mirrors/cpython
1
0
Fork 0
mirror of https://github.com/python/cpython.git synced 2025-11-03 19:34:08 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions 9

Adding unittest.mock documentation

Browse source
This commit is contained in:
Michael Foord 2012-03-25 23:12:55 +01:00
parent e58a562d93
commit 944e02d055
8 changed files with 3516 additions and 5 deletions
Download patch file Download diff file Expand all files Collapse all files

6
Doc/library/development.rst
View file

@ -19,5 +19,11 @@ The list of modules described in this chapter is:
pydoc.rst
doctest.rst
unittest.rst
unittest.mock.rst
unittest.mock-patch.rst
unittest.mock-magicmethods.rst
unittest.mock-helpers.rst
unittest.mock-getting-started.rst
unittest.mock-examples.rst
2to3.rst
test.rst

887
Doc/library/unittest.mock-examples.rst Normal file
View file

@ -0,0 +1,887 @@
.. _further-examples:
:mod:`unittest.mock` --- further examples
=========================================
.. module:: unittest.mock
:synopsis: Mock object library.
.. moduleauthor:: Michael Foord <michael@python.org>
.. currentmodule:: unittest.mock
.. versionadded:: 3.3
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:
>>> 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 :meth:`~Mock.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 :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:
>>> 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`.
>>> 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:
>>> 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]
.. [#] 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`:
>>> @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.
>>> 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.
:meth:`unittest.TestCase.addCleanup` makes this easier:
>>> 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:
>>> 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
:meth:`assert_called_once_with` method that also asserts that the
:attr:`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
:attr:`~Mock.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 :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`:
>>> 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 :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.
>>> from copy import deepcopy
>>> from unittest.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 :func:`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`.
Multiple calls with different effects
-------------------------------------
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:
>>> 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 unittest `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:
>>> 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:
>>> 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`
[#]_. 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
<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:
>>> 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.
>>> 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 :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:
>>> 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 :meth:`~Mock.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
: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.
>>> 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 :data:`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:
>>> 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
<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>`_).

419
Doc/library/unittest.mock-getting-started.rst Normal file
View file

@ -0,0 +1,419 @@
:mod:`unittest.mock` --- getting started
========================================
.. module:: unittest.mock
:synopsis: Mock object library.
.. moduleauthor:: Michael Foord <michael@python.org>
.. currentmodule:: unittest.mock
.. versionadded:: 3.3
.. _getting-started:
Using Mock
----------
Mock Patching Methods
~~~~~~~~~~~~~~~~~~~~~
Common uses for :class:`Mock` objects include:
* Patching methods
* Recording method calls on objects
You might want to replace a method on an object to check that
it is called with the correct arguments by another part of the system:
>>> real = SomeClass()
>>> real.method = MagicMock(name='method')
>>> real.method(3, 4, 5, key='value')
<MagicMock name='method()' id='...'>
Once our mock has been used (`real.method` in this example) it has methods
and attributes that allow you to make assertions about how it has been used.
.. note::
In most of these examples the :class:`Mock` and :class:`MagicMock` classes
are interchangeable. As the `MagicMock` is the more capable class it makes
a sensible one to use by default.
Once the mock has been called its :attr:`~Mock.called` attribute is set to
`True`. More importantly we can use the :meth:`~Mock.assert_called_with` or
:meth`~Mock.assert_called_once_with` method to check that it was called with
the correct arguments.
This example tests that calling `ProductionClass().method` results in a call to
the `something` method:
>>> class ProductionClass(object):
... def method(self):
... self.something(1, 2, 3)
... def something(self, a, b, c):
... pass
...
>>> real = ProductionClass()
>>> real.something = MagicMock()
>>> real.method()
>>> real.something.assert_called_once_with(1, 2, 3)
Mock for Method Calls on an Object
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
In the last example we patched a method directly on an object to check that it
was called correctly. Another common use case is to pass an object into a
method (or some part of the system under test) and then check that it is used
in the correct way.
The simple `ProductionClass` below has a `closer` method. If it is called with
an object then it calls `close` on it.
>>> class ProductionClass(object):
... def closer(self, something):
... something.close()
...
So to test it we need to pass in an object with a `close` method and check
that it was called correctly.
>>> real = ProductionClass()
>>> mock = Mock()
>>> real.closer(mock)
>>> mock.close.assert_called_with()
We don't have to do any work to provide the 'close' method on our mock.
Accessing close creates it. So, if 'close' hasn't already been called then
accessing it in the test will create it, but :meth:`~Mock.assert_called_with`
will raise a failure exception.
Mocking Classes
~~~~~~~~~~~~~~~
A common use case is to mock out classes instantiated by your code under test.
When you patch a class, then that class is replaced with a mock. Instances
are created by *calling the class*. This means you access the "mock instance"
by looking at the return value of the mocked class.
In the example below we have a function `some_function` that instantiates `Foo`
and calls a method on it. The call to `patch` replaces the class `Foo` with a
mock. The `Foo` instance is the result of calling the mock, so it is configured
by modify the mock :attr:`~Mock.return_value`.
>>> def some_function():
... instance = module.Foo()
... return instance.method()
...
>>> with patch('module.Foo') as mock:
... instance = mock.return_value
... instance.method.return_value = 'the result'
... result = some_function()
... assert result == 'the result'
Naming your mocks
~~~~~~~~~~~~~~~~~
It can be useful to give your mocks a name. The name is shown in the repr of
the mock and can be helpful when the mock appears in test failure messages. The
name is also propagated to attributes or methods of the mock:
>>> mock = MagicMock(name='foo')
>>> mock
<MagicMock name='foo' id='...'>
>>> mock.method
<MagicMock name='foo.method' id='...'>
Tracking all Calls
~~~~~~~~~~~~~~~~~~
Often you want to track more than a single call to a method. The
:attr:`~Mock.mock_calls` attribute records all calls
to child attributes of the mock - and also to their children.
>>> mock = MagicMock()
>>> mock.method()
<MagicMock name='mock.method()' id='...'>
>>> mock.attribute.method(10, x=53)
<MagicMock name='mock.attribute.method()' id='...'>
>>> mock.mock_calls
[call.method(), call.attribute.method(10, x=53)]
If you make an assertion about `mock_calls` and any unexpected methods
have been called, then the assertion will fail. This is useful because as well
as asserting that the calls you expected have been made, you are also checking
that they were made in the right order and with no additional calls:
You use the :data:`call` object to construct lists for comparing with
`mock_calls`:
>>> expected = [call.method(), call.attribute.method(10, x=53)]
>>> mock.mock_calls == expected
True
Setting Return Values and Attributes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Setting the return values on a mock object is trivially easy:
>>> mock = Mock()
>>> mock.return_value = 3
>>> mock()
3
Of course you can do the same for methods on the mock:
>>> mock = Mock()
>>> mock.method.return_value = 3
>>> mock.method()
3
The return value can also be set in the constructor:
>>> mock = Mock(return_value=3)
>>> mock()
3
If you need an attribute setting on your mock, just do it:
>>> mock = Mock()
>>> mock.x = 3
>>> mock.x
3
Sometimes you want to mock up a more complex situation, like for example
`mock.connection.cursor().execute("SELECT 1")`. If we wanted this call to
return a list, then we have to configure the result of the nested call.
We can use :data:`call` to construct the set of calls in a "chained call" like
this for easy assertion afterwards:
>>> mock = Mock()
>>> cursor = mock.connection.cursor.return_value
>>> cursor.execute.return_value = ['foo']
>>> mock.connection.cursor().execute("SELECT 1")
['foo']
>>> expected = call.connection.cursor().execute("SELECT 1").call_list()
>>> mock.mock_calls
[call.connection.cursor(), call.connection.cursor().execute('SELECT 1')]
>>> mock.mock_calls == expected
True
It is the call to `.call_list()` that turns our call object into a list of
calls representing the chained calls.
Raising exceptions with mocks
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
A useful attribute is :attr:`~Mock.side_effect`. If you set this to an
exception class or instance then the exception will be raised when the mock
is called.
>>> mock = Mock(side_effect=Exception('Boom!'))
>>> mock()
Traceback (most recent call last):
...
Exception: Boom!
Side effect functions and iterables
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
`side_effect` can also be set to a function or an iterable. The use case for
`side_effect` as an iterable is where your mock is going to be called several
times, and you want each call to return a different value. When you set
`side_effect` to an iterable every call to the mock returns the next value
from the iterable:
>>> mock = MagicMock(side_effect=[4, 5, 6])
>>> mock()
4
>>> mock()
5
>>> mock()
6
For more advanced use cases, like dynamically varying the return values
depending on what the mock is called with, `side_effect` can be a function.
The function will be called with the same arguments as the mock. Whatever the
function returns is what the call returns:
>>> vals = {(1, 2): 1, (2, 3): 2}
>>> def side_effect(*args):
... return vals[args]
...
>>> mock = MagicMock(side_effect=side_effect)
>>> mock(1, 2)
1
>>> mock(2, 3)
2
Creating a Mock from an Existing Object
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
One problem with over use of mocking is that it couples your tests to the
implementation of your mocks rather than your real code. Suppose you have a
class that implements `some_method`. In a test for another class, you
provide a mock of this object that *also* provides `some_method`. If later
you refactor the first class, so that it no longer has `some_method` - then
your tests will continue to pass even though your code is now broken!
`Mock` allows you to provide an object as a specification for the mock,
using the `spec` keyword argument. Accessing methods / attributes on the
mock that don't exist on your specification object will immediately raise an
attribute error. If you change the implementation of your specification, then
tests that use that class will start failing immediately without you having to
instantiate the class in those tests.
>>> mock = Mock(spec=SomeClass)
>>> mock.old_method()
Traceback (most recent call last):
...
AttributeError: object has no attribute 'old_method'
If you want a stronger form of specification that prevents the setting
of arbitrary attributes as well as the getting of them then you can use
`spec_set` instead of `spec`.
Patch Decorators
----------------
.. note::
With `patch` it matters that you patch objects in the namespace where they
are looked up. This is normally straightforward, but for a quick guide
read :ref:`where to patch <where-to-patch>`.
A common need in tests is to patch a class attribute or a module attribute,
for example patching a builtin or patching a class in a module to test that it
is instantiated. Modules and classes are effectively global, so patching on
them has to be undone after the test or the patch will persist into other
tests and cause hard to diagnose problems.
mock provides three convenient decorators for this: `patch`, `patch.object` and
`patch.dict`. `patch` takes a single string, of the form
`package.module.Class.attribute` to specify the attribute you are patching. It
also optionally takes a value that you want the attribute (or class or
whatever) to be replaced with. 'patch.object' takes an object and the name of
the attribute you would like patched, plus optionally the value to patch it
with.
`patch.object`:
>>> original = SomeClass.attribute
>>> @patch.object(SomeClass, 'attribute', sentinel.attribute)
... def test():
... assert SomeClass.attribute == sentinel.attribute
...
>>> test()
>>> assert SomeClass.attribute == original
>>> @patch('package.module.attribute', sentinel.attribute)
... def test():
... from package.module import attribute
... assert attribute is sentinel.attribute
...
>>> test()
If you are patching a module (including `__builtin__`) then use `patch`
instead of `patch.object`:
>>> mock = MagicMock(return_value = sentinel.file_handle)
>>> with patch('__builtin__.open', mock):
... handle = open('filename', 'r')
...
>>> mock.assert_called_with('filename', 'r')
>>> assert handle == sentinel.file_handle, "incorrect file handle returned"
The module name can be 'dotted', in the form `package.module` if needed:
>>> @patch('package.module.ClassName.attribute', sentinel.attribute)
... def test():
... from package.module import ClassName
... assert ClassName.attribute == sentinel.attribute
...
>>> test()
A nice pattern is to actually decorate test methods themselves:
>>> class MyTest(unittest2.TestCase):
... @patch.object(SomeClass, 'attribute', sentinel.attribute)
... def test_something(self):
... self.assertEqual(SomeClass.attribute, sentinel.attribute)
...
>>> original = SomeClass.attribute
>>> MyTest('test_something').test_something()
>>> assert SomeClass.attribute == original
If you want to patch with a Mock, you can use `patch` with only one argument
(or `patch.object` with two arguments). The mock will be created for you and
passed into the test function / method:
>>> class MyTest(unittest2.TestCase):
... @patch.object(SomeClass, 'static_method')
... def test_something(self, mock_method):
... SomeClass.static_method()
... mock_method.assert_called_with()
...
>>> MyTest('test_something').test_something()
You can stack up multiple patch decorators using this pattern:
>>> class MyTest(unittest2.TestCase):
... @patch('package.module.ClassName1')
... @patch('package.module.ClassName2')
... def test_something(self, MockClass2, MockClass1):
... self.assertTrue(package.module.ClassName1 is MockClass1)
... self.assertTrue(package.module.ClassName2 is MockClass2)
...
>>> MyTest('test_something').test_something()
When you nest patch decorators the mocks are passed in to the decorated
function in the same order they applied (the normal *python* order that
decorators are applied). This means from the bottom up, so in the example
above the mock for `test_module.ClassName2` is passed in first.
There is also :func:`patch.dict` for setting values in a dictionary just
during a scope and restoring the dictionary to its original state when the test
ends:
>>> foo = {'key': 'value'}
>>> original = foo.copy()
>>> with patch.dict(foo, {'newkey': 'newvalue'}, clear=True):
... assert foo == {'newkey': 'newvalue'}
...
>>> assert foo == original
`patch`, `patch.object` and `patch.dict` can all be used as context managers.
Where you use `patch` to create a mock for you, you can get a reference to the
mock using the "as" form of the with statement:
>>> class ProductionClass(object):
... def method(self):
... pass
...
>>> with patch.object(ProductionClass, 'method') as mock_method:
... mock_method.return_value = None
... real = ProductionClass()
... real.method(1, 2, 3)
...
>>> mock_method.assert_called_with(1, 2, 3)
As an alternative `patch`, `patch.object` and `patch.dict` can be used as
class decorators. When used in this way it is the same as applying the
decorator indvidually to every method whose name starts with "test".
For some more advanced examples, see the :ref:`further-examples` page.

