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()

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,

'Return a > b. Computed by @total_ordering from (not a < b) and (a != b).'

op_result = self.__lt__(other)

if op_result is NotImplemented:

return op_result

'Return a <= b. Computed by @total_ordering from (a < b) or (a == b).'

op_result = self.__lt__(other)

if op_result is NotImplemented:

return op_result

'Return a >= b. Computed by @total_ordering from (not a < b).'

op_result = self.__lt__(other)

if op_result is NotImplemented:

return op_result

'Return a >= b. Computed by @total_ordering from (not a <= b) or (a == b).'

op_result = self.__le__(other)

if op_result is NotImplemented:

return op_result

'Return a < b. Computed by @total_ordering from (a <= b) and (a != b).'

op_result = self.__le__(other)

if op_result is NotImplemented:

return op_result

'Return a > b. Computed by @total_ordering from (not a <= b).'

op_result = self.__le__(other)

if op_result is NotImplemented:

return op_result

'Return a < b. Computed by @total_ordering from (not a > b) and (a != b).'

op_result = self.__gt__(other)

if op_result is NotImplemented:

return op_result

'Return a >= b. Computed by @total_ordering from (a > b) or (a == b).'

op_result = self.__gt__(other)

if op_result is NotImplemented:

return op_result

'Return a <= b. Computed by @total_ordering from (not a > b).'

op_result = self.__gt__(other)

if op_result is NotImplemented:

return op_result

'Return a <= b. Computed by @total_ordering from (not a >= b) or (a == b).'

op_result = self.__ge__(other)

if op_result is NotImplemented:

return op_result

'Return a > b. Computed by @total_ordering from (a >= b) and (a != b).'

op_result = self.__ge__(other)

if op_result is NotImplemented:

return op_result

'Return a < b. Computed by @total_ordering from (not a >= b).'

op_result = self.__ge__(other)

if op_result is NotImplemented:

return op_result

'__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)]

"""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)

"""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

"""

reduce(function, sequence[, initial]) -> value

Apply a function of two arguments cumulatively to the items of a sequence,

from left to right, so as to reduce the sequence 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 sequence in the calculation, and serves as a default when the

sequence is empty.

"""

it = iter(sequence)

if initial is _initial_missing:

try:

value = next(it)

except StopIteration:

raise TypeError("reduce() of empty sequence with no initial value") from None

else:

value = initial

for element in it:

value = function(value, element)

"""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

"""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)

""" 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):

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]

"""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_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)

# 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

"""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 candidate is None:

raise RuntimeError("Inconsistent hierarchy")

result.append(candidate)

# remove the chosen candidate

for seq in sequences:

if seq[0] == candidate:

"""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]

"""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)

"""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

"""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) -> <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:

if isinstance(cls, type):

return lambda f: register(cls, f)

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 isinstance(cls, type):

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)

"""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):

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