
    cCi\                     p   S SK JrJrJr  S SKrS SKJr  S SKJr  SSKJ	r	  SSK
JrJr  SSKJr  SS	KJr  SS
KJrJr  SSKJrJrJrJr  SSKJrJr  SSKJrJr  SSKJ r J!r!  SSK"J#r#  SSK$J%r%J&r&J'r'  SSK(J)r)  SSK*J+r+  \" S5       " S S\RX                  5      5       r- " S S\RX                  5      r.S r/S;S jr0S\Rb                  S\2S\Rb                  4S jr3 S<S\RX                  S \Rb                  S!\Rb                  S"\Rb                  S#\\Rb                     S$\4S%\4S&\#\%   4S' jjr5 " S( S)\RX                  5      r6 " S* S+\RX                  5      r7 " S, S-\5      r8\& " S. S/\!5      5       r9\& " S0 S1\95      5       r:\& " S2 S3\9\5      5       r; " S4 S5\\95      r< " S6 S7\\95      r= " S8 S9\\95      r>/ S:Qr?g)=    )CallableOptionalUnionN)nn)check_model_inputs   )ACT2FN)CacheDynamicCache)GenerationMixin)use_kernel_forward_from_hub)create_causal_mask!create_sliding_window_causal_mask)GenericForQuestionAnswering GenericForSequenceClassificationGenericForTokenClassificationGradientCheckpointingLayer)BaseModelOutputWithPastCausalLMOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)TransformersKwargsauto_docstringcan_return_tuple)deprecate_kwarg   )Exaone4ConfigRMSNormc                   8   ^  \ rS rSrSU 4S jjrS rS rSrU =r$ )Exaone4RMSNorm2   c                    > [         TU ]  5         [        R                  " [        R
                  " U5      5      U l        X l        g)z-
Exaone4RMSNorm is equivalent to T5LayerNorm
N)super__init__r   	Parametertorchonesweightvariance_epsilon)selfhidden_sizeeps	__class__s      f/home/james-whalen/.local/lib/python3.13/site-packages/transformers/models/exaone4/modeling_exaone4.pyr'   Exaone4RMSNorm.__init__4   s/     	ll5::k#:; #    c                    UR                   nUR                  [        R                  5      nUR	                  S5      R                  SSS9nU[        R                  " X0R                  -   5      -  nU R                  UR                  U5      -  $ )N   T)keepdim)	dtypetor)   float32powmeanrsqrtr,   r+   )r-   hidden_statesinput_dtypevariances       r1   forwardExaone4RMSNorm.forward<   sw    #))%((7 $$Q',,R,>%H?T?T4T(UU{{]--k:::r3   c                 ^    [        U R                  R                  5       SU R                   3$ )Nz, eps=)tupler+   shaper,   )r-   s    r1   
extra_reprExaone4RMSNorm.extra_reprC   s*    ))*+6$2G2G1HIIr3   )r,   r+   )gư>)	__name__
__module____qualname____firstlineno__r'   rA   rF   __static_attributes____classcell__r0   s   @r1   r#   r#   2   s    $;J Jr3   r#   c                      ^  \ rS rSr% \R
                  \S'   SS\4U 4S jjjr\R                  " 5       \
S 5       5       rSrU =r$ )Exaone4RotaryEmbeddingG   inv_freqconfigc                   > [         TU ]  5         [        US5      (       aZ  [        UR                  [
