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Multiple Python Inheritance with abstract class calls __new__ method wrong number of arguments

I have the following situation:
class AbstractSpeaker(ABC):
#abstractmethod
def say_hello(self)->None:
pass
"""... other abstract and maybe even concrete methods ..."""
class BasicGreeter:
"""implements a basic say_hello method so that Speakers
don't need to implement it"""
def __new__(cls):
r=object.__new__(cls)
r._language=Language.English #assume that Language is an appropriate Enum
return r
#property
def language(self)->Language:
return self._language
#language.setter
def language(self, newLanguage:Language):
self._language=newLanguage
def say_hello(self)->None:
if self.language is Language.English:
print("Hello")
elif self.language is Language.German:
print("Hallo")
elif self.language is Language.Italian:
print("Ciao")
else:
print("Hi")
class Person(BasicGreeter, AbstractSpeaker):
def __init__(self, name):
self.name=name
"""goes on to implement everything AbstractSpeaker speaker commands but should
use the say_hello method from BasicGreeter, who should also hold the language
information.
"""
But when I want to generate a new Person I get an TypeError:
>>> peter=Person('Peter')
Traceback (most recent call last):
File "<pyshell#19>", line 1, in <module>
peter=Person('Peter')
TypeError: __new__() takes 1 positional argument but 2 were given
Now I get that it has something to do with the fact that I have implemented a new method in BasicGreeter because when I rewrite BasicGreeter to use an init method instead of the new-method and then explicitly call super.init() in the Person's init it works. But I wanted to know if there is any way to do this with the new method as having to invoke super.init() in every Speakers init can be a bug source if ever forgotten. Any ideas, can this (a class implementing basic compliance in some areas for an abstract class so that children of that abstract class don't need to implement the same code) be done? I know that I could always go the 3 generation route making the BasicGreeter an abstract class, but I think that is more elegant way as it allows BasicGreeter to be used for other families f.e. I might have a class Robot with a subclass FriendlyRobot that could also make use of inheriting from BasicGreeter but a Robot is not necessarily a speaker and vice versa.

The error gives you the solution, simply let the new method to take the arguments even if you don't use them:
from abc import ABC, abstractmethod
class Language():
English = 'english'
class AbstractSpeaker(ABC):
#abstractmethod
def say_hello(self)->None:
pass
"""... other abstract and maybe even concrete methods ..."""
class BasicGreeter:
"""implements a basic say_hello method so that Speakers don't need to implement it"""
def __new__(cls, *args, **kwargs): # Fix to your code
# print(args,kwargs)
r=object.__new__(cls)
r._language=Language.English
return r
#property
def language(self)->Language:
return self._language
#language.setter
def language(self, newLanguage:Language):
self._language=newLanguage
def say_hello(self)->None:
if self.language is Language.English:
print("Hello")
elif self.language is Language.German:
print("Hallo")
elif self.language is Language.Italian:
print("Ciao")
else:
print("Hi")
class Person(BasicGreeter, AbstractSpeaker):
def __init__(self, name):
self.name=name
"""goes on to implement everything AbstractSpeaker speaker commands but should
use the say_hello method from BasicGreeter, who should also hold the language
information.
"""
This piece of code works, said that, I'm not sure in what you need such a structure inside new, is a way of making a singleton?

Python lambdas for __bool__, __str__, etc

Often in Python it is helpful to make use of duck typing, for instance, imagine I have an object spam, whose prompt attribute controls the prompt text in my application. Normally, I would say something like:
spam.prompt = "fixed"
for a fixed prompt. However, a dynamic prompt can also be achived - while I can't change the spam class to use a function as the prompt, thanks to duck typing, because the userlying spam object calls str, I can create a dynamic prompt like so:
class MyPrompt:
def __str__( self ):
return eggs.get_user_name() + ">"
spam.prompt = MyPrompt()
This principal could be extended to make any attribute dynamic, for instance:
class MyEnabled:
def __bool__( self ):
return eggs.is_logged_in()
spam.enabled = MyEnabled()
Sometimes though, it would be more succinct to have this inline, i.e.
spam.prompt = lambda: eggs.get_user_name() + ">"
spam.enabled = eggs.is_logged_in
These of course don't work, because neither the __str__ of the lambda or the __bool__ of the function return the actual value of the call.
I feel like a solution for this should be simple, am I missing something, or do I need to wrap my function in a class every time?

