
    Z jT                        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	  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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'  SSK(J)r)J*r*  SSK+J,r,  SSK-J.r.   " S S\R^                  5      r0\" S5       " S S\R^                  5      5       r1 " S S\R^                  5      r2S r3\" S5      S@S j5       r4S \Rj                  S!\6S"\Rj                  4S# jr7 SAS$\R^                  S%\Rj                  S&\Rj                  S'\Rj                  S(\Rj                  S-  S)\8S*\8S+\$\&   4S, jjr9\" \45       " S- S.\R^                  5      5       r: " S/ S0\5      r;\ " S1 S2\"5      5       r<\ " S3 S4\<5      5       r=\" S5S69 " S7 S8\<\5      5       r>\" S5S69 " S9 S:\\<5      5       r?\" S5S69 " S; S<\\<5      5       r@\" S5S69 " S= S>\\<5      5       rA/ S?QrBg)B    )Callable)OptionalN)nn)auto_docstring   )ACT2FN)CacheDynamicCache)GenerationMixin)use_kernel_forward_from_hubuse_kernel_func_from_hubuse_kernelized_func)create_causal_mask)GenericForQuestionAnswering GenericForSequenceClassificationGenericForTokenClassificationGradientCheckpointingLayer)BaseModelOutputWithPastCausalLMOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)TransformersKwargscan_return_tuple)maybe_autocastmerge_with_config_defaults)capture_outputs   )ArceeConfigc                   .   ^  \ rS rSrU 4S jrS rSrU =r$ )ArceeMLP2   c                   > [         TU ]  5         Xl        UR                  U l        UR                  U l        [
        R                  " U R                  U R                  UR                  S9U l        [
        R                  " U R                  U R                  UR                  S9U l	        [        UR                     U l        g )Nbias)super__init__confighidden_sizeintermediate_sizer   Linearmlp_biasup_proj	down_projr   
hidden_actact_fnselfr*   	__class__s     y/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/arcee/modeling_arcee.pyr)   ArceeMLP.__init__3   s    !--!'!9!9yy!1!143I3IPVP_P_`4#9#94;K;KRXRaRabV../    c                 `    U R                  U R                  U R                  U5      5      5      $ N)r0   r2   r/   )r4   xs     r6   forwardArceeMLP.forward<   s"    ~~dkk$,,q/:;;r8   )r2   r*   r0   r+   r,   r/   )__name__
__module____qualname____firstlineno__r)   r<   __static_attributes____classcell__r5   s   @r6   r#   r#   2   s    0< <r8   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$ )ArceeRMSNorm@   epsreturnNc                    > [         TU ]  5         [        R                  " [        R
                  " U5      5      U l        X l        g)z+
ArceeRMSNorm is equivalent to T5LayerNorm
N)r(   r)   r   	Parametertorchonesweightvariance_epsilon)r4   r+   rI   r5   s      r6   r)   ArceeRMSNorm.__init__B   s/     	ll5::k#:; #r8   hidden_statesc                    UR                   nUR                  [        R                  5      nUR	                  S5      R                  SSS9nU[        R                  " X0R                  -   5      -  nU R                  UR                  U5      -  $ )N   T)keepdim)	dtypetorM   float32powmeanrsqrtrP   rO   )r4   rR   input_dtypevariances       r6   r<   ArceeRMSNorm.forwardJ   sw    #))%((7 $$Q',,R,>%H?T?T4T(UU{{]--k:::r8   c                 ^    [        U R                  R                  5       SU R                   3$ )Nz, eps=)tuplerO   shaperP   )r4   s    r6   
extra_reprArceeRMSNorm.extra_reprQ   s*    ))*+6$2G2G1HIIr8   )rP   rO   )gư>)r>   r?   r@   rA   floatr)   rM   Tensorr<   rc   rB   rC   rD   s   @r6   rG   rG   @   sB    $ $$ $ $;U\\ ;ell ;J Jr8   rG   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$ )ArceeRotaryEmbeddingU   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)r4   r*   devicerope_init_fnrj   r5   s        r6   r)   ArceeRotaryEmbedding.__init__X   s    "("@"@$*$B$B!44[A!%!E!E>>Y&.t~~>L+7V+L((ZeD0(..2BuUr8   rx   ztorch.deviceseq_lenrJ   ztorch.Tensorc           	         U R                   S   n[        U SS5      =(       d    U R                  U R                  -  nSnSU[        R
                  " SUS[        R                  S9R                  U[        R                  S9U-  -  -  nXe4$ )	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   rT   rW   )rx   rW   )	rs   getattrr+   num_attention_headsrM   arangeint64rX   re   )r*   rx   r{   basedimattention_factorrj   s          r6   rt   4ArceeRotaryEmbedding.compute_default_rope_parametersh   s    & %%l3fj$/c63E3EIcIc3c U\\!S!5;;?BB&X]XcXcBdgjjk
 ))r8   c                 L   U R                   S S S 2S 4   R                  5       R                  UR                  S   SS5      R	                  UR
                  5      nUS S 2S S S 24   R                  5       n[        UR
