
    Z jW                        S SK Jr  S SK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Jr  SSKJr  SSK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$J%r%J&r&J'r'  SSK(J)r)J*r*  SSK+J,r,  SSK-J.r.   " S S\R^                  5      r0 " S S\Rb                  5      r2 " S S\Rb                  5      r3 " S S\Rb                  5      r4S r5\" S5      S=S  j5       r6S!\Rn                  S"\8S#\Rn                  4S$ jr9 S>S%\Rb                  S&\Rn                  S'\Rn                  S(\Rn                  S)\Rn                  S-  S*\:S+\:S,\#\%   4S- jjr;\" \65       " S. S/\Rb                  5      5       r< " S0 S1\5      r=\& " S2 S3\!5      5       r>\& " S4 S5\>5      5       r?\& " S6 S7\>\5      5       r@ " S8 S9\\>5      rA " S: S;\\>5      rB/ S<QrCg)?    )Callable)OptionalN)nn   )initialization)ACT2FN)CacheDynamicCache)GenerationMixin)use_kernel_func_from_hubuse_kernelized_func)create_causal_mask) GenericForSequenceClassificationGenericForTokenClassificationGradientCheckpointingLayer)BaseModelOutputWithPastCausalLMOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)TransformersKwargsauto_docstringcan_return_tuple)maybe_autocastmerge_with_config_defaults)capture_outputs   )GemmaConfigc            	       l   ^  \ rS rSrSrSS\S\S\S\4U 4S jjjrS\R                  4U 4S	 jjr
S
rU =r$ )GemmaTextScaledWordEmbedding2   zT
This module overrides nn.Embeddings' forward by multiplying with embeddings scale.
num_embeddingsembedding_dimpadding_idxembed_scalec                 |   > [         TU ]  XU5        X@l        U R                  S[        R
                  " U5      SS9  g )Nr'   F
persistent)super__init__scalar_embed_scaleregister_buffertorchtensor)selfr$   r%   r&   r'   	__class__s        y/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/gemma/modeling_gemma.pyr,   %GemmaTextScaledWordEmbedding.__init__7   s7    D"-]ELL,ERWX    	input_idsc                    > [         TU ]  U5      U R                  R                  U R                  R
                  5      -  $ N)r+   forwardr'   toweightdtype)r1   r6   r2   s     r3   r9   $GemmaTextScaledWordEmbedding.forward<   s2    wy)D,<,<,?,?@Q@Q,RRRr5   )r-   )      ?)__name__
__module____qualname____firstlineno____doc__intfloatr,   r/   Tensorr9   __static_attributes____classcell__r2   s   @r3   r"   r"   2   sM    Ys Y3 YS Y_d Y Y
S S Sr5   r"   c                   J   ^  \ rS rSrS	S\S\4U 4S jjjrS rS rS r	Sr
U =r$ )
GemmaRMSNorm@   dimepsc                    > [         TU ]  5         X l        [        R                  " [
        R                  " U5      5      U l        g r8   )r+   r,   rN   r   	Parameterr/   zerosr;   )r1   rM   rN   r2   s      r3   r,   GemmaRMSNorm.__init__A   s,    ll5;;s#34r5   c                     U[         R                  " UR                  S5      R                  SSS9U R                  -   5      -  $ )N   T)keepdim)r/   rsqrtpowmeanrN   )r1   xs     r3   _normGemmaRMSNorm._normF   s4    5;;quuQx}}R}>IJJJr5   c                     U R                  UR                  5       5      nUSU R                  R                  5       -   -  nUR                  U5      $ )Nr>   )r[   rE   r;   type_as)r1   rZ   outputs      r3   r9   GemmaRMSNorm.forwardI   sC    AGGI& 3!2!2!445~~a  r5   c                 ^    [        U R                  R                  5       SU R                   3$ )Nz, eps=)tupler;   shaperN   )r1   s    r3   
