
    cCia                     @   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	J
r
  SSKJ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Jr  SSKJrJr  SSKJr  SSKJrJ r J!r!J"r"  SSK#J$r$  SSK%J&r&  SSK'J(r(  \"RR                  " \*5      r+S r,S5S jr-S\R\                  S\/S\R\                  4S jr0 S6S\Rb                  S\R\                  S\R\                  S\R\                  S\\R\                     S\2S \2S!\\   4S" jjr3 " S# S$\Rb                  5      r4\" S%5       " S& S'\Rb                  5      5       r5 " S( S)\Rb                  5      r6 " S* S+\5      r7\  " S, S-\5      5       r8 " S. S/\Rb                  5      r9\  " S0 S1\85      5       r:\  " S2 S3\8\5      5       r;/ S4Qr<g)7    )CallableOptionalUnionN)nn   )ACT2FN)CacheDynamicCache)GenerationMixin)use_kernel_forward_from_hub)create_causal_mask)GradientCheckpointingLayer)BaseModelOutputWithPastCausalLMOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)TransformersKwargsauto_docstringcan_return_tuplelogging)deprecate_kwarg)check_model_inputs   )GraniteConfigc                     U SSU R                   S   S-  24   nU SU R                   S   S-  S24   n[        R                  " U* U4SS9$ )z*Rotates half the hidden dims of the input..N   dim)shapetorchcat)xx1x2s      f/home/james-whalen/.local/lib/python3.13/site-packages/transformers/models/granite/modeling_granite.pyrotate_halfr*   .   sZ    	
3"!''"+"""	#B	
3q ""	#B99rc2YB''    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kcossinposition_idsunsqueeze_dimq_embedk_embeds           r)   apply_rotary_pos_embr6   5   sS    ( --
&C
--
&Cw;q>C/0Gw;q>C/0Gr+   hidden_states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)r#   expandreshape)r7   r8   batchnum_key_value_headsslenhead_dims         r)   	repeat_kvrA   P   s_    
 2?1D1D.Ez!!Qa"23::5W\dlmM  e(CTTTr+   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$ )Nr    r   r   )r"   dtype)ptrainingr   )rA   num_key_value_groupsr$   matmul	transposer#   r   
functionalsoftmaxfloat32torL   rH   rN   
contiguous)rB   rC   rD   rE   rF   rG   rH   rI   
key_statesvalue_statesattn_weightscausal_maskattn_outputs                r)   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$$r+   c                   >  ^  \ rS rSrSrSS\S\\   4U 4S jjjr\	" SSSS	9  SS
\
R                  S\\
R                  \
R                  4   S\\
R                     S\\   S\\
R                     S\\   S\\
R                  \
R                  4   4S jj5       rSrU =r$ )GraniteAttentionv   z=Multi-headed attention from 'Attention Is All You Need' paperconfig	layer_idxc                 J  > [         TU ]  5         Xl        X l        [	        USUR
                  UR                  -  5      U l        UR                  UR                  -  U l	        UR                  U l        UR                  U l        SU l        [        R                  " UR
                  UR                  U R                  -  UR                   S9U l        [        R                  " UR
                  UR                  U R                  -  UR                   S9U l        [        R                  " UR
                  UR                  U R                  -  UR                   S9U l        [        R                  " UR                  U R                  -  UR
                  UR                   S9U l        g )Nr@   Tbias)super__init__r`   ra   getattrhidden_sizenum_attention_headsr@   r>   rO   attention_multiplierrG   attention_dropout	is_causalr   Linearattention_biasq_projk_projv_projo_projselfr`   ra   	__class__s      r)   rf   GraniteAttention.__init__y   sF   "
F4F4F&JdJd4de$*$>$>&B\B\$\!22!'!9!9ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii&&68J8JQWQfQf
r+   past_key_valuepast_key_values4.58new_nameversionr7   position_embeddingsrF   cache_positionrI   r9   c                 4   UR                   S S n/ UQSPU R                  P7nU R                  U5      R                  U5      R	                  SS5      n	U R                  U5      R                  U5      R	                  SS5      n
U R                  U5      R                  U5      R	                  SS5      nUu  p[        XX5      u  pUb$  XUS.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                   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$ )Nr   r   r    )r1   r0   r~   eager        )rH   rG   )r#   r@   ro   viewrQ   rp   rq   r6   updatera   r\   r`   _attn_implementationr   rN   rk   rG   r<   rV   rr   )rt   r7   r}   rF   rx   r~   rI   input_shapehidden_shapequery_statesrW   rX   r0   r1   cache_kwargsattention_interfacer[   rY   s                     r)   forwardGraniteAttention.forward   s    $))#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&#7RU#[ &#&nUL'6'='=jX\XfXfht'u$J(?;;++w6"9$++:Z:Z"[$7	%
  $}}C$2H2HLL	%
 	%
!\ "));;;;FFHkk+.L((r+   )rk   r`   r@   rl   rp   ra   rO   rr   ro   rG   rq   N)NN)__name__
__module____qualname____firstlineno____doc__r   r   intrf   r   r$   Tensortupler	   
LongTensorr   r   r   __static_attributes____classcell__ru   s   @r)   r^   r^   v   s    G
} 
# 
 
