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How to mock a Python class during testing for runtime typing checks?

I have some application method that uses the #typeguard.typechecked decorator for performing runtime checks of the passed parameters:
class SalesDeal:
pass
#typechecked
def do_something(deal: SalesDeal):
pass
Inside a test I have fake class FakeSalesDeal which implements a minimal mock for SalesDeal (which is a very complicated class in reality):
class FakeSalesDeal:
pass
def test_foo():
deal = FakeSalesDeal()
do_something(deal)
This test will of course fail because the #typechecked decorator will raise an error due to a different class.
Is there a way to mock/fake the class of FakeSalesDeal in order make the test pass?

You can use MagicMock with spec set to SalesDeal instead of creating a fake class.
isinstance(mock, SalesDeal) will be True for that mock object & you should be able to bypass the typecheck.
from unittest.mock import MagicMock
# ...
def test_foo():
deal = MagicMock(spec=SalesDeal)
print(isinstance(deal, SalesDeal))
do_something(deal)
test_foo()
This prints:
True
& doesn't throw any type check error.
This works because typechecked explicitly checks for Mock objects being passed with:
if expected_type is Any or isinstance(value, Mock):
return
Code from here
So if you use proper mocks, typechecked should not be causing any issues for you.

My final solution:
class FakeSalesDeal(MagicMock):
pass

Python mocking the right class/import

I am trying to implement unittests for my python program. The problem is, that my program is using several imported classes, which I would like to replace by a mocked object/class to verify single functions/methods.
I do not get any errors with my mocked class. But it appears that the mock itself didn't replace the object I wanted to replace.
This is basically my structure:
First the class I want to mock. Might look like that:
class ToMock():
def getSomething(self):
return "something"
The class I want to test looks like this:
from x.y import ToMock
class ClassToTest():
def __init__(self):
self.obj = ToMock()
def returnStuff():
return self.obj.getSomething()
As you can imagine, I want to test the returnStuff method. Therfore I want to mock .getSomething, or better said the whole ToMock object.
The unittest should therefore test the ClassToTest with the mocked ToMock class. I tried several mock.patch variants, but I couldn't get it to run/test properly.
import unittest
from unittest import mock
from a.b import ClassToTest
class TestObject(unittest.TestCase):
def setUp(self):
with mock.patch('x.y.ToMock') as mock_obj:
mock_obj.return_value.getSomething().return_value="mocked return value"
self.test_class = ClassToTest()
result = self.test_class.returnStuff() # This should return now 'mocked return value', I guess?
mock_obj.return_value.getSomething.assert_called_once_with("")
The problem I face is, that the self.test_class.returnStuff() is not "calling" the mocked object, but imports the real class etc. and therefore I am running into timeouts, or similar stuff.
I am sure, that I provide the wrong path for the object which should be mocked. Perhaps someone can hint me into the right direction.
Thanks
-GreNait

The issue is that you are not patching in the correct place. You are patching where the object is defined as opposed to where it is looked up.
a.py
-> Defines ToMock
b.py
-> from a import ToMock
-> some_function/class instantiates ToMock
In your code shown you are patching a.ToMock however you should be patching b.ToMock. That is why it is not running your mock object when testing. You can read more about where to patch here.

