"unittest.mock" — getting started
*********************************

New in version 3.3.


Using Mock
==========


Mock Patching Methods
---------------------

Common uses for "Mock" objects include:

* Patching methods

* Recording method calls on objects

You might want to replace a method on an object to check that it is
called with the correct arguments by another part of the system:

>>> real = SomeClass()
>>> real.method = MagicMock(name='method')
>>> real.method(3, 4, 5, key='value')
<MagicMock name='method()' id='...'>

Once our mock has been used ("real.method" in this example) it has
methods and attributes that allow you to make assertions about how it
has been used.

Note:

  In most of these examples the "Mock" and "MagicMock" classes are
  interchangeable. As the "MagicMock" is the more capable class it
  makes a sensible one to use by default.

Once the mock has been called its "called" attribute is set to "True".
More importantly we can use the "assert_called_with()" or
"assert_called_once_with()" method to check that it was called with
the correct arguments.

This example tests that calling "ProductionClass().method" results in
a call to the "something" method:

>>> class ProductionClass:
...     def method(self):
...         self.something(1, 2, 3)
...     def something(self, a, b, c):
...         pass
...
>>> real = ProductionClass()
>>> real.something = MagicMock()
>>> real.method()
>>> real.something.assert_called_once_with(1, 2, 3)


Mock for Method Calls on an Object
----------------------------------

In the last example we patched a method directly on an object to check
that it was called correctly. Another common use case is to pass an
object into a method (or some part of the system under test) and then
check that it is used in the correct way.

The simple "ProductionClass" below has a "closer" method. If it is
called with an object then it calls "close" on it.

>>> class ProductionClass:
...     def closer(self, something):
...         something.close()
...

So to test it we need to pass in an object with a "close" method and
check that it was called correctly.

>>> real = ProductionClass()
>>> mock = Mock()
>>> real.closer(mock)
>>> mock.close.assert_called_with()

We don’t have to do any work to provide the ‘close’ method on our
mock. Accessing close creates it. So, if ‘close’ hasn’t already been
called then accessing it in the test will create it, but
"assert_called_with()" will raise a failure exception.


Mocking Classes
---------------

A common use case is to mock out classes instantiated by your code
under test. When you patch a class, then that class is replaced with a
mock. Instances are created by *calling the class*. This means you
access the “mock instance” by looking at the return value of the
mocked class.

In the example below we have a function "some_function" that
instantiates "Foo" and calls a method on it. The call to "patch()"
replaces the class "Foo" with a mock. The "Foo" instance is the result
of calling the mock, so it is configured by modifying the mock
"return_value".

   >>> def some_function():
   ...     instance = module.Foo()
   ...     return instance.method()
   ...
   >>> with patch('module.Foo') as mock:
   ...     instance = mock.return_value
   ...     instance.method.return_value = 'the result'
   ...     result = some_function()
   ...     assert result == 'the result'


Naming your mocks
-----------------

It can be useful to give your mocks a name. The name is shown in the
repr of the mock and can be helpful when the mock appears in test
failure messages. The name is also propagated to attributes or methods
of the mock:

>>> mock = MagicMock(name='foo')
>>> mock
<MagicMock name='foo' id='...'>
>>> mock.method
<MagicMock name='foo.method' id='...'>


Tracking all Calls
------------------

Often you want to track more than a single call to a method. The
"mock_calls" attribute records all calls to child attributes of the
mock - and also to their children.

>>> mock = MagicMock()
>>> mock.method()
<MagicMock name='mock.method()' id='...'>
>>> mock.attribute.method(10, x=53)
<MagicMock name='mock.attribute.method()' id='...'>
>>> mock.mock_calls
[call.method(), call.attribute.method(10, x=53)]

If you make an assertion about "mock_calls" and any unexpected methods
have been called, then the assertion will fail. This is useful because
as well as asserting that the calls you expected have been made, you
are also checking that they were made in the right order and with no
additional calls:

You use the "call" object to construct lists for comparing with
"mock_calls":

>>> expected = [call.method(), call.attribute.method(10, x=53)]
>>> mock.mock_calls == expected
True

However, parameters to calls that return mocks are not recorded, which
means it is not possible to track nested calls where the parameters
used to create ancestors are important:

>>> m = Mock()
>>> m.factory(important=True).deliver()
<Mock name='mock.factory().deliver()' id='...'>
>>> m.mock_calls[-1] == call.factory(important=False).deliver()
True


Setting Return Values and Attributes
------------------------------------

Setting the return values on a mock object is trivially easy:

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

Of course you can do the same for methods on the mock:

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

The return value can also be set in the constructor:

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

If you need an attribute setting on your mock, just do it:

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

Sometimes you want to mock up a more complex situation, like for
example "mock.connection.cursor().execute("SELECT 1")". If we wanted
this call to return a list, then we have to configure the result of
the nested call.

