from __future__ import absolute_import
import functools
from collections import namedtuple
from threading import RLock
_CacheInfo = namedtuple("_CacheInfo", ["hits", "misses", "maxsize", "currsize"])
@functools.wraps(functools.update_wrapper)
def update_wrapper(
wrapper,
wrapped,
assigned=functools.WRAPPER_ASSIGNMENTS,
updated=functools.WRAPPER_UPDATES,
):
"""
Patch two bugs in functools.update_wrapper.
"""
# workaround for http://bugs.python.org/issue3445
assigned = tuple(attr for attr in assigned if hasattr(wrapped, attr))
wrapper = functools.update_wrapper(wrapper, wrapped, assigned, updated)
# workaround for https://bugs.python.org/issue17482
wrapper.__wrapped__ = wrapped
return wrapper
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},
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.
"""
# All of code below relies on kwds preserving the order input by the user.
# Formerly, we sorted() the kwds before looping. The new way is *much*
# faster; however, it means that f(x=1, y=2) will now be treated as a
# distinct call from f(y=2, x=1) which will be cached separately.
key = args
if kwds:
key += kwd_mark
for item in kwds.items():
key += item
if typed:
key += tuple(type(v) for v in args)
if kwds:
key += tuple(type(v) for v in kwds.values())
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(decimal.Decimal("3.0")) and f(3.0) will be treated as
distinct calls with distinct results. Some types such as str and int may
be cached separately even when typed is false.
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: https://en.wikipedia.org/wiki/Cache_replacement_policies#Least_recently_used_(LRU)
"""
# 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).
if isinstance(maxsize, int):
# Negative maxsize is treated as 0
if maxsize < 0:
maxsize = 0
elif callable(maxsize) and isinstance(typed, bool):
# The user_function was passed in directly via the maxsize argument
user_function, maxsize = maxsize, 128
wrapper = _lru_cache_wrapper(user_function, maxsize, typed, _CacheInfo)
wrapper.cache_parameters = lambda: {'maxsize': maxsize, 'typed': typed}
return update_wrapper(wrapper, user_function)
elif maxsize is not None:
raise TypeError('Expected first argument to be an integer, a callable, or None')
def decorating_function(user_function):
wrapper = _lru_cache_wrapper(user_function, maxsize, typed, _CacheInfo)
wrapper.cache_parameters = lambda: {'maxsize': maxsize, 'typed': typed}
return update_wrapper(wrapper, user_function)
return decorating_function
def _lru_cache_wrapper(user_function, maxsize, typed, _CacheInfo):
# 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
cache = {}
hits = misses = 0
full = False
cache_get = cache.get # bound method to lookup a key or return None
cache_len = cache.__len__ # get cache size without calling len()
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
if maxsize == 0:
def wrapper(*args, **kwds):
# No caching -- just a statistics update
nonlocal misses
misses += 1
result = user_function(*args, **kwds)
return result
elif maxsize is None:
def wrapper(*args, **kwds):
# Simple caching without ordering or size limit
nonlocal hits, misses
key = make_key(args, kwds, typed)
result = cache_get(key, sentinel)
if result is not sentinel:
hits += 1
return result
misses += 1
result = user_function(*args, **kwds)
cache[key] = result
return result
else:
def wrapper(*args, **kwds):
# Size limited caching that tracks accesses by recency
nonlocal root, hits, misses, full
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 = root[PREV]
last[NEXT] = root[PREV] = link
link[PREV] = last
link[NEXT] = root
hits += 1
return result
misses += 1
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 full:
# 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 = 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
# Use the cache_len bound method instead of the len() function
# which could potentially be wrapped in an lru_cache itself.
full = cache_len() >= maxsize
return result
def cache_info():
"""Report cache statistics"""
with lock:
return _CacheInfo(hits, misses, maxsize, cache_len())
def cache_clear():
"""Clear the cache and cache statistics"""
nonlocal hits, misses, full
with lock:
cache.clear()
root[:] = [root, root, None, None]
hits = misses = 0
full = False
wrapper.cache_info = cache_info
wrapper.cache_clear = cache_clear
return wrapper