/* 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. */
public static void BrotliHistogramRemap(HistogramDistance *in_,
size_t in_size, uint *clusters, size_t num_clusters,
HistogramDistance *out_, uint *symbols)
{
size_t i;
for (i = 0; i < in_size; ++i)
{
uint best_out = i == 0 ? symbols[0] : symbols[i - 1];
double best_bits =
BrotliHistogramBitCostDistance(&in_[i], &out_[best_out]);
size_t j;
for (j = 0; j < num_clusters; ++j)
{
double cur_bits =
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)
{
HistogramDistance.HistogramClear(&out_[clusters[i]]);
}
for (i = 0; i < in_size; ++i)
{
HistogramDistance.HistogramAddHistogram(&out_[symbols[i]], &in_[i]);
}
}
public static unsafe void InitBlockSplitter(
ref MemoryManager m, BlockSplitterDistance *self, size_t alphabet_size,
size_t min_block_size, double split_threshold, size_t num_symbols,
BlockSplit *split, HistogramDistance **histograms, size_t *histograms_size)
{
size_t max_num_blocks = num_symbols / min_block_size + 1;
/* We have to allocate one more histogram than the maximum number of block
* types for the current histogram when the meta-block is too big. */
size_t max_num_types =
Math.Min(max_num_blocks, BROTLI_MAX_NUMBER_OF_BLOCK_TYPES + 1);
self->alphabet_size_ = alphabet_size;
self->min_block_size_ = min_block_size;
self->split_threshold_ = split_threshold;
self->num_blocks_ = 0;
self->split_ = split;
self->histograms_size_ = histograms_size;
self->target_block_size_ = min_block_size;
self->block_size_ = 0;
self->curr_histogram_ix_ = 0;
self->merge_last_count_ = 0;
BrotliEnsureCapacity(ref m, sizeof(byte), (void **)&split->types, &split->types_alloc_size, max_num_blocks);
BrotliEnsureCapacity(ref m, sizeof(uint), (void **)&split->lengths, &split->lengths_alloc_size, max_num_blocks);
self->split_->num_blocks = max_num_blocks;
*histograms_size = max_num_types;
*histograms = (HistogramDistance *)BrotliAllocate(ref m, *histograms_size * sizeof(HistogramDistance));
self->histograms_ = *histograms;
/* Clear only current histogram. */
HistogramDistance.HistogramClear(&self->histograms_[0]);
self->last_histogram_ix_0 = self->last_histogram_ix_1 = 0;
}
/* Adds the next symbol to the current histogram. When the current histogram
* reaches the target size, decides on merging the block. */
public static unsafe void BlockSplitterAddSymbol(BlockSplitterDistance *self, size_t symbol)
{
HistogramDistance.HistogramAdd(&self->histograms_[self->curr_histogram_ix_], symbol);
++self->block_size_;
if (self->block_size_ == self->target_block_size_)
{
BlockSplitterFinishBlock(self, /* is_final = */ false);
}
}
private static unsafe void BrotliBuildHistogramsWithContext(
Command *cmds, size_t num_commands,
BlockSplit *literal_split, BlockSplit *insert_and_copy_split,
BlockSplit *dist_split, byte *ringbuffer, size_t start_pos,
size_t mask, byte prev_byte, byte prev_byte2,
ContextType *context_modes, HistogramLiteral *literal_histograms,
HistogramCommand *insert_and_copy_histograms,
HistogramDistance *copy_dist_histograms)
{
size_t pos = start_pos;
BlockSplitIterator literal_it;
BlockSplitIterator insert_and_copy_it;
BlockSplitIterator dist_it;
size_t i;
InitBlockSplitIterator(&literal_it, literal_split);
InitBlockSplitIterator(&insert_and_copy_it, insert_and_copy_split);
InitBlockSplitIterator(&dist_it, dist_split);
for (i = 0; i < num_commands; ++i)
{
Command *cmd = &cmds[i];
size_t j;
BlockSplitIteratorNext(&insert_and_copy_it);
HistogramCommand.HistogramAdd(&insert_and_copy_histograms[insert_and_copy_it.type_],
cmd->cmd_prefix_);
for (j = cmd->insert_len_; j != 0; --j)
{
size_t context;
BlockSplitIteratorNext(&literal_it);
context = context_modes != null ?