extra_reprGemmaRMSNorm.extra_reprP   s'    ))*+6$((<<r5   )rN   r;   )gư>)r?   r@   rA   rB   rD   rE   r,   r[   r9   rd   rG   rH   rI   s   @r3   rK   rK   @   s0    5C 5e 5 5
K!= =r5   rK   c                   .   ^  \ rS rSrU 4S jrS rSrU =r$ )GemmaMLPT   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 NFbias)r+   r,   confighidden_sizeintermediate_sizer   Linear	gate_projup_proj	down_projr   
hidden_actact_fnr1   rm   r2   s     r3   r,   GemmaMLP.__init__U   s    !--!'!9!94#3#3T5K5KRWXyy!1!143I3IPUV4#9#94;K;KRWXV../r5   c                     U R                  U R                  U R                  U5      5      U R                  U5      -  5      nU$ r8   )rs   ru   rq   rr   )r1   rZ   rs   s      r3   r9   GemmaMLP.forward_   s6    NN4;;t~~a/@#ADLLQRO#ST	r5   )ru   rm   rs   rq   rn   ro   rr   )r?   r@   rA   rB   r,   r9   rG   rH   rI   s   @r3   rg   rg   T   s    0 r5   rg   c                      ^  \ rS rSr% \R
                  \S'   SS\4U 4S jjjr\	   SS\S-  S\
S   S\S-  S	\S
\4   4S jj5       r\R                  " 5       \S 5       5       rSrU =r$ )GemmaRotaryEmbeddingd   inv_freqNrm   c                   > [         TU ]  5         UR                  U l        UR                  U l        Xl        U R
                  R                  S   U l        U R                  nU R                  S:w  a  [        U R                     nU" U R
                  U5      u  o@l
        U R                  SUSS9  U R                  SUR                  5       SS9  g )N	rope_typedefaultr}   Fr)   original_inv_freq)r+   r,   max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenrm   rope_parametersr   compute_default_rope_parametersr   attention_scalingr.   clone)r1   rm   devicerope_init_fnr}   r2   s        r3   r,   GemmaRotaryEmbedding.__init__g   s    "("@"@$*$B$B!44[A!%!E!E>>Y&.t~~>L+7V+L((ZeD0(..2BuUr5   r   ztorch.deviceseq_lenreturnztorch.Tensorc           	         U R                   S   n[        U SS5      =(       d    U R                  U R                  -  nSnSU[        R
                  " SUS[        R                  S9R                  U[        R                  S9U-  -  -  nXe4$ )	aH  
Computes the inverse frequencies according to the original RoPE implementation
Args:
    config ([`~transformers.PreTrainedConfig`]):
        The model configuration.
    device (`torch.device`):
        The device to use for initialization of the inverse frequencies.
    seq_len (`int`, *optional*):
        The current sequence length. Unused for this type of RoPE.
Returns:
    Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
    post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).

