Merging debian version 1.9.4-1.

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

1 files changed, 0 insertions, 297 deletions

diff --git a/debian/vendor-h2o/deps/brotli/enc/cluster.h b/debian/vendor-h2o/deps/brotli/enc/cluster.h
deleted file mode 100644
index 4f6c06e..0000000
--- a/debian/vendor-h2o/deps/brotli/enc/cluster.h
+++ /dev/null
@@ -1,297 +0,0 @@
-/* Copyright 2013 Google Inc. All Rights Reserved.
-
-   Distributed under MIT license.
-   See file LICENSE for detail or copy at https://opensource.org/licenses/MIT
-*/
-
-// Functions for clustering similar histograms together.
-
-#ifndef BROTLI_ENC_CLUSTER_H_
-#define BROTLI_ENC_CLUSTER_H_
-
-#include <math.h>
-#include <algorithm>
-#include <map>
-#include <utility>
-#include <vector>
-
-#include "./bit_cost.h"
-#include "./entropy_encode.h"
-#include "./fast_log.h"
-#include "./histogram.h"
-#include "./port.h"
-#include "./types.h"
-
-namespace brotli {
-
-struct HistogramPair {
-  uint32_t idx1;
-  uint32_t idx2;
-  double cost_combo;
-  double cost_diff;
-};
-
-inline bool operator<(const HistogramPair& p1, const HistogramPair& p2) {
-  if (p1.cost_diff != p2.cost_diff) {
-    return p1.cost_diff > p2.cost_diff;
-  }
-  return (p1.idx2 - p1.idx1) > (p2.idx2 - p2.idx1);
-}
-
-// Returns entropy reduction of the context map when we combine two clusters.
-inline double ClusterCostDiff(size_t size_a, size_t size_b) {
-  size_t size_c = size_a + size_b;
-  return static_cast<double>(size_a) * FastLog2(size_a) +
-      static_cast<double>(size_b) * FastLog2(size_b) -
-      static_cast<double>(size_c) * FastLog2(size_c);
-}
-
-// 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.
-template<typename HistogramType>
-void CompareAndPushToQueue(const HistogramType* out,
-                           const uint32_t* cluster_size,
-                           uint32_t idx1, uint32_t idx2,
-                           std::vector<HistogramPair>* pairs) {
-  if (idx1 == idx2) {
-    return;
-  }
-  if (idx2 < idx1) {
-    uint32_t t = idx2;
-    idx2 = idx1;
-    idx1 = t;
-  }
-  bool store_pair = false;
-  HistogramPair p;
-  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_;
-    store_pair = true;
-  } else if (out[idx2].total_count_ == 0) {
-    p.cost_combo = out[idx1].bit_cost_;
-    store_pair = true;
-  } else {
-    double threshold = pairs->empty() ? 1e99 :
-        std::max(0.0, (*pairs)[0].cost_diff);
-    HistogramType combo = out[idx1];
-    combo.AddHistogram(out[idx2]);
-    double cost_combo = PopulationCost(combo);
-    if (cost_combo < threshold - p.cost_diff) {
-      p.cost_combo = cost_combo;
-      store_pair = true;
-    }
-  }
-  if (store_pair) {
-    p.cost_diff += p.cost_combo;
-    if (!pairs->empty() && (pairs->front() < p)) {
-      // Replace the top of the queue if needed.
-      pairs->push_back(pairs->front());
-      pairs->front() = p;
-    } else {
-      pairs->push_back(p);
-    }
-  }
-}
-
-template<typename HistogramType>
-void HistogramCombine(HistogramType* out,
-                      uint32_t* cluster_size,
-                      uint32_t* symbols,
-                      size_t symbols_size,
-                      size_t max_clusters) {
-  double cost_diff_threshold = 0.0;
-  size_t min_cluster_size = 1;
-
-  // Uniquify the list of symbols.
-  std::vector<uint32_t> clusters(symbols, symbols + symbols_size);
-  std::sort(clusters.begin(), clusters.end());
-  std::vector<uint32_t>::iterator last =
-      std::unique(clusters.begin(), clusters.end());
-  clusters.resize(static_cast<size_t>(last - clusters.begin()));
-
-  // We maintain a heap of histogram pairs, ordered by the bit cost reduction.
-  std::vector<HistogramPair> pairs;
-  for (size_t idx1 = 0; idx1 < clusters.size(); ++idx1) {
-    for (size_t idx2 = idx1 + 1; idx2 < clusters.size(); ++idx2) {
-      CompareAndPushToQueue(out, cluster_size, clusters[idx1], clusters[idx2],
-                            &pairs);
-    }
-  }
-
-  while (clusters.size() > min_cluster_size) {
-    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.
-    uint32_t best_idx1 = pairs[0].idx1;
-    uint32_t best_idx2 = pairs[0].idx2;
-    out[best_idx1].AddHistogram(out[best_idx2]);
-    out[best_idx1].bit_cost_ = pairs[0].cost_combo;
-    cluster_size[best_idx1] += cluster_size[best_idx2];
-    for (size_t i = 0; i < symbols_size; ++i) {
-      if (symbols[i] == best_idx2) {
-        symbols[i] = best_idx1;
-      }
-    }
-    for (std::vector<uint32_t>::iterator cluster = clusters.begin();
-         cluster != clusters.end(); ++cluster) {
-      if (*cluster >= best_idx2) {
-        clusters.erase(cluster);
-        break;
-      }
-    }
-
-    // Remove pairs intersecting the just combined best pair.
-    size_t copy_to_idx = 0;
-    for (size_t i = 0; i < pairs.size(); ++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 (pairs.front() < p) {
-        // Replace the top of the queue if needed.