537
Doc/library/unittest.mock-helpers.rst Normal file
View file

@ -0,0 +1,537 @@
:mod:`unittest.mock` --- helpers
================================
.. module:: unittest.mock
:synopsis: Mock object library.
.. moduleauthor:: Michael Foord <michael@python.org>
.. currentmodule:: unittest.mock
.. versionadded:: 3.3
sentinel
--------
.. data:: sentinel
The ``sentinel`` object provides a convenient way of providing unique
objects for your tests.
Attributes are created on demand when you access them by name. Accessing
the same attribute will always return the same object. The objects
returned have a sensible repr so that test failure messages are readable.
Sometimes when testing you need to test that a specific object is passed as an
argument to another method, or returned. It can be common to create named
sentinel objects to test this. `sentinel` provides a convenient way of
creating and testing the identity of objects like this.
In this example we monkey patch `method` to return `sentinel.some_object`:
>>> real = ProductionClass()
>>> real.method = Mock(name="method")
>>> real.method.return_value = sentinel.some_object
>>> result = real.method()
>>> assert result is sentinel.some_object
>>> sentinel.some_object
sentinel.some_object
DEFAULT
-------
.. data:: DEFAULT
The `DEFAULT` object is a pre-created sentinel (actually
`sentinel.DEFAULT`). It can be used by :attr:`~Mock.side_effect`
functions to indicate that the normal return value should be used.
call
----
.. function:: call(*args, **kwargs)
`call` is a helper object for making simpler assertions, for comparing
with :attr:`~Mock.call_args`, :attr:`~Mock.call_args_list`,
:attr:`~Mock.mock_calls` and:attr: `~Mock.method_calls`. `call` can also be
used with :meth:`~Mock.assert_has_calls`.
>>> m = MagicMock(return_value=None)
>>> m(1, 2, a='foo', b='bar')
>>> m()
>>> m.call_args_list == [call(1, 2, a='foo', b='bar'), call()]
True
.. method:: call.call_list()
For a call object that represents multiple calls, `call_list`
returns a list of all the intermediate calls as well as the
final call.
`call_list` is particularly useful for making assertions on "chained calls". A
chained call is multiple calls on a single line of code. This results in
multiple entries in :attr:`~Mock.mock_calls` on a mock. Manually constructing
the sequence of calls can be tedious.
:meth:`~call.call_list` can construct the sequence of calls from the same
chained call:
>>> m = MagicMock()
>>> m(1).method(arg='foo').other('bar')(2.0)
<MagicMock name='mock().method().other()()' id='...'>
>>> kall = call(1).method(arg='foo').other('bar')(2.0)
>>> kall.call_list()
[call(1),
call().method(arg='foo'),
call().method().other('bar'),
call().method().other()(2.0)]
>>> m.mock_calls == kall.call_list()
True
.. _calls-as-tuples:
A `call` object is either a tuple of (positional args, keyword args) or
(name, positional args, keyword args) depending on how it was constructed. When
you construct them yourself this isn't particularly interesting, but the `call`
objects that are in the :attr:`Mock.call_args`, :attr:`Mock.call_args_list` and
:attr:`Mock.mock_calls` attributes can be introspected to get at the individual
arguments they contain.
The `call` objects in :attr:`Mock.call_args` and :attr:`Mock.call_args_list`
are two-tuples of (positional args, keyword args) whereas the `call` objects
in :attr:`Mock.mock_calls`, along with ones you construct yourself, are
three-tuples of (name, positional args, keyword args).
You can use their "tupleness" to pull out the individual arguments for more
complex introspection and assertions. The positional arguments are a tuple
(an empty tuple if there are no positional arguments) and the keyword
arguments are a dictionary:
>>> m = MagicMock(return_value=None)
>>> m(1, 2, 3, arg='one', arg2='two')
>>> kall = m.call_args
>>> args, kwargs = kall
>>> args
(1, 2, 3)
>>> kwargs
{'arg2': 'two', 'arg': 'one'}
>>> args is kall[0]
True
>>> kwargs is kall[1]
True
>>> m = MagicMock()
>>> m.foo(4, 5, 6, arg='two', arg2='three')
<MagicMock name='mock.foo()' id='...'>
>>> kall = m.mock_calls[0]
>>> name, args, kwargs = kall
>>> name
'foo'
>>> args
(4, 5, 6)
>>> kwargs
{'arg2': 'three', 'arg': 'two'}
>>> name is m.mock_calls[0][0]
True
create_autospec
---------------
.. function:: create_autospec(spec, spec_set=False, instance=False, **kwargs)
Create a mock object using another object as a spec. Attributes on the
mock will use the corresponding attribute on the `spec` object as their
spec.
Functions or methods being mocked will have their arguments checked to
ensure that they are called with the correct signature.
If `spec_set` is `True` then attempting to set attributes that don't exist
on the spec object will raise an `AttributeError`.
If a class is used as a spec then the return value of the mock (the
instance of the class) will have the same spec. You can use a class as the
spec for an instance object by passing `instance=True`. The returned mock
will only be callable if instances of the mock are callable.
`create_autospec` also takes arbitrary keyword arguments that are passed to
the constructor of the created mock.
See :ref:`auto-speccing` for examples of how to use auto-speccing with
`create_autospec` and the `autospec` argument to :func:`patch`.
ANY
---
.. data:: ANY
Sometimes you may need to make assertions about *some* of the arguments in a
call to mock, but either not care about some of the arguments or want to pull
them individually out of :attr:`~Mock.call_args` and make more complex
assertions on them.
To ignore certain arguments you can pass in objects that compare equal to
*everything*. Calls to :meth:`~Mock.assert_called_with` and
:meth:`~Mock.assert_called_once_with` will then succeed no matter what was
passed in.
>>> mock = Mock(return_value=None)
>>> mock('foo', bar=object())
>>> mock.assert_called_once_with('foo', bar=ANY)
`ANY` can also be used in comparisons with call lists like
:attr:`~Mock.mock_calls`:
>>> m = MagicMock(return_value=None)
>>> m(1)
>>> m(1, 2)
>>> m(object())
>>> m.mock_calls == [call(1), call(1, 2), ANY]
True
FILTER_DIR
----------
.. data:: FILTER_DIR
`FILTER_DIR` is a module level variable that controls the way mock objects
respond to `dir` (only for Python 2.6 or more recent). The default is `True`,
which uses the filtering described below, to only show useful members. If you
dislike this filtering, or need to switch it off for diagnostic purposes, then
set `mock.FILTER_DIR = False`.
With filtering on, `dir(some_mock)` shows only useful attributes and will
include any dynamically created attributes that wouldn't normally be shown.
If the mock was created with a `spec` (or `autospec` of course) then all the
attributes from the original are shown, even if they haven't been accessed
yet:
>>> dir(Mock())
['assert_any_call',
'assert_called_once_with',
'assert_called_with',
'assert_has_calls',
'attach_mock',
...
>>> from urllib import request
>>> dir(Mock(spec=request))
['AbstractBasicAuthHandler',
'AbstractDigestAuthHandler',
'AbstractHTTPHandler',
'BaseHandler',
...
Many of the not-very-useful (private to `Mock` rather than the thing being
mocked) underscore and double underscore prefixed attributes have been
filtered from the result of calling `dir` on a `Mock`. If you dislike this
behaviour you can switch it off by setting the module level switch
`FILTER_DIR`:
>>> from unittest import mock
>>> mock.FILTER_DIR = False
>>> dir(mock.Mock())
['_NonCallableMock__get_return_value',
'_NonCallableMock__get_side_effect',
'_NonCallableMock__return_value_doc',
'_NonCallableMock__set_return_value',
'_NonCallableMock__set_side_effect',
'__call__',
'__class__',
...
Alternatively you can just use `vars(my_mock)` (instance members) and
`dir(type(my_mock))` (type members) to bypass the filtering irrespective of
`mock.FILTER_DIR`.
mock_open
---------
.. function:: mock_open(mock=None, read_data=None)
A helper function to create a mock to replace the use of `open`. It works
for `open` called directly or used as a context manager.
The `mock` argument is the mock object to configure. If `None` (the
default) then a `MagicMock` will be created for you, with the API limited
to methods or attributes available on standard file handles.
`read_data` is a string for the `read` method of the file handle to return.
This is an empty string by default.
Using `open` as a context manager is a great way to ensure your file handles
are closed properly and is becoming common::
with open('/some/path', 'w') as f:
f.write('something')
The issue is that even if you mock out the call to `open` it is the
*returned object* that is used as a context manager (and has `__enter__` and
`__exit__` called).
Mocking context managers with a :class:`MagicMock` is common enough and fiddly
enough that a helper function is useful.
>>> m = mock_open()
>>> with patch('__main__.open', m, create=True):
... with open('foo', 'w') as h:
... h.write('some stuff')
...
>>> m.mock_calls
[call('foo', 'w'),
call().__enter__(),
call().write('some stuff'),
call().__exit__(None, None, None)]
>>> m.assert_called_once_with('foo', 'w')
>>> handle = m()
>>> handle.write.assert_called_once_with('some stuff')
And for reading files:
>>> with patch('__main__.open', mock_open(read_data='bibble'), create=True) as m:
... with open('foo') as h:
... result = h.read()
...
>>> m.assert_called_once_with('foo')
>>> assert result == 'bibble'
.. _auto-speccing:
Autospeccing
------------
Autospeccing is based on the existing `spec` feature of mock. It limits the
api of mocks to the api of an original object (the spec), but it is recursive
(implemented lazily) so that attributes of mocks only have the same api as
the attributes of the spec. In addition mocked functions / methods have the
same call signature as the original so they raise a `TypeError` if they are
called incorrectly.
Before I explain how auto-speccing works, here's why it is needed.
`Mock` is a very powerful and flexible object, but it suffers from two flaws
when used to mock out objects from a system under test. One of these flaws is
specific to the `Mock` api and the other is a more general problem with using
mock objects.
First the problem specific to `Mock`. `Mock` has two assert methods that are
extremely handy: :meth:`~Mock.assert_called_with` and
:meth:`~Mock.assert_called_once_with`.
>>> mock = Mock(name='Thing', return_value=None)
>>> mock(1, 2, 3)
>>> mock.assert_called_once_with(1, 2, 3)
>>> mock(1, 2, 3)
>>> mock.assert_called_once_with(1, 2, 3)
Traceback (most recent call last):
...
AssertionError: Expected to be called once. Called 2 times.
Because mocks auto-create attributes on demand, and allow you to call them
with arbitrary arguments, if you misspell one of these assert methods then
your assertion is gone:
.. code-block:: pycon
>>> mock = Mock(name='Thing', return_value=None)
>>> mock(1, 2, 3)
>>> mock.assret_called_once_with(4, 5, 6)
Your tests can pass silently and incorrectly because of the typo.
The second issue is more general to mocking. If you refactor some of your
code, rename members and so on, any tests for code that is still using the
*old api* but uses mocks instead of the real objects will still pass. This
means your tests can all pass even though your code is broken.
Note that this is another reason why you need integration tests as well as
unit tests. Testing everything in isolation is all fine and dandy, but if you
don't test how your units are "wired together" there is still lots of room
for bugs that tests might have caught.
`mock` already provides a feature to help with this, called speccing. If you
use a class or instance as the `spec` for a mock then you can only access
attributes on the mock that exist on the real class:
>>> from urllib import request
>>> mock = Mock(spec=request.Request)
>>> mock.assret_called_with
Traceback (most recent call last):
...
AttributeError: Mock object has no attribute 'assret_called_with'
The spec only applies to the mock itself, so we still have the same issue
with any methods on the mock:
.. code-block:: pycon
>>> mock.has_data()
<mock.Mock object at 0x...>
>>> mock.has_data.assret_called_with()
Auto-speccing solves this problem. You can either pass `autospec=True` to
`patch` / `patch.object` or use the `create_autospec` function to create a
mock with a spec. If you use the `autospec=True` argument to `patch` then the
object that is being replaced will be used as the spec object. Because the
speccing is done "lazily" (the spec is created as attributes on the mock are
accessed) you can use it with very complex or deeply nested objects (like
modules that import modules that import modules) without a big performance
hit.
Here's an example of it in use:
>>> from urllib import request
>>> patcher = patch('__main__.request', autospec=True)
>>> mock_request = patcher.start()
>>> request is mock_request
True
>>> mock_request.Request
<MagicMock name='request.Request' spec='Request' id='...'>
You can see that `request.Request` has a spec. `request.Request` takes two
arguments in the constructor (one of which is `self`). Here's what happens if
we try to call it incorrectly:
>>> req = request.Request()
Traceback (most recent call last):
...
TypeError: <lambda>() takes at least 2 arguments (1 given)
The spec also applies to instantiated classes (i.e. the return value of
specced mocks):
>>> req = request.Request('foo')
>>> req
<NonCallableMagicMock name='request.Request()' spec='Request' id='...'>
`Request` objects are not callable, so the return value of instantiating our
mocked out `request.Request` is a non-callable mock. With the spec in place
any typos in our asserts will raise the correct error:
>>> req.add_header('spam', 'eggs')
<MagicMock name='request.Request().add_header()' id='...'>
>>> req.add_header.assret_called_with
Traceback (most recent call last):
...
AttributeError: Mock object has no attribute 'assret_called_with'
>>> req.add_header.assert_called_with('spam', 'eggs')
In many cases you will just be able to add `autospec=True` to your existing
`patch` calls and then be protected against bugs due to typos and api
changes.
As well as using `autospec` through `patch` there is a
:func:`create_autospec` for creating autospecced mocks directly:
>>> from urllib import request
>>> mock_request = create_autospec(request)
>>> mock_request.Request('foo', 'bar')
<NonCallableMagicMock name='mock.Request()' spec='Request' id='...'>
This isn't without caveats and limitations however, which is why it is not
the default behaviour. In order to know what attributes are available on the
spec object, autospec has to introspect (access attributes) the spec. As you
traverse attributes on the mock a corresponding traversal of the original
object is happening under the hood. If any of your specced objects have
properties or descriptors that can trigger code execution then you may not be
able to use autospec. On the other hand it is much better to design your
objects so that introspection is safe [#]_.
A more serious problem is that it is common for instance attributes to be
created in the `__init__` method and not to exist on the class at all.
`autospec` can't know about any dynamically created attributes and restricts
the api to visible attributes.
>>> class Something(object):
... def __init__(self):
... self.a = 33
...
>>> with patch('__main__.Something', autospec=True):
... thing = Something()
... thing.a
...
Traceback (most recent call last):
...
AttributeError: Mock object has no attribute 'a'
There are a few different ways of resolving this problem. The easiest, but
not necessarily the least annoying, way is to simply set the required
attributes on the mock after creation. Just because `autospec` doesn't allow
you to fetch attributes that don't exist on the spec it doesn't prevent you
setting them:
>>> with patch('__main__.Something', autospec=True):
... thing = Something()
... thing.a = 33
...
There is a more aggressive version of both `spec` and `autospec` that *does*
prevent you setting non-existent attributes. This is useful if you want to
ensure your code only *sets* valid attributes too, but obviously it prevents
this particular scenario:
>>> with patch('__main__.Something', autospec=True, spec_set=True):
... thing = Something()
... thing.a = 33
...
Traceback (most recent call last):
...
AttributeError: Mock object has no attribute 'a'
Probably the best way of solving the problem is to add class attributes as
default values for instance members initialised in `__init__`. Note that if
you are only setting default attributes in `__init__` then providing them via
class attributes (shared between instances of course) is faster too. e.g.
.. code-block:: python
class Something(object):
a = 33
This brings up another issue. It is relatively common to provide a default
value of `None` for members that will later be an object of a different type.
`None` would be useless as a spec because it wouldn't let you access *any*
attributes or methods on it. As `None` is *never* going to be useful as a
spec, and probably indicates a member that will normally of some other type,
`autospec` doesn't use a spec for members that are set to `None`. These will
just be ordinary mocks (well - `MagicMocks`):
>>> class Something(object):
... member = None
...
>>> mock = create_autospec(Something)
>>> mock.member.foo.bar.baz()
<MagicMock name='mock.member.foo.bar.baz()' id='...'>
If modifying your production classes to add defaults isn't to your liking
then there are more options. One of these is simply to use an instance as the
spec rather than the class. The other is to create a subclass of the
production class and add the defaults to the subclass without affecting the
production class. Both of these require you to use an alternative object as
the spec. Thankfully `patch` supports this - you can simply pass the
alternative object as the `autospec` argument:
>>> class Something(object):
... def __init__(self):
... self.a = 33
...
>>> class SomethingForTest(Something):
... a = 33
...
>>> p = patch('__main__.Something', autospec=SomethingForTest)
>>> mock = p.start()
>>> mock.a
<NonCallableMagicMock name='Something.a' spec='int' id='...'>
.. [#] This only applies to classes or already instantiated objects. Calling
a mocked class to create a mock instance *does not* create a real instance.
It is only attribute lookups - along with calls to `dir` - that are done.

