class_docstr: 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(), 3))
  0.866
  >>>
  
  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.
code: def __chain_b(self):
      # Because isjunk is a user-defined (not C) function, and we test
      # for junk a LOT, it's important to minimize the number of calls.
      # Before the tricks described here, __chain_b was by far the most
      # time-consuming routine in the whole module!  If anyone sees
      # Jim Roskind, thank him again for profile.py -- I never would
      # have guessed that.
      # The first trick is to build b2j ignoring the possibility
      # of junk.  I.e., we don't call isjunk at all yet.  Throwing
      # out the junk later is much cheaper than building b2j "right"
      # from the start.
      b = self.b
      self.b2j = b2j = {}
  
      for i, elt in enumerate(b):
          indices = b2j.setdefault(elt, [])
          indices.append(i)
  
      # Purge junk elements
      self.bjunk = junk = set()
      isjunk = self.isjunk
      if isjunk:
          for elt in b2j.keys():
              if isjunk(elt):
                  junk.add(elt)
          for elt in junk:  # separate loop avoids separate list of keys
              del b2j[elt]
  
      # Purge popular elements that are not junk
      self.bpopular = popular = set()
      n = len(b)
      if self.autojunk and n >= 200:
          ntest = n // 100 + 1
          for elt, idxs in b2j.items():
              if len(idxs) > ntest:
                  popular.add(elt)
          for elt in popular:  # ditto; as fast for 1% deletion
              del b2j[elt]
  
imports: from heapq import nlargest
  from collections import namedtuple
  from types import GenericAlias
  import re
label_desc: Use image node_id 5 for calling the SequenceMatcher obj's underlying member method code with example usage: obj.__chain_b() without return types