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	# Copyright (c) 2013, Mahmoud Hashemi
	#
	# Redistribution and use in source and binary forms, with or without
	# modification, are permitted provided that the following conditions are
	# met:
	#
	# * Redistributions of source code must retain the above copyright
	# notice, this list of conditions and the following disclaimer.
	#
	# * Redistributions in binary form must reproduce the above
	# copyright notice, this list of conditions and the following
	# disclaimer in the documentation and/or other materials provided
	# with the distribution.
	#
	# * The names of the contributors may not be used to endorse or
	# promote products derived from this software without specific
	# prior written permission.
	#
	# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
	# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
	# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
	# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
	# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
	# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
	# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
	# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
	# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
	# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
	# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

	"""``cacheutils`` contains consistent implementations of fundamental
	cache types. Currently there are two to choose from:

	* :class:`LRI` - Least-recently inserted
	* :class:`LRU` - Least-recently used

	Both caches are :class:`dict` subtypes, designed to be as
	interchangeable as possible, to facilitate experimentation. A key
	practice with performance enhancement with caching is ensuring that
	the caching strategy is working. If the cache is constantly missing,
	it is just adding more overhead and code complexity. The standard
	statistics are:

	* ``hit_count`` - the number of times the queried key has been in
	the cache
	* ``miss_count`` - the number of times a key has been absent and/or
	fetched by the cache
	* ``soft_miss_count`` - the number of times a key has been absent,
	but a default has been provided by the caller, as with
	:meth:`dict.get` and :meth:`dict.setdefault`. Soft misses are a
	subset of misses, so this number is always less than or equal to
	``miss_count``.

	Additionally, ``cacheutils`` provides :class:`ThresholdCounter`, a
	cache-like bounded counter useful for online statistics collection.

	Learn more about `caching algorithms on Wikipedia
	<https://en.wikipedia.org/wiki/Cache_algorithms#Examples>`_.

	"""

	# TODO: TimedLRI
	# TODO: support 0 max_size?


	import heapq
	import weakref
	import itertools
	from operator import attrgetter

	try:
	from threading import RLock
	except Exception:
	class RLock:
	'Dummy reentrant lock for builds without threads'
	def __enter__(self):
	pass

	def __exit__(self, exctype, excinst, exctb):
	pass

	try:
	from .typeutils import make_sentinel
	_MISSING = make_sentinel(var_name='_MISSING')
	_KWARG_MARK = make_sentinel(var_name='_KWARG_MARK')
	except ImportError:
	_MISSING = object()
	_KWARG_MARK = object()

	PREV, NEXT, KEY, VALUE = range(4) # names for the link fields
	DEFAULT_MAX_SIZE = 128


	class LRI(dict):
	"""The ``LRI`` implements the basic Least Recently Inserted strategy to
	caching. One could also think of this as a ``SizeLimitedDefaultDict``.

	on_miss is a callable that accepts the missing key (as opposed
	to :class:`collections.defaultdict`'s "default_factory", which
	accepts no arguments.) Also note that, like the :class:`LRI`,
	the ``LRI`` is instrumented with statistics tracking.

	>>> cap_cache = LRI(max_size=2)
	>>> cap_cache['a'], cap_cache['b'] = 'A', 'B'
	>>> from pprint import pprint as pp
	>>> pp(dict(cap_cache))
	{'a': 'A', 'b': 'B'}
	>>> [cap_cache['b'] for i in range(3)][0]
	'B'
	>>> cap_cache['c'] = 'C'
	>>> print(cap_cache.get('a'))
	None
	>>> cap_cache.hit_count, cap_cache.miss_count, cap_cache.soft_miss_count
	(3, 1, 1)
	"""
	def __init__(self, max_size=DEFAULT_MAX_SIZE, values=None,
	on_miss=None):
	if max_size <= 0:
	raise ValueError('expected max_size > 0, not %r' % max_size)
	self.hit_count = self.miss_count = self.soft_miss_count = 0
	self.max_size = max_size
	self._lock = RLock()
	self._init_ll()

	if on_miss is not None and not callable(on_miss):
	raise TypeError('expected on_miss to be a callable'
	' (or None), not %r' % on_miss)
	self.on_miss = on_miss

	if values:
	self.update(values)

