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Module difflib -- helpers for computing deltas between objects.

Function get_close_matches(word, possibilities, n=3, cutoff=0.6):
    Use SequenceMatcher to return list of the best "good enough" matches.

Function context_diff(a, b):
    For two lists of strings, return a delta in context diff format.

Function ndiff(a, b):
    Return a delta: the difference between `a` and `b` (lists of strings).

Function restore(delta, which):
    Return one of the two sequences that generated an ndiff delta.

Function unified_diff(a, b):
    For two lists of strings, return a delta in unified diff format.

Class SequenceMatcher:
    A flexible class for comparing pairs of sequences of any type.

Class Differ:
    For producing human-readable deltas from sequences of lines of text.

Class HtmlDiff:
    For producing HTML side by side comparison with change highlights.
)�get_close_matches�ndiff�restore�SequenceMatcher�Differ�IS_CHARACTER_JUNK�IS_LINE_JUNK�context_diff�unified_diff�
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SequenceMatcher is a flexible class for comparing pairs of sequences of
any type, so long as the sequence elements are hashable.  The basic
algorithm predates, and is a little fancier than, an algorithm
published in the late 1980's by Ratcliff and Obershelp under the
hyperbolic name "gestalt pattern matching".  The basic idea is to find
the longest contiguous matching subsequence that contains no "junk"
elements (R-O doesn't address junk).  The same idea is then applied
recursively to the pieces of the sequences to the left and to the right
of the matching subsequence.  This does not yield minimal edit
sequences, but does tend to yield matches that "look right" to people.

SequenceMatcher tries to compute a "human-friendly diff" between two
sequences.  Unlike e.g. UNIX(tm) diff, the fundamental notion is the
longest *contiguous* & junk-free matching subsequence.  That's what
catches peoples' eyes.  The Windows(tm) windiff has another interesting
notion, pairing up elements that appear uniquely in each sequence.
That, and the method here, appear to yield more intuitive difference
reports than does diff.  This method appears to be the least vulnerable
to syncing up on blocks of "junk lines", though (like blank lines in
ordinary text files, or maybe "<P>" lines in HTML files).  That may be
because this is the only method of the 3 that has a *concept* of
"junk" <wink>.

Example, comparing two strings, and considering blanks to be "junk":

>>> s = SequenceMatcher(lambda x: x == " ",
...                     "private Thread currentThread;",
...                     "private volatile Thread currentThread;")
>>>

.ratio() returns a float in [0, 1], measuring the "similarity" of the
sequences.  As a rule of thumb, a .ratio() value over 0.6 means the
sequences are close matches:

>>> print(round(s.ratio(), 2))
0.87
>>>

If you're only interested in where the sequences match,
.get_matching_blocks() is handy:

>>> for block in s.get_matching_blocks():
...     print("a[%d] and b[%d] match for %d elements" % block)
a[0] and b[0] match for 8 elements
a[8] and b[17] match for 21 elements
a[29] and b[38] match for 0 elements

Note that the last tuple returned by .get_matching_blocks() is always a
dummy, (len(a), len(b), 0), and this is the only case in which the last
tuple element (number of elements matched) is 0.

If you want to know how to change the first sequence into the second,
use .get_opcodes():

>>> for opcode in s.get_opcodes():
...     print("%6s a[%d:%d] b[%d:%d]" % opcode)
 equal a[0:8] b[0:8]
insert a[8:8] b[8:17]
 equal a[8:29] b[17:38]

See the Differ class for a fancy human-friendly file differencer, which
uses SequenceMatcher both to compare sequences of lines, and to compare
sequences of characters within similar (near-matching) lines.

See also function get_close_matches() in this module, which shows how
simple code building on SequenceMatcher can be used to do useful work.

Timing:  Basic R-O is cubic time worst case and quadratic time expected
case.  SequenceMatcher is quadratic time for the worst case and has
expected-case behavior dependent in a complicated way on how many
elements the sequences have in common; best case time is linear.
Nc�Z�XlS
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)a�Construct a SequenceMatcher.

