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PK!��CfMIMI	heapq.pyc+
c��RtRt.ROtRtRtRtRtRtRtRt	R	t
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t]t]t]R8Xd^RIt]!]P<!44R#R# ]dL9i;i)�Heap queue algorithm (a.k.a. priority queue).

Heaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for
all k, counting elements from 0.  For the sake of comparison,
non-existing elements are considered to be infinite.  The interesting
property of a heap is that a[0] is always its smallest element.

Usage:

heap = []            # creates an empty heap
heappush(heap, item) # pushes a new item on the heap
item = heappop(heap) # pops the smallest item from the heap
item = heap[0]       # smallest item on the heap without popping it
heapify(x)           # transforms list into a heap, in-place, in linear time
item = heappushpop(heap, item) # pushes a new item and then returns
                               # the smallest item; the heap size is unchanged
item = heapreplace(heap, item) # pops and returns smallest item, and adds
                               # new item; the heap size is unchanged

Our API differs from textbook heap algorithms as follows:

- We use 0-based indexing.  This makes the relationship between the
  index for a node and the indexes for its children slightly less
  obvious, but is more suitable since Python uses 0-based indexing.

- Our heappop() method returns the smallest item, not the largest.

These two make it possible to view the heap as a regular Python list
without surprises: heap[0] is the smallest item, and heap.sort()
maintains the heap invariant!
�xHeap queues

[explanation by François Pinard]

Heaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for
all k, counting elements from 0.  For the sake of comparison,
non-existing elements are considered to be infinite.  The interesting
property of a heap is that a[0] is always its smallest element.

The strange invariant above is meant to be an efficient memory
representation for a tournament.  The numbers below are 'k', not a[k]:

                                   0

                  1                                 2

          3               4                5               6

      7       8       9       10      11      12      13      14

    15 16   17 18   19 20   21 22   23 24   25 26   27 28   29 30


In the tree above, each cell 'k' is topping '2*k+1' and '2*k+2'.  In
a usual binary tournament we see in sports, each cell is the winner
over the two cells it tops, and we can trace the winner down the tree
to see all opponents s/he had.  However, in many computer applications
of such tournaments, we do not need to trace the history of a winner.
To be more memory efficient, when a winner is promoted, we try to
replace it by something else at a lower level, and the rule becomes
that a cell and the two cells it tops contain three different items,
but the top cell "wins" over the two topped cells.

If this heap invariant is protected at all time, index 0 is clearly
the overall winner.  The simplest algorithmic way to remove it and
find the "next" winner is to move some loser (let's say cell 30 in the
diagram above) into the 0 position, and then percolate this new 0 down
the tree, exchanging values, until the invariant is re-established.
This is clearly logarithmic on the total number of items in the tree.
By iterating over all items, you get an O(n ln n) sort.

A nice feature of this sort is that you can efficiently insert new
items while the sort is going on, provided that the inserted items are
not "better" than the last 0'th element you extracted.  This is
especially useful in simulation contexts, where the tree holds all
incoming events, and the "win" condition means the smallest scheduled
time.  When an event schedules other events for execution, they are
scheduled into the future, so they can easily go into the heap.  So, a
heap is a good structure for implementing schedulers (this is what I
used for my MIDI sequencer :-).

Various structures for implementing schedulers have been extensively
studied, and heaps are good for this, as they are reasonably speedy,
the speed is almost constant, and the worst case is not much different
than the average case.  However, there are other representations which
are more efficient overall, yet the worst cases might be terrible.

Heaps are also very useful in big disk sorts.  You most probably all
know that a big sort implies producing "runs" (which are pre-sorted
sequences, whose size is usually related to the amount of CPU memory),
followed by a merging passes for these runs, which merging is often
very cleverly organised[1].  It is very important that the initial
sort produces the longest runs possible.  Tournaments are a good way
to achieve that.  If, using all the memory available to hold a
tournament, you replace and percolate items that happen to fit the
current run, you'll produce runs which are twice the size of the
memory for random input, and much better for input fuzzily ordered.

