
    Z j9[                        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$J%r%  SSK&J'r'J(r(J)r)  SSK*J+r+J,r,  SSK-J.r.  SSK/J0r0  \" S5       " S S\Rb                  5      5       r2 " S S\Rb                  5      r3 " S S\Rb                  5      r4S r5\" S5      S>S j5       r6S \Rn                  S!\8S"\Rn                  4S# jr9 S?S$\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< " 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   )Qwen3Config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$ )Qwen3RMSNorm1   epsreturnNc                    > [         TU ]  5         [        R                  " [        R
                  " U5      5      U l        X l        g)z+
Qwen3RMSNorm is equivalent to T5LayerNorm
N)super__init__r   	Parametertorchonesweightvariance_epsilon)selfhidden_sizer(   	__class__s      y/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/qwen3/modeling_qwen3.pyr,   Qwen3RMSNorm.__init__3   s/     	ll5::k#:; #    hidden_statesc                    UR                   nUR                  [        R                  5      nUR	                  S5      R                  SSS9nU[        R                  " X0R                  -   5      -  nU R                  UR                  U5      -  $ )N   T)keepdim)	dtypetor.   float32powmeanrsqrtr1   r0   )r2   r8   input_dtypevariances       r5   forwardQwen3RMSNorm.forward;   sw    #))%((7 $$Q',,R,>%H?T?T4T(UU{{]--k:::r7   c                 ^    [        U R                  R                  5       SU R                   3$ )Nz, eps=)tupler0   shaper1   )r2   s    r5   
extra_reprQwen3RMSNorm.extra_reprB   s*    ))*+6$2G2G1HIIr7   )r1   r0   )gư>)__name__
__module____qualname____firstlineno__floatr,   r.   TensorrE   rJ   __static_attributes____classcell__r4   s   @r5   r&   r&   1   sB    $ $$ $ $;U\\ ;ell ;J Jr7   r&   c                   .   ^  \ rS rSrU 4S jrS rSrU =r$ )Qwen3MLPF   c                   > [         TU ]  5         Xl        UR                  U l        UR                  U l        [
        R                  " U R                  U R                  SS9U l        [
        R                  " U R                  U R                  SS9U l        [
        R                  " U R                  U R                  SS9U l	        [        UR                     U l        g NFbias)r+   r,   configr3   intermediate_sizer   Linear	gate_projup_proj	down_projr   
hidden_actact_fnr2   r\   r4   s     r5   r,   Qwen3MLP.__init__G   s    !--!'!9!94#3#3T5K5KRWXyy!1!143I3IPUV4#9#94;K;KRWXV../r7   c                     U R                  U R                  U R                  U5      5      U R                  U5      -  5      nU$ N)ra   rc   r_   r`   )r2   xra   s      r5   rE   Qwen3MLP.forwardQ   s6    NN4;;t~~a/@#ADLLQRO#ST	r7   )rc   r\   ra   r_   r3   r]   r`   )rL   rM   rN   rO   r,   rE   rR   rS   rT   s   @r5   rV   rV   F   s    0 r7   rV   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$ )Qwen3RotaryEmbeddingV   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defaultrm   F)
persistentoriginal_inv_freq)r+   r,   max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenr\   rope_parametersro   compute_default_rope_parametersr   attention_scalingregister_bufferclone)r2   r\   devicerope_init_fnrm   r4   s        r5   r,   Qwen3RotaryEmbedding.__init__Y   s    "("@"@$*$B$B!44[A!%!E!E>>Y&.t~~>L+7V+L((ZeD0(..2BuUr7   r{   ztorch.deviceseq_lenr)   ztorch.Tensorc           	         U R                   S   n[        U SS5      =(       d    U R                  U R                  -  nSnSU[        R
                  " SUS[        R                  S9R                  U[        R                  S9U-  -  -  nXe4$ )	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   r:   r=   )r{   r=   )	rv   getattrr3   num_attention_headsr.   arangeint64r>   rP   )r\   r{   r~   basedimattention_factorrm   s          r5   rw   4Qwen3RotaryEmbedding.compute_default_rope_parametersi   s    & %%l3fj$/c63E3EIcIc3c U\\!S!5;;?BB&X]XcXcBdgjjk
 ))r7   c                 L   U R                   S S S 2S 4   R                  5       R                  UR                  S   SS5      R	                  UR
                  5      nUS S 2S S S 24   R                  5       n[        UR
                  R                  [        5      (       a0  UR
                  R                  S:w  a  UR