Python 2.7 mock a void method of a class

I am trying to use unittests.mock to mock a void method call of an object.
My package is like below
common
baseupgradehandler.py
baseupgradehandler.py
class BaseUpgradeHandler(object):
def __init__(self, upgrade_config, upgrade_state, system_config, pre_step, main_step, post_step):
...
# Method call to be supressed
def start(self, service_manifest, upgrade_bundle):
# type: (service_version_pb2.ServiceManifest, str) -> ()
...
In my test code I am trying to mock the call to start() like below as explained in the documentation.
from workflow.upgradeworkflow import UpgradeWorkflow
from common.serviceregistry import ServiceRegistry
# The above imports are at the start of the test file
...
with patch('common.baseupgradehandler.BaseUpgradeHandler') as handler_mock: # type: Mock
handler_mock.return_value.start.return_value = ''
wf = UpgradeWorkflow(ServiceRegistry(self.service_bundle, config, sys_config, state),
config,
state,
sys_config)
BaseUpgradeHandler object is returned by get_upgrade_handler() method of ServiceRegistry. When I am executing the above code in test I am seeing the BaseUpgradeHandler.start() is still getting called.
Can someone let me know how can I mock the call to a start() so that the method is not called?
EDIT
If I change my patching code like below it is working as expected and BaseUpgradeHandler is getting mocked and start is not getting called.
with patch('common.baseupgradehandler.BaseUpgradeHandler') as handler_mock: # type: Mock
handler_mock.return_value.start.return_value = ''
with patch('common.serviceregistry.ServiceRegistry') as serviceregistry_mock: # type: Mock
serviceregistry_mock.return_value.get_upgrade_handler.return_value = handler_mock
wf = UpgradeWorkflow(ServiceRegistry(self.service_bundle, config, sys_config, state), config, state, sys_config)
wf.start()
Can someone explain me why do I have to patch ServiceRegistry as well?

The code you provided is not enough to see the part that causes the issue. We'd need to see the module serviceregistry to be sure but I'd take an educated guess:
You have a file a.py (aka baseupgradehandler) like this:
class A:
def method(self):
print("It's real!")
And a file b.py (aka serviceregistry) like this:
from a import A
class B:
def get_A(self):
return A()
In your test files you do this:
import unittest
from unittest.mock import patch
from b import B
from a import A
GAME OVER!
The B module right now has already got its reference to the original A class. When, afterwards, you patch('a.A') only the reference in the a module is changed, but patch has no way to know that B has its own reference to the original A.
You can fix this in three ways:
patch the method: this will modify the existing class so all references to that class will be automatically patched
patch b.A too:
with patch('a.A') as h_a, patch('b.A') as h_b:
h_a.return_value.method.return_value = ''
h_b.return_value.method.return_value = ''
Avoid importing the modules before patching (probably not feasible or a good idea):
import unittest
from unittest.mock import patch
class MyTest(unittest.TestCase):
def test_one(self):
with patch('a.A') as h:
h.return_value.method.return_value = ''
from b import B
B().get_A().method()

I have been using unittest.mocks for a while, and I have been re-inventing the wheel sometimes. I decided to make mockito part of my project and now things look way better. Any kind of mock verification is really simple, if you can, I definitively encourage you to make mockito part of your libraries. This library has a good documentation and so far it has been easier than unittest.mock IMHO.

unittest blacklist namespace and fail any attempt to reference it

in the case of unit testing a wrapper library, testing the wrapper without depending/exercising the upstream library is a goal; In a known case, all calls to the upstream library can be mocked and that's what I've done, but I've been frustrated by changes to the wrapper that introduce more calls to the upstream library being missed by the mock tools;
How can I best fail any test that tries to use a given namespace?
My idea currently is to change all the unittest methods to have a monkey patch like
#unittest.mock.patch('wrapper_namespace.upsteam_namespace')
and reply the upstream library with a mock that can be asserted untouched; I'm hoping for an option that works globally, so that I
don't have to add a monkeypatch to every test method, though this level of granularity is acceptable; but also don't have to perform the assertion that the mock was never used in the test methods (or make a decorator to do all that either)
prohibits access to the upstream library from any part of the software
(e.g, Wrapper calls B calls Upstream, B's call to upstream might not be caught)