What you want are computed attributes. Python's support for computed attributes is the descriptor protocol, which has a generic implementation as the builtin property type.
Now the trick is that, as documented (cf link above), descriptors only work when they are class attributes. Your code snippet is incomplete as it doesn't contains the definition of the spam object but I assume it's a class instance, so you cannot just do spam.something = property(...) - as the descriptor protocol wouldn't then be invoked on property().
The solution here is the good old "strategy" design pattern: use properties (or custom descriptors, but if you only have a couple of such attributes the builtin property will work just fine) that delegates to a "strategy" function:
def default_prompt_strategy(obj):
return "fixed"
def default_enabled_strategy(obj):
return False
class Spam(object):
def __init__(self, prompt_strategy=default_prompt_strategy, enabled_strategy=default_enabled_strategy):
self.prompt = prompt_strategy
self.enabled = enabled_strategy
#property
def prompt(self):
return self._prompt_strategy(self)
#prompt.setter
def prompt(self, value):
if not callable(value):
raise TypeError("PromptStrategy must be a callable")
self._prompt_strategy = value
#property
def enabled(self):
return self._enabled_strategy(self)
#enabled.setter
def enabled(self, value):
if not callable(value):
raise TypeError("EnabledtStrategy must be a callable")
self._enabled_strategy = value
class Eggs(object):
def is_logged_in(self):
return True
def get_user_name(self):
return "DeadParrot"
eggs = Eggs()
spam = Spam(enabled_strategy=lambda obj: eggs.is_logged_in())
spam.prompt = lambda obj: "{}>".format(eggs.get_user_name())

AttributeError: 'super' object has no attribute

I wrote the following code. When I try to run it as at the end of the file I get this stacktrace:
AttributeError: 'super' object has no attribute do_something
class Parent:
def __init__(self):
pass
def do_something(self, some_parameter, next_parameter):
if type(some_parameter) is not int:
raise AttributeError("Some message")
if type(next_parameter) is not int:
raise AttributeError("Some message")
class Child(Parent):
def __init__(self):
super(Parent).__init__()
def do_something(self, some_parameter, next_parameter):
super(Parent).do_something(some_parameter, next_parameter)
return some_parameter + next_parameter
object = Child()
object.do_something(2, 2)
How should I solve this and where did I the mistake in this simply inheritance sample?

You're passing the wrong argument to super. If you're going to pass arguments at all, they need to be the current class and instance, not the parent class you're expecting to call. Or assuming you're using Python 3, you can skip the arguments completely and the compiler will make it work for you anyway. Calling super with one argument is allowed, but it returns an "unbound super object" which is almost never useful.
Change your calls to use one of these styles:
class Child(Parent):
def __init__(self):
super().__init__() # no arguments is almost always best in Python 3
def do_something(self, some_parameter, next_parameter):
super(Child, self).do_something(some_parameter, next_parameter) # name the current class
return some_parameter + next_parameter
I'd also note that your type checks in Parent.do_something are rather awkward. Rather than type(some_parameter) is not int, use isinstance(some_parameter, int) (unless you deliberately want to exclude subtypes).