Optional arg isjunk is None (the default), or a one-argument
function that takes a sequence element and returns true iff the
element is junk.  None is equivalent to passing "lambda x: 0", i.e.
no elements are considered to be junk.  For example, pass
    lambda x: x in " \t"
if you're comparing lines as sequences of characters, and don't
want to synch up on blanks or hard tabs.

Optional arg a is the first of two sequences to be compared.  By
default, an empty string.  The elements of a must be hashable.  See
also .set_seqs() and .set_seq1().

Optional arg b is the second of two sequences to be compared.  By
default, an empty string.  The elements of b must be hashable. See
also .set_seqs() and .set_seq2().

Optional arg autojunk should be set to False to disable the
"automatic junk heuristic" that treats popular elements as junk
(see module documentation for more information).
N)�isjunk�
a�
b�autojunk�set_seqs)�selfrrrr s     r�__init__�SequenceMatcher.__init__xs)��v
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)zsSet the two sequences to be compared.

>>> s = SequenceMatcher()
>>> s.set_seqs("abcd", "bcde")
>>> s.ratio()
0.75
N)�set_seq1�set_seq2)r"rrs   rr!�SequenceMatcher.set_seqs�s��	
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Set the first sequence to be compared.

The second sequence to be compared is not changed.

>>> s = SequenceMatcher(None, "abcd", "bcde")
>>> s.ratio()
0.75
>>> s.set_seq1("bcde")
>>> s.ratio()
1.0
>>>

SequenceMatcher computes and caches detailed information about the
second sequence, so if you want to compare one sequence S against
many sequences, use .set_seq2(S) once and call .set_seq1(x)
repeatedly for each of the other sequences.

See also set_seqs() and set_seq2().
N)r�matching_blocks�opcodes)r"rs  rr&�SequenceMatcher.set_seq1�s$��*
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Set the second sequence to be compared.

The first sequence to be compared is not changed.

>>> s = SequenceMatcher(None, "abcd", "bcde")
>>> s.ratio()
0.75
>>> s.set_seq2("abcd")
>>> s.ratio()
1.0
>>>

SequenceMatcher computes and caches detailed information about the
second sequence, so if you want to compare one sequence S against
many sequences, use .set_seq2(S) once and call .set_seq1(x)
repeatedly for each of the other sequences.

See also set_seqs() and set_seq1().
N)rr*r+�
fullbcount�_SequenceMatcher__chain_b)r"rs  rr'�SequenceMatcher.set_seq2�s5��*
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setdefault�append�set�bjunkr�keys�add�bpopular�lenr �items)r"rr5�
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(aXYU-XjU-:XaM9[X�U5$)a9Find longest matching block in a[alo:ahi] and b[blo:bhi].

By default it will find the longest match in the entirety of a and b.

If isjunk is not defined:

Return (i,j,k) such that a[i:i+k] is equal to b[j:j+k], where
    alo <= i <= i+k <= ahi
    blo <= j <= j+k <= bhi
and for all (i',j',k') meeting those conditions,
    k >= k'
    i <= i'
    and if i == i', j <= j'

In other words, of all maximal matching blocks, return one that
starts earliest in a, and of all those maximal matching blocks that
start earliest in a, return the one that starts earliest in b.

>>> s = SequenceMatcher(None, " abcd", "abcd abcd")
>>> s.find_longest_match(0, 5, 0, 9)
Match(a=0, b=4, size=5)

If isjunk is defined, first the longest matching block is
determined as above, but with the additional restriction that no
junk element appears in the block.  Then that block is extended as
far as possible by matching (only) junk elements on both sides.  So
the resulting block never matches on junk except as identical junk
happens to be adjacent to an "interesting" match.

