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# Further Examples

For comprehensive examples, see the unit tests included in the full source distribution.

Here are some more examples for some slightly more advanced scenarios than in the getting started guide.

## Mocking chained calls

Mocking chained calls is actually straightforward with mock once you understand the `return_value` attribute. When a mock is called for the first time, or you fetch its return_value before it has been called, a new Mock is created.

This means that you can see how the object returned from a call to a mocked object has been used by interrogating the return_value mock:

    >>> mock = Mock()
    >>> mock().foo(a=2, b=3)
    <Mock name='mock().foo()' id='...'>
    >>> mock.return_value.foo.assert_called_with(a=2, b=3)

From here it is a simple step to configure and then make assertions about chained calls. Of course another alternative is writing your code in a more testable way in the first place...

So, suppose we have some code that looks a little bit like this:

    >>> class Something(object):
    ...     def __init__(self):
    ...         self.backend = BackendProvider()
    ...     def method(self):
    ...         response = self.backend.get_endpoint('foobar').create_call('spam', 'eggs').start_call()
    ...         # more code

Assuming that BackendProvider is already well tested, how do we test method()? Specifically, we want to test that the code section # more code uses the response object in the correct way.

As this chain of calls is made from an instance attribute we can monkey patch the backend attribute on a Something instance. In this particular case we are only interested in the return value from the final call to start_call so we don’t have much configuration to do. Let’s assume the object it returns is ‘file-like’, so we’ll ensure that our response object uses the builtin file as its spec.

To do this we create a mock instance as our mock backend and create a mock response object for it. To set the response as the return value for that final start_call we could do this:

> mock_backend.get_endpoint.return_value.create_call.return_value.start_call.return_value = mock_response.

We can do that in a slightly nicer way using the `configure_mock()` method to directly set the return value for us:

    >>> something = Something()
    >>> mock_response = Mock(spec=file)
    >>> mock_backend = Mock()
    >>> config = {'get_endpoint.return_value.create_call.return_value.start_call.return_value': mock_response}
    >>> mock_backend.configure_mock(**config)

With these we monkey patch the “mock backend” in place and can make the real call:

    >>> something.backend = mock_backend
    >>> something.method()

Using `mock_calls` we can check the chained call with a single assert. A chained call is several calls in one line of code, so there will be several entries in mock_calls. We can use `call.call_list()` to create this list of calls for us:

    >>> chained = call.get_endpoint('foobar').create_call('spam', 'eggs').start_call()
    >>> call_list = chained.call_list()
    >>> assert mock_backend.mock_calls == call_list

## Partial mocking

In some tests I wanted to mock out a call to datetime.date.today() to return a known date, but I didn’t want to prevent the code under test from creating new date objects. Unfortunately datetime.date is written in C, and so I couldn’t just monkey-patch out the static date.today method.

I found a simple way of doing this that involved effectively wrapping the date class with a mock, but passing through calls to the constructor to the real class (and returning real instances).

The `patch decorator` is used here to mock out the date class in the module under test. The `side_effect` attribute on the mock date class is then set to a lambda function that returns a real date. When the mock date class is called a real date will be constructed and returned by side_effect.

    >>> from datetime import date
    >>> with patch('mymodule.date') as mock_date:
    ...     mock_date.today.return_value = date(2010, 10, 8)
    ...     mock_date.side_effect = lambda *args, **kw: date(*args, **kw)
    ...
    ...     assert mymodule.date.today() == date(2010, 10, 8)
    ...     assert mymodule.date(2009, 6, 8) == date(2009, 6, 8)
    ...

Note that we don’t patch datetime.date globally, we patch date in the module that uses it. See where to patch.

When date.today() is called a known date is returned, but calls to the date(...) constructor still return normal dates. Without this you can find yourself having to calculate an expected result using exactly the same algorithm as the code under test, which is a classic testing anti-pattern.

Calls to the date constructor are recorded in the mock_date attributes (call_count and friends) which may also be useful for your tests.

An alternative way of dealing with mocking dates, or other builtin classes, is discussed in this blog entry.

## Mocking a Generator Method

A Python generator is a function or method that uses the yield statement to return a series of values when iterated over [1].

A generator method / function is called to return the generator object. It is the generator object that is then iterated over. The protocol method for iteration is __iter__, so we can mock this using a MagicMock.

