bCi$SrSSKJr SSKrSSKrSSKJr SSKr SSK J r SSK J r JrJrJr SSKJrJrJrJrJrJr SS KJrJr SS KJrJrJrJrJ r J!r! S S K"J#r# \ RH"\%5r&S r'Sr(/SQr)Sr*Sr+\"SS\55r,"SS\RZR\5r/"SS\RZR\5r0"SS\RZR\5r1"SS\RZR\5r2"SS\RZR\5r3"SS\RZR\5r4"SS \RZR\5r5"S!S"\RZR\5r6"S#S$\RZR\5r7\"S%S&\RZR\55r8"S'S(\5r9S)r:S*r;\"S+\:5"S,S-\955r<"S.S/\RZR\5r="S0S1\RZR\5r>"S2S3\RZR\5r?\"S4\:5"S5S6\955r@\"S7\:5"S8S9\9\55rA\"S:\:5"S;S<\955rB/S=QrCg)>zTensorFlow DeiT model.) annotationsN) dataclass)get_tf_activation)TFBaseModelOutputTFBaseModelOutputWithPoolingTFImageClassifierOutputTFMaskedImageModelingOutput)TFPreTrainedModelTFSequenceClassificationLossget_initializerkeraskeras_serializable unpack_inputs) shape_liststable_softmax) ModelOutputadd_code_sample_docstringsadd_start_docstrings%add_start_docstrings_to_model_forwardloggingreplace_return_docstrings) DeiTConfigrz(facebook/deit-base-distilled-patch16-224)riztabby, tabby catc`\rSrSr%SrSrS\S'SrS\S'SrS\S'Sr S\S 'Sr S\S 'S r g) -TFDeiTForImageClassificationWithTeacherOutputBa Output type of [`DeiTForImageClassificationWithTeacher`]. Args: logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`): Prediction scores as the average of the cls_logits and distillation logits. cls_logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`): Prediction scores of the classification head (i.e. the linear layer on top of the final hidden state of the class token). distillation_logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`): Prediction scores of the distillation head (i.e. the linear layer on top of the final hidden state of the distillation token). hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Ntf.Tensor | Nonelogits cls_logitsdistillation_logitsztuple[tf.Tensor] | None hidden_states attentions) __name__ __module__ __qualname____firstlineno____doc__r __annotations__r!r"r#r$__static_attributes__r%c/home/james-whalen/.local/lib/python3.13/site-packages/transformers/models/deit/modeling_tf_deit.pyrrBsA, $F ##'J ',0)0-1M*1*.J'.r-rcn^\rSrSrSrSS U4SjjjrS SjrS SjrS S SjjrSr U=r $)TFDeiTEmbeddingsazn Construct the CLS token, distillation token, position and patch embeddings. Optionally, also the mask token. c >[TU]"S0UD6 XlX l[ USS9Ul[ RRURSS9Ul g)Npatch_embeddings)confignamedropoutr5r%) super__init__r4use_mask_tokenTFDeiTPatchEmbeddingsr3rlayersDropouthidden_dropout_probr6)selfr4r:kwargs __class__s r.r9TFDeiTEmbeddings.