# `unittest.mock` — Bibliothèque d'objets simulacres

Nouveau dans la version 3.3.

Code source : Lib/unittest/mock.py

`unittest.mock` est une bibliothèque pour tester en Python. Elle permet de remplacer des parties du système sous tests par des objets simulacres et faire des assertions sur la façon dont ces objets ont été utilisés.

`unittest.mock` fournit une classe `Mock` pour ne pas avoir besoin de créer manuellement des objets factices dans la suite de tests. Après avoir effectué une action, on peut faire des assertions sur les méthodes / attributs utilisés et les arguments avec lesquels ils ont été appelés. On peut également spécifier des valeurs renvoyées et définir les attributs nécessaires aux tests.

De plus, mock fournit un décorateur `patch()` qui est capable de patcher les modules et les classes dans la portée d'un test, ainsi que `sentinel` pour créer des objets uniques. Voir le guide rapide quick guide pour quelques exemples d'utilisation de `Mock`, `MagicMock` et `patch()`.

Mock is designed for use with `unittest` and is based on the 'action -> assertion' pattern instead of 'record -> replay' used by many mocking frameworks.

Il y a un portage de `unittest.mock` pour les versions antérieures de Python, disponible sur PyPI.

## Guide rapide

Les classes `Mock` et `MagicMock` créent tous les attributs et méthodes au fur et à mesure des accès et stockent les détails de la façon dont ils ont été utilisés. On peut les configurer, pour spécifier des valeurs de renvoi ou limiter les attributs utilisables, puis faire des assertions sur la façon dont ils ont été utilisés :

    >>> 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')

L'attribut `side_effect` permet de spécifier des effets de bords, y compris la levée d'une exception lorsqu'un objet simulacre est appelé :

    >>> 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)

Il existe beaucoup d'autres façons de configurer et de contrôler le comportement de Mock. Par exemple, l'argument spec configure le mock pour qu'il utilise les spécifications d'un autre objet. Tenter d'accéder à des attributs ou méthodes sur le mock qui n'existent pas sur l'objet spec lève une `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

Lorsque l'on imbrique des décorateurs de patchs, les mocks sont transmis à la fonction décorée dans le même ordre qu'ils ont été déclarés (l'ordre normal Python des décorateurs est appliqué). Cela signifie du bas vers le haut, donc dans l'exemple ci-dessus, l'objet simulacre pour `module.ClassName1` est passé en premier.

Avec `patch()`, il est important de patcher les objets dans l'espace de nommage où ils sont recherchés. C'est ce qui se fait normalement, mais pour un guide rapide, lisez où patcher.

Comme tout décorateur, `patch()` peut être utilisé comme gestionnaire de contexte avec une instruction with :

    >>> 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)

Il existe également `patch.dict()` pour définir des valeurs d'un dictionnaire au sein d'une portée et restaurer ce dictionnaire à son état d'origine lorsque le test se termine :

    >>> foo = {'key': 'value'}
    >>> original = foo.copy()
    >>> with patch.dict(foo, {'newkey': 'newvalue'}, clear=True):
    ...     assert foo == {'newkey': 'newvalue'}
    ...
    >>> assert foo == original

Mock gère le remplacement des méthodes magiques de Python. La façon la plus simple d'utiliser les méthodes magiques est la classe `MagicMock`. Elle permet de faire des choses comme :

    >>> mock = MagicMock()
    >>> mock.__str__.return_value = 'foobarbaz'
    >>> str(mock)
    'foobarbaz'
    >>> mock.__str__.assert_called_with()

Mock permet d'assigner des fonctions (ou d'autres instances Mock) à des méthodes magiques et elles seront appelées correctement. La classe `MagicMock` est juste une variante de Mock qui a toutes les méthodes magiques pré-créées (enfin, toutes les méthodes utiles).

L'exemple suivant est un exemple de création de méthodes magiques avec la classe Mock ordinaire :

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

Pour être sûr que les objets simulacres dans vos tests ont la même API que les objets qu'ils remplacent, utilisez l'auto-spécification. L'auto-spécification peut se faire via l'argument autospec de patch ou par la fonction `create_autospec()`. L'auto-spécification crée des objets simulacres qui ont les mêmes attributs et méthodes que les objets qu'ils remplacent, et toutes les fonctions et méthodes (y compris les constructeurs) ont les mêmes signatures d'appel que l'objet réel.

