ij)dZddlZddlZddlZddlZddlZddlZddlZddlZddl Z ddl Z ddl Z ddl Z ddlmZddlmZddlmZddlmZddlmZddlmZdd lmZdd lmZdd lmZdd lmZdd lmZddlm Z ddl!m"Z"ddl#m$Z$ddl#m%Z%ddl&m'Z( ddl)Z) ddl+Z+dZ,dZ-dZ.e.dZ/e.dZ0dZ1dZ2dZ3dZ4dZ5dFdZ6dZ7dZ8d Z9d!Z:dGd"Z;d#ZdZ?d%Z@d&ZAd'ZB dHd(ZC dId)ZDdJd*ZEd+ZFd,ZGd-ZHd.ZI dKd/ZJd0ZKd1ZL dLd2ZM dLd3ZNGd4d5ZOd6ZPdZQd7ZRd8ZSGd9d:ZTGd;dZVd?ZWd@ZXdAZYdBZZdCZ[dDZ\dEZ]y#e*$rdZ)YwxYw#e*$rdZ+YwxYw)Mz3Python-based idempotent model-saving functionality.N)format)backend) global_state)ObjectSharingScope)deserialize_keras_object)serialize_keras_object) dtype_utils) file_utils)io_utils)naming) plot_model) check_pydot)h5py)readable_memory_size)weight_memory_size) __version__ config.jsonz metadata.jsonz model.weightsz.h5z.npzassetsg?lda --- library_name: keras --- This model has been uploaded using the Keras library and can be used with JAX, TensorFlow, and PyTorch backends. This model card has been generated automatically and should be completed by the model author. See [Model Cards documentation](https://huggingface.co/docs/hub/model-cards) for more information. For more details about the model architecture, check out [config.json](./config.json).cJ|dk(rt td|jstjddt |t jrt|||yt|}|jd}|r|jdstd ||s|jdrtd ||r|rtd |d ||rt|||y|st|||ytj |r`t j"}t|||tj$|d 5}|j'|j)dddyt+|d 5}t|||dddy#1swYyxYw#1swYyxYw)aSave a zip-archive representing a Keras model to the given file or path. The zip-based archive contains the following structure: - JSON-based configuration file (config.json): Records of model, layer, and other saveables' configuration. - H5-based saveable state files, found in respective directories, such as model/states.npz, model/dense_layer/states.npz, etc. - Metadata file. The states of Keras saveables (layers, optimizers, loss, and metrics) are automatically saved as long as they can be discovered through the attributes returned by `dir(Model)`. 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Received: filepath=z , zipped=wb)r ImportErrorbuiltwarningswarn isinstanceioIOBase_save_model_to_fileobjstr startswithendswith ValueError_upload_model_to_hf_save_model_to_dirr is_remote_pathBytesIOFilewritegetvalueopen)modelfilepathweights_formatzippedis_hf zip_filepathfs p/var/www/html/emotional.easysim.app/public_html/venv/lib/python3.12/site-packages/keras/src/saving/saving_lib.py save_modelr:Ks0$,LMM ;;    (BII&uh?8}H    (E h''1 ""* -   h''1 33;* >  % 33;*IfX O   E8^< 5(N;  $ $X .:: 6u =>**23KJJ*"++//1::;NO M  %%>>s A??BcBtj|stj|t|\}}t tj |t d5}|j|dddt tj |td5}|j|dddtj |t}tj |t} |dk(rt|d}n!|dk(rt|d}ntd|t|d} t||| dt!|j#| j#y#1swYxYw#1swYxYw#j# j#wxYw)NwrmodenpzTUnknown `weights_format` argument. Expected 'h5' or 'npz'. Received: weights_format= weights_store assets_store inner_pathvisited_saveables)r existsmakedirsrFr1join_METADATA_FILENAMEr/_CONFIG_FILENAME_VARS_FNAME_H5_ASSETS_DIRNAME H5IOStore NpzIOStorer) DiskIOStore _save_statesetclose) r2dirpathr4rDrEr8weights_filepathassert_dirpathrO asset_stores r9r+r+sh   W %G$!9%!