Python functools.wraps equivalent for classes

Question

When defining a decorator using a class, how do I automatically transfer over__name__, __module__ and __doc__? Normally, I would use the @wraps decorator from functools. Here's what I did instead for a class (this is not entirely my code):

class memoized:
    """Decorator that caches a function's return value each time it is called.
    If called later with the same arguments, the cached value is returned, and
    not re-evaluated.
    """
    def __init__(self, func):
        super().__init__()
        self.func = func
        self.cache = {}

    def __call__(self, *args):
        try:
            return self.cache[args]
        except KeyError:
            value = self.func(*args)
            self.cache[args] = value
            return value
        except TypeError:
            # uncacheable -- for instance, passing a list as an argument.
            # Better to not cache than to blow up entirely.
            return self.func(*args)

    def __repr__(self):
        return self.func.__repr__()

    def __get__(self, obj, objtype):
        return functools.partial(self.__call__, obj)

    __doc__ = property(lambda self:self.func.__doc__)
    __module__ = property(lambda self:self.func.__module__)
    __name__ = property(lambda self:self.func.__name__)

Is there a standard decorator to automate the creation of name module and doc? Also, to automate the get method (I assume that's for creating bound methods?) Are there any missing methods?

Answer 1

Everyone seems to have missed the obvious solution. Using functools.update_wrapper:

>>> import functools
>>> class memoized(object):
    """Decorator that caches a function's return value each time it is called.
    If called later with the same arguments, the cached value is returned, and
    not re-evaluated.
    """
    def __init__(self, func):
        self.func = func
        self.cache = {}
        functools.update_wrapper(self, func)  ## TA-DA! ##
    def __call__(self, *args):
        pass  # Not needed for this demo.

>>> @memoized
def fibonacci(n):
    """fibonacci docstring"""
    pass  # Not needed for this demo.

>>> fibonacci
<__main__.memoized object at 0x0156DE30>
>>> fibonacci.__name__
'fibonacci'
>>> fibonacci.__doc__
'fibonacci docstring'

Answer 2

I'm not aware of such things in stdlib, but we can create our own if we need to.

Something like this can work :

from functools import WRAPPER_ASSIGNMENTS


def class_wraps(cls):
    """Update a wrapper class `cls` to look like the wrapped."""

    class Wrapper(cls):
        """New wrapper that will extend the wrapper `cls` to make it look like `wrapped`.

        wrapped: Original function or class that is beign decorated.
        assigned: A list of attribute to assign to the the wrapper, by default they are:
             ['__doc__', '__name__', '__module__', '__annotations__'].

        """

        def __init__(self, wrapped, assigned=WRAPPER_ASSIGNMENTS):
            self.__wrapped = wrapped
            for attr in assigned:
                setattr(self, attr, getattr(wrapped, attr))

            super().__init__(wrapped)

        def __repr__(self):
            return repr(self.__wrapped)

    return Wrapper

Usage:

@class_wraps
class memoized:
    """Decorator that caches a function's return value each time it is called.
    If called later with the same arguments, the cached value is returned, and
    not re-evaluated.
    """

    def __init__(self, func):
        super().__init__()
        self.func = func
        self.cache = {}

    def __call__(self, *args):
        try:
            return self.cache[args]
        except KeyError:
            value = self.func(*args)
            self.cache[args] = value
            return value
        except TypeError:
            # uncacheable -- for instance, passing a list as an argument.
            # Better to not cache than to blow up entirely.
            return self.func(*args)

    def __get__(self, obj, objtype):
        return functools.partial(self.__call__, obj)


@memoized
def fibonacci(n):
    """fibonacci docstring"""
    if n in (0, 1):
       return n
    return fibonacci(n-1) + fibonacci(n-2)


print(fibonacci)
print("__doc__: ", fibonacci.__doc__)
print("__name__: ", fibonacci.__name__)

Output:

<function fibonacci at 0x14627c0>
__doc__:  fibonacci docstring
__name__:  fibonacci

EDIT:

And if you are wondering why this wasn't included in the stdlib is because you can wrap your class decorator in a function decorator and use functools.wraps like this:

def wrapper(f):

    memoize = memoized(f)

    @functools.wraps(f)
    def helper(*args, **kws):
        return memoize(*args, **kws)

    return helper


@wrapper
def fibonacci(n):
    """fibonacci docstring"""
    if n <= 1:
       return n
    return fibonacci(n-1) + fibonacci(n-2)

Answer 3

Turns out there's a straightforward solution using functools.wraps itself:

import functools

def dec(cls):
    @functools.wraps(cls, updated=())
    class D(cls):
        decorated = 1
    return D


@dec
class C:
    """doc"""

print(f'{C.__name__=} {C.__doc__=} {C.__wrapped__=}')