Ceci garantit que vos objets simulacres échouent de la même manière que votre code de production s'ils ne sont pas utilisés correctement :

    >>> 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)

La fonction `create_autospec()` peut aussi être utilisée sur les classes, où elle copie la signature de la méthode `__init__`, et sur les objets appelables où elle copie la signature de la méthode `__call__`.

## La classe Mock

La classe `Mock` est un objet simulacre flexible destiné à remplacer l'utilisation d'objets bouchons et factices dans votre code. Les Mocks sont appelables et créent des attributs comme de nouveaux Mocks lorsque l'on y accède [1]. L'accès au même attribut renvoie toujours le même mock. Les simulacres enregistrent la façon dont ils sont utilisés, ce qui permet de faire des assertions sur ce que le code leur a fait.

La classe `MagicMock` est une sous-classe de `Mock` avec toutes les méthodes magiques pré-créées et prête à l'emploi. Il existe également des variantes non appelables, utiles lorsque l'on simule des objets qui ne sont pas appelables : `NonCallableMock` et `NonCallableMagicMock`

Le décorateur `patch()` facilite le remplacement temporaire de classes d'un module avec un objet `Mock`. Par défaut `patch()` crée un `MagicMock`. On peut spécifier une classe alternative de `Mock` en utilisant le paramètre new_callable de `patch()`.

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

  Crée un nouvel objet `Mock`. `Mock` prend plusieurs arguments optionnels qui spécifient le comportement de l'objet Mock :

  - spec : une liste de chaînes de caractères ou un objet existant (une classe ou une instance) qui sert de spécification pour l'objet simulacre. Si on passe un objet, alors une liste de chaînes de caractères est formée en appelant la fonction dir sur l'objet (à l'exclusion des attributs et méthodes magiques non pris en charge). L'accès à un attribut qui n'est pas dans cette liste entraîne la levée d'une exception `AttributeError`.

    Si spec est un objet (plutôt qu'une liste de chaînes de caractères) alors `__class__` renvoie la classe de l'objet spécifié. Ceci permet aux mocks de passer les tests `isinstance()`.

  - spec_set : variante plus stricte de spec. S'il est utilisé, essayer d'utiliser la fonction set ou tenter d’accéder à un attribut sur le mock qui n'est pas sur l'objet passé comme spec_set lève une exception `AttributeError`.

  - side_effect : fonction à appeler à chaque fois que le Mock est appelé. Voir l'attribut `side_effect`. Utile pour lever des exceptions ou modifier dynamiquement les valeurs de retour. La fonction est appelée avec les mêmes arguments que la fonction simulée et, à moins qu'elle ne renvoie `DEFAULT`, la valeur de retour de cette fonction devient la valeur de retour de la fonction simulée.

    side_effect peut être soit une classe, soit une instance d'exception. Dans ce cas, l'exception est levée lors de l'appel de l'objet simulacre.

    Si side_effect est un itérable alors chaque appel au mock renvoie la valeur suivante de l’itérable.

    Utilisez `None` pour remettre à zéro un side_effect.

  - return_value : valeur renvoyée lors de l'appel de l'objet simulacre. Par défaut, il s'agit d'un nouveau Mock (créé lors du premier accès). Voir l'attribut `return_value`.

  - unsafe: By default, accessing any attribute whose name starts with assert, assret, asert, aseert or assrt will raise an `AttributeError`. Passing `unsafe=True` will allow access to these attributes.

    Nouveau dans la version 3.5.

  - wraps : élément que le simulacre doit simuler. Si wraps n'est pas `None` alors appeler Mock passe l'appel à l'objet simulé (renvoyant le résultat réel). L'accès à un attribut sur le mock renvoie un objet Mock qui simule l'attribut correspondant de l'objet simulé (donc essayer d'accéder à un attribut qui n'existe pas lève une exception `AttributeError`).

    Si l'objet simulacre a un ensemble explicite de return_value alors les appels ne sont pas passés à l'objet simulé et c'est return_value qui est renvoyée à la place.

  - name : Si le mock a un nom, il est alors utilisé par la fonction repr du mock. C'est utile pour le débogage. Le nom est propagé aux enfants de l'objet mock.

  Les mocks peuvent aussi être appelés avec des arguments par mots-clés arbitraires. Ceux-ci sont utilisés pour définir les attributs sur le mock après sa création. Voir la méthode `configure_mock()` pour plus de détails.

  assert_called()

    Asserter que le mock a été appelé au moins une fois.

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

    Nouveau dans la version 3.6.

  assert_called_once()

    Asserter que le mock a été appelé exactement une fois.

        >>> 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.