@K joog'9:C @A   joog'78# >!  !w?__Wo>N T !%&6SAM u $&'7cBM,,:+;=  ".s;  '$!e   70 s%E$E0*AE<$E-0E9<"Fc t|\}}tj|d5}|jtd5}|j |j ddd|jtd5}|j |j dddd}d}d} d} |dk(rx t|rtt|d}nXtj|jj} tj | }t|jd}d} n&|dk(rt#t$|d}nt'd |t)t*|d} t-||| d t/  |r|j3| r| j3| r"|r |j |jt|r|j3 dddy#1swYxYw#1swYRxYw#tt|d}YxYw#d} |rd|_xYw#|r|j3| r| j3| r"|r |j |jt|r|j3wwxYw#1swYyxYw) NrHFrarchiverJdirrITrKrLrMrN)rFzipfileZipFiler1rVr/encoderWis_memory_sufficientrZrXpathlibPathnameparenttempfileNamedTemporaryFiler[_VARS_FNAME_NPZr)r\rYr]r^rfr_) r2fileobjr4rDrEzfr8weights_file_pathrOrcwrite_zf working_dirs r9r%r%sw!9%!@K # &G*" WW' - , GGM((* + , WW%s + *q GGK&&( ) *!  = *%+E2)2*BS) '.ll7<<&@&G&G ,4,G,G +-))2-22) $( 5( *#Rc! !00>/?A &orLK +("%%  ##%!!#-*//@ !'')OG*G* , , * *8%.&%M. H(, % ##%!!#-*//@ !'')!MG*G*szJ GJ: G*J+H1A6G7'AH9AJG' "J*G4 /J7H  HHH!!AI>>JJ ct td|}|jdr|dd}|jddkDrt d|tj dt }|j|d }|j}tj5}t|||t}trJtj|d d } t!|| d d d t#|j$dkr|dz }n|dz }t'tj|dd5} | j)|ddd|j+||dt-j.d|d|ddddy#1swYBxYw#1swYyxYw)NzWTo save models to the Hugging Face Hub, you must install the `huggingface_hub` package.r/zVInvalid `hf_path` argument: expected `namespace/model_name` format. Received: hf_path=keras) library_namelibrary_versionTexist_okrzsummary_plot.png)to_fileshow_layer_names show_shapes show_dtype z ![](./assets/summary_plot.png)zCA plot of the model can be found [here](./assets/summary_plot.png).z README.mdrHzSave model using Keras.)repo_id folder_pathcommit_messagez%Model saved to the Hugging Face Hub: z< To load back the model, use `keras.saving.load_model('hf://z')`)huggingface_hubrr'countr)HfApir< create_reporrqTemporaryDirectoryr+_MODEL_CARD_TEMPLATErr rUr lenlayersr1r/ upload_folderr print_msg) r2hf_pathr4original_hf_pathapirepo_urlrtmp_dir model_card plot_pathr8s r9r*r*s >  '"!"+}}SA **:); =    m Cw6HG  $ $ &" '5'>:) =";MNI !!%   5<< B&BB 9 *//';7 =  GGJ   4  3H:>..5Yc ; =" " ,  -" " s%BF"E548F5E> :FF cZt|tjrt||||St |j dr@t td|dd}t j|dt}t||||St |}|jds^tj|xr)tjtj|d}|rt||||St!d |t#|d 5}t||||cdddS#1swYyxYw) z.Load a zip archive representing a Keras model.rNzYTo load models from the Hugging Face Hub, you must install the `huggingface_hub` package.rzr})rr~rrrzDInvalid filename: expected a `.keras` extension. Received: filepath=rb)r"r#r$_load_model_from_fileobjr&r'rrsnapshot_downloadr<_load_model_from_dirr(r isdirrSrUr)r1)r3custom_objectscompile safe_moderr is_keras_dirr8s r9 load_modelrKsG(BII&' ngy   X ! !' *  "B  12,%77 ) $ )  x=  *%++H5*:K:K-8;L+ngy&&.Z1 (D ! Q+>7I   s D!!D*c tj|std|tj|std|t tj |t d5}|j}dddt|||}tj|} t|vr(tj |t}t|d} nJt|vr(tj |t}t|d} ntdtdtdt|dkDr&ttj |t d} nd} t#} i} t%|| | d t#| | | j'| r| j' | r t)| |S#1swY6xYw# j' r| j'wwxYw) NzDirectory doesn't exist: zPath isn't a directory: rrI Expected a  or  file.rMrOrPrQrRfailed_saveables error_msgs)r rSr)rr1rUrWread_model_from_configlistdirrXrZrsr[rr\rYr^ _load_stater__raise_loading_failure) r`rrrr8rDr2 all_filenamesrvrOrcrrs r9rrus   W %4WI>??   