The Tor Browser / file revision

 .. _further-examples:

 ==================

  Further Examples

 ==================

 .. currentmodule:: mock

 .. testsetup::

     from datetime import date

     BackendProvider = Mock()

     sys.modules['mymodule'] = mymodule = Mock(name='mymodule')

     def grob(val):

         "First frob and then clear val"

         mymodule.frob(val)

         val.clear()

     mymodule.frob = lambda val: val

     mymodule.grob = grob

     mymodule.date = date

     class TestCase(unittest2.TestCase):

         def run(self):

             result = unittest2.TestResult()

             out = unittest2.TestCase.run(self, result)

             assert result.wasSuccessful()

     from mock import inPy3k

 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 :ref:`getting started <getting-started>` guide.

 Mocking chained calls

 =====================

 Mocking chained calls is actually straightforward with mock once you

 understand the :attr:`~Mock.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:

 .. doctest::

     >>> 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:

 .. doctest::

     >>> 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 :meth:`~Mock.configure_mock`

 method to directly set the return value for us:

 .. doctest::

     >>> 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:

 .. doctest::

     >>> something.backend = mock_backend

     >>> something.method()

 Using :attr:`~Mock.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 :meth:`call.call_list` to create

 this list of calls for us:

 .. doctest::

     >>> 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()

 <http://docs.python.org/library/datetime.html#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 :func:`patch decorator <patch>` is used here to

 mock out the `date` class in the module under test. The :attr:`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`.

 .. doctest::

     >>> 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 :ref:`where to patch <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

 <http://williamjohnbert.com/2011/07/how-to-unit-testing-in-django-with-mocking-and-patching/>`_.

 Mocking a Generator Method

 ==========================

 A Python generator is a function or method that uses the `yield statement

 <http://docs.python.org/reference/simple_stmts.html#the-yield-statement>`_ to

 return a series of values when iterated over [#]_.

 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__

 <http://docs.python.org/library/stdtypes.html#container.__iter__>`_, so we can

 mock this using a `MagicMock`.

 Here's an example class with an "iter" method implemented as a generator:

 .. doctest::

     >>> 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()`.

 .. doctest::

     >>> mock_foo = MagicMock()

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

     >>> list(mock_foo.iter())

     [1, 2, 3]

 .. [#] There are also generator expressions and more `advanced uses

     <http://www.dabeaz.com/coroutines/index.html>`_ 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

     <http://www.dabeaz.com/generators/>`_.

 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`:

 .. doctest::

     >>> @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 :ref:`start-and-stop`.

 These allow you to move the patching into your `setUp` and `tearDown` methods.

 .. doctest::

     >>> 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

 <http://pypi.python.org/pypi/unittest2>`_ cleanup functions make this simpler:

 .. doctest::

     >>> 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 :func:`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:

 .. doctest::

     >>> 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.

 .. doctest::

     >>> 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

 :meth:`assert_called_once_with` method that also asserts that the

 :attr:`call_count` is one.

 .. doctest::

     >>> 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

 :attr:`~Mock.call_args_list`:

 .. doctest::

     >>> 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 :data:`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`:

 .. doctest::

     >>> 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:

 .. doctest::

     >>> 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 :attr:`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.

 .. doctest::

     >>> 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.

     .. doctest::

         >>> 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

 <http://docs.python.org/library/copy.html#copy.deepcopy>`_) the arguments.

 Here's an example implementation:

 .. doctest::

     >>> 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 :class:`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:

 .. doctest::

     >>> 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 :attr:`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:

 .. doctest::

     >>> 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:

 .. doctest::

     >>> 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:

 .. doctest::

     >>> 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 :ref:`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:

 .. doctest::

     >>> 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 :class:`MagicMock`, which will behave like a dictionary,

 and using :data:`~Mock.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

 :data:`~Mock.call_args_list` to assert about how the dictionary was used:

 .. doctest::

     >>> 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:

     .. doctest::

         >>> 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:

     .. doctest::

         >>> 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.

 .. doctest::

     >>> 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:

 .. doctest::

     >>> 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:

 .. doctest::

     >>> 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`

 [#]_. 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.

 .. doctest::

     >>> 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

 <https://code.google.com/p/mock/issues/detail?id=105>`_ is subclassing mock to

 created a `Twisted adaptor

 <http://twistedmatrix.com/documents/11.0.0/api/twisted.python.components.html>`_.

 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:

 .. doctest::

     >>> 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)

 .. [#] 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 :func:`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.

 .. doctest::

     >>> 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:

 .. doctest::

     >>> 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:

 .. doctest::

     >>> 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 :class:`Mock` class allows you to track the *order* of method calls on

 your mock objects through the :attr:`~Mock.method_calls` attribute. This

 doesn't allow you to track the order of calls between separate mock objects,

 however we can use :attr:`~Mock.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:

 .. doctest::

     >>> 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:

 .. doctest::

     >>> 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 :meth:`~Mock.attach_mock` method. After

 attaching calls will be recorded in `mock_calls` of the manager.

 .. doctest::

     >>> 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

 :meth:`~Mock.assert_has_calls` method. This takes a list of calls (constructed

 with the :data:`call` object). If that sequence of calls are in

 :attr:`~Mock.mock_calls` then the assert succeeds.

 .. doctest::

     >>> 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`:

 .. doctest::

     >>> 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 :meth:`~Mock.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:

 .. doctest::

     >>> 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:

 .. doctest::

     >>> 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:

 .. doctest::

     >>> 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:

 .. doctest::

     >>> 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:

 .. doctest::

     >>> 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

 <http://pypi.python.org/pypi/PyHamcrest>`_ provides similar functionality,

 that may be useful here, in the form of its equality matcher

 (`hamcrest.library.integration.match_equality

 <http://packages.python.org/PyHamcrest/integration.html#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 :meth:`~Mock.configure_mock` method.

 Matching any argument in assertions

 ===================================

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

 Mocking Properties

 ==================

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

 Mocking open

 ============

 This example is now built in to mock. See :func:`mock_open`.

 Mocks without some attributes

 =============================

 This example is now built in to mock. See :ref:`deleting-attributes`.