AVh3dZddlZddlmZddlmZddlmZddlm Z ddlm Z ddl m Z dd l mZd Zd Zej"gZej&ee d g ej(dedZej&ee dg  ddZdZddZdZdZ ddZ ddZddZdZy)z3FeatureColumn serialization, deserialization logic.N)feature_column_v2_types)init_ops) deprecation) tf_decorator) tf_inspect) tf_export) doc_controlsa Warning: tf.feature_column is not recommended for new code. Instead, feature preprocessing can be done directly using either [Keras preprocessing layers](https://www.tensorflow.org/guide/migrate/migrating_feature_columns) or through the one-stop utility [`tf.keras.utils.FeatureSpace`](https://www.tensorflow.org/api_docs/python/tf/keras/utils/FeatureSpace) built on top of them. See the [migration guide](https://tensorflow.org/guide/migrate) for details. zUse Keras preprocessing layers instead, either directly or via the `tf.keras.utils.FeatureSpace` utility. Each of `tf.feature_column.*` has a functional equivalent in `tf.keras.layers` for feature preprocessing when training a Keras model.z4__internal__.feature_column.serialize_feature_column)v1ct|tjr|St|tjr'|j j |jdStdj|)aSerializes a FeatureColumn or a raw string key. This method should only be used to serialize parent FeatureColumns when implementing FeatureColumn.get_config(), else serialize_feature_columns() is preferable. This serialization also keeps information of the FeatureColumn class, so deserialization is possible without knowing the class type. For example: a = numeric_column('x') a.get_config() gives: { 'key': 'price', 'shape': (1,), 'default_value': None, 'dtype': 'float32', 'normalizer_fn': None } While serialize_feature_column(a) gives: { 'class_name': 'NumericColumn', 'config': { 'key': 'price', 'shape': (1,), 'default_value': None, 'dtype': 'float32', 'normalizer_fn': None } } Args: fc: A FeatureColumn or raw feature key string. Returns: Keras serialization for FeatureColumns, leaves string keys unaffected. Raises: ValueError if called with input that is not string or FeatureColumn.  class_nameconfigz#Instance: {} is not a FeatureColumn) isinstancesix string_typesfc_types FeatureColumn __class____name__ get_config ValueErrorformatfcs ^/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/python/feature_column/serialization.pyserialize_feature_columnr-s\ZC$$% I"h,,-,,//2==? KK :AA"E FFz6__internal__.feature_column.deserialize_feature_columncrt|tjr|StDcic]}|j|}}|i}t |||d\}}t |tjstdj||j|||}|jt||Scc}w)anDeserializes a `config` generated with `serialize_feature_column`. This method should only be used to deserialize parent FeatureColumns when implementing FeatureColumn.from_config(), else deserialize_feature_columns() is preferable. Returns a FeatureColumn for this config. Args: config: A Dict with the serialization of feature columns acquired by `serialize_feature_column`, or a string representing a raw column. custom_objects: A Dict from custom_object name to the associated keras serializable objects (FeatureColumns, classes or functions). columns_by_name: A Dict[String, FeatureColumn] of existing columns in order to avoid duplication. Raises: ValueError if `config` has invalid format (e.g: expected keys missing, or refers to unknown classes). Returns: A FeatureColumn corresponding to the input `config`. feature_column_v2module_objectscustom_objectsprintable_module_namez/Expected FeatureColumn class, instead found: {})r"columns_by_name)rrr_FEATURE_COLUMNSr-_class_and_config_for_serialized_keras_object issubclassrrrr from_config setdefault_column_name_with_class_name)rr"r$clsmodule_feature_column_classes cls_config new_instances