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How to use from_config method in avocado

Best Python code snippet using avocado_python

config.py

Source:config.py

...97            @configurable98            def __init__(self, a, b=2, c=3):99                pass100            @classmethod101            def from_config(cls, cfg):   # 'cfg' must be the first argument102                # Returns kwargs to be passed to __init__103                return {"a": cfg.A, "b": cfg.B}104        a1 = A(a=1, b=2)  # regular construction105        a2 = A(cfg)       # construct with a cfg106        a3 = A(cfg, b=3, c=4)  # construct with extra overwrite107        # Usage 2: Decorator on any function. Needs an extra from_config argument:108        @configurable(from_config=lambda cfg: {"a: cfg.A, "b": cfg.B})109        def a_func(a, b=2, c=3):110            pass111        a1 = a_func(a=1, b=2)  # regular call112        a2 = a_func(cfg)       # call with a cfg113        a3 = a_func(cfg, b=3, c=4)  # call with extra overwrite114    Args:115        init_func (callable): a class's ``__init__`` method in usage 1. The116            class must have a ``from_config`` classmethod which takes `cfg` as117            the first argument.118        from_config (callable): the from_config function in usage 2. It must take `cfg`119            as its first argument.120    """121    if init_func is not None:122        assert (123            inspect.isfunction(init_func)124            and from_config is None125            and init_func.__name__ == "__init__"126        ), "Incorrect use of @configurable. Check API documentation for examples."127        @functools.wraps(init_func)128        def wrapped(self, *args, **kwargs):129            try:130                from_config_func = type(self).from_config131            except AttributeError as e:132                raise AttributeError(133                    "Class with @configurable must have a 'from_config' classmethod."134                ) from e135            if not inspect.ismethod(from_config_func):136                raise TypeError("Class with @configurable must have a 'from_config' classmethod.")137            if _called_with_cfg(*args, **kwargs):138                explicit_args = _get_args_from_config(from_config_func, *args, **kwargs)139                init_func(self, **explicit_args)140            else:141                init_func(self, *args, **kwargs)142        return wrapped143    else:144        if from_config is None:145            return configurable  # @configurable() is made equivalent to @configurable146        assert inspect.isfunction(147            from_config148        ), "from_config argument of configurable must be a function!"149        def wrapper(orig_func):150            @functools.wraps(orig_func)151            def wrapped(*args, **kwargs):152                if _called_with_cfg(*args, **kwargs):153                    explicit_args = _get_args_from_config(from_config, *args, **kwargs)154                    return orig_func(**explicit_args)155                else:156                    return orig_func(*args, **kwargs)157            return wrapped158        return wrapper159def _get_args_from_config(from_config_func, *args, **kwargs):160    """161    Use `from_config` to obtain explicit arguments.162    Returns:163        dict: arguments to be used for cls.__init__164    """165    signature = inspect.signature(from_config_func)166    if list(signature.parameters.keys())[0] != "cfg":167        if inspect.isfunction(from_config_func):168            name = from_config_func.__name__169        else:170            name = f"{from_config_func.__self__}.from_config"171        raise TypeError(f"{name} must take 'cfg' as the first argument!")172    support_var_arg = any(173        param.kind in [param.VAR_POSITIONAL, param.VAR_KEYWORD]...