We can use "call" to construct the set of calls in a “chained call”
like this for easy assertion afterwards:

>>> mock = Mock()
>>> cursor = mock.connection.cursor.return_value
>>> cursor.execute.return_value = ['foo']
>>> mock.connection.cursor().execute("SELECT 1")
['foo']
>>> expected = call.connection.cursor().execute("SELECT 1").call_list()
>>> mock.mock_calls
[call.connection.cursor(), call.connection.cursor().execute('SELECT 1')]
>>> mock.mock_calls == expected
True

It is the call to ".call_list()" that turns our call object into a
list of calls representing the chained calls.


Raising exceptions with mocks
-----------------------------

A useful attribute is "side_effect". If you set this to an exception
class or instance then the exception will be raised when the mock is
called.

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


Side effect functions and iterables
-----------------------------------

"side_effect" can also be set to a function or an iterable. The use
case for "side_effect" as an iterable is where your mock is going to
be called several times, and you want each call to return a different
value. When you set "side_effect" to an iterable every call to the
mock returns the next value from the iterable:

>>> mock = MagicMock(side_effect=[4, 5, 6])
>>> mock()
4
>>> mock()
5
>>> mock()
6

For more advanced use cases, like dynamically varying the return
values depending on what the mock is called with, "side_effect" can be
a function. The function will be called with the same arguments as the
mock. Whatever the function returns is what the call returns:

>>> vals = {(1, 2): 1, (2, 3): 2}
>>> def side_effect(*args):
...     return vals[args]
...
>>> mock = MagicMock(side_effect=side_effect)
>>> mock(1, 2)
1
>>> mock(2, 3)
2


Mocking asynchronous iterators
------------------------------

Since Python 3.8, "AsyncMock" and "MagicMock" have support to mock
Asynchronous Iterators through "__aiter__". The "return_value"
attribute of "__aiter__" can be used to set the return values to be
used for iteration.

>>> mock = MagicMock()  # AsyncMock also works here
>>> mock.__aiter__.return_value = [1, 2, 3]
>>> async def main():
...     return [i async for i in mock]
...
>>> asyncio.run(main())
[1, 2, 3]


Mocking asynchronous context manager
------------------------------------

Since Python 3.8, "AsyncMock" and "MagicMock" have support to mock
Asynchronous Context Managers through "__aenter__" and "__aexit__". By
default, "__aenter__" and "__aexit__" are "AsyncMock" instances that
return an async function.

>>> class AsyncContextManager:
...     async def __aenter__(self):
...         return self
...     async def __aexit__(self, exc_type, exc, tb):
...         pass
...
>>> mock_instance = MagicMock(AsyncContextManager())  # AsyncMock also works here
>>> async def main():
...     async with mock_instance as result:
...         pass
...
>>> asyncio.run(main())
>>> mock_instance.__aenter__.assert_awaited_once()
>>> mock_instance.__aexit__.assert_awaited_once()


Creating a Mock from an Existing Object
---------------------------------------

One problem with over use of mocking is that it couples your tests to
the implementation of your mocks rather than your real code. Suppose
you have a class that implements "some_method". In a test for another
class, you provide a mock of this object that *also* provides
"some_method". If later you refactor the first class, so that it no
longer has "some_method" - then your tests will continue to pass even
though your code is now broken!

"Mock" allows you to provide an object as a specification for the
mock, using the *spec* keyword argument. Accessing methods /
attributes on the mock that don’t exist on your specification object
will immediately raise an attribute error. If you change the
implementation of your specification, then tests that use that class
will start failing immediately without you having to instantiate the
class in those tests.

>>> mock = Mock(spec=SomeClass)
>>> mock.old_method()
Traceback (most recent call last):
   ...
AttributeError: object has no attribute 'old_method'

Using a specification also enables a smarter matching of calls made to
the mock, regardless of whether some parameters were passed as
positional or named arguments:

   >>> def f(a, b, c): pass
   ...
   >>> mock = Mock(spec=f)
   >>> mock(1, 2, 3)
   <Mock name='mock()' id='140161580456576'>
   >>> mock.assert_called_with(a=1, b=2, c=3)

If you want this smarter matching to also work with method calls on
the mock, you can use auto-speccing.

If you want a stronger form of specification that prevents the setting
of arbitrary attributes as well as the getting of them then you can
use *spec_set* instead of *spec*.


Patch Decorators
================

Note:

  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.

A common need in tests is to patch a class attribute or a module
attribute, for example patching a builtin or patching a class in a
module to test that it is instantiated. Modules and classes are
effectively global, so patching on them has to be undone after the
test or the patch will persist into other tests and cause hard to
diagnose problems.

mock provides three convenient decorators for this: "patch()",
"patch.object()" and "patch.dict()". "patch" takes a single string, of
the form "package.module.Class.attribute" to specify the attribute you
are patching. It also optionally takes a value that you want the
attribute (or class or whatever) to be replaced with. ‘patch.object’
takes an object and the name of the attribute you would like patched,
plus optionally the value to patch it with.

