
    Z jNY                        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	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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$J%r%  SSK&J'r'J(r(J)r)  SSK*J+r+J,r,  SSK-J.r.  SSK/J0r0   " S S\Rb                  5      r2S r3\" S5      S>S j5       r4S\Rj                  S\6S\Rj                  4S jr7 S?S\Rb                  S \Rj                  S!\Rj                  S"\Rj                  S#\Rj                  S-  S$\8S%\8S&\%\'   4S' jjr9\" \45       " S( S)\Rb                  5      5       r:\" S*5       " S+ S,\Rb                  5      5       r; " S- S.\Rb                  5      r< " S/ S0\5      r=\( " S1 S2\#5      5       r>\( " S3 S4\>5      5       r?\( " S5 S6\>\5      5       r@ " S7 S8\\>5      rA " S9 S:\\>5      rB " S; S<\\>5      rC/ S=QrDg)@    )Callable)OptionalN)nn   )ACT2FN)CacheDynamicCache)GenerationMixin)use_kernel_forward_from_hubuse_kernel_func_from_hubuse_kernelized_func)create_causal_mask!create_sliding_window_causal_mask)FlashAttentionKwargs)GenericForQuestionAnswering 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   )SmolLM3Configc                      ^  \ 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$ )SmolLM3RotaryEmbedding1   inv_freqNconfigc                   > [         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'   F)
persistentoriginal_inv_freq)super__init__max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenr(   rope_parametersr*   compute_default_rope_parametersr   attention_scalingregister_bufferclone)selfr(   devicerope_init_fnr'   	__class__s        }/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/smollm3/modeling_smollm3.pyr/   SmolLM3RotaryEmbedding.__init__4   s    "("@"@$*$B$B!44[A!%!E!E>>Y&.t~~>L+7V+L((ZeD0(..2BuU    r9   ztorch.deviceseq_lenreturnztorch.Tensorc           	         U R                   S   n[        U SS5      =(       d    U R                  U R                  -  nSnSU[        R
                  " SUS[        R                  S9R                  U[        R                  S9U-  -  -  nXe4$ )	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_dimNg      ?r      dtype)r9   rF   )	r3   getattrhidden_sizenum_attention_headstorcharangeint64tofloat)r(   r9   r?   basedimattention_factorr'   s          r<   r4   6SmolLM3RotaryEmbedding.compute_default_rope_parametersD   s    & %%l3fj$/c63E3EIcIc3c U\\!S!5;;?BB&X]XcXcBdgjjk
 ))r>   c                 L   U R                   S S S 2S 4   R                  5       R                  UR                  S   SS5      R	                  UR
                  5      nUS S 2S S S 24   R                  5       n[        UR
                  R                  [        5      (       a0  UR
                  R                  S:w  a  UR
                  R                  OSn[        USS9   UR                  5       UR                  5       -  R                  SS5      n[        R                  " Xf4SS	9nUR                  5       U R                  -  nUR                  5       U R                  -  n	S S S 5        WR	                  UR                   S
9W	R	                  UR                   S
94$ ! , (       d  f       N@= f)Nr   r"   mpscpuF)device_typeenabledrD   rP   rE   )r'   rN   expandshaperM   r9   
isinstancetypestrr   	transposerJ   catcosr5   sinrF   )
r8   xposition_idsinv_freq_expandedposition_ids_expandedrW   freqsembra   rb   s
             r<   forwardSmolLM3RotaryEmbedding.forwardb   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#)r5   r(   r1   r2   r*   N)NNN)__name__
__module____qualname____firstlineno__rJ   Tensor__annotations__r#   r/   staticmethodr   inttuplerN   r4   no_gradr   ri   __static_attributes____classcell__r;   s   @r<   r%   r%   1   s    llV} V V  '++/"*$*(* t* 
~u$	%	* *: ]]_<  <r>   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$ )z*Rotates half the hidden dims of the input..NrT   rD   rY   )r[   rJ   r`   )rc   x1x2s      r<   rotate_halfr|   r   sZ    	
3"!''"+"""	#B	
3q ""	#B99rc2YB''r>   rotary_pos_embc                     UR                  U5      nUR                  U5      nX-  [        U 5      U-  -   nX-  [        U5      U-  -   nXV4$ )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kra   rb   unsqueeze_dimq_embedk_embeds          r<   apply_rotary_pos_embr   y   sS    & --
&C
--
&Cw;q>C/0Gw;q>C/0Gr>   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)r[   rZ   reshape)r   r   batchnum_key_value_headsslenrC   s         r<   	repeat_kvr      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  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$ )NrD   r   rT   )rP   rF   )ptrainingr"   )r   num_key_value_groupsrJ   matmulr_   r   
functionalsoftmaxfloat32rM   rF   r   r   
contiguous)r   r   r   r   r   r   r   r   
key_statesvalue_statesattn_weightsattn_outputs               r<   eager_attention_forwardr      s     3 ; ;<JU$?$?@L<<';';Aq'ABWLL!#4==((2U]](SVVW\WbWbcL==((6??([L,,|:K''1-88:K$$r>   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	\R                  S-  S
\S-  S\\   S\
\R                  \R                  S-  4   4S jjrSrU =r$ )SmolLM3Attention   z=Multi-headed attention from 'Attention Is All You Need' paperr(   	layer_idxc                   > [         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        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        UR(                  U   U l        UR,                  (       a%  UR.                  U   S:X  a  UR0                  U l        g S U l        g )NrC   g      Tbiassliding_attention)r.   r/   r(   r   rG   rH   rI   rC   r   r   r   attention_dropout	is_causalr   Linearattention_biasq_projk_projv_projo_projno_rope_layersuse_ropeuse_sliding_windowlayer_typessliding_windowr8   r(   r   r;   s      r<   r/   SmolLM3Attention.__init__   s   "
F4F4F&JdJd4de$*$>$>&B\B\$\!}}d*!'!9!9ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii&&68J8JQWQfQf
 --i8 ((V-?-?	-JNa-a !! 	  	r>   Nr   position_embeddingsr   past_key_valuesr   r@   c                 @   UR                   S S n/ UQSPU R                  P7nU R                  U5      R                  U5      R	                  SS5      nU R                  U5      R                  U5      R	                  SS5      n	U R                  U5      R                  U5      R	                  SS5      n
U R                  (       a  Uu  p[        XX5      u  pUb  UR                  XU R                  5      u  p[        R                  " U R                  R                  [        5      nU" U UU	U
U4U R                   (       d  SOU R"                  U R$                  U R&                  S.UD6u  pUR(                  " / UQSP76 R+                  5       nU R-                  U5      nX4$ )NrT   r"   rD           )r   r   r   )r[   rC   r   viewr_   r   r   r   r   updater   r   get_interfacer(   _attn_implementationr   r   r   r   r   r   r   r   )r8   r   r   r   r   r   input_shapehidden_shapequery_statesr   r   ra   rb   attention_interfacer   r   s                   r<   ri   SmolLM3Attention.forward   s    $))#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==*HC';LVY'_$L&'6'='=jX\XfXf'g$J(?(M(MKK,,.E)
 %8
%
  $}}C$2H2HLL..
%
 
