&Vf0eddlmZddlmZddlmZddlmZddlmZddlm Z Gddej Z d Z d Z d Zd ZGd dej ZGddejZdS))backend)lossesmetrics)ops)tree)get_object_namecPeZdZd fd Zd dZdZdZdZedZ xZ S) MetricsList metrics_listNcht|||_||_dS)Nname)super__init__r output_name)selfrrr __class__s ]/var/www/html/software/conda/lib/python3.11/site-packages/keras/src/trainers/compile_utils.pyrzMetricsList.__init__ s3 d### &cJ|jD]}||||dS)N) sample_weight)r update_state)ry_truey_predrms rrzMetricsList.update_states< H HA NN66N G G G G H HrcB|jD]}|dSN)r reset_staterrs rrzMetricsList.reset_states,  A MMOOOO  rc$d|jDS)NcBi|]}|j|S)rresult.0rs r z*MetricsList.get_result..s$999q 999rrrs r get_resultzMetricsList.get_results99DL9999rctrNotImplementedErrorr(s r get_configzMetricsList.get_config!!rctrr+clsconfigs r from_configzMetricsList.from_config!!r)r Nr) __name__ __module__ __qualname__rrrr)r- classmethodr3 __classcell__rs@rr r s'''''' HHHH:::"""""["""""rr c^|dSt|trdSt|rdSdS)NTF) isinstancestrcallable)values ris_function_liker@"s; }t%tt 5rct|j}t|j}|jd}|jd}|dk}||kp |dko|dk}||fS)N)lenshape)rry_t_ranky_p_rank y_t_last_dim y_p_last_dim is_binaryis_sparse_categoricals ris_binary_or_sparse_categoricalrL,sq6<  H6<  H<#L<#L!I8E|q0E\A5E + ++rc4|dSt|dvrtj|}nt ||\}}|r#tjt|}nG|r#tjt|}n"tjt|}t|tr|}nt|}t|tj stj |}||_ |S)N)accuracyaccr) r=lowermetrics_modulegetrLBinaryAccuracySparseCategoricalAccuracyCategoricalAccuracyr<r MetricMeanMetricWrapperr) identifierrr metric_objrJrK metric_names r get_metricr[9s%t :&999#' 33 +J F, , ( (  '6C OOLLLJJ " 'A__JJ(;__J*c""2 %j11 j."7 8 8B#5jAA !JO rc|dSt|dvrtj|}n=t ||\}}|r tj}n|r tj}n tj}t|tj s=t|tr|}nt|}tj ||}|S)N) crossentropycer) r=rP losses_modulerRrLbinary_crossentropysparse_categorical_crossentropycategorical_crossentropyr<Lossr LossFunctionWrapper)rXrrloss_objrJrK loss_names rget_lossrg\st :&<<< $Z00+J F, , ( (  >$8HH " >$DHH$=H h 2 3 3O j# & & 2"II'11I 4XINNN OrceZdZ dfd ZedZedZdZdZdZ dd Z d Z d Z d Z ed ZxZS)CompileMetricscompile_metricNct||rDt|ttt fs"t d|dt||rDt|ttt fs"t d|dt|||_||_ d|_ d|_ ||_ dS)NrzTExpected `metrics` argument to be a list, tuple, or dict. Received instead: metrics= of type zfExpected `weighted_metrics` argument to be a list, tuple, or dict. Received instead: weighted_metrics=Fcompile_metrics) rrr<listtupledict ValueErrortype _user_metrics_user_weighted_metricsbuiltr output_names)rrweighted_metricsrrvrs rrzCompileMetrics.__init__ys  d###  :geT/BCC O-4OO?CG}}OO   J tUD1% %  4z'CompileMetrics.build..-@@A71.//@@@@@@rc0g|]}|jjjSr#r operationrrs r z(CompileMetrics.build..!PPPA 0 : ?PPPrrCr) argument_namerwT)rvr<rpsortedrnkeysrorDall _flatten_yrsrt_build_metrics_setryrzru)rrrrv num_outputsrrws rbuildzCompileMetrics.buildsc   ,LL  % % !$v{{}}"5"566LL u . . f++K@@@@@@@ $PPPPP # LK  ,l++K(((($6!44     # 5  '+&=&=     , '>' ' # rc g}t|tr6|D]!}||vrtd|d|d|d|"|dkr|s|dnt|trt j|}t|ts|g}td|Dstd|d|d||tfd |Dn6t|ttfrt|tkr9td |d |d|d t|d td tt|D]\} \} t| ts| g} |r|| nd}td| Dstd|d| |t fd| D|nt|tr|td|d|d||D]f}t||ts ||g||<td||Dstd|d|d||gt|D]W\} ||vr8|t fd||D|B|dX|S)NzIn the dict argument `z`, key 'z5' does not correspond to any model output. Received: =rCc34K|]}t|VdSrr@r%s rrz4CompileMetrics._build_metrics_set..s+@@1+A..