Update prebuilt Clang to r416183b from Android.
https://android.googlesource.com/platform/prebuilts/clang/host/
linux-x86/+/06a71ddac05c22edb2d10b590e1769b3f8619bef
clang 12.0.5 (based on r416183b) from build 7284624.
Change-Id: I277a316abcf47307562d8b748b84870f31a72866
Signed-off-by: Olivier Deprez <olivier.deprez@arm.com>
diff --git a/linux-x64/clang/python3/lib/python3.9/functools.py b/linux-x64/clang/python3/lib/python3.9/functools.py
new file mode 100644
index 0000000..5cab497
--- /dev/null
+++ b/linux-x64/clang/python3/lib/python3.9/functools.py
@@ -0,0 +1,980 @@
+"""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', 'cache', 'cmp_to_key', 'lru_cache', 'reduce',
+ 'partial', 'partialmethod', 'singledispatch', 'singledispatchmethod',
+ 'cached_property']
+
+from abc import get_cache_token
+from collections import namedtuple
+# import types, weakref # Deferred to single_dispatch()
+from reprlib import recursive_repr
+from _thread import RLock
+from types import GenericAlias
+
+
+################################################################################
+### update_wrapper() and wraps() decorator
+################################################################################
+
+# update_wrapper() and wraps() are tools to help write
+# wrapper functions that can handle naive introspection
+
+WRAPPER_ASSIGNMENTS = ('__module__', '__name__', '__qualname__', '__doc__',
+ '__annotations__')
+WRAPPER_UPDATES = ('__dict__',)
+def update_wrapper(wrapper,
+ wrapped,
+ assigned = WRAPPER_ASSIGNMENTS,
+ updated = WRAPPER_UPDATES):
+ """Update a wrapper function to look like the wrapped function
+
+ wrapper is the function to be updated
+ wrapped is the original function
+ assigned is a tuple naming the attributes assigned directly
+ from the wrapped function to the wrapper function (defaults to
+ functools.WRAPPER_ASSIGNMENTS)
+ updated is a tuple naming the attributes of the wrapper that
+ are updated with the corresponding attribute from the wrapped
+ function (defaults to functools.WRAPPER_UPDATES)
+ """
+ for attr in assigned:
+ try:
+ value = getattr(wrapped, attr)
+ except AttributeError:
+ pass
+ else:
+ setattr(wrapper, attr, value)
+ for attr in updated:
+ getattr(wrapper, attr).update(getattr(wrapped, attr, {}))
+ # Issue #17482: set __wrapped__ last so we don't inadvertently copy it
+ # from the wrapped function when updating __dict__
+ wrapper.__wrapped__ = wrapped
+ # Return the wrapper so this can be used as a decorator via partial()
+ return wrapper
+
+def wraps(wrapped,
+ assigned = WRAPPER_ASSIGNMENTS,
+ updated = WRAPPER_UPDATES):
+ """Decorator factory to apply update_wrapper() to a wrapper function
+
+ Returns a decorator that invokes update_wrapper() with the decorated
+ function as the wrapper argument and the arguments to wraps() as the
+ remaining arguments. Default arguments are as for update_wrapper().
+ This is a convenience function to simplify applying partial() to
+ update_wrapper().
+ """
+ return partial(update_wrapper, wrapped=wrapped,
+ assigned=assigned, updated=updated)
+
+
+################################################################################
+### total_ordering class decorator
+################################################################################
+
+# The total ordering functions all invoke the root magic method directly
+# rather than using the corresponding operator. This avoids possible
+# infinite recursion that could occur when the operator dispatch logic
+# detects a NotImplemented result and then calls a reflected method.
+
+def _gt_from_lt(self, other, NotImplemented=NotImplemented):
+ 'Return a > b. Computed by @total_ordering from (not a < b) and (a != b).'
+ op_result = 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)
+ if op_result is NotImplemented:
+ return op_result
+ return op_result or self == other
+
+def _ge_from_lt(self, other, NotImplemented=NotImplemented):
+ 'Return a >= b. Computed by @total_ordering from (not a < b).'
+ op_result = 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)
+ if op_result is NotImplemented:
+ return op_result
+ return op_result or self == other
+
+def _le_from_gt(self, other, NotImplemented=NotImplemented):
+ 'Return a <= b. Computed by @total_ordering from (not a > b).'
