// 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 <jxl/memory_manager.h>

#include <algorithm>

#include <array>

#include <cstddef>

#include <cstdint>

#include <cstdlib>

#include <limits>

#include <queue>

#include <utility>

#include <vector>

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

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

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

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

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

#include "lib/jxl/enc_ans.h"

#include "lib/jxl/enc_ans_params.h"

#include "lib/jxl/enc_aux_out.h"

#include "lib/jxl/enc_bit_writer.h"

#include "lib/jxl/enc_fields.h"

#include "lib/jxl/fields.h"

#include "lib/jxl/image.h"

#include "lib/jxl/image_ops.h"

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

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

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

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

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

#include "lib/jxl/modular/modular_image.h"

#include "lib/jxl/modular/options.h"

#include "lib/jxl/pack_signed.h"

namespace jxl {

namespace {

// Plot tree (if enabled) and predictor usage map.

constexpr bool kWantDebug = true;

// constexpr bool kPrintTree = false;

inline std::array<uint8_t, 3> PredictorColor(Predictor p) {

  switch (p) {

    case Predictor::Zero:

      return {{0, 0, 0}};

    case Predictor::Left:

      return {{255, 0, 0}};

    case Predictor::Top:

      return {{0, 255, 0}};

    case Predictor::Average0:

      return {{0, 0, 255}};

    case Predictor::Average4:

      return {{192, 128, 128}};

    case Predictor::Select:

      return {{255, 255, 0}};

    case Predictor::Gradient:

      return {{255, 0, 255}};

    case Predictor::Weighted:

      return {{0, 255, 255}};

      // TODO(jon)

    default:

      return {{255, 255, 255}};

  };

}

// `cutoffs` must be sorted.

Tree MakeFixedTree(int property, const std::vector<int32_t> &cutoffs,

                   Predictor pred, size_t num_pixels, int bitdepth) {

  size_t log_px = CeilLog2Nonzero(num_pixels);

  size_t min_gap = 0;

  // Reduce fixed tree height when encoding small images.

  if (log_px < 14) {

    min_gap = 8 * (14 - log_px);

  }

  const int shift = bitdepth > 11 ? std::min(4, bitdepth - 11) : 0;

  const int mul = 1 << shift;

  Tree tree;

  struct NodeInfo {

    size_t begin, end, pos;

  };

  std::queue<NodeInfo> q;

  // Leaf IDs will be set by roundtrip decoding the tree.

  tree.push_back(PropertyDecisionNode::Leaf(pred));

  q.push(NodeInfo{0, cutoffs.size(), 0});

  while (!q.empty()) {

    NodeInfo info = q.front();

    q.pop();

    if (info.begin + min_gap >= info.end) continue;

    uint32_t split = (info.begin + info.end) / 2;

    int32_t cutoff = cutoffs[split] * mul;

    tree[info.pos] = PropertyDecisionNode::Split(property, cutoff, tree.size());

    q.push(NodeInfo{split + 1, info.end, tree.size()});

    tree.push_back(PropertyDecisionNode::Leaf(pred));

    q.push(NodeInfo{info.begin, split, tree.size()});

    tree.push_back(PropertyDecisionNode::Leaf(pred));

  }

  return tree;

}

Status GatherTreeData(const Image &image, pixel_type chan, size_t group_id,

                      const weighted::Header &wp_header,

                      const ModularOptions &options, TreeSamples &tree_samples,

                      size_t *total_pixels) {

  const Channel &channel = image.channel[chan];

  JxlMemoryManager *memory_manager = channel.memory_manager();

  JXL_DEBUG_V(7, "Learning %" PRIuS "x%" PRIuS " channel %d", channel.w,

              channel.h, chan);

  std::array<pixel_type, kNumStaticProperties> static_props = {

      {chan, static_cast<int>(group_id)}};

  Properties properties(kNumNonrefProperties +

                        kExtraPropsPerChannel * options.max_properties);

  double pixel_fraction = std::min(1.0f, options.nb_repeats);

  // a fraction of 0 is used to disable learning entirely.

  if (pixel_fraction > 0) {

    pixel_fraction = std::max(pixel_fraction,

                              std::min(1.0, 1024.0 / (channel.w * channel.h)));

  }

  uint64_t threshold =

      (std::numeric_limits<uint64_t>::max() >> 32) * pixel_fraction;

  uint64_t s[2] = {static_cast<uint64_t>(0x94D049BB133111EBull),

                   static_cast<uint64_t>(0xBF58476D1CE4E5B9ull)};