Moreover, if you output the 0'th item on disk and get an input which
may not fit in the current tournament (because the value "wins" over
the last output value), it cannot fit in the heap, so the size of the
heap decreases.  The freed memory could be cleverly reused immediately
for progressively building a second heap, which grows at exactly the
same rate the first heap is melting.  When the first heap completely
vanishes, you switch heaps and start a new run.  Clever and quite
effective!

In a word, heaps are useful memory structures to know.  I use them in
a few applications, and I think it is good to keep a 'heap' module
around. :-)

--------------------
[1] The disk balancing algorithms which are current, nowadays, are
more annoying than clever, and this is a consequence of the seeking
capabilities of the disks.  On devices which cannot seek, like big
tape drives, the story was quite different, and one had to be very
clever to ensure (far in advance) that each tape movement will be the
most effective possible (that is, will best participate at
"progressing" the merge).  Some tapes were even able to read
backwards, and this was also used to avoid the rewinding time.
Believe me, real good tape sorts were quite spectacular to watch!
From all times, sorting has always been a Great Art! :-)
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>>> list(merge([1,3,5,7], [0,2,4,8], [5,10,15,20], [], [25]))
[0, 1, 2, 3, 4, 5, 5, 7, 8, 10, 15, 20, 25]

If *key* is not None, applies a key function to each element to determine
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'super' call mechanisms.  abstractmethod() may be used to declare
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ABCs as 'virtual subclasses' -- these and their descendants will
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Regular expressions can contain both special and ordinary characters.
Most ordinary characters, like "A", "a", or "0", are the simplest
regular expressions; they simply match themselves.  You can
concatenate ordinary characters, so last matches the string 'last'.

The special characters are:
    "."      Matches any character except a newline.
    "^"      Matches the start of the string.
    "$"      Matches the end of the string or just before the newline at
             the end of the string.
    "*"      Matches 0 or more (greedy) repetitions of the preceding RE.
             Greedy means that it will match as many repetitions as possible.
    "+"      Matches 1 or more (greedy) repetitions of the preceding RE.
    "?"      Matches 0 or 1 (greedy) of the preceding RE.
    *?,+?,?? Non-greedy versions of the previous three special characters.
    {m,n}    Matches from m to n repetitions of the preceding RE.
    {m,n}?   Non-greedy version of the above.
    "\\"     Either escapes special characters or signals a special sequence.
    []       Indicates a set of characters.
             A "^" as the first character indicates a complementing set.
    "|"      A|B, creates an RE that will match either A or B.
    (...)    Matches the RE inside the parentheses.
             The contents can be retrieved or matched later in the string.
    (?aiLmsux) The letters set the corresponding flags defined below.
    (?:...)  Non-grouping version of regular parentheses.
    (?P<name>...) The substring matched by the group is accessible by name.
    (?P=name)     Matches the text matched earlier by the group named name.
    (?#...)  A comment; ignored.
    (?=...)  Matches if ... matches next, but doesn't consume the string.
    (?!...)  Matches if ... doesn't match next.
    (?<=...) Matches if preceded by ... (must be fixed length).
    (?<!...) Matches if not preceded by ... (must be fixed length).
    (?(id/name)yes|no) Matches yes pattern if the group with id/name matched,
                       the (optional) no pattern otherwise.

The special sequences consist of "\\" and a character from the list
below.  If the ordinary character is not on the list, then the
resulting RE will match the second character.
    \number  Matches the contents of the group of the same number.
    \A       Matches only at the start of the string.
    \z       Matches only at the end of the string.
    \b       Matches the empty string, but only at the start or end of a word.
    \B       Matches the empty string, but not at the start or end of a word.
    \d       Matches any decimal digit; equivalent to the set [0-9] in
             bytes patterns or string patterns with the ASCII flag.
             In string patterns without the ASCII flag, it will match the whole
             range of Unicode digits.
    \D       Matches any non-digit character; equivalent to [^\d].
    \s       Matches any whitespace character; equivalent to [ \t\n\r\f\v] in
             bytes patterns or string patterns with the ASCII flag.
             In string patterns without the ASCII flag, it will match the whole
             range of Unicode whitespace characters.
    \S       Matches any non-whitespace character; equivalent to [^\s].
    \w       Matches any alphanumeric character; equivalent to [a-zA-Z0-9_]
             in bytes patterns or string patterns with the ASCII flag.
             In string patterns without the ASCII flag, it will match the
             range of Unicode alphanumeric characters (letters plus digits
             plus underscore).
             With LOCALE, it will match the set [0-9_] plus characters defined
             as letters for the current locale.
    \W       Matches the complement of \w.
    \\       Matches a literal backslash.

