--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/mupdf-source/thirdparty/brotli/c/enc/cluster_inc.h	Mon Sep 15 11:43:07 2025 +0200
@@ -0,0 +1,325 @@
+/* NOLINT(build/header_guard) */
+/* Copyright 2013 Google Inc. All Rights Reserved.
+
+   Distributed under MIT license.
+   See file LICENSE for detail or copy at https://opensource.org/licenses/MIT
+*/
+
+/* template parameters: FN, CODE */
+
+#define HistogramType FN(Histogram)
+
+/* Computes the bit cost reduction by combining out[idx1] and out[idx2] and if
+   it is below a threshold, stores the pair (idx1, idx2) in the *pairs queue. */
+BROTLI_INTERNAL void FN(BrotliCompareAndPushToQueue)(
+    const HistogramType* out, HistogramType* tmp, const uint32_t* cluster_size,
+    uint32_t idx1, uint32_t idx2, size_t max_num_pairs, HistogramPair* pairs,
+    size_t* num_pairs) CODE({
+  BROTLI_BOOL is_good_pair = BROTLI_FALSE;
+  HistogramPair p;
+  p.idx1 = p.idx2 = 0;
+  p.cost_diff = p.cost_combo = 0;
+  if (idx1 == idx2) {
+    return;
+  }
+  if (idx2 < idx1) {
+    uint32_t t = idx2;
+    idx2 = idx1;
+    idx1 = t;
+  }
+  p.idx1 = idx1;
+  p.idx2 = idx2;
+  p.cost_diff = 0.5 * ClusterCostDiff(cluster_size[idx1], cluster_size[idx2]);
+  p.cost_diff -= out[idx1].bit_cost_;
+  p.cost_diff -= out[idx2].bit_cost_;
+
+  if (out[idx1].total_count_ == 0) {
+    p.cost_combo = out[idx2].bit_cost_;
+    is_good_pair = BROTLI_TRUE;
+  } else if (out[idx2].total_count_ == 0) {
+    p.cost_combo = out[idx1].bit_cost_;
+    is_good_pair = BROTLI_TRUE;
+  } else {
+    double threshold = *num_pairs == 0 ? 1e99 :
+        BROTLI_MAX(double, 0.0, pairs[0].cost_diff);
+    double cost_combo;
+    *tmp = out[idx1];
+    FN(HistogramAddHistogram)(tmp, &out[idx2]);
+    cost_combo = FN(BrotliPopulationCost)(tmp);
+    if (cost_combo < threshold - p.cost_diff) {
+      p.cost_combo = cost_combo;
+      is_good_pair = BROTLI_TRUE;
+    }
+  }
+  if (is_good_pair) {
+    p.cost_diff += p.cost_combo;
+    if (*num_pairs > 0 && HistogramPairIsLess(&pairs[0], &p)) {
+      /* Replace the top of the queue if needed. */
+      if (*num_pairs < max_num_pairs) {
+        pairs[*num_pairs] = pairs[0];
+        ++(*num_pairs);
+      }
+      pairs[0] = p;
+    } else if (*num_pairs < max_num_pairs) {
+      pairs[*num_pairs] = p;
+      ++(*num_pairs);
+    }
+  }
+})
+
+BROTLI_INTERNAL size_t FN(BrotliHistogramCombine)(HistogramType* out,
+                                                  HistogramType* tmp,
+                                                  uint32_t* cluster_size,
+                                                  uint32_t* symbols,
+                                                  uint32_t* clusters,
+                                                  HistogramPair* pairs,
+                                                  size_t num_clusters,
+                                                  size_t symbols_size,
+                                                  size_t max_clusters,
+                                                  size_t max_num_pairs) CODE({
+  double cost_diff_threshold = 0.0;
+  size_t min_cluster_size = 1;
+  size_t num_pairs = 0;
+
+  {
+    /* We maintain a vector of histogram pairs, with the property that the pair
+       with the maximum bit cost reduction is the first. */
+    size_t idx1;
+    for (idx1 = 0; idx1 < num_clusters; ++idx1) {
+      size_t idx2;
+      for (idx2 = idx1 + 1; idx2 < num_clusters; ++idx2) {
+        FN(BrotliCompareAndPushToQueue)(out, tmp, cluster_size, clusters[idx1],
+            clusters[idx2], max_num_pairs, &pairs[0], &num_pairs);
+      }
+    }
+  }
+
+  while (num_clusters > min_cluster_size) {
+    uint32_t best_idx1;
+    uint32_t best_idx2;
+    size_t i;
+    if (pairs[0].cost_diff >= cost_diff_threshold) {
+      cost_diff_threshold = 1e99;
+      min_cluster_size = max_clusters;
+      continue;
+    }
+    /* Take the best pair from the top of heap. */
