mirror of
https://github.com/go-gitea/gitea.git
synced 2024-08-22 21:14:18 -04:00
* update github.com/alecthomas/chroma v0.8.0 -> v0.8.1 * github.com/blevesearch/bleve v1.0.10 -> v1.0.12 * editorconfig-core-go v2.1.1 -> v2.3.7 * github.com/gliderlabs/ssh v0.2.2 -> v0.3.1 * migrate editorconfig.ParseBytes to Parse * github.com/shurcooL/vfsgen to 0d455de96546 * github.com/go-git/go-git/v5 v5.1.0 -> v5.2.0 * github.com/google/uuid v1.1.1 -> v1.1.2 * github.com/huandu/xstrings v1.3.0 -> v1.3.2 * github.com/klauspost/compress v1.10.11 -> v1.11.1 * github.com/markbates/goth v1.61.2 -> v1.65.0 * github.com/mattn/go-sqlite3 v1.14.0 -> v1.14.4 * github.com/mholt/archiver v3.3.0 -> v3.3.2 * github.com/microcosm-cc/bluemonday 4f7140c49acb -> v1.0.4 * github.com/minio/minio-go v7.0.4 -> v7.0.5 * github.com/olivere/elastic v7.0.9 -> v7.0.20 * github.com/urfave/cli v1.20.0 -> v1.22.4 * github.com/prometheus/client_golang v1.1.0 -> v1.8.0 * github.com/xanzy/go-gitlab v0.37.0 -> v0.38.1 * mvdan.cc/xurls v2.1.0 -> v2.2.0 Co-authored-by: Lauris BH <lauris@nix.lv>
593 lines
14 KiB
Go
Vendored
593 lines
14 KiB
Go
Vendored
package brotli
|
|
|
|
import "math"
|
|
|
|
/* Copyright 2010 Google Inc. All Rights Reserved.
|
|
|
|
Distributed under MIT license.
|
|
See file LICENSE for detail or copy at https://opensource.org/licenses/MIT
|
|
*/
|
|
|
|
/* Entropy encoding (Huffman) utilities. */
|
|
|
|
/* A node of a Huffman tree. */
|
|
type huffmanTree struct {
|
|
total_count_ uint32
|
|
index_left_ int16
|
|
index_right_or_value_ int16
|
|
}
|
|
|
|
func initHuffmanTree(self *huffmanTree, count uint32, left int16, right int16) {
|
|
self.total_count_ = count
|
|
self.index_left_ = left
|
|
self.index_right_or_value_ = right
|
|
}
|
|
|
|
/* Input size optimized Shell sort. */
|
|
type huffmanTreeComparator func(huffmanTree, huffmanTree) bool
|
|
|
|
var sortHuffmanTreeItems_gaps = []uint{132, 57, 23, 10, 4, 1}
|
|
|
|
func sortHuffmanTreeItems(items []huffmanTree, n uint, comparator huffmanTreeComparator) {
|
|
if n < 13 {
|
|
/* Insertion sort. */
|
|
var i uint
|
|
for i = 1; i < n; i++ {
|
|
var tmp huffmanTree = items[i]
|
|
var k uint = i
|
|
var j uint = i - 1
|
|
for comparator(tmp, items[j]) {
|
|
items[k] = items[j]
|
|
k = j
|
|
if j == 0 {
|
|
break
|
|
}
|
|
j--
|
|
}
|
|
|
|
items[k] = tmp
|
|
}
|
|
|
|
return
|
|
} else {
|
|
var g int
|
|
if n < 57 {
|
|
g = 2
|
|
} else {
|
|
g = 0
|
|
}
|
|
for ; g < 6; g++ {
|
|
var gap uint = sortHuffmanTreeItems_gaps[g]
|
|
var i uint
|
|
for i = gap; i < n; i++ {
|
|
var j uint = i
|
|
var tmp huffmanTree = items[i]
|
|
for ; j >= gap && comparator(tmp, items[j-gap]); j -= gap {
|
|
items[j] = items[j-gap]
|
|
}
|
|
|
|
items[j] = tmp
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
/* Returns 1 if assignment of depths succeeded, otherwise 0. */
|
|
func setDepth(p0 int, pool []huffmanTree, depth []byte, max_depth int) bool {
|
|
var stack [16]int
|
|
var level int = 0
|
|
var p int = p0
|
|
assert(max_depth <= 15)
|
|
stack[0] = -1
|
|
for {
|
|
if pool[p].index_left_ >= 0 {
|
|
level++
|
|
if level > max_depth {
|
|
return false
|
|
}
|
|
stack[level] = int(pool[p].index_right_or_value_)
|
|
p = int(pool[p].index_left_)
|
|
continue
|
|
} else {
|
|
depth[pool[p].index_right_or_value_] = byte(level)
|
|
}
|
|
|
|
for level >= 0 && stack[level] == -1 {
|
|
level--
|
|
}
|
|
if level < 0 {
|
|
return true
|
|
}
|
|
p = stack[level]
|
|
stack[level] = -1
|
|
}
|
|
}
|
|
|
|
/* Sort the root nodes, least popular first. */
|
|
func sortHuffmanTree(v0 huffmanTree, v1 huffmanTree) bool {
|
|
if v0.total_count_ != v1.total_count_ {
|
|
return v0.total_count_ < v1.total_count_
|
|
}
|
|
|
|
return v0.index_right_or_value_ > v1.index_right_or_value_
|
|
}
|
|
|
|
/* This function will create a Huffman tree.