    Nouveau dans la version 3.6.

  assert_called_with(*args, **kwargs)

    This method is a convenient way of asserting that the last call has been 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 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)

    Asserter que le simulacre a été appelé avec les arguments spécifiés.

    Asserter que le simulacre a bien été appelé avec les arguments au cours de la vie du simulacre. Contrairement à `assert_called_with()` et `assert_called_once_with()` qui passent seulement si l'appel demandé correspond bien au dernier appel, et dans le cas de `assert_called_once_with()` l'appel au simulacre doit être unique.

        >>> 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)

    Asserter que le simulacre a été appelé avec les appels spécifiés. L'attribut `mock_calls` est comparé à la liste des appels.

    If any_order is false then the calls must be sequential. There can be extra calls before or after the specified calls.

    Si any_order est vrai alors les appels peuvent être dans n'importe quel ordre, mais ils doivent tous apparaître dans `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()

    Asserter que le simulacre n'a jamais été appelé.

        >>> 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.

    Nouveau dans la version 3.5.

  reset_mock(*, return_value=False, side_effect=False)

    La méthode reset_mock réinitialise tous les attributs d'appel sur un simulacre :

        >>> mock = Mock(return_value=None)
        >>> mock('hello')
        >>> mock.called
        True
        >>> mock.reset_mock()
        >>> mock.called
        False

    Modifié dans la version 3.6: Added two keyword-only arguments to the reset_mock function.

    Utile pour faire une série d'assertions qui réutilisent le même objet. Attention `reset_mock()` ne réinitialise pas la valeur de retour, les `side_effect` ou tout attribut enfant que vous avez défini en utilisant l'affectation normale par défaut. Pour réinitialiser return_value ou `side_effect`, utiliser les paramètres correspondants avec la valeur `True`. Les simulacres enfants et le simulacre de valeur de retour (le cas échéant) seront également réinitialisés.

    Note

    return_value, and `side_effect` are keyword-only arguments.

  mock_add_spec(spec, spec_set=False)

    Ajoute une spécification à un simulacre. spec peut être un objet ou une liste de chaînes de caractères. Seuls les attributs de la spécification spec peuvent être récupérés en tant qu'attributs du simulacre.

    Si spec_set est vrai, seuls les attributs de la spécification peuvent être définis.

  attach_mock(mock, attribute)

    Attache un simulacre comme attribut de l'instance courante, en remplaçant son nom et son parent. Les appels au simulacre attaché sont enregistrés dans les attributs `method_calls` et `mock_calls` de l'instance courante.

  configure_mock(**kwargs)

    Définir les attributs sur le simulacre à l'aide d'arguments nommés.

    Les attributs, les valeurs de retour et les effets de bords peuvent être définis sur des simulacres enfants en utilisant la notation par points standard et en dépaquetant un dictionnaire dans l'appel de méthode :

        >>> 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

    La même chose peut être réalisée en utilisant le constructeur des simulacres :

        >>> 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()` existe pour faciliter la configuration après la création du simulacre.

  __dir__()

    Les objets `Mock` limitent les résultats de `dir(un_mock)` à des résultats utiles. Pour les simulacres avec une spécification spec, cela inclut tous les attributs autorisés du simulacre.

    Voir `FILTER_DIR` pour savoir ce que fait ce filtrage, et comment le désactiver.

  _get_child_mock(**kw)

    Crée les simulacres enfants pour les attributs et la valeur de retour. Par défaut, les objets simulacre enfants sont du même type que le parent. Les sous-classes de Mock peuvent surcharger cette méthode pour personnaliser la façon dont les simulacres enfants sont créés.

    Pour les simulacres non appelables, la variante appelable est utilisée (plutôt qu'une sous-classe personnalisée).

  called

    Un booléen représentant si le simulacre a bien été appelé ou non :

        >>> mock = Mock(return_value=None)
        >>> mock.called
        False
        >>> mock()
        >>> mock.called
        True

  call_count

    Un entier indiquant combien de fois le simulacre a été appelé :

        >>> mock = Mock(return_value=None)
        >>> mock.call_count
        0
        >>> mock()
        >>> mock()
        >>> mock.call_count
        2

  return_value

    Définir cette option pour configurer la valeur renvoyé par appel du simulacre :

        >>> mock = Mock()
        >>> mock.return_value = 'fish'
        >>> mock()
        'fish'

    La valeur de revoie par défaut est un simulacre configurable normalement :

        >>> mock = Mock()
        >>> mock.return_value.attribute = sentinel.Attribute
        >>> mock.return_value()
        <Mock name='mock()()' id='...'>
        >>> mock.return_value.assert_called_with()

    L'attribut `return_value` peut également être défini dans le constructeur :

        >>> mock = Mock(return_value=3)
        >>> mock.return_value
        3
        >>> mock()
        3

  side_effect

    C'est soit une fonction à appeler lors de l'appel du simulacre, soit une exception (classe ou instance) à lever.

