kc3-lang/harfbuzz/src/hb-ot-var-common.hh

Branch - branch -main

• Show log

  Commit

• Hash : e1fd2bee
  Author :
  Date : 2025-09-18T11:18:09
  

  [alloc-pool] Reduce memory usage by reclaiming some discards
  

Download

• src/hb-ot-var-common.hh

• /*
   * Copyright © 2021  Google, Inc.
   *
   *  This is part of HarfBuzz, a text shaping library.
   *
   * Permission is hereby granted, without written agreement and without
   * license or royalty fees, to use, copy, modify, and distribute this
   * software and its documentation for any purpose, provided that the
   * above copyright notice and the following two paragraphs appear in
   * all copies of this software.
   *
   * IN NO EVENT SHALL THE COPYRIGHT HOLDER BE LIABLE TO ANY PARTY FOR
   * DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES
   * ARISING OUT OF THE USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN
   * IF THE COPYRIGHT HOLDER HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH
   * DAMAGE.
   *
   * THE COPYRIGHT HOLDER SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING,
   * BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
   * FITNESS FOR A PARTICULAR PURPOSE.  THE SOFTWARE PROVIDED HEREUNDER IS
   * ON AN "AS IS" BASIS, AND THE COPYRIGHT HOLDER HAS NO OBLIGATION TO
   * PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
   *
   */
  
  #ifndef HB_OT_VAR_COMMON_HH
  #define HB_OT_VAR_COMMON_HH
  
  #include "hb-ot-layout-common.hh"
  #include "hb-alloc-pool.hh"
  #include "hb-priority-queue.hh"
  #include "hb-subset-instancer-iup.hh"
  
  
  namespace OT {
  
  using rebase_tent_result_scratch_t = hb_pair_t<rebase_tent_result_t, rebase_tent_result_t>;
  
  /* https://docs.microsoft.com/en-us/typography/opentype/spec/otvarcommonformats#tuplevariationheader */
  struct TupleVariationHeader
  {
    friend struct tuple_delta_t;
    unsigned get_size (unsigned axis_count) const
    { return min_size + get_all_tuples (axis_count).get_size (); }
  
    unsigned get_data_size () const { return varDataSize; }
  
    const TupleVariationHeader &get_next (unsigned axis_count) const
    { return StructAtOffset<TupleVariationHeader> (this, get_size (axis_count)); }
  
    bool unpack_axis_tuples (unsigned axis_count,
                             const hb_array_t<const F2DOT14> shared_tuples,
                             const hb_map_t *axes_old_index_tag_map,
                             hb_hashmap_t<hb_tag_t, Triple>& axis_tuples /* OUT */) const
    {
      const F2DOT14 *peak_tuple = nullptr;
      if (has_peak ())
        peak_tuple = get_peak_tuple (axis_count);
      else
      {
        unsigned int index = get_index ();
        if (unlikely ((index + 1) * axis_count > shared_tuples.length))
          return false;
        peak_tuple = shared_tuples.sub_array (axis_count * index, axis_count).arrayZ;
      }
  
      const F2DOT14 *start_tuple = nullptr;
      const F2DOT14 *end_tuple = nullptr;
      bool has_interm = has_intermediate ();
  
      if (has_interm)
      {
        start_tuple = get_start_tuple (axis_count);
        end_tuple = get_end_tuple (axis_count);
      }
  
      for (unsigned i = 0; i < axis_count; i++)
      {
        float peak = peak_tuple[i].to_float ();
        if (peak == 0.f) continue;
  
        hb_tag_t *axis_tag;
        if (!axes_old_index_tag_map->has (i, &axis_tag))
          return false;
  
        float start, end;
        if (has_interm)
        {
          start = start_tuple[i].to_float ();
          end = end_tuple[i].to_float ();
        }
        else
        {
          start = hb_min (peak, 0.f);
          end = hb_max (peak, 0.f);
        }
        axis_tuples.set (*axis_tag, Triple ((double) start, (double) peak, (double) end));
      }
  
      return true;
    }
  
    HB_ALWAYS_INLINE
    double calculate_scalar (hb_array_t<const int> coords, unsigned int coord_count,
  			   const hb_array_t<const F2DOT14> shared_tuples,
  			   hb_scalar_cache_t *shared_tuple_scalar_cache = nullptr) const
    {
      unsigned tuple_index = tupleIndex;
  
      const F2DOT14 *peak_tuple;
  
      bool has_interm = tuple_index & TuppleIndex::IntermediateRegion; // Inlined for performance
      if (unlikely (has_interm))
        shared_tuple_scalar_cache = nullptr;
  
      if (unlikely (tuple_index & TuppleIndex::EmbeddedPeakTuple)) // Inlined for performance
      {
        peak_tuple = get_peak_tuple (coord_count);
        shared_tuple_scalar_cache = nullptr;
      }
      else
      {
        unsigned int index = tuple_index & TuppleIndex::TupleIndexMask; // Inlined for performance
  
        float scalar;
        if (shared_tuple_scalar_cache &&
  	  shared_tuple_scalar_cache->get (index, &scalar))
  	return (double) scalar;
  
        if (unlikely ((index + 1) * coord_count > shared_tuples.length))
          return 0.0;
        peak_tuple = shared_tuples.arrayZ + (coord_count * index);
  
      }
  
      const F2DOT14 *start_tuple = nullptr;
      const F2DOT14 *end_tuple = nullptr;
  
      if (has_interm)
      {
        start_tuple = get_start_tuple (coord_count);
        end_tuple = get_end_tuple (coord_count);
      }
  
      double scalar = 1.0;
      for (unsigned int i = 0; i < coord_count; i++)
      {
        int peak = peak_tuple[i].to_int ();
        if (!peak) continue;
  
        int v = coords[i];
        if (!v) { scalar = 0.0; break; }
        if (v == peak) continue;
  
        if (has_interm)
        {
          int start = start_tuple[i].to_int ();
          int end = end_tuple[i].to_int ();
          if (unlikely (start > peak || peak > end ||
                        (start < 0 && end > 0 && peak))) continue;
          if (v < start || v > end) { scalar = 0.0; break; }
          if (v < peak)
          { if (peak != start) scalar *= (double) (v - start) / (peak - start); }
          else
          { if (peak != end) scalar *= (double) (end - v) / (end - peak); }
        }
        else if (v < hb_min (0, peak) || v > hb_max (0, peak)) { scalar = 0.0; break; }
        else
          scalar *= (double) v / peak;
      }
      if (shared_tuple_scalar_cache)
        shared_tuple_scalar_cache->set (get_index (), scalar);
      return scalar;
    }
  
    bool           has_peak () const { return tupleIndex & TuppleIndex::EmbeddedPeakTuple; }
    bool   has_intermediate () const { return tupleIndex & TuppleIndex::IntermediateRegion; }
    bool has_private_points () const { return tupleIndex & TuppleIndex::PrivatePointNumbers; }
    unsigned      get_index () const { return tupleIndex & TuppleIndex::TupleIndexMask; }
  
    protected:
    struct TuppleIndex : HBUINT16
    {
      enum Flags {
        EmbeddedPeakTuple   = 0x8000u,
        IntermediateRegion  = 0x4000u,
        PrivatePointNumbers = 0x2000u,
        TupleIndexMask      = 0x0FFFu
      };
  
      TuppleIndex& operator = (uint16_t i) { HBUINT16::operator= (i); return *this; }
      DEFINE_SIZE_STATIC (2);
    };
  
    hb_array_t<const F2DOT14> get_all_tuples (unsigned axis_count) const
    { return StructAfter<UnsizedArrayOf<F2DOT14>> (tupleIndex).as_array ((has_peak () + has_intermediate () * 2) * axis_count); }
    const F2DOT14* get_all_tuples_base (unsigned axis_count) const
    { return StructAfter<UnsizedArrayOf<F2DOT14>> (tupleIndex).arrayZ; }
    const F2DOT14* get_peak_tuple (unsigned axis_count) const
    { return get_all_tuples_base (axis_count); }
    const F2DOT14* get_start_tuple (unsigned axis_count) const
    { return get_all_tuples_base (axis_count) + has_peak () * axis_count; }
    const F2DOT14* get_end_tuple (unsigned axis_count) const
    { return get_all_tuples_base (axis_count) + has_peak () * axis_count + axis_count; }
  
    HBUINT16      varDataSize;    /* The size in bytes of the serialized
                                   * data for this tuple variation table. */
    TuppleIndex   tupleIndex;     /* A packed field. The high 4 bits are flags (see below).
                                     The low 12 bits are an index into a shared tuple
                                     records array. */
    /* UnsizedArrayOf<F2DOT14> peakTuple - optional */
                                  /* Peak tuple record for this tuple variation table — optional,
                                   * determined by flags in the tupleIndex value.
                                   *
                                   * Note that this must always be included in the 'cvar' table. */
    /* UnsizedArrayOf<F2DOT14> intermediateStartTuple - optional */
                                  /* Intermediate start tuple record for this tuple variation table — optional,
                                     determined by flags in the tupleIndex value. */
    /* UnsizedArrayOf<F2DOT14> intermediateEndTuple - optional */
                                  /* Intermediate end tuple record for this tuple variation table — optional,
                                   * determined by flags in the tupleIndex value. */
    public:
    DEFINE_SIZE_MIN (4);
  };
  
  struct optimize_scratch_t
  {
    iup_scratch_t iup;
    hb_vector_t<bool> opt_indices;
    hb_vector_t<int> rounded_x_deltas;
    hb_vector_t<int> rounded_y_deltas;
    hb_vector_t<float> opt_deltas_x;
    hb_vector_t<float> opt_deltas_y;
    hb_vector_t<unsigned char> opt_point_data;
    hb_vector_t<unsigned char> opt_deltas_data;
    hb_vector_t<unsigned char> point_data;
    hb_vector_t<unsigned char> deltas_data;
    hb_vector_t<int> rounded_deltas;
  };
  
  struct tuple_delta_t
  {
    static constexpr bool realloc_move = true;  // Watch out when adding new members!
  
