带参数的装饰器？

I have a problem with the transfer of variable ‘insurance_mode’ by the decorator. I would do it by the following decorator statement:

 @execute_complete_reservation(True)
 def test_booking_gta_object(self):
     self.test_select_gta_object()

but unfortunately, this statement does not work. Perhaps maybe there is better way to solve this problem.

def execute_complete_reservation(test_case,insurance_mode):
    def inner_function(self,*args,**kwargs):
        self.test_create_qsf_query()
        test_case(self,*args,**kwargs)
        self.test_select_room_option()
        if insurance_mode:
            self.test_accept_insurance_crosseling()
        else:
            self.test_decline_insurance_crosseling()
        self.test_configure_pax_details()
        self.test_configure_payer_details

    return inner_function

The syntax for decorators with arguments is a bit different – the decorator with arguments should return a function that will take a function and return another function. So it should really return a normal decorator. A bit confusing, right? What I mean is:

def decorator_factory(argument):
    def decorator(function):
        def wrapper(*args, **kwargs):
            funny_stuff()
            something_with_argument(argument)
            result = function(*args, **kwargs)
            more_funny_stuff()
            return result
        return wrapper
    return decorator

Here you can read more on the subject – it’s also possible to implement this using callable objects and that is also explained there.

Edit : for an in-depth understanding of the mental model of decorators, take a look at this awesome Pycon Talk. well worth the 30 minutes.

One way of thinking about decorators with arguments is

@decorator
def foo(*args, **kwargs):
    pass

translates to

foo = decorator(foo)

So if the decorator had arguments,

@decorator_with_args(arg)
def foo(*args, **kwargs):
    pass

translates to

foo = decorator_with_args(arg)(foo)

decorator_with_args is a function which accepts a custom argument and which returns the actual decorator (that will be applied to the decorated function).

I use a simple trick with partials to make my decorators easy

from functools import partial

def _pseudo_decor(fun, argument):
    def ret_fun(*args, **kwargs):
        #do stuff here, for eg.
        print ("decorator arg is %s" % str(argument))
        return fun(*args, **kwargs)
    return ret_fun

real_decorator = partial(_pseudo_decor, argument=arg)

@real_decorator
def foo(*args, **kwargs):
    pass

Update:

Above, foo becomes real_decorator(foo)

One effect of decorating a function is that the name foo is overridden upon decorator declaration. foo is “overridden” by whatever is returned by real_decorator. In this case, a new function object.

All of foo‘s metadata is overridden, notably docstring and function name.

>>> print(foo)
<function _pseudo_decor.<locals>.ret_fun at 0x10666a2f0>

functools.wraps gives us a convenient method to “lift” the docstring and name to the returned function.

from functools import partial, wraps

def _pseudo_decor(fun, argument):
    # magic sauce to lift the name and doc of the function
    @wraps(fun)
    def ret_fun(*args, **kwargs):
        #do stuff here, for eg.
        print ("decorator arg is %s" % str(argument))
        return fun(*args, **kwargs)
    return ret_fun

real_decorator = partial(_pseudo_decor, argument=arg)

@real_decorator
def bar(*args, **kwargs):
    pass

>>> print(bar)
<function __main__.bar(*args, **kwargs)>

I’d like to show an idea which is IMHO quite elegant. The solution proposed by t.dubrownik shows a pattern which is always the same: you need the three-layered wrapper regardless of what the decorator does.

So I thought this is a job for a meta-decorator, that is, a decorator for decorators. As a decorator is a function, it actually works as a regular decorator with arguments:

def parametrized(dec):
    def layer(*args, **kwargs):
        def repl(f):
            return dec(f, *args, **kwargs)
        return repl
    return layer

This can be applied to a regular decorator in order to add parameters. So for instance, say we have the decorator which doubles the result of a function:

def double(f):
    def aux(*xs, **kws):
        return 2 * f(*xs, **kws)
    return aux

@double
def function(a):
    return 10 + a

print function(3)    # Prints 26, namely 2 * (10 + 3)

With @parametrized we can build a generic @multiply decorator having a parameter

@parametrized
def multiply(f, n):
    def aux(*xs, **kws):
        return n * f(*xs, **kws)
    return aux

@multiply(2)
def function(a):
    return 10 + a

print function(3)    # Prints 26

@multiply(3)
def function_again(a):
    return 10 + a

print function(3)          # Keeps printing 26
print function_again(3)    # Prints 39, namely 3 * (10 + 3)

Conventionally the first parameter of a parametrized decorator is the function, while the remaining arguments will correspond to the parameter of the parametrized decorator.