Here’s an example class with an “iter” method implemented as a generator:

    >>> class Foo(object):
    ...     def iter(self):
    ...         for i in [1, 2, 3]:
    ...             yield i
    ...
    >>> foo = Foo()
    >>> list(foo.iter())
    [1, 2, 3]

How would we mock this class, and in particular its “iter” method?

To configure the values returned from the iteration (implicit in the call to list), we need to configure the object returned by the call to foo.iter().

    >>> mock_foo = MagicMock()
    >>> mock_foo.iter.return_value = iter([1, 2, 3])
    >>> list(mock_foo.iter())
    [1, 2, 3]

[1] | There are also generator expressions and more advanced uses of generators, but we aren’t concerned about them here. A very good introduction to generators and how powerful they are is: Generator Tricks for Systems Programmers.

## Applying the same patch to every test method

If you want several patches in place for multiple test methods the obvious way is to apply the patch decorators to every method. This can feel like unnecessary repetition. For Python 2.6 or more recent you can use patch (in all its various forms) as a class decorator. This applies the patches to all test methods on the class. A test method is identified by methods whose names start with test:

    >>> @patch('mymodule.SomeClass')
    ... class MyTest(TestCase):
    ...
    ...     def test_one(self, MockSomeClass):
    ...         self.assertTrue(mymodule.SomeClass is MockSomeClass)
    ...
    ...     def test_two(self, MockSomeClass):
    ...         self.assertTrue(mymodule.SomeClass is MockSomeClass)
    ...
    ...     def not_a_test(self):
    ...         return 'something'
    ...
    >>> MyTest('test_one').test_one()
    >>> MyTest('test_two').test_two()
    >>> MyTest('test_two').not_a_test()
    'something'

An alternative way of managing patches is to use the patch methods: start and stop. These allow you to move the patching into your setUp and tearDown methods.

    >>> class MyTest(TestCase):
    ...     def setUp(self):
    ...         self.patcher = patch('mymodule.foo')
    ...         self.mock_foo = self.patcher.start()
    ...
    ...     def test_foo(self):
    ...         self.assertTrue(mymodule.foo is self.mock_foo)
    ...
    ...     def tearDown(self):
    ...         self.patcher.stop()
    ...
    >>> MyTest('test_foo').run()

If you use this technique you must ensure that the patching is “undone” by calling stop. This can be fiddlier than you might think, because if an exception is raised in the setUp then tearDown is not called. unittest2 cleanup functions make this simpler:

    >>> class MyTest(TestCase):
    ...     def setUp(self):
    ...         patcher = patch('mymodule.foo')
    ...         self.addCleanup(patcher.stop)
    ...         self.mock_foo = patcher.start()
    ...
    ...     def test_foo(self):
    ...         self.assertTrue(mymodule.foo is self.mock_foo)
    ...
    >>> MyTest('test_foo').run()

## Mocking Unbound Methods

Whilst writing tests today I needed to patch an unbound method (patching the method on the class rather than on the instance). I needed self to be passed in as the first argument because I want to make asserts about which objects were calling this particular method. The issue is that you can’t patch with a mock for this, because if you replace an unbound method with a mock it doesn’t become a bound method when fetched from the instance, and so it doesn’t get self passed in. The workaround is to patch the unbound method with a real function instead. The `patch()` decorator makes it so simple to patch out methods with a mock that having to create a real function becomes a nuisance.

If you pass autospec=True to patch then it does the patching with a real function object. This function object has the same signature as the one it is replacing, but delegates to a mock under the hood. You still get your mock auto-created in exactly the same way as before. What it means though, is that if you use it to patch out an unbound method on a class the mocked function will be turned into a bound method if it is fetched from an instance. It will have self passed in as the first argument, which is exactly what I wanted:

    >>> class Foo(object):
    ...   def foo(self):
    ...     pass
    ...
    >>> with patch.object(Foo, 'foo', autospec=True) as mock_foo:
    ...   mock_foo.return_value = 'foo'
    ...   foo = Foo()
    ...   foo.foo()
    ...
    'foo'
    >>> mock_foo.assert_called_once_with(foo)

If we don’t use autospec=True then the unbound method is patched out with a Mock instance instead, and isn’t called with self.

