Edit: /usr/lib64/python3.6/types.py (8870B)
"""
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 _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
ModuleType = type(sys)
try:
raise TypeError
except TypeError:
tb = sys.exc_info()[2]
TracebackType = type(tb)
FrameType = type(tb.tb_frame)
tb = None; del tb
# For Jython, the following two types are identical
GetSetDescriptorType = type(FunctionType.__code__)
MemberDescriptorType = type(FunctionType.__globals__)
del sys, _f, _g, _C, _c, # 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."""
meta, ns, kwds = prepare_class(name, bases, kwds)
if exec_body is not None:
exec_body(ns)
return meta(name, bases, ns, **kwds)
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
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 (see Enum for an example).
"""
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
import functools as _functools
import collections.abc as _collections_abc
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__
func.__code__ = CodeType(
co.co_argcount, co.co_kwonlyargcount, co.co_nlocals,
co.co_stacksize,
co.co_flags | 0x100, # 0x100 == CO_ITERABLE_COROUTINE
co.co_code,
co.co_consts, co.co_names, co.co_varnames, co.co_filename,
co.co_name, co.co_firstlineno, co.co_lnotab, co.co_freevars,
co.co_cellvars)
return func
# The following code is primarily to support functions that
# return generator-like objects (for instance generators
# compiled with Cython).
@_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
__all__ = [n for n in globals() if n[:1] != '_']