                  R                  [        5      (       a0  UR
                  R                  S:w  a  UR
                  R                  OSn[        USS9   UR                  5       UR                  5       -  R                  SS5      n[        R                  " Xf4SS	9nUR                  5       U R                  -  nUR                  5       U R                  -  n	S S S 5        WR	                  UR                   S
9W	R	                  UR                   S
94$ ! , (       d  f       N@= f)Nr   rU   r    mpscpuF)device_typeenabledrT   r   r   )rj   re   expandrb   rX   rx   
isinstancetypestrr   	transposerM   catcosru   sinrW   )
r4   r;   position_idsinv_freq_expandedposition_ids_expandedr   freqsembr   r   s
             r6   r<   ArceeRotaryEmbedding.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?   r@   rA   rM   rf   __annotations__r!   r)   staticmethodr   intra   re   rt   no_gradr   r<   rB   rC   rD   s   @r6   rh   rh   U   s    llV{ V V  %)+/"*d"*(* t* 
~u$	%	* *: ]]_<  <r8   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$ )z*Rotates half the hidden dims of the input..NrU   rT   r   )rb   rM   r   )r;   x1x2s      r6   rotate_halfr      sZ    	
3"!''"+"""	#B	
3q ""	#B99rc2YB''r8   rotary_pos_embc                     UR                  U5      nUR                  U5      nX-  [        U 5      U-  -   nX-  [        U5      U-  -   nXV4$ )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          r6   apply_rotary_pos_embr      sS    & --
&C
--
&Cw;q>C/0Gw;q>C/0Gr8   rR   n_reprJ   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)rR   r   batchnum_key_value_headsslenr~   s         r6   	repeat_kvr      s_    
 2?1D1D.Ez!!Qa"23::5W\dlmM  e(CTTTr8   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$ )NrT   r   rU   )r   rW   )ptrainingr    )r   num_key_value_groupsrM   matmulr   r   
functionalsoftmaxrY   rX   rW   r   r   
contiguous)r   r   r   r   r   r   r   r   
key_statesvalue_statesattn_weightsattn_outputs               r6   eager_attention_forwardr      s     3 ; ;<JU$?$?@L<<';';Aq'ABWLL!#4==((2U]](SVVW\WbWbcL==((6??([L,,|:K''1-88:K$$r8   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-  S	\R                  S-  S
\S-  S\\   S\
\R                  \R                  4   4S jjrSrU =r$ )ArceeAttention   z=Multi-headed attention from 'Attention Is All You Need' paperr*   	layer_idxc                 P  > [         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        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_projr4   r*   r   r5   s      r6   r)   ArceeAttention.__init__   sI   "
F4F4F&JdJd4de$*$>$>&B\B\$\!}}d*!'!9!9ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii&&68J8JQWQfQf
r8   NrR   position_embeddingsr   past_key_valuesr   rJ   c                    UR                   S S n/ UQSPU R                  P7nU R                  U5      R                  U5      R	                  SS5      nU R                  U5      R                  U5      R	                  SS5      n	U R                  U5      R                  U5      R	                  SS5      n
Uu  p[        XX5      u  pUb  UR                  XU R                  5      u  p[        R                  " U R                  R                  [        5      nU" U UU	U
U4U R                  (       d  SOU R                   U R"                  S.UD6u  pUR$                  " / UQSP76 R'                  5       nU R)                  U5      nX4$ )NrU   r    rT           )r   r   )rb   r~   r   viewr   r   r   r   updater   r   get_interfacer*   _attn_implementationr   r   r   r   r   r   r   )r4   rR   r   r   r   r   input_shapehidden_shapequery_statesr   r   r   r   attention_interfacer   r   s                   r6   r<   ArceeAttention.forward   s~    $))#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$2H2HLL	%
 	%
! "));;;;FFHkk+.((r8   )r   r*   r~   r   r   r   r   r   r   r   r   r   )r>   r?   r@   rA   __doc__r!   r   r)   rM   rf   ra   r	   r   r   r<   rB   rC   rD   s   @r6   r   r      s    G
{ 
s 
4 IM.2(,&)||&) #5<<#=>E&) t+	&)
 &) +,&) 
u||U\\)	*&) &)r8   r   c                     ^  \ rS rSrS\S\4U 4S jjr     SS\R                  S\R                  S-  S\R                  S-  S	\
S-  S
\S-  S\\R                  \R                  4   S-  S\\   S\R                  4S jjrSrU =r$ )ArceeDecoderLayeri   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   rI   )r(   r)   r+   r   	self_attnr#   mlprG   rms_norm_epsinput_layernormpost_attention_layernormr   s      r6   r)   ArceeDecoderLayer.__init__!  sj    !--'vKF#+F,>,>FDWDWX(4V5G5GVM`M`(a%r8   NrR   r   r   r   	use_cacher   r   rJ   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$ )N)rR   r   r   r   r   r    )r   r   r   r   )
r4   rR   r   r   r   r   r   r   residual_s
             r6   r<   ArceeDecoderLayer.forward+  s     !,,];>> 
')%+ 3
 