    public:
    hb_hashmap_t<hb_tag_t, Triple> axis_tuples;
  
    /* indices_length = point_count, indice[i] = 1 means point i is referenced */
    hb_vector_t<bool> indices;
  
    hb_vector_t<float> deltas_x;
    /* empty for cvar tuples */
    hb_vector_t<float> deltas_y;
  
    /* compiled data: header and deltas
     * compiled point data is saved in a hashmap within tuple_variations_t cause
     * some point sets might be reused by different tuple variations */
    hb_vector_t<unsigned char> compiled_tuple_header;
    hb_vector_t<unsigned char> compiled_deltas;
  
    hb_vector_t<F2DOT14> compiled_peak_coords;
    hb_vector_t<F2DOT14> compiled_interm_coords;
  
    tuple_delta_t (hb_alloc_pool_t *pool = nullptr) {}
    tuple_delta_t (const tuple_delta_t& o) = default;
    tuple_delta_t& operator = (const tuple_delta_t& o) = default;
  
    friend void swap (tuple_delta_t& a, tuple_delta_t& b) noexcept
    {
      hb_swap (a.axis_tuples, b.axis_tuples);
      hb_swap (a.indices, b.indices);
      hb_swap (a.deltas_x, b.deltas_x);
      hb_swap (a.deltas_y, b.deltas_y);
      hb_swap (a.compiled_tuple_header, b.compiled_tuple_header);
      hb_swap (a.compiled_deltas, b.compiled_deltas);
      hb_swap (a.compiled_peak_coords, b.compiled_peak_coords);
    }
  
    tuple_delta_t (tuple_delta_t&& o)  noexcept : tuple_delta_t ()
    { hb_swap (*this, o); }
  
    tuple_delta_t& operator = (tuple_delta_t&& o) noexcept
    {
      hb_swap (*this, o);
      return *this;
    }
  
    void copy_from (const tuple_delta_t& o, hb_alloc_pool_t *pool = nullptr)
    {
      axis_tuples = o.axis_tuples;
      indices.allocate_from_pool (pool, o.indices);
      deltas_x.allocate_from_pool (pool, o.deltas_x);
      deltas_y.allocate_from_pool (pool, o.deltas_y);
      compiled_tuple_header.allocate_from_pool (pool, o.compiled_tuple_header);
      compiled_deltas.allocate_from_pool (pool, o.compiled_deltas);
      compiled_peak_coords.allocate_from_pool (pool, o.compiled_peak_coords);
      compiled_interm_coords.allocate_from_pool (pool, o.compiled_interm_coords);
    }
  
    void remove_axis (hb_tag_t axis_tag)
    { axis_tuples.del (axis_tag); }
  
    bool set_tent (hb_tag_t axis_tag, Triple tent)
    { return axis_tuples.set (axis_tag, tent); }
  
    tuple_delta_t& operator += (const tuple_delta_t& o)
    {
      unsigned num = indices.length;
      for (unsigned i = 0; i < num; i++)
      {
        if (indices.arrayZ[i])
        {
          if (o.indices.arrayZ[i])
          {
            deltas_x[i] += o.deltas_x[i];
            if (deltas_y && o.deltas_y)
              deltas_y[i] += o.deltas_y[i];
          }
        }
        else
        {
          if (!o.indices.arrayZ[i]) continue;
          indices.arrayZ[i] = true;
          deltas_x[i] = o.deltas_x[i];
          if (deltas_y && o.deltas_y)
            deltas_y[i] = o.deltas_y[i];
        }
      }
      return *this;
    }
  
    tuple_delta_t& operator *= (float scalar)
    {
      if (scalar == 1.0f)
        return *this;
  
      unsigned num = indices.length;
      if (deltas_y)
        for (unsigned i = 0; i < num; i++)
        {
  	if (!indices.arrayZ[i]) continue;
  	deltas_x[i] *= scalar;
  	deltas_y[i] *= scalar;
        }
      else
        for (unsigned i = 0; i < num; i++)
        {
  	if (!indices.arrayZ[i]) continue;
  	deltas_x[i] *= scalar;
        }
      return *this;
    }
  
    void change_tuple_var_axis_limit (hb_tag_t axis_tag, Triple axis_limit,
  				    TripleDistances axis_triple_distances,
  				    hb_vector_t<tuple_delta_t>& out,
  				    rebase_tent_result_scratch_t &scratch,
  				    hb_alloc_pool_t *pool = nullptr)
    {
      // May move *this out.
  
      out.reset ();
      Triple *tent;
      if (!axis_tuples.has (axis_tag, &tent))
      {
        out.push (std::move (*this));
        return;
      }
  
      if ((tent->minimum < 0.0 && tent->maximum > 0.0) ||
          !(tent->minimum <= tent->middle && tent->middle <= tent->maximum))
        return;
  
      if (tent->middle == 0.0)
      {
        out.push (std::move (*this));
        return;
      }
  
      rebase_tent_result_t &solutions = scratch.first;
      rebase_tent (*tent, axis_limit, axis_triple_distances, solutions, scratch.second);
      for (unsigned i = 0; i < solutions.length; i++)
      {
        auto &t = solutions.arrayZ[i];
  
        tuple_delta_t new_var;
        if (i < solutions.length - 1)
  	new_var.copy_from (*this, pool);
        else
  	new_var = std::move (*this);
  
        if (t.second == Triple ())
          new_var.remove_axis (axis_tag);
        else
          new_var.set_tent (axis_tag, t.second);
  
        new_var *= t.first;
        out.push (std::move (new_var));
      }
    }
  
    bool compile_coords (const hb_map_t& axes_index_map,
  		       const hb_map_t& axes_old_index_tag_map,
  		       hb_alloc_pool_t *pool= nullptr)
    {
      unsigned cur_axis_count = axes_index_map.get_population ();
      if (pool)
      {
        if (unlikely (!compiled_peak_coords.allocate_from_pool (pool, cur_axis_count)))
  	return false;
      }
      else if (unlikely (!compiled_peak_coords.resize (cur_axis_count)))
        return false;
  
      hb_array_t<F2DOT14> start_coords, end_coords;
  
      unsigned orig_axis_count = axes_old_index_tag_map.get_population ();
      unsigned j = 0;
      for (unsigned i = 0; i < orig_axis_count; i++)
      {
        if (!axes_index_map.has (i))
          continue;
  
        hb_tag_t axis_tag = axes_old_index_tag_map.get (i);
        Triple *coords = nullptr;
        if (axis_tuples.has (axis_tag, &coords))
        {
  	float min_val = coords->minimum;
  	float val = coords->middle;
  	float max_val = coords->maximum;
  
  	compiled_peak_coords.arrayZ[j].set_float (val);
  
  	if (min_val != hb_min (val, 0.f) || max_val != hb_max (val, 0.f))
  	{
  	  if (!compiled_interm_coords)
  	  {
  	    if (pool)
  	    {
  	      if (unlikely (!compiled_interm_coords.allocate_from_pool (pool, 2 * cur_axis_count)))
  		return false;
  	    }
  	    else if (unlikely (!compiled_interm_coords.resize (2 * cur_axis_count)))
  	      return false;
  	    start_coords = compiled_interm_coords.as_array ().sub_array (0, cur_axis_count);
  	    end_coords = compiled_interm_coords.as_array ().sub_array (cur_axis_count);
  
  	    for (unsigned k = 0; k < j; k++)
  	    {
  	      signed peak = compiled_peak_coords.arrayZ[k].to_int ();
  	      if (!peak) continue;
  	      start_coords.arrayZ[k].set_int (hb_min (peak, 0));
  	      end_coords.arrayZ[k].set_int (hb_max (peak, 0));
  	    }
  	  }
  
  	}
  
  	if (compiled_interm_coords)
  	{
  	  start_coords.arrayZ[j].set_float (min_val);
  	  end_coords.arrayZ[j].set_float (max_val);
  	}
        }
  
        j++;
      }
  
      return !compiled_peak_coords.in_error () && !compiled_interm_coords.in_error ();
    }
  
    /* deltas should be compiled already before we compile tuple
     * variation header cause we need to fill in the size of the
     * serialized data for this tuple variation */
    bool compile_tuple_var_header (const hb_map_t& axes_index_map,
                                   unsigned points_data_length,
                                   const hb_map_t& axes_old_index_tag_map,
                                   const hb_hashmap_t<const hb_vector_t<F2DOT14>*, unsigned>* shared_tuples_idx_map,
  				 hb_alloc_pool_t *pool = nullptr)
    {
      /* compiled_deltas could be empty after iup delta optimization, we can skip
       * compiling this tuple and return true */
      if (!compiled_deltas) return true;
  
      unsigned cur_axis_count = axes_index_map.get_population ();
      /* allocate enough memory: 1 peak + 2 intermediate coords + fixed header size */
      unsigned alloc_len = 3 * cur_axis_count * (F2DOT14::static_size) + 4;
      if (unlikely (!compiled_tuple_header.allocate_from_pool (pool, alloc_len, false))) return false;
  
      unsigned flag = 0;
      /* skip the first 4 header bytes: variationDataSize+tupleIndex */
      F2DOT14* p = reinterpret_cast<F2DOT14 *> (compiled_tuple_header.begin () + 4);
      F2DOT14* end = reinterpret_cast<F2DOT14 *> (compiled_tuple_header.end ());
      hb_array_t<F2DOT14> coords (p, end - p);
  
      if (!shared_tuples_idx_map)
        compile_coords (axes_index_map, axes_old_index_tag_map); // non-gvar tuples do not have compiled coords yet
  
