o ?eD*@sdZddlZddlZddlZddlmZddlmZddlmZddlm Z ddl m Z ddl m Z dd lmZgaGd d d Ze jd d ZddZGdddeZGddde jedZedGdddeZGdddeZdS)z7Definitions for resource-type trackable object classes.N)context) def_function)ops)tensor)base) tf_contextlib) tf_exportc@s2eZdZdZdgZddZeddZddZd S) ResourceTrackerz*An object that tracks a list of resources. _resourcescCs g|_dSNr selfre/home/www/facesmatcher.com/pyenv/lib/python3.10/site-packages/tensorflow/python/trackable/resource.py__init__&s zResourceTracker.__init__cC|jSr r r rrr resources)zResourceTracker.resourcescCs|j|dSr )r append)rresourcerrr add_resource-szResourceTracker.add_resourceN) __name__ __module__ __qualname____doc__ __slots__rpropertyrrrrrrr !s  r ccs,tt}t|zdVW|adS|aw)aA context to manage resource trackers. Use this in order to collect up all resources created within a block of code. Example usage: ```python resource_tracker = ResourceTracker() with resource_tracker_scope(resource_tracker): resource = TrackableResource() assert resource_tracker.resources == [resource] Args: resource_tracker: The passed in ResourceTracker object Yields: A scope in which the resource_tracker is active. N)list_RESOURCE_TRACKER_STACKr)resource_trackeroldrrrresource_tracker_scope1s  r"csfdd}|S)z"To avoid capturing loop variables.csg|Ri|Sr r)argskwargscaptured_gettercaptured_previousrrgetterQsz_make_getter..getterr)r&r'r(rr%r _make_getterNsr)c@seZdZdZddZdS)_ResourceMetaclassz!Metaclass for CapturableResource.csLfddfdd}tj}|D]}t||}q||i|S)Ncs8|dusJjg|Ri|}|j|i||Sr )__new__r)Z next_creatorakwobjclsrrdefault_resource_creator\s z=_ResourceMetaclass.__call__..default_resource_creatorcsdg|Ri|Sr r)r,r-)r1rrbsz-_ResourceMetaclass.__call__..)rget_default_graphZ_resource_creator_stack_resource_typer))r0r#r$Zprevious_getterZresource_creator_stackr(r)r0r1r__call__Zs    z_ResourceMetaclass.__call__N)rrrrr5rrrrr*Ws r*cseZdZdZdddZeddZeddZej d dZd d Z ed d Z e j dd Z ddZ ddZ eddZddZejjffdd ZddZZS)CapturableResourceazHolds a Tensor which a tf.function can capture. `CapturableResource`s are discovered by traversing the graph of object attributes, e.g. during `tf.saved_model.save`. 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A TrackableResource is most useful for stateful Tensors that require initialization, such as `tf.lookup.StaticHashTable`. `TrackableResource`s are discovered by traversing the graph of object attributes, e.g. during `tf.saved_model.save`. A TrackableResource has three methods to override: * `_create_resource` should create the resource tensor handle. * `_initialize` should initialize the resource held at `self.resource_handle`. * `_destroy_resource` is called upon a `TrackableResource`'s destruction and should decrement the resource's ref count. For most resources, this should be done with a call to `tf.raw_ops.DestroyResourceOp`. Example usage: >>> class DemoResource(tf.saved_model.experimental.TrackableResource): ... def __init__(self): ... super().__init__() ... self._initialize() ... def _create_resource(self): ... return tf.raw_ops.VarHandleOp(dtype=tf.float32, shape=[2]) ... def _initialize(self): ... tf.raw_ops.AssignVariableOp( ... resource=self.resource_handle, value=tf.ones([2])) ... def _destroy_resource(self): ... tf.raw_ops.DestroyResourceOp(resource=self.resource_handle) >>> class DemoModule(tf.Module): ... def __init__(self): ... self.resource = DemoResource() ... def increment(self, tensor): ... return tensor + tf.raw_ops.ReadVariableOp( ... resource=self.resource.resource_handle, dtype=tf.float32) >>> demo = DemoModule() >>> demo.increment([5, 1]) r7cs&tD]}||qtj|ddS)acInitialize the `TrackableResource`. Args: device: A string indicating a required placement for this resource, e.g. "CPU" if this resource must be created on a CPU device. 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