/snap/core22/2437/usr/lib/python3.10
Edit: /snap/core22/2437/usr/lib/python3.10/functools.py (38076B)
"""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
,
# Raymond Hettinger ,
# and Łukasz Langa .
# 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', 'cache', 'cmp_to_key', 'lru_cache', 'reduce',
'partial', 'partialmethod', 'singledispatch', 'singledispatchmethod',
'cached_property']
from abc import get_cache_token
from collections import namedtuple
# import types, weakref # Deferred to single_dispatch()
from reprlib import recursive_repr
from _thread import RLock
from types import GenericAlias
################################################################################
### 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
################################################################################
# The total ordering functions all invoke the root magic method directly
# rather than using the corresponding operator. This avoids possible
# infinite recursion that could occur when the operator dispatch logic
# detects a NotImplemented result and then calls a reflected method.
def _gt_from_lt(self, other, NotImplemented=NotImplemented):
'Return a > b. Computed by @total_ordering from (not a < b) and (a != b).'
op_result = type(self).__lt__(self, other)
if op_result is NotImplemented:
return op_result
return not op_result and self != other
def _le_from_lt(self, other, NotImplemented=NotImplemented):
'Return a <= b. Computed by @total_ordering from (a < b) or (a == b).'
op_result = type(self).__lt__(self, other)
if op_result is NotImplemented:
return op_result
return op_result or self == other
def _ge_from_lt(self, other, NotImplemented=NotImplemented):
'Return a >= b. Computed by @total_ordering from (not a < b).'
op_result = type(self).__lt__(self, other)
if op_result is NotImplemented:
return op_result
return not op_result
def _ge_from_le(self, other, NotImplemented=NotImplemented):
'Return a >= b. Computed by @total_ordering from (not a <= b) or (a == b).'
op_result = type(self).__le__(self, other)
if op_result is NotImplemented:
return op_result
return not op_result or self == other
def _lt_from_le(self, other, NotImplemented=NotImplemented):
'Return a < b. Computed by @total_ordering from (a <= b) and (a != b).'
op_result = type(self).__le__(self, other)
if op_result is NotImplemented:
return op_result
return op_result and self != other
def _gt_from_le(self, other, NotImplemented=NotImplemented):
'Return a > b. Computed by @total_ordering from (not a <= b).'
op_result = type(self).__le__(self, other)
if op_result is NotImplemented:
return op_result
return not op_result
def _lt_from_gt(self, other, NotImplemented=NotImplemented):
'Return a < b. Computed by @total_ordering from (not a > b) and (a != b).'
op_result = type(self).__gt__(self, other)
if op_result is NotImplemented:
return op_result
return not op_result and self != other
def _ge_from_gt(self, other, NotImplemented=NotImplemented):
'Return a >= b. Computed by @total_ordering from (a > b) or (a == b).'
op_result = type(self).__gt__(self, other)
if op_result is NotImplemented:
return op_result
return op_result or self == other
def _le_from_gt(self, other, NotImplemented=NotImplemented):
'Return a <= b. Computed by @total_ordering from (not a > b).'
op_result = type(self).__gt__(self, other)
if op_result is NotImplemented:
return op_result
return not op_result
def _le_from_ge(self, other, NotImplemented=NotImplemented):
'Return a <= b. Computed by @total_ordering from (not a >= b) or (a == b).'
op_result = type(self).__ge__(self, other)
if op_result is NotImplemented:
return op_result
return not op_result or self == other
def _gt_from_ge(self, other, NotImplemented=NotImplemented):
'Return a > b. Computed by @total_ordering from (a >= b) and (a != b).'
op_result = type(self).__ge__(self, other)
if op_result is NotImplemented:
return op_result
return op_result and self != other
def _lt_from_ge(self, other, NotImplemented=NotImplemented):
'Return a < b. Computed by @total_ordering from (not a >= b).'
op_result = type(self).__ge__(self, other)
if op_result is NotImplemented:
return op_result
return not op_result
_convert = {
'__lt__': [('__gt__', _gt_from_lt),
('__le__', _le_from_lt),
('__ge__', _ge_from_lt)],
'__le__': [('__ge__', _ge_from_le),
('__lt__', _lt_from_le),
('__gt__', _gt_from_le)],
'__gt__': [('__lt__', _lt_from_gt),
('__ge__', _ge_from_gt),
('__le__', _le_from_gt)],
'__ge__': [('__le__', _le_from_ge),
('__gt__', _gt_from_ge),
('__lt__', _lt_from_ge)]
}
def total_ordering(cls):
"""Class decorator that fills in missing ordering methods"""
# 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
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
__hash__ = None
return K
try:
from _functools import cmp_to_key
except ImportError:
pass
################################################################################
### reduce() sequence to a single item
################################################################################
_initial_missing = object()
def reduce(function, sequence, initial=_initial_missing):
"""
reduce(function, iterable[, initial]) -> value
Apply a function of two arguments cumulatively to the items of a sequence
or iterable, from left to right, so as to reduce the iterable to a single
value. For example, reduce(lambda x, y: x+y, [1, 2, 3, 4, 5]) calculates
((((1+2)+3)+4)+5). If initial is present, it is placed before the items
of the iterable in the calculation, and serves as a default when the
iterable is empty.
