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from __future__ import absolute_import
import sys
from functools import *
from .version_info import PY2, PY3
if PY2:
reduce = reduce
if sys.version_info <= (3, 2):
from .collections import namedtuple
try:
from threading import RLock
except ImportError:
from dummy_threading import RLock
if PY3:
integer_types = (int, )
else:
integer_types = (int, long)
_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=set([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, integer_types):
raise TypeError('Expected maxsize to be an integer or None')
def decorating_function(user_function):
cache = dict()
stats = [0, 0] # make statistics updateable non-locally
HITS, MISSES = 0, 1 # names for the stats fields
make_key = _make_key # build a key from the function arguments
cache_get = cache.get # bound method to lookup key or return None
_len = len # localize the global len() function
lock = RLock() # because linkedlist updates aren't threadsafe
root = [] # root of the circular doubly linked list
root[:] = [root, root, None, None] # initialize by pointing to self
nonlocal_root = [root] # make updateable non-locally
PREV, NEXT, KEY, RESULT = 0, 1, 2, 3 # names for the link fields
if maxsize == 0:
def wrapper(*args, **kwds):
# No caching -- just a statistics update after a successful call
result = user_function(*args, **kwds)
stats[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, root)
if result is not root:
stats[HITS] += 1
return result
result = user_function(*args, **kwds)
cache[key] = result
stats[MISSES] += 1
return result
else:
def wrapper(*args, **kwds):
# Size limited caching that tracks accesses by recency
key = make_key(args, kwds, typed) if kwds or typed else args
with lock:
link = cache_get(key)
if link is not None:
# Move the link to the front of the circular queue
root, = nonlocal_root
link_prev, link_next, key, result = link
link_prev[NEXT] = link_next
link_next[PREV] = link_prev
last = root[PREV]
last[NEXT] = root[PREV] = link
link[PREV] = last
link[NEXT] = root
stats[HITS] += 1
return result
result = user_function(*args, **kwds)
with lock:
root, = nonlocal_root
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 _len(cache) >= maxsize:
# Use the old root to store the new key and result.
oldroot = 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.
root = nonlocal_root[0] = oldroot[NEXT]
oldkey = root[KEY]
root[KEY] = 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 = root[PREV]
link = [last, root, key, result]
last[NEXT] = root[PREV] = cache[key] = link
stats[MISSES] += 1
return result
def cache_info():
"""Report cache statistics"""
with lock:
return _CacheInfo(stats[HITS], stats[MISSES], maxsize, len(cache))
def cache_clear():
"""Clear the cache and cache statistics"""
with lock:
cache.clear()
root = nonlocal_root[0]
root[:] = [root, root, None, None]
stats[:] = [0, 0]
wrapper.__wrapped__ = user_function
wrapper.cache_info = cache_info
wrapper.cache_clear = cache_clear
return update_wrapper(wrapper, user_function)
return decorating_function
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