qi dZddlZddlmZddlZddlmZddlmZ ddl m Z ddl mZdd lmZdd lmZdd lmZmZmZmZd d lmZej4eZeedGddeZeedGddeZGddej>Z Gddej>Z!Gddej>Z"Gddej>Z#d>dejHde%de&d ejHfd!Z'Gd"d#ej>Z(Gd$d%eZ)Gd&d'ej>Z*Gd(d)ejVZ,Gd*d+ej>Z-Gd,d-ej>Z.eGd.d/eZ/eGd0d1e/Z0d2ejHd3e1d ejHfd4Z2d2ejHd5e1d6e1d ejHfd7Z3Gd8d9ej>Z4ed:Gd;dsg &*D%  d ")+/J!!D(/59M5**+d2926Je''(4/6r!r$c(eZdZdZfdZdZxZS)SegGptPatchEmbeddingsz 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. ct||j|j}}|j|j }}t |tjjr|n||f}t |tjjr|n||f}|d|dz|d|dzz}||_||_||_||_ tj|||||_ y)Nrr) kernel_sizestride)super__init__ image_size patch_size num_channels hidden_size isinstance collectionsabcIterable num_patchesrConv2d projection)selfconfigr.r/r0r1r6 __class__s r"r-zSegGptPatchEmbeddings.__init__`s !'!2!2F4E4EJ $*$7$79K9Kk #-j+//:R:R#SZZdfpYq #-j+//:R:R#SZZdfpYq !!} 1 5*Q-:VW=:XY $$(&))L+:^hir!c N|j\}}}}||jk7r td||jdk7s||jdk7r2td|d|d|jdd|jdd |j |j ddd d}|S) NzeMake sure that the channel dimension of the pixel values match with the one set in the configuration.rrzInput image size (*z) doesn't match model ().r)shaper0 ValueErrorr.r8permute)r9 pixel_values batch_sizer0heightwidth embeddingss r"forwardzSegGptPatchEmbeddings.forwardns2>2D2D/ L&% 4,, ,w  T__Q' '5DOOA4F+F$VHAeW4KDOO\]L^K__`aeapapqras`ttvw __\2::1aAF r!)rrrrr-rH __classcell__r;s@r"r(r(Ys j r!r(c eZdZdZdeddffd ZdededejfdZ dd ejd ejd ejdzd e dzdejf d Z xZ S)SegGptEmbeddingszX Construct the embeddings from patch, position embeddings for input and prompt. r:returnNc~t|tjt j ddd|j |_tjt j ddd|j |_tjt j ddd|j |_ tjt j ddd|j |_ tjt j ddd|j |_ t||_ |j|jzdzdz}tjt j d||j |_tj$|j&|_y)Nrr?)r,r-r Parameterrzerosr1 mask_tokensegment_token_inputsegment_token_prompttype_token_semantictype_token_instancer(patch_embeddingspretrain_image_sizer/randnposition_embeddingsDropouthidden_dropout_probdropout)r9r: num_positionsr;s r"r-zSegGptEmbeddings.__init__s3 ,,u{{1aF@SUWX``abdeghjkle_# O#**1aA6 6"**1feR@ @r!rCprompt_pixel_valuesbool_masked_posembedding_typecR|j|}|j|}|j\}}} } |jj||| d} |j dj | j d|| d} |d| z z| | zz}||nd}|j|| } ||jz}||jz}|| z}|| z}|dk(r |j}n |dk(r |j}ntd|||z}||z}tj||fd}|S)Nr`rinstancesemanticzBEmbedding type should be either 'semantic' or 'instance', but got rdim)rVr@rQexpand unsqueezetype_asrirlrRrSrTrUrArcat)r9rCrmrnroinput_embeddingsprompt_embeddingsrD patch_height patch_width_rQw pos_embedtype_embeddingrGs r"rHzSegGptEmbeddings.forwardsh 00> 112EF3C3I3I0 L+q__++J kSUV  % %b ) 1 1* = E Eb,Xcef g-Q7*q.H+9+E:11, L ,d.F.FF-0I0II,i7- 9 Z '!55N z )!55Nabpaqrs s+n<->YY 02CD!L r!)NN)rrrrrr-intrTensorrl BoolTensorstrrHrIrJs@r"rLrL|s>|>> AsA3A5<<A,48%) +ll+#\\+))D0 + d +  +r!rLc8eZdZdZfdZdededejdejfdZdejd ejd ejd ejde eefde eefdejfd Z dd ejdejfdZ xZ S)SegGptAttentionz=Multi-head Attention block with relative position embeddings.ct||j|j}}t |t j jr|n||f}t |t j jr|n||f}|d|jz|d|jzf}|j|jz}|j|_ |dz|_ tj|j|jdz|j|_tj|j|j|_|j |_|j r|| t#dtj$t'j(d|dzdz ||_tj$t'j(d|dzdz ||_yy)NrrgrbiaszBInput size must be provided if using relative positional encoding.r?)r,r-r.r/r2r3r4r5r1num_attention_headsscalerLinearqkv_biasqkvproj use_relative_position_embeddingsrArOrrP rel_pos_h rel_pos_w)r9r:r.r/ input_sizehead_dimr;s r"r-zSegGptAttention.