B ӻd@sdZddlZddlZddlZddlZddlZddlZddlZddlZddl Z ddl Z ddl Z ddl Z ddlZddlmZddlmZddlmZddlmZddlmZiZiZdad Zed d Gd d d eZedddZdZe Z!e a"e a#ddZ$ddZ%ddZ&GdddeZ'GdddeZ(GdddeZ)Gddde*Z+Gd d!d!eZ,d_d"d#Z-ed$d`d&d'Z.ed(d)d*Z/ej0d+d,Z1ed-dad.d/Z2Gd0d1d1e3Z4ed2d3d4Z5dbd5d6Z6dcd8d9Z7ed:ddd;d<Z8d=d>Z9ded?d@Z:dfdAdBZ;edCGdDdEdEeZdJdKZ?dLdMZ@dNdOZAdPdQZBdhdSdTZCdUdVZDdWdXZEdYdZZFd[d\ZGGd]d^d^e jHZIeZJdS)iz#Python utilities required by Keras.N) tf_contextlib) tf_inspect)nest) tf_decorator) keras_exportFzlayer was saved without configzkeras.utils.custom_object_scopezkeras.utils.CustomObjectScopec@s(eZdZdZddZddZddZdS) CustomObjectScopeaExposes custom classes/functions to Keras deserialization internals. Under a scope `with custom_object_scope(objects_dict)`, Keras methods such as `tf.keras.models.load_model` or `tf.keras.models.model_from_config` will be able to deserialize any custom object referenced by a saved config (e.g. a custom layer or metric). Example: Consider a custom regularizer `my_regularizer`: ```python layer = Dense(3, kernel_regularizer=my_regularizer) config = layer.get_config() # Config contains a reference to `my_regularizer` ... # Later: with custom_object_scope({'my_regularizer': my_regularizer}): layer = Dense.from_config(config) ``` Args: *args: Dictionary or dictionaries of `{name: object}` pairs. cGs||_d|_dS)N)custom_objectsbackup)selfargsr ]/var/www/html/venv/lib/python3.7/site-packages/tensorflow/python/keras/utils/generic_utils.py__init__MszCustomObjectScope.__init__cCs(t|_x|jD]}t|qW|S)N)_GLOBAL_CUSTOM_OBJECTScopyr rupdate)r objectsr r r __enter__Qs  zCustomObjectScope.__enter__cOstt|jdS)N)rclearrr )r r kwargsr r r __exit__WszCustomObjectScope.__exit__N)__name__ __module__ __qualname____doc__rrrr r r r r2srzkeras.utils.get_custom_objectscCstS)aRetrieves a live reference to the global dictionary of custom objects. Updating and clearing custom objects using `custom_object_scope` is preferred, but `get_custom_objects` can be used to directly access the current collection of custom objects. Example: ```python get_custom_objects().clear() get_custom_objects()['MyObject'] = MyObject ``` Returns: Global dictionary of names to classes (`_GLOBAL_CUSTOM_OBJECTS`). )rr r r r get_custom_objects\srshared_object_idcCs ttddS)zFGet whether shared object handling is disabled in a threadsafe manner.disabledF)getattrSHARED_OBJECT_DISABLEDr r r r _shared_object_disabledsr cCsttdtS)zBGet the current shared object saving scope in a threadsafe manner.scope)rSHARED_OBJECT_LOADINGNoopLoadingScoper r r r _shared_object_loading_scopesr$cCs ttddS)zBGet the current shared object saving scope in a threadsafe manner.r!N)rSHARED_OBJECT_SAVINGr r r r _shared_object_saving_scopesr&c@s eZdZdZddZddZdS)DisableSharedObjectScopezA context manager for disabling handling of shared objects. Disables shared object handling for both saving and loading. Created primarily for use with `clone_model`, which does extra surgery that is incompatible with shared objects. cCsdt_t|_t|_dS)NT)rrr$_orig_loading_scoper&_orig_saving_scope)r r r r rsz"DisableSharedObjectScope.__enter__cOsdt_|jt_|jt_dS)NF)rrr(r"r!r)r%)r r rr r r rsz!DisableSharedObjectScope.__exit__N)rrrrrrr r r r r'sr'c@s eZdZdZddZddZdS)r#zThe default shared object loading scope. It does nothing. Created to simplify serialization code that doesn't care about shared objects (e.g. when serializing a single object). cCsdS)Nr )r Zunused_object_idr r r getszNoopLoadingScope.getcCsdS)Nr )r object_idobjr r r setszNoopLoadingScope.setN)rrrrr*r-r r r r r#sr#c@s0eZdZdZddZddZddZdd Zd S) SharedObjectLoadingScopeaMA context manager for keeping track of loaded objects. During the deserialization process, we may come across objects that are shared across multiple layers. 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N)r/r*)r r+r r r r*s zSharedObjectLoadingScope.getcCs|dkr dS||j|<dS)zr=r?)r r4r,rAr r r create_config-s z%SharedObjectSavingScope.create_configcOst|ddsdt_dS)Nr:F)rr%r!)r r rr r r r:s z SharedObjectSavingScope.