mayx/blog
1
0
Fork 0
mirror of https://github.com/Mabbs/mabbs.github.io synced 2026-10-14 20:55:20 +02:00
Code Issues Packages Projects Releases Wiki Activity
Files
7f6baa0c8d5d51a20bfec7867a88f381bf99ab6a
blog/_posts/2026-10-01-quine.md
T

1914 lines
80 KiB
Markdown
Raw Normal View History

Update 5 files
2026-09-30 16:01:13 +00:00
---
layout: post
title: 如何制作一个“完整”的博客压缩包？
tags: [压缩包, Quine, LZMA2, 7-Zip]
---
让AI做出真正的“完整”将不再是难事……<!--more-->
# 起因
在上次[用AI制作了各种东西](/2026/09/01/vibe-coding2.html)之后，我已经完全理解了AI确实是无所不能的。既然如此，那就让它帮我解决曾经未能解决的事情吧？
去年，我为了让下载全站压缩包的按钮不断链，让这个压缩包也包含它本身而研究了[ZIP Quine](/2025/09/01/quine.html)，但限于DEFLATE的回溯窗口大小没能做到……但那是人做的东西，人还是太弱小了，现在换AI来试试，也许一切将变得不一样？
# 制作基于LZMA2的博客压缩包
首先，我把[Ruben Van Mello](https://github.com/ruvmello)写的那篇论文《[A Generator for Recursive Zip Files](https://www.mdpi.com/2076-3417/14/21/9797)》以及生成器[zip-quine-generator](https://github.com/ruvmello/zip-quine-generator)发给了AI，问它这个限制是不是真的，是不是真的无法创造出超过32KiB的ZIP Quine？没过多久它分析完了，告诉我确实存在这样的问题，但并不是无法解决的，最简单的办法就是换个回溯窗口更大的压缩算法。我看了一下它给我列出的几个算法，看起来LZMA2和zstd比较符合要求。不过zstd感觉不是很知名，一般的解压软件应该处理不了，所以我就选择让它基于LZMA2来制作了。
不过LZMA2只是算法，还得选个容器，考虑到7z算是最出名，而且LZMA2本来就是7-Zip的作者开发的，所以就直接让它做7z版本的了。另外因为[Nate Choe](https://github.com/NateChoe1)做了通过扩展欧几里得求逆元来计算CRC32的[PR](https://github.com/ruvmello/zip-quine-generator/pull/3)，所以我也告诉它要用这种方法在多项式时间内解出CRC32的值，而不是爆破。就这样AI花了两个小时左右成功把代码写出来了，效果非常完美，另外它还偷懒地将计算CRC32的算法改成了高斯消元法😆，因为7z格式有3个相互影响的CRC32值，用扩展欧几里得会更复杂一点，不过对7z来说不写入文件的CRC32也不影响，只是会不校验罢了，至少它还是按我要求做了……但其实这个问题也并不是完全不能用扩展欧几里得算法，后来我又强烈要求了一下，AI还是给我写出来了。以下是代码片段，有兴趣的人可以参考一下（不过这个看起来写的就是很复杂，所以我自己只用了高斯消元法）：
```python
RU_POLY = 0x104C11DB7 # x^32+x^26+x^23+...+1（含 x^32 首项）
def _find_probe(p):
ret = 0
for i in range(64):
if (1 << i) & p:
ret = i
return ret
def _mul_raw(p1, p2):
"""多项式乘法（无模）。"""
ret = 0
probe = _find_probe(p1)
for i in range(64):
if p2 & (1 << i):
assert probe + i < 64, "多项式乘法溢出"
ret ^= p1 << i
return ret
def _poly_divmod(dividend, divisor):
"""多项式长除法，返回 (商, 余)。"""
probe = _find_probe(divisor)
probe_bit = 1 << probe
quot = 0
rem = dividend
for i in range(63 - probe, -1, -1):
if rem & (probe_bit << i):
quot |= 1 << i
rem ^= divisor << i
return quot, rem
def _mul(p1, p2, mod):
"""p1*p2 % mod。"""
return _poly_divmod(_mul_raw(p1, p2), mod)[1]
def _xgcd(p1, p2):
"""扩展欧几里得：返回 (k1,k2,gcd)，满足 p1*k1+p2*k2=gcd。"""
if _find_probe(p1) < _find_probe(p2):
k1, k2, g = _xgcd(p2, p1)
return k2, k1, g
if p2 == 0:
return p1, 0, p1
q, r = _poly_divmod(p1, p2)
c1, c2, g = _xgcd(p2, r)
return c2, c1 ^ _mul_raw(c2, q), g
def _minv(p, mod):
"""p 在环上的乘法逆元；不存在时返回 0。"""
k1, _, g = _xgcd(p, mod)
if g != 1:
return 0
return _poly_divmod(k1, mod)[1]
def _bitrev32(x):
"""32 位比特反转。CRC 的输出循环冗余（binascii 反射）↔ 本环（非反射）互为 bit-reverse。"""
return int(format(x & 0xFFFFFFFF, "032b")[::-1], 2)
def _ring_pow_x(k):
"""环内 x^k mod P（P=RU_POLY），二进制幂 O(log k)。变量列就是单个环幂。"""
res, base, mod, e = 1, 2, RU_POLY, k
while e:
if e & 1:
res = _mul(res, base, mod)
base = _mul(base, base, mod)
e >>= 1
return res
def solve_crc_system(files):
"""解析解 CRC 定点系统
- 常数列 ：对“变量清零”的数据做一次 binascii.crc32，再 bit-reverse 32 位
（binascii 反射 CRC 与本环非反射 CRC 互为 bit-reverse，见 _bitrev32）。
- 变量列 ：单个环幂 x^(32 + 8*(len-pos-4))，二进制幂 O(log N) 一步算出；
同一变量出现在多处时按位异或累加。
各自 O(#rows) 次 CRC + 少量环幂即可建出整矩阵，Gauss 消元不变。
"""
n = len(files)
mod = RU_POLY
matrix = [[0] * (n + 1) for _ in range(n)]
for file in range(n):
data = files[file][0]
offsets = files[file][1]
N = len(data)
zero = bytearray(data)
for pos in offsets: # 未知量占 4 字节，清零后求常数项
zero[pos:pos + 4] = b"\x00\x00\x00\x00"
matrix[file][n] = _bitrev32(crc32(bytes(zero)))
for pos, vid in offsets.items():
rest = N - pos - 4 # 变量被 4 字节（x^32）+ 其后字节（x^8/个）推到底
matrix[file][vid] ^= _ring_pow_x(32 + 8 * rest)
matrix[file][file] ^= 1
for sr in range(n):
ex = sr
while ex < n and matrix[ex][sr] == 0:
ex += 1
if ex == n:
raise RuntimeError("CRC 多项式系统奇异 @row%d" % sr)
if ex != sr:
matrix[sr], matrix[ex] = matrix[ex], matrix[sr]
inv = _minv(matrix[sr][sr], mod)
for er in range(sr + 1, n):
mult = _mul(inv, matrix[er][sr], mod)
for ec in range(n + 1):
matrix[er][ec] ^= _mul(mult, matrix[sr][ec], mod)
res = [0] * n
for row in range(n - 1, -1, -1):
v = matrix[row][n]
for col in range(row + 1, n):
v ^= _mul(res[col], matrix[row][col], mod)
res[row] = _mul(v, _minv(matrix[row][row], mod), mod)
return [_impl_val(res[i]) for i in range(n)]
def _impl_val(math_val):
imp = 0
for bit in range(32):
if math_val & (1 << bit):
imp |= 1 << (31 - bit)
return imp
class BlogQuine:
def solve_crc(self, F, lay, seed):
T, h, d = lay["T"], lay["h"], lay["d"]
k, n_sub, crc_base = lay["k"], lay["n_sub"], lay["crc_base"]
hoff = lay["total"] - h
vout_f = lay["total"] - 2 * T
hcopy = vout_f + (T - h)
# 已知 CRC：seed + 各文件（quine 的是未知量 D）
known = [crc32(seed)] + [crc32(data) for _, data in self.files]
assert len(known) == n_sub - 1
for i, e in enumerate(known):
struct.pack_into("<I", F, hoff + crc_base + 4 * i, e)
struct.pack_into("<I", F, hcopy + crc_base + 4 * i, e)
# 未知量 D N S：D=整个文件、N=头部 F[hoff:hoff+h]、S=签名 F[12:32]
dpos = crc_base + 4 * (n_sub - 1)
ncopy = 32 + 3 * k + d # C1k 载荷里 F[0:35) 副本起点
groups = [[hoff + dpos, hcopy + dpos], # D 出现两处
[28, ncopy + 28], # N
[8, ncopy + 8]] # S
# 构造 3 个“文件”喂给解析解系统：
# 文件0 = 整个 F，三个未知量各自出现在 groups 两处
# 文件1 = 头部块，只有 D 出现在相对 dpos（另一处 hcopy+dpos 在头外）
# 文件2 = 签名块，只有 N 出现在相对 16（28-12）
whole = bytes(F)
hdr = bytes(F[hoff:hoff + h])
sig = bytes(F[12:32])
f0_off = {hoff + dpos: 0, hcopy + dpos: 0,
28: 1, ncopy + 28: 1,
8: 2, ncopy + 8: 2}
f1_off = {dpos: 0}
f2_off = {28 - 12: 1}
vals = solve_crc_system([(whole, f0_off), (hdr, f1_off), (sig, f2_off)])
for g in range(3):
for p in groups[g]:
F[p:p + 4] = struct.pack("<I", vals[g])
real = (crc32(F), crc32(F[hoff:hoff + h]), crc32(F[12:32]))
assert real == tuple(vals), "CRC 定点失败: %s != %s" % (real, vals)
return vals
```
于是我第一时间就把原来的TGZ压缩命令换掉，换成了AI给我写的[blogquine.py](https://github.com/Mabbs/mabbs.github.io/blob/master/_tools/blogquine.py)，现在就可以通过[这里](https://mayx.eu.org/MayxBlog.7z)下载到“完整”包含我博客所有内容的压缩包了。
不过唯一的问题就是这样做出来的压缩包并没有压缩😂，相当于给做成了普通的归档了。当然我的博客本身倒是不大，没压缩也多不了多少空间，但相比于能做出“完整”的效果来说，这点浪费的空间也是小问题了。
Update 2 files
2026-10-07 06:16:54 +00:00
## 基于博客压缩包的离线模式（2026.10.03更新）
既然现在的AI什么都能做了，那上次我因为技术不足仅仅只是用[Service Worker做了个代理](/2025/08/01/sw-proxy.html)，现在也可以升级成下载全站之后让全站离线的功能吧？不过之前做的压缩包并没有压缩，导致整个压缩包有接近20MiB的大小，后来我仔细想了一下我的需求，我只是想让下载全站压缩包的按钮不断链，并没有说解压后的文件一定就必须是它本身，所以我只要用相同的文件名再压缩一次应该就可以了，这样既能满足我的想法，又可以让其他人下载的时候不会花费太多的时间。
在我直接压缩之后，整个压缩包占用大概9MiB的空间，其实我已经很满意了，不过压之前是18MiB，也就是说压缩率是50%……虽然也不是不能接受，但我在想这是不是合理的结果呢？我让AI分析了一下50%的压缩率正不正常，它看了一眼之后告诉我文本部分的10MiB压缩完之后有1MiB，图片因为压不动所以直接占用了8MiB😅，这时候我才意识到原来我一直存着一堆占着大量空间的垃圾，而且还是用的PNG/JPG这种压缩率很低的格式……考虑到兼容性和压缩效果，我把占用比较大的图片转换为了WebP格式，最终整个压缩包就只占用了4MiB的空间，一口气又节省了50%的空间。
最后做完对压缩包的处理之后，我直接让AI给我基于那个压缩包制作离线模式，它轻轻松松就[做出来](https://mayx.eu.org/offline.html)了，最终使用的是[libarchive库](https://github.com/nika-begiashvili/libarchivejs)，在启用离线模式之后整个网站的速度简直就是0延迟，点击就能瞬间跳转，效果算是相当不错了。