__init__fsO "6" , 5VJ\ ]||++F,F,FY+W r-cURSSURR4[RR 5SSS9UlURSSURR4[RR 5SSS9UlSUlUR(aJURSSURR4[RR 5SSS9UlURRnURSUS-URR4[RR 5SSS9Ul UR(agSUl [US S5bN[R "URR"5 URR%S5 SSS5 [US S5bO[R "UR&R"5 UR&R%S5 SSS5 gg!,(df  Nl=f!,(df  g=f) NrT cls_token)shape initializer trainabler5distillation_token mask_tokenposition_embeddingsr3r6) add_weightr4 hidden_sizer initializerszerosrDrHrIr:r3 num_patchesrKbuiltgetattrtf name_scoper5buildr6)r? input_shaperPs r.rUTFDeiTEmbeddings.buildmsa001**002 )  #'//a001**002% #2#     "oo!T[[445!..446! .DO ++77 #'??kAot{{'>'>?**002& $3$   ::  4+T 2 >t4499:%%++D1; 4D ) 5t||001 ""4(21 6;:21s+H-H>- H;> I c URSS- nURRSS- nXE:XaX#:Xa UR$URSS2SSS24nURSS2SSS24nURSS2SS2SS24nURSn X RR-n X0RR-n U S-U S-p[R "US[ [R"U55[ [R"U55U 45n[RRU[ U 5[ U 54SS9n[R"U/SQS 9n[R "USSU 45n[R"[R"USS 9[R"USS 9U/SS 9$) NrrJrg?bicubic)sizemethodrrJrrpermaxis)rErKr4 patch_sizerSreshapeintmathsqrtimageresize transposeconcat expand_dims) r? embeddingsheightwidthrP num_positionsclass_pos_embeddist_pos_embedpatch_pos_embeddimh0w0s r.interpolate_pos_encoding)TFDeiTEmbeddings.interpolate_pos_encodings &&q)A- 0066q9A=  'FO++ +221a7;11!Q':221ab!8<r" {{-- - kk,, ,c28B** aTYY}%=!>DIImD\@]_bc ((///R#b'@R[d/e,,\J**_q"clCyy ^^O! 4bnn^Z[6\^m nuv  r-cLURupVpuURU5n[U5upnUbc[R"UR XS/5n [R "USS9n [R"XRS9n USU - -X--n[R"URU SS9n [R"URU SS9n[R"XU4SS9nURnU(aURXU5nX-nURXS9nU$) NrrYr`dtypeg?r)repeatsratraining)rEr3rrStilerIrkcastrzrepeatrDrHrjrKrvr6)r? pixel_valuesbool_masked_posr}rv_rmrnrl batch_size seq_length mask_tokensmask cls_tokensdistillation_tokensposition_embeddings r.callTFDeiTEmbeddings.calls+005**<8 $.z$:!   &''$//JA3NOK>>/;D774'8'89D#sTz2[5GGJYYt~~zJ  ii(?(?Z[\YY LSTU !55 #!%!>!>zSX!Y 4 \\*\@ r-) rQrDr4rHr6rIr3rKr:F)r4rr:boolreturnNoneN)rl tf.Tensorrmrdrnrdrr)NFF) rrrrr}rrvrrr) r&r'r(r)r*r9rUrvrr, __classcell__rAs@r.r0r0asdXX%)N <-1). *  #'   r-r0cD^\rSrSrSrSU4SjjrSSjrS SjrSrU=r $) r;z This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a Transformer. c >[TU]"S0UD6 URURpCURUR pe[ U[RR5(aUOX34n[ U[RR5(aUOXD4nUSUS-USUS--nX0lX@lXPlXpl [RRXdUSS9Ulg)Nrr projection) kernel_sizestridesr5r%)r8r9 image_sizerb num_channelsrM isinstance collectionsabcIterablerPrr<Conv2Dr) r?r4r@rrbrrMrPrAs r.r9TFDeiTPatchEmbeddings.__init__s "6"!'!2!2F4E4EJ$*$7$79K9Kk#-j+//:R:R#S#SZZdYq #-j+//:R:R#S#SZZdYq !!} 1 5*Q-:VW=:XY $$(&,,-- ,. r-c[U5up#pE[R"5(aXPR:wa [ S5eUR U5n[U5up#pE[R "XbX4-U45nU$)NzeMake sure that the channel dimension of the pixel values match with the one set in the configuration.)rrSexecuting_eagerlyr ValueErrorrrc)r?rrrmrnrxs r.rTFDeiTPatchEmbeddings.callsu2<\2J/ E   ! !l6G6G&Gw  OOL )2[TU]"S 0UD6 URUR-S:wa&[ SURSURS35eURUl[ URUR- 5UlURUR -Ul[R"UR 5Ul [RRUR[UR5SS9Ul[RRUR[UR5SS9Ul[RRUR[UR5SS9Ul[RR'UR(S 9UlXlg) NrzThe hidden size (z6) is not a multiple of the number of attention heads ()queryunitskernel_initializerr5keyvaluerater%)r8r9rMnum_attention_headsrrdattention_head_size all_head_sizererfsqrt_att_head_sizerr<Denser initializer_rangerrrr=attention_probs_dropout_probr6r4r?r4r@rAs r.r9TFDeiTSelfAttention.