""" Define names for built-in types that aren't directly accessible as a builtin. """ import sys # Iterators in Python aren't a matter of type but of protocol. A large # and changing number of builtin types implement *some* flavor of # iterator. Don't check the type! Use hasattr to check for both # "__iter__" and "__next__" attributes instead. def _f(): pass FunctionType = type(_f) LambdaType = type(lambda: None) # Same as FunctionType CodeType = type(_f.__code__) MappingProxyType = type(type.__dict__) SimpleNamespace = type(sys.implementation) def _cell_factory(): a = 1 def f(): nonlocal a return f.__closure__[0] CellType = type(_cell_factory()) def _g(): yield 1 GeneratorType = type(_g()) async def _c(): pass _c = _c() CoroutineType = type(_c) _c.close() # Prevent ResourceWarning async def _ag(): yield _ag = _ag() AsyncGeneratorType = type(_ag) class _C: def _m(self): pass MethodType = type(_C()._m) BuiltinFunctionType = type(len) BuiltinMethodType = type([].append) # Same as BuiltinFunctionType WrapperDescriptorType = type(object.__init__) MethodWrapperType = type(object().__str__) MethodDescriptorType = type(str.join) ClassMethodDescriptorType = type(dict.__dict__['fromkeys']) ModuleType = type(sys) try: raise TypeError except TypeError as exc: TracebackType = type(exc.__traceback__) FrameType = type(exc.__traceback__.tb_frame) GetSetDescriptorType = type(FunctionType.__code__) MemberDescriptorType = type(FunctionType.__globals__) del sys, _f, _g, _C, _c, _ag, _cell_factory # Not for export # Provide a PEP 3115 compliant mechanism for class creation def new_class(name, bases=(), kwds=None, exec_body=None): """Create a class object dynamically using the appropriate metaclass.""" resolved_bases = resolve_bases(bases) meta, ns, kwds = prepare_class(name, resolved_bases, kwds) if exec_body is not None: exec_body(ns) if resolved_bases is not bases: ns['__orig_bases__'] = bases return meta(name, resolved_bases, ns, **kwds) def resolve_bases(bases): """Resolve MRO entries dynamically as specified by PEP 560.""" new_bases = list(bases) updated = False shift = 0 for i, base in enumerate(bases): if isinstance(base, type): continue if not hasattr(base, "__mro_entries__"): continue new_base = base.__mro_entries__(bases) updated = True if not isinstance(new_base, tuple): raise TypeError("__mro_entries__ must return a tuple") else: new_bases[i+shift:i+shift+1] = new_base shift += len(new_base) - 1 if not updated: return bases return tuple(new_bases) def prepare_class(name, bases=(), kwds=None): """Call the __prepare__ method of the appropriate metaclass. Returns (metaclass, namespace, kwds) as a 3-tuple *metaclass* is the appropriate metaclass *namespace* is the prepared class namespace *kwds* is an updated copy of the passed in kwds argument with any 'metaclass' entry removed. If no kwds argument is passed in, this will be an empty dict. """ if kwds is None: kwds = {} else: kwds = dict(kwds) # Don't alter the provided mapping if 'metaclass' in kwds: meta = kwds.pop('metaclass') else: if bases: meta = type(bases[0]) else: meta = type if isinstance(meta, type): # when meta is a type, we first determine the most-derived metaclass # instead of invoking the initial candidate directly meta = _calculate_meta(meta, bases) if hasattr(meta, '__prepare__'): ns = meta.__prepare__(name, bases, **kwds) else: ns = {} return meta, ns, kwds def _calculate_meta(meta, bases): """Calculate the most derived metaclass.""" winner = meta for base in bases: base_meta = type(base) if issubclass(winner, base_meta): continue if issubclass(base_meta, winner): winner = base_meta continue # else: raise TypeError("metaclass conflict: " "the metaclass of a derived class " "must be a (non-strict) subclass " "of the metaclasses of all its bases") return winner def get_original_bases(cls, /): """Return the class's "original" bases prior to modification by `__mro_entries__`. Examples:: from typing import TypeVar, Generic, NamedTuple, TypedDict T = TypeVar("T") class Foo(Generic[T]): ... class Bar(Foo[int], float): ... class Baz(list[str]): ... Eggs = NamedTuple("Eggs", [("a", int), ("b", str)]) Spam = TypedDict("Spam", {"a": int, "b": str}) assert get_original_bases(Bar) == (Foo[int], float) assert get_original_bases(Baz) == (list[str],) assert get_original_bases(Eggs) == (NamedTuple,) assert get_original_bases(Spam) == (TypedDict,) assert get_original_bases(int) == (object,) """ try: return cls.__dict__.get("__orig_bases__", cls.__bases__) except AttributeError: raise TypeError( f"Expected an instance of type, not {type(cls).