//没有评论

""".........................:~+?7$$$ZZZZZZZ$$$7I+=:,............................
........................~+7ZZZZZOZZOOZOZZOZOZOOZZZZZ7?~:........................
......................,~7$ZZOOOOOZOZOZOZZOOZZOZOOOOOZ$$I,.......................
...................,=I$OOZOZOZZOOOZZOZOOOOZOZZZOOZZZOZZOZI=:....................
.................:?$ZZOOZZOZOZZOOOZZZOOZOZOZZZZZZZOZZOZOOOZ$I~..................
................IZOOOZOOOZZZOZZZZOZZOZOOOOZOZZZOOZZZZOOZOZZZOZ7=................
...............~ZZOZZOZOOZOOZOZOZZOZOZOZZZZZOZOZZOZOOZOZZOOOOZZ7................
.............:IZOOZOZZZZOZOZZOZOOZOZOZOZZOZOOZOOOOZOZZZZZOZOZZOOI~..............
...........,+$ZOOZZOZOZOZOZOZZOZOZOOZZOZZOZZOZOOOOZOZZOZZOOZOOOOO$?:............
..........:IZZOOOZOZZZZOOZOOZOZOZZOZOZZZZOZOOZOZZOZOZOZOOOOOOOZZZOZ7~...........
..........+$OOZZZOZZOOZOOZZZZOZZOZOZZOZOOOZOZOZZOZOZOZOOOOOZ$$77I77$+:..........
........,?$OOZZZZZZZOZOOOZOZZOZZZOOZOZOOOOZOZZZOOZOOZOOO7?~:,.......,...........
........+ZOOZZZZZOZOOZOOZZZZOZZOOOZZZOZOZOOZZOZOZZZOOO$?........................
........$ZOZZZOZZZZOZOOZZZOZOZZOOOOOOOOOOOZOZOZZOZOO$?,.........................
.......:ZOOZOZOZZOOZZOZOZOZOOOZOOOOOOOOOOOOOOOZOZOOZI:..........................
.......+OOOZOOZOZOZOZZZOOZOOZOOO$I+=~:::~+I$OOOOOOZ?:........,:=,...............
......:7ZOOZOZZOOOZOZOZOOZOOZ$I=............:?$OOZ7:.......:IZOOZ?,.............
......=$OZOZOOZOOOOOZOZZOOZ7=,................:?O$+.......~7OOOOOZ+,............
.....,?$OOOOOZZZZOOOOOOZOZ?,....................ZZ=.......=$OOZOOZ+,............
.....:IZOZZ$777I7$ZOOOOOZ7~.....................$Z=.......~7OOOOO7=.............
.....:+?~:,.......,~IZOO7~........~+II?=........?$?,.......:I$ZZ?:..............
.....................+ZO=,......:IOOOOOZ:.......=7$~............................
.....................:IO~.......=OOZOZOO=,......~7O7~...........................
...........:~:.......:IO~.......+OOOOZOO=.......~78Z?,.................,:.......
..........:IZ7~......+ZO~.......:7OOOOO$,.......+$OOZ7=,.............:?$=.......
...........,,.....,=7ZOO+,.......,=II?=:........7OOOOOOZ=:,.....,:=I$ZOO=.......
....................,:+$7=.....................~OOOZZZOOOZZ$$7$$ZOOOOOOZ=.......
......................:?Z?,...................:?OZOOZOOZOOOOOOOOOOZOZOZO=.......
............,::,.......,OO7:................,+$OOZOZOOZOZZOZOZZOOZOZOZOO=.......
...........~$8OI........$OOZI~,.........,:=IZOOZOZOZOZOOOZOZOZOOOZZZOZOO=.......
...........:??=:.......:OOOOOZZ7+=~~==+?$ZOOOOZOOOZOZOZOOZOZOZZOZZOZOZZO=.......
............::,.......,+OOZOOOOO$7777$$ZOOOOOZOZZZZOZOZZZOOZOZZOOOZOOZOO=.......
.....................=7OOZOOZOOZOOOOOOOOOZZZOZOZZOZOZOZOOOZOZOZZOZOOZOOO=.......
................,:=I$OOOZZOOOZOOOOOZOZOZZZZZOOZZZOZOZZZOOZOOZOZOZOZOZOOZ=.......
...........:~+?7ZOOOOOOZZZOZOOZOZOOZOZOZZOZZOZOZZZZOZOZZOZOZOZZOZOOZOOOZ=.......
........$$ZOOOOOOOOZOZOZZZZOZOZOOOZZZOZZZOZOOZOZZZZZZZZOOOZOOZZZOZOOZOOZ=.......
.......~OOZOOZZOOZZZZZZOOZOZOZOZZOOZOOZZZOZZOZOZZOZZZOZOOOOOZOZOZOOZOOOZ=.......
.......~OOZOOZZOZZOZOZZOZZOZOOZOZOOZOZOZZOZOOZOZZOZOZOZOZOOZOZOOOZOOZOZO=.......
.......~OOZZZOZOOOZOZOZZOZOZOZOZOOZOOZOOOOZOZOOZOOOZOOOZOZZOZOZOOZZOOOOZ=.......
.......~OOZZOZOZZZOOZOOZOZOZOZZOZZZZOZZZZOZOZZOOOOZ$ZZZZZZOZZZOZZOZOZZZO=.......
.......~OOZZOO$??$OOZOOZZOOZOZOZ+~IZOOOZOZOOZZOOZI==IZOZZOZOOZOZZOZI~=7O=.......
.......~OOZO$I:..~IZZZOZOZOZOZ$+...=7ZOOZOOZZOZZ=,..,=$ZZOZZZZZOZI~...,?=.......
.......~OOOZI:....:IZOOOZZOOO$+:....~7ZOZOZOZOZ$,....,=$OOZOOOZOI~.....:~.......
.......~OZI~........~IZZZOZ$?:........=IOOZZZ$+,.......,$ZOOOZZ7................
.......=7~............~IOZI:............7ZO$+:..........,=7ZZ7=,................
.......,,...............=~...............~=:..............,~=...................
                                                               GlassGiant.com""" 
print "Hello World!"