__init__s "6"    : : :a ?#F$6$6#78''-'A'A&B!E  $*#=#= #&v'9'9F[TU]"S0UD6 [RR UR [ UR5SS9Ul[RRURS9Ul Xl gNdenserrr% r8r9rr<rrMr rrr=r>r6r4rs r.r9TFDeiTSelfOutput.__init__]m "6"\\''$$IaIa9bip(  ||++1K1K+L  r-cBURUS9nURXS9nU$Nrrrr6r?r# input_tensorr}s r.rTFDeiTSelfOutput.callfs( - 8  M M r-c@UR(agSUl[USS5bf[R"URR 5 URR SSURR/5 SSS5 gg!,(df  g=fNTr rQrRrSrTrr5rUr4rMrs r.rUTFDeiTSelfOutput.buildlm ::  4$ ' 3tzz/   $dkk.E.E!FG0/ 4// 3B BrQr4rr6rrr#rrrr}rrrrrrs@r.rrWs  HHr-rc^^\rSrSrSU4SjjrSrSS SjjrS SjrSrU=r $) TFDeiTAttentionivc b>[TU]"S0UD6 [USS9Ul[ USS9Ulg)N attentionr7outputr%)r8r9rself_attentionr dense_outputrs r.r9TFDeiTAttention.__init__ws1 "6"1&{K,V(Cr-c[erNotImplementedError)r?headss r. prune_headsTFDeiTAttention.prune_heads}s!!r-c^URXX4S9nURUSXS9nU4USS-nU$)Nr#rrr}rr#rr}r)rr)r?rrrr} self_outputsrrs r.rTFDeiTAttention.calls[**&O`+  ,,&q/ - $% QR(88r-cUR(agSUl[USS5bN[R"URR 5 URR S5 SSS5 [USS5bO[R"URR 5 URR S5 SSS5 gg!,(df  Nl=f!,(df  g=f)NTrr)rQrRrSrTrr5rUrrs r.rUTFDeiTAttention.builds ::  4)4 0 <t22778##))$/9 4 . :t00556!!''-76 ;9876C.C% C"% C3)rQrrrr) rrrrrrr}rrrr) r&r'r(r)r9rrrUr,rrs@r.rrvsRD "      " . .r-rc@^\rSrSrSU4SjjrSSjrSSjrSrU=r$) TFDeiTIntermediateic J>[TU]"S0UD6 [RR UR [ UR5SS9Ul[UR[5(a[UR5Ul OURUl Xlg)Nrrr%)r8r9rr<rintermediate_sizer rrr hidden_actstrrintermediate_act_fnr4rs r.r9TFDeiTIntermediate.__init__s "6"\\''**vOgOg?hov(   f'' - -'89J9J'KD $'-'8'8D $ r-cFURUS9nURU5nU$)Nr)rr)r?r#s r.rTFDeiTIntermediate.calls( - 8 00? r-c@UR(agSUl[USS5bf[R"URR 5 URR SSURR/5 SSS5 gg!,(df  g=frrrs r.rUTFDeiTIntermediate.buildrr)rQr4rrrr#rrrr r&r'r(r)r9rrUr,rrs@r.rrs  HHr-rcD^\rSrSrSU4SjjrSSSjjrS SjrSrU=r$) TFDeiTOutputic  >[TU]"S0UD6 [RR UR [ UR5SS9Ul[RRURS9Ul Xl grrrs r.r9TFDeiTOutput.__init__rr-cJURUS9nURXS9nX-nU$rrrs r.rTFDeiTOutput.calls0 - 8  M M %4 r-c@UR(agSUl[USS5bf[R"URR 5 URR SSURR/5 SSS5 gg!,(df  g=fr) rQrRrSrTrr5rUr4rrs r.rUTFDeiTOutput.buildsm ::  4$ ' 3tzz/   $dkk.K.K!LM0/ 4//rrrrrrrrs@r.rrsNNr-rc\^\rSrSrSrSU4SjjrSS SjjrS SjrSrU=r $) TFDeiTLayeriz?This corresponds to the Block class in the timm implementation.c @>[TU]"S0UD6 [USS9Ul[ USS9Ul[ USS9Ul[RRURSS9Ul [RRURSS9Ul Xlg) Nrr7 intermediaterlayernorm_beforeepsilonr5layernorm_afterr%)r8r9rrrr)r deit_outputrr<LayerNormalizationlayer_norm_epsr*r-r4rs r.r9TFDeiTLayer.