unittest.mock — mock object library¶New in version 3.3.
Source code: Lib/unittest/mock.py
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 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 patch() decorator that handles patching
module and class level attributes within the scope of a test, along with
sentinel for creating unique objects. See the quick guide for
some examples of how to use Mock, MagicMock and
patch().
Mock is very easy to use and is designed for use with 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.
Mock and 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')
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 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 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 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 magic methods. The
easiest way of using magic methods is with the 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 auto-speccing.
Auto-speccing can be done through the autospec argument to patch, or the
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.
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 [1]. 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.
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:
NonCallableMock and NonCallableMagicMock
The 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().
unittest.mock.Mock(spec=None, side_effect=None, return_value=DEFAULT, wraps=None, name=None, spec_set=None, unsafe=False, **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
__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 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 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
return_value attribute.
unsafe: By default if any attribute starts with assert or
assret will raise an AttributeError. Passing unsafe=True
will allow access to these attributes.
New in version 3.5.
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). 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
configure_mock() method for details.
assert_called(*args, **kwargs)¶Assert that the mock was called at least once.
>>> mock = Mock()
>>> mock.method()
<Mock name='mock.method()' id='...'>
>>> mock.method.assert_called()
New in version 3.6.
assert_called_once(*args, **kwargs)¶Assert that the mock was called exactly once.
>>> mock = Mock()
>>> mock.method()
<Mock name='mock.method()' id='...'>
>>> mock.method.assert_called_once()
>>> mock.method()
<Mock name='mock.method()' id='...'>
>>> mock.method.assert_called_once()
Traceback (most recent call last):
...
AssertionError: Expected 'method' to have been called once. Called 2 times.
New in version 3.6.
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')
assert_called_once_with(*args, **kwargs)¶Assert that the mock was called exactly once and that that call was with the specified arguments.
>>> mock = Mock(return_value=None)
>>> mock('foo', bar='baz')
>>> mock.assert_called_once_with('foo', bar='baz')
>>> mock('other', bar='values')
>>> mock.assert_called_once_with('other', bar='values')
Traceback (most recent call last):
...
AssertionError: Expected 'mock' to be called once. Called 2 times.
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
assert_called_with() and assert_called_once_with() that
only pass if the call is the most recent one, and in the case of
assert_called_once_with() it must also be the only call.
>>> mock = Mock(return_value=None)
>>> mock(1, 2, arg='thing')
>>> mock('some', 'thing', 'else')
>>> mock.assert_any_call(1, 2, arg='thing')
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 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)
assert_not_called()¶Assert the mock was never called.
>>> m = Mock()
>>> m.hello.assert_not_called()
>>> obj = m.hello()
>>> m.hello.assert_not_called()
Traceback (most recent call last):
...
AssertionError: Expected 'hello' to not have been called. Called 1 times.
New in version 3.5.
reset_mock(*, return_value=False, side_effect=False)¶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
Changed in version 3.6: Added two keyword only argument to the reset_mock function.
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, side_effect or any child attributes you have
set using normal assignment by default. In case you want to reset
return_value or side_effect, then pass the corresponding
parameter as True. Child mocks and the return value mock
(if any) are reset as well.
Note
return_value, and side_effect are keyword only
argument.
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.
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
method_calls and mock_calls attributes of this one.
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.
__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 FILTER_DIR for what this filtering does, and how to
switch it off.
_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).
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
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
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
side_effect¶This can either be a function to be called when the mock is called, an iterable 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 DEFAULT singleton the
call to the mock will then return whatever the function returns. If the
function returns DEFAULT then the mock will return its normal
value (from the return_value).
If you pass in an iterable, it is used to retrieve an iterator which
must yield a value on every call. This value can either be an exception
instance to be raised, or a value to be returned from the call to the
mock (DEFAULT handling is identical to the function case).
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)
Using a callable:
>>> 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
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 call_args_list,
method_calls and mock_calls are call objects.
These are tuples, so they can be unpacked to get at the individual
arguments and make more complex assertions. See
calls as tuples.
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
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 call objects. These can be
unpacked as tuples to get at the individual arguments. See
calls as tuples.
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 call objects. These can be
unpacked as tuples to get at the individual arguments. See
calls as tuples.