This module exports the following functions:
    match     Match a regular expression pattern to the beginning of a string.
    fullmatch Match a regular expression pattern to all of a string.
    search    Search a string for the presence of a pattern.
    sub       Substitute occurrences of a pattern found in a string.
    subn      Same as sub, but also return the number of substitutions made.
    split     Split a string by the occurrences of a pattern.
    findall   Find all occurrences of a pattern in a string.
    finditer  Return an iterator yielding a Match object for each match.
    compile   Compile a pattern into a Pattern object.
    purge     Clear the regular expression cache.
    escape    Backslash all non-alphanumerics in a string.

Each function other than purge and escape can take an optional 'flags' argument
consisting of one or more of the following module constants, joined by "|".
A, L, and U are mutually exclusive.
    A  ASCII       For string patterns, make \w, \W, \b, \B, \d, \D
                   match the corresponding ASCII character categories
                   (rather than the whole Unicode categories, which is the
                   default).
                   For bytes patterns, this flag is the only available
                   behaviour and needn't be specified.
    I  IGNORECASE  Perform case-insensitive matching.
    L  LOCALE      Make \w, \W, \b, \B, dependent on the current locale.
    M  MULTILINE   "^" matches the beginning of lines (after a newline)
                   as well as the string.
                   "$" matches the end of lines (before a newline) as well
                   as the end of the string.
    S  DOTALL      "." matches any character at all, including the newline.
    X  VERBOSE     Ignore whitespace and comments for nicer looking RE's.
    U  UNICODE     For compatibility only. Ignored for string patterns (it
                   is the default), and forbidden for bytes patterns.

This module also defines exception 'PatternError', aliased to 'error' for
backward compatibility.

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>>> p[0] + p[1]                     # indexable like a plain tuple
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>>> x, y = p                        # unpack like a regular tuple
>>> x, y
(11, 22)
>>> p.x + p.y                       # fields also accessible by name
33
>>> d = p._asdict()                 # convert to a dictionary
>>> d['x']
11
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Point(x=100, y=22)

�,� �_�*Type names and field names must be strings�6Type names and field names must be valid identifiers: �0Type names and field names cannot be a keyword: �-Field names cannot start with an underscore: �"Encountered duplicate field name: �(Got more default values than field names�, r�c3�*"�TF	qR2x�K	R#5i)�=%rNr+)�.0�names  r&�	<genexpr>�namedtuple.<locals>.<genexpr>�����D����s�|����r��
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or multiset.  Elements are stored as dictionary keys and their counts
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>>> c = Counter('abcdeabcdabcaba')  # count elements from a string

>>> c.most_common(3)                # three most common elements
[('a', 5), ('b', 4), ('c', 3)]
>>> sorted(c)                       # list all unique elements
['a', 'b', 'c', 'd', 'e']
>>> ''.join(sorted(c.elements()))   # list elements with repetitions
'aaaaabbbbcccdde'
>>> sum(c.values())                 # total of all counts
15

>>> c['a']                          # count of letter 'a'
5
>>> for elem in 'shazam':           # update counts from an iterable
...     c[elem] += 1                # by adding 1 to each element's count
>>> c['a']                          # now there are seven 'a'
7
>>> del c['b']                      # remove all 'b'
>>> c['b']                          # now there are zero 'b'
0

>>> d = Counter('simsalabim')       # make another counter
>>> c.update(d)                     # add in the second counter
>>> c['a']                          # now there are nine 'a'
9

>>> c.clear()                       # empty the counter
>>> c
Counter()

Note:  If a count is set to zero or reduced to zero, it will remain
in the counter until the entry is deleted or the counter is cleared:

>>> c = Counter('aaabbc')
>>> c['b'] -= 2                     # reduce the count of 'b' by two
>>> c.most_common()                 # 'b' is still in, but its count is zero
[('a', 3), ('c', 1), ('b', 0)]

c�J<�\SV`4VP!V3/VBR#)��Create a new, empty Counter object.  And if given, count elements
from an input iterable.  Or, initialize the count from another mapping
of elements to their counts.