  // Xorshift128+ adapted from xorshift128+-inl.h

  auto use_sample = [&]() {

    auto s1 = s[0];

    const auto s0 = s[1];

    const auto bits = s1 + s0;  // b, c

    s[0] = s0;

    s1 ^= s1 << 23;

    s1 ^= s0 ^ (s1 >> 18) ^ (s0 >> 5);

    s[1] = s1;

    return (bits >> 32) <= threshold;

  };

  const intptr_t onerow = channel.plane.PixelsPerRow();

  JXL_ASSIGN_OR_RETURN(

      Channel references,

      Channel::Create(memory_manager, properties.size() - kNumNonrefProperties,

                      channel.w));

  weighted::State wp_state(wp_header, channel.w, channel.h);

  tree_samples.PrepareForSamples(pixel_fraction * channel.h * channel.w + 64);

  const bool multiple_predictors = tree_samples.NumPredictors() != 1;

  auto compute_sample = [&](const pixel_type *p, size_t x, size_t y) {

    pixel_type_w pred[kNumModularPredictors];

    if (multiple_predictors) {

      PredictLearnAll(&properties, channel.w, p + x, onerow, x, y, references,

                      &wp_state, pred);

    } else {

      pred[static_cast<int>(tree_samples.PredictorFromIndex(0))] =

          PredictLearn(&properties, channel.w, p + x, onerow, x, y,

                       tree_samples.PredictorFromIndex(0), references,

                       &wp_state)

              .guess;

    }

    (*total_pixels)++;

    if (use_sample()) {

      tree_samples.AddSample(p[x], properties, pred);

    }

    wp_state.UpdateErrors(p[x], x, y, channel.w);

  };

  for (size_t y = 0; y < channel.h; y++) {

    const pixel_type *JXL_RESTRICT p = channel.Row(y);

    PrecomputeReferences(channel, y, image, chan, &references);

    InitPropsRow(&properties, static_props, y);

    // TODO(veluca): avoid computing WP if we don't use its property or

    // predictions.

    if (y > 1 && channel.w > 8 && references.w == 0) {

      for (size_t x = 0; x < 2; x++) {

        compute_sample(p, x, y);

      }

      for (size_t x = 2; x < channel.w - 2; x++) {

        pixel_type_w pred[kNumModularPredictors];

        if (multiple_predictors) {

          PredictLearnAllNEC(&properties, channel.w, p + x, onerow, x, y,

                             references, &wp_state, pred);

        } else {

          pred[static_cast<int>(tree_samples.PredictorFromIndex(0))] =

              PredictLearnNEC(&properties, channel.w, p + x, onerow, x, y,

                              tree_samples.PredictorFromIndex(0), references,

                              &wp_state)

                  .guess;

        }

        (*total_pixels)++;

        if (use_sample()) {

          tree_samples.AddSample(p[x], properties, pred);

        }

        wp_state.UpdateErrors(p[x], x, y, channel.w);

      }

      for (size_t x = channel.w - 2; x < channel.w; x++) {

        compute_sample(p, x, y);

      }

    } else {

      for (size_t x = 0; x < channel.w; x++) {

        compute_sample(p, x, y);

      }

    }

  }

  return true;

}

StatusOr<Tree> LearnTree(

    TreeSamples &&tree_samples, size_t total_pixels,

    const ModularOptions &options,

    const std::vector<ModularMultiplierInfo> &multiplier_info = {},

    StaticPropRange static_prop_range = {}) {

  Tree tree;

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

    if (static_prop_range[i][1] == 0) {

      static_prop_range[i][1] = std::numeric_limits<uint32_t>::max();

    }

  }

  if (!tree_samples.HasSamples()) {

    tree.emplace_back();

    tree.back().predictor = tree_samples.PredictorFromIndex(0);

    tree.back().property = -1;

    tree.back().predictor_offset = 0;

    tree.back().multiplier = 1;

    return tree;

  }

  float pixel_fraction = tree_samples.NumSamples() * 1.0f / total_pixels;

  float required_cost = pixel_fraction * 0.9 + 0.1;

  tree_samples.AllSamplesDone();

  JXL_RETURN_IF_ERROR(ComputeBestTree(

      tree_samples, options.splitting_heuristics_node_threshold * required_cost,

      multiplier_info, static_prop_range, options.fast_decode_multiplier,

      &tree));

  return tree;