((literal_it.type_ << BROTLI_LITERAL_CONTEXT_BITS) +
Context(prev_byte, prev_byte2, context_modes[literal_it.type_])) :
literal_it.type_;
HistogramLiteral.HistogramAdd(&literal_histograms[context],
ringbuffer[pos & mask]);
prev_byte2 = prev_byte;
prev_byte = ringbuffer[pos & mask];
++pos;
}
pos += CommandCopyLen(cmd);
if (CommandCopyLen(cmd) != 0)
{
prev_byte2 = ringbuffer[(pos - 2) & mask];
prev_byte = ringbuffer[(pos - 1) & mask];
if (cmd->cmd_prefix_ >= 128)
{
size_t context;
BlockSplitIteratorNext(&dist_it);
context = (dist_it.type_ << BROTLI_DISTANCE_CONTEXT_BITS) +
CommandDistanceContext(cmd);
HistogramDistance.HistogramAdd(©_dist_histograms[context],
cmd->dist_prefix_);
}
}
}
}
/* What is the bit cost of moving histogram from cur_symbol to candidate. */
public static double BrotliHistogramBitCostDistance(
HistogramDistance *histogram, HistogramDistance *candidate)
{
if (histogram->total_count_ == 0)
{
return(0.0);
}
else
{
HistogramDistance tmp = *histogram;
HistogramDistance.HistogramAddHistogram(&tmp, candidate);
return(BitCostDistance.BrotliPopulationCost(&tmp) - candidate->bit_cost_);
}
}
public void DifferentImages(string imageSet, string image1Name, string image2Name)
{
using (var image1 = new Bitmap(Path.Combine("Images", imageSet, image1Name)))
using (var image2 = new Bitmap(Path.Combine("Images", imageSet, image2Name)))
{
var img1 = Bitmap2ByteArray(image1);
var img2 = Bitmap2ByteArray(image2);
var distance = HistogramDistance.Calculate(img1, img2, false);
Assert.True(distance > 100000);
distance = HistogramDistance.Calculate(img1, img2, true);
Assert.True(distance > 0.1);
}
}
public static size_t BrotliHistogramCombine(HistogramDistance *out_,
uint *cluster_size,
uint *symbols,
uint *clusters,
HistogramPair *pairs,
size_t num_clusters,
size_t symbols_size,
size_t max_clusters,
size_t max_num_pairs)
{
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)
{
BrotliCompareAndPushToQueue(out_, cluster_size, clusters[idx1],
clusters[idx2], max_num_pairs, &pairs[0], &num_pairs);
}
}
}
while (num_clusters > min_cluster_size)
{
uint best_idx1;
uint 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;
HistogramDistance.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(uint));
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)
{
BrotliCompareAndPushToQueue(out_, cluster_size, best_idx1, clusters[i],
max_num_pairs, &pairs[0], &num_pairs);
}
}
return(num_clusters);
}
static void BrotliCompareAndPushToQueue(
HistogramDistance *out_, uint *cluster_size, uint idx1,
uint idx2, size_t max_num_pairs, HistogramPair *pairs,
size_t *num_pairs)
{
bool is_good_pair = false;
HistogramPair p = new HistogramPair();
if (idx1 == idx2)
{
return;
}
if (idx2 < idx1)
{
uint 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 = true;
}
else if (out_[idx2].total_count_ == 0)
{
p.cost_combo = out_[idx1].bit_cost_;
is_good_pair = true;
}
else
{
double threshold = *num_pairs == 0 ? 1e99 : Math.Max(0.0, pairs[0].cost_diff);
HistogramDistance combo = out_[idx1];
double cost_combo;
HistogramDistance.HistogramAddHistogram(&combo, &out_[idx2]);
cost_combo = BitCostDistance.BrotliPopulationCost(&combo);
if (cost_combo < threshold - p.cost_diff)
{
p.cost_combo = cost_combo;
is_good_pair = 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);
}
}
}
private static unsafe void BrotliBuildMetaBlock(ref MemoryManager m,
byte *ringbuffer,
size_t pos,
size_t mask,
BrotliEncoderParams *params_,
byte prev_byte,
byte prev_byte2,
Command *cmds,
size_t num_commands,
ContextType literal_context_mode,
MetaBlockSplit *mb)
{