rope_thetahead_dimNr>   r   rT   r<   )r   r<   )	r   getattrrn   num_attention_headsr/   arangeint64r:   rE   )rm   r   r   baserM   attention_factorr}   s          r3   r   4GemmaRotaryEmbedding.compute_default_rope_parametersw   s    & %%l3fj$/c63E3EIcIc3c U\\!S!5;;?BB&X]XcXcBdgjjk
 ))r5   c                 L   U R                   S S S 2S 4   R                  5       R                  UR                  S   SS5      R	                  UR
                  5      nUS S 2S S S 24   R                  5       n[        UR
                  R                  [        5      (       a0  UR
                  R                  S:w  a  UR
                  R                  OSn[        USS9   UR                  5       UR                  5       -  R                  SS5      n[        R                  " Xf4SS	9nUR                  5       U R                  -  nUR                  5       U R                  -  n	S S S 5        WR	                  UR                   S
9W	R	                  UR                   S
94$ ! , (       d  f       N@= f)Nr   rU   r   mpscpuF)device_typeenabledrT   rM   r   )r}   rE   expandrc   r:   r   
isinstancetypestrr   	transposer/   catcosr   sinr<   )
r1   rZ   position_idsinv_freq_expandedposition_ids_expandedr   freqsembr   r   s
             r3   r9   GemmaRotaryEmbedding.forward   sN    !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   rm   r   r   r   r8   NNN)r?   r@   rA   rB   r/   rF   __annotations__r    r,   staticmethodr   rD   rb   rE   r   no_gradr   r9   rG   rH   rI   s   @r3   r{   r{   d   s    llV{ V V  %)+/"*d"*(* t* 
~u$	%	* *: ]]_<  <r5   r{   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$ )z*Rotates half the hidden dims of the input..NrU   rT   r   )rc   r/   r   )rZ   x1x2s      r3   rotate_halfr      sZ    	
3"!''"+"""	#B	
3q ""	#B99rc2YB''r5   rotary_pos_embc                     UR                  U5      nUR                  U5      nX-  [        U 5      U-  -   nX-  [        U5      U-  -   nXV4$ )aI  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.
    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kr   r   unsqueeze_dimq_embedk_embeds          r3   apply_rotary_pos_embr      sS    & --
&C
--
&Cw;q>C/0Gw;q>C/0Gr5   hidden_statesn_repr   c                     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)rc   r   reshape)r   r   batchnum_key_value_headsslenr   s         r3   	repeat_kvr      s_    
 2?1D1D.Ez!!Qa"23::5W\dlmM  e(CTTTr5   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  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$ )NrT   r   rU   )rM   r<   )ptrainingr   )r   num_key_value_groupsr/   matmulr   r   
functionalsoftmaxfloat32r:   r<   r   r   
contiguous)r   r   r   r   r   r   r   r   
key_statesvalue_statesattn_weightsattn_outputs               r3   eager_attention_forwardr      s     3 ; ;<JU$?$?@L<<';';Aq'ABWLL!#4==((2U]](SVVW\WbWbcL==((6??([L,,|:K''1-88:K$$r5   c                     ^  \ rS rSrSrS\S\4U 4S jjr   SS\R                  S\
\R                  \R                  4   S-  S	\R                  S-  S
\S-  S\\   S\
\R                  \R                  4   4S jjrSrU =r$ )GemmaAttention   z=Multi-headed attention from 'Attention Is All You Need' paperrm   	layer_idxc                 p  > [         TU ]  5         Xl        X l        [	        USUR
                  UR                  -  5      U l        UR                  UR                  -  U l	        U R                  S-  U l
        UR                  U l        [	        USS5      (       + 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   g      use_bidirectional_attentionFrk   )r+   r,   rm   r   r   rn   r   r   r   r   r   attention_dropout	is_causalr   rp   attention_biasq_projk_projv_projo_projr1   rm   r   r2   s      r3   r,   GemmaAttention.__init__   sV   "
F4F4F&JdJd4de$*$>$>&B\B\$\!}}d*!'!9!9$V-JERRii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii&&68J8JQWQfQf
r5   Nr   position_embeddingsr   past_key_valuesr   r   c                    UR                   S S n/ UQSPU R                  P7nU R                  U5      R                  U5      R	                  SS5      nU R                  U5      R                  U5      R	                  SS5      n	U R                  U5      R                  U5      R	                  SS5      n
Uu  p[        XX5      u  pUb  UR                  XU R                  5      u  p[        R                  " U R                  R                  [        5      nU" U UU	U
U4U R                  (       d  SOU R                   U R"                  S.UD6u  pUR$                  " / UQSP76 R'                  5       nU R)                  U5      nX4$ )NrU   r   rT           )r   r   )rc   r   r   viewr   r   r   r   updater   r   get_interfacerm   _attn_implementationr   r   r   r   r   r   r   )r1   r   r   r   r   r   input_shapehidden_shapequery_statesr   r   r   r   attention_interfacer   r   s                   r3   r9   GemmaAttention.forward  s~    $))#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#[ &'6'='=jX\XfXf'g$J(?(M(MKK,,.E)
 %8	%
  $}}C$2H2HLL	%
 	%
! "));;;;FFHkk+.((r5   )r   rm   r   r   r   r   r   r   r   r   r   r   )r?   r@   rA   rB   rC   r    rD   r,   r/   rF   rb   r	   r   r   r9   rG   rH   rI   s   @r3   r   r      s    G
{ 
s 
4 IM.2(,&)||&) #5<<#=>E&) t+	&)
 &) +,&) 
u||U\\)	*&) &)r5   r   c                     ^  \ rS rSrS\S\4U 4S jjr     SS\R                  S\R                  S-  S\R                  S-  S	\
S-  S
\S-  S\\R                  \R                  4   S-  S\\   S\R                  4S jjrSrU =r$ )GemmaDecoderLayeri/  rm   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)rm   r   rN   )r+   r,   rn   r   	self_attnrg   mlprK   rms_norm_epsinput_layernormpost_attention_layernormr   s      r3   r,   GemmaDecoderLayer.__init__0  sj    !--'vKF#+F,>,>FDWDWX(4V5G5GVM`M`(a%r5   Nr   r   r   r   	use_cacher   r   r   c           
          UnU R                  U5      nU R                  " SUUUUUUS.UD6u  pX-   nUnU R                  U5      nU R                  U5      nX-   nU$ )N)r   r   r   r   r  r    )r   r   r   r   )
r1   r   r   r   r   r  r   r   residual_s
             r3   r9   GemmaDecoderLayer.forward:  s     !,,];>> 
')%+ 3
 
