eThcSrSSKJr SSKrSSKrSSKrSSKrSSKrSSKrSSK r SSK r SSK J r SSK Jr SSKJrJrJrJrJrJrJr SSKrSSKrSSKrSSKJr SS KJrJ r SS K!J"r" SS K#J$r$ SS K%J&r& SS K'J(r(J)r) SSK*J+r+J,r,J-r-J.r.J/r/ SSK0J1r1J2r2J3r3J4r4J5r5J6r6J7r7J8r8J9r9J:r:J;r;Jr>J?r?J@r@JArAJBrBJCrCJDrD SSKEJFrFJGrG \@"5(a SSKHJIrI SSKJJKrL \(aSSKJMrM \BR"\O5rPS\R;aS\RS'O$\RSS:wa\PRS5 SSKSrTSSKSJUrV \R"5r\\\\R\\R\\_\R4\\_\R4\R\R4r`Sra"SS5rbSrc"SS5rd"S S!5re"S"S#5rf"S$S%5rg"S&S'5rh"S(S)\d5ri"S*S+5rjS,rkS-rlS.rmSBS/jrnS0\44SCS1jjroSDS2jrpSES3jrqSDS4jrrSES5jrsSES6jrtSES7jruS8rv"S9S:\TR\b\)\95rx"S;S<\TRR5r{"S=S>\TRR5r|"S?S@\TRR5r}SFSGSAjjr~g!\W\X4a6 SSKTrTSSKTJUrV \"\TR5RS:a\["S5eGNf=f)HzTF general model utils.) annotationsN)Mapping)Path) TYPE_CHECKINGAnyCallableDictListOptionalUnion)parse)DataCollatorWithPaddingDefaultDataCollator)get_tf_activation)PretrainedConfig)custom_object_save)GenerationConfigTFGenerationMixin)convert_batch_encoding expand_1dload_attributes_from_hdf5_groupsave_attributes_to_hdf5_group shape_list)SAFE_WEIGHTS_INDEX_NAMESAFE_WEIGHTS_NAMETF2_WEIGHTS_INDEX_NAMETF2_WEIGHTS_NAMETF_WEIGHTS_NAMEWEIGHTS_INDEX_NAME WEIGHTS_NAME ModelOutputPushToHubMixin cached_file download_url find_labelshas_fileis_offline_mode is_remote_urlis_safetensors_availableis_tf_symbolic_tensorloggingrequires_backendsworking_or_temp_dir)convert_file_size_to_intget_checkpoint_shard_files) safe_open) save_file)PreTrainedTokenizerBaseTF_USE_LEGACY_KERAS1zTransformers is only compatible with Keras 2, but you have explicitly set `TF_USE_LEGACY_KERAS` to `0`. This may result in unexpected behaviour or errors if Keras 3 objects are passed to Transformers models.)backendzYour currently installed version of Keras is Keras 3, but this is not yet supported in Transformers. Please install the backwards-compatible tf-keras package with `pip install tf-keras`.cURRS::aU$[[SURR55n[R "XS9$)Nraxis)shaperanklistrangetf reduce_mean)y_truey_predreduction_axess V/var/www/auris/envauris/lib/python3.13/site-packages/transformers/modeling_tf_utils.py dummy_lossrEvsC ||A eAv||'8'89:~~f::c&\rSrSrSrSSSjjrSrg)TFModelUtilsMixin~z; A few utilities for `keras.Model`, to be used as a mixin. czU(a%[[SUR555$UR5$)a Get the number of (optionally, trainable) parameters in the model. Args: only_trainable (`bool`, *optional*, defaults to `False`): Whether or not to return only the number of trainable parameters Returns: `int`: The number of parameters. c3~# UH3n[R"URR55v M5 g7fN)npprodr;as_list).0ws rD 3TFModelUtilsMixin.num_parameters..s)X?W!277177??#455?Ws;=)intsumtrainable_variables count_params)selfonly_trainables rDnum_parameters TFModelUtilsMixin.num_parameterss2 sXt?W?WXXY Y$$& &rFN)F)rYboolreturnrT)__name__ __module__ __qualname____firstlineno____doc__rZ__static_attributes__r\rFrDrHrH~s''rFrHc^^^TRm[TSS5mTc [S5e[R"T5UU4Sj5nUTl[ TS5(d [ S5e[ TRS5(a U4SjnUTlS Tl[ [RS 5(a$[RR5"T5mT$) aT Decorate a Keras Layer class to support Keras serialization. This is done by: 1. Adding a `transformers_config` dict to the Keras config dictionary in `get_config` (called by Keras at serialization time. 2. Wrapping `__init__` to accept that `transformers_config` dict (passed by Keras at deserialization time) and convert it to a config object for the actual layer initializer. 