
    Z j]U                     t   S SK Jr  S SKJr  S SKrS SK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  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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\RZ                  5      r.\" S5       " S S\RZ                  5      5       r/ " S S\RZ                  5      r0S r1\" S5      S9S j5       r2S\Rf                  S \4S!\Rf                  4S" jr5 S:S#\RZ                  S$\Rf                  S%\Rf                  S&\Rf                  S'\Rf                  S-  S(\6S)\6S*\!\#   4S+ jjr7\" \25       " S, S-\RZ                  5      5       r8 " S. S/\5      r9\$ " S0 S1\5      5       r:\$ " S2 S3\:5      5       r;\$ " S4 S5\:\5      5       r< " S6 S7\\:5      r=/ S8Qr>g);    )Callable)OptionalN)nn   )ACT2CLSACT2FN)CacheDynamicCache)GenerationMixin)use_kernel_forward_from_hubuse_kernel_func_from_hubuse_kernelized_func)create_causal_mask)GenericForTokenClassificationGradientCheckpointingLayer)BaseModelOutputWithPastCausalLMOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)TransformersKwargsauto_docstringcan_return_tuple)maybe_autocastmerge_with_config_defaults)capture_outputs   )ApertusConfigc                   .   ^  \ rS rSrU 4S jrS rSrU =r$ )
ApertusMLP+   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        [        UR                     U l        UR                  S:X  a  [        S   " UR                  S9U l        g g )NFbiasxieludtype)super__init__confighidden_sizeintermediate_sizer   Linearup_proj	down_projr   
hidden_actact_fnr   r)   selfr,   	__class__s     }/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/apertus/modeling_apertus.pyr+   ApertusMLP.__init__,   s    !--!'!9!9yy!1!143I3IPUV4#9#94;K;KRWXV../'!'*>DK (    c                 `    U R                  U R                  U R                  U5      5      5      $ N)r1   r3   r0   )r5   xs     r7   forwardApertusMLP.forward7   s"    ~~dkk$,,q/:;;r9   )r3   r,   r1   r-   r.   r0   )__name__
__module____qualname____firstlineno__r+   r=   __static_attributes____classcell__r6   s   @r7   r"   r"   +   s    	?< <r9   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$ )ApertusRMSNorm;   epsreturnNc                    > [         TU ]  5         [        R                  " [        R
                  " U5      5      U l        X l        g)z-
ApertusRMSNorm is equivalent to T5LayerNorm
N)r*   r+   r   	Parametertorchonesweightvariance_epsilon)r5   r-   rJ   r6   s      r7   r+   ApertusRMSNorm.__init__=   s/     	ll5::k#:; #r9   hidden_statesc                    UR                   nUR                  [        R                  5      nUR	                  S5      R                  SSS9nU[        R                  " X0R                  -   5      -  nU R                  UR                  U5      -  $ )N   T)keepdim)	r)   torN   float32powmeanrsqrtrQ   rP   )r5   rS   input_dtypevariances       r7   r=   ApertusRMSNorm.forwardE   sw    #))%((7 $$Q',,R,>%H?T?T4T(UU{{]--k:::r9   c                 ^    [        U R                  R                  5       SU R                   3$ )Nz, eps=)tuplerP   shaperQ   )r5   s    r7   
extra_reprApertusRMSNorm.extra_reprL   s*    ))*+6$2G2G1HIIr9   )rQ   rP   )gư>)r?   r@   rA   rB   floatr+   rN   Tensorr=   rc   rC   rD   rE   s   @r7   rH   rH   ;   sB    $ $$ $ $;U\\ ;ell ;J Jr9   rH   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$ )ApertusRotaryEmbeddingP   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defaultrj   F)
persistentoriginal_inv_freq)r*   r+   max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenr,   rope_parametersrl   compute_default_rope_parametersr   attention_scalingregister_bufferclone)r5   r,   devicerope_init_fnrj   r6   s        r7   r+   ApertusRotaryEmbedding.__init__S   s    "("@"@$*$B$B!44[A!%!E!E>>Y&.t~~>L+7V+L((ZeD0(..2BuUr9   rx   ztorch.deviceseq_lenrK   ztorch.Tensorc           	         U R                   S   n[        U SS5      =(       d    U R                  U R                  -  nSnSU[        R
                  " SUS[        R                  S9R                  U[        R                  S9U-  -  -  nXe4$ )	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   rU   r(   )rx   r)   )	rs   getattrr-   num_attention_headsrN   arangeint64rX   re   )r,   rx   r{   basedimattention_factorrj   s          r7   rt   6ApertusRotaryEmbedding.compute_default_rope_parametersc   s    & %%l3fj$/c63E3EIcIc3c U\\!S!5;;?BB&X]XcXcBdgjjk
 ))r9   c                 L   U R                   S S S 2S 4   R                  5       R                  UR                  S   SS5      R	                  UR
                  5      nUS S 2S S S 24   R                  5       n[        UR
                  R                  [        5      (       a0  UR