%
! "));;;;FFHkk+.((r>   )r   r(   rC   r   r   r   r   r   r   r   r   r   r   rk   )rl   rm   rn   ro   __doc__r#   rs   r/   rJ   rp   rt   r   r   r   ri   rv   rw   rx   s   @r<   r   r      s    G
} 
 
F )-()||() #5<<#=>() t+	()
 () -.() 
u||U\\D00	1() ()r>   r   RMSNormc                   x   ^  \ rS rSrS
S\SS4U 4S jjjrS\R                  S\R                  4S jrS r	S	r
U =r$ )SmolLM3RMSNormi  epsr@   Nc                    > [         TU ]  5         [        R                  " [        R
                  " U5      5      U l        X l        g)z-
SmolLM3RMSNorm is equivalent to T5LayerNorm
N)r.   r/   r   	ParameterrJ   onesweightvariance_epsilon)r8   rH   r   r;   s      r<   r/   SmolLM3RMSNorm.__init__  s/     	ll5::k#:; #r>   r   c                    UR                   nUR                  [        R                  5      nUR	                  S5      R                  SSS9nU[        R                  " X0R                  -   5      -  nU R                  UR                  U5      -  $ )NrD   rT   T)keepdim)	rF   rM   rJ   r   powmeanrsqrtr   r   )r8   r   input_dtypevariances       r<   ri   SmolLM3RMSNorm.forward  sw    #))%((7 $$Q',,R,>%H?T?T4T(UU{{]--k:::r>   c                 ^    [        U R                  R                  5       SU R                   3$ )Nz, eps=)rt   r   r[   r   )r8   s    r<   
extra_reprSmolLM3RMSNorm.extra_repr  s*    ))*+6$2G2G1HIIr>   )r   r   )gư>)rl   rm   rn   ro   rN   r/   rJ   rp   ri   r   rv   rw   rx   s   @r<   r   r     sB    $ $$ $ $;U\\ ;ell ;J Jr>   r   c                   .   ^  \ rS rSrU 4S jrS rSrU =r$ )
SmolLM3MLPi  c                   > [         TU ]  5         Xl        UR                  U l        UR                  U l        [
        R                  " U R                  U R                  UR                  S9U l        [
        R                  " U R                  U R                  UR                  S9U l	        [
        R                  " U R                  U R                  UR                  S9U l
        [        UR                     U l        g )Nr   )r.   r/   r(   rH   intermediate_sizer   r   mlp_bias	gate_projup_proj	down_projr   
hidden_actact_fnr8   r(   r;   s     r<   r/   SmolLM3MLP.__init__  s    !--!'!9!94#3#3T5K5KRXRaRabyy!1!143I3IPVP_P_`4#9#94;K;KRXRaRabV../r>   c                     U R                  U R                  U R                  U5      5      U R                  U5      -  5      nU$ rk   )r   r   r   r   )r8   rc   r   s      r<   ri   SmolLM3MLP.forward%  s6    NN4;;t~~a/@#ADLLQRO#ST	r>   )r   r(   r   r   rH   r   r   )rl   rm   rn   ro   r/   ri   rv   rw   rx   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\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$ )SmolLM3DecoderLayeri*  r(   r   c                   > [         TU ]  5         UR                  U l        [        XS9U l        [        U5      U l        [        UR                  UR                  S9U l	        [        UR                  UR                  S9U l