__init__s !'!2!2F4E4EJ #-j+//:R:R#SZZdfpYq #-j+//:R:R#SZZdfpYq  mv'8'88*Q-6K\K\:\] %%)C)CC#)#=#= t^ 99V//1C1Ca1Gfoo^IIf00&2D2DE 060W0W-  0 0! !eff \\%++a*Q-6G!6KX*VWDN\\%++a*Q-6G!6KX*VWDN 1r!q_sizek_sizerel_posrMctdt||zdz }tj|j d|j ddj ddd|d}|j d|j dd}tj|dddft||z dz}tj|dddft||z dz}||z |dz t||z dzz}||jS) a Get relative positional embeddings according to the relative positions of query and key sizes. Args: q_size (int): size of the query. k_size (int): size of key k. rel_pos (`torch.Tensor`): relative position embeddings (L, channel). Returns: Extracted positional embeddings according to relative positions. r?rrr`linear)rbrcNg?) rmaxrgrhrir@rBrarangelong) r9rrr max_rel_distrel_pos_resizedq_coordsk_coordsrelative_coordss r" get_rel_poszSegGptAttention.get_rel_poss 1s6622Q67 -- OOAw}}Q/ 4 < bhwkzbhwc,wkc->bhwkN)rr@rireinsum)r9rrrrrr query_height query_width key_height key_widthrelative_position_heightrelative_position_widthrDr}rtreshaped_queryrel_hrel_ws r"add_decomposed_rel_posz&SegGptAttention.add_decomposed_rel_pos s>%+! k & I#'#3#3L*i#X "&"2"2; 9"U"[[ Asz<cR -~?WX -~?VW||J k:yYeAq!Q,--aAtQ6F0GG||J {(BJQZDZ[ r!rc |j\}}}}|j|j|||zd|jdj ddddd}|jd||jz||zdj d\}} } ||j z| jddz} |jr.|j| ||j|j||f||f} tjjj| tj dj#|j$} |rE| j'||j||zd} | j'||jz||zd} nd} | | zj||j||d} | j dddddj|||d} |j)| } | | fS) Nrr`r?rr)dtypert)r@rrirrBunbindr transposerrrrrrrsoftmaxfloat32torviewr)r9routput_attentionsrDrErFr}rrkeyvalue attn_weightsattn_weights_reshaped attn_outputs r"rHzSegGptAttention.forward9s'4':':$ FE1 HH] # WZ%D4L4Lb Q WQ1a #  KK:8P8P+PRX[`R`bdellmnosE *cmmB.CC  0 066eT^^T^^fe_W]_dVeLxx**22,>FDYDYZr!r ensemble_condfeature_ensemblerc|j|j||}|d}|dd}|r|jddz|k\r|j|jddzd\}} |dk(ra|jddz} | j d| d} | j ddj | } | j|j} n"| j ddj | } tj|| gd}|j||z}|} |j|}|j|}| |j|z}|f|z}|S) N)rrrr?rsr`T)rtkeepdim) rrr@splitrimean expand_asrrxrrr) r9rrrrself_attention_outputsattention_outputoutputspromptinputs num_promptsresiduals r"rHzSegGptLayer.forwardsz"&  ! !- 0/"0" 2!4(,  0 6 6q 9Q >- O-334D4J4J14MQR4RXY3ZNFF!.44Q71< ;;D9CCFK'6D9CCFK$yy&&)9qA '78=H  ,,]; /  4>>-#@@  "W,r!)FF) rrrrrr-rrrboolrrHrIrJs@r"rrs[|[U[t["'"' #||## #  # u||U\\) *U5<<-@ @ #r!rcleZdZdeddffd Z d dej dededed edee zf d Z xZ S) SegGptEncoderr:rMNc t|||_tjd|j |j dDcgc]}|j}}tjt|j Dcgc]}t|||c}|_ tj|j|j|_d|_ycc}wcc}w)Nrcpu)rrF)r,r-r:rlinspacernum_hidden_layersitemr ModuleListrangerlayersrr1r layernormgradient_checkpointing)r9r:xdprir;s r"r-zSegGptEncoder.__init__s  !