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Re-commit r83327 now that the release is done.
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‎Doc/library/functools.rst‎

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@@ -37,6 +37,57 @@ The :mod:`functools` module defines the following functions:
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.. versionadded:: 3.2
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.. decorator:: lfu_cache(maxsize)
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Decorator to wrap a function with a memoizing callable that saves up to the
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*maxsize* most frequent calls. It can save time when an expensive or I/O
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bound function is periodically called with the same arguments.
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The *maxsize* parameter defaults to 100. Since a dictionary is used to cache
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results, the positional and keyword arguments to the function must be
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hashable.
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The wrapped function is instrumented with two attributes, :attr:`hits`
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and :attr:`misses` which count the number of successful or unsuccessful
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cache lookups. These statistics are helpful for tuning the *maxsize*
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parameter and for measuring the cache's effectiveness.
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The wrapped function also has a :attr:`clear` attribute which can be
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called (with no arguments) to clear the cache.
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A `LFU (least frequently used) cache
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<http://en.wikipedia.org/wiki/Cache_algorithms#Least-Frequently_Used>`_
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is indicated when the pattern of calls does not change over time, when
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more the most common calls already seen are the best predictors of the
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most common upcoming calls.
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.. versionadded:: 3.2
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.. decorator:: lru_cache(maxsize)
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Decorator to wrap a function with a memoizing callable that saves up to the
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*maxsize* most recent calls. It can save time when an expensive or I/O bound
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function is periodically called with the same arguments.
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The *maxsize* parameter defaults to 100. Since a dictionary is used to cache
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results, the positional and keyword arguments to the function must be
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hashable.
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The wrapped function is instrumented with two attributes, :attr:`hits`
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and :attr:`misses` which count the number of successful or unsuccessful
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cache lookups. These statistics are helpful for tuning the *maxsize*
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parameter and for measuring the cache's effectiveness.
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The wrapped function also has a :attr:`clear` attribute which can be
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called (with no arguments) to clear the cache.
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A `LRU (least recently used) cache
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<http://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used>`_
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is indicated when the pattern of calls changes over time, such as
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when more recent calls are the best predictors of upcoming calls.
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.. versionadded:: 3.2
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.. decorator:: total_ordering
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Given a class defining one or more rich comparison ordering methods, this