        g )N)r(   r   r   )r.   r/   rH   r   	self_attnr   mlpr   rms_norm_epsinput_layernormpost_attention_layernormr   s      r<   r/   SmolLM3DecoderLayer.__init__+  sj    !--)Mf%-f.@.@fFYFYZ(6v7I7IvObOb(c%r>   Nr   r   rd   r   	use_cacher   r   r@   c           
          UnU R                  U5      nU R                  " SUUUUUUS.UD6u  pX-   nUnU R                  U5      nU R                  U5      nX-   nU$ )N)r   r   rd   r   r   r    )r   r   r   r   )
r8   r   r   rd   r   r   r   r   residual_s
             r<   ri   SmolLM3DecoderLayer.forward5  s     !,,];>> 
')%+ 3
 
 !0 !55mD/ 0r>   )rH   r   r   r   r   )NNNFN)rl   rm   rn   ro   r#   rs   r/   rJ   rp   
LongTensorr   boolrt   r   r   ri   rv   rw   rx   s   @r<   r   r   *  s    d} d d /304(,!&HL|| t+ &&-	
  $; #5<<#=>E +, 
 r>   r   c                   R    \ rS rSr% \\S'   SrSrS/rS/r	Sr
SrSrSrSr\\S.rSrg	)
SmolLM3PreTrainedModeliU  r(   modelTr   r   )r   
attentionsr   N)rl   rm   rn   ro   r#   rq   base_model_prefixsupports_gradient_checkpointing_no_split_modules_skip_keys_device_placement_supports_flash_attn_supports_sdpa_supports_flex_attn_can_compile_fullgraph_supports_attention_backendr   r   _can_record_outputsrv   r   r>   r<   r   r   U  sQ    &*#./#4"5N!"&,&r>   r   c                     ^  \ rS rSrS\4U 4S jjr\\\      SS\	R                  S-  S\	R                  S-  S\	R                  S-  S\S-  S	\	R                  S-  S
\S-  S\\   S\4S jj5       5       5       rSrU =r$ )SmolLM3Modelih  r(   c           	      D  > [         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        SU R(                  R*                  ;   U l        U R/                  5         g s  snf )Nr   r(   Fr   )r.   r/   pad_token_idpadding_idx
vocab_sizer   	EmbeddingrH   embed_tokens
ModuleListrangenum_hidden_layersr   layersr   r   normr%   
rotary_embgradient_checkpointingr(   r   has_sliding_layers	post_initr   s      r<   r/   SmolLM3Model.__init__j  s     !.. ++LL):):F<N<NPTP`P`ammEJ6KcKcEdeEd	 3Ede
 #6#5#56;N;NO	0?&+#"59P9P"P 	 fs   DN	input_idsr   rd   r   inputs_embedsr   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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=n	[        5      (       d>  U R                  UUUUS.n
S[        S0 U
D60n	U R                  (       a  [        S0 U
D6U	S'   UnU R                  X5      n[!        U R"                  S U R                  R$                   5       H-  u  pU" U4XR                  R&                  U      UUUUS	.UD6nM/     U R)                  U5      n[+        UU(       a  US
9$ S S
9$ )Nz:You must specify exactly one of input_ids or inputs_embedsr  r   r"   )r9   )r(   r   r   r   rd   full_attentionr   )r   r   rd   r   r   )last_hidden_stater   r   )
ValueErrorr  r	   r(   get_seq_lengthrJ   rK   r[   r9   r   r\   dictr   r  r   r  	enumerater  r  r   r  r   )r8   r  r   rd   r   r   r   r   past_seen_tokenscausal_mask_mappingmask_kwargsr   r   idecoder_layers                  r<   ri   SmolLM3Model.forward{  s    -t";<YZZ  --i8M0*$++>OCRC^==?de <<(;(;A(>}G[G[\_ooL'11!4L ?-FF ++!."0#2 ,K !"4"C{"C# &&;\;k_j;k#$78%"oomJ )$++6U8U8U*V WA)2;;3J3J13MN$7) /# M !X 		-0&+/8O
 	
