Python标准库unittest.mock模拟对象库

python lib

3.3 新版功能.

源代码: Lib/unittest/mock.py

unittest.mock 是一个用于测试的Python库。它允许使用模拟对象来替换受测系统的部分,并对它们如何已经被使用进行断言。

unittest.mock 提供了一个核心类 Mock 用于消除了在整个测试套件中创建大量存根(stub)的需求。创建后,就可以断言调用了哪些方法/属性及其参数。还可以以常规方式指定返回值并设置所需的属性。

此外,mock 提供了用于修补测试范围内模块和类级别属性的 patch() 装饰器,和用于创建独特对象的 sentinel 。 阅读 quick guide 中的案例了解如何使用 MockMagicMockpatch()

Mock 是为 unittest 而设计,且简单易用。模拟基于 'action -> assertion' 模式,而不是许多模拟框架所使用的 'record -> replay'模式。

在 Python 的早期版本要单独使用 unittest.mock ,在 PyPI 获取 mock

快速上手¶

当您访问对象时, MockMagicMock 将创建所有属性和方法,并保存他们在使用时的细节。你可以通过配置,指定返回值或者限制可访问属性,然后断言他们如何被调用。

>>> fromunittest.mockimportMagicMock

>>> 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')

通过 side_effect 设置副作用(side effects) ,可以是一个 mock 被调用是抛出的异常

>>> mock=Mock(side_effect=KeyError('foo'))

>>> mock()

Traceback (most recent call last):

...

KeyError: 'foo'

>>> values={'a':1,'b':2,'c':3}

>>> defside_effect(arg):

... returnvalues[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 还可以通过其他方法配置和控制其行为。例如 mock 可以通过设置 spec 参数来从一个对象中获取其规格(specification)。如果访问 mock 的属性或方法不在 spec 中,会报 AttributeError 错误。

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:

>>> fromunittest.mockimportpatch

>>> @patch('module.ClassName2')

... @patch('module.ClassName1')

... deftest(MockClass1,MockClass2):

... module.ClassName1()

... module.ClassName2()

... assertMockClass1ismodule.ClassName1

... assertMockClass2ismodule.ClassName2

... assertMockClass1.called

... assertMockClass2.called

...

>>> test()

注解

当你嵌套 patch 装饰器时,mock 将以执行顺序传递给装饰器函数(Python 装饰器正常顺序)。由于从下至上,因此在上面的示例中,首先 mock 传入的 module.ClassName1

在查找对象的名称空间中修补对象使用 patch() 。使用起来很简单,阅读 在哪里打补丁 来快速上手。

patch() 也可以 with 语句中使用上下文管理。

>>> withpatch.object(ProductionClass,'method',return_value=None)asmock_method:

... thing=ProductionClass()

... thing.method(1,2,3)

...

>>> mock_method.assert_called_once_with(1,2,3)

还有一个 patch.dict() 用于在一定范围内设置字典中的值,并在测试结束时将字典恢复为其原始状态:

>>> foo={'key':'value'}

>>> original=foo.copy()

>>> withpatch.dict(foo,{'newkey':'newvalue'},clear=True):

... assertfoo=={'newkey':'newvalue'}

...

>>> assertfoo==original

Mock支持 Python 魔术方法 。使用模式方法最简单的方式是使用 MagicMock class. 。它可以做如下事情:

>>> mock=MagicMock()

>>> mock.__str__.return_value='foobarbaz'

>>> str(mock)

'foobarbaz'

>>> mock.__str__.assert_called_with()

Mock 能指定函数(或其他 Mock 实例)为魔术方法,它们将被适当地调用。 MagicMock 是预先创建了所有魔术方法(所有有用的方法) 的 Mock 。

下面是一个使用了普通 Mock 类的魔术方法的例子

>>> mock=Mock()

>>> mock.__str__=Mock(return_value='wheeeeee')

>>> str(mock)

'wheeeeee'

使用 auto-speccing 可以保证测试中的模拟对象与要替换的对象具有相同的api 。在 patch 中可以通过 autospec 参数实现自动推断,或者使用 create_autospec() 函数。自动推断会创建一个与要替换对象相同的属相和方法的模拟对象,并且任何函数和方法(包括构造函数)都具有与真实对象相同的调用签名。

这么做是为了因确保不当地使用 mock 导致与生产代码相同的失败:

>>> fromunittest.mockimportcreate_autospec

>>> deffunction(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)

在类中使用 create_autospec() 时,会复制 __init__ 的签名,另外在可调用对象上使用时,会复制 __call__ 的签名。

Mock 类¶

Mock 是一个可以灵活的替换存根 (stubs) 的对象,可以测试所有代码。 Mock 是可调用的,在访问其属性时创建一个新的 mock 1 。访问相同的属性只会返回相同的 mock 。 Mock 保存调用记录,可以通过断言获悉代码的调用。

