
    Z jX                     ~   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  SS	K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$J%r%J&r&J'r'  SSK(J)r)J*r*  SSK+J,r,  SSK-J.r.   " S S\R^                  5      r0 " S S\5      r1S\Rd                  S\3S\Rd                  4S jr4 S:S\R^                  S\Rd                  S\Rd                  S \Rd                  S!\Rd                  S-  S"\5S#\5S$\#\%   4S% jjr6S& r7S;S' jr8\" \85       " S( S)\R^                  5      5       r9 " S* S+\R^                  5      r:\" S,5       " S- S.\R^                  5      5       r;\& " S/ S0\!5      5       r<\& " S1 S2\<5      5       r=\& " S3 S4\<\5      5       r> " S5 S6\\<5      r? " S7 S8\\<5      r@/ S9QrAg)<    )Callable)OptionalN   )ACT2FN)CacheDynamicCache)GenerationMixin)use_kernel_forward_from_hubuse_kernelized_func)create_causal_mask)FlashAttentionKwargs) 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   )
Glm4Configc                   b   ^  \ rS rSrU 4S jrS\R                  S\R                  4S jrSrU =r	$ )Glm4MLP1   c                    > [         TU ]  5         Xl        [        R                  " UR
                  SUR                  -  SS9U l        [        R                  " UR                  UR
                  SS9U l        [        UR                     U l        g )N   Fbias)super__init__confignnLinearhidden_sizeintermediate_sizegate_up_proj	down_projr   
hidden_actactivation_fnselfr)   	__class__s     w/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/glm4/modeling_glm4.pyr(   Glm4MLP.__init__2   sn    IIf&8&8!f>V>V:V]bc6#;#;V=O=OV[\#F$5$56    hidden_statesreturnc                     U R                  U5      nUR                  SSS9u  p2X R                  U5      -  nU R                  U5      $ )Nr$   dim)r.   chunkr1   r/   )r3   r8   	up_statesgates       r5   forwardGlm4MLP.forward:   sH    %%m4	#//!/4 2 24 88	~~i((r7   )r1   r)   r/   r.   )
__name__
__module____qualname____firstlineno__r(   torchFloatTensorrA   __static_attributes____classcell__r4   s   @r5   r!   r!   1   s,    7)U%6%6 )5;L;L ) )r7   r!   c                   T  ^  \ rS rSrS\S\4U 4S jjr     SS\R                  S\R                  S-  S\R                  S-  S	\
S-  S
\S-  S\\R                  \R                  4   S-  S\\   S\\R                  \\R                  \R                  4   S-  4   4S jjrSrU =r$ )Glm4DecoderLayerC   r)   	layer_idxc                   > [         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R                  S9U l        [        UR                  UR                  S9U l        g )N)r)   rO   eps)r'   r(   r,   Glm4Attention	self_attnr!   mlpGlm4RMSNormrms_norm_epsinput_layernormpost_attention_layernormpost_self_attn_layernormpost_mlp_layernormr3   r)   rO   r4   s      r5   r(   Glm4DecoderLayer.__init__D   s    !--&fJ6?*6+=+=6CVCVW(3F4F4FFL_L_(`%(3F4F4FFL_L_(`%"-f.@.@fFYFY"Zr7   Nr8   attention_maskposition_idspast_key_values	use_cacheposition_embeddingskwargsr9   c           
          UnU R                  U5      nU R                  " S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U R                  U5      nX-   nU$ )N)r8   r^   r_   r`   ra   rb    )rX   rT   rZ   rY   rU   r[   )
r3   r8   r^   r_   r`   ra   rb   rc   residual_s
             r5   rA   Glm4DecoderLayer.forwardO   s     !,,];>> 
')%+ 3
 
