
    3jX                     d   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  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      r/ " S S\5      r0S\Rb                  S\2S\Rb                  4S jr3 S:S\R\                  S\Rb                  S\Rb                  S \Rb                  S!\Rb                  S-  S"\4S#\4S$\"\$   4S% jjr5S& r6S;S' jr7 " S( S)\R\                  5      r8 " S* S+\R\                  5      r9\" 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r@g)<    )Callable)OptionalN   )ACT2FN)CacheDynamicCache)GenerationMixin)use_kernel_forward_from_hub)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     `/home/wildlama/miniconda3/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-   chunkr0   r.   )r2   r7   	up_statesgates       r4   forwardGlm4MLP.forward:   sH    %%m4	#//!/4 2 24 88	~~i((r6   )r0   r(   r.   r-   )
__name__
__module____qualname____firstlineno__r'   torchFloatTensorr@   __static_attributes____classcell__r3   s   @r4   r    r    1   s,    7)U%6%6 )5;L;L ) )r6   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$ )Glm4DecoderLayerC   r(   	layer_idxc                   > [         TU ]  5         UR                  U l        [        XS9U l        [        U5      U l        [        UR                  UR                  S9U l	        [        UR                  UR                  S9U l
        [        UR                  UR                  S9U l        [        UR                  UR                  S9U l        g )N)r(   rN   eps)r&   r'   r+   Glm4Attention	self_attnr    mlpGlm4RMSNormrms_norm_epsinput_layernormpost_attention_layernormpost_self_attn_layernormpost_mlp_layernormr2   r(   rN   r3   s      r4   r'   Glm4DecoderLayer.__init__D   s    !--&fJ6?*6+=+=6CVCVW(3F4F4FFL_L_(`%(3F4F4FFL_L_(`%"-f.@.@fFYFY"Zr6   Nr7   attention_maskposition_idspast_key_values	use_cacheposition_embeddingskwargsr8   c           
          UnU R                  U5      nU R                  " SUUUUUUS.UD6u  pU R                  U5      nX-   nUnU R                  U5      nU R	                  U5      nU R                  U5      nX-   nU$ )N)r7   r]   r^   r_   r`   ra    )rW   rS   rY   rX   rT   rZ   )
r2   r7   r]   r^   r_   r`   ra   rb   residual_s
             r4   r@   Glm4DecoderLayer.forwardO   s     !,,];>> 
')%+ 3
 
