BVh,*`dZddlZddlZddlZddlmZddlmZddl m Z ddl m Z ddl m Z ddl mZdd lmZdd lmZed ed Z dd ZdZdZdZdZdZdZdZdZGddej<ZeZ e jBdZ"dZ#e jBdZ$y)zKUtility functions shared between SavedModel saving/loading implementations.N)context)backend)base_layer_utils)control_flow_util) tf_contextlib) tf_inspect) LazyLoader) tf_decorator training_libz'tensorflow.python.keras.engine.trainingc t}t|dr|j}|j}n|}t ||||\ } fd}t j |||}rtj|S|S)a:Creates fn that adds the losses returned by call_fn & returns the outputs. Args: layer: A Keras layer object call_fn: tf.function that takes layer inputs (and possibly a training arg), and returns a tuple of (outputs, list of losses). default_training_value: Default value of the training kwarg. If `None`, the default is `K.learning_phase()`. return_method: Whether to return a method bound to the layer. Returns: function that calls call_fn and returns the outputs. Losses returned by call_fn are added to the layer losses. original_layer_callcr|dd}|i|\}}j|dtjr0jD]}|ustj g|_|S)zFReturns the outputs from the layer call function, and adds the losses.NT)inputs)add_lossrexecuting_eagerly_flatten_layersrREVIVED_LOSS_PLACEHOLDER _eager_losses)argskwargsoutputslossesifnlayer return_methods `/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/python/keras/saving/saved_model/utils.pyreturn_outputs_and_add_lossesz7use_wrapped_call..return_outputs_and_add_losses@sz !"Xd$)&)OGV NN6$N'  "$$&H! E>-FFG!/H N)targetdecorator_funcdecorator_argspec) layer_uses_training_boolhasattrr __call__maybe_add_training_argr make_decoratortypes MethodType) rcall_fndefault_training_valuerexpects_training_arg original_callarg_specr decoratedrs ` ` @ruse_wrapped_callr1&s 2%8 W+,//MGM'W24JL,"h,)) 2 ")    Iu -- r c|jry|h}t|}|rQ|j}||vrt|ddry|j ||j t||rQy)zHReturns whether this layer or any of its children uses the training arg.T_expects_training_argF)r3list_all_layerspopgetattraddextend)rvisitedto_visits rr$r$aso    G' U #( LLNE u-t4  KK OOOE*+  r ct|tjr |jSt |j ddS)NF) include_self recursive) isinstancer Modellayerslistr)objs rr4r4rs9\''( :: ##%#H IIr ct|g}|jtjj dt |D|S)Nc32K|]}t|ywN)list_all_layers_and_sublayers).0rs r z0list_all_layers_and_sublayers..}s)N/4#E*)Ns)setupdate itertoolschain from_iterabler4)rBss rrFrF{sE 3%j!((9?? ( ()N8G8L)N NO (r c j|sdfSfd}tj}|jt|jng}|j}|j xsi}d|j vr|jd|d<nc|j jd} t|j | z } |jr$t|j| k\r || || <tj|j |j|j||||j} || fS)aDecorate call and optionally adds training argument. If a layer expects a training argument, this function ensures that 'training' is present in the layer args or kwonly args, with the default training value. Args: original_call: Original call function. wrapped_call: Wrapped call function. expects_training_arg: Whether to include 'training' argument. default_training_value: Default value of the training kwarg to include in the arg spec. If `None`, the default is `K.learning_phase()`. Returns: Tuple of ( function that calls `wrapped_call` and sets the training arg, Argspec of returned function or `None` if the argspec is unchanged) Nctt}|xstj}t j fdt j|fdfdS)z.wrap_with_training_arg..replace_training_and_calls"x!3T6B 4 *6 **r cdSNTrVsrzHmaybe_add_training_arg..wrap_with_training_arg..s 3D9r cdSNFrYrZsrr[zHmaybe_add_training_arg..wrap_with_training_arg..s )%0r )get_training_arg_indexget_training_argKlearning_phaserAcopyr smart_cond)rrrSrVrTr,r.rUs`` @@rwrap_with_training_argz6maybe_add_training_arg..wrap_with_training_argsm/ > 2D&AH'=1+;+;+=h :D [[]F+  ' '90 22r rS)rvarargsvarkwdefaults kwonlyargskwonlydefaults annotations)rgetfullargspecrgrArhrirappendindexlen FullArgSpecrerfrj) r.rUr-r,rdr/rgrhrirmtraining_default_indexr#s `` ` rr'r's0&   2( & &} 5((0(9(9(ET(## $2(""***0b.x}}$j!!7N: MM   +E /%7 H  "88(()1*@h&&' ,, == NN#&&( !2 22r c*tj|}|jrd|jvs |jryy|j }tj |r|dd}d|vr|jdSd|jvs |jryy)abReturns the index of 'training' in the layer call function arguments. Args: call_fn: Call function. Returns: - n: index of 'training' in the call function arguments. - -1: if 'training' is not found in the arguments, but layer.call accepts variable keyword arguments - None: if layer doesn't expect a training argument. rSNr)rrkrerhrfrismethodrm)r+argspecarg_lists rr^r^s  % %g .' __W'''7==  ||H7#!"hX ^^J '' w)) )W]]  r cP||dkst||kr ||d<||fS|||<||fSNrrS)rn)rSrmrrs rrRrRsC ]eai3t9#5!F: vDK vr cZ||dkst||kr|jddS||Srw)rngetrmrrs rr_r_s3 ]eai3t9#5 ::j$ '' ;r cv||dkst||kr|jddy|j|yrw)rnr5rzs rremove_training_argr|s1 ]eai3t9#5 JJz4 HHUOr ceZdZfdZxZS)SaveOptionsContextc8tt| d|_yrX)superr~__init__ save_traces)self __class__s rrzSaveOptionsContext.__init__s d,.Dr )__name__ __module__ __qualname__r __classcell__)rs@rr~r~s r r~c#Ktj} |t_d|t_y#|t_wxYwwrE_save_options_contextr)rprevious_values rkeras_option_scopers4(44.7(3% (6%%s?/ ? <?c"tjS)zHWhether to trace layer functions-can be disabled in the save_traces arg.rrYr rshould_save_tracesrs  * **r c#dKt|dd}d|_ d||_y#||_wxYww)aA context that disables automatic dependency tracking when assigning attrs. Objects that inherit from Autotrackable automatically creates dependencies to trackable objects through attribute assignments, and wraps data structures (lists or dicts) with trackable classes. This scope may be used to temporarily disable this behavior. This works similar to the decorator `no_automatic_dependency_tracking`. Example usage: ``` model = tf.keras.Model() model.arr1 = [] # Creates a ListWrapper object with no_automatic_dependency_tracking_scope(model): model.arr2 = [] # Creates a regular, untracked python list ``` Args: obj: A trackable object. Yields: a scope in which the object doesn't track dependencies. _setattr_trackingTFN)r6r)rBrs r&no_automatic_dependency_tracking_scopers703 3T:.#+ *CNCs0$0 -0r])%__doc__rK threadingr)tensorflow.python.eagerrtensorflow.python.kerasrr`tensorflow.python.keras.enginertensorflow.python.keras.utilsrrr+tensorflow.python.keras.utils.generic_utilsr tensorflow.python.utilr globalsr r1r$r4rFr'r^rRr_r|localr~rcontextmanagerrrrrYr rrsR +0;;74B/GI-/ =A#(8v"J B3J>+,77+ ++r