|
|
|
|
The catch here is that the tree cannot be arbitrarily deep.
|
|
Brotli specifies a maximum depth of 15 bits for "code trees"
|
|
and 7 bits for "code length code trees."
|
|
|
|
count_limit is the value that is to be faked as the minimum value
|
|
and this minimum value is raised until the tree matches the
|
|
maximum length requirement.
|
|
|
|
This algorithm is not of excellent performance for very long data blocks,
|
|
especially when population counts are longer than 2**tree_limit, but
|
|
we are not planning to use this with extremely long blocks.
|
|
|
|
See http://en.wikipedia.org/wiki/Huffman_coding */
|
|
func createHuffmanTree(data []uint32, length uint, tree_limit int, tree []huffmanTree, depth []byte) {
|
|
var count_limit uint32
|
|
var sentinel huffmanTree
|
|
initHuffmanTree(&sentinel, math.MaxUint32, -1, -1)
|
|
|
|
/* For block sizes below 64 kB, we never need to do a second iteration
|
|
of this loop. Probably all of our block sizes will be smaller than
|
|
that, so this loop is mostly of academic interest. If we actually
|
|
would need this, we would be better off with the Katajainen algorithm. */
|
|
for count_limit = 1; ; count_limit *= 2 {
|
|
var n uint = 0
|
|
var i uint
|
|
var j uint
|
|
var k uint
|
|
for i = length; i != 0; {
|
|
i--
|
|
if data[i] != 0 {
|
|
var count uint32 = brotli_max_uint32_t(data[i], count_limit)
|
|
initHuffmanTree(&tree[n], count, -1, int16(i))
|
|
n++
|
|
}
|
|
}
|
|
|
|
if n == 1 {
|
|
depth[tree[0].index_right_or_value_] = 1 /* Only one element. */
|
|
break
|
|
}
|
|
|
|
sortHuffmanTreeItems(tree, n, huffmanTreeComparator(sortHuffmanTree))
|
|
|
|
/* The nodes are:
|
|
[0, n): the sorted leaf nodes that we start with.
|
|
[n]: we add a sentinel here.
|
|
[n + 1, 2n): new parent nodes are added here, starting from
|
|
(n+1). These are naturally in ascending order.
|
|
[2n]: we add a sentinel at the end as well.