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Received: filepath=rHrIc32K|]}t|ywNid.0os r9 z$save_weights_only..R#CaBqE#CrMrNT ignore_errors)rr)r&r(r r, get_temp_dirospathrUbasenameShardedH5IOStorerZr^r]r_copyshutilrmtree) r2r3max_shard_sizeobjects_to_skip filepath_strrremote_filepathlocal_filepathrOrRs r9save_weights_onlyr)s ;; "  x=LGOl&;&;M&J "". 1    #L,A,A&- --9N <  7  $ $X ."nGWW\\'2773C3CH3MNN&O%H  %,X~CPM%hS9M  & ##C?#C C  #  '/      OOHo 6 MM' 6 7  OOHo 6 MM' 6 s *B=E2F c ||js tdd}d}t|} tj|rat }t jj|t jj|}tj|||x}}|jdrt|d}nX|jdrt|d}n9|jdr(tj|d}tt |d}t#} |t#d |D} n t#} i} t%|dd || | | |j'|r|j'| r t)| | |t+j,|d yy#|t+j,|d wwxYw)znLoad the weights of a model from a filepath (.keras or .weights.h5). Note: only supports h5 for now. zYou are loading weights into a model that has not yet been built. 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Example error message for object z: z, List of objects that could not be loaded: )listkeysrvaluesr r!r))rr first_key ex_saveableex_errorxmsgs r9rrsZ__&'*I&y1K c*o&'e *6$++- .QAaD . /  1 co /s A>c:tj|s|j||ytj|D][}tj||j dd}tj||j dd}t |||]y)N\r{)r rr/rrUreplace_write_to_zip_recursively)zipfile_to_save system_pathzip_path file_namesystem_file_path zip_file_paths r9rrs   K (k84#++K8 I){IFNNc  'OOHi@HHcM &!1=  rc2|jdrd|S|S)z3Make sure that private attributes are visited last._~r')ros r9 _name_keyrs s4&z Krc#jKddlm}t||std|dt ||j }t |}|dvrd|jftt|dD]+}|jd s||vr t||}||f-y#t$rY:wxYww) Nr KerasSaveablez>Expected object to be an instance of `KerasSaveable`, but got of type ) Sequential Functionalrct|Sr)rrs r9z _walk_saveable..s )A,r)key__) keras.src.saving.keras_saveabler r"r)type _obj_typeget_attr_skipsetrsortedrhr'getattr Exception)saveabler obj_type attr_skipset child_attr child_objs r9_walk_saveablers= h . *Id8n%5 7  !!#H#H-L //''S]0FG$   &* *D  *5I)##$    s*B B3 B$ B3$ B0-B3/B00B3c ddlm}t|ttt t dfstd|dt |t|tt dfstd|dt |t||vryt|dr[|rYt|dr)t|jtrd|ji}nd}|j|j||t|d r"|r |j|j||j!t|t#|D]\}}t||r5t%|||t'j(||j+d d | Gt|t,t.t0fsct3|||t'j(||j+d d | y) NrrrExpected `weights_store` to be an instance of `H5IOStore`, `ShardedH5IOStore`, `NpzIOStore`, or `None`. 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Received: save_own_variablesrometadata save_assetsrr{rQrR)rr r"rZrr[rr)r\rhasattrror&r"maker%addrr]r rUrrdicttuple_save_container_state) rrOrPrQrRr r$rrs r9r]r]s>  #3ZdL  &ym1D0E G  l[$t*$= > %i\0B/C E   (|((x-.= 8V $HMM3)G .HH##   zH  = x'L\..z:;"X,'"0!9 I i / %??:zBJJ##4  D$#6 7 !%??:zBJJ##4 rc ddlm}t|ttt t dfstd|dt |t|tt dfstd|dt ||rt||vryd} t|drH|rF|s|" |j|j|n |j|j|t|d rH|rF|s|" |j|j|n |j|j|| t!