                  R                  S:w  a  UR
                  R                  OSn[        USS9   UR                  5       UR                  5       -  R                  SS5      n[        R                  " Xf4SS	9nUR                  5       U R                  -  nUR                  5       U R                  -  n	S S S 5        WR	                  UR                   S
9W	R	                  UR                   S
94$ ! , (       d  f       N@= f)Nr   rV   r   mpscpuF)device_typeenabledrU   r   r(   )rj   re   expandrb   rX   rx   
isinstancetypestrr   	transposerN   catcosru   sinr)   )
r5   r<   position_idsinv_freq_expandedposition_ids_expandedr   freqsembr   r   s
             r7   r=   ApertusRotaryEmbedding.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#)ru   r,   rq   rr   rl   r;   )NNN)r?   r@   rA   rB   rN   rf   __annotations__r    r+   staticmethodr   intra   re   rt   no_gradr   r=   rC   rD   rE   s   @r7   rh   rh   P   s    llV} V V  '++/"*$*(* t* 
~u$	%	* *: ]]_<  <r9   rh   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$ )z*Rotates half the hidden dims of the input..NrV   rU   r   )rb   rN   r   )r<   x1x2s      r7   rotate_halfr      sZ    	
3"!''"+"""	#B	
3q ""	#B99rc2YB''r9   rotary_pos_embc                     UR                  U5      nUR                  U5      nX-  [        U 5      U-  -   nX-  [        U5      U-  -   nXV4$ )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          r7   apply_rotary_pos_embr      sS    & --
&C
--
&Cw;q>C/0Gw;q>C/0Gr9   rS   n_reprK   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)rb   r   reshape)rS   r   batchnum_key_value_headsslenr~   s         r7   	repeat_kvr      s_    
 2?1D1D.Ez!!Qa"23::5W\dlmM  e(CTTTr9   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$ )NrU   r   rV   )r   r)   )ptrainingr   )r   num_key_value_groupsrN   matmulr   r   
functionalsoftmaxrY   rX   r)   r   r   
contiguous)r   r   r   r   r   r   r   r   
key_statesvalue_statesattn_weightsattn_outputs               r7   eager_attention_forwardr      s     3 ; ;<JU$?$?@L<<';';Aq'ABWLL!#4==((2U]](SVVW\WbWbcL==((6??([L,,|:K''1-88:K$$r9   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	\R                  S-  S
\S-  S\\   S\
\R                  \R                  4   4S jjrSrU =r$ )ApertusAttention   z=Multi-headed attention from 'Attention Is All You Need' paperNr,   	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R*                  5      U l        [)        U R                  UR*                  5      U l        g )Nr~   g      Tr%   )r*   r+   r,   r   r   r-   r   r~   r   r   r   attention_dropout	is_causalr   r/   attention_biasq_projk_projv_projo_projrH   rms_norm_epsq_normk_normr5   r,   r   r6   s      r7   r+   ApertusAttention.__init__   s{   "
F4F4F&JdJd4de$*$>$>&B\B\$\!}}d*!'!9!9ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii&&68J8JQWQfQf
 %T]]F4G4GH$T]]F4G4GHr9   rS   position_embeddingsr   past_key_valuesr   rK   c                 L   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                  U5      nU R                  U	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$                  U R&                  S.UD6u  pUR(                  " / UQSP76 R+                  5       nU R-                  U5      nX4$ )NrV   r   rU           )r   r   )rb   r~   r   viewr   r   r   r   r   r   updater   r   get_interfacer,   _attn_implementationr   r   r   r   r   r   r   )r5   rS   r   r   r   r   input_shapehidden_shapequery_statesr   r   r   r   attention_interfacer   r   s                   r7   r=   ApertusAttention.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{{<0[[,
&#7RU#[ &'6'='=jX\XfXf'g$J(?(M(MKK,,.E)
 %8	%
  $}}C$2H2HLL	%
 	%
! "));;;;FFHkk+.((r9   )r   r,   r~   r   r   r   r   r   r   r   r   r   r   r;   )r?   r@   rA   rB   __doc__r    r   r+   rN   rf   ra   r	   r   r   r=   rC   rD   rE   s   @r7   r   r      s    GI} It I I< )-()||() #5<<#=>() t+	()
 () +,() 
u||U\\)	*() ()r9   r   c                     ^  \ rS rSrS\S\4U 4S jjr     SS\R                  S\R                  S-  S\R                  S-  S	\
S-  S
\S-  S\\R                  \R                  4   S-  S\\   S\R                  4S jjrSrU =r$ )ApertusDecoderLayeri  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   rJ   )r*   r+   r-   r   	self_attnr"   mlprH   r   attention_layernormfeedforward_layernormr   s      r7   r+   ApertusDecoderLayer.__init__   sj    !--)Mf%#1&2D2D&J]J]#^ %3F4F4FFL_L_%`"r9   NrS   r   r   r   	use_cacher   r   rK   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$ )N)rS   r   r   r   r   r    )r   r   r   r   )
r5   rS   r   r   r   r   r   r   residual_s
             r7   r=   ApertusDecoderLayer.forward*  s     !00?>> 
')%+ 3
 
