"unittest.mock" --- 入門
************************

バージョン 3.3 で追加.


Mock を使う
===========


Mock のパッチ用メソッド
-----------------------

一般的な "Mock" の使い方の中には次のものがあります:

* メソッドにパッチを当てる

* オブジェクトに対するメソッド呼び出しを記録する

システムの他の部分からメソッドが正しい引数で呼び出されたかどうかを確認
するために、そのオブジェクトのメソッドを置き換えることができます:

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

モック(上の例では "real.method")が利用された場合、どう使われたかを
assert できるようにする属性やメソッドがモックにあります。

注釈:

  この例のような場合、たいてい "Mock" と "MagicMock" は互換です。
  "MagicMock" の方が強力なので、デフォルトではこちらを使うといいでしょ
  う。

モックが呼び出されると、その "called" 属性が "True" に設定されます。そ
して "assert_called_with()" や "assert_called_once_with()" メソッドを
使ってそのメソッドが正しい引数で呼び出されたかどうかをチェックできます
。

次の例では "ProductionClass().method" が "something" メソッドを呼び出
したことをテストしています:

>>> 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)


オブジェクトのメソッド呼び出しに対するモック
--------------------------------------------

上の例ではオブジェクトのメソッドに対して直接パッチを当てて、それが正し
く呼び出されていたかどうかをテストしていました。もう一つのよくあるユー
スケースが、モックをメソッド (またはテスト対象のシステムのどこか) に渡
して、それが正しく利用されたかどうかをチェックする方法です。

次の例で、"ProductionClass" は "closer" メソッドを持っています。このメ
ソッドは渡されたオブジェクトの "close" メソッドを呼び出します。

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

ですからこれををテストするには、"close" メソッドを持ったオブジェクトを
渡して、それが正しく呼び出されたかどうかをテストしなければなりません。

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

モックに *close* メソッドを持たせるために何か準備する必要はありません
。 *close* メソッドにアクセスすると自動的にそれが作られます。なので、
もし *close* が呼び出されなかったとしてもテスト時に生成されるのですが
、 "assert_called_with()" が failure 例外を発生させます。


クラスをモックする
------------------

他のよくあるユースケースが、テスト対象のコードによってインスタンス化さ
れているクラスをモックに置き換えることです。クラスに patch すると、そ
のクラスがモックに置き換えられます。インスタンスは *クラスを呼び出した
時に* 作られます。なので、モックの戻り値を使うことで、「モックのインス
タンス」にアクセスできます。

次の例では、 "some_function" という関数が "Foo" をインスタンス化し、そ
の method を呼び出しています。 "patch()" を呼び出すと "Foo" クラスをモ
ックに置き換えます。 "Foo" のインスタンスはモックを呼び出して作られる
ので、モックの "return_value" を変更することでカスタマイズできます。

>>> 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'


モックに名前をつける
--------------------

モックに名前をつけると便利なことがあります。その名前はモックを repr し
たときに表示されるので、モックがテスト失敗のメッセージ内に現れた時に便
利です。また、モックの名前はそのモックの属性やメソッドにも伝播します:

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


全ての呼び出しのトラッキング
----------------------------

メソッドの複数回の呼び出しをトラックしたいことがあります。
"mock_calls" 属性は、そのモックの子属性やさらにその子孫に対する呼び出
しすべてを記録しています。

>>> 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)]

"mock_calls" に対して assert すると、予期していないメソッド呼び出しが
あったときにその assert が失敗します。これはあるメソッド呼び出しが期待
通りに実行されたかどうかだけでなく、その呼び出し順序や期待した以外の呼
び出しが起こらなかったことまでテストできるので便利です:

"mock_calls" と比較するリストを作るために "call" オブジェクトを利用で
きます:

>>> 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


戻り値や属性を設定する
----------------------

モックオブジェクトに戻り値を設定するのはとっても簡単です:

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

もちろん同じことがモックのメソッドに対しても行えます:

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

コンストラクターで戻り値を設定することもできます:

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

モックに属性を設定したかったら、普通に設定するだけです:

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

"mock.connection.cursor().execute("SELECT 1")" のような複雑なケースで
モックを使いたい場合もあります。この呼出があるリストを返すようにしたい
場合、このネストした呼び出しを構成しなければなりません。