## 受大佬启发制作了能压缩的7z Quine（2026.10.05更新）
做完博客压缩包之后我突发奇想，虽然东西是AI做的，但也是在我的手中做出了世界上第一个LZMA2 Quine，那么如果我向全世界第一个制作ZIP Quine的人展示一下我的作品会怎么样呢？目前可以知道，世界上第一个制作ZIP Quine的人是[Erling Ellingsen](https://infosec.exchange/@steike)，所以我向他发了个帖子[展示了一下](https://infosec.exchange/@mayx/117385801954308033)。大佬不愧是大佬，一眼就能看出理论上能压缩的7z Quine是可以做出来的，对我来说，我是完全不知道7-Zip居然还支持链式编解码，能可以通过“double lzma2”的方式做到既可以自包含、又能真正被压缩的压缩包。既然大佬说理论上可以，那我自然是相信的，于是就让AI给我使劲折腾想办法把它做出来。在折腾了一晚上之后AI终于做出来了，真是强得可怕：
```python
# ============================================================
# 双层 LZMA2 的 7z quine（"double lzma2"）
#
# F = S32 ‖ P ‖ H，folder 的 coder 链 = [LZMA2(外层), LZMA2(内层)]，
# 解码管线：P --外层--> I --内层--> U。
#
# I = Cseg ‖ LBseg ‖ Mseg ‖ 0x00
# Cseg : 真实 LZMA2 对 content 的压缩流（内容真正的压缩数据 C）
# LBseg: 内层 store 链，载荷 = seed（seed 内含一份 C 的参照副本）
# Mseg : 内层 match 链，输出 quine 区（= F 的逐字节副本）
# U = content ‖ seed ‖ F
#
# 外层把 I 再压一遍：Cseg 用 store 链原样承载；seed 里的 C 参照副本与 Cseg
# 逐字节相同，被外层的 rep0 回溯匹配消掉，于是成品里只剩一份压缩数据。
# quine 区回引 P 的字节时，源全在 seed 里（C 副本 / 头样本表 / 机械串）。
# ============================================================
def lzma_chunk(tokens, out_pos):
"""编一个 LZMA chunk（0xC0: state+props reset，无 dict reset，无 end marker）。
match token 的编码字节只取决于 (dist,len,pos)，与历史内容无关（不维护输出历史）。
解压长度用 21 位（控制字节低 5 位存高 5 位），故单 chunk 输出上限 2 MiB。"""
enc = LzmaEncoder()
enc.pos = out_pos
total = 0
for t in tokens:
if t[0] == "m":
enc.match(t[1], t[2])
total += t[2]
else:
enc.rep_match(t[1])
total += t[1]
data = enc.finish()
u = total - 1
c = len(data) - 1
assert 0 <= u < (1 << 21) and 0 <= c < 65536, (total, len(data))
hdr = bytes([0xC0 | ((u >> 16) & 0x1F)]) + (u & 0xFFFF).to_bytes(2, "big") + \
(c & 0xFFFF).to_bytes(2, "big") + bytes([PROPS_BYTE])
return hdr + data, total
LZ2_MAXU = 1 << 21 # LZMA2 单个 LZMA chunk 的解压上限
def lzma2_raw(data, dict_size, preset=6):
"""FORMAT_RAW + LZMA2 压缩；去掉结尾 0x00 便于后面继续追加 chunk。"""
c = lzma.LZMACompressor(
format=lzma.FORMAT_RAW,
filters=[{"id": lzma.FILTER_LZMA2, "dict_size": dict_size, "preset": preset}])
out = c.compress(data) + c.flush()
assert out and out[-1] == 0x00, "LZMA2 流应以 0x00 结尾"
return out[:-1]
def split_len(n, cap):
"""按 cap 上限切分 n 字节。末片若为 1 字节则从前一片借 1（match 最短为 2）。"""
Ls, rem = [], n
while rem > cap:
Ls.append(cap)
rem -= cap
if rem == 1 and Ls:
Ls[-1] -= 1
rem = 2
Ls.append(rem)
return Ls
class DoubleQuine(BlogQuine):
"""双层 LZMA2 版本：folder = [LZMA2(外层), LZMA2(内层)]。"""
def entries(self):
"""U 的顺序是 内容文件… ‖ seed ‖ quine，条目顺序必须与之对应。"""
ent = [{"name": r, "dir": isd} for r, isd in self.walk_entries]
ent.append({"name": self.seed_name, "dir": False})
ent.append({"name": self.quine_name, "dir": False})
return ent
# ---------- 头部：双 coder + BindPair ----------
def build_header(self, n, total, d, entries, sub_sizes, size_I):
n_sub = len(sub_sizes)
N = len(entries)
h = bytearray()
h += b"\x01\x04" # kHeader, kMainStreamsInfo
h += b"\x06" + varint(0) + varint(1) + b"\x09" + varint(n) + b"\x00"
h += b"\x07" # kUnpackInfo
h += b"\x0b" + varint(1) + b"\x00" # kFolder: 1, local
h += varint(2) # NumCoders = 2
h += b"\x21\x21" + varint(1) + bytes([self.dict_prop_out]) # coder0 = 外层
h += b"\x21\x21" + varint(1) + bytes([self.dict_prop_in]) # coder1 = 内层
h += varint(1) + varint(0) # BindPair = (InIndex=1, OutIndex=0)
h += b"\x0c" + varint(size_I) + varint(d + total) # 两个 coder 的输出大小
h += b"\x00"
h += b"\x08" # SubStreamsInfo
h += b"\x0d" + varint(n_sub)
h += b"\x09" + b"".join(varint(s) for s in sub_sizes[:-1])
h += b"\x0a" + b"\x01"
crc_base = len(h)
h += b"\x00" * (4 * n_sub)
h += b"\x00\x00"
h += b"\x05" + varint(N) # FilesInfo
bits = bytearray((N + 7) // 8)
for i, e in enumerate(entries):
if e["dir"]:
bits[i // 8] |= 1 << (7 - i % 8)
h += b"\x0e" + varint(len(bits)) + bytes(bits)
mt = b"\x01\x00" + struct.pack("<Q", self.mtime_ft) * N
h += b"\x14" + varint(len(mt)) + mt
names = bytearray(b"\x00")
for e in entries:
names += e["name"].encode("utf-16-le") + b"\x00\x00"
h += b"\x11" + varint(len(names)) + bytes(names)
attrs = b"".join(struct.pack("<I", 0x10 if e["dir"] else 0x20)
for e in entries)
h += b"\x15" + varint(2 + len(attrs)) + b"\x01\x00" + attrs
h += b"\x00\x00"
return bytes(h), crc_base
# ---------- 布局：三层坐标的不动点迭代 ----------
def layout(self):
content = self.content
dc = len(content)
dict_c = max(1 << 20, 1 << max(20, dc.bit_length()))
C = lzma2_raw(content, dict_c)
self._C = C # 内容压缩只做一次，finalize 复用
lc = len(C)
# 迭代变量：Y = plant 长度, H = 头部长度, M = Mseg 字节数
Y, H, M = 96, 256, 256
seen = set()
for _ in range(200):
ds = lc + 32 + H + Y # seed = C副本‖S32‖H‖plant
d = dc + ds
Ls_LB = split_len(ds, CHUNK)
lLB = 3 * len(Ls_LB) + ds
off_in = lc + lLB
M1 = M + 1 # Mseg ‖ 内层流终止符 0x00
Ls_M = split_len(M1, CHUNK)
Li = off_in + M1
self.dict_prop_out, _ = dict_prop_for(max(Li, 1 << 20))
self.dict_prop_in, _ = dict_prop_for(d + 32 + H + 8192)
st = self._build_streams(C, lc, ds, d, Ls_LB, lLB, off_in, Ls_M,
M1, H)
if st is None:
Y += 8
continue
mseg = self._build_mseg(st, d, lc, ds, H, M)
if mseg is None:
Y += 8
continue
M_new = len(mseg)
Y_new = len(st["hdr_bytes"]) + len(st["mech"]) + M_new + 2
total = st["n"] + 32 + H
file_sizes = [len(x) for _, x in self.files]
sub_sizes = file_sizes + [ds] + [total]
entries = self.entries()
hdr, crc_base = self.build_header(st["n"], total, d, entries,
sub_sizes, Li)
H_new = len(hdr)
key = (Y_new, H_new, M_new)
if key in seen: # 再次见到同一组值 = 已收敛
Y, H, M = Y_new, H_new, M_new
break
seen.add(key)
Y, H, M = Y_new, H_new, M_new
else:
raise RuntimeError("double layout 不收敛")
return {"Y": Y, "H": H, "M": M}
# ---------- 定稿：用收敛后的 (Y, H, M) 重算全部结构 ----------
def finalize(self, Y, H, M):
content = self.content
dc = len(content)
C = self._C # layout 里已压缩过
lc = len(C)
ds = lc + 32 + H + Y
d = dc + ds
Ls_LB = split_len(ds, CHUNK)
lLB = 3 * len(Ls_LB) + ds
off_in = lc + lLB
M1 = M + 1
Ls_M = split_len(M1, CHUNK)
Li = off_in + M1
self.dict_prop_out, ds_out = dict_prop_for(max(Li, 1 << 20))
self.dict_prop_in, ds_in = dict_prop_for(d + 32 + H + 8192)
st = self._build_streams(C, lc, ds, d, Ls_LB, lLB, off_in, Ls_M, M1, H)
mseg = self._build_mseg(st, d, lc, ds, H, M)
assert len(mseg) == M, "Mseg %d != %d" % (len(mseg), M)
total = st["n"] + 32 + H
file_sizes = [len(x) for _, x in self.files]
sub_sizes = file_sizes + [ds] + [total]
entries = self.entries()
hdr, crc_base = self.build_header(st["n"], total, d, entries,
sub_sizes, Li)
assert len(hdr) == H, "header %d != %d" % (len(hdr), H)
plant = st["hdr_bytes"] + bytes(st["mech"]) + mseg + b"\x00\x00"
assert len(plant) == Y, "plant %d != %d" % (len(plant), Y)
return {"C": C, "lc": lc, "dc": dc, "ds": ds, "d": d, "Li": Li,
"ochunks": st["ochunks"], "hdr_tab": st["hdr_tab"],
"mseg": mseg, "M": M, "H": H, "n": st["n"], "total": total,
"header": hdr, "crc_base": crc_base, "plant": plant,
"dict_size_in": ds_in, "dict_size_out": ds_out,
"n_sub": len(sub_sizes)}
# ---------- 外层流 P 的构造 ----------
def _build_streams(self, C, lc, ds, d, Ls_LB, lLB, off_in, Ls_M, M1, H):
hdr_tab, hdr_idx = [], {}
def hidx(b):
assert len(b) == 3 and b[0] in (0x01, 0x02), b.hex()