"patch.object":

   >>> original = SomeClass.attribute
   >>> @patch.object(SomeClass, 'attribute', sentinel.attribute)
   ... def test():
   ...     assert SomeClass.attribute == sentinel.attribute
   ...
   >>> test()
   >>> assert SomeClass.attribute == original

   >>> @patch('package.module.attribute', sentinel.attribute)
   ... def test():
   ...     from package.module import attribute
   ...     assert attribute is sentinel.attribute
   ...
   >>> test()

If you are patching a module (including "builtins") then use "patch()"
instead of "patch.object()":

>>> mock = MagicMock(return_value=sentinel.file_handle)
>>> with patch('builtins.open', mock):
...     handle = open('filename', 'r')
...
>>> mock.assert_called_with('filename', 'r')
>>> assert handle == sentinel.file_handle, "incorrect file handle returned"

The module name can be ‘dotted’, in the form "package.module" if
needed:

   >>> @patch('package.module.ClassName.attribute', sentinel.attribute)
   ... def test():
   ...     from package.module import ClassName
   ...     assert ClassName.attribute == sentinel.attribute
   ...
   >>> test()

A nice pattern is to actually decorate test methods themselves:

>>> class MyTest(unittest.TestCase):
...     @patch.object(SomeClass, 'attribute', sentinel.attribute)
...     def test_something(self):
...         self.assertEqual(SomeClass.attribute, sentinel.attribute)
...
>>> original = SomeClass.attribute
>>> MyTest('test_something').test_something()
>>> assert SomeClass.attribute == original

If you want to patch with a Mock, you can use "patch()" with only one
argument (or "patch.object()" with two arguments). The mock will be
created for you and passed into the test function / method:

>>> class MyTest(unittest.TestCase):
...     @patch.object(SomeClass, 'static_method')
...     def test_something(self, mock_method):
...         SomeClass.static_method()
...         mock_method.assert_called_with()
...
>>> MyTest('test_something').test_something()

You can stack up multiple patch decorators using this pattern:

   >>> class MyTest(unittest.TestCase):
   ...     @patch('package.module.ClassName1')
   ...     @patch('package.module.ClassName2')
   ...     def test_something(self, MockClass2, MockClass1):
   ...         self.assertIs(package.module.ClassName1, MockClass1)
   ...         self.assertIs(package.module.ClassName2, MockClass2)
   ...
   >>> MyTest('test_something').test_something()

When you nest patch decorators the mocks are passed in to the
decorated function in the same order they applied (the normal *Python*
order that decorators are applied). This means from the bottom up, so
in the example above the mock for "test_module.ClassName2" is passed
in first.

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

"patch", "patch.object" and "patch.dict" can all be used as context
managers.

Where you use "patch()" to create a mock for you, you can get a
reference to the mock using the “as” form of the with statement:

>>> class ProductionClass:
...     def method(self):
...         pass
...
>>> with patch.object(ProductionClass, 'method') as mock_method:
...     mock_method.return_value = None
...     real = ProductionClass()
...     real.method(1, 2, 3)
...
>>> mock_method.assert_called_with(1, 2, 3)

As an alternative "patch", "patch.object" and "patch.dict" can be used
as class decorators. When used in this way it is the same as applying
the decorator individually to every method whose name starts with
“test”.


Further Examples
================

Here are some more examples for some slightly more advanced scenarios.


Mocking chained calls
---------------------

Mocking chained calls is actually straightforward with mock once you
understand the "return_value" attribute. When a mock is called for the
first time, or you fetch its "return_value" before it has been called,
a new "Mock" is created.

This means that you can see how the object returned from a call to a
mocked object has been used by interrogating the "return_value" mock:

>>> mock = Mock()
>>> mock().foo(a=2, b=3)
<Mock name='mock().foo()' id='...'>
>>> mock.return_value.foo.assert_called_with(a=2, b=3)

From here it is a simple step to configure and then make assertions
about chained calls. Of course another alternative is writing your
code in a more testable way in the first place…

So, suppose we have some code that looks a little bit like this:

>>> class Something:
...     def __init__(self):
...         self.backend = BackendProvider()
...     def method(self):
...         response = self.backend.get_endpoint('foobar').create_call('spam', 'eggs').start_call()
...         # more code

Assuming that "BackendProvider" is already well tested, how do we test
"method()"? Specifically, we want to test that the code section "#
more code" uses the response object in the correct way.

As this chain of calls is made from an instance attribute we can
monkey patch the "backend" attribute on a "Something" instance. In
this particular case we are only interested in the return value from
the final call to "start_call" so we don’t have much configuration to
do. Let’s assume the object it returns is ‘file-like’, so we’ll ensure
that our response object uses the builtin "open()" as its "spec".