 !0 !55mD/ 0r5   )rn   r   r   r   r   )NNNFN)r?   r@   rA   rB   r    rD   r,   r/   rF   
LongTensorr	   boolrb   r   r   r9   rG   rH   rI   s   @r3   r   r   /  s    b{ bs b /304(,!&HL|| t+ &&-	
  $; #5<<#=>E +, 
 r5   r   c                      ^  \ 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&                  " 5       U 4S j5       rS	rU =r$ )
GemmaPreTrainedModeliZ  rm   modelTr   r   )r   
attentionsc                   > [         TU ]  U5        SUR                  R                  ;   a!  [        R
                  " UR                  5        g [        U[        5      (       a,  [        R                  " UR                  UR                  5        g g )NRMSNorm)r+   _init_weightsr2   r?   initzeros_r;   r   r"   	constant_r'   r-   )r1   r   r2   s     r3   r  "GemmaPreTrainedModel._init_weightsl  sa    f%((111KK& <==NN6--v/H/HI >r5   r  )r?   r@   rA   rB   r    r   base_model_prefixsupports_gradient_checkpointing_no_split_modules_skip_keys_device_placement_supports_flash_attn_supports_sdpa_supports_flex_attn_can_compile_fullgraph_supports_attention_backendr   r   _can_record_outputsr/   r   r  rG   rH   rI   s   @r3   r  r  Z  sn    &*#,-#4"5N!"&*$
 ]]_J Jr5   r  c                     ^  \ rS rSrS\4U 4S jjr\\\      SS\	R                  S-  S\	R                  S-  S\	R                  S-  S\S-  S	\	R                  S-  S
\S-  S\\   S\4S jj5       5       5       rSrU =r$ )
GemmaModeliv  rm   c           	        > [         TU ]  U5        UR                  U l        UR                  U l        [        UR                  UR                  U R                  U R                  R                  S-  S9U l        [        R                  " [        UR                  5       Vs/ s H  n[        X5      PM     sn5      U l        [        UR                  UR                   S9U l        [%        US9U l        SU l        U R+                  5         g s  snf )Ng      ?)r'   r   rm   F)r+   r,   pad_token_idr&   
vocab_sizer"   rn   rm   embed_tokensr   
ModuleListrangenum_hidden_layersr   layersrK   r   normr{   
rotary_embgradient_checkpointing	post_initr   s      r3   r,   GemmaModel.__init__x  s     !.. ++8v1143C3CQUQ\Q\QhQhjmQm
 mmCHIaIaCbcCbiv1Cbc
 !!3!39L9LM	.f=&+# 	 ds    D
Nr6   r   r   r   inputs_embedsr  r   r   c           
      T   US L US L-  (       a  [        S5      eUc  U R                  U5      nU(       a  Uc  [        U R                  S9nUcU  Ub  UR	                  5       OSn[
        R                  " UR                  S   UR                  S9U-   nUR                  S5      n[        U R                  UUUUS9n	Un
U R                  XS9nU R                  S U R                  R                    H  nU" U
4U	UUUUS.UD6n
M     U R                  U
5      n
[        U
U(       a  US	9$ S S	9$ )
Nz:You must specify exactly one of input_ids or inputs_embedsr"  r   r   )r   )rm   r/  r   r   r   )r   )r   r   r   r  r   )last_hidden_stater   )
ValueErrorr%  r
   rm   get_seq_lengthr/   r   rc   r   r   r   r+  r)  r(  r*  r   )r1   r6   r   r   r   r/  r  r   past_seen_tokenscausal_maskr   r   decoder_layers                r3   r9   GemmaModel.forward  sU    -t";<YZZ  --i8M0*$++>OCRC^==?de <<(;(;A(>}G[G[\_ooL'11!4L(;;')+%
 &"oomoW![[)H4;;+H+HIM)*) /#$7 M J 		-0&+/8O
 	