 55mD 0 55mD///> 0r7   )r,   rX   rU   rY   r[   rZ   rT   )NNNFN)rC   rD   rE   rF   r   intr(   rG   Tensor
LongTensorr   booltupler   r   rH   rA   rI   rJ   rK   s   @r5   rM   rM   C   s    	[z 	[c 	[ /304(,!&HL|| t+ &&-	
  $; #5<<#=>E -. 
u  %(9(95;L;L(L"MPT"TT	U r7   rM   r8   n_repr9   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)shapeexpandreshape)r8   rn   batchnum_key_value_headsslenhead_dims         r5   	repeat_kvrw   q   s_    
 2?1D1D.Ez!!Qa"23::5W\dlmM  e(CTTTr7   modulequerykeyvaluer^   scalingdropoutrc   c                    [        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$ )Nr$   r   r;   )r=   dtype)ptrainingr   )rw   num_key_value_groupsrG   matmul	transposer*   
functionalsoftmaxfloat32tor   r}   r   
contiguous)rx   ry   rz   r{   r^   r|   r}   rc   
key_statesvalue_statesattn_weightsattn_outputs               r5   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$$r7   c                 x    U SSSS24   nU SSSS24   n[         R                  " U* U4SS9R                  S5      $ )	z*Rotates half the hidden dims of the input..r   Nr$   r   r;   r<   )rG   stackflatten)xx1x2s      r5   rotate_halfr      sJ    	
319B	
319B;;Ryb)11"55r7   c                    UR                  U5      nUR                  U5      nUSSUR                  S   S-  24   R                  SSS9nUSSUR                  S   S-  24   R                  SSS9nUR                  S   nU SSU24   U SUS24   pvUSSU24   USUS24   pXb-  [        U5      U-  -   n
X-  [        U5      U-  -   n[        R
                  " X/SS9n
[        R
                  " X/SS9nX4$ )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.
.Nr;   r$   r<   )	unsqueezerp   repeat_interleaver   rG   cat)qkcossinunsqueeze_dim
rotary_dimq_rotq_passk_rotk_passq_embedk_embeds               r5   apply_rotary_pos_embr      s6   $ --
&C
--
&C c'SYYr]a'''
(
:
:1"
:
EC
c'SYYr]a'''
(
:
:1"
:
EC 2Jc;J;&'3
+;)<6c;J;&'3
+;)<6 {{51C78G{{51C78G ii)r2Gii)r2Gr7   c                     ^  \ rS rSrSrSS\S\S-  4U 4S j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$ )rS      z=Multi-headed attention from 'Attention Is All You Need' paperNr)   rO   c                 <  > [         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
                  SS9U l        g )Nrv   g      Tr%   F)r'   r(   r)   rO   getattrr,   num_attention_headsrv   rt   r   r|   attention_dropout	is_causalr*   r+   attention_biasq_projk_projv_projo_projr\   s      r5   r(   Glm4Attention.__init__   s@   "
F4F4F&JdJd4de$*$>$>&B\B\$\!}}d*!'!9!9ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii : :T]] JFL^L^ejkr7   r8   rb   r^   r`   rc   r9   c                    UR                   S S n/ UQSPU R                  P7nU R                  U5      R                  U5      R	                  SS5      nU R                  U5      R                  U5      R	                  SS5      n	U R                  U5      R                  U5      R	                  SS5      n
Uu  p[        XX5      u  pUb  UR                  XU R                  5      u  p[        R                  " U R                  R                  [        5      nU" U UU	U
U4U R                  (       d  SOU R                   U R"                  S.UD6u  pUR$                  " / UQSP76 R'                  5       nU R)                  U5      nX4$ )Nr;   r   r$           )r}   r|   )rp   rv   r   viewr   r   r   r   updaterO   r   get_interfacer)   _attn_implementationr   r   r   r|   rr   r   r   )r3   r8   rb   r^   r`   rc   input_shapehidden_shapequery_statesr   r   r   r   attention_interfacer   r   s                   r5   rA   Glm4Attention.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$2H2HLL	%
 	%
! "));;;;FFHkk+.((r7   )r   r)   rv   r   r   rO   r   r   r   r|   r   NNNN)rC   rD   rE   rF   __doc__r   ri   r(   rG   rj   rm   r   r   r   rA   rI   rJ   rK   s   @r5   rS   rS      s    Glz lcDj l l0 IM.2(,&)||&) #5<<#=>E&) t+	&)
 &) +,&) 
u||U\\)	*&) &)r7   rS   c                      ^  \ rS rSr% \R
                  \S'   SS\4U 4S jjjr\	   SS\S-  S\
S   S\S-  S	\S
\4   4S jj5       r\R                  " 5       \S 5       5       rSrU =r$ )Glm4RotaryEmbeddingi  inv_freqNr)   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   F)
persistentoriginal_inv_freq)r'   r(   max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenr)   rope_parametersr   compute_default_rope_parametersr   attention_scalingregister_bufferclone)r3   r)   devicerope_init_fnr   r4   s        r5   r(   Glm4RotaryEmbedding.__init__
  s    "("@"@$*$B$B!44[A!%!E!E>>Y&.t~~>L+7V+L((ZeD0(..2BuUr7   r   ztorch.deviceseq_lenr9   ztorch.Tensorc           	      j   U R                   S   nU R                   R                  SS5      n[        U SS5      =(       d    U R                  U R                  -  n[        XT-  5      nSnSU[        R                  " SUS[        R                  S9R                  U[        R                  S	9U-  -  -  nX4$ )