>B
 	
r>   )r  r  r  r  r  r  r  r  )NNNNNN)rl   rm   rn   ro   r#   r/   r    r!   r   rJ   r   rp   r   FloatTensorr   r   r   r   ri   rv   rw   rx   s   @r<   r  r  h  s    } "   .2.204(,26!%<
##d*<
 t+<
 &&-	<

 <
 ((4/<
 $;<
 +,<
 
!<
    <
r>   r  c                   P  ^  \ rS rSrSS0rSS0rSS/S/40rU 4S jr\\	        SS
\
R                  S	-  S\
R                  S	-  S\
R                  S	-  S\S	-  S\
R                  S	-  S\
R                  S	-  S\S	-  S\\
R                  -  S\\   S\4S jj5       5       rSrU =r$ )SmolLM3ForCausalLMi  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 )NFr   )
r.   r/   r  r   r  r   r   rH   r1  r  r   s     r<   r/   SmolLM3ForCausalLM.__init__  sU     !&)
 ++yy!3!3V5F5FUS 	r>   Nr  r   rd   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, SmolLM3ForCausalLM

>>> model = SmolLM3ForCausalLM.from_pretrained("meta-smollm3/SmolLM3-2-7b-hf")
>>> tokenizer = AutoTokenizer.from_pretrained("meta-smollm3/SmolLM3-2-7b-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."
```)r  r   rd   r   r   r   N)r3  r6  r  )lossr3  r   r   r  r   )r   r#  r\   rs   slicer1  loss_functionr(   r  r   r   r   r  )r8   r  r   rd   r   r   r6  r   r7  r   outputsr   slice_indicesr3  r9  s                  r<   ri   SmolLM3ForCausalLM.forward  s    > ,0:: ,
)%+',
 ,
  118B>SV8W8W~ot4]kmA}a,?@A%%pVt{{OeOepiopD%#33!//))
 	
r>   )r1  r   r  )NNNNNNNr   )rl   rm   rn   ro   _tied_weights_keys_tp_plan_pp_planr/   r   r   rJ   r   rp   r   r.  r   rs   r   r   r   ri   rv   rw   rx   s   @r<   r0  r0    s   *,GH23H_-z:;H  .2.204(,26*.!%-.6
##d*6
 t+6
 &&-	6

 6
 ((4/6
   4'6
 $;6
 ell*6
 +,6
 
 6
  6
r>   r0  c                       \ rS rSrSrg) SmolLM3ForSequenceClassificationi  r   Nrl   rm   rn   ro   rv   r   r>   r<   rC  rC        r>   rC  c                       \ rS rSrSrg)SmolLM3ForTokenClassificationi  r   NrD  r   r>   r<   rG  rG    rE  r>   rG  c                       \ rS rSrSrSrg)SmolLM3ForQuestionAnsweringi  transformerr   N)rl   rm   rn   ro   r  rv   r   r>   r<   rI  rI    s    %r>   rI  )r   r  r0  rC  rG  rI  )r"   )r   )Ecollections.abcr   typingr   rJ   r   activationsr   cache_utilsr   r	   
generationr
   integrationsr   r   r   masking_utilsr   r   modeling_flash_attention_utilsr   modeling_layersr   r   r   r   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   r   utils.genericr   r    utils.output_capturingr!   configuration_smollm3r#   Moduler%   r|   r   rp   rs   r   rN   r   r   r   r   r   r   r  r0  rC  rG  rI  __all__r   r>   r<   <module>r^     s  * %    ! . ) f f R B  P K F & I I G 5 0><RYY ><B( *+ ,2	UU\\ 	U# 	U%,, 	U& %II%<<% 
% <<	%
 LL4'% % % '(%2 )*I)ryy I) +I)X Y'JRYY J (J(  (4 (V _  $ Q
) Q
 Q
h F
/ F
 F
R	'GI_ 		$ACY 	&"=?U &r>   