$ python3 t.py 
C.__name__='C' C.__doc__='doc' C.__wrapped__=<class '__main__.C'>

Note that updated=() is needed to prevent an attempt to update the class's __dict__ (this output is without updated=()):

$ python t.py
Traceback (most recent call last):
  File "t.py", line 26, in <module>
    class C:
  File "t.py", line 20, in dec
    class D(cls):
  File "/usr/lib/python3.8/functools.py", line 57, in update_wrapper
    getattr(wrapper, attr).update(getattr(wrapped, attr, {}))
AttributeError: 'mappingproxy' object has no attribute 'update'

Answer 4

I needed something that would wrap both classes and functions and wrote this:

def wrap_is_timeout(base):
    '''Adds `.is_timeout=True` attribute to objects returned by `base()`.

    When `base` is class, it returns a subclass with same name and adds read-only property.
    Otherwise, it returns a function that sets `.is_timeout` attribute on result of `base()` call.

    Wrappers make best effort to be transparent.
    '''
    if inspect.isclass(base):
        class wrapped(base):
            is_timeout = property(lambda _: True)

        for k in functools.WRAPPER_ASSIGNMENTS:
            v = getattr(base, k, _MISSING)
            if v is not _MISSING:
                try:
                    setattr(wrapped, k, v)
                except AttributeError:
                    pass
        return wrapped

    @functools.wraps(base)
    def fun(*args, **kwargs):
        ex = base(*args, **kwargs)
        ex.is_timeout = True
        return ex
    return fun

Answer 5

All we really need to do is modify the behavior of the decorator so that it is "hygienic", i.e. it is attribute-preserving.

#!/usr/bin/python3

def hygienic(decorator):
    def new_decorator(original):
        wrapped = decorator(original)
        wrapped.__name__ = original.__name__
        wrapped.__doc__ = original.__doc__
        wrapped.__module__ = original.__module__
        return wrapped
    return new_decorator

This is ALL you need. In general. It doesn't preserve the signature, but if you really want that you can use a library to do that. I also went ahead and rewrote the memoization code so that it works on keyword arguments as well. Also there was a bug where failure to convert it to a hashable tuple would make it not work in 100% of cases.

Demo of rewritten memoized decorator with @hygienic modifying its behavior. memoized is now a function that wraps the original class, though you can (like the other answer) write a wrapping class instead, or even better, something which detects if it's a class and if so wraps the __init__ method.

@hygienic
class memoized:
    def __init__(self, func):
        self.func = func
        self.cache = {}

    def __call__(self, *args, **kw):
        try:
            key = (tuple(args), frozenset(kw.items()))
            if not key in self.cache:
                self.cache[key] = self.func(*args,**kw)
            return self.cache[key]
        except TypeError:
            # uncacheable -- for instance, passing a list as an argument.
            # Better to not cache than to blow up entirely.
            return self.func(*args,**kw)

In action:

@memoized
def f(a, b=5, *args, keyword=10):
    """Intact docstring!"""
    print('f was called!')
    return {'a':a, 'b':b, 'args':args, 'keyword':10}

x=f(0)  
#OUTPUT: f was called!
print(x)
#OUTPUT: {'a': 0, 'b': 5, 'keyword': 10, 'args': ()}                 

y=f(0)
#NO OUTPUT - MEANS MEMOIZATION IS WORKING
print(y)
#OUTPUT: {'a': 0, 'b': 5, 'keyword': 10, 'args': ()}          

print(f.__name__)
#OUTPUT: 'f'
print(f.__doc__)
#OUTPUT: 'Intact docstring!'

Answer 6

Another solution using inheritance:

import functools
import types

class CallableClassDecorator:
    """Base class that extracts attributes and assigns them to self.

    By default the extracted attributes are:
         ['__doc__', '__name__', '__module__'].
    """

    def __init__(self, wrapped, assigned=functools.WRAPPER_ASSIGNMENTS):
        for attr in assigned:
            setattr(self, attr, getattr(wrapped, attr))
        super().__init__()

    def __get__(self, obj, objtype):
        return types.MethodType(self.__call__, obj)

And, usage:

class memoized(CallableClassDecorator):
    """Decorator that caches a function's return value each time it is called.
    If called later with the same arguments, the cached value is returned, and
    not re-evaluated.
    """
    def __init__(self, function):
        super().__init__(function)
        self.function = function
        self.cache = {}

    def __call__(self, *args):
        try:
            return self.cache[args]
        except KeyError:
            value = self.function(*args)
            self.cache[args] = value
            return value
        except TypeError:
            # uncacheable -- for instance, passing a list as an argument.
            # Better to not cache than to blow up entirely.
            return self.function(*args)