Here's the same example as before, but considering blanks to be
junk.  That prevents " abcd" from matching the " abcd" at the tail
end of the second sequence directly.  Instead only the "abcd" can
match, and matches the leftmost "abcd" in the second sequence:

>>> s = SequenceMatcher(lambda x: x==" ", " abcd", "abcd abcd")
>>> s.find_longest_match(0, 5, 0, 9)
Match(a=1, b=0, size=4)

If no blocks match, return (alo, blo, 0).

>>> s = SequenceMatcher(None, "ab", "c")
>>> s.find_longest_match(0, 2, 0, 1)
Match(a=0, b=0, size=0)
rr4)	rrr5r:�__contains__r>�range�getr
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�E�1�(�E�(�,��E�!�(�k�e�k��!�!�G�*�%�%��a��j�A�A�g�J�&�%*�1�W�e�A�g�x�
�z�(�E��k�e�k��!�!�G�*�%�%��a��j�A�A�g�J�&��n�s�"�u�~��';��!�(�N�+�,�,��h���1�8�^�#4�4��
�M�H��n�s�"�u�~��';��!�(�N�+�,�,��h���1�8�^�#4�4��k�e�k��a�a��j�!�!��a��j�A�A�g�J�&�%*�1�W�e�A�g�x�
�z�(�E��k�e�k��a�a��j�!�!��a��j�A�A�g�J�&��n�s�"�u�~��';��a�h��'�(�(��h���1�8�^�#4�4��!�|�H��n�s�"�u�~��';��a�h��'�(�(��h���1�8�^�#4�4��U�8�,�,rc
�<�URbUR$[UR5
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 [[[RU55
UlUR$)a�Return list of triples describing matching subsequences.

Each triple is of the form (i, j, n), and means that
a[i:i+n] == b[j:j+n].  The triples are monotonically increasing in
i and in j.  New in Python 2.5, it's also guaranteed that if
(i, j, n) and (i', j', n') are adjacent triples in the list, and
the second is not the last triple in the list, then i+n != i' or
j+n != j'.  IOW, adjacent triples never describe adjacent equal
blocks.

The last triple is a dummy, (len(a), len(b), 0), and is the only
triple with n==0.

>>> s = SequenceMatcher(None, "abxcd", "abcd")
>>> list(s.get_matching_blocks())
[Match(a=0, b=0, size=2), Match(a=3, b=2, size=2), Match(a=5, b=4, size=0)]
r)r*r>rr�popr\r8�sort�list�mapr
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��&���+��'�'�'��T�V�V��c�$�&�&�k�B��R��B�� �����!&�����C�c��1�1�#�C�E�E�G�A�!���&�&�q�)��7�s�w��L�L�#�#�!1�2��3��9���s���L�L�!�#�s�a�C��!5�6��e�	����
����R���)�J�B��B��w�"�}���B���b���
� �'�'����5���R����*��������-����b�a�[�*�#�C����\�$B�C����#�#�#rc
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UlnUR5HcupEnSnX:aX%:aSnOX:aSnOX%:aSnU(aURXqXBU45
 XF-XV-p!U(dMOURSXAXR45
 Me U$)a�Return list of 5-tuples describing how to turn a into b.

Each tuple is of the form (tag, i1, i2, j1, j2).  The first tuple
has i1 == j1 == 0, and remaining tuples have i1 == the i2 from the
tuple preceding it, and likewise for j1 == the previous j2.