&63H3H&JbJbkp!qrAqvvxrrmm%PVPhPhJi$jQ[Q%@$jk f&8&8f>S>ST&+#s$js CC$rrroutput_hidden_states return_dictc6|rdnd}|rdnd}g}t|jD]\} } |r||fz}|jj| kDrdnd} | || ||} | d}| |jjk(r.|d|jddz||jddzdzdz}| |jj vr |j |j||s|| dfz}|r||fz}|std||||fDSt||||S)Nr r?rrr_c3$K|]}|| ywrr ).0vs r" z(SegGptEncoder.forward..s=s)rrrr) enumerater r: merge_indexr@!intermediate_hidden_state_indicesappendr rr) r9rrrrrall_hidden_statesall_self_attentionsrr layer_moduler layer_outputss r"rHzSegGptEncoder.forwardsr#7BD$5b4%'"(5 POA|#$58H$H!"&!8!81! channels_last)r data_formatc \t||fd|i||dvrtd|||_y)Nr)r channels_firstzUnsupported data format: )r,r-NotImplementedErrorr!)r9normalized_shaperr!kwargsr;s r"r-zSegGptLayerNorm.__init__s? )=s=f= A A%(A+&OP P&r!featuresrMc|jdk(r9|jdddd}t| |}|jdddd}|St| |}|S)z Args: features: Tensor of shape (batch_size, channels, height, width) OR (batch_size, height, width, channels) r#rr?rr)r!rBr,rH)r9r'r;s r"rHzSegGptLayerNorm.forwardsj   / /''1a3Hwx0H''1a3Hwx0Hr!) rrrrr-rrrHrIrJs@r"rrs4 15/'    r!rc>eZdZfdZdej fdZxZS)SegGptDecoderHeadcTt|tj|j|jdd|_t |j|jd|_t|j|_ tj|jddd|_ y)Nrr)r*paddingr#)r%rr!T)r*r) r,r-rr7decoder_hidden_sizeconvrrr rract_fctheadrs r"r-zSegGptDecoderHead.__init__s II  & &  & &   )#77V=R=R`p f//0 IIf88!QUV r!rc|j|}|j|}|j|}|j|}|Sr)r.r r/r0rs r"rHzSegGptDecoderHead.forward"s@ -0 }5  ]3  -0 r!)rrrr-rrrHrIrJs@r"r*r*s WU%6%6r!r*cveZdZfdZdej dej fdZdej fdZxZS) SegGptDecodercBt|tj|jt |j z|jdz|jzd|_ t||_ |j|_|j|_||_ y)Nr?Tr) r,r-rrr1lenrr/r- decoder_embedr* decoder_predr:rs r"r-zSegGptDecoder.__init__,s YY   V%M%M!N N   q 6#=#= =  .f5 ++#)#=#=  r!rrMc|j\}}}}|j||||j|j|j}|j dddddd}|j|d||jz||jzf}|S) Nrrrr?rr`r@)r@rir/r-rB)r9rrDr{r|r}s r"_reshape_hidden_statesz$SegGptDecoder._reshape_hidden_states8s3@3F3F0 L+q%--  k4??DOOUYUmUm &--aAq!Q? %--r<$//#A;QUQ`Q`C`a. r!cl|j|}|j|}|j|}|Sr)r6r;r7rs r"rHzSegGptDecoder.forwardDs8**=9 33MB ))-8 r!) rrrr-rrr;rHrIrJs@r"r3r3+s9  E4E4E %J[J[ U%6%6r!r3c|eZdZUeed<dZdZdZdZddgZ e jde jd d fd Zy ) SegGptPreTrainedModelr:modelrC)imageTrLrmodulerMNc:|jj}t|tjtj frOt j|jd||j t j|jyyt|tjtfr?t j|jt j|jyt|trEt j|jd|t j|j d|yt|t"rt j|j$d|t j&|j(|t j&|j*|t j&|j,|t j&|j.|t j&|j0|yy)zInitialize the weightsr)rstdN)rC)r:initializer_ranger2rrr7init trunc_normal_weightrzeros_rrones_rrrrLrYnormal_rQrRrSrTrU)r9rArCs r" _init_weightsz#SegGptPreTrainedModel._init_weightsUsXkk++ fryy"))4 5   v}}3C @{{& FKK('  ? @ KK $ JJv}} %  0   v//cs C   v//cs C  0 1   v99 M LL** 4 LL33 = LL44# > LL33 = LL33 = 2r!)rrrrrbase_model_prefixmain_input_nameinput_modalitiessupports_gradient_checkpointing_no_split_modulesrno_gradrModulerKr r!r"r>r>LsV $O!&*#+];U]]_>BII>$>>r!r>ceZdZdeffd ZdefdZe ddejdejdejd ejdzd e dzd e dzd ejdzd e dzde dzde dzdeezfdZxZS) SegGptModelr:ct||||_t||_t ||_|jyr)r,r-r:rLrGrencoder post_initrs r"r-zSegGptModel.__init__ns;   *62$V,  r!rMc.