__exit__N)rrrrrrBrCrr r r r r9s  r9cCsV||d}|dk r||t<tdk rR|dk rRt|}|dkrNt||S|S|S)z=Returns the serialization of the class with the given config.) class_nameconfigN)r6r&rBrC)cls_name cls_configr,rr4rAr r r serialize_keras_class_and_config@s  rHz'keras.utils.register_keras_serializableCustomcsfdd}|S)a|Registers an object with the Keras serialization framework. This decorator injects the decorated class or function into the Keras custom object dictionary, so that it can be serialized and deserialized without needing an entry in the user-provided custom object dict. It also injects a function that Keras will call to get the object's serializable string key. Note that to be serialized and deserialized, classes must implement the `get_config()` method. Functions do not have this requirement. The object will be registered under the key 'package>name' where `name`, defaults to the object name if not passed. Args: package: The package that this class belongs to. name: The name to serialize this class under in this package. If None, the class' name will be used. Returns: A decorator that registers the decorated class with the passed names. csdk r n|j}d|}t|r:t|ds:td|tkrVtd|t|f|tkrrtd|t|f|t|<|t|<|S)z9Registers a class with the Keras serialization framework.N>rBzACannot register a class that does not have a get_config() method.z$%s has already been registered to %s)rrisclasshasattr ValueErrorr_GLOBAL_CUSTOM_NAMES)argrDZregistered_name)namepackager r decoratorps z.register_keras_serializable..decoratorr )rQrPrRr )rPrQr register_keras_serializableXsrSzkeras.utils.get_registered_namecCs|tkrt|S|jSdS)aReturns the name registered to an object within the Keras framework. This function is part of the Keras serialization and deserialization framework. It maps objects to the string names associated with those objects for serialization/deserialization. Args: obj: The object to look up. Returns: The name associated with the object, or the default Python name if the object is not registered. N)rNr)r,r r r get_registered_namesrTccst}zdadVWd|aXdS)NT)_SKIP_FAILED_SERIALIZATION)prevr r r skip_failed_serializations  rWz!keras.utils.get_registered_objectcCs<|tkrt|S|r$||kr$||S|r8||kr8||SdS)aReturns the class associated with `name` if it is registered with Keras. This function is part of the Keras serialization and deserialization framework. It maps strings to the objects associated with them for serialization/deserialization. Example: ``` def from_config(cls, config, custom_objects=None): if 'my_custom_object_name' in config: config['hidden_cls'] = tf.keras.utils.get_registered_object( config['my_custom_object_name'], custom_objects=custom_objects) ``` Args: name: The name to look up. custom_objects: A dictionary of custom objects to look the name up in. Generally, custom_objects is provided by the user. module_objects: A dictionary of custom objects to look the name up in. Generally, module_objects is provided by midlevel library implementers. Returns: An instantiable class associated with 'name', or None if no such class exists. N)r)rPrmodule_objectsr r r get_registered_objects  rYc@s eZdZdS)CustomMaskWarningN)rrrr r r r rZsrZz"keras.utils.serialize_keras_objectc Csnt|\}}|dkrdSt|ddp:t|do:t|j }|rXt|jrXtjdt dt|drLt |j }y |}Wn8t k r}zt rt|tdiS|Wdd}~XYnXi}x||D]p\}}t|tr|||<qy4t|} t| trt|tsd| d <| ||<Wqtk r0|||<YqXqWt |j }t|||St|d r`t |Std |dS) aSerialize a Keras object into a JSON-compatible representation. 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If you need a deserialization utility that has preexisting knowledge of built-in Keras objects, use e.g. `keras.layers.deserialize(config)`, `keras.metrics.deserialize(config)`, etc. Calling `deserialize_keras_object` while underneath the `SharedObjectLoadingScope` context manager will cause any already-seen shared objects to be returned as-is rather than creating a new object. Args: identifier: the serialized form of the object. module_objects: A dictionary of built-in objects to look the name up in. Generally, `module_objects` is provided by midlevel library implementers. custom_objects: A dictionary of custom objects to look the name up in. Generally, `custom_objects` is provided by the end user. printable_module_name: A human-readable string representing the type of the object. Printed in case of exception. Returns: The deserialized object. Example: A mid-level library implementer might want to implement a utility for retrieving an object from its config, as such: ```python def deserialize(config, custom_objects=None): return deserialize_keras_object( identifier, module_objects=globals(), custom_objects=custom_objects, name="MyObjectType", ) ``` This is how e.g. `keras.layers.deserialize()` is implemented. N from_configr)rzUnknown {}: {}. Please ensure this object is passed to the `custom_objects` argument. See https://www.tensorflow.org/guide/keras/save_and_serialize#registering_the_custom_object for details.z%Could not interpret serialized %s: %s)rfrirwr*r6r$rLrgetfullargspecrxr rsrrerr-rgrMrrrKru) identifierrXrrqrErvrGrZ shared_objectarg_specZdeserialized_objZ object_namer,r r r rt]sX0                  rtcCstjdkr2t|jdd}t|dd}nt|j}t|dd}|j }|j rrt dd|j D}nd}|||fS) zSerializes a user defined function. Args: func: the function to serialize. 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