26.5. "unittest.mock" --- mock object library
*********************************************

버전 3.3에 추가.

**Source code:** Lib/unittest/mock.py

======================================================================

"unittest.mock" is a library for testing in Python. It allows you to
replace parts of your system under test with mock objects and make
assertions about how they have been used.

"unittest.mock" provides a core "Mock" class removing the need to
create a host of stubs throughout your test suite. After performing an
action, you can make assertions about which methods / attributes were
used and arguments they were called with. You can also specify return
values and set needed attributes in the normal way.

Additionally, mock provides a "patch()" decorator that handles
patching module and class level attributes within the scope of a test,
along with "sentinel" for creating unique objects. See the quick guide
for some examples of how to use "Mock", "MagicMock" and "patch()".

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

There is a backport of "unittest.mock" for earlier versions of Python,
available as mock on PyPI.


26.5.1. Quick Guide
===================

"Mock" and "MagicMock" objects create all attributes and methods as
you access them and store details of how they have been used. You can
configure them, to specify return values or limit what attributes are
available, and then make assertions about how they have been used:

>>> from unittest.mock import MagicMock
>>> thing = ProductionClass()
>>> thing.method = MagicMock(return_value=3)
>>> thing.method(3, 4, 5, key='value')
3
>>> thing.method.assert_called_with(3, 4, 5, key='value')

"side_effect" allows you to perform side effects, including raising an
exception when a mock is called:

>>> mock = Mock(side_effect=KeyError('foo'))
>>> mock()
Traceback (most recent call last):
 ...
KeyError: 'foo'

>>> values = {'a': 1, 'b': 2, 'c': 3}
>>> def side_effect(arg):
...     return values[arg]
...
>>> mock.side_effect = side_effect
>>> mock('a'), mock('b'), mock('c')
(1, 2, 3)
>>> mock.side_effect = [5, 4, 3, 2, 1]
>>> mock(), mock(), mock()
(5, 4, 3)

Mock has many other ways you can configure it and control its
behaviour. For example the *spec* argument configures the mock to take
its specification from another object. Attempting to access attributes
or methods on the mock that don't exist on the spec will fail with an
"AttributeError".

The "patch()" decorator / context manager makes it easy to mock
classes or objects in a module under test. The object you specify will
be replaced with a mock (or other object) during the test and restored
when the test ends:

>>> from unittest.mock import patch
>>> @patch('module.ClassName2')
... @patch('module.ClassName1')
... def test(MockClass1, MockClass2):
...     module.ClassName1()
...     module.ClassName2()
...     assert MockClass1 is module.ClassName1
...     assert MockClass2 is module.ClassName2
...     assert MockClass1.called
...     assert MockClass2.called
...
>>> test()

참고:

  When you nest patch decorators the mocks are passed in to the
  decorated function in the same order they applied (the normal
  *python* order that decorators are applied). This means from the
  bottom up, so in the example above the mock for "module.ClassName1"
  is passed in first.With "patch()" it matters that you patch objects
  in the namespace where they are looked up. This is normally
  straightforward, but for a quick guide read where to patch.

As well as a decorator "patch()" can be used as a context manager in a
with statement:

>>> with patch.object(ProductionClass, 'method', return_value=None) as mock_method:
...     thing = ProductionClass()
...     thing.method(1, 2, 3)
...
>>> mock_method.assert_called_once_with(1, 2, 3)

There is also "patch.dict()" for setting values in a dictionary just
during a scope and restoring the dictionary to its original state when
the test ends:

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

Mock supports the mocking of Python magic methods. The easiest way of
using magic methods is with the "MagicMock" class. It allows you to do
things like:

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

Mock allows you to assign functions (or other Mock instances) to magic
methods and they will be called appropriately. The "MagicMock" class
is just a Mock variant that has all of the magic methods pre-created
for you (well, all the useful ones anyway).

The following is an example of using magic methods with the ordinary
Mock class:

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

For ensuring that the mock objects in your tests have the same api as
the objects they are replacing, you can use auto-speccing. Auto-
speccing can be done through the *autospec* argument to patch, or the
"create_autospec()" function. Auto-speccing creates mock objects that
have the same attributes and methods as the objects they are
replacing, and any functions and methods (including constructors) have
the same call signature as the real object.

This ensures that your mocks will fail in the same way as your
production code if they are used incorrectly:

>>> from unittest.mock import create_autospec
>>> def function(a, b, c):
...     pass
...
>>> mock_function = create_autospec(function, return_value='fishy')
>>> mock_function(1, 2, 3)
'fishy'
>>> mock_function.assert_called_once_with(1, 2, 3)
>>> mock_function('wrong arguments')
Traceback (most recent call last):
 ...
TypeError: <lambda>() takes exactly 3 arguments (1 given)

"create_autospec()" can also be used on classes, where it copies the
signature of the "__init__" method, and on callable objects where it
copies the signature of the "__call__" method.


26.5.2. The Mock Class
======================

"Mock" is a flexible mock object intended to replace the use of stubs
and test doubles throughout your code. Mocks are callable and create
attributes as new mocks when you access them [1]. Accessing the same
attribute will always return the same mock. Mocks record how you use
them, allowing you to make assertions about what your code has done to
them.

"MagicMock" is a subclass of "Mock" with all the magic methods pre-
created and ready to use. There are also non-callable variants, useful
when you are mocking out objects that aren't callable:
"NonCallableMock" and "NonCallableMagicMock"

The "patch()" decorators makes it easy to temporarily replace classes
in a particular module with a "Mock" object. By default "patch()" will
create a "MagicMock" for you. You can specify an alternative class of
"Mock" using the *new_callable* argument to "patch()".

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

   Create a new "Mock" object. "Mock" takes several optional arguments
   that specify the behaviour of the Mock object:

   * *spec*: This can be either a list of strings or an existing
     object (a class or instance) that acts as the specification for
     the mock object. If you pass in an object then a list of strings
     is formed by calling dir on the object (excluding unsupported
     magic attributes and methods). Accessing any attribute not in
     this list will raise an "AttributeError".

