2VhMXddlZddlZddlZddlZddlmZddlmZddlm Z ddl m Z ddl m Z ddl mZddl mZdd lmZdd lmZ ddlZd Zdd Zdd ZdZdZdZdZdZ ddZddZdZ dZ!dZ"dZ#y#e$rdZY8wxYw)N)logging)backend) optimizers) global_state) json_utils)saving_options) saving_utils)object_registration)io_utilsic:t tdt|tjs|s7tj j |rtj|}|sytj j|}|r6tj j|st j|dtj|d}d}n|}d} tjd5tj||}|j!D]q\} } t| t"t$t&frBt)j*| t,j.j1d |j2| <c| |j2| <s|j5d } t7| ||r"t9|d rt;||j<ddd|j?|r|jAyy#1swY-xYw#|r|jAwwxYw) NzD`save_model()` using h5 format requires h5py. Could not import h5py.T)exist_okwmodeFuse_legacy_config)defaultutf8 model_weights optimizer)!h5py ImportError isinstanceFileospathisfiler ask_to_proceed_with_overwritedirnameexistsmakedirsrkeras_option_scoper model_metadataitemsdictlisttuplejsondumpsr get_json_typeencodeattrs create_groupsave_weights_to_hdf5_grouphasattr$save_optimizer_weights_to_hdf5_grouprflushclose) modelfilepath overwriteinclude_optimizerproceeddirpathfopened_new_filer#kvmodel_weights_groups X/home/dcms/DCMS/lib/python3.12/site-packages/keras/src/legacy/saving/legacy_h5_format.pysave_model_to_hdf5r?s |   h *RWW^^H5<>'2 KK$ / IIhS )   . . F I)88(N',,. #1a$e!45!%:#;#;"fVnGGAJ"#AGGAJ  ##$.."A  &':E B!WUK%@4QH% I(   GGI - I I,  GGI s%H1CG9 H9H>HHct td|si}tj}t j di}i|||}t |tj }|rtj|d}n|}d} |jjd}|td|dt|d r|jd }tj|}tjd 5t!j"|| }t%|d|ddd|rO|jjd} t| d r| jd } | *t'j(d||r|j+SStj| } |j,dit!j.| |t!j0|d|vr t |j2t4j6r&|j2j9|j:n%|j2j=|j:tC|} |j2jE| |r|j+|S#1swYpxYw#t>t@f$rt'j(dYnwxYw#t$rt'j(dYkwxYw#|r|j+wwxYw)axLoads a model saved via `save_model_to_hdf5`. Args: filepath: One of the following: - String, path to the saved model - `h5py.File` object from which to load the model custom_objects: Optional dictionary mapping names (strings) to custom classes or functions to be considered during deserialization. compile: Boolean, whether to compile the model after loading. Returns: A Keras model instance. If an optimizer was found as part of the saved model, the model is already compiled. Otherwise, the model is uncompiled and a warning will be displayed. When `compile` is set to `False`, the compilation is omitted without any warning. Raises: ImportError: if h5py is not available. ValueError: In case of an invalid savefile. NzD`load_model()` using h5 format requires h5py. Could not import h5py.custom_objects_scope_dictrr model_configz%No model config found in the file at .decodezutf-8Tr)custom_objectsrtraining_configzgNo training configuration found in the save file, so the model was *not* compiled. Compile it manually.optimizer_weightszError when creating the weights of optimizer {}, making it impossible to restore the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.zuError in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.)#rrr GLOBAL_CUSTOM_OBJECTSrget_global_attributerrr,get ValueErrorr/rErrr"r model_from_configload_weights_from_hdf5_grouprwarningr2compile!compile_args_from_training_configtry_build_compiled_argumentsrr Optimizerbuild_trainable_variables_create_all_weightsNotImplementedErrorAttributeError&load_optimizer_weights_from_hdf5_group set_weights) r4rFrQgcotlcor:r9r3rCrGoptimizer_weight_valuess r>load_model_from_hdf5r_Ls2 |     3 3C  , ,-H" MD66#66N$Xtyy99O IIhS )  EGww{{>2  7zC  < *'..w7L!