+ op_result = 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__(self, func, /, *args, **keywords):
+ if not callable(func) and not hasattr(func, "__get__"):
+ raise TypeError("{!r} is not callable or a descriptor"
+ .format(func))
+
+ # func could be a descriptor like classmethod which isn't callable,
+ # so we can't inherit from partial (it verifies func is callable)
+ if isinstance(func, partialmethod):
+ # flattening is mandatory in order to place cls/self before all
+ # other arguments
+ # it's also more efficient since only one function will be called
+ self.func = func.func
+ self.args = func.args + args
+ self.keywords = {**func.keywords, **keywords}
+ else:
+ self.func = func
+ self.args = args
+ self.keywords = keywords
+
+ def __repr__(self):
+ args = ", ".join(map(repr, self.args))
+ keywords = ", ".join("{}={!r}".format(k, v)
+ for k, v in self.keywords.items())
+ format_string = "{module}.{cls}({func}, {args}, {keywords})"
+ return format_string.format(module=self.__class__.__module__,
+ cls=self.__class__.__qualname__,
+ func=self.func,
+ args=args,
+ keywords=keywords)
+
+ def _make_unbound_method(self):
+ def _method(cls_or_self, /, *args, **keywords):
+ keywords = {**self.keywords, **keywords}
+ return self.func(cls_or_self, *self.args, *args, **keywords)
+ _method.__isabstractmethod__ = self.__isabstractmethod__
+ _method._partialmethod = self
+ return _method
+
+ def __get__(self, obj, cls=None):
+ get = getattr(self.func, "__get__", None)
+ result = None
+ if get is not None:
+ new_func = get(obj, cls)
+ if new_func is not self.func:
+ # Assume __get__ returning something new indicates the
+ # creation of an appropriate callable
+ result = partial(new_func, *self.args, **self.keywords)
+ try:
+ result.__self__ = new_func.__self__
+ except AttributeError:
+ pass
+ if result is None:
+ # If the underlying descriptor didn't do anything, treat this
+ # like an instance method
+ result = self._make_unbound_method().__get__(obj, cls)
+ return result
+
+ @property
+ def __isabstractmethod__(self):
+ return getattr(self.func, "__isabstractmethod__", False)
+
+ __class_getitem__ = classmethod(GenericAlias)
+
+
+# Helper functions
+
+def _unwrap_partial(func):
+ while isinstance(func, partial):
+ func = func.func
+ return func
+
+################################################################################
+### LRU Cache function decorator
+################################################################################
+
+_CacheInfo = namedtuple("CacheInfo", ["hits", "misses", "maxsize", "currsize"])
+
+class _HashedSeq(list):
+ """ This class guarantees that hash() will be called no more than once
+ per element. This is important because the lru_cache() will hash
+ the key multiple times on a cache miss.
+
+ """
+
+ __slots__ = 'hashvalue'
+
+ def __init__(self, tup, hash=hash):
+ self[:] = tup
+ self.hashvalue = hash(tup)
+
+ def __hash__(self):
+ return self.hashvalue
+
+def _make_key(args, kwds, typed,
+ kwd_mark = (object(),),
+ fasttypes = {int, str},
+ tuple=tuple, type=type, len=len):
+ """Make a cache key from optionally typed positional and keyword arguments
+
+ The key is constructed in a way that is flat as possible rather than
+ as a nested structure that would take more memory.
+
+ If there is only a single argument and its data type is known to cache
+ its hash value, then that argument is returned without a wrapper. This
+ saves space and improves lookup speed.
+
+ """
+ # All of code below relies on kwds preserving the order input by the user.
+ # Formerly, we sorted() the kwds before looping. The new way is *much*
+ # faster; however, it means that f(x=1, y=2) will now be treated as a
+ # distinct call from f(y=2, x=1) which will be cached separately.
+ key = args
+ if kwds:
+ key += kwd_mark
+ for item in kwds.items():
+ key += item
+ if typed:
+ key += tuple(type(v) for v in args)
+ if kwds:
+ key += tuple(type(v) for v in kwds.values())
+ elif len(key) == 1 and type(key[0]) in fasttypes:
+ return key[0]
+ return _HashedSeq(key)
+
+def lru_cache(maxsize=128, typed=False):
+ """Least-recently-used cache decorator.
+
+ If *maxsize* is set to None, the LRU features are disabled and the cache
+ can grow without bound.
+
+ If *typed* is True, arguments of different types will be cached separately.
+ For example, f(3.0) and f(3) will be treated as distinct calls with
+ distinct results.
+
+ Arguments to the cached function must be hashable.
+
+ View the cache statistics named tuple (hits, misses, maxsize, currsize)
+ with f.cache_info(). Clear the cache and statistics with f.cache_clear().
+ Access the underlying function with f.__wrapped__.