226
Doc/library/unittest.mock-magicmethods.rst Normal file
View file

@ -0,0 +1,226 @@
:mod:`unittest.mock` --- MagicMock and magic method support
===========================================================
.. module:: unittest.mock
:synopsis: Mock object library.
.. moduleauthor:: Michael Foord <michael@python.org>
.. currentmodule:: unittest.mock
.. versionadded:: 3.3
.. _magic-methods:
Mocking Magic Methods
---------------------
:class:`Mock` supports mocking the Python protocol methods, also known as
"magic methods". This allows mock objects to replace containers or other
objects that implement Python protocols.
Because magic methods are looked up differently from normal methods [#]_, this
support has been specially implemented. This means that only specific magic
methods are supported. The supported list includes *almost* all of them. If
there are any missing that you need please let us know.
You mock magic methods by setting the method you are interested in to a function
or a mock instance. If you are using a function then it *must* take ``self`` as
the first argument [#]_.
>>> def __str__(self):
... return 'fooble'
...
>>> mock = Mock()
>>> mock.__str__ = __str__
>>> str(mock)
'fooble'
>>> mock = Mock()
>>> mock.__str__ = Mock()
>>> mock.__str__.return_value = 'fooble'
>>> str(mock)
'fooble'
>>> mock = Mock()
>>> mock.__iter__ = Mock(return_value=iter([]))
>>> list(mock)
[]
One use case for this is for mocking objects used as context managers in a
`with` statement:
>>> mock = Mock()
>>> mock.__enter__ = Mock(return_value='foo')
>>> mock.__exit__ = Mock(return_value=False)
>>> with mock as m:
... assert m == 'foo'
...
>>> mock.__enter__.assert_called_with()
>>> mock.__exit__.assert_called_with(None, None, None)
Calls to magic methods do not appear in :attr:`~Mock.method_calls`, but they
are recorded in :attr:`~Mock.mock_calls`.
.. note::
If you use the `spec` keyword argument to create a mock then attempting to
set a magic method that isn't in the spec will raise an `AttributeError`.
The full list of supported magic methods is:
* ``__hash__``, ``__sizeof__``, ``__repr__`` and ``__str__``
* ``__dir__``, ``__format__`` and ``__subclasses__``
* ``__floor__``, ``__trunc__`` and ``__ceil__``
* Comparisons: ``__cmp__``, ``__lt__``, ``__gt__``, ``__le__``, ``__ge__``,
``__eq__`` and ``__ne__``
* Container methods: ``__getitem__``, ``__setitem__``, ``__delitem__``,
``__contains__``, ``__len__``, ``__iter__``, ``__getslice__``,
``__setslice__``, ``__reversed__`` and ``__missing__``
* Context manager: ``__enter__`` and ``__exit__``
* Unary numeric methods: ``__neg__``, ``__pos__`` and ``__invert__``
* The numeric methods (including right hand and in-place variants):
``__add__``, ``__sub__``, ``__mul__``, ``__div__``,
``__floordiv__``, ``__mod__``, ``__divmod__``, ``__lshift__``,
``__rshift__``, ``__and__``, ``__xor__``, ``__or__``, and ``__pow__``
* Numeric conversion methods: ``__complex__``, ``__int__``, ``__float__``,
``__index__`` and ``__coerce__``
* Descriptor methods: ``__get__``, ``__set__`` and ``__delete__``
* Pickling: ``__reduce__``, ``__reduce_ex__``, ``__getinitargs__``,
``__getnewargs__``, ``__getstate__`` and ``__setstate__``
The following methods exist but are *not* supported as they are either in use
by mock, can't be set dynamically, or can cause problems:
* ``__getattr__``, ``__setattr__``, ``__init__`` and ``__new__``
* ``__prepare__``, ``__instancecheck__``, ``__subclasscheck__``, ``__del__``
Magic Mock
----------
There are two `MagicMock` variants: `MagicMock` and `NonCallableMagicMock`.
.. class:: MagicMock(*args, **kw)
``MagicMock`` is a subclass of :class:`Mock` with default implementations
of most of the magic methods. You can use ``MagicMock`` without having to
configure the magic methods yourself.
The constructor parameters have the same meaning as for :class:`Mock`.
If you use the `spec` or `spec_set` arguments then *only* magic methods
that exist in the spec will be created.
.. class:: NonCallableMagicMock(*args, **kw)
A non-callable version of `MagicMock`.
The constructor parameters have the same meaning as for
:class:`MagicMock`, with the exception of `return_value` and
`side_effect` which have no meaning on a non-callable mock.
The magic methods are setup with `MagicMock` objects, so you can configure them
and use them in the usual way:
>>> mock = MagicMock()
>>> mock[3] = 'fish'
>>> mock.__setitem__.assert_called_with(3, 'fish')
>>> mock.__getitem__.return_value = 'result'
>>> mock[2]
'result'
By default many of the protocol methods are required to return objects of a
specific type. These methods are preconfigured with a default return value, so
that they can be used without you having to do anything if you aren't interested
in the return value. You can still *set* the return value manually if you want
to change the default.
Methods and their defaults:
* ``__lt__``: NotImplemented
* ``__gt__``: NotImplemented
* ``__le__``: NotImplemented
* ``__ge__``: NotImplemented
* ``__int__`` : 1
* ``__contains__`` : False
* ``__len__`` : 1
* ``__iter__`` : iter([])
* ``__exit__`` : False
* ``__complex__`` : 1j
* ``__float__`` : 1.0
* ``__bool__`` : True
* ``__index__`` : 1
* ``__hash__`` : default hash for the mock
* ``__str__`` : default str for the mock
* ``__sizeof__``: default sizeof for the mock
For example:
>>> mock = MagicMock()
>>> int(mock)
1
>>> len(mock)
0
>>> list(mock)
[]
>>> object() in mock
False
The two equality method, `__eq__` and `__ne__`, are special.
They do the default equality comparison on identity, using a side
effect, unless you change their return value to return something else:
>>> MagicMock() == 3
False
>>> MagicMock() != 3
True
>>> mock = MagicMock()
>>> mock.__eq__.return_value = True
>>> mock == 3
True
The return value of `MagicMock.__iter__` can be any iterable object and isn't
required to be an iterator:
>>> mock = MagicMock()
>>> mock.__iter__.return_value = ['a', 'b', 'c']
>>> list(mock)
['a', 'b', 'c']
>>> list(mock)
['a', 'b', 'c']
If the return value *is* an iterator, then iterating over it once will consume
it and subsequent iterations will result in an empty list:
>>> mock.__iter__.return_value = iter(['a', 'b', 'c'])
>>> list(mock)
['a', 'b', 'c']
>>> list(mock)
[]
``MagicMock`` has all of the supported magic methods configured except for some
of the obscure and obsolete ones. You can still set these up if you want.
Magic methods that are supported but not setup by default in ``MagicMock`` are:
* ``__subclasses__``
* ``__dir__``
* ``__format__``
* ``__get__``, ``__set__`` and ``__delete__``
* ``__reversed__`` and ``__missing__``
* ``__reduce__``, ``__reduce_ex__``, ``__getinitargs__``, ``__getnewargs__``,
``__getstate__`` and ``__setstate__``
* ``__getformat__`` and ``__setformat__``
.. [#] Magic methods *should* be looked up on the class rather than the
instance. Different versions of Python are inconsistent about applying this
rule. The supported protocol methods should work with all supported versions
of Python.
.. [#] The function is basically hooked up to the class, but each ``Mock``
instance is kept isolated from the others.