To do this we create a mock instance as our mock backend and create a
mock response object for it. To set the response as the return value
for that final "start_call" we could do this:

   mock_backend.get_endpoint.return_value.create_call.return_value.start_call.return_value = mock_response

We can do that in a slightly nicer way using the "configure_mock()"
method to directly set the return value for us:

   >>> something = Something()
   >>> mock_response = Mock(spec=open)
   >>> mock_backend = Mock()
   >>> config = {'get_endpoint.return_value.create_call.return_value.start_call.return_value': mock_response}
   >>> mock_backend.configure_mock(**config)

With these we monkey patch the “mock backend” in place and can make
the real call:

   >>> something.backend = mock_backend
   >>> something.method()

Using "mock_calls" we can check the chained call with a single assert.
A chained call is several calls in one line of code, so there will be
several entries in "mock_calls". We can use "call.call_list()" to
create this list of calls for us:

   >>> chained = call.get_endpoint('foobar').create_call('spam', 'eggs').start_call()
   >>> call_list = chained.call_list()
   >>> assert mock_backend.mock_calls == call_list


Partial mocking
---------------

In some tests I wanted to mock out a call to "datetime.date.today()"
to return a known date, but I didn’t want to prevent the code under
test from creating new date objects. Unfortunately "datetime.date" is
written in C, and so I couldn’t just monkey-patch out the static
"date.today()" method.

I found a simple way of doing this that involved effectively wrapping
the date class with a mock, but passing through calls to the
constructor to the real class (and returning real instances).

The "patch decorator" is used here to mock out the "date" class in the
module under test. The "side_effect" attribute on the mock date class
is then set to a lambda function that returns a real date. When the
mock date class is called a real date will be constructed and returned
by "side_effect".

   >>> from datetime import date
   >>> with patch('mymodule.date') as mock_date:
   ...     mock_date.today.return_value = date(2010, 10, 8)
   ...     mock_date.side_effect = lambda *args, **kw: date(*args, **kw)
   ...
   ...     assert mymodule.date.today() == date(2010, 10, 8)
   ...     assert mymodule.date(2009, 6, 8) == date(2009, 6, 8)

Note that we don’t patch "datetime.date" globally, we patch "date" in
the module that *uses* it. See where to patch.

When "date.today()" is called a known date is returned, but calls to
the "date(...)" constructor still return normal dates. Without this
you can find yourself having to calculate an expected result using
exactly the same algorithm as the code under test, which is a classic
testing anti-pattern.

Calls to the date constructor are recorded in the "mock_date"
attributes ("call_count" and friends) which may also be useful for
your tests.

An alternative way of dealing with mocking dates, or other builtin
classes, is discussed in this blog entry.


Mocking a Generator Method
--------------------------

A Python generator is a function or method that uses the "yield"
statement to return a series of values when iterated over [1].

A generator method / function is called to return the generator
object. It is the generator object that is then iterated over. The
protocol method for iteration is "__iter__()", so we can mock this
using a "MagicMock".

Here’s an example class with an “iter” method implemented as a
generator:

>>> class Foo:
...     def iter(self):
...         for i in [1, 2, 3]:
...             yield i
...
>>> foo = Foo()
>>> list(foo.iter())
[1, 2, 3]

How would we mock this class, and in particular its “iter” method?

To configure the values returned from the iteration (implicit in the
call to "list"), we need to configure the object returned by the call
to "foo.iter()".

>>> mock_foo = MagicMock()
>>> mock_foo.iter.return_value = iter([1, 2, 3])
>>> list(mock_foo.iter())
[1, 2, 3]

[1] There are also generator expressions and more advanced uses of
    generators, but we aren’t concerned about them here. A very good
    introduction to generators and how powerful they are is: Generator
    Tricks for Systems Programmers.


Applying the same patch to every test method
--------------------------------------------

If you want several patches in place for multiple test methods the
obvious way is to apply the patch decorators to every method. This can
feel like unnecessary repetition. For Python 2.6 or more recent you
can use "patch()" (in all its various forms) as a class decorator.
This applies the patches to all test methods on the class. A test
method is identified by methods whose names start with "test":

   >>> @patch('mymodule.SomeClass')
   ... class MyTest(unittest.TestCase):
   ...
   ...     def test_one(self, MockSomeClass):
   ...         self.assertIs(mymodule.SomeClass, MockSomeClass)
   ...
   ...     def test_two(self, MockSomeClass):
   ...         self.assertIs(mymodule.SomeClass, MockSomeClass)
   ...
   ...     def not_a_test(self):
   ...         return 'something'
   ...
   >>> MyTest('test_one').test_one()
   >>> MyTest('test_two').test_two()
   >>> MyTest('test_two').not_a_test()
   'something'

An alternative way of managing patches is to use the patch methods:
start and stop. These allow you to move the patching into your "setUp"
and "tearDown" methods.

   >>> class MyTest(unittest.TestCase):
   ...     def setUp(self):
   ...         self.patcher = patch('mymodule.foo')
   ...         self.mock_foo = self.patcher.start()
   ...
   ...     def test_foo(self):
   ...         self.assertIs(mymodule.foo, self.mock_foo)
   ...
   ...     def tearDown(self):
   ...         self.patcher.stop()
   ...
   >>> MyTest('test_foo').run()

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('mymodule.foo')
   ...         self.addCleanup(patcher.stop)
   ...         self.mock_foo = patcher.start()
   ...
   ...     def test_foo(self):
   ...         self.assertIs(mymodule.foo, self.mock_foo)
   ...
   >>> MyTest('test_foo').run()


Mocking Unbound Methods
-----------------------

Whilst writing tests today I needed to patch an *unbound method*
(patching the method on the class rather than on the instance). I
needed self to be passed in as the first argument because I want to
make asserts about which objects were calling this particular method.
The issue is that you can’t patch with a mock for this, because if you
replace an unbound method with a mock it doesn’t become a bound method
when fetched from the instance, and so it doesn’t get self passed in.
The workaround is to patch the unbound method with a real function
instead. The "patch()" decorator makes it so simple to patch out
methods with a mock that having to create a real function becomes a
nuisance.