. %0A6R ,059))||)) #5<<#=>)) !.	))
 "%)) !!1!12)) +,)) 
u||U\\)	*)) S))r+   r^   RMSNormc                   8   ^  \ rS rSrSU 4S jjrS rS rSrU =r$ )GraniteRMSNorm   c                    > [         TU ]  5         [        R                  " [        R
                  " U5      5      U l        X l        g)z-
GraniteRMSNorm is equivalent to T5LayerNorm
N)re   rf   r   	Parameterr$   onesweightvariance_epsilon)rt   rh   epsru   s      r)   rf   GraniteRMSNorm.__init__   s/     	ll5::k#:; #r+   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      -  $ )Nr    r   T)keepdim)	rL   rU   r$   rT   powmeanrsqrtr   r   )rt   r7   input_dtypevariances       r)   r   GraniteRMSNorm.forward   sw    #))%((7 $$Q',,R,>%H?T?T4T(UU{{]--k:::r+   c                 ^    [        U R                  R                  5       SU R                   3$ )Nz, eps=)r   r   r#   r   )rt   s    r)   
extra_reprGraniteRMSNorm.extra_repr   s*    ))*+6$2G2G1HIIr+   )r   r   )gư>)	r   r   r   r   rf   r   r   r   r   r   s   @r)   r   r      s    $;J Jr+   r   c                   .   ^  \ rS rSrU 4S jrS rSrU =r$ )
GraniteMLP   c                   > [         TU ]  5         Xl        UR                  U l        UR                  U l        [
        R                  " U R                  U R                  UR                  S9U l        [
        R                  " U R                  U R                  UR                  S9U l	        [
        R                  " U R                  U R                  UR                  S9U l
        [        UR                     U l        g )Nrc   )re   rf   r`   rh   intermediate_sizer   rm   mlp_bias	gate_projup_proj	down_projr   
hidden_actact_fnrt   r`   ru   s     r)   rf   GraniteMLP.__init__   s    !--!'!9!94#3#3T5K5KRXRaRabyy!1!143I3IPVP_P_`4#9#94;K;KRXRaRabV../r+   c                     U R                  U R                  U R                  U5      5      U R                  U5      -  5      nU$ r   )r   r   r   r   )rt   r&   r   s      r)   r   GraniteMLP.forward   s6    NN4;;t~~a/@#ADLLQRO#ST	r+   )r   r`   r   r   rh   r   r   )r   r   r   r   rf   r   r   r   r   s   @r)   r   r      s    0 r+   r   c                     ^  \ 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\
\   S\
\R                     S\
\\R                  \R                  4      S\\R                  \
\\R                  \R                  4      4   4S jj5       rSrU =r$ )GraniteDecoderLayer   r`   ra   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
        UR                  U l        g )N)r`   ra   r   )re   rf   rh   r^   	self_attnr   mlpr   rms_norm_epsinput_layernormpost_attention_layernormresidual_multiplierrs   s      r)   rf   GraniteDecoderLayer.__init__   sx    !--)Mf%-f.@.@fFYFYZ(6v7I7IvObOb(c%#)#=#= r+   rw   rx   ry   rz   r7   rF   r2   output_attentions	use_cacher~   r}   r9   c	                    Un
U R                  U5      nU R                  " SUUUUUUUUS.U	D6u  pXU R                  -  -   nUn
U R                  U5      nU R	                  U5      nXU R                  -  -   nU4nU(       a  X4-  nU$ )at  
Args:
    hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
    attention_mask (`torch.FloatTensor`, *optional*):
        attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
        query_sequence_length, key_sequence_length)` if default attention is used.
    output_attentions (`bool`, *optional*):
        Whether or not to return the attentions tensors of all attention layers. See `attentions` under
        returned tensors for more detail.
    use_cache (`bool`, *optional*):
        If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
        (see `past_key_values`).
    past_key_values (`Cache`, *optional*): cached past key and value projection states
    cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
        Indices depicting the position of the input sequence tokens in the sequence
    position_embeddings (`tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
        Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
        with `head_dim` being the embedding dimension of each attention head.
    kwargs (`dict`, *optional*):
        Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
        into the model
)r7   rF   r2   rx   r   r   r~   r}    )r   r   r   r   r   )rt   r7   rF   r2   rx   r   r   r~   r}   rI   residualself_attn_weightsoutputss                r)   r   GraniteDecoderLayer.forward   s    F !,,]; ,0>> 
,
')%+/) 3
,
 