>>> c = Counter()                           # a new, empty counter
>>> c = Counter('gallahad')                 # a new counter from an iterable
>>> c = Counter({'a': 4, 'b': 2})           # a new counter from a mapping
>>> c = Counter(a=4, b=2)                   # a new counter from keyword args

N��superrdr�)r%r�r[r�s   �r&rd�Counter.__init__Z�!���	�������H�%��%r5c��^#)�1The count of elements not in the Counter is zero.r+)r%r9s  r&�__missing__�Counter.__missing__h���r5c�4�\VP44#)�Sum of the counts��sumr�)r%s r&�total�
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>>> Counter('abracadabra').most_common(3)
[('a', 5), ('b', 2), ('r', 2)]

NT�r9�reverse�r9��sortedr�r!�heapq�nlargest)r%r�s  r&�most_common�Counter.most_commonq�I��
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>>> c = Counter('ABCABC')
>>> sorted(c.elements())
['A', 'A', 'B', 'B', 'C', 'C']

Knuth's example for prime factors of 1836:  2**2 * 3**3 * 17**1

>>> import math
>>> prime_factors = Counter({2: 2, 3: 3, 17: 1})
>>> math.prod(prime_factors.elements())
1836

Note, if an element's count has been set to zero or is a negative
number, elements() will ignore it.

��_chain�
from_iterable�_starmap�_repeatr�)r%s r&�elements�Counter.elements��"��&�#�#�H�W�d�j�j�l�$C�D�Dr5c��\R4h)�@Counter.fromkeys() is undefined.  Use Counter(iterable) instead.��NotImplementedError)rYr��vs   r&r��Counter.fromkeys����"�N�P�	Pr5c�:<�Ve{\V\P4'dPV'd8VPpVP	4FwrEWS!V^4,W&K	M\
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Source can be an iterable, a dictionary, or another Counter instance.

>>> c = Counter('which')
>>> c.update('witch')           # add elements from another iterable
>>> d = Counter('watch')
>>> c.update(d)                 # add elements from another counter
>>> c['h']                      # four 'h' in which, witch, and watch
4

N�r�r�Mappingr�r�r�r�r�)r%r�r[�self_getr��countr�s      �r&r��Counter.update��{���(���(�$4�$<�$<�=�=��#�x�x�H�'/�~�~�'7���%*�X�d�A�->�%>��
�(8��G�N�8�,���/���K�K���r5c�*�VetVPp\V\P4'd-VP	4FwrEV!V^4V,
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W&K	V'dVPV4R#R#)�_Like dict.update() but subtracts counts instead of replacing them.
Counts can be reduced below zero.  Both the inputs and outputs are
allowed to contain zero and negative counts.

Source can be an iterable, a dictionary, or another Counter instance.

>>> c = Counter('which')
>>> c.subtract('witch')             # subtract elements from another iterable
>>> c.subtract(Counter('watch'))    # subtract elements from another counter
>>> c['h']                          # 2 in which, minus 1 in witch, minus 1 in watch
0
>>> c['w']                          # 1 in which, minus 1 in witch, minus 1 in watch
-1