3. Registering the class as a custom object in Keras (if the Tensorflow version supports this), so that it does not need to be supplied in `custom_objects` in the call to `keras.models.load_model`. Args: cls (a `keras.layers.Layers subclass`): Typically a `TF.MainLayer` class in this project, in general must accept a `config` argument to its initializer. Returns: The same class object, with modifications for Keras deserialization. config_classNz2Must set `config_class` to use @keras_serializablec>U(a[US[5(aUSOURSS5n[U[5(aTR U5nT"X/UQ70UD6 OI[U[5(a)[ U5S:a T"U/UQ70UD6 OT"X/UQ70UD6 O [ S5eX0lX lg)Nrconfigz?Must pass either `config` (PretrainedConfig) or `config` (dict)) isinstancerpopdict from_dictlen ValueError_config_kwargs)rXargskwargsrhrf initializers rD wrapped_init(keras_serializable..wrapped_inits ZQ9I%J%JaPVPZPZ[ceiPj fd # #!++F3F  6t 6v 6  0 1 14y1}D24262D:4:6:^_ _  rF get_configz=Only use @keras_serializable on keras.layers.Layer subclasses _is_defaultc>[TU]5nURR5US'UR UR 5 U$)Nrh)superrvroto_dictupdaterp)rXcfgclss rDrv&keras_serializable..get_configs=T-/C LL002CM JJt|| $JrFTregister_keras_serializable) __init__getattrAttributeError functoolswrapshasattr TypeErrorrv_keras_serializablekerasutilsr)r}rtrvrfrss` @@rDkeras_serializablers*,,K35LQRR__[!"" CL 3 % %WXXs~~}--  $"Cu{{9::kk557< JrFc\rSrSrSrSrSrg)TFCausalLanguageModelingLossz Loss function suitable for causal language modeling (CLM), that is, the task of guessing the next token. Any label of -100 will be ignored (along with the corresponding logits) in the loss computation. c  [RRS[RRRS9nUR R (a[R"[R"US5S5n[R"[R"US[U5S45U5n[R"[R"US5U5nU"X5$U"[RRU5U5n[R"US:gURS9nXg-n[R "U5[R "U5- n [R"U S5$) NT from_logits reductionrr7dtyper)rlossesSparseCategoricalCrossentropy ReductionNONErhtf_legacy_lossr? not_equalreshape boolean_maskrnnrelucastr reduce_sum rXlabelslogitsloss_fn active_lossreduced_logits unmasked_loss loss_mask masked_lossreduced_masked_losss rDhf_compute_loss,TFCausalLanguageModelingLoss.hf_compute_losss,,<<Y^YeYeYoYoYtYt<u ;; % %,,rzz&%'@$GK__RZZZPVEWXYEZ@[-\^ijN__RZZ%> LF62 2  6 2F; GGFdN-2E2EF #/  mmK82==;SSzz-t44rFr\Nr_r`rarbrcrrdr\rFrDrrs 5rFrc\rSrSrSrSrSrg)TFQuestionAnsweringLossz0 Loss function suitable for question answering. c[RRS[RRRS9nU"USUS5nU"USUS5nXE-S- $)NTrstart_positionr end_positionrg@rrrrr)rXrrr start_lossend_losss rDr'TFQuestionAnsweringLoss.hf_compute_losssg,,<<Y^YeYeYoYoYtYt<uV$45vayA 6.16!9=%,,rFr\Nrr\rFrDrrs -rFrc\rSrSrSrSrSrg)TFTokenClassificationLossz Loss function suitable for token classification. Any label of -100 will be ignored (along with the