o ?e3@sdZddlZddlmZddlmZddlmZddlm Z ddlm Z ddl m Z dd l mZd Zd ZejgZeee d gd ededdZeee dgd   d$ddZddZd%ddZddZddZ   d&ddZ   d&ddZd$d d!Zd"d#ZdS)'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)v1cCs<t|tjr|St|tjr|jj|dStd |)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 ValueErrorformatfcro/home/www/facesmatcher.com/pyenv/lib/python3.10/site-packages/tensorflow/python/feature_column/serialization.pyserialize_feature_column-s - rz6__internal__.feature_column.deserialize_feature_columncCsxt|tjr|SddtD}|duri}t|||dd\}}t|tjs,td ||j |||d}| t ||S)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). 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Raises: ValueError if called with input that is not a list of FeatureColumns. cSsg|]}t|qSr)r)rrrrr sz-serialize_feature_columns..r)Zfeature_columnsrrrserialize_feature_columnssr-csifdd|DS)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. csg|]}t|qSr)r+)rcr#r!rrr,s z/deserialize_feature_columns..r)Zconfigsr!rr/rdeserialize_feature_columnss r0cCs|jjd|jS)aReturns a unique name for the feature column used during deduping. 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Returns: A unique name as a string. :)rrnamerrrrr)sr)c Cst|\}}|dur dSt|drZ|jj}|}i}|D]4\}}t|tj r/|||<q zt |}t|t rBt|t sBd|d<|||<Wq t yT|||<Yq w||dSt|drb|jSt d|)z?Serialize a Keras object into a JSON-compatible representation.NrT__passive_serialization__r rzCannot serialize) runwraphasattrrrritemsr rr_serialize_keras_objectdictr)instance_r2r Zserialization_configkeyitemZserialized_itemrrrr7s.        r7objectc Cs|durdSt|trG|}t||||\}}t|drr?r3Z config_itemr) r r8rstr_get_registered_objectr6rHrrrrE) r r r!r"r rr*Zdeserialized_objectsr;r<rrrr%+s8  r%cCs,|r ||vr ||S|r||vr||SdSNr)r2r!r rrrrJVs  rJcCst||S)z;Decorator that registers a FeatureColumn for serialization.)r$appendrrrrregister_feature_column^s rM)NNrK)NNr=)__doc__rZ tensorflow.python.feature_columnrrZtensorflow.python.opsrZtensorflow.python.utilrrrZ tensorflow.python.util.tf_exportrZtensorflow.tools.docsrZ#_FEATURE_COLUMN_DEPRECATION_WARNINGZ+_FEATURE_COLUMN_DEPRECATION_RUNTIME_WARNINGZTruncatedNormalr$header deprecatedrr+r-r0r)r7rHr%rJrMrrrrsN         0 9   4 +