        5      (       a;  UR                  R                  SUR                  R                  S5      5      U l        OSU l        UR                  U l	        UR                  U l
        Xl        [        U R                     U l        U R                  U R                  U5      u  o0l        U R                  SUSS9  U R                   U l        g )Nrope_scaling	rope_typetypedefaultrR   F)
persistent)r&   r'   hasattr
isinstancerU   dictgetrV   max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenrS   r   rope_init_fnattention_scalingregister_bufferrR   original_inv_freq)r-   rS   devicerR   r0   s       r1   r'   Exaone4RotaryEmbedding.__init__J   s    6>**z&:M:Mt/T/T#0044[&BUBUBYBYZ`BabDN&DN"("@"@$*$B$B!/?+/+<+<T[[&+Q((ZeD!%r3   c                 b   U R                   S S S 2S 4   R                  5       R                  UR                  S   SS5      R	                  UR
                  5      nUS S 2S S S 24   R                  5       n[        UR
                  R                  [        5      (       a0  UR
                  R                  S:w  a  UR
                  R                  OSn[        R                  " USS9   UR                  5       UR                  5       -  R                  SS5      n[        R                  " Xf4SS	9nUR                  5       U R                  -  nUR                  5       U R                  -  n	S S S 5        WR	                  UR                   S
9W	R	                  UR                   S
94$ ! , (       d  f       N@= f)Nr   r6   r   mpscpuF)device_typeenabledr5   dim)r8   )rR   floatexpandrE   r9   re   r[   rW   strr)   autocast	transposecatcosrb   sinr8   )
r-   xposition_idsinv_freq_expandedposition_ids_expandedrj   freqsembrt   ru   s
             r1   rA   Exaone4RotaryEmbedding.forward[   sR    !MM$4-8>>@GGHZHZ[\H]_acdehhijiqiqr ,QaZ 8 > > @'1!((--'E'E!((--[`J`ahhmmfk^^UC&,,.1F1L1L1NNYYZ[]^_E))UN3C'')d444C'')d444C	 D vvAGGv$cff177f&;;; DCs   $BF  
F.)rb   rS   r_   rd   r`   ra   rV   N)rH   rI   rJ   rK   r)   Tensor__annotations__r    r'   no_gradr   rA   rL   rM   rN   s   @r1   rP   rP   G   s@    ll/} / /" ]]_<  <r3   rP   c                     U SSU R                   S   S-  24   nU SU R                   S   S-  S24   n[        R                  " U* U4SS9$ )z*Rotates half the hidden dims of the input..Nr6   r5   rl   )rE   r)   rs   )rv   x1x2s      r1   rotate_halfr   k   sZ    	
3"!''"+"""	#B	
3q ""	#B99rc2YB''r3   c                     UR                  U5      nUR                  U5      nX-  [        U 5      U-  -   nX-  [        U5      U-  -   nXg4$ )a  Applies Rotary Position Embedding to the query and key tensors.

