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LRUCache.py
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LRUCache.py
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from functools import update_wrapper
from collections import namedtuple
# This is a backport of the Python 3.4 implementation.
try:
from thread import RLock
except:
class RLock:
'Dummy reentrant lock for builds without threads'
def __enter__(self):
pass
def __exit__(self, exctype, excinst, exctb):
pass
################################################################################
### LRU Cache function decorator
################################################################################
_CacheInfo = namedtuple("CacheInfo", ["hits", "misses", "maxsize", "currsize"])
class _HashedSeq(list):
""" This class guarantees that hash() will be called no more than once
per element. This is important because the lru_cache() will hash
the key multiple times on a cache miss.
"""
__slots__ = 'hashvalue'
def __init__(self, tup, hash=hash):
self[:] = tup
self.hashvalue = hash(tup)
def __hash__(self):
return self.hashvalue
def _make_key(args, kwds, typed, kwd_mark=(object(), ),
fasttypes={int, str, frozenset, type(None)},
sorted=sorted, tuple=tuple, type=type, len=len):
"""Make a cache key from optionally typed positional and keyword arguments
The key is constructed in a way that is flat as possible rather than
as a nested structure that would take more memory.
If there is only a single argument and its data type is known to cache
its hash value, then that argument is returned without a wrapper. This
saves space and improves lookup speed.
"""
key = args
if kwds:
sorted_items = sorted(kwds.items())
key += kwd_mark
for item in sorted_items:
key += item
if typed:
key += tuple(type(v) for v in args)
if kwds:
key += tuple(type(v) for k, v in sorted_items)
elif len(key) == 1 and type(key[0]) in fasttypes:
return key[0]
return _HashedSeq(key)
def lru_cache(maxsize=128, typed=False):
"""Least-recently-used cache decorator.
If *maxsize* is set to None, the LRU features are disabled and the cache
can grow without bound.
If *typed* is True, arguments of different types will be cached separately.
For example, f(3.0) and f(3) will be treated as distinct calls with
distinct results.
Arguments to the cached function must be hashable.
View the cache statistics named tuple (hits, misses, maxsize, currsize)
with f.cache_info(). Clear the cache and statistics with f.cache_clear().
Access the underlying function with f.__wrapped__.
See: http://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used
"""
# Users should only access the lru_cache through its public API:
# cache_info, cache_clear, and f.__wrapped__
# The internals of the lru_cache are encapsulated for thread safety and
# to allow the implementation to change (including a possible C version).
# Early detection of an erroneous call to @lru_cache without any arguments
# resulting in the inner function being passed to maxsize instead of an
# integer or None.
if maxsize is not None and not isinstance(maxsize, int):
raise TypeError('Expected maxsize to be an integer or None')
# Constants shared by all lru cache instances:
sentinel = object() # unique object used to signal cache misses
make_key = _make_key # build a key from the function arguments
PREV, NEXT, KEY, RESULT = 0, 1, 2, 3 # names for the link fields
def decorating_function(user_function):
cache = {}
d = {
"hits": 0,
"misses": 0,
"full": False,
"root": [] # root of the circular doubly linked list
}
cache_get = cache.get # bound method to lookup a key or return None
lock = RLock() # because linkedlist updates aren't threadsafe
d["root"][:] = [d["root"], d["root"], None, None] # initialize by pointing to self
if maxsize == 0:
def wrapper(*args, **kwds):
# No caching -- just a statistics update after a successful call
result = user_function(*args, **kwds)
d["misses"] += 1
return result
elif maxsize is None:
def wrapper(*args, **kwds):
# Simple caching without ordering or size limit
key = make_key(args, kwds, typed)
result = cache_get(key, sentinel)
if result is not sentinel:
d["hits"] += 1
return result
result = user_function(*args, **kwds)
cache[key] = result
d["misses"] += 1
return result
else:
def wrapper(*args, **kwds):
# Size limited caching that tracks accesses by recency
key = make_key(args, kwds, typed)
with lock:
link = cache_get(key)
if link is not None:
# Move the link to the front of the circular queue
link_prev, link_next, _key, result = link
link_prev[NEXT] = link_next
link_next[PREV] = link_prev
last = d["root"][PREV]
last[NEXT] = d["root"][PREV] = link
link[PREV] = last
link[NEXT] = d["root"]
d["hits"] += 1
return result
result = user_function(*args, **kwds)
with lock:
if key in cache:
# Getting here means that this same key was added to the
# cache while the lock was released. Since the link
# update is already done, we need only return the
# computed result and update the count of misses.
pass
elif d["full"]:
# Use the old root to store the new key and result.
oldroot = d["root"]
oldroot[KEY] = key
oldroot[RESULT] = result
# Empty the oldest link and make it the new root.
# Keep a reference to the old key and old result to
# prevent their ref counts from going to zero during the
# update. That will prevent potentially arbitrary object
# clean-up code (i.e. __del__) from running while we're
# still adjusting the links.
d["root"] = oldroot[NEXT]
oldkey = d["root"][KEY]
oldresult = d["root"][RESULT]
d["root"][KEY] = d["root"][RESULT] = None
# Now update the cache dictionary.
del cache[oldkey]
# Save the potentially reentrant cache[key] assignment
# for last, after the root and links have been put in
# a consistent state.
cache[key] = oldroot
else:
# Put result in a new link at the front of the queue.
last = d["root"][PREV]
link = [last, d["root"], key, result]
last[NEXT] = d["root"][PREV] = cache[key] = link
d["full"] = (len(cache) >= maxsize)
d["misses"] += 1
return result
def cache_info():
"""Report cache statistics"""
with lock:
return _CacheInfo(d["hits"], d["misses"], maxsize, len(cache))
def cache_clear():
"""Clear the cache and cache statistics"""
with lock:
cache.clear()
d["root"][:] = [d["root"], d["root"], None, None]
d["hits"] = d["misses"] = 0
d["full"] = False
wrapper.cache_info = cache_info
wrapper.cache_clear = cache_clear
return update_wrapper(wrapper, user_function)
return decorating_function