      /* encode peak coords */
      unsigned peak_count = 0;
      unsigned *shared_tuple_idx;
      if (shared_tuples_idx_map &&
          shared_tuples_idx_map->has (&compiled_peak_coords, &shared_tuple_idx))
      {
        flag = *shared_tuple_idx;
      }
      else
      {
        peak_count = encode_peak_coords(coords, flag);
        if (!peak_count) return false;
      }
  
      /* encode interim coords, it's optional so returned num could be 0 */
      unsigned interim_count = encode_interm_coords (coords.sub_array (peak_count), flag);
  
      /* pointdata length = 0 implies "use shared points" */
      if (points_data_length)
        flag |= TupleVariationHeader::TuppleIndex::PrivatePointNumbers;
  
      unsigned serialized_data_size = points_data_length + compiled_deltas.length;
      TupleVariationHeader *o = reinterpret_cast<TupleVariationHeader *> (compiled_tuple_header.begin ());
      o->varDataSize = serialized_data_size;
      o->tupleIndex = flag;
  
      unsigned total_header_len = 4 + (peak_count + interim_count) * (F2DOT14::static_size);
      compiled_tuple_header.shrink_back_to_pool (pool, total_header_len);
      return true;
    }
  
    unsigned encode_peak_coords (hb_array_t<F2DOT14> peak_coords,
                                 unsigned& flag) const
    {
      hb_memcpy (&peak_coords[0], &compiled_peak_coords[0], compiled_peak_coords.length * sizeof (compiled_peak_coords[0]));
      flag |= TupleVariationHeader::TuppleIndex::EmbeddedPeakTuple;
      return compiled_peak_coords.length;
    }
  
    /* if no need to encode intermediate coords, then just return p */
    unsigned encode_interm_coords (hb_array_t<F2DOT14> coords,
                                   unsigned& flag) const
    {
      if (compiled_interm_coords)
      {
        hb_memcpy (&coords[0], &compiled_interm_coords[0], compiled_interm_coords.length * sizeof (compiled_interm_coords[0]));
        flag |= TupleVariationHeader::TuppleIndex::IntermediateRegion;
      }
      return compiled_interm_coords.length;
    }
  
    bool compile_deltas (hb_vector_t<int> &rounded_deltas_scratch,
  		       hb_alloc_pool_t *pool = nullptr)
    { return compile_deltas (indices, deltas_x, deltas_y, compiled_deltas, rounded_deltas_scratch, pool); }
  
    static bool compile_deltas (hb_array_t<const bool> point_indices,
  			      hb_array_t<const float> x_deltas,
  			      hb_array_t<const float> y_deltas,
  			      hb_vector_t<unsigned char> &compiled_deltas, /* OUT */
  			      hb_vector_t<int> &rounded_deltas, /* scratch */
  			      hb_alloc_pool_t *pool = nullptr)
    {
      if (unlikely (!rounded_deltas.resize_dirty  (point_indices.length)))
        return false;
  
      unsigned j = 0;
      for (unsigned i = 0; i < point_indices.length; i++)
      {
        if (!point_indices[i]) continue;
        rounded_deltas.arrayZ[j++] = (int) roundf (x_deltas.arrayZ[i]);
      }
      rounded_deltas.resize (j);
  
      if (!rounded_deltas) return true;
      /* Allocate enough memory: this is the correct bound:
       * Worst case scenario is that each delta has to be encoded in 4 bytes, and there
       * are runs of 64 items each. Any delta encoded in less than 4 bytes (2, 1, or 0)
       * is still smaller than the 4-byte encoding even with their control byte.
       * The initial 2 is to handle length==0, for both x and y deltas. */
      unsigned alloc_len = 2 + 4 * rounded_deltas.length + (rounded_deltas.length + 63) / 64;
      if (y_deltas)
        alloc_len *= 2;
  
      if (unlikely (!compiled_deltas.allocate_from_pool (pool, alloc_len, false))) return false;
  
      unsigned encoded_len = compile_deltas (compiled_deltas, rounded_deltas);
  
      if (y_deltas)
      {
        /* reuse the rounded_deltas vector, check that y_deltas have the same num of deltas as x_deltas */
        unsigned j = 0;
        for (unsigned idx = 0; idx < point_indices.length; idx++)
        {
          if (!point_indices[idx]) continue;
          int rounded_delta = (int) roundf (y_deltas.arrayZ[idx]);
  
          if (j >= rounded_deltas.length) return false;
  
          rounded_deltas[j++] = rounded_delta;
        }
  
        if (j != rounded_deltas.length) return false;
        encoded_len += compile_deltas (compiled_deltas.as_array ().sub_array (encoded_len), rounded_deltas);
      }
      compiled_deltas.shrink_back_to_pool (pool, encoded_len);
      return true;
    }
  
    static unsigned compile_deltas (hb_array_t<unsigned char> encoded_bytes,
  				  hb_array_t<const int> deltas)
    {
      return TupleValues::compile_unsafe (deltas, encoded_bytes);
    }
  
    bool calc_inferred_deltas (const contour_point_vector_t& orig_points,
  			     hb_vector_t<unsigned> &scratch)
    {
      unsigned point_count = orig_points.length;
      if (point_count != indices.length)
        return false;
  
      unsigned ref_count = 0;
  
      hb_vector_t<unsigned> &end_points = scratch.reset ();
  
      for (unsigned i = 0; i < point_count; i++)
      {
        ref_count += indices.arrayZ[i];
        if (orig_points.arrayZ[i].is_end_point)
          end_points.push (i);
      }
      /* all points are referenced, nothing to do */
      if (ref_count == point_count)
        return true;
      if (unlikely (end_points.in_error ())) return false;
  
      hb_bit_set_t inferred_idxes;
      unsigned start_point = 0;
      for (unsigned end_point : end_points)
      {
        /* Check the number of unreferenced points in a contour. If no unref points or no ref points, nothing to do. */
        unsigned unref_count = 0;
        for (unsigned i = start_point; i < end_point + 1; i++)
          unref_count += indices.arrayZ[i];
        unref_count = (end_point - start_point + 1) - unref_count;
  
        unsigned j = start_point;
        if (unref_count == 0 || unref_count > end_point - start_point)
          goto no_more_gaps;
        for (;;)
        {
          /* Locate the next gap of unreferenced points between two referenced points prev and next.
           * Note that a gap may wrap around at left (start_point) and/or at right (end_point).
           */
          unsigned int prev, next, i;
          for (;;)
          {
            i = j;
            j = next_index (i, start_point, end_point);
            if (indices.arrayZ[i] && !indices.arrayZ[j]) break;
          }
          prev = j = i;
          for (;;)
          {
            i = j;
            j = next_index (i, start_point, end_point);
            if (!indices.arrayZ[i] && indices.arrayZ[j]) break;
          }
          next = j;
         /* Infer deltas for all unref points in the gap between prev and next */
          i = prev;
          for (;;)
          {
            i = next_index (i, start_point, end_point);
            if (i == next) break;
            deltas_x.arrayZ[i] = infer_delta ((double) orig_points.arrayZ[i].x,
                                              (double) orig_points.arrayZ[prev].x,
                                              (double) orig_points.arrayZ[next].x,
                                              (double) deltas_x.arrayZ[prev], (double) deltas_x.arrayZ[next]);
            deltas_y.arrayZ[i] = infer_delta ((double) orig_points.arrayZ[i].y,
                                              (double) orig_points.arrayZ[prev].y,
                                              (double) orig_points.arrayZ[next].y,
                                              (double) deltas_y.arrayZ[prev], (double) deltas_y.arrayZ[next]);
            inferred_idxes.add (i);
            if (--unref_count == 0) goto no_more_gaps;
          }
        }
      no_more_gaps:
        start_point = end_point + 1;
      }
  
      for (unsigned i = 0; i < point_count; i++)
      {
        /* if points are not referenced and deltas are not inferred, set to 0.
         * reference all points for gvar */
        if ( !indices[i])
        {
          if (!inferred_idxes.has (i))
          {
            deltas_x.arrayZ[i] = 0.0;
            deltas_y.arrayZ[i] = 0.0;
          }
          indices[i] = true;
        }
      }
      return true;
    }
  
    bool optimize (const contour_point_vector_t& contour_points,
                   bool is_composite,
  		 optimize_scratch_t &scratch,
                   double tolerance = 0.5 + 1e-10)
    {
      unsigned count = contour_points.length;
      if (deltas_x.length != count ||
          deltas_y.length != count)
        return false;
  
      hb_vector_t<bool> &opt_indices = scratch.opt_indices.reset ();
      hb_vector_t<int> &rounded_x_deltas = scratch.rounded_x_deltas;
      hb_vector_t<int> &rounded_y_deltas = scratch.rounded_y_deltas;
  
      if (unlikely (!rounded_x_deltas.resize_dirty  (count) ||
                    !rounded_y_deltas.resize_dirty  (count)))
        return false;
  
      for (unsigned i = 0; i < count; i++)
      {
        rounded_x_deltas.arrayZ[i] = (int) roundf (deltas_x.arrayZ[i]);
        rounded_y_deltas.arrayZ[i] = (int) roundf (deltas_y.arrayZ[i]);
      }
  