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partial_rotary_factorg      ?rv   Nr   r$   r   )r   r   )r   getr   r,   r   ri   rG   arangeint64r   float)	r)   r   r   baser   rv   r=   attention_factorr   s	            r5   r   3Glm4RotaryEmbedding.compute_default_rope_parameters  s    & %%l3 & 6 6 : :;RTW X6:t4h8J8JfNhNh8h(23 U\\!S!5;;?BB&X]XcXcBdgjjk
 ))r7   c                 L   U R                   S S S 2S 4   R                  5       R                  UR                  S   SS5      R	                  UR
                  5      nUS S 2S S S 24   R                  5       n[        UR
                  R                  [        5      (       a0  UR
                  R                  S:w  a  UR
                  R                  OSn[        USS9   UR                  5       UR                  5       -  R                  SS5      n[        R                  " Xf4SS	9nUR                  5       U R                  -  nUR                  5       U R                  -  n	S S S 5        WR	                  UR                   S
9W	R	                  UR                   S
94$ ! , (       d  f       N@= f)Nr   r;   r   mpscpuF)device_typeenabledr$   r<   r   )r   r   rq   rp   r   r   
isinstancetypestrr   r   rG   r   r   r   r   r   )
r3   r   r_   inv_freq_expandedposition_ids_expandedr   freqsembr   r   s
             r5   rA   Glm4RotaryEmbedding.forward:  sN    !MM$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$cff177f&;;; DCs   BF
F#)r   r)   r   r   r   r   r   )rC   rD   rE   rF   rG   rj   __annotations__r   r(   staticmethodr   ri   rm   r   r   no_gradr   rA   rI   rJ   rK   s   @r5   r   r     s    llVz V V  $(+/"*T!*(* t* 
~u$	%	* *> ]]_<  <r7   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$ )rV   iJ  rR   r9   Nc                    > [         TU ]  5         [        R                  " [        R
                  " U5      5      U l        X l        g)z*
Glm4RMSNorm is equivalent to T5LayerNorm
N)r'   r(   r*   	ParameterrG   onesweightvariance_epsilon)r3   r,   rR   r4   s      r5   r(   Glm4RMSNorm.__init__L  s/     	ll5::k#:; #r7   r8   c                    UR                   nUR                  [        R                  5      nUR	                  S5      R                  SSS9nU[        R                  " X0R                  -   5      -  nU R                  UR                  U5      -  $ )Nr$   r;   T)keepdim)	r   r   rG   r   powmeanrsqrtr   r   )r3   r8   input_dtypevariances       r5   rA   Glm4RMSNorm.forwardT  sw    #))%((7 $$Q',,R,>%H?T?T4T(UU{{]--k:::r7   c                 ^    [        U R                  R                  5       SU R                   3$ )Nz, eps=)rm   r   rp   r   )r3   s    r5   
extra_reprGlm4RMSNorm.extra_repr[  s*    ))*+6$2G2G1HIIr7   )r   r   )gư>)rC   rD   rE   rF   r   r(   rG   rj   rA   r  rI   rJ   rK   s   @r5   rV   rV   J  sB    $ $$ $ $;U\\ ;ell ;J Jr7   rV   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	)
Glm4PreTrainedModeli_  r)   modelTrM   r`   )r8   
attentionsre   N)rC   rD   rE   rF   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_backendrM   rS   _can_record_outputsrI   re   r7   r5   r  r  _  sQ    &*#+,#4"5N!"&)#r7   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$ )	Glm4Modelir  r)   c           	        > [         TU ]  U5        UR                  U l        UR                  U l        [
        R                  " UR                  UR                  U R                  5      U l        [
        R                  " [        UR                  5       Vs/ s H  n[        X5      PM     sn5      U l        [        UR                  UR                  S9U l        [#        US9U l        SU l        U R)                  5         g s  snf )NrQ   r)   F)r'   r(   pad_token_idpadding_idx
vocab_sizer*   	Embeddingr,   embed_tokens
ModuleListrangenum_hidden_layersrM   layersrV   rW   normr   
rotary_embgradient_checkpointing	post_initr\   s      r5   r(   Glm4Model.__init__t  s     !.. ++LL):):F<N<NPTP`P`ammBGH`H`BabBaYf0Bab
   2 28K8KL	-V<&+# 	 cs   C?N	input_idsr^   r_   r`   inputs_embedsra   rc   r9   c           
      >   US L US L-  (       a  [        S5      eUc  U R                  U5      nU(       a  Uc  [        U R                  S9nUcU  Ub  UR	                  5       OSn[
        R                  " UR                  S   UR                  S9U-   nUR                  S5      n[        U R                  UUUUS9n	Un
U R                  XS9nU R                  S U R                  R                    H  nU" U
4U	UUUUS.UD6n
M     U R                  U
5      n
[        U
US	9$ )
Nz:You must specify exactly one of input_ids or inputs_embedsr  r   r   )r   )r)   r&  r^   r`   r_   )r_   )r^   rb   r_   r`   ra   )last_hidden_stater`   )
ValueErrorr  r   r)   get_seq_lengthrG   r   rp   r   r   r   r!  r  r  r   r   )r3   r%  r^   r_   r`   r&  ra   rc   past_seen_tokenscausal_maskr8   rb   decoder_layers                r5   rA   Glm4Model.forward  sF    -t";<YZZ *.*;*;I*FM0*$++>OCRC^==?de <<(;(;A(>}G[G[\_ooL'11!4L(;;')+%
 &"oomoW![[)H4;;+H+HIM)*$7) /# M J 		-0&++
 	