__init__s "6"(kB.vNK'X> % ? ?H]H]dv ? w$||>>vG\G\ct>u r-cURURXS9UUUS9nUSnXa-nURXS9nURXtS9nUR XUS9nU4USS-n U $)Nr)rrrr}r)r#r}r r)rr*r-r)r.) r?r#rrr}attention_outputsr layer_outputintermediate_outputrs r.rTFDeiTLayer.calls!NN..m.W/ + -Q/)8 ++=+T "//l/^''-T\(  /$5ab$99r-cUR(agSUl[USS5bN[R"URR 5 URR S5 SSS5 [USS5bN[R"URR 5 URR S5 SSS5 [USS5bN[R"URR 5 URR S5 SSS5 [USS5be[R"URR 5 URR SSURR/5 SSS5 [USS5bf[R"URR 5 URR SSURR/5 SSS5 gg!,(df  GN=f!,(df  GNe=f!,(df  GN=f!,(df  N=f!,(df  g=f)NTrr)r.r*r-) rQrRrSrTrr5rUr)r.r*r4rMr-rs r.rUTFDeiTLayer.builds ::  4d + 7t~~223$$T*4 4 . :t00556!!''-7 4 - 9t//445  &&t,6 4+T 2 >t4499:%%++T49P9P,QR; 4*D 1 =t33889$$**D$ 8O8O+PQ:9 >437665;::9s<H.H( H:&3I 3I H%( H7: I  I I+)rrQr4r.r)r-r*rrrrrrs@r.r'r'sRI         @RRr-r'c`^\rSrSrSU4SjjrSSSjjrS SjrSrU=r$) TFDeiTEncoderic >[TU]"S0UD6 [UR5Vs/sHn[ USU3S9PM snUlgs snf)Nzlayer_._r7r%)r8r9rangenum_hidden_layersr'layer)r?r4r@irAs r.r9TFDeiTEncoder.__init__sF "6"HMfNfNfHghHg1k&!~>Hgh hsA c&U(aSOSnU(aSOSn[UR5H2upU(aXq4-nU "UX)UUS9n U SnU(dM*XS4-nM4 U(aXq4-nU(d[SXU455$[XUS9$)Nr%r rrc3.# UH ocMUv M g7frr%).0vs r. %TFDeiTEncoder.call..@sh$Vq$Vs )last_hidden_stater#r$) enumerater>tupler) r?r#rroutput_hidden_states return_dictr}all_hidden_statesall_attentionsr? layer_module layer_outputss r.rTFDeiTEncoder.call s#7BD0d(4OA#$58H$H!(+#,"3! M *!,M  !/3C2E!E 5 14D D h]~$Vhh h +Yg  r-cUR(agSUl[USS5bNURH=n[R"UR 5 UR S5 SSS5 M? gg!,(df  MR=f)NTr>)rQrRr>rSrTr5rU)r?rVr>s r.rUTFDeiTEncoder.buildFs` ::  4$ ' 3]]5::.KK%/.$ 4..s A77 B )rQr>rr)r#rrrrrrJrrKrr}rrz$TFBaseModelOutput | tuple[tf.Tensor]rrrs@r.r:r:sbi$  $ $  $ # $  $ $  .$ L&&r-r:c^\rSrSr\rS S U4SjjjrS SjrSrSr \ S S Sjj5r SSjr Sr U=r$)TFDeiTMainLayeriPc >[TU]"S0UD6 Xl[XSS9Ul[ USS9Ul[RRURSS9Ul U(a[USS9Ul gSUl g) Nrl)r:r5encoderr7 layernormr+poolerr%)r8r9r4r0rlr:rVrr<r/r0rW TFDeiTPoolerrXr?r4add_pooling_layerr:r@rAs r.r9TFDeiTMainLayer.__init__Tsp "6" *6Wcd$V)< 88AVAV]h8i=Nl69 TX r-c.URR$r)rlr3)r?s r.get_input_embeddings$TFDeiTMainLayer.get_input_embeddings`s///r-c[e)z Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel r)r?heads_to_prunes r. _prune_headsTFDeiTMainLayer._prune_headscs "!r-cLUb[eS/URR-nU$r)rr4r=)r?rs r. get_head_maskTFDeiTMainLayer.get_head_maskjs*  % %!>!