    Si vous passez une fonction, elle est appelée avec les mêmes arguments que la fonction simulée et à moins que la fonction ne renvoie le singleton `DEFAULT` l'appel le la fonction simulée renvoie ce que la fonction renvoie. Si la fonction renvoie `DEFAULT` alors le simulacre renvoie sa valeur normale (celle de `return_value`).

    Si vous passez un itérable, il est utilisé pour récupérer un itérateur qui doit renvoyer une valeur à chaque appel. Cette valeur peut être soit une instance d'exception à lever, soit une valeur à renvoyer à l'appel au simulacre (le traitement `DEFAULT` est identique au renvoie de la fonction simulée).

    Un exemple d'un simulacre qui lève une exception (pour tester la gestion des exceptions d'une API) :

        >>> mock = Mock()
        >>> mock.side_effect = Exception('Boom!')
        >>> mock()
        Traceback (most recent call last):
          ...
        Exception: Boom!

    Utiliser `side_effect` pour renvoyer une séquence de valeurs :

        >>> mock = Mock()
        >>> mock.side_effect = [3, 2, 1]
        >>> mock(), mock(), mock()
        (3, 2, 1)

    Utilisation d'un objet appelable :

        >>> mock = Mock(return_value=3)
        >>> def side_effect(*args, **kwargs):
        ...     return DEFAULT
        ...
        >>> mock.side_effect = side_effect
        >>> mock()
        3

    Un attribut `side_effect` peut être défini dans le constructeur. Voici un exemple qui ajoute un à la valeur du simulacre appelé et qui le renvoie :

        >>> side_effect = lambda value: value + 1
        >>> mock = Mock(side_effect=side_effect)
        >>> mock(3)
        4
        >>> mock(-8)
        -7

    Positionner `side_effect` sur `None` l'efface :

        >>> 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, which can also be accessed through the `args` property, is any ordered arguments the mock was called with (or an empty tuple) and the second member, which can also be accessed through the `kwargs` property, 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.call_args.args
        (3, 4)
        >>> mock.call_args.kwargs
        {}
        >>> mock(3, 4, 5, key='fish', next='w00t!')
        >>> mock.call_args
        call(3, 4, 5, key='fish', next='w00t!')
        >>> mock.call_args.args
        (3, 4, 5)
        >>> mock.call_args.kwargs
        {'key': 'fish', 'next': 'w00t!'}

    L'attribut `call_args`, ainsi que les éléments des listes `call_args_list`, `method_calls` et `mock_calls` sont des objets `call`. Ce sont des n-uplets, que l'on peut dépaqueter afin de faire des affirmations plus complexes sur chacun des arguments. Voir appels comme *n*-uplets.

    Modifié dans la version 3.8: Added `args` and `kwargs` properties.

  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.

    Note

    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:

    >>> 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.

Modifié dans la version 3.4: 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.

      >>> 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()

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

  An asynchronous version of `MagicMock`. The `AsyncMock` object will behave so the object is recognized as an async function, and the result of a call is an awaitable.

      >>> mock = AsyncMock()
      >>> asyncio.iscoroutinefunction(mock)
      True
      >>> inspect.isawaitable(mock())
      True

  The result of `mock()` is an async function which will have the outcome of `side_effect` or `return_value` after it has been awaited:

  - if `side_effect` is a function, the async function will return the result of that function,

  - if `side_effect` is an exception, the async function will raise the exception,

  - if `side_effect` is an iterable, the async function will return the next value of the iterable, however, if the sequence of result is exhausted, `StopAsyncIteration` is raised immediately,

  - if `side_effect` is not defined, the async function will return the value defined by `return_value`, hence, by default, the async function returns a new `AsyncMock` object.

  Setting the spec of a `Mock` or `MagicMock` to an async function will result in a coroutine object being returned after calling.

      >>> async def async_func(): pass
      ...
      >>> mock = MagicMock(async_func)
      >>> mock
      <MagicMock spec='function' id='...'>
      >>> mock()
      <coroutine object AsyncMockMixin._mock_call at ...>

  Setting the spec of a `Mock`, `MagicMock`, or `AsyncMock` to a class with asynchronous and synchronous functions will automatically detect the synchronous functions and set them as `MagicMock` (if the parent mock is `AsyncMock` or `MagicMock`) or `Mock` (if the parent mock is `Mock`). All asynchronous functions will be `AsyncMock`.