|jjSr)rGrVrs r"get_input_embeddingsz SegGptModel.get_input_embeddingsxs///r!NrCrm prompt_masksrnrrolabelsrrrc  ||n|jj}| | n|jj} | | n|jj} ||nd}|jj j jj} |j| }|j| }tj||fd}|tj||fdntj||fd}||tjd||jj j} tj| dztj |j"}tj$| | dzz tj |j"}tj||g}|j'd}|j ||||}|j)|||| | }|S) ax prompt_pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Prompt pixel values. Prompt pixel values can be obtained using [`AutoImageProcessor`]. See [`SegGptImageProcessor.__call__`] for details. prompt_masks (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Prompt mask. Prompt mask can be obtained using [`AutoImageProcessor`]. See [`SegGptImageProcessor.__call__`] for details. bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`, *optional*): Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). feature_ensemble (`bool`, *optional*): Boolean indicating whether to use feature ensemble or not. If `True`, the model will use feature ensemble if we have at least two prompts. If `False`, the model will not use feature ensemble. This argument should be considered when doing few-shot inference on an input image i.e. more than one prompt for the same image. embedding_type (`str`, *optional*): Embedding type. Indicates whether the prompt is a semantic or instance embedding. Can be either instance or semantic. labels (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`, `optional`): Ground truth mask for input images. Examples: ```python >>> from transformers import SegGptImageProcessor, SegGptModel >>> from PIL import Image >>> import httpx >>> from io import BytesIO >>> image_input_url = "https://raw.githubusercontent.com/baaivision/Painter/main/SegGPT/SegGPT_inference/examples/hmbb_2.jpg" >>> image_prompt_url = "https://raw.githubusercontent.com/baaivision/Painter/main/SegGPT/SegGPT_inference/examples/hmbb_1.jpg" >>> mask_prompt_url = "https://raw.githubusercontent.com/baaivision/Painter/main/SegGPT/SegGPT_inference/examples/hmbb_1_target.png" >>> with httpx.stream("GET", image_input_url) as response: ... image_input = Image.open(BytesIO(response.read())) >>> with httpx.stream("GET", image_prompt_url) as response: ... image_prompt = Image.open(BytesIO(response.read())) >>> with httpx.stream("GET", mask_prompt_url) as response: ... mask_prompt = Image.open(BytesIO(response.read())).convert("L") >>> checkpoint = "BAAI/seggpt-vit-large" >>> model = SegGptModel.from_pretrained(checkpoint) >>> image_processor = SegGptImageProcessor.from_pretrained(checkpoint) >>> inputs = image_processor(images=image_input, prompt_images=image_prompt, prompt_masks=mask_prompt, return_tensors="pt") >>> outputs = model(**inputs) >>> list(outputs.last_hidden_state.shape) [1, 56, 28, 1024] ``` Fr?rszLabels were provided, but bool_masked_pos were not. It will be set to default value. If you're training the model, make