Understanding Python metaclasses

转载：https://blog.ionelmc.ro/2015/02/09/understanding-python-metaclasses/

None of the existing articles [1] give a comprehensive explanation of how metaclasses work in Python so I’m making my own. Metaclasses are a controversial topic [2] in Python, many users avoid them and I think this is largely caused by the arbitrary workflow and lookup rules which are not well explained. There are few key concepts that you need to understand to efficiently work with metaclasses.

If you haven’t heard of metaclasses at all, they present lots of interesting opportunities for reducing boilerplate and nicer APIs. To make this as brief as possible I’m going to assume the creative types [3] will read this. This means I’m going to skip the whole very subjective “use metaclasses for this but not for that” conundrum.

This is written with Python 3 in mind, if there something specific to Python 2 worth mentioning it’s going to be in the footnotes [4], in itty-bitty font size 🙂

A quick overview

A high level explanation is necessary before we get down to the details.

A class is an object, and just like any other object, it’s an instance of something: a metaclass. The default metaclass is type. Unfortunately, due to backwards compatibility, type is a bit confusing: it can also be used as a function that return the class [13] of an object:

>>> class Foobar:
...     pass
...
>>> type(Foobar)
<class 'type'>
>>> foo = Foobar()
>>> type(foo)
<class '__main__.Foobar'>

If you’re familiar with the isinstance builtin then you’ll know this:

>>> isinstance(foo, Foobar)
True
>>> isinstance(Foobar, type)
True

To put this in picture:

But lets go back to making classes …

Simple metaclass use

We can use type directly to make a class, without any class statement:

>>> MyClass = type('MyClass', (), {})
>>> MyClass
<class '__main__.MyClass'>

The class statement isn’t just syntactic sugar, it does some extra things, like setting an adequate __qualname__ and __doc__ properties or calling __prepare__.

We can make a custom metaclass:

>>> class Meta(type):
...     pass

And then we can use it [15]:

>>> class Complex(metaclass=Meta):
...     pass
>>> type(Complex)
<class '__main__.Meta'>

Now we got a rough idea of what we’ll be dealing with …

Magic methods

One distinctive feature of Python is magic methods: they allow the programmer to override behavior for various operators and behavior of objects. To override the call operator you’d do this:

>>> class Funky:
...     def __call__(self):
...         print("Look at me, I work like a function!")
>>> f = Funky()
>>> f()
Look at me, I work like a function!

Metaclasses rely on several magic methods so it’s quite useful to know a bit more about them.

The slots

When you define a magic method in your class the function will end up as a pointer in a struct that describes the class, in addition to the entry in __dict__. That struct [7] has a field for each magic method. For some reason these fields are called type slots.

Now there’s another feature, implemented via the __slots__ attribute [17]. A class with __slots__ will create instances that don’t have a __dict__ (they use a little bit less memory). A side-effect of this is that instances cannot have other fields than what was specified in __slots__: if you try to set an unexpected field you’ll get an exception.

For the scope of this article when slots are mentioned it will mean the type slots, not__slots__.

Object attribute lookup

Now this is something that’s easy to get wrong because of the many slight differences to old-style objects in Python 2. [5]

Assuming Class is the class and instance is an instance of Class, evaluatinginstance.foobar roughly equates to this:

• Call the type slot for Class.__getattribute__ (tp_getattro). The default does this: [10]
  • Does Class.__dict__ have a foobar item that is a data descriptor [8]?
    • If yes, return the result ofClass.__dict__['foobar'].__get__(instance, Class). [6]
  • Does instance.__dict__ have a foobar item in it?
    • If yes, return instance.__dict__['foobar'].
  • Does Class.__dict__ have a foobar item that is not a data descriptor [9]?
    • If yes, return the result ofClass.__dict__['foobar'].__get__(instance, klass). [6]
  • Does Class.__dict__ have a foobar item?
    • If yes, return the result of Class.__dict__['foobar'].
• If the attribute still wasn’t found, and there’s a Class.__getattr__, callClass.__getattr__('foobar').

Still not clear? Perhaps a diagram normal attribute lookup helps:

To avoid creating confusions with the “.” operator doing crazy things I’ve used “:” in this diagram to signify the location.

Class attribute lookup

Because classes needs to be able support the classmethod and staticmethod properties [6]when you evaluate something like Class.foobar the lookup is slightly different than what would happen when you evaluate instance.foobar.