      >>> class ExampleClass:
      ...     def sync_foo():
      ...         pass
      ...     async def async_foo():
      ...         pass
      ...
      >>> a_mock = AsyncMock(ExampleClass)
      >>> a_mock.sync_foo
      <MagicMock name='mock.sync_foo' id='...'>
      >>> a_mock.async_foo
      <AsyncMock name='mock.async_foo' id='...'>
      >>> mock = Mock(ExampleClass)
      >>> mock.sync_foo
      <Mock name='mock.sync_foo' id='...'>
      >>> mock.async_foo
      <AsyncMock name='mock.async_foo' id='...'>

  Nouveau dans la version 3.8.

  assert_awaited()

    Assert that the mock was awaited at least once. Note that this is separate from the object having been called, the `await` keyword must be used:

        >>> mock = AsyncMock()
        >>> async def main(coroutine_mock):
        ...     await coroutine_mock
        ...
        >>> coroutine_mock = mock()
        >>> mock.called
        True
        >>> mock.assert_awaited()
        Traceback (most recent call last):
        ...
        AssertionError: Expected mock to have been awaited.
        >>> asyncio.run(main(coroutine_mock))
        >>> mock.assert_awaited()

  assert_awaited_once()

    Assert that the mock was awaited exactly once.

        >>> mock = AsyncMock()
        >>> async def main():
        ...     await mock()
        ...
        >>> asyncio.run(main())
        >>> mock.assert_awaited_once()
        >>> asyncio.run(main())
        >>> mock.method.assert_awaited_once()
        Traceback (most recent call last):
        ...
        AssertionError: Expected mock to have been awaited once. Awaited 2 times.

  assert_awaited_with(*args, **kwargs)

    Assert that the last await was with the specified arguments.

        >>> mock = AsyncMock()
        >>> async def main(*args, **kwargs):
        ...     await mock(*args, **kwargs)
        ...
        >>> asyncio.run(main('foo', bar='bar'))
        >>> mock.assert_awaited_with('foo', bar='bar')
        >>> mock.assert_awaited_with('other')
        Traceback (most recent call last):
        ...
        AssertionError: expected call not found.
        Expected: mock('other')
        Actual: mock('foo', bar='bar')

  assert_awaited_once_with(*args, **kwargs)

    Assert that the mock was awaited exactly once and with the specified arguments.

        >>> mock = AsyncMock()
        >>> async def main(*args, **kwargs):
        ...     await mock(*args, **kwargs)
        ...
        >>> asyncio.run(main('foo', bar='bar'))
        >>> mock.assert_awaited_once_with('foo', bar='bar')
        >>> asyncio.run(main('foo', bar='bar'))
        >>> mock.assert_awaited_once_with('foo', bar='bar')
        Traceback (most recent call last):
        ...
        AssertionError: Expected mock to have been awaited once. Awaited 2 times.

  assert_any_await(*args, **kwargs)

    Assert the mock has ever been awaited with the specified arguments.

        >>> mock = AsyncMock()
        >>> async def main(*args, **kwargs):
        ...     await mock(*args, **kwargs)
        ...
        >>> asyncio.run(main('foo', bar='bar'))
        >>> asyncio.run(main('hello'))
        >>> mock.assert_any_await('foo', bar='bar')
        >>> mock.assert_any_await('other')
        Traceback (most recent call last):
        ...
        AssertionError: mock('other') await not found

  assert_has_awaits(calls, any_order=False)

    Assert the mock has been awaited with the specified calls. The `await_args_list` list is checked for the awaits.

    If any_order is false then the awaits must be sequential. There can be extra calls before or after the specified awaits.

    If any_order is true then the awaits can be in any order, but they must all appear in `await_args_list`.

        >>> mock = AsyncMock()
        >>> async def main(*args, **kwargs):
        ...     await mock(*args, **kwargs)
        ...
        >>> calls = [call("foo"), call("bar")]
        >>> mock.assert_has_awaits(calls)
        Traceback (most recent call last):
        ...
        AssertionError: Awaits not found.
        Expected: [call('foo'), call('bar')]
        Actual: []
        >>> asyncio.run(main('foo'))
        >>> asyncio.run(main('bar'))
        >>> mock.assert_has_awaits(calls)

  assert_not_awaited()

    Assert that the mock was never awaited.