if b not in hdr_idx:
hdr_idx[b] = len(hdr_tab)
hdr_tab.append(b)
return hdr_idx[b]
ochunks = []
f, io = 32, 0
mech = bytearray()
def put_lzma(tokens, ioff):
blk, t = lzma_chunk(tokens, ioff)
ochunks.append({"kind": "lzma", "foff": f, "size": len(blk),
"ioff": ioff, "olen": t, "bytes": blk,
"mech": len(mech)})
mech.extend(blk)
return len(blk), t
def put_store(ioff, olen, hi, src):
ochunks.append({"kind": "store", "foff": f, "size": 3 + olen,
"ioff": ioff, "olen": olen, "hdr": hi, "src": src})
return 3 + olen
# 段 1：store 链承载 Cseg（I[0:lc] = C 的字节；源 = seed 的 C 参照副本）
Ls_C = split_len(lc, CHUNK)
c_off = 0
for L in Ls_C:
f += put_store(io, L, hidx(store_hdr(L, first=(c_off == 0))),
("seed", c_off))
io += L
c_off += L
assert io == lc
# 段 2：LBseg（I[lc : lc+lLB]）—— 内层 store 链，载荷 = seed
lb_off = lc
seed_off = 0
for j, L in enumerate(Ls_LB):
lb_hdr = store_hdr(L, first=False)
if j == 0 or L != CHUNK:
# 没有可回引的相同样本，用外层 store chunk 原样携带这 3 字节
f += put_store(lb_off, 3, hidx(store_hdr(3, first=False)),
("tab", hidx(lb_hdr)))
io += 3
else:
# 与 chunk0 的头逐字节相同，直接从 I[lc:lc+3] 复制
n, t = put_lzma([("m", lb_off - lc, 3)], lb_off)
assert t == 3
f += n
io += 3
# 载荷：C 参照副本区走 rep0 回溯匹配（去重发生处），其余走 store
pay = lb_off + 3
i = 0
while i < L:
if seed_off + i < lc:
a = min(L - i, lc - (seed_off + i), LZ2_MAXU)
dist = (pay + i) - (seed_off + i)
n, t = put_lzma(matches_for(dist, a), pay + i)
assert t == a
f += n
io += a
i += a
else:
a = min(L - i, LZ2_MAXU)
f += put_store(pay + i, a, hidx(store_hdr(a, first=False)),
("seed", seed_off + i))
io += a
i += a
lb_off += 3 + L
seed_off += L
assert lb_off == lc + lLB and io == lc + lLB
# 段 3：store 链承载 Mseg ‖ 0x00（0x00 是内层流的终止符）
m_off = 0
for L in Ls_M:
f += put_store(off_in + m_off, L,
hidx(store_hdr(L, first=False)), ("mseg", m_off))
io += L
m_off += L
# 外层流自己的终止符 0x00
ochunks.append({"kind": "term", "foff": f, "size": 1, "ioff": io,
"olen": 0, "bytes": b"\x00"})
f += 1
return {"ochunks": ochunks, "mech": mech, "hdr_tab": hdr_tab,
"hdr_bytes": b"".join(hdr_tab), "n": f - 32}
# ---------- 内层 Mseg：把 quine 区的每个 F 区段映射成 match 链 ----------
def _build_mseg(self, st, d, lc, ds, H, M):
dc = d - ds
s_S32 = dc + lc # seed 里 S32 副本
s_H = dc + lc + 32 # seed 里 H 副本
s_tab = dc + lc + 32 + H # seed 里头样本表
s_mech = s_tab + len(st["hdr_bytes"]) # seed 里机械串
s_mseg = s_mech + len(st["mech"]) # seed 里 Mseg 副本
total = st["n"] + 32 + H
hoff = 32 + st["n"]
segs = [(0, 32, s_S32)] # F[0:32) 签名头
for c in st["ochunks"]:
if c["kind"] == "store":
segs.append((c["foff"], 3, s_tab + 3 * c["hdr"]))
src = c["src"]
if src[0] == "seed":
u = dc + src[1]
elif src[0] == "tab":
u = s_tab + 3 * src[1]
else: # ("mseg", off)
u = s_mseg + src[1]
segs.append((c["foff"] + 3, c["olen"], u))
elif c["kind"] == "lzma":
segs.append((c["foff"], c["size"], s_mech + c["mech"]))
else: # term：并入前一个载荷段
pf, pl, pu = segs[-1]
segs[-1] = (pf, pl + 1, pu)
segs.append((hoff, H, s_H)) # F[hoff:] 头部
pos = 0
for (fo, ln, u) in segs:
assert fo == pos, "段不连续: %d != %d" % (fo, pos)
assert ln >= 2, "段太短 %d @%d" % (ln, fo)
pos += ln
assert pos == total, "段总长 %d != total %d" % (pos, total)
# 生成 match 链，按 2 MiB 上限切成多个 0xC0 chunk
mseg = bytearray()
out_pos = d
cur, cur_len = [], 0
chunks = []
for (fo, ln, u) in segs:
dist = (d + fo) - u
assert 0 < dist, "dist=%d @%d" % (dist, fo)
toks = matches_for(dist, ln)
if cur_len + ln > LZ2_MAXU - MAX_MATCH:
chunks.append((cur, out_pos, cur_len))
out_pos += cur_len
cur, cur_len = [], 0
cur.extend(toks)
cur_len += ln
if cur:
chunks.append((cur, out_pos, cur_len))
out_pos += cur_len
assert out_pos == d + total, "Mseg 输出 %d != %d" % (out_pos, d + total)
for (toks, op, cl) in chunks:
blk, t = lzma_chunk(toks, op)
assert t == cl
mseg.extend(blk)
return bytes(mseg)
# ---------- F 中承载 seed[soff] 的那个字节的偏移 ----------
def _f_off_of_seed(self, lay, soff):
"""返回 (F 偏移, 该 store 载荷内从 soff 起还剩多少字节)。
seed_head 超过 64 KiB 时会被切成多片，所以调用方要循环推进。"""
for c in lay["ochunks"]:
if c["kind"] == "store" and c["src"][0] == "seed":
s0 = c["src"][1]
if s0 <= soff < s0 + c["olen"]:
return c["foff"] + 3 + (soff - s0), s0 + c["olen"] - soff
raise RuntimeError("seed 偏移 %d 未被 store 载荷覆盖" % soff)
def _f_pos_list(self, lay, soff, length):
"""seed[soff:soff+length] 在 F 中对应的字节位置列表（可能跨多片）。"""
res = []
while length > 0:
p, avail = self._f_off_of_seed(lay, soff)
a = min(length, avail)
res.extend(range(p, p + a))
soff += a
length -= a
return res
# ---------- 装配 ----------
def assemble(self, lay):
total, H = lay["total"], lay["H"]
hoff = total - H
C, plant = lay["C"], lay["plant"]
F = bytearray(total)
# pass A: 签名头 + 所有非 store 载荷字节
F[0:6] = SIG
F[6:8] = VER
struct.pack_into("<Q", F, 12, lay["n"])
struct.pack_into("<Q", F, 20, H)
for c in lay["ochunks"]:
if c["kind"] == "store":
F[c["foff"]:c["foff"] + 3] = lay["hdr_tab"][c["hdr"]]
else:
F[c["foff"]:c["foff"] + c["size"]] = c["bytes"]
F[hoff:hoff + H] = lay["header"]
# pass B: seed = C副本 ‖ S32副本 ‖ H副本 ‖ plant
seed = bytearray()
seed += C
seed += bytes(F[0:32])
seed += bytes(F[hoff:hoff + H])
seed += plant
assert len(seed) == lay["ds"], "seed %d != %d" % (len(seed), lay["ds"])
# pass C: 各 store 的载荷
msegz = lay["mseg"] + b"\x00"
for c in lay["ochunks"]:
if c["kind"] != "store":
continue
p = c["foff"] + 3
src, L = c["src"], c["olen"]
if src[0] == "seed":
F[p:p + L] = seed[src[1]:src[1] + L]
elif src[0] == "tab":
F[p:p + 3] = lay["hdr_tab"][src[1]]
else:
F[p:p + L] = msegz[src[1]:src[1] + L]
return F, bytes(seed)
# ---------- CRC 定点（128x128 GF(2)：seed/quine/Next/Start 四个 CRC） ----------
def solve_crc(self, F, lay, C, plant):
H, hoff = lay["H"], lay["total"] - lay["H"]
n_sub, crc_base = lay["n_sub"], lay["crc_base"]
lc = lay["lc"]
# seed 里 S32 / H 副本在 F 中的镜像（可能跨多片，逐字节列出）
scopy0 = self._f_pos_list(lay, lc, 32)
Fv = memoryview(F)
def seed_crc():
v = crc32(C)
v = crc32(Fv[0:32], v) # S32 副本
v = crc32(Fv[hoff:hoff + H], v) # H 副本
return crc32(plant, v)
def targets():
return (seed_crc(), crc32(Fv), crc32(Fv[hoff:hoff + H]),
crc32(Fv[12:32]))
# 已知：各内容文件的 CRC，写本体 + seed 里 H 副本的镜像两处
known = [crc32(data) for _, data in self.files]
for i, e in enumerate(known):
slot = crc_base + 4 * i
b = struct.pack("<I", e)
for k in range(4):
F[hoff + slot + k] = b[k]
mir = self._f_pos_list(lay, lc + 32 + slot, 4)
assert len(mir) == 4
for k in range(4):
F[mir[k]] = b[k]
# 未知量：D_seed(seed 的 CRC)、D(quine 的 CRC)、N、S
# 每个未知量 4 字节，每字节有"本体 + seed 镜像"两个落点
qs = crc_base + 4 * len(known) # seed 子流的 CRC 槽
q = crc_base + 4 * (n_sub - 1) # quine 子流的 CRC 槽
groups = []
for slot in (qs, q):
groups.append([list(range(hoff + slot, hoff + slot + 4)),
self._f_pos_list(lay, lc + 32 + slot, 4)])
groups.append([list(range(28, 32)), scopy0[28:32]]) # NextHeaderCRC
groups.append([list(range(8, 12)), scopy0[8:12]]) # StartHeaderCRC
nb = 32 * len(groups)
base = targets()
basevec = 0
for i, v in enumerate(base):
basevec |= v << (32 * i)
cols = []
for g in range(len(groups)):
own, mir = groups[g]
for kb in range(32):
bi, bb = kb // 8, kb % 8