+    best_idx1 = pairs[0].idx1;
+    best_idx2 = pairs[0].idx2;
+    FN(HistogramAddHistogram)(&out[best_idx1], &out[best_idx2]);
+    out[best_idx1].bit_cost_ = pairs[0].cost_combo;
+    cluster_size[best_idx1] += cluster_size[best_idx2];
+    for (i = 0; i < symbols_size; ++i) {
+      if (symbols[i] == best_idx2) {
+        symbols[i] = best_idx1;
+      }
+    }
+    for (i = 0; i < num_clusters; ++i) {
+      if (clusters[i] == best_idx2) {
+        memmove(&clusters[i], &clusters[i + 1],
+                (num_clusters - i - 1) * sizeof(clusters[0]));
+        break;
+      }
+    }
+    --num_clusters;
+    {
+      /* Remove pairs intersecting the just combined best pair. */
+      size_t copy_to_idx = 0;
+      for (i = 0; i < num_pairs; ++i) {
+        HistogramPair* p = &pairs[i];
+        if (p->idx1 == best_idx1 || p->idx2 == best_idx1 ||
+            p->idx1 == best_idx2 || p->idx2 == best_idx2) {
+          /* Remove invalid pair from the queue. */
+          continue;
+        }
+        if (HistogramPairIsLess(&pairs[0], p)) {
+          /* Replace the top of the queue if needed. */
+          HistogramPair front = pairs[0];
+          pairs[0] = *p;
+          pairs[copy_to_idx] = front;
+        } else {
+          pairs[copy_to_idx] = *p;
+        }
+        ++copy_to_idx;
+      }
+      num_pairs = copy_to_idx;
+    }
+
+    /* Push new pairs formed with the combined histogram to the heap. */
+    for (i = 0; i < num_clusters; ++i) {
+      FN(BrotliCompareAndPushToQueue)(out, tmp, cluster_size, best_idx1,
+          clusters[i], max_num_pairs, &pairs[0], &num_pairs);
+    }
+  }
+  return num_clusters;
+})
+
+/* What is the bit cost of moving histogram from cur_symbol to candidate. */
+BROTLI_INTERNAL double FN(BrotliHistogramBitCostDistance)(
+    const HistogramType* histogram, const HistogramType* candidate,
+    HistogramType* tmp) CODE({
+  if (histogram->total_count_ == 0) {
+    return 0.0;
+  } else {
+    *tmp = *histogram;
+    FN(HistogramAddHistogram)(tmp, candidate);
+    return FN(BrotliPopulationCost)(tmp) - candidate->bit_cost_;
+  }
+})
+
+/* Find the best 'out' histogram for each of the 'in' histograms.
+   When called, clusters[0..num_clusters) contains the unique values from
+   symbols[0..in_size), but this property is not preserved in this function.
+   Note: we assume that out[]->bit_cost_ is already up-to-date. */
+BROTLI_INTERNAL void FN(BrotliHistogramRemap)(const HistogramType* in,
+    size_t in_size, const uint32_t* clusters, size_t num_clusters,
+    HistogramType* out, HistogramType* tmp, uint32_t* symbols) CODE({
+  size_t i;
+  for (i = 0; i < in_size; ++i) {
+    uint32_t best_out = i == 0 ? symbols[0] : symbols[i - 1];
+    double best_bits =
+        FN(BrotliHistogramBitCostDistance)(&in[i], &out[best_out], tmp);
+    size_t j;
+    for (j = 0; j < num_clusters; ++j) {
+      const double cur_bits =
+          FN(BrotliHistogramBitCostDistance)(&in[i], &out[clusters[j]], tmp);
+      if (cur_bits < best_bits) {
+        best_bits = cur_bits;
+        best_out = clusters[j];
+      }
+    }
+    symbols[i] = best_out;
+  }
+
+  /* Recompute each out based on raw and symbols. */
+  for (i = 0; i < num_clusters; ++i) {
+    FN(HistogramClear)(&out[clusters[i]]);
+  }
+  for (i = 0; i < in_size; ++i) {
+    FN(HistogramAddHistogram)(&out[symbols[i]], &in[i]);
+  }
+})
+
+/* Reorders elements of the out[0..length) array and changes values in
+   symbols[0..length) array in the following way:
+     * when called, symbols[] contains indexes into out[], and has N unique
+       values (possibly N < length)
+     * on return, symbols'[i] = f(symbols[i]) and
+                  out'[symbols'[i]] = out[symbols[i]], for each 0 <= i < length,
+       where f is a bijection between the range of symbols[] and [0..N), and
+       the first occurrences of values in symbols'[i] come in consecutive
+       increasing order.