((6  . . F D 22^E )?);U C  D ggkk*;A4K"2'JAK"AK"A/J K"J>J K"$J;8K":J;;K">KK"KK""K7c tddlm}t|djDcgc]}|jj dc}t j j d|jd<t|j d|jd<tjdD]4}|j|j}t|}t||6tfd jjzD}|jd }t||y cc}w) z|Saves the weights of a list of layers to a HDF5 group. Args: f: HDF5 group. model: Model instance. r) __version__ layer_namesrr keras_versionc|jSNname)xs r>z,save_weights_to_hdf5_group..s AFF)keyc3>K|]}|jvr|ywreweights.0r<r3s r> z-save_weights_to_hdf5_group..s%   top_level_model_weightsN) keras.srcrasave_attributes_to_hdf5_grouplayersrgr+rr,strsortedr-_legacy_weights!save_subset_weights_to_hdf5_groupr&rV_non_trainable_variables)r9r3rclayergrns ` r>r.r.s7! =%,,O5::,,V4O!*11&9AGGI"=188@AGGO *:;6 NN5:: &!%()!W56++e.L.LLG 01A%a1#Ps"D5 c~|Dcgc]}tj|}}|Dcgc]&}t|jj d(}}t |d|t ||D]D\}}|j||j|j}|js||d<@||ddFycc}wcc}w)zSave top-level weights of a model to a HDF5 group. Args: f: HDF5 group. weights: List of weight variables. r weight_namesdtyperIN) rconvert_to_numpyrwrr+ruzipcreate_datasetshaper)r9rnr weight_valuesrrgval param_dsets r>rzrzs;BBQW--a0BMB8?@1CK&&v.@L@!!^\B}5 c%%dCIISYY%G yy JrNJqM  C@s B5+B:c t|tjr |j}n t |d}|r|j d}|Dcgc]&}t |jjd(}}t|d||Dcgc]}tj|}}t||D]D\}}|j||j|j} |js|| d<@|| ddFyycc}wcc}w)zSaves optimizer weights of a optimizer to a HDF5 group. Args: hdf5_group: HDF5 group. optimizer: optimizer instance. rnrHrrrrIN)rrrT variablesgetattrr-rwrr+rurrrrrr) hdf5_grouprsymbolic_weights weights_grouprrrrgrrs r>r0r0s)Z112$.."9i8"//0CD < ?OO11!4O O\=9 $ID#&55ciisyy6J99!$ 2 # 1  $MPs +C< Dc|Dcgc]}t|tkDs|}}|rtdtd|tj|}d}tj ||}t d|Dr.|dz }tj ||}t d|Dr.|dkDr(t|D]\}} | |jd||fz<y||j|<ycc}w)aSaves attributes (data) of the specified name into the HDF5 group. This method deals with an inherent problem of HDF5 file which is not able to store data larger than HDF5_OBJECT_HEADER_LIMIT bytes. Args: group: A pointer to a HDF5 group. name: A name of the attributes to save. data: Attributes data to store. Raises: RuntimeError: If any single attribute is too large to be saved. zSThe following attributes cannot be saved to HDF5 file because they are larger than z bytes: c3BK|]}|jtkDywre)nbytesHDF5_OBJECT_HEADER_LIMIT)rprhs r>rqz0save_attributes_to_hdf5_group..3s Haahh11 Hsz%s%dN) lenr RuntimeErrornpasarray array_splitany enumerater,) grouprgdatarhbad_attributesdata_npy num_chunks chunked_datachunk_id chunk_datas r>rurus""&KAQ2J)JaKNK $$<#=>$% '  zz$HJ>>(J7L  H< H Ha ~~h ;   H< H HA~$-l$; @ Hj5?EKK$!11 2 @! D1Ls C$C$cd|jvr-|jd}t|dr|jd}nd}d|jvr-|jd}t|dr|jd}nd}g}jD]!}t |}|s|j |#t |d}g}|D]'} || } t | d} | s|j | )|}t|t|k7r$td t|d t|d t|D]\} } || } || }t |} t| }t|t| k7r7td | d |jd| dt| dt|d t|| |d| d |jdd|vrtfdjjzD} t|d}t|t| k7r$tdt| dt|dt| |dyy)zImplements topological (order-based) weight loading. Args: f: A pointer to a HDF5 group. model: Model instance. Raises: ValueError: in case of mismatch between provided layers and weights file. rcrEr1rNrbrzDLayer count mismatch when loading weights from file. Model expected z layers, found z saved layers.!Weight count mismatch for layer # (named z in the current model, z" in the save file). Layer expects  weight(s). Received  saved weight(s)layer #)rfrsc3>K|]}|jvr|ywrermros r>rqz/load_weights_from_hdf5_group..s' EMM!  rrzZWeight count mismatch for top-level weights when loading weights from file. Model expects  top-level weight(s). Received  saved top-level weight(s)top-level model)r,r/rErvryappendload_attributes_from_hdf5_grouprrMr#load_subset_weights_from_hdf5_grouprg _set_weightsr&rVr{)r9r3original_keras_versionoriginal_backendfiltered_layersr|rnrbfiltered_layer_namesrgr}rr;rrs ` r>rOrO>s!''!!"!9 )8 4%;%B%B6%J "!$AGG779- #X ./66v> O*!%(   " "5 )* 2!]CK. dG6q.I  ' ' - . 'K ;3// !/23?;  0  [) 4 dG"*51;A> } %5!6 63A3huzzlK&&*V,!!$%5!6 77L}%&&68     1#Xejj\3   &!A% //%2P2PP   < ' (  } %5!6 6!!$%5!6 78 .