o i e8@sdZddlZddlmZddlmZddlmZddlmZddlm Z ddlm Z dd l m Z dd l m Z dd lmZe jZd d ZddZedgdZedddgZddZddZedgd      dddZdS)zUtility to lift subgraphs.N) func_graph)ops)tensor) array_ops) op_selector)resource_variable_ops)compat)object_identity) tf_exportcCst|tjr |jS|SN) isinstance tensor_libTensorop op_or_tensorrT/var/www/myenv/lib/python3.10/site-packages/tensorflow/python/eager/lift_to_graph.py _as_operation"s rcCstddtt|DS)Ncss*|]}t|jdkot|j VqdS)ConstN)rtypecontrol_inputs).0irrr )s   z#_constant_inputs..)allr graph_inputsrrrrr_constant_inputs(s r_InputMutation copied_op input_indexold_graph_tensor_ControlMutationr old_graph_opc sg}g}g}t|jD]'\}}||d} | dur-tjd|j|jd} |td||d|| q g} |j D]} || d} | durN|t d| dq8| | q8t | St |j =|j|jd} | dur{t| j|jvr{| ||j|j|dd|jDdd |jjD|jd Wdn1swYWdn1swY||<t|jD] \}}j|||<qfd d|Dfd d|DfS) aCopy an op directly to a given graph. Generally `op`'s inputs should already have been copied. If this is not the case, for example with v1 while_loops, then `_copy_non_source` inserts placeholders for the unavailable Tensors and returns a list of required mutations. Args: op: The op to be copied. graph: The destination graph. op_map: A dict mapping ops and tensors in the old graph to the new one. base_graph: The graph we're copying from, for any necessary functions. Returns: A tuple of (required_inputs, required_control_inputs): required_inputs: A list of `_InputMutation` tuples containing inputs to `copied_op` which must be updated once `old_graph_tensor` has been copied. required_control_inputs: A list of `_ControlMutation` tuples containing control inputs to `copied_op` which must be added once `old_graph_op` has been copied. Nunused_control_flow_input)nameshapedtyper)r r$cSsg|]}|jqSr)r(rxrrr ~z$_copy_non_source..cSs*i|]\}}|ds|ds||qS)_class_tpu_replicate) startswith)rkeyvaluerrr sz$_copy_non_source..)op_typeinputsdtypesattrsr&cg|]}|jdqSr _replacermutationr9rrr+cr7r8r:r<r9rrr+r>) enumerater4getr placeholderr'r(appendrrr#rcontrol_dependenciesdevice _functionsrras_strr& add_to_graph create_opoutputsnode_defattritems)rgraphop_map base_graphinput_mutationscontrol_mutations copied_inputsr!original_input copied_inputcopied_control_inputsoriginal_control_inputcopied_control_inputfrorr9r_copy_non_source<sl         rZc Cs8|r||vr|j|||jjd}nn|jjdkr]t|r]|jjd}t|j|||d\}} |s1| r8td|t |jj t j |||j |jjd}Wdn1sWwYn#t |jj t j|j|j |jjd}Wdn1s{wYt|} | jrtj|| d d |||<|j||j<dS) aCreate a source in a graph based on a Tensor from a different graph. This function creates a placeholder analog of `s` in a graph with the following behavior: 1) If s is a captured Tensor or Variable and handle_captures is set to True, simply capture it in the new graph as well. 2) If s is a PlaceholderWithDefault whose default is a constant, preserve said default in the new graph. 3) When applicable, copy resource variable metadata from `s` to the newly created placeholder. Args: s: The source of interest. graph: The destination graph. op_map: A dict mapping ops and tensors in the old graph to the new one. handle_captures: A boolean indicating whether to re-capture s in the new graph or simply create a vanilla placeholder. inverse_captures: A dict mapping s back to the Tensor or Variable that it captures. base_graph: The graph being copied from. )r&PlaceholderWithDefaultrrrMrNrOz4Could not copy source node {} because it has inputs.)inputr'r&N)r(r'r&T) graph_mode)capturerr&rrr4rZAssertionErrorformatrrDrplaceholder_with_defaultr'rAr(rget_resource_handle_datashape_and_type_set_handle_shapes_and_types) srMrNhandle_capturesinverse_capturesrOcopied_placeholder default_valueunavailable_inputsunavailable_control_inputs base_handlerrr _copy_sourcesB    rnz__internal__.lift_to_graph)v1Fc sNg}g} |D]} t| tjr|| q| | q|p | dj}|p&t}t|p,g}tdd|D} t t| D]} | t j | ||| |dq>g} tgfdd| D}t|}|r|r|}|vroqd|| |t |D]#}|jdkrq~||tfdd|Dr||vr||q~|sf||r|tt||sd| jd d d g}t}g}t|tjrt|tjr|j}|D]\}}|||<q|j}||D]}|||<qt}|D]}||vr||||jt||||||d q|D]}||jt||||||d qg}g}t| D]#}||vs=||vr?q1t ||||d \}}|!||!|q1|"1|D]}|j#$|j%||j&q\|D]}|j'jdkrxqm|j#(||j'qmWdn 1swY|WdS1swYdS)aCopies the tensor and all its inputs recursively to the outer graph. Args: tensors: The Tensors to lift. graph: The graph to lift to. sources: Optional sequence of nodes to start from. If omitted the whole subgraph which feeds into `init_tensor` is lifted. disallowed_placeholders: An optional set of ops which may not appear in the lifted graph. Defaults to all placeholders. add_sources: A boolean indicating whether placeholders which are not in sources should be allowed. handle_captures: A boolean indicating whether to re-capture s in the new graph or simply create a vanilla placeholder. base_graph: The graph from which to lift ops. This will be inferred if not specified. op_map: A map contains all the existing nodes that have been lifted to the destination graph, so they won't be lifted and copied again. Returns: A mapping from ops in the current default graph to ops in `graph`. Raises: UnliftableError: If a placeholder blocks lifting. rcss|]}|jVqdSr rr)rrrrsz lift_to_graph..) init_tensorsourcesdisallowed_placeholders visited_ops op_outputs add_sourcescs g|] }t|st|qSr)r)rt)rurrr+ s z!lift_to_graph..TPUReplicateMetadatac3s|]}|vVqdSr rr)) marked_opsrrrscSstt|dkS)Nr)lenrrrprrr(r,zlift_to_graph..)r0)rfrMrNrgrhrOr\N))r rResourceVariablerBrMr ObjectIdentityDictionaryObjectIdentitySetset collections defaultdictupdater map_subgraphpopaddrrrdifference_updatenextitersortr FuncGraphcapturesinternal_captures as_defaultremoverrnreversedrZextend_mutation_lockr _update_inputr!r"r$_add_control_input)tensorsrMrrrsrvrgrOrNvariable_init_tensors init_tensorsrrtrq ops_to_copy ops_to_visit unvisited_opsrinprrhrexternal_captureinternal_capturer source_opsrfrPrQnew_input_mutationsnew_control_mutationsr=r)ryrur lift_to_graphs!                             &r)NNFFNN)__doc__rtensorflow.python.frameworkrrrr tensorflow.python.opsrrrtensorflow.python.utilrr tensorflow.python.util.tf_exportr UnliftableErrorrr namedtuplerr#rZrnrrrrrs@         S <