 !0 !55mD/ 0r8   )r+   r   r   r   r   )NNNFN)r>   r?   r@   rA   r!   r   r)   rM   rf   
LongTensorr	   boolra   r   r   r<   rB   rC   rD   s   @r6   r   r      s    b{ bs b /304(,!&HL|| t+ &&-	
  $; #5<<#=>E +, 
 r8   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	)
ArceePreTrainedModeliK  r*   modelTr   r   )rR   
attentionsr   N)r>   r?   r@   rA   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_outputsrB   r   r8   r6   r   r   K  sQ    &*#,-#4"5N!"&*$r8   r   c                     ^  \ rS rSrS\4U 4S jjr\\\      SS\	R                  S-  S\	R                  S-  S\	R                  S-  S\S-  S	\	R                  S-  S
\S-  S\\   S\4S jj5       5       5       rSrU =r$ )
ArceeModeli^  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   layersrG   r   normrh   
rotary_embgradient_checkpointing	post_initr   s      r6   r)   ArceeModel.__init__`  s     !.. ++LL):):F<N<NPTP`P`ammCHIaIaCbcCbiv1Cbc
 !!3!39L9LM	.f=&+# 	 ds   C?N	input_idsr   r   r   inputs_embedsr   r   rJ   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$ )
Nz:You must specify exactly one of input_ids or inputs_embedsr	  r   r    )rx   )r*   r  r   r   r   )r   )r   r   r   r   r   )last_hidden_stater   )
ValueErrorr  r
   r*   get_seq_lengthrM   r   rb   rx   r   r   r  r  r  r  r   )r4   r  r   r   r   r  r   r   past_seen_tokenscausal_maskrR   r   decoder_layers                r6   r<   ArceeModel.forwardp  sF    -t";<YZZ *.*;*;I*FM0*$++>OCRC^==?de <<(;(;A(>}G[G[\_ooL'11!4L(;;')+%
 &"oomoW![[)H4;;+H+HIM)*$7) /# M J 		-0&++
 	