poss = [own[bi], mir[bi]]
for p in poss:
F[p] ^= (1 << bb)
t = targets()
for p in poss:
F[p] ^= (1 << bb)
delta = 0
for i, v in enumerate(t):
delta |= (v ^ base[i]) << (32 * i)
cols.append(delta)
eqs = []
for i in range(nb):
coeff = 0
for j in range(nb):
if (cols[j] >> i) & 1:
coeff |= 1 << j
coeff ^= 1 << i
eqs.append([coeff, (basevec >> i) & 1])
for j in range(nb):
p = next((i for i in range(j, nb) if (eqs[i][0] >> j) & 1), None)
if p is None:
raise RuntimeError("GF(2) 奇异 @bit%d" % j)
eqs[j], eqs[p] = eqs[p], eqs[j]
for i in range(nb):
if i != j and ((eqs[i][0] >> j) & 1):
eqs[i][0] ^= eqs[j][0]
eqs[i][1] ^= eqs[j][1]
x = 0
for i in range(nb):
assert eqs[i][0] == (1 << i)
if eqs[i][1]:
x |= 1 << i
vals = [(x >> (32 * i)) & 0xFFFFFFFF for i in range(len(groups))]
for g in range(len(groups)):
own, mir = groups[g]
b = struct.pack("<I", vals[g])
for k in range(4):
F[own[k]] = b[k]
F[mir[k]] = b[k]
t = targets()
assert t == tuple(vals), "CRC 定点失败: %s != %s" % (t, vals)
return vals
def build(self):
l0 = self.layout()
lay = self.finalize(l0["Y"], l0["H"], l0["M"])
F, seed = self.assemble(lay)
crcs = self.solve_crc(F, lay, lay["C"], lay["plant"])
# CRC 定点会改写 H / S32，而 seed 是它们的镜像，定点后需重建
hoff = lay["total"] - lay["H"]
seed = lay["C"] + bytes(F[0:32]) + bytes(F[hoff:hoff + lay["H"]]) \
+ lay["plant"]
return bytes(F), lay, seed, crcs
def verify_double(F, lay, files, seed, content):
"""串联两个原始 LZMA2 解码器还原 U，逐段校验。"""
d, total = lay["d"], lay["total"]
dec_o = lzma.LZMADecompressor(
format=lzma.FORMAT_RAW,
filters=[{"id": lzma.FILTER_LZMA2, "dict_size": lay["dict_size_out"]}])
I = dec_o.decompress(F[32:32 + lay["n"]])
ok = True
if len(I) != lay["Li"]:
print("[verify] 外层输出 %d != I %d" % (len(I), lay["Li"]))
return False
dec_i = lzma.LZMADecompressor(
format=lzma.FORMAT_RAW,
filters=[{"id": lzma.FILTER_LZMA2, "dict_size": lay["dict_size_in"]}])
U = dec_i.decompress(I)
exp = d + total
if len(U) != exp:
print("[verify] 内层输出 %d != %d" % (len(U), exp))
return False
dc, ds = lay["dc"], lay["ds"]
if U[:dc] != content:
print("[verify] 内容不匹配")
ok = False
pos = 0
for rel, data in files: # 内容文件排在 U 的最前面
if U[pos:pos + len(data)] != data:
print("[verify] 文件不匹配:", rel)
ok = False
pos += len(data)
assert pos == dc
if U[dc:dc + ds] != seed:
print("[verify] seed 不匹配")
ok = False
if U[d:] != F:
print("[verify] quine 自复制不匹配")
ok = False
print("[verify] 自解码: |I|=%d, |U|=%d, seed/文件/quine 全部匹配: %s"
% (len(I), len(U), ok))
return ok
```
仅仅是大佬的一句话，AI就能做出来，实在是太可怕了。最终我试了一下，官方的7-Zip可以正常解压，但是Windows资源管理器不行……Windows资源管理器使用的也是[libarchive](https://github.com/libarchive/libarchive)，似乎是因为libarchive只支持2个解码器，并且[第二个解码器只能是像BCJ这样的过滤器](https://github.com/libarchive/libarchive/blob/8bb3bbdc7b117a1e22086a2260f2087aafa90687/libarchive/archive_read_support_format_7zip.c#L1475)……明明7-Zip官方是可以解压的，不知道如果把这个问题当作BUG报告给libarchive他们会不会解决？但目前存在兼容性问题的话我博客压缩包就先不用这个方案了吧🥲……
Update 5 files
2026-09-30 16:01:13 +00:00
## 对TXZ格式的尝试
在做完7z格式的压缩包之后，我发现了一个问题，虽然7z确实很流行，但是在Linux下解压起来有点麻烦，7-Zip历史上主要面向Windows，Linux上长期以来更多依赖p7zip等第三方移植，因此生态集成度不如tar.xz，想要解压7z文件还得额外安装。
不过Linux下也有个支持LZMA2算法的压缩软件，那就是前些年出过[后门](https://tukaani.org/xz-backdoor/)的XZ Utils，配合tar就可以做出TXZ（tar.xz）文件，甚至用我[博客终端](https://mabbs.github.io/linux/)中的BusyBox也能解压。我想了一下反正有AI，干脆一句话让AI帮我把blogquine.py改成tar.xz格式的好了，结果倒也没费多少功夫，AI就这样写出来了：
<details markdown="1">
<summary markdown="span">
Show Code
</summary>
```python
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import argparse
import binascii
import io
import lzma
import os
import struct
import sys
import tarfile
import time
import ctypes
import ctypes.util
# ============================================================
# Part 1: 最小 LZMA1 range coder（只做编码）
# ============================================================
kNumBitModelTotalBits = 11
kBitModelTotal = 1 << kNumBitModelTotalBits # 2048
kNumMoveBits = 5
kTopValue = 1 << 24 # 0x1000000
kNumStates = 12
kNumLitStates = 7
kNumPosBitsMax = 4
kNumLenToPosStates = 4
kNumAlignBits = 4
kEndPosModelIndex = 14
kNumFullDistances = 1 << (kEndPosModelIndex >> 1) # 128
kMatchMinLen = 2
kNumLowLenBits = 3
kNumMidLenBits = 3
kNumHighLenBits = 8
kNumLowLenSymbols = 1 << kNumLowLenBits # 8
kNumMidLenSymbols = 1 << kNumMidLenBits # 8
kNumPosSlotBits = 6
PROB_INIT = kBitModelTotal >> 1 # 1024
class RangeEncoder:
"""LZMA 的区间编码器。low 是 64 位（要容纳进位），range 是 32 位。"""
def __init__(self):
self.low = 0
self.range = 0xFFFFFFFF
self.cache = 0
self.cache_size = 1 # 初值 1 -> 第一个输出字节恒为 0x00
self.buf = bytearray()
def _shift_low(self):
if (self.low & 0xFFFFFFFF) < 0xFF000000 or (self.low >> 32) != 0:
temp = self.cache
while True:
self.buf.append((temp + (self.low >> 32)) & 0xFF)
temp = 0xFF
self.cache_size -= 1
if self.cache_size == 0:
break
self.cache = (self.low >> 24) & 0xFF
self.cache_size += 1
self.low = ((self.low & 0xFFFFFFFF) << 8) & 0xFFFFFFFF
def encode_bit(self, probs, idx, bit):
p = probs[idx]
bound = (self.range >> kNumBitModelTotalBits) * p
if bit == 0:
self.range = bound
probs[idx] = p + ((kBitModelTotal - p) >> kNumMoveBits)
else:
self.low += bound
self.range -= bound
probs[idx] = p - (p >> kNumMoveBits)
while self.range < kTopValue:
self.range = (self.range << 8) & 0xFFFFFFFF
self._shift_low()
def encode_direct_bits(self, value, num_bits):
for i in range(num_bits - 1, -1, -1):
self.range >>= 1
if (value >> i) & 1:
self.low += self.range
while self.range < kTopValue:
self.range = (self.range << 8) & 0xFFFFFFFF
self._shift_low()
def bittree_encode(self, probs, off, num_bits, symbol):
m = 1
for i in range(num_bits - 1, -1, -1):
bit = (symbol >> i) & 1
self.encode_bit(probs, off + m, bit)
m = (m << 1) | bit
def bittree_reverse_encode(self, probs, off, num_bits, symbol):
m = 1
for i in range(num_bits):
bit = symbol & 1
symbol >>= 1
self.encode_bit(probs, off + m, bit)
m = (m << 1) | bit
def finish(self):
for _ in range(5):
self._shift_low()
return bytes(self.buf)
def _pos_slot_and_bits(d):
if d < 4:
return d, 0, 0
for slot in range(4, 64):
n = (slot >> 1) - 1
base = (2 | (slot & 1)) << n
if d < base + (1 << n):
return slot, n, d - base
raise ValueError("distance too large: %d" % d)
# pb 必须与写进每个 LZMA2 chunk 头的 PROPS_BYTE(0x5D) 一致。
# lc/lp 只影响 literal 编码的概率表索引，而本编码器不实现 literal 路径，故无需常量。
PB = 2
class LzmaEncoder:
"""只实现 quine 用到的两条路径：match 与 rep0-match。
literal / rep-g1 / rep-g2 三条分支从未被构造，因此它们对应的概率表
（p_lit、p_is_rep_g1、p_is_rep_g2）以及 prev_byte 都不必存在。
reps 同理：编码器从不回读（rep0 的距离由解码器自行维护），故不保存。
"""
def __init__(self):
self.pos_mask = (1 << PB) - 1
self.rc = RangeEncoder()
self.pos = 0
self.state = 0
n = PROB_INIT
self.p_is_match = [n] * (kNumStates << kNumPosBitsMax)
self.p_is_rep = [n] * kNumStates
self.p_is_rep_g0 = [n] * kNumStates
self.p_rep0_long = [n] * (kNumStates << kNumPosBitsMax)
self.p_pos_slot = [n] * (kNumLenToPosStates << kNumPosSlotBits)
self.p_spec_pos = [n] * (kNumFullDistances - kEndPosModelIndex)
self.p_align = [n] * (1 << kNumAlignBits)
self.p_len_choice = [n] * 2