/* Histogram ids need to fit in one byte. */
size_t kMaxNumberOfHistograms = 256;
HistogramDistance *distance_histograms;
HistogramLiteral * literal_histograms;
ContextType * literal_context_modes = null;
size_t literal_histograms_size;
size_t distance_histograms_size;
size_t i;
size_t literal_context_multiplier = 1;
BrotliSplitBlock(ref m, cmds, num_commands,
ringbuffer, pos, mask, params_,
&mb->literal_split,
&mb->command_split,
&mb->distance_split);
if (!params_->disable_literal_context_modeling)
{
literal_context_multiplier = 1 << BROTLI_LITERAL_CONTEXT_BITS;
literal_context_modes =
(ContextType *)BrotliAllocate(ref m, mb->literal_split.num_types * sizeof(ContextType));
for (i = 0; i < mb->literal_split.num_types; ++i)
{
literal_context_modes[i] = literal_context_mode;
}
}
literal_histograms_size =
mb->literal_split.num_types * literal_context_multiplier;
literal_histograms =
(HistogramLiteral *)BrotliAllocate(ref m, literal_histograms_size * sizeof(HistogramLiteral));
HistogramLiteral.ClearHistograms(literal_histograms, literal_histograms_size);
distance_histograms_size =
mb->distance_split.num_types << BROTLI_DISTANCE_CONTEXT_BITS;
distance_histograms =
(HistogramDistance *)BrotliAllocate(ref m, distance_histograms_size * sizeof(HistogramDistance));
HistogramDistance.ClearHistograms(distance_histograms, distance_histograms_size);
mb->command_histograms_size = mb->command_split.num_types;
mb->command_histograms =
(HistogramCommand *)BrotliAllocate(ref m, mb->command_histograms_size * sizeof(HistogramCommand));
HistogramCommand.ClearHistograms(mb->command_histograms, mb->command_histograms_size);
BrotliBuildHistogramsWithContext(cmds, num_commands,
&mb->literal_split, &mb->command_split, &mb->distance_split,
ringbuffer, pos, mask, prev_byte, prev_byte2, literal_context_modes,
literal_histograms, mb->command_histograms, distance_histograms);
BrotliFree(ref m, literal_context_modes);
mb->literal_context_map_size =
mb->literal_split.num_types << BROTLI_LITERAL_CONTEXT_BITS;
mb->literal_context_map =
(uint *)BrotliAllocate(ref m, mb->literal_context_map_size * sizeof(uint));
mb->literal_histograms_size = mb->literal_context_map_size;
mb->literal_histograms =
(HistogramLiteral *)BrotliAllocate(ref m, mb->literal_histograms_size * sizeof(HistogramLiteral));
ClusterLiteral.BrotliClusterHistograms(ref m, literal_histograms, literal_histograms_size,
kMaxNumberOfHistograms, mb->literal_histograms,
&mb->literal_histograms_size, mb->literal_context_map);
BrotliFree(ref m, literal_histograms);
if (params_->disable_literal_context_modeling)
{
/* Distribute assignment to all contexts. */
for (i = mb->literal_split.num_types; i != 0;)
{
size_t j = 0;
i--;
for (; j < (1 << BROTLI_LITERAL_CONTEXT_BITS); j++)
{
mb->literal_context_map[(i << BROTLI_LITERAL_CONTEXT_BITS) + j] =
mb->literal_context_map[i];
}
}
}
mb->distance_context_map_size =
mb->distance_split.num_types << BROTLI_DISTANCE_CONTEXT_BITS;
mb->distance_context_map =
(uint *)BrotliAllocate(ref m, mb->distance_context_map_size * sizeof(uint));
mb->distance_histograms_size = mb->distance_context_map_size;
mb->distance_histograms =
(HistogramDistance *)BrotliAllocate(ref m, mb->distance_histograms_size * sizeof(HistogramDistance));
ClusterDistance.BrotliClusterHistograms(ref m, distance_histograms,
mb->distance_context_map_size,
kMaxNumberOfHistograms,
mb->distance_histograms,
&mb->distance_histograms_size,
mb->distance_context_map);
BrotliFree(ref m, distance_histograms);
}
/// <summary>
/// Returns the distance between two histograms. 0 is identical, 1 is completely different.