        >>> mock = AsyncMock()
        >>> mock.assert_not_awaited()

  reset_mock(*args, **kwargs)

    See `Mock.reset_mock()`. Also sets `await_count` to 0, `await_args` to None, and clears the `await_args_list`.

  await_count

    An integer keeping track of how many times the mock object has been awaited.

        >>> mock = AsyncMock()
        >>> async def main():
        ...     await mock()
        ...
        >>> asyncio.run(main())
        >>> mock.await_count
        1
        >>> asyncio.run(main())
        >>> mock.await_count
        2

  await_args

    This is either `None` (if the mock hasn’t been awaited), or the arguments that the mock was last awaited with. Functions the same as `Mock.call_args`.

        >>> mock = AsyncMock()
        >>> async def main(*args):
        ...     await mock(*args)
        ...
        >>> mock.await_args
        >>> asyncio.run(main('foo'))
        >>> mock.await_args
        call('foo')
        >>> asyncio.run(main('bar'))
        >>> mock.await_args
        call('bar')

  await_args_list

    This is a list of all the awaits made to the mock object in sequence (so the length of the list is the number of times it has been awaited). Before any awaits have been made it is an empty list.

        >>> mock = AsyncMock()
        >>> async def main(*args):
        ...     await mock(*args)
        ...
        >>> mock.await_args_list
        []
        >>> asyncio.run(main('foo'))
        >>> mock.await_args_list
        [call('foo')]
        >>> asyncio.run(main('bar'))
        >>> mock.await_args_list
        [call('foo'), call('bar')]

### 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:

    >>> 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

### Deleting Attributes

Mock objects create attributes on demand. This allows them to pretend to be objects of any type.

You may want a mock object to return `False` to a `hasattr()` call, or raise an `AttributeError` when an attribute is fetched. You can do this by providing an object as a `spec` for a mock, but that isn't always convenient.

You "block" attributes by deleting them. Once deleted, accessing an attribute will raise an `AttributeError`.

    >>> mock = MagicMock()
    >>> hasattr(mock, 'm')
    True
    >>> del mock.m
    >>> hasattr(mock, 'm')
    False
    >>> del mock.f
    >>> mock.f
    Traceback (most recent call last):
        ...
    AttributeError: f

### 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()
    >>> 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 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

Note

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 an `AsyncMock` if the patched object is an async function or a `MagicMock` otherwise. 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 `AsyncMock` is used for async functions and `MagicMock` for the rest.

  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!

  Note

  Modifié dans la 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 `AsyncMock` if the patched object is asynchronous, to `MagicMock` otherwise or to new_callable if specified.

  `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

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)
    ... def test():
    ...     assert sys.non_existing_attribute == 42
    ...
    >>> test()
    Traceback (most recent call last):
      ...
    AttributeError: <module 'sys' (built-in)> does not have the attribute 'non_existing_attribute'

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)
    ... def test(mock_stdout):
    ...     assert sys.non_existing_attribute == 42
    ...
    >>> test()

Modifié dans la version 3.8: `patch()` now returns an `AsyncMock` if the target is an async function.

### 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')
    ... 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

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.

  Modifié dans la version 3.8: `patch.dict()` now returns the patched dictionary when used as a context manager.

`patch.dict()` can be used as a context manager, decorator or class decorator:

    >>> foo = {}
    >>> @patch.dict(foo, {'newkey': 'newvalue'})
    ... def test():
    ...     assert foo == {'newkey': 'newvalue'}
    >>> test()
    >>> assert foo == {}

When used as a class decorator `patch.dict()` honours `patch.TEST_PREFIX` (default to `'test'`) for choosing which methods to wrap:

    >>> import os
    >>> import unittest
    >>> from unittest.mock import patch
    >>> @patch.dict('os.environ', {'newkey': 'newvalue'})
    ... class TestSample(unittest.TestCase):
    ...     def test_sample(self):
    ...         self.assertEqual(os.environ['newkey'], 'newvalue')

If you want to use a different prefix for your test, you can inform the patchers of the different prefix by setting `patch.TEST_PREFIX`. For more details about how to change the value of see TEST_PREFIX.

`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'}) as patched_foo:
    ...     assert foo == {'newkey': 'newvalue'}
    ...     assert patched_foo == {'newkey': 'newvalue'}
    ...     # You can add, update or delete keys of foo (or patched_foo, it's the same dict)
    ...     patched_foo['spam'] = 'eggs'
    ...
    >>> assert foo == {}
    >>> assert patched_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

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 manager 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
    ...

