
    <h}\                     d   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$J%r%J&r&J'r'  SSK(J)r)  \" S5       " S S\RT                  5      5       r+ " S S\RT                  5      r,S r-S:S jr.S\R^                  S\0S\R^                  4S jr1 S;S\RT                  S\R^                  S \R^                  S!\R^                  S"\\R^                     S#\2S$\2S%\#\%   4S& jjr3 " S' S(\RT                  5      r4 " S) S*\RT                  5      r5 " S+ S,\5      r6\& " S- S.\!5      5       r7\& " S/ S0\75      5       r8\& " S1 S2\7\5      5       r9 " S3 S4\\75      r: " S5 S6\\75      r; " S7 S8\\75      r</ S9Q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   )Exaone4ConfigRMSNormc                   8   ^  \ rS rSrSU 4S jjrS rS rSrU =r$ )Exaone4RMSNorm1   c                    > [         TU ]  5         [        R                  " [        R
                  " U5      5      U l        X l        g)z-
Exaone4RMSNorm is equivalent to T5LayerNorm
N)super__init__r   	Parametertorchonesweightvariance_epsilon)selfhidden_sizeeps	__class__s      d/var/www/html/shao/venv/lib/python3.13/site-packages/transformers/models/exaone4/modeling_exaone4.pyr&   Exaone4RMSNorm.__init__3   s/     	ll5::k#:; #    c                    UR                   nUR                  [        R                  5      nUR	                  S5      R                  SSS9nU[        R                  " X0R                  -   5      -  nU R                  UR                  U5      -  $ )N   T)keepdim)	dtypetor(   float32powmeanrsqrtr+   r*   )r,   hidden_statesinput_dtypevariances       r0   forwardExaone4RMSNorm.forward;   sw    #))%((7 $$Q',,R,>%H?T?T4T(UU{{]--k:::r2   c                 ^    [        U R                  R                  5       SU R                   3$ )Nz, eps=)tupler*   shaper+   r,   s    r0   
extra_reprExaone4RMSNorm.extra_reprB   s*    ))*+6$2G2G1HIIr2   )r+   r*   )gư>)	__name__
__module____qualname____firstlineno__r&   r@   rF   __static_attributes____classcell__r/   s   @r0   r"   r"   1   s    $;J Jr2   r"   c                   l   ^  \ rS rSrSS\4U 4S jjjr\R                  " 5       \S 5       5       r	Sr
U =r$ )Exaone4RotaryEmbeddingF   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defaultinv_freqF)
persistent)r%   r&   hasattr
isinstancerT   dictgetrU   max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenrR   r   rope_init_fnattention_scalingregister_bufferrX   original_inv_freq)r,   rR   devicerX   r/   s       r0   r&   Exaone4RotaryEmbedding.__init__G   s    6>**z&:M:Mt/T/T#0044[&BUBUBYBYZ`BabDN&DN"("@"@$*$B$B!/?+/+<+<T[[&+Q((ZeD!%r2   c                 b   U R                   S S S 2S 4   R                  5       R                  UR                  S   SS5      R	                  UR
                  5      nUS S 2S S S 24   R                  5       n[        UR
                  R                  [        5      (       a0  UR
                  R                  S:w  a  UR
                  R                  OSn[        R                  " USS9   UR                  5       UR                  5       -  R                  SS5      n[        R                  " Xf4SS	9nUR                  5       U R                  -  nUR                  5       U R                  -  n	S S S 5        WR	                  UR                   S