## Checking multiple calls with mock

mock has a nice API for making assertions about how your mock objects are used.

    >>> mock = Mock()
    >>> mock.foo_bar.return_value = None
    >>> mock.foo_bar('baz', spam='eggs')
    >>> mock.foo_bar.assert_called_with('baz', spam='eggs')

If your mock is only being called once you can use the `assert_called_once_with()` method that also asserts that the `call_count` is one.

    >>> mock.foo_bar.assert_called_once_with('baz', spam='eggs')
    >>> mock.foo_bar()
    >>> mock.foo_bar.assert_called_once_with('baz', spam='eggs')
    Traceback (most recent call last):
        ...
    AssertionError: Expected to be called once. Called 2 times.

Both assert_called_with and assert_called_once_with make assertions about the most recent call. If your mock is going to be called several times, and you want to make assertions about all those calls you can use `call_args_list`:

    >>> mock = Mock(return_value=None)
    >>> mock(1, 2, 3)
    >>> mock(4, 5, 6)
    >>> mock()
    >>> mock.call_args_list
    [call(1, 2, 3), call(4, 5, 6), call()]

The `call` helper makes it easy to make assertions about these calls. You can build up a list of expected calls and compare it to call_args_list. This looks remarkably similar to the repr of the call_args_list:

    >>> expected = [call(1, 2, 3), call(4, 5, 6), call()]
    >>> mock.call_args_list == expected
    True

## Coping with mutable arguments

Another situation is rare, but can bite you, is when your mock is called with mutable arguments. call_args and call_args_list store references to the arguments. If the arguments are mutated by the code under test then you can no longer make assertions about what the values were when the mock was called.

Here’s some example code that shows the problem. Imagine the following functions defined in ‘mymodule’:

    def frob(val):
        pass

    def grob(val):
        "First frob and then clear val"
        frob(val)
        val.clear()

When we try to test that grob calls frob with the correct argument look what happens:

    >>> with patch('mymodule.frob') as mock_frob:
    ...     val = set([6])
    ...     mymodule.grob(val)
    ...
    >>> val
    set([])
    >>> mock_frob.assert_called_with(set([6]))
    Traceback (most recent call last):
        ...
    AssertionError: Expected: ((set([6]),), {})
    Called with: ((set([]),), {})

One possibility would be for mock to copy the arguments you pass in. This could then cause problems if you do assertions that rely on object identity for equality.

Here’s one solution that uses the `side_effect` functionality. If you provide a side_effect function for a mock then side_effect will be called with the same args as the mock. This gives us an opportunity to copy the arguments and store them for later assertions. In this example I’m using another mock to store the arguments so that I can use the mock methods for doing the assertion. Again a helper function sets this up for me.

    >>> from copy import deepcopy
    >>> from mock import Mock, patch, DEFAULT
    >>> def copy_call_args(mock):
    ...     new_mock = Mock()
    ...     def side_effect(*args, **kwargs):
    ...         args = deepcopy(args)
    ...         kwargs = deepcopy(kwargs)
    ...         new_mock(*args, **kwargs)
    ...         return DEFAULT
    ...     mock.side_effect = side_effect
    ...     return new_mock
    ...
    >>> with patch('mymodule.frob') as mock_frob:
    ...     new_mock = copy_call_args(mock_frob)
    ...     val = set([6])
    ...     mymodule.grob(val)
    ...
    >>> new_mock.assert_called_with(set([6]))
    >>> new_mock.call_args
    call(set([6]))

copy_call_args is called with the mock that will be called. It returns a new mock that we do the assertion on. The side_effect function makes a copy of the args and calls our new_mock with the copy.

Note

If your mock is only going to be used once there is an easier way of checking arguments at the point they are called. You can simply do the checking inside a side_effect function.