### patch methods: start and stop

All the patchers have `start()` and `stop()` methods. These make it simpler to do patching in `setUp` methods or where you want to do multiple patches without nesting decorators or with statements.

To use them call `patch()`, `patch.object()` or `patch.dict()` as normal and keep a reference to the returned `patcher` object. You can then call `start()` to put the patch in place and `stop()` to undo it.

If you are using `patch()` to create a mock for you then it will be returned by the call to `patcher.start`.

    >>> patcher = patch('package.module.ClassName')
    >>> from package import module
    >>> original = module.ClassName
    >>> new_mock = patcher.start()
    >>> assert module.ClassName is not original
    >>> assert module.ClassName is new_mock
    >>> patcher.stop()
    >>> assert module.ClassName is original
    >>> assert module.ClassName is not new_mock

A typical use case for this might be for doing multiple patches in the `setUp` method of a `TestCase`:

    >>> class MyTest(unittest.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()

Prudence

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(unittest.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`.

### patch builtins

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

### 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')
    ... 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

### Nesting Patch Decorators

If you want to perform multiple patches then you can simply stack up the decorators.

You can stack up multiple patch decorators using this pattern:

    >>> @patch.object(SomeClass, 'class_method')
    ... @patch.object(SomeClass, 'static_method')
    ... def test(mock1, mock2):
    ...     assert SomeClass.static_method is mock1
    ...     assert SomeClass.class_method is mock2
    ...     SomeClass.static_method('foo')
    ...     SomeClass.class_method('bar')
    ...     return mock1, mock2
    ...
    >>> mock1, mock2 = test()
    >>> mock1.assert_called_once_with('foo')
    >>> mock2.assert_called_once_with('bar')

Note that the decorators are applied from the bottom upwards. This is the standard way that Python applies decorators. The order of the created mocks passed into your test function matches this order.

### Where to patch

`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')

### 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)
    >>> 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__` et `__subclasses__`

- `__round__`, `__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__`, `__exit__`, `__aenter__` and `__aexit__`

- Unary numeric methods: `__neg__`, `__pos__` and `__invert__`

- The numeric methods (including right hand and in-place variants): `__add__`, `__sub__`, `__mul__`, `__matmul__`, `__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__`

- File system path representation: `__fspath__`

- Asynchronous iteration methods: `__aiter__` and `__anext__`

Modifié dans la version 3.8: Added support for `os.PathLike.__fspath__()`.

Modifié dans la version 3.8: Added support for `__aenter__`, `__aexit__`, `__aiter__` and `__anext__`.

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`

- `__int__`: `0`

- `__iter__`: `iter([])`

- `__exit__`: `False`

- `__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

Par exemple :

    >>> 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__` et `__delete__`

- `__reversed__` et `__missing__`

- `__reduce__`, `__reduce_ex__`, `__getinitargs__`, `__getnewargs__`, `__getstate__` and `__setstate__`

- `__getformat__`

[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.

  Modifié dans la version 3.7: 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()
    >>> assert result is sentinel.some_object
    >>> result
    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
    >>> kall.args
    (1, 2, 3)
    >>> kall.kwargs
    {'arg': 'one', 'arg2': 'two'}
    >>> kall.args is kall[0]
    True
    >>> kall.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
    {'arg': 'two', 'arg2': 'three'}
    >>> name is m.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()`.

Modifié dans la version 3.8: `create_autospec()` now returns an `AsyncMock` if the target is an async function.

### 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

`ANY` is not limited to comparisons with call objects and so can also be used in test assertions:

    class TestStringMethods(unittest.TestCase):

        def test_split(self):
            s = 'hello world'
            self.assertEqual(s.split(), ['hello', ANY])

### FILTER_DIR

unittest.mock.FILTER_DIR

`FILTER_DIR` is a module level variable that controls the way mock objects respond to `dir()`. 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',
     'assert_called_once',
     'assert_called_once_with',
     'assert_called_with',
     'assert_has_calls',
     'assert_not_called',
     'attach_mock',
     ...
    >>> from urllib import request
    >>> dir(Mock(spec=request))
    ['AbstractBasicAuthHandler',
     'AbstractDigestAuthHandler',
     'AbstractHTTPHandler',
     'BaseHandler',
     ...