corresponding logits) in the loss computation. c F[RRS[RRRS9n[ R "5(a=[ RRUS:H5(a[ R"S5 URR(a[ RRUS:H5(a1[ R"S5 [ R"US5S:gnO[ R"US5S:gn[ R"[ R"US[U5S45U5n[ R"[ R"US5U5nU"X5$U"[ RR!U5U5n[ R""US:UR$S 9nXg-n[ R&"U5[ R&"U5- n [ R"U S 5$) NTrrzSUsing `-1` to mask the loss for the token is deprecated. Please use `-100` instead.rrr7rrr)rrrrrr?executing_eagerlymath reduce_anyprintrhrrrrrrrrrrs rDr)TFTokenClassificationLoss.hf_compute_loss s,,<<Y^YeYeYoYoYtYt<u   ! !ww!!&B,//no ;; % %ww!!&B,//no jj72=  jj74? __RZZZPVEWXYEZ@[-\^ijN__RZZ%> LF62 2  6 2F; GGFaK}/B/BC $/  mmK82==;SSzz-t44rFr\Nrr\rFrDrrs 5rFrc\rSrSrSrSrSrg)TFSequenceClassificationLossi)z5 Loss function suitable for sequence classification. cURRS:XdURSS:Xao[RR [RR R S9nURRS:Xa[R"USS9nO@[RRS[RR R S9nU"X5$)Nr)rrr9Tr) r;r<rrMeanSquaredErrorrrr? expand_dimsrrXrrrs rDr,TFSequenceClassificationLoss.hf_compute_loss.s <<   !V\\!_%9ll33ell>T>T>Y>Y3ZG||  A%R8ll@@ ELL,B,B,G,GAGv&&rFr\Nrr\rFrDrr)s  'rFrc\rSrSrSrSrSrg)TFMultipleChoiceLossi<z1Loss function suitable for multiple choice tasks.c[RRS[RRRS9nU"X5$)NTrrrs rDr$TFMultipleChoiceLoss.hf_compute_loss?s9,,<<Y^YeYeYoYoYtYt<uv&&rFr\Nrr\rFrDrr<s ;'rFrc\rSrSrSrSrg)TFMaskedLanguageModelingLossiDz Loss function suitable for masked language modeling (MLM), that is, the task of guessing the masked tokens. Any label of -100 will be ignored (along with the corresponding logits) in the loss computation. r\N)r_r`rarbrcrdr\rFrDrrDsrFrc\rSrSrSrSrSrg)TFNextSentencePredictionLossiPz Loss function suitable for next sentence prediction (NSP), that is, the task of guessing the next sentence. Any label of -100 will be ignored (along with the corresponding logits) in the loss computation. ch[RRS[RRRS9nUR R (a[R"[R"US5S5n[R"[R"US5U5n[R"[R"US5U5nU"Xe5$U"[RRU5US9n[R"US:gURS9nXx-n U $)NTrrr)rr7)rArBr)rrrrrrhrr?rrrrrrr) rXrrrnext_sentence_active_lossnext_sentence_reduced_logitsnext_sentence_labelunmasked_ns_loss ns_loss_maskmasked_ns_losss rDr,TFNextSentencePredictionLoss.hf_compute_loss[s,,<<Y^YeYeYoYoYtYt<u ;; % %)+ RZZ5NPT(U %+-??2::fg;VXq+r ("$//"**VU2KMf"g .M M #"%%**V*DM=R=^F=$9djdvdvN=!f#)+#6#BF; PVXceiHj {# rFc^^[R"T5m[R"T5UU4Sj5nTUlU$)a Decorator that processes the inputs to a Keras layer, passing them to the layer as keyword arguments. 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Returns: A callable that wraps the original `func` with the behavior described above. c  >UR5VVs0sH"up4U[T R5;dM X4_M$ nnnUR5VVs0sHup4X5;dM X4_M nnnURSU05 UR[[ T R R SSU555 SURR;aSnO URn[T U40UD6nT "U40UD6$s snnfs snnf)N kwargs_callrEncoderDecoder) itemsrk parametersr{zip__code__ co_varnames __class__r_rhinput_processing) rXrqrrkeyvalrfn_args_and_kwargsrhunpacked_inputsfuncoriginal_signatures rDrun_call_with_unpacked_inputs4unpack_inputs..run_call_with_unpacked_inputss17 qHC#TRdRoRoMpBpxsx q7=||~`~83I_hch~`!!=+">? !!$s4==+D+DQR+H$'O"PQ t~~66 6F[[F*4N;MND,O,,r`sC9C9 C?