     If *spec* is an object (rather than a list of strings) then
     "__class__" returns the class of the spec object. This allows
     mocks to pass "isinstance()" tests.

   * *spec_set*: A stricter variant of *spec*. If used, attempting to
     *set* or get an attribute on the mock that isn't on the object
     passed as *spec_set* will raise an "AttributeError".

   * *side_effect*: A function to be called whenever the Mock is
     called. See the "side_effect" attribute. Useful for raising
     exceptions or dynamically changing return values. The function is
     called with the same arguments as the mock, and unless it returns
     "DEFAULT", the return value of this function is used as the
     return value.

     Alternatively *side_effect* can be an exception class or
     instance. In this case the exception will be raised when the mock
     is called.

     If *side_effect* is an iterable then each call to the mock will
     return the next value from the iterable.

     A *side_effect* can be cleared by setting it to "None".

   * *return_value*: The value returned when the mock is called. By
     default this is a new Mock (created on first access). See the
     "return_value" attribute.

   * *unsafe*: By default if any attribute starts with *assert* or
     *assret* will raise an "AttributeError". Passing "unsafe=True"
     will allow access to these attributes.

     버전 3.5에 추가.

   * *wraps*: Item for the mock object to wrap. If *wraps* is not
     "None" then calling the Mock will pass the call through to the
     wrapped object (returning the real result). Attribute access on
     the mock will return a Mock object that wraps the corresponding
     attribute of the wrapped object (so attempting to access an
     attribute that doesn't exist will raise an "AttributeError").

     If the mock has an explicit *return_value* set then calls are not
     passed to the wrapped object and the *return_value* is returned
     instead.

   * *name*: If the mock has a name then it will be used in the repr
     of the mock. This can be useful for debugging. The name is
     propagated to child mocks.

   Mocks can also be called with arbitrary keyword arguments. These
   will be used to set attributes on the mock after it is created. See
   the "configure_mock()" method for details.

   assert_called(*args, **kwargs)

      Assert that the mock was called at least once.

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

      버전 3.6에 추가.

   assert_called_once(*args, **kwargs)

      Assert that the mock was called exactly once.

      >>> mock = Mock()
      >>> mock.method()
      <Mock name='mock.method()' id='...'>
      >>> mock.method.assert_called_once()
      >>> mock.method()
      <Mock name='mock.method()' id='...'>
      >>> mock.method.assert_called_once()
      Traceback (most recent call last):
      ...
      AssertionError: Expected 'method' to have been called once. Called 2 times.

      버전 3.6에 추가.

   assert_called_with(*args, **kwargs)

      This method is a convenient way of asserting that calls are made
      in a particular way:

      >>> mock = Mock()
      >>> mock.method(1, 2, 3, test='wow')
      <Mock name='mock.method()' id='...'>
      >>> mock.method.assert_called_with(1, 2, 3, test='wow')

   assert_called_once_with(*args, **kwargs)

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

      >>> mock = Mock(return_value=None)
      >>> mock('foo', bar='baz')
      >>> mock.assert_called_once_with('foo', bar='baz')
      >>> mock('other', bar='values')
      >>> mock.assert_called_once_with('other', bar='values')
      Traceback (most recent call last):
        ...
      AssertionError: Expected 'mock' to be called once. Called 2 times.

   assert_any_call(*args, **kwargs)

      assert the mock has been called with the specified arguments.

      The assert passes if the mock has *ever* been called, unlike
      "assert_called_with()" and "assert_called_once_with()" that only
      pass if the call is the most recent one, and in the case of
      "assert_called_once_with()" it must also be the only call.

      >>> mock = Mock(return_value=None)
      >>> mock(1, 2, arg='thing')
      >>> mock('some', 'thing', 'else')
      >>> mock.assert_any_call(1, 2, arg='thing')

   assert_has_calls(calls, any_order=False)

      assert the mock has been called with the specified calls. The
      "mock_calls" list is checked for the calls.

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

      If *any_order* is true then the calls can be in any order, but
      they must all appear in "mock_calls".

      >>> mock = Mock(return_value=None)
      >>> mock(1)
      >>> mock(2)
      >>> mock(3)
      >>> mock(4)
      >>> calls = [call(2), call(3)]
      >>> mock.assert_has_calls(calls)
      >>> calls = [call(4), call(2), call(3)]
      >>> mock.assert_has_calls(calls, any_order=True)

   assert_not_called()

      Assert the mock was never called.

      >>> m = Mock()
      >>> m.hello.assert_not_called()
      >>> obj = m.hello()
      >>> m.hello.assert_not_called()
      Traceback (most recent call last):
        ...
      AssertionError: Expected 'hello' to not have been called. Called 1 times.

      버전 3.5에 추가.

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

      The reset_mock method resets all the call attributes on a mock
      object:

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

      버전 3.6에서 변경: Added two keyword only argument to the
      reset_mock function.

      This can be useful where you want to make a series of assertions
      that reuse the same object. Note that "reset_mock()" *doesn't*
      clear the return value, "side_effect" or any child attributes
      you have set using normal assignment by default. In case you
      want to reset *return_value* or "side_effect", then pass the
      corresponding parameter as "True". Child mocks and the return
      value mock (if any) are reset as well.

      참고:

        *return_value*, and "side_effect" are keyword only argument.

   mock_add_spec(spec, spec_set=False)

      Add a spec to a mock. *spec* can either be an object or a list
      of strings. Only attributes on the *spec* can be fetched as
      attributes from the mock.

      If *spec_set* is true then only attributes on the spec can be
      set.

   attach_mock(mock, attribute)

      Attach a mock as an attribute of this one, replacing its name
      and parent. Calls to the attached mock will be recorded in the
      "method_calls" and "mock_calls" attributes of this one.

   configure_mock(**kwargs)

      Set attributes on the mock through keyword arguments.

