![]() If spec is an object (rather than a list of strings) then ![]() The object (excluding unsupported magic attributes and methods).Īccessing any attribute not in this list will raise an AttributeError. You pass in an object then a list of strings is formed by calling dir on Spec: This can be either a list of strings or an existing object (aĬlass or instance) that acts as the specification for the mock object. That specify the behaviour of the Mock object: Mock ( spec = None, side_effect = None, return_value = DEFAULT, wraps = None, name = None, spec_set = None, unsafe = False, ** kwargs ) ¶Ĭreate a new Mock object. You can specify an alternative class of Mock using By default patch() will createĪ MagicMock for you. In a particular module with a Mock object. The patch() decorators makes it easy to temporarily replace classes When you are mocking out objects that aren’t callable: There are also non-callable variants, useful MagicMock is a subclass of Mock with all the magic methods Mocks record how you use them, allowing you to makeĪssertions about what your code has done to them. Mocks are callable and create attributes as Mock is a flexible mock object intended to replace the use of stubs and The _init_ method, and on callable objects where it copies the signature of TypeError: () takes exactly 3 arguments (1 given)Ĭreate_autospec() can also be used on classes, where it copies the signature of assert_called_once_with ( 1, 2, 3 ) > mock_function ( 'wrong arguments' ) Traceback (most recent call last). > mock_function = create_autospec ( function, return_value = 'fishy' ) > mock_function ( 1, 2, 3 ) 'fishy' > mock_function. > from unittest.mock import create_autospec > def function ( a, b, c ). YouĬan configure them, to specify return values or limit what attributes areĪvailable, and then make assertions about how they have been used: Methods as you access them and store details of how they have been used. Mock and MagicMock objects create all attributes and There is a backport of unittest.mock for earlier versions of Python, Is based on the ‘action -> assertion’ pattern instead of ‘record -> replay’ Mock is designed for use with unittest and Some examples of how to use Mock, MagicMock and Module and class level attributes within the scope of a test, along with You can also specify return values andĪdditionally, mock provides a patch() decorator that handles patching After performing anĪction, you can make assertions about which methods / attributes were usedĪnd arguments they were called with. Unittest.mock provides a core Mock class removing the need toĬreate a host of stubs throughout your test suite. Replace parts of your system under test with mock objects and make assertions Unittest.mock is a library for testing in Python.
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