>B
 	
r5   )r%  r,  r)  r*  r&   r+  r$  )NNNNNN)r?   r@   rA   rB   r    r,   r   r   r   r/   r  rF   r	   FloatTensorr	  r   r   r   r9   rG   rH   rI   s   @r3   r   r   v  s    { $   .2.204(,26!%2
##d*2
 t+2
 &&-	2

 2
 ((4/2
 $;2
 +,2
 
!2
    2
r5   r   c                   P  ^  \ rS rSrSS0rSS0rSS/S/40rU 4S jr\\	        SS
\
R                  S	-  S\
R                  S	-  S\
R                  S	-  S\S	-  S\
R                  S	-  S\
R                  S	-  S\S	-  S\\
R                  -  S\\   S\4S jj5       5       rSrU =r$ )GemmaForCausalLMi  zlm_head.weightzmodel.embed_tokens.weightlm_headcolwise_gather_outputr   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 rj   )
r+   r,   r   r  r$  r   rp   rn   r;  r-  rv   s     r3   r,   GemmaForCausalLM.__init__  sU     '
 ++yy!3!3V5F5FUS 	r5   Nr6   r   r   r   r/  labelsr  logits_to_keepr   r   c	           
      |   U R                   " SUUUUUUS.U	D6n
U
R                  n[        U[        5      (       a  [	        U* S5      OUnU R                  USS2USS24   5      nSnUb)  U R                  " SXU R                  R                  S.U	D6n[        UUU
R                  U
R                  U
R                  S9$ )a  
Example:

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

>>> model = GemmaForCausalLM.from_pretrained("google/gemma-7b")
>>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")

>>> prompt = "What is your favorite condiment?"
>>> 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]
"What is your favorite condiment?"
```)r6   r   r   r   r/  r  N)r=  r@  r$  )lossr=  r   r   r  r  )r  r1  r   rD   slicer;  loss_functionrm   r$  r   r   r   r  )r1   r6   r   r   r   r/  r@  r  rA  r   outputsr   slice_indicesr=  rC  s                  r3   r9   GemmaForCausalLM.forward  s    > ,0:: ,
)%+',
 ,
  118B>SV8W8W~ot4]kmA}a,?@A%%pVt{{OeOepiopD%#33!//))
 	
r5   )r;  r  r$  )NNNNNNNr   )r?   r@   rA   rB   _tied_weights_keys_tp_plan_pp_planr,   r   r   r/   r  rF   r	   r8  r	  rD   r   r   r   r9   rG   rH   rI   s   @r3   r:  r:    s   *,GH23H_-z:;H  .2.204(,26*.!%-.6
##d*6
 t+6
 &&-	6

 6
 ((4/6
   4'6
 $;6
 ell*6
 +,6
 
 6
  6
r5   r:  c                       \ rS rSrSrg)GemmaForSequenceClassificationi  r  Nr?   r@   rA   rB   rG   r  r5   r3   rM  rM        r5   rM  c                       \ rS rSrSrg)GemmaForTokenClassificationi  r  NrN  r  r5   r3   rQ  rQ    rO  r5   rQ  )r   r:  rM  rQ  r  )r   )r   )Dcollections.abcr   typingr   r/   r    r   r  activationsr   cache_utilsr	   r
   
generationr   integrationsr   r   masking_utilsr   modeling_layersr   r   r   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   r   utils.genericr   r   utils.output_capturingr   configuration_gemmar    	Embeddingr"   ModulerK   rg   r{   r   r   rF   rD   r   rE   r   r   r   r  r   r:  rM  rQ  __all__r  r5   r3   <module>rf     s  . %    & ! . ) I / 
 P K F & I I G 5 ,S2<< S=299 =(ryy  ><299 ><B( *+ ,2	UU\\ 	U# 	U%,, 	U& %II%<<% 
% <<	%
 LL4'% % % '(%2 )*@)RYY @) +@)F(2 (V J? J J6 H
% H
 H
V F
+_ F
 F
R	%EG[ 		"?AU 	r5   