|} nd} t#|D]\} } t| |r8t%| ||t'j(|| j+d d |||| Jt| t,t.t0fsft3| ||t'j(|| j+d d |||| |t!|| z }nd}| sg||dkr|jt||Ct||vr5|j5t||j7t|yyyy#t$r9} ||jt||| f|t|<d} Yd} ~ d} ~ wwxYw#t$r9} ||jt||| f|t|<d} Yd} ~ d} ~ wwxYw) Nrrr r r!Fload_own_variablesT load_assetsrr{rQrrRrr)rr r"rZrr[rr)r\rr'r.getrr)r/rrrr rUrrr*r+_load_container_stateremovepop)rrOrPrQrrRrrr failureecurrently_failedrr newly_faileds r9rr(s>  #3ZdL  &ym1D0E G  l[$t*$= > %i\0B/C E  R\->>Gx-.= ,8 ++M,=,=j,IJ  ' ' (9(9*(E Fx'L ,8 $$\%5%5j%AB  !1!1*!= >#/0"0!9 I i / %??:zBJJ#,"3!1%  D$#6 7 !%??:zBJJ#,"3!1% 8#+,/??    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Defaults to `"r"`. Nc|dvrtd|t|ttjfrtj||_nJt|t jr| td||_ntd|dt|d||_ ||_ d|_ |j|j |_d|_i|_d|_d|_y)NrHr.`mode` should be either 'w' or 'r'. Received: zHWhen `path_or_io` is an `io.BytesIO` object, `archive` should be `None`.z\`path_or_io` should be a `str`, `pathlib.Path` or `io.BytesIO` object. Received: path_or_io=r .F)r)r"r&rmrn path_or_ior#r- TypeErrorrrJrfr _get_h5_fileh5_file_h5_entry_path_h5_entry_group_h5_entry_metadata_h5_entry_initialized)rOrrfrJs r9rPzH5IOStore.__init__s z !@G  j3 "5 6%ll:6DO  BJJ /" ()DO==GLIZ(),     ((9 #!"&%*"rc6|jjSr)r__bool__r]s r9rzH5IOStore.__bool__ s||$$&&rc`|xs |j}|dvrtd||jrj|dk(rtj|_n*|jj t|d|_tj|j |Stj||S)N)rrHaz3`mode` should be either 'r', 'w' or 'a'. Received: rHrrI) rJr)rfr#r-rr1r&rr.)rOrrJs r9rzH5IOStore._get_h5_files tyy  &EdVL  <<s{!zz| #||00Z#F 99T\\5 599Zd3 3rc|jdk7r tdt|tt dfstd|||_|r|j |||Si|_d|_|S)aMake a new H5 entry group. This method is only available in write mode. It defers the creation of the H5 entry group until `__setitem__` is called, preventing the creation of empty groups. Args: path: `str`. The variable path. metadata: Optional `dict`. The metadata to save with the H5 entry group. Defaults to `None`. rHz%`make` is only allowed in write mode.Nz1`metadata` should be a dict or `None`. Received: r#F) rJr)r"r*rr_create_h5_grouprrrOrr$s r9r(zH5IOStore.make!s 99 DE E(T4:$67CH:N #   ! !$ ! :  $&D ).D & rc|jdk7r td||_i|_d|_|s+d|j vrt |j d|_|S||j vr-t |j |}d|vrt |d|_|Sd|j vrK|jdd}||j vr+t |j |}d|vrt |d|_|S)zGet the H5 entry group. This method is only available in read mode. Args: path: `str`. The variable path. rz#`get` is only allowed in read mode.Tvars_layer_checkpoint_dependenciesr)rJr)rrrrrnr)rOr path_groups r9r1z H5IOStore.get>s 99 BC C"!%)"%'8v'N$K 4<< *4<<>J#'8V'L$  ,t|| ;<<*JKDt||#.t||TB Z'+zH5IOStore.has_pathbst||##rcJ|jj|jdk(rS|jrG|jj t |j |jj|jr|jjyyr\) rr_rJrfwritestrr&rrr0r]s r9r_zH5IOStore.closefsj  99  LL ! !#doo"6 8M8M8O P << LL    rc|s!|jjd|_n/|jj|jd|_|r1|jD]\}}||jj|< d|_y)NrT)r create_groupritemsattrsr)rOrr$kvs r9rzH5IOStore._create_h5_groupos#'<<#<# E7 #5;;& G$ 2HHU)*<*<=E E2::.HHUOE rcj|jdvr td|js|j|jt j |}t j|jdk(r-|jj||}d|jd<y||j|<y)NrHrz.Setting a value is only allowed in write mode.r)datarv) rJr)rrrrconvert_to_numpystandardize_dtypervrcreate_datasetr)rOrrdss r9 __setitem__zH5IOStore.