"""
it = iter(sequence)
if initial is _initial_missing:
try:
value = next(it)
except StopIteration:
raise TypeError(
"reduce() of empty iterable with no initial value") from None
else:
value = initial
for element in it:
value = function(value, element)
return value
try:
from _functools import reduce
except ImportError:
pass
################################################################################
### partial() argument application
################################################################################
# Purely functional, no descriptor behaviour
class partial:
"""New function with partial application of the given arguments
and keywords.
"""
__slots__ = "func", "args", "keywords", "__dict__", "__weakref__"
def __new__(cls, func, /, *args, **keywords):
if not callable(func):
raise TypeError("the first argument must be callable")
if hasattr(func, "func"):
args = func.args + args
keywords = {**func.keywords, **keywords}
func = func.func
self = super(partial, cls).__new__(cls)
self.func = func
self.args = args
self.keywords = keywords
return self
def __call__(self, /, *args, **keywords):
keywords = {**self.keywords, **keywords}
return self.func(*self.args, *args, **keywords)
@recursive_repr()
def __repr__(self):
qualname = type(self).__qualname__
args = [repr(self.func)]
args.extend(repr(x) for x in self.args)
args.extend(f"{k}={v!r}" for (k, v) in self.keywords.items())
if type(self).__module__ == "functools":
return f"functools.{qualname}({', '.join(args)})"
return f"{qualname}({', '.join(args)})"
def __reduce__(self):
return type(self), (self.func,), (self.func, self.args,
self.keywords or None, self.__dict__ or None)
def __setstate__(self, state):
if not isinstance(state, tuple):
raise TypeError("argument to __setstate__ must be a tuple")
if len(state) != 4:
raise TypeError(f"expected 4 items in state, got {len(state)}")
func, args, kwds, namespace = state
if (not callable(func) or not isinstance(args, tuple) or
(kwds is not None and not isinstance(kwds, dict)) or
(namespace is not None and not isinstance(namespace, dict))):
raise TypeError("invalid partial state")
args = tuple(args) # just in case it's a subclass
if kwds is None:
kwds = {}
elif type(kwds) is not dict: # XXX does it need to be *exactly* dict?
kwds = dict(kwds)
if namespace is None:
namespace = {}
self.__dict__ = namespace
self.func = func
self.args = args
self.keywords = kwds
try:
from _functools import partial
except ImportError:
pass
# Descriptor version
class partialmethod(object):
"""Method descriptor with partial application of the given arguments
and keywords.
Supports wrapping existing descriptors and handles non-descriptor
callables as instance methods.
"""
def __init__(self, func, /, *args, **keywords):
if not callable(func) and not hasattr(func, "__get__"):
raise TypeError("{!r} is not callable or a descriptor"
.format(func))
# func could be a descriptor like classmethod which isn't callable,
# so we can't inherit from partial (it verifies func is callable)
if isinstance(func, partialmethod):
# flattening is mandatory in order to place cls/self before all
# other arguments
# it's also more efficient since only one function will be called
self.func = func.func
self.args = func.args + args
self.keywords = {**func.keywords, **keywords}
else:
self.func = func
self.args = args
self.keywords = keywords
def __repr__(self):
args = ", ".join(map(repr, self.args))
keywords = ", ".join("{}={!r}".format(k, v)
for k, v in self.keywords.items())
format_string = "{module}.{cls}({func}, {args}, {keywords})"
return format_string.format(module=self.__class__.__module__,
cls=self.__class__.__qualname__,
func=self.func,
args=args,
keywords=keywords)
def _make_unbound_method(self):
def _method(cls_or_self, /, *args, **keywords):
keywords = {**self.keywords, **keywords}
return self.func(cls_or_self, *self.args, *args, **keywords)
_method.__isabstractmethod__ = self.__isabstractmethod__
_method._partialmethod = self
return _method
def __get__(self, obj, cls=None):
get = getattr(self.func, "__get__", None)
result = None
if get is not None:
new_func = get(obj, cls)
if new_func is not self.func:
# Assume __get__ returning something new indicates the
# creation of an appropriate callable
result = partial(new_func, *self.args, **self.keywords)
try:
result.__self__ = new_func.__self__
except AttributeError:
pass
if result is None:
# If the underlying descriptor didn't do anything, treat this
# like an instance method
result = self._make_unbound_method().__get__(obj, cls)
return result
@property
def __isabstractmethod__(self):
return getattr(self.func, "__isabstractmethod__", False)
__class_getitem__ = classmethod(GenericAlias)
# Helper functions
def _unwrap_partial(func):
while isinstance(func, partial):
func = func.func
return func
################################################################################
### 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},
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(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: 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]
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
# 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
try:
from _functools import _lru_cache_wrapper
except ImportError:
pass
################################################################################
### cache -- simplified access to the infinity cache
################################################################################
def cache(user_function, /):
'Simple lightweight unbounded cache. Sometimes called "memoize".'
return lru_cache(maxsize=None)(user_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 https://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 candidate is None:
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 not isinstance(typ, GenericAlias)
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.