}

Status EncodeModularChannelMAANS(const Image &image, pixel_type chan,

                                 const weighted::Header &wp_header,

                                 const Tree &global_tree, Token **tokenpp,

                                 size_t group_id, bool skip_encoder_fast_path) {

  const Channel &channel = image.channel[chan];

  JxlMemoryManager *memory_manager = channel.memory_manager();

  Token *tokenp = *tokenpp;

  JXL_ENSURE(channel.w != 0 && channel.h != 0);

  Image3F predictor_img;

  if (kWantDebug) {

    JXL_ASSIGN_OR_RETURN(predictor_img,

                         Image3F::Create(memory_manager, channel.w, channel.h));

  }

  JXL_DEBUG_V(6,

              "Encoding %" PRIuS "x%" PRIuS

              " channel %d, "

              "(shift=%i,%i)",

              channel.w, channel.h, chan, channel.hshift, channel.vshift);

  std::array<pixel_type, kNumStaticProperties> static_props = {

      {chan, static_cast<int>(group_id)}};

  bool use_wp;

  bool is_wp_only;

  bool is_gradient_only;

  size_t num_props;

  FlatTree tree = FilterTree(global_tree, static_props, &num_props, &use_wp,

                             &is_wp_only, &is_gradient_only);

  MATreeLookup tree_lookup(tree);

  JXL_DEBUG_V(3, "Encoding using a MA tree with %" PRIuS " nodes", tree.size());

  // Check if this tree is a WP-only tree with a small enough property value

  // range.

  // Initialized to avoid clang-tidy complaining.

  auto tree_lut = jxl::make_unique<TreeLut<uint16_t, false, false>>();

  if (is_wp_only) {

    is_wp_only = TreeToLookupTable(tree, *tree_lut);

  }

  if (is_gradient_only) {

    is_gradient_only = TreeToLookupTable(tree, *tree_lut);

  }

  if (is_wp_only && !skip_encoder_fast_path) {

    for (size_t c = 0; c < 3; c++) {

      FillImage(static_cast<float>(PredictorColor(Predictor::Weighted)[c]),

                &predictor_img.Plane(c));

    }

    const intptr_t onerow = channel.plane.PixelsPerRow();

    weighted::State wp_state(wp_header, channel.w, channel.h);

    Properties properties(1);

    for (size_t y = 0; y < channel.h; y++) {

      const pixel_type *JXL_RESTRICT r = channel.Row(y);

      for (size_t x = 0; x < channel.w; x++) {

        size_t offset = 0;

        pixel_type_w left = (x ? r[x - 1] : y ? *(r + x - onerow) : 0);

        pixel_type_w top = (y ? *(r + x - onerow) : left);

        pixel_type_w topleft = (x && y ? *(r + x - 1 - onerow) : left);

        pixel_type_w topright =

            (x + 1 < channel.w && y ? *(r + x + 1 - onerow) : top);

        pixel_type_w toptop = (y > 1 ? *(r + x - onerow - onerow) : top);

        int32_t guess = wp_state.Predict</*compute_properties=*/true>(

            x, y, channel.w, top, left, topright, topleft, toptop, &properties,

            offset);

        uint32_t pos =

            kPropRangeFast +

            jxl::Clamp1(properties[0], -kPropRangeFast, kPropRangeFast - 1);

        uint32_t ctx_id = tree_lut->context_lookup[pos];

        int32_t residual = r[x] - guess;

        *tokenp++ = Token(ctx_id, PackSigned(residual));

        wp_state.UpdateErrors(r[x], x, y, channel.w);

      }

    }

  } else if (tree.size() == 1 && tree[0].predictor == Predictor::Gradient &&

             tree[0].multiplier == 1 && tree[0].predictor_offset == 0 &&

             !skip_encoder_fast_path) {

    for (size_t c = 0; c < 3; c++) {

      FillImage(static_cast<float>(PredictorColor(Predictor::Gradient)[c]),

                &predictor_img.Plane(c));

    }

    const intptr_t onerow = channel.plane.PixelsPerRow();

    for (size_t y = 0; y < channel.h; y++) {

      const pixel_type *JXL_RESTRICT r = channel.Row(y);

      for (size_t x = 0; x < channel.w; x++) {

        pixel_type_w left = (x ? r[x - 1] : y ? *(r + x - onerow) : 0);

        pixel_type_w top = (y ? *(r + x - onerow) : left);

        pixel_type_w topleft = (x && y ? *(r + x - 1 - onerow) : left);

        int32_t guess = ClampedGradient(top, left, topleft);

        int32_t residual = r[x] - guess;