>>Ir-c .UbUOURRnUbUOURRnUbUOURRnUc [ S5e[ R "US5nURU5nURUUUUS9n URU UUUUUS9n U Sn URXS9n URbURXS9OSn U(dU bX4OU 4n XSS-$[U U U RU RS9$) Nz You have to specify pixel_valuesr])rr}rv)rrrJrKr}rr|r)rG pooler_outputr#r$)r4rrJuse_return_dictrrSrirerlrVrWrXrr#r$)r?rrrrrJrKrvr}embedding_outputencoder_outputssequence_output pooled_output head_outputss r.rTFDeiTMainLayer.callrsR2C1N-TXT_T_TqTq$8$D $++JjJj &1%; ; z((r-rTc$\rSrSrSr\rSrSrSr g)TFDeiTPreTrainedModelizz An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. deitrr%N) r&r'r(r)r*rrvbase_model_prefixmain_input_namer,r%r-r.rxrxs L$Or-rxaR This model is a TensorFlow [keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer). Use it as a regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and behavior. Parameters: config ([`DeiTConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. a Args: pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`DeiTImageProcessor.__call__`] for details. head_mask (`tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. interpolate_pos_encoding (`bool`, *optional*, defaults to `False`): Whether to interpolate the pre-trained position encodings. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. z^The bare DeiT Model transformer outputting raw hidden-states without any specific head on top.c ^\rSrSrSS U4Sjjjr\\"\5\"\ \ \ S\ S9S S Sjj555r S SjrSrU=r$) TFDeiTModelic H>[TU]"U40UD6 [XUSS9Ulg)Nryr[r:r5)r8r9rTryrZs r.r9TFDeiTModel.__init__s+ *6*# ]c  r-vision) checkpoint output_typervmodalityexpected_outputc 2URUUUUUUUUS9n U $)N)rrrrrJrKrvr})ry) r?rrrrrJrKrvr}rs r.rTFDeiTModel.calls6())%+/!5#%=  r-cUR(agSUl[USS5bO[R"URR 5 URR S5 SSS5 gg!,(df  g=f)NTry)rQrRrSrTryr5rUrs r.rUTFDeiTModel.build sZ ::  4 & 2tyy~~. %/. 3..s A88 B)rQryrrrsrt)rrrrrrrrurJrurKrurvrr}rrz$tuple | TFBaseModelOutputWithPoolingr)r&r'r(r)r9rrDEIT_INPUTS_DOCSTRINGr_CHECKPOINT_FOR_DOCr_CONFIG_FOR_DOC_EXPECTED_OUTPUT_SHAPErrUr,rrs@r.r}r}s Z_   59 RV   *+@A&0$. *.,0&*)-,0#').&*$  '  * !#' .B.&&r-r}c@^\rSrSrSU4SjjrSSjrSSjrSrU=r$) rYi*c >[TU]"S0UD6 [RR UR [ UR5URSS9Ul Xl g)Nr)rr activationr5r%) r8r9rr<rpooler_output_sizer r pooler_actrr4rs r.r9TFDeiTPooler.__init__+sX "6"\\''++.v/G/GH(( (   r-c6USS2S4nURUS9nU$)Nrr)r)r?r#first_token_tensorrms r.rTFDeiTPooler.call6s*+1a40 *< = r-c@UR(agSUl[USS5bf[R"URR 5 URR SSURR/5 SSS5 gg!,(df  g=frrrs r.rUTFDeiTPooler.build>rr)rQr4rrrrrrs@r.rYrY*s HHr-rYc:^\rSrSrSrSU4SjjrSSjrSrU=r$)TFDeitPixelShuffleiGz0TF layer implementation of torch.nn.PixelShufflec >[TU]"S0UD6 [U[5(aUS:a[ SU35eXlg)NrJz1upscale_factor must be an integer value >= 2 got r%)r8r9rrdrupscale_factor)r?rr@rAs r.r9TFDeitPixelShuffle.__init__JsB "6".#...12DPQ_P`ab b,r-c Un[U5un pEURS-n[XV- 5n[R"[ U5VV s/sHn[ U5H oX--PM M sn n/5n [R "U[R"XS/5SS9n[RRX RSS9nU$s sn nf)NrJrrY)paramsindices batch_dimsNHWC) block_size data_format) rrrdrSconstantr<gatherr~nndepth_to_space) r?rr#rrnum_input_channelsblock_size_squared output_depthr?j permutations r.rTFDeitPixelShuffle.callPs /9-/H, Aq!00!3-BC kk278J2K i2KQUZ[gUhPQ!