AlkantarClanX12
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"""functools.py - Tools for working with functions and callable objects
"""
# Python module wrapper for _functools C module
# to allow utilities written in Python to be added
# to the functools module.
# Written by Nick Coghlan <ncoghlan at gmail.com>
# and Raymond Hettinger <python at rcn.com>
# Copyright (C) 2006-2010 Python Software Foundation.
# See C source code for _functools credits/copyright
__all__ = ['update_wrapper', 'wraps', 'WRAPPER_ASSIGNMENTS', 'WRAPPER_UPDATES',
'total_ordering', 'cmp_to_key', 'lru_cache', 'reduce', 'partial']
from _functools import partial, reduce
from collections import namedtuple
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
################################################################################
### update_wrapper() and wraps() decorator
################################################################################
# update_wrapper() and wraps() are tools to help write
# wrapper functions that can handle naive introspection
WRAPPER_ASSIGNMENTS = ('__module__', '__name__', '__qualname__', '__doc__',
'__annotations__')
WRAPPER_UPDATES = ('__dict__',)
def update_wrapper(wrapper,
wrapped,
assigned = WRAPPER_ASSIGNMENTS,
updated = WRAPPER_UPDATES):
"""Update a wrapper function to look like the wrapped function
wrapper is the function to be updated
wrapped is the original function
assigned is a tuple naming the attributes assigned directly
from the wrapped function to the wrapper function (defaults to
functools.WRAPPER_ASSIGNMENTS)
updated is a tuple naming the attributes of the wrapper that
are updated with the corresponding attribute from the wrapped
function (defaults to functools.WRAPPER_UPDATES)
"""
wrapper.__wrapped__ = wrapped
for attr in assigned:
try:
value = getattr(wrapped, attr)
except AttributeError:
pass
else:
setattr(wrapper, attr, value)
for attr in updated:
getattr(wrapper, attr).update(getattr(wrapped, attr, {}))
# Return the wrapper so this can be used as a decorator via partial()
return wrapper
def wraps(wrapped,
assigned = WRAPPER_ASSIGNMENTS,
updated = WRAPPER_UPDATES):
"""Decorator factory to apply update_wrapper() to a wrapper function
Returns a decorator that invokes update_wrapper() with the decorated
function as the wrapper argument and the arguments to wraps() as the
remaining arguments. Default arguments are as for update_wrapper().
This is a convenience function to simplify applying partial() to
update_wrapper().
"""
return partial(update_wrapper, wrapped=wrapped,
assigned=assigned, updated=updated)
################################################################################
### total_ordering class decorator
################################################################################
def total_ordering(cls):
"""Class decorator that fills in missing ordering methods"""
convert = {
'__lt__': [('__gt__', lambda self, other: not (self < other or self == other)),
('__le__', lambda self, other: self < other or self == other),
('__ge__', lambda self, other: not self < other)],
'__le__': [('__ge__', lambda self, other: not self <= other or self == other),
('__lt__', lambda self, other: self <= other and not self == other),
('__gt__', lambda self, other: not self <= other)],
'__gt__': [('__lt__', lambda self, other: not (self > other or self == other)),
('__ge__', lambda self, other: self > other or self == other),
('__le__', lambda self, other: not self > other)],
'__ge__': [('__le__', lambda self, other: (not self >= other) or self == other),
('__gt__', lambda self, other: self >= other and not self == other),
('__lt__', lambda self, other: not self >= other)]
}
# Find user-defined comparisons (not those inherited from object).
roots = [op for op in convert if getattr(cls, op, None) is not getattr(object, op, None)]
if not roots:
raise ValueError('must define at least one ordering operation: < > <= >=')
root = max(roots) # prefer __lt__ to __le__ to __gt__ to __ge__
for opname, opfunc in convert[root]:
if opname not in roots:
opfunc.__name__ = opname
opfunc.__doc__ = getattr(int, opname).__doc__
setattr(cls, opname, opfunc)
return cls
################################################################################
### cmp_to_key() function converter
################################################################################
def cmp_to_key(mycmp):
"""Convert a cmp= function into a key= function"""
class K(object):
__slots__ = ['obj']
def __init__(self, obj):
self.obj = obj
def __lt__(self, other):
return mycmp(self.obj, other.obj) < 0
def __gt__(self, other):
return mycmp(self.obj, other.obj) > 0
def __eq__(self, other):
return mycmp(self.obj, other.obj) == 0
def __le__(self, other):
return mycmp(self.obj, other.obj) <= 0
def __ge__(self, other):
return mycmp(self.obj, other.obj) >= 0
def __ne__(self, other):
return mycmp(self.obj, other.obj) != 0
__hash__ = None
return K
try:
from _functools import cmp_to_key
except ImportError:
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).
# 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 = {}
hits = misses = 0
full = False
cache_get = cache.get # bound method to lookup a key or return None
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 after a successful call
nonlocal misses
result = user_function(*args, **kwds)
misses += 1
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
result = user_function(*args, **kwds)
cache[key] = result
misses += 1
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
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]
oldresult = root[RESULT]
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
full = (len(cache) >= maxsize)
misses += 1
return result
def cache_info():
"""Report cache statistics"""
with lock:
return _CacheInfo(hits, misses, maxsize, len(cache))
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 update_wrapper(wrapper, user_function)
return decorating_function