      Attributes plus return values and side effects can be set on
      child mocks using standard dot notation and unpacking a
      dictionary in the method call:

      >>> mock = Mock()
      >>> attrs = {'method.return_value': 3, 'other.side_effect': KeyError}
      >>> mock.configure_mock(**attrs)
      >>> mock.method()
      3
      >>> mock.other()
      Traceback (most recent call last):
        ...
      KeyError

      The same thing can be achieved in the constructor call to mocks:

      >>> attrs = {'method.return_value': 3, 'other.side_effect': KeyError}
      >>> mock = Mock(some_attribute='eggs', **attrs)
      >>> mock.some_attribute
      'eggs'
      >>> mock.method()
      3
      >>> mock.other()
      Traceback (most recent call last):
        ...
      KeyError

      "configure_mock()" exists to make it easier to do configuration
      after the mock has been created.

   __dir__()

      "Mock" objects limit the results of "dir(some_mock)" to useful
      results. For mocks with a *spec* this includes all the permitted
      attributes for the mock.

      See "FILTER_DIR" for what this filtering does, and how to switch
      it off.

   _get_child_mock(**kw)

      Create the child mocks for attributes and return value. By
      default child mocks will be the same type as the parent.
      Subclasses of Mock may want to override this to customize the
      way child mocks are made.

      For non-callable mocks the callable variant will be used (rather
      than any custom subclass).

   called

      A boolean representing whether or not the mock object has been
      called:

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

   call_count

      An integer telling you how many times the mock object has been
      called:

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

   return_value

      Set this to configure the value returned by calling the mock:

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

      The default return value is a mock object and you can configure
      it in the normal way:

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

      "return_value" can also be set in the constructor:

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

   side_effect

      This can either be a function to be called when the mock is
      called, an iterable or an exception (class or instance) to be
      raised.

      If you pass in a function it will be called with same arguments
      as the mock and unless the function returns the "DEFAULT"
      singleton the call to the mock will then return whatever the
      function returns. If the function returns "DEFAULT" then the
      mock will return its normal value (from the "return_value").

      If you pass in an iterable, it is used to retrieve an iterator
      which must yield a value on every call.  This value can either
      be an exception instance to be raised, or a value to be returned
      from the call to the mock ("DEFAULT" handling is identical to
      the function case).

      An example of a mock that raises an exception (to test exception
      handling of an API):

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

      Using "side_effect" to return a sequence of values:

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

      Using a callable:

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

      "side_effect" can be set in the constructor. Here's an example
      that adds one to the value the mock is called with and returns
      it:

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

      Setting "side_effect" to "None" clears it:

      >>> m = Mock(side_effect=KeyError, return_value=3)
      >>> m()
      Traceback (most recent call last):
       ...
      KeyError
      >>> m.side_effect = None
      >>> m()
      3

   call_args

      This is either "None" (if the mock hasn't been called), or the
      arguments that the mock was last called with. This will be in
      the form of a tuple: the first member is any ordered arguments
      the mock was called with (or an empty tuple) and the second
      member is any keyword arguments (or an empty dictionary).

      >>> mock = Mock(return_value=None)
      >>> print(mock.call_args)
      None
      >>> mock()
      >>> mock.call_args
      call()
      >>> mock.call_args == ()
      True
      >>> mock(3, 4)
      >>> mock.call_args
      call(3, 4)
      >>> mock.call_args == ((3, 4),)
      True
      >>> mock(3, 4, 5, key='fish', next='w00t!')
      >>> mock.call_args
      call(3, 4, 5, key='fish', next='w00t!')

      "call_args", along with members of the lists "call_args_list",
      "method_calls" and "mock_calls" are "call" objects. These are
      tuples, so they can be unpacked to get at the individual
      arguments and make more complex assertions. See calls as tuples.

   call_args_list

      This is a list of all the calls made to the mock object in
      sequence (so the length of the list is the number of times it
      has been called). Before any calls have been made it is an empty
      list. The "call" object can be used for conveniently
      constructing lists of calls to compare with "call_args_list".

      >>> mock = Mock(return_value=None)
      >>> mock()
      >>> mock(3, 4)
      >>> mock(key='fish', next='w00t!')
      >>> mock.call_args_list
      [call(), call(3, 4), call(key='fish', next='w00t!')]
      >>> expected = [(), ((3, 4),), ({'key': 'fish', 'next': 'w00t!'},)]
      >>> mock.call_args_list == expected
      True

      Members of "call_args_list" are "call" objects. These can be
      unpacked as tuples to get at the individual arguments. See calls
      as tuples.

   method_calls

      As well as tracking calls to themselves, mocks also track calls
      to methods and attributes, and *their* methods and attributes:

      >>> mock = Mock()
      >>> mock.method()
      <Mock name='mock.method()' id='...'>
      >>> mock.property.method.attribute()
      <Mock name='mock.property.method.attribute()' id='...'>
      >>> mock.method_calls
      [call.method(), call.property.method.attribute()]

      Members of "method_calls" are "call" objects. These can be
      unpacked as tuples to get at the individual arguments. See calls
      as tuples.

   mock_calls

      "mock_calls" records *all* calls to the mock object, its
      methods, magic methods *and* return value mocks.

      >>> mock = MagicMock()
      >>> result = mock(1, 2, 3)
      >>> mock.first(a=3)
      <MagicMock name='mock.first()' id='...'>
      >>> mock.second()
      <MagicMock name='mock.second()' id='...'>
      >>> int(mock)
      1
      >>> result(1)
      <MagicMock name='mock()()' id='...'>
      >>> expected = [call(1, 2, 3), call.first(a=3), call.second(),
      ... call.__int__(), call()(1)]
      >>> mock.mock_calls == expected
      True

      Members of "mock_calls" are "call" objects. These can be
      unpacked as tuples to get at the individual arguments. See calls
      as tuples.