self.p_len_low = [n] * (16 * kNumLowLenSymbols)
self.p_len_mid = [n] * (16 * kNumMidLenSymbols)
self.p_len_high = [n] * (1 << kNumHighLenBits)
self.p_rep_len_choice = [n] * 2
self.p_rep_len_low = [n] * (16 * kNumLowLenSymbols)
self.p_rep_len_mid = [n] * (16 * kNumMidLenSymbols)
self.p_rep_len_high = [n] * (1 << kNumHighLenBits)
@property
def pos_state(self):
return self.pos & self.pos_mask
def _encode_len(self, choice, low, mid, high, length):
ps = self.pos_state
l = length - kMatchMinLen
if l < kNumLowLenSymbols:
self.rc.encode_bit(choice, 0, 0)
self.rc.bittree_encode(low, ps << kNumLowLenBits, kNumLowLenBits, l)
else:
self.rc.encode_bit(choice, 0, 1)
l -= kNumLowLenSymbols
if l < kNumMidLenSymbols:
self.rc.encode_bit(choice, 1, 0)
self.rc.bittree_encode(mid, ps << kNumMidLenBits, kNumMidLenBits, l)
else:
self.rc.encode_bit(choice, 1, 1)
self.rc.bittree_encode(high, 0, kNumHighLenBits, l - kNumMidLenSymbols)
def _write_dist(self, dist, lts):
d = dist - 1
slot, n, low_bits = _pos_slot_and_bits(d)
self.rc.bittree_encode(self.p_pos_slot, lts << kNumPosSlotBits,
kNumPosSlotBits, slot)
if slot >= 4:
if slot < kEndPosModelIndex:
base = (2 | (slot & 1)) << n
off = base - slot - 1
self.rc.bittree_reverse_encode(self.p_spec_pos, off, n, low_bits)
else:
self.rc.encode_direct_bits(low_bits >> kNumAlignBits, n - kNumAlignBits)
self.rc.bittree_reverse_encode(self.p_align, 0, kNumAlignBits,
low_bits & ((1 << kNumAlignBits) - 1))
def match(self, dist, length):
assert kMatchMinLen <= length <= 273, length
ps = self.pos_state
self.rc.encode_bit(self.p_is_match, (self.state << kNumPosBitsMax) + ps, 1)
self.rc.encode_bit(self.p_is_rep, self.state, 0)
lts = min(length - kMatchMinLen, kNumLenToPosStates - 1)
self._encode_len(self.p_len_choice,
self.p_len_low, self.p_len_mid, self.p_len_high, length)
self._write_dist(dist, lts)
self.state = 7 if self.state < kNumLitStates else 10
self.pos += length
def rep_match(self, length):
assert kMatchMinLen <= length <= 273, length
ps = self.pos_state
self.rc.encode_bit(self.p_is_match, (self.state << kNumPosBitsMax) + ps, 1)
self.rc.encode_bit(self.p_is_rep, self.state, 1)
self.rc.encode_bit(self.p_is_rep_g0, self.state, 0)
self.rc.encode_bit(self.p_rep0_long, (self.state << kNumPosBitsMax) + ps, 1)
self._encode_len(self.p_rep_len_choice,
self.p_rep_len_low, self.p_rep_len_mid, self.p_rep_len_high, length)
self.state = 8 if self.state < kNumLitStates else 11
self.pos += length
def finish(self):
return self.rc.finish()
# ============================================================
# Part 2: tar + xz 格式原语
# ============================================================
MAX_MATCH = 273 # LZMA1 单个 match 长度上限
CHUNK = 65536 # LZMA2 单 chunk 解压上限
PROPS_BYTE = 0x5D # lc=3, lp=0, pb=2
SEED_NOTE = ("\nMayx's Blog!").encode("utf-8")
# xz 格式常量
XZ_MAGIC = b"\xFD7zXZ\x00"
XZ_FOOTER_MAGIC = b"YZ"
XZ_CHECK_CRC64 = 0x04
LZMA2_FILTER_ID = 0x21
CHECK_SIZE = 8 # CRC64 占 8 字节
_XZ_FLAGS = bytes([0x00, XZ_CHECK_CRC64]) # Stream Flags: reserved + check_type
# tar 格式常量
TAR_BLOCK = 512
def round_up_512(n):
"""长度按 tar 块 512 字节向上取整。"""
return (n + TAR_BLOCK - 1) // TAR_BLOCK * TAR_BLOCK
def crc32(b):
return binascii.crc32(b) & 0xFFFFFFFF
# ---------- CRC64 (ECMA-182, reflected) ----------
_CRC64_POLY = 0xC96C5795D7870F42 # reflected ECMA-182
_CRC64_INIT = 0xFFFFFFFFFFFFFFFF
_CRC64_XOROUT = 0xFFFFFFFFFFFFFFFF
_CRC64_NON_REFLECTED = 0x142F0E1EBA9EA3693 # 非反射多项式 (65 bit)
_CRC64_MOD_BIT = 1 << 64 # x^64 对应的 bit
_liblzma = None
try:
_lzma_name = ctypes.util.find_library('lzma')
if _lzma_name:
_liblzma = ctypes.CDLL(_lzma_name)
_liblzma.lzma_crc64.argtypes = [ctypes.c_char_p, ctypes.c_size_t, ctypes.c_uint64]
_liblzma.lzma_crc64.restype = ctypes.c_uint64
# 验证：CRC64("123456789") 应为 0x995dc9bbdf1939fa
if _liblzma.lzma_crc64(b'123456789', 9, 0) != 0x995dc9bbdf1939fa:
_liblzma = None
except (OSError, AttributeError):
_liblzma = None
def _make_crc64_table():
table = []
for i in range(256):
crc = i
for _ in range(8):
crc = (crc >> 1) ^ _CRC64_POLY if (crc & 1) else crc >> 1
table.append(crc)
return table
_CRC64_TABLE = _make_crc64_table()
def _crc64_pure(data):
"""纯 Python CRC64（ECMA-182 反射，init/xorout = 0xFFFF...FFFF）。"""
crc = _CRC64_INIT
for byte in data:
crc = (crc >> 8) ^ _CRC64_TABLE[(crc ^ byte) & 0xFF]
return crc ^ _CRC64_XOROUT
def _crc64_lib(data):
"""liblzma CRC64（init=0，内部处理 init/xorout）。"""
return _liblzma.lzma_crc64(data, len(data), 0)
crc64 = _crc64_lib if _liblzma else _crc64_pure
# ---------- GF(2^64) 多项式运算 ----------
# 用于 CRC64 自引用求解。CRC64 的线性贡献可表示为
# bit_reverse(contribution(D)) = D_poly * P (mod G_non)
# 其中 D_poly = bit_reverse(D)，P 是位置决定的多项式。
# 方程 D_poly * (1 ^ P1 ^ P2) = bit_reverse(CRC64(W)) 通过多项式逆元求解。
def _bit_reverse64(v):
r = 0
for _ in range(64):
r = (r << 1) | (v & 1)
v >>= 1
return r
def _poly_mul_mod(a, b):
"""GF(2) 多项式乘法 mod G（非反射）。"""
result = 0
while b:
if b & 1:
result ^= a
b >>= 1
a <<= 1
if a & _CRC64_MOD_BIT:
a ^= _CRC64_NON_REFLECTED
return result
def _poly_pow(base, exp):
"""多项式幂：base^exp mod G。"""
result = 1
while exp:
if exp & 1:
result = _poly_mul_mod(result, base)
base = _poly_mul_mod(base, base)
exp >>= 1
return result
def _poly_minv(p):
"""多项式逆元 p^(-1) mod G，用扩展欧几里得算法。"""
if p == 0:
return 0
old_r, r = p, _CRC64_NON_REFLECTED
old_s, s = 1, 0
while r:
# poly divmod(old_r, r) -> (q, rem)
probe_r = r.bit_length() - 1
rem = old_r
q = 0
while rem.bit_length() - 1 >= probe_r and rem:
shift = rem.bit_length() - 1 - probe_r
q |= 1 << shift
rem ^= r << shift
old_r, r = r, rem
# s = old_s ^ q*s (raw 多项式乘法)
prod = 0
a, b = q, s
while b:
if b & 1:
prod ^= a
b >>= 1
a <<= 1
old_s, s = s, old_s ^ prod
if old_r != 1:
return 0
# old_s mod G
probe_m = _CRC64_NON_REFLECTED.bit_length() - 1
rem = old_s
while rem.bit_length() - 1 >= probe_m and rem:
shift = rem.bit_length() - 1 - probe_m
rem ^= _CRC64_NON_REFLECTED << shift
return rem
def store_hdr(payload_len, first=False):
"""LZMA2 uncompressed chunk 头（3 字节，大端 size-1）。"""
assert 1 <= payload_len <= 65536
return bytes([0x01 if first else 0x02]) + (payload_len - 1).to_bytes(2, "big")
def matches_for(dist, total):
"""dist 固定、总长 total 的 token 序列：首个 match + 后续 rep0。"""
assert total >= 2
toks = []
first = min(MAX_MATCH, total)
if total - first == 1:
first -= 1
toks.append(("m", dist, first))
total -= first
while total > 0:
l = min(MAX_MATCH, total)
if total - l == 1:
l -= 1
toks.append(("r0", l))
total -= l
return toks
def _add_gear(chunks, o, f, q):
"""追加一组标准 gear：store 载荷 x = slip+3，紧跟一个 lzma 把那 x 字节复制一遍。
lzma chunk 解压出的字节数正好等于 x，所以齿轮转完之后 slip (f - (o-q)) 精确
归约为 len(cb)（很小的正数，x 被 CHUNK 截断时则按 CHUNK 缩减）。
_layout_iterate 反复套用把 slip 压到 <= 16，_trim_layouts 用它做相位微调。
返回追加后的 (o, f)。
"""
x = min(f - (o - q) + 3, CHUNK)
chunks.append({"kind": "store", "foff": f, "size": 3 + x,
"ooff": o, "olen": x})
o += x
f += 3 + x
cb, cu = lzma_chunk(matches_for(x, x), o)
chunks.append({"kind": "lzma", "foff": f, "size": len(cb),
"ooff": o, "olen": cu, "bytes": cb})