((Uh(2K i j   bcUd@ertu ,,]GZGZhn,o  js #C )r)rrdrr)rrrr) r&r'r(r)r*r9rr,rrs@r.rrGs:- r-rcD^\rSrSrSU4SjjrSSSjjrS SjrSrU=r$) TFDeitDecoderiac >[TU]"S0UD6 [RR UR S-UR -SSS9Ul[UR SS9Ul Xl g)NrJr0)filtersrr51r7r%) r8r9rr<rencoder_striderconv2dr pixel_shuffler4rs r.r9TFDeitDecoder.__init__bsi "6"ll))))1,v/B/BBPQX[*  00E0ECP r-cNUnURU5nURU5nU$r)rr)r?rr}r#s r.rTFDeitDecoder.calljs+  M2 **=9 r-cUR(agSUl[USS5bf[R"URR 5 URR SSSURR/5 SSS5 [USS5bO[R"URR 5 URR S5 SSS5 gg!,(df  Nl=f!,(df  g=f)NTrr) rQrRrSrTrr5rUr4rMrrs r.rUTFDeitDecoder.buildps ::  44 ( 4t{{//0 !!4tT[[5L5L"MN1 4$ / ;t11667""((.87 <1087s4C,C=, C:= D )rQr4rrrr)rrr}rrrrrrs@r.rras / /r-rz~DeiT Model with a decoder on top for masked image modeling, as proposed in [SimMIM](https://huggingface.co/papers/2111.09886).c^\rSrSrSU4Sjjr\\"\5\"\ \ S9SS Sjj555r S Sjr Sr U=r$) TFDeiTForMaskedImageModelingi|cd>[TU]U5 [USSSS9Ul[ USS9Ulg)NFTryrdecoderr7)r8r9rTryrrr?r4rAs r.r9%TFDeiTForMaskedImageModeling.__init__s2  #FeTX_ef $V)< r-rrvc UbUOURRnURUUUUUUUUS9n U Sn U SS2SS24n [U 5upn [ U S-5=p[ R "XXU 45n URXS9n[ R"US5nSnUGbURRURR-n[ R "USUU45n[ R"X RRS5n[ R"UURRS 5n[ R"US5n[ R"U[ R5n[R R#[ R"US 5[ R"US 55n[ R"US5n[ R$"UU-5n[ R$"U5S -URR&-nUU- n[ R "US 5nU(dU4U SS-nUbU4U-$U$[)UUU R*U R,S 9$)a bool_masked_pos (`tf.Tensor` of type bool and shape `(batch_size, num_patches)`): Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). Returns: Examples: ```python >>> from transformers import AutoImageProcessor, TFDeiTForMaskedImageModeling >>> import tensorflow as tf >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> image_processor = AutoImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224") >>> model = TFDeiTForMaskedImageModeling.from_pretrained("facebook/deit-base-distilled-patch16-224") >>> num_patches = (model.config.image_size // model.config.patch_size) ** 2 >>> pixel_values = image_processor(images=image, return_tensors="tf").pixel_values >>> # create random boolean mask of shape (batch_size, num_patches) >>> bool_masked_pos = tf.cast(tf.random.uniform((1, num_patches), minval=0, maxval=2, dtype=tf.int32), tf.bool) >>> outputs = model(pixel_values, bool_masked_pos=bool_masked_pos) >>> loss, reconstructed_pixel_values = outputs.loss, outputs.reconstruction >>> list(reconstructed_pixel_values.shape) [1, 3, 224, 224] ```N)rrrrJrKrvr}rrrYg?r|)rrrrJrJ)rrJrrgh㈵>)r)lossreconstructionr#r$)r4riryrrdrSrcrrirrbrrkrfloat32rlossesmean_absolute_error reduce_sumrr r#r$)r?rrrrrJrKrvr}rrlrsequence_lengthrrmrnreconstructed_pixel_valuesmasked_im_lossr[rreconstruction_loss total_lossnum_masked_pixelsrs r.r!TFDeiTForMaskedImageModeling.callsIV&1%4O1 \_c122**_6R^6_`&*\\/\%U"&(\\2Ll%["  &;;))T[[-C-CCD jj2tT:JKO99_kk.D.DaHD99T4;;#9#91=D>>$*D774,D"',,"B"B \<8 7F#  #%..1Da"H ':T'ABJ!