You don't have to patch every test method. You can easily patch over the class if you're using unittest, or just assign the module to whatever you want to patch over it with. Here's a workable example:
A fake lib in some_lib.py:
def some_lib_func():
raise ValueError("I've been called.")
def some_other_lib_func():
raise ValueError("I've been called.")
class SomeClass:
def __init__(self):
raise ValueError("I've been constructed.")
wrapper.py:
import some_lib
def wrapper1():
some_lib.some_lib_func()
def wrapper2():
some_lib.some_other_lib_func()
def wrapper3():
x = some_lib.SomeClass()
test.py:
from unittest.mock import patch, MagicMock
import unittest
import wrapper
# Alternative:
# wrapper.some_lib = MagicMock()
# Can patch an entire class
#patch('wrapper.some_lib', MagicMock())
class TestWrapper(unittest.TestCase):
def test_wrapper1(self):
wrapper.wrapper1()
def test_wrapper2(self):
wrapper.wrapper2()
def test_wrapper3(self):
wrapper.wrapper3()
if __name__ == "__main__":
unittest.main()
We would explode if the functions/classes in some_lib were called, but they aren't:
Matthews-MacBook-Pro:stackoverflow matt$ python test.py
...
----------------------------------------------------------------------
Ran 3 tests in 0.001s
OK
Feel free to comment out the patch and comment in wrapper.some_lib = MagicMock(). You'll get the same result in this toy example, but there is a major difference between the two approaches:
When using #patch('wrapper.some_lib', MagicMock()) the patch is only live for that Test Case class.
When using wrapper.some_lib = MagicMock(), however, that patch will stay live for the entire length of your python program, unless you save off the original module and patch it back manually at some point. Everything that is using the wrapper module will get the mocked version.
So you could so something like:
original_lib = wrapper.some_lib
wrapper.some_lib = MagicMock()
...
# call some test suite, every call to the wrapper module will be mocked out
...
wrapper.some_lib = original_lib
...
# call some other test suite that actually needs the real thing
...
HTH.
EDIT: Misread your question slightly, but you can inspect MagicMock objects to see if they've been called, and if so, fail the test. Or just patch over with something that fails when called (instead of MagicMock). I can provide code to do this if requested (just leave a comment), but hopefully the above can get you started. I think the crux of the question was really about the global patching. Cheers!

Custom exceptions in unittests

I have created my custom exceptions as such within errors.py
mapper = {
'E101':
'There is no data at all for these constraints',
'E102':
'There is no data for these constraints in this market, try changing market',
'E103':
'There is no data for these constraints during these dates, try changing dates',
}
class DataException(Exception):
def __init__(self, code):
super().__init__()
self.msg = mapper[code]
def __str__(self):
return self.msg
Another function somewhere else in the code raises different instances of DataException if there is not enough data in a pandas dataframe. I want to use unittest to ensure that it returns the appropriate exception with its corresponding message.
Using a simple example, why does this not work:
from .. import DataException
def foobar():
raise DataException('E101')
import unittest
with unittest.TestCase.assertRaises(DataException):
foobar()
As suggested here: Python assertRaises on user-defined exceptions
I get this error:
TypeError: assertRaises() missing 1 required positional argument: 'expected_exception'
Or alternatively:
def foobar():
raise DataException('E101')
import unittest
unittest.TestCase.assertRaises(DataException, foobar)
results in:
TypeError: assertRaises() arg 1 must be an exception type or tuple of exception types
Why is it not recognizing DataException as an Exception? Why does the linked stackoverflow question answer work without supplying a second argument to assertRaises?

You are trying to use methods of the TestCase class without creating an instance; those methods are not designed to be used in that manner.
unittest.TestCase.assertRaises is an unbound method. You'd use it in a test method on a TestCase class you define:
class DemoTestCase(unittest.TestCase):
def test_foobar(self):
with self.assertRaises(DataException):
foobar()
The error is raised because unbound methods do not get self passed in. Because unittest.TestCase.assertRaises expects both self and a second argument named expected_exception you get an exception as DataException is passed in as the value for self.
You do now have to use a test runner to manage your test cases; add
if __name__ == '__main__':
unittest.main()
at the bottom and run your file as a script. Your test cases are then auto-discovered and executed.
It is technically possible to use the assertions outside such an environment, see Is there a way to use Python unit test assertions outside of a TestCase?, but I recommend you stick to creating test cases instead.
To further verify the codes and message on the raised exception, assign the value returned when entering the context to a new name with with ... as <target>:; the context manager object captures the raised exception so you can make assertions about it:
with self.assertRaises(DataException) as context:
foobar()
self.assertEqual(context.exception.code, 'E101')
self.assertEqual(
context.exception.msg,
'There is no data at all for these constraints')
See the TestCase.assertRaises() documentation.
Last but not least, consider using subclasses of DataException rather than use separate error codes. That way your API users can just catch one of those subclasses to handle a specific error code, rather than having to do additional tests for the code and re-raise if a specific code should not have been handled there.