If you pass "autospec=True" to patch then it does the patching with a
*real* function object. This function object has the same signature as
the one it is replacing, but delegates to a mock under the hood. You
still get your mock auto-created in exactly the same way as before.
What it means though, is that if you use it to patch out an unbound
method on a class the mocked function will be turned into a bound
method if it is fetched from an instance. It will have "self" passed
in as the first argument, which is exactly what I wanted:

>>> class Foo:
...   def foo(self):
...     pass
...
>>> with patch.object(Foo, 'foo', autospec=True) as mock_foo:
...   mock_foo.return_value = 'foo'
...   foo = Foo()
...   foo.foo()
...
'foo'
>>> mock_foo.assert_called_once_with(foo)

If we don’t use "autospec=True" then the unbound method is patched out
with a Mock instance instead, and isn’t called with "self".


Checking multiple calls with mock
---------------------------------

mock has a nice API for making assertions about how your mock objects
are used.

>>> mock = Mock()
>>> mock.foo_bar.return_value = None
>>> mock.foo_bar('baz', spam='eggs')
>>> mock.foo_bar.assert_called_with('baz', spam='eggs')

If your mock is only being called once you can use the
"assert_called_once_with()" method that also asserts that the
"call_count" is one.

>>> mock.foo_bar.assert_called_once_with('baz', spam='eggs')
>>> mock.foo_bar()
>>> mock.foo_bar.assert_called_once_with('baz', spam='eggs')
Traceback (most recent call last):
    ...
AssertionError: Expected to be called once. Called 2 times.

Both "assert_called_with" and "assert_called_once_with" make
assertions about the *most recent* call. If your mock is going to be
called several times, and you want to make assertions about *all*
those calls you can use "call_args_list":

>>> mock = Mock(return_value=None)
>>> mock(1, 2, 3)
>>> mock(4, 5, 6)
>>> mock()
>>> mock.call_args_list
[call(1, 2, 3), call(4, 5, 6), call()]

The "call" helper makes it easy to make assertions about these calls.
You can build up a list of expected calls and compare it to
"call_args_list". This looks remarkably similar to the repr of the
"call_args_list":

>>> expected = [call(1, 2, 3), call(4, 5, 6), call()]
>>> mock.call_args_list == expected
True


Coping with mutable arguments
-----------------------------

Another situation is rare, but can bite you, is when your mock is
called with mutable arguments. "call_args" and "call_args_list" store
*references* to the arguments. If the arguments are mutated by the
code under test then you can no longer make assertions about what the
values were when the mock was called.

Here’s some example code that shows the problem. Imagine the following
functions defined in ‘mymodule’:

   def frob(val):
       pass

   def grob(val):
       "First frob and then clear val"
       frob(val)
       val.clear()

When we try to test that "grob" calls "frob" with the correct argument
look what happens:

   >>> with patch('mymodule.frob') as mock_frob:
   ...     val = {6}
   ...     mymodule.grob(val)
   ...
   >>> val
   set()
   >>> mock_frob.assert_called_with({6})
   Traceback (most recent call last):
       ...
   AssertionError: Expected: (({6},), {})
   Called with: ((set(),), {})

One possibility would be for mock to copy the arguments you pass in.
This could then cause problems if you do assertions that rely on
object identity for equality.

Here’s one solution that uses the "side_effect" functionality. If you
provide a "side_effect" function for a mock then "side_effect" will be
called with the same args as the mock. This gives us an opportunity to
copy the arguments and store them for later assertions. In this
example I’m using *another* mock to store the arguments so that I can
use the mock methods for doing the assertion. Again a helper function
sets this up for me.

   >>> from copy import deepcopy
   >>> from unittest.mock import Mock, patch, DEFAULT
   >>> def copy_call_args(mock):
   ...     new_mock = Mock()
   ...     def side_effect(*args, **kwargs):
   ...         args = deepcopy(args)
   ...         kwargs = deepcopy(kwargs)
   ...         new_mock(*args, **kwargs)
   ...         return DEFAULT
   ...     mock.side_effect = side_effect
   ...     return new_mock
   ...
   >>> with patch('mymodule.frob') as mock_frob:
   ...     new_mock = copy_call_args(mock_frob)
   ...     val = {6}
   ...     mymodule.grob(val)
   ...
   >>> new_mock.assert_called_with({6})
   >>> new_mock.call_args
   call({6})

"copy_call_args" is called with the mock that will be called. It
returns a new mock that we do the assertion on. The "side_effect"
function makes a copy of the args and calls our "new_mock" with the
copy.