#t!4t!;t{{?W?W W '*;;NZZ=N02WQR[@F3A3M^%. YSY Y*5!//))   r-cUR(agSUl[USS5bN[R"URR 5 URR S5 SSS5 [USS5bO[R"URR 5 URR S5 SSS5 gg!,(df  Nl=f!,(df  g=f)NTryr)rQrRrSrTryr5rUrrs r.rU"TFDeiTForMaskedImageModeling.builds ::  4 & 2tyy~~. %/ 4D ) 5t||001 ""4(21 6/.21r)rQrryrrt)rrrrrrrrurJrurKrurvrr}rrz#tuple | TFMaskedImageModelingOutputr)r&r'r(r)r9rrrrr rrrUr,rrs@r.rr|s = *+@A+FUde*.,0&*)-,0#').a &a *a $ a ' a * a !a #'a a  -a fBa F ) )r-rz DeiT Model transformer with an image classification head on top (a linear layer on top of the final hidden state of the [CLS] token) e.g. for ImageNet. c^\rSrSrSU4Sjjr\\"\5\"\ \ S9SS Sjj555r S Sjr Sr U=r$) TFDeiTForImageClassificationic(>[TU]U5 URUl[USSS9UlURS:a([ R RURSS9O[ R RSSS9Ul Xl g)NFryr[r5r classifierr7linear) r8r9 num_labelsrTryrr<r Activationrr4rs r.r9%TFDeiTForImageClassification.__init__s   ++#Fe&Q   1$ LL  v00|  D(( (E   r-rc DUbUOURRnURUUUUUUUS9n U Sn URU SS2SSS245n UcSOUR X;5n U(dU 4U SS-n U bU 4U -$U $[ U U U R U RS9$)a labels (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for computing the image classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). Returns: Examples: ```python >>> from transformers import AutoImageProcessor, TFDeiTForImageClassification >>> import tensorflow as tf >>> from PIL import Image >>> import requests >>> keras.utils.set_random_seed(3) # doctest: +IGNORE_RESULT >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> # note: we are loading a TFDeiTForImageClassificationWithTeacher from the hub here, >>> # so the head will be randomly initialized, hence the predictions will be random >>> image_processor = AutoImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224") >>> model = TFDeiTForImageClassification.from_pretrained("facebook/deit-base-distilled-patch16-224") >>> inputs = image_processor(images=image, return_tensors="tf") >>> outputs = model(**inputs) >>> logits = outputs.logits >>> # model predicts one of the 1000 ImageNet classes >>> predicted_class_idx = tf.math.argmax(logits, axis=-1)[0] >>> print("Predicted class:", model.config.id2label[int(predicted_class_idx)]) Predicted class: little blue heron, Egretta caerulea ```NrrrJrKrvr}rr)rr r#r$)r4riryrhf_compute_lossr r#r$)r?rrlabelsrrJrKrvr}rrlr rrs r.r!TFDeiTForImageClassification.calls^&1%