,
( !43K3K#KK !55mD/ 43K3K#KK "++Gr+   )rh   r   r   r   r   r   )NNNFFNN)r   r   r   r   r   r   rf   r   r$   r   r   r   r	   boolr   FloatTensorr   r   r   r   s   @r)   r   r      s   >} > > %0A6R 2637+/,1$)59KO?||? !.? u//0	?
 "%? $D>? D>? !!1!12? &eELL%,,,F&GH? 
u  (51B1BEDUDU1U+V"WW	X? S?r+   r   c                   R    \ 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Srg	)
GranitePreTrainedModeli0  r`   modelTr   rx   )r7   
attentionsr   N)r   r   r   r   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_outputsr   r   r+   r)   r   r   0  sQ    &*#./#4"5N!"&,&r+   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$ )GraniteRotaryEmbeddingiC  inv_freqr`   c                   > [         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defaultr   F)
persistent)re   rf   hasattr
isinstancer   dictgetr   max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenr`   r   rope_init_fnattention_scalingregister_bufferr   original_inv_freq)rt   r`   devicer   ru   s       r)   rf   GraniteRotaryEmbedding.__init__F  s    6>**z&:M:Mt/T/T#0044[&BUBUBYBYZ`BabDN&DN"("@"@$*$B$B!/?+/+<+<T[[&+Q((ZeD!%r+   c                 b   U R                   S S S 2S 4   R                  5       R                  UR                  S   SS5      R	                  UR
                  5      nUS S 2S S S 24   R                  5       n[        UR
                  R                  [        5      (       a0  UR
                  R                  S:w  a  UR
                  R                  OSn[        R                  " USS9   UR                  5       UR                  5       -  R                  SS5      n[        R                  " Xf4SS	9nUR                  5       U R                  -  nUR                  5       U R                  -  n	S S S 5        WR	                  UR                   S
9W	R	                  UR                   S
94$ ! , (       d  f       N@= f)Nr   r   r   mpscpuF)device_typeenabledr    r!   )rL   )r   floatr;   r#   rU   r   r   r   strr$   autocastrQ   r%   r0   r   r1   rL   )
rt   r&   r2   inv_freq_expandedposition_ids_expandedr   freqsembr0   r1   s
             r)   r   GraniteRotaryEmbedding.forwardW  sR    !MM$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.)r   r`   r   r   r   r   r   r   )r   r   r   r   r$   r   r   r   rf   no_gradr   r   r   r   r   s   @r)   r   r   C  s@    ll/} / /" ]]_<  <r+   r   c                   :  ^  \ rS rSrS\4U 4S jjr\" 5       \         SS\\	R                     S\\	R                     S\\	R                     S\\   S\\	R                     S	\\   S
\\   S\\   S\\	R                     S\\   S\4S jj5       5       rSrU =r$ )GraniteModelig  r`   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(                  U l        U R+                  5         g s  snf )Nr   r`   F)re   rf   pad_token_idpadding_idx
vocab_sizer   	Embeddingrh   embed_tokens
ModuleListrangenum_hidden_layersr   layersr   r   normr   
rotary_embgradient_checkpointingembedding_multiplier	post_initrs   s      r)   rf   GraniteModel.__init__i  s     !.. ++LL):):F<N<NPTP`P`ammEJ6KcKcEdeEd	 3Ede
 #6#5#56;N;NO	0?&+#$*$?$?! 	 fs   D	input_idsrF   r2   rx   inputs_embedsr   r   output_hidden_statesr~   rI   r9   c
                 ,   Ub  UOU R                   R                  nUb  UOU R                   R                  nUb  UOU R                   R                  nUS L US L-  (       a  [	        S5      eU R
                  (       a/  U R                  (       a  U(       a  [        R                  S5        SnUc  U R                  U5      nXPR                  -  nU(       a  Uc  [        U R                   S9nU	cD  Ub  UR                  5       OSn[        R                  " XUR                  S   -   UR                   S9n	Uc  U	R#                  S5      n[%        U R                   UUU	UUS9nUnU R'                  X5      nU(       a  S	OS nU(       a  S	OS nU R(                  S U R                   R*                    H7  nU(       a  X4-  nU" U4UUUUUU	US
.U
D6nUS   nU(       d  M.  UUS   4-  nM9     U R-                  U5      nU(       a  X4-  n[/        UU(       a  UOS UUS9$ )Nz:You must specify exactly one of input_ids or inputs_embedszX`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.Fr
  r   r   )r   )r`   input_embedsrF   r~   rx   r2   r   )rF   r2   rx   r   r   r~   r}   )last_hidden_staterx   r7   r   )r`   r   r  r   
ValueErrorr  rN   loggerwarning_oncer  r  r
   get_seq_lengthr$   aranger#   r   r-   r   r  r  r  r  r   )rt   r  rF   r2   rx   r  r   r   r  r~   rI   past_seen_tokensrZ   r7   r}   all_hidden_statesall_self_attnsdecoder_layerlayer_outputss                      r)   r   GraniteModel.forwardz  sB    2C1N-TXT_T_TqTq$8$D $++JjJj 	 "+!6IDKK<Q<Q	-t";<YZZ&&4==Yj I  --i8M%(A(AA0*$++>O!CRC^==?de"\\ ]5H5H5K"KTaThThN )33A6L(;;&))+%
 & #oomJ #7BD0d![[)H4;;+H+HIM#!%55!)
*) /"3#-$7
 