__setitem__s 99J &MN N))  ! !$"5"5 6((/  $ $U[[ 1Z ?%%44Su4EB *BHHW (-D  %rcP|jdvr td|j|=y)Nrz/Deleting a value is only allowed in write mode.)rJr)r)rOrs r9 __delitem__zH5IOStore.__delitem__s( 99J &NO O   %rc||jvSr)r)rOitems r9 __contains__zH5IOStore.__contains__st++++rNrr)rEr_r`rarPrrr(r1r>r_rrrrrrrr:rr9rZrZsO +B' 4:"H$! *.+  .& ,rrZceZdZdZddZdfd ZfdZdZdZdZ dZ d Z d Z d Z d Zfd ZfdZfdZdZxZS)raVSharded numerical variable store backed by HDF5. Args: path_or_io: `str` or `pathlib.Path` object. The path where to save the model. max_shard_size: `int` or `float`. Maximum size in GB for each sharded file. If `None`, no sharding will be done. Defaults to `None`. archive: Optional `zipfile.ZipFile` object. If specified, the h5 file will be saved inside the archive and `path_or_io` will be used as the filename. mode: `str`. One of {'r', 'w'}. The mode to open the h5 file. Defaults to `"r"`. c |dvrtd|t|ttjfst d|dt |dtj||_||_||_ d|_ t|dz|_ |jjjdd|_|jj d k7ro|jd k(rd nd }|jj#d }t%j&|d t|jdt|d||_d|_i|_d|_d|_d|_d|_d|_d|_g|_|jd k(r ddiid|_n|jrBt=j>tA|jt|j|_n9tC|jd5}t=jD||_ddd|jG|_$y#1swYxYw)NrrzL`path_or_io` should be a `str`, `pathlib.Path` object. Received: path_or_io=r r@z.weightsrMz.jsonrHSavingLoadingzH sharded weights requires `*.json` as the extension. The original path: z will be renamed to Fr total_size)r$ weight_mapr)%r)r"r&rmrnrrrrJrfrfloatrstemr base_namesuffix with_suffixr r!rrrrcurrent_shard_indexcurrent_shard_sizetotal_shard_sizecurrent_shard_pathcurrent_shard_filenamessharding_configr>rrr1load_create_new_shard_filer)rOrrrfrJmethodnew_pathmap_files r9rPzShardedH5IOStore.__init__s z !@G *sGLL&9:((2|9T*=MD(?224 ??s H;;Icg|_|jL|jjtj|jj j t|!||S)aMake a new H5 entry group. This method is only available in write mode. It defers the creation of the H5 entry group until `__setitem__` is called, preventing the creation of empty groups. The information about the current shard is reset. Args: path: `str`. The variable path. metadata: Optional `dict`. The metadata to save with the H5 entry group. Defaults to `None`. ) rrappendrmrnfilenamerosuperr()rOrr$rDs r9r(zShardedH5IOStore.makesZ(*$ << #  ( ( / / T\\22388 w|D(++rc|sd}n|}|jd}|j|xs|jd|d}| t|ts|g}||_|d}n g|_d}|i||j j k7rP|j|jj||_|j|j |_ t||S)zGet the H5 entry group. This method is only available in read mode. If the path is not found in the current shard, it will switch to the correct shard. Args: path: `str`. The variable path. z/varsrr{Nr) rr1r"rrrror_r with_namerrr)rOr parsed_pathr filenamesrrDs r9r1zShardedH5IOStore.gets!KK)),7 NN;/ :>> }E "4   i.&K +4D ( |H+-D (H  H0G0G0L0L$L JJL&*ii&9&9(&CD #,,T-D-DEDLw{4  rc||jjdi}d|dtfd|DS)zReturn True if any shard has a group under `path`. Resolves via the shard weight-map rather than the currently open shard, so the answer is independent of which shard is active. rr{c3NK|]}|k(xs|jywrr)rrleadingprefixs r9rz,ShardedH5IOStore.has_path..,s&LA1<71<<#77Ls"%)rr1any)rOrrrrs @@r9r>zShardedH5IOStore.has_path#sD ))--lB? dV*9ALLLLrc|j!