~Wh +ndZddlmcmZddlmZddlmZ ddlm Z ddlm Z dZ dZd Zd Zd Zd ZdS) z%Utilities for keras functional model.N)backend) input_layer) keras_tensor)nodezFound unexpected instance while processing input tensors for keras functional model. Expecting KerasTensor which is from tf.keras.Input() or output from keras layer call(). Got: {}ctj|s'tt||jjS)aCheck if tensor is directly generated from `tf.keras.Input`. This check is useful when constructing the functional model, since we will need to clone Nodes and KerasTensors if the model is building from non input tensor. Args: tensor: A `KerasTensor` as inputs to the functional model. Returns: bool. Whether the tensor is directly generated from `tf.keras.Input`. Raises: ValueError: if the tensor is not a KerasTensor instance. ) node_moduleis_keras_tensor ValueError"_KERAS_TENSOR_TYPE_CHECK_ERROR_MSGformatris_inputtensors c/var/www/html/movieo_spanner_bot/venv/lib/python3.11/site-packages/keras/engine/functional_utils.pyis_input_keras_tensorrs@  &v . .L;BB6JJKKK ; c tj|}tj|}||zD]=}tj|s't t |>td|D}t g}g}t}|D]}| |j |r| d} t| |vr)| t| | | | jD]{} t| |vr# t| 6| j } | jr"t d| | | ||| kr0 fd|D} t d| |S)a6Fetch all Nodes in the graph defined by "inputs" and "outputs". This method is used to find and then clone Nodes when creating a new sub-model from an existing functional model. Args: inputs: A nested structure of KerasTensor to use as model inputs. outputs: A nested structure of KerasTensor to use as model outputs. Returns: A list of Nodes that are connected to the inputs and outputs. Raises: ValueError: when inputs and outputs are disconnected or in case of unexpected objects in the inputs/outputs. c,g|]}t|Sid).0kts r z4find_nodes_by_inputs_and_outputs..Ts666b2b66666rrzFound input tensor cannot be reached given provided output tensors. Please make sure the tensor {} is included in the model inputs when building functional model.c6g|]}t|v|Srr)rr end_ids_founds rrz4find_nodes_by_inputs_and_outputs..~s0   bffM.I.IB.I.I.IrzHFound unvisited input tensors that are disconnected from the outputs: {})tfnestflattenrr r r r setappendrpopradd keras_inputsr )inputsoutputsstart_keras_tensorsend_keras_tensorstend_idsnodes_to_visitnodes_in_graphnode_id_visitedrr inbound_nodeunvisited_inputsrs @r find_nodes_by_inputs_and_outputsr04s;4'//'22// #4 4KK*1-- K?FFqIIJJ J K66$566677GEEMNNeeO &&af%%%% 0!!!$$ d88 & & BtHH%%%d#### 0 0B"vv  !!"R&&)))7L $  ()/r   ! !, / / / /3 0:-    *    $f%566    rct||}g}g}i}tj|D]}|jjr(||||t|<6t|}tj |}||||t|<tj ||}tj|D]D} t| }| j |_ ||||t| <Etj ||}|D]^} t| j|} t| j|} t| j|} t#j| j| | | _||fS)aClone the `Node` between the inputs and output tensors. This function is used to create a new functional model from any intermediate keras tensors. The clone of the nodes mimic the behavior of reconstructing the functional graph network by re-executing all the __call__ methods. The cloned nodes will be appended to the layers. Note that a new tf.keras.Inputs will be created for any items in the `inputs` Args: inputs: A nested structure of keras_tensors. outputs: A nested structure of keras_tensors. Returns: A pair of inputs and outputs, with cloned keras_tensors. They can be used to create a new functional model. r) call_args call_kwargsr&)r0rrrrr r!r_clone_keras_tensorinput_layer_moduleInputpack_sequence_as_keras_historyclone_keras_tensorsoutput_tensorsr2r3rNodelayer)r%r&nodes_to_clone cloned_inputscloned_outputs kt_id_mappingkt_inputcpy cloned_input kt_outputr output_copycall_args_copycall_kwargs_copys rclone_graph_nodesrHs&6fgFFNMN MGOOF++ 7 7 = ! 7   * * **2M"X,, ' '&h//C-33???L   . . .*6M"X,, ' 'G,,V]CCMW__W--++ !),, '5c"""'* bmm$$W--g~FFN  *$*=}MM ,T^]KK.t/?OO  J$(      . ((rcg}tj|D]}tj|rjt ||vr|t |}n-t |}|j|_||t |<||||tj ||S)aClone the keras tensors from the inputs. For any KerasTensor instance in the `args`, a new copy of KerasTensor will be created if it has not been cloned yet (by checking the `keras_tensor_mapping`). For any other types, the instance will be unchanged. This function is useful for cloning the Nodes since KerasTensor can't be reused across the models. Args: args: A nested structure of objects, which could contain KerasTensor. keras_tensor_mapping: A dict contains the ID of original KerasTensor, and the cloned KerasTensor instance. The dict will be updated with newly copied KerasTensor instances within this method. Returns: Same structure as inputs, with KerasTensor cloned. ) rrrrr rr4r8r!r7)argskeras_tensor_mappingresultobjrBs rr9r9s"Fwt$$    &s + + #ww...*2c773*#..%(%7"03$RWW- MM#     MM#     7 # #D& 1 11rc tj5}|5tj|}tj|cdddcdddS#1swxYwY ddddS#1swxYwYdS)a(Create an identical keras_tensor based on the input. We use keras_tensor_to_placeholder and keras_tensor_from_tensor to make sure inferred shape are not lost during the copy. Args: kt: the input KerasTensor. Returns: An identical copy of the input KerasTensor. N)r_scratch_graph as_defaultrkeras_tensor_to_placeholderkeras_tensor_from_tensor)r scratch_graph placeholders rr4r4sS   ! !F]  % % ' ' F F&B2FFK8EE F F F F F F FFFFFFFFF F F F F F F F F FFFFFFFFFFFFFFFFFFFs4B(A* B*A. .B1A. 2BB B)__doc__tensorflow.compat.v2compatv2rkerasr keras.enginerr5rrrr rr0rHr9r4rrrr[s,+!!!!!!!!!::::::%%%%%%,,,,,,1#   *QQQhH)H)H)V222BFFFFFr