Assuming Class is an instance of Metaclass, evaluating Class.foobar roughly equates to this:

• Call the type slot for Metaclass.__getattribute__ (tp_getattro). The default does this: [11]
  • Does Metaclass.__dict__ have a foobar item that is a data descriptor [8]?
    • If yes, return the result ofMetaclass.__dict__['foobar'].__get__(Class, Metaclass). [6]
  • Does Class.__dict__ have a foobar item that is a descriptor (of any kind)?
    • If yes, return the result of Class.__dict__['foobar'].__get__(None,Class). [6]
  • Does Class.__dict__ have a foobar item in it?
    • If yes, return Class.__dict__['foobar'].
  • Does Metaclass.__dict__ have a foobar item that is not a data descriptor[9]?
    • If yes, return the result ofMetaclass.__dict__['foobar'].__get__(Class, Metaclass). [6]
  • Does Metaclass.__dict__ have any foobar item?
    • If yes, return Metaclass.__dict__['foobar'].
• If the attribute still wasn’t found, and there’s a Metaclass.__getattr__, callMetaclass.__getattr__('foobar').

The whole shebang would look like this in a diagram:

To avoid creating confusions with the “.” operator doing crazy things I’ve used “:” in this diagram to signify the location.

Magic method lookup

For magic methods the lookup is done on the class, directly in the big struct with the slots: [12]

• Does the object’s class have a slot for that magic method (roughly object->ob_type->tp_<magicmethod> in C code)? If yes, use it. If it’s NULL then the operation is not supported.

In C internals parlance:

• object->ob_type is the class of the object.
• ob_type->tp_<magicmethod> is the type slot.

This looks much simpler, however, the type slots are filled with wrappers around your functions, so descriptors work as expected: [12]

>>> class Magic:
...     @property
...     def __repr__(self):
...         def inner():
...             return "It works!"
...         return inner
...
>>> repr(Magic())
'It works!'

Thats it. Does that mean there are places that don’t follow those rules and lookup the slot differently? Sadly yes, read on …

The __new__ method

One of the most common point of confusion with both classes and metaclasses is the__new__ method. It has some very special conventions.

The __new__ method is the constructor (it returns the new instance) while __init__ is just a initializer (the instance is already created when __init__ is called).

Suppose have a class like this: [20]

class Foobar:
    def __new__(cls):
        return super().__new__(cls)

Now if you recall the previous section, you’d expect that __new__ would be looked up on the metaclass, but alas, it wouldn’t be so useful that way [19] so it’s looked upstatically.

When the Foobar class wants this magic method it will be looked up on the same object (the class), not on a upper level like all the other magic methods. This is very important to understand, because both the class and the metaclass can define this method:

• Foobar.__new__ is used to create instances of Foobar
• type.__new__ is used to create the Foobar class (an instance of type in the example)

The __prepare__ method

This method is called before the class body is executed and it must return a dictionary-like object that’s used as the local namespace for all the code from the classbody. It was added in Python 3.0, see PEP-3115.

If your __prepare__ returns an object x then this:

class Class(metaclass=Meta):
    a = 1
    b = 2
    c = 3

Will make the following changes to x:

x['a'] = 1
x['b'] = 2
x['c'] = 3

This x object needs to look like a dictionary. Note that this x object will end up as an argument to Metaclass.__new__ and if it’s not an instance of dict you need to convert it before calling super().__new__. [21]

Interestingly enough this method doesn’t have __new__‘s special lookup. It appears it doesn’t have it’s own type slot and it’s looked up via the class attribute lookup, if you read back a bit. [6]

Putting it all together

To start things off, a diagram of how instances are constructed:

How to read this swim lane diagram:

• The horizontal lanes is the place where you define the functions.
• Solid lines mean a function call.
  • A line from Metaclass.__call__ to Class.__new__ meansMetaclass.__call__ will call Class.__new__.
• Dashed lines means something is returned.
  • Class.__new__ returns the instance of Class.
  • Metaclass.__call__ returns whatever Class.__new__ returned (and if it returned an instance of Class it will also call Class.__init__ on it). [16]
• The number in the red circle signifies the call order.

Creating a class is quite similar:

Few more notes:

• Metaclass.__prepare__ just returns the namespace object (a dictionary-like object as explained before).
• Metaclass.__new__ returns the Class object.
• MetaMetaclass.__call__ returns whatever Metaclass.__new__ returned (and if it returned an instance of Metaclass it will also call Metaclass.__init__ on it). [16]

So you see, metaclasses allow you to customize almost every part of an object life-cycle.