|jjd|_|jdk(r|j|jdd<t j |jd}|jrw|jjt|j||jjt|j|jjn0t|jd5}|j|ddd|jr|jjyy#1swY1xYw)NrHr$r)indent)rr_rJrrr>r?rfrr&rrrr0r1r/)rOjson_strr8s r9r_zShardedH5IOStore.close.s << # LL   DL 99 %%   ,\ :zz$"6"6qAH|| %%c$))nh? %%//0$,,2G2G2I$))S)&QGGH%& << LL    &&s EEcr|jd|jdd}|xjdz c_|jj||_|j |j}|j jtj|jjd|_ |S)z6Create a new shard file and return the H5 file object.r05rr|F) rrrrrrrrrmrnrror)rOnew_shard_pathrs r9rz'ShardedH5IOStore._create_new_shard_fileDs~~a 8 844 :H422777**$$XC$8T1166899  :  rcR||jvrt| |S|jD]`}||jj k(r|j |d||jvs?t| |}|j|cStd|d|j)NrrIKey '"' not found in any of the shards: ) rrrrrrorrrh)rOrrrrDs r9rzShardedH5IOStore.__getitem__s $&& &7&s+ +44 H422777   4d***w*3/%%'  C5:++, .  rc |jtj|}tj|j}t j |j}tj|}||zdz }|xj|z c_ ||jkDr5t|}t|j}td|d|d|d|xj|z c_|j|jkDr,|j|j!|_||_t$ |M|||j(j*} |j,j*} |j.d} | | vr| | | <yt1| | t2s | | g| | <| | | vr| | j5| yy)Nz The size of z is z& which exceeds the maximum shard size zJ. You can increase the `max_shard_size` parameter to accommodate the size.r)rrrrrvrsrtrur dtype_sizerrrr)rr_rrrrrrorrr"rr) rOrrrv weight_countsper_param_size value_sizevalue_size_strmax_shard_size_str variable_pathshard_filenamerrDs r9rzShardedH5IOStore.__setitem__s ((/))%++6 %++. $//6"^3a7  + ++ +1*=N!5d6I6I!J se4'7822D1EF  :-  " "T%8%8 8 JJL668DL&0D # C',,11 0055)),7  *(6J} %j7>-7 -F,G =)Z %>>=)00@?rcP||jvrt| |y|jD]^}||jj k(r|j |d||jvs?t| ||jytd|d|j)NrrIrr) rrrrrrorrrh)rOrrrDs r9rzShardedH5IOStore.__delitem__s $&& & G  $ 44 H422777   4d***#C(%%' C5:++, .  rc||jvry|jD]O}||jjk(r|j |d||jvs?|j y|j y)NTrrIF)rrrrorr)rOrrs r9rzShardedH5IOStore.__contains__s 4'' '44 H422777   4t+++%%'   r)rzNrr)rEr_r`rarPr(r1r>r_rrrrrrrrrr __classcell__)rDs@r9rrsY 75r,* !D M!, 0 J /   "'AR $ rrc4eZdZddZd dZdZdZdZdZy) r[Nc||_||_||_|dk(ri|_y|jr|j |d|_nt |d|_t j|j d|_y)aNumerical variable store backed by NumPy.savez/load. If `archive` is specified, then `root_path` refers to the filename inside the archive. If `archive` is not specified, then `root_path` refers to the path of the npz file on disk. rHrrIrF) allow_pickleN)rLrJrfcontentsr1r8rrrNs r9rPzNpzIOStore.__init__sf#  3;DM|| ic:id3GGDFF?DMrc~|si|jd<|jdSi|j|<|j|SN__root__rrs r9r(zNpzIOStore.makes?(*DMM* %==, ,  d}}T""rc|s(d|jvrt|jdSiS||jvr.|j||j|jSiSr)rr*_reject_npz_bombtolistrXs r9r1zNpzIOStore.get saT]]*DMM*566I 4==  ! !$ '==&--/ / rc Xt|jdd}|y |j|d}|j |5}t j |\}}tt d|dt j}||\}}} dddtj jz} t|jd} | tkDr>| t| zkDr1td|dt!| d t!