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356 lines
14 KiB
Python
356 lines
14 KiB
Python
"""functools.py - Tools for working with functions and callable objects
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"""
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# Python module wrapper for _functools C module
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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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# 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', 'lru_cache', 'reduce', 'partial']
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try:
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from _functools import reduce
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except ImportError:
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pass
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from collections import namedtuple
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try:
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from _thread import RLock
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except:
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class RLock:
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'Dummy reentrant lock for builds without threads'
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def __enter__(self): pass
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def __exit__(self, exctype, excinst, exctb): pass
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################################################################################
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### update_wrapper() and wraps() decorator
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################################################################################
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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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WRAPPER_ASSIGNMENTS = ('__module__', '__name__', '__qualname__', '__doc__',
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'__annotations__')
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WRAPPER_UPDATES = ('__dict__',)
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def update_wrapper(wrapper,
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wrapped,
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assigned = WRAPPER_ASSIGNMENTS,
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updated = WRAPPER_UPDATES):
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"""Update a wrapper function to look like the wrapped function
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wrapper is the function to be updated
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wrapped is the original function
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assigned is a tuple naming the attributes assigned directly
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from the wrapped function to the wrapper function (defaults to
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functools.WRAPPER_ASSIGNMENTS)
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updated is a tuple naming the attributes of the wrapper that
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are updated with the corresponding attribute from the wrapped
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function (defaults to functools.WRAPPER_UPDATES)
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"""
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wrapper.__wrapped__ = wrapped
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for attr in assigned:
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try:
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value = getattr(wrapped, attr)
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except AttributeError:
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pass
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else:
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setattr(wrapper, attr, value)
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for attr in updated:
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getattr(wrapper, attr).update(getattr(wrapped, attr, {}))
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# Return the wrapper so this can be used as a decorator via partial()
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return wrapper
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def wraps(wrapped,
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assigned = WRAPPER_ASSIGNMENTS,
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updated = WRAPPER_UPDATES):
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"""Decorator factory to apply update_wrapper() to a wrapper function
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Returns a decorator that invokes update_wrapper() with the decorated
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function as the wrapper argument and the arguments to wraps() as the
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remaining arguments. Default arguments are as for update_wrapper().
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This is a convenience function to simplify applying partial() to
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update_wrapper().
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"""
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return partial(update_wrapper, wrapped=wrapped,
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assigned=assigned, updated=updated)
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################################################################################
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### total_ordering class decorator
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################################################################################
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def total_ordering(cls):
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"""Class decorator that fills in missing ordering methods"""
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convert = {
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'__lt__': [('__gt__', lambda self, other: not (self < other or self == other)),
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('__le__', lambda self, other: self < other or self == other),
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('__ge__', lambda self, other: not self < other)],
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'__le__': [('__ge__', lambda self, other: not self <= other or self == other),
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('__lt__', lambda self, other: self <= other and not self == other),
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('__gt__', lambda self, other: not self <= other)],
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'__gt__': [('__lt__', lambda self, other: not (self > other or self == other)),
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('__ge__', lambda self, other: self > other or self == other),
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('__le__', lambda self, other: not self > other)],
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'__ge__': [('__le__', lambda self, other: (not self >= other) or self == other),
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('__gt__', lambda self, other: self >= other and not self == other),
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('__lt__', lambda self, other: not self >= other)]
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}
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# Find user-defined comparisons (not those inherited from object).
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roots = [op for op in convert if getattr(cls, op, None) is not getattr(object, op, None)]
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if not roots:
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raise ValueError('must define at least one ordering operation: < > <= >=')
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root = max(roots) # prefer __lt__ to __le__ to __gt__ to __ge__
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for opname, opfunc in convert[root]:
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if opname not in roots:
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opfunc.__name__ = opname
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opfunc.__doc__ = getattr(int, opname).__doc__
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setattr(cls, opname, opfunc)
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return cls
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################################################################################
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### cmp_to_key() function converter
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################################################################################
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def cmp_to_key(mycmp):
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"""Convert a cmp= function into a key= function"""
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class K(object):
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__slots__ = ['obj']
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def __init__(self, obj):
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self.obj = obj
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def __lt__(self, other):
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return mycmp(self.obj, other.obj) < 0
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def __gt__(self, other):
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return mycmp(self.obj, other.obj) > 0
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def __eq__(self, other):
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return mycmp(self.obj, other.obj) == 0
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def __le__(self, other):
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return mycmp(self.obj, other.obj) <= 0
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def __ge__(self, other):
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return mycmp(self.obj, other.obj) >= 0
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def __ne__(self, other):
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return mycmp(self.obj, other.obj) != 0
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__hash__ = None
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return K
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try:
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from _functools import cmp_to_key
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except ImportError:
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pass
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################################################################################
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### partial() argument application
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################################################################################
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def partial(func, *args, **keywords):
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"""new function with partial application of the given arguments
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and keywords.
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"""
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def newfunc(*fargs, **fkeywords):
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newkeywords = keywords.copy()
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newkeywords.update(fkeywords)
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return func(*(args + fargs), **newkeywords)
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newfunc.func = func
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newfunc.args = args
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newfunc.keywords = keywords
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return newfunc
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try:
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from _functools import partial
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except ImportError:
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pass
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################################################################################
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### LRU Cache function decorator
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################################################################################
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_CacheInfo = namedtuple("CacheInfo", ["hits", "misses", "maxsize", "currsize"])
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class _HashedSeq(list):
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""" This class guarantees that hash() will be called no more than once
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per element. This is important because the lru_cache() will hash
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the key multiple times on a cache miss.
