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    "info": {
        "author": "Zach Kelling",
        "author_email": "zk@monoid.io",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4"
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        "description": "=======\nDecorum\n=======\n\n.. image:: https://travis-ci.org/zeekay/decorum.png?branch=master\n    :target: https://travis-ci.org/zeekay/decorum\n\nDecorum is a library which aims to make it easier to write flexible and simple\ndecorators:\n\n* use decorators just like you need: don't bother with parentheses, pass\n  options when you need them.\n\n* write decorators just like you think: they get initialized with or without\n  options, they wrap a function, they are callables.\n\n* test decorators: it's really easy, you have no excuse!\n\n\nInherit from Decorum\n====================\n\nIn order to write your own decorators, just subclass ``decorum.Decorum``:\n\n.. code:: pycon\n\n   >>> from decorum import Decorum\n\n   >>> class empty_decorator(Decorum):\n   ...     \"\"\"Just inherit from Decorum's builtins.\"\"\"\n\nOf course you will want to customize decorator's behaviour! There are only\nthree methods to be aware of:\n\n* ``init()`` setups the decorator itself;\n\n* ``wraps()`` decorates the function;\n\n* ``call()`` runs the wrapped function and returns result.\n\nHere is a simple decorator to illustrate Decorum usage:\n\n.. code:: pycon\n\n   >>> from __future__ import print_function\n\n   >>> class verbose_decorator(Decorum):\n   ...    \"\"\"Print info about decoration process.\"\"\"\n   ...    def init(self, *args, **kwargs):\n   ...        print('Initializing decorator with args={} and kwargs={}'\n   ...              .format(args, kwargs))\n   ...        return super(verbose_decorator, self).init(*args, **kwargs)\n   ...\n   ...    def wraps(self, f):\n   ...        print('Wrapping function \"{}\"'.format(f.__name__))\n   ...        return super(verbose_decorator, self).wraps(f)\n   ...\n   ...    def call(self, *args, **kwargs):\n   ...        print('Running wrapped function with args={} and kwargs={}'\n   ...              .format(args, kwargs))\n   ...        return super(verbose_decorator, self).call(*args, **kwargs)\n\nThen you can use it as any decorator:\n\n.. code:: pycon\n\n   >>> @verbose_decorator\n   ... def foo(x):\n   ...     print(x)\n   Initializing decorator with args=() and kwargs={}\n   Wrapping function \"foo\"\n\n   >>> foo('bar')\n   Running wrapped function with args=('bar',) and kwargs={}\n   bar\n\n.. note::\n\n   You can also use ``decorum.decorator`` to turn classes into decorators.\n\n   .. code:: pycon\n\n      >>> from decorum import decorator\n\n      >>> @decorator\n      ... class noop:\n      ...     \"\"\"Override init(), wraps() or call() if you like.\"\"\"\n\n      >>> @noop\n      ... def foo():\n      ...     \"\"\"Do nothing.\"\"\"\n\n   The result is a class that inherits from the original class and Decorum:\n\n   .. code:: pycon\n\n      >>> isinstance(foo, noop)\n      True\n      >>> isinstance(foo, Decorum)\n      True\n\n\nDon't bother with parentheses\n=============================\n\nDecorum lets you write decorators with and without arguments in a unified way.\nThen your decorator can be used with or without arguments, called or not, and\nit will work the same way:\n\n.. code:: pycon\n\n   >>> @verbose_decorator\n   ... def foo(x):\n   ...     print(x)\n   Initializing decorator with args=() and kwargs={}\n   Wrapping function \"foo\"\n\nIs identical to:\n\n.. code:: pycon\n\n   >>> @verbose_decorator()\n   ... def foo(x):\n   ...     print(x)\n   Initializing decorator with args=() and kwargs={}\n   Wrapping function \"foo\"\n\n\nInitialize decorator with options\n=================================\n\nTo implement a decorator that accepts a custom options, just change ``init()``:\n\n.. code:: pycon\n\n   >>> class configurable_decorator(Decorum):\n   ...    def init(self, custom_option='default', *args, **kwargs):\n   ...        print('Initializing decorator with custom_option=\"{}\"'\n   ...              .format(custom_option))\n   ...        # Remember the option, typically for use in wraps() or call().\n   ...        self.custom_option = custom_option\n   ...        # Call super().init() with remaining arguments.\n   ...        return super(configurable_decorator, self).init(*args, **kwargs)\n\nAs we used a keyword argument, it is optional:\n\n.. code:: pycon\n\n   >>> @configurable_decorator\n   ... def foo(x):\n   ...     