An interesting usage example could be a type-safe assertive decorator:

import itertools as it

@parametrized
def types(f, *types):
    def rep(*args):
        for a, t, n in zip(args, types, it.count()):
            if type(a) is not t:
                raise TypeError('Value %d has not type %s. %s instead' %
                    (n, t, type(a))
                )
        return f(*args)
    return rep

@types(str, int)  # arg1 is str, arg2 is int
def string_multiply(text, times):
    return text * times

print(string_multiply('hello', 3))    # Prints hellohellohello
print(string_multiply(3, 3))          # Fails miserably with TypeError

A final note: here I’m not using functools.wraps for the wrapper functions, but I would recommend using it all the times.

Here is a slightly modified version of t.dubrownik’s answer. Why?

1. As a general template, you should return the return value from the original function.
2. This changes the name of the function, which could affect other decorators / code.

So use @functools.wraps():

from functools import wraps

def decorator(argument):
    def real_decorator(function):
        @wraps(function)
        def wrapper(*args, **kwargs):
            funny_stuff()
            something_with_argument(argument)
            retval = function(*args, **kwargs)
            more_funny_stuff()
            return retval
        return wrapper
    return real_decorator

I presume your problem is passing arguments to your decorator. This is a little tricky and not straightforward.

Here’s an example of how to do this:

class MyDec(object):
    def __init__(self,flag):
        self.flag = flag
    def __call__(self, original_func):
        decorator_self = self
        def wrappee( *args, **kwargs):
            print 'in decorator before wrapee with flag ',decorator_self.flag
            original_func(*args,**kwargs)
            print 'in decorator after wrapee with flag ',decorator_self.flag
        return wrappee

@MyDec('foo de fa fa')
def bar(a,b,c):
    print 'in bar',a,b,c

bar('x','y','z')

Prints:

in decorator before wrapee with flag  foo de fa fa
in bar x y z
in decorator after wrapee with flag  foo de fa fa

See Bruce Eckel’s article for more details.

def decorator(argument):
    def real_decorator(function):
        def wrapper(*args):
            for arg in args:
                assert type(arg)==int,f'{arg} is not an interger'
            result = function(*args)
            result = result*argument
            return result
        return wrapper
    return real_decorator

Usage of the decorator

@decorator(2)
def adder(*args):
    sum=0
    for i in args:
        sum+=i
    return sum

Then the

adder(2,3)

produces

10

but

adder('hi',3)

produces

---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
<ipython-input-143-242a8feb1cc4> in <module>
----> 1 adder('hi',3)

<ipython-input-140-d3420c248ebd> in wrapper(*args)
      3         def wrapper(*args):
      4             for arg in args:
----> 5                 assert type(arg)==int,f'{arg} is not an interger'
      6             result = function(*args)
      7             result = result*argument

AssertionError: hi is not an interger

This is a template for a function decorator that does not require () if no parameters are to be given:

import functools


def decorator(x_or_func=None, *decorator_args, **decorator_kws):
    def _decorator(func):
        @functools.wraps(func)
        def wrapper(*args, **kws):
            if 'x_or_func' not in locals() \
                    or callable(x_or_func) \
                    or x_or_func is None:
                x = ...  # <-- default `x` value
            else:
                x = x_or_func
            return func(*args, **kws)

        return wrapper

    return _decorator(x_or_func) if callable(x_or_func) else _decorator

an example of this is given below:

def multiplying(factor_or_func=None):
    def _decorator(func):
        @functools.wraps(func)
        def wrapper(*args, **kwargs):
            if 'factor_or_func' not in locals() \
                    or callable(factor_or_func) \
                    or factor_or_func is None:
                factor = 1
            else:
                factor = factor_or_func
            return factor * func(*args, **kwargs)
        return wrapper
    return _decorator(factor_or_func) if callable(factor_or_func) else _decorator