@@@@@@rzExpected all entries in the `z/` list to be metric objects. Received instead: cNg|]!}|t|dd"S)Nrr[)r&rrrs rrz5CompileMetrics._build_metrics_set..s: ! }'q&)VAY??,}}rz7For a model with multiple outputs, when providing the `zY` argument as a list, it should have as many entries as the model has outputs. Received: of length  whereas the model has outputs.c34K|]}t|VdSrrr&es rrz4CompileMetrics._build_metrics_set..s+@@q/22@@@@@@rz$All entries in the sublists of the `zQ` list should be metric objects. Found the following sublist with unknown types: c6g|]}|t|Srrr&rypyts rrz5CompileMetrics._build_metrics_set..s2$%#$=!+1b" 5 5#0==r)rz Argument `zY` can only be provided as a dict when the model also returns a dict of outputs. Received c34K|]}t|VdSrrrs rrz4CompileMetrics._build_metrics_set..#s+JJq/22JJJJJJrz)` dict should be metric objects. At key 'z3', found the following sublist with unknown types: c6g|]}|t|Srrrs rrz5CompileMetrics._build_metrics_set...s2!"!"!"()'(}%/q"b$9$9'4}}r)r<rprrqr|rflattenrnrr rorD enumeratezip) rrrrvrrr flat_metricsridxmlsrrs `` @@rrz!CompileMetrics._build_metrics_sets& gt $ $    |++$HHH HH.;HH>EHH, !   ##D))))gt,,4"l733G!'400(&iG@@@@@@@$5 55(55+255 ##%,'D%=11? 2w<<3v;;..$2/<220=22@G22#&g,, 22 v;; 222+400++&C#r2&c400$"e0<F<,,$D@@C@@@@@(, -,,'*,, !''#), )-     .GT** 2'$>]>>$1>>4;>> $LLNN  D%gdmT::8)0 JJGDMJJJJJ(C -CC'+CC4;4=CC%( ff$E$E 2 2LD"bw$++'!"!"!"!"!"-4T]!"!"!" -1     %++D1111rct|tr4|jr-g}|jD]!}||vr|||"|St j|Srr<rprvr|rrryr$rs rrzCompileMetrics._flatten_y:h a   4#4 F) + +199MM!D'***M|Arc||js|||||}||}t|j||D]\}}}|r|||^|t t |kr(fdtt |Dn&dtt |Dt|j||D] \}}}}|r||||!dS)Nc g|] }d Srr#r&_rs rrz/CompileMetrics.update_state..O N N Naq!1 N N Nrcg|]}dSrr#r&rs rrz/CompileMetrics.update_state..Qs>>>aT>>>r) rurrrryrrDrangerz)rrrrry_ty_ps_ws ` rrzCompileMetrics.update_stateCsVz ' JJvv & & &((((t166BB ) )KAsC )sC(((  $ OOM::M=!!CKK// N N N N5V;M;M N N N >>5V+=+=>>>M #  '! !  . . AsC .sC---  . .rc|jsdS|jD]}|r||jD]}|r|dSr)ruryrrzr s rrzCompileMetrics.reset_stateXskz  F#  A  ,  A    rc|jstdi}i}|jD]}|s|jD]s}|j}|jr |jd|}||vr|||<d||<=||}||xxdz cc<|d|}|||<t|jD]}|s|jD]}|j}|jr |jd|}||vr|||<d||<=d|j}|jr |jd|}||vrd||<n||}||xxdz cc<|d|}|||<|S)Nz.rrc0g|]}|jjjSr#rrs rrz%CompileLoss.build..rrrCz"In the dict argument `loss`, key 'z:' does not correspond to any model output. Received: loss=rzcWhen there is only a single output, the `loss` argument must be a callable. Received instead: loss=rlcSrr#)rrs rz#CompileLoss.build..srzwWhen there is only a single output, the `loss_weights` argument must be a Python float. Received instead: loss_weights=?zFor a model with multiple outputs, when providing the `loss` argument as a list, it should have as many entries as the model has outputs. Received: loss=rrrc34K|]}t|VdSrrrs rrz$CompileLoss.build..s+99q'**999999rzFor a model with multiple outputs, when providing the `loss` argument as a list, each list entry should be a callable (the loss function corresponding