538
Doc/library/unittest.mock-patch.rst Normal file
View file

@ -0,0 +1,538 @@
:mod:`unittest.mock` --- the patchers
=====================================
.. module:: unittest.mock
:synopsis: Mock object library.
.. moduleauthor:: Michael Foord <michael@python.org>
.. currentmodule:: unittest.mock
.. versionadded:: 3.3
The patch decorators are used for patching objects only within the scope of
the function they decorate. They automatically handle the unpatching for you,
even if exceptions are raised. All of these functions can also be used in with
statements or as class decorators.
patch
-----
.. note::
`patch` is straightforward to use. The key is to do the patching in the
right namespace. See the section `where to patch`_.
.. function:: patch(target, new=DEFAULT, spec=None, create=False, spec_set=None, autospec=None, new_callable=None, **kwargs)
`patch` acts as a function decorator, class decorator or a context
manager. Inside the body of the function or with statement, the `target`
(specified in the form `'package.module.ClassName'`) is patched
with a `new` object. When the function/with statement exits the patch is
undone.
The `target` is imported and the specified attribute patched with the new
object, so it must be importable from the environment you are calling the
decorator from. The target is imported when the decorated function is
executed, not at decoration time.
If `new` is omitted, then a new `MagicMock` is created and passed in as an
extra argument to the decorated function.
The `spec` and `spec_set` keyword arguments are passed to the `MagicMock`
if patch is creating one for you.
In addition you can pass `spec=True` or `spec_set=True`, which causes
patch to pass in the object being mocked as the spec/spec_set object.
`new_callable` allows you to specify a different class, or callable object,
that will be called to create the `new` object. By default `MagicMock` is
used.
A more powerful form of `spec` is `autospec`. If you set `autospec=True`
then the mock with be created with a spec from the object being replaced.
All attributes of the mock will also have the spec of the corresponding
attribute of the object being replaced. Methods and functions being mocked
will have their arguments checked and will raise a `TypeError` if they are
called with the wrong signature. For mocks
replacing a class, their return value (the 'instance') will have the same
spec as the class. See the :func:`create_autospec` function and
:ref:`auto-speccing`.
Instead of `autospec=True` you can pass `autospec=some_object` to use an
arbitrary object as the spec instead of the one being replaced.
By default `patch` will fail to replace attributes that don't exist. If
you pass in `create=True`, and the attribute doesn't exist, patch will
create the attribute for you when the patched function is called, and
delete it again afterwards. This is useful for writing tests against
attributes that your production code creates at runtime. It is off by by
default because it can be dangerous. With it switched on you can write
passing tests against APIs that don't actually exist!
Patch can be used as a `TestCase` class decorator. It works by
decorating each test method in the class. This reduces the boilerplate
code when your test methods share a common patchings set. `patch` finds
tests by looking for method names that start with `patch.TEST_PREFIX`.
By default this is `test`, which matches the way `unittest` finds tests.
You can specify an alternative prefix by setting `patch.TEST_PREFIX`.
Patch can be used as a context manager, with the with statement. Here the
patching applies to the indented block after the with statement. If you
use "as" then the patched object will be bound to the name after the
"as"; very useful if `patch` is creating a mock object for you.
`patch` takes arbitrary keyword arguments. These will be passed to
the `Mock` (or `new_callable`) on construction.
`patch.dict(...)`, `patch.multiple(...)` and `patch.object(...)` are
available for alternate use-cases.
Patching a class replaces the class with a `MagicMock` *instance*. If the
class is instantiated in the code under test then it will be the
:attr:`~Mock.return_value` of the mock that will be used.
If the class is instantiated multiple times you could use
:attr:`~Mock.side_effect` to return a new mock each time. Alternatively you
can set the `return_value` to be anything you want.
To configure return values on methods of *instances* on the patched class
you must do this on the `return_value`. For example:
>>> class Class(object):
... def method(self):
... pass
...
>>> with patch('__main__.Class') as MockClass:
... instance = MockClass.return_value
... instance.method.return_value = 'foo'
... assert Class() is instance
... assert Class().method() == 'foo'
...
If you use `spec` or `spec_set` and `patch` is replacing a *class*, then the
return value of the created mock will have the same spec.
>>> Original = Class
>>> patcher = patch('__main__.Class', spec=True)
>>> MockClass = patcher.start()
>>> instance = MockClass()
>>> assert isinstance(instance, Original)
>>> patcher.stop()
The `new_callable` argument is useful where you want to use an alternative
class to the default :class:`MagicMock` for the created mock. For example, if
you wanted a :class:`NonCallableMock` to be used:
>>> thing = object()
>>> with patch('__main__.thing', new_callable=NonCallableMock) as mock_thing:
... assert thing is mock_thing
... thing()
...
Traceback (most recent call last):
...
TypeError: 'NonCallableMock' object is not callable
Another use case might be to replace an object with a `StringIO` instance:
>>> from StringIO import StringIO
>>> def foo():
... print 'Something'
...
>>> @patch('sys.stdout', new_callable=StringIO)
... def test(mock_stdout):
... foo()
... assert mock_stdout.getvalue() == 'Something\n'
...
>>> test()
When `patch` is creating a mock for you, it is common that the first thing
you need to do is to configure the mock. Some of that configuration can be done
in the call to patch. Any arbitrary keywords you pass into the call will be
used to set attributes on the created mock:
>>> patcher = patch('__main__.thing', first='one', second='two')
>>> mock_thing = patcher.start()
>>> mock_thing.first
'one'
>>> mock_thing.second
'two'
As well as attributes on the created mock attributes, like the
:attr:`~Mock.return_value` and :attr:`~Mock.side_effect`, of child mocks can
also be configured. These aren't syntactically valid to pass in directly as
keyword arguments, but a dictionary with these as keys can still be expanded
into a `patch` call using `**`:
>>> config = {'method.return_value': 3, 'other.side_effect': KeyError}
>>> patcher = patch('__main__.thing', **config)
>>> mock_thing = patcher.start()
>>> mock_thing.method()
3
>>> mock_thing.other()
Traceback (most recent call last):
...
KeyError
patch.object
------------
.. function:: patch.object(target, attribute, new=DEFAULT, spec=None, create=False, spec_set=None, autospec=None, new_callable=None, **kwargs)
patch the named member (`attribute`) on an object (`target`) with a mock
object.
`patch.object` can be used as a decorator, class decorator or a context
manager. Arguments `new`, `spec`, `create`, `spec_set`, `autospec` and
`new_callable` have the same meaning as for `patch`. Like `patch`,
`patch.object` takes arbitrary keyword arguments for configuring the mock
object it creates.
When used as a class decorator `patch.object` honours `patch.TEST_PREFIX`
for choosing which methods to wrap.
You can either call `patch.object` with three arguments or two arguments. The
three argument form takes the object to be patched, the attribute name and the
object to replace the attribute with.
When calling with the two argument form you omit the replacement object, and a
mock is created for you and passed in as an extra argument to the decorated
function:
>>> @patch.object(SomeClass, 'class_method')
... def test(mock_method):
... SomeClass.class_method(3)
... mock_method.assert_called_with(3)
...
>>> test()
`spec`, `create` and the other arguments to `patch.object` have the same
meaning as they do for `patch`.
patch.dict
----------
.. function:: patch.dict(in_dict, values=(), clear=False, **kwargs)
Patch a dictionary, or dictionary like object, and restore the dictionary
to its original state after the test.
`in_dict` can be a dictionary or a mapping like container. If it is a
mapping then it must at least support getting, setting and deleting items
plus iterating over keys.
`in_dict` can also be a string specifying the name of the dictionary, which
will then be fetched by importing it.
`values` can be a dictionary of values to set in the dictionary. `values`
can also be an iterable of `(key, value)` pairs.
If `clear` is True then the dictionary will be cleared before the new
values are set.
`patch.dict` can also be called with arbitrary keyword arguments to set
values in the dictionary.
`patch.dict` can be used as a context manager, decorator or class
decorator. When used as a class decorator `patch.dict` honours
`patch.TEST_PREFIX` for choosing which methods to wrap.
`patch.dict` can be used to add members to a dictionary, or simply let a test
change a dictionary, and ensure the dictionary is restored when the test
ends.
>>> foo = {}
>>> with patch.dict(foo, {'newkey': 'newvalue'}):
... assert foo == {'newkey': 'newvalue'}
...
>>> assert foo == {}
>>> import os
>>> with patch.dict('os.environ', {'newkey': 'newvalue'}):
... print os.environ['newkey']
...
newvalue
>>> assert 'newkey' not in os.environ
Keywords can be used in the `patch.dict` call to set values in the dictionary:
>>> mymodule = MagicMock()
>>> mymodule.function.return_value = 'fish'
>>> with patch.dict('sys.modules', mymodule=mymodule):
... import mymodule
... mymodule.function('some', 'args')
...
'fish'
`patch.dict` can be used with dictionary like objects that aren't actually
dictionaries. At the very minimum they must support item getting, setting,
deleting and either iteration or membership test. This corresponds to the
magic methods `__getitem__`, `__setitem__`, `__delitem__` and either
`__iter__` or `__contains__`.
>>> class Container(object):
... def __init__(self):
... self.values = {}
... def __getitem__(self, name):
... return self.values[name]
... def __setitem__(self, name, value):
... self.values[name] = value
... def __delitem__(self, name):
... del self.values[name]
... def __iter__(self):
... return iter(self.values)
...
>>> thing = Container()
>>> thing['one'] = 1
>>> with patch.dict(thing, one=2, two=3):
... assert thing['one'] == 2
... assert thing['two'] == 3
...
>>> assert thing['one'] == 1
>>> assert list(thing) == ['one']
patch.multiple
--------------
.. function:: patch.multiple(target, spec=None, create=False, spec_set=None, autospec=None, new_callable=None, **kwargs)
Perform multiple patches in a single call. It takes the object to be
patched (either as an object or a string to fetch the object by importing)
and keyword arguments for the patches::
with patch.multiple(settings, FIRST_PATCH='one', SECOND_PATCH='two'):
...
Use :data:`DEFAULT` as the value if you want `patch.multiple` to create
mocks for you. In this case the created mocks are passed into a decorated
function by keyword, and a dictionary is returned when `patch.multiple` is
used as a context manager.
`patch.multiple` can be used as a decorator, class decorator or a context
manager. The arguments `spec`, `spec_set`, `create`, `autospec` and
`new_callable` have the same meaning as for `patch`. These arguments will
be applied to *all* patches done by `patch.multiple`.
When used as a class decorator `patch.multiple` honours `patch.TEST_PREFIX`
for choosing which methods to wrap.
If you want `patch.multiple` to create mocks for you, then you can use
:data:`DEFAULT` as the value. If you use `patch.multiple` as a decorator
then the created mocks are passed into the decorated function by keyword.
>>> thing = object()
>>> other = object()
>>> @patch.multiple('__main__', thing=DEFAULT, other=DEFAULT)
... def test_function(thing, other):
... assert isinstance(thing, MagicMock)
... assert isinstance(other, MagicMock)
...
>>> test_function()
`patch.multiple` can be nested with other `patch` decorators, but put arguments
passed by keyword *after* any of the standard arguments created by `patch`:
>>> @patch('sys.exit')
... @patch.multiple('__main__', thing=DEFAULT, other=DEFAULT)
... def test_function(mock_exit, other, thing):
... assert 'other' in repr(other)
... assert 'thing' in repr(thing)
... assert 'exit' in repr(mock_exit)
...
>>> test_function()
If `patch.multiple` is used as a context manager, the value returned by the
context manger is a dictionary where created mocks are keyed by name:
>>> with patch.multiple('__main__', thing=DEFAULT, other=DEFAULT) as values:
... assert 'other' in repr(values['other'])
... assert 'thing' in repr(values['thing'])
... assert values['thing'] is thing
... assert values['other'] is other
...
.. _start-and-stop:
patch methods: start and stop
-----------------------------
All the patchers have `start` and `stop` methods. These make it simpler to do
patching in `setUp` methods or where you want to do multiple patches without
nesting decorators or with statements.
To use them call `patch`, `patch.object` or `patch.dict` as normal and keep a
reference to the returned `patcher` object. You can then call `start` to put
the patch in place and `stop` to undo it.
If you are using `patch` to create a mock for you then it will be returned by
the call to `patcher.start`.
>>> patcher = patch('package.module.ClassName')
>>> from package import module
>>> original = module.ClassName
>>> new_mock = patcher.start()
>>> assert module.ClassName is not original
>>> assert module.ClassName is new_mock
>>> patcher.stop()
>>> assert module.ClassName is original
>>> assert module.ClassName is not new_mock
A typical use case for this might be for doing multiple patches in the `setUp`
method of a `TestCase`:
>>> class MyTest(TestCase):
... def setUp(self):
... self.patcher1 = patch('package.module.Class1')
... self.patcher2 = patch('package.module.Class2')
... self.MockClass1 = self.patcher1.start()
... self.MockClass2 = self.patcher2.start()
...
... def tearDown(self):
... self.patcher1.stop()
... self.patcher2.stop()
...
... def test_something(self):
... assert package.module.Class1 is self.MockClass1
... assert package.module.Class2 is self.MockClass2
...
>>> MyTest('test_something').run()
.. caution::
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.
:meth:`unittest.TestCase.addCleanup` makes this easier:
>>> class MyTest(TestCase):
... def setUp(self):
... patcher = patch('package.module.Class')
... self.MockClass = patcher.start()
... self.addCleanup(patcher.stop)
...
... def test_something(self):
... assert package.module.Class is self.MockClass
...
As an added bonus you no longer need to keep a reference to the `patcher`
object.
In fact `start` and `stop` are just aliases for the context manager
`__enter__` and `__exit__` methods.
TEST_PREFIX
-----------
All of the patchers can be used as class decorators. When used in this way
they wrap every test method on the class. The patchers recognise methods that
start with `test` as being test methods. This is the same way that the
:class:`unittest.TestLoader` finds test methods by default.
It is possible that you want to use a different prefix for your tests. You can
inform the patchers of the different prefix by setting `patch.TEST_PREFIX`:
>>> patch.TEST_PREFIX = 'foo'
>>> value = 3
>>>
>>> @patch('__main__.value', 'not three')
... class Thing(object):
... def foo_one(self):
... print value
... def foo_two(self):
... print value
...
>>>
>>> Thing().foo_one()
not three
>>> Thing().foo_two()
not three
>>> value
3
Nesting Patch Decorators
------------------------
If you want to perform multiple patches then you can simply stack up the
decorators.
You can stack up multiple patch decorators using this pattern:
>>> @patch.object(SomeClass, 'class_method')
... @patch.object(SomeClass, 'static_method')
... def test(mock1, mock2):
... assert SomeClass.static_method is mock1
... assert SomeClass.class_method is mock2
... SomeClass.static_method('foo')
... SomeClass.class_method('bar')
... return mock1, mock2
...
>>> mock1, mock2 = test()
>>> mock1.assert_called_once_with('foo')
>>> mock2.assert_called_once_with('bar')
Note that the decorators are applied from the bottom upwards. This is the
standard way that Python applies decorators. The order of the created mocks
passed into your test function matches this order.
.. _where-to-patch:
Where to patch
--------------
`patch` works by (temporarily) changing the object that a *name* points to with
another one. There can be many names pointing to any individual object, so
for patching to work you must ensure that you patch the name used by the system
under test.
The basic principle is that you patch where an object is *looked up*, which
is not necessarily the same place as where it is defined. A couple of
examples will help to clarify this.
Imagine we have a project that we want to test with the following structure::
a.py
-> Defines SomeClass
b.py
-> from a import SomeClass
-> some_function instantiates SomeClass
Now we want to test `some_function` but we want to mock out `SomeClass` using
`patch`. The problem is that when we import module b, which we will have to
do then it imports `SomeClass` from module a. If we use `patch` to mock out
`a.SomeClass` then it will have no effect on our test; module b already has a
reference to the *real* `SomeClass` and it looks like our patching had no
effect.
The key is to patch out `SomeClass` where it is used (or where it is looked up
). In this case `some_function` will actually look up `SomeClass` in module b,
where we have imported it. The patching should look like::
@patch('b.SomeClass')
However, consider the alternative scenario where instead of `from a import
SomeClass` module b does `import a` and `some_function` uses `a.SomeClass`. Both
of these import forms are common. In this case the class we want to patch is
being looked up on the a module and so we have to patch `a.SomeClass` instead::
@patch('a.SomeClass')
Patching Descriptors and Proxy Objects
--------------------------------------
Both patch_ and patch.object_ correctly patch and restore descriptors: class
methods, static methods and properties. You should patch these on the *class*
rather than an instance. They also work with *some* objects
that proxy attribute access, like the `django setttings object
<http://www.voidspace.org.uk/python/weblog/arch_d7_2010_12_04.shtml#e1198>`_.