The tags are strings, with these meanings:

'replace':  a[i1:i2] should be replaced by b[j1:j2]
'delete':   a[i1:i2] should be deleted.
            Note that j1==j2 in this case.
'insert':   b[j1:j2] should be inserted at a[i1:i1].
            Note that i1==i2 in this case.
'equal':    a[i1:i2] == b[j1:j2]

>>> a = "qabxcd"
>>> b = "abycdf"
>>> s = SequenceMatcher(None, a, b)
>>> for tag, i1, i2, j1, j2 in s.get_opcodes():
...    print(("%7s a[%d:%d] (%s) b[%d:%d] (%s)" %
...           (tag, i1, i2, a[i1:i2], j1, j2, b[j1:j2])))
 delete a[0:1] (q) b[0:0] ()
  equal a[1:3] (ab) b[0:2] (ab)
replace a[3:4] (x) b[2:3] (y)
  equal a[4:6] (cd) b[3:5] (cd)
 insert a[6:6] () b[5:6] (f)
r��replace�delete�insert�equal)r+ror8)r"r@rZ�answer�ai�bj�size�tags        r�get_opcodes�SequenceMatcher.get_opcodes�
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)a9Isolate change clusters by eliminating ranges with no changes.

Return a generator of groups with up to n lines of context.
Each group is in the same format as returned by get_opcodes().

>>> from pprint import pprint
>>> a = list(map(str, range(1,40)))
>>> b = a[:]
>>> b[8:8] = ['i']     # Make an insertion
>>> b[20] += 'x'       # Make a replacement
>>> b[23:28] = []      # Make a deletion
>>> b[30] += 'y'       # Make another replacement
>>> pprint(list(SequenceMatcher(None,a,b).get_grouped_opcodes()))
[[('equal', 5, 8, 5, 8), ('insert', 8, 8, 8, 9), ('equal', 8, 11, 9, 12)],
 [('equal', 16, 19, 17, 20),
  ('replace', 19, 20, 20, 21),
  ('equal', 20, 22, 21, 23),
  ('delete', 22, 27, 23, 23),
  ('equal', 27, 30, 23, 26)],
 [('equal', 31, 34, 27, 30),
  ('replace', 34, 35, 30, 31),
  ('equal', 35, 38, 31, 34)]]
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�(��C�R�R��C��q�D�M�2�s�2�!�t�}�b�@�E�!�H���9�Q�<�7�"�"'��)��C�R�R���R�A����C��q�D�M�A�E�"�I�
�U����#(��C�R�R��g�~�"�%�"�*����c�s�2�!�t�}�b�#�b�Q�$�-�H�I������R�A����B�1��
�B��L�L�#�2�2�.�/�$)��#�e�*�a�-�E�!�H�Q�K�7�,B��K�-C
�5�s�D4D6
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-5$)aRReturn a measure of the sequences' similarity (float in [0,1]).

Where T is the total number of elements in both sequences, and
M is the number of matches, this is 2.0*M / T.
Note that this is 1 if the sequences are identical, and 0 if
they have nothing in common.

.ratio() is expensive to compute if you haven't already computed
.get_matching_blocks() or .get_opcodes(), in which case you may
want to try .quick_ratio() or .real_quick_ratio() first to get an
upper bound.

>>> s = SequenceMatcher(None, "abcd", "bcde")
>>> s.ratio()
0.75
>>> s.quick_ratio()
0.75
>>> s.real_quick_ratio()
1.0
c
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This isn't defined beyond that it is an upper bound on .ratio(), and
is faster to compute.
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z�Return an upper bound on ratio() very quickly.

This isn't defined beyond that it is an upper bound on .ratio(), and
is faster to compute than either .ratio() or .quick_ratio().
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 My [X$5nUVVs/sHupvUPM	 snn$s snnf)awUse SequenceMatcher to return list of the best "good enough" matches.

word is a sequence for which close matches are desired (typically a
string).

possibilities is a list of sequences against which to match word
(typically a list of strings).

Optional arg n (default 3) is the maximum number of close matches to
return.  n must be > 0.

Optional arg cutoff (default 0.6) is a float in [0, 1].  Possibilities
that don't score at least that similar to word are ignored.

The best (no more than n) matches among the possibilities are returned
in a list, sorted by similarity score, most similar first.