Metaclasses are callables

If you look again at the diagrams, you’ll notice that making an instance goes throughMetaclass.__call__. This means you can use any callable as the metaclass:

>>> class Foo(metaclass=print):  # pointless, but illustrative
...     pass
...
Foo () {'__module__': '__main__', '__qualname__': 'Foo'}
>>> print(Foo)
None

If you use a function as the metaclass then subclasses won’t inherit your functionmetaclass, but the type of whatever that function returned.

Subclasses inherit the metaclass

One advantage compared to class decorators is the fact that subclasses inherit the metaclass.

This is a consequence of the fact that Metaclass(...) returns an object which usually has Metaclass as the __class__.

Restrictions with multiple metaclasses

In the same tone of classes allowing you to have multiple baseclasses, each one of those baseclasses may have a different metaclass. But with a twist: everything has to be linear – the inheritance tree must have a single leaf.

For example, this is not accepted because there would be two leafs (Meta1 and Meta2):

>>> class Meta1(type):
...     pass
...
>>> class Meta2(type):
...     pass
...
>>> class Base1(metaclass=Meta1):
...     pass
...
>>> class Base2(metaclass=Meta2):
...     pass
...
>>> class Foobar(Base1, Base2):
...     pass
...
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: metaclass conflict: the metaclass of a derived class must be a (non-strict) subclass of the metaclasses of all its bases

This will work (and will use the leaf as the metaclass):

>>> class Meta(type):
...     pass
...
>>> class SubMeta(Meta):
...     pass
...
>>> class Base1(metaclass=Meta):
...     pass
...
>>> class Base2(metaclass=SubMeta):
...     pass
...
>>> class Foobar(Base1, Base2):
...     pass
...
>>> type(Foobar)
<class '__main__.SubMeta'>

The method signatures

There are still few important details missing, like the method signatures. Lets look at class and metaclass with all the important stuff implemented.

Note the extra **kwargs – those are the extra keywords arguments you can pass in theclass statement. [22]

>>> class Meta(type):
...     @classmethod
...     def __prepare__(mcs, name, bases, **kwargs):
...         print('  Meta.__prepare__(mcs=%s, name=%r, bases=%s, **%s)' % (
...             mcs, name, bases, kwargs
...         ))
...         return {}

As mentioned before, __prepare__ can return objects that are not dict instances, so you need to make sure your __new__ handles that. [21]

...     def __new__(mcs, name, bases, attrs, **kwargs):
...         print('  Meta.__new__(mcs=%s, name=%r, bases=%s, attrs=[%s], **%s)' % (
...             mcs, name, bases, ', '.join(attrs), kwargs
...         ))
...         return super().__new__(mcs, name, bases, attrs)

It’s uncommon to see __init__ being implemented in a metaclass because it’s not that powerful – the class is already constructed when __init__ is called. It roughly equates to having a class decorator with the difference that __init__ would get run when making subclasses, while class decorators are not called for subclasses.

...     def __init__(cls, name, bases, attrs, **kwargs):
...         print('  Meta.__init__(cls=%s, name=%r, bases=%s, attrs=[%s], **%s)' % (
...             cls, name, bases, ', '.join(attrs), kwargs
...         ))
...         return super().__init__(name, bases, attrs)

The __call__ method will be called when you make instances of Class.

...     def __call__(cls, *args, **kwargs):
...         print('  Meta.__call__(cls=%s, args=%s, kwargs=%s)' % (
...             cls, args, kwargs
...         ))
...         return super().__call__(*args, **kwargs)
...

Using Meta, note the extra=1: [22]

>>> class Class(metaclass=Meta, extra=1):
...     def __new__(cls, myarg):
...         print('  Class.__new__(cls=%s, myarg=%s)' % (
...             cls, myarg
...         ))
...         return super().__new__(cls)
...
...     def __init__(self, myarg):
...         print('  Class.__init__(self=%s, myarg=%s)' % (
...             self, myarg
...         ))
...         self.myarg = myarg
...         return super().__init__()
...
...     def __str__(self):
...         return "<instance of Class; myargs=%s>" % (
...             getattr(self, 'myarg', 'MISSING'),
...         )
  Meta.__prepare__(mcs=<class '__main__.Meta'>, name='Class', bases=(),
                   **{'extra': 1})
  Meta.__new__(mcs=<class '__main__.Meta'>, name='Class', bases=(),
               attrs=[__qualname__, __new__, __init__, __str__, __module__],
               **{'extra': 1})
  Meta.__init__(cls=<class '__main__.Class'>, name='Class', bases=(),
                attrs=[__qualname__, __new__, __init__, __str__, __module__],
                **{'extra': 1})

Note that Meta.__call__ is called when we make instance of Class:

>>> Class(1)
  Meta.__call__(cls=<class '__main__.Class'>, args=(1,), kwargs={})
  Class.__new__(cls=<class '__main__.Class'>, myarg=1)
  Class.__init__(self=<instance of Class; myargs=MISSING>, myarg=1)
<instance of Class; myargs=1>

Ending notes

Now that I wrote this all down, it seems there’s lots of thing going on, more than I’d like. I also suspect I’ve missed some significant details 🙂

In a future article the practical applications of all this metaclass theory will be tackled …

>>> def foo(*args):
...     print(args)
...
>>> foo
<function foo at 0x00000000047AD6A8>
>>> foo(1)
(1,)

>>> foo.__get__("stuff", None)
<bound method str.foo of 'stuff'>
>>> foo.__get__("stuff", None)(1)
('stuff', 1)

>>> class Desc:
...     def __init__(self, value):
...         self.value = value
...
...     def __get__(self, instance, owner):
...         return "%r from descriptor" % self.value
...
>>> class Foo:
...     pass
...
>>> Foo.a = Desc('a')
>>> Foo.a
"'a' from descriptor"

>>> Foo.x = property(lambda: "whatever")
>>> Foo.x
<property object at 0x0000000003767B88>

>>> prop = property(lambda: "whatever")
>>> prop.__get__(None, 123) is prop
True

[13]

(1, 2) There were acute differences between types (classes implemented in C) and classes (implemented in pure Python) in Python 2 and especially when using old-style classes but it’s more streamlined and consistent now in Python 3. In other words, in Python 3 class and type mean the same thing. Or at least in theory … 收藏 0 赏 微信扫一扫 支付宝扫一扫 点赞 457 本文网址：https://www.zhankr.net/141150.html attrcleardescriptorsklassresult 程序员文波 贡献者 生成海报 分享 上一篇： 【BZOJ 3032】 七夕祭 下一篇： Flask-SQLAlchemy 学习总结 相关推荐 python开发_常用的python模块及安装方法 adodb：我们领导推荐的数据库连接组件bsddb3：BerkeleyDB的连接组件Cheetah-1.0：我比较喜欢这个版本的cheeta… 程序员润宾 技术 日期：2022-11-24 点赞：877 阅读：6,234 Educational Codeforces Round 11 C. Hard Process 二分 C. Hard Process题目连接：http://www.codeforces.com/contest/660/problem/CDes… 程序员春广 技术 日期：2022-11-24 点赞：806 阅读：4,053 下载Ubuntn 17.04 内核源代码 zengkefu@server1:/usr/src$ uname -aLinux server1 4.10.0-19-generic #21… 程序员峰军 技术 日期：2022-11-24 点赞：566 阅读：4,858 可用Active Desktop Calendar V7.86 注册码序列号 可用Active Desktop Calendar V7.86 注册码序列号Name: www.greendown.cn Code: &nb… 程序员天赐 技术 日期：2022-11-24 点赞：732 阅读：4,790 Android调用系统相机、自定义相机、处理大图片 Android调用系统相机和自定义相机实例本博文主要是介绍了android上使用相机进行拍照并显示的两种方式，并且由于涉及到要把拍到的照片显… 程序员爱鹏 技术 日期：2022-11-24 点赞：512 阅读：5,881 Struts的使用 一、Struts2的获取　　Struts的官方网站为：http://struts.apache.org/　　下载完Struts2的jar包,… 程序员红卫 技术 日期：2022-11-24 点赞：671 阅读：3,572 程序员文波 贡献者 这家伙很懒，只想把你留下。 文章 78 收藏 0 人气 221,799 粉丝 关注Ta 发私信 进主页 文章展示 [ 技术 ] python开发_常用的python模块及安装方法 [ 技术 ] Educational Codeforces Round 11 C. Hard Process 二分 [ 技术 ] 下载Ubuntn 17.04 内核源代码 [ 技术 ] 可用Active Desktop Calendar V7.86 注册码序列号 [ 技术 ] Android调用系统相机、自定义相机、处理大图片 [ 技术 ] Struts的使用 立即升级VIP 你的前景，远超我们想象 申请入驻 助力内容变现 将您的收入提升到一个新的水平 发文章 提问题 发帖子 发布资源 点击联系客服 在线时间：8:00-16:00 客服电话 400-888-8888 客服邮箱 ceotheme@ceo.com 扫描二维码 关注微信公众号 扫描二维码 手机访问本站 开通VIP 该资源仅限VIP专享下载 立即开通 首页 首页 菜单 首页 首页