sure to provide a bool_masked_pos.rr)rorn)rrrr)r:rruse_return_dictrGrVr8rGrrrrxlogger warning_oncer6rPrronesrvrV)r9rCrmrZrnrror[rrrr&expected_dtyper6bool_masked_pos_zerosbool_masked_pos_onesembedding_outputencoder_outputss r"rHzSegGptModel.forward{sD2C1N-TXT_T_TqTq$8$D $++JjJj &1%@T(UVO-77:O?? -n^m+ ,, -/!5# ' r!NNNNNNN)rrrrr-r(rYr rrrrrrrrrHrIrJs@r"rTrTls|0&;0 48(,%)+/)-,0#'rllr#\\rll r ))D0 r + rd r!!D(r $;r#TkrD[r $ $rrr!rTtensorr/c|j\}}}}||z}||z}|j||||||f}|jdddddd}|j|||z|dzdzf}|S)Nr:rr?rrr9r)r@rirB)rgr/rDr0rErFr{r|s r"patchifyris.4ll+J feZ'L:%K ^^:|\:Wbdn"o^ pF ^^Aq!Q1 -F ^^:|k/I:WX=[\K\"]^ ^F Mr!r{r|c b|jd}t|jddz dz}||z|jdk7r"td|jdd|d|d |j|||||df }|j dd ddd d }|j|d||z||zf }|S)Nrr`rr_rzNumber of patches z does not match patch height (z ) and width (r>r:r9r?r)r@rrArirB)rgr{r|rDr/s r" unpatchifyrksaJfll2&*s23Jk!V\\!_4 a 11OP\~]jkvjwwy z  ^^:|[*V`bc"d^ eF ^^Aq!Q1 -F ^^:q,2K[[eMe"f^ gF Mr!ceZdZfdZdej dej dej dej fdZxZS) SegGptLosscft||j|_|j|_yr)r,r-betar/rs r"r-zSegGptLoss.__init__ s& KK  ++r!rZr&r[rnctj||fd}|dddddfjdd|jdzdz}t ||j d|jz|j d|jz}t j||d|j}||zj|jz }|S)aNComputes the L1 loss between the predicted masks and the ground truth masks. Args: prompt_masks (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values from mask prompt. pred_masks (`torch.FloatTensor` of shape `(batch_size, num_channels, 2*height, width)`): Predicted masks. labels (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Ground truth mask for input images. bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`): Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). Returns: `torch.FloatTensor`: The mean L1 loss between the predicted masks and the ground truth masks. r?rsNrrnone) reductionro) rrxrepeatr/rkr@rgsmooth_l1_lossrosum)r9rZr&r[rn ground_truthmaskr%s r"rHzSegGptLoss.forwards2yy,!7Q? q!Tz*11!Q8JQ8NO$ 2 21 5 H,J\J\]^J_cgcrcrJrs LFQUQZQZ[t   "TXXZ/ r!) rrrr-rrrrHrIrJs@r"rmrm sK, !''!%%!!! ! )) !r!rmzM SegGpt model with a decoder on top for one-shot image segmentation. ceZdZdeffd Ze ddejdejdejdejdzde dzd e dzd ejdzd e dzd e dzd e dzde e zfdZxZS)SegGptForImageSegmentationr:ct||||_t||_t ||_|jyr)r,r-r:rTr?r3decoderrWrs r"r-z#SegGptForImageSegmentation.__init__<s;    ( $V,  r!NrCrmrZrnrror[rrrrMc ||n|jj}| | n|jj} | | n|jj} ||jj j j} tj| dztj|j} tj| | dzz tj|j}tj| |g}|jd}|j ||||||||| |  }| r |jn|d}tj|d}|j!|}d}| t#|j}|||||}| s)|f}| r ||dfz}|r| rdnd}|||fz}||f|z}|St%|||j&|j( S) a prompt_pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Prompt pixel values. Prompt pixel values can be obtained using [`AutoImageProcessor`]. See [`SegGptImageProcessor.__call__`] for details. prompt_masks (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Prompt mask. Prompt mask can be obtained using [`AutoImageProcessor`]. See [`SegGptImageProcessor.