@multiplying
def summing(x): return sum(x)

print(summing(range(10)))
# 45


@multiplying()
def summing(x): return sum(x)

print(summing(range(10)))
# 45


@multiplying(10)
def summing(x): return sum(x)

print(summing(range(10)))
# 450

In my instance, I decided to solve this via a one-line lambda to create a new decorator function:

def finished_message(function, message="Finished!"):

    def wrapper(*args, **kwargs):
        output = function(*args,**kwargs)
        print(message)
        return output

    return wrapper

@finished_message
def func():
    pass

my_finished_message = lambda f: finished_message(f, "All Done!")

@my_finished_message
def my_func():
    pass

if __name__ == '__main__':
    func()
    my_func()

When executed, this prints:

Finished!
All Done!

Perhaps not as extensible as other solutions, but worked for me.

Writing a decorator that works with and without parameter is a challenge because Python expects completely different behavior in these two cases! Many answers have tried to work around this and below is an improvement of answer by @norok2. Specifically, this variation eliminates the use of locals().

Following the same example as given by @norok2:

import functools

def multiplying(f_py=None, factor=1):
    assert callable(f_py) or f_py is None
    def _decorator(func):
        @functools.wraps(func)
        def wrapper(*args, **kwargs):
            return factor * func(*args, **kwargs)
        return wrapper
    return _decorator(f_py) if callable(f_py) else _decorator


@multiplying
def summing(x): return sum(x)

print(summing(range(10)))
# 45


@multiplying()
def summing(x): return sum(x)

print(summing(range(10)))
# 45


@multiplying(factor=10)
def summing(x): return sum(x)

print(summing(range(10)))
# 450

Play with this code.

The catch is that the user must supply key,value pairs of parameters instead of positional parameters and the first parameter is reserved.

It is well known that the following two pieces of code are nearly equivalent:

@dec
def foo():
    pass    foo = dec(foo)

############################################
foo = dec(foo)

A common mistake is to think that @ simply hides the leftmost argument.

@dec(1, 2, 3)
def foo():
    pass    
###########################################
foo = dec(foo, 1, 2, 3)

It would be much easier to write decorators if the above is how @ worked. Unfortunately, that’s not the way things are done.

Consider a decorator Waitwhich haults program execution for a few seconds. If you don’t pass in a Wait-time then the default value is 1 seconds. Use-cases are shown below.

##################################################
@Wait
def print_something(something):
    print(something)

##################################################
@Wait(3)
def print_something_else(something_else):
    print(something_else)

##################################################
@Wait(delay=3)
def print_something_else(something_else):
    print(something_else)

When Wait has an argument, such as @Wait(3), then the call Wait(3) is executed before anything else happens.

That is, the following two pieces of code are equivalent

@Wait(3)
def print_something_else(something_else):
    print(something_else)

###############################################
return_value = Wait(3)
@return_value
def print_something_else(something_else):
    print(something_else)

This is a problem.

if `Wait` has no arguments:
    `Wait` is the decorator.
else: # `Wait` receives arguments
    `Wait` is not the decorator itself.
    Instead, `Wait` ***returns*** the decorator

One solution is shown below:

Let us begin by creating the following class, DelayedDecorator:

class DelayedDecorator:
    def __init__(i, cls, *args, **kwargs):
        print("Delayed Decorator __init__", cls, args, kwargs)
        i._cls = cls
        i._args = args
        i._kwargs = kwargs
    def __call__(i, func):
        print("Delayed Decorator __call__", func)
        if not (callable(func)):
            import io
            with io.StringIO() as ss:
                print(
                    "If only one input, input must be callable",
                    "Instead, received:",
                    repr(func),
                    sep="\n",
                    file=ss
                )
                msg = ss.getvalue()
            raise TypeError(msg)
        return i._cls(func, *i._args, **i._kwargs)

Now we can write things like:

 dec = DelayedDecorator(Wait, delay=4)
 @dec
 def delayed_print(something):
    print(something)

Note that:

• dec does not not accept multiple arguments.

