forgejo-federation/vendor/github.com/andybalholm/brotli/block_splitter_distance.go

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package brotli
import "math"
/* Copyright 2013 Google Inc. All Rights Reserved.
Distributed under MIT license.
See file LICENSE for detail or copy at https://opensource.org/licenses/MIT
*/
func initialEntropyCodesDistance(data []uint16, length uint, stride uint, num_histograms uint, histograms []histogramDistance) {
var seed uint32 = 7
var block_length uint = length / num_histograms
var i uint
clearHistogramsDistance(histograms, num_histograms)
for i = 0; i < num_histograms; i++ {
var pos uint = length * i / num_histograms
if i != 0 {
pos += uint(myRand(&seed) % uint32(block_length))
}
if pos+stride >= length {
pos = length - stride - 1
}
histogramAddVectorDistance(&histograms[i], data[pos:], stride)
}
}
func randomSampleDistance(seed *uint32, data []uint16, length uint, stride uint, sample *histogramDistance) {
var pos uint = 0
if stride >= length {
stride = length
} else {
pos = uint(myRand(seed) % uint32(length-stride+1))
}
histogramAddVectorDistance(sample, data[pos:], stride)
}
func refineEntropyCodesDistance(data []uint16, length uint, stride uint, num_histograms uint, histograms []histogramDistance) {
var iters uint = kIterMulForRefining*length/stride + kMinItersForRefining
var seed uint32 = 7
var iter uint
iters = ((iters + num_histograms - 1) / num_histograms) * num_histograms
for iter = 0; iter < iters; iter++ {
var sample histogramDistance
histogramClearDistance(&sample)
randomSampleDistance(&seed, data, length, stride, &sample)
histogramAddHistogramDistance(&histograms[iter%num_histograms], &sample)
}
}
/* Assigns a block id from the range [0, num_histograms) to each data element
in data[0..length) and fills in block_id[0..length) with the assigned values.
Returns the number of blocks, i.e. one plus the number of block switches. */
func findBlocksDistance(data []uint16, length uint, block_switch_bitcost float64, num_histograms uint, histograms []histogramDistance, insert_cost []float64, cost []float64, switch_signal []byte, block_id []byte) uint {
var data_size uint = histogramDataSizeDistance()
var bitmaplen uint = (num_histograms + 7) >> 3
var num_blocks uint = 1
var i uint
var j uint
assert(num_histograms <= 256)
if num_histograms <= 1 {
for i = 0; i < length; i++ {
block_id[i] = 0
}
return 1
}
for i := 0; i < int(data_size*num_histograms); i++ {
insert_cost[i] = 0
}
for i = 0; i < num_histograms; i++ {
insert_cost[i] = fastLog2(uint(uint32(histograms[i].total_count_)))
}
for i = data_size; i != 0; {
i--
for j = 0; j < num_histograms; j++ {
insert_cost[i*num_histograms+j] = insert_cost[j] - bitCost(uint(histograms[j].data_[i]))
}
}
for i := 0; i < int(num_histograms); i++ {
cost[i] = 0
}
for i := 0; i < int(length*bitmaplen); i++ {
switch_signal[i] = 0
}
/* After each iteration of this loop, cost[k] will contain the difference
between the minimum cost of arriving at the current byte position using
entropy code k, and the minimum cost of arriving at the current byte
position. This difference is capped at the block switch cost, and if it
reaches block switch cost, it means that when we trace back from the last
position, we need to switch here. */
for i = 0; i < length; i++ {
var byte_ix uint = i
var ix uint = byte_ix * bitmaplen
var insert_cost_ix uint = uint(data[byte_ix]) * num_histograms
var min_cost float64 = 1e99
var block_switch_cost float64 = block_switch_bitcost
var k uint
for k = 0; k < num_histograms; k++ {
/* We are coding the symbol in data[byte_ix] with entropy code k. */
cost[k] += insert_cost[insert_cost_ix+k]
if cost[k] < min_cost {
min_cost = cost[k]
block_id[byte_ix] = byte(k)
}
}
/* More blocks for the beginning. */
if byte_ix < 2000 {
block_switch_cost *= 0.77 + 0.07*float64(byte_ix)/2000
}
for k = 0; k < num_histograms; k++ {
cost[k] -= min_cost
if cost[k] >= block_switch_cost {
var mask byte = byte(1 << (k & 7))
cost[k] = block_switch_cost
assert(k>>3 < bitmaplen)
switch_signal[ix+(k>>3)] |= mask
/* Trace back from the last position and switch at the marked places. */
}
}
}
{
var byte_ix uint = length - 1
var ix uint = byte_ix * bitmaplen
var cur_id byte = block_id[byte_ix]
for byte_ix > 0 {
var mask byte = byte(1 << (cur_id & 7))
assert(uint(cur_id)>>3 < bitmaplen)
byte_ix--
ix -= bitmaplen
if switch_signal[ix+uint(cur_id>>3)]&mask != 0 {
