), 1);

    std::vector<uint32_t> renumbering(out->size());

    std::iota(renumbering.begin(), renumbering.end(), 0);

    // Try to pair up clusters if doing so reduces the total cost.

    struct HistogramPair {

      // validity of a pair: p.version == max(version[i], version[j])

      float cost;

      uint32_t first;

      uint32_t second;

      uint32_t version;

      // We use > because priority queues sort in *decreasing* order, but we

      // want lower cost elements to appear first.

      bool operator<(const HistogramPair& other) const {

        return std::make_tuple(cost, first, second, version) >

               std::make_tuple(other.cost, other.first, other.second,

                               other.version);

      }

    };

    // Create list of all pairs by increasing merging cost.

    std::priority_queue<HistogramPair> pairs_to_merge;

    for (uint32_t i = 0; i < out->size(); i++) {

      for (uint32_t j = i + 1; j < out->size(); j++) {

        Histogram histo;

        histo.AddHistogram((*out)[i]);

        histo.AddHistogram((*out)[j]);

        JXL_ASSIGN_OR_RETURN(float cost, histo.ANSPopulationCost());

        cost -= (*out)[i].entropy + (*out)[j].entropy;

        // Avoid enqueueing pairs that are not advantageous to merge.

        if (cost >= 0) continue;

        pairs_to_merge.push(

            HistogramPair{cost, i, j, std::max(version[i], version[j])});

      }

    }

    // Merge the best pair to merge, add new pairs that get formed as a

    // consequence.

    while (!pairs_to_merge.empty()) {

      uint32_t first = pairs_to_merge.top().first;

      uint32_t second = pairs_to_merge.top().second;

      uint32_t ver = pairs_to_merge.top().version;

      pairs_to_merge.pop();

      if (ver != std::max(version[first], version[second]) ||

          version[first] == 0 || version[second] == 0) {

        continue;

      }

      (*out)[first].AddHistogram((*out)[second]);

      JXL_ASSIGN_OR_RETURN((*out)[first].entropy,

                           (*out)[first].ANSPopulationCost());

      for (uint32_t& item : renumbering) {

        if (item == second) {

          item = first;

        }

      }

      version[second] = 0;

      version[first] = next_version++;

      for (uint32_t j = 0; j < out->size(); j++) {

        if (j == first) continue;

        if (version[j] == 0) continue;

        Histogram histo;

        histo.AddHistogram((*out)[first]);

        histo.AddHistogram((*out)[j]);

        JXL_ASSIGN_OR_RETURN(float merge_cost, histo.ANSPopulationCost());

        merge_cost -= (*out)[first].entropy + (*out)[j].entropy;

        // Avoid enqueueing pairs that are not advantageous to merge.

        if (merge_cost >= 0) continue;

        pairs_to_merge.push(

            HistogramPair{merge_cost, std::min(first, j), std::max(first, j),

                          std::max(version[first], version[j])});

      }

    }

    std::vector<uint32_t> reverse_renumbering(out->size(), -1);

    size_t num_alive = 0;

    for (size_t i = 0; i < out->size(); i++) {

      if (version[i] == 0) continue;

      (*out)[num_alive++] = (*out)[i];

      reverse_renumbering[i] = num_alive - 1;

    }

    out->resize(num_alive);

    for (uint32_t& item : *histogram_symbols) {

      item = reverse_renumbering[renumbering[item]];

    }

  }

  // Convert the context map to a canonical form.

  HistogramReindex(out, prev_histograms, histogram_symbols);

  return true;

}

}  // namespace jxl

#endif  // HWY_ONCE