      if (!iup_delta_optimize (contour_points, rounded_x_deltas, rounded_y_deltas, opt_indices, scratch.iup, tolerance))
        return false;
  
      unsigned ref_count = 0;
      for (bool ref_flag : opt_indices)
         ref_count += ref_flag;
  
      if (ref_count == count) return true;
  
      hb_vector_t<float> &opt_deltas_x = scratch.opt_deltas_x.reset ();
      hb_vector_t<float> &opt_deltas_y = scratch.opt_deltas_y.reset ();
      bool is_comp_glyph_wo_deltas = (is_composite && ref_count == 0);
      if (is_comp_glyph_wo_deltas)
      {
        if (unlikely (!opt_deltas_x.resize (count) ||
                      !opt_deltas_y.resize (count)))
          return false;
  
        opt_indices.arrayZ[0] = true;
        for (unsigned i = 1; i < count; i++)
          opt_indices.arrayZ[i] = false;
      }
  
      hb_vector_t<unsigned char> &opt_point_data = scratch.opt_point_data.reset ();
      if (!compile_point_set (opt_indices, opt_point_data))
        return false;
      hb_vector_t<unsigned char> &opt_deltas_data = scratch.opt_deltas_data.reset ();
      if (!compile_deltas (opt_indices,
                           is_comp_glyph_wo_deltas ? opt_deltas_x : deltas_x,
                           is_comp_glyph_wo_deltas ? opt_deltas_y : deltas_y,
                           opt_deltas_data,
  			 scratch.rounded_deltas))
        return false;
  
      hb_vector_t<unsigned char> &point_data = scratch.point_data.reset ();
      if (!compile_point_set (indices, point_data))
        return false;
      hb_vector_t<unsigned char> &deltas_data = scratch.deltas_data.reset ();
      if (!compile_deltas (indices, deltas_x, deltas_y, deltas_data, scratch.rounded_deltas))
        return false;
  
      if (opt_point_data.length + opt_deltas_data.length < point_data.length + deltas_data.length)
      {
        indices = std::move (opt_indices);
  
        if (is_comp_glyph_wo_deltas)
        {
          deltas_x = std::move (opt_deltas_x);
          deltas_y = std::move (opt_deltas_y);
        }
      }
      return !indices.in_error () && !deltas_x.in_error () && !deltas_y.in_error ();
    }
  
    static bool compile_point_set (const hb_vector_t<bool> &point_indices,
                                   hb_vector_t<unsigned char>& compiled_points /* OUT */)
    {
      unsigned num_points = 0;
      for (bool i : point_indices)
        if (i) num_points++;
  
      /* when iup optimization is enabled, num of referenced points could be 0 */
      if (!num_points) return true;
  
      unsigned indices_length = point_indices.length;
      /* If the points set consists of all points in the glyph, it's encoded with a
       * single zero byte */
      if (num_points == indices_length)
        return compiled_points.resize (1);
  
      /* allocate enough memories: 2 bytes for count + 3 bytes for each point */
      unsigned num_bytes = 2 + 3 *num_points;
      if (unlikely (!compiled_points.resize_dirty  (num_bytes)))
        return false;
  
      unsigned pos = 0;
      /* binary data starts with the total number of reference points */
      if (num_points < 0x80)
        compiled_points.arrayZ[pos++] = num_points;
      else
      {
        compiled_points.arrayZ[pos++] = ((num_points >> 8) | 0x80);
        compiled_points.arrayZ[pos++] = num_points & 0xFF;
      }
  
      const unsigned max_run_length = 0x7F;
      unsigned i = 0;
      unsigned last_value = 0;
      unsigned num_encoded = 0;
      while (i < indices_length && num_encoded < num_points)
      {
        unsigned run_length = 0;
        unsigned header_pos = pos;
        compiled_points.arrayZ[pos++] = 0;
  
        bool use_byte_encoding = false;
        bool new_run = true;
        while (i < indices_length && num_encoded < num_points &&
               run_length <= max_run_length)
        {
          // find out next referenced point index
          while (i < indices_length && !point_indices[i])
            i++;
  
          if (i >= indices_length) break;
  
          unsigned cur_value = i;
          unsigned delta = cur_value - last_value;
  
          if (new_run)
          {
            use_byte_encoding = (delta <= 0xFF);
            new_run = false;
          }
  
          if (use_byte_encoding && delta > 0xFF)
            break;
  
          if (use_byte_encoding)
            compiled_points.arrayZ[pos++] = delta;
          else
          {
            compiled_points.arrayZ[pos++] = delta >> 8;
            compiled_points.arrayZ[pos++] = delta & 0xFF;
          }
          i++;
          last_value = cur_value;
          run_length++;
          num_encoded++;
        }
  
        if (use_byte_encoding)
          compiled_points.arrayZ[header_pos] = run_length - 1;
        else
          compiled_points.arrayZ[header_pos] = (run_length - 1) | 0x80;
      }
      return compiled_points.resize_dirty  (pos);
    }
  
    static double infer_delta (double target_val, double prev_val, double next_val, double prev_delta, double next_delta)
    {
      if (prev_val == next_val)
        return (prev_delta == next_delta) ? prev_delta : 0.0;
      else if (target_val <= hb_min (prev_val, next_val))
        return (prev_val < next_val) ? prev_delta : next_delta;
      else if (target_val >= hb_max (prev_val, next_val))
        return (prev_val > next_val) ? prev_delta : next_delta;
  
      double r = (target_val - prev_val) / (next_val - prev_val);
      return prev_delta + r * (next_delta - prev_delta);
    }
  
    static unsigned int next_index (unsigned int i, unsigned int start, unsigned int end)
    { return (i >= end) ? start : (i + 1); }
  };
  
  template <typename OffType = HBUINT16>
  struct TupleVariationData
  {
    bool sanitize (hb_sanitize_context_t *c) const
    {
      TRACE_SANITIZE (this);
      // here check on min_size only, TupleVariationHeader and var data will be
      // checked while accessing through iterator.
      return_trace (c->check_struct (this));
    }
  
    unsigned get_size (unsigned axis_count) const
    {
      unsigned total_size = min_size;
      unsigned count = tupleVarCount.get_count ();
      const TupleVariationHeader *tuple_var_header = &(get_tuple_var_header());
      for (unsigned i = 0; i < count; i++)
      {
        total_size += tuple_var_header->get_size (axis_count) + tuple_var_header->get_data_size ();
        tuple_var_header = &tuple_var_header->get_next (axis_count);
      }
  
      return total_size;
    }
  
    const TupleVariationHeader &get_tuple_var_header (void) const
    { return StructAfter<TupleVariationHeader> (data); }
  
    struct tuple_iterator_t;
    struct tuple_variations_t
    {
      hb_vector_t<tuple_delta_t> tuple_vars;
  
      private:
      /* referenced point set->compiled point data map */
      hb_hashmap_t<const hb_vector_t<bool>*, hb_vector_t<unsigned char>> point_data_map;
      /* referenced point set-> count map, used in finding shared points */
      hb_hashmap_t<const hb_vector_t<bool>*, unsigned> point_set_count_map;
  
      /* empty for non-gvar tuples.
       * shared_points_bytes is a pointer to some value in the point_data_map,
       * which will be freed during map destruction. Save it for serialization, so
       * no need to do find_shared_points () again */
      hb_vector_t<unsigned char> *shared_points_bytes = nullptr;
  
      /* total compiled byte size as TupleVariationData format, initialized to 0 */
      unsigned compiled_byte_size = 0;
      bool needs_padding = false;
  
      /* for gvar iup delta optimization: whether this is a composite glyph */
      bool is_composite = false;
  
      public:
      tuple_variations_t () = default;
      tuple_variations_t (const tuple_variations_t&) = delete;
      tuple_variations_t& operator=(const tuple_variations_t&) = delete;
      tuple_variations_t (tuple_variations_t&&) = default;
      tuple_variations_t& operator=(tuple_variations_t&&) = default;
      ~tuple_variations_t () = default;
  
      explicit operator bool () const { return bool (tuple_vars); }
      unsigned get_var_count () const
      {
        unsigned count = 0;
        /* when iup delta opt is enabled, compiled_deltas could be empty and we
         * should skip this tuple */
        for (auto& tuple: tuple_vars)
          if (tuple.compiled_deltas) count++;
  
        if (shared_points_bytes && shared_points_bytes->length)
          count |= TupleVarCount::SharedPointNumbers;
        return count;
      }
  
      unsigned get_compiled_byte_size () const
      { return compiled_byte_size; }
  
      bool create_from_tuple_var_data (tuple_iterator_t iterator,
                                       unsigned tuple_var_count,
                                       unsigned point_count,
                                       bool is_gvar,
                                       const hb_map_t *axes_old_index_tag_map,
                                       const hb_vector_t<unsigned> &shared_indices,
                                       const hb_array_t<const F2DOT14> shared_tuples,
  				     hb_alloc_pool_t *pool = nullptr,
                                       bool is_composite_glyph = false)
      {
        hb_vector_t<unsigned> private_indices;
        hb_vector_t<int> deltas_x;
        hb_vector_t<int> deltas_y;
        do
        {
          const HBUINT8 *p = iterator.get_serialized_data ();
          unsigned int length = iterator.current_tuple->get_data_size ();
          if (unlikely (!iterator.var_data_bytes.check_range (p, length)))
            return false;
  