                  R                  OSn[        USS9   UR                  5       UR                  5       -  R                  SS5      n[        R                  " Xf4SS	9nUR                  5       U R                  -  nUR                  5       U R                  -  n	S S S 5        WR	                  UR                   S
9W	R	                  UR                   S
94$ ! , (       d  f       N@= f)Nr   r;   r"   mpscpuF)device_typeenabledr:   r   r   )rm   rP   expandrI   r>   r{   
isinstancetypestrr   	transposer.   catcosrx   sinr=   )
r2   rh   position_idsinv_freq_expandedposition_ids_expandedr   freqsembr   r   s
             r5   rE   Qwen3RotaryEmbedding.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#)rx   r\   rt   ru   ro   rg   )NNN)rL   rM   rN   rO   r.   rQ   __annotations__r#   r,   staticmethodr   intrH   rP   rw   no_gradr   rE   rR   rS   rT   s   @r5   rk   rk   V   s    llV{ V V  %)+/"*d"*(* t* 
~u$	%	* *: ]]_<  <r7   rk   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$ )z*Rotates half the hidden dims of the input..Nr;   r:   r   )rI   r.   r   )rh   x1x2s      r5   rotate_halfr      sZ    	
3"!''"+"""	#B	
3q ""	#B99rc2YB''r7   rotary_pos_embc                     UR                  U5      nUR                  U5      nX-  [        U 5      U-  -   nX-  [        U5      U-  -   nXV4$ )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          r5   apply_rotary_pos_embr      sS    & --
&C
--
&Cw;q>C/0Gw;q>C/0Gr7   r8   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)rI   r   reshape)r8   r   batchnum_key_value_headsslenr   s         r5   	repeat_kvr      s_    
 2?1D1D.Ez!!Qa"23::5W\dlmM  e(CTTTr7   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$ )Nr:   r   r;   )r   r=   )ptrainingr"   )r   num_key_value_groupsr.   matmulr   r   
functionalsoftmaxr?   r>   r=   r   r   
contiguous)r   r   r   r   r   r   r   r   
key_statesvalue_statesattn_weightsattn_outputs               r5   eager_attention_forwardr      s     3 ; ;<JU$?$?@L<<';';Aq'ABWLL!#4==((2U]](SVVW\WbWbcL==((6??([L,,|:K''1-88:K$$r7   c                     ^  \ 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$ )Qwen3Attention   z=Multi-headed attention from 'Attention Is All You Need' paperr\   	layer_idxc                 |  > [         TU ]  5         [        US5      (       a  UR                  U   OS U l        Xl        X l        [        USUR                  UR                  -  5      U l
        UR                  UR                  -  U l        U R                  S-  U l        UR                  U l        SU l        [         R"                  " UR                  UR                  U R                  -  UR$                  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R0                  S9U l        [/        U R                  UR0                  S9U l        U R                  S:X  a  UR6                  U l        g S U l        g )Nlayer_typesr   g      TrZ   r(   sliding_attention)r+   r,   hasattrr   
layer_typer\   r   r   r3   r   r   r   r   r   attention_dropout	is_causalr   r^   attention_biasq_projk_projv_projo_projr&   rms_norm_epsq_normk_normsliding_windowr2   r\   r   r4   s      r5   r,   Qwen3Attention.__init__   s   ;B6=;Y;Y&,,Y7_c"
F4F4F&JdJd4de$*$>$>&B\B\$\!}}d*!'!9!9ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii&&68J8JQWQfQf
 #4==f6I6IJ"4==f6I6IJ7;J]7]f33cgr7   Nr8   position_embeddingsr   past_key_valuesr   r)   c                 Z   UR                   S S n/ UQSPU R                  P7nU R                  U R                  U5      R	                  U5      5      R                  SS5      nU R                  U R                  U5      R	                  U5      5      R                  SS5      n	U R                  U5      R	                  U5      R                  SS5      n
Uu  p[        XX5      u  pUb  UR                  XU R                  5      u  p[        R                  " U R                  R                  [         5      nU" U UU	U
U4U R"                  (       d  SOU R$                  U R&                  U R(                  S.UD6u  pUR*                  " / UQSP76 R-                  5       nU R/                  U5      nX4$ )Nr;   r"   r:           )r   r   r   )rI   r   r   r   viewr   r   r   r   r   updater   r   get_interfacer\   _attn_implementationr   r   r   r   r   r   r   r   )r2   r8   r   r   r   r   input_shapehidden_shapequery_statesr   r   r   r   attention_interfacer   r   s                   r5   rE   Qwen3Attention.forward   s    $))#2.88b8$--8{{4;;}#=#B#B<#PQ[[\]_`a[[]!;!@!@!NOYYZ[]^_
{{=166|DNNqRST&#7RU#[ &'6'='=jX\XfXf'g$J(?(M(MKK,,.E)
 %8
%
  $}}C$2H2HLL..
%
 