	# TODO: fromkeys()?

	# linked list manipulation methods.
	#
	# invariants:
	# 1) 'anchor' is the sentinel node in the doubly linked list. there is
	# always only one, and its KEY and VALUE are both _MISSING.
	# 2) the most recently accessed node comes immediately before 'anchor'.
	# 3) the least recently accessed node comes immediately after 'anchor'.
	def _init_ll(self):
	anchor = []
	anchor[:] = [anchor, anchor, _MISSING, _MISSING]
	# a link lookup table for finding linked list links in O(1)
	# time.
	self._link_lookup = {}
	self._anchor = anchor

	def _print_ll(self):
	print('***')
	for (key, val) in self._get_flattened_ll():
	print(key, val)
	print('***')
	return

	def _get_flattened_ll(self):
	flattened_list = []
	link = self._anchor
	while True:
	flattened_list.append((link[KEY], link[VALUE]))
	link = link[NEXT]
	if link is self._anchor:
	break
	return flattened_list

	def _get_link_and_move_to_front_of_ll(self, key):
	# find what will become the newest link. this may raise a
	# KeyError, which is useful to __getitem__ and __setitem__
	newest = self._link_lookup[key]

	# splice out what will become the newest link.
	newest[PREV][NEXT] = newest[NEXT]
	newest[NEXT][PREV] = newest[PREV]

	# move what will become the newest link immediately before
	# anchor (invariant 2)
	anchor = self._anchor
	second_newest = anchor[PREV]
	second_newest[NEXT] = anchor[PREV] = newest
	newest[PREV] = second_newest
	newest[NEXT] = anchor
	return newest

	def _set_key_and_add_to_front_of_ll(self, key, value):
	# create a new link and place it immediately before anchor
	# (invariant 2).
	anchor = self._anchor
	second_newest = anchor[PREV]
	newest = [second_newest, anchor, key, value]
	second_newest[NEXT] = anchor[PREV] = newest
	self._link_lookup[key] = newest

	def _set_key_and_evict_last_in_ll(self, key, value):
	# the link after anchor is the oldest in the linked list
	# (invariant 3). the current anchor becomes a link that holds
	# the newest key, and the oldest link becomes the new anchor
	# (invariant 1). now the newest link comes before anchor
	# (invariant 2). no links are moved; only their keys
	# and values are changed.
	oldanchor = self._anchor
	oldanchor[KEY] = key
	oldanchor[VALUE] = value

	self._anchor = anchor = oldanchor[NEXT]
	evicted = anchor[KEY]
	anchor[KEY] = anchor[VALUE] = _MISSING
	del self._link_lookup[evicted]
	self._link_lookup[key] = oldanchor
	return evicted

	def _remove_from_ll(self, key):
	# splice a link out of the list and drop it from our lookup
	# table.
	link = self._link_lookup.pop(key)
	link[PREV][NEXT] = link[NEXT]
	link[NEXT][PREV] = link[PREV]

	def __setitem__(self, key, value):
	with self._lock:
	try:
	link = self._get_link_and_move_to_front_of_ll(key)
	except KeyError:
	if len(self) < self.max_size:
	self._set_key_and_add_to_front_of_ll(key, value)
	else:
	evicted = self._set_key_and_evict_last_in_ll(key, value)
	super().__delitem__(evicted)
	else:
	link[VALUE] = value
	super().__setitem__(key, value)
	return

	def __getitem__(self, key):
	with self._lock:
	try:
	link = self._link_lookup[key]
	except KeyError:
	self.miss_count += 1
	if not self.on_miss:
	raise
	ret = self[key] = self.on_miss(key)
	return ret

	self.hit_count += 1
	return link[VALUE]

	def get(self, key, default=None):
	try:
	return self[key]
	except KeyError:
	self.soft_miss_count += 1
	return default

	def __delitem__(self, key):
	with self._lock:
	super().__delitem__(key)
	self._remove_from_ll(key)

	def pop(self, key, default=_MISSING):
	# NB: hit/miss counts are bypassed for pop()
	with self._lock:
	try:
	ret = super().pop(key)
	except KeyError:
	if default is _MISSING:
	raise
	ret = default
	else:
	self._remove_from_ll(key)
	return ret

	def popitem(self):
	with self._lock:
	item = super().popitem()
	self._remove_from_ll(item[0])
	return item

	def clear(self):
	with self._lock:
	super().clear()
	self._init_ll()

	def copy(self):
	return self.__class__(max_size=self.max_size, values=self)

	def setdefault(self, key, default=None):
	with self._lock:
	try:
	return self[key]
	except KeyError:
	self.soft_miss_count += 1
	self[key] = default
	return default