        *tokenp++ = Token(tree[0].childID, PackSigned(residual));

      }

    }

  } else if (is_gradient_only && !skip_encoder_fast_path) {

    for (size_t c = 0; c < 3; c++) {

      FillImage(static_cast<float>(PredictorColor(Predictor::Gradient)[c]),

                &predictor_img.Plane(c));

    }

    const intptr_t onerow = channel.plane.PixelsPerRow();

    for (size_t y = 0; y < channel.h; y++) {

      const pixel_type *JXL_RESTRICT r = channel.Row(y);

      for (size_t x = 0; x < channel.w; x++) {

        pixel_type_w left = (x ? r[x - 1] : y ? *(r + x - onerow) : 0);

        pixel_type_w top = (y ? *(r + x - onerow) : left);

        pixel_type_w topleft = (x && y ? *(r + x - 1 - onerow) : left);

        int32_t guess = ClampedGradient(top, left, topleft);

        uint32_t pos =

            kPropRangeFast +

            std::min<pixel_type_w>(

                std::max<pixel_type_w>(-kPropRangeFast, top + left - topleft),

                kPropRangeFast - 1);

        uint32_t ctx_id = tree_lut->context_lookup[pos];

        int32_t residual = r[x] - guess;

        *tokenp++ = Token(ctx_id, PackSigned(residual));

      }

    }

  } else if (tree.size() == 1 && tree[0].predictor == Predictor::Zero &&

             tree[0].multiplier == 1 && tree[0].predictor_offset == 0 &&

             !skip_encoder_fast_path) {

    for (size_t c = 0; c < 3; c++) {

      FillImage(static_cast<float>(PredictorColor(Predictor::Zero)[c]),

                &predictor_img.Plane(c));

    }

    for (size_t y = 0; y < channel.h; y++) {

      const pixel_type *JXL_RESTRICT p = channel.Row(y);

      for (size_t x = 0; x < channel.w; x++) {

        *tokenp++ = Token(tree[0].childID, PackSigned(p[x]));

      }

    }

  } else if (tree.size() == 1 && tree[0].predictor != Predictor::Weighted &&

             (tree[0].multiplier & (tree[0].multiplier - 1)) == 0 &&

             tree[0].predictor_offset == 0 && !skip_encoder_fast_path) {

    // multiplier is a power of 2.

    for (size_t c = 0; c < 3; c++) {

      FillImage(static_cast<float>(PredictorColor(tree[0].predictor)[c]),

                &predictor_img.Plane(c));

    }

    uint32_t mul_shift =

        FloorLog2Nonzero(static_cast<uint32_t>(tree[0].multiplier));

    const intptr_t onerow = channel.plane.PixelsPerRow();

    for (size_t y = 0; y < channel.h; y++) {

      const pixel_type *JXL_RESTRICT r = channel.Row(y);

      for (size_t x = 0; x < channel.w; x++) {

        PredictionResult pred = PredictNoTreeNoWP(channel.w, r + x, onerow, x,

                                                  y, tree[0].predictor);

        pixel_type_w residual = r[x] - pred.guess;

        JXL_DASSERT((residual >> mul_shift) * tree[0].multiplier == residual);

        *tokenp++ = Token(tree[0].childID, PackSigned(residual >> mul_shift));

      }

    }

  } else if (!use_wp && !skip_encoder_fast_path) {

    const intptr_t onerow = channel.plane.PixelsPerRow();

    Properties properties(num_props);

    JXL_ASSIGN_OR_RETURN(

        Channel references,

        Channel::Create(memory_manager,

                        properties.size() - kNumNonrefProperties, channel.w));

    for (size_t y = 0; y < channel.h; y++) {

      const pixel_type *JXL_RESTRICT p = channel.Row(y);

      PrecomputeReferences(channel, y, image, chan, &references);

      float *pred_img_row[3];

      if (kWantDebug) {

        for (size_t c = 0; c < 3; c++) {

          pred_img_row[c] = predictor_img.PlaneRow(c, y);