"""
# There are many programs that use functools without singledispatch, so we
# trade-off making singledispatch marginally slower for the benefit of
# making start-up of such applications slightly faster.
import types, weakref
registry = {}
dispatch_cache = weakref.WeakKeyDictionary()
cache_token = None
def dispatch(cls):
"""generic_func.dispatch(cls) ->
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 _is_valid_dispatch_type(cls):
return isinstance(cls, type) and not isinstance(cls, GenericAlias)
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 _is_valid_dispatch_type(cls):
if func is None:
return lambda f: register(cls, f)
else:
if func is not None:
raise TypeError(
f"Invalid first argument to `register()`. "
f"{cls!r} is not a class."
)
ann = getattr(cls, '__annotations__', {})
if not ann:
raise TypeError(
f"Invalid first argument to `register()`: {cls!r}. "
f"Use either `@register(some_class)` or plain `@register` "
f"on an annotated function."
)
func = cls
# only import typing if annotation parsing is necessary
from typing import get_type_hints
argname, cls = next(iter(get_type_hints(func).items()))
if not _is_valid_dispatch_type(cls):
raise TypeError(
f"Invalid annotation for {argname!r}. "
f"{cls!r} is not a class."
)
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):
if not args:
raise TypeError(f'{funcname} requires at least '
'1 positional argument')
return dispatch(args[0].__class__)(*args, **kw)
funcname = getattr(func, '__name__', 'singledispatch function')
registry[object] = func
wrapper.register = register
wrapper.dispatch = dispatch
wrapper.registry = types.MappingProxyType(registry)
wrapper._clear_cache = dispatch_cache.clear
update_wrapper(wrapper, func)
return wrapper
# Descriptor version
class singledispatchmethod:
"""Single-dispatch generic method descriptor.
Supports wrapping existing descriptors and handles non-descriptor
callables as instance methods.
"""
def __init__(self, func):
if not callable(func) and not hasattr(func, "__get__"):
raise TypeError(f"{func!r} is not callable or a descriptor")
self.dispatcher = singledispatch(func)
self.func = func
def register(self, cls, method=None):
"""generic_method.register(cls, func) -> func
Registers a new implementation for the given *cls* on a *generic_method*.
"""
return self.dispatcher.register(cls, func=method)
def __get__(self, obj, cls=None):
def _method(*args, **kwargs):
method = self.dispatcher.dispatch(args[0].__class__)
return method.__get__(obj, cls)(*args, **kwargs)
_method.__isabstractmethod__ = self.__isabstractmethod__
_method.register = self.register
update_wrapper(_method, self.func)
return _method
@property
def __isabstractmethod__(self):
return getattr(self.func, '__isabstractmethod__', False)
################################################################################
### cached_property() - computed once per instance, cached as attribute
################################################################################
_NOT_FOUND = object()
class cached_property:
def __init__(self, func):
self.func = func
self.attrname = None
self.__doc__ = func.__doc__
self.lock = RLock()
def __set_name__(self, owner, name):
if self.attrname is None:
self.attrname = name
elif name != self.attrname:
raise TypeError(
"Cannot assign the same cached_property to two different names "
f"({self.attrname!r} and {name!r})."
)
def __get__(self, instance, owner=None):
if instance is None:
return self
if self.attrname is None:
raise TypeError(
"Cannot use cached_property instance without calling __set_name__ on it.")
try:
cache = instance.__dict__
except AttributeError: # not all objects have __dict__ (e.g. class defines slots)
msg = (
f"No '__dict__' attribute on {type(instance).__name__!r} "
f"instance to cache {self.attrname!r} property."
)
raise TypeError(msg) from None
val = cache.get(self.attrname, _NOT_FOUND)
if val is _NOT_FOUND:
with self.lock:
# check if another thread filled cache while we awaited lock
val = cache.get(self.attrname, _NOT_FOUND)
if val is _NOT_FOUND:
val = self.func(instance)
try:
cache[self.attrname] = val
except TypeError:
msg = (
f"The '__dict__' attribute on {type(instance).__name__!r} instance "
f"does not support item assignment for caching {self.attrname!r} property."
)
raise TypeError(msg) from None
return val
__class_getitem__ = classmethod(GenericAlias)