|
|
There will be (2n+1) elements at the end. */
|
|
tree[n] = sentinel
|
|
|
|
tree[n+1] = sentinel
|
|
|
|
i = 0 /* Points to the next leaf node. */
|
|
j = n + 1 /* Points to the next non-leaf node. */
|
|
for k = n - 1; k != 0; k-- {
|
|
var left uint
|
|
var right uint
|
|
if tree[i].total_count_ <= tree[j].total_count_ {
|
|
left = i
|
|
i++
|
|
} else {
|
|
left = j
|
|
j++
|
|
}
|
|
|
|
if tree[i].total_count_ <= tree[j].total_count_ {
|
|
right = i
|
|
i++
|
|
} else {
|
|
right = j
|
|
j++
|
|
}
|
|
{
|
|
/* The sentinel node becomes the parent node. */
|
|
var j_end uint = 2*n - k
|
|
tree[j_end].total_count_ = tree[left].total_count_ + tree[right].total_count_
|
|
tree[j_end].index_left_ = int16(left)
|
|
tree[j_end].index_right_or_value_ = int16(right)
|
|
|
|
/* Add back the last sentinel node. */
|
|
tree[j_end+1] = sentinel
|
|
}
|
|
}
|
|
|
|
if setDepth(int(2*n-1), tree[0:], depth, tree_limit) {
|
|
/* We need to pack the Huffman tree in tree_limit bits. If this was not
|
|
successful, add fake entities to the lowest values and retry. */
|
|
break
|
|
}
|
|
}
|
|
}
|
|
|
|
func reverse(v []byte, start uint, end uint) {
|
|
end--
|
|
for start < end {
|
|
var tmp byte = v[start]
|
|
v[start] = v[end]
|
|
v[end] = tmp
|
|
start++
|
|
end--
|
|
}
|
|
}
|
|
|
|
func writeHuffmanTreeRepetitions(previous_value byte, value byte, repetitions uint, tree_size *uint, tree []byte, extra_bits_data []byte) {
|
|
assert(repetitions > 0)
|
|
if previous_value != value {
|
|
tree[*tree_size] = value
|
|
extra_bits_data[*tree_size] = 0
|
|
(*tree_size)++
|
|
repetitions--
|
|
}
|
|
|
|
if repetitions == 7 {
|
|
tree[*tree_size] = value
|
|
extra_bits_data[*tree_size] = 0
|
|
(*tree_size)++
|
|
repetitions--
|
|
}
|
|
|
|
if repetitions < 3 {
|
|
var i uint
|
|
for i = 0; i < repetitions; i++ {
|
|
tree[*tree_size] = value
|
|
extra_bits_data[*tree_size] = 0
|
|
(*tree_size)++
|
|
}
|
|
} else {
|
|
var start uint = *tree_size
|
|
repetitions -= 3
|
|
for {
|
|
tree[*tree_size] = repeatPreviousCodeLength
|
|
extra_bits_data[*tree_size] = byte(repetitions & 0x3)
|
|
(*tree_size)++
|
|
repetitions >>= 2
|
|
if repetitions == 0 {
|
|
break
|
|
}
|
|
|
|
repetitions--
|
|
}
|
|
|
|
reverse(tree, start, *tree_size)
|
|
reverse(extra_bits_data, start, *tree_size)
|
|
}
|
|
}
|
|
|
|
func writeHuffmanTreeRepetitionsZeros(repetitions uint, tree_size *uint, tree []byte, extra_bits_data []byte) {
|
|
if repetitions == 11 {
|
|
tree[*tree_size] = 0
|
|
extra_bits_data[*tree_size] = 0
|
|
(*tree_size)++
|
|
repetitions--
|
|
}
|
|
|
|
if repetitions < 3 {
|
|
var i uint
|
|
for i = 0; i < repetitions; i++ {
|
|
tree[*tree_size] = 0
|
|
extra_bits_data[*tree_size] = 0
|
|
(*tree_size)++
|
|
}
|
|
} else {
|
|
var start uint = *tree_size
|
|
repetitions -= 3
|
|
for {
|
|
tree[*tree_size] = repeatZeroCodeLength
|
|
extra_bits_data[*tree_size] = byte(repetitions & 0x7)
|
|
(*tree_size)++
|
|
repetitions >>= 3
|
|
if repetitions == 0 {
|
|
break
|
|
}
|
|
|
|
repetitions--
|
|
}
|
|
|
|
reverse(tree, start, *tree_size)
|
|
reverse(extra_bits_data, start, *tree_size)
|
|
}
|
|
}
|
|
|
|
/* Change the population counts in a way that the consequent
|
|
Huffman tree compression, especially its RLE-part will be more
|
|
likely to compress this data more efficiently.
|
|
|
|
length contains the size of the histogram.
|
|
counts contains the population counts.
|
|
good_for_rle is a buffer of at least length size */
|
|
func optimizeHuffmanCountsForRLE(length uint, counts []uint32, good_for_rle []byte) {
|
|
var nonzero_count uint = 0
|
|
var stride uint
|
|
var limit uint
|
|
var sum uint
|
|
var streak_limit uint = 1240
|
|
var i uint
|
|
/* Let's make the Huffman code more compatible with RLE encoding. */
|
|
for i = 0; i < length; i++ {
|
|
if counts[i] != 0 {
|
|
nonzero_count++
|
|
}
|
|
}
|
|
|
|
if nonzero_count < 16 {
|
|
return
|
|
}
|
|
|
|
for length != 0 && counts[length-1] == 0 {
|
|
length--
|
|
}
|
|
|
|
if length == 0 {
|
|
return /* All zeros. */
|
|
}
|
|
|
|
/* Now counts[0..length - 1] does not have trailing zeros. */
|
|
{
|
|
var nonzeros uint = 0
|
|
var smallest_nonzero uint32 = 1 << 30
|
|
for i = 0; i < length; i++ {
|
|
if counts[i] != 0 {
|
|
nonzeros++
|
|
if smallest_nonzero > counts[i] {
|
|
smallest_nonzero = counts[i]
|
|
}
|
|
}
|
|
}
|
|
|
|
if nonzeros < 5 {
|
|
/* Small histogram will model it well. */
|
|
return
|
|
}
|
|
|
|
if smallest_nonzero < 4 {
|
|
var zeros uint = length - nonzeros
|
|
if zeros < 6 {
|
|
for i = 1; i < length-1; i++ {
|
|
if counts[i-1] != 0 && counts[i] == 0 && counts[i+1] != 0 {
|
|
counts[i] = 1
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
if nonzeros < 28 {
|
|
return
|
|
}
|
|
}
|
|
|
|
/* 2) Let's mark all population counts that already can be encoded
|
|
with an RLE code. */
|
|
for i := 0; i < int(length); i++ {
|
|
good_for_rle[i] = 0
|
|
}
|
|
{
|
|
var symbol uint32 = counts[0]
|
|
/* Let's not spoil any of the existing good RLE codes.