r7   )r  r"  r  r   r  r!  r  )NNNNNN)rC   rD   rE   rF   r   r(   r   r   r   rG   rk   rj   r   rH   rl   r   r   r   rA   rI   rJ   rK   s   @r5   r  r  r  s    z     .2.204(,26!%2
##d*2
 t+2
 &&-	2

 2
 ((4/2
 $;2
 +,2
 
!2
    2
r7   r  c                   V  ^  \ rS rSrSS0rSS0rSS/S/40rU 4S jr\\	        SS
\
R                  S	-  S\
R                  S	-  S\
R                  S	-  S\S	-  S\
R                  S	-  S\
R                  S	-  S\S	-  S\\
R                  -  S\\   S\\-  4S jj5       5       rSrU =r$ )Glm4ForCausalLMi  zlm_head.weightzmodel.embed_tokens.weightlm_headcolwise_gather_outputr8   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+   r,   r1  r#  r2   s     r5   r(   Glm4ForCausalLM.__init__  sU     v&
 ++yy!3!3V5F5FUS 	r7   Nr%  r^   r_   r`   r&  labelsra   logits_to_keeprc   r9   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  
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 AutoTokenizer, Glm4ForCausalLM

>>> model = Glm4ForCausalLM.from_pretrained("THUDM/GLM-4-9B-0414")
>>> tokenizer = AutoTokenizer.from_pretrained("THUDM/GLM-4-9B-0414")

>>> 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^   r_   r`   r&  ra   N)r3  r6  r  )lossr3  r`   r8   r  re   )r  r(  r   ri   slicer1  loss_functionr)   r  r   r`   r8   r  )r3   r%  r^   r_   r`   r&  r6  ra   r7  rc   outputsr8   slice_indicesr3  r9  s                  r5   rA   Glm4ForCausalLM.forward  s    H ,0:: ,
)%+',
 ,
  118B>SV8W8W~ot4]kmA}a,?@A%%pVt{{OeOepiopD%#33!//))
 	
r7   )r1  r  r  )NNNNNNNr   )rC   rD   rE   rF   _tied_weights_keys_tp_plan_pp_planr(   r   r   rG   rk   rj   r   rH   rl   ri   r   r   rm   r   rA   rI   rJ   rK   s   @r5   r0  r0    s   *,GH23H_-z:;H  .2.204(,26*.!%-.;
##d*;
 t+;
 &&-	;

 ;
 ((4/;
   4';
 $;;
 ell*;
 +,;
 
'	';
  ;
r7   r0  c                       \ rS rSrSrg)Glm4ForSequenceClassificationi  re   NrC   rD   rE   rF   rI   re   r7   r5   rC  rC        r7   rC  c                       \ rS rSrSrg)Glm4ForTokenClassificationi  re   NrD  re   r7   r5   rG  rG    rE  r7   rG  )r  r  r0  rC  rG  )r   )r   )Bcollections.abcr   typingr   rG   torch.nnr*   activationsr   cache_utilsr   r   
generationr	   integrationsr
   r   masking_utilsr   modeling_flash_attention_utilsr   modeling_layersr   r   r   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   r   utils.genericr   r   utils.output_capturingr   configuration_glm4r   Moduler!   rM   rj   ri   rw   r   r   r   r   rS   r   rV   r  r  r0  rC  rG  __all__re   r7   r5   <module>r\     s  , %    ! . ) L / B 
 P K F & I I G 5 *)bii )$+1 +\	UU\\ 	U# 	U%,, 	U& %II%<<% 
% <<	%
 LL4'% % % '(%26%P )*>)BII >) +>)B@<")) @<F Y'J")) J (J( /  $ F
# F
 F
R K
)? K
 K
\	$DFY 		!>@S 	r7   