>>> get_close_matches("appel", ["ape", "apple", "peach", "puppy"])
['apple', 'ape']
>>> import keyword as _keyword
>>> get_close_matches("wheel", _keyword.kwlist)
['while']
>>> get_close_matches("Apple", _keyword.kwlist)
[]
>>> get_close_matches("accept", _keyword.kwlist)
['except']
rzn must be > 0: grzcutoff must be in [0.0, 1.0]: )	�
ValueErrorrr'r&r�r�r�r8�	_nlargest)�word�
possibilitiesrE�cutoff�result�
srg�scores        rrr�s���:
�
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�����6�)��=�=�?�f�$��7�7�9����M�M�1�7�7�9�a�.�)���q�
!�F�$�%�f�(�%�A�f�%�%��%s�C0c�D�S
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$)zAReplace whitespace with the original whitespace characters in `s`rrc
3�d# �UH&upUS:XaU
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Differ is a class for comparing sequences of lines of text, and
producing human-readable differences or deltas.  Differ uses
SequenceMatcher both to compare sequences of lines, and to compare
sequences of characters within similar (near-matching) lines.

Each line of a Differ delta begins with a two-letter code:

    '- '    line unique to sequence 1
    '+ '    line unique to sequence 2
    '  '    line common to both sequences
    '? '    line not present in either input sequence

Lines beginning with '? ' attempt to guide the eye to intraline
differences, and were not present in either input sequence.  These lines
can be confusing if the sequences contain tab characters.

Note that Differ makes no claim to produce a *minimal* diff.  To the
contrary, minimal diffs are often counter-intuitive, because they synch
up anywhere possible, sometimes accidental matches 100 pages apart.
Restricting synch points to contiguous matches preserves some notion of
locality, at the occasional cost of producing a longer diff.

Example: Comparing two texts.

First we set up the texts, sequences of individual single-line strings
ending with newlines (such sequences can also be obtained from the
`readlines()` method of file-like objects):

>>> text1 = '''  1. Beautiful is better than ugly.
...   2. Explicit is better than implicit.
...   3. Simple is better than complex.
...   4. Complex is better than complicated.
... '''.splitlines(keepends=True)
>>> len(text1)
4
>>> text1[0][-1]
'\n'
>>> text2 = '''  1. Beautiful is better than ugly.
...   3.   Simple is better than complex.
...   4. Complicated is better than complex.
...   5. Flat is better than nested.
... '''.splitlines(keepends=True)

Next we instantiate a Differ object:

>>> d = Differ()

Note that when instantiating a Differ object we may pass functions to
filter out line and character 'junk'.  See Differ.__init__ for details.

Finally, we compare the two:

>>> result = list(d.compare(text1, text2))

'result' is a list of strings, so let's pretty-print it:

>>> from pprint import pprint as _pprint
>>> _pprint(result)
['    1. Beautiful is better than ugly.\n',
 '-   2. Explicit is better than implicit.\n',
 '-   3. Simple is better than complex.\n',
 '+   3.   Simple is better than complex.\n',
 '?     ++\n',
 '-   4. Complex is better than complicated.\n',
 '?            ^                     ---- ^\n',
 '+   4. Complicated is better than complex.\n',
 '?           ++++ ^                      ^\n',
 '+   5. Flat is better than nested.\n']

As a single multi-line string it looks like this:

>>> print(''.join(result), end="")
    1. Beautiful is better than ugly.
-   2. Explicit is better than implicit.
-   3. Simple is better than complex.
+   3.   Simple is better than complex.
?     ++
-   4. Complex is better than complicated.
?            ^                     ---- ^
+   4. Complicated is better than complex.
?           ++++ ^                      ^
+   5. Flat is better than nested.
Nc��XlX l
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)aU
Construct a text differencer, with optional filters.

The two optional keyword parameters are for filter functions:

- `linejunk`: A function that should accept a single string argument,
  and return true iff the string is junk. The module-level function
  `IS_LINE_JUNK` may be used to filter out lines without visible
  characters, except for at most one splat ('#').  It is recommended
  to leave linejunk None; the underlying SequenceMatcher class has
  an adaptive notion of "noise" lines that's better than any static
  definition the author has ever been able to craft.