  	hb_hashmap_t<hb_tag_t, Triple> axis_tuples;
          if (!iterator.current_tuple->unpack_axis_tuples (iterator.get_axis_count (), shared_tuples, axes_old_index_tag_map, axis_tuples)
              || axis_tuples.is_empty ())
            return false;
  
  	private_indices.reset ();
          bool has_private_points = iterator.current_tuple->has_private_points ();
          const HBUINT8 *end = p + length;
          if (has_private_points &&
              !TupleVariationData::decompile_points (p, private_indices, end))
            return false;
  
          const hb_vector_t<unsigned> &indices = has_private_points ? private_indices : shared_indices;
          bool apply_to_all = (indices.length == 0);
          unsigned num_deltas = apply_to_all ? point_count : indices.length;
  
          if (unlikely (!deltas_x.resize_dirty  (num_deltas) ||
                        !TupleVariationData::decompile_deltas (p, deltas_x, end)))
            return false;
  
          if (is_gvar)
          {
            if (unlikely (!deltas_y.resize_dirty  (num_deltas) ||
                          !TupleVariationData::decompile_deltas (p, deltas_y, end)))
              return false;
          }
  
          tuple_delta_t var;
          var.axis_tuples = std::move (axis_tuples);
          if (unlikely (!var.indices.allocate_from_pool (pool, point_count) ||
                        !var.deltas_x.allocate_from_pool (pool, point_count, false)))
            return false;
  
          if (is_gvar && unlikely (!var.deltas_y.allocate_from_pool (pool, point_count, false)))
            return false;
  
          for (unsigned i = 0; i < num_deltas; i++)
          {
            unsigned idx = apply_to_all ? i : indices[i];
            if (idx >= point_count) continue;
            var.indices[idx] = true;
            var.deltas_x[idx] = deltas_x[i];
            if (is_gvar)
              var.deltas_y[idx] = deltas_y[i];
          }
          tuple_vars.push (std::move (var));
        } while (iterator.move_to_next ());
  
        is_composite = is_composite_glyph;
        return true;
      }
  
      bool create_from_item_var_data (const VarData &var_data,
                                      const hb_vector_t<hb_hashmap_t<hb_tag_t, Triple>>& regions,
                                      const hb_map_t& axes_old_index_tag_map,
                                      unsigned& item_count,
                                      const hb_inc_bimap_t* inner_map = nullptr)
      {
        /* NULL offset, to keep original varidx valid, just return */
        if (&var_data == &Null (VarData))
          return true;
  
        unsigned num_regions = var_data.get_region_index_count ();
        if (!tuple_vars.alloc (num_regions)) return false;
  
        item_count = inner_map ? inner_map->get_population () : var_data.get_item_count ();
        if (!item_count) return true;
        unsigned row_size = var_data.get_row_size ();
        const HBUINT8 *delta_bytes = var_data.get_delta_bytes ();
  
        for (unsigned r = 0; r < num_regions; r++)
        {
          /* In VarData, deltas are organized in rows, convert them into
           * column(region) based tuples, resize deltas_x first */
          tuple_delta_t tuple;
          if (!tuple.deltas_x.resize_dirty  (item_count) ||
              !tuple.indices.resize_dirty  (item_count))
            return false;
  
          for (unsigned i = 0; i < item_count; i++)
          {
            tuple.indices.arrayZ[i] = true;
            tuple.deltas_x.arrayZ[i] = var_data.get_item_delta_fast (inner_map ? inner_map->backward (i) : i,
                                                                     r, delta_bytes, row_size);
          }
  
          unsigned region_index = var_data.get_region_index (r);
          if (region_index >= regions.length) return false;
          tuple.axis_tuples = regions.arrayZ[region_index];
  
          tuple_vars.push (std::move (tuple));
        }
        return !tuple_vars.in_error ();
      }
  
      private:
      static int _cmp_axis_tag (const void *pa, const void *pb)
      {
        const hb_tag_t *a = (const hb_tag_t*) pa;
        const hb_tag_t *b = (const hb_tag_t*) pb;
        return (int)(*a) - (int)(*b);
      }
  
      bool change_tuple_variations_axis_limits (const hb_hashmap_t<hb_tag_t, Triple>& normalized_axes_location,
                                                const hb_hashmap_t<hb_tag_t, TripleDistances>& axes_triple_distances,
  					      hb_alloc_pool_t *pool = nullptr)
      {
        /* sort axis_tag/axis_limits, make result deterministic */
        hb_vector_t<hb_tag_t> axis_tags;
        if (!axis_tags.alloc (normalized_axes_location.get_population ()))
          return false;
        for (auto t : normalized_axes_location.keys ())
          axis_tags.push (t);
  
        // Reused vectors for reduced malloc pressure.
        rebase_tent_result_scratch_t scratch;
        hb_vector_t<tuple_delta_t> out;
  
        axis_tags.qsort (_cmp_axis_tag);
        for (auto axis_tag : axis_tags)
        {
          Triple *axis_limit;
          if (!normalized_axes_location.has (axis_tag, &axis_limit))
            return false;
          TripleDistances axis_triple_distances{1.0, 1.0};
          if (axes_triple_distances.has (axis_tag))
            axis_triple_distances = axes_triple_distances.get (axis_tag);
  
          hb_vector_t<tuple_delta_t> new_vars;
          for (tuple_delta_t& var : tuple_vars)
          {
  	  // This may move var out.
  	  var.change_tuple_var_axis_limit (axis_tag, *axis_limit, axis_triple_distances, out, scratch, pool);
            if (!out) continue;
  
            unsigned new_len = new_vars.length + out.length;
  
            if (unlikely (!new_vars.alloc (new_len, false)))
              return false;
  
            for (unsigned i = 0; i < out.length; i++)
              new_vars.push (std::move (out[i]));
          }
          tuple_vars = std::move (new_vars);
        }
        return true;
      }
  
      /* merge tuple variations with overlapping tents, if iup delta optimization
       * is enabled, add default deltas to contour_points */
      bool merge_tuple_variations (contour_point_vector_t* contour_points = nullptr)
      {
        hb_vector_t<tuple_delta_t> new_vars;
        // The pre-allocation is essential for address stability of pointers
        // we store in the hashmap.
        if (unlikely (!new_vars.alloc (tuple_vars.length)))
  	return false;
        hb_hashmap_t<const hb_hashmap_t<hb_tag_t, Triple>*, unsigned> m;
        for (tuple_delta_t& var : tuple_vars)
        {
          /* if all axes are pinned, drop the tuple variation */
          if (var.axis_tuples.is_empty ())
          {
            /* if iup_delta_optimize is enabled, add deltas to contour coords */
            if (contour_points && !contour_points->add_deltas (var.deltas_x,
                                                               var.deltas_y,
                                                               var.indices))
              return false;
            continue;
          }
  
          unsigned *idx;
          if (m.has (&(var.axis_tuples), &idx))
          {
            new_vars[*idx] += var;
          }
          else
          {
  	  auto *new_var = new_vars.push ();
  	  if (unlikely (new_vars.in_error ()))
  	    return false;
            hb_swap (*new_var, var);
            if (unlikely (!m.set (&(new_var->axis_tuples), new_vars.length - 1)))
              return false;
          }
        }
        m.fini (); // Just in case, since it points into new_vars data.
  		 // Shouldn't be necessary though, since we only move new_vars, not its
  		 // contents.
        tuple_vars = std::move (new_vars);
        return true;
      }
  
      /* compile all point set and store byte data in a point_set->hb_bytes_t hashmap,
       * also update point_set->count map, which will be used in finding shared
       * point set*/
      bool compile_all_point_sets ()
      {
        for (const auto& tuple: tuple_vars)
        {
          const hb_vector_t<bool>* points_set = &(tuple.indices);
          if (point_data_map.has (points_set))
          {
            unsigned *count;
            if (unlikely (!point_set_count_map.has (points_set, &count) ||
                          !point_set_count_map.set (points_set, (*count) + 1)))
              return false;
            continue;
          }
  
          hb_vector_t<unsigned char> compiled_point_data;
          if (!tuple_delta_t::compile_point_set (*points_set, compiled_point_data))
            return false;
  
          if (!point_data_map.set (points_set, std::move (compiled_point_data)) ||
              !point_set_count_map.set (points_set, 1))
            return false;
        }
        return true;
      }
  
      /* find shared points set which saves most bytes */
      void find_shared_points ()
      {
        unsigned max_saved_bytes = 0;
  
        for (const auto& _ : point_data_map.iter_ref ())
        {
          const hb_vector_t<bool>* points_set = _.first;
          unsigned data_length = _.second.length;
          if (!data_length) continue;
          unsigned *count;
          if (unlikely (!point_set_count_map.has (points_set, &count) ||
                        *count <= 1))
          {
            shared_points_bytes = nullptr;
            return;
          }
  
          unsigned saved_bytes = data_length * ((*count) -1);
          if (saved_bytes > max_saved_bytes)
          {
            max_saved_bytes = saved_bytes;
            shared_points_bytes = &(_.second);
          }
        }
      }
  
      bool calc_inferred_deltas (const contour_point_vector_t& contour_points,
  			       hb_vector_t<unsigned> &scratch)
      {
        for (tuple_delta_t& var : tuple_vars)
          if (!var.calc_inferred_deltas (contour_points, scratch))
            return false;
  
        return true;
      }
  
      bool iup_optimize (const contour_point_vector_t& contour_points,
  		       optimize_scratch_t &scratch)
      {
        for (tuple_delta_t& var : tuple_vars)
        {
          if (!var.optimize (contour_points, is_composite, scratch))
            return false;
        }
        return true;
      }
  
      public:
      bool instantiate (const hb_hashmap_t<hb_tag_t, Triple>& normalized_axes_location,
                        const hb_hashmap_t<hb_tag_t, TripleDistances>& axes_triple_distances,
  		      optimize_scratch_t &scratch,
  		      hb_alloc_pool_t *pool = nullptr,
                        contour_point_vector_t* contour_points = nullptr,
                        bool optimize = false)
      {
        if (!tuple_vars) return true;
        if (!change_tuple_variations_axis_limits (normalized_axes_location, axes_triple_distances, pool))
          return false;
        /* compute inferred deltas only for gvar */
        if (contour_points)
        {
  	hb_vector_t<unsigned> scratch;
  	if (!calc_inferred_deltas (*contour_points, scratch))
  	  return false;
        }
  