/// </summary>
/// <param name="histogramA">The first histogram.</param>
/// <param name="histogramB">The second histogram.</param>
/// <param name="model">The computation model.</param>
/// <returns>A distance between 0 and 1.</returns>
public static float HistogramDistance(Histogram histogramA, Histogram histogramB, HistogramDistance model)
{
switch (model)
{
case Unity.QuickSearch.HistogramDistance.CityBlock: return(CityBlockDistance(histogramA, histogramB));
case Unity.QuickSearch.HistogramDistance.Euclidean: return(EuclideanDistance(histogramA, histogramB));
case Unity.QuickSearch.HistogramDistance.Bhattacharyya: return(BhattacharyyaDistance(histogramA, histogramB));
case Unity.QuickSearch.HistogramDistance.MDPA: return(MDPA(histogramA, histogramB));
}
return(1.0f);
}
public static double BrotliPopulationCost(HistogramDistance *histogram)
{
const double kOneSymbolHistogramCost = 12;
const double kTwoSymbolHistogramCost = 20;
const double kThreeSymbolHistogramCost = 28;
const double kFourSymbolHistogramCost = 37;
size_t data_size = HistogramDistance.HistogramDataSize();
int count = 0;
size_t *s = stackalloc size_t[5];
double bits = 0.0;
size_t i;
if (histogram->total_count_ == 0)
{
return(kOneSymbolHistogramCost);
}
for (i = 0; i < data_size; ++i)
{
if (histogram->data_[i] > 0)
{
s[count] = i;
++count;
if (count > 4)
{
break;
}
}
}
if (count == 1)
{
return(kOneSymbolHistogramCost);
}
if (count == 2)
{
return(kTwoSymbolHistogramCost + (double)histogram->total_count_);
}
if (count == 3)
{
uint histo0 = histogram->data_[s[0]];
uint histo1 = histogram->data_[s[1]];
uint histo2 = histogram->data_[s[2]];
uint histomax =
Math.Max(histo0, Math.Max(histo1, histo2));
return(kThreeSymbolHistogramCost +
2 * (histo0 + histo1 + histo2) - histomax);
}
if (count == 4)
{
uint *histo = stackalloc uint[4];
uint h23;
uint histomax;
for (i = 0; i < 4; ++i)
{
histo[i] = histogram->data_[s[i]];
}
/* Sort */
for (i = 0; i < 4; ++i)
{
size_t j;
for (j = i + 1; j < 4; ++j)
{
if (histo[j] > histo[i])
{
uint tmp = histo[j];
histo[j] = histo[i];
histo[i] = tmp;
}
}
}
h23 = histo[2] + histo[3];
histomax = Math.Max(h23, histo[0]);
return(kFourSymbolHistogramCost +
3 * h23 + 2 * (histo[0] + histo[1]) - histomax);
}
{
/* In this loop we compute the entropy of the histogram and simultaneously
* build a simplified histogram of the code length codes where we use the
* zero repeat code 17, but we don't use the non-zero repeat code 16. */
size_t max_depth = 1;
uint * depth_histo = stackalloc uint[BROTLI_CODE_LENGTH_CODES];
memset(depth_histo, 0, BROTLI_CODE_LENGTH_CODES * sizeof(uint));
double log2total = FastLog2(histogram->total_count_);
for (i = 0; i < data_size;)
{
if (histogram->data_[i] > 0)
{
/* Compute -log2(P(symbol)) = -log2(count(symbol)/total_count) =
* = log2(total_count) - log2(count(symbol)) */
double log2p = log2total - FastLog2(histogram->data_[i]);
/* Approximate the bit depth by round(-log2(P(symbol))) */
size_t depth = (size_t)(log2p + 0.5);
bits += histogram->data_[i] * log2p;
if (depth > 15)
{
depth = 15;
}
if (depth > max_depth)
{
max_depth = depth;
}
++depth_histo[depth];
++i;
}
else
{
/* Compute the run length of zeros and add the appropriate number of 0
* and 17 code length codes to the code length code histogram. */
uint reps = 1;
size_t k;
for (k = i + 1; k < data_size && histogram->data_[k] == 0; ++k)
{
++reps;
}
i += reps;
if (i == data_size)
{
/* Don't add any cost for the last zero run, since these are encoded
* only implicitly. */
break;
}
if (reps < 3)
{
depth_histo[0] += reps;
}
else
{
reps -= 2;
while (reps > 0)
{
++depth_histo[BROTLI_REPEAT_ZERO_CODE_LENGTH];
/* Add the 3 extra bits for the 17 code length code. */
bits += 3;
reps >>= 3;
}
}
}
}
/* Add the estimated encoding cost of the code length code histogram. */
bits += (double)(18 + 2 * max_depth);