#C?)inspect signaturerr __signature__)rrrs` @rD unpack_inputsrsC!**40__T--*3E!/ ((rFc  [[R"U5R5n[ UR SS55nUR SS5 [ UR55nUSnUR US5n0n[R[[[[[ [[R4n SUS;a2[R "S["5 USR S5US'S US;a2[R "S ["5 USR S 5US 'S US;a9S U;a3[R "S ["5 USR S 5US 'O&S US;aS U;aUSR S 5US 'U(aUR S05US'OL[%US5S:a)['S[ USR55S35eUR S5 UR)5HUup[+X5(d[R,"U 5(dU cXU 'M9['S[/U 5SU SU S35e [+U[[ 45(a[1U5Hup[3U 5(a1U R4R7S5SnX;aXU'M>XX\'MF[+X5(dU cXX\'Ma['S[/U 5SU SX\S35e GOA[+U[85(aSU;a/[R "S["5 UR S5US'S U;a/[R "S ["5 UR S 5US '[U5R)5Hcup[+X5(dU cXU 'MX;a$SU;a[:R=SU SUS35 MG['S[/U 5SU SU S35e OA[R,"U5(dUcXxU'O['S[/U5SU SUS35eUHJnU[ UR55;dM"US:wdM*UR XUR>5X'ML SU;aMUSb<[3US5(a)USR4R7S5SnUSX'OUSUS'US SU;aUS 0nUR)5Hunn[+U[R5(aHUR@[RB:Xa*[RD"U[RF5UU'Mm[+U[R5(aBUR@[RB:Xa$URI[RF5UU'MUUU'M UnAUbHUR)5V V s0sHupU S;dM X_M nn n URK[MSSU0UD65 U$s sn n f)av Process the input of each TensorFlow model including the booleans. In case of a list of symbolic inputs, each input has to be named accordingly to the parameters name, i.e. `input_ids = keras.Input(shape=(128,), dtype='int32', name="input_ids")` otherwise the order of the tensors will not be guaranteed during the training. Args: func (`callable`): The callable function of the TensorFlow model. config ([`PretrainedConfig`]): The config of the running model. **kwargs: The inputs of the model. Returns: Two lists, one for the missing layers, and another one for the unexpected layers. rrNrXrinputsrzeThe `inputs` argument is deprecated and will be removed in a future version, use `input_ids` instead. input_idsdecoder_cached_stateszzThe `decoder_cached_states` argument is deprecated and will be removed in a future version, use `past_key_values` instead.past_key_valuespastziThe `past` argument is deprecated and will be removed in a future version, use `past_key_values` instead.zAThe following keyword arguments are not supported by this model: .z Data of type z is not allowed only z is accepted for :rqzThe parameter z( does not belongs to the parameter list z and will be ignored.)rrrrrhr\)'rkrrrr]rjr=keysr?TensorrTr"tuplerMndarraywarningswarn FutureWarningrmrnrri is_tensortype enumerater+namesplitrloggerwarningdefaultrint64rint32astyper{r)rrhrrr has_kwargsparameter_namesmain_input_name main_inputoutput allowed_typeskviinput tensor_namer  cast_outputrr boolean_dicts rDrrs "W&&t,778IimmHd34J MM&$9>>+,O%a(OOT2J FYYc;tT2::VM6-(( s  %]377A{&"77  *  %+=$9$=$=>U$V !  &&+<+O    %+=$9$=$=f$E ! f]3 3/8Q .223DEv!::mR8x vm$ % ) .33567q:   =!  a ' '2<<??ai1I}T!WI5J=/Yjkljmmnop p  *udm,,!*-HA$U++$jj..s3A6 1*/;'16?-.E11U]-2)* #DK=0Em_U'*+1..$ J ( ( z ! MM  #-..":F;  "j 0 MM.  )37N(OF$ %$**,DA!++qyq )fO.K$QC'OP_O``uv =a 9N}o]nopnqqr!stt- << # #z'9&0? #Z 011F}oV#$A'    tFKKM* *tv~!::ddO,C,CDFL  &> %*?v*O*O .