        }

      }

      InitPropsRow(&properties, static_props, y);

      for (size_t x = 0; x < channel.w; x++) {

        PredictionResult res =

            PredictTreeNoWP(&properties, channel.w, p + x, onerow, x, y,

                            tree_lookup, references);

        if (kWantDebug) {

          for (size_t i = 0; i < 3; i++) {

            pred_img_row[i][x] = PredictorColor(res.predictor)[i];

          }

        }

        pixel_type_w residual = p[x] - res.guess;

        JXL_DASSERT(residual % res.multiplier == 0);

        *tokenp++ = Token(res.context, PackSigned(residual / res.multiplier));

      }

    }

  } else {

    const intptr_t onerow = channel.plane.PixelsPerRow();

    Properties properties(num_props);

    JXL_ASSIGN_OR_RETURN(

        Channel references,

        Channel::Create(memory_manager,

                        properties.size() - kNumNonrefProperties, channel.w));

    weighted::State wp_state(wp_header, channel.w, channel.h);

    for (size_t y = 0; y < channel.h; y++) {

      const pixel_type *JXL_RESTRICT p = channel.Row(y);

      PrecomputeReferences(channel, y, image, chan, &references);

      float *pred_img_row[3];

      if (kWantDebug) {

        for (size_t c = 0; c < 3; c++) {

          pred_img_row[c] = predictor_img.PlaneRow(c, y);

        }

      }

      InitPropsRow(&properties, static_props, y);

      for (size_t x = 0; x < channel.w; x++) {

        PredictionResult res =

            PredictTreeWP(&properties, channel.w, p + x, onerow, x, y,

                          tree_lookup, references, &wp_state);

        if (kWantDebug) {

          for (size_t i = 0; i < 3; i++) {

            pred_img_row[i][x] = PredictorColor(res.predictor)[i];

          }

        }

        pixel_type_w residual = p[x] - res.guess;

        JXL_DASSERT(residual % res.multiplier == 0);

        *tokenp++ = Token(res.context, PackSigned(residual / res.multiplier));

        wp_state.UpdateErrors(p[x], x, y, channel.w);

      }

    }

  }

  /* TODO(szabadka): Add cparams to the call stack here.

  if (kWantDebug && WantDebugOutput(cparams)) {

    DumpImage(

        cparams,

        ("pred_" + ToString(group_id) + "_" + ToString(chan)).c_str(),

        predictor_img);

  }

  */

  *tokenpp = tokenp;

  return true;

}

}  // namespace

Tree PredefinedTree(ModularOptions::TreeKind tree_kind, size_t total_pixels,

                    int bitdepth, int prevprop) {

  switch (tree_kind) {

    case ModularOptions::TreeKind::kJpegTranscodeACMeta:

      // All the data is 0, so no need for a fancy tree.

      return {PropertyDecisionNode::Leaf(Predictor::Zero)};

    case ModularOptions::TreeKind::kTrivialTreeNoPredictor:

      // All the data is 0, so no need for a fancy tree.

      return {PropertyDecisionNode::Leaf(Predictor::Zero)};

    case ModularOptions::TreeKind::kFalconACMeta:

      // All the data is 0 except the quant field. TODO(veluca): make that 0

      // too.

      return {PropertyDecisionNode::Leaf(Predictor::Left)};

    case ModularOptions::TreeKind::kACMeta: {

      // Small image.

      if (total_pixels < 1024) {

        return {PropertyDecisionNode::Leaf(Predictor::Left)};

      }

      Tree tree;

      // 0: c > 1

      tree.push_back(PropertyDecisionNode::Split(0, 1, 1));

      // 1: c > 2

      tree.push_back(PropertyDecisionNode::Split(0, 2, 3));

      // 2: c > 0

      tree.push_back(PropertyDecisionNode::Split(0, 0, 5));

      // 3: EPF control field (all 0 or 4), top > 3

      tree.push_back(PropertyDecisionNode::Split(6, 3, 21));

      // 4: ACS+QF, y > 0

      tree.push_back(PropertyDecisionNode::Split(2, 0, 7));

      // 5: CfL x

      tree.push_back(PropertyDecisionNode::Leaf(Predictor::Gradient));

      // 6: CfL b

      tree.push_back(PropertyDecisionNode::Leaf(Predictor::Gradient));

      // 7: QF: split according to the left quant value.

      tree.push_back(PropertyDecisionNode::Split(7, 5, 9));