 !0 !22=A/ 0r9   )r   r   r-   r   r   )NNNFN)r?   r@   rA   rB   r    r   r+   rN   rf   
LongTensorr	   boolra   r   r   r=   rC   rD   rE   s   @r7   r   r     s    a} a a /304(,!&HL|| t+ &&-	
  $; #5<<#=>E +, 
 r9   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	)
ApertusPreTrainedModeliI  r,   modelTr   r   )rS   
attentionsr   N)r?   r@   rA   rB   r    r   base_model_prefixsupports_gradient_checkpointing_no_split_modules_skip_keys_device_placement_supports_flash_attn_supports_sdpa_supports_flex_attn_can_compile_fullgraph_supports_attention_backendr   r   _can_record_outputsrC   r   r9   r7   r   r   I  sQ    &*#./#4"5N!"&,&r9   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$ )ApertusModeli\  r,   c           	        > [         TU ]  U5        UR                  U l        UR                  U l        [
        R                  " UR                  UR                  U R                  5      U l        [
        R                  " [        UR                  5       Vs/ s H  n[        X5      PM     sn5      U l        [        UR                  UR                  S9U l        [#        US9U l        SU l        U R)                  5         g s  snf )Nr   r,   F)r*   r+   pad_token_idpadding_idx
vocab_sizer   	Embeddingr-   embed_tokens
ModuleListrangenum_hidden_layersr   layersrH   r   normrh   
rotary_embgradient_checkpointing	post_initr   s      r7   r+   ApertusModel.__init__^  s     !.. ++LL):):F<N<NPTP`P`ammEJ6KcKcEdeEd	 3Ede
 #6#5#56;N;NO	0?&+# 	 fs   C?N	input_idsr   r   r   inputs_embedsr   r   rK   c           
      >   US L US L-  (       a  [        S5      eUc  U R                  U5      nU(       a  Uc  [        U R                  S9nUcU  Ub  UR	                  5       OSn[
        R                  " UR                  S   UR                  S9U-   nUR                  S5      n[        U R                  UUUUS9n	Un
U R                  XS9nU R                  S U R                  R                    H  nU" U
4U	UUUUS.UD6n
M     U R                  U
5      n
[        U
US	9$ )
Nz:You must specify exactly one of input_ids or inputs_embedsr	  r   r   )rx   )r,   r  r   r   r   )r   )r   r   r   r   r   )last_hidden_stater   )
ValueErrorr  r
   r,   get_seq_lengthrN   r   rb   rx   r   r   r  r  r  r  r   )r5   r  r   r   r   r  r   r   past_seen_tokenscausal_maskrS   r   decoder_layers                r7   r=   ApertusModel.forwardn  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&++
 	