return o + cu, f + len(cb)
def _forward_vac(F, vac, q):
"""vac_store：把紧随其后的等长字节块前移覆盖自身。"""
fo = vac["ooff"] - q
olen = vac["olen"]
F[fo:fo + olen] = F[fo + olen:fo + 2 * olen]
def dict_prop_for(maxdist):
"""选最小的 LZMA2 dict prop 使字典 >= maxdist。"""
for p in range(41):
if (2 | (p & 1)) << (p // 2 + 11) >= maxdist:
return p, (2 | (p & 1)) << (p // 2 + 11)
raise ValueError("distance too large")
def lzma_chunk(tokens, out_pos):
"""编一个 LZMA chunk（0xC0: state+props reset，无 dict reset，无 end marker）。"""
enc = LzmaEncoder()
enc.pos = out_pos
total = 0
for t in tokens:
if t[0] == "m":
enc.match(t[1], t[2])
total += t[2]
else:
enc.rep_match(t[1])
total += t[1]
data = enc.finish()
assert total - 1 < 65536 and len(data) - 1 < 65536
hdr = bytes([0xC0]) + (total - 1).to_bytes(2, "big") + \
(len(data) - 1).to_bytes(2, "big") + bytes([PROPS_BYTE])
return hdr + data, total
# ---------- tar 原语 ----------
def tar_header(name, size, mtime, mode=0o644, typeflag=b'0', uid=0, gid=0):
"""构建 512 字节 POSIX ustar tar header。
注意：tar 数值字段包含 null 终止符，slice 范围必须精确匹配字段宽度。
mode/uid/gid 各 8 字节（7 octal + NUL），size/mtime 各 12 字节（11 octal + NUL）。
"""
h = bytearray(512)
name_bytes = name.encode('utf-8')[:100]
h[0:len(name_bytes)] = name_bytes
h[100:108] = b'%07o\x00' % mode # 8 bytes (field 100-107)
h[108:116] = b'%07o\x00' % uid # 8 bytes (field 108-115)
h[116:124] = b'%07o\x00' % gid # 8 bytes (field 116-123)
h[124:136] = b'%011o\x00' % size # 12 bytes (field 124-135)
h[136:148] = b'%011o\x00' % mtime # 12 bytes (field 136-147)
h[148:156] = b' ' # checksum placeholder (8 spaces)
h[156:157] = typeflag
h[257:263] = b'ustar\x00'
h[263:265] = b'00'
assert len(h) == 512, "header length %d != 512" % len(h)
# 计算校验和：所有字节之和，checksum 字段视为空格
chksum = sum(h) & 0o7777777
h[148:156] = b'%06o\x00 ' % chksum # 6 octal + NUL + space = 8 bytes
assert len(h) == 512, "header length %d != 512 after checksum" % len(h)
return bytes(h)
# ---------- xz 原语 ----------
def xz_varint(v):
"""xz 变长整数编码：每字节 7 位数据（LSB first），MSB 为续位。"""
b = []
while v >= 0x80:
b.append((v & 0x7F) | 0x80)
v >>= 7
b.append(v & 0x7F)
return bytes(b)
def xz_stream_header():
"""12 字节 xz stream header (CHECK_CRC64)。
Stream Flags = [reserved(0x00), check_type(CRC64=0x04)]。
"""
flags = _XZ_FLAGS
crc = crc32(flags)
return XZ_MAGIC + flags + struct.pack('<I', crc)
def xz_block_header(dict_size):
"""xz block header（单 LZMA2 filter）。
返回 (header_bytes, 实际字典大小)；header 长度即 block header 总长度。
"""
prop, real_dict = dict_prop_for(dict_size)
# filter flags: filter_id(1) + props_size(1) + props(1=prop byte)
filter_flags = bytes([LZMA2_FILTER_ID, 1, prop])
# block header content: flags(1, 0x00=1 filter) + filter_flags
content = bytes([0x00]) + filter_flags
# 填充到 (1 + len(content) + 4) 是 4 的倍数
total = 1 + len(content) + 4
pad = (4 - total % 4) % 4
content += b'\x00' * pad
bh = 1 + len(content) + 4
assert bh % 4 == 0, "bh not 4-aligned: %d" % bh
crc = crc32(bytes([bh // 4 - 1]) + content)
header = bytes([bh // 4 - 1]) + content + struct.pack('<I', crc)
assert len(header) == bh
return header, real_dict
def xz_index(records):
"""xz index。records = [(unpadded_size, uncompressed_size), ...]。"""
data = bytes([0x00]) # index indicator
data += xz_varint(len(records))
for unpadded, uncompressed in records:
data += xz_varint(unpadded)
data += xz_varint(uncompressed)
# 填充到 (len(data) + 4) 是 4 的倍数
pad = (4 - (len(data) + 4) % 4) % 4
data += b'\x00' * pad
return data + struct.pack('<I', crc32(data))
def xz_stream_footer(backward_size):
"""12 字节 xz stream footer (CHECK_CRC64)。
backward_size = (index_size / 4) - 1。
Stream Flags = [reserved(0x00), check_type(CRC64=0x04)]。
CRC32 覆盖 Backward Size + Stream Flags（6 字节，不含 CRC32 和 Footer Magic）。
"""
flags = _XZ_FLAGS
crc_data = struct.pack('<I', backward_size) + flags # 6 bytes, no magic
crc = crc32(crc_data)
return struct.pack('<I', crc) + crc_data + XZ_FOOTER_MAGIC
# ============================================================
# Part 3: 多文件 tar.xz quine 构造器
# ============================================================
class TarXzQuine:
def __init__(self, root, quine_name="quine.tar.xz", seed_name=".this_is_mayx_blog",
src_dir=""):
self.quine_name = quine_name
self.seed_name = seed_name
self.src_dir = src_dir.strip("/")
self.mtime = int(time.time())
# walk 源目录
self.walk_entries = []
self.files = []
if self.src_dir:
parts = self.src_dir.split("/")
for i in range(1, len(parts) + 1):
self.walk_entries.append(("/".join(parts[:i]), True))
for dirpath, dirnames, filenames in os.walk(root):
dirnames.sort()
for dn in dirnames:
rel = os.path.relpath(os.path.join(dirpath, dn), root).replace(os.sep, "/")
self.walk_entries.append((self._prefixed(rel), True))
for fn in sorted(filenames):
p = os.path.join(dirpath, fn)
rel = os.path.relpath(p, root).replace(os.sep, "/")
with open(p, "rb") as fp:
data = fp.read()
self.walk_entries.append((self._prefixed(rel), False))
self.files.append((self._prefixed(rel), data))
# rel -> data 映射，避免下面三处按 rel 回头线性搜索整个 files 列表
self.file_map = dict(self.files)
self.dirs = [r for r, isd in self.walk_entries if isd]
self.content = b"".join(data for _, data in self.files)
def _prefixed(self, rel):
return self.src_dir + "/" + rel if self.src_dir else rel
def tar_entries(self, d_seed, xz_size):
"""计算 tar 归档中每个条目的 (header, data_size, is_dir)。
顺序：seed, walk_entries (interleaved files+dirs), quine。
"""
entries = []
# seed 文件
seed_hdr = tar_header(self.seed_name, d_seed, self.mtime)
entries.append({"name": self.seed_name, "hdr": seed_hdr,
"data_size": d_seed, "is_dir": False})
# walk 顺序的文件和目录
for rel, isd in self.walk_entries:
if isd:
hdr = tar_header(rel, 0, self.mtime, mode=0o755, typeflag=b'5')
entries.append({"name": rel, "hdr": hdr,
"data_size": 0, "is_dir": True})
else:
# file_map 与 walk_entries 在 __init__ 里同步构造，这里必有
data = self.file_map[rel]
entries.append({"name": rel, "hdr": tar_header(rel, len(data), self.mtime),
"data_size": len(data), "is_dir": False})
# quine 文件（放在最后）
qhdr = tar_header(self.quine_name, xz_size, self.mtime)
entries.append({"name": self.quine_name, "hdr": qhdr,
"data_size": xz_size, "is_dir": False})
return entries
# ---------- 结构计算 ----------
def _compute_q(self, d_seed):
"""计算 xz_bytes 在 tar 中的偏移 q。
q = seed + files + dirs 的 tar 占用 + quine tar header。
q 不依赖 xz_size（quine 的 data 在 q 之后）。"""
q = 0
# seed 文件
q += TAR_BLOCK # seed tar header
q += round_up_512(d_seed) # seed data + pad
# walk 顺序的文件和目录
for rel, isd in self.walk_entries:
q += TAR_BLOCK # tar header
if not isd:
q += round_up_512(len(self.file_map[rel]))
# quine tar header
q += TAR_BLOCK
return q
def _trim_layouts(self, base, o_g, f_g, q, w_plant_off, d_seed):
"""枚举 trim 配置，产出一批「相位」不同的候选布局。
trim = [可选的裸 store chunk，载荷长度 x0] + 一组标准 gear
（store 载荷 x = s+3，紧接着一个 lzma 复制这 x 字节）。
裸 store 把 slip 抬高 3、把 f 抬高 3+x0；随后这组 gear 把 slip 重新归一到
len(cb)，jump 再归一到 -3。净效果是 (o_pre, f_pre) 被整体平移
δ ≈ s_g + 6 + len(cb) + 3 + x0，而 δ 决定了自洽方程的模 4 相位。