 55mD 0 55mD///> 0r6   )r+   rW   rT   rX   rZ   rY   rS   )NNNFN)rB   rC   rD   rE   r   intr'   rF   Tensor
LongTensorr   booltupler   r   rG   r@   rH   rI   rJ   s   @r4   rL   rL   C   s    	[z 	[c 	[ /304(,!&HL|| t+ &&-	
  $; #5<<#=>E -. 
u  %(9(95;L;L(L"MPT"TT	U r6   rL   r7   n_repr8   c                     U R                   u  p#pEUS:X  a  U $ 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      $ )z
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
r   N)shapeexpandreshape)r7   rm   batchnum_key_value_headsslenhead_dims         r4   	repeat_kvrv   q   s_    
 2?1D1D.Ez!!Qa"23::5W\dlmM  e(CTTTr6   modulequerykeyvaluer]   scalingdropoutrb   c                    [        X R                  5      n[        X0R                  5      n	[        R                  " XR	                  SS5      5      U-  n
Ub  X-   n
[
        R                  R                  U
S[        R                  S9R                  UR                  5      n
[
        R                  R                  XU R                  S9n
[        R                  " X5      nUR	                  SS5      R                  5       nX4$ )Nr#   r   r:   )r<   dtype)ptrainingr   )rv   num_key_value_groupsrF   matmul	transposer)   
functionalsoftmaxfloat32tor~   r|   r   
contiguous)rw   rx   ry   rz   r]   r{   r|   rb   
key_statesvalue_statesattn_weightsattn_outputs               r4   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$$r6   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      $ )	z*Rotates half the hidden dims of the input..r   Nr#   r   r:   r;   )rF   stackflatten)xx1x2s      r4   rotate_halfr      sJ    	
319B	
319B;;Ryb)11"55r6   c                    UR                  U5      nUR                  U5      nUSSUR                  S   S-  24   R                  SSS9nUSSUR                  S   S-  24   R                  SSS9nUR                  S   nU SSU24   U SUS24   pvUSSU24   USUS24   pXb-  [        U5      U-  -   n
X-  [        U5      U-  -   n[        R
                  " X/SS9n
[        R
                  " X/SS9nX4$ )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;   )	unsqueezero   repeat_interleaver   rF   cat)qkcossinunsqueeze_dim
rotary_dimq_rotq_passk_rotk_passq_embedk_embeds               r4   apply_rotary_pos_embr      s6   $ --
&C
--
&C c'SYYr]a'''
(
:
:1"
:
EC
c'SYYr]a'''
(
:
:1"
:
EC 2Jc;J;&'3
+;)<6c;J;&'3
+;)<6 {{51C78G{{51C78G ii)r2Gii)r2Gr6   c                     ^  \ rS rSrSrSS\S\S-  4U 4S jjjr   SS\R                  S\
\R                  \R                  4   S-  S	\R                  S-  S
\S-  S\\   S\
\R                  \R                  4   4S jjrSrU =r$ )rR      z=Multi-headed attention from 'Attention Is All You Need' paperNr(   rN   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 )Nru   g      Tr$   F)r&   r'   r(   rN   getattrr+   num_attention_headsru   rs   r   r{   attention_dropout	is_causalr)   r*   attention_biasq_projk_projv_projo_projr[   s      r4   r'   Glm4Attention.__init__   s@   "
F4F4F&JdJd4de$*$>$>&B\B\$\!}}d*!'!9!9ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii : :T]] JFL^L^ejkr6   r7   ra   r]   r_   rb   r8   c                    UR                   S S n/ UQSPU R                  P7nU R                  U5      R                  U5      R	                  SS5      nU R                  U5      R                  U5      R	                  SS5      n	U R                  U5      R                  U5      R	                  SS5      n
Uu  p[        XX5      u  pUb  UR                  XU R                  5      u  p[        R                  " U R                  R                  [        5      nU" U UU	U
U4U R                  (       d  SOU R                   U R"                  S.UD6u  pUR$                  " / UQSP76 R'                  5       nU R)                  U5      nX4$ )Nr:   r   r#           )r|   r{   )ro   ru   r   viewr   r   r   r   updaterN   r   get_interfacer(   _attn_implementationr   r   r   r{   rq   r   r   )r2   r7   ra   r]   r_   rb   input_shapehidden_shapequery_statesr   r   r   r   attention_interfacer   r   s                   r4   r@   Glm4Attention.forward   s~    $))#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$2H2HLL	%
 	%
! "));;;;FFHkk+.((r6   )r   r(   ru   r   r   rN   r   r   r   r{   r   NNNN)rB   rC   rD   rE   __doc__r   rh   r'   rF   ri   rl   r   r   r   r@   rH   rI   rJ   s   @r4   rR   rR      s    Glz lcDj l l0 IM.2(,&)||&) #5<<#=>E&) t+	&)
 &) +,&) 
u||U\\)	*&) &)r6   rR   c                      ^  \ rS rSr% \R
                  \S'   SS\4U 4S jjjr\	   SS\S-  S\
S   S\S-  S	\S
