571 lines
22 KiB
Python
571 lines
22 KiB
Python
"""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>,
|
|
# Raymond Hettinger <python at rcn.com>,
|
|
# and Łukasz Langa <lukasz at langa.pl>.
|
|
# Copyright (C) 2006-2013 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',
|
|
'singledispatch']
|
|
|
|
try:
|
|
from _functools import reduce
|
|
except ImportError:
|
|
pass
|
|
from abc import get_cache_token
|
|
from collections import namedtuple
|
|
from types import MappingProxyType
|
|
from weakref import WeakKeyDictionary
|
|
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)
|
|
"""
|
|
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, {}))
|
|
# Issue #17482: set __wrapped__ last so we don't inadvertently copy it
|
|
# from the wrapped function when updating __dict__
|
|
wrapper.__wrapped__ = wrapped
|
|
# 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
|
|
|
|
|
|
################################################################################
|
|
### partial() argument application
|
|
################################################################################
|
|
|
|
def partial(func, *args, **keywords):
|
|
"""new function with partial application of the given arguments
|
|
and keywords.
|
|
"""
|
|
def newfunc(*fargs, **fkeywords):
|
|
newkeywords = keywords.copy()
|
|
newkeywords.update(fkeywords)
|
|
return func(*(args + fargs), **newkeywords)
|
|
newfunc.func = func
|
|
newfunc.args = args
|
|
newfunc.keywords = keywords
|
|
return newfunc
|
|
|
|
try:
|
|
from _functools import partial
|
|
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
|
|
|
|
|
|
################################################################################
|
|
### singledispatch() - single-dispatch generic function decorator
|
|
################################################################################
|
|
|
|
def _c3_merge(sequences):
|
|
"""Merges MROs in *sequences* to a single MRO using the C3 algorithm.
|
|
|
|
Adapted from http://www.python.org/download/releases/2.3/mro/.
|
|
|
|
"""
|
|
result = []
|
|
while True:
|
|
sequences = [s for s in sequences if s] # purge empty sequences
|
|
if not sequences:
|
|
return result
|
|
for s1 in sequences: # find merge candidates among seq heads
|
|
candidate = s1[0]
|
|
for s2 in sequences:
|
|
if candidate in s2[1:]:
|
|
candidate = None
|
|
break # reject the current head, it appears later
|
|
else:
|
|
break
|
|
if not candidate:
|
|
raise RuntimeError("Inconsistent hierarchy")
|
|
result.append(candidate)
|
|
# remove the chosen candidate
|
|
for seq in sequences:
|
|
if seq[0] == candidate:
|
|
del seq[0]
|
|
|
|
def _c3_mro(cls, abcs=None):
|
|
"""Computes the method resolution order using extended C3 linearization.
|
|
|
|
If no *abcs* are given, the algorithm works exactly like the built-in C3
|
|
linearization used for method resolution.
|
|
|
|
If given, *abcs* is a list of abstract base classes that should be inserted
|
|
into the resulting MRO. Unrelated ABCs are ignored and don't end up in the
|
|
result. The algorithm inserts ABCs where their functionality is introduced,
|
|
i.e. issubclass(cls, abc) returns True for the class itself but returns
|
|
False for all its direct base classes. Implicit ABCs for a given class
|
|
(either registered or inferred from the presence of a special method like
|
|
__len__) are inserted directly after the last ABC explicitly listed in the
|
|
MRO of said class. If two implicit ABCs end up next to each other in the
|
|
resulting MRO, their ordering depends on the order of types in *abcs*.
|
|
|
|
"""
|
|
for i, base in enumerate(reversed(cls.__bases__)):
|
|
if hasattr(base, '__abstractmethods__'):
|
|
boundary = len(cls.__bases__) - i
|
|
break # Bases up to the last explicit ABC are considered first.
|
|
else:
|
|
boundary = 0
|
|
abcs = list(abcs) if abcs else []
|
|
explicit_bases = list(cls.__bases__[:boundary])
|
|
abstract_bases = []
|
|
other_bases = list(cls.__bases__[boundary:])
|
|
for base in abcs:
|
|
if issubclass(cls, base) and not any(
|
|
issubclass(b, base) for b in cls.__bases__
|
|
):
|
|
# If *cls* is the class that introduces behaviour described by
|
|
# an ABC *base*, insert said ABC to its MRO.
|
|
abstract_bases.append(base)
|
|
for base in abstract_bases:
|
|
abcs.remove(base)
|
|
explicit_c3_mros = [_c3_mro(base, abcs=abcs) for base in explicit_bases]
|
|
abstract_c3_mros = [_c3_mro(base, abcs=abcs) for base in abstract_bases]
|
|
other_c3_mros = [_c3_mro(base, abcs=abcs) for base in other_bases]
|
|
return _c3_merge(
|
|
[[cls]] +
|
|
explicit_c3_mros + abstract_c3_mros + other_c3_mros +
|
|
[explicit_bases] + [abstract_bases] + [other_bases]
|
|
)
|
|
|
|
def _compose_mro(cls, types):
|
|
"""Calculates the method resolution order for a given class *cls*.