MagicMockMock 的子类,它有所有预创建且可使用的魔术方法。在需要模拟不可调用对象时,可以使用 NonCallableMock  和 NonCallableMagicMock

patch() 装饰器使得用 Mock 对象临时替换特定模块中的类非常方便。 默认情况下 patch() 将为你创建一个 MagicMock。 你可以使用 patch()new_callable 参数指定替代 Mock 的类。

class unittest.mock.Mock(spec=None, side_effect=None, return_value=DEFAULT, wraps=None, name=None, spec_set=None, unsafe=False, **kwargs)

创建一个新的 Mock 对象。通过可选参数指定 Mock 对象的行为:

  • spec: 可以是要给字符串列表,也可以是充当模拟对象规范的现有对象(类或实例)。如果传入一个对象,则通过在该对象上调用 dir 来生成字符串列表(不支持的魔法属性和方法除外)。访问不在此列表中的任何属性都将引发 AttributeError

    如果 spec 是一个对象(而不是字符串列表),则 __class__ 返回 spec 对象的类。 这允许模拟程序通过 isinstance() 测试。

  • spec_setspec 的更严格的变体。如果使用了该属性,尝试模拟 setget 的属性不在 spec_set 所包含的对象中时,会抛出 AttributeError

  • side_effect :每当调用 Mock 时都会调用的函数。 参见 side_effect 属性。 对于引发异常或动态更改返回值很有用。 该函数使用与 mock 函数相同的参数调用,并且除非返回 DEFAULT ,否则该函数的返回值将用作返回值。

    另外, side_effect 可以是异常类或实例。 此时,调用模拟程序时将引发异常。

    如果 side_effect 是可迭代对象,则每次调用 mock 都将返回可迭代对象的下一个值。

    设置 side_effectNone 即可清空。

  • return_value :调用 mock 的返回值。 默认情况下,是一个新的Mock(在首次访问时创建)。 参见 return_value 属性 。

  • unsafe :默认情况下,如果任何以 assertassret 开头的属性都将引发 AttributeError 。 当 unsafe=True 时可以访问。

    3.5 新版功能.

  • wraps :要包装的 mock 对象。 如果 wraps 不是 None ,那么调用 Mock 会将调用传递给 wraps 的对象(返回实际结果)。 对模拟的属性访问将返回一个 Mock 对象,该对象包装了 wraps 对象的相应属性(因此,尝试访问不存在的属性将引发 AttributeError )。

    如果该 mock 明确指定 return_value ,调用是,不会返回包装对象,而是返回 return_value

  • name :mock 的名称。 在调试时很有用。 名称会传递到子 mock 。

还可以使用任意关键字参数来调用 mock 。 创建模拟后,将使用这些属性来设置 mock 的属性。 有关详细信息,请参见 configure_mock() 方法。

assert_called()

断言该 mock 至少被调用过一次。

>>> mock=Mock()

>>> mock.method()

<Mock name='mock.method()' id='...'>

>>> mock.method.assert_called()

3.6 新版功能.

assert_called_once()

断言仅被调用一次。

>>> 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.

3.6 新版功能.

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)

断言仅被调用一次,并且该调用是使用指定的参数进行的。

>>> 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)

断言使用指定的参数调用。

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 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.

3.5 新版功能.

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

在 3.6 版更改: 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.

注解

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)

>>> defside_effect(*args,**kwargs):

... returnDEFAULT

...

>>> 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=lambdavalue: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.

注解

The way mock_calls are recorded means that where nested

calls are made, the parameters of ancestor calls are not recorded

and so will always compare equal:

>>> mock=MagicMock()

>>> mock.top(a=3).bottom()

<MagicMock name='mock.top().bottom()' id='...'>

>>> mock.mock_calls

[call.top(a=3), call.top().bottom()]

>>> mock.mock_calls[-1]==call.top(a=-1).bottom()

True

__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 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:

>>> deff(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.

在 3.4 版更改: Added signature introspection on specced and autospecced mock objects.

class 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.

>>> classFoo:

... @property

... deffoo(self):

... return'something'

... @foo.setter

... deffoo(self,value):

... pass

...

>>> withpatch('__main__.Foo.foo',new_callable=PropertyMock)asmock_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()

Calling¶

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:

>>> defside_effect(value):

... returnvalue+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()

>>> defside_effect(*args,**kwargs):

... returnm.return_value

...

>>> m.side_effect=side_effect

>>> m.return_value=3

>>> m()

3

>>> defside_effect(*args,**kwargs):

... returnDEFAULT

...