|
|
Mark any seq of 0's that is longer as 5 as a good_for_rle.
|
|
Mark any seq of non-0's that is longer as 7 as a good_for_rle. */
|
|
|
|
var step uint = 0
|
|
for i = 0; i <= length; i++ {
|
|
if i == length || counts[i] != symbol {
|
|
if (symbol == 0 && step >= 5) || (symbol != 0 && step >= 7) {
|
|
var k uint
|
|
for k = 0; k < step; k++ {
|
|
good_for_rle[i-k-1] = 1
|
|
}
|
|
}
|
|
|
|
step = 1
|
|
if i != length {
|
|
symbol = counts[i]
|
|
}
|
|
} else {
|
|
step++
|
|
}
|
|
}
|
|
}
|
|
|
|
/* 3) Let's replace those population counts that lead to more RLE codes.
|
|
Math here is in 24.8 fixed point representation. */
|
|
stride = 0
|
|
|
|
limit = uint(256*(counts[0]+counts[1]+counts[2])/3 + 420)
|
|
sum = 0
|
|
for i = 0; i <= length; i++ {
|
|
if i == length || good_for_rle[i] != 0 || (i != 0 && good_for_rle[i-1] != 0) || (256*counts[i]-uint32(limit)+uint32(streak_limit)) >= uint32(2*streak_limit) {
|
|
if stride >= 4 || (stride >= 3 && sum == 0) {
|
|
var k uint
|
|
var count uint = (sum + stride/2) / stride
|
|
/* The stride must end, collapse what we have, if we have enough (4). */
|
|
if count == 0 {
|
|
count = 1
|
|
}
|
|
|
|
if sum == 0 {
|
|
/* Don't make an all zeros stride to be upgraded to ones. */
|
|
count = 0
|
|
}
|
|
|
|
for k = 0; k < stride; k++ {
|
|
/* We don't want to change value at counts[i],
|
|
that is already belonging to the next stride. Thus - 1. */
|
|
counts[i-k-1] = uint32(count)
|
|
}
|
|
}
|
|
|
|
stride = 0
|
|
sum = 0
|
|
if i < length-2 {
|
|
/* All interesting strides have a count of at least 4, */
|
|
/* at least when non-zeros. */
|
|
limit = uint(256*(counts[i]+counts[i+1]+counts[i+2])/3 + 420)
|
|
} else if i < length {
|
|
limit = uint(256 * counts[i])
|
|
} else {
|
|
limit = 0
|
|
}
|
|
}
|
|
|
|
stride++
|
|
if i != length {
|
|
sum += uint(counts[i])
|
|
if stride >= 4 {
|
|
limit = (256*sum + stride/2) / stride
|
|
}
|
|
|
|
if stride == 4 {
|
|
limit += 120
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
func decideOverRLEUse(depth []byte, length uint, use_rle_for_non_zero *bool, use_rle_for_zero *bool) {
|
|
var total_reps_zero uint = 0
|
|
var total_reps_non_zero uint = 0
|
|
var count_reps_zero uint = 1
|
|
var count_reps_non_zero uint = 1
|
|
var i uint
|
|
for i = 0; i < length; {
|
|
var value byte = depth[i]
|
|
var reps uint = 1
|
|
var k uint
|
|
for k = i + 1; k < length && depth[k] == value; k++ {
|
|
reps++
|
|
}
|
|
|
|
if reps >= 3 && value == 0 {
|
|
total_reps_zero += reps
|
|
count_reps_zero++
|
|
}
|
|
|
|
if reps >= 4 && value != 0 {
|
|
total_reps_non_zero += reps
|
|
count_reps_non_zero++
|
|
}
|
|
|
|
i += reps
|
|
}
|
|
|
|
*use_rle_for_non_zero = total_reps_non_zero > count_reps_non_zero*2
|
|
*use_rle_for_zero = total_reps_zero > count_reps_zero*2
|
|
}
|
|
|
|
/* Write a Huffman tree from bit depths into the bit-stream representation
|
|
of a Huffman tree. The generated Huffman tree is to be compressed once
|
|
more using a Huffman tree */
|
|
func writeHuffmanTree(depth []byte, length uint, tree_size *uint, tree []byte, extra_bits_data []byte) {
|
|
var previous_value byte = initialRepeatedCodeLength
|
|
var i uint
|
|
var use_rle_for_non_zero bool = false
|
|
var use_rle_for_zero bool = false
|
|
var new_length uint = length
|
|
/* Throw away trailing zeros. */
|
|
for i = 0; i < length; i++ {
|
|
if depth[length-i-1] == 0 {
|
|
new_length--
|
|
} else {
|
|
break
|
|
}
|
|
}
|
|
|
|
/* First gather statistics on if it is a good idea to do RLE. */
|
|
if length > 50 {
|
|
/* Find RLE coding for longer codes.