-        HistogramPair front = pairs.front();
-        pairs.front() = p;
-        pairs[copy_to_idx] = front;
-      } else {
-        pairs[copy_to_idx] = p;
-      }
-      ++copy_to_idx;
-    }
-    pairs.resize(copy_to_idx);
-
-    // Push new pairs formed with the combined histogram to the heap.
-    for (size_t i = 0; i < clusters.size(); ++i) {
-      CompareAndPushToQueue(out, cluster_size, best_idx1, clusters[i], &pairs);
-    }
-  }
-}
-
-// -----------------------------------------------------------------------------
-// Histogram refinement
-
-// What is the bit cost of moving histogram from cur_symbol to candidate.
-template<typename HistogramType>
-double HistogramBitCostDistance(const HistogramType& histogram,
-                                const HistogramType& candidate) {
-  if (histogram.total_count_ == 0) {
-    return 0.0;
-  }
-  HistogramType tmp = histogram;
-  tmp.AddHistogram(candidate);
-  return PopulationCost(tmp) - candidate.bit_cost_;
-}
-
-// Find the best 'out' histogram for each of the 'in' histograms.
-// Note: we assume that out[]->bit_cost_ is already up-to-date.
-template<typename HistogramType>
-void HistogramRemap(const HistogramType* in, size_t in_size,
-                    HistogramType* out, uint32_t* symbols) {
-  // Uniquify the list of symbols.
-  std::vector<uint32_t> all_symbols(symbols, symbols + in_size);
-  std::sort(all_symbols.begin(), all_symbols.end());
-  std::vector<uint32_t>::iterator last =
-      std::unique(all_symbols.begin(), all_symbols.end());
-  all_symbols.resize(static_cast<size_t>(last - all_symbols.begin()));
-
-  for (size_t i = 0; i < in_size; ++i) {
-    uint32_t best_out = i == 0 ? symbols[0] : symbols[i - 1];
-    double best_bits = HistogramBitCostDistance(in[i], out[best_out]);
-    for (std::vector<uint32_t>::const_iterator k = all_symbols.begin();
-         k != all_symbols.end(); ++k) {
-      const double cur_bits = HistogramBitCostDistance(in[i], out[*k]);
-      if (cur_bits < best_bits) {
-        best_bits = cur_bits;
-        best_out = *k;
-      }
-    }
-    symbols[i] = best_out;
-  }
-
-
-  // Recompute each out based on raw and symbols.
-  for (std::vector<uint32_t>::const_iterator k = all_symbols.begin();
-       k != all_symbols.end(); ++k) {
-    out[*k].Clear();
-  }
-  for (size_t i = 0; i < in_size; ++i) {
-    out[symbols[i]].AddHistogram(in[i]);
-  }
-}
-
-// Reorder histograms in *out so that the new symbols in *symbols come in
-// increasing order.
-template<typename HistogramType>
-void HistogramReindex(std::vector<HistogramType>* out,
-                      std::vector<uint32_t>* symbols) {
-  std::vector<HistogramType> tmp(*out);
-  std::map<uint32_t, uint32_t> new_index;
-  uint32_t next_index = 0;
-  for (size_t i = 0; i < symbols->size(); ++i) {
-    if (new_index.find((*symbols)[i]) == new_index.end()) {
-      new_index[(*symbols)[i]] = next_index;
-      (*out)[next_index] = tmp[(*symbols)[i]];
-      ++next_index;
-    }
-  }
-  out->resize(next_index);
-  for (size_t i = 0; i < symbols->size(); ++i) {
-    (*symbols)[i] = new_index[(*symbols)[i]];
-  }
-}
-
-// Clusters similar histograms in 'in' together, the selected histograms are
-// placed in 'out', and for each index in 'in', *histogram_symbols will
-// indicate which of the 'out' histograms is the best approximation.
-template<typename HistogramType>
-void ClusterHistograms(const std::vector<HistogramType>& in,
-                       size_t num_contexts, size_t num_blocks,
-                       size_t max_histograms,
-                       std::vector<HistogramType>* out,
-                       std::vector<uint32_t>* histogram_symbols) {
-  const size_t in_size = num_contexts * num_blocks;
-  assert(in_size == in.size());
-  std::vector<uint32_t> cluster_size(in_size, 1);
-  out->resize(in_size);
-  histogram_symbols->resize(in_size);
-  for (size_t i = 0; i < in_size; ++i) {
-    (*out)[i] = in[i];
-    (*out)[i].bit_cost_ = PopulationCost(in[i]);
-    (*histogram_symbols)[i] = static_cast<uint32_t>(i);
-  }
-
-
-  const size_t max_input_histograms = 64;
-  for (size_t i = 0; i < in_size; i += max_input_histograms) {
-    size_t num_to_combine = std::min(in_size - i, max_input_histograms);
-    HistogramCombine(&(*out)[0], &cluster_size[0],
-                     &(*histogram_symbols)[i], num_to_combine,
-                     max_histograms);
-  }
-
-  // Collapse similar histograms.
-  HistogramCombine(&(*out)[0], &cluster_size[0],
-                   &(*histogram_symbols)[0], in_size,
-                   max_histograms);
-
-  // Find the optimal map from original histograms to the final ones.
-  HistogramRemap(&in[0], in_size, &(*out)[0], &(*histogram_symbols)[0]);
-
-  // Convert the context map to a canonical form.
-  HistogramReindex(out, histogram_symbols);
-
-}
-
-
-}  // namespace brotli
-
-#endif  // BROTLI_ENC_CLUSTER_H_