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"""
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__slots__ = 'hashvalue'
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def __init__(self, tup, hash=hash):
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self[:] = tup
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self.hashvalue = hash(tup)
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def __hash__(self):
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return self.hashvalue
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def _make_key(args, kwds, typed,
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kwd_mark = (object(),),
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fasttypes = {int, str, frozenset, type(None)},
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sorted=sorted, tuple=tuple, type=type, len=len):
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"""Make a cache key from optionally typed positional and keyword arguments
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The key is constructed in a way that is flat as possible rather than
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as a nested structure that would take more memory.
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If there is only a single argument and its data type is known to cache
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its hash value, then that argument is returned without a wrapper. This
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saves space and improves lookup speed.
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"""
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key = args
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if kwds:
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sorted_items = sorted(kwds.items())
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key += kwd_mark
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for item in sorted_items:
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key += item
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if typed:
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key += tuple(type(v) for v in args)
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if kwds:
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key += tuple(type(v) for k, v in sorted_items)
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elif len(key) == 1 and type(key[0]) in fasttypes:
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return key[0]
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return _HashedSeq(key)
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def lru_cache(maxsize=128, typed=False):
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"""Least-recently-used cache decorator.
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If *maxsize* is set to None, the LRU features are disabled and the cache
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can grow without bound.
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If *typed* is True, arguments of different types will be cached separately.
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For example, f(3.0) and f(3) will be treated as distinct calls with
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distinct results.
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Arguments to the cached function must be hashable.
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View the cache statistics named tuple (hits, misses, maxsize, currsize)
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with f.cache_info(). Clear the cache and statistics with f.cache_clear().
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Access the underlying function with f.__wrapped__.
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See: http://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used
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"""
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# Users should only access the lru_cache through its public API:
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# cache_info, cache_clear, and f.__wrapped__
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# The internals of the lru_cache are encapsulated for thread safety and
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# to allow the implementation to change (including a possible C version).
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# Constants shared by all lru cache instances:
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sentinel = object() # unique object used to signal cache misses
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make_key = _make_key # build a key from the function arguments
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PREV, NEXT, KEY, RESULT = 0, 1, 2, 3 # names for the link fields
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def decorating_function(user_function):
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cache = {}
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hits = misses = 0
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full = False
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cache_get = cache.get # bound method to lookup a key or return None
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lock = RLock() # because linkedlist updates aren't threadsafe
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root = [] # root of the circular doubly linked list
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root[:] = [root, root, None, None] # initialize by pointing to self
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if maxsize == 0:
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def wrapper(*args, **kwds):
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# No caching -- just a statistics update after a successful call
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nonlocal misses
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result = user_function(*args, **kwds)
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misses += 1
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return result
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elif maxsize is None:
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def wrapper(*args, **kwds):
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# Simple caching without ordering or size limit
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nonlocal hits, misses
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key = make_key(args, kwds, typed)
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result = cache_get(key, sentinel)
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if result is not sentinel:
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hits += 1
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return result
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result = user_function(*args, **kwds)
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cache[key] = result
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misses += 1
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return result
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else:
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def wrapper(*args, **kwds):
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# Size limited caching that tracks accesses by recency
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nonlocal root, hits, misses, full
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key = make_key(args, kwds, typed)
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with lock:
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link = cache_get(key)
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if link is not None:
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# Move the link to the front of the circular queue
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link_prev, link_next, _key, result = link
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link_prev[NEXT] = link_next
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link_next[PREV] = link_prev
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last = root[PREV]
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last[NEXT] = root[PREV] = link
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link[PREV] = last
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link[NEXT] = root
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hits += 1
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return result
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result = user_function(*args, **kwds)
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with lock:
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if key in cache:
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# Getting here means that this same key was added to the
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# cache while the lock was released. Since the link
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# update is already done, we need only return the
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# computed result and update the count of misses.
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pass
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elif full:
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# Use the old root to store the new key and result.
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oldroot = root
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oldroot[KEY] = key
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oldroot[RESULT] = result
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# Empty the oldest link and make it the new root.
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# Keep a reference to the old key and old result to
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# prevent their ref counts from going to zero during the
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# update. That will prevent potentially arbitrary object
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# clean-up code (i.e. __del__) from running while we're
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# still adjusting the links.
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root = oldroot[NEXT]
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oldkey = root[KEY]
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oldresult = root[RESULT]
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root[KEY] = root[RESULT] = None
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# Now update the cache dictionary.
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del cache[oldkey]
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# Save the potentially reentrant cache[key] assignment
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# for last, after the root and links have been put in
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# a consistent state.
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cache[key] = oldroot
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else:
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# Put result in a new link at the front of the queue.
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last = root[PREV]
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link = [last, root, key, result]
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last[NEXT] = root[PREV] = cache[key] = link
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full = (len(cache) >= maxsize)
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misses += 1
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return result
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def cache_info():
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"""Report cache statistics"""
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with lock:
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return _CacheInfo(hits, misses, maxsize, len(cache))
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def cache_clear():
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"""Clear the cache and cache statistics"""
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nonlocal hits, misses, full
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with lock:
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cache.clear()
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root[:] = [root, root, None, None]
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hits = misses = 0
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full = False
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wrapper.cache_info = cache_info
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wrapper.cache_clear = cache_clear
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return update_wrapper(wrapper, user_function)
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return decorating_function
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