	def update(self, E, **F):
	# E and F are throwback names to the dict() __doc__
	with self._lock:
	if E is self:
	return
	setitem = self.__setitem__
	if callable(getattr(E, 'keys', None)):
	for k in E.keys():
	setitem(k, E[k])
	else:
	for k, v in E:
	setitem(k, v)
	for k in F:
	setitem(k, F[k])
	return

	def __eq__(self, other):
	with self._lock:
	if self is other:
	return True
	if len(other) != len(self):
	return False
	if not isinstance(other, LRI):
	return other == self
	return super().__eq__(other)

	def __ne__(self, other):
	return not (self == other)

	def __repr__(self):
	cn = self.__class__.__name__
	val_map = super().__repr__()
	return ('%s(max_size=%r, on_miss=%r, values=%s)'
	% (cn, self.max_size, self.on_miss, val_map))


	class LRU(LRI):
	"""The ``LRU`` is :class:`dict` subtype implementation of the
	Least-Recently Used caching strategy.

	Args:
	max_size (int): Max number of items to cache. Defaults to ``128``.
	values (iterable): Initial values for the cache. Defaults to ``None``.
	on_miss (callable): a callable which accepts a single argument, the
	key not present in the cache, and returns the value to be cached.

	>>> cap_cache = LRU(max_size=2)
	>>> cap_cache['a'], cap_cache['b'] = 'A', 'B'
	>>> from pprint import pprint as pp
	>>> pp(dict(cap_cache))
	{'a': 'A', 'b': 'B'}
	>>> [cap_cache['b'] for i in range(3)][0]
	'B'
	>>> cap_cache['c'] = 'C'
	>>> print(cap_cache.get('a'))
	None

	This cache is also instrumented with statistics
	collection. ``hit_count``, ``miss_count``, and ``soft_miss_count``
	are all integer members that can be used to introspect the
	performance of the cache. ("Soft" misses are misses that did not
	raise :exc:`KeyError`, e.g., ``LRU.get()`` or ``on_miss`` was used to
	cache a default.

	>>> cap_cache.hit_count, cap_cache.miss_count, cap_cache.soft_miss_count
	(3, 1, 1)

	Other than the size-limiting caching behavior and statistics,
	``LRU`` acts like its parent class, the built-in Python :class:`dict`.
	"""
	def __getitem__(self, key):
	with self._lock:
	try:
	link = self._get_link_and_move_to_front_of_ll(key)
	except KeyError:
	self.miss_count += 1
	if not self.on_miss:
	raise
	ret = self[key] = self.on_miss(key)
	return ret

	self.hit_count += 1
	return link[VALUE]


	### Cached decorator
	# Key-making technique adapted from Python 3.4's functools

	class _HashedKey(list):
	"""The _HashedKey guarantees that hash() will be called no more than once
	per cached function invocation.
	"""
	__slots__ = 'hash_value'

	def __init__(self, key):
	self[:] = key
	self.hash_value = hash(tuple(key))

	def __hash__(self):
	return self.hash_value

	def __repr__(self):
	return f'{self.__class__.__name__}({list.__repr__(self)})'


	def make_cache_key(args, kwargs, typed=False,
	kwarg_mark=_KWARG_MARK,
	fasttypes=frozenset([int, str, frozenset, type(None)])):
	"""Make a generic key from a function's positional and keyword
	arguments, suitable for use in caches. Arguments within args and
	kwargs must be `hashable`_. If typed is ``True``, ``3`` and
	``3.0`` will be treated as separate keys.

	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.

	>>> tuple(make_cache_key(('a', 'b'), {'c': ('d')}))
	('a', 'b', _KWARG_MARK, ('c', 'd'))

	.. _hashable: https://docs.python.org/2/glossary.html#term-hashable
	"""

	# key = [func_name] if func_name else []
	# key.extend(args)
	key = list(args)
	if kwargs:
	sorted_items = sorted(kwargs.items())
	key.append(kwarg_mark)
	key.extend(sorted_items)
	if typed:
	key.extend([type(v) for v in args])
	if kwargs:
	key.extend([type(v) for k, v in sorted_items])
	elif len(key) == 1 and type(key[0]) in fasttypes:
	return key[0]
	return _HashedKey(key)