Note:

  If your mock is only going to be used once there is an easier way of
  checking arguments at the point they are called. You can simply do
  the checking inside a "side_effect" function.

  >>> def side_effect(arg):
  ...     assert arg == {6}
  ...
  >>> mock = Mock(side_effect=side_effect)
  >>> mock({6})
  >>> mock(set())
  Traceback (most recent call last):
      ...
  AssertionError

An alternative approach is to create a subclass of "Mock" or
"MagicMock" that copies (using "copy.deepcopy()") the arguments.
Here’s an example implementation:

>>> from copy import deepcopy
>>> class CopyingMock(MagicMock):
...     def __call__(self, /, *args, **kwargs):
...         args = deepcopy(args)
...         kwargs = deepcopy(kwargs)
...         return super(CopyingMock, self).__call__(*args, **kwargs)
...
>>> c = CopyingMock(return_value=None)
>>> arg = set()
>>> c(arg)
>>> arg.add(1)
>>> c.assert_called_with(set())
>>> c.assert_called_with(arg)
Traceback (most recent call last):
    ...
AssertionError: Expected call: mock({1})
Actual call: mock(set())
>>> c.foo
<CopyingMock name='mock.foo' id='...'>

When you subclass "Mock" or "MagicMock" all dynamically created
attributes, and the "return_value" will use your subclass
automatically. That means all children of a "CopyingMock" will also
have the type "CopyingMock".


Nesting Patches
---------------

Using patch as a context manager is nice, but if you do multiple
patches you can end up with nested with statements indenting further
and further to the right:

   >>> class MyTest(unittest.TestCase):
   ...
   ...     def test_foo(self):
   ...         with patch('mymodule.Foo') as mock_foo:
   ...             with patch('mymodule.Bar') as mock_bar:
   ...                 with patch('mymodule.Spam') as mock_spam:
   ...                     assert mymodule.Foo is mock_foo
   ...                     assert mymodule.Bar is mock_bar
   ...                     assert mymodule.Spam is mock_spam
   ...
   >>> original = mymodule.Foo
   >>> MyTest('test_foo').test_foo()
   >>> assert mymodule.Foo is original

With unittest "cleanup" functions and the patch methods: start and
stop we can achieve the same effect without the nested indentation. A
simple helper method, "create_patch", puts the patch in place and
returns the created mock for us:

   >>> class MyTest(unittest.TestCase):
   ...
   ...     def create_patch(self, name):
   ...         patcher = patch(name)
   ...         thing = patcher.start()
   ...         self.addCleanup(patcher.stop)
   ...         return thing
   ...
   ...     def test_foo(self):
   ...         mock_foo = self.create_patch('mymodule.Foo')
   ...         mock_bar = self.create_patch('mymodule.Bar')
   ...         mock_spam = self.create_patch('mymodule.Spam')
   ...
   ...         assert mymodule.Foo is mock_foo
   ...         assert mymodule.Bar is mock_bar
   ...         assert mymodule.Spam is mock_spam
   ...
   >>> original = mymodule.Foo
   >>> MyTest('test_foo').run()
   >>> assert mymodule.Foo is original


Mocking a dictionary with MagicMock
-----------------------------------

You may want to mock a dictionary, or other container object,
recording all access to it whilst having it still behave like a
dictionary.

We can do this with "MagicMock", which will behave like a dictionary,
and using "side_effect" to delegate dictionary access to a real
underlying dictionary that is under our control.

When the "__getitem__()" and "__setitem__()" methods of our
"MagicMock" are called (normal dictionary access) then "side_effect"
is called with the key (and in the case of "__setitem__" the value
too). We can also control what is returned.

After the "MagicMock" has been used we can use attributes like
"call_args_list" to assert about how the dictionary was used:

>>> my_dict = {'a': 1, 'b': 2, 'c': 3}
>>> def getitem(name):
...      return my_dict[name]
...
>>> def setitem(name, val):
...     my_dict[name] = val
...
>>> mock = MagicMock()
>>> mock.__getitem__.side_effect = getitem
>>> mock.__setitem__.side_effect = setitem

Note:

  An alternative to using "MagicMock" is to use "Mock" and *only*
  provide the magic methods you specifically want:

  >>> mock = Mock()
  >>> mock.__getitem__ = Mock(side_effect=getitem)
  >>> mock.__setitem__ = Mock(side_effect=setitem)

  A *third* option is to use "MagicMock" but passing in "dict" as the
  *spec* (or *spec_set*) argument so that the "MagicMock" created only
  has dictionary magic methods available:

  >>> mock = MagicMock(spec_set=dict)
  >>> mock.__getitem__.side_effect = getitem
  >>> mock.__setitem__.side_effect = setitem

With these side effect functions in place, the "mock" will behave like
a normal dictionary but recording the access. It even raises a
"KeyError" if you try to access a key that doesn’t exist.