      참고:

        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.

   버전 3.4에서 변경: 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()


26.5.2.1. 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


26.5.2.2. 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


26.5.2.3. 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"


26.5.2.4. 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.


26.5.3. 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.


26.5.3.1. patch
---------------

참고:

  "patch()" is straightforward to use. The key is to do the patching
  in the right namespace. See the section where to patch.

unittest.mock.patch(target, new=DEFAULT, spec=None, create=False, spec_set=None, autospec=None, new_callable=None, **kwargs)

   "patch()" acts as a function decorator, class decorator or a
   context manager. Inside the body of the function or with statement,
   the *target* is patched with a *new* object. When the function/with
   statement exits the patch is undone.

   If *new* is omitted, then the target is replaced with a
   "MagicMock". If "patch()" is used as a decorator and *new* is
   omitted, the created mock is passed in as an extra argument to the
   decorated function. If "patch()" is used as a context manager the
   created mock is returned by the context manager.

   *target* should be a string in the form
   "'package.module.ClassName'". The *target* is imported and the
   specified object replaced with the *new* object, so the *target*
   must be importable from the environment you are calling "patch()"
   from. The target is imported when the decorated function is
   executed, not at decoration time.

   The *spec* and *spec_set* keyword arguments are passed to the
   "MagicMock" if patch is creating one for you.

   In addition you can pass "spec=True" or "spec_set=True", which
   causes patch to pass in the object being mocked as the
   spec/spec_set object.

   *new_callable* allows you to specify a different class, or callable
   object, that will be called to create the *new* object. By default
   "MagicMock" is used.

   A more powerful form of *spec* is *autospec*. If you set
   "autospec=True" then the mock will be created with a spec from the
   object being replaced. All attributes of the mock will also have
   the spec of the corresponding attribute of the object being
   replaced. Methods and functions being mocked will have their
   arguments checked and will raise a "TypeError" if they are called
   with the wrong signature. For mocks replacing a class, their return
   value (the 'instance') will have the same spec as the class. See
   the "create_autospec()" function and Autospeccing.

   Instead of "autospec=True" you can pass "autospec=some_object" to
   use an arbitrary object as the spec instead of the one being
   replaced.

   By default "patch()" will fail to replace attributes that don't
   exist. If you pass in "create=True", and the attribute doesn't
   exist, patch will create the attribute for you when the patched
   function is called, and delete it again afterwards. This is useful
   for writing tests against attributes that your production code
   creates at runtime. It is off by default because it can be
   dangerous. With it switched on you can write passing tests against
   APIs that don't actually exist!

   참고:

     버전 3.5에서 변경: If you are patching builtins in a module then
     you don't need to pass "create=True", it will be added by
     default.

   Patch can be used as a "TestCase" class decorator. It works by
   decorating each test method in the class. This reduces the
   boilerplate code when your test methods share a common patchings
   set. "patch()" finds tests by looking for method names that start
   with "patch.TEST_PREFIX". By default this is "'test'", which
   matches the way "unittest" finds tests. You can specify an
   alternative prefix by setting "patch.TEST_PREFIX".

   Patch can be used as a context manager, with the with statement.
   Here the patching applies to the indented block after the with
   statement. If you use "as" then the patched object will be bound to
   the name after the "as"; very useful if "patch()" is creating a
   mock object for you.

   "patch()" takes arbitrary keyword arguments. These will be passed
   to the "Mock" (or *new_callable*) on construction.

   "patch.dict(...)", "patch.multiple(...)" and "patch.object(...)"
   are available for alternate use-cases.

"patch()" as function decorator, creating the mock for you and passing
it into the decorated function:

>>> @patch('__main__.SomeClass')
... def function(normal_argument, mock_class):
...     print(mock_class is SomeClass)
...
>>> function(None)
True

Patching a class replaces the class with a "MagicMock" *instance*. If
the class is instantiated in the code under test then it will be the
"return_value" of the mock that will be used.

If the class is instantiated multiple times you could use
"side_effect" to return a new mock each time. Alternatively you can
set the *return_value* to be anything you want.

To configure return values on methods of *instances* on the patched
class you must do this on the "return_value". For example:

>>> class Class:
...     def method(self):
...         pass
...
>>> with patch('__main__.Class') as MockClass:
...     instance = MockClass.return_value
...     instance.method.return_value = 'foo'
...     assert Class() is instance
...     assert Class().method() == 'foo'
...

If you use *spec* or *spec_set* and "patch()" is replacing a *class*,
then the return value of the created mock will have the same spec.

>>> Original = Class
>>> patcher = patch('__main__.Class', spec=True)
>>> MockClass = patcher.start()
>>> instance = MockClass()
>>> assert isinstance(instance, Original)
>>> patcher.stop()

The *new_callable* argument is useful where you want to use an
alternative class to the default "MagicMock" for the created mock. For
example, if you wanted a "NonCallableMock" to be used:

>>> thing = object()
>>> with patch('__main__.thing', new_callable=NonCallableMock) as mock_thing:
...     assert thing is mock_thing
...     thing()
...
Traceback (most recent call last):
  ...
TypeError: 'NonCallableMock' object is not callable

Another use case might be to replace an object with an "io.StringIO"
instance:

>>> from io import StringIO
>>> def foo():
...     print('Something')
...
>>> @patch('sys.stdout', new_callable=StringIO)
... def test(mock_stdout):
...     foo()
...     assert mock_stdout.getvalue() == 'Something\n'
...
>>> test()

When "patch()" is creating a mock for you, it is common that the first
thing you need to do is to configure the mock. Some of that
configuration can be done in the call to patch. Any arbitrary keywords
you pass into the call will be used to set attributes on the created
mock:

>>> patcher = patch('__main__.thing', first='one', second='two')
>>> mock_thing = patcher.start()
>>> mock_thing.first
'one'
>>> mock_thing.second
'two'

As well as attributes on the created mock attributes, like the
"return_value" and "side_effect", of child mocks can also be
configured. These aren't syntactically valid to pass in directly as
keyword arguments, but a dictionary with these as keys can still be
expanded into a "patch()" call using "**":

>>> config = {'method.return_value': 3, 'other.side_effect': KeyError}
>>> patcher = patch('__main__.thing', **config)
>>> mock_thing = patcher.start()
>>> mock_thing.method()
3
>>> mock_thing.other()
Traceback (most recent call last):
  ...
KeyError


26.5.3.2. 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()".