    >>> def side_effect(arg):
    ...     assert arg == set([6])
    ...
    >>> mock = Mock(side_effect=side_effect)
    >>> mock(set([6]))
    >>> mock(set())
    Traceback (most recent call last):
        ...
    AssertionError

An alternative approach is to create a subclass of Mock or MagicMock that copies (using copy.deepcopy) the arguments. Here’s an example implementation:

    >>> from copy import deepcopy
    >>> class CopyingMock(MagicMock):
    ...     def __call__(self, *args, **kwargs):
    ...         args = deepcopy(args)
    ...         kwargs = deepcopy(kwargs)
    ...         return super(CopyingMock, self).__call__(*args, **kwargs)
    ...
    >>> c = CopyingMock(return_value=None)
    >>> arg = set()
    >>> c(arg)
    >>> arg.add(1)
    >>> c.assert_called_with(set())
    >>> c.assert_called_with(arg)
    Traceback (most recent call last):
        ...
    AssertionError: Expected call: mock(set([1]))
    Actual call: mock(set([]))
    >>> c.foo
    <CopyingMock name='mock.foo' id='...'>

When you subclass Mock or MagicMock all dynamically created attributes, and the return_value will use your subclass automatically. That means all children of a CopyingMock will also have the type CopyingMock.

## Raising exceptions on attribute access

You can use `PropertyMock` to mimic the behaviour of properties. This includes raising exceptions when an attribute is accessed.

Here’s an example raising a ValueError when the ‘foo’ attribute is accessed:

    >>> m = MagicMock()
    >>> p = PropertyMock(side_effect=ValueError)
    >>> type(m).foo = p
    >>> m.foo
    Traceback (most recent call last):
    ....
    ValueError

Because every mock object has its own type, a new subclass of whichever mock class you’re using, all mock objects are isolated from each other. You can safely attach properties (or other descriptors or whatever you want in fact) to type(mock) without affecting other mock objects.

## Multiple calls with different effects

Note

In mock 1.0 the handling of iterable side_effect was changed. Any exceptions in the iterable will be raised instead of returned.

Handling code that needs to behave differently on subsequent calls during the test can be tricky. For example you may have a function that needs to raise an exception the first time it is called but returns a response on the second call (testing retry behaviour).

One approach is to use a `side_effect` function that replaces itself. The first time it is called the side_effect sets a new side_effect that will be used for the second call. It then raises an exception:

    >>> def side_effect(*args):
    ...   def second_call(*args):
    ...     return 'response'
    ...   mock.side_effect = second_call
    ...   raise Exception('boom')
    ...
    >>> mock = Mock(side_effect=side_effect)
    >>> mock('first')
    Traceback (most recent call last):
        ...
    Exception: boom
    >>> mock('second')
    'response'
    >>> mock.assert_called_with('second')

Another perfectly valid way would be to pop return values from a list. If the return value is an exception, raise it instead of returning it:

    >>> returns = [Exception('boom'), 'response']
    >>> def side_effect(*args):
    ...   result = returns.pop(0)
    ...   if isinstance(result, Exception):
    ...     raise result
    ...   return result
    ...
    >>> mock = Mock(side_effect=side_effect)
    >>> mock('first')
    Traceback (most recent call last):
        ...
    Exception: boom
    >>> mock('second')
    'response'
    >>> mock.assert_called_with('second')

Which approach you prefer is a matter of taste. The first approach is actually a line shorter but maybe the second approach is more readable.

## Nesting Patches

Using patch as a context manager is nice, but if you do multiple patches you can end up with nested with statements indenting further and further to the right:

    >>> class MyTest(TestCase):
    ...
    ...     def test_foo(self):
    ...         with patch('mymodule.Foo') as mock_foo:
    ...             with patch('mymodule.Bar') as mock_bar:
    ...                 with patch('mymodule.Spam') as mock_spam:
    ...                     assert mymodule.Foo is mock_foo
    ...                     assert mymodule.Bar is mock_bar
    ...                     assert mymodule.Spam is mock_spam
    ...
    >>> original = mymodule.Foo
    >>> MyTest('test_foo').test_foo()
    >>> assert mymodule.Foo is original

With unittest2 cleanup functions and the patch methods: start and stop we can achieve the same effect without the nested indentation. A simple helper method, create_patch, puts the patch in place and returns the created mock for us:

    >>> class MyTest(TestCase):
    ...
    ...     def create_patch(self, name):
    ...         patcher = patch(name)
    ...         thing = patcher.start()
    ...         self.addCleanup(patcher.stop)
    ...         return thing
    ...
    ...     def test_foo(self):
    ...         mock_foo = self.create_patch('mymodule.Foo')
    ...         mock_bar = self.create_patch('mymodule.Bar')
    ...         mock_spam = self.create_patch('mymodule.Spam')
    ...
    ...         assert mymodule.Foo is mock_foo
    ...         assert mymodule.Bar is mock_bar
    ...         assert mymodule.Spam is mock_spam
    ...
    >>> original = mymodule.Foo
    >>> MyTest('test_foo').run()
    >>> assert mymodule.Foo is original

## Mocking a dictionary with MagicMock

You may want to mock a dictionary, or other container object, recording all access to it whilst having it still behave like a dictionary.