\4   4S jj5       r\R                  " 5       \S 5       5       rSrU =r$ )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)r2   r(   devicerope_init_fnr   r3   s        r4   r'   Glm4RotaryEmbedding.__init__	  s    "("@"@$*$B$B!44[A!%!E!E>>Y&.t~~>L+7V+L((ZeD0(..2BuUr6   r   ztorch.deviceseq_lenr8   ztorch.Tensorc           	      j   U R                   S   nU R                   R                  SS5      n[        U SS5      =(       d    U R                  U R                  -  n[        XT-  5      nSnSU[        R                  " SUS[        R                  S9R                  U[        R                  S	9U-  -  -  nX4$ )
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      ?ru   Nr   r#   r~   )r   r~   )r   getr   r+   r   rh   rF   arangeint64r   float)	r(   r   r   baser   ru   r<   attention_factorr   s	            r4   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
 ))r6   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$ ! , (       d  f       N@= f)Nr   r:   r   mpscpuF)device_typeenabledr#   r;   r   )r   r   rp   ro   r   r   
isinstancetypestrr   r   rF   r   r   r   r   r~   )
r2   r   r^   inv_freq_expandedposition_ids_expandedr   freqsembr   r   s
             r4   r@   Glm4RotaryEmbedding.forward9  sN    !MM$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$cff177f&;;; DCs   BF
F#)r   r(   r   r   r   r   r   )rB   rC   rD   rE   rF   ri   __annotations__r   r'   staticmethodr   rh   rl   r   r   no_gradr   r@   rH   rI   rJ   s   @r4   r   r     s    llVz V V  $(+/"*T!*(* t* 
~u$	%	* *> ]]_<  <r6   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$ )rU   iI  rQ   r8   Nc                    > [         TU ]  5         [        R                  " [        R
                  " U5      5      U l        X l        g)z*
Glm4RMSNorm is equivalent to T5LayerNorm
N)r&   r'   r)   	ParameterrF   onesweightvariance_epsilon)r2   r+   rQ   r3   s      r4   r'   Glm4RMSNorm.__init__K  s/     	ll5::k#:; #r6   r7   c                    UR                   nUR                  [        R                  5      nUR	                  S5      R                  SSS9nU[        R                  " X0R                  -   5      -  nU R                  UR                  U5      -  $ )Nr#   r:   T)keepdim)	r~   r   rF   r   powmeanrsqrtr   r   )r2   r7   input_dtypevariances       r4   r@   Glm4RMSNorm.forwardS  sw    #))%((7 $$Q',,R,>%H?T?T4T(UU{{]--k:::r6   c                 ^    [        U R                  R                  5       SU R                   3$ )Nz, eps=)rl   r   ro   r   )r2   s    r4   
extra_reprGlm4RMSNorm.extra_reprZ  s*    ))*+6$2G2G1HIIr6   )r   r   )gư>)rB   rC   rD   rE   r   r'   rF   ri   r@   r  rH   rI   rJ   s   @r4   rU   rU   I  sB    $ $$ $ $;U\\ ;ell ;J Jr6   rU   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(   modelTrL   r_   )r7   
attentionsrd   N)rB   rC   rD   rE   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_backendrL   rR   _can_record_outputsrH   rd   r6   r4   r  r  ^  sQ    &*#+,#4"5N!"&)#r6   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$ )	Glm4Modeliq  r(   c           	        > [         TU ]  U5        UR                  U l        UR                  U l        [
        R                  " UR                  UR                  U R                  5      U l        [
        R                  " [        UR                  5       Vs/ s H  n[        X5      PM     sn5      U l        [        UR                  UR                  S9U l        [#        US9U l        SU l        U R)                  5         g s  snf )NrP   r(   F)r&   r'   pad_token_idpadding_idx
vocab_sizer)   	Embeddingr+   embed_tokens
ModuleListrangenum_hidden_layersrL   layersrU   rV   normr   
rotary_embgradient_checkpointing	post_initr[   s      r4   r'   Glm4Model.__init__s  s     !.. ++LL):):F<N<NPTP`P`ammBGH`H`BabBaYf0Bab
   2 28K8KL	-V<&+# 	 cs   C?N	input_idsr]   r^   r_   inputs_embedsr`   rb   r8   c           
      >   US L US L-  (       a  [        S5      eUc  U R                  U5      nU(       a  Uc  [        U R                  S9nUcU  Ub  UR	                  5       OSn[
        R                  " UR                  S   UR                  S9U-   nUR                  S5      n[        U R                  UUUUS9n	Un
U R                  XS9nU R                  S U R                  R                    H  nU" U
4U	UUUUS.UD6n
M     U R                  U
5      n
[        U
US	9$ )
Nz:You must specify exactly one of input_ids or inputs_embedsr  r   r   )r   )r(   r%  r]   r_   r^   )r^   )r]   ra   r^   r_   r`   )last_hidden_stater_   )
ValueErrorr  r   r(   get_seq_lengthrF   r   ro   r   r   r   r   r  r  r  r   )r2   r$  r]   r^   r_   r%  r`   rb   past_seen_tokenscausal_maskr7   ra   decoder_layers                r4   r@   Glm4Model.forward  sF    -t";<YZZ *.*;*;I*FM0*$++>OCRC^==?de <<(;(;A(>}G[G[\_ooL'11!4L(;;')+%
 &"oomoW![[)H4;;+H+HIM)*$7) /# M J 		-0&++
 	