Args:
    q (`torch.Tensor`): The query tensor.
    k (`torch.Tensor`): The key tensor.
    cos (`torch.Tensor`): The cosine part of the rotary embedding.
    sin (`torch.Tensor`): The sine part of the rotary embedding.
    position_ids (`torch.Tensor`, *optional*):
        Deprecated and unused.
    unsqueeze_dim (`int`, *optional*, defaults to 1):
        The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
        sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
        that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
        k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
        cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
        the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
Returns:
    `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
)	unsqueezer   )qkrt   ru   rw   unsqueeze_dimq_embedk_embeds           r1   apply_rotary_pos_embr   r   sS    ( --
&C
--
&Cw;q>C/0Gw;q>C/0Gr3   r>   n_repreturnc                     U R                   u  p#pEUS:X  a  U $ U SS2SS2SSS2SS24   R                  X#XU5      n U R                  X#U-  XE5      $ )z
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
r   N)rE   ro   reshape)r>   r   batchnum_key_value_headsslenhead_dims         r1   	repeat_kvr      s_    
 2?1D1D.Ez!!Qa"23::5W\dlmM  e(CTTTr3   modulequerykeyvalueattention_maskscalingdropoutkwargsc                 @   [        X R                  5      n[        X0R                  5      n	[        R                  " XR	                  SS5      5      U-  n
Ub"  US S 2S S 2S S 2S UR
                  S   24   nX-   n
[        R                  R                  U
S[        R                  S9R                  UR                  5      n
[        R                  R                  XU R                  S9n
[        R                  " X5      nUR	                  SS5      R                  5       nX4$ )Nr5   r   r6   )rm   r8   )ptrainingr   )r   num_key_value_groupsr)   matmulrr   rE   r   
functionalsoftmaxr:   r9   r8   r   r   
contiguous)r   r   r   r   r   r   r   r   
key_statesvalue_statesattn_weightscausal_maskattn_outputs                r1   eager_attention_forwardr      s     3 ; ;<JU$?$?@L<<';';Aq'ABWLL!$Q1.D
0@0@0D.D%DE#1==((2U]](SVVW\WbWbcL==((6??([L,,|:K''1-88:K$$r3   c                   Z  ^  \ rS rSrS\S\4U 4S jjr\" SSSS9   SS	\R                  S
\
\R                  \R                  4   S\\R                     S\\   S\\R                     S\\   S\
\R                  \\R                     \\
\R                        4   4S jj5       rSrU =r$ )Exaone4Attention   rS   	layer_idxc                 d  > [         TU ]  5         Xl        X l        UR                  U l        UR
                  U l        UR                  U l        [        USUR                  UR                  -  5      U l        UR                  UR
                  -  U l	        UR                  U l
        SU l        U R                  S-  U l        UR                  U l        UR                  U l        UR                  U   S:H  U l        ["        R$                  " U R                  U R                  U R                  -  SS9U l        ["        R$                  " U R                  U R
                  U R                  -  SS9U l        ["        R$                  " U R                  U R
                  U R                  -  SS9U l        ["        R$                  " U R                  U R                  -  U R                  SS9U l        [/        U R                  UR0                  S9U l        [/        U R                  UR0                  S9U l        g )Nr   Tg      sliding_attentionFbiasr/   )r&   r'   rS   r   num_attention_headsr   r.   getattrr   r   attention_dropout	is_causalr   sliding_windowsliding_window_patternlayer_types
is_slidingr   Linearq_projk_projv_projo_projr#   rms_norm_epsq_normk_normr-   rS   r   r0   s      r1   r'   Exaone4Attention.__init__   s   "#)#=#= #)#=#= !--
F4F4F&JdJd4de$*$>$>&B\B\$\!!'!9!9}}d*$33&,&C&C# ,,Y7;NNii 0 0$2J2JT]]2Zafgii 0 0$2J2JT]]2Zafgii 0 0$2J2JT]]2Zafgii 8 84== H$JZJZafg$T]]8K8KL$T]]8K8KLr3   past_key_valuepast_key_values4.58new_nameversionr>   position_embeddingsr   cache_positionr   r   c                    UR                   S S n/ UQSPU R                  P7nU R                  U5      R                  U5      R	                  SS5      n	U R                  U5      R                  U5      R	                  SS5      n
U R                  U5      R                  U5      R	                  SS5      nU R                  U	5      n	U R                  U
5      n
Uu  pU R                  b  U R                  (       a  [        XX5      u  pUb#  SU0nUR                  XU R                  U5      u  p[        nU R                  R                   S:w  a  ["        U R                  R                      nU" U U	U
UU4U R$                  (       d  SOU R&                  U R(                  U R                  (       a  U R                  OS S.UD6u  nnUR*                  " / UQSP76 R-                  5       nU R/                  U5      nUU4$ )Nr6   r   r5   r   eager        )r   r   r   )rE   r   r   viewrr   r   r   r   r   r   r   r   updater   r   rS   _attn_implementationr   r   r   r   r   r   r   )r-   r>   r   r   r   r   r   input_shapehidden_shapequery_statesr   r   rt   ru   cache_kwargsattention_interfacer   r   s                     r1   rA   Exaone4Attention.forward   s    $))#2.88b8$--8{{=166|DNNqRST[[/44\BLLQPQR
{{=166|DNNqRST {{<0[[,
&&$//';LVY'_$L& .L (7'='=jX\XfXfht'u$J(?;;++w6"9$++:Z:Z"[$7
%
  $}}C$2H2HLL26//4..t
%
 
