o 8wi2 @sxddlZddlZddlmZddlmZmZddlmZm Z ddl m Z m Z m Z ddlmZddlmZde e eee fefd eee ffd d Zdeee fd eee ffd d Zd dee e fdeded eee ffddZdeee fd eee ffddZdeee fd eee ffddZde d efddZdeee eeffdeded eee eefffddZdS)!N) Namespace)MappingMutableMapping)asdict is_dataclass)AnyOptionalUnion)Tensor)_NUMPY_AVAILABLEparamsreturncCs"t|tr t|}|duri}|S)zEnsure parameters are a dict or convert to dict if necessary. Args: params: Target to be converted to a dictionary Returns: params as a dictionary N) isinstancervarsr r^/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/lightning_fabric/utilities/logger.py_convert_paramss rcs(dtdtfddfdd|DS)zSanitize callable params dict, e.g. ``{'a': } -> {'a': 'function_****'}``. Args: params: Dictionary containing the hyperparameters Returns: dictionary with all callables sanitized valr cSsXt|r|jSt|r*z|}t|r|jWS|WSty)t|ddYSw|S)N__name__)inspectisclassrcallable Exceptiongetattr)r_valrrr_sanitize_callable:s  z5_sanitize_callable_params.._sanitize_callablecsi|] \}}||qSrr).0keyrrrr Jsz-_sanitize_callable_params..)ritemsrrrr_sanitize_callable_params/s r"/ delimiter parent_keyc Csi}|D]f\}}|r||t|nt|}t|r&t|ts&t|}n t|tr/t|}t|tr@i|t |||d}qt|t rht dd|Drht |D]\}}i|t ||d||d}qRq|||<q|S)aFlatten hierarchical dict, e.g. ``{'a': {'b': 'c'}} -> {'a/b': 'c'}``. Args: params: Dictionary containing the hyperparameters delimiter: Delimiter to express the hierarchy. Defaults to ``'/'``. Returns: Flattened dict. Examples: >>> _flatten_dict({'a': {'b': 'c'}}) {'a/b': 'c'} >>> _flatten_dict({'a': {'b': 123}}) {'a/b': 123} >>> _flatten_dict({5: {'a': 123}}) {'5/a': 123} >>> _flatten_dict({"dl": [{"a": 1, "c": 3}, {"b": 2, "d": 5}], "l": [1, 2, 3, 4]}) {'dl/0/a': 1, 'dl/0/c': 3, 'dl/1/b': 2, 'dl/1/d': 5, 'l': [1, 2, 3, 4]} )r&r%css|]}t|tVqdS)N)rr)ritemrrr msz _flatten_dict..r#) r!strrrtyperrrr _flatten_dictlistall enumerate) r r%r&resultkvnew_keyir'rrrr+Ms   " r+cCsn|D]2}trddl}t|||j|j|jfr||||<t||tt t t t fvr4t ||||<q|S)aReturns params with non-primitvies converted to strings for logging. >>> import torch >>> params = {"float": 0.3, ... "int": 1, ... "string": "abc", ... "bool": True, ... "list": [1, 2, 3], ... "namespace": Namespace(foo=3), ... "layer": torch.nn.BatchNorm1d} >>> import pprint >>> pprint.pprint(_sanitize_params(params)) # doctest: +NORMALIZE_WHITESPACE {'bool': True, 'float': 0.3, 'int': 1, 'layer': "", 'list': '[1, 2, 3]', 'namespace': 'Namespace(foo=3)', 'string': 'abc'} rN) r numpyrbool_integerfloatingr'r*boolintfloatr)r )r r0nprrr_sanitize_paramsusr<cCsdd|DS)z5Convert non-serializable objects in params to string.cSs&i|]\}}|t|st|n|qSr)_is_json_serializabler)rr0r1rrrr s&z._convert_json_serializable..r!rrrr_convert_json_serializablesr@valuec CsL|dust|ttttttfrdSzt|WdSt t fy%YdSw)z/Test whether a variable can be encoded as json.NTF) rr8r9r:r)r,dictjsondumps TypeError OverflowError)rArrrr=s r=metricsprefix separatorcs s|Sfdd|DS)alInsert prefix before each key in a dict, separated by the separator. Args: metrics: Dictionary with metric names as keys and measured quantities as values prefix: Prefix to insert before each key separator: Separates prefix and original key name Returns: Dictionary with prefix and separator inserted before each key cs"i|] \}}||qSrrr>rHrIrrr s"z_add_prefix..r?)rGrHrIrrJr _add_prefixsrK)r#r$)rrCargparsercollections.abcrr dataclassesrrtypingrrr torchr "lightning_fabric.utilities.importsr rBr)rr"r+r<r@r8r=r:rKrrrrs.   .","("!