9W	R	                  UR                   S
94$ ! , (       d  f       N@= f)Nr   r5   r   mpscpuF)device_typeenabledr4   dim)r7   )rX   floatexpandrD   r8   re   r[   rV   strr(   autocast	transposecatcosrb   sinr7   )
r,   xposition_idsinv_freq_expandedposition_ids_expandedrj   freqsembrt   ru   s
             r0   r@   Exaone4RotaryEmbedding.forwardX   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   $BF  
F.)rb   rR   r_   rd   r`   ra   rU   N)rH   rI   rJ   rK   r   r&   r(   no_gradr   r@   rL   rM   rN   s   @r0   rP   rP   F   s6    /} / /" ]]_<  <r2   rP   c                     U SSU R                   S   S-  24   nU SU R                   S   S-  S24   n[        R                  " U* U4SS9$ )z*Rotates half the hidden dims of the input..Nr5   r4   rl   )rD   r(   rs   )rv   x1x2s      r0   rotate_halfr   h   sZ    	
3"!''"+"""	#B	
3q ""	#B99rc2YB''r2   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           r0   apply_rotary_pos_embr   o   sS    ( --
&C
--
&Cw;q>C/0Gw;q>C/0Gr2   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)rD   ro   reshape)r=   r   batchnum_key_value_headsslenhead_dims         r0   	repeat_kvr      s_    
 2?1D1D.Ez!!Qa"23::5W\dlmM  e(CTTTr2   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$ )Nr4   r   r5   )rm   r7   )ptrainingr   )r   num_key_value_groupsr(   matmulrr   rD   r   
functionalsoftmaxr9   r8   r7   r   r   
contiguous)r   r   r   r   r   r   r   r   
key_statesvalue_statesattn_weightscausal_maskattn_outputs                r0   eager_attention_forwardr      s     3 ; ;<JU$?$?@L<<';';Aq'ABWLL!$Q1.D
0@0@0D.D%DE#1==((2U]](SVVW\WbWbcL==((6??([L,,|:K''1-88:K$$r2   c                   D  ^  \ rS rSrS\S\4U 4S jjr   SS\R                  S\	\R                  \R                  4   S\
\R                     S\
\   S	\
\R                     S
\\   S\	\R                  \
\R                     \
\	\R                        4   4S jjrSrU =r$ )Exaone4Attention   rR   	layer_idxc                 d  > [         TU ]  5         Xl        X l        UR                  U l        UR
                  U l        UR                  U l        [        USUR                  UR                  -  5      U l        UR                  UR
                  -  U l	        UR                  U l
        SU l        U R                  S-  U l        UR                  U l        UR                  U l        UR                  U   S:H  U l        ["        R$                  " 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&   rR   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,   rR   r   r/   s      r0   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r2   r=   position_embeddingsr   past_key_value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$                  (       d  SOU R&                  U R(                  U R                  (       a  U R                  OS S.UD6u  nnUR*                  " / UQSP76 R-                  5       nU R/                  U5      nUU4$ )Nr5   r   r4   r   eager        )r   r   r   )rD   r   r   viewrr   r   r   r   r   r   r   r   updater   r   rR   _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                     r0   r@   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 (6'<'<ZW[WeWegs't$J(?;;++w6"9$++:Z:Z"[$7
%
  $}}C$2H2HLL26//4..t
%
 