We can do this with `MagicMock`, which will behave like a dictionary, and using `side_effect` to delegate dictionary access to a real underlying dictionary that is under our control.

When the __getitem__ and __setitem__ methods of our MagicMock are called (normal dictionary access) then side_effect is called with the key (and in the case of __setitem__ the value too). We can also control what is returned.

After the MagicMock has been used we can use attributes like `call_args_list` to assert about how the dictionary was used:

    >>> my_dict = {'a': 1, 'b': 2, 'c': 3}
    >>> def getitem(name):
    ...      return my_dict[name]
    ...
    >>> def setitem(name, val):
    ...     my_dict[name] = val
    ...
    >>> mock = MagicMock()
    >>> mock.__getitem__.side_effect = getitem
    >>> mock.__setitem__.side_effect = setitem

Note

An alternative to using MagicMock is to use Mock and only provide the magic methods you specifically want:

    >>> mock = Mock()
    >>> mock.__setitem__ = Mock(side_effect=getitem)
    >>> mock.__getitem__ = Mock(side_effect=setitem)

A third option is to use MagicMock but passing in dict as the spec (or spec_set) argument so that the MagicMock created only has dictionary magic methods available:

    >>> mock = MagicMock(spec_set=dict)
    >>> mock.__getitem__.side_effect = getitem
    >>> mock.__setitem__.side_effect = setitem

With these side effect functions in place, the mock will behave like a normal dictionary but recording the access. It even raises a KeyError if you try to access a key that doesn’t exist.

    >>> mock['a']
    1
    >>> mock['c']
    3
    >>> mock['d']
    Traceback (most recent call last):
        ...
    KeyError: 'd'
    >>> mock['b'] = 'fish'
    >>> mock['d'] = 'eggs'
    >>> mock['b']
    'fish'
    >>> mock['d']
    'eggs'

After it has been used you can make assertions about the access using the normal mock methods and attributes:

    >>> mock.__getitem__.call_args_list
    [call('a'), call('c'), call('d'), call('b'), call('d')]
    >>> mock.__setitem__.call_args_list
    [call('b', 'fish'), call('d', 'eggs')]
    >>> my_dict
    {'a': 1, 'c': 3, 'b': 'fish', 'd': 'eggs'}

## Mock subclasses and their attributes

There are various reasons why you might want to subclass Mock. One reason might be to add helper methods. Here’s a silly example:

    >>> class MyMock(MagicMock):
    ...     def has_been_called(self):
    ...         return self.called
    ...
    >>> mymock = MyMock(return_value=None)
    >>> mymock
    <MyMock id='...'>
    >>> mymock.has_been_called()
    False
    >>> mymock()
    >>> mymock.has_been_called()
    True

The standard behaviour for Mock instances is that attributes and the return value mocks are of the same type as the mock they are accessed on. This ensures that Mock attributes are Mocks and MagicMock attributes are MagicMocks [2]. So if you’re subclassing to add helper methods then they’ll also be available on the attributes and return value mock of instances of your subclass.

    >>> mymock.foo
    <MyMock name='mock.foo' id='...'>
    >>> mymock.foo.has_been_called()
    False
    >>> mymock.foo()
    <MyMock name='mock.foo()' id='...'>
    >>> mymock.foo.has_been_called()
    True

Sometimes this is inconvenient. For example, one user is subclassing mock to created a Twisted adaptor. Having this applied to attributes too actually causes errors.