|
|
|
|
Includes relevant abstract base classes (with their respective bases) from
|
|
the *types* iterable. Uses a modified C3 linearization algorithm.
|
|
|
|
"""
|
|
bases = set(cls.__mro__)
|
|
# Remove entries which are already present in the __mro__ or unrelated.
|
|
def is_related(typ):
|
|
return (typ not in bases and hasattr(typ, '__mro__')
|
|
and issubclass(cls, typ))
|
|
types = [n for n in types if is_related(n)]
|
|
# Remove entries which are strict bases of other entries (they will end up
|
|
# in the MRO anyway.
|
|
def is_strict_base(typ):
|
|
for other in types:
|
|
if typ != other and typ in other.__mro__:
|
|
return True
|
|
return False
|
|
types = [n for n in types if not is_strict_base(n)]
|
|
# Subclasses of the ABCs in *types* which are also implemented by
|
|
# *cls* can be used to stabilize ABC ordering.
|
|
type_set = set(types)
|
|
mro = []
|
|
for typ in types:
|
|
found = []
|
|
for sub in typ.__subclasses__():
|
|
if sub not in bases and issubclass(cls, sub):
|
|
found.append([s for s in sub.__mro__ if s in type_set])
|
|
if not found:
|
|
mro.append(typ)
|
|
continue
|
|
# Favor subclasses with the biggest number of useful bases
|
|
found.sort(key=len, reverse=True)
|
|
for sub in found:
|
|
for subcls in sub:
|
|
if subcls not in mro:
|
|
mro.append(subcls)
|
|
return _c3_mro(cls, abcs=mro)
|
|
|
|
def _find_impl(cls, registry):
|
|
"""Returns the best matching implementation from *registry* for type *cls*.
|
|
|
|
Where there is no registered implementation for a specific type, its method
|
|
resolution order is used to find a more generic implementation.
|
|
|
|
Note: if *registry* does not contain an implementation for the base
|
|
*object* type, this function may return None.
|
|
|
|
"""
|
|
mro = _compose_mro(cls, registry.keys())
|
|
match = None
|
|
for t in mro:
|
|
if match is not None:
|
|
# If *match* is an implicit ABC but there is another unrelated,
|
|
# equally matching implicit ABC, refuse the temptation to guess.
|
|
if (t in registry and t not in cls.__mro__
|
|
and match not in cls.__mro__
|
|
and not issubclass(match, t)):
|
|
raise RuntimeError("Ambiguous dispatch: {} or {}".format(
|
|
match, t))
|
|
break
|
|
if t in registry:
|
|
match = t
|
|
return registry.get(match)
|
|
|
|
def singledispatch(func):
|
|
"""Single-dispatch generic function decorator.
|
|
|
|
Transforms a function into a generic function, which can have different
|
|
behaviours depending upon the type of its first argument. The decorated
|
|
function acts as the default implementation, and additional
|
|
implementations can be registered using the register() attribute of the
|
|
generic function.
|
|
|
|
"""
|
|
registry = {}
|
|
dispatch_cache = WeakKeyDictionary()
|
|
cache_token = None
|
|
|
|
def dispatch(cls):
|
|
"""generic_func.dispatch(cls) -> <function implementation>
|
|
|
|
Runs the dispatch algorithm to return the best available implementation
|
|
for the given *cls* registered on *generic_func*.
|
|
|
|
"""
|
|
nonlocal cache_token
|
|
if cache_token is not None:
|
|
current_token = get_cache_token()
|
|
if cache_token != current_token:
|
|
dispatch_cache.clear()
|
|
cache_token = current_token
|
|
try:
|
|
impl = dispatch_cache[cls]
|
|
except KeyError:
|
|
try:
|
|
impl = registry[cls]
|
|
except KeyError:
|
|
impl = _find_impl(cls, registry)
|
|
dispatch_cache[cls] = impl
|
|
return impl
|
|
|
|
def register(cls, func=None):
|
|
"""generic_func.register(cls, func) -> func
|
|
|
|
Registers a new implementation for the given *cls* on a *generic_func*.
|
|
|
|
"""
|
|
nonlocal cache_token
|
|
if func is None:
|
|
return lambda f: register(cls, f)
|
|
registry[cls] = func
|
|
if cache_token is None and hasattr(cls, '__abstractmethods__'):
|
|
cache_token = get_cache_token()
|
|
dispatch_cache.clear()
|
|
return func
|
|
|
|
def wrapper(*args, **kw):
|
|
return dispatch(args[0].__class__)(*args, **kw)
|
|
|
|
registry[object] = func
|
|
wrapper.register = register
|
|
wrapper.dispatch = dispatch
|
|
wrapper.registry = MappingProxyType(registry)
|
|
wrapper._clear_cache = dispatch_cache.clear
|
|
update_wrapper(wrapper, func)
|
|
return wrapper
|