+
+ See: 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)
+
+ return decorating_function
+
+def _lru_cache_wrapper(user_function, maxsize, typed, _CacheInfo):
+ # Constants shared by all lru cache instances:
+ sentinel = object() # unique object used to signal cache misses
+ make_key = _make_key # build a key from the function arguments
+ PREV, NEXT, KEY, RESULT = 0, 1, 2, 3 # names for the link fields
+
+ cache = {}
+ hits = misses = 0
+ full = False
+ cache_get = cache.get # bound method to lookup a key or return None
+ cache_len = cache.__len__ # get cache size without calling len()
+ lock = RLock() # because linkedlist updates aren't threadsafe
+ root = [] # root of the circular doubly linked list
+ root[:] = [root, root, None, None] # initialize by pointing to self
+
+ if maxsize == 0:
+
+ def wrapper(*args, **kwds):
+ # No caching -- just a statistics update
+ nonlocal misses
+ misses += 1
+ result = user_function(*args, **kwds)
+ return result
+
+ elif maxsize is None:
+
+ def wrapper(*args, **kwds):
+ # Simple caching without ordering or size limit
+ nonlocal hits, misses
+ key = make_key(args, kwds, typed)
+ result = cache_get(key, sentinel)
+ if result is not sentinel:
+ hits += 1
+ return result
+ misses += 1
+ result = user_function(*args, **kwds)
+ cache[key] = result
+ return result
+
+ else:
+
+ def wrapper(*args, **kwds):
+ # Size limited caching that tracks accesses by recency
+ nonlocal root, hits, misses, full
+ key = make_key(args, kwds, typed)
+ with lock:
+ link = cache_get(key)
+ if link is not None:
+ # Move the link to the front of the circular queue
+ link_prev, link_next, _key, result = link
+ link_prev[NEXT] = link_next
+ link_next[PREV] = link_prev
+ last = root[PREV]
+ last[NEXT] = root[PREV] = link
+ link[PREV] = last
+ link[NEXT] = root
+ hits += 1
+ return result
+ misses += 1
+ result = user_function(*args, **kwds)
+ with lock:
+ if key in cache:
+ # Getting here means that this same key was added to the
+ # cache while the lock was released. Since the link
+ # update is already done, we need only return the
+ # computed result and update the count of misses.
+ pass
+ elif full:
+ # Use the old root to store the new key and result.
+ oldroot = root
+ oldroot[KEY] = key
+ oldroot[RESULT] = result
+ # Empty the oldest link and make it the new root.
+ # Keep a reference to the old key and old result to
+ # prevent their ref counts from going to zero during the
+ # update. That will prevent potentially arbitrary object
+ # clean-up code (i.e. __del__) from running while we're
+ # still adjusting the links.
+ root = oldroot[NEXT]
+ oldkey = root[KEY]
+ oldresult = root[RESULT]
+ root[KEY] = root[RESULT] = None
+ # Now update the cache dictionary.
+ del cache[oldkey]
+ # Save the potentially reentrant cache[key] assignment
+ # for last, after the root and links have been put in
+ # a consistent state.
+ cache[key] = oldroot
+ else:
+ # Put result in a new link at the front of the queue.
+ last = root[PREV]
+ link = [last, root, key, result]
+ last[NEXT] = root[PREV] = cache[key] = link
+ # Use the cache_len bound method instead of the len() function
+ # which could potentially be wrapped in an lru_cache itself.
+ full = (cache_len() >= maxsize)
+ return result
+
+ def cache_info():
+ """Report cache statistics"""
+ with lock:
+ return _CacheInfo(hits, misses, maxsize, cache_len())
+
+ def cache_clear():
+ """Clear the cache and cache statistics"""
+ nonlocal hits, misses, full
+ with lock:
+ cache.clear()
+ root[:] = [root, root, None, None]
+ hits = misses = 0
+ full = False
+
+ wrapper.cache_info = cache_info
+ wrapper.cache_clear = cache_clear
+ return wrapper
+
+try:
+ from _functools import _lru_cache_wrapper
+except ImportError:
+ pass
+
+
+################################################################################
+### cache -- simplified access to the infinity cache
+################################################################################
+
+def cache(user_function, /):
+ 'Simple lightweight unbounded cache. Sometimes called "memoize".'
+ return lru_cache(maxsize=None)(user_function)
+
+
+################################################################################
+### singledispatch() - single-dispatch generic function decorator
+################################################################################
+
+def _c3_merge(sequences):
+ """Merges MROs in *sequences* to a single MRO using the C3 algorithm.
+
+ Adapted from 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
+
+ __class_getitem__ = classmethod(GenericAlias)