900
Doc/library/unittest.mock.rst Normal file
View file

@ -0,0 +1,900 @@
:mod:`unittest.mock` --- mock object library
============================================
.. module:: unittest.mock
:synopsis: Mock object library.
.. moduleauthor:: Michael Foord <michael@python.org>
.. currentmodule:: unittest.mock
.. versionadded:: 3.3
:mod:`unittest.mock` is a library for testing in Python. It allows you to
replace parts of your system under test with mock objects and make assertions
about how they have been used.
`unittest.mock` provides a core :class:`Mock` class removing the need to
create a host of stubs throughout your test suite. After performing an
action, you can make assertions about which methods / attributes were used
and arguments they were called with. You can also specify return values and
set needed attributes in the normal way.
Additionally, mock provides a :func:`patch` decorator that handles patching
module and class level attributes within the scope of a test, along with
:const:`sentinel` for creating unique objects. See the `quick guide`_ for
some examples of how to use :class:`Mock`, :class:`MagicMock` and
:func:`patch`.
Mock is very easy to use and is designed for use with :mod:`unittest`. Mock
is based on the 'action -> assertion' pattern instead of `'record -> replay'`
used by many mocking frameworks.
There is a backport of `unittest.mock` for earlier versions of Python,
available as `mock on PyPI <http://pypi.python.org/pypi/mock>`_.
**Source code:** :source:`Lib/unittest/mock.py`
Quick Guide
-----------
:class:`Mock` and :class:`MagicMock` objects create all attributes and
methods as you access them and store details of how they have been used. You
can configure them, to specify return values or limit what attributes are
available, and then make assertions about how they have been used:
>>> from unittest.mock import MagicMock
>>> thing = ProductionClass()
>>> thing.method = MagicMock(return_value=3)
>>> thing.method(3, 4, 5, key='value')
3
>>> thing.method.assert_called_with(3, 4, 5, key='value')
:attr:`side_effect` allows you to perform side effects, including raising an
exception when a mock is called:
>>> mock = Mock(side_effect=KeyError('foo'))
>>> mock()
Traceback (most recent call last):
...
KeyError: 'foo'
>>> values = {'a': 1, 'b': 2, 'c': 3}
>>> def side_effect(arg):
... return values[arg]
...
>>> mock.side_effect = side_effect
>>> mock('a'), mock('b'), mock('c')
(1, 2, 3)
>>> mock.side_effect = [5, 4, 3, 2, 1]
>>> mock(), mock(), mock()
(5, 4, 3)
Mock has many other ways you can configure it and control its behaviour. For
example the `spec` argument configures the mock to take its specification
from another object. Attempting to access attributes or methods on the mock
that don't exist on the spec will fail with an `AttributeError`.
The :func:`patch` decorator / context manager makes it easy to mock classes or
objects in a module under test. The object you specify will be replaced with a
mock (or other object) during the test and restored when the test ends:
>>> from unittest.mock import patch
>>> @patch('module.ClassName2')
... @patch('module.ClassName1')
... def test(MockClass1, MockClass2):
... module.ClassName1()
... module.ClassName2()
... assert MockClass1 is module.ClassName1
... assert MockClass2 is module.ClassName2
... assert MockClass1.called
... assert MockClass2.called
...
>>> test()
.. note::
When you nest patch decorators the mocks are passed in to the decorated
function in the same order they applied (the normal *python* order that
decorators are applied). This means from the bottom up, so in the example
above the mock for `module.ClassName1` is passed in first.
With `patch` it matters that you patch objects in the namespace where they
are looked up. This is normally straightforward, but for a quick guide
read :ref:`where to patch <where-to-patch>`.
As well as a decorator `patch` can be used as a context manager in a with
statement:
>>> with patch.object(ProductionClass, 'method', return_value=None) as mock_method:
... thing = ProductionClass()
... thing.method(1, 2, 3)
...
>>> mock_method.assert_called_once_with(1, 2, 3)
There is also :func:`patch.dict` for setting values in a dictionary just
during a scope and restoring the dictionary to its original state when the test
ends:
>>> foo = {'key': 'value'}
>>> original = foo.copy()
>>> with patch.dict(foo, {'newkey': 'newvalue'}, clear=True):
... assert foo == {'newkey': 'newvalue'}
...
>>> assert foo == original
Mock supports the mocking of Python :ref:`magic methods <magic-methods>`. The
easiest way of using magic methods is with the :class:`MagicMock` class. It
allows you to do things like:
>>> mock = MagicMock()
>>> mock.__str__.return_value = 'foobarbaz'
>>> str(mock)
'foobarbaz'
>>> mock.__str__.assert_called_with()
Mock allows you to assign functions (or other Mock instances) to magic methods
and they will be called appropriately. The `MagicMock` class is just a Mock
variant that has all of the magic methods pre-created for you (well, all the
useful ones anyway).
The following is an example of using magic methods with the ordinary Mock
class:
>>> mock = Mock()
>>> mock.__str__ = Mock(return_value='wheeeeee')
>>> str(mock)
'wheeeeee'
For ensuring that the mock objects in your tests have the same api as the
objects they are replacing, you can use :ref:`auto-speccing <auto-speccing>`.
Auto-speccing can be done through the `autospec` argument to patch, or the
:func:`create_autospec` function. Auto-speccing creates mock objects that
have the same attributes and methods as the objects they are replacing, and
any functions and methods (including constructors) have the same call
signature as the real object.
This ensures that your mocks will fail in the same way as your production
code if they are used incorrectly:
>>> from unittest.mock import create_autospec
>>> def function(a, b, c):
... pass
...
>>> mock_function = create_autospec(function, return_value='fishy')
>>> mock_function(1, 2, 3)
'fishy'
>>> mock_function.assert_called_once_with(1, 2, 3)
>>> mock_function('wrong arguments')
Traceback (most recent call last):
...
TypeError: <lambda>() takes exactly 3 arguments (1 given)
`create_autospec` can also be used on classes, where it copies the signature of
the `__init__` method, and on callable objects where it copies the signature of
the `__call__` method.
The Mock Class
--------------
`Mock` is a flexible mock object intended to replace the use of stubs and
test doubles throughout your code. Mocks are callable and create attributes as
new mocks when you access them [#]_. Accessing the same attribute will always
return the same mock. Mocks record how you use them, allowing you to make
assertions about what your code has done to them.
:class:`MagicMock` is a subclass of `Mock` with all the magic methods
pre-created and ready to use. There are also non-callable variants, useful
when you are mocking out objects that aren't callable:
:class:`NonCallableMock` and :class:`NonCallableMagicMock`
The :func:`patch` decorators makes it easy to temporarily replace classes
in a particular module with a `Mock` object. By default `patch` will create
a `MagicMock` for you. You can specify an alternative class of `Mock` using
the `new_callable` argument to `patch`.
.. class:: Mock(spec=None, side_effect=None, return_value=DEFAULT, wraps=None, name=None, spec_set=None, **kwargs)
Create a new `Mock` object. `Mock` takes several optional arguments
that specify the behaviour of the Mock object:
* `spec`: This can be either a list of strings or an existing object (a
class or instance) that acts as the specification for the mock object. If
you pass in an object then a list of strings is formed by calling dir on
the object (excluding unsupported magic attributes and methods).
Accessing any attribute not in this list will raise an `AttributeError`.
If `spec` is an object (rather than a list of strings) then
:attr:`__class__` returns the class of the spec object. This allows mocks
to pass `isinstance` tests.
* `spec_set`: A stricter variant of `spec`. If used, attempting to *set*
or get an attribute on the mock that isn't on the object passed as
`spec_set` will raise an `AttributeError`.
* `side_effect`: A function to be called whenever the Mock is called. See
the :attr:`~Mock.side_effect` attribute. Useful for raising exceptions or
dynamically changing return values. The function is called with the same
arguments as the mock, and unless it returns :data:`DEFAULT`, the return
value of this function is used as the return value.
Alternatively `side_effect` can be an exception class or instance. In
this case the exception will be raised when the mock is called.
If `side_effect` is an iterable then each call to the mock will return
the next value from the iterable.
A `side_effect` can be cleared by setting it to `None`.
* `return_value`: The value returned when the mock is called. By default
this is a new Mock (created on first access). See the
:attr:`return_value` attribute.
* `wraps`: Item for the mock object to wrap. If `wraps` is not None then
calling the Mock will pass the call through to the wrapped object
(returning the real result and ignoring `return_value`). Attribute access
on the mock will return a Mock object that wraps the corresponding
attribute of the wrapped object (so attempting to access an attribute
that doesn't exist will raise an `AttributeError`).