- `charjunk`: A function that should accept a string of length 1. The
  module-level function `IS_CHARACTER_JUNK` may be used to filter out
  whitespace characters (a blank or tab; **note**: bad idea to include
  newline in this!).  Use of IS_CHARACTER_JUNK is recommended.
N��linejunk�charjunk)r"r�r�s   rr#�Differ.__init__*s��(!�
� �
rc	#�t
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Compare two sequences of lines; generate the resulting delta.

Each sequence must contain individual single-line strings ending with
newlines. Such sequences can be obtained from the `readlines()` method
of file-like objects.  The delta generated also consists of newline-
terminated strings, ready to be printed as-is via the writelines()
method of a file-like object.

Example:

>>> print(''.join(Differ().compare('one\ntwo\nthree\n'.splitlines(True),
...                                'ore\ntree\nemu\n'.splitlines(True))),
...       end="")
- one
?  ^
+ ore
?  ^
- two
- three
?  -
+ tree
+ emu
rsrt�
-ru�
+rvr��unknown tag N)rr�r|�_fancy_replace�_dumpr�)
r"rr�cruncherr{rNrOrPrQ�
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          r�compare�Differ.compareAs����4#�4�=�=�!�7��'/�';�';�'=�#�C�c���i���'�'�
���A�
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�����J�J�s�A�C�0�
�����J�J�s�A�C�0�
� �S�!:�;�;��L�L�(>�
�s�B*B8
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c#�J# �[
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rL)r"r{rg�lo�hir@s      rr��Differ._dumpjs ����r��A� �!�$�'�'��s�!#
c#�# �X#:aXV:de
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          r�_plain_replace�Differ._plain_replaceos�����y�S�Y�&�&��9�s�y� ��Z�Z��Q�S�1�E��Z�Z��Q�S�1�F��Z�Z��Q�S�1�E��Z�Z��Q�S�1�F���A��L�L���s�A/A=
�1
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A=
c#�"# �S
upx[
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U	RU
5
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cX�p�MU	R	U5
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5U:�dMPU	R5U:�dMfU	R5X�nnnM{ M� Xx:a%U
cURXX4XV5Shv
�N gX�SnnnOSn
URXWXEW5Shv
�N U
UUUnnU
c�S=
nnU	RUU5 U	R5HpunnnnnUU-
UU-
nnUS:XaUSU--
nUSU--
nM+US:Xa
US	U--
nM;US
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USU--
nMKUS:XaUS
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When replacing one block of lines with another, search the blocks
for *similar* lines; the best-matching pair (if any) is used as a
synch point, and intraline difference marking is done on the
similar pair. Lots of work, but often worth it.

Example:

>>> d = Differ()
>>> results = d._fancy_replace(['abcDefghiJkl\n'], 0, 1,
...                            ['abcdefGhijkl\n'], 0, 1)
>>> print(''.join(results), end="")
- abcDefghiJkl
?    ^  ^  ^
+ abcdefGhijkl
?    ^  ^  ^
)g�G�z��?g�?�NNNrrrrs�
^rtr�rur�rvr�r��  r4)rr�rLr'r&r�r�r�r��
_fancy_helperr!r|r��_qformat)r"rrNrOrrPrQ�
best_ratior�r��eqi�eqjrZryr@rx�best_i�best_j�aelt�belt�atags�btagsr{�ai1�ai2�bj1�bj2rdres                             rr��Differ._fancy_replace}sb���*(��
�"�4�=�=�1�����
�s��A���B����b�!��3�_�
��T���8��{�#$�S���!�!�"�%��,�,�.��;��*�*�,�z�9��n�n�&��3�19���1A�1���J��!%�!�(���{��.�.�q�s�s�H�H�H��),�3�J�F�F�J��C��%�%�a�f�a�f�E�E�E��v�Y�
�&�	�d���;���E�E����d�D�)�+3�+?�+?�+A�'��S�#�s�C��s��C�#�I�B���)�#��S�2�X�%�E��S�2�X�%�E��H�_��S�2�X�%�E��H�_��S�2�X�%�E��G�^��S�2�X�%�E��S�2�X�%�E�$��%>�?�?�,B
��}�}�T�4���>�>�>���+���%�%�a��
��3�6�!�8�S�I�I�I�Q
I
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�
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c#�# �/nX#:a-XV:aUR
XX4XV5nO,URS
XU5nOXV:aURSXEU5nUShv
�N gN7f
)Nr�r�)r�r�)r"rrNrOrrPrQr�s        rr��Differ._fancy_helper�sX����
��9��y��'�'�
���A�
��J�J�s�A�C�0�
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�Y��
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��,�A����s�AA
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-v� U(aSUS3v� SU-v� U(a	SUS3v� gg7f
)a:

Format "?" output and deal with tabs.

Example:

>>> d = Differ()
>>> results = d._qformat('\tabcDefghiJkl\n', '\tabcdefGhijkl\n',
...                      '  ^ ^  ^      ', '  ^ ^  ^      ')
>>> for line in results: print(repr(line))
...
'- \tabcDefghiJkl\n'
'? \t ^ ^  ^\n'
'+ \tabcdefGhijkl\n'
'? \t ^ ^  ^\n'
�- z? �

�+ N)r��rstrip)r"�aline�bliner�r�s     rr��Differ._qformat�sh��� "�%�/�6�6�8��!�%�/�6�6�8���U�l����u�g�R�.� ��U�l����u�g�R�.� ��s�A!A#
)r�r�r�)
r�r�r�r�r�r#r�r�r�r�r�r�r�rrrrr�s0��S
�j!�.'�R
(�
�\
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Return True for ignorable line: iff `line` is blank or contains a single '#'.

Examples:

>>> IS_LINE_JUNK('\n')
True
>>> IS_LINE_JUNK('  #   \n')
True
>>> IS_LINE_JUNK('hello\n')
False
Nr)�line�pats  rrrs���t�9�D� � rc�
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Return True for ignorable character: iff `ch` is a space or tab.

Examples:

>>> IS_CHARACTER_JUNK(' ')
True
>>> IS_CHARACTER_JUNK('\t')
True
>>> IS_CHARACTER_JUNK('\n')
False
>>> IS_CHARACTER_JUNK('x')
False
r)�ch�wss  rrr%s�� �8�Orc�|�US
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�format��start�stop�	beginningrs    r�_format_range_unifiedr�<sE���
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nSU-v� M M$US;aUUUH
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Compare two sequences of lines; generate the delta as a unified diff.

Unified diffs are a compact way of showing line changes and a few
lines of context.  The number of context lines is set by 'n' which
defaults to three.

By default, the diff control lines (those with ---, +++, or @@) are
created with a trailing newline.  This is helpful so that inputs
created from file.readlines() result in diffs that are suitable for
file.writelines() since both the inputs and outputs have trailing
newlines.

For inputs that do not have trailing newlines, set the lineterm
argument to "" so that the output will be uniformly newline free.

The unidiff format normally has a header for filenames and modification
times.  Any or all of these may be specified using strings for
'fromfile', 'tofile', 'fromfiledate', and 'tofiledate'.
The modification times are normally expressed in the ISO 8601 format.