M *!,M  =#3"55' J* 		-0  !11&+/8Od+%	
 	
r+   )r  r  r  r  r  r  r  r  )	NNNNNNNNN)r   r   r   r   r   rf   r   r   r   r$   r   r   r	   r   r   r   r   r   r   r   r   r   s   @r)   r  r  g  s   } "  151537+/59$(,0/359_
E,,-_
 !._
 u//0	_

 "%_
   1 12_
 D>_
 $D>_
 'tn_
 !!1!12_
 +,_
 
!_
  _
r+   r  c                     ^  \ rS rSrS/rSS0rSS/S/40rU 4S jr\\	           SS\
\R                     S	\
\R                     S
\
\R                     S\
\\\\R"                     4      S\
\R"                     S\
\R                     S\
\   S\
\   S\
\   S\
\R                     S\\\R                  4   S\\   S\4S jj5       5       rSrU =r$ )GraniteForCausalLMi  zlm_head.weightlm_headcolwise_repr7   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 )NFrc   )
re   rf   r  r   r  r   rm   rh   r-  r  r   s     r)   rf   GraniteForCausalLM.__init__  sU     !&)
 ++yy!3!3V5F5FUS 	r+   r  rF   r2   rx   r  labelsr   r   r  r~   logits_to_keeprI   r9   c                     Ub  UOU R                   R                  nU	b  U	OU R                   R                  n	U R                  " SUU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UU R                   R                  -  nSnUb)  U R                  " SUX`R                   R                  S.UD6n[        UUUR                  UR                  UR                  S9$ )a{  
Example:

```python
>>> from transformers import AutoTokenizer, GraniteForCausalLM

>>> model = GraniteForCausalLM.from_pretrained("meta-granite/Granite-2-7b-hf")
>>> tokenizer = AutoTokenizer.from_pretrained("meta-granite/Granite-2-7b-hf")

>>> prompt = "Hey, are you conscious? Can you talk to me?"
>>> inputs = tokenizer(prompt, return_tensors="pt")

>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
```N)	r  rF   r2   rx   r  r   r   r  r~   )r/  r2  r  )lossr/  rx   r7   r   r   )r`   r   r  r   r  r   r   slicer-  logits_scalingloss_functionr  r   rx   r7   r   )rt   r  rF   r2   rx   r  r2  r   r   r  r~   r3  rI   r   r7   slice_indicesr/  r5  s                     r)   r   GraniteForCausalLM.forward  s,   D 2C1N-TXT_T_TqTq$8$D $++JjJj 	
 ,0:: ,
)%+'/!5),
 ,
  118B>SV8W8W~ot4]kmA}a,?@A$++444%%pVF{{OeOepiopD%#33!//))
 	
r+   )r-  r   r  )NNNNNNNNNNr   )r   r   r   r   _tied_weights_keys_tp_plan_pp_planrf   r   r   r   r$   r   r   r   r	   listr   r   r   r   r   r   r   r   r   r   s   @r)   r,  r,    sw   *+=)H_-z:;H  151537KO59-1$(,0/35934C
E,,-C
 !.C
 u//0	C

 "%tE4E4E/F(F"GHC
   1 12C
 ))*C
 D>C
 $D>C
 'tnC
 !!1!12C
 c5<</0C
 +,C
 
 C
  C
r+   r,  )r,  r  r   )Nr   )r   )=typingr   r   r   r$   r   activationsr   cache_utilsr	   r
   
generationr   integrationsr   masking_utilsr   modeling_layersr   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   r   r   utils.deprecationr   utils.genericr   configuration_graniter   
get_loggerr   r!  r*   r6   r   r   rA   Moduler   r\   r^   r   r   r   r   r   r  r,  __all__r   r+   r)   <module>rQ     s  , - ,   ! . ) 7 / 9 O K F & R R 0 / 0 
		H	%(6	UU\\ 	U# 	U%,, 	U& %II%<<% 
% <<	%
 U\\*% % % '(%4D)ryy D)N Y'JRYY J (J(  K4 K\ _  $!<RYY !<H s
) s
 s
l S
/ S
 S
l Kr+   