if cur_id != block_id[byte_ix] {
cur_id = block_id[byte_ix]
num_blocks++
}
}
block_id[byte_ix] = cur_id
}
}
return num_blocks
}
var remapBlockIdsDistance_kInvalidId uint16 = 256
func remapBlockIdsDistance(block_ids []byte, length uint, new_id []uint16, num_histograms uint) uint {
var next_id uint16 = 0
var i uint
for i = 0; i < num_histograms; i++ {
new_id[i] = remapBlockIdsDistance_kInvalidId
}
for i = 0; i < length; i++ {
assert(uint(block_ids[i]) < num_histograms)
if new_id[block_ids[i]] == remapBlockIdsDistance_kInvalidId {
new_id[block_ids[i]] = next_id
next_id++
}
}
for i = 0; i < length; i++ {
block_ids[i] = byte(new_id[block_ids[i]])
assert(uint(block_ids[i]) < num_histograms)
}
assert(uint(next_id) <= num_histograms)
return uint(next_id)
}
func buildBlockHistogramsDistance(data []uint16, length uint, block_ids []byte, num_histograms uint, histograms []histogramDistance) {
var i uint
clearHistogramsDistance(histograms, num_histograms)
for i = 0; i < length; i++ {
histogramAddDistance(&histograms[block_ids[i]], uint(data[i]))
}
}
var clusterBlocksDistance_kInvalidIndex uint32 = math.MaxUint32
func clusterBlocksDistance(data []uint16, length uint, num_blocks uint, block_ids []byte, split *blockSplit) {
var histogram_symbols []uint32 = make([]uint32, num_blocks)
var block_lengths []uint32 = make([]uint32, num_blocks)
var expected_num_clusters uint = clustersPerBatch * (num_blocks + histogramsPerBatch - 1) / histogramsPerBatch
var all_histograms_size uint = 0
var all_histograms_capacity uint = expected_num_clusters
var all_histograms []histogramDistance = make([]histogramDistance, all_histograms_capacity)
var cluster_size_size uint = 0
var cluster_size_capacity uint = expected_num_clusters
var cluster_size []uint32 = make([]uint32, cluster_size_capacity)
var num_clusters uint = 0
var histograms []histogramDistance = make([]histogramDistance, brotli_min_size_t(num_blocks, histogramsPerBatch))
var max_num_pairs uint = histogramsPerBatch * histogramsPerBatch / 2
var pairs_capacity uint = max_num_pairs + 1
var pairs []histogramPair = make([]histogramPair, pairs_capacity)
var pos uint = 0
var clusters []uint32
var num_final_clusters uint
var new_index []uint32
var i uint
var sizes = [histogramsPerBatch]uint32{0}
var new_clusters = [histogramsPerBatch]uint32{0}
var symbols = [histogramsPerBatch]uint32{0}
var remap = [histogramsPerBatch]uint32{0}
for i := 0; i < int(num_blocks); i++ {
block_lengths[i] = 0
}
{
var block_idx uint = 0
for i = 0; i < length; i++ {
assert(block_idx < num_blocks)
block_lengths[block_idx]++
if i+1 == length || block_ids[i] != block_ids[i+1] {
block_idx++
}
}
assert(block_idx == num_blocks)
}
for i = 0; i < num_blocks; i += histogramsPerBatch {
var num_to_combine uint = brotli_min_size_t(num_blocks-i, histogramsPerBatch)
var num_new_clusters uint
var j uint
for j = 0; j < num_to_combine; j++ {
var k uint
histogramClearDistance(&histograms[j])
for k = 0; uint32(k) < block_lengths[i+j]; k++ {
histogramAddDistance(&histograms[j], uint(data[pos]))
pos++
}
histograms[j].bit_cost_ = populationCostDistance(&histograms[j])
new_clusters[j] = uint32(j)
symbols[j] = uint32(j)
sizes[j] = 1
}
num_new_clusters = histogramCombineDistance(histograms, sizes[:], symbols[:], new_clusters[:], []histogramPair(pairs), num_to_combine, num_to_combine, histogramsPerBatch, max_num_pairs)
if all_histograms_capacity < (all_histograms_size + num_new_clusters) {
var _new_size uint
if all_histograms_capacity == 0 {
_new_size = all_histograms_size + num_new_clusters
} else {
_new_size = all_histograms_capacity
}
var new_array []histogramDistance
for _new_size < (all_histograms_size + num_new_clusters) {
_new_size *= 2
}
new_array = make([]histogramDistance, _new_size)
if all_histograms_capacity != 0 {
copy(new_array, all_histograms[:all_histograms_capacity])
}
all_histograms = new_array
all_histograms_capacity = _new_size
}
brotli_ensure_capacity_uint32_t(&cluster_size, &cluster_size_capacity, cluster_size_size+num_new_clusters)
for j = 0; j < num_new_clusters; j++ {
all_histograms[all_histograms_size] = histograms[new_clusters[j]]
all_histograms_size++
cluster_size[cluster_size_size] = sizes[new_clusters[j]]
cluster_size_size++
remap[new_clusters[j]] = uint32(j)
}
for j = 0; j < num_to_combine; j++ {
histogram_symbols[i+j] = uint32(num_clusters) + remap[symbols[j]]
}
num_clusters += num_new_clusters