26.5.3.3. patch.dict
--------------------

patch.dict(in_dict, values=(), clear=False, **kwargs)

   Patch a dictionary, or dictionary like object, and restore the
   dictionary to its original state after the test.

   *in_dict* can be a dictionary or a mapping like container. If it is
   a mapping then it must at least support getting, setting and
   deleting items plus iterating over keys.

   *in_dict* can also be a string specifying the name of the
   dictionary, which will then be fetched by importing it.

   *values* can be a dictionary of values to set in the dictionary.
   *values* can also be an iterable of "(key, value)" pairs.

   If *clear* is true then the dictionary will be cleared before the
   new values are set.

   "patch.dict()" can also be called with arbitrary keyword arguments
   to set values in the dictionary.

   "patch.dict()" can be used as a context manager, decorator or class
   decorator. When used as a class decorator "patch.dict()" honours
   "patch.TEST_PREFIX" for choosing which methods to wrap.

"patch.dict()" can be used to add members to a dictionary, or simply
let a test change a dictionary, and ensure the dictionary is restored
when the test ends.

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

>>> import os
>>> with patch.dict('os.environ', {'newkey': 'newvalue'}):
...     print(os.environ['newkey'])
...
newvalue
>>> assert 'newkey' not in os.environ

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

>>> mymodule = MagicMock()
>>> mymodule.function.return_value = 'fish'
>>> with patch.dict('sys.modules', mymodule=mymodule):
...     import mymodule
...     mymodule.function('some', 'args')
...
'fish'

"patch.dict()" can be used with dictionary like objects that aren't
actually dictionaries. At the very minimum they must support item
getting, setting, deleting and either iteration or membership test.
This corresponds to the magic methods "__getitem__()",
"__setitem__()", "__delitem__()" and either "__iter__()" or
"__contains__()".

>>> class Container:
...     def __init__(self):
...         self.values = {}
...     def __getitem__(self, name):
...         return self.values[name]
...     def __setitem__(self, name, value):
...         self.values[name] = value
...     def __delitem__(self, name):
...         del self.values[name]
...     def __iter__(self):
...         return iter(self.values)
...
>>> thing = Container()
>>> thing['one'] = 1
>>> with patch.dict(thing, one=2, two=3):
...     assert thing['one'] == 2
...     assert thing['two'] == 3
...
>>> assert thing['one'] == 1
>>> assert list(thing) == ['one']


26.5.3.4. 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 manger is a dictionary where created mocks are keyed by
name:

>>> with patch.multiple('__main__', thing=DEFAULT, other=DEFAULT) as values:
...     assert 'other' in repr(values['other'])
...     assert 'thing' in repr(values['thing'])
...     assert values['thing'] is thing
...     assert values['other'] is other
...


26.5.3.5. 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(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()

조심:

  If you use this technique you must ensure that the patching is
  "undone" by calling "stop". This can be fiddlier than you might
  think, because if an exception is raised in the "setUp" then
  "tearDown" is not called. "unittest.TestCase.addCleanup()" makes
  this easier:

  >>> class MyTest(TestCase):
  ...     def setUp(self):
  ...         patcher = patch('package.module.Class')
  ...         self.MockClass = patcher.start()
  ...         self.addCleanup(patcher.stop)
  ...
  ...     def test_something(self):
  ...         assert package.module.Class is self.MockClass
  ...

  As an added bonus you no longer need to keep a reference to the
  "patcher" object.

It is also possible to stop all patches which have been started by
using "patch.stopall()".

patch.stopall()

   Stop all active patches. Only stops patches started with "start".


26.5.3.6. 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


26.5.3.7. 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


26.5.3.8. 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.


26.5.3.9. 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')


26.5.3.10. 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.


26.5.4. MagicMock and magic method support
==========================================


26.5.4.1. 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".

참고:

  If you use the *spec* keyword argument to create a mock then
  attempting to set a magic method that isn't in the spec will raise
  an "AttributeError".

The full list of supported magic methods is:

* "__hash__", "__sizeof__", "__repr__" and "__str__"

* "__dir__", "__format__" and "__subclasses__"

* "__floor__", "__trunc__" and "__ceil__"

* Comparisons: "__lt__", "__gt__", "__le__", "__ge__", "__eq__" and
  "__ne__"

* Container methods: "__getitem__", "__setitem__", "__delitem__",
  "__contains__", "__len__", "__iter__", "__reversed__" and
  "__missing__"

* Context manager: "__enter__" and "__exit__"

* Unary numeric methods: "__neg__", "__pos__" and "__invert__"

* The numeric methods (including right hand and in-place variants):
  "__add__", "__sub__", "__mul__", "__matmul__", "__div__",
  "__truediv__", "__floordiv__", "__mod__", "__divmod__",
  "__lshift__", "__rshift__", "__and__", "__xor__", "__or__", and
  "__pow__"

* Numeric conversion methods: "__complex__", "__int__", "__float__"
  and "__index__"

* Descriptor methods: "__get__", "__set__" and "__delete__"

* Pickling: "__reduce__", "__reduce_ex__", "__getinitargs__",
  "__getnewargs__", "__getstate__" and "__setstate__"

The following methods exist but are *not* supported as they are either
in use by mock, can't be set dynamically, or can cause problems:

* "__getattr__", "__setattr__", "__init__" and "__new__"

* "__prepare__", "__instancecheck__", "__subclasscheck__", "__del__"


26.5.4.2. Magic Mock
--------------------

There are two "MagicMock" variants: "MagicMock" and
"NonCallableMagicMock".

class unittest.mock.MagicMock(*args, **kw)

   "MagicMock" is a subclass of "Mock" with default implementations of
   most of the magic methods. You can use "MagicMock" without having
   to configure the magic methods yourself.