If the mock has an explicit `return_value` set then calls are not passed
to the wrapped object and the `return_value` is returned instead.
* `name`: If the mock has a name then it will be used in the repr of the
mock. This can be useful for debugging. The name is propagated to child
mocks.
Mocks can also be called with arbitrary keyword arguments. These will be
used to set attributes on the mock after it is created. See the
:meth:`configure_mock` method for details.
.. method:: assert_called_with(*args, **kwargs)
This method is a convenient way of asserting that calls are made in a
particular way:
>>> mock = Mock()
>>> mock.method(1, 2, 3, test='wow')
<Mock name='mock.method()' id='...'>
>>> mock.method.assert_called_with(1, 2, 3, test='wow')
.. method:: assert_called_once_with(*args, **kwargs)
Assert that the mock was called exactly once and with the specified
arguments.
>>> mock = Mock(return_value=None)
>>> mock('foo', bar='baz')
>>> mock.assert_called_once_with('foo', bar='baz')
>>> mock('foo', bar='baz')
>>> mock.assert_called_once_with('foo', bar='baz')
Traceback (most recent call last):
...
AssertionError: Expected to be called once. Called 2 times.
.. method:: assert_any_call(*args, **kwargs)
assert the mock has been called with the specified arguments.
The assert passes if the mock has *ever* been called, unlike
:meth:`assert_called_with` and :meth:`assert_called_once_with` that
only pass if the call is the most recent one.
>>> mock = Mock(return_value=None)
>>> mock(1, 2, arg='thing')
>>> mock('some', 'thing', 'else')
>>> mock.assert_any_call(1, 2, arg='thing')
.. method:: assert_has_calls(calls, any_order=False)
assert the mock has been called with the specified calls.
The `mock_calls` list is checked for the calls.
If `any_order` is False (the default) then the calls must be
sequential. There can be extra calls before or after the
specified calls.
If `any_order` is True then the calls can be in any order, but
they must all appear in :attr:`mock_calls`.
>>> mock = Mock(return_value=None)
>>> mock(1)
>>> mock(2)
>>> mock(3)
>>> mock(4)
>>> calls = [call(2), call(3)]
>>> mock.assert_has_calls(calls)
>>> calls = [call(4), call(2), call(3)]
>>> mock.assert_has_calls(calls, any_order=True)
.. method:: reset_mock()
The reset_mock method resets all the call attributes on a mock object:
>>> mock = Mock(return_value=None)
>>> mock('hello')
>>> mock.called
True
>>> mock.reset_mock()
>>> mock.called
False
This can be useful where you want to make a series of assertions that
reuse the same object. Note that `reset_mock` *doesn't* clear the
return value, :attr:`side_effect` or any child attributes you have
set using normal assignment. Child mocks and the return value mock
(if any) are reset as well.
.. method:: mock_add_spec(spec, spec_set=False)
Add a spec to a mock. `spec` can either be an object or a
list of strings. Only attributes on the `spec` can be fetched as
attributes from the mock.
If `spec_set` is `True` then only attributes on the spec can be set.
.. method:: attach_mock(mock, attribute)
Attach a mock as an attribute of this one, replacing its name and
parent. Calls to the attached mock will be recorded in the
:attr:`method_calls` and :attr:`mock_calls` attributes of this one.
.. method:: configure_mock(**kwargs)
Set attributes on the mock through keyword arguments.
Attributes plus return values and side effects can be set on child
mocks using standard dot notation and unpacking a dictionary in the
method call:
>>> mock = Mock()
>>> attrs = {'method.return_value': 3, 'other.side_effect': KeyError}
>>> mock.configure_mock(**attrs)
>>> mock.method()
3
>>> mock.other()
Traceback (most recent call last):
...
KeyError
The same thing can be achieved in the constructor call to mocks:
>>> attrs = {'method.return_value': 3, 'other.side_effect': KeyError}
>>> mock = Mock(some_attribute='eggs', **attrs)
>>> mock.some_attribute
'eggs'
>>> mock.method()
3
>>> mock.other()
Traceback (most recent call last):
...
KeyError
`configure_mock` exists to make it easier to do configuration
after the mock has been created.
.. method:: __dir__()
`Mock` objects limit the results of `dir(some_mock)` to useful results.
For mocks with a `spec` this includes all the permitted attributes
for the mock.
See :data:`FILTER_DIR` for what this filtering does, and how to
switch it off.
.. method:: _get_child_mock(**kw)
Create the child mocks for attributes and return value.
By default child mocks will be the same type as the parent.
Subclasses of Mock may want to override this to customize the way
child mocks are made.
For non-callable mocks the callable variant will be used (rather than
any custom subclass).
.. attribute:: called
A boolean representing whether or not the mock object has been called:
>>> mock = Mock(return_value=None)
>>> mock.called
False
>>> mock()
>>> mock.called
True
.. attribute:: call_count
An integer telling you how many times the mock object has been called:
>>> mock = Mock(return_value=None)
>>> mock.call_count
0
>>> mock()
>>> mock()
>>> mock.call_count
2
.. attribute:: return_value
Set this to configure the value returned by calling the mock:
>>> mock = Mock()
>>> mock.return_value = 'fish'
>>> mock()
'fish'
The default return value is a mock object and you can configure it in
the normal way:
>>> mock = Mock()
>>> mock.return_value.attribute = sentinel.Attribute
>>> mock.return_value()
<Mock name='mock()()' id='...'>
>>> mock.return_value.assert_called_with()
`return_value` can also be set in the constructor:
>>> mock = Mock(return_value=3)
>>> mock.return_value
3
>>> mock()
3
.. attribute:: side_effect
This can either be a function to be called when the mock is called,
or an exception (class or instance) to be raised.
If you pass in a function it will be called with same arguments as the
mock and unless the function returns the :data:`DEFAULT` singleton the
call to the mock will then return whatever the function returns. If the
function returns :data:`DEFAULT` then the mock will return its normal
value (from the :attr:`return_value`.
An example of a mock that raises an exception (to test exception
handling of an API):
>>> mock = Mock()
>>> mock.side_effect = Exception('Boom!')
>>> mock()
Traceback (most recent call last):
...
Exception: Boom!
Using `side_effect` to return a sequence of values:
>>> mock = Mock()
>>> mock.side_effect = [3, 2, 1]
>>> mock(), mock(), mock()
(3, 2, 1)
The `side_effect` function is called with the same arguments as the
mock (so it is wise for it to take arbitrary args and keyword
arguments) and whatever it returns is used as the return value for
the call. The exception is if `side_effect` returns :data:`DEFAULT`,
in which case the normal :attr:`return_value` is used.
>>> mock = Mock(return_value=3)
>>> def side_effect(*args, **kwargs):
... return DEFAULT
...
>>> mock.side_effect = side_effect
>>> mock()
3
`side_effect` can be set in the constructor. Here's an example that
adds one to the value the mock is called with and returns it:
>>> side_effect = lambda value: value + 1
>>> mock = Mock(side_effect=side_effect)
>>> mock(3)
4
>>> mock(-8)
-7
Setting `side_effect` to `None` clears it:
>>> m = Mock(side_effect=KeyError, return_value=3)
>>> m()
Traceback (most recent call last):
...
KeyError
>>> m.side_effect = None
>>> m()
3
.. attribute:: call_args
This is either `None` (if the mock hasn't been called), or the
arguments that the mock was last called with. This will be in the
form of a tuple: the first member is any ordered arguments the mock
was called with (or an empty tuple) and the second member is any
keyword arguments (or an empty dictionary).
>>> mock = Mock(return_value=None)
>>> print mock.call_args
None
>>> mock()
>>> mock.call_args
call()
>>> mock.call_args == ()
True
>>> mock(3, 4)
>>> mock.call_args
call(3, 4)
>>> mock.call_args == ((3, 4),)
True
>>> mock(3, 4, 5, key='fish', next='w00t!')
>>> mock.call_args
call(3, 4, 5, key='fish', next='w00t!')
`call_args`, along with members of the lists :attr:`call_args_list`,
:attr:`method_calls` and :attr:`mock_calls` are :data:`call` objects.
These are tuples, so they can be unpacked to get at the individual
arguments and make more complex assertions. See
:ref:`calls as tuples <calls-as-tuples>`.
.. attribute:: call_args_list
This is a list of all the calls made to the mock object in sequence
(so the length of the list is the number of times it has been
called). Before any calls have been made it is an empty list. The
:data:`call` object can be used for conveniently constructing lists of
calls to compare with `call_args_list`.
>>> mock = Mock(return_value=None)
>>> mock()
>>> mock(3, 4)
>>> mock(key='fish', next='w00t!')
>>> mock.call_args_list
[call(), call(3, 4), call(key='fish', next='w00t!')]
>>> expected = [(), ((3, 4),), ({'key': 'fish', 'next': 'w00t!'},)]
>>> mock.call_args_list == expected
True
Members of `call_args_list` are :data:`call` objects. These can be
unpacked as tuples to get at the individual arguments. See
:ref:`calls as tuples <calls-as-tuples>`.
.. attribute:: method_calls
As well as tracking calls to themselves, mocks also track calls to
methods and attributes, and *their* methods and attributes:
>>> mock = Mock()
>>> mock.method()
<Mock name='mock.method()' id='...'>
>>> mock.property.method.attribute()
<Mock name='mock.property.method.attribute()' id='...'>
>>> mock.method_calls
[call.method(), call.property.method.attribute()]
Members of `method_calls` are :data:`call` objects. These can be