‎Lib/functools.py‎

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@@ -4,10 +4,17 @@
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# to allow utilities written in Python to be added
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# to the functools module.
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# Written by Nick Coghlan <ncoghlan at gmail.com>
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# Copyright (C) 2006 Python Software Foundation.
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# and Raymond Hettinger <python at rcn.com>
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# Copyright (C) 2006-2010 Python Software Foundation.
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# See C source code for _functools credits/copyright
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__all__ = ['update_wrapper', 'wraps', 'WRAPPER_ASSIGNMENTS', 'WRAPPER_UPDATES',
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'total_ordering', 'cmp_to_key', 'lfu_cache', 'lru_cache']
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from _functools import partial, reduce
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from collections import OrderedDict, Counter
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from heapq import nsmallest
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from operator import itemgetter
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# update_wrapper() and wraps() are tools to help write
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# wrapper functions that can handle naive introspection
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def __hash__(self):
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raise TypeError('hash not implemented')
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return K
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def lfu_cache(maxsize=100):
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'''Least-frequently-used cache decorator.
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Arguments to the cached function must be hashable.
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Cache performance statistics stored in f.hits and f.misses.
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Clear the cache using f.clear().
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http://en.wikipedia.org/wiki/Cache_algorithms#Least-Frequently_Used
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'''
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def decorating_function(user_function):
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cache = {} # mapping of args to results
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use_count = Counter() # times each key has been accessed
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kwd_mark = object() # separate positional and keyword args
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@wraps(user_function)
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def wrapper(*args, **kwds):
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key = args
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if kwds:
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key += (kwd_mark,) + tuple(sorted(kwds.items()))
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use_count[key] += 1 # count a use of this key
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try:
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result = cache[key]
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wrapper.hits += 1
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except KeyError:
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result = user_function(*args, **kwds)
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cache[key] = result
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wrapper.misses += 1
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if len(cache) > maxsize:
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# purge the 10% least frequently used entries
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for key, _ in nsmallest(maxsize // 10,
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use_count.items(),
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key=itemgetter(1)):
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del cache[key], use_count[key]
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return result
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def clear():
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'Clear the cache and cache statistics'
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cache.clear()
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use_count.clear()
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wrapper.hits = wrapper.misses = 0
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wrapper.hits = wrapper.misses = 0
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wrapper.clear = clear
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return wrapper
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return decorating_function
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def lru_cache(maxsize=100):
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'''Least-recently-used cache decorator.
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Arguments to the cached function must be hashable.
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Cache performance statistics stored in f.hits and f.misses.
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Clear the cache using f.clear().
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http://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used
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'''
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def decorating_function(user_function):
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cache = OrderedDict() # ordered least recent to most recent
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kwd_mark = object() # separate positional and keyword args
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@wraps(user_function)
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def wrapper(*args, **kwds):
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key = args
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if kwds:
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key += (kwd_mark,) + tuple(sorted(kwds.items()))
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try:
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result = cache.pop(key)
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wrapper.hits += 1
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except KeyError:
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result = user_function(*args, **kwds)
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wrapper.misses += 1
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if len(cache) >= maxsize:
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cache.popitem(0) # purge least recently used cache entry
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cache[key] = result # record recent use of this key
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return result
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def clear():
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'Clear the cache and cache statistics'
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cache.clear()
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wrapper.hits = wrapper.misses = 0
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wrapper.hits = wrapper.misses = 0
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wrapper.clear = clear
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return wrapper
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return decorating_function

‎Lib/test/test_functools.py‎

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from test import support
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from weakref import proxy
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import pickle
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from random import choice
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@staticmethod
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def PythonPartial(func, *args, **keywords):
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class A:
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pass
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class TestLRU(unittest.TestCase):
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def test_lru(self):
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def orig(x, y):
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return 3*x+y
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f = functools.lru_cache(maxsize=20)(orig)
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domain = range(5)
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for i in range(1000):
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x, y = choice(domain), choice(domain)
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actual = f(x, y)
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expected = orig(x, y)
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self.assertEquals(actual, expected)
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self.assert_(f.hits > f.misses)
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self.assertEquals(f.hits + f.misses, 1000)
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f.clear() # test clearing
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self.assertEqual(f.hits, 0)
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self.assertEqual(f.misses, 0)
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f(x, y)
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self.assertEqual(f.hits, 0)
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self.assertEqual(f.misses, 1)
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def test_lfu(self):
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def orig(x, y):
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return 3*x+y
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f = functools.lfu_cache(maxsize=20)(orig)
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domain = range(5)
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for i in range(1000):
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x, y = choice(domain), choice(domain)
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actual = f(x, y)
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expected = orig(x, y)
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self.assertEquals(actual, expected)
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self.assert_(f.hits > f.misses)
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self.assertEquals(f.hits + f.misses, 1000)
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f.clear() # test clearing
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self.assertEqual(f.hits, 0)
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self.assertEqual(f.misses, 0)
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f(x, y)
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self.assertEqual(f.hits, 0)
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self.assertEqual(f.misses, 1)
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def test_main(verbose=None):
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test_classes = (
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TestPartial,
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TestPartialSubclass,
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TestPythonPartial,
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TestUpdateWrapper,
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TestWraps,
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TestReduce
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TestReduce,
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TestLRU,
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)
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support.run_unittest(*test_classes)
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‎Misc/NEWS‎

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- Issue #4179: In pdb, allow "list ." as a command to return to the currently
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debugged line.
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- Add lfu_cache() and lru_cache() decorators to the functools module.
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- Issue #4108: In urllib.robotparser, if there are multiple 'User-agent: *'
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entries, consider the first one.
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