      // 8: ACS: split in 4 segments (8x8 from 0 to 3, large square 4-5, large

      // rectangular 6-11, 8x8 12+), according to previous ACS value.

      tree.push_back(PropertyDecisionNode::Split(7, 5, 15));

      // QF

      tree.push_back(PropertyDecisionNode::Split(7, 11, 11));

      tree.push_back(PropertyDecisionNode::Split(7, 3, 13));

      tree.push_back(PropertyDecisionNode::Leaf(Predictor::Left));

      tree.push_back(PropertyDecisionNode::Leaf(Predictor::Left));

      tree.push_back(PropertyDecisionNode::Leaf(Predictor::Left));

      tree.push_back(PropertyDecisionNode::Leaf(Predictor::Left));

      // ACS

      tree.push_back(PropertyDecisionNode::Split(7, 11, 17));

      tree.push_back(PropertyDecisionNode::Split(7, 3, 19));

      tree.push_back(PropertyDecisionNode::Leaf(Predictor::Zero));

      tree.push_back(PropertyDecisionNode::Leaf(Predictor::Zero));

      tree.push_back(PropertyDecisionNode::Leaf(Predictor::Zero));

      tree.push_back(PropertyDecisionNode::Leaf(Predictor::Zero));

      // EPF, left > 3

      tree.push_back(PropertyDecisionNode::Split(7, 3, 23));

      tree.push_back(PropertyDecisionNode::Split(7, 3, 25));

      tree.push_back(PropertyDecisionNode::Leaf(Predictor::Zero));

      tree.push_back(PropertyDecisionNode::Leaf(Predictor::Zero));

      tree.push_back(PropertyDecisionNode::Leaf(Predictor::Zero));

      tree.push_back(PropertyDecisionNode::Leaf(Predictor::Zero));

      return tree;

    }

    case ModularOptions::TreeKind::kWPFixedDC: {

      std::vector<int32_t> cutoffs = {

          -500, -392, -255, -191, -127, -95, -63, -47, -31, -23, -15,

          -11,  -7,   -4,   -3,   -1,   0,   1,   3,   5,   7,   11,

          15,   23,   31,   47,   63,   95,  127, 191, 255, 392, 500};

      return MakeFixedTree(kWPProp, cutoffs, Predictor::Weighted, total_pixels,

                           bitdepth);

    }

    case ModularOptions::TreeKind::kGradientFixedDC: {

      std::vector<int32_t> cutoffs = {

          -500, -392, -255, -191, -127, -95, -63, -47, -31, -23, -15,

          -11,  -7,   -4,   -3,   -1,   0,   1,   3,   5,   7,   11,

          15,   23,   31,   47,   63,   95,  127, 191, 255, 392, 500};

      return MakeFixedTree(

          prevprop > 0 ? kNumNonrefProperties + 2 : kGradientProp, cutoffs,

          Predictor::Gradient, total_pixels, bitdepth);

    }

    case ModularOptions::TreeKind::kLearn: {

      JXL_DEBUG_ABORT("internal: kLearn is not predefined tree");

      return {};

    }

  }

  JXL_DEBUG_ABORT("internal: unexpected TreeKind: %d",

                  static_cast<int>(tree_kind));

  return {};

}

StatusOr<Tree> LearnTree(

    const Image *images, const ModularOptions *options, const uint32_t start,

    const uint32_t stop,

    const std::vector<ModularMultiplierInfo> &multiplier_info = {}) {

  TreeSamples tree_samples;

  JXL_RETURN_IF_ERROR(tree_samples.SetPredictor(options[start].predictor,

                                                options[start].wp_tree_mode));

  JXL_RETURN_IF_ERROR(

      tree_samples.SetProperties(options[start].splitting_heuristics_properties,

                                 options[start].wp_tree_mode));

  uint32_t max_c = 0;

  std::vector<pixel_type> pixel_samples;

  std::vector<pixel_type> diff_samples;

  std::vector<uint32_t> group_pixel_count;

  std::vector<uint32_t> channel_pixel_count;

  for (uint32_t i = start; i < stop; i++) {

    max_c = std::max<uint32_t>(images[i].channel.size(), max_c);

    CollectPixelSamples(images[i], options[i], i, group_pixel_count,

                        channel_pixel_count, pixel_samples, diff_samples);