%
!\ "));;;;FFHkk+.L((r2   )r   rR   r   r-   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   )NNN)rH   rI   rJ   rK   r   intr&   r(   TensorrC   r   r
   
LongTensorr   r   r@   rL   rM   rN   s   @r0   r   r      s    M} M M8 26*.591)||1) #5<<#=>1) !.	1)
 !1) !!1!121) +,1) 
u||Xell3XeELL>Q5RR	S1) 1)r2   r   c                   .   ^  \ rS rSrU 4S jrS rSrU =r$ )
Exaone4MLP   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&   rR   r-   intermediate_sizer   r   	gate_projup_proj	down_projr	   
hidden_actact_fnr,   rR   r/   s     r0   r&   Exaone4MLP.__init__   s    !--!'!9!94#3#3T5K5KRWXyy!1!143I3IPUV4#9#94;K;KRWXV../r2   c                     U R                  U R                  U R                  U5      5      U R                  U5      -  5      nU$ r}   )r   r   r   r   )r,   rv   r   s      r0   r@   Exaone4MLP.forward  s6    NN4;;t~~a/@#ADLLQRO#ST	r2   )r   rR   r   r   r-   r   r   )rH   rI   rJ   rK   r&   r@   rL   rM   rN   s   @r0   r   r      s    0 r2   r   c                   t  ^  \ rS rSrS\S\4U 4S jjr      SS\R                  S\	\R                     S\	\R                     S\	\   S	\	\   S
\	\R                     S\	\\R                  \R                  4      S\\   S\\R                   \	\\R                   \R                   4      4   4S jjrSrU =r$ )Exaone4DecoderLayeri  rR   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)rR   r   r   )r%   r&   r-   r   	self_attnr   mlpr"   r   post_attention_layernormpost_feedforward_layernormr   s      r0   r&   Exaone4DecoderLayer.__init__  sk    !--)Mf%(6v7I7IvObOb(c%*89K9KQWQdQd*e'r2   r=   r   rw   r   	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$ )N)r=   r   rw   r   r   r   r    )r   r   r   r   )r,   r=   r   rw   r   r   r   r   r   residual_s              r0   r@   Exaone4DecoderLayer.forward  s     !>> 	
')%)) 3	
 	
 55mD 0 !/77F 0r2   )r-   r   r   r   r   )NNNFNN)rH   rI   rJ   rK   r   r   r&   r(   r   r   r   r
   boolrC   r   r   FloatTensorr@   rL   rM   rN   s   @r0   r   r     s   f} f f 2637*.$)59KO|| !. u//0	
 ! D> !!1!12 &eELL%,,,F&GH +, 
u  (51B1BEDUDU1U+V"WW	X r2   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	)
Exaone4PreTrainedModeli8  rR   modelTr   past_key_values)r=   
attentionsr   N)rH   rI   rJ   rK   r   __annotations__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   r2   r0   r   r   8  sX    &*#./#4"5N!"&,& !Lr2   r   c                     ^  \ rS rSrS\4U 4S jjr\       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$ )Exaone4ModeliL  rR   c           	        > [         TU ]  U5        UR                  U l        UR                  U l        [
        R                  " UR                  UR                  U R                  5      U l        [
        R                  " [        UR                  5       Vs/ sH  n[        X5      PM     sn5      U l        [        UR                  UR                  S9U l        [#        US9U l        SU l        U R)                  5         g s  snf )Nr   )rR   F)r%   r&   pad_token_idpadding_idx
vocab_sizer   	Embeddingr-   embed_tokens
ModuleListrangenum_hidden_layersr   layersr"   r   normrP   
rotary_embgradient_checkpointing	post_initr   s      r0   r&   Exaone4Model.__init__N  s     !.. ++LL):):F<N<NPTP`P`ammEJ6KcKcEdeEd	 3Ede
 #6#5#56;N;NO	0?&+# 	 fs   C>	input_idsr   rw   r   inputs_embedsr   r   r   r   c                    US L US L-  (       a  [        S5      eUc  U R                  U5      nU(       a  Uc
  [        5       nUcD  Ub  UR                  5       OSn	[        R
                  " XUR                  S   -   UR                  S9nUc  UR                  S5      n[        U=n
[        5      (       dH  U R                  UUUUUS.nS[        S
0 UD60n
SU R                  R                  ;   a  [        S
0 UD6U
S'   UnU R                  X5      n[!        U R"                  5       H1  u  pU R                  R                  U   nU" U4UU
U   UUUUS.UD6nM3     U R%                  U5      n['        UU(       a  US	9$ S S	9$ )Nz:You must specify exactly one of input_ids or inputs_embedsr   r   )re   )rR   input_embedsr   r   r   rw   full_attentionr   )r   r   rw   r   r   r   )last_hidden_stater   r   )
ValueErrorr  r   get_seq_lengthr(   arangerD   re   r   r[   r\   rR   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                    r0   r@   Exaone4Model.forward^  s    -t";<YZZ  --i8M0*nO!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)	$72:>).#-	 	M !7 		-0&+/8O
 	