	# for backwards compatibility in case someone was importing it
	_make_cache_key = make_cache_key


	class CachedFunction:
	"""This type is used by :func:`cached`, below. Instances of this
	class are used to wrap functions in caching logic.
	"""
	def __init__(self, func, cache, scoped=True, typed=False, key=None):
	self.func = func
	if callable(cache):
	self.get_cache = cache
	elif not (callable(getattr(cache, '__getitem__', None))
	and callable(getattr(cache, '__setitem__', None))):
	raise TypeError('expected cache to be a dict-like object,'
	' or callable returning a dict-like object, not %r'
	% cache)
	else:
	def _get_cache():
	return cache
	self.get_cache = _get_cache
	self.scoped = scoped
	self.typed = typed
	self.key_func = key or make_cache_key

	def __call__(self, args, *kwargs):
	cache = self.get_cache()
	key = self.key_func(args, kwargs, typed=self.typed)
	try:
	ret = cache[key]
	except KeyError:
	ret = cache[key] = self.func(args, *kwargs)
	return ret

	def __repr__(self):
	cn = self.__class__.__name__
	if self.typed or not self.scoped:
	return ("%s(func=%r, scoped=%r, typed=%r)"
	% (cn, self.func, self.scoped, self.typed))
	return f"{cn}(func={self.func!r})"


	class CachedMethod:
	"""Similar to :class:`CachedFunction`, this type is used by
	:func:`cachedmethod` to wrap methods in caching logic.
	"""
	def __init__(self, func, cache, scoped=True, typed=False, key=None):
	self.func = func
	self.__isabstractmethod__ = getattr(func, '__isabstractmethod__', False)
	if isinstance(cache, str):
	self.get_cache = attrgetter(cache)
	elif callable(cache):
	self.get_cache = cache
	elif not (callable(getattr(cache, '__getitem__', None))
	and callable(getattr(cache, '__setitem__', None))):
	raise TypeError('expected cache to be an attribute name,'
	' dict-like object, or callable returning'
	' a dict-like object, not %r' % cache)
	else:
	def _get_cache(obj):
	return cache
	self.get_cache = _get_cache
	self.scoped = scoped
	self.typed = typed
	self.key_func = key or make_cache_key
	self.bound_to = None

	def __get__(self, obj, objtype=None):
	if obj is None:
	return self
	cls = self.__class__
	ret = cls(self.func, self.get_cache, typed=self.typed,
	scoped=self.scoped, key=self.key_func)
	ret.bound_to = obj
	return ret

	def __call__(self, args, *kwargs):
	obj = args[0] if self.bound_to is None else self.bound_to
	cache = self.get_cache(obj)
	key_args = (self.bound_to, self.func) + args if self.scoped else args
	key = self.key_func(key_args, kwargs, typed=self.typed)
	try:
	ret = cache[key]
	except KeyError:
	if self.bound_to is not None:
	args = (self.bound_to,) + args
	ret = cache[key] = self.func(args, *kwargs)
	return ret

	def __repr__(self):
	cn = self.__class__.__name__
	args = (cn, self.func, self.scoped, self.typed)
	if self.bound_to is not None:
	args += (self.bound_to,)
	return ('<%s func=%r scoped=%r typed=%r bound_to=%r>' % args)
	return ("%s(func=%r, scoped=%r, typed=%r)" % args)


	def cached(cache, scoped=True, typed=False, key=None):
	"""Cache any function with the cache object of your choosing. Note
	that the function wrapped should take only `hashable`_ arguments.

	Args:
	cache (Mapping): Any :class:`dict`-like object suitable for
	use as a cache. Instances of the :class:`LRU` and
	:class:`LRI` are good choices, but a plain :class:`dict`
	can work in some cases, as well. This argument can also be
	a callable which accepts no arguments and returns a mapping.
	scoped (bool): Whether the function itself is part of the
	cache key. ``True`` by default, different functions will
	not read one another's cache entries, but can evict one
	another's results. ``False`` can be useful for certain
	shared cache use cases. More advanced behavior can be
	produced through the key argument.
	typed (bool): Whether to factor argument types into the cache
	check. Default ``False``, setting to ``True`` causes the
	cache keys for ``3`` and ``3.0`` to be considered unequal.

	>>> my_cache = LRU()
	>>> @cached(my_cache)
	... def cached_lower(x):
	... return x.lower()
	...
	>>> cached_lower("CaChInG's FuN AgAiN!")
	"caching's fun again!"
	>>> len(my_cache)
	1

	.. _hashable: https://docs.python.org/2/glossary.html#term-hashable

	"""
	def cached_func_decorator(func):
	return CachedFunction(func, cache, scoped=scoped, typed=typed, key=key)
	return cached_func_decorator


	def cachedmethod(cache, scoped=True, typed=False, key=None):
	"""Similar to :func:`cached`, ``cachedmethod`` is used to cache
	methods based on their arguments, using any :class:`dict`-like
	cache object.