Many of the not-very-useful (private to `Mock` rather than the thing being mocked) underscore and double underscore prefixed attributes have been filtered from the result of calling `dir()` on a `Mock`. If you dislike this behaviour you can switch it off by setting the module level switch `FILTER_DIR`:

    >>> from unittest import mock
    >>> mock.FILTER_DIR = False
    >>> dir(mock.Mock())
    ['_NonCallableMock__get_return_value',
     '_NonCallableMock__get_side_effect',
     '_NonCallableMock__return_value_doc',
     '_NonCallableMock__set_return_value',
     '_NonCallableMock__set_side_effect',
     '__call__',
     '__class__',
     ...

Alternatively you can just use `vars(my_mock)` (instance members) and `dir(type(my_mock))` (type members) to bypass the filtering irrespective of `mock.FILTER_DIR`.

### mock_open

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.

  Modifié dans la version 3.4: Added `readline()` and `readlines()` support. The mock of `read()` changed to consume read_data rather than returning it on each call.

  Modifié dans la version 3.5: read_data is now reset on each call to the mock.

  Modifié dans la version 3.8: 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:

    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

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)  # Intentional typo!

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  # Intentional typo!
    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()  # Intentional typo!

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  # Intentional typo!
    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.

### 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.

  Nouveau dans la version 3.7.

## Order of precedence of `side_effect`, `return_value` and wraps

The order of their precedence is:

1. `side_effect`

2. `return_value`

3. wraps

If all three are set, mock will return the value from `side_effect`, ignoring `return_value` and the wrapped object altogether. If any two are set, the one with the higher precedence will return the value. Regardless of the order of which was set first, the order of precedence remains unchanged.

    >>> from unittest.mock import Mock
    >>> class Order:
    ...     @staticmethod
    ...     def get_value():
    ...         return "third"
    ...
    >>> order_mock = Mock(spec=Order, wraps=Order)
    >>> order_mock.get_value.side_effect = ["first"]
    >>> order_mock.get_value.return_value = "second"
    >>> order_mock.get_value()
    'first'

As `None` is the default value of `side_effect`, if you reassign its value back to `None`, the order of precedence will be checked between `return_value` and the wrapped object, ignoring `side_effect`.

    >>> order_mock.get_value.side_effect = None
    >>> order_mock.get_value()
    'second'

If the value being returned by `side_effect` is `DEFAULT`, it is ignored and the order of precedence moves to the successor to obtain the value to return.

    >>> from unittest.mock import DEFAULT
    >>> order_mock.get_value.side_effect = [DEFAULT]
    >>> order_mock.get_value()
    'second'

When `Mock` wraps an object, the default value of `return_value` will be `DEFAULT`.

    >>> order_mock = Mock(spec=Order, wraps=Order)
    >>> order_mock.return_value
    sentinel.DEFAULT
    >>> order_mock.get_value.return_value
    sentinel.DEFAULT

The order of precedence will ignore this value and it will move to the last successor which is the wrapped object.

As the real call is being made to the wrapped object, creating an instance of this mock will return the real instance of the class. The positional arguments, if any, required by the wrapped object must be passed.

    >>> order_mock_instance = order_mock()
    >>> isinstance(order_mock_instance, Order)
    True
    >>> order_mock_instance.get_value()
    'third'

    >>> order_mock.get_value.return_value = DEFAULT
    >>> order_mock.get_value()
    'third'

    >>> order_mock.get_value.return_value = "second"
    >>> order_mock.get_value()
    'second'

But if you assign `None` to it, this will not be ignored as it is an explicit assignment. So, the order of precedence will not move to the wrapped object.

    >>> order_mock.get_value.return_value = None
    >>> order_mock.get_value() is None
    True

Even if you set all three at once when initializing the mock, the order of precedence remains the same:

    >>> order_mock = Mock(spec=Order, wraps=Order,
    ...                   **{"get_value.side_effect": ["first"],
    ...                      "get_value.return_value": "second"}
    ...                   )
    ...
    >>> order_mock.get_value()
    'first'
    >>> order_mock.get_value.side_effect = None
    >>> order_mock.get_value()
    'second'
    >>> order_mock.get_value.return_value = DEFAULT
    >>> order_mock.get_value()
    'third'

If `side_effect` is exhausted, the order of precedence will not cause a value to be obtained from the successors. Instead, `StopIteration` exception is raised.

    >>> order_mock = Mock(spec=Order, wraps=Order)
    >>> order_mock.get_value.side_effect = ["first side effect value",
    ...                                     "another side effect value"]
    >>> order_mock.get_value.return_value = "second"

    >>> order_mock.get_value()
    'first side effect value'
    >>> order_mock.get_value()
    'another side effect value'

    >>> order_mock.get_value()
    Traceback (most recent call last):
     ...
    StopIteration