N�r�r�rr�r��subtract)r%r�r[r�r�r�s      r&r��Counter.subtract���� ���x�x�H��(�$4�$<�$<�=�=�#+�>�>�#3�K�D�!)�$��!2�U�!:�D�J�$4�%�D�!)�$��!2�Q�!6�D�J�%���M�M�$��r5c�$�VPV4#)�Return a shallow copy.r�)r%s r&r��Counter.copy�r�r5c�2�VP\V433#)N�r�rS)r%s r&r��Counter.__reduce__�����~�~��T�
�}�,�,r5c�6<�W9d\SV`V4R#R#)�GLike dict.__delitem__() but does not raise KeyError for missing values.N�r�ry)r%r�r�s  �r&ry�Counter.__delitem__������<��G���%�r5c���V'gVPPR2#\VP44pVPPRV:R2# \d\T4pL5i;i)r�r�r��r�r-rSr�rG)r%�ds  r&r��Counter.__repr__��o����n�n�-�-�.�b�1�1�	��T�%�%�'�(�A��.�.�)�)�*�!�A�5��2�2���	��T�
�A�	���A�A2�1A2c�aa�\S\4'g\#\;QJd"VV3RlSS34F'dKR#	R#!VV3RlSS344#)�=True if all counts agree. Missing counts are treated as zero.c3�Z<"�TF qFpSV,SV,8Hx�K	K"	R#5i)Nr+)r6�c�ercr%s   ��r&r8�!Counter.__eq__.<locals>.<genexpr>!�'����I�
�1�q�!�4��7�e�A�h�&�q�&�
���(+FT�r�rrr�)r%rcs``r&r��Counter.__eq__�G����%��)�)�!�!��s�I��u�
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�I�I�Ir5c�D�\V\4'g\#W8X*#)�@True if any counts disagree. Missing counts are treated as zero.�r�rr)r%rcs  r&r�Counter.__ne__#����%��)�)�!�!�� � r5c�aa�\S\4'g\#\;QJd"VV3RlSS34F'dKR#	R#!VV3RlSS344#)�:True if all counts in self are a subset of those in other.c3�Z<"�TF qFpSV,SV,8*x�K	K"	R#5i)Nr+)r6r�r�rcr%s   ��r&r8�!Counter.__le__.<locals>.<genexpr>-r�r�FTr�)r%rcs``r&�__le__�Counter.__le__)r�r5c�Z�\V\4'g\#W8*;'dW8g#)�ATrue if all counts in self are a proper subset of those in other.r)r%rcs  r&�__lt__�Counter.__lt__/�'���%��)�)�!�!��}�.�.���.r5c�aa�\S\4'g\#\;QJd"VV3RlSS34F'dKR#	R#!VV3RlSS344#)�<True if all counts in self are a superset of those in other.c3�Z<"�TF qFpSV,SV,8�x�K	K"	R#5i)Nr+)r6r�r�rcr%s   ��r&r8�!Counter.__ge__.<locals>.<genexpr>9r�r�FTr�)r%rcs``r&�__ge__�Counter.__ge__5r�r5c�Z�\V\4'g\#W8�;'dW8g#)�CTrue if all counts in self are a proper superset of those in other.r)r%rcs  r&�__gt__�Counter.__gt__;r
r5c��\V\4'g\#\4pVP4F!wr4WAV,,pV^8�gKWRV&K#	VP4Fwr4W09gK
V^8�gKWBV&K	V#)�gAdd counts from two counters.

>>> Counter('abbb') + Counter('bcc')
Counter({'b': 4, 'c': 2, 'a': 1})

�r�rrr�)r%rcr�r�r��newcounts      r&�__add__�Counter.__add__A�x���%��)�)�!�!�����:�:�<�K�D��T�{�*�H��!�|�'�t��(�!�;�;�=�K�D���E�A�I�$�t��)��
r5c�&�\V\4'g\#\4pVP4F!wr4WAV,,
pV^8�gKWRV&K#	VP4F!wr4W09gK
V^8gK^V,
W#&K#	V#)�Subtract count, but keep only results with positive counts.

>>> Counter('abbbc') - Counter('bccd')
Counter({'b': 2, 'a': 1})

r)r%rcr�r�r�rs      r&�__sub__�Counter.__sub__T�|���%��)�)�!�!�����:�:�<�K�D��T�{�*�H��!�|�'�t��(�!�;�;�=�K�D���E�A�I� �5�y���)��
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V^8�gKWBV&K	V#)��Union is the maximum of value in either of the input counters.