r9   )r  r  r  r  r  r  r  )NNNNNN)r?   r@   rA   rB   r    r+   r   r   r   rN   r   rf   r	   FloatTensorr   r   r   r   r=   rC   rD   rE   s   @r7   r  r  \  s    }     .2.204(,26!%2
##d*2
 t+2
 &&-	2

 2
 ((4/2
 $;2
 +,2
 
!2
    2
r9   r  c                   P  ^  \ rS rSrSS0rSS0rSS/S/40rU 4S jr\\	        SS
\
R                  S	-  S\
R                  S	-  S\
R                  S	-  S\S	-  S\
R                  S	-  S\
R                  S	-  S\S	-  S\\
R                  -  S\\   S\4S jj5       5       rSrU =r$ )ApertusForCausalLMi  zlm_head.weightzmodel.embed_tokens.weightlm_headcolwise_gather_outputrS   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-   r%  r  r4   s     r7   r+   ApertusForCausalLM.__init__  sU     !&)
 ++yy!3!3V5F5FUS 	r9   Nr  r   r   r   r  labelsr   logits_to_keepr   rK   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, ApertusForCausalLM

>>> model = ApertusForCausalLM.from_pretrained("swiss-ai/Apertus-8B-Instruct-2509")
>>> tokenizer = AutoTokenizer.from_pretrained("swiss-ai/Apertus-8B-Instruct-2509")

>>> 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)r'  r*  r  )lossr'  r   rS   r   r   )r   r  r   r   slicer%  loss_functionr,   r  r   r   rS   r   )r5   r  r   r   r   r  r*  r   r+  r   outputsrS   slice_indicesr'  r-  s                  r7   r=   ApertusForCausalLM.forward  s    H ,0:: ,
)%+',
 ,
  118B>SV8W8W~ot4]kmA}a,?@A%%pVt{{OeOepiopD%#33!//))
 	
r9   )r%  r   r  )NNNNNNNr   )r?   r@   rA   rB   _tied_weights_keys_tp_plan_pp_planr+   r   r   rN   r   rf   r	   r"  r   r   r   r   r   r=   rC   rD   rE   s   @r7   r$  r$    s   *,GH23H_-z:;H  .2.204(,26*.!%-.;
##d*;
 t+;
 &&-	;

 ;
 ((4/;
   4';
 $;;
 ell*;
 +,;
 
 ;
  ;
r9   r$  c                       \ rS rSrSrg)ApertusForTokenClassificationi  r   N)r?   r@   rA   rB   rC   r   r9   r7   r7  r7    s    r9   r7  )r  r$  r7  r   )r   )r   )?collections.abcr   typingr   rN   r   activationsr   r   cache_utilsr	   r
   
generationr   integrationsr   r   r   masking_utilsr   modeling_layersr   r   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   r   utils.genericr   r   utils.output_capturingr   configuration_apertusr    Moduler"   rH   rh   r   r   rf   r   r   re   r   r   r   r   r  r$  r7  __all__r   r9   r7   <module>rJ     s  * %    * . ) f f / X O K F & I I G 5 0< <  Y'JRYY J (J(><RYY ><B( *+ ,2	UU\\ 	U# 	U%,, 	U& %II%<<% 
% <<	%
 LL4'% % % '(%2 )*D)ryy D) +D)N'4 'T _  $ F
) F
 F
R K
/ K
 K
\	$ACY 	 lr9   