   The constructor parameters have the same meaning as for "Mock".

   If you use the *spec* or *spec_set* arguments then *only* magic
   methods that exist in the spec will be created.

class unittest.mock.NonCallableMagicMock(*args, **kw)

   A non-callable version of "MagicMock".

   The constructor parameters have the same meaning as for
   "MagicMock", with the exception of *return_value* and *side_effect*
   which have no meaning on a non-callable mock.

The magic methods are setup with "MagicMock" objects, so you can
configure them and use them in the usual way:

>>> mock = MagicMock()
>>> mock[3] = 'fish'
>>> mock.__setitem__.assert_called_with(3, 'fish')
>>> mock.__getitem__.return_value = 'result'
>>> mock[2]
'result'

By default many of the protocol methods are required to return objects
of a specific type. These methods are preconfigured with a default
return value, so that they can be used without you having to do
anything if you aren't interested in the return value. You can still
*set* the return value manually if you want to change the default.

Methods and their defaults:

* "__lt__": NotImplemented

* "__gt__": NotImplemented

* "__le__": NotImplemented

* "__ge__": NotImplemented

* "__int__": 1

* "__contains__": False

* "__len__": 0

* "__iter__": iter([])

* "__exit__": False

* "__complex__": 1j

* "__float__": 1.0

* "__bool__": True

* "__index__": 1

* "__hash__": default hash for the mock

* "__str__": default str for the mock

* "__sizeof__": default sizeof for the mock

For example:

>>> mock = MagicMock()
>>> int(mock)
1
>>> len(mock)
0
>>> list(mock)
[]
>>> object() in mock
False

The two equality methods, "__eq__()" and "__ne__()", are special. They
do the default equality comparison on identity, using the
"side_effect" attribute, unless you change their return value to
return something else:

   >>> MagicMock() == 3
   False
   >>> MagicMock() != 3
   True
   >>> mock = MagicMock()
   >>> mock.__eq__.return_value = True
   >>> mock == 3
   True

The return value of "MagicMock.__iter__()" can be any iterable object
and isn't required to be an iterator:

>>> mock = MagicMock()
>>> mock.__iter__.return_value = ['a', 'b', 'c']
>>> list(mock)
['a', 'b', 'c']
>>> list(mock)
['a', 'b', 'c']

If the return value *is* an iterator, then iterating over it once will
consume it and subsequent iterations will result in an empty list:

>>> mock.__iter__.return_value = iter(['a', 'b', 'c'])
>>> list(mock)
['a', 'b', 'c']
>>> list(mock)
[]

"MagicMock" has all of the supported magic methods configured except
for some of the obscure and obsolete ones. You can still set these up
if you want.

Magic methods that are supported but not setup by default in
"MagicMock" are:

* "__subclasses__"

* "__dir__"

* "__format__"

* "__get__", "__set__" and "__delete__"

* "__reversed__" and "__missing__"

* "__reduce__", "__reduce_ex__", "__getinitargs__", "__getnewargs__",
  "__getstate__" and "__setstate__"

* "__getformat__" and "__setformat__"

[2] Magic methods *should* be looked up on the class rather than the
    instance. Different versions of Python are inconsistent about
    applying this rule. The supported protocol methods should work
    with all supported versions of Python.

[3] The function is basically hooked up to the class, but each "Mock"
    instance is kept isolated from the others.


26.5.5. Helpers
===============


26.5.5.1. 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.

      The "sentinel" attributes don't preserve their identity when
      they are "copied" or "pickled".

Sometimes when testing you need to test that a specific object is
passed as an argument to another method, or returned. It can be common
to create named sentinel objects to test this. "sentinel" provides a
convenient way of creating and testing the identity of objects like
this.

In this example we monkey patch "method" to return
"sentinel.some_object":

>>> real = ProductionClass()
>>> real.method = Mock(name="method")
>>> real.method.return_value = sentinel.some_object
>>> result = real.method()
>>> assert result is sentinel.some_object
>>> sentinel.some_object
sentinel.some_object


26.5.5.2. 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.


26.5.5.3. call
--------------

unittest.mock.call(*args, **kwargs)

   "call()" is a helper object for making simpler assertions, for
   comparing with "call_args", "call_args_list", "mock_calls" and
   "method_calls". "call()" can also be used with
   "assert_has_calls()".

   >>> m = MagicMock(return_value=None)
   >>> m(1, 2, a='foo', b='bar')
   >>> m()
   >>> m.call_args_list == [call(1, 2, a='foo', b='bar'), call()]
   True

call.call_list()

   For a call object that represents multiple calls, "call_list()"
   returns a list of all the intermediate calls as well as the final
   call.

"call_list" is particularly useful for making assertions on "chained
calls". A chained call is multiple calls on a single line of code.
This results in multiple entries in "mock_calls" on a mock. Manually
constructing the sequence of calls can be tedious.

"call_list()" can construct the sequence of calls from the same
chained call:

>>> m = MagicMock()
>>> m(1).method(arg='foo').other('bar')(2.0)
<MagicMock name='mock().method().other()()' id='...'>
>>> kall = call(1).method(arg='foo').other('bar')(2.0)
>>> kall.call_list()
[call(1),
 call().method(arg='foo'),
 call().method().other('bar'),
 call().method().other()(2.0)]
>>> m.mock_calls == kall.call_list()
True

A "call" object is either a tuple of (positional args, keyword args)
or (name, positional args, keyword args) depending on how it was
constructed. When you construct them yourself this isn't particularly
interesting, but the "call" objects that are in the "Mock.call_args",
"Mock.call_args_list" and "Mock.mock_calls" attributes can be
introspected to get at the individual arguments they contain.