>>> 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)

>>> defside_effect(*args,**kwargs):

... return3

...

>>> 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

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

>>> delmock.m

>>> hasattr(mock,'m')

False

>>> delmock.f

>>> mock.f

Traceback (most recent call last):

...

AttributeError: f

Mock names and the name attribute¶

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"

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 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()

>>> withpatch('__main__.thing1',return_value=None)aschild1:

... withpatch('__main__.thing2',return_value=None)aschild2:

... 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 patchers¶

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¶

注解

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 after the patched function has exited. 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!

注解

在 3.5 版更改: 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')

... deffunction(normal_argument,mock_class):

... print(mock_classisSomeClass)

...

>>> function(None)

True

Patching a class replaces the class with a MagicMockinstance. 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:

>>> classClass:

... defmethod(self):

... pass

...

>>> withpatch('__main__.Class')asMockClass:

... instance=MockClass.return_value

... instance.method.return_value='foo'

... assertClass()isinstance

... assertClass().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()

>>> assertisinstance(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()

>>> withpatch('__main__.thing',new_callable=NonCallableMock)asmock_thing:

... assertthingismock_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:

>>> fromioimportStringIO

>>> deffoo():

... print('Something')

...

>>> @patch('sys.stdout',new_callable=StringIO)

... deftest(mock_stdout):

... foo()

... assertmock_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

By default, attempting to patch a function in a module (or a method or an

attribute in a class) that does not exist will fail with AttributeError:

>>> @patch('sys.non_existing_attribute',42)

... deftest():

... assertsys.non_existing_attribute==42

...

>>> test()

Traceback (most recent call last):

...

AttributeError: <module 'sys' (built-in)> does not have the attribute 'non_existing'

but adding create=True in the call to patch() will make the previous example

work as expected:

>>> @patch('sys.non_existing_attribute',42,create=True)

... deftest(mock_stdout):

... assertsys.non_existing_attribute==42

...

>>> test()

patch.object¶

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')

... deftest(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¶

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={}

>>> withpatch.dict(foo,{'newkey':'newvalue'}):

... assertfoo=={'newkey':'newvalue'}

...

>>> assertfoo=={}

>>> importos

>>> withpatch.dict('os.environ',{'newkey':'newvalue'}):

... print(os.environ['newkey'])

...

newvalue

>>> assert'newkey'notinos.environ

Keywords can be used in the patch.dict() call to set values in the dictionary:

>>> mymodule=MagicMock()

>>> mymodule.function.return_value='fish'

>>> withpatch.dict('sys.modules',mymodule=mymodule):

... importmymodule

... 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__().

>>> classContainer:

... def__init__(self):

... self.values={}

... def__getitem__(self,name):

... returnself.values[name]

... def__setitem__(self,name,value):

... self.values[name]=value

... def__delitem__(self,name):

... delself.values[name]

... def__iter__(self):

... returniter(self.values)

...

>>> thing=Container()

>>> thing['one']=1

>>> withpatch.dict(thing,one=2,two=3):

... assertthing['one']==2

... assertthing['two']==3

...

>>> assertthing['one']==1

>>> assertlist(thing)==['one']

patch.multiple¶

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:

withpatch.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)

... deftest_function(thing,other):

... assertisinstance(thing,MagicMock)

... assertisinstance(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)

... deftest_function(mock_exit,other,thing):

... assert'other'inrepr(other)

... assert'thing'inrepr(thing)

... assert'exit'inrepr(mock_exit)

...

>>> test_function()

If patch.multiple() is used as a context manager, the value returned by the

context manager is a dictionary where created mocks are keyed by name:

>>> withpatch.multiple('__main__',thing=DEFAULT,other=DEFAULT)asvalues:

... assert'other'inrepr(values['other'])

... assert'thing'inrepr(values['thing'])

... assertvalues['thing']isthing

... assertvalues['other']isother

...

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')

>>> frompackageimportmodule

>>> original=module.ClassName

>>> new_mock=patcher.start()

>>> assertmodule.ClassNameisnotoriginal

>>> assertmodule.ClassNameisnew_mock

>>> patcher.stop()

>>> assertmodule.ClassNameisoriginal

>>> assertmodule.ClassNameisnotnew_mock

A typical use case for this might be for doing multiple patches in the setUp

method of a TestCase:

>>> classMyTest(TestCase):

... defsetUp(self):

... self.patcher1=patch('package.module.Class1')

... self.patcher2=patch('package.module.Class2')

... self.MockClass1=self.patcher1.start()

... self.MockClass2=self.patcher2.start()

...

... deftearDown(self):

... self.patcher1.stop()

... self.patcher2.stop()

...