"call" を使って "chained call" 内の呼び出しを構成して、 assert で使う
ことができます:

>>> 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

call object を chained call を表す list にするために ".call_list()" を
使います。


モックから例外を発生させる
--------------------------

"side_effect" という便利な属性があります。この属性に例外クラスやそのイ
ンスタンスを設定すると、モックが呼ばれた時にその例外を発生させます。

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


side_effect の関数と iterable
-----------------------------

"side_effect" には関数や iterable を設定することもできます。
"side_effect" に iterable を設定するユースケースは、そのモックが複数回
呼び出され、そのたびに違う値を返したい場合です。"side_effect" に
iterable を設定すると、そのモックに対するすべての呼び出しは iterable
の次の値を返します:

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

より高度なユースケースとして、mock が呼び出された時の引数によって戻り
値を変化させたい場合は、"side_effect" に関数を設定することができます。
その関数は mock と同じ引数で呼び出されます。その関数の戻り値がそのモッ
ク呼び出しの戻り値になります:

>>> 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


既存のオブジェクトから Mock を作る
----------------------------------

mock を使いすぎることの問題の一つは、テストが実際のコードではなく mock
の実装をテストするようになってしまうことです。"some_method" というメソ
ッドを実装したクラスがあるとします。他のクラスをテストするときに、
"some_method" を提供する mock を使います。最初のクラスをリファクタリン
グして "some_method" がなくなった時、コードは壊れているのにテストは通
る状態になってしまいます

"Mock" は *spec* というキーワード引数で mock の定義となるオブジェクト
を指定できます。定義オブジェクトに存在しないメソッドや属性にアクセスす
ると AttributeError を発生させます。定義となるクラスの実装を変更した場
合、テストの中でそのクラスをインスタンス化させなくても、テストを失敗さ
せる事ができます。

>>> 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.

任意の属性の参照だけでなく代入も禁止するより強い定義を利用したい場合は
、*spec* の代わりに *spec_set* を使います。


patch デコレータ
================

注釈:

  "patch()" では探索される名前空間内のオブジェクトにパッチをあてること
  が重要です。通常は単純ですが、クイックガイドには where-to-patch を読
  んでください。

テストの中でクラス属性やモジュール属性、例えば組み込み関数や、テスト対
象モジュールにあるインスタンス化されるクラスに対してパッチしたいことが
あります。モジュールやクラスは実際はグローバルなので、パッチするときは
必ずテスト後にパッチを解除しないと、そのパッチが永続化されて他のテスト
に影響を与え、解析しにくい問題になります。

mock はこのために3つの便利なデコレータを提供しています: "patch()",
"patch.object()", "patch.dict()" です。"patch" はパッチ対象を指定する
"package.module.Class.attribute" の形式の文字列を引数に取ります。オプ
ションでその属性 (やクラスなど) を置き換えるオブジェクトを渡すことがで
きます。 'patch.object' はオブジェクトとパッチしたい属性名、それにオプ
ションで置き換えるオブジェクトを受け取ります。

"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()

モジュール ("builtins" を含む) をパッチしようとする場合、
"patch.object()" の代わりに "patch()" を使用してください:

>>> 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"

モジュール名は必要に応じて "package.module" のようにドットを含むことが
できます:

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

テストメソッド自体をデコレートするのは良いパターンです:

>>> 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

Mock を使ってパッチしたい場合は、 "patch()" を1引数で (または
"patch.object()" を2引数で) 使うことができます。mock が自動で生成され
、テスト関数/メソッドに渡されます:

>>> 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()

次のパターンのように patch デコレータを重ねることができます:

>>> 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()

patch デコレータをネストした際、モックは (デコレータを適用する
*Python* の通常の) 順に適用されます。つまり引数は下から上の順になり、
よって上記の例では "test_module.ClassName2" が先になります。

また、 "patch.dict()" を使うと、スコープ内だけで辞書に値を設定し、テス
ト終了時には元の状態に復元されます:

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

"patch", "patch.object", "patch.dict" は全てコンテキストマネージャーと
しても利用できます。

"patch()" に mock を生成させる場合、その参照を with 文の as を使って受
け取れます:

>>> 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)

他の方法として、"patch", "patch.object", "patch.dict" はクラスデコレー
タとしても利用できます。その場合、"test" で始まる全てのメソッドにデコ
レータを適用するのと同じになります。


さらなる例
==========

より高度なシナリオを想定した例をあげていきます。


chained call をモックする
-------------------------

chained call を mock するのは、一度 "return_value" 属性を理解してしま
えば簡単です。mock が最初に呼ばれた時や、呼び出される前に
"return_value" を参照した場合、新しい "Mock" が生成されます。

つまり、戻り値のオブジェクトがどう利用されたかは、"return_value" mock
を調べれば分かります:

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

これをもとに、mock を構成して chained call に対する assert を行うのは
簡単です。もちろん、元のコードを最初からもっとテストしやすく書くという
選択肢もありますが...

では、例として次のようなコードがあるとします:

>>> 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

"BackendProvider" はすでに十分テストされているとします。"method()" を
どうテストしましょうか？特に、response オブジェクトを使う "# more
code" の部分のコードをテストしたいとします。

この chained call はインスタンス属性を起点にしているので、 "Something"
インスタンスの "backend" 属性に対してモンキーパッチすることができます
。今回の場合だと、最後の "start_call" の呼び出しが返す値にだけ興味があ
るので、あまり多くの構成は必要ありません。このメソッドが返すのが
'file-like' オブジェクトだとしましょう。そうすると、 response オブジェ
クトは組み込みの "open()" を "spec" として利用できます。

これをするために、backend のモックとしてモックインスタンスを作成し、そ
れに対するモックの response オブジェクトを作成します。最終的な
"start_call" の返り値として response をセットすると、このようにするこ
とができます:

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

これより少し良いやり方として、返り値を直接セットする
"configure_mock()" メソッドを使用して次のようにすることができます:

>>> 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)

これらによって「モックの backend」をその場で monkey patch して、実際の
呼び出しを行うことができます:

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

"mock_calls" を使用すると、チェーンされた呼び出しを単一のアサーション
でチェックすることができます。チェーンされた呼び出しは、1行のコードの
中で行われる複数の呼び出しです。したがって、 "mock_calls" には複数のエ
ントリーがあるでしょう。 "call.call_list()" を使用することで、この呼び
出しのリストを作成することができます:

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


部分的なモック
--------------

テストによっては、 "datetime.date.today()" の呼び出しを既知の date を
返すようにモックしたいと思うことがありました。しかし、テスト対象のコー
ドが新しい date オブジェクトを生成するのを妨げたくはありません。不運に
も、 "datetime.date" は C で書かれています。したがって、静的な
"date.today()" メソッドを単にモンキーパッチすることはできませんでした
。

私はこれを行う単純な方法を見つけました。それは、date クラスをモックで
事実上ラップして、しかしコンストラクタの呼び出しを実際のクラスへと素通
りさせる (そして、実際のインスタンスを返す) というものです。

ここで、テスト対象のモジュール中の "date" クラスをモックするために
"patch decorator" が使用されています。そして、モックの date クラスの
"side_effect" 属性は、本物の date を返すラムダ関数にセットされます。モ
ックの date クラスが呼ばれる時、 "side_effect" によって本物の date が
構築されて返されます。

>>> 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.


ジェネレータ method をモックする
--------------------------------

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.


同じパッチを全てのメソッドに適用する
------------------------------------

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(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 のメソッド:
start と stop. These allow you to move the patching into your "setUp"
and "tearDown" methods.

>>> class MyTest(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(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".


モックで複数回の呼び出しをチェックする
--------------------------------------

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


ミュータブルな引数に対処する
----------------------------

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.

注釈:

  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".


patch をネストする
------------------

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(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 のメソッド: start と
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(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


MagicMock で辞書をモックする
----------------------------

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

注釈:

  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, 'c': 3, 'b': 'fish', 'd': 'eggs'}


Mock のサブクラスと属性
-----------------------

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.


patch.dict で import をモックする
---------------------------------

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.

>>> 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)
...

全てをつなぎ合わせて:

>>> 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).