The "call" objects in "Mock.call_args" and "Mock.call_args_list" are
two-tuples of (positional args, keyword args) whereas the "call"
objects in "Mock.mock_calls", along with ones you construct yourself,
are three-tuples of (name, positional args, keyword args).

You can use their "tupleness" to pull out the individual arguments for
more complex introspection and assertions. The positional arguments
are a tuple (an empty tuple if there are no positional arguments) and
the keyword arguments are a dictionary:

>>> m = MagicMock(return_value=None)
>>> m(1, 2, 3, arg='one', arg2='two')
>>> kall = m.call_args
>>> args, kwargs = kall
>>> args
(1, 2, 3)
>>> kwargs
{'arg2': 'two', 'arg': 'one'}
>>> args is kall[0]
True
>>> kwargs is kall[1]
True

>>> m = MagicMock()
>>> m.foo(4, 5, 6, arg='two', arg2='three')
<MagicMock name='mock.foo()' id='...'>
>>> kall = m.mock_calls[0]
>>> name, args, kwargs = kall
>>> name
'foo'
>>> args
(4, 5, 6)
>>> kwargs
{'arg2': 'three', 'arg': 'two'}
>>> name is m.mock_calls[0][0]
True


26.5.5.4. 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()".


26.5.5.5. 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


26.5.5.6. FILTER_DIR
--------------------

unittest.mock.FILTER_DIR

"FILTER_DIR" is a module level variable that controls the way mock
objects respond to "dir()" (only for Python 2.6 or more recent). The
default is "True", which uses the filtering described below, to only
show useful members. If you dislike this filtering, or need to switch
it off for diagnostic purposes, then set "mock.FILTER_DIR = False".

With filtering on, "dir(some_mock)" shows only useful attributes and
will include any dynamically created attributes that wouldn't normally
be shown. If the mock was created with a *spec* (or *autospec* of
course) then all the attributes from the original are shown, even if
they haven't been accessed yet:

>>> dir(Mock())
['assert_any_call',
 'assert_called_once_with',
 'assert_called_with',
 'assert_has_calls',
 'attach_mock',
 ...
>>> 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".


26.5.5.7. 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.

   버전 3.4에서 변경: Added "readline()" and "readlines()" support.
   The mock of "read()" changed to consume *read_data* rather than
   returning it on each call.

   버전 3.5에서 변경: *read_data* is now reset on each call to the
   *mock*.

Using "open()" as a context manager is a great way to ensure your file
handles are closed properly and is becoming common:

   with open('/some/path', 'w') as f:
       f.write('something')

The issue is that even if you mock out the call to "open()" it is the
*returned object* that is used as a context manager (and has
"__enter__()" and "__exit__()" called).

Mocking context managers with a "MagicMock" is common enough and
fiddly enough that a helper function is useful.

>>> m = mock_open()
>>> with patch('__main__.open', m):
...     with open('foo', 'w') as h:
...         h.write('some stuff')
...
>>> m.mock_calls
[call('foo', 'w'),
 call().__enter__(),
 call().write('some stuff'),
 call().__exit__(None, None, None)]
>>> m.assert_called_once_with('foo', 'w')
>>> handle = m()
>>> handle.write.assert_called_once_with('some stuff')

And for reading files:

>>> with patch('__main__.open', mock_open(read_data='bibble')) as m:
...     with open('foo') as h:
...         result = h.read()
...
>>> m.assert_called_once_with('foo')
>>> assert result == 'bibble'


26.5.5.8. Autospeccing
----------------------

Autospeccing is based on the existing "spec" feature of mock. It
limits the api of mocks to the api of an original object (the spec),
but it is recursive (implemented lazily) so that attributes of mocks
only have the same api as the attributes of the spec. In addition
mocked functions / methods have the same call signature as the
original so they raise a "TypeError" if they are called incorrectly.

Before I explain how auto-speccing works, here's why it is needed.

"Mock" is a very powerful and flexible object, but it suffers from two
flaws when used to mock out objects from a system under test. One of
these flaws is specific to the "Mock" api and the other is a more
general problem with using mock objects.

First the problem specific to "Mock". "Mock" has two assert methods
that are extremely handy: "assert_called_with()" and
"assert_called_once_with()".

>>> mock = Mock(name='Thing', return_value=None)
>>> mock(1, 2, 3)
>>> mock.assert_called_once_with(1, 2, 3)
>>> mock(1, 2, 3)
>>> mock.assert_called_once_with(1, 2, 3)
Traceback (most recent call last):
 ...
AssertionError: Expected 'mock' to be called once. Called 2 times.

Because mocks auto-create attributes on demand, and allow you to call
them with arbitrary arguments, if you misspell one of these assert
methods then your assertion is gone:

   >>> mock = Mock(name='Thing', return_value=None)
   >>> mock(1, 2, 3)
   >>> mock.assret_called_once_with(4, 5, 6)

Your tests can pass silently and incorrectly because of the typo.

The second issue is more general to mocking. If you refactor some of
your code, rename members and so on, any tests for code that is still
using the *old api* but uses mocks instead of the real objects will
still pass. This means your tests can all pass even though your code
is broken.

Note that this is another reason why you need integration tests as
well as unit tests. Testing everything in isolation is all fine and
dandy, but if you don't test how your units are "wired together" there
is still lots of room for bugs that tests might have caught.