rdeserialize_feature_columnr/bs6(() M$4#cllC##OC 2#/ 13 C// 0 9@@E GG#%!',  # #"<0, @@1#sB4c>|Dcgc] }t|c}Scc}w)aSerializes a list of FeatureColumns. Returns a list of Keras-style config dicts that represent the input FeatureColumns and can be used with `deserialize_feature_columns` for reconstructing the original columns. Args: feature_columns: A list of FeatureColumns. Returns: Keras serialization for the list of FeatureColumns. Raises: ValueError if called with input that is not a list of FeatureColumns. )r)feature_columnsrs rserialize_feature_columnsr2s 2A A2 "2 & AA AscFi}|Dcgc]}t|||c}Scc}w)aIDeserializes a list of FeatureColumns configs. Returns a list of FeatureColumns given a list of config dicts acquired by `serialize_feature_columns`. Args: configs: A list of Dicts with the serialization of feature columns acquired by `serialize_feature_columns`. custom_objects: A Dict from custom_object name to the associated keras serializable objects (FeatureColumns, classes or functions). Returns: FeatureColumn objects corresponding to the input configs. Raises: ValueError if called with input that is not a list of FeatureColumns. )r/)configsr"r$cs rdeserialize_feature_columnsr6s4$/  !NOD  scN|jjdz|jzS)aReturns a unique name for the feature column used during deduping. Without this two FeatureColumns that have the same name and where one wraps the other, such as an IndicatorColumn wrapping a SequenceCategoricalColumn, will fail to deserialize because they will have the same name in columns_by_name, causing the wrong column to be returned. Args: fc: A FeatureColumn. Returns: A unique name as a string. :)rrnamers rr*r*s"    $rww ..rctj|\}}|yt|dr|jj}|j }i}|j D][\}}t|tjr|||<& t|}t|trt|tsd|d<|||<]||dSt|dr |jStd|#t$r|||<YwxYw)z?Serialize a Keras object into a JSON-compatible representation.NrT__passive_serialization__r rzCannot serialize) runwraphasattrrrritemsrrr_serialize_keras_objectdictr)instance_r9rserialization_configkeyitemserialized_items rr?r?s ##H-+!X   X|$    & &D  "F\\^ ) T D#** +$(S!)1$7 ot ,Zd5K9=/5 6$3S! )*> ?? Xz"   %x00 )$(S!)s5C''C87C8c |yt|tr|}t||||\}}t|drwt j |j }|xsi}d|jvr3|j |tt|jS|j |S|xsi}|di|St|tjr_|}|r||vr|j|} n'|j|} | td|zdz|zt j| r| S| St j|r|Std|d|) zGTurns the serialized form of a Keras object back into an actual object.Nr(r")r"Unknown : zCould not interpret serialized )rr@r&r=rgetfullargspecr(argslistr>rrgetrisclass isfunction) identifierr!r"r#rr+r-arg_spec object_nameobjs r_deserialize_keras_objectrUsr   D! FE0EGS*sM"**3??;h%+n X]] * tD1E1E1G,H'IK K __Z (( &+n  : *c../K+7   { +c   { +c &;;dB$%& &# Ul JZ(  +Z9 ::rct|trd|vsd|vrtdt|z|d}t |||}|td|zdz|z|d}i}|j D]v\}} t| trd| vrt | ||d ||<-t| tjsHtjt | |sht | |||<x|j D] \}} ||||<||fS) z@Returns the class name and config for a serialized keras object.r rzImproper config format: )r"r!rHrIr; config_itemr ) rr@rstr_get_registered_objectr>rUrrrrP) rr!r"r#r r+r-deserialized_objectsrDrEs rr&r&+s? VT "l&&@f /#f+= >>l#* P#[ Z"77$>K LLh*##%Oic4$"="E"; '' - #/3 T3++ ,    6t^ L M#9~"N3#O$(--/0ic4*3/JsO0 z rc0|r ||vr||S|r ||vr||SyNrJ)r9r"r!s rrYrYVs/. $ $.0 $  rc0tj||S)z;Decorator that registers a FeatureColumn for serialization.)r%appendrs rregister_feature_columnr_^s" )r)NNr\)NNobject)__doc__r tensorflow.python.feature_columnrrtensorflow.python.opsrtensorflow.python.utilrrr tensorflow.python.util.tf_exportrtensorflow.tools.docsr #_FEATURE_COLUMN_DEPRECATION_WARNING+_FEATURE_COLUMN_DEPRECATION_RUNTIME_WARNINGTruncatedNormalr%header deprecatedrr/r2r6r*r?rUr&rYr_rJrrrls: P*./-6.'##, ,,-89 :  IJ-GK : -G`89 CK.2/36@L:6@rB&2/"1@.2-14</:h" (V r