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.
docstr: 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().
docstr_tok: ["Return","an","upper","bound","on","ratio","(",")","very","quickly",".","This","is","n't","defined","beyond","that","it","is","an","upper","bound","on",".ratio","(",")",",","and","is","faster","to","compute","than","either",".ratio","(",")","or",".quick_ratio","(",")","."]
code: def real_quick_ratio(self):
      """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().
      """
  
      la, lb = len(self.a), len(self.b)
      # can't have more matches than the number of elements in the
      # shorter sequence
      return _calculate_ratio(min(la, lb), la + lb)
  
imports: from heapq import nlargest
  from collections import namedtuple
  from types import GenericAlias
  import re
label_desc: Use image node_id 12 for calling the SequenceMatcher obj's underlying member method code with example usage: obj.real_quick_ratio() and returns: _calculate_ratio