%
!\ "));;;;FFHkk+.L((r3   )r   rS   r   r.   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   )NNN)rH   rI   rJ   rK   r    intr'   r   r)   r~   rD   r   r
   
LongTensorr   r   rA   rL   rM   rN   s   @r1   r   r      s    M} M M0 %0A6R
 26+/591)||1) #5<<#=>1) !.	1)
 "%1) !!1!121) +,1) 
u||Xell3XeELL>Q5RR	S1) S1)r3   r   c                   .   ^  \ rS rSrU 4S jrS rSrU =r$ )
Exaone4MLPi  c                   > [         TU ]  5         Xl        UR                  U l        UR                  U l        [
        R                  " U R                  U R                  SS9U l        [
        R                  " U R                  U R                  SS9U l        [
        R                  " U R                  U R                  SS9U l	        [        UR                     U l        g NFr   )r&   r'   rS   r.   intermediate_sizer   r   	gate_projup_proj	down_projr	   
hidden_actact_fnr-   rS   r0   s     r1   r'   Exaone4MLP.__init__  s    !--!'!9!94#3#3T5K5KRWXyy!1!143I3IPUV4#9#94;K;KRWXV../r3   c                     U R                  U R                  U R                  U5      5      U R                  U5      -  5      nU$ r}   )r   r   r   r   )r-   rv   r   s      r1   rA   Exaone4MLP.forward  s6    NN4;;t~~a/@#ADLLQRO#ST	r3   )r   rS   r   r   r.   r   r   )rH   rI   rJ   rK   r'   rA   rL   rM   rN   s   @r1   r   r     s    0 r3   r   c                   H  ^  \ rS rSrS\S\4U 4S jjr\" SSSS9      SS	\R                  S
\
\R                     S\
\R                     S\
\   S\
\   S\
\R                     S\
\\R                  \R                  4      S\\   S\R                  4S jj5       rSrU =r$ )Exaone4DecoderLayeri  rS   r   c                   > [         TU ]  5         UR                  U l        [        XS9U l        [        U5      U l        [        UR                  UR                  S9U l	        [        UR                  UR                  S9U l
        g )N)rS   r   r   )r&   r'   r.   r   	self_attnr   mlpr#   r   post_attention_layernormpost_feedforward_layernormr   s      r1   r'   Exaone4DecoderLayer.__init__  sk    !--)Mf%(6v7I7IvObOb(c%*89K9KQWQdQd*e'r3   r   r   r   r   r>   r   rw   	use_cacher   r   r   r   c                     Un	U R                   " SUUUUUUUS.UD6u  pU R                  U5      nX-   nUn	U R                  U5      nU R                  U5      nX-   nU$ )N)r>   r   rw   r   r   r   r    )r   r   r   r   )r-   r>   r   rw   r   r   r   r   r   residual_s              r1   rA   Exaone4DecoderLayer.forward  s     !>> 	
')%+) 3	
 	