r8   )r  r  r  r  r  r  r  )NNNNNN)r>   r?   r@   rA   r!   r)   r   r   r   rM   r   rf   r	   FloatTensorr   r   r   r   r<   rB   rC   rD   s   @r6   r  r  ^  s    {     .2.204(,26!%2
##d*2
 t+2
 &&-	2

 2
 ((4/2
 $;2
 +,2
 
!2
    2
r8   r  zarcee-ai/AFM-4.5B)
checkpointc                   P  ^  \ rS rSrSS0rSS0rSS/S/40rU 4S jr\\	        SS
\
R                  S	-  S\
R                  S	-  S\
R                  S	-  S\S	-  S\
R                  S	-  S\
R                  S	-  S\S	-  S\\
R                  -  S\\   S\4S jj5       5       rSrU =r$ )ArceeForCausalLMi  zlm_head.weightzmodel.embed_tokens.weightlm_headcolwise_gather_outputrR   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  r3   s     r6   r)   ArceeForCausalLM.__init__  sU     '
 ++yy!3!3V5F5FUS 	r8   Nr  r   r   r   r  labelsr   logits_to_keepr   rJ   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$ )ao  
Example:

```python
>>> from transformers import AutoTokenizer, ArceeForCausalLM

>>> model = ArceeForCausalLM.from_pretrained("meta-arcee/Arcee-2-7b-hf")
>>> tokenizer = AutoTokenizer.from_pretrained("meta-arcee/Arcee-2-7b-hf")

>>> prompt = "Hey, are you conscious? Can you talk to me?"
>>> inputs = tokenizer(prompt, return_tensors="pt")

>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
```)r  r   r   r   r  r   N)r(  r+  r  )lossr(  r   rR   r   r   )r   r  r   r   slicer&  loss_functionr*   r  r   r   rR   r   )r4   r  r   r   r   r  r+  r   r,  r   outputsrR   slice_indicesr(  r.  s                  r6   r<   ArceeForCausalLM.forward  s    > ,0:: ,
)%+',
 ,
  118B>SV8W8W~ot4]kmA}a,?@A%%pVt{{OeOepiopD%#33!//))
 	
r8   )r&  r   r  )NNNNNNNr   )r>   r?   r@   rA   _tied_weights_keys_tp_plan_pp_planr)   r   r   rM   r   rf   r	   r"  r   r   r   r   r   r<   rB   rC   rD   s   @r6   r%  r%    s   *,GH23H_-z:;H  .2.204(,26*.!%-.6
##d*6
 t+6
 &&-	6

 6
 ((4/6
   4'6
 $;6
 ell*6
 +,6
 
 6
  6
r8   r%  c                       \ rS rSrSrg)ArceeForSequenceClassificationi  r   Nr>   r?   r@   rA   rB   r   r8   r6   r8  r8        r8   r8  c                       \ rS rSrSrSrg)ArceeForQuestionAnsweringi  transformerr   N)r>   r?   r@   rA   r   rB   r   r8   r6   r<  r<    s    %r8   r<  c                       \ rS rSrSrg)ArceeForTokenClassificationi  r   Nr9  r   r8   r6   r?  r?    r:  r8   r?  )r%  r<  r8  r?  r  r   )r    )r   )Ccollections.abcr   typingr   rM   r   transformers.utilsr   activationsr   cache_utilsr	   r
   
generationr   integrationsr   r   r   masking_utilsr   modeling_layersr   r   r   r   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   utils.genericr   r   utils.output_capturingr   configuration_arceer!   Moduler#   rG   rh   r   r   rf   r   r   re   r   r   r   r   r  r%  r8  r<  r?  __all__r   r8   r6   <module>rS     s_  * %    - ! . ) f f /  P K F & 9 G 5 ,<ryy < Y'J299 J (J(><299 ><B( *+ ,2	UU\\ 	U# 	U%,, 	U& %II%<<% 
% <<	%
 LL4'% % % '(%2 )*@)RYY @) +@)F(2 (V ?  $ F
% F
 F
R ./F
+_ F
 0F
R ./	%EG[ 	 0	 ./& ;=Q & 0& ./	"?AU 	 0	r8   