ijjJddlZddlmZddlmZddlmZddlmZddlm Z ddl m Z edGd d e Z d Z d Zd ZdZy)N)tree) keras_export KerasTensor)backend)is_nnx_enabled) Operationzkeras.FunctionceZdZdZdfd ZedZedZedZdZ dZ d Z d Z d Z d dfd ZdZxZS)FunctionaClass that encapsulates a computation graph of Keras operations. You can use a `Function` to capture the computation graph linking some input tensors to some output tensors, and reapply the same computation on new inputs. A `Function` is similar to a Functional Model, with the difference that it is stateless (it does not track state variables) and does not implement the `Layer` API. Example: ```python input_1 = keras.KerasTensor(shape=(None, 2, 3)) input_2 = keras.KerasTensor(shape=(None, 2, 3)) x = input_1 + input_2 output = keras.ops.sigmoid(x) fn = keras.Function(inputs=[input_1, input_2], outputs=output) input_1_val = np.random.random((4, 2, 3)) input_2_val = np.random.random((4, 2, 3)) output_val = fn([input_1_val, input_2_val]) ``` Args: inputs: `KerasTensor` instance or nested structured of `KerasTensor` instances. outputs: `KerasTensor` instance or nested structured of `KerasTensor` instances. They should be computable given only the values of `inputs`. name: String. The name of the function. Nct ||tdk(rt|dd}d|_t j d||_t j d||_t j||_ t j||_ |jstd|d ||jstd |d |tdk(r|_t|j|j\}}}}||_||_||_||_d } |j%dgt'|jz| t)r|j+yy) N)name tensorflow_self_setattr_trackingTFc|SNxs k/var/www/html/emotional.easysim.app/public_html/venv/lib/python3.12/site-packages/keras/src/ops/function.pyz#Function.__init__..:s1c|Srrrs rrz#Function.__init__..;sArz4`inputs` argument cannot be empty. Received: inputs=z outputs=z5`outputs` argument cannot be empty. Received: inputs=cZdgttj|jzSr)lenrflattenoutput)opargskwargss rempty_op_outputsz+Function.__init__..empty_op_outputsWs!6C RYY 788 8r)call_fn)super__init__rgetattrrr map_structure_inputs_struct_outputs_structr_inputs_outputs ValueError map_graph_nodes_nodes_by_depth _operations_operations_by_depth_run_through_graphrr_setup_nnx_op_mapping) selfinputsoutputsr rnodesnodes_by_depth operationsoperations_by_depthr __class__s rr#zFunction.__init__.s d# 9 $ &-.& "+0D '"00fE#11+wG||F+  W- || "")%  }} "")%  9 $*@D 'CL LL$--D @ ,? -%$7! 9  FS& &0@     & & ( rc |jddSr)r.r2s rr7zFunction.operationsbs""rc|jS)z1Flat list of the symbolic inputs of the Function.)r(r;s rr3zFunction.inputsfs||rc|jS)z2Flat list of the symbolic outputs of the Function.)r)r;s rr4zFunction.outputsks}}rci|_|jdd|_|jD]}||jt|<y)zSetup operation mapping for NNXN)_nnx_op_mappingr.nnx_operationsid)r2 operations rr1zFunction._setup_nnx_op_mappingpsJ "#..q1)) .s+AGG177Crc|jSr)compute_output_specrs rrz.Function.compute_output_spec..s B,B,Br) operation_fn) _assert_input_compatibilityziprrr(rIr%r'r0)r2r3shortcutrx_refs rrLzFunction.compute_output_specs ((0DLL0$,,? HAuww%++%   %%C$$ && !B'  rc&tjd|}ttj||jD]'\}}|j |_|j|_)|j|}tjd|S)Nct|S)NrIrrs rrz/Function.compute_output_shape..s k*rc|jSrrUrs rrz/Function.compute_output_shape..s AGGr) rmap_shape_structurerPrr(rJ_dtypesparse_sparserLr%)r2 input_shapeinput_shape_structrrR output_specs rcompute_output_shapezFunction.compute_output_shapes"55 *K  DLL);sbrc6tj|}it|j|D]\}}|t |<|j }t |j}|jd|D]}||} | D]} | jr | jrtfd| jDr;| jj\} } ||| j} || g| i| }n!|j| }||} | | i| }t| j tj|D]\}}|t |<g}t#|j D]{\}}t |vrLtj$|j&|d}dj)d|D}t+d|d|j-t |}tj.|j&|S) zExecute the graph. At each node we compute outputs via `operation_fn(node.operation)(*args, **kwargs)`. Treversec38K|]}t|vywrrA).0r tensor_dicts r z.Function._run_through_graph..sLAr!uK/Lsr.c32K|]}t|ywr)str)rgps rriz.Function._run_through_graph..s51A5szOutput with path `z` is not connected to `inputs`)rrrPr3rAr-listkeyssortrBis_inputany input_tensors argumentsfill_inrFr4 enumerateflatten_with_pathr'joinr*appendpack_sequence_as)r2r3rNr!ryr6 depth_keysdepthr5rErrrr4rBoutput_tensorsipathrhs @rr0zFunction._run_through_graphsf%  V, #DAq!"K1  #--.--/0 % +E"5)E +~~L9K9KLL#~~55kB f&%dnn5B%b:4:6:G!