  }

  StaticPropRange range;

  range[0] = {{0, max_c}};

  range[1] = {{start, stop}};

  tree_samples.PreQuantizeProperties(

      range, multiplier_info, group_pixel_count, channel_pixel_count,

      pixel_samples, diff_samples, options[start].max_property_values);

  size_t total_pixels = 0;

  for (size_t i = 0; i < images[start].channel.size(); i++) {

    if (i >= images[start].nb_meta_channels &&

        (images[start].channel[i].w > options[start].max_chan_size ||

         images[start].channel[i].h > options[start].max_chan_size)) {

      break;

    }

    total_pixels += images[start].channel[i].w * images[start].channel[i].h;

  }

  total_pixels = std::max<size_t>(total_pixels, 1);

  weighted::Header wp_header;

  for (size_t i = start; i < stop; i++) {

    size_t nb_channels = images[i].channel.size();

    if (images[i].w == 0 || images[i].h == 0 || nb_channels < 1)

      continue;  // is there any use for a zero-channel image?

    if (images[i].error) return JXL_FAILURE("Invalid image");

    JXL_ENSURE(options[i].tree_kind == ModularOptions::TreeKind::kLearn);

    JXL_DEBUG_V(

        2, "Encoding %" PRIuS "-channel, %i-bit, %" PRIuS "x%" PRIuS " image.",

        nb_channels, images[i].bitdepth, images[i].w, images[i].h);

    // encode transforms

    Bundle::Init(&wp_header);

    if (options[i].predictor == Predictor::Weighted) {

      weighted::PredictorMode(options[i].wp_mode, &wp_header);

    }

    // Gather tree data

    for (size_t c = 0; c < nb_channels; c++) {

      if (c >= images[i].nb_meta_channels &&

          (images[i].channel[c].w > options[i].max_chan_size ||

           images[i].channel[c].h > options[i].max_chan_size)) {

        break;

      }

      if (!images[i].channel[c].w || !images[i].channel[c].h) {

        continue;  // skip empty channels

      }

      JXL_RETURN_IF_ERROR(GatherTreeData(images[i], c, i, wp_header, options[i],

                                         tree_samples, &total_pixels));

    }

  }

  // TODO(veluca): parallelize more.

  JXL_ASSIGN_OR_RETURN(Tree tree,

                       LearnTree(std::move(tree_samples), total_pixels,

                                 options[start], multiplier_info, range));

  return tree;

}

Status ModularCompress(const Image &image, const ModularOptions &options,

                       size_t group_id, const Tree &tree, GroupHeader &header,

                       std::vector<Token> &tokens, size_t *width) {

  size_t nb_channels = image.channel.size();

  if (image.w == 0 || image.h == 0 || nb_channels < 1)

    return true;  // is there any use for a zero-channel image?

  if (image.error) return JXL_FAILURE("Invalid image");

  JXL_DEBUG_V(

      2, "Encoding %" PRIuS "-channel, %i-bit, %" PRIuS "x%" PRIuS " image.",

      nb_channels, image.bitdepth, image.w, image.h);

  // encode transforms

  Bundle::Init(&header);

  if (options.predictor == Predictor::Weighted) {

    weighted::PredictorMode(options.wp_mode, &header.wp_header);

  }

  header.transforms = image.transform;

  header.use_global_tree = true;

  size_t image_width = 0;

  size_t total_tokens = 0;

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

    if (i >= image.nb_meta_channels &&

        (image.channel[i].w > options.max_chan_size ||

         image.channel[i].h > options.max_chan_size)) {

      break;

    }

    if (image.channel[i].w > image_width) image_width = image.channel[i].w;

    total_tokens += image.channel[i].w * image.channel[i].h;

  }

  if (options.zero_tokens) {

    tokens.resize(tokens.size() + total_tokens, {0, 0});

  } else {

    // Do one big allocation for all the tokens we'll need,

    // to avoid reallocs that might require copying.

    size_t pos = tokens.size();

    tokens.resize(pos + total_tokens);

    Token *tokenp = tokens.data() + pos;

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

      if (i >= image.nb_meta_channels &&

          (image.channel[i].w > options.max_chan_size ||

           image.channel[i].h > options.max_chan_size)) {

        break;

      }

      if (!image.channel[i].w || !image.channel[i].h) {

        continue;  // skip empty channels

      }

      JXL_RETURN_IF_ERROR(

          EncodeModularChannelMAANS(image, i, header.wp_header, tree, &tokenp,

                                    group_id, options.skip_encoder_fast_path));

