"""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',
 'partialmethod', 'singledispatch']

try:
 from _functools import reduce
except ImportError:
 pass
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


################################################################################
### 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 = self.__lt__(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 = self.__lt__(other)
 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 = self.__lt__(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 = self.__le__(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 = self.__le__(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 = self.__le__(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 = self.__gt__(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 = self.__gt__(other)
 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 = self.__gt__(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 = self.__ge__(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 = self.__ge__(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 = self.__ge__(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


################################################################################
### 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__(*args, **keywords):
 if not args:
 raise TypeError("descriptor '__new__' of partial needs an argument")
 if len(args) < 2:
 raise TypeError("type 'partial' takes at least one argument")
 cls, func, *args = args
 if not callable(func):
 raise TypeError("the first argument must be callable")
 args = tuple(args)

 if hasattr(func, "func"):
 args = func.args + args
 tmpkw = func.keywords.copy()
 tmpkw.update(keywords)
 keywords = tmpkw
 del tmpkw
 func = func.func

 self = super(partial, cls).__new__(cls)

 self.func = func
 self.args = args
 self.keywords = keywords
 return self

 def __call__(*args, **keywords):
 if not args:
 raise TypeError("descriptor '__call__' of partial needs an argument")
 self, *args = args
 newkeywords = self.keywords.copy()
 newkeywords.update(keywords)
 return self.func(*self.args, *args, **newkeywords)

 @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.copy()
 self.keywords.update(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(*args, **keywords):
 call_keywords = self.keywords.copy()
 call_keywords.update(keywords)
 cls_or_self, *rest = args
 call_args = (cls_or_self,) + self.args + tuple(rest)
 return self.func(*call_args, **call_keywords)
 _method.__isabstractmethod__ = self.__isabstractmethod__
 _method._partialmethod = self
 return _method

 def __get__(self, obj, cls):
 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)


################################################################################
### 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)},
 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: 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).

 # Early detection of an erroneous call to @lru_cache without any arguments
 # resulting in the inner function being passed to maxsize instead of an
 # integer or None.
 if maxsize is not None and not isinstance(maxsize, int):
 raise TypeError('Expected maxsize to be an integer or None')

 def decorating_function(user_function):
 wrapper = _lru_cache_wrapper(user_function, maxsize, typed, _CacheInfo)
 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 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
 # 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)
 misses += 1
 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


################################################################################
### 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 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 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) -> <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()))
 assert isinstance(cls, type), (
 f"Invalid annotation for {argname!r}. {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):
 return dispatch(args[0].__class__)(*args, **kw)

 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