r6   )r  r!  r  r  r  r   r  )NNNNNN)rB   rC   rD   rE   r   r'   r   r   r   rF   rj   ri   r   rG   rk   r   r   r   r@   rH   rI   rJ   s   @r4   r  r  q  s    z     .2.204(,26!%2
##d*2
 t+2
 &&-	2

 2
 ((4/2
 $;2
 +,2
 
!2
    2
r6   r  c                   V  ^  \ rS rSrSS0rSS0rSS/S/40rU 4S jr\\	        SS
\
R                  S	-  S\
R                  S	-  S\
R                  S	-  S\S	-  S\
R                  S	-  S\
R                  S	-  S\S	-  S\\
R                  -  S\\   S\\-  4S jj5       5       rSrU =r$ )Glm4ForCausalLMi  zlm_head.weightzmodel.embed_tokens.weightlm_headcolwise_gather_outputr7   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$   )
r&   r'   r  r  r  r)   r*   r+   r0  r"  r1   s     r4   r'   Glm4ForCausalLM.__init__  sU     v&
 ++yy!3!3V5F5FUS 	r6   Nr$  r]   r^   r_   r%  labelsr`   logits_to_keeprb   r8   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$  r]   r^   r_   r%  r`   N)r2  r5  r  )lossr2  r_   r7   r  rd   )r  r'  r   rh   slicer0  loss_functionr(   r  r   r_   r7   r  )r2   r$  r]   r^   r_   r%  r5  r`   r6  rb   outputsr7   slice_indicesr2  r8  s                  r4   r@   Glm4ForCausalLM.forward  s    H ,0:: ,
)%+',
 ,
  118B>SV8W8W~ot4]kmA}a,?@A%%pVt{{OeOepiopD%#33!//))
 	
r6   )r0  r  r  )NNNNNNNr   )rB   rC   rD   rE   _tied_weights_keys_tp_plan_pp_planr'   r   r   rF   rj   ri   r   rG   rk   rh   r   r   rl   r   r@   rH   rI   rJ   s   @r4   r/  r/    s   *,GH23H_-z:;H  .2.204(,26*.!%-.;
##d*;
 t+;
 &&-	;

 ;
 ((4/;
   4';
 $;;
 ell*;
 +,;
 
'	';
  ;
r6   r/  c                       \ rS rSrSrg)Glm4ForSequenceClassificationi
  rd   NrB   rC   rD   rE   rH   rd   r6   r4   rB  rB  
      r6   rB  c                       \ rS rSrSrg)Glm4ForTokenClassificationi  rd   NrC  rd   r6   r4   rF  rF    rD  r6   rF  )r  r  r/  rB  rF  )r   )r   )Acollections.abcr   typingr   rF   torch.nnr)   activationsr   cache_utilsr   r   
generationr	   integrationsr
   masking_utilsr   modeling_flash_attention_utilsr   modeling_layersr   r   r   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   r   utils.genericr   r   utils.output_capturingr   configuration_glm4r   Moduler    rL   ri   rh   rv   r   r   r   r   rR   r   rU   r  r  r/  rB  rF  __all__rd   r6   r4   <module>r[     s  , %    ! . ) 7 / B 
 P K F & I I G 5 *)bii )$+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 	r6   