% < JJrc  tj||jt tj ||j D]\}}t|jt|jk7rIt|jjd|jd|jd|jdt |j|jD]Y\}}| | ||k7st|jjd|jd|jd|jdy#t$rtd|jd|wxYw)NzOFunction was called with an invalid input structure. Expected input structure: z Received input structure: z, was passed incompatible inputs. For input 'z', expected shape z+, but received instead a tensor with shape rj) rassert_same_structurer&r*rPrr(rrIr9__name__r )r2r3rrRdimref_dims rrOz$Function._assert_input_compatibilitysr   & &vt/B/B CDLL0$,,? HAu177|s5;;// ~~../077 !$AGGU[[ 9  W&3?g~(#~~6678??DzzlK..3kk];;;<77)1F    --1-@-@,AB--3H6  s E%E)r)r __module__ __qualname____doc__r#propertyr7r3r4r1rFrLr^r`r0rO __classcell__)r9s@rr r sxB2)h## < , B/ $1$5Knrr c"t|d|S)Nz_ib-rf)r node_indexs r make_node_keyrsfXT* &&rc t||\}t|}|Dchc];}t|j|jjj |=}}i}i}t |D]}|j|d}|j|jd} t|| }|||j<|||<|jD]+} | |vr|j| d} t|dz| || <-|D]u} | jd} | s| |vs| jj} | j| }|jsLd|| <d| <d||<|jt| | wtj t"}|j%D]\}}||j'|tj t"}|j%D]\}}||j'|t#|j)}|j+dg}|D]-}||}|j+fd|j-|/t#|j)}|j+dt}|D]}|j|g}|D]}||D]}t/j0|j2D]K}||vr |j}t5d|d |d ||j'|jj6Mt/j0|j8D]}|j||Dcgc]}|j6}}|D]7}|j;|dk7st5d |d |j;|d ||||fScc}wcc}w)a Validates a graph's topology and gather its operations and nodes. Args: inputs: List of input tensors. outputs: List of outputs tensors. Returns: A tuple `(nodes, nodes_by_depth, operations, operations_by_depth)`. - nodes: set of Node instances - nodes_by_depth: dict mapping ints (depth) to lists of node instances. - operations: list of Operation instances. - operations_by_depth: dict mapping ints (depth) to lists of Operation instances. rTrcc|Srr)roperation_indicess rrzmap_graph..\s 0A!0Dr)keyz2Graph disconnected: cannot find parent for tensor z at operation 'zB'. The following previous operations were accessed without issue: z The name "z " is used z: times in the model. All operation names should be unique.) _build_mapsetrrB_inbound_nodesindexreversed setdefaultgetmax parent_nodes_keras_historyrrqadd collections defaultdictrnitemsryrorpextendrrrsr*r r4count)r3r4nodes_in_decreasing_depthnodes_in_graphrE network_nodes nodes_depthsoperations_depthsr}previous_depthnode_depinput_tinput_operationrr6r8rBr|r7operations_for_depthcomputable_tensorsroperations_with_complete_input all_namesr rs @rr+r+s>"4>fg3N0023N.  dnndnn&C&C&I&I$&OPM L23D''a0+..t~~qA E>*,1$..)" T )) DH~-)--h:N%(N%CL "  D%D6 N!003 6GG //::J"11*=D}}56!/257!/2%& T"!!-"LM N!,,T2N#))++ eu$$T*+&11$7-3355 5E")))45)..01JOODO!J0259 !!&D!E./ 0n))+,JOODO!  "q!"&("*"5) *D\\$"4"45 K.. $I$""#OI;?**H)IK /55dnn6I6IJ K\\$,,/ *"&&q) * **$2<'Oc t}t}g}i}tj|D]}t||||||||fS)aTopologically sort nodes in order from inputs to outputs. It uses a depth-first search to topologically sort nodes that appear in the _keras_history connectivity metadata of `outputs`. Args: outputs: the output tensors whose _keras_history metadata should be walked. This may be an arbitrary nested structure. Returns: A tuple like (ordered_nodes, operation_to_first_traversal_index) ordered_nodes: list of nodes appearing in the keras history, topologically sorted from original inputs to the `outputs`. (If outputs have different sets of ancestors, the inputs to one output may appear after a different output). operation_to_first_traversal_index: A dict mapping operation to the traversal index in the DFS where it is seen. Note: if a operation is shared by several nodes, the dict will onlystore the index corresponding to the *first* time the operation seen. )rrr_build_map_helper)r3r4finished_nodesnodes_in_progressrrrs rrrs_,UN ",,w'      %     %&7 77rc |j\}}}|sy|j|} | |vrytj|} | js|| vr|j | y| |vrt d|d|jd||vrt|||<|j | | js!| jD]} t|| |||||j | |j| |j| y)z"Recursive helper for `_build_map`.NzTensor z from operation 'z' is part of a cycle.) rrrrrqrr*r rrsrremovery) r3tensorrrrrrBr_rE flat_inputs input_tensors rrrs0     # #J /D ~ ,,v&K ==V{24    fX.y~~.>?   ))'*+<'=)$$ == .. L !)!   tT"$$T*r)r keras.srcrkeras.src.api_exportrkeras.src.backendrkeras.src.backend.configrrkeras.src.ops.operationr r rr+rrrrrrsW-),3-qyq qh'@JF#8L9+r