--33C8;K"(.F #).F;  6N6 8 KLLNS c299 % %#))rxx*?!wwsBHH5K  RZZ ( (SYY"((-B"zz"((3K "K  #F   &]] AD&        M s  Y!YcUb?URU5(a)U[U5SnURS5(aUSSnSU;aA[URS55S:a#SRURS5SS5nU$)N/rmodel.) startswithrmr join)r _prefixs rDstrip_model_name_and_prefixr&ls~tw77CLN# ??3  8DtDJJsO 4q 8xx 3+, KrF10GBc x[U5n/n/nSnSnUHjnUR5RURR-nXX-U:aUR U5 /nSnUR U5 XX- nXh- nMl UR U5 [ U5S:XaX#S0S4$0n 0n [ U5HpupURSSU S-SS[ U5SS35n U RSSU S-SS[ U5SS35n XU 'U HnURnXU'M Mr S U0nUU S .nU U4$) a Splits a model state dictionary in sub-checkpoints so that the final size of each sub-checkpoint does not exceed a given size. The sub-checkpoints are determined by iterating through the `state_dict` in the order of its keys, so there is no optimization made to make each sub-checkpoint as close as possible to the maximum size passed. For example, if the limit is 10GB and we have weights of sizes [6GB, 6GB, 2GB, 6GB, 2GB, 2GB] they will get sharded as [6GB], [6+2GB], [6+2+2GB] and not [6+2+2GB], [6+2GB], [6GB]. If one of the model's weight is bigger that `max_shard_size`, it will end up in its own sub-checkpoint which will have a size greater than `max_shard_size`. Args: weights (`Dict[str, tf.RessourceVariable]`): The list of tf.RessourceVariable of a model to save. max_shard_size (`int` or `str`, *optional*, defaults to `"10GB"`): The maximum size of each sub-checkpoint. If expressed as a string, needs to be digits followed by a unit (like `"5MB"`). rrNz.h5-05dz-of- .safetensors total_size)metadata weight_map) r/numpysizerappendrmr replacer )weightsmax_shard_size weights_namesharded_state_dicts current_blockcurrent_block_sizer,item weight_sizer.shardsidxshard shard_fileweight weight_namer-indexs rDtf_shard_checkpointrBvs..n=NMJjjl''$**//9   +n <  & &} 5M!" T")! }- 1$!45t;;J F 34 !))%1S1WSMcJ]F^_bEccf1gh '' aa}D5H1I#0Nl[ #zF ++K&0{ # 5j)H! saved_weight_names_setunexpected_keys_setmismatched_keys_set missing_keys error_messagestr_missing_keysstr_unexpected_keyss rDload_tf_sharded_weightsras#,eOJeOJO%--(VV  :#8#8#A#A#CLN3J#**3/JJ&#j.>.>s.C*D*I*"2"23"7";V>V=WX | q "xx<(Hmissing_layersunexpected_layersmismatched_layersr]r^rr_r`s rD(load_tf_sharded_weights_from_safetensorsrWst2eOeO! ?_  $; @ <+< N 340101 "##%56L 3|$q(C,@1,D=eoo>V>V=WX | q "xx<(HU rFc ./n[R"US5n[[US55n[ UR Vs1sHowR iM snU- 5n[ X`R Vs1sHowR iM sn- 5n [5n [5n /n UR GHnUR U;dMXWR n URUR-n0n[U S5H^nSRURS5SS5nUbUS-U-n[R"U U5UU'U RU5 M` UGH_nUbj[URS55nSRUR RS5SUUR RS5US-S-5nO-SRUR RS5SS5nURUS5nUc0UR!S5(aUSSS-nURUS5nU RU5 UcM["R$"U5UR&:wa-[R("U["R$"U55nOUnU R-UU45 GMb GM SSS5 ["R."W 5 WR1[ W W - 55 W R1[ X- 55 XU4$s snfs snf![*aGnU(a9UR-UUR&["R$"U545 SnAGMUeSnAff=f!,(df  N=f) Nrcrd weight_namesr!rz embeddings:0izweight:0)rgrhrJrr=layersr trainable_weightsnon_trainable_weightsr$r rMrirLrmgetrrjrkr;rrnr1rlextend)rQrrrEr%rrtrulayerrrrZsymbolic_weights_namesrvrwsymbolic_weightsrsr@r rx delimitersymbolic_weight_nameryrzr{s rDrrsn (# .2I%()HI`bo)p%q"u||D|ezz|DGaab!!;WcWc>dWcezzWc>d!de!$!