        /* if iup delta opt is on, contour_points can't be null */
        if (optimize && !contour_points)
          return false;
  
        if (!merge_tuple_variations (optimize ? contour_points : nullptr))
          return false;
  
        if (optimize && !iup_optimize (*contour_points, scratch)) return false;
        return !tuple_vars.in_error ();
      }
  
      bool compile_bytes (const hb_map_t& axes_index_map,
                          const hb_map_t& axes_old_index_tag_map,
                          bool use_shared_points,
                          bool is_gvar = false,
                          const hb_hashmap_t<const hb_vector_t<F2DOT14>*, unsigned>* shared_tuples_idx_map = nullptr,
  			hb_alloc_pool_t *pool = nullptr)
      {
        // return true for empty glyph
        if (!tuple_vars)
          return true;
  
        // compile points set and store data in hashmap
        if (!compile_all_point_sets ())
          return false;
  
        /* total compiled byte size as TupleVariationData format, initialized to its
         * min_size: 4 */
        compiled_byte_size += 4;
  
        if (use_shared_points)
        {
          find_shared_points ();
          if (shared_points_bytes)
            compiled_byte_size += shared_points_bytes->length;
        }
        hb_vector_t<int> rounded_deltas_scratch;
        // compile delta and tuple var header for each tuple variation
        for (auto& tuple: tuple_vars)
        {
          const hb_vector_t<bool>* points_set = &(tuple.indices);
          hb_vector_t<unsigned char> *points_data;
          if (unlikely (!point_data_map.has (points_set, &points_data)))
            return false;
  
          /* when iup optimization is enabled, num of referenced points could be 0
           * and thus the compiled points bytes is empty, we should skip compiling
           * this tuple */
          if (!points_data->length)
            continue;
          if (!tuple.compile_deltas (rounded_deltas_scratch, pool))
            return false;
  
          unsigned points_data_length = (points_data != shared_points_bytes) ? points_data->length : 0;
          if (!tuple.compile_tuple_var_header (axes_index_map, points_data_length, axes_old_index_tag_map,
                                               shared_tuples_idx_map,
  					     pool))
            return false;
          compiled_byte_size += tuple.compiled_tuple_header.length + points_data_length + tuple.compiled_deltas.length;
        }
  
        if (is_gvar && (compiled_byte_size % 2))
        {
          needs_padding = true;
          compiled_byte_size += 1;
        }
  
        return true;
      }
  
      bool serialize_var_headers (hb_serialize_context_t *c, unsigned& total_header_len) const
      {
        TRACE_SERIALIZE (this);
        for (const auto& tuple: tuple_vars)
        {
          tuple.compiled_tuple_header.as_array ().copy (c);
          if (c->in_error ()) return_trace (false);
          total_header_len += tuple.compiled_tuple_header.length;
        }
        return_trace (true);
      }
  
      bool serialize_var_data (hb_serialize_context_t *c, bool is_gvar) const
      {
        TRACE_SERIALIZE (this);
        if (is_gvar && shared_points_bytes)
        {
          hb_ubytes_t s (shared_points_bytes->arrayZ, shared_points_bytes->length);
          s.copy (c);
        }
  
        for (const auto& tuple: tuple_vars)
        {
          const hb_vector_t<bool>* points_set = &(tuple.indices);
          hb_vector_t<unsigned char> *point_data;
          if (!point_data_map.has (points_set, &point_data))
            return_trace (false);
  
          if (!is_gvar || point_data != shared_points_bytes)
          {
            hb_ubytes_t s (point_data->arrayZ, point_data->length);
            s.copy (c);
          }
  
          tuple.compiled_deltas.as_array ().copy (c);
          if (c->in_error ()) return_trace (false);
        }
  
        /* padding for gvar */
        if (is_gvar && needs_padding)
        {
          HBUINT8 pad;
          pad = 0;
          if (!c->embed (pad)) return_trace (false);
        }
        return_trace (true);
      }
    };
  
    struct tuple_iterator_t
    {
      unsigned get_axis_count () const { return axis_count; }
  
      void init (hb_bytes_t var_data_bytes_, unsigned int axis_count_, const void *table_base_)
      {
        var_data_bytes = var_data_bytes_;
        var_data = var_data_bytes_.as<TupleVariationData> ();
        tuples_left = var_data->tupleVarCount.get_count ();
        axis_count = axis_count_;
        current_tuple = &var_data->get_tuple_var_header ();
        data_offset = 0;
        table_base = table_base_;
      }
  
      bool get_shared_indices (hb_vector_t<unsigned int> &shared_indices /* OUT */)
      {
        if (var_data->has_shared_point_numbers ())
        {
          const HBUINT8 *base = &(table_base+var_data->data);
          const HBUINT8 *p = base;
          if (!decompile_points (p, shared_indices, (const HBUINT8 *) (var_data_bytes.arrayZ + var_data_bytes.length))) return false;
          data_offset = p - base;
        }
        return true;
      }
  
      bool is_valid ()
      {
        if (unlikely (tuples_left <= 0))
  	return false;
  
        current_tuple_size = TupleVariationHeader::min_size;
        if (unlikely (!var_data_bytes.check_range (current_tuple, current_tuple_size)))
  	return false;
  
        current_tuple_size = current_tuple->get_size (axis_count);
        if (unlikely (!var_data_bytes.check_range (current_tuple, current_tuple_size)))
  	return false;
  
        return true;
      }
  
      HB_ALWAYS_INLINE
      bool move_to_next ()
      {
        data_offset += current_tuple->get_data_size ();
        current_tuple = &StructAtOffset<TupleVariationHeader> (current_tuple, current_tuple_size);
        tuples_left--;
        return is_valid ();
      }
  
      // TODO: Make it return (sanitized) hb_bytes_t
      const HBUINT8 *get_serialized_data () const
      { return &(table_base+var_data->data) + data_offset; }
  
      private:
      signed tuples_left;
      const TupleVariationData *var_data;
      unsigned int axis_count;
      unsigned int data_offset;
      unsigned int current_tuple_size;
      const void *table_base;
  
      public:
      hb_bytes_t var_data_bytes;
      const TupleVariationHeader *current_tuple;
    };
  
    static bool get_tuple_iterator (hb_bytes_t var_data_bytes, unsigned axis_count,
                                    const void *table_base,
                                    hb_vector_t<unsigned int> &shared_indices /* OUT */,
                                    tuple_iterator_t *iterator /* OUT */)
    {
      iterator->init (var_data_bytes, axis_count, table_base);
      if (!iterator->get_shared_indices (shared_indices))
        return false;
      return iterator->is_valid ();
    }
  
    bool has_shared_point_numbers () const { return tupleVarCount.has_shared_point_numbers (); }
  
    static bool decompile_points (const HBUINT8 *&p /* IN/OUT */,
  				hb_vector_t<unsigned int> &points /* OUT */,
  				const HBUINT8 *end)
    {
      enum packed_point_flag_t
      {
        POINTS_ARE_WORDS     = 0x80,
        POINT_RUN_COUNT_MASK = 0x7F
      };
  
      if (unlikely (p + 1 > end)) return false;
  
      unsigned count = *p++;
      if (count & POINTS_ARE_WORDS)
      {
        if (unlikely (p + 1 > end)) return false;
        count = ((count & POINT_RUN_COUNT_MASK) << 8) | *p++;
      }
      if (unlikely (!points.resize_dirty  (count))) return false;
  
      unsigned n = 0;
      unsigned i = 0;
      while (i < count)
      {
        if (unlikely (p + 1 > end)) return false;
        unsigned control = *p++;
        unsigned run_count = (control & POINT_RUN_COUNT_MASK) + 1;
        unsigned stop = i + run_count;
        if (unlikely (stop > count)) return false;
        if (control & POINTS_ARE_WORDS)
        {
          if (unlikely (p + run_count * HBUINT16::static_size > end)) return false;
          for (; i < stop; i++)
          {
            n += *(const HBUINT16 *)p;
            points.arrayZ[i] = n;
            p += HBUINT16::static_size;
          }
        }
        else
        {
          if (unlikely (p + run_count > end)) return false;
          for (; i < stop; i++)
          {
            n += *p++;
            points.arrayZ[i] = n;
          }
        }
      }
      return true;
    }
  
    template <typename T>
    static bool decompile_deltas (const HBUINT8 *&p /* IN/OUT */,
  				hb_vector_t<T> &deltas /* IN/OUT */,
  				const HBUINT8 *end,
  				bool consume_all = false,
  				unsigned start = 0)
    {
      return TupleValues::decompile (p, deltas, end, consume_all, start);
    }
  