>>> Counter('abbb') | Counter('bcc')
Counter({'b': 3, 'c': 2, 'a': 1})

r)r%rcr�r�r��other_countrs       r&r�Counter.__or__g����%��)�)�!�!�����:�:�<�K�D��+�K�&+�&9�{�u�H��!�|�'�t��	(�
!�;�;�=�K�D���E�A�I�$�t��)��
r5c���\V\4'g\#\4pVP4F$wr4W,pWE8dTMTpV^8�gK WbV&K&	V#)�nIntersection is the minimum of corresponding counts.

>>> Counter('abbb') & Counter('bcc')
Counter({'b': 1})

r)r%rcr�r�r�r&rs       r&�__and__�Counter.__and__{�X���%��)�)�!�!�����:�:�<�K�D��+�K� %� 3�u��H��!�|�'�t��	(�
�
r5c�f�\4pVP4Fwr#V^8�gKW1V&K	V#)�EAdds an empty counter, effectively stripping negative and zero counts�rr�)r%r�r�r�s    r&�__pos__�Counter.__pos__��0������:�:�<�K�D��q�y�$�t��(��
r5c�t�\4pVP4Fwr#V^8gK^V,
W&K	V#)�kSubtracts from an empty counter.  Strips positive and zero counts,
and flips the sign on negative counts.

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>>> class Color(Enum):
...     RED = 1
...     BLUE = 2
...     GREEN = 3

Access them by:

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  >>> Color.RED
  <Color.RED: 1>

- value lookup:

  >>> Color(1)
  <Color.RED: 1>

- name lookup:

  >>> Color['RED']
  <Color.RED: 1>

Enumerations can be iterated over, and know how many members they have:

>>> len(Color)
3

>>> list(Color)
[<Color.RED: 1>, <Color.BLUE: 2>, <Color.GREEN: 3>]

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    PYTHONPATH=Tools/peg_generator python3 -m pegen.keywordgen         Grammar/python.gram         Grammar/Tokens         Lib/keyword.py

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This exports:
  - all functions from posix or nt, e.g. unlink, stat, etc.
  - os.path is either posixpath or ntpath
  - os.name is either 'posix' or 'nt'
  - os.curdir is a string representing the current directory (always '.')
  - os.pardir is a string representing the parent directory (always '..')
  - os.sep is the (or a most common) pathname separator ('/' or '\\')
  - os.extsep is the extension separator (always '.')
  - os.altsep is the alternate pathname separator (None or '/')
  - os.pathsep is the component separator used in $PATH etc
  - os.linesep is the line separator in text files ('\n' or '\r\n')
  - os.defpath is the default search path for executables
  - os.devnull is the file path of the null device ('/dev/null', etc.)

Programs that import and use 'os' stand a better chance of being
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Super-mkdir; create a leaf directory and all intermediate ones.  Works
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rightmost) will be created if it does not exist.  If the target
directory already exists, raise an OSError if exist_ok is False.
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Super-rmdir; remove a leaf directory and all empty intermediate
ones.  Works like rmdir except that, if the leaf directory is
successfully removed, directories corresponding to rightmost path
segments will be pruned away until either the whole path is
consumed or an error occurs.  Errors during this latter phase are
ignored -- they generally mean that a directory was not empty.

N�rWrxryr)rr�r�s   r�
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Super-rename; create directories as necessary and delete any left
empty.  Works like rename, except creation of any intermediate
directories needed to make the new pathname good is attempted
first.  After the rename, directories corresponding to rightmost
path segments of the old name will be pruned until either the
whole path is consumed or a nonempty directory is found.

Note: this function can fail with the new directory structure made
if you lack permissions needed to unlink the leaf directory or
file.

N�rxryrzr{rRr�r)�old�newr�r�s    r�renamesr�	�q�����C��J�D���T�[�[��.�.����
�3�����C��J�D���	��t���t���	��	���B�B$�#B$c#�"�\P!RWW#4\V4.p\P\P
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'gK�V	PVP4K�	RRR4T'dNYT3x�\#T4F5pT!TT4pT'gT!T4'dK$TPT4K7	EK�TPYT34\#T	4FpTPT4K	EK�R# \dRpELGi;i \dRpL�i;i +'giL�;i \dpTe	T!T4Rp?EK0Rp?ii;i5i)�
Directory tree generator.