	Args:
	cache (str/Mapping/callable): Can be the name of an attribute
	on the instance, any Mapping/:class:`dict`-like object, or
	a callable which returns a Mapping.
	scoped (bool): Whether the method itself and the object it is
	bound to are part of the cache keys. ``True`` by default,
	different methods will not read one another's cache
	results. ``False`` can be useful for certain shared cache
	use cases. More advanced behavior can be produced through
	the key arguments.
	typed (bool): Whether to factor argument types into the cache
	check. Default ``False``, setting to ``True`` causes the
	cache keys for ``3`` and ``3.0`` to be considered unequal.
	key (callable): A callable with a signature that matches
	:func:`make_cache_key` that returns a tuple of hashable
	values to be used as the key in the cache.

	>>> class Lowerer(object):
	... def __init__(self):
	... self.cache = LRI()
	...
	... @cachedmethod('cache')
	... def lower(self, text):
	... return text.lower()
	...
	>>> lowerer = Lowerer()
	>>> lowerer.lower('WOW WHO COULD GUESS CACHING COULD BE SO NEAT')
	'wow who could guess caching could be so neat'
	>>> len(lowerer.cache)
	1

	"""
	def cached_method_decorator(func):
	return CachedMethod(func, cache, scoped=scoped, typed=typed, key=key)
	return cached_method_decorator


	class cachedproperty:
	"""The ``cachedproperty`` is used similar to :class:`property`, except
	that the wrapped method is only called once. This is commonly used
	to implement lazy attributes.

	After the property has been accessed, the value is stored on the
	instance itself, using the same name as the cachedproperty. This
	allows the cache to be cleared with :func:`delattr`, or through
	manipulating the object's ``__dict__``.
	"""
	def __init__(self, func):
	self.__doc__ = getattr(func, '__doc__')
	self.__isabstractmethod__ = getattr(func, '__isabstractmethod__', False)
	self.func = func

	def __get__(self, obj, objtype=None):
	if obj is None:
	return self
	value = obj.__dict__[self.func.__name__] = self.func(obj)
	return value

	def __repr__(self):
	cn = self.__class__.__name__
	return f'<{cn} func={self.func}>'


	class ThresholdCounter:
	"""A bounded dict-like Mapping from keys to counts. The
	ThresholdCounter automatically compacts after every (1 /
	threshold) additions, maintaining exact counts for any keys
	whose count represents at least a threshold ratio of the total
	data. In other words, if a particular key is not present in the
	ThresholdCounter, its count represents less than threshold of
	the total data.

	>>> tc = ThresholdCounter(threshold=0.1)
	>>> tc.add(1)
	>>> tc.items()
	[(1, 1)]
	>>> tc.update([2] * 10)
	>>> tc.get(1)
	0
	>>> tc.add(5)
	>>> 5 in tc
	True
	>>> len(list(tc.elements()))
	11

	As you can see above, the API is kept similar to
	:class:`collections.Counter`. The most notable feature omissions
	being that counted items cannot be set directly, uncounted, or
	removed, as this would disrupt the math.

	Use the ThresholdCounter when you need best-effort long-lived
	counts for dynamically-keyed data. Without a bounded datastructure
	such as this one, the dynamic keys often represent a memory leak
	and can impact application reliability. The ThresholdCounter's
	item replacement strategy is fully deterministic and can be
	thought of as Amortized Least Relevant. The absolute upper bound
	of keys it will store is (2/threshold), but realistically
	(1/threshold) is expected for uniformly random datastreams, and
	one or two orders of magnitude better for real-world data.

	This algorithm is an implementation of the Lossy Counting
	algorithm described in "Approximate Frequency Counts over Data
	Streams" by Manku & Motwani. Hat tip to Kurt Rose for discovery
	and initial implementation.

	"""
	# TODO: hit_count/miss_count?
	def __init__(self, threshold=0.001):
	if not 0 < threshold < 1:
	raise ValueError('expected threshold between 0 and 1, not: %r'
	% threshold)

	self.total = 0
	self._count_map = {}
	self._threshold = threshold
	self._thresh_count = int(1 / threshold)
	self._cur_bucket = 1

	@property
	def threshold(self):
	return self._threshold

	def add(self, key):
	"""Increment the count of key by 1, automatically adding it if it
	does not exist.

