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+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.
+
+mock is now part of the Python standard library, available as `unittest.mock <
+http://docs.python.org/py3k/library/unittest.mock.html#module-unittest.mock>`_
+in Python 3.3 onwards.
+
+mock provides a core `MagicMock` 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.
+
+mock is tested on Python versions 2.4-2.7 and Python 3. mock is also tested
+with the latest versions of Jython and pypy.
+
+The mock module also provides utility functions / objects to assist with
+testing, particularly monkey patching.
+
+* `PDF documentation for 1.0 beta 1
+  <http://www.voidspace.org.uk/downloads/mock-1.0.0.pdf>`_
+* `mock on google code (repository and issue tracker)
+  <http://code.google.com/p/mock/>`_
+* `mock documentation
+  <http://www.voidspace.org.uk/python/mock/>`_
+* `mock on PyPI <http://pypi.python.org/pypi/mock/>`_
+* `Mailing list (testing-in-python@lists.idyll.org)
+  <http://lists.idyll.org/listinfo/testing-in-python>`_
+
+Mock is very easy to use and is designed for use with
+`unittest <http://pypi.python.org/pypi/unittest2>`_. Mock is based on
+the 'action -> assertion' pattern instead of 'record -> replay' used by many
+mocking frameworks. See the `mock documentation`_ for full details.
+
+Mock 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 mock import Mock
+    >>> real = ProductionClass()
+    >>> real.method = Mock(return_value=3)
+    >>> real.method(3, 4, 5, key='value')
+    3
+    >>> real.method.assert_called_with(3, 4, 5, key='value')
+
+`side_effect` allows you to perform side effects, return different values or
+raise 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')
+   (3, 2, 1)
+   >>> 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 mock import patch
+    >>> @patch('test_module.ClassName1')
+    ... @patch('test_module.ClassName2')
+    ... def test(MockClass2, MockClass1):
+    ...     test_module.ClassName1()
+    ...     test_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 `test_module.ClassName2` 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
+   <http://www.voidspace.org.uk/python/mock/patch.html#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') as mock_method:
+    ...     mock_method.return_value = None
+    ...     real = ProductionClass()
+    ...     real.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 the
+scope of a test 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::
+
+    >>> from mock import MagicMock
+    >>> mock = MagicMock()
+    >>> mock.__str__.return_value = 'foobarbaz'
+    >>> str(mock)
+    'foobarbaz'
+    >>> mock.__str__.assert_called_once_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::
+
+    >>> from mock import Mock
+    >>> mock = Mock()
+    >>> mock.__str__ = Mock(return_value = 'wheeeeee')
+    >>> str(mock)
+    'wheeeeee'
+
+For ensuring that the mock objects your tests use 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 mock import create_autospec
+   >>> def function(a, b, c):
+   ...     pass
+   ...
+   >>> mock_function = create_autospec(function, return_value='fishy')
+   >>> mock_function(1, 2, 3)
+   'fishy'
+   >>> mock_function.assert_called_once_with(1, 2, 3)
+   >>> mock_function('wrong arguments')
+   Traceback (most recent call last):
+    ...
+   TypeError: <lambda>() takes exactly 3 arguments (1 given)
+
+`create_autospec` can also be used on classes, where it copies the signature of
+the `__init__` method, and on callable objects where it copies the signature of
+the `__call__` method.
+
+The distribution contains tests and documentation. The tests require
+`unittest2 <http://pypi.python.org/pypi/unittest2>`_ to run.
+
+Docs from the in-development version of `mock` can be found at
+`mock.readthedocs.org <http://mock.readthedocs.org>`_.