# Source code for functools

    """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', '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

    ################################################################################
    ### 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

    ################################################################################
    ### reduce() sequence to a single item
    ################################################################################

    _initial_missing = object()

    def reduce(function, sequence, initial=_initial_missing):
        """
        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)

        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__(*args, **keywords):
            if len(args) >= 2:
                self, func, *args = args
            elif not args:
                raise TypeError("descriptor '__init__' of partialmethod "
                                "needs an argument")
            elif 'func' in keywords:
                func = keywords.pop('func')
                self, *args = args
                import warnings
                warnings.warn("Passing 'func' as keyword argument is deprecated",
                              DeprecationWarning, stacklevel=2)
            else:
                raise TypeError("type 'partialmethod' takes at least one argument, "
                                "got %d" % (len(args)-1))
            args = tuple(args)

            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
        __init__.__text_signature__ = '($self, func, /, *args, **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)

    # 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:  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)
            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)
            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

    ################################################################################
    ### 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()))
                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)
        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