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 call objects. These can be
unpacked as tuples to get at the individual arguments. See
calls as tuples.
__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
unittest.mock.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 isinstance() 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 magic
methods for the full details.
The mock classes and the 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
A callable mock which was created with a spec (or a spec_set) will introspect the specification object’s signature when matching calls to the mock. Therefore, it can match the actual call’s arguments regardless of whether they were passed positionally or by name:
>>> def f(a, b, c): pass
...
>>> mock = Mock(spec=f)
>>> mock(1, 2, c=3)
<Mock name='mock()' id='140161580456576'>
>>> mock.assert_called_with(1, 2, 3)
>>> mock.assert_called_with(a=1, b=2, c=3)
This applies to assert_called_with(),
assert_called_once_with(), assert_has_calls() and
assert_any_call(). When Autospeccing, it will also
apply to method calls on the mock object.
Changed in version 3.4: Added signature introspection on specced and autospecced mock objects.
unittest.mock.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:
... @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)]
Because of the way mock attributes are stored you can’t directly attach a
PropertyMock to a mock object. Instead you can attach it to the mock type
object:
>>> m = MagicMock()
>>> p = PropertyMock(return_value=3)
>>> type(m).foo = p
>>> m.foo
3
>>> p.assert_called_once_with()
Mock objects are callable. The call will return the value set as the
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 call_args and call_args_list.
If 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
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 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
If any members of the iterable are exceptions they will be raised instead of returned:
>>> iterable = (33, ValueError, 66)
>>> m = MagicMock(side_effect=iterable)
>>> m()
33
>>> m()
Traceback (most recent call last):
...
ValueError
>>> m()
66
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
Since “name” is an argument to the Mock constructor, if you want your
mock object to have a “name” attribute you can’t just pass it in at creation
time. There are two alternatives. One option is to use
configure_mock():
>>> mock = MagicMock()
>>> mock.configure_mock(name='my_name')
>>> mock.name
'my_name'
A simpler option is to simply set the “name” attribute after mock creation:
>>> mock = MagicMock()
>>> mock.name = "foo"
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 method_calls and 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 patch() are automatically given names. To
attach mocks that have names to a parent you use the 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')]
| [1] | The only exceptions are magic methods and attributes (those that have
leading and trailing double underscores). Mock doesn’t create these but
instead 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 magic methods. |
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.
Note
patch() is straightforward to use. The key is to do the patching in the
right namespace. See the section where to patch.
unittest.mock.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
is patched with a new object. When the function/with statement exits
the patch is undone.
If new is omitted, then the target is replaced with a
MagicMock. If patch() is used as a decorator and new is
omitted, the created mock is passed in as an extra argument to the
decorated function. If patch() is used as a context manager the created
mock is returned by the context manager.
target should be a string in the form 'package.module.ClassName'. The
target is imported and the specified object replaced with the new
object, so the target must be importable from the environment you are
calling patch() from. The target is imported when the decorated function
is executed, not at decoration time.
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 will 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 create_autospec() function and
Autospeccing.
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
default because it can be dangerous. With it switched on you can write
passing tests against APIs that don’t actually exist!
Note
Changed in version 3.5: If you are patching builtins in a module then you don’t
need to pass create=True, it will be added by default.
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.
patch() as function decorator, creating the mock for you and passing it into
the decorated function:
>>> @patch('__main__.SomeClass')
... def function(normal_argument, mock_class):
... print(mock_class is SomeClass)
...
>>> function(None)
True
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
return_value of the mock that will be used.
If the class is instantiated multiple times you could use
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:
... 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 MagicMock for the created mock. For example, if
you wanted a 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 an io.StringIO instance:
>>> from io 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
return_value and 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(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(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:
... 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(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 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
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
...
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.
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.
It is also possible to stop all patches which have been started by using
patch.stopall().
patch.stopall()¶Stop all active patches. Only stops patches started with start.
You can patch any builtins within a module. The following example patches
builtin ord():
>>> @patch('__main__.ord')
... def test(mock_ord):
... mock_ord.return_value = 101
... print(ord('c'))
...
>>> test()
101
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
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:
... def foo_one(self):
... print(value)
... def foo_two(self):
... print(value)
...