unpacked as tuples to get at the individual arguments. See
:ref:`calls as tuples <calls-as-tuples>`.
.. attribute:: mock_calls
`mock_calls` records *all* calls to the mock object, its methods, magic
methods *and* return value mocks.
>>> mock = MagicMock()
>>> result = mock(1, 2, 3)
>>> mock.first(a=3)
<MagicMock name='mock.first()' id='...'>
>>> mock.second()
<MagicMock name='mock.second()' id='...'>
>>> int(mock)
1
>>> result(1)
<MagicMock name='mock()()' id='...'>
>>> expected = [call(1, 2, 3), call.first(a=3), call.second(),
... call.__int__(), call()(1)]
>>> mock.mock_calls == expected
True
Members of `mock_calls` are :data:`call` objects. These can be
unpacked as tuples to get at the individual arguments. See
:ref:`calls as tuples <calls-as-tuples>`.
.. attribute:: __class__
Normally the `__class__` attribute of an object will return its type.
For a mock object with a `spec` `__class__` returns the spec class
instead. This allows mock objects to pass `isinstance` tests for the
object they are replacing / masquerading as:
>>> mock = Mock(spec=3)
>>> isinstance(mock, int)
True
`__class__` is assignable to, this allows a mock to pass an
`isinstance` check without forcing you to use a spec:
>>> mock = Mock()
>>> mock.__class__ = dict
>>> isinstance(mock, dict)
True
.. class:: NonCallableMock(spec=None, wraps=None, name=None, spec_set=None, **kwargs)
A non-callable version of `Mock`. The constructor parameters have the same
meaning of `Mock`, with the exception of `return_value` and `side_effect`
which have no meaning on a non-callable mock.
Mock objects that use a class or an instance as a `spec` or `spec_set` are able
to pass `isintance` tests:
>>> mock = Mock(spec=SomeClass)
>>> isinstance(mock, SomeClass)
True
>>> mock = Mock(spec_set=SomeClass())
>>> isinstance(mock, SomeClass)
True
The `Mock` classes have support for mocking magic methods. See :ref:`magic
methods <magic-methods>` for the full details.
The mock classes and the :func:`patch` decorators all take arbitrary keyword
arguments for configuration. For the `patch` decorators the keywords are
passed to the constructor of the mock being created. The keyword arguments
are for configuring attributes of the mock:
>>> m = MagicMock(attribute=3, other='fish')
>>> m.attribute
3
>>> m.other
'fish'
The return value and side effect of child mocks can be set in the same way,
using dotted notation. As you can't use dotted names directly in a call you
have to create a dictionary and unpack it using `**`:
>>> attrs = {'method.return_value': 3, 'other.side_effect': KeyError}
>>> mock = Mock(some_attribute='eggs', **attrs)
>>> mock.some_attribute
'eggs'
>>> mock.method()
3
>>> mock.other()
Traceback (most recent call last):
...
KeyError
.. class:: PropertyMock(*args, **kwargs)
A mock intended to be used as a property, or other descriptor, on a class.
`PropertyMock` provides `__get__` and `__set__` methods so you can specify
a return value when it is fetched.
Fetching a `PropertyMock` instance from an object calls the mock, with
no args. Setting it calls the mock with the value being set.
>>> class Foo(object):
... @property
... def foo(self):
... return 'something'
... @foo.setter
... def foo(self, value):
... pass
...
>>> with patch('__main__.Foo.foo', new_callable=PropertyMock) as mock_foo:
... mock_foo.return_value = 'mockity-mock'
... this_foo = Foo()
... print this_foo.foo
... this_foo.foo = 6
...
mockity-mock
>>> mock_foo.mock_calls
[call(), call(6)]
Calling
~~~~~~~
Mock objects are callable. The call will return the value set as the
:attr:`~Mock.return_value` attribute. The default return value is a new Mock
object; it is created the first time the return value is accessed (either
explicitly or by calling the Mock) - but it is stored and the same one
returned each time.
Calls made to the object will be recorded in the attributes
like :attr:`~Mock.call_args` and :attr:`~Mock.call_args_list`.
If :attr:`~Mock.side_effect` is set then it will be called after the call has
been recorded, so if `side_effect` raises an exception the call is still
recorded.
The simplest way to make a mock raise an exception when called is to make
:attr:`~Mock.side_effect` an exception class or instance:
>>> m = MagicMock(side_effect=IndexError)
>>> m(1, 2, 3)
Traceback (most recent call last):
...
IndexError
>>> m.mock_calls
[call(1, 2, 3)]
>>> m.side_effect = KeyError('Bang!')
>>> m('two', 'three', 'four')
Traceback (most recent call last):
...
KeyError: 'Bang!'
>>> m.mock_calls
[call(1, 2, 3), call('two', 'three', 'four')]
If `side_effect` is a function then whatever that function returns is what
calls to the mock return. The `side_effect` function is called with the
same arguments as the mock. This allows you to vary the return value of the
call dynamically, based on the input:
>>> def side_effect(value):
... return value + 1
...
>>> m = MagicMock(side_effect=side_effect)
>>> m(1)
2
>>> m(2)
3
>>> m.mock_calls
[call(1), call(2)]
If you want the mock to still return the default return value (a new mock), or
any set return value, then there are two ways of doing this. Either return
`mock.return_value` from inside `side_effect`, or return :data:`DEFAULT`:
>>> m = MagicMock()
>>> def side_effect(*args, **kwargs):
... return m.return_value
...
>>> m.side_effect = side_effect
>>> m.return_value = 3
>>> m()
3
>>> def side_effect(*args, **kwargs):
... return DEFAULT
...
>>> m.side_effect = side_effect
>>> m()
3
To remove a `side_effect`, and return to the default behaviour, set the
`side_effect` to `None`:
>>> m = MagicMock(return_value=6)
>>> def side_effect(*args, **kwargs):
... return 3
...
>>> m.side_effect = side_effect
>>> m()
3
>>> m.side_effect = None
>>> m()
6
The `side_effect` can also be any iterable object. Repeated calls to the mock
will return values from the iterable (until the iterable is exhausted and
a `StopIteration` is raised):
>>> m = MagicMock(side_effect=[1, 2, 3])
>>> m()
1
>>> m()
2
>>> m()
3
>>> m()
Traceback (most recent call last):
...
StopIteration
.. _deleting-attributes:
Deleting Attributes
~~~~~~~~~~~~~~~~~~~
Mock objects create attributes on demand. This allows them to pretend to be
objects of any type.
You may want a mock object to return `False` to a `hasattr` call, or raise an
`AttributeError` when an attribute is fetched. You can do this by providing
an object as a `spec` for a mock, but that isn't always convenient.
You "block" attributes by deleting them. Once deleted, accessing an attribute
will raise an `AttributeError`.
>>> mock = MagicMock()
>>> hasattr(mock, 'm')
True
>>> del mock.m
>>> hasattr(mock, 'm')
False
>>> del mock.f
>>> mock.f
Traceback (most recent call last):
...
AttributeError: f
Attaching Mocks as Attributes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
When you attach a mock as an attribute of another mock (or as the return
value) it becomes a "child" of that mock. Calls to the child are recorded in
the :attr:`~Mock.method_calls` and :attr:`~Mock.mock_calls` attributes of the
parent. This is useful for configuring child mocks and then attaching them to
the parent, or for attaching mocks to a parent that records all calls to the
children and allows you to make assertions about the order of calls between
mocks:
>>> parent = MagicMock()
>>> child1 = MagicMock(return_value=None)
>>> child2 = MagicMock(return_value=None)
>>> parent.child1 = child1
>>> parent.child2 = child2
>>> child1(1)
>>> child2(2)
>>> parent.mock_calls
[call.child1(1), call.child2(2)]
The exception to this is if the mock has a name. This allows you to prevent
the "parenting" if for some reason you don't want it to happen.
>>> mock = MagicMock()
>>> not_a_child = MagicMock(name='not-a-child')
>>> mock.attribute = not_a_child
>>> mock.attribute()
<MagicMock name='not-a-child()' id='...'>
>>> mock.mock_calls
[]
Mocks created for you by :func:`patch` are automatically given names. To
attach mocks that have names to a parent you use the :meth:`~Mock.attach_mock`
method:
>>> thing1 = object()
>>> thing2 = object()
>>> parent = MagicMock()
>>> with patch('__main__.thing1', return_value=None) as child1:
... with patch('__main__.thing2', return_value=None) as child2:
... parent.attach_mock(child1, 'child1')
... parent.attach_mock(child2, 'child2')
... child1('one')
... child2('two')
...
>>> parent.mock_calls
[call.child1('one'), call.child2('two')]
.. [#] The only exceptions are magic methods and attributes (those that have
leading and trailing double underscores). Mock doesn't create these but
instead of raises an ``AttributeError``. This is because the interpreter
will often implicitly request these methods, and gets *very* confused to
get a new Mock object when it expects a magic method. If you need magic
method support see :ref:`magic methods <magic-methods>`.

8
Lib/unittest/mock.py
View file

@ -1577,11 +1577,9 @@ right = ' '.join('r%s' % n for n in numerics.split())
# __del__ is not supported at all as it causes problems if it exists
_non_defaults = set('__%s__' % method for method in [
'cmp', 'getslice', 'setslice', 'coerce', 'subclasses',
'format', 'get', 'set', 'delete', 'reversed',
'missing', 'reduce', 'reduce_ex', 'getinitargs',
'getnewargs', 'getstate', 'setstate', 'getformat',
'setformat', 'repr', 'dir'
'get', 'set', 'delete', 'reversed', 'missing', 'reduce', 'reduce_ex',
'getinitargs', 'getnewargs', 'getstate', 'setstate', 'getformat',
'setformat', 'repr', 'dir', 'subclasses', 'format',
])