$ \\Ezz77"9**"E#(#:#:U=X=X#X " $C?Tb#cK88K$5$5c$:12$>?D*&}t3*,**_[5Q*RM$'+..t4$d(8O*$' c(:$; /2xx+0066s;JYG-2288=i!moNO0, 03xx8L8L8R8RSV8WXYXZ8[/\, *7):):;OQU)V&*16J6S6STb6c6c/CDS/IJ/V,-:->->?SUY-Z*+../CD*5;;7;M;S;SS ,(* 3Eq{{SbGc(d%7E,22OU3KL[(81" /n)*$58NNOPT"8"QRS .? ??qE?e@$.,#:$5$<$<)=?Q?W?WYZYdYdetYu(v%&%-*+G,Q / .sf)NL( N5L- =N FN$N+L2- N( N2 N <9M> 5N<M> >N N Nc [USS9n/nURVs/sHn[URUS9PM nn[ UR 55n[ [ U5[ U5- 5n [ [ U5[ U5- 5n URHn [U RUS9n X;dMURU 5n [R"U 5U R:wa,[R"U [R"U 55n [R""X5 M SSS5 W W W4$s snf![[RR4aFnU(a8UR!XR[R"U 545 SnAGMUeSnAff=f!,(df  N=f)Nr? framework)r%)r1r3r&r r=rrJ get_tensorrjrkr;r?rrnerrorsInvalidArgumentErrorr1 set_value)rQrrrEr%safetensors_archiverrQrloaded_weight_namesrrr?r@ weight_valuer{s rDr~r~ sw (D 9=PV[VcVcdVcQR3AFFGLVc d"#6#;#;#=>c,/#6I2JJK %8!9C old_num_tokens. A mask is also computed in order to know which weight in the embeddings should be kept or not. Example: - if new_num_tokens=5 and old_num_tokens=4 and old_embeddings=[w1,w2,w3,w4] - mask=[True,True,True,True,False] and current_weights=[w1,w2,w3,w4,-1] - if new_num_tokens=4 and old_num_tokens=5 and old_embeddings=[w1,w2,w3,w4,w5] - mask=[True,True,True,True] and current_weights=[w1,w2,w3,w4] rrconstant_valuesrTF) rr?rgreaterpadvalueconvert_to_tensorminfillslice)old_embeddingsnew_num_tokensold_num_tokensold_embedding_dim size_diffcurrent_weightsnum_tokens_to_copymasks rDinit_copy_embeddingsr-s7)3>(B%N/I wwy!$$&&  "B$8$81i.1a&9Q$Rdf !@wwr++-?,CDdKvvdB001i.1a&1IJ\ab   ((  "  !Q (  .!D E  wwr++^Q,?@$G   rFc ^\rSrSrSrSrSrSrSrSr Sr Sr Sr Sr \S>Sj5rSr\S?S j5rS@S jrU4S jrS r\R,"\R0R25U4S j5r\R,"\R0R45U4Sj5r\R,"\R0R65U4Sj5r\R,"\R0R85U4Sj5r\R,"\R0R:5U4Sj5r\R,"\R0R<5U4Sj5r\S5r \S5r!SASjr"Sr#\$RJS5r&\SBSj5r'Sr(\SCSj5r)SDSjr*Sr+SESFSjjr,SGU4Sjjr-U4Sjr.S r/S!r0S"r1SHSIS#jjr2S$r3SJS%jr4S&r5SJS'jr6SKS(jr7SLS)jr8S*r9SDS+jr:S@SMS,jjr;S@SNS-jjrS0r?S1r@SOS2jrAS3rBS@SPS4jjrCSQS5jrDS6rESRSSS7jjrF\SSSSSSS8SS9.STS:jj5rGSUSVS;jjrH\SWS<j5rIS=rJU=rK$)XTFPreTrainedModeliUa} Base class for all TF models. [`TFPreTrainedModel`] takes care of storing the configuration of the models and handles methods for loading, downloading and saving models as well as a few methods common to all models to: - resize the input embeddings, - prune heads in the self-attention heads. Class attributes (overridden by derived classes): - **config_class** ([`PretrainedConfig`]) -- A subclass of [`PretrainedConfig`] to use as configuration class for this model architecture. - **base_model_prefix** (`str`) -- A string indicating the attribute associated to the base model in derived classes of the same architecture adding modules on top of the base model. - **main_input_name** (`str`) -- The name of the principal input to the model (often `input_ids` for NLP models, `pixel_values` for vision models and `input_values` for speech models). NrFc0nURR5H|up#URVs/sH oDbUOSPM nnURScSUS'[R"XSR S9X'US:XdMb[R "X5X'M~ URR(aS[R"UR5R;aaSU;a[URS:Xa@[R"SSURR4[RSS9US'U$[!S 5eU$s snf) z\ Dummy inputs to build the network. Returns: `Dict[str, tf.Tensor]`: The dummy inputs. r7rr)r;rtoken_type_idsencoder_hidden_statesrr;rr zModel has cross-attention but we couldn't infer the shape for the encoder hidden states. Please manually override dummy_inputs!)input_signaturerr;r?onesr zeros_likerhadd_cross_attentionrrcallrr hidden_sizefloat32NotImplementedError)rXdummiesrspecdim dummy_shapes rD dummy_inputsTFPreTrainedModel.dummy_inputsxs(--335ICDHJJOJS/3q8JKOzz!