|jd yy#t$rYywxYw#1swYxYw) aGuard against npz shape/decompression bombs. Reading `self.contents[path]` makes NumPy allocate an array sized to the `.npy` header's declared shape before the stored data is validated, so a tiny member can declare a huge shape and drive an unbounded allocation. Reject a member whose declared size hugely exceeds the number of bytes actually stored for it. zipNz.npyread_array_header__0r|zRefusing to load npz weight 'z': its header declares rzD is stored on disk; refusing to load a potential decompression bomb.)rrrrhr1 npy_format read_magicread_array_header_2_0rsrtrwmaxr_NPZ_MEMBER_BOMB_FLOOR_BYTES_NPZ_MEMBER_MAX_EXPANSIONr)r) rOrzip_filer member_filemajorr read_headerrurvr}r~s r9r zNpzIOStore._reject_npz_bombsI4==%6    ##tfDM2D]]4  7K!,,[9HE1!$UG2.00K *+6OE1e 75)ENN:4--q1 9 9!:\!II/v5L'78 '(:(:;<=II J :    7 7sDAD  DD D)c||jvS)z:Return True if `path` exists as a key in the npz contents.rrXs r9r>zNpzIOStore.has_path8st}}$$rcV|jdk(r|jr.|jj|jdd|_nt|jd|_t j |jfi|j|jjy)NrHT)rJ force_zip64rrI) rJrfr1rLr8rsavezrr_r]s r9r_zNpzIOStore.close<su 99 ||**NN$+dnn48 HHTVV -t}} -  rrr) rEr_r`rPr(r1r r>r_r:rr9r[r[s"@*##J% rr[cztj}tj|}|j|S)Nrg)rqmkdtemp TemporaryFiler_)temp_dirtestfiles r9rrHs/!H%%(3H NN OrcRtjd|d}||Stdg}|dk(r)ddlm}|}|j t |n|dk(r)ddlm}|}|j t |n|dk(r.ddlm}|}|j t |d d gznU|d k(r-ddlm}|}|j t |d gzn#|d k(rCddl m }ddl m }|}|gg}|j t |t |zn|dk(r:ddl m} | d}|j t ||jdn|dk(r(ddlm} | }|j t |no|dk(r*ddlm} | dd}|j t |n@|dk(r,ddlm} | ddd}|j t |nt+d|d tj,d!|||S)"Nsaving_attr_skiplist_$_self_unconditional_dependency_names Operationr)r&Layer)r'r  operations _operationsr  _functionalMetric)r+)CompileMetrics Optimizer)r-g? variablesLoss)r/Cross)r0r:r|Feature)r1int32c|Srr:rs r9rz"get_attr_skipset..sQrintz&get_attr_skipset got invalid obj_type=z. Accepted values for `obj_type` are ['Operation', 'Layer', 'Functional', 'Sequential', 'Metric', 'Optimizer', 'Loss', 'Cross', 'Feature']saving_attr_skipset_)rget_global_attributer^keras.src.ops.operationr&updaterhkeras.src.layers.layerr'keras.src.metrics.metricr+ keras.src.trainers.compile_utilsr,keras.src.optimizers.optimizerr-r3keras.src.losses.lossr/,keras.src.layers.preprocessing.feature_spacer0r1r)set_global_attribute) rskipsetr&ref_objr'r+r, ref_obj_a ref_obj_br-r/r0r1s r9rrOs// z*DG 2 G ;5+s7|$ W 0's7|$ \ !0's7||]&CCD \ !0's7|}o56 X 3CH "2r* s9~I67 [ <C.s7|${# V .&s7|$ W FA,s7|$ Y H';6s7|$5H;77 7  %% xj)7 Nrctd}ntjj}t|j|t zkS)zCheck if there is sufficient memory to load the model into memory. If psutil is installed, we can use it to determine whether the memory is sufficient. Otherwise, we use a predefined value of 1 GB for available memory. r)psutilvirtual_memory availablerr._MEMORY_UPPER_BOUND)r2available_memorys r9rlrlsC~-!002<<5??