+   Returns N, the number of unique values in symbols[]. */
+BROTLI_INTERNAL size_t FN(BrotliHistogramReindex)(MemoryManager* m,
+    HistogramType* out, uint32_t* symbols, size_t length) CODE({
+  static const uint32_t kInvalidIndex = BROTLI_UINT32_MAX;
+  uint32_t* new_index = BROTLI_ALLOC(m, uint32_t, length);
+  uint32_t next_index;
+  HistogramType* tmp;
+  size_t i;
+  if (BROTLI_IS_OOM(m) || BROTLI_IS_NULL(new_index)) return 0;
+  for (i = 0; i < length; ++i) {
+      new_index[i] = kInvalidIndex;
+  }
+  next_index = 0;
+  for (i = 0; i < length; ++i) {
+    if (new_index[symbols[i]] == kInvalidIndex) {
+      new_index[symbols[i]] = next_index;
+      ++next_index;
+    }
+  }
+  /* TODO(eustas): by using idea of "cycle-sort" we can avoid allocation of
+     tmp and reduce the number of copying by the factor of 2. */
+  tmp = BROTLI_ALLOC(m, HistogramType, next_index);
+  if (BROTLI_IS_OOM(m) || BROTLI_IS_NULL(tmp)) return 0;
+  next_index = 0;
+  for (i = 0; i < length; ++i) {
+    if (new_index[symbols[i]] == next_index) {
+      tmp[next_index] = out[symbols[i]];
+      ++next_index;
+    }
+    symbols[i] = new_index[symbols[i]];
+  }
+  BROTLI_FREE(m, new_index);
+  for (i = 0; i < next_index; ++i) {
+    out[i] = tmp[i];
+  }
+  BROTLI_FREE(m, tmp);
+  return next_index;
+})
+
+BROTLI_INTERNAL void FN(BrotliClusterHistograms)(
+    MemoryManager* m, const HistogramType* in, const size_t in_size,
+    size_t max_histograms, HistogramType* out, size_t* out_size,
+    uint32_t* histogram_symbols) CODE({
+  uint32_t* cluster_size = BROTLI_ALLOC(m, uint32_t, in_size);
+  uint32_t* clusters = BROTLI_ALLOC(m, uint32_t, in_size);
+  size_t num_clusters = 0;
+  const size_t max_input_histograms = 64;
+  size_t pairs_capacity = max_input_histograms * max_input_histograms / 2;
+  /* For the first pass of clustering, we allow all pairs. */
+  HistogramPair* pairs = BROTLI_ALLOC(m, HistogramPair, pairs_capacity + 1);
+  /* TODO(eustas): move to "persistent" arena? */
+  HistogramType* tmp = BROTLI_ALLOC(m, HistogramType, 1);
+  size_t i;
+
+  if (BROTLI_IS_OOM(m) || BROTLI_IS_NULL(cluster_size) ||
+      BROTLI_IS_NULL(clusters) || BROTLI_IS_NULL(pairs)|| BROTLI_IS_NULL(tmp)) {
+    return;
+  }
+
+  for (i = 0; i < in_size; ++i) {
+    cluster_size[i] = 1;
+  }
+
+  for (i = 0; i < in_size; ++i) {
+    out[i] = in[i];
+    out[i].bit_cost_ = FN(BrotliPopulationCost)(&in[i]);
+    histogram_symbols[i] = (uint32_t)i;
+  }
+
+  for (i = 0; i < in_size; i += max_input_histograms) {
+    size_t num_to_combine =
+        BROTLI_MIN(size_t, in_size - i, max_input_histograms);
+    size_t num_new_clusters;
+    size_t j;
+    for (j = 0; j < num_to_combine; ++j) {
+      clusters[num_clusters + j] = (uint32_t)(i + j);
+    }
+    num_new_clusters =
+        FN(BrotliHistogramCombine)(out, tmp, cluster_size,
+                                   &histogram_symbols[i],
+                                   &clusters[num_clusters], pairs,
+                                   num_to_combine, num_to_combine,
+                                   max_histograms, pairs_capacity);
+    num_clusters += num_new_clusters;
+  }
+
+  {
+    /* For the second pass, we limit the total number of histogram pairs.
+       After this limit is reached, we only keep searching for the best pair. */
+    size_t max_num_pairs = BROTLI_MIN(size_t,
+        64 * num_clusters, (num_clusters / 2) * num_clusters);
+    BROTLI_ENSURE_CAPACITY(
+        m, HistogramPair, pairs, pairs_capacity, max_num_pairs + 1);
+    if (BROTLI_IS_OOM(m)) return;
+
+    /* Collapse similar histograms. */
+    num_clusters = FN(BrotliHistogramCombine)(out, tmp, cluster_size,
+                                              histogram_symbols, clusters,
+                                              pairs, num_clusters, in_size,
+                                              max_histograms, max_num_pairs);
+  }
+  BROTLI_FREE(m, pairs);
+  BROTLI_FREE(m, cluster_size);
+  /* Find the optimal map from original histograms to the final ones. */
+  FN(BrotliHistogramRemap)(in, in_size, clusters, num_clusters,
+                           out, tmp, histogram_symbols);
+  BROTLI_FREE(m, tmp);
+  BROTLI_FREE(m, clusters);
+  /* Convert the context map to a canonical form. */
+  *out_size = FN(BrotliHistogramReindex)(m, out, histogram_symbols, in_size);
+  if (BROTLI_IS_OOM(m)) return;
+})
+
+#undef HistogramType