旧实现 δ 恒为 0，相位一旦不对就永远无解 —— 而 d_seed += 1 根本不改变
布局（q 只在跨 512 字节块时才变），所以那 80 次重试是纯粹的无效空转。
注意：不能只插一个裸的 lzma 复制块。lzma 复制块的输出必须等于
F[o-q : o-q+olen]，而这只能靠前面那个「载荷 = F[o-q : o-q+x] 且 x = s+3」
的 store chunk 把字节搬到正确位置来实现；裸插复制块解压出来和 F 对不上。
返回 [(chunks, o_before_jump, f_before_jump, y, jb), ...]，按文件体积升序。
"""
s_g = f_g - (o_g - q)
out = []
seen = set()
for x0 in range(0, s_g + 4):
chunks = list(base)
o, f = o_g, f_g
if x0:
# 裸 store：载荷 = F[o-q : o-q+x0]，要求 x0 <= s+3 以免读到尚未定稿的字节
chunks.append({"kind": "store", "foff": f, "size": 3 + x0,
"ooff": o, "olen": x0})
o += x0
f += 3 + x0
o, f = _add_gear(chunks, o, f, q) # 一组标准 gear
# jump 不动点：y = s + 3 + len(jb)
s = f - (o - q)
y = max(2, s + 17)
jb = ju = None
for _ in range(64):
try:
jb, ju = lzma_chunk([("m", o - w_plant_off, y)], o)
except AssertionError:
jb = None
break
y2 = s + 3 + len(jb)
if y2 == y:
break
if y2 < 2 or y2 > 273:
jb = None
break
y = y2
if jb is None or ju != y:
continue
if y + w_plant_off - TAR_BLOCK > d_seed:
continue
key = (o + y, f + len(jb))
if key in seen:
continue
seen.add(key)
out.append((chunks, o, f, y, jb))
out.sort(key=lambda t: t[2] + len(t[4]))
return out
def _solve_gadget(self, chunks_pre, o_j, f_j, y, jb, q, bh, FEW,
block_hdr, dict_size, hdrA, hdrB, d_seed, k):
"""在一个给定相位的布局上求解 gadget（T / gb / suffix / tz / k）。
关键简化：tz 只依赖 u = gb + suffix 与 k；T = u + tz + 4 也只依赖 (u, k)；
而 gb 又由 T 唯一确定（len(lzma_chunk(matches_for(T, T))))。
因此只需一维枚举 u 即可遍历全部解，既不漏解也远快于原来的三重暴力。
"""
o_pre = o_j + y
f_pre = f_j + len(jb)
if f_pre != (o_pre - q) - 3: # jump 应把 slip 归一到 -3
return None
chunks = list(chunks_pre)
chunks.append({"kind": "lzma", "foff": f_j, "size": len(jb),
"ooff": o_j, "olen": y, "bytes": jb, "jump": True})
best = None
for u in range(8, 512):
# 512k 必须落在 [f_pre + 2u + 2059, f_pre + 2u + 3595)
lo = f_pre + 2 * u + 2059
hi = f_pre + 2 * u + 3595
for k_cand in range(max(1, (lo - 512) // 512), (hi + 512) // 512 + 2):
num = k_cand * 512 - (o_pre + 2 * u + 8 - q - 1024)
if num % 3 != 0:
continue
tz_cand = num // 3
if tz_cand < 1024:
continue
T = u + tz_cand + 4
if T < 2:
continue
gb, gu = lzma_chunk(matches_for(T, T), o_pre + T)
gb_len = len(gb)
suffix = u - gb_len
if suffix < 1:
continue
# 精确自检（不再用带 9 字节偏差的预筛公式）
f_final = f_pre + 3 + T + gb_len + 3 + tz_cand + 1
n = f_final - (12 + bh)
bp = (4 - (12 + bh + n) % 4) % 4
unpadded = bh + n + CHECK_SIZE
idx = xz_index([(unpadded, q + k_cand * 512 + 1024)])
ix = len(idx)
suffix_actual = bp + CHECK_SIZE + ix + 12
if suffix_actual != suffix:
continue
xz_size = 12 + bh + n + bp + CHECK_SIZE + ix + 12
xz_pad_actual = round_up_512(xz_size)
if xz_pad_actual != k_cand * 512:
continue
total_w_actual = q + xz_pad_actual + 1024
o_final = o_pre + 2 * T + tz_cand
if o_final != total_w_actual:
continue
if best is not None and xz_size >= best["xz_size"]:
continue # 已找到更小的解，跳过
backward_size = ix // 4 - 1
final = list(chunks)
final.append({"kind": "store", "foff": f_pre, "size": 3 + T,
"ooff": o_pre, "olen": T, "vac": True})
final.append({"kind": "lzma", "foff": f_pre + 3 + T,
"size": gb_len, "ooff": o_pre + T, "olen": gu,
"bytes": gb})
final.append({"kind": "store",
"foff": f_pre + 3 + T + gb_len,
"size": 3 + tz_cand, "ooff": o_pre + 2 * T,
"olen": tz_cand, "trailing": True})
final.append({"kind": "term",
"foff": f_pre + 3 + T + gb_len + 3 + tz_cand,
"size": 1, "ooff": o_final, "olen": 0,
"bytes": b"\x00"})
best = {
"q": q, "d_seed": d_seed, "k": k,
"chunks": final, "block_hdr": block_hdr, "bh": bh,
"n": n, "xz_size": xz_size,
"T": T, "suffix": suffix_actual,
"bp": bp, "ix": ix,
"dict_size": dict_size,
"FEW": FEW, "hdrA": hdrA,
"hdrB": hdrB, "jump_y": y, "index": idx,
"stream_hdr": xz_stream_header(),
"footer": xz_stream_footer(backward_size),
"entries": self.tar_entries(d_seed, xz_size),
}
return best
def _layout_iterate(self, d_seed, maxdist_hint):
"""核心布局。
阶段 1a：C1 / repro / gears（只依赖 q，所有 trim 配置共用，只算一次）
阶段 1b：插入 trim 微调块，平移 (o_pre, f_pre) 改变模 4 相位
阶段 2 ：按 u = gb + suffix 一维搜索自洽解
"""
block_hdr, dict_size = xz_block_header(maxdist_hint)
bh = len(block_hdr)
FEW = 12 + bh + 3
w_hdrA_off = TAR_BLOCK + 12 + bh
w_hdrB_off = TAR_BLOCK + 12 + bh + 3
w_plant_off = TAR_BLOCK + 12 + bh + 6
q = self._compute_q(d_seed)
# ---------- 阶段 1a：C1 + repro + gears ----------
base = []
o, f = 0, 12 + bh
Ls = []
rem = q + FEW
while rem > CHUNK:
Ls.append(CHUNK)
rem -= CHUNK
Ls.append(rem)
assert Ls[-1] >= 2, "末 C1 chunk 载荷 %d 太小" % Ls[-1]
k = len(Ls)
hdrA = store_hdr(CHUNK, first=False)
hdrB = store_hdr(Ls[-1], first=False)
S = 0
for j, L in enumerate(Ls):
base.append({"kind": "store", "foff": f, "size": 3 + L,
"ooff": o, "olen": L, "first": j == 0,
"c1": True, "S": S})
o += L
f += 3 + L
S += L
assert o == q + FEW
S = 0
for j, L in enumerate(Ls):
if j > 0:
srcpos = w_hdrA_off if (j < k - 1 or L == CHUNK) else w_hdrB_off
dist_h = (q + FEW + S + 3 * (j - 1)) - srcpos
hb, hu = lzma_chunk([("m", dist_h, 3)], o)
base.append({"kind": "lzma", "foff": f, "size": len(hb),
"ooff": o, "olen": hu, "bytes": hb})
o += hu
f += len(hb)
cb, cu = lzma_chunk(matches_for(q + FEW + 3 * j, L), o)
base.append({"kind": "lzma", "foff": f, "size": len(cb),
"ooff": o, "olen": cu, "bytes": cb})
o += cu
f += len(cb)
S += L
# gears：把 slip 压到 <= 16
while f - (o - q) > 16:
o, f = _add_gear(base, o, f, q)
o_g, f_g = o, f
# ---------- 阶段 1b + 阶段 2 ----------
# 相位按文件体积升序枚举；第一个成功的相位通常已经足够好，但不同相位的
# (u, tz) 不同，xz_size 仍可能相差几百字节，所以再多看几个取最小的。
tried = 0
best = None
since_hit = 0
for chunks_pre, o_j, f_j, y, jb in self._trim_layouts(
base, o_g, f_g, q, w_plant_off, d_seed):
lay = self._solve_gadget(chunks_pre, o_j, f_j, y, jb, q, bh, FEW,
block_hdr, dict_size, hdrA, hdrB,
d_seed, k)
tried += 1
if lay is not None and (best is None
or lay["xz_size"] < best["xz_size"]):
best = lay
if best is not None:
since_hit += 1
if since_hit >= 10:
break
elif tried >= 80: # 一直无解，放弃后续相位
break
if best is not None:
return best
raise RuntimeError("layout 不收敛: 无自洽解 (q=%d, f_pre=%d, o_pre=%d)"
% (q, f_g, o_g))
# ---------- 装配 ----------
def assemble(self, lay):
"""构建 xz 文件 F 和 seed。"""
F = bytearray(lay["xz_size"])
q = lay["q"]
d_seed = lay["d_seed"]
bh = lay["bh"]
FEW = lay["FEW"]
# bytearray 已全零初始化：block padding、CRC64 占位、trailing-zero 载荷
# 都不必再显式写 0（后面几处赋值只会写到各自独立的区间，不会交叉覆盖）。
# pass A: 写 xz stream header + block header + 所有 chunk bytes + suffix
F[0:12] = lay["stream_hdr"]
F[12:12+bh] = lay["block_hdr"]
jump_c = None
for c in lay["chunks"]:
if c["kind"] == "store":
F[c["foff"]:c["foff"] + 3] = store_hdr(c["olen"],
first=c.get("first", False))
else:
F[c["foff"]:c["foff"] + c["size"]] = c["bytes"]
if c.get("jump"):
jump_c = c
# block padding + CRC64(pass D 填) + index + footer
bp_off = 12 + bh + lay["n"]
ix_off = bp_off + lay["bp"] + CHECK_SIZE
F[ix_off:ix_off + lay["ix"]] = lay["index"]
sf_off = ix_off + lay["ix"]
F[sf_off:sf_off + 12] = lay["footer"]
assert jump_c is not None
# pass B: 构建 seed
y = lay["jump_y"]
plant = bytes(F[jump_c["ooff"] - q: jump_c["ooff"] - q + y])
seed = bytearray(d_seed)