Mock (in all its flavours) uses a method called _get_child_mock to create these “sub-mocks” for attributes and return values. You can prevent your subclass being used for attributes by overriding this method. The signature is that it takes arbitrary keyword arguments (**kwargs) which are then passed onto the mock constructor:

    >>> class Subclass(MagicMock):
    ...     def _get_child_mock(self, **kwargs):
    ...         return MagicMock(**kwargs)
    ...
    >>> mymock = Subclass()
    >>> mymock.foo
    <MagicMock name='mock.foo' id='...'>
    >>> assert isinstance(mymock, Subclass)
    >>> assert not isinstance(mymock.foo, Subclass)
    >>> assert not isinstance(mymock(), Subclass)

[2] | An exception to this rule are the non-callable mocks. Attributes use the callable variant because otherwise non-callable mocks couldn’t have callable methods.

## Mocking imports with patch.dict

One situation where mocking can be hard is where you have a local import inside a function. These are harder to mock because they aren’t using an object from the module namespace that we can patch out.

Generally local imports are to be avoided. They are sometimes done to prevent circular dependencies, for which there is usually a much better way to solve the problem (refactor the code) or to prevent “up front costs” by delaying the import. This can also be solved in better ways than an unconditional local import (store the module as a class or module attribute and only do the import on first use).

That aside there is a way to use mock to affect the results of an import. Importing fetches an object from the sys.modules dictionary. Note that it fetches an object, which need not be a module. Importing a module for the first time results in a module object being put in sys.modules, so usually when you import something you get a module back. This need not be the case however.

This means you can use `patch.dict()` to temporarily put a mock in place in sys.modules. Any imports whilst this patch is active will fetch the mock. When the patch is complete (the decorated function exits, the with statement body is complete or patcher.stop() is called) then whatever was there previously will be restored safely.

Here’s an example that mocks out the ‘fooble’ module.

    >>> mock = Mock()
    >>> with patch.dict('sys.modules', {'fooble': mock}):
    ...    import fooble
    ...    fooble.blob()
    ...
    <Mock name='mock.blob()' id='...'>
    >>> assert 'fooble' not in sys.modules
    >>> mock.blob.assert_called_once_with()

As you can see the import fooble succeeds, but on exit there is no ‘fooble’ left in sys.modules.

This also works for the from module import name form:

    >>> mock = Mock()
    >>> with patch.dict('sys.modules', {'fooble': mock}):
    ...    from fooble import blob
    ...    blob.blip()
    ...
    <Mock name='mock.blob.blip()' id='...'>
    >>> mock.blob.blip.assert_called_once_with()

With slightly more work you can also mock package imports:

    >>> mock = Mock()
    >>> modules = {'package': mock, 'package.module': mock.module}
    >>> with patch.dict('sys.modules', modules):
    ...    from package.module import fooble
    ...    fooble()
    ...
    <Mock name='mock.module.fooble()' id='...'>
    >>> mock.module.fooble.assert_called_once_with()

## Tracking order of calls and less verbose call assertions

The `Mock` class allows you to track the order of method calls on your mock objects through the `method_calls` attribute. This doesn’t allow you to track the order of calls between separate mock objects, however we can use `mock_calls` to achieve the same effect.

Because mocks track calls to child mocks in mock_calls, and accessing an arbitrary attribute of a mock creates a child mock, we can create our separate mocks from a parent one. Calls to those child mock will then all be recorded, in order, in the mock_calls of the parent:

    >>> manager = Mock()
    >>> mock_foo = manager.foo
    >>> mock_bar = manager.bar

    >>> mock_foo.something()
    <Mock name='mock.foo.something()' id='...'>
    >>> mock_bar.other.thing()
    <Mock name='mock.bar.other.thing()' id='...'>

    >>> manager.mock_calls
    [call.foo.something(), call.bar.other.thing()]

We can then assert about the calls, including the order, by comparing with the mock_calls attribute on the manager mock:

    >>> expected_calls = [call.foo.something(), call.bar.other.thing()]
    >>> manager.mock_calls == expected_calls
    True

If patch is creating, and putting in place, your mocks then you can attach them to a manager mock using the `attach_mock()` method. After attaching calls will be recorded in mock_calls of the manager.