You have a few issues here. Firstly there was an indentation error for the parent do_something definition. This meant that it was defined as a function in and of itself, rather than being a method of the class Parent.
Secondly, class methods should usually have self as their first parameter, as when they are called, the object that they refer to is passed as the first variable.
Thirdly, when you call super() you do not need to specify what the super is, as that is inherent in the class definition for Child.
Below is a fixed version of your code which should perform as you expect.
class Parent:
def __init__(self):
pass
def do_something(self, some_parameter, next_parameter):
if type(some_parameter) is not int:
raise AttributeError("Some message")
if type(next_parameter) is not int:
raise AttributeError("Some message")
class Child(Parent):
def __init__(self):
super(Parent).__init__()
def do_something(self, some_parameter, next_parameter):
super().do_something(some_parameter, next_parameter)
return some_parameter + next_parameter
test = Child()
test.do_something(2, 2)

Proxy class can't call methods on child

I'm writing a Python class to wrap/decorate/enhance another class from a package called petl, a framework for ETL (data movement) workflows. Due to design constraints I can't just subclass it; every method call has to be sent through my own class so I can control what kind of objects are being passed back. So in principle this is a proxy class, but I'm having some trouble using existing answers/recipes out there. This is what my code looks like:
from functools import partial
class PetlTable(object):
"""not really how we construct petl tables, but for illustrative purposes"""
def hello(name):
print('Hello, {}!'.format(name)
class DatumTable(object):
def __init__(self, petl_tbl):
self.petl_tbl = petl_tbl
def __getattr__(self, name):
"""this returns a partial referencing the child method"""
petl_attr = getattr(self.petl_tbl, name, None)
if petl_attr and callable(petl_attr):
return partial(self.call_petl_method, func=petl_attr)
raise NotImplementedError('Not implemented')
def call_petl_method(self, func, *args, **kwargs):
func(*args, **kwargs)
Then I try to instantiate a table and call something:
# create a petl table
pt = PetlTable()
# wrap it with our own class
dt = DatumTable(pt)
# try to run the petl method
dt.hello('world')
This gives a TypeError: call_petl_method() got multiple values for argument 'func'.
This only happens with positional arguments; kwargs seem to be fine. I'm pretty sure it has to do with self not being passed in, but I'm not sure what the solution is. Can anyone think of what I'm doing wrong, or a better solution altogether?

This seems to be a common issue with mixing positional and keyword args:
TypeError: got multiple values for argument
To get around it, I took the positional arg func out of call_petl_method and put it in a kwarg that's unlikely to overlap with the kwargs of the child function. A little hacky, but it works.
I ended up writing a Proxy class to do all this generically:
class Proxy(object):
def __init__(self, child):
self.child = child
def __getattr__(self, name):
child_attr = getattr(self.child, name)
return partial(self.call_child_method, __child_fn__=child_attr)
#classmethod
def call_child_method(cls, *args, **kwargs):
"""
This calls a method on the child object and wraps the response as an
object of its own class.
Takes a kwarg `__child_fn__` which points to a method on the child
object.
Note: this can't take any positional args or they get clobbered by the
keyword args we're trying to pass to the child. See:
https://stackoverflow.com/questions/21764770/typeerror-got-multiple-values-for-argument
"""
# get child method
fn = kwargs.pop('__child_fn__')
# call the child method
r = fn(*args, **kwargs)
# wrap the response as an object of the same class
r_wrapped = cls(r)
return r_wrapped

This will also solve the problem. It doesn't use partial at all.
class PetlTable(object):
"""not really how we construct petl tables, but for illustrative purposes"""
def hello(name):
print('Hello, {}!'.format(name))
class DatumTable(object):
def __init__(self, petl_tbl):
self.petl_tbl = petl_tbl
def __getattr__(self, name):
"""Looks-up named attribute in class of the petl_tbl object."""
petl_attr = self.petl_tbl.__class__.__dict__.get(name, None)
if petl_attr and callable(petl_attr):
return petl_attr
raise NotImplementedError('Not implemented')
if __name__ == '__main__':
# create a petl table
pt = PetlTable()
# wrap it with our own class
dt = DatumTable(pt)
# try to run the petl method
dt.hello('world') # -> Hello, world!