|
|
Shorter codes seem not to benefit from RLE. */
|
|
decideOverRLEUse(depth, new_length, &use_rle_for_non_zero, &use_rle_for_zero)
|
|
}
|
|
|
|
/* Actual RLE coding. */
|
|
for i = 0; i < new_length; {
|
|
var value byte = depth[i]
|
|
var reps uint = 1
|
|
if (value != 0 && use_rle_for_non_zero) || (value == 0 && use_rle_for_zero) {
|
|
var k uint
|
|
for k = i + 1; k < new_length && depth[k] == value; k++ {
|
|
reps++
|
|
}
|
|
}
|
|
|
|
if value == 0 {
|
|
writeHuffmanTreeRepetitionsZeros(reps, tree_size, tree, extra_bits_data)
|
|
} else {
|
|
writeHuffmanTreeRepetitions(previous_value, value, reps, tree_size, tree, extra_bits_data)
|
|
previous_value = value
|
|
}
|
|
|
|
i += reps
|
|
}
|
|
}
|
|
|
|
var reverseBits_kLut = [16]uint{
|
|
0x00,
|
|
0x08,
|
|
0x04,
|
|
0x0C,
|
|
0x02,
|
|
0x0A,
|
|
0x06,
|
|
0x0E,
|
|
0x01,
|
|
0x09,
|
|
0x05,
|
|
0x0D,
|
|
0x03,
|
|
0x0B,
|
|
0x07,
|
|
0x0F,
|
|
}
|
|
|
|
func reverseBits(num_bits uint, bits uint16) uint16 {
|
|
var retval uint = reverseBits_kLut[bits&0x0F]
|
|
var i uint
|
|
for i = 4; i < num_bits; i += 4 {
|
|
retval <<= 4
|
|
bits = uint16(bits >> 4)
|
|
retval |= reverseBits_kLut[bits&0x0F]
|
|
}
|
|
|
|
retval >>= ((0 - num_bits) & 0x03)
|
|
return uint16(retval)
|
|
}
|
|
|
|
/* 0..15 are values for bits */
|
|
const maxHuffmanBits = 16
|
|
|
|
/* Get the actual bit values for a tree of bit depths. */
|
|
func convertBitDepthsToSymbols(depth []byte, len uint, bits []uint16) {
|
|
var bl_count = [maxHuffmanBits]uint16{0}
|
|
var next_code [maxHuffmanBits]uint16
|
|
var i uint
|
|
/* In Brotli, all bit depths are [1..15]
|
|
0 bit depth means that the symbol does not exist. */
|
|
|
|
var code int = 0
|
|
for i = 0; i < len; i++ {
|
|
bl_count[depth[i]]++
|
|
}
|
|
|
|
bl_count[0] = 0
|
|
next_code[0] = 0
|
|
for i = 1; i < maxHuffmanBits; i++ {
|
|
code = (code + int(bl_count[i-1])) << 1
|
|
next_code[i] = uint16(code)
|
|
}
|
|
|
|
for i = 0; i < len; i++ {
|
|
if depth[i] != 0 {
|
|
bits[i] = reverseBits(uint(depth[i]), next_code[depth[i]])
|
|
next_code[depth[i]]++
|
|
}
|
|
}
|
|
}
|