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    "info": {
        "author": "Ge Yang",
        "author_email": "yangge1987@gmail.com",
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        "description": ".. figure:: https://github.com/episodeyang/waterbear/blob/master/figures/waterbear_resized.jpg?raw=true\n   :width: 355px\n   :height: 266px\n   :scale: 50%\n   :alt: waterbear\\_is\\_a\\_bear\n\n   waterbear\\_is\\_a\\_bear\n\n``waterbear``, A Base Classs That Makes Python Dictionary Accessible With The Dot Notation, Recursively and with Default Values\n===============================================================================================================================\n\nNow introducing the smallest bear! **Waterbear**.\n\nWaterbear makes it easy to use python dictionaries with dot notation!\n\nWhat does ``Waterbear``:bear: do?\n---------------------------------\n\n``Waterbear`` is like ``defaultdict`` + ``SimpleNameSpace`` +\n``namedtuples``.\n\n``Waterbear`` is similar in usage to ``namedtuples`` or ``recordtypes``,\nbut it is not a tuple or array type but a dictionary. The distinction is\nthat ``Waterbear`` attributes are accessible via ``key`` strings instead\nof index numbers.\n\n``Waterbear`` is more similar to ``types.SimpleNamespace``. However, a\nmajor difference is that ``Waterbear`` enables:\n\n-  setting default values via a ``default_factory`` during instantiation\n-  all attributes are recognized by IDE's static type-checking so they\n   have auto-completion without having to be used first.\n-  work recursively\n\nNow with all of these three, there isn't an alternative solution\navailable. libraries like ``Munch`` has bad support for pythonic idioms.\nIn this case ``Waterbear`` allows you to:\n\n-  use ``vars(bear)`` to convert the bear object into a dictionary.\n-  use ``dict(bear)`` for the same purpose\n-  use ``print(bear)`` and get a dictionary string\n-  ... all methods that are available in a python ``dict`` object\n\nTODOs\n-----\n\n-  [ ] fix class extension usage pattern\n-  [ ] [STRIKEOUT:merge ``python2.7`` version with ``python3``]\n-  [ ] [STRIKEOUT:make another package called ``tardigrade``]\n\nInstallation\n------------\n\n.. code-block:: python\n\n    pip install waterbear\n\nUsage\n-----\n\nFor more usage examples, take a look at the\n`test.py <https://github.com/episodeyang/waterbear/blob/master/waterbear/test_waterbear.py>`__!\n\nThere are two classes, the ``Bear`` and the ``DefaultBear``. Default\nBear allows you to pass in a default factory as the first argument.\n``Bear`` allows you do do so via a keyword argument ``__default``\n\nExample usage below:\n\n.. code-block:: python\n\n    # Waterbear is a bear!\n    from waterbear import Bear\n\n    waterbear = Bear(**{\"key\": 100})\n    assert waterbear.key == 100, 'now waterbear.key is accessible!'\n    assert waterbear['key'] == 100, 'item access syntax is also supported!'\n\nSimilar to ``collection.defaultdict``, there is ``DefaultBear``\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n.. code-block:: python\n\n    bear = DefaultBear(None, a=10, b=100)\n    assert vars(bear) == {'a': 10, 'b': 100}\n\n    assert bear.does_not_exist is None, \"default value works\"\n\nDefaultBear like ``defaultdict``\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nYou can use the ``DefaultBear`` class and pass in a default factor as\nthe first parameter.\n\n.. code-block:: python\n\n    bear = DefaultBear(tuple, a=10, b=100)\n    assert bear.does_not_exist is (), \"default factory also works!\"\n\nYou can also use it with ``vars``, ``str``, ``print(repr)``, ``dict`` etc.\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n.. code-block:: python\n\n    bear = Bear(a=10, b=100)\n    assert str(bear) == \"{'a': 10, 'b': 100}\"\n    assert dir(bear) == ['a', 'b']\n    assert list(iter(bear)) == ['a', 'b']\n    assert dict(bear) == {'a': 10, 'b': 100}\n\nAs Bool in Condition Logic\n~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nWhen used in conditional logic, ``Bear`` and ``DefaultBear`` behaves\nexactly like an ordinary dictionary!\n\n.. code-block:: python\n\n    def test_dict_comparison():\n        bear = Bear()\n        assert not {}, 'empty dictionary are treated as False value.'\n        assert not bear, 'bear should be treated as False value too!'\n\nUsing with Pickle\n~~~~~~~~~~~~~~~~~\n\nWhen using with default factories, only non-callables are picklable.\n\n.. code-block:: python\n\n    def test_pickle_setstate_getstate():\n        # create a default bear with a default factory\n        bear = DefaultBear('hey', a=10, b=100)\n        pickle_string = pickle.dumps(bear)\n        bear_reborn = pickle.loads(pickle_string)\n        assert type(bear_reborn) == DefaultBear\n        assert vars(bear_reborn) == {'a': 10, 'b': 100}\n\n        bear = DefaultBear(lambda: 'hey', a=10, b=100)\n        function_fails = False\n        try:\n            pickle.dumps(bear)\n        except AttributeError as e:\n            function_fails = True\n        assert function_fails\n\nUsing deepcopy\n~~~~~~~~~~~~~~\n\nYou can just do ``copy.deepcopy(bear)``!