>>> mock['a']
1
>>> mock['c']
3
>>> mock['d']
Traceback (most recent call last):
    ...
KeyError: 'd'
>>> mock['b'] = 'fish'
>>> mock['d'] = 'eggs'
>>> mock['b']
'fish'
>>> mock['d']
'eggs'

After it has been used you can make assertions about the access using
the normal mock methods and attributes:

>>> mock.__getitem__.call_args_list
[call('a'), call('c'), call('d'), call('b'), call('d')]
>>> mock.__setitem__.call_args_list
[call('b', 'fish'), call('d', 'eggs')]
>>> my_dict
{'a': 1, 'b': 'fish', 'c': 3, 'd': 'eggs'}


Mock subclasses and their attributes
------------------------------------

There are various reasons why you might want to subclass "Mock". One
reason might be to add helper methods. Here’s a silly example:

>>> class MyMock(MagicMock):
...     def has_been_called(self):
...         return self.called
...
>>> mymock = MyMock(return_value=None)
>>> mymock
<MyMock id='...'>
>>> mymock.has_been_called()
False
>>> mymock()
>>> mymock.has_been_called()
True

The standard behaviour for "Mock" instances is that attributes and the
return value mocks are of the same type as the mock they are accessed
on. This ensures that "Mock" attributes are "Mocks" and "MagicMock"
attributes are "MagicMocks" [2]. So if you’re subclassing to add
helper methods then they’ll also be available on the attributes and
return value mock of instances of your subclass.

>>> mymock.foo
<MyMock name='mock.foo' id='...'>
>>> mymock.foo.has_been_called()
False
>>> mymock.foo()
<MyMock name='mock.foo()' id='...'>
>>> mymock.foo.has_been_called()
True

Sometimes this is inconvenient. For example, one user is subclassing
mock to created a Twisted adaptor. Having this applied to attributes
too actually causes errors.

"Mock" (in all its flavours) uses a method called "_get_child_mock" to
create these “sub-mocks” for attributes and return values. You can
prevent your subclass being used for attributes by overriding this
method. The signature is that it takes arbitrary keyword arguments
("**kwargs") which are then passed onto the mock constructor:

>>> class Subclass(MagicMock):
...     def _get_child_mock(self, /, **kwargs):
...         return MagicMock(**kwargs)
...
>>> mymock = Subclass()
>>> mymock.foo
<MagicMock name='mock.foo' id='...'>
>>> assert isinstance(mymock, Subclass)
>>> assert not isinstance(mymock.foo, Subclass)
>>> assert not isinstance(mymock(), Subclass)

[2] An exception to this rule are the non-callable mocks. Attributes
    use the callable variant because otherwise non-callable mocks
    couldn’t have callable methods.


Mocking imports with patch.dict
-------------------------------

One situation where mocking can be hard is where you have a local
import inside a function. These are harder to mock because they aren’t
using an object from the module namespace that we can patch out.

Generally local imports are to be avoided. They are sometimes done to
prevent circular dependencies, for which there is *usually* a much
better way to solve the problem (refactor the code) or to prevent “up
front costs” by delaying the import. This can also be solved in better
ways than an unconditional local import (store the module as a class
or module attribute and only do the import on first use).

That aside there is a way to use "mock" to affect the results of an
import. Importing fetches an *object* from the "sys.modules"
dictionary. Note that it fetches an *object*, which need not be a
module. Importing a module for the first time results in a module
object being put in *sys.modules*, so usually when you import
something you get a module back. This need not be the case however.

This means you can use "patch.dict()" to *temporarily* put a mock in
place in "sys.modules". Any imports whilst this patch is active will
fetch the mock. When the patch is complete (the decorated function
exits, the with statement body is complete or "patcher.stop()" is
called) then whatever was there previously will be restored safely.

Here’s an example that mocks out the ‘fooble’ module.

>>> import sys
>>> mock = Mock()
>>> with patch.dict('sys.modules', {'fooble': mock}):
...    import fooble
...    fooble.blob()
...
<Mock name='mock.blob()' id='...'>
>>> assert 'fooble' not in sys.modules
>>> mock.blob.assert_called_once_with()

As you can see the "import fooble" succeeds, but on exit there is no
‘fooble’ left in "sys.modules".

This also works for the "from module import name" form:

>>> mock = Mock()
>>> with patch.dict('sys.modules', {'fooble': mock}):
...    from fooble import blob
...    blob.blip()
...
<Mock name='mock.blob.blip()' id='...'>
>>> mock.blob.blip.assert_called_once_with()

With slightly more work you can also mock package imports:

>>> mock = Mock()
>>> modules = {'package': mock, 'package.module': mock.module}
>>> with patch.dict('sys.modules', modules):
...    from package.module import fooble
...    fooble()
...
<Mock name='mock.module.fooble()' id='...'>
>>> mock.module.fooble.assert_called_once_with()


Tracking order of calls and less verbose call assertions
--------------------------------------------------------

The "Mock" class allows you to track the *order* of method calls on
your mock objects through the "method_calls" attribute. This doesn’t
allow you to track the order of calls between separate mock objects,
however we can use "mock_calls" to achieve the same effect.