//IK     "  #&rjc t|D]\}}||j}|j}||k7rW|r1tjd|d||jd|d|dYt d|d||jd|d|||j |t|d r|r|jy y y ) a2Safely set weights into a model or a layer. Args: instance: Model or layer instance, symbolic_weights: symbolic tensors representing the weights of the variables to load, weight_values: values of the weights to load, skip_mismatch: Boolean, whether to skip loading of weights where there is a mismatch in the shape of the weights, name: name used to identify the group. Raises: ValueError: in case of mismatch between provided model/layer and weights. zSkipping loading weights for z$due to mismatch in shape for weight z. Weight expects shape z#. Received saved weight with shape  stacklevelzShape mismatch in z for weight finalize_stateN) rrwarningswarnrrMassignr/r) instancerrrg skip_mismatchi weight_valueexpected_shapereceived_shapes r>rrs $%]31<)!,22%++ ^ + 3D6.q16678,,:+;<""0!1 3 !$TF.q16678((6'78,- /  ""<0-10x)*/?!0@*rjcd|jvr-|jd}t|dr|jd}nd}d|jvr-|jd}t|dr|jd}nd}t|d}i}|jD]:}|j s|j |j gj|<t|D]\}} || } t| } |j| gD]}t|} t| t| k7rw|rAtjd|d |j d t| d t| d d htd|d |j dt| d t| d t!|| | |d|d |j dd|vr|j"|j$z} t|d} t| t| k7rW|r1tjdt| dt| dd ytdt| dt| dt!|| | |dyy)aImplements name-based weight loading (instead of topological loading). Layers that have no matching name are skipped. Args: f: A pointer to a HDF5 group. model: Model instance. skip_mismatch: Boolean, whether to skip loading of layers where there is a mismatch in the number of weights, or a mismatch in the shape of the weights. Raises: ValueError: in case of mismatch between provided layers and weights file and skip_match=False. rcrErrrNrbz'Skipping loading of weights for layer #rz6) due to mismatch in number of weights. Layer expects rrrrrz). Layer expects rr)rrgrszaSkipping loading top-level weights for model due to mismatch in number of weights. Model expects rrzDWeight count mismatch for top-level weights of model. Model expects r)r,r/rErrvrg setdefaultrrrrLryrrrrMrrVr{) r9r3rrrrbindexr|r;rgr}rrs r>$load_weights_from_hdf5_group_by_namers !''!!"!9 )8 4%;%B%B6%J "!$AGG779- #X ./66v> 2!]CK E; ::   UZZ , 3 3E :;[)4 dG;A> YYtR( E.u5 =!S)9%:: MMA!H ::,')),-=)>(?@$$' $6#77GI$%  7s;#jj\*%%()9%:$;< #M 233CE  +qc%**Q7  ' <!A%  & &)G)G G < ' (  } %5!6 6 %%()9%:$;< #M 233M O ! !%%()9%:$;< #M 233MO  +&  5&rjcpt|d}|Dcgc]}tj||c}Scc}w)a Load layer weights of a model from hdf5. Args: f: A pointer to a HDF5 group. Returns: List of NumPy arrays of the weight values. Raises: ValueError: in case of mismatch between provided model and weights file. r)rrr)r9r weight_names r>rr,s131nEL:F G;BJJq~ & GG Gs3cT|d}t|d}|Dcgc]}|| c}Scc}w)zLoad optimizer weights from a HDF5 group. Args: hdf5_group: A pointer to a HDF5 group. Returns: data: List of optimizer weight names. rHr)r)rroptimizer_weight_namesrs r>rZrZ=sD23M<~7M '2 k"  s %c ||jvr;|j|Dcgc]!}t|dr|jdn|#}}|Sg}d}|||jvre|j|j||Dcgc]!}t|dr|jdn|#c}|dz }|||jvre|Scc}wcc}w)acLoads attributes of the specified name from the HDF5 group. This method deals with an inherent problem of HDF5 file which is not able to store data larger than HDF5_OBJECT_HEADER_LIMIT bytes. Args: group: A pointer to a HDF5 group. name: A name of the attributes to load. Returns: data: Attributes data. rErrr)r,r/rEextend)rrgnrrs r>rrOs u{{[[& !(8 4AHHV ! ;   Kxj!U[[0 KK#[[D6()<=)08(ryryps  " "U%@%@ @@rj)TT)NT)F)$r(rrnumpyrabslrrtrrkeras.src.backend.commonrkeras.src.legacy.savingrrr keras.src.savingr keras.src.utilsr rrrr?r_r.rzr0rurOrrrrZrryrIrjr>rs  1.200$ !/dup2< &$:)!XV tDI+"\dNH"$BA{ DsA;;BB