\n\n.. code-block:: python\n\n    def test_deepcopy():\n        from copy import deepcopy\n        original = Bear(a=1, b={'ha': 0})\n        copy = deepcopy(original)\n        copy.b.ha += 1\n        assert copy.b.ha == 1\n        assert original.b.ha == 0\n\nAs A Base Class\n~~~~~~~~~~~~~~~\n\nWaterbear is completely rewritten to play well with class extension!\n\n.. code-block:: python\n\n    class ExtendBear(Bear):\n        @property\n        def _hidden_stuff(self):\n            return \"._hidden_stuff\"\n\n        @property\n        def __mangled_stuff(self):\n            return \".__mangled_stuff\"\n\n        @property\n        def __dict__(self):\n            return \".__dict__\"\n\n    e = ExtendBear()\n    assert e.__dict__ == \".__dict__\"\n    assert e._hidden_stuff == '._hidden_stuff'\n    assert e._ExtendBear__mangled_stuff == \".__mangled_stuff\"\n\nOrder Preserving SimpleNamespace\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nIn tensorflow, you frequently need order preserving namespaces that you\ncan use for ``sess.run([tensors...``. We built ``OrderedBear`` exactly\nfor this purpose. It is an extension of the ``types.SimpleNamespace``\nclass.\n\n::\n\n    # First declare the typings (namespace) for your model\n    class Reporting:\n        loss=None\n        entropy=None\n        mean_kl=None\n\n    # Now, you can instantiate this with new values\n\n    ... inside model\n\n        r = Reporting(entropy=-5, loss=1)\n        # Notice that 1. we are putting values in out-of-order, and 2. We are missing `mean_kl` in our construction.\n\n        tems = r.items()\n        assert items[0] == ('loss', 1), 'order follows class declaration.'\n        assert items[1] == ('entropy', -5), 'entropy goes after loss even though this is the second atrribute'\n        assert items[2] == ('mean_kl', None), 'undefined falls back to the default'\n\n        values = r.values()\n        assert values[0] == 1, 'order follows class declaration.'\n        assert values[1] == -5, 'entropy goes after loss even though this is the second atrribute'\n        assert values[2] == None, 'undefined falls back to the default'\n\n        keys = r.keys()\n        assert keys[0] == 'loss', 'order follows class declaration.'\n        assert keys[1] == 'entropy', 'entropy goes after loss even though this is the second atrribute'\n        assert keys[2] == 'mean_kl', 'undefined falls back to the default'\n\nMore Usages Could Be Found in The Tests!\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nFor more usage examples, take a look at\n`test.py <https://github.com/episodeyang/waterbear/blob/master/waterbear/test_waterbear.py>`__.\n\n.. code-block:: python\n\n    test_dict = {\n        'a': 0,\n        'b': 1\n    }\n\n    # Use spread operators to construct with a dictionary!\n    test_args = Bear(**test_dict)\n    assert test_args.a == 0\n    assert test_args.b == 1\n    # the value should now be accessible through the key name.\n    test_args.haha = 0\n    assert test_args.haha == 0\n\n\n    # You can also use a nested dictionary.\n    test_args.haha = {'a': 1}\n    assert test_args.haha != {'a': 1}\n    assert vars(test_args.haha) == {'a': 1}\n    assert test_args.haha.a == 1\n    assert test_args.__dict__['haha']['a'] == 1\n    assert vars(test_args)['haha']['a'] == 1\n    assert str(test_args) == \"{'a': 0, 'b': 1, 'haha': {'a': 1}}\", \\\n        'test_args should be this value \"{\\'a\\': 0, \\'b\\': 1, \\'haha\\': {\\'a\\': 1}}\"'\n\n    # To set recursion to false, use this `__recursive` parameter.\n    test_args = Bear(__recursive=False, **test_dict)\n    assert test_args.__is_recursive == False\n    assert test_args.a == 0\n    assert test_args.b == 1\n    test_args.haha = {'a': 1}\n    assert test_args.haha['a'] == 1\n    assert test_args.haha == {'a': 1}\n\n    # Some other usage patterns\n    test_args = Bear(**test_dict, **{'ha': 'ha', 'no': 'no'})\n    assert test_args.ha == 'ha', 'key ha should be ha'\n\nTo Develop\n----------\n\n.. code-block:: python\n\n    git clone https://github.com/episodeyang/waterbear.git\n    cd waterbear\n    make dev\n\nThis ``make dev`` command should build the wheel and install it in your\ncurrent python environment. Take a look at the\n`https://github.com/episodeyang/waterbear/blob/master/Makefile <https://github.com/episodeyang/waterbear/blob/master/Makefile>`__ for details.\n\n**To publish**, first update the version number, then do:\n\n.. code-block:: bash\n\n    make publish\n\n\\* image credit goes to BBC `waterbear: The Smallest\nBear! <http://www.bbc.com/earth/story/20150313-the-toughest-animals-on-earth>`__\n\ud83d\ude1b |tardigrade|\n\n.. |tardigrade| image:: https://github.com/episodeyang/waterbear/blob/master/figures/waterbear_2_resized.jpg?raw=true\n   :width: 355px\n   :height: 266px\n   :scale: 50%\n\n\n",
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