seed[0:12] = lay["stream_hdr"]
seed[12:12+bh] = lay["block_hdr"]
seed[12+bh:12+bh+3] = lay["hdrA"]
seed[12+bh+3:12+bh+6] = lay["hdrB"]
seed[12+bh+6:12+bh+6+y] = plant
pad = (SEED_NOTE * (d_seed // len(SEED_NOTE) + 2))[:d_seed - 12 - bh - 6 - y]
seed[12+bh+6+y:] = pad
# 构建 tar 前缀 T（含 seed data）
T = self._build_tar_prefix_with_seed(lay, seed)
F_prefix = bytes(F[0:FEW])
P = T + F_prefix
assert len(P) == q + FEW, "P 长度 %d != q+FEW %d" % (len(P), q + FEW)
# pass C: 填充 store payloads
for c in lay["chunks"]:
if c["kind"] != "store":
continue
ooff, olen = c["ooff"], c["olen"]
if c.get("c1"):
F[c["foff"] + 3:c["foff"] + 3 + olen] = P[c["S"]:c["S"] + olen]
elif c.get("trailing"):
# 载荷是尾部零：F 全零初始化，且唯一会写到 chunk 之外的 vac_store
# 目标是 [f_pre+3, f_pre+3+T)（f_pre == (o_pre-q)-3 已由 _solve_gadget
# 断言），与本区块不重叠，因此无需写入。
continue
elif c.get("vac"):
_forward_vac(F, c, q)
else:
# 其余 store 的载荷 = 输出流自身对应位置的字节（自引用复制）
fo = ooff - q
F[c["foff"] + 3:c["foff"] + 3 + olen] = F[fo:fo + olen]
# pass D: 求解 CRC64 自引用
self._solve_crc64(F, lay, T)
return bytes(F), bytes(seed)
def _build_tar_prefix_with_seed(self, lay, seed):
"""构建 tar 前缀 T（含 seed data）。"""
entries = lay["entries"]
T = bytearray()
for e in entries[:-1]:
T += e["hdr"]
if not e["is_dir"] and e["data_size"] > 0:
data = seed if e["name"] == self.seed_name else self.file_map[e["name"]]
T += data
T += b'\x00' * (round_up_512(e["data_size"]) - e["data_size"])
T += entries[-1]["hdr"] # quine tar header
return bytes(T)
def _solve_crc64(self, F, lay, T_prefix):
"""求解 CRC64 自引用。
CRC64 值 D = CRC64(W)，W = T + F + 尾部零。
D 在 W 中出现两次：F 的 CRC64 字段 + vac_store 副本。
解法（多项式逆元）：
CRC64 的线性贡献可表示为 GF(2^64) 多项式乘法：
bit_reverse(contribution(D)) = D_poly * P (mod G)
其中 D_poly = bit_reverse(D)，P = x^(64+trailing) mod G。
方程 D_poly * (1 ^ P1 ^ P2) = bit_reverse(CRC64(W)) (mod G)
用扩展欧几里得求 (1 ^ P1 ^ P2) 的逆元，一步求解。
"""
q = lay["q"]
bh = lay["bh"]
n = lay["n"]
bp = lay["bp"]
xz_size = lay["xz_size"]
T = lay["T"]
# CRC64 位置 1：在 F 中（block padding 后）
crc_f_off = 12 + bh + n + bp
crc_w1 = q + crc_f_off
# CRC64 位置 2：vac_store 副本
crc_f_copy = crc_f_off - T
crc_w2 = q + crc_f_copy
# 构造 W = T + F + 尾部零（先把两处 CRC64 置 0）
xz_pad = round_up_512(xz_size)
tz = xz_pad - xz_size + 1024
W = bytearray(T_prefix) + F + b'\x00' * tz
W[crc_w1:crc_w1 + CHECK_SIZE] = b'\x00' * CHECK_SIZE
W[crc_w2:crc_w2 + CHECK_SIZE] = b'\x00' * CHECK_SIZE
# CRC64(W) 的多项式表示
crc_poly = _bit_reverse64(crc64(bytes(W)))
# P1 = x^(64 + 位置1之后的bit数) mod G
trailing1 = (len(W) - crc_w1 - 8) * 8
P1 = _poly_pow(2, 64 + trailing1)
# P2 = x^(64 + 位置2之后的bit数) mod G
trailing2 = (len(W) - crc_w2 - 8) * 8
P2 = _poly_pow(2, 64 + trailing2)
# D_poly = crc_poly * inv(1 ^ P1 ^ P2)
coeff = 1 ^ P1 ^ P2
inv = _poly_minv(coeff)
if inv == 0:
raise RuntimeError("CRC64 多项式无逆元")
D_poly = _poly_mul_mod(crc_poly, inv)
D = _bit_reverse64(D_poly)
# 写入 D 到 F 的 CRC64 位置
struct.pack_into('<Q', F, crc_f_off, D)
# 重新执行 vac_store 副本
_forward_vac(F, next(c for c in lay["chunks"] if c.get("vac")), q)
# 验证
W2 = bytearray(T_prefix) + F + b'\x00' * tz
assert crc64(bytes(W2)) == D, "CRC64 求解验证失败"
def build(self, maxdist_hint=None):
if maxdist_hint is None:
maxdist_hint = 2 * (len(self.content) + 128) + (1 << 21)
last_err = None
# d_seed 在同一 512 字节块内不改变 q（seed 数据按 512 对齐），所以旧的
# "d_seed += 1" 重试完全不会改变布局，只是空转 80 次。真正需要 d_seed
# 变化的情况只有两种：seed 装不下 jump 种植串，或需要跨块改变 q 作为兜底。
for d_seed in (64, 96, 128, 160, 192, 256, 320, 384, 448,
576, 704, 832, 960, 1088, 1600, 2112, 2624, 3648):
try:
lay = self._layout_iterate(d_seed, maxdist_hint)
F, seed = self.assemble(lay)
return F, lay, seed
except (RuntimeError, AssertionError, ValueError) as e:
last_err = e
raise RuntimeError("build 多次失败: %s" % last_err)
# ============================================================
# Part 4: 验证 + main
# ============================================================
def verify(F, lay, files, quine_name):
"""验证：解压 xz → 解析 tar → 检查文件 + quine 自复制。"""
# 1. 解压 xz
try:
dec = lzma.LZMADecompressor(format=lzma.FORMAT_XZ)
tar_data = dec.decompress(F)
except Exception as e:
print("[verify] xz 解压失败: %s" % e)
return False
# 2. 检查 quine 自复制
if tar_data[lay["q"]:lay["q"] + lay["xz_size"]] != F:
print("[verify] quine 自复制不匹配")
a = tar_data[lay["q"]:lay["q"] + lay["xz_size"]]
for i in range(min(len(a), len(F))):
if a[i] != F[i]:
print("[verify] 首个不匹配在偏移 %d: tar=%02x vs xz=%02x" % (i, a[i], F[i]))
break
return False
# 3. 解析 tar
try:
tf = tarfile.open(fileobj=io.BytesIO(tar_data))
members = tf.getmembers()
except Exception as e:
print("[verify] tar 解析失败: %s" % e)
return False
print("[verify] tar 条目: %s" % [m.name for m in members])
# 4. 检查源文件
ok = True
for rel, data in files:
try:
f = tf.extractfile(rel)
if f is None:
print("[verify] 文件 %s 是目录或不存在" % rel)
ok = False
continue
extracted = f.read()
if extracted != data:
print("[verify] 文件 %s 内容不匹配" % rel)
ok = False
except KeyError:
print("[verify] 文件 %s 不在 tar 中" % rel)
ok = False
# 5. 检查 quine 文件
try:
qf = tf.extractfile(quine_name)
if qf:
qdata = qf.read()
if qdata != F:
print("[verify] quine 文件内容不匹配 (%d vs %d)" % (len(qdata), len(F)))
ok = False
else:
print("[verify] quine 文件匹配!")
except KeyError:
print("[verify] quine 文件不在 tar 中")
ok = False
print("[verify] 结果: %s" % ("通过" if ok else "失败"))
return ok
def main():
ap = argparse.ArgumentParser(description="多文件 tar.xz quine 生成器")
ap.add_argument("srcdir")
ap.add_argument("output")
ap.add_argument("--quine-name", default=None,
help="归档内 quine 文件名（默认 = output 的文件名）")
ap.add_argument("--seed-name", default=".this_is_mayx_blog")
ap.add_argument("--src-dir", default="",
help="源文件在归档内的前缀目录")
ap.add_argument("--quine-dir", default="",
help="quine 在归档内所在目录")
args = ap.parse_args()
quine_name = args.quine_name or os.path.basename(args.output)
if args.quine_dir:
quine_dir = args.quine_dir.strip("/")
quine_name = quine_dir + "/" + quine_name
bq = TarXzQuine(args.srcdir, quine_name=quine_name, seed_name=args.seed_name,
src_dir=args.src_dir)
print("[blogquine-tarxz] %d 个文件, %d 个目录, 内容 %d 字节"
% (len(bq.files), len(bq.dirs), len(bq.content)))
F, lay, seed = bq.build()
with open(args.output, "wb") as fp:
fp.write(F)
print("[blogquine-tarxz] written %s: %d 字节 (q=%d, k=%d chunks, n=%d, xz_size=%d)"
% (args.output, len(F), lay["q"], lay["k"], lay["n"], lay["xz_size"]))
print("[blogquine-tarxz] T=%d, suffix=%d (bp=%d, ix=%d), dict_size=%d"
% (lay["T"], lay["suffix"], lay["bp"], lay["ix"], lay["dict_size"]))
ok = verify(F, lay, bq.files, quine_name)
sys.exit(0 if ok else 1)
if __name__ == "__main__":
main()
```
</details>
不过对我来说，相比于TXZ格式我还是更喜欢7z一点，一是因为LZMA2本来就是7-Zip的作者发明的，XZ感觉像是摘桃子的，二是TXZ这个名字听起来有点怪，感觉不像压缩包，三是XZ Utils出过后门，尽管整个Linux社区都在使用XZ，但是我还是稍微有点偏见，所以这份TXZ的生成器我就粘贴出来给需要的人吧，我博客用7z格式就好了。
# 感想
以前总是有人说AI没有创新能力，只是对曾经在网络上出现的东西进行重组，但人何尝不是这样呢？像这次制作的7z/TXZ Quine生成器在整个网络上没有任何公开信息，当然我也知道这并不是理论上的创新，但谁说组合创新不是创新呢？再看看最近OpenAI又解决了一大堆数学难题，完全可以相信AI是真的拥有智能，所以我相信总有一天AI将能完成人类能做的所有事情，人类将不再需要额外的思考，只需要做自己想做的事情吧。
Reference in New Issue Copy Permalink