+++ /dev/null
-"""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
-from types import MappingProxyType
-from weakref import WeakKeyDictionary
-from reprlib import recursive_repr
-try:
- from _thread import RLock
-except ImportError:
- class RLock:
- 'Dummy reentrant lock for builds without threads'
- def __enter__(self): pass
- def __exit__(self, exctype, excinst, exctb): pass
-
-
-################################################################################
-### 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.
-
- """
- 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.
-
- """
- registry = {}
- dispatch_cache = 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:
- return lambda f: register(cls, f)
- 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 = MappingProxyType(registry)
- wrapper._clear_cache = dispatch_cache.clear
- update_wrapper(wrapper, func)
- return wrapper
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