    bool has_data () const { return tupleVarCount; }
  
    bool decompile_tuple_variations (unsigned point_count,
                                     bool is_gvar,
                                     tuple_iterator_t iterator,
                                     const hb_map_t *axes_old_index_tag_map,
                                     const hb_vector_t<unsigned> &shared_indices,
                                     const hb_array_t<const F2DOT14> shared_tuples,
                                     tuple_variations_t& tuple_variations, /* OUT */
  				   hb_alloc_pool_t *pool = nullptr,
                                     bool is_composite_glyph = false) const
    {
      return tuple_variations.create_from_tuple_var_data (iterator, tupleVarCount,
                                                          point_count, is_gvar,
                                                          axes_old_index_tag_map,
                                                          shared_indices,
                                                          shared_tuples,
  							pool,
                                                          is_composite_glyph);
    }
  
    bool serialize (hb_serialize_context_t *c,
                    bool is_gvar,
                    const tuple_variations_t& tuple_variations) const
    {
      TRACE_SERIALIZE (this);
      /* empty tuple variations, just return and skip serialization. */
      if (!tuple_variations) return_trace (true);
  
      auto *out = c->start_embed (this);
      if (unlikely (!c->extend_min (out))) return_trace (false);
  
      if (!c->check_assign (out->tupleVarCount, tuple_variations.get_var_count (),
                            HB_SERIALIZE_ERROR_INT_OVERFLOW)) return_trace (false);
  
      unsigned total_header_len = 0;
  
      if (!tuple_variations.serialize_var_headers (c, total_header_len))
        return_trace (false);
  
      unsigned data_offset = min_size + total_header_len;
      if (!is_gvar) data_offset += 4;
      if (!c->check_assign (out->data, data_offset, HB_SERIALIZE_ERROR_INT_OVERFLOW)) return_trace (false);
  
      return tuple_variations.serialize_var_data (c, is_gvar);
    }
  
    protected:
    struct TupleVarCount : HBUINT16
    {
      friend struct tuple_variations_t;
      bool has_shared_point_numbers () const { return ((*this) & SharedPointNumbers); }
      unsigned int get_count () const { return (*this) & CountMask; }
      TupleVarCount& operator = (uint16_t i) { HBUINT16::operator= (i); return *this; }
      explicit operator bool () const { return get_count (); }
  
      protected:
      enum Flags
      {
        SharedPointNumbers= 0x8000u,
        CountMask         = 0x0FFFu
      };
      public:
      DEFINE_SIZE_STATIC (2);
    };
  
    TupleVarCount tupleVarCount;  /* A packed field. The high 4 bits are flags, and the
                                   * low 12 bits are the number of tuple variation tables
                                   * for this glyph. The number of tuple variation tables
                                   * can be any number between 1 and 4095. */
    OffsetTo<HBUINT8, OffType>
                  data;           /* Offset from the start of the base table
                                   * to the serialized data. */
    /* TupleVariationHeader tupleVariationHeaders[] *//* Array of tuple variation headers. */
    public:
    DEFINE_SIZE_MIN (2 + OffType::static_size);
  };
  
  // TODO: Move tuple_variations_t to outside of TupleVariationData
  using tuple_variations_t = TupleVariationData<HBUINT16>::tuple_variations_t;
  struct item_variations_t
  {
    using region_t = const hb_hashmap_t<hb_tag_t, Triple>*;
    private:
    /* each subtable is decompiled into a tuple_variations_t, in which all tuples
     * have the same num of deltas (rows) */
    hb_vector_t<tuple_variations_t> vars;
  
    /* num of retained rows for each subtable, there're 2 cases when var_data is empty:
     * 1. retained item_count is zero
     * 2. regions is empty and item_count is non-zero.
     * when converting to tuples, both will be dropped because the tuple is empty,
     * however, we need to retain 2. as all-zero rows to keep original varidx
     * valid, so we need a way to remember the num of rows for each subtable */
    hb_vector_t<unsigned> var_data_num_rows;
  
    /* original region list, decompiled from item varstore, used when rebuilding
     * region list after instantiation */
    hb_vector_t<hb_hashmap_t<hb_tag_t, Triple>> orig_region_list;
  
    /* region list: vector of Regions, maintain the original order for the regions
     * that existed before instantiate (), append the new regions at the end.
     * Regions are stored in each tuple already, save pointers only.
     * When converting back to item varstore, unused regions will be pruned */
    hb_vector_t<region_t> region_list;
  
    /* region -> idx map after instantiation and pruning unused regions */
    hb_hashmap_t<region_t, unsigned> region_map;
  
    /* all delta rows after instantiation */
    hb_vector_t<hb_vector_t<int>> delta_rows;
    /* final optimized vector of encoding objects used to assemble the varstore */
    hb_vector_t<delta_row_encoding_t> encodings;
  
    /* old varidxes -> new var_idxes map */
    hb_map_t varidx_map;
  
    /* has long words */
    bool has_long = false;
  
    public:
    bool has_long_word () const
    { return has_long; }
  
    const hb_vector_t<region_t>& get_region_list () const
    { return region_list; }
  
    const hb_vector_t<delta_row_encoding_t>& get_vardata_encodings () const
    { return encodings; }
  
    const hb_map_t& get_varidx_map () const
    { return varidx_map; }
  
    bool instantiate (const ItemVariationStore& varStore,
                      const hb_subset_plan_t *plan,
                      bool optimize=true,
                      bool use_no_variation_idx=true,
                      const hb_array_t <const hb_inc_bimap_t> inner_maps = hb_array_t<const hb_inc_bimap_t> ())
    {
      if (!create_from_item_varstore (varStore, plan->axes_old_index_tag_map, inner_maps))
        return false;
      if (!instantiate_tuple_vars (plan->axes_location, plan->axes_triple_distances))
        return false;
      return as_item_varstore (optimize, use_no_variation_idx);
    }
  
    /* keep below APIs public only for unit test: test-item-varstore */
    bool create_from_item_varstore (const ItemVariationStore& varStore,
                                    const hb_map_t& axes_old_index_tag_map,
                                    const hb_array_t <const hb_inc_bimap_t> inner_maps = hb_array_t<const hb_inc_bimap_t> ())
    {
      const VarRegionList& regionList = varStore.get_region_list ();
      if (!regionList.get_var_regions (axes_old_index_tag_map, orig_region_list))
        return false;
  
      unsigned num_var_data = varStore.get_sub_table_count ();
      if (inner_maps && inner_maps.length != num_var_data) return false;
      if (!vars.alloc (num_var_data) ||
          !var_data_num_rows.alloc (num_var_data)) return false;
  
      for (unsigned i = 0; i < num_var_data; i++)
      {
        if (inner_maps && !inner_maps.arrayZ[i].get_population ())
            continue;
        tuple_variations_t var_data_tuples;
        unsigned item_count = 0;
        if (!var_data_tuples.create_from_item_var_data (varStore.get_sub_table (i),
                                                        orig_region_list,
                                                        axes_old_index_tag_map,
                                                        item_count,
                                                        inner_maps ? &(inner_maps.arrayZ[i]) : nullptr))
          return false;
  
        var_data_num_rows.push (item_count);
        vars.push (std::move (var_data_tuples));
      }
      return !vars.in_error () && !var_data_num_rows.in_error () && vars.length == var_data_num_rows.length;
    }
  
    bool instantiate_tuple_vars (const hb_hashmap_t<hb_tag_t, Triple>& normalized_axes_location,
                                 const hb_hashmap_t<hb_tag_t, TripleDistances>& axes_triple_distances)
    {
      optimize_scratch_t scratch;
      for (tuple_variations_t& tuple_vars : vars)
        if (!tuple_vars.instantiate (normalized_axes_location, axes_triple_distances, scratch))
          return false;
  
      if (!build_region_list ()) return false;
      return true;
    }
  
    bool build_region_list ()
    {
      /* scan all tuples and collect all unique regions, prune unused regions */
      hb_hashmap_t<region_t, unsigned> all_regions;
      hb_hashmap_t<region_t, unsigned> used_regions;
  
      /* use a vector when inserting new regions, make result deterministic */
      hb_vector_t<region_t> all_unique_regions;
      for (const tuple_variations_t& sub_table : vars)
      {
        for (const tuple_delta_t& tuple : sub_table.tuple_vars)
        {
          region_t r = &(tuple.axis_tuples);
          if (!used_regions.has (r))
          {
            bool all_zeros = true;
            for (float d : tuple.deltas_x)
            {
              int delta = (int) roundf (d);
              if (delta != 0)
              {
                all_zeros = false;
                break;
              }
            }
            if (!all_zeros)
            {
              if (!used_regions.set (r, 1))
                return false;
            }
          }
          if (all_regions.has (r))
            continue;
          if (!all_regions.set (r, 1))
            return false;
          all_unique_regions.push (r);
        }
      }
  
      /* regions are empty means no variation data, return true */
      if (!all_regions || !all_unique_regions) return true;
  
      if (!region_list.alloc (all_regions.get_population ()))
        return false;
  
      unsigned idx = 0;
      /* append the original regions that pre-existed */
      for (const auto& r : orig_region_list)
      {
        if (!all_regions.has (&r) || !used_regions.has (&r))
          continue;
  
        region_list.push (&r);
        if (!region_map.set (&r, idx))
          return false;
        all_regions.del (&r);
        idx++;
      }
  
      /* append the new regions at the end */
      for (const auto& r: all_unique_regions)
      {
        if (!all_regions.has (r) || !used_regions.has (r))
          continue;
        region_list.push (r);
        if (!region_map.set (r, idx))
          return false;
        all_regions.del (r);
        idx++;
      }
      return (!region_list.in_error ()) && (!region_map.in_error ());
    }
  