    >>> manager = MagicMock()
    >>> with patch('mymodule.Class1') as MockClass1:
    ...     with patch('mymodule.Class2') as MockClass2:
    ...         manager.attach_mock(MockClass1, 'MockClass1')
    ...         manager.attach_mock(MockClass2, 'MockClass2')
    ...         MockClass1().foo()
    ...         MockClass2().bar()
    ...
    <MagicMock name='mock.MockClass1().foo()' id='...'>
    <MagicMock name='mock.MockClass2().bar()' id='...'>
    >>> manager.mock_calls
    [call.MockClass1(),
     call.MockClass1().foo(),
     call.MockClass2(),
     call.MockClass2().bar()]

If many calls have been made, but you’re only interested in a particular sequence of them then an alternative is to use the `assert_has_calls()` method. This takes a list of calls (constructed with the `call` object). If that sequence of calls are in `mock_calls` then the assert succeeds.

    >>> m = MagicMock()
    >>> m().foo().bar().baz()
    <MagicMock name='mock().foo().bar().baz()' id='...'>
    >>> m.one().two().three()
    <MagicMock name='mock.one().two().three()' id='...'>
    >>> calls = call.one().two().three().call_list()
    >>> m.assert_has_calls(calls)

Even though the chained call m.one().two().three() aren’t the only calls that have been made to the mock, the assert still succeeds.

Sometimes a mock may have several calls made to it, and you are only interested in asserting about some of those calls. You may not even care about the order. In this case you can pass any_order=True to assert_has_calls:

    >>> m = MagicMock()
    >>> m(1), m.two(2, 3), m.seven(7), m.fifty('50')
    (...)
    >>> calls = [call.fifty('50'), call(1), call.seven(7)]
    >>> m.assert_has_calls(calls, any_order=True)

## More complex argument matching

Using the same basic concept as ANY we can implement matchers to do more complex assertions on objects used as arguments to mocks.

Suppose we expect some object to be passed to a mock that by default compares equal based on object identity (which is the Python default for user defined classes). To use `assert_called_with()` we would need to pass in the exact same object. If we are only interested in some of the attributes of this object then we can create a matcher that will check these attributes for us.

You can see in this example how a ‘standard’ call to assert_called_with isn’t sufficient:

    >>> class Foo(object):
    ...     def __init__(self, a, b):
    ...         self.a, self.b = a, b
    ...
    >>> mock = Mock(return_value=None)
    >>> mock(Foo(1, 2))
    >>> mock.assert_called_with(Foo(1, 2))
    Traceback (most recent call last):
        ...
    AssertionError: Expected: call(<__main__.Foo object at 0x...>)
    Actual call: call(<__main__.Foo object at 0x...>)

A comparison function for our Foo class might look something like this:

    >>> def compare(self, other):
    ...     if not type(self) == type(other):
    ...         return False
    ...     if self.a != other.a:
    ...         return False
    ...     if self.b != other.b:
    ...         return False
    ...     return True
    ...

And a matcher object that can use comparison functions like this for its equality operation would look something like this:

    >>> class Matcher(object):
    ...     def __init__(self, compare, some_obj):
    ...         self.compare = compare
    ...         self.some_obj = some_obj
    ...     def __eq__(self, other):
    ...         return self.compare(self.some_obj, other)
    ...

Putting all this together:

    >>> match_foo = Matcher(compare, Foo(1, 2))
    >>> mock.assert_called_with(match_foo)

The Matcher is instantiated with our compare function and the Foo object we want to compare against. In assert_called_with the Matcher equality method will be called, which compares the object the mock was called with against the one we created our matcher with. If they match then assert_called_with passes, and if they don’t an AssertionError is raised:

    >>> match_wrong = Matcher(compare, Foo(3, 4))
    >>> mock.assert_called_with(match_wrong)
    Traceback (most recent call last):
        ...
    AssertionError: Expected: ((<Matcher object at 0x...>,), {})
    Called with: ((<Foo object at 0x...>,), {})

With a bit of tweaking you could have the comparison function raise the AssertionError directly and provide a more useful failure message.

As of version 1.5, the Python testing library PyHamcrest provides similar functionality, that may be useful here, in the form of its equality matcher (hamcrest.library.integration.match_equality).

## Less verbose configuration of mock objects

This recipe, for easier configuration of mock objects, is now part of Mock. See the `configure_mock()` method.

## Matching any argument in assertions

This example is now built in to mock. See `ANY`.

## Mocking Properties

This example is now built in to mock. See `PropertyMock`.

## Mocking open

This example is now built in to mock. See `mock_open()`.

## Mocks without some attributes

This example is now built in to mock. See Deleting Attributes.

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