o ci-@sddlmZddlmZddlZddlZddlZddlZddlZddl m Z ddl m Z m Z e dddZe dd Zd d ZGd d d ZeZdS))deepcopy)partialN) DeveloperAPI) force_list merge_dictsc Ks|durt|ttfr|}d}n t|trt||r|Szt|}Wn ty+Ynwt|trJ|dd}|durBt|trB|}|}||n|}|durYd|vrY|d}|}t|dg}|dur|durt |dr|j dur|gkrt |j ddr|j dj dus|j dj |j ur|j }t|t rt ||j}t |jfi|}i}n|}nt||}|durn|dus|dur|St|r|}nt|trtd|rt||g|Ri|St|}t|trt||Sz t|t|}W|StjyYnw|dd kr2|dd \}} z t|} t| | }Wn t t!t"fy1Ynw|durt|trEt#d |d z t|j$} t| |}Wn0t t!t"fyzt|j$j%} t&| |gd } t| |}Wn t t!t"fyYnwYnw|durt#d|d|j$d|j'd|st(d)|z ||i|} Wn t(y} zt*d| j+drWYd} ~ dS| d} ~ wwt|j'dkrt| t|t r|jn|sJ| S)aUses the given config to create an object. If `config` is a dict, an optional "type" key can be used as a "constructor hint" to specify a certain class of the object. If `config` is not a dict, `config`'s value is used directly as this "constructor hint". The rest of `config` (if it's a dict) will be used as kwargs for the constructor. Additional keys in **kwargs will always have precedence (overwrite keys in `config` (if a dict)). Also, if the config-dict or **kwargs contains the special key "_args", it will be popped from the dict and used as *args list to be passed separately to the constructor. The following constructor hints are valid: - None: Use `cls` as constructor. - An already instantiated object: Will be returned as is; no constructor call. - A string or an object that is a key in `cls`'s `__type_registry__` dict: The value in `__type_registry__` for that key will be used as the constructor. - A python callable: Use that very callable as constructor. - A string: Either a json/yaml filename or the name of a python module+class (e.g. "ray.rllib. [...] .[some class name]") Args: cls: The class to build an instance for (from `config`). config (Optional[dict, str]): The config dict or type-string or filename. Keyword Args: kwargs: Optional possibility to pass the constructor arguments in here and use `config` as the type-only info. Then we can call this like: from_config([type]?, [**kwargs for constructor]) If `config` is already a dict, then `kwargs` will be merged with `config` (overwriting keys in `config`) after "type" has been popped out of `config`. If a constructor of a Configurable needs *args, the special key `_args` can be passed inside `kwargs` with a list value (e.g. kwargs={"_args": [arg1, arg2, arg3]}). Returns: any: The object generated from the config. Ntype_args__default_constructor__rFz\.(yaml|yml|json)$.zFull classpath specifier (zS) must be a valid full [module].[class] string! E.g.: `my.cool.module.MyCoolClass`.)fromlistzString specifier (z@) must be a valid filename, a [module].[class], a class within 'z', or a key into z.__type_registry__!z/Invalid type '{}'. Cannot create `from_config`.z Can't instantiate abstract classfunction), isinstancedictstrrr Exceptionpopupdaterhasattrr __bases__rrkeywordsfunc _lookup_typecallableresearch from_fileyaml safe_load from_configjsonloadsJSONDecodeErrorfindrsplit importlib import_modulegetattrModuleNotFoundError ImportErrorAttributeError ValueError __module__ __package__ __import____name__ TypeErrorformatmatchargs)clsconfigkwargstype_ ctor_kwargs ctor_args constructorobj module_name function_namemodule package_nameobject_erCO/home/ubuntu/.local/lib/python3.10/site-packages/ray/rllib/utils/from_config.pyr s/                       r cOstjt|}tj|std|t|d}|ds&|dr,t |}nt |}Wdn1s;wY||d<t |fd|i|S)z Create object from config saved in filename. Expects json or yaml file. Args: filename: File containing the config (json or yaml). Returns: any: The object generated from the file. zFile '{}' not found!rtz.yamlz.ymlNrr6)ospathjoingetcwdisfileFileNotFoundErrorr2openendswithrrr!loadr )r5filenamer4r7rGfpr6rCrCrDrs    rcCs~|dur=t|dr=t|jtr=||jvs%t|tr=tdd||jvr=|j|}|dur;|jtdd|}|SdS)N__type_registry__z[\W_]) rrrQrrrsublowerget)r5r8available_class_for_typerCrCrDrs    rc@s*eZdZdZGdddZdZddZdS) _NotProvidedadSingleton class to provide a "not provided" value for AlgorithmConfig signatures. Using the only instance of this class indicates that the user does NOT wish to change the value of some property. .. testcode:: :skipif: True from ray.rllib.algorithms.algorithm_config import AlgorithmConfig config = AlgorithmConfig() # Print out the default learning rate. print(config.lr) .. testoutput:: 0.001 .. testcode:: :skipif: True # Print out the default `preprocessor_pref`. print(config.preprocessor_pref) .. testoutput:: "deepmind" .. testcode:: :skipif: True # Will only set the `preprocessor_pref` property (to None) and leave # all other properties at their default values. config.training(preprocessor_pref=None) config.preprocessor_pref is None .. testoutput:: True .. testcode:: :skipif: True # Still the same value (didn't touch it in the call to `.training()`. print(config.lr) .. testoutput:: 0.001 c@s eZdZdS)z_NotProvided.__NotProvidedN)r0r- __qualname__rCrCrCrD __NotProvided8srYNcCstjdur tt_dSdSN)rWinstance_NotProvided__NotProvided)selfrCrCrD__init__=s z_NotProvided.__init__)r0r-rX__doc__r\r[r^rCrCrCrDrWs 2 rWrZ)copyr functoolsrr&r!rFrrray.rllib.utils.annotationsrray.rllib.utilsrrr rrrW NotProvidedrCrCrCrDs"   H  @