assert(num_clusters == cluster_size_size)
assert(num_clusters == all_histograms_size)
}
histograms = nil
max_num_pairs = brotli_min_size_t(64*num_clusters, (num_clusters/2)*num_clusters)
if pairs_capacity < max_num_pairs+1 {
pairs = nil
pairs = make([]histogramPair, (max_num_pairs + 1))
}
clusters = make([]uint32, num_clusters)
for i = 0; i < num_clusters; i++ {
clusters[i] = uint32(i)
}
num_final_clusters = histogramCombineDistance(all_histograms, cluster_size, histogram_symbols, clusters, pairs, num_clusters, num_blocks, maxNumberOfBlockTypes, max_num_pairs)
pairs = nil
cluster_size = nil
new_index = make([]uint32, num_clusters)
for i = 0; i < num_clusters; i++ {
new_index[i] = clusterBlocksDistance_kInvalidIndex
}
pos = 0
{
var next_index uint32 = 0
for i = 0; i < num_blocks; i++ {
var histo histogramDistance
var j uint
var best_out uint32
var best_bits float64
histogramClearDistance(&histo)
for j = 0; uint32(j) < block_lengths[i]; j++ {
histogramAddDistance(&histo, uint(data[pos]))
pos++
}
if i == 0 {
best_out = histogram_symbols[0]
} else {
best_out = histogram_symbols[i-1]
}
best_bits = histogramBitCostDistanceDistance(&histo, &all_histograms[best_out])
for j = 0; j < num_final_clusters; j++ {
var cur_bits float64 = histogramBitCostDistanceDistance(&histo, &all_histograms[clusters[j]])
if cur_bits < best_bits {
best_bits = cur_bits
best_out = clusters[j]
}
}
histogram_symbols[i] = best_out
if new_index[best_out] == clusterBlocksDistance_kInvalidIndex {
new_index[best_out] = next_index
next_index++
}
}
}
clusters = nil
all_histograms = nil
brotli_ensure_capacity_uint8_t(&split.types, &split.types_alloc_size, num_blocks)
brotli_ensure_capacity_uint32_t(&split.lengths, &split.lengths_alloc_size, num_blocks)
{
var cur_length uint32 = 0
var block_idx uint = 0
var max_type byte = 0
for i = 0; i < num_blocks; i++ {
cur_length += block_lengths[i]
if i+1 == num_blocks || histogram_symbols[i] != histogram_symbols[i+1] {
var id byte = byte(new_index[histogram_symbols[i]])
split.types[block_idx] = id
split.lengths[block_idx] = cur_length
max_type = brotli_max_uint8_t(max_type, id)
cur_length = 0
block_idx++
}
}
split.num_blocks = block_idx
split.num_types = uint(max_type) + 1
}
new_index = nil
block_lengths = nil
histogram_symbols = nil
}
func splitByteVectorDistance(data []uint16, length uint, literals_per_histogram uint, max_histograms uint, sampling_stride_length uint, block_switch_cost float64, params *encoderParams, split *blockSplit) {
var data_size uint = histogramDataSizeDistance()
var num_histograms uint = length/literals_per_histogram + 1
var histograms []histogramDistance
if num_histograms > max_histograms {
num_histograms = max_histograms
}
if length == 0 {
split.num_types = 1
return
} else if length < kMinLengthForBlockSplitting {
brotli_ensure_capacity_uint8_t(&split.types, &split.types_alloc_size, split.num_blocks+1)
brotli_ensure_capacity_uint32_t(&split.lengths, &split.lengths_alloc_size, split.num_blocks+1)
split.num_types = 1
split.types[split.num_blocks] = 0
split.lengths[split.num_blocks] = uint32(length)
split.num_blocks++
return
}
histograms = make([]histogramDistance, num_histograms)
/* Find good entropy codes. */
initialEntropyCodesDistance(data, length, sampling_stride_length, num_histograms, histograms)
refineEntropyCodesDistance(data, length, sampling_stride_length, num_histograms, histograms)
{
var block_ids []byte = make([]byte, length)
var num_blocks uint = 0
var bitmaplen uint = (num_histograms + 7) >> 3
var insert_cost []float64 = make([]float64, (data_size * num_histograms))
var cost []float64 = make([]float64, num_histograms)
var switch_signal []byte = make([]byte, (length * bitmaplen))
var new_id []uint16 = make([]uint16, num_histograms)
var iters uint
if params.quality < hqZopflificationQuality {
iters = 3
} else {
iters = 10
}
/* Find a good path through literals with the good entropy codes. */
var i uint
for i = 0; i < iters; i++ {
num_blocks = findBlocksDistance(data, length, block_switch_cost, num_histograms, histograms, insert_cost, cost, switch_signal, block_ids)
num_histograms = remapBlockIdsDistance(block_ids, length, new_id, num_histograms)
buildBlockHistogramsDistance(data, length, block_ids, num_histograms, histograms)
}
insert_cost = nil
cost = nil
switch_signal = nil
new_id = nil
histograms = nil
clusterBlocksDistance(data, length, num_blocks, block_ids, split)
block_ids = nil
}
}