我曾经参与过Windows 3.0的源代码。(我赶紧补充一句，作为一名微软员工，不是!)在那里，我遇到了一个文件加载器，它可以多次重新输入，并且有一个讨厌的双关语的例子(只是为了显示作者有多聪明)。

这些乱七八糟的可重入代码是用Intel程序集jmp指令(在C代码中间)执行的，它的标签是“we_are_not_in_kansas_any_more_toto”。

在头文件的顶部:

/* Project : XYZ (Please somebody shoot me!)
 *
 * File : $Id: defs.h,v 1.1 $
 *
 * Purpose : Create havoc rather than peace among many nations
 *
 * History : Back-ported changes that were not in CVS.  Please somebody,
 *  shoot us and put us all out of our misery.
 */

“XYZ项目”(已改名)是一个长达七年的折磨。最后这句话是由一个从头到尾都参与其中的坚定的灵魂写的。

    /* Mark: If there's one thing you learn from this code, it is this...
   Never, ever fly Air France.  Their customer service is absolutely
   the worst.  I've never heard the words "That's not my problem" as 
   many times as I have from their staff -- It should, without doubt
   be their corporate motto if it isn't already.  Don't bother giving 
   them business because you're just a pain in their side and they
   will be sure to let you know the first time you speak to them.

   If you ever want to make me happy just tell me that you, too, will
   never fly Air France again either (in spite of their excellent
   cuisine). 

   Update by oej: The merger with KLM has transferred this
   behaviour to KLM as well. 
   Don't bother giving them business either...

   Only if you want to travel randomly without luggage, you
   might pick either of them.
   */

有人抱怨说，“最好的”评论会带来最糟糕的评论。恕我直言，他们更有趣，所以“更好”，但这是我读过的最好的评论:

/*
Major subtleties ahead:  Most hash schemes depend on having a "good" hash
function, in the sense of simulating randomness.  Python doesn't:  its most
important hash functions (for strings and ints) are very regular in common
cases:

>>> map(hash, (0, 1, 2, 3))
[0, 1, 2, 3]
>>> map(hash, ("namea", "nameb", "namec", "named"))
[-1658398457, -1658398460, -1658398459, -1658398462]
>>>

This isn't necessarily bad!  To the contrary, in a table of size 2**i, taking
the low-order i bits as the initial table index is extremely fast, and there
are no collisions at all for dicts indexed by a contiguous range of ints.
The same is approximately true when keys are "consecutive" strings.  So this
gives better-than-random behavior in common cases, and that's very desirable.