"mock" already provides a feature to help with this, called speccing.
If you use a class or instance as the "spec" for a mock then you can
only access attributes on the mock that exist on the real class:

>>> from urllib import request
>>> mock = Mock(spec=request.Request)
>>> mock.assret_called_with
Traceback (most recent call last):
 ...
AttributeError: Mock object has no attribute 'assret_called_with'

The spec only applies to the mock itself, so we still have the same
issue with any methods on the mock:

   >>> mock.has_data()
   <mock.Mock object at 0x...>
   >>> mock.has_data.assret_called_with()

Auto-speccing solves this problem. You can either pass "autospec=True"
to "patch()" / "patch.object()" or use the "create_autospec()"
function to create a mock with a spec. If you use the "autospec=True"
argument to "patch()" then the object that is being replaced will be
used as the spec object. Because the speccing is done "lazily" (the
spec is created as attributes on the mock are accessed) you can use it
with very complex or deeply nested objects (like modules that import
modules that import modules) without a big performance hit.

Here's an example of it in use:

>>> from urllib import request
>>> patcher = patch('__main__.request', autospec=True)
>>> mock_request = patcher.start()
>>> request is mock_request
True
>>> mock_request.Request
<MagicMock name='request.Request' spec='Request' id='...'>

You can see that "request.Request" has a spec. "request.Request" takes
two arguments in the constructor (one of which is *self*). Here's what
happens if we try to call it incorrectly:

>>> req = request.Request()
Traceback (most recent call last):
 ...
TypeError: <lambda>() takes at least 2 arguments (1 given)

The spec also applies to instantiated classes (i.e. the return value
of specced mocks):

>>> req = request.Request('foo')
>>> req
<NonCallableMagicMock name='request.Request()' spec='Request' id='...'>

"Request" objects are not callable, so the return value of
instantiating our mocked out "request.Request" is a non-callable mock.
With the spec in place any typos in our asserts will raise the correct
error:

>>> req.add_header('spam', 'eggs')
<MagicMock name='request.Request().add_header()' id='...'>
>>> req.add_header.assret_called_with
Traceback (most recent call last):
 ...
AttributeError: Mock object has no attribute 'assret_called_with'
>>> req.add_header.assert_called_with('spam', 'eggs')

In many cases you will just be able to add "autospec=True" to your
existing "patch()" calls and then be protected against bugs due to
typos and api changes.

As well as using *autospec* through "patch()" there is a
"create_autospec()" for creating autospecced mocks directly:

>>> from urllib import request
>>> mock_request = create_autospec(request)
>>> mock_request.Request('foo', 'bar')
<NonCallableMagicMock name='mock.Request()' spec='Request' id='...'>

This isn't without caveats and limitations however, which is why it is
not the default behaviour. In order to know what attributes are
available on the spec object, autospec has to introspect (access
attributes) the spec. As you traverse attributes on the mock a
corresponding traversal of the original object is happening under the
hood. If any of your specced objects have properties or descriptors
that can trigger code execution then you may not be able to use
autospec. On the other hand it is much better to design your objects
so that introspection is safe [4].

A more serious problem is that it is common for instance attributes to
be created in the "__init__()" method and not to exist on the class at
all. *autospec* can't know about any dynamically created attributes
and restricts the api to visible attributes.

>>> class Something:
...   def __init__(self):
...     self.a = 33
...
>>> with patch('__main__.Something', autospec=True):
...   thing = Something()
...   thing.a
...
Traceback (most recent call last):
  ...
AttributeError: Mock object has no attribute 'a'

There are a few different ways of resolving this problem. The easiest,
but not necessarily the least annoying, way is to simply set the
required attributes on the mock after creation. Just because
*autospec* doesn't allow you to fetch attributes that don't exist on
the spec it doesn't prevent you setting them:

>>> with patch('__main__.Something', autospec=True):
...   thing = Something()
...   thing.a = 33
...

There is a more aggressive version of both *spec* and *autospec* that
*does* prevent you setting non-existent attributes. This is useful if
you want to ensure your code only *sets* valid attributes too, but
obviously it prevents this particular scenario:

>>> with patch('__main__.Something', autospec=True, spec_set=True):
...   thing = Something()
...   thing.a = 33
...
Traceback (most recent call last):
 ...
AttributeError: Mock object has no attribute 'a'

Probably the best way of solving the problem is to add class
attributes as default values for instance members initialised in
"__init__()". Note that if you are only setting default attributes in
"__init__()" then providing them via class attributes (shared between
instances of course) is faster too. e.g.

   class Something:
       a = 33

This brings up another issue. It is relatively common to provide a
default value of "None" for members that will later be an object of a
different type. "None" would be useless as a spec because it wouldn't
let you access *any* attributes or methods on it. As "None" is *never*
going to be useful as a spec, and probably indicates a member that
will normally of some other type, autospec doesn't use a spec for
members that are set to "None". These will just be ordinary mocks
(well - MagicMocks):

>>> class Something:
...     member = None
...
>>> mock = create_autospec(Something)
>>> mock.member.foo.bar.baz()
<MagicMock name='mock.member.foo.bar.baz()' id='...'>

If modifying your production classes to add defaults isn't to your
liking then there are more options. One of these is simply to use an
instance as the spec rather than the class. The other is to create a
subclass of the production class and add the defaults to the subclass
without affecting the production class. Both of these require you to
use an alternative object as the spec. Thankfully "patch()" supports
this - you can simply pass the alternative object as the *autospec*
argument:

>>> class Something:
...   def __init__(self):
...     self.a = 33
...
>>> class SomethingForTest(Something):
...   a = 33
...
>>> p = patch('__main__.Something', autospec=SomethingForTest)
>>> mock = p.start()
>>> mock.a
<NonCallableMagicMock name='Something.a' spec='int' id='...'>

[4] This only applies to classes or already instantiated objects.
    Calling a mocked class to create a mock instance *does not* create
    a real instance. It is only attribute lookups - along with calls
    to "dir()" - that are done.