/* Add the entropy of the code length code histogram. */
bits += BitsEntropy(depth_histo, BROTLI_CODE_LENGTH_CODES);
}
return(bits);
}
/* Does either of three things:
* (1) emits the current block with a new block type;
* (2) emits the current block with the type of the second last block;
* (3) merges the current block with the last block. */
public static unsafe void BlockSplitterFinishBlock(
BlockSplitterDistance *self, bool is_final)
{
BlockSplit * split = self->split_;
double * last_entropy = self->last_entropy_;
HistogramDistance *histograms = self->histograms_;
self->block_size_ =
Math.Max(self->block_size_, self->min_block_size_);
if (self->num_blocks_ == 0)
{
/* Create first block. */
split->lengths[0] = (uint)self->block_size_;
split->types[0] = 0;
last_entropy[0] =
BitsEntropy(histograms[0].data_, self->alphabet_size_);
last_entropy[1] = last_entropy[0];
++self->num_blocks_;
++split->num_types;
++self->curr_histogram_ix_;
if (self->curr_histogram_ix_ < *self->histograms_size_)
{
HistogramDistance.HistogramClear(&histograms[self->curr_histogram_ix_]);
}
self->block_size_ = 0;
}
else if (self->block_size_ > 0)
{
double entropy = BitsEntropy(histograms[self->curr_histogram_ix_].data_,
self->alphabet_size_);
HistogramDistance *combined_histo = stackalloc HistogramDistance[2];
double * combined_entropy = stackalloc double[2];
double * diff = stackalloc double[2];
size_t j;
for (j = 0; j < 2; ++j)
{
size_t last_histogram_ix = j == 0 ? self->last_histogram_ix_0 : self->last_histogram_ix_1;
combined_histo[j] = histograms[self->curr_histogram_ix_];
HistogramDistance.HistogramAddHistogram(&combined_histo[j],
&histograms[last_histogram_ix]);
combined_entropy[j] = BitsEntropy(
&combined_histo[j].data_[0], self->alphabet_size_);
diff[j] = combined_entropy[j] - entropy - last_entropy[j];
}
if (split->num_types < BROTLI_MAX_NUMBER_OF_BLOCK_TYPES &&
diff[0] > self->split_threshold_ &&
diff[1] > self->split_threshold_)
{
/* Create new block. */
split->lengths[self->num_blocks_] = (uint)self->block_size_;
split->types[self->num_blocks_] = (byte)split->num_types;
self->last_histogram_ix_1 = self->last_histogram_ix_0;
self->last_histogram_ix_0 = (byte)split->num_types;
last_entropy[1] = last_entropy[0];
last_entropy[0] = entropy;
++self->num_blocks_;
++split->num_types;
++self->curr_histogram_ix_;
if (self->curr_histogram_ix_ < *self->histograms_size_)
{
HistogramDistance.HistogramClear(&histograms[self->curr_histogram_ix_]);
}
self->block_size_ = 0;
self->merge_last_count_ = 0;
self->target_block_size_ = self->min_block_size_;
}
else if (diff[1] < diff[0] - 20.0)
{
/* Combine this block with second last block. */
split->lengths[self->num_blocks_] = (uint)self->block_size_;
split->types[self->num_blocks_] = split->types[self->num_blocks_ - 2];
size_t tmp = self->last_histogram_ix_0;
self->last_histogram_ix_0 = self->last_histogram_ix_1;
self->last_histogram_ix_1 = tmp;
histograms[self->last_histogram_ix_0] = combined_histo[1];
last_entropy[1] = last_entropy[0];
last_entropy[0] = combined_entropy[1];
++self->num_blocks_;
self->block_size_ = 0;
HistogramDistance.HistogramClear(&histograms[self->curr_histogram_ix_]);
self->merge_last_count_ = 0;
self->target_block_size_ = self->min_block_size_;
}
else
{
/* Combine this block with last block. */
split->lengths[self->num_blocks_ - 1] += (uint)self->block_size_;
histograms[self->last_histogram_ix_0] = combined_histo[0];
last_entropy[0] = combined_entropy[0];
if (split->num_types == 1)
{
last_entropy[1] = last_entropy[0];
}
self->block_size_ = 0;
HistogramDistance.HistogramClear(&histograms[self->curr_histogram_ix_]);
if (++self->merge_last_count_ > 1)
{
self->target_block_size_ += self->min_block_size_;
}
}
}
if (is_final)
{
*self->histograms_size_ = split->num_types;
split->num_blocks = self->num_blocks_;
}
}