    }

    // Make sure we actually wrote all tokens

    JXL_ENSURE(tokenp == tokens.data() + tokens.size());

  }

  *width = image_width;

  return true;

}

Status ModularGenericCompress(const Image &image, const ModularOptions &opts,

                              BitWriter &writer, AuxOut *aux_out,

                              LayerType layer, size_t group_id) {

  size_t nb_channels = image.channel.size();

  if (image.w == 0 || image.h == 0 || nb_channels < 1)

    return true;  // is there any use for a zero-channel image?

  if (image.error) return JXL_FAILURE("Invalid image");

  ModularOptions options = opts;  // Make a copy to modify it.

  if (options.predictor == kUndefinedPredictor) {

    options.predictor = Predictor::Gradient;

  }

  size_t bits = writer.BitsWritten();

  JxlMemoryManager *memory_manager = image.memory_manager();

  JXL_DEBUG_V(

      2, "Encoding %" PRIuS "-channel, %i-bit, %" PRIuS "x%" PRIuS " image.",

      nb_channels, image.bitdepth, image.w, image.h);

  // encode transforms

  GroupHeader header;

  Bundle::Init(&header);

  if (options.predictor == Predictor::Weighted) {

    weighted::PredictorMode(options.wp_mode, &header.wp_header);

  }

  header.transforms = image.transform;

  JXL_RETURN_IF_ERROR(Bundle::Write(header, &writer, layer, aux_out));

  // Compute tree.

  Tree tree;

  if (options.tree_kind == ModularOptions::TreeKind::kLearn) {

    JXL_ASSIGN_OR_RETURN(tree, LearnTree(&image, &options, 0, 1));

  } else {

    size_t total_pixels = 0;

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

      if (i >= image.nb_meta_channels &&

          (image.channel[i].w > options.max_chan_size ||

           image.channel[i].h > options.max_chan_size)) {

        break;

      }

      total_pixels += image.channel[i].w * image.channel[i].h;

    }

    total_pixels = std::max<size_t>(total_pixels, 1);

    tree = PredefinedTree(options.tree_kind, total_pixels, image.bitdepth,

                          options.max_properties);

  }

  Tree decoded_tree;

  std::vector<std::vector<Token>> tree_tokens(1);

  JXL_RETURN_IF_ERROR(TokenizeTree(tree, tree_tokens.data(), &decoded_tree));

  JXL_ENSURE(tree.size() == decoded_tree.size());

  tree = std::move(decoded_tree);

  /* TODO(szabadka) Add text output callback

  if (kWantDebug && kPrintTree && WantDebugOutput(aux_out)) {

    PrintTree(*tree, aux_out->debug_prefix + "/tree_" + ToString(group_id));

  } */

  // Write tree

  EntropyEncodingData code;

  JXL_ASSIGN_OR_RETURN(

      size_t cost,

      BuildAndEncodeHistograms(memory_manager, options.histogram_params,

                               kNumTreeContexts, tree_tokens, &code, &writer,

                               LayerType::ModularTree, aux_out));

  JXL_RETURN_IF_ERROR(WriteTokens(tree_tokens[0], code, 0, &writer,

                                  LayerType::ModularTree, aux_out));

  size_t image_width = 0;

  std::vector<std::vector<Token>> tokens(1);

  // it puts `use_global_tree = true` in the header, but this is not used

  // further

  JXL_RETURN_IF_ERROR(ModularCompress(image, options, group_id, tree, header,

                                      tokens[0], &image_width));

  // Write data

  code = {};

  HistogramParams histo_params = options.histogram_params;

  histo_params.image_widths.push_back(image_width);

  JXL_ASSIGN_OR_RETURN(

      cost, BuildAndEncodeHistograms(memory_manager, histo_params,

                                     (tree.size() + 1) / 2, tokens, &code,

                                     &writer, layer, aux_out));

  (void)cost;

  JXL_RETURN_IF_ERROR(WriteTokens(tokens[0], code, 0, &writer, layer, aux_out));

  bits = writer.BitsWritten() - bits;

  JXL_DEBUG_V(4,

              "Modular-encoded a %" PRIuS "x%" PRIuS

              " bitdepth=%i nbchans=%" PRIuS " image in %" PRIuS " bytes",

              image.w, image.h, image.bitdepth, image.channel.size(), bits / 8);

  (void)bits;

  return true;

}

}  // namespace jxl