>>>
>>> Thing().foo_one()
not three
>>> Thing().foo_two()
not three
>>> value
3
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.
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 in the module and so we have to patch a.SomeClass instead:
@patch('a.SomeClass')
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 settings object.
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 [2], 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 [3].
>>> 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 method_calls, but they
are recorded in 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____lt__, __gt__, __le__, __ge__,
__eq__ and __ne____getitem__, __setitem__, __delitem__,
__contains__, __len__, __iter__, __reversed__
and __missing____enter__ and __exit____neg__, __pos__ and __invert____add__, __sub__, __mul__, __matmul__, __div__, __truediv__,
__floordiv__, __mod__, __divmod__, __lshift__,
__rshift__, __and__, __xor__, __or__, and __pow____complex__, __int__, __float__
and __index____get__, __set__ and __delete____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__There are two MagicMock variants: MagicMock and NonCallableMagicMock.
unittest.mock.MagicMock(*args, **kw)¶MagicMock is a subclass of 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 Mock.
If you use the spec or spec_set arguments then only magic methods that exist in the spec will be created.
unittest.mock.NonCallableMagicMock(*args, **kw)¶A non-callable version of MagicMock.
The constructor parameters have the same meaning as for
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__: 0__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 mockFor example:
>>> mock = MagicMock()
>>> int(mock)
1
>>> len(mock)
0
>>> list(mock)
[]
>>> object() in mock
False
The two equality methods, __eq__() and __ne__(), are special.
They do the default equality comparison on identity, using the
side_effect attribute, 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__| [2] | 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. |
| [3] | The function is basically hooked up to the class, but each Mock
instance is kept isolated from the others. |
unittest.mock.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.
The sentinel attributes don’t preserve their identity when they are
copied or pickled.
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
unittest.mock.DEFAULT¶The DEFAULT object is a pre-created sentinel (actually
sentinel.DEFAULT). It can be used by side_effect
functions to indicate that the normal return value should be used.
unittest.mock.call(*args, **kwargs)¶call() is a helper object for making simpler assertions, for comparing with
call_args, call_args_list,
mock_calls and method_calls. call() can also be
used with 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
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 mock_calls on a mock. Manually constructing
the sequence of calls can be tedious.
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
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 Mock.call_args, Mock.call_args_list and
Mock.mock_calls attributes can be introspected to get at the individual
arguments they contain.
The call objects in Mock.call_args and Mock.call_args_list
are two-tuples of (positional args, keyword args) whereas the call objects
in 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
unittest.mock.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 Autospeccing for examples of how to use auto-speccing with
create_autospec() and the autospec argument to patch().
unittest.mock.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 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 assert_called_with() and
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
mock_calls:
>>> m = MagicMock(return_value=None)
>>> m(1)
>>> m(1, 2)
>>> m(object())
>>> m.mock_calls == [call(1), call(1, 2), ANY]
True
unittest.mock.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.
unittest.mock.mock_open(mock=None, read_data=None)¶A helper function to create a mock to replace the use of
open(). It works foropen()called directly or used as a context manager.The mock argument is the mock object to configure. If
None(the default) then aMagicMockwill 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(),readline(), andreadlines()methods of the file handle to return. Calls to those methods will take data from read_data until it is depleted. The mock of these methods is pretty simplistic: every time the mock is called, the read_data is rewound to the start. If you need more control over the data that you are feeding to the tested code you will need to customize this mock for yourself. When that is insufficient, one of the in-memory filesystem packages on PyPI can offer a realistic filesystem for testing.
Changed in version 3.4: Added readline() and readlines() support.
The mock of read() changed to consume read_data rather
than returning it on each call.
Changed in version 3.5: read_data is now reset on each call to the mock.
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 MagicMock is common enough and fiddly
enough that a helper function is useful.
>>> m = mock_open()
>>> with patch('__main__.open', m):
... 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')) as m:
... with open('foo') as h:
... result = h.read()
...
>>> m.assert_called_once_with('foo')
>>> assert result == 'bibble'
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: assert_called_with() and
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 'mock' 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:
>>> 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:
>>> 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
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 [4].
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:
... 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.
class Something:
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:
... 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:
... 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='...'>
| [4] | 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. |