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+/* 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, 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);
+ HistogramType combo = out[idx1];
+ double cost_combo;
+ FN(HistogramAddHistogram)(&combo, &out[idx2]);
+ cost_combo = FN(BrotliPopulationCost)(&combo);
+ 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,
+ 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, 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, 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) CODE({
+ if (histogram->total_count_ == 0) {
+ return 0.0;
+ } else {
+ HistogramType 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, 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]);
+ size_t j;
+ for (j = 0; j < num_clusters; ++j) {
+ const double cur_bits =
+ FN(BrotliHistogramBitCostDistance)(&in[i], &out[clusters[j]]);
+ 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)) 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: 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)) 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);
+ size_t i;
+
+ if (BROTLI_IS_OOM(m)) 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, 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, 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, histogram_symbols);
+ 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