OTOH, when collisions occur, the tendency to fill contiguous slices of the
hash table makes a good collision resolution strategy crucial.  Taking only
the last i bits of the hash code is also vulnerable:  for example, consider
[i << 16 for i in range(20000)] as a set of keys.  Since ints are their own
hash codes, and this fits in a dict of size 2**15, the last 15 bits of every
hash code are all 0:  they *all* map to the same table index.

But catering to unusual cases should not slow the usual ones, so we just take
the last i bits anyway.  It's up to collision resolution to do the rest.  If
we *usually* find the key we're looking for on the first try (and, it turns
out, we usually do -- the table load factor is kept under 2/3, so the odds
are solidly in our favor), then it makes best sense to keep the initial index
computation dirt cheap.

The first half of collision resolution is to visit table indices via this
recurrence:

    j = ((5*j) + 1) mod 2**i

For any initial j in range(2**i), repeating that 2**i times generates each
int in range(2**i) exactly once (see any text on random-number generation for
proof).  By itself, this doesn't help much:  like linear probing (setting
j += 1, or j -= 1, on each loop trip), it scans the table entries in a fixed
order.  This would be bad, except that's not the only thing we do, and it's
actually *good* in the common cases where hash keys are consecutive.  In an
example that's really too small to make this entirely clear, for a table of
size 2**3 the order of indices is:

    0 -> 1 -> 6 -> 7 -> 4 -> 5 -> 2 -> 3 -> 0 [and here it's repeating]

If two things come in at index 5, the first place we look after is index 2,
not 6, so if another comes in at index 6 the collision at 5 didn't hurt it.
Linear probing is deadly in this case because there the fixed probe order
is the *same* as the order consecutive keys are likely to arrive.  But it's
extremely unlikely hash codes will follow a 5*j+1 recurrence by accident,
and certain that consecutive hash codes do not.

The other half of the strategy is to get the other bits of the hash code
into play.  This is done by initializing a (unsigned) vrbl "perturb" to the
full hash code, and changing the recurrence to:

    j = (5*j) + 1 + perturb;
    perturb >>= PERTURB_SHIFT;
    use j % 2**i as the next table index;

Now the probe sequence depends (eventually) on every bit in the hash code,
and the pseudo-scrambling property of recurring on 5*j+1 is more valuable,
because it quickly magnifies small differences in the bits that didn't affect
the initial index.  Note that because perturb is unsigned, if the recurrence
is executed often enough perturb eventually becomes and remains 0.  At that
point (very rarely reached) the recurrence is on (just) 5*j+1 again, and
that's certain to find an empty slot eventually (since it generates every int
in range(2**i), and we make sure there's always at least one empty slot).

Selecting a good value for PERTURB_SHIFT is a balancing act.  You want it
small so that the high bits of the hash code continue to affect the probe
sequence across iterations; but you want it large so that in really bad cases
the high-order hash bits have an effect on early iterations.  5 was "the
best" in minimizing total collisions across experiments Tim Peters ran (on
both normal and pathological cases), but 4 and 6 weren't significantly worse.

Historical:  Reimer Behrends contributed the idea of using a polynomial-based
approach, using repeated multiplication by x in GF(2**n) where an irreducible
polynomial for each table size was chosen such that x was a primitive root.
Christian Tismer later extended that to use division by x instead, as an
efficient way to get the high bits of the hash code into play.  This scheme
also gave excellent collision statistics, but was more expensive:  two
if-tests were required inside the loop; computing "the next" index took about
the same number of operations but without as much potential parallelism
(e.g., computing 5*j can go on at the same time as computing 1+perturb in the
above, and then shifting perturb can be done while the table index is being
masked); and the dictobject struct required a member to hold the table's
polynomial.  In Tim's experiments the current scheme ran faster, produced
equally good collision statistics, needed less code & used less memory.

Theoretical Python 2.5 headache:  hash codes are only C "long", but
sizeof(Py_ssize_t) > sizeof(long) may be possible.  In that case, and if a
dict is genuinely huge, then only the slots directly reachable via indexing
by a C long can be the first slot in a probe sequence.  The probe sequence
will still eventually reach every slot in the table, but the collision rate
on initial probes may be much higher than this scheme was designed for.
Getting a hash code as fat as Py_ssize_t is the only real cure.  But in
practice, this probably won't make a lick of difference for many years (at
which point everyone will have terabytes of RAM on 64-bit boxes).
*/