>B
 	
r2   )r  r  r  r  r  r  r  )NNNNNNN)rH   rI   rJ   rK   r   r&   r   r(   r   r   r   r
   r   r   r   r   r   rC   r   r@   rL   rM   rN   s   @r0   r	  r	  L  s    }    '+1537+/59$(59E
##E
 !.E
 u//0	E

 "%E
   1 12E
 D>E
 !!1!12E
 +,E
 
u--	.E
 E
r2   r	  c                   ~  ^  \ rS rSrS/rSS0rSS/S/40rU 4S jrS rS	 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$ )Exaone4ForCausalLMi  zlm_head.weightlm_headcolwise_repr=   logitsc                    > [         TU ]  U5        [        U5      U l        UR                  U l        [
        R                  " UR                  UR                  SS9U l        U R                  5         g r   )
r%   r&   r	  r   r  r   r   r-   r,  r  r   s     r0   r&   Exaone4ForCausalLM.__init__  sU     !&)
 ++yy!3!3V5F5FUS 	r2   c                     Xl         g r}   r   )r,   decoders     r0   set_decoderExaone4ForCausalLM.set_decoder  s    
r2   c                     U R                   $ r}   r2  rE   s    r0   get_decoderExaone4ForCausalLM.get_decoder  s    zzr2   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-Instruct")
>>> tokenizer = AutoTokenizer.from_pretrained("LGAI-EXAONE/EXAONE-4.0-Instruct")

>>> 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"
```

NOTE: `EXAONE-4.0-Instruct` is a placeholder model ID. The exact model ID will be updated in the future.)r  r   rw   r   r  r   r   N)r.  r9  r  )lossr.  r   r=   r   r   )r   r  r[   r   slicer,  loss_functionrR   r  r   r   r=   r   )r,   r  r   rw   r   r  r9  r   r   r:  r   outputsr=   slice_indicesr.  r<  s                   r0   r@   Exaone4ForCausalLM.forward  s    ^ ,0:: 	,
)%+')	,
 	,
  118B>SV8W8W~ot4]kmA}a,?@A%%pVt{{OeOepiopD%#33!//))
 	
r2   )r,  r   r  )	NNNNNNNNr   )rH   rI   rJ   rK   _tied_weights_keys_tp_plan_pp_planr&   r4  r7  r   r   r   r(   r   r   r
   r   r   r   r   r   r   r   r@   rL   rM   rN   s   @r0   r+  r+    sG   *+=)H_-z:;H  151537+/59-1$(5934G
E,,-G
 !.G
 u//0	G

 "%G
   1 12G
 ))*G
 D>G
 !!1!12G
 c5<</0G
 +,G
 
 G
  G
r2   r+  c                       \ rS rSrSrg) Exaone4ForSequenceClassificationi  r   NrH   rI   rJ   rK   rL   r   r2   r0   rF  rF        r2   rF  c                       \ rS rSrSrg)Exaone4ForTokenClassificationi  r   NrG  r   r2   r0   rJ  rJ    rH  r2   rJ  c                       \ rS rSrSrSrg)Exaone4ForQuestionAnsweringi  transformerr   N)rH   rI   rJ   rK   r   rL   r   r2   r0   rL  rL    s    %r2   rL  )r   r	  r+  rF  rJ  rL  )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   configuration_exaone4r   Moduler"   rP   r   r   r   r   r   rn   r   r   r   r   r   r	  r+  rF  rJ  rL  __all__r   r2   r0   <module>r^     s  . - ,   9 ! . ) 7 R  P K F & I I 0 Y'JRYY J (J(<RYY <D(6	UU\\ 	U# 	U%,, 	U& %II%<<% 
% <<	%
 U\\*% % % '(%4J)ryy J)Z  (4 (V !_ ! !& W
) W
 W
t ]
/ ]
 ]
@	'GI_ 		$ACY 	&"=?U &r2   