__name__!r}" ) from None class DynamicClassAttribute: """Route attribute access on a class to __getattr__. This is a descriptor, used to define attributes that act differently when accessed through an instance and through a class. Instance access remains normal, but access to an attribute through a class will be routed to the class's __getattr__ method; this is done by raising AttributeError. This allows one to have properties active on an instance, and have virtual attributes on the class with the same name. (Enum used this between Python versions 3.4 - 3.9 .) Subclass from this to use a different method of accessing virtual attributes and still be treated properly by the inspect module. (Enum uses this since Python 3.10 .) """ def __init__(self, fget=None, fset=None, fdel=None, doc=None): self.fget = fget self.fset = fset self.fdel = fdel # next two lines make DynamicClassAttribute act the same as property self.__doc__ = doc or fget.__doc__ self.overwrite_doc = doc is None # support for abstract methods self.__isabstractmethod__ = bool(getattr(fget, '__isabstractmethod__', False)) def __get__(self, instance, ownerclass=None): if instance is None: if self.__isabstractmethod__: return self raise AttributeError() elif self.fget is None: raise AttributeError("unreadable attribute") return self.fget(instance) def __set__(self, instance, value): if self.fset is None: raise AttributeError("can't set attribute") self.fset(instance, value) def __delete__(self, instance): if self.fdel is None: raise AttributeError("can't delete attribute") self.fdel(instance) def getter(self, fget): fdoc = fget.__doc__ if self.overwrite_doc else None result = type(self)(fget, self.fset, self.fdel, fdoc or self.__doc__) result.overwrite_doc = self.overwrite_doc return result def setter(self, fset): result = type(self)(self.fget, fset, self.fdel, self.__doc__) result.overwrite_doc = self.overwrite_doc return result def deleter(self, fdel): result = type(self)(self.fget, self.fset, fdel, self.__doc__) result.overwrite_doc = self.overwrite_doc return result class _GeneratorWrapper: # TODO: Implement this in C. def __init__(self, gen): self.__wrapped = gen self.__isgen = gen.__class__ is GeneratorType self.__name__ = getattr(gen, '__name__', None) self.__qualname__ = getattr(gen, '__qualname__', None) def send(self, val): return self.__wrapped.send(val) def throw(self, tp, *rest): return self.__wrapped.throw(tp, *rest) def close(self): return self.__wrapped.close() @property def gi_code(self): return self.__wrapped.gi_code @property def gi_frame(self): return self.__wrapped.gi_frame @property def gi_running(self): return self.__wrapped.gi_running @property def gi_yieldfrom(self): return self.__wrapped.gi_yieldfrom cr_code = gi_code cr_frame = gi_frame cr_running = gi_running cr_await = gi_yieldfrom def __next__(self): return next(self.__wrapped) def __iter__(self): if self.__isgen: return self.__wrapped return self __await__ = __iter__ def coroutine(func): """Convert regular generator function to a coroutine.""" if not callable(func): raise TypeError('types.coroutine() expects a callable') if (func.__class__ is FunctionType and getattr(func, '__code__', None).__class__ is CodeType): co_flags = func.__code__.co_flags # Check if 'func' is a coroutine function. # (0x180 == CO_COROUTINE | CO_ITERABLE_COROUTINE) if co_flags & 0x180: return func # Check if 'func' is a generator function. # (0x20 == CO_GENERATOR) if co_flags & 0x20: # TODO: Implement this in C. co = func.__code__ # 0x100 == CO_ITERABLE_COROUTINE func.__code__ = co.replace(co_flags=co.co_flags | 0x100) return func # The following code is primarily to support functions that # return generator-like objects (for instance generators # compiled with Cython). # Delay functools and _collections_abc import for speeding up types import. import functools import _collections_abc @functools.wraps(func) def wrapped(*args, **kwargs): coro = func(*args, **kwargs) if (coro.__class__ is CoroutineType or coro.__class__ is GeneratorType and coro.gi_code.co_flags & 0x100): # 'coro' is a native coroutine object or an iterable coroutine return coro if (isinstance(coro, _collections_abc.Generator) and not isinstance(coro, _collections_abc.Coroutine)): # 'coro' is either a pure Python generator iterator, or it # implements collections.abc.Generator (and does not implement # collections.abc.Coroutine). return _GeneratorWrapper(coro) # 'coro' is either an instance of collections.abc.Coroutine or # some other object -- pass it through. return coro return wrapped GenericAlias = type(list[int]) UnionType = type(int | str) EllipsisType = type(Ellipsis) NoneType = type(None) NotImplementedType = type(NotImplemented) __all__ = [n for n in globals() if n[:1] != '_']