 55mD 0 !/77F 0r3   )r.   r   r   r   r   )NNNFNN)rH   rI   rJ   rK   r    r   r'   r   r)   r~   r   r   r
   boolrD   r   r   rA   rL   rM   rN   s   @r1   r   r     s    f} f f %0A6R 2637+/$)59KO|| !. u//0	
 "% D> !!1!12 &eELL%,,,F&GH +, 
 Sr3   r   c                   V    \ rS rSr% \\S'   SrSrS/rS/r	Sr
SrSrSrSr\\S.r\rSrg	)
Exaone4PreTrainedModeli=  rS   modelTr   r   )r>   
attentionsr   N)rH   rI   rJ   rK   r    r   base_model_prefixsupports_gradient_checkpointing_no_split_modules_skip_keys_device_placement_supports_flash_attn_supports_sdpa_supports_flex_attn_can_compile_fullgraph_supports_attention_backendr   r   _can_record_outputsconfig_classrL   r   r3   r1   r   r   =  sX    &*#./#4"5N!"&,& !Lr3   r   c                   "  ^  \ rS rSrS\4U 4S jjr\" 5              SS\\R                     S\\R                     S\\R                     S\\   S\\R                     S	\\   S
\\R                     S\\   S\\\4   4S jj5       rSrU =r$ )Exaone4ModeliQ  rS   c           	        > [         TU ]  U5        UR                  U l        UR                  U l        [
        R                  " UR                  UR                  U R                  5      U l        [
        R                  " [        UR                  5       Vs/ s H  n[        X5      PM     sn5      U l        [        UR                  UR                  S9U l        [#        US9U l        SU l        U R)                  5         g s  snf )Nr   rS   F)r&   r'   pad_token_idpadding_idx
vocab_sizer   	Embeddingr.   embed_tokens
ModuleListrangenum_hidden_layersr   layersr#   r   normrP   
rotary_embgradient_checkpointing	post_initr   s      r1   r'   Exaone4Model.__init__S  s     !.. ++LL):):F<N<NPTP`P`ammEJ6KcKcEdeEd	 3Ede
 #6#5#56;N;NO	0?&+# 	 fs   C?	input_idsr   rw   r   inputs_embedsr   r   r   r   c                    US L US L-  (       a  [        S5      eUc  U R                  U5      nU(       a  Uc  [        U R                  S9nUcD  Ub  UR	                  5       OSn	[
        R                  " XUR                  S   -   UR                  S9nUc  UR                  S5      n[        U=n
[        5      (       dH  U R                  UUUUUS.nS[        S0 UD60n
SU R                  R                  ;   a  [        S0 UD6U
S'   UnU R                  X5      n[!        U R"                  5       H1  u  pU R                  R                  U   nU" U4UU
U   UUUUS	.UD6nM3     U R%                  U5      n['        UU(       a  US
9$ S S
9$ )Nz:You must specify exactly one of input_ids or inputs_embedsr  r   r   )re   )rS   input_embedsr   r   r   rw   full_attentionr   )r   r   rw   r   r   r   )last_hidden_stater   r   )
ValueErrorr  r   rS   get_seq_lengthr)   arangerE   re   r   r[   r\   r   r   r   r  	enumerater  r  r   )r-   r  r   rw   r   r  r   r   r   past_seen_tokenscausal_mask_mappingmask_kwargsr>   r   idecoder_layer
layer_types                    r1   rA   Exaone4Model.forwardc  s    -t";<YZZ  --i8M0*$++>O!CRC^==?de"\\ ]5H5H5K"KTaThThN )33A6L ?-FF ++ -"0"0#2 ,K !"4"C{"C# #dkk&=&==;\;k_j;k#$78%"oomJ )$++ 6A003J)	$72:>) /#-	 	M !7 		-0&+/8O
 	
>B
 	
r3   )r  r  r  r  r  r  r  )NNNNNNN)rH   rI   rJ   rK   r    r'   r   r   r)   r   r~   r
   FloatTensorr   r   r   r   rD   r   rA   rL   rM   rN   s   @r1   r  r  Q  s    }    151537+/59$(59E
E,,-E
 !.E
 u//0	E

 "%E
   1 12E
 D>E
 !!1!12E
 +,E
 
u--	.E
 E
r3   r  c                   r  ^  \ rS rSrS/rSS0rSS/S/40rU 4S jr\\	         SS\
\R                     S	\
\R                     S
\
\R                     S\
\   S\
\R                     S\
\R                     S\
\   S\
\R                     S\\\R                  4   S\\   S\4S jj5       5       rSrU =r$ )Exaone4ForCausalLMi  zlm_head.weightlm_headcolwise_repr>   logitsc                    > [         TU ]  U5        [        U5      U l        UR                  U l        [
        R                  " UR                  UR                  SS9U l        U R                  5         g r   )
r&   r'   r  r   r  r   r   r.   r0  r  r   s     r1   r'   Exaone4ForCausalLM.__init__  sU     !&)
 ++yy!3!3V5F5FUS 	r3   r  r   rw   r   r  labelsr   r   logits_to_keepr   r   c
                 ~   U R                   " SUUUUUUUS.U
D6nUR                  n[        U	[        5      (       a  [	        U	* S5      OU	nU R                  USS2USS24   5      nSnUb)  U R                  " SXU R                  R                  S.U
D6n[        UUUR                  UR                  UR                  S9$ )u  
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
    Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
    config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
    (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