}$!" A77JJGGL&&!}}W\: 6 ;; * */F'J[J[\`\e\eJfJqJq/q&g5'';679ww !T[[%<%<=RZZVm8G34.Z%PsE c[R"UR5 URSS9 SSS5 g!,(df  g=f)N) input_shape)r? name_scoper buildrXs rDbuild_in_name_scope%TFPreTrainedModel.build_in_name_scopes, ]]499 % JJ4J (& % %s : Acg)z3 :str: Identifies that this is a TensorFlow model. r?r\rs rDrTFPreTrainedModel.frameworks rFcgrLr\rXrs rDrTFPreTrainedModel.builds rFc>[TU]"U0UD6 [U[5(d:[ SUR R SUR R S35eXlURUlUR5(a[R"U5OSUl URUR5 g)NzParameter config in `zt(config)` should be an instance of class `PretrainedConfig`. To create a model from a pretrained model use `model = z(.from_pretrained(PRETRAINED_MODEL_NAME)`)ryrrirrrr_rh name_or_path can_generaterfrom_model_configgeneration_config_set_save_specr)rXrhrrrrs rDrTFPreTrainedModel.__init__s &+F+&"233'(?(?'@A NN3344\^   "//OSO`O`ObOb!1!C!CF!Khl D001rFc6URR5$rL)rhrzrs rDrvTFPreTrainedModel.get_configs{{""$$rFc>>[U0UD6up[TU]"U0UD6$rL)rryfitrXrqrrrs rDrTFPreTrainedModel.fits(-t>v> w{D+F++rFc>>[U0UD6up[TU]"U0UD6$rL)rrytrain_on_batchrs rDr TFPreTrainedModel.train_on_batchs)-t>v> w%t6v66rFc>>[U0UD6up[TU]"U0UD6$rL)rry test_on_batchrs rDrTFPreTrainedModel.test_on_batchs)-t>v> w$d5f55rFc>>[U0UD6up[TU]"U0UD6$rL)rrypredict_on_batchrs rDr"TFPreTrainedModel.predict_on_batchs)-t>v> w'888rFc>>[U0UD6up[TU]"U0UD6$rL)rrypredictrs rDrTFPreTrainedModel.predicts(-t>v> w///rFc>>[U0UD6up[TU]"U0UD6$rL)rryevaluaters rDrTFPreTrainedModel.evaluates)-t>v> w000rFc [U[5(aUR"U40UD6$URURR"U40UD65$rL)rir _from_configrfrlr}rhrrs rD from_configTFPreTrainedModel.from_configsM f. / /##F5f5 5 0 0 : :6 LV LMMrFc U"U40UD6$)zJ All context managers that the model should be initialized under go here. r\rs rDrTFPreTrainedModel._from_configs 6$V$$rFc>UbURX5nU$S/U-nU$)a Prepare the head mask if needed. Args: head_mask (`tf.Tensor` with shape `[num_heads]` or `[num_hidden_layers x num_heads]`, *optional*): The mask indicating if we should keep the heads or not (1.0 for keep, 0.0 for discard). num_hidden_layers (`int`): The number of hidden layers in the model. Returns: `tf.Tensor` with shape `[num_hidden_layers x batch x num_heads x seq_length x seq_length]` or list with `[None]` for each layer. N)_convert_head_mask_to_5drX head_masknum_hidden_layerss rD get_head_maskTFPreTrainedModel.get_head_masks7  55iSI!22IrFc|URRS:Xa"USSSS2SS4n[R"XSS9nO(URRS:XaUSS2SSS2SS4nURRS:XdSUR 535e[R "U[R 5nU$)zD-> [num_hidden_layers x batch x num_heads x seq_length x seq_length]rNr)repeatsr:r7zhead_mask.dim != 5, instead )r;r<r?repeatrrrrs rDr*TFPreTrainedModel._convert_head_mask_to_5ds ??  1 $!$at";