Example:

>>> for line in unified_diff('one two three four'.split(),
...             'zero one tree four'.split(), 'Original', 'Current',
...             '2005-01-26 23:30:50', '2010-04-02 10:20:52',
...             lineterm=''):
...     print(line)                 # doctest: +NORMALIZE_WHITESPACE
--- Original        2005-01-26 23:30:50
+++ Current         2010-04-02 10:20:52
@@ -1,4 +1,4 @@
+zero
 one
-two
-three
+tree
 four
FNT�	{}rr�
--- {}{}{}z
+++ {}{}{}rrr4�r��z@@ -{} +{} @@{}rvr�>rtrsr�>rursr�)�_check_typesrr�r�r�)rr�fromfile�tofile�fromfiledate�
tofiledaterE�lineterm�startedr��fromdate�todater��last�file1_range�file2_ranger{rhrlrirmr�s                      rr
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��x��8�L��G� ��a�*�>�>�q�A����G�6B�v�}�}�\�2��H�2<�V�]�]�:�.�"�F��%�%�h�(�C�C��%�%�f�h�?�?��A�h��b�	�t�+�E�!�H�d�1�g�>��+�E�!�H�d�1�g�>���&�&�{��J�J�#(��C��R��R��g�~��b��H�D���*�$�%���+�+��b��H�D���*�$�%��+�+��b��H�D���*�$�%�$)�B
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Compare two sequences of lines; generate the delta as a context diff.

Context diffs are a compact way of showing line changes and a few
lines of context.  The number of context lines is set by 'n' which
defaults to three.

By default, the diff control lines (those with *** or ---) are
created with a trailing newline.  This is helpful so that inputs
created from file.readlines() result in diffs that are suitable for
file.writelines() since both the inputs and outputs have trailing
newlines.

For inputs that do not have trailing newlines, set the lineterm
argument to "" so that the output will be uniformly newline free.

The context diff format normally has a header for filenames and
modification times.  Any or all of these may be specified using
strings for 'fromfile', 'tofile', 'fromfiledate', and 'tofiledate'.
The modification times are normally expressed in the ISO 8601 format.
If not specified, the strings default to blanks.

Example:

>>> print(''.join(context_diff('one\ntwo\nthree\nfour\n'.splitlines(True),
...       'zero\none\ntree\nfour\n'.splitlines(True), 'Original', 'Current')),
...       end="")
*** Original
--- Current
***************
*** 1,4 ****
  one
! two
! three
  four
--- 1,4 ----
+ zero
  one
! tree
  four
r�r�z! r�)rurtrsrvFNTr�rrz
*** {}{}{}r�rrz***************r4r�z
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isinstance�str�	TypeError�typer�)rr�args�args    rr

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Compare `a` and `b`, two sequences of lines represented as bytes rather
than str. This is a wrapper for `dfunc`, which is typically either
unified_diff() or context_diff(). Inputs are losslessly converted to
strings so that `dfunc` only has to worry about strings, and encoded
back to bytes on return. This is necessary to compare files with
unknown or inconsistent encoding. All other inputs (except `n`) must be
bytes rather than str.
c
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�linesr�s            rrr�s����*�	
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Compare `a` and `b` (lists of strings); return a `Differ`-style delta.

Optional keyword parameters `linejunk` and `charjunk` are for filter
functions, or can be None:

- linejunk: A function that should accept a single string argument and
  return true iff the string is junk.  The default is None, and is
  recommended; the underlying SequenceMatcher class has an adaptive
  notion of "noise" lines.

- charjunk: A function that accepts a character (string of length
  1), and returns true iff the character is junk. The default is
  the module-level function IS_CHARACTER_JUNK, which filters out
  whitespace characters (a blank or tab; note: it's a bad idea to
  include newline in this!).

Tools/scripts/ndiff.py is a command-line front-end to this function.

Example:

>>> diff = ndiff('one\ntwo\nthree\n'.splitlines(keepends=True),
...              'ore\ntree\nemu\n'.splitlines(keepends=True))
>>> print(''.join(diff), end="")
- one
?  ^
+ ore
?  ^
- two
- three
?  -
+ tree
+ emu
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linejunk -- passed on to ndiff (see ndiff documentation)
charjunk -- passed on to ndiff (see ndiff documentation)

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from/to line tuple -- (line num, line text)
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boolean flag -- None indicates context separation, True indicates
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