to that output). Received: loss=c6g|]}|t|Sr)rg)r&fnrrs rrz%CompileLoss.build..s-13r~VV,,~~~rzIf the `loss` argument is provided as a list/tuple, the `loss_weight` argument should also be provided as a list/tuple, of equal length. Received: loss_weights=zFor a model with multiple outputs, when providing the `loss_weights` argument as a list, it should have as many entries as the model has outputs. Received: loss_weights=z of length c3NK|] }t|ttfV!dSr)r<intrrs rrz$CompileLoss.build..s0MM1:a#u66MMMMMMrzFor a model with multiple outputs, when providing the `loss_weights` argument as a list, each list entry should be a Python int or float (the weighting coefficient corresponding to the loss for that output). Received: loss_weights=cg|]}dSrr#rs rrz%CompileLoss.build.."s$7$7$7QS$7$7$7rzlArgument `loss` can only be provided as a dict when the model also returns a dict of outputs. Received loss=zFor a model with multiple outputs, when providing the `loss` argument as a dict, each dict entry should be a callable (the loss function corresponding to that output). At key 'z', received invalid type: zIf the `loss` argument is provided as a dict, the `loss_weight` argument should also be provided as a dict. Received: loss_weights=z*In the dict argument `loss_weights`, key 'zB' does not correspond to any model output. Received: loss_weights=zFor a model with multiple outputs, when providing the `loss_weights` argument as a dict, each dict entry should be a Python float (the weighting coefficient corresponding to the loss for that output). At key 'cg|]}dSrr#rs rrz%CompileLoss.build..Zs$>$>$>QS$>$>$>rT)rvr<rprrnrrorDrrrrrqrrr@rr is_nested map_structurer|rgrr flat_lossesflat_loss_weightsru) rrrrvrrrrrrrrs `` @rrzCompileLoss.buildsZ   ,LL  % % !$v{{}}"5"566LL u . . f++K@@@@@@@ $PPPPP # LK  ,l++K((.   dD ! !    |++$2 22+/22, !  $%% *|D))$%% #d))q..Aw#D))  K/3KK>B4jjKK &$'' #CKK1,<,< D ! ! >dnV&<&< >%nnnnf==D D ! !v ?   xff== > > > .!,66$5;G55!%\ 2 2 55"((6666!((---- tUm , ,h ?<%%D4yyCKK'' D(,DD:=TDD.1[[ DDD99D99999  -'+ --7;K 8!,u >>$A3?AA |$$F 33$2JMM &l4&8%@@(L?C LL 8DD7I LL)66D|++)00d1CDDDD)005555 6 %?$>+$>$>$>!&!2 rctj|j5||||cdddS#1swxYwYdSr)r name_scopercall)rrrrs r__call__zCompileLoss.__call___s ^DI & & < <99VV];; < < < < < < < < < < < < < < < < < AAct|tr4|jr-g}|jD]!}||vr|||"|St j|Srrrs rrzCompileLoss._flatten_ycrrc ||js|||||}||}^|tt|kr(fdt t|Dn d|Dg}t |j|||jD]T\}}}}|rJ|tj |||tj z} | | U|rt|} | SdS)Nc g|] }d Srr#rs rrz$CompileLoss.call..wrrcg|]}dSrr#rs rrz$CompileLoss.call..ys222aT222r)dtype)rurrrDrrrrrcastrfloatxr|sum) rrrr loss_valuesrrr loss_weightr? total_losss ` rrzCompileLoss.calllsez ' JJvv & & &((((  $ OOM::M=!!CKK// N N N N5V;M;M N N N 226222M :=      "  ; ; * * 6D#sK *#chDc=119I9I'''""5)))  [))J trctrr+r(s rr-zCompileLoss.get_configr.rctrr+r0s rr3zCompileLoss.from_configr4r)NrNr) r5r6r7rrrrrr-r8r3r9r:s@rrrs' CCCCCC.mmm^<<<<B"""""["""""rrN) keras.srcrrr_rrQrrkeras.src.utils.namingr rVr r@rLr[rgrircrr#rrrsR------//////222222""""".'"""2 , , ,   F8]"]"]"]"]"^*]"]"]"@ z"z"z"z"z"-$z"z"z"z"z"r