• dec only accepts the function to be wrapped.

  import inspect class PolyArgDecoratorMeta(type): def call(Wait, *args, **kwargs): try: arg_count = len(args) if (arg_count == 1): if callable(args[0]): SuperClass = inspect.getmro(PolyArgDecoratorMeta)[1] r = SuperClass.call(Wait, args[0]) else: r = DelayedDecorator(Wait, *args, **kwargs) else: r = DelayedDecorator(Wait, *args, **kwargs) finally: pass return r

  import time class Wait(metaclass=PolyArgDecoratorMeta): def init(i, func, delay = 2): i._func = func i._delay = delay

  def __call__(i, *args, **kwargs):
      time.sleep(i._delay)
      r = i._func(*args, **kwargs)
      return r 
  

The following two pieces of code are equivalent:

@Wait
def print_something(something):
     print (something)

##################################################

def print_something(something):
    print(something)
print_something = Wait(print_something)

We can print "something" to the console very slowly, as follows:

print_something("something")

#################################################
@Wait(delay=1)
def print_something_else(something_else):
    print(something_else)

##################################################
def print_something_else(something_else):
    print(something_else)

dd = DelayedDecorator(Wait, delay=1)
print_something_else = dd(print_something_else)

##################################################

print_something_else("something")

Final Notes

It may look like a lot of code, but you don’t have to write the classes DelayedDecorator and PolyArgDecoratorMeta every-time. The only code you have to personally write something like as follows, which is fairly short:

from PolyArgDecoratorMeta import PolyArgDecoratorMeta
import time
class Wait(metaclass=PolyArgDecoratorMeta):
 def __init__(i, func, delay = 2):
     i._func = func
     i._delay = delay

 def __call__(i, *args, **kwargs):
     time.sleep(i._delay)
     r = i._func(*args, **kwargs)
     return r

define this “decoratorize function” to generate customized decorator function:

def decoratorize(FUN, **kw):
    def foo(*args, **kws):
        return FUN(*args, **kws, **kw)
    return foo

use it this way:

    @decoratorize(FUN, arg1 = , arg2 = , ...)
    def bar(...):
        ...

Great answers above. This one also illustrates @wraps, which takes the doc string and function name from the original function and applies it to the new wrapped version:

from functools import wraps

def decorator_func_with_args(arg1, arg2):
    def decorator(f):
        @wraps(f)
        def wrapper(*args, **kwargs):
            print("Before orginal function with decorator args:", arg1, arg2)
            result = f(*args, **kwargs)
            print("Ran after the orginal function")
            return result
        return wrapper
    return decorator

@decorator_func_with_args("foo", "bar")
def hello(name):
    """A function which prints a greeting to the name provided.
    """
    print('hello ', name)
    return 42

print("Starting script..")
x = hello('Bob')
print("The value of x is:", x)
print("The wrapped functions docstring is:", hello.__doc__)
print("The wrapped functions name is:", hello.__name__)

Prints:

Starting script..
Before orginal function with decorator args: foo bar
hello  Bob
Ran after the orginal function
The value of x is: 42
The wrapped functions docstring is: A function which prints a greeting to the name provided.
The wrapped functions name is: hello

In case both the function and the decorator have to take arguments you can follow the below approach.

For example there is a decorator named decorator1 which takes an argument

@decorator1(5)
def func1(arg1, arg2):
    print (arg1, arg2)

func1(1, 2)

Now if the decorator1 argument has to be dynamic, or passed while calling the function,

def func1(arg1, arg2):
    print (arg1, arg2)


a = 1
b = 2
seconds = 10

decorator1(seconds)(func1)(a, b)

In the above code

• seconds is the argument for decorator1
• a, b are the arguments of func1