... deftest_something(self):

... assertpackage.module.Class1isself.MockClass1

... assertpackage.module.Class2isself.MockClass2

...

>>> MyTest('test_something').run()

警告

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:

>>> classMyTest(TestCase):

... defsetUp(self):

... patcher=patch('package.module.Class')

... self.MockClass=patcher.start()

... self.addCleanup(patcher.stop)

...

... deftest_something(self):

... assertpackage.module.Classisself.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.

patch builtins¶

You can patch any builtins within a module. The following example patches

builtin ord():

>>> @patch('__main__.ord')

... deftest(mock_ord):

... mock_ord.return_value=101

... print(ord('c'))

...

>>> test()

101

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

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')

... classThing:

... deffoo_one(self):

... print(value)

... deffoo_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')

... deftest(mock1,mock2):

... assertSomeClass.static_methodismock1

... assertSomeClass.class_methodismock2

... SomeClass.static_method('foo')

... SomeClass.class_method('bar')

... returnmock1,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¶

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

->DefinesSomeClass

b.py

->fromaimportSomeClass

->some_functioninstantiatesSomeClass

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 realSomeClass 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 fromaimport

SomeClass module b does importa 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')

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 settings object.

MagicMock and magic method support¶

Mocking Magic Methods¶

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)

>>> withmockasm:

... assertm=='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.

注解

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: __lt__, __gt__, __le__, __ge__,

    __eq__ and __ne__

  • Container methods: __getitem__, __setitem__, __delitem__,

    __contains__, __len__, __iter__, __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__, __matmul__, __div__, __truediv__,

    __floordiv__, __mod__, __divmod__, __lshift__,

    __rshift__, __and__, __xor__, __or__, and __pow__

  • Numeric conversion methods: __complex__, __int__, __float__

    and __index__

  • 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 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.

class 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 mock

例如:

>>> mock=MagicMock()

>>> int(mock)

1

>>> len(mock)

0

>>> list(mock)

[]

>>> object()inmock

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.

Helpers¶

sentinel¶

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.

在 3.7 版更改: The sentinel attributes now 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()

>>> assertresultissentinel.some_object

>>> sentinel.some_object

sentinel.some_object

DEFAULT¶

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.

call¶

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'}

>>> argsiskall[0]

True

>>> kwargsiskall[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'}

>>> nameism.mock_calls[0][0]

True

create_autospec¶

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().

ANY¶

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

FILTER_DIR¶

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',

...

>>> fromurllibimportrequest

>>> 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:

>>> fromunittestimportmock

>>> 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¶

unittest.mock.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(),

readline(), and readlines() 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.

在 3.4 版更改: Added readline() and readlines() support.

The mock of read() changed to consume read_data rather

than returning it on each call.

在 3.5 版更改: read_data is now reset on each call to the mock.

在 3.7.1 版更改: Added __iter__() to implementation so that iteration (such as in for

loops) correctly consumes read_data.

Using open() as a context manager is a great way to ensure your file handles

are closed properly and is becoming common:

withopen('/some/path','w')asf:

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()

>>> withpatch('__main__.open',m):

... withopen('foo','w')ash:

... 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:

>>> withpatch('__main__.open',mock_open(read_data='bibble'))asm:

... withopen('foo')ash:

... result=h.read()

...

>>> m.assert_called_once_with('foo')

>>> assertresult=='bibble'

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: 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:

>>> fromurllibimportrequest

>>> 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:

>>> fromurllibimportrequest

>>> patcher=patch('__main__.request',autospec=True)

>>> mock_request=patcher.start()

>>> requestismock_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:

>>> fromurllibimportrequest

>>> 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.

>>> classSomething:

... def__init__(self):

... self.a=33

...

>>> withpatch('__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:

>>> withpatch('__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:

>>> withpatch('__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.

classSomething:

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):

>>> classSomething:

... 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:

>>> classSomething:

... def__init__(self):

... self.a=33

...

>>> classSomethingForTest(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.

Sealing mocks¶

unittest.mock.seal(mock)

Seal will disable the automatic creation of mocks when accessing an attribute of

the mock being sealed or any of its attributes that are already mocks recursively.

If a mock instance with a name or a spec is assigned to an attribute

it won't be considered in the sealing chain. This allows one to prevent seal from

fixing part of the mock object.

>>> mock=Mock()

>>> mock.submock.attribute1=2

>>> mock.not_submock=mock.Mock(name="sample_name")

>>> seal(mock)

>>> mock.new_attribute# This will raise AttributeError.

>>> mock.submock.attribute2# This will raise AttributeError.

>>> mock.not_submock.attribute2# This won't raise.

3.7 新版功能.

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