%
! "));;;;FFHkk+.((r7   )r   r\   r   r   r   r   r   r   r   r   r   r   r   r   r   rg   )rL   rM   rN   rO   __doc__r#   r   r,   r.   rQ   rH   r   r   r   rE   rR   rS   rT   s   @r5   r   r      s    Gh{ hs h@ )-')||') #5<<#=>') t+	')
 ') -.') 
u||U\\D00	1') ')r7   r   c                     ^  \ rS rSrS\S\4U 4S jjr     SS\R                  S\R                  S-  S\R                  S-  S	\
S-  S
\S-  S\\R                  \R                  4   S-  S\\   S\R                  4S jjrSrU =r$ )Qwen3DecoderLayeri&  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,   r3   r   	self_attnrV   mlpr&   r   input_layernormpost_attention_layernormr   s      r5   r,   Qwen3DecoderLayer.__init__'  sj    !--'vKF#+F,>,>FDWDWX(4V5G5GVM`M`(a%r7   Nr8   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$ )N)r8   r   r   r   r   r    )r   r   r   r   )
r2   r8   r   r   r   r   r   r   residual_s
             r5   rE   Qwen3DecoderLayer.forward1  s     !,,];>> 
')%+ 3
 
 !0 !55mD/ 0r7   )r3   r   r   r   r   )NNNFN)rL   rM   rN   rO   r#   r   r,   r.   rQ   
LongTensorr   boolrH   r   r   rE   rR   rS   rT   s   @r5   r   r   &  s    b{ bs b /304(,!&HL|| t+ &&-	
  $; #5<<#=>E +, 
 r7   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	)
Qwen3PreTrainedModeliQ  r\   modelTr   r   )r8   
attentionsr   N)rL   rM   rN   rO   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_outputsrR   r   r7   r5   r  r  Q  sQ    &*#,-#4"5N!"&*$r7   r  c                     ^  \ rS rSrS\4U 4S jjr\\\      SS\	R                  S-  S\	R                  S-  S\	R                  S-  S\S-  S	\	R                  S-  S
\S-  S\\   S\4S jj5       5       5       rSrU =r$ )
Qwen3Modelid  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   	Embeddingr3   embed_tokens
ModuleListrangenum_hidden_layersr   layersr&   r   normrk   
rotary_embgradient_checkpointingr\   r   has_sliding_layers	post_initr   s      r5   r,   Qwen3Model.__init__f  s     !.. ++LL):):F<N<NPTP`P`ammCHIaIaCbcCbiv1Cbc
 !!3!39L9LM	.f=&+#"59P9P"P 	 ds   DN	input_idsr   r   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$ )Nz:You must specify exactly one of input_ids or inputs_embedsr  r   r"   )r{   )r\   r#  r   r   r   full_attentionr   )r   r   r   r   r   )last_hidden_stater   r   )
ValueErrorr  r	   r\   get_seq_lengthr.   r   rI   r{   r   r   dictr   r  r   r  	enumerater  r  r   r  r   )r2   r"  r   r   r   r#  r   r   past_seen_tokenscausal_mask_mappingmask_kwargsr8   r   idecoder_layers                  r5   rE   Qwen3Model.forwardw  s    -t";<YZZ  --i8M0*$++>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
 	
