:j dZddlZddlZddlZddlmZddlmZddlm Z m Z ddl m Z m Z mZddlmZddlmZdd lmZdd lmZdd lmZGd d eeZGddeZdej6fde e dee de e egefdefdZy)z.Abstract base class used to build new loggers.N)ABC) defaultdict)MappingSequence)AnyCallableOptional)override)Logger)_DummyExperiment)rank_zero_experiment)ModelCheckpointc<eZdZdZdeddfdZedeefdZ y)r z"Base class for experiment loggers.checkpoint_callbackreturnNcy)zCalled after model checkpoint callback saves a new checkpoint. Args: checkpoint_callback: the model checkpoint callback instance N)selfrs u/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/lightning/pytorch/loggers/logger.pyafter_save_checkpointzLogger.after_save_checkpoint#s cy)zvReturn the root directory where experiment logs get saved, or `None` if the logger does not save data locally.Nrrs rsave_dirzLogger.save_dir,sr) __name__ __module__ __qualname____doc__rrpropertyr strrrrrr r s7,  T (3-rr ceZdZdZdfd ZedefdZede de ddfdZ ede de ddfd Z eede fd Z eede fd Zd eddfd Zde defdZxZS) DummyLoggerzDummy logger for internal use. It is useful if we want to disable user's logger for a feature, but still ensure that user code can run rNc@t|t|_yN)super__init__DummyExperiment _experiment)r __class__s rr&zDummyLogger.__init__:s *,rc|jS)z9Return the experiment object associated with this logger.)r(rs r experimentzDummyLogger.experiment>srargskwargscyr$rrr,r-s r log_metricszDummyLogger.log_metricsC rcyr$rr/s rlog_hyperparamszDummyLogger.log_hyperparamsGr1rcy)zReturn the experiment name.rrs rnamezDummyLogger.nameKrcy)zReturn the experiment version.r5rrs rversionzDummyLogger.versionQr7ridxc|Sr$r)rr:s r __getitem__zDummyLogger.__getitem__Ws rr6c*dtdtddfd}|S)zUAllows the DummyLogger to be called with arbitrary methods, to avoid AttributeErrors.r,r-rNcyr$r)r,r-s rmethodz'DummyLogger.__getattr__..method^sr)r)rr6r?s r __getattr__zDummyLogger.__getattr__[s# #    r)rN)rrrrr&rr'r+r rr0r3r r6r9intr<rr@ __classcell__)r)s@rr"r"3s - O        S C D   c s}rr"dicts agg_key_funcs default_funcrc |xsi}ttjtj|Dcgc]}t |j c}}tt}|D]u}|j|}|Dcgc]}|j|c}D cgc]} | |  } } t| dtrt| ||||<g|xs|| ||<wt|Scc}wcc}wcc} w)a#Merge a sequence with dictionaries into one dictionary by aggregating the same keys with some given function. Args: dicts: Sequence of dictionaries to be merged. agg_key_funcs: Mapping from key name to function. This function will aggregate a list of values, obtained from the same key of all dictionaries. If some key has no specified aggregation function, the default one will be used. Default is: ``None`` (all keys will be aggregated by the default function). default_func: Default function to aggregate keys, which are not presented in the `agg_key_funcs` map. Returns: Dictionary with merged values. Examples: >>> import pprint >>> d1 = {'a': 1.7, 'b': 2.0, 'c': 1, 'd': {'d1': 1, 'd3': 3}} >>> d2 = {'a': 1.1, 'b': 2.2, 'v': 1, 'd': {'d1': 2, 'd2': 3}} >>> d3 = {'a': 1.1, 'v': 2.3, 'd': {'d3': 3, 'd4': {'d5': 1}}} >>> dflt_func = min >>> agg_funcs = {'a': statistics.mean, 'v': max, 'd': {'d1': sum}} >>> pprint.pprint(merge_dicts([d1, d2, d3], agg_funcs, dflt_func)) {'a': 1.3, 'b': 2.0, 'c': 1, 'd': {'d1': 3, 'd2': 3, 'd3': 3, 'd4': {'d5': 1}}, 'v': 2.3} r) list functoolsreduceoperatoror_setkeysrdictget isinstance merge_dicts) rCrDrEdrMd_outkfnd_inv values_to_aggs rrQrQesL"'RM    u/M!AFFH /MN ODd#E ;   q !=B$CTTXXa[$CUqq}U U mA& -"="lCE!H**lM:E!H; ;0N%DUs C-C2C7'C7)rrHrJ statisticsabcr collectionsrcollections.abcrrtypingrrr typing_extensionsr lightning.fabric.loggersr FabricLoggerlightning.fabric.loggers.loggerr r'r ,lightning.pytorch.callbacks.model_checkpointrr"meanfloatrNrQrrrres5#-**&;O@H\3&.&.h(,7A2 G 2G$2HUO,e342 2r