o 2h3@sldZddlmZddlmZddlmZeZddZ ddZ d d Z d d Z d dZ ddZddZdS)aJUtilities for handling Keras model in graph plugin. Two canonical types of Keras model are Functional and Sequential. A model can be serialized as JSON and deserialized to reconstruct a model. This utility helps with dealing with the serialized Keras model. They have distinct structures to the configurations in shapes below: Functional: config name: Name of the model. If not specified, it is 'model' with an optional suffix if there are more than one instance. input_layers: Keras.layers.Inputs in the model. output_layers: Layer names that are outputs of the model. layers: list of layer configurations. layer: [*] inbound_nodes: inputs to this layer. Sequential: config name: Name of the model. If not specified, it is 'sequential' with an optional suffix if there are more than one instance. layers: list of layer configurations. layer: [*] [*]: Note that a model can be a layer. Please refer to https://github.com/tensorflow/tfjs-layers/blob/master/src/keras_format/model_serialization.ts for more complete definition. )GraphDef)dtypes) tb_loggingccszd|fV|ddr9|dd}|ddD]}t|D]\}}|r0d||fn|}||fVq$qdSdS)aYWalks the nested keras layer configuration in preorder. Args: keras_layer: Keras configuration from model.to_json. Yields: A tuple of (name_scope, layer_config). name_scope: a string representing a scope name, similar to that of tf.name_scope. layer_config: a dict representing a Keras layer configuration. configlayersname%s/%sN)get _walk_layers) keras_layer name_scopelayersub_name_scopesublayerr_/var/www/html/chatgem/venv/lib/python3.10/site-packages/tensorboard/plugins/graph/keras_util.pyr 4s  r cCs|rd||fS|S)a/Returns scoped name for a node as a string in the form '/'. Args: name_scope: a string representing a scope name, similar to that of tf.name_scope. node_name: a string representing the current node name. Returns A string representing a scoped name. r r)r node_namerrr _scoped_nameLs rcCs|ddduS)zReturns True if layer is a model. Args: layer: a dict representing a Keras model configuration. Returns: bool: True if layer is a model. rrN)r )rrrr _is_model\s rcCst|dtfr |S|gS)aNormalizes to a list of layers. Args: maybe_layers: A list of data[1] or a list of list of data. Returns: List of list of data. [1]: A Functional model has fields 'inbound_nodes' and 'output_layers' which can look like below: - ['in_layer_name', 0, 0] - [['in_layer_is_model', 1, 0], ['in_layer_is_model', 1, 1]] The data inside the list seems to describe [name, size, index]. r) isinstancelist) maybe_layersrrr_norm_to_list_of_layershsrcCsg}|ddurH|dgD]8}t|tsq|dgD])}t|ts'|g}|D]}|didg}t|dkrsz!_update_dicts..)r ValueErrorrr%boolziprr)r model_layerinput_to_in_layermodel_name_to_outputprev_node_name layer_configr&r'ris_functional_modelis_parent_functional_model input_layer inbound_nodeinput_layer_nameinbound_node_namer layer_names last_layerlast_layer_name output_noderr*r _update_dictssP            r?c Csi}i}t}d}t|D]\}}t|r!t|||||\}}}q |d}t||d}|j} || _|ddurK|d d} | | j d_ |d} d} d} t | t rfd| vrf| dd} n| durl| } | durzt| }|j| j d_Wntyd } Ynw| durd } | rtd | |d durt|D]%\}}}t||}|||g}|t|kr||n|d }| j|qn |dur| j|||vr| j||| j}q |S) aReturns a GraphDef representation of the Keras model in a dict form. Note that it only supports models that implemented to_json(). Args: keras_layer: A dict from Keras model.to_json(). Returns: A GraphDef representation of the layers in the model. Nrr class_nameascii keras_classdtypeFTz8Unsupported dtype value in graph model config (json): %srr,)rr rr?r rnodeaddrencodeattrsrrras_dtypeas_datatype_enumtype TypeErrorloggerwarningr%rinputr )r input_to_layerr2gr3r rr4rnode_defkeras_cls_namedtype_or_policyrChas_unsupported_valuetf_dtypersizeindex inbound_nameinbound_node_names input_namerrrkeras_model_to_graph_defs          r\N)__doc__"tensorboard.compat.proto.graph_pb2r"tensorboard.compat.tensorflow_stubrtensorboard.utilr get_loggerrMr rrrr%r?r\rrrrs     I