__call__`] for details. bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`, *optional*): Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). feature_ensemble (`bool`, *optional*): Boolean indicating whether to use feature ensemble or not. If `True`, the model will use feature ensemble if we have at least two prompts. If `False`, the model will not use feature ensemble. This argument should be considered when doing few-shot inference on an input image i.e. more than one prompt for the same image. embedding_type (`str`, *optional*): Embedding type. Indicates whether the prompt is a semantic or instance embedding. Can be either instance or semantic. labels (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`, `optional`): Ground truth mask for input images. Examples: ```python >>> from transformers import SegGptImageProcessor, SegGptForImageSegmentation >>> from PIL import Image >>> import httpx >>> from io import BytesIO >>> image_input_url = "https://raw.githubusercontent.com/baaivision/Painter/main/SegGPT/SegGPT_inference/examples/hmbb_2.jpg" >>> image_prompt_url = "https://raw.githubusercontent.com/baaivision/Painter/main/SegGPT/SegGPT_inference/examples/hmbb_1.jpg" >>> mask_prompt_url = "https://raw.githubusercontent.com/baaivision/Painter/main/SegGPT/SegGPT_inference/examples/hmbb_1_target.png" >>> with httpx.stream("GET", image_input_url) as response: ... image_input = Image.open(BytesIO(response.read())) >>> with httpx.stream("GET", image_prompt_url) as response: ... image_prompt = Image.open(BytesIO(response.read())) >>> with httpx.stream("GET", mask_prompt_url) as response: ... mask_prompt = Image.open(BytesIO(response.read())).convert("L") >>> checkpoint = "BAAI/seggpt-vit-large" >>> model = SegGptForImageSegmentation.from_pretrained(checkpoint) >>> image_processor = SegGptImageProcessor.from_pretrained(checkpoint) >>> inputs = image_processor(images=image_input, prompt_images=image_prompt, prompt_masks=mask_prompt, return_tensors="pt") >>> outputs = model(**inputs) >>> result = image_processor.post_process_semantic_segmentation(outputs, target_sizes=[(image_input.height, image_input.width)])[0] >>> print(list(result.shape)) [170, 297] ``` Nr?rr) rCrmrZrnrror[rrrr`rsr)r%r&rr)r:rrr]r?rGrVr6rrPrrr`rxrvrr{rmr$rr)r9rCrmrZrnrror[rrrr&r6rbrcrrr&r%loss_fnridxs r"rHz"SegGptForImageSegmentation.forwardFsD2C1N-TXT_T_TqTq$8$D $++JjJj &1%@T(UVO-77:O**% 3%+-)/!5#  LWW%G%G\cdf\g"%*YY/Ir%R"\\"<=    -G<V_MD ]F#71:-/ /aQ73ry)rF)7rcollections.abcr3 dataclassesrrrtorch.nnrrgrrE activationsrmodeling_layersr modeling_utilsr utilsr r r rconfiguration_seggptr get_loggerrr^rr$rRr(rLrrrrrrrrrrrr*r3r>rTrrirkrmry__all__r r!r"rs;! $&!9-DD.   H %  G+G  G,  7K7  7* BII FRryyRjK4biiK4^   U\\ e T V[VbVb  %RYY %,,,^9 BII9 zbll4 0BIIB>O>>>A'AAH U\\ s u||  u|| 3 S U\\ ''T D !6D  D N Qr!