Because mocks track calls to child mocks in "mock_calls", and
accessing an arbitrary attribute of a mock creates a child mock, we
can create our separate mocks from a parent one. Calls to those child
mock will then all be recorded, in order, in the "mock_calls" of the
parent:

>>> manager = Mock()
>>> mock_foo = manager.foo
>>> mock_bar = manager.bar

>>> mock_foo.something()
<Mock name='mock.foo.something()' id='...'>
>>> mock_bar.other.thing()
<Mock name='mock.bar.other.thing()' id='...'>

>>> manager.mock_calls
[call.foo.something(), call.bar.other.thing()]

We can then assert about the calls, including the order, by comparing
with the "mock_calls" attribute on the manager mock:

>>> expected_calls = [call.foo.something(), call.bar.other.thing()]
>>> manager.mock_calls == expected_calls
True

If "patch" is creating, and putting in place, your mocks then you can
attach them to a manager mock using the "attach_mock()" method. After
attaching calls will be recorded in "mock_calls" of the manager.

   >>> manager = MagicMock()
   >>> with patch('mymodule.Class1') as MockClass1:
   ...     with patch('mymodule.Class2') as MockClass2:
   ...         manager.attach_mock(MockClass1, 'MockClass1')
   ...         manager.attach_mock(MockClass2, 'MockClass2')
   ...         MockClass1().foo()
   ...         MockClass2().bar()
   <MagicMock name='mock.MockClass1().foo()' id='...'>
   <MagicMock name='mock.MockClass2().bar()' id='...'>
   >>> manager.mock_calls
   [call.MockClass1(),
   call.MockClass1().foo(),
   call.MockClass2(),
   call.MockClass2().bar()]

If many calls have been made, but you’re only interested in a
particular sequence of them then an alternative is to use the
"assert_has_calls()" method. This takes a list of calls (constructed
with the "call" object). If that sequence of calls are in "mock_calls"
then the assert succeeds.

>>> m = MagicMock()
>>> m().foo().bar().baz()
<MagicMock name='mock().foo().bar().baz()' id='...'>
>>> m.one().two().three()
<MagicMock name='mock.one().two().three()' id='...'>
>>> calls = call.one().two().three().call_list()
>>> m.assert_has_calls(calls)

Even though the chained call "m.one().two().three()" aren’t the only
calls that have been made to the mock, the assert still succeeds.

Sometimes a mock may have several calls made to it, and you are only
interested in asserting about *some* of those calls. You may not even
care about the order. In this case you can pass "any_order=True" to
"assert_has_calls":

>>> m = MagicMock()
>>> m(1), m.two(2, 3), m.seven(7), m.fifty('50')
(...)
>>> calls = [call.fifty('50'), call(1), call.seven(7)]
>>> m.assert_has_calls(calls, any_order=True)


More complex argument matching
------------------------------

Using the same basic concept as "ANY" we can implement matchers to do
more complex assertions on objects used as arguments to mocks.

Suppose we expect some object to be passed to a mock that by default
compares equal based on object identity (which is the Python default
for user defined classes). To use "assert_called_with()" we would need
to pass in the exact same object. If we are only interested in some of
the attributes of this object then we can create a matcher that will
check these attributes for us.

You can see in this example how a ‘standard’ call to
"assert_called_with" isn’t sufficient:

>>> class Foo:
...     def __init__(self, a, b):
...         self.a, self.b = a, b
...
>>> mock = Mock(return_value=None)
>>> mock(Foo(1, 2))
>>> mock.assert_called_with(Foo(1, 2))
Traceback (most recent call last):
    ...
AssertionError: Expected: call(<__main__.Foo object at 0x...>)
Actual call: call(<__main__.Foo object at 0x...>)

A comparison function for our "Foo" class might look something like
this:

>>> def compare(self, other):
...     if not type(self) == type(other):
...         return False
...     if self.a != other.a:
...         return False
...     if self.b != other.b:
...         return False
...     return True
...

And a matcher object that can use comparison functions like this for
its equality operation would look something like this:

>>> class Matcher:
...     def __init__(self, compare, some_obj):
...         self.compare = compare
...         self.some_obj = some_obj
...     def __eq__(self, other):
...         return self.compare(self.some_obj, other)
...

Putting all this together:

>>> match_foo = Matcher(compare, Foo(1, 2))
>>> mock.assert_called_with(match_foo)

The "Matcher" is instantiated with our compare function and the "Foo"
object we want to compare against. In "assert_called_with" the
"Matcher" equality method will be called, which compares the object
the mock was called with against the one we created our matcher with.
If they match then "assert_called_with" passes, and if they don’t an
"AssertionError" is raised:

>>> match_wrong = Matcher(compare, Foo(3, 4))
>>> mock.assert_called_with(match_wrong)
Traceback (most recent call last):
    ...
AssertionError: Expected: ((<Matcher object at 0x...>,), {})
Called with: ((<Foo object at 0x...>,), {})

With a bit of tweaking you could have the comparison function raise
the "AssertionError" directly and provide a more useful failure
message.

As of version 1.5, the Python testing library PyHamcrest provides
similar functionality, that may be useful here, in the form of its
equality matcher (hamcrest.library.integration.match_equality).