+ 0 0 1rcg} tjj|\}}|r|j|n|r|j|nn|}N|j |S)zSplit a relative path into a list of individual components. Uses ``os.path.split`` iteratively so the result is independent of the platform path separator. Example:: _split_path_components("a/b/c.txt") -> ["a", "b", "c.txt"] )rrrRrreverse)rpartsheadtails r9_split_path_componentsrOs` E WW]]4( d  LL   LL     MMO Lrcd|jD]\}}tjj|vs6tjjr*tjj|vrt d|t j||}|t d|t|tr$tj|dt||t|tjst|d5}|j|j!dddy#1swYxYw)avRecursively write a nested dict of numpy arrays to a directory tree. Each dict key becomes a directory or filename. Leaf values (numpy arrays) are written as binary files. Args: tree: A nested dictionary of numpy arrays. base_dir: The base directory to write the assets to. Must be resolved via `file_utils.resolve_path` first. zInvalid asset path: NTrr)rrrsepaltsepr)r rSr"r*rT_write_nested_dict_to_dirrrr1r/tobytes)treebase_dirrr child_pathr8s r9rSrSsjjl ) U 77;;# "''..RWW^^s5J3C59: :003?  3C59: : eT " KK T 2 %eZ 8 rzz *j$' )1 ( ) ) ) ) )s 9 D%%D/ ctdd} t|d|dti}|j}t j |D]\}}}|D]}tj j||}tj j||} t| } t|d5} tj| jtj} ddd|} | dd D]}| j|i}  | | d <|r|nd |j!S#1swYJxYw#|j!wxYw) aSave model assets to a nested dictionary. Collects assets (e.g. vocabulary files) from the model using the Keras ``_save_state`` mechanism and returns them as a nested dictionary that mirrors the directory hierarchy. Leaf values are numpy uint8 arrays containing file contents. For example, a file at ``layer/sublayer/vocab.txt`` is stored as:: {"layer": {"sublayer": {"vocab.txt": np.array([...])}}} Using a nested structure instead of flat path keys avoids platform-specific path separator issues and zip-slip vulnerabilities. Args: model: The model whose assets should be collected. Returns: A nested dictionary of numpy uint8 arrays mirroring the asset directory tree, or ``None`` if the model has no assets. rrHrINrMrNrr)r\r]r^rxrwalkrrUrelpathrOr1r frombufferruint8 setdefaultr_)r2rP assets_treerxrootdirsfilesfname file_pathrelrLr8rnodeparts r9_save_assets_to_dictrhs=,xc2L %!e   ".. !#!5 ' D$ 'GGLLu5 ggooi=.s3)T*Ca==BDC"!#2J5D??44D5"&U2Y ' '*{t3CC s$BD6-4D*!7D6*D3 /D66Ec |sytj5}tj|}t ||t |d}t |d|dtti|jdddy#1swYyxYw)aLoad assets from a nested dictionary into the model. Reconstructs the asset directory tree from a nested dictionary produced by ``_save_assets_to_dict`` and loads the assets into the model via the Keras ``_load_state`` mechanism. Args: model: The model to load assets into. assets_dict: Nested dictionary mirroring the asset directory tree, with numpy uint8 arrays as leaf values. NrrIrMr) rqrr rTrSr\rr^r_)r2 assets_dictrrPs r9_load_assets_from_dictrks   $ $ &'))'2!+w7"75  %!e U  s A!BB )rT)NTTr^)FN)F)FNNNr)^rar@r#r>rsrrmrrqr rirnumpyr numpy.librr keras.srcrkeras.src.backend.commonr"keras.src.saving.serialization_librrrkeras.src.utilsr r r r r #keras.src.utils.model_visualizationrkeras.src.utils.module_utilsrkeras.src.utils.summary_utilsrrkeras.src.versionrr<rErrrWrV _VARS_FNAMErXrsrYrHrrrr:rFr+r%r*rrrrrrrrrrrrrrr]rr<r?r,r2r\rnryrzrrZrr[rrrlrOrSrhrkr:rr9rws9  *1AGE'&$"&;-><: !$ =$ M& '!"FAR &DJ*Z8 v'T1h . '  L`;?77v;?;7|"  $<>Ld)N $ -r_DV,V,rB ' )XB,B,JEyEP YYxGT"2)40fY? FOs$EE&E#"E#&E0/E0