test_framework_agnostic_components.py

Source:test_framework_agnostic_components.py

...34        return tf.add(self.prop_b, value)35class NonAbstractChildOfDummyComponent(DummyComponent):36    pass37class AbstractDummyComponent(DummyComponent, metaclass=ABCMeta):38    """Used for testing `from_config()`.39    """40    @abstractmethod41    def some_abstract_method(self):42        raise NotImplementedError43class TestFrameWorkAgnosticComponents(unittest.TestCase):44    """45    Tests the Component base class to implement framework-agnostic functional46    units.47    """48    def test_dummy_components(self):49        # Bazel makes it hard to find files specified in `args`50        # (and `data`).51        # Use the true absolute path.52        script_dir = Path(__file__).parent53        abs_path = script_dir.absolute()54        for fw, sess in framework_iterator(session=True):55            fw_ = fw if fw != "tfe" else "tf"56            # Try to create from an abstract class w/o default constructor.57            # Expect None.58            test = from_config({59                "type": AbstractDummyComponent,60                "framework": fw_61            })62            check(test, None)63            # Create a Component via python API (config dict).64            component = from_config(65                dict(66                    type=DummyComponent,67                    prop_a=1.0,68                    prop_d="non_default",69                    framework=fw_))70            check(component.prop_d, "non_default")71            # Create a tf Component from json file.72            config_file = str(abs_path.joinpath("dummy_config.json"))73            component = from_config(config_file, framework=fw_)74            check(component.prop_c, "default")75            check(component.prop_d, 4)  # default76            value = component.add(3.3)77            if sess:78                value = sess.run(value)79            check(value, 5.3)  # prop_b == 2.080            # Create a torch Component from yaml file.81            config_file = str(abs_path.joinpath("dummy_config.yml"))82            component = from_config(config_file, framework=fw_)83            check(component.prop_a, "something else")84            check(component.prop_d, 3)85            value = component.add(1.2)86            if sess:87                value = sess.run(value)88            check(value, np.array([2.2]))  # prop_b == 1.089            # Create tf Component from json-string (e.g. on command line).90            component = from_config(91                '{"type": "ray.rllib.utils.tests.'92                'test_framework_agnostic_components.DummyComponent", '93                '"prop_a": "A", "prop_b": -1.0, "prop_c": "non-default", '94                '"framework": "' + fw_ + '"}')95            check(component.prop_a, "A")96            check(component.prop_d, 4)  # default97            value = component.add(-1.1)98            if sess:99                value = sess.run(value)100            check(value, -2.1)  # prop_b == -1.0101            # Test recognizing default module path.102            component = from_config(103                DummyComponent, '{"type": "NonAbstractChildOfDummyComponent", '104                '"prop_a": "A", "prop_b": -1.0, "prop_c": "non-default",'105                '"framework": "' + fw_ + '"}')106            check(component.prop_a, "A")107            check(component.prop_d, 4)  # default108            value = component.add(-1.1)109            if sess:110                value = sess.run(value)111            check(value, -2.1)  # prop_b == -1.0112            # Test recognizing default package path.113            scope = None114            if sess:115                scope = tf1.variable_scope("exploration_object")116                scope.__enter__()117            component = from_config(118                Exploration, {119                    "type": "EpsilonGreedy",120                    "action_space": Discrete(2),121                    "framework": fw_,122                    "num_workers": 0,123                    "worker_index": 0,124                    "policy_config": {},125                    "model": None126                })127            if scope:128                scope.__exit__(None, None, None)129            check(component.epsilon_schedule.outside_value, 0.05)  # default130            # Create torch Component from yaml-string.131            component = from_config(132                "type: ray.rllib.utils.tests."133                "test_framework_agnostic_components.DummyComponent\n"134                "prop_a: B\nprop_b: -1.5\nprop_c: non-default\nframework: "135                "{}".format(fw_))136            check(component.prop_a, "B")137            check(component.prop_d, 4)  # default138            value = component.add(-5.1)139            if sess:140                value = sess.run(value)141            check(value, np.array([-6.6]))  # prop_b == -1.5142    def test_unregistered_envs(self):143        """Tests, whether an Env can be specified simply by its absolute class.144        """145        env_cls = "ray.rllib.examples.env.stateless_cartpole.StatelessCartPole"146        env = from_config(env_cls, {"config": 42.0})147        state = env.reset()148        self.assertTrue(state.shape == (2, ))149if __name__ == "__main__":150    import pytest151    import sys...

utils.py

Source:utils.py

1import json2import numpy as np3import datetime4class NpEncoder(json.JSONEncoder):5    def default(self, obj):6        if isinstance(obj, np.integer):7            return int(obj)8        elif isinstance(obj, np.floating):9            return float(obj)10        elif isinstance(obj, np.ndarray):11            return obj.tolist()12        else:13            return super(NpEncoder, self).default(obj)14def filter_config(from_config, to_config):  15    """16    Exclude all except type: (dict, list, bool, int, loat, str)17    """18    if isinstance(from_config, dict):19        for key in list(from_config.keys()):            20            if isinstance(from_config[key], dict):21                to_config[key] = {}  22                to_config[key] =  filter_config(from_config[key], to_config[key])23            if isinstance(from_config[key], list):24                to_config[key] = []25                to_config[key] = filter_config(from_config[key], to_config[key])26            if isinstance(from_config[key], bool):27                to_config[key] = from_config[key]28            if isinstance(from_config[key], int):29                to_config[key] = from_config[key]30            if isinstance(from_config[key], float):31                to_config[key] = from_config[key]32            if isinstance(from_config[key], str):33                to_config[key] = from_config[key]34    if isinstance(from_config, list):35        for value in from_config:36            if isinstance(value, dict):37                tmp_to_config = {}38                to_config.append(filter_config(value, tmp_to_config))39            if isinstance(value, list):40                tmp_to_config = []41                to_config.append(filter_config(value, tmp_to_config))42            if isinstance(value, bool):43                to_config.append(value)44            if isinstance(value, int):45                to_config.append(value)46            if isinstance(value, float):47                to_config.append(value)48            if isinstance(value, str):49                to_config.append(value)50                ...

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