For each directory in the directory tree rooted at top (including top
itself, but excluding '.' and '..'), yields a 3-tuple

    dirpath, dirnames, filenames

dirpath is a string, the path to the directory.  dirnames is a list of
the names of the subdirectories in dirpath (including symlinks to
directories, and excluding '.' and '..').
filenames is a list of the names of the non-directory files in dirpath.
Note that the names in the lists are just names, with no path
components.  To get a full path (which begins with top) to a file or
directory in dirpath, do os.path.join(dirpath, name).

If optional arg 'topdown' is true or not specified, the triple for a
directory is generated before the triples for any of its subdirectories
(directories are generated top down).  If topdown is false, the triple
for a directory is generated after the triples for all of its
subdirectories (directories are generated bottom up).

When topdown is true, the caller can modify the dirnames list in-place
(e.g., via del or slice assignment), and walk will only recurse into the
subdirectories whose names remain in dirnames; this can be used to prune
the search, or to impose a specific order of visiting.  Modifying
dirnames when topdown is false has no effect on the behavior of
os.walk(), since the directories in dirnames have already been generated
by the time dirnames itself is generated. No matter the value of
topdown, the list of subdirectories is retrieved before the tuples for
the directory and its subdirectories are generated.

By default errors from the os.scandir() call are ignored.  If
optional arg 'onerror' is specified, it should be a function; it
will be called with one argument, an OSError instance.  It can
report the error to continue with the walk, or raise the exception
to abort the walk.  Note that the filename is available as the
filename attribute of the exception object.

By default, os.walk does not follow symbolic links to subdirectories on
systems that support them.  In order to get this functionality, set the
optional argument 'followlinks' to true.

Caution:  if you pass a relative pathname for top, don't change the
current working directory between resumptions of walk.  walk never
changes the current directory, and assumes that the client doesn't
either.

Example:

import os
from os.path import join, getsize
for root, dirs, files in os.walk('python/Lib/xml'):
    print(root, "consumes ")
    print(sum(getsize(join(root, name)) for name in files), end=" ")
    print("bytes in", len(files), "non-directory files")
    if '__pycache__' in dirs:
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�os.walkF��follow_symlinksTN��sys�audit�fspathrx�islink�join�popr}�tupler`�_walk_symlinks_as_files�is_dir�is_junctionr�appendr�
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�	3��3���D�	��"������	����AI>�2I>�I�I�"H�?H�I�H�&I�/<I�0I�:I�I�H2�I�'I�I�I>�0I>�AI>�
H/	�+I�.H/	�/I�2
I	�?I�I	�I�I	�I�I>�I�I;�#I6�/I>�6I;�;I>r�r��dir_fdc#�"�\P!RWW#V4\V4p\RW@VR33.p\	V\
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KV'd-VP4wrxV\8XgK'\V4K4R#L= T'd-TP4wrxT\8XgK'\T4K4i;i5i)�]Directory tree generator.

This behaves exactly like walk(), except that it yields a 4-tuple

    dirpath, dirnames, filenames, dirfd

`dirpath`, `dirnames` and `filenames` are identical to walk() output,
and `dirfd` is a file descriptor referring to the directory `dirpath`.

The advantage of fwalk() over walk() is that it's safe against symlink
races (when follow_symlinks is False).

If dir_fd is not None, it should be a file descriptor open to
a directory, and top should be relative; top will then be relative to
that directory.  (dir_fd is always supported for fwalk.)

Caution:
Since fwalk() yields file descriptors, those are only valid until the
next iteration step, so you should dup() them if you want to keep them
for a longer period.

Example:

import os
for root, dirs, files, rootfd in os.fwalk('python/Lib/xml'):
    print(root, "consumes", end="")
    print(sum(os.stat(name, dir_fd=rootfd).st_size for name in files),
          end="")
    print("bytes in", len(files), "non-directory files")
    if '__pycache__' in dirs:
        dirs.remove('__pycache__')  # don't visit __pycache__ directories
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