print(x)\n   Initializing decorator with custom_option=\"default\"\n\n   >>> foo.custom_option\n   'default'\n\nAnd we can pass this option when decorating a function. Either as positional\nargument...\n\n.. code:: pycon\n\n   >>> @configurable_decorator('positional')\n   ... def foo(x):\n   ...     print(x)\n   Initializing decorator with custom_option=\"positional\"\n\n   >>> foo.custom_option\n   'positional'\n\n... or keyword argument:\n\n.. code:: pycon\n\n   >>> @configurable_decorator(custom_option='keyword')\n   ... def foo(x):\n   ...     print(x)\n   Initializing decorator with custom_option=\"keyword\"\n\n   >>> foo.custom_option\n   'keyword'\n\nOf course, you cannot pass arguments that are not declared as ``init()``\noptions:\n\n.. code:: pycon\n\n   >>> @configurable_decorator(wrong_option=True)  #\u00a0doctest: +ELLIPSIS\n   ... def foo(x):\n   ...     print(x)\n   Traceback (most recent call last):\n     ...\n   TypeError: init() got an unexpected keyword argument 'wrong_option'\n\n.. note::\n\n   In most cases, ``init()`` should accept additional arguments and and proxy\n   them to parent via ``super(...).init(*args, **kwargs)``. This way, options\n   of ancestor classes are supported.\n\n   As an example, ``Decorum`` base class declares ``assigned`` keyword argument\n   in ``init()`` (see section `wrap function <#wrap-function>`_ below).\n\n\nWrap function\n=============\n\nThe ``wraps()`` method allows you to handle the decorated function. It receives\nthe function to decorate as single positional argument, and returns a callable\n(typically ``self``).\n\nIn most cases, ``wraps()`` function will ``return super(..., self).wraps(f)``.\n\nBy default, the base ``Decorum.wraps()`` will try to keep assign certain\nattributes to the wrapped function for you, namely ``__doc__`` and\n``__name__``. This feature uses ``functools.wraps``.\n\n.. code:: pycon\n\n   >>> class identity(Decorum):\n   ...     \"\"\"Noop decorator: does nothing!\"\"\"\n\n   >>> @identity\n   ... def my_function():\n   ...     \"\"\"My function's docstring.\"\"\"\n\n   >>> print(my_function.__name__)\n   my_function\n   >>> print(my_function.__doc__)\n   My function's docstring.\n\nThe optional ``assigned`` keyword argument can be used to specify which\nattributes of the original function are assigned directly to the matching\nattributes on the wrapper function. This defaults to\n``functools.WRAPPER_ASSIGNMENTS``. You can specify ``False`` or ``None`` to\ndisable this.\n\n.. code:: pycon\n\n   >>> @identity(assigned=None)\n   ... def my_function():\n   ...     \"\"\"My function's docstring.\"\"\"\n\n   >>> print(my_function.__name__)\n   identity\n   >>> print(my_function.__doc__)\n   Noop decorator: does nothing!\n\n\nRun decorated function\n======================\n\n``Decorum.call()`` method receives ``*args`` and ``**kwargs`` as input. It runs\nthe wrapped function, and returns the result.\n\nHere is a simple decorator that repeats the result of decorated function:\n\n.. code:: pycon\n\n   >>> class repeat(Decorum):\n   ...     def call(self, *args, **kwargs):\n   ...         result = super(repeat, self).call(*args, **kwargs)\n   ...         return ' '.join([result] * 2)\n\n   >>> @repeat\n   ... def parrot(word):\n   ...     return word\n\n   >>> parrot('hello')\n   'hello hello'\n\n\nTest decorators\n===============\n\nDecorum makes it easy to test custom decorators.\n\nAssert a decorator has expected behaviour\n-----------------------------------------\n\nDecorators are defined as classes, so you have fine-grained control over what\nyou test. And your tests can focus on what you customized.\n\nLet's check ``repeat`` decorator from the section before. Since we just\noverrode ``call()``, let's focus on it:\n\n.. code:: pycon\n\n   >>> decorator = repeat(lambda x: x.upper())\n\n   >>> result = decorator.call('input')\n   >>> assert result == 'INPUT INPUT'\n\nThat's quite useful to unit test decorators.\n\n\nAssert a function has been decorated\n------------------------------------\n\nDecorators are instances of ``Decorum`` or subclasses. So you can inspect\ndecorated functions.\n\nLet's inspect ``my_function`` from the examples above:\n\n.. code:: pycon\n\n   >>> assert isinstance(my_function, Decorum)\n   >>> assert isinstance(my_function, identity)\n\n\nKnown limitations\n=================\n\nDecorum has little known limitations:\n\n* your decorator's ``init()`` cannot receive a single positional argument that\n  is a callable. 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