r7   )r  r  r  r  r  r  r  r  )NNNNNN)rL   rM   rN   rO   r#   r,   r    r!   r   r.   r   rQ   r   FloatTensorr   r   r   r   rE   rR   rS   rT   s   @r5   r  r  d  s    { "   .2.204(,26!%<
##d*<
 t+<
 &&-	<

 <
 ((4/<
 $;<
 +,<
 
!<
    <
r7   r  c                   P  ^  \ rS rSrSS0rSS0rSS/S/40rU 4S jr\\	        SS
\
R                  S	-  S\
R                  S	-  S\
R                  S	-  S\S	-  S\
R                  S	-  S\
R                  S	-  S\S	-  S\\
R                  -  S\\   S\4S jj5       5       rSrU =r$ )Qwen3ForCausalLMi  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 rY   )
r+   r,   r  r  r  r   r^   r3   r4  r   rd   s     r5   r,   Qwen3ForCausalLM.__init__  sU     '
 ++yy!3!3V5F5FUS 	r7   Nr"  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  
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, Qwen3ForCausalLM

>>> model = Qwen3ForCausalLM.from_pretrained("Qwen/Qwen3-8B")
>>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")

>>> 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)r6  r9  r  )lossr6  r   r8   r  r   )r  r&  r   r   slicer4  loss_functionr\   r  r   r   r8   r  )r2   r"  r   r   r   r#  r9  r   r:  r   outputsr8   slice_indicesr6  r<  s                  r5   rE   Qwen3ForCausalLM.forward  s    H ,0:: ,
)%+',
 ,
  118B>SV8W8W~ot4]kmA}a,?@A%%pVt{{OeOepiopD%#33!//))
 	
r7   )r4  r  r  )NNNNNNNr   )rL   rM   rN   rO   _tied_weights_keys_tp_plan_pp_planr,   r   r   r.   r   rQ   r   r1  r   r   r   r   r   rE   rR   rS   rT   s   @r5   r3  r3    s   *,GH23H_-z:;H  .2.204(,26*.!%-.;
##d*;
 t+;
 &&-	;

 ;
 ((4/;
   4';
 $;;
 ell*;
 +,;
 
 ;
  ;
r7   r3  c                       \ rS rSrSrg)Qwen3ForSequenceClassificationi  r   NrL   rM   rN   rO   rR   r   r7   r5   rF  rF        r7   rF  c                       \ rS rSrSrg)Qwen3ForTokenClassificationi  r   NrG  r   r7   r5   rJ  rJ    rH  r7   rJ  c                       \ rS rSrSrSrg)Qwen3ForQuestionAnsweringi  transformerr   N)rL   rM   rN   rO   r  rR   r   r7   r5   rL  rL    s    %r7   rL  )r3  rL  r  r  rF  rJ  )r"   )r   )Ecollections.abcr   typingr   r.   r   activationsr   cache_utilsr   r	   
generationr
   integrationsr   r   r   masking_utilsr   r   modeling_flash_attention_utilsr   modeling_layersr   r   r   r   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   r   utils.genericr   r    utils.output_capturingr!   configuration_qwen3r#   Moduler&   rV   rk   r   r   rQ   r   r   rP   r   r   r   r  r  r3  rF  rJ  rL  __all__r   r7   r5   <module>ra     s  * %    ! . ) f f R B  P K F & I I G 5 , Y'J299 J (J(ryy  ><299 ><B( *+ ,2	UU\\ 	U# 	U%,, 	U& %II%<<% 
% <<	%
 LL4'% % % '(%2 )*E)RYY E) +E)P(2 (V ?  $ Q
% Q
 Q
h K
+_ K
 K
\	%EG[ 		"?AU 	& ;=Q &r7   