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