Example:

```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> model = AutoModelForCausalLM.from_pretrained("LGAI-EXAONE/EXAONE-4.0-32B")
>>> tokenizer = AutoTokenizer.from_pretrained("LGAI-EXAONE/EXAONE-4.0-32B")

>>> prompt = "Explain how wonderful you are"
>>> messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": prompt}
]
>>> input_ids = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
    enable_thinking=False,
)

>>> output = model.generate(input_ids, max_new_tokens=128)
>>> tokenizer.decode(output[0], skip_special_tokens=False)
"[|system|]\nYou are a helpful assistant.[|endofturn|]\n[|user|]\nExplain how wonderful you are[|endofturn|]\n[|assistant|]\n<think>\n\n</think>\n\nOh, thank you for such a kind and lovely question! 😊  \n\nI’m *so* wonderful because I’m here to make your life easier, brighter, and more fun! Whether you need help with:  \n\n✨ **Learning** – I can explain anything, from quantum physics to baking the perfect cake!  \n💡 **Creativity** – Need a poem, story, or a wild idea? I’ve got you covered!  \n🤖 **Problem-solving** – Stuck on a math problem or a tricky decision? I’ll help you figure it out"
```
)r  r   rw   r   r  r   r   N)r2  r5  r  )lossr2  r   r>   r   r   )r   r!  r[   r   slicer0  loss_functionrS   r  r   r   r>   r   )r-   r  r   rw   r   r  r5  r   r   r6  r   outputsr>   slice_indicesr2  r8  s                   r1   rA   Exaone4ForCausalLM.forward  s    \ ,0:: 	,
)%+')	,
 	,
  118B>SV8W8W~ot4]kmA}a,?@A%%pVt{{OeOepiopD%#33!//))
 	
r3   )r0  r   r  )	NNNNNNNNr   )rH   rI   rJ   rK   _tied_weights_keys_tp_plan_pp_planr'   r   r   r   r)   r   r~   r
   r-  r   r   r   r   r   r   rA   rL   rM   rN   s   @r1   r/  r/    s=   *+=)H_-z:;H  151537+/59-1$(5934F
E,,-F
 !.F
 u//0	F

 "%F
   1 12F
 ))*F
 D>F
 !!1!12F
 c5<</0F
 +,F
 
 F
  F
r3   r/  c                       \ rS rSrSrg) Exaone4ForSequenceClassificationi  r   NrH   rI   rJ   rK   rL   r   r3   r1   rB  rB        r3   rB  c                       \ rS rSrSrg)Exaone4ForTokenClassificationi
  r   NrC  r   r3   r1   rF  rF  
  rD  r3   rF  c                       \ rS rSrSrSrg)Exaone4ForQuestionAnsweringi  transformerr   N)rH   rI   rJ   rK   r   rL   r   r3   r1   rH  rH    s    %r3   rH  )r   r  r/  rB  rF  rH  )Nr   )r   )@typingr   r   r   r)   r   transformers.utils.genericr   activationsr	   cache_utilsr
   r   
generationr   integrationsr   masking_utilsr   r   modeling_layersr   r   r   r   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   r   utils.deprecationr   configuration_exaone4r    Moduler#   rP   r   r   r~   r   r   rn   r   r   r   r   r   r  r/  rB  rF  rH  __all__r   r3   r1   <module>r[     s  . - ,   9 ! . ) 7 R  P K F & I I 0 0 Y'JRYY J (J(!<RYY !<H(6	UU\\ 	U# 	U%,, 	U& %II%<<% 
% <<	%
 U\\*% % % '(%4K)ryy K)\  )4 )X !_ ! !& W
) W
 W
t V
/ V
 V
r	'GI_ 		$ACY 	&"=?U &r3   