	Cache compaction is triggered every 1/threshold additions.
	"""
	self.total += 1
	try:
	self._count_map[key][0] += 1
	except KeyError:
	self._count_map[key] = [1, self._cur_bucket - 1]

	if self.total % self._thresh_count == 0:
	self._count_map = {k: v for k, v in self._count_map.items()
	if sum(v) > self._cur_bucket}
	self._cur_bucket += 1
	return

	def elements(self):
	"""Return an iterator of all the common elements tracked by the
	counter. Yields each key as many times as it has been seen.
	"""
	repeaters = itertools.starmap(itertools.repeat, self.iteritems())
	return itertools.chain.from_iterable(repeaters)

	def most_common(self, n=None):
	"""Get the top n keys and counts as tuples. If n is omitted,
	returns all the pairs.
	"""
	if not n or n <= 0:
	return []
	ret = sorted(self.iteritems(), key=lambda x: x[1], reverse=True)
	if n is None or n >= len(ret):
	return ret
	return ret[:n]

	def get_common_count(self):
	"""Get the sum of counts for keys exceeding the configured data
	threshold.
	"""
	return sum([count for count, _ in self._count_map.values()])

	def get_uncommon_count(self):
	"""Get the sum of counts for keys that were culled because the
	associated counts represented less than the configured
	threshold. The long-tail counts.
	"""
	return self.total - self.get_common_count()

	def get_commonality(self):
	"""Get a float representation of the effective count accuracy. The
	higher the number, the less uniform the keys being added, and
	the higher accuracy and efficiency of the ThresholdCounter.

	If a stronger measure of data cardinality is required,
	consider using hyperloglog.
	"""
	return float(self.get_common_count()) / self.total

	def __getitem__(self, key):
	return self._count_map[key][0]

	def __len__(self):
	return len(self._count_map)

	def __contains__(self, key):
	return key in self._count_map

	def iterkeys(self):
	return iter(self._count_map)

	def keys(self):
	return list(self.iterkeys())

	def itervalues(self):
	count_map = self._count_map
	for k in count_map:
	yield count_map[k][0]

	def values(self):
	return list(self.itervalues())

	def iteritems(self):
	count_map = self._count_map
	for k in count_map:
	yield (k, count_map[k][0])

	def items(self):
	return list(self.iteritems())

	def get(self, key, default=0):
	"Get count for key, defaulting to 0."
	try:
	return self[key]
	except KeyError:
	return default

	def update(self, iterable, **kwargs):
	"""Like dict.update() but add counts instead of replacing them, used
	to add multiple items in one call.

	Source can be an iterable of keys to add, or a mapping of keys
	to integer counts.
	"""
	if iterable is not None:
	if callable(getattr(iterable, 'iteritems', None)):
	for key, count in iterable.iteritems():
	for i in range(count):
	self.add(key)
	else:
	for key in iterable:
	self.add(key)
	if kwargs:
	self.update(kwargs)


	class MinIDMap:
	"""
	Assigns arbitrary weakref-able objects the smallest possible unique
	integer IDs, such that no two objects have the same ID at the same
	time.

	Maps arbitrary hashable objects to IDs.

	Based on https://gist.github.com/kurtbrose/25b48114de216a5e55df
	"""
	def __init__(self):
	self.mapping = weakref.WeakKeyDictionary()
	self.ref_map = {}
	self.free = []

	def get(self, a):
	try:
	return self.mapping[a][0] # if object is mapped, return ID
	except KeyError:
	pass

	if self.free: # if there are any free IDs, use the smallest
	nxt = heapq.heappop(self.free)
	else: # if there are no free numbers, use the next highest ID
	nxt = len(self.mapping)
	ref = weakref.ref(a, self._clean)
	self.mapping[a] = (nxt, ref)
	self.ref_map[ref] = nxt
	return nxt

	def drop(self, a):
	freed, ref = self.mapping[a]
	del self.mapping[a]
	del self.ref_map[ref]
	heapq.heappush(self.free, freed)

	def _clean(self, ref):
	print(self.ref_map[ref])
	heapq.heappush(self.free, self.ref_map[ref])
	del self.ref_map[ref]

	def __contains__(self, a):
	return a in self.mapping

	def __iter__(self):
	return iter(self.mapping)

	def __len__(self):
	return self.mapping.__len__()

	def iteritems(self):
	return iter((k, self.mapping[k][0]) for k in iter(self.mapping))


	# end cacheutils.py