    /* main algorithm ported from fonttools VarStore_optimize() method, optimize
     * varstore by default */
  
    struct combined_gain_idx_tuple_t
    {
      uint64_t encoded;
  
      combined_gain_idx_tuple_t () = default;
      combined_gain_idx_tuple_t (unsigned gain, unsigned i, unsigned j)
          : encoded ((uint64_t (0xFFFFFF - gain) << 40) | (uint64_t (i) << 20) | uint64_t (j))
      {
        assert (gain < 0xFFFFFF);
        assert (i < 0xFFFFFFF && j < 0xFFFFFFF);
      }
  
      bool operator < (const combined_gain_idx_tuple_t& o)
      {
        return encoded < o.encoded;
      }
  
      bool operator <= (const combined_gain_idx_tuple_t& o)
      {
        return encoded <= o.encoded;
      }
  
      unsigned idx_1 () const { return (encoded >> 20) & 0xFFFFF; };
      unsigned idx_2 () const { return encoded & 0xFFFFF; };
    };
  
    bool as_item_varstore (bool optimize=true, bool use_no_variation_idx=true)
    {
      /* return true if no variation data */
      if (!region_list) return true;
      unsigned num_cols = region_list.length;
      /* pre-alloc a 2D vector for all sub_table's VarData rows */
      unsigned total_rows = 0;
      for (unsigned major = 0; major < var_data_num_rows.length; major++)
        total_rows += var_data_num_rows[major];
  
      if (!delta_rows.resize (total_rows)) return false;
      /* init all rows to [0]*num_cols */
      for (unsigned i = 0; i < total_rows; i++)
        if (!(delta_rows[i].resize (num_cols))) return false;
  
      /* old VarIdxes -> full encoding_row mapping */
      hb_hashmap_t<unsigned, const hb_vector_t<int>*> front_mapping;
      unsigned start_row = 0;
      hb_vector_t<delta_row_encoding_t> encoding_objs;
  
      /* delta_rows map, used for filtering out duplicate rows */
      hb_vector_t<const hb_vector_t<int> *> major_rows;
      hb_hashmap_t<const hb_vector_t<int>*, unsigned> delta_rows_map;
      for (unsigned major = 0; major < vars.length; major++)
      {
        /* deltas are stored in tuples(column based), convert them back into items
         * (row based) delta */
        const tuple_variations_t& tuples = vars[major];
        unsigned num_rows = var_data_num_rows[major];
  
        if (!num_rows) continue;
  
        for (const tuple_delta_t& tuple: tuples.tuple_vars)
        {
          if (tuple.deltas_x.length != num_rows)
            return false;
  
          /* skip unused regions */
          unsigned *col_idx;
          if (!region_map.has (&(tuple.axis_tuples), &col_idx))
            continue;
  
          for (unsigned i = 0; i < num_rows; i++)
          {
            int rounded_delta = roundf (tuple.deltas_x[i]);
            delta_rows[start_row + i][*col_idx] += rounded_delta;
            has_long |= rounded_delta < -65536 || rounded_delta > 65535;
          }
        }
  
        major_rows.reset ();
        for (unsigned minor = 0; minor < num_rows; minor++)
        {
  	const hb_vector_t<int>& row = delta_rows[start_row + minor];
  	if (use_no_variation_idx)
  	{
  	  bool all_zeros = true;
  	  for (int delta : row)
  	  {
  	    if (delta != 0)
  	    {
  	      all_zeros = false;
  	      break;
  	    }
  	  }
  	  if (all_zeros)
  	    continue;
  	}
  
  	if (!front_mapping.set ((major<<16) + minor, &row))
  	  return false;
  
  	if (delta_rows_map.has (&row))
  	  continue;
  
  	delta_rows_map.set (&row, 1);
  
  	major_rows.push (&row);
        }
  
        if (major_rows)
  	encoding_objs.push (delta_row_encoding_t (std::move (major_rows), num_cols));
  
        start_row += num_rows;
      }
  
      /* return directly if no optimization, maintain original VariationIndex so
       * varidx_map would be empty */
      if (!optimize)
      {
        encodings = std::move (encoding_objs);
        return !encodings.in_error ();
      }
  
      /* NOTE: Fonttools instancer always optimizes VarStore from scratch. This
       * is too costly for large fonts. So, instead, we retain the encodings of
       * the original VarStore, and just try to combine them if possible. This
       * is a compromise between optimization and performance and practically
       * works very well. */
  
      // This produces slightly smaller results in some cases.
      encoding_objs.qsort ();
  
      /* main algorithm: repeatedly pick 2 best encodings to combine, and combine them */
      using item_t = hb_priority_queue_t<combined_gain_idx_tuple_t>::item_t;
      hb_vector_t<item_t> queue_items;
      unsigned num_todos = encoding_objs.length;
      for (unsigned i = 0; i < num_todos; i++)
      {
        for (unsigned j = i + 1; j < num_todos; j++)
        {
          int combining_gain = encoding_objs.arrayZ[i].gain_from_merging (encoding_objs.arrayZ[j]);
          if (combining_gain > 0)
            queue_items.push (item_t (combined_gain_idx_tuple_t (combining_gain, i, j), 0));
        }
      }
  
      hb_priority_queue_t<combined_gain_idx_tuple_t> queue (std::move (queue_items));
  
      hb_bit_set_t removed_todo_idxes;
      while (queue)
      {
        auto t = queue.pop_minimum ().first;
        unsigned i = t.idx_1 ();
        unsigned j = t.idx_2 ();
  
        if (removed_todo_idxes.has (i) || removed_todo_idxes.has (j))
          continue;
  
        delta_row_encoding_t& encoding = encoding_objs.arrayZ[i];
        delta_row_encoding_t& other_encoding = encoding_objs.arrayZ[j];
  
        removed_todo_idxes.add (i);
        removed_todo_idxes.add (j);
  
        encoding.merge (other_encoding);
  
        for (unsigned idx = 0; idx < encoding_objs.length; idx++)
        {
          if (removed_todo_idxes.has (idx)) continue;
  
          const delta_row_encoding_t& obj = encoding_objs.arrayZ[idx];
  	// In the unlikely event that the same encoding exists already, combine it.
          if (obj.width == encoding.width && obj.chars == encoding.chars)
          {
  	  // This is straight port from fonttools algorithm. I added this branch there
  	  // because I thought it can happen. But looks like we never get in here in
  	  // practice. I'm not confident enough to remove it though; in theory it can
  	  // happen. I think it's just that our tests are not extensive enough to hit
  	  // this path.
  
            for (const auto& row : obj.items)
              encoding.add_row (row);
  
            removed_todo_idxes.add (idx);
            continue;
          }
  
          int combined_gain = encoding.gain_from_merging (obj);
          if (combined_gain > 0)
            queue.insert (combined_gain_idx_tuple_t (combined_gain, idx, encoding_objs.length), 0);
        }
  
        auto moved_encoding = std::move (encoding);
        encoding_objs.push (moved_encoding);
      }
  
      int num_final_encodings = (int) encoding_objs.length - (int) removed_todo_idxes.get_population ();
      if (num_final_encodings <= 0) return false;
  
      if (!encodings.alloc (num_final_encodings)) return false;
      for (unsigned i = 0; i < encoding_objs.length; i++)
      {
        if (removed_todo_idxes.has (i)) continue;
        encodings.push (std::move (encoding_objs.arrayZ[i]));
      }
  
      return compile_varidx_map (front_mapping);
    }
  
    private:
    /* compile varidx_map for one VarData subtable (index specified by major) */
    bool compile_varidx_map (const hb_hashmap_t<unsigned, const hb_vector_t<int>*>& front_mapping)
    {
      /* full encoding_row -> new VarIdxes mapping */
      hb_hashmap_t<const hb_vector_t<int>*, unsigned> back_mapping;
  
      for (unsigned major = 0; major < encodings.length; major++)
      {
        delta_row_encoding_t& encoding = encodings[major];
        /* just sanity check, this shouldn't happen */
        if (encoding.is_empty ())
          return false;
  
        unsigned num_rows = encoding.items.length;
  
        /* sort rows, make result deterministic */
        encoding.items.qsort (_cmp_row);
  
        /* compile old to new var_idxes mapping */
        for (unsigned minor = 0; minor < num_rows; minor++)
        {
          unsigned new_varidx = (major << 16) + minor;
          back_mapping.set (encoding.items.arrayZ[minor], new_varidx);
        }
      }
  
      for (auto _ : front_mapping.iter ())
      {
        unsigned old_varidx = _.first;
        unsigned *new_varidx;
        if (back_mapping.has (_.second, &new_varidx))
          varidx_map.set (old_varidx, *new_varidx);
        else
          varidx_map.set (old_varidx, HB_OT_LAYOUT_NO_VARIATIONS_INDEX);
      }
      return !varidx_map.in_error ();
    }
  
    static int _cmp_row (const void *pa, const void *pb)
    {
      /* compare pointers of vectors(const hb_vector_t<int>*) that represent a row */
      const hb_vector_t<int>** a = (const hb_vector_t<int>**) pa;
      const hb_vector_t<int>** b = (const hb_vector_t<int>**) pb;
  
      for (unsigned i = 0; i < (*b)->length; i++)
      {
        int va = (*a)->arrayZ[i];
        int vb = (*b)->arrayZ[i];
        if (va != vb)
          return va < vb ? -1 : 1;
      }
      return 0;
    }
  };
  
  
  } /* namespace OT */
  
  
  #endif /* HB_OT_VAR_COMMON_HH */
  

Download