Better solution to access class that owns the method from a method decorator

Recently, I faced a problem which was similar to this question:
Accessing the class that owns a decorated method from the decorator
My rep was not high enough to comment there, so I am starting a new question to address some improvements to the answer to that problem.
This is what I needed:
def original_decorator(func):
# need to access class here
# for eg, to append the func itself to class variable "a", to register func
# or say, append func's default arg values to class variable "a"
return func
class A(object):
a=[]
#classmethod
#original_decorator
def some_method(self,a=5):
''' hello'''
print "Calling some_method"
#original_decorator
def some_method_2(self):
''' hello again'''
print "Calling some_method_2"
The solution would need to work both with class methods and instance methods, the method returned from the decorator should work and behave just the same way if it was undecorated i.e. method signature should be preserved.
The accepted answer for that question returned a Class from the decorator and the metaclass identified that specific Class, and did the "class-accessing" operations.
The answer did mention itself as a rough solution, but clearly it had a few caveats :
Decorator returned a class and it was not callable. Obviously, it can be made callable easily, but the returned value is still a class - it just behaves the same way while calling, but its properties and behaviors would be different. Essentially, it would not work the same way as the undecorated method.
It forced the decorator to return a custom-type class and all the "class-accessing" code was put inside the metaclass directly. It is simply not nice, writing the decorator should not enforce touching the metaclass directly.
I have tried to come up with a better solution, documented in the answer.

Here is the solution.
It uses a decorator (which would work on "class-accessing" decorators) and a metaclass, which would fulfill all my requirements and address the problems of that answer. Probably the best advantage is that the "class-accessing" decorators can just access the class, without even touching the metaclass.
# Using metaclass and decorator to allow class access during class creation time
# No method defined within the class should have "_process_meta" as arg
# Potential problems: Using closures, function.func_globals is read-only
from functools import partial
import inspect
class meta(type):
def __new__(cls, name, base, clsdict):
temp_cls = type.__new__(cls, name, base, clsdict)
methods = inspect.getmembers(temp_cls, inspect.ismethod)
for (method_name, method_obj) in methods:
tmp_spec = inspect.getargspec(method_obj)
if "__process_meta" in tmp_spec.args:
what_to_do, main_func = tmp_spec.defaults[:-1]
f = method_obj.im_func
f.func_code, f.func_defaults, f.func_dict, f.func_doc, f.func_name = main_func.func_code, main_func.func_defaults, main_func.func_dict, main_func.func_doc, main_func.func_name
mod_func = what_to_do(temp_cls, f)
f.func_code, f.func_defaults, f.func_dict, f.func_doc, f.func_name = mod_func.func_code, mod_func.func_defaults, mod_func.func_dict, mod_func.func_doc, mod_func.func_name
return temp_cls
def do_it(what_to_do, main_func=None):
if main_func is None:
return partial(do_it, what_to_do)
def whatever(what_to_do=what_to_do, main_func=main_func, __process_meta=True):
pass
return whatever
def original_classmethod_decorator(cls, func):
# cls => class of the method
# appends default arg values to class variable "a"
func_defaults = inspect.getargspec(func).defaults
cls.a.append(func_defaults)
func.__doc__ = "This is a class method"
print "Calling original classmethod decorator"
return func
def original_method_decorator(cls, func):
func_defaults = inspect.getargspec(func).defaults
cls.a.append(func_defaults)
func.__doc__ = "This is a instance method" # Can change func properties
print "Calling original method decorator"
return func
class A(object):
__metaclass__ = meta
a = []
#classmethod
#do_it(original_classmethod_decorator)
def some_method(cls, x=1):
''' hello'''
print "Calling original class method"
#do_it(original_method_decorator)
def some_method_2(self, y=2):
''' hello again'''
